period_count.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "superior-final",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "import seaborn\n",
    "from matplotlib import pyplot\n",
    "\n",
    "import swasputils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "signed-management",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifications = swasputils.AggregatedClassifications()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "alike-genealogy",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifications.add_classification_labels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "motivated-general",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SWASP ID</th>\n",
       "      <th>Period Number</th>\n",
       "      <th>Period</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Period Uncertainty</th>\n",
       "      <th>Classification Count</th>\n",
       "      <th>Classification Label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21813536</th>\n",
       "      <td>1SWASPJ000000.15+320847.6</td>\n",
       "      <td>1</td>\n",
       "      <td>24802.357</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>EW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21813537</th>\n",
       "      <td>1SWASPJ000000.74+561358.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1884039.250</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Junk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21813538</th>\n",
       "      <td>1SWASPJ000000.74+561358.9</td>\n",
       "      <td>3</td>\n",
       "      <td>428081.250</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Junk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21813539</th>\n",
       "      <td>1SWASPJ000000.74+561358.9</td>\n",
       "      <td>5</td>\n",
       "      <td>275859.969</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Junk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21813540</th>\n",
       "      <td>1SWASPJ000001.10+562043.7</td>\n",
       "      <td>2</td>\n",
       "      <td>323290.344</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Junk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             SWASP ID  Period Number       Period  \\\n",
       "subject_id                                                          \n",
       "21813536    1SWASPJ000000.15+320847.6              1    24802.357   \n",
       "21813537    1SWASPJ000000.74+561358.9              1  1884039.250   \n",
       "21813538    1SWASPJ000000.74+561358.9              3   428081.250   \n",
       "21813539    1SWASPJ000000.74+561358.9              5   275859.969   \n",
       "21813540    1SWASPJ000001.10+562043.7              2   323290.344   \n",
       "\n",
       "            Classification  Period Uncertainty  Classification Count  \\\n",
       "subject_id                                                             \n",
       "21813536                 3                   0                     4   \n",
       "21813537                 6                   1                     3   \n",
       "21813538                 6                   1                     6   \n",
       "21813539                 6                   1                     3   \n",
       "21813540                 6                   1                     3   \n",
       "\n",
       "           Classification Label  \n",
       "subject_id                       \n",
       "21813536                     EW  \n",
       "21813537                   Junk  \n",
       "21813538                   Junk  \n",
       "21813539                   Junk  \n",
       "21813540                   Junk  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifications.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "enormous-bhutan",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Period</th>\n",
       "      <th>Period Number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWASP ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1SWASPJ000000.15+320847.6</th>\n",
       "      <td>2.480236e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1SWASPJ000000.74+561358.9</th>\n",
