{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot all genotypes/global divergence tree\n",
    "\n",
    "In order to show where the divergent genotype G viruses cluster with other viruses and where the other genotypes land on the tree, I ran an extra nextstrain tree with all genomes we generated and would like to plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, subprocess, glob, os, shutil, re, importlib,json\n",
    "from subprocess import call\n",
    "import imp\n",
    "bt = imp.load_source('baltic', '../baltic/baltic/baltic.py')\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.patheffects as path_effects\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import matplotlib.colors as clr\n",
    "import textwrap as textwrap\n",
    "from textwrap import wrap\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.special import binom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "mpl.rc('text', usetex='false') \n",
    "mpl.rcParams.update({'font.size': 22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_tree_json(tree_path):\n",
    "    \n",
    "    with open(tree_path, \"r\") as json_file:\n",
    "        tree_json = json.load(json_file)\n",
    "    tree_object=tree_json['tree']\n",
    "    meta=tree_json['meta']\n",
    "    json_translation={'absoluteTime':lambda k: k.traits['node_attrs']['div'],'name':'name'} ## allows baltic to find correct attributes in JSON, height and name are required at a minimum\n",
    "\n",
    "    tree=bt.loadJSON(tree_object,json_translation)\n",
    "    \n",
    "    return(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in metadata dictionary\n",
    "def generate_metadata_dictionary(metadata_path):\n",
    "    metadata = {}\n",
    "\n",
    "    with open(metadata_path, \"r\") as infile: \n",
    "        for line in infile: \n",
    "            if \"MuV_genotype\" not in line:\n",
    "                strain = line.split(\"\\t\")[0].replace(\"?\",\"_\")  #iqtree will do this replacement\n",
    "                division = line.split(\"\\t\")[6]\n",
    "                region = line.split(\"\\t\")[4]\n",
    "                country = line.split(\"\\t\")[5]\n",
    "                date = line.split(\"\\t\")[3]\n",
    "                if date == \"?\":\n",
    "                    date1 = \"XXXX-XX-XX\"\n",
    "                else:\n",
    "                    date1 = date\n",
    "\n",
    "                metadata[strain] = {\"division\":division, \"date\":date1, \"country\":country, \"region\":region}\n",
    "    metadata[\"KM597072.1\"] = {\"division\":\"reference\", \"date\":\"2013-XX-XX\", \"country\":\"reference\", \"region\":\"Africa\"}\n",
    "    return(metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"using baltic's subtree function, enumerate the subtree descending from the supplied node\"\"\"\n",
    "\n",
    "def return_subtree(node, tree):\n",
    "    subtree=tree.subtree(node) ## this function returns a new baltic object that contains a trait-traversed subtree, starting from node k, for as long as the traversal stays within the starting trait value state\n",
    "            \n",
    "    if subtree != None:\n",
    "        subtree.traverse_tree()\n",
    "        subtree.sortBranches()\n",
    "        return(subtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_full_genome_divergence_tree_triangle(tree,metadata,colors,division_order,output_name, tips_list1,genotype_G_node):\n",
    "    fig,ax = plt.subplots(figsize=(10,12),facecolor='w')\n",
    "\n",
    "    divergence = [0,0.01,0.02,0.03,0.04,0.05]\n",
    "    #[ax.axvline(i,ls='--',lw=2,color='grey',zorder=0, alpha=0.6) for i in divergence]\n",
    "\n",
    "    # # this sets the vertical dashed lines on the tree; plot a dashed line every other year from 1990 to 2020\n",
    "    branchWidth=2 ## default branch width\n",
    "    tipSize = 40\n",
    "\n",
    "    # k objects are tips, nodes, branches\n",
    "    for k in tree.Objects: ## iterate over objects in tree\n",
    "        x=k.traits['node_attrs']['div']\n",
    "        y=k.y \n",
    "\n",
