04-plotting.ipynb

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    "### Notebook index\n",
    "\n",
    "[3 Minimal examples](03-minimal.ipynb)  \n",
    "[4 Plotting chromosomes](04-plotting.ipynb)   \n",
    "[5 Multiple OTUs](05-multiple.ipynb)    \n",
    "[6 Changing units](06-units.ipynb)  \n",
    "[7 GISH](07-gish.ipynb)  \n",
    "[8 Groups](08-groups.ipynb)  \n",
    "[9 Circular Plots](09-circular.ipynb)  \n",
    "[10 Plotting alongside phylogeny](10-phylogeny.ipynb)  \n",
    "[11 Citrus](11-citrushelp.ipynb)  \n",
    "[12 Human Karyotype](12-human.ipynb)  "
   ]
  },
  {
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   "id": "b9886154",
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   "source": [
    "# Plotting chromosomes\n",
    "\n",
    "This guide shows how to plot idiograms of measured karyotypes and optionally marks."
   ]
  },
  {
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   "source": [
    "# General jupyter instructions"
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    "# press Ctrl + Enter to RUN active cell\n",
    "\n",
    "# SHOW keyboard shortcuts Ctrl + M then H (only jupyter notebooks and colab)\n",
    "\n",
    "# Ctrl + D (or X) DELETE a line of code\n",
    "\n",
    "# press Ctrl + M to escape from cell (opposite to ENTER)\n",
    "\n",
    "# then !\n",
    "# B to ADD cell below or A above\n",
    "# M to turn code cell into MARKDOWN\n",
    "# Y turn markdown to CODE\n",
    "# O to toggle OUTPUT\n",
    "# D D to DELETE cell\n",
    "\n",
    "# If in jupyter-lab:\n",
    "# collapse cell clicking in the blue left bar\n",
    "# use the settings menu to use the dark theme\n",
    "\n",
    "# if in colab:\n",
    "# remove bar at 80 in tools, preferences, 80 -> 0"
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   "source": [
    "# install if necessary\n",
    "\n",
    "if (system.file(package = \"idiogramFISH\") == '') {\n",
    "  install.packages(\"idiogramFISH\")\n",
    "}\n",
    "\n",
    "library(idiogramFISH)"
   ]
  },
  {
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   "source": [
    "## Plot monocentrics\n",
    "\n",
    "Initially you have to put chromosome data in data.frames.\n",
    "\n",
    "Main three data.frames: \n",
    "\n",
    "* One for chr. sizes (parameter `dfChrSize`)\n",
    "* One for marks' positions  (parameter `dfMarkPos`)\n",
    "* One (optional) for mark style (parameter `dfMarkColor`)\n",
    "\n",
    "For a way to start with only one data.frame of chr. size and marks' pos., see Chapter 8.\n",
    "\n",
    "### data.frame of chr. size"
   ]
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  {
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     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OTU</th><th scope=col>chrName</th><th scope=col>shortArmSize</th><th scope=col>longArmSize</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Species one</td><td>1</td><td>1.5</td><td>2.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>2</td><td>2.0</td><td>2.5</td></tr>\n",
       "\t<tr><td>Species one</td><td>3</td><td>1.0</td><td>1.5</td></tr>\n",
       "\t<tr><td>Species one</td><td>B</td><td>2.0</td><td>3.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 4\n",
       "\\begin{tabular}{llll}\n",
       " OTU & chrName & shortArmSize & longArmSize\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Species one & 1 & 1.5 & 2.0\\\\\n",
       "\t Species one & 2 & 2.0 & 2.5\\\\\n",
       "\t Species one & 3 & 1.0 & 1.5\\\\\n",
       "\t Species one & B & 2.0 & 3.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 4\n",
       "\n",
       "| OTU &lt;chr&gt; | chrName &lt;chr&gt; | shortArmSize &lt;dbl&gt; | longArmSize &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| Species one | 1 | 1.5 | 2.0 |\n",
       "| Species one | 2 | 2.0 | 2.5 |\n",
       "| Species one | 3 | 1.0 | 1.5 |\n",
       "| Species one | B | 2.0 | 3.5 |\n",
       "\n"
      ],
      "text/plain": [
       "  OTU         chrName shortArmSize longArmSize\n",
       "1 Species one 1       1.5          2.0        \n",
       "2 Species one 2       2.0          2.5        \n",
       "3 Species one 3       1.0          1.5        \n",
       "4 Species one B       2.0          3.5        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example data.frame to write in R, use: (column OTU is optional if only 1 OTU)\n",
    "mydfChrSize<-read.table(text=\n",
    "\"            OTU chrName shortArmSize longArmSize \n",
    "  \\\"Species one\\\"   1     1.5         2.0  \n",
    "  \\\"Species one\\\"   2     2.0         2.5  \n",
    "  \\\"Species one\\\"   3     1.0         1.5\n",
    "  \\\"Species one\\\"   B     2.0         3.5\"  ,  header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "mydfChrSize"
   ]
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   "source": [
    "### Loading saved data\n",
    "\n",
    "If you use RStudio, in the menu \"Session\", use \"Set working directory\" for choosing your desired folder or:\n",
    "\n",
    "If you use colab (google) use the menu to the left to upload files to the virtual machine\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a10a1c9-4b51-440c-8091-568d7e496062",
   "metadata": {},
   "source": [
    "`\n",
    "setwd(\"~/folder/subfolder\")\n",
    "`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ec77ef",
   "metadata": {},
   "source": [
    "Open your chromosome data data.frame importing it from a .csv (read.csv) or .xls file (readxl). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2554c400-8426-4ebc-b5e7-58ccecc5db53",
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   "source": [
    "`mydfChrSize<-read.csv(\"somefile.csv\")`"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "\n",
    "Editing a data.frame:"
   ]
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   "source": [
    "`bigdfOfChrSize <- edit(bigdfOfChrSize, edit.row.names = FALSE)`"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "For fixing column names use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5510416c",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mydfChrSize)<-c(\"OTU\", \"chrName\",\"shortArmSize\",\"longArmSize\")"
   ]
  },
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   "source": [
    "### Marks' position data\n",
    "\n",
    "Open or write your mark positions in a data.frame. This data.frame has the marks present in all karyotypes with position info.\n",
    "This data.frame has also the centromeric marks present in all karyotypes.\n",
    "\n",
    "Column `chrRegion` defines the arm or region of occurrence (`p`, `q`, `w`, `cen`). Distance to centromere is defined in `markDistCen`.\n"
   ]
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    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 11 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OTU</th><th scope=col>chrName</th><th scope=col>markName</th><th scope=col>chrRegion</th><th scope=col>markSize</th><th scope=col>markDistCen</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Species one</td><td>1</td><td>45S    </td><td>p  </td><td> NA</td><td> NA</td></tr>\n",
       "\t<tr><td>Species one</td><td>1</td><td>5S     </td><td>q  </td><td>0.5</td><td>0.5</td></tr>\n",
       "\t<tr><td>Species one</td><td>B</td><td>B mark </td><td>w  </td><td> NA</td><td> NA</td></tr>\n",
       "\t<tr><td>Species one</td><td>B</td><td>cB mark</td><td>q  </td><td> NA</td><td>1.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>2</td><td>cgene4 </td><td>p  </td><td>1.0</td><td>1.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>2</td><td>cgene1 </td><td>q  </td><td>1.0</td><td>1.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>2</td><td>cgene2 </td><td>q  </td><td>0.5</td><td>2.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>3</td><td>DAPI   </td><td>q  </td><td> NA</td><td>1.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>3</td><td>cgene3 </td><td>p  </td><td> NA</td><td>0.5</td></tr>\n",
       "\t<tr><td>Species one</td><td>1</td><td>DAPI   </td><td>cen</td><td> NA</td><td> NA</td></tr>\n",
       "\t<tr><td>Species one</td><td>3</td><td>CMA    </td><td>cen</td><td> NA</td><td> NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 11 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " OTU & chrName & markName & chrRegion & markSize & markDistCen\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Species one & 1 & 45S     & p   &  NA &  NA\\\\\n",
       "\t Species one & 1 & 5S      & q   & 0.5 & 0.5\\\\\n",
       "\t Species one & B & B mark  & w   &  NA &  NA\\\\\n",
       "\t Species one & B & cB mark & q   &  NA & 1.0\\\\\n",
       "\t Species one & 2 & cgene4  & p   & 1.0 & 1.0\\\\\n",
       "\t Species one & 2 & cgene1  & q   & 1.0 & 1.0\\\\\n",
       "\t Species one & 2 & cgene2  & q   & 0.5 & 2.0\\\\\n",
       "\t Species one & 3 & DAPI    & q   &  NA & 1.0\\\\\n",
       "\t Species one & 3 & cgene3  & p   &  NA & 0.5\\\\\n",
       "\t Species one & 1 & DAPI    & cen &  NA &  NA\\\\\n",
       "\t Species one & 3 & CMA     & cen &  NA &  NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 11 × 6\n",
       "\n",
       "| OTU &lt;chr&gt; | chrName &lt;chr&gt; | markName &lt;chr&gt; | chrRegion &lt;chr&gt; | markSize &lt;dbl&gt; | markDistCen &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Species one | 1 | 45S     | p   |  NA |  NA |\n",
       "| Species one | 1 | 5S      | q   | 0.5 | 0.5 |\n",
       "| Species one | B | B mark  | w   |  NA |  NA |\n",
       "| Species one | B | cB mark | q   |  NA | 1.0 |\n",
       "| Species one | 2 | cgene4  | p   | 1.0 | 1.0 |\n",
       "| Species one | 2 | cgene1  | q   | 1.0 | 1.0 |\n",
       "| Species one | 2 | cgene2  | q   | 0.5 | 2.0 |\n",
       "| Species one | 3 | DAPI    | q   |  NA | 1.0 |\n",
       "| Species one | 3 | cgene3  | p   |  NA | 0.5 |\n",
       "| Species one | 1 | DAPI    | cen |  NA |  NA |\n",
       "| Species one | 3 | CMA     | cen |  NA |  NA |\n",
       "\n"
      ],
      "text/plain": [
       "   OTU         chrName markName chrRegion markSize markDistCen\n",
       "1  Species one 1       45S      p          NA       NA        \n",
       "2  Species one 1       5S       q         0.5      0.5        \n",
       "3  Species one B       B mark   w          NA       NA        \n",
       "4  Species one B       cB mark  q          NA      1.0        \n",
       "5  Species one 2       cgene4   p         1.0      1.0        \n",
       "6  Species one 2       cgene1   q         1.0      1.0        \n",
       "7  Species one 2       cgene2   q         0.5      2.0        \n",
       "8  Species one 3       DAPI     q          NA      1.0        \n",
       "9  Species one 3       cgene3   p          NA      0.5        \n",
       "10 Species one 1       DAPI     cen        NA       NA        \n",
