plot_EIGENSTRAT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EIGENSTRAT / SMARTPCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: To examine patterns of population structure in duplicated loci."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take away:\n",
    "* PCAs of genetic data:\n",
    "    - full data coded as codominant genotypes (normal bi-allelic SNPs)\n",
    "    - full data coded as dominant alleles\n",
    "    - paralogs coded as dominant alleles\n",
    "\n",
    "### Programs used:\n",
    "* [EIGENSOFT](http://www.hsph.harvard.edu/alkes-price/software/) (specifically SMARTPCA)\n",
    " - Patterson N, Price AL, Reich D (2006) Population Structure and Eigenanalysis. PLoS Genet 2(12): e190. doi: 10.1371/journal.pgen.0020190"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ryan\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "import collections\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "from IPython.core.pylabtools import figsize\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load EIGENSTRAT results \n",
    "eigenvectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_evecs(evec_file):\n",
    "    evecs = pd.read_csv(evec_file, sep = ' ', skipinitialspace = True)\n",
    "    evecs.columns = ['ind', 'PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7', 'PC8', 'PC9', 'PC10', 'junk']\n",
    "    evecs = evecs[['ind', 'PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7', 'PC8', 'PC9', 'PC10']]\n",
    "    evecs['POP'] = [int(xx.split(':')[0]) for xx in evecs['ind']]\n",
    "    return(evecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "complete_codom_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','complete.codom.evec'))\n",
    "complete_dom_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','complete.dom.evec'))\n",
    "on_map_codom_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','on_map.codom.evec'))\n",
    "on_map_dom_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','on_map.dom.evec'))\n",
    "paralogs_dom_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','paralogs.dom.evec'))\n",
    "\n",
    "complete_codom_subsample_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','complete.codom.subsample.evec'))\n",
    "complete_dom_subsample_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','complete.dom.subsample.evec'))\n",
    "on_map_codom_subsample_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','on_map.codom.subsample.evec'))\n",
    "on_map_dom_subsample_evecs = load_evecs(os.path.join('results','batch_4','EIGENSOFT','on_map.dom.subsample.evec'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_evals(eval_file):\n",
    "    evals = pd.read_csv(eval_file, sep = ' ', skipinitialspace = True, header = None)\n",
    "    evals['percent_explained'] = 100*evals[0]/np.sum(evals[0])\n",
    "    return(evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "complete_dom_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','complete.dom.eval'))\n",
    "paralogs_dom_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','paralogs.dom.eval'))\n",
    "complete_dom_subsample_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','complete.dom.subsample.eval'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot EIGENSOFT results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rotate PC1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#complete_dom_subsample_evecs['PC1'] = -complete_dom_subsample_evecs['PC1']\n",
    "#paralogs_dom_evecs['PC2'] = -paralogs_dom_evecs['PC2']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fonts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "font_paths = mpl.font_manager.findSystemFonts()\n",
    "font_objects = mpl.font_manager.createFontList(font_paths)\n",
    "font_names = [f.name for f in font_objects]\n",
    "#print font_names\n",
    "for xx in font_names:\n",
    "    if xx.startswith('Rob'):\n",
    "        pass\n",
    "        #print xx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(font=\"Roboto Slab\")\n",
    "sns.set(font_scale=4)\n",
    "#sns.set_context(\"poster\")\n",
    "sns.set_style(\"white\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manually color populations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop_colors = ['purply pink', 'golden', 'leaf green', 'blood', 'vomit', 'primary blue', \n",
    "              'bright sky blue', 'dark cyan', 'grey', 'red']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## new colors after reorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
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       "<matplotlib.figure.Figure at 0xa3daf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_colors = ['purply pink',\n",
    " 'bright sky blue',\n",
    " 'golden',\n",
    " 'blood',\n",
    " 'vomit',\n",
    " 'primary blue',\n",
    " 'grey',\n",
    " 'red',\n",
    " 'dark cyan',\n",
    " 'leaf green']\n",
    "sns.palplot(sns.xkcd_palette(pop_colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABGCAYAAADIKU4UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa47a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_colors = ['purply pink', 'golden', 'leaf green', 'blood', 'vomit', 'primary blue', \n",
    "              'bright sky blue', 'dark cyan', 'grey', 'red']\n",
    "sns.palplot(sns.xkcd_palette(pop_colors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLUSTER</th>\n",
       "      <th>SILLI</th>\n",
       "      <th>POPNAME</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>TIMING</th>\n",
       "      <th>REGION</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "      <th>map_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CMHAMM10</td>\n",
       "      <td>Hamma Hamma River</td>\n",
       "      <td>2010</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Hood Canal</td>\n",
       "      <td>47.586900</td>\n",
       "      <td>-123.120100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CMLILLIW11</td>\n",
       "      <td>Lilliwaup Creek</td>\n",
       "      <td>2011</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Hood Canal</td>\n",
       "      <td>47.463500</td>\n",
       "      <td>-123.113000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>CMUW10</td>\n",
       "      <td>Hoodsport Hatchery</td>\n",
       "      <td>2010</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Hood Canal</td>\n",
       "      <td>47.404332</td>\n",
       "      <td>-123.138869</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>CMSHERW94F</td>\n",
       "      <td>Sherwood River Fall</td>\n",
       "      <td>1994</td>\n",
       "      <td>Fall</td>\n",
       "      <td>South Puget Sound</td>\n",
       "      <td>47.350000</td>\n",
       "      <td>-122.894100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>CMSHERW94S</td>\n",
       "      <td>Sherwood River Summer</td>\n",
       "      <td>1994</td>\n",
       "      <td>Summer</td>\n",
       "      <td>South Puget Sound</td>\n",
       "      <td>47.300000</td>\n",
       "      <td>-122.894100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>CMSKOO10</td>\n",
       "      <td>Skookum Creek</td>\n",
       "      <td>2010</td>\n",
       "      <td>Fall</td>\n",
       "      <td>South Puget Sound</td>\n",
       "      <td>47.142200</td>\n",
       "      <td>-123.022480</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>CMKALA03</td>\n",
       "      <td>Nisqually R. Kalama Creek</td>\n",
       "      <td>2003</td>\n",
       "      <td>Winter</td>\n",
       "      <td>South Puget Sound</td>\n",
       "      <td>47.089300</td>\n",
       "      <td>-122.699100</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>CMSTILL10</td>\n",
       "      <td>Stillaguamish River</td>\n",
       "      <td>2010</td>\n",
       "      <td>Fall</td>\n",
       "      <td>North Puget Sound</td>\n",
       "      <td>48.239156</td>\n",
       "      <td>-122.376980</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>CMSNOH10</td>\n",
       "      <td>Snohomish River</td>\n",
       "      <td>2010</td>\n",
       "      <td>Fall</td>\n",
       "      <td>North Puget Sound</td>\n",
       "      <td>47.943337</td>\n",
       "      <td>-122.174279</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>CMSQUA10</td>\n",
       "      <td>Squakum Creek</td>\n",
       "      <td>2010</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Fraser River BC</td>\n",
       "      <td>49.233200</td>\n",
       "      <td>-122.000150</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CLUSTER       SILLI                    POPNAME  YEAR  TIMING  \\\n",
       "1        1    CMHAMM10          Hamma Hamma River  2010  Summer   \n",
       "2        3  CMLILLIW11            Lilliwaup Creek  2011    Fall   \n",
       "3       10      CMUW10         Hoodsport Hatchery  2010    Fall   \n",
       "7        4  CMSHERW94F        Sherwood River Fall  1994    Fall   \n",
       "8        5  CMSHERW94S      Sherwood River Summer  1994  Summer   \n",
       "9        6    CMSKOO10              Skookum Creek  2010    Fall   \n",
       "6        2    CMKALA03  Nisqually R. Kalama Creek  2003  Winter   \n",
       "5        9   CMSTILL10        Stillaguamish River  2010    Fall   \n",
       "4        7    CMSNOH10            Snohomish River  2010    Fall   \n",
       "0        8    CMSQUA10              Squakum Creek  2010    Fall   \n",
       "\n",
       "              REGION        LAT        LONG  map_order  \n",
       "1         Hood Canal  47.586900 -123.120100          1  \n",
       "2         Hood Canal  47.463500 -123.113000          2  \n",
       "3         Hood Canal  47.404332 -123.138869          3  \n",
       "7  South Puget Sound  47.350000 -122.894100          4  \n",
       "8  South Puget Sound  47.300000 -122.894100          5  \n",
       "9  South Puget Sound  47.142200 -123.022480          6  \n",
       "6  South Puget Sound  47.089300 -122.699100          7  \n",
       "5  North Puget Sound  48.239156 -122.376980          8  \n",
       "4  North Puget Sound  47.943337 -122.174279          9  \n",
       "0    Fraser River BC  49.233200 -122.000150         10  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "POPINFO = pd.read_csv(os.path.join('data', 'POPINFO.latlong.txt.csv'), sep = '\\t')\n",
    "POPINFO = POPINFO.sort_values('CLUSTER')\n",
    "POPINFO['map_order'] = [1,7,2,4,5,6,9,10,8,3]\n",
    "POPINFO = POPINFO.sort_values('map_order')\n",
    "POPINFO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ind</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>...</th>\n",
       "      <th>POP</th>\n",
       "      <th>CLUSTER</th>\n",
       "      <th>SILLI</th>\n",
       "      <th>POPNAME</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>TIMING</th>\n",
       "      <th>REGION</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "      <th>map_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>4:CMSHERW94F_0083</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-0.0545</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>-0.0329</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>-0.0058</td>\n",
       "      <td>0.0565</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.0174</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>CMSHERW94F</td>\n",
       "      <td>Sherwood River Fall</td>\n",
       "      <td>1994</td>\n",
       "      <td>Fall</td>\n",
       "      <td>South Puget Sound</td>\n",
       "      <td>47.35</td>\n",
       "      <td>-122.8941</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ind    PC1     PC2    PC3     PC4     PC5     PC6     PC7  \\\n",
       "84  4:CMSHERW94F_0083  0.035 -0.0545 -0.102 -0.0329  0.0379 -0.0058  0.0565   \n",
       "\n",
       "      PC8     PC9    ...      POP  CLUSTER       SILLI              POPNAME  \\\n",
       "84  0.071  0.0174    ...        4        4  CMSHERW94F  Sherwood River Fall   \n",
       "\n",
       "    YEAR  TIMING             REGION    LAT      LONG  map_order  \n",
       "84  1994    Fall  South Puget Sound  47.35 -122.8941          4  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_plot = pd.merge(complete_dom_evecs, POPINFO, left_on ='POP', right_on = 'CLUSTER' ).sort_values('map_order')\n",
    "stray = to_plot[to_plot['map_order']==4]\n",
    "stray = stray[stray['PC1']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TtUVkFBqq3gvHcVh583VUV1fz2ANrvb1mYg6uz9tr5opbr1ZPyDihQKR/Cjrcb+jDuR3L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cAAAO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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb4faf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S6iHgYUKDkGVAdX+L7Qkhskc2pUkPX7way9q967l84ZUp7pGIhwQifXPY96f/A708WsMI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VX1olNOUxCRwcAhwEeB9wEFHihQqj7P3tewwoJgBQVfbUhHnZbhRe8cpwGIyHXASTUcUgY2AGux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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba46f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RSl0D/CX4MBRoHAA+BCqAZiAHu3rucdjrP0LtDgJTOppuUUpNBH4CfJXwwlkL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\nOIXx/xJAw+HgH/MHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa518f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mymarkers = ['o', 's', 's', 's', 'o', 's', '^', 's', 's', 's']\n",
    "\n",
    "to_plot = pd.merge(complete_dom_evecs, POPINFO, left_on ='POP', right_on = 'CLUSTER' ).sort_values('map_order')\n",
    "\n",
    "\n",
    "sns.lmplot(data = to_plot, x='PC1', y='PC2', \n",
    "           hue = 'map_order', \n",
    "           palette=sns.xkcd_palette(pop_colors), \n",
    "           markers=mymarkers,\n",
    "           fit_reg=False, size = 7, aspect = 1.1,\n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "\n",
    "# select the possible migrant\n",
    "stray = to_plot[to_plot['map_order']==4]\n",
    "stray = stray[stray['PC1']>0]\n",
    "# mark the stray as a dot\n",
    "plt.scatter(x = stray['PC1'], y = stray['PC2'], \n",
    "            c = sns.xkcd_palette(pop_colors)[3], \n",
