{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example usage of Millefy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“replacing previous import ‘IRanges::shift’ by ‘data.table::shift’ when loading ‘millefy’”Registered S3 method overwritten by 'xts':\n",
      "  method     from\n",
      "  as.zoo.xts zoo \n",
      "Warning message:\n",
      "“replacing previous import ‘IRanges::distance’ by ‘destiny::distance’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘IRanges::collapse’ by ‘dplyr::collapse’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘data.table::last’ by ‘dplyr::last’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘IRanges::union’ by ‘dplyr::union’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘IRanges::slice’ by ‘dplyr::slice’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘IRanges::intersect’ by ‘dplyr::intersect’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘IRanges::setdiff’ by ‘dplyr::setdiff’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘data.table::first’ by ‘dplyr::first’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘IRanges::desc’ by ‘dplyr::desc’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘data.table::between’ by ‘dplyr::between’ when loading ‘millefy’”Warning message:\n",
      "“replacing previous import ‘magrittr::extract’ by ‘tidyr::extract’ when loading ‘millefy’”"
     ]
    }
   ],
   "source": [
    "library(millefy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BigWig files (scRNA-seq samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"/opt/conda/lib/R/library/millefy/extdata/RamDA_00h_A06.bw\"\n",
      "[2] \"/opt/conda/lib/R/library/millefy/extdata/RamDA_00h_A07.bw\"\n",
      "[3] \"/opt/conda/lib/R/library/millefy/extdata/RamDA_00h_A08.bw\"\n",
      "[4] \"/opt/conda/lib/R/library/millefy/extdata/RamDA_12h_A06.bw\"\n",
      "[5] \"/opt/conda/lib/R/library/millefy/extdata/RamDA_12h_A07.bw\"\n",
      "[6] \"/opt/conda/lib/R/library/millefy/extdata/RamDA_12h_A08.bw\"\n"
     ]
    }
   ],
   "source": [
    "# Path to bigWig files\n",
    "bwfiles = Sys.glob(file.path(system.file(\"extdata\", package=\"millefy\"), \"*.bw\"))\n",
    "print(bwfiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group labels for bigWig files (same length as bwfiles)\n",
    "groups = c(\"00h\", \"00h\", \"00h\", \"12h\", \"12h\", \"12h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      00h       12h \n",
      "\"#FFFF00\" \"#FF7F00\" \n"
     ]
    }
   ],
   "source": [
    "# Color labels for bigWig files (A named vector with the same length as the number of kinds of \\\\code{groups})\n",
    "color_labels <- colorRampPalette(c(\"yellow\", \"red\"))(length(unique(groups))+1)[1:length(unique(groups))]\n",
    "names(color_labels)  <- unique(groups)\n",
    "print(color_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GTF file (gene models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load gene models (It takes a little time)\n",
    "path_gtf = system.file(\"extdata\", \"example.gtf\", package=\"millefy\")\n",
    "dt_gtf_exon <- gtfToDtExon(path_gtf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set tracks\n",
    "## Single-cell track\n",
    "max_value = 7000\n",
    "scTrackBw <- list(path_bam_files = bwfiles, groups = groups, group_colors = color_labels, max_value = max_value, isBw=TRUE)\n",
    "\n",
    "## Gene annotation track\n",
    "geneTrack1 <- list(path_gtf = path_gtf, dt_gtf = dt_gtf_exon, label = \"GENCODE\")\n",
    "\n",
    "\n",
    "# Prepare arguments for millefyPlot()\n",
    "## List of tracks\n",
    "tdlist <- list(scTrackBw, geneTrack1)\n",
    "\n",
    "## List of track types\n",
    "tt <- c(\"sc\", \"gene\")\n",
    "\n",
    "## List of track hights\n",
    "heights = c(12, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set a location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Location to visualize\n",
    "chr =  \"chr19\" # character\n",
    "start = 5824708 # integer\n",
    "end = 5845478 # integer\n",
    "\n",
    "text_main = \"mESC 00h, 12h (Neat1)\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default\n",
    "When we don't set the sc_sort_destiny parameter (default), the order of single cells is the order of bwfiles.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Begin millefyPlot: 2019-12-30 02:19:04\"\n",
      "[1] \"title\" \"sc\"    \"avg\"   \"gene\"  \"axis\" \n",
      "[1] \"Importing BigWig: 2019-12-30 02:19:04\"\n",
      "[1] \"Finished millefyPlot: 2019-12-30 02:19:05\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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tSnj7ZtU1mZhgxRMqkNGxSNavRoSfL7cw+S5eVqb5fNJr9fwaAaGuRyqaREVVUq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\nEf8P9in83Pn0oXIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l <- millefyPlot(track_data=tdlist, track_type=tt, heights=heights,\n",
    "          sc_type = \"heatmap\",\n",
    "          chr = chr, start = start, end = end,\n",
    "          sc_avg = TRUE, sc_avg_height = 1,\n",
    "          title = text_main)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replot ('all-cell' mode)\n",
    "When we set sc_sort_destiny = 'all', all single cells are reordered by diffusion maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Begin millefyPlot: 2019-12-30 02:19:05\"\n",
