V2-R_plots-Figure9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(tidyverse)\n",
    "library(ggplot2)\n",
    "library(gapminder)\n",
    "library(socviz)\n",
    "library(ggsci)\n",
    "library(\"gridExtra\")\n",
    "library(stringr)\n",
    "library(scales)\n",
    "library(\"ggrepel\")\n",
    "library(cowplot)\n",
    "library(gg.gap)\n",
    "library(ggpol)\n",
    "options(warn=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 9B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Coqyr3TAhW49/86Gj10O/ecT88saBJkGD+m233DOyrdFAAAqH8qD9BuT88OTz31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\nps51gQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data input and reshape\n",
    "df <- read.csv(\"/Share2/home/qitiancong/00.project/04.Song_Lab/01.R_plots/01.data/9B.csv\",\n",
    "                header=TRUE,\n",
    "                stringsAsFactors=TRUE)\n",
    "\n",
    "# set df for sig note\n",
    "df$max <- apply(df[,c(2:7)],1,max) + 20\n",
    "\n",
    "# transformat df\n",
    "df_long <- gather(df, replicate, value, R1:R6)\n",
    "\n",
    "# set genotype order\n",
    "df_long$genotype <- factor(df_long$genotype, levels=c(\"Col-0\",\n",
    "                                                      \"ein3 eil1\", \n",
    "                                                      \"gl3 egl3\",                                                                                                           \n",
    "                                                      \"ein3 eil1 gl3 egl3\"))\n",
    "\n",
    "\n",
    "# set theme function \n",
    "theme_custom <- function (base_size = 11, base_family = \"\", base_line_size = base_size/22, \n",
    "    base_rect_size = base_size/22) \n",
    "{\n",
    "    theme_bw(base_size = base_size, base_family = base_family, \n",
    "        base_line_size = base_line_size, base_rect_size = base_rect_size) %+replace% \n",
    "        theme(panel.border = element_blank(), \n",
    "              panel.grid.major = element_blank(), \n",
    "              panel.grid.minor = element_blank(), \n",
    "              axis.line = element_line(colour = \"black\", size = rel(1)),\n",
    "              axis.ticks.length=unit(.15, \"cm\"),\n",
    "              legend.key = element_blank(), \n",
    "              strip.background = element_rect(fill = \"white\", colour = \"black\", size = rel(2)), \n",
    "              complete = TRUE,\n",
    "#               axis.ticks.x = element_blank(),\n",
    "              axis.text.x = element_text(size=16, angle=0, color=\"black\",\n",
    "#                                          hjust=1, vjust=1,\n",
    "                                         margin = margin(t = 25, r = 0, b = 0, l = 0)),\n",
    "              axis.text.y = element_text(size=16, color=\"black\", margin = margin(t = 0, r = 5, b = 0, l = 0)),\n",
    "              axis.title.x = element_blank(),\n",
    "              axis.title.y = element_text(size=20, angle=90, margin = margin(t = 0, r = 15, b = 0, l = 0)),\n",
    "              strip.text.x = element_blank(),\n",
    "#               legend.title = element_blank(),\n",
    "#               legend.position = c(0.82,0.7),\n",
    "#               legend.position = \"bottom\",\n",
    "#               legend.justification = \"center\",\n",
    "#               legend.text = element_text(size = 18),\n",
    "#               legend.text.align = 0\n",
    "             )\n",
    "}\n",
    "\n",
    "# set figure size\n",
    "options(repr.plot.width = 8, repr.plot.height = 6)\n",
    "\n",
    "# generate x-tick label vector\n",
    "xtick_labs <- c(\"Col-0\\n\",\n",
    "               expression(italic(\"ein3 eil1\\n\")),\n",
    "               expression(italic(\"gl3 egl3\\n\")),                              \n",
    "               expression(italic(\"ein3 eil1 \\n gl3 egl3\"))\n",
    "               )\n",
    "\n",
    "\n",
    "p <- ggplot(df_long, aes(genotype, value, fill=genotype, color=genotype))\n",
    "B9 <- p + stat_summary(fun=\"mean\", \n",
    "                 geom=\"col\", \n",
    "                 position = position_dodge(0.7),\n",
    "                 width = 0.3,\n",
    "                 alpha=0.4,\n",
    "                 show.legend=FALSE) + \n",
    "    stat_summary(fun.data = mean_se, \n",
    "                 geom = \"errorbar\", \n",
    "                 position = position_dodge(width=0.7),\n",
    "                 width = 0.15,\n",
    "                 size=1,\n",
    "                show.legend=FALSE) +\n",
    "    geom_jitter(alpha=0.8, \n",
    "                width=0.1,\n",
    "                show.legend=FALSE) +\n",
    "    theme_custom() +\n",
    "    scale_fill_aaas() +\n",
    "    scale_color_aaas() +\n",
    "    scale_x_discrete(labels=xtick_labs) +\n",
    "    ylab(quote(\"Root hair length (μm)\")) +\n",
    "    geom_text(data=df,aes(x=genotype,y=max,label=sig),size=8,color=\"black\",show.legend=FALSE) +\n",
    "    scale_y_continuous(expand = expansion(mult = c(0, 0.05)))\n",
    "#     annotate(geom=\"text\", x=7, y=40,size=10,\n",
    "#             label=expression(italic(\"S. exigua\")))\n",
    "B9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 9C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wEAAAC1ipxl7AAAAIB6S84T6OjoaBcXF5VKpVQqJUmyeSYAAACg1pL/JkIAAACg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\naAAAAMCA/w9XYzLnSIxJHQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data input and reshape\n",
    "df <- read.csv(\"/Share2/home/qitiancong/00.project/04.Song_Lab/01.R_plots/01.data/9C.csv\",\n",
    "                header=TRUE,\n",
    "                stringsAsFactors=TRUE)\n",
    "\n",
    "# set df for sig note\n",
    "df$max <- apply(df[,c(2:7)],1,max) + 5\n",
    "\n",
    "# transformat df\n",
    "df_long <- gather(df, replicate, value, R1:R6)\n",
    "\n",
    "# set genotype order\n",
    "df_long$genotype <- factor(df_long$genotype, levels=c(\"Col-0\",\n",
    "                                                      \"ein3eil1\", \n",
    "                                                      \"g3eg3\",                                                                                                           \n",
    "                                                      \"e3e1g3eg3\"))\n",
    "\n",
    "\n",
    "# set theme function \n",
