social adult play in howler monkeys study RScripts .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b1238f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 26</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>SITE</th><th scope=col>Subsp</th><th scope=col>HABITAT</th><th scope=col>AREA</th><th scope=col>DENS</th><th scope=col>GR</th><th scope=col>FEM</th><th scope=col>MAL</th><th scope=col>⋯</th><th scope=col>PLAYSEC</th><th scope=col>OBSSEC</th><th scope=col>IM.RATIO</th><th scope=col>NOPLAYSECS</th><th scope=col>ad_ad_play</th><th scope=col>ad_im_play</th><th scope=col>mix_play</th><th scope=col>non_ad_ad_play</th><th scope=col>non_ad_im_play</th><th scope=col>non_mix_play</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>amp</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>14</td><td>5</td><td>⋯</td><td> 2043</td><td>118800</td><td>0.2105263</td><td>116757</td><td>1383</td><td> 240</td><td> 420</td><td>117417</td><td>118560</td><td>118380</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>amr</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>6</td><td>⋯</td><td>12705</td><td>496800</td><td>0.2777778</td><td>484095</td><td>1310</td><td>9775</td><td>1620</td><td>495490</td><td>487025</td><td>495180</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>azt</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>6</td><td>⋯</td><td> 3990</td><td>331200</td><td>0.2777778</td><td>327210</td><td>2390</td><td>1250</td><td> 350</td><td>328810</td><td>329950</td><td>330850</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>cha</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>13</td><td>7</td><td>⋯</td><td> 1770</td><td>136800</td><td>0.2500000</td><td>135030</td><td>1480</td><td>  40</td><td> 250</td><td>135320</td><td>136760</td><td>136550</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>chu</td><td>m</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>20</td><td>12</td><td>5</td><td>⋯</td><td>  725</td><td>176040</td><td>0.2941176</td><td>175315</td><td> 335</td><td> 390</td><td>   0</td><td>175705</td><td>175650</td><td>176040</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>cri</td><td>m</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>8</td><td>⋯</td><td>  360</td><td> 75600</td><td>0.2500000</td><td> 75240</td><td> 360</td><td>   0</td><td>   0</td><td> 75240</td><td> 75600</td><td> 75600</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 26\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & ID & SEX & SITE & Subsp & HABITAT & AREA & DENS & GR & FEM & MAL & ⋯ & PLAYSEC & OBSSEC & IM.RATIO & NOPLAYSECS & ad\\_ad\\_play & ad\\_im\\_play & mix\\_play & non\\_ad\\_ad\\_play & non\\_ad\\_im\\_play & non\\_mix\\_play\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <int> & <int> & <int> & ⋯ & <int> & <int> & <dbl> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & amp & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 14 & 5 & ⋯ &  2043 & 118800 & 0.2105263 & 116757 & 1383 &  240 &  420 & 117417 & 118560 & 118380\\\\\n",
       "\t2 & amr & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 6 & ⋯ & 12705 & 496800 & 0.2777778 & 484095 & 1310 & 9775 & 1620 & 495490 & 487025 & 495180\\\\\n",
       "\t3 & azt & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 6 & ⋯ &  3990 & 331200 & 0.2777778 & 327210 & 2390 & 1250 &  350 & 328810 & 329950 & 330850\\\\\n",
       "\t4 & cha & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 13 & 7 & ⋯ &  1770 & 136800 & 0.2500000 & 135030 & 1480 &   40 &  250 & 135320 & 136760 & 136550\\\\\n",
       "\t5 & chu & m & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 20 & 12 & 5 & ⋯ &   725 & 176040 & 0.2941176 & 175315 &  335 &  390 &    0 & 175705 & 175650 & 176040\\\\\n",
       "\t6 & cri & m & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 8 & ⋯ &   360 &  75600 & 0.2500000 &  75240 &  360 &    0 &    0 &  75240 &  75600 &  75600\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 26\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | SEX &lt;chr&gt; | SITE &lt;chr&gt; | Subsp &lt;chr&gt; | HABITAT &lt;chr&gt; | AREA &lt;dbl&gt; | DENS &lt;dbl&gt; | GR &lt;int&gt; | FEM &lt;int&gt; | MAL &lt;int&gt; | ⋯ ⋯ | PLAYSEC &lt;int&gt; | OBSSEC &lt;int&gt; | IM.RATIO &lt;dbl&gt; | NOPLAYSECS &lt;int&gt; | ad_ad_play &lt;int&gt; | ad_im_play &lt;int&gt; | mix_play &lt;int&gt; | non_ad_ad_play &lt;int&gt; | non_ad_im_play &lt;int&gt; | non_mix_play &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | amp | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 14 | 5 | ⋯ |  2043 | 118800 | 0.2105263 | 116757 | 1383 |  240 |  420 | 117417 | 118560 | 118380 |\n",
       "| 2 | amr | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 6 | ⋯ | 12705 | 496800 | 0.2777778 | 484095 | 1310 | 9775 | 1620 | 495490 | 487025 | 495180 |\n",
       "| 3 | azt | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 6 | ⋯ |  3990 | 331200 | 0.2777778 | 327210 | 2390 | 1250 |  350 | 328810 | 329950 | 330850 |\n",
       "| 4 | cha | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 13 | 7 | ⋯ |  1770 | 136800 | 0.2500000 | 135030 | 1480 |   40 |  250 | 135320 | 136760 | 136550 |\n",
       "| 5 | chu | m | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 20 | 12 | 5 | ⋯ |   725 | 176040 | 0.2941176 | 175315 |  335 |  390 |    0 | 175705 | 175650 | 176040 |\n",
       "| 6 | cri | m | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 8 | ⋯ |   360 |  75600 | 0.2500000 |  75240 |  360 |    0 |    0 |  75240 |  75600 |  75600 |\n",
       "\n"
      ],
      "text/plain": [
       "  ID  SEX SITE Subsp    HABITAT AREA DENS GR FEM MAL ⋯ PLAYSEC OBSSEC IM.RATIO \n",
       "1 amp f   AGA  mexicana SEMIDEC 8.3  9.5  24 14  5   ⋯  2043   118800 0.2105263\n",
       "2 amr f   AGA  mexicana SEMIDEC 8.3  9.5  24 12  6   ⋯ 12705   496800 0.2777778\n",
       "3 azt f   AGA  mexicana SEMIDEC 8.3  9.5  24 12  6   ⋯  3990   331200 0.2777778\n",
       "4 cha f   AGA  mexicana SEMIDEC 8.3  9.5  24 13  7   ⋯  1770   136800 0.2500000\n",
       "5 chu m   AGA  mexicana SEMIDEC 8.3  9.5  20 12  5   ⋯   725   176040 0.2941176\n",
       "6 cri m   AGA  mexicana SEMIDEC 8.3  9.5  24 12  8   ⋯   360    75600 0.2500000\n",
       "  NOPLAYSECS ad_ad_play ad_im_play mix_play non_ad_ad_play non_ad_im_play\n",
       "1 116757     1383        240        420     117417         118560        \n",
       "2 484095     1310       9775       1620     495490         487025        \n",
       "3 327210     2390       1250        350     328810         329950        \n",
       "4 135030     1480         40        250     135320         136760        \n",
       "5 175315      335        390          0     175705         175650        \n",
       "6  75240      360          0          0      75240          75600        \n",
       "  non_mix_play\n",
       "1 118380      \n",
       "2 495180      \n",
       "3 330850      \n",
       "4 136550      \n",
       "5 176040      \n",
       "6  75600      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "setwd(\"~/Documents/R scripts and data/PLAY\")#set directory\n",
    "\n",
    "data.ptime<-read.csv(\"playdatabytype.csv\",header=T)\n",
    "head(data.ptime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98b63a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dl-inline {width: auto; margin:0; padding: 0}\n",
       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
       "</style><dl class=dl-inline><dt>AGA</dt><dd>0.231705574795845</dd><dt>CH</dt><dd>0.173499431448993</dd><dt>CP</dt><dd>0.0547141431741351</dd><dt>LIZ</dt><dd>0.0269444444444444</dd><dt>PLA</dt><dd>0.408657407407407</dd><dt>SE</dt><dd>0</dd><dt>SN</dt><dd>0.215685098556635</dd></dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[AGA] 0.231705574795845\n",
       "\\item[CH] 0.173499431448993\n",
       "\\item[CP] 0.0547141431741351\n",
       "\\item[LIZ] 0.0269444444444444\n",
       "\\item[PLA] 0.408657407407407\n",
       "\\item[SE] 0\n",
       "\\item[SN] 0.215685098556635\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "AGA\n",
       ":   0.231705574795845CH\n",
       ":   0.173499431448993CP\n",
       ":   0.0547141431741351LIZ\n",
       ":   0.0269444444444444PLA\n",
       ":   0.408657407407407SE\n",
       ":   0SN\n",
       ":   0.215685098556635\n",
       "\n"
      ],
      "text/plain": [
       "       AGA         CH         CP        LIZ        PLA         SE         SN \n",
       "0.23170557 0.17349943 0.05471414 0.02694444 0.40865741 0.00000000 0.21568510 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Uu3eek6n1w6ePAfESvXji78aw7AFGU7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",
      "text/plain": [
       "Plot with title “(b)”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####FIGURE 3\n",
    "########\n",
    "\n",
    "par(mfrow=c(1,2))\n",
    "data.ptime$X.ad<-(data.ptime$ad_ad_play/data.ptime$OBSSEC)*100\n",
    "boxplot(X.ad~ SITE, data = data.ptime,las=1,\n",
    "        xlab = \"Group ID\", ylab = \"%Adult-adult play\",\n",
    "        varwidth = TRUE,\n",
    "        col = \"lightgray\",outline=F,medcol=\"red\")\n",
    "title(\"(a)\", adj = 0.5, line = -2)\n",
    "means1 <- tapply(data.ptime$X.ad,data.ptime$SITE,mean)\n",
    "points(means1,pch=3,\n",
    "       cex=1.5)\n",
    "\n",
    "\n",
    "####ad.im %play\n",
    "####\n",
    "data.ptime$X.im<-(data.ptime$ad_im_play/data.ptime$OBSSEC)*100\n",
    "boxplot(X.im~ SITE, data = data.ptime,las=1,\n",
    "        xlab = \"Group ID\", ylab = \"%Adult-immature play\",\n",
    "        varwidth = TRUE,\n",
    "        col = \"lightgray\",outline=F,medcol=\"red\",\n",
    "        ylim=c(0,1.2))\n",
    "title(\"(b)\", adj = 0.5, line = -2)\n",
    "means2 <- tapply(data.ptime$X.im,data.ptime$SITE,mean)\n",
    "means2\n",
    "points(means2,pch=3,\n",
    "       cex=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aeb8a92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>CLASS</th><th scope=col>GROUP</th><th scope=col>OBSTIMESECS</th><th scope=col>TOTAL_PLAY</th><th scope=col>MONTHS</th><th scope=col>PLAY.P</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>rme</td><td>I</td><td>AGA</td><td>510000</td><td> 8655</td><td>4</td><td>1.6970588</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>vrd</td><td>I</td><td>AGA</td><td> 66000</td><td>  420</td><td>4</td><td>0.6363636</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>rds</td><td>J</td><td>AGA</td><td>612000</td><td>14247</td><td>7</td><td>2.3279412</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>rxy</td><td>I</td><td>AGA</td><td> 90667</td><td> 4590</td><td>7</td><td>5.0624814</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>sbt</td><td>I</td><td>AGA</td><td>184696</td><td> 6515</td><td>7</td><td>3.5274180</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>cht</td><td>I</td><td>AGA</td><td>187200</td><td> 1260</td><td>9</td><td>0.6730769</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & ID & CLASS & GROUP & OBSTIMESECS & TOTAL\\_PLAY & MONTHS & PLAY.P\\\\\n",
       "  & <chr> & <chr> & <chr> & <int> & <int> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & rme & I & AGA & 510000 &  8655 & 4 & 1.6970588\\\\\n",
       "\t2 & vrd & I & AGA &  66000 &   420 & 4 & 0.6363636\\\\\n",
       "\t3 & rds & J & AGA & 612000 & 14247 & 7 & 2.3279412\\\\\n",
       "\t4 & rxy & I & AGA &  90667 &  4590 & 7 & 5.0624814\\\\\n",
       "\t5 & sbt & I & AGA & 184696 &  6515 & 7 & 3.5274180\\\\\n",
       "\t6 & cht & I & AGA & 187200 &  1260 & 9 & 0.6730769\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | CLASS &lt;chr&gt; | GROUP &lt;chr&gt; | OBSTIMESECS &lt;int&gt; | TOTAL_PLAY &lt;int&gt; | MONTHS &lt;int&gt; | PLAY.P &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | rme | I | AGA | 510000 |  8655 | 4 | 1.6970588 |\n",
       "| 2 | vrd | I | AGA |  66000 |   420 | 4 | 0.6363636 |\n",
       "| 3 | rds | J | AGA | 612000 | 14247 | 7 | 2.3279412 |\n",
       "| 4 | rxy | I | AGA |  90667 |  4590 | 7 | 5.0624814 |\n",
       "| 5 | sbt | I | AGA | 184696 |  6515 | 7 | 3.5274180 |\n",
       "| 6 | cht | I | AGA | 187200 |  1260 | 9 | 0.6730769 |\n",
       "\n"
      ],
      "text/plain": [
       "  ID  CLASS GROUP OBSTIMESECS TOTAL_PLAY MONTHS PLAY.P   \n",
       "1 rme I     AGA   510000       8655      4      1.6970588\n",
       "2 vrd I     AGA    66000        420      4      0.6363636\n",
       "3 rds J     AGA   612000      14247      7      2.3279412\n",
       "4 rxy I     AGA    90667       4590      7      5.0624814\n",
       "5 sbt I     AGA   184696       6515      7      3.5274180\n",
       "6 cht I     AGA   187200       1260      9      0.6730769"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "AGA PLA \n",
       " 33   4 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "     \n",
       "       f  i  I  j  J  m\n",
       "  AGA  7  0  9  0  5 12\n",
       "  PLA  0  2  0  1  0  1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = play.cb ~ poly(MONTHS, 2), family = binomial(link = \"logit\"), \n",
       "    data = data.playage)\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-112.820   -36.532    -4.637    24.221    80.016  \n",
       "\n",
       "Coefficients:\n",
       "                  Estimate Std. Error  z value Pr(>|z|)    \n",
       "(Intercept)      -4.480596   0.003499 -1280.46   <2e-16 ***\n",
       "poly(MONTHS, 2)1 -5.217114   0.025766  -202.48   <2e-16 ***\n",
       "poly(MONTHS, 2)2 -1.587286   0.021075   -75.31   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 142130  on 36  degrees of freedom\n",
       "Residual deviance:  73772  on 34  degrees of freedom\n",
       "AIC: 74138\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Waiting for profiling to be done...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 3 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-4.487468</td><td>-4.473751</td></tr>\n",
       "\t<tr><th scope=row>poly(MONTHS, 2)1</th><td>-5.267722</td><td>-5.166721</td></tr>\n",
       "\t<tr><th scope=row>poly(MONTHS, 2)2</th><td>-1.628639</td><td>-1.546024</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 3 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -4.487468 & -4.473751\\\\\n",
       "\tpoly(MONTHS, 2)1 & -5.267722 & -5.166721\\\\\n",
       "\tpoly(MONTHS, 2)2 & -1.628639 & -1.546024\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 3 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % |\n",
       "|---|---|---|\n",
       "| (Intercept) | -4.487468 | -4.473751 |\n",
       "| poly(MONTHS, 2)1 | -5.267722 | -5.166721 |\n",
       "| poly(MONTHS, 2)2 | -1.628639 | -1.546024 |\n",
       "\n"
      ],
      "text/plain": [
       "                 2.5 %     97.5 %   \n",
       "(Intercept)      -4.487468 -4.473751\n",
       "poly(MONTHS, 2)1 -5.267722 -5.166721\n",
       "poly(MONTHS, 2)2 -1.628639 -1.546024"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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oaGlpZWP91LX4LEDoDBqNN5/jPVsZnfj9dfmcDlZZP7Zo2UgsLFkJCQpqam7/Mk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\n6y+jAAAVW0lEQVT6i5r+rai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1j2jcTu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\nAOAIJHYAAAAAHIHEDgAAAIAj/g+pbcIjn8sXRgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#only individuals with known age\n",
    "data.playage<-read.csv(\"monthsPLAYsecond.csv\",header=T)\n",
    "head(data.playage)\n",
    "table(data.playage$GROUP)#33 individuals from AGA the other 4 from PLA\n",
    "table(data.playage$GROUP,data.playage$CLASS)\n",
    "\n",
    "#use cbind for seconds playing vs second not playing for a binomial regression on social play\n",
    "play.cb<-cbind(data.playage$TOTAL_PLAY,data.playage$OBSTIMESECS)\n",
    "#build the model\n",
    "model.con4b<-glm(play.cb~poly(MONTHS,2),\n",
    "                 data=data.playage,\n",
    "                 family = binomial(link = \"logit\"))\n",
    "#poly(MONTHS,3)\n",
    "#MONTHS+I(MONTHS^2)\n",
    "\n",
    "summary(model.con4b)\n",
    "confint(model.con4b, level=0.95)\n",
    "par(mfrow=c(2,2))\n",
    "plot(model.con4b)\n",
    "par(mfrow=c(1,1))#all looks fine\n",
    "\n",
    "#plot the age model\n",
    "newdata.months<- data.frame(MONTHS= seq(3,120,1))\n",
    "predicted.months<- predict(model.con4b,newdata=newdata.months,                           type=\"response\",se=T)#regression line\n",
    "predicted.months.int<- predict(model.con4b,\n",
    "                           newdata=newdata.months,\n",
    "                           type=\"response\",ic=T)#CIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31dd5981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tKruskal-Wallis rank sum test\n",
       "\n",
       "data:  X.PLAYTOTAL by CLASS\n",
       "Kruskal-Wallis chi-squared = 18.978, df = 3, p-value = 0.0002762\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AN8J8mLvwb3XvWo1c9Xtvc0k+LzYWFh7u7uZdnzzp07Uhz/e7VqRX//XXSjhQVV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llxQXR2PHUqdO5OFBO3ZQYKB04gQJTIyM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\nEsHEjoiIiEgimNgRERERScT/Aca7afiGQ1wkAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “(b)”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####FIGURE 1\n",
    "#par(mar = c(bottom, left, top, right)) \n",
    "#two plots together\n",
    "par(mar=c(5,4.5,2,1),cex.lab=2)\n",
    "par(mfrow=c(1,2))\n",
    "\n",
    "\n",
    "setwd(\"~/Documents/R scripts and data/PLAY\")#set directory\n",
    "data.ptime<-read.csv2(\"playage.csv\",header=T)#enter data play by age and class\n",
    "\n",
    "boxplot(data.ptime$X.PLAYTOTAL ~ data.ptime$CLASS,las=1,\n",
    "                   ylab=\"%Social play\" ,\n",
    "                   xlab=\"Age class\",outline=FALSE,\n",
    "        cex.lab=1.5,medcol=\"red\",\n",
    "        varwidth = TRUE,\n",
    "        col = \"lightgray\")\n",
    "title(\"(a)\", adj = 0.5, line = -2)\n",
    "points(c(0.67,1.40,2.16,0.842),pch=3,cex=1.5)\n",
    "plot(data.playage$MONTHS,data.playage$PLAY.P,las=1,\n",
    "     pch = 16,\n",
    "     col=\"blue\",xlab = \"age (months)\",cex=0.76, \n",
    "     ylab = \"%Social play\",cex.lab=1.5)\n",
    "title(\"(b)\", adj = 0.5, line = -2)\n",
    "lines(newdata.months$MONTHS, predicted.months$fit*100,lwd=2,col=2)\n",
    "lines(newdata.months$MONTHS, predicted.months$fit*100+\n",
    "        predicted.months.int*100,lty=3)\n",
    "lines(newdata.months$MONTHS, predicted.months$fit*100-\n",
    "        predicted.months.int*100,lty=3)\n",
    "\n",
    "kruskal.test(X.PLAYTOTAL ~ CLASS,data=data.ptime)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8bd4cc0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 26</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>SITE</th><th scope=col>Subsp</th><th scope=col>HABITAT</th><th scope=col>AREA</th><th scope=col>DENS</th><th scope=col>GR</th><th scope=col>FEM</th><th scope=col>MAL</th><th scope=col>⋯</th><th scope=col>PLAYSEC</th><th scope=col>OBSSEC</th><th scope=col>IM.RATIO</th><th scope=col>NOPLAYSECS</th><th scope=col>ad_ad_play</th><th scope=col>ad_im_play</th><th scope=col>mix_play</th><th scope=col>non_ad_ad_play</th><th scope=col>non_ad_im_play</th><th scope=col>non_mix_play</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>amp</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>14</td><td>5</td><td>⋯</td><td> 2043</td><td>118800</td><td>0.2105263</td><td>116757</td><td>1383</td><td> 240</td><td> 420</td><td>117417</td><td>118560</td><td>118380</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>amr</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>6</td><td>⋯</td><td>12705</td><td>496800</td><td>0.2777778</td><td>484095</td><td>1310</td><td>9775</td><td>1620</td><td>495490</td><td>487025</td><td>495180</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>azt</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>6</td><td>⋯</td><td> 3990</td><td>331200</td><td>0.2777778</td><td>327210</td><td>2390</td><td>1250</td><td> 350</td><td>328810</td><td>329950</td><td>330850</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>cha</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>13</td><td>7</td><td>⋯</td><td> 1770</td><td>136800</td><td>0.2500000</td><td>135030</td><td>1480</td><td>  40</td><td> 250</td><td>135320</td><td>136760</td><td>136550</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>chu</td><td>m</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>20</td><td>12</td><td>5</td><td>⋯</td><td>  725</td><td>176040</td><td>0.2941176</td><td>175315</td><td> 335</td><td> 390</td><td>   0</td><td>175705</td><td>175650</td><td>176040</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>cri</td><td>m</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>8</td><td>⋯</td><td>  360</td><td> 75600</td><td>0.2500000</td><td> 75240</td><td> 360</td><td>   0</td><td>   0</td><td> 75240</td><td> 75600</td><td> 75600</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 26\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & ID & SEX & SITE & Subsp & HABITAT & AREA & DENS & GR & FEM & MAL & ⋯ & PLAYSEC & OBSSEC & IM.RATIO & NOPLAYSECS & ad\\_ad\\_play & ad\\_im\\_play & mix\\_play & non\\_ad\\_ad\\_play & non\\_ad\\_im\\_play & non\\_mix\\_play\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <int> & <int> & <int> & ⋯ & <int> & <int> & <dbl> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & amp & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 14 & 5 & ⋯ &  2043 & 118800 & 0.2105263 & 116757 & 1383 &  240 &  420 & 117417 & 118560 & 118380\\\\\n",
       "\t2 & amr & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 6 & ⋯ & 12705 & 496800 & 0.2777778 & 484095 & 1310 & 9775 & 1620 & 495490 & 487025 & 495180\\\\\n",
       "\t3 & azt & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 6 & ⋯ &  3990 & 331200 & 0.2777778 & 327210 & 2390 & 1250 &  350 & 328810 & 329950 & 330850\\\\\n",
       "\t4 & cha & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 13 & 7 & ⋯ &  1770 & 136800 & 0.2500000 & 135030 & 1480 &   40 &  250 & 135320 & 136760 & 136550\\\\\n",
       "\t5 & chu & m & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 20 & 12 & 5 & ⋯ &   725 & 176040 & 0.2941176 & 175315 &  335 &  390 &    0 & 175705 & 175650 & 176040\\\\\n",
       "\t6 & cri & m & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 8 & ⋯ &   360 &  75600 & 0.2500000 &  75240 &  360 &    0 &    0 &  75240 &  75600 &  75600\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 26\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | SEX &lt;chr&gt; | SITE &lt;chr&gt; | Subsp &lt;chr&gt; | HABITAT &lt;chr&gt; | AREA &lt;dbl&gt; | DENS &lt;dbl&gt; | GR &lt;int&gt; | FEM &lt;int&gt; | MAL &lt;int&gt; | ⋯ ⋯ | PLAYSEC &lt;int&gt; | OBSSEC &lt;int&gt; | IM.RATIO &lt;dbl&gt; | NOPLAYSECS &lt;int&gt; | ad_ad_play &lt;int&gt; | ad_im_play &lt;int&gt; | mix_play &lt;int&gt; | non_ad_ad_play &lt;int&gt; | non_ad_im_play &lt;int&gt; | non_mix_play &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | amp | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 14 | 5 | ⋯ |  2043 | 118800 | 0.2105263 | 116757 | 1383 |  240 |  420 | 117417 | 118560 | 118380 |\n",
       "| 2 | amr | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 6 | ⋯ | 12705 | 496800 | 0.2777778 | 484095 | 1310 | 9775 | 1620 | 495490 | 487025 | 495180 |\n",
       "| 3 | azt | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 6 | ⋯ |  3990 | 331200 | 0.2777778 | 327210 | 2390 | 1250 |  350 | 328810 | 329950 | 330850 |\n",
       "| 4 | cha | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 13 | 7 | ⋯ |  1770 | 136800 | 0.2500000 | 135030 | 1480 |   40 |  250 | 135320 | 136760 | 136550 |\n",