       "      <td>8.626602e+05</td>\n",
       "      <td>887808.689601</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1SWASPJ000001.10+562043.7</th>\n",
       "      <td>2.216910e+05</td>\n",
       "      <td>57779.079031</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1SWASPJ000001.37+455849.8</th>\n",
       "      <td>3.006162e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1SWASPJ000001.54+551206.8</th>\n",
       "      <td>3.505436e+05</td>\n",
       "      <td>76109.963778</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Period                Period Number\n",
       "                                   mean            std      <lambda>\n",
       "SWASP ID                                                            \n",
       "1SWASPJ000000.15+320847.6  2.480236e+04            NaN             1\n",
       "1SWASPJ000000.74+561358.9  8.626602e+05  887808.689601             3\n",
       "1SWASPJ000001.10+562043.7  2.216910e+05   57779.079031             5\n",
       "1SWASPJ000001.37+455849.8  3.006162e+06            NaN             1\n",
       "1SWASPJ000001.54+551206.8  3.505436e+05   76109.963778             3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period_counts = classifications.df.pivot_table(\n",
    "    index='SWASP ID',\n",
    "    values=['Period Number', 'Period'],\n",
    "    aggfunc={\n",
    "        'Period Number': lambda x: len(x.unique()),\n",
    "        'Period': [numpy.mean, numpy.std],\n",
    "    },\n",
    ")\n",
    "period_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "collect-semiconductor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Number of Detected Periods Per SWASP Source'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "period_counts['Period Number'].plot.hist(\n",
    "    title='Number of Detected Periods Per SWASP Source',\n",
    "    log=True,\n",
    "    xlabel='Number of Periods',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fourth-congress",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='<lambda>', ylabel='mean'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "means = period_counts['Period Number'].copy().merge(period_counts['Period']['mean'], left_index=True, right_index=True)\n",
    "means.plot.scatter(x='<lambda>', y='mean', logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "light-application",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x213b4a537c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NXUgbrHBARFTtbBt8Il43kv48vnzhGiSyBoIeF9zOwteJiKi62Tb4ZPMSV97xdEm6tc/twPcvXT+DV0VERONh28Ki3bGMcp9PTywzQ1dERETjZdvg0xr2Kvf5tITYz4eIqNrZdtrNKYDPn78K+/qGm8kJYFFzEE7bhlMiotph2+ATy+TQH8+WNJP7+FuWoyHAhAMiompn23GCz+3Cl+99vqSw6JfvfR4+VjggIqp6tg0+g6mcMuFgKJWboSsiIqLxsu20W3NQ3UyuKciEAyKiamfb4JPK5fCpjSuQN4FExkDQ50LXnBVI5TjyISKqdrYNPhGfBwcHYmWdTOfW+7XnmKbES/0JdEfTaIv4sKgpCIdDVPCqiYgIsPGaTzKbV3YyTWbVzeRMU+KeXYfx9hsfxru/9RjefuPDuGfXYZgmO58SEVWabYNPfyKrTDjoT6hbKrzUn8DWHz9VEqy2/vgpvNSfmPZrJSKiUrYNPm2aCgdtYXXCQXc0rSnHk562ayQiIjXbrvk4HMANF62BOSrhwCEKX1dpi/jgczvKCpG2hn0VumIiIhph25FPKmcgmjLw8Z8+jX/52TP4+E+eRjRlIK1Z81nUFMQNF60tabt9w0VrsagpWMnLJiIi2Hjk43a4cM3dfypZw7nm7l249RJ1SwWHQ+Ds5a34wWWvx+FoGnMiPqyaW8dsNyKiGWDb4NMXV7dU6IurWyoYhom7nnkVn/nFzmJq9nVbVmLLmnlwuWw7ACQisiXbPnWbQh5lwkFTyKM8ftehoWLgAQqB6jO/2Ildh4am/VqJiKiUbYOP3+3E1Zu6StZwrt7UBb9LXVj00JA62+3wELPdiIgqzbbTbg4BNARc+PKFa5DIGgh6XHA59dluc+r8ymy39jpmuxERVZptRz5hnxuprFHytVTWQNin7ufTNSeC67asLBkpXbdlJbrm1E37tRIRUSnbjnw6GoPY0xvHXw4OwZSFkdDq+XXoaFSnTrtcDmxZMw9LW0M4PJRGe50PXXPqmGxARDQDbBt8HA6BDcvb0NkcQk8sjdbwsQuFOhwCYZ8byWweYZ+badZERDPEtsEHKBQLjaVzGEzm4He7YJpSG1BGCouO1Hcb2WS6saudQYiIqMJsO+dkGCZ+vesQ7n+uBztfjeKB57rx612HYBim8ngWFiUiqh62Hfk83x3FwYEUtu/YWxzJXHnWUjzfHUXXvPqy460Ki3a2hCp01UREBNh45HMkmcPX7t9dMpL52v27MZBUdzIdKSw6GguLEhHNDNsGn0TGUI5k4hlDeTwLixIRVQ/bTru1hr34wBsX4Oyueegdznb77a5X0BJS9/NxOAQ2drVjxRWnjzs7joiIpodtg0/E58DqjiY8+mIfTAns6YljdUcT6vzHHsxJds4mIppRtg0+8bSJdNbAstZwsZlcMpNDPK3OdmOqNRFR9bBt8DGkhCkFPv7Tp4vB5JpNXTA0wxpdqvWKK05nthsRUYXZNvjkTYlr7t6laCb3OuXx3dE0GgIenH/SfIjhgc6dTx5kqjUR0QywbfDpjWc1zeSyyuPn1PnwvlMXFtOzR/YFtUeYak1EVGm2TbVuCqqbyTUG1c3k8iaU+4Ly6iUiIiKaRrYNPgGPuplcwK1uJmdV4YCIiCrLttNubqdA45hmcm4n4HKqM9e8LoeymZzbadv4S0RkW7Z98vbGMxib1yYB9CUyyuNjmSyu2LC0ZKR0xYaliGfU5XiIiGj62Hbk43O58MHv/6lsJHPrJeuVx4e9HtzxxAFcdlonhChsNL3jiQP4yjvWVuiKiYhoxLQFHyGED8AOAN7h9/mplPLqqXr9aCqnXMOJptQjmbyUeOe6Dtz4wNFstys2LIUsGz8REdF0G3fwEUK8AcCi0edIKW+zOCUDYIOUMi6EcAN4RAjxaynlHyZ7saM1BN3KNZz6gFt5vNflxFMv9+ObF5+MgUQOjUE3fvCHfXjjkqapuBwiIpqAcQUfIcT3ARwH4CkA+eEvSwDa4COllADiw//pHv4zZcOMVDaPKzYsLRvJpHN55fFCSJxzwlx88PtPFo//3Hkrwco6RESVN96RzzoAJwwHlHETQjgBPAlgCYCbpZSPTfD6tBxCKNdwVs9fpTw+l5f47C93luzz+ewvd+KHH3j9VF0SERGN03iDz04A7QAOTeTFpZR5AGuFEPUAfi6EWCml3Dn6GCHE5QAuB4COjo5xv7bTAXzsnKWoD3hxZHgabXlbAC5N/l5/PKMsr9OvqYhARDTdRj//FixYMMNXU1njDT7NAP4qhPgjCms5AAAp5ebxnCylHBRCPARgIwqBbPTfbQewHQDWrVs37pFVXcCNA0dSuHzUNNq1m7tQ51ev+dT53cryOnV+2yb8EZHNjX7+nXTSSTWV/TTefT7XANgC4N8BfGXUHy0hRMvwiAdCCD+AswE8N8nrLBNL5XHVXaWFRa+6axdiafWaD6Aur0NERJU3ro/9UsrfTeK15wC4dXjdxwHgx1LKX03idZS6YxlNuRz1JtMhTWr2UErddpuIiKbPeLPdTgHwnwCOB+AB4ASQkFJGdOdIKf8C4MSpuEiVtohXmWrdGlG30W4Kqo/XFSIlIqLpM95pt5sAvBvAbgB+AB8Y/tqMCXic2La5tLDots1dCHjUhUUBic+dt7Lk+M+dtxLiGNnfpimxtzeO37/Yh729cZhmTU3LEhFNi3Gvtksp9wghnMMZbN8VQjw6jdd1TC8PpHDPM4fwzYtPxmAyh/qAG997ZB/q/G6snFdfdnwym0c6l8flZ3TClIBDAOlcHsmsfo2IrbeJiKbHeINPUgjhAfCUEOJLKKRcB6fvso6tzu/C7/cdwYMv9BW/5nM78PdndCqPj/jc+Lf/92zZtNsdf3+K9j3YepuIaHqMd9rt4uFjPwIgAWABgAum66LGw+10YOs5y0qm0baeswxuzUafZC6vTDhIaioiAOwBREQ0Xcab7bZ/OF16jpRy2zRf07gkMwa8TkfJNJrX6UAyo85ea4v4sLDJj3NXzytuMr376VfQZtFGuy3iUyc1hNl6m4jotRhvttsmAF9GIdNtsRBiLYBrx7vJdDr4PS58/p7nygLDbZeqWyrMr/PjY29Zjj09cZgScArgY29Zjvl1fu17LGoK4oaL1pat+SxqmtEZRyIi2xvvms81ANYDeAgApJRPCSEWTc8ljU9/XL3Ppz+u3ufzQm8Mh4fS2L5jbzGQbD1nGV7ojSkTFADA4RB4y/FtuOPyU3BoKI05dT50zaljsgER0Ws03uBjSCmHhKieh25D0KNpqaDetzOUyuGG375Qkjxww29fQNfcddr3ME2Jh3b34C8Hh2BK4NlDUfTGM9iwvI0BiIjoNRhvwsFOIcR7ADiFEEuFEP8JYEZTrb0uB67eVLrPZ/R/jxXPGMqRUiKjTzg4cCSB3d1xbN+xFzc9sAff3LEXu7vjOHAkMXU3QkRUg8Y78vknAP8XhaKi/w3gNwA+N10XNR6DqSyCboHtF59crGo9mMhgKKnuZBpwO5UjJb92UyrQHc0o68Gd1NGARc1MtSYimqzxjnxOGP7jAuADcB6Ax6frosajwe/BYMrA5d9/Ev98x1O4/PtPYjBloF5T1drvceLKs5aWjJSuPGsp/JqREgAksurRUjLLenBERK/FeEc+PwTwcRTaIZjHOLYijLzEv//6uZJRyb//+jn84DJ1tlvI50R7na8kNbu9zoeQVz/yWdgYVI6WOhqZ7UZE9FqMd+TTK6W8W0q5T0q5f+TPtF7ZMRxJqqtUH0mop91i6TwCHgdO7WzCsrYQTu1sQsDjQNxizWdxcyHVevRo6YaL1mJxM4MPEdFrMd6Rz9VCiG8DuB+lzeR+Ni1XNQ5+z8TWcFrDXrzYk8CVtz9dTLW+ZlMXVrRrC3PD4RDY2NWOFVecjp5YGq1hHxY1BZnpRkT0Go135HMJgLUodCLdNPzn3Gm6pnFxOaBcw9G10R5I5HDN3aXN5665excGNCOlEQ6HQGdLCKd0NqOzJcTAQ0Q0BcY78lkjpVw1rVcyQT6XE01BN7584RokMgaCPheSmRx8LvXI59WhlHKa7tWhFNaioRKXTEREw8YbfP4ghDhBSvnXab2aCUjnDQjhwMd/enQa7XPnrUQmr85Eaw6pm8k1B9XN54iIaPqMN/icBuDvhBD7UFjzEQCklHL1tF3ZMTiFE5/95c6SabTP/nInbrtEne3WXufFFy9YhRd7E8Xabp0tQbTXM/gQEVXaeIPPxmm9ikkYTGaV02iDqazyeMOQ6I5mymq7GQY7kxIRVdq4WypM94VMVENAXdutQVPb7VA0razttnJuBJ2t4YpcMxERFYy7jXa1iWVyuOrc43E4milOo7VFvIhn1NlrutpuVvt8iIhoetg2+LSGvOiP50qm0T533kq0hNRrOEGPSzlSCljUdiMioukx3n0+VSc5nGAwNuEgmVNX/wl6ndi2ubQK9rbNXQhalNchIqLpYduRT09U3UyuJ6ZuJmdK4CdPHMCXLlyDVNZAwOPCrY/uxaffdkIlLpeIiEaxbfBpjaj37bRqpt0SmRw2rGjHJ0ftC7piw1Iks9YVDoiIaOrZNvhImNi2uQtX37WrGEy2be4ChHrazed24Y4nDuCy0zox0pD1jicOYN2iNRW8aiIiAmwcfExT4OsP7SkGEymBrz+0B184X73vdSiVw3vWL8RX73uhGKw+evYyDKU48iEiqjTbBp9YOof9/Snc/OCeMV9Xl9dpCLiLgQcorA999b4X8INL1RURiIho+tg2+DQFPVjY5Me5q+cVp9HufvoVNAXVnUwHEur+PwOatttERDR9bBt8XA6BD79pKa66a2dxGu3azSvh0rQ8CHjV/X8CTLUmIqo42+7zyZkoBh6gMIq56q6d0Gzzgd/tVPb/8bsZfIiIKs22I58jCfU+nyMJ9T6f3ngWAbcTl5/RCVMCDgEE3E70JdSFSEeYpsRL/Ql0R9Noi7CTKRHRVLDtyKd+uLDoaD63A/V+dWHRpqAbtzy6D/nheJU3gVse3YcGv3qNCCgEnnt2Hcbbb3wY7/7WY3j7jQ/jnl2HYZqshE1E9FrYNvikDQNXn1taLufqc7u0zeRMKfGu13XgO4/sxU0P7MF3HtmLd72uAxL6QPJSfwJbf/xUydTe1h8/hZf6E1N/Q0RENcS2025+lwt3/ml3Wbmcf9l4vPL4/ngOt/1+f8m+oNt+vx+LmoLa9+iOpjUlfNLobAlN6f0QEdUS2wafRDaHTavnYU9PrNhSYdPqeUhk1SOflrAHA8lsyb4gn9uB5pB6mg4A2iI+dQmfsG/qboSIqAbZNvgEvW74PRksC4WRyBgI+lxIZnIIetW3FPE7cf2Fq7G7J14MVktaQ6gL6LPdFjUF8ZV3rMXHfvJUMZ37K+9YazlaIiKiY7Nt8HEIIC8FPj6qUOjVm7qgS0TL5oCMYZb0/7luy0pY1RU1TQlTmiUZcqY0YZqSGW9ERK+BbYNP1pC488nhFgkZAwFvYc3nY29ZoTw+bZj4zC9K9wV95hc78YPLXq99j12HhvCJn/6lbNqtozGANQsapvaGiIhqiG2Dj2Hm8c7XLSxpkfC581Yib6rbYvfF1fuC+uPqfUEAcGhInXBwaCiNNQte+z0QEdUq26ZaBz1uZSdTv0e9byfgcSr3Bfkt2mi3hLzKc5o1PYOIiGh8bBt8+uNZdYWDuLpiQcTnUpbXifj0g7+8zCtbb5tSPboiIpos0zQhZe1sYJ+2aTchxAIAtwFoB2AC2C6l/NpUvX7Y71amQUc0FQu8bgfmNfhLkgfmNfjLRjajSekoab3t97hw26N78Ym3qvcSERFN1t6eISSTSQSDtZFNO51rPgaAj0kp/ySECAN4UgjxWynlX6