    "theme_custom <- function (base_size = 11, base_family = \"\", base_line_size = base_size/22, \n",
    "    base_rect_size = base_size/22) \n",
    "{\n",
    "    theme_bw(base_size = base_size, base_family = base_family, \n",
    "        base_line_size = base_line_size, base_rect_size = base_rect_size) %+replace% \n",
    "        theme(panel.border = element_blank(), \n",
    "              panel.grid.major = element_blank(), \n",
    "              panel.grid.minor = element_blank(), \n",
    "              axis.line = element_line(colour = \"black\", size = rel(1)),\n",
    "              axis.ticks.length=unit(.15, \"cm\"),\n",
    "              legend.key = element_blank(), \n",
    "              strip.background = element_rect(fill = \"white\", colour = \"black\", size = rel(2)), \n",
    "              complete = TRUE,\n",
    "#               axis.ticks.x = element_blank(),\n",
    "              axis.text.x = element_text(size=16, angle=0, color=\"black\",\n",
    "#                                          hjust=1, vjust=1,\n",
    "                                         margin = margin(t = 25, r = 0, b = 0, l = 0)),\n",
    "              axis.text.y = element_text(size=16, color=\"black\", margin = margin(t = 0, r = 5, b = 0, l = 0)),\n",
    "              axis.title.x = element_blank(),\n",
    "              axis.title.y = element_text(size=20, angle=90, margin = margin(t = 0, r = 15, b = 0, l = 0)),\n",
    "              strip.text.x = element_blank(),\n",
    "#               legend.title = element_blank(),\n",
    "#               legend.position = c(0.82,0.7),\n",
    "#               legend.position = \"bottom\",\n",
    "#               legend.justification = \"center\",\n",
    "#               legend.text = element_text(size = 18),\n",
    "#               legend.text.align = 0\n",
    "             )\n",
    "}\n",
    "\n",
    "# set figure size\n",
    "options(repr.plot.width = 8, repr.plot.height = 6)\n",
    "\n",
    "# generate x-tick label vector\n",
    "xtick_labs <- c(\"Col-0\\n\",\n",
    "               expression(italic(\"ein3 eil1\\n\")),\n",
    "               expression(italic(\"gl3 egl3\\n\")),                              \n",
    "               expression(italic(\"ein3 eil1 \\n gl3 egl3\"))\n",
    "               )\n",
    "\n",
    "\n",
    "p <- ggplot(df_long, aes(genotype, value, fill=genotype, color=genotype))\n",
    "C9 <- p + stat_summary(fun=\"mean\", \n",
    "                 geom=\"col\", \n",
    "                 position = position_dodge(0.7),\n",
    "                 width = 0.3,\n",
    "                 alpha=0.4,\n",
    "                 show.legend=FALSE) + \n",
    "    stat_summary(fun.data = mean_se, \n",
    "                 geom = \"errorbar\", \n",
    "                 position = position_dodge(width=0.7),\n",
    "                 width = 0.15,\n",
    "                 size=1,\n",
    "                show.legend=FALSE) +\n",
    "    geom_jitter(alpha=0.8, \n",
    "                width=0.1,\n",
    "                show.legend=FALSE) +\n",
    "    theme_custom() +\n",
    "    scale_fill_aaas() +\n",
    "    scale_color_aaas() +\n",
    "    scale_x_discrete(labels=xtick_labs) +\n",
    "    ylab(quote(\"Root hair number/mm\")) +\n",
    "    geom_text(data=df,aes(x=genotype,y=max,label=sig),size=8,color=\"black\",show.legend=FALSE) +\n",
    "    scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +\n",
    "    annotate(geom = \"line\",\n",
    "           x = c(1, 1),\n",
    "           y = c(40, 50)) +\n",
    "    annotate(geom = \"line\",\n",
    "           x = c(2, 2),\n",
    "           y = c(35, 50)) +\n",
    "    annotate(geom = \"line\",\n",
    "           x = c(1, 2),\n",
    "           y = c(50, 50)) +\n",
    "    annotate(geom=\"text\", x=1.5, y=52,size=10,\n",
    "            label=expression(\"**\"))\n",
    "C9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 9D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wIChBA3btyQMBAAAABgbaQE6MzMTCGEh4dHZTo3a9ZMCFHuImkAAACgxpESoB0c\nHIQQGRkZlemsUqmEEK6urhIGAgAAAKyNlADt6ekphEhISKhM5127dgkhmjdvLmEgAAAAwNpI\nCdB9+/YVQkRERGRlZVXcMyEhITIyUggRGBgoYSAAAADA2kgJ0JMmTRJCpKWlDRgw4MKFC+X2\nUavVK1as6N+/v1qtlslkur04AAAAgJpOykmEXbt2nTRpUmRkZFJSkq+vb69evTp27Ki7FRMT\ns3379suXL8fFxeXk5OgaJ0+e3KVLF7OVDAAAAFiOxKO8w8PDVSrVpk2btFptfHx8fHy8rn3l\nypWleo4cOTIiIsKkGgEAAACrIWUJhxBCqVTGxsauXr36ueeee1Kfrl27rl27duPGjTY2EmM6\nAAAAYG1MirbBwcHBwcGXLl2Kj49PTU1VqVRyudzNza1Vq1bdu3f39vY2V5UAUHW0ubna+/eF\ng4PQai1dCwCgBjDD3LCPj4+Pj4/p7wGAaqbVakv27Ss5dUpz755MoxH29pauCABQA0hcwgEA\ntYDm+PGiHTuEWi1v3Fjm7q4tKJBZuiQAgPV7+gz0uXPnzDKSr6+vWd4DAOZScuKEzNVV2Nr+\n57eNjVCrydAAgIo9PUDrt6gzkZbFhQCsiba4WPvokazMV85aIcjQAIAKsIQDQB0ls7ERjo6i\nuNiwkfQMAHiqp89A9+vXrxrqAIDqp+jWrTg2VtjY/GceurhYRoYGADzN0wP0oUOHqr4MALAA\nm169RF5e0Y4dQi4XWq2wt9eq1ZYuCgBg7TjiBEAdJpPZvPKKTc+emnv3hL198Y4dxadPW7om\nAIC1I0ADqPNcXeWurkIIIWPtBgDg6fiIEAAAADACARoAAAAwAgEaAAAAMAIBGgAAADACARoA\nAAAwAgEaAAAAMAIBGgAAADACARoAAAAwAgEaAAAAMAIBGgAAADCCSUd5Z2dnnzlz5v79+48e\nPdJqtRV3njBhgiljAYBkmhs3tI8fP71bbq7uQltUVPLnn5V5s+K55zgAHADqGokB+ubNmzNn\nzty+fXtJSUklHyFAA7AU9c8/a27dMuKBhw8Lw8Iq09Fx2TICNADUNVIC9P3793v37n337l2z\nVwMAAABYOSkBevHixfr03LFjR19fXzc3N4VCYdbCAAAAAGskJUDv3LlTCOHi4rJ9+/Z+/fqZ\nuyQAMDO70aMrswZaCtZvAEDdIyVAp6SkCCGmT59OegZQI8hbtrR0CQCA2kPKNlp87LgAACAA\nSURBVHaFhYVCCH9/f3MXAwAAAFg7KQG6cePGQggbG5O2wAMAAABqIikBun///kKIS5cumbsY\nAAAAwNpJCdAzZ86Uy+VRUVG6tRwAAABA3SElQPv7+3/33XdXrlx56623Hj58aPaaAAAAAKsl\nZR1zSUnJhAkTXFxcZsyY4e3tHRwc3KNHj8aNG1e8KrpPnz5SiwQAAACshZQAXSoof/vtt5V5\nSqvVShgLAAAAsCpSlnAAAAAAdZaUGeh+/frZ29vb2NgoFAoZp3ABqGuKi4uPH9ckJ2sfPpQ1\naKDo0UPRqRNHEgJA3SElQB86dMjcZQBAjVG8c2fxwYMyFxehUGhv31ZfuGA3erTihRcsXRcA\noJqwhAMAjKBJTS3at080aCDs7YWtrXB0lNWrV3zokJZtPQGgziBAA4ARtPfuyWxtZQqFvkWm\nVGrv3tVmZFiwKgBAdTLPcdxarTY3N1e3J3S9evWcnZ3N8loAsDYyW1uh0Ri2aLVarRAyOztL\nlQQAqGYmzUCnp6cvXLiwX79+9erVc3Nza9GiRYsWLVxcXBo2bPjKK68sX748Pz/fXIUCgDWQ\nt2wpPDxEQcH/NeXlKdq3F40aWa4oAEC1kh6gf/jhB29v7zlz5hw5cqTUeYRZWVl79uyZOnWq\nt7f3rl27TC4SAKyGk5Pdq68KZ2dtVpb24UNtZqasUSObwYPZkggA6g6JSzjCwsJmzZpVqtHB\nwUEIUWAwMZOenv76669v27Zt0KBBkksEAKsi79DBrmlTzblzmocP5Q0byjt3ljk6WrooAED1\nkTIDfevWrTlz5uiuhw4dunHjxuvXr5eUlDx69OjRo0fFxcVXrlxZs2bNiy++KIQoKSkZN25c\nbm6uOat+sjt37rzyyisymUwmk2VnZ1f+wc6dO8ueLDg4uOpqBlDjyBo0UAQE2L7+uqJnT9Iz\nANQ1Umagly9fXlhYaGtru2nTpjfeeKPUXYVC4e3t7e3tPWbMmOjo6EmTJmVmZkZGRn744Yfm\nKLgi//rXv2bNmpWTkyPhWV3adnR0tLW1LXvXycnJ1OIAAABQK0iZgT5w4IAQ4p133imbnkuZ\nOHHi22+/LYSo6pXQaWlpr732