       "| 5 | chu | m | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 20 | 12 | 5 | ⋯ |   725 | 176040 | 0.2941176 | 175315 |  335 |  390 |    0 | 175705 | 175650 | 176040 |\n",
       "| 6 | cri | m | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 8 | ⋯ |   360 |  75600 | 0.2500000 |  75240 |  360 |    0 |    0 |  75240 |  75600 |  75600 |\n",
       "\n"
      ],
      "text/plain": [
       "  ID  SEX SITE Subsp    HABITAT AREA DENS GR FEM MAL ⋯ PLAYSEC OBSSEC IM.RATIO \n",
       "1 amp f   AGA  mexicana SEMIDEC 8.3  9.5  24 14  5   ⋯  2043   118800 0.2105263\n",
       "2 amr f   AGA  mexicana SEMIDEC 8.3  9.5  24 12  6   ⋯ 12705   496800 0.2777778\n",
       "3 azt f   AGA  mexicana SEMIDEC 8.3  9.5  24 12  6   ⋯  3990   331200 0.2777778\n",
       "4 cha f   AGA  mexicana SEMIDEC 8.3  9.5  24 13  7   ⋯  1770   136800 0.2500000\n",
       "5 chu m   AGA  mexicana SEMIDEC 8.3  9.5  20 12  5   ⋯   725   176040 0.2941176\n",
       "6 cri m   AGA  mexicana SEMIDEC 8.3  9.5  24 12  8   ⋯   360    75600 0.2500000\n",
       "  NOPLAYSECS ad_ad_play ad_im_play mix_play non_ad_ad_play non_ad_im_play\n",
       "1 116757     1383        240        420     117417         118560        \n",
       "2 484095     1310       9775       1620     495490         487025        \n",
       "3 327210     2390       1250        350     328810         329950        \n",
       "4 135030     1480         40        250     135320         136760        \n",
       "5 175315      335        390          0     175705         175650        \n",
       "6  75240      360          0          0      75240          75600        \n",
       "  non_mix_play\n",
       "1 118380      \n",
       "2 495180      \n",
       "3 330850      \n",
       "4 136550      \n",
       "5 176040      \n",
       "6  75600      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>62</li><li>26</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 62\n",
       "\\item 26\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 62\n",
       "2. 26\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 62 26"
      ]
     },
     "metadata": {},
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     "data": {
      "text/plain": [
       "\n",
       " f  m \n",
       "40 22 "
      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tFriedman rank sum test\n",
       "\n",
       "data:  play.per and type and id\n",
       "Friedman chi-squared = 39.909, df = 2, p-value = 2.157e-09\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tKruskal-Wallis rank sum test\n",
       "\n",
       "data:  X.PLAY by Subsp\n",
       "Kruskal-Wallis chi-squared = 13.839, df = 1, p-value = 0.0001991\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tKruskal-Wallis rank sum test\n",
       "\n",
       "data:  ad_ad_play by SITE\n",
       "Kruskal-Wallis chi-squared = 28.992, df = 6, p-value = 6.104e-05\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tKruskal-Wallis rank sum test\n",
       "\n",
       "data:  ad_im_play by SITE\n",
       "Kruskal-Wallis chi-squared = 19.124, df = 6, p-value = 0.003959\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tKruskal-Wallis rank sum test\n",
       "\n",
       "data:  ad_ad_play by Subsp\n",
       "Kruskal-Wallis chi-squared = 0.11525, df = 1, p-value = 0.7342\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tKruskal-Wallis rank sum test\n",
       "\n",
       "data:  ad_im_play by Subsp\n",
       "Kruskal-Wallis chi-squared = 0.51396, df = 1, p-value = 0.4734\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NbqgzGPMjPuRu9bPmNIt+a1tQTZD8/s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\niAwECzsiIiIiA/H/3AUzUGmjKdMAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “(c)”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "setwd(\"~/Documents/R scripts and data/PLAY\")\n",
    "data.ptime<-read.csv(\"playdatabytype.csv\",header=T)\n",
    "head(data.ptime)\n",
    "dim(data.ptime)\n",
    "table(data.ptime$SEX)\n",
    "\n",
    "#FIGURE2\n",
    "#arrange plots\n",
    "par(mar=c(5,5,2,2),cex.lab=1.5)\n",
    "layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))\n",
    "\n",
    "\n",
    "\n",
    "###adult social play by SITE\n",
    "boxplot(X.PLAY ~ SITE, data = data.ptime,\n",
    "        xlab = \"Group ID\", ylab = \"%Social play\",\n",
    "        varwidth = TRUE,\n",
    "        col = \"lightgray\",outline=F,medcol=\"red\",\n",
    "        las=1)\n",
    "title(\"(a)\", adj = 0.5, line = -2)\n",
    "means <- tapply(data.ptime$X.PLAY,data.ptime$SITE,mean)\n",
    "points(means,pch=3,\n",
    "       cex=1.5)\n",
    "\n",
    "####adult social play subspecies\n",
    "boxplot(X.PLAY ~ Subsp, data = data.ptime,\n",
    "        xlab = \"Subspecies\", ylab = \"%Social play\",\n",
    "        varwidth = TRUE,\n",
    "        col = \"lightgray\",outline=F,medcol=\"red\",\n",
    "        xaxt = \"n\",las=1)\n",
    "xtick<-seq(1, 2, by=1)\n",
    "title(\"(b)\", adj = 0.5, line = -2)\n",
    "axis(side=1, at=xtick, labels = c(expression(italic(A.p.mexicana)),\n",
    "                                  expression(italic(A.p.palliata))))\n",
    "means <- tapply(data.ptime$X.PLAY,data.ptime$Subsp,mean)\n",
    "points(c(0.83,0.37),pch=3,\n",
    "       cex=1.5)\n",
    "#https://r-coder.com/plot-r/\n",
    "#https://r-graphics.org\n",
    "\n",
    "\n",
    "####adult social play by type\n",
    "data.types<-read.csv(\"play types onlyb.csv\",\n",
    "                     header=T)\n",
    "boxplot(play.per ~ type, data = data.types,\n",
    "        xlab = \"Play bout composition\", ylab = \"%Social play\",\n",
    "        varwidth = TRUE,\n",
    "        col = \"lightgray\",outline=F,medcol=\"red\",las=1)\n",
    "title(\"(c)\", adj = 0.5, line = -2)\n",
    "means <- tapply(data.types$play.per,data.types$X,mean)\n",
    "points(c(0.34,0.175,0.108),pch=3,\n",
    "       cex=1.5)\n",
    "#ylab=expression(italic(\"Sciurus vulgaris  \") (Presence/Absence)))\n",
    "\n",
    "\n",
    "\n",
    "####adult social play by type repeated measures Friedman ANOVA test\n",
    "friedman.test(play.per~type |id,data=data.types)\n",
    "kruskal.test(X.PLAY~Subsp,data=data.ptime)#adult play by subspecies\n",
    "kruskal.test(ad_ad_play~SITE,data=data.ptime)#adult adult play by site\n",
    "kruskal.test(ad_im_play~SITE,data=data.ptime)#adult immature play by site\n",
    "kruskal.test(ad_ad_play~Subsp,data=data.ptime)#adult adult play by subsp\n",
    "kruskal.test(ad_im_play~Subsp,data=data.ptime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a31a3198",
   "metadata": {},
   "outputs": [],
   "source": [
    "####SOCIAL PLAY AMONG ADULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b35b29d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 27</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ID</th><th scope=col>SEX</th><th scope=col>SITE</th><th scope=col>Subsp</th><th scope=col>HABITAT</th><th scope=col>AREA</th><th scope=col>DENS</th><th scope=col>GR</th><th scope=col>FEM</th><th scope=col>MAL</th><th scope=col>⋯</th><th scope=col>OBSSEC</th><th scope=col>IM.RATIO</th><th scope=col>NOPLAYSECS</th><th scope=col>ad_ad_play</th><th scope=col>ad_im_play</th><th scope=col>mix_play</th><th scope=col>non_ad_ad_play</th><th scope=col>non_ad_im_play</th><th scope=col>non_mix_play</th><th scope=col>adults</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>amp</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>14</td><td>5</td><td>⋯</td><td>118800</td><td>0.2105263</td><td>116757</td><td>1383</td><td> 240</td><td> 420</td><td>117417</td><td>118560</td><td>118380</td><td>20</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>amr</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>6</td><td>⋯</td><td>496800</td><td>0.2777778</td><td>484095</td><td>1310</td><td>9775</td><td>1620</td><td>495490</td><td>487025</td><td>495180</td><td>19</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>azt</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>6</td><td>⋯</td><td>331200</td><td>0.2777778</td><td>327210</td><td>2390</td><td>1250</td><td> 350</td><td>328810</td><td>329950</td><td>330850</td><td>19</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>cha</td><td>f</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>13</td><td>7</td><td>⋯</td><td>136800</td><td>0.2500000</td><td>135030</td><td>1480</td><td>  40</td><td> 250</td><td>135320</td><td>136760</td><td>136550</td><td>19</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>chu</td><td>m</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>20</td><td>12</td><td>5</td><td>⋯</td><td>176040</td><td>0.2941176</td><td>175315</td><td> 335</td><td> 390</td><td>   0</td><td>175705</td><td>175650</td><td>176040</td><td>15</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>cri</td><td>m</td><td>AGA</td><td>mexicana</td><td>SEMIDEC</td><td>8.3</td><td>9.5</td><td>24</td><td>12</td><td>8</td><td>⋯</td><td> 75600</td><td>0.2500000</td><td> 75240</td><td> 360</td><td>   0</td><td>   0</td><td> 75240</td><td> 75600</td><td> 75600</td><td>19</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 27\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & ID & SEX & SITE & Subsp & HABITAT & AREA & DENS & GR & FEM & MAL & ⋯ & OBSSEC & IM.RATIO & NOPLAYSECS & ad\\_ad\\_play & ad\\_im\\_play & mix\\_play & non\\_ad\\_ad\\_play & non\\_ad\\_im\\_play & non\\_mix\\_play & adults\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <int> & <int> & <int> & ⋯ & <int> & <dbl> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & amp & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 14 & 5 & ⋯ & 118800 & 0.2105263 & 116757 & 1383 &  240 &  420 & 117417 & 118560 & 118380 & 20\\\\\n",
       "\t2 & amr & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 6 & ⋯ & 496800 & 0.2777778 & 484095 & 1310 & 9775 & 1620 & 495490 & 487025 & 495180 & 19\\\\\n",
       "\t3 & azt & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 6 & ⋯ & 331200 & 0.2777778 & 327210 & 2390 & 1250 &  350 & 328810 & 329950 & 330850 & 19\\\\\n",
       "\t4 & cha & f & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 13 & 7 & ⋯ & 136800 & 0.2500000 & 135030 & 1480 &   40 &  250 & 135320 & 136760 & 136550 & 19\\\\\n",
       "\t5 & chu & m & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 20 & 12 & 5 & ⋯ & 176040 & 0.2941176 & 175315 &  335 &  390 &    0 & 175705 & 175650 & 176040 & 15\\\\\n",
       "\t6 & cri & m & AGA & mexicana & SEMIDEC & 8.3 & 9.5 & 24 & 12 & 8 & ⋯ &  75600 & 0.2500000 &  75240 &  360 &    0 &    0 &  75240 &  75600 &  75600 & 19\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 27\n",
       "\n",
       "| <!--/--> | ID &lt;chr&gt; | SEX &lt;chr&gt; | SITE &lt;chr&gt; | Subsp &lt;chr&gt; | HABITAT &lt;chr&gt; | AREA &lt;dbl&gt; | DENS &lt;dbl&gt; | GR &lt;int&gt; | FEM &lt;int&gt; | MAL &lt;int&gt; | ⋯ ⋯ | OBSSEC &lt;int&gt; | IM.RATIO &lt;dbl&gt; | NOPLAYSECS &lt;int&gt; | ad_ad_play &lt;int&gt; | ad_im_play &lt;int&gt; | mix_play &lt;int&gt; | non_ad_ad_play &lt;int&gt; | non_ad_im_play &lt;int&gt; | non_mix_play &lt;int&gt; | adults &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | amp | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 14 | 5 | ⋯ | 118800 | 0.2105263 | 116757 | 1383 |  240 |  420 | 117417 | 118560 | 118380 | 20 |\n",
       "| 2 | amr | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 6 | ⋯ | 496800 | 0.2777778 | 484095 | 1310 | 9775 | 1620 | 495490 | 487025 | 495180 | 19 |\n",
       "| 3 | azt | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 6 | ⋯ | 331200 | 0.2777778 | 327210 | 2390 | 1250 |  350 | 328810 | 329950 | 330850 | 19 |\n",
       "| 4 | cha | f | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 13 | 7 | ⋯ | 136800 | 0.2500000 | 135030 | 1480 |   40 |  250 | 135320 | 136760 | 136550 | 19 |\n",
       "| 5 | chu | m | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 20 | 12 | 5 | ⋯ | 176040 | 0.2941176 | 175315 |  335 |  390 |    0 | 175705 | 175650 | 176040 | 15 |\n",
       "| 6 | cri | m | AGA | mexicana | SEMIDEC | 8.3 | 9.5 | 24 | 12 | 8 | ⋯ |  75600 | 0.2500000 |  75240 |  360 |    0 |    0 |  75240 |  75600 |  75600 | 19 |\n",
       "\n"
      ],
      "text/plain": [
       "  ID  SEX SITE Subsp    HABITAT AREA DENS GR FEM MAL ⋯ OBSSEC IM.RATIO \n",
       "1 amp f   AGA  mexicana SEMIDEC 8.3  9.5  24 14  5   ⋯ 118800 0.2105263\n",
       "2 amr f   AGA  mexicana SEMIDEC 8.3  9.5  24 12  6   ⋯ 496800 0.2777778\n",
       "3 azt f   AGA  mexicana SEMIDEC 8.3  9.5  24 12  6   ⋯ 331200 0.2777778\n",
       "4 cha f   AGA  mexicana SEMIDEC 8.3  9.5  24 13  7   ⋯ 136800 0.2500000\n",
       "5 chu m   AGA  mexicana SEMIDEC 8.3  9.5  20 12  5   ⋯ 176040 0.2941176\n",
       "6 cri m   AGA  mexicana SEMIDEC 8.3  9.5  24 12  8   ⋯  75600 0.2500000\n",
       "  NOPLAYSECS ad_ad_play ad_im_play mix_play non_ad_ad_play non_ad_im_play\n",
       "1 116757     1383        240        420     117417         118560        \n",
       "2 484095     1310       9775       1620     495490         487025        \n",
       "3 327210     2390       1250        350     328810         329950        \n",
       "4 135030     1480         40        250     135320         136760        \n",
       "5 175315      335        390          0     175705         175650        \n",
       "6  75240      360          0          0      75240          75600        \n",
       "  non_mix_play adults\n",
       "1 118380       20    \n",
       "2 495180       19    \n",
       "3 330850       19    \n",
       "4 136550       19    \n",
       "5 176040       15    \n",
       "6  75600       19    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: Matrix\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Generalized linear mixed model fit by maximum likelihood (Laplace\n",
       "  Approximation) [glmerMod]\n",
       " Family: binomial  ( logit )\n",
       "Formula: cb.pt ~ z.ir + z.gr + z.fe + z.tr + z.dens + SEX + z.area + (1 |  \n",
       "    SITE)\n",
       "   Data: data.ptime\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       " 25783.3  25802.4 -12882.6  25765.3       53 \n",
       "\n",
       "Scaled residuals: \n",
       "    Min      1Q  Median      3Q     Max \n",
       "-47.885 -12.357  -0.999  11.867  83.132 \n",
       "\n",
       "Random effects:\n",
       " Groups Name        Variance Std.Dev.\n",
       " SITE   (Intercept) 0.8235   0.9074  \n",
       "Number of obs: 62, groups:  SITE, 7\n",
       "\n",
       "Fixed effects:\n",
       "             Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -6.435018   0.425854 -15.111  < 2e-16 ***\n",
       "z.ir        -1.320789   0.029372 -44.968  < 2e-16 ***\n",
       "z.gr         0.529555   0.023859  22.195  < 2e-16 ***\n",
       "z.fe         0.158159   0.014718  10.746  < 2e-16 ***\n",
       "z.tr         0.011533   0.003727   3.094  0.00197 ** \n",
       "z.dens      -0.849464   0.708977  -1.198  0.23086    \n",
       "SEXm        -0.371757   0.008263 -44.993  < 2e-16 ***\n",
       "z.area      -0.641625   0.534188  -1.201  0.22970    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "       (Intr) z.ir   z.gr   z.fe   z.tr   z.dens SEXm  \n",
       "z.ir    0.016                                          \n",
       "z.gr   -0.026  0.049                                   \n",
       "z.fe   -0.001 -0.328 -0.057                            \n",
       "z.tr    0.005 -0.072 -0.307 -0.022                     \n",
       "z.dens  0.559  0.042 -0.056 -0.006  0.011              \n",
       "SEXm   -0.004 -0.183 -0.147  0.050  0.359 -0.001       \n",
       "z.area  0.301  0.039 -0.034 -0.012  0.006  0.765 -0.002"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 method overwritten by 'broom.mixed':\n",
      "  method      from \n",
      "  tidy.gamlss broom\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>effect</th><th scope=col>group</th><th scope=col>term</th><th scope=col>estimate</th><th scope=col>std.error</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>conf.low</th><th scope=col>conf.high</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>(Intercept)</td><td>-6.43501769</td><td>0.425854164</td><td>-15.110848</td><td> 1.373599e-51</td><td>-7.269676509</td><td>-5.60035886</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.ir       </td><td>-1.32078867</td><td>0.029371596</td><td>-44.968230</td><td> 0.000000e+00</td><td>-1.378355944</td><td>-1.26322140</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.gr       </td><td> 0.52955501</td><td>0.023858864</td><td> 22.195315</td><td>3.811674e-109</td><td> 0.482792499</td><td> 0.57631753</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.fe       </td><td> 0.15815893</td><td>0.014717517</td><td> 10.746305</td><td> 6.168341e-27</td><td> 0.129313125</td><td> 0.18700473</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.tr       </td><td> 0.01153295</td><td>0.003727066</td><td>  3.094378</td><td> 1.972261e-03</td><td> 0.004228036</td><td> 0.01883787</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.dens     </td><td>-0.84946410</td><td>0.708976664</td><td> -1.198155</td><td> 2.308566e-01</td><td>-2.239032826</td><td> 0.54010463</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 9\n",
       "\\begin{tabular}{lllllllll}\n",
       " effect & group & term & estimate & std.error & statistic & p.value & conf.low & conf.high\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t fixed & NA & (Intercept) & -6.43501769 & 0.425854164 & -15.110848 &  1.373599e-51 & -7.269676509 & -5.60035886\\\\\n",
       "\t fixed & NA & z.ir        & -1.32078867 & 0.029371596 & -44.968230 &  0.000000e+00 & -1.378355944 & -1.26322140\\\\\n",
       "\t fixed & NA & z.gr        &  0.52955501 & 0.023858864 &  22.195315 & 3.811674e-109 &  0.482792499 &  0.57631753\\\\\n",
       "\t fixed & NA & z.fe        &  0.15815893 & 0.014717517 &  10.746305 &  6.168341e-27 &  0.129313125 &  0.18700473\\\\\n",
       "\t fixed & NA & z.tr        &  0.01153295 & 0.003727066 &   3.094378 &  1.972261e-03 &  0.004228036 &  0.01883787\\\\\n",
       "\t fixed & NA & z.dens      & -0.84946410 & 0.708976664 &  -1.198155 &  2.308566e-01 & -2.239032826 &  0.54010463\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 9\n",
       "\n",
       "| effect &lt;chr&gt; | group &lt;chr&gt; | term &lt;chr&gt; | estimate &lt;dbl&gt; | std.error &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | conf.low &lt;dbl&gt; | conf.high &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| fixed | NA | (Intercept) | -6.43501769 | 0.425854164 | -15.110848 |  1.373599e-51 | -7.269676509 | -5.60035886 |\n",
       "| fixed | NA | z.ir        | -1.32078867 | 0.029371596 | -44.968230 |  0.000000e+00 | -1.378355944 | -1.26322140 |\n",
       "| fixed | NA | z.gr        |  0.52955501 | 0.023858864 |  22.195315 | 3.811674e-109 |  0.482792499 |  0.57631753 |\n",
       "| fixed | NA | z.fe        |  0.15815893 | 0.014717517 |  10.746305 |  6.168341e-27 |  0.129313125 |  0.18700473 |\n",
       "| fixed | NA | z.tr        |  0.01153295 | 0.003727066 |   3.094378 |  1.972261e-03 |  0.004228036 |  0.01883787 |\n",
       "| fixed | NA | z.dens      | -0.84946410 | 0.708976664 |  -1.198155 |  2.308566e-01 | -2.239032826 |  0.54010463 |\n",
       "\n"
      ],
      "text/plain": [
       "  effect group term        estimate    std.error   statistic  p.value      \n",
       "1 fixed  NA    (Intercept) -6.43501769 0.425854164 -15.110848  1.373599e-51\n",
       "2 fixed  NA    z.ir        -1.32078867 0.029371596 -44.968230  0.000000e+00\n",
       "3 fixed  NA    z.gr         0.52955501 0.023858864  22.195315 3.811674e-109\n",
       "4 fixed  NA    z.fe         0.15815893 0.014717517  10.746305  6.168341e-27\n",
       "5 fixed  NA    z.tr         0.01153295 0.003727066   3.094378  1.972261e-03\n",
       "6 fixed  NA    z.dens      -0.84946410 0.708976664  -1.198155  2.308566e-01\n",
       "  conf.low     conf.high  \n",
       "1 -7.269676509 -5.60035886\n",
       "2 -1.378355944 -1.26322140\n",
       "3  0.482792499  0.57631753\n",
       "4  0.129313125  0.18700473\n",
       "5  0.004228036  0.01883787\n",
       "6 -2.239032826  0.54010463"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 7 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>effect</th><th scope=col>group</th><th scope=col>term</th><th scope=col>estimate</th><th scope=col>std.error</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>conf.low</th><th scope=col>conf.high</th><th scope=col>low.dif</th><th scope=col>up.dif</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.ir  </td><td>-1.32078867</td><td>0.029371596</td><td>-44.968230</td><td> 0.000000e+00</td><td>-1.378355944</td><td>-1.26322140</td><td>0.057567270</td><td>0.057567270</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.gr  </td><td> 0.52955501</td><td>0.023858864</td><td> 22.195315</td><td>3.811674e-109</td><td> 0.482792499</td><td> 0.57631753</td><td>0.046762514</td><td>0.046762514</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.fe  </td><td> 0.15815893</td><td>0.014717517</td><td> 10.746305</td><td> 6.168341e-27</td><td> 0.129313125</td><td> 0.18700473</td><td>0.028845804</td><td>0.028845804</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.tr  </td><td> 0.01153295</td><td>0.003727066</td><td>  3.094378</td><td> 1.972261e-03</td><td> 0.004228036</td><td> 0.01883787</td><td>0.007304916</td><td>0.007304916</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.dens</td><td>-0.84946410</td><td>0.708976664</td><td> -1.198155</td><td> 2.308566e-01</td><td>-2.239032826</td><td> 0.54010463</td><td>1.389568728</td><td>1.389568728</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>SEXm  </td><td>-0.37175740</td><td>0.008262550</td><td>-44.993060</td><td> 0.000000e+00</td><td>-0.387951702</td><td>-0.35556310</td><td>0.016194300</td><td>0.016194300</td></tr>\n",
       "\t<tr><td>fixed</td><td>NA</td><td>z.area</td><td>-0.64162473</td><td>0.534188250</td><td> -1.201121</td><td> 2.297043e-01</td><td>-1.688614462</td><td> 0.40536500</td><td>1.046989732</td><td>1.046989732</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " effect & group & term & estimate & std.error & statistic & p.value & conf.low & conf.high & low.dif & up.dif\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t fixed & NA & z.ir   & -1.32078867 & 0.029371596 & -44.968230 &  0.000000e+00 & -1.378355944 & -1.26322140 & 0.057567270 & 0.057567270\\\\\n",
       "\t fixed & NA & z.gr   &  0.52955501 & 0.023858864 &  22.195315 & 3.811674e-109 &  0.482792499 &  0.57631753 & 0.046762514 & 0.046762514\\\\\n",
       "\t fixed & NA & z.fe   &  0.15815893 & 0.014717517 &  10.746305 &  6.168341e-27 &  0.129313125 &  0.18700473 & 0.028845804 & 0.028845804\\\\\n",
       "\t fixed & NA & z.tr   &  0.01153295 & 0.003727066 &   3.094378 &  1.972261e-03 &  0.004228036 &  0.01883787 & 0.007304916 & 0.007304916\\\\\n",
       "\t fixed & NA & z.dens & -0.84946410 & 0.708976664 &  -1.198155 &  2.308566e-01 & -2.239032826 &  0.54010463 & 1.389568728 & 1.389568728\\\\\n",
       "\t fixed & NA & SEXm   & -0.37175740 & 0.008262550 & -44.993060 &  0.000000e+00 & -0.387951702 & -0.35556310 & 0.016194300 & 0.016194300\\\\\n",
       "\t fixed & NA & z.area & -0.64162473 & 0.534188250 &  -1.201121 &  2.297043e-01 & -1.688614462 &  0.40536500 & 1.046989732 & 1.046989732\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 11\n",
       "\n",
       "| effect &lt;chr&gt; | group &lt;chr&gt; | term &lt;chr&gt; | estimate &lt;dbl&gt; | std.error &lt;dbl&gt; | statistic &lt;dbl&gt; | p.value &lt;dbl&gt; | conf.low &lt;dbl&gt; | conf.high &lt;dbl&gt; | low.dif &lt;dbl&gt; | up.dif &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| fixed | NA | z.ir   | -1.32078867 | 0.029371596 | -44.968230 |  0.000000e+00 | -1.378355944 | -1.26322140 | 0.057567270 | 0.057567270 |\n",