       "11 Species one 3       CMA      cen        NA       NA        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will use column OTU if data.frame because chromosome size df has it\n",
    "mydfOfMarks<-read.table(text=\n",
    "\"            OTU chrName markName chrRegion markSize markDistCen\n",
    "\\\"Species one\\\"      1      45S       p       NA         NA     # no measure (NA) means whole arm\n",
    "\\\"Species one\\\"      1       5S       q      0.5         0.5\n",
    "\\\"Species one\\\"      B  \\\"B mark\\\"    w       NA         NA     # w for whole chromosome\n",
    "\\\"Species one\\\"      B  \\\"cB mark\\\"   q       NA         1.0    \n",
    "\\\"Species one\\\"      2    cgene4      p        1         1.0\n",
    "\\\"Species one\\\"      2    cgene1      q        1         1\n",
    "\\\"Species one\\\"      2    cgene2      q      0.5         2.0\n",
    "\\\"Species one\\\"      3     DAPI       q       NA         1\n",
    "\\\"Species one\\\"      3    cgene3      p       NA         0.5 \n",
    "\\\"Species one\\\"      1     DAPI       cen\n",
    "\\\"Species one\\\"      3      CMA       cen\", header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "mydfOfMarks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616b5071",
   "metadata": {},
   "source": [
    "For fixing column names use something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3be4a119",
   "metadata": {},
   "outputs": [],
   "source": [
    "if(F){\n",
    "colnames(mydfMarkColor)<-c(\"OTU\", \"chrName\",\"markName\",\"chrRegion\",\"markSize\",\"markDistCen\") \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961eba56",
   "metadata": {},
   "source": [
    "### Marks' general data \n",
    "\n",
    "This data.frame is optional. It has the marks present in all karyotypes without position info.\n",
    "\n",
    "It includes color and style. If the `style` column is not present, param. `defaultStyleMark = \"square\"` will be used during plotting.\n",
    "\n",
    "Note the `cenStyle` to make constrictions, mimicking centromeres.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f4cb3e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>markName</th><th scope=col>markColor</th><th scope=col>style</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5S     </td><td>red           </td><td>dots     </td></tr>\n",
       "\t<tr><td>45S    </td><td>chartreuse3   </td><td>square   </td></tr>\n",
       "\t<tr><td>cgene1 </td><td>chocolate     </td><td>upArrow  </td></tr>\n",
       "\t<tr><td>cgene2 </td><td>salmon        </td><td>downArrow</td></tr>\n",
       "\t<tr><td>cgene3 </td><td>#056522       </td><td>cMLeft   </td></tr>\n",
       "\t<tr><td>DAPI   </td><td>dodgerblue    </td><td>cM       </td></tr>\n",
       "\t<tr><td>cB mark</td><td>black         </td><td>cenStyle </td></tr>\n",
       "\t<tr><td>CMA    </td><td>darkgoldenrod1</td><td>square   </td></tr>\n",
       "\t<tr><td>B mark </td><td>black         </td><td>square   </td></tr>\n",
       "\t<tr><td>cgene4 </td><td>cornflowerblue</td><td>exProtein</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 3\n",
       "\\begin{tabular}{lll}\n",
       " markName & markColor & style\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 5S      & red            & dots     \\\\\n",
       "\t 45S     & chartreuse3    & square   \\\\\n",
       "\t cgene1  & chocolate      & upArrow  \\\\\n",
       "\t cgene2  & salmon         & downArrow\\\\\n",
       "\t cgene3  & \\#056522        & cMLeft   \\\\\n",
       "\t DAPI    & dodgerblue     & cM       \\\\\n",
       "\t cB mark & black          & cenStyle \\\\\n",
       "\t CMA     & darkgoldenrod1 & square   \\\\\n",
       "\t B mark  & black          & square   \\\\\n",
       "\t cgene4  & cornflowerblue & exProtein\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 3\n",
       "\n",
       "| markName &lt;chr&gt; | markColor &lt;chr&gt; | style &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 5S      | red            | dots      |\n",
       "| 45S     | chartreuse3    | square    |\n",
       "| cgene1  | chocolate      | upArrow   |\n",
       "| cgene2  | salmon         | downArrow |\n",
       "| cgene3  | #056522        | cMLeft    |\n",
       "| DAPI    | dodgerblue     | cM        |\n",
       "| cB mark | black          | cenStyle  |\n",
       "| CMA     | darkgoldenrod1 | square    |\n",
       "| B mark  | black          | square    |\n",
       "| cgene4  | cornflowerblue | exProtein |\n",
       "\n"
      ],
      "text/plain": [
       "   markName markColor      style    \n",
       "1  5S       red            dots     \n",
       "2  45S      chartreuse3    square   \n",
       "3  cgene1   chocolate      upArrow  \n",
       "4  cgene2   salmon         downArrow\n",
       "5  cgene3   #056522        cMLeft   \n",
       "6  DAPI     dodgerblue     cM       \n",
       "7  cB mark  black          cenStyle \n",
       "8  CMA      darkgoldenrod1 square   \n",
       "9  B mark   black          square   \n",
       "10 cgene4   cornflowerblue exProtein"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From scratch:\n",
    "mydfMarkColor<-read.table(text=\n",
    "\" markName      markColor      style\n",
    "        5S      red            dots\n",
    "       45S      chartreuse3    square\n",
    "    cgene1      chocolate      upArrow\n",
    "    cgene2      salmon         downArrow\n",
    "    cgene3      \\\"#056522\\\"    cMLeft    \n",
    "      DAPI      dodgerblue     cM   \n",
    "\\\"cB mark\\\"     black          cenStyle   \n",
    "       CMA      darkgoldenrod1 square\n",
    "\\\"B mark\\\"      black          square\n",
    "    cgene4      cornflowerblue exProtein\",  header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "mydfMarkColor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe865b7",
   "metadata": {},
   "source": [
    "For fixing column names use:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "83f63ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mydfMarkColor)<-c(\"markName\", \"markColor\",\"style\") \n",
    "# if style column is not present it will be filled with \"square\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87136579",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Add some text to the right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3170cf96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OTU</th><th scope=col>note</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Species one</td><td>Author notes</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1 × 2\n",
       "\\begin{tabular}{ll}\n",
       " OTU & note\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Species one & Author notes\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1 × 2\n",
       "\n",
       "| OTU &lt;chr&gt; | note &lt;chr&gt; |\n",
       "|---|---|\n",
       "| Species one | Author notes |\n",
       "\n"
      ],
      "text/plain": [
       "  OTU         note        \n",
       "1 Species one Author notes"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will use column note to add a note to the right of the karyotype of the \n",
    "# OTU in column OTU\n",
    "notesdf<-read.table(text=\n",
    "\"            OTU    note\n",
    "\\\"Species one\\\"   \\\"Author notes\\\"  \", header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "notesdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9fcf83",
   "metadata": {},
   "source": [
    "For adding just the OTU name (from column `OTU` of data.frame of chr. size) to the right, use `OTUasNote=TRUE`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4cf69d",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "You can plot without marks (use only 1st data.frame), but we will use all 4 data.frames created.\n",
    "By default the function will calculate indices [@Zarco1986new;@Watanabe1999] and morphological categories of [Guerra](https://ferroao.gitlab.io/guerra1986/Guerra1986.pdf) [-@Guerra1986d] and Levan [-@Levan1964].\n",
    "Use parameters `chrIndex` and `morpho` of the function `plotIdiograms` to modify that. See `?plotIdiograms`.\n",
    "\n",
    "The `cM` style of mark always adds the name as if `legend=\"inline\"`, even when `legend=\"aside\"` (default).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ae754f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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ACQqtjHFACA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\nT59WOgWqO3bsWKNrUIxUXl6+a9cu+eELL7xg5IFTp06V24cOH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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 900,
       "width": 1500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mar = c(0, 0, 0, 0))\n",
    "\n",
    "options(repr.plot.width = 15, repr.plot.height = 9, repr.plot.res = 200)\n",
    "\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize= mydfChrSize,     # chr. size data.frame\n",
    "              dfMarkPos= mydfOfMarks,     # mark position data.frame (inc. cen.)\n",
    "              dfMarkColor=mydfMarkColor,  # mark style d.f.\n",
    "              \n",
    "              chrSpacing=.6,              # separ. among chr.\n",
    "              distTextChr = .7,           # separation among text and chr. names and ind.        \n",
    "              orderChr = \"name\",          # order chr. by name\n",
    "              karHeiSpace=2               # vertical size of karyotype including spacer\n",
    "              \n",
    "              ,arrowhead = .5             # proportion of head of arrow\n",
    "              \n",
    "              ,fixCenBorder = TRUE        # use chrColor as border color of cen. or cen. marks\n",
    "              ,legendWidth = .8           # legend item width\n",
    "              ,legendHeight = .5          # legend item height\n",
    "              ,markLabelSpacer = 2        # legend spacer\n",
    "              ,lwd.mimicCen = 2.5         # constric. mark line width\n",
    "              \n",
    "              ,rulerPos=0                 # ruler position\n",
    "              ,ruler.tck=-0.01            # ticks of ruler size and orientation\n",
    "              ,xPosRulerTitle = 2.5       # ruler units pos.\n",
    "              \n",
    "              ,markPer = \"45S\"            # show mark % of chr.  under kar.\n",
    "              ,showMarkPos = TRUE         # show position of marks under kar. See bToRemove\n",
    "              ,bToRemove = c(\"B mark\",\"cB mark\") # bands to remove from pos. calc. See showMarkPos\n",
    "              \n",
    "              ,notes=notesdf              # data.frame with notes \n",
    "              ,notesTextSize = 1.3        # font size of notes\n",
    "              ,notesPosX = .2             # space from chr. (right) to note\n",
    "              \n",
    "              ,ylimBotMod = 2             # modify ylim bottom argument\n",
