{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual EDA\n",
    "\n",
    "The [first page of this chapter](04b-whyplot) discussed the reasons we plot our data. \n",
    "1. Data cleaning: To find issues in the data that need to get fixed before we can do larger analysis\n",
    "2. Data exploration: Learning about each of the variables, how they covary, and what further questions you can ask of the data\n",
    "3. Analysis and presentation\n",
    "\n",
    "## EDA on a classic firm financial dataset\n",
    "\n",
    "In [the Pandas EDA](02e_eda_golden) page, I explored Compustat by producing summary stats to get a sense of the variables involved, look for missing values, and look for problematic outliers. We noted that some variables, like $delaycon$, had a lot of missing values and decided we'd look into it. \n",
    "\n",
    "Let's continue exploring that dataset. First, let's download our slice of it. The variables are listed and described in a csv file in the [repo's data folder.](https://github.com/LeDataSciFi/ledatascifi-2022/tree/main/data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide_input"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# these three are used to download the file\n",
    "from io import BytesIO\n",
    "from zipfile import ZipFile\n",
    "from urllib.request import urlopen\n",
    "\n",
    "url = 'https://github.com/LeDataSciFi/ledatascifi-2022/blob/main/data/CCM_cleaned_for_class.zip?raw=true'\n",
    "\n",
    "#firms = pd.read_stata(url)   \n",
    "# <-- that code would work, but GH said it was too big and\n",
    "# forced me to zip it, so here is the work around to download it:\n",
    "\n",
    "with urlopen(url) as request:\n",
    "    data = BytesIO(request.read())\n",
    "\n",
    "with ZipFile(data) as archive:\n",
    "    with archive.open(archive.namelist()[0]) as stata:\n",
    "        ccm = pd.read_stata(stata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The mystery of the poorly populated variables\n",
    "\n",
    "Again, there are some variables with lots of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_80760_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >% missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row0\" class=\"row_heading level0 row0\" >privdelaycon</th>\n",
       "      <td id=\"T_80760_row0_col0\" class=\"data row0 col0\" >74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row1\" class=\"row_heading level0 row1\" >debtdelaycon</th>\n",
       "      <td id=\"T_80760_row1_col0\" class=\"data row1 col0\" >74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row2\" class=\"row_heading level0 row2\" >equitydelaycon</th>\n",
       "      <td id=\"T_80760_row2_col0\" class=\"data row2 col0\" >74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row3\" class=\"row_heading level0 row3\" >delaycon</th>\n",
       "      <td id=\"T_80760_row3_col0\" class=\"data row3 col0\" >74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row4\" class=\"row_heading level0 row4\" >prodmktfluid</th>\n",
       "      <td id=\"T_80760_row4_col0\" class=\"data row4 col0\" >60.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row5\" class=\"row_heading level0 row5\" >tnic3tsimm</th>\n",
       "      <td id=\"T_80760_row5_col0\" class=\"data row5 col0\" >56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row6\" class=\"row_heading level0 row6\" >tnic3hhi</th>\n",
       "      <td id=\"T_80760_row6_col0\" class=\"data row6 col0\" >56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row7\" class=\"row_heading level0 row7\" >smalltaxlosscarry</th>\n",
       "      <td id=\"T_80760_row7_col0\" class=\"data row7 col0\" >33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row8\" class=\"row_heading level0 row8\" >largetaxlosscarry</th>\n",
       "      <td id=\"T_80760_row8_col0\" class=\"data row8 col0\" >33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row9\" class=\"row_heading level0 row9\" >invopps_FG09</th>\n",
       "      <td id=\"T_80760_row9_col0\" class=\"data row9 col0\" >13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row10\" class=\"row_heading level0 row10\" >short_debt</th>\n",
       "      <td id=\"T_80760_row10_col0\" class=\"data row10 col0\" >12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row11\" class=\"row_heading level0 row11\" >sales_g</th>\n",
       "      <td id=\"T_80760_row11_col0\" class=\"data row11 col0\" >11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_80760_level0_row12\" class=\"row_heading level0 row12\" >l_laborratio</th>\n",
       "      <td id=\"T_80760_row12_col0\" class=\"data row12 col0\" >10.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x175ab1e8af0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    ( # these lines do the calculation - what % of missing values are there for each var\n",
    "        ccm.isna()      # ccm.isna() TURNS every obs/variable = 1 when its missing and 0 else\n",
    "       .sum(axis=0)     # count the number of na for each variable (now data is 1 obs per column = # missing)\n",
    "        /len(ccm)       # convert # missing to % missing \n",
    "        *100            # report as percentage\n",
    "    ) \n",
    "    # you can stop here and report this...\n",
    "    # but I wanted to format it a bit...\n",
    "    .sort_values(ascending=False)[:13]\n",
    "    .to_frame(name='% missing') # the next line only works on a frame, and because pandas sees only 1 variable at this pt\n",
    "    .style.format(\"{:.1f}\")     # in the code, it calls this a \"series\" type object, so convert it to dataframe type object\n",
    ")\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When variables missing that much in a dataset, something systematic is going on and you need to figure it out. \n",
    "\n",
    "One way you could investigate why those variables are missing. Maybe it's a data issue, as if some data for variable $x$ isn't available in all years. E.g. perhaps a variable isn't available before 1995 for some reason.\n",
    "\n",
    "A way you could get a start on that is to plot the % missing by year for each variable. This legend is UGGGGLY, because the plot has 40+ series, which is why it's a **spaghetti chart**. It would take extra work to unravel the spaghetti and figure out what variables are what. But CLEARLY some variables only become available in 1995 so they can be used after that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "ccm\n",
    "    .groupby('fyear')\n",
    "    [['privdelaycon','debtdelaycon','equitydelaycon','delaycon',\n",
    "     'prodmktfluid','tnic3tsimm','tnic3hhi']]\n",
    "    .apply(lambda x: 100*(x.isna().sum(axis=0))  / len(x) ) \n",
    "    .plot.line(title=\"These variables didn't exist before 1997!\",\n",
    "               ylabel=\"Fraction of missing observations\")\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributions \n",
    "\n",
    "Among the first things I do with new data, besides the statistical EDA [we covered in the Pandas section](02e_eda_golden), is plot the distribution of each variable to get a sense of the data. \n",
    "\n",
    "I generally want to know \n",
    "1. If a variable is numerical\n",
    "    1. How many observations are missing and is it systematic (like in the example above)?\n",
    "    1. Is it continuous or discrete?\n",
    "    1. What is the shape of a distribution (normal, binary, skewed left, skewed right, fat-tailed, etc.)?\n",
    "    2. How prevalent are outliers?\n",
    "2. If a variable is categorical\n",
    "    1. What are the common values? \n",
    "    2. Are the averages of numerical variables different for different categories? \n",
    "\n",
    "```{warning}\n",
    "Remember the `gsector` variable! Just because a variable is a \"number\" doesn't mean the numbers have a mathematical meaning! \n",
    "```\n",
    "\n",
    "Four functions come in handy as a starting point, and you should look at their documentation and the example galleries: [`sns.displot`](https://seaborn.pydata.org/generated/seaborn.displot.html), [`sns.boxplot`](https://seaborn.pydata.org/generated/seaborn.boxplot.html), [`sns.catplot`](https://seaborn.pydata.org/generated/seaborn.catplot.html), and the built in pandas plot function [`df[<columnName>].plot()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html)\n",
    "\n",
    "```{tip}\n",
    "1. Quick syntax help: Remember to type <kbd>SHIFT</kbd> + <kbd>TAB</kbd> when the cursor inside a function!\n",
    "2. Better syntax help: Go the official seaborn page for your function and look at the examples to figure out what argument creates the change you want.\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LYEkXSVol6Y4xtkvSv0laJuk2SfvXVQt4KrKZpafOFvAiYN44248Gdi9v84HP1liLpyKbWXJqC+CIuAF4ZJxdjge+HIWbge0k7VRXPa0EHhqq7QhmZpPSZB/wbGB55fmK8rVatPp+fUkiM0tFkwGsDq91TEdJ8yUtkbRkcHBwow7mmXBmlpomA3gFsHPl+RxgZacdI2JhRAxExEB/f/9GHcyroZlZapoM4CuBU8rREC8B/hARv6vrYD4JZ2apmVHXB0u6BDgMmCVpBfBhoA8gIhYAVwPHAMuAPwFvq6uW4pjlfZ0HMTObhNoCOCJOnGB7AO+q6/htxxp+7IkYZpaKLGbCVTPXJ+HMLBV5BHD1sVvAZpaIPAK4ErrOXzNLRRYBXO128EQMM0tFFgFcDV1PRTazVOQRwG4Bm1mCsgjgKo+CMLNUZBHAo1rAPgtnZonIIoCHPArCzBKURQBXM9cz4cwsFXkEcLUF3GAdZmZVXQWwpMskvVrSJhnYo1vAjZVhZjZKt4H6WeAk4NeSPi5prxprmnIRYz0xM2tOVwEcEd+LiLcA+wP3A9dJuknS2yT11VngVBi9GlqDhZiZVXTdpSBpB+A04HTgF8C/UgTydbVUNoVGr4bmBDazNHS1HrCky4G9gK8Ax1WuXPE1SUvqKm6quAfCzFLU7YLsF0bE1dUXJD0tIv4SEQM11DWlvCC7maWo2y6Ij3V47cdTWUidHLlmlqJxW8CSng3MBraU9CJGLiW/LbBVzbVNmSG3gM0sQRN1QRxFceJtDvCpyuuPAx+qqaapN2otiObKMDOrGjeAI+JLwJckvSEiLpummqacpyKbWYom6oI4OSIuBnaV9D/bt0fEpzq8LTnhFrCZJWiiLoity/un111InaqLsDt/zSwVE3VBfK68/+j0lFOPIU/EMLMEdbsYz79I2lZSn6TvS1ot6eS6i5sqviqymaWo23HAR0bEY8CxwApgD+ADtVU1xTwV2cxS1G0AtxbcOQa4JCIeqame2jl/zSwV3U5F/rake4A/A2dK6gf+u76yplar1dvbI18TzsyS0e1ylOcALwUGIuJJ4Ang+IneJ2mepKWSlkk6p8P2Z0j6tqRfSrpT0tsm+wV0o5W5vZKXozSzZHTbAgbYm2I8cPU9Xx5rZ0m9wAXAERT9xrdIujIi7qrs9i7grog4rmxVL5X01YhYO4m6JtTKXGn0kDQzsyZ1uxzlV4DnAbcC68uXg3ECGDgQWBYR95afcSlFq7kawAFsI0kUY40fAdZNov6uRKULwi1gM0tFty3gAWBuTK4DdTawvPJ8BXBQ2z7nA1cCK4FtgL+NiKFJHKMrraJ7JZ+EM7NkdDsK4g7g2ZP8bHV4rT3+jqJoVT8H2A84X9K2G3yQNF/SEklLBgcHJ1nGSAu4xyfhzCwh3QbwLOAuSddKurJ1m+A9K4CdK8/nULR0q94GXB6FZcB9FFfeGCUiFkbEQEQM9Pf3d1ly9f3FfY88FdnM0tFtF8RHNuKzbwF2l7Qb8CBwAsWVlaseAF4J/FDSjsCewL0bcaxxDXdB9Ih17gQ2s0R0FcARcb2kvwJ2j4jvSdoK6J3gPesknQVcW+57UUTcKemMcvsC4DxgkaTbKboszo6I1U/h6xmjluK+R2JoaMq7mM3MNkq3oyDeAcwHtqcYDTEbWEDReh1TeR25q9teW1B5vBI4cnIlT15r6Flvj9wFYWbJ6LYP+F3Ay4DHACLi18Cz6ipqqrUavT0eBWFmCek2gP9SnRxRTsbYZKKs1QLu6cGjIMwsGd0G8PWSPkRxcc4jgK8D366vrKnlqchmlqJuA/gcYBC4HXgnRb/uP9ZVVF16euSpyGaWjG5HQQxJugK4IiImPxOiYW4Bm1mKxm0Bq/ARSauBeygWyxmUdO70lDc1vBylmaVooi6I91KMfjggInaIiO0p1nN4maT31V3cVBlZDc2jIMwsHRMF8CnAiRFxX+uFcnWzk8ttm4SR1dB8SSIzS8dEAdzXaWZa2Q/c12H/JI1aDa3RSszMRkwUwOMtjD6li6bXafRqaA0XY2ZWmmgUxL6SHuvwuoAtaqinFtW1IIrngdRptUwzs+kzbgBHxLgL7mwqql0QAEMBvc5fM2tYtxMxNmnDLeDyq/WJODNLQSYBPDIOuHjeZDVmZoUsAniorQ/YLWAzS0EWATy8GppPvJlZQrII4NZZuFYXhFvAZpaCLAK4Fbdl/roP2MySkEcAuwVsZgnKIoCH2kZBeElKM0tBFgFcXQ1t1AtmZg3KI4BbLWAPQzOzhOQRwOX98ESM5koxMxuWRQDjiRhmlqAsArgVuB6GZmYpySKA24eh+bpwZpaCPAK4vO/xMDQzS0geAdw2CiJ8Gs7MEpBHAJf3nohhZimpNYAlzZO0VNIySeeMsc9hkm6VdKek6+uoo9UCHp6H4T5gM0vARNeE22iSeoELgCOAFcAtkq6MiLsq+2wHfAaYFxEPSHpWHbUMn4STF2Q3s3TU2QI+EFgWEfdGxFrgUuD4tn1OAi6PiAcAImJVHYW0n4RzAJtZCuoM4NnA8srzFeVrVXsAz5S0WNLPJJ3S6YMkzZe0RNKSwcHBSRfi1dDMLEV1BnCny0+0J98M4MXAq4GjgH+StMcGb4pYGBEDETHQ398/6UKGvBaEmSWotj5gihbvzpXnc4CVHfZZHRFPAE9IugHYF/jVVBaywYLsU/nhZmYbqc4W8C3A7pJ2kzQTOAG4sm2fbwEvlzRD0lbAQcDdU11Ia9RDj2fCmVlCamsBR8Q6SWcB1wK9wEURcaekM8rtCyLibknXALcBQ8CFEXFHXTX1eBSEmSWkzi4IIuJq4Oq21xa0Pf8k8Ml66yjuPRHDzFKSxUy4kdXQPBXZzNKRRQCPtICL+6Gh5moxM2vJI4DLey/IbmYpySOA27ogzMxSkEcAl/eeCWdmKckjgDcYB9xkNWZmhUwCuLj3VGQzS0keAVzeeyqymaUkjwD2RTnNLEF5BHDZ5vVMODNLSRYB3ApcrwVhZinJIoBpGwXhk3BmloIsAniDk3DOXzNLQB4BvMFFOZ3AZta8TAK4bSJGk8WYmZWyCOAhT8QwswRlEcAbrgXRXC1mZi15BHDZ4pVGPzcza1IWAdzS68V4zCwhWQRwq8+315ckMrOEZBHArRbv8EQMX5LIzBKQRwCX9yMX5TQza14eAdx+UU53AptZAvIIYNouS+8ANrME5BHAG6wH3GAxZmalTAJ4dAvYEzHMLAWZBHBx3+NhaGaWkDwCuLz3VGQzS0mtASxpnqSlkpZJOmec/Q6QtF7SG+uoo30UhE/CmVkKagtgSb3ABcDRwFzgRElzx9jvE8C1ddUyFO2jIOo6kplZ9+psAR8ILIuIeyNiLXApcHyH/f4OuAxYVVch7RMxPA7YzFJQZwDPBpZXnq8oXxsmaTbwOmDBeB8kab6kJZKWDA4OTr4St4DNLEF1BrA6vNYefZ8Gzo6I9eN9UEQsjIiBiBjo7++fdCFBcT241nKUbgGbWQpm1PjZK4CdK8/nACvb9hkALlWRjLOAYySti4grprKQCJA0sh7wVH64mdlGqjOAbwF2l7Qb8CBwAnBSdYeI2K31WNIi4KqpDl8oWrwqjtE67lQfwsxs0moL4IhYJ+ksitENvcBFEXGnpDPK7eP2+05pLRTdD74svZmlpM4WMBFxNXB122sdgzciTquvDhAaPgm33glsZgnIZCZclC3g1oLsDmAza14eARxFF8TMcirc2vUOYDNrXiYBHAjRN6NoAa9b72sSmVnzMgngogU8o6f4cp90AJtZAvIIYIpZIX29RQvYXRBmloI8AjiKE3CS6OuVuyDMLAlZBPBQMQ4NKLoh3AVhZinIIoBhZGGKvl7xpLsgzCwBWQRwRAxPQ545wy1gM0tDHgHMyEpofb0OYDNLQx4BHCOz4Ga4C8LMEpFFALdWQwO3gM0sHVkEcLULYqYD2MwSkUcAB7TGQbgLwsxSkUUAU66GBu6CMLN0ZBHAxUm44rED2MxSkUUAD5WroYEnYphZOrII4NZqaFC0gL0WhJmlII8AhlHD0LwampmlII8AjpErIhddEG4Bm1nz8gjgtlEQ7oIwsxTkEcDRvhaEuyDMrHmZBPDoURBr3QI2swTkEcB4FISZpSePAK6shuYuCDNLRRYBXF0NbYa7IMwsEVkEcGUtHma6C8LMEpFFABOjJ2IMBawfcjeEmTWr1gCWNE/SUknLJJ3TYftbJN1W3m6StG8ddRTjgEeWowQ8GcPMGldbAEvqBS4AjgbmAidKmtu2233AoRGxD3AesLCOWqqroc3sLb5kB7CZNa3OFvCBwLKIuDci1gKXAsdXd4iImyLi0fLpzcCcOgoZvRpaK4DdBWFmzaozgGcDyyvPV5SvjeXtwHc6bZA0X9ISSUsGBwcnXUh1Jpy7IMwsFXUGsDq81rHZKelwigA+u9P2iFgYEQMRMdDf3z/pQqoH7XMXhJklYkaNn70C2LnyfA6wsn0nSfsAFwJHR8TDdRRSXQ1tprsgzCwRdbaAbwF2l7SbpJnACcCV1R0k7QJcDrw1In5VXykx6pJE4BawmTWvthZwRKyTdBZwLdALXBQRd0o6o9y+ADgX2AH4TNlCXRcRA1Ndy5D7gM0sQXV2QRARVwNXt722oPL4dOD0OmsojzM8CsJdEGaWiixmwrWvhgZuAZtZ8/II4MpUZHdBmFkq8ghgqteEcxeEmaUhjwCOkWvCDfcBr3ML2MyalUkAb9gFsW7IAWxmzcojgCurobW6INa6C8LMGpZHAHdaDc1dEGbWsGwCuDUO2F0QZpaKLAJ4qNIJ7C4IM0tFFgEcjJyEcxeEmaUiiwDGa0GYWYKyCOAg6GkbBbHOF+U0s4ZlEcDV1dD6yhbwWndBmFnDsgjg6mpokpjRI3dBmFnj8ghgRlrAUHRDuAvCzJqWRwC3ZW1fr9wFYWaNyyOAYfgkHBQtYHdBmFnT8gjgympoUHZBeCKGmTUskwAemYgB0DfDJ+HMrHl5BHBlNTSAvp4e1jqAzaxheQRwewvYXRBmloB8ArjaAnYXhJklIIsAHmo7CTfDXRBmloAsAhhGd0HMdBeEmSUgiwCOaJsJN0NuAZtZ4/IIYEbWggB49rZbcu/gH1nnEDazBuURwAE9la/0FXs9i0f/9CQ/f2BNYzWZmWURwEMxugV8yB6z6OsV37v79w1WZWa5qzWAJc2TtFTSMknndNguSf9Wbr9N0v511BEw6izcNlv08ZLn7uAANrNG1RbAknqBC4CjgbnAiZLmtu12NLB7eZsPfLaWYtomYgAcOXdH7h18gvOuuouVa/5MtC+ZZmZWsxk1fvaBwLKIuBdA0qXA8cBdlX2OB74cRfrdLGk7STtFxO+mspBiPeDREfzmA3bmnoce5ws33scXbryP3p6ik6K123CXReWufZs0Euytzx8+Sqdtoz5r9P7F0w33GznO2J9BZb/x9jezp+YVe+3Iuce1tyM3Xp0BPBtYXnm+Ajioi31mA6MCWNJ8ihYywB8lLZ1sMYth1vknsXqy76vZLEiqJtczsdRqcj3jm9J6rgc+vHFvvSYi5rW/WGcAd2p0tf+d380+RMRCYOFTKkZaEhEDT+UzplpqNbmeiaVWk+sZX2r1tKvzJNwKYOfK8znAyo3Yx8xss1RnAN8C7C5pN0kzgROAK9v2uRI4pRwN8RLgD1Pd/2tmlqrauiAiYp2ks4BrgV7gooi4U9IZ5fYFwNXAMcAy4E/A2+qqh6fYhVGT1GpyPRNLrSbXM77U6hlFHn5lZtaMLGbCmZmlyAFsZtaQLAJ4oinR03D8iyStknRH5bXtJV0n6dfl/TOnsZ6dJf2XpLsl3SnpPQnUtIWkn0r6ZVnTR5uuqTx+r6RfSLqq6Xok3S/pdkm3SlrSdD3l8beT9A1J95S/Ty9tqiZJe5bfm9btMUnvbfp7NJ7NPoC7nBJdt0VA+yDsc4DvR8TuwPfL59NlHfD+iNgbeAnwrvJ70mRNfwFeERH7AvsB88qRMU3WBPAe4O7K86brOTwi9quMbW26nn+lmGSwF7AvxfeqkZoiYmn5vdkPeDHFif1vNlVPVyJis74BLwWurTz/IPDBBurYFbij8nwpsFP5eCdgaYPfo28BR6RSE7AV8HOKmZON1UQxLv37wCuAq5r+uQH3A7PaXmuynm2B+yhP5qdQU6WGI4EfpVLPWLfNvgXM2NOdm7ZjlGOey/tnNVGEpF2BFwE/abqm8s/9W4FVwHUR0XRNnwb+Hqiu3N9kPQF8V9LPyun5TdfzXGAQ+GLZTXOhpK0brqnlBOCS8nEK9XSUQwB3Nd05R5KeDlwGvDciHmu6nohYH8Wfj3OAAyW9oKlaJB0LrIqInzVVQwcvi4j9KbrT3iXpkIbrmQHsD3w2Il4EPEECf96XE79eA3y96VomkkMApzrd+feSdgIo71dN58El9VGE71cj4vIUamqJiDXAYop+86ZqehnwGkn3A5cCr5B0cYP1EBEry/tVFH2bBzZZD8W/rRXlXyoA36AI5KZ/j44Gfh4RrQW/m65nTDkEcDdToptwJXBq+fhUin7YaSFJwBeAuyPiU4nU1C9pu/LxlsCrgHuaqikiPhgRcyJiV4rfmR9ExMlN1SNpa0nbtB5T9HHe0VQ9ABHxELBc0p7lS6+kWG62sZpKJzLS/UAC9Yyt6U7oaeqQPwb4FfAb4B8aOP4lFEtsPknRang7sAPFCZ5fl/fbT2M9B1N0w9wG3Frejmm4pn2AX5Q13QGcW77eWE2V2g5j5CRcI/VQ9Lf+srzd2fo9bvr7QzFiZUn5c7sCeGbDv0dbAQ8Dz6i81vjv0Fg3T0U2M2tIDl0QZmZJcgCbmTXEAWxm1hAHsJlZQxzAZmYNcQCbmTXEAWybtXK5xDPH2LZI0hunuyazFgewbe62AzoGsFnTarsop1kiPg48r1xl7TpgS4rlJe+j80JNwySdCxxXvucm4J3hmUs2hdwCts3dOcBvolhl7cfAnsALgXcAfzPBe8+PiAMi4gUUIXxsnYVafhzAlpNDgEuiWPZyJfCDCfY/XNJPJN1O0Wp+fu0VWlbcBWG56aoLQdIWwGeAgYhYLukjwBZ1Fmb5cQvYNnePA9uUj28ATiivvLETcPg472uF7epy4XqPlrAp5xawbdYi4mFJPyqvSP0diiUJb6dYnvT6cd63RtLny33vp1hX2mxKeTlKM7OGuAvCzKwh7oKw7En6JrBb28tnR8S1TdRj+XAXhJlZQ9wFYWbWEAewmVlDHMBmZg1xAJuZNeT/A2S2cFmMAgrIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=ccm,\n",
    "            x='td_a',\n",
    "            kind='kde').set(title='A density (kind=\"kde\") graph')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time, let's query the dataset to eliminate outliers that distort the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1DY5Zn22NQVUikiwUwM1wzlHZji4IgIMG96R7t3TeXLo5BpWJSLJQADejurYB56JbjL2pzPQAU8cU8cqijRqOJiLNUgA3o3FL+pxWtqRvzqkH9Ke8spaZKzQaQkQiUwA3I1gXarlmtTOAjx1VSF63dF6cv6EzyxKRJKIAbkZHA7hbehpfHteXGQs37mlNi4iEUwA3o7o2tBh7t/T2v0XnHzqYXcE6nvlkXWeVJSJJJGYBbGaDzOxNM1tiZovM7LsRjjEzu93MVpjZfDM7OFb1tFVjC7hblIuxRzJxSE/G9u/Bw7PWaLdkEdlHLFvAdcB1zrkxwOHAN81sbJNjTgb28z6uAu6KYT1tEvRawFnp7euCADAzLps8lGWbdjFzhcYEi8gXxSyAnXMbnHNzvM93AUuA4iaHTQMediHvAwVm1j9WNbVFdSe0gAHOmDCAAflZ3DJjqVrBIvIFXdIHbGZDgYOAD5rcVQysDfu6lH1DGjO7ysxmm9nssrKuWWMhuKcPuP0tYAhdxPt/Xx7JvNIdvLRAIyJEZK+YB7CZdQeeBr7nnGu6ZXCkzdb2aSY65+51zk10zk0sLCyMRZn72DsKouNv0f8cPJDR/fL49YuL2V5R0+HnE5HkENMANrMMQuH7T+fcvyMcUgoMCvt6ILA+ljVFq7qTWsAAaQHjD2ePZ1tFDdc/s0BdESICxHYUhAH3A0ucc7c2c9jzwCXeaIjDgR3Oubj4O33PKIgODEMLt39xPtedMIqXF27ktleXd8pzikhia/tSX9E7ErgYWGBmc73brgcGAzjn7gamA6cAK4BK4PIY1tMmewO44y3gRlcfM5yVZbu5/Y0VdMtI49opJVFveS8iySdmAeyce5fIfbzhxzjgm7GqoSP2dEF0Qh9wIzPjt189gGBdA7fMWEbp9kpuOG1cuxb8EZHEF8sWcELr7C6IRulpAW47ZwIDe2Zz55uf8eGqbfzp3IM4YGB+p76OiMQ/TUVuRrCunsz0QEy6CAIB4wcnjuZfVx5GZU09X/3rTG5+Zal20BBJMQrgZgRrG8jq5NZvU5NH9OGV7x7DtAnF3PXWZxzz+ze57dXllG6vjOnrikh8UBdEM4J19XRr50pobZGfk8EfzxnPpZOHcPvrn/Jn7+OgwQWcekB/vjSmL0P75Ma8DhHpegrgZgRrGzq9/7clBw4s4L5LJ7FmawUvLdjAS/M3cNNLS7jppSUML8xl6ugipo7py6FDexEIaOSESDJQADcjWNfQ7rWAO2JI71yunTKCa6eMYO22St5YupnXlmzioVlr+Ns7qxjUK5vzJg3m7EMGUtQjq8vrE5HOowBuRnVtfZe2gCMZ1CuHSycP5dLJQ9kdrOP1JZt47MO13DJjGbe9upwT9+/HJYcP4dBhvTSeWCQBKYCbEazr2i6I1nTvls60CcVMm1DMyrLd/OuDz3li9lpemr+BUX3zuOjwwZxyQH96d+/md6kiEiVLtHUJJk6c6GbPnh3z1zn77llkpAX419cPj/lrtVdVTT0vzFvPw++vZuG6naQFjMOH9+Kkcf2YMqqIQb1y/C5RREIi/omqFnAzqmsb6N4tvt+e7Mw0zpk0iLMnDmTJhl1MX7CB6Qs28PPnFgGLKCnM5bhRRUwZVcSkYT07dVq1iHRcfCeMj4J19QkTWGbG2AE9GDugB9edMJKVWyp4c+lm/ru8jIffW8N9764iJzONySW9OXZUEcePLqK4INvvskVSngK4GaFREPHTBxwtM6OksDslhd258ujhVNbU8d5nW3lz2WbeWlbGa0s28wuDrx40kB+eNIq+Gkkh4hsFcDNCoyASowXckpzMdKaO6cvUMX1xzvFZWQWPf/Q5D723hjeWbuIPZ49n6pi+fpcpkpISr4nXRYJ1DZ26Elo8MDNGFHXnp6eO5eXvHs2AgmyufHg2//rgc79LE0lJyZUwnairZ8J1tZLC7jx1zWSmjCzk+mcW8OiHCmGRrpa8CdMBzjmq6+p9mQnXlbIz07j3kokcN6qQnz6zgFcXb/K7JJGUogCOoLbe4VznrwUcjzLSAtx54cEcUJzPdx/7hKUbm+6bKiKxkvwJ0w7Bus7bkDMR5GSmc+8lE+neLZ2vPzxbOzeLdBEFcASduSV9oujbI4u7Lz6ETTuCfOvROdTVN/hdkkjSS52EaYPO3JI+kRw8uCc3fXV/Zq7Yym+nL/W7HJGkp3HAEezZDy6FWsCNzpk4iCUbdvLAzFWM6Z/H2RMH+V2SSNJKvYSJQrC287ekTyQ/PWUMk0t689NnFjLn8+1+lyOStBTAEVTXdf6W9IkkPS3AnRccTL/8LK7+x8esK6/yuySRpJSaCdOKvS3g1H17euZm8rdLJlJdW88l93+gkREiMZC6CdOCxmFoyT4RozWj+uVx3yUTWbu9issf/IjKmjq/SxJJKgrgCKrVAt7jsOG9+cv5BzG/tJxr/zmHWg1PE+k0SpgIUm0iRmtOHNePm75yAG8tK+NHT82noSGxdlERiVcahhZBKk7EaM0Fhw1my+4gt766nMK8bvzklDF+lySS8BTAEQRTdCJGa759/AjKdgW55+2V9Oneja8fM9zvkkQSmgI4glSeiNESM+PGM8axtSLIb6YvoXf3TP7n4IF+lyWSsJQwEewJYF2E20dawLjt3AlMLunND5+az5vLNvtdkkjCUsJEUF1bjxlkpuntiaRbehr3XHwII/vmce0jc5hfWu53SSIJSQkTQbAutBuGmfldStzKy8rgwa9NomdOBtf+cw47qmr9Lkkk4SiAIwgmyYacsVaUl8VfLjiYjTuq+dFT83FOw9NE2kIBHEGibknvh0OG9OSHJ43ilUUbeWjWar/LEUkoSpkIkmVL+q5y5VHDmTq6iN9MX8Li9drSSCRaCuAIGvuAJTqBgHHL2ePJz87g+0/O03RlkSgpZSIIdUGoBdwWvXIzuekrB7B4w07ufHOF3+WIJAQFcAShLgi9NW110v79mDZhAHe8sYJF63f4XY5I3FPKRBCsa9AsuHa68fRxFORk8oMn52tjT5FWKGUiCNbVk6WLcO3SMzeTX08bx+INO3n4vTV+lyMS1xTAEVTXqgXcESft349jRxZy66vL2bSz2u9yROKWUiaCYJ2GoXWEmfHLM8ZRU9/Ab15a4nc5InFLARxBsFYTMTpqaJ9cvnFsCc/PW8/MFVv8LkckLillItBEjM7xjSklDOmdwy+eX6SxwSIRKIAj0ESMzpGVkcbPTx3Lis27dUFOJAKlTBPOOQVwJ5o6pohjRhbyp9eWs3V30O9yROKKUqaJvbthqAuiM5gZN5w2lqqaev7wn2V+lyMSV2IWwGb2gJltNrOFzdw/xcx2mNlc7+OGWNXSFtoNo/ONKOrOpZOH8thHa1m4TjPkRBrFMmUeBE5q5Zh3nHMTvI9fxbCWqO3Zkl4t4E71nan70SsnkxufX6R1g0U8MQtg59zbwLZYPX+sBGu9LenVAu5U+dkZ/ODEUcxes53n5633uxyRuOB3yhxhZvPM7GUzG9fcQWZ2lZnNNrPZZWVlMS1ILeDYOXviIPYv7sH/TV9KZU2d3+WI+C6qADazp83sVDPrzMCeAwxxzo0H/gI829yBzrl7nXMTnXMTCwsLO7GEfVXXqg84VtICxo2nj2Pjzmr++uZnfpcj4rtoU+Yu4ALgUzP7nZmN7ugLO+d2Oud2e59PBzLMrE9Hn7ejGi/CaT3g2Jg4tBdfmTCAe99ZyedbK/0uR8RXUQWwc+4159yFwMHAauBVM5tlZpebWUZ7XtjM+pm37bCZHerVsrU9z9WZgrVeF4RawDHz45PHkB4wfjN9sd+liPgq6pQxs97AZcCVwCfAnwkF8qvNHP8o8B4wysxKzewKM7vGzK7xDjkLWGhm84DbgfNcHFwe1zC02OuXn8U3jxvBjEWbtE6EpLT0aA4ys38Do4F/AKc75zZ4dz1uZrMjPcY5d35Lz+mcuwO4ow21donGi3DqgoitK44axuMfreWXLyzipe8cTUaafuFJ6on2f/19zrmxzrn/awxfM+sG4JybGLPqfKCLcF0jKyONn546huWbdvPI+1onQlJTtClzU4Tb3uvMQuKFhqF1nRPG9uXo/fpw26taJ0JSU4sB7F0oOwTINrODzOxg72MKkNMVBXY19QF3ncZ1Iipq6vnjq8v9Lkeky7XWB3wioQtvA4Fbw27fBVwfo5p8VV2rPuCutF/fPC45YggPzlrNRYcNYeyAHn6XJNJlWmzmOececs4dB1zmnDsu7OMM59y/u6jGLhVUH3CX+97UkfTIyuCWGUv9LkWkS7XYAjazi5xzjwBDzex/m97vnLs1wsMSWrCugYBBesD8LiVl5OdkcM2xJdz8ylI+Wr2NSUN7+V2SSJdorZmX6/3bHciL8JF0qmvrycpIw5sjIl3ksslDKczrxu9fWarV0iRltNgCds7d4/37y64px3/aDcMf2ZlpfOf4Efz8uUW8tbyM40YV+V2SSMxFuxjP782sh5llmNnrZrbFzC6KdXF+0Jb0/jl30mAG9crmlleW0dCgVrAkv2ibeic453YCpwGlwEjgBzGrykfBOm1J75fM9ADfmzqSxRt28vrSzX6XIxJz0SZN44I7pwCPOucSbqH1aGlLen9NmzCAQb2yuePNFeoLlqQXbQC/YGZLgYnA62ZWCFTHriz/BOsa6KYWsG/S0wJcc2wJ89aWM+sz3xfHE4mpaJej/DFwBDDROVcLVADTYlmYX4K1DWSpBeyrMw8eSFFeN+58c4XfpYjEVFuaemOAc83sEkJLSZ4Qm5L8VV1Xrxawz7Iy0rjqmOHM+mwrcz7f7nc5IjET7SiIfwB/AI4CJnkfSbUKWqNgrYahxYPzDx1MQU6Gti6SpBbVesCEwnZsPCyYHmvBunqthBYHcrulc+kRQ/nz65+ysmw3wwu7+12SSKeLtqm3EOgXy0LiRbVawHHjosOHkJkW4O8zV/tdikhMRJs0fYDFZjbDzJ5v/IhlYX4JzYRTCzgeFOZ1Y9qEATz1cSnllTV+lyPS6aLtgrgxlkXEk9BMOLWA48UVRw/jyY9LefTDtXxjSonf5Yh0qmiHof2X0G7IGd7nHwFzYliXb4K1DVoLOI6M7teDo0b04aFZq6mtb/C7HJFOFe0oiK8DTwH3eDcVA8/GqCbfNDQ4aurVBxxvrjhqGBt3VjN9wYbWDxZJINEmzTeBI4GdAM65T4GkW66qxmthaRxwfDl2ZCHDC3O5/91Vmp4sSSXapAk65/ZcBTGzdCDpfhIad8PQTLj4EggYXztyGPNLdzB7jSZmSPKINoD/a2bXE9qc88vAk8ALsSvLH9V7dkRWCzjenHnwQApyMrj/nVV+lyLSaaJNmh8DZcAC4GpgOvCzWBXll737wakFHG+yM9O44NDB/GfxRj7fWul3OSKdItpREA2ELrpd65w7yzn3t2ScFResa9wRWS3geHTp5KGkBYy/z1IrWJJDi0ljITea2RZgKbDMzMrM7IauKa9rVasFHNf69sji9AMH8MRHa9lRVet3OSId1lpT73uERj9Mcs71ds71Ag4DjjSz/xfr4rpaYwtYw9Di19eOGkZFTT2Pf/S536WIdFhrSXMJcL5zbs/ffM65lcBF3n1JJVjnjYLQRIy4tX9xPocP78WDM1dTp4kZkuBaC+AM59yWpjc658rYu01R0qiuVQs4EVx51HDW76jm5YUb/S5FpENaS5qWVkBJutVRGlvAGoYW344fXcSwPrnc985KTcyQhNZa0ow3s50RPnYBB3RFgV1pzygIXYSLa6GJGUOZV7qDjzUxQxJYiwHsnEtzzvWI8JHnnEvCLgi1gBPFmYcMJD87g/vf1ZA0SVxKmjDBPX3AagHHu5zMdC48bDAzFmlihiQuBXCYPX3AugiXEC45YigB08QMSVxKmjB7J2LobUkE/fKzOH18aGLGzmpNzJDEo6QJE6yrJz1gpKfpbUkUV3gTMx77UBMzJPEoacKE9oPTW5JI9i/O57BhmpghiUlpEyZYV69ZcAnoyqM1MUMSkwI4jLakT0xTRxcxtHcO92nHDEkwSpswwboGuqkFnHACAeNrRw1j3tpy5nyuiRmSOBTAYYK12pI+UZ2liRmSgJQ2YarVAk5YOZnpnH/oYF5ZuJG12zQxQxKDAjiMWsCJ7dLJQ0ITM2au9rsUkagobcIE6xo0CiKB9c/P5tQD+/PEbE3MkMSgAA5TrRZwwrviqGHsDtbx+Idr/S5FpFVKmzA1moiR8A4cWMChQ3vx4CxNzJD4F7O0MbMHzGyzmS1s5n4zs9vNbIWZzTezg2NVS7TUBZEcvnbUUNaVV/Hakk1+lyLSolg29x4ETmrh/pOB/byPq4C7YlhLVNQFkRy+PLYfA3tm88C7q/0uRaRFMUsb59zbwLYWDpkGPOxC3gcKzKx/rOqJRmgtCLWAE11awLhs8lA+XL2Nhet2+F2OSLP8bO4VA+FXSkq92/ZhZleZ2Wwzm11WVhazgoJ19doNI0mcM2kQuZlpPDBTEzMkfvmZNhbhtogT+Z1z9zrnJjrnJhYWFsakmPoGR229035wSaJHVgZnHTKQF+atZ/Ouar/LEYnIzwAuBQaFfT0QWO9TLXs25FQLOHlcduQwausdj7yvtYIlPvmZNs8Dl3ijIQ4HdjjnNvhVTFC7YSSdYX1ymTq6iH++v4Zqb78/kXgSy2FojwLvAaPMrNTMrjCza8zsGu+Q6cBKYAXwN+DaWNUSjcb94DQMLbl87ahhbK2o4YV5vv1xJdKs9Fg9sXPu/Fbud8A3Y/X6bVW9Z0dktYCTyeSS3ozqm8cDM1dz1iEDMYt06UHEH0obz94dkdUCTiZmxteOGsqSDTt5f2VLoyJFup4C2KMWcPKaNqGYnjkZGpImcUdp42kM4OxMtYCTTVZGGhceNoTXlmxi1ZYKv8sR2UMB7KnWRbikdsnkIWSkBbj37c/8LkVkDwWwp6om1ALO0jjgpFSUl8XZhwzk6Y/XsWmnJmZIfFDaePZ0QagFnLSuPqaEuoYGHtC+cRInFMCeKvUBJ73BvXM47cABPPL+GnZUascM8Z8C2KMWcGq45tgSKmrqeeSDNX6XIqIAbtTYAtZFuOQ2dkAPpowq5IF3V2l6svhOAeyprtE44FTxjWNL2FpRw5OztW+c+Etp46mqrSc7I01TVVPAocN6cfDgAu55e6X2jRNfKYA91bUNugCXIsyMb0wZQen2Kl6c79sCfCIK4EZVtfVkqfshZUwdXcR+Rd25663PCK0LJdL1lDieqtp6stQCThmBgHHNsSUs27SLN5Zu9rscSVEKYE91Tb2GoKWYMyYMoLggmzvfXKFWsPhCAeyprqvXELQUk5EW4JpjhzPn83LeW7nV73IkBSmAPVVqAaeksycOojCvG3e8scLvUiQFKYA9VbUNagGnoKyMNK4+ZjizPtvKx2u2+12OpBgFsCdYW69haCnqgsMG0zMngzvfVCtYupYC2KNhaKkrJzOdK48ezhtLN7Nw3Q6/y5EUosTxVKkFnNIuPmIIeVnpagVLl1IAe3QRLrX1yMrg8slDeXnhRpZv2uV3OZIiFMBAQ4MjWKeLcKnu8iOHkZOZxl/VCpYuogBm75b0CuDU1jM3k4sPH8Lz89azWpt3ShdQABO2G4b2g0t5Vxw9jIy0AHe9pc07JfaUOGg7ItmrKC+L8w8dzNNzSlm7rdLvciTJKYDZux2RuiAEQtsWpQWMP732qd+lSJJTABO+Jb0CWKBffhaXHDGEZz4p5VONiJAYUgCjDTllX9+YMoLsjDRufXW536VIElMAoz5g2Vev3EyuPHo4Ly/cyPzScr/LkSSlACa0HRFAVroCWPa68uhhFORkcMuMZVovWGJCAUx4C1hvh+yVl5XBt44bwTufbuHNZdo1QzqfEoe9W9LrIpw0dckRQxlemMuvX1xCTZ12UJbOpQAmtBsG6CKc7CszPcDPTxvLqi0VPPzear/LkSSjAEbD0KRlx40q4rhRhfz5tU/ZsjvodzmSRBTA7O0DVgBLc3522liqauv53ctL/S5FkogCmFAAZ6YHSAuY36VInCop7M5VxwznqY9LeffTLX6XI0lCAQwEaxu0G4a06jtT92N4n1x+8sx8Kmvq/C5HkoBSB6gI1pGTme53GRLnsjLS+N2ZB7J2WxW3/kcz5KTjFMBARU0dud3U/yutO3RYLy46fDAPzFzFJ59rF2XpGAUwUBGsp3s3tYAlOj86aTT987O57ol5e0bQiLSHAphQF0SuAliilJeVwS1nH8jKLRXc/IpGRUj7KYCB3eoDljaaXNKHyyYP5cFZq5m1QqMipH0UwIT6gLurD1ja6EcnjWZ4n1y+/+Q8dlbX+l2OJCAFMFAZrFcXhLRZdmYat547gU27gvzqhcV+lyMJSAFMqAtCF+GkPSYMKuDaKSU89XEp/1m00e9yJMGkfADX1TcQrGtQH7C027eP349xA3pw/TML2Kq1IqQNUj6AK4KhYUQaByztlZke4NZzJrCzqo7rn1mgxdslajENYDM7ycyWmdkKM/txhPunmNkOM5vrfdwQy3oiqfCmlKoLQjpiVL88rjthJDMWbeKZT9b5XY4kiJgFsJmlAXcCJwNjgfPNbGyEQ99xzk3wPn4Vq3qaUxEMBbAuwklHXXn0cCYN7ckvnl/E+vIqv8uRBBDLFvChwArn3ErnXA3wGDAthq/XLruDagFL50gLGH84ezz1DY4fPjWfhgZ1RUjLYhnAxcDasK9LvduaOsLM5pnZy2Y2LtITmdlVZjbbzGaXlZV1apGNfcA52hFZOsGQ3rn89NQxvLtiC498sMbvciTOxTKAIy2u27RJMAcY4pwbD/wFeDbSEznn7nXOTXTOTSwsLOzUIhv7gNUFIZ3lgkMHc+zIQn47fQmrtlT4XY7EsVgGcCkwKOzrgcD68AOcczudc7u9z6cDGWbWJ4Y17aNCXRDSycyMm888kMy0AP/v8blU12rBHokslgH8EbCfmQ0zs0zgPOD58APMrJ+Zmff5oV49W2NY0z50EU5ioV9+FjefeSBz15Zz3RPz1B8sEcUsgJ1zdcC3gBnAEuAJ59wiM7vGzK7xDjsLWGhm84DbgfNcFw+i3O31AasFLJ3t5AP689NTxvDSgg386sXFGh8s+4hp6njdCtOb3HZ32Od3AHfEsobWVATrCBhkZaT8nBSJga8fM5yNO6u5/91VFOZ145vHjfC7JIkjKd/sq6ipIzczHa8nRKTT/fSUMWzZHeSWGcuorW/gu1P30/83ARTAWoxdYi4QMG49ZwIZaQH+9NqnVNXU8+OTRyuERQFcEazXOhASc2kB4/dnHkh2Rhr3vL2S8spabvrq/mSkqesrlaV8AGspSukqgYDxq2njKMjJ4C9vrGDDzmruvOAg8rIy/C5NfJLyv34ra7QdkXQdM+O6E0Zx85kHMHPFFs6++z027NC6Eakq5QN4t3bDEB+cO2kwf79sEqXbq/jqnbNYvH6n3yWJD1I+gCuC2g9O/HHMyEKeuPoIAM66exavLd7kc0XS1RTAGgUhPho7oAfPfetISgq78/V/zOZvb6/UhI0UkvIBvFsBLD7r2yOLJ64+gpP378dvpi/hx08voKauwe+ypAukdABX19YTrGsgP1tXocVf2Zlp3HH+wXzruBE8PnstlzzwAeWVNX6XJTGW0gFcXlkLQM+cTJ8rEQkNU/v+iaO47dzxzFlTzlfunMlqLWeZ1FI6gLd7LYyCHLWAJX589aCBPHrVYeyoquXMu2Yxb2253yVJjCiAUQBL/DlkSC+e+sZksjPTOO/e93lz2Wa/S5IYSOkA3qEuCIljJYXd+fc3JjOsTy5XPjSbJ2evbf1BklBSOoC3K4AlzhX1yOLxqw/niOG9+cFT87njjU81TC2JpHgAqwtC4l9eVgYPXDaJr0wYwB/+s5yfP7eQeu2wkRRSegBseWUNWRkBsjI0E07iW2Z6gFvPmUDfHlnc8/ZKNu0Mctu5E7SQVIJL6RZweWWtuh8kYQQCxk9OGcMvTh/L60s2Me2Od1mxeZffZUkHpHQAb6+spUABLAnm8iOH8cgVh1FeWcu0O2by3Nx1fpck7ZTSAVxeWUOBZsFJApo8og8vfedoRvfvwXcfm8u3/jWH7RWaOZdoUjqAt1fW0DNXASyJqV9+Fo9fdTjfP2EkMxZt5IQ/vc3rS7SiWiJJ6QDeUaUuCEls6WkBvnX8fjz7zSPpnZvJFQ/N5vtPzmObWsMJIWUD2DnnXYRTC1gS37gB+Tz3rSP55nElPPvJOqb+8S2emL2WBg1Xi2spG8C7gnXUNTgKstUCluTQLT2NH5w4mpe+czQjirrzw6fmc+6977Fso0ZKxKuUDeDGaciahCHJZlS/PB6/6gh+f+aBrNi8m1Nvf4f/e3kJFcE6v0uTJlI2gBtnwWkcsCSjQMA4Z9IgXr9uCv9zcDH3/Hclx/z+Te5/dxXVtfV+lyeeFA5gbx0IjYKQJNYrN5PfnzWeZ795JGP69+DXLy5myi1vceebKyjbFfS7vJSXsgG8rSL0n0+jICQVTBhUwCNXHsa/vn4YJUW53DJjGZN/9zrffvQT3v10iy7W+SRlJ5KvL68GoH9+ls+ViHSdySV9mFzShxWbd/PI+2t45pN1vDBvPQN7ZnP2IYM4e+JABhRk+11mykjZFvC68ip65WaSk5myv4MkhY0o6s6NZ4zjg+uncvv5BzG0dy63vbaco25+g28/+gkL1+3wu8SUkLLps768igEFav1KasvKSOOM8QM4Y/wA1m6r5JH31/CvDz7nhXnrmVzSm6uPLeGY/fpgZn6XmpRStwW8vYpi/aklssegXjn85JQxzPzJ8fzk5NF8VrabSx/4kNPveJdXFm5UP3EMpGQAO+e8FrACWKSpHlkZXH1sCe/88HhuPvMAdlXXcc0jH3PSn9/mubnrqKtv8LvEpJGSAbyjqpaKmnq1gEVakJke4NxJg3n9f4/lz+dNwDn47mNz+dKt/+WJj9ZSU6cg7qiUDODS7VUADOypABZpTXpagGkTipnxvWO4+6JD6J6Vzg+fns9xf3iLf7y3WhM7OiAlA3h9eSiA1QUhEr1AwDhp/3688K2j+Pvlk+iXn8XPn1vE0b9/k7ve+owtuzWxo61SchTEOi+A1QUh0nZmxnGjipgyspD3V27jzjdXcPMrS7n11WWcMLYf5x86mMklvQkENHKiNSkZwOvLq8jKCNArV7PgRNrLzDiipDdHlPRmxeZdPPbhWp6aU8pLCzZQXJDNieP6cdL+/ThkSE/SFMYRpWQAr/NGQGhso0jnGFGUx89OG8v3TxzFjEUbeW7ueh55fw0PzFxFn+6ZHD+6iMklfThseC/65+svz0YpGcBLN+xieGGu32WIJJ2sjDSmTShm2oRidlXX8tayMmYs2sjLCzfyxOxSAAbkZzGqXx4j++Uxqm8eI/vmMaxPLrndUi+OUu6Md1TWsnJLBWceMtDvUkSSWl5WBqePH8Dp4wdQ3+BYsmEn76/cyoJ1O1i2cRczV2ylJmxMcf/8LEoKu1NSmEtJUXeG9+lOSVEu/XpkJe1fqykXwHNLy4HQ6lAi0jXSAsb+xfnsX5y/57ba+gbWbK3g0027Wbmlgs827+azst38e846doUtHt+9Wzr7F/dg/KACDhpUwIRBPemXJItopV4Af16OGRw4ML/1g0UkZjLSAowoymNEUd4XbnfOUbYryIqy3awsq2D5pl3MK93BA++uorY+NB16RFF3jh9dxNTRRUwa2ithR1ykXgCv3c5+Rd3Jy9JC7CLxyMwo6pFFUY8sJpf02XN7sK6exet38vGa7by1rIy/z1zFvW+vZEB+FtMOKuZ/Dipmv755LTxz/EmpAHbOMXdtOV8e29fvUkSkjbqlp3HQ4J4cNLgnVx49nN3BOl5fsolnPlnHvW+v5K63PuOA4nzOnjiQaeOLyU+A/R5TKoDnri1ne2UtE4f08rsUEemg7t3S94y4KNsV5Pl563n641JueG4RN720hBPH9eOciQM5sqRP3HZRpFQA/33mavK6pXPKgf39LkVEOlFhXjeuOGoYVxw1jIXrdvDUx6V7dvsoLsjmzEMGcvYhAxnUK8fvUr/AnEusNT4nTpzoZs+e3ebHbdpZzZG/e4NLjhjKDaePjUFlIhJPqmvreW3JJp6YXco7n5bhHBw5ojfnTBzEieP6kZWR1pXlRGyCx7QFbGYnAX8G0oD7nHO/a3K/efefAlQClznn5nR2HbX1Dfzs2YU0OMelk4d09tOLSBzKykjjtAMHcNqBA1hXXsW/Py7liY/X8t3H5pKXlc60CQM4Z+IgDijO922cccxawGaWBiwHvgyUAh8B5zvnFocdcwrwbUIBfBjwZ+fcYS09b1tbwPUNju89PpcX5q3nl2eM49LJQ9t8LiKSHBoaHB+s2saTs9cyfeEGqmsbKC7I5rBhvTh0WC9KirpTXJBN3x5Znb1+RZe3gA8FVjjnVgKY2WPANGBx2DHTgIdd6LfA+2ZWYGb9nXMbOqsI5xwBg5+cPFrhK5LiAoG9CwjdOG0cL83fwNvLy/jv8jL+/cm6LxzbLT0Q+shIIzMtQFrA+NKYvp3ahRnLAC4G1oZ9XUqoldvaMcXAFwLYzK4CrvK+3G1my9pT0DVtf0gfYEt7XivOJMN5JMM5QHKcRzKcA7TjPN4BftG+13rFOXdS0xtjGcCRmtxN+zuiOQbn3L3AvZ1RVFuY2Wzn3MSuft3OlgznkQznAMlxHslwDhAf5xHLHTFKgUFhXw8E1rfjGBGRpBTLAP4I2M/MhplZJnAe8HyTY54HLrGQw4Edndn/KyISz2LWBeGcqzOzbwEzCA1De8A5t8jMrvHuvxuYTmgExApCw9Auj1U97dTl3R4xkgznkQznAMlxHslwDhAH55FwEzFERJJFSu6KLCISDxTAIiI+UQATmjJtZsvMbIWZ/TjC/WZmt3v3zzezg/2osyVRnMOFXu3zzWyWmY33o87WtHYeYcdNMrN6MzurK+uLRjTnYGZTzGyumS0ys/92dY3RiOL/VL6ZvWBm87zziLdrOJjZA2a22cwWNnO/vz/bzrmU/iB0gfAzYDiQCcwDxjY55hTgZULjlg8HPvC77nacw2Sgp/f5yfF2DtGeR9hxbxC6iHuW33W343tRQGhG6GDv6yK/627neVwP3Ox9XghsAzL9rr1JjccABwMLm7nf159ttYDDpkw752qAxinT4fZMmXbOvQ8UmFk8rWnZ6jk452Y557Z7X75PaMx1vInmewGh9UOeBjZ3ZXFRiuYcLgD+7Zz7HMA5l6jn4YA8b1Gt7oQCuI444px7m1BdzfH1Z1sB3Px06LYe46e21ncFod/68abV8zCzYuCrwN1dWFdbRPO9GAn0NLO3zOxjM7uky6qLXjTncQcwhtDkqQXAd51zDSQWX3+2U2pB9mZ02pRpH0Vdn5kdRyiAj4ppRe0TzXn8CfiRc64+Trcqj+Yc0oFDgKlANvCemb3vnFse6+LaIJrzOBGYCxwPlACvmtk7zrmdMa6tM/n6s60ATo4p01HVZ2YHAvcBJzvntnZRbW0RzXlMBB7zwrcPcIqZ1Tnnnu2SClsX7f+nLc65CqDCzN4GxhNavjVeRHMelwO/c6HO1BVmtgoYDXzYNSV2Cn9/tv3uJPf7g9AvoZXAMPZebBjX5JhT+WJH/Yd+192OcxhMaMbhZL/r7ch5NDn+QeLvIlw034sxwOvesTnAQmB/v2tvx3ncBdzofd4XWAf08bv2COcylOYvwvn6s53yLWCXBFOmozyHG4DewF+91mOdi7MVraI8j7gWzTk455aY2SvAfKCB0G4xEYdJ+SXK78WvgQfNbAGhAPuRcy6ulqk0s0eBKUAfMysltJpkBsTHz7amIouI+ESjIEREfKIAFhHxiQJYRMQnCmAREZ8ogEVEfKIAFhHxiQJYkpqZFZjZtc3c92A8LmcpqUMBLMmuAIgYwCJ+S/mZcJL0fgeUmNlc4FVCi98cD6wi8kIse5jZDcDp3mNmAVc7zVySTqQWsCS7HwOfOecmAO8Bo4ADgK8TWqS+JXc45yY55/YnFMKnxbJQST0KYEklxwCPOufqnXPrCe2q0ZLjzOwDb62D44FxMa9QUoq6ICTVRNWFYGZZwF+Bic65tWZ2I5AVy8Ik9agFLMluF5Dnff42cJ6ZpXnbzhzXwuMaw3aLmXUHNFpCOp1awJLUnHNbzWymtyvuy8CnhLbPWQ40uxuxc67czP7mHbsa+KgLypUUo+UoRUR8oi4IERGfqAtCUp6ZPUNo651wP3LOzfCjHkkd6oIQEfGJuiBERHyiABYR8YkCWETEJwpgERGf/H8it/l6F1CeMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=ccm.query('td_a < 1 & td_a > 0'),\n",
    "            x='td_a',\n",
    "            kind='kde').set(title='Used .query() to filter outliers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now I'll add a histogram on top:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=ccm.query('td_a < 1 & td_a > 0'),\n",
    "            x='td_a', \n",
    "            # kind=hist is the default, so I'm not even typing it\n",
    "            kde=True).set(title='kind=\"hist\", kde=True --> histogram + kde' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the distribution of leverage (`td_a`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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"
    }
   ],
   "source": [
    "sns.boxplot(data=ccm, \n",
    "            x='td_a').set(title='Outliers can distort graph until useless')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's query the dataset to eliminate outliers that distort the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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"
    }
   ],
   "source": [
    "sns.boxplot(data=ccm.query('td_a < 1 & td_a > 0'), \n",
    "            x='td_a').set(title='With query, outliers are filtered, main patterns visible')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data = ccm,\n",
    "            x = 'div_d', kde=True\n",
    "           ).set(title='div_d is a binary variable: Does the firm pay dividends?')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the counts of categorical variables, it's easy to do with pandas. \n",
    "- Note 1: You need to manually count the variable before calling the plot.\n",
    "- Note 2: What is industry 40? It's a great idea to replace the numeric category labels with the corresponding industry names (Finance)\n",
    "- Note 3: I like horizontal bars, because if you replace the industry code with the industry name, it's easier to read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
    }
   ],
   "source": [
    "# pandas built in plot \n",
    "ccm['gsector'].value_counts().plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.catplot is powerful, but it's overkill for a categorical count\n",
    "sns.catplot(data=ccm,\n",
    "            y='gsector',\n",
    "            kind='count',\n",
    "            # sns wants to use all diff colors, which is distracting  \n",
    "            # on this particular graph with no value. \n",
    "            # so I force all colors to grey by adding this:\n",
    "            color = 'grey', \n",
    "            # to sort the categories, need to tell sns the order to sort:\n",
    "            order = ccm['gsector'].value_counts().index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariances and relationships between variables\n",
    "\n",
    "To get a quick sense of relationships, I like to use I like to use `pairplot` and `heatmap` to get a quick since of relationships.\n",
    "\n",
    "### Getting the big picture with [**Pairplot**](https://seaborn.pydata.org/generated/seaborn.pairplot.html)\n",
    "\n",
    "I like passing `corner=True` or using the `x_vars` and `y_vars` parameters to make the info shown more usable. \n",
    "\n",
    "```{warning}\n",
    "With pairplot, \n",
    "1. Use 7 or fewer variables at a time. If your dataset has a lot of variables, do them part by part.\n",
    "2. Don't plot all of the data points! This will oversaturate your graphs and make it harder to draw any conclusions. Below, I randomly sample a piece of the dataset. \n",
    "```\n",
    "\n",
    "**It's clear from running these two plots that some extreme outliers are hiding patterns by messing with the scales and influencing the regression lines.**\n",
    "\n",
    "(We should deal with these outliers later.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# every time you run this, you'll get diff figures... why?!\n",
    "f1 = sns.pairplot(ccm[['capx_a', 'xrd_a', 'cash_a','td_a']].sample(500),\n",
    "                kind='reg',\n",
    "                 corner=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the \"kind\" is a histogram. In 2D, a histogram is squares with colors corresponding to frequency at that spot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2 = sns.pairplot(ccm[['capx_a', 'xrd_a', 'cash_a','td_a']].sample(500),\n",
    "                kind='hist',\n",
    "                 corner=True) # hist handles a lot of datapoints well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the big picture with [**Heatmap with correlations**](https://seaborn.pydata.org/generated/seaborn.pairplot.html)\n",
    "\n",
    "After some pairplots (and often before), I like to look at correlations.\n",
    "\n",
    "```{warning}\n",
    "This analysis step doesn't help for categorical variables!\n",
    "\n",
    "Make sure you don't include categorical variables that are numbers!\n",
    "\n",
    "(E.g. industry classifications are numbers that have no meaning.)\n",
    "```\n",
    "\n",
    "Seeing the correlations between variables is nice.\n",
    "\n",
    "A correlation table is ugly and hard to work with:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>gvkey</th>\n",
       "      <th>fyear</th>\n",
       "      <th>lpermno</th>\n",
       "      <th>sic</th>\n",
       "      <th>sic3</th>\n",
       "      <th>age</th>\n",
       "      <th>at</th>\n",
       "      <th>me</th>\n",
       "      <th>l_a</th>\n",
       "      <th>l_sale</th>\n",
       "      <th>...</th>\n",
       "      <th>tnic3hhi</th>\n",
       "      <th>tnic3tsimm</th>\n",
       "      <th>prodmktfluid</th>\n",
       "      <th>delaycon</th>\n",
       "      <th>equitydelaycon</th>\n",
       "      <th>debtdelaycon</th>\n",
       "      <th>privdelaycon</th>\n",
       "      <th>l_emp</th>\n",
       "      <th>l_ppent</th>\n",
       "      <th>l_laborratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gvkey</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.534930</td>\n",
       "      <td>0.475523</td>\n",
       "      <td>0.107321</td>\n",
       "      <td>0.107265</td>\n",
       "      <td>-0.276126</td>\n",
       "      <td>0.010126</td>\n",
       "      <td>0.014763</td>\n",
       "      <td>0.126591</td>\n",
       "      <td>0.038674</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021473</td>\n",
       "      <td>0.036015</td>\n",
       "      <td>0.244347</td>\n",
       "      <td>0.212391</td>\n",
       "      <td>0.225956</td>\n",
       "      <td>-0.110047</td>\n",
       "      <td>0.276941</td>\n",
       "      <td>-0.090190</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.103596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fyear</th>\n",
       "      <td>0.534930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402615</td>\n",
       "      <td>0.098469</td>\n",
       "      <td>0.098408</td>\n",
       "      <td>0.449878</td>\n",
       "      <td>0.080264</td>\n",
       "      <td>0.142570</td>\n",
       "      <td>0.345151</td>\n",
       "      <td>0.243784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047983</td>\n",
       "      <td>-0.084418</td>\n",
       "      <td>0.060319</td>\n",
       "      <td>-0.028163</td>\n",
       "      <td>-0.037806</td>\n",
       "      <td>0.029978</td>\n",
       "      <td>-0.048051</td>\n",
       "      <td>0.029245</td>\n",
       "      <td>0.184322</td>\n",
       "      <td>0.257928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lpermno</th>\n",
       "      <td>0.475523</td>\n",
       "      <td>0.402615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110724</td>\n",
       "      <td>0.110660</td>\n",
       "      <td>-0.195393</td>\n",
       "      <td>0.019407</td>\n",
       "      <td>-0.021205</td>\n",
       "      <td>0.029452</td>\n",
       "      <td>-0.042433</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019426</td>\n",
       "      <td>0.049461</td>\n",
       "      <td>0.168210</td>\n",
       "      <td>0.132109</td>\n",
       "      <td>0.150432</td>\n",
       "      <td>-0.039851</td>\n",
       "      <td>0.188820</td>\n",
       "      <td>-0.155718</td>\n",
       "      <td>-0.079054</td>\n",
       "      <td>0.054108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sic</th>\n",
       "      <td>0.107321</td>\n",
       "      <td>0.098469</td>\n",
       "      <td>0.110724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>-0.072601</td>\n",
       "      <td>0.036937</td>\n",
       "      <td>-0.016392</td>\n",
       "      <td>0.050434</td>\n",
       "      <td>-0.031385</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052579</td>\n",
       "      <td>0.138981</td>\n",
       "      <td>0.111862</td>\n",
       "      <td>-0.034781</td>\n",
       "      <td>-0.025027</td>\n",
       "      <td>-0.067122</td>\n",
       "      <td>0.046615</td>\n",
       "      <td>-0.030025</td>\n",
       "      <td>-0.152852</td>\n",
       "      <td>-0.223692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sic3</th>\n",
       "      <td>0.107265</td>\n",
       "      <td>0.098408</td>\n",
       "      <td>0.110660</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.072562</td>\n",
       "      <td>0.036988</td>\n",
       "      <td>-0.016361</td>\n",
       "      <td>0.050608</td>\n",
       "      <td>-0.031159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052559</td>\n",
       "      <td>0.138958</td>\n",
       "      <td>0.111700</td>\n",
       "      <td>-0.034839</td>\n",
       "      <td>-0.025132</td>\n",
       "      <td>-0.066992</td>\n",
       "      <td>0.046468</td>\n",
       "      <td>-0.029792</td>\n",
       "      <td>-0.152593</td>\n",
       "      <td>-0.223594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.276126</td>\n",
       "      <td>0.449878</td>\n",
       "      <td>-0.195393</td>\n",
       "      <td>-0.072601</td>\n",
       "      <td>-0.072562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.079051</td>\n",
       "      <td>0.191595</td>\n",
       "      <td>0.376925</td>\n",
       "      <td>0.394578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052359</td>\n",
       "      <td>-0.154524</td>\n",
       "      <td>-0.243894</td>\n",
       "      <td>-0.210383</td>\n",
       "      <td>-0.239992</td>\n",
       "      <td>0.108078</td>\n",
       "      <td>-0.296800</td>\n",
       "      <td>0.304658</td>\n",
       "      <td>0.366293</td>\n",
       "      <td>0.187915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>at</th>\n",
       "      <td>0.010126</td>\n",
       "      <td>0.080264</td>\n",
       "      <td>0.019407</td>\n",
       "      <td>0.036937</td>\n",
       "      <td>0.036988</td>\n",
       "      <td>0.079051</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.418377</td>\n",
       "      <td>0.250631</td>\n",
       "      <td>0.192991</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034427</td>\n",
       "      <td>0.026975</td>\n",
       "      <td>0.060690</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>-0.004631</td>\n",
       "      <td>0.014204</td>\n",
       "      <td>-0.031252</td>\n",
       "      <td>0.221481</td>\n",
       "      <td>0.174699</td>\n",
       "      <td>0.058018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>me</th>\n",
       "      <td>0.014763</td>\n",
       "      <td>0.142570</td>\n",
       "      <td>-0.021205</td>\n",
       "      <td>-0.016392</td>\n",
       "      <td>-0.016361</td>\n",
       "      <td>0.191595</td>\n",
       "      <td>0.418377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.359011</td>\n",
       "      <td>0.338825</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027534</td>\n",
       "      <td>-0.029282</td>\n",
       "      <td>0.013475</td>\n",
       "      <td>-0.005837</td>\n",
       "      <td>-0.010836</td>\n",
       "      <td>-0.018789</td>\n",
       "      <td>-0.018693</td>\n",
       "      <td>0.391248</td>\n",
       "      <td>0.349469</td>\n",
       "      <td>0.142494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_a</th>\n",
       "      <td>0.126591</td>\n",
       "      <td>0.345151</td>\n",
       "      <td>0.029452</td>\n",
       "      <td>0.050434</td>\n",
       "      <td>0.050608</td>\n",
       "      <td>0.376925</td>\n",
       "      <td>0.250631</td>\n",
       "      <td>0.359011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872928</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.244952</td>\n",
       "      <td>0.135873</td>\n",
       "      <td>0.070614</td>\n",
       "      <td>-0.065039</td>\n",
       "      <td>-0.139895</td>\n",
       "      <td>0.171411</td>\n",
       "      <td>-0.221926</td>\n",
       "      <td>0.718865</td>\n",
       "      <td>0.832030</td>\n",
       "      <td>0.373117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_sale</th>\n",
       "      <td>0.038674</td>\n",
       "      <td>0.243784</td>\n",
       "      <td>-0.042433</td>\n",
       "      <td>-0.031385</td>\n",
       "      <td>-0.031159</td>\n",
       "      <td>0.394578</td>\n",
       "      <td>0.192991</td>\n",
       "      <td>0.338825</td>\n",
       "      <td>0.872928</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108592</td>\n",
       "      <td>-0.162672</td>\n",
       "      <td>-0.164440</td>\n",
       "      <td>-0.174759</td>\n",
       "      <td>-0.276642</td>\n",
       "      <td>0.242195</td>\n",
       "      <td>-0.359774</td>\n",
       "      <td>0.805165</td>\n",
       "      <td>0.832674</td>\n",
       "      <td>0.196937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prof_a</th>\n",
       "      <td>-0.032376</td>\n",
       "      <td>-0.016935</td>\n",
       "      <td>-0.024876</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.036991</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>0.015373</td>\n",
       "      <td>0.076535</td>\n",
       "      <td>0.111897</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006247</td>\n",
       "      <td>-0.017040</td>\n",
       "      <td>-0.048775</td>\n",
       "      <td>-0.101483</td>\n",
       "      <td>-0.144560</td>\n",
       "      <td>0.069140</td>\n",
       "      <td>-0.155657</td>\n",
       "      <td>0.057073</td>\n",
       "      <td>0.068003</td>\n",
       "      <td>0.040532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mb</th>\n",
       "      <td>0.015236</td>\n",
       "      <td>0.024092</td>\n",
       "      <td>0.018649</td>\n",
       "      <td>0.009941</td>\n",
       "      <td>0.009921</td>\n",
       "      <td>-0.008643</td>\n",
       "      <td>-0.007318</td>\n",
       "      <td>0.007431</td>\n",
       "      <td>-0.072881</td>\n",
       "      <td>-0.053002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004663</td>\n",
       "      <td>-0.017202</td>\n",
       "      <td>0.012462</td>\n",
       "      <td>0.114049</td>\n",
       "      <td>0.168112</td>\n",
       "      <td>-0.140150</td>\n",
       "      <td>0.215779</td>\n",
       "      <td>-0.038118</td>\n",
       "      <td>-0.055769</td>\n",
       "      <td>-0.052968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ppe_a</th>\n",
       "      <td>-0.135813</td>\n",
       "      <td>-0.171166</td>\n",
       "      <td>-0.127377</td>\n",
       "      <td>-0.265391</td>\n",
       "      <td>-0.265214</td>\n",
       "      <td>0.021262</td>\n",
       "      <td>-0.044536</td>\n",
       "      <td>0.014267</td>\n",
       "      <td>0.046263</td>\n",
       "      <td>0.111666</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010736</td>\n",
       "      <td>-0.274219</td>\n",
       "      <td>-0.140591</td>\n",
       "      <td>0.011186</td>\n",
       "      <td>-0.049586</td>\n",
       "      <td>0.180126</td>\n",
       "      <td>-0.179432</td>\n",
       "      <td>0.160349</td>\n",
       "      <td>0.487191</td>\n",
       "      <td>0.611810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capx_a</th>\n",
       "      <td>-0.066717</td>\n",
       "      <td>-0.163322</td>\n",
       "      <td>-0.048126</td>\n",
       "      <td>-0.128922</td>\n",
       "      <td>-0.128830</td>\n",
       "      <td>-0.126680</td>\n",
       "      <td>-0.034281</td>\n",
       "      <td>-0.014750</td>\n",
       "      <td>-0.081184</td>\n",
       "      <td>-0.062048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025464</td>\n",
       "      <td>-0.112381</td>\n",
       "      <td>-0.014537</td>\n",
       "      <td>0.069741</td>\n",
       "      <td>0.042308</td>\n",
       "      <td>0.041066</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.012417</td>\n",
       "      <td>0.147684</td>\n",
       "      <td>0.295649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xrd_a</th>\n",
       "      <td>0.092295</td>\n",
       "      <td>0.080038</td>\n",
       "      <td>0.064314</td>\n",
       "      <td>-0.056463</td>\n",
       "      <td>-0.056631</td>\n",
       "      <td>-0.048923</td>\n",
       "      <td>-0.019533</td>\n",
       "      <td>-0.017695</td>\n",
       "      <td>-0.180619</td>\n",
       "      <td>-0.214395</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042739</td>\n",
       "      <td>0.009194</td>\n",
       "      <td>0.169561</td>\n",
       "      <td>0.179756</td>\n",
       "      <td>0.256839</td>\n",
       "      <td>-0.156769</td>\n",
       "      <td>0.285156</td>\n",
       "      <td>-0.130468</td>\n",
       "      <td>-0.166178</td>\n",
       "      <td>-0.072052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cash_a</th>\n",
       "      <td>0.224050</td>\n",
       "      <td>0.153596</td>\n",
       "      <td>0.142805</td>\n",
       "      <td>0.039442</td>\n",
       "      <td>0.039296</td>\n",
       "      <td>-0.120339</td>\n",
       "      <td>-0.024931</td>\n",
       "      <td>-0.029058</td>\n",
       "      <td>-0.240905</td>\n",
       "      <td>-0.303408</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001263</td>\n",
       "      <td>-0.061901</td>\n",
       "      <td>0.231409</td>\n",
       "      <td>0.250270</td>\n",
       "      <td>0.356693</td>\n",
       "      <td>-0.398662</td>\n",
       "      <td>0.491385</td>\n",
       "      <td>-0.242722</td>\n",
       "      <td>-0.316222</td>\n",
       "      <td>-0.156323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>div_d</th>\n",
       "      <td>-0.143810</td>\n",
       "      <td>-0.105791</td>\n",
       "      <td>-0.176155</td>\n",
       "      <td>-0.034704</td>\n",
       "      <td>-0.034589</td>\n",
       "      <td>0.209333</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.133055</td>\n",
       "      <td>0.434820</td>\n",
       "      <td>0.411841</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.097187</td>\n",
       "      <td>0.125090</td>\n",
       "      <td>-0.061720</td>\n",
       "      <td>-0.124230</td>\n",
       "      <td>-0.146363</td>\n",
       "      <td>0.095661</td>\n",
       "      <td>-0.188719</td>\n",
       "      <td>0.378149</td>\n",
       "      <td>0.409395</td>\n",
       "      <td>0.127227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>td</th>\n",
       "      <td>0.003857</td>\n",
       "      <td>0.068688</td>\n",
       "      <td>0.011306</td>\n",
       "      <td>0.033958</td>\n",
       "      <td>0.033992</td>\n",
       "      <td>0.078200</td>\n",
       "      <td>0.830751</td>\n",