       "| fixed | NA | z.gr   |  0.52955501 | 0.023858864 |  22.195315 | 3.811674e-109 |  0.482792499 |  0.57631753 | 0.046762514 | 0.046762514 |\n",
       "| fixed | NA | z.fe   |  0.15815893 | 0.014717517 |  10.746305 |  6.168341e-27 |  0.129313125 |  0.18700473 | 0.028845804 | 0.028845804 |\n",
       "| fixed | NA | z.tr   |  0.01153295 | 0.003727066 |   3.094378 |  1.972261e-03 |  0.004228036 |  0.01883787 | 0.007304916 | 0.007304916 |\n",
       "| fixed | NA | z.dens | -0.84946410 | 0.708976664 |  -1.198155 |  2.308566e-01 | -2.239032826 |  0.54010463 | 1.389568728 | 1.389568728 |\n",
       "| fixed | NA | SEXm   | -0.37175740 | 0.008262550 | -44.993060 |  0.000000e+00 | -0.387951702 | -0.35556310 | 0.016194300 | 0.016194300 |\n",
       "| fixed | NA | z.area | -0.64162473 | 0.534188250 |  -1.201121 |  2.297043e-01 | -1.688614462 |  0.40536500 | 1.046989732 | 1.046989732 |\n",
       "\n"
      ],
      "text/plain": [
       "  effect group term   estimate    std.error   statistic  p.value      \n",
       "1 fixed  NA    z.ir   -1.32078867 0.029371596 -44.968230  0.000000e+00\n",
       "2 fixed  NA    z.gr    0.52955501 0.023858864  22.195315 3.811674e-109\n",
       "3 fixed  NA    z.fe    0.15815893 0.014717517  10.746305  6.168341e-27\n",
       "4 fixed  NA    z.tr    0.01153295 0.003727066   3.094378  1.972261e-03\n",
       "5 fixed  NA    z.dens -0.84946410 0.708976664  -1.198155  2.308566e-01\n",
       "6 fixed  NA    SEXm   -0.37175740 0.008262550 -44.993060  0.000000e+00\n",
       "7 fixed  NA    z.area -0.64162473 0.534188250  -1.201121  2.297043e-01\n",
       "  conf.low     conf.high   low.dif     up.dif     \n",
       "1 -1.378355944 -1.26322140 0.057567270 0.057567270\n",
       "2  0.482792499  0.57631753 0.046762514 0.046762514\n",
       "3  0.129313125  0.18700473 0.028845804 0.028845804\n",
       "4  0.004228036  0.01883787 0.007304916 0.007304916\n",
       "5 -2.239032826  0.54010463 1.389568728 1.389568728\n",
       "6 -0.387951702 -0.35556310 0.016194300 0.016194300\n",
       "7 -1.688614462  0.40536500 1.046989732 1.046989732"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>terms</th><th scope=col>estimate</th><th scope=col>low</th><th scope=col>up</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>immature to adult ratio</td><td>-1.32078867</td><td>0.057567270</td><td>0.057567270</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>group size             </td><td> 0.52955501</td><td>0.046762514</td><td>0.046762514</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>%feeding fruit         </td><td> 0.15815893</td><td>0.028845804</td><td>0.028845804</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>%travel                </td><td> 0.01153295</td><td>0.007304916</td><td>0.007304916</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>howler density         </td><td>-0.84946410</td><td>1.389568728</td><td>1.389568728</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>sex                    </td><td>-0.37175740</td><td>0.016194300</td><td>0.016194300</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & terms & estimate & low & up\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & immature to adult ratio & -1.32078867 & 0.057567270 & 0.057567270\\\\\n",
       "\t2 & group size              &  0.52955501 & 0.046762514 & 0.046762514\\\\\n",
       "\t3 & \\%feeding fruit          &  0.15815893 & 0.028845804 & 0.028845804\\\\\n",
       "\t4 & \\%travel                 &  0.01153295 & 0.007304916 & 0.007304916\\\\\n",
       "\t5 & howler density          & -0.84946410 & 1.389568728 & 1.389568728\\\\\n",
       "\t6 & sex                     & -0.37175740 & 0.016194300 & 0.016194300\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | terms &lt;chr&gt; | estimate &lt;dbl&gt; | low &lt;dbl&gt; | up &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | immature to adult ratio | -1.32078867 | 0.057567270 | 0.057567270 |\n",
       "| 2 | group size              |  0.52955501 | 0.046762514 | 0.046762514 |\n",
       "| 3 | %feeding fruit          |  0.15815893 | 0.028845804 | 0.028845804 |\n",
       "| 4 | %travel                 |  0.01153295 | 0.007304916 | 0.007304916 |\n",
       "| 5 | howler density          | -0.84946410 | 1.389568728 | 1.389568728 |\n",
       "| 6 | sex                     | -0.37175740 | 0.016194300 | 0.016194300 |\n",
       "\n"
      ],
      "text/plain": [
       "  terms                   estimate    low         up         \n",
       "1 immature to adult ratio -1.32078867 0.057567270 0.057567270\n",
       "2 group size               0.52955501 0.046762514 0.046762514\n",
       "3 %feeding fruit           0.15815893 0.028845804 0.028845804\n",
       "4 %travel                  0.01153295 0.007304916 0.007304916\n",
       "5 howler density          -0.84946410 1.389568728 1.389568728\n",
       "6 sex                     -0.37175740 0.016194300 0.016194300"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create number of adults variable\n",
    "adults<-data.ptime$GR-data.ptime$IMM\n",
    "data.ptime$adults<-adults\n",
    "head(data.ptime)\n",
    "\n",
    "#put together ad-ad play seconds and non play seconds (rest of obs time)\n",
    "cb.pt<-cbind(data.ptime$ad_ad_play,data.ptime$non_ad_ad_play)\n",
    "\n",
    "#standarize predictors\n",
    "#substract mean and divide by SD\n",
    "#density\n",
    "mean.dens<-mean(data.ptime$DENS)\n",
    "sd.dens<-sd(data.ptime$DENS)\n",
    "z.dens<-(data.ptime$DENS-mean.dens)/sd.dens\n",
    "\n",
    "#group size (adults)\n",
    "mean.gr<-mean(data.ptime$adults)\n",
    "sd.gr<-sd(data.ptime$adults)\n",
    "z.gr<-(data.ptime$GR-mean.gr)/sd.gr\n",
    "\n",
    "#feeding on fruit\n",
    "mean.fe<-mean(data.ptime$X.FE)\n",
    "sd.fe<-sd(data.ptime$X.FE)\n",
    "z.fe<-(data.ptime$X.FE-mean.fe)/sd.fe\n",
    "\n",
    "#travel\n",
    "mean.tr<-mean(data.ptime$X.TR)\n",
    "sd.tr<-sd(data.ptime$X.TR)\n",
    "z.tr<-(data.ptime$X.TR-mean.tr)/sd.tr\n",
    "\n",
    "#immature ratio\n",
    "mean.ir<-mean(data.ptime$IM.RATIO)\n",
    "sd.ir<-sd(data.ptime$IM.RATIO)\n",
    "z.ir<-(data.ptime$IM.RATIO-mean.ir)/sd.ir\n",
    "\n",
    "#area log and standarized\n",
    "log.a<-log10(data.ptime$AREA)\n",
    "mean.area<-mean(log.a)\n",
    "sd.area<-sd(log.a)\n",
    "z.area<-(log.a-mean.area)/sd.area\n",
    "\n",
    "library(lme4)\n",
    "#full model adult-adult play\n",
    "full.ad.ad<-glmer(cb.pt~z.ir+z.gr+z.fe+z.tr+z.dens+SEX+z.area+(1|SITE),family=binomial,data=data.ptime)\n",
    "\n",
    "summary(full.ad.ad)\n",
    "\n",
    "library(ggplot2)\n",
    "library(broom.mixed)\n",
    "library(dplyr)\n",
    "\n",
    "# Extract coefficents\n",
    "lmer_coef <-\n",
    "  tidy(full.ad.ad, conf.int = TRUE)\n",
    "head(lmer_coef)\n",
    "\n",
    "\n",
    "# first 5 observations\n",
    "DATA <- lmer_coef[2:8,]\n",
    "DATA$low.dif<-abs(DATA$estimate-DATA$conf.low)\n",
    "DATA$up.dif<-abs(DATA$conf.high-DATA$estimate)\n",
    "  \n",
    "head(DATA,7)\n",
    "\n",
    "par(mfrow=c(1,1))\n",
    "my.data<-data.frame(terms=c(\"immature to adult ratio\",\"group size\",\n",
    "                            \"%feeding fruit\",\"%travel\",\"howler density\",\n",
    "                            \"sex\",\"study area\"),estimate=DATA$estimate,\n",
    "                    low=DATA$low.dif,up=DATA$up.dif)\n",
    "head(my.data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce67c9e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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si1TpcPfuv2dJM61Yq62aWzX9yVQ5uX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kT1i3V3N7ZYfqNj4LwuIyslNvH5zQ1NSwxTAJPp4AAAw4SURBVMLbmsorsAox7m3+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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(full.ad.ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2fb00be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for easier plotting % of social adult adult play\n",
    "data.ptime$X.playim<-data.ptime$ad_ad_play/data.ptime$OBSSEC\n",
    "#GROUP SIZE FEMALES\n",
    "#Create a temporary data frame of hypothetical values\n",
    "\n",
    "newdata.gr.f.aga <- data.frame(z.gr = seq(-1.99, 2.45,0.1),z.fe=mean(z.fe),\n",
    "                                  z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.liz <- data.frame(z.gr = seq(-1.99, 2.45,0.1),z.fe=mean(z.fe),\n",
    "                                  z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.pla <- data.frame(z.gr = seq(-1.99,2.45,0.1),z.fe=mean(z.fe),\n",
    "                                  z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.ch <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"CH\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.cp <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"CP\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.se <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"SE\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.sn <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"SN\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.gr.f.aga <- predict(full.ad.ad,newdata=newdata.gr.f.aga,type=\"response\")\n",
    "predicted.gr.f.liz <- predict(full.ad.ad,newdata=newdata.gr.f.liz,type=\"response\")\n",
    "predicted.gr.f.pla <- predict(full.ad.ad,newdata=newdata.gr.f.pla,type=\"response\")\n",
    "predicted.gr.f.ch <- predict(full.ad.ad,newdata=newdata.gr.f.ch,type=\"response\")\n",
    "predicted.gr.f.cp <- predict(full.ad.ad,newdata=newdata.gr.f.cp,type=\"response\")\n",
    "predicted.gr.f.se <- predict(full.ad.ad,newdata=newdata.gr.f.se,type=\"response\")\n",
    "predicted.gr.f.sn <- predict(full.ad.ad,newdata=newdata.gr.f.sn,type=\"response\")\n",
    "\n",
    "newdata.gr.m.aga <- data.frame(z.gr = seq(-1.99, 2.45,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.liz <- data.frame(z.gr = seq(-1.99, 2.45,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.pla <- data.frame(z.gr = seq(-1.99,2.45,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.ch <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CH\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.cp <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CP\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.se <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SE\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.sn <- data.frame(z.gr = seq(-1.99,2.45, 0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SN\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "\n",
    "predicted.gr.m.aga <- predict(full.ad.ad,newdata=newdata.gr.m.aga,type=\"response\")\n",
    "predicted.gr.m.liz <- predict(full.ad.ad,newdata=newdata.gr.m.liz,type=\"response\")\n",
    "predicted.gr.m.pla <- predict(full.ad.ad,newdata=newdata.gr.m.pla,type=\"response\")\n",
    "predicted.gr.m.ch <- predict(full.ad.ad,newdata=newdata.gr.m.ch,type=\"response\")\n",
    "predicted.gr.m.cp <- predict(full.ad.ad,newdata=newdata.gr.m.cp,type=\"response\")\n",
    "predicted.gr.m.se <- predict(full.ad.ad,newdata=newdata.gr.m.se,type=\"response\")\n",
    "predicted.gr.m.sn <- predict(full.ad.ad,newdata=newdata.gr.m.sn,type=\"response\")\n",
    "\n",
    "#FEMALES IR\n",
    "newdata.ir.f.aga <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"AGA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.liz <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"LIZ\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.pla <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"PLA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.ch <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CH\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.cp <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CP\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.se <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SE\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.sn <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SN\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "\n",
    "predicted.ir.f.aga <- predict(full.ad.ad,newdata=newdata.ir.f.aga,type=\"response\")\n",
    "predicted.ir.f.liz <- predict(full.ad.ad,newdata=newdata.ir.f.liz,type=\"response\")\n",
    "predicted.ir.f.pla <- predict(full.ad.ad,newdata=newdata.ir.f.pla,type=\"response\")\n",
    "predicted.ir.f.ch <- predict(full.ad.ad,newdata=newdata.ir.f.ch,type=\"response\")\n",
    "predicted.ir.f.cp <- predict(full.ad.ad,newdata=newdata.ir.f.cp,type=\"response\")\n",
    "predicted.ir.f.se <- predict(full.ad.ad,newdata=newdata.ir.f.se,type=\"response\")\n",
    "predicted.ir.f.sn <- predict(full.ad.ad,newdata=newdata.ir.f.sn,type=\"response\")\n",
    "\n",
    "######IR ON MALES\n",
    "# Create a temporary data frame of hypothetical values\n",
    "\n",
    "newdata.ir.m.aga <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"AGA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.liz <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"LIZ\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.pla <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"PLA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.ch <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CH\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.cp <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CP\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.se <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SE\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.sn <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SN\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "\n",
    "predicted.ir.m.aga <- predict(full.ad.ad,newdata=newdata.ir.m.aga,type=\"response\")\n",
    "predicted.ir.m.liz <- predict(full.ad.ad,newdata=newdata.ir.m.liz,type=\"response\")\n",
    "predicted.ir.m.pla <- predict(full.ad.ad,newdata=newdata.ir.m.pla,type=\"response\")\n",
    "predicted.ir.m.ch <- predict(full.ad.ad,newdata=newdata.ir.m.ch,type=\"response\")\n",
    "predicted.ir.m.cp <- predict(full.ad.ad,newdata=newdata.ir.m.cp,type=\"response\")\n",
    "predicted.ir.m.se <- predict(full.ad.ad,newdata=newdata.ir.m.se,type=\"response\")\n",
    "predicted.ir.m.sn <- predict(full.ad.ad,newdata=newdata.ir.m.sn,type=\"response\")\n",
    "\n",
    "###########FEMALES feeding\n",
    "# Create a temporary data frame of hypothetical values\n",
    "\n",
    "newdata.fe.f.aga <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                               z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.f.liz <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                               z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.f.pla <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                               z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.f.ch <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"CH\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.f.cp <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"CP\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.f.se <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"SE\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.f.sn <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"SN\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.fe.f.aga <- predict(full.ad.ad,newdata=newdata.fe.f.aga,type=\"response\")\n",
    "predicted.fe.f.liz <- predict(full.ad.ad,newdata=newdata.fe.f.liz,type=\"response\")\n",
    "predicted.fe.f.pla <- predict(full.ad.ad,newdata=newdata.fe.f.pla,type=\"response\")\n",
    "predicted.fe.f.ch <- predict(full.ad.ad,newdata=newdata.fe.f.ch,type=\"response\")\n",
    "predicted.fe.f.cp <- predict(full.ad.ad,newdata=newdata.fe.f.cp,type=\"response\")\n",
    "predicted.fe.f.se <- predict(full.ad.ad,newdata=newdata.fe.f.se,type=\"response\")\n",
    "predicted.fe.f.sn <- predict(full.ad.ad,newdata=newdata.fe.f.sn,type=\"response\")\n",
    "\n",
    "#######NOW MALES FEEDING\n",
    "\n",
    "newdata.fe.m.aga <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                               z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.m.liz <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                               z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.m.pla <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                               z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.m.ch <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"CH\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.m.cp <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"CP\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.m.se <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"SE\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.fe.m.sn <- data.frame(z.gr = mean(z.gr),z.fe=seq(-1.71,7, 0.1),\n",
    "                              z.ir=mean(z.ir),SITE=\"SN\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.fe.m.aga <- predict(full.ad.ad,newdata=newdata.fe.m.aga,type=\"response\")\n",
    "predicted.fe.m.liz <- predict(full.ad.ad,newdata=newdata.fe.m.liz,type=\"response\")\n",
    "predicted.fe.m.pla <- predict(full.ad.ad,newdata=newdata.fe.m.pla,type=\"response\")\n",
    "predicted.fe.m.ch <- predict(full.ad.ad,newdata=newdata.fe.m.ch,type=\"response\")\n",
    "predicted.fe.m.cp <- predict(full.ad.ad,newdata=newdata.fe.m.cp,type=\"response\")\n",
    "predicted.fe.m.se <- predict(full.ad.ad,newdata=newdata.fe.m.se,type=\"response\")\n",
    "predicted.fe.m.sn <- predict(full.ad.ad,newdata=newdata.fe.m.sn,type=\"response\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "393f40c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 7 × 6 of type dbl</caption>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>4</td><td>4</td><td>5</td><td>5</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>4</td><td>4</td><td>5</td><td>5</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>6</td><td>6</td><td>7</td><td>7</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>6</td><td>6</td><td>7</td><td>7</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>8</td><td>8</td><td>9</td><td>9</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>8</td><td>8</td><td>9</td><td>9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 7 × 6 of type dbl\n",
       "\\begin{tabular}{llllll}\n",
       "\t 1 & 1 & 1 & 2 & 2 & 2\\\\\n",
       "\t 3 & 3 & 4 & 4 & 5 & 5\\\\\n",
       "\t 3 & 3 & 4 & 4 & 5 & 5\\\\\n",
       "\t 3 & 3 & 6 & 6 & 7 & 7\\\\\n",
       "\t 3 & 3 & 6 & 6 & 7 & 7\\\\\n",
       "\t 3 & 3 & 8 & 8 & 9 & 9\\\\\n",
       "\t 3 & 3 & 8 & 8 & 9 & 9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 7 × 6 of type dbl\n",
       "\n",
       "| 1 | 1 | 1 | 2 | 2 | 2 |\n",
       "| 3 | 3 | 4 | 4 | 5 | 5 |\n",
       "| 3 | 3 | 4 | 4 | 5 | 5 |\n",
       "| 3 | 3 | 6 | 6 | 7 | 7 |\n",
       "| 3 | 3 | 6 | 6 | 7 | 7 |\n",
       "| 3 | 3 | 8 | 8 | 9 | 9 |\n",
       "| 3 | 3 | 8 | 8 | 9 | 9 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3] [,4] [,5] [,6]\n",
       "[1,] 1    1    1    2    2    2   \n",
       "[2,] 3    3    4    4    5    5   \n",
       "[3,] 3    3    4    4    5    5   \n",
       "[4,] 3    3    6    6    7    7   \n",
       "[5,] 3    3    6    6    7    7   \n",
       "[6,] 3    3    8    8    9    9   \n",
       "[7,] 3    3    8    8    9    9   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xt7S93O6dOnxRt5k54oLvQjICLKzMzs27evOKtTVlY+d+7ctm3bin/GukaNGnv3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w57H3Xxv2bOfm/kI3oyg6eCgboMOIkTFu\nX/pI7KABsZ+kXbl8mYVgBEDYbe43I7cO3LRhZH/czgZPXDmnU329ChyNg++fMuydlGHvLFan\n/7V5/foNm3ZdOP3LnJ8HcQsSAADYGZuFNstx5faKzZf1Z220NVaS4Nq4uWqoCYaLEMdV2Ssl\nrFHFa+7PWHtROoIs/0w4OLxi1QvG5R7Dyhj9MsV7H+nm8FHULklRKHtLZZ0QfnD43eZA0/St\nW7cuXbp08eLFS5cuXblyxWz2rLvlDkEQrVq16tSpU+fOnTt37qxU3svh5yStkjxbYpcLUDGB\nzDyid7i97FdZanZzEIDXWkpOlzgCJGhKAK9NAC/G604WYZX4sbqnaK3OnnvbkX/bkV+MKWTK\nl59Xr9poOX/NvQ9LM2R5lfyFvvpt+3AfJT8uSpAQJYiLwv0aVk+9XnB29OQot5fcMKUjACp+\n4IqCBRbaDABSXGakDABgY6x2x52DpRhCjAt9+7Q2zc+eG2VLj8wZ2ZfV2RFeGq9liv1cTY4S\nAABgAC2XddulaNfScVkliZPKOyL8sLuGoDIMk52d7W6hd63U4gLH8ZYtW3bs2LFr166dO3e+\nr5gz2JkbGkrGR0ga3j+mz9KQSgGqFKB5Ok+92DaQd6HM4SVAX4wTzWgjrbIw3kIUe9InlThh\n91ST9MHGhWkvfPTXjrkTdswFnk9C52efHz7hlZe7hfABnMHbp2cEIjPq3qnVmgEkAACKAYu+\n7Ldjwh4zqXhx8Ve965ndmouxe2AIn8SB0xcOnP6lLuvgr+tvhz7p+TwpSNZxy5SpI7XNpMkb\nSlZfNVxgWCZM2MRZBALcwnEYhAiQdGFph51Br4t6FTCwVveiH1UOcMA12se6Ty7w2riPT6P8\nan5TR+RP30jCBciLd52DxWLJyMi4evXq1atXr1y5cu3atXsrOQDw8vJq3759u3btOnXq1KZN\nm3ufgSg10Tc05A01dUND8jAkQo4vu3hnHaq7g4Mi8FEH2blSh5cQ7d9E2D6IdxcDftSwLFmu\nRjAU9/N25BdXLfqRLK8Vz4b7KBlTrRT8CIGL2jQXpSQiREvFsIZV3nwQODt6PJzTnUgzXKRZ\nKkYcv6nkB+cZcxTBXPkgHYyDAcKG+SsxeQo/SGDNMrG4BVPkV52apvnCfSg+6/Cny1hAnMKO\nRngmIsQh7+nrO/hzWTsAABjs8elWqzUjIyMtLc1poWlpaXUrtHqgUCicFtqxY8d27drd9wwE\nzcCmTMu5UnuGmnTWaAEALyHq9M9V2xidrcZRhyIQqcCfixJOaykhGZb/t5T7l5RX5oTd0420\nzbt7ssdc2rPtz917Dxw4evHQhoxDG5atmXPs1Mct7+GMBnBLpmXPTc+2AAAYs64X0S94cWEq\njwXGWJh2Jc8ojUxu0fSZV764/w3/JW7bCs5oj1aTmtaKDr+V/lzlqAAAASq0MXe0QpE1jwXE\njnqTqKyrOIChINiSFkYWRFbN47N2ANhpv4azlB9V7jFyJep/1mtqS8wWQhWKvfooZW1QcXMp\nwnNf7xmGycvLS09Pz8jIuHbtWlpaWm5u7j12V50QBJGYmNi2bds2bdq0a9cuNjYW8cwV7/aA\nBvpUsZ1moXUA7/X91XVf9F2EyPBCPcUCeAvR5n5Esh+vexg/3ocYm1jfs3gPEfOpS9ZrWWRh\nqeN2GWt3AIL4TBttS7/loeoAABEKvCa9oNuyCyFwfnQEPyacFxGCEI9/QfmftqNHAcMyxbYC\nK22NEEX9VvbzZf05O2NrLm11TnfC2eGC7pTzBxYQPi+KptQyqvSmZDQiaD1Z81EH81anhbow\nI55hDnZEcNp/bhhf3pxAvDE+Km4uRfm15sAwBQUFTgtNS0u7du1adnb2fS0Ux/GEhIS2bdu2\nbdu2Xbt2cXFx/2ShJgebo6NYlo1Q4LOP608VOzAUklU8Z7ScO9a/80HyMWRZL8XlClKII0kq\nIlnFc2UP5j8WBx1tMAFNY0p5fTpzwu7ph6dq9fxrrZ5/bS44qtJ2fjl1/OKz85f8NWtDlJ8f\nQHW/tcbd4++xRNDpX722PJsNjIqqzrm24NXlY06/FVWPf6ZcjN2D4Lj586Qh0zdkmgAApM3G\nfvvn9y9H1SOy8amDBbbQmqcjtZGi6Iu60wfUuzSOqhRFB+dqAQCu1QIAMSFykh8bTBn9wR5H\nV3nT5TKq8BfJy0pL8WDDWqi9a9POfmq2cn5f627nS78RV5mEiYQ4sU/wjIG4BAKWuXpSFJWf\nn52RkZGVlZWRkXHjxo0bN25YLJb7Th7DsNjY2NatW7dq1ap169bJycl3ja120Gx6FZlTTeXp\nqFtayk6zvcIFC84aKAYAwHkmzuOWTsH8XB1lIdn2QbxPO8mdZ10DJY/vlYq1O+y38h3F5WRx\nOXm7nKUor3FDyNJK9bcbavdjLReuCRNjTcfu5IDFvOT8iBBBi6ayvp0BRf1mTnlsc74L/zN2\n9KgpsuZf1J0y0cY2ik7ri1eX20sAQEl4V5N3sjme050AQGyotxULEAqaB1CFXaxn/ChtuP03\nOaMDAC2ZvUEyrrv1oMfIFkRUoBzuZz7Fp3UWYSwhSvBRdFFI207Aa2JPaZrOz8nJzMx02mZm\nZmZmZuZ9XeYAgKJoTEyMy0Jbtmx5V8e5wc7YaPAVoRuum48U2nOqqVLTHY3onluurqoLlGCr\n+yqLDDTDQvtAnpcQ7R3xWCOVrJczLJczyOJysrCUNpoAQbwnvSh9pvN9b+SE3dPMkWk+PVYG\nfpZxbXY8AADwfFsMfW/MisVnj1RVmeG59u0VkHV6z17d+KE1RlSysmfUtMMJC/MuvhsBwOZ/\n89r8a5Rs8MITn+U8m/zJ2dnTf3zxr0kNyJTK0WDIMx8OmLghO+D5b/bMTro6Z8Qb68Y/p4q5\nvqDdU22NDMuYaIMEk1XYS49o/tKR1fHSFpf1Z68brwCAFJeZKKNz++ZM9TESlbB4qBzwAGCC\nWJuSzDwgHh5DlfY1/hlC18oYMrP6CzMq9lB1FEKkeU3trepb4nesCatGRQkiwk8FAABqtfpK\ndvrNmzdv3bp18+bNrKysnJycuxZ7qAuPx0tISEhOTk5OTk5KSkpKSpJIajkbSIa1USDlIUeL\n7AfybRaK7Rcp+PSkobx23lH3lAcyHqK1sQwLAKASY0298ZQA3rhE8WMr/8DaHWR5FVlaSZVW\nsiQp6dURKLrs/a9pY62dLO26P0WtPVM8oAK+tEd7YatmRGgAayd54UH1dBg8Fv6bdvRIcTCO\nbHOmmTbFS5rvV+88U33UTJlSFB3PVB+lWAoAjmn2O42UQkQloECJ4Fi2WkXrW7FlBG32s6et\nkb2aBso52lc8wuO8aI0PXenaXQVAtLxQoygFC3w7RRbv3lOj0aRdzHKap9NCs7Oz62mhBEHE\nx8cn/01SUpJUWit4iGKg2Ejl6+hcHWWn2GciBbNP6J322Mqfd6nc81Pcj0FMaiGusjA6OxPn\nTcT74Ak+RJgcRwASfB75uwKtN9oyc6iySrKkkiytYGla+dIAAKj4YlWtfixrOnGBE3b/dVp2\naC9YuWvpxHeilr7eq1mg1FZ47tdPlpwECE9KUgDW85XJUT8v/P31IR+jiyd3j1Hais7/9vn0\nWYdtot5TRkUAAJStm/7JSSuv7WdfveQfZV8+9afOK/bOemfb4M1D7xfszMXYNZ4zG9ZlM5Ay\nY9n0zqHQeemM9dvfvfjzhjML2t3fXv9NWGnLOd0JjaMqSty03F7yZ/lGB+MIEoSaaZOO1ALA\nRf1pAHCgcgKEcrIgDJEwWGAGr7kOC33e8sdQ40b3pAY9HJl4neKtAFBGBJ8KXdu/6lMjzbD8\nCJEkUSVpJhQ3G4hJSktL8/L0p3MKc3MP5+Tk5Obm5uTkVFdX1/8RgoODExISWrRokZiY2Lx5\n87i4