{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36dbe51a-88f2-4a25-819d-e3db5cfbd22e",
   "metadata": {},
   "source": [
    "### 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)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c43233",
   "metadata": {},
   "source": [
    "# Minimal examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3729a054-36df-4968-8b4b-20ecc34b3e4e",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# General jupyter instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "54627046-3d2d-40f7-a51e-c66d6e431701",
   "metadata": {
    "tags": [],
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d10cf1",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# to run 1 time in a common R session (not here)\n",
    "install.packages(\"IRkernel\")\n",
    "IRkernel::installspec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d52ffe4-d948-4671-98b9-05647521e63f",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "version 2.0.6 See help: ?idiogramFISH - browseVignettes('idiogramFISH') - https://ferroao.gitlab.io/manualidiogramfish\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# install if missing\n",
    "if (system.file(package = \"idiogramFISH\") == '') {\n",
    "  install.packages(\"idiogramFISH\")\n",
    "}\n",
    "library(idiogramFISH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfc3afb-00a2-410d-8e6a-c884ec9e4e05",
   "metadata": {},
   "source": [
    "## Monocentrics\n",
    "\n",
    "Define your plotting window size with something like `par(pin=c(10,6))`, or with `svg()`, `png()`, etc.\n",
    "Add chromosome morphology according to [Guerra](https://ferroao.gitlab.io/guerra1986/Guerra1986.pdf) [-@Guerra1986d] or [@Levan1964]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7621a6c7",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(dfOfChrSize) # chromosome data\n",
    "data(dfMarkColor) # mark general data\n",
    "data(dfOfMarks2)  # mark position data (inc. cen.)\n",
    "\n",
    "# column Mbp not for plotting purposes\n",
    "dfOfChrSize$Mbp<-(dfOfChrSize$shortArmSize+dfOfChrSize$longArmSize)*100\n",
    "\n",
    "# svg(\"dfOfChrSize.svg\", width=10, height=6 )\n",
    "# png(\"dfOfChrSize.png\", width=500, height=400)\n",
    "options(repr.plot.width = 10, repr.plot.height = 6, repr.plot.res = 100)\n",
    "\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize  =dfOfChrSize,  # data.frame of chr. size\n",
    "              dfMarkColor=dfMarkColor,  # d.f of mark style <- Optional\n",
    "              dfMarkPos=dfOfMarks2,     # df of mark positions (includes cen. marks)\n",
    "              \n",
    "              karHeight=5,              # kar. height\n",
    "              chrWidth = 1.2,           # chr. width\n",
    "              chrSpacing = 1,           # space among chr.\n",
    "              \n",
    "              morpho=\"Guerra\",          # chr. morpho. classif. (Guerra, Levan, both, \"\" ) ver. >= 1.12 only\n",
    "              chrIndex=\"CI\",            # cen. pos. (CI, AR, both, \"\" ) ver. >= 1.12 only\n",
    "              chrSize = TRUE,           # add chr. sizes under chr.\n",
    "              chrSizeMbp = TRUE,        # add Mbp sizes under chr. (see above)\n",
    "              \n",
    "              rulerPos= 0,              # position of ruler\n",
    "              ruler.tck=-0.01,          # size and orientation of ruler ticks\n",
    "              rulerNumberSize=.8        # font size of rulers\n",
    "              ,xPosRulerTitle = 3       # pos of ruler title\n",
    "              \n",
    "              ,legendWidth=1            # width of legend items\n",
    "              ,fixCenBorder = TRUE      # use chrColor as border color of cen. or cen. marks\n",
    "              ,distTextChr = 1.2        # chr. text separation\n",
    "              \n",
    "              ,xlimLeftMod = 2          # xlim left param.\n",
    "              ,ylimBotMod = 0           # modify ylim bottom argument\n",
    "              ,ylimTopMod = 0           # modify ylim top argument\n",
    ")\n",
    ")\n",
    "# dev.off() # close svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3909908d",
   "metadata": {},
   "source": [
    "###  Let's explore the data.frames for monocentrics: {-}\n",
    "\n",
    "If only one species, column OTU is optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfa46794",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>chrName</th><th scope=col>shortArmSize</th><th scope=col>longArmSize</th><th scope=col>Mbp</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>3</td><td>4</td><td>700</td></tr>\n",