       "      <td>0.361632</td>\n",
       "      <td>0.224592</td>\n",
       "      <td>0.173297</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019110</td>\n",
       "      <td>0.015334</td>\n",
       "      <td>0.048355</td>\n",
       "      <td>0.013929</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.017482</td>\n",
       "      <td>-0.017984</td>\n",
       "      <td>0.194655</td>\n",
       "      <td>0.149301</td>\n",
       "      <td>0.067114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>td_a</th>\n",
       "      <td>-0.041912</td>\n",
       "      <td>-0.058602</td>\n",
       "      <td>-0.025629</td>\n",
       "      <td>-0.007453</td>\n",
       "      <td>-0.007468</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.005640</td>\n",
       "      <td>-0.005687</td>\n",
       "      <td>0.036377</td>\n",
       "      <td>0.044912</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001006</td>\n",
       "      <td>-0.067247</td>\n",
       "      <td>-0.014955</td>\n",
       "      <td>0.013493</td>\n",
       "      <td>-0.003257</td>\n",
       "      <td>0.179656</td>\n",
       "      <td>-0.090580</td>\n",
       "      <td>0.038511</td>\n",
       "      <td>0.104232</td>\n",
       "      <td>0.156660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>td_mv</th>\n",
       "      <td>-0.095096</td>\n",
       "      <td>-0.136315</td>\n",
       "      <td>-0.078023</td>\n",
       "      <td>0.058698</td>\n",
       "      <td>0.058789</td>\n",
       "      <td>0.008723</td>\n",
       "      <td>0.094805</td>\n",
       "      <td>-0.027080</td>\n",
       "      <td>0.264727</td>\n",
       "      <td>0.180944</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083947</td>\n",
       "      <td>0.224841</td>\n",
       "      <td>0.046017</td>\n",
       "      <td>-0.053045</td>\n",
       "      <td>-0.115435</td>\n",
       "      <td>0.356015</td>\n",
       "      <td>-0.276715</td>\n",
       "      <td>0.127690</td>\n",
       "      <td>0.225602</td>\n",
       "      <td>0.158872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dltt_a</th>\n",
       "      <td>-0.025243</td>\n",
       "      <td>-0.027216</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>-0.016189</td>\n",
       "      <td>-0.016199</td>\n",
       "      <td>0.034247</td>\n",
       "      <td>-0.006857</td>\n",
       "      <td>-0.004115</td>\n",
       "      <td>0.088419</td>\n",
       "      <td>0.091396</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019466</td>\n",
       "      <td>-0.093693</td>\n",
       "      <td>-0.017095</td>\n",
       "      <td>0.016273</td>\n",
       "      <td>-0.009877</td>\n",
       "      <td>0.185298</td>\n",
       "      <td>-0.099023</td>\n",
       "      <td>0.069760</td>\n",
       "      <td>0.168018</td>\n",
       "      <td>0.214947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dv_a</th>\n",
       "      <td>0.000433</td>\n",
       "      <td>0.002155</td>\n",
       "      <td>-0.006613</td>\n",
       "      <td>0.011560</td>\n",
       "      <td>0.011547</td>\n",
       "      <td>0.017824</td>\n",
       "      <td>-0.002553</td>\n",
       "      <td>0.008590</td>\n",
       "      <td>-0.002643</td>\n",
       "      <td>0.010263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020645</td>\n",
       "      <td>-0.013909</td>\n",
       "      <td>-0.015901</td>\n",
       "      <td>-0.016225</td>\n",
       "      <td>-0.023186</td>\n",
       "      <td>-0.008109</td>\n",
       "      <td>-0.027834</td>\n",
       "      <td>0.004705</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.029392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>invopps_FG09</th>\n",
       "      <td>0.014087</td>\n",
       "      <td>0.025920</td>\n",
       "      <td>0.019696</td>\n",
       "      <td>0.013651</td>\n",
       "      <td>0.013632</td>\n",
       "      <td>-0.008012</td>\n",
       "      <td>-0.009023</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>-0.068388</td>\n",
       "      <td>-0.053791</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000954</td>\n",
       "      <td>0.017745</td>\n",
       "      <td>0.018953</td>\n",
       "      <td>0.119372</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-0.153368</td>\n",
       "      <td>0.230717</td>\n",
       "      <td>-0.040975</td>\n",
       "      <td>-0.057058</td>\n",
       "      <td>-0.047338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales_g</th>\n",
       "      <td>0.002118</td>\n",
       "      <td>-0.000244</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>-0.000393</td>\n",
       "      <td>-0.000395</td>\n",
       "      <td>-0.006840</td>\n",
       "      <td>-0.001200</td>\n",
       "      <td>-0.001852</td>\n",
       "      <td>-0.009331</td>\n",
       "      <td>-0.010363</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.000550</td>\n",
       "      <td>0.009896</td>\n",
       "      <td>-0.001228</td>\n",
       "      <td>0.001472</td>\n",
       "      <td>-0.008093</td>\n",
       "      <td>0.007809</td>\n",
       "      <td>-0.008432</td>\n",
       "      <td>-0.009231</td>\n",
       "      <td>0.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short_debt</th>\n",
       "      <td>0.017008</td>\n",
       "      <td>-0.031044</td>\n",
       "      <td>0.031285</td>\n",
       "      <td>0.076769</td>\n",
       "      <td>0.076772</td>\n",
       "      <td>-0.112489</td>\n",
       "      <td>0.025343</td>\n",
       "      <td>-0.023332</td>\n",
       "      <td>-0.195373</td>\n",
       "      <td>-0.231229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056781</td>\n",
       "      <td>0.135594</td>\n",
       "      <td>0.030166</td>\n",
       "      <td>0.020471</td>\n",
       "      <td>0.063244</td>\n",
       "      <td>-0.129376</td>\n",
       "      <td>0.124318</td>\n",
       "      <td>-0.177918</td>\n",
       "      <td>-0.307513</td>\n",
       "      <td>-0.216896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_debt_dum</th>\n",
       "      <td>-0.120833</td>\n",
       "      <td>-0.118462</td>\n",
       "      <td>-0.097384</td>\n",
       "      <td>-0.048407</td>\n",
       "      <td>-0.048333</td>\n",
       "      <td>0.057693</td>\n",
       "      <td>0.039313</td>\n",
       "      <td>0.054351</td>\n",
       "      <td>0.247078</td>\n",
       "      <td>0.254331</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051041</td>\n",
       "      <td>0.064945</td>\n",
       "      <td>-0.015834</td>\n",
       "      <td>-0.058475</td>\n",
       "      <td>-0.097171</td>\n",
       "      <td>0.251156</td>\n",
       "      <td>-0.196334</td>\n",
       "      <td>0.229960</td>\n",
       "      <td>0.307833</td>\n",
       "      <td>0.141860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atr</th>\n",
       "      <td>0.055605</td>\n",
       "      <td>-0.021006</td>\n",
       "      <td>0.048001</td>\n",
       "      <td>-0.057143</td>\n",
       "      <td>-0.057163</td>\n",
       "      <td>-0.130810</td>\n",
       "      <td>-0.037131</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>-0.292325</td>\n",
       "      <td>-0.280625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080345</td>\n",
       "      <td>-0.100169</td>\n",
       "      <td>0.119474</td>\n",
       "      <td>0.184233</td>\n",
       "      <td>0.242827</td>\n",
       "      <td>-0.087503</td>\n",
       "      <td>0.254502</td>\n",
       "      <td>-0.176234</td>\n",
       "      <td>-0.205942</td>\n",
       "      <td>-0.049847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smalltaxlosscarry</th>\n",
       "      <td>0.121731</td>\n",
       "      <td>0.228745</td>\n",
       "      <td>0.087134</td>\n",
       "      <td>0.011192</td>\n",
       "      <td>0.011204</td>\n",
       "      <td>0.144917</td>\n",
       "      <td>0.050115</td>\n",
       "      <td>0.090566</td>\n",
       "      <td>0.198132</td>\n",
       "      <td>0.181862</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014424</td>\n",
       "      <td>-0.055757</td>\n",
       "      <td>-0.032283</td>\n",
       "      <td>-0.042433</td>\n",
       "      <td>-0.053955</td>\n",
       "      <td>0.006375</td>\n",
       "      <td>-0.043721</td>\n",
       "      <td>0.125611</td>\n",
       "      <td>0.167890</td>\n",
       "      <td>0.094071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>largetaxlosscarry</th>\n",
       "      <td>0.131454</td>\n",
       "      <td>0.176323</td>\n",
       "      <td>0.103508</td>\n",
       "      <td>-0.010460</td>\n",
       "      <td>-0.010525</td>\n",
       "      <td>-0.011025</td>\n",
       "      <td>-0.030574</td>\n",
       "      <td>-0.068973</td>\n",
       "      <td>-0.263600</td>\n",
       "      <td>-0.311908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064159</td>\n",
       "      <td>0.109787</td>\n",
       "      <td>0.180862</td>\n",
       "      <td>0.158367</td>\n",
       "      <td>0.210634</td>\n",
       "      <td>-0.070300</td>\n",
       "      <td>0.219635</td>\n",
       "      <td>-0.242570</td>\n",
       "      <td>-0.237480</td>\n",
       "      <td>0.027022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tnic3hhi</th>\n",
       "      <td>-0.021473</td>\n",
       "      <td>0.047983</td>\n",
       "      <td>-0.019426</td>\n",
       "      <td>-0.052579</td>\n",
       "      <td>-0.052559</td>\n",
       "      <td>0.052359</td>\n",
       "      <td>-0.034427</td>\n",
       "      <td>-0.027534</td>\n",
       "      <td>-0.244952</td>\n",
       "      <td>-0.108592</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.301292</td>\n",
       "      <td>-0.310163</td>\n",
       "      <td>-0.114673</td>\n",
       "      <td>-0.116476</td>\n",
       "      <td>0.051374</td>\n",
       "      <td>-0.106777</td>\n",
       "      <td>-0.053393</td>\n",
       "      <td>-0.129702</td>\n",
       "      <td>-0.132895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tnic3tsimm</th>\n",
       "      <td>0.036015</td>\n",
       "      <td>-0.084418</td>\n",
       "      <td>0.049461</td>\n",
       "      <td>0.138981</td>\n",
       "      <td>0.138958</td>\n",
       "      <td>-0.154524</td>\n",
       "      <td>0.026975</td>\n",
       "      <td>-0.029282</td>\n",
       "      <td>0.135873</td>\n",
       "      <td>-0.162672</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.301292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.396461</td>\n",
       "      <td>0.303963</td>\n",
       "      <td>0.385465</td>\n",
       "      <td>-0.180947</td>\n",
       "      <td>0.382518</td>\n",
       "      <td>-0.165729</td>\n",
       "      <td>-0.149036</td>\n",
       "      <td>0.048841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prodmktfluid</th>\n",
       "      <td>0.244347</td>\n",
       "      <td>0.060319</td>\n",
       "      <td>0.168210</td>\n",
       "      <td>0.111862</td>\n",
       "      <td>0.111700</td>\n",
       "      <td>-0.243894</td>\n",
       "      <td>0.060690</td>\n",
       "      <td>0.013475</td>\n",
       "      <td>0.070614</td>\n",
       "      <td>-0.164440</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.310163</td>\n",
       "      <td>0.396461</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.317672</td>\n",
       "      <td>0.383794</td>\n",
       "      <td>-0.179151</td>\n",
       "      <td>0.396171</td>\n",
       "      <td>-0.192621</td>\n",
       "      <td>-0.108475</td>\n",
       "      <td>0.119382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delaycon</th>\n",
       "      <td>0.212391</td>\n",
       "      <td>-0.028163</td>\n",
       "      <td>0.132109</td>\n",
       "      <td>-0.034781</td>\n",
       "      <td>-0.034839</td>\n",
       "      <td>-0.210383</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>-0.005837</td>\n",
       "      <td>-0.065039</td>\n",
       "      <td>-0.174759</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.114673</td>\n",
       "      <td>0.303963</td>\n",
       "      <td>0.317672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910246</td>\n",
       "      <td>-0.129176</td>\n",
       "      <td>0.530678</td>\n",
       "      <td>-0.122729</td>\n",
       "      <td>-0.067429</td>\n",
       "      <td>0.095262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equitydelaycon</th>\n",
       "      <td>0.225956</td>\n",
       "      <td>-0.037806</td>\n",
       "      <td>0.150432</td>\n",
       "      <td>-0.025027</td>\n",
       "      <td>-0.025132</td>\n",
       "      <td>-0.239992</td>\n",
       "      <td>-0.004631</td>\n",
       "      <td>-0.010836</td>\n",
       "      <td>-0.139895</td>\n",
       "      <td>-0.276642</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116476</td>\n",
       "      <td>0.385465</td>\n",
       "      <td>0.383794</td>\n",
       "      <td>0.910246</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.199101</td>\n",
       "      <td>0.723611</td>\n",
       "      <td>-0.184878</td>\n",
       "      <td>-0.155000</td>\n",
       "      <td>0.064953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debtdelaycon</th>\n",
       "      <td>-0.110047</td>\n",
       "      <td>0.029978</td>\n",
       "      <td>-0.039851</td>\n",
       "      <td>-0.067122</td>\n",
       "      <td>-0.066992</td>\n",
       "      <td>0.108078</td>\n",
       "      <td>0.014204</td>\n",
       "      <td>-0.018789</td>\n",
       "      <td>0.171411</td>\n",
       "      <td>0.242195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051374</td>\n",
       "      <td>-0.180947</td>\n",
       "      <td>-0.179151</td>\n",
       "      <td>-0.129176</td>\n",
       "      <td>-0.199101</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.490911</td>\n",
       "      <td>0.180458</td>\n",
       "      <td>0.221762</td>\n",
       "      <td>0.067969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>privdelaycon</th>\n",
       "      <td>0.276941</td>\n",
       "      <td>-0.048051</td>\n",
       "      <td>0.188820</td>\n",
       "      <td>0.046615</td>\n",
       "      <td>0.046468</td>\n",
       "      <td>-0.296800</td>\n",
       "      <td>-0.031252</td>\n",
       "      <td>-0.018693</td>\n",
       "      <td>-0.221926</td>\n",
       "      <td>-0.359774</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.106777</td>\n",
       "      <td>0.382518</td>\n",
       "      <td>0.396171</td>\n",
       "      <td>0.530678</td>\n",
       "      <td>0.723611</td>\n",
       "      <td>-0.490911</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.257807</td>\n",
       "      <td>-0.276021</td>\n",
       "      <td>-0.030851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_emp</th>\n",
       "      <td>-0.090190</td>\n",
       "      <td>0.029245</td>\n",
       "      <td>-0.155718</td>\n",
       "      <td>-0.030025</td>\n",
       "      <td>-0.029792</td>\n",
       "      <td>0.304658</td>\n",
       "      <td>0.221481</td>\n",
       "      <td>0.391248</td>\n",
       "      <td>0.718865</td>\n",
       "      <td>0.805165</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053393</td>\n",
       "      <td>-0.165729</td>\n",
       "      <td>-0.192621</td>\n",
       "      <td>-0.122729</td>\n",
       "      <td>-0.184878</td>\n",
       "      <td>0.180458</td>\n",
       "      <td>-0.257807</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.787591</td>\n",
       "      <td>0.028813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_ppent</th>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.184322</td>\n",
       "      <td>-0.079054</td>\n",
       "      <td>-0.152852</td>\n",
       "      <td>-0.152593</td>\n",
       "      <td>0.366293</td>\n",
       "      <td>0.174699</td>\n",
       "      <td>0.349469</td>\n",
       "      <td>0.832030</td>\n",
       "      <td>0.832674</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129702</td>\n",
       "      <td>-0.149036</td>\n",
       "      <td>-0.108475</td>\n",
       "      <td>-0.067429</td>\n",
       "      <td>-0.155000</td>\n",
       "      <td>0.221762</td>\n",
       "      <td>-0.276021</td>\n",
       "      <td>0.787591</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.556455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l_laborratio</th>\n",
       "      <td>0.103596</td>\n",
       "      <td>0.257928</td>\n",
       "      <td>0.054108</td>\n",
       "      <td>-0.223692</td>\n",
       "      <td>-0.223594</td>\n",
       "      <td>0.187915</td>\n",
       "      <td>0.058018</td>\n",
       "      <td>0.142494</td>\n",
       "      <td>0.373117</td>\n",
       "      <td>0.196937</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.132895</td>\n",
       "      <td>0.048841</td>\n",
       "      <td>0.119382</td>\n",
       "      <td>0.095262</td>\n",
       "      <td>0.064953</td>\n",
       "      <td>0.067969</td>\n",
       "      <td>-0.030851</td>\n",
       "      <td>0.028813</td>\n",
       "      <td>0.556455</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      gvkey     fyear   lpermno       sic      sic3       age  \\\n",
       "gvkey              1.000000  0.534930  0.475523  0.107321  0.107265 -0.276126   \n",
       "fyear              0.534930  1.000000  0.402615  0.098469  0.098408  0.449878   \n",
       "lpermno            0.475523  0.402615  1.000000  0.110724  0.110660 -0.195393   \n",
       "sic                0.107321  0.098469  0.110724  1.000000  0.999999 -0.072601   \n",
       "sic3               0.107265  0.098408  0.110660  0.999999  1.000000 -0.072562   \n",
       "age               -0.276126  0.449878 -0.195393 -0.072601 -0.072562  1.000000   \n",
       "at                 0.010126  0.080264  0.019407  0.036937  0.036988  0.079051   \n",
       "me                 0.014763  0.142570 -0.021205 -0.016392 -0.016361  0.191595   \n",
       "l_a                0.126591  0.345151  0.029452  0.050434  0.050608  0.376925   \n",
       "l_sale             0.038674  0.243784 -0.042433 -0.031385 -0.031159  0.394578   \n",
       "prof_a            -0.032376 -0.016935 -0.024876  0.000183  0.000213  0.036991   \n",
       "mb                 0.015236  0.024092  0.018649  0.009941  0.009921 -0.008643   \n",
       "ppe_a             -0.135813 -0.171166 -0.127377 -0.265391 -0.265214  0.021262   \n",
       "capx_a            -0.066717 -0.163322 -0.048126 -0.128922 -0.128830 -0.126680   \n",
       "xrd_a              0.092295  0.080038  0.064314 -0.056463 -0.056631 -0.048923   \n",
       "cash_a             0.224050  0.153596  0.142805  0.039442  0.039296 -0.120339   \n",
       "div_d             -0.143810 -0.105791 -0.176155 -0.034704 -0.034589  0.209333   \n",
       "td                 0.003857  0.068688  0.011306  0.033958  0.033992  0.078200   \n",
       "td_a              -0.041912 -0.058602 -0.025629 -0.007453 -0.007468  0.001203   \n",
       "td_mv             -0.095096 -0.136315 -0.078023  0.058698  0.058789  0.008723   \n",
       "dltt_a            -0.025243 -0.027216 -0.017234 -0.016189 -0.016199  0.034247   \n",
       "dv_a               0.000433  0.002155 -0.006613  0.011560  0.011547  0.017824   \n",
       "invopps_FG09       0.014087  0.025920  0.019696  0.013651  0.013632 -0.008012   \n",
       "sales_g            0.002118 -0.000244  0.003401 -0.000393 -0.000395 -0.006840   \n",
       "short_debt         0.017008 -0.031044  0.031285  0.076769  0.076772 -0.112489   \n",
       "long_debt_dum     -0.120833 -0.118462 -0.097384 -0.048407 -0.048333  0.057693   \n",
       "atr                0.055605 -0.021006  0.048001 -0.057143 -0.057163 -0.130810   \n",
       "smalltaxlosscarry  0.121731  0.228745  0.087134  0.011192  0.011204  0.144917   \n",
       "largetaxlosscarry  0.131454  0.176323  0.103508 -0.010460 -0.010525 -0.011025   \n",
       "tnic3hhi          -0.021473  0.047983 -0.019426 -0.052579 -0.052559  0.052359   \n",
       "tnic3tsimm         0.036015 -0.084418  0.049461  0.138981  0.138958 -0.154524   \n",
       "prodmktfluid       0.244347  0.060319  0.168210  0.111862  0.111700 -0.243894   \n",
       "delaycon           0.212391 -0.028163  0.132109 -0.034781 -0.034839 -0.210383   \n",
       "equitydelaycon     0.225956 -0.037806  0.150432 -0.025027 -0.025132 -0.239992   \n",
       "debtdelaycon      -0.110047  0.029978 -0.039851 -0.067122 -0.066992  0.108078   \n",
       "privdelaycon       0.276941 -0.048051  0.188820  0.046615  0.046468 -0.296800   \n",
       "l_emp             -0.090190  0.029245 -0.155718 -0.030025 -0.029792  0.304658   \n",
       "l_ppent            0.002419  0.184322 -0.079054 -0.152852 -0.152593  0.366293   \n",
       "l_laborratio       0.103596  0.257928  0.054108 -0.223692 -0.223594  0.187915   \n",
       "\n",
       "                         at        me       l_a    l_sale  ...  tnic3hhi  \\\n",
       "gvkey              0.010126  0.014763  0.126591  0.038674  ... -0.021473   \n",
       "fyear              0.080264  0.142570  0.345151  0.243784  ...  0.047983   \n",
       "lpermno            0.019407 -0.021205  0.029452 -0.042433  ... -0.019426   \n",
       "sic                0.036937 -0.016392  0.050434 -0.031385  ... -0.052579   \n",
       "sic3               0.036988 -0.016361  0.050608 -0.031159  ... -0.052559   \n",
       "age                0.079051  0.191595  0.376925  0.394578  ...  0.052359   \n",
       "at                 1.000000  0.418377  0.250631  0.192991  ... -0.034427   \n",
       "me                 0.418377  1.000000  0.359011  0.338825  ... -0.027534   \n",
       "l_a                0.250631  0.359011  1.000000  0.872928  ... -0.244952   \n",
       "l_sale             0.192991  0.338825  0.872928  1.000000  ... -0.108592   \n",
       "prof_a             0.001021  0.015373  0.076535  0.111897  ... -0.006247   \n",
       "mb                -0.007318  0.007431 -0.072881 -0.053002  ...  0.004663   \n",
       "ppe_a             -0.044536  0.014267  0.046263  0.111666  ... -0.010736   \n",
       "capx_a            -0.034281 -0.014750 -0.081184 -0.062048  ... -0.025464   \n",
       "xrd_a             -0.019533 -0.017695 -0.180619 -0.214395  ... -0.042739   \n",
       "cash_a            -0.024931 -0.029058 -0.240905 -0.303408  ... -0.001263   \n",
       "div_d              0.076352  0.133055  0.434820  0.411841  ... -0.097187   \n",
       "td                 0.830751  0.361632  0.224592  0.173297  ... -0.019110   \n",
       "td_a               0.005640 -0.005687  0.036377  0.044912  ... -0.001006   \n",
       "td_mv              0.094805 -0.027080  0.264727  0.180944  ... -0.083947   \n",
       "dltt_a            -0.006857 -0.004115  0.088419  0.091396  ... -0.019466   \n",
       "dv_a              -0.002553  0.008590 -0.002643  0.010263  ...  0.020645   \n",
       "invopps_FG09      -0.009023  0.005050 -0.068388 -0.053791  ... -0.000954   \n",
       "sales_g           -0.001200 -0.001852 -0.009331 -0.010363  ... -0.001085   \n",
       "short_debt         0.025343 -0.023332 -0.195373 -0.231229  ...  0.056781   \n",
       "long_debt_dum      0.039313  0.054351  0.247078  0.254331  ... -0.051041   \n",
       "atr               -0.037131 -0.076904 -0.292325 -0.280625  ...  0.080345   \n",
       "smalltaxlosscarry  0.050115  0.090566  0.198132  0.181862  ... -0.014424   \n",
       "largetaxlosscarry -0.030574 -0.068973 -0.263600 -0.311908  ...  0.064159   \n",
       "tnic3hhi          -0.034427 -0.027534 -0.244952 -0.108592  ...  1.000000   \n",
       "tnic3tsimm         0.026975 -0.029282  0.135873 -0.162672  ... -0.301292   \n",
       "prodmktfluid       0.060690  0.013475  0.070614 -0.164440  ... -0.310163   \n",
       "delaycon           0.004913 -0.005837 -0.065039 -0.174759  ... -0.114673   \n",
       "equitydelaycon    -0.004631 -0.010836 -0.139895 -0.276642  ... -0.116476   \n",
       "debtdelaycon       0.014204 -0.018789  0.171411  0.242195  ...  0.051374   \n",
       "privdelaycon      -0.031252 -0.018693 -0.221926 -0.359774  ... -0.106777   \n",
       "l_emp              0.221481  0.391248  0.718865  0.805165  ... -0.053393   \n",
       "l_ppent            0.174699  0.349469  0.832030  0.832674  ... -0.129702   \n",
       "l_laborratio       0.058018  0.142494  0.373117  0.196937  ... -0.132895   \n",
       "\n",
       "                   tnic3tsimm  prodmktfluid  delaycon  equitydelaycon  \\\n",
       "gvkey                0.036015      0.244347  0.212391        0.225956   \n",
       "fyear               -0.084418      0.060319 -0.028163       -0.037806   \n",
       "lpermno              0.049461      0.168210  0.132109        0.150432   \n",
       "sic                  0.138981      0.111862 -0.034781       -0.025027   \n",
       "sic3                 0.138958      0.111700 -0.034839       -0.025132   \n",
       "age                 -0.154524     -0.243894 -0.210383       -0.239992   \n",