    "            s = 150,alpha = .8, edgecolor = 'black', linewidths = .5, marker = 's')\n",
    "\n",
    "plt.xlabel('PC1: {0:.3g}%'.format(complete_dom_evals['percent_explained'][0]))\n",
    "plt.ylabel('PC2: {0:.3g}%'.format(complete_dom_evals['percent_explained'][1]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .24, s = 'b', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('/home/ipseg/Desktop/waples/chum_populations/paper/figures/PCA_b.png')\n",
    "plt.savefig('C:/Users/Ryan/Documents/GitHub/chum_populations/paper/figures/PCA_b.png')\n",
    "\n",
    "\n",
    "to_plot = pd.merge(paralogs_dom_evecs, POPINFO, left_on ='POP', right_on = 'CLUSTER' ).sort_values('map_order')\n",
    "sns.lmplot(data = to_plot, x='PC1', y='PC2', hue = 'map_order', palette=sns.xkcd_palette(pop_colors),\n",
    "                      markers=mymarkers,\n",
    "           fit_reg=False, size = 7, aspect = 1.1, \n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "# select the possible migrant\n",
    "stray = to_plot[to_plot['map_order']==4]\n",
    "stray = stray[stray['PC1']>0]\n",
    "# mark the stray as a dot\n",
    "plt.scatter(x = stray['PC1'], y = stray['PC2'], \n",
    "            c = sns.xkcd_palette(pop_colors)[3], \n",
    "            s = 150,alpha = .8, edgecolor = 'black', linewidths = .5, marker = 's')\n",
    "plt.xlabel('PC1: {0:.3g}%'.format(paralogs_dom_evals['percent_explained'][0]))\n",
    "plt.ylabel('PC2: {0:.3g}%'.format(paralogs_dom_evals['percent_explained'][1]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .24, s = 'a', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('/home/ipseg/Desktop/waples/chum_populations/paper/figures/PCA_a.png')\n",
    "plt.savefig('C:/Users/Ryan/Documents/GitHub/chum_populations/paper/figures/PCA_a.png')\n",
    "\n",
    "\n",
    "to_plot = pd.merge(complete_dom_subsample_evecs, POPINFO, left_on ='POP', right_on = 'CLUSTER' ).sort_values('map_order')\n",
    "sns.lmplot(data = to_plot, x='PC1', y='PC2', hue = 'map_order', palette=sns.xkcd_palette(pop_colors),\n",
    "                      markers=mymarkers,\n",
    "           fit_reg=False, size = 7, aspect = 1.1, \n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "# select the possible migrant\n",
    "stray = to_plot[to_plot['map_order']==4]\n",
    "stray = stray[stray['PC1']>0]\n",
    "# mark the stray as a dot\n",
    "plt.scatter(x = stray['PC1'], y = stray['PC2'], \n",
    "            c = sns.xkcd_palette(pop_colors)[3], \n",
    "            s = 150,alpha = .8, edgecolor = 'black', linewidths = .5, marker = 's')\n",
    "plt.xlabel('PC1: {0:.3g}%'.format(complete_dom_subsample_evals['percent_explained'][0]))\n",
    "plt.ylabel('PC2: {0:.3g}%'.format(complete_dom_subsample_evals['percent_explained'][1]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .20, s = 'c', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('/home/ipseg/Desktop/waples/chum_populations/paper/figures/PCA_c.png')\n",
    "plt.savefig('C:/Users/Ryan/Documents/GitHub/chum_populations/paper/figures/PCA_c.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## subsequent axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PT2DHvlYuGzuCZpcbT4qHYW7v/Y6fbLqkeS5fVczKDUU0ptQwaHQzAK+8koR7SSa3zVrA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\n16STlL+J58t+y9mk1vNCSWNLI/UN9ay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DBIC5i+9/DLwKvOHz+f4ZGAG+gB1IfWuZ/iXS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\nfwn2H72zss/5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c8aae390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A5aMz8VobxohEJNQC3bL5R2Atzqh4TMcq9gbgPmvtsfCEJiLiKC6vpHGiIT7I9Y0TDcXl\npX2y80bdQzJQBRod/74xZirOZNaP4LT91gIlwIvW2r3hD1FEpP9T91DvUdIXmfx22UQ6Y8ww4Mc4\nCdMY4AiwGvihtfZAEK+f2/r6y3C6h/YAzwM/s9ae8vO6NNRlI9JrvNbyucJyomYvCmp9y1treWre\nZLIyg7/N4yZ1D/WOTpO+3Zb0A1ZJ34UJaZdNUAmJMWbQuQPPjDEfAz4JTADqgC3AcmvtkVAG6Cem\nRJzD/gzwX8BmIBN4ADgMfNRae8LP6+8C/oyz2/M7oBq4GcgF3gbmWms7/cNRQiLS+25f+giVufcH\ntTYt/1Ge+/63whxRaOSvK2Dp4biABbu+oo18f2Sj3jQvkJK+sAhpQhJwdLwx5mc4b/xtv/YYYx4D\n/gb8C/AF4KvAr4FKY8znQxmgH/+A083zLWvtA9baZ6y1/wp8DkgHftjVC40x8cBvceaoXGGtfdRa\n+6S19hbgL8AsILiPYiLSKxZfNh1fUWHAdb6ijSy+rO80+ql7KPyCbhmPm0D+Os31dIvfhMQY8wXg\nB8DQsx7+LvAl4BTw3zjTWr8HPIdz2+MPxphPhCXajj4P1OCMrm9nrX0R+BA4b3bKWUbj3Jp50Fp7\n8pzn1rR+nRGiOEUkBHIXzue2ht34ijZ2ucZXtJHbGvb0mV0EdQ/1DiV9fUOgLpuvAftwCloxxniA\n+3FqNT5mrd199mJjzBU4E12/C7wR8mjPfJ9knFs1b1hrGztZ8jcg1xiTbq2tOPfJ1rjv6eLyKa1f\nq0MSrIiEzJK772LqugLy8h89rw4g44Bl8eUzyF3Yd7bcB1L3kFvakr5gT5JtS/r0Z9z7AiUkWThj\n4NvqQkbiFI9+99xkBMBa+64x5gng9tCGeZ5JrV+76vJpiy0dOC8h6YoxJg74Is7uz18uODoRCZvc\nhfPJXTi/tVOiFICcuRlkm+tdjkwiUW8mfere6ZlACUkMHXcK6lu/npeMnGUvMKgnQQVhcOvXrk4W\nPnXOuoCMMVHAYzhJ2D8G06UjIu7JNqbP/8DPyUgjttBCanpQ62N3W6bPmxzmqKS71LIdGoF2sbxA\n+8cOa20VsIPWWzhduJpu7EpEgtaOnedxCmJ/Za39pcshicgAkG0MGQd3BL0+4+COPtPKHClyMtKI\n3R183U3sbsv0ycEliOB07yw9HEdl7v1EzV5EfGo68anpRM1exK5Pf4ulh+NY9uTTFxL6gBMoIfkd\n8HFjzKPGmITWx34C3GeM+ejZC40xg1tPB74GeCbkkXbUtmtzURfPJ52zrkvGmJHAa8DfAf9qrQ2u\nr1BEJAT6a/dQpAhn0qfundDym5BYa/8HeBL4BrDLGPM/wHhgE1BojFlhjPm1MeZlnNs4X8NpEX4o\nrFE7OzA+oKshIG01JqX+LmKMGQ28CVwK3G2t/UmoAhQRCUZ/7B6KNOFK+tS9E1rBDka7E1gCXOxn\n2QmcHZWfnztELRyMMUXAVGC4tbb+rMejcTqDaq21aX5en4zTEWSAXGvtK9343mloMJqIhFBXU0TP\ndA8pGekJZzDaBDwz53X6fFvSF+xgtAuZHvznuRl9vu7pHCEdjBaoqBUAa+3/Av/b+kY8E2c662Cg\nCTgGFAObumjBDZc/AI/iDGV79KzHP4vTDfSjtgeMMVlAnbW28qx1jwCX0M1kREQkHNQ9FF6hbhlX\ny3boBZWQtGl9Q68MSyTd9zvgLuBhY8wk4D0gB/g2zhj7h89aux2wQDaAMeZinAmz24EYY8ytnVz/\nkLU2bLNUREQ60x+6hyKVkr7IFnRC0tqJ8jFgGs7k1kE4bbdHgPeB/7PWtoQjyM5Ya5uMMdfiFNne\nglPnchCndffH1tq6c15y9r2pma1fs4HlXXyLDTgFuiIifYrmYfgXiqRPLduhF7CGpLXW4uc4o9qT\n/Sw9CPwGeMha2xCyCCOQakhEJBLpNNve1V8PfOyG3jvt1xgzBCjE2RWpwxnJXopTN1KPc3bNCJyd\nho8B0a3rr+1kh6LfUEIiIpFGp9n2Pp3U3LtFrf+Mk4w8iHMQXU1XC1vnefwb8GXgO8DPQhWkiIh0\nLeh5GEUbmbquoD++Mboid+F8Sp98muVFGwN27/SlM5bcEmgwWi6wylr7A3/JCIC19rC19l6cQ/U+\nE6oARUTEP83DcM+Su+/i+yMbSct/lJa31lK/t4L6vRW0vLWWtBce4fsjG7UrFaRAOyQTgSe6ec31\nwA8uLBwREekOnWbrPnXvhEaghOQUMKqb1xwJ1F5YOCIi0h2ahxE51LLdM4GS6neBu4wxE4O5mDEm\nE+eAund6GpiIiIgMHIF2SB7GuQVTZIz5PbAOKMMZE98AxAHDcbpsFgF3A4k4xa0iIhJmmoch/UWg\nw/VeA74ExAPfxUlOKoDjOLdzjuMkKCtxBpM1Ap+x1r4ZxphFRKRVOE+zFelNAeugrLVPAFNwEpJX\ngA9xJrTS+nU3sAq4H5hirX0+PKGKiEhnenKarddaVqx5hRVrXsFrbbhCFAkoqNN+pSMNRhORSNPd\n02w11VVCoPcmtUrnlJCISCTqKsk4c5qtk2RoqquESOQmJMaYWCAdwFob/E3NPkYJiYhEMn+H62nc\nuYRQr46O767JwHack3WjQ3xtEREJgr95GHmbtuEJ4kA4z8x55OU/qoREek2ww/2C1YhT5Lo7xNcV\nEZEeapvqGqy2qa4ivSGkOyTW2p1AWiivKSIi/vm7RXM2TXWVSBbqWzYiItJLOi1iLbSkr1ijThnp\nc5SQiIj0Qe2dMrn3EwVndj1S09nFIpYWFVL65NMdOmU01VUiWahrSDDGPGCMaQ71dUVExJG/rsBv\n2y6AZ+ZclsdNIH9dQftjmuoqkSzkCUmrkLYCiYjIGXmbtgVs24XWTplN2zo81jbVtbZiB8cKX+VY\n4avUVpyfpHQ21VUknMKVkIiISBiEolOm4a/5VBR/wP6xU9k/dioV5eXsfPIRjr/9GnBmqqtqUKQ3\n+a0hMcZ8AWemSHd89MLDERERf3rSKdNWdxL/j/+PsdXVHDtVS31sAr7LrqbusqvZ/7d1JC79e376\n+VvJXagprdK7AhW1PnGB19U8ehGRCHJu3cnQ5GSGJidTW1dHXX0dAAlzFxA/PAVnpJRI7wqUkOwH\nRgH/S/BJRg5waU+CEhERx7kzRi60U+ZHK9Z0OqE1MSGBxISEMw9oQqu4JFBC8mVgFVBirX0wmAsa\nYx5At21ERHqkyxkjByzxBw/TOHtRUNfJOLgDny+D8tGZQRcNttWdaCCa9Ca//z6ttWuAx4CfGGOU\nZIiI9IJlTz7N0sNxVObeT9TsRcSnphOfmk7U7EXs+vS3OJgyhv3rXwx4nbZOmba6k2A5dSeVPfgd\niHRfMAnzPwF7gaeNMYlhjkdEZEALZsbIsDu/ga9sG8dfX9XlGnXKSF8TcFKrtbbGGJMDJAANQVzz\nLeCnPQ1MRGQgCvY03nFf+wExv/xHUo/t7HhbZ7cl44Bl8eUz2jtlNKFV+oKgRsdba+uAuiDXvoWT\nlIiISDe0zRgJttaj4fJr+encDACKy0sByJmbQba5vsO6bGPIeH4tlQRfd5KVeX3ghSIhpLNsREQi\nxIXOGLn1+usCFqAuvmw6S4sKA0541YRWcYsmtYqIDAC5C+dzW8NufEUbu1yjuhNxk3ZIREQiRLhr\nPZbcfRdT1xWQl/9owLqT7jh3VoraheVCKCEREYkQvVHrkbtwPrkL57cmEV3XnQSjy1kpK9Zw5+Uz\ntNMi3eLx+TTlvbuMMWlARUFBAampqW6HIyL9SP66ApYejguq1uP7Ixtde9NvOxenqzh9RYXc1rCb\nJXfrTJx+zBPKi6mGREQkgvSFWo9gZqV4Zs5ledwE8tcV9GJk0pcpIRERiTBL7r6L749sJC3/UVre\nWkv93grq91bQ8tZa0l54hO+PbHR15yFv07aAOzgAnpnzyNu0rRcikv5ANSQiIhEolLUeodTdWSn9\n/VwcFfSGTtAJiTHmbZwTf++w1u7pYs1E4FnAZ62dHZoQRUQGrmxjIupN7kJnpUTS7yEUVNAbet3Z\nIbmi9au/82wGnbVOREJMn8ZE3Nde0Jt7P1FwJjlLTWcXi1haVEjpk0+roLebupOQXIOzQ9Lp7kir\nXWetE5EQ0acxiRQD/VycoAt6izYydV2B/t/shqATEmvthiDW1AIB14lI8PRpTCLJQD8XJ9jDDz0z\n55GX/6gSkm4ISZeNMWaiMWZIKK4lImeovVIi0eLLpuMrKgy4rr+di9NW0BustoJeCU6o2n4rge+H\n6Foi0krtlRKJ+sKslHBoK+gNllPQWxm+gPoZtf2KRCi1V0okC9e5ODJw+U1IjDEtBF+g+oAx5p/a\nfmGtje5JYCIDndorJdJF6qyUcBnoBb3hFswOiQc4CNT7WTMRqAZOtP5aXTYiIgNEpM1KCZeBXtAb\nboESkvuBZUAT8E/W2uc7W9S6k/J7a+2SEMcnMmDlZKTRvP59TiQ59eIJCQkkJHQ9BkifxkTCb/Fl\n01laVBjU4Yf9qaC3N/hNSKy1vzLGrAP+DCw3xuQB91lrT/h7XW8xxgwDfgx8ChgDHAFWAz+01h4I\n8hpTgP8FPgbcY639Y5jCFQna2rX5bCxcjmfXaZouzgbg+AloaY4jKWkoKSlDz3uNPo2JhF/uwvmU\nPvk0y4s24pk5r9M1Zwp6VUPTHQHr5ay1FpgN/BS4HdhmjLku3IEFYoxJxJl58jVgOfAF4L+BO4A3\ng2lDNsbcAxQBWa0P6VaTuO6xxx7GW/JLZs85zN/lHCWm+C1iY6MYlBhFUlITdXWHOHykY76tT2Mi\nvSfSDz/sq4LqsrHWNgE/NcaswtktWWOM+W+c2zinwxmgH/8ATAf+3lr7u7YHjTEfAPnAD4F/6uK1\nGGPuBX4HPAoU4yQzIq5auzafmlMvk2niAJh1SQL7Xv8zhT5f+6exhAQPdXVVVFXFk5IyVJ/GRFww\n0Ap6e0O32n6ttZuNMZcCDwHfABYYYz4flsgC+zxQA/zh7AettS8aYz4EPoufhARnN+RT1tqXjDF3\nhy1KkW7YWLic2XPiOjx2y5WxjPvgj7z+9GoOpF9Jc9p0ooGT217jkugGtVeKuGigFPT2hm7PIWkd\nD3+/MeYl4AlcGBVvjEkGDPCGtbaxkyV/A3KNMenW2orOrmGtfSycMYp0V0mJl6SkvdBJo++sSxKY\ndclJdu9dzm7vcwDUVTXy1a/8kays7F6OVEQk9C54Uqu1dj0wA1jR+pAnJBEFZ1Lr171dPL+79Wtw\nzeIiEaCsbDujRrf4XTMxNZG5lw5i7qWDmD49jrKy7b0UnYhIePVoUmtrt81drf/1psGtX7uqXzl1\nzjoRERGJYEHtkBhjYowxMwOs+Zwxpjd3SUT6lSlTpnHoYPCblocORjF1ak4YIxIR6T0Bf/oZY8YD\n7wFP+llzA/BHYENrO264Vbd+vaiL55POWScS8bKysqmpSQ16/amaCRiTFXihiEgf4DchMcbE4NSI\nzACajTEJXSz14hS3zgP+FMoAu1CB0yXT1U/vthqT0l6IRSRk5s29jR22IeA6axuZO/fWXohIxD+v\n17JixSusWPEKXq91OxzpwwLVkHwKuAJ4AbjdWttpxZ21tsIYcy3O/I9bjDGzrLVvhzbUDt/vlDFm\nC/BRY0y8tbb9nB1jTDTOILfd1tquil5FItKiRbns2bsTa1diTGyna6xtZHDSTSxalNvL0YmckZ9f\nwLN5W6msnEJjQyYAsXE7mJS2ms8svpjc3PkuRyh9TaBbNncBJ4EvdZWMtGkdnnYPTkHpfaEJz68/\nAIOAr57z+GeBkcDjbQ8YY7KMMWm9EJNIj33lyw8wLetbvP3mKLZ8UM+BA7UcOFDLlg/qeatwJNOy\nvsVXvvyA22HKALZs2VM89GA0e3bdR7TnOhLi00mITyfacx17d32DZf8ezS+WPe12mNLHBNohyQFe\nttZWBXMxa+0RY8yLgP9Th0LjdzgJ08PGmEk4dS45wLeBLcDDZ63dDligfWCDMeZ6ztSgfKz162XG\nmLbOnUPW2jfCF75I1xYtymXRolxKSrztrb1zZk3TzBFxXX5+AS+sSCXaM6fLNVHM4YXnC5kytUA7\nJRK0QAkMET6qAAAgAElEQVTJOKCsm9csBW67sHCCZ61tar1N9BPgFpzJsQeBx4AfW2vrznnJuefU\n/IYztSZtz9/Hmd2dDcA1oY1apHuysrKVhEhEeTZvK9GewJvgUczl2Wd+rYREghYoIfE/palzsUB9\nwFUhYK09iTMe3t+IeKy1592astZqaJqISDd4vZbKyilEBzngobJiCl6vJTtbo9UlsEAJyQHOus0R\npEuB/RcWjohI6NgSS8UO5/SI9Mx0TJbeGHuiuLiSxoZMos8/3aBTjQ2ZFBdXKCGRoARKSN4AbjfG\njLLWHgp0MeMMRViAM5NERMQV61etZ8v6IjJ8aUxJzgDA+9oWVvIilyy8lAU3LnA5QhE5V6Aumydx\nhoz90XTVg9jKGDMceA4nyfmfkEQnItJNT/3mT7S8Xs+9afewIP1q0oZPIm34JBakX81X07+I7/V6\nnvptb4xL6n9yctKIjdsR9PrYuB1Mn6674xIcvwmJtbYQJ8m4DthsjPm7cxMTY8wQY8zXgK3AdODJ\ncM4gERHpyvpV6xlVOZzZ46/ocs2s8VcwqmIY61et78XIeqakxMtfVq7gLytXUFLidS2O7GxDWlrw\nfQ5p6WVkZWWGMSLpT4I5XO9LOIfUXY8z+KzeGFMB1ABDgTQgunXtcs6fCyIi0iu2rC/i3rR7Aq6b\nPf7j/H79ExF/62bl2nzWvJlHXXIlg0Y7E3xffjmO+KfSuGHuYm5yYTjeHYtn8NCDb/pt+wVooZA7\nPjOjl6KS/iDgWTbW2lPATcDngU1AHJCFM7tjcuuyjcAt1to7rLWNYYpVRKRLtsSS4UsLen2GLw1b\nErmjzv/r8WWsLH2Q4Z/YxfiPeBg6Np6hY+MZ/xEPI67cxcrSf+dXjy/r9bhyc+dzy60f0kJhl2ta\nKOTTt+xTy690SzA7JFhrfcBTwFPGmGScXZHBOIfX7bTWnvbzchGJECVeLzuKiwHIzMkhK7v/zDip\n2FHRXsAajCnJGVTsqIjIzpuVa/Mprl3BqKyu+2tHZcHWkudZuXZqr++UfGfJXUyZWsCzz/yayoqO\no+PT0kq5Y/HF5Obe1asxSd8XVEJyNmttNc4kVBHpI1bl51OQl4dvZzkpDc7W/1/j4iAjnQV33smN\nuToXJ5KseTOPUZ8IPOxjdBasfeMZV27d5ObOJzd3Pl6vpbjYaa3OyZlKdvZNvR6L9A9BJSTGmESc\nk3zHAUeBN4IdJy8i7vrtsmVUPrecdACPB+KdIRLDASoqeWPpUnaXlvL1JUtcjLLn0jPT8b62hbTh\nkwIvBsqqy5lmLglzVN1XUuKlLrkSCG76WG1yBSUlXtcm+mZnG80ZkZAIWENijPkksAtYi9PO+yKw\n1xjz7TDHJiI9tCo/n8rnlpPqZ02qDyqeW86q/PxuX7/E6+WlFSt4acUKSrzudX8AmCxDuacy6PXl\nnkoyTeR1gJSUFbcXsAZj0OgGSsqKwxgReK1lxZpXWLHmFbw2cutupG/zu0NijJmG0/YbB2wDSoAx\nwBU4h9ods9ZqCJpIhCrIyyOYKRATWtcGe+smEm8BeUtKOJHSwENv/opPTbkOM3pql2vf+vAdLl4w\nsxej65vy1xWQt2kb5aMzaZzo7ILEFlrSV6zhzstnkLtQRasSOoFu2fwTTjJyr7X28bYHjTGZwHrg\n52gqq0hEKvF68e0sd27TBMG3s5wSrzdgoWuk3QLKX72aZzdsYFdMLI3DhnF8ci0v7H2K7O0x3DJ+\nNjdndnzTfOvDdziUfozP3vj5Xomvu7Km5PDyy3EMHRvc+tMH48i+YnrI41j25NMsj5uIJ/d+ooD2\nafGp6exiEUuLCil98mmW3N3/ile91lJcXglATkYa2Ua3pHpDoITkKuD1s5MRAGvtDmPMvwK/N8bM\nsNZuDVeAInJhdhQXO7sX8cEdPJLS0MCO4mK/CUm3bgFNnRr2nZJlv/tv8g8cIDpnOtE4A5HGjhxJ\n9ZhqPjhZy/aKt9n8+jbumf4pyqrL2UkFlyy8NCzJSKjOzcnKyibh6TScO+WBJVan45zaETr56wqc\nZGTm3C7XeGbOZXnRRqauK+g3OyXaEXJXoIRkHM6ws84UtX4djzOlVUT6uXDdAroQ+atXO8lI+vkR\nJQ9JJnlIMnUjUlhbYrkoei13fe1WPpV1W8jjCMe5OdfPWczKkgf9tv0CHCyBm+d85oLi9idv0zY8\nufcHXOeZOY+8/Ef7xRv1QN4RihSBilrjga4O1Tva+jUudOGISKhk5uRQFRf8/55VcXGY6V1v/bff\nAgpS2y2gcHl2w4ZOk5GzJSQmMHTmJZSeqgrLvJFwnZtz06JcpifeysGSrtccLIEZibeEvOXXay3l\no4Mv9i0fndnnC12D3hGKm0D+uoJejGxgCdhlIyJ9U1Z2Np7JwQ8K80zOwGR1vfXffgsoSG23gMLB\nW1LCrhi/5312UBkTi7fEz7v7BQj3uTnf+PISbp76PY69kcaH7/s4vr+e4/vr+fB9H0dfn8TNU7/H\nN74c+jqd4vLK9tsVwWicaNrrLfqqvE3b/CYjbTwz55G3aVsvRDQwdXswmoj0HfMXL+aNf1vqt+YD\nYE/r2r6iuLSUxmHD2g/RCqRx2DCKS0vJ9pNwdVdvnJtz06JcblqUS0mJt721N+vyHNdmjvRHbTtC\nwX46b9sRUqFr6PUkIfGFLAoRCYsbc3PZXVpKxXPLmdDFmj1A+u23Baz3yMzJ4a9xcU43TRAC3QLq\ny7p7bk7s8TgeffwJxo0fd0FdG1lZ2b2WhORkpBFbaCE1mGohiN1tmT5vcuCFvciZHlsJQE5Omt/B\nbW07QsGVfrftCJUqIQmDYBKSbxpjbu3k8YTWrw8ZY7537pPW2tk9ikwkREpKvJSVbQdgypRpA+7T\n5deXLGHV1KnnzQ2piouDyRksWLw4qOLT9ltA5RVBfd9At4B6ImfqVGLffgdGjgxqfeyxY0zPDN0Q\ntGDPzXl550aWV5eyIyOD00OnMjhmcMR3bWQbQ8bza6lkUVDrMw7uICvz+jBHFZz8/AKezdtKZWXH\n83Umpa3mM4sv1mF/ES6YhGRS639dUZooEWnt2nw2Fi4nKWkvo0a3ALB1WxQ1J1OZN+82Frlw/odb\nbszN5cbc3B4frhcpt4Cys7KY1NTI3iDXpzU1ktXLn2j/c2s++dMn4/nIt/E01xM7qIn4ISl9omtj\n8WXTWVpUGLCuwle0kcWXRcYu2LJlT/HCilSiPfcR7YHo9i2PdPbuuo5l//4mZaVP850lHf+8+8OO\nUH8RKCG55gKvq9s54qrHHnuYmlMvM3tOHGc18DFmDMBhvCWPsGfvTr7y5QfcCtEVWdnZ7UlISYmX\nlSufB4LfOQrlLaCeuuOqq1j2t00BO21aKiq4/aqrQvq9A52b8/LOja3JyDwA6pvrSUgY1GFNJM/x\nyF04n9Inn2Z50UY8M+d1usZXtJHbGvaQu9D9hCo/v6A1GZnT5Zoo5vDC84VMmVrQYaekL+8I9Td+\nExJr7YZeikMkZNauzafm1Mtkmq5bXjNNLNauZO3ayQNqpwR6vnMUqltAPZV7ww2U7dlDfkUFUV0k\nJS0VFeSOHUPuDTeE9HubLMMqz0tdPr+8uhTPR84c91VPA0MShp23LpLneCy5+y6mrisgL//RjoPC\ndlsyDlgWXz4jIpIRgGfzthLtuS/guijm8uwzvz7v1k1f3BHqjzw+nzYzussYkwZUFBQUkJoaaPNa\netsP/uVOZs85HNTat98cxc9//nSYI4ocbTtHXSVrO2wjSUk3Bb1z1NNbQKGQv3o1z23YQGXr6Hhw\nakYmNTZyx9VXhTwZabN+1XpaXq8/r+3XHtrJV5IO4ZnlfOI+1VgDg6NITknu9Dotb63lz3MzIrpI\nMpJHqXu9lnu+UEq057qg1jf7XuGJP049r9DVGYw2IeCOUCTeYnNRcOdSBEltv9KvlJR4SUraC0HW\nzF+UtMfVo9t7Uzh2js6+BeSW3BtuIPeGG/CWlFBcWgo4Ra+hbPHtzIIbF/DU7j/xVsU7zB7/8fbH\nvVV7aJh+MfE4yUhjfDPDU4Z0eZ2+0LWRbUzExldcXEljQ+ZZNSP+NTZkUlxccV5C0pd2hPorJSTS\nr5SVbW+/DRGMUaNbKCvbPiASko2Fy1travwzJpbCwhV97lZWdlZW2JOQc332659n/ar1/H79E+2j\n4w+dPMypxhqqG