       "\t<tr><td>2</td><td>4</td><td>5</td><td>900</td></tr>\n",
       "\t<tr><td>3</td><td>2</td><td>3</td><td>500</td></tr>\n",
       "\t<tr><td>X</td><td>1</td><td>2</td><td>300</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 4\n",
       "\\begin{tabular}{llll}\n",
       " chrName & shortArmSize & longArmSize & Mbp\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 3 & 4 & 700\\\\\n",
       "\t 2 & 4 & 5 & 900\\\\\n",
       "\t 3 & 2 & 3 & 500\\\\\n",
       "\t X & 1 & 2 & 300\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 4\n",
       "\n",
       "| chrName &lt;chr&gt; | shortArmSize &lt;dbl&gt; | longArmSize &lt;dbl&gt; | Mbp &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 3 | 4 | 700 |\n",
       "| 2 | 4 | 5 | 900 |\n",
       "| 3 | 2 | 3 | 500 |\n",
       "| X | 1 | 2 | 300 |\n",
       "\n"
      ],
      "text/plain": [
       "  chrName shortArmSize longArmSize Mbp\n",
       "1 1       3            4           700\n",
       "2 2       4            5           900\n",
       "3 3       2            3           500\n",
       "4 X       1            2           300"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# chromosome data, if only 1 species, column OTU is optional\n",
    "dfOfChrSize\n",
    "# mark general data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c8dbb97",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 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>DAPI</td><td>blue          </td><td>square</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: 4 × 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 DAPI & blue           & square\\\\\n",
       "\t CMA  & darkgoldenrod1 & square\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 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",
       "| DAPI | blue           | square |\n",
       "| CMA  | darkgoldenrod1 | square |\n",
       "\n"
      ],
      "text/plain": [
       "  markName markColor      style \n",
       "1 5S       red            dots  \n",
       "2 45S      chartreuse3    square\n",
       "3 DAPI     blue           square\n",
       "4 CMA      darkgoldenrod1 square"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mark general data\n",
    "dfMarkColor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852b879d",
   "metadata": {},
   "source": [
    "`p, q` and `w` marks can have empty columns `markDistCen` and `markSize` since v. 1.9.1 to plot whole arms (`p`, `q`) and whole chr. `w`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "374b41cc",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></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></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><th scope=row>1</th><td>1</td><td>5S  </td><td>p  </td><td> 1</td><td>0.5</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>45S </td><td>q  </td><td> 1</td><td>0.5</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>X</td><td>45S </td><td>p  </td><td>NA</td><td> NA</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>3</td><td>DAPI</td><td>q  </td><td> 1</td><td>1.0</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>1</td><td>DAPI</td><td>cen</td><td>NA</td><td> NA</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>X</td><td>CMA </td><td>cen</td><td>NA</td><td> NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & chrName & markName & chrRegion & markSize & markDistCen\\\\\n",
       "  & <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 5S   & p   &  1 & 0.5\\\\\n",
       "\t2 & 1 & 45S  & q   &  1 & 0.5\\\\\n",
       "\t3 & X & 45S  & p   & NA &  NA\\\\\n",
       "\t4 & 3 & DAPI & q   &  1 & 1.0\\\\\n",
       "\t5 & 1 & DAPI & cen & NA &  NA\\\\\n",
       "\t6 & X & CMA  & cen & NA &  NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | chrName &lt;chr&gt; | markName &lt;chr&gt; | chrRegion &lt;chr&gt; | markSize &lt;dbl&gt; | markDistCen &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 1 | 5S   | p   |  1 | 0.5 |\n",
       "| 2 | 1 | 45S  | q   |  1 | 0.5 |\n",
       "| 3 | X | 45S  | p   | NA |  NA |\n",
       "| 4 | 3 | DAPI | q   |  1 | 1.0 |\n",
       "| 5 | 1 | DAPI | cen | NA |  NA |\n",
       "| 6 | X | CMA  | cen | NA |  NA |\n",
       "\n"
      ],
      "text/plain": [
       "  chrName markName chrRegion markSize markDistCen\n",
       "1 1       5S       p          1       0.5        \n",
       "2 1       45S      q          1       0.5        \n",
       "3 X       45S      p         NA        NA        \n",
       "4 3       DAPI     q          1       1.0        \n",
       "5 1       DAPI     cen       NA        NA        \n",