    "        if x==None: ## matplotlib won't plot Nones, like root\n",
    "            x=0.0\n",
    "        if 'node_attrs' in k.parent.traits:\n",
    "            xp=k.parent.traits['node_attrs']['div'] ## get x position of current object's parent\n",
    "        else:\n",
    "            xp = x\n",
    "        \n",
    "        #### clades are classified as leaf objects\n",
    "        if isinstance(k,bt.leaf) or k.branchType=='leaf': ## if leaf...\n",
    "            \n",
    "            # first, are you a clade? \n",
    "            if isinstance(k, bt.clade):\n",
    "                if k.numName == \"G_clade1\":\n",
    "                    c=colors['G_clade1']\n",
    "                    s=15\n",
    "                    z=11\n",
    "                    label = \"North American\\nmumps genomes\\nshown Figure 2\"\n",
    "                    \n",
    "                end_x=k.traits['node_attrs']['div'] ## get beginning of triangle\n",
    "                start_x=k.lastHeight ## get height of last child in clade\n",
    "                lower_left=[start_x,y-0.0005*len(tree.Objects)]\n",
    "                upper_left=[start_x,y+0.0005*len(tree.Objects)]\n",
    "                upper_right=[end_x,y+k.width/3.0]\n",
    "                lower_right=[end_x,y-k.width/3.0]\n",
    "    \n",
    "                clade=plt.Polygon((lower_left,upper_left,upper_right,lower_right),facecolor=c,edgecolor='k',zorder=12) ## define a triangl\n",
    "                #clade=plt.Polygon(([x,y-0.001*len(tree.Objects)],[x,y+0.001*len(tree.Objects)],[k.lastHeight,y+k.width/3.0],[k.lastHeight,y-k.width/3.0]),facecolor=c,edgecolor='k',zorder=12) ## define a triangle polygon\n",
    "                ax.add_patch(clade)\n",
    "                ax.text(x+0.002, y-3, label, fontsize=18)\n",
    "   \n",
    "            \n",
    "            # no? I guess you are a normal leaf then\n",
    "            else:\n",
    "                division = metadata[k.numName]['division'].lower().replace(\" \",\"_\")\n",
    "                country = metadata[k.numName]['country'].lower().replace(\" \",\"_\")\n",
    "                region = metadata[k.numName]['region'].lower().replace(\" \",\"_\")\n",
    "\n",
    "                if division == \"washington\":\n",
    "                    region = \"washington\"\n",
    "                elif \"asia\" in region or \"japan_korea\" in region or \"china\" in region:\n",
    "                    region = \"asia\"\n",
    "                else:\n",
    "                    region = region\n",
    "\n",
    "                c=colors[region]\n",
    "            \n",
    "                if metadata[k.numName]['division'].lower() == \"reference\":\n",
    "                    s=0\n",
    "                    z=0\n",
    "                elif division == \"washington\": \n",
    "                    s=tipSize ## tip size can be fixed\n",
    "                    z=12\n",
    "                else:\n",
    "                    s=tipSize\n",
    "                    z=11\n",
    "                \n",
    "                ax.scatter(x,y,s=s,facecolor=c,edgecolor='none',zorder=z) ## plot circle for every tip\n",
    "                ax.scatter(x,y,s=s+0.8*s,facecolor='k',edgecolor='none',zorder=10) ## plot black circle underneath\n",
    "\n",
    "        elif isinstance(k,bt.node) or k.branchType=='node': ## if node...\n",
    "            c=\"#696969\"\n",
    "            \n",
    "            if k == genotype_G_node:\n",
    "                label = \"Genotype\\n   G\"\n",
    "                ax.plot([x,x],[k.children[-1].y,k.children[0].y],lw=branchWidth,color=c,ls='-',zorder=9) #color=node_colors[node_types[k][\"node_community_status\"]]\n",
    "                ax.scatter(x,y,s=100,facecolor=\"black\",edgecolor='none',zorder=z) ## plot a circle at the genotype G marker\n",
    "                ax.text(x-0.008, y-6, label, fontsize=18)\n",
    "                \n",
    "            else:\n",
    "                # this is the vertical line\n",
    "                ax.plot([x,x],[k.children[-1].y,k.children[0].y],lw=branchWidth,color=c,ls='-',zorder=9) #color=node_colors[node_types[k][\"node_community_status\"]]\n",