       "at                   0.026975      0.060690  0.004913       -0.004631   \n",
       "me                  -0.029282      0.013475 -0.005837       -0.010836   \n",
       "l_a                  0.135873      0.070614 -0.065039       -0.139895   \n",
       "l_sale              -0.162672     -0.164440 -0.174759       -0.276642   \n",
       "prof_a              -0.017040     -0.048775 -0.101483       -0.144560   \n",
       "mb                  -0.017202      0.012462  0.114049        0.168112   \n",
       "ppe_a               -0.274219     -0.140591  0.011186       -0.049586   \n",
       "capx_a              -0.112381     -0.014537  0.069741        0.042308   \n",
       "xrd_a                0.009194      0.169561  0.179756        0.256839   \n",
       "cash_a              -0.061901      0.231409  0.250270        0.356693   \n",
       "div_d                0.125090     -0.061720 -0.124230       -0.146363   \n",
       "td                   0.015334      0.048355  0.013929        0.006329   \n",
       "td_a                -0.067247     -0.014955  0.013493       -0.003257   \n",
       "td_mv                0.224841      0.046017 -0.053045       -0.115435   \n",
       "dltt_a              -0.093693     -0.017095  0.016273       -0.009877   \n",
       "dv_a                -0.013909     -0.015901 -0.016225       -0.023186   \n",
       "invopps_FG09         0.017745      0.018953  0.119372        0.177258   \n",
       "sales_g             -0.000550      0.009896 -0.001228        0.001472   \n",
       "short_debt           0.135594      0.030166  0.020471        0.063244   \n",
       "long_debt_dum        0.064945     -0.015834 -0.058475       -0.097171   \n",
       "atr                 -0.100169      0.119474  0.184233        0.242827   \n",
       "smalltaxlosscarry   -0.055757     -0.032283 -0.042433       -0.053955   \n",
       "largetaxlosscarry    0.109787      0.180862  0.158367        0.210634   \n",
       "tnic3hhi            -0.301292     -0.310163 -0.114673       -0.116476   \n",
       "tnic3tsimm           1.000000      0.396461  0.303963        0.385465   \n",
       "prodmktfluid         0.396461      1.000000  0.317672        0.383794   \n",
       "delaycon             0.303963      0.317672  1.000000        0.910246   \n",
       "equitydelaycon       0.385465      0.383794  0.910246        1.000000   \n",
       "debtdelaycon        -0.180947     -0.179151 -0.129176       -0.199101   \n",
       "privdelaycon         0.382518      0.396171  0.530678        0.723611   \n",
       "l_emp               -0.165729     -0.192621 -0.122729       -0.184878   \n",
       "l_ppent             -0.149036     -0.108475 -0.067429       -0.155000   \n",
       "l_laborratio         0.048841      0.119382  0.095262        0.064953   \n",
       "\n",
       "                   debtdelaycon  privdelaycon     l_emp   l_ppent  \\\n",
       "gvkey                 -0.110047      0.276941 -0.090190  0.002419   \n",
       "fyear                  0.029978     -0.048051  0.029245  0.184322   \n",
       "lpermno               -0.039851      0.188820 -0.155718 -0.079054   \n",
       "sic                   -0.067122      0.046615 -0.030025 -0.152852   \n",
       "sic3                  -0.066992      0.046468 -0.029792 -0.152593   \n",
       "age                    0.108078     -0.296800  0.304658  0.366293   \n",
       "at                     0.014204     -0.031252  0.221481  0.174699   \n",
       "me                    -0.018789     -0.018693  0.391248  0.349469   \n",
       "l_a                    0.171411     -0.221926  0.718865  0.832030   \n",
       "l_sale                 0.242195     -0.359774  0.805165  0.832674   \n",
       "prof_a                 0.069140     -0.155657  0.057073  0.068003   \n",
       "mb                    -0.140150      0.215779 -0.038118 -0.055769   \n",
       "ppe_a                  0.180126     -0.179432  0.160349  0.487191   \n",
       "capx_a                 0.041066      0.000323  0.012417  0.147684   \n",
       "xrd_a                 -0.156769      0.285156 -0.130468 -0.166178   \n",
       "cash_a                -0.398662      0.491385 -0.242722 -0.316222   \n",
       "div_d                  0.095661     -0.188719  0.378149  0.409395   \n",
       "td                     0.017482     -0.017984  0.194655  0.149301   \n",
       "td_a                   0.179656     -0.090580  0.038511  0.104232   \n",
       "td_mv                  0.356015     -0.276715  0.127690  0.225602   \n",
       "dltt_a                 0.185298     -0.099023  0.069760  0.168018   \n",
       "dv_a                  -0.008109     -0.027834  0.004705  0.000153   \n",
       "invopps_FG09          -0.153368      0.230717 -0.040975 -0.057058   \n",
       "sales_g               -0.008093      0.007809 -0.008432 -0.009231   \n",
       "short_debt            -0.129376      0.124318 -0.177918 -0.307513   \n",
       "long_debt_dum          0.251156     -0.196334  0.229960  0.307833   \n",
       "atr                   -0.087503      0.254502 -0.176234 -0.205942   \n",
       "smalltaxlosscarry      0.006375     -0.043721  0.125611  0.167890   \n",
       "largetaxlosscarry     -0.070300      0.219635 -0.242570 -0.237480   \n",
       "tnic3hhi               0.051374     -0.106777 -0.053393 -0.129702   \n",
       "tnic3tsimm            -0.180947      0.382518 -0.165729 -0.149036   \n",
       "prodmktfluid          -0.179151      0.396171 -0.192621 -0.108475   \n",
       "delaycon              -0.129176      0.530678 -0.122729 -0.067429   \n",
       "equitydelaycon        -0.199101      0.723611 -0.184878 -0.155000   \n",
       "debtdelaycon           1.000000     -0.490911  0.180458  0.221762   \n",
       "privdelaycon          -0.490911      1.000000 -0.257807 -0.276021   \n",
       "l_emp                  0.180458     -0.257807  1.000000  0.787591   \n",
       "l_ppent                0.221762     -0.276021  0.787591  1.000000   \n",
       "l_laborratio           0.067969     -0.030851  0.028813  0.556455   \n",
       "\n",
       "                   l_laborratio  \n",
       "gvkey                  0.103596  \n",
       "fyear                  0.257928  \n",
       "lpermno                0.054108  \n",
       "sic                   -0.223692  \n",
       "sic3                  -0.223594  \n",
       "age                    0.187915  \n",
       "at                     0.058018  \n",
       "me                     0.142494  \n",
       "l_a                    0.373117  \n",
       "l_sale                 0.196937  \n",
       "prof_a                 0.040532  \n",
       "mb                    -0.052968  \n",
       "ppe_a                  0.611810  \n",
       "capx_a                 0.295649  \n",
       "xrd_a                 -0.072052  \n",
       "cash_a                -0.156323  \n",
       "div_d                  0.127227  \n",
       "td                     0.067114  \n",
       "td_a                   0.156660  \n",
       "td_mv                  0.158872  \n",
       "dltt_a                 0.214947  \n",
       "dv_a                   0.029392  \n",
       "invopps_FG09          -0.047338  \n",
       "sales_g                0.000193  \n",
       "short_debt            -0.216896  \n",
       "long_debt_dum          0.141860  \n",
       "atr                   -0.049847  \n",
       "smalltaxlosscarry      0.094071  \n",
       "largetaxlosscarry      0.027022  \n",
       "tnic3hhi              -0.132895  \n",
       "tnic3tsimm             0.048841  \n",
       "prodmktfluid           0.119382  \n",
       "delaycon               0.095262  \n",
       "equitydelaycon         0.064953  \n",
       "debtdelaycon           0.067969  \n",
       "privdelaycon          -0.030851  \n",
       "l_emp                  0.028813  \n",
       "l_ppent                0.556455  \n",
       "l_laborratio           1.000000  \n",
       "\n",
       "[39 rows x 39 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccm.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But a lazily made figure of that exact same info is somewhat workable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f3 = sns.heatmap(ccm.corr()) # v1, use the nicer version below!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning that and making it more useful is easy:\n",
    "1. Drop the numerical variables that don't make sense in a correlation matrix\n",
    "2. Make the figure large enough to see\n",
    "3. Colors: cold for negative corr, hot for positive corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dont plot identifying type info or categorical vars\n",
    "corr = ccm.drop(columns=['gvkey','lpermno','sic3','fyear','sic']).corr()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9,9)) # make a big space for the figure\n",
    "ax = sns.heatmap(corr,\n",
    "                 # cmap for the colors, \n",
    "                 center=0,square=True,\n",
    "                 cmap=sns.diverging_palette(230, 20, as_cmap=True),\n",
    "                 # mask to hide the upper diag (redundant)\n",
    "                 mask=np.triu(np.ones_like(corr, dtype=bool)),\n",
    "                 # shrink the heat legend\n",
    "                 cbar_kws={\"shrink\": .5},\n",
    "                 #optional: vmax and vmin will \"cap\" the color range\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is an information DENSE figure, but we somehow managed to get it on screen decently! Still, it's a ton of variables, and doing this in parts would be a good idea.\n",
    "\n",
    "```{tip}\n",
    "If you're feeling frisky, and your data is in good shape, you can push this farther by using [`sns.clustermap`](https://seaborn.pydata.org/generated/seaborn.clustermap.html) to find clusters of similar variables. \n",
    "```\n",
    "\n",
    "Also - don't take these correlations as gospel yet: They should *point* you towards further relationships to explore, which you should do one plot at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digging in with [**lmplot**](https://seaborn.pydata.org/generated/seaborn.relplot.html) and [**Jointplot**](https://seaborn.pydata.org/generated/seaborn.jointplot.html)\n",
    "\n",
    "These are good for digging into the relationships between two continuous variables. \n",
    "\n",
    "Let's dig into a strong correlation suggested by our heatmap.\n",
    "\n",
    "```{warning}\n",
    "\n",
    "Jointplot can be slow - it's doing a lot. \n",
    "\n",
    "Again, don't plot all of the data points! As your sample size goes up, either randomly sample data, or use \"hex\" style graphs. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GEkluf6Sbrv4Q562Zzzmr2tJ9Wn958emFBa9XngEFLK687PSzeMe/fJMf7Oykwe/lg1tO4c/OX0293zv1N2SMKQULXnlYqvwsiCWS/PSRbrr7Q7xy/SLWLWmd2hNpsuAgd90bz8Q3r4O2l72Hf43E+dz/3kf/9m8ysnsbmTF+2fIVdD53cGrlMcaYErHgVWSZgeviDYtYt3iKgWsysoJcV1+Q3+2tw/enf8UZV/w9F6xdwLK2RsCp9RljTKWxGTaKKKnKL3YdcWpcGxbPTuDKYVlbI5efs5xXblhEOJbgRw8f4s7HDzMYjpWkPMYYM11W8yqi3z59jGePj7DltIWcvrilpGUREdYtbuWUhc08dLCPnQf7ePb4CHNefDnhWML6w4wxFcVqXkXScs7reKxrgLNXzOX5y+eWujhpfq+HF62Zz7tetJLVC5qY+9J38Cf/9lt+8fhhW6XZGFMxLHgVwW+e6qHtwvdxansz55+6oNTFyam1wc+rn7eEI9/9O1rqfXzwOw/z9lv+yFNHhkpdNGOMmZAFrxnW2RvkY7c+QuzYQS5ev2jUFE/lKNL5OHd8+HyuvWwDu7sHefUXf8enb9/NQND6w4wx5cuC1wwKxxJ88DsPkVTl2G3/jN9bGafX5/XwrvNWse2vt3DF5uV88/4DbPnCb/jOHw+SSFpTojGm/FjCxgy65qe72HVokFvetYmLPnOk1MUpjHjG1A79C1cTfMWV/EMwxt/c9FN6f3kzCz3DNh7MGFM2LHjNkFsffI4f7Ozi6gtP5RXrF5W6OIXLM/BZVdnbM8zv6nwE3vF5Rp74LYcHQiyZY/MlGmNKrzLatcrcY139XPPT3bx07QI+ftFppS7OjBARTlvUwrvOW8nm1fNoWPsizv30HbRuugzxeBGRvD/LV6wsdfGNMVXOal7T1DsS5YPffpiFLXXccPkL8FbZwpB+r4fz1szntk+8nhf/w/c4+IorWff6D/Pyde15V3S2WTuMMcVmNa9pSCSVj33/EY4NRfjS28+ecD2uShYfOMplz1/KqzcuZiQS5/s7Otn2VA+ReKLURTPG1CCreU3DF3+9l+1PH+OfX7+xrAYiF4uIsHZRCyvmN3L/Myd4tGuAfceG2XJaO6e2N5e6eMaYGmI1ryn6zVM9fPHevbzx7GW8bfOKUhdnVtX5vGw5vZ23nrOcRr+Pnz9+mDse62Y4Ei910YwxNcKC1yQtX7ES35xFvOtLvyZy9Fn+4+3n4vF4xiQt1ILFrfW89ZzlvOSU+Rw4EeRbDxxk16EBbAkiY0yxWbPhJHV1H+acz9xJfyjGFS9/HnOveCznfrWStOD1CJtWzePU9mZ+vaeHX+/pYdEV/8L+Y8OsWWhNicaY4rCa1yTNe8WV9AxFuHj9IuY2Vm+CxmTNbQzwhhd08Ioz2vG3r+aSG37Hjb/ZRywx8crPxhgzWRa8JuGb9x+g5axXsWllG6dYrWIMEWHD0jl03/IBLjpjEf9691O89r/u49HO/lIXzRhTZSx4FWj708f4zM+eILj3j5x3yvxSF6esJUf6ufHtZ7P1nS+kPxjj9V/6PZ+94wmCUUvoMMbMDAteBdjXM8yHvvswa9ubOX7HF/DUSELGdF28YTG//MsLePu5K/nKfc9y0b9v5+7dR2zdMGPMtFnCxgSODIR5z9cepM7n4ZZ3b2L5x0OlLlL5yzHZb13HeqKXfIirvhUi3Lmbvm1fpd0bssl+jTFTYsFrHL0jUd7xlT/SH4zx3T8/l2VtjaUuUmXIM9lvMqnsPjzIAwEv9cv/jdAzO9lxoJdzVs0rQSGNMZXMglceg+EY7/7qg3T2BvnG+zZz5rK5pS5SxfN4hOd1zOH0RS082tXP70ZO5c033c85q9p494tXcfH6xQR81pJtjJmYBa8c+kaivPfrO3jy8CBb3/VCXrTGEjRmUsDn4ZxV87j1L7bwlW1P8pX7nuXq7/4fC5oDvPGFy3jtmUvZsLS1ZgZ7G2Mmz4JXlkP9Id71lT/S2RfiS28/m5evq6C1uSqMxiO89yWrefd5q9i+9xjffuA5bvnds9z82/2snN/Iq5+3hFdvXMKGpa14qmy2fmPM9FjwyvDk4UHe9/UdDIfjfPN9m63GNUs8HmHL6e1sOb2d3pEo9+w+ws8fP8zW7fv58rZnWNAc4PxTF3D+2oW8dO2CvEuxGGNqhwUvnFWDv7+jk0/dvps5DX6+f9V5rF/aWupi1aR5TQEu37yCyzevoHckyq+fPMp9+45z377j/OSRbgBOW9TMi09ZwAtXtrFpVZut7mxMDar54DUSifOPP9nFbf93iNCB/2Pfz/6NDf/YX+pi1YYcKfW5eH1+EvE4/oWraFh9Fo+uegF7Otfz9YBTA4sP9BA59CSRQ08yJ97Hsw9vx+e1xA9jqlnNB69HO/v52aPd9G//Fp/89Gfw/Nlbxt2/VibcnRV5Uuqz/eXFp4/ZL5FUjg9HODwQpru/mcPtSxle/zIANn76btYtbmVjRysbls5hw9JWTlvUQr3fW5S3YYyZfTUfvF586gJ+89dbWPG51+CRa0tdHFMgr0dY1FrPotZ6zlo+F1VlKBLn8397NXWLT6V/0Sk8vGgNnromADQRJ95/mFjvIWK9h2ghxA+/eiOrFjSyoKnOEkKMqTA1H7wAls+zwceVTkRorfcTfGIb//SfNwNOX+ZAKMaxoQg9QxH6gnPoD55CfzBGQpW33Hw/AH6v0N5Sz6LWOha11jO3MUBrg4/Wej+tDX4a/F78XiHg9eD3evD7PKPvez0EfB7nvs/d7vO4x1nzpTHFYMHLVC0RYW5jgLmNAdYuaklvT6ryt295Gb/842McPBHkyGCYowNhjgyGefroEAOhOIOhGNEZWM4lGQuTjATRSJBkJEgyOkK9B97y+tfSXO+jpc7n/K7305y67f5urvPRGPBR5/NQ5/NYP54xGSx4mZrjESE+cJQtp7ePu184lmAwFKNjxSr+7uu/IqFKIqkk3d+JpJJQJZl0+uC+/fn/x1v/6rr0Y7Fkkmjc+Ym4v6OJJAefeYr79h1nOBxnOBqn0HmKvR5JB7I6n5c6f8Ztn8e97y18n1H7597X7/Wg6tRiFVB1gr9zW93HQFGSWfs5nBvOPqRvO49kPJZjG+Psn3rm1CTPo5977H4nXyfX/iACPo/gzfjxeTzub/e+VxAERdPvJ3UOTr7HrMfc7QGvhxXzrYVnJlnwMrWpwEzHlLamiRceHdl1Lxs75ky431/+w8Uc1lStTpBAPZ66RjyBRsT97ak7+YN4EV8A8fqd3z73tzcA7m2Prw68vpOPewNZt/2IxxJWSkUHjnDwy39W6mJUFamm5SlE5C5gwQS7LQCOz0JxpsrKN3XlXDaw8k1HOZcNile+46p6SRGet+JVVfAqhIjsVNVNpS5HPla+qSvnsoGVbzrKuWxQ/uWrRtYDbIwxpuJY8DLGGFNxajF4bS11ASZg5Zu6ci4bWPmmo5zLBuVfvqpTc31exhhjKl8t1ryMMcZUOAtexhhjKo4FL2OMMRXHgpcxxpiKU1XB65JLLlHcacXsx37sx36q4KdgVXr9y6uqgtfx4+U8e4wxxhRPrV3/ih68ROQSEXlKRPaJyN+Os985IpIQkTdN9lhjjDG1pajBS0S8wI3Aq4D1wBUisj7PftcDd0/2WGOMMbWn2DWvzcA+Vd2vqlHgVuCyHPt9GPgR0DOFY40xxtSYYgevDqAz436Xuy1NRDqA1wM3TfZY9/grRWSniOw8duzYjBTaGGMqQS1f/4odvHKt9pedQfKfwCdUNTGFY1HVraq6SVU3LVy4cGqlNMaYClTL179ir6TcBSzPuL8M6M7aZxNwq7uq7QLg1SISL/BYY4wxNajYwWsHsFZEVgOHgMuBt2XuoKqrU7dF5OvAHar6ExHxTXSsMcaY2lTU4KWqcRG5GieL0At8VVV3i8gH3Mez+7kmPLaY5TXGGFMZil3zQlXvBO7M2pYzaKnqeyY61hhjjCl68DLlbdueHm7evp/OviDL2xq56oI1bFnXXupiGWPMuKpqeigzOdv29HDN7bvpGQozt8FPz1CYa27fzbY9PRMfbIwxJWTBq4bdvH0/fq/QGPAh4vz2e4Wbt+8vddGMMZM0HImXugizyoJXDevsC9Lg947a1uD30tUXLFGJjDFTNRCMlLoIs8r6vGrY8rZGeobCNAZOfgxCsQTL2hpnrQzW52aMmQqredWwqy5YQyyhBKNxVJ3fsYRy1QVrZuX1rc/NGDNVFrxq2JZ17Vx76QbaW+oZCMVob6nn2ks3zFrNx/rcjDFTZc2GNW7LuvaSNdN19gWZ2+Aftc363IwxhbCalymZ5W2NhGKj52Oe7T43Y0xlsuBlSqbUfW7GmMplwcuUTKn73Iwxlcv6vExJlbLPzRhTuazmZYwxpuJY8DLGGFNxLHgZY4ypOBa8jDHGVBwLXsYYYyqOBS9jjDEVx4KXMcaYimPByxhjTMWx4GWMMabi2AwbpmLYwpXGmBSreZmKYAtXGjMBLXUBZpcFL1MRbOFKY0wmC16mInT2BWnwe0dts4UrjaldFrxMRbCFK40xmSx4mYpgC1caYzIVPXiJyCUi8pSI7BORv83x+GUi8piIPCIiO0Xk/IzHDojI46nHil1WU75s4UpjTKaipsqLiBe4EbgI6AJ2iMjtqvpExm6/Bm5XVRWRM4EfAOsyHr9QVY8Xs5ymMtjClcaYlGLXvDYD+1R1v6pGgVuByzJ3UNVhVU0leTZRcwmfxhhjJqvYg5Q7gM6M+13Audk7icjrgc8B7cBrMh5S4B4RUeBmVd2a49grgSsBVqxYMXMlN6YC2MDt2pZ5/ZuzZFVpCzPLil3zkhzbxtSsVPU2VV0HvA74bMZDL1HVs4FXAR8SkQtyHLtVVTep6qaFCxfOULGNKX82cNtkXv8a6utLXZxZVezg1QUsz7i/DOjOt7OqbgdOEZEF7v1u93cPcBtOM6QxBhu4bWpbsYPXDmCtiKwWkQBwOXB75g4icqqIiHv7bCAAnBCRJhFpcbc3ARcDu4pcXmMqhg3cNrWsqH1eqhoXkauBuwEv8FVV3S0iH3Afvwl4I/AuEYkBIeCtbubhIuA2N675gO+q6l3FLK8xlWR5WyM9Q2EaAyf/jW3gtqkVRZ9VXlXvBO7M2nZTxu3rgetzHLcfeH6xy2dMpbrqgjVcc/tugtE4DX4voVjCBm6bmmEzbBhToWzgtqlltp6XMRXMBm6bWmU1L2OMMRXHgpcxxpiKY82GVcRmWzDG1AqreVUJm23BGFNLLHhVCZttwRhTS6zZsIKM1yzY2RdkboN/1P4224IxtaPWluOwmleFmKhZcHlbI6FYYtQxNtuCMaZaWfCqEBM1C151wRpiCSUYjaPq/LbZFowx1cqCV4WYaBJWm23BGFNLrM+rQhQyCavNtmCMqRVW86oQ1ixojDEnWc2rQmxZ1861OH1fXX1Bltkg5EmxAdzGVBcLXhXEmgWnJpWp6ffKqEzNa8HOpzEVypoNTdWzAdzGVB+redWQWm06swHcxlQfq3nViFqe+9AGcBtTfSx41YhabjqzTE1jqo81G9aImWg6q9RmR8vUNKb6WPCqEYUMch5PpWfsWaamMdXFmg1rxHSbzmq52dEYU34seNWI6c59ONHcisYYM5us2bCGTKfpbLrNjsYYM5Os5mUKYhl7xphyYsHLFMSWXDHGlJOiNxuKyCXADYAXuEVVr8t6/DLgs0ASiAMfU9X7CjnWzC7L2DPGlIui1rxExAvcCLwKWA9cISLrs3b7NfB8VT0LeB9wyySONcYYU4OKXfPaDOxT1f0AInIrcBnwRGoHVR3O2L8J0EKPNdNTqYOOjTGm2H1eHUBnxv0ud9soIvJ6EdkD/Byn9jWZY68UkZ0isvPYsWMzVvBqV8tzHRpTLTKvf+FwuNTFmVXFDl6SY5uO2aB6m6quA16H0/81mWO3quomVd20cOHC6ZS1ptigY2MqX+b1r76+vtTFmVXFDl5dwPKM+8uA7nw7q+p24BQRWTDZY83k2KBjY0wlK3bw2gGsFZHVIhIALgduz9xBRE4VEXFvnw0EgBOFHGumzpYJMcZUsqIGL1WNA1cDdwNPAj9Q1d0i8gER+YC72xuBXSLyCE524VvVkfPYYpa3ltigY2NMJRPVMd1IFWvTpk26c+fOUhejYqSyDW2ZEGPKVq6+/5wWrzlDj+x/sphlKYW879/mNqxhNujYGFOpbHooY4wxFceClzHGmIpjwcsYY0zFseBljDGm4ljwMsYYU3EseBljjKk4FryMMcZUHAtexhhjKo4FL2OMMRXHgpcxxpiKY8HLGGNMxbHgZYwxpuJY8DLGGFNxLHgZY4ypOBa8jDHGVBwLXsYYYyqOBS9jjDEVx4KXMcaYimPByxhjTMXxlboAxmTatqeHm7fvp7MvyPK2Rq66YA1b1rWXuljGmDJjNS9TNrbt6eGa23fTMxRmboOfnqEw19y+m217ekpdNGNMmbHgZcrGzdv34/cKjQEfIs5vv1e4efv+UhfNGFNmLHiZstHZF6TB7x21rcHvpasvWKISGWPKlQUvUzaWtzUSiiVGbQvFEixrayxRiYwx5cqClykbV12whlhCCUbjqDq/YwnlqgvWlLpoxpgyY8HLlI0t69q59tINtLfUMxCK0d5Sz7WXbrBsQ2PMGEVPlReRS4AbAC9wi6pel/X424FPuHeHgQ+q6qPuYweAISABxFV1U7HLa0pry7p2C1bGmAkVNXiJiBe4EbgI6AJ2iMjtqvpExm7PAi9T1T4ReRWwFTg34/ELVfV4MctpjDGmshS72XAzsE9V96tqFLgVuCxzB1X9g6r2uXcfAJYVuUzGGGMqXLGbDTuAzoz7XYyuVWX7M+AXGfcVuEdEFLhZVbdmHyAiVwJXAqxYsWLaBa4GNkuFMbUh8/o3Z8mq0hZmlhW75iU5tmnOHUUuxAlen8jY/BJVPRt4FfAhEblgzJOpblXVTaq6aeHChTNR5opms1QYUzsyr3/19fWlLs6sKnbw6gKWZ9xfBnRn7yQiZwK3AJep6onUdlXtdn/3ALfhNEOacdgsFcaYWlDs4LUDWCsiq0UkAFwO3J65g4isAH4MvFNVn87Y3iQiLanbwMXAriKXt+LZLBXGmFpQ1D4vVY2LyNXA3Tip8l9V1d0i8gH38ZuAa4D5wJdEBE6mxC8CbnO3+YDvqupdxSxvNVje1kjPUJjGwMk/bfYsFdYnZoypdKKaswuqIm3atEl37txZ6mKUVKrPy+8VGvxeQrEEsYSmB/tO9LgxpqzkyhvIafGaM/TI/ieLWZZSyPv+bYaNKjPRLBXWJ2aMqQa2GGUVGm+Wis6+IHMb/KO2WZ+YMabSWM2rxtjM7caYamDBq8bYzO3GmGpgwavG2MztxphqYH1eNchmbjfGVDqreRljjKk4VvOqUTZQ2ZjqUj0jdgtjNa8aZJP3GmMqndW8alDmQGWAxoCPYDTOzdv3l2Xty2qJxphsVvOqQZU0ea/VEo0xuVjwqkGVNFDZprMyxuRiwasGVdJA5UqqJRpjZo8FrxpUSQOVK6mWaIyZPZawUaMqZaDyVRes4ZrbdxOMxkct4VKOtURjSqrGcuWt5mXKWq5a4pvO7uDm7fs5//p7uWLrA5a8YUwNspqXKXuZtcTMxTQzsw+vdfczxtQGC15VolbGQlXaGLXx1MrfzJhisGbDKlBLY6GqJfuwlv5mxhSDBa8qUEtjoaol+7CW/mbGFIMFrypQLbWRQlTSGLXx1NLfzMyOGks2tOBVDaqlNlKIShqjNp5a+psZUwyWsFEFUmOhjg+HGQjGiCSS+DweLnv+0lIXrSgqZYzaeGz8mpl5tVX3sppXFdiyrp03nd1B70iMaEKp93lpa/Tzw4cPWQJAmaqWGqQxpWI1rypx//5elrU1pFPIgYpNIa8V1VCDNKZUCg5eIvIaYANQn9qmqtcWo1Bm8jr7gsxt8I/aZgkAxphqVVCzoYjcBLwV+DAgwJuBlUUsl5kkSwAwxtSSQvu8Xqyq7wL6VPUzwHnA8kIOFJFLROQpEdknIn+b4/G3i8hj7s8fROT5hR5rTqqWFHJjjClEocEr5P4OishSIAasnuggEfECNwKvAtYDV4jI+qzdngVepqpnAp8Ftk7iWOOyBABjTC0ptM/rDhGZC/wr8DBOTub/FHDcZmCfqu4HEJFbgcuAJ1I7qOofMvZ/AFhW6LFmtGpMALD5/4wxuRRU81LVz6pqv6r+CKeva52qXpN6XEQuynNoB9CZcb/L3ZbPnwG/mMyxInKliOwUkZ3Hjh2b+M2YimHz/xkzvszrXyQSKXVxZtWkx3mpakRVB7I2X59nd8n1FDl3FLkQJ3h9YjLHqupWVd2kqpsWLlyYpxhmItv29HDF1gfKao0sm//PmPFlXv/q6upKXZxZNVODlHMFGnBqS5mJHcuA7jEHi5wJ3AJcpqonJnOsmb5yreHY/H/GmHxmKnjlm5dkB7BWRFaLSAC4HLg9cwcRWQH8GHinqj49mWPNzCjXGo6l/xtj8inq9FCqGgeuBu4GngR+oKq7ReQDIvIBd7drgPnAl0TkERHZOd6xxSxvrSrXGo6l/xtj8pmp6aEO5HtAVe8E7szadlPG7fcD7y/0WDPzlrc10jMUHjW1VDnUcLasa+danJphV1+QZZZtaExeWlvz8o4fvETkDeM9rqo/dn+Pu58pb+U8w3m5pv9bCr8xpTVRzeu17u924MXAve79C4FtOH1VpsJZDWdyUgkufq+MSnC5FuycGTNLxg1eqvpeABG5A1ivqofd+0twZr8wVaJcazjlKDPBBaAx4LMZ/I2ZZYX2ea1KBS7XUeC0IpSn5ljzU+WxGfyNKb1Csw23icjdIvIeEXk38HPgN0UsV00o1/FVZnyWwm9M6RU6PdTVwE3A84GzgK2q+uEilqsmzMT4qnKcGaPaWQq/MaU3YbOhiHiAx1R1I3Bb8YtUO6bb/GSJA6VhCS7GlN6EwUtVkyLyqIisUNXnZqNQtWK646sscaB0LMHFmNIqtM9rCbBbRH4tIreLyM9ExKZqmqbpNj+V68wYxhhTbIVmG/4I+EzWtnyT8ZoCTbf5qVxnxjDGmGIrNHh9EPgW8Hmg3v29CTivSOWqGdNpfirnmTGMMaaYCm02PBdneZI/4Mz23g28pFiFMoXZsq6day/dQHtLPQOhGO0t9Vx76QbrizHGVL1Ca14xIAQ04NS8nlXVZNFKZQpmiQPGmFpUaPDaAfwUOAdn+ZKbReRNqvqmopXMzCqb6cMYU0kKDV5/pqo73dtHgMtE5J1FKpOZBV/81dPcct+zjEQT1Pk8eAXaW+ttvJgxFarGVkQpeIaNnTm2fWvmi2Nmwxd/9TQ33LuPUCyBzwPBaIKhSILBUKysVlI2xph8ZmoxSlNkM9msd8t9z+IR8Hmc7y5CAgVOjERZPKcBsPFixsyEcMz5Uji/uQ6vx0YXzaRCsw1NCc30BL4j0QSZ/0fi3k5mtDvYeDFjpkZVGQzH6OoL0t0fYjgSL3WRqpLVvCrAdKeByq611fk8xBLJdADzeTxE3fuqmnO82GRrfpYAYmpNLJFkKBxnKBwjkay1HqjZZ8GrAkx1At9te3q47hdPsvfYMH6Ph0WtdfQMhfEKhJMKuAEL8Agsba1jIBQbM9PHZCcAtgmDTS0JRRMMhmOMWA1rVlnwqgATTQOVq5YDpJsavSIocHggwtK59bS31hOJxhmMJBiJJmgKeHn/+av5yCtyry862ZqfTRhsql0yqQxF4gyGYsQSNuS1FCx4VYDxpoHKV8tp9Hvwe4VEUvGKICIkUY4NRVi9oIloPMljn35FQa8/2ZqfrTRsqlU0nmQoHGMoHCep1jRYSpawUQHGmwYq34KWz55wZpwPeD2k/sdEIJpITjoZY7IrB9tKw6baBKNxjgyE6eoLMhCKWeAqA1bzKmOFJD3kq+WAEzAWNNfRPRCCJChOLWyyk/dOdgLgzP3jiSRHhyLEEkrA62Hbnh5rOjQVIZlUhsJxBsPWNFiOrOZVpgpNj89Xy1mzoIlYQvF5haVz6hEPJBRWL2ia9OS9k50AOLW/3yN09YdAYdnceqKJ5LRS/I2ZDbFEkuPDEZ7rDXJiJFI5gavGKoNW8ypThSY95KsVffI169LP09UX5AXL26aVrj7ZCYBTTZqrkjoq0cQSN0y5CscSDIQsa7BSWPAqU4UmPUy0oGUpg4Qlbphyp6oMR+IMhGJE4xVSwzLALAQvEbkEuAHwAreo6nVZj68DvgacDfyDqn4h47EDwBCQAOKquqnY5S0Xk1kluVyXRbGVnk25SiSVwVCMwSoaUFwd76JwRe3zEhEvcCPwKmA9cIWIrM/arRf4CPAFcrtQVc+qpcAFTnNgLKEEo3FUnd+Vtkpysd7Dtj09XLH1Ac6//l6u2PqA9aGZgkXiCXqGwjzXG6QvGK2awFWLip2wsRnYp6r7VTUK3ApclrmDqvao6g6cBS+NqxpWSS7Ge5jpeR5NbQhG4xweCHGoL8Rw2PkyZSpbsZsNO4DOjPtdwLmTOF6Be0REgZtVdWv2DiJyJXAlwIoVK6ZR1PJTrs2BkzHT78Fm7zCFqoVZMDKvf81LVpe4NLOr2DWvXGsATOYrz0tU9WycZscPicgFY55MdauqblLVTQsXLpxqOU2F6OwLpsexpVgSiMkUTyQ5kUp1H66gVPcpyLz+BfyBUhdnVhW75tUFLM+4vwzoLvRgVe12f/eIyG04zZDbZ7SEFa7WZm+3JBCTT2rtLFuCpDYUu+a1A1grIqtFJABcDtxeyIEi0iQiLanbwMXArqKVtALVYv9PNSSymJmTSnU/1B+ytbNqTFFrXqoaF5GrgbtxUuW/qqq7ReQD7uM3ichiYCfQCiRF5GM4mYkLgNvEWSnRB3xXVe8qZnkrTS32/0w0rs3UhkRSGQrHGAzFiSert1nQ5Ff0cV6qeidwZ9a2mzJuH8FpTsw2CDy/uKWrbLU6CLgaElnM1ETjSQbcpkHLGKxtNsNGBZtu/0+t9ZeZyhWMxhkMxQlGrVnQOGxi3go2nf6fWuwvM5VFVRkMx+jsDXJkIGyBy4xiNa8KNlH/z3g1q1rsLzOVIZ5IMhiOM1RFUzeZmWfBq8Ll6//Jt8Lyte4xtdpfZspXOJZgMBxjJJKw/iwzIWs2rFL5Vli+eft+wFY7NuVjJBKnO5XqblM3mQJZ8KpSE81EYeOlTCklk8pA0OnPOjoYJpz1RcpMXq2FfGs2rFITZSLaeClTCrGEm+oejpO0GpaZBgteVSrfCsuZNSsbL2VmSyia6s+yjEEzMyx4VSmrWZlSs1WKTTFZ8KpiVrMypVCNqxSb8mPByxgzIyLxBAMhS3Uvndo65xa8aohNB2WKYSQSZzAcIxS1jEEzeyxVvkbYdFBmJiWSSn8wmk51t8BlZpsFrxox0aBlYwoRjiXoGQrzXG+Q3pFoVa9SbMqbNRvWCJsOykxVKmtwMBwnYoOJy1dtdXlZ8Co3xeqXmu7yKab22AS5ppxZ8CojE02mO5nnyQ6AhQxaNgZsQLGpDNbnVUZmol8qX2IGwLWXbqC9pZ6BUIz2lnquvXSDZRsawJ1rMOTMNXh4IGSBy5Q9q3mVkZnolxpvna7vXfmiGQlWs5Vyb6n9xReNJxkM21yD1aDW/npW8yojM7FMyUSzyU/XbKXcW2p/8aQSMA4PhOjqCzIYilngMhXHglcZmYllSoq9Ttdspdxbav/MiyeS9I5Eea43SI+NzTIVzoJXGdmyrn3a/VLFXqer2DW72X6dWhCOJegZDNPZF6I/GLXMQVMVrM+rzEx3Mt1izyY/Wyn3lto/ParKUCTOoM3obqqUBa8qVMzZ5Keacj/Z5AtL7Z8am9Hd1AoLXkVQzVlyU6nZTWX8mq1HNjk2o7upNVJNH/RNmzbpzp07S1qGzAt1Zo1hpsZUVWJgvGLrA2OaAIPROO0t9XzvyheVsGSVz2Z0rwwr5zfh9chUDi34oLYVp2vfc09N5TXKWd73bzWvGTbeOKvpBpmpzsBR6oBn8yrOrGRSGQo7Qcsmxi1vRwfD7Do0SFdfiH/80zPwey1HbqYUPXiJyCXADYAXuEVVr8t6fB3wNeBs4B9U9QuFHluOinmhnkpgnKkpp6bDki9mRiyRZCBkA4rLVSKpPHNsmF2HBtndPcCuQ4McG46kH3/jCzs4c9nc0hWwyhQ1eImIF7gRuAjoAnaIyO2q+kTGbr3AR4DXTeHYslPMC/VUAmMxa4KFsuSL6QlFnf6sYNSmbConw5E4Tx4eZNehAXZ1D/Lk4UHCsbE14Qa/l7NXziVuCTQzqtg1r83APlXdDyAitwKXAekApKo9QI+IvGayx5ajiS7U02nCm0pgLIcmO0u+mLxUqvtA0JoGy4GqcnQwwi63RrWre4Bnj43knJKpvaWODUtb2dgxh41LW1mzsJk1C5un2udl8ih28OoAOjPudwHnzuSxInIlcCXAihUrplbKGTTehXq6TXhTqcGUS5NdMdP3q4ktQ1IeEkllX89wuvnv8e4BTgxHx+znEVizsJmNbrDasLSVRa31s1bOzOtf0+LaaskodvDK9VWj0P/Igo5V1a3AVnCyDQsvWvHku1BPtwlvKjUYa7KrDOFYgsFQjJGopbqXwnAkzhPdTo1q9wRNgOuXtLDBrVWtX9o66ovhbMu8/s1dcXpNfXCKfda7gOUZ95cB3bNwbFmaiSa8ydZgpttkV+pMxWpmKxSXhqpyxM0C3NU9wO5Dgzx7PH8TYKr5b4PbBGjNf+Wh2MFrB7BWRFYDh4DLgbfNwrFlaSaa8KYSTKbaZFcOmYrVKDULxlA4Tjxp/VnFFk8k2edmAaaC1YmR3E2ApyxsTgerjR1zWNhSV4ISm0IUNXipalxErgbuxkl3/6qq7haRD7iP3yQii4GdQCuQFJGPAetVdTDXscUsb7FNtwlvtoNJOWQqVhObBWN2DIfj7D48kE5Z33N4iHCO+R0bA17WL2llY0crG5fO4YwlrTQEvDme0ZSjojfWquqdwJ1Z227KuH0Ep0mwoGMr2XSb8IodTLJrdU8fHWTJnIZR+9jg4skbicQZCMUIW9PgjFNVDg+E2dU9yG43Zf1AnibARa11bFw6Jx2sVi2Y8qwXpgzYDBuzbDpZd8VMe89VqxuOJDg+HGFhy8nsKRtcXJhEUhkKxxgMWdPgTIonkuztGR4VrHrzNAGe2t6cDlYblloTYLWx4FVBptpnVkg/Wa5a3bwmP70jMZrqfGWTqVjuCSShaIKhsGUNzpShcIzd3YPs7nYGA+85MkQkRxNgU8DL+qVOjWpDRytnLLYmwGpnwauCTKXPrNB+sly1uvlNdcQSSntLfc4xa7MdRMo1gSSRVIZtrsFpU1W6B8LpGtWuQwMcOJG7VWFxa/3JgcAdraya+sS3pkJZ8KogU+kzK7SfLF+tbm17y5iZ30sVRMotgcTGZk1PLJFkX89wenqlXYcG6AvGxuznEVjb3sIGt69qY0crC5qtCXCMGvsIWvCqMJPtMyu0n2wytbpSBZFymOrKallTl2oC3HVogMcPDfLU0aGcqzw31XnZsKQ1PRB43ZJWGvzWBGhGs+BV5QrtJ5tMra5UQaSUU12FYwkGw5bmXihVpbs/PGouwIN5mgCXzKkfNRfgqgVNeMSaAM34LHhVgfH6nyZToyq0VleqIDLbU10lk87kuEPhWM4agjkplkiy9+hwOljt7s7dBOj1iJsFeHIuQGsCnBm19pXKgleFm6j/qRgzupdqvsTZmp3ealkTGww5TYCPHxpwBgIfGSKWGHuumut8bhagE6zWLW6h3poAzQyQavrn3LRpk+7cubPUxZhVV2x9YEwtKBiN095SPybRYialanvVssSJ1bLyU1UO9YdGTa90sDd3E+DSufVOurobrFbOb7QmQGDl1LMhCz5ozvLTdaDzqam8RjnL+/6t5lXhStX/VC1LnETjSXfKJludOCUaT7K3ZygdrJ7oHszbBLi2vdmZsaJjDhuXzmFeU6AEJTZgzYamTBQ6jipX/9OJkQgjkQTnX39vWQ7kLQfhWIL+oK1ODDAQirHr0EA6E/Cpo/mbAJ0alZOyfro1AZoSsuBVhiYzjiq7/+nESISeoSgLmwNFG4NV7rNcjGckEqc/FKvZJUhUla6+0KixVZ19oZz7dsxtSAerDUutCbDs1VjVy4JXGSpkHFVmAGkOeBGR9IzlC5sD6fkIizF5bznOcjEeVac/ayBYe2OzovEkTx8dSs8FuLt7kP7Q2CZAn0dYuyg1F6DTZ2VNgJVFayx6WfAqQxP1Y2UHECfbL8lnL9vIP/50V1H7wMptlovxJJPKYI1NjtsfjKab/3Z1D/J0nibAlnq3CdCdC3DdohbqrAnQVBALXiWWqwluonFU4wWQYo/B2tszRDASJ5ZUAl4PC1vqaK7zldUyKfGEk4QxFK7uJAxVpTPVBOgmV3TlaQJc1taQDlYbO1pZPs+aAE1ls+BVQvma4N50dgc/fPhQ3nFUuWpm8USSh5/ro6Xex1A4TlujnwXNdTM6Bmvbnp50QPB6hHjSmUVhfrOfVfObp/3801Xtcw2mmwDdWtXu7kEG8jQBnraoJZ1YsaGjlbZGawKsdtX3iR+fBa8SyleDun9/L9deuiHvOKrs2tVQOMah/jA+r7C4tR6/N0LvSIx4IsnaRa0zllBx8/b9tDX6OTESRZMgAkmU3pEYn3t9aZZJqea5BvuD0XSNatehQfb25G4CbK33pZcDeV6HkwUY8HlKUGJTUjUWvSx4ldB4fVvjjaPKzjA8MhAGYFFLPSLCguZ6GgO+GR+o3NkXZEFzHXU+L8eHI0QTSQJeDw1+z6z3d1XbulmqynO9wZMDgbsHrQnQmHFY8CqhqfZPZU+TpEDH3HpaMwJhMQYqp8rb2uBPv1ZqNo/ZkFqdeCgcr/haVjSe5KkjQ+zqHuDxQ85A4MHw2DFnfq/bBOjOWLF+qTUBmtwq/yvc5FjwKqHpzBGYWTNLTRGVqRgT5ZZqTsNIPJEeBlCptay+VBOgm64+XhNganb1jR1zOG2RNQEak4sFr2mazoDdmZpodraCymxNjJsyEokzEIoRrrABxcmMJsDdbn/Vof7cTYDL2xrS46o2Lp3D8nkNiDUBGjMhm5h3GjKzBTODxrWXbpj1PqBqmShXVRkMxxkMVU4CRiSWYM/RIXZn9FcN5WkCPH1Ry6hgNafRn+MZTbWZjYl5G5eepsHup6fyGuXMJuYthmIM2N22p4frfvEkz7oL961Z0MQnLlk34fNNdqLcYk7xNNnnTiaVkWicYDRBKJoo+7FZvSPR9Ozqu7oH2Ht0mHhybJnnNPjZuPTkisDWBGjMzLHgNQ2Fzuhe6MV8254e/vqHj9IfjJH6kra3Z5i/+eGj/Oubnj9hcJnM6xQ6xdNkA9FknjsYjTMcjpd1xmBSlYMngunmv13dA3T3h3Puu2Jeo1ujcvqrlrVZE6AxxWLBaxoKyRaczMX85u37GY7E8YrgcaOXqNIfjPKRW/+P1gZ/3gAy2dcppMY4lXkMJ3ruWCLJUNgJWuU4ZVM4lkhnAe46NMgTh/M3Aa5b3MIGN119wxJrAjSlZXMb1rjJ1DQKSZSYTNNiZ1+QRFLxZnxbjyeSJBRi4TjxRJKRSIyrvv0QLfU+1ra3pMs32dcppMY4lWbRXM9d7/PwXO8IhwdChKLllXzROxJ1Z6xIDQQeJpGjCXBug58NHSfHVq1ttyZAY0rJgleGydY0Csm+m8xikc0BL91JJaaKR8AjQiqb2iMQTSQJxpJ4PRCMxEeV7+mjg4RjyfTA4QXNdbTU555zsNDxZVNZ6DLzuVWVhNuftbC5vuSBK9UEmLkcyOGB3E2AK+c1jgpWHXOtCdCUtzJteS+aogcvEbkEuAHwAreo6nVZj4v7+KuBIPAeVX3YfewAMAQkgLiqbipmWadS05goUaLQQLFtTw8nRqLpkYZJZVTigs/jSTezqUIsqenyXX/XHoYjTqKDV4R4QukeCDE/HmD1grFzDhaaWj+VQdRXXbCGT/50F/FElIDPQziWJJ5ULj9ned5jiiUcS7DnyMm5AJ/oHmQ4MrYJMODzuFmArekl7FsbrAnQVJYai13FDV4i4gVuBC4CuoAdInK7qj6RsdurgLXuz7nAl93fKReq6vFiljNlKjWNiRQaKG7evp/WBj9NdT4O94eIJk62YAsQSyTT95MKDV5Punx7e4aZ1+TnxHAMBcQDJKEvGOO6HGO9Ch2vNdnxY8FonNOXtPChLady645OjgyGWNzawOXnLGfzmnlTPoeFOjEcSdeodnUPsi9PE2Bboz/dV7Vx6RzWLmrG77UmQFP5kklN95dXu2LXvDYD+1R1P4CI3ApcBmQGr8uAb6qTbvaAiMwVkSWqerjIZRujGMuJFBooUoFTRGhZ7ATQpw4PEE3m/ka1sKUuXT6A+U3OnIPHhpw5B/0eobHON+UaY6FlzzVl0+Y184oerJKqHDg+kg5Wu7sH8zcBzm9MN/9tXDqHpXPrrQnQTIlHBK9HEAGvR/CKIO42rwjiAW/mPunbs/N5iyWT1HlqY122YgevDqAz434Xo2tV+fbpAA7jXLfvEREFblbVrdkvICJXAlcCrFixYlqFLdZMFYUEilyBM4mQK3T5PEJzndNkGEsoq+c3EoolaKn301I/s3MO5it7KJpgMBwjOEtp7qFYgj2HB0+uCHx4kJHI2D60gM/DusUZcwEusSZAM5bXI3jcrF6fezs7ADmPOwEr9Xi5ybz+BRafSiyh1NVIJkOx32auv3b2lW68fV6iqt0i0g78UkT2qOr2UTs6AW0rODNsTKewsz39EZzMbtzbMzRqHa7jwxHiSU2P98ps/UoklSOD4XS2ITAm6A6EYgS8Hs6//t4ZG4Q8m8uPHBuKOGOrugfZ7S4HkqMFkLZG/6i5AE9ttybAWiEi6cQmjxt4UsEmNdzEIxmBKhWgyjAITVXm9a9uyVqNxuLUSvQq9rvsAjJ76pcB3YXuo6qp3z0ichtOM+R2imiyM1VMR2Z2Y/Y6XNGEEvAKgqAwKljU+T00BnyjAlJm0G0KeBGc7MRCx2eNJxxzalnFmhg3kUw1AZ4cCHx0MDJmPwFWLWhKDwTe0DGHpXOsCbDSiaSa30gHl+wmt5NNdCeb62qlb2cynus6xLx1p5S6GLOi2MFrB7BWRFYDh4DLgbdl7XM7cLXbH3YuMKCqh0WkCfCo6pB7+2Kca3TVyM5uTK3DFfB62H98hHhC0RxDDxe11OPzyqgsyOxZ5lPZiDC1aavuffIoX/7tM3T2BVncMrNJF6FYgicPD6anV3qie5CRHGn0dakmwIy5AJvra+NbZSUSOVn7GVPbybE9VSuyLx8zJ9dKBdWqqFcCVY2LyNXA3Tip8l9V1d0i8gH38ZuAO3HS5PfhpMq/1z18EXCb+8H2Ad9V1buKWd7Zliu7MZ5IcuBEEL9X8HmFpCpxt9IlQMArtDb4UdW8WZDTyZoMRRPcvesI19+9B59HaKnzcWIkwg337uWjrJ1SADs2FEknVezqHmBfz3DOJsB5TYFRcwFaE2BpeDL6e8oxKcHkt3DR4lIXYdYU/Wusqt6JE6Ayt92UcVuBD+U4bj/w/GKXrxgKnaVjeVsjB04MMxiKpwcXxxJJ/F5hUUs93QMhvOIhjhO9fF5h8ZwGYPwsyEKnrUqVsWNuA28/dwVnLW8jnkzy9T8cwOdxZsoH0v1ot+7onDB4JZLKs8dHRg0E7hnK3wSYGayWWBPgjMqVGedJJSdk9g9lJCx4BPsbVDCreZkpm8wsHeetmceDB3rdTmenjyqWUBY2+NIZcseHI8RVSCSV+U0BWupPZhnmy4KcKGty254ePvnTXXg9QqPfS3d/iM/9Yg8ffblTszo8GKI1q3mu3u/hyODYNalCUacJMHMuwGCeJsAzloyeC9CaACcvFWR8Xve3x5PuJ/JZEKp5Q+FYqYswa+zqMcMmM0vH/ft7WdgcYCh8suaFJhgKJ1g8B1ob/LQ2+AlG4wS8HuY2Bth7dNBJ5vB5uHn7fmBsUMyXNXn+2gX0B6N88dd7AZzXw6lZ9QUjfPbnT9Bc72M4HCeZTNLWWJd+znAsyeLWBnoGw04GoFureuZY7ibA+U2BUdMrnbqwGZ81AeaV6i/yep0glApGmQHK57EkBTO+A11HOHtl8ScEKAcWvGbYZPqbOvuCLGiuY2HGeKzBUJSu/lC61nRixMlAbK7zoqqICAtbAjT4vePW6lIJHLFEkmA0QTAa57lepwyHBkbXrEaicfpGnNk5ls6tJ5FIcmIkhqrSEPAxFE4QjicYDMe5/H/+OOZ9CLB6YVM6qWJjRyuLW2uzCTAzfVskd4JCOq3bbbpLBSdjpitYYauOT4cFrxk2mVk6svcdDMU4OhTBg3BsKIKqEk1oeuzXvmPDxBNKU50PERlVqwOnpvVc7wgdcxt554tW8IKVbUTjY8djLWlt4MRIhAa/l5FonO7+cDqjsXckCjgX1+MjcRg5ORdgOOY8V73fwxlLTq5bdcaSVpqrdGxJ5mDWdMZcxnihzGY7a6ozpVbXPKfURZg11XnFKaHJzNKRuW88keSQu8hhx9x6fF4PXX0h2hr96ZpZwh20fGwokp5Jo8Hv5emjg/zjT3bh9TjNlIcHQvzLL/ZwyfpF/PbpY3T2O31Vy9saufKla7j8nOXccO9ejg+H6Q/GR6XinxgZ22a+oDlwcnqljjmcsrC5ImsKqUDk855MXsisAVXzYFZTG3LNOlOtLHjNsMnM0rFlXTtv6urnlvueZTAcR4D2ljpaGwIAxJPOwo0LW5z9U9mIkXiCRFJRdZYbCceSNAagzncyO7B3JMI3HzgIOMkgqnDg+AifuWM3a9tbCEYTDITGzrAOTjNgU52X+U11fO6Nz2NRS92YGsWD+3u5dUcnhwdDLJnFyXfhZJARYVSTnMjJfqFUTSid4GCp3KYGOC0ntcGCVxFMNEtHKk396aODDEcSzGvyE4olEJyZ4BsCXlrq/dR5PYRjCZJJJanK3EY/RwbCeD1CLJFILzfi9wr1/tHJEMOROAkFDxDn5Fo/oViSxw4NpPcTcYNiPIl4nMClCi31fj74slNY3Dp2fsQH9/dyw7178XmE1vrpjwNLBZ3MpIRU8kJmWrf1DRmTn88jOWemqVYWvGZZZip9OJYkqcqJ4RheEZJJBVF6BsM0+L001fuIBWMMhmPU+z14xBmg3NboZCimlhv53oPP0TPk9FuFYknCsQSRuBOtkjBmNsl6n4f3v3QN9+w+QjAapzHgYyQap3ckSiSepMHvTafN53Lrjs6CxoGNSuuWk31E6cGvGX1Gxpjp8XqEo0O5V1aoRha8Zlkqlb7B7yUST+AVQVGndgWIQiSuDEfi+Dwe3rF5Bf/XOZBeG+tDW07lhavaeObYMLsODXDX7iMc6A0yEMo/viPVfAaKR4R1i1t5w9kdLJvbwA337iUUS9AY8OKROuJJHTdwARwZDNFa78fjkfSsys11Po4Nh1k6t8Ga6YwpAb/VvExKoTNlZEsklVjCadKLJ5IkkkpC1Z19YpiWeh/ReBK/10M8oYjHmTW+vaWe4yMRPOqsz5XqR3p9JO4MBD40wPcf6uTTd+xOZ/5l8ojTdNDWGOD5y+bw4IFehsJxd2Z6JanQVO9Lr2q8ec08PsraMQtHnnvKfPxeIeD14Pd68HmdGpLPHYO0ekEzPUNh6rwn1w0KRuOsmNdEvb821hIyptz4vEJnbzA9pKbaWfDKY6KZMlIBKppIEk+oOxO8czs5zszrizPS1NsaA05zX5J0n8+8xgDvetEqfD7hD/tPsPW+/Tx7bCTngpTtLXXO2Cp3eqU1WVmAD+7vZev2Z9LZhivnOdmGm9fMw+sR/F4Pf7J+EZc8bzE+rwefW2OaaDBxsdY9M8ZMXZ1XGI7EOTIYZok7jVw1s+CVw7Y9PXz4ew8zEk1Q7/OwoKWO5jo/8WScL967l9ULm3IuLw/wrT8c4AcPdRGKJWjwe3nLC5fxzhevSj+eSlMPxRI01XmZE/PRH44jIgyF4yRV+fw9T415Xo/AmoXNbFjayvPcYNWeI5ki0+Y18zjv1Pn43RpUwOsh4PO4k/5OfbaLUqx7ZowZn8edA3Xv0WELXrUiHEswEomTUOW+p4/zhXueIhhN4PVALKkc7g/T3qo0Bpx5AMcLXN944KA7kwJE4gm+dv9BfrH7CIozOHjDkhZUlUP94VHTKoWSSUIZTYGNAW/WQOCWUQOfs4kIAZ+HOp8TqFK/i5UMMZvrnhljJjY0EqIF2NczzAWnLSx1cYrOghfO5LKphIdv3n8Qr8cJBPGE4vEISZTekSgeqWNxa/5vND94qMvpX1LI7JI6MhihMeDh+HCER7r6cx7rNOMJr1y/mNecuYTVC5rGDTwBn4d6v5eAz6lR1fk8NdHObYzJTZMJ2hq87OoemHjnKmDBK0tqRvVUf1TSTQGMxJV4UtPJDrkEo85YrbGpFBCMjt4qqR8Bv1dYMa+JUCzBwRNBTm1vBkYPBF46p4F3n7eSl69fRKPfa5PcGmNGUU2yflEj//dcf6mLMisseGVJzfvnzNVXT18wSjSuOcc+DYfj7D7sLAWyu3sAZcyQqjE8OMEtta9fIO62H2YuO/Lg/l6+eO9eAj4P85sCDIZj/Puv9jK30Zl9I18W5FQzJCdrtl7HGFMYEQ8bFzXy+wNH6R2JMq8pUOoiFZUFryzZCRVejzP26SMXnkrHvAbueeIou92FFg8cz50FOJ5U/cttXSSWdFZHBmfi26VzG5jfVMdtjxyiIeDNWFrFQzAa5/q79jASTeTMggQKXktsqrbt6eH6u/bwdM+wu2hmHc8eH+aqbz9ES72Pte0tNRPILICbcqKaZMNip1vj4YN9vGL9ohKXqLgseGVJjX363oPP0dkfJBZPMhJN8Hc/2ZVzf4/Aqe3NbFg6h+d1tPLZO56cMKAJ4BWIp3ZUJ+0e4CMvX8ucRj+H+kN4BfYfG06v9bWgOUBXX4hlbQ051wsDCl5LbCpSwwd6BsOgSiSmHOwNpc9DMBIvSsAsR5NZdNSY2eAP1HPGoib8XuGh5yx41YyhcIzdGYss7jk6RCTHciIAHXPquXjDYjZ0tHLG4lYaAicH5n77gec4cHwkZ79XipIRuIBo0llp+FOvOiN94Wup87G3Zzg9jVI8qelZ5xuyBgKn1gtTyBPwxq4lNhWp2UFiSR2zAGVSIRxPznjALFeTWXTUmNkQj0Xxe2DD0jk8dLCv1MUpupoPXl/89V5++sghnjk2knefzMUFk0mlPxTjneetzLnvy9Yu4LneESfRI8O8Rj/D4RjRHFHN74Fg1owZmhronK6duft6xZ3Oaex6Yf3BaM6At9ZNAJmu1EKb6bKl2j6z5Ft8s5pMZtFRY2aDqnMNeeHKNr79wEGi8SQBX/UmdlXvOyvQob5QOnB5BE5f1MIbzu7gmj9djwB+L+nxUql1n0J5Vit9cH8vdz1xlLkNfhqyZnnvDY4OXKlsQwCvxxk4nGr6AxiOJuiYWw8o4XiSiDvdVCSWZCAUIxiNo6oEo/H07BajAl5G9oiOM+NHobbt6WEwFOPJI4Oouk+d9bSpVP18i29Wk+VtjWM+B7Xwvk35EnGuOS9YMZdIPMnTR4dKXKLiqvma12ufv5T5zQFOWdjEuiWto5rk/u0eZ/JcMoZPuV1TXPE/D4xZxyo123pLUx3zm4XnTgwTiucOHIo7F6EICdUx39qXtzXy7PFhEtnNczhr9vQHo3g9HtYsaOKTr1nHlnXt/PUPH8WDs/oyOE2RHXPqGYlOb4G6VP9OY8CZCirhRq3Monk9zmz1mcG0mtkUWabcpGpeZ3bMBeCxrgE2dlTvyso1X/M6f+0C/vyla3jBirYxfUlveeEykgqJZJKkJoklks7ktgHvqHWsHtzfCzizrTfVuQOHfZ68gQucwFLn8+IRZwLc7G/tV12whr5gjHhSR9XSwFlvq87nZVlbQzowbdvTw1A4jopQ53deP6kQTSSnXRtI9e8sbKln6ZwG6tymCI/AynkNLJlTh8/jocHvob2lnmsv3VD1/T5b1rVz7aUbaG+pZyAUq5n3bcpXqua1fF4Dcxr8PH6ov7QFKrKar3mNJzUnYWquQoB6n9OX9OzxEfxeD00BL//7UBevO7sjPdu63zvxTBfJpJIUJ7i11PuJJZTz1szjiq0PpFOv63yedNKIu6JJujUwmkiOyTRsa/RzYiRKPJ4kiZNEcWQwwhXnrJjWecjs32lt8NPa4GcwFOXIYISkwqr5zXzu9bWXJm5TZJlykqp5iQhnLpvD44eqe6YNC14TeOeLV/HOF6/iwf29/MNPHiccd8KHTyCRcJI3ODFMvd87pilpPKkMxYDXWWLkvDXz+OHDh0alXkfiSQJeZ10sjwhhN4AKzurHMDrTcEFzHYmkcnw4irr7eYAfPnyIM5fNzXuhnWi80vK2RnqGwqOSRHxeD2evaON7V75oimfWGDOTUjUvgPVLWvna7w+QTDpT3FUjC14FSC17n5kenlDweARVpS8U5/zr72V5WyNvOruD+/f30tUXZEGTn+MjYxeJ9ABL59Rz18dflt52xdYHxqRetzX6OTYcIZHV8SXiBCqA48MRgtEEkXiS48MRVJ3A5vEISVV87pyJ+VK4CxmvNF7/jg3UNaY8pGpeAMvaGogmkhwfidDeMv7qE5XKglcBfvBQpzvxrdPflPoeE0sk3cQLTV/4f/jwoVF9H+v+8U63tnaSAk8dHWbbnp70fk8fHSQcS2aMz6qjzuch6c7AEUtoOkGitd5HS72PY0Nhjg1HaW8J0Nbo51B/mHhS8XsA9aAKC1vqxk3hLmS8Ur4lUCD/jB6p584OahbsZp6dUwPOIGWPx6l9pZZE6e4PV23wkplIox73BUQuAW4AvMAtqnpd1uPiPv5qIAi8R1UfLuTYbJs2bdKdO3dOqnzb9vTw37/ZR2dfcFT24M5ne/mf+/Zz8EQwnb1nZpbgrP46p8HP2vYWzlszj/v397LjwAnyjA+nwe/hgy87hY+84rQxF+3U8an7i1sD3LX7KKFY0mlq9XkIeIWEQjSewOvxsLDJT0tDgKFIfMILf+brtdT5UFWGo4n0ccCY8vzvQ1109YXQrLJPR2Y5mgNeToxEaW3wj6oVlzp5pJQBNfXaTx8dJJZQAj5PyaYtm4HzUHCb37zFy/Tnt/0vHR0d7D4S5IM/fobrX7OS81a2Tr7gJbRs2bJ0EGac91/U4CUiXuBp4CKgC9gBXKGqT2Ts82rgwzjB61zgBlU9t5Bjs002eKWazDzuzO7hWJKEKm84q4NbdzxHfygOaN4LqZkZdV5hXnOAnqEoJJWJEvsFeN1ZS3jouQH8XqHB7+X4cCRdC53fVEdXX9D9++WXmiTZg5Oh5fN68l74M5tX44lkeraTjrn1+LweBkIxBNJB5PhwhKODkTFjuAX4+CvWTjmAZZajwe9l37Fh4gllWVsDLfVOUk0wGqe9pb5k/ZHZZZzNgJp67Wg8wYmRqLNRYUFLAL/XO6tBfYbOQ8HBa8HSFfrB67+OeDwcDya485kwF66sY3lr5TSwDRw/yl+9/jxWrEgnmeV9/8VOld8M7FPV/aoaBW4FLsva5zLgm+p4AJgrIksKPHZaMpvMfB4Pc9wLzzcfOMhwJIHXI2OmQTIzL5pQBkNxPMKEgSvl9seOpP92qVWoPQKDIWdV6sHw+IELTk6SrALHh6M0BnxjBounZH5Wjg9HnVlMRNLHDUfiDIXjo8qT+uiIuNmirlvue7bAdzlWZjlEhERS8QgcG4qk9yn1TB/ZZRzvvBbrtYfCcTwIPo/T/zsYis9aGbLLMlvnwevzI56Tw1jA6eaoVsUOyR1AZ8b9Lpza1UT7dBR4LCJyJXAlkBmtC5JKAXdaLp2/doPfy0g0gaD4vE6/UaqvyxRPNJGk0KQoBRJJHZXRmTo+6o4in8yXDtWTx+W78GcOF4gmkumFQlPHJZI6aiaT1PZcZZ/OoPHsaakCXg+xRHLU65V6po9STp2Veu1oIonX/cYg7uditoP6bJyHzOtfR0cH73+p03x9ZCjKsB7m8rMWsn5RJc36soZly5YVtGexg1euy1GulpRc+xRyLKq6FdgKTrPhZAqXKwU8FEvQFPASjSctcM2SOp8z/Va+C342wcn0zJzjMeD1pJNdwPnmWWgAEzk59CDfhT/zsxLwepw12PTkcV6POFU4lxNUxgYpwRnkPlXZn9kFzXUc6g/hczNfy2Gmj3z/V7MRUFOvHfA6K6Gn/n9zTQQwW2Up5nnIvv6lvsCvADZvOHXGXqccFbvZsAvIXHp4GdBd4D6FHDstV12whlhCx8wT+P7zV9NS70s3yVjsKq5FrXW0NvhIqpOZU4hLz1w86m/XUu8c39rgJFK01k/8vSz14ReFBc2Bcae2yvysLGgOkEgqCdX0cc11TgZoZnlSoUx19Beg95+/usB3OVb2Z9bnFeY2+lm9oKlsZvrI9381GwE19dot9T6SKPFkkmRSaW3wzXpQL+V5qAXFTtjw4SRd/AlwCCfp4m2qujtjn9cAV3MyYeOLqrq5kGOzTTXbMDsFPJXSff1de9h/fCQ9XsrngXjSWTTSAlphfO7s9rm01HlY1tbEcCTOsoxswZ0HThCbRLZh6m+XOj51fzLZhqkyFJJt2NUXpNnNNhyJJkYNHcguTzGzDbM/s+WklGVMvfbeo4NEyyTbcBrnoeCEjalc/ypAabINIZ1N+J84X6q/qqr/LCIfAFDVm9xU+f8GLsFJlX+vqu7Md+x4r1WlfzxjTO2y4JVH0XMoVfVO4M6sbTdl3FbgQ4Uea4wxxtT8rPLGGGMqjwUvY4wxFceClzHGmIpjwcsYY0zFseBljDGm4ljwMsYYU3EseBljjKk4FryMMcZUnKLPsDGbROQYcHCC3RYAx2ehOFNl5Zu6ci4bWPmmo5zLBsUr33FVvaSQHUXkrkL3rQZVFbwKISI7VXVTqcuRj5Vv6sq5bGDlm45yLhuUf/mqkTUbGmOMqTgWvIwxxlScWgxeW0tdgAlY+aaunMsGVr7pKOeyQfmXr+rUXJ+XMcaYyleLNS9jjDEVzoKXMcaYilNTwUtELhGRp0Rkn4j8banLk01EDojI4yLyiIiUdElUEfmqiPSIyK6MbfNE5Jcistf93VZm5fu0iBxyz98j7krcpSjbchH5jYg8KSK7ReSj7vayOH/jlK9czl+9iDwoIo+65fuMu73k52+cspXFuaslNdPnJSJe4GngIqAL2AFcoapPlLRgGUTkALBJVUs+GFNELgCGgW+q6kZ32+eBXlW9zg3+bar6iTIq36eBYVX9QinKlFG2JcASVX1YRFqAh4DXAe+hDM7fOOV7C+Vx/gRoUtVhEfED9wEfBd5Aic/fOGW7hDI4d7Wklmpem4F9qrpfVaPArcBlJS5T2VLV7UBv1ubLgG+4t7+Bc8EriTzlKwuqelhVH3ZvDwFPAh2Uyfkbp3xlQR3D7l2/+6OUwfkbp2xmltVS8OoAOjPud1FG/7AuBe4RkYdE5MpSFyaHRap6GJwLINBe4vLkcrWIPOY2K5asWTNFRFYBLwD+SBmev6zyQZmcPxHxisgjQA/wS1Utm/OXp2xQJueuVtRS8JIc28rtG9NLVPVs4FXAh9ymMVO4LwOnAGcBh4F/K2VhRKQZ+BHwMVUdLGVZcslRvrI5f6qaUNWzgGXAZhHZWKqyZMtTtrI5d7WiloJXF7A84/4yoLtEZclJVbvd3z3AbThNneXkqNtfkuo36SlxeUZR1aPuhSUJ/A8lPH9uf8iPgO+o6o/dzWVz/nKVr5zOX4qq9gPbcPqUyub8weiyleO5q3a1FLx2AGtFZLWIBIDLgdtLXKY0EWlyO88RkSbgYmDX+EfNutuBd7u33w38tIRlGSN1YXO9nhKdP7dT/yvAk6r67xkPlcX5y1e+Mjp/C0Vkrnu7AXgFsIcyOH/5ylYu566W1Ey2IYCbvvqfgBf4qqr+c2lLdJKIrMGpbQH4gO+Wsnwi8j1gC85SD0eBTwE/AX4ArACeA96sqiVJmshTvi04zTYKHACuSvWRzHLZzgd+BzwOJN3Nf4/Tr1Ty8zdO+a6gPM7fmTgJGV6cL9g/UNVrRWQ+JT5/45TtW5TBuaslNRW8jDHGVIdaajY0xhhTJSx4GWOMqTgWvIwxxlQcC17GGGMqjgUvY4wxFceClzGAiLxHRP57kscM59i2SkTelrXte+60QR8Xka+LyJsmeN5LJc+qB7le05haZMHLVDV3NYHZtApIBy8RWQy8WFXPVNX/KOQJVPV2Vb2uSOUzpipY8DIVy63l7BGRb7g1mx+KSKM466JdIyL3AW8WkSvEWSdtl4hcn3H8e0XkaRH5LfCSjO1fF5Evi7Pm1X4ReZk72eqTIvL1HOVYICL3i8hrgOuAl7prOn0cuAdod++/NOu4AyKywL29SUS2ubfTtUB3Rpj7RWSHiHx2ps+hMZXKgpepdKcDW1X1TGAQ+At3e1hVzwe2A9cDL8eZAeEcEXmdO53PZ3CC1kXA+qznbXOP+TjwM+A/gA3A80TkrNROIrII+Dlwjar+HPhb4HeqepZb07oUeMa9/7spvL8bgC+r6jnAkSkcb0xVsuBlKl2nqv7evf1t4Hz39vfd3+cA21T1mKrGge8AFwDnZmyPZuyf8jN1pp95HDiqqo+7k67uxmkaBGctp18D/09Vf1mE9wZOcP2ee/tbRXoNYyqOBS9T6bLnN0vdH3F/51oKJ9+xmSLu72TG7dR9n3s7jrMK8SsnLmZOcU7+D9aPs5/N4WZMFgteptKtEJHz3NtX4CzLnumPwMvcfimvu89v3e1bRGS+uzzIm6fw2gq8D1iXkR04BLQUePwB4IXu7Tfm2ef3OCsgALx9CmU0pipZ8DKV7kng3SLyGDAPZ1HANHdm778DfgM8Cjysqj91t38auB/4FfDwVF5cVRM4weVCEfkL4DEgLiKPugkb4/kMcIOI/A5I5NnnozgLk+4A5kyljMZUI5tV3lQscZawv0NVy2aVXWPM7LCalzHGmIpjNS9jjDEVx2pexhhjKo4FL2OMMRXHgpcxxpiKY8HLGGNMxbHgZYwxpuL8f0bHck7k7nhcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1 = sns.jointplot(data=ccm.query('xrd_a<.4').sample(1000),\n",
    "                  x=\"prodmktfluid\", y=\"xrd_a\", kind='reg') \n",
    "# notice: most firms have 0 R&D!\n",
    "# set_title doesn't work with jointplots\n",
    "f1.fig.suptitle('Strongly positive, even with zero R&D firms in sample')\n",
    "f1.fig.subplots_adjust(top=0.95) # Reduce plot to make room "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f2 = sns.jointplot(data=ccm.query('xrd_a<.4 & xrd_a > 0').sample(1000),\n",
    "                  x=\"prodmktfluid\", y=\"xrd_a\", kind='reg')\n",
    "\n",
    "f2.fig.suptitle('Among R&D firms, even stronger relationship')\n",
    "f2.fig.subplots_adjust(top=0.95) # Reduce plot to make room "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'd pencil this as a relationship to look into more (Do firms do more R&D _**because**_ of the fluidity of their product market?) and then continue exploring. \n",
    "\n",
    "`lmplot` will plot regressions as well, but it makes it easy add [facets](04d-whichplot) to see if the relationship depends on a third (categorical) variable with the `hue`, `col`, and `row` parameters. (And you can combine `hue`, `col`, and `row` to see several cuts!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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meyjr5YMd/Hgahl2LharaUoyZ/HwHQMhnEE/brN/SOOhmOVPKfrNMN0WCYjKZq7KOdiXZ8Hgjm3c2owk4f0UtK2eVT/SyJh3KkCjGzGhlTtTmOrOZzA2GScth47YDbNx2gLTt5WpOnlc5sYuaxChDohgz00neXjE+TNYGQyklm99qZv2WRpq6UwDUhH18es0S1p2/BE0TE7zCyYkyJIoxc/aSam7bvBvHlfgNjUjAwGfo0zLfoRg7kzWU9faxbn742G5ePdQJgKkLrnznPFbWR7j/pcP819Z9SpVgEJQhUYyJzTuauPeFQ1SHTTrjFknbwY5LPrd2gfpjU/RisoayOuJp7npqL7975QhZMfpzl9Vw4wVLOdSW4NZNb2NoSpVgKJQhUYyJbKK9IhigtiwAQDxt80xj2+QcfauYECZjKMt2XH790mF+9sxeYimvFH1hTYjPrV3KqkXVAPzr73diaIKgqecaZ4crJJmJlNyQCCEuAW4FdOBOKeV3+jz/UeDLmW+jwGeklC9nntsLdAMOYEspV5V6vYqRMe3niUwQ00nkcTKGsrbtbeO2x3azv837PS3zG1x7ziIuP3UOel4e5EhXgvJA721S/X73p6SGRAihA7cBFwMHgW1CiAeklG/kHbYHuEBK2S6EuBTYAKzOe/5CKWVLKdepGD0q0V58povI42QMZR1qT/Cjzbt5prEVAE3AZSfP4bpzFlERMvsdP7s8SGss1UuFQf1+96fUDYlnAruklI1SyjSwEbg8/wAp5dNSyvbMt1uBeSVek6KIzKTGwvFiOuiQTbYGw3jaZsOWRq67e1vOiJw6v4L1H38nX3j38gGNCMBVZ8zHdiUJy1G/30NQ6tDWXOBA3vcH6e1t9OVTwMN530vgD0IICayXUm4o/hIVY2GmNRaOB1M9XNgRT9MetybFHHVXSv7w+jHufHJPLrzWUO7nxguWsmZ5LUIMXc575pJqbmY5G7cdoDWWUr/fg1BqQzLQT2nA3y4hxIV4huS8vIfPlVIeFkLUA48KIXZIKbf0OW8dsA5gwYIFxVm1YkSoxsLiMli4sMxvcPWGrZM2bzLZQllvHuniPzbtYsfRbgD8hsZfnrmAv1g1D38fwdChOHNJNWcuqWZJXVmpljrlKbUhOQjMz/t+HnC470FCiJOBO4FLpZSt2cellIczX5uEEPfjhcp6GZKMl7IBYNWqVRN/C6RQMLZk+UDjh7sSFhJIO+6kzJtMpqqslmiKHz+xh0ffOJZ77MLj6rhhzRLqywMTuLLpS6kNyTZguRBiMXAIuAr4y/wDhBALgPuAj0spd+Y9HgY0KWV35t9/BtxS4vUqisB0qjgaDWNNlg8ULjQ1geXKEemZjReTpSorbbvc+/xB/uvZfSQtz6Atqy/j8xcu46R5FRO8uulNSQ2JlNIWQtwE/B6v/PcuKeXrQogbM8/fAXwdqAF+lIlXZst8G4D7M48ZwP9IKR8p5XoVY2fzjib+9t6X6U7a2K5LU1eST/9nG+UBgxUN5cUzKjsfhadvhY59ULkQzrkZVlw89usWgdGIWPalb7jwvO9umnR5E9txaY6mSKSd4Q8uIVJKntrVyu2P7+ZIZxKAyqDJp85bzCUnzupVzqsoDSXvI5FSPgQ81OexO/L+fT1w/QDnNQKnlHp9iuLy3Ud20B630DWBJgRpx4s2RlNO8cIxOx+Fh78Emg8CVdB9zPue7xXdmIzGuypFsnyylVnH0zbN3Skcd2KjyXtbY9y2aRfP7+8AQNcEHzptDtectYiygOq3Hi/UPBJFUWlsiaEJ0ITAdmWu2iJtu8UrY336Vs+I+EIghPdV83mPF5HRzpUvxdCuyVJmLaWkNZriaGdyQo1Id9Lih5t2cf3PtueMyJmLqvjJNav47NplyoiMM+rTVpQMKfHq9vL2m6KEYzr2eZ5IPmYQOvaP7bp9GG2IaqBk+Vg3/clQZm05Lk3dKVLWxIWyHFfy4CtH+OlTe+hKetVh86qCfHbtUs5aUjNh65rpKEOiKCqLa0Lsao4hMner2VYCXYPG5igp2yXk09m8o2n0m2DlQi+c5cu7w7cSUNlT/l2MhP9Y5qyUYtOfyDLr7qRFazSNO4G9IS8d6OCHj+2isTkGQMin8/GzFnLF6XMxdRVcmUiUIVEUla9c+g6+dO/LRFM2mhS4GYPiSq90VQBhvz62XMk5N3s5kTSeJ2IlwE17j1M8iZGx5CWmS2+N60paYimiyYnrDTnaleSOx3ezZaenlCSAS06cxafOW0x12Ddh61L0ICZD92mxWLVqldy+fftEL2PGk/UGDrbHKfMb7G2NkXZcAoZOXcRPJGDSEk0SSzmUB83ReQy5qq39nieSV7V19Yat/QxAPG1THwlwz7qzRvQ+sgYpP0Q1U+atp2yvN8RyJqY3JGE5bHxuP7/YfjA3pfD42eV8/qJlHDcrMu7rGWVD4owoGVMeiaKoDBRS+upvXqMyaObkKLqTFi3daSSwoDo0Oo9hxcWDVmgVq2pqMuQlJorOuEVbPD0hMidSSjbtaGbDlkaao5kphWU+blizhHetrB9W1kQx/ihDoigag4WUynze3XzWQ2juToEAv66VZMZDMUtlp0uIqlAcV9ISTRFLTUwoa+exbm57bBevHuoCvCmFf7FqPn955gKCvsJlTRTjizIkiqIxWJWTEALLcXNVTEnbQROC2jJ/7tyReAzDJdJLUTU1EyiWzMlzjW1s3HaAI10JZpcHueqM+Zy5pHrIc9rjaX7y5B4efvVorsjv/OW13LBmCXMqg2Naj6L0KEOiKBqDhZQ6ExbfuvzEXIgo7DMI+XTK844t1GMoJJE+k0NSo2UkoayhDMVzjW250bTlAYPWWIpbN73NzSwf0JhYmSmFP8+bUrioJsRNFy7j9IVV/Y5XTE6UIVEUjaFCSvkhoqwxGI3HUGhvx0wLSY2WkSr2DmcoNm47kBtNC+R+vhu3HehnSJ7b08Ztj+3iQHsCgEjAm1L4gVPmKFmTKYYyJOPETBAyLDSkNFKPIf+za+5OMavc3+v5idacmqok0g7N3SMLZQ1nKAYaTRswNY52JXLfH2yP86PNu9na2AZ4Uwrff/Icrj13ERXBgQdMKSY3ypCMA9NldOpwjMRAFOox9P3sWqIpDnUkEUIQCXibzqjlRyax8GMpkVLSFkvTmbBGfO5whmKg0bRJy2VWeZBYyua/tu7jVy8cws70F506v5KbLlyqZn1McZQhGQeKoQY7Wvp6QmcvqeaZxraSeUYjMRCFeGh9P7uGSIBDHQmOdiYp8xujT6SPo/DjZCJtuzR1J3N9GSNlKEMB3mjaWze9TcJyCJgaScvFclyW1oW55q7naI97xmtWeYAb1y7h/GXDTylUTH6UrsA4cKA93usPD8YnHNNXdHBPS5RbN+1ib2t0RCKEpV7XUOvo+9mVB03mVgaQQGfCoj4SGF2D4DgJP04mOhMWhzoSozYi0GeGOd5X25VcdYY3v+7MJdXcfNFyasJ+upM2AUNH1zR+9eIh2uMWAUPjunMX8dNrV7FmeZ0yItME5ZGMAxMlAd73br47aaMJ6ErY1JYFJmw40kg8tIE+O0PXOH1BVcFd6gN6P+Mk/DgZKOYI3PwZ5ke7EswaoLz3zCXVLK4L8+MnGvnjmz03B+9aWc+6NUuoi/gHurRiCqMMyTgwUX0Nfctx046LJryvWSYiUT2SzvOxfnaD5ad+UzaHKqttSOHH6UAp5oZkZ5gPRNp2+d/nD/Dfz+7PTSlc0VDGTRcu48S5akrhdEUZknFgovoa+t7N+3SNtOPiy1NKnYjhSCPx0Mb62Q3q/diX8RV556DCjwMxlSrvxpJQH+3r9Z1SWBXqmVKoTfEQllIXHhol2jhZKEEFUV/RwZZoiuZomvqIj5qwf8JECMdTDDE7ojY/Fi+lpDNh8cSHnEGFHydyzWNlrAn1kbKnJcZtj+3ihbwphVecNpePn72QMv/UvlcN+nTKAybh0b+PqW1BC0QZkslAfgVR/t3xpWOvIMpX4p2XV7U10R3ffddVqnUMpAQ8GuXhYikKl5rxnBvSlbD46dN7+e3Lh8lGzs5aUs2NFyxlQfXEjAAuBpoQlAUMygMmPmPMnogyJFONKWtI7r6s/6CmdBwiDXDtgxO3rmlAX0+iNZaiqTtNXZmP2rLCvbIhPZsvXzQeb2VI3IzYYnQcxBa9KYWH+elTe6fVlEJT1ygPmkT8BlrxOutnhCGZ2n7ndGEGVRCNN31zLLGUQ12Zj7pIACi8p2eiKu8KIWl5HeqlnhvyXGMbP36ikX1t8VxDYdin8/GzF/Kh06bulMKw3/M+lLrw6FGGZDJQwOhYxejJb5LMehb5FFK5NlkVhTsTFm2x0s8NeeTVo/xg09sk7d4Vf19413LedXxDSV+7FOiap4wQCRhT1gBOJtQnOBk452YvJ5KOe0PO0/FhK4gUo2N+VYiE5fR6rBDPYu3Kem75wAnURwJja4QsEq4raepK0hpNldSIJCyHu57aw7/84a2cEQmYGguqgtSW+fjdq0dL9tqlwGdo1EX8LKgOUR32KSNSJAr2SIQQ7wNOAALZx6SUt5RiUTOOFRcD3yu4gkgxPIOV6o7Fs5gsisLjMQLXm1LofYYt0TQAhiaoLfMR8RsIIZDIXmKMkxUhBOHM2IKAqcJXpaAgQyKEuAMIARcCdwJXAs+VcF0zjyFGxypGxnAimVN5Vsl4hLJ2HuvmPzbt4vXDPVMKq0M+DF0QzssR5WtsTUYMTSMSMCgPmkqWvsQU6pGcI6U8WQjxipTym0KIfwXuK+XCFBPPVGrAy2c4CZbJ4lmMhGLKnAxGWyzNXU/u4eHXek8pvPGCJRxoTfQTY8zX2JpMBEzP+wj7dKXlNU4Uakiy/mtcCDEHaAUWF3KiEOIS4FZAB+6UUn6nz/MfBb6c+TYKfEZK+XIh5ypKx1SWvh+JBMtUYDRzQ0aC5bjc/+Ih/vOZfcTSXv5oSW2Yz164lNMXeNWEsyuCw2psTSRCCMr8BuVBA7+hwlfjTaGG5EEhRCXwL8ALgAR+PNxJQggduA24GDgIbBNCPCClfCPvsD3ABVLKdiHEpcAGYHWB5ypKxHcf2UFTVxJHSny6l6A0dTHuAo+jYTKX6o4EKSXtcYuOeLpkr7G1sZUfbd7NwcyUwvKAwXXnLuKyk/tPKRxKY2uiMHWN8oBJWcBQ4asJpCBDIqX8VuafvxJCPAgEpJSd2eeFEBdLKR8d4NQzgV1SysbMcRuBy4GcMZBSPp13/FZgXqHnzmRKGXbavKOJnU1RdOGVSdqu5HBHktkV/ilxVz9ZS3VHguW4NHWnSPWpMCsW+9vi3L55N8/u6ZlS+IFT5nDtOYsonwJTCkM+z/vIv1lQTBwj/ilIKVNAqs/D3wUGMiRzgQN53x8EVg9x+U8BD4/kXCHEOmAdwIIFM6PvotRhp2yOQbogEAgBLpJjXSlOW1A1/AUmmKmeUC+lzEk0ZfOfz+zjvhcP5RSBT1tQyU0XLmNxbbjor1dMNCGIBAwixZEuURSRYpnzwXzKgR4f8K9DCHEhniE5byTnSik34IXDWLVq1fTRexmCUk9cPNAepyHi53BnElxv7pOUEltOnbv6qZhQd11JSyxFNFn8hLrjSn7/+lF+8uSe3JTC2RUBbrxgKectq5nUSWmf4UmXlPmKKl0y5RBCfAMvj1wObJFS/nEM17oWWCWlvKkYayuWIRlsAz8I5Jd1zAMO9z1ICHEyXlnxpVLK1pGcOxPJTyafntrOFclf0eAe41CsDnZ+Y8xlxNkcw5yKIC3RFGnHRdcES6rDw27OU7XSa6IppczJa4c6+eFju9h5LAp4DYUfXb2AP3/n/El7Zy+EIJRR3lXSJb2RUn59otfQl1IHGLcBy4UQi4FDwFXAX+YfIIRYgFdK/HEp5c6RnDtTyW7058mXuDF+OxYGnTJMg9ZZlLnj2RyDqQsW14ZzOYavXPqOIc8rVchtshqn/HVdFnydG4wHqUofHvEYgI54mva4VfTekObuFBu2NPKnvBHG735HPZ8+f/JOKcxKl5QHDAzVdY4Q4h+Aa/DC/M3A80KIu4EHgRhwnZTyLzLHrgX+Rkr5/kGudR3w98ARYCf9UxSjpliGZO9AD0opbSHETcDv8Up475JSvi6EuDHz/B3A14Ea4EcZ99qWUq4a7NwirXdKk93oP5i6FwuDBH6kgIryCtDSXof8GAzJaHMMIw65FTCDZbKWIeev6yL9Za5pv51ODIyKSiLdxwoy6I4raepOkkgXN6Geshx++fxB7nl2f07W5LiGCDddtJQT5kzOKYV+U6c8YFCW6ZpXgBDinXg30Kfh7dUvAM/nHfIosF4IEZZSxoCPAL8Y5FqzgW8C7wQ6gceAF4u11iENiRDiiqGel1Lel/k66HFSyoeAh/o8dkfev68Hri/03JnGYHfjtwBz/7eJNjeMz9SoK/MTCRgg9aKoBo8mxzCi/o38GSyBKk+0coDNt9T5oOEY7PPPX9eHO+/DFiYp/DTH0kRqw97kxSEMeilG4EopeWJXC3dsbuRoV8+UwuvPX8J7TmiYdFMKhRCE/V74SkmXDMj5wP1SyjiAEOKB/CczN9uPAO8XQtwLvA/4u0GutRrYLKVszlzrF8CKYi10OI8k6yLVA+cAmzLfXwhsRnW3l5Rh78bnr6B+EqkGj6h/4+lbPSOSXbsvNODm+3ZTN/GUjeX29LOU+Y1xKUMe6vPPN5oN7jG6KUMTomcq4SBjAEo1ArexOcptm3fzYmZKoaEJPnz6XD521sKxTPcrCYamUR70qq9U78ewDHen8Qvgc0AbsE1K2T2Ga42aIYOQUsrrpJTXZRZwvJTyw1LKD+OJNypKTP5dr5d8NHJNgcCkUw2+Yc0SLEcST9tI6X0dtH+jY5+32ebTZ/PdvKOJ7qRnRPL7WVpjqXFpLhzq889XET6mNeAnhStlT/J6AIOetl0OdSSKakQ6Exa3/ult1v3n8zkjctaSau66dhU3XLB0UhmRoE+noTzAgpoQlSGfMiLDswX4kBAiKISI0HNjn89m4HTg0wwS1srwLLBWCFEjhDCBPy/mQgvNZi2SUh7J+/4YRXSLFANzoD1OsI/L3ytUtOJibxxvpAGSHd7XIoznHS0jklqvXOhttvn02XzXb2mkKuTd9Xs9LSCRtMWscSlDHurzzzeavwpcgSEt/DJJXdg3oEHvSloc6kgUbY6640p+/eIhrrnrOX7zkjfqdkF1iO9ccRL/+KGTJk0XvyYE5UGTeVUhZlcEJ5Vhm+xIKV/AMw4vAb8CnhjgGAcv8X5p5utg1zoCfAN4BvgjXr6laBT6U90shPg9cA+ed3IVXrJGUUIKChVNMtXgQnMrLy/4BLOe+ioJJ4ZrBGkISsKG02vzPdAep7bMj9/Qc2XIPl0jaGrjkh8Z6vPPL0h4rP0UQlWf5wbjQSLpIxDpGQPgZEbgxoo4AveFfe388LFd7G31bijCPp1rzlnEh06dM2kqnbLSJZHAzO79GCtSym8D3x7mmJuAYftBpJQ/BX5apKX1olCJlJuEEB8C1mQe2iClvL8UC1L0UEypj8lUQrt5RxNf317LOeY6rha/pt45yo5YA8Z5N3NKnlHMbuTlQTMn2xFP29RHAoNduqgM9/n3NpoXAZ/vdX6xE+qHOxLc8XgjT+5qATwP7b0nzeaT5y2iKuQrymuMlZDPoCKoej9mGmK42nUhhAa8IqU8cXyWNHpWrVolt2/fPtHLKCpZAzAWqY/8pHH+hjhRE/6u3rC1351+1kDcs+6sYdd95elzeaaxbVyM4mg+fyklrbE0XUXKhSTSDv/z3H5+uf0AluP9vZ44p5ybLlrGioZIUV5jLOhaVnnXVBMH+zPu7pgQ4lmgb6PQx6WUr5bqNYf1SKSUrhDiZSHEAinl2OtKFSOioFDRMP0Y+UnjbCd8vXOU1vtmw5VfG/fQ2HBlwvneU8RvIKWkM2ExryrE2UuqufeFQ+PWVzLSMui07dLUnSxKLkRKyZ8yn0VrZkphXZmfdWuWcNHKugnvt8hKl0RU78ekQko5lJ5hSSg0RzIbeF0I8RxeN6UApJTyAyVbmaIwCujHyG7cp6e25zrhoyJCWbqlKJ3wI2Wo3EPfktusF/Kty09k7cp6rt6wdUL7SoaiM27RFi/O9MK3jnpTCt844k0p9BkaH1k1j6vOXNCvAGA8UWNrFQNRqCH5FV5XZD7qFmQyUEA/RnbjviL5KywMUiKAKyWuGfL0wwvthC+gE70Qhso9DNeAOBmHVtmOS3M0VZQO9bZYmjuf2MMjrx/NPbZmRS03rlnKrIrxyQ0NRHZsbURJlygGoFBD8hngP4F/BgKZr6uAs0u0LkWhdOzzPJF8+vRjZDfueucoURHBlRKJFybBLLATvsBO9EIYSoLlq795bUhDUcqhVaMpSIilbFqiY0+oW47Lr144xH9t3Uc8O6WwLsxNFy7j1PmVY7r2WFBjaxWFUKghWY03c+RpIAL8N3BuqRY1XRiXSqnKhd6mPkR3e3bjbr1vNmXpFlwz1COpko4X1glfYCd6oQyWexjOUJRqaNVINb2klLRE03Qnx5ZQl1Ly7J62flMKP3neYt530uwJadpTY2unJwWMPReZ598LxIFrM70sw1KoIbHw5rYH8TySPVLK0gyQniaMm9jgOTd7nkEazxOxEgN2t69dWe8l1h/+khfOMvWRdcIX4PkUg0JKbksxtGokml4p26Gpa+yS7/tb4/xo8y6e29sOeD+Wy0+dyyfOXjghUwrV2NrJw6Kv/O4S4G+BxXjjyP9l73fe98hor1fg6PJLgeWZ/1YDtzP0IMIchRqSbcBvgDPwlHrXCyGulFJeWeD5M45xExtccTHwvUzuYr/nXQyWuxjJsX0pwPMpBllD8Z2H3+TtJm9+xpI+k/tKMbSq0NxLMSTfo0mbn2/dy/0vHs6FxN65oJLPTtCUwqBPpyJoqrG1k4SMEbkNT+a9Da/Y6bZFX/nd58ZgTAoZXX458HPp/XJvFUJUCiFm91E1GZBCf3M+JaXMNmgcBS4XQny84LcwAxnXpPBIuttH2wlfoOdTLFpiaYTwpED2tMb40r0v870rTylZZdZwITXLcWnuTpEcwwx1x5U88po3pbAj0TOl8LNrl3LO4gqMZCsO42NIstVXFSFTha8mH3+LZ0Sym0U87/HRGpJCRpcPdMxcvPklQ1JoZ3u/Lj8p5X8Wcu5MpZRJ4dEyppzNWLyZEfKdh9+kI26hC4EuvNnxHXGL7zz8ZskMyVAhtWLMUH/1YCf/8dgudjX1TCn82OqFXPnOefhlEj16iPEohMzOPa8Imqr6avKyGM8TySeeeXy0FDK6vODR6H1RvmyJKFVSeLQMlLP50r0vU1fmpztlF2ZYiqzrNZhh29MaBySWK5HSmxmvCTKPl4aBci/Xn7eYlbPLae4e/SC5pq6kp8f1VnPusYuPb+DT5y+mNuxDS7ahpzoAkKJ0f46GplERVNpXU4Q9eOGs/F/4UObx0VLI6PJRjzdXhqREDJcUHm/tq745G9uRdMQtokmbZfVl4z55cKhiBNeV2Bm1X4SnkG9LEKUbpwD0zr1kvZB4enRiiynL4RfbD3DPcwdIZbrcV86KcNOFyzh+Tjm4Nnr0MJqTLNr6B8JvevkPVb47pfgXvBwJeMYkhCd58i9juGYho8sfAG7K5E9WA52F5EdAGZKSMlhSeCLGx/bN2bREU2gCHClzszaKXQwwlLEcqhjB1DUsN5OLyLMd46Hj5LiS1miK6CjVeqWUPL6zhTse301TxpOpDvv49PmLufh4b0qhsOLo8SaELO6I3Sxq8uDUZu933vfIoq/87nMUsWqrwLHnD+GV/u7CM2DXFXp9ZUgmgIkYH9s3Z5N2XATgy9ucCy4GKMKs9aGKEYI+jZTj4LqeHRGArkHIp+WuXQpvLp62aelOY7ujK+vd3Rzltsd28dKBTgBMXfDh0+fxsbMW5D53LdGGnmof81oHQk0enD5kjMaoDcdAFDD2XOJNWxwxypBMABMh89E3Z6NrAtuR1EV6REILKgYo0qz14YoR9rRE6U7auRkkkYDB4tqyknhzjitpjaWIJkfnhXTGLe56eg+/e+UI2Qb3c5bWcOMFS3o+T9dGjx0rSSjLZ3j5jzIlnqiYIJQhmQAmoqKrb85mUXWI1lgaXRNIKQsvBiiww304YzlcMcLXH3idWRXGiLW4RspYJE5sx+WBl49w99N7c6GwhdUhPnvhUs5YVJ07TlgJ9PixooaylHiiYjKhDMkEMFEVXX1zNqOadVJgh/twxnK4YoTRanEVylhzIdv3tnHb5t3sy04p9Ot84uxFfLDPlEIt2YaeLF4oS4knKiYjypBMAKWS+RjNOkb8mgV2uBdiLId6/dFqcRVCd9KiLZYelRdyqCPBHZt389TuVsDL31x28myuO3cRlflTCl0bPd6EZicGvtAIUdVXisnMsBMSpxLTcULipCM/R5Lf4X5pfxXgYkx37EvfqYmtsRRtMYsyv86KhvIhX8NxJc3dqVGV9MbTNv/97H7uff5gbkrhSXMr+PxFy1hWX9br2NGGsqQwsCsW9lxHha+mAzPC6itDohg5uaqt4nS4j7QKK3v8203ddCdtqkImtWX+IUcIJ9IOzd2pEVdkuVLyxzeO8eMn9tAa86YU1kf83HjBEi5Y0X9K4VhCWVlDognhzalX4avpgDIkRXmB4aWLVwI/BU4H/kFK+b285/YC3YAD2FLKVUO9ljIkAzPezY8jYSzz5AuZ/S6lpC2WpnMU89PfPNLFDx/bxZtHugGvOuqqM+Zz1Rnz+3sIroMePzamUJam+yibtUx1n08vJs0PUghxF3AZ0CSlPHGA50suIz8qCpQubgP+CvjgIJe5UErZUsp1TmcmovlxJIylCmu4yrCU7XkhI52f3hpNceeTe/j968dyj61dUccNFyyhobz/lEJhJ9BjTQg5usS9qWuUBQzCgQAiNP7y8YpJyDcq+snI843OsfaV3A38EPj5IM+XXEZ+tAwrXSylbAKahBDvK/FaZiQT0fw4EsbSUzNU4n00cu9Pv93CbY/v5mhnMtdQv7QuzE0XLeOUeZUDnqMl23n97T387tUjNEVT1Jf5ed9Jszl5fsWwrxf0GUT8aniUog+eEeknI883Kj43FmMipdwihFg0xCEll5EfLYVIFw+FBP4ghJDAeinlhr4HCCHWAesAFiwo7myMqcRg4atSND/2fa2zl1TzTGPboN8PFUobSxXWQJVhadvlytPn0pbJZxSClJKfPb2P/35uf66SSxNQ5jf41LmLBzYiroMeb+K1PUf42TP7MDQo8+l0xNP87Jl9fIKFgxgTQdhnUBYweqkKKBR5lEJGvhBKKyM/BkYtS5zhXCnlYSFEPfCoEGKHlHJLr4t5xmUDeDmS0S916jJU+KrYzY99X2tPS5Tn9rZRH/FRE/azt9X7vq7MR22Zf9hQ2lh6avqWUc8qD/Dhd87jtIVVw56bZV9rjB9t3s22vT0J8sqgSU3YR9px+eX2g5y1tIbnGtvYuO0AR7oSLIjoXHOij1PnlfG7V49gaOS8Cr+hg+3wu1eP9DIkhqZR5jcI+Q10Vb6rGJpSyMgXwqSVkR+1LDGAlPJw5muTEOJ+vFDZlqHPmpqMJSE+VPiq2M2PfV+rO2mjCehK2NSWBehKeN93J23qIoFhQ2lj7alZu7Ke85bX0hxNkUgXXm4bTdrc/cxefv3ioZysScjUqIsE8BuepxDQNI52JXiusY1bN72NoQnm+JMY0Q7+82mJdvZCmqIpyny9Q1M+Q6M56gk2+k2diN8gaKqWLUXBlEJGvhAmrYx8IdLFAyKECAOalLI78+8/w2t4nnYMdJd/w389TyRgsLw+MuzGOlT4aqwbdV8D93ZTN7PyEs5px0UT3teBvs9fy2CMZXTuSIdOOa7k4deO8JMn9+YqueZUBvDrOpbj5IwIQNJymVUeZOO2A/iEy2y9m6CbBEMjlfE66sv8dMTTvfIcadulIRKgoTyAT1f5D8WIKYWMfCFMThn5QqSLhRCzgO1AOeAKIb4AHA/UAvdn6vQN4H+klKWMD04Y+Xf5XQkr168QT9kFVVkVIkcymo16oJBZd9LG1FMZ78PCcSWWJOOFWPh0LSe0ONBaRvr6g3lpo5E4eeVgBz/ctJtdzd6UwqCp8/GzFnDF6fN4aX8Ht256m4TlEDA1kpaL7UquOmM+t/3xNRb5uzHyGgyzXse1Zy/iZ8/sA9vBZ+i5PpWPn7VQGRHF6PhG5yN8o6KfjPxYq7aEEPcAa4FaIcRB4P8CJkwBGfkCpIuP4rlQfekCTint6iYH+R5FSzSFhkBoYLlyyNDQcI15Y9XuGihkVhUyaYmmaelOYeVV1QoBh9oTRAIGSdslEjBGJgbZh6HyPmcuqR6R3PuxriQb+kwpfM8JDVx/3mJqyjz14zOXVHMzy9m47QBHuxLMKg9y1RnzOWuuzuNlUTrjaYw+XkddmZ+T51dwrVjIw68dpaU7xazKIFetms/qJTUjer8KRS88o1FsGfmrh3leychPZfI9irTjDeGwLS9U09gcpbbM1y80lL/RzioPYOqeVIjtuCwfRiqkUAYKmfkNDduRZPPFQoCQYGoatiuxXcnNFy3jmca2MUmjDGTEYimL/9i0i3+pObmgayQth19sO8DGbT1TCt8x25tS+I7Z5f2OP3NJNWcuyaj2Shc93oyWaOeyk2bleR0aadvFduH9J8+hKuzj0pNm896T5ozo/SkU0wllSCYB+QlxTQhSmfyCTxfYruRQR5LlffSc+m60tWVeYjvb1b15RxNXb9g6quR91tNp7k7REk3REAlQnjEox7pT+A0NR0p8QiCEwJUSXRMsqy+jM2HxV+9ewV+N8TPpa8RcKTE0jUMdw5cte1MKm7nj8cbclMKasI9Pr1nCu99RjzZc1ZSTwog1IVwvxHjy/Ao+wUJ+9+oRmjOfx0fOmMf5yye+D0ehmAwoQzIJyE+I72uNAWBoAk0Isjnkvo11QyXYx9LN3tvT8XOoI8mhjgQgMXQNy5HMqwzQEk3nPBORSa6fGH+Oj7m/ge/fDJULeXnBJ/jOrvmjMmZZLy1o6jiuxHG9MNms8uCQ5+1qivLDx3bxysGeKYVXvnMeH129oFcOaTC0VCdaorXffPhT5ldyzrJaT75dU/0fCkU+6i9ikrB2ZT33rDuLWRVBFlYHc3f9hi6YWxkg1qe0dX5ViITV+7FsUjvfW8nOYzd1wfotjcOuI//c8qCPeVVBDE1wtCtFfSTAivoyDF2jtsyPi8R1vf8u0F7i86n1LPZHIVBFrPUwtU/8AwvbnuplzDbvaCro87hhzRJStktX0sJ2XRKWk0t+D0Rn3OLvf/Uq6/7z+ZwROX52OXddewafPn/J8EZEuuixY+iJll5GRBOC8oCP2ZVBqkI+ZUQUigFQfxWTjPlVIQxdY0ldGStnlbOkztu4+1Y93bBmCZYjiadtpPS+ZpPaB9rjBPuIChbazd733EjAZFl9GfURP/esO4svX7ISy/EM3JyKAEIDR8JN/oeoLCsjXBYBITiW1LAwuNr+9YiNmZSSk+ZVcNPaZVSH/HQnbWrCfm6+aHlPDiOD7bj86oWDXH3nVp7d6/Vw+XRBXZmP9niaQ20FiCg6KYzuQ2hWNPeQoWlUhX3MqQxSETRVE6FCMQQqtDUJyC9zjfiNXH/DUA2EQ/WHRB4x2NUU9fIYukZdxI+uiYJKcEc62fC0+VXcsGYJpz7S2WtyYtp20UWABrdH+LAQY5a0PKFFy3F7J78HYNveNn702G72tXnXFAJqwz4qgyZCCBKWw8ZtB4a8hkh19fJCTF0jEjAJFxAGUygUHuqvZYLpm89IWA4CMDVBZ8IasuppoP6QzTuaaI6msF2JJsByXA62J6gKmXztfccPu55RTzbc2ntyos/Q0Ow4x/SG3CH5Bqlvj8inz1/MyfMr6cqTe8+XJZmdKcc9c0k1h9oT3P74bp7OTCnUhFdNNrsi0Cv0FDC9zvQByVZlWVHA85jKlICiQjEq1GCrCaaQmRqjuZ7tSFqiKdKOi64JFlWHeOSLFxR0jfzJhtnRrt0pe+iEed7kxG7HpK2rE5w033Kv4+3IWblE/S0fOAGg1wySWNomZbn8VV7oKl+WJNsgmHZcVjaU83RjS25K4SnzKrjpwmXc9thuWmOpXmG5hOVQE/bzbx/p3Y4k7CR6vAkTh7DfIDxZ9K80HapLLaekGGcmwS9W6VE5kglmLPmMoa5XHjRzeZZldWX9kvVDkU38f+vyE4lnNvBhE+YrLoZLv0e7Xk13ZzNtooof+tfxpDyNgx0JTE3khlVlE/pBU8d2JaamoWuCjdt6hEc3bjuAoXnHIL1QWXN3isffbsZyJPURP1+/7Hj+7S9OYWl9GVedMR87U9klkYMm57VkB2WpJupCGrMrgpQHJlH+I9UFR16Z6FUoFCNGhbYmmGKr8xbzeiOeZbLiYj67OUJTWc/rL494HlZV2J87J5sLyjZfQv8w1JGuBOUBg0QmZ5LMG0517TkL+ciq+fjzDPBgnem5/IjrsHPXbh55aTfHupLMqihNB/qzja1s3H6Ao52Jfq8x6HPpGDz+Xdj5iJfomb8azv3imMYXKxTjiTIkE0yx1XmLeb3RzDIZ7hzXlTREArkekSxZgcQstWE/e1pjxPM8qZCps6gmzDVnLxrwtQdKzgshCGtp3m7cxc+feAtTF0QCJm2xFLduepuboWjG5NnGVm7d9PaArwH0e+6OP73G7N1vsKDxHrBimaM06DjghQn5njImiimBCm1NMGtX1nPLB06gPhKgM2FRHwkUNK98PK43VK9KIed0Jy0am6O8ebSLzoTF7189wqGOBH/+znmDhqHStsv/PLuft5ujOSPiNzTqy3xUhEyuOXthQWvXhKAiaDLfn6DebeXe5/dh6oKA6fXWBEyvHHnj9gPDX6xANm4/MOhr5D/nw+J9zma+n/q/LHjrzowRERCsgfrjIVQNmg+evrVoa1MoSonySCYBI1XnHW52yVhk2fMZjXeTPaclmqSlOw3Cyzb6DY1vPPhGrhekbxjqI6vmYbkun/zZNg53JAEI+3SqQj4sx2F2Rah3qGoQTF2jPGAS8YEWawLLC5cd7UwQCfTVDdM51llAn0mBDPUaEqjyw/mpx7gk+TBVsgOANAY+0w+R2aCboGX+JM0gdOwv2toUilKiDMkUYyzyJyNlNLNMsuf81cYXcaXEr2tUhvyU+Y1efR35Yai9rTFue2w3z+/zphTqmuBDp83hmrMWURYo7FfU1L0GwjK/4eUcuo5BnjrwrIogbbEUgbwBUynboaFiaMmVkTDYa8wuNzkz+RTv7v4tNdJ7jzY6m41zebLiA3wjcA/EWj1DksVKQOXMHR2tmFooQzLFGHECfOejXoikYx9ULoRzbh5R3H003s3alfVEAkavAVjQP6HenbT42dP7+PVLPVMKz1hUxWfXLmVhTbig1/IZGpWhjAGREmItkOjod9xVq+Zz66a3ARu/oZOyPe/qqlUDS66Mhr6vYVlpzk4/y7XuHwkkjgJgo/G0eQ73G5dwTFZx8+rloPlI/ukfae9K0ekGKdMtagIQfM/NQ7+gQjFJUIZkinGgPY4uPHn57ACpgWTmgV69HQSqvIbBIidxB2ssrCsL9OvryCbUHVfyu1ePcNeTe+hKeoOp5lYG+ezapZy1pBpRQDmuz9CoCvkI+zO/wo4F3UfBTg14/OolNdyMl8c41pmgoQRVW9nX+MW2fSxtf5I/l49Q7xwDGxAazXPexfrku3gjWkZDJMjNmdd/tnElj9lX8H7+RI3WwRHq+b71QS5zT2Ft0VY39RnLOGpFaVENiVOMS/79cXY1x9CFQAjvJtyRkmV14f4Nh3df1qvbHIB0HCINcO2DY15Lfpitb2Mh0K+h0HYl7z95Nn/a0cTuZq9KKeTT+dhZC7nitLn4jOFrP/oZEPBCWdHeoawJQbqw+0/w3AZoy2qKCVjxHjhjHVQNXCjwxV++RFsshWn6OKbPBsbWlDod6fu7ls3XjaUwZZyYJE1KpUV5JFOM3N26oOdXVDLwXXzHvl76V0BRk7gDNRbammTjtgP820dO6ZVQrw750XXBhif25M6/5IRZXH/+YqrDvmFfK2DqVIbM/iq+8Tbvv4lEStizGZ5dD61v9zy+7N1w5g1QPXTpdTZJn28Gx9KUOh0ZcUhXMa4oQzLF6E7ZzM3MA8mGtmaV+weeXV65sJ9HEotF2ZOq5MbvbhpzeGB/W4zygDloY+GZS6o5eX4FG5/zyl/TmabC42dHuOmiZayc1X9KYZasztbR7iQLqkJ8du3S3ut0Hc8LSU/gZisl7HsKnr0Dmt/seXzxBbD6RqhdUdBlskl6s4+8y2C6ZDMxpDOanibF+KEMyRQj27m+pK5nYmI2DNKPc272ciJpwAzS2tlJLB7nX+0radfT2I476oqvpOXQEAnQHB04DyKl5LG3mln/eCPN0cyUwjIfnz5/6CmFzzW2sWHLbva2xTE1wawKL9fSa512CrqPgDOA8RwPpISDz8HW2+HYqz2PLzwXzrwRGoYXx8wnm6R3cJBa7zn341mlN5kptgKEoriohsQpxlBzSPqR0b8i0kCiu5W3Y2G+4VzHM+I0bEfSGkuTtp2CZoRkcV1PDPJwR4K/WDWwvtX5y2v5wi9e5v/97k2aoylMXfDR1Qv4+XVn8mfHNwxpRH7w2Nsc6kxgaN7oxSOdKRxX9swySXVD58GJMyKHXoD718FvPttjROavhg//FN7/gxEbEcgk6S9aTk3Y36+JdCxDyqYTI/q9V4w7yiOZYgzU23H2kmrWb2nkq795rX/oY8XFsOJiPrlhK8+1t2LqGgIvUY8L3Um74PBALGXTGk1jZ5LafRsLa8N+Qn6dHz62Kzdj8Lxltdx4wRLmVA7dr+E3de578RBBU8eVZIoJBC6S5u4Ui2tCRNuOQHft6D64sXLkZS+EdfC5nsfmnA6rPwNzTx/z5VcvqWH1svp+6r/TKaQzlhDdaHqaFOOHMiTjSLFi3fm9HYWGPg60x/HrGo70dAHB+5qy3WHDA5bj0hpNE0/39wLOXFLN6Qsruf+lw/z8mb3EUp6syaKaEDdduIzTF1b1Oycfv6nz2oEO7n5mH8/vbydgaLlZ9dl58I5jEUkfY1b5BBTAHHuDjs23UtncUw3YXfkOIhd8Huad2fNhlojpEtIpRoiuWIoNiuKjDMk4UapYd6HVLPOrQjiuS2vUwkV6G7SU6JoYNDwgpaQzYdEetxisTPy5PW38aPNu9memFJb5Da49ZxGXnzoHXRt8kw2YnvzJs42tfPvhHZi6wK8L0o6L40qEEBgu+EnSIDoQjslVq5aP9mMaOS07PQ9kz+NUZh7aqy/kV8ZlPJ94Bzdby1g9DvLzxRb1nChU1dX0RhmScaJUf0iFhj6yG1JNGXTGLVKOi6FpfK5vNVSGWMqmLZbGcgbuzTjYHudHm3eztdErvdUEXHbyHK47ZxEVod7rySbQD3QkEMDi2jB/f+k7+uUA6ssDHO5IomugIanSugk7URZWh/j0+d7G+cVfvjSgRPtQDCXt3o/W3fDceq8fJMM+bR4PBj/AK8YpIASmZbNx+4GiS9APxHQJ6UynEJ2iPyU3JEKIS4BbAR24U0r5nT7PrwR+CpwO/IOU8nuFnjuVKOYfUn6IrCthYTsudZmqra6ExbHuJFJ60xKzm87alfVcebCDO5/cQ8qRhH0G15+3mL96d+8S1UTaoS2eJmUNPAgrlrL572f3c+/zB7Ezuianzq/gcxcuY2leJVmW5xrb+O7vd9CVsNAEIAS7m2P87b0v8y9XntLrc4kETOZUQmtXjAqnnTNmB7lq1YmZ7u/BJdqH2tALPq99r9dI+PYfyNUyVy/h37suZEdolTe9MEOxxR6HYzqEdKZLiE4xMCU1JEIIHbgNuBg4CGwTQjwgpXwj77A24K+AD47i3CnDcH9IheZP+obIHNelqTsNeAq7hzLKuXMrA73CZ68c7OC2zbtxXEnA0Aj5dO594RAnz6tk7cp6UrZDWyxNYpBJiq6U/OH1Y/z4iUba495cdU3AgqoQH3nn/AGNCMAvnz9APG1jaBpaJtQlpKQ76XljfT+XWr/DvPJu6sJB/v0vTs1dJ1+GHSBgGpxkvUzVo/8BgU4onwOnfQIWndvr9Qc6D/I8is4D8NyPYefDXmc6eP03Z66DZRez/95XScZSBPLqG4st9jgTmC4hOsXAlNojORPYJaVsBBBCbAQuB3LGQErZBDQJId430nOnEkP9IY0kf9I3RFZb5nkisZRDe9zCyPReZOXM42mb7zz8Jnta47hSYmgiV/pbE/Zx++O7OX5Oeb+GxmxD4JGuBOV+k4TtcLDduwsXQCRgUBfxkbYlP3hsFzcL0UviPWDqVId9NHWnMlVYPdcWAmzH5WB7nG9dfiJfuvdlDnXEqXC7qNJiuD6Dqy7ovcH0lWg/If0qV1m/IC01qKj21HO3fBf4ci9jMpi0u9NxCDb9Fnb81mtuBCifC2d8Go67NCfnPpTY44hCZjOc6RKiUwxMqQ3JXCB/ctBBYHUxzxVCrAPWASxYMHllt4f6Q7p6w9aC8ycDhchqwn4MzUIClUGzl1xK0NR5uymK7bq9Sn+l4yXSdx7tYt3Pn+dIV4LZmfG04OlkCSCesjnW1SOEWBU0Cfh0Ihmtq6BJL3n4gKnz6sEO7n56Xy70JiBXhUXm34am5bwxU1rMkS1oMg1SDChO1Fei/eLUH0hLDWkEPctmBsACXvxZL0PS97xKt50/iz/IBfbT8EbGgERmwarrYeVlvaXcGVzsMfsZlXLi4nRjOoToFANTakMy0J5QqEpkQedKKTcAG8ATbSx8aePPYH9II8mfDBciG+g5IFf6m/sIBTkhxdZYivKAQWtmM/QbGvGUTVfKJlusZeqChdVhulMWZf6efAGA47q8caSTj/3kWSoCJs3RFBVBMxd6607aaEKiZ36kroSqkMkNa5bw882vsizQSSjiB/wAJAdIZvf1DGqcZroJ0RDq0emKOgapI3u56cfP5DyE7HkVbisfcB5lTXoLPjLeV7gOVn0Sjv8g6IPrfa1eUtPPOHzxly8NHTJTKGYQpTYkB4H8gQ/zgMPjcO6UYiSJyOFizQM9t6Q2THs8RWvU8iYWCrAciQQcx+tUrwr5CPt02uIWRzqTudfThaC2zEckoBNNWbzX/xqXRf+XubKJI1oDPxeX80DseAxNUB3ysaspiu16yXzhE7nQW0fcys0cWV4X5svvWcHauS7r2w8XNLmwr2fQYdYzzxcjlDFqLbE0sViUNllJh5XGcSW3bnqbvzmvnu/N2Uxt42/w4eWSLF8l5pmfghOvAGMAaZkCGI+JiwrFVKHUhmQbsFwIsRg4BFwF/OU4nDt+jHFwFIwsETlcrLnvc9ees5BYyuFf/vAWQZ9Gd9Lp5dbpOtiO5FhXEiFErhILoMyv0xAJoGuChOVwqf81/tr+MS2upIMyqmU7X7Q30KFdx57KsxFC4EiJJqAlmqI842V5oTeNJ758kXfhdNwTXEw5I5pc2Msz2Ot6ORErSdQxiMWimNjcKy/CleDEO7jK/wQn/OlxAjITmgtUwOnXYp70554K8hgYj4mLxUaJPypKRcnnkQgh3gt8H6+E9y4p5beFEDcCSCnvEELMArYD5YALRIHjpZRdA5071GuN+zyS/MFRZtAbj+qmPX2rERqT7B95sRKR0ZRNRzydU9z9z6f38l/P7cdxJW6fH7kGvSTMswr1QkBDuR9d07Bdya/D36HKbSMm/bTF01i2i+Em6dKr+L9V3wW8gVuW4yIhp+6bm63x6dUQb+01wTC/PNfOdNBbrmRRdYh1a5YOHSba+xS8+DNaj+xlv13JvfIidrCYD4rHuUI8TlhkPCt/BE77OJx8FfgKm7w4HPnrzk/C33zR8tGHtjS9n0RKsZjC8zymOjNiHokabDUWSjw4aqRs3tHEPz70JntbY0hgfmWQdWuWcuaSav76Fy/TGvMEEA/nha76oguYVRFASmiJpZASTphdwVVnzOfKp96LFqzOlfGCZziCThfrqn8KeONzD7YnMHTBsrqy3Ib1rctWcMFcCVb/1362sZUfP9HIntY4pg41YR+GrhW8MV/942dIJ7q5TD7BldpmIsLLLUVlgCfDf8YlH/1rz5gUmWzVVtEmLpbQkFy9YWu/8KkanjUuzAhDojrbx0KJB0eNhN+/eoS/v/9VOrONf8C+tjjf/f0OvvyelV4Zb8DIlfAOxqwKL2fQHk9j2xIX2Nsa5VcvHuQ9gbn44s0cS2qkbRefoVFh2ux1G3JhOV0TVIVMasI+OhMW86pCfPbses5vSILVc9PSt3RWSsm8qkCvUFFByWsrwdXG46zVHqacKAAJ6ePX7vn8Sq7lb9euLokRgYGT8JMV1VmuKCXKkIyFAQZHYSWgcvzKkONpm/a4xe2PNxJN2Wia6JFpdyWxtM3GbQeYXR7kWFeCpDX0ONqk5dCVsJHIXLgrmrI50Bbn6/ZFfNn9CQIDW/owUgliaZvt82+k3g3kwnJfe9/xXrjEdSHWBKlor3q7gbrND7YnmFXuh7y9bsjktZ2C1++D53/KB+Kt3tqlyW+cc7nHuZCYFuHjZy0oykY/HfpFVGe5opQoQzIW+gyOyuVIzrm56C/VN1F67dkLOXF+ZU7K5EhXAseV6Hmdf0J480MOd8Y5cU4lLx3sGPZ1WmMWpgaZ1ApmxjB1J20elScTl9dyvfYg80QTh6hng3MZT+1dxPqP9cnpWEmIHh1wbshA3eamJmiNpSkL9JThtsfTJC2Hq/PKeVcvLIc3fg3b7/KMFOBqJg/Jc/hv50JaZDm6ARGfwXENg09gHI6s8djbEiWedrxy5pBvyvaLqM5yRSlROZKxkqva2u95IqOo2hqO/ESpT9eIZYb63HzR8lw3+V//4mXeONKJhJxHYtkuff0PXRM4fbPtgCHAkZ7jYGjguN5XQ9eRUuJkfk8sR+LPSL2DpxBsu5Ll9WVUhnwcaI+zssLh0++sYPXi6n6vA15OIxLo3TgZTaY52pVmXlUAv6HTHk/TFrOoCXsbuG2lOSu9lU+Yf8SfOOadpBlw/OV848hZ7EmGe4XFkpZNddjfS2alUPI9pubuFJYjEUB9uZ+w3xzTtYekhDkSKH5Bh6IgVI5EUQCZwVGl5I7Hd6MJrxvc08rSkdJhwxONORmTsKnjMzRPK0tIHJd+RkQTUObTSNouKdszDAIvwS40gQ/wGRr15QGOdiZzpcC24xmk7D2HzGtTl9IzTjuboiyp9rPU34XVHefWPx0bNFGeXzobTVq0x9OkbYnPEBhCEE1aJC2HmrBJTcjgTGsrlyV/S53bAjYgdHjH+2HVp6B8Dm/9+Bkigd5NkmPp6cj3mCwniS48A9sWSxP2m+PWL1Lscl3VWa4oFcqQTGKklHQlbfa2xogEjF4zQRzX5VBHgrmVQcoDBknLk0AJhnVaYulevSIBU8O2XRCQdiTlAYOWqCe8aGTECF1XUhn28fGzFvKfW/dhOS5pp+cq2YiZxLuGKV20zARDXEmZbrPQiKHjQqbLe8OW3QPmFrLd5vF0io6MAKQAIn6DhO1y87tWcOsfd3Ch/jLv7/4ts1zPA3ERPKmdwZqP/h+o6OlVLXZPR36zoakLHEciNM8bG+u1C0XNaldMJZQhmYS4rqeO25mweHpXC91Jm6buFH5DozrsI+wzaImmMTI9AQABQ6MrKWmNWTnPwacL6iN+WmNphOZpWFmOS1XIS7B2Jx1c6Q2RWlYb5suXrARAsg9NE+iSXBjM1DVmVQSIpWxaMr0eYZ9GxG9gx1o4Lmyj05PfsB2Xg10p5kk5oBbVzcD/e+gNXCl73pffJJVO88aT9/M95wHmpA95nweC7eYq7jMuJRVZyJqKfMGDoYUVR0O+YaoKecKTZEJ9Scse07ULRQ2CUkwllCGZRNiOS2fCojtp40rJc41t3LrpbYKmltkcXY51JqkKm9iu9Kqc8GaENEVTuTvmSMCgPGAikIR8Bke7kmiaQLqeQQCv29xv2Dz5lXf1WsPVG7ZSETSZnbnj3nG0ywt/aV6FVSRgEvLpHO1KUR8SnFgeJ+6X2NK7bjRp0RJN5byZox1J6jK5hfxy3tVLaijzG8yuCHq5Eik52XqZ9ycfYIHbo9W5TT+V3wU/wB45y8sLDbCBDyasONpkeL5hCvsNKh2XroRF0GdQHfaPS9WWKtdVTCWUISkBI41tp22XjkSaWMrpFb7auO0AhiaIhP34MgnotO0ST7ssqg4RtxwOdSSI5c0QqQ37+PEnVvHWkW5u3fQ2CcvxpOMznkV1yI9paCQth/nV/bu8+25gPl3LhLl6Mi6GrnHuPB93/fkikDKXnI6nU7TF0r0659Ou5EhnktkV3l11fm5hVkWQtmiSd4q3eH/yARY7e3tOXHwBr875CP/ztj9jHIbewIvZ09HXMM2rCnHayRW8eKCTo50JNm4/kDuuVKhyXcVUQhmSIjOS2HbScuhMWMRS/UtkgVwTIXiz0Mv8BhKZa/Tbsqsld6wQgITyoMlbR7o5c0k1N7OcjdsOEE3ZRFM2VSGTqrBvyNLPfoOmyvwc6khgaAIpJcm0RcTp4IbT5+ay771DVf3fh5ReolrXRE9uQUrWLWpB27ae47yRMwC8rL0D/azPcOLp53IS8O+nFfa5D8Vo+kDyDdNopzOOBVWuq5hKKENSZIaLbbuuJJq26U7ag46zzTK7PEhrLJXLg0gpaYml6U7aOSNiZMp5DQF15X4sx81scl5pcHZGyBuHO/nJk3uHLf28Yc2SzKCpTF+KJgia3uyQWDzOieUJPrZqbr8NdPWSmkzYrP97kkDKdntyC4dfhK23847Dz+eOeU2sYFPFFaw+56Kibs7FMALDTlksBoYPzB5vQw2CUkwllCEpMoPFtg+0xWiNpnL5j0K46oz5ufCURHKsqycPEjA0/nL1Arbvbac9ns4ZG+gZNLV6aQ3VYR8VQZM5lUHeffysgl5XAMhMma8UBEydv3/3QtbMtntqgAfAcgbvmnclnCD2svK5n0DLCz1PzD4VVt/IifPO4MSCVtebrLdR37adD4vHWGC0E6ielxu7WwwjUHTJeCG8BlYj0POfpvU7TJXrKqYKypAUmb6hISkl0ZRNbSRAZ8Ia0bXOXFLNtYlF3LFld25OOsC731HPp89fQl3Ez+9ePZILf2UJmBrHur3SYJ/Rf4MaivVbGikPmszKK2/VUh3cv2U7a/7ilCHPNbSBe69WiANcp/+esxJvQnbvbTgRVt8I88/qGZ04QrLexunuG1zj/C9pqXHI8jG7o4lQZuzu0U5tTEbg2cZWoimb5u40PkNQFfJRFjBHVgKsmxnD4Qcj6HkfCsU0QhmSIpONbcdSFj5DI54eXSlq2nb55fYD/M+z+0lm9EqOa4jwuQuXcuLcitxxfcNfCLBsycLq8IiNCPT2qAxpUel2YOopjnUObwQX1Zbx2sEOMr2OLBGHuVb/Pefrr+WOecudxz3ae7n09A+zekHtiNeXT9bbeJ/1R2x0bM2PkJLWlEaoTIcXf8asis8W1GMyUB4FyFTN6STTXmiuqTtF2vF6dgb8mRbobSgU0wllSIrMectr+dKfreCup/Z6m1JmDnpWymQogvs2UfX0P7KptYp/TH+Eg7IO8MbSXn/eYt5z4qweQcYM+eGvoKl7XegSbrxg6ajWn/Wo6owE5dIr/U0WePd91ar5/HNrjEjiANfqf2Ct/nLuud3ubH7qvIdt4iRsG3Y+toubhRhTjiEbcqp1W4jh5Rc0IbwZLEYYuo5w1fnD95gMlkcJGlrmMT8+Q6MtliZluyQthy9dfJy3duVtKBTKkBQLy3Fpj3slvCfOreDfhgkD9SW4bxNtf/gX/jp6Bc+4JwBgYvMJ/+NcdfFatGWzBzwvW5117wsHOdqZYH51eFRJ2WzJ8t5jbfhTrThBCSEfyxOvcIn1KMvcTri/J/cwEKurY6xv+DXVhx5DE55bss+t527nPTzunowQGhpeedmxriT/76E3+Op7jx+1Mck2DrZotVS4HaTx40rpeWJ2EspnF9RjMlge5WBHgoU1Xol02G8S8vtIYdKSFKw+9STlbSgUGZQhGSPZHpBocuAS3kLoSlj86KFXuT/+tzh4IaoLtZf5B/89LNGbSb72JkeXXTTguT5D44Onz+Wq1aOXrs+WLFdpMU4qi9KheSXGJ1mvcq12H6FQkECwCmKt3njbpsvg0HboOgzlc+C4y+DwC/DWQ9RKBwQcEfXcq13Cg+mTSUvPixKA7Up0IdA1SKTdMZXRZhsHf6e/m2vcX+C6SVL4qPG7noT9aZ8A8kp59z4FL/4InjgML8/JGcXBkukWBm12AN0XJC182ML0hkFVB3qPDlAoZjjKkIyStO3SEU8THaQHpBAcV/Lblw9z5xN7iFunArBIHOEfjP/hIv0FNCnBFvja3u53riYEVZmKrLHy48ffZrZopdJwAEFV2E/Qp3NVegu14TIwvWFXmAGIxeH5n0JkDhghaH4LDvWU8VI+F864nv3mWTS+cJhwaww7YZMdSKILgakLXNcTaTR1Meoy2h5vI8BP2tyeqq3KATynvU95RlAzwV/RYxT5MrMqwrTELDQzgCV8pIWPjrSGv9agKe1g2pnxtBnVZdXLoVD0RhmSEVIMAwLwwv52bntsN3taYgCESPIF414+of8en3CQeFuvALR0F8F9m0gs9LySoE+nrsyPofcPq2RDVDuPdWE5XphneX1k8HBXOobVtpfKgE6+4rXf0KmINYHR5xwr6s0YSXZAspPcxCrdhPP/zlPl1U1WA6uXeedmcxDHupLomqcl5gK1Id+YlXR7GgdPBa4f/MAXf+YZkZxRDIKtwSv38MEL7uRrv3sb08mbZ+5KvprRHlO9HArF0ChDUiAp26Ezbo3ZgBzpTHDH44088bbXUCiA+UY7P9H/icXisKeem3ncMyYajr+aihdvJ7noXVSFvPkc+TIsEb/Rq1kxaGo52ZRE2mFva7R/d71jQ7wFUlHmVvgHrGzqNOtpsJM9m69re8O7kJ4hAW8mSLAGhAYnXjHge87vfE+kXXyGoDZTRpu07JIr6SIEdB/xxiJrGYMpBOg+6DrMBSfM4xbdN6jBUIZDoRgaZUiGIWU7dMQHlzHpS3DfJipevB2z6wBW+Xw6T/sMiYUXkbAc/ufZ/fxy+4FcU+GJc8q56aJlzPnNX2BakqOigTnySM4vkFJwSNSRToWY3baPOZXe0Kd8GRZdwNtN3qxyDYkUgs6EjS7ANHRcKelK2MyqMHqUYxPtEG/LNRcOpp7rnPIx2Hk78VgaOxGlTHaR84GEDqEaCFaCnYbwwKGp/LLa+jI/HQmLsoCB39BLp6Q7UCVV9VJvLLKeFwrMG4usmv8UitGjDMkgjNSAgGdEarf8A1Lz4forMWJN1Dz+D/zP4n/kh28EaImmAagr87NuzRIuWlmHEIJ6o5UDlh+BIEkQHRtHgoGkww0SdBO8nK7kWz98ii9fsrKXDEtjcxRd8yYvpR2J3xRYTs+8dSEg7bgETZ0jbV3QccCbd54hu9EnLYfupIupCRbWlnHVqvm8Y45Jy6E/UnXkcUKZEFYKkzQm3bKMRMxHONFFpR8C532i3+fRt6w2ZXshO1PTiCatMav05t5g1mCYAe/rQJVU4zgWWaGYaShD0ofhhBSHouLF25GaD5nRTHpFLuEfOy7ihec0II3P0PjIqnlcdeaCXpImWvVC5nQcoTml0WJX0CBb0JGkMQmSwofNeucydjdHc0J+s8q9cFPacT1DkkFKLyyWFU+U0vNUmpqPUiGjfPF/unKb95tbH6Zh+4/4Lt7s8yOigXv0y3n3KRezqvU++ON/UZv2vJ2YCPFH/7t5kDXUdL/FVfpjzNbaOGJX8/3UhbQ87uPTbmsBZbUQCZrc+YkzRvz5AhnDEehp+jODhXXGr7gY+F7JxyIrFDMRNbM9Qzxt05WwiadHnwOZ//OzcP2VtLhh/q3zAu6LnYzMBKouWFHHDWuWMKsi0O+8fE9GGkFamw9TTRddMsguOY/1zmVscU8FYEldmObuFHURf84jsV1v2LoQngFxXK8p0dAFupOmTnTgFw4N5T4MXcNyJF87sZ05279LiBgSDZAIXCxMNKERkEnvcyHAJv+7+KP/YhJaiANtMWzHm+GuCZE50xvjW13m7zVed7DZ7C3RNNVhX2FKvJqWZzgy4apRSqooFBPAjPhlnfEeieW4HO1MDik4WCixyELuaV7KD6NriUlv6NRxxlG+Uv8MC9///UHPSyy8iJY1387lVvbJBr7mXs8m+xSvcivvVzFo6vh0L3wVT9vUlvk41OFt+nMrAqQdl7aYhU+XVIo4fjoxBNRGssOlAGz0l/8LP0kkGhLQkehITNKeZTACcPJH+Pb+0zmYMAloRubzkrm/DA28gVl4M+L7lvL2HYEbTVoc60pjaAyuxKtpngpuznj4x/pjUSgUJabkhkQIcQlwK6ADd0opv9PneZF5/r1AHLhWSvlC5rm9QDeeNrktpVxV7PU5rswZkcES5YWwtbGV25s+w4Got81Wagm+UPZHrtI3AdXoPz9ryGsmFl6Es/TdNJQH+OYPnmBXc4xcaa3ncBAwNBKWw/KGcm5YsyRXZbS8vgwpJbG0w6KaMv75snrOn6eBY+e8gnja5kBbDMuRGBpU6E3oGcl3E7cnwQ8kCRC85rcQquYDs1p7JeJ1AZ70l8gZOCk9I9K3lLdvEr815uVmaiN+TrRe4+LUH6hxWmh7tAEu/zKsfO+0lxgZ6dAzhWIqUFJDIoTQgduAi4GDwDYhxANSyjfyDrsUWJ75bzVwe+ZrlgullC2UmIES5bVb/oGWNd8e0pjsb4vzo827eW5PGyDQheSq8lf4gnE/5QEdPe7gutaw16wImlSHfQgh+Mql7+BL975MZzyN5Xqbuy6gKmzmGuIGrDKyUxBrBisJGeMY9hnsbYlhuZ4nYWgCXJu00DG03gbERWChk64+jmDI0wYbaFpgWzxNPG3juiCQSAnVYV8/McS+50oJNeVhTha7+IvEL0hj0qlVErA64U//F3zhXM5iOm64Ixl6plBMJUrtkZwJ7JLSG4EnhNgIXA7kG5LLgZ9LL1mzVQhRKYSYLaU8UuK19aJvolyaIbC8x3ONgHkeS1t4Kd83r+eXu3WcTGb79AWVrFlex+a3qris60x+nP4mC30RAkNcU9dELt+RZe3Ker535Sms39LI28e6SGcaCxfVlA28oboOxFsh2dXr4WcbW2mLp7Ey6xM4vIvnucZ4lNmizVsTngFx8PIdmi9CxTmf6nWdvmNsn21sZcOW3exti2MKQW3Eh6554bZ1C47A/T/KyaesXnU9qz/xZ2D4+ejdr3A0avGexG2khY+UCOBKiWuGvCTL07fCioun7YY73NCz0TIdja5ialFqQzIXOJD3/UF6exuDHTMXOIK3z/1BCCGB9VLKDaVaqNl1ANdf2esxaQQxu7ylZT0WW/j5hb2Gf99/Aa2ul2qeXRHgMxcsxdQEP3hsF4YmKA8Y1HYf5bBVRoNuE/Yb/a4Z9hvUlvl7VV1lGaqvIbdxtMVYWeny6XdWsnpxVb/jNm4/QCRg0BVPcaH2Ip/Q/8A8zXPuHKnxiljOaRVJ9OhhdAlULYKzPz+oKGOWrGHJlg7va41hJW3O4A1qX7iPWLCMcLgGEl2eDEm4DlZczKcvWM7XH3ideucoURHBlRKJVw6NqXvVVIx9wy31xtr3+mcvqeaZxrZhX2+woWcH2+NjWst0NLqKqUWpDclAFQt9y8SGOuZcKeVhIUQ98KgQYoeUckuvk4VYB6wDWLBg9MKFVvl8jFhTziMBEHYCq9xrlqt48Xa22cv4dvdlvG55kwZDIsWnq17g0o//HT5D469/8TKGJnKlvce0BqqcNtriWs6QCDuBXT6fuoifyP7NmXLUfVC5sKBy1OzGEdFSrPBHsbpT3PqnY72qpbIc64hxgf4ql/geYKE4BoArBX9yT+e/3T+j1Wzgtx8/v6DPZ6i559/btBcZCPLB5NPEXD+tCZ25PogEwpCO5zyN7PjY1vtmU5ZuwTVD1JX5iQQM77hMc2B2w+1OWjRn539ooqDBYKXeWPtef09LlOf2tlEf8VET9g/5en2HnoE3zXJe1egFIEvl5SgUI6HUGtgHgfy25XnA4UKPkVJmvzYB9+OFynohpdwgpVwlpVxVV1c36oV2nvYZhJtGWHGQEmHFEW6aztM+Q3N3ir87eC5/2XZ9zohcHnqNR2Zt4Cb917kBUke6EgTMno/0Je0kZtPEQnsPRsdutFgzmrTwrfmiZ0Qe/pLXbR2o8r4+/CXY+eiQ67zr8TeZLVqZZ3RiCoeAaeSqpfI+FNi9ie85/8wNiR/njMhjzilcZ/0t/2T/JYeoQ0qXL/7yJa7+8TN88Zcv8Wxj64CvmW0sbIuliARMWmNpvrvpAE8ehh+8aNNmNmAHqqmXzVgigACao5mmRzOY8zTA21xPvPJrLKoyWVIuiPh1z4jkNQdG/AY7j3WztzVO3HIQgOVKupM2m3c0Dfn55G+sQnhfTV2wfkvjkOcVSt/rdydtNAFdCXvY17thzZJctZ2UkpZokoPtCXYe6+LqDVuHfW8DcaA93qsnCcbu5SgUI6XUhmQbsFwIsVgI4QOuAh7oc8wDwDXC4yygU0p5RAgRFkJEAIQQYeDPgNcoEdkSXDtcj5bqwA7Xc/Dcb7Ph8FI+cddzPJg6DYCTfIfZWP9z/rnmQWbJ5pzHAt60wqTlJblXWc/zHnsT7ZRjYSLsJEaqDeO0j6If92feXbrm8+TIhfC+aj740zfg7svg+yd5X7OGJR2DzoNYbfupMHr3uuSqpaSEvU/ALz8GD/8tc5yDADzlnsinrb/hW/Y17JcNmLrA0CBuSV492ElnwuJge5xbN709oDHZuP0AaRf2dus82+Lj+e4qjshKbtvawr6OVC8PzE8KTQhMqxtad0HT654uV76BXHExXPo9iDR4z0UavO8z+ZHmaIp0RkZGZjr2XekN+BrOIJR6Y+17/bTjomXUA4Z7vbUr67nlAydQHwlwtCtJW8yiKmQyuyKY82RGakzmV4VIWE6vx8bq5SgUI6WkoS0ppS2EuAn4PV75711SyteFEDdmnr8DeAiv9HcXXvnvdZnTG4D7M81sBvA/UspHSrnexMKLSCy8CCkldz25l18+cADL2QtAuenyfwK/4oOh1xBmAGElch5LlvxphX+evo+01EmIML7yBgJBn+ft7H0C+LIXzgp4eY3upE1zNIVpdTObJlKRxYTDVdB1FB76a4j9Pcz3nLFZFUEOtceJpjydKlMXlPl0LgrvgXvvgGN5tnbhebw672p++orOntY4PgNqwj7SjqQlmkYTYGjgOJKOuEVlyOzpA8nr53i2NUhzIoSmCYQG0oWW7jSW083y+kguXHNf4MPcGL8dn9NGDV1gC0CAr8zztvheT+huxcUDhvHWb2mkImjSFvPkZLL9soaA2jL/sAahFOGjoa7v0zXSjosvT4l5qNfL5r6u3rC113VGG5LKjnaOp+0e5WIlda8YZ0reRyKlfAjPWOQ/dkfevyXwuQHOawRGNmawCOxujvJPD+2gMSPvDl6oJejXmXXSxbhHjw3aZ5KdVrhhy25q7aN0yDA+AwxNQxP0DvNULoTuY3S7Pg51JhDAHNqxMDgQlczTUpT5DHAt2H5nzpCcNr+CVw52ogmv0Ol4dyefSD/CSfYe6MwsZP5ZsPpGmHUSJwF3ntaT4zjWmSBpOWjC6/0Q2X4QVxJNuezpAiKzvFLcTKNI1DFAOLkxv0KAKyRp2+WGNUt48L6f89H4r5ktm4gSZA7t6EJ6TYVl9eAv75UrGYpsfiRo6tiu1z0vpddJX4hBKPXG2vf6kYBBczRNedBTYC709YqVeM/mnZTUvWIimfGd7Vk6ExY/fWovD75yOKdTFc7M/fBlGgG/v28R//aR/x32WgnbpUlvoJYOkuh0drQS0rsw3JS3Qe98NCci2NmVQEiToEhjYNFMDT4s2mMOZb6MnlRXTyX0iwc6qQ6bLEzt5KM8zGnarp4XnvtOWP0ZmHNavzXll/Be/eNncrPdXaERJ0BUBIjaJmdU14C/rNe5pi5IWN4cESF6vASfLlirvcxq825aHehww5TpFgFho1UugEBF3kV650oGIzczPuLncEcSF4mUMldePNwGXeqNte/1F9eW8ZdnelVbI3m9YnpOSrlYMdHMeENiOy73v3iIu5/eS3dmXK6uCRoiPsr8PXeMAVPjaNfwA5g2bvOECn9jXMlnEncQlEkqZTvYAC4ku70cxnl/DZd+j2Mbv8FcmmgVNRwWszGw0YQgbWdi7pnZ41nC7W/yf+VDnKj1tOLs0peyUXsvX/3QtQW955qKCjqkxZGYTgqvCdLJbNYDbdQrGsrZ0xKlO2nnwjiRgMni2jJ4+v8RDASZV563ATa3QPfR3oYkI9k+XGlu9o7f1AWzK/wc60phS8mS6jBfufQdBW2Ypd5YB7r+X43wGiokpZhOzGhDsrWxla/9+rXcPI+wX+easxfx9NsttMXTvY5NWi6zyocfwHS0O0FNyMeL2hncIQRfiv0zOhKB9GZ46KY3IOqJf4Ur7uQndX9PLNpJwDQ4If0qVyXuwXWT6HrA61BPdXnn3PUecC3+n9WZe6235ALudi7hRfs45lWFh16YGQB/BHxlXHlROV9/4HXCZQ523CLluBiaxufWLh1wA85uerMqjP6b3iMD5HrscmbTQiraTThclpNsf3nBJ4Ytze17x3/agqppGapRISnFdGJGG5K9LTHebooigPedPJtPnruIypCPhVWhXNI8YGokLRfblVx1xtADmCpDPhbXlGVCFhov+FcRi5ehSwcDB59heHEhKcFNwa8+yT9VHs/t6XN5kxN5zTyRn9sf4RLrUZb5OonaIWQihZ7YT4ieGSJHqOWH1uVs43hA4Epoi6d5trG3jDu64eUn/JFeA53yN7E57hNcJ37LUrOV4MHFsPl8ryCgYx/tvjmsty/j3q6VxFMOacdB1zSW1Ib52vtWepve1v65HkMz2Svn0RkLcbzbSrBuMZxzM9/ZHMHUh08wT+ZQTTGbHSfz+1QoRsKMNiR/vmo+Lx7o4KLj6ljeEMk9nk2ab9x2gKNdCWaVB7nqjPmcuaR6wOuYukZdxE/A1PuFLI6IempoRtPMjAFxQGbKd6Wkwu3kC8Z9/Mzw8XhyGYcqTye26nLeiu5n4ZYvUkEs17GZwgR/OR3pKp7XTkBmFHerQj6MrPLusjqe2p9kw9Ymdne6g252a1fWs1Z7GR7+L6/s2KyBlt2w/xkoa6Bbq6Sz+QAfk//BTutanuBUhBBUhczcGF9gwFyPic1Pwp/mSXEq9ZEA91x7FgAH7t9U9M7u8UR1kSsUAzOjDYmuCb75gRM43NE/93HmkupBDUc+ZQGD2rAfLSNzkn+3f6ytky2Ryzi58y106YDUvCos79U9pVszQAC4IfwEN3z8494Ew2234r71UEbgHdIYdIoKumUQ09aolq0sqA73mvNhSZ03u/xsPhbm63/YV9hml9/LApDuAgQkO2mWIVIEMN0E12u/ZZPlFdA1daeoj/h7vIjMwKhjG7/BXNHMMa2B+wIf5gX/KoJS9jISxUgwT6SulOoiVygGZkYbkrEQ2v8Yta/cgdG5v0feZOmFYMVZOw/W/vkscGqBpfBcGzz/U88bAUDz+jQyCrsYAc+A/OkW2PEgSAdvpqJJjCCdogKEQBMgnCSdZj0p28F2JMfikiYnTFoLsqg6xPon9hS82SWa93AkFSDtdHuikE4KiYabThJ1Pa/Jwsc80Zw7x5VwfPw5PnP4Qfh+d+69f2/Wvw5rJMaaYJ5oj6AUWlkKxXRAGZJRUH7ocWqe+CpC94G/EjoPw+++AOf/3cCCh2eug/oT4MWfwdFXAA3K6rxGPceCaBOko/Dmb7zjg9UcTfvwO92ESeBKnW7KMGSKgObSfcrHOPaiwYG4SVz6Pb/FcTnSlaQ5mmJ2Re+igKCp83ZTN1dv2NpLaPDseCVVbjuW9GG7kjg6PmFh40PgCZ4FSXNQ9kjPXKC9xDf0u7EwIVCbk3b5yklf5fPba4c0EqNNMGe9kBf2tyOAWRWBnBzJeHoEpW52HAlK8VcxmVCGZIRUBE2qX/wRQuheMttNg2GC5XiGYjDl3EXnev/tfcpTxJV4m3CyveeYcD0sWQt7n6Rc0zkSq8Kmmwqi6NLmoJiNtup6jr/4GmKvP0Ms5oXkNOGF6WIpJ9fzkr/ZLe96hq+mf83CI8006bO4p+2D/GDvSl7kvXzTvNuLZkkfnYSop4MOQkgkQdK5efFZbtAfJC0N0sLfI+2ShlP2/4xbPnDXsEZisATzYBtjvhfiSm+myuGOJHMqvSmL4+kRTJaS3Yn2zBSKvihDMgh9pyV2n3oDkePWEHI6oG03+Cs8rZAsfRoHB6XheKg9DhofIydy7C/3vJYTroDffh50H6FAgAbdoT2uk7L9SM3P3JpKqnb+EJp+y3HRczimn4qu9UhzOK6LZbs5YcCgqXNi/Dk+a/0YRzOJighVbhs3pzdwhGvY7J7Kt9xP8inxW+aJJva5s7jHuYjz9DeZQxMHZR3rnct4PDMvHmC+aKaDMEJCY0vMU+/1e82Go61CGmpjzM9L+HQN25EgoLnbE5Asikew89GCVJgnS8muytUoJhvKkAxAcN8mah//P0jNxDXLMLsPUb/l/yDElz2vonwOxFq93owsfRoHc+x9yvNUOg8CAhJt4GR6VAKV8M5r4cQrvc5v8AZC+b1GvjK/Tpk/DGlwuw5yuA2OOSZl3Xv5uvYW35TX8TSn93o5TRPc8oETcpvdx9zfYAsTR/eunyIAMskN+oM87p7Kk5zGk/I0kOTE/97SXuGT4rfMF03coD8IwNPiNFwpOSDrqBcd2HoQy3E51JlgfhjCNcNL+A/mdQy2MX7n4TfZ0xrHcV38hk7Yp9OesBASUrZLPG2P3SPY+ainA6b5eqsw5+uC5TEZSnZVrkYx2VCGpA/CTlK57ftIBGgmupDo/iDCSsKLP+PNo11Ejh5jlnMIBwM3UEsw4PeqsU77RO+L7X0KHv8nT2cq1UXOAxEaHPdeWPPlnoqpLDkjFQTNAE3HbmvEkYJqp4UGLCzHpIMQnxK/5Un3NISA83iRv9bvYal2BP+9Gmurl8GHvgkPtdPoBnEzulUAKfzM17wEuit7y55coL3E17S7SWPSIcPUiQ5uMe7mO5rOo+mTuIv38039bhxSpPBjyiTRuEP4/TcP+bkO5XUMtDHajsve1gSmrqEJge1KOhI2VUGTrpSNkIL6SGDsHkHfyrVMqK4QXbCJYjLlahQKUIakP66NET2E9JVj6F6lFABGgHRTI9WH/h0LnVZqqKQTM3mUqLGIsrVf7p0fScdgy3cgeqxXCMxFw5Ia7Tue5vYd/8nz8h0IITB1jYU1YdYtvIJ37LyDWCLFsaQFdoKFMoWGQGLjCA0dm1rRganZCOAc+SL/qN9BJVEknryK0fwW2q8+Ba7NIidJQvpopYIuGaJKdlIputnh9wxfo5zNv3E1j+un8jnzd6SlSUJ60im2FiSopbl9/pOcd+xMjgbPZ0M6xBXJX9HgHuOY3sB/iA/wr8NsukOFYwbaGI91pzA1jYZIgMOdCYQEkHQlberLA9zygROK4xnkqTDnKFAXbKKYLLkahSKLMiQDICNzMJNtCNE7dGVZKSzKsLUANpAghOEmiaf8rAC4fx20NoIV86qxeg2D1HCEjiVBwyXpGlwu/8QCdx9/rj9OSKawmkzamsqJmS6OlaIcg7fdudTTgilsXARI0ISGwMUvbBZVh/h0+28pJ4qO60mx4LkYMtWJC0h0/Fg0yGZChKgU3ZkeFIkGrBT7+Q9uJRqZR1W8kbTw0a5XESWEBCoi5dCxP7fhv+BfxQv+VQDE0zb1kbzPaRCGCsd86/ITB9wY51UGKM+c05KZUSKheEYEcirMvTzDjC7YZGWy5GoUiizKkOSha4LaiB/fqmu9yioLL4luJ8G1SKNhC3+vc2zhp846CI9/xwtfpaN9rpqZyaEbOI5EQ2JJnSQmy8RBTjIacRE4CPwyzRzRwjGrgqSoRHctfuK+n3/SbsfEQcPFRaBlJFfCIsa3u/+BZWIvfhwyN+0IIXM2TBNgI7GljoFDBTFcvH+TMTsakgBJ9Pg+kvgwsahzm9H0esrKq9HsBK+lKnk72k130qYqZFJb5h/RnfBQ4Zi1K+u58mAHtz++m4TlIgCfoZHKCFeWB03Kg2bOaBV1w8x05pPG80QyumDZaY2TlfxcTTb39NXfvKZKgRUTQqknJE4ZyvwG86pC3mz1Red6+YtwDclYOztjAf4p/gH2MwtdpnqdZ8ikp4MVPdbHiGigmV4+hIy+VuZrm4wQwCIo0hkjomNkPAkJ1Mku6mUbs2jlX/UfksCHBHzYBLDQcbARJKSfareNMhIZuyE8OZU8R8hF4KLjoPGmnO9pYeH0XlLuvVg0uxUgPW+ljg40O0FHNMp/aZczqzxAddikPW5xtDPBxear/KbsO6x95F29pzkOQN8xs/mJ8s07mvj51n1YjsSnC0xdYDsuTd0pmruT/Y4vKkNMa5wKZHNPTd3JXrmn0YztVShGi5BSDn/UFGHVqlVy+/btBR+/eUcTtz++mwPtcRZWh707ucVBL9RBz6xy23HpTlqc4rzB5437cDQDFx8VdFJODJ28MmCheUlywHVdWqiiUragZQxGkxvBxcDAZpZo8/SzEARIAwKE1yuRxsSVYJIdqyuw0TDx8iKO0Djo1hETYVayJyen0iOa4mEJE0cKNFz2y3rqRScRYr0Mj8h8JxG8wWLKiFFLJwGRZofvJJ6TJ/BO+ZqXE9EauMf4IIYm+HxqPQlHxzWCNAQlYcMZchPO3jm/3dRN2nYxdcGKhnLaYyn2tsWRLjmpGVd6c0gCpk5F0FThm0HoO2kResKN96w7awJXpsjQ909yWjJjQ1v5VUTVIV/uTu7bly7k/EwV78btB7Adl2WJV/gL7TFmma1EpY+Im6RGtGAIz4BIwDHC3p2+64AQOI5LSmqkEezWV/C6WM4l9p+YLdqJST93Ou/lU/ojBEUKJyM0LzJGBDIGREiEBBew0bEx8GPjCnAwiIkwUkpSmh+/TGFhZgxSxsvwUioIXBw0fNj82L6Uzxv3YXojo8i1sAOpzK9DlDC21GmRVfyvfD9fcu7EwqCbMqrcNj6fWk+n4yeh61i6VxF2IAbzwzrhIaqdskbg6w+8TkVQEDR1mrqT7G2NISW9xtUKAY4rqQiaPPHliwa8nkKVAismBzM2tJVfRZSV2zB1wV1P7c0dc7QzwYrkK9yk/4oq0YlEsFC00KB1YggvX/G0eTZ3mh+jy9ZwHNsrA7ZTSOkQI4hPuLylrWCNs5UOymkUC2gWNVypP8kf3dPQkOg4uELkjEjWSxCy53sTm2ZZQVwEsDHQcdE1gRDC2/iFwSHq2CvrsYWOI3RaqMCRAh2XvXIW35bX8UPnCv7DvgIbLRcG84Jl0E4EkARI4RM2d8rLuE78lpTUSYkACEFKBEi4BkvEEaxMMYKWWfuxhBi22mnAzz3TVJnvHEvpjSie8JLWnY96YbvvnzRs+G4imF8VyvX/ZFGlwIrxZsYakgPtcYKm3uuxoKlzKE8JeFZFkA+LP2FiM4t2Zos2TOEgJbTJMr4R+SY/C19HwqzyinOlAHS8kJFLUitjY/BqjnN3YmGQ1MpwhUFDdRUYPuZpHdzmfpi0FsDA9ean561Hiky6QgBCUCc6aZaVaMiMIZDMDrmgm9zFh2gVlZhCspt57JJzSbo+XnKXcaP9Jd5vfYc/ZBR8f+hcwaetL/GWXEA6k8Y/JGvplGVUEKXJreBr1rU87pxCtXWEbsckZbs4UuJKSUKaCAH+vBkpEnCtBM93Rbh6w9ZBY/QDfe4N5V4BgyMljutm/pNEAsbElrRmmxW7j+WaFRO/+SLf+cF/cN53Nw35PseLoXJPCsV4MWNDW4NVES2ozHSYS8ln5h9g8bF9mKLnjs+WXkYhTJJap5lj+iwuTv2BhAjTJqtYWudNKmxu7aCbMK/7TuKqxD20yQh2Zhj8ka4ktmuyUG/my8G/5EedV/BH/a/oJExEJFkojnp356InwCqRBEQaW+p0EiGqV7EwkCYZnscPou9hY8dxaHzQy9ZIcPK8mdoyH/N8Bvvb4r0KkkMkOSjrSOAjJCzCJPmqdR1b3FNzx+21a6kXHSSkH8uW+E2dcsPmgJxDmBTIJHHpw3CS+ITNb0JXDqz9lJEhuS+9k0