      "[1] \"title\" \"sc\"    \"avg\"   \"gene\"  \"axis\" \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in DiffusionMap(as.ExpressionSet(as.data.frame(mat))):\n",
      "“You have 990 genes. Consider passing e.g. n_pcs = 50 to speed up computation.”"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalue of DC1: 0.274219\n",
      "[1] \"Finished millefyPlot: 2019-12-30 02:19:06\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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2ps1IwZOvdcxWKd15h13Hw+DRumwkKFQioo0I4dGjJEW7equVlerwIBuVx64gmV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\nAACAlvj/ORvaBRQavRUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millefy_adjust(sc_sort_destiny = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replot ('group-wise' mode)\n",
    "When we set sc_sort_destiny = 'group', all single cells in each group are reordered by diffusion maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Begin millefyPlot: 2019-12-30 02:19:07\"\n",
      "[1] \"title\" \"sc\"    \"avg\"   \"gene\"  \"axis\" \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in DiffusionMap(as.ExpressionSet(as.data.frame(mat))):\n",
      "“You have 990 genes. Consider passing e.g. n_pcs = 50 to speed up computation.”"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalue of DC1: 0.274219\n",
      "[1] \"Finished millefyPlot: 2019-12-30 02:19:08\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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u9WXZ369NG2bSor09ChSqW0fr3a2jR6tCQFg/aTZHm52tvlcCgYVDishgZ5PCop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ffnju3Lm33nqLKfz2229LSkpsbGx4PJ5Q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\niP8PJRg2VDHyCsgAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millefy_adjust(sc_sort_destiny = 'group')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replot (Change scale of averaged single-cell read coverage track)\n",
    "Using `sc_avg_scale`, you can change the scale of the averaged single-cell read coverage tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Begin millefyPlot: 2019-12-30 02:19:08\"\n",
      "[1] \"title\" \"sc\"    \"avg\"   \"gene\"  \"axis\" \n",
      "[1] \"Finished millefyPlot: 2019-12-30 02:19:08\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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80a9eunbdtUVqYRI5RKacMGtbZq7FhJCgbtJ8nycrW1yeFQMKhwWHV18nhUUqJB\ng1RUpPJyrVmjVEoNDUql9Kc/KRrVt7+tCRPU1KTaWo0apcpK3XWX1qyRpLIyeb0aPVpVVXK7\nNX68vF4tXarCQk2apLVrNWOGHA6tWKF02n7GGzhQgwbJ4bAfX9YD3+22d836G4cIXrEDAAAw\nBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAA\nYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4A\nAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQd\nAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEI\nOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABD\nEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAA\nhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAA\nAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgB\nAAAYgrADAAAwBGEHAABgCMIOAADAEAW5XK63x4C8gt4eQGd7vDBaWxWJKBbTa6/p+ed1zDEa\nM0YjRmjZMiWTkpTJaN06DR2qjg55vRo6VE1NSqdVW6s//UmSfvYzbd2qs86S06l0WsGg1q1T\nLKbWVgUCev55rV9vr9PvVyCgoiJt2KBnnlE4rIYGTZ5sr+drX9ODD2rSJI0YocmT9Z3vSJLH\no+ZmSSork9utWExXXCGvV6tXa9MmjR2rmhq1t6u+XpGIgkG99prWrVNZmeJxud0KBpVOa8UK\nDRmiBx5QQ4MaGhQIqKlJXq8CAY0erQ0blMmosVHRqFpbFY1qzRqNHauRI3XCCQoE1NGhZFJt\nbUqnlUgonVYopMZGJZOKxVRZqRNPVDCo9eu1aZOWLlU0qtNOUzSq55+XpCFD9NOfauZMNTZq\n8WJ5PPL71diotjY1N9vb8vu1dq3cbpWXa906RSKaOVO1tfL7NWSIqqoUjdrnooaf2MsAACAA\nSURBVLFRoZCCQW3atOvUVFaqpkbV1XrqKXm9mjpVkYi2b1drq7xenX66nntOtbWaMkXJpFwu\n+7y/+KJ8Pv3rX/aBrazUqFEKBLR1q+bMUSikjRs1aJCef17xuFpaFI3q6qtVVaWLLtJrr2nO\nHLlcevZZrVmjo47Sb3+rE05QY6O2bNFPfqLaWk2frrFj9eKLKitTebliMS1ZomhUl1+uwkLd\ncIOyWU2YoHRap5wiv1+xmH73O8Vicrk0aZKeekqSLrxQM2eqvV3ptCor9ac/qaREY8bI49Ho\n0Wpr07JlcrvV1KRAQD6fSkvV1KRgUOXlevppLVmiESMUDMrlUkODpkzRWWcpFlM6LZ9PLpc2\nblRdnUaMkMslt1uSIhE9/bSOPlqRiBYuVFWVhg5VSYkGDVJlpZqbtWSJJDU1ado0TZyooUO1\ncKHGjtWGDVq3ToGAMhklkxo9WvG4Ghp0zjlaskQlJfJ6lU7L7VZ9vbZu1bhxSiYVj6u9XS6X\nWlq0bZsGD5bXK6dTU6Zo9Gh7nPmjV1qq4cPV1iZJRUVKJhUOq7FRzc2KxTRnjpJJNTWptFQb\nN2rdOo0fr1mztHSpamvV2qrmZo0YoaoqtbYqFNIrr6hfP516qmIx3XWX/YQwbpwCAblc9oGV\ndMklGjVKd94pl0uzZ8vt1qpV2rpVNTX65S9VVaWqKqVSqq3VxRersVHV1fL7tWmTqqrsx6/X\nq44ONTRo3jxJ8vt1yilqbFRDg7Zv1+jR8vkUCmnrVqXT9kMmGNSKFfL5NHGi0mn7tD77rAYO\n1MKFuuIKvfKKPWDrpFgjLy1VPK7nn9eQIbrzTnV0qLZWNTVasULPPKPSUgWDamnRo49KUr9+\n2rlTF1ygP/5RLS3KZhWJaM0aeTyqq1M0qqYmjRypaFSplFasUDgsSSNHqq5OJ50kr1ePPiqP\nR3PmyO1WW5vWrVNVldasUUODJF19tTweZTKSVFurWExTpsjn0z33KJlUSYmqqzVjhjwerVmj\nxkb5/dq6VS6XVq2S262iIm3bZq/K4vHI5VIwqA0b7C+tp2hJkyZp7Fj7WXf2bK1Zoxtv1Pjx\n+vGP1dAgp1M+n776VUlyOHTeebroIi1ZovXrVVqqE06Q3y+3W1VV2rhRY8eqokIeT0//acHH\njVfsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAA\nAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgB\nAAAYgrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKw\nAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAE\nYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABg\nCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAA\nwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0A\nAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7\nAAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMERBLpfr7THgA/MKensEnczb44WRSEhScbES\nCSWTkuR2q6NDhYVyueR2K5GQ2y2nU4mEwmGVlMjtliSnU263UillMkomlU7L45HLpbY2xeMK\nBOT1SlIkYq/T61UioeJidXQonZakbFahkFwuOZ0aOFDptGIxud3y+ZRMKhZTPK5RoySpo0PJ\npFwuSUql5HYrmVR5+d72xRqbJZuVw2HvRff53W6Fw5JUUiKnUw6HJKXTKi62Z2ttVTq9a3P5\npayDICmTUSqleFzRqBwOBYP2OHfuVDwuv18+X9dNdx6Dy6XCQkUi9q5lMnI6lcnI67XPiNer\nTEbptDIZud32wPI723lV1lEqLd3buc5Lp+VyKRpVJqOhQ+2td16VJI9n1+aSyV1fdmbtu3XW\nOjrk9SqdVjRqH1LrKorH7ZMeDquy0j6VXcbfmXVIu8+QP/hdjmdjozIZlZSorU1ut4qKdh2o\nLotb11sq1fXQWWeh85TuW7FYF3CXxa0v43ElEvZS1gWTv9rzrOszf7Tz221qksez61rNP1L2\ndIi67Je1Wqnr5dHlRt5rr8nhkM+n0lL7oRqPS7IPu9ereFwej6QPnfcdO+R0yuWSy7XrAWsd\ncGuoO3Z86Mjnd8E6LKmUtm6Vw6Hycrlc9iGyBt/UpFBIFRUqKVEspvJyZTLaulU+n8rK7Idz\nOm0/z0hqaFAqpaFDVVcnh0OBgBob7R1xuxWLacIE+0R0dKiuTk6nhg9XQ4M9fuvRVFyssrLd\nH+H8yCMRhcP2w3PQIJWUaONGtberpkZtbfL5VFSktjY5nfJ49Pzzeu45NTdrzBh9/eu7TnFt\nrTIZxeOKRLR9uyZNUjqtkhIdeaRSKW3YoIEDFQho3Tpls/L7lUiooUEVFQqF5HbL4VAkovZ2\nVVXJ71djo5xOeb3246uxUeXl8nrtVY0erUxGknbu1OjR9u643WpqUjqtcFiZjKqqFI2qrExO\np30WrEdxJKKKCvsZDIcIXrEDAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDs\nAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB\n2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAY\ngrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAA\nMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcA\nAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIO\nAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCE\nHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAh\nCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEAW5XK63x4APzCvo\n7RF0Mm8fF0YqpVhMJSUqLt79DKGQJAUCkpTJKJNRYaFSKaXT8np3zRaP77q38/SeiMflcimd\nlsslt1vxuBwONTbK71cyqWxWiYT8fkn2yuNxSXI4lM3at30+hcNqaVFFhTweud3q6FBhoTwe\npVK7di2VUiIhp1NOp7xeey8cDrlcam9XKiWHQ/37Kx7ftbjT2XWoFo9HyaQ9Jb+/1g1rnt0e\nBGuL1t+RiDweDRu2695MRsmkvdF4XEVFam+XpERC2exuzlE8rkTCHrnfv5uD39qqZFJ+v5xO\nZTJqbFRbm/x++2xaaygqUmHhrrFJu86CdVK83l0Ds3Y5v4lQyB5t541GIvZKrOnW2cxm1dKi\nQEDFxbs5PomEkkm53bsmJhJqa/vQOLscT2u11tpaWxWNqrLSXsPmzSotlc+3m0s0f+Ksfexy\nV+eZd+ywD2b+Uk+ltGWLBg3adQV2GVJHh5LJroeo8zFMp+2zMHSofai775Q1sfvjq7v8pZif\nLR5XPK6mJo0apbY2eTwqLVVHh9rb7dO0Y4e2b5fLJZdL5eUqKlI8rp075XDYJ8vr/dD81nnx\n+Xb/KMhfGJlM12tAUiIht3vXgqGQIhF5vSovt5e1rmpJGzdKUk2NolF5PAqF5PXK6bTH3/nE\nhcP2ILtIJNTcrExGgYBcrl0PE+tJyVpV5/MSiSidth8XnQ9jPL7rIW8Nz+tVS4va2lRZaV9s\n1qq83g89Meb30boafT4NG2YfnI4OZbNyu3ctmD96nW80Nam01F5bPK5USq2tcjrldisQUCik\n1lYVF6usTG1t9pGvq9OyZTr6aE2cqGTSfhJob7evbevRIe06R7GY4nGNGrX7s9nlEYFDAa/Y\nAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiC\nsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAw\nBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAA\nYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4A\nAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQd\nAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEI\nOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABD\nEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAA\nhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYIg+vT0AHGbicUnyepXJSFI6rfp6ORwqLVVrq6JR\nxePy+VRSomhU6bTicXk8SqeVTOq55xQOa/JkVVerrk7BoMrL5fUqlVIspmxWqZTSaXtKKCSX\nS5KcTnk8am5WPK62Ng0eLKdTfr88HtXWyu+X2y2HQ9msJPl8ika1YYO9aZ9PgYAcDlVVqaND\n994rn0/V1aqsVCCgjg5t2KBIRIGA1q5VKKRRo3TiiVq6VBs26IILVFysLVsUDqukRG63Nm6U\n260TTlAopGRSLpfKylRYqExG6bRCIYVC8nolye9XMimvV/G44nF7tkRC6bQaGpRKaexYuVxK\nJOyj5PMpFFIwqHTaPgj5NSeTSiSUzcrnUyKhUEglJaqokKTWVjU0aMQIxWKKRFRero4OOZ1K\npRSPq7RUgYAkZbMKhew1h8Oqr9fo0aqokMNhHyWnU+3tKipSNGpvMZWSx6NIRG63wmE1N8vh\nUGWlPQCn074YNmxQUZGcTmUyGjtWksJhRSKS5PGouFiRiIJB1dersVFVVTr6aHvBUEj19Zo5\nU62tisWUTsvjUSxmr9nj0fr1klRVpWRSsZgkBQJqaFBrq3w++XxKJlVSYh+WWEwdHSosVCgk\nh0PBoFpalM0qkZDTaZ8R69L1euX1qrVV8bg2bpTHo+pqSWppUUeHvF6FQlq/3l7K61VDgyor\nVV2tZNK+7K31xONqaJDfb0/JZJRKyetVNqv2dvXvry1blEzK51M8rqIi+zhbc8Zi2rLFvvLz\nrKvFmpJIqK1Nfr+9VFGRmpr02mvKZOR0qrLSftC1tqq4WH6/XC45nXI47E3k99S60dKitjZ5\nvSopUTqtcFgjRtibsB4a2ax9oktLVVcnt1tFRfaqli7VkUeqslIul8JhZTLy+dTYqPp6ZbPy\nejVkiCS1tcnlUnm5PB57BzduVHOzKiuVTOq889Tebp/EgQNVW6stWxQMKh5Xc7PKy1VUpIoK\nlZTI65XDocJCSerXT5K2bVMqpcpKlZZq7Vp5vWpuViSi8ePl8cjl0jPPKBbThAnaulWtrUom\n5fEoldLGjfZF1dysbFYTJqix0d7NkhLFYlqzRqmUZs7U00+rrk7V1ZoyRW1t8vmUTquxUeGw\nPB6tWqWGBg0dqqoqe/ejUZWV6cQT7Ud3LKbNm+2BWYf3ySeVyejRR1VZaV/SNTXyeu2nqWRS\n4bASCU2bJr9fP/qRSkq0bZsknXWWRo7UrFn2MfR6tXixJLlcWrdOjz2mkhL95S/y+7VihR56\nSB6PxoxRZaU2bVJVlST9v/+ntrZdF1W/ftq5035AJZM65hil09q0yb53+nQ9+aR8Pv361woG\n5ffL75fPp9mzdd99knTBBRo37kNP/qedpm9/W06nysqUTKq8XImEtm9XTY0GDdrdvxboJbxi\nBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAI\nwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADA\nEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAA\ngCEIOwAAAEMQdgAAAIYg7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsA\nAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2\nAAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEMQdgAAAIYg\n7AAAAAxB2AEAABiCsAMAADAEYQcAAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAM\nQdgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAA\nGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEKcrlcb48BAAAABwGv2AEAABiCsAMAADAEYQcA\nAGAIwg4AAMAQhB0AAIAhCDsAAABDEHYAAACGIOwAAAAMQdgBAAAYgrADAAAwBGEHAABgiD69\nPQB0VtDbA+iMXyIMAMBhhlfsAAD4tJg/v7dHgI8Zr9gBAGCU+fN13XW9PIbHH398y5YtTzzx\nxDe+8Y3m5uZEInHkkUc2NjZ2vnHNNddIevjhh7tM7zID9guv2AEAgIPstNNOO+mkk2bNmvXK\nK6/84Ac/KCgoeOaZZ7rcSCQSkmpra/c+A/YLYQcAAA6+++677+yzz7Zu53K5Pd3o4QzoIcIO\nAAAcfO+9957D4aipqbn55psdDscJJ5zQ5caqVat27NixlxmKi4t7eycOP7zHDgAAHHy/+c1v\nJJ111ll7n22fM2C/8IodAACAIQg7AAA+FayfdcJPPDEbYQcAAGAIwg4AcLjixac94ch8ahF2\nAAAAhiDsAAAADEHY4RPFdwcAoFfw9PspQdgBAA5LH6VUDqnK+ZgG03m1h9T+4mNF2AEADmNm\nJMtB3AszDggOGGF3MKVSqYKCgrVr1