WmhoqEwmCw0NlfAGlUolhFi3bl12eZYvX27ukgEAAFAjSQnQ\n169fF0L85S9/qUznESNGCCHOnz8vYaDK27Bhw86dO4OCgpKTk4cOHWrs4yUlJbpFJvXr16+C\n6gAAAFB7SAnQusnapk2bVqazl5eXECIzM1PCQJVnb2//zTff7N+/v0WLFhIe1/2JhBD16tUz\na10AAACobaSsgXZwcCgqKqrkd4GPHz8WQthV8REDkydPlsulb8mn/9yQGWgAAABUTEro1M09\nHz9+vDKddd2aNWsmYaDKMyU9C4MZ6MLCwq+++iowMNDHx6dLly6jRo3avHmzVqs1R40AAACo\nDaTMQPfp0+fSpUvfffddSEhIowoP37p//35YWJjuEYkFVgt9gPbz8zM8FObMmTPr168PDAzc\nvHlzxZPTJ06cKNvIwQoAAAC1j5QAPXr06Ojo6NTU1ICAgIiIiAEDBpTto9Fodu3aNWPGjLt3\n7wohxo0bZ2qlVUkfoD08PJYvX96/f/969epdu3btf//3f1etWnXo0KExY8bs3LmzgjdMnz5d\no9GUarSxkXhODQAAAKyWlITXv3//119//ddff7148eKLL77o5eXVvXv3li1bOjs7a7Xa3Nzc\na9eu/f7772lpabr+I0aMCAgIMGvZZtazZ88tW7bI5fKXX37Z3t5e19iuXbuVK1d6eHgsWLDg\nt99+O3jwYFBQ0JPeMH369FIrPSIiIkpKSqq2bgAAAFQ7iVOkP//886uvvhofHy+EuHnz5s2b\nN5/U86WXXlq5cqXE6qqLp6enp6dnubc+++yzH3/8UaVS/fLLLxUE6LJT7BEREWXnpAEAAFDT\nSfz2zsXF5fDhw//85z+fffbZJ/Vp27btsmXLdu/e7ViTz7m1t7fv1KmTEKKCvyQAAACg7pC+\nSFehUMyaNWvmzJlnzpw5derUrVu3cnJyZDKZm5ubp6dn9+7dfX19a8dXdEVFRaLqd+IDAABA\njWDqV24ymaxLly5dunQxSzWWsnnz5suXL7dr127w4MGlbhUUFCQnJwsh2rZta4nSAAAAYF3Y\nJkIIIbZs2bJmzRpPT8+goCBXV1fDWwsWLMjLyxNCDBkyxELVAQAAwIo8PUBfvHhRCGFvb69f\n7qxrMdZzzz0n4amqEBYWtn79eqVSefjwYV3L+++///PPP9+6devVV18NDw/38/MTQuTm5v7w\nww8LFiwQQrz99tv+/v6WLBoAAADW4ekBul27dkKIzp07JyUlGbYYq0rP83N3d9edGS6EKC4u\n1l14eXnpF2HPmjXr888/112npKT88ccfSqVS/3j37t2XLVs2ffr0Y8eO+fv7N2zY0NHRMS0t\nTfeq1157LSoqquqKBwAAQA1i0gnY1iM7Ozvn/8vPz9c1Pnz4UN9YUFBQ8RsmT5585syZqVOn\n+vj4FBQU3Lt3r3HjxkOGDNm6dev27dudnJyq/g+BioSFhclkMplM1rx5c0vXAgAA6rSnz0D3\n7t1bCNGmTZtSLVZFP/1cGWFhYboDxktp167dsmXLzFcUAAAAaqGnB+ijR48+tQUAAACoI2rJ\nEg4AAACgehCgAQAAACOYtA+0VqstLCy0t7cv1X7kyJHk5GQHB4eePXu2b9/elCEAAAAAqyJ9\nBnrp0qXNmjVbv369YeP9+/eDgoL69ev3/vvvv/POOx06dHjzzTcfPXpkcp0AAACAVZAYoGfM\nmDFjxoz09PSUlBR9o0ajGTJkyKFDhwx7xsbGjh8/3oQKgf+i39s7KSnp/fff9/X1bdCggaOj\nY8uWLUePHr1r1y7LlgcAAGo9KUs4Tp48uXTpUiGEi4uLl5eXvn39+vXHjx8XQri7u0+bNs3R\n0XHlypXJycmbNm06evRonz59zFU06jK5XC6E+Pvf//71118bns6TkpKSkpKybt26t99+e9Wq\nVba2tparEQAA1GZSAvRPP/0khHB2dj5x4oThAd0//PCDEMLOzu7AgQO60wqnTp3aoUOHlJSU\ntWvXEqBhFra2tj/88MNXX30lhGjSpImfn5+Tk1NKSkpCQoIuT69fv97R0TE6OtrSlQIAgNpJ\nyhKOY8eOCSEmTJhgmJ7v3bunax85cqT+rG9HR8fRo0cLIX7//XczFAsIUVBQMHfuXBcXl59/\n/jktLW3nzp2xsbEnT55MTk5u27atrs9PP/104sQJy9YJAABqKykBWrfuOTAw0LDx4MGDuvm/\noUOHGrZ36dJF/whgutTU1JycnI0bN44aNUq/HloI4evru23bNhub//yjim6VEQAAgNlJCdB5\neXlCiIYNGxo2Hjx4UAghl8uDgoIM23XddI8AZvHyyy8PHDiwbLuPj8/w4cN11zt27CguLq7e\nugAAQJ0gJUArlUohhFqtNmw8cOCAEMLPz69BgwaG7bm5uUII/bwgYLpRo0Y96ZY+WGdnZ1+5\ncqW6KgIAAHWIlADdpEkTIcTly5f1LQkJCVevXhVCDBo0qFTn27dvizLT1YApevXq9aRbHTt2\n1F9funSpWsoBAAB1i5QArVvWHBMTU1hYqGtZvHix7mLw4MGlOm/ZskUI4ePjI71GwIBCoWjV\nqtWT7np6euqv09LSqqUiAABQt0hZWTFs2LCtW7cmJCT06tVr4MCBZ86c2bFjhxAiICDAz8/P\nsGdMTIxuacdLL71klnIBZ2fnClYEOTs7669ZeQ8AAKqClAA9atSoxYsXJycnJyYmJiYm6hrl\ncvmXX35p2G306NHr1q0TQjg7O4eEhJheKyCEcHR0rOCuboG+jv5fSAAAAMxIyhIOGxubnTt3\n9u7dW9/i4OAQHR3dr18/w24PHjzQdY6KitItmwZMV+rr1VIMQ3PFURsAAEAaiZtjeHh4HD16\nNDk5+c8//3RwcOjTp0+pzTeEEP7+/oWFhfPnzzeM2oCJKl6YkZ+fr782XM4BAABgLibtLtep\nU6dOnTo96e7XX38tl0uZ4QYqUFhYmJaW1rRp03Lv3rp1S39t+EEhAACAuVRhwCU9o4qcPn36\nSbcuXLigv9YfKQ8AAGBGJs1Aa7XawsJCe3v7Uu1HjhxJTk52cHDo2bNn+/btTRkCKGvTpk1l\ndxzX2b17t+6iefPmzEADAICqIH2SeOnSpc2aNVu/fr1h4/3794OCgvr16/f++++/8847HTp0\nePPNNx89emRyncD/WbNmzcWLF8u237x5MzY2Vnc9dOhQmUxWvXUBAIA6QWKAnjFjxowZM9LT\n01NSUvSNGo1myJAhhw4dMuwZGxs7fvx4EyoEhBBCo9HoLho0aGBra/vqq6+WWshx586doUOH\n6nbhsLW1nTFjhgWqBAAAdYCUJRwnT55cunSpEMLFxcXLy0vfvn79+uPHjwsh3N3dp02b5ujo\nuHLlyuTk5E2bNh09erRPnz7mKhp1UHFxse6iadOmISEhs2fPfv7553v06NGpUyd7e/sbN27s\n3btX/28dn3zySevWrS1XLAAAqM2kBOiffvpJCOHs7HzixInnnntO3/7DDz8IIezs7A4cOKD7\nfmvq1KkdOnRISUlZu3YtARqm0G//7OLi8tFHH92/f3/RokXHjh07duyYYTeZTDZjxowvvvjC\nEjUCAIA6QcoSDl1kmTBhgmF6vnfvnq595MiR+t0PHB0dR48eLYT4/fffzVAs6jD97LKbm5sQ\nYuHChadOnXr33Xfbt2/v5uZmb2/fsmXL8ePHx8XFffvttxatFAAA1HJSZqB1654DAwMNGw8e\nPKjVaoUQQ4cONWzv0qWL/hFAsvnz58+fP9+wxd/f39/f31L1AACAOkvKDLTuKLiGDRsaNh48\neFAIIZfLg4KCDNt13So+PQ4AAACoKaQEaKVSKQzWpOocOHBACOHn51fqTO/c3FwhhI2NSRtO\nAwAAAFZCSoBu0qSJEOLy5cv6loSEhKtXrwohyh5vcfv2bVFmuhoAAACooaQEaN2y5piYGN2e\nu0KIxYsX6y4GDx5cqvOWLVuEED4+PtJrBAAAAKyGlJUVw4YN27p1a0JCQq9evQYOHHjmzJkd\nO3YIIQICAvz8/Ax7xsTE6JZ2vPTSS2YpFwAAALAsKQF61KhRixcvTk5OTkxMTExM1DXK5fIv\nv/zSsNvo0aPXrVsnhHB2dg4JCTG9VgAAAMDipCzhsLGx2blzZ+/evfUtDg4O0dHR/fr1M+z2\n4MEDXeeoqCjdsmkAAACgppO4OYaHh8fRo0eTk5P//PNPBweHPn36lNp8Qwjh7+9fWFg4f/58\nw6gNAAAA1Ggm7S7XqVOnTp06Penu119/LZdLmeEGAAAArFYVBlzSMwAAAGofc2ZctVpdUlJi\nxhcCAAAA1sakAJ2XlxcTEzN8+PA2bdoolUqlUhkXF6e/e/bsWf0eHQAAAEDtID1Ab926tW3b\ntiEhIZs3b7569Wqpk72FEFFRUf7+/lOmTGFaGgAAALWGxI8IN27cOGrUKI1GU0Ef3ekqK1as\ncHBwCAsLkzYQAAAAYFWkzEBnZGRMnTpVo9EoFIrQ0NCDBw/m5uaW7RYZGdmyZUshRHh4+Pnz\n502tFAAAALACUgJ0VFSUSqVSKBTbtm2Ljo4ODAx0dnYu2y0oKGjv3r1OTk4ajSY6OtrkUgEA\nAADLkxKgdWszJkyYMGjQoIp7tm7dWneI9+HDhyUMBAAAAFgbKQH62rVrQojBgwdXpnNAQIAQ\n4saNGxIGAgAAAKyNlACdmZkphPDw8KhM52bNmgkhyl0kDQAAANQ4UgK0g4ODECIjI6MynVUq\nlRDC1dVVwkAAAACAtZESoD09PYUQCQkJlem8a9cuIUTz5s0lDAQAAABYGykBum/fvkKIiIiI\nrKysinsmJCRERkYKIQIDAyUMBAAAAFgbKQF60qRJQoi0tLQBAwZcuHCh3D5qtXrFihX9+/dX\nq9UymUy3FwcAAABQ00k5ibBr166TJk2KjIxMSkry9fXt1atXx44ddbdiYmK2b99++fLluLi4\nnJwcXePkyZO7dOlitpIBAAAAy5F4lHd4eLhKpdq0aZNWq42Pj4+Pj9e1r1y5slTPkSNHRkRE\nmFQjAAAAYDWkLOEQQiiVytjY2NWrVz/33HNP6tO1a9e1a9du3LjRxkZiTAcAAACsjUnRNjg4\nODg4+NKlS/Hx8ampqSqVSi6Xu7m5tWrVqnv37t7e3uaqEgAAALASZpgb9vHx8fHxMf09AAAA\ngPWTEqC/+uqr7OxsDw+PWbNmmb0gAAAAwJpJWQP95ZdfLlmyZPfu3WavBgAAALByUgK0m5ub\nECI/P9/cxQAAAADWTkqAfvPNN4UQJ0+eTEtLM3c9AAAAgFWTEqAXLFgwcODAwsLCN9544+bN\nm2avCQAAALBaUj4idHFx+fe//71hw4Yff/yxbdu2b7zxRp8+fdzd3Rs1amRnZ/ekp/r06WNC\nnYAUafceXrn+QGi1bVo1auruaulyAABAbSAlQMvl/zVvvWnTpk2bNj31Ka1WK2EsQLJN284s\njzlua6sQQhQVlUwe33Pk4M6WLgoAANR4nBGI2ulE4q0VMb+7N3axs7MRQqjVJZGrfm/hUa/H\n816WLg0AANRsUgJ0nz597O3tbW1tbWxsSs1GA1Yi7vh1V1d7XXoWQtjZKVxd7OOOXydAAwAA\nE0kJ0HFxcWavAzCv3LxCheK//nZnYyN/mPvYUvUAAIBag/lj1E5NGjkXFhYZthQWFrk34TtC\nAABgKgI0aqdBL7V3b+zyMPexRqvVaLW5eYVNGrm89lI7S9cFAABqPPME6JKSkgcPHty8efPO\nnTsqlcos7wRM4dWi/ruhvVt6Nbx9J/v2nWyvFvWnhvZ+1rOBpesCAAA1nkm7cBw+fHjdunWH\nDx++fv26Wq3Wtzs5Ofn4+PTv33/s2LGdOnUyuUhAis6+zTp1aPog65FWq32mgZNcLrN0RQAA\noDaQGKCzs7ODg4N37NhR7t38/PzExMTExMQlS5aMGzdu2bJlDg4OJhQJSCSTyRo1dLJ0FQAA\noFaREqCLiopefvnlkydPGjbK5XIHBwetVvv48WONRqNr1Gq1K1euvHPnzp49e9jwDgAAALWA\nlFC7fPlyXXq2sbGZOHHizp07U1NTi4uL8/Ly8vPzi4qKUlNTf/vttylTpuhO9t6/f//KlSvN\nXDgAAABgCVJmoNevXy+EUCqVe/bsCQgIKHVXLpc3bdq0adOmAwcOnDhxYv/+/fPy8lauXBkS\nEmKGegEAAACLkjIDffHiRSHE5MmTy6bnUrp16/bxxx8LIf78808JAwEAAADWRkqAzs3NFUL0\n6dOnMp2DgoKEEDk5ORIGAgAAAKyNlAD9zDPPCCGUSmVlOuv232jSpImEgQAAAABrIyVAd+7c\nWQhx9erVynTWdfP395cwEAAAAGBtpATo0NBQIcSqVauKi4uf2jkmJkYIMX78eAkDAQAAANZG\nSoAeMWLEpEmTkpOTR40aVcHB3YWFhR9++OHu3bvHjBkzePBgE4oEAAAArIXEg1Tmz5/ftGnT\n+fPn79mzZ9iwYT179mzTpo2Li4utrW1eXt6tW7dOnjwZGxt79+7dd9999/33379w4YL+dBVD\nvr6+Jv8RAAAAgOojJUDrjkfRefjwYUxMjG6dRrmWLVu2bNmyJ93VarUSCgAAAAAsheO1AQAA\nACNImYHu0qWLg4ODQqFQKBRmLwhYuf7kjj1VcvLO5399ucNz7lXxZgAAUHdICdCnT582ex2A\n3sPcx6npVXLyTqH66fvGAAAAVIwlHAAAAIARpMxAS5CVldWgQYPqGQs1nXfLZ4L6ej+12527\n2VeuP9Bd9+jm5WBv+9RH6rs5mlocAACo86QE6KlTpy5ZssTJyamS/X/77bd33nnn7t27EsZC\nHfTqi+1efbHdU7vF/nLmu+VHdNcfvRfYtIlrFdcFAAAghLQlHMuXL+/cuXN8fPxTe+bl5U2Z\nMmXQoEGpqakSBgIAAACsjcQ10NeuXQsICJgzZ45arX5Sn7i4uM6dO69YsUJqbQAAAIDVkRKg\n33rrLSGERqNZuHDh888/f+bMmVIdCgsL//rXvwYGBl6/fl0I4enpuWPHDtNrBQAAACxOSoBe\nv379rl27WrVqJYQ4e/Zs9+7dFyxYUFJSort7+vRpf3//b775RqPRyOXy999///z584MGDTJn\n1QAAAICFSFzC8corr5w7d+6TTz6xtbVVq9Vz587t27fvxYsXv/rqqxdeeOH8+fNCCF9f3/j4\n+KVLlzo7O5u1ZgAAAMBipO8D7eDgMH/+/NOnT/fu3VsIcfz48Xbt2v39738vKipSKpVffPFF\nYmJijx49zFcqAAAAYHmmHqTSoUOHuLi4hQsX6luaNm16+vTpzz77zNb26fvyAgAAADWLGU4i\nXLdu3ZIlS/Q/09LSZs2adfPmTdPfDAAAAFgbkwL0tWvXXn755TFjxty/f1+hULz//vvt2rUT\nQuzevdvX13fp0qUajcZMdQIAAABWQWKALioqWrBgQceOHffu3SuEaNOmTVxc3NKlSxMTEz/6\n6CO5XJ6XlzdjxozevXtfuHDBrAUDAAAAliQlQB8/ftzPz2/u3LkFBQUymWzatGlJSUk9e/YU\nQtjb2y9evPjIkSPe3t5CiN9//71r165ffPFFBeetAAAAADWIlADdu3fvc+fOCSE8PT337t0b\nERHh6OhYqsOZM2emTZsmk8nUavX//M//+Pv7m6deAAAAwKKkBGitViuEGD9+/NmzZwcMGFBu\nH0dHx4iIiH379nl5eQkhdIEbAAAAqOmkBOgmTZr88ssvMTExrq6uFffs379/cnLyxIkTJdUG\nAAAAWB0bCc+cO3fumWeeqWRnV1fXqKioYcOGSRgIAAAAsDZSAnTl07PeoEGDJAyE2uFeRu6F\ni/fM/tor1x7or4+fvFnfzcHsQwT0aqVQmGGvdAAAUJtICdBPolarFQqFQqEw4ztRC5w5l/rl\nN3uqdIh//nCoKl67599THB3squLNAACg5jIpQOfl5W3atGn79u3Jycm3bt1Sq9UHDx4MDAzU\n3T179mxRUZGfn58ZykTN593qGQ93NzO+8E5qzrWU/0xCv+Dvaa8059HxyefTVDmPzPhCAABQ\na0gP0Fu3bn3vvffS0tKe1CEqKmrp0qWTJ0/+4YcfmJaGV/P6fp2bm/GFCsUdfYDu7Ovh5mpv\nxpffvJ1FgAYAAOWSGKA3btw4atSoik/q3rFjhxBixYoVDg4OYWFh0gYCAAAArIqUD6QyMjKm\nTp2q0WgUCkVoaOjBgwdzc3PLdouMjGzZsqUQIjw8/Pz586ZWCgAAAFgBKQE6KipKpVIpFIpt\n27ZFR0cHBgY6OzuX7RYUFLR3714nJyeNRhMdHW1yqQAAAIDlSQnQurUZEyZMeOrmdK1btw4J\nCRFCHD58WMJAAAAAgLWREqCvXbsmhBg8eHBlOgcEBAghbty4IWEgCe7cufPKK6/IZDKZTJad\nnW3UsyUlJatXr37ppZcaNWpkZ2fn7u4+ZMiQPXuqdv81AAAA1CxSAnRmZqYQwsPDozKdmzVr\nJoQod5G02f3rX//y9fWVFnkLCwsHDx48bty4ffv2PXr0yN3dPTs7+5dffnnllVdmz55t9lIB\nAABQQ0kJ0A4ODkKIjIyMynRWqVRCCFdXVwkDVV5aWtprr70WGhoqk8lCQ0MlvOHzzz/fsWOH\ng4PDqlWrsrOzb926pVKpFi1aJJPJlixZsn79erPXDAAAgJpISoD29PQUQiQkJFSm865du4QQ\nzZubcwPgsjZs2LBz586goKDk5OShQ4ca+3hmZua3334rhFi8ePHYsWNtbW2FEA4ODh9//PF7\n770nhJg3b55WqzV72QAAAKhxpATovn37CiEiIiKysrIq7pmQkBAZGSmE0B9PWEXs7e2/+eab\n/fv3t2jRQsLjsbGxarXazc3tnXfeKXVr5syZQojr16/Hx8eboVBUSF1YUvBYbekqAAAAKiIl\nQE+aNEkIkZaWNmDAgAsXLpTbR61Wr1ixon///mq1WiaT6fbiqDqTJ0+ePXu2TCaT9vixY8eE\nEH379rWzsyt1y9vbWzd9ruuDKvIgK//X3RfWbzn9479+//f2s7fuqCxdEQAAQPmknETYtWvX\nSZMmRUZGJiUl+fr69urVq2PHjrpbMTEx27dvv3z5clxcXE5Ojq5x8uTJXbp0MVvJ5ZHLpfxN\nQO/cuXNCCB8fn3Lvtm3b9s6dO8nJyaYMgQrk5xfuO3QlO6fAXmnTwM0xMzNv8/azo4b7NWlc\nzv7iAAAAliXxKO/w8HCVSrVp0yatVhsfH69f3rBy5cpSPUeOHBkREWFSjVVPt69IkyZNyr3r\n7u6u7/MkDx8+rIrC6ojzF9Mzs/JdnJW6n/b2thqtOHMu9eX+bS1bGAAAQFkSA7RSqYyNjV2z\nZs3XX3998eLFcvt07dp19uzZo0ePNqG8aqLbZU+3u0hZuvaKI/KLL76o0WhKNeo+RsRT5eQW\n2tgoDFvsbOQ5DwssVQ8AAEAFJAZoneDg4ODg4EuXLsXHx6empqpUKrlc7ubm1qpVq+7du3t7\ne5urSsvS7b9R8QLrbt26ldqm48SJE2zcUUn2djal/vpRotUqlSb9jxMAAKCKmCGj+Pj4PGn1\ncE3h6uqqUqkePXpU7l1de8VbWX///felWrp161ZcXGyuCmu3Vq0aJpy5o7SzsbGRCyE0Wm1B\nQVHrlg0tXRcAAEA5mOQTQohGjRrdvHkzPT293LupqaniySukYToPd7f+fdscOHrFRqEQQltU\nVNK4kcuDrPzDx65V8FT6/f873vKPhFtKpaKCzjqdOzSr51b+Qh0AAIBKIkALIUSnTp1OnTr1\n559/lr2l1Wp17X5+ftVeVx3Sybepl2e99Hu5xSWalNtZFy7eS03PqfzjU5eBSAAAIABJREFU\nZ87drUy3ll4NCNAAAMBEJu3+VmsEBAQIIeLi4goKSn+4lpiYqDu0vKrPgoGbq4NPm8YdnnN3\ndJD88aXEjcABAAAqjwAthBDDhw93dnbOz89ftmxZqVsLFy4UQjz//PP6va5hrWTi/3+1qdUK\nra4FAADA3OriEo6wsLD169crlcrDhw/rWpydnefNm/fJJ5/MnTu3fv36wcHBtra2Dx8+/Mc/\n/hEbGyuEWLx4sUVLrluC+rQJ6tPGqEcePy7avutCpuqRbva6RKPNyyvs2c3r+a5SjnYHAACo\nQC0J0O7u7o8fP9Zd6/e+8PLy0u89N2vWrM8//1x3nZKS8scffyiVSsM3fPzxx+fPn1+zZk1o\naOj06dMbNmyYnp5eVFQkk8nCwsL69etXXX8USJFyW3Xvfq6r63/+myrkMidHu0tX73ft3Fwh\nZx4aAACYUy0J0NnZ2YWFhaUaDY8+Kbu4uRSFQrF69eo33ngjKioqISEhPT29cePGffv2/fDD\nD7t162b+imFW+flquUJmuGZDrpDdf5Bf+LjY0ZHjbAAAgDnVkgCtn36ujLCwsLCwsHJvjRw5\ncuTIkWYqCtXH2UlZUqIRQqvP0JoSTZNGzkr7p+9tBwAAYBQ+IkRt8KxX/abubvmPinRfEZaU\naPPy1T7ejRVy/hcOAADMjHiB2kBpZxPUp3Wzpm4qVYEqpyAnt6Cbn2fXjh6WrgsAANRC5lzC\noVarFQqFQsE/msMCGjZwemNgB1X2o8ePi+u52Ts62lm6IgAAUDuZNAOdl5cXExMzfPjwNm3a\nKJVKpVIZFxenv3v27NnExESTKwQqSyYTDeo7NmvqSnoGAABVR3qA3rp1a9u2bUNCQjZv3nz1\n6lW1Wl2qQ1RUlL+//5QpU0pKSkwrEgAAALAWEpdwbNy4cdSoURqNpoI+O3bsEEKsWLHCwcHh\nSbteAAAAADWLlBnojIyMqVOnajQahUIRGhp68ODB3Nzcst0iIyNbtmwphAgPDz9//ryplQIA\nAABWQEqAjoqKUqlUCoVi27Zt0dHRgYGBzs7OZbsFBQXt3bvXyclJo9FER0ebXCoAAABgeVIC\ntG5txoQJEwYNGlRxz9atW4eEhAghDh8+LGEgAAAAwNpICdDXrl0TQgwePLgynQMCAoQQN27c\nkDAQAAAAYG2kBOjMzEwhhIdHpU6paNasmRCi3EXSAAAAQI0jJUA7ODgIITIyMirTWaVSCSFc\nXV0lDAQAAABYGykB2tPTUwiRkJBQmc67du0SQjRv3lzCQAAAAIC1kRKg+/btK4SIiIjIysqq\nuGdCQkJkZKQQIjAwUMJAAAAAgLWREqAnTZokhEhLSxswYMCFCxfK7aNWq1esWNG/f3+1Wi2T\nyXR7cQAAAAA1nZSTCLt27Tpp0qTIyMikpCRfX99evXp17NhRdysmJmb79u2XL1+Oi4vLycnR\nNU6ePLlLly5mKxkAAACwHIlHeYeHh/+/9u40sMkq/fv4laZp0hba0gUKLVWgpRbLvgygbCoI\nAlIXFCwgixsO8qgzwAgzwMwflU1FZERUBmVRhqK0owiyVXZR2RehFIRa21K6F7qmyfPinsl0\n0jUhadLw/bxKz31ycpXcpL+cnJw7Nzd38+bNRqPx4MGDBw8eVNo//fRTs56jR49esWLFLdUI\nAAAAOA1rlnCIiFarjYuLW7du3V133VVTn65du27YsGHTpk3u7lbGdAAAAMDZ3FK0HTdu3Lhx\n4y5cuHDw4MG0tLTc3Fw3NzdfX9+2bdv26tUrPDzcVlUCAAAATsIGc8ORkZGRkZG3Pg4AAADg\n/KxZwrFu3bqbN2/avBQAAADA+VkToCdMmBAcHPz000/v2rXLYDDYvCYAAADAaVn5JcIbN26s\nXbt28ODBYWFhs2bNOnv2rG3LAgAAAJyTNQH6/vvvV6vVyu3ffvtt8eLF0dHR3bp1W7ZsWWZm\npk3LAwAAAJyLNQF6165daWlpf//73/v166dSqZTG48ePv/LKKyEhIcOHD//nP/9ZXFxs0zoB\nAAAAp2DlEo7mzZu/+OKL+/bt+/XXX995553evXsr7Xq9/ptvvhkzZkxwcPAzzzyzd+9eo9Fo\nu2oBAAAAB7MyQJuEhIS8/PLLhw8fvnLlyqJFi7p166a0FxQUrF69euDAgW3atPnzn/+clJR0\ny6UCAAAAjnerAdrkjjvumDlz5tGjRy9evLhgwYJOnTop7VevXn399dfZKBoAAACuwWYB2iQ8\nPHzOnDknT55MTk5+++23w8LCbP4QAAAAgKPY4EqEVeXl5W3fvn3btm3bt29nXw4AAAC4ElsG\n6Nzc3ISEhLi4uJ07d5aXl5vadTrdyJEjbfhAAAAAgKPYIEDn5OTEx8fHxcXt3r27cm5Wq9WD\nBg2KjY199NFHfXx8bv2BAAAAAIezPkBnZWUpuXnPnj16vb7yoe7du8fGxo4ZM6Zly5a3XCEA\nAADgRKwJ0B9++OHmzZsTExPNcnPbtm2feuqpcePGsecGAAAAXJU1Afr555+v/GNQUNATTzwR\nGxvbp08fG1UFAAAAOCnrl3B4e3uPGjUqNjZ2yJAh7u522c0DAAAAcDbWBN9hw4bFxsbGxMR4\ne3vbvCAAAADAmVkToL/55hub1wEAAAA0CnUH6PPnz4uITqe78847K7dY6q677rLiXgAAAIBT\nqTtAR0VFiUjnzp1