OIK48yVup1m9nZUAnCt1/JVnLdTTBXqqxETTDHQP4x8ttDuXr9Rsa91hk1U8M8kU6OlE\nX+KjDjIRgVSY6abehLfw0cbfMBYrVa4mK9SszS5qnYjweZVfrbGmZQFbswZbr96Q9OzgoeoA\nAPfzkj7T2VFYSquriZAAXngQERbICw9GBXwAEMRHP9KZN4r/iB09IirtZUbKECwMu2m6flx7\nwEDpUxQdj6r3ldmLAUCCy5z55ADgcPUFBvWNonJFiEKGeQdRVWLGvEU24gaRnFrRR84Y3Id9\nV//lDK/lHqqOBVQj7WoInnvJPjCSKvCTxBPixGBMWlZWlpeedyZnXe7fZGdn3yM9UF0CAwPd\nLTQ+Pp7Hq5XpukBPHS2ymxxsvyaCheeMhwpt7vm6v7lkpP7+ta6qk/HRL7vJKYYtNzNJfkQr\n/0bn0K4vLE2TRWVkeRVVoaYq1IzNIevXFVPISt+dz5hrfZNof9omG1gnPx+KSrq28Wy8G5yw\ne5qRj/j8vYV7Pzu7eFS7xTWtSODwr2a0BgCs7acbPz7aa86RuUOS57ouowEDflw9KQAAdDtm\nzNxthMh3lrwRhQAIOn2+6KUtQzdveev9yX3X9HgCET7/PfIunJW0bOdXe4/NeX7r74NZ3t7e\nrrZ/PZf1534r+1ntqIgUxbAsm2u5CQAIIAiCMixNAKqjWBkr9kVFpbyIU/xug20HJxq+UzA1\nYotBUBsiFDGe+ywIi5ZhKjsiDKdybajUSAQ7+NG4tHWg34sD9EiR5aPCwsKCgoKCgrT8/O15\neXn5+fl3Lel4D/z9/RMSEpo2bdqsWbOEhIRmzZoplUoWoEhPFRvpLBN98KrNYLcMiBKuS7f8\nlWdlWOgYzD9bavco7XCksCaEKFiK0Sw4i7FGKvAmCqyZLzE8TuSRf+ThJq9i7Q6qSktVahir\nTdi8KSoRaX7caj55kTHV7C/zIoIVI56zXr3hcS9jsRGV92YAACAASURBVAqT4zEvBa3VoUIB\nEaQigv35UWGS7u0QPs/3rXEPcZ4Pj/+aHT1EtKTaQOkD+cEltqKjmn0GSpcsb3tVf+Gq4TwA\n+PEDtI4qpzcux5zFIHwRFqoEVslSoaydRfADwueuCjrsrujlQ9dK9tZSc2WG93IPVQcA5VhA\nlrjbQXpyc+spShiJipJ8pYkOe7C61BZx6ODFgoJthYV5eTvy8/Pz8vKs1ru89twDlUoVHx8f\nFxfXrFmz+Pj4xMRELy8vkmHNJKvgo1kacmWa7bbRcttAFxtpo515Pka4PdtqIVkAWHHZVDfm\nQcJDbRRro1gE4LVWEj6G5FRTEXI82guPVuLhcvwhVvGqBcuSpZVUpcZlp5Lu7Yhg/7JZC8ni\nWmHB9sxs5bihHqoOAIgAX0nnFHtWHllaSQT4EiEBvJAAXmQIF2P3vwCWNHvfAflHX/y090J2\nuYkV+ka27D1q5ucz+90pfiRq89mRc3FzP/5606GMclISENW679j3PnmjWwAKYDn8wZtbqsB7\n+IKP2t8JXFUOWTivV+rUgz9M+2xi2ldt7/X+wsXY1Yuy30cPndh2xuIv3+wV4ooObtutm+CX\nvSc2rs8f81po0bqNJwCE3bq1fZLzrC9bSn8spygx4l9tM8lZtiXincVLOCPo1td2bLphiYyp\nWVVZM/woVY81/khArUxsKMvQCFaIh4VRdypu6YlARBAlUo4uNMYaS9RXSm7l5FUUFxcXFRUV\nFa0sLHyvocsDABAEERkZGRsb27Rp09jY2KZx8T5hMVZcarAzzf2Ia5Xkb1mWH06x/aMsu3Js\np4prxXpvyrC4FgiPS076NxE0UeKXy8lgGTahuThYimmsjEqMPdwQapaiaa2O0upodTVjtQuT\n4nBfr+oN242HzzDGGllMBPv7vDbKuPe4x+1kaSUvJAAVChjrHfmL+yiJIH/F8H64jzJ45WeM\nwYh53T/H3r+D/5odNRQW2BJbkYHUhQgjNGTVwapdeqo6Udqy2FZ4UnsIAHx5KjNtcqYdOWcu\npBBlCBaqANqHNCWzuACY44Ke1Qj7oeG7IGq/+8h9rQe2Sl7yUHUAQCN4Gq/lJX5KK/sFPe5P\nCKMJIuK23eu2tuXEAweuFkp2FScUFRXdvr22oKCgPhGrHhAEER4e3rRpU6eFxsfHR0XHVrDS\nMhNdaqLLTPR+B0tYBduP6LbfstIsdAnhXyxzWKha0m1TZs3nEijgKGL5+6yDQoBGK/G3U6SB\nUuyWlmzqRQQ9gvJ6LEU78oqoqmpaU02pqxmzVdy1jSAhqvzjpfZb+e49zacu+b493kPVAQDg\nuLBFU15kiCPvNu6twAP9iAA/XmiguGsbhM/zeX1M4ybGCbunHFTV7e3vu739zx3EcSPm/z5i\nft0Loh4rC9iVHo1BrxwwvfJQJ/i/TccFGRd6Lpv1VkpCxOQFX88aGisGAJ+xS79N7fVq6vQW\nkd8oq7OLqNDBq5e9/AgyfT18okE827wBhZo9U9by52Sf6DcNSyVMLV8JApDkuJzPaxLjyHK1\n6TG/NLxHmrW7oYxUaU9X3Cq/nmXIzS8tLj6m16c2bkrOFaJJkyZhsYlBkbEto4K8w6LT7H56\nErqF8jPV5Npr5op0mkqzA9gBIFCCaayMnWYB4OTddBuBId48pNLCAECPMH5rf97JYoePCA2X\nY+FyvIkCT/D1rAfU+HMPLOsoKqXV1bTOSGl1rNUmTGkuiGuiWbPFePA0MDWuQkwpV330mn6H\nZ3A6WVKByiSogM/Yap4F91EqhvfH/bwDl3xIFpZg3koiwNe9WiuCY0+PqoP/nh3VhWQdBMIj\nWUea4aKB1EWIoq2MdXfF7zpKGytuZqXNzmNGElyKAKJhEDvmdcJ+CUeErVB/L9YeaCsRAOVF\nV1zkJ6EgSrBfTnJcch+/p/WYEZXKGb3H5yLAbpSM6W/Z4bykx/x0oDLT8pNlXbqe2P5jZsgX\npdjt27dLSk7odDsb92gEQYSFhTVp0iQsJsEvMkEZGi3yDeFJvbqEiTLU5MYMS5qd6U8Il5y0\nXyqvpS+3ZtW8Yh2/fRdTbeFHRCnxMyUOPxH6aktJrDdxvYoMkGDhMkwhqPHFPXjFZNposl68\nTmmq6Wojra1mzDZx51aSHh3KPljkyKsVE2w+c1n18eseqg4AgGH5TcKIQBVZWgEAmFJOqHzw\nID9Z/+6oSBj41XvAsvDPp+wbCifsOBoFF2NXT/ghfWZuTBt39vvZb/douuyFOUtmj23lFTtx\nR0abbT9tO5mtl7/aedj4Ic1kT3qe9WOw8Xd3VQcACLBxZEYW0bS1/byr0QKSfDZ8cd4EtDQ/\n3uatKay4cjovLaPa5qgGuAmwqs7A9wNBMYmXf0hEqJcgODIWmvaUeAe0C+aLvAO3l4rKTEy+\nlbnFsABw1kyYrjLFRhMArE0zMyzrsT9TaqLRv78/RQQSLscz1SQA8DEkSIqFyLAxCeIWfsR1\nNekjRJt6EygCryQ3eL4AwNI0a7OjYhEA0Fq9+cwVulpH64y0wcSYLKK2LeQDe1bMXWlNq7Vb\navjreMAX7xj3n/QYjdYZEIJApWJ3dx0qFsqf7034+wZ8NdOWmYMpZITKB1f5ILw7kYK4jxL3\nuX+25IcIY6FpI41JMONJHVXpIMvslNpBVjlwb17gR5FYo1Pw/7fsSO2oPKjeVU1qIkUxIky8\npXStlbaECiNFmDjLlI4Bg2N+YkSMseRtPPiMVRLE0AOBF04Vix23ZIxZArZCPOIKr2U8mdHM\nUStHdBxVcNdPZBHkd+mE0cY1fMYMAFZEbGGFebaQA0Xxjl/W9bnZBLl9NTu/2ubQAdwCAIBd\nDX0oVCT3b5KgimjqExajUAW18kMl/pHnyGASE/QMFxboqc1OB1slQCUAmJddttgo1hkD50rr\n6A6GIj4C1HlqtW0gr20g73Ch3UuABkuxEBkWKsO7hHqWRW6QhmNphjEYaaOZMVt54UGoUGDP\nKTQeOEnrjLRWR+uNjNkqbpfs/erIsve/pmqLTtuNHCI4wEPVAQAgCBGs4oUHOQpKAAAVCnBf\nL9zfR9a/OyoRBS7+gNZUYwqZ+1uW68b6z/y+cMKOg+ORg/m1m7r6zEvXN8+dMSzh267vL/7i\nta6JQ99MHPqkJ9ZQSkRdok17nD8zgOpZyXVL+KZD5E8FqrZmka28Kq+YKSxj9CYdQDHASQD4\n894jIggmUqBiBSr2QvkiR8kNgVji33uS2CcomKnw8VPd8u5gRYVmBnceLBXJcQMG2VoKGDhV\nDEQpYqVqbfVmaWp+JRlWKUCra6cVHRgtHBglTM2xKvjooGhhvA+Rr6PkAtQjs2iXkFqJtepC\nllUxeiNtNDMmM2O1C+KjeOFBlovphtRDtM5IG4zOiDd+bKT/p9PLZi/xWBjst/IFsZEeqg4A\nWIpCRQIiwJcs+zt1HILgXnLpM12IQL/ABTNt6bdQmRj39cJ9vZyqEQCIQBURqLr3hB8QxspQ\nGgdjonEVz3xaT5bZKS1JaUlK46A0JGOgcRXPeFhLGykAQAUoY/Msu4SgEPxVzIPM4T9jRyuK\nFtPWKjGgtwyZUtbRlzZl8uM380f1sR6ea7+icItsYwH5TDlvonFNCFVLQ0STN6PJmw7E88Am\nBVgxHqrGVK3tZxlAjSCuskvz9LJtV5VbVq+eqS1TeaF6E6szOgt55QAcqdeMEYQfnIDJ/FCx\nkq/wlfsG+NpK/IJC1RF9EKE0SW7HRfJdpXwA0AM4vYJZKAKVYCZZAPJ8mWdlPAAwOVgBjlAM\nCwAKPhrvQ5wusQOAUoD6i7EgKTYiTpSsIq5UkF5CtJkvgSHwZut6JtD/+/8eSdnSb9J6I2M0\n00azM8BU1DrRsPOwfscBWlejJnFfr8Cv3qv4fAVjrrWtbDp2TtKjHVWh8RgZFfB5oQHC5Hjr\nlUxAEEwhw70VuK+XtE8nTCoJ+HImVaXFpGJUUiv9CoJjuOph1da7F5yw+++zffv2BQsWXL9+\nXaVSDRs27MMPP/Q4It4YuBi7hiNv9tLC/YOn7lo0c2rLlfGvff3Vu881uVe5gn8hsz/QGy0E\nSULBTU25mqFoLUARwHEAcO09IDgPFQkZqwEAETRpjclUqEiGCeWIUGLLOc/ajIpnpvOVKn5V\nloLHauOHMWjNt5CUAKUQKzLQJoCbAPkYYqdZdxdhvr4m0y/FAK/O+3mcNzGsqXDLDYuEQJ+P\nEfYK5x8utAtxJFCCqcRogARzZhPtGlqj26K9cABwfvszFitjtjI2Oz8qDPf1IovLDXuPM3oj\nbTIzJgtrtQtaxHlPekG9cqPpyFn3z0UIImjZR+pl61wxbU7sN/PsWXl0ledJQIQgcH9fUUpz\ny4VrAIDwCMxLjnspJN3b4SqfgAUz7TfzUaEA81FiShmC3XlO3M9b0rN9ff9a94PWUYyFxn0I\nyxUjpSYZG80LFhiPV1NqB62nqGqSrqaoapKxMjx/vum8HhgWABAM8fSC1qGuqgMAxHMHu5H8\nB+yoo+laB9sZ95ae9hNlePQbhm8Vtc8rIMCONf7gEagKAHbgHxf2uMwmjLeutVEYSbHXNYqS\nrKo/t5Ve0WtJU4nArLc7WJKq9Q+PAigsY+44hxCEFxCLSX0cxRmAIJKUwZjMFxXJUZEC5YuQ\n21fkQh7TfhzNl/rbSyR8LBsJch9Ki4IZQywkCyzs1QHowAMzybqOJhAoEu11xzUOADgKKjE2\nJEbYJ0KwN88mJpA+kYJwOV5lYWQ8RFDbCdc9zO0Vi2XtebcZg+mOnVptgoRoflSYLf2Wfuch\nWmdgTBbGZGGsNmFyvN97UyrmrbRdv+U+mnH/ycBF71f/sp2tfSSKqtI6bpeydZL6Y15yXpNQ\n+ZA+poOnEQEfV8oxbwXurRB3ao2KRaoPXmVMFkQkcFnonb8ajhEBvp7/RxoFWeGgqhy0nnL+\nh6t4sh73L27LCbv/OCtXrpw2bRqKogzDmEymBQsWHD169MSJE660DhyPHHPOvo1bj+UapU26\nvDCqb9RzH27rO/H4yg/f7hy/bNS8xR+NTJQ/6cqC9SYtK0/r1xsTK22yywhU+vaaikq9UYEE\nFcpQvsSWe54sz1EOfA/BCVadL7Cq7SEp7rcjACKcNVMIAFAhLTQe6ekAjCQYyTs6jgUQEoi9\ntoaIUuLD40SbMiwEBn0jBYOihTuyrQIM8RNj3kLUj89G+wgwFMY1E9lzixhTJXPNNtBiYymK\nHx3GCwx1FJWqdx5mDCbGamMsNtZmFyTGeE0artucqt9xyD0tCCoUBC75sOKLVVRlrZd1srxK\n3D7JfPw81IYlSVqrR0UCD2GHq3x40eFeE4YZ9hxDCBxTyjGZFFNKRe2SMYXUb+bk/2fvPsOj\nqLoAAJ8p23fTGymkEVoIhN4JQZpCKAKCgPQiKlhREP1EBRVRUESxA1Kkg4ChiPRA6CVBSoAQ\nEkjvm61Tvh+TXkiy2c2GzXl/5JmdnZl7Zjd358ydO3eYrFxSJiHlZYY+JuUyWfvWUDNsPkOp\naMNDHZNpZPMZroDlClg2j+E0rMRPpj6fy2YzbD4jCZCpz+SyWUY2n2FzGTaPKSqL4jTVjOlv\neFBy/0q1WR0A0C5iNttISEjaTSxyF9MuIllLpce7fjXco8rZUD1qZ4ipOFPK6W6KW/XUnSo9\nMwOcpqe+4Zz/4CX5YalR/SAu9/715Eza7XQMJN3dD1zkJ5pcIAixV2tKKSNlbUhpV5FSzD64\noudlds9OIWUq3b0LTG6q47NvUPZuQlUlaLHhwWVak0m2HgAANKNRgC6XLpsuhAxgix52lyyt\n5DltLAdQKm9r40pfSS3JPp1l5HOB0uFB8l23NVKaGBwgFW5aUgDrSDLOYKCcHQiK4o1GP/IB\nl6XmkvV5Wr1Ip+eDgyC0lS72Tu7uw8JFUk6j5fUGaZvmbvNnZqzeUBBVpu8gQVGe33yQvuL3\nciP4aC/f0N24q795t3zcPA88L/LyMDx8XHq2rF1LSYtAt3dmqI+eJcQi0l5FO9lT9nbSti1I\nqcTxxQhhYLnyCIJUVT9+BJvLcAUsp+fEnhLN1Xwmy8jpOIm/rOBcrtAKzqpZNtvI5rOcjhM3\nlRZE5zCZRjaflfjL9PfK35jivby52xyfJ5eIiZ0ty8zMfOutt4SsrnjmuXPnfvzxx7lz59Zp\n09jHrmb4pJ2T+47fcI93dFOq0z5f/OVLm4+vH+Xt0WfebxdfurL+47eGtF713AcrP5nVw63G\nnUO4xP0LZ777+6l4g0vI8HdWr57bpWLPoposY4LWC3bcNjgCAPAcbVAzkjJbFbkHFE8TLv56\n8C+3Og/AAQmFT5mEFk6im5llmiI6eYjHNzFsvMvSJNdHoXvWXnOE9BA7quxZnfy/Ww66/KY5\nueQxbQTDSloG2nXqqzl/fcyW/WxePq83ChlVWmBT98Xzsn7brj5+rvSWCYry/Hph+pe/lHs6\nljElXd41NO/AydJZHQBwWp0xMZk3lG8pIUQ07eas6N25ePuEREypFPKu7SQt/N0/mltw8gJQ\nFGWnoOztSHuVJMCHkIhVg/qoBpWqKTywuQybw1D2NKeRGh4aeUMup2E5LcfpOV7LSgLlmqv5\nTLaR13KSILnmSh6TZuB0HKfj2FyG13GclpU0kxecyxXyM5Gb2JhWoyFeK6o2qytH7C1lsoyE\niKBdxLSziHYS0c4i2lWsCnPkNCwhImlXsbytkpCYcyQJG6tHv1xu/1roWRaIbNYum5FpGOll\nte+G4+Q2XVgfjjbei03OZAr8ns02uCbHXjemrXB4dt5VxduEWEZKVdCa1MdfMbjd8J3+LkGL\nDI9vGR7fUXYaVjZuhtTmcgpnAFD1HE8Dy0CZz0Xs16F4mqHluVC+yZMA8FRRiXksANhJyL6e\n1N54IwCISMJRSjhIyXCl5jl53l8ZEpplekvyQ4M97vb0pHJyxKfPqvJzSLWBT9Dy/3BvtQxw\nHDdUfTQ65YO9zjn5AKAFSAIQebg2+WJ++qr12ss3Spebu+cfz+UL0letZ7PK3OehvXxDF3tH\nc75Mh0IA4FmWzcohZBIom9iRKoXY19P++UG5uw4DRVJ2SlKpoOxV8q7txL5eHp+8obkUS4hF\nlL2KslNS9iohP5O1b118QsXmMDzPM1kMk6UVNZHobhWw2Uae5SX+Ms2VfDaPYXMYzsBxGpZT\nszzDSwJkBefzhDMrSTO55nJRnS1VvyrtovAEFbM6ANBer35IH5pl2YyMDGEMoMzMTCgaDwin\nbWD63Llzen35m4kIgjhx4sSLL75Yl+3bG41Ci19GRkbD2V9LT/N89W0VZeVsnjt1wz3o8fmV\nowua3f28X4f3N0ydN/iZXeMdAAjH9lO+OTbmld1fvDO57ffj98Z+XKOhJyFu+ajRO1r+HpX8\nvPPdH8f3H/Jq07sbhtvXfhlTFEhcwMACABAkIbUr1+BGAd+aKohhlQDgQHPPt1atjylgeVDw\nRiVnVLH6LprHo9z0kSG9mMepbaKPtUu+ddmuKcHxdozWyUXZfOFM47a9+RtPtyu1zQEikedX\nCzK+3yjcaFbccFRw9oq4aZPMX7eWOwDo7z3UXbuluVC+RYRnWSYts+I3SFAU7eak6NpOffwi\nz4oAgBAZeEYmad4SRD6qIS+pT17gjRQpkUgCRMYsBe3ulX+SkfccwYu6M5kMQYt5juA0rDYe\nEt+8I22t0F4PZHMZngNJgEwbm89kXBXSOJ4HNscIAJJAuTZWzes5AKCdRUxmJd2PakIfX9KK\nZnJWBwCUPc3mMcADISFVvRyETJFyEFEONO0oohxFtCOtCnNi8xiggHYSSQLlRL08HLMUW6tH\nv0f3+A1+AgA2N1WfGCNv0x8ApA4AAKdgPO/BsDkptJM3ALh1AU6nJqVlnoAicvGFziOEabFn\nS7Fny/IFkDShKLk/WEERuWWzdxXB9oH0v3kPAGjnJhrjrFkbqwWWs+P0CqNOyej62hsGj+j3\n94EY3akLbdLjXfT5U0VyEc+qnJRNPn8n6/cdQsvZhKINplNky+ULMn5cZ3j4uHSTte5GnLRV\ns+xNf7F5ZRIvY0q67kac/naFu0d5nstXk3JZuXpNyqS0u7syLCzv4CmeJYCnKCcgCCdxQAtO\n7+7y+uzsTRd4huJ5CSGVkLSEtHfJ3pknbd5D3q8dm2PktJy0pUIbk5+9n83cdoPXczwjZdUs\nQL4kgNHGJjFpBiiqm8ZUfblfNlJGctpaJGQF58vffSyoVVYHAEASQv8HASEmpc3k7vN8q12P\n1ul0MTExoaGhABATEwMAOG0z01WN1il86XXZvsPw4W3EYt3cuVbfx/qcFrLkGj79BgAAzh4+\nnA8wYPorwRKA4Fem9Xn/zJHDh6Nh/OCiJRTNR366d8isI+dqOlZb9E+rL3ZesHd8czuADm8s\nHL9s0C+7Vw+fYl/bZUwyL+/a9xoPI0mFZidEJF7e492JYMUuHZqL78SJklNDchKD8lNuO7uy\nRolfdpa/34SJN3cb72UQjIhnSi4OvDy5ifrfvcYklmcc2ifnkcp8tkDF39FofrquPprCabwA\ngGdoSpXHFiiBpdLWxOtvkYakAGBoAOBZmucJAEhdk8dmNtfd0fKMCAAoZT6rVgEnTVnBGh4N\nMCQVXkKl7TWc2h7ECt08tajJc5qr2bwBeB3FAwABvJHMbHlD1rql7rYPb+QBQOQmMqYb4Qik\n/CBcby3qWkQITY2PAcpcxKktzaWSTlQmZ3XlEGKSN5Q6ZpAEZUdRdrSiqz2by/BGnnYSKbrY\nszlG4IFUUpQdTTmKKDuasqdlrRXAA5AEaeEHY9SBrdUjh57js1kAAMreXW5f/q4XgqQljp7F\nmZhSqdSU7fpF8WwrY3as2AUA7IzaPmk3//ZszxMEAEg4Ri4TddE+Hnjl6M6mXYCHDtnxvVNu\nH3Vvr+zXg/vviuxBsp2WCcxJszdqptNSkud8X3sxf+uOLgkFvEEMBE8r1KxWzhvEesKpS+Rx\nTl0AIGVAqpSr2QIHTRKdvuY/3cUkQ4oXbyzqz8MTPCNK+/ahIV5hSArkDRLgCUqVyxaoeKM4\n5ZsCw70uTEY2z5HAkZQyj9UoCVA8+tRIq54tuJrC68U8QRIkyfMUQUoLxmdKAgdpruZwGgII\nEHuThoc8p4WM3VcBVADPFX4IDjSbwwDA4+UXCJrgmeLc1whgBCj/bJWq5Je6e6T0yVJptcrq\nnoB2EjE5DHA8QRGKrvZMthF4oJQUqaIpO5qyoygVrezpIPws0E4iRWd7Np8BAih7mrKjSXlN\nm6NpqVQaEhLi4OAAACEhIQCA0zYzHRYWRtM0y7KlGyp4ng8LC6vj9kVqtUgkopXKkODghrO/\nlp4WHnVQ7pk2TySVSgE0arUawA5AeK61VFrhOdJin/41fQzSwzNnkgLDenkUvuzSpbNh2/lr\nMKVPLZcxTcj2uK8vsTwrAhBTyuAZGhI4DkAYqc4FwCUPwEui4/QSBoi4Y0mEpD2vL9/fI+9s\nNkH24rniVh8egAAA9dUCgJCKhRbcKABoW3G+7n4egHfF+fpHOQAkQGHnZaYwwQNDkhquQoX+\nJzwAaGNLDgPGtCqSrdo219ZApZdmKCUl72TPpBt4I0+qKEVHOybdAACElCRlFGVPk1KSVFCK\nLvZsNsMZONqBlrVTcfksz/GUiiLlVM0PAE8JW6tHL16743xGqdSwFHBgry7QOkgNHACQwJM8\np9QZVQb9PW+5YxZvpzUq5OIklVGRRdAMT/I8rScojqOA46QZBmWBJIcgGHIyXCeUBZCvEE5y\nAACg/ZtgBABS7sjpwvtzJPx6i1SQnCYAeABoLrQpsQB3jyWTom6cseh/huSBIwAg73wuQCUj\n/aivqgF6VJyvuVMAUP5xwwCgS8gFsAconw/rHwkhuJVfIV2rj9cW9+DT3a684glZnYBnLFA5\nS6EcaDaXEX4BZCFKJsPIaVnaUUTQBKmkSAVNSklFF3s228jpOcqeVnSyM6YbCCAIKUkqKMqe\nJuUkpaAlQXLgAXi+5jVUBNXcnl8pmqIoF5fC+2+LJ3DaNqY9PDyWLl363nvvle5mFxISMnfu\n3NK/iqZsf/VqOHyYMBhcDh1qOPtr6Wmi1kMN9Rj3YtOfvz/77YJN/T5pd+1/354F8B03ri63\nNaakpICra/ENV3JnZ2lmenqpjsw1XKYYy7KRkZEVH8/1+PFjACjdOxMAtHdbFD/QlVWrKo2Q\n05f8a1XM6grnc6U/yYbV550QEbyxkuOExF9mTDVwGpYQEYpO9oZkPTA8ISIICUnKSCAJyo6W\nt1MZ0wy8liVklKKDiskwcgaOIAnKjgYSKDuaoAlFZ3tjmgE4HmhCHqJi1Qxv5EkxSSooQkqS\nUlObzZxs+HYoW6tHA3brmNyif/skMUDFrlSSljeFmkZxeaxnilBkcU0heQDCyEnyZVB0xsFn\nO1S6p5ympHc/V1B5llCS1QEA17DqY1VK3/Qj8pAwGYbi9I6gCFJFERQh72BnTNHzep5UUvJQ\nlTFVz2k4SkkRNEFISFJOERSh6GJvTDMAywNFKDrbGVMLL8jQDrRQqQmakATJgeV5I19Y2Rs8\nvHnCxs2fP9/FxeWLL764d++enZ3dmDFjlixZUsm5LrIISfiX+9Zkjn17w8SQDQAgbzl2zdYv\n+5pyClZa6VuaSZKkRKKKPzU1WQYAAI4dOzZs2LDK3wOIjy/TCcYu3D17Z+qToyudGFV1x6XI\nXcxkGIX7K8V+MmOyXuhzVhKygpK3VRmSdGw+Q9CkoqPK8EjPFbAgNFxJSQAgKEIeamdI1nEa\njiBB3t7OmKwXiiPEJKmgAIAgCXkHleGxntOwBE0oOtgZkvVcPksqKGH0DeH3Xd5OZUzV8zoO\nSEIWomQyDFwBR4gJ4cSaVFCkhKTszfZrKW1Vcqw1fczeRsTW6pHY34m5WusH5ZmIKGlsrmqo\nGkpJserCeko7i5hspnTXLoE0SG54pOc0LJCEyCuzawAAIABJREFUPFRlfKzndCVVm7KnCZKQ\nhagMiVpezxMSQhaiMjzUcRpWqIkAQNAEpaRkbZSGJL0wXxaiNCTqeANPiAkAKDwFIkAeqjKm\nGDgtS1CEvL3KkGzgNCxBEZQw/jBNUEpa2lzO5jKcniNIQuQl4Rme13GUHQXk05GYWhT+ptg4\ngiCmTZs2bdo0vV4vkdT1l7AEjmNXQ/K2L/95bfTHly/dybUP6tihhUvNr+NWysPDQ7ibQ2hI\n0GZmatz93Wu/TLHw8PC9e/dWbGn4448/9u/fP2fOnNIz/X4Ndp7QhC1gCQrkbVWGR/oyXbsA\ngCJkIUomzcBpOCBAGiQ3phnYHKZ0+kJQhMhTwhs43sAL3Ud4lq/3/vjlSQJKBhyhbbn16+lk\nW/UocFvHjHWPhKvwstZKw0Mdqy7TjY52EElbKAyJWqEeyUKUhgc64b4WUkkRRSO9yUJUhoda\nNpchaELewc6QqCt3ZZ+gCHkHlVFIyAgoTKoK2NKXAgkKJM3kbB7DaTmCAJGXlFUzvI4jZaWa\n8WiCMvm5eXUmq6QjBgBAccoIAARNgMmt3TYHE7vGwpxZHaodsUvzboPqNOR+CZ/u3b3fjY7O\ngZYOAMBdOH9JFTalfe2XKUZRVERERMX5586d279/f7nxDgkRYTew5G47sZ+swnoAALRjyVoS\n/8qXIWiqeIwFq2d16GlgO/VI5Clp8n5AxYVNoOhcMgCLrI2y0mXE3iWXaOgqUuLSqV7p+oue\nRpjhIpMsXQr798PSpdaOo9Ehuk+bGXz883d2xufnxkcufGstPf3lweLaL4NQY4b1CNkwTOwQ\neroEL9yz6dn4BR3cPDq8dqbLmgNf9BJOx28tbkN4v3H6icsghARYj5DNwkuxyCTYx856RP6j\nvvl31DflZ7dcHMsvrmaZWouJiUlLSyt+aTQa161b5+vrS5L1cU7Icdzdu3ebNWtWP8XVf4k2\nX1xCQsKUKVPKXYisanzNeob1yGZKtPnialuPMLFDCFWCoigAGDJkiLUDQU+3n376qeJMLy+v\nijNtEtYjZBa1qkeY2CGT4LNibd2rr75a8Slq169f37x5c69evXx9q3+sTd0lJCScPn263oqr\n/xIbQ3Hjx49v27b8bY3t2rWrdBXbg/UIizNLcbWrR8HBwTxCtTVwIA/ADxxo7TjqlVarBYBJ\nkyZZOxCr2bZtGwBs27bNJour/xKxuMbJ5r8I297Bhl8cttghk2AfO4QQQqjhwbtiEUIIIYRs\nBLbYIZNgHzuEEEKo4cEWO4QQQgghG4Etdsgk2McOIYQQaniwxQ4hVFMymaz4r+0VV/8lYnGN\nk81/Eba9g09BcTjcCTIFDnfSKDEMc+jQIYZhbLK4+i8Ri2ucbP6LsO0dbPjFEcHBwbGxsRbK\nNJHNGjQIDh+GgQPh0CFrh1J/dDqdTCabNGnS+vXrrR0LQgghVAnsY4dMgn3sEEIIoYYH+9gh\nhBBCCNkIbLFDJsFx7BBCCKGGB1vsEEIIIYRsBLbYIZNgHzuEEEKo4cEWO4QQQgghG4Etdsgk\n2McOIYQQaniwxQ4hhBBCyEZgix0yCfaxQwghhBoebLFDCCGEELIRmNghkyxdCvv3w9Kl1o4D\n1R8ucf97g1u7KmT2vl0mfXc+z8LFRb3pQ5TSZrGlnnzIHXvVreUHpbdu0T2tWJyF9pR9dGBR\nRFsPpczOq2XfWb9czy8KwGJ7V1WJ9fZVPhWwHlmoOKxHJYKDgy338FpkswYO5AH4gQOtHUe9\n0mq1ADBp0iRrB2IVd77oLAl4adPtXG3GpZUDHV0m7smxZHGadUNFo7foLVkEz2rS7kRtfrWj\nHFosiimZbak9raI4C+3p/RU9pL7j1sZmFWT/t31qc9Jz6t/5PG/J77GqEuvjq3x6YD2yUHFY\nj0pgYodMcuIEP3Qof+KEteOoV406sTv7tjfRa3Vy4atjr3qIh6y15BEpZlGLoA+vWbAAnjdu\nGF50glv6CGGpPa2iOAvt6Z2lIdD920eFr7J/GQSKl/7mLPk9VlVifXyVTw+sRxYqDutRKXgp\nFiFUvYdnziQF9urlUfiyS5fOhvPnr1muPDYuLt5w5L2Ovk5KR98Ooz/5J5k1exn0xD08z/OJ\nK3uWnmuxPa28OEvt6e07cY5t2ngWvpKrVBRJURb9HqsosV6+yqcG1iMLFYf1qDRM7JBJsI9d\nI5OSkgKurq5FL+XOztLM9HTOYuUlxMWxvM+Iny8mJV//Y0Le90OGfnaNsVhppdnIng5dp836\nuZ8wzSb8+uthv6lT+hCW3LsqSrTiV9kA2ch/Vw3YyJ4+nfUIEzuEUM2IRKLiaZIkKZHIcr8f\nAe9eZhK2zu7oKlf5hL3924LOl39ae9FipZVnQ3uqidvz/sD+P3p9f2B5mEyYZeG9K1+idb/K\nBsiG/ruqYUN7+nTVI0zskEkWLYKhQ2HRImvHgeqJh4cHZGZmFr3UZmZq3N3d66t0n2bNxGmp\nqXx9lGU7e8okRX44sEXvpfkz9p1bNzFIDGDpvausxHLq86tsgGznv6s6trOnT2E9wsQOIVQ9\nn+7dvW9FR+cIr7gL5y+pwsLaW6y4hK+7EyEf3y56eScmxtCyVSvCYuWVYit7mrx5XOfxB9v8\nfPHsdy+2lBXNteTeVV6iFb/KBshW/ruqZyt7+nTWI7wrFpkChztpdGI/bitpPn3H/byc+3+/\n21Hp98YprQVLi/u2u9z1ua9PPszOeXj8i37OiiFrk6tfyySJK3uWvb3OsntavjjL7Clz/DUP\n+5FbMyu+Y6m9q7LEevwqnwZYjyxTHNajUjCxQybBxK7xMdzf8Xq/Zg5SqYN/rzkbbuosWxr3\n6J8lozv62EkkDk07j/vieLrFSqpwQLLsnlYoziJ7Gru4TYWz+FHbeZ632N5VXWL9fZVPBaxH\nlikO61EJIjg4ODa2UY8Djkxx8iQsXw7z50OfPtYOpf7odDqZTDZp0qT169dbOxaEEEKoEtjH\nDiGEEELIRtDWDgA9nZYuhcOHwWBoVC12CCGEUAOHLXYIIYQQQjYCW+yQSRYtArEY5s+3dhwI\nIYQQKoEtdgghhBBCNgJb7JBJsI8dQggh1PBgix1CCCGEkI3AFjtkEuxjhxBCCDU82GKHEEII\nIWQjsMUOmQT72CGEEEIND7bYIYQQQgjZCGyxQybBPnYIIYRQw4MtdgghhBBCNgJb7JBJsI8d\nQggh1PBgix1CCCGEkI3AxA6ZZNEiGDoUFi2ydhzoKfDgq24EQRB0m/9dMVZ4M+WbXgRBdPsm\nyXLl31rchiA8XjttuRJqIWn3mwNauavElGzKfjNuNmN1X4JQTjlYs6X3TJQSROgXd80YALI4\nrEelYD16EkzsEEL1gr3x5csr7nDWDsO6rv44/5sjt9LURo63diil7BhHEESnBniAQhVhPQKs\nR9WwgcSO2TiCIIheq1Pqqbx6TNJrd/ZQN8LH2OmrBzVbfOlS2L8fli61aEzIlpAkqT//ySs/\nJ1g7EKvKyckBcJm0O4/RrRtq7WDQUwjrEQDWo2rYQGKHLKPBnHwg29B92qwQWvPvwnl/pj1h\nqZwf+1c8T9s/UUoQoUvuAhSdgfT9MSP34o8zewc6K+082w7/8J9HHPCpRz4b27mpg0zm6N9t\n/IqorPLb1t7a9Obg1m4KqdK9Vfj0lSdTy7R6sGmnvpszqK23vUxm79lm4OwVRx8zxW/umSgl\niMHr8lKOLB7Rxl3e+oOrVe6B+r9NC8d0DXRRSGQO3m0Hz/nmVDIrvPHrYIII/z4TIOOPkXa0\n9AmXkNi0c7++NbxTUBOV3M6zecchr605k8KUXoBJOrhkfI9AZ4XM3qv9iA/+TmBr8xmWlvJN\nL4IYsxUALi0MIog2S24J8w0PDy97KSzE11kuUTj7tX92zqpTaQ2pcaTRwnqE9ah6wcHB/NPN\nuGE4APT8Lrmeyts9QQLQ7vO4eigq/bswAMXkA/VQVEXbxwJAx6r288QJfuhQ/sSJ+o3JyrRa\nLQBMmjTJ2oE8ZeKXdwWAsO8eRL0VRAB4TNyTU/Jm8sqeANB1ZaLwMnvNMxWr874JEoB2n8bx\nfHF9f21Bb2Wp3zFpz6V/vN26zE3+9kM3pgqr3/woGMD1uYkDHcv89tH+s/7OLCyBS9g0pmm5\n01zCuf8Pt4zC+7snSADCZr7aRgwAAC0WXal8V/NOv9deUe43lmwSsTaO4fn8XwaVni2ZvK+K\nzyv30ExfqtxGCI/ntzwqWiB1z3gfosyutJgwtlPxb0W1n2HpHzHh8y85IHx6k+d5/sHaIU7l\njxV0q3ej9E/6npFlYT3CelRDttRil3X2u2m9m7kpZKomwf1fWxujKf1m1en/uqEigmjyZlTx\nolfeCyQIgh65qaBoDhc5zZEg2n52p4qSzXSO8sSzh2oLEs7ARu8A9eXfXu3fyk0hVbg27zPj\nhwt5Jes/4eyh9KXYiicfhl3jVQQhGroup0w8N3/0JQiq3w/pVe0SQiUkPT7+YZo3pGyct+hY\nQfWLP8mZ71cmDf7m33tZuSmXfxnpCbqoRZNWZIV/sve/9Py8B8fe76GA3Mj1ezJK1kiP3Bjd\ndNYvJ+9mFBQ8vrR+RhsJE//zy4vPsAAAD3+ePmv7Q3HQmG//uZWm1mbHR/0yNViceeSNWWse\nlWzi5K8/5wxefvhWqubmktDKotIff3/CsisFjj3m77j6uECnybj974oxgWTyvpenrn4IyhkH\nef7Yq86Fx7MqLyGlbvzi1wTWo/+SAzeT83UF6XdOfDvGj0jZ9cF3lwAAQHvw/TmbE3nHHu/s\nuJZSoE7778AXA9Rbtl407ZP0eON06bO42A9aAsCFVR/9nSXr9M7+mxkagy4v7c6pn8b6MTe/\n+/5QI+/a1SBgPcJ6VB1babHrOuuN7vIye+Y6cU9W4SJPSv/5hK+7A0D3rxMKF077QRiZzf3V\nY0VFXH+/OYDXvDM8z1dssTPXOUo1Zw/VFiR8DhHvL+la9nNwHL0lTdjAE88ehNU7Lo/nKz35\nyN/8vARAPmarVtjWwIE8QEygEoDo/W1SXb6/pwi22JmmqKUhmef5jB3jnAHIlu9eKDxpNa2l\nAVovuswUvX36DW8AsH9hW37RHMPWMRSA//xzPM8XtjSA8/hd2SWbZK8uaA4ALq8d53n+2oIg\nAHGXr26zJQtwtz5uRxQHtnuCBAD85p180ql2/taRcgDpoLVppWYaL81vDgCtPr7B83zpA1KV\n/pntDNDhy/tlPsGOAOQL23ie53M3RUgBFEM2pJe8b7i0sDmY1tLA83yF5nnDpggACFp47QlR\nonqH9QjrUQ3ZTIvd+V9WP+zz8e7rj3Nzk65smN5SBOlbf96TB1Bd+g9Nw8MDAC5HRxsAAICN\njr4E9vb2kHr2bLyw7YyoqDugCu/XubKCzXSOUv3ZQ80K+vuLjxL6fBp5I1Wtzbmzc3ZrCrJ3\n/7Y7G6A2Zw+VnHwon43oS4PmnwMnSnVRiMxQA3QaMcKrRl8RQgDOo1Z8+awdd2vly1/dqMNZ\nq8+QYe2Lr7K4uroCQOd+/YovKomcnJQA+fn5JWvYRUwa7lDykmw3aWJbgIzr15Ph4ZF/4wA6\njRvfvNTvIdFieEQLgCuXLxf3iHEbNrq3+AlBnTt2XAOK4TPGuZaaSXd4YWQgwM0LF9Q13Lf+\nP2bw/KX5/kWvjTm3j11IBOBYFgD46OMndKB8ftaLLiWriDpMHNuqhpuvAVHr4GYAcStGj/hw\n7eFrKVrsW9fgYD2qVmOuRzaT2PFBb+7c+78RIU3s7LxCJ6766DkRGGNjbwOA+q9VvyeAdNCK\nPV+OatdELpE5N+/35uZtbzYH/emf1v0H0C483An0585dAwCA69HRBarhs150h+vR0RoAAGNU\n1AUge/cLq+wxHdfXfH2kQNxlyf4t8/q3cFVIHfx6zPht5/vtCMPJTdtLhhTifeds3v7OgBZu\nMqKSjQDk7f5hUzIohqz6a/motu5yhWurwe/9tffd5rUuiAuct3PPB8+2dlNI7YOe/2rRcyJg\nb92KAwBjXNxDAO8BLw1p6SwTSVSuQb1mbYnnec2miJr8FzgMiehFQs7ByLM8AMCiRTAwJDIf\noP2IEU1rsDpChZpMXb2kp8x4acmcNfE1/KXj+fILenp6lrwgSRIA7O3tn7Sad9Nyzd0BAQEA\nkJWVBQkJCQBw5i1PoozQJbcADFlZxVe7mjRp8qQo8x8+zAbwb9VKWna+j48PAGRmVuiEXjXD\no7N/frNo9tjBPdo0dVA4tpy2rbijfPaDB3kAfoGBZXsP+fn5PXmTFT/DJwh9f9PyZ5tC3F9L\npg0KbeLg1qb/pA9+OZ6or/kWkKVhPapeo61HNpPYeUU831VU/Eru6+taeEd09ek/2bNfmAQe\nRkcnA0DW2bN3iM7d5vbqTjPRZy8CAFyJitJA+/BwB6jITOco1Z891LSggDEv9ZAUL6AMCmoC\nkJubC3U/e3CNiOgKkBIZeVl4nZeWdoaDkBEjAmq5IdTIEQGvrHm/A609tWjuuhoNUmTIzzeU\nm0XTtX0aosFQbht6vR4AJBIJMAxT6SqVr1clo9EIAFJpueNRYYOHRCKpZJ3KpEfO69Cyx/g3\nP/t526GzNx5TQUMX//5el6J3hWCFY3ApBQVP7m1VyWf4JKou70TGJVzc/f2iGRFdmuhv/rth\n6azw1r0+vVybjSCLwnr0ZI25HtlOYudV5nqgSCQCYFm2Rum/LDy8K8Dlc+eMwEVHX4Sgbt18\nevRoC2nR0fcBkqOi4sG/Xz+/yoo10zlK9WcPNS3Izc2t8s+h7mcPTSMi2gLcPXDgLgAs/fTw\n1VQjyEeMaF3T9REqRIW8++MbLcjcv999a1e582+CIKD8afH9+/frXuaj+/fL/pjeu3cPgPLz\n8xIqDfHc7+rKeqscnlWhY2oV7F1cRADx9+6VPWcy/vffXQCFn59rFeuVxR3/cMp3N9TKkLH/\n+/mvUzce5abd2PdRH8fi2B3d3UUAD+Liyh5ES39GZvoMxe4dR7yy5Je95x5kpFzd8VY3lfri\n5ysPVHzmAbIWrEdVa9z1yGYSO5FIVPkbNUn/m4SHtwBddPQ1iI2Ozpd36xYC/j16NIGrZ8/q\n9FFRl8CxX79Ke8aZ6xyl+rOHmhZEUeXv7y5R17OHoIiIFgDXIiMfA/DZkQAgcxvRtmbrIlSa\nuPPiH2b7QMafr793pEyfGblCAQCJiYnFc/ibO3b9V/cStYe37i11Tzd77o9Nd4Do0qeXDFp2\n7+4A/JnIg2Xu+X70wzMyguj0VXxNS6C6dO1IQOaOdftKn/Sr92/6KxfEvfv2qNmP7YPz59MB\nen56eMvHM4f1au2poIBPPHS4+BMQdeveiYC8Pet25ZaspDv82+aS/hh1/gyPvepCEG0/KV5D\n7Npu1HsvdQLQpqfX8TZMZFZYj6rSuOuRzSR2VapR+t+mXz83uHfu3O3o6Djo3L0bDdCpR3ex\nMTr60sWoKD0dFt6r8k/KTOco1Z89mKmgup09tImI8Ac4d+BAFj/c/yBA04j5HWpaMEJlKPp9\n9u14d0ja//e10rNFzZv7AyT+/sGyM481enXS+XUzRy/9T/ykvtY1Q+RunzPyf3tj0zSGgkcX\nfps25fsEsIuYM8EbgHpm9sxmRM6O157/cPfVhzma/JRb/66aMOCdozr5gFkT/KvfdiHvF2cO\nVkD2ljmjPztwK0Nn1GXeObxs7NxteeA+/rXRjtVvAADAxdWVALgVueFUQr5Bl/3g/PalY/q9\nd5IDgOyMdAPLeY2fPUQF+bvnjf4k8maGzqB+eHLVuJl/5EqKf6Jq/xnSNAWQEnc7T+iH36FH\ndynEfDP97c3n4tMK9NrMO8d/eH3laQC/0NDKeqQg68F6VLlGXo9sZbiTcvckX3jbt+im5cRv\nuhEAji/+VTonyt/1gh2AePC6whFR9NtekIFywisv2UPge8It1A++6gbQ8eWXOwP0WPW4ZNUy\ndzgzh6Y6ADiM3lHqBnCeT/q+nxSg4/L7FZavStK33QkA1citpUac1B6a6QZFe1F9QZV+Dlfe\nCyzawtFXnAFCCm8WL5T6fTgADPolu+xwJzzPVz5AMXdirjuA9PnNpyd6A0jmfXfsiXtla3C4\nE9OUHqahjJQNQwo7ahcP08BzsZ+1K9v67jvr85f9yw+sWnpjcZ93BIBRfxpLZv0z2x7AefY/\nPM8XDtMQMvm1Pqoy23V59vf7XOHyBef+17nsu8KISPcKB26o4bDk7L21Q9zL3x9F+U/el1q0\nRPXDNGTtnlC23wbh+uyada/5AAAIv07cgw3DPMqUIm2/ePELkuKhkar7DCvszpVFgUUHhE9v\n8jzPXPmoXYXOV4Tn2G2plYaM6gXWI6xHNWT7LXY1S//FfcN7EOo963flKrp1CwEAAN8ePbzg\n2vr1V6Blv35V9ZAz1zlKtWcPdS+otmcP5U4+AACA6BkxxAl0R1a/uycJQD9q7+c13kOEKnCf\nuHppuKzsPCL43f17Px7Vsam9RCR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O88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\nbOndMt6vqtNkF3aZa6X8xE9XAwBpLmY5ZLA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y9fWlEaqu69fpYcPo69eNHUe9ksvlABAUFGTsQOqF+shIACDHn3zz+MBIAOixYOl7wmIf\nINwea/d/1rJEz79o2MF0g19C0/kXZ7mReh9ThMPoo69pmk79sUeJT6/Vz2iazt3WDwB6bE0t\nHv3ZSRwAv9WxNE3T9MlJHIBes+a3YgMAQPPgBzRN09rEQ4Guej9tCev+v0ary6qOMndN0wU3\nvmgn0IuW4Th8T6ym3Iqt6ABpmqbp9FMTXYjizzKbTxrfEUAw5Txd9eP1WxdbftillF1F1X4v\ndO94h40Jb7ZrQP00MphHJWAeVeW9qGkeYcMOVUl6evrUqVOtrKysra2DgoLST53qZ2Njb2mp\ne3bSpEkAkJ+fv2LFCjs7u+DgYN16sVi8dOnSpk2bstnsJk2azJ07NyUlpWib/fr1A4DU1BKJ\nxeFw/Pz8dMu+vr729vZqtXr16tWurq58Pr9Xr143b96sONSLFy/27NnTysqKz+e3bdt2y5Yt\nWq1W99TIkSMBICEhgaZptVoN5bhz546uvEaj+emnn1q3bs3lch0dHWfMmAHvesOOIDgeYzdf\njc/JT7u/c5RT4UfUgG/OPM0UF7wM+7K7AIAxYHumoS+h037pQwA49F9z/lmqWCHNjLm+JdCd\nAdBs6d3CQELGA0CHdbFvAq3iBzRBsJxHbLwUnS7T/TUk/tZfAMD1CtxyOTpDIs9N+HfnNF8O\nALvnT6/KqxP9XSvCFrgBgGX3JccfpkgVsqznVzcFejIBOP6bE8vZRmUHKDs/wxEALLsvPh6Z\nJpVkPD2/fkgTEsCwL6Qywy5DmVVU/fdC7wvJkPppbDCPSsM8quM8woYdqlxmZmazZs2KN31a\nCYXdAezZbF0BXcNu/vz5umd1DbuCgoJ27drptZkcHR1jYwv/fKvYsJsyZUrxLXA4nGvXrpUX\n6p49e0o31D7//HPds1Vs2N2/f5+maa1WGxgYWPrZgICA2q1eE1X2FxK0DL5f9CPxxifOACAa\nd0z8Zo3qj0ASwGPJLUNfQl+eYw3Q/rsXxSJJ2NgBgDHuWOFDA7+QwP3jf5RvC0Qu9QJgd/7+\nOfV2nTZ6lR8B0OXH5HLqRG/X4j9G8QG4g/ZkFK+3e0u8AcBn1ZOyt1HJAeYfGs4FEAw9kPn2\nedW9Zd718YWkV0UGvBclv5AMqp/GBvOoNMyjOs4jHGOHKrd06dK4uLjBgwc/e/asoKDgzJkz\nWWp1RKliO3bs2LhxY3p6+po1awDgyy+/fPDgQffu3R8+fKhQKJ4/fx4YGJiamjpt2rSq7zoj\nI+PPP/88evSoRCJJSkqaPn26UqmcMWOGVqsts/yKFSt4PF5oaKhMJisoKAgPD3d3d9+6dWvp\n8kwmUy8ZFi5cCAABAQG69uiOHTtCQkK8vLwuX74skUgSEhKCgoIA4ObNm1WPv9FxGTqiXdFJ\nBVtbWwDo1Ldv0RkilpWVEEAsFhv8kv6/ZdH0vSUeb55W5z0Pu5MMoKVqODDLbsTY99hFj5Ku\nXI0F6Dhhonexz0Ci+cjhzQEe3L9PV2mTt8KuyUAwcuYE22Irme3HjfIEeHbnjqTMF1V8gPR/\n164rQDh69gc2b1/Caj95vE+VIqqZklVU8/fCoPp5N2AeFYN5VLHq1w9eFYsqIZFIDh06ZGdn\nd/z4cYFAAADDhw8//sMP/gsWgKDEWf958+YtXry46FW7d+/mcrmnTp3SfQZ5e3sfPnw4MjLy\nxo0bT58+bdmyZVX2TtP0unXrxo8fDwACgWDXrl0PHjx48OBBRESEv7+/XmG1Wp2UlOTl5TV0\n6FDdGn9//4SEhKrsKCQkZOvWrZ6ennv37tWt+eGHH9hsdmhoqLe3t27vv/322/79+9PT06uy\nwUbKycnp7QMGgwEAIpFIrxBN0zV5ier1zT9DQq/dvPf4ydOnMcn55XatVqBkCAAAjo6OxR4l\nJiYCQMQiJ2JR6Rfn5EgBhKXX6xEnJeUCtPLx4ZZc7+LiAhCfnZ1T3kYqOMDcly8LAFp5epYc\nkuPu7g6QVEEspY/XACWrqLJQK2Vw/bwDMI/ewjyqmAH1gw07VIn79+8rFIoJEyYIijXjerRu\n7crjKUuWHDt2bNHyrVu3ZDLZ+PHjda06HSaTOWrUqA0bNty5c6eKDTsAGDduXNEyQRDjxo3T\nte1KN+xYLFazZs1iY2MDAgI++uij9957j8fjVWUXMTExM2bM4HK5ISEhus/KpKSk2NjY7t27\n61p1RXuvYsyNF5NZ7c+M6r0k89zHfcZvfVL4K5S0ajls5WLfc9M33K7WPlVisarCAhpNeZft\nAYBKVfGLC+nO5nO5ep+3hb0mHA6nzFdVfIC6uHRf28VIpdIKQ6n0eA1Sw/fCsPp5N2AevYV5\nVDED6gdPxaJKvHr1CgA8PT1LrF271k0uh5JZUvx3SlJSEgD4+Oh3fLu4uABAdnZ2ebvT+8Vk\nbm5uZ2dXfI1uCrqcnBIX9hc5dOiQq6vr6dOnBw0aZGFh0b9//507dyqVyjIL68hksjFjxojF\n4q1btxYNCiz8IRoRQRRTxWYiMpz22ldTtz6RCFuP/3rH6fAnr/Mznpxd0dOywg9bXXu75B/O\nixcvKt6RnZ0dAPH+bklZA2UuzbaqSrAiGxsWQEJ8fMkf+eqnT+MABO7utmW8prIDtLS3ZwG8\njI0t+X1Z/HgMOl4DGPJelGBI/aBagXn0bucRNuxQJXQ/F1gslt76itpKhv4IU6lUej/ySm9B\nl4jltbE6d+4cGxt78uTJmTNnOjo6Xr16dfbs2f7+/hX8dpw3b15UVFRQUNDMmTOLVlb4QxTV\nmZe3b2cC9Fh96eiqWSP8WzoJSKCTL156WtFr+AIBACQnJxetoZ8dP1HhSwBadOtmAXTEuQt5\nxde+/rUfjyA6fl+ls/dAdu7SgYDs43vPFv+BIwk9dDof2O/17l7Wp2tlB8jq2q0jAQWn9p7I\nf/sixaXfD7+q2fEawJD3ogRD6gfVCsyjdzuPMLVQJXSnJnX9dkWoZctiWSy9MXbF2djYAEB8\nfLze+qdPn8KbXrfSv5hK/17KyclRKBTF18TExACAg4NDebtms9kBAQE7d+58+fLlw4cPu3bt\nevfu3fPnz5dZeOfOnfv372/VqtW2bduKr9d1E77//vvFf4AWzWNX3q5RTdnY2hIA0ecOhCeK\nVYrcl7dD1gb2/eIfLQDkZmWqKC0AMJkkQFrs84LC62FY3t4eAMm7l2+ISJEpJa9u7501du1T\nNrui/QDZb86sZkTe8QWjvzr5MClPJk6LvvrTpAGL/1bwB8ye5FHOq/R27fzBrMECyD06b+y3\n56OzFGpFdsylDeMXHisA+4kLxloadIBNJs4Zagbikx+P/ebcsyyFSpL0z08TZu3P5xR9Vlf/\nePVrrGoMeS9KMqB+UK3APHrH8winO0EVi42NBQA3Nze1+u2Ek8dWrQIAvXnsiuYxoWk6OTmZ\nIAhLS0uJ5G0nvVgsNjc3Z7PZOTk59JvJR27dulVUYPXq1QBQfLoTADhy5EhRAZVKpRv0FhMT\nUzrUv//+GwBWrVpVfOUvv/wCADt37qRLTndC0/SDBw+4XK6Zmdnz58/1NqXRaCwsLCwsLHJz\nc4tW6hp2AFB5rTUC5UysWnx2gNh1HQBgzJFiM5FeniMCsJ5z2dCX5JycVHLgMWE7ZNveBS4A\nAOD5xQOaph8EvxkWUDhNqDbqW7+SHcpus9fN9Sh/2gId6a2vO5npfR4yHIfviafo8pTaNRW/\nZ6i9/shL0mPK2fTyNlH5AWpfHhjhUGKb3HYrV47jvJmmofrHWyrs0sqqIgPeC/2JVatfP40O\n5lFpmEd1nEfYY4cq0axZMz8/v8TExA8//DAhIUEikRw7dmz26tUEAJQ/EtXZ2Xnw4MG5ublj\nx46Njo5Wq9UxMTHjx48vKCiYOHGipaUlAOiaaMuXL09JSZFIJHv37l27di271C+mhQsXnj17\nVi6XJyYmjh8/PiYmpkePHl5eXqV32r59ey6Xu3nz5sOHD2dkZMjl8mvXrv34448A0LZtW73C\n+fn5Y8eOVSgUu3btKn6FhA5JkrNmzcrLyxs9evTDhw9lMll0dPTUqVOhzEueUK2xDNgRdvjT\nIa2cRTyhXfPeUzdevn92btCnq8Z5mrPNHe0EANB23ndzujiZvR1JTvh+Hnpm1ZgOriIOi2fX\ncuiS439vHSCs9Oo2fudVYbeOLB3TwdWCyxbYuPkNnrPp6r2TU5uW/6lYateMplNP3L7w7ZRe\nze0EbBbfyq39sIW/3ri9e5hdeZuo/AAJt8khN898NaZ9EyGHZ9XUP2jT1Ssrin8DVft4S4Vd\nNYa8F3qqXz+oVmAevdt5hD12qFIRERF6Y9q6ikRtAJy5XF2B0j12NE3Hx8fb29vr/b15eHik\npxf+yoiKitIbujd79mwPD4/iPXYCgWDYsGHFy5iZmUVGRpYX6ooVK0r/kY8fP173bPEeuw0b\nNpSXRbruPalU2qlTp9LPjho1qpbrFyGEEKol2GOHKtetW7eIiIjBgwfz+XwLC4sJEyaEHjyY\nx+NZOztX8KqmTZvevn17ypQpdnZ2LBbLzc1t4cKFt2/fLrrK1dfX98yZMx06dOBwOHZ2dkuW\nLNm6dStdah6hI0eOzJkzx8bGxsbGZtSoUXfv3m3Tpk15O/366683bdqkuwkYh8Px8fFZs2bN\ngQMHSpes9FJ8Pp8fFha2dOlSV1dXNpvt5uamu7rCzEz/vANCCCFkIghfX9+oqChjh4EamKSQ\nEPdx48b07h0SFlZ3e2nVqtXLly8lElOZoV6hUPB4vKCgoH379hk7FoQQQqgM2GOHKqHVas3M\nzCwsLC5cuCAWi5VK5a1bt8bMnEkDBJQ/HR1CCCGE6h/eeQJVgsFgLFiwYP369UOGDCm+vi/A\nB+XPOVLr0tLSnj59amVl1apVKwNmbUcIIYTeBfgFiSr37bffent779q169mzZwqFwsPD44Nu\n3T5PTWV88UU97F0mk/3vf//bvXu3VqsFgKZNm+7cubNv3771sGuEEEKoYcFTsahyBEFMmzbt\n33//zcnJkclkT548WR4UxNa/DV/ti4qKkkgkn3766a5du3StOgB4+fLlyJEjS099jBBCCCEm\nRVFZWVnW1tbw5g6euIzLlS6LVq5khYWBSpXVsmWd7isyMnLnzp3F/2S1Wq1EIpk7d+7Bgwd1\nF9jW27GXvmIXIYQQMilMhULx+PFj3fStjx8/hjdTueIyLlew7CcWWwGo1eq63tdvv/1WZnPq\nypUrBw4cmDFjRn0eu1KphCrMk4IQQggZC9GiRYvw8HCj9wDhcsNaZt28Kdqxg168ONvXt073\ndfny5UGDBun/1RJEhw4dQkND67nHTqFQ8Pn8d2S6k6NHjy5cuJCiKGMHghqhQYMGHTlyxNhR\n1AfMI1R3yssjnMcOGeSff2DjRliyBHr2rNP9UBTl7e398uXLojF2OocPH/7ggw/qdNelvVPz\n2C1evPiHH37w9/fncrnGjgU1Kvfu3WOz2WlpacYOpD5gHqE6UlEe4S3FkCEGDqQB6IED62FX\nt2/fFgqFAEAQhXdBDgoKqof9liaXy4249yKaV+e+HNbaXsA1c2rea9aOyIIyylBJZz8f5GPD\n55q7dvrwp1v51d/LZ599BgBJSUk1jhehEnr16mVvb2/sKDCPUMNWQR7hVbHI1HXq1Ck2NjY4\nOHjYsGEzZsw4derUu9BhVr6En8aNPiRcdDU5O+nKmqbX5w7537lSt+aI3Thm7HG75f+m5r44\nOTF9xdD5p/ONESpCJgvzCDVa2LBDBgkOhmHDIDi4fvbm4OCwZs2aM2fO7Nq1a+TIkfWzUxMV\ne2RPRLtFP0z1teRb+IzdtGRAyrGj/+hdXvLf9p/vdlr03URvc651+0+WTWQe23kSv5EQegvz\nCDVe2LBDqEF5HhNr2aqVU+EjvpkZySDJkkWSIiJeefr7v7ktSOfOnVS3b0fWZ5AImTjMI9R4\n4Z0nkEHWroVLl0ClquuLJxqFi1/0OUCMGhUQMLiLq4Co4caG7ZXnvFmmEnftuuQ+7UzPkhtN\nS0sDW1vbNw/51tbc7MxMbdm/4yiKOnfunEKh0Fv//PlzAFCr1TWMF6FagnmEUJVgww6huiaO\nv3boz2uHNvyP69i+/4iAgICRw/u2sWPXbKOy2FNr5i4Jdfnl/MZevNJPs1isomUGg0GyWOX0\nzoeFhY0YMaK8vWzbtm3jxo01ixShWoF5hFCVYMMOGSQ4GNhsWLLE2HE0CEO33D494K9z586d\nv3ovdPv90O1fM8w8ur0fMGrUqFHv92hqVt0BEZpX51ZNn7MzN+DHs7c+aFH628jBwUE3+Z6u\ns0GenS2z97AvZ2N9+vQ5c+ZM6Z6GDRs23Lt3z8PDo5rBIVRHMI8Qqhqc7gQZ4vp1etgw+vp1\nY8dRr2o+3Yky4/GlfRs+Gd/L27KwI4Dj0HbEgh9OR+VQVd1GyqExDqKOn55LVpdXQhuxyJkc\ntCdX94i6vtDZbNJJZTVDHT58OABs27atmq9DqBI1n+4E8wghnO4E1ba1ayE0FNauNXYcDQzb\nttWAoM9/PHrteWZWXHjIT8um9bF59dfPn41s1aRpn9k/nH1easIFfdT1bz+70m3HxU1DnMvt\nbie6TZ/le23d4j8TxPkJ55Yt2sOcMXdwDc9YIWQ6MI8QqgA27BCqf7QsLS76+fPo58+fJ+VS\nAADyxH8PLB/V0r3TnOMJ2gpeGX3tWlr+yfHWRDFjjwMARK9sRTh/cgMAAHyXnTo0JGFpezuH\n9gsiOm87v94fZ71HjQ/mEUJlwDF2yCA4xs4AmrzYm5fPnzt37vyF65FpusE4LKvmfScNHz58\n+PDBXeBoQMe5OybPbNvj6jzHcrbhu+IxvaLMZ1qsjKJXvnnA8hiz+eqYzbV9BAgZH+YRQhXC\nhh1CdS39v717Dp87d/7yzbg8DQAAMK2a9x4zfPjw4SMG92gmKpo+a3pQz7kXzty+/QjK/UJC\n6F2FeYRQlWDDDhkE57GrhvDvpy37EwCYls17D9H1KvTwsiBLFywgLHx9fS2bWtd7iAiZPMwj\nhKoEG3YI1TW7Th981n348OFDenhZVJhx1hP2RU2or6gQalgwjxCqEmzYIYPgGLtq6PnF4fL7\nNZWnZ/t+9neTGSevL2tdjzEh1NBgHiFUJdiwQ6g+aFL+2bn10I0XOUqqxJ3GtTmPL8XHS7Pi\n0wHwCwmhimEeIVQpbNghg+AYu2pJC5nQYfyfaXRZz5FCl87TV32G9YhQJTCPEKoCnMcOobqm\nurbmsz/TaLfxO28nJD/ZPc4OHMftfZQY9/Dizx+2EDj1C9716zQfnPgUoQphHiFUJdiwQwYJ\nDoZhwyA42NhxNAjPLl5MBmg7b+3MTu7OLSeP68lKPfb9Cbmn38D5e08stTnz0Ygl16TGDhIh\nE4d5hFCVYMMOobr2+vVrAHB2dgYAAFbLll4AUZcupQIAw2fSB+20L39bvT/dqCEiZPIwjxCq\nEmzYIYPgvWKrwcnJCQASEhJ0D718fdkAD+/cURc9qb5zJ9KIASLUAGAeIVQl2LBDqK61GjHC\nA+DJjx+vu55QoAFmt24dAWTnDp/OB1DevPkAACwsLIwdJWoE1Gq1TCbTaDRSaeM7KYl5hFCV\nYMMOGQTH2FUDs/sXP052I3Muf9m76fRTAC6BU/sKIPuPaR38+3Yc++trILwCx7Q1dpSo4VIq\nlWfOnPnwww9FIpFQKORwOEKh0Nvbe9u2benpjebkJOYRQlWC050gVPfsR+5/9Hj8wWNXI+3c\nAcB55p6j0R9M3xLxbxgQQt9Jm46s7oFX86Hqkkqlp0+fPn/+/NmzZ/Pz84vW0zQNALGxsR99\n9NHChQt79uw5atSowYMHe3l5GS/Y2oB5hIxKqVRKpVIrKytjB1IJbNghg+A8dtVEmPsM+2jF\nsDePXIf98G/amoyXr1U27s7mmIao2h49etSxY0e1Wl18JYvFYjAYBEGo1WqKogCAoqiwsLCw\nsDAA+PLLL9c28HGxmEfIWLKzs5s1a5aXl0eSJJPJZDKZLBaLIAi9YkqlUqVSBQQEhISEGCVO\nwIYdQkbD4Nk1bWbsIFCDFB8fP2jQoKJWHZfLHTRoUGBg4PDhw83NzQGAoqjw8PCQkJATJ06k\npaXpiq1fv97Hx2fy5MlGi7suYB6hehETE5OXlwcAFEVRFKVUKisofPLkyfqKqwzYsEMGwXvF\nVk6aFpcqqWphoWMzB0FdRoMajfj4+D59+uiaa0Kh8Ntvv50yZYquPVeEJMnevXv37t37p59+\nCg8PnzNnTkxMjFarnTp1KgA0qLYd5hEyCV27dh08ePDly5d1feGlFfXe0TTN5XLrMTR92LBD\nqI6cX+AV+GdVC48JoY+PrctoUOMQFxfXt2/f5ORkAHB1db127ZqHh0cF5XUtvKdPn06dOvXg\nwYMURU2dOlUsFs+bN6++Qq4hzCNkEgiCOH/+PACkpKRcu3bt2rVr169fj4mJKSqgG9sqEAik\nUimPvcAiOgAAIABJREFUxzNaoNiwQwbCMXaVc+s5aVKVf7V1dqvLUFDjkJ6e7uPjo9FoAMDD\nwyMsLMzNrUp/OCRJ7t27V6PRHD16lKKojz76SCqVLl68uI7jrRWYR8i0ODk5TZw4ceLEiQ8f\nPuzbt29ubm7xZ3XNO+PChh1CdaTTxwcPGjsG1KjoGmcAYG5uXvVWnQ5JkgcPHszMzLx69SoA\n7N69u4E07DCPkCmKjIwcMGCAXqsOAFQqlVHiKQ4bdsggOMbOANr82IiwGw9iU3JlwBVZOTT1\n6+bfycuKZey4UEMRHh6uW9i6dWu1WnU6JEmeOXPGyspKqVS+ePEiPz9fJBLVdox1D/MIGVtk\nZGT//v2zsrJKP4U9dgi9I+i0v9fOmrUu9IWs5Hq2k//Utb9umtoaB3yjSuTl5V25cgUALCws\nJkyYYNhG+Hz+uHHjDhw4oFQqQ0NDJ02aVKsx1jXMI2R85bXqeDyeXC4v79KK+oQNO2QQHGNX\nHfKIZX2HbHimYli3GztlXD8/dxseLU6NuXPh8MHzN3ZM6/FMcu/6Ai/9+ZAQKu706dO6GRYC\nAgLYbMMn4g0MDDxw4AAAhISENKyGHeYRMrqHDx8OGDCgdKvOz8+va9eu27dvN0pUerBhh1Bd\nS9u17PtnKvCccebmzqG2b793pn385ZK943tMOxn+5dKjH/75QQM8K4bqT9F8p4GBgTXZzsCB\nA0UiUX5+/oULFxrU2VjMI2RkDx8+7N+/f3Z2tt56Pz+/K1eu3Lhxw0QadnivWGQQvFdsNdy/\nc4cC6LJgZfFvIwAAYHlM3bSoA4A4PDzSOLGhhqGgoODixYsAwOPx+vfvX5NNcTicESNGAIBS\nqfz4449rJ776gHmEjKniVp2NjU3Pnj0ZDJNoU5lEEAg1ajY2NgBQzg0G7ezs6jkc1ABlZGTo\nroclCKIm52F1xo8fr1sw7vz41YR5hOqERCLJyMiQyWSpqanllam0VQcAVlZWbdq00a3XarV1\nF3ClsGGHDLJ2LYSGQgO/72R96ThtVlsSboaG5pR+TnLlyi0A59GjO9V/XKjhcHBw0M1rXytd\nAm3bttVtrfSdLk0Y5hGqTWKx+PDhw6NGjbKysrK3txcKhU5OTi4uLuvWrYuLiytesiqtOp3e\nvXvrFvRu4lzPsGGHUF1jtF52Yv8Uh9AZfab/fOFJhkJ3NTytSLt7dOmQGUfNhv94+vv+xpyn\nHJm8lJQU3TQKFd+hsooOHjyo25opTM1QZZhHqBZkZmb++uuvo0aNsrOzmzRp0qlTp3SNMF0u\nvHr16ssvv/Ty8mrfvv2qVavu3r1b9VYdAPTr10+3YNyfTHjxBDIIzmNXDaGznGeeBRVIc/cs\nHLJnIck1txRxKXFOrkwDACTn7+U9HZYXKz/qoORAgLGCRSbJ29vbxsYmKytLrVbHx8d7enrW\nZGvHjh3TLXz00Ue1EV39wDxCNfXgwYNOnTrpzUjC5XJ17TC1Wq0b8KAr+eDBg5UrV3K5XIVC\nobedMlt1APD++++///77ERERwUYdgI4NO4TqGsfMxsYGwMbGqfhaG1uncsrbVmUyLm3YfId5\nln9Hr2lV1rP/furiv/lV0UPfFY+jVpZZEDUY06dP/+677wDgxIkTS2rwm+rFixf3798HgKZN\nm65fv77W4qtzmEeoRp4/fz5o0KCiVp25ufnw4cMDAwMHDRrE5Rbete7hw4chISEhISGxsbG6\nNVVv1QEAg8H466+/6uwIqgobdsggOI9dNQzYFBVVi5vTyjPjH1zZsmRvJnxaThF5XFz62KPK\nkPE1HWWPTEdgYKCuYRcSElKThl1Rd53BsxwbCeYRMtzz58/79OmTmZkJACKRaPPmzRMmTChq\nzxVp27Zt27Zt165d+/Dhw5kzZ967d0+3nsVi6c7Ytm3b9sqVK9bW1vUcf7XgGDuEGhbN4Q/s\nvHtM/OWerPwy8bGx7j4++G3UqHTs2FF3BvbOnTvx8fEGb+fo0aO6hRrOh9fAYR69QyIjI3v1\n6qW76NXLy+vJkydTp04t3aorrm3btnfu3FmwYIHuYQNq1QE27JCBcB47o2FOPkXTNJ38Y49y\ni1CxsQmqK190cLMSWrq1H/vN5VTj3+QG1YLhw4frFhYuXGjYFk6ePBkZGQkATk5Obdu2rbXI\nGh7Mo3dFenp6hw4d0tPTAcDLyyssLKxJkyZVeSFBED/99NO8efOK1vTp08f0W3WAp2IRaoQS\nY2Mp2itgx+k/vRV3dswYN3QYcefWV35lZjtFUefOnSs9jiQlJQWMPRsT0tO7d+/NmzcDwPnz\n59euXVvdAdq3bt0KCgrSLTdv3rz242tkMI8ahSNHjujG1YlEoqq36nQIgvjll1/S09NPnDgB\nAKdPn960aVNdBVp7sGGHDIJj7ExZ08/vaz4vXO712e9LT7j+sOfuV5u7llU2LCxMdx+CMsXE\nxNRJhMggI0eOHDJkyPnz5wFg+fLlAFD1tt2tW7cGDRokkUgAwNHRUXe7WFQRzKNG4e+//9Yt\nbN26tVqtOh2CIP744w8rKyuxWJyYmJiRkWH6k2Fjww6hRs6lWTN2Rno6DVDWzEp9+vQ5c+ZM\n6Z6GDRs23Lt3z9vbuz5CRFV27ty5r776as2aNQCwfPnyzMxMXR9exa5evTpmzJj8/HwAaN++\n/eXLl8u5gQMqF+ZRQ1RQUHD58mUAsLCwGDdunGEbYTKZEydO3L59O0VRp06dmj17dq3GWPuw\nYYcMgvPYmbDEH7q57x0c/XiF7mRbzOPHqhYBPuXMl0mSZNHIreIOHDhw7949E7n1ISpu9erV\narV6w4YNALBly5aQkJAZM2YEBga2bt1ar2RmZuaJEycOHToUHh6uW9OpU6dLly5ZWFjUd9AN\nEOZRI3D69Glda3vEiBEcDsfg7QQGBm7fvh0AQkJCTL9hh39tCNU1cUp0dHR0itiQZw3hNvKD\nbi9+WbQpPDkvP/n6hnmbYoYumo09Bo3J+vXrZ86cqVtOSUlZvXp1mzZtfHx8vv7666dPn+bk\n5Gzfvr1///6Ojo5z584tatU1adKkIbfqMI9QtYWEhOgWangNeO/evXVnYMPCwjIyMmohsrqE\nDTtkELxXbDVc/NjHx8fn44tlPqk9v6CCZ6shemUrwvmTGwAAzRYeP/0//uFJrRzs2wT9abfk\nr71THWq6eWRidu7cOXnyZFtb26I10dHRq1ev/j975xkfRdU18DMz21t67yQQQiihBQKBhBZ6\nACkCgqKCiIoNGw/6il1EFEWFB3xUpEpv0kuAAAGSQAiBFNID6WX77uzs3PfDhpCe3SWFcv+/\nfJidOXPm3NmcnTP3nntucHCwo6Pjq6++eurUqZparFKptG/fvnFxcY9IVEfySCOydIop9iOM\nZSiVSlNCqlAoHDVq1MOooihqypQpAGA0Gt9+++3Wsa/NwEOxGEzbkbbv272pcDOtevvbO/UF\nkC5j/2kAkMpkFqr2fDsW1fl56br8JlpevU24j1y2Mx4Xo3nCMU2ASE9PN9XKN9UxgVorwDo7\nOz/zzDPTp0+PiIigKKrDDK0LJSYH/9xdY1RLOWb+12M/wlhDVVVVzRJhDzMOa2LmzJmm0dhD\nhw49rGVtDA7sMFaBc+zMInnz0qW7q7dvblm6tAkxTqfZMxqdaofBtEiXLl2WLVu2bNmy9PT0\nHj160DRt2i8UCu/du/foxHMAgABladJlQ3hSv+ZqwzYA+xHGGuzt7QmCQAi1So5j586dTdoe\nXlVbgwM7DKbtCHtv586ZELdq+qo4GLhk55KGTx2CI7T37T0oxP1hXycxTz1dunSRyWRlZWUk\nSbIsKxaLH6moTsHI1+Z+l6ZKsRtrUVQH2I8w1lFeXm6Kw/R6/cNr27Jly2MR1QEO7DBWguvY\nmYXHwGnTAByTBsdRMGzCtGmRHW0QBtNR7CncnKZKMW1nbr8n/Fpk9qnYjzDW4O3t7ebmVlhY\nyDDMrVu3unXr9jDaahZZfvXVV1vDujYEB3YYTFsT+UVsbEfbgMF0DApGvqlgXZIiniKquw8N\nJcbsvYWcbyx9+mA/wljMwoULly9fDgA7d+789NNPrdaTnZ2dmJgIAJ06dTJVGnqUwYEdxipw\njl3LqIvuFKrMFZa4BbiK29IaDKZDWJ31eY42EwAYZIh0GG3Hdfjxg7V0lcFsBdiPMNYzc+ZM\nU2C3ffv2hwnsduzYYRqHnTFjBkE0UczwkQEHdh1F2S+RTovjXzii+muMGdL75gimbOn6Tcb1\njwLa3LK6MJsnc+fu77syO/4933a+9OPOkTc6T9/dspiJqTvRrmltaQ0G054oGPneoi1Vhopc\nbZZpj5vAc47HKyRBrdL/Zokm7EcY6wkMDAwODk5JSUlNTX2Y0dht27aZNh6yHl778GgEdrtm\nEtP/6ftNRny7hy0deemHoe3MNlMzzrFrGZ+hzz1ndpZ4qE9bmoJ5wlizZs1XX31VMwG2hqqq\nKri/5Hx5eXnDdcNkMtmSJUsWL17cpuYpGcVn6e9WGsoBQEiJ7LgO/qLA6W4vkIQVkzmwH2Ee\niujo6JSUFAB45ZVXYq0azd+1a5eplpCLi0ufPn1a2b424NEI7DCPLpw5+9Ccjjbi8aT/m5s3\nd7QNmCeTVatWFRcXNy+DEKqsrKy3s7KyctWqVW0X2MmZypjyY2V0iSmqA4Du0t6LfB4mZwP7\nEeahiIqK+uabbwDgwoUL77777g8//GDR6bGxsS+88IJpOygoqPXtawPIlJScdWXy+HULhvg7\nSGTuPSd9cuIuC6j45NfP9ve2FQrt/AbO/uFCxX15ZvNkgiAiLTkFAMBYcvn3dyf16+wmFcnc\nu/Qd/8bai0WmsoFFq8MJYvo/AJCwtDNBdP8yFQCq1o0kCCL8l6LaOg7NERBEyJem0pT75ggI\nYsxfiqKTyyd3dxF1+/h6zZXOr1k0uqenjVBo4949auEPp+8xTbS90UsDAIDq1pal0wf4O4r5\nQlvPnmMWrT5f2EKR9KYbeP/GFRz9cvYgfwex0Maj9+SP/82trbDl9ppldl2aukUWfhemb7zf\n9zm1VKtubXGiB4h44jMxZt4fDAbTiixZssTFxcWuAbWzfwiCaCjg4+OzZMmSNrIqW5Pxceri\n/UXbL1ScNk2V6G87+HnPRW10OQzGHCIjI+fOnWva/vHHH999913zzz1//vzYsWM1Gg0AeHp6\n1gzIPuJwAFDKqonvf3velJ2afODL6HLh+oFbXlp1y/S0z7m8bcl4pUv6weeca85iLDtFcXxR\n6LgN90MZVUZiYcZrR3af3Jaw+1n3hzFed/E/ozb+epMGMC3SDChv66whc3fmsaaPhSkn1i85\nufvIrxeOLAo0u29SeeGjiNErrqmrP95NPrbunRP7T//v3N55AU2MI7TYwJL9LwyasjXfVAJH\nc33/V5NTc6ZKLW6w5TS4RQ//XVhxf552Tr4f8t6JFmRGfX995ch2sQbzBLB48eJGe92cnJxq\n6tg5ODiUlpa2jz3ldGmaOuVy5Tm1sXqew2yPBaG24WJK0noXwX6EsZK///47JCTE9Erz448/\nFhUVbd68ucWqxXv27Hn++efVajUAhIWFHT16VGbxyiYdB8H3m7b6VGaFvChxwxTT051wHfX5\ngVulSkXOmf8MEgOQo/5bihBCyLBpEgAQlpyCin4dRgC4jvzyyO1CpU5dmn72p+m+JEDAR/Em\nAbTzWQDo+01G9UdUuXYEAAxeU4hqcfA5PkCvL0xCe5/jAxAE1zN65fHUYg2LEEIod91IMYCg\n8/SfTqSWqLSV2Rc2vBjMB+AN/bkANUH9S+vOvOEDAHaD3t91/Z5apylLO/XDdH8OAD98dW4T\nOlpqoObIy24AYDfovV1JRWpVya0j3471oAAAxC8csaC9vR6YWd/sRmj0Fln+XZi+8b4rs+ve\nH1vfXQDqkSPNuT9PDFqtFgCef/55C8/bM4tfFx7nQb8Kz97bz8/XZ9HhNjHZeiZOnAgAa9eu\n7WhDMBbg6OgIAKYnlqOjY/tc9ELF6YU3Zrx4fVLN34bc1QaWbko+IiLCxcXF8utgP8I8FN99\n913N/4uNjc3ixYvPnTtnNBrrieXk5Hz//fe1c+kGDRokl8s7xOZmaMaPAAC6LUtk7n+OfdsT\nAGxm7FDe30P/M50C8Hv/MkLo/mPeolPQiYUOAH2+y6p12eyVfQHIGTuqP1oZ2IHvm+f0DwSS\nPuoMwAv9Pq3W98SmftaLABjwY34T96bepZX/TBEBCEb/WVJLxpDwfhcACPospXEdLTRQvmWi\nAEA8flPpg+N0wtIu7RHY1btFVnwXdQO7mvsTGYUAUFSUOffnicHawK4RjHpl4e3zW7+YEiCw\n6fPK1nR9y6e0M/iB9DjSzoFdqb44T5tdE9W9dH3yTcW1XE1W82dZG9g1AvYjjEUsbbAenbu7\n++LFiy9cuHDv3r3vv/9+wIAB9aqZeHt7P4JRHWrWjzgA3PHRvWuG0JycnAAK+g8fXtOBzrW3\nlwAolcpaLfWy6JSR68rQulpnG6rSzlzNB2CND5mY5Rw9bQiv5lPeyVMZAINmzu5Sq3+VCJw0\nMfDTpGuJiQg8zag9c/lMjAbEz86f6VRrJ6fPjCn+K1fcvnpVBd0aGVlovoEoLuasDiRzX5nl\n+ECE22fOs0HffJFnblOtpe4tevjvoub+vOYMK+/XsWvp/mAaQvIkrl3DZ30cHukxofNLcyba\n+t1cMRBPZcI8LrCI3Vm48UTZQRZVZ74QQEx0mREsDWlPM7AfYSzi66+/1uv1e/bsycvLM00e\nv3fv3po1a9asWdNwHVgnJ6du3brt2LHjcRqBBQAADgDHvVZylelVz8bGpp5c3Qa7W3oKfffS\n7p2HYi4lJKfcupWeLze/OGVTJgAAuLm51fqUm5sLABffdScaSY2sqFADtBxzKPPyKgG6BwXV\nm13v5eUFkFleXtGUkmYaWJmTowDo7u9fNwHN19cXoLnArmF7raDuLWrJ1BapdX/Ka+9v8f5g\nmsJt2tTBL/17fOPmuBUDwzvaGAzGHKoMFWnqlGOl+00f/USdw+wi/UVd/ESdO8ok7EcYM1m1\natWqVasKCwv37Nmzc+fO8+fPmyK8mieur6/v9OnTp0+f3r9//w611Ho4AATH4hccjkWnlB5+\nc9iza1Kqc2op+24Tlr8XfPilFVcsuiatVNav2VQXhmlq+isANCz41CgGgwEABIL6VZNMvY98\nfuMLTDffQJNdDfI0TRmZzVjcUnut4iG/iwf3p24du+bvD6Y5FAoFAMjl8o42BINpETlT+Wf+\nLzcUCTUV6bgEb5LLsz1l/TrWMOxHGItwc3N7/fXXX3/99cLCwrfeeuvIkSMIIS6XO3/+/JUr\nV3a0dQ9L23daszGfzFuTopL0ePbdxbNHDe7XO9BdTGWu2NfcOaYx7rpdVllZWc1fyNnZGaBy\n3B/Kf1+0ekkZG0dHLkB2ZiYC/1oDt4Zbt+4AiH19nRo5p6UG2rm4cAFyMjIYCKp1u2u3x6r2\nWoE130Udat0fMPf+YEBfVVSpa7gbGeT58duWfRMHAD16BLe7WZgnDy6X2+j2w4MAGVj6r/xf\nbygSAIBFxjC7CBnHNtJhtAv/oaobmA32I0zr4+bmtmPHjo62opVp+8Au58qVUoDBXxzf/rZr\n9S6Uf+z4rebOEYnFAJCfnw9QPZKIbu/a0+wpAF3Dwmwh9eLho1UvTrWt2Xv3txEBr58OXpkV\n/55fy8ZSoQP6Etvidv118Meo6JrwUHVoy3458MZEDmpsdnRLDeQODOtHbL+076898ugZ98er\ndcf/t7UAQPwQ7bUCa76LOjy4P9uXRdesFdv8/cHAwfluza6JxPVf9Pl83/ayBvMEs3Tp0jVr\n1vTq1SspKakVqxDfUib9fXddGV1MQnVHnQPPaab7y1KOlblHpIOjHiG+ZWtuYj/CYMyi7QM7\nRycnAiD18KbzU14d4MLcu3Fyy3f/+fYcCwCVZaW00YFHkRwOBVCUkaZgA2QkAHC7dPEDyP7j\n4xWT/1rcV1aRtGv5i1/d4vFA38yFqBELFwT8tXLXG898Qv6wYFgXO13elR1fLP7otE406pXn\nmorq6l3ac9aCMcvijmxfNK0798dXhvlL1Nnnfn9n8Q4FuMx7Y5qdVQ30mL1w/MeXDu19c9rn\nkp9fG+4vKIn7480Ff8v5NWGQ5e2tf8fMw5rvoi419+cTCVdED2NodfrxFu4PBlx6jhhR1XA3\nQfLE9m5dQqNfmD8p+DFLzsU8mjRV385qEKAKuuzH7M+NyAgAPJI/wG6ILcd+rPMUEWXN0AiD\nkGbsONmkyWqLAzvsRxiMuYhqV9nI+KYvAEzdZniw68RCGwCHhScQQveLXwy25JSKvc/VTeAn\nnMau/esNLwAA8P/wGkLo2jL/6mPBX9xGCCH25te96g4j+Lzyzat+TZf/MKG+/H/965f9Jd0m\n/plZv1DNAxpc2pj553iX+j83lN8LB4ubUtFyA9mcTdGudXQKei9fPoN/v9yJ5e1tYHZDGrtF\nVnwX9erYWXF/nhxasdzJow8u0/CUw7DM7nubFt6Y8UbyczUF6jbmP9T/Q7xOP76weFBO0p7r\ns8v1JS2f8PiD/QjTRjRT7qQdxs7sJq8/s/Wdsd09bYQS58DIeStPJB589fl3PpvhL+PJ3JzF\nABCy6LuFA9ylD7oPieAPDh34bGpfbxs+V+jcbfz7u06vGSVpcZaoKPSzM5e3fTS1r7etgCd2\n9Ok1ZuEPpxL2zuvUdDMbXJrsNG/PlaNfvxAR6CzmcUX2Pn0mLP4t9sofE5ybUtFyAwmfOTsv\nHfhkah8PCV9o3yn8+R9Onfy0diRncXsbmG0e1nwX9ai+P+52gQA8gjLj/mAwmMePvUVbD5Xs\nolm92qjyE3UOloa86PXGXM+F1mljECo3sgtLiwZVbjhQNHa09jAwFS2fhsFgLIcIDg6+efNm\nR5uBedw4dw5WroT33zfNin1K0Ol0QqHw+eef37hxoxni++ZK5uw1V/eUzapNkx/CtlYnOjr6\n4MGDa9euffXVVzvaFkz7kafN3nJ3faYmnU/ytUYNAHAI7tKAr60uZaJi0X+Vys1KNYeAL0sX\nDdGdNe0nesQJxYFmKMB+hME0QmRkZGpqalFRUcNDuJQjBtNG2HXq1+9BEQh1zpX4XC0IXIJC\nuvvaU+qSgqz0tAKFke8bMWmwJ7e/RwdaisEAGJGRZvUrMpeZ4jmtURMi608RnGjXZ70EvtYo\nBNij1vwkV0h0mVP0F2KEIwfrzpsOIQRCrpn5cNiPMBjLwIEdxirq1rHDNEbEZzExpi02d+eL\nUS+c85ry3/3/m9/b7n5mgDrz6Jq3Fnx8/o77p6fefFxLYWIef1hk3F/8z7HS/RRBmaI6AAiW\nhrzpt8xqnRd0+u+qFOX6wtcVP0/U7CURu0C5VuE427Z8W0YB/+NfDQdPNyie3jjYjzAYy8CB\nXePI5fIrV65UVVX16tWrS5cuHW0O5rGmcutbL/+dLltwbMsrvYW19ov9x3y0a1NBwLCVM9+N\nzP1jXP262BhMG4MAaYzq02WHDxZXl/LyEvoKSVEPWZ8op2jrdBYajf9XUXVFq3xe9cdLyg18\nVF17ToY0Ir9viICf3vpweGpqquWKsR9hMGaBA7tG2Lp16+uvv15VVQUABEHMnDnz999/F4lE\nHW3Xo8SyZVBTxw7TAvFnzigBwsPDhQ2PCcLCesOvhw4ciINxke1uGeZpJk2V8vfdtYW6Ah5Z\nvWYMh+DMcp/fVdLdOoVKllUjtKS88pqe/l/ZyyF04oNjJI/vs4KgHmbJQexHGIxZ4MCuPleu\nXHnhhRdMi8cBAEJo27ZtMpls3bp1HWsY5rGFoigAyMrMBOjR4KBpiREz17zDYFqDMroEAP2Y\n/TnN6gGAZvWhtuEAMM55qrfQjEruDVCy7O9K1d9KdT/tuQj6si03pBd9reYoZT+J5/MFwfd6\nOKuxH2EwZsExGo1lZWUODg4AUF5eDgBP87atre2SJUuMRiOqs7wXrF+/Pjw8fNSoURRFPQp2\ndvi2zfLl3DNngKbLunV7FOxpn+16/xVmM3D0aJsN29N+ev3zMXuXhTlQNQeQ/Nr3i1ffApCO\nHh1mlWoMxiLURtWf+b8kyuNIgmRR9etrb5sBr/q8Z51CPUJbVer1ClUn3dW18lU96SQAmMDx\nLhP0cKQzOdL+XK+PSUnf1rAd+xEGYxYcnU6XnJwcEhICAMnJyQDwNG/L5fLY2NiGtwkhNHfu\n3JUrV/bt2/dRsLPDt3splfYABoPhEbGnfbb1ej1Y0ykgeua7ddNi5+w6/+kg381RU6JC/D3t\nSfndjIQje0/eUSHSffK6VTPqV9bGYFqVSkP5XV1egvxSojwOAFjEBkqCZRzb7tLeg+2GW6HQ\nCLBPrflFrgR9wWdVn4frz9UcskcKYc8rQHCAoJrRYCHYjzAY8+jatWtpaSnLsizLlpaWPuXb\nBoOhd+/eRGML3axfv76oqOgRsbPDt6sOHEATJrAxMY+IPe2zrdFowNqVJ4zFsatfHuxeL6+b\nkAZGf3Ikj7ZG4enXnAKXJTd1NO/gB6ODHEUCmXf/uT9flluuH1fMf5I4WLRjQdK0mgUkXrw+\nafHNOQXaXKsVZtKGcYXFXfPu9sjLybvir75kW/OnudqJqYpp5txmKua3CPYjDMZEM37EoSjK\n0dHR5B01G0/z9rvvvjt37lyoBUEQYWFhCxYsqL2zw+3s4G0bGwAgCOJRsaddthuN+M2EdB78\n1u+xi3/ITbh09VZuiZLhyFw69RwY1stDbKlSVluaee3kT+//VQrvNCGSsXLqtF1d/7hQ+IzD\nnXWzR45/3fvOpkk2VhuPeUxRMYorVbEkQe4p2mLawyG4E12mA8AwhzESjsUrqyKACzp9hsGQ\nqsqcW7ram8kvJx0cjOWmowQl5Hh8wHVdCGQj8xtaBexHGEyL4MkT9ZkzZ87Zs2d///33mj09\nYpEfAAAgAElEQVQeHh6bN2/uQJMeRXAdO6sgZT79R/vUKbWl3/9K8JLTHi/vPbu0YUZ4YzBb\nZznP3Q8AAE3V7Y/77y/x/T86MLuLDKDP20tnrxi9Ye8vk+bhJ9JTxS3VjV9zvq2pSwcAJEE+\n4zp7jPMU6xSe0+l/kStKtAWvKVb/R3OYCwwA3OP46nleQr4HJQvnuL1GcOxax/pmwX6EwTQD\nDuwaYcOGDTNnzjx69GhFRUWfPn1efPFFXOsE85Aw985tWLMlNqtCb6wzA4OtSD6emakuyyxu\nbKpfY3Dm7ENzAApWh3s1MVE77+LFAv+IcNfqj6Gh/ekdV5JgHo7BnwY0RnVM+bESfWG2JqMm\nqhvjNNme59hd2tuVb83SDLE6/S9yZRJNP6f+e7H8Ry56kGbqIfIQBu0Dop0eJdiPMJgWwYFd\n44wYMWLEiBEdbcUjDK5jZxFFO2f2fXZ3UaOTaimJV+hLny1pxadFUVERODk53f8ocnAQlJeW\nsgBkI8JGo/Hw4cM6na7e/nv37gEAe7/uD+axQMUoPk1/p9JQXntnf9vBk11n1RSrs4hYnf5X\nuTJdV8UBwwj95XfkK4n7c2mBoDgOE7m+q9otqsN+hMHUAwEioH4aAg7sMJi2ho75csnuIuTz\n7Iad344Rn1ky7KXzkX8dWxnOph5d9daHMYHLfv/txSBe616Ty+XWbJMkSXG5jT2NAODMmTPR\n0U2uMZCent66dmHaAq1Rc7R0X6I8jkfya6I6f3HgLPeX+aTAQ+BthU4EcFCt/an05gvK39er\ndxMAKkJ8P6ojKYeJPK+PCUFA6zWiRbAfYTB1CF7sp2KU0gbJsjiww1gFzrGzgNvHjuUDhCz6\nan5/X4A5M4Yu3LHj+z2fv/Bp1Ot/7ansF/xa9PuBN3+OFLfW9VxdXU3F90ydDdryco2Ln0sT\nwsOGDTtw4EDDnoYVK1YkJCTg9fQecWhWzyBmfd6PNxTxpj1CSqQzartJe83xeMWF726pQhbg\nuEb7h1Kl0GW/pvhtv/oQCUbTIb2wK4gD+TZDSUlfQmBNHeOHA/sRBgMAkCC/dLLskPubEp5X\n4wmfOLDDYNqau3fvAoCnpycAAHC7desMcPP48cJPA93IoOdm9f7kg3Vf/L0sclFTzwxL8QoL\n8/wgLq4KutoCAHv1SoI0Yl7vJoQpijJVZKjHpk2bEhISSLKJDgpMR8MgZmfhxrPlx1jEIqge\nneQSvE87r5JybISUxWnBDEKHNNoNSlWWgQky3NpYNl/Kyh8cJkhPj0WUw+TWst9ysB9hnmoQ\nIIWhKlV987+5qwBA4M8x6hof4seBHcYqcI6dBbi7uwNkZmdnA3QFgM7BwTy4df3qVQNEc00H\nE69eTYJFUa10PSLspQXBfb95b3fEjyPh/Nfv/sl5+ciYVh6iwnQchfqCLHV6KV18ovSgaY8j\nz0VICl347uNdpjrz3SxVqENol1rzp1LlorkyX/WPgpKN1B57ENURFMdxGsd9CSns3IqtsBzs\nR5inl8tV53cV/l1Ol/LJB1UcMzblSwc0UrQIv0ZgMG1N9+hoP4CUH9/85my2ggFOWFg/AM3h\nrfvlAPpLl64BgK2t7cNeJXV5d8LzbdO6KcFL920Zm/1RH2fXPm9cDF175NtwQQtnYx4Ttt7d\n8HHq4v/l/3yg+B/THgKIqW5zPgtc/ZrvBz5Cf0sV3qINowqLLtzb+3Xhs7+XPj9a++901TYK\nIQNlA3wvynmWMCSe57+uo6M6wH6EeQpRMcrzFSfj5Rc35K0up0sBQM/qukq6+4oCyv7RZu8p\navQs3GOHsQqcY2cBnEEf/jhnz9TNJ/4T2SlhJ9o1bfq84Z9cPP3Pi30LfhOmxtwFovP0qSEW\nKvV8Oxa9XXtH1+U30fL7H7h+U1efmrq6NazHdDgI0HX5lUuVZ8Ucydny46adJEFOdpmlY7WD\n7Ia5C7ws1VnAGLeo1JkGA8uUrSmc18WQWvuovdfbXMdZBNepqdM7AuxHmKeLa/LL6/N+1LN1\nEjf72oS97vshAERejERs48uX48AOg2l7XCb9fSP52c07TiU5+wKA5/w/t6fOeumnixfOACEJ\nfu6HbV8MxkM8mIYgQAaWPla6f2/RVtMeAggEiEfyJrvOHuNkTcZbMm34U6m6pCruQV8roZxX\nl7/ubCx+cJgUcz3e5Lq+3qrLvLYS2I8wTwE52sxTZf8qGXmR7m5NVBfhEMUj+EHSHr1k/Zs/\nHXBgh7ESnGNnIYQsaMJrn064/8l7wqoLRV+W5NylHX09ZdgNMfVBgA6X7D5WekBrVNcuQTfP\n63Vbrr2fMMDSBcFYgDNa3Z9KVak6Y5Zq0/9pdvMQrScEfKQDIAAQwbHjeizhOM8DqtUmlrY6\n2I8wTyoIUJWhospQ8dWdj1hkBABTgToCiN42A2a5v2x+KUrsCRhMB0EKnTu1ZxkwzONBvjYn\nTX3TiIy7C6tXMiSBlHJkQkoc5Rg9xH6kFTpv0YZ3yit1+nsfVX0+VHempp4pH+kMduOkbosQ\nMlKyQUBwm9PyaIL9CPP4kyiP++fen6V0sYAUmqI6AAizixjmOFZCSS2tW4QDO4xV4Bw76zn5\nfsh7J2DU99dXWvOIxjzRbCpYd6b8aL2dUU7RE11mWKEtj2F2qDRVRvoeXYF0Fb9VvOFnyKwt\nwHGYKuq8ARpUrn8cwH6Eeewp0RdelV+QcWy33F1PszQA6Fitr9BfwcgH2A2Z4DzdirpFgAM7\nDKbdqcpOSkqCgKqOtgPziMAgw8nSQ3FV5204tsnKRNNOAoiJLjNURmV/m8GBkmCLFCKAizr9\nZpU6Q5U1VfXPi+ptAqRRkRIpq6yWILkch+kct9dIkWWaHyWwH2Eeby5Wnvkjfw2L6tSi6yrp\n/r7/Fw1XCbMIHNhhrALn2GEwD81dXZ6SkSfKL58sO2TaQxGUERn5pGCiy4xxzs9YqlDJsvvU\n2m0qNVebskj5c7j2HAnVjw0hwdHxAyR2IwmeM8fpOYLr3JotwWAw5nG2/PjJskMqo5IAoiaq\nG+E4nkvy/EVdessGPGRUBziww2AwmPZHa9T8kvPtbdUNuJ8ibeI1nw9suHbuAi8BKbRU5161\n5qvKKjWCobqYLyo+kiBl7aNCl7lcr48fhSw6g8GgVqsVCoVMZtn8Dwzm8SVPm52lSUeANhWs\nM+3hkwICCIrgDLKLnOH+ApewbEo3Eor4U6ZqERIS9QNBHNhhrALn2GEwllNhKDtZeihLky6k\nRKaoDgAceM6OPGcBKRjmOLaHtI9FCvUIHdFoD2m0AfTNLhWbTmoOKsBWBpU8ZKiWICjKbjzX\n7VVSGta6bbGC2NjYtWvXxsXFsSyr1+s72hwMpp1Yn/dDXOW5ejtHOI4b5TSRR/AtTaS7TRu2\nq9Xa1d9EcrK0LCOk6r+t4cAOg2lnRv98+/aXIPPoaDsw7QaDmCxNmi3HfkXmx5WGctNOU0U6\nISWa7fFyiCzUUp2ZBmaHWr1frR2i2vuu8ndfJsu03xFKyjheAq6tjOdIinpw3RYSPMum1LU6\nlZWVmzZtWr9+fUpKSutpxX6EeaRRMYqDJTsT5XEOXOd0dfV/PkmQIxzGVxjKQm3D+9kOsmjU\nVYPQYY12h0pNqa5MU217W3eCj2id22UQdakniQM7jFXgHDsLUN5LvasAmUdXdykAgNS9a1f3\nJo9injRuKBL+yP9ZwchJgqopZCDlyN7y+7jSUB4sDbFo1FWH0FGNdpdak6xTMQTny4oPR2sP\n1xZABOXZeTUpCwcLR3ZaHYTQ+fPnN2zYsGvXLp1OV+8o0WD8qCWwH2EeA2hWf11xFQGKqzyb\npIgHgHK6lENwGMQISOEk15mjnSZZoXaPWvNNlbyP5vT/yVd3Yu7U7Cfqzr0wgQM7DKatOfZm\n0PTdMHUn2jWt4UH2yBtBz+5t6ijmsSWu8tyFytMAUKwvVDByAGCRsbM4SM5U+Yu6jHeeZuk6\nYKVGo5gkXyrMCFbsXaba5sPk3uF2CTCkP5CgpJTDFK7ba6QwsFWbYjFFRUUbN278448/0tPT\n6x1ycHAQCoU6nc7R0dFCrdiPMI86Bbrc7zI/UTGK2jspgvow4CuEkJfQl09asNywgmUParTH\nNOqe9I1suopHBa4sf5sDTG0ZW24jiao4sMNYBc6xM4u0fd/uTYWbadXb396pL4B0GftPA4AU\np5E/IWRq0kr1RSqjauvdDaY9NaMtvkL/t/w+Flm4rkMFyx5Sa/eoNTptxpvKXzZojlH3J7oG\nGNJVHCdS0FnKEVC2YznOs4G0pupVa2EwGP79998///zz8OHDDMPUOzpkyJCFCxdOnTp1zJgx\nqampjWpoAuxHmEeaS5Vnj5XuK9YX2nHta6K6zuIgMSWRcWyHO471FnYyXxsLcEmn36PW3FJl\nTlJuXa455sreA4AY4fAHUR0p2nde/Os2+YX4RhItcGCHwbQdyZuXLt1dvX1zy9KlTYhxOs2e\nMbC9bMK0EQZEf5/5aYb6NtSd6DrBZbq30M+GY+cvDjQ/pYZB6JxOv1etSVPl6ICyYys2ls2T\nsnV6ApCkt3PQAaAkrdgK60hKStq4ceOWLVtKSkrqHXJ0dHz++ecXLFjQtWtXa9VjP8I8crCI\nvaGMT1OlOPCctt793bSzhC42VSzyEfq/6LXY1cIVI+4xxp1qzT61Rs6ofipb9BkdTwKqOdoP\nSiu8v3ahc0lJf8p+wvoPRmcWKBrVgwM7jFXgHDuzCHtv586ZELdq+qo4GLhk55KGTx2CI7T3\n7T0oxN3cVQAxjxiZmrR9Rdsy1Ldd+e552mzTTjFHGiDqqjYqB9gOjXSIIgnKfIU0Qj/LlaeU\n90LV/87RHOhB32CBUpDSWlEdQcoGcZxmcRymgdnLR7YFxcXFW7du3bhxY1JSUr1DJEmOGjXq\npZdemjx5Mo/3kNl+2I8wjxAl+kIpx+a/eT/cUMTXO9RbFjrbY76e1bnyLZjVI2fZm7TBhSK+\nyz8Soovtwg0ZrTnSl75aW4bg2Dh2XmtmRXEc2GEwbYfHwGnTAByTBsdRMGzCtGmRHW0QprW4\nrbpxXXHVmee6v3i7ilECQJ42m0NwGWQQU5LZ7vMH2lmWpVBoNB7WaAsYI8+o6HLvP/9ojtYM\nu5BgLOYFUVAlFQdw+Z05zrMJgX/rN8lsNBrN/v37N2/efPz48YZDrp06dZo3b968efO8vCxL\nImwa7EeYR4IczZ0/8tcU6HKFlEhr1Jh2EkBMcp1Zoi/sJg0JtQ3nEOaGVQaEzun0+9WaPFVK\nlOrAFM3O1awcABBBXOAPMckgguLYhHNcXqVsR4LZmnFgh7EKnGNnAZFfxMZ2tA2Y1uGuLg8A\nrlTFHizeUe+QPdfxP52/LadLfUX+5tcaVbDsMa3ukFqFFJf60FcuicZ9U/5eoKFuChoh6OX/\nf6R0QMcu6mo0Gk+cOLF169Z9+/Yplcp6RyUSydSpU+fNmxcREWH5jFdzwH6E6QAQoEuVMZcq\nzyLEypkq0y+A1qhx4DmV06WOPJcJLtOG2o+yRCEk6umDGu1RjVbOsv+pWv6temdtAQIhB5+l\nHP1YkmNH2QwnuE6W2owDOwymjVAX3SlUmSsscQtwtSypHtO+lNEla7K/ztflAEDtuG2U40S1\nUekp9A23Gy7hyOy5Zk32ZBA6odX9q9EWK66NV+/5UnfMwVgOAHNVf/HYB5V7SWkox2k2ZT+Z\n4Ni0cnvMBiF06dKlbdu27dixo2EKHUmSw4YNe/7555955hmJpC2y/bAfYToAmqWTFFfL6RIW\n0K7Cv+sdteHafej/pZRjY9EsVwBIoQ0fl2YHKY+M0J14jsldY/POFM3u2gIE14Hj9mpfu4EA\n1ueL4sAOYxU4x65ljrzRefrulsVM4DINjyixFafOlB+tMlTYcGxNUR0A2HBtlYxCSImG2o+K\ndnmWJEgztdEIJehpZ4r6Ua44pdX9VrZggP5ibQEhqy33+82VuUtQUsp2FCHwa93mWERiYuL2\n7dt37NiRm5vb8GhwcPCcOXOee+651htybRTsR5j2g0GGYn2hhCP9MuODcrq03tHRTpNc+R4i\nStxL1p9Hmtsrn80whzXaYxptX9Xh4Zr9GzXnOVBdz3J5xcdZvOAAfTIi+RybERyXFyib4eYP\nuTYFDuwwmDbCZ+hzz5n9Nhfq05amYCxCbVRdrDiTq81y4bvtLdpq2ilnKk0bYkqywPsdf1Gg\n+fGcAaGLOv1RrS5dcXuQ5kiU9ugixAYIo+pFdYiS8r3+z8tlZiu2xQqSkpJ27ty5Y8eOjIyM\nhkc9PDxmzpw5Z86ckJCQdjEH+xGmnThauu9g8Q6tUSOmJGpjdTexPdexk6iL2qjqaxMW4RBF\nmT0RKodhjmq0Z9WVKm02TfACDbffr1hST0ZvF92jyzrQ3iZ43mBhIaRmwIEdxipwjl3L9H9z\n8+aOtgFjARWGsjRViqfQ59fsb0voonpH+9kMGuE4Xm1UdZP0MvNlnUbokk5/TKs7o1FPUfw+\nW3vI3/CgBNsA+gpDcDiIQZSYIxvKcXmZsol4+Jd1q7l+/fquXbt27dqVlpbW8Kidnd3UqVNn\nz54dERFBkuZGtK0B9iNMG1JGFx8o3nFLmeQl9L2hSECAAEBtVNlw7eSGSneB12yPBd0kPS3S\neUSj/buq2EV1apJ6z7N0PB/RLJDx/AG1ZQhJH67TLDfnFwEAhEGt1yAAHNhhMJinHLmhkkfy\nT5X9u7doKwJEEiR7f5UePimY6DK9QJfXTdJzoN1QDlF/se1G0SFUbDTyCGJZQayTLuWMYOQS\n+TcTNfvqifV3n0vZ/gkABNe5o6qWIISuXr26Z8+e3bt337nToO4vgEQiiY6Onjlz5ujRox+6\nagkG80iAACUp4q/JL0s5NinK67naTACoMJTVlJnsKeu32HepkpHbcu3N1JlmMJzQ6OL1dBCP\ny7/7/XrVb9T99QMBgARWKeqronRSwsixn8xxnELwfVu7WQ/AgR3GKnCOnQWcfD/kvRMtyIz6\n/vrKke1iDaaGfG3OH/lrcrWZQkpkREbTyzqL2C7ibnna7E6izuNdpgWZ/bKuYNkYrf6UVi1X\nXB6sOTFO8+8vbBkAvEmuqv0Tjwgux3YUx3Ea5TClLRplDgzDnDt3bu/evfv27SsoKGgoIBKJ\nJkyYMGPGjHHjxgmFFixl25ZgP8I8FBqjOlmZyLCGCkNZTYpFDRyC+3anT0r093yE/n6izgDQ\nYlTHAiTT9AmNLkGV1015uC99pSurz+IGPKfaSNQqLAwAIIuYGPhFq7amOXBgh8G0Ncq79VZQ\nQkaaZqr9nmfv7WFDsmpDR1j2FMIgw4Gif+LlFwGAIjg1xQuElAgASIIMtQ1/2est8zNpiozG\n01rdKa3uip5GLPNP6RQ/Q2ZtARmS7/fZPr7kC5HQn7IbS9mNIaiOWaderVYfO3bswIEDhw4d\nKi8vbyggFovHjx8/bdq08ePHi0QduTpZY2A/wlhDpaG8wlDGJbgrMj82FZ+rvQBMhEOUzqi1\n5dqH24/wEHibOeqqZNmf5MozGsUo+Z9jtYcXGdJqNA7Sx+q5LnxDMQCAIIBjP57jNJMUWr3s\nijXgwA5jFTjHzgKmbNXp6u1iaVVJ1vUzu374v69Oy0Z9tX3NrM4dYtpTQ6oq+ULlGbmhUsqR\nXao8a9pZ8/suosQfBnwFCGy59lJOy8uNIoBsA+NIkdsr83LKDg7Vnv6Svk4A2iSdVy+qYymB\nwHPpHNeR4NphHUn37t07dOjQgQMHTp06pWvwrwgANjY2EyZMeOaZZ8aOHfvI9M81BPsRxlwQ\nIK1RwyW5v2R/m6xMBAABKdSxWtNRd4GXETEEkKF24ROcp5v5FlfJsue0uvNajZsugcNq5drC\nufSNGert9cQUsiiXwPVs5XFCHEIKO+b/EQd2GEwHQPIkrl3DZ30cHukxofNLcyba+t1cMRB7\nY+uiZBSXq84X6Qs8Bb6bCtaheoMjAFNcZ9txHTgkt6e0r6nHrnl0CMXp9DE6fYxWZ6NN+brq\nw9mGLBLYGoFx6oMqylZirGI5tlzpII7zc5TNcLCw0lWrgBBKTEw8dOjQwYMHExMTEarfdgBw\ndnaOjo6eMmXKiBEj+PzHci0u7EeYhpwsO3SkZG+lodyN71mor840QIBMy0X4Cv1f9F7sJfA1\nU9sdAxOj1V3UlDvJjwzQx36oOytmqyfMKonaXe8ESHrznOe5Os8FAMpxeus1yGKwC2CsAufY\ntRJu06YOfunf4xs3x60YGN7RxjwBIEBpqps52kx/UeBvud/JDZX1BAIlwZ3F3VSMMkTWv4es\nD2HGWg5FRuMZre6iRk7KT4foLwWzKifKZYT2hDeTU1uMBY59wFpnWU9Wl0MKAzpkfqtCoThx\n4sSRI0cOHz5cWFjYqIy/v//kyZMnT548aNCg9p3f2oZgP3rKydKkHyjekaVJ6yQKTL4/ubVQ\nX2Ba5Y8kqCmus4c6jNKzOhuOnZk6/1Cq9imK7rDCwbpzn1V+4sDWyV4wEtxcm4nebIktUnGc\nZlJ2Ywmuc+s3zCpwYIfBdCgKhQIA5HJ5RxvyWFNpKL+jTvUQeO8s3JikiAcAAoiaLjohJepv\nMzhPlx0k6THaabLMjFUcTJWEq1hWRhKH8/43Qbl7jCGFD3SNQD7H+/4mgQR+PMcZHMcZEoEf\nAJCids2nAYDk5OSjR48ePXo0NjaWpumGAiRJhoaGRkdHR0dHBwebtY74Ywb2o6cPJaM4Wrr3\ntvKGh9DnpvKa6S3uhiK+5m0tUBL8ktebBdqczuIgCUcGAAKyuUyDXIY5r9NfUZdLlOdGGS6N\nUR2ZaazMo7y9jPn1J0NwHIV+K0IdnmmrtrVERUVFaWmpQqGorKy0s6sfquLADmMVOMfOAvRV\nRZWNZDYhgzw/ftuyb+IAoEePJ/Fh27YYEJ2jyZRxbBLll3cV/o0AcQgOg6rXpEeAgqUhGerb\nnUSdo12e7SrpYY7OHIa5oNNf0ZQTinN2hkKa4NuiymXy1fXE1JRTZZfdXhV/8kSdKdsogufe\nym0zg4qKilOnTh07duzo0aN3795tVEYikYwaNWrChAnjx493cXFpZwsbYmQh7p5eHvKccHjX\nCi1rL7SovxD7EQaMyBhbcSpZmSChZCV0YarqJgDkaB8ktoopySLfD7I06T7CTsGSXiRBOfGa\n+8/XIHRZpz+v01/Q6ctoxUuq9V8q/+QAUyPgbcxDQCg5TlKmlOX58B2nUHajSUl/MHt+VWvB\nsmx8fPzRo0ePHTt2+fJlo9EIAAzDNJTEgR0G09YcnO/W7JpIXP9Fn8/3NVsdm39o6YIP/jif\nTTv2mPTeL78sDm2Y7X/hHa/w1Q/KWAR/mnxzeXcLTH6EuafL5xCcIv29/+at0ho1JEFxCMrU\nOccgxpRVI+XIRjlOnOBibprLbdrwj1qTocoapNjWV3c1mkk2FShBQOiJOhlyRq4L3/EZR48P\nnDi2IPuq1VvXPAaDIS4u7vjx4ydOnIiPjzf9sjckICBg/Pjx48ePHzp06KOQPJdewaSUGXq7\ncBefqLpVZoCuE6waAMZ+9PRyU3ktUX6ZAGCBPVt+vN5RAohZHi8XaHOd+a4D7SLsuY7NT25l\nAdINBluSjFfmXSw+2ld/8RXt+Qh+Vy5r6EVfq9c5pxKFyHy/dJEOREYlwbFt/ba1RF5e3okT\nJ06cOHHy5MlGJ7M3BAd2GKvAOXYW4NJzxIiqhrsJkie2d+sSGv3C/EnBLc/EvE/GyqnTdnX9\n40LhMw531s0eOf517zubJtUbW9TeuVM8bbt+57NPSEXZSkO5gaUZxPyc/VUJXUQAIaLEpsoF\nLDIKKCnN0gQQvWT9XvV5X8/qJBxp88lzeoSu0/Qlnb5cnR7Bpv3IBI5U7/1BvUXK1hnKMxIc\ntd0EEVfAqm9y7SdTdlHtXLbAxM2bN0+dOnXy5MmYmBiVStWojEAgiIiIGDNmzLhx47p06dLO\nFjakWG1MKjEE2nN+ilftz9ACAJckDGwjczjMBvvR00WeNitefknNKB14zrsK/24oEOkwWsFU\nCSlxmG1EN2mvFhXmMswlnT5OT8fp9L3Up15V/TacvjX8/tH+usty0gYBSYARAFieF892BGUb\nJbIbCQQXANozqqusrIyJiTl58uTJkyfT09ObEuNwOI2+ueHADoNpa4b838mTraYs7r+/xPf/\n6MDsLjKAPm8vnb1i9Ia9v0yaV/eJlJmR4RsU9Hg/jVSMUsnIxRzpmuyvMzVpAGBa5AcAECD2\n/lxUF777kk7LDSwt5chMaTRNrfdlBEih6cs6Ol6ntK06NExzZJLhtouxCAD6AY8H9VPTDDxv\nkc+nMofJAB0wwyAnJ+f06dOnTp06ffp0UVH99c1qCAwMjIqKGjt2bERERIdXnjMiOJOrO5ev\nv3SXzqpiAEDAIXT3K80ZWORnw8mWM5ROrk87L+TOt1A99qMnn1K6+ErV+UpDua8w4K+C31hU\nv0+6h7SPETEIoLdN6HCHceas13xGq4vRKBTyS37auIH6i33YClr2/reV73FQnaKHiCALvX+U\nCV34SE0K/AhBQGs2zAw0Gk1sbOzp06dPnz6dmJjYVH+8VCodMWLE6NGj//rrr5ycHJmskZcZ\nHNhhrALn2HUQeRcvFvhHhLtWfwwN7U/vuJIE8+p8DcaMjGw6/8O+Gy+nKaRdRry8Ys2yUW7t\nnRBiDWqjqoIuc+a7rsv93rRuox3XodJQPfqgM1aXoRJR4jf9lvFIPkVQngKfZjrnWIBMA2ND\nErc0d48VnU6jPGjgrah818+QVVuMB7SWlBYK+3vqk3mirjyHaMomUtTuv+z5+fkxMTExMTFn\nzpzJzs5uSszOzm748OFRUVFRUVG+vr7taGAjIIDYfP2FAr2DkMpVMNtuaWof1THITUIVqox8\nipjZTfTxIFmpxvjshNmpt28L1y7oKJvhSfejxws5UxlXea5If7erpMfG/N9qqs3V4D/qU94A\nACAASURBVCX0FVMSBSPvLu090WWGmJI0r7DcyF7R66/qaQXLBpDabtkvvm24xq8Vxi2Rr6DQ\ng9Q0RtRdIBvMcZrZRxzSiu0yB61WGxcXFxMTc/r06StXrjQ68wkASJLs27evyeXDwsK4XC4A\nbN9ev4ReDTiww2DaiH1zJXP2mis8ZbNq02Qz5IqKisDJyen+R5GDg6C8tJSt26mUm5FhRJ0n\nr9+/u4vu6vqXZ4yfQFy9/EmvRr3daDQePny4Yd3ae/fuAQDLso2d1JqoGGWhvsBb6LexYO3l\nynMIkC3XvspQYTqqZBSmxVs5BGeu50I3vqeW1QaIujbVLQcARoDbtCFer7+q1wmqjvbVxAyi\nL4QyRaEACAgVKZWyitryCEiu83P23p87dEQCTW5u7tmzZ2NiYs6dO5eZmdmUGI/HGzRo0MiR\nI0eNGtW3b1+K6sj4okrHxhfRV+7RFElwSfg1sfHRYZKASG/+z6Ps7qmM7hJKyCEAwEVMQWNF\n9ZoG+9ETiIpRXKg8k6/NDpL03F202eTvtZPnbLn23kK/fG1OoCR4ssssZ75b8wpLjMZ4PX1N\nq5Qrr/qoYrrTyfOYzBOiMV3otL701XrCVbKRjF2UDyrjcqSkbAjBdWpUZxuh0Wji4uJMXn/5\n8mW9Xt+UpL+//4gRI0aOHDl8+HAHBwfzL4EDO4xV4By7lrHr1K9fv5pP6pwr8blaELgEhXT3\ntafUJQVZ6WkFCiPfN2LSYE9ufw/zFZve10yQJElxufWGIzp9kMh8UL0dseR/H+3xXvVn/Cer\nBzam7MyZM9HR0U1dqpn0joehylCRp832EXU6WLzjTNlRBEjKkSmZ6nhLbqjkkTxT2txEl+kh\nstByQ2mAKFDS7JoQRoDtKvUtRRqhvCxmK7ZJ5qwvndeTvl5bhgCUwusVaMwUIj1FSQT2Yziy\nYaRsMNFSH0ArghBKTU09f/78+fPnz507l5eX15QkSZK9e/cePnz4iBEjhgwZ0uEjrQwLPyco\nT2TrMiqYRkOzQR789AqDlEcO8OAN8uCHufNM8179bR/mQYP96AlBxSjPVRzP1tzxFwdeqDht\nWs3vYmVMjQCP5PWS9U9T3fQTdY52eda0YGsz5DPGLIOhF5/3SUXFKa3+q8r339Cc4Naa0DpL\ntTlN0L/mo5GSkrLhPJshfV1eaOcyk5WVlRcuXDB5fXx8vMHQ5NJ3rq6uw4YNM3m9n5+fdZfD\ngR0G00ZEfBYTY9pic3e+GPXCOa8p/93/v/m97e4/PtSZR9e8teDj83fcPz31Zv+m1NTF1dUV\nysvLAUzvmNryco2LX/N1LLwCAnglxcUIGh2xHDZs2IEDBxr2NKxYsSIhIaEV0/CL9Hcz1Wne\nok6XKs8eK9lXUwjedFTJKPikQM/qACDSYXSUU3SBLreTqIsd1wEAvIS+DRXKWfaank6k6UQ9\nHarYOUh3fqT6whRUHR1GaY4EGVLqnaIT9hza7X8cSgZku84VpWk6ISHhwoULsbGxFy9eLC0t\nbUqSIIju3bubftmHDh3asEJVu8Ei0DGIzyF+vKL8N1NbqWN7u/DO5dfvXQh14xWpjQjBMB/B\n+wOlIk7LBZ8tBPvRY4zaqDpRejBdfctd4Jmnzb6jTgWABPmlGgECiDC7iBRlkofAa4zzlO7S\n3s1oMwKk0YYEPX1HU8AqL3XWXO5HXwbj3aWE2E88e7TmSD15FcfFrce/vJINQPJJ6QBS1K09\n82Xz8vJiY2NN8VxKSkozvbYODg5Dhw41eX2rlJnEgR3GKnCOnQVUbn3r5b/TZQuObXmld+3y\nmGL/MR/t2lQQMGzlzHcjc/8YZ87KU15hYZ4fxMVVQVdbAGCvXkmQRsyr91uYuyrM968xqcmf\nBgIAQHpyMt11clATD1yKoiZOnNhw/6ZNmxISEh5yZYJMdVqG+pYT3zVXm3WoeCcAUASFAJmq\nk2iNGjElURtVANDfdvBs9/mZmnRvoZ8jzxkAXPiNFIerZNkYre6WplSvuuyoueZlzC3jhfUy\nls9X/lRPUgTaa+IxgfokimsjEHXn2Y8lZeGidiwNX1paeunSpYsXL168eDE+Pl6rrZ85VANJ\nkj169IiIiIiIiBg6dKijo2O7GVkPIwvxRXR8EX2tmE4sMij0bG9XXmJRdd5PvajOS0oN8eIv\nCZXaCtrnYfn0+tHjhYpRHijenqZKkXFtjYgxlZpLVSXXlolwiLqtvOHIdxnmMKavTVgz2nQI\nCQjimk67q/zGeaODwFj+rHrzh6rNtWV4iJ6i2c0QHA5iAMBICpB0qMgu0slpNkFxwe21Nmhl\nIxgMhuvXr5u8/sKFCwUFBc0IOzk5DRkyJCIiIjIysnv37q37H4IDOwymrYk/c0YJEB4e3kjR\nc0FYWG/49dCBA3EwLtIMXUTYSwuC+37z3u6IH0fC+a/f/ZPz8pEx9dLNfCbNCvu/L9/9Yfi6\nl3pC0rpFP6SP//6V9ukxQIAS5XFpqhQpR6Zn9YdLquuO1UxeMyJjzXyI3jYD5nu9la3JcOG7\nO/CcAKCPzYB6ClUsSqbp6zR9lzFGCAVpOd+OVe2MMhbW1JqK0h7NpjrVsYGgDDyfgMDNXFFQ\nmza2HgzDJCcnX7p0KS4uLi4uLiMjoxlhDofTp0+f8PDwyMjI8PDwDuyZUxvQyRxdVhUT6c1f\ndlaeVlGn3um1ogfZ3FO6CAPtuXdVTC9n3gB3nruknfP8niI/eozQGjUssEZk3Hp3Q4ryOp8U\nOPCcMtS3AQDqdl+OdJxwR5Mq49iG2UUMsB3SlELThKcbNH1dr0PyM77qcyFMamf99WWI1oEQ\nCMRDDZLSKIk8YIctjydUXqBkoe3ZM1dYWBgXF2fy+oSEBI1G04ywh4fH0KFDhwwZMnTo0G7d\nuhFEq3dvV4MDO4xV4Bw7CzBlumdlZgI0XP4gKysLAJqaDdUIwUv3bamY/1Ef5wJw6zdr7ZFv\nw009FKnLuwf9PvJ8wepwCFi8a7/yrY+e6/6fEtKl55j3/10zz7UFrQ+BERlPlx++rbzBJXkS\nSnqm/GhDGTe+5z1dPkGQvWWh873fKtLfteM6mpb2aliAKt1gSKINmZq7emW8k/baQN2F0Wyl\nmhAfF41bpPi5/tWBu99zw4vF79hxZRxJX1I6kJQNIigptAv5+flXrlyJi4u7fPlyiz/rEolk\nwIAB4eHhQ4YMGThwoFgsbh8j61GlZ2+UGJJKDHcqmSGevA1J6juVDAD8lqhqWGZunL9gTCfh\n7XJDL2fuMG8B1ZEdT0+4Hz0usIi9q8ujkd6R67w29/sM9S2SIN34ngW6XDBNbDeU1QiPc556\nT5fHJwV9bAb2tx3cjFoNQlvk5UXK6yLV1VzKVUsKo1W7R+jqFLgRgBYhIocb4GfIAAA9348n\n7Mp3GE/ZTQgxrRMoDmyLJtexU6NJTEy8fJ9mcmQBgCCIoKCg8PtYnTNnKTiww2DamoGjR9ts\n2J720+ufj9m7LMzhQT8Hkl/7fvHqWwDS0aObG42oC9dv6upTU+uvcgVdl99Ey6u3CfeRy3bG\nL3tYw5uGZundhZtuKBMQIC+Bb+2kmRoCJcE5mkwOwekl6z/Xc6GO1QpIIZ8UAICP0L+2ZB7D\nJNOGfF3FIJS7hXHPUGdMV/+zWL2nXgn4F5UbgCBq5lTSlAPYDJa6LVwm7Q2uMW3V1LqUl5fH\nx8dfuXLl6tWrV69ebabCnAkvL69BgwYNGjRo8ODBvXr14nA64CdXy6A7lYynlLpaSK+8rDRV\nmDNx6E6dAeIgB+7tcoMNn+ztwu3jwgt15/Vz4xEA4/zNGd5sa55AP3pcUDHKFNV1NaMMlHRf\nk/11CV0EADKOjYKRA4ARGU0zIUxMc5tbZahgAfWU9ukp69eoQiXLptCGm7ThBk3f1RU9q/yz\nnzZmJp1NQXXxNobgkA2K2Kl53grX97q4zqI0Nwi+Z/tkVjAMc/Pmzav3uXnzZqOreNUgFAr7\n9es3ePBgk+NbNJu1tcCBHcYqcI6dBYie+W7dtNg5u85/Osh3c9SUqBB/T3tSfjcj4cjek3dU\niHSfvG7VjHbqYmolzpYfO1F20LRdoi+s2d/XJqyULmIRGywNmeI6m0vyaorMmUI6E0VGY5Ke\nTjEYMnUV3lW7+mrP+jP5w5g8APQeIRIhbf0ltwFoUlTst6ELl2+sOETKBlPSUBHfu41bCQBQ\nWVmZkJCQmJgYHx8fHx/fTHk5E3w+v0+fPgMHDgwLCwsLC/P09GwHI+uhY1BqBXO7zOBjQ+Up\njF9cUOgYJOURAKCk69xYkoDRfoKTOXofG+qlnuLpXUWlGqOzmGqrIaKH4gn0o0eZPG32larY\nKkN5b5sBf+X/asqFrb0cs46tHmolgJjr+SqLWA2r7ibp2UnU+Hg1AjikUd+W36ZUV230qT3p\n62FIdcnmg3XlS8RIXU/4HuXJkmJ3Jp8kgJANFUr7k9KBIkkfJ9NsVkmfNmo1ADAMc/v27YSE\nhISEhPj4+KSkpGYSZE34+PiE3SckJKT2fOsOAQd2GEybQ3jN/CfBa81/Pvxuy4XjmzIeFGsi\npIHRb6/+5ZMxXo/kk7Rp+NSDKC3SYYwB0Vqjuos4eLjjWA5R/0eNBchjmNu0IUdXGmS4zVUn\na+Xnkvh9iiiP9xQ/mNZ+qEGMNABQRLm5GgsBQMdzJ/n+YvsooeMMW64zAFC2I9q0aSUlJdeu\nXUu8j2mIr3k6deo04D69e/du/+VZtQyiCFDQaE28Mr7IkFFpMN6fgcejCNqIAEBJIy754N/M\nQUj2cubO6Coa5VenN85F/OhW4H3y/OgRAQFSMFUyjm2C/NLZ8uPldEkfm4HHSw8yyAB1K5II\nKKGBNehZHZ8UvOC5yJ7nWEGXdxYHmXJka6Ng2Vu0IZXWlKpSK/Ql8YL+4yDr2fznR7CVtcWW\nV35SL6pjgauxGebr94OA7wwNfkzaApqmb968WeP15kRyUqm0X79+AwcOHDBgQGhoqJtbC2X2\n2gJG7MTp7qNlkLDBVHQc2GGsAufYWQjpPPit32MX/5CbcOnqrdwSJcORuXTqOTCsl4f4cXwW\nDbEfybCGMkNpF3G3XrJ+Ddd+yGGYBD19T52TwnLjGck41fZZqr8jmJwagUH680agakZeaiCl\noQrXdzzshnHVNwiBT1uPtiCEsrKyrl27lpSUdP369WvXrt29e7fFs5ycnPr379+/f//Q0NDQ\n0ND2n8daomFTyw0CDmEvIN87XZVSZpDwSC8plVJWvz6WLZ8o0SAACLDj/Bpld/Gu3kFI9nLm\neUof3QCuGZ4wP+pA8rRZRfp7ncVB5ypOHC89oDVq/MWB2ZoMFrEAcLhkT42kDcfOhmuXr812\nF3hNd3vBVxRQaShzF3hxCR4AwP1MUdP01SMa7WGN1kZxrIf6ZGc6dSqTRiEjAORyfMVIbVs3\nqgOADHFkAc/RE6lcKCBEwZRsMCnsKmnjInNVVVU3bty4fv26yeVTUlKaKSxngsvl9uzZMzQ0\n1OT1QUFB7T/TmWHhSJb2erHBTUIxLCqc8BOfILQGHNhhMB0KKfPpP9qnQaktpM7N1/p4d1iV\nC8shgBjuOK7mY4nRmG5gUmlDhoHurT4UpEtCyosRTL4IqREQv8tefVnxXxLql3E6LZ3SyVjo\nwJYLCJJvM0wg7UOKQwhBp+oeJKmZJcksQ6VSJScn37hxIykpKSkpKTk5WalUtniWra1tv1r4\n+Pi0hW1NYWCRkQUOSfxxQ3WhgL5dbijXVt9MWwFZpWMBQKFnU/R17rCAQwzx4q+ItMmqYsRc\nMsCOQxIQYPck/OY/MX7UnpgWe7inyw+S9IiXX0yUXwYAESXWGjWm8kOZ6rQaYUeecx+bgSnK\n654Cn9HOk32F/gZEV0dyADKOjQ6hNNqQZjAUarJp1Q2x7mYn/U0vUPBArJXOWVFav8KIF5N3\nXTbVUbELACGC0HF9SGmoWBoy3OVlINp2NV6WZbOyskz+bnL8nJycFs/icDhBQUE1Lt+rV692\n7ok3TXK6XWa4Xc6kVxjshKSTkDp4PymWAICmJ9U+CU6O6QBwjl1rYChLPrZz69Yt2/Zf7LeR\n3TWto+2xiLNazS1lBq1JkakTHQz5MpDfFk7pYbgRrdpaW4wAFKU5zBDcB0UKOP/P3nnHR1Vs\nD/zM3Lp9N5tkN71CIIQOigiCFBsqdhELIIj12Z8/9T19dn329tQnVhAs4BMRC4KVJgJKTyC9\n97K93Pb7Y0OySdaQhJQlzPePfHZn586cmc25e+7MmXP0MpsM5svmxt3a11EJRFHMzc09cODA\nvn37An8LCwuVLqS0MpvN48aNGzdu3Pjx48eNG5eWlnbMS3qRcod0pEH0iMqEGPb/fmraVu5D\ngCbEMNvL2x/6DGyzAgCF4elpxgO1ggJKViSTFcUMMTGBQ6xjLYM8jf0JrUd9gU/2VvnKzUxU\ntb9yXdWnJd7CdPWwGn9lqacIADY3tB41dUsuHqsC6VlT1EMusFyxx/a7lYs9zTTdwLSG4JEB\nKiVcIfpGUq6tDX/scRT8SI8xSbU3218/1/dbcNeJAAlC+4OiIrAVllsnJT+EPXeB5MTqTA3V\nh0fCa2tr9+/ff+DAgf379+/du/fQoUMuV3sfvo4wDDNixIixY8cGtH7MmDEqVYjAOn2HArCx\n0Lujws9QkBXJ3PeTzSf95Z0qK4rZX+MDCB0yhRh2BEJ/o7iKt3758cpVq1Zv2F8fcERmJp9Y\nv70/Nx5KzZ07Ua4LLhzv22nH7dOtyogujn9O5KNSxFwV5rFmFOL6arlLluXCwsIDBw4cOnRo\n//79hw4dys7O7mIEjJSUlNGjR48ZM2bMmDFjx45NTOyPkxkBql2S3aekmeh397o2FHpzGwXn\n0SMOeg7bm9fhlI5WXYKeenWWqcYteUVlQgxr1VCXZvTrT9HAMgj0qLeQFGmP/fcST2E0axUV\ncVX5O4LiV1FqFnE2sRHaJnsAgDRNRr7rMI3oyaYzL7bOP+jcE8la0tXDMMJj9BMhcLzJ6833\ne46IqMzXpHX9fqXzg+G+A0hxTAGYAnAjsApSWKX9DqaMmG+t/z7Tucbqz6G1E3Wa4ZR2PFZn\n6QNnp/ogtGRjY+PBIPbv399JZpdgtFptsMpnZWX155pclUvaXu7PbRRzG4TcBhEQnBLDfn64\nM9++q0eo/6wWAGBKPHfnRO3sORflZh803ZzXsSYx7Ag9gvjY9QChbv/3q1euWvXxl1tLXIFf\nbi5m/Jz51123YP55HSNzhTP62g8i21p1ANCALe/HvHFb1S1qJoJSpSuaUYx2HNaOP6c5qtyY\n3pVBFMX8/PxDhw7l5OQcPHgw8OKYXs8BeJ4fMWLEqFGjRo8eHbi5G43tTdK+QAGocEgFTWJu\no9jkk89J4R/dYt9V5QeAjAi6XXBgAJBkBUHzCeHbJ2jdglLtkodG0MPNzPBI2tp80GGAj+D1\nN4NIj3qAX/aVe0t0tB4A/a/qoyJ3nomJ1NK6nU1bAxUwwgE/OY/k9kKzOtCIOd9y2c6mrYH4\nwFMiZjYK9TraQCNaAojXTc0XxM1Od7Xffrb3e79jp+jJSxKLJkg1e7gJnOwZKmRTbV0pGPB7\nQF2DI6LlagDwMlEym6g3TmOirrmDTwE4t4+GX1tbe/DgwZycnMBj28GDBysrK499GQAAJCYm\njho1atSoUWPGjBk9enR6enr/+Mm5BMUvKUYef3LI/VOxzysps5P5J7fZhbZxI0vtrfeu4WYm\nv0kMXDXcTA83M5Pi2JlJbc45Ya9N8TlD9kgMOwKhj1FcxVvXfbxq5crV3x9o49s+5alD6+4b\nbjoRndiHWObKje8FAk35KaNPPYbVn5pkXfoIEwHxh/qiR5vNdvjw4ZycnJycnMOHD2dnZ+fn\n53dxNQ4hlJycnJWVNXLkyFGjRo0cOXLo0KH9E1LO4Ve2lPmavPK0RO7NP5xrcz1uofVu/tYf\nzpbNliMdrLpYLfXIVL2Bw6V2aWQUMzjc43rOYNSjrqCA8lvjr4XuXA2tjeMT3yt5zSt7MMJm\nJqrWXw0AVb6K4PqRTHQgzpyVi70h8a4/bDvUlGaMfmIMH3+h5UoJgAJokOX1jjq3c7PKs4fx\nVyYLeY2UdaXhrk+r5waOpbcwxrdLRrhd+CEJ86XmJVzU5YmaIZTnEOJT1HRErw9cFMWioqKc\nnJzs7OwjR45kZ2fn5OTU19d38XK9Xh94eBs5cmRA8fvn4U2SgcKwtcy3qciX3yQWNImVTgkB\nTIxhf69svl9tLeuQPAPg8mHqapdU55HHW5nbx+swBkGCKHVPTM+T+05B6DHEx+7YiHX7v1+9\nauXKj7/cVhxYWKBMw2ZdfOW8efPkN4YvXQuWISfsr5HOcLoy5g/FX4FUw9WBmO+9hyAIBQUF\nR4LIyck5ZijgYCwWS1ZW1ogRIwLG3IgRI3S6Pg9wJsjKzkp/YZNU0CQWNIkNXvnsFP6zbHep\nQwIANYOCTboAagZRCDX5ZIzgxjHaJAN1oFZINtAZZjojgjGrmu/pE07qfAeDWY/aUeOvKnTn\ncphL4FPeL30t331ETWnS1Bm7bNsCFRCgwEEHWZHr/DUtF86MnLPfvhshPFI37rKY6yq8JSzm\novn4ClGKMiVWeGu+bshVvOuwt9wG9Gbj9Usan77U8Wk776whwpF2Vh0AOKiIrVEPRDNsimeP\ngTWodBOxOhNxiZkt3rHa8b0z9pqaw4cPB/Q9Nzc3Jyen609uAMDz/PDhwzMzM7OOkpyc3CuC\ndYLTrxTZxEKbaPMps5K5l3Y6v8n3eEXl3FT+2wJv8HqcAtBi1QHAmGimwSuXO6REPT00gh4S\nQY+1sGckcLiXjnYTw45A6CPW3jTq8s8BAJAuZcqFV8y7ct7lZ4+JZgEA1vx3YEXrDRCXiI47\nRLAgCEVFRfn5+Xl5eblHKSoq6jy2eztiY2MzMzOHDx8+YsSIzMzMESNGRET0/vpBMNUuqdYt\npxjpg3XCh/td1S55RhL3Q5F3T00bl6MDta1vfaKiZVGL85yBwxkR9M3jtCOjmCMNYoqRjlZj\nALh8WJ8KfiIymPVIVMR1VZ8ccWXTiBqpH7+68sPALqqG0gYCAvtkb4tVBwAxfHylt0wBxcCY\nbkq8N9u5TwY5Uzt6iDZrmuX6UlGs8NV/WvNbglSh4uLvqczjpbpnGu6ZILc5932Oa12SVNLR\nhFhtuHG+8wOLVKOiTW4ukdKeolUPURlnXtLsSnFdb426rq4uLy8vLy/vyJEjLYpvs9m63oJa\nrR42bFjAkhs+fHhWVlZqamog5VzfYffJW8v9Nq98RiL30UH3/w67a92t29P//q31ye3rfG/H\ny28YrdGyOLdRHBpBX52pNvJYlIHum61gYtgRegTxses6xjGX3HDz0msunpxwEjm2d8TtdhcU\nFOQfJS8vLz8/v7i4uFs2HE3TKSkpw4cPD9zWAy8Mhl5eMgym1CFVOKQhJrrYLi3b4yy0icU2\nKXBaLUZLOfxywFb7szrE0oKRx5Ni2Y2F3mgNtWS0ZmYSv7vKb9VQaSY6eIfl1NiTzuOfRhhk\nGbrl4TQY9ejn+g3ra9YEXh9y7mspb8nuAADnRV9S7ClwS64Udfql1msbREepKKZyUVqpbi9E\n75MMK+x6Y9Xa6x1vZPn3j1ZaTYp3ADVQkTq5fTQfGTM16tkWxwYAEJHKyQ9FqmEa49THoq8G\nWBKo0ysp5GRZLisr66j13bLhAMBsNgeUPSMjY8SIERkZGcnJyaGPg/YSDr/yc4m32CYV28Vi\nm9Tolc9N49flekrtEgAEP6G1IMrA08grKgAwK5lPMVB7aoRYLZVuolON9NAIOtXY3tzqI6sO\niGFHIPQZU+9+6wF2+cq12/788MGrP/ynLn36Zdddd921l05L1g60aH2JJEllZWVFRUUFBQWF\nhYUtf7u1lxogMjJy6NChGRkZgb/Dhg1LS0tj2b4yg/bXCoVNYqlDKrNLVS5prIVt9MrLD7gA\nwMBhCkODp43/eMB1JgBPoynx3KYir5pBKQY6xUinGekLhvAphjb32AT94DFKuoSi+Esrxep6\nsbpOrKmX7E7N5HEUQu+OPlNyuil9VzRhMOsRhVoXmUboxpR5ix2iLYaLX5x4R72/xiHaEJvu\nAH0MVVymsN+huC3Fv57t/CjTv5+XKkARzweYAyifGRIvlvBK+1UiBEoFFWuWmo+IKkA5uCSv\nKsua8nIkZ1L8ZYA4xETpe2MgTU1NhUcJVvyu76UG4DguLS0toPJDhgwZNmxYRkZGHwUA94pK\nmUMqsUt1HmliDLuvRnhnr6vOLc1I5ndV+vMa2zxw/md36zEFt6AEe1ZQCOJ01G3jdaOjmb01\nQpyWmhjLDmxWPmLYEXoE8bE7NpbJNz41+cYnHQU/r1m+fPlHn//y4/sP//j+v25NmnrJ8C4d\nxw9vFEUpKSkpLi4uLi4uCqK0tPSYYdw7YjQa09PT09PThwwZMmTIkKFDhw4dOtRkMh37ym5S\n45YrnVK1S0ox0JKivLzLWWqXMiJojkKf5bRxMPq1tNXB2eaTO7q/jLeyd03UfZXn0XNoTppq\nZBTjFRW+QxT4kwShtFKoqRdr6qXaBsnuVI0boZk8rvqpNz1/tjlM497+ZyqtytB2/ZsdzHo0\nLWK2W3IXCw6aSY7h0+O9lZUyHMQJdze476p/KU2uVPvzVXLzYckPtYuvcn3UGg8SAAAQKFap\nkoI2VoiCENKMr468njNe5Hf9rKNozKchLlETlNEBsT3JYlxfX9+i9YWFhS2639TU1N2maJpO\nTk4OqHzAjBsyZEhSUlKv76jKCmAEdp/8TYG3xiVPiGFKbNKru53Vrta0NxoGCXJzVMhPDrV3\nNASASBU+NZbbUOiJUlOLRmmmJXC7Kv1mFU420EkGqsWOC5MTTmEhBIEweEG61DMXPXLmokfe\nKNn6xUfLV6z4bOOvK4oBAGDjw3Pvqly65JpzRpxwvt+5ubkzZswoKyvrwbWRb1YVjAAAIABJ\nREFUkZFpaWlpaWnpQURFtc81eTz4JYWlkCjDhkJvUZOYZKAsGuqRLfaCJjE4oq+ZxzVuGQBy\n6kNboqccPcg21sI+eJruy1yPmsFJeirJQCcZqFgtBQCnxbWuIA5+q05RfPklYnW9WNcg1TdK\njXZ+1DDd7NNrX3rPtfWP4IrOX36noyLaWXUAAAiqJf+OxuqE7nU8OPWoXHApVetm+/8wSo0a\npXlN6AgzzIU0Y/2721U+17O+nQEHAG7K9Gvsq5G0Ol4qipRtOu0IxKVgPgkQmxKowXc78ogg\nCBUVFaWlpcXFxSUlJYEXAZzO0PE1OofjuJSUlBaVT0tLC9hwDNObkXoaPHKVS0rU03a/vHy/\nq9gulTukCqfU6JXHWhhJgX01AgBQCDBC7UKNuITWRzIjj89N5b/K8xg5nKinEw1Usp4+N42P\n11EAredqw8SGC0n4SkYIa4iPXbdRJZ4+/8HT5z/4atXOr1YuX77ik+/2Zq97+fZ1L98XP/my\n6x944tHz+zVN1fGxatWqY1p1HMclJSWlpqampKSkpKSkpqampqampaXp9b2y+QMAUOOWi2yi\nVUMZOPT2Hldeo1jplKpcUr1HtmqorChmU1Hz/pSKRh6xbcgGGeq9zVurCOC8NNV3BR4AsGqp\neB2VoKenJ3LnpvIVTomjUOCA6jjryeIMp4iSZ2+2WF0n1TeJ9U2y3cENTTFeOaf2lQ9dW3YF\n13Rt/5NNinVt+7NjI5ROqxqf5dl9IPCatphpi1k74zT7HTffmbP5Sl3Pcg8MKj0qLX1lhvvL\ndoVDhMPZ7Ih2hRLQv8Q8ZcaqFGGvSREMnIXXDMNcspqNuwz1xJ4VRbGqqqqkpKS8vLy8vLy4\nuLi8vLysrKy4uLiqqkqW2ycA7CIWiyWg8qlBxMXF9WLEOEmBrWW+IptY7ZKrXFKtW84001oW\nv7LLIStg5LGRw0W2NhZwIK5vy+V6DjV6W+8GBg5fkM4vHKX5rsCrZ/GZSVyslnrijD703O1r\niGFHIPQvnHXiZfdMvOyeF+v2f/vx8uUrVq3fue2jxz6Ye0L9IM2cOfPJJ58MbLkyDJOQkJCc\nnJyUlJScnBww45KTk2NjY4//bt7gkdUMYim0sch7pEGsdknVLqnaJUdr8Glx3L+32yUFMII0\nE53bNg5clUuqCtpq4ag2hh0CGGtlH5+q/zrfiwBOi+NOi2O9ooHGqJ1Hc2BZbpChCAJiGACQ\n3R7nzzvE6jqpwSY22CSbnUtNjLzt2rrXlru2t7HVPPsOc8PT3Dv3tW8LIcRz2qkTnL/uBACs\nUdNREbTFrJkygbZGWu6/UaxvwhoVVrVxx1egs0yXXWJQ6JFFFduuRETMTtPCw9EPmOqeSGDU\nmLUiPhVxyZhPWRBI3tCd2L92u728vLyioiJgvVVWVpaUlFRWVpaVlR2P9QYAUVFRLSqflJQU\nUPmUlBS1Wt3jNluodkkldilgtNW65RQjFaelHttqL3NIQ0x0vI76tqCNQ2FwWLgmr9zkbT8u\nA4cXj9Z8ftjDUzA7hV80UvNTiU/NoAQdFaejDFyzzt889sR32wQAYtgRegjxsTtumMiRF/7t\nuQv/9kxTzqZPl5f2Xwar3mDKlCl5eXklJSVJSUmxsbE9dotRAtmsAfbXCkcaxCqnVOeRq12S\nWYVnp/D3/dRU65bVNJocz7WsvQU4WAc/FTffzWUF8jpE96Ux3D5Bty7XI8gwzsI8eJr+l1Kf\nKCuxWipOR8Vqm91ihplbN4MG00aq7PJIjTbJ7mCsUVSEUaypb/r8O7GyTrLZpQab7PEyMVHW\nx+6sf+tj9+4DwReKVXXqU0Z5cwrat4gQ1mr0506zfbkJACijno6KoCNN6klj2MTYyNsXGK+e\ni1UcVqvaXUVH9r6jZDAntB6lxV7fgCSP+zCnztBzCTSfhPnkmVg9EwCML3Slhbq6uurq6srK\nysrKyqqqqoqKiqqqqvLy8sBftzuEu1jXoSgqJiYmKSkpKSkpMTExMTEx8Po4DTifpJQ7pHqP\nHIjHm6Snk43UM9sdJXYx2UBnRTIv73K03SkFlkIBJ4r9tcL+2hCOE+Ot7O4qPwAkG+gHTtOt\nzvFQCGJ1VLyOitVS462sWYVvHddqt108dDAfYyKGHYEwIMiO4r1/Fjh0qWNHDzv7xqcGWpzu\nE7jRd7FyXqOYXS/Ue+Q6t1zjlowcnpOu+uevtsP1go7F56fzHx1s/wv08SF34N7uFpV2Vl2A\naYncljKfJINFQz0z3fBNvtctKFYNjtVRMVpqRCQTr6MG8a1carKL1XWS3Sk12CS7k4mN1pw+\n3pudX//2J2JVnXL0/ApiaOsTdzeuWOs9cCT4cqGy1v37PqGmQxx/jJnYaOMV5zau+BJRFGU2\n0GYTFWlSjRvBpSVyaYmGy85BNI3o9qY8be6PsP4dONH1CEXELO3kY4fDUVFRUVtbG7Deamtr\nAwZcTU1NeXl5TU1Nd4+dhpAAIYvFEh8fHxcXl5iYmJCQEB8fn5CQkJiYGBsb24MELYKs1Ljk\nGrfc4G12ihhmZl743VHUJCboqUmx7CNb7O38Iow8DiyzHWkQvy8Moew6FtV7FACgMSwaqVmd\n41EALBocq6WsWmpyHDcnjc9vEjGCRB1NYZiV3CsBW8IIRZJlu6NZ3x0uJjYaAFLVesXnR1x7\nFxFi2BF6BPGxOx78hz9YcsnfVhxyAgDosha8/sWy69JPwISfHlGhELAUyq4XDtWJ9R6pzi03\neGUNgy7OUD+6xXaoTtAy+JIM1Qf7Xe0ewT844JJkAIAmn9zRqgOA9Ijm3VUKw72n6L4r8Dr8\nSqQKx+qoKDXOimTmpKtsPtknKlFqCiM4I6H/Enj3KYokyXYn1qoRwyii5Nq8U6iqlZrsst0p\n2Zy0xWxecqX7tz11b60Cpe2cKopjw2ahtE3qTEUQfYfyoMNaJOJYPjOdiY1uXLkOMKZMetps\npCKMfNZQNjWRTU3UzZ4SUjzMh808Dwo9qqys3LdvX01NTV1dXU1NTXV1dW1tbU1NTcCM83pD\nWDk9gGXZmJiY+Pj42NjYuLi4hISEmJiYgA0XGxvbrRBCTr8SOJFQ65YavLJFQ423ss/tcBQ2\niVYNnpnMP/SrzdU2yUq0uvmU0q4q+OJIiGzO3iA7b0YS93OJT1aAQhCppiwaPM7C3jFB+1OJ\nT1ZgjIVJMdD3nxbCTzetQ6C4Ew9F8R7MParvLsnuZGKiDJee49m9v+61FbI36EA0QpmM5qz0\ncbLPTxHDjkAYaITt/7hg8YrcmIte/ebhMXsem3f7h4vOtww98O9JJ5A2ljuk679pyGsUOQpd\nnKH6LNvdzm77+JA7cPy0ySe/t8/VsQWrhip3NPvALRip+bHIW+OWo9U4WkNFqnCGmV46RlvY\nJNp9SoqRsmiopWNCuL8YOQxhY2Z0EanJIdbUSQ6X7HDJdidlNmkmjxWr6+vfWy1W1Uo2p+xy\nAwBWqyz/+ptryy77Vz8GX+47UsjGx3j25bS36gCkRjsTZ/Vm5wcX0tZI9Wlj1aeOtq3dCJJM\nmY2UUU9FGLi0RMpkYBJiYp45QR/PBoMebd++/cwzz/T5QmQO7S4Mw0RHR8fFxVksltjY2JiY\nmLi4uIAxZ7VaLRZLF9sptUv5TWKjV270yk1e2cTjWSn8U9vs+Y1ipBqfn656cpvd23a9zazC\n9UdDPIZMuhB8UmGshWk5yoAAzCo8Mpp5aLJ+fb7XJymjo5mZSXy9RxZlJVJFUUEOr3OHnJAr\n7ookyTaHZHfJDpfkdNFmIzc0RWqyN332jVBZI9tdksMp2520NcrywE3On3c0rf62XQuUQefc\nvKuNVQcAipJIq+ze0Ou1J5AKEMIJ4mPXc7av+DBXhol3v/K3qYkw9eW7l6+9d9cHK7b/e9LU\ngZas63xxxBMI4OmTlJBhn2K0VNlRu21epvrXEl+VS4rgsVmFozVUioG+Y4L2cINY7ZLSTXRm\nJPPw6SEewTMjT4zlF9nrk51u2emSHW7KpGfirYog2Nf/JFTUBAolp4syGSJvudqfX1L70vuK\nJLW53HGFN6fA88fBNoVuj2vrbrEqRKQ2yqTXnDHReygvYNthFU9FGLj0JO2syYim+awhiiBS\nBh0VYaAMOsqoDxxTMC+d15dz0P8MBj36/vvvu27Vmc1mi8USHR0dGxsbHR1ttVpjYmICZlx0\ndHQnpptLUHLqBQOHY7TUzyW+w/VCo0+2eZVGn2zm8SUZqke22I80CBE8viRD/fYeZ7uHtBd3\nOgLBePObYEdFCEvC4Q9ttwGAmkaZkcyjU/Xr87wuQRkeSV+WoS60iW5BiVZjs4pqOasU7DXR\nkiU5bFEkWXa6ZKeb0muxTqNIsmvLTrGyVnK4ZadTdrqpCGPEwkt8ecW1z78re9oYu1H3LHb/\nvs+1eWdwoVBW5fzpN+/hwhCdYawaOdR3uNXtFdEUNzRly5bs3Lzca1UhNp2JYUcg9B0FO3/T\njpsU3dYdyeFwAMDRdKZms7ml7MRhnJXBCAI/AAtHaraV+0rskonHUWpsVuEEHf238dpCm1jm\nkBL19FhLaPssnFNpyR6v7PLILjel01ARRlAU19bdQnm17HLLLo/sdGOjznT1XKmusebf/xXr\n20Rnjbpjob+43LZ2Y3ChUFZl/+oHqdHezqoDALGmnjLq2hUihlZPyMJqtWR3KR4vNmgpo57S\na9nURO0ZEwEh9YSRiiBSBi1qGwxMM2VC701DmDBo9WjevHlvvfVWVVUVwzBRUVFRUVEB0y3w\nwmKxBF7ExMRERUW1bJi6BcXul+0+RceiGC21o8L/XY2gt3nOSuH/+6czp160+WSbX7Z5ZT2H\n52eqn//d4RYUjGBqAvdLSXs7cnVO8+J6jVt+688QYeqCkyhMTeC2lQUy6gECiFDhdBP96BTD\nNwWeJq+SEUFfPlxdZBPtPjlSTUWqsOrogaTgU0rhuWeqiJLscmMVj1gGALz7DwsVNYGbgOz2\nUEa94aLZYm1D7avLxcqaFlsNMXT0/Tf5svOb1nRYaTMZhNLKdlYdAPjzi1HHcAEIcRmp3PA0\noaxK8Xgpox4bdJRey6UnaWdORhRWTRip+AVKr6WMeqxVA4Bz+lqb4EdMiMkMx/klnAAQH7su\nUbnmmksXn3r3i8/cMavVA+zU6dP5j77bvHJ54bW3JJZ8uHIzgGr69FMHUs5uMzmO+/aKqFK7\nFKejhkaEvo1EqNjx1n6WKzSKX5DdXtnrVdwerNXQ0WZQFNdve4TSStntlV1u2e2l9FrjvDmy\ny1P7/Dv+sipoiQSBcdSdC6X6poYP/9euWcQwil9oZ9UBgGf/4Y5nCwCAtkbxWRnunfsUUQpc\nTuk1TGKs/oIZWKumLZGyy03ptJRRh3VaJtYSsPZinrw75KAoQ3tbcPAyaPUoIyOjuKS0osEZ\nFWHQMKjELh2uF+x+JZCDWGHQ5KGq//7pPPSnqGdd12bB09vt2fWCePR/EwHMSuE3Hj1t0OLK\n1kKNW35sqz3wWlago1UHALE6KpACFQCuHK7+tsBr98kshUw8NvE4zUg9cJr+pxJfnVtOM9Hn\npfG1btnmlU0qHMHjlnQsd0S0/jeGhd2mKLLbI7s8WKPGGhUAePcfESqqA4Wyy0OZ9Ia5sySb\no+4/HwmVNbLLo/j8AIA41nL/TUJ5Vf07n7VvUpKl+iZ/QUmbQkF079jT0XoDAKxRqcYMbxMh\nCCE2KVZ31lSk4rBBq7i92KCldBqs07Ip8WxiLAAkLHsy5IC49G6E8QmDL4BAGLSc/u+DO2e+\ncv+dE0ek3PDv5++/NEMDAJELXn593ayb1/1tdOqrpsbcEjHx4rdeuc480LJ2l3QTPSCx12Wv\nT6ypV7w+2esPrKjxI4aAojg2bvEXVcher+LxyW4v1qkjFlyiCGL1U2+K1XWt1yMUecvVil+o\nX/Zp+6YVBTG0v6SibX+ye8deHCqaLuY5Ji3R+eP24EJKr9WdNYWONClen2R3Yq2G0muwVsMk\nxGgmjQGEEt59Wvb4KJ2m3Vk2/bnTjmteBjODVo+KbeLVXzVUOiUae64Ypv40xy21DcH2+m5n\nk6+5aEOht90mqQKwMegMabArWwtnJnHbyvw+SeEodP9puq/zvNUuScdhI4dNPE4xUkvHaPfX\nCFUuKcVIj45mHp9q8EqKhmlz3GZ+Zmtwk2g1jlb331Zp4DSo7PUrHo/s8mKDlk2MBUVxbdnt\nL6tUPD7Z41W8PqzTGOedr7i9tS+9J1QErajRVNS9SySbo/7NVe1b9vpkr897MLdNoc/v/GVH\n6CCLksQktH9URRyrmTKeMuil+ibJ4aK0aqzTYJ2GTYjVnTsNUZjPTJddnkAhpdO0tBxx3cW9\nMj8hIYYdoUcQH7suwiWcdd/KvQt/W/bwXTOGvXL5Yy89vGB8RMbiLw+e8vn7n2/JtRlunnrZ\nokuyei0XQ1ij+AVFEGSnW/ELSK0KBMjwHSn0F1coPp/iE2SPF2vVurOnAkKNH37hLy5XfH7Z\n65NdbkqvjbzlGqCp6kdfa/d8bLrqAqTiGt5b0647xDCUSd/GqgMARXHv3EdbQ2QwQyzNprTP\ndIVYRjv9VCYhRqysEeubsEaNtWpKo6Jjow0XzkIswybFio12SqvGWg3WqimdBjAGgMjbF4Sc\nBKxRY00vBHENExSfLHtkySbKXkl2y5JdpKMY2SF5Djllj0xHMpJN9Ga7ZK8sO0TJIcku6e5D\nS2p8tbJLwpouhz8cpHr0Za630ikBgCjDqlC+ql6p1VZLNtAFTW3iNTIY3XOqbkOBt8QuJuqp\nh043/FDkLXVIBg4ZOGzgcKKempHE2/1ynVu2aigti67LCvGIMikoLR6FQdMxL3KPURTZ7VF8\nfqTiA0GqhbIqf2ml4vUpfr/s8VE6rWbaKSDLTZ997S+tUnx+2e1VPB5s0EXeeJUiStWPvy7Z\n22wQm5fOUySp4d3V7fuSFcTQvvy2K2qi5Nq8M6TGKYIY4j6AsXriKNoa6cstkhrtWKvGGjWl\nVTHxVsOlZ2MVz8RZJJuD0mqwRo11Gjo6IhC70frYnSEngEmI6daE9QrEsCMQ+hwqetJNb22/\n6sDHT9x92YjXpz3w4lO3TBt56R0jLx1owY4Hf2GZv6Rc8Yuy2wOihFScbuZkoCjbmm8Dhprs\n8SmCiFVcxOIrsIqrfuI/QmXQUQCEzEvnUUZdzbPL2h3wFCtraUukY+OW4ELZ6W764ns2wdpx\n18OXW8Smhkg9Suk1/LBU+/qfgttHHKubOZlNiRdKKsTaBqxWYY0Kq1V0TJThotlYxdPWKKmh\nCat4rFFhjZoyGwM/SJaHbgs5D2xqYvi6Cv41UpMo+2TZLckeSfEpkkNkoljZI3kPu2WPRJsZ\n2SN7DjkVryx7ZckmKn5ZcgbKJe8hl+yRaTMjuSR/UYjoFZ0TDzHxECN7umPYAcBg1KPT4tj/\n/AGBrdWFIzUbi7zlDomnkY5FOhbH6aj7J+l+LfUV26UYDbVwpCa3USi1SzoW6zmkZ7FFg/Uc\nvmF0q602OjqEP6uRw0aue2tsstsDkiy7PQHLBgCEimqhpFKRJNntVfwC1qo1U8YDIPtXPwgl\nFYogym6PIohYo45YcAlQuOa5ZUJJRcDrAAAQTUXesRCxTM0z/22n7/7yKkqjtq3d1EaCytqm\n1d9SRn07qw4APHtzaEuIlVnE0ExS+0weiMKayeOY+Bjf4UKxvjGwM4vVKsYaZbziPKxV02aT\n1NiE1arAR3RMdOCBM+7lf4acGfXEUV2fxoGCGHaEHkF87LqPIeuq576/+Kb1L9x307g3Mm95\n/tl7z087UVduPHuyq598o/0NuricTYprWvNdu8qNK7/k0pLaWHUAoCiubX/wGakhwnY43WxK\niKNeXEq8amym/ZtfFH/rsTvabNTPncUmxYp1jVJdA+I4rOKQimcskbqzpyKOjX3+frGmAav5\ngK1GGfWBPVDLP28NOTR+eFpXZ6FfUARFrPfLHpky0iAqviKv7BQVUZGcEkiKZBfpKBZkxZvr\nluwiE8UqouI94pKdkuyXZbeseGTZJ9ERjCIonmyXIsi0iZH9slB+vCE2hKpeCNLRAwaTHk2M\nYX+aH11sk+J0VKKeeuh0vSQD1dYGCz52MNbCjg06/KpIEshyYIVYdnvEqjoARXZ5FAWwmg94\nZXn25viLy0GWZbcXZBmrVbpzz0A01fTxen9xuSLJstsLoog1qoglV2AVX/3Um0JZVUsXiMKR\nt12LddoQ+l5URkdGNH7UPtctoinaGuUvKA0uVETJ+dNvbLw1hL7XNlChXB1oaxQ/LNX+3a8Q\nlPoMq1X6c6YycVahvFqqa0QqDqt4zHN0bHTzg1lUhFTXhNQ8VquwmqejIiijHgBiX3gg5Feg\nOX1cyPKBQpEU2SEBAGKRIiq+Qg/IQBlpkBTPIZciKbJDVERFdkszq05PFmIUUUEdUuYQw45A\n6GNceRtWrv4l36FLO+Pyq89JP/8fn5+z+Nc3/nHX1MxXrn7yxX/OH2k48TJZ+fKKQtygG2yQ\nHN+xMh1t5oanAUJtVs4orD1zEp+Z7tlzSKisxTyLWAapVLTZGHHtRXR0hKIoYk09Yhms4rFa\nRUUY1OOzAKGEZU9KjXbEc80JrI76rETecnVIUdmkODYprpfGDYqkIASKDGKtX/bKtIkBAF+h\nJ3DDBQDZJcmCQkcyCCPvYZfslmgzi2jk3u8AANkpKaKiiIrsEOkoFlHIk+NSvDIVQQNC3oNO\nRQGpSQAAyS6BrNAmBigk1vkBANEIKKT4ep7fM4DU1D79Wu9C6WgmjhOqfJin2CSeS1L5K3yY\nx5SOwnoaq6hPv/x0f8PBD0wzutfuYNQjADAeztaUVsoeb6MkgywjFa8/bzpimabV3/gLy0CS\nFFmR3V5Kr4lYcAnWaWpffM9fXBHwagAAzHORdyygzcaqf73abjHbOO98Js5S+8K77XoUKmvY\nhBjbuh/alTet+opNiQ+26gBAkWTnzzvYtMSO+i5W1YY8wYN1mhAr6AhpJo3hMlJdv+0Ra+oR\nx2KWRWqeNpuMV86hLZGKIIrV9YhjsFqFVTwVadJMmYAoHP/6v8T6JsxzWM0jNY/VakRhALA8\ncFPI+VSNHh6y/HiQXZIiKIqsYA4DgL/cK7tkOpIBBL5ctyIpkkOCgHYLMh3JIgzeI27ZLVFm\nBtHIc8AJCkhNIihK4HGLjmQAwJfnlmwiE80BgCfHqQiKHJTeGvMYKNRcggDQ0TAER5kDMwBm\nSE0CHdkhQLHT6dy0aROEQhTFTz75JD4+/vgzeR8TWZYLCwtTUlJIXydEX9etXJleUJB36NDy\nq0P/mvYi/TyHZWVl8+bNC5lIpwfZe5SyzxdMn78iXzFFa501Tz/y7LWrfv7w0njrGbe/u+va\nPz989O45ma+e98+XHls6ObrLu1Jy6foHbrjvvc2F/siRc+99/fW/ndLRs6grdY4H7ZmnuXfs\nFStrgaKwRoVoio40ma69iImzyC6PWFGNOBapeETTdFSEdvopiGHiXvqHWF0HDIM1KswylMkQ\nOLT/VwFyWw8TyAoASHZJqBWwmkKYkwWD3CAqkgTgDFhUtIXFPOU54FQkhTYzmMeeA04AABkk\ne7MdQ+lpxCJvnlu2S3Q0gzjsPehSBBkAZI8s+2QAoI00YrA31yU7JdrMIhX2HHAG6rTYQ5SO\nRjwWa5uNLcQg2XO8xtZfITa2rk0qogJiCO/43gJrKDaO95d6EIfZRBWbyAvlXsxRSIUpPY05\njLUUG8+zibxQ4UM85hJ5NpH3l/qwGmMdjVUYqynKQFN6uuMSQjDrd/2Q48hBVDfssMGqR67t\nf4YwvCpquCFJtv993668AX3BZaR4D+W1kdDrs3/9Mz8sraOLgvfgkY7WGADITjeEjJERbeaG\nprR7AAOE1JPH8cPTXVt2izX1gDFW8YhjAgYZExMtNTnE6jpEU1ijBpqio836s6cijqWfukes\nqUc0jdQqzDGU2RTIGhz/xqMhp8J4xXmtErokRVBkh4xVQBlMkkutAABNg4Q8+1wAIDnFgC4o\ngkIZaKzC3nyP1CQwFg7x2HvQqQgKAMheSfbKAECbGMRi72GXIiqUgcYc9ua5JZsIACArgVsE\nHcEgBnsPu2S/TBsZxKKAb2iwkIjDmEGSs9nYQhRSjlslfQWhnRnadK1AyK/yr6CLi4tnz559\nnJIRTjamAKQDFBQUPP744wMtS+/z4YcfdvJpd9LvNK3626IV+TD56T9/vD897+kZ4x5csej2\nc2b+b74RAJnGLnz5p8tv+eKZexeM+s/8dQcePaVLbeY+d+lla4a9t7XyEnPeW/Nnzbk1MW/F\nXEP36xwXtNmonnq955BLdkuMhaO0lDffXf2yQxHtAMkAyYpfDvzGe7911S3PwXqaUlOeHAFA\nkJ12RVQASgGAMjBYhb05LgCgDDTmKU+OEwBkh6QcdRundDTWUIGNP8RizOMWW60NCKB3zZ7c\n0AnUJYcIR6OlBZbferXXDrQEDMSI0lCSo3XsiEZYSzFWjk3kffkeSk8xFo5N4v1FHqymEIex\nmsI8Rjxm43g2kRfKfYCBTVaxCbxQ5kUcxioKqzFiMaWnKT2NmG4veqlG9UPUlUGrRyEDUCtu\nD6JC2KeUyRBy7ZnPzFCNy7R//bNklwAAUSKAAsikGjeNSYyjE0qk+kbZTzMRGGgAOoofP41N\nNuvPEzwHbYAR5lhQKIXTgiFdcvOmRXe6/6gFmaEjGdrMCdXYc4R37/Uw6dcz6QAAWE9RasqT\n46p9z0sZKrB6ore8ObWMZBdBUhpWH6bNLFZjzwFKURTaLGC17DnQHE+7ZV2KNjNYRXkOOBVF\nOVrf2U6FsQpjngo83iAKIbYPH6KC+StHBcUnSy2fKNC3uo8Aczhg3mEeUxGMUOEDAEpHAwWU\nlq6srawQq8aopoe49L///e9fNbtr165ly5bNmjUrNTW1z2RvpqCgYNNv8PHtAAAfXklEQVSm\nTaSvE6Uv9R9/WHftqpowwT2uzx0U+n8Ob7jhhgkT/jLK63nnnRcfH2LDMRTfLtCct9w9+13b\n99frAWzvzjYu2aRZ8K3zg3PaVPOXbtrhmTV1aFea/O3ehMk77q/YfKsVAODn22LOLnq6Zv1C\nQ3frHIsLL7zwq6++evPNN2+6KcSWR+Oa6sKFB7rT3uCixdhCgNVU8AZKACaaZRN5b54bc5iJ\n59k4zl/qBQDMU4jHmEVYTdHRLBvP+wo9mMdMLMfG8/4SL2Cg9DQAUEYaEGJjOTaB95d6EYuZ\naJaOZIRaP6WlEYO6e/IgfJg+fXpOTk5VVdWxqzYzaPVIqHbmX7zJXw4IY8TJmJPEBg3WaRBF\nyS6PIkmKBJQKsBr8VRSiaTqCAUby5frbGUCUlsJaSqjyAwDmMVZhsbHDww9GmEUt60CI7oXV\nppMQxOGAOwRiEKWjxYbWNfXAQxRj4dh4zlfgQRxm4zgmjg/oPqWlEI0Qj7GKYqwsG89789yU\ngWaiOSaO8xd5MI+RikIUonQUIGCTVHQkI9YJAMBEs1hDgQLBSZ870SN66dKlfzUAk8m0bNmy\npUuXXn755b03LaFZvXr1pk2bSF8nUF9XXHHFZ/fdN/jGtWnTptmzZ/dSXzzPA7idTieAHsDl\nch0tawubMKuraZBKtm0rS5s25Wg0pVNOmej/7Pe9sPCMbtY5TvwlvZObvKsErca13FiDwSpM\nm1l/mRcA6CiWjeV8xc0bHJSRCbjhMbEcm8B7D7uwimKsHBPL+Yo8mEGBNrGaAgDGyrEJvDfX\nhXmKiWGZWN5f5AmsY1F6OhCDn0vkmRjOV+TBKoqOZOgIRqjxAwBlpBFCiMO4txMiMbGtCXHZ\n+BDHSgY7g1aPGj6ud+/rsOTZ2OLyQQGA1BoAW2zePeyA5JSa9wcBZK/cbgOxGVmRgwLdnShW\nXasBihFW4Y4PUZSBZmI4b46L0lJ0FNus1yyGo0vaAMBYOSaO8+V7EAI2ScXEsN5sF2WgAR9V\nbQyMhWMTOF+eByjEJvJMDOvL9wRuC5jHmMcAwCapaDPjL/ZgHU2bGKyjAgcdqD6LyUzpglru\n8no6OTxBIPQdk+ddlfj2f7a/cv/KGY+N3vvwK9sBkubNO+04WqyqqoKoqJbgS2qzma+vrZUB\ncPfqtCBJ0jfffOP1tjfUKioqAECWQ298RC6Oc/1h9+x3UnqKMjKBGytmMTpq0yAMbALPWDnv\nYRcAMLEcE8f78txw9Lk2UI2N5ZjY5nImhmPiOV+uGwACO4mtdeI4X64bKMTG87SZ8RV4sAYj\nprkCbWKwCiMOK4LSg83EnhFsbHEpJ2R68hOHQatHjIULWd4Tgh9+Qq3GIQphdfNWfmDzPXAo\nJxjazDBWzpvjwjqKNjGMlfNkOxGNKV3r8jATyzExnC//qM7Gcr785ocorMYBiypgYAUe/5ho\nlrayLY+CCKPAmjQdxTIxzeXND2NH67QYUrSFZaJZf6kPABgLSxlpodoPR7doAaBlhbsv0E4y\nhiwPdj/oO5PueAhHmQiEwQJ35rNfvVl/5T0rrhm5AgDUw65889Nnpx/vvZwJSg6KMaYYpuMv\nTVfqAADATz/9dOGFF/5VV4WFoZJSA1AGOvWjkV0VuDvopppClgevVKnHhvbr6jerjtC/DFo9\nirjKChg8B50QWFWysv6itqYhAjaRp6PY5oefaJaJaTWAmqtgYON5OooNGFt0NEtHse28xLCW\noiMYrKGESh8A0JEMVjevNgV6oQwDbwyox4c+mhJ86pM8RHWFgf8uCYTBjHrUTR/vvezRP3Yf\nsRmGjB+X0eFgejexWq1QX18PEFhI8NTXuy0plu7XaeHMM89ct25dx5WG5cuXr1+//uabbz4+\neQmE3mDw6lHElV1KqKw7o/WB568MIDax9eGHiQ49Q8GGUXiuNhGOH/K9Egh9DRs5dNLZXfLo\nPjYJp50Wf99vvzXBMCMAyDt/362btnBs9+u0QFHUBRdc0LF8x44d69evD16wIBAGFKJHBEKX\n6MzJV6VStfzta0hfpK+Tra+egU67/oYRPz997+eFDlvhNw/c/T69+KZz2O7XIRBOZogeEQYz\nyl8jiuKGDRtEUeykTm9B+iJ9nWx99Rh/wZo7ZqQbed6YMuXmFdne5uLsf42AuDs2d1qnG9xz\nzz0AUFJS0mtyEwiKoijKtGnTLBbLQEtB9IhwYtOJHiGlO+GMCQTCScK99977wgsvfP311xZL\nq1uRIAgffPBBUlJSP2QBOSayLOfl5aWnpw+4MOEjSVgJI8tycXHxwoUL221ELl26tLy8vDtx\n7E5giB6diJKElTA90CPiY0cgEEJAURQAzJkzZ6AFIZzYhIyBHxfXa9l7wxyiR4ReoVt6RAw7\nAoEQgltvvdVsNrdb0d+3b9+qVaumTJmSlJQ0UIK1UFxcvGXLlnAQJnwkCSthApLMnz9/1KhR\n7T4aPXr0gIjU/xA9OhElCStheqJH/bklTCAQTmg+++wzAPjss88GWhBFCSdhwkcSJZyECR9J\nwo2wmpnwESZ8JFHCSZgeSDLwO9kEAoFAIBAIhF6BGHYEAoFAIBAIgwRi2BEIBAKBQCAMEohh\nRyAQCAQCgTBIIIYdgUAgEAgEwiCBGHYEAqGrhFXKtfARJnwkgXASJnwkCTfCambCR5jwkQTC\nSZieSNL2kKxUtPbvZw2LVvG6mGEz71id62vzqX/lxVTrleYbfzquM7yd96U0bnl2blaUmtcn\nT75heU9SuXRE/uFmy7TXKjuU9+64Ou+rl8fVaWu9Nq7OZT4RRxREP31Tg4OwSrkWPsKEjyRK\nOAkTPpKEG2E1M+EjTPhIooSTMD2QpK1hV/nfM9Xpiz8+UOtoylm9eAg37tnDwR/nPj563LMF\nvSLqsfpyfHV1ZNQFr+2saijd9MBEdda/Dhxfb4KtZNf//j5JC6F+wnt1XJ331bvjOkZrvTSu\nzns5EUd0lP77pggEAoFA6BfaGHbCZ1fgU549mqw4+18j4Jx3moI+37DIfPnnvWS/dt6Xa9Ul\nTMY/9gfeeD67hEt98I/j6a3w6fHNqzyhDLveHFfnffXuuI7VWu+Mq/NeTsQRHaX/vikCgUAg\nEPqHNj529OWfSjv+ngAAAGL+9z8WxA4fbmj9uDY/X8h55Yw0s9aYMOHKZzfXdmPHtwOd93Vo\n714YOzYr8IYfOTK98PBh4Th6S75/l6Iota9NC/Vhr46r8756d1zHaK2XxtV5LyfiiI7Sf98U\ngUAgEAj9Q8jDE968L+6fMfkhxy0fPjQ5qLigoAC00x//tbTq4MdX2Z87/5r3y49fgNB92Ww2\nvaHFztPpdIrD4Tz+zkLTJ+MKTe+O6xit9dK4Ou/lRBxRL0hCIBAIBEJYgnOeGIMQQghZb/sZ\nAMC2++VLs8bfvnvKf//47flZpuC6pz5Xatv26Iw4tTZuyj3P3Rizad3Pnm511vW+TCaTx9PS\nuMvlwiaToX1z3eqrE3p9XH9N747rGK0d97i6IvPxj6jrffXWiHpBEgKBQCAQwhI87J97Apuy\nVa9PByn73+fMeJ2+b/OhjU9dlMZ1dqHfLzA6XadVOtD1vtIyM8X9+wsCb3wHD+aPGju2e5FZ\n2vTVDY57XJ3Su+PqTms9GVdXZD7+EXW9r7b0fES9LQmBQCAQCGFDsMOdZ+01uhH/2CeEdscr\neXkKO+rW9YfrXY6KLc+dZbYu2uDsuXNf530p9v9dYUy4etURm61w3R2j9ae/VNjzrlqofW1a\nCDf53h1X53318rg6ba3XxtW5zCfiiNrQL98UgUAgEAj9QhvDbsffU9qZfWcva1SUjYsNcOnH\nguLP/fiOszItWl4XM+yMJe/sdRxPx8foS1FqfnxsznAzzxnSpt/5RXGvnIRs9xPeJ+M6Rl+9\nPa5QrfX+uDrr5cQcURD99E0RCAQCgdAPIEVR+mtxkEAgEAgEAoHQhxC3IQKBQCAQCIRBAjHs\nCAQCgUAgEAYJxLAjEAhdQi5d/3/nZEZpVIakU6577Xf7gMjw063Rw/55YKClksq//ccFo6xa\nlT5u2PSly/Y5BkoYufjL/zt3hFXDq81J4y7+x1cl8kBJEoz7x1tS0PTX61qkDIP/nPAhHGaD\n6FFbBp0eDbSTH4FAOCE48sxELvXalYdtnrrdL51lirxmbdOxL+o9JHfNka2rbh2vhpZUbwMm\nVcGLk/mkee8faHA1Hlq9aCiOXfS1Y0CEKX7tdDbukje3l9mcVb+/Nieanf56sTIgkgTh3nJX\nGgKY9lptc8EA/+eEGUSPWiB61BnHo0fEsCMQCF1g+z3xaMrrR08P/3SrlZ3zfj/+Igkr5h59\nGA3+QRoQqY48ORJOe6W8+V3jsrNBc+3X8gAIY3/vPBj+0MHmd/LnV3GGxd8qyoB+Wb6dfx8e\nPWFCSusP0gD/54QZRI9aIHrUCcenR2QrlkAgHJuSbdvK0qZMsTa/PeWUif7ff9/bf/3T16xV\nFEUpfen0gZfq8JFcU1ZWbPM7tU5HYYoaCGF0i75WDj2WCQCCrWjrq+/8wE2fPhYG8ssS/3xi\nyQfpz710aURL0UD/54QXAz0bRI9CMPj0iO5zCQkEwolPVVUVREVFHX2rNpv5+tpaeYDddAdG\nqvM/8DQcfS0Vv/PO98mL1p2BYP9ATVHTO7NMN/wAoBp71zfnWGDgvizp4LNL3rA8s+c660ev\nthSG53/OQBGes0H0CGBQ6dFJqVsEAqEHMAzT8hpjTDFMONw/Bk4qd+7aB8+a9Vbcf759bppq\nAIUxLtkkOCv3fn49vDnnmneqB0oSOfeFJS8aH3vr+vgOH4Xnf85AEZ6zQfRoEOlROPxDEQiE\ncMdqtUJ9ff3Rt576erfFYhlIiQAGUCqx7JuHzsqY+qRjyVc7PrhmCDugwgAArbGOuuSF++b4\nfti4TRkYSQrfXPos9eDbN7dPKRSe/zkDRXjOBtGjZgaLHhHDjkAgHJuE006Lz/ntt6bAO3nn\n77t106aNHViZBkyqylXzJs7/LuvtXdtfu2qYauCE+eHmKP6Kz4SjbwW/X4m2WtHATMvuzZvr\nt96TjhFCaMgDu+GXv0WhYY8cCNP/nIEiPGeD6NFg06O+PttBIBAGBQceHcUNXbymwN5U8PV9\n47XJd2729L8QpS+d3jZMwwBIJf58m9Vw8af1HT/pb2Gc3y+NVY+7d112rdNRufODq9LYUQ/v\n9g+EJO0ofO7UoDANYfGfEzaExWwQPQpm8OkRMewIBEKX8BesuWNGupHnjSlTbl6R7R0IGTr8\nIA2AVAceyerwgHzp6oERxv7nOzdOHxanV2kt6ade+cSGUqH5g4H9str+IIXFf074EA6zQfSo\nLYNNj5CiKL23iEggEAgEAoFAGDCIjx2BQCAQCATCIIEYdgQCgUAgEAiDBGLYEQgEAoFAIAwS\niGFHIBAIBAKBMEg4aQw715HPH1swIyveqGJ5fUzmmQue3lAiHl+Tnj9evWhkjJY59wNn8Os1\nlyF0zjtNf33dMSsMIN4PzkfGJd+F/rDTOcx5YgxC53zg7C9JCQQCgUAgdODkMOxsv943dfy1\nn4gXv7DxSIO9Jvv7p8+sfHXOKVesKD6eI8G/vPHwl3jJb85vFmqDX1+2RlG+W2L86+uOWaFz\nmt6aheh5a3p6eU/pmzkkEAgEAoHQi9ADLUA/4PnunnnPlZ275uDKSwMZdONGzn14ra5qxMzb\n7lp9/v+uMPWwXafTCfGpaRxq93ow0ldzSCAQCAQCoRc5CVbsHJ+/vrxy6A0PNFskzahn3HjN\nEPtXn23wAgDYdr9106xh0RpeFzNs8rwnvyv2t1QUCtf+49JxCQae18WNOvf2t3c2AQCUPT8J\nXf6pBN9cr0ZZWVktr89psxXb9Ptri6elm1W8Pmb4zBvf2xPYpwyqELrxQB1+/itf/N+Fo2O1\nHG9MnrJ0Ra4IADmPZJlu/gGkTy9HxiWb2o3UtvvtW87KjNHzKmPM8DOue/7XxmO0BgAgl333\nz4tGx2g4lSl1ypJl+909nUMCgUAgEAgDTj9ETx5gfvlbFPDXrZP+soKU8/xkjX7ivWsPVjtd\nVX8sXzSUjrr8k0pFURSlZs1VsfpJ93x5oNZpr9i7+q6xWtXpLx0OXLf6SgrmrhDav159KcDZ\nyxoV6dC/T1XrJz+4sdDuajjw0dWpKHbJRldQhc4aX30pANJNuGdtdoPLVrjulqGYOffdOkVR\nFKXxzZlAXbm6wzCcX1wdwQy/8fPsepezbPuL55kh8taf5GO0Jmc/OZ4xTr7/q5w6e93B1beO\n4QEMi7/t9hwqSvbjowHOft/R6XdBIBAIBAKhLzkZVuwcDjCYTH85UvH7557YlnTvB8/NzYzW\nqC1jr33z2Xn+1U+/kwcA+19/8GP9Xcuev3BEpEYXM+qyF15eFLH13Y8Pd6Fb8fsXntoRe9vr\nT8xK1qlNI65+6b5plZ+v2Sa31jhW4+m3vvXs3GEmtT75gtvnZwp79hzstMNdGzY0TLvjyUuG\nRag1cZPuXDqbrcvPt7d8HLI1/7fPPrd7xP99+PT5GWadOfOyl19bHN+TOSQQCAQCgRAOnAQ+\ndiaTCeqrqyUAKuTnh3fsaIqaNi2zpYAbPXoYrM7JATBt23YEch4diR4NvsBYVgaQcaxuj/z+\nu001afLYo153UTf+JN8YXKH+WI0PGz78qCWl0WjA7Q65S9rCtDdrFcVbl7dne3FZWcmBT3f4\nYWhQvriQreXt2tUUefpp6Ucr0ePGj4KPOrZ9rDkkEAgEAoEQDpwEazCjJkxgxD1/7m9/eLPu\n7RkUddbbdRhjYFk26BO32w0REREAgiAAzFzW2HaVs3HZzC52zbRpth3HbBzj7nw7SvmX95wR\nb06fvfiBl1Z8d4iyxDFtPg/Zmt/vB4SCTnzIYsgYMMeaw27ISSAQCAQCoc84CQw77YWLr4zK\nefPptbbgUnHff978RTXrqgsi07Ky+Mrffitp+aj211+z+fHjMwEso0ZFw5/bt7ccDqh5+yyV\n9orPPF3oNm3ECN7+558FR9+XvzoZx9yxudU0Op7GO6JsemzBi2UXf11auPvn9Z+++9ytk43S\nsa8aOnIkV7tjR4uQvt927A1V71hz2CORCQQCgUAg9DIngWEH2rkvfLgk8otFs299f3N+g1fw\n1uV89/zlFz5ZOffNZYtigL3w/+7L2v3kwse/z6t3Nhb++vLiR35OufOh+UYANPX2f5whrLz3\nto/3Vjuain98bsHDv2T8/f65qi70ys39v7sz975wy/Oby+zO6p2v3vT07hE3LJzcujrW08Z5\nlQrkuuradnYbx3HgayirtPs8tTkbX1n0+EYAj9sldxplTn3pg3dn7v334sc25tU76g9+cvu9\nnzaE/Jc4xhwSCAQCgUAIB04Gww4g+txlO3a8PK3qrQWnxuvVEUPPfvBH690b/1h9bSIAADXq\n4a/X32pet3hcrCl23IJV3I1fbnziFAYAAKXf/tUvr0wpeHhGcmTc+BvWmu/ZsOHhcVyXOqXH\nPPr12kXs8sszoiwjrlipuunLr/81NthFrYeN82ctXJSx++5oc5twJ+iMRz57atKh+yZEG2NP\nWfB23byVH980dPe9Y+/8uXMhxz72zRdLNJ/MHxljzbx8heGRZ678C8Oy8zkkEAgEAoEQDvTf\nAVwCgUDoXTbeaOjk7jbiX9l91XHta9MANAu+VRRFEVbMBYDxzxX2VWeKoihK6f/unDUsWstg\nfsFXPW6kf0QlnGgQPeoe4a5HJ8eKHYFACHPch5bfNntopIrTxo655OENZW1dDXzb70gfct9v\n/r+4+CRgz1t/f3lTTo1T6Ny7orusmYcQmvBMXm+2SRhAiB51zsmhRydBuBMCgRDu1H++eMaC\nT6oBAKBy7xePz9nf+OOe187QBD5VDr9y15pTnz006S9Omc9537N+Id9PonaEvmatck1fd9LU\n1AQQed0XBe9dpOt50KF+EZUwYBA9OhYnhx6RFTsCgTDQHPnvo5/UZ1z77s5yp7spf9NT50Tk\nvfHEyqNhdGpX3fsi/9DTF3e2W9Q5Us3m124+e1S8QaUyxGaddeOLP1aI3aogln33xPzJaWaN\nyhA39qJ/fl0cvBAifnQRQmjC80XBby9bA84/3r111vBoDa+JGnrGkjd22oOukWs2v7BoarpZ\no4lMPX3BC1tqGt+ahZD1ti0hpHe+cw5CZ/6nHqBu+cV6ml+4HgBg7TU8Qud8YK/a9MhFWRZ1\n5j/3ADS9NQshNOX1quDL11/DIzTmibz2ola9PAWhyz8FgN0PDEEo64mcnkwtIYwgekT0CADI\nih2BQBh4jhw5AqOfeOz6CbEAkDrzgQcvf/m77/ILASIBPJse+kfhDV/c0ONTOkrJqqumXru6\npDnrS+XBjW/fs+nzb/+z9dubM+guVaj5csHki1eVBvZu3Hu+fPL/27vToKauKADA5wVCSICC\nIgEEWY1WQbFqFbStUwWlUJRhESqidaxQB+u4oGjdWNx1tGrL6LSASm1tQbDKuFBQUUHpjFDU\nAhqVLSIgBUISCGG5/REHEkpCDLYkcr5/ee+8+/Iyc95NXu4917e03N9I+Vklhbvcl27Nl1YV\nb+HeSoiYxzd9lBJkBgBQfzls5vyEJ9I+ryzvdKR7wePgd9S4NnHe1x6nvnsoUaFkOnr7YR5h\nHkkN9iA/hNCQV7rTGXSlTxpa+U+z9niaAc39eB0hpONB7ATrZVcVrUEsHfTtndSquO2K4+4G\nAPqcwCO/l9YJWxvLcr9f5sQA0PvoKE+lgJbLyy0BYNiMyNSiGpGwrvjy3k+sdAAUDfqWvqTR\ndCw84y79VStsbXp8Lny8DoCOx4mGVw1aAFAWnnFXSupFzS8eXIidY0EDADCPuKXoMq5HmAKM\n3VLYvSE9hAFAUXTr+QcyS2tbugiRLiQNMPPYC9kjL4YwAFziuH28VUJSggBgyh6u4s8PaQ/M\nI8wjQggh+MUOITToXv4SyJb9wUmzX3lNQAipPuFh7pVYrfA4JbP5HKMKCSGkaBMHQG/awUed\nPUd1lca4UADTD1epEMA/46MPYOCd/LJnv+Te5jH9dEjAWZsr7j5AcMaHDjBqbT4hRHDWVx+A\nvfSysKdB8e01dup0SGC3+mZbT5DWd0hoYDCPMI8IwVmxCCENMGJhUlbiytmjhzPoLPOJC7Zc\nvHH0Y0NoztgcW7/q4NIBlMCuzMrmAkwNXjRG5l5HjV3gMxagsKCA9BtA7t7IEYOhX9hnMgus\n0CcvDhqn/MQOgaEzeopSGnI4lgB8Ph8ACnJzxWDsFTjXoCeaMXORnzr/kbHnB3yoZN1CNMRg\nHmEeAeDkCYSQRjCYsCw+m/u3WCKqKTq/08tGFzoKdm3I8Tu8fhwN2rip672c2CwGy9zJJyq9\nrF3+2D7/QnqydxIAVFRUAEDeupGUnEk7SwEkDQ2ifgMay8ubAewcHeWn0NnZ2Sm/HjZb7tEJ\nnU4H6OzsBGjn8V4C2Dk6yt98bW1t1fjULC3776wJeaOFHZBGwzzCPMLJEwghzVQWv/b0+N0P\nZzGhOSvCPTBBuppza3HGfv+i2t+KTvoMU6WVjo4OxTslEkm/AdL9NFqv38AikUj5iXV0FBRT\nkLS3w6sOSlZbW5vyBtUlEQiGcNkyhHn0ZmhVHuETO4SQ5mk8t3FvW9T+YFOA50kxCTybgG9z\nKwUtzeW3D/ta807FJVWo1g6bzQagvBKF/34UQUhm2PB+A4aZm9MByrlc+X7r2bNnal4Zy9hY\nF4DH48lt7Swp4arZoAyKoqD3owX13ynSfphH6tD6PMIvdgghTSO5HRN1P+SbCEcKALhcLnBC\nd0TMGGXINLKduWZ7qD08fariXfZdNzcTIHmXrjTJbn0eP4dJUVMPlvUfQHd1m0pB8/mTafye\n3eLMhJ/kOxTVUc7OTgA1F9PuyHRxDWkJ5xvVbFAGy8AAAKqqqrq3kJLUtOKBN4y0EuaRerQ+\nj/CLHUJIs5DHR9amTDuw1VX6LwuHwwFucmx8XpVQLKjMPRKbXAYODg6qtaUzJ3zFaKopdZXf\ntvQ/K5taBDWl2UdDPCKviVkeYSH2KgRYLQr3NgJB+uqA2Esl9WKJsPLm0eAVp/kMdW+eo/0C\nXCioOL4kNP52WWOLsLrg142fhqU2UWq2J4M+Zow9QFXi1n151S1tQt4fJ1cE7CrWUzIsXFdX\nB6CG+6i5a+BnR5oF80hd2p9HfU+WRQihwVF3ysd8dnxFzwb+1WXWcnctq9DzDdJdKtTfIqL8\n7e/3roJKs/RJetqpYkBXefJ8C7nuQv+96OiFDKVlGnrVSiiMcuwu60BEeVtdmHKnY7qGh04E\nsF5zV9FV9F2mwaVXgYWuh7td5Acd2Ybt+dJecZmGwi2OrwKd4v6zld7RYMA86svQyCN8YocQ\n0iAt2du3PfviUJhM0YJ35sZnnV3jOW6EPp1pNs4rMiXnhwUqjfiWYk2LuZ7/8yb/KTYm+noG\nI2xdPMMPZd9L/9yBpmIAZbs45c6Fbf6TrQwZzOEOHyw5lJ21o9d9/7Ww3OJy8hK/8nQ2Z+np\nm9hOD96XmbHcrAnA1NRU/VYBACinjRkXYvyn2Bgz6Ez2eO8NqdeOeRgqmc03aeX+8OkjjXAW\n3dsG82ggtD2PKKJFU3gRQuitVHnY1W7dff+zDSlBg7cKO0JaDvMIAHCMHUII/a+6Li0xoiiT\nWdFXimsFkva2pvL8H1f5R+cT5jzfeUO5N0LoNWAeKfYPSlLPqW2SK8EAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat <- matrix(c(1,1,1,2,2,2,\n",
    "                3,3,4,4,5,5,\n",
    "                3,3,4,4,5,5,\n",
    "                3,3,6,6,7,7,\n",
    "                3,3,6,6,7,7,\n",
    "                3,3,8,8,9,9,\n",
    "                3,3,8,8,9,9),\n",
    "              nrow = 7, byrow = TRUE)\n",
    "mat\n",
    "\n",
    "layout(mat)\n",
    "par(mar = c(2, 2, 0, 2))\n",
    "plot(1:10, 1:10, axes=FALSE, ylab=\"\", xlab=\"\", type=\"n\")\n",
    "text(4,2,\"(a)\",cex=1.8)\n",
    "plot(1:10, 1:10, axes=FALSE, ylab=\"\", xlab=\"\", type=\"n\")\n",
    "text(4,2,\"(b)\",cex=1.8)\n",
    "\n",
    "\n",
    "par(mar = c(5.1, 0, 0.5, 0.5))\n",
    "dotchart(my.data$estimate,xlim=c(-2.5,1),pch=21,\n",
    "         cex=0.60,bg=\"black\",\n",
    "         xlab = \"Coefficient and CI\",\n",
    "         cex.lab=1.25)\n",
    "segments(my.data$estimate-my.data$low, 1:7, my.data$estimate+my.data$up, 1:7)\n",
    "abline(v=0,col=\"red\",lwd=1.5,lty=3)\n",
    "text(x = c(my.data$estimate),\n",
    "     y = c(1,2,3,4,5,6,7),\n",
    "     labels = my.data$terms,pos = 3,cex = 1.5)\n",
    "\n",
    "par(mar = c(4, 4.5, 0.5, 0.5))\n",
    "#bottom, left, top, and right. The default is c(5.1, 4.1, 4.1, 2.1).\n",
    "\n",
    "#group size on females\n",
    "plot((z.gr*sd.gr)+mean.gr,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Number of adults\",ylab=\"%Adult-adult play\", \n",
    "     ylim=c(0,2),xlim=c(0,25),cex.lab=1.5,las=1)\n",
    "lines((newdata.gr.f.aga$z.gr*sd.gr)+mean.gr, predicted.gr.f.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.gr.f.liz$z.gr*sd.gr)+mean.gr, predicted.gr.f.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.gr.f.pla$z.gr*sd.gr)+mean.gr, predicted.gr.f.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.gr.f.ch$z.gr*sd.gr)+mean.gr, predicted.gr.f.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.gr.f.cp$z.gr*sd.gr)+mean.gr, predicted.gr.f.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.gr.f.se$z.gr*sd.gr)+mean.gr, predicted.gr.f.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.gr.f.sn$z.gr*sd.gr)+mean.gr, predicted.gr.f.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "\n",
    "text(20,1.5,labels='\\\\VE',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "legend(0.25,2, legend=c(\"AGA\",\"LIZ\",\"PLA\",\"CH\",\"CP\",\"SE\",\"SN\"),\n",
    "       col=c(1,2,3,4,5,6,7), lty=c(1,2,3,4,5,6,2), cex=0.8,\n",
    "       box.lty=1,lwd=1)\n",
    "#group size and males\n",
    "plot((z.gr*sd.gr)+mean.gr,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Number of adults\",ylab=\"%Adult-adult play\", \n",
    "     ylim=c(0,2),xlim=c(0,25),cex.lab=1.5,las=1)\n",
    "lines((newdata.gr.m.aga$z.gr*sd.gr)+mean.gr, predicted.gr.m.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.gr.m.liz$z.gr*sd.gr)+mean.gr, predicted.gr.m.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.gr.m.pla$z.gr*sd.gr)+mean.gr, predicted.gr.m.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.gr.m.ch$z.gr*sd.gr)+mean.gr, predicted.gr.m.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.gr.m.cp$z.gr*sd.gr)+mean.gr, predicted.gr.m.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.gr.m.se$z.gr*sd.gr)+mean.gr, predicted.gr.m.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.gr.m.sn$z.gr*sd.gr)+mean.gr, predicted.gr.m.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(20,1.5,labels='\\\\MA',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "#IR and males\n",
    "plot((z.ir*sd.ir)+mean.ir,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Immature to adult ratio\",ylab=\"%Adult-adult play\", \n",
    "     ylim=c(0,5),xlim=c(0,1.5),cex.lab=1.5,las=1)\n",
    "lines((newdata.ir.f.aga$z.ir*sd.ir)+mean.ir, predicted.ir.f.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.ir.f.liz$z.ir*sd.ir)+mean.ir, predicted.ir.f.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.ir.f.pla$z.ir*sd.ir)+mean.ir, predicted.ir.f.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.ir.f.ch$z.ir*sd.ir)+mean.ir, predicted.ir.f.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.ir.f.cp$z.ir*sd.ir)+mean.ir, predicted.ir.f.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.ir.f.se$z.ir*sd.ir)+mean.ir, predicted.ir.f.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.ir.f.sn$z.ir*sd.ir)+mean.ir, predicted.ir.f.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "\n",
    "text(1.25,3.85,labels='\\\\VE',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "#ir and females\n",
    "plot((z.ir*sd.ir)+mean.ir,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Immature to adult ratio\",ylab=\"%Adult-adult play\", \n",
    "     ylim=c(0,5),xlim=c(0,1.5),cex.lab=1.5,las=1)\n",
    "lines((newdata.ir.m.aga$z.ir*sd.ir)+mean.ir, predicted.ir.m.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.ir.m.liz$z.ir*sd.ir)+mean.ir, predicted.ir.m.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.ir.m.pla$z.ir*sd.ir)+mean.ir, predicted.ir.m.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.ir.m.ch$z.ir*sd.ir)+mean.ir, predicted.ir.m.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.ir.m.cp$z.ir*sd.ir)+mean.ir, predicted.ir.m.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.ir.m.se$z.ir*sd.ir)+mean.ir, predicted.ir.m.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.ir.m.sn$z.ir*sd.ir)+mean.ir, predicted.ir.m.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(1.25,3.85,labels='\\\\MA',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "plot(((z.fe*sd.fe)+mean.fe)*100,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"%Feeding fruit\",las=1,\n",
    "     ylab = \"%Adult-adult play\",ylim=c(0,2),xlim=c(0,40),cex.lab=1.5)\n",
    "lines(((newdata.fe.f.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.f.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines(((newdata.fe.f.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.f.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines(((newdata.fe.f.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.f.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines(((newdata.fe.f.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.f.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines(((newdata.fe.f.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.f.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines(((newdata.fe.f.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.f.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines(((newdata.fe.f.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.f.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(30,1.5,labels='\\\\VE',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "######PLOTS\n",
    "plot(((z.fe*sd.fe)+mean.fe)*100,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"%Feeding fruit\", las=1,\n",
    "     ylab = \"%Adult-adult play\",ylim=c(0,2),xlim=c(0,40),cex.lab=1.5)\n",
    "lines(((newdata.fe.m.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.m.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines(((newdata.fe.m.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.m.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines(((newdata.fe.m.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.m.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines(((newdata.fe.m.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.m.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines(((newdata.fe.m.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.m.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines(((newdata.fe.m.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.m.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines(((newdata.fe.m.aga$z.fe*sd.fe)+mean.fe)*100, predicted.fe.m.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(30,1.5,labels='\\\\MA',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "91ee78e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generalized linear mixed model fit by maximum likelihood (Laplace\n",
       "  Approximation) [glmerMod]\n",
       " Family: binomial  ( logit )\n",
       "Formula: cb.pt ~ z.ir + z.gr + z.fe + z.tr + z.dens + SEX + z.area + (1 |  \n",
       "    SITE)\n",
       "   Data: data.ptime\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       " 76379.2  76398.3 -38180.6  76361.2       53 \n",
       "\n",
       "Scaled residuals: \n",
       "    Min      1Q  Median      3Q     Max \n",
       "-45.538 -17.707  -8.360   5.812 201.195 \n",
       "\n",
       "Random effects:\n",
       " Groups Name        Variance Std.Dev.\n",
       " SITE   (Intercept) 4.666    2.16    \n",
       "Number of obs: 62, groups:  SITE, 7\n",
       "\n",
       "Fixed effects:\n",
       "             Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -7.393359   1.021475  -7.238 4.56e-13 ***\n",
       "z.ir         0.355127   0.032905  10.792  < 2e-16 ***\n",
       "z.gr         0.633186   0.049748  12.728  < 2e-16 ***\n",
       "z.fe        -0.224840   0.024071  -9.341  < 2e-16 ***\n",
       "z.tr         0.511816   0.004729 108.230  < 2e-16 ***\n",
       "z.dens      -0.852713   1.687035  -0.505    0.613    \n",
       "SEXm        -0.336071   0.009582 -35.073  < 2e-16 ***\n",
       "z.area      -1.077219   1.278625  -0.842    0.400    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "       (Intr) z.ir   z.gr   z.fe   z.tr   z.dens SEXm  \n",
       "z.ir    0.009                                          \n",
       "z.gr    0.007  0.036                                   \n",