    "              ,ylimTopMod = 0             # modify ylim top argument\n",
    "              ,xlimLeftMod = 2            # modify left xlim\n",
    "              ,xlimRightMod = 3           # modify right xlim\n",
    "              ,lwd.cM = 2                 # thickness of cM marks \n",
    "              ,pattern = \"^c\"             # regex pattern to remove from mark names\n",
    "              ,remSimiMarkLeg=TRUE        # remove pseudoduplicated mark names from legend (same after pattern removal)\n",
    "              ,legendYcoord = -1.2\n",
    "              # ,legend=\"inline\"          # legend next to chr.\n",
    "              ,bannedMarkName = c(\"B mark\",\"cB mark\")  # remove from legends\n",
    ")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3a83df",
   "metadata": {},
   "source": [
    "Vertices when `centromereSize=0` are rounded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e286d7b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ty501gvrdraWrOkdo2MjIysrCxzHb3lqqqqWmnVdl1cv37dNPd0tpKioqLr16+b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08PCIiYn5+OOPVSVyufzVV18dOnRoYmJi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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#png(\"mydfChrSize2.png\", width=550, height=550)\n",
    "options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 100)\n",
    "par(mar = c(0, 0, 0, 0))\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize   = bigdfOfChrSize[1:8,],  # chr. size data.frame\n",
    "              dfMarkColor = mydfMarkColor,# mark style df\n",
    "              dfMarkPos   = bigdfOfMarks, # mark position df\n",
    "              \n",
    "              centromereSize = 0,         # <- HERE\n",
    "              \n",
    "              squareness=3,               # vertices squareness  \n",
    "              chrSpacing = .7,            # space among chr.\n",
    "              karHeight = 2,              # karyotype rel. height \n",
    "              karHeiSpace=4,              # vertical size of karyotype including spacer\n",
    "              amoSepar= 2.5,              # separation among karyotype\n",
    "              \n",
    "              indexIdTextSize=.8,         # font size of chr. name and indices\n",
    "              karIndexPos = .1,           # position of kar. index\n",
    "              markLabelSize=.7,           # font size of mark legends\n",
    "              fixCenBorder = FALSE,       # do not use chrColor as border color of cen. or cen. marks\n",
    "              distTextChr = .8,           # separation among chr. and ind.\n",
    "              \n",
    "              rulerPos= 0,                # ruler position\n",
    "              ruler.tck=-0.01,            # ticks of ruler size and orientation\n",
    "              xPosRulerTitle = 2.6,       # ruler units pos.\n",
    "              \n",
    "              xlimLeftMod = 2,            # modify xlim left argument \n",
    "              ylimBotMod = 0.4,           # modify ylim bottom argument\n",
    "              ylimTopMod = 0              # modify ylim top argument\n",
    "              ,lwd.cM = 2                 # thickness of cM marks \n",
    "              )\n",
    ")\n",
    "#dev.off()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d0fcd2",
   "metadata": {},
   "source": [
    "There is no need to add `dfMarkColor` and you can also use the parameter\n",
    "`mycolors` (optional too), to establish marks' colors. Colors are assigned depending on the order of marks, i.e.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a124430f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'45S'</li><li>'5S'</li><li>'B mark'</li><li>'cB mark'</li><li>'cgene4'</li><li>'cgene1'</li><li>'cgene2'</li><li>'DAPI'</li><li>'cgene3'</li><li>'CMA'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '45S'\n",
       "\\item '5S'\n",
       "\\item 'B mark'\n",
       "\\item 'cB mark'\n",
       "\\item 'cgene4'\n",
       "\\item 'cgene1'\n",
       "\\item 'cgene2'\n",
       "\\item 'DAPI'\n",
       "\\item 'cgene3'\n",
       "\\item 'CMA'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '45S'\n",
       "2. '5S'\n",
       "3. 'B mark'\n",
       "4. 'cB mark'\n",
       "5. 'cgene4'\n",
       "6. 'cgene1'\n",
       "7. 'cgene2'\n",
       "8. 'DAPI'\n",
       "9. 'cgene3'\n",
       "10. 'CMA'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"45S\"     \"5S\"      \"B mark\"  \"cB mark\" \"cgene4\"  \"cgene1\"  \"cgene2\" \n",
       " [8] \"DAPI\"    \"cgene3\"  \"CMA\"    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(mydfOfMarks$markName) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45426936",
   "metadata": {},
   "source": [
    "Let's use the `cM` style of marks. A protruding line.\n",
    "\n",
    "`cM` style does not apply to centromere marks. To make something similar, use `centromereSize=0`, `legend=\"inline\"` and `fixCenBorder = FALSE`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "027b29b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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ISYCgAAAABQEGIqAAAAAEBBiKkAAAAA\nAAUhpgIAAAAAFISVfgEAQ5LVEnj2dICxQtNQo2ptUneYhBAiINARFmEfFmOP09uSM+wjRnm1\nBM35+oCqMk3tWU1To7q9Vdi6hBCOEJ0UMcw+Mt6WmNqlTxfaIK/WAACAAhFTAQBDi/piQ9Ch\nT7Wnjwqrpfdzti518wV184XAypNi/4f2EaOs13zPOm6SUGs8WYHDrj1xWPvNAc35+u8or8Mk\nOkyac9Xa0i+ENsiaPsEyeYYjeqQnCwAAQNmIqQCAoUJl6Qza/2FQ6Zd97K85Xx+yZ3vQV590\nfH+hLTnDIzUEVJWFfPw3dfOFPvW2WrTHvtYe+9oy8TrL9XOloGCP1AAAgMIRUwEAQ0JAnSHk\n/S3q9pb+HqhuvqDb+ZZ14tTO7y+QNO5fVlXZ7cEf79KWfuHGsUGlXwZWnuqYv8w2OsntAgAA\nGCxYQgkA4P8CTx/VFax3I6N205Z+oduxQXXpPOG+UVktuh0b3MuoTur2Fl3B+sDTR90eAQCA\nwYKYCgDwc4FnjoW+v3ng42iMFbqdbzkXOuofW5du51saY8XAawh9f3PgmWMDHwcAACUjpgIA\n/JmmoSZ01ybPjKUSmtqzobuL+ntc6O4iTe1ZofJMFSF7t2saajwzFgAAikRMBQD4LZWtK/SD\nv3p2zMDTpdqjB/veX3v0YODpUg8WoOrsCP3gryo3LuoCADBIEFMBAH4r6Mu/93VN3f4I/vTD\nf+6zeiXqDlPwpx96vAB184WgL//u8WEBAFAIYioAwD85Wpu1Rz73xsgqS4f24Md96ak9+LHK\n0uGNGrRHPne0NntjZAAAZEdMBQD4J+v+PW4vzHtF2mNf2U3trvvYTe3aY195qQCV1WLdv8dL\ngwMAIC9iKgDAH0kOy5efeG94ldXS/Nk+132aP9vnvZwshLB8+YmQHN4bHwAAuRBTAQB+yHzm\nlNRy0aunaP2yeIAdBkhquWg+c8qrpwAAQBbEVACAH2o7esjbpzCdKHF1MVNymE6UeLsGH7xM\nAAB8j5gKAPBDHVVnvH0Ke4fZUl97uWct9bX2DrO3a/DBywQAwPeIqQAAP2RtqPfFWRrPufGU\nJwvwycsEAMDHAuQuAAAAz7Ob2uQ9i+wFAArkcDgsFovNZutLZ41GExwcrFZ7+JpKV1eXxWKR\nJOmKPVUqlVar1Wq1ni0AQF8QUwE3SZKkUqnkrgKArFz8pduHP4KBIUKSpHPnzplMJoPB4Mbh\nGRkZo0ePHmBeNZvN9fX1Z8+edePYpKSk2NjYsLCwgRQAoF+IqYArbW1td999t9Vqvfnmm++7\n777udmfL3r17ZawNgAvqkFB5zyJ7AYBCtLa2fv311wMZoaysrKys7Oqrr46OjnbjcIfDYTAY\nKisr3S7AYDAYDIakpKTk5GSNRuP2OAD6jntTAVc2bNiwdOnSHTt27Ny502z+52oojY2Nd911\nV01Njby1AXBBO2KkL84SE+vGU54swCcvE3BbY2PjADNqtyNHjtTV1fX3KLvdfubMmYFk1G4G\ng+GTTz6xWq0DHwrAFXE1FXCltLQ0NzdXCJGenm4wGDIzM4UQMTExmzdvnjNnzqX9Dx48WFRU\n1LOlvLzcN6UC6ClYnyb27/PqKdRabdDohMs9GzQ6Qa3VOrz8F22wPs2r4wMD0dLScvToUQ8O\nePLkycDAwBEjRvT9kIqKCs9+rFxZWZmenu7xO2YB9EJMBVxxOBzdN6D2ZcmH7Ozs7Ozsni2r\nV6/2SmUAXAobd7W3TxEyZrxKc9n/RlWagJAx403Hv/FqDT54mYB7HA7HoUOe39e3tLR0xowZ\ngYGBfelcV1dXXV3t2QJqa2tDQkKSkpI8OyyAXvgoCHAlKyurpKRECFFRUZGSkiJ3OQD6Kmz8\nVapQnVdPEZF9/QA7DJAqVBc2/iqvngJwmxsTdPuotvay+xX3JEnSyZMnvVFAeXl5HxcrBuA2\nYirgSn5+/ubNm5cvXz5v3jydTldcXPy73/1O7qIAXJlKE6Cd9D2vnmLYjNkD7DBA2knfc3E5\nF5BXWVmZl0auqKjoS7eLFy96qQAhRGNjo/cGByCY9Au4FhkZuWnTpu4vc3JycnJynI8/+ugj\nmYoC0CdBN8y17N/jpcGtmdcERl/hBrnA6BHWzGu0J7017zfohrleGhkYIG9fbDSbzaGhV1jm\nurW11asFeG9wAIKrqQAAf6WOGW3NutZLg1umzvJgNzdYs65Vx4z20uDAAHV0dHh1/L6kRMmb\nexe7twEsgL4jpgIA/Fbn9Tc5Qjx/h2rn1FmOqOF96emIGt7phaTqCNF1Xn+Tx4cFPMVut3t1\nfIfD4dXxAciOmAoA8FtSiK5jzq2eHdMep7dcN7Pv/S3XzbTH6T1bQ8ecWyUvxG9gsPDqlVIA\nSkBMBQD4M1vK2M6cmz01miNquGnBHaJfWyaq1aYFd/Tx6mtfdObcbEsZ66nRAABQIGIqAMDP\nWSbP6Jw+Z+DjOIaNMN1ytxQa1t8DpdAw0y13O4ZdYcmlvuicPscyecbAxwEAQMmIqQAA/2fJ\nvtF802IpKMTtEWyJae1L7nOER7p3uCM8sn3JfbbENLcLkIJCzDcttmTf6PYIAAAMFmxIAwAY\nErrGXeMYnRC8Z0dATWW/DpSCQyxTZ1mv+Z4kVAMpQAoONd+yQvvNgaAv9qk6+7cOqi0+pXN2\nnj3KA9djAQBQPmIqAGCosEeNMN12T2DFCe1XnwTUXXk/CSkouCvr2s4pOW5M9P3uAYXKcs10\na8ZVwV8XBx77SmXpvOIhttFJ1mtv6Eod55ECAAAYFIipAIChpSt1XFfqOM2FhoDyYwHGck1D\nncr6f3FREipH1HBHXFJXcoYtJVMKCPR4AVJoWEfOzZ3fmx1QeTKwqkxda1A3X1CJ/1u5VNIG\n20eOtiWm2dKy7MNHerwAAAAUjpgKABiK7MNH2oePdG4to+o0q6wWIYRQqSVdmKTW+KAAKSCw\nK31iV/pEIYTKYVeZ2oXkEEJI2iApONQHBQAAoFjEVADAUCcFh8qbDCW1RnJ3cSYAAPwPK/0C\nAAAAABSEmAoAAAAAUBBiKgAol91ul7sEXNYV3x3ePiXj3RnU+vL2nT171geVAPASYioAyMZs\nNrvu0NbW5ptK4IbW1tYBdoCMrvjDdcUfT8ioo+MKOw/zMQQw2BFTAUA2586dc/GsJElnzpzx\nWTHor6ampgF2gIzOnDkjSZKLDq5/PCEvg8Fgs9lcdODtAwY7YioAyKaqqqqlpeVyzxoMBm8X\noFb78/8CKpXKq+NXV1e7+FP43Llz1dXVXi3Av/ngm9PFj1hLS0tVVZW3C/Bj3v7pE0JUVlZe\n7qnOzs5Tp055uwAAXuXPf6AAgPIdOnTowoULvRolSTp79mxFRYW3zx4UFOTtU8hIq9V6+xTH\njx+vq6u7tL22tvb48ePePrt/v30+eHUVFRVnz5699JrqhQsXDh065O2z+7fg4GBvn6K6uvrM\nmTOXTu5tbW09cOCAt8+u1+u9fQpgiGPfVAC4rLCwMB+cpaSkJDExMSQkJCQkRJKk9vZ2HwRU\np/DwcN+cSBaRkb7YifTkyZMnT54cM2aMTqcTQphMJp9N1fbNC5SLb745KyoqKioqUlNTw8LC\nVCpVR0dHR0eH0Wj0wal98+tFLr75DMVoNBqNxuTk5PDwcI1G09nZaTKZfPP2aTQaH5wFGMqI\nqQBwWcOHD/fNiXzzd1UviYmJAQH+/L+AL2OALHcR+3fOCQgISExM9M2Phs8+GOrJZ79e5JKS\nkuJiXq4HyTI9e+TIkb4/KTCkMOkXAC5Lo9GkpqbKXYW3DIU/s8aNGyd3Cd7ixy+tmx9/i6am\npvr95bjRo0fLXYK3OOe/yF0F4OeIqQDgSkJCgtwleIVer4+IiJC7Cq8bNWpUfHy83FV4Xnx8\n/KhRo+SuwusiIiL89Q5Af/3F0lNQUFBGRobcVXhFYmKiZwc0mUyvv/668/HUqVNV//KLX/xC\nCHHo0KGZM2cOHz48IyNj5cqVFovFs2cHlImYCgCuaDSaadOmyV2Fh8XHxycnJ8tdhY+kpKTI\nXYLn+eWL+k7Jycn+90HDtGnT/P5SqlN8fLz/fdAwadIkj995+7Of/Wzt2rXOx5WVlc3NzTab\nzWazPffcczabbcGCBcuXLzcajbt377bb7cuXL/fs2QFlIqYC/dbW1rZkyZK8vLz169fLXQt8\nISQkxJ+Sql6vHzNmjH9vRdNTYGDgjBkz/CbqxMfHz5gxIzAwUO5CfEStVo8ZMyYpKUnuQjxm\n2rRpQ2q+aGpqalpamtxVeMyUKVOioqI8O+b27dsPHjzofNze3q5SqSIjIzUajUajUavVNTU1\nra2tt99+e2hoaHJy8tNPP+3fS3wD3VSu97YGcKmXXnopLi7u1ltvnT9//tatW0NDQ7ufstls\nbW1tPTs/99xzf/jDH3xeIzxPkqTa2tqysjK5C3FfQkLCiBEjhg0bJnch8mhoaDh67JjZPlhX\njQrV2CZkZfnx7ZquNTU1nT9/flBvRZuRkREXF+eDDUUVqL29vb6+vsJ4SlL13j/GS5pN9ccN\n+yTJM6dTCXVQUHBgYKB7b59GHbBo5gPJcVmXPlVbW3vzzTe/+OKL999//4kTJ0pKSmbPnj16\n9Oiampobbrhh/fr1MTExmZmZCQkJDz744KxZs/x7eXagp8H6vzUgo9LS0tzcXCFEenq6wWDI\nzMzs+dTu3bt7dh4K948NESqVKj4+3m63l5eXy12Lm+Lj43t+qjLUjBw58mJt8CMfD9Y7ctfc\n0Dpy5GAtfuCGDRsWFBQ0eGNqWlqa31zSd0NYWFhKqv751nSfnVHTEhdo8uhVXPOAjq6sPXpp\nTJUkacWKFWvWrImOjv7nSczmnJycV155JTo6+s4773z44Yc3btz45Zdfrlu37qmnnlqyZMns\n2bPXrl3b8w8PwF8RU4F+czgc3Z+n2my2nk9NmjRp0qRJchQFH9FqtXKX4L5BXbxHjAofxDcE\njo4YxMV7xKD+Bh7UxXuERu3Tmer2hFopol3YPXN3w8SgW36QtMrtwwM02pT4CZe2/+lPfxo3\nbtycOXOOHDnibJk2bVpRUZHz8QMPPHDLLbcIISIiIp588sknn3zy3Llzzz333A033FBXVzdE\nbm/GUEZMBfotKyurpKREr9dXVFQMnYVM4OTxu5J8yb93Se2La5MG8T2Bg7p4jxjU38CD+leH\np1wXdM+Xltd9dDKVcES2emqwKbHzx8Zf66nRuh08eHDXrl1vvfWW3W43mUzDhg0rLCyMioq6\n9tprhRBarTYkJOSNN97YtGnT3r17hRCxsbFr16596623Tp06NX78eI/XAyjKUFlCA/Cg/Pz8\nzZs3L1++fN68eTqdTu5y4FPBwcGDdCeJ9HTfTbdTrOBA9Y/Sm+Wuwh0/Sm8ODuS/7MH6bZyQ\nkBAcHCx3FfIbHzlb7hLcdO3of/PGsBs3bmxqampqaiouLs7IyGhqarp48eJtt91mMBhsNtvL\nL7+cl5d30003HTx4cMuWLRaLxW63v/322zqdzl93+gF6GsQfTAJyiYyM3LRpk9xVQDYjR44c\njDfIjR49Wu4SFOEnU0M3npa7iP77ydShe1NxT6NHjz59evC9f0N24atepiUsOdy080