/b2QAAA2CPiz2AGht3KlSsLCgouuOCCg7K2lpaWM888\ns6ysrLKy8tJLL02n03ufDjPwrHeImD+fc/FpxwXw0XEMP1UMDLu77rpr5syZDz744LvvvvvR\n13buuec6HI4XXnhh6dKlzzzzzHXXXbf36QCAQ9wnFjrdN/QJN1bP/2u029kowsORaWHX2tr6\n0EMPXXvttcOGDVu0aJE1cezYsT/5yU/y8/zoRz8aP368pBdeeGH48OEej2fq1Km333778OHD\nu6zt3//+98qVK2+99dbhw4cfe+yx8+fPv/POO3O53J6mW0tt2bJl3LhxRUVFo0aN2rx58yey\n3wDwqdBbqWEVEqGDQ59pYXfvvfcOHz581KhRs2bNuvvuu62JZ5999pIlS/LzPPLII+eee25H\nR8e0adPOPPPMSCRy0UUXzZ8/3+HoejRqa2sHDRpUUVFhfTl27NhoNPrmm2/uabr15a233vp/\n//d/oVBo4MCB11xzzce6v9iTA3j+5VkbODR93B8a3ef6D2wAe1rqkHqe2ecnZw+p0aInTAu7\nu+++e9asWZL+8z//8+WXX3711VclnX322Zs2bXrjjTckbd68+bXXXps5c+Zzzz3X1tZ2zTXX\nFBcXT5s2bfLkyd3XtmPHDp/Pl/+yf//+kqLR6J6mW19ecsklI0eO7N+//8yZM3nF7rBA0n3C\nDu7R5tz1Ig7+oYYzAqPC7qWXXnr11VfPO+88SRUVFSeffPJdd90lafDgwWPHjrVetHv44Ycn\nTZo0cODAt956q7y83Ov1Wssee+yx+1y/9c3WgoKCvU8fOnSodcPj8aRSqYOya582PDcBh4te\n/N7oPqf0os4vB/b8pcFPEv+hNZVRYXfXXXdls9lhw4b169evX79+y5cvv/fee9vb29Xpu7EP\nP/zwOeecIymXyxUWFuaX7f59WEnl5eWRSCT/5dtvv21N3NN068vu5YeDbk9PSZ3fB3Ngbxnm\nyQ74WH30x5dhD9KefAPUpP3Fx82csEskEvfff/+tt976ygfWr1///vvvWz139tlnv/jii2vX\nrt20adOMGTMkDRgwoKWl5b333rMWX79+ffd1jhkz5q233mpqarK+fOGFFyoqKoLB4J6mfxL7\niT07xP8Hvyef8L9Sh+wxOWQHhoOod38Xwif2AYiDson9XcnHt2s8Ng8v5oTd4sWL+/TpM3v2\n7KoPjBw58pxzzrE+QlFZWfmlL33pv//7v0855RTr7XETJ07s06fPr371q46Ojn/84x9PP/10\n93UO+//t3XtcjOn/P/BrOiuHTNNJJe2ms2rUDJ1kxaKics5WbFjWfpK1Yq2wdr+sXeGL2LXC\n2hy/IrtLWCzSOtPkkNXuJhJJakhqV3X//rg/e//unabMTFNzej0f/rjnmvu+rvd9zd3M23Uf\nrt69w8LCpk2b9vvvv1+6dGnhwoUffPBBK+UgC+U+YF3LvnG0b4/am8TorA5evdfxj89Q4K32\naE5Z9bc0wN+u50/V8+SsBHWODVqhPYldRkZGXFycsbExu3DKlCknTpyg71cdP3782bNn6fOw\nhBAzM7Ndu3bt3LmTx+N99913c+fOlXo2dufOnWZmZv379x8zZszo0aMXLFjQejkoi8q/U+QK\nQLlnlxS7pbcjyZiAduTwDHLiDvDa84PtOmjUSuUSb0n9U+qAO+Vbz3plr0r9vwFAnRmoOgCl\nOXfuXPNCoVDY1NRELycnJycnJ7PfHTZsWHh4uIGBASHk888/t7Oza16DtbX1/v37ZSw3MTFh\nnmZHCImLi4uLi5NzP7SZUrKfNm7OPEmavawUSq+wlYYIkdLWawNo6VIeecNW1cBMSzuuKTrs\nCOkY6plJdMxp1lY+x7Zkb61v27zdjj+itOwY1lbaM2Inr4aGhl69ei1atOjly5c3b97MyMgI\nDw9XdVCgfMr6P7S6YQYwFBjq65i9bmk4R1P6XN3ibGV4TFm9qtyrVFXbgfLeRKVxVLVf2tqf\n2kR3EzsDA4OsrKxTp07xeLzhw4ePHz8e18mpiVbOtiilciXmFh1/xkS5LbZfMO337a/mvysK\nh6fm+9U6BU5ZavT+SqXCezI64MJlrfzItJX2nIpVQGBg4IULF1QdhS5Sk9N/SqHAmReJM8Lt\nFA+7h1Xee/IOJTY/5dRhOvhkk8LNKeXCBgXO3ctYs1xN4Oxe6zrg+lTQJjqd2IEKSf3GV+z7\nRVWXfbTrt2F775TCXa2s1WTZwVbGJ9rSOS2lj8rSyrGt8GHfYT+9bflcFN6qAz4RLSPv1RdK\nbx25uDrT3VOx0K7a6Yof9alN3qY7vvX2+/VVVluth6FwutP2s9Xtd8mavGE0X6GVawnkPRkq\n4wpq+LesbtrvD1xVJ0Bx4lWjIbED5VNJEtMejS6VdoMC85bU9eWtXynrtFFLP+EyhqeUc4Jq\nQmou1R7VtrIme+UOPgfdxhWUTn0ODAANglOxoExy/SZp7uU1yh0akaUGupfaI3GUsWl1I++l\njVL3QuGLHZt3i0SJpmQkMv4NasruKEsH/4FrIrX9ZgCCxA7aQuLKmDZ+wan/96MSv8s6PkVT\nmHb/yCkx4LafAlZWtextlXUla1tikGtllacLGncMq4rm/s9c6yGxAxVr/WtUKV/0uL74tdp+\nCrKVGx10WbvepKIsavUZqVUwakjd+kezvuh0BBI7UHdq9UUm13VIahW57DQ0bLaOuTBRKXfm\ntjcFzhFrwQGgGJ3dcdAySOxACfCFCGoOp40AOtiBAwfu3btXW1ubmprKfunh4SGxQK8AyoK7\nYkFBSObam448caCDb/xU4j28yn0gSPvRlDhBQ7V0XF29evXDDz/kcDi1tbXsl7m5uRIL9Aqg\nLBixA0XgFwLaTguOona6YULptKCr1RM69rUoipL6svkCKAsHfapOOKoOgK3FAwPfZaBTmJu+\nlXL3dwfQlDhBa0i9yOHAgQNlZWU1NTVCoZDP5585c4Z+6ebmJrHwySefdHjI2gyJnVrRgMQO\nvxags5AwAUiFq1fVChI7taLWiR1+zwAAQCrkduoDiZ06+VSdErtPcWAAAABoGNwVCwAAAKAl\ncFcsAAAAKFlOTk5xcfGRI0cmT5784MGDVp5gh0fcKRdG7AAAAEDJwsPDBw0aFB8fLxKJWn+C\nHR5xp1xI7AAAAED5du3aNW7cOHr5tU+wwyPulAU3T6gT3DwBAADa4qOPPlq1ahXzQLvmT7DD\nI+7aAxI7dYLEDgAAANoAp2IBAAAAtAQSOwAAAAAtgcQOAAAAQEsgsQMAAADQEkjsAAAAdAVm\n/dZ6SOwAAAAAtAQSOwAAAB2CQTvthsQOAAAAQEsgsQMAAADQEkjslKm+vp7D4Vy5ckXVgQAA\nAIAu0sLE7syZMxwOZ9KkScqq8OLFi87OzsHBwezC4uLikSNHdu/e3draetKkSVVVVcpqDgAA\ndAouegMl0sLELiMjY+zYsVlZWc+fP297bd9+++348eM9PDzYhRRFjRgxwxFgrQAAIABJREFU\nwsDA4MKFCzk5Ofn5+cnJyW1vCwAAQFmQL+ombUvsxGLx/v37Fy9e3Lt37927d9OFQqFw0aJF\nzDqffPJJQEAAIeTcuXMuLi6mpqYjRozYtGmTi4tL8woNDAyuXLkiFArZhY8fP3Zxcfnmm29c\nXV39/Pxmz56dm5vLvFtcXNy/f39jY2MvL6/CwsJ22U8AANAKyk2/kMyBtiV2O3bscHFx8fLy\nio+P37JlC104bty4gwcPMutkZ2fHxsb+/fffI0eOjImJefLkybRp05YuXaqnJ6U3EhMTeTye\nRKGNjU12draVlRX98uHDh05OTsy7GzZs2LZt28OHD21tbVNTU5W8hwAAAK+DDE9naVtit2XL\nlvj4eELIO++8c+3atRs3bhBCxo0bd+vWrT///JMQUlhYWFRUNHbs2Ly8vGfPnqWmppqZmY0c\nOTIsLEyxFgsKCtLS0pay/oZmzpzp7u5uYWExduxYjNgBAEBHWrr0/2d1bU/v2LWBRtCqxO7y\n5cs3btyYOHEiIcTGxmbw4MEZGRmEkJ49ewqFQnrQ7sCBAwMHDrS1tS0rK7O0tOzSpQu9bd++\nfRVo8dSpU0OGDPn6669DQ0OZwjfeeINeMDU1ra+vb+NOAQAAyOK1GZi8KRpSOk2kVYldRkZG\nU1NT7969O3fu3Llz519++WXHjh1//fUXYZ2NPXDgwIQJEwghFEUZGRkx20o9D9u6Xbt2jR07\ndseOHbGxsexyDofT1j0BAADNJ3ti1PqabUmw6CG3lgbekLppH+1J7Gpra/fs2bNhwwbRPwoK\nChoaGuh8bty4cRcuXLhy5cqtW7dGjx5NCLGysiovL6+rq6M3LygokKu5Q4cOffjhhydOnHj7\n7beVvi8AAADth53PIbfTMtqT2O3du9fAwCAxMdH5H+7u7hMmTKBvobC3txcIBHPmzBkyZAiX\nyyWEhISEGBgYrFy58u+//z527NjJkyelVvvw4cMHDx48f/7877//fvDgwYMHDxobG2tqat57\n773PP/+cx+M9+EdTU1OH7jAAAOgM1aZfSP40iIGqA1CajIyMuLg4Y2NjduGUKVP69+9fUlLS\nq1ev8ePHz549OzMzk37LzMxs165dKSkpaWlpERERc+fO/frrr5tX6+Hh8ezZM3rZwcGBEFJa\nWlpQUPDo0aPp06ez13zy5Ennzp3bZd8AAEDTdHAyhNwLaNqT2J07d655oVAoZAbSkpOTJR4j\nPGzYsPDwcAMDA0LI559/bmdn17wGsVjcvNDe3p6iKKlhsMvj4uLi4uJk3gMAANASMqZZyMZA\n6bTnVKy8GhoaevXqtWjRopcvX968eTMjIyM8PFzVQQEAAHQ0PNNEm+huYmdgYJCVlXXq1Cke\njzd8+PDx48d/8MEHqg4KAAA0nsStCRI5k+wPmVOru1mR+WkK7TkVq4DAwMALFy6oOgoAANBy\ndFa0ZIkya2OWFau2eaKmcFWgVnR3xA4AADSaUsaQOnIgqvUznhgSA6XQ6RE7AAAA5VJWfqb0\naSRARyCxAwAAHSVL8qSDZyeRMmo0nIoFAADdIlfi0h4rK/cuVNzTCmxI7EBr4ZsOQFXU/6+P\nnQwpa0bX9qb+vQrqAIkdAIAGUPmPurwBtLS+vOmUqnZctcNgmrLXKj8soTlcYwcdR7k3/MvS\nlkSLUi+XYX8x0e92ZJw6S/ZLl3TzIid10LznJX7FW/pcZL/qX4EPt5VNmGpbr1OBXKTD0pfX\ndojKhwzZ4SGrU09I7ECTyPIz0Pp3TVuyhPbIMDo+a1Hsp5S0f6arEV2hKh3wC9p6b7T+jNxW\nMq3WU0MZ25V6BLLfan1z9n/tWlmz7SmLLiQ6sn8bHDhw4N69e7W1tampqeyXHh4eEgv0CqAs\nOBUL7UWuIX1Z1mz+PS7RhAKPaJc3QgUuylGu5rsssSBvbW0Jg/1ProZaOUnH3qNWHtnfdlIz\nDIlGO+y5FWqiLcdDB7TLfDSvPeSaf47tfV5VifVrxJ0QsgR59erVDz/8kMPh1NbWsl/m5uZK\nLNArgLJgxA4U1/z/61J/LF+7lVxtkfY8HSBvXth8TRkHFFuPvy3DSM3PLDd/t3lq2HwsRN7B\nm+bbynIeXK762+PXTpaUVGrYyh3tk7G25kd+ew8ht/1T0z4aNNDbMVrvEIqipL5svgDKwkGf\nqpFPOaqOgOXT1x8YEimaAt/sEhtKTRAVrrz1Rps39Nrw5Fqh9Yv5XqulZELGwKRW9dodaUsr\nCkTSxsOmOalJs8LXgbXeXEsnpKTmtS0tt1RbK/91aenTfG0izrQusdC8qlbC7jAqbJQmyzeD\nLGvqCKmH34EDB8rKympqaoRCIZ/PP3PmDP3Szc1NYuGTTz7p8JC1GRI7daJRiV17fJ2p5Ntc\nPSnxl0PGXu2AzpclxWxLzVJT0jZm2BJNtFKJvB3YvDfa+BE0z9harw1/bs3J9cciy5o6AkOY\nagWJnTrRhMQOX2SgMGQSHQZdDR0JiZ1aQWIHAAAAoCVwVywAAACAlkBiBwAAAKAlkNgBAAAA\naAkkdgAAALIqLS39/fff6eVZs2ZxuVw+n8/n88PDw2fNmtWpU6eqqipCiEAgyMvLE4lETk5O\nAQEBHh4eiYmJ9Ar0tjk5ORwOp76+furUqe+//z5d6O3tnZOTI7FVZGQkn883MDDg8/kJCQmn\nTp1ydnbu27dvTEyMvMulpaUBAQFWVlaEkKysLA8PjwEDBgwcOHDatGm2trZ8Pj8qKoq9/pw5\nc+i9MzY2/u2335htma6QKCGEREZGbty4saGhISAgwNPT09vbu1evXhcuXJDamVlZWU5OTj4+\nPkFBQey2EhISpMbDjlNqJS3t7IABA7KyspR1DKg7CgAAAF5n3bp1/v7+ISEhd+7coUuSkpJi\nY2OZFZKSkgIDA/38/CiK8vf3P3v2bGpqalBQUGNjI0VRR44cSUpKsrS0XLlyJUVRQqGQy+XW\n1dVNmTJlxowZdA19+vQ5fPiwxFb0W1wul17o27fv4cOHKYrq0aOHt7e3XMv5+fkPHjywsrKi\nKCoyMnL16tUURVlYWERERPTu3dvHx2f+/Pns+mtraymKWrx48bvvvnvnzh1mW1rzkkuXLrm7\nu1MUNWXKlLFjx9KFxcXFJSUlI0aMEAqFAwcOdHd3nzBhgr29fW1tbXZ2Nt2Z3bt3p1em22op\nniFDhjDlTKPsStgrs3f26dOnDg4ObfjwNQlmngAAAHgNPz8/Dodz+vTpzp07s8uPHj3K5/MJ\nIW+99RYhJCAg4Nq1a1999RX97pIlSx49euTt7U1R1KRJkwghUVFRmzdvTkxMrKurk6iKIbHV\nsGHD2O9WVlbSLXbt2rWkpESu5cbGRjs7O7qelJSUqKior7/+2sPDY/ny5ZaWltbW1lZWVkZG\nRsz6t2/f9vT03Lp167179/T0JE/xubi4SJSkp6fTY2kikeijjz4ihCQkJBQUFPB4vK5du0ZF\nRc2bN8/a2nr37t2+vr5XrlyJjo5uamoaOnQo3Tn19fV0Wzdv3pQaT0JCQlhYGF3+ySefdO3a\nlRDCruTAgQNSd5bL5err65eVlTElWgynYgEAAF7j2LFjfn5+wcHBsbGxFRUVTPmwYcPy8/Pz\n8/NXr15Nl+Tk5Kxdu7ampoYQkp2dvXDhwps3b+bn5y9btowQ0q1bN3Nz8/j4+JkzZ9Lr29vb\nl5aW0ssVFRXu7u7Nt2KzsrK6du0aIUQsFjs5Ocm17O7uztQzc+bMrKysoqKiP//884cfftDT\n09PT0zM0NORyuez1V6xYERoa2jyrk+rhw4dvvPEGIUQgEOzZs4cQ8v333+/du/fevXuEEFNT\nUwMDAwMDA0IIh8N59eqVWCz28vKaOHHimjVrCCFMW3l5eVLjefLkCVM+Y8YMb2/vrKwsdiXs\nzmHvLCGEy+UWFhbKsheaDokdAADAa/B4vE2bNolEoqlTpzJ5GPlnxI5Gl5iYmGzZsqWoqIgQ\n0tTUNHDgQIFAIBQKBw0aRK+wfPny3NzcGTNm0C/nzZtXUlLi6+vr6uoaEhLi5OQkdSvGmjVr\nkpOT+Xz+gAED0tPT5Vreu3evQCCorq4WCATR0dHJycmhoaGWlpZcLrd///6+vr7+/v7ffPMN\ns76pqenJkyfDwsIIIdu2bWO2Xb9+fXx8PLtELBYTQmxtbYuLiwkhGzZsqKys9PT09PHxiYiI\nWLRokdReHTVqVFVVVXp6ukAgePHiBdMWRVFS4zEyMmLKd+3adf369TFjxrArWbZsmdSdFYvF\nVVVVbm5uyjsi1BceUAwAAABKcPHixcmTJ9++fVvVgUiqqqry8fFhZ+RaDCN2AAAAuuvJkyd2\ndnZcLpfL5dJXCiqsX79+b7755saNG5UVm7JER0fTZ3t1AUbsAAAAdFdRUZGrq+umOFL4iPxf\noe3Dhw9VHRG0CUbsAAAAdFd9XR0hJLIP6edEap4/V3U40FZI7AAAAHSXSadOhJDrZeRuJTFr\n4QksoEHwHDsAAADd1blzZ319/eHrGgkhbm7dVR0OtBWusQMAANBpL168ePXqFSGkU6dOJiYm\nqg4H2gSnYgEAANpE0yeQHTJkiKura/fu3Q8dOsRMIJuZmeno6Ojr6xsaGsqeWJaOytHRUWI+\nVqYrlDiBLLvdluJ/9OgRn8/v169fZGSk1Ep0bgJZVc5nBgAAoMm0eALZ7Ozs4uLixsZGLpfL\nLqcoKioqKiIiIjo6ur0nkGW329I+jhw5Mjg4ODAwcO7cuUyjujyBLEbsAAAAFOHn57d9+/ZT\np07l5uayJ05lpqOYM2cOISQgIKBr167sCWTd3Ny8vb09PT2vX79O/plAtqqqqvUJZCW2YlPi\nBLKfffaZi4uLh4dHdHT06dOne/ToERISwi5ft24dh8OJjo4mhLi4uEjMvtq8hD2BbExMDCEk\nISEhOjo6MTFRX18/Kirq+PHjjx8/3r17t4WFBT2BrLOzMz33K7vdlvbx2rVrQ4cO/fXXX7dv\n3/7y5Uu6UXYl7A2lTiAr+yeuEZDYAQAAKEK7J5D98ssvY2JiysvLz58/n5iYyJSvX7++urp6\n5cqVeXl5ly9ffm0vtWUCWXY8ZmZmUuPv3bs33ZCRkdF7772HCWRx8wQAAECbnDx50tzc3M/P\njxAya9asHTt2ODo60m+FhIQYGRmlpaUdPXo0PDw8Nze3rKxs3rx5VlZWr169cnJycnBwMDIy\nGj58eHR0dE1NjaOj4507d5qamoRCoYGBQV1dnbe39759+/bu3cveKjs7mxBiYWHx9OlTQkhe\nXt7kyZO7dOni4uKSlJQk1/KwYcM2btxYUFDg4+MzdOjQgwcPWlhYPHv2LDY2dvPmzSYmJhRF\nxcXF7dy5ky4XiUSEkIyMjMOHD48cOZLZNiEh4dKlS4MGDWJKjh8/bm5unpCQYGdn98UXXzQ1\nNYWEhIjFYgMDgxcvXixevPjgwYOhoaGzZ8+2trZ+/Pgxn89PS0tbtmxZYWGhg4MDISQ8PHz/\n/v10u+np6VLjX7JkSUREBJfLtbCw+Pnnn+luHzRoEFPJsmXLZs6c2Xxnjx8/zufzc3Nz6dW0\nBhI7AAAAaC+YQLaD4Tl2AAAAICk7O/vJkyeEEGNj4/Hjxyv8GBRmAlnmRLOa0NYJZDFiBwAA\nAP9SXV3N5XLtuxMjfXL3KTl27OchQ4aoOiiQCW6eAAAAgH9pamoihOQkkT+XEWMDUvfP3aag\n/pDYAQAAwL/QiR3jJRI7zYFr7AAAAOBf9PT0CCGfHSJcM/KqkZh3xxyyGgOJHQAAAPyLubn5\ne++9V11dXU3I+AmG3t7eqo4IZIWbJwAAAAC0BK6xAwAA+JfS0tLff/+dXp41axaXy6WnCAsP\nD581a1anTp2qqqoIIQKBIC8vTyQSOTk5BQQEeHh4JCYm0ivQ2+bk5HA4nPr6+qlTp77//vt0\nobe3d05OjsRWkZGRfD7fwMCAz+cnJCS0NOG9LMvsee6zsrI8PDwGDBgwcODAzMxMR0dHX1/f\n0NBQdnlL9TBdwdTGiIyM3LhxY0NDQ0BAgKenp7e3d69evS5cuCC1M7OyspycnHx8fIKCgubM\nmUP3pLGxcUJCgq2tLZ/Pj4qKYsfGXpZaSUs7O2DAgKysLCUeBppKpTPVAgAAqJF169b5+/uH\nhITQU8hTFJWUlBQbG8uskJSUFBgY6OfnR1GUv7//2bNnU1NTg4KCGhsbKYo6cuRIUlKSpaXl\nypUrKYoSCoVcLreurm7KlCkzZsyga+jTp8/hw4cltqLf4nK59EJLE97Lssye5z4yMnL16tUU\nRVlYWGRnZxcXFzc2NnK5XHZ5S23V1tZSFHXnzh2mNtqlS5fc3d0pipoyZcrYsWPpwuLi4pKS\nkhEjRgiFwoEDB7q7u0+YMMHe3r62tjY7O5vuzO7du9MrL168+N13342IiOjdu7ePj8/8+fPZ\nsbGXmUbZlbADZu/s06dPHRwc2nwIaDyM2AEAABBCiJ+f3/bt20+dOpWbm+vi4sKUHz16lB5n\nmjNnDiEkICCga9euX331Ff3ukiVL3NzcvL29PT09r1+/TgiJioravHlzVVVVXV1d586dpbbV\nfCu2lia8l2WZPc99SkrKZ5995uLi4uHhER0dffr06R49eoSEhLDLW2qLnivCxcWFqY2Wnp4e\nFRVFCBGJRPTAXkJCQnR0dGJior6+flRU1PHjxx8/frx7924LC4srV65ER0c7OzsPHTp00qRJ\nhJD6+vqtW7dmZGQsX778zJkz165dy8jIGDRoEBMbO06mUXYl7IDZO8vlcvX19cvKyuT93LUM\nEjsAAABCCDl27Jifn19wcHBsbGxFRQVTPmzYsPz8/Pz8/NWrV9MlOTk5a9eurampIYRkZ2cv\nXLjw5s2b+fn5y5YtI4R069bN3Nw8Pj6emWvB3t6embqqoqLC3d29+VZs7Hnr2RPey7LMnud+\n5syZWVlZRUVFf/7555dffhkTE1NeXn7+/PnExESm3MzM7LX1sD18+PCNN94ghAgEgj179hBC\nvv/++7179967d48QYmpqamBgYGBgQAjhcDivXr