Ppen4dvDJXQmzoGsc1RJBSsqfVC8ksrwvz5Usyw7MKlDApOn2aFbtdH63xGO9J/oInK/5pUmhcqVJgxWRgxhqSwZq6PnnOQm9C4LN3sKL5zdxOnpAmBg6u0LweDSG4KnEPG4Fat4VuGcRnaJCKQqKNejdNFS3UJxo5KGZR7rRh4cc0NNK2S0CmOKjXc6gjie1KDmp11IkOpJReiW+2nEqAjeaFsnBZKI5i1q+k9t3fZLN7Sq5iRxcSy5X0rZ2QQEs0zYJqI1dSa2jCE1/EIIEfn65RX1lGU3s7NxoPstU5Ldezsd65jFuMuwGBpfkJkaImKPiadQ2GJrja/jXV1hEOUsf9gQ/zpv8MQtBb+ylPhiQYqaWio41Pdd/G/vZreUacTiRg8C9XntJr88sm5n/7q5/xd+6dlIXChMPDS5iMlgHlW/o0KzZHU1j4mS2bJkR9eDAmg2yLYmYzYw3JQHdyXz7uGKduvR4Ov9BzYP3xpDqPoaW6MsEnLyfQTCWOFHwoeR9BGaeSdjRHhy4HNB1NCHShcZN9N/dY53OF9jiGJrAIUE4n1aKLCifKz/VvsZ7LWO9cxr+Y66kkipM1JHi5ApHxTBw0hBHwmh0f/hJbxfWY+kk4rkTP1znJ4wLtJW7QH2R+tJluLQimpFxLUksHR6kmKb1KqUjA4BB+FhutGGikHM8L2yJP5ev2tXzG+B3zZBPHZAPzLv8Gl7mnsH5LIze1n0ZTMsWscj/lQR+np7bnut4Pxepg5zd639knbQwcaungduPfeUmu4L/dy4FTcmvOL4T4inyAhKvTHoO5hkMkUHwJk8HkW35TNocqqy3nkaRtl5BIc0xryJ2rEtsKxQwv/+3HQ38Hz633/j1/NZx5I8w+2dPMeuhvMhlgH4Sqicog8e4OqmULXaKSStmFRu+kJ7o3XrfL9fNr/+Wc4rzGAmc/QTdGm4zQQjlB0pQTo5UKloqDGHgeiY6L9KqBEQJsDDRNRxcS6o+HdJy3W1PEjQpqrCMclPXcYb+PzRmlXgGs0V7iFuNu0ply47miFfDyO3WiCw1JSvhoppr5s2dxsKmVZir5i+T/wXElrsylZ/AZGme7L/A34d9zYqi9V4gpW4J6nnyJG+O3Y2GQkD7CmkVNAFKJbo661fhMHb8Tpc5tzjRCwhFtFoa0+HnV5/nKJSvh6VtpOrCTQ3jhr8/Ef0Q3Zbh4I42X1Ia9n0OyA77wyuh/1nkMVkJ7sfkqX5F3ZkYRBznY3Ibmplkf/my/zv7pXGqrZp+MiRlR/qsMST7dR+G+dXDqR2HuO3s/d/+6/oq/bXu8r9WLIdYM8db+19RMbNelSdSyPvxZrkj+igqnlW7HB0CYOPNECzY6Oo5XBoynyaVlyoF9WOR+Hw0/1K2ERCd2x34Oilkk8WM4SSLESOKjQXR4JclS0EkZrVSwRBzByHTH67i4QsvUanmlxGZZPQmp8zXrWh5MnIBlu7k8i6kJztde4hv6T6kpjxAOl/V0gF/6vVyI7dbU16iR7STwhm1VhXwkYl3UyA4SIkCZjBOQSRCed2Xh45A+F79MknJ1llcJ0HzsaLUJad7s9wQBDGxSIoDjSlbOingimJEGuPbB0f+s8zjvu94s+fzRxVJKOhMWT3zIyeRn9tPum823Oy7mRf+qXuHQokq2TDLyvbWZ8p6LzIwwJDM2tDUgkVlw1X/nGhJ7cdonSP7pH+noShJzDcKaTb1roUXmeM9b2WqvvMYMBLg2GCHSjsEHE/cy39mPnxRzNBsLA006uAgMXFKYmNi5nhIt05PuNaB7RkU4FiQ7IXoUdJOkDCAl6DjUiG40XNIYCCAsLHy0k5YmPuxMQ6OTCZMZSAQGjufl2DGCV/yEy9xTeO3hN3m7OYopQNM0HFeyTnuQcChEuCzivbU8ldy11z7ILcDc/22izQ3jMzXqyvy5nAJIqmUHua4bCYZw6MQzygnpYzEHQFsMvhA+M0bCyUjRCIEpbVw3ga4HPSMyjITJSO+gh1TTXXFWLoRWBVy2o4lDMyixrWafKAqh5OW/QohLhBBvCSF2CSG+MsDzQgjxg8zzrwghTi/03PHkWXcl37evoI1yykWCNsrZzyzibkYKxbW9r/T18CRGeQM1FZUsdxupoIsQSUwcwppNQFhowtv8W2SFd/sipOc1ZK7gogECKyP8SPdRcNMY5bOZWxHEcSW1ohOROcNFy/wHOpI60UkaAy1jjGSul13gGiG0hhO92SjZDTPspzJoEvQZRAIGZyyq5pRIJzUVFb3fWp5K7tqV9dTPX8HKGoMltWEiAYO07RIUaRCCNiqx8WWaJL33EiKJKyV+Ut6ArsxMlroyz6NJSB8hGedW3zpaRTWz/clhJUxGIxkykhLatSvruWfdWTzx5Yu4Z91Z034zHaxMXuWFFPmU1CMRQujAbcDFwEFgmxDiASnlG3mHXQosz/y3GrgdWF3guePGxu0HaPOdyB7z1NxjixKv88nERkKBFOg+sFOePIp0e6TjjQD4y4nEmoGEJ6GSfc51MqEria9mDmX+ckiGoPsoaduTRjlCHXG9jJCMU+W2I9y0l1ivewfYaSKZO0V/RjolGxqTeN3wJg5+LA5RyxxaAHCEIKgDCChvyKnd5ocxZlcEe4kyBrcuHl4lt48AYpmWRnMtbEw6tUo6RRVBN0Y93iwUA4uwSFMT1NDLlnvX84WIBAzmEqSzq5NDso591efSvebjBAvYtEdzB61KaAdHzT5RFEKpQ1tnAruklI0AQoiNwOVAvjG4HPi59JI1W4UQlUKI2cCiAs4dN452JogEeqQooqKMp0LvpiUa4V8jj0Oi3fNKQjVecrbrgHdgZLYXjom3eru7YXqGxLUzBkVkyrIML4msmRBp4O+jH+Ma+QBVbhsAcREirocwnQSL5i/pvWlnGhQNvNxDFonAEgZJ6UfHZbecS5mWYK5o8wxaZJb3uplQ0frNQ2zCawtQyV1xMfC9XE4hXDOXb3dczEedX3u5E+knKkJUh+cSSjYBLvPmLeq5Rt71I1qaSLnOvEu/wT0rCk9kj1YyRJXQDoyafaIohFIbkrnAgbzvD+J5HcMdM7fAcxFCrAPWASxYULoZErMqgrTFUpimjw6typMKSdscrjsfrv1b76Bc49x+by67EJDq9sIxiTbQkl7prqZ7/3nt4F7yPFjlnVe5AM65mcObI9zTpnFzegPIZG4aYVB38hryvE27tu0t9joNzBFthEgivMkl6ELi+qu4PfwFfpc4IXenPV97uWedkYbc9Q7cv2nwTbiPkcius1+IacXF/XIKv/+Dn4+3/QdhXVARKSekW2AOFKIq4PrDoO6gi4vy1hSFUGpDMlDFQt8kwmDHFHIuUsoNwAbwqrZGusBCuWrVfL676QDdogq/z0dioDh63ibaj7svg5bdkGgB1/WMjOt4obB3faPfeTe4TXz9gQT41nG1/WvqnaMcFg0Y595MQ/bYzOvt2NHEl+59mVNT2/gbcQ+LxREQkKxYSuR93+YrKy6md4Jp4HUOuwkP9f4GwbvT/zzsXJlnvIY3QqNF3UEXH+WtKYaj1IbkIDA/7/t5wOECj/EVcO74IASrTzqOvypfPPo7s2woilpId3nDsjQDzvvrATfPnjvBIDe1n8a8+sFfb+3Ker535Sms3xJhXftZo75rLOkmXAQjUQjqDlqhGH9K2kcihDCAncC7gEPANuAvpZSv5x3zPuAm4L14oasfSCnPLOTcvoy5jwS8UFR++a9uerkEwz/4OYWSH/oaZeim1GRLZ9UmrFAUBdVHMlaklLYQ4ibg93iyuHdJKV8XQtyYef4O4CE8I7ILiAPXDXVuKdfbD38Eyuq9MFQxGKe78rGgwhgKhWKkqM72vqS6IdoE4VoIVAx/vEKhUAyO8khmJJoBFfOKE8pSKBSKGYAyJH3JdFcrFAqFojBm7IREhUKhUBQHZUgUCoVCMSaUIVEoFArFmFCGRKFQKBRjQhkShUKhUIwJZUgUCoVCMSaUIVEoFArFmFCGRKFQKBRjQhkShUKhUIwJZUgUCoVCMSaUIVEoFArFmFCGRKFQKBRjQhkShUKhUIyJaTWPRAjRDOwDaoGWCV7OeKPe88xAveepRYuU8pKJXkSpmVaGJIsQYruUctVEr2M8Ue95ZqDes2IyokJbCoVCoRgTypAoFAqFYkxMV0OyYaIXMAGo9zwzUO9ZMemYljkShUKhUIwf09UjUSgUCsU4oQyJQqFQKMbEtDMkQohLhBBvCSF2CSG+MtHrGQ+EEHuFEK8KIV4SQmyf6PWUAiHEXUKIJiHEa3mPVQshHhX/v717C7GqiuM4/v2hRlQSlmhhihVSWcl0sZv3oKICNWog68HqoUADkyCqB1Mi0IeahEIoipEyK7qp2YMlXkYRESVvTHQRSdPGoActKBn997DX2GGaozZnztln9vw+L3vtdfY6+79nMed/9uWsJf2QloPyjLGnlTnm+ZJ+SX39raT78oyxJ0kaLmmdpFZJeyXNSfWF7uciKFQikdQPeBO4FxgNzJA0Ot+oamZKRDQU+Hn7ZqDzD7ueB9ZGxChgbVovkmb+e8wATamvGyLiqxrHVE3twLMRcQ1wGzA7/f8WvZ97vUIlEuAW4MeI2BcRx4EPgWk5x2Q9ICI2Ar93qp4GLE3lpcD0WsZUbWWOubAi4nBE7EjlY0ArMIyC93MRFC2RDAMOlKwfTHVFF8AaSdslPZl3MDU0NCIOQ/YhBAzJOZ5aeVrSrnTpq5CXeSSNBG4AttJ3+7nXKFoiURd1feH55nERcSPZJb3ZkibmHZBVzRLgSqABOAy8mms0VSDpAuBT4JmIOJp3PHZmRUskB4HhJeuXAYdyiqVmIuJQWh4BPie7xNcXtEm6FCAtj+QcT9VFRFtEnIiIk8DbFKyvJQ0gSyLLIuKzVN3n+rm3KVoi2QaMknS5pHOAh4GVOcdUVZLOlzSwowzcDew5favCWAnMTOWZwIocY6mJjg/U5AEK1NeSBLwDtEbEayUv9bl+7m0K98v29Djk60A/4N2IeCXfiKpL0hVkZyEA/YEPinjMkpYDk8mGFG8DXgK+AD4GRgA/A40RUZib02WOeTLZZa0A9gNPddw/6O0kjQdagN3AyVT9Itl9ksL2cxEULpGYmVltFe3SlpmZ1ZgTiZmZVcSJxMzMKuJEYmZmFXEiMTOzijiRWJ8g6TFJb/zPNn90UTdS0iOd6panIUvmSmqW9NAZ3ndquZGpu9qnWb1zIrFeLY34XEsjgVOJRNIlwB0RMSYims7mDSJiZUQsrFJ8ZjXnRGJ1K337/07S0vSN/xNJ56X5V+ZJ2gQ0SpqR5mPZI2lRSfvHJX0vaQMwrqS+WdKSNPfFPkmT0gCIrZKau4hjsKQtku4HFgIT0lwgc4E1wJC0PqFTu/2SBqfyzZLWp/Kps6M0CsMWSdskvdzTf0OzWnAisXp3FfBWRIwBjgKzUv1fETEe2AgsAu4k+8X3WEnT01AiC8gSyF1k89OUGpTazAVWAU3AtcD1kho6NpI0FFgNzIuI1WRzYbSkuUCagKnAT2m9pRvHtxhYEhFjgV+70d4sd04kVu8ORMTmVH4fGJ/KH6XlWGB9RPwWEe3AMmAicGtJ/fGS7TusimxYh91AW0TsTgMh7iW7fAUwgGwipeci4usqHBtkiW55Kr9XpX2YVZUTidW7zmP4dKz/mZZdTR1Qrm2pv9PyZEm5Y71/KrcD24F7zhxml9r593/s3NNs53GKrFdzIrF6N0LS7ak8A9jU6fWtwKR0H6Nf2mZDqp8s6eI0NHljN/YdwBPA1SVPWR0DBp5l+/3ATan8YJltNpONUg3waDdiNMudE4nVu1ZgpqRdwEVkEzudkka+fQFYB+wEdkTEilQ/H9gCfAPs6M7OI+IE2Qf9FEmzgF1Au6Sd6Wb76SwAFktqAU6U2WYO2WRk24ALuxOjWd48+q/VrTTd6pcRcV3esZhZeT4jMTOziviMxMzMKuIzEjMzq4gTiZmZVcSJxMzMKuJEYmZmFXEiMTOzivwDCyXYPoJVCfQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 412x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f3 = sns.lmplot(data=ccm.query('xrd_a<.4 & xrd_a > 0').sample(1000),\n",
    "                  x=\"prodmktfluid\", y=\"xrd_a\", hue='div_d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `col` instead of `hue` \"works\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jeq8druVa8OXOsh08ppzYE0KIapORWNSlL/z4ZYamYxwbi3BkJIxl6wWvRNcIZROlqIVVwMRME5EEp6Zi1T4MIYQQSIAs6tDuA0McGg7hOBpTKSxbc2IyimU7C+4msZyWbF1uXwhqXdJ2ODERZSycQMtyHUIIUROkxELUnbv29OM1DDSglEIpwIHT03EuWd+5oNus97KJUjRCHXWjmIwmGQ8ncCQyFkKImiIBsqg7A+MRVrb5OTkZx0GjFGg0lo1kQYu0nL4Q1CLLdhgOxYkm7Pk3FkIIseSkxELUnfWdTXhMgzUdATyGwnY0hlJs6W2RoE/UvOlYksHxqATHQghRwyRAFnUnXUNrGopN3c1s6Gqity3Ap2/YWu1DE2JWtqM5PRVjeDouJRVCCFHjpMRC1J2lqKGVleZEOYXjFiOhOLYjgbEQQtQDCZBFXapkDW05eyyL5c1xNCPhOKGYVe1DEUIIUQIJkEXF1Vs2thFXmqu316ARRBM2w9NxLMep9qEIIYQokdQgi4pKZ2OHpmM52djdB4aqfWizGhiPEPSaOZfV80pz9fga1DPH0YyE4pycjEpwLIQQdUoCZFFR2dlYpdx/F7ri3VJptJXm6vE1qFexpM3xiShT0WS1D0UIIcQiSIAsKqoes7GNttJcPb4G9UZrzWgozomJKElbssZCCFHvJEAWFVWP2dhGW3q6Hl+DehK3bAbHo0xK1lgIIRqGTNITFXXbjj4+d/+LRBIWQa9JNGnXRTa2kVaaq9fXoNZprZmIJJmIJtHS11gIIRqKBMiioorpWSwdFiprKfpGLzcJy10qOp6U1fCEEKIRSYAsKm6ubKz0HF4ajZQRr7bJSJKxSEKyxkII0cCkBllUlXRYEPUiaTucmIgyGo5LcCyEEA1OMsiiqgbGI3QEvTmXFdNhQcoyxFKaiiUZCyVwJDAWQohlQQJkUVXrO5sYmo5lVq2D+TssLNeyDPlSsPQs22EklCCSkKWihRBiOZESC1FVC+k5vBzLMmQ1vKUXilscn4hKcCyEEMuQBMiiqhbSc3g5LnyxHL8UVIvtaIamYgxNxbCd2i2p0FpLLbQQQlSIlFiIqiu1w8JCyjLq3UJrtUVpIgmL4el4zQfGaUqpKh6JEEI0Lskgi7rTaEtBF0NWw6ssx9EMTcc4NVm7WeP8wFiCYyGEqJyKB8hKqRuUUq8opQ4rpT5T4Pr3KqWeT/3/uFLqoqzrjiqlfq2UelYptb/Sxyrqw2KXgt59YIhb7t7LtXc8xC13762LOt7l+KVgqUQTNscnooRi9VFrLIGxEEJUXkVLLJRSJvAl4M3AILBPKXW/1vqlrM2OANdrrceVUm8F7gauyLr+DVrrkUoep6iMSnZdWOjCF7XWAWP3gSG+8OOXOTLqlkr0dTfz6Ru2zjgWWQ2v/BxHMxpOMB1LVvtQiiKBsRBCLJ1K1yBvBw5rrfsBlFK7gJuATICstX48a/u9wLoKH5NYArUWiKZlT3YDaPJ5iCQs7trTv+THtfvAEJ+67zkmIkmMVOxzaCjEn933HH/9zosKBskSEJdHJGExMp3AcpxqH0pBWmsJiIUQoooqXWKxFhjI+n0wddlsPgT8OOt3DfxUKfWUUurWQjsopW5VSu1XSu0fHh5e9AGL8qjVrguV6oCxkLKNu/b0E4pbmEphGkbqf8V0zKr689SosmuNazE4fuXUNA8fXJpxTMZOIYSYXaUzyIVSIAVnwCil3oAbIF+bdfE1WusTSqle4GdKqQNa6z05N6b13bhlGWzbtq02Z9csQ7XadaESHTAWmi0fGI9gOxozK1OolLs4RaHnSRYKWZxowmZ4Ol6TgfHgeIR7Hz3K7oPDrGkP8NCndhLI+yJXbjJ2CiHE7CqdQR4E1mf9vg44kb+RUupC4B7gJq31aPpyrfWJ1L9DwA9wSzZEHajVrguVmOxWSrY8O9M8FU2igOxWtlqDxzBmPE+yUMjCaa0ZCcU5ORmtueB4NBTn735+kA98bT+7U5njtqCX01OxKh+ZEEIsb5XOIO8DNiulNgHHgZuB92RvoJTaAPwr8Ida64NZlzcDhtZ6OvXzW4DbK3y8okxu29HH5+5/kUjCIug1iSbtsnVdWEwmtRKT3YrNludnmm3HYTpmYSiNmTrZ4mjobPLOeJ5qqXa6nsSSbtY4addWYByKWXx3/wDff2qQmOUe26q2AB++bhMfvGYThiH1x0IIUU0VDZC11pZS6uPATwATuFdr/aJS6qOp678MfA5YAfxDalKKpbXeBqwEfpC6zAN8W2v9YCWPV5RPpboulGPyX7knuxVbtpEf5Ha3BACYiCRJt97d3FO4i0WtlqzUKq0145EkE5FEtQ8lRzxp82/PnuDbTx5jOtVWriPo5Q+u3MBvXbiGZr9HgmMhhKgBFV9JT2v9APBA3mVfzvr5w8CHC+zXD1yUf7moH5XoulCLmdRis+WFgtwVzX48hsEjn37jnPexHFcPXKhazBrbjuYnL57i64+/xnAoDrhfcH5/2zretW1dzusqhBCi+mRUFnWlWpnUuco6is2WLybIrWTJSqOoxayx1ppHD49y76NHeG3M/Rv1GIobL1rDe6/cQGeTr8pHKIQQohAJkEVdqUYmtZiyjmKy5YsJcmWhkLnVYtb4uYEJvvJIPy+dnAbclj5vOm8l77/6LFa3B6t7cEIIUWa2ozEbqERMAmRRUK22FKtGJrVcZR2LDXJloZCZajFrfHgoxD2P9PPk0fHMZVf2dfGhazdxdk9LFY9MCCHKT2vNRCRJzLIb6su/BMhihlpdBQ+qk0ktZ1mHBLnlU2tZ4+MTUf7psaM8lNV67/w1bXzkuk1cuK6jegcmhBAVErfccThhORXv3b7UJEAWM9TiRLhsCwkyF5MRlwlytUVrzVg4wWQ0We1DAWAsnOCf977Gj54/iZ1qR3LWiiY+fO0mrj57hSwZLYRoOOms8UQ0idaNuc6QBMhihlqcCLfY211MRrxRJ8jVahnNXGopaxyOu72M73tqkFjSPZ7eVj/vv3ojbz5vZUPV4gkhRFp21riRSYAsZqjViXALtdiMeCNOkKvlMppCailrnLAcfvjscb71q2NMpXoZtwU8vPfKs7jpojX4PJVeoFQIIZZees7HZANnjbNJgCxmqETGdL5sZSXLOsqREW+02uFaL6PJVitZY9vR/Oyl03zt8aMMTbu9jAMeg3dtW8e7tq2nxS/DqRCiMcWSNiOhxs8aZ5MRXcxQroxpOig+NDTNdMyis8lLd4u/YLaykmUdUkM8Uz2szFcrWWOtNY+/Oso9jx7htVH3+TENxW9duJo/vPIsupqll7EQojHVYqegpSIBsihosRnT7FP4kbiFozWj4QR+j0lb0DsjW1nJILZRa4gXo9a/NNRK1vj5wQm+8sgRXjwxlbnsjVt7+cA1G1nb0TjtjIQQIl+tjMPVIgGyqIjsU/jJVPNw7cBIKE5b0DsjW1nJILYRa4gXq1a/NNRK1vjV4RBfffQIe/vHMpdt39jJh6/r45xe6WUshGhcyzlrnE0CZFER2afwfaaB5WiUgkTqm2h+trLSQWyj1RAvVi1+aaiFbMXJSbeX8S9eHiI9BeV1q1v5yHV9XLy+o2rHJYQQS6EWxuFaIQGyqIjsU/g9rX5OTMRw0PhMg0jCKpitlCB2aZX6fFeqLVwtZI3HIwm+tfcY9z93AivVy3hDVxMfunYT154jvYyFEI2tFsbhWiMBsqiI7FP4LX4PK1q8jIWTBL0Gva2BqmcrRWkq1RaulGzFk/1j7No3wMmpKKvbgtx8+Xq293Ut+L4BIgmL7+0f5F/2DxJN2gD0tPh539Vn8Zvnr5JexkKIhidZ48IkQBYVkX8Kf+OKFv7nOyQorlflbgtXarbiyf4x7nzoEB5D0RbwMBqOc+dDh/gkmxcUJCcsh39//gTf3HsscwytAQ+3bN/AOy5eg7/BlkwVQoh8OjV5fkqyxgVJgCwqRkomGkc528ItJFuxa98AHkMRTAWu6YmFu/YNlBQg247mFweG+NpjRzk1FQPA7zH4vUvXcvPlG2gJyJAohGh8kjWen3wa1Lh6XA5YNJ5ytIVbTI3byakobXnBa8BrcGoqWvR9/+rIGPc8coT+kTAAhoK3X7iaP7ryLFa0+Es+JiGEqDeOoxmLSNa4GBIg17B6Ww5YNK7FtoVbbLZidVuQ0XA8k0F2b9NhVdv8vYhfOD7JVx45wq+PT2Yue8O5PXzgmo010/dZCCEqLZpwV8OTrHFxJECuYfW0HLBobAttC+c4bo3bdGxx2YqbL1/PnQ8dIpq0CXgNYkkHy9HcfPn6Wfc5MhLmq48e4fFXRzOXXXZWJx+5bhNbVrYu6niEEKJelGscXm4kQK5h9bAcsFg+Sq0pD8ctRkMJLGfx2YrtfV18ks3s2jfAqakoq+boYnFqKsbXHz/KT188nellfO7KVj5y3SYuPatz0ccihBD1Ippwz96VYxxebiRArmG1vhywEIVYtsNoOEE4bpX1drf3dc05IW8ykuRbT77GD589QdJ2Q+N1nUE+dO0mdmzull7GQohlQ7LGiycBcg2r1eWAhZjNVCzJWCiBo/X8G5dJNGFz39ODfG/fAOGE28t4RYuP9121kbdeIL2MhRDLSyRhMTJdnrN3y5kEyDWsFpcDFqKQhOUwEooTSy22sRSStsN/PH+Sf977GuMRN0vS4vdwy/b1vOOStQQK9DKuxGIj5eI1DdrySqqEmI10OBL5HEczEo4TipX37N1yJQFyjZNewqIWpT+cj42FWd0e5J2XrWP7pqUJNB2t+eWBIe597CgnJ91exj6Pwe9espZbtq+nNVA4yCz3YiPlYChFs99Da8BTMKAXohDpcCTySda4/CRAFkKUJP3h7DGg2efh9FSMO39xiE++sbKBptaafUfHueeRIxweDgFuL+O3vX41f3jlWfS0zt3LuFyLjZRD0GfSGvDS7DOlNlqUTDociTTb0YxK1rgiJEAWy1r2acpWvwetNaGELacs5/Dlh1/FUOAxDdBLE2i+fHKKrzzSz7MDZ3oZ79jSzQev2cSGruImrS52sZHF8poGrQEPLX6P+9wJsUDS4UhAeTsFiZmKDpCVUm8HzgcC6cu01rcXsd8NwJ2ACdyjtf5C3vXvBT6d+jUE/Cet9XPF7CvEYmSfpjQVHBpys5JrOwJyynIWsaTN0dEwrQEPZM3Dq1SgeWw0wlcfO8Ijh0Yyl126oYMPX7eJravaSrqtxSw2slBSQiEqQTocLW+2oxkNxQmVuVOQyFVUGkMp9WXg3cB/ARTwLuCsIvYzgS8BbwXOA25RSp2Xt9kR4Hqt9YXA54G7S9hXiAXLPk05EkpgGgpTKUZCCZp8Hrym4q49/dU+zJqgtTsgn5hwexDHkrkZi3IHmsPTcf7mp6/wwa/vywTHm3tb+F+/93r+5l0XlRwcg7vYiOVookkbjfvvfIuNLJTfa9Ld6mdDVxM9rX4JjkVZ3bajj6StiSQstHb/lQ5Hy0MobjE4HpHgeAkUm0G+Wmt9oVLqea31Xyml/hb41yL22w4c1lr3AyildgE3AS+lN9BaP561/V5gXbH7CrEY2acpE7aTaQeWSC3DKacsXfnLky5kVbtiTUWTfOfJY/zrM8czvYzXdgT54DUbuf7cHoxF1OuWstjIQiilaPabtAW8EhCLipIOR8uP7WhGQvGy95cXsys2QE6fO40opdYAo8CmIvZbCwxk/T4IXDHH9h8CflzKvkqpW4FbATZs2FDEIQnhyj5N6TMNLEeDBl+qPnS2U5bLpb3SbJM/KhFoxpI2//r0cb6z7xjhuNsqrqvZxx9eeRZvf/2qstXszrfYyEJ4TYO2gJeWgKeuei7L2FnfpMPR8hGKW4yG4tjO0vWXF8UHyD9SSnUAfw08jVt9+JUi9iv0aVHwFVZKvQE3QL62lH211neTKsvYtm2b/PWIomUvxNLd4uP4hNsybFWLf9ZTlsulvdJ8A3K5Ak3LdnjghVP88xOvMRpOANDsM7l5+3p+99J1OfXCtSSdLW71ewn6avMY5yNjpxC1rVKrkoriFBUga60/n/rx+0qpHwEBrXVmOrlS6s1a658V2HUQyD7vug44kb+RUupC4B7grVrr0VL2FWKh8k9Tbu5tQWtNOGHT2xoomBleyvZK1chUW7bDSChBJFHZAdnRmj0Hh7n3saMMjrsnqLym4h2XrOWW7Rtor9EFMwJek5aAhxafB6OOssVCiPpSjVVJRa6S27xpreNAPO/iO4BCAfI+YLNSahNwHLgZeE/2BkqpDbj1zH+otT5Yyr5i6TRqWcFcpyl3Hxjilrv35jzmpWqvVI1M9VINyPuPjnHPo0c4ePpML+PfPH8V77vqLHrbAvPsvfS8pkGL30NLwINX2rMJISqoGquSisLK1Qe5YCpFa20ppT4O/AS3Vdu9WusXlVIfTV3/ZeBzwArgH1IN8y2t9bbZ9i3T8YoSLJeygmyzPeYWn9vzt9LtlZYyU71UA/KBU1Pc88gRnj42kbns2nO6+eC1G9m4ormi910qac8mhFhKWmsmo0nGI0m0ZI1rQrkC5FlfTa31A8ADeZd9OevnDwMfLnZfsfSW46pNsz1mpRRJ2yGSsDILZFSivdJSZKq11kxEkkxEKzsgHxuLcO9jR9hz8Ewv44vWtfOR6/o4b03p7doqqcnnZoplhTshxFKJJd1OQQlLFvyoJbKSnpjXcly1abbHPBlN8vmbLqh4e6VKLwQQS9oMT59p3VYJI6E4X3/8NX78wknSc/3O7mnmI9f1cfnGzpoJQGWFOyFENTiOZjySYDKarPahiALKFSAfLdPtiBq0HFdtmusxL0V7pewOG+XMVDuOZjScYDpWuQF5OpbkO08O8K/PHM9kRFa3B/jgNRt5w9beRfUyLhelFM0+k7ag9CwWQiy9SMJdJrqSSQqxOHMGyEqp353req31v6b+nXM7Ud8qFawtxcS/hd5HpR5zsSqxEEA04WaNLacyA3I8afODZ47znX0DTKd6J3c2ed1exheurokJbh7DoC3ooTXgrauexUKIxjBbf3lRe9RctYdKqX9K/dgLXA08lPr9DcDuWguMt23bpvfv31/tw2hI6UCzXMFa9iS47AD09hvPL1uQvNj7KPdjrpZKD8i2o3nwhVN8/YmjjITcXsZNPpN3b1vPOy9bVxN9goM+d4W7Zn9DV5UtOOKXsVOIypuOJRkLJxp2wY+A12RNR7Dah7EQBcfOOT8ttNYfAEj1Pj5Pa30y9ftq4EvlPkJRe/IzsJ+/6YKyBIlLMfFvsffRCCtVVXIFJq01jxwa4auPHmEgq5fxTRev4T3bN9DR5Cv7fZbCUIqWgIe2gBefp/rZayHEwtVzq9Gl6i8vyqvYdMrGdHCcchrYUoHjETWkku3dlmLi33KcXJhm2Q7//twJvv74a5ycirK6DMtBZ3v62DhfeeQIr5yaBtyv3285fyXvu3ojq6rcy9jnMWgLemUxDyEaRD23Gp2MJhkPy4If9ajYAHm3UuonwHdwW7rdDPyyYkclakIls7xLMfFvOU4uBPc03gPPn+T/+8UhPIaiLeBhNBznzocO8Uk2LypIPnh6mnseOcL+18Yzl1199go+dO0mNnVXr5exTLoTonHVY6vRhOUwHIoTlwU/6laxS01/XCn1DmBH6qK7tdY/qNxhiVpQyQxsqZPgFnJ6rdoT7ZZa9mm87zw5gMdwa6+BzOPftW9gQQHy8fEo9z52hF++Mpy57PVr2/jIdX1csLa9bI+hVB7DbdHWGpAWbeKMej4dL2aqp7OBS9VfXlTevAGyUsoAntdaXwBIULyMVDIDW0qXhoWeXqtEJ4halX8a7+RUlLZA7ts74DU4NRUt6XZHQ3H+ee8x/uPXJzN1zH3dzXz4uk1csamrar2Mgz6T1oBXFvQQM9Tz6XhRWL2cDVyK/vJi6cwbIGutHaXUc0qpDVrrY0txUKI2zJaBvaqvi1vu3rvo7Eyxk+AWc3ptrvsoJctUqxmp2ZaJXt0WZDQcz2SQAWJJh1Vtxc0wDsUtvrtvgO8/NUgs1ct4ZZufD1y9kd943cqqtEiTSXeiGPV4Ol7MrdbPBjqOZiySYEoW/GgoxdYgrwZeVEo9CYRx5+RorfWNFTsyUXWFMrBX9XVx39PHlzQ7U4nTa6VkmWo1IzURSTAeKXwa7+bL13PnQ4eIJm0CXoNY0sFyNDdfvn7O20xYDv/27HG+/atjTKXawnUEvfzBlRv4rQvXVCUwDXjNzEp3ki0W86mn0/GiOLV8NlAW/GhcxQbI3wf+Ku8y+aRaBvIzsLfcvXfJszOVOL1WSpap1jJSxUz+2N7XxSfZzK59A5yairJqni4WtqP56Yun+NrjrzEcigNuUPGubev4/W3rcp77peAxDFpStcW1sMCIqB/1cjpelKbW2m7Kgh+Nr9hPvf8E/DPwv4BA6t9twFUVOi5RoyqZnZmtjGG+02sLKX8o5XHUUkZqMpJkLJIoavLH9r6ueSfkaa157PAoX330CK+NuY/HYyhuvGgN771yA51L2Ms43YmiJeBZ8oBcNI5aPx0v6l8l+8uL2lHsp9AVwB3A40Ar8C3gmkodlKhd5czOZAe2LT6T0XCCtqC3YBnDbKfXFlr+UMrjqIWMVCxpMxKKk7DKdxrvuYEJvvJIPy+dPNPL+Dde18sHrtnI6valWw3JNBStAS9tNdKJolbrzUVxavl0vKhvsuDH8lJsgJwEokAQN4N8RGstBTfLULmyM/mB7eHhEJataU7VmeaXMcx2em2h5Q+lPI5qZqQcRzMaTjAdK9/kj8NDIe55pJ8nj57pZXxlXxcfunYTZ/e0FH07T/aPsWvfwIIXIkkv6NFaQ7XF5ao3lyC7umrtdLyof7Lgx/JTbIC8D/ghcDmwArhLKfVOrfU7K3ZkoibNNnHvrj39/MUPXyg6GMgPbG1HYygYno7TGnDLGYopY1ho+UMpWab5MtiVCoTK3TLo+ESUf3rsKA8dGMpcdt7qNj6yYxMXreso6bae7B/jzocWthBJk89De9BL0Fd7C3qUo968Vid1CiFKF7dsRkOJGZ2CROMrNkD+kNZ6f+rnU8BNSqk/rNAxiRqXnZ1ZaDCQH9j6TIOk7ZDICgaLKWNYTPlDKVmmQttWMhCaq0NFqcbCCb659zX+/fkzvYzPWtHEh6/dxNVnr1hQ9nbXvtIWIkm3aGsPemt60l056s1rbVKnEKJ0jqMZjySYilmy4McyVexKevsLXPbP5T8cUW8WGgzkB7bdLX6OT0TxGAqtddFlDNUsf1joY58r62zZboeKaGLx2Ypw3OJ7+wf4l6cGiSXdLx69rX7ef/VG3nze4noZF7sQidc0aAt4aQ14MKrQO7lU5ag3r6VJnUKI0kUSFiPTCSxHKkmXM5kqLhZlocFAfmDrMRUdTV56WvxMRpNFT6yp5oSchTz2ubLO2/u6GJ5e/MzohOVw/3Mn+NavjjGZalzfFvDw3is2cNPFa8vSy3i+hUgCXpP2oJdmf30NMeX4wlULkzqFEKWzHc1oKE4oLpPwhATIYpEWGgykA9s7HjzAoaEQAJtWNPHpG7aWVOuZnYl912XreKJ/zK2F3lP5QDn/sU/HkpyajKFx+0UXuv9CWedwPMn/eegwf73iwkUdj+1ofv7yaf7psaMMTbu9jAMeg9+7bB3vvnw9LWUMVmdbiOT9V29kbWcQv6f26ouLUY4vXNJmTIj6Mx1LMhZOSOs2kSEBsliUxQYD4YTNus5gZt9ia3jzM7FHR0M8eXSMnhYf3S3+JZkYlf3YLdvh+EQMgLUdgVnvPz/r7Dgaj2FwfGLhp9+11jz+6ih//9BhTqcCY4ArN3Xxqd88l67m8vcyzl6I5PRUlLWdTfzn68/mjeetLPt9LbXFdkCQNmNC1I+k7TBSprI20VgkQBaLsphgYDGTmfL3nYpaGAqmYxY9rYElmRiV/difPjaOx1SsbA3QlgqAC91/Ousc9JpYjsZxNGPhONGkwy1f2Vtyu7RfD07ylUf6eeHEVOayFr9Ji9/Da2MRDp8OldR6rRQ7zu3hty5eQ7PPrJk2bbVC2owJUftKWXhJLD8SIItFW2gwsJjJTAPjEUwF/cMhEraDZWs8BjldMMo5MWq2iXXp/6+94yE6gt6cQLHQ/d+2o4+/+OELJG0Hv8dgPJxgLJJkRbO3pHZp/cMh7nn0CHv7xzKXBTwGva1+Aqm64Lm6SixUuhtFW8BbllpmIYRYanHLbaFZzoWXROORAFmUpJx9fxczmanV7+HQUAjTUJiGwrI1SQf8njMBarkmRhXTzq2Yx5KwHM5d3crHd57Drn0DnJqKEk06rGj20tnkB+Zvl3ZqMsY/PX6Un790mnTOY+uqVk5Pxehu8aE48/gLdZVYKK95ZlGPeuhGsdRkYRAhap/WmvFIksloeVpoisYmKSBRtHSgODQdywkUd2ctPFGK23b0kbQ1kYTbZzKSsIquX84Mbtr9P92yzHGckm9rPtnlHOlV/rym4q49/UU9Fq014+EExyeiRBM22/u6+N/vvohvf+RKWgIeOppya4QLBbYTkQR//9Bh/ujeJ/lZKjhe3xnkL288jy+95xLO6mrOtHJLy+4qsVBNPg+r2gOs72qiPeiV4LiAcr8vhBDlF7dsjk9EmZCSClGkimeQlVI3AHcCJnCP1voLeddvBf4JuBT4c63132RddxSYBmzA0lpvq/TxNqJyZbfKvQDCYuqXQwmbtR0BRkIJEqlyhU6fwVTMLqlNXDGKKQWZ7bFcefYKBsejs66Gl98uLRS3GAnH0Rr+9LvP8Y6L19A/GuZ7+weJplZy6m7x8b6rNnLDBasyXwxm6ypx8+XrS368Sila/O6iHlJGMT9ZGESI2qW1ZiKSZEKyxqJEFQ2QlVIm8CXgzcAgsE8pdb/W+qWszcaATwC/M8vNvEFrPVLJ42xk5VztrRILICy0fjld0tDX05K5LJKwOKc3wHduvXLBxzPXfWWXT4yE4kQSNtfe8dCMmmRwu1OMRRKcmJi7xCE7sLUdh9NTbheK3lYfR0fD/NV/vES661BrwMMt2zfwjovX4PfmtlHL7ipxairKqhIn+wF4DIO2oIfWgHdRi4gsN7IwiBC1KZa0GQktXa3xk/1j7No3wMmpaMkTrkXtqXQGeTtwWGvdD6CU2gXcBGQCZK31EDCklHp7hY9lWSpndqsWFkBIZ8MPnp4iFLfpavayotlf0V6z+a3sRkJxhkMJelt9Bb90lLIKU3Zg++LJSQwFLQEvI6EkyVRkrBTccvl6br58Ay2B2d+y2/u6FjQY+9OLekg3igWphfeFEOIMrTVj4URmoaSl8GT/GHc+dAiPoUqacC1qV6XPn64FBrJ+H0xdViwN/FQp9ZRS6tZCGyilblVK7VdK7R8eHl7EoTamgfFIzmpnsPDs1mJqhsshu9ZzdXuQziYvY+Ekp6Zi9LYGuP3G8ytySnvn1l5uv/F8elsDTEaTRBI2va0+ulsCOTXJX374VYamY5yajJW0ROn2vi7+9vcvdANUw2A8ciY4bgt46G728eHr+uYMjvM92T/Gn373OW75yl7+9LvP8WRWtwtwyyhaA17WdARZ2xGkxe+R4HiBqv2+WCgZO0UjiiZsBsejSxocA+zaN4DHUAS9Jgr3X4+h2LVvYP6dRU2qdAa50CduKUVA12itTyileoGfKaUOaK335NyY1ncDdwNs27ZNCozylDO7tdgFEBZbC52fDe9pDdDs99DbWv6yinzZ5RPplm7ZfKbBa6NhQrHSlyh94fgkX3nkCBPRM/u2+N3A2NaaFc1uh4tiT9/Nlcm4+pxu2oJumzaZcFce9bowiIydopE4jmY0nGA6trSBcdrJqShteUmMcnYSEkuv0gHyIJA9S2gdcKLYnbXWJ1L/DimlfoBbsrFn7r1EtnIve7vQmuFy1ELXSq1n9pcOR2usVPZwZYkdI46MhLn30SM89upo5jKfx6Az6KUt6MmZaFfK6bvsTAa4z1HMsvn+M4O8e/t6yRRXgCwMIkT1hOIWY6HiytoqJX/CNZSnk5ConkqXWOwDNiulNimlfMDNwP3F7KiUalZKtaZ/Bt4CvFCxI21Q+eUBlSxFmEsxrdLms76zKdPJIa0atZ637egjYTlMxZIkLNtdarqEjhGnp2Lc8eABPvKN/Zng+NyVrfzNOy/k9t8+n9XtQaZjFiua/XzyjW4AXMrpu5NTUQJe961tGMrtYRzwcmoyJsGxEKJhWLbD6akYQ1OllbVVws2Xr8dyNNGkjcb9d6GdhERtqGgGWWttKaU+DvwEt83bvVrrF5VSH01d/2Wl1CpgP9AGOEqpPwbOA7qBH6Q+0D3At7XWD1byeBtVLWS3ypH9LXc2fKEu39TFf3nDOXz7ydI6RkxGk3z7V8f4t2ePk7TdM9rrOoN88JpNXL+lOxO8FrqdUk7frW4PMh5J0OI3MVK3GUlYMmlMCNEwpmJJxkIJnBpp3VaOTkKitlS8D7LW+gHggbzLvpz18ync0ot8U8BFlT06sVTKUQtd7VpP29GMhuOEYhbbNnWxbVNxA180aXPfU4N8b98A4YSbAV/R7ON9V5/FDeevwmPOfyKnmNN3pqFoD3r5xBvO4S9/9BKxpF3VLxJCCFFuSdthJBQnmrDn33iJLbSTkKhNstR0HWiEZWzLlf2tVjZ8KpZkPJzAdorPViRthwd+fZJvPPEa4xF34kiz3+SWyzfwu5euJZDXXWQucy0E4jEM2lN1y0op3njeSgxD1d2kMSGEmMtkJMmYrIQnlohqpD+0bdu26f3791f7MMoqe3JbdmBZjTrixUoH+vUUtMUtm5FQgniy+GyFozW/PDDMPz1+hBMTMcCdfPeOi9dwy/YNtOWVmhQr3cUiffruD67cwA2vX70s+xc3wpfGCljwH0Ejjp2icSQsh+FQvKRxWCwdR2sOnJzm0cMjrG4P8sk3ba72IZWq4NgpGeQa10jL2NZCLXSxtNaMR5JMlrA8qdaa/a+N85VHjnB4KASAoeCGC1bxvqs20tPqn7FPKSsvpU/fNaeWgS4lA10tlQhky7k6pBCitknWuDY5WvPSiSkePjjMnoMjDIfcVWC7W/x8/I3nNMRqrBIg17hqtTZbzhm6aMJdnjRpFz8r+uWTU3zlkX6eHZjMXLZjczcfvGYTG1YUrrMupXWboRStAQ9tQS/eImqWa0GlAtlG+tIohCgsaTsMT8eJSda4ZqSD4t0Hh3kkKyhO6+tp5qaL1pKwHIK+2k/gzEcC5BpXjWVs8wObo6MhbvvmU7T4TbasbGvYYDl7El6xjo1G+OpjR3jk0EjmsovXd/CR6zbxutVtc+5bqF9xNGmza99AJkD2mgZtQS+tfk/dLeyRHchORZOMhOLELYdP7HqGL958yYL/hmqlH7YQojJqrUPFcuZozYvHU5niQ8OMhBI51/f1NHP9lh6u39zDllWtrOlonL7PEiDXuGq0NssObKZjSUZDSTSaWNJp2NPZpU7CG56O8/UnjvLgC6dI73JObwsfuW4T287qLKomeK7Wbc1+d7W7ev4Wng5kp6JJTkxGMVCYBoQT1qL+hqrxpVEIUXmW7TASShBJlL4iqSgf29G8cGKSh18Z5pFDI4yGc4Pis3ua2bGlh+u39LChq3HHXQmQa1w1WptlZ+iGp+MoBQaKhO003OnsuGUzGkoUfRpvKprkO08e4wfPniBhuSUYazoCfPCaTew8tyfTd7gY+a3blFIkLIeNK5pZ2RYo/cHUmHQgOxKKY6AwDIWjIeAxMovELORvqFb6YQshyicUtxgNxUvqFCTKx3Y0vz6eCooPjzCWFxSf09vCzi097NjSvWySERIg14GlntyWnaFL2A5oTSoWpH84RHeLr+5PZzuOZjySYCpmFTX5I5a0+eLPD/HTl09nMsYtfg8funYTb399cb2M86Vbt8Utmyafh7hl42j46PVnl3xbtSgdyMYtB9MAR4PW0NPqX1RJRLX7YYv6t5znWNQa29GMhuKE4pI1Xmq2o3l+cIKHD47wyKHhTDvStM29LW75xJYe1nY2TulEsSRAFjNkZ+iU1iRSwbHXUFi25vhEjHN6mqt7kIswHUsyHk4WXJo0v6vEuy5bx0g4zj2PHmE6VZtsKGj1ewj4TFa3BRYUHLv9intZ0eLj3seONmSglw5kP7HrGcIJi4DHoKfVT2vAu+iV/eqpI4qoLdIFpXZEEhYj04mqLxO9nNiO5rmBCR4+6JZPTERzg+JzV7Zy/ZZudmzpaah64oWQAFnMkJ2hOzYWATQeQ2GaCq0BTc313S0mIzRfOUV2V4lWv8nAeITP/fuLOaf8OoNeupp9mIaaMaGuGKahaAt4aQt6MQ3Fm89fxZvPX7WwB10Hdm7t5Ys3X5LTyzuSsKQkQlSNdEGpPsfRjIYTTMeS828sFs12NM+mguJHCwXFq1pTmeJuVrcv76A4mwTIoqB0hu7aOx7CVDASSpCwHXymwao2f02dDpsvI1RsOUW6q4SjNQPjbseFNAWYCqbjSWKWTWeTj2a/yampKDB/P2Ofx13trsXvqbkvF5UmJRGilkgXlOqKJW2Gp0troylKZ9kOz2QFxVN53ZnOW93Kji097NjSw6oGmPNSCRIgizml65H7eloyl0USFr2ttfOGmisjdOnGzqK7UxwbDxNN2ESTuYExgAYs7b5hLFszNB2jw/KyrrN5zn7Gb3hdb9UX9aiFekspiRC1QrqgnLGUY4PWmrFwgsmoZI0rxbIdnj7mBsWPHS4UFLdx/bk97Njc3RATwStNAuQGVM5BL7se2bIdTk/HSdoan2mw+8BQWQbTxR5voYyQ3zQ4OhJiZDo+y15Z+49FuPexo4yFzwzchnKDYyNVd+0xwHLcINlngnZgMmbxqcvXz+xn7DNJWA4/ePY4N1+xoejHUQlSbylELumC4lrKsUGyxpWTtB2eem2cPQdHeOzVkcxcmbTz17Rx/RY3KO6VoLgkEiA3mHIPeunT41/48cscHY3iNQzWdQRI2E5ZBtNCx/tn9z3HimYfoYRdVMCcnRHSWmM5mnDcYmXb3LVUI6E4//zEa/zHr09mOlN4DEVHk5fxcAJDAdoNlg1D4dEaS7v1cx5DEfR52N7Xxd/94iBtAbd0wjQUhgKfaXBiIlrU469kBkfqLYXIJSU/rqUYG7TWTESSTESTslR0GaWDYjdTPJpT8qhwg+Kd5/Zw3eYeelr91TvQOicBcoOpxKC3c2svd+3pZ6Ojc05LlmMwzT9e29GMR5JMxy3O6WkpKsC/bUcfn/3hC9hOEq+piCUdLEdz8+XrC24fill8Z98x/vXp45k649XtAT5wzUZafB6+t3+QyWgShduSbCycwHI0SimCHsX6ziaiSZsVze7As7YjyHgkQYv/TDeLYro0LEUGR+othZipWiU/tVDulFbpsSFhOQyH4sRlqeiySFhZQfGrI4TjZ55XBVywtt3NFG/pprtFguJykAC5wVRq0Fuq2x2ejmMoN1BWSs0Z4O8+MMSXH36VY2MRgl4TrTXTMYtVBSbJAcSTNj945jjf2TeQOQ3VEfTyB1eexW9ftBpvql3blWevyNQVG0rR2eTl9JRbqtHZ5COatLEczfuuPos1HUE+8cbNCzpluxQZHKm3FKI21Fq5UyXHhslIkrFIQrLGi5SwHPYdHWPPoREePzxCOJEbFF+4zg2Kr9vczQoJistOAuQGU6lBb6luN2E7KNwShbRCgfjuA0P8xQ9fwFDQ7DczWeM//o0tMwJj29E8+MIpvv7E0cw68k0+k9/fto53XrYu5zGlbe/r4pNsZte+AU5NRTlrRTNoTTRps7ItwEev7+NN57nt2RZ6ynYpsrtSbylEbZjtC/EdDx6oSla5EmND0nYYCcWJJiRrvFAJy+HJI2M8fHCYJ/pHiWQ9l4aCC9d1cP2Wbq7b3ENXs6+KR9r4JEBuMJUKiJbqdk2lsBydc4ooPxCPJW2++NAhFBDwpCbGpY4puy+x1ppHDo3w1UePMDDu1gN7TcWNF63hvVdsoKNp7sFle19X5ra8pkFb0JupNc63kFO2S5HdlXrL2lBLp9ZFdRT6QmzZDkdHI2xc0bTkWeVyjw1TsSRjoQSOZI1LFk/aPHnULZ944tVRosncoPii9R1cv6WHa8/plqB4CUmA3GAqFRClb/eOBw9waCgEwKYViw/k8o93U3czw6E4HlOhUxnbdCBu2Q5j4QShuMXxiShtgdw/34DXyPQlfnZggrv39HPg1DTgno5683kref/VG1nVXvxMXq9p0NFUmf7FS5XdlRZr1VVrp9ZFdRT6Qnx6Kl7VSbTlGBss22EklCCSqJ3e+PUglrRzMsWxrPaihoJL1newY0sP127upnOeZI6oDAmQG1AlA6JwwmZdZzAT0GV/0C80S5Z/vOnbSQf4H7luExeu72BgPJqpaVvdFmQ0HM+0VgOIJR3aAl4+/f3n2Xd0PHP5VX0r+NC1G3N6Oc8n6DNpD3oLll+Ui2R3lwfpJFJdtZK9L/iF2HFYl7ecbz1Nog3FLUZD8aL6zAs3KP7VkTEefmWYvUcKBMUbOt2a4nO6aW/yznFLYilIgCyKNtcHPVC2LFk6YNZaMxWzmIgkmIgkclara/aamdY2Aa/BdMxiIprMGXAuWNPGR67r4/Xr2ou6X6UUzX43MPZ7ilvYY7EfvpLdbXzSSaR6ail7X+gLsddQJPOCy3qYRGs7mtFQvKZWVK1V0aTNr/pHefjgCL/qHyWWtUKraSguWd/BznN7uOacbtqDEhTXEgmQRdFB3lwf9OXOkoXiFuPhRKaxfP5qdelAWCkYHI/mrH63cUUTH75uE1f1rUApNe8y0B7DoDXgoTXgwZM1OXC+56eWPnxF7ZJOItVTa9n7QmfL6m0SrdQazy+asNnbP8rDB4f51ZGxTDtRcIPiy87q5PrN3VxzTjdtEhTXLAmQl7lSgry5PujLlSWLJmxGw3ESVu6KS/mr1flMA9vRDI5HM4t8rGzz84GrN/Ibr1uJabj1wnMtA73j3B7agl6afeas9cVzPT+19uErapN0EqmeWs/e11OZ1Wxjs3BFEhZPvOrWFD95dCznefKkguIdW3q45uwVEhTXCQmQl7lSgry5Pujv2tO/qCxZ3LIZDydnnehxcsqdlOdozUQ0yVg4kQmM24Ne3nvFBm68aA0+T24GuNAy0HHLLnoZ6Lmen1r/8BW1oZ6CoEZTzex9sWfmar3MSibhzS4ct9jbP8rug8M8eWSMpH0mq+4xFNs2ujXFV5+9gtaABMX1RgLkZa6UIG++D/pSsmTpD49jY2FWtQV512XrZvQvzraqNcCx8QjTMQsrFRkroLfVzz3v20azv/CfcjqwRoGZWgq62GWgYe7nR06di2LVehDUqKqVvV9s+VWtTCycjiUZlXKKHKG4xROvuuUT+47mBsVeU7HtrC6uP7eHq/tW0BKQEKueyau3zJUa5M32QZ8Onr/w45czbeD6upsL3sbuA0N89ocvYBqKJp/J0HQsU/aQHyRrrXns8Cgnp2KMR5KZy1v8Jk0+D3/ypi2zBscAa9rTy0CfKaMoZhnotLmeHzl1LkRtq1b2fjHlV7Uwt8F2NCOhOGGZhAdAKGbx+Ksj7D44zFOvjc8IirdvdIPiq/pWzPl5JOpLxV9JpdQNwJ2ACdyjtf5C3vVbgX8CLgX+XGv9N8XuKxav3EFeJOnM2gYOwHE0f//Lwyh1ZrW8Qot8ADw3OMFX9hzhpZNTmcs6g15MU7Guo6ngctJp6f7Fn3jjOfzf//4S0aS9oMc31/Mjp86FqH3VyN4vpvyq2nMbIgmL4Wlp3TYdS/J4KlNcMCje1MXOLT1cKUFxw6roq6qUMoEvAW8GBoF9Sqn7tdYvZW02BnwC+J0F7CsWqZxB3lwD+44tPUzFkkxGkwyMR+Zc5OPVoRBfefQITx4Zy1y/fVMXH752E+f0zt3LOB0Yp+u93vC6lSilFvz45nt+5NS5ECLfYsqvlnJuQ3Ypx7qOILds31B0W8xGNB1L8thht6b46dfGM+V8AD6PwRWbutixuYerzu6qaI98URsq/QpvBw5rrfsBlFK7gJuATJCrtR4ChpRSby91X1Ee5QryCg3sAY/BsdEwA+ORTEZitkU+Opt8/L8PvMwvXh4iPSydt7qVj1zXx0XrO+a8b69p0Nnso6XAN/nFPj4JgoUQpVjMmbmlmtuQXcrRFvBwYjLKFx48wCffOLPUrZFNRpM8fnjEzRQfm8jJnPtTQfH1qUxx0Fdcf3zRGCodIK8FBrJ+HwSuKOe+SqlbgVsBNmyYvyuBqJzsgV1rjaPdCQ09rYGcQefmy9dz50OHiCZtAl6DcNxmIprkxESMl1NLQ5/V1cSHrt3ENeesmHOJ57kC42qplQk2xainYxXlJWNn5SzmzNxSzW24a08/HsMtdbNsTcBjovXMUrdGNBlJ8mgqKH5mIDcoDngMruhbwfVberiirysnkSOWl0pHFYUim2ILm4raV2t9N3A3wLZt25Z30VSV3bajj8/+8AVsJ4nPNIgmbSxHc/Pl63O2297XxSfZzLd+dYz+kRDRpJ1p2dbb6ud9V2/kLeed6WVciGkoOoI+2oKeOQPocigliKyFCTbFqqdjFeUnY2dlLfTM01LNbTg2FqbZ58kNDrNK3RrNRCTBo4fdmuJnjo2TXWId8Bhc2beCnef2sH1TFwEJigWVD5AHgezoaB1wYgn2FUtMa80lZ3XyX954Dt/+1QCnpqKsSq1aB/Cn330us0S0BoZDcaJJJzM4twU8vOeKDfzOxWtn9DLO5jEM