jXZrkL\n6Z9bhr3ksxnLixcvrqysvPHGGy0Wy8033/zss8/qdLri4uJHHnnkpz/9qcPhmDx58nvvvTeo\nZxYAfcRKvwDQb1VVVZWVlXJX0Q+TJk1izmG31w+0PP1lpNxV9MNvprbcPW0wFexVzc3Nhw8f\nlruKfkhJSRk62xRfkaHl6DPlE+Wuoh8ma++8b8JbclcBDEXMIAKAftPr9YNo6m9mZiYZtad7\nvhd597hBM/U3P7OZjNpTVFTUIFrmNCEhQa/Xy12FgiRFTrhn9Ha5q+irqwKX/Hjsn+SuAhii\nuJoKAO6QJKm8vNxoNMpdyBWMGzeOXZG+0zMfNf/luNLT+31Zzf85W+lFyqK+vv7EiRNyV3EF\niYmJaWlpQ3OjVNcO1u14vW6R3FVcwVUBy+4c+3J40HC5CwGGKGIqALivurpayXfKTZ48OTKS\nC3GXtemrlsf3K/ff5/fXt9xxrXLLk53CZ/+mp6cPojkXvlfV/M3vK5S7f9uMkJ6td8kAACAA\nSURBVJ/ePvb5APVQ30YIkBExFQAGpL29vba2tqamRu5CviUpKUmv17PMxhVVXbC++Kl5e5Wy\nrljm6ZsfmhGSMiJI7kKUzmazGQyGs2fPyl3It8THx8fFxYWFhcldiNJZ7R1Fp5/8h/mPchfS\n2z2jtmfH5cldBTDUEVMBwAPOnTt3/PhxuasQQoiEhIRRo0aFh4fLXchgsutY2/17lPIv9ufZ\nbQuylFLMoNDW1lZfX6+Q9bfHjx8fGxsrdxWDSXXL8f+tXnfQ8obchQghxLzwpxak/kKrYfcg\nQH7EVADwDEmSGhsbW1pa5PpzOTExMTY2loDqHkmI94+17S23F1XKc2X1luTm2WM0BFS3tba2\nNjY2GgwGWc6ekJAQGRkZExPDnajuMbSU7qt57UDnn+UqYEHEH+YmPxAUwF7ogFIQUwHAw0wm\nU2Njoy93rElPT4+NjQ0M9NHOfv6t8rxlyzcdrx3zXVhdmdW89JqQVKb4ekJXV9e5c+d8ecd4\nSkpKTEyMTke88QBzV8snxreqO0p8dnH1e8GrJg3Puzr2Jt+cDkDfEVMBwFtMJtPFixetVqs3\nrvAkJSXpdLro6GitlkU+vKLsXOf/nuqsaRVbyj0fWZemNSeEi3mZwRmxTC/0CqvVevHiRZPJ\n5KWfvsDAwOHDh5NOvaTVcv6ruh1nzV9/3vmaxwe/LugnIZqIidE3Z8XcqFZpPD4+AI8gpgKA\n10mSZDab29razGazEMK9FV/0er1arQ4LCwsPDw8K4sqbT52s7/y62nqy0WFziE1n3Emtd4xp\nDlCLzBj1lARt5iiiqU9ZLJa2trb29naHw+H2T58QIjQ0NDw8PDQ0lJm9vtTUUXfq4qdVbV93\nOFrcS63TgleqhGq4NnlM1LT06Gms3wsMCsRUAPARq9Xa3t5uNputVqvoT1jV6/UqlSo4OFin\n0+l0Oo2Gj/9lcMFkO2zsPNloq2sTQoiNp/saVn+U3iyEGB0uMkcGTEoIHq5j+WUZ2O12k8lk\nMpk6OzslSerXT58QQqvVhoaGhoWFMXlBFlZ7R0XT14a2I+c6T9ulLqvD/LV1Q18OdAbUUE10\ngm5CcuSkuPCxXq4UgMcQUwHAi9rb25uamjw+79c543fYsGFcVvWqk/Wde093GlvFZreuoF7O\n7WOaEyPED9KDuazqVV6a95uUlKTVaocNG8aWM17V3HHum8b3q9q/8uy6SlOD7gvVRI0f9oOs\nmJkaNZ8ZAcpFTAUAD5Mkqbm5uampyTfbOaampsbExISGhvrgXEPE55Xm909Z3zrli1WUlo9t\nvnmsdloKb5/HmM3mxsbGiooKH5xLr9cPGzYsKiqKacCe0mCqLK55q6mr2gerKE0Nujc9YsbU\nuMXsQAMoEDEVADzGarXW1dX55u/jXhITE8PCwkaOHKlWq31/dv/QbLZtONj+wjfybEjz8DXN\ny7PDokK5vOMmh8PR0NDQ3t5uNBp9f/bU1NTRo0czJdhtDsm+v3rzyZZ9X1n/n+/Pfn3IgzfG\n3ZsUOcH3pwZwOcRUAPAAi8VSU1Pjm8unrqWnp8fFxRFW++WCyfbn/e2vn5AnoPZ0z7jm+68P\n4/7VfnE4HLW1tb7chOZy9Hp9fHw8U/H7xe6w7Tn76ramn8pdiLhWmz8rflXasGvlLgSAEMRU\nABggh8NhNBpluYLqwoQJE2JiYuSuYhCwO6RX97c8f0j+gNrTo5OaV82IDFAzj/TKGhsbjx49\nKncV35KampqYmMhHRX1xuP6D12rny13Ft0wLXrko5bdRIaPkLgQY6oipAOC+tra2r776Su4q\nvltCQkJycnJgYKDchShXeaNlXXHH34zKyqhOCxKbH8kJSYvhutxldXV1VVVVVVdXy13Id7v2\n2mvDw8PlrkK5LDbzX0//an/HS3IX8t2Wj9zyvYSlclcBDGnEVABwU319/YkTJ+Su4gquu+46\nnU4ndxVK9OHxtns/UnqK+MuctrnjlV6kLEwm05dffil3FVcwfvz42NhYuatQovNmw5Yzvyi1\nFchdiCs/0P3nkrHPyF0FMHQRUwHAHTU1NWVlZXJX0SfZ2dnsnNHLjpK2n/19cMS//5rZlnfV\n4CjVZ9rb2w8ePCh3FX2SkZERHx8vdxXKcs5U8ZtTaXJX0SfTgx+4a7xCr/cCfo8bJwBc1sMP\nP6xSqa6//nq5C1GcxsbGwZJRhRAHDx60WCxyV6Egn1eaB0tGFUL87O/hX1SZ5a5CQSwWy2DJ\nqEKIsrKyxsZGuatQEHNX62DJqEKIzzpfLip7Uu4qgCGKmArgu0mSVFRUFBAQcODAgZqamn4d\ne+DAgZkzZ0ZFRV133XUFBa6mdRUUFMyaNSsqKio5Ofn++++/cOGCG4P4mMViUdqSLVekhCWI\nFcJsdSx9d5BtUrpkZ6jZ6pC7CqUYdN/MR48e5XOibgWnH5e7hP7Z3f67Y43/kLsKYCgipgL4\nbp9//rnRaHzsscckSdq2bVvfD/zqq69mzpwZFBS0bt26sWPHLlu27HIh8y9/+cuyZcsiIyNf\neumle+65Z9OmTQsWLLDZbP0axPcUu2SLCzU1NefPn5e7CkX48/5WuUtwxyAt2+POnz/f30/N\nlGAw1uwN5U1fHej8s9xV9Ftx/RtylwAMRdybCuC7/fznP3/ttdcaGhquueaauLi4Tz75pI8H\nLl68+MSJE4cOHXLudJ+bm2swGI4cOdKrmyRJo0aNmjp16s6dO1UqlRDib3/7W25u7rvvvpub\nm9vHQXzPZrMVFxfLXYU7EhMTx4wZI3cVMpOESP4vuYtwV9XPBBvUnDlzxmg0yl2FO3JycgIC\nhvp2uG8df+CzzlfkrsIdj+g/yxj+PbmrAIYWrqYC/dbW1rZkyZK8vLz169fLXYu3SJK0devW\nBQsWhIWFLVq0aP/+/XV1dX05sKOjY/v27UuXLnXGSyHEnXfeWVJScvjw4V496+vrGxoabr31\nVmdGFULceOONQohjx471fRDfa25ulrsENxmNRodjqE8cPVbbKXcJ7hvUxXuEc49iuatw0+D9\n1eFBgzSjCiG+Of83uUsAhhxiKtBvGzZsWLp06Y4dO3bu3Gk2f2tpkwsXLnz9baWlpXLVORDO\nGb9LliwRQixatMjhcGzfvr0vB9bW1trt9quuuqq7ZeLEiUKI48eP9+oZHR199OjRW265pbvl\n008/FUKkpqb2fRDf6/WODy6dnUM955xqsMpdgvsGdfEeMai/gQf1rw6PsDtscpfgvlbbOblL\nAIacoT7/BHBDaWlpbm6uECI9Pd1gMGRmZnY/1dLSUlFR0bPz//7v/77xxuC7raWwsFCn0y1Y\nsEAIMW3atFGjRhUWFt5///1XPNB5C9bw4cO7W5yPL72SEBQUlJWV1f3l4cOH8/Pzx48fv2jR\nogMHDvRxEB8zm82D+g/lxsbG+Pj4ITvz0GKxJGsv/od+sF6OS9bqLJagoKAguQuRh81mG9RL\n5nZ2dprN5tDQQbZ8l6c4HI76+nMzzE9Jwi53Le5ICbmmtbU1IiJC7kKAIWSI/rECDITD4eie\np+pc76dbampqampqz5ZvvvnGd5V5iHPG77hx4/7+9787WzIzMz/55JOGhoaRI0e6PvZy00rV\n6svO3TCbzc8888yaNWuuvvrqbdu2BQYGujGItzkcDoPBUFlZKVcBHlFRUVFRUTFhwoSYmBi5\na/G17n1usyPlLsVdrefEZ+cqh+Y+nI2NjYNuee1eampqampqUlJSkpKSZPxVJouWlpZDhw4J\nIRLEDLlrcZOtXnxd/3ViYmJycvKQ/aQP8DF+0oB+y8rKKikp0ev1FRUVKSkpcpfjeV988YXR\naDQajQsXLuzZvn379vvuu8/1saNHjxZCNDU1dbdcvHhRCBEXF/ed/YuLi1esWNHS0rJmzZpV\nq1Y5//vv7yDeZrfby8vL/WatzqNHj44ZMyYxMVHuQnxEkqSKigqDwSB3IZ5RVlbW2dmZmpra\n/WGZ3zMajWfOnJG7Cs+orKy0Wq1paWkajUbuWnykoaHh2LFjclfhGc7/Ga+//vrudRMAeM/Q\n+jwP8Ij8/PzNmzcvX7583rx5Op1O7nI8r6CgICAg4MKFC9K/WK3WqKiowsLCKx4bHx+v0Wh6\n3kR68uRJIYRer7+08969e2fPnp2dnX369OkHH3yw+yPqfg3iAxUVFX6TUZ3OnDlTX18vdxU+\nYjAY/CajOvnfK3Khvr7ebzKqU01NTa97Q/xYc3Oz32TUbpWVlSxHB/gAG9IA3rV69epnnnlG\n7ir6QZIkvV6fmZm5e/funu0/+tGPtmzZUl9fP2LECNcj5OXl1dTUfPHFF86JbT/84Q8PHTp0\n4sSJXt0cDkdaWtr48eN37drl9iA+cO7cOSUs3eQN06dP9/sbHVtbW7/++mu5q/CKKVOm+P2d\nchaL5bPPPpO7Cq8YP358bGys3FV4l8Ph+Pjjj+WuwitSU1Pl+tgUGDq4mgrgW5wzfm+99dZe\n7Xl5eXa7fceOHVcc4bHHHistLc3Pz//oo49+9atfbd68+de//rXzqVdffXXOnDlVVVVCiAMH\nDlRVVY0aNeqVb3NujupiEF+SJMlfM6oQora2Vu4SvO7cOb9dn7OhoUHuErzOj79Fjx8/7vfX\nCfq4jdlgVFFR0WtlCgAex72pAL6lsLBQrVbn5eX1ap83b15wcHBhYeE999zjeoTp06e///77\nTz311OLFi8eOHbtx48Y77rjD+VRZWdmePXtMJpMQwjmR780333zzzTd7Hv7iiy9effXVLgbx\npZaWFp+dKykpyXm3oSRJvpnSWVVVlZyc7Me3OFoslurqat+cy/dvn9FoTExM9OPr4ZIkOT/S\n8gHfv31CiJaWlqioKN+cSxbORct8w3lt05dvX0NDg1zLJQBDBJN+Ae8adJN+0VNVVZUPVvfV\n6/VJSUk9V480mUzV1dU+uJTk3xNHfTNhOyEhISkpqWdctFgsBoPBBwnZvyeO+mbCdlxcXEJC\nQs9VBmw2m8FgOHv2rLdPnZKSkpyc7O2zyMVmsxUXF/vgRJMmTeqZ9h0OR3V1dXl5ubfPm5SU\nlJaW5u2zAEMZk34B4LJ8sE5Genp6ampqrx0OdDpdenq6D/Yd6ejo8PYpZOSDV6fX69PT03td\n0gwKCkpPT/fBrWu8fQMUHx+fnp7eayW8gICA1NTU9PR0b5/dv5fh8c03Z05OTq8r0mq1Oikp\naeLEid4+9dBZxgyQCzEVAGSTkJCQkJDwnU+p1WofbHdkt9u9fQoZ+SAGuHiPfPD2+XfO8cE3\nZ0pKyuW2MHXxs4m+8MHbd911111uC9MRI0b48ZVqYIggpgKAbGJiYlw8GxgYyF9aSjZ27FgX\nd/aqVKrMzExf1oN+SU5ODgwMdNHB9Y8n5JWYmOh6QzhuHAUGO2IqAMgmMjLSdQc/vnHUDwwb\nNsx1B/9eIGewu+IP1xV/PCGj4OBg1x38eHUxYIggpgKAbK64yq5Wq/VNJXDDFd+dwfX2mUym\n119/3fl46tSpqn/5xS9+IYQ4dOjQzJkzhw8fnpGRsXLlSovFImuxHnDFd8ePF8H2A66vhDux\ntSkwqBFTAQCA+NnPfrZ27Vrn48rKyubmZpvNZrPZnnvuOZvNtmDBguXLlxuNxt27d9vt9uXL\nl8taLADAzxFTAQAY6rZv337w4EHn4/b2dpVKFRkZqdFoNBqNWq2uqalpbW29/fbbQ0NDk5OT\nn376aWZUAgC86rtXSAMAAAPU2FRddf4r743fJlXWtx/tY+fw0OiJY2Z851O1tbVPPvnkiy++\neP/99wshKioqJEm6+uqra2pqbrjhhvXr1+v1+sTExPnz5z/44IOzZs0aNWrUhg0bPPUqAAC4\nFDEVAACv+OtHf6yoKfXiCc72r/sv7/p/cTFpvRolSVqxYsWaNWuio6OdLWazOScn55VXXomO\njr7zzjsffvjhjRs3fvnll+vWrXvqqaeWLFkye/bstWvXso4xAMB7iKkAAHjFjVOWWMwOyWvj\n63Q613ty9BSpGx4bnXRp+5/+9Kdx48bNmTPnyJEjzpZp06YVFRU5Hz/wwAO33HKLECIiIuLJ\nJ5988sknz50799xzz91www11dXUajcYTrwMAgN6IqQC+5eGHH37xxRenT5++f/9+X57XarU2\nNDQkJCT48qSAV01Im3GxxuG98fV6fWpq6gAHOXjw4K5du9566y273W4ymYYNG1ZYWBgVFXXt\ntdcKIbRabUhIyBtvvLFp06a9e/cKIWJjY9euXfvWW2+dOnVq/PjxHngZAABcgiWUAPwfSZKK\niooCAgIOHDhQU1PT69mCgoJZs2ZFRUUlJyfff//9Fy5cuNw4Bw4cmDlzZlRU1HXXXVdQUNCX\nU69atSo7O7v7S6vV+vTTT48bN06n040bN+73v/+91Wp170UBcGHjxo1NTU1NTU3FxcUZGRlN\nTU0XL1687bbbDAaDzWZ7+eWX8/LybrrppoMHD27ZssVisdjt9rffflun02VkZMhdOwDAbxFT\nAfyfzz//3Gg0PvbYY5Ikbdu2redTf/nLX5YtWxYZGfnSSy/dc889mzZtWrBggc1mu3SQr776\naubMmUFBQevWrRs7duyyZcuumFSLiorefPPNni2//OUvn3zyyfnz57/xxhsLFy789a9//fjj\njw/8BQK4osWLF69aterGG2/U6/VarfbZZ59NTEwsLi5+/fXXExMTY2Nj33nnnffeey8ggAlZ\nAABv4f8YAP+nsLAwJCTk8ccf/+tf/1pYWPjggw862yVJ+vWvf71w4cJt27Y5t7y/5pprcnNz\nP/jgg9zc3F6DPPfcc2PGjHn33Xe1Wm1+fn5zc/MzzzyzZMmSy520pqbm3nvvTUtLM5lMzhar\n1frKK6/89re//c1vfiOEWLZsWXNz8xtvvNG9qSMAj7v66qtPnDghhFCpVL/85S9/+ctf9np2\nz549MpUGABhyuJoK4J8kSdq6deuCBQvCwsIWLVq0f//+uro651P19fUNDQ233nqrM6MKIW68\n8UYhxLFjx3oN0tHRsX379qVLl2q1WmfLnXfeWVJScvjw4cud9K677po1a1ZeXl53Y11d3ZQp\nU+bOndvdkpSUZLFYmPcLAAAwFBBTAfyTc8av87LnokWLHA7H9u3bnU9FR0cfPXrUueCn06ef\nfiqEuHT5ltraWrvdftVVV3W3TJw4UQhx/Pjx7zzpunXrTpw4sX79+u4ALITQ6/UHDhyYOnWq\n88uysrJ33nln/vz53dEXAAAAfoyYCuCfCgsLdTrdggULhBDTpk0bNWpUYWGh86mgoKCsrKzw\n8HDnl4cPH87Pzx8/fvyiRYt6DeJceGn48OHdLc7Hzc3Nl57xm2++Wb169Ztvvtm9YWMvf/vb\n38LDw8eOHRsaGvo///M/A32FAAAAGAyIqQCE+NeM33Hjxv3973/ftWvXBx98kJmZ+cknnzQ0\nNPTsZjabV69ePW3atISEhA8//DAwMLDXOA7Hd2+/oVb3/m3T0dHxwx/+8Cc/+UnPyb29ZGdn\nb968+fnnn6+rq5s3b54keW8HSgAAACgFSygBbpIkqec81cHuiy++MBqNRqNx4cKFPdu3b99+\n3333OR8XFxevWLGipaVlzZo1q1at+s51PkePHi2EaGpq6m65ePGiECIuLq5XzxdffPHs2bPT\np0/fuXOnEOL06dOdnZ07d+6Mi4vr3pkmNjZ24cKFCxcuvOqqq+bOnXv48OHJkyd77DUDAABA\nkbiaCrjS1ta2ZMmSvLy89evX92y3Wq2zZ8+WqypvKCgoCAgIuHDhgvQvVqs1Kiqqe97v3r17\nZ8+enZ2dffr06QcffPBye1HEx8drNJqed6KePHlSCKHX63v1vHjxoslkuuOOO/Ly8vLy8nbu\n3Nnc3JyXl/fHP/5xx44dOTk5bW1t3Z2zsrKEEEeOHPHsqwYAAIACEVMBVzZs2LB06dIdO3bs\n3LnTbDY7GxsbG++66y7nTZj+wTnjd+bMmT3vEQ0MDFywYME//vGP8+fPOxyOe+65Z/bs2Zs3\nb46KinIxVFhY2MKFC7du3do9+7egoCAzM/Oaa67p1fP555+Xenj00UdHjRolSVJhYeHw4cM/\n/fTT3bt3d3f+xz/+If4VVgEAAODfmPQLuFJaWurcFzQ9Pd1gMGRmZgohYmJiNm/ePGfOnEv7\nnzhxori4uGeL0Wj0TakD4Zzxu3r16l7teXl577zzzo4dO8aNG1dVVTVr1qxXXnmlZ4cZM2Zc\nffXVvY567LHHZs2alZ+f/8Mf/nDv3r2bN2/euHGj86lXX31127Zt//3f/52cnOyinhkzZixc\nuPAnP/nJqVOnMjIyjh49um7dun/7t3+77rrrBvpSAcC/hISEOG9C0Wq1U6dOfemll8aOHet8\nqqurKzY2Nj09/YsvvujuHx4ebrVa1Wq1JEmpqanr1q2bN2/euHHjXnvtte9///syvQgA6I2Y\nCrjicDi6b0C12WxX7J+YmNhrMrBzyqvCFRYWqtXqnjuXOs2bNy84OLiwsPCOO+4QQrz55ptv\nvvlmzw4vvvjipTF1+vTp77///lNPPbV48eKxY8du3LjRebgQoqysbM+ePSaTyXU9KpWqqKjo\n8ccfLywsPH36dHJy8qOPPvrYY48N6EUCgJ8qLy+Pj49vbW198803Z8yYcebMmcjISCHEnj17\n4uPjT548efbs2Z53Xhw/fjwtLc1kMv3pT3+6/fbb6+vr5asdAL4bMRVwJSsrq6SkRK/XV1RU\npKSkXLF/WFhYWFhYz5aQkBCvVecxa9euXbt27aXtYWFhHR0dzsd33XVX3wecNWvWrFmzLm1/\n4YUXXnjhhe885Pnnn3/++ee7v9Rqtd9ZEgDgO0VERDz00EMff/zxX/7yl1/84hdCiIKCghUr\nVhw6dGjr1q2PPPJIr/46ne6nP/3pE088YTAY5KgXAFwhpgKu5Ofnr1q1qqioaN68eTqdrri4\neN++fb/97W/lrgsA4F3vnxa/OzXVLnlrRfeAMwGBn/S1c1SwePlmoY+8cs8f/OAHn332mRDC\narW+9957zz77bFpa2u9///tLY2pnZ+ef//zn0NDQhISE/pUOAN5HTAVciYyM3LRpU/eXOTk5\nOTk5zscfffSRTEUBALzuy1pRbwn14gms/ehraBGVTX2KqTExMXV1dUKI3bt3T5kyZfTo0XPn\nzr3zzjsNBkNSUpKzz1VXXaVWq61Wa1xc3KZNmwbFrB8AQw0xFQAAoLfVM8SotsM2h7eupsbG\nxjo3mu6LYSEiK6ZPPc+fP+8ctqCgYP/+/c7HZrN569atP//5z519SkpK0tLS3CkaAHyFmAoA\nANBboEaM1TV7b3x9TERqkueH3bdv39SpUy0Wy7vvvltWVhYXFyeEKCoqWrNmTXdMBQDlY99U\nAACAQc9sNv/5z38uLi6+7777Pvzww/T0dGdGFULMmTPn0KFDg2KDNABwIqYCAAAMYhkZGeHh\n4XFxcdu2bSsuLo6MjCwoKHBu+u0UERExY8aMrVu3ylgkAPQLk34BAAAGq+5tw3rauHFjr5a9\ne/c6H7S1tV3a/8SJEx4vDAAGgqupAAAAAAAFIaYCAAAAABSEmAoAAAAAUBBiKgAAAABAQYip\nAAAAAAAFIaYCAAAAABSEmAoAAAAAUBBiKgAAAABAQYipAAAAAAAFIaYCAAAAABSEmAoAAAAA\nUBBiKgAAAABAQYipAAAAAAAFIaYCAAAAABSEmAoAAAAAUBBiKgAAAABAQYipAAAAAAAFIaYC\nAAAAABQkQO4CAEVra2u7++67rVbrzTfffN999zkb6+rqVq1aFRgYmJKS8sc//lHeCgEAUBqV\nSjWoxwcgO66mAq5s2LBh6dKlO3bs2Llzp9lsdja++uqrq1atKiwsrK+vLykpkbdCAACUJigo\naFCPf0V6vV7eAgC/x9VUwJXS0tLc3FwhRHp6usFgyMzMFEI8+uijOp2uvb3daDSmpqb27H/w\n4MGioqKeLeXl5b4sGAAA2QUHB3t1/LCwsCv28WqUVau50gN4FzEVcMXhcHTPLLLZbM4HERER\nH3zwwUsvvbRu3bpe/1NmZ2dnZ2f3bFm9erVvSgUAQDn0ev3Zs2e9MXJiYqJGo7litxEjRpSV\nlXmjAOfgXhoZgBMfBQGuZGVlOaf1VlRUpKSkOBv37Nmzffv2d999d9KkSbJWBwCAQo0aNcpL\nI8fExPSlW1BQUFJSkjcKSEhI6MvlXAADwdVUwJX8/PxVq1YVFRXNmzdPp9MVFxfv27evvr6+\nrKxs0aJFQogXXnhhzJgxcpcJAICyhIaGpqamVlRUeHbYpKSkyMjIPnZOTEw0GAyeLUAIER8f\n7/ExAfRCTAVciYyM3LRpU/eXOTk5OTk5MtYDAMBgkZSUZLFYampqPDVgXFxccnJy3/trtdpJ\nkyYdPnzYUwUIISZMmBAaGurBAQF8Jyb9AgAAwPNUKtWYMWM8NfM2ISFhzJgxfbkrtaeoqKjJ\nkyd7pAAhxMSJE/s45RjAAHE1FQAAAF6hVqvT0tKioqIGuH9bVlbWyJEj3Ts2MjJyxowZ1dXV\nVVVVbheQlJSUkJAg+0Y4wNBBTAUAAIAXDR8+fObMmSaTqbm52WKxSJLUl6NUKlVgYGBUVFR4\nePgACwgMDExJSUlKSmpqampvb7fb7X08UK1W63S66OjogAD+ZgZ8ih85AAAAeJ1Op9PpdDIW\noNFoRowYwV4ywKDAvakAAAAAAAUhpgIAAAAAFISYCgAAAABQEGIqAAAAAEBBiKkAAAAAAAUh\npgIAAAAAFISYCgAAAABQEGIqAAAAAEBBiKkAAAAAAAUhpgIAAAAAFISYCgAAAABQEGIqAAAA\nAEBBiKkAAAAAAAUhpgIAAAAAFISYCgAAAABQEGIqAAAAAEBBiKkAAAAAAAUhpgIAAAAAFISY\nCgAAAABQEGIq4EpbW9uSJUvy8vLWr1/f3Wg0GlesWLFs2bKXX35ZxtoABtYmswAAIABJREFU\nAAAAv0RMBVzZsGHD0qVLd+zYsXPnTrPZ7GzctWvXQw89tGXLlm3btslbHgAAAOB/AuQuAFC0\n0tLS3NxcIUR6errBYMjMzBRCrFy58vz580888URWVlav/gcPHiwqKurZUl5e7rNqAQAAAD9A\nTAVccTgcKpXK+dhms3W3R0REzJ8///HHHzeZTDqdrrs9Ozs7Ozu75wirV6/2TakA4E+6f/cO\n0vEBKJZd6rI42uWuwiNUoZoouWvwFmIq4EpWVlZJSYler6+oqEhJSXE2Pvroow888MD06dNH\njBhx7ty51NRUeYsEAP+j1WoH9fgAFOtvdU+9X///yV2FZ/xhgmGYNlHuKryCmAq4kp+fv2rV\nqqKionnz5ul0uuLi4n379v34xz++9957Y2NjJ02aREYFAG8ICwvz6vh9ial6vf7s2bNeKiAo\nKMhLIwNwTRIOuUvwGIcfvZZeiKmAK5GRkZs2ber+MicnJycnRwixe/du+YoCAP/n1RSn1+v7\n0i0yMtJ7NYwYMcJ7gwPAYMdKvwAAQInGjx/vpZFjY2P70i06OtpLBSQmJnI1FYAPSJL04osv\nTpw4MSwsbMKECVu2bBFCnD17VqVSPf744z17Tps2rfsGNyFEV1dXdHT01KlTfV3xvxBTAQCA\nEo0cOTI+Pt7jwyYnJ/dc+s4FlUp19dVXe7wAIURCQoI3hgWAXp5++umCgoKCgoKmpqaXX375\noYce2r9/vxAiNDR0+/bt3d1qa2uPHz/e88A9e/bEx8efPHnSe/c+uEZMBQAASqRSqfo4O7fv\n4uLi+jVmdHR0WlqaZ2uYOHFicHCwZ8cEgEs1NDS88MILhYWF48aNCwwMvPHGG3/zm998/PHH\nQojo6Ojo6Ohjx445e+7YsWPevHk9jy0oKFixYkVubu7WrVtlKJ2YCgAAFCsoKGjatGmeGi0+\nPn7MmDFqdf/++ElKShozZoynapg4cSJ3pQLwja+//jorK6vntJT777//P//zP52Pb7311m3b\ntjkfb9++fdGiRd3drFbre++9d/vtty9evLiwsNCXNXcjpgIAAOUKCQn5/ve/P/Bl1TMzMzMy\nMjQajRvHJiYmTp48eYAFJCYmTps2jYwKwGeqqqpGjx59uWcXLVrkjKlNTU1VVVWTJk3qfmr3\n7t1TpkwZPXr03LlzT5w4YTAYfFHut7HSLwAAUDS1Wq3X6+Pj4y9cuGAymYQQkiT15UCVSiWE\niIyMjI6Odj52W2Rk5MyZM9va2i5evGi32/tYgLOGwMDAmJgYJvoC8LHExMS6urqeLTU1NQcO\nHMjOzhZCOD/+q6io2L9//8KFC3v+kiwoKNi/f78z4prN5q1bt/785z/3be3EVAAAMBgEBAT0\ncYVe7wkPDw8PD5e3BgDoo8mTJx85csRoNCYmJjpbNmzY8M033zhjqhDilltu2b59+/79+x96\n6KHuoywWy7vvvltWVhYXFyeEKCoqWrNmje9jKpN+AQAAAMDfxMXFPfDAA7fddtvx48e7urp2\n7979/PPPr1y5srvDLbfc8s477xw6dOj666/vbvzwww/T09OdGVUIMWfOnEOHDhmNRh8XT0wF\nAAAAAD/09NNP5+bm/vu//3t0dPSjjz762muv/eAHP+h+Nisry2QyzZ49u+d9+wUFBbm5ud1f\nRkREzJgxw/fr/TLpFwAAAAD8UEBAwBNPPPHEE0/0bNTr9d1XR0+dOuV8MHbs2MrKSiHExo0b\new2yd+9e71faG1dTAQAAAAAKQkwFAAAAACgIk34BAAAADBXXROUN1+rlrsIDVEIdESDz+ufe\nQ0wFAAAAMFQkh2Ynh2bLXQWugEm/AAAAAAAFIaYCAAAAABSEmPr/t3enYVFc6d/Hq2kW2VEk\nICAg2MiiuCKokCiaiAqKu0aNBiVxi9EZ1BB1MmKUGNdkXKImRscdxRWTwaijDxrcFzSIICpE\nhcRdZBW6nxf9T18MKptAVcP386r7rtOn7u5zKfyo6ioAAAAAgIQQUwEAAAAAEkJMBQAAAABI\nCDEVAAAAACAhxFQAAAAAgIQQUwEAAAAAEkJMBQAAAABICDEVKEt2dvaQIUNCQkLWrFlTatOG\nDRtWr14tSlcAAABAHUZMBcqyYcOGoUOH7t27d9++fbm5uZp6WlraihUrSlYAAAAAVAtdsRsA\nJO3KlSvBwcGCICgUioyMDDc3N0EQioqKvvjii88++yw9Pb3U+LNnz8bExJSspKWl1Vq3AAAA\nQB1ATAXKolQqZTKZ+nFRUZH6wVdffTVp0qRnz569PN7b29vb27tkZdasWTXdJAAAACroQa6Q\n+VzsJqqJh5Ugl4ndRM0gpgJl8fT0TExMdHR0vHnzZrNmzdTF3NzcpUuXZmVlPX36tEuXLr6+\nvuI2CQAAgAr68ZKw4qzYTVSTX0MFO1Oxm6gZxFSgLKGhoRMmTIiJiQkMDDQ2No6Pjz969OiC\nBQsEQYiLi7t69SoZFQAAAKhexFSgLObm5lu3btU89ff39/f3Vz/u2bNnz549ReoLAAAAqLO4\n0i8AoG6Sy+Xizi96AwCAes7Q0LBBgwaGhobm5ubvvffe9evXNZtevHjRqFEjHx+fkuNNTU0N\nDAzUr/Lw8PjPf/4jCIK7u/vx48druXNiKgCgbjIxMRG7hZpV598gAODNpaWl5eXl/f777717\n9/bz83v69Km6fvjwYTs7u+Tk5FK3rkhKSsrLy3v48OGoUaOGDx9eUFAgRtfEVABAHWVhYVFz\nkzs7O1fjsKqp0TcIAKhLzMzMpk6d6ufnt3btWnUlOjr6ww8/DA4O3rVr18vjjY2Np0yZ8uzZ\ns4yMjNrt9P8QUwEAdZNcLnd0dKyhyW1sbKpxWBU4OTlx0i8AoFK6d+9+8eJFQRAKCwsPHDgw\nfPjwwYMH79y58+WR+fn5q1atMjIysre3r/U2BYGYCgCow2roh6uzs7OBgUFFRhoYGNTQAVU7\nO7uamBYAUIdZWVllZmYKgnDo0KH27ds3adKkZ8+e165dK3nI1MvLy9TU1NzcfNWqVVu3bjU0\nNBSlVWIqAKDO0tfX9/Lyqt457ezsHBwcKj7ewcGh2iOll5eXvr5+9c4JAKjzHjx40KRJE0EQ\noqOjT5482aRJk2bNmuXm5pY87zcxMTE7O7ugoODWrVvBwcFitUpMBQDUZZaWli1btqyu2ezs\n7FxcXGQyWcVfIpPJXFxcqjGptmzZ0tLSsrpmAwDUH0ePHm3Tpk1BQcH+/ftTUlIyMzMzMzO3\nb9/+yvN+xUVMBQDUcVZWVh07dnzzE4AVCoVCoajCN0Llcrn6tW/YgL29vY+Pj5WV1RvOAwCo\nb3Jzc1etWhUfH//xxx/HxcUpFApbW1v1pnfffffChQu///67uB2Woit2AwAA1DhjY2OFQmFt\nbf3w4cPbt29X6rUODg5GRkZWVla6ulX/oSmTyezt7W1sbO7fv5+bm1vZCyc6OTlZWlqamZlV\nuQEAQP3k6uqqo6Mjl8s7dOgQHx9vbm4eHR1d8mxeMzMzPz+/Xbt2TZs2TcQ+SyGmAgDqCzMz\nMzMzs2bNmonVgK6urvpLQS4uLmL1AACoP/Ly8l4ubt68uVTlyJEj6gfZ2dkvj7927Vq1N1Yu\nTvoFAAAAAEgIMRUAAAAAICGc9AsAAACgvhjlJQQ2F7uJ6iAThCYmYjdRY4ipAAAAAOoLGxPB\npu6muzqDk34BAAAAABJCTAUAAAAASAgxFQAAAAAgIcRUAAAAAICEEFMBAAAAABJCTAUAAAAA\nSAgxFQAAAAAgIcRUAAAAAICEEFMBAAAAABJCTAXKkp2dPWTIkJCQkDVr1miKSUlJzZs3f/fd\nd4ODg0XsDQAAAKiTdMVuAJC0DRs2DB06dODAgb179x41apSRkZEgCGlpaREREWPHjn15/IsX\nL54/f16yUlRUVEu9AgAAAHUCMRUoy5UrV9SHTBUKRUZGhpubmyAIaWlpv/zyy65duxQKxbff\nflty/NWrVw8dOlSykpGRUZsNAwAAANqOmAqURalUymQy9WPNcdFhw4aNGzfOxMRkyJAhiYmJ\nXl5emvFt27Zt27ZtyRlmzZpVa90CAACguhQ9ffzkTHxeWkpR9hO5qblRM4V5R3+9hpbVu5ec\n61efXTxdkHlHUKn0raxNWrYzbe0t06nv380kpgJl8fT0TExMdHR0vHnzZrNmzdTFX375pWvX\nriYmJrq6ugYGBuJ2CAAAgOqlzM+7t2Xtw8MHivNy/2fD6q+teg2wHTVBbmT85nvJuf7b72uX\n5KYl/081ZpOBta196KfmPv5vvgvtRUwFyhIaGjphwoSYmJjAwEBjY+P4+PijR48OHDhw/Pjx\nOjo67dq1a9Gihdg9AgAAoNq8ePzwxhef5mXcfOXW+z/vzk4813zuN/qNrd9kLw+PHEz/1/xX\nbir4415a1EybQaNtR378JrvQasRUoCzm5uZbt27VPPX39/f39xcE4eDBg+I1BQAAgBqhevEi\n7cvw12VUtfy7GTci/+a26AcdgwZV28uzi6dfl1E1snZt1GvYyKrP4KrtQtvV95OeAQAAAEDt\nj31bc9OulzssP+NW1q5/V20XysLCjJVfVWTkva3rCh/8UbW9qPn4+Mj+Mn36dEEQLly40K1b\nN0tLS1dX1/HjxxcUFLzJ/DWHmAoAAAAAgqq46M/9Oyo4+P7PMcrCqmS8xyd+qWD4LM55fv+n\nmCrsQuPWrVtPnjwpKioqKipauHBhUVFRnz59xowZ8/vvvx86dKi4uHjMmDFvMn/NIaYCAAAA\ngJCTfLXo2ZMKDi5+nv386sUq7OXp6fhKDD5zogq7UHv+/LlMJjM3N5fL5XK5XEdH5+7du8+e\nPRs+fLiRkZGTk9O8efMkezVQYioASJeubs1eQaCm5wcAQIvk/X5TUFVq/K0q7eV2xQcX3MtQ\nFRdVYS+CINy8eVOlUrVu3bpx48YDBgy4f/++o6Nj06ZNe/fuvW/fvuzsbBsbmw0bNlRt8ppG\nTAWA15LL5TU3uYODQ7ljGjSo4rUZKsjYuBqupw8AQN2gLCgQZJUan1+lveRVfLBKqVS9eFGF\nvQiCkJub6+/vHxcXd+/ePX19/WnTpgmCcObMGT8/v8jIyMaNG/fp0yc5ObnceURBTAWA12rU\nqFHNTW5kZFTuGJlM1rRp05rrgZgKAICGnnnDGh3/f6+yqMRvFzoNDKt8PWFfX9+YmBgbGxt9\nff3JkycfOnRIEAQzM7N//vOf58+fz8jIaNGixdtvv11cXFy1+WsUMRUAXsvU1LTmJreysqrI\nsIYNq/IjsCJcXFxqaGYAALSRsatHJcd7VmUvikq8yljhLsgqc4S3hDNnzpw7d079WF9f39DQ\n8Icffujevbu6Ym1tvWTJkuLi4uvXy7+yce0jpgJAWdq0aVMT0yoUigp+L9TS0tLOzq4merC1\nta2JaQEA0FIGtg7GioomVUMHZ8NmiirspeE771V8cKN3elZhF2q3b98eNGhQRkZGUVHRihUr\nQkJC3nvvvbNnz27fvr2goKC4uPjf//63sbGxq6trlXdRc4ipAFCWhg0bNmvWrHrntLOzq1Ty\nrMi3WCurTZs2XD8JAIBSmoz4qIIjbUd+XLVdmLh7mXfoXJGRDeydGnUNrNpeBEEYPHjwhAkT\nunbt6ujoqK+vv2DBgqZNm8bHx3///fdNmza1trbesmXLgQMHpPn7gEylqszVrABU0qxZs+bP\nny92F3gjKpXq1q1b6enp1TKbnZ2di4tLZS/O9Pz587Nnz1ZLA4IgtGzZsoKnHAMAUN/c/feq\nP3ZvLnuMVZ/BTcOmVXkXLx4/vD5jXOH9su6eqmtqrpj3L0On5lXei1bjaCoAlEMmkzk7O7u7\nu7/5VC4uLgqFogoXEDYxMencubO9vf2b9+Dt7U1GBQDgdexGTWgy5MMyBlj3H9F03Kdvsgu9\nhpauX64sI4LqWb7l8o8l9TajChxNBWoaR1PrkuLi4qysrPz8/IyMjMq+1tnZuUmTJvr6+m/Y\nw7Nnzx48eFCFQ7sODg4WFhaWlpZv2AAAAPVBTmpS1s6NT8/Elyyat+9kPWi0ibtXtexCVfTi\n/k8x93+KKci6qynqNbS07BFs3f99uZFJtexFSxFTgZpFTK2rlEqlUqksd5hMJqu5m68WFVXo\nft9yuVxW1YsEAgBQnxXn5eanpykL8mX6BoaOLnKjGrmRW8Ef9wr/uCeoVHqWVg3sHAQZZ7wK\nUvy+LABIn46Ojo6OyD9FpHnNAwAA6gy5oZGxW6ua3ouBta2BNZff/x8kdQAAAACAhBBTAQAA\nAAASQkwFAAAAAEgIMRUAAAAAICHEVAAAAACAhBBTAQAAAAASQkwFAAAAAEgIMRUAAAAAICHE\nVAAAAACAhOiK3QAgadnZ2WPHji0sLOzVq9fHH3+sLr548WLKlCmPHj1q27btZ599Jm6HAAAA\nQB1DTAXKsmHDhqFDhw4cOLB3796jRo0yMjISBCE6Otrb2zs0NHTlypUvXrzQ09PTjL9//35G\nRkbJGZ4+fVrbTQMAAADajJN+gbJcuXKlffv2giAoFApN/kxISHj27NnEiRNdXFxKZlRBEPLz\n8x//rwEDBojQNwAAAKC1OJoKlEWpVMpkMvXjoqIi9YPnz58/efJk9uzZI0aM8PDwcHBw0Ixv\n2rRp06ZNRWgUAAAAqCs4mgqUxdPTMzExURCEmzdvNmvWTF10dXX18fGxtbV1dHR8/PixqA0C\nAAAAdY1MpVKJ3QMgXU+fPp0wYYK+vr63t/ekSZPi4+OPHj06ZcqUDz/8UKlUNm/efOnSpWL3\nCAAAANQpxFQAAAAAgIRw0i+A17p8+fKQIUPc3NyMjY09PT2nT59e8iRnd3f36dOni9geIDVK\npTIvL0/sLoB64c6dO2ZmZu3atSsuLi5Znz59ukwm+/nnn8VqDEC1IKYCeLX169d37NgxOTl5\nwIABUVFRHTt2/Oabb7p06ZKTkyN2a3VQdHR0QECAhYWFk5PTxIkTHz58KHZHqIqNGzcGBASI\n3QUqp7CwcN68ee7u7sbGxu7u7lFRUYWFhWI3hfLZ29svWLDg4sWLq1ev1hSvXr26fPnykSNH\n9urVS8TeALw5TvoF8AqZmZmurq69e/fetGmTvr6+upiQkPD2229Pnz59wYIFgiC4u7sHBQUt\nWrRI1E7rgrVr144fP75fv34DBgxIT09fvHixm5vbiRMndHW5GLs2uXPnTkBAgKWlZUJCgti9\noBKmTZv27bffTp061dvb+/z588uWLfv000+XLFkidl8on1Kp7NKly7Vr15KTk21sbFQqlb+/\nf0pKSlJSUuPGjcXuDsAbIaYCeIVx48bt2LEjIyOjYcOGJeu9evW6ceNGamqqULGYqlKp8vLy\njIyMarZdbaZSqWxsbHx8fPbt26e++1FsbGxwcPD+/fuDg4PF7g4VcubMmUmTJl26dKmoqMjX\n15eYqkUKCwtNTExmz579j3/8Q10JCwvbuXPnkydPxG0MFXT16tV27doNGTJk8+bNP/zww7hx\n47Zu3Tp8+HCx+wLwpjjpF8ArHDp0aPDgwaUyqiAI+/btu3TpUsnKunXrWrdubWJi0rlz53Pn\nzqmLCoVi5cqVK1ascHR0PHDgQC01rZ2ysrL+/PPPgQMHau7Q27VrV0EQfvvtNzHbQmVYWVmN\nGTNm+fLlXl5eYveCysnMzGzfvn3Pnj01FQcHh4KCAs771RYtW7acMWPGli1bdu3aNXPmzD59\n+pBRgbqBo6kASsvJyTE1NY2Kipo5c2YZw9zd3Z8/f+7k5BQeHp6ZmRkVFSWTyW7fvi0IgkKh\nsLe3z8vLmzx5co8ePWxsbGqpdS1UUFBw48YNBwcHU1NTdeU///lPr169duzYMWTIEHF7Q2X1\n798/KyuLo6naKyUlpW/fvp6enjExMWL3gooqKCjw8vJKTU01MTFJSkqyt7cXuyMA1YAvPgEo\nLT09XX0markjCwsL4+Li1Of0Ghsbf/DBBw8ePFB/I+jatWs3b97kdN9yGRgYeHp6ap5evHgx\nNDTUw8Ojf//+InYF1DexsbHDhw9//vx527ZtN23aJHY7qAQDA4NBgwYtWLCgW7duZFSgzuCk\nXwClqX/MZ2VllTvyvffe0wTRt956SxAEzY0BunfvTkatlNzc3FmzZvn6+trb28fFxenp6Ynd\nEVCPeHt7b9u2bdGiRZmZmYGBgZxrpkVu3bq1fPlyW1vb/fv3HzlyROx2AFQPYiqA0szMzJo0\naXL9+vWXN23cuLHkJmtr69dNwom+lRIfH+/l5bV27drFixf/+uuvHBAAapm1tXVQUFB4ePjG\njRvj4+MvXrwodkeoqI8++sjAwOD06dNubm4fffRRbm6u2B0BqAbEVACv4O/vHx0d/eDBg1L1\nAwcO5OfnKxQK9VPNVX9epqPDfy8VdeTIkR49enh7e6empn7yySfchwaoNXv37vX398/OztZU\n1CfhX758WbymUAk//vjj4cOHv/rqK3t7+5UrV968eVNz0WYAWo3fIwG8QlRUVHFxcVhYWF5e\nnqYYExOzd+/eYcOGEUGrkVKpHDduXI8ePbZt22ZhYSF2O0D9YmlpeeLEiUOHDmkqx44dE/4K\nq5C4rKysv//97z4+PmFhYYIgBAQEjBgxYvny5WfPnhW7NQBvir/ZA3gFZ2fnBQsWhIeHt2/f\nvl+/fra2tgkJCTExMQ4ODgsXLqzUVKtXr969e/e6deucnJxqplntlpCQcPv27YCAgJUrV5as\n+/n5tW7dWqyugHrCz88vKCgoLCzs+vXrrq6uV69eXbp0ad++fTt27Ch2ayjf5MmTnz179t13\n32lO7VmyZMnBgwfHjh17/vx5vuEPaDViKoBXmzZtmq+v7/z587dv337//n0XF5dPP/10zpw5\nmvumVFBKSsrhw4dzcnJqqE9td+PGDUEQ1q9fv379+pL15cuXE1OBmiaTyWJiYiIiInbu3Jma\nmqq+w9aMGTPE7gvl27NnT0xMzLRp09q0aaMpWltbz58/f9KkSQsXLpw9e7aI7QF4Q9w3FQAA\nAAAgIXzBDAAAAAAgIcRUAAAAAICEEFMBAAAAABJCTAUAAAAASAgxFQAAAAAgIcRUAAAAAICE\nEFMBAAAAABJCTAUAAAAASAgxFQAAAAAgIcRUAAAAAICEEFMBAAAAABJCTAVQCZcvXx4yZIib\nm5uxsbGnp+f06dMfP36s2bpjxw6ZTHby5EkRO5S4wsLCO3fuVHZTlUeievHJ1z2sqZQVFhbO\nmzfP3d3d2NjY3d09KiqqsLCw3E0VnwSAlBFTAVTU+vXrO3bsmJycPGDAgKioqI4dO37zzTdd\nunTJyckRuzWtMWHCBG9v78puqvJIVK+XP/no6OiAgAALCwsnJ6eJEyc+fPjwlS9UqVSrVq1q\n166dmZlZ586dN2/eXCv9onwvr2lCQkK3bt0sLCw6duwYHR0tVmMQBGHmzJn//Oc/e/fu/cMP\nPwQFBc2ZMyciIqLcTRWfBICkqQCgAu7du2diYjJkyJCCggJN8ddff9XV1Y2IiFA/3b59uyAI\nJ06cEKlHqdu1a5cgCDY2NpXaVOWRqF4vf/Jr1qyRyWQhISH//ve/582bZ25u7uPj8+LFi5df\n+7e//U1PTy88PHzbtm2ffPKJrq7uqlWrarF3vNrLa3r27FkDA4OePXv+8MMPI0eOlMlkO3bs\nELHD+qygoEBPT2/u3Lmayrhx48zNzcveVPFJAEgcMRVAhYwdO9bExOTRo0el6oGBgc2bN1c/\nJqaW4c6dO40aNXJxcXk5YZaxqcojUb1e/uSVSuVbb70VHBysVCrVlQMHDgiCsH///lKvzcjI\n0NHRWbRokaYyd+5cKyurvLy82mker/TKf02DBg3y9PTU/DEuKCjIy8tLpAbru9u3b/v6+p46\ndUpTiYyMbNCgQUFBQRmbKj5JTfcP4A1x0i+ACjl06NDgwYMbNmxYqr5v375Lly6J0pIWUalU\no0ePDggICAkJqfimKo9E9XrlJ5+VlfXnn38OHDhQJpOpK127dhUE4bfffiv18vPnzyuVysDA\nQE2lX79+9+/fP378eI23jtd45Zrm5eXt2bNn6NCh+vr66soHH3yQmJh48eJFkdqs1xwdHRMS\nEnx8fNRPU1JStmzZ0rt3b319/TI2VXySWnsjAKqGmAqgfDk5OXfu3GnRosXLm/T19Y2NjWu/\nJe2ydOnSa9euqc8RrfimKo9E9XrlJ9+oUaOrV68OGDBAUzlx4oQgCM7OzqVe3rhxY0EQMjIy\nNJX09HRBEO7evVujbaMMr1zTe/fuFRcXe3l5aSqtWrUSBCEpKUmEFvGX2NhYU1PTFi1aGBkZ\nbdq0qYKbKj4JAGkipgIoX3p6ukqlsrGxEbsRrXTp0qVZs2atX7++UaNGFd9U5ZGoXq/75A0M\nDDw9PU1NTdVPL168GBoa6uHh0b9//1IzeHt7u7q6/v3vfz937lxubu6xY8emTp0qCMLrrreE\nmva6NVX/4cDS0lJTUT9+8uRJLXeIkry9vbdt27Zo0aLMzMzAwECVSlWRTRWfBIA0EVMBlM/e\n3l4QhKysLLEb0T55eXkjRowICwvr2bNnxTdVeSSqV0U++dzc3FmzZvn6+trb28fFxenp6ZUa\nYGBgEBMTI5fLvb29jY2Ne/bsOX78eOGvo6yoZWWsqVKpfOVLdHT4ZUlM1tbWQUFB4eHhGzdu\njI+PL3kOdhmbKj4JAGnif14A5TMzM2vSpMn169df3rRx48bXbYIgCMuXL09PT+/cufO+ffv2\n7duXmpqan5+/b9++s2fPlrGp4pOI8qbqj3I/+fj4eC8vr7Vr1y5evPjXX39V/0HnZS1btrx8\n+fKVK1eOHTt2//79Pn36CILQpEmT2nsn+EsZa6pekZL3gn706JEgCLa2tqK1W4/t3bvX398/\nOztbU/H09BQE4fLly2VsqvgkNdo8gDenK3YDALSDv79/dHT011+x2mrMAAAYB0lEQVR/XeoQ\n0IEDB/Lz8xUKhViNSdyjR49ycnLef//9ksWQkJBBgwY5OTm9btPOnTsrOEmpkaheZX/yR44c\n6d2794ABA1avXm1hYfG6SYqKitLS0mxsbFq2bKmunD59WkdHR/MUtamMNf3xxx/lcnlSUlJw\ncLC6npycLAiCo6OjCI3We5aWlidOnDh06NDAgQPVlWPHjgmCoL4U8+s2VXySWngLAN6IaNcY\nBqBV0tLSGjRoEBISkpubqynu2rVLLpePHz9e/ZQb0pQrPDz8dfeSKWNTlUeiepX85IuLi52c\nnHr37l3uq4qLiy0tLQcNGqR+WlhY6OPjU5EXohaU+tfUr1+/Dh06FBcXq5++//77bm5uIrVW\n3ymVyqCgoIYNG86fP3/nzp1ffPGFqalp3759y96kUqlWrVrVo0ePW7dulTsSgJRxNBVAhTg7\nOy9YsCA8PLx9+/b9+vWztbVNSEiIiYlxcHBYuHCh2N3VQatXr969e/e6deucnJzE7gWvkJCQ\ncPv27YCAgJUrV5as+/n5tW7duuTy6ejoTJ48OTIyMiwsrEuXLtu2bUtOTv7ll1/E6hxlmDFj\nRkBAQGho6IgRI44cObJt27bNmzeL3VQ9JZPJYmJiIiIidu7cmZqa6uTkFB4ePmPGjLI3CYKQ\nkpJy+PDhnJycckcCkDJiKoCKmjZtmq+v7/z587dv337//n0XF5dPP/10zpw5moudohqV/E0L\nEnTjxg1BENavX79+/fqS9eXLl7du3brU8s2ZM0dHR2f9+vV79uzx8/P773//27ZtWxGaRnk6\nd+78008/RUZGDh48uEWLFps3by51ejBqk76+/pIlSyq7admyZcuWLavISABSJlNxSW4AAAAA\ngGRwpV8AAAAAgIQQUwEAAAAAEkJMBQAAAABICDEVAAAAACAhxFQAAAAAgIQQUwEAAAAAEkJM\nBQAAAABICDEVAAAAACAhxFQAAAAAgIQQUwEAAAAAEkJMBQAAAABICDEVAESgVCrz8vLE7gJV\nxwpqNZZPS0VHRwcEBFhYWDg5OU2cOPHhw4evG5mQkNCtWzcLC4uOHTtGR0fXZpMAqgUxFcCb\nUqlUubm5YnehZTZu3BgQEFDGgMLCwnnz5rm7uxsbG7u7u0dFRRUWFtZaeygXK6jVyl2+//f/\n/p/sfzVs2LDW2sMrrV27dtiwYebm5v/617/GjRu3devWPn36FBUVvTzy3Llz3bp1MzAwWLp0\naYsWLYYNG0ZSBbSOrtgNANBWCoVi6tSpKpXq66+/XrRo0dChQ8XuSGvcuXMnKirK0tKyjDEz\nZ8789ttvp06d6u3tff78+Tlz5jx48GDJkiW11iTKwApqtYosX1pamlwu/+qrr2QymbpiYGBQ\nK93h1VQq1Zw5c4KCgnbv3q1elDZt2gQHB//888/BwcGlBi9cuLB58+b79+/X19cPDQ198uTJ\n/PnzhwwZIkbjAKpKBQBV0rx5865du/r4+GzatCkzM1PsdrTD6dOnO3TooKurKwiCr6/v64YV\nFBTo6enNnTtXUxk3bpy5uXmt9IiysIJarYLLp1KpPv/8cxcXl1prDOW6d++eIAgbNmzQVLKz\nswVBiIqKKjUyNzdXLpdHRkZqKupDqRcuXKilXgFUB46mAqi6a9eu3bx508jISOxGtIaVldWY\nMWMEQVi7dm0ZwzIzM9u3b9+zZ09NxcHBoaCgoLCwUF9fv6abRBlYQa1WweUTBOHGjRvNmzev\njZ5QMY0aNbp69aqDg4OmcuLECUEQnJ2dS428d+9ecXGxl5eXptKqVStBEJKSktq2bVsrzQKo\nBsRUAFXXvXt3MmqlNGvWbNKkSYIgHD58OCsr63XDHB0dExISNE9TUlK2bNnSu3dvEo7oWEGt\nVsHlEwThxo0bRkZGXbt2PX/+vI2NTd++fSMjI42NjWurU5RmYGDg6empeXrx4sXQ0FAPD4/+\n/fuXGnn37l1BEEqe1K1+/OTJk1rpFED14BJKAKrOxsZG7BbquNjYWFNT0xYtWhgZGW3atEns\ndlBprKCWSktLO3PmjI+Pz+rVq/v167dixYqgoCCVSiV2XxByc3NnzZrl6+trb28fFxenp6dX\naoBSqXzlC3V0+KUX0CYcTQVQdfzUr2ne3t7btm1LTk5esmRJYGDg8ePHNRd0gVZgBbVRcXHx\nN99806pVq3bt2gmCMHLkSE9Pz9DQ0Li4uMDAQLG7q9fi4+M//PDDp0+fLl68eMKECeqvGZfS\npEkTQRAeP36sqTx69EgQBFtb21rrE8Cb41dMAJAua2vroKCg8PDwjRs3xsfHX7x4UeyOUDms\noDaSy+WjR49WZ1S1IUOGyGSyCxcuiNgVjhw50qNHD29v79TU1E8++eSVGVUQBDs7O7lcnpSU\npKkkJycLguDo6FhLjQKoDsRUAJCcvXv3+vv7q69jqab+Utbly5fFawqVwApqtfv3758+fbrk\nfW51dHRkMpmpqamIXdVzSqVy3LhxPXr02LZtm4WFRRkjTUxMgoKCdu3apTn7Nzo62s3NrU2b\nNrXSKYDqQUwFAMmxtLQ8ceLEoUOHNJVjx44Jf0UdSB8rqNVu3Ljh6+u7bNkyTWX//v1KpbJT\np04idlXPJSQk3L5928bGZuX/Uv/pZ/Xq1e++++7t27fVg2fMmHHlypXQ0NBffvnls88+27Zt\n25w5c8TsHkDl8d1UAFWUmpoqdgt1yurVq3fv3r1u3TonJyc/P7+goKCwsLDr16+7urpevXp1\n6dKlffv27dixo9ht4rVYQa1Wcvk6deoUHBwcGRmZnp7u5+eXlJS0bNmyDz/8sEOHDmK3WX/d\nuHFDEIT169evX7++ZH358uWtW7dOSUk5fPhwTk6Outi5c+effvopMjJy8ODBLVq02Lx58/vv\nvy9C0wDeADEVACSh5K9ZMpksJiYmIiJi586dqampTk5O4eHhM2bMELtHlIUV1Gqlcs6ePXsW\nLFiwd+/eTZs2ubi4zJs3b+rUqeJ2WM+NHj169OjRr9u6bNmykke/BUEICAgICAio+b4A1BQZ\nV1cHAAAAAEgH300FAAAAAEgIMRUAAAAAICHEVAAAAACAhBBTAQAAAAASQkwFAAAAAEgIMRUA\nAAAAICHEVAAAAACAhBBTAQAAAAASQkwFAAAAAEgIMRUAAAAAICHEVKD+un79+siRI93d3Y2M\njJydnQcMGHDq1CmxmwIAAEB9R0wF6qkff/yxZcuW//3vf995552vv/56xIgRly9f7tSp05o1\na8RurR7p1q2bTCabPn36y5vGjBkjk8n69+9f+12hglg+rXPjxo3Ro0crFIoGDRrY2dmNGDEi\nKSlJs5UFlTiWD6hXiKlAfXTx4sWwsLB33nknMTHxu+++mzx58rx585KTk4ODgydNmpSSkiJ2\ng/XL7t27S1WKi4tjY2NFaQaVxfJpi7S0tPbt2//6668ff/zxhg0bPvnkk1OnTnl7eycmJpYc\nxoJKE8sH1DfEVKA++sc//mFoaLhlyxZLS0tNUU9Pb8WKFY0aNdq/f3+17EWlUuXm5pZdQbNm\nzW7evFnqN60TJ048evTI0dFRrK5QQSyfFpk/f76ent6pU6fCw8OHDRv22WefnT9/vlGjRiWP\nv7GgksXyAfUNMRWod54/f37o0KEPPvjA2tq61CYHB4c///wzPDxc/dTKymrJkiWarVu2bJHJ\nZAUFBeqnBw8e9PPzMzMzc3Jy+vzzzzV1hUKxcuXKFStWODo6Hjhw4OVKYWHhvHnzWrVqZWRk\nZGdn9+GHHz58+LDG37ZUeXh4KBSKUkcA9u7d6+vr26RJE7G6QgWxfFrkypUrLVu2LPm3OQsL\ni9GjR5f82xkLKlksH1DfEFOBeic1NbWwsLBVq1ZvMsnWrVuDg4NdXFzWrFkzevTob775ZuDA\ngZqtu3bt2rx584IFC955552XK1OmTJk7d27Pnj3XrVs3duzYvXv3Tpky5Y3ekjZTf2Nqz549\nJYv79u3ja1RageXTItbW1mfOnDl69GjJ4pdffhkfH695yoJKFssH1DfEVKDeefr0qSAIJQ+l\nHj9+XFZC27Zty56hsLBwxowZH3300caNG4cPHz537tz169cfPHhQ8+vCtWvXjh49OnLkSBsb\nm5cr2dnZn3/++eLFi0eMGBEZGRkaGnru3Lmaea/aoX///omJiWlpaeqniYmJt27d6tevn7hd\noYJYPm0xe/ZsuVzevXv3Nm3azJ49+/jx44WFhS8PY0GlieUD6htiKlDvNGvWTBCEW7duaSpu\nbm4b/vLee++VO0NKSsrdu3cDAgLS/9K6dWuZTKa5n0337t2NjIxKvqRkZcuWLZGRkQUFBSkp\nKfv374+NjVUqldX29rSQj4+Pra2t5gjA3r17PTw8XF1dxe0KFcTyaQtfX98rV67MmjVLR0dn\nwYIFXbt2tbS0nDhxovovdxosqDSxfEB9Q0wF6h0HBwdDQ8NLly5pKtbW1qP/YmhoWO4M6og7\ndOhQp7+4u7urVKonT56oB2gOomqUrJw/f75Tp05GRkadO3deuHChmZlZNbwrbSaTyfr166f5\nPhWnqGkXlk+LODk5ffnllxcuXPjzzz+3bNnSuXPn1atXBwYGqlQqzRgWVLJYPqBeIaYC9Y5M\nJgsODt6xY0fJA6pqT548OXny5Ote+PjxY/WDt956SxCE3377TfW/5s+frx6go1P6/xZNJTs7\n28/PT309xgcPHpw8ebJnz57V8r60Wv/+/U+dOpWZmZmRkXHhwoWQkBCxO0IlsHzS9+zZs337\n9v3555/qp40bN37//ffj4uIiIiJOnTp1+vTpkoNZUKlh+YB6iJgK1EdRUVGCIISGhj548EBT\nzM/P//jjj0tW5HL5tWvXNE8PHjyofuDh4WFiYrJ9+3bNptjYWIVCUfJO669z/vz5/Pz8yZMn\na24PUOrmAfVT165dzc3N9+7du2/fPnt7+/bt24vdESqB5ZO+nJyckJAQ9bXHS+revbsgCNnZ\n2SWLLKjUsHxAPaQrdgMARODs7Lxx48Zx48a1atVq0KBBHh4ed+7c2bVrl0ql+vTTT48fP64e\n1q5du+3bt7u6urZt23b79u2HDx9W101NTb/44ovp06dnZWUFBAQkJSWtWLHC29vbw8Oj3F27\nubkZGhrOmjVr2rRpKpXq+++/P3nyZFFR0dmzZ729vWvwPUubnp5eUFDQnj17lEplSEiITCYT\nuyNUAssnfTY2Nm5ubsuWLQsJCSl5U5OdO3fq6uqW+s+HBZUalg+oh4ipQD01bNiw1q1bf/31\n10ePHt20aVPbtm1HjBjxt7/97fbt25oTdL/77rsJEyZ8+eWX2dnZTZo0Wbp0qebOMeHh4VZW\nVitWrNi6dWvjxo3Hjh37xRdfVGS/NjY2MTExERERw4cPb968eVhY2JIlS3r16jVixIiUlJSa\nerfaoH///sOGDVOpVBEREWL3gkpj+SROJpOtXbt2wIABrVq1GjlyZPPmzfPy8g4fPhwbG7to\n0SILC4tS41lQSWH5gHpIVvJ75wDwSn/88YelpaWuLn/YqmbdunUzMTFRn8mWm5vbuHFjAwOD\n+/fvqz/qTp062djYlLoHIKSD5dM6GRkZc+fOvXTpUnJycsOGDd3c3CZNmqS5xA4LKnEsH1Cv\nEFMBAAAAABLCJZQAAAAAABJCTAUAAAAASAgxFQAAAAAgIcRUAAAAAICEEFMBAAAAABJCTAUA\nAAAASAgxFQAAAAAgIcRUAAAAAICEEFMBAAAAABJCTAUAAAAASAgxFQAAAAAgIcRUoL4bPHhw\ns2bNxO4CAAAA+D/EVAAQX0FBgdgtoOpYPq3G8tUZLCVQlxBTAUA07u7u8+fPDwoKatCggbW1\n9fjx41+8eDF37lyFQmFubj5o0KCnT5+K3SNei+XTaiyf1rlx48agQYOsra3NzMzefvvtkydP\nqussJVAnEVMB1LaioiKxW5CQefPmNWzY8ODBgyEhIWvWrFEoFBcvXvzmm29CQ0NjYmIWL14s\ndoMoC8un1Vg+LVJUVPTee+8lJiZGRER8/fXXT58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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281.25,
       "width": 625
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "charVectorCol <- c(\"tomato3\",\"darkolivegreen4\",\"dfsd\",\"dodgerblue\",\"chartreuse3\")\n",
    "\n",
    "# Modify size of kar. to use rulerInterval and ceilingFactor (>= 1.13)  \n",
    "quo<-9\n",
    "dfOfChrSize2<-dfOfChrSize\n",
    "dfOfChrSize2$shortArmSize<-dfOfChrSize$shortArmSize/quo\n",
    "dfOfChrSize2$longArmSize<-dfOfChrSize$longArmSize/quo\n",
    "dfOfMarks2b<-dfOfMarks2\n",
    "dfOfMarks2b$markSize<-dfOfMarks2$markSize/quo\n",
    "dfOfMarks2b$markDistCen<-dfOfMarks2$markDistCen/quo\n",
    "\n",
    "#png(\"dfOfChrSizeVector.png\", width=1000, height=450)\n",
    "par(mar=rep(0,4))\n",
    "\n",
    "options(repr.plot.width = 1000/80, repr.plot.height = 450/80, repr.plot.res = 100)\n",
    "\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize = dfOfChrSize2,    # d.f. of chr. sizes\n",
    "              dfMarkPos = dfOfMarks2b,     # d.f. of marks' positions\n",
    "              defaultStyleMark = \"cM\",     # forces \"cM\" style in d.f dfMarkColor (exc. 5S)\n",
    "              \n",
    "              mycolors = charVectorCol,    # colors to use\n",
    "              \n",
    "              distTextChr = 0.5,           # separ. text and chr.\n",
    "              \n",
    "              markLabelSize=.7,            # font size for labels (legend)\n",
    "              lwd.cM=2,                    # width of cM marks\n",
    "              legendWidth=0.9,             # legend item width\n",
    "              legendHeight=.5,\n",
    "              \n",
    "              rulerPos= 0,                 # ruler position\n",
    "              ruler.tck=-0.01,             # ruler tick orientation and length\n",
    "              rulerNumberSize=.5           # ruler font size\n",
    "              ,xPosRulerTitle = 2.8        # ruler units pos.\n",
    "              \n",
    "              ,xlimRightMod = 1            # modify xlim right arg.\n",
    ")\n",
    ")\n",
    "#dev.off() # close png\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1104e9c",
   "metadata": {},
   "source": [
    "## Plot holocentrics\n",
    "\n",
    "Initially you have to put your chromosome data in data.frames. \n",
    "\n",
    "Main three data.frames: \n",
    "\n",
    "* One for chr. sizes (parameter `dfChrSize`)\n",
    "* One for marks' positions  (parameter `dfMarkPos`)\n",
    "* One (optional) for mark style (parameter `dfMarkColor`)\n",
    "\n",
    "For simplicity, I call a holocen. any karyotype without centromeres. \n",
    "\n",
    "Accordingly, they lack the columns: `shortArmSize` and `longArmSize`.\n",
    "\n",
    "### From scratch: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b97c78a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OTU</th><th scope=col>chrName</th><th scope=col>chrSize</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Species one</td><td>1</td><td>6.5</td></tr>\n",
       "\t<tr><td>Species one</td><td>2</td><td>5.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>3</td><td>4.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>4</td><td>4.0</td></tr>\n",
       "\t<tr><td>Species one</td><td>X</td><td>3.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 3\n",
       "\\begin{tabular}{lll}\n",
       " OTU & chrName & chrSize\\\\\n",
       " <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Species one & 1 & 6.5\\\\\n",
       "\t Species one & 2 & 5.0\\\\\n",
       "\t Species one & 3 & 4.0\\\\\n",
       "\t Species one & 4 & 4.0\\\\\n",