       "6 X       CMA      cen       NA        NA        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mark position data (inc. cen.) \n",
    "dfOfMarks2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cee561",
   "metadata": {},
   "source": [
    "## Holocentrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b8d4068",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(idiogramFISH)\n",
    "\n",
    "# load some package data.frames - optional\n",
    "data(dfChrSizeHolo, dfMarkColor, dfMarkPosHolo)\n",
    "\n",
    "# column Mbp not for plotting purposes\n",
    "dfChrSizeHolo$Mbp<-dfChrSizeHolo$chrSize*100\n",
    "\n",
    "# svg(\"testing.svg\",width=14,height=8 )\n",
    "par(mar = c(0, 0, 0, 0), omi=rep(0,4) )\n",
    "\n",
    "# png(\"dfOfChrSize.png\", width=500, height=400)\n",
    "options(repr.plot.width = 10, repr.plot.height = 6, repr.plot.res = 100)\n",
    "\n",
    "suppressMessages(\n",
    "\n",
    "plotIdiograms(dfChrSize  =dfChrSizeHolo, # data.frame of chr. size\n",
    "              dfMarkColor=dfMarkColor,   # df of mark style\n",
    "              dfMarkPos  =dfMarkPosHolo, # df of mark positions\n",
    "              \n",
    "              addOTUName=FALSE,        # do not add OTU names\n",
    "              distTextChr = 1,         # chr. name distance to chr.\n",
    "              chrSize = TRUE,          # show chr. size under chr.\n",
    "              chrSizeMbp = TRUE,       # show chr. size in Mbp under chr. requires Mbp column\n",
    "              \n",
    "              rulerPos=-0.1,           # position of ruler\n",
    "              rulerNumberPos=.9        # position of numbers of rulers\n",
    "              ,xPosRulerTitle = 3      # pos. of ruler title (units)\n",
    "              \n",
    "              ,xlimLeftMod=2           # modify xlim left argument of plot\n",
    "              ,ylimBotMod=.2           # modify ylim bottom argument of plot\n",
    "              ,legendHeight=.5         # height of legend labels\n",
    "              ,legendWidth = 1.2       # width of legend labels\n",
    "              ,xModifier = 20          # separ. among chromatids\n",
    "              )\n",
    "    )\n",
    "    #dev.off() # close svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec95c4c",
   "metadata": {},
   "source": [
    "### Let's explore the data.frames for holocentrics:\n",
    "\n",
    "* chromosome data, if only 1 species, column OTU is optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dc9f711b",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>chrName</th><th scope=col>chrSize</th><th scope=col>Mbp</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>3</td><td>300</td></tr>\n",
       "\t<tr><td>2</td><td>4</td><td>400</td></tr>\n",
       "\t<tr><td>3</td><td>2</td><td>200</td></tr>\n",
       "\t<tr><td>4</td><td>5</td><td>500</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 3\n",
       "\\begin{tabular}{lll}\n",
       " chrName & chrSize & Mbp\\\\\n",
       " <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 3 & 300\\\\\n",
       "\t 2 & 4 & 400\\\\\n",
       "\t 3 & 2 & 200\\\\\n",
       "\t 4 & 5 & 500\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 3\n",
       "\n",
       "| chrName &lt;dbl&gt; | chrSize &lt;dbl&gt; | Mbp &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 3 | 300 |\n",
       "| 2 | 4 | 400 |\n",
       "| 3 | 2 | 200 |\n",
       "| 4 | 5 | 500 |\n",
       "\n"
      ],
      "text/plain": [
       "  chrName chrSize Mbp\n",
       "1 1       3       300\n",
       "2 2       4       400\n",
       "3 3       2       200\n",
       "4 4       5       500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfChrSizeHolo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6af561ca",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 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>DAPI</td><td>blue          </td><td>square</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: 4 × 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 DAPI & blue           & square\\\\\n",
       "\t CMA  & darkgoldenrod1 & square\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 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",
       "| DAPI | blue           | square |\n",
       "| CMA  | darkgoldenrod1 | square |\n",
       "\n"
      ],
      "text/plain": [
       "  markName markColor      style \n",
       "1 5S       red            dots  \n",
       "2 45S      chartreuse3    square\n",
       "3 DAPI     blue           square\n",
       "4 CMA      darkgoldenrod1 square"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mark general data\n",