44wKHmQ32REIkvuwvnkLpzfuiPUmtzOzSDbqGakNyghERkAtHMUPgtuXMCCGxe0\nH66XNDaWpJO7SLxsZlCvV9dGz+TkpBEbtwOCOmXJaQOePt1/C3gk7wj1Z0pIpF+ZMmUaW7dFtXbT\nBHboYBRzZ+f06Hv29q2CCxHJO0ehOiXXbSbLtMe+fukjVAb5OnVt9Ex2tiEtbQ17dgVXQ5KWXkZW\n1k1hjkouhBIS6VeysrKpqUkFgitqPVUz4YKPbs9fvZpnN2xg19nFlG+/w6TGBu64+uqgiykjoTDU\nDeE4JTdSqGujd92xeAYPPfim37ZfgBYKueMzM3opKukuJSTS78ybexvekl/6Ld4EsLaRuXM7G0Ic\n2LLf/Tf5Bw4QnTOdaDhzpsrIkewFfvG3zZTt2cN3vvrVLq+xKj+f537/e45W7mJQUxMjo6L4a1IS\nZKSz4M47Q9o668bOkT9P/eZPjKocft5ZMGnDJ7GAq3n79Xd5avef+OzXPx+2GMKpr83x6Otyc+dT\nVvo0LzxfSBSdJ4EtFPLpW/aRm6s/70iltt8LoLbfyPfY4w9zsmYlxnR+Iqy1jQzuRnvr2fJXrw56\nINd3Lr+s052Sr33lXt7YvJn6tDRaRo0GIOrQQYZUVnJpQwPDBiWSdtttfH1J6E5zjZR26HPbZXcc\n2kH5cef4+oyhhsxRzv39tz58h6grE/r0Tkn+ugLyNm3z07URefNH+rL8/AKefWYrlRUdR8enpZVy\nh0bHh0NI236VkFyA3kpIBvoZLD21dm0+hYUruChpT3v9xKGDPR8d/5klS9ibE9w2+4TibeQtW9bh\nsS9+85usP3SYhLQuhnlVVjB961YyBw3iyh98P2Q7JWvX5lP0/kOkTXL+LOIS4klISDhvnbWNTMv6\nVti6bP7j27/g3rR7WFe6mncOv0Ty+P2MGtsMwKH90VTvHcvHR32ShVNv4PeVT/CP//mdsMTRmyJ5\njkd/1J3D9aRHlJC4LdwJSWeTNEPxRjpQhTKx85aU8MU//ZnoIN9Qmq3lfz7/ufZC1/zVq7n/mWdJ\nHDvO7+taKsq5uriYwTnT+I/nnrvgeNu01Wvs3rmVUaaEzKxY6pvrqaeeQcmDGNw6tKsnO0fBsCUW\n7++28OGJ7dSOfRXTxW01axtJ3L+Q8UOmkf21i/tsoatIP6fBaP2ZzmAJvVAe3V5cWkrjsGFnakYC\naBw2jOLS0vaE5PGXXiIqQDICEJWeQVFpKfN2llPi9fao0LVDvUYarCtdzbuvvsTg8fsZNTaGg0dr\nee/IKeISLg57wluxo4JDx3dTO67rZAScWSiWdRzal0TCjsFKSEQGACUkEURnsPRv3pISyj1RRPt8\n4An8weJ4WhoN27ezo7j4ghOS9avWM6pyeIfx5gun3sDCqTc4tRsVlqFAUkMdk66ZyoS0Cax9aS0Q\n2hbctl2q9/+viJJ9r3PH1cENaHvVW8BEwldcK71Ht1EkECUkEaS/T9LsD3KmTiX27Xdg5Mig1sce\nO8b0TKe4rri0lLqUIdQ3NhPlgVgPxEZ1nZi0jBrN4W3buh2j9XqpaG0jfv2ld/jBR7/X6brMUZnt\nBaTVVdX868NLWXTJdSFtwW27/RgbU8bQofVEJ9SSMvUYL6yPJ2PcMD4ybYTf1w8avwdie++2sr83\nzf4yL6W35ecX8GzeViorOxaaTkpbzWdUaCpnUUISITRJs2/IzspiUlMje4Ncn9bUSJYx5OcX8J+P\nrOJY5ih8iWNbb7zWEeM5yaCYBhKjO09MTsfEYKYHV0C7Lj+f9/PymLRzJxkNDdTX15NW1cR/bSll\n+qWf5upLbuz0dUcPHyWmIZqFY69h4qAJpA2f5MTewxbcxx57mH37nmbyxCbiiSc+Ooma2BZGXhpD\nYlwUO3Yc5q9v13H1rK7rsIaNb6Cp+XS3vu+F8PemmZ3pI/lUc7+clxJuy5Y9xQsrUon23Ee0h7PO\nm0ln767rWPbvb1JW+jTfWaJWXIEotwMQx4VO0uwPSkq8rFz5PCtXPk9JidftcLrUFufkpAROeQPH\n2VJRwe1XXcWyZU/x0IPRnD72DaKONEFUPB5PHB5PMs2Mp7pxJNWN578+6tBBhqdNwgQx+fVPy5ZR\n/2//xt3l5Vzt8TApPp7RLXB9dAL3HNuFb/0veabgv857XXVVNTEN0STFJjFl6BR2H9193ppZ469g\nVMUw1q9aHzCONmvX5rOr8o9kjotlZNwIkuMGEx8dR0x0DNFRUbT4fGRmxpE04iTvbz/S6TVqGmtI\nTEoM+nteqLa/nz277iPacx0J8ekkxKcT7bmODzYvZvkTGdTuTGFB+tWkDZ/kJGrpV/PV9C/ie72e\np377p7DH2Bfl5xe0JiNdDyuLYg4vPD+O/PyCXoxMIpV2SMQ1nXUTbd0W+m6ikpISSsuc0e5Tp0wl\nqxuj3UtKvDy74gkqy99l4sRaJk6KI2MCjH2lhuKj+0iZ8VGSOzk8raWigtyxY6A+vv2H8qBEiK1s\ngMkxtDQ2tpenezxJ1DZDbNThDjsl8ZWV3P6jHwaMcV1+PsOee44r/Ky5HB/vFr3IX0dkdNgpOV19\nmpHx/m+bAMwe/3F+v/6JoHYDSrxefv/bn3DDgmZiPB3Lf+OjE6hriiY6uoUWXwvGxLH+laN8ZNoI\n6prqqG+uA6C5xUfURVGcOprM1KnhqyHx96ZZXVVNTH0MSXHX8dKWQjJGbuTmizsOOZs1/greqniH\n9avWa6fkHM/mbSXac1/AdVHM5dlnfq1bN6KEJFJE2iTNcAt3N1FJiZfly/MoLStn7NhxpE5wbgts\n3ryJurp6rrryKq6/vuvzQ1auzWfNm3mUVrxLTvpprrgmjpZGDw3NcaQkDeO+L4zk7fdKWPPadg6M\nuoQhmc6ts9hjx5jU2MgdV19F7g038Jnbf9nhh/Lwxis5sOc9PONT8TU3d0hKTjdVkRjtbJXUVpRz\n48c+FtQMkvfz8ri7k8fjE+KoO1lLXIxTl3SFB54oym9PSOrq6ojnTM1S2fEyMtK7PuQtw5eGLbFd\n1k6sys+nIC+PY9uLGT3jQ3ynBnH0dBXExDF40FCSE1NIiEng+KlYYhKbaPa10ORrYujoOv5vp2Vc\nahTRsS348NHcEgUNCewsS77g0f7B8PemeXay5vHM5fn/e/S8hAS6l6wNFF6vpbJyCl3ciTxPZcUU\nvF57XqGrtV4qdzr1UGmTczBGt6j7MyUkEaI3z2BxWzi7idp2Xcp3ehk7fgZXfHwKjU3Q2FhDUtJQ\npuVMA6DogyL27NnNvfeeP9r9vx5fRnHtCmqTTnBJZh2ZxkmYomMBGqiqO0D9kXpmfXQMsz4Kb7yx\nhdjTY/nIzMs7HK7X2Q/lYYMWUbdtDyda9uKZNJHmpibw+fD4fDSSxKmWY0Qd2Me1o0fxu/86/xbL\nuazXy6SdOzvt2klISKCKEwxmcPtjaUcq2HFgB5ljMqmvryfhzE19dlZXcN2oa7v8XlOSM6jYUdFp\nQvLbZcuofG456cDJhhomToglxuNxfsA0NVBTfZCGpnpGDB7FRTFDqKs/TEJ8FD5aGDfew8FD9SQk\nJRIVFY3H49xJtiW1xMcf4bHHHw5Lm7u/N00nWetYz7XrmMEe2IkZc37SFihZ64/8FQAXF1fS2JB5\nVs2If40NmRQXV7Rfo+CVfLa8m8fkERVkjneSdFsQy6pn0rnk44uZf93ALejvz0maEpII0htnsESC\ncHUTte26JCbWMTHtYjImO28cMbEATdTVHaKhsZ6RI8YweXIGO8t2smbNmg47JSvX5lNcu4JRWR62\nPFfF3AXnj56PSYDauuNUVcWTkjKUT3wihbffLOfWm3/WYV1XP5THXfQVErxrOVr2BqTF0jJiCD6f\nD9+B/YyJ3su/3PvFoA/mqyguJqOhAeI7/8mfmDyIUydPcVHcRQBMbm5g14ESMsd0PH79rb3vMG3C\nhf1gW5WfT+Vzy2krTW1qaMRzzrykOKDh9AmqY+JJTkzhaG09p1qOE5fYTJTHg8fjIzr6zO0dW9JI\nTc1gbrhpbHtimp42LaRdLv7eNJ1kreO/0cZmg/dAcacJib9krb8Jd9fMU08sYyzL+foNUThljs5f\nUNp4uJZdvLntQZ56opTP3hO6YxX6goGQpCkhiSCLFuWyZ+9OrA18BktfbfkNVzfR2bsuzz3bwOy5\n579pxCd4qKurak8kJk+ZzOtvvN4hIVnzZh6jPuHh6J5aJozqut00NgGqTh4nJWVot+JsM2zQIoax\niNNlltodTn1LVPMl/PPPPOTeENwx6m3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MAxAFDEYjNpsBj0cmNVWv2PTZ59N58RKFMXw2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T2fL/rfmE4kPl96F/5z/iE+Sq2nYGL4ySR7y+Azud/n1hj36J2rn9t5QD0kIiLBVG+oYHDJCZLr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\nERERkZj7P5yzyHhltBXeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c865a0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hJ9gcj8LD2ynYkzGsk5JQCWag3WgPvZsIpz1CiPAjAYkIO0PN8RhKQJKSno0uMJGSFNz6\nktMm7LdeebVJMAPtxnrond2eJUGIEGLMSEAiwspwczyCTXRVKoudz6QSbG+RsnNp3J15ZdUmwQy0\ne/r5J4nJqZOhd0KICUvKfkVYqSw72p3AGozMpLbuCpxgLb5+PQcOewZdV3jYy+Lrr6zaJJiBdubZ\nF2hI2MfFlgt+18jQOyFEuJOARIg+clflUc1aCg/7T/guPOylhnuuOD8lmIF2jc31JC0xcrrsfMB1\nMvROCBHO5JGNCCvB5niUVDo5Ue3k0HEP77vdNuTvc98DGyjYk8HjO7eSPqO8+1Sm5LSJ43W+suL7\nrjAYCWagndPlwhvhBgzYEls5d9LJjLn+fz0y9E5MRsVac7S8EoDstBSylArthsSwSEAiwspgOR6v\nvtXAsdLzpCe1kjnXS6zVRH35D/nZj/57yL1Jclflkbsqr1fjNZWbzd1X2HitSzAD7VxuJ0aTFzAw\nbTY01AQOSGTonZhM8l8pYMvbRyhPzKRtni8IMRVqUrfv4t6lOeTdlhviHYqhkIBEhB1fjsej/cp+\nt+6sJjm+gYfyfImfzU4DM2bOJDra2Gv+zFB7kyiVdcXdX4UQI2vjU0+zzTwPQ96Xe0ybApJTOcFq\nHi0qpPSpp9nwyY+FcJdiKCSHRISdgXI8Xn2rgeT4Bpbl+B5/NDuhjViio2O713TNnynYMz4SP+0L\nsmmpNQdcY7XY8LT5fk0XqyFuduDHTy21ZrIyZOidmNjyXynwBSNLVvhdY1iygm3mueS/UjCGOxNX\nQgISEZbue2ADhjnf5vGdKbz8loe3D53lWuWl6ZKXs41mMM0iPn5Wv+tW5Bg4dHB8JH4GM9DOZrVi\n6PD9289ZayY+OXBAIkPvxGSw5e0jAYORLoYlN7Ll7SNjsCMxEiQgEWErd1UeX/v+M5hTf0Dq/Bm0\nGuZgjU5l1uz0XicjfXX1JhkPbl++nrOOwOMbYqLiOF3kISk9PuC6WgeslqF3YoIr1pryxMyg15cn\nZlKs9SjuSIwUCUhE2DN4XVy3MIqYmFislsEraobTm2S03Lk6j0W2e6h1+F/jrp5O7NmbiJriP8iS\noXdisjhaXtmdwBqMtnmquwJHjG+S1CrGpZ6VLSnp2RM6qfSLD25gx+4Mdu8beKDdXSvWc+eXfK3j\nA66RYEQIEcYkIBHjSsGefA4d3EL6jIru3h+6wMRLW1P9lu2Gav7MSApmoJ0MvRPC12fEVKghOTWo\n9aYqzaIb00d5V2IkSEAixo3NmzYym22dg/MuF/KlJBGwbDcU82dGSzAD7WTonZjMspQi7bndVLI6\nqPVptSXYM28f5V2JkSA5JGJcKNiTz2y29est0lOgst2xnD8jhAit9dctwltUOOg6b9F+1l83vk5D\nhX8SkIhx4dDBLQGDkS7+ynbHcv6MECK08m7LZW1rFd6i/X7XeIv2s7b1pHRrDSPyyEaEnNbFpM+o\nINj4uKtst2+i61jMnwknvXJNFkiuiZhYNnzyY2S8UsCW/F/2bh1fpUmr0axfmkPebdKlNZwYvN7A\nPRBEf0qpFKCioKCA5OTkUG8n7O3ZuZ35bY+QkuR/pktPlafdnDB9l1Vr7vG7ZjJV6fS1Y3c+uw5s\nwRVd2asax9KYwhqpxhETkAzXCyt+p4nKCYmYkCbr/JlfPbmRo87tJNzkm+fTlRgcOxvgBDscP6Hy\nVClffHBo83yEGM+ylJIgZAKQHBIRcinp2ZScNgW9vuS0idQFkqjW147d+b5gxO73HyAk2OGw8zl2\n7B4f83yEEKKLBCQi5JTKouxccD0FwFe2mzlOy3ZDadeBLQGDkS6Jdth9YHzM8xFCiC4SkIhxQcp2\nr4zDUYwrujLo9c7oChyO8THPRwghQAISMU5I2e6VcRw/2p3AGowpia3dFThCCDEeSFKrGDdGq2x3\nMlfcCCFEuJCARIwruavyyF2V1yuIULnZ3D3EIELrYl587o+cKC1k4byL3HKN7496z7k4ySn2CROo\n2Bdk8+KL5s5qmsG11JrJul4Sg4UQ44cEJGJcGm7Zbtdwvlje5Ob59UxNN1By0sCOV81kZ8azcmks\nprfeY+8zBaiUKdxwVTww+AC/8c5uz8L6dArBzvOxNaWilCQGCyHGD2mMNgzSGG186hrOlzO/Edqq\nibL1rjh547CXl96I4I5lHSzLMdDsBEyziI6O7V5z4IiXau89/Qb4hYMdu/PZUfrjQSttah1wV8a3\npEGaECIU/P4FJUmtYkLoOZzPeam+XzAC0Nrazs2Lm8lJ6wAgygbOS/W91gQa4Dfe3bk6j0W2e6h1\n+F9T64Ac20ckGBFCjDsSkIgJoWs4n8vtxBLpHnDNsYp2Vl5vxOnu6H7NEunG5Xb2WudvgF84+OKD\nG7gr41vU70vh9HteGqrdNFS7Of2el/Ovz+eujG9Jl1YhxLgkOSQi7PUczud2ubCYvPQ9FSyt6mBB\nsu91i8mDy+3BajFiMXlxu1xYLbZe6/0N8AsHd67O487Veb2H6y2V4XpCiPFNAhIR9irLjnaWCPsf\nzneixkPmXF+QYjWDu80XkPiTmdRGZdnRsAxIutjtWRKECCHChjyyEROKxWrF3TZ4+/Qu7jYDFqt1\nFHckhBAiGBKQiLDXczif1WLD3d7/pGT+LCMlJ30VZa5WMJt9f/Td7ZZ+j2tABvgJIcRYk4BEhL2+\nw/lsU+NodvYuZ8+YF8HxU76TE3ebEavZSLPTt3YgMsBPCCHGlgQkYkLoOZwvOjqWNuJ8fUZ6WJga\nycsHPdgsETQ7oY3YXj1Iugx3gJ/WxezZuZ09O7ejtQyuE0KIoZCkVjEh5K7KY/OmUgoPb2dFjoH4\n+Fk0NVmoa6zHEunGYvLi8RrZ/vpUWtpN3Pr+OOL9BCM13DOkmTld3WHTZ1R0z98J986vQggx1qRT\n6zBIp9bxyxcc9B7Od6i8g3dKppEwbzl3ffhTnKp09FvTc4DfUAKIru6wy3MGPmwM586vQggxCvxW\nHUhAMgwSkIx/wUz4vdIpwAV78uHMo36DkS6Fh70Y5nxbTkqEEEICkpElAcnEMtzA5LGH1/HQmuCG\n2T2+M4Wvff+ZYe9RCCEmCL8BieSQiEnrSnI/enaHDUY4d34VQoixIAGJmJS6cj8eWmPEF1T4epek\nJMFKTnDgyI/ZvKnUb+5HMN1he5oInV+FEGI0SdmvmHR6Tgb2J5yn/gohRDiSgERMOl2TgQcTaOpv\nz+6wwZDOr0IIEZg8shHj0pVWwAS670jkfiiVxc5nUoHgklrLzqVx9xh0fu014XeBTPgVQoQPCUjE\nuDKaTca0LuaFbU8ys6OOkspoMlP6z7DpK1Duh687bHBlv8Pp/DoUO3bns+vAFlzRlUxJbAXgxRfN\nWDansGbFeu5cLSXHQojxTcp+h0HKfkfHaDUZ6xnkzIo+h9lbS2VtBMdPmcnOjOeWpf07tnapPO3m\nhOm7rFpzj989z8LXHXYg3Z1fR7Ex2q+e3MhR53YS7APv4awDFtk+whcflOZsQoiQkz4kI0kCkpE3\nWk3G+gY5LrcTV1MF0VN9PxNvHPZyqj6Wj94+e8DrX37Lg/3W/wk4aG+g7rDD7fw6VDt257Oj9Md+\ng5EutQ64K+NbclIihAg1v39ZSVKrGBdGItG0r4GqaawWG+72y6W6y3IMJMU28OpbDQPeI5ipv7mr\n8vja959B5f6JE6bvcsL0XVTun/j6D54d9e6suw5sGTQYAUi0w+4Dwf2+CSFEKEhAIkLucqJpcLoS\nTQfjL8ixTY2j2Xn5ZHD5YgPHSs/3WzfU3A+lsli15h5WrblnTPqNOBzFuKIrg17vjK7A4ZApxEKI\n8UkCEhFyl5uMBacr0TSQQEFOdHQsbcTR7Lz8WnpSKyWVl1/oyv0Yz/NnHMePdiewBmNKYmt3BY4Q\nQow3UmUjJqTBOqnGx8+iqclCXWM9lkg3KYkdvHO0icqzlu7cj/vGcTAihBATTVgHJEqpOOAHwIeA\nWcA5YCfwPa11TZD3WAD8GXgf8IDW+r9HabvCj5T0bHSBiZSk4NaXnDZhv/XKm4xFR8cSHR2Ly+2k\n9fxF6iI+zPLcB7k7TNq72xdk8+KLZmIHzsftp6XWTNb10pxNCDE+he0jG6WUDXgN+BywDfgE8Fvg\nn4EDSqnpQdzjAaAI6MpalJKjEFAqi7JzqUGvDybRdCidVK0WGzVNM8hb9+mwmjVjt2dhbUoJer2t\nKRWlRr85mxBCDEfYBiTAV4FFwFe01t/UWm/VWj8MfBxIBb4X6GKl1GeAP3T+7xujvVkRmK/JmGfQ\ndcEmmo5GkDMe3b58PWcdg8fRtQ5YvXx0m7MJIcSVCOeA5H6gGV9A0U1r/b/AaeC+Qa73Ah/SWn8V\nCD6jUoyK3FV5VLOWwsP+P1yHmmg60kHOeHTn6jwW2e6h1uF/Ta0DcmwfkR4kQohxLSxzSJRS0YAC\n9mmtBwom3gLylFKpWusBSy201r8fzT2KobvvgQ0U7Mng8Z3+m4wNJdE0d1UemzeVUng4iE6qYZzA\n+sUHN7Bjdwa7923FGV3RXXnTUmvG2pjCXdI6XggRBsIyIAHmd3495ef9qs6vqUDwDS5EyOWuyiN3\nVV6v4XoqN3vYiaYjHeR0Ga3hf8N15+o87lyd13u43lIZrieECB/hGpBM6/za4uf9S33WiTCjVNaI\nfciPZJAzmsP/RoLdniVBiBAiLIVrQCImseGeTlxpkNM1F+ehNUZ86Ve+HicpSbCSExw48mM2byod\n1UF6QggxUYVrQNLU+XWqn/ej+qwTE0AoTycGmovT1/JFBgoPb6dgT0bIT0qEGK+KteZoeSUA2Wkp\nZCkV2g2JcSNcA5IKfFUy/kbtduWYlI7NdsRoC+XphNbFvLB1IyuvuUBJpY3MFJvftStyDDy+c6sE\nJEL0kf9KAVvePkJ5YiZt83xBiKlQk7p9F/cuzSHvttwQ71CEWliW/WqtLwGHgGuVUr16gyulIoBl\nQJXW2l/SqwgjwZ5OzGI7BXvyR/T7PvbwOv6xcx0fv+kQ6QnVlFdW8OvNx/1OB4bgh/8JMVlsfOpp\nHq0zU5n3ZYzLVmNJTsWSnIpx2WpOfPgrPFpnZuNTT4d6myLEwjIg6fQHYArw2T6v3wfMBJ7sekEp\nZVdKpYzd1sRI8je1t68VOQYOHdw64HtaF7Nn53b27NweVLCwedNGOPMoD605wc2L3WTMNZAy28jK\npQY+/+E2TN5qtu6qHvDaYIb/CTFZ5L9SwDbzPAxLVvhdY1iygm3mueS/UjCGOxPjTbg+sgF4AvgY\n8FOl1HzgXSAb+Bq+05Of9lh7DNBAd0ajUup2LuegvK/z63VKqa7KnbNa632jt30RjMtTe4OLnbtO\nJ7qSV4eTdxLMicyyHAMHDjXw6ltWblkaO/RfmBCTxJa3j2DI+/Kg6wxLbmRL/i/l0c0kFrYnJFrr\ndmAl8CvgI8AmfG3jfw98QGvt6nNJ3xagvwGe7fzf5zvf/0KP1344WnsXwbs8tTc4PU8nep5yrFxq\nJCXJQkqShZVLjXz+jhNQ/WPfmj76nshYrFbcbf0bqy1fbOBY6fl+r5ecNpG6QIbYCVGsNeWJmUGv\nL0/MpFjrUdyRGM/C+YQErfVFfHNoAs6i0Vr3C7y01sEPOhFhZ7hVMVoXkzStmMZGN+ALRqwWG43t\nFqC13z3Sk1opqXT2SnQtO5fG3WE4F0eIkXa0vJK2eQrL4EsBaJunOFpeKpU3k1TYnpCIyWEoU3vh\n8unEcPJOCvbk8+uffIqU6eWYvWcwe8/gaqqgproMDBaanf3n7GTO9XKi2tn93+E8F0cIIUJJAhIx\nrg1naq/X6+3MOwlO+oxyHvuPb8KZR1l5zTmiphiwmI1YzEaipxpIiGnFZrpI4yUTzU7/9xnq8D8h\nJrrstBRMVcE/gjFVaRaly+H1ZCUBiRj3hjq1d6h5Jw31daRYXmB5jpH5s22UnOyfLxJlg5ip7Tjb\nplHXaKbpkhd3q4cjZV5On7fwm5fmY5jzbenSKkQPWUqRVlsy6Dqny0VDYyOW9/bh9fqf+C0mNglI\nxLiXuyqPatZSeNj/X1RXcjpx4tQFlnU+3slMsVF22jzguigb4HWTODsda3QqrYY5vHvqam5Zv42v\n/+BZORkiDE0IAAAgAElEQVQRYgDrr1uEt6hwwPcampooq66lotnN6ffe5vS8HD5eWM7af/+5lABP\nQhKQiLBw3wMbMMz5No/vTOHltzxUnnZTedrNy295+p1ODCXvpKTSScrsdixWa/drCzPiecNP8GOJ\ndONyO7FabByums7Kf/pmyCf9CjGe5d2Wy9rWKrxF+3u9XnPuPDXtBlpjE6HkEPFNZ0m4c700S5vE\nDHI8NnSdTdYqCgoKSE72171ejJZghus99vA6HlpzYtB7vfJmPbHWc7xvSe/SxK27qkmKbWD54t6P\nb9ytHloNczhcNZ0a7pFHNEIEqWfr+HNxc6hrN2CoPY3lpCZO5RD7/g/2u8ZbtJ9/ndkmvUkmlv7P\nxLvekIBk6CQgGf8K9uTDmUcHrbT51TPn+cDVHeSohH7vvfpWA8dKz5Oe1ErmXN/PyZEyL++cvIpV\nH/oXeUQjxDAUa82nfvob6pauwZaUgi01cJ+SlPxf8uy/fmWMdifGgN+AZNh9SJRSMcA1+Nq0G4Ba\n4N3O3iBChFTuqjw2byql8PB2VuQM/Oe/8LAX4v6J6sZ95Azw/i1LY7llaSwllU7KOkt7z7stPPi1\nP5IpfUaEGLa2G1YTt2xlUGu7mqVJb5KJb8gBiVJqGvA4sG6A691KqaeAr2it+3eREmIM3ffABgr2\nZPD4zq2kzyjvrrwpOW3ieJ2vdfy/PJDHYw+vA/w/3slMuTzh9/GdKUEHI8E8WhIinBRrzdHySsBX\n0jucIEGapQl/hnNC8l/A7cD/A4qABnzJsbM7X/8svjbsnx+hPQoxbLmr8shdldcrOFC52dzdIzjw\nlRUP/ngn2KZnw5mfI8R41jP/o22eLzAwFWpSt+/i3qU5kuMhRkTAHBKlVITWuqPPa2eAL2mtn/Nz\nzZ+ANVrrGSO603FEckgmns2bNjKLwI93gkli3bxpY8CW9QeOeKn23sN1y+6S0xMRFjY+9XTAab3e\nokLWtlax4ZMfC+p+xVrz8cJyjMtWB7Xe88ZuNt+Yjj0z+Jk4Ylzzm0MyWNnvW0qpvo/XI4G+g+t6\nqgesAd4XYtwZSlmxP8HMz8mZdwHjmY08/8RdzG97hPltj6AL7udnP1rnS8QVYhzJf6UgYDACYFiy\ngm3muUH3DQm2WVqXtNoSCUYmicEe2ZiAd5RSPwYe6Zyw+ybwa6XULOAdoJHLj2xW43tU8+LobVmI\n0RHM4x3wnxty6OAWHlrjPxg5f74GE/V86EYDj+c3kJIU77tHEqzkBAeO/JjNm0qllFiMG1vePoIh\n78uDrjMsuZEt+b8M+tHN+usW8WhRYcBAB3xlv+uvk8nZk8VgAcm1wHeBbwN5SqkHgC8AzwO/93NN\nIfDQiO1QiDGmVNaAj1AC5YbMmHdj5/ycgQOSpqYGTNQTZfOdVg40JXig6cNChEqx1pQnZgbdPXMo\n1TB5t+VS+tTTbCvaj2HJjQOu8RbtZ23rSfJuC+5RkAh/AQMSrXUb8AOl1HPAJuAgvmTWG4EcfGW/\nXbkiZ4GDWuvDo7ddIUKjKzfEdwJihM4aga7TjV2FRRw/dYmVS+cOeL3zUj0zYy4/Os2c66WsundA\nAr7pw4/v3CoBiQi50a6G2fDJj5HxSgFb8n/ZO1m2SpNWo1m/NEeCkUkmqCobrfUhpdT1wAbg+8CH\ngE9prX83mpsTYiQNtww3mNyQZTkGTJ5GXn0riluWxvZ6z+V2Yol0AwZ0ZQfHKz2cbfBinOIe8F7p\nM8rRulgSXcWEl3dbLnm35XaWE5cCkL0ijSx1e4h3JkIh6LLfzvyRR5VS+cAfgUKl1C+A72itAyW5\nChFSV1qGO1huCIDFauV9WUaefvl8v4DE7XKx72Abha93YDjjIaYNmp3QMuUcGw5e4pZVcdx+S1z3\n+sykNirLjkpAIkIqOy0FU6GG5NSg1puqNItuTB/W98pSSvqMiKEP19NaFwPL8Z2WPAQcUkoN/BBQ\niFHgcBSzY8dz7NjxHA5HccC1mzdthDOP8tCaE6xcaiQlyUJKkoWVS418/o4TUP1j3xo/tC7uzA0J\nzGqx4W63dOeG9PTfz9Rz8M9tpFd7STMYiDcbmOIxkmUxkn7GzZubqvndn6qD+8ULMUaCrYZxulw0\nNDZieW8fMopEXImgTkiUUpGAAmKAFqBEa/0zpdRfgD8ArymlfgP8X611y6jtVkxqu3fns79wG1FR\np0hI9OB2uync76a2ZjrLb1zLgw9+qdf6YB61DJZIWll2tPNUZfAn6bapcSTNPM2JHrkhu16tp3Zf\nC0ney/kjzS1gtV7+0UsCTu6tZ9dcC7ffEkfJaRP2W32VBQ5HMcePHwNgwYKF2O1yaiLGTqBqmIam\nJuovOXGbrHiOvk3bvBw+XlguzdLEsAU8IVFKWZVSv8ZX2nsYXwXN34ELnYmuLuADwJeBTwJHlFLy\np1CMuN///qcUO37OsuV1zJ/fQnvLGYyXqkmfeY5lOcc59tYj3H3z1byUf7mXx6GDWwbtvgq+RNJD\nB7de8R6jo2NpZzqOqsv/SnxtTz3zjEa8nb2AmlugrSOCaVMjel2bDLz2cj0AZefSKC8v5jvfvZf8\nFz5Lbd1j1NY9Rv4Ln+U737mX3bulX4kYG3m35bK2tQpv0f5er9ecO09Nu4HW2EQoOUR801kS7lyP\ncdlqTnz4KzxaZ2bjU0+HaNciXA3WqfU3wOeAHcDrwAV8Tc+uAT4KVAGLtNYdSqn5wJNALvCk1voz\no7z3kJFOrWNr9+58ih0/J1OZqaupobWhAdMA67Sjjao3o7nx45/mg/90F7rgflYuDe6p5MtveVC5\nf+qXt6F18ZDv02C9n/pThVjaj3L4z+WkYaDF1YHT1UHU1Mh+wUiXcq+Xq+9Lw3HhWmYlVZCpzAOu\nK9FtREXdyacf/GZQexLiSvVsHX8ubg517QYMtaexnNTEqRxi3//Bftd4i/bzrzPb5KRE9DXsTq13\nAn/WWt+ttf5PrfUftNb/pbX+P/hORBSwCEBrfUJrfRvwGWDtyOxbCNhfuI1MZabxQoPfYARA