    "        \n",
    "        # this is the horizonal lines connecting the tips to other parts of the tree\n",
    "        ax.plot([xp,x],[y,y],lw=branchWidth,color=c,ls='-',zorder=9)\n",
    "\n",
    "        # add in a legend\n",
    "        han_list = []\n",
    "\n",
    "\n",
    "        # bbox to anchor puts a bounding box around where you want the legend to go, prop part is for text size\n",
    "        #ax.legend(handles = han_list, markerfirst = True, frameon=False, bbox_to_anchor=[0.8, 1], loc=2, prop={'size': 24})\n",
    "    for key in division_order:\n",
    "        marker = mlines.Line2D(range(1), range(1), color = colors[key], marker='o', markerfacecolor = colors[key], label = key.replace(\"_\",\" \").title().replace(\"Usa\",\"USA\").replace(\"And\",\"and\"), markersize = 12)\n",
    "        han_list.append(marker)\n",
    "\n",
    "    # set axis limits, remove border lines         \n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "#     ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "    ax.set_xlim(0,0.012)\n",
    "    ax.set_ylim(-5,tree.ySpan+5)\n",
    "    ax.tick_params(axis='y',labelsize=0,size=0)\n",
    "    ax.tick_params(axis='x',labelsize=24,size=5, width=2,color='grey')\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticks(divergence)\n",
    "    ax.set_xlabel(\"\\nDivergence (substitutions per site)\", fontsize=24)\n",
    "\n",
    "    # in order to get the legend to plot without being transparent, over the plot, it needs to be here with frame set to true\n",
    "    # bbox arguments are: x, y, with 0 being furthest left and bottom\n",
    "    ax.legend(handles = han_list, markerfirst = True, edgecolor=\"white\", framealpha=1, bbox_to_anchor=[-0.05, 0.02], loc=3,prop={'size': 24}, facecolor='w')\n",
    "\n",
    "    fig.tight_layout()\n",
    "    plt.gcf().subplots_adjust(right=0.88)\n",
    "    plt.savefig(output_name)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run \n",
    "\n",
    "In Figtree, I manually rooted the tree (midpoint) and ordered the nodes to be in descending order. Then, export as newick and check \"export as displayed\" or something like that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the current date \n",
    "from datetime import date\n",
    "today = date.today()\n",
    "current_date = str(today.strftime(\"%Y-%m-%d\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try instead, clustering into regions and plotting it that way; we could do: west, 2 midwests, 2 souths, northeast\n",
    "\n",
    "colors = {\"washington\":\"#2664A5\",\n",
    "          \"north_america\":\"#93B2D2\",\n",
    "          \"asia\":\"#EEA160\",\n",
    "          \"china\":\"#5CA7A4\",\n",
    "          \"oceania\":\"#CF7E86\",\n",
    "         \"europe\":\"#5CA7A4\",#\"#544370\", \n",
    "         \"africa\":\"#B2313D\",\n",
    "         \"G_clade1\":\"#93B2D2\",\n",
    "         \"G_clade2\":\"#93B2D2\"}\n",
    "\n",
    "uncertainty_color = \"#B9B9B9\"\n",
    "\n",
    "\n",
    "division_order = [\"washington\",\"north_america\",\"africa\",\"europe\",\"asia\",\"oceania\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_path = \"../auspice/mumps_global.json\"\n",
    "metadata_path = \"../auspice/metadata.tsv\"\n",
    "output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/individual-PDFs/global-all-genomes-divergence-tree-\"+current_date+\".pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clade\n"
     ]
    }
   ],
   "source": [