PnDhRucVSRqPRinsBAAAATsXN0QUAAAAAjUndM9D33HOPiERERJi1AAAA\nALehugP0gQMH6mwBAAAAbhN2X8JhMBj0er3BYLD3AwEAAAANwJoAPXTo0KFDh6anp9en8xtv\nvKHRaEaMGGHFAwEAAADOxpp9oL/99lsRuXnzZn06t27dWkROnTplxQMBAAAAzsbuSziSkpJE\nJDs7294PBAAAADSA+s5AL1y40Kxl1apVAQEBtdxFr9dfvHhx48aNIuLr62tdfQAAAIBTqW+A\nfu2118xali5dWv+HYec7AAAAuIb6LuF4/vnnu3Tp4u5uzZrpqKioZcuWWXFHAAAAwNnUNxB/\n8MEHIlJUVHT06NH+/fuLyB//+Mfal3CIiJ+fX3h4+KBBg9Rq9S0WCgAAADgDy2aUvby8+vXr\np9x+/vnnw8PD7VASAAAA4LysWZIxb948EfH397d1MQAAAICzsyZAz58/v/6dU1NTV6xY0aVL\nlzFjxljxWAAAAIBTsSZAWyQnJ2fRokUREREEaAAAALgA+15IJTc39+9//7uI/Prrr3Z9IAAA\nAKBhWD8DnZqa+u677+7evTstLa2kpKRqB71eb7rcd3BwsNUPBAAAADgPKwN0YmLiqFGjCgsL\n69l/3Lhx1j0QAAAA4FSsCdDXr18fPXp0fdJzYGBgVFTU2LFjn332WSseCAAAAHA21qyBXrVq\nVXZ2tog88cQThw4dys3NzcjIUA4VFxffuHHj9OnTf/vb3/z9/UNDQ1esWDF16lTrLmEIAAAA\nOBtrAvT27dtFpFevXhs3buzTp4+fn59Wq1UO6XQ6b2/v6Ojov/zlL6dPnzYYDL169dq/f78t\nSwYAAAAcx5oAff78eRH5/e9/r1KpaunWqlWrrVu3ajSaUaNGKTPWAAAAQGNnTYDOz88XkbCw\nsKqH9Hp95R9DQ0NfeOGF3NzcVatWWVcfAAAA4FSsCdDKgubKW9d5e3srs9E5OTlmnYcNGyYi\nmzZtsr5GAAAAwGlYE6ADAgJE5PLly6YWjUbTrFkzEUlNTTXr3Lx5cxFJTk62vkYAAADAaVgT\noKOjo0VkzZo1ZWVlpkblUinbtm0z66xcg7ByTwAAAKDxsiZAjxw5UkR++umnIUOGfP3110pj\nr169RGTJkiVnz5419dTr9UuXLhWRFi1a2KBYAAAAwNGs2Z554sSJb7zxRlpa2t69ewsKCkaM\nGCEisbGxn3zySX5+/u9+97snn3wyKioqJycnISHh3LlzItK/f38bFw4AAAA4gjUB2tvbOz4+\n/qGHHsrKylJWbojIAw88MHLkyK+++urmzZv/+Mc/Kvf38PCYOXOmDYoFAAAAHM2aJRwi0rNn\nz3Pnzs2fP3/QoEGmxg0bNjz++ONmPQMCAr744ovOnTtbXyMAAADgNKy/wnZQUNC8efMqtzRt\n2jQuLu7UqVM7d+5MT0/XarUdO3YcOXKkt7f3LdcJAAAAOAXrA3RNOnXq1KlTJ5sPCwAAADgD\nK5dwAAAAALenumegz5w5Y5NHUnaPBgAAABq1ugN0x44dbfJIRqPRJuMAAAAADsQSDgAAAMAC\ndc9ADxgwoAHqAAAAABqFugP0d999Z/8yAAAAgMaBJRwAAACABQjQAAAAgAVsGaDLysoqKips\nOCAAAADgbG4pQN+4ceOTTz557LHHIiIitFqtVqvdv3+/6ejp06ePHTt2yxUCAAAATsT6AB0f\nH9++fftJkyZ9+eWXycnJZWVlZh0+/vjj7t27P//880xLAwAAwGXUvQtHtTZt2jR27FiDwVBL\nn61bt4rIhx9+6OnpuWzZMuseCAAAAHAq1sxAX79+/YUXXjAYDGq1evLkyYmJiYWFhVW7ffTR\nR23atBGR99577+zZs7daKQAAAOAErAnQH3/8cW5urlqt/te//rV69eqBAwc2adKkardBgwbt\n3LnT29vbYDCsXr36lksFAAAAHM+aAK2szZg4ceJDDz1Ue8927dpNmjRJRPbu3WvFAwEAAADO\nxpoAfenSJREZNWpUfTr3799fRH755RcrHggAAABwNtYE6OzsbBEJCQmpT+dWrVqJSLWLpAEA\nAIBGx5oA7enpKSLXr1+vT+fc3FwR8fHxseKBAAAAAGdjTYAOCwsTkaNHj9an8/bt20UkNDTU\nigcCAAAAnI01Abpfv34ismLFipycnNp7Hj169KOPPhKRgQMHWvFAAAAAgLOxJkA/++yzIpKe\nnn7//fefO3eu2j5lZWUffvjhfffdV1ZWplKplL04AAAAgMbOmisRdu3a9dlnn/3oo49OnDgR\nHR3dt2/fjh07Koc++eSTr776Kikpaf/+/fn5+Urjc88916VLF5uVDAAAADiOlZfyfu+993Jz\nczdv3mw0Gg8ePHjw4EGl/dNPPzXrOXr06BUrVtxSjQAAAIDTsGYJh4hotdq4uLh169bddddd\nNfXp2rXrhg0bNm3a5O5uZUwHAAAAnM0tRdtx48aNGzfuwoULBw8eTEtLy83NdXNz8/X1bdu2\nba9evcLDw21VJQAAAOAkbDA3HBkZGRkZeevjAAAAAM7PyiUcAAAAwO2pgQJ0Pa+6AgAAADg5\ny5Zw6PX6xMTEAwcOZGVlNW3atEePHsOHD1eu7F2T0tLS+fPnL1myRK/X31qpAAAAgONZEKAP\nHDgwZcqUpKSkyo3+/v5vvfXWxIkTq73L4cOHJ0+efP78+VspEQAAAHAe9V3CceDAgSFDhpil\nZxHJycmZNGnSokWLzNqLiopeffXVe++9l/QMAAAAV1KvGeiysrKnn366uLhYRLy8vB566KE2\nbdrk5OTs2LHj119/FZHZs2ffd999PXv2VPrv3bt3ypQply5dUn4MDAxcsmSJfeoHAAAAGlS9\nAnR8fPzly5dFJDIycseOHWFhYUp7eXn5zJkzly1bZjAY3nzzzS+//LKwsHDWrFkffPCB0WhU\n+jz99NNvvfVWQECAnX4BAAAAoCHVawlHQkKCcuOzzz4zpWcR0Wg0b7/99oABA0Tk22+//frr\nrzt27Lhy5UolPUdGRiYmJn7yySekZwAAALiMegXon376SUR69OjRrVs3s0MqlWr69OkiUlRU\nNHLkyKtXr4qIVqudN2/eyZMnBw4caON6AQAAAIeq1xKOjIwMEenRo0e1R/v27Vv5x0GDBn3w\nwQft27e/9eIAAAAAZ1OvGejCwkIRCQwMrPZoUFCQciMwMHDNmjV79uwhPQMAAMBV1StAK2ua\n1Wp1tUdN7X/4wx9q2hAaAAAAcA0NdCnvBlBRUbFu3brBgwcHBQV5eHgEBwfHxMTs2LGjnnfv\n3Lmzqmbjxo2za/EAAABoLCy7lLfTKi0tfeyxx7Zu3SoiXl5ewcHBmZmZCQkJCQkJf/jDH5Yu\nXVrnCHl5ecp9NRpN1aPe3t42rxkAAACNkYvMQM+bN2/r1q2enp5r167Ny8tLSUnJzc1dvHix\nSqV66623Nm7cWOcIubm5IvL555/nVWfVqlX2/yUAAADQCLhCgM7Ozn7nnXdEZOnSpePHj1em\nkD09PWfMmPHiiy+KyJw5c0wXdqlWRUWF8kXJZs2aNUjJAAAAaKxcIUDHxcWVlZX5+vo+88wz\nZodefvllEbl8+fLBgwdrGUGZfhYRPz8/OxUJAAAA1+AKAfrQoUMi0q9fPw8PD7ND4eHhoaGh\npj41URZACzPQAAAAqIsFXyJcsWJF7YuJ33777U8++aSmo+fPn6//Y1nkzJkzIhIZGVnt0fbt\n26empp46daqWEUwz0KWlpQsWLNi1a1d6erqnp2dUVNTo0aMfeeQRlUpl87IBAADQGFkQoLOz\ns7Ozs2vpcP369evXr99ySRZTqmrRokW1R4ODg019amIK0N26dSsoKDC1nzx5cuPGjQMHDvzy\nyy+ZnAYAAIC4xjZ2yvf/PD09qz2qtFeOxVWZAnRISMiqVavuu+8+Pz+/S5cuLVy4cO3atd99\n911sbOw333xTywiPPPKIwWCo3GI0Gt3dXeGfFwAAAJXVK+Ht3LnT3nXYj7L/Ru1rMPr06bNl\nyxY3N7chQ4bodDqlMSoq6tNPPw0JCXnzzTe3bduWmJg4aNCgmkYoKCiofaMPAAAAuIZ6BegH\nHnjA3nXcCh8fn9zc3KKiomqPKu0+Pj61jBAWFhYWFlbtoblz537wwQe5ubkJCQm1BOjdu3eb\ntfTs2VOv19dROgAAABobV9iFIygoSEQyMjKqPZqWliY1r5Cuk06n69Spk4hcvXrV2gIBAADg\nOlwhQCsB9+eff656yGg0Ku3dunWzevzy8nIRqbpHHgAAAG5DrhCg+/fvLyL79+8vLi42O3Ts\n2DFlY5CBAwfWMsKXX365cOHChISEqoeKi4uVLfDat29vq4IBAADQeLlCgH7ssceaNGly8+bN\nlStXmh1atGiRiPTo0aNjx461jLBly5bXXntt+vTpVTfrePPNN2/cuCEiMTExNq0aAAAAjZIr\nBOgmTZrMmTNHRGbPnr1mzRplxUVBQcGMGTPi4uJEZOnSpZX7L1u2rHfv3gMGDDC1vPTSS25u\nbikpKcOGDTt27JjSWFhYuGjRojfffFNExowZ07179wb7jQAAAOC0XGSj4hkzZpw9e3b9+vWT\nJ0+eNm1aQEBARkZGeXm5SqVatmxZ5awsIleuXDly5IhWqzW19OrVa+XKldOmTTt06FD37t0D\nAgK8vLzS09OVbTSGDx/+8ccfN/SvBAAAAKfkCjPQIqJWq9etW7dp06YhQ4Z4enpmZGQ0b958\nzJgxR44cmT59en1GeO65506ePPnCCy9ERkYWFxdfu3atefPmMTEx8fHxX331lbe3t71/BQAA\nADQKLjIDrRg9evTo0aPr7LZs2bJly5ZVbY+Kiqq6ihoAAACozEVmoAEAAICGQYAGAAAALECA\nBgAAACxAgAYAAAAsQIAGAAAALECABgAAACxAgAYAAAAsQIAGAAAALECABgAAACxAgAYAAAAs\nQIAGAAAALECABgAAACxAgAYAwPb27dv34osvduzYMSAgQKfTtWrVasSIEatXry4tLXV0aQBu\nlbujCwAAwKWkpaU99dRTe/furdyYnp6+devWrVu3vvHGG59//nmvXr0cVR6AW0eABgDAZtLT\n0/v06ZOSkqL82KJFi6ioKJVKlZSU9Ntvv4nI5cuX77///l27dv3ud79zaKUArMcSDgAAbGbC\nhAlKevb19f3888/T0tISExP37NmTmpr6xRdfBAYGisiNGzfGjRtXVlbm6GIBWIkADQCAbezY\nsWPXrl3K7c8++2zMmDFubv/9O/voo4+uX79euZ2cnLxx40YHlAjAFgjQAADYxkcffaTc6Nu3\n70MPPVS1w4MPPtixY0fldlxcXMNVBsCmWAMNAIAN6PX6nTt3KrdHjhxZU7e1a9fm5eX5+/s3\nb968oUqDi9i3b9/GjRv379+flpZ28+ZNf3//bt26PfLII+PGjdNqtY6u7vZCgAYAwAaSk5Pz\n8/OV2126dKmpWy2HgJqwtYuzIUADAGADly9fNt0ODQ11YCVwMWzt4oRYAw0AgA2Ypp9FpGnT\npg6sBC6GrV2cEAEaAAAbKCkpMd12d+cDXtgGW7s4JwI0AAA24OXlZbqdk5PjwErgStjaxTnx\nFhkAABvw8/Mz3c7KynJgJXAZbO3itAjQAADYQEREhOl2RkaGAyuBy2BrF6dFgAYAwAbatGnj\n4+NTUFAgIt9///3YsWOr7bZnz54dO3aISNOmTefMmdOgJaKxYWsXp8UaaAAAbEClUo0YMUK5\nvXnz5vLy8mq7LV68eNGiRYsWLdqzZ08DVodGia1dnBYBGgAA25g4caJyIy0tbcmSJVU7fP/9\n96bc/OSTTzZYYWik2NrFaRGgAQCwjcGDBw8dOlS5PXfu3DfffLO4uNh0dMuWLY888ogyM33n\nnXdOmDDBMVWi8WBrF6dFgAYAwGbWrFkTHh4uIhUVFbNnz27ZsuU999wzYMCAli1bPvroo8qX\nC3U63fr163U6naOLhbNjaxenRYAGAMBmgoODExMTBw8erPyYn59/6NChffv2mfblCAsL2717\n9z333OO4GtFosLWL02I9DQAAthQaGrpjx47t27dv2rTp8OHDGRkZRUVFAQEBnTt3jomJmThx\nolardXSNaBzY2sVpEaABALC9oUOHmtZDA9ZRtnb57LPPRGTz5s1Lly7VaDRVuy1evPjbb78V\nkfvuu48A3TBYwgEAAOCk2NrFORGgAQAAnBRbuzgnAjQAAIDzYmsXJ0SABgAAcF5s7eKE+BIh\nAACAU2NrF2dDgAYAAGgE2NrFebCEAwAAALAAARoAAACwAAEaAAAAsAABGgAAALAAARoAAACw\nAAEaAAAAsAABGgAAALAAARoAAACwAAEaAAAAsAABGgAAALAAARoAAACwAAEaAAAAsAABGgAA\nALAAARoAAACwAAEaAAAAsAABGgAAALAAARoAAACwAAEaAAAAsAABGgAAALAAARoAAACwAAEa\nAAAAsAABGgAAALAAARoAAACwAAEaAAAAsAABGgAAALAAARoAAACwAAEaAAAAsIC7owsAAMA1\nFZQVrTr59aHfzpVWlHcIuGNqlxHt/Fo5uigANkCABgDA9koryp/79p3dKcf9dU3dVKqT1y9/\nn/7z2odmtvVt6ejSANwqlnAAAGB7m87v3XHlaFjTFk09vLw1ni29/a/kZyz9Ic7RdQGwAQI0\nAAC2dybrSlMPT5Xqvy0+Wu8zWVccVhAA2yFAAwBgex5qd4PRWLnFaDRo3TWOqgeADRGgAQCw\nvXtC7i4ou6k3VJhasosL7w2JdmBJAGyFLxECAGB7Q9v0fLHrw38//pWv1ttN5EZ5Sb/Q6Fd6\nPObouuAibuYV71lz+OL3V8uKy4IjggZN7H1HR/Z4aTgEaAAA7OKNeycPat3lcNq5ovLS6MA7\nR0f217l7OLooND6/HE89813SzZyioDsDeo7q6BPYRF+mXzcr/sLBy95+Xm5q1bm9F9MvZI5b\nNKr13ezx0kAI0AAA2IVKpRpyZ/chd3Z3dCFoBHJ+yzuTmFS1Pen7Kz99dcZD5+6mVunLDbtX\nH+ozumvOr3mndp5vEuBdXlouIiqVKuNy1rqZCfeO7VZ1hLDoVnd2CbX7L3CbIUADAGCZYn3p\nw1vm2WPk6MA73xn0gj1GhpNL/fna2hnx1RwwiqhU+jK98lPxjdKExbuU9pzivModL+emXD6W\nUnWA4f9vEAHa5gjQAABYpsJo2HX1mD1GLtGX2WNYNGIqlch/t3NRiVFEJaIS1f/s8fI/nWB/\n7MIBAADgrIxmwVgl/47R/9urocqBghloAAAs4yZu9bwi99XCaxUGg4g00Xg29/Krs3+rJgG3\nWhwap7vuafvm9380a/z1XPrqaXH+rXzd3P8943kjpzg/s0CJy+4ataevTuXmVl5U3rZH69F/\nGab1ruZbql6+nvYt/bZEgAYAwDJeGu2lZz+tT8/m74++XpQvIo9E3LP2oZl2rgtOpDD75px7\n3r71cQwGY3pyluo/KwaMBuN/12u4qUpvlhmNRjc39eXjqYsf/ejWH05EnlnxRKcHIm0ylAsj\nQAMAANiY0WAsyi/20GmaBHjfyjg6b4/S4nJ9id5gMLi5u2l07jeyi5RDWk+N1ltri2L/raSw\npKigRF+ut+GYrooADQAAYBfN2wUOevp39elZerM0Jy2/zm43cou/33xcuR0W3SokqkWdd2nq\n713PEH8mMen4tnP16QkCNAAAgINlpeTtWXPYorv8fODSzwcu1dmt432RXYZGWVsXqscuHAAA\nWKCgtMjRJQC1YEOOhsAMNAAAsunC3rzSm7V0KDfo96WePp5x8WphZhuf4J4tI/uGdFCr1LUP\na9rX+WLubx+e+sZm5f5Hx8A7+7TqYPNh0ZgpXzFUOboMF0eABgBA5h1cez7n13p2vpCbeiE3\ndf253fUf//v0n79P/9mq0mrzcvdHCdCuITDM74Fn+1p8N6Oc2nUh//oND0+N0qAv1Xv56DoN\niVS7//vdXVP/W/oWI6pFgAYAQETEQ+3+ROSAag8VlBV9c+mHJh6ebqp/T+xVGAzuavWg1p11\n7tXsvGuyOWlfib5cRNr6tuwbYsukm1VcsP2XH204IBxL661tGdHc0nvlXSvM+S2/afMmpgln\njVZdkH1To9W0aBto2wpRGQEaAAAREXeVul9IdLWHzmRd0bq7+2q9KjdmFuW182tZ+xVVEpIP\nlUi5iDT38qtpcOtczk8nQENfVq4yvav7N5VKJeWlbEVnX3yJEADR0SEvAAAgAElEQVSAOmjV\nGoPRWPnbWUajGIxGnbq26WfA3pr4e/s0b1KhN5haDBUGg97YNJBlG/ZFgAYAoA53+LZo69vy\nhr743z8bpbCsqIN/WHATf4fWhdudzlvbpktoUX5ReaneYDDoy/RF+SV39WvrG9TU0aW5OAI0\nAAB18HBzfzi8T6Cnb1ZxQV7pzeslBS2b+MdE3OPGXgdwtMi+bXoM7+jt51mYddOzia7TA3d1\nvJ8Lcdsda6ABAKjbnT4tXug04lzO1fzSIn9tkw6Bd2jVGkcXBYib2i38d3eE/+6O8hK9Rkeu\nayD8QwMAUC9eGm2PFu1FpKxC/9fD6+tzl5tlJcqN45mXkg98Umf/O31aPNtp2C3UiNsX6bkh\n8W8NAIBljGLMKs636C6lFWWlxWV1dvPT8t0voBFgDTQAAABgAWagAQCwjJtKFeUflltSmF92\ns1Rf7qHWeLp7BHr6urvVcWXvOrVqEmCTCgHYFQEaAADLaNzcuwdHfJF0wFfXROvmXmEwFJQV\n+Wq9JkU/eOsZGoDzYwkHAACW0RsMh9N+9tV6ad3cRUTt5uanbXI2++r5nF8dXRqAhkCABgDA\nMjfKi34tzPRQ//dTXJVKNG7uOSWFDqwKQIMhQAMAYBmd2qOVd0CFwVC5scJo8NJoHVUSgIZE\ngAYAwDI6d49I/9YFpUUGo0FExCg3y0tDmwZGNmvt6NIANAS+RAgAgMWGtelRWFZ0LPOiu0pt\nEGNY0+YPh/dp6uHp6LoANAQCNAAAFvN0107o8MCA1p0yi3KbaLza+gXr1B6OLgpAAyFAAwBg\nDZVKdadPizt9Wji6EAANjTXQAAAAgAUI0AAAAIAFCNAAAACABQjQAAAAgAUI0AAAAIAFXCdA\nV1RUrFu3bvDgwUFBQR4eHsHBwTExMTt27GjIEQAAAODyXCRAl5aWjho1asKECbt27SoqKgoO\nDs7Ly0tISHjwwQf/+Mc/NswIAAAAuB24SICeN2/e1q1bPT09165dm5eXl5KSkpubu3jxYpVK\n9dZbb23cuLEBRgAAAMDtwBUCdHZ29jvvvCMiS5cuHT9+vEajERFPT88ZM2a8+OKLIjJnzhyj\n0WjXEQAAAHCbcIUAHRcXV1ZW5uvr+8wzz5gdevnll0Xk8uXLBw8etOsIAAAAuE24QoA+dOiQ\niPTr18/Dw8PsUHh4eGhoqKmP/UYAAADAbcIVAvSZM2dEJDIystqj7du3F5FTp07ZdQQAAADc\nJlwhQGdnZ4tIixYtqj0aHBxs6mO/EQAAAHCbcHd0ATZQWFgoIp6entUeVdoLCgrsOsIbb7xR\ntdFoND7//PO13Os2kZqWd+lMavplj806jaNrqa/CwpLy8oqXpp1yd3eFN5muR79/f4WI28qV\n4t5oXsQMer0qJcWD1wRnlXHhYEnpzXlfveboQuqr3KCXksLdezc8v/G4o2tBNcpKyr8vOO5+\n1H3bpThH11JfZUVlJTdKc99LDoxv5uhaHC8wMPD111+v6Wij+dtjNWX3DJVKZdcR4uPjDQaD\nWWPTpk0//PBDqx8XdQoMDGzWrFlqampxcbE9xv/HP1j47tyysmw+ZFhYmFarvXjxos1HFhHJ\nyhJeE5xbhqTadkCVShUeHl5cXJyaauORFacl9bQcsMfIsI0ikTwbD+nl5RUSEpKTk2Onz8bP\nbzthj2EbnbZt27p4gPbx8cnNzS0qKqr2qNLu4+Nj1xG2bNlStbGsrIzN7+xq/fr1//rXv+Li\n4qKiohxdC1zE7NmzL1++fOnSJUcXAhdRXl4eGxvbu3fv+fPnO7oWuIgTJ0688cYbU6ZMGT16\ntKNrcWXKpsY1cYUAHRQUdPXq1YyMjGqPpqWlSc3rm201QkhISH3Lhe34+fmJSKtWrdq2bevo\nWuAitFqtiHBGwVbKyspExNPTk5MKtnLt2jURadasGSeVA7nC+s5OnTqJyM8//1z1kNFoVNq7\ndetm1xEAAABwm3CFAN2/f38R2b9/f9WFsMeOHbt+/bqIDBw40K4jAAAA4DbhCgH6sccea9Kk\nyc2bN1euXGl2aNGiRSLSo0ePjh072nUEAAAA3CZcIUA3adJkzpw5IjJ79uw1a9aUl5eLSEFB\nwYwZM+Li4kRk6dKllfsvW7asd+/eAwYMsHoEAAAA3LZUrrFNREVFxcSJE9evXy8iXl5eAQEB\nGRkZ5eXlKpVq2bJl06dPr9z55Zdffvfdd7VabUlJiXUjwElcvHjx6tWrPXr0UL5NCNy6H374\nobCw8P7773d0IXARBoNhz549zZo16969u6NrgYvIzs4+fvx427Zt+RKhA7lIgFbExcV9/PHH\nR48eLSgoaN68eb9+/V599dWePXuadas2QFs0AgAAAG5bLhWgAQAAAHtzhTXQAAAAQIMhQAMA\nAAAWIEADAAAAFiBAAwAAABYgQAMAAAAWIEADAAAAFiBAAwAAABYgQAMAAAAWIEADAAAAFiBA\nAwAAABYgQAMAAAAWIEADAAAAFiBAAwAAABYgQAMA8F8tWrRQqVTLly93dCH10riqvR00b95c\npVKtWLHC0YXUS9VqOaPqiQANAMC/paamZmZmikj37t0rt2dmZs6fP79nz56+vr4ajSY4ODgm\nJmbnzp0OKvPfGle1t4MrV65cv35dRHr27Gn1IA329FWttqYzylTYpEmT3NzcVCrVsWPHbF5P\nI2MEXMKPP/6onNIXL150dC2wu6CgIBF57733HF1IvVSttnnz5iLy7rvvOrAqVCs+Pl5E3Nzc\nbty4YWrcsmVL06ZNlVcYnU7n7+9v+hv6l7/8hWphcuPGjePHjx8/fry8vNy6ERry6atabbVn\nlNFo1Ov1y5cv9/X1VSrx8PAoLS21bTGNDjPQaFB6vf7zzz8fP358+/bt/fz8NBqNv79/9+7d\np0+ffuTIkQYupqKiYt26dYMHDw4KCvLw8FDe5e/YsaOBy4ClmOOB/Sj/5pGRkd7e3krLvn37\nRo8eXVhYOGTIkB9//LGoqCg7OzspKalfv34ismDBAgc+TY2r2tuBt7d3ly5dunTp4u7ubsXd\nG/jpq1pt1TNKRA4ePKj8mfb09OzcubOIdOzY0cPDw4aVNEqOTvC4jezcubNNmza1nI0PP/zw\n9evXrRvc0hnokpKS4cOHK3fx8vJq3bq1VqtVfvzDH/5gXQ1oGMzxwH6Ul4Vx48YpPxoMhg4d\nOojI8OHDzc63tLQ05ZmaP3++Iyo1GhtbtaidMzx9ZmeUolWrVmq1etq0aXl5ef379xeR5557\nzq5lNArMQKOBrF+/fujQob/88ou3t/fMmTOPHDmSn59vMBgyMzM3bdqkvL3+17/+NWDAgIKC\nggaoZ968eVu3bvX09Fy7dm1eXl5KSkpubu7ixYtVKtVbb721cePGBqgB1mGOB5Y6duxYbGys\n8j45JCRkwoQJFy9eFJHevXurVKqFCxdW7imVPhxISUkJDw/v06fPkiVLzM63li1b+vn5iUhO\nTk6dBZSWli5fvvzee+8NCAjQarWtWrWKiYn5+uuvnbNamKn/0xcQEKBSqd5//33lR4PBoNPp\nVCrV7t27s7OzX3vttaioKG9v75CQkJiYmHPnzlW+b8OfbGbVSpUzSkSuXbsWFhb2448/vvfe\nez4+PsePH5caPj277Tg6weO2cPLkSZ1OJyKRkZG//PJLtX3eeOMN5ZycNGmSFQ9h0Qx0VlaW\nEk3+/ve/mx36/e9/LyJt27Y1GAxWlAEnxxzPbWjVqlVqtVpE/P39+/fv36tXLw8PD39//6NH\njyoxZd++fUrP9PR05RwwtdQiKytL6fzOO+/U3vPMmTPt2rUTEZ1O16NHj/vuuy8sLEy575Qp\nU8xeahxeLczU/+n75ZdflPbDhw8rLZcuXVJaduzYcccdd3h4eHTt2rV9+/ZKo7+//7Vr1+pT\ngz1OtqrVVntGFRcXm+51/vx5pcOPP/5Yn7JdGwEaDWHEiBEi4u3tXXu6HTt2bLt27WbNmlX5\nP3lhYeHixYv79u3r7++v0WgCAgL69++/bNmyoqKiyve1KECvXLlSRHx9fat+RK7M9IjI/v37\nLfkVcatKSkrefffde+65x9/f38PDo2XLlqNGjfrqq6+q9lRWXJje/FRUVCjLb3bt2pWVlfWn\nP/3prrvu8vLyatWq1ahRo86ePVv5vleuXHn44Yf79Olz7ty5qiMrczzTp0+3X7VGo7Fly5Zm\nfwgzMjJ69+597Ngxo9FoMBiU5SWrVq2qswzUad++fUoefe2118rKypTGpKSkiIgIJWdUXkuj\nzNKpVKqCgoI6R37ppZdExNPTMzU1tZZuycnJyjzfrFmzsrKylEaDwfDpp5+qVCoR2bBhg/NU\nCzMWPX1ffPGFiKjVatOfpy1btih/UNq1a/fcc8/l5OQo7d98841y97feeqs+ZdjjZKtabZ1n\n1IYNG0REo9GUlJTUp2zXRoCG3aWkpCj/detcW2z6g2Fy6dKl8PBw5c9GRETEoEGDwsPD3dzc\nRKRjx47p6emmnhYF6PHjx4vIiBEjqj0aGhoqIosWLapzHNgKczzM8dhJjx49RGTo0KFm7du2\nbVP+nTt06GBq/Otf/yoikZGRNY1WUVGRlZX17bffxsTEKCfA5s2bay+gT58+IjJ37tyqh5RB\nhg8f7jzVwoxFT9+cOXNEJDo62tQyd+5c5Ymr+rY8MjJSRKZOnVrTQ9v7ZKtabZ1n1Kuvvioi\nXbt2rb2M2wQBGnb3ySefKK8gR48eteiOFRUV3bp1U/4/X7hwwdR+4sQJZQ5v2LBhpkaLAnTX\nrl1rCfT33XefiMTGxlpULazGHE9lzPHY0A8//KCcG4mJiWaHTN+1mDBhgqlx1KhRIvLUU09V\nHaqkpESj0Sh3UalU0dHRr7zySnJycu0FfPvttyISHBxc7fdB//znP1dOMA6vFmYsevqMRuOw\nYcPMniNlvVZgYKDZR6ZGozEqKkpEZs6cWXXkBjjZqq22ljNKMWDAABF55plnaq/kNmHNV3AA\ni/z8888i4uHhoXw1qv62bt2qfKFh7dq1pglFEencufPbb789duzYbdu2nTlzJjo62tKSsrOz\nRaRFixbVHg0ODjb1QQMYP358dnb23LlzlfkPhUqlmjBhwpYtW+Lj4z/77LOnnnpKaVdOiaio\nKE9PT6VF+VKLiAwfPvzdd981jTBs2LD27dtfuHAhOTm5poc2GAy5ublHjx5duXJlfHy8Tqdb\nt25dSEiI/ao9evSoiLRv3960DYgZpUN0dLRpWxhYLSEhQUSUdV9mh7y9vd3c3AwGgzLpq1D+\n8av9gtTZs2cNBoNKpTIajWq1OjQ0tH///soHEbVYt26diOj1+iFDhlQ9evnyZRExnRsOrxZm\nLHr65D+vRcq8j0J5BXjssccqdxMRg8Fw5coVEVFitJkGONmqrbaWM0pEjEbjiRMnaulw23Fs\nfsft4JlnnhGR4OBgS+/43HPPiUhERETVQyUlJUq8WLJkidJi0Qx0s2bNpObLcEyZMkVE+vbt\na2nBsAJzPGaY47EhJYmOHDmy6iHTd0YPHTqktCibc0t1E8AmZWVlycnJf/3rX5WTZ/bs2bUX\ncMcdd9T5V9j0dVKHVwszFj19pufI9P2ZjIwMpWXr1q1mIyvzSiJy8uTJmh7dridb1WrrPKOS\nkpKUDkeOHKnjH+72wAw07E75XLuiosLSO54+fVpEqp231mq17dq1O3fu3NmzZ2u6e1lZmcFg\nqNyiVqtNmakWRqPRVDbsjTmeyozM8djUmTNnROTuu++uekh5atRqdZcuXZQW5alRqVTKEq9q\naTSadu3azZ0719/f/6WXXlq4cOHTTz9d+fOxyioqKlJSUkRk+/btDz74oJNXCzOWPn3KM+Lm\n5mb2HEl1l3xSDul0OmVToGrZ9WSrqdpaziilg7u7e6dOneoc/3ZAgIbdBQYGikhOTk5JSYmy\nmV09KRteKrPFVSnXm8jNza3p7r169Tp58mTlluHDhysrUH18fHJzc4uKiqq9o9Lu4+NT/1Jh\ntf3794tIVlbW3r17a+qjfNtGRNLT05VJHVO+vHbtmvK1vIcfftjsXklJScXFxfK/+dWkW7du\ner2+vLw8JSVlw4YNCxYs2L59++zZs19//XX7VXv9+vXU1NSaShKR5OTk/Pz8Wjqg/oqKipTX\nEOVrwWaUl4IOHTqY3vAo78TCw8NN17KpxZNPPvnSSy8ZDIajR4/WlGny8/OVd+OVN/x22mph\nxqKnT/7zjERERDRp0qRyS+vWrYOCgsw6//TTTyLSuXPn+mxmb/OTrZZqazmjlAB99913W/R3\n3IVxIRXYnfJutaKi4tChQ1bcvaaZ4FuZJ1Zezkyfr5lRPtuqaYU0bKjyrEktn5Qp875izzme\nt99+W0QWLlxo+pjSftUyx9MwSkpKlBtVXyWys7PXr18vIrUsKb506dK4ceOioqJu3rxZdXDl\nfY6I1PKhlpeXl3JDeddkprS01KmqhRmLnj75TwCt58dNjj3ZLK22nh1uNwRo2N2AAQOUjedW\nr15de8+ysrL333+/sLBQ+TEgIEBq/jKfMvdc+SLMZk6cOGGWbEyXYlLSiWkVWmXG/6xOYwqw\nATjVHI+IKHM89q6WOZ6G4efnp/zLV13oNW3aNOV1pnIkNYsUQUFB8fHx58+f/9Of/lR18Pfe\ne09E1Gq1cgHLaul0OmW+0LQJnUlpaWnv3r1HjBihvCVzhmphxqKnT6q7hp/SUvlZUxgMBrOV\nWg18stVSbS1/+LgGoTmrV08D9ff444+LiJubW+1XzJo5c6aING/ePC8vz2g0Tp06VUTCw8Or\n9rxx44aSit59912lxaIvESo763l7e1f92pmSukTk1KlT9f31YC1liYWIfP7551WPVt3HTfkG\n3tixY00tyuamMTExVe+u/LF58cUXlR+Tk5NjY2Pvuusu03UoKjNdQCcuLs5+1T766KMiMmbM\nmJoeYtCgQSIyefLkmjrAIsoXgr29vX/66SelJTc3d9KkSREREcpTafo6lOmN+u7du013nz9/\nvtI4duxY0wtLamrqK6+8oswTT5s2rfYCli5dqrz0ffTRR6bGCxcuKCfn3XffXfm0cXi1MFP/\np8+0i/yePXvMWrZt22Y2rGnupvIsT0OebFWrrfaMqsy0l5FpS3sQoNEQLly4oEyuBAYG1vTf\n729/+5vy//P//u//lJbt27crLQcOHDDrrExmu7m5mbZNsChAFxYWKvVU3SF49OjRItKjRw/L\nfkNYS5k1qbxPhaKkpKRLly7Dhw+/evWqqbF169YisnTpUlOLcgWTBQsWmN29oqJC2Sdu9erV\nSkt+fr4yc1zt36Hp06eLiFqtzsjIsF+1d955p4gsXry4pvGVSyFWvcI8rJOZmalsSuju7t69\ne/cBAwZ4eXlFRkYqLyCVN9veuXOn8gKSm5truntFRYXyNl7RrFkz04cJIhIbG1v12k9m9Hq9\naXV+q1atBg4cGB0drXwi16lTp7S0NKeqFmbq//Qp+/NUfkZ27NihtGRmZpoNqyzI0el05eXl\npsaGPNmqVlv1jCopKQkICPD9D1MxPj4+psbKL263IQI0GsiXX37p4eGhZJRnnnkmMTExNzfX\nYDBkZWVt2rSpV69eyn/OkSNHml5TDAaDcl2liIiIyvuLHTp0SFndMXHiRFOjRQHaaDS++eab\nIqLVav/xj38oL0z5+fl//OMflUG+++47m/72qBFzPCbM8dhDdnb25MmTmzdvrtPpIiMjZ86c\nmZ2dPW/ePPnf98kLFy4UkbZt21YdITEx8YknnggNDfXw8PDy8mrXrt348eNr2TzOTEVFxZo1\nawYMGODn56dWq5s1a3bvvfcuX768uLjYCauFmXo+fVWfEaUlLCys6pivvPKKiPTq1avqoYY5\n2WqqtnLLkSNHpC7ffPNNPQtzSQRoNJwDBw5Uu6GYwsPD47XXXqv8jtxoNF69elXZ00Cj0XTq\n1Gnw4MGmTzMfeOCBwsJCU09LA7Rerx83bpxyFy8vr9atWyvfz1CpVKZlIWgAzPEwx9PwlHfm\ns2bNcnQh9dK4qgVuEwRoNCi9Xr9p06bx48dHRET4+vq6u7v7+/v37dt37ty5V65cqfYuN27c\nWLx4ce/evZX+QUFBDz744Lp16/R6feVulgZoxaZNm4YMGRIQEKDRaEJCQsaMGfPDDz/c0m8I\nyzHHwxyPPZw+fXrlypVnz541a1feyahUqjNnzjiksGo1rmoBqIxGY52v4AAANC7Dhg3bvn17\n7969t2zZEhwcrDR+/fXXU6ZMyczMnDx5cp37AjWkxlUtAAI0AMAFnT59euDAgTk5ORqNJjo6\nWqfT/fLLL8ru78OGDfviiy/MLl3pWI2rWgAEaACAa0pOTl66dOnu3btTU1MrKioCAwO7d+8+\nYcKExx9/3LprMNlV46oWuM0RoAEAAAALcCVCAAAAwAIEaAAAAMACBGgAAADAAgRoAAAAO2rR\nooVKpVq+fLmjC6mXxlWtoxCgAQAA7CU1NTUzM1NEunfvXrk9MzNz/vz5PXv29PX11Wg0wcHB\nMTExpkuWOkrjqtaB2IUDAADAXhISEmJiYtzc3AoKCry9vZXG+Pj4CRMmFBYWiohOp/Py8srJ\nyVEO/eUvf/nb3/5GtU6OGWgAAAB7OXbsmIhERkaa8ui+fftGjx5dWFg4ZMiQH3/8saioKDs7\nOykpqV+/fiKyYMEC5S5U68wI0AAAAPZy9OhRqbQiwmg0Tp06Va/XDx8+fOvWrT169FAulBMR\nEfHPf/5T6fDVV19RrZMjQAMAAFjm2LFjsbGxrVu31mq1ISEhEyZMuHjxooj07t1bpVItXLiw\nck+pFElTUlLCw8P79OmzZMkSd3f3ymO2bNnSz89PREwLJGpRWlq6fPnye++9NyAgQKvVtmrV\nKiYm5uuvv3bOal2Pe91dAAAA8B8ffvjhiy++WFFR4e/v37t375KSkn/+859bt27duXOnMoN7\nzz33KD0zMjLS09OlUiS94447EhISqh02Ozs7Ly9PRNq0aVN7AWfPnh01atSlS5d0Ol10dLSP\nj09ycnJCQkJCQsKUKVM++uijypd/d3i1rskIAACA+tm3b59arRaR1157raysTGlMSkqKiIho\n166diLi5ud24cUNpV6aEVSpVQUFBnSO/9NJLIuLp6ZmamlpLt+Tk5ICAAJVKNWvWrKysLKXR\nYDB8+umnSm7esGGD81TrqgjQAAAA9dWjRw8RGTp0qFn7tm3blKnJDh06mBr/+te/ikhkZGRN\no1VUVGRlZX377bcxMTEiotPpNm/eXHsBffr0EZG5c+dWPaQMMnz4cOep1lWxhAMAAKBefvzx\nx59++klEZs2aZXbItBBCyawKsyXFlZWWljZt2rS8vFxEVCrV3Xff/corr/z+979XJoZrsmPH\njsOHDwcHB8+ZM6fq0ejo6Pj4+KtXrzpJtS6MAA0AAFAvyoLggICA/v37mx3y9vZ2c3MzGAyV\nI6nZphaVnT171mAwqFQqo9GoVqtDQ0P79+9fZx5dt26diOj1+iFDhlQ9evnyZRHx9PR0kmpd\nGAEaAACgXvbv3y8iffv2dXMz38fs2rVrBoNBKs3pXr9+PTU1VUS6detWdahu3brp9fry8vKU\nlJQNGzYsWLBg+/bts2fPfv311+ssICsra+/evTX1iYyMdJJqXRjb2AEAANTLmTNnROTuu++u\neuj48eMiolaru3TporQoE7oqlapr1641DajRaNq1azd37ty3335bRBYuXJiUlFRT54qKipSU\nFBHZvn17LctzlVlqh1fr2gjQAAAAdSsqKlL2PA4NDa16VNnCokOHDqYVFMqS4vDwcF9f3zoH\nf/LJJ0XEYDAoQbZa+fn5RqNRREyXCXTmal0bARoAAKBuJSUlyo3KuywrsrOz169fL//7nTyz\nJcWXLl0aN25cVFTUzZs3qw6en5+v3NBoNDUV4OXlpdxQ1lqYKS0tdapqXRsBGgAAoG5+fn5N\nmjQRkbNnz5odmjZtWmFhoVS3qYVpSXFQUFB8fPz58+f/9Kc/VR38vffeExG1Wt2vX7+aCtDp\ndO3btxcR0yZ0JqWlpb179x4xYoSyxsMZqnVtBGgAAIC6ubm5KUsXPv30U9PShby8vMmTJ5t+\nNEXSnJycK1euSKU5XR8fnxkzZojIihUrnnrqqeTkZKX9t99+e/XVV5VIOnXq1BYtWtRSw3PP\nPSci69ev//jjj02NSUlJgwcPPnHixJUrV0x3d4ZqXVnDbDcNAADQ2GVmZoaEhIiIu7t79+7d\nBwwY4OXlFRkZuXr1ahHRaDQlJSVKz507dypBKzc313T3ioqKqVOnmjJYs2bNlEliRWxsrOli\ngTXR6/UPP/yw0r9Vq1YDBw6Mjo5WNtno1KlTWlqaU1XrwpiBBgAAqJegoKBTp05NnjzZ39//\n7NmzGRkZ06ZNO3TokLJwonPnzlqtVumpzPK2bdvWz8/PdHc3N7f3338/MTHxiSeeCA0NvXnz\npsFgaNeu3fjx4xMTE9evX1/nkmK1Wr1ly5Y1a9YMGDCgqKho//79v/32W9++fZcvX37kyJGW\nLVs6VbUuTGU0Gh1dAwAAQCPWt2/fw4cPz5o1a+HChY6upW6Nq1rnxAw0AABA3c6cOfPBBx+c\nO3fOrH3Xrl2HDx9WqVTjx493SGHValzVNjoEaAAAgLrNmDFj6tSpU6ZMycjIMDV+/fXXsbGx\nIjJp0qRqL1niKI2r2kaHJRwAAAB1O3369MCBA3NycjQaTXR0tE6n++WXX5R4OmzYsC+++MJ0\nURJn0LiqbXQI0AAAAPWSnJy8dOnS3bt3p6amVlRUBAYGdu/efcKECY8//njV65U4XOOqtnEh\nQAMAAAAWYA00AAAAYAECNAAAAGABAjQAAABgAQI0AAAAYAECNAAAAGABAjQAAABgAQI0AAAA\nYAECNAAAAGABAjQAAABgAQI0AAAAYAECNAAAAGCB/w/BRTtc148AAAACSURBVAe9hmrkTAAA\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 480