       "z.fe    0.001 -0.206 -0.110                            \n",
       "z.tr   -0.005  0.011 -0.237 -0.063                     \n",
       "z.dens  0.555  0.019 -0.041  0.004  0.006              \n",
       "SEXm   -0.005 -0.129 -0.123 -0.067  0.356  0.001       \n",
       "z.area  0.311  0.019 -0.024 -0.002  0.004  0.761  0.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#########\n",
    "#########\n",
    "########\n",
    "#adult immature social play\n",
    "cb.pt<-cbind(data.ptime$ad_im_play,data.ptime$non_ad_im_play)\n",
    "\n",
    "#GR, group size, rest is the same\n",
    "mean.gr<-mean(data.ptime$GR)\n",
    "sd.gr<-sd(data.ptime$GR)\n",
    "z.gr<-(data.ptime$GR-mean.gr)/sd.gr\n",
    "\n",
    "full.ad.im<-glmer(cb.pt~z.ir+z.gr+z.fe+z.tr+z.dens+SEX+z.area+(1|SITE),family=binomial,data=data.ptime)\n",
    "\n",
    "summary(full.ad.im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5f4be662",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prepare predictor dataframes\n",
    "newdata.gr.f.aga <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                                z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.liz <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                                  z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.pla <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                                  z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.ch <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"CH\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.cp <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"CP\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.se <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"SE\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.f.sn <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"SN\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.gr.f.aga <- predict(full.ad.im,newdata=newdata.gr.f.aga,type=\"response\")\n",
    "predicted.gr.f.liz <- predict(full.ad.im,newdata=newdata.gr.f.liz,type=\"response\")\n",
    "predicted.gr.f.pla <- predict(full.ad.im,newdata=newdata.gr.f.pla,type=\"response\")\n",
    "predicted.gr.f.ch <- predict(full.ad.im,newdata=newdata.gr.f.ch,type=\"response\")\n",
    "predicted.gr.f.cp <- predict(full.ad.im,newdata=newdata.gr.f.cp,type=\"response\")\n",
    "predicted.gr.f.se <- predict(full.ad.im,newdata=newdata.gr.f.se,type=\"response\")\n",
    "predicted.gr.f.sn <- predict(full.ad.im,newdata=newdata.gr.f.sn,type=\"response\")\n",
    "\n",
    "######GROUP SIZE OMALES\n",
    "newdata.gr.m.aga <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.liz <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.pla <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.ch <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CH\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.cp <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CP\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.se <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SE\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.gr.m.sn <- data.frame(z.gr = seq(-2.1,3.47,0.1),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SN\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.gr.m.aga <- predict(full.ad.im,newdata=newdata.gr.m.aga,type=\"response\")\n",
    "predicted.gr.m.liz <- predict(full.ad.im,newdata=newdata.gr.m.liz,type=\"response\")\n",
    "predicted.gr.m.pla <- predict(full.ad.im,newdata=newdata.gr.m.pla,type=\"response\")\n",
    "predicted.gr.m.ch <- predict(full.ad.im,newdata=newdata.gr.m.ch,type=\"response\")\n",
    "predicted.gr.m.cp <- predict(full.ad.im,newdata=newdata.gr.m.cp,type=\"response\")\n",
    "predicted.gr.m.se <- predict(full.ad.im,newdata=newdata.gr.m.se,type=\"response\")\n",
    "predicted.gr.m.sn <- predict(full.ad.im,newdata=newdata.gr.m.sn,type=\"response\")\n",
    "\n",
    "#FEMALES\n",
    "newdata.ir.f.aga <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"AGA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.liz <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"LIZ\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.pla <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"PLA\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.ch <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CH\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.cp <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CP\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.se <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SE\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.f.sn <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SN\",SEX=\"f\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.ir.f.aga <- predict(full.ad.im,newdata=newdata.ir.f.aga,type=\"response\")\n",
    "predicted.ir.f.liz <- predict(full.ad.im,newdata=newdata.ir.f.liz,type=\"response\")\n",
    "predicted.ir.f.pla <- predict(full.ad.im,newdata=newdata.ir.f.pla,type=\"response\")\n",
    "predicted.ir.f.ch <- predict(full.ad.im,newdata=newdata.ir.f.ch,type=\"response\")\n",
    "predicted.ir.f.cp <- predict(full.ad.im,newdata=newdata.ir.f.cp,type=\"response\")\n",
    "predicted.ir.f.se <- predict(full.ad.im,newdata=newdata.ir.f.se,type=\"response\")\n",
    "predicted.ir.f.sn <- predict(full.ad.im,newdata=newdata.ir.f.sn,type=\"response\")\n",
    "\n",
    "# Create a temporary data frame of hypothetical values\n",
    "newdata.ir.m.aga <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"AGA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.liz <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"LIZ\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.pla <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=seq(-2.08,7.4,0.1),SITE=\"PLA\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.ch <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CH\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.cp <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"CP\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.se <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SE\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.ir.m.sn <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=seq(-2.08,7.4,0.1),SITE=\"SN\",SEX=\"m\",z.tr=mean(z.tr),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.ir.m.aga <- predict(full.ad.im,newdata=newdata.ir.m.aga,type=\"response\")\n",
    "predicted.ir.m.liz <- predict(full.ad.im,newdata=newdata.ir.m.liz,type=\"response\")\n",
    "predicted.ir.m.pla <- predict(full.ad.im,newdata=newdata.ir.m.pla,type=\"response\")\n",
    "predicted.ir.m.ch <- predict(full.ad.im,newdata=newdata.ir.m.ch,type=\"response\")\n",
    "predicted.ir.m.cp <- predict(full.ad.im,newdata=newdata.ir.m.cp,type=\"response\")\n",
    "predicted.ir.m.se <- predict(full.ad.im,newdata=newdata.ir.m.se,type=\"response\")\n",
    "predicted.ir.m.sn <- predict(full.ad.im,newdata=newdata.ir.m.sn,type=\"response\")\n",
    "\n",
    "newdata.tr.f.aga <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"f\",z.tr=seq(-1.71,7, 0.1),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.f.liz <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"f\",z.tr=seq(-1.71,7, 0.1),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.f.pla <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"f\",z.tr=seq(-1.71,7, 0.1),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.f.ch <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CH\",SEX=\"f\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.f.cp <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CP\",SEX=\"f\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.f.se <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SE\",SEX=\"f\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.f.sn <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SN\",SEX=\"f\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "\n",
    "predicted.tr.f.aga <- predict(full.ad.im,newdata=newdata.tr.f.aga,type=\"response\")\n",
    "predicted.tr.f.liz <- predict(full.ad.im,newdata=newdata.tr.f.liz,type=\"response\")\n",
    "predicted.tr.f.pla <- predict(full.ad.im,newdata=newdata.tr.f.pla,type=\"response\")\n",
    "predicted.tr.f.ch <- predict(full.ad.im,newdata=newdata.tr.f.ch,type=\"response\")\n",
    "predicted.tr.f.cp <- predict(full.ad.im,newdata=newdata.tr.f.cp,type=\"response\")\n",
    "predicted.tr.f.se <- predict(full.ad.im,newdata=newdata.tr.f.se,type=\"response\")\n",
    "predicted.tr.f.sn <- predict(full.ad.im,newdata=newdata.tr.f.sn,type=\"response\")\n",
    "\n",
    "newdata.tr.m.aga <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"AGA\",SEX=\"m\",z.tr=seq(-1.71,7, 0.1),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.m.liz <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"LIZ\",SEX=\"m\",z.tr=seq(-1.71,7, 0.1),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.m.pla <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                               z.ir=mean(z.ir),SITE=\"PLA\",SEX=\"m\",z.tr=seq(-1.71,7, 0.1),\n",
    "                               z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.m.ch <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CH\",SEX=\"m\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.m.cp <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"CP\",SEX=\"m\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.m.se <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SE\",SEX=\"m\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "newdata.tr.m.sn <- data.frame(z.gr = mean(z.gr),z.fe=mean(z.fe),\n",
    "                              z.ir=mean(z.ir),SITE=\"SN\",SEX=\"m\",z.tr=seq(-1.71,7, 0.1),\n",
    "                              z.dens=mean(z.dens),z.area=mean(z.area))\n",
    "\n",
    "predicted.tr.m.aga <- predict(full.ad.im,newdata=newdata.tr.m.aga,type=\"response\")\n",
    "predicted.tr.m.liz <- predict(full.ad.im,newdata=newdata.tr.m.liz,type=\"response\")\n",
    "predicted.tr.m.pla <- predict(full.ad.im,newdata=newdata.tr.m.pla,type=\"response\")\n",
    "predicted.tr.m.ch <- predict(full.ad.im,newdata=newdata.tr.m.ch,type=\"response\")\n",
    "predicted.tr.m.cp <- predict(full.ad.im,newdata=newdata.tr.m.cp,type=\"response\")\n",
    "predicted.tr.m.se <- predict(full.ad.im,newdata=newdata.tr.m.se,type=\"response\")\n",
    "predicted.tr.m.sn <- predict(full.ad.im,newdata=newdata.tr.m.sn,type=\"response\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6c283c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 7 × 6 of type dbl</caption>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>4</td><td>4</td><td>5</td><td>5</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>4</td><td>4</td><td>5</td><td>5</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>6</td><td>6</td><td>7</td><td>7</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>6</td><td>6</td><td>7</td><td>7</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>8</td><td>8</td><td>9</td><td>9</td></tr>\n",
       "\t<tr><td>3</td><td>3</td><td>8</td><td>8</td><td>9</td><td>9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 7 × 6 of type dbl\n",
       "\\begin{tabular}{llllll}\n",
       "\t 1 & 1 & 1 & 2 & 2 & 2\\\\\n",
       "\t 3 & 3 & 4 & 4 & 5 & 5\\\\\n",
       "\t 3 & 3 & 4 & 4 & 5 & 5\\\\\n",
       "\t 3 & 3 & 6 & 6 & 7 & 7\\\\\n",
       "\t 3 & 3 & 6 & 6 & 7 & 7\\\\\n",
       "\t 3 & 3 & 8 & 8 & 9 & 9\\\\\n",
       "\t 3 & 3 & 8 & 8 & 9 & 9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 7 × 6 of type dbl\n",
       "\n",
       "| 1 | 1 | 1 | 2 | 2 | 2 |\n",
       "| 3 | 3 | 4 | 4 | 5 | 5 |\n",
       "| 3 | 3 | 4 | 4 | 5 | 5 |\n",
       "| 3 | 3 | 6 | 6 | 7 | 7 |\n",
       "| 3 | 3 | 6 | 6 | 7 | 7 |\n",
       "| 3 | 3 | 8 | 8 | 9 | 9 |\n",
       "| 3 | 3 | 8 | 8 | 9 | 9 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3] [,4] [,5] [,6]\n",
       "[1,] 1    1    1    2    2    2   \n",
       "[2,] 3    3    4    4    5    5   \n",
       "[3,] 3    3    4    4    5    5   \n",
       "[4,] 3    3    6    6    7    7   \n",
       "[5,] 3    3    6    6    7    7   \n",
       "[6,] 3    3    8    8    9    9   \n",
       "[7,] 3    3    8    8    9    9   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g+qAv0IoEwYYwdywRi7cki5E7j3UEhI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MTExMjFLZYpFMgs7Il1cKBhOvMyKTWRIV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\niocNSHZ29rlz57DkTYlN8mnTp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UFYX+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\nLCG8M61FuUypBQReotBUS14pywgJ9KTSZ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Bw7U5luYIGrcVyjjIwiBERlGafjOsoYOWDRbcvIKYKuKlN1TIpIQtC8u+dj7pYnhjyfkpLy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\nF0PECYCSAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Adult immature social play\n",
    "#####ADIM PLOTS##############\n",
    "############################3\n",
    "\n",
    "mat <- matrix(c(1,1,1,2,2,2,\n",
    "                3,3,4,4,5,5,\n",
    "                3,3,4,4,5,5,\n",
    "                3,3,6,6,7,7,\n",
    "                3,3,6,6,7,7,\n",
    "                3,3,8,8,9,9,\n",
    "                3,3,8,8,9,9),\n",
    "              nrow = 7, byrow = TRUE)\n",
    "mat\n",
    "\n",
    "layout(mat)\n",
    "par(mar = c(2, 2, 0, 2))\n",
    "plot(1:10, 1:10, axes=FALSE, ylab=\"\", xlab=\"\", type=\"n\")\n",
    "text(4,2,\"(a)\",cex=1.75)\n",
    "plot(1:10, 1:10, axes=FALSE, ylab=\"\", xlab=\"\", type=\"n\")\n",
    "text(4,2,\"(b)\",cex=1.75)\n",
    "\n",
    "\n",
    "par(mar = c(5.1, 0, 0.5, 0.5))\n",
    "\n",
    "####******************************************************\n",
    "\n",
    "dotchart(my.data$estimate,xlim=c(-2.5,1),pch=21,\n",
    "         cex=0.6,bg=\"black\",xlab = \"Coefficient and CI\",\n",
    "         cex.lab=1.25)\n",
    "segments(my.data$estimate-my.data$low,\n",
    "         1:7, my.data$estimate+my.data$up, 1:7)\n",
    "abline(v=0,col=\"red\",lwd=1.5,lty=3)\n",
    "text(x = c(my.data$estimate),\n",
    "     y = c(1,2,3,4,5,6,7),\n",
    "     labels = my.data$terms,pos = 3,\n",
    "     cex=1.5)\n",
    "\n",
    "par(mar = c(4, 4.5, 0.5, 0.5))\n",
    "\n",
    "\n",
    "#group size on females\n",
    "plot((z.gr*sd.gr)+mean.gr,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Group size\",ylab=\"%Adult-Imm. play\", \n",
    "     ylim=c(0,2),xlim=c(0,30),cex.lab=1.5,las=1)\n",
    "lines((newdata.gr.f.aga$z.gr*sd.gr)+mean.gr, predicted.gr.f.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.gr.f.liz$z.gr*sd.gr)+mean.gr, predicted.gr.f.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.gr.f.pla$z.gr*sd.gr)+mean.gr, predicted.gr.f.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.gr.f.ch$z.gr*sd.gr)+mean.gr, predicted.gr.f.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.gr.f.cp$z.gr*sd.gr)+mean.gr, predicted.gr.f.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.gr.f.se$z.gr*sd.gr)+mean.gr, predicted.gr.f.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.gr.f.sn$z.gr*sd.gr)+mean.gr, predicted.gr.f.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "\n",
    "text(25,1.5,labels='\\\\VE',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "legend(0.25,1.75, legend=c(\"AGA\",\"LIZ\",\"PLA\",\"CH\",\"CP\",\"SE\",\"SN\"),\n",
    "       col=c(1,2,3,4,5,6,7), lty=c(1,2,3,4,5,6,2), cex=0.8,\n",
    "       box.lty=1,lwd=1)\n",
    "\n",
    "#group size males\n",
    "plot((z.gr*sd.gr)+mean.gr,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Group size\",ylab=\"%Adult-Imm. play\", \n",
    "     ylim=c(0,2),xlim=c(0,30),cex.lab=1.5,las=1)\n",
    "lines((newdata.gr.m.aga$z.gr*sd.gr)+mean.gr, predicted.gr.m.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.gr.m.liz$z.gr*sd.gr)+mean.gr, predicted.gr.m.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.gr.m.pla$z.gr*sd.gr)+mean.gr, predicted.gr.m.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.gr.m.ch$z.gr*sd.gr)+mean.gr, predicted.gr.m.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.gr.m.cp$z.gr*sd.gr)+mean.gr, predicted.gr.m.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.gr.m.se$z.gr*sd.gr)+mean.gr, predicted.gr.m.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.gr.m.sn$z.gr*sd.gr)+mean.gr, predicted.gr.m.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(25,1.5,labels='\\\\MA',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "plot((z.ir*sd.ir)+mean.ir,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Immature to adult ratio\",ylab=\"%Adult-Imm. play\", \n",
    "     ylim=c(0,5),xlim=c(0,1.5),cex.lab=1.5,las=1)\n",
    "lines((newdata.ir.f.aga$z.ir*sd.ir)+mean.ir, predicted.ir.f.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.ir.f.liz$z.ir*sd.ir)+mean.ir, predicted.ir.f.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.ir.f.pla$z.ir*sd.ir)+mean.ir, predicted.ir.f.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.ir.f.ch$z.ir*sd.ir)+mean.ir, predicted.ir.f.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.ir.f.cp$z.ir*sd.ir)+mean.ir, predicted.ir.f.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.ir.f.se$z.ir*sd.ir)+mean.ir, predicted.ir.f.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.ir.f.sn$z.ir*sd.ir)+mean.ir, predicted.ir.f.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "\n",
    "text(1.2,3.85,labels='\\\\VE',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "\n",
    "######\n",
    "plot((z.ir*sd.ir)+mean.ir,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"Immature to adult ratio\",ylab=\"%Adult-Imm. play\", \n",
    "     ylim=c(0,5),xlim=c(0,1.5),cex.lab=1.5,las=1)\n",
    "lines((newdata.ir.m.aga$z.ir*sd.ir)+mean.ir, predicted.ir.m.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines((newdata.ir.m.liz$z.ir*sd.ir)+mean.ir, predicted.ir.m.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines((newdata.ir.m.pla$z.ir*sd.ir)+mean.ir, predicted.ir.m.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines((newdata.ir.m.ch$z.ir*sd.ir)+mean.ir, predicted.ir.m.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines((newdata.ir.m.cp$z.ir*sd.ir)+mean.ir, predicted.ir.m.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines((newdata.ir.m.se$z.ir*sd.ir)+mean.ir, predicted.ir.m.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines((newdata.ir.m.sn$z.ir*sd.ir)+mean.ir, predicted.ir.m.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(1.25,3.85,labels='\\\\MA',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "######PLOTS\n",
    "\n",
    "plot(((z.tr*sd.tr)+mean.tr)*100,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"%Travelling\",las=1, \n",
    "     ylab = \"%Adult-Imm. play\",ylim=c(0,5),xlim=c(0,40),cex.lab=1.5)\n",
    "lines(((newdata.tr.f.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.f.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines(((newdata.tr.f.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.f.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines(((newdata.tr.f.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.f.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines(((newdata.tr.f.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.f.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines(((newdata.tr.f.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.f.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines(((newdata.tr.f.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.f.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines(((newdata.tr.f.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.f.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(30,3.85,labels='\\\\VE',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)\n",
    "\n",
    "######PLOTS\n",
    "plot(((z.tr*sd.tr)+mean.tr)*100,data.ptime$X.playim*100, pch = 16,\n",
    "     col=\"white\",xlab = \"%Travelling\",las=1, \n",
    "     ylab = \"%Adult-Imm. play\",ylim=c(0,5),xlim=c(0,40),cex.lab=1.5)\n",
    "lines(((newdata.tr.m.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.m.aga*100,\n",
    "      lwd=2,col=1,lty=1,type=\"l\",pch=1)\n",
    "lines(((newdata.tr.m.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.m.liz*100,\n",
    "      lwd=2,col=2,lty=2,type=\"l\",pch=2)\n",
    "lines(((newdata.tr.m.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.m.pla*100,\n",
    "      lwd=2,col=3,lty=3,type=\"l\",pch=3)\n",
    "lines(((newdata.tr.m.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.m.ch*100,\n",
    "      lwd=2,col=4,lty=4,type=\"l\",pch=4)\n",
    "lines(((newdata.tr.m.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.m.cp*100,\n",
    "      lwd=2,col=5,lty=5,type=\"l\",pch=5)\n",
    "lines(((newdata.tr.m.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.m.se*100,\n",
    "      lwd=2,col=6,lty=6,type=\"l\",pch=6)\n",
    "lines(((newdata.tr.m.aga$z.tr*sd.tr)+mean.tr)*100, predicted.tr.m.sn*100,\n",
    "      lwd=2,col=7,lty=2,type=\"o\",pch=18,cex=0.5)\n",
    "text(30,3.85,labels='\\\\MA',\n",
    "     vfont=c(\"sans serif\",\"bold\"),cex=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "737836af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne Sample t-test\n",
       "\n",
       "data:  pref.data$ADULT.PREF\n",
       "t = -0.73111, df = 56, p-value = 0.4678\n",
       "alternative hypothesis: true mean is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4605658  0.2142746\n",
       "sample estimates:\n",
       " mean of x \n",
       "-0.1231456 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWelch Two Sample t-test\n",
       "\n",
       "data:  ADULT.PREF by SEX\n",
       "t = -0.78044, df = 46.333, p-value = 0.4391\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.9469085  0.4177155\n",
       "sample estimates:\n",
       "mean in group f mean in group m \n",
       "    -0.22062853      0.04396798 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5o3b650CgBAhll3sbNpsyJWt0LpFDAn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\npwMCrletavjwYfq0abVMTF76xRw71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Cgtrr6+fzW/PsWeYPP5wyN9f/5pumv//+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\n/5CixrRktAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "################\n",
    "################preferences\n",
    "#################\n",
    "\n",
    "\n",
    "setwd(\"~/Documents/R scripts and data/PLAY\")\n",
    "pref.data<-read.csv(\"playprefCORR.csv\",header=TRUE)\n",
    "\n",
    "boxplot(ADULT.PREF ~ SEX, data = pref.data,\n",
    "        xlab = \"Sex\", ylab = \"Adult play pref. index\",\n",
    "        varwidth = TRUE,\n",
    "        col = \"lightgray\",outline=F,las=1,\n",
    "        cex.lab=1.5,medcol=\"red\",ylim=c(-2,3.1))\n",
    "#abline(h=0,lty=4,lwd=3,col=\"black\")\n",
    "positive<-subset(pref.data, ADULT.PREF>0)\n",
    "negative<-subset(pref.data, ADULT.PREF<0)\n",
    "stripchart(ADULT.PREF ~ SEX, data = positive,\n",
    "           vertical = TRUE, \n",
    "           method = \"jitter\", \n",
    "           add = TRUE, pch = 17, col = 'black',\n",
    "           cex=1.1)\n",
    "stripchart(ADULT.PREF ~ SEX, data = negative,\n",
    "           vertical = TRUE, \n",
    "           method = \"jitter\", \n",
    "           add = TRUE, pch = 6, col = 'dark blue',\n",
    "           cex=1.1)\n",
    "means <- tapply(pref.data$ADULT.PREF,pref.data$SEX,mean)\n",
    "points(c(-0.22,-0.043),pch=3,\n",
    "       cex=4)\n",
    "\n",
    "t.test(pref.data$ADULT.PREF)\n",
    "t.test(ADULT.PREF~SEX,data=pref.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96dd62fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}