    "all_figure2 = [\"Massachusetts.USA/26.15/1/G\",\"NewJersey.USA/49.14/G\",\"Virginia.USA/51.14/G\",\"Pennsylvania.USA/51.14/G\",\"NewYork/10.14/2/G\",\"NewYork.USA/51.14/G\",\"MountIsa.AUS/10.17/G\",\"BritishColumbia.CAN/34.16/3/G\",\"BritishColumbia.CAN/33.16/3/G\",\"BritishColumbia.CAN/36.16/G\",\"BritishColumbia.CAN/16.16/4/G\",\"BritishColumbia.CAN/28.16/5/G\",\"BritishColumbia.CAN/31.16/2/G\",\"BritishColumbia.CAN/17.16/G\",\"BritishColumbia.CAN/27.16/3/G\",\"BritishColumbia.CAN/29.16/4/G\",\"BritishColumbia.CAN/24.16/3/G\",\"BritishColumbia.CAN/25.16/3/G\",\"BritishColumbia.CAN/26.16/5/G\",\"BritishColumbia.CAN/26.16/1/G\",\"BritishColumbia.CAN/28.16/6/G\",\"BritishColumbia.CAN/29.16/5/G\",\"BritishColumbia.CAN/28.16/1/G\",\"BritishColumbia.CAN/25.16/5/G\",\"BritishColumbia.CAN/21.16/2/G\",\"BritishColumbia.CAN/21.16/6/G\",\"BritishColumbia.CAN/27.16/7/G\",\"BritishColumbia.CAN/29.16/3/G\",\"BritishColumbia.CAN/22.16/1/G\",\"BritishColumbia.CAN/30.16/3/G\",\"BritishColumbia.CAN/21.16/3/G\",\"BritishColumbia.CAN/24.16/4/G\",\"BritishColumbia.CAN/31.16/4/G\",\"BritishColumbia.CAN/29.16/6/G\",\"BritishColumbia.CAN/42.16/G\",\"BritishColumbia.CAN/24.16/7/G\",\"BritishColumbia.CAN/23.16/2/G\",\"BritishColumbia.CAN/24.16/1/G\",\"BritishColumbia.CAN/30.16/2/G\",\"BritishColumbia.CAN/24.16/5/G\",\"BritishColumbia.CAN/30.16/1/G\",\"BritishColumbia.CAN/31.16/1/G\",\"BritishColumbia.CAN/27.16/_/_\",\"BritishColumbia.CAN/13.16/G\",\"BritishColumbia.CAN/33.16/2/G\",\"BritishColumbia.CAN/20.16/1/G\",\"BritishColumbia.CAN/18.16/G\",\"BritishColumbia.CAN/28.16/4/G\",\"BritishColumbia.CAN/27.16/4/G\",\"BritishColumbia.CAN/19.16/3/G\",\"BritishColumbia.CAN/18.16/3/G\",\"BritishColumbia.CAN/19.16/2/G\",\"BritishColumbia.CAN/22.16/2/G\",\"BritishColumbia.CAN/24.16/2/G\",\"BritishColumbia.CAN/18.16/4/G\",\"BritishColumbia.CAN/18.16/2/G\",\"BritishColumbia.CAN/19.16/5/G\",\"BritishColumbia.CAN/23.16/1/G\",\"BritishColumbia.CAN/19.16/1/G\",\"BritishColumbia.CAN/21.16/4/G\",\"Illinois.USA/26.15/2/G\",\"Missouri.USA/29.15/FH114/G\",\"Wisconsin.USA/42.15/FH115/G\",\"Wisconsin.USA/2.16/FH118/G\",\"Massachusetts.USA/12.16/5/G\",\"Michigan.USA/4.16/G\",\"Ohio.USA/19.16/FH119/G\",\"Wisconsin.USA/19.16/FH121/G\",\"Wisconsin.USA/24.16/FH120/G\",\"Massachusetts.USA/21.16/1/G\",\"Louisiana.USA/29.17/G\",\"Texas.USA/27.17/G\",\"Massachusetts.USA/46.16/4/G\",\"Massachusetts.USA/46.16/2/G\",\"Texas.USA/18.17/G\",\"Texas.USA/15.17/G\",\"Massachusetts.USA/45.16/7/G\",\"Massachusetts.USA/45.16/5/G\",\"Ohio.USA/46.17/FH157/G\",\"NorthCarolina.USA/11.17/FH136/G\",\"Alabama.USA/50.17/FH156/G\",\"BritishColumbia.CAN/8.17/3/G\",\"Wisconsin.USA/42.15/FH116/G\",\"Wisconsin.USA/46.15/FH117/G\",\"Wisconsin.USA/51.15/FH163/G\",\"Massachusetts.USA/19.16/6/G\",\"Massachusetts.USA/16.16/12/G\",\"Pennsylvania.USA/19.16/G\",\"NewHampshire.USA/8.16/1/G\",\"Massachusetts.USA/10.16/14/G\",\"Massachusetts.USA/13.16/3/G\",\"Massachusetts.USA/11.16/6/G\",\"Massachusetts.USA/14.16/3/G\",\"Massachusetts.USA/11.16/2/G\",\"Massachusetts.USA/9.16/3/G\",\"Massachusetts.USA/11.16/1/G\",\"Massachusetts.USA/10.16/5/G\",\"Massachusetts.USA/10.16/6/G\",\"Massachusetts.USA/9.16/2/G\",\"Massachusetts.USA/12.16/1/G\",\"Massachusetts.USA/13.16/2/G\",\"Massachusetts.USA/10.16/10/G\",\"Washington.USA/4.17/FH48/G\",\"Washington.USA/12.17/FH76/G\",\"Springdale_745/2016/G\",\"Springdale_754/2016/G\",\"Springdale_730/2016/G\",\"Arkansas.USA/47.16/G\",\"Arkansas.USA/41.16/G\",\"Arkansas.USA/5.17/G\",\"Ohio.USA/2.17/FH128/G\",\"Ohio.USA/7.17/FH130/G\",\"Arkansas.USA/49.16/G\",\"Arkansas.USA/50.16/G\",\"Arkansas.USA/52.16/G\",\"Washington.USA/16.17/FH63/G\",\"Washington.USA/17.17/FH38/G\",\"Washington.USA/16.17/FH36/G\",\"Arkansas.USA/12.17/G\",\"Arkansas.USA/48.16/G\",\"Arkansas.USA/13.17/G\",\"Arkansas.USA/51.16/G\",\"Arkansas.USA/43.16/G\",\"Arkansas.USA/44.16/G\",\"Kansas.USA/8.17/G\",\"Wisconsin.USA/50.16/FH126/G\",\"Washington.USA/18.17/FH147/G\",\"Washington.USA/15.17/FH146/G\",\"Washington.USA/2.17/FH84/G\",\"Washington.USA/11.17/FH143/G\",\"Washington.USA/3.17/FH62/G\",\"Washington.USA/3.17/FH67/G\",\"Washington.USA/12.17/FH144/G\",\"Washington.USA/7.17/FH15/G\",\"Arkansas.USA/46.16/G\",\"BritishColumbia.CAN/18.17/2/G\",\"Washington.USA/50.16/FH3/G\",\"Washington.USA/5.17/FH27/G\",\"Washington.USA/4.17/FH68/G\",\"Washington.USA/51.16/FH41/G\",\"Washington.USA/1.17/FH7/G\",\"Washington.USA/2.17/FH47/G\",\"Arkansas.USA/45.16/G\",\"Washington.USA/6.17/FH12/G\",\"Washington.USA/5.17/FH72/G\",\"Washington.USA/29.17/FH83/G\",\"Washington.USA/19.17/FH80/G\",\"Arkansas.USA/38.16/G\",\"Arkansas.USA/40.16/G\",\"Washington.USA/1.17/FH29/G\",\"Washington