       "\t Species one & X & 3.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 3\n",
       "\n",
       "| OTU &lt;chr&gt; | chrName &lt;chr&gt; | chrSize &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Species one | 1 | 6.5 |\n",
       "| Species one | 2 | 5.0 |\n",
       "| Species one | 3 | 4.0 |\n",
       "| Species one | 4 | 4.0 |\n",
       "| Species one | X | 3.0 |\n",
       "\n"
      ],
      "text/plain": [
       "  OTU         chrName chrSize\n",
       "1 Species one 1       6.5    \n",
       "2 Species one 2       5.0    \n",
       "3 Species one 3       4.0    \n",
       "4 Species one 4       4.0    \n",
       "5 Species one X       3.0    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example data.frame written in R, use: (column OTU is optional if only 1 OTU)\n",
    "mydfChrSizeHolo<-read.table(text=\n",
    "\"            OTU chrName chrSize  \n",
    "\\\"Species one\\\"   1     6.5      \n",
    "\\\"Species one\\\"   2     5.0      \n",
    "\\\"Species one\\\"   3     4.0\n",
    "\\\"Species one\\\"   4     4.0\n",
    "\\\"Species one\\\"   X     3.0    \"  ,  header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "mydfChrSizeHolo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b9ca2d",
   "metadata": {},
   "source": [
    "For fixing column names use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "51112e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mydfChrSize)<-c(\"OTU\", \"chrName\",\"chrSize\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b37bdb",
   "metadata": {},
   "source": [
    "### Get marks general data \n",
    "\n",
    "Put your mark data in a data.frame. This data.frame has the marks present in all karyotypes without position info.\n",
    "If the `style` column is not present, param. `defaultStyleMark = \"square\"` will be used during plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2389b288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>markName</th><th scope=col>markColor</th><th scope=col>style</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5S     </td><td>red           </td><td>dots     </td></tr>\n",
       "\t<tr><td>45S    </td><td>chartreuse3   </td><td>square   </td></tr>\n",
       "\t<tr><td>gene2  </td><td>salmon        </td><td>downArrow</td></tr>\n",
       "\t<tr><td>gene3  </td><td>#056522       </td><td>cMLeft   </td></tr>\n",
       "\t<tr><td>gene4  </td><td>darkmagenta   </td><td>cM       </td></tr>\n",
       "\t<tr><td>DAPI   </td><td>dodgerblue    </td><td>square   </td></tr>\n",
       "\t<tr><td>protein</td><td>cornflowerblue</td><td>exProtein</td></tr>\n",
       "\t<tr><td>B      </td><td>black         </td><td>square   </td></tr>\n",
       "\t<tr><td>gene1  </td><td>chocolate     </td><td>upArrow  </td></tr>\n",
       "\t<tr><td>CMA    </td><td>darkgoldenrod1</td><td>square   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 3\n",
       "\\begin{tabular}{lll}\n",
       " markName & markColor & style\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 5S      & red            & dots     \\\\\n",
       "\t 45S     & chartreuse3    & square   \\\\\n",
       "\t gene2   & salmon         & downArrow\\\\\n",
       "\t gene3   & \\#056522        & cMLeft   \\\\\n",
       "\t gene4   & darkmagenta    & cM       \\\\\n",
       "\t DAPI    & dodgerblue     & square   \\\\\n",
       "\t protein & cornflowerblue & exProtein\\\\\n",
       "\t B       & black          & square   \\\\\n",
       "\t gene1   & chocolate      & upArrow  \\\\\n",
       "\t CMA     & darkgoldenrod1 & square   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 3\n",
       "\n",
       "| markName &lt;chr&gt; | markColor &lt;chr&gt; | style &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 5S      | red            | dots      |\n",
       "| 45S     | chartreuse3    | square    |\n",
       "| gene2   | salmon         | downArrow |\n",
       "| gene3   | #056522        | cMLeft    |\n",
       "| gene4   | darkmagenta    | cM        |\n",
       "| DAPI    | dodgerblue     | square    |\n",
       "| protein | cornflowerblue | exProtein |\n",
       "| B       | black          | square    |\n",
       "| gene1   | chocolate      | upArrow   |\n",
       "| CMA     | darkgoldenrod1 | square    |\n",
       "\n"
      ],
      "text/plain": [
       "   markName markColor      style    \n",
       "1  5S       red            dots     \n",
       "2  45S      chartreuse3    square   \n",
       "3  gene2    salmon         downArrow\n",
       "4  gene3    #056522        cMLeft   \n",
       "5  gene4    darkmagenta    cM       \n",
       "6  DAPI     dodgerblue     square   \n",
       "7  protein  cornflowerblue exProtein\n",
       "8  B        black          square   \n",
       "9  gene1    chocolate      upArrow  \n",
       "10 CMA      darkgoldenrod1 square   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From scratch:\n",
    "mydfMarkColorHolo<-read.table(text=\n",
    "\"  markName    markColor      style\n",
    "     5S        red            dots\n",
    "     45S       chartreuse3    square\n",
    "     gene2     salmon         downArrow\n",
    "     gene3     \\\"#056522\\\"    cMLeft\n",
    "     gene4     darkmagenta    cM\n",
    "     DAPI      dodgerblue     square\n",
    "     protein   cornflowerblue exProtein\n",
    "     B         black          square\n",
    "     gene1     chocolate      upArrow\n",
    "     CMA       darkgoldenrod1 square\"  ,  header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "mydfMarkColorHolo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f7a44a",
   "metadata": {},
   "source": [
    "For fixing column names use:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3434df90",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mydfMarkColorHolo) <- c(\"markName\", \"markColor\",\"style\") \n",
    "# if style column not present it will be filled with \"square\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82a4e1c",
   "metadata": {},
   "source": [
    "### Get marks positions data\n",
    "\n",
    "Open or write your mark positions in a data.frame. This data.frame has the marks present in all karyotypes with position info.\n",
    "\n",
    "For holocentrics, position is defined with column `markPos`, while in monocentrics it is `markDistCen`.\n",
    "\n",
    "Column `chrRegion` can be absent for holocentrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6e8306e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 11 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OTU</th><th scope=col>chrName</th><th scope=col>markName</th><th scope=col>markPos</th><th scope=col>markSize</th><th scope=col>chrRegion</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Species one</td><td>4</td><td>B      </td><td> NA</td><td> NA</td><td>w</td></tr>\n",
       "\t<tr><td>Species one</td><td>3</td><td>DAPI   </td><td>2.0</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>1</td><td>45S    </td><td>2.0</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>2</td><td>protein</td><td>3.5</td><td>1.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>2</td><td>gene1  </td><td>1.0</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>1</td><td>gene2  </td><td>1.0</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>4</td><td>gene3  </td><td>3.0</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>3</td><td>gene4  </td><td>1.0</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>X</td><td>CMA    </td><td>2.0</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>X</td><td>5S     </td><td>0.5</td><td>0.5</td><td> </td></tr>\n",
       "\t<tr><td>Species one</td><td>X</td><td>5S     </td><td>0.5</td><td>0.5</td><td> </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 11 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " OTU & chrName & markName & markPos & markSize & chrRegion\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t Species one & 4 & B       &  NA &  NA & w\\\\\n",
       "\t Species one & 3 & DAPI    & 2.0 & 0.5 &  \\\\\n",
       "\t Species one & 1 & 45S     & 2.0 & 0.5 &  \\\\\n",
       "\t Species one & 2 & protein & 3.5 & 1.5 &  \\\\\n",
       "\t Species one & 2 & gene1   & 1.0 & 0.5 &  \\\\\n",
       "\t Species one & 1 & gene2   & 1.0 & 0.5 &  \\\\\n",
       "\t Species one & 4 & gene3   & 3.0 & 0.5 &  \\\\\n",
       "\t Species one & 3 & gene4   & 1.0 & 0.5 &  \\\\\n",
       "\t Species one & X & CMA     & 2.0 & 0.5 &  \\\\\n",
       "\t Species one & X & 5S      & 0.5 & 0.5 &  \\\\\n",
       "\t Species one & X & 5S      & 0.5 & 0.5 &  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 11 × 6\n",
       "\n",
       "| OTU &lt;chr&gt; | chrName &lt;chr&gt; | markName &lt;chr&gt; | markPos &lt;dbl&gt; | markSize &lt;dbl&gt; | chrRegion &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Species one | 4 | B       |  NA |  NA | w |\n",
       "| Species one | 3 | DAPI    | 2.0 | 0.5 | <!----> |\n",
       "| Species one | 1 | 45S     | 2.0 | 0.5 | <!----> |\n",
       "| Species one | 2 | protein | 3.5 | 1.5 | <!----> |\n",
       "| Species one | 2 | gene1   | 1.0 | 0.5 | <!----> |\n",
       "| Species one | 1 | gene2   | 1.0 | 0.5 | <!----> |\n",
       "| Species one | 4 | gene3   | 3.0 | 0.5 | <!----> |\n",
       "| Species one | 3 | gene4   | 1.0 | 0.5 | <!----> |\n",
       "| Species one | X | CMA     | 2.0 | 0.5 | <!----> |\n",
       "| Species one | X | 5S      | 0.5 | 0.5 | <!----> |\n",
       "| Species one | X | 5S      | 0.5 | 0.5 | <!----> |\n",
       "\n"
      ],
      "text/plain": [
       "   OTU         chrName markName markPos markSize chrRegion\n",
       "1  Species one 4       B         NA      NA      w        \n",
       "2  Species one 3       DAPI     2.0     0.5               \n",
       "3  Species one 1       45S      2.0     0.5               \n",
       "4  Species one 2       protein  3.5     1.5               \n",
       "5  Species one 2       gene1    1.0     0.5               \n",
       "6  Species one 1       gene2    1.0     0.5               \n",
       "7  Species one 4       gene3    3.0     0.5               \n",
       "8  Species one 3       gene4    1.0     0.5               \n",
       "9  Species one X       CMA      2.0     0.5               \n",
       "10 Species one X       5S       0.5     0.5               \n",
       "11 Species one X       5S       0.5     0.5               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will use column OTU if data.frame because chromosome size df has it\n",
    "mydfMarkPosHolo<-read.table(text=\n",
    "\"          OTU  chrName markName markPos markSize chrRegion\n",
    "\\\"Species one\\\"       4        B      NA       NA        w    # w = whole chromosome mark \n",
    "\\\"Species one\\\"       3     DAPI     2.0      0.5\n",
    "\\\"Species one\\\"       1      45S     2.0      0.5\n",