    "\n",
    "dfMarkColor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79d0f82e",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>chrName</th><th scope=col>markName</th><th scope=col>markPos</th><th scope=col>markSize</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&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>3</td><td>5S  </td><td>1.0</td><td>0.5</td></tr>\n",
       "\t<tr><td>3</td><td>DAPI</td><td>1.5</td><td>0.5</td></tr>\n",
       "\t<tr><td>1</td><td>45S </td><td>2.0</td><td>0.5</td></tr>\n",
       "\t<tr><td>2</td><td>DAPI</td><td>2.0</td><td>0.5</td></tr>\n",
       "\t<tr><td>4</td><td>CMA </td><td>2.0</td><td>0.5</td></tr>\n",
       "\t<tr><td>4</td><td>5S  </td><td>0.5</td><td>0.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{llll}\n",
       " chrName & markName & markPos & markSize\\\\\n",
       " <dbl> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 3 & 5S   & 1.0 & 0.5\\\\\n",
       "\t 3 & DAPI & 1.5 & 0.5\\\\\n",
       "\t 1 & 45S  & 2.0 & 0.5\\\\\n",
       "\t 2 & DAPI & 2.0 & 0.5\\\\\n",
       "\t 4 & CMA  & 2.0 & 0.5\\\\\n",
       "\t 4 & 5S   & 0.5 & 0.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| chrName &lt;dbl&gt; | markName &lt;chr&gt; | markPos &lt;dbl&gt; | markSize &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 3 | 5S   | 1.0 | 0.5 |\n",
       "| 3 | DAPI | 1.5 | 0.5 |\n",
       "| 1 | 45S  | 2.0 | 0.5 |\n",
       "| 2 | DAPI | 2.0 | 0.5 |\n",
       "| 4 | CMA  | 2.0 | 0.5 |\n",
       "| 4 | 5S   | 0.5 | 0.5 |\n",
       "\n"
      ],
      "text/plain": [
       "  chrName markName markPos markSize\n",
       "1 3       5S       1.0     0.5     \n",
       "2 3       DAPI     1.5     0.5     \n",
       "3 1       45S      2.0     0.5     \n",
       "4 2       DAPI     2.0     0.5     \n",
       "5 4       CMA      2.0     0.5     \n",
       "6 4       5S       0.5     0.5     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mark position data, if only 1 species, column OTU is optional (mandatory if in d.f of Chr. Size)\n",
    "\n",
    "dfMarkPosHolo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66fbbdfb",
   "metadata": {},
   "source": [
    "## Plotting both mono. and holo.\n",
    "\n",
    "Merge data.frames with `plyr` [@plyr2011]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86dc9426",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: plyr\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# chromosome data, if only 1 species, column OTU is optional\n",
    "require(plyr)\n",
    "dfOfChrSize$OTU   <- \"Species mono\"\n",
    "dfChrSizeHolo$OTU <- \"Species holo\"\n",
    " \n",
    "monoholoCS <- plyr::rbind.fill(dfOfChrSize,dfChrSizeHolo)\n",
    "\n",
    "dfOfMarks2$OTU     <-\"Species mono\"\n",
    "dfOfMarks2[which(dfOfMarks2$markName==\"5S\"),]$markSize<-.7\n",
    "dfMarkPosHolo$OTU <-\"Species holo\"\n",
    "\n",
    "monoholoMarks <- plyr::rbind.fill(dfOfMarks2,dfMarkPosHolo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac50d645",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 600
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(idiogramFISH)\n",
    "\n",
    "#svg(\"testing.svg\",width=14,height=10 )\n",
    "#png(\"monoholoCS.png\", width=700, height=600)\n",
    "options(repr.plot.width = 12, repr.plot.height = 10, repr.plot.res = 100)\n",
    "\n",
    "par(mar=rep(0,4))\n",
    "\n",
    "suppressMessages(\n",
    "\n",
    "plotIdiograms(dfChrSize  = monoholoCS,   # data.frame of chr. size\n",
    "              dfMarkColor= dfMarkColor,  # df of mark style\n",
    "              dfMarkPos  = monoholoMarks,# df of mark positions, includes cen. marks\n",
    "              \n",
    "              chrSize = TRUE,            # show chr. size under chr.\n",
    "              \n",
    "              squareness = 4,            # vertices squareness\n",
    "              cenFormat = \"triangle\",    # triangular cen.\n",
    "              addOTUName = TRUE,         # add OTU names\n",
    "              OTUTextSize = .7,          # font size of OTU\n",
    "              distTextChr = .5,          # separ. among chr. and text and among chr. name and indices\n",
    "              \n",
    "              karHeiSpace = 4,           # karyotype height inc. spacing\n",
    "              karIndexPos = .2,          # move karyotype index\n",
    "              \n",
    "              legendHeight= 1,           # height of legend labels\n",
    "              legendWidth = 1,           # width of legend labels\n",