.USA/11.17/FH56/G\",\"Washington.USA/3.17/FH24/G\",\"Washington.USA/6.17/FH13/G\",\"Washington.USA/4.17/FH49/G\",\"Washington.USA/49.16/FH45/G\",\"Washington.USA/50.16/FH2/G\",\"Washington.USA/15.17/FH20/G\",\"Washington.USA/20.17/FH73/G\",\"Washington.USA/21.17/FH69/G\",\"Washington.USA/17.17/FH39/G\",\"Washington.USA/20.17/FH148/G\",\"Washington.USA/23.17/FH150/G\",\"Washington.USA/30.17/FH32/G\",\"Washington.USA/23.17/FH149/G\",\"Washington.USA/1.17/FH160/G\",\"Washington.USA/50.16/FH4/G\",\"Washington.USA/7.17/FH106/G\",\"Washington.USA/12.17/FH97/G\",\"Washington.USA/50.16/FH33/G\",\"Washington.USA/2.17/FH66/G\",\"Washington.USA/49.16/FH55/G\",\"Washington.USA/48.16/FH1/G\",\"Washington.USA/49.16/FH158/G\",\"Washington.USA/4.17/FH60/G\",\"Washington.USA/1.17/FH77/G\",\"Washington.USA/10.17/FH43/G\",\"Washington.USA/9.17/FH42/G\",\"Washington.USA/52.16/FH75/G\",\"Washington.USA/1.17/FH5/G\",\"Washington.USA/2.17/FH26/G\",\"Washington.USA/5.17/FH10/G\",\"Washington.USA/51.16/FH46/G\",\"Washington.USA/5.17/FH11/G\",\"Washington.USA/4.17/FH103/G\",\"Washington.USA/20.17/FH82/G\",\"Washington.USA/20.17/FH81/G\",\"Washington.USA/7.17/FH61/G\",\"Washington.USA/2.17/FH21/G\",\"Washington.USA/7.17/FH166/G\",\"Washington.USA/5.17/FH161/G\",\"Washington.USA/5.17/FH30/G\",\"Washington.USA/2.17/FH22/G\",\"Washington.USA/5.17/FH162/G\",\"Washington.USA/12.17/FH34/G\",\"Washington.USA/2.17/FH23/G\",\"Washington.USA/3.17/FH59/G\",\"Washington.USA/4.17/FH70/G\",\"Washington.USA/1.17/FH65/G\",\"Washington.USA/5.17/FH9/G\",\"Washington.USA/3.17/FH25/G\",\"Washington.USA/22.17/FH74/G\",\"Washington.USA/16.17/FH152/G\",\"Washington.USA/10.17/FH52/G\",\"Washington.USA/4.17/FH71/G\",\"Washington.USA/52.16/FH28/G\",\"Washington.USA/2.17/FH54/G\",\"Washington.USA/1.17/FH6/G\",\"Washington.USA/12.17/FH57/G\",\"Washington.USA/2.17/FH58/G\",\"Washington.USA/1.17/FH8/G\",\"Washington.USA/8.17/FH17/G\",\"Washington.USA/9.17/FH31/G\",\"Washington.USA/9.17/FH18/G\",\"Washington.USA/50.16/FH64/G\",\"Washington.USA/6.17/FH14/G\",\"Washington.USA/6.17/FH105/G\",\"Washington.USA/8.17/FH19/G\",\"Washington.USA/12.17/FH35/G\",\"Massachusetts.USA/20.16/2/G\",\"Massachusetts.USA/37.15/1/G\",\"Ohio.USA/2.18/FH102/G\",\"Massachusetts.USA/46.16/5/G\",\"Montana.USA/11.16/G\",\"Massachusetts.USA/52.16/G\",\"Ontario.CAN/09.17/4/G\",\"Ontario.CAN/06.17/1/G\",\"Ontario.CAN/12.17/4/G\",\"Ontario.CAN/09.17/9/G\",\"Ontario.CAN/06.17/3/G\",\"Ontario.CAN/06.17/4/G\",\"BritishColumbia.CAN/8.17/5/G\",\"BritishColumbia.CAN/8.17/2/G\",\"BritishColumbia.CAN/8.17/8/G\",\"Washington.USA/11.17/FH96/G\",\"Ontario.CAN/07.17/1/G\",\"Ontario.CAN/09.17/2/G\",\"Ontario.CAN/08.17/2/G\",\"Ontario.CAN/08.17/3/G\",\"Ontario.CAN/13.17/2/G\",\"BritishColumbia.CAN/11.16/G\",\"Massachusetts.USA/13.16/4/G\",\"Rockhampton.AUS/41.16/G\",\"Washington.USA/16.17/FH79/G\",\"Missouri.USA/15.17/FH135/G\",\"Manitoba.CAN/43.16/11/G\",\"Manitoba.CAN/44.16/15/G\",\"Manitoba.CAN/43.16/5/G\",\"Manitoba.CAN/47.16/4/G\",\"Washington.USA/19.17/FH53/G\",\"Ontario.CAN/12.17/5/G\",\"Ontario.CAN/09.17/3/G\",\"Ontario.CAN/09.17/7/G\",\"Ontario.CAN/04.17/1/G\",\"Ontario.CAN/12.17/3/G\",\"Ontario.CAN/09.17/8/G\",\"Ontario.CAN/06.17/2/G\",\"Ontario.CAN/09.17/6/G\",\"Ontario.CAN/12.17/2/G\",\"Ontario.CAN/12.17/1/G\",\"Washington.USA/17.17/FH40/G\",\"Ontario.CAN/09.17/10/G\",\"Ontario.CAN/13.17/1/G\",\"Ontario.CAN/09.17/1/G\",\"Massachusetts.USA/27.16/1/G\",\"Massachusetts.USA/30.16/1/G\",\"Massachusetts.USA/45.16/2/G\",\"Massachusetts.USA/45.16/4/G\",\"Massachusetts.USA/50.16/1/G\",\"Massachusetts.USA/43.16/1/G\",\"Massachusetts.USA/45.16/3/G\",\"Massachusetts.USA/42.16/1/G\",\"Massachusetts.USA/45.16/1/G\",\"Massachusetts.USA/46.16/7/G\",\"Massachusetts.USA/44.16/1/G\",\"Massachusetts.USA/43.16/2/G\",\"Massachusetts.USA/39.16/1/G\",\"Massachusetts.USA/46.16/1/G\",\"Massachusetts.USA/46.16/6/G\",\"Massachusetts.USA/10.16/3/G\",\"Massachusetts.USA/17.16/7/G\",\"Massachusetts.USA/12.16/3/G\",\"Massachusetts.USA/10.16/13/G\",\"Massachusetts.USA/8.16/1/G\",\"Massachusetts.USA/19.16/10/G\",\"Massachusetts.USA/11.16/3/G\",\"Massachusetts.USA/11.16