    "\\\"Species one\\\"       2     protein  3.5      1.5\n",
    "\\\"Species one\\\"       2     gene1    1.0      0.5\n",
    "\\\"Species one\\\"       1     gene2    1.0      0.5\n",
    "\\\"Species one\\\"       4     gene3    3.0      0.5\n",
    "\\\"Species one\\\"       3     gene4    1.0      0.5\n",
    "\\\"Species one\\\"       X      CMA     2.0      0.5\n",
    "\\\"Species one\\\"       X       5S     0.5      0.5\n",
    "\\\"Species one\\\"       X       5S     0.5      0.5\"  ,  header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "mydfMarkPosHolo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180e9029",
   "metadata": {},
   "source": [
    "For fixing column names use something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b4a8f6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(mydfMarkPosHolo)<-c(\"OTU\", \"chrName\",\"markName\",\"markPos\",\"markSize\",\"chrRegion\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7c3dcf",
   "metadata": {},
   "source": [
    "### Plotting \n",
    "\n",
    "You can plot without marks (passing only 1st data.frame), but we will use all 4 data.frames created:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "37728629",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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7Ny/WWt1Wpnzpw5cODAbdu2KRQKIUTHjh0HDRq0d+/eQYMGVXObNYoVDQAAAAAA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 700
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mar=c(0,0,0,1)) # bottom left top right\n",
    "\n",
    "options(repr.plot.width = 14, repr.plot.height = 6, repr.plot.res = 100)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize  = mydfChrSizeHolo,    # data.frame of chr. sizes\n",
    "              dfMarkColor= mydfMarkColorHolo,  # df of mark style\n",
    "              dfMarkPos  = mydfMarkPosHolo,    # df of mark positions\n",
    "              chrSpacing = 1,\n",
    "              addOTUName=FALSE,            # add OTU names\n",
    "              \n",
    "              xlimLeftMod= 2,              # modify xlim left argument\n",
    "              ylimTopMod= -1,              # modify ylim top argument\n",
    "              ylimBotMod= -2               # modify ylim bottom argument\n",
    "              ,rulerPos = 0                # ruler position\n",
    "              ,ruler.tck = -0.01           # ruler ticks size and orient.\n",
    "              ,xPosRulerTitle=2.6\n",
    "              \n",
    "              ,legendWidth=1               # width of legend\n",
    "              ,legendHeight=.5             # height of legend item \n",
    "              ,xModifier = 50              # separation among chromatids\n",
    ")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da551c18",
   "metadata": {},
   "source": [
    "\n",
    "It is not mandatory to use `dfMarkColor` and you can also use the parameter\n",
    "`mycolors` (optional too), to establish marks' colors. When using `mycolors`, colors are assigned depending on the order of marks, i.e.:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "18c4abd6",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'5S'</li><li>'DAPI'</li><li>'45S'</li><li>'CMA'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '5S'\n",
       "\\item 'DAPI'\n",
       "\\item '45S'\n",
       "\\item 'CMA'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '5S'\n",
       "2. 'DAPI'\n",
       "3. '45S'\n",
       "4. 'CMA'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"5S\"   \"DAPI\" \"45S\"  \"CMA\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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8cbuoH3Nw5KRJX9JcNNq4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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 700
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique(dfMarkPosHolo$markName)\n",
    "\n",
    "par(mar=rep(0,4)) \n",
    "\n",
    "options(repr.plot.width = 14, repr.plot.height = 6, repr.plot.res = 100)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize = dfChrSizeHolo,  # d.f. of chr. size\n",
    "              dfMarkPos = dfMarkPosHolo,  # d.f. of marks' positions\n",
    "              \n",
    "              mycolors   = c(\"red\",\"dodgerblue\",\"fdsjkfds\",\"chartreuse3\",\"darkgoldenrod1\"),  # colors for marks\n",
    "\n",
    "              addOTUName=FALSE,           # do not add OTU name\n",
    "              ruler=FALSE,                # do not add ruler\n",
    "              xlimLeftMod=1,              # modify left xlim arg.\n",
    "              xlimRightMod=3,             # modify right xlim arg.\n",
    "              ylimBotMod=.2               # modify bottom ylim\n",
    "              ,chromatids=FALSE           # do not show separ. chromatids\n",
    "\n",
    ")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ff1db7",
   "metadata": {},
   "source": [
    "## ggplot holocentrics\n",
    "\n",
    "With function `mapGGChrMark` and the data.frames of chr. (`dfChrSizeHolo`) and marks' position (`dfMarkPosHolo`) for one OTU, you get a list of data.frames for ggplot [@R-ggplot2] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e62a230d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: ggplot2\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ovFarUWFBScOHHC6/WWlJTExsampKRc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\nJw76KwFH2xZxAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chrAndMarksMap <- mapGGChrMark(dfChrSizeHolo, dfMarkPosHolo, chrSpacing = .5)\n",
    "\n",
    "require(ggplot2)\n",
    "\n",
    "myColors <- c(\"chartreuse3\",\"red\",\"darkgoldenrod1\",\"dodgerblue\")\n",
    "names(myColors) <- c(\"45S\",\"5S\",\"CMA\",\"DAPI\")\n",
    "\n",
    "options(repr.plot.width = 10, repr.plot.height = 6, repr.plot.res = 100)\n",
    "\n",
    "ggplot() + \n",
    "  geom_polygon(aes(x=x,\n",
    "                   y=y,\n",
    "                   group=Chr\n",
    "                   ) \n",
    "                ,data=chrAndMarksMap$dataChr\n",
    "                ,color=\"gray\"\n",
    "                ,fill=\"gray\"\n",
    "  ) +\n",
    "  geom_polygon(aes(x=x,y=y,\n",
    "                   group=id,\n",
    "                   color=markName,\n",
    "                   fill=markName\n",
    "                   ) \n",
    "                ,data=chrAndMarksMap$dataMark\n",
    "  ) +\n",
    "  theme_classic() +\n",
    "  scale_color_manual(\n",
    "      values = myColors\n",
    "    ) +\n",
    "  scale_fill_manual(\n",
    "      values = myColors\n",
    "    ) +\n",
    "  scale_x_continuous(breaks = seq(1, nrow(dfChrSizeHolo), 1 )\n",
    "  ) +\n",
    "  scale_y_continuous(breaks = seq(0, 5, 0.5)\n",
    "  ) +\n",
    "  geom_segment(aes(y=0, yend=5, x=-Inf, xend=-Inf)\n",
    "  )+\n",
    "  theme(axis.line   = element_blank(),\n",
    "        axis.ticks.x= element_blank(),\n",
    "        axis.title  = element_blank()\n",
    "        ,title      = element_blank()\n",
    "  )\n",
    "\n",
    "#require(svglite)\n",
    "#ggsave(file=\"ggplot.svg\",width=6, height=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bbd1856",
   "metadata": {},
   "source": [
    "## Plot holo. and mono. in the same karyotype\n",
    "\n",
    "To accomplish this, we will use `cenStyle` marks in a \"holocen.\" karyotype. The ruler is continuous as in holocen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3728d343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2pgf7PXv2PH78eIcOHYQQP/744/bt2+ud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\nWFhYV1cnOwUAdAeBQ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 750,
       "width": 1500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mark general characteristics' data.frame:\n",
    "\n",
    "mydfMarkColorHolo2 <- read.table(text=\n",
    "\"  markName    markColor      style\n",
    "     5S        red            dots\n",
    "     45S       chartreuse3    square\n",
    "     gene2     salmon         downArrow\n",
    "     gene3     \\\"#056522\\\"    cMLeft\n",
    "     gene4     darkmagenta    cM\n",
    "     DAPI      dodgerblue     square\n",
    "     protein   cornflowerblue exProtein\n",
    "     B         black          square\n",
    "     gene1     chocolate      upArrow\n",
    "     CMA       darkgoldenrod1 square\n",
    "     c45S      chartreuse3    cenStyle    # <- simulate Cen.\n",
    "     myCen2    red            cenStyle\n",
    "     myCen     chartreuse2    cenStyle\n",
    "     constr    NA             cenStyle\"  ,  header=TRUE, stringsAsFactors=FALSE,fill=TRUE)\n",
    "\n",
    "\n",
    "# add new cenStyle marks to data.frame of marks' position, created above\n",
    "\n",
    "mydfMarkPosHolo2<-plyr::rbind.fill(mydfMarkPosHolo,\n",
    "                                   data.frame(OTU=\"Species one\",\n",
    "                                              chrName=c(1:3,\"X\"),\n",
    "                                              markName=c(\"c45S\",\"myCen2\",\"constr\",\"myCen\"), # <- use new mark\n",
    "                                              markPos= c(rep(2.5,3),1),\n",
    "                                              markSize=NA \n",
    "                                              )\n",
    "                  )\n",
    "\n",
    "# png(\"mydChrSizeHolo.png\", width=600, height=300)\n",
    "par(mar=c(0,0,0,1)) # bottom left top right\n",
    "\n",
    "options(repr.plot.width = 1200/80, repr.plot.height = 600/80, repr.plot.res = 200)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize  = mydfChrSizeHolo,     # data.frame of chr. sizes\n",
    "              dfMarkColor= mydfMarkColorHolo2,  # df of mark style\n",
    "              dfMarkPos  = mydfMarkPosHolo2,    # df of mark positions\n",
    "              addOTUName=FALSE,             # add OTU names\n",
    "              \n",
    "              xlimLeftMod= 2,              # modify xlim left argument\n",
    "              ylimTopMod= -1,              # modify ylim top argument\n",
    "              ylimBotMod= -2               # modify ylim bottom argument\n",
    "              ,rulerPos = 0\n",
    "              ,ruler.tck = -0.01\n",
    "              ,xPosRulerTitle = 2.6\n",
    "              \n",
    "              ,legendWidth=1               # width of legend\n",
    "              ,legendHeight=.5             # height of legend item \n",
    "              ,lwd.mimicCen=2.5            # line width of const. mark\n",
    "              ,pattern=\"^c\"                # regex pattern to remove from mark names\n",
    "              ,remSimiMarkLeg = TRUE       # remove pseudoduplicated mark names (got equal after pattern removal)\n",
    "              ,bannedMarkName = c(\"myCen\",\"myCen2\",\"B\") # hide label\n",
    "              # ,legend=\"inline\"           # legends inline\n",
    ")\n",
    ")"
   ]
  }
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