0Si8VeXl4TJ06kp3lYsWJFaGionp5eXl6e\nnp6enp6eoaHh5s2bmdhWrVrFLE+cONHb2zsrK4tdCbtzJILkcrmFhYVyfuzaBokdAAAAIYTw\neLxNmzaJRKKpU6eypxBlRuzogSJCiImJyZYtW4qKigghTU1NAwcOFAgEQqFw0KBB9ArLly/P\nzc2dMWMG/XLevHklJSW+vr6urq4hISFOTk5St2KsWbMmOTmZz+cPGDAgPT1druW9e/cKBILq\n6mqBQBAdHZ2cnBwaGmppaUkI8ff39/Ly4vF4U6ZMYcq//fbbluqJj4/ftm0bU5tYLCaE2Nra\nFhcXE0I2bNhQWVnp6enp4+MTERGxaNEiqb06atSoqqqq9PR0gUDw4sWLkydPhoWFEUIoiurf\nv7+vr6+/v39DQwMTG3t5165d169fHzNmDLuSZcuWSd1ZsVhcVVXl5uamvCNCI+GuWAAAAJDV\nxYsXJ0+eTJ+oVStVVVU+Pj7sjFw34Tl2AAAAWu7ly5eRkZHPnz+nX9rY2Pz0008cDkeBqvr1\n6/fmm29u3LiROdGsJqKjo+mzvToOI3YAAABarqyszN7efu7bhNeZlFSSb3JJQ0ODvr6+quMC\n5cOIHQAAgJZraGgghLwXQnpbkbO/k29yVR0QtBvcPAEAAKDl6NtUQRcgsWurwsLCd955x9bW\n1tjY2MHBIS4u7s6dO8y7bm5unGbS0tLot2xsbOibjGhXrlwxMTFhXt6+ffudd96xtrY2NjZ2\ncnKaPXs2/axzdrs2NjZGRkYODg7vvfce+4pRpl1DQ0N7e/u4uDiRSCRLVAAAoH2MjIwIIS6L\nCGc6GZBGDA0NFbvADtQfErs2OX/+vEAgqKioyMzMzM/Pz8jIEIvF/fv3Z+aiIYR8/PHHv//b\n1KlT6bcaGhpSU1Ol1nzp0iWBQFBeXr5z585r166lpaWdPn06KCjo5cuXhJBz584JBILKysod\nO3aIRKJvvvnm6tWrfn5+f/zxB1NDampqaWnp7du3N2/eXFNTIxQKf/rpJ1miAgAALWNpaXnr\n1q0r/7h165aeHhIALaXimS80nI+Pz7Bhw5qampiSpqamefPmnT17ln7p6upKTyzTnKura1pa\nmomJydWrV+mSy5cvGxsb08t+fn6DBw+mZ5uhicXi4cOHX716tampycPDIyoqit3uX3/91adP\nn+HDh7fU7kcffWRtbV1XV9d6VAAAAKC5kLArrri4uKCgYN68eewBbQ6H8+WXXwYHB8tSg7Oz\n86xZs2bOnNnU1MQuv3///tWrV+fPn8/+H1W3bt1ycnL69u1769atwsLCjz/+mN2ukZFRSkrK\nzz//zNzNLuHjjz+uqKjIzcUVswAAbVJaWsqclpk1axaXy6WfXRweHj5r1qxOnTrRl80IBIK8\nvDyRSOTk5BQQEODh4ZGYmEivQG+bk5PD4XDq6+unTp36/vvv04Xe3t45OTkSW0VGRvL5fAMD\nAz6fn5CQcOrUKWdn5759+8bExMi7XFpaGhAQYGVlRQiZM2cOHbmxsfGLFy8IIY6OjjExMVlZ\nWR4eHgMGDBg4cGBWVpaTk5OPj09QUND69et79+7t7+9PTw5GdwVTGyMyMnLjxo3Nu6Kl/ly1\natWCBQukVtXSXmRmZjo6Ovr6+oaGhg4YMCArK0sZH6y2UHVmqcGOHz9OCCkvL29lHVdXVyMj\nI7N/e/HiBf1Wdnb2ixcvHBwcNm3aRLFG7E6dOkUIefTokdQ6Dx48SAipqamRKC8oKCCEFBQU\nUC2MyVlbW3/zzTetRwUAAC1Zt26dv79/SEgIPSE9RVFJSUmxsbHMCklJSYGBgX5+fhRF+fv7\nnz17NjU1NSgoiD79cuTIkaSkJEtLS/r7WSgUcrncurq6KVOmzJgxg66hT58+hw8fltiKfovL\n5dILffv2PXz4MEVRPXr08Pb2lms5Pz//wYMHVlZWTMyLFy9+9913KYqKioqKiIiIjo6OjIxc\nvXo1RVEWFhbZ2dn0znbv3t3Hx+fHH3+kKIrH49Gx3blzR6K2S5cuubu7S+2KQ4cO0b0XGBiY\nmZnp5uYWHBwcGRnZu3fvN95449dff5WoqpU93bVrV3FxcWNjI5fLffr0qYODQ5s/W+2B22QU\nRw+YNTY20i9//fXX0NBQetnNze3mzZv08pw5c6ZMmcLe0NTUlFk2MzNbvXr19OnTR40aJVEz\nfXd6SyQG+SS2laqxsZF5alHrUQEAgAQ/Pz8Oh3P69OnOnTuzy+kJxwghb731FiEkICDg2rVr\nX331Ff3ukiVLHj165O3tTVEUPdAVFRW1efPmxMTEuro6iaoYElsNGzaM/W5lZSXdYteuXUtK\nSuRabmxstLOzY6qqr6/funXrvXv31q1bx+FwoqOjDx8+nJKSEhUV9fXXX3t4eERHRzc1NQ0d\nOnTSpEnR0dGzZ89eu3ZtfX19ZWWllZWVi4uLROTp6elRUVH0skRXLFiwwNDQUE9Pr7i4uFu3\nbjwez8LC4vTp01FRUT169AgMDGzeDy3tqYuLy+nTpxcsWBASEsLlcvX19cvKytj7pctwKlZx\nrq6uhJDr16/TL/l8vkgkEolE//M//8POySwsLJz/TSL3GjNmjL+/P/vUqrOzMyGEfR8r7dWr\nV83bZdy6dcvQ0JCem7m5u3fvVlZWMpPovTYqAABgO3bsmJ+fX3BwcGxsbEVFBVM+bNiw/Pz8\n/Pz81atX0yU5OTlr166tqakhhGRnZy9cuPDmzZv5+fnLli0jhHTr1s3c3Dw+Pp6ZucHe3p55\nrEFFRYW7u3vzrdisrKyuXbtGCBGLxU5OTnItu7u7s6tasWJFaGionp7e1q1bq6urV65cmZeX\nN27cuKysrKKioj///PO3337z8vKaOHHimjVrGhoaMjMzf/75ZwMDA4lzpoyHDx+yf4bYXUEI\n+fjjj0+fPr1///6PP/74s88+++6775jBEbbp06d7e3tnZWW1tKcXLlyIiYkpLy8/f/58ZWUl\nl8stLCx87SeoI5DYKc7e3j4wMHDZsmX0cWlqaurl5eXl5WVjYyNvVenp6Tt37rx8+TL90s7O\nLiAg4LPPPqMzOdrz58+9vb0PHjzo5ubm4+OzfPlyijVrSENDw6pVq0aMGGFmZia1iQULFjg5\nOfXv31/e2AAAgBDC4/E2bdokEommTp3KfrwUPWJHo0tMTEy2bNlSVFRECGlqaho4cKBAIBAK\nhYMGDaJXWL58eW5u7owZM+iX8+bNKykp8fX1dXV1DQkJcXJykroVY82aNcnJyXw+f8CAAenp\n6XIt7927VyAQVFdXCwQCsVh88uTJsLAwQohIJDp9+nRKSkpwcHBycnJycnJoaKilpeXMmTOr\nqqrS09MFAkFVVdWQIUPc3NymTp26bdu2+Pj4bdu2sWsjhNja2hYXFzOhsrviiy++SElJEQqF\ns2fPDgwMnD59+vjx401NTcVicWZm5tKlS5mqvvzyy+vXr48ZM6alPX3x4oW/v7+XlxePx+Px\neFVVVcywBWBKsTYpKCgIDQ319PRcsmSJk5NTZWXliRMn1qxZM3HixPT0dEKIm5vb6NGjp02b\nxt6qU6dO1tbWbm5uK1asiI6OpgtTU1Ppp6XU19cTQvLz80NDQ93c3JYuXdqzZ8/ffvtt6dKl\nZmZmp06dMjExuXjxYlhYWFBQ0IIFC+zs7EpKSpYsWXLv3r3z58/37NmTbnfs2LHTp09vbGz8\n/fff165de+LEiSNHjgwcOLD1qDqk2wAAoK1evnx5+/Ztxba1sLDo1asXIeTp06clJSVMuZGR\nUZ8+fejlBw8ePH782MPDg7nbg1mf2by5ixcvTp48WeHAFFBVVeXj48NOtXWdai/x0wJFRUVx\ncXG2traGhoaWlpYRERE//fQT8y592lRCWFgY9c/NE8yaL1++7NW5TYBDAAAVeklEQVSrF/O4\nE4qiCgsLx40bZ2lpaWxs7OzsvHDhQvb9DTdu3BgzZoylpaWhoaGDg8OMGTPKysqktsvlcmNi\nYkQikSxRAQCARli+fLnCP/3W1tZ0JbGxsRJv3b59m36LzvBWrVrFbnTixImEEFtb21YCi4iI\n2LBhQzvtdXMhISH79u3rsObUH0bsAAAANM8nCxacP7Di1Edyb5idTybt7HIs5wgh5JPU1D56\np9dNIISQqlpiMYd8/9125zffJIRMeGfi/fv3Z7w3PSE+ntl24aJFv+WfquOYV1dXK2tHQLlw\nVywAAIDmYd/AIa+//vrr2w0bCCF3i//s4/yvt/bv2dPd3JwQ8qxaTAi5dOFCPevxqHf//IMQ\n8vdffyncNLQ3JHYAAACax97B4VQV+fKY3BveekjMzMy27d5FCBkzZsxVUSldycu/CSHksy9X\neHt7E0LOu7k9u/N8zITxCxYsYLYdO3bssUOlnczweCz1hcQOAABA8/j5+R1y9Nt3T5Fthw//\n75XWQUFBO0tKmEqCgkx69OhBLw8ePLhLly7Mrb7M+nfv3sUtqOoM19gBAAAAaAk8xw4AAHSd\nLkz/ykz5yp4Ktvn6qpr+lR0hpn9tE1XflgsAAKAyOjL9K3vKV/ZUsBLrUyqd/pWJENO/tgWu\nsQMAAB2lO9O/sqd8jYmJYaaCZa9Pb67C6V+dnZ3pCDH9a1vgVCwAAOgo3Zn+9eTJk8yUrzNn\nzmSmgn306BGzfku91DHTv/bo0YOJkBCC6V8VhpsnAABA1508edLc3NzPz48QMmvWrB07djg6\nOtJvhYSEGBkZpaWlHT16NDw8PDc3t6ysbN68eVZWVq9evXJycnJwcDAyMho+fHh0dHRNTY2j\no+OdO3eampqEQqGBgUFdXZ23t/e+ffv27t3L3io7O5sQYmFh8fTpU0JIXl7e5MmTu3Tp4uLi\nkpSUJNfysGHDNm7cWFBQ4OPjc/z48REjRiQmJr777rt0/BkZGYcPH3727FlhYaGDgwMhJDw8\nfP/+/RYWFs+ePROJRCEhIfT627Zt++WXXwYNGsSuzdzcPCEhwc7O7osvvpg1a5ZEVzx79uyD\nDz6wsrLS19f38vI6c+aMk5OTSCTq37//1atXp02bdujQIXZVrezpkydPmAhPnTrVp0+f3Nxc\n+iXIBYkdAAAAtAjTv2oWXGMHAACgNJcvX161alVTU5OqA1GEqanp+vXru3TpQghZuXLl5cuX\n6fJnz575+fm9+eabUrfS09NLSUmhxzuVIjo6mj4hCwrAiB0AAIDSfPXVV6v+Z360r6rjkN9f\nDWT7eVJQUEDPPOHm5mbVdMfd5vUbHsgnH3+a9tFH8k9