/5/G\",\"Massachusetts.USA/16.16/13/G\",\"Massachusetts.USA/17.16/1/G\",\"Massachusetts.USA/17.16/10/G\",\"Massachusetts.USA/17.16/11/G\",\"Massachusetts.USA/10.16/2/G\",\"Massachusetts.USA/16.16/1/G\",\"Massachusetts.USA/12.16/4/G\",\"Massachusetts.USA/22.16/2/G\",\"Massachusetts.USA/17.16/8/G\",\"Massachusetts.USA/11.16/4/G\",\"Massachusetts.USA/8.16/3/G\",\"Massachusetts.USA/10.16/7/G\",\"Massachusetts.USA/10.16/8/G\",\"Massachusetts.USA/12.16/2/G\",\"Massachusetts.USA/14.16/1/G\",\"Massachusetts.USA/13.16/1/G\",\"Massachusetts.USA/16.16/4/G\",\"Massachusetts.USA/9.16/4/G\",\"Massachusetts.USA/8.16/5/G\",\"Massachusetts.USA/10.16/4/G\",\"Massachusetts.USA/10.16/9/G\",\"Massachusetts.USA/10.16/1/G\",\"Massachusetts.USA/8.16/2/G\",\"Massachusetts.USA/16.16/9/G\",\"Massachusetts.USA/14.16/2/G\",\"Massachusetts.USA/10.16/12/G\",\"Massachusetts.USA/8.16/6/G\",\"Massachusetts.USA/9.16/1/G\",\"Massachusetts.USA/17.16/12/G\",\"Massachusetts.USA/15.16/2/G\",\"Massachusetts.USA/15.16/1/G\",\"Massachusetts.USA/17.16/2/G\",\"Massachusetts.USA/17.16/6/G\",\"Massachusetts.USA/19.16/1/G\",\"Massachusetts.USA/17.16/3/G\",\"Massachusetts.USA/16.16/2/G\",\"Massachusetts.USA/16.16/5/G\",\"Massachusetts.USA/16.16/16/G\",\"Massachusetts.USA/19.16/7/G\",\"Massachusetts.USA/17.16/13/G\",\"Massachusetts.USA/19.16/8/G\",\"Massachusetts.USA/18.16/2/G\",\"Massachusetts.USA/19.16/9/G\",\"Massachusetts.USA/16.16/6/G\",\"Massachusetts.USA/17.16/4/G\",\"Massachusetts.USA/16.16/14/G\",\"Massachusetts.USA/16.16/7/G\",\"Massachusetts.USA/16.16/15/G\",\"Massachusetts.USA/19.16/3/G\",\"Massachusetts.USA/16.16/19/G\",\"Massachusetts.USA/16.16/3/G\",\"Massachusetts.USA/18.16/3/G\",\"Massachusetts.USA/16.16/18/G\",\"Massachusetts.USA/18.16/1/G\",\"Massachusetts.USA/22.16/1/G\",\"Massachusetts.USA/16.16/10/G\",\"Massachusetts.USA/20.16/1/G\",\"Massachusetts.USA/19.16/5/G\",\"Massachusetts.USA/17.16/9/G\",\"Massachusetts.USA/19.16/2/G\",\"Massachusetts.USA/18.16/4/G\",\"Massachusetts.USA/19.16/4/G\",\"Massachusetts.USA/25.17/4/G\",\"Massachusetts.USA/25.17/3/G\",\"Massachusetts.USA/22.17/3/G\",\"Massachusetts.USA/13.17/2/G\",\"Massachusetts.USA/22.17/6/G\",\"Massachusetts.USA/26.17/G\",\"Massachusetts.USA/24.17/3/G\",\"Massachusetts.USA/22.17/G\",\"Massachusetts.USA/25.17/2/G\",\"Massachusetts.USA/22.17/5/G\",\"Massachusetts.USA/21.17/2/G\",\"Massachusetts.USA/25.17/7/G\",\"Massachusetts.USA/22.17/8/G\",\"Georgia.USA/13.17/G\",\"Massachusetts.USA/24.17/G\",\"Massachusetts.USA/22.17/2/G\",\"Massachusetts.USA/24.17/4/G\",\"Massachusetts.USA/19.17/G\",\"Massachusetts.USA/19.17/3/G\",\"Massachusetts.USA/25.17/G\",\"Massachusetts.USA/24.17/2/G\",\"Massachusetts.USA/21.17/G\",\"Massachusetts.USA/22.17/4/G\",\"Massachusetts.USA/19.17/4/G\",\"Massachusetts.USA/16.16/17/G\",\"Massachusetts.USA/16.16/8/G\",\"Massachusetts.USA/19.16/11/G\",\"Massachusetts.USA/22.16/3/G\",\"NewHampshire.USA/40.16/G\",\"Missouri.USA/32.16/FH88/G\",\"Massachusetts.USA/33.16/1/G\",\"Massachusetts.USA/27.16/2/G\",\"Indiana.USA/48.16/G\",\"Washington.USA/17.17/FH37/G\",\"Missouri.USA/2.17/FH127/G\",\"Missouri.USA/46.16/FH124/G\",\"Washington.USA/5.17/FH50/G\",\"Missouri.USA/46.16/FH123/G\",\"Missouri.USA/7.17/FH129/G\",\"Missouri.USA/50.16/FH125/G\",\"Kansas.USA/13.17/2/G\",\"Indiana.USA/1.17/G\",\"Indiana.USA/49.16/G\",\"Illinois.USA/14.17/G\",\"Missouri.USA/19.17/FH140/G\",\"NorthDakota.USA/15.17/G\",\"NorthDakota.USA/14.17/G\",\"NorthDakota.USA/9.17/G\",\"Massachusetts.USA/14.17/2/G\",\"Kansas.USA/10.17/G\",\"Kansas.USA/8.17/4/G\",\"Kansas.USA/9.17/2/G\",\"Kansas.USA/15.17/G\",\"Washington.USA/6.17/FH51/G\",\"Alabama.USA/11.17/FH90/G\",\"Alabama.USA/19.17/FH92/G\",\"Missouri.USA/24.17/FH165/G\",\"Missouri.USA/33.17/FH155/G\",\"Massachusetts.USA/8.17/G\",\"Massachusetts.USA/14.17/G\",\"Missouri.USA/41.16/FH122/G\",\"Alabama.USA/7.17/FH164/G\",\"Alabama.USA/15.17/FH139/G\",\"Wisconsin.USA/19.17/FH141/G\",\"Missouri.USA/28.17/FH101/G\",\"Wisconsin.USA/15.17/FH89/G\",\"Missouri.USA/11.17/FH133/G\",\"Massachusetts.USA/23.17/G\",\"Illinois.USA/11.17/G\",\"Massachusetts.USA/22.17/9/G\",\"Massachusetts.USA/17.17/G\",\"Massachusetts.USA/25.17/6/G\",\"Massachusetts.USA/25.17/5/G\",\"Washington.USA/19.17/FH44/G\",\"Missouri.USA/15.17/FH138/G\",\"Missouri.USA/15.17/FH134/G\",\"Ohio.USA/11.17/FH132/G\",\"Wisconsin.USA/15.17/FH137/G\",\"Ontario.CAN/37.09/G\",\"BritishColumbia.CAN/14.11/2/G\",\"New_York.USA/53.09/3\",\"New_York.USA/40.09/1\",\"Ontario.CAN/38.09/2/G\",\"Ontario.CAN/40.09/1/G\",\"Ontario.CAN/50.09/1/G\",\"New_York.USA/01.10\",\"New_York.USA/40.09/4\",\"Ontario.CAN/50.09/3/G\",\"California.USA/40.11/G\",\"NewJersey.USA/16.14/2/G\",\"Indiana.USA/5.14/G\",\"Wisconsin.USA/20.14/FH111/G\",\"Wisconsin.USA/16.14/FH110/G\",\"Wisconsin.USA/29.14/FH113/G\",\"Wisconsin.USA/20.14/FH86/G\",\"Wisconsin.USA/16.14/FH85/G\",\"Wisconsin.USA/24.14/FH112/G\",\"NewYork.USA/10.14/3/G\",\"NewYork.USA/10.14/2/G\",\"NewYork.USA/8.14/G\",\"NewYork.USA.USA/51.14/G\",\"Ohio.USA/13.14/10/G\",\"Ohio.USA/13.14/6/G\",\"Ohio.USA/12.14/9/G\",\"Michigan.USA/12.14/G\",\"Ohio.USA/11.14/2/G\",\"Ohio.USA/13.14/7/G\",\"Ohio.USA/16.14/2/G\",\"Ohio.USA/16.14/1/G\", \"Wisconsin.USA/7.07/FH107/G\",\"Wisconsin.USA/11.07/FH108/G\",\"Wisconsin.USA/7.07/FH154/G\",\"Wisconsin.USA/11.07/FH109/G\",\"Ontario.CAN/04.10/G\",\"Ontario.CAN/08.17/1/G\",\"Massachusetts.USA/12.17/G\",\"Massachusetts.USA/5.17/G\",\"Georgia.USA/2.17/G\",\"Massachusetts.USA/18.17/G\",\"Massachusetts.USA/22.17/7/G\",\"Massachusetts.USA/23.17/2/G\",\"Massachusetts.USA/19.17/2/G\", \"Wisconsin.USA/15.06/FH131/G\",\"Iowa.USA/06/G\",\"Wisconsin.USA/28.06/FH159/G\",\"Wisconsin.USA/41.06/FH153/G\",\"Wisconsin.USA/37.06/FH93/G\",\"Wisconsin.USA/41.06/FH99/G\"]\n",
    "\n",
    "for k in tree.Objects: \n",
    "    if k.branchType == \"node\":\n",
    "        if k.leaves == set(all_G):\n",
    "            genotype_G_node = k\n",
    "\n",
    "            \n",
    "for k in tree.Objects: \n",
    "    if k.branchType == \"node\":\n",
    "        if k.leaves == set(all_figure2):\n",
    "            ancestor = k\n",
    "            print(k)\n",
    "            tree.collapseSubtree(ancestor,'G_clade1',widthFunction=lambda x:len(x.leaves)/10)\n",
    "\n",
    "# check to make sure we did collapse a clade\n",
    "for k in tree.Objects: \n",
    "    if isinstance(k, bt.clade):\n",
    "        print(\"clade\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/global-all-genomes-divergence-tree-triangles-\"+current_date+\".pdf\"\n",
    "plot_full_genome_divergence_tree_triangle(tree,metadata,colors,division_order,output_name, G_clade1,genotype_G_node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pull out the subtrees for the divergent Washington sequences "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"using baltic's subtree function, enumerate the subtree descending from the supplied node\"\"\"\n",
    "\n",
    "def return_subtree(node, tree):\n",
    "    subtree=tree.subtree(node) ## this function returns a new baltic object that contains a trait-traversed subtree, starting from node k, for as long as the traversal stays within the starting trait value state\n",
    "            \n",
    "    if subtree != None:\n",
    "        subtree.traverse_tree()\n",
    "        subtree.sortBranches()\n",
    "        return(subtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_subtree(tree,metadata,colors,division_order,output_name, width, height):\n",
    "    fig,ax = plt.subplots(figsize=(width, height),facecolor='w')\n",
    "\n",
    "    divergence = [0,0.01,0.02,0.03,0.04,0.05]\n",
    "    #[ax.axvline(i,ls='--',lw=2,color='grey',zorder=0, alpha=0.6) for i in divergence]\n",
    "\n",
    "    # # this sets the vertical dashed lines on the tree; plot a dashed line every other year from 1990 to 2020\n",
    "    branchWidth=2 ## default branch width\n",
    "\n",
    "\n",
    "    # k objects are tips, nodes, branches\n",
    "    for k in tree.Objects: ## iterate over objects in tree\n",
    "        x=k.x ## or use absolute time instead\n",
    "        y=k.y ## get y position from .drawTree that was run earlier, but could be anything else\n",
    "\n",
    "        xp=k.parent.x ## get x position of current object's parent\n",