    "              fixCenBorder = TRUE,       # use chrColor as border color of cen. or cen. marks\n",
    "              \n",
    "              rulerPos= 0,               # position of ruler\n",
    "              ruler.tck=-0.02,           # size and orientation of ruler ticks\n",
    "              rulerNumberPos=.9,         # position of numbers of rulers\n",
    "              xPosRulerTitle = 3.5,      # ruler title (units) position\n",
    "              \n",
    "              xlimLeftMod=1,             # modify xlim left argument of plot\n",
    "              xlimRightMod=3,            # modify xlim right argument of plot\n",
    "              ylimBotMod= .2             # modify ylim bottom argument of plot\n",
    "              \n",
    "              ,chromatids=FALSE          # do not show separ. chromatids\n",
    "              \n",
    "              # ,useOneDot=TRUE\n",
    "              \n",
    "              # ,circularPlot = TRUE       # circularPlot\n",
    "              # ,shrinkFactor = .9         # percentage 1 = 100% of circle with chr.\n",
    "              # ,circleCenter = 3          # X coordinate of circleCenter (affects legend pos.)\n",
    "              # ,chrLabelSpacing = .9      # chr. names spacing\n",
    "              \n",
    "              # ,OTUsrt = 0                # angle for OTU name (or number)\n",
    "              # ,OTUplacing = \"number\"     # Use number and legend instead of name\n",
    "              # ,OTULabelSpacerx = -0.6    # modify position of OTU label, when OTUplacing=\"number\" or \"simple\"\n",
    "              # ,OTUlegendHeight = 1.5     # space among OTU names when in legend - OTUplacing\n",
    "              # ,separFactor = 0.75        # alter separ. of kar.\n",
    ")\n",
    ")\n",
    "#dev.off() # close png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9abcf20b-5540-4fe9-a833-e2648459cc74",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(idiogramFISH)\n",
    "\n",
    "#svg(\"testing.svg\",width=14,height=10 )\n",
    "#png(\"monoholoCS.png\", width=700, height=600)\n",
    "options(repr.plot.width = 6, repr.plot.height = 6, repr.plot.res = 100)\n",
    "\n",
    "par(mar=rep(0,4))\n",
    "\n",
    "suppressMessages(\n",
    "\n",
    "plotIdiograms(dfChrSize  = monoholoCS,   # data.frame of chr. size\n",
    "              dfMarkColor= dfMarkColor,  # df of mark style\n",
    "              dfMarkPos  = monoholoMarks,# df of mark positions, includes cen. marks\n",
    "              \n",
    "              chrSize = TRUE,            # show chr. size under chr.\n",
    "              \n",
    "              squareness = 4,            # vertices squareness\n",
    "              addOTUName = TRUE,         # add OTU names\n",
    "              OTUTextSize = .7,          # font size of OTU\n",
    "              distTextChr = .5,          # separ. among chr. and text and among chr. name and indices\n",
    "              \n",
    "              legendHeight= 1,           # height of legend labels\n",
    "              legendWidth = 1,           # width of legend labels\n",
    "              fixCenBorder = TRUE,       # use chrColor as border color of cen. or cen. marks\n",
    "              \n",
    "              xlimLeftMod=-2,            # modify xlim left argument of plot\n",
    "              xlimRightMod=2,            # modify xlim right argument of plot\n",
    "              ylimBotMod= .2,            # modify ylim bottom argument of plot\n",
    "              ylimTopMod=-2\n",
    "              \n",
    "              ,chromatids=FALSE          # do not show separ. chromatids\n",
    "              \n",
    "              ,useOneDot=TRUE\n",
    "              \n",
    "              ,circularPlot = TRUE       # circularPlot\n",
    "              ,shrinkFactor = .9         # percentage 1 = 100% of circle with chr.\n",
    "              ,circleCenter = 3          # X coordinate of circleCenter (affects legend pos.)\n",
    "              ,chrLabelSpacing = .9      # chr. names spacing\n",
    "              \n",
    "              ,OTUsrt = 0                # angle for OTU name (or number)\n",
    "              ,OTUplacing = \"number\"     # Use number and legend instead of name\n",
    "              ,OTULabelSpacerx = -0.6    # modify position of OTU label, when OTUplacing=\"number\" or \"simple\"\n",
    "              ,OTUlegendHeight = 1.5     # space among OTU names when in legend - OTUplacing\n",
    "              ,separFactor = 0.75        # alter separ. of kar.\n",
    ")\n",
    ")\n",
    "#dev.off() # close png"
   ]
  }
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