bC+0AI3YAAABKU1H+uJcF2RSn6jjk\nV1VLtp8na1emWfJ4hJAnFRUfhpPpA16/4dX7pPzRo3aPD2SDxA4AAEBpzLpIf9yJpvD29e3l\n6EgI2fF/ewmplnGrzl26tGdQIAckdgAAAErTtWvXggfkzYWqjkN+TRQhhIQNfdvLy4sQsmDx\notQfHn517PUblolJXLdu7RwdyAqJHQAAgNLEx8ebm5tLfZab+uvcuTPzVLyMjIybN2/KspW+\nvv7IkSPbMy6QA26eAAAAANASmHkCADqCVk6yvmnTJkdHR19f39DQ0EePHvH5/H79+kVGRrLX\nyczMZKY2Z7pCJZOss1fGJOsAWkuVE9UCgA7Q4knWs7Ozi4uLGxsbuVzumDFjgoODAwMD586d\nK7EOM7U5Xa6qSdbZe4FJ1gG0Fa6xA4B2pN2TrOvp6W3btm3BggUhISF//PHH6NGjU1NTrays\nli5dSs/USa/DTL5Ob66qSdbZe4FJ1gG0FU7FAkA70u5J1jMyMmJiYsrLy8+fP8+km0ZGRvX1\n9cw6YrGYPbW5VB0zybrEXmCSdQCthMQOANoRj8fbtGmTSCSaOnUqe+ZHesSORpeYmJhs2bKl\nqKiIENLU1DRw4ECBQCAUCgcNGkSvsHz58tzc3BkzZtAv582bV1JS4uvr6+rqGhIS4uTkJHUr\nxpo1a5KTk/l8/oABA9LT0+Va3rt3r0AgqK6uFggEYrH45MmTYWFhhJCnT5/6+/t7eXnRu7ll\nyxY/Pz8PDw8ul8usM2rUqKqqqvT0dIFAsHHjxvj4+G3btrFrI4TY2toWFxczobK74osvvkhJ\nSREKhbNnzw4MDJw+ffr48eNNTU3FYnFmZubSpUuZqr788svr16+PGTOmpT2V2Iuqqio3N7f2\n+dgBQGVwVyxI9/z5cw29XR9A41y9enXmzJkXL17ssBbFYnFwcLCMD7MAeC1zc3MOh6PqKIAQ\nJHYg1bVr15Q4nTMAAGi31atXf/jhh6qOAgjBA4pBqmfPnnE45PICVccBAABqb/pOUl1Vpeoo\n4L+Q2IEUlRUVhBA/R1XHAQAAaq+LMSm5e1fVUcB/IbEDKXhWVoSQfVdVHQcAAKi9ihoS6uys\n6ijgv5DYgRQ2NjY8nuX0fQ2qDgQAADSAk5OTqkOA/8LNEwAAAABaAs+xAwAAANASSOwAAAAA\ntAQSOwAAAAAtgcQOAAAAQEsgsQMAAADQEkjsAAAAALQEEjsAAAAALYHEDgAAAEBLYOYJkK6q\nqqqhATNPAADAa3A4HAsLCz09DBWpBXwMAAAAAFoCU4oBAAAAaAmM2AEAAABoCSR2AAAAAFoC\niR0AAACAlkBiBwAAAKAlkNgBAAAAaAkkdgAAAABaAokdAAAAgJZAYgcAAACgJZDYAQAAAGgJ\nJHYAAAAAWgKJHQAAAICWQGIHAAAAoCWQ2AEAAABoCSR2AAAAAFoCiR0AtIv6+noOh3PlyhVV\nBwIAoEOQ2AGA6l28eNHZ2Tk4OJhdeOvWraFDh5qbm1tZWX3yyScURTXf0NHRkcMSFxdHl1+7\ndm3QoEHdunWztraOjY0tLy+ny4uLi0eOHNm9e3dra+tJkyZVVVVJVPif//yHw+E0NDQQQo4e\nPcppprKykhBSXl4eExPD4/Hs7e3/85//vHr1qvV22xi21PBaqUfe3ZExbABQf0jsAEDFvv32\n2/Hjx3t4eLALX758OXz4cAcHB5FItG/fvl27dv3v//5v823FYvG+fftK/7F+/XpCyKtXr8LD\nwwMDA8vLy2/cuFFdXT1t2jRCCEVRI0aMMDAwuHDhQk5OTn5+fnJyMru2y5cv7969m3n51ltv\nlbKkpaX17duXy+USQmJjY/X09M6dO/fjjz/m5uYuWbKklXbbGHZL4bVUj7y7I3vYAKABKACA\nNisqKho8eLCpqWmvXr02bNhAUVRdXR0hZO/evf369TMyMvL09Lx16xZFUc+ePSOEbNu2jcfj\npaenUxS1ZcuWJ0+efP7550FBQUyFp0+fNjIyqq+vp19u3brV1dWVXh46dOiUKVMoimpsbORw\nOAUFBRLBFBcXE0KKi4vpl9u2bbOzs6Mo6tGjR9HR0Y8fP6bLt2zZ0rNnT2arhoYGPp+/bNky\nQsirV68k6hSLxba2thcuXKAo6vbt2xwO59GjR/RbBw4c4PF4TU1NLbUrQd6wWwqvpXrk3R0Z\nwwYAjYAROwBoq6ampqioKA8Pj3v37mVmZs6fP//nn3+m39qwYcO2bdsePnxoa2ubmppKCDEy\nMiKE7N69Oy8vb/LkyYSQxMREHo8nUedff/2lr69Pr0wI6dGjR1FR0cuXLwkhcXFxI0eOJISI\nxWKKojZu3Ojp6eni4pKcnPzixQtCiKOjY58+fb755psXL148efJk3759kZGRhBAbG5vs7Gwr\nKyu6zocPHzo5OTEtrlu3jsfjjRs3Tuo+fvbZZ2FhYf369SOEXL161cHBwcbGhn5LKBRWVlaW\nlJS01K4EecNuKbyW6pF3d2QMGwA0g4oTSwDQfOfOnTMwMHj27Bn98siRIwUFBfSI3Z49e+jC\nTZs20UNudHlmZqZEJRIjdlVVVV27dl2xYkVjY2NVVdWIESMIIQ8ePGBvcv/+fTs7u7lz5964\ncePo0aO9e/ceN24c/dadO3d69uxJf8sFBwfX1NRINCcSibp163b69Gn6ZWlpKY/HKyoq+v33\n30mzIa7y8vLOnTvfvn2bfrl27VpfX1/mXXqPLl26JEu7ioUtNbxW6pFrd2QMGwA0AhI7AGir\nzMxMa2triUJ2ukOv4+joyJSfO3dOYn2JxI6iqJ9++qlHjx7Gxsa2trarV68mhDBnUaWihwmf\nPXtWV1fn5uaWmpoqFovLyspiYmIiIyPZa/7yyy+Wlpa7du1iSqKjoz/99FOKoqRmQgsXLoyO\njmZeSiR29Dji5cuXW2p3+/bt+v948eKFAmG3Hp5EPfLuzmu7CwA0CBI7AGirHTt2WFpaShTS\nCdzly5fplxKJHVPOaJ7Y0Z4+fdrQ0HDs2DETE5OGhoZWwrh//z4hpLCw8IcffujWrVtjYyNd\nXlhYyE4Kd+7caWFhcezYMWbDH3/80dXVlb6eT2om5OzsvGPHDublrl272FehlZSUEEJKSkpa\nare6uvrGP5h3ZQ/7teFJ1CPv7rTeXQCgWXCNHQC01Ztvvvn06dMnT57QL7Oysk6cONHGOv/+\n++9du3bV1NRwuVx9ff0jR44EBATo6+uz17l27VpSUhLz8ubNm3p6eo6Ojo2NjU1NTUw58ywS\nQsihQ4c+/PDDEydOvP3220zh999/X1paamdnx+PxhEIhIcTGxmbPnj30uzdu3Pjjjz8iIiKY\n9f39/cvKykpLS+mX586ds7GxaaVdc3Nzr3/o6enJG3ZL4bVUj7y700p3AYDmUXVmCQAar6mp\nydPTMyEhoays7OzZs926dTty5IjsI3Z0kpSSkiIQCOgncdAjcy4uLu+///6jR4927NhhaGh4\n6NAhpqoffviB3rBLly5z584tLS29cOGCu7t7YmIiRVEVFRXdu3dfsmRJbW1tZWXlmDFjAgMD\nKYp6/vy5ra3tpk2b2E/9aGxsfPr0KfPy7NmzhJCSkpLa2lq6ua1bt1pYWEjsclhY2NChQ4uK\nii5evOjk5PT555+30q4EecNuKbyW6pF3d2QMGwA0AhI7AFCCe/fuDRw40MTEpFevXuvXr6fk\nORXbrVs3if9wlpaWUhQlEomEQqGJiYmTk9OWLVuY9ZnHnVAUlZeXFxAQYGZm1rNnz1mzZr18\n+ZIuv3TpUmhoqLm5ubW19dixY+kKDx061Pw/t0+ePGHvSPNzl8uXL/f09JTY3/Ly8lGjRnG5\nXAcHh7lz5zLniKW225xcYbcSXkv1yLs7MoYNAOqPQ0l7mDsAAAAAaBxcYwcAAACgJZDYAQAA\nAGgJJHYAAAAAWgKJHQAAAICWQGIHAAAAoCWQ2AEAAABoCSR2AAAAAFoCiR0AAACAlkBiBwAA\nAKAlkNgBAAAAaAkkdgAAAABaAokdAAAAgJZAYgcAAACgJZDYAQAAAGgJJHYAAAAAWsJA1QGA\nJomNjW1oaOByuaoORBc9ePCgoaGhV69eqg5EFz1//vzevXt9+vRRdSC6qLGx8erVq/7+/np6\nGIlQgYKCgrS0tODgYFUHArLC3wnIITc39+HDh6qOQkfdvXu3uLhY1VHoqPLy8hs3bqg6Ch1V\nW1srEonq6upUHYiOun79ekFBgaqjADlgxA7kYGFhMWHChKSkJFUHootmzJjx/PnzTZs2qToQ\nXbR9+/a7d++i81Xijz/+2LNnz4oVK+zt7VUdiy46ePCgpaWlqqMAOWDEDgAAAEBLILEDAAAA\n0BJI7AAAAAC0BBI7AAAAAC2h/+mnn6o6BtAkoaGhVlZWqo5CFxkaGrq5ubm5uak6EF3UuXPn\n7t27BwQEqDoQXWRqatrQ0BAZGamvr6/qWHQRRVFvv/22ubm5qgMBWXEoilJ1DAAAAACgBDgV\nCwAAAKAlkNgBAAAAaAkkdgAAAABaAokdAAAAgJZAYgcAAACgJZDYAQAAAGgJJHY65+rVq+bm\n5vQT0RITEwkhr169io+PHzx4cFBQ0A8//EAIoSjqP//5j7e3t6+v76lTp6RuRQiZO3eur6+v\nv7//5cuXZS/RZbJ0fn19/TvvvOPr6+vr60uXEEW7Gp3Ppljn48hXClk6n1ZbW9uzZ89Dhw7R\nL3HkK4Vi/Y+DX1NRoGN++eWX6dOns0v27dv33nvvURRVWVn5xhtvUBR16NCh0aNHUxRVVFTU\np08fqVudOXMmPDy8qanpxo0bQqFQxhIdJ0vn79+/f/HixRRF3b9/ny5RrKvR+RIU63wc+Uoh\nS+fT5s6d6+/v/9NPP1E48pVHsf7Hwa+hDFSdWEJHq6mp6dy5M7vEwsKiqqqKfsvCwoIQ8tZb\nbwUHBxNCevToIRaLpW71yy+/jB49msPheHl51dTUPH/+XJaSrl27dtyuqh9ZOn/UqFGjRo0i\nhJSUlNjZ2RFFuxqdL0GxzseRrxSydD4h5ObNm6WlpWFhYfRLHPnKolj/4+DXUDgVq3OeP3+e\nl5cXGBjYr1+/3NxcQshbb72lr6/v5uYmFAq/+uorQoipqWm3bt0IIevWrUtISJC61ePHj62t\nrek6e/ToUV5eLktJh++uepGl82nu7u7jx49fv349UbSr0fkSFOt8HPlKIUvnUxSVkpKSlpbG\nbIUjX1kU638c/BoKI3Y6x9fXNyUlZdSoUTdu3BgxYkRxcfGRI0f09PRu3759//79iIiI69ev\n6+npEUK2bNly9uxZ+vKL5lux66QoisPhyFuig2Tv/Nu3b1+6dCkuLu769evsGhTranQ+UbTz\nceQrhSyd/91334WFhdnb20utAUd+WyjW/zj4NRQSO53j5eXl5eVFCPH29rawsKioqPj111+H\nDh3K4XAcHR0NDQ0rKyutrKx27tx54MCBAwcOGBoaSt3K1tb24cOHdJ1lZWU2NjaylKhgh9WJ\nLJ3/4MEDLpfbq1cvoVDY2Nj45MkTxboanS9Bsc7Hka8UsnT+4cOHS0tLs7KySktLDx48aG1t\njSNfWRTrf4FAgINfE+FUrM7ZvHnzypUrCSH379+vrq62trZ2dna+cuUKIUQsFldXV1tYWJSV\nla1duzYrK8vExKSlrQYPHrx3716Koq5du8bj8bp06SJLiQp3XB3I0vnnz59ftmwZIaSsrKym\npobH4ynW1eh8CYp1Po58pZCl8/fv33/p0qULFy7Ex8enpaUJBAIc+cqiWP/j4NdU7XtvBqgf\nsVgcERHB5/P5fP7PP/9MUVR9fX18fPygQYOCgoIOHjxIUdTy5cstLS09//H8+fPmW1EUNX/+\nfF9f3379+olEItlLdJksnV9fXx8XF+ft7e3l5fXjjz/SGyrW1eh8NsU6H0e+UsjS+Yz58+fT\nd2VSOPKVRLH+x8GvoTgURak6twQAAAAAJcCpWAAAAAAt8f8A0QnSll4g/2gAAAAASUVORK5C\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millefy_adjust(sc_avg_scale = 7000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sessionInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.6.1 (2019-07-05)\n",
       "Platform: x86_64-conda_cos6-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 18.04.2 LTS\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /opt/conda/lib/R/lib/libRblas.so\n",
       "\n",
       "locale:\n",
       "[1] en_US.UTF-8\n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] millefy_0.1.9\n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       "  [1] bitops_1.0-6                matrixStats_0.55.0         \n",
       "  [3] destiny_3.0.0               xts_0.11-2                 \n",
       "  [5] GenomeInfoDb_1.22.0         repr_1.0.1                 \n",
       "  [7] tools_3.6.1                 backports_1.1.5            \n",
       "  [9] irlba_2.3.3                 R6_2.4.1                   \n",
       " [11] BiocGenerics_0.32.0         lazyeval_0.2.2             \n",
       " [13] colorspace_1.4-1            nnet_7.3-12                \n",
       " [15] sp_1.3-2                    smoother_1.1               \n",
       " [17] tidyselect_0.2.5            curl_4.2                   \n",
       " [19] compiler_3.6.1              Biobase_2.46.0             \n",
       " [21] DelayedArray_0.12.1         rtracklayer_1.46.0         \n",
       " [23] scales_1.1.0                DEoptimR_1.0-8             \n",
       " [25] lmtest_0.9-37               hexbin_1.27.3              \n",
       " [27] robustbase_0.93-5           proxy_0.4-23               \n",
       " [29] stringr_1.4.0               pbdZMQ_0.3-3               \n",
       " [31] digest_0.6.23               Rsamtools_2.2.1            \n",
       " [33] foreign_0.8-74              rio_0.5.16                 \n",
       " [35] XVector_0.26.0              base64enc_0.1-3            \n",
       " [37] pkgconfig_2.0.3             htmltools_0.3.6            \n",
       " [39] TTR_0.23-5                  ggthemes_4.2.0             \n",
       " [41] rlang_0.4.2                 readxl_1.3.1               \n",
       " [43] zoo_1.8-6                   jsonlite_1.6               \n",
       " [45] BiocParallel_1.20.1         dplyr_0.8.3                \n",
       " [47] zip_2.0.4                   car_3.0-6                  \n",
       " [49] RCurl_1.95-4.12             magrittr_1.5               \n",
       " [51] GenomeInfoDbData_1.2.2      Matrix_1.2-17              \n",
       " [53] Rcpp_1.0.3                  IRkernel_1.0.2             \n",
       " [55] munsell_0.5.0               S4Vectors_0.24.1           \n",
       " [57] abind_1.4-5                 lifecycle_0.1.0            \n",
       " [59] scatterplot3d_0.3-41        stringi_1.4.3              \n",
       " [61] carData_3.0-3               MASS_7.3-51.4              \n",
       " [63] SummarizedExperiment_1.16.1 zlibbioc_1.32.0            \n",
       " [65] grid_3.6.1                  parallel_3.6.1             \n",
       " [67] forcats_0.4.0               crayon_1.3.4               \n",
       " [69] lattice_0.20-38             IRdisplay_0.7.0            \n",
       " [71] Biostrings_2.54.0           haven_2.2.0                \n",
       " [73] hms_0.5.2                   zeallot_0.1.0              \n",
       " [75] pillar_1.4.3                ranger_0.11.2              \n",
       " [77] GenomicRanges_1.38.0        uuid_0.1-2                 \n",
       " [79] boot_1.3-24                 RcppHNSW_0.2.0             \n",
       " [81] codetools_0.2-16            stats4_3.6.1               \n",
       " [83] XML_3.98-1.20               glue_1.3.1                 \n",
       " [85] evaluate_0.14               pcaMethods_1.78.0          \n",
       " [87] data.table_1.12.8           laeken_0.5.0               \n",
       " [89] vcd_1.4-4                   vctrs_0.2.1                \n",
       " [91] VIM_4.8.0                   cellranger_1.1.0           \n",
       " [93] tidyr_1.0.0                 gtable_0.3.0               \n",
       " [95] purrr_0.3.3                 knn.covertree_1.0          \n",
       " [97] assertthat_0.2.1            ggplot2_3.2.1              \n",
       " [99] openxlsx_4.1.4              RcppEigen_0.3.3.7.0        \n",
       "[101] e1071_1.7-3                 RSpectra_0.16-0            \n",
       "[103] class_7.3-15                SingleCellExperiment_1.8.0 \n",
       "[105] tibble_2.1.3                GenomicAlignments_1.22.1   \n",
       "[107] IRanges_2.20.1              ggplot.multistats_1.0.0    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}