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data input and reshape\n",
    "df <- read.csv(\"/Share2/home/qitiancong/00.project/04.Song_Lab/01.R_plots/01.data/9D.csv\",\n",
    "                header=TRUE,\n",
    "                stringsAsFactors=TRUE)\n",
    "\n",
    "# set df for sig note\n",
    "df$max <- apply(df[,c(2:4)],1,max) + 0.2\n",
    "\n",
    "# transformat df\n",
    "df_long <- gather(df, replicate, value, R1:R3)\n",
    "\n",
    "# set genotype order\n",
    "df_long$genotype <- factor(df_long$genotype, levels=c(\"Col-0\",\n",
    "                                                      \"ein3eil1\", \n",
    "                                                      \"gl3egl3\",                                                                                                           \n",
    "                                                      \"gl3egl3ein3eil1\"))\n",
    "\n",
    "\n",
    "# set theme function \n",
    "theme_custom <- function (base_size = 11, base_family = \"\", base_line_size = base_size/22, \n",
    "    base_rect_size = base_size/22) \n",
    "{\n",
    "    theme_bw(base_size = base_size, base_family = base_family, \n",
    "        base_line_size = base_line_size, base_rect_size = base_rect_size) %+replace% \n",
    "        theme(panel.border = element_blank(), \n",
    "              panel.grid.major = element_blank(), \n",
    "              panel.grid.minor = element_blank(), \n",
    "              axis.line = element_line(colour = \"black\", size = rel(1)),\n",
    "              axis.ticks.length=unit(.15, \"cm\"),\n",
    "              legend.key = element_blank(), \n",
    "              strip.background = element_rect(fill = \"white\", colour = \"black\", size = rel(2)), \n",
    "              complete = TRUE,\n",
    "#               axis.ticks.x = element_blank(),\n",
    "              axis.text.x = element_text(size=16, angle=0, color=\"black\",\n",
    "#                                          hjust=1, vjust=1,\n",
    "                                         margin = margin(t = 25, r = 0, b = 0, l = 0)),\n",
    "              axis.text.y = element_text(size=16, color=\"black\", margin = margin(t = 0, r = 5, b = 0, l = 0)),\n",
    "              axis.title.x = element_blank(),\n",
    "              axis.title.y = element_text(size=20, angle=90, margin = margin(t = 0, r = 15, b = 0, l = 0)),\n",
    "              strip.text.x = element_blank(),\n",
    "#               legend.title = element_blank(),\n",
    "#               legend.position = c(0.82,0.7),\n",
    "#               legend.position = \"bottom\",\n",
    "#               legend.justification = \"center\",\n",
    "#               legend.text = element_text(size = 18),\n",
    "#               legend.text.align = 0\n",
    "             )\n",
    "}\n",
    "\n",
    "# set figure size\n",
    "options(repr.plot.width = 8, repr.plot.height = 6)\n",
    "\n",
    "# generate x-tick label vector\n",
    "xtick_labs <- c(\"Col-0\\n\",\n",
    "               expression(italic(\"ein3 eil1\\n\")),\n",
    "               expression(italic(\"gl3 egl3\\n\")),                              \n",
    "               expression(italic(\"ein3 eil1 \\n gl3 egl3\"))\n",
    "               )\n",
    "\n",
    "\n",
    "p <- ggplot(df_long, aes(genotype, value, fill=genotype, color=genotype))\n",
    "D9 <- p + stat_summary(fun=\"mean\", \n",
    "                 geom=\"col\", \n",
    "                 position = position_dodge(0.7),\n",
    "                 width = 0.3,\n",
    "                 alpha=0.4,\n",
    "                 show.legend=FALSE) + \n",
    "    stat_summary(fun.data = mean_se, \n",
    "                 geom = \"errorbar\", \n",
    "                 position = position_dodge(width=0.7),\n",
    "                 width = 0.15,\n",
    "                 size=1,\n",
    "                show.legend=FALSE) +\n",
    "    geom_jitter(alpha=0.8, \n",
    "                width=0.1,\n",
    "                show.legend=FALSE) +\n",
    "    theme_custom() +\n",
    "    scale_fill_aaas() +\n",
    "    scale_color_aaas() +\n",
    "    scale_x_discrete(labels=xtick_labs) +\n",
    "    ylab(quote(\"Relative expression level\")) +\n",
    "    geom_text(data=df,aes(x=genotype,y=max,label=sig),size=8,color=\"black\",show.legend=FALSE) +\n",
    "    scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +\n",
    "#     annotate(geom = \"line\",\n",
    "#            x = c(1, 1),\n",
    "#            y = c(40, 50)) +\n",
    "#     annotate(geom = \"line\",\n",
    "#            x = c(2, 2),\n",
    "#            y = c(35, 50)) +\n",
    "#     annotate(geom = \"line\",\n",
    "#            x = c(1, 2),\n",
    "#            y = c(50, 50)) +\n",
    "    annotate(geom=\"text\", x=4, y=2.3,size=10,\n",
    "            label=expression(italic(\"GL2\")))\n",
    "D9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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vO88cTk75Ib1935bjXh0W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NRxjm5OfiE+j/8cs9lSeu39RCMPnyrof53e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\nZam//aaazbrAQMXPrygxUTGbJ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 720,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 16, repr.plot.height = 12)\n",
    "fig9 <- plot_grid(NULL,B9,C9,D9,\n",
    "          ncol=2,\n",
    "#           rel_heights=c(4,6,10),\n",
    "          labels = c('A','B','C','D'),\n",
    "          label_colour = 'red',\n",
    "          label_x = 0.01,\n",
    "          label_y = 1,\n",
    "          label_fontface = 1,\n",
    "          label_size = 28,\n",
    "                 scale=0.9)\n",
    "fig9\n",
    "ggsave2(\"/Share2/home/qitiancong/00.project/04.Song_Lab/01.R_plots/01.data/9_fig_done/Figure9.png\",\n",
    "      fig9, height=12, width=16, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}