2oNeWgMejDkC6HIpNYis9gSbUtTTsQqxnFS6rGs8nKCnNcBoaGap26q2YMXud6mNRxI8esjNFD87MJEbFHsNrupbwfXn9rB9owTFYqZKB8j7gM1KqU3AceBm4D1LsO+ytpSnzbXWTMUsJiNJLMdh28Yutm08c3oueyU7A82R0UjOaQAF7Dy3hz9585Y5yyRMQ9HZ7KPVX/mMcbZSg8h6Wjykno5VCLF4CcthOBQnnrS5eVtuqVss6RQ841dvxsIJHjk0wp5DwzyXFxQHvSZXne2WT2zf2IlfgmIxh4oGyFprSyn1ceAnuK3a7tVav6iU+mjq+i8rpVYB+4E2wFFK/TFwntZ6qtC+lTzeRlDu0+ZzBdvTsSQTkSRJO3fp0Sf7x9i1b4CTU1FCMYuAR6G8Hk5NJXKC4/agh2afh/FwctbgWCl3Aklnk6/sGeNivkiUGkTW0+Ih9XSsQojFmYwkGYsk0KlFP9Klbrv25Z7xq8f647Fwgj0Hh9lzaJjnBydzguImn8nVqaD48o1dc56hFCJbxWc2aa0fAB7Iu+zLWT+fwi2fKGpfMbdynjYvFGx/9ocv8N8Tr+OCde0zAmPIzRi3BTwMTcUJJwDONJw3lJs5XtkaQKNnrXlrSQXGXnPmgLbYLHmxXyRKDSLrafGQejpWIcTCxJI2o+EE8aQ947rtfV11GRADjIbi7EmVT/x6cDIn+dLsM7n6nG6u39LNtrMkKBYLUztT/0VZlPO0eXawrbXG7zFJ2g537ennf7/7ooL77No34JZTKDgxEcsZtAwFBqAMhSeVDS5U89bi99DR5Jt1UCtHlrzYLxKlBpGVnGBT7tIZWehEiMZlO5rRcJxQrHFWwxuejvPIoWEePjjCC8fzgmK/ybXndLNjcw+XndUpQbFYNAmQG8xCTpvPFngNjEdoD3iwbAdba9BuX8i5ZjkPTkSIWw7TeYOyAaxq83N6Ko52NJ0tvhldLpr9HjqavPg9c9eFlSNLXuwXiYUEkZWYYFOu0plCr/V3br2yrMcqRL1Kvz8ODU2TsBy8pmLLyra6++IYiluMhhpjNbzh6Th7Dg3z8CvDvHBiKue6Fr+Ha85xyycuO6uz4NlGIRZKAuQGU2rGc7bA63O2w8rWAEPTsaJmOU9EEnzrV8cYDZ2pM/aaiu5mP0nbIpp0+yKf1dUEShFJWPQ2+7n58vW84XW9RQXGaeXIkpfyRaIWFgopx5cCaesmxOzS74+kbTMZSYKCaBKOjITq5n1iO5qRUJxwvL6zxkNTMR4+NMKeg8O8mBcUtwY8XHN2N9ef282lGyQoFpUjAXKDKTXjmR94Bbwmlp3k73/56owFPQrNco4mbP7lqQG+t3+QSMKtcTMUtAe9rGj2Erc0KA+fesvM1ZmafB46m4sPjNPKMbms0vW35S6HKMeXAmnrJsTs0u+P0ZCFYSgMpXAczXTMYlW7p+bfJ5GExfB0/WaNT0/F2HNwmIcPDvPSyemc69oCHq7d3M31W3q4ZH0HHgmKxRKQALkBlZLxTAdejqOxtcZxNL5UGcVcs5yTtsOPnj/JN/e+xngkCbinu96zfT3rO5v4/tPHZ50Z7TUNVrT4FryW/UKy5IWC1UrWCpc7U1uOLwXS1k2ImdLjw5NHx/Cbirit8ZruHAmlIGE7Nf0+sWyHsXCCUB1mjU9Nxng4FRQfOJUbFLcHvW5N8ZZuCYpFVUiAvMytaQ9yaipKwFO4jCJ/lrOjNT9/+TT/9NhRTk7GALcu+aq+FYyE4vzwuROsnqVdkNc0aG/yltTLeLHBbXawaip4ZmCcD31jP1t6W/j0DVsrUn9biUxtOTLe0tZNiFzZ40PAY5CwHRxHY+GWiGlNZlXQWnufaK2ZilqMRxI4un6yxicnozx80O0+8UqBoPi6VKb44vUdc66mKkSlSYC8DKUX95iKJvndS9Zy50OH0HruZvFaa548OsY9jxzh1eEw4JZSvO31q7lwTTv/9MTRTGu30XCcOx86xCdxyypMQ9HR5KMtkBsYz1eGMF8mtphgMx2sWrbmxGQMAzdQPjISrlhdYSUyteXIeEtbNyFyZX+Z7W7xc2IyimGA5WiUcttYtga8FXufLLQUK5qwGQnFC7barEUnJqKZTPHB06Gc6zqbvJnyiYvWSVAsaocEyMuI7WimY0kmo8lMnVoxzeJfOjHF3Y/08/zgZOayHVu6+eA1m9jQ1cSffvc5PIbKTOZLB1+79g/w5vNXFlzko5gyhPSHl+1ojoyESdgOplLc8eCBkktIjkyGMVAYhkKnnguvqSpSV1ipTO1iJwtKWzchcmV/mW1L/TsSihNN2gS9Jj5Tsam7pSLvk4WUYsUtm7FwgmhiZk/jWnN8/ExQfGhoZlC8Y3MP15/bw+vXtktQXILshbhmO1srykMC5GUgYTlMRpOE4lZmFaVC8q95bTTMPY8e4bHDo5nLLt3QwYev28TWVW2Zy05ORWkL5P4pNflMRqZjrGjxF7yvYsoQBsYjmApOTsZRyl1u2nE0B4dC7D4wVNQHVjpYTQfXQOa0aaXqCms5U1sLHTmEqBX5X2bbgl48pqK3NVDx9oellGIlbYfxSKIiPY3LGXANjkfcoPiVEQ4P5wbFXc0+rtvczc4tPVwgQfGC5C/ElX+2VpSXBMgNLG657YrmmrxR6A33v39+kPWdTTwzMJ5ZsnNzbwsfuW4T2zbOfBOubgsyGo4T9Joo5S4CErNs1nc1z3q/xZQhrO9s4plj4ygFRiq4VYDXpOjMbzpYTQfXCjdA7mn1V6yuUDK15e/iIUQlFPNltlJ/y8WMgbajGY8kmI7NndxYqHIEXANjblC8++Aw/anyu7QVzT52bOlhx5ZuLlgjQfFipRfimnG2dt+ABMgVIAFyg9FaE0nYTMcsIon5sw3Zbzjb0YRiFuPRJEPTcQDWdgT50LUb2bGlJxOk5rv58vV88aFDJGyHFr+nqIxpMWUIt+3o40Pf2Iep3LIIrcFBs6Y1wOB4pKgPrnSw+oUfv8yh4RBepVjV7sc0VFmyurMdQ3aZyMB4hLv29GeOp9FJv2VRL+b7MlvJv+XsMXAqmmQkFCduOTT5THYfGOLyTV2MhhJYTuXqjBcacL02GmZPaqJd/0huUNzd4nPLJ7b0cP7atlk/N0TpCp2tDXjnXrxLLJwEyA3Csh2mYhahmFXSgHpyKkqL32Q0nEjNhnYvNxR84jc287YLVs3ZXkcpxVsuWEVvm5+//skrmVqzTSvmzswWk7nZubWXzT0t9I+EiVvuY/KZioTt0B7wFv3BlQ5Y08Hs4HiE3tbAojNBc314AjUbJFY6uyv9lkU9mavsqJJ/y+kxcHg6xmg4AbhnyJp8Bn/+by/wltf18szAZEVrTUsJuI6OhlN9ikc4Uigo3tLDzi09nLdGguJKyT5bmzbb4l1LzWMYDbe8twTIdS6asJmKJRe0cpJlO3gNgyMjkZzAuNXvYUNXMzdetGbWfZ/sH+Nfnhrk1FSUDV3NXNXXRThhs64zmAl45woIiy1DeNvrV3PnQ4fxmApDgaNhaDqReTMW+uACZs3qLlUgCNRkkLgU2V3ptywaRSX/ltNj4Cd2PYOjNX7TYEWLn6DPZCwU55tPHmNVW6CitaZzBVxaa46ORjIT7V4bzX3Mva1+dmxxu0+8brUExUuhmMW7KsVjGHhMt4TSY2b9bBh4TVV069Z6IgFyHdJaE4pbTEaTJCyn5EkWjtY8/Mow9z52lOMTbqZAAR1NXoJeEw2894oNs+7/zGvjfGn3YXweg84mH0PTMb740CGMVCmEzzTobvHP2yWimID1if4xelp8TMcsoskzM7cHJ6Js6Mz91hz0mhw6PbVkmdu5Pjw11GSQuBTZXem3LBpFpf+Wd27tpS3oZW1HEEe7Yzsawgkb29Hzlj4sdoJdfsAVTdjELIeuZh8f+Np+jo3ljlcr2/zs2NzDznN72LqqtSGDolpWTNephcoEwKbK/Ow1DExDNWwAPB8JkOuIZTtMxyymYmfatJUyyUJrzf7XxrnnkSOZUghDwWUbOgnHbcYicXpbA7O+4UxD0dns476nj+dkby1bYzmg0Pg9RqrncJQ17YFFB4QD4xG6W/z4PabboxQFShO3NMcnYkQSNpGE7XapMBQGivampcnczvfhWYtB4lJkdxfaxUMm9olaU+mONAnLobfVz/B0bhY3YTn4zNyAJL/0oRwT7Lb3dfEJfQ5fe/w1BiciWI4mbjk89MpQZptVbYFMpliC4urLX7yrWNkBsNcwMFP/pjPB8rrOJAFyHYhbNpPRJOG4PWMmc7GTLA6cmuIrjxzhmWMTmcuu29zNB6/ZyFkrZu82kdYS8LCi2Z3clh9kjYTibncI3JpkpQAHTk/HuWR952IeeiYIHQnFM32MHQ0BjyJpO4yEEu63W9xA3dEOVl7z/FICwFKCtPk+PGux1dtSZHcX0sVDJvaJWlDo/X/7jeeXvSNNdneK379s5mlzw1C05tUG59eaLqajgdaaV4fDmfKJwfHcmuPV7QGu3+JOtNuyskWCpxqX7h7lMZWb8c0EvhIAL4YEyDUsFHdXu4slZ28KP98ki2NjEe599Ah7Do1krr94fTsfua6P161uYz5e06Cn1U8gK7uRH2S52VuwHbd8QynQaCybRQeE6SA0brn34Z6GhNUdAU5ORInbOlPW0dPq59RUjNNTcdqCvsxtFBsAlhqkzRcI1mKrt6Xq0VxqvbdM7BPVNuv7/8bzy9oTeTKaZCKSmHOxpjdt7eXBl07PWWtaakcDrTWHhkKZiXbp8rq0dFC889weNvdKUFxLsgPgdM2vW/pgZGqCRflJgFxj0qvdTUWL60Yx2ySLrmY/f/PTV3jwhVOZCXjn9LTwkR2b2HZW57yDn6EUHU1e2oPeGdvmB1mmobBs6G31MRlNEk86aNzFQhYreyJLOGER8LiBcGvAy4CO0Owz6etpyWyvtWZwIrqgAHAhQdpcgWAtLspRqz2aZWKfqLZKf0mLWzbD03ES1sxxvdBp83NXtc1Za1pMRwOtNQdPhzKZ4pOTsZz7WNsR5PpU+cQ5EhRXjSdT8uAGvunf3UlwEgBXiwTINWKuMoq55E+yiMRtJqJJTk7GeOnkFOBmBj54zSbesHX2XsbZWvweupp9s74p84OsjV1NjIYTGIYb4HtStXNtAU9ZTpPv3NrLF2++JJPdCXpNIgkLj2HMOA3pMQ0297TQ2ewvOQCslyBtsbW6tRi4y8Q+UW2VfP9PRpKMRRIlje3z1ZrO1tHg3dvWceDUFA+/MsyeQyMzguJ1nUE3U7ylh76eZgmKKyid+TVTga5hnKn/TQe/ppQ/1CwJkKtIa004Yc9bRjGX9Om5b//qGP2jIaIJBzs1CHc2efmjq87iba9fjbeIb6DeVPeJYBGZ3/wga/eBIT6x6xk04E/dTlvQW7YMTKHM500XreG+p4/PyBZ/9u3nLej+6iFIa9Ra3VpenlssD5V4/0cSFuORJPEFju9zyS7NODkZoS3oY3VbgDsfOsypqdygeH1nkOvPdWuK+7olKC4XM93yLCsI9ppnOkAYsnJgXZMAuQocRzNVQhnFXCzbYSgU4/hUlFDcHYSbfCbvvnw977x0XVHBrqEUnU0+2oKeogbO2TKYbUEvG7qacm6jnBnYQpnPC9d1lK1coB6CtEat1a3V0g+xfJTz/R9JWIyFEwXLKcpFa01LwMPmlS2cmIxyaCiU6U4EcFZXEzu2dLPz3F42rmiSoHgBsgPgdKmD1yyu96905al/qhLru1fLtm3b9P79+6t9GLNK2g6T0SShmIWzyOdda83DB0e497EjmRnIXlNx08VreO/2s2hv8s5zC65mv4cVc5RT5MvOYGZ/iKRneudnYEZCMcJxm7agty4GiezV9moxSLv2jofoyKsL11ozGU3yyKffWMUjEzVgwRFQrY+dS2Wx7/9Y0mYsnFjwGcH5OFrz0okpHj44zCOHRhiajudcv3FFE9dv6WHHlh42dc/fnUjktj/zmUYmIPaZC88Az/U5WUufJyKj4AstGeQlEEum64tLX+2ukKdfG+crjxzhldPTgNvL+M3nreT9V29kZVugqNvwmgYrWnw5wWwx5spg5mdgRsNxhqYT9LT4ylYOUOlv5bVYn5utHspAhKhXC33/W7bDWCRBKFaeMT5bOijefXCYPQeHGQklcq7f1N2cmWhXTMvO5chjGHg9ZzK/lS6DaNQzfcuNBMgVlF7trlz1ZwdPT/O3Pz2YcxrtvNVt/Ne3bCk6W6CU21S+q9k37ym3QsHoXBNZ8k+Th+M2PS0+elrdoH2xg0Sj1t+Woh7KQIRYLtJnbyYiyUWfFczmaM0Lxyd5+OAIew4NM5oXFPf1NLt9ijf3sGGFfDkGN+mTHfhmB8RLXV5SLxO+xdwkQC4zx9GZ1e6SdnnqzwbHI9z76FF2HxzOXBbwGrQFPIxHEgxPxYsKkP1ek+4WH37P/HXJswWjaM3hoRC21pipQSfpODT7POw+MJSTgUmXA2QrZpCYLUu8kG/ljVYHJrW6QtSGqViSiXBy0fNI0mxH88KJSR5+xS2fGA3nBsVnp4PiLT2s71qeQXG6K4TP4wbD7r9uOUQt1VjLmb7GIAFymVip+uLpMtQXp42G4nxj72v8x/MnM72Mvaaip8VPs89EKVXUykmGUnS1+GgLzF+XnA4onz42jlKwsjWA8rlB6UgoxkgogfvwNDHbPSjTcCcG5mdzFzJIzJUlLvVbeaNmnGu9DESIRha3bEZCibKcGbQdza+Pp4LiwyOM5QXF5/S2ZMonlktwpZTKBL2ZjHCdLYghZ/oaQ8UDZKXUDcCdgAnco7X+Qt71KnX924AI8H6t9dOp644C04ANWFrrbZU+3lLFkm6btlCZ6osBQjGLXfuO8f2njxNPzYJe1RYgkrDobfVhqDODRMBr8NpoiD/97nOcnIqyOq+hfIvfw4oWd4no+WQHlLbjYCjFiUl3AmBb0MtkJInWbnP5gVRQqgCPUvS0BmZkcxcySMyVJS414K5UHVijZaWFEPNL2g4TkSTTseSibsd2NM8PTvDwwREeOTTMeCT39rasbGHHZjdTvLYzOMut1D9DKbxZGeB0iYTPUx9B8FzkTF9jqGiArJQygS8BbwYGgX1Kqfu11i9lbfZWYHPq/yuAf0z9m/YGrfUINSaWtJmIJIkkyhcYx5M2//bsCb795DGmU5M9OoJe/uDKDfzWhWv4zPd/nVo56cw+4+EEoYTNaDhOW8DDaDjOnQ8d4k+NLbz9otUlTcLLDij9HhPL0SgNI6E4bUEvcdsh4DFpC3oxJxU+Q4Em03c5P5u7kEFirizx52+6oKSAuxJ1YI2alRZCFBZL2kzFSl/EKZvtaJ4bmMh0n5iI5gbF565s5fot3ezY0sOajuoGxU/2j7m9lQskXBYiPUEuEwBnZYUbmZzpq3+VziBvBw5rrfsBlFK7gJuA7AD5JuAb2h159iqlOpRSq7XWJyt8bAsSS9qMRxJEE+Vr42M7mp+8eIqvP/4awyG3bU/Qa/L729bxrm3rMkFuoZWTJmMWHUFPZrnRoM8kYTl8/+lB3nX5+pKOIzug7Gn1c2IiBmgStp6xcp3PNLAcDdr9GdxsbrPP5Ja79+ZkV79z65VFH8NcWeJSA+5K1IHJ7GQhGl+6V/10zFrwXBLb0TybFRRP5gfFq1pTNcXdrG6vjUzxk/1j3PnQITyGykm4fJLN85bxeT0G3tRCGd4arQ0WohSVDpDXAgNZvw+Smx2ebZu1wElAAz9VSmngLq313fl3oJS6FbgVYMOGDeU78jzRhBsYl7O/pdaaRw6PcO+jRzk25mY1PYbixovX8N4rNtDZ5MvZPnvlpFNTUVa1BQnFLTpS2yl1ppfj8Ylozr7FlAW0+j2ZCXg+06CzyctkNIkGelsD3HTRGr6x9zUODU2TtDWOo1HAqvYgkYTbsUMBSUcvOLs6X1lGKd/KK1EHJrOTRaNYqrGzntiOZiqaZDK6sK4Ulu3wTCoofvTQCFN5bd9et7o106d4VZEtOZfSrn0DeAx1JuGSGjd37RvgirNXZFaK85rpgDi1cEaDZ4PF8lTpALnQV8f8UWeuba7RWp9QSvUCP1NKHdBa78nZ0A2a7wa32f1iDzhfuVu1pT1zzO1lfOCU28tYAW86byUfuHojq9pnHzi393XlfJP/0+8+x1gkTovfm6kzjiSsnCxpMWUBuw8MMRyKYzkaQ7n1dqPhBJ1NXv76nRexc2svuw8MoXgNNCg0XlOhgXDcYvPKNnymQcJ2FpVdLWftViXqwGR2smgUlR4768liyigs2+HpY25Q/NjhmUHxeavbuP7cHnZs7ua1kQi79g3wg2eOl6V8oRyUUphKYZqK09Mx2gMelKFw/wOPqRgJxWThEbHsVDpAHgSyz/OvA04Uu43WOv3vkFLqB7glG3uosFjSJhy3CMftsrXwSTt0epp7Hj3CvqPjmcuu7Oviw9duoq+npeTbe//VG/nbn71C3LJnzZIWUxZw155+2oNemn0eRkJxEraDx1SsaPblbNMW9LIq63SgO3EwwHduvXJGW7fpWJKhqRhHRyPccvfeooPTctZulbsOTGYnC1E95Zwgazua0AJbciZth6deG2fPwREee3UkM2ck7YI1bezY4gbFvalM8ULLF8rBNHJrgE1T5SyfnLZxRbObAMhqBRpJ2KzvkuBYLD+VDpD3AZuVUpuA48DNwHvytrkf+HiqPvkKYFJrfVIp1QwYWuvp1M9vAW6v1IFqrZmOW0xFkySs8gbFAMcnovzTY0d56MBQ5rLz17Rx63V9vH5de8m35zUNulv89PW00NHknTNLWkxZQHob5VO0pbZNN8Ev9nays6vTsSQnJmJoNAGPkclav3Nwgif6x5a8A0S5PljLmZWWbhhCFK8cE2QdRxNN2oTiFpFEcdni9KS1o6MhokkHR2sc58zkZHDPAF6wto3rt/Rw3eYeelr9M25nrvKFcgXI+S3SfB4j0zO4GJIAEOKMigbIWmtLKfVx4Ce4bd7u1Vq/qJT6aOr6LwMP4LZ4O4zb5u0Dqd1XAj9IFfh7gG9rrR+s1LEeG4tgO+U/yzgWTvDPT7zGj359MnP7G1c0sWNzD88NTPD//vjlkk+1tQe9dDb5MktkzpclLaYsoBzbZA+uQ1NucAzQ3eLP9FH+0u5XWdcZXNIOEOXuPFGOrHShY/rUfc/R0+JnOm5JwCxEnoVOkP3ly6f5h4dfZWA8yqq2ADdvK36sffzQCH/784NEEhZxa+bnw6YVzbz9wtVct7m7YFCc7eRUlLZA7kduwGtwaurMfJFiO0ikF8zIDoJ9ZWiRJu3JhDij4n2QtdYP4AbB2Zd9OetnDXyswH79wEWVPr60csfGobjF9/YPcN/+QWKpjHRvq58PXLOR9oCX//PLwyWfaitlJbxsxWQFyrVNs8+kfyRM3HLwm4pV7cFMRnoyksR29JL3Ja7FzhP5x2TZmolIklDM4pzeFmkfJ0SeUibI2o4mbtn8/MXT/M8HD+AxFK1+k9HQ/GNtwnLYd3SMhw8O89CBoRmfDQp3MnVPq5/2oJffvXRtUce/ui2YatN5ZvyOJR1Wtbkla3OVYFyzuRu/1yDgNQl4zIr2Cpb2ZEK4ZCW9MktYDj989jjf+tWxzGQN01AEvQar2gJ0Bn0ln2ozlKKz2Ud7cP6V8AopNivQ5DU4Mup+2PR1N/PZt2/N2Wau28nOiG7ubeHwUAjL0UQTFqenYsQtB4374TIdS9KaWtVvKfoSZ3+wTseSDE/HiVs2g+PRzPLYSy3/w34kFMdQ7mlbpVRNBPFC1JK5zmBprYlbDpGETSRhZcrk7n3s6Iyxdiwc5/P/8RItAU8mS3vR+nb2HR1nz6FhHn91lEheG08FGAaYqZZljtY0+82c7O98CrXptBzNzal2nLv2DeA1FUGvB6WgNWASTVr827PHufkK6TIixFKTALlMbEfzs5dO87XHjzI07fYydk95KXymIpp0ePHEJJ+9/wW8BqzN63ow24p4b3hdLyuafYtuozNXViA/uI0mbcKz9Hme7XbyM6Kr2gMMjEUYCuUunaqBgbEI67uaaA14i+oAMVeGuFB2eHg6xid2PUNb0Mv6ziZafO4XENvRnJiIoZT7pUMpZs3SVro+OP/DPmE7KM70lAZpHydEtvQZrHA8ScBrEknYJGyH371kLUdGwgX3yS9rCMUtJqJJHA2r2v0MjEf4yx+9iKOZMfekxe8h4FH4PCbjkYS7cJJSOI7Gaxo52d9iZLfpPD0VZXVHkPdftZGdr+vFaxgMh2J0Nvly+gY3+zwzWnaWk8yDEGJ2EiAvktaax18d5auPHuHo6Jlexr914WoOnQ5xeirKRDSJQmEaCkdrIkl3Bbyu5jM1a/kr4o1F4vz97sOsag9UfMAqJsgsdQW81oAXj6Gwbff8pKHcLIyt3XKWoakYpqHmnQAyX4Y4/36noklGwwkcrdnQ5QahU6lezqGYBWjQbnu6la0BPKaakaVditXy8stVTENh2TqnjlHaxwnhBq5xy+aCde188o3n8M+/OsapSbcP/M2Xr+eyjZ2z7ru6LcjxiTChuE3SdrCcVDZYQf9IhOw5eoYCj2ngNdyERzRhEUuC37Rp8ZuMh5PYyt2h2efJyf7OJVMnbBr89sVreOe2dQUTHhu6mpe0haSsCirE3CRAXoTnByf4yiNHePHEVOayN27t5QPXbGRtR5BbvrKXcMJGoTIT6gwFhqOZjFkEfZ4ZK+LZjub4eDTVZs3gCz9+ueKDVTFB5nwDZ6HTn5Z2FxLxe41UR023Z6jlaOK2prc1MG/gPV/9cP79jqRWIgx4zEypAriZ2emYhQa8pqK7JUBb0IvWekaWtlw1y3NlZ/LLVTZ2NTEaTmAaCq11WWePS5ZI1Iq5/hZtRxNL2sRTAbFlayxH53SauOSsTi45a/aAON8l69t5bnACpc4019e4X9TTmrwGHtNgQ2cTpwokNOK2w0p/AMtxg3WfqVjX2VxwAp3XNPB7DfweE38qKE6P/fNZ6g4StTg3Q4haIgHyArw6HOKeR47wqyNjmcu2b3J7GZ/Te6aX8eq2ICPT8cwCHgBag8+jCPo8rGj256yI5zMNTk/HMJWBxzBwHM2h4dCi6mSLCY6KCTLTAyfAF378ck6t8qdv2FpwcPcYBg4OWkP6rKGhFH6v4pL1nUUtQT3fxJz8+41bbqlCdiY26DWZjCa5dENnURmacqyWV0x2Jr9cJf1alXP2uGSJRK3I/ltsD3g4NRXlL/7tBT71li1ctrEr04u42E4Oc4kmbX7VP8oPnj2BBvK7uRkKelr8tPg9JGyHFc1+Tk5FCyY0LFvTFvRxz/svz7sNhd/rBsEBrxsQL6YUbqk7SMiqoELMTQLkEpycdHsZ/+LloUw24nWrW/nIdX1cvL5jxvY3X76eF09O4mh3dTqtAQ2tQQ9rO5r53+92m3QYSvFn//Icvz4xianOZBwU4FUzSwCKVWxwVGyQeWhomk/d9xwTkSTpmP/QUIg/u+85/vqdF3H7jefnDO43XbSGrz52hMmolXk8hoLOoK/orEg6eLcdzfC0u4CJqVRmVaf8D5Umn4lpwPB0nOMTUXymQWvAw6bulqIzNOVYLe+uPf0kbZvRkEXCdvCZBm1Bz5yvZSVmj0uWSFSb42gStsOXfnkYhcZjmCRtjdcwsJTN1x5/jQtT4+eT/WPc8ZMDhBMWjqMZjyS44ydhPv2bW+cNkqMJmyf6R9lzcJhfHRkjnldTbCh3HIslbTTQFvTkTJTbtW+gcELDVJyaiuI1DYI+MxMMF9tbeD7VOsMjq4IKMTcJkIswHknwzb3H+PfnTmClev5s6GriQ9du4tpzVuRMqsi2va+LP9i+gW8+eQzL1vhMRWvQg8c0M7VrzX4PK5p9fOwN5/Chb+zHTJ0K1Nr9f1W7f0Hf6HcfGOITu54hkrDxe9xFRdqC3oLBUaEgs9lvZjpNgDtwRuI2McvG0e6Hjanc05DTMfc2r+rr4sUTk4QTNpPRJKvafKmVm9xaY42bSf6jK88q+gPgth19/Nl9zzGeCsoVYDma4VA8k1nPDiy/+POD3PnQYbeURbmT34ZDCd6zvavoDE05TnUeGppmMpLEMNznyHI0I9MJkvZ00bdRDpIlEtWgteb0VJyE5WRWIz02HqEt4MkpmcjvA3z3nleZiiZBg4P7Xk9YSf7uZwf4zm1Xz7ifSMLiiVfH+LdnjvPSySmyE8WmcksklAI3llUkLIdmv4ntwHTMytQxp4PvF09OZso60rfl8xmE4xa3fGVv2QPYap7hkUVBhJibBMhziCQsvrd/kH/ZP0g06XZ16Gnx8/6rz+It56/KyTTM5g+v3si5q9rYtW8gU05x8+XrufqcbrpbfZlv7zu39rKlt4UjI2FsR+MzDXpa/ZiGorc1UNJxpwfdcMLCk5r8dWLS/RBqDXgKBkfZQWZ6/+yBcyqaJJrKvIA70c7RGm9qOZAXjk/w5NExd6KL4QbUP3j2JB1BD+euast5Tp/oH+MTRT6WnVvdLh7TcSvzvHS3+AtOrgN4on+M3lYfU9HczG36PovJ0pbjVGciNRvISH15UgocpSuySuNcJEskqiWSyF1+eb4+wAADE1F0KjjOdjqU5Mn+Mbb3daWC4lF2HxzmySNjJLMKitNzHvwek46Ah3DC7VqBdrvWOGiiSYfP33jBjIy0aShafAbj0TMdfAwF4YRDR9BTdABbSka4mmd4ZFEQIeYmAXIBCcvhR8+f4Jt7j7mDK25g+Z7tG/idi9fg95a2UMf2vq6cwTh/Jby0T9+wNZNNmOsbfbELYwQ8Jpaj3ftx4PRUjNNTbob6lrv3zjoYFho4valMcdx2SDc01hosDX7TIJJ0UsGxe9rRUJC07Uwv6DTLdnj62DjX3vFQ0dmYUMLmnJ6WnEx9ocl14GZMVzT76W4JzLvtXBZb7uA1FdGke3pZqTM1kD6zuAk75SJZIlEr5usD/GR/brCb7x92v8r9z59g39GZQbHXdDvTOI47MdhrwImpGBtXNGXatCVtB69pEPAYmfE44DVp9nnYd3SU//PLw0QtjekOl5n3rDuWFdefvNSMcLXP8JSzrEsmA4tGIwFyFtvR/OLAEF977CinpmIA+D0G77xsHe/etp6WwOKerqdfG+dfnhrk+ES04ABSzDf6UhbG6Gn1c2IihoPG0Q5J221Bt7YjMO/AnT9wXnvHQ6xs83N8IuZ2p0h9eGjtfnkYDSdmBPwKN9PcPxwiYTsYSpG0HHweo6TTiaVkQReaMS334L5lZRtHRkJMx85kslsDXjZ1t8y/cxlJlkjUiuw+wNln07b3dWVWkfMakJzlJMux8QjHUoGj11Rs39jFji09/MMvD2Um16XbJU5Ek2jtZqhb/B5a/O54EE3a9LT6WdHip9lnZibVffXRo3hNhZ3qcaxS5RkJy8HnUSTsMwc1VwBbaka4Uc7wyGRg0YgkQMbNMD7x6gj3PHKE/lTDeUPBb124hj+8cgMrWvzz3MLclFK8ODjJnb84NG9wON83+mIG4PSg2xrwsqbDnbAWttzHtLbjzNLPpZzKS9/mus4gpyZjmQkwTT6Tv37nRXxi1zOE4xYaO6drBZAKjsns0xbwlLRaXClZ0IVkTCsxuKePY1W7p+qZW1k6VtSK/LNpaenVRVe0+Dk1FS+4rwLaAl600pzV2cxvX7iG7X1dfPEXhwAyX9C11qQrmU5NxWgPeFjR4s+UN33ijZtnrEqaTir4TAPLds/6pMcwR+cu4DNXAFtqRricZ3iqmcGVycCiEVVuQfc6cfD0NL9/1xP89x+8kAmO33BuD1/7wOX88Zs2Lzo49nkM1nQE+NaTx/B5DJp8Z4LDhGXziV3PcO0dD3HL3XvZfWBo3tsbGI/k1PDBzAH4th19JG1NJGHR4vewqj2AacD6zjPBcaH95pK+TdNQnNPbQl9PMxu6mviH91zKzq29/MbWnswiIJoz//pN98PF0e4HnMcgZ5W+Yo5h59Zebr/xfHpbA0xGk/S2Brj9xvNnLw8pctu07ME9/dp4UzXOC7WQ4xBiOZqOJekfCTEaTswaHIM7nkSTFi0+k4logjsfOsST/WP4PIY7qU9rHMch6ZxZnKiryctUzGI0FGdVe5DP33TBrJncaNKmu8XvnnFzdGrFPIWTOkumtTumzhXApm8n21wBdbnGifSX/KHpWM6X/GI+U8qhmM8lIerNss8gP/PaOPuPjgPQ6vfwvqs28ruXrS3LbXc0+ehs8qKUmpFZODUZZSSUQOPW5dqOU5Zyg3QWIZKwMk3tN69sw2uozAdH9n7NPpNb7t47a9YhOyvR4nP7Ip+ajJKwNT6PkQkiT00l6GzyMBm1Ml0utAaPadLX45YV9A+HSNpOzunK+U4n5mdFZvuAy1ZqxrRSdYCSuRUiV7rH8fGJCAGvSdBn8uqwOzE5TQGeVOebfAlbc2oyzuqOAEnL5vP/8ZK7TLsCtM6UZ5jKLY/rbQvSEvDS2xqYs+96OpPrNRVr2gOcno5j2bClt4W3XrCKJ/rHiipRWkhGuBzjRLUzuI1SKiJEtmUdIO8+MMSXdr9KV7OPgNfAUIrvPzPIus5gyY3ps/lSbdUCWd+osweQqWgyExwDRJMOiekEPa2+TMA526myuQbg3QeG+NR9zxFKdXwwDYXP48kMzvn7TUaTKCDp6IKlBfmlB+4+CZRS9LT6CHrNzD7heJK1HU2s6zxTW/Hq0HSmrGIqmsR2NAnb7Qk9FU3gMY05PzyKLX344s8Pcs+jRwgn3ID/w9du4hNv2lL06yWDuxCV98uXh/jiLw8RTzrE8rq5eE23fthrKJK2Q8yafbKeA5yaiKEM90zVqjY/p1OZ5/RZKpSiJ9X9p+izVJyp1b9kfWfOuFtK151q1Pxnf8mfjiUZno4Tt2wGx6OLWmiqWDIZWDSiZR0gp791r+kIErds0G5gtGvfwIICZKUUnU1e2oPeGb2RsweQU1OxTHCc3spyNGMht0/uXEFhegD+wo9f5tBQCHBXsyN12UQk6fYnVgrtwEQkyRd+/DIP/sn1MwZun2mQsJ1Zsw6FshLHJ6KgYVV7MGefpO0uj5wdZLY3eRkLJxmejjEaTgBuZsc0FIMTUTb3tPDZt5+3qBZI2X2P0+3l7nzoMEDRQbIM7kJUxkQkwaOHR3j4lWGeOjaRc51S6a4uivagBwWpJe7d6w1mtntLswEj1VBnaDqOJ7UstAOYpsHK1kCmnKzYL7tzZXLnqu/Nv+6q1GfH7CF++WUvqHRiIoZKtZhUiiWZLCeTgUUjdjFZ1gFyoVPr+Y3rixX0mXS3+GddXSl7AHl1OHzmilSErDTEbY3fcmgPzn+qLJJ0WNcZzAR0n7v/RU5MRN0FMtIr8SnQjs4sC12oM8VcpQWFnh871UQ/m2U7RBM2R0fDeA2DlW1+PKaB1zT52M4N3PPoERytCXhMejr8tAbcBUs6m/1zvoGKKX2459EjM9rLWY7DPY8eKTpAlsF98RpxcBQLMx5O8MCvT/L9pwd5dmCC7MoupaDFl+oqoTTDU3E0mvagl9fGImgNq9sDjIUTRGdrZ5Hi4H4pdvvRuysstfpNOpp8eEyF1nrOVplf+PHLmbGxr7uZT9+wFXDHgUND0yQsB6+p6GnxMxpO0Bb0zkhaADkJjSMjIZ486vZiX9HsX7JuDukv+UNTMUCDdtverWwNzNozvtykpGz5atQuJss6QC50aj2/cf18TEPR2eyjLeCdd9v0AHLuX/zYrTvWZ3ptpqV7IGfLDwpny6zajsbI67NrOxpH64J9h/Mf/3QsyanJWKZPcqvfMyMrbBoK9Jn7mI4lOT4Rw2Mq1rS6tXuDEzG29Lbw2bdvZefWXr731CAbuppysurFnPYspvQhnLDdU6pZ3Ob+uRNl5iOD+8I16uAoFuaFE5P8+b+9kPk96DW5+uwVvJqag9Ccej8PjEdAuZN4FQrH0WhNJgM6H0OB1zSxHY3lOO7iRQmbD1+7ds6a4XQp2kRqdU6AQ0MhPvndZ/CaBl5TMRlJgoJo0l1xz9Huqqf53XeAnLF4OmalSsgsulsCS1YLnP6Sf9s3n0Knjqm7xc2iL6QPvBClqHYNfKUs6wA5+9S6oZjRuH4+LX63fdB8K+rlZ9d6mr2cmJw5W3tdu5/1K1rmnYT39LFxbMfB7zHpaXUzskGviSc141qllle1bI2t3d7HY6E4JyejPH1snI/tPJtPvGlLzuO3bIfjE27v53Sf5HSNMrgfcqPhOJbtfhAdOj3Nyqzav/Qpzbagj0jCoqPJN2sgnv+YZlNM6UOzzyQUtyCvvVy676movEYdHMXCXNW3gg1dTWxZ2cL1W3q4fGMXPo+R6XU8HokzHbMydcZtATchoDhTUuE1FIk5Fg0B972etN0xO007mvuePj5rJ4jdB4b4xK5nmIpZKEAZCo9poLRmKmrh9xqYSmEYCkO5QXvCdjPJw9NxWlOJkPQXfA05Z7liloPjaJK2Tf9wiJ5WPy3+wquXltvOrb1cuqFT5lOIJVftBW8qZVm3ectusTMds1jR7OeTb9xcsP74yf4x/vS7z3HLV/byX7/3HIdOT9PbFigqOM5vvzMVs2bUpxkKfv/yDTkt2vLbCqVvK11fZqXqzaZj7jLQZ3c3u10zSHfG0KluEm6g7DUNHK350u5XMxM30o//1FQcj6lSfZLdJbDbg15WNPtS18cYCyfpbvGzvjMICgYnYliOZm1HYM72cXM9pmJfn9laIP3G1p7Ustdn2ss52r1cLA1p8SSyeUyDX/7X6/nvb3sd15zT7bZhw+2BfMN5K5mIuu//VGEE0zGLcMLKbZ6emlNQiMKtXdaO29oN3PHTo8DrMRiajnHbN5+a0TozPX6GU0tga9wJylaqC4aG1ERiJ3Mo+f3c09JBZ3Zbt6lo0s2Cky71csfn0XB8yQLUhY61QixGqe0N68WyT7OlT60fGQnPqK1NS2c+PIaiM+hlMprgfzxwAL/HnDdDVii7lkj11/SmJsn5TIO2oIcn+sf4xJu2zFoPe8vde/GaipWtAU5MRlOr2WlOTcbobQvw2befl7nPwfEIQ9NxtOOgDLdDB7iT5CxHZ7J76f/T9cj5ZRCT0SQP/smV3HL33pzMRDpTPDwdz6xGlZZ+YxRqETcZTZZU4ztf6cOpqQRdTV4moslMe7mOoJdTU4l5b1uUh3QBEfnyV9VMe2ZgklVtAYJe98zP0HQMjWY0FMdxNAZuizcntaJdW8BkKpok3dQi4DFY2eZ2pzg+EcVyNH6PW/ZlaU0ytSiR5cAT/aM80T/KunY//887LsyMxQGPSSTpTsrWgK01hlYo3BIyM5V8SC8R7zNV5kxcobrm9FmukVA8s4+hVCojrhkLJ/mf71iaAFXmU4hqaNSJ7ss+QC7Grn0DeE1Fa8CLoRRej1n0KeS5Jrql+wMDOXViswWF6dtSPvfDZyQUJ2G7GYvszGr631vu3suTR0fxZn1Wae32B83P7s0X5Mx2CsXnMTIZi+w3xlV9XTNaxCVth8/fdAHgDuB/8cMXFj2ha2A8wpqOIGuzgrFiau5kUln5NOrgKBYn3fP45FSU1allpU9ORWkLuGOMWwYVYHg6lim38BrQ0+qn2efBMBQJy+bcVW1nxr6c4mTNsfEotoMbJNsaG5UpuVC4AfDxyTifuu85AFa1Behp9TM4HsVKJUQc7Y7JbUFPpgZ5ZDqB42YgWNHsw3I0PS3+gl/w0wHp0dEIAY9Bi99DOGFnkh9Br1GRsWW2Mayc8ylknBTFaNQvZhIgz8M0FEOhGF1NvpInmUHhwDN/ohsUl3HLvi233tftBtHbGij4h3jbjr5UvbLGTGU2HDStAe+M+5ovyJktgN7c28ptO/pmvDFmq0v9wo9fJpJ0yjahayHZS5lUVl6NOjiKhUl3iDg4FMJjKrqbfYyG49z50CGavSaxpJNTkuOksrTdLT5OT8U5NRljbUcAr8fEcsiMJ/nvc49psHVlK+GEjddUHBuL5ATH6QhZA6G4hc80iCbt1PgHpyZjmX7MPo+B1zToafGjtSZp68xCS5u6W+b8e04HpPln2YDM+FxuSzGGyTgpStGIE90lQJ5Da8CtwT2rq3nBp5ALBZ4tfrfnZ6kZt1IzdTu39vKxnWfzpd2vpk5FGrQGvPg85ox9CgU5V/V1ZTK9LT73VCcw474LvTH+4ocvzMg4W7ZD/4jbii6QNcFwMRO6rurr4ku7X8XOPD5PwceXTSaVlV8jDo6idOmg6vRk1P1S7rh9intbA3gMBUphOQ7RpE3AazASdif5tgY8jEfcMiml4ORknG0bu3IC00Jj32fffqY12+B4lOzuw9kVc0lb0+JTmbNdLX4PazuDmYnIbUFvzu3+zTsvKvnvOT0+j4RiTEaSxG0Hj2Fw00VrSn4O58vazjWGpa9fbNb3jgcPMDQVw9Yan2nQ0+rHW+aWcZKhFrVMAuQCvKa7El7Q52Y5FnMKuVDgmV8rPFvGrdDgcfuN55eUqfvEm7Zw4bqOovbJDnIKraKncVsyFVNHnJ/ZnYq67eDc5afPTDBc08GCZ3nvPjDEfU8fp6vZy2QkScyySYQdVrb63fKNPYWPsVFn3ApRbenAzdYaw1Cp9m0wHkmwrjPIdMzij39jC7v2DXBqKorW0B70MhW1UAr8XgPHcScVZ7935ztLkV7587ZvPkXccmZMgrYdzXjUorvFnzMXYr7FkuaTP0ZftqGdB144je24fd9bAx7ue/o4F67rKPr2isnazjaGHRqae6GpYu0+MMTBoVBmYaf0eL263V+2cVIy1JUlXz4WT802Ma0ebdu2Te/fv39B+6Yn6bUHvXQ1+2ashJf+Y1uqU8jZg0d2UD5b+6Jym+t0YfqU51xvvPzjPzwcwrJ1aqKLO4nH0e7vq9oD9LYG+M6tVy7qGKdjSQbHo3hMxTk9LbM+Z4Ue20goRjhu0xb0LslgIoPX3OT5WZAiugcXtpixM1t6sm//cIhkqrVMMlX24DMV6zqbuOd92zLbf+p7z/HiiUls7XakSH8ceU3FZWd1zTomzPb38cWfH+Tvfn5o1lXsVrf78ZpmZkwoNDlZa81kNMkjn37jnI9194Eh/uy+55iOWViOmy1OOo7bGkqpTNY1btlFjy1zjbvZz8Vs2w1Px+lp9c+7/3xuuXsvzwyMo50zEy4d7XYeuWRDZ8lj9Wz3UcxjFaWrdvxQhwqOnZJBTkk3Vg94zTknPyyVSpQBZD+uVr8HrTWhhF1w0J41Q3F6qqhv/fkZH63d/sqgODEZdRueKk3McubNxs/2euQf4/B0HEO52aL8hv7Zx5Z/RmA0HGdoOkFPi6/kTMZCAjnJnMxNnp/6lT5zlJ4Il93K2HY045EET/aPccXZK+gIevkvbzyHD31jf85qe+B22jl0emrG7e8+MMQdDx7g4FAo1dHnzGp17xyc4In+sUwniUJOT8UxlOITu57hizdfUnAOw2g4TjhuF1xcKdsdDx5gPJLETPVStmyN7bhDm9eESMLmaGqlPkPBhq6mGX/L+ePHoaFpVrXl1iwXOrt1244+/uy+5zg+Hs0E560BT1ELTRVjYDzCylY/JyZj4KRWZdUaS5dv8q2cyascKSMsj4r3QVZK3aCUekUpdVgp9ZkC1yul1BdT1z+vlLq02H3LaW1HMBMc5/ct/tz9L+b001wK5e4tm/24TOWuHHV4OIypKPgYZ+trmLB15o2XDkLTdWnp+7nl7r1ce8dD3LWnn9t29PHIp9/IpRs68ZgGbUEva9qDeEyF7UCzzzPnt9q5Xo/8Y0z3KfVltZ0r9Jzl91cOx216Wnz0tAYKPqZintNS/layB69S7m+5kOenfqX78JqGyqxSB253ipVtAVr8Hr731ABrO4J0Nvt4w+tW4vcYmZ7IhnIXCTELLBSSfr8dGQln6ptPTMawbE3CsvnS7lcZmo4RmK2BMu6EQNOAcMLic/e/yFV9XTl9g0dCMYamEzT5zHnf0/0jYQyVbummMj2ZNW7Nczat3X7P2X/LhcaP6ZjFaDh3EanZ5rto3CdNKZVeaZueFn9Z+tGu72zCYxpnxupUyczmnpayBViN2ju3Fkhv+vKoaICslDKBLwFvBc4DblFKnZe32VuBzan/bwX+sYR9y3msQO18OJd78Mh+XCOhRKbf50goUfAxztZw3ucxZn3jzRUwZt9ea8DDqvYAazqCfPHmS+YccOd6PfKP0VTuSoLdLf55n7OdW3v5zq1X8sin30hb0JuzT/ZjKvY5LeVvRQavucnzU7+yv3zaGoJegzXtATZ1t9Aa8NLq9zA8Hc8sHgLuEs4eQ+HzGPg8hntKX5OzDcysbzYMhYFiJBTPlDk0+Tz0tgVmrTUxUu0tAh4Tr6l4on+sLF+WITdrrSGnzMNUbltOOPO3XGj86GzyMhZOzrvQx117+mkPetnc28rWVW1s7m2lPVUqUo6FQtJjq8dUbOpuZkNXE72tAT7z1teVdDvF3IcsalJ+8uWjPCqdQd4OHNZa92utE8Au4Ka8bW4CvqFde4EOpdTqIvctu1r5cC734JH9uNIrRSl1Juua/xhnW8Vuc2/rrG+8uQLGYlbFm++409LHmn+bm7qb6Wjy4jFVSc/ZQgeThf6tyOA1N3l+6lv6y+eFazvobvHTEvBgGgqvqYjbzozXcXNvK92tPjyGwnbceQndrT4297bmbJd+v/lMIxOMpsewuOXgT505ag146W0984U3O1g2U3Mgelr9OePIQr4sb1rR5K7cmeprn81QuffrSU0GhDN/y4XGj+4WP61+c95xcraxJxS3FjTO5lvoeF1r97FcyZeP8qh0DfJaYCDr90HgiiK2WVvkviilbsXNPLNhw4ZFH3CtrApW7t6y2Y/LZxpuv1B9phyh0GOcre56to4ehVq7ZX+4LKSOe77XI/82FzKZcqFdShb6tyILa8xNnp+lUe6xM9/Nl6/n//zyMJbt4PN5Zn0d06/3qnbPnK93dn3ziYkYDm5gmg5625vOjD29bQEMA8JxG5+pGI9akNWuLN1eMv+9Wsp7+jNvfR2fuu85QnELO7U6ajJVggagcLPdWutMq7TsQKVQb+do0mbzyrZ5J6nNdZzlmi+zFPNupD1kZUhv+vKodAa50Jmu/OkTs21TzL5ore/WWm/TWm/r6elZwCHmqqVvXtmZje/ceuWi/rizH1d3iw/bcQft7hZfSY9xrm/9lcj8lfp6LOQ5W2gmY6F/K5I5mZs8P0uj3GNnvndctpb/8TsXsLItOOfrWOzrnV3fvLrdj8JdJnpjVxMf23k2XtPMeS96TZMv3nwJ+z/7Fr76R9tY29nEqna3Dnq292op7+mdW3v5m3dexCXrO1nVFuCys7r4kzdt5uyeFmwNynAnJve2+TGUIug1ch7bYj5raulzStSmcsYPy1VF27wppa4C/lJr/Zup3/8bgNb6f2ZtcxewW2v9ndTvrwA7gY3z7ZuvXK2Klrql21LJflwtqS4W4YRdtsdYqdYytfx61PKxiWWn6m3eKm2u99t878Vi36vleE8vxX3J2CNE2RQcOysdIHuAg8BvAMeBfcB7tNYvZm3zduDjwNtwSyi+qLXeXsy++eplkG9kMmgLUTUNHyALIUQFLH0fZK21pZT6OPATwATu1Vq/qJT6aOr6LwMP4AbHh4EI8IG59q3k8YrFk5oyIYQQQtS7ii8UorV+ADcIzr7sy1k/a+Bjxe4rhBBCCCFEJVV8oRAhhBBCCCHqiQTIQgghhBBCZJEAWQghhBBCiCwSIAshhBBCCJFFAmQhhBBCCCGySIAshBBCCCFEFgmQhRBCCCGEyCIBshBCCCGEEFkkQBZCCCGEECKLcheyawxKqWHgtXk26wZGluBwSiHHVBw5puLIMRWn0Y5pRGt9w0J2LGLsbLTnqlLkmIojx1QcOabiLPaYCo6dDRUgF0MptV9rva3ax5FNjqk4ckzFkWMqjhxT8WrxuOSYiiPHVBw5puIsp2OSEgshhBBCCCGySIAshBBCCCFEluUYIN9d7QMoQI6pOHJMxZFjKo4cU/Fq8bjkmIojx1QcOabiLJtjWnY1yEIIIYQQQsxlOWaQhRBCCCGEmJUEyEIIIYQQQmRp2ABZKXWDUuoVpdRhpdRnClyvlFJfTF3/vFLq0gofz3ql1C+VUi8rpV5USn2ywDY7lVKTSqlnU/9/rpLHlLrPo0qpX6fub3+B65f6eTo36/E/q5SaUkr9cd42FX+elFL3KqWGlFIvZF3WpZT6mVLqUOrfzln2nfNvr8zH9NdKqQOp1+YHSqmOWfad83Uu8zH9pVLqeNbr87ZZ9l3K5+m7WcdzVCn17Cz7Vup5Kvj+r/bfVKn3I+Nm5j5l3Cx8HDJuLvyYZNycebvVHTe11g33P2ACrwJ9gA94Djgvb5u3AT8GFHAl8KsKH9Nq4NLUz63AwQLHtBP40RI/V0eB7jmuX9LnqcDreAo4a6mfJ2AHcCnwQtZl/wv4TOrnzwB3LORvr8zH9BbAk/r5jkLHVMzrXOZj+kvgU0W8tkv2POVd/7fA55b4eSr4/q/231Sp9yPjZnF/JzJuyri5gGOScXPm7VZ13GzUDPJ24LDWul9rnQB2ATflbXMT8A3t2gt0KKVWV+qAtNYntdZPp36eBl4G1lbq/spoSZ+nPL8BvKq1nm91xLLTWu8BxvIuvgn4eurnrwO/U2DXYv72ynZMWuufaq2t1K97gXXluK/FHFORlvR5SlNKKeD3ge+U475KOKbZ3v9V/ZtawP3IuFkcGTfPkHGziGMqkoybSzhuNmqAvBYYyPp9kJmDajHbVIRSaiNwCfCrAldfpZR6Tin1Y6XU+UtwOBr4qVLqKaXUrQWur9rzBNzM7G/IpX6eAFZqrU+C+8YFegtsU83n64O4WatC5nudy+3jqdOX985y+qtaz9N1wGmt9aFZrq/485T3/q+lvykZN4sn42bxaulvvBAZN+e3LMfNRg2QVYHL8vvZFbNN2SmlWoDvA3+stZ7Ku/pp3NNiFwH/B/i3Sh8PcI3W+lLgrcDHlFI78q6v1vPkA24E/qXA1dV4nopVrefrzwEL+NYsm8z3OpfTPwJnAxcDJ3FPzeWryvME3MLcWZCKPk/zvP9n3a3AZZV4rmTcLJ6Mm+Ul46aMm7Oq1rjZqAHyILA+6/d1wIkFbFNWSikv7ov8La31v+Zfr7We0lqHUj8/AHiVUt2VPCat9YnUv0PAD3BPS2Rb8ucp5a3A01rr0/lXVON5SjmdPk2a+neowDbV+Lt6H/BbwHt1qvgqXxGvc9lorU9rrW2ttQN8ZZb7qsbz5AF+F/jubNtU8nma5f1fS39TMm4WScbNktTS33iGjJvFWc7jZqMGyPuAzUqpTalv1DcD9+dtcz/wR8p1JTCZTtlXQqqG56vAy1rr/z3LNqtS26GU2o77+oxW8JialVKt6Z9xJy68kLfZkj5PWWb9xrrUz1OW+4H3pX5+H/DDAtsU87dXNkqpG4BPAzdqrSOzbFPM61zOY8qutXzHLPe1pM9TypuAA1rrwUJXVvJ5muP9X0t/UzJuFndMMm6Wppb+xgEZN0u0fMdNXeZZh7XyP+4s4oO4sxj/PHXZR4GPpn5WwJdS1/8a2Fbh47kWN73/PPBs6v+35R3Tx4EXcWdb7gWurvAx9aXu67nU/Vb9eUrdZxPuwN2eddmSPk+4HzIngSTuN9EPASuAXwCHUv92pbZdAzww199eBY/pMG6dVfpv6sv5xzTb61zBY/rn1N/K87gD0upqP0+py7+W/hvK2napnqfZ3v9V/ZsqcJwybs5/TDJuzn4MMm4u/Jhk3Jx5TFUdN2WpaSGEEEIIIbI0aomFEEIIIYQQCyIBshBCCCGEEFkkQBZCCCGEECKLBMhCCCGEEEJkkQBZCCGEEEKILBIgCzELpdT7lVJ/X+I+oQKXbVRKvSfvsu8od0nRP1FKfU0p9c55bvdGpdRnir1PIYSoFhk7RSOQAFksO0opc4nvciOQGeSVUqtwe5BeqLX+u2JuQGt9v9b6CxU6PiGEmJeMnWI5kQBZNJRUxuGAUurrqSzDfUqpJqXUUaXU55RSjwLvUkrdopT6tVLqBaXUHVn7f0ApdVAp9TBwTdblX1NK/aNS6pdKqX6l1PVKqXuVUi8rpb5W4Di6lVJPKKXeDnwBuE4p9axS6k+AnwK9qd+vy9vvqEot/6qU2qaU2p36OZORSa0M9IRSap9S6vPlfg6FEMuPjJ1C5JIAWTSic4G7tdYXAlPAf05dHtNaXwvsAe4A3ghcDFyulPod5S71+Ve4g/ubgfPybrcztc+fAP8O/B1wPvB6pdTF6Y2UUiuB/wA+p7X+D+AzwCNa64tTWY8bgVdTvz+ygMd3J/CPWuvLgVML2F8IIQqRsVOIFAmQRSMa0Fo/lvr5m7jLVQJ8N/Xv5cBurfWw1toCvgXsAK7IujyRtX3av2t36clfA6e11r/WWju4y2tuTG3jxV368v/SWv+sAo8N3A+h76R+/ucK3YcQYvmRsVOIFAmQRSPKXz89/Xs49a8qYd9s8dS/TtbP6d89qZ8t4CngN+c/zIIszrwvA3NsJ2vECyHKTcZOIVIkQBaNaINS6qrUz7cAj+Zd/yvg+lStm5na5uHU5TuVUiuUUl7gXQu4bw18ENiaNXN6Gmgtcv+jwGWpn39vlm0eA25O/fzeBRyjEEIUImOnECkSIItG9DLwPqXU80AX8I/ZV2qtTwL/Dfgl8BzwtNb6h6nL/xJ4Avg58PRC7lxrbeMOwm9QSv1n4HnAUko9l5poMpe/Au5USj0C2LNs80ngY0qpfUD7Qo5RCCEKkLFTiBTllgUJ0RiUUhuBH2mtL6j2sQghRL2QsVOIXJJBFkIIIYQQIotkkIUQQgghhMgiGWQhhBBCCCGySIAshBBCCCFEFgmQhRBCCCGEyCIBshBCCCGEEFkkQBZCCCGEECLL/w8XmKa4V/bfbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f4 = sns.lmplot(data=ccm.query('xrd_a<.4 & xrd_a > 0').sample(1000),\n",
    "                  x=\"prodmktfluid\", y=\"xrd_a\", col='div_d')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}