fixaPpHK7YsBQ3PrC7pC12NK1OX0tlTXzl3ueLLRjyJvCVe5/HVy9aq32P/kQGT+wfwhP7/1z2dSKiqbSwKYRAIDDTl1Ex0xZ8pJSHABwa/veYEOJZAPMATEnwaQy4S9piS1loi/3lC9VtsfvjWWXzuX7NNB0AtNepN5m2RbjJlIimltNZWxX2K5LtJoRYBOBEAI8p/u5yAJcDQEdHx7hf0+dy4kNnLsG2u3eV7PPRtUgI+VxYt7AO73tDZ0lqdshizSfoceDazV246q5do3oGdSHkte1SGRFVkdHPP1+kCYlEAqFQbZTumvbgI4QIAbgTwD9LKaNj/15KuR3AdgBYt27duFfbDkfT+NFj+0vWY76940W0nLUUKxXHu5xSmZrtdurfciiVx80P7SkZXd380B5crxldERFNxOjnX2TOotrJNsA0Bx8hhBuFwPNDKeXPpvK1I343XuiJ44ofHV2P8bkdiPg0LRKkAzc9uLsYSADgpgd3a6fpAKAnllFO1fXEuOZDRFOrs62+ZpINgOnNdhMAvgPgWSnlDVP9+gNJdcLBQEqdcBBN5/Ce9Qvx1fteKB7/0bOXIZbR19dpDnmUaz5NQX0xUiKiyXA4HBCidsp2TefixRsBXAxggxDiqeE/b5+qF28IuPHUy/345sUn42vvXItvXnwynnq5X9scLux1FwMPUNgT9NX7XkBIsykVKLTevnrTmJ5Bm7oQsNiYSkQ0GdznM0WklI8AmLYw3hh04uzj5+KD339yVDLASjSG1IFhMKXelDqoGSkBwCuDKbxwaBDfu2Q9emNptIZ9uOeZV1Dnd1nWdmPrbSKaqH09Ue7zsYPBZB5X3VVaXuequ3biB5etVx4f8LiUU2hWo5iOxgDi6Xq8/7t/LMl262jQ5+KPtN4e6YDqcztww0VrsbGr3TIAMWAR1bbFrRHu87GD7qi6UGh3VJ0MEPY68cULVuHF3kSxn09nSxBhrz74pLL5Ypr1yOtfddcufP9SdYADgH196tbby//pdBzXqk6hnGzAIqLZo9bWfGwbfFrDXixs8hcrFgDA3U+/gpawuuinKYG+eLakn88n3rrcchTTHVMHOKtst/39CeU5+/sT2uDzUr86YK244nS26yaiWcm2uyV9bgc+dOYSfOeRvbjpgT349sN78aEzl8CvqdWWyOZx/W+eL3nAX/+b55HI6ouETqaqtcflUJ7jcem/1d1RdeuGnlhaew4RzS61lnBg2+AzlDKK1Q2AwsN62927MJQylMfHM4byAZ/I6INPzjRw9bljst3O7YKR15/jcQtsPWdZyTlbz1kGj0s/nG6L+JQBqzXMMj5EtWIk4aBW2HbaTRdM4hl18Al71QkHQYs1n4DbjYgvjdsuWY/uWCERoDcah9+j/7aFPC60hL0l1bNbwl6EvPpzFjUFccNFa8vWfBY11UbWCxEx4cA26vzqYFLnV99SyOfElWctxdfuP7op9cqzliLs0wefhoADBwdceOTFPpgS2NMTx3EtITQE9APGRCaPT//smbLrskpScDgENna1Y8UVp6NnOKWb2W5EtYUJBzbhc6mDic+lDiZ5Ewh6nCUjkqDHibypPBwAMJA0MZgsrXo9mMxiIOnHYs05hzVZeIc1WXgjHA6BzpYQEwyIqCbYNvgcSWbxx72FCgeDiRzqg25875F9WNamfngfjqbxX7/bi/NPml/s5/Nfv9uLz56rbwyXNvJIZPMlGXJXnrUUaUO/5tMW8arbMGiy8IiIapFtg09z0IstJ83Dk/sHCvt2+oAtJ81Dc1D9kG8NezGQzJYUCfW5HWixyFzLmyiOrIDCCOZr9+/Gt963TntOU9CJ67asxGd+sbMYsK7bshJNmsoLRES1yLbBJy8lBCTWLWzAkUQOjUE3BpMZ5DWpirm8qZymM0z9vFs6m1dOoaUt0rMNE/A4HSXTex6nA4bF9B7ACgdEVFtsG3wcAkjlJD45qrbbts1d0D2vY+k8bvv9/pLePLf9fj+Oa+nSvkeTpqp1Y0hf1Xoomccn7/xL2Tm3XaJPODBNifuf68YzrwwVqy+snFeHs1a0MQAR0axk230+GUPi6jGlb66+axcyhnrk0xL2FKfdbnpgD25+cA8Gklk0WwQSQ5r4xFuXl+zZ+cRblyMv9cMYXVWEbouqCPv64vA4gVM7m7CsNYRTOpvgcRa+TkQ0G9l25NOjecj3ah7y0gSu3XwCAh43EhkDQZ8LyUwOsNhQnM3l0Rj0lEyhNQY9yOX0wactoi77Y5VwYJh5dEezuOquP5cUMG2v4yZToloxUuGgVtKtbRt8dFllrRH1Qz6ezcHtcuKFnlhxamtRcxCJnL6lgsfpwqd/9qcJTaE1Bpz48JuWFAuSjgSSxqA+4WAoqS5gavU+RDS77O0ZQiKRQChUG9stbDvtls3nsW1zaembbZu7kNWUvgl63OiNZbB9R6EW3Dd37EVvLIOAW99MrjeeQUPAgw+/eQk+sqHwpyHgQW9cP4XWn1AHkv6EPklhMlN1RDTL1FBdN8DGI5+QxwW/W2D7xSdjIJlDQ6CQ7RZ0q28plcvjht+WdjK94bcv4NsWadNtES/ed+rCsgy5Ns3oCgD6E+pAciShDyTcG0REnW31NdNIDrDxyMcwga/8djce2zeA3T1x/PGlAXzlt7uR13x4SGTVteCSFmnTgFDu87Fq0Noanngl7IaAE9eOGcVdu7kLDRZTdUQ0u7C8jk30J7J457oO3PjA0VHJFRuW4kgiqzw+NIlOpke0oxj1ewCAYUrlfiLTYkgd8rqwsNmHWy9ZX6ztBpG3LEZKRGRntn26NQbcuPKB0lHJjQ/s1hbwDHqduHpTV7ENg8/twNWbuiyrWtcFPMrMtTq/fp0omspp9hPpFxG7ozk8/XKsOC040obB63RjTr3+e8CNqURkV7YNPoPJnHJUMphUZ68lsnl843d7SoLCN363B5//m1Xa93AK4ENnLikLWE6Lycr6gEdZxqc+oA9YsbShXI+yKuPD1ttEZGe2DT4Br1M5jebXjGTiGQPZURtQhQCyhtT2/wGArGEqG9bd8ncWQUFKfPH8lXA6nMX9RPl83nLaLZE1sKw1hA+ccRxSGQMBrwvf2vEikhbXxtbbRGRntg0+frcTW89ZVjZVFXCrg09bWJ251mqRCBDPqGu7WXU/9TiBjAFcddfTo/b5rITF0hLm1vnw3lMW4pM/PXrOts1dmGOxybQ7mlYGrJ5YmsGHiKqebYPPYCoL75gCnl6nA0MpdTJAMptXZq6tsRjFhH3qJIWQT/9tM0yBmx/aXZzeA4CbH9qN6y9coz0nbZjKUkG3WTSgm1s/8YBFRFQtbBt8wl43Pn/Pn8urD2ge2NGMemorltaPYjKGgSs2LC3LqMtY9POJpnPKLLxoWl9JoT+eVY6w+i2y6gYTOWXAuuPvT8HCJu1pRERVwbbBpz+hfmAPaB7YcyM+vPv1pSOFqzd1YU6dftot4HHjjicOlCQp3PHEActRTJ3PXQw8I9d04wO7LUcxEc0IK2yRan04mkZDwFNsjgcAdz55EN3RtPYcIqJqYdvgE/K6lGnQQc0DO61JHrAKCqbMY+s5y/BibwKmBFwOYOs5y2BK/chnokERKKSB/+vbVqAvkS3WnWsKehCyWCiqD7iVa1h1Fll1RETVwrbBx+9xKNOg/R51HnRPVL1htMeifprH6UJ3NFbSRnvrOcuwoCGgPSfo1WxmtRjFxLMG/B4Xtv/6ueL7fO68lYjn9NlugMDtjx8oWVu6/fEDOKmjweIcIqLqYNvyOlIK5UhGSvUel5aIuuyNVRvteEa9/8YqPdvnFrjyrKUlpXIK/63fe+NxOPHZX+4seZ/P/nInPA79yCeRLawtfeeRQqHUbz+8F+9c14FkVr+2RERULWwbfHrj6pFMn6bidNBTqHAwOigcq8JBUtNG26oenFs4EPa5cPkZnfjIhiW4/IxOhH0uuIX+Wz2QUk/VDWoy94BCwoVqbSno1U+7mabE3t44fv9iH/b2xmGatVVFl4iqh22Dz8gi/Wg+d+HBrzKQzBUrHHxkwxJcdlonvvG7PRjQVEQAgJbQxIuE9sazuPnBF5EfjiV5E7j5wRfRG9cHkoaAR/k+9QF9l9VoWl3hIa7JqhupiPD2Gx/Gu7/1GN5+48O4Z9dhBiAimhG2XfMJTHCTaTxtYH9/qqTszcjXdSJ+B67bshKf+cXO4ntct2UlIn59zA76nMryOlYjrMFkTpnSPZjSB8awz63Zg6Qe+ezrU1dEWP5Pp+O4Vm5KJaLKsm3wiaZzyk2muv00I60Oxj6sWyx65iTSJpIZo+Q9khkDybS+jbbX6cCXLliJ+oAXRxI5NAYLfYa8Ln3Aqg9oUrov0Kd0p7IGvnD+KuztSxQz5BY3B5HKqoPp/v6EMjV7f3+CwYeoCrCNtk34PS58/p7nxr3J1OEAtm3uKm7MHKkIYFUkNJbN499/Xf4eVgU/DWlCQuCJ/QOFoNAHdLYEYUh9wErlDPzDmUtwzajMvWs2dSFl6Edl9QEPDkczJZl42zZ3aafqgl6nMjXbakRGRJVTa220bRt8BpOaRXrNGs4rg2n88A/78aUL1yA1nNr87R0v4oNnHocTO9TvkdYkHKQtEw6ceHUwXRIUrjxrKRbU69Oz6/2FgPHd978OffEMmkNeZPN51Pv0az7JbF5Z4eC773+d8niHUDfG07WgIKIKYxttewhMsDlcW8SLF3riuOJHfy45vtVi2i0SUK+rRCz6+cSyhnL/TdfciP5mpMSrg5myPUv17fr3GdAE34GkOrFBf7x1ajZ7BhFVRq210bZx8NEkHGiCT8jjxJcuWI09vfHiGslxLSGELKadoil1IoBVnTYBqaztBug/1SRzJh587hC+efHJGBheJ/rBH/ZhUdNx2nN0a1i6Kt0Rvy6Q6n8E2DOIqHLYRtsmYml1VeuYJjBkDYneeOkaySfeuhwLG/XTYXX+idd287ldE67tJqWJs4+fgw9+/8lRbRi6IC3WifKadt15zdDd7XAoj3c79Ite7BlEVDlMOLCJgGeCVa3TBq7/zfMlD9Lrf/O8ZfJA3szjI29eWqw+MFL2Jm9R2603NrHNrwDgdjpx1Zj1m6vu2mW5HjM0wXbdh6Jp5fEdFsG3W1O89Fg9gzhVRzRxe3uGkEwma2bqzbbB54g24UC95pHIGupqBRalcvxuF4x8smR0ZeTz8Dv137amkEc5vdVgsWG0W1N3rtui7lzE74bHdfSBLgTgcQntNFpr2Ks83qq80Jw6Hz785uPQE8sUpyo//Obj0B7R9wziVB3R5CxsCiEQ0H8YnG1sG3yC2oQD9S01BdVBoTGoDwqJbB53/ukg3veGzmIPoFsf3YuPnrNce45/OFlgbPJAQFPwFACaw+praw7pr01KE//4piVlqeOAeqquIeDEh9+0pDjCGpnaawjq17x01Q+sqiLs60vgi/c8W5Jw8cV7nsXytrDlfiKOlqjWud3umplyA6Yx+AghbgFwLoAeKeXKqX59l0Mo1zBcTvX/vEQ2h4+evQxfve9ogsJHz16GpGZTJgCY0sTfnrIIe3pixU/+f3vKIpgWazGQwNw6L7ZffHJxk6mU0jKL0udSByyfxcZUp8M5oe6nsbSJHz9xoJBqPiqQLmk9Qfse/cksYmmjLG28P5lFp+acVweTyoSLQ0NJbfDhaImIaz5T6XsAbgJw23S9QdDjLJkSC3qc2qSysNcDvztVcrzf7UDIohBn0OPGzqFY2cPXap3E5XSgN54tK8lzrHpw9+4sZLsNJnKoD7rxvUf2oTm0UH+OZm2pVzNVF8/k8N7XlwbS975+EeIWVbAzw3uBxu4N+rbFOpnH5VQmXNx6iX79iokNRMC+nijXfKaClHKHEGLRdL2+RzMq0H09lVNXK7B6kCazedz/7OGy0cKqeXXac1K5fDHwAIUH6Wd+sRM/uEz/8G0KerC+s6kk2+3Ks5aiyWJKUJtqrdm3FPK68dzheFlvooVN+kAa16yTJTL6hIuhlLrgadSiTl13NK3ptWSd2EA0myxujXDNp5KEEJcDuBwAOjo0pQYU3E6BtjofGoNeJDIGgj4X3E4Bt3baTZdwoH+QCiGVowUh9HNofXF1IkSfRVXrtKEOcmsW6IOcQwDXnteFq345ag3nvC7oZqmS2byyN5FV8A1r1tWsSvIEvU5NMz39Oa1hn7ruXkif2EA0G4x+/i1YsKBmptyAKgg+UsrtALYDwLp168ZdX8LnFoiljbLpLV+z+n9enV/ddjsS0H8L/C4XDkejZdNuVnuDAh7Nw9eiJbYpTVxwUgc++dOnj675nNsFabFQlDPz8LlK9zn5XA4YpjqYJrIGTl3ciPeftri4kfW7j+yzDL7e4dHR2I28Xrd+LSrkdSnX4kKaRBAAcDmhXPNysewczXKjn39r166VXPOxgYFkHvfuerWsKkBH4xLl8UGPS5ntFbQICvGsoVzz2H7xydpz6v1ufP78Vdg3qtr0ouYg6i1K8rgdLmz71Z9K3mfbr3ZZrpM4hBOfvPOZce9zaq/z4m2ryjeyttXpp/bcTgfmN/jx5QvXIJE1EPS44HAAHotqrN3RzIT2HwHA4aEM7nzyQLHuXsBTGPl1NgexsInTblQbuOZjEybyOGtMVYBtm7sgof4kP7JxcyIbOVM5TWHRnD7bzZQSvbFM2drKYou1lb4JdmUFgB7N3qAeTcJBNicnfP+DySziaaMsYOv2UgGA36PuZ+S3CPJZI48NK9pLRn5XbFiKXF4/KmNqNs02HU1B+P3+mb6MipnOVOsfAXgTgGYhxEEAV0spvzNVr+/AxFKNuzXZYbqHNVAorzPRemixdB4/fGx/yT6XHz62H8fP0RcWbZlEr6HmsEc5jahLUujRZcdZBLiAx4WbH9pVci83P7QHX7YsL6Qu4zO2U+vY9xldxggolDF6/eK1yuNNU+KB57vxl4NDxdHlqvl12LC8jQGIbGt/fxzJZJItFV4rKeW7p+u1AX1VgJ6o+mGqbSZnkQLtdAh8auMKfGG4b5DP7cCnNq6A0+IBl83nlftcshaf4p0Oif9674lwOhzFvUF504TLoV/zCXmdyk2mYa/6f2lzSH3/TUH9/ZtS4j3rF5btjbJai4plcgi4S1PgA26nZcfYWCar/J7FMuoMuQNHEtjbmygbXS5pSWBRc2384tIsxJYK9tAWmViqsUNAuRZj1UzO43SgIegueZA2BN3wWBTjjPjcyn0uVtNbPpcLe2LJskDSGtZnew2lDOXI7zt/p85eS+XUm2xTOX0KtMvpKB4/8h5fve8F/PADr9ee0+j3YNujfy2OyPImcMuj+/Af71yrPSfk9Si/Zz+4TP0+PbGMcnR54oJ6Bh+yrYXNYaZa24GRz+Pqc7uw7VejMqTO7dJmew2lcsgZpX+XM/IYsmiP4HGKsswTIURJjbSxBjX7XAYt9rnEM+rGcFYJB9GUOnU8phlh1Ps9mFOXw/bhBI2G4fbeVg3r+jVp40cs0sbDPjf+acMS+FyuYgr84uYliPj0CRdZxTpaOmcia6jX1lJZQzlSsqpWUUlcj6LJ4LSbTRgmcOefDpR0Jr3t0b244qxlyuObg1680B0vS5te3qZfixlMGfjkT/9SNrrSdQsFAL9bnWrtd+sX3LXrMRbrUW0Rr3LNRzeN6BBAIivxyTuPJmhcvanL8qEY8av3+YR8+h+bgWQGeRP4+KjkgW2buzCQzAAIK88J+ia2n8iva1thEawrhaWCaNI47WYPYa8LZx1fmiF15VlLEdKseUQz6rRpq2oFRxJZZUuBIwn9J/+Iz6XcsxKxeGC3a6YQ2yL69RiP04EPnbmk7H28mgoPyZyJb/xuT8lU1Td+twdfvGC19j3SuTw+vXEF+pPZ4lRlY8CDrGGVhSbw9YdK3+frD+3B9RfokxSyec375NUjH92G4UQVjHxYKogmi51MbWJOnQPz6v0l6zHz6v2YW6d++MbTE5umAoC5dT78w5md6EscfSj+w5mdmFunX4vJ5ksf8lIWHvJfeYf+4et2ahq9WSxIDaRyxcAzci/b7t6l7U8UTaunqqIW9x/yuuAcUzHC6RQIaAL8yHWp3sdq2tHvdiLoc6IpFC5O1aVzhna0qE2esKgCXiksFUSTxU6mNvHqUB5rFgTRHvGhO5ZGW9iHtjonXh3Ko72+/HhtgoLF6CIvJRLZfNlUna5bKAD0xrLY358q2ecy8nWdQ9E0fv3MoZLyOt/a8SIWN+s/BaWyhnJUltJ8+q/TJEJYrSvlpURyzP1vPcc6260h4FamTVt1f82bErk88JlfPF0yitO1bsgapjJY54yZn7Zoi6hLBVkljxDVItsGH0NK/PlADHv7kjAlsLsnjs7mAObWqzdpuRxCOR3mtpiHH5maGztVZ1UPLaRZv7BaJ5kTKVQfGDuF2Ga1zyfkxafftgz1AW8xPXtZa0BbPTuaVidC6NqOA0A+L5X14G7RZNQVjjFw6RsWl0yhXfqGxUjn9COsVC6vHMV97xL12tpQWt3FdUW7ek1pRCUSARY1BXHDRWvL1nwWNdXOdArReNg2+DQFXOioD2NuXaAw8on40NHghG45ZiCVU06HXXuevtVQUleMNKtf8wh7nfjXt60omaprCnoQtiismTHUQe4kzcMXAJwOIJWT+OSYCg+6mbrwJIJiLKO+/7hFPbiQ142xMwdCACGPPttNl1XXr8mqW9gYxKp5Iaxf3FAsrbS7exAdjfoHfKUSARwOgY1d7VhxxenoiaXRGma2G5GKbYNP0APs2B3DVXcdLSx67eaVOGOp+tNvOpdXTodlsvpSOZFJVDjwexwIeF3Y/uujG1Ov27ISfosd/v2axIZ+i8SGdE5OKD3b73Liui0rywqx+i2qd+oy96yqFWSMvHKqMmOxyVY3JapLuJgX8eHs4+eOqVO3EvMs2nu/1K/usLqiPTzlazEOh0BnS4hrPDQhbCZnEwcG8sXAA4zUKtuJ2y5dr1zzada10Q7pP5Gncwau2LC0bPE8ndM/SGNpU9PPR78xsz3iw/tOXVi2htFusU6gbSanKZfjdBR6HY1O0PC4HJabbINel3IUF7SoUJ03MeGpSp/biS9dsAp7eo9uAD6uJQifJuFg5+Go8v99Z0sQ6xY1Ks/pT2SU04FHEhkGCaoKe3uGkEgkuM+n2ukevrpinG6nulW1VYVmj8tZsnguZWHx/PN/s0p7Tl88oxzFWBUJzeZN3P546SL97Y8fwIkd9dpzWidY4SGVM5V7lqwrLzjg95SO4j533krLkU9aW4xVH7CTOQPZvCwZLV23ZSVSmnUiXUZZdzStfQ+/24lkrnxEpgtwRBVnSstkntnGtsFHV4xTt+A+qFnzuc5izSfgdir30lj15qnzu5SjmDqLqbp0Tp0GbfXATmUNZYUH3QNb1+TOamovljHw2V+WjjA++8ud2kQAwGqqUj/CFFIoR4u6wDjRLq4AkMjk1etqHQ3ac4ho+lhMulS33HB5nZFP4cXyOpq1hXTOLK753PTAHtz84B7s708hldOv+SSy+WKfmS+evwrXX7gGdz55wDLhwO10KB9ybot6cD7Njn2rT+V+j6tY4eGLF6zCly5cgzv/dAB+tzrINYXcZSMWn9uBxqA+KEymK2ssncMVG5aW/H+5YsNSxDVFQoHCmpeyjE9CfY7LKZT/712aLraARSfbKtiYSlRQO6MewMYjH5fDiW/sGDOS2bEHX9LspK8LqDuZ1ll8Is8YeWxaPa+kjfam1fOQsdjhP5jU1HazSGmOaurBRVP6B2Mubyi7n+p64LiEQ7l+4xL6oNgcUq+T6do2AIDX5cRTL/eXNflbPV9fSSKoadeta70d8bqVpZW+cL5+L9HCxqDyPawy5IgqqZam3AAbB5/+REaZvdaf0DRTM/L40BlLyqapcnn9A77e78GefHnp/jq//uEb9qmnncJefZDTPnwtpvfypsCDzx8qPuQbgm788A/78LenLFYe35vIKouk9lpMuwU8TuU6mVX313q/S9nkr95i2tHjEsp23R7NSCaZy2PLifNLPhRsOXE+UhbTlIubg7jpPSeW9QCy2shLVEnC4oPgbGTb4NOkyV7TfSr3utStqnXN54DCQ061yVLXtgAobOZUZchFLUY+DUE3rt3cVdYx1GpKrD7gVj/kA+pzWkIedA+lSr4mpUSLRUkaBwTaIp5CJexkDg0BNwzThMMiFTQ5nPI9NgXc6vsc8DjREvaWZOK1hL3a4HskkUHI6yppBBjyunBE88FjRNYoTWq44aK1lsdPlmGY2HVoCIeG0phT50fXnAhcmpp7RLXKtsEn4HHimk1duGbUp/JrNnVp64H1J9RZaFYPrERGXcImkdZ/wq73u5UZclbdP+OZPG5+qHQK8eaH9liWpMloHvK6RfqcaSr33+RM/ZpXysjj4JFU+YZZi/YI2hRwiwrdyWwe/3HfC8UpUVMC/3HfC/iSpuhp2OvGUCqLdQsbitUdBpMZy9FlpQp+GoaJXzz9Stl+qi1r5jEAkTWBmtnjA9g4+BxJZOF0AF++cA0SWQNBjwupnIGBpHoaaY5mL02bxV6aOr8bHzh9Ma7/zfPFcz7x1uWIBCymkJxCmSGnm0ICCl1ZVVOIuq6sANA7wZTunCEnvP/GyBeuf1nr0YKfyUwOhqbaNKAv+qnLQgQKRU9Ve3B0RU+FQyqrOwiLzq+VKvi569CQMnNvaWsIaxYws470aq2qtW0/ioV9btz04B48eziGlwdSePZwDDc9uAchzadfXQkbXdl+oJC5NhJ4Rs65/jfPW2auvTqUweN7+3DL+1+H/3z3Wtzy/tfh8b19OGQRSEZSh0fzuR1osUgdHtmY+p1H9uKmB/bg2w/vxftOXYg2zS7/yZQK8jgd8LqdeKEnhpcHU9jdHYPX7bTcGwWRL9kLNLI3CMIiYAU9xT04Nz2wB9/csRfJXB7NminUrAHlqM8qcW2k4Odo01HwU78HyXpKkIhVrW2iszWAazafAKdwFKde1i2qw3Ft6ja0unReq30uvbGJbxhtDXtw+vJW/GFvf+FTfE8cpy9vtVxbqQ84lWs+9QH9wn46p963oiv6WafZf2O1/0hAoDuaKUu4WFCvb/XrcbiQMZIl6zcZIw+PQ38viaz6XlZpRmUTre4AVK7gZ1NQPfJrtMgQJKpFtg0+PUNpxFJ57Okdzl7qA45rCaFnKI16f/mn2RbNdJCu8yeg3zBqVdvNIQRiY3rXxFI5OBr1D+wjCQP3PVvIXBtM5lAfKGSuLbA4Z0jTRls3VZXN55WZa1mLmmspY+IJF/FsHtf9z7Nl32er6b24toCp+l60teAsRoqVKvhpQioTTmSN7eGgicvlcqztZgfxjInD0XTZp/L5DeqWComsOgvNqvuly+FQlr1Zt1CfCJDWFNZMW+wNOpLI4t6/9uHev/aVfP28tQu05zQG1SMZXbab1+XCN363q6zCg1VSQ2wSDfh0Tft0gQSY+H6ikU2mY9PmrTaZApMr+DnRNgxNQa8y4WTjyvZxvyfVpv29MdZ2s4NkVv2pXPcJ2+d2KR8KVm2kU5qyN7oSNgBgaApr6jqMAvpSQVajsqDXhesvXI3dPfHiIv2S1pC2jfhQKqdMaohadBit86v3H1m1BG8JT3xj6kAyh4+evQxfve/oPp+Pnr0MQ5r09IjPo9xk+sUL1mrfYzIm04ZhUVMQ/7LxePbzoUmppY2mtg0+qWxe08lTPcJIZjWBxGLkoyt7Y7VnZTKFNR3CVFZ1djj0i/SARDpnloywPnfeSuhKdEymn4/P5VRu/tSlswOASwh1S3CL0UJjwINv/nVPMZgEPC7c+uhenLzwBOXxTgdw1vHtZc33rPIgJmMy6dns50OvRTKZRCgUqompN9sGn6aQB/9wZmfJHpR/OLMTTZqFfa+mQvW/W1SoPqKtOaZPUqjXLOzXW5TxcQoneuPZkkDyibcux4IG/ZpPImMqi35q+/m41dUKrPr5DKSy8DpL2zB4nQ4MpPT3fziWUXYZ7bSoJOB2Clxw8phSQRbp6YeG0sr3OLGjHoua9VMWE938Odn0bPbzoUlxCLz/m7/DT7a+vSam3mwbfHSbJg3Npkm/24l3va6j7BO51af4sFddD043tQUAsYx6bSlmUVgzns0rU7qtpup6JriZcyiVKxZJHT26mHfWMu17BDwufP6e58oCqVVV65DXhYFktmR6z+d2IGjxPRtKGco22rr7bw374HEdDUxCFEr0tIT0adOGYeLXuw4VpymfPRTFgSMJvK1rjjYAjaRnj73/qU7PJhqRz2VqZurNtsEnm1NvmvzWxeoH1kAyi3l1Xmy/+OSju+ITGe2mVKAwVfUPZy4pq6JgtebhdapHWP+2RT/CSmXVlRR0U4hAIeNLFRhbNd0/89LEhhWlU1VXbFgK0+IHParJqItZFDwdGbWN3Zhr1QNIl+2W0CQpuJxQbuS1GMThue4oDg6kyj6sPNcdxcp59cpzOhoCyu6vHRYjUiIaH9sGn1RO/cDSFZes87vRF8+W7YqfazEdlsrli4Fn5PWvuXsXbrP45O/3qEdYVkVCm0NeZUp3s8XeII9T4sNvWlK2N8jrVAeToMc94fUrv8ehDHA+i3sJuJ2Y3+AvqTzhdgkELEaYTSGP8n0aA+r7PzyUUY6Ubr1kPRY2qacrBpM59V6iefpq2wcHk0hmjJJpx2TGwMHBpOX0HhEdm22DT31Al2qsuyWh3BVv1d5a2x4hqf/k3zOJNY+kZpOl1d6YbF4UA8/IOVfdtQu3adZ8hjT3MpS0yHbzuZUjjDqLkV8qZ6J7KF1WD645qM/cC/uc+PCblhZbYxcC6UqE/eqApa/WoP//MpkKD93RDO56+hW87w2dSGUMBLyFqcrl7RHL4DPR9GwiAMO5QhLJZBLhcHimr2ba2Tb4OIV6esepKUs+0bbbQCGdeSJ9ZgCgMehRrnk0WKQaT3STJWDRrltTKDXocypHF0GLe0lk1Wsx332/Pijm8hMvYBpP54uBZ+R9rrpL38k0omlbEbEoeBr2u/CWE5rx3lMWl/QZCvv09y9h4tI3FkY9QGFt6dI3dgLQ34tpSjzwfHdZ64YNy9sYgMiSNPP4xiWn1Ux9N9vWdhtIZvHth/fhstM68ZENS3DZaZ349sP7tGs4oeFAMtqxFsIDnkKq8eg6ZVvPWWY5hZbOGspOnmmLvUFz6tS13do16zfA0UKpY2u7zdHUdgt6XPjns5cV05GdAvjns5dZ3r9+k6l+tJDLq9ficnmLop+aDwY9muQJj0vi2s2l9eOu3bwSXpf+PRoDbpy7eh6e3D+APb1xPLl/AOeunmfZtiLocWMobeDjP30a//KzZ/DxnzyNobSBgEd/zoEjCfRG0yVf642mceBIQnsOEVDo59PS0lITmW6AjUc+LWGPJuNJPcIIep3K/SdBi7WII0lNqrFFkoLPo97MalUVARDKNGirXH/dQ/5kzbRbKpfH4aF02YhkTp0+c6shoN4w2qCpolB4H81anMX0Vqtuk62mXE4mB/z4if1lmXuf2ni89j0SaROvDJbf/3yLOnXxjG7kp1/zG0xmMZgyyt5n0OJnhmhEMpmEY7hwcSAQmNX7fWwbfJzCoVyPcGoqTrscAnPrfSWBZG69z7IkS8Trxkfu+XPZQ1G3rlJg4iNvXlrcgzOeqs6DqRy+8bs9ZaVvCptG1XrjmmlEzbSbLlhZpXNDQBmwYfH70KBp8mcVsAIedWFVXZLCUDqnbG8+ZJnObihLJXXNjWjPmcw+r3hGUyR1nsX3GVwnIgAOgcu+90cIIZDPpPC9D55ZMgU324KRbYPPwOh9K6MWg+c3LFceH8/kEPS4sG5hQ7HtdNYwEbd4YEXT6kV6q66kLuFEKluaIZXKGnAJ6wrVqtI3Y997NF1JHl3fnJRmwd1qRNIdzeDXzxwqGWF8a8eL6LAoeJo1DGUgsSpgms7lEfK5SjLkHA5o6+FF/G4IUdqVVQhYrvnkpYn3rF9YVsLHKtVcW/bIooDpZOrhTaaMD81O+dzwh0chcOl3H4NreJ7cyKZxx5UbZ9V6kG2Dj5Qm/vHNS+ByOGCaEnV+F/7xzUsgpfqB7XW58PTBI2Wf4tcvatS+R6P2U7w+eSCWNfCd/91X0pXzO/+7z3IUE9HUUAtbZJXpNrPqgmlIV17HIuFgfr0Pb1s1p6yMzVyLqTqXw4WbH9pV1pX1Sxfopx1zeYmtP3667Np001umKZE2zLKisqapDyRBj7sYeIBCQPjqfS9YpppHfOqqEFZJCrradlYtNSrVZZXsJRsfxHc+8pbiGlAgMLv2l9k2+DSHvHjmlWgxfXpk384qzTRKImsop0O2X3yy9j1ypqmcdjI0AQ4ATGkqa8hZfcKGhDJzT1enDQACmkKpXzhfXSjV7VRnB7otysvkTKmu6r1I35GzP5FB1jh63UIAWUOi36Jdua7Xkm56yzClsqisrpcRYJU2rx/FDqUNOIUsGZElszlt2woA8Lmd+OIFq/DiqDp9nS1By71R+gZ0+jI+Ey0VBHBqz44CgcCsGu2MZtvgE0vn8fWH9pQ8GL9u8Qk7nTOVv+BWU1uDydzRaafhqb1v7XgRx1l8Gg153CVBASgEhS9btC7wupxwO0TJVJ3bIeC12LLvdgr845uWlAVft2YNK2sYaAx4St6jMeBB1qLVQzyTUwZSXeUB4GiH1bEBu12ThQdMPOFAN7VlFRT8HnWqud8iKAwmc/jqfXuK6exSAj/700Fcs1ld8BQopMAfSeRKRmX/snEF6vz64BvSlHHSpcEbholfPP1KWeWFLWvmaQPQZKf2ZlPAqvp7kYCZz8PhdGBkYXUkAWE2BiDbBp/BlPrBOKhpEaDrf9NosRBe53Mpp50iFtMusUxOubZgtbY0kMqi3u/CouZgSemfQYsCnl6XAz53aSaez+3Qtrj2Ol349M//VHb/ur00AOB3qat6W9V2y5vqxAar98kYBr5w/irs7Ts6WljcHETWUAcT3TSlVdmjkNepTFCxmnZsCGj2bFlMuwY9LnxxVD28dM7EF+95zvL+pZTKa9MNfP96aKgYeEbe4zO/2IllrSGsXqAelU5mam82rUXZ4l6khJGM4Vsf3DBrp9pGs23waQhMrFyMrmfMgEU/G5fDoXyQ/sDiQRLyuPHff9xfMvL57z/ux/UWax5NAQ96o6Wlf67Z1IUFTfqHXNYoTDWNXlu64bcv4IZ3qN+nbxKZW9F0TrmRNWpR201X8FS3ZwcoTCG+apS26962uQt+t/rH0+dy4l/ftqKsioLPIm0+mc2XZBQChYxCq6rmSU0DQqtKCgOa6b2BY0zvTaSw6qtD6mm6Q0MZrNb0H+yOppX/L60qdM+mtShb3ItDwOnxIhgMlmW5zUa2DT6xlPqXPKbJRKv3u/Hk/r5Cq+pEDvXBQqvqkzrqte9xJKl5YFs8SJKaBnRJi02mubxU1pCz+rSczBrKEZbuwaif2tH/CNQH3copNH0Jo8lliOUllKWPdB8koukssnlZEqw+/pbliKb1gTSXzyv/v+Ty+mlXp8OBB547XJZRuWq+vh6cz+1Q3r9lYVXNNKJuelOfoKIPvnPqfLhm0/HI5YFExkDQ58I1m463nA49rAlyVmtRAJBK5fDM4Si6oxm0RbxY1R6B36KGIjDxKbFKtceoNKfXX0y3BgBpZGZdltsI2wafkM+FdQvryupu6dodmMjj6s3L8cqRPKKOXKG/zeblODigDyQRv1tTksWiqrXLqXxgnWgR5HpiGby9qw0Xvq4DfbEMWsJe/OTxA5ajBZ/bpRxh6daWwj4Xtp6zrGQhfOs5yyzvRWq6st5qMe2WzefxpQtXY8+oDqvHtYaQs0i17ompSwXp2kMEPW58+d7SFhRfvvd5y2CtK6xqdU6dz63sM2RV2y7gUY/KrNaWWjUVynWdbMNelyYLT39deTOPZM7EvlFTm4uag8ib+v8vYZ86kAa9+od8KpXDr3YdLtvndm5XuzYAmabEH/b1wsgDRxI55PImDkeTOGVxizIAzfb2GCPp1vlMCt+89DT4/f4ZvqLpMa3BRwixEcDXADgBfFtK+YWpem2vy4F3rCt9MGzb3AWf5odvYYMHO/bEcNWoX4prz1uJM5boC/iFvA6cu3o+ntw/UPiF7QPOXT0fYZ/+l09KE+9av7Dkuq7bshLSoh7YcS0BLDx9EZIZCSEAp0Pg0tMXaTfMAkA8m8P//T/Hw+dyFteJVs+PIJFVB1MpJbqjmbL05IUWe3biGQPLWkP4wBnHlSRcWNWcC3vdODxU+j7XbVmJkFf/ybc94sXH3rKsbAFd1x5iMJXDqYsb8f7Tjn4o+O4j+7TrfSPnvHvdPLx11Tz0DncYveeZVyzPSebyyukwqzUvt0PA73Fh+6+fK3n4eqw+xZt5ZYXyvFQHhqGUodyUbNW2YyBpIDbmXmOpHAYsiuQaefUmY4vPEdh1OKpscri4KYB1i5uU57w8kMDLR9JlyTPz6hPKKuWTaY+xqCmIGy5aO+H25jOZpOD0+vGP338ct34oWDbymQ0bTqct+AghnABuBnAOgIMAHhdC3CWl/OtUvH5qeGpm7FSNbm/IywP5YuAZOf6qXxaKV7bXq98jawCvDJb/kM9v0H8SCXhc+MwvnixbDL797/XVs/0e4LG98bKHz+s79bvv59b58NTLUVx911Mlv7BrF6jPSWbzE05Pnt/gw7tfv