2U10\nTG2i4tltbPufP3YnsAajK5G03z1VFmXngkvqA9/pxic+9SW+9v1nMMQ/yJT2mbQaZjNlegbWqFkY\nDAMHIwCRTV72vBMfMBgByFQmLjbvkJMSMWbybsvl2X/9Cv+zIo3ZO//I7AvVpKalkf7JrwwYjEBn\ns7S3j4zxTkU4GywgmQZU+nnveOfX6T1f1Fo/SWeQIsSVcjiKiYo6BcDF+nq/wUiXOcqDxe3k2OsH\nRmwPvuF7nkHX9R2+l5SUzLRpMUTHxGK1WomfMQvMs6lrtNB0CVpbvbS2emm6BHUXLGBOwBbjDRiM\ndFHKRGHh9iv6dQkxHG03rCZuxUpsqYO3c+9qliZEMAYLSIqAh5RSH1JKxUB3Xsky4BdAK77ckl60\n1qdHfKdiUjp+/BgJiR5cLhdeV+ug62cnR1DvcRHT0MRf320P+vuUnDaRumDgODp3VR7VrKXwcP/H\nm7rCyUt76/npH8/z9zM39kqMzczOptHcO7iIjp5O4uw0rNNSaTXMptUwG+u0VBLnpHF+6lTmpzj7\nfgu/pkadHLTKSIiR1NUsLVi+ZmmVo7chMaEMVmXzdWA3vlbxqN514q3AF7TW9aOzNSEuczudRHg9\nYAgulyPRYOQ9PRUY/GQDfI9a7s60+33/vgc2ULAng8d3biV9Rjk1J+t4a98FOk62MdVlwjJ1Op64\n1/nae2u59d57uSMvD3tWFob0NCjvXzZstVqxWnvPoLyUMJ258/0+Xu0nIdHD8ePHpPJGCDEhBPzb\nXWv9HpABfAJ4DF9DtP8CvgQs0Fr/YdR3KCa1BQsWcrY2+HY51ac6iDP6PujnZ6wY1qMWf3JX5fG1\n7z9DUfnNFG2LJPXsDBZGp6HmZZASP5M0g4G0ikr2Pfooj2/09TbJXb+eU0Hs+ySQs2J5ECuFCJ3s\ntBRMVcE/gjFVaRalB5+DJSa3QfuQaK0vAv/T+T8hxpTdnkVzczIW20maDMZB/8Ce0UZWWGyUe718\n7r7/w9tvzqTw8HZW5Ax88lB42EsN93BfkLNjXsrPp+W111k4PdbvmmQvVDy7jZcyMrgjL4+q0lIq\nnt3GwFNufMFI6rq13HL3XeS/8DdmzQpqK5ytNbJiWXZwi4UYAVlKkfbcbipZHdT6tNoS7JnSBl4E\nJ9jGaFbgKiAC+HugVvFKqfcDt2mtHx6ZLYrJ7sYVayl2/JwpVjO4/eeRaEcbCc3RYAVDehrKbkfZ\n7b0etXRV3pScNnG8ztc6PthgBKBgyxaC+ffe3M61d+Tl8dCGDbyUkUHBli14y8qJafX9GhrNZkhP\n49b167kjz7eH5uZkoC6ovVxqnotS/h8zCTEaAjVL68lbtJ/110l9gwjeoAGJUuoLwI+Bqfjqhy8o\npR7WWv/czyXLgR8AEpCIEbF6dR4nT5Vx6uRmkmLO+e1BUvOmhWut0zmJ71FJl9xVeeSuyus1XE/l\nZnP3EGfFOIqL8ZaVgyG4PA9vWTmO4mLsWVnckZfHHXl5OIqLKTnq20Nmdjb2rN576Aq+Bqu00bqN\nFSvuGdL+hRgJebflUvrU02wr2o9hycBTQ7xF+1nbelKm9YohCRiQKKVuB34FeIEy4CKwGPiZUmop\n8HGtdccAlwafmSdEED794DfZvTudp558hPipVcxP9vXzqD7VwWmHkcRL0d3BSOq6td0nDj0plXVF\nA+tKjh71nW5YghvIHtPaSsnRo72CDntWVr8gpKeu4EvrHSg1cJGz1m1Mi7qT1auDP9kRYiRt+OTH\nyHilgC35v6Q8MbO78sZUpUmr0axfmiPByAjxTUKuBHw5PBN5COFgJyRfAdqA27XWfwVQSiUDm/B1\nam3Dl/AqxKhbvTqP1avz+O2vf8Xe57fira1hdnsEC81WGi1mytNSez3+CJZ2aCpKfJUwqZmpKHto\nf+C7gq/Cwu1MjTpJQqIvMfdsrZHmi8mkpr6fhOmz+cv27QOesggxFvJuy8U+L5k9+wo5+dZh5s6a\nyaoVy8hSkjMyEnp2x+0O+Ar1hJ4VNFjr+HPAS1rrT/R53QTkA2uAH2it/63He98ENmqthzxJOFxI\n6/jxYbDHH4PZ+9JeDu0tIs2bwoLoNACON5VTRgVX3XYNt95xa7/v99v7Pk5akI9syr1ePvf0ZpR9\n+HkePYfrna48g963v38eSlpqd6mxEGNhwA/LKk1qjZ6wH5ZjaeNTT7PNPM9vno63qJC1rVVs+GRY\nnkL5/Qt0sICkFXhYa/3IAO9NBfbhS3b9qNZ6e+frEpCIcW/zb/5EQmU8y5KuH/D9N08fpDb1PPc9\ndH+v17++bh2pA/QVGUhFWio/e/bZK94rwOMbN1L57Db8/Wk7ZYCUtWt5aMOGEfl+QvgzwT8sQy7/\nlQIerTMHlTQcprOChj3LpgHfvJp+tNaXgLuAWuC/lVKBf/eEGCf2vrQ3YDAC8P6k60moiGPvS3t7\nvT6UviI9E2uvxEv5+QGDEehRapwv823E6Ml/pSBgMAJgWLKCbea55L9SMIY7mzi2vH1k0GAEJuas\noMECkv3A2s5S3n601meAu4F2YJdSag2+BFghxq1De4sCBiNdliXdwKG9Rb1euyMvj5R1azkZ4LpA\nibXDUbBlS8BgpEtXqbEQo2Uyf1iOhWKtKU8cfEZQl4k2K2iwgOQ/8CW+vqqUen6gBVrrd4FbABfw\nIr4urhKUiHFJOzRp3pSg16d5U9CO3j/wD23YwM3f+Vcq0lIp93o573Zz3u2m3OulPC2Vm7/zryP2\n6MRRXIzz2DEuNDRwoaEBl8tvCyDgcqmxECNtsn9YjoXJPisoYJWN1vptpdR9wO/x9Rfxt+7vnaco\nTwPXjewWhRg5FSUV3QmswVgQnUZFSUW/yptg+4pciZfy8/njxo3El5XT0vnaRaMRLGai4+KJmT69\n3zUDlRoLMRK6PiyDK3rv+rAsndBlqmJkBdM6fqtSajewdJB1x5VSNwBrAfnbUARtND/UB+J2u2ls\nbATAYrH0G3I3FIP1FRmuriTWmQ31RAKRRt9hZiSAu5WL1dW0ulzMDLbPvBBi3MtOS8FUqCE5uPk/\npirNohvTR3lXYyeo1vFa6wvAy0Gs8wIjU1YgJryX8vP7tVN/dRTLWPe+tJfXn3+VzItpzFuQBICz\nqYULXGBK9BSiY6L7XXO8qZyF6qoR3cdgeiaxmqw2KowG+k7OMQPuhgYardZeJyWNZjNqkbTrFiNv\nsn9YjoXJPitoxEtzlVLJSqmbRvq+YmJ5fONG9v37o6SWV5BmMBBvsRBvsQw4MXckbP7Nn/C87uY7\nS/6FOvc5zJEWzJEWoi3RzLTMgGYP5+vOd693uVw0NjbyRvVBApXGj4aeSayJNhtNfjrDmoGm+vpe\nr3XN8BFipGUpRVptSdDrfR+WweecCJ/11y3CW1Q46LqJOCtoNHqFfBR4dRTuKyaIsS5j7Vvmu3Bu\nFm+eOthrzVRTFJGtEdRU11BzsgZnXQvv6HexmzIofuIQ//nVjf1KgEdD97ycHubFxnHKX1Dkdncn\nup4E1E03smPHc+zY8RwOhyS3ipE1mT8sx0rebbmsba3CW7Tf75rLs4LCrgdJQEE9shkGmWUj/BrO\nxNxg+MtFObS3iM+kPNC97gP2m3jmre28cepvLEu+oft1q8HKxYsXmT19Nm+c+hvnqOefr/MNsLuV\nW3jz9YNsrvpTr2ZpDoeD0uOlAGQsyMB+hacTA83LscfGctDt4mR9A3P7dImN8HhwOZ38HRferGhi\nm7cRWedrNX/4iK/V/I03rpW5N2JEyGC9sTFZZwWNVkAixICuZGKuP4FyURbedPOAZb7/vPQeXnPs\n47f/+APp0amkRaeCx0tR7SHKdSU3ZCzln+29p+m+P+l63qj4G3tf2kubsY3X972GxWJhxswZALzz\nztu4XG4+cPMHuP32kX2ue/2s2TgsVt5rqCfa7SbW4zsxqQNqozu4+e5olt4Q1+saX75rHcWOX3Dy\nVBmffvCbI7onMTlN1g/LsZZ3Wy55t+V2Dtfz/aMne0XahJ4VNFjr+E0MvafIIuBarXXElWxsPJPW\n8cP3l+3b+fuPHiY+yIm5591urvnB97n7nt7BQbHDwdHSUva88L94Dr7F1daB73fsQiMJKbfzL3d9\nz+/3KKk9zqGSQ8RETiMiwoQnysOtCz/od/3n9n6FhcuzSU/3lQ87XU7cbt9jE4vFypnT1cyIi+cz\nn/lsUL/GnoKZl1PrdHLO5QTgZEQbKz41h8VXTQt4X63bWGj/ipyUiBE1mSbRihHj9y+3wU5IhjvJ\nVxqjiVGRv3Mnz7z2GiciTdRHRNIcacKasYD3Kiu5prWVxTZbr/VJQO2xV9gz7xpWXXXHgPecF5PM\nzJQ4oi3RVF6ooqK90u/3f6WkgDlzZ5OUNIfGxgaamxswRrRi6vxJcjrBajVzvKKUXbt2DfmkxJ6V\nhSE9DQLMy0m02Ujs/HWeijk/aDACoJSJwsLtEpCIEZWllAQhYsQMFpC8BVwLfBNoDPKeq4F1V7Ip\nMXFlZmfzqtlMfJDre5axbnzit+TX1BCRvYgIwFVWztTp02H6dC5mKl6trKDu8GFyp0zpvt5itZBw\n0cnrRfl+AxK32401wnfCcrzhOGmp/ksVD9T+jUU3LqKurgaz2c3UKAM9c8MjTQDtJEdOY9NTvxvW\no5vc9evZ9++PDtou/h9uJ4uuiwr6vlOjTuJwFGO3S5sgIcT4M1hAcj/wHnC91vreYG6olJqBBCTC\nj2BOAHrqKmPN37nTF4yk+tJhXU4XuN291hpTUjni9TLz6NHukxKr1UojF+BcJSU1JWTOClyGWNZU\nwaqElQO+V3K2FFvCFJrdF4mwtGCx+v/xsVgNxMVb+P3vf82NK3L568t7OFt3ijlz53LTzSv9BgWO\n4mI6Ojposdspfe89Mvw0bTsJ2G5YQoYK7vcRICHRw/HjxyQgEUKMS4O1ji9RSv1f4BdKqZ1a681j\ntC8xgQV7AtBzYu4zr71GRPblMkKXy0mE19svOdaYmkZRaSmLe7xmi56C80Ijx2scAwYkFosFZ1ML\n79QUsXCu/w/rsvoK4hPjae64wPQpvlORtjYPbW0dAJhMEZhMl09L5iTN5Lf/9Qj/++TDpCkPs5ON\nnD1h5Jc/+3+4WtNZ99Gvdj9C6ZuYGw8ccbZw+MIFUi0W5nWe+jSazZCexq3r1+M1eaite2yQ30Uh\nhAgPwVTZ/Bp4P/CgUuovWuumUd6TmODuyMujqrSUime3MdfPmp4Tc4sdDk5Emgg2S7ohJYWa0uPM\n6kx0jY6J5mxLDbq+hDUDrLdarew6tYfWiLZ+lTV9uTvcRJrbcTqNtLpbiTR5MZk633O3c6nZgNkS\nidnkpaOtneR5rVyfbWLWzMsnHUkzoI0yXn/t25w8VUZ7vYfKZ7f5SqENhu6S35s7vx5yOam7ajGr\nPvShXuXMDkcxh48YCbZ7/NlaIyuWZQe3WAghxlgws2y8QFCPazptBd4Z9o7EpPDQhg28lJHRr1y3\n5wlAV/+Ro6WltMXF9QpIrFYbFw2GAf8AexISqdaanp/TxlmJzL09k9/pTaR5U7oH7B1vKqeMCgwL\nI1jUHLiHSHpcKq9U/ZW0aTMxRrYzLdpIz4TxyEjABi5XGxcveqirO4PdDuca2pk109TrXiYgJaGZ\nkuInOL3HwPut/QfldVlstXHSoYmIiOhV/my3Z9HcnAzU4XK5cLt9lTcWi23A+TyXmueilHRxFUKM\nTyPeh0RrfQo4NdL3FRPPlUzMtdqsvpOEPnkk/hjS0/jk5z4FgHZoKkp8uRdZmYv5kH0tAJsf/xNv\nVPyNZUk3DHiPXUd38PfKN1FX34nFbMDr8XQ/MjL0eHRksYDXA5WVDnI/YORM08BFZ2ZgTsJFqqKA\ndv8BCfhvEhc1dT6vvlpIVhZERvq+j9NpoKPDxLSoWKJjfFNwtG5jxYrApz9CCBFK0hhNhNxgE3Oz\nMzIwvfk3mDkTl9OFq7MHhzUqCrfbjbnPeuPZWuYYL5+n9MxFAVB2hbL3L1W876H72fvSXn63t/8p\nyjPvbGJ6TRlTrfWcOllORuaCXtd6vV7fWYnBAF4vp0+V09Fey5kzMDN24B4pXo+HyPZ2EjIMnH3P\nSYLFNuA6gLNuJ1VH3+V3v/1Fd1Ls73//U6y2N/Cet1F14iLK7juFMZnA622l+VI1jY0XqK+PZ+ZM\n6dYqhBjfRjQgUUqZwPcoXGsd/BQmIQLIstsxV1ZSGWkCt9uXzAp0GAy4vV5aPR6iIi4HILGVlSR2\n5o/0zEUJxq133Mqtd9za6xSl7kQt8wrrMNtMXHMN1J99gzIDpGdcDkoMdDbf8XopO15Gc/Mb5CwG\nx6E2snJteD0eDMbeo6M8Hg+RQEKSgZq/u0igf0BS4rrA2agm5mR7uDbJQIvzSfJfeI5jRyE9o5Hr\nrksgU82jqKiOvXvPER/nJiHBt4+aM+3oY424Ki+Rs/ggL8Xnj/gEZSGEGCkjfUKSDhzD93fzhO3U\nKsbW4xs3EvePf3C6owNjSmr3Y5JIwGIw4AQavV6sBgOG8jLSL12ifNq0frkoQ9HzFOXr69aRCjg8\nLuYnR6BiPehT+9j32hFmzMwkIXEOAGdrz1BXV8K8ufUsX+7h9GkDU8xGDB4DHo8HLx0YIoxERAT3\no/Fuay2zl7u5RvlOPto9HqYmWIiJtVB6vJh584ycO+dhxoxZLFkyk+TZHZwsOUvN8XYAZsRaWLQq\nit3PtnVPUK4qLeWhDRuG/PshhBCjbaQDkjagCunUKkZI12TgD9pseA8f5ojXizE1rdcaW0QEEYDp\nYhMrEmaS9+i/B52LMpiBZu9YjBEsSPaQZT9PQ8MB6s75Aoy5czq49hojeMHp9NLeBnNnmTEaDHTF\n5x0dHto97USaIjEajbQDZ097iTP2TkItcV1g9nI3mepyMmyHwYjFZuPUqUskJHRgtUbgcjXS1GjB\n64WTjrM017QRCUyfZepOpJ2jPJz9u5Nki803QTkjQ05KhBDjzogGJFrrMiBlJO8pJreek4Fzp0xh\n5tGjFJWW0pCSgichEfDljMRXVpIUO51Nf/3riH7/ntN344xWqk81MjM+kilGE+7mDqZYYEG6B5PZ\nABhwtXhxugwYvFBZ3o7RGcGR4y5mxEQwa6YJo8GIx9NBW2sbkZERtEdGcrYUMvvkj5yNauo+Geli\nsJqxWq3U1pxl1izf4x+r1UDRvpNc/Fszi+vauKHd92+B45EG3o6LIOpqG3FJEbz75jlymMFci21I\nE5SFEGKsSFKrGLcGOp1YbLOxGKgpPU611gDMNkYwy2qhvLFp0MnAVyLBYqNEG1l8le+/LcYILETQ\n3uKhtcVDR3sHEURw6mQEp2ubmBYDWdf6fsTOVLdy+C0XSfGRZKXZ8HjacLV7KC030nrWCD0G9Z51\nO5mT7en1vVuBaXG9p/kCvPOXRha/0cJ1HZ0N7CN9v1dJeLmpvp23X2tiX5aZmdeZaY6sRTuMcKxh\nVH+fxNiTIXdiIgjbgEQpFQf8APgQMAs4B+wEvqe1rgni+mXA94DrARtQAvxea/3rUdu0GJKepxN9\nzbJa6NsPLKa1lZKjR0f0g7bv7J2E5mhKdFOvRymRRiMRHvAAbx52k5RqJndxFM2N7URNM2CxwoyZ\nZq5abEDrVg4ebuGqRRbKTkwhu/UTWOae4L3K17h6pu+71HfmqnRpBSyxscRM95XwJs6aSkXFWRoq\nW1jytxbeZwBPj661HrwYjBBhhBsw4Cxq5fByE4tXWFh8FRwrbuS3j/+Ez37+Wxw/fgyABQsWSkv5\nMJT/SgFb3j5CeWImbfN8QYipUJO6fRf3Ls0h77bcEO9QiOCNeECilPom8B9a61FLalVK2YDXAAX8\nCl8jtkx8QwA/qJS6Vmt9IcD1HwR2ASfwBTX1+AKbXyql0rXWXxutvYvw0nf2TqZ1Ou++4cbrdXeX\n2T8AQm8AACAASURBVAJ4PF4c5a0kzjOQkmLG2BZN4jQL55vP0nLJjdUGJrOX1HQTl1rayH9uKiuS\nPswnPvgFWAK/ePUx/nb+AAktzVxsa6fD68HtBaxmouPiuoMRgOTkqRw8GMHUI40sxYCnR8qWtzMY\n6VnQc02bl8PlbbCic68RHVScfImn/lRERkY0AG+/00pVhZXUlBtY99EH5PQkDGx86mm2medhyPsy\nRqA7bE9O5QSrebSokNKnnmbDJz8Wwl0KEbzROiExDL7kinwVWAR8Xmv9RNeLSql/APn4Tj6+EeD6\n3wAtwI1a69rO155WSuUDX1ZKbdJaHxqdrYtgXclk4JHUd/bOtZZESt64wO5DTcxRHmYnR9DR0cHR\ncjd3Zsdh6ZhJtC0GAFvkFDo6OrjobqK9rQ1ThJnlC6fRfnw6V826rvt7fOWWr/G7yums+cyd/PXl\nPVSd+SOZaTEDdlwFiImKYn7VWbAa8HoBg4G2di9t7V6MEb6usSaTAZfLiznSQM75dmpq26isbiU+\nCdbebKPl0iVsVjMX6+uZPa2V5BwPJbqYDeu2km6/nlvvvVdyTcap/FcKfMHIkhV+1xiWrGBb0X4y\nXimQkxIRFoyDLxmX7geagT/0fFFr/b/AaeA+fxcqpa7Hd5rybI9gpMuv8QVTfq8XY6f7dCJIXZOB\nR4p2aHb/ZTcRETamfuADnOzxXqZ1Oiva5xH190ROvBDFq89EMjcphnkxC4i2+IIRj9eDAQOWSAsz\nps5kVtQc4m0zsERamJJ8Eq+hd55ImjcFg8HI57/yVSJMi/wGIwAzpphIb/MFHC6Xl+ZLHto9XsxW\nwADNLXC2zkNzsxerxciCdi9H3mthRhJk2i0YDEY8nhYunD6D2d2KxeB79LQwy8LC97tpL36PfY8+\nyuMbN47Y76cYOVvePhIwGOliWHIjW94+MgY7EuLKBTwhUUp9gqGX8F47/O0MTikVje9RzT6tddsA\nS94C8pRSqVrrgWazL+38+qafa3uuESEWaDKwy+XC7fK1jq+zWljZoxvrUPVshFZTXUO9o65Xt1ZX\n+1J2zarldGMVCS3N3bN3ms1WzOkLWWFPwWL931739Hq8GP3E/HGzW+moc/Z6bUF0GhUlFSi74sYV\nayl2/JxM1bcP7WVRU01camjFYoVp0UZanF4aLkXQHmEBiwksBrxtrVRfcOM0dVDf1M5tagoRkZF0\ndHQQaezAg5G+/y5RdtP/Z+/Mw6O6z3v/OefMqn2XkIR26UhIgLGNMTbYEMA2dpxWiZcQp27SLL5p\nmvS2t6Zpmts2fRq3cfvcNk3Txo6buI4dgjdlA7yAzWpWI7MI6WgboX3fpVnPOfePMxok0DKAMNv5\nPI+eQTO/85vfGTQz3/P+3vf78tbJYVYF4swy4WuQakWhMbUo7KvJxtQiqhXFTHQ1ueaZa8vmZ5c4\n75X0IckO3s7UL6c5eJsLTCdIcmY6XlGUEVmWh4DwL8tNrijTdQYeHhrGPTyOHTt2yU4LOlraUmr2\n1GG37WT9Q+vDnn/ntp2c3FkZEh+D/YMIXX78414sWRI5icafW05iNutz13Kw7TCVjlMULzFcWif8\nTn73uzdobHyHUf8YUdbI0PzegAevaogmh8WJ3WJnzDeGwzq9nfwEDzxQTktrA4ryO+RgAm1L6xjN\nHYYGtzskPhxTWR8hEhVlo38owCh2hAjblL46gsWJbndwxOOlzyYiShKSJOGbowfQhHeJWSZ87VHV\n2IQ/S2b2v6Bz+LNkqhrrTEFics0zlyDpAFKAXxC+yCgFbr2cRc1BdPB2fIbHx84bdynHz3SsyVVg\ncmfgvkNHSXJDpDWaHsmGnpTNmmWf5v6lDwFwcM9hXm5+ic9/7ck55335P18ipSmRr+Z8ETCETnxE\nLIuLSo25Wg+z9cjrPH7HuaZ0KzNWoLfpiE7HFOFTULCIU6djiI8bZdQ7ihZQGR4bgIAXS9DqfkwQ\nCYgiVquTsf5k8hOmbi/VDzdit0Txm9dfB2D13Q/RdDaf/375P6kbGmR0QRZaUhpqAPSuAfKFZtaL\nXkbHVcYFJ6LNhj7N21QQBfakpNC3qIzK6qPcvlgDXcfv07HMcJ29IFPi7DHDzl5vaDTLhE1MTK44\ncwmSLwPbgBpFUf4xnAmDVTZXdNvG5ObjofJy7LZozkatJVo3rg0L0oopSiuaMm5lxgo+cB1i57bZ\nIyU7t+0kpSmRuzJWhO4bHx4n2Z5EbW8d9T01AGiqn901e1lTfE9o3F0Zd/L8zp9Nmb+4uITR0UwS\nkntobWoiMDSMHQFBF0AQ0HUNUQsw2BOgZ3icU8f9bHzgnHh4+8Q2frr//1G0zxraDnrfZuOY00n/\n7bfjXFWC02ts8djtTsiDNtXH2+++z3K3GzEuuLUzzWXDYXTabilGX5jL4ZM13Fo6jKDr+D0C9vP6\n6+iahqZpqKqKz+8H55Uppza5dErzcrDuVyAzd+7BgLVZoWx1/hVelYnJ5TOrIFEUZYcsyz8B/k6W\n5XcURfnwY1rXbAwHbyNneDzqvHGXcvzQJazL5ApzcmclX136RWo7a6nvrKG+0xAN54uS6QTDtHMF\nIyNg5KMcqN3Dsbq3EQbPEhswRMGQxcabaHg3/kUoCgNGAqpSo0zpGrx61aMcPPh3ZMS6ibBZ0TWN\nQEAFHWpdfjr6A6QtFEnNkohPGOWX/V9BqCthtFNCU45zV4xIjGAL+a60ud10FBQQsDsIDA2SnHbO\neWVocABxmczP21vxnqhllTZR6htq8wfAYeAXtxQj3lLI+LhOX1w+bW1HiIvWEQMCumg0/VMDKqoa\nAE1DANpcPmgdo8Uxghodc5H/UyZXkhJZJu+Nt2jigbDG53XVUly08QqvysTk8gmn7Pf/AOsxymKX\nKYrinuuAK4wL4xN3ujxHOJdjUjfD443B2wuOl2U5FojB8DUxuYZQahSG6rr49vtfgr6zxKmGYNgv\n2dATs1lz66fnFAyT58rTc6bc98KuH9J9cgf5gggIYDFEQSKQ5Btn947v09LbyJfXfQOYmoA6wQMP\nlPNf//RdvEV+5GIrgihitYl88OEIKQtF7l/pxOPR8Y8IRNgseFPGiV7SyNHDzbS3CiyKnbqmSpsN\nMScXG+DtH2DI7iA2Pm7KmGE5j/+ITeWd2lrWDw8iqwF0HWosIgcS42hfVoywpAjBr6MGdAIJqTQ2\naxRnqkSpAn7UkHyZbBzUejLAYkECr48abw/HDx3iU488gsm1wablZTxTuX/OShu9ch+bls9/KbyJ\nyZVgTkGiKMqoLMulgAPDNHIuPgC+e7kLm2U9Y7IsnwRuk2XZrihKKDtPlmUJuAtoVhRlpqTXA8Hb\nVVyYtLs6eLt/Ptdscvn81z89i3b8IPkWu+FIOkkw0N/MoZ3/NqdgmMBV6wpVz4CxXdJVtYOsGexz\nHJKdlICP1spf83ZS3hThM5ma6mryRiyMfRAT8igZ1wIkpIsszLIw0Kci+QXsCAQI4Pf66fSPU1pi\nwWHTqf1gkCKHITg6PV4GCgtCIsEGDPf3hwSJ3e7EHbw08MhFVC1bRsdIO/a+PgD8KQlISXEg6KDr\n+Hwauq7jduvglZB0EDDKjgVdR9dBE0AUBGpq/MRM8jr22O2M797DtooKM7n1GqF8wzrqXnyF1yr3\nISxbPe0YvXIfj/paKN9gGqOZXB+EVTmmKIpHUZRBRVHUMMZ+oCjKFRMkQf4biACeOu/+zwPJwAsT\nd8iyXCzLcs6k9Z0AjgOPyrKcMWmcAPwZhuj6nyu2cpOLZltFBZ79+8icxW8vEwzBcGLbRc+/5/ib\nZAkCGtqs4xYKAnsqKwAjATVPnlqMNWF1P9mjpKUGspJt6EMCkQELTsGCgNERGF3DpmkIOuTnQZu9\nLzRXh6aGmgeG8HrxuD0AOJwOVM2GPSEWenvQLRa0hESkW0qQbinBkZ6K1WZHkuzouoR7BKICNrTG\nbvThADZJMq4uggm3QnCnp7rGR/M7PrJ9hhRq0XWy4hNYiNHo0OTaYfMXnuDbyX5yKv4d7YO38La6\n8La60D54i5w3f8C3k/2mS6vJdcX12svmx8ATwL/IspwNfIhR3fNnwEngXyaNPQMowOSMvD8G3gf2\nyrL8bxg5I58F1gLfmcG/xOQqsWvLFvIddjwjbuwWI3mz2+dh2G+ECGKsTlJsjpBguH/pQ9QPN7JI\nNrrg1dTUUFdv7OAVFhSSW5RL9XsnyUnMprazFvrOIooiKtPrbY/qxW4LFln2NvH26Xd4q30H4ikB\nTVNnTfYsKBKwayKSKKIHv/xFQQBBRNBA0jV0HSyihUzZT+2eDooWLJh2LknX8XjcOJyGYVp0VDxu\niw/LmUq0vIJpjxFFgdERFYduwSKIZDa34fDHs/vsMDF5KhlpOjYB2psDNJ/wY2/XKNaN+Vt0nUBC\nPMXxhm29WW1z7VG+YR3lG9YFm+sZf+Olq/Iokc2cEZPrj+tSkCiKEpBl+T7g74DPAH8CdAE/Af5W\nURTPeYfo5x1/RJble4C/D/7YMYTLFxVFMaMj1xATHX8dDgdDDOIaVxkfH2BJwMdq3Yho1AsiJyw2\nIiPiQdep7aylUWjC3hjFT1/8b+x2O0nJSQAcO3YUj8fLcNsg63PXUt9ZY+SjWOyIkoimaojC1MCh\nV/USa41haHwI92gvP3n1zymNj+D4d0/wvs0Gebms/9znLrC6bxsdoDBDQhLEUDmuMCnKowE2UUJA\nQNVV0jMlTnrG6GhrJyE6CrG7C6+m4Rs2cqyl6Bgi0s+JlZjYOHw+L/akBMbq67AtCgX8QoyPq3hH\nIEaQ0FyNLPP5KHLGURSIo35/J/v7OxnRNRLdOtkI+NCptamMO51kJSSExAiY1TbXMiWybPqMmFz3\nXJeCBAwTM4yE29l61qAoyrTbUsGKoemTAUyuGSZ3/G0NeFg1PMydQjCzIigcMoE1AR9Hhrt4zzvO\ntjO/YTgvgOeEn2W3LpsyX0pKCgAfCR/xd+9/jzviF+P1exnyGdEWSbDilBwhUTLqH8Npc9Az3I1/\nfBCrFiApykFOkiE7EgFcTex95hmaH310SiM+r9uLNLFWfaoYARAtdjRNRdRVAgE/qqqC141/aIyG\n8VH6bDYC0dGImYYlnNrTjWfvXtSCfCLzCvB5fdisTmKLl6Fv/Tk+qx+hyCgFDfhB1WyIRBDBMJqr\nkbLTp1kSERF6/iSrneWqFYso0mtTGdJ1RnWNiOQk7ks9v5eyiYmJyZXluhUkJjcXjQP93Ose41YR\nNO3CKAYYfv9e9yC/bnufe1b+Hvmz9MG55dZb2DPwPv++/V9IG+qgMHh/AIEByUqkPQZRtODXfIii\ngGesDws6/RaBgtSUC+bL1MH16mvErfsErY0ukjwekgJOOlo9JCVc+Pw+IC4ikb7RLkTVjwh0tKjE\n+0SOR0dQd88qInLzGI2OQbDbEQQBITERzWKhtaGRyF37yFh2t7HmhnoeyLqDaGc0JytddMfG4VyQ\nid1hZ6yhAcuBA9ypqlPECIDD6WREFLEASZJEEjCgaQRs09vVX6nmhSYmJiZgChKTa5yJbZDxtlbu\nEAQI9mEJqAEERCMfA9B0HR2N2y0iewf7ZhUjAN2dneSnp9EWb2WoCwRNQxBEbOjoAQ8jATdCVBRR\nMdEMdHcRKeiIksSY00Gq0zntnAsBl8tF4WOPcuL5F8izxXCsysPiJcZ2zcS2jR+wBqtpRF0zfgda\nTvqw2xzUrV6FlJ2LRYfA6CgedHS7A0EUQQN7XgFewcWYq45YawRPeLP409VPcrDtMCvu7uO2tXdQ\nVWfkEzgXlbDzyBHyphEZDocD7HZ6x8cZCua3jAiw1DH9+c1380ITExOTyZiCxOSaprikhKHkJG6t\nqQ7dJwX7sWiaNiVRVBSt+C0WVoyP097aRnrmhTkVAIODA/gGBhA0jYSiQkZ7+6ju66NkIs9DEHHo\nOu6RYQbGRpECATTgrN+PA2jo6cIfZXzBJ4gOUuznvsD1hkbWfve79A2MoPzmEIHuMaqqR5BlG6og\ngGQjwh6DVbLRM9aBTQCfKFJzxkN0q85HKclIOeccOKM0DUZGcHu8SJHRYDWqgcQFC3C/8z5/e8uX\neXi50Vp+wqW2LauVRx5+ODTHrhdfDG0jTaZmYIAGn5don4+k4H0jgsBH7e1kn5c/0oLR6PBmw0gW\nbQIMh1QzT8PE5MphChKTa578xYvJ2bNnwoo0hHje7z7AFhlJCbC/pWVGQdLX3Q3oWJwOMooKCGgq\ngq5z6HQVGV09ZLrdaKqKXdcZUTXsGJ0Y2yJE4haq+Jf0k55tx2qz0tE6hFIjkjoWQ5EjLpT4+egf\nfp79Z9MpcOTyat1/0xRRxeLSSPy6j3FtCK/kIzI+gADUnvRz+JgfvyDRU1QUapqmAqog4LBYsARU\n4sUotGAhkNMSjaXkVopis6ac23QutdN1TD7c2YG1f4DlgkDAYkFTVQK6TrHFgsPno7Wjg8NeDyvS\nFtAC5D726E3lQVLx7i62HD1NY2oR/ixDhFj3K+S+voPP3bGY8g3rrvIKTUxuPExBYnLNc9uKFYg/\n/zm+kRGmz24wxIg9Ph6r0wljYzOMgvaOdixWG84o42t/fGyc6KgoSvLzKCsr5Ux1Dft3vkfpoFHZ\n0g70As4EC8sfdIQ672qAIGkkL7WzZCnU1gzz4UEvORhbMS0NLShddTxyZzlZMX/P+81v83rVC2SW\ndZOTZ0H3qbS1+uho0chItPClJ2N4ay+0ZqYTEEUki4RNELGKIpquIagCaBrxEeecWj1JCVT3NyKn\nFk4+xQtcas/vmFwzMIC1f4DM4HaXxWrFLQr4RRGHIBLQNNKAs/0D7MnK4otPPz3vYuRajjw8++Ir\nvGbLQij/JiKc66qbmctZHuCZyv3UvfiK6fFhYjLPhC1IZFk+iFE++7iiKC0zjMkCtgK6oih3zc8S\nTW52cktLOZGRQWZPD8MDAxAIYNV1LIKIKgpgtxOTkEBsXBwej4fqsTESs7IumGdoaAhNVXHYz3XF\n7ezsIinG6NXitFhYXFSITdep3/4OKV4/EbqG6hRYNkmMgOEoqAab0EmSRFGxFR0vZz4YZ1NZGdue\n+y33FK3infqdLEksw26xsXJ5LBFxTrpbxhn3jxPvUClbbkfVVVRNJS1dxBapI1pEnNK5t6au6Yiz\nmMKdz3QutZM7Jp95t4E7dZ2ArqOKItjtxCUsCL1+nqAF7DKnk47MzHkVI9d65KHi3V2GGJnFkl1Y\ntorXKvdR+O6uq75eE5MbiYuJkEy0RZ0+480gYtI4E5N5obGpid+NjSEHAkTHx6PpGj6fH28gQFR0\nNMlJSaGxDoeDD2Nj+GRG+gXzjIyOYLVa0QOBUAlud3sn8i1L8fv8hl2ppiEX5NOevoD85nYGdI2z\nC/UpYmQCQdfRAoYgAZCLrZxu0GhsaKS/toPxlEReOPYKX7nli5zw72SVbAWsZKZG4g146BvyoepG\nHowuiiTE+XEMdjGWHHvBmywgCNitU++19PazKPXesF/Hh8rLyS8u5gdnzhARcnx1Gsmtk16/yb/P\npxna9RB52HL0NEL5N+ccJyxbzZaKfw8Jkms54nMjYr7eNyYXI0g+gREhmTY6EuTspHEmJpfNc88/\nR19/H3mffJCTFb/ijuD9tmDViNfrpaenh+TkZAAOAYs/8wgNDY1TKm3cHg+aqiFIInpQjDQ0NJAe\nnxDyCvGrfnRdR0AgbVExfc1ttAiw+BaHkZ9y3tq6e1X6hlVES4DkRCux8QJpC/288Mdfo9Bt40P9\nN8SOj/CzD/6ZrA39jHhTsAd78HhVHx50rKhoNh2rE3KTJGyvVDHmtTPgBzEqltjkVARRQEXCYbVP\nef7sXi9Fiy90aJ3sUns+tVVVpAoicZMSVmdjvszQrofIQ7Wi0JhaFF4/DaAxtYgfvvgS+9oHrtmI\nz43GtR5hM7k8whYkiqLsDmOMG5hznInJTCg1Cq5aoyKktauVvv5gCW9+Hkc7u9APHpwSgrPb7Xi9\nXoaGhqiOjWXgscfYvHkzzz//HA31DeQX5AMw2D+AVbIiiRKaptJQX8dAexe3LyoNzSUKIiogAKnp\nC2iyWum3BFidbUcXdXyaig2ocfmpaQ4gRtmJTxBJjdHo8XipPBIg3mklRpBIsEQx4hmhzBZBpz5M\nZpqPcd/ouXVLdhx2J91DA4z0anz0EXgDKdyaU0JcRg6C1UZ7ZxfVp44zGpuAlD9VEKhNLj6dsXLa\n17BRaOL35Ufn479j3rjUyMPHSVVjE/4sGfvcQwFoa6znBwtzSSj/g2s24nMjcT1E2Ewuj3lJag3m\njgwrijI4H/OZ3Hzs3LaTkzsrydNzQp14Xznyc26/bznDQ8PExMaw/PcepjotlWMHPmBRVzeFAT8A\ndTY7BywST/71X/NkMN/hq199ih07drBn7x7sdhvecQ92h4P+vn462jtIioqcIkYABEEAhFB4rw+d\nZHsUAJGSFY8gsmWXhy5LGr7cQrQkw8Zd7O0kob+eO3MHcODjhNVLntuLqqpE2iIBD1ZA842iWyOI\nccRy8HQzzT19LMgQOP6RjeKSe1lUmo+uw/CYgFuMJD65hNKyEurqGzlUd5rRJfFEWSNRm1x8ajg+\nVO47mQ/aDrFk/bIL7p/gfHv7uZgPM7RLiTxUK8o1HYYfOPgeQ1klRC29fcYxVzvicyNxPUTYTC6f\n+aqyacJoaLd5nuYzuYl4+T9fIqUpka/mfDF0X213HTl5OSTbkhgbGaPP10diciIlK+6AFXfQ3trG\n7hZj9zBh4ULyhoZYeN62wsaNG9m4cSM7duzgdz/4NS32VpYtvYWVJXfSO9KNb3xwyjaMKIhGHomu\n09regVVyUmpJoKOll6QEna3vQ2PuWiKL87Bg7EsKCJAYyzAybzW5KBs+ROm9Puq2DZEjRaDrGvGq\nnY7WUZITYNQzyIcnR3AmD/HAcgdv7QhQsuheSkuNSA4CxEbqWNyjuMc8BCQHBbnZEFDZv3cbCSTx\nVMHDM4qR7tx+Pv/QkzO+1sUlJVPs7ediPszQLjby4M+SqWqs+9gFSWleDtb9CmTmzjm2XzmN+Mk/\nxGmf/ayuZsTnRuJ6iLCZXD7hXrSYmFwRdm7bSUpTIndlTM2Fbuh3kZhiXMdHWiOxeCV6e3oZHBhg\ncGCA+KQEylbeSdnKO0nPzCApOSnU0fd8BL/A1+74CgusqaSnGMmuSdEpRMWk4rXY8AIBXTN+RAsj\nQHuti7XRqcRrEq2ndY7XQ03qnUQWn8tLMSIq59KlxJxcTkSswGexMZquIkgC7oCHFMlB22ljTE3d\nMJHJw8jFxrVAe0fiOTEyMa8IkRGQFKOS7BgjRhvi9sI0ivVRvvPNL9Bha2an632a+s7S1HeWna73\nec71U8R7HXz+azOLkQnWbdpE65yjbj4ztBJZJq+rds5xblct3oVF2P2eKQnAMzER8TG5NCYibOFi\nvt7XL7NGSGRZ1gg/QfUvZFkONbpTFEW6nIWZ3Byc3Fk5JTIyHcPuIUbGBwj4x3FYjD+rEcEo941O\nTCAuLrwEzbtT7+R0Qw35+YYAiHHGEuOMxRPw4PUbVSdxVgcHP/wAW98QSPFYBAspvdG8dcaK44Fp\n7OiDERUATQcpJ4+jH9SwssxPf2cAu9tLnB5Lcm8UijJK94iPZXdFIQgaJ096ycufGn2Y6AksiqCq\nYLUKWCzG0+TlZ+AXffz5vz49JdempGgJv18cfs7I+b4knW4vHR7j2mSBQyPNaZ9XM7SLiTwAWJsV\nylbnzz3wCrBpeRnPVO6fdWvA3daEnppBQuRsBYfnuFoRnxuF6yXCZnL5hLNlIwBdgHeWMVnAMDCR\nQ2JW2ZjMiVKjkKfnTPtYfkIuJ7urEO3gGx/EDlgQEDEcWi0APh+jHZ34PF56+/q5b/39086VW5RL\n9Xsn2VC0jtYP22nQzyW7AjgsDhwW40q3ob4BsUPEnlbK0ZFRkr0jqB6R/vgC4kPvlqAniH7un5oO\numpY2PclFSBae+l1WPC5rZwdHSXXbkd5e4z0T50LSjY3i8jF58qTJ940Qb8yBEFH18/5j2Skp3H4\n8AE2bnwQuVie4jNysXxt82a+0+fnR1tOMtJ/O4K+yFiDcIbo+KM88rmlfG3z/OzAlsgyeW+8RRMP\nhDU+r6uW4qKN8/LcF0v5hnXUvfgKr1XuQ1i2etoxuusMsUuTiA/615iYmMwPcwmSbwLPAgHg/yiK\n8sZ0g4KRlOcVRTFzSG5QqmtqQg3bSgsLKZmHJmuuWlcogfV8ilIKcR1uIDZRCuV5iIhomj7FQd4G\n+AYGaGpwIc9wRSQXy2wTfgPAF2/7A96t3cWB3YdwpkSEtoX6uvsY7xpjVdpK2tQWXvryy+yu2cvu\nM3toHWzBkZmFoEuoqo4g6Aii4WSiaTq6BoJOqNHfWFQab/wuC1/nI1iFpQQCfg75z2Af34mtS2Fk\nUMDi9KIGbeDPqXchJEbA2BKaKEMWpfndXX322ZfZ+f7dJC7YTES8G2/Ql8TuXI3T8b/ZtfsAsc++\nwtOb56diIZzIA4BeuY9Ny69uR+HNX3iCwnd3saXi36eWlzYr5HUqfL4wgZdGu8Oe72pGfG4ErqcI\nm8nlMasgURTlP2RZfhf4OfCaLMtbgK+b1TQ3DxXbt7N1927OWqz4ExIAsB48RLbfx+Nr11L+4INX\n7Lndbe20NFjm7NzbUteAu7191jFL1i/jgz2HuStjBRuK1rGhaB213XU0dBnbHvckrKRILuS9+t1k\nJRgur2uK72FN8T3856H/odLaSyCgEWkztow0XQNA1EFDRRAMWTE8LDE6loxt5PNEiWsRVA27IKAL\nCxnT13PwyAGs/JIV97aRvsBPa2s7qWlxwXwUQJ8sUIwQjI6AIIi0tXfy6d/7wrTnV1NdTW1VFWBU\n0szmG1JRsYs3X89EEu4GwOlw4jyvw6/I3bz5xn4KCndRXn75CYJhRR4q9/Gor4XyDVe/bLN8PdL2\nJQAAIABJREFUwzrKN6wLGnAFhfiqPEpkI3Kz/5kfXBcRnxuB6ynCZnJ5zLlloyiKIsvyXcBfA98B\n7pVl+UuKorx9xVdnclV59sfPUdHZiVRahgSEkoKSk2kF/vnIMepbWnj6qacuaf6JrRSf30t9Zw0A\nBWnFFKUVUdtZS8boMAMffEiDrpFfUGB0uT2voV5DXQMDlR+RYXfM6ii6/qH1vNz8Eh+4DnFXxp2A\nEYUpSjnXB+aDtkMc5SMeL52aN7Eu9y5e7/4V3QEHomCENcRgPrgmBJ1WNRX3uMC4Oxm9qw3dnYWo\n6aAJoTWLugDqvRz5yEps4o9YtkLnlS0Kt99eGupaPIEAqBrouo4kGmKlvaWLBx6Y+kG7raKCXVu2\noDc0EuvzAfC+zQZ5uaz/3OemzQHZuuUUkvD1Gf5XziGyiq2//NG8CBKYO/Kw6Y7F14QYmUyJLE+b\ni3A9RXxuBMzX++YgrLJfRVECwHdlWd6GES3ZIcvycxjbOONXcoEmV4eK7dsNMZI7c5hUzM2hwuWi\nYPv2S4qU1Fef4Sfb/4F8t0Cc6qPX7+UXmooencyClEJSVB/5goPa/ZXsPlVDQlE2mdkLAehq76C7\nvpF0r5fbHU76vN45HUU//7Un2bltJ8/v/NkUv5P64UYacLF0w63cra6CY1OPk1MLyTrjZTw7DY+n\nB4fjvK7DkoguwNi4FYhCrxslQs9FUzUsovEW8+ngjEjAr7qxSvdy6tS7LL+tjpTkHqpO17OoLH/a\nbjWSKKDpKpXHq5DlqR+0//XsszS9+hq5YCSeBEtQEwFcTex95hma6+qm5IJUVys0NRUghdkap8lV\nQHW1QknJ/CQIzhV5uF640hEf0xp9KtdbhM3k0rgoHxJFUY7Jsnwr8H3gT4D1sizPXWdoct2xdfdu\npNK5rzTE3Fxe3b37ogXJxJfpXbqH+pERGn3jxAb8ZOoaDHdxquUkHlHAEptOUUQs6b2j9I6eoFup\nZdDnx6vrxNpsxIgXV8y1/qH1rH9o/YxVKkqNQvV7J8lJzJ5y3GcyVvJvHafxpcTgYQSHw/hG13Ud\nURDx6yKqFkugsRGpcwmiLqCh49UBi43oiDhiIuIY9gzhdnfR0XUXHW0Ka+6M5s0de9EFnbLSczbw\nmga6BrqkU32mHqV6PytWpvDXf/05Vq9+FNUNTa++RuYs55qpg+vV19hWWBiKlFRVNeH3FSGFWbLg\n9xVRVeWaN0EywUyRh+uJKxHxMa3RZ+Z6jLCZXBwXbYwWtIf/pizLvwF+hmkVf8NRXVPDWYuVcL/q\nmyxWqmtqwk503VZREfoyPeweQxjvY4kuIgmiYcIBLBJ0egN+hgfbGfWOEJEQzWhkBJU2GwNFMlpK\nKgBidxdxTU1kqCqbLsJRdKYqlckJsJN5uGgdjUfb+A0DjC1IYnRoENHmBymA2jWIp30ErVnF2ZzP\nwuw/ZKCvH4tHItIRhcNqVPAMjw8yMj6IVw1giSjibOMYEfFuntgk8fY7e3ht60nyCopZkJ4Ouk5H\nRwcN9TVkZAzwlaciGBsdIz2jh+qaH7D7V4Os58KGf+ezENi1Zcu8duw1Ocf5EZ/W1lYQRTJvLaE4\naza5eCGmNfrc3CgRNpPpuWSnVkVRdsqyvBj4EbAJLqI/usk1TVVdHf6EhLAFiT8hgaq6urAFya4t\nW8gFagYGsPYPkGm1oqoqfjWAGEzgTBBF6jXI0fxo/lEOSTHUl5Uh5uQiAYKuoWs6REczVFhId2cn\nv9mzh6fnofpncgLsZP50+ZPk1e7izQ8PcirCQ/9IB4nuMeS0FNITk9Bi4+hO8tHY+H+x2u8iSrgf\nW7CZXu9wNz63Ub5sx8LoiMiH+/2IUT7yC6wszFTx+/o4emgfmhYgNc1CYoLOJzc6SE4zhEd3zxjN\nzW0AxKd0UtubSJEjbs7zmdyxt7Q0B6utFgizYsFWS1lZ+KZUNys1za28WllzyZEN0xr94rgRImwm\nF3JZ1vHBapsngj8mJnNSU12N3tAIgkDzQD+3BKtLJElCkiQ0TQsleCZbJNoDKl6bjTNLluLIyUVV\nVdRAAHQ91HXGi4CekcHPqqsvOZ9lMtMlwE7wcNE6ulrO0L3719x97wqKblsSilP0+xJJiouhpAQa\nGs9w/IM2Evgaw+OD+NyTbOp1HSc15I9b6W+34WpMZMHCIvIKFpCVozM03El/Xy2JSX109XrZ+Z4X\nu0OksMhC+oJ+AMYzND5q7oBR5hQlkzv2lpTI5OTsoOXs9J4t55OTW09x8Scv4tW7+ZiPyIZpjW5i\nEqYgkWXZAixWFKVyljF/ALysKIppinadU1pYiPXgIUhODmu8tb+fsqLwrqJrq6qI9fno0jRivF7O\nD6xNrqLJlyROaF5OJyUhZmfj9/vQVW1SvwMBHyBIElGazrjdyTM/fu6SBElNTU3Ier6woHDGBNjX\njm/hyNFfsmTDaorOO2dJGEHVYxCA/LxsdM1F9eF3GBkvmuIyqQPx4vsMRlgoylnLajmf0XENiw3s\ndgFRTARKaWio59Spvdy+HLJzJAJ+iIo23rKJcTYKC63UVw7w4SEvt9lTwz7Xxzct5vv/eCBU9jsT\nGvt5/LOLw573ZmQ+Ihs3YvNBE5NLYc73gCzLGcCHwIuzjHkQ+B9gtyzL4fkpm1yzlBQXkx3spBsO\nOQE/xRf54djrcROvza1dUy0WxktKGAU8ARUNnQA6XmBcEBCtViKtRozCBnRKEj/+j/8Iex07duzg\nW3/1l2z55S9oOuui6ayLLb/8BX/5rb/EL/r58399mpL/tYTW23tovb2H9kAVkQvTLhAjABGSHxgF\nDJmVX5CHEPkeBHxTxmnafhZENJJ/1wqyFubh9upYg2JksjFaekYeeQWr8fttOBwikkXA49EQRQFd\nELDaBPJlC2krvdR6ZrYGOr9jb3n5Oj7zSBsa+2c8RmM/n/5M+7yV/N6obDl6es5SVAhGNo6envax\nCWv0cDGs0ZvCHm9icr0wqyAJRkZeBxYDqizLM3WSqsZIbl0NvDSfCzS5Ojy+Zg2qa+6OsJrLxWNr\n1oQ9b1FpKUM229wDg7gsFiyCgNTajDg6jG61olut2Gw2Yux2HNLUTBdLSirbXn89rLmfe/45Kk9U\nsuzWZSwqXURKSgopKSksKl3Erbcto/JEJc8//xxyscwDn3qAnMJs/K2t2JISqa86Q33VGbp7ekLz\nRVgEnFIf50SJTp5sR9UaQmM0bR+LHS9iS4tALijE69bxB/QLSokB/D6j8qa52TCkczgEfD4VYaIr\ncRC52Ep31PCM5zldx96nNz/B5m9pLMz5Ear+Nh6vC4/Xhaq/zcLs/2Dzt7R5c2m9UTGbvpmYzC9z\nbdn8PrACeBN4TFEUbbpBiqK4ZFm+D6gAPiPL8kpFUQ7O71JNPk7KH3yQ+pYWKlwuxBm8SDSXi/IF\naRe1RVJcUoKQn0dS1RlcosBMbfGaLBYG0lJIW1RCUUkxCCKd3d3Un6liNDYOS1b2DEeC3tU9q0ka\nGJGRvv6+WV1g8/PzaKhvYMeOHWzcuJGKn/6UsSYXd/d0MTFznWThZHwckYsXk7eomBgrWIRextUh\nAlo0CzLi0W0n8foU4oX3uCWig1Srl8HCMjq6O+nr7UaKhJTcZFIWJKLrhtbw+3UsVkN0JCYV0d62\nn/QMC1arjs+nIVksuMe8WILXFOmyRvdxNyn2qQHK2Tr2lpevo7x8HdXVClVVhvgsLS2kpMTMGQmH\n+Wr6Zlqjm5gYzCVIngBGgC/NJEYmUBQlIMvyFwEX8HXAFCTXOU8/9RQF27fz6u7dNE22ju/vJ9vv\n5/G1ay4pX2Pdpk3s/d4zDNvt4PVd8PiZyAgWrLmHW/Lz0CwWBiIiEOx2EpISWbSohIaGRo4oNUjy\nhRU1YncX2Zo2p0nanr27WXbrsjnXml+Qz569e+g5dYqsN99kXSCAHQFLsOtwJrB2cJDD+/ZxuL+P\nslV3E2ERiLAEcKu9BKID5MX/ijXRNuIEHa/Hx5GhIeJPnyJ9bIx71ACCBPVWiZNJcUTcVkLhbcUE\nAjrWoCBJSVlAT49EeobR/TcQ0LDZLAQ0G2LQxXVBpsTZYx5SOCdIwu3YW1Iiz7vPiEn4mNboJiYG\ncwmSUuC3iqIMhTOZoii9siz/Gph7U9XkuqD8wQcpf/DBeW2u91B5Oc11dTQ99zytHR1kTtp+aLJY\nWLDmHnLz8xAsFhxWK4LXC/Zzu4X5+Xmgw+HmsxdESuKbmkieY0uopqYGuz3c61robWwk8vRvucPp\npEMQ8Pv9IUEywQpAP32Gj+LjWVhihPE1QQRdo6g0l7SeXgA+6unhE8ND3CuCRZJAsiBYDL+Qtb1D\nHH77IEd6Bym45w503egirOmGUdpkPB6dpKQF6DqM9Pfj0wOM+AP0iV5jSyw/j/WbNpn+I1eQ+Yxs\nmNboJiZzC5J0oP4i56wDHr205Zhcq5QUF89Lh98JvrZ5M1mFhfzob/8Wb30D2cFS3960FG4pLECU\nJCwWCz4g0ulgzO+DSUIjvyCPMzvfxz1pTs3VyDKfjyGHY0oS5/nU1deRlJwU9lqlujqW+Hw4YmOx\nOJ34vF5wTk2n0nSd5QIcrDmFfaXhuOoN6Jw+doSVd67kvVd/i2t8nM/6vCyOjMSraghWK2gaogYT\nqTArENCOVXM0Ipb8W2WsVoGe7g5SUwIMDYmAgM0mYLXGEhNrbHjFxsXT3jPEyj95goz0jDmb682G\nsX3TBEBpac68RE6uxJzXAvMZ2TCt0U1M5hYks27TzIAV8F7CcSY3GQ+Vl/NQeTnP//CHvLdlC76W\nVjKWlCFaraiCgGq3EZ2QQGZcPDU1NfhhiigpSE/jeH8/1oQENFcjZadPsyQiAlde7gVJnJdKe2sb\ni3t7QxGamIQE+tra8Hq9oSiLqusIEkgC3DIwSHdvHylpSXS1nWXlSjdHqt/i2O3LKDp8lBXBeTzu\ncRxOJ0giqq6BpiFJRiTkTkHgmKKgLpURgL6+WlasMN6qY2M6uu4gKSltyjrVQCFf++afXvJ5VlTs\nYuuWUzQ1FeD3GREeq62W7JztfHbTkkuqtrkSc15rzGdkw7RGN7nZmUuQdAIXe6l1K9BxacsxuRn5\n6je+wVe/8Q1+/OP/oq+/l4ikJOxOB07HuXyItLRUetva8Xk8qHY7usVCWnIS2v6DRI+McqvPx5KI\niFmTOCcoLCjk2LGjpKSkzLm2/pYWVnq8OGJiAYiNi8Pn8TDU1wc6WO02BAmCqRwUBlTqO3oYdY+i\n67V4dImGlFLsQgSrR0bQRMHYg5EsIVEjCKKh/FUNMVhAU9YzQNtAHy0tg2Rn9yMIApoGUVE2/H4f\nw0MDoQiJovhZteqRS3npAXj22Zd58/VMJOHrSAKT+tzk0nr2fp79pwPU171yUVU3V2LOa5H5jmyY\n1ugmNzNzCZK9wGOyLKcoitI912SyLBcD6zE8SUxMLoqMzEz8qp+4+Atrb+Li4vF5vPgGBhA8HvxA\nYGyMuzs6WRkbC05n2EmcxcXFeL3hB/E0XcfhOLdFk5yWhs3hoLe7mzHPOBGRdiwWI3Lj8XqpUU6w\namMyy26N59lfDCMuz8H5UTXFAT+2iEjDjdZqZXx8DMHnw+6wo+simqaFuvAWBlQOVJ1hMODittv8\naJoVQZCCjrYwOmoIEkXxEx31SR544NJyRSoqdgWFw8wmaSJ38+Yb+yko3BVWVONKzHktcyUiG6Y1\nusnNyFyC5EXgj4D/kWX5U4qizOiWJctyIvBqcM6fztsKTW4a5opcpKSlMeiwM9Lfj+Tx0t/RSaIo\n0qjrF53Eee89a6g8UTlr2S/AuK7TEXehNXtsXBx2h4P+fhei5CXg9yKIAq1RAp9+Mo2MhZG0tI7R\nHZN17k0m6GiaZrjRikBMDG63G8/IKDaLBaskgl9DDfgZ944jijU88mgUVad1dr2rkZ6pk5YWAMDV\n6OHUSQcPPvjHlyxGALZuOYUkfH3OcSKr2PrLH00rHs5PeJ6POa83zMiGicnlM6sgURRlvyzLrwKP\nAcdkWf4bYPtkYSLLchzwWeBvgDTgRdODxORSCCdyERcXT1xcPG6Pm5P1jTz8vX+4pCTOjRs30tLS\nTEN9A/kF01c+NNQ3IJcsYrCtAxobL3jc43UHE0ytEAygNKQ5WbcwEoDmDj9akmHpHkhNosYisUAN\nAJaQRb7odILTidvjYcznJkrwEhkh0hVl4bZ7FhAXl8C69cbkra1jdHWOAZCSKrF08RcuS4xUVys0\nNRWEojJz0eQqoLpaCSWlVmzfztbduzk7qSRcfW83rfVdJPnfIiFi7mTP8+e83jEjGyYml044vWy+\nBEQDGzGMz7yyLLsw7CjjgRwINYZ9DXhq/pdpcrMQbuSivbWdTZ97go0bL/0K9KtffYodO3awZ+8e\n7HZbqPKmt6cXj8fLmnvXsHHjRt5NTuHw977HijnmOwSk3Du1p4yqqvh8XsSkaA4kxLKmbwhV1VAD\nAqJkQQqW1whWKwIqdtGPwy5Rm+ZkXUn6lLkyMyPJzDTETmenm8ulqqoJv69oUn7H7Ph9RVRVuSgp\nkXn2x89R0dmJVFqGxLkPgAGLHW29TOfZ43iqWkiP/ErYc5qYmNzczClIFEUZk2X5kxgmad8Abgcm\nlzCowD7g3xRFqbgiqzS5aQg3cpGUmHRZYmTy823cuHFKc70N6+6jeFKVzobycl6qq+PQq68yufev\nw+5kwC1gtRpi5Mw9qdx99zlBEm0fRmhtRUosBgTOlskc23uUFSKAjqb50XUNi8WKIAjofj+2KGFa\nYXM+3V0iq+4qvezzvxQqtm83xMgMDr4AUm4Og7oLR3V4kRITExOTsLr9Bjv4vgy8LMtyDEZUJBoY\nBhoURRm/Yis0uekIN3IxnxQXF08RIefz5ObNvFtYyItbtpDd0ECezweCwD7VSnOaldR7p4qR4aEB\nUlPdJFc2MhjU7165iC1jY3C8mhWCjiiCpqmoqmgkq/oDHLeKFwib6RgbXYg8jVPtxVBamoPVVguE\naexlq6WsrIi/++kLSKXTl7A6HTYE0QvYkfJy6avfS8IsPh0Tc5qYmJiEJUgmoyjKMHDyCqzFxCRE\nOJGLCebTRXY2NpSXs6G8HKW6GldVFQDR/e1kjb9BkTzVHXZkdICoKIEVecPsaHLhS8vBbregrlvJ\nz5Piefd4Fat6B5G1AJpfpUGy0JC1kNjlAvc/kj7d04e43DLfCUpKZHJydtBy9v6wxufk1qNTwFmL\nFWmGMQ6nA5ttGF+wI4A/x8p4vUKEc/otmZzceoqLzd45JiYmYQoSWZadGJ1804E+YG+4dvImJpfD\nbJGL6ZIqrQcPke338fjatZfUZycc5JIS5ElJtD95wYei/A5ZtgLg8XiQJD8gsHyJyOnXDlEzKhC3\n1PhSFpYW07W0mF929iB29uJp6WJd8TL+54c/5Ccv/MuUuc5ncpnvfDigPr5pMd//xwOzlugCaOzn\n8c8upqquDn9CwoyCBCAhwUln5ygQBSkJuGvriODCtU3Meb1iVNQ0AYaNvJnMamJyecwpSGRZ/hTw\nAjDZa3tMluW/URTlX6/YykxMZmGmpEqSk2kF/vnIMepbWnj6qSufY/2VL/8Fb72Vz/79rxMZ1YIz\nYpBAwE9dHXR3Sdx7WxIrtGb2HW2mOyYrVHkjDvWT7G+lbKmPjWvWTDtXSqphltzdJTI6ksnq1Y/i\ndsfw2cf+bV4cUMvL11Ff9wpvvrEfkVWMuxXc7rMAOJ3ZRDhlNPbz6c+0U17+BK//9rdzzhkXH43H\n28/gwOiMYybPeb1R8e4uthw9PdVzZL9C7us7+NwdiynfcP2XMZuYXA0EPdhDZDpkWV4EHAdswGmg\nBqO0dwWGmPkjRVFuOhM0WZZzANeuXbvIzMy82su56ajYvp1njxydNakSQHO5ePqO5VcsUjIdNTXV\nbN36U4aGf4dcHB+qigFoax6jprKX3gGViBQntyyJYWFmJJ2dblKT/4xPfvIzF8xVX38GgIKCRRQX\nl0xyQJ0+oqFxgE9/pu2iHVC/8pW/Ysf2KsbHivH5ViEKuYjiSWLj9vPZzxXx/e//GWBsj/3RSz9H\nCiMaMDgwQs/BStKql2MR1wKGcMrJqePx69Q6/tkXX+E1W9aMVvF65X4e9TWz+QvXjtAyIzkm1xgz\nGg3MJUj+G/gi8FVFUV6YdH8RsBMQFEVZOI8LvS4wBcnV5bObN9M6Q1Ll+SysOs2WZ5+97Oe8mDyV\nmppqKn71FEuWGvW0pw500b2ni5JONwV+I+JRbxWpTnOScm8qelQcny5/fs4k1YqKXXz/H6Wwtlc2\nf0sL6wt/x44dPPf8S9jtaSxYkIOma7S39VJfO4bPdw/xMQ9fIHIu9vX/my9+6YZorlfx7i6e6bGF\n1bfm28n+qx4pmTaS06yQ26mYkRyTq8mMgmSuLZs1wJ7JYgRAUZRaWZb/HnheluXFiqKcuvw1mtzs\nTE5gLSwonDGBdbakyvNpsliprqm55ETXS8lTKS4uYXQ0E+jhwGtNlO3rIpSCajUM0RYCazvdHN7a\nxPasfP7qW3Ovb74dUJ97/jkaGlzcsuwTCESF7k9MSGPxYmhoqObE8S6iIr4csnkvKUknP8rByY8q\niZBLcJzX9XgymsvFY2vWUFIiX7ciZDJbjp5GKP/mnOOEZavZUvHvV/ULPxTJKf8mIhCymsnM5SwP\n8EzlfupefOWaiuSYmMwlSNIxzM6mozJ4mwGYgsTkkjFKfHdjt9tDJb7Hjh2dtsQ3nKTKyfgTEqiq\nq7skQXI5eSqrVz3K9le/w537urhjludY6tGhZYR3KyrYMIvt/eW6qp7Pjh076OvvIz5hAT5v1LRj\n8vPz0PUGTn30FgH/Cr7znW9y3/2NeDw6sY3jtA53YMkvIToqnpjYqfb6mstF+YK0j3W77EpSrSg0\nphYhhjm+MbWIakW5KtsjFe/umnVbCUBYtorXKvdR+O4uM1Jics0w1/vLDszUVK8veGub4XETkzl5\n7vnnqDxRybJbl7GodBEpKSmkpKSwqHQRt962jMoTlTz//HMf+7rCMf8Sc3Oo6OikYvv2Cx574IFy\n/CctLPHMvCXq8eiIUhz3xcTx0ZYts65nwlV1OrzuGjx9b+DpewOvuwaYcEBtmnG+PXt3k56Rjtd7\noU1rX08bZ6sOc7bqMPGxDnTeZaC/k/aWlfz6zfXsfPsf6K37f+jvxzP+5jv0HNpDu1KNp6cHVVHI\nPH2ap+9Y/rEkFH9cVDU2hbY9wsGfJYfyNj5uthw9Pee2EgQjOUdPfwwrMjEJj4v2ITExmS8mrtJn\ns4nPz8+job6BHTt2sHHjRkoLC7EePATJyWE9h7W/n7Kiizfe2rp794zmX5MRc3N5dffuCyIBSnU1\na3ULDmcqoyMDiJIPq8UQJ/6AgKbaiIqOJzbGiCxkNzSgVFdPKSeeC1//r0jq28pKrwtZM3oAKaKd\ng/ZcxmLWAIXTHldTU4Pdbsfj9qLrDoRg1KX1zBECpw6ydKCbItVoV6VIFpoFGx2DKQSkT+H3H8Hp\nNPKmbPwJjIHn8OsknK3g/vsf5okn/+CK+cCYzM31FMkxMTmfcP9uTUzmnT17d8/ZswYgvyCfPXv3\nAFBSXEx2YMam0xeQE/BTfJEfthN5KuEykacyGVdVFXk+H7ExcWRk5BIfl43FmorFmkp8XDYZGbkh\nMQKQ5/OFzNam45yrqoHa/i883v5PfNvrYi2QLtpJF+2sBb7tdfH5nh9w9tCuaeeqq68LbY1N0LD/\nt9yx71f878Fu1gqQYbGSYbGyVtf5jn+cb6g/I8H/s2nnc9geYaz3a9ScrLxhxUhpXg7WZiXs8dZm\nhbL88Bxw55PrKZJjYnI+lyNIZo5Fm5jMwcRVerjY7TZqgl/6j69Zg+pyzXnMRFLlxTKRpxIuE3kq\ns+FwOIiLjScuNh6HY+ZE0JkwXFXrASMy8kj/61Oa/Wm6h4A6TEAdRtM9rLZ7KNr9Hu9WzNxeyuG0\nIwgeWs8cYXXVQe44P/ldP/cmXwF8Sf0NzkDTtHMJ4irq6iRqaqov+tw+DqqrFV5//W1ef/1tqqvD\nFxYTlMgyeV21cw8MktdVS/ElROZMTG5mwtmy+YYsy9P5VE98qn5fluVvnf+goih3XdbKTG5oprtK\nn42k5CTq6usoLi6m/MEHqW9pocLlQpwhx+NqJ1XmlpZywmYjO8zxjTYbt5TNvkU04aqa1Lc1JEYC\n6giBgBtdd3CulmIcUQwgD47w6y1bLkiWLSwo5Nixo6SkpGC3DxE4NY0YAXR0QMDvC4AezZ3CEHvd\nx+hg07TrGxu5h/fe20lxcfjbTleaiopdbN1y6rJN5KoVhWwxwPE923CuWIdzFlGpV+5j0/LwyqLn\nm9K8HKz7FcgMsz9Rs0LZ6umbWJqYfNyEI0iygz8zYW4+mnzsPP3UUxRs386ru3fTNLkkt7+fbL+f\nx9euuWQxMh95KnJJCb/Jz4fGxrDmOJufz2NzbHeUl6/j0P5niaw6BUTh8/ejqjYEUibJCRXJEg16\nPJ0do9g/OHpBbkpxcTFer5Fz4vMOsnSgA4SZ6pYEPB4ddD+Sxcrdvha2eBuw2i/8ElPVRbQ0Hwnr\nfD8OzpnIfR1JACkUkMul9ez9PPtPB6ive2VWE7kpXh63lTP2/jY6fTuxly0nIdJJfEzMlPF65T4e\n9bVQvuHqlNOWyDJ5b7xF0ywNDSdjRHLmt1GlicmlMpcg+cQlzmtu55jMyuSr9HDo7enlvvVTm8CV\nP/gg5Q8+OO/N9SbyVFrDHD9TnsotmzZx+Hvfm7K1Mh2HgmPDYe3KPKyv+6GvFVWNBiLR0RHQEEQN\nSRKxSMbbWiCK1L4Btv50C3/zz38/ZZ5771lD5YlKAoMDFGP03ZluB9fr9eD3S4iSB0mKoVjzIXob\nYBpBEgjAgvRrwyexomLXrI62ACJ3h/xVpouUTOflsfAP/oSBg+/R//ZWOpLSGcvKIznDSeDOAAAg\nAElEQVQhHmuzQl6nwqY7Fl81MTLBpuVlPFO5PywDt6sVyTExmY5ZBYmiKLs/pnWY3GRMXKW3t7XT\n3W1UlqekpJCeMX2nW6/XhzxDcmpJcfFFiZBwBMzja9aEbU8/U57KhvJyXqqr49Crr3LnDMcfAgYe\ne4wnZ/EgOZ/YmEjUET+aFoOmGc6voigiThvliODAgeYL7t24cSMtLc0cO3kKq0XCKuoEAgF0XQDd\nECZer5uRETeSFI8kRV4wx4XUc9994XUOvtJcroncbF4e8Ss/QfzKT+B21TK+fzv3xaj8UfnDlMjX\nRqShfMM66l58hdcq9yEsWz3tmKsdyTExmQ6z7NfkqrBjxw46OzrpHxxg4UKjjNTlcnH02FHycvNY\nvORcF9iG+gbuvefey37Oi3Fdna88lSc3b+bdwkJe3LKF7IYG8nw+wMgZacrPZ9mmTRclRnJLSzmk\n6+R6HYiCgCjNbhFXjfX/t3fn8VVW977HPwmZkEBECYOiBAn8EqOtWpWKoFRKhVNtjYLDC7W1V2lv\nPT2dLLb3tLantcdzW9uqV62KnbVWpU2rVmItdYpWqUMFJVlAGpAgMjggQQOZ7h/r2WEn2TvZSfbO\ns5N8369XXo95hp2VJcn+5nnW+i021h/DDTf8jDPPnN2hUNrSpZ8low3W/O0xxmVmkpWVRWtbC/v3\nN9Lc3MLo0fk07Yf9+w+EkZqMHFpzi7sUpmtsbGNa8WuUlIQ/kDMZReQSqco6cuoMRk6dweaKm9Ju\n2uyyTy9h+qOruKfipi6l49PlTo5IZ92uZSOxaS2b/rn9jtvb64/s3LmT5uZmcvMOzLip3VjL+++9\nz2mnn0btxlrGHTqOpUv7V2Srvepq3HCxifJJE7oU86pI4jgVV13dPrV3allZr2qORC+098A3r2PJ\n+kYyM+LPUmpuaaGlpZVvt01lbe61jCt8nvHjC5lStKHLYM4fnn8+F65bR2MwriQvN7d9JtDb7+yh\nfksTMJLMTLg2+wi2Hf6jDl+rsbGNjIy1/PBH49NiwbwVKx7humunkpeb2MDOxn11fOObdSxa5O/u\nVDvHJVX/InNWYuMwWp+p5Dezj0q7UBKhxfUkzfR5LRuRpOpcDK2wsJDdu3ezp2EPGRkZZGdlM2XK\nFGpqavhjxZ+49JJLO5SO74uEq67W1VH88MNd7pQka5yKlZb2KoQAVFZW8FTV/eTn1zN+gn88Uz96\nN083ZTArK4sRmV3vkDQ1N9PSnMGzZFOXs4TMzBxyssczIuNjMQdzHnfRRbz8/e8zM8bMkbEHj2Zf\n41vs2rWPZ1pGsil/AZnBAoHNTdDaksNB+a9y2WcSW8xvMIjU8kh0Urqv5bEhbd/oS83Stm0i0RRI\nZEA98eTjHH/C8R32FRQUUFBQwPuNjezb1wjASSefTM26mn6HEeh/1VXo/TiVZFi+/Hoa9j7IrFNz\niFoejbMvnsBPa8bCu//ilNZWsqKKuDW3tARhZAS/yF7EvpxzoPURRo48UICu82DOnsa6TJx4CP/M\n28OTB00ln8nsbfD/j8YUbKakZAsXXPQBysvPTVU39NqBInIJTn3NWc8xx4T/qElkuBu0gcTMZgHf\nwtdsGgmsB5Y7527u5etcAtwMjHbOqXJtCvVUDG1kXl6H+g6RYmixVv1N1ECvDpwslZUVNOx9kBnW\ndamoI48cydjSLO6qXcqj76zktJZtHJ3RAsCa5kwez5xBXdb5PowAOTkbOGhkx8cPnQdzJjLWpbK8\nnOpq175GTlnZsZSWxipRFC5fRG4lWzYnNsC2aOpGSkrOav9ctTxEwjEoA4mZnQGsBDYD3wbeAs4B\nbjKzac65LyfwGgXAT4ELgPfRVOWU608xtL4ayNWBk+mpqvuDOyOxzTl9F7/fMZIdY37Ez9/azPjR\n77K/aT/1u6aRmXVS+3ktrVWMP7Qs5mt0Hsw5v7yc+eXlHca6fLCsjPOjHjOVllrcFYTTSaSIXHfT\nfgFaqeKCC4/tsE+1PETCMSgDCXAr8B4wxzm3Pdh3t5lVAP9hZr9wzq3p4TVWAsXAWcDVQOz5cSIp\nED1Itbj46A7VTWtqqsnPr4duRjGcMiuP11+/m2er2sjOm0XbqCm0vL8fMg9c09JaxcFj6zlkbOwa\nJ35F4LouAaMvY13STXn5PDZuuJs//L6KTGLX42ilinPPe53y8q6zTVTLQ2TgDbpAYmYzgRn4xzPb\nOx2+GfgkcDGwrIeXWgsscs69bmZXJ7+l0lkyiqH1VqTqamNTE2zd6ncefjh5h8Wud9LX1YETFWuQ\n6tpXMmnYM5k5cxazYEE5Gzeuaz/WnfMW5XDYYXfx6F/+zO53FtPcdDQtrTm0sYnsnPWMP/SYuGFk\nOPjasiUUT1/Fvb+7hU11HUvHFxVtCMa+xJ76qloeIgNv0AUS4ORg+/cYx1Z3Oicu51z/5pFKr0WX\nLE9Ed8XQElVfXc1hv1/BB/ftx4JVgl1WNn8fX8ibMz9Mzokndji/L6sDJyreINWJEwF2Ul1zI1vq\na5k0MfFVYk+ZlcfUo95i3/vvsH//Gm69eRujDvpElzEjsQyHwZzl5fMoL58XjH3xCzKWlU2ntPSs\nHq5ULQ+RgTYYA0lRsO1S2ds5t8fMdgM9r2kvoYiULI9M+40nGcXQfv2DH3DIffdxXWsrb+zfR0Yw\noPYw4CM7d/LcQw+yYsd2sv7t40DfVwdORHeDVCNmWDbOPQTbzmPXm5lBUOnZju2ZnFs+H7MSnn3m\nBrZsTixQdR7MOZT1dexL+fx5lM+fF9TyCKZ9zz4qbaqyigwlaRFIzOziBE7b6px7DBgdfP5enPP2\nRp0jaSZSsrx2Yy3TimPPTIgUQ+vPlN9HKyo45L77/DoyBQXs27+fd/btg6hZPjMBVq/mvvETyDr0\n0JSuDtzTINUIs2z+/vSztDEZ2JnQa+9tOAIzPwi3P4M5JT7V8hBJvbQIJMCvEzinEngs1Q2R1Fu6\n9LOsXLmSJ558gtzcnPaZN7t27qKxcR9zT5/b7/oj/7znHj4d9fnEwkJyd+/m7T172JeRQVuW/6d/\ncnMzz/zlL1zykx+nLIwkMkg12qj8LRQWLma9W9HtHRUA55qYPfvA1NuSkslMt9/w3LMvMGrkmRw0\nsuubaHeDOUVEwpIugeTgBM5pCrbvBtt4q33lA7v73SJJqYULF7Jw4UJqamrYsNHfCp8/72P9muIb\n4aqrmVJbCxkdKxSPLShgbFCALbpM+oW5ueS1tbHiwQeB5KwYHC3RQaoR4ye0MqHwMNrazsa5hzDL\njnmec02Mzj+LBQvKqahYxb33rGXTpmKa9l9Ka0sDW7euppVfUzD6WA4+eGZCgzlFRMKSFoHEOfdu\nz2e1+1ew7bKITFBbZAzwfDLaJalXUlKSlBASre7VV31hrzhF2CIF2BobG3nz7behoYFlN9xA7pl+\nIGi8BfdSrb5+L29sa+Kdd5pomrGVz33uKiorp1FVtYJR+VvaQ82O7R1n5fzgB3fxhxWTGZFxJSMy\nYEQu5BXChMJjeb+xkffe+xtlx97FN/7PkoQGc4qIhCEtAkkvPR1sZwO/6HQsMj+vauCaI4NNZO2c\nlpZWMrJGMDIvl0lZI3jn+X/QdOSR5JUdQz3ww9XPs3HLli4L7vVWcfHRrH0l/iDVl158lw0bMhl7\nSBGFhRPJH9NK3aZ6rv761cw9fS7XXnt3h7olp55yoG5JRcWqIIzEHjPiw9e/sWH9GGpq6gdFUTMR\nGZ4GXSBxzr1sZi8Ci83sGufcVgAzywC+DOwHfhU538zG4CdWbHfOvR1Gm2VgTS0r4+WcHKbEOLZz\n506aW5rJzc2lqbUVMjKozc6mdOZJHDJxIrUba1nzzNPkzzo17oJ7vVVSUkpDQ+xBqn999G1ycsuY\nNfvAAN+9DVkcfrifhfTSyy+xZctrLF362Q7F0yLuvWctIzKu7LENnUvFi4ikm8G6dsvngRHAk2b2\nBTO7FPgz8BHgu865uqhzzwXWAUsjO8xsnJktinwAhUCGmZ0Xtf/Igft2JJmstJTN07rO4Nm9e3d7\nGGluaW0fY/LyuHEcEty+mFY8jWPzR7H31VeAAwvu9dec2YtZ7/Z32PfSi++Sk1vGUVFtbWxsIz9/\nbPvn06Ydxa43d7Fy5cour1ld7di0qTjhNkRKxYuIpKNBGUicc6uB04Aa4LvAbcB44DLn3H93Or0t\n6iPiGOC+qI+S4Pj9wef3AnNT9x1Iqh130UU812nfnoY95Obm0tra1v6P4Vkg66QPdTivePo0sre8\n1v55ZMG9/liwoJz8/LNxrql934YNmV3CyIgRBRQUjO1w7bTiaTzx5BNdXvPVVze1Vx9NhC8Vv6n3\njRcRGQCD7pFNhHPuBeDjCZz3K6Ie4QT7HmeQhjFJzPzycn69YQPP3ncfHwYaGxvJzPD/y9vaWiHD\nh5GnP3QC0447rsv1UwsPpXbbNvImTUragntXXH5gkOr77zvyR0+juamVpmZobckhP39slzASkYyV\nj0VE0tmgDSQiPbl02TIenT6dX95zDwUvvsgUIKupiZqMTFYfeig5M0+KGUYAJo0fj9u1EyZNSmqb\nFiwoZ8GCcm67/ae8+dYusrLHMSo/j5F5I7u9LtbKx2VlRWTnrAcSKzU/HErFi8jgpUAiQ9r88nLm\nl5dz26238PALLzCmYAyjJoxnam4uGXl5Cb1GKhbcO/zwyTQ1N3FwnDsiiSgtNYqKVrJlc2ILEA6n\nUvEiMvjosYUMC3PPmMfBR0zmmFM+zNSjjiK3ra3b87ft2EHWuEIgNQvuTS+ezq6duxI+f9fOXcyY\n3jUUXXDRsbS0PR3jio5UKl5E0p0CiQwLnVcaHjt6NG3drDxct/NN8iZNStmCe8la+bi8fB7nLdpK\nazeldw6UiteUXxFJXwokMmycftpcamt9od+xBQWMzc6OGUo2bqil6Ygjaa2rS+mCe9Ht6U5PKx9/\nbdkSln29lSOKbqGl7REa99XRuK+OlrZHOGLKzSz7eitfW6ZS8SKS3jLaerh1LV2ZWRFQt2rVKiZP\n7lLBXtLYHXfczq43d7WvNPx29IJ7I0ZQW1vLms31HF08nQs+MjflpeM7t6ezyMrHS5cmVi22utq1\nT+0tKytSZVYRSTcZcQ8okPSeAsngFmul4fot9dS/vo1pxdO56Pzzk7q4Xl/ak8yVj0VE0ogCSTIp\nkAwN0SsNTy+eHnqNj3Rrj4hICiiQJJMCiYiISJ/EDSQa1CoiIiKhUyARERGR0CmQiIiISOgUSERE\nRCR0CiQiIiISOgUSERERCZ0CiYiIiIROgURERERCp0AiIiIioVMgERERkdApkIiIiEjoFEhEREQk\ndAokIiIiEjoFEhEREQmdAomIiIiEToFEREREQqdAIiIiIqFTIBEREZHQKZCIiIhI6BRIREREJHQK\nJCIiIhI6BRIREREJnQKJiIiIhE6BREREREKnQCIiIiKhywq7ASLDSU1NNRs3rgOguPhoSkpKQ26R\niEh6UCARGQCVlRU8VXU/+fn1jJ/QCsDaVzJp2DOZOXMWs2BBecgtFBEJlwKJSIotX349DXsfZNap\nOUBu+/6JEwF2Ul1zI1vqa7ni8qvCaqKISOg0hkQkhSorK2jY+yAzLCfuOTMsmz0ND1FZWTGALRMR\nSS8KJCIp9FTV/d2GkQizbKqqVgxAi0RE0pMCiUiK1NRUk59fn/D5o/K3UFNTncIWiYikLwUSkRTZ\nuHFd+wDWRIyf0No+A0dEZLhRIBEREZHQKZCIpEhx8dHs2J74j9iO7ZlMn16WwhaJiKQvBRKRFCkp\nKaWhYXLC5+9tOAKzkhS2SEQkfSmQiKTQnNmLWe/293iec03Mnr1oAFokIpKeFEhEUmjBgnLy88/G\nuaa45zjXxOj8s1StVUSGNVVqFUmxKy6/isrKaVRVrWBU/pb2mTc7tqt0vIhIREZbW1vYbRh0zKwI\nqFu1ahWTJyc+RkBEi+uJyDCXEe+A7pCIDKCSklKFEBGRGDSGREREREKnQCIiIiKhUyARERGR0CmQ\niIiISOgUSERERCR0CiQiIiISOgUSERERCZ0CiYiIiIROgURERERCp0AiIiIioVMgERERkdApkIiI\niEjoFEhEREQkdAokIiIiEjoFEhEREQmdAomIiIiEToFEREREQqdAIiIiIqFTIBEREZHQKZCIiIhI\n6BRIREREJHQKJCIiIhK6rLAbICLx1dRUs3HjOgCKi4+mpKQ05BaJiKSGAolIGqqsrOCpqvvJz69n\n/IRWANa+kknDnsnMmbOYBQvKQ26hiEhyKZCIpJnly6+nYe+DzDo1B8ht3z9xIsBOqmtuZEt9LVdc\nflVYTRQRSTqNIRFJI5WVFTTsfZAZlhP3nBmWzZ6Gh6isrBjAlomIpJYCiUgaearq/m7DSIRZNlVV\nKwagRSIiA0OBRCRN1NRUk59fn/D5o/K3UFNTncIWiYgMHAUSkTSxceO69gGsiRg/obV9Bo6IyGCn\nQCIiIiKhUyARSRPFxUezY3viP5I7tmcyfXpZClskIjJwFEhE0kRJSSkNDZMTPn9vwxGYlaSwRSIi\nA0eBRCSNzJm9mPVuf4/nOdfE7NmLBqBFIiIDQ4FEJI0sWFBOfv7ZONcU9xznmhidf5aqtYrIkKJK\nrSJp5orLr6KychpVVSsYlb+lfebNju0qHS8iQ1dGW1tb2G0YdMysCKhbtWoVkycn/sxfpLe0uJ6I\nDDEZ8Q7oDolIGispKVUIEZFhQWNIREREJHQKJCIiIhI6BRIREREJnQKJiIiIhE6BREREREKnQCIi\nIiKhUyARERGR0CmQiIiISOgUSERERCR0CiQiIiISOgUSERERCZ0CiYiIiIROgURERERCp0AiIiIi\noVMgERERkdApkIiIiEjoFEhEREQkdAokIiIiEjoFEhEREQmdAomIiIiEToFEREREQqdAIiIiIqFT\nIBEREZHQKZCIiIhI6BRIREREJHQKJCIiIhI6BRIREREJnQKJiIiIhE6BREREREKnQCIiIiKhUyAR\nERGR0CmQiIiISOgUSERERCR0CiQiIiISOgUSERERCZ0CiYiIiIROgURERERCp0AiIiIioVMgERER\nkdApkIiIiEjoFEhEREQkdAokIiIiEjoFEhEREQmdAomIiIiEToFEREREQqdAIiIiIqFTIBEREZHQ\nKZCIiIhI6BRIREREJHQKJCIiIhI6BRIREREJXVbYDRikRgC88cYbYbdDRERk0Jg3b14RUO+ca+58\nTIGkbyYBLFmyJOx2iIiIDCZ1wFRgU+cDCiR98w9gDrANaAm5LSIiIoNJfaydGW1tbQPdEBEREZEO\nNKhVREREQqdAIiIiIqFTIBEREZHQKZCIiIhI6BRIREREJHSa9tsDM5sFfAuYCYwE1gPLnXM3J3h9\nDvDvwBX4udf7gZeBG51zK1LS6JD1t8+iXucS4GZgtHNuyIRnMzsE+DZwDjAR2AU8DHzLOddjtb1k\n9e9g0t8+C16jGPgtcCJwmXPuVylqbtpIwr+12cH1JwF5wBbg98D3nHN7U9VuGZ6GzC/5VDCzM4DH\ngGn4H8rL8b/8bzKznyRwfSbwJ+B64CXgs8A1+MJq95nZZ1PU9ND0t8+C1ygws98Cv8RXxR0yc9PN\nbCTwOPA54H7gU8DtwAXA02Z2cA/X97t/B5v+9lnwGpfhfwZLgl1D5t9UPEn4t7YEeBI4HP9763PA\nGmAZ8Bczy0hZ42VY0h2S7t0KvAfMcc5tD/bdbWYVwH+Y2S+cc2u6uf5C4EzgJufclyI7zexe/JvI\nd/C/IIaS/vYZwEqgGDgLuBpfhG6o+BJwDPB559xtkZ1m9jJQgb/z8dVurk9G/w42/eozM1sK3Abc\nBLzK0PuZi6fP/WZmucBPgdeAmc65PcGhX5rZH/B3XBbgf1ZFkkJ3SOIws5nADOC+qF/8ETcDGcDF\nPbxMC3BvcH4759w2/F8aE8xsfHJaHL4k9RnAWuA459xQ/GV3KdAA/Cx6p3PuT8BWuumfJPbvYNPn\nPgu0AecEfxQ0paSF6ak//TYB/2jmuqgwEhH5uTw2Se0UAXSHpDsnB9u/xzi2utM5MTnn7sUHklgK\n8IFlKD2H7XefATjnhtyjLAAzGwMY8KRzLtYb42qg3MymOufqYhxPSv8OJknoM5xzy1PZxnTU335z\nzr0GXBbn5QuC7btJaaxIQHdI4isKtl1q7gd/MewGjurLCwcDxY4GHhhiA8OKgm3S+2yImBJsY67j\ngL89Dn7wcyxF8a4fwv3b3z4brlLSb8Eg/c/g/5D6Y9+aJhLbsLpDYmaJ3M7e6px7DBgdfP5enPP2\nRp3TmzYUAfcAbwNX9fb6gZYOfTaEJNI/0ef15fqh1r/97bPhKun9FgzSX44fGPyVRGc3iSRqWAUS\n4NcJnFOJn8WQdGZ2PH7K3SjgrHi3mNNMqH0mIuELZuz8FvgkcLNz7oaQmyRD0HALJD1OD+TAoLfI\n89FRcc7Lx98iT4iZLQTuw//FMs85949Erw1ZaH02BCXSP9Hn9eX6oda//e2z4Spp/WZmhcAD+Lo3\n33XOfaffrROJYVgFEudcb35p/SvYTu58wMwKgDHA84m8kJktxj+m2QAsdM5t6kU7QhVWnw1RdfgZ\nH136JxB57r8hzvHh2L/97bPhKin9ZmYTgKeC8z/tnEvkjqlIn2hQa3xPB9vZMY5F6mJU9fQiZvZR\n4C58UaZTB1MY6YOk9NlQFQxgXgN8KKjz0M7MRgCzgNecc/EGIg67/k1Cnw1Lyei3YKZOJT7UfEJh\nRFJNgSQO59zLwIvAYjM7PLI/qE74ZXwJ+F9F7R9jZiVmNjZqXyH+uetrwJnOubcGqv1hSEafDQM/\nAw7CV+2NdjFQCNwZ2RH0TVHk89727xDS5z4b5vrbbzcCHwQucs49ksJ2igDD7JFNH3weP1jzSTO7\nAf98/kLgI8A3Ow1KPRf4OfAN4P8G+74OjMPXIjnDzGJ9jdXBnP+hol99ZmbjgLlR5xQCGWZ2Hr7w\nFwzuPrsNWAJcb2ZTgBeAMnygWINfZiBiHeCA0qh9venfoaJffRaM34qMpTgx2J5kZpEZKDucc0+m\nrvmh6XO/mdkH8KXm1wFZZrYoxusP1X6TkCiQdMM5t9rMTgO+G3zk4n9AYy3M1Rb1EXFC8PmVwUdn\nbfjiQ0PmVmgS+uwY/ODfzufcH/X5oO0z51yzmX0Mv2zAefiFF7fjp1N+2znX2OmStk7X96Z/h4T+\n9hm+3P6UTsejfyYfB85IbqvD189+Oz7YlnLgZ6+zxxmC/SbhyWhrG/JrTImIiEia0xgSERERCZ0C\niYiIiIROgURERERCp0AiIiIioVMgERERkdApkIiIiEjoFEhEREQkdAokIiIiEjoFEhEREQmdSseL\nDCNmNhf4W5zD+4DX8SXBf+ScW9fN68wGPg2cBkzE/3HzOvAqcDfwR+dcc4zrZgFfAE4GDgMag2t+\nB9wW65q+CNavuROYRB/L6pvZBcD/xpdRzwN24Pvmv51z1Qlcfz3wFWCzc25qp2PHAT8OXnsP8BDw\nlRjl3DGzbPw6NE3Ayc65lt5+LyKDgQKJyPC0Gfh/nfaNB2bi1wpaYmbnO+ceiD7BzEbhV5E9H2gA\nVgKRN+di4Gzgk8BzZrbIObc16tql+AXf3sUvOLkZH2YuBG4CFgIf7883ZWb5wI+AK4DIG3ev18cw\ns1vwYWQXfgXlXcCp+MXqzjWz051zz3dz/en4Rey6fH0zGw1UAtnALUAR8DmgFb/eTGffBAw4UWFE\nhjIFEpHhaZtz7sexDpjZ5cAdwJ1mNsU5936wPwO/8OFC4BHgU865HZ2uLcAHlnOBh83sQ8Eib0fg\n33zfwr+xboq65vvAWmChmc1xzj3Vl2/IzPKAl4HJwH8CJcAlfXidj+LDyCbgJOfcm1HHvgNcA/wP\n8NE414/Bh5gXOLC6cLRz8OHvXOfcH6OuuczMvhR9lyhYdffrwHXOubW9/V5EBhONIRGRDpxzdwKv\nAOOAOVGHluDDyFrgE53DSHDtbvzdk38Gu44Ntrn41Ym/GB1Ggmu2A5EQ0r4qr5nNNbNWM/t7gk3P\nwz/WmOWcu44+3BkJnALsBm6KDiOB24Ptqd1cfyP+cdQX4hw/Ltg+FrXvCWAk/k4IAGaWBfwCqAGu\nTajlIoOY7pCISCw1wDH4N9aIyBvsNc65pngXOudazWxW9HgI59xG4Huxzg/GSEzHP7J4MepQW6dt\nT/YAx0fu6PSVc+57xGkr/jEVxPljzszOAT6Fv6PxnJnFOq0g+Dq7o/a9E30ssAz4ADAzWWNrRNKZ\nAomIxDIx2L4J7Y8UTsQPfH24p4tjDc6MZmZF+DsCRwNfwj9e+UangbSrg/0JBYxgfEW/wkgCImNc\nHut8wMzG4x91rQG+3c1rRB6B5UX1U16wfS84Vop/NPRDYIeZ/Ql/56YZ+CtwpXNuT/++FZH0okAi\nIh2Y2Qz84Na9wJPB7iIgA6jr7u5IL/wr6r/XAWc65zq8yQd3OtYn4WslhZkdjh8w24Ifo9LZcmA0\ncEkPdzRqgu0JwDNR/90MrDezTODn+D76L/zjrEL8YOM8fOjJBC7uz/cjkm4USESGpzwzm4IPGRGH\n4qehXoP/3fD1qMcKo4NtA8lxDv4OSRn+jfURM/sv59z3k/T6SWVm0/F3hiYCn3fOvdDp+P/CzzD6\nRgKDT/+EDzb/Y2ZfxM9OuhB40Dn3npl9BTgJmI0fg3MicIVz7s/B1zoB+JqZXdnpsY/IoKZBrSLD\n03FAHf6v8MjHP/B/fWcAn3HO/STq/HeD7dhkfHHn3APOuXudc9fgx6q8AnzPzOYn4/WTKai58gz+\nLtG/O+du73R8KvCT4Jwf9PR6zrl64Ep8LZYX8FOgXwO+aGbF+AGsNzrnnsXfOQGInmL8Ej4wnoDI\nEKI7JCLDkwOu7rRvP7A1zl/4tfhHFUVmdrBz7p0Y5/StIf6uwE/wU2UvBR5N1iEXvZkAAAL6SURB\nVGv3l5l9Ch/S3gfOidyliDqeiW93K3Cpcy6hAbjOuZ+ZWQX+Dsg7+EDWii+8Vs+BR0Ljgm30nZC3\ng21hb78fkXSmQCIyPL3duehZd4LQ8Ax+GvCF+AJncQV3DUqdcw8Hnx+PvyOw2jn3UoxLItNrD0+0\nTakWVY9lM/DxOJVrj8A/WgHYGGdWTZGZtQI459rvSjvn3sJP9418vSuD15obY1BwZoz/zkBkCFEg\nEZFE3YAPJNeY2f0xanQA7fUzfgbMNbNPOed+g6/bcRPwS+AzMS4rCbavJ73VfWBm5+JrjqzHB4Q3\n4py6Gz8eJNadkQzgq/jHXXf08PWm4Iut3dqpMFyk1sskDgwEnhRst/fwbYgMKgokIpIQ51yFmd0P\nLAYeC0rL10SfY2bj8MW85uJnh9wVHHoA/8a9xMzuCMZHRK4p5ECZ9T9E7R+JL5T2nnPutVR8T2Y2\nDV/CvTYyeyiYTfNz/KORed2EEYJHV1/r5vW/CrzlnFvWQ1PuBHbS9THac8F2AfB08N8L8dOvX0Bk\nCFEgEZHeuBg/HfjTwBoz+yu+mFkTvrjZJ4FRwO+JGlPhnHvNzL6MXz/nCTNbgR/HMhFf2fUQYIVz\n7g9RX2smfiHA5/A1OLoVLNw3K2pXWbBdGNQIIWjL9VHnrAKOxN+hiUwx/k9gDL7WyEVxHsMA/C4Y\noNovwaOhecB859x70cecc2vNrAq4OlgD5xDgAuB21SGRoUaBRGR46Ws5dQCCuwifMbPl+Ecvp+HH\nPeQC2/BTWm9xznUp9+6cu9XM1uBXwD0Df6dlL76Q2K+ccz+P09ZE2zyfjgXJ2oKPC4KPyL7rY5wT\nrTTY95HgI5Y2fOG2fgWS4G7MD4E7nXOr4py2CPgpcDl+deTb8MXkRIaUjLa2fv1+EhEREek31SER\nERGR0CmQiIiISOgUSERERCR0CiQiIiISOgUSERERCZ0CiYiIiIROgURERERCp0AiIiIioVMgERER\nkdApkIiIiEjoFEhEREQkdP8fqlh/j3efueYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15c934d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "to_plot = pd.merge(complete_dom_evecs, POPINFO, left_on ='POP', right_on = 'CLUSTER' ).sort_values('map_order')\n",