7Dsk//8Bv2nxbwplYvh//0B/f173UJ5zk8+pD5nTp0PG1fNwQfHtMewKhXU0ejDwNx6vP+7fyz5Hnc06s9JTLDPEACkDTmhDrMAAOlQVij/nuaceMZQbkq2+lCgLfhqMe14KKqu0G71wO6Nq6eqe+MW3W+HMsrfZV2LjAFNe4yVFu0xJtPevCqSFBwOXPrdx0oCjar6gUq1B6jpHPmsB7BHSrkXAIQQtwM4D8CUBJ+J7g2ZaPFKAIhlJr436HBU/T7dUf0vX/dQXtseYaH6wyIGk3ntL6xKdBLpyUMp9Sd/q42Z+g6r+vt/dUB9zqGBLFbNKz8+mVHfu1XZo6GU5ntscS9hTfXssMUobkCzTmhVD7A/ob7/I5q9US2hiVW3AABDM4VqVV4p4lN3pbWa3tNuZrY4Z6JdedO5vPL4tKaX14iJtjevliSFYtWDEcPVD6wCy3gD1HQYb9CbzuAzD8DLo/77IAD9x98Jag6pd5I3aloXNOmqFVi0Okhl1T/kqaz+02LA41QvOHssFkM1v3zdFoFxollluvYQVgkH3dpAapG5Non792vO8WrOmcz3azL3MpBUZ7tZZUjqfi6tGgPq9jm1apI0Al6HssOq1VpMWvOznLYor1Tndymn3SJ+/c9MxKs+J2zxc9Zep77/Ns20a2dTUHn84mNMoU1UpZMUBASc4x1R5bMWW9ABh9OBv7/1jxUf+UykDNB0Bh/VXZd9v4QQlwO4HAA6OjrG/eIBt7r0ia7dQcCjPj5otRCsq58W1j9IQpP45WuLTOyXbzLnhDVVvUMW9z+Z65rM/U/0nEp8v4BCOr+qisT1FhuG59Y78bnzVpYtuM+ttyrJ48S1560sW49sCavPCXqB9ogH333/69AXz6A55EXezMPicxTaIuqg2BbRn7SkKYT9R1Ile8kWNAawVNMtFihkSM5v8JecM7/BjwaL1hWr2utw7eaVZc0EV81RT6MtbgnhK+9Yi4/95Oh02FfesXbKA0IlkhRGP/8WLFiAO67cOGWvPVPGmxoupFXZl9dACHEqgGuklG8d/u9PA4CU8vO6c9atWyefeOKJcb3+4cFBPH0whVxeHm3X7BRYM9+P9vr613z8yDmPvBgr+4R52nFhy3P++FICe3rjxV++JS0hrF8UtDxnx+5Y2S/fGUut3mcIO3ZHFedE0F5f/kt7eHAIf9wfL2ahOQSwpDWE9QtDyuMBYDCVxr07e8ve4y0rW1DvV/8CDqbS2PF8f9n9n7G8acrOmcx1TfT79VrufyiZRvdQHt2xNNrCPrTVOVEX8GnPiafSeGUohaGkWTynLuDAvDo/Qopz0mkD+wdjZccvrA/Dp5neSqVy+M1z3fjUz54p3ssXzl+Ft65os0yDnmja9OjMtYFkDg0BN1xOaDPXRt/TM4eGjr7PnDrtvYy8z0v9iXGv30zGFK35jPvAiTz/bER7/9MZfFwAXgBwFoBXADwO4D1Syl26cybyzR9MpZHOpHHgyNFf8o5GJ3xe9S/5RI+v5DmpVA5DmUTZOXXeoGV6ak90qOyc1kid8hdjosePvp8XDieKD4Vl7UHtfVTynIkeX+33H0+l8ddR55zQHlQGnhETfVgDk9t/MxmVCAyVMgX3wuCjMW3TblJKQwjxEQC/QSHV+harwDNR9X4fBgFAJAAIQMDyAT/R4yt5TuEBEAREtHiOVeABCounrZE6JHMJmJBoDnvRGtH/Ykz0+NH3s37xxKYZKnHORI+v9vsPTfAcn8+F12lSl3X8fjfWT/CcyZjown41m033Um2mbeQzGbM08hNR7eLIR8O6BjsREdE0YPAhIqKKY/AhIqKKY/AhIqKKY/AhIqKKY/AhIqKKY/AhIqKKY/AhIqKKY/AhIqKKq6oKB0KIXgAJAH0zfS0zqBm8f95/7Zpt998npRxXqWohxD3jPXY2qKrgAwBCiCeklPoOV7Mc75/3z/uv3fuvJZx2IyKiimPwISKiiqvG4LN9pi9ghvH+axvvn2pC1a35EBHR7FeNIx8iIprlGHyIiKjiqib4CCE2CiGeF0LsEUJ8aqavp9KEEC8JIZ4RQjwlhJh17QzHEkLcIoToEULsHPW1RiHEb4UQu4f/2TCT1zidNPd/jRDileGfgaeEEG+fyWucTkKIBUKIB4UQzwohdgkhrhz+es38DNS6qgg+QggngJsBvA3ACQDeLYQ4YWavaka8WUq5tkb2OXwPwNgNdZ8CcL+UcimA+4f/e7b6HsrvHwC+OvwzsFZK+f8qfE2VZAD4mJTyeACnAPjw8O98Lf0M1LSqCD4A1gPYI6XcK6XMArgdwHkzfE00jaSUOwAcGfPl8wDcOvzvtwLYUslrqiTN/dcMKeUhKeWfhv89BuBZAPNQQz8Dta5ags88AC+P+u+Dw1+rJRLAvUKIJ4UQl8/0xcyQNinlIaDwcALQOsPXMxM+IoT4y/C0XE1MOQkhFgE4EcBj4M9AzaiW4CMUX6u1HPA3SilPQmHq8cNCiDNm+oKo4v4LwHEA1gI4BOArM3o1FSCECAG4E8A/SymjM309VDnVEnwOAlgw6r/nA3h1hq5lRkgpXx3+Zw+An6MwFVlruoUQcwBg+J89M3w9FSWl7JZS5qWUJoBvYZb/DAgh3CgEnh9KKX82/OWa/hmoJdUSfB4HsFQIsVgI4QHwLgB3zfA1VYwQIiiECI/8O4C3ANhpfdasdBeAvxv+978D8MsZvJaKG3noDvsbzOKfASGEAPAdAM9KKW8Y9Vc1/TNQS6qmwsFwWul/AHACuEVK+W8ze0WVI4ToRGG0AwAuAP892+9fCPEjAG9CoYR+N4CrAfwCwI8BdAA4AOAdUspZuSivuf83oTDlJgG8BOCDI+sfs40Q4jQADwN4BoA5/OV/RWHdpyZ+Bmpd1QQfIiKqHdUy7UZERDWEwYeIiCqOwYeIiCqOwYeIiCqOwYeIiCqOwYdsQwjxkBDiNRddFUK8SQjxq3Ec934hxE2v9f2IqByDD1U1IYRneONtVRJC1Akh+HtENEH8paGqJIQ4XgjxFQDPA1im+Pv/EkI8MdwLZtuor78khPh3IcTvh//+JCHEb4QQLwohPjTqJSJCiJ8LIf4qhPjGSAARQlwihHhBCPE7AG8c9bqbhBCPCSH+LIS4TwjRNvxXpwF4frgXT8d0fC+IZiMGH6oaw2WGLhFCPALg2yiU2V8tpfyz4vD/O9z3aDWAM4UQq0f93ctSylNR2EH/PQAXotAz5tpRx6wH8DEAq1Ao5nn+cHmbbSgEnXNQ6C014hEAp0gpT0Sh5ccnAUBK+T8ATgUwCOCXw4HuHcNloohIwzXTF0A0yiEAfwHwASnlc8c49qLh1hMuAHNQCBR/Gf67kbqAzwAIDfeLiQkh0kKI+uG/+6OUci9QLHVzGgoNzh6SUvYOf/0OHB11zQdwx3CA8gDYN3IhUso+FEpD/YcQ4lQAtwD4LAqBkYgUOPKhanIhgFcA/FwIcZUQYqHqICHEYgAfB3CWlHI1gP8B4Bt1SGb4n+aofx/575EPXGPrSknN10f8J4CbpJSrAHxwzPtBCHGCEOJ6AN8H8CiAv9e8DhGBwYeqiJTyXinlO1EYhQyhMI1133CzsdEiABIAhobXXt42ibdbP1xF3QHgnShMqz0G4E1CiKbhcv/vGHV8HQqBEThadRnDa0p/QGGa8DkAa6WUl0kpH5vENRHVDE67UdWRUvYD+BqArwkh1gPIj/n7p4UQfwawC8BeAP87ibf5PYAvoLDmswPAz6WUphDimuG/OwTgTyhUWQeAawD8RAjxCoA/AFg8/PUUgEuklM9O4hqIaharWhMRUcVx2o2IiCqOwYeIiCqOwYeIiCqOwYeIiCqOwYeIiCqOwYeIiCqOwYeIiCru/wOpardDY1XGegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.jointplot(\n",
    "    data=means,\n",
    "    x='<lambda>',\n",
    "    y='mean',\n",
    "   # kind='hex'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aquatic-indie",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}