    "sns.lmplot(data = to_plot, x='PC3', y='PC4', hue = 'map_order', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 7, aspect = 1.1,\n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "plt.xlabel('PC3: {0:.3g}%'.format(complete_dom_evals['percent_explained'][2]))\n",
    "plt.ylabel('PC4: {0:.3g}%'.format(complete_dom_evals['percent_explained'][3]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .24, s = 'b', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "#plt.savefig(os.path.join('paper','figures', 'supplemental','PCA_b_3_4.png'), bbox_inches='tight')\n",
    "\n",
    "to_plot = pd.merge(paralogs_dom_evecs, POPINFO, left_on ='POP', right_on = 'CLUSTER' ).sort_values('map_order')\n",
    "\n",
    "sns.lmplot(data = to_plot, x='PC3', y='PC4', hue = 'map_order', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 7, aspect = 1.1, \n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "plt.xlabel('PC3: {0:.3g}%'.format(paralogs_dom_evals['percent_explained'][2]))\n",
    "plt.ylabel('PC4: {0:.3g}%'.format(paralogs_dom_evals['percent_explained'][3]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .24, s = 'a', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "#plt.savefig(os.path.join('paper','figures', 'supplemental','PCA_a_3_4.png'), bbox_inches='tight')\n",
    "\n",
    "\n",
    "to_plot = pd.merge(complete_dom_subsample_evecs, POPINFO, left_on ='POP', right_on = 'CLUSTER' ).sort_values('map_order')\n",
    "\n",
    "sns.lmplot(data = to_plot, x='PC3', y='PC4', hue = 'map_order', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 7, aspect = 1.1, \n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "plt.xlabel('PC3: {0:.3g}%'.format(complete_dom_subsample_evals['percent_explained'][2]))\n",
    "plt.ylabel('PC4: {0:.3g}%'.format(complete_dom_subsample_evals['percent_explained'][3]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .20, s = 'c', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "#plt.savefig(os.path.join('paper','figures', 'supplemental','PCA_c_3_4.png'), bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+jJLrK4lPtABweTy43W56e7oJdndz9KiDPx3Kp7P7Q5jhZYQNi3f7j/NW5z5y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HZGtmmz/WOH/X8EpgGb9FDXIZTJqjsC/0mZE3MNsDPwrz3cvXIYZYTjax1Gco4DrqZc6rkXWES5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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f5739b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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M5t+6Td9NTY1F0xpJSgomIsJZtGzmzDBmzuw/briq0o50uvKVclfwzR9r0IyU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\n9+cO/qwde4ckSe2iKDqBgrH3KVycnOvWykfr1kX05rwBeG3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8SDY0q62p5SNtClH3Ph44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dxefmG3IAQiigLG2GHTU6CwAJPguplkO52pL5FI8QKRcuA4cLW19rXwQWPMMuBZ4FOAAhEZsoqL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20b7b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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IFV3XNwKfAf4E7AEW4j9v5tqy798CDKXUtVVYr77Xa8DjBDj0Ttf1ZKAsPz+f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RCE4l1LWXyBM3V3zqQ5+F+awfGSoDu7KL5w74YxRf2uZ5PMMvSlpQspLV3oLnrnhMiSkkKKi5cm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\niIh45v8D5GoUi3/+yGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2111f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data = complete_dom_evecs, x='PC5', y='PC6', hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 7, aspect = 1.1,\n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "plt.xlabel('PC5: {0:.3g}%'.format(complete_dom_evals['percent_explained'][4]))\n",
    "plt.ylabel('PC6: {0:.3g}%'.format(complete_dom_evals['percent_explained'][5]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .24, s = 'b', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join('paper','figures', 'supplemental','PCA_b_5_6.png'), bbox_inches='tight')\n",
    "\n",
    "\n",
    "sns.lmplot(data = paralogs_dom_evecs, x='PC5', y='PC6', hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 7, aspect = 1.1, \n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "plt.xlabel('PC5: {0:.3g}%'.format(paralogs_dom_evals['percent_explained'][4]))\n",
    "plt.ylabel('PC6: {0:.3g}%'.format(paralogs_dom_evals['percent_explained'][5]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .24, s = 'a', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join('paper','figures', 'supplemental','PCA_a_5_6.png'), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "sns.lmplot(data = complete_dom_subsample_evecs, x='PC5', y='PC6', hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 7, aspect = 1.1, \n",
    "           scatter_kws={\"s\": 150., 'alpha': .8, 'edgecolor':\"black\", 'linewidths':.5}, legend = False)\n",
    "plt.xlabel('PC5: {0:.3g}%'.format(complete_dom_subsample_evals['percent_explained'][4]))\n",
    "plt.ylabel('PC6: {0:.3g}%'.format(complete_dom_subsample_evals['percent_explained'][5]))\n",
    "plt.xticks([-.2, -.1, 0, .1, .2 ])\n",
    "plt.yticks([-.1, 0, .1, .2 ])\n",
    "plt.text(-.16, .20, s = 'c', family = 'sans-serif', fontsize=60, fontweight = 'bold')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join('paper','figures', 'supplemental','PCA_c_5_6.png'), bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-6e6df518a476>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "assert False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_evecs(data, ax1 = 'PC1', ax2 = 'PC2', title = 'untitled', figname = 'fig0.png', pop_colors = pop_colors):\n",
    "    sns.lmplot(data = data, x=ax1, y=ax2, hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 7, scatter_kws={\"s\": 150., 'alpha': .6} )\n",
    "    plt.title(title)\n",
    "    sns.despine()    \n",
    "    plt.savefig('/home/ipseg/Desktop/waples/chum_populations/paper/figures/{}'.format(figname))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plot_evecs(complete_codom_evecs, title ='PCA - complete - codom', figname= 'PCA_complete_codom.png')\n",
    "plot_evecs(complete_dom_evecs, title ='PCA - complete - dom', figname='PCA_complete_dom.png')\n",
    "\n",
    "plot_evecs(on_map_codom_evecs, title ='PCA - on_map - codom', figname='PCA_on_map_codom.png')\n",
    "plot_evecs(on_map_dom_evecs, title ='PCA - on_map - dom', figname='PCA_on_map_dom.png')\n",
    "\n",
    "plot_evecs(paralogs_dom_evecs, title ='PCA - paralogs', figname='PCA_paralogs.png')\n",
    "\n",
    "plot_evecs(complete_codom_subsample_evecs, title ='PCA - complete - codom - subsample', figname='PCA_complete_codom_subsample.png')\n",
    "plot_evecs(complete_dom_subsample_evecs, title ='PCA - complete - dom - subsample', figname='PCA_complete_dom_subsample.png')\n",
    "\n",
    "plot_evecs(on_map_codom_subsample_evecs, title ='PCA - on_map - codom - subsample', figname='PCA_on_map_codom_subsample.png')\n",
    "plot_evecs(on_map_dom_subsample_evecs, title ='PCA - on_map - dom - subsample', figname='PCA_on_map_dom_subsample.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Scree plot of eigenvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_evals(eval_file):\n",
    "    evals = pd.read_csv(eval_file, sep = ' ', skipinitialspace = True, header = None)\n",
    "    evals['percent_explained'] = 100*evals[0]/np.sum(evals[0])\n",
    "    return(evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "complete_codom_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','complete.codom.eval'))\n",
    "complete_dom_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','complete.dom.eval'))\n",
    "\n",
    "on_map_codom_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','on_map.codom.eval'))\n",
    "on_map_dom_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','on_map.dom.eval'))\n",
    "\n",
    "paralogs_dom_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','paralogs.dom.eval'))\n",
    "\n",
    "complete_codom_subsample_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','complete.codom.subsample.eval'))\n",
    "complete_dom_subsample_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','complete.dom.subsample.eval'))\n",
    "on_map_codom_subsample_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','on_map.codom.subsample.eval'))\n",
    "on_map_dom_subsample_evals = load_evals(os.path.join('results','batch_4','EIGENSOFT','on_map.dom.subsample.eval'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(8,8)\n",
    "   \n",
    "plt.plot(complete_codom_evals['percent_explained'], label = 'complete_codom_evals', c = sns.xkcd_palette(pop_colors)[0])\n",
    "plt.plot(complete_dom_evals['percent_explained'], label = 'complete_dom_evals', c = sns.xkcd_palette(pop_colors)[1])\n",
    "plt.plot(on_map_codom_evals['percent_explained'], label = 'on_map_codom_evals', c = sns.xkcd_palette(pop_colors)[2])\n",
    "plt.plot(on_map_dom_evals['percent_explained'], label = 'on_map_dom_evals', c = sns.xkcd_palette(pop_colors)[3])\n",
    "plt.plot(paralogs_dom_evals['percent_explained'], label = 'paralogs_dom_evals', c = sns.xkcd_palette(pop_colors)[4])\n",
    "\n",
    "plt.plot(complete_codom_subsample_evals['percent_explained'], label = 'complete_codom_subsample_evals', c = sns.xkcd_palette(pop_colors)[5])\n",
    "plt.plot(complete_dom_subsample_evals['percent_explained'], label = 'complete_dom_subsample_evals', c = sns.xkcd_palette(pop_colors)[6])\n",
    "plt.plot(on_map_codom_subsample_evals['percent_explained'], label = 'on_map_codom_subsample_evals', c = sns.xkcd_palette(pop_colors)[7])\n",
    "plt.plot(on_map_dom_subsample_evals['percent_explained'], label = 'on_map_dom_subsample_evals', c = sns.xkcd_palette(pop_colors)[8])\n",
    "\n",
    "plt.xlim(-.5,7)\n",
    "plt.xticks(range(0,8), range(1,9))\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.xlabel('PC axis')\n",
    "plt.ylabel('Percent explained')\n",
    "sns.despine()\n",
    "plt.savefig('/home/ipseg/Desktop/waples/chum_populations/paper/figures/supplemental/PCA_eigenvalues.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot snp weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_snpweights(snpweights_file):\n",
    "    snpweights = pd.read_csv(snpweights_file, sep = ' ', skipinitialspace = True, header = None)\n",
    "    snpweights.columns = ['locus', 'LG', 'pos'] +  ['wPC{}'.format(x) for x in range(1,11)]\n",
    "    return(snpweights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "complete_codom_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','complete.codom.snpweights'))\n",
    "complete_dom_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','complete.dom.snpweights'))\n",
    "\n",
    "on_map_codom_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','on_map.codom.snpweights'))\n",
    "on_map_dom_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','on_map.dom.snpweights'))\n",
    "\n",
    "paralogs_dom_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','paralogs.dom.snpweights'))\n",
    "\n",
    "complete_codom_subsample_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','complete.codom.subsample.snpweights'))\n",
    "complete_dom_subsample_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','complete.dom.subsample.snpweights'))\n",
    "on_map_codom_subsample_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','on_map.codom.subsample.snpweights'))\n",
    "on_map_dom_subsample_snpweights = load_snpweights(os.path.join('results','batch_4','EIGENSOFT','on_map.dom.subsample.snpweights'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.pairplot(complete_codom_snpweights, \n",
    "             vars = ['wPC{}'.format(x) for x in range(1,7)], \n",
    "             plot_kws={'alpha':.7, 's':30, 'linewidth':0 }, diag_kind=\"hist\", markers = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.pairplot(complete_dom_snpweights, \n",
    "             vars = ['wPC{}'.format(x) for x in range(1,7)], \n",
    "             plot_kws={'alpha':.7, 's':30, 'linewidth':0 }, diag_kind=\"hist\", markers = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.pairplot(paralogs_dom_snpweights, \n",
    "             vars = ['wPC{}'.format(x) for x in range(1,7)], \n",
    "             plot_kws={'alpha':.7, 's':30, 'linewidth':0 }, diag_kind=\"hist\", markers = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.pairplot(on_map_codom_snpweights, \n",
    "             vars = ['wPC{}'.format(x) for x in range(1,7)], \n",
    "             plot_kws={'alpha':.5, 's':30, 'linewidth':0 }, diag_kind=\"hist\", markers = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from skbio.stats.spatial import procrustes\n",
    "help(procrustes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"disparity\" is equivalent to the \"sum of squared differences between X and Yrot\" as reported by the vegan R pacakge.  Use R for a more developed method including residuals, and permutation-based significance testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mtx1, mtx2, disparity = procrustes(complete_dom_evecs.iloc[:,1:7], complete_codom_evecs.iloc[:,1:7])\n",
    "disparity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mtx1, mtx2, disparity = procrustes(complete_dom_subsample_evecs.iloc[:,1:11], paralogs_dom_evecs.iloc[:,1:11])\n",
    "disparity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formally test for population structure\n",
    "using the twstats program from EIGENSTRAT.\n",
    "\n",
    "This program compares the distribution of output eigenvalues to those generated by the Tracy-Widom distribution, see Patterson (2006)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### run twstats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "! /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/bin/twstats \\\n",
    "-t /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/POPGEN/twtable \\\n",
    "-i /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/paralogs.dom.eval \\\n",
    "-o /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/paralogs.dom.tw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/bin/twstats \\\n",
    "-t /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/POPGEN/twtable \\\n",
    "-i /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.eval \\\n",
    "-o /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.tw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!/home/ipseg/Programs/EIGENSOFT/EIG6.0.1/bin/twstats \\\n",
    "-t /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/POPGEN/twtable \\\n",
    "-i /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/complete.dom.eval \\\n",
    "-o /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/complete.dom.tw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!/home/ipseg/Programs/EIGENSOFT/EIG6.0.1/bin/twstats \\\n",
    "-t /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/POPGEN/twtable \\\n",
    "-i /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.subsample.eval \\\n",
    "-o /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.subsample.tw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "! /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/bin/twstats \\\n",
    "-t /home/ipseg/Programs/EIGENSOFT/EIG6.0.1/POPGEN/twtable \\\n",
    "-i /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/paralogs.dom.eval \\\n",
    "-o /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/paralogs.dom.tw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Tracy-Widom stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "File /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.tw does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-b116c1f07970>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m tw_onmap_codom = pd.read_csv(\n\u001b[0;32m      2\u001b[0m     \u001b[1;34m'/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.tw'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m            sep = ' ', skipinitialspace = True)\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\IPSEG\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)\u001b[0m\n\u001b[0;32m    489\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    490\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    492\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    493\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\IPSEG\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 268\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\IPSEG\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    581\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    582\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 583\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    584\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_options_with_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\IPSEG\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m    722\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    723\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 724\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    725\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    726\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\IPSEG\\Anaconda\\lib\\site-packages\\pandas\\io\\parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1091\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1093\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1095\u001b[0m         \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas\\parser.c:3229)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas\\parser.c:6042)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: File /home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.tw does not exist"
     ]
    }
   ],
   "source": [
    "tw_onmap_codom = pd.read_csv(\n",
    "    '/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.tw',\n",
    "           sep = ' ', skipinitialspace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tw_onmap_dom = pd.read_csv(os.path.join('results','batch_4','EIGENSOFT', 'on_map.dom.tw'), sep = ' ', skipinitialspace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tw_paralogs_dom = pd.read_csv(os.path.join('results','batch_4','EIGENSOFT', 'paralogs.dom.tw'),\n",
    "           sep = ' ', skipinitialspace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tw_complete_dom = pd.read_csv(os.path.join('results','batch_4','EIGENSOFT', 'complete.dom.tw'),\n",
    "           sep = ' ', skipinitialspace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tw_subsample_codom = pd.read_csv(os.path.join('results','batch_4','EIGENSOFT', 'on_map.codom.subsample.tw'),\n",
    "           sep = ' ', skipinitialspace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#N</th>\n",
       "      <th>eigenvalue</th>\n",
       "      <th>difference</th>\n",
       "      <th>twstat</th>\n",
       "      <th>p-value</th>\n",
       "      <th>effect.</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.819728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.892</td>\n",
       "      <td>7.155550e-133</td>\n",
       "      <td>1402.534</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.527649</td>\n",
       "      <td>-0.292079</td>\n",
       "      <td>51.406</td>\n",
       "      <td>1.022560e-108</td>\n",
       "      <td>1604.912</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.030819</td>\n",
       "      <td>-0.496830</td>\n",
       "      <td>24.982</td>\n",
       "      <td>4.447890e-38</td>\n",
       "      <td>1791.222</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.841122</td>\n",
       "      <td>-0.189697</td>\n",
       "      <td>14.272</td>\n",
       "      <td>1.776790e-17</td>\n",
       "      <td>1879.609</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.722421</td>\n",
       "      <td>-0.118701</td>\n",
       "      <td>7.334</td>\n",
       "      <td>5.373120e-08</td>\n",
       "      <td>1937.363</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #N  eigenvalue  difference  twstat        p-value   effect.   n\n",
       "0   1    2.819728         NaN  58.892  7.155550e-133  1402.534 NaN\n",
       "1   2    2.527649   -0.292079  51.406  1.022560e-108  1604.912 NaN\n",
       "2   3    2.030819   -0.496830  24.982   4.447890e-38  1791.222 NaN\n",
       "3   4    1.841122   -0.189697  14.272   1.776790e-17  1879.609 NaN\n",
       "4   5    1.722421   -0.118701   7.334   5.373120e-08  1937.363 NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_paralogs_dom.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YIzfbtzzwetq3l09fTrOzu080sw2IMedT3H1A2pfV0u7u7uPa8u+RtpEPuGb2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\nEhY+FAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x216b1c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(8,4)\n",
    "#plt.plot(-np.log10(tw_onmap_codom['p-value']), label = 'all_codom')\n",
    "#plt.plot(-np.log10(tw_onmap_dom['p-value']), label = 'all_dom')\n",
    "#plt.plot(-np.log10(tw_paralogs_dom['p-value']), label = 'paralogs')\n",
    "#plt.xlim(-1,10)\n",
    "#plt.bar(1, np.sum(tw_onmap_codom['p-value'] < .01), label = 'all_codom', color = 'green', align='center', width=0.3)\n",
    "plt.bar(1, np.sum(tw_paralogs_dom['p-value'] < .05), label = 'paralogs', color = 'black', align='center', width=0.3)\n",
    "plt.bar(2, np.sum(tw_complete_dom['p-value'] < .05), label = 'all_dom', color = 'black', align='center', width=0.3)\n",
    "plt.bar(3, np.sum(tw_subsample_codom['p-value'] < .05), label = 'subsample', color = 'black', align='center', width=0.3)\n",
    "#plt.gcf().tight_layout()\n",
    "#plt.legend()\n",
    "plt.gcf().subplots_adjust(bottom=0.15)\n",
    "plt.xticks([1,2,3],['a (paralogs)', 'b (all)', 'c (all_subsample)'], rotation= 50)\n",
    "sns.despine()\n",
    "plt.savefig(os.path.join('paper','figures', 'supplemental','TW_stats.png'), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "figsize(10, 10)\n",
    "sns.lmplot('PC2', 'PC3', data= eigenvec_on_map_dom, hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 8, scatter_kws={\"s\": 200., 'alpha': .6} )\n",
    "plt.title('mapped loci - dominance coding from haplotypes file')\n",
    "plt.show()\n",
    "\n",
    "sns.lmplot('PC2', 'PC3', data= eigenvec_on_map_codom, hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 8, scatter_kws={\"s\": 200., 'alpha': .6} )\n",
    "plt.title('mapped loci - codiminant genotypes')\n",
    "plt.show()\n",
    "\n",
    "sns.lmplot('PC2', 'PC3', data= eigenvec_paralogs, hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 8, scatter_kws={\"s\": 200., 'alpha': .6} )\n",
    "plt.title('paralogs - dominance coding')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.lmplot('PC2', 'PC4', data= eigenvec_paralogs, hue = 'POP', palette=sns.xkcd_palette(pop_colors),\n",
    "           fit_reg=False, size = 8, scatter_kws={\"s\": 200., 'alpha': .6} )\n",
    "plt.title('paralogs - dominance coding')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### TODO \n",
    "   * look at evalues - include on plots\n",
    "   * down sample to same number of loci\n",
    "   * population names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Examine the inferred relationship matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "paralogs_rel = pd.read_csv('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/paralogs.dom.rel', header = None, sep = ' ')\n",
    "paralogs_rel.columns = ['ind1', 'ind2', 'numloci', 'cov']\n",
    "paralogs_rel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.spatial.distance\n",
    "from sklearn.cross_decomposition import CCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_rel_matrix(rel_file):\n",
    "    rel = pd.read_csv(rel_file, header = None, sep = ' ')\n",
    "    rel.columns = ['ind1', 'ind2', 'numloci', 'cov']\n",
    "    rel_mat = np.zeros(shape = (max(rel['ind1']), max(rel['ind1'])))\n",
    "    for idx, row in rel.iterrows():\n",
    "        rel_mat[rel['ind1']-1, rel['ind2']-1] = rel['cov']\n",
    "        rel_mat[rel['ind2']-1, rel['ind1']-1] = rel['cov']\n",
    "    np.fill_diagonal(rel_mat, 0)\n",
    "    #rel_sq = scipy.spatial.distance.squareform(rel['cov'])\n",
    "    sns.heatmap(rel_mat, linewidth = 0, square = True, xticklabels = False, yticklabels = False, robust = True)\n",
    "    plt.show()\n",
    "    #return(rel_mat)\n",
    "\n",
    "def get_rel_mat(rel_file):\n",
    "    rel = pd.read_csv(rel_file, header = None, sep = ' ')\n",
    "    rel.columns = ['ind1', 'ind2', 'numloci', 'cov']\n",
    "    rel_mat = np.zeros(shape = (max(rel['ind1']), max(rel['ind1'])))\n",
    "    for idx, row in rel.iterrows():\n",
    "        rel_mat[rel['ind1']-1, rel['ind2']-1] = rel['cov']\n",
    "        rel_mat[rel['ind2']-1, rel['ind1']-1] = rel['cov']\n",
    "    np.fill_diagonal(rel_mat, 1)\n",
    "    return(rel_mat)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rel_mat1 = get_rel_mat('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.rel')\n",
    "rel_mat2 = get_rel_mat('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.dom.rel')\n",
    "rel_mat3 = get_rel_mat('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/paralogs.dom.rel')\n",
    "rel_mat4 = get_rel_mat('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.subsample.rel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cd /home/ipseg/Desktop/waples/chum_populations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savetxt(fname = os.path.join('results', 'batch_4', 'EIGENSOFT', 'on_map.codom.relmatrix'), X = rel_mat1, fmt = '%10.7f')\n",
    "np.savetxt(fname = os.path.join('results', 'batch_4', 'EIGENSOFT', 'on_map.dom.relmatrix'), X = rel_mat2, fmt = '%10.7f')\n",
    "np.savetxt(fname = os.path.join('results', 'batch_4', 'EIGENSOFT', 'paralogs.dom.relmatrix'), X = rel_mat3, fmt = '%10.7f')\n",
    "np.savetxt(fname = os.path.join('results', 'batch_4', 'EIGENSOFT', 'on_map.codom.subsample.relmatrix'), X = rel_mat4, fmt = '%10.7f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print rel_mat1.shape\n",
    "print rel_mat2.shape\n",
    "print rel_mat3.shape\n",
    "print rel_mat4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#rel_mat1 = rel_mat1[0:171,0:171]\n",
    "#rel_mat2 = rel_mat2[0:171,0:171]\n",
    "#rel_mat3 = rel_mat3[0:171,0:171]\n",
    "#rel_mat4 = rel_mat4[0:171,0:171]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.heatmap(np.corrcoef(rel_mat1, rel_mat2), linewidths=0, square = True, xticklabels = False, yticklabels = False, robust = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cca = CCA(scale = True, n_components=2)\n",
    "my_fit = cca.fit(X = rel_mat1, Y= rel_mat2)\n",
    "t_rel_mat1, t_rel_mat2 = my_fit.transform(X = rel_mat1, Y= rel_mat2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot(t_rel_mat1[:, 0], t_rel_mat2[:, 0], \"ob\")\n",
    "plt.show()\n",
    "np.corrcoef(t_rel_mat1[:, 0], t_rel_mat2[:, 0])[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cca = CCA(scale = True, n_components=2)\n",
    "my_fit_b  = cca.fit(rel_mat3, rel_mat4)\n",
    "t_rel_mat3, t_rel_mat4 = my_fit_b.transform(rel_mat3, rel_mat4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.plot(t_rel_mat3[:, 0], t_rel_mat4[:, 0], \"ob\")\n",
    "plt.show()\n",
    "np.corrcoef(t_rel_mat3[:, 0], t_rel_mat4[:, 0])[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "figsize(6,6)\n",
    "plot_rel_matrix('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.rel')\n",
    "plot_rel_matrix('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.dom.rel')\n",
    "plot_rel_matrix('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/paralogs.dom.rel')\n",
    "plot_rel_matrix('/home/ipseg/Desktop/waples/chum_populations/results/batch_4/EIGENSOFT/on_map.codom.subsample.rel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename=os.path.join('paper','figures','CCA.png') )"
   ]
  }
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