    "        if x==None: ## matplotlib won't plot Nones, like root\n",
    "            x=0.0\n",
    "        if xp==None:\n",
    "            xp=x\n",
    "\n",
    "        if isinstance(k,bt.leaf) or k.branchType=='leaf': ## if leaf...\n",
    "            #x=decimalDate(k.name.split('_')[-1],variable=True) ## get x position from name\n",
    "            \n",
    "            division = metadata[k.numName]['division'].lower().replace(\" \",\"_\")\n",
    "            country = metadata[k.numName]['country'].lower().replace(\" \",\"_\")\n",
    "            region = metadata[k.numName]['region'].lower().replace(\" \",\"_\")\n",
    "            \n",
    "            if k.numName == \"KM597072.1\":\n",
    "                label = \"\"\n",
    "            else:\n",
    "                label = k.numName\n",
    "            \n",
    "            if division == \"washington\":\n",
    "                region = \"washington\"\n",
    "            elif \"asia\" in region or \"japan_korea\" in region:\n",
    "                region = \"asia\"\n",
    "            else:\n",
    "                region = region\n",
    "            \n",
    "            c=colors[region]\n",
    "            \n",
    "            if metadata[k.numName]['division'].lower() == \"reference\":\n",
    "                s = 0\n",
    "            elif division == \"washington\": \n",
    "                s=45 ## tip size can be fixed\n",
    "            else:\n",
    "                s=45\n",
    "\n",
    "            ax.scatter(x,y,s=s,facecolor=c,edgecolor='none',zorder=11) ## plot circle for every tip\n",
    "            ax.scatter(x,y,s=s+0.8*s,facecolor='k',edgecolor='none',zorder=10) ## plot black circle underneath\n",
    "            ax.text(x + 0.001,y-0.2,label, fontsize=14,zorder=13)\n",
    "\n",
    "        elif isinstance(k,bt.node) or k.branchType=='node': ## if node...\n",
    "            c=\"#696969\"\n",
    "            ax.plot([x,x],[k.children[-1].y,k.children[0].y],lw=branchWidth,color=c,ls='-',zorder=9) #color=node_colors[node_types[k][\"node_community_status\"]]\n",
    "\n",
    "        ax.plot([xp,x],[y,y],lw=branchWidth,color=c,ls='-',zorder=9)\n",
    "\n",
    "        # add in a legend\n",
    "        han_list = []\n",
    "\n",
    "\n",
    "        # bbox to anchor puts a bounding box around where you want the legend to go, prop part is for text size\n",
    "        #ax.legend(handles = han_list, markerfirst = True, frameon=False, bbox_to_anchor=[0.8, 1], loc=2, prop={'size': 24})\n",
    "#     for key in division_order:\n",
    "#         marker = mlines.Line2D(range(1), range(1), color = colors[key], marker='o', markerfacecolor = colors[key], label = key.replace(\"_\",\" \").title().replace(\"Usa\",\"USA\").replace(\"And\",\"and\"), markersize = 8)\n",
    "#         han_list.append(marker)\n",
    "\n",
    "    # set axis limits, remove border lines         \n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "    #ax.set_xlim(0.035,0.06)\n",
    "#     ax.set_ylim(-10,tree.ySpan+5)\n",
    "    ax.tick_params(axis='y',labelsize=0,size=0)\n",
    "    ax.tick_params(axis='x',labelsize=0,size=0)\n",
    "#     ax.tick_params(axis='x',labelsize=20,size=5, width=2,color='grey')\n",
    "    ax.set_yticklabels([])\n",
    "    ax.set_xticklabels([])\n",
    "\n",
    "#     ax.set_xticks(divergence)\n",
    "\n",
    "    # in order to get the legend to plot without being transparent, over the plot, it needs to be here with frame set to true\n",
    "    # bbox arguments are: x, y, with 0 being furthest left and bottom\n",
    "#     ax.legend(handles = han_list, markerfirst = True, edgecolor=\"white\", framealpha=1, bbox_to_anchor=[0.04, 0.1], loc=3,prop={'size': 20}, facecolor='w')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    #plt.gcf().subplots_adjust(right=0.88)\n",
    "    plt.savefig(output_name)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<baltic.tree object at 0x7f81fe642128> <baltic.tree object at 0x7f81fea68518> <baltic.tree object at 0x7f81ff75af28>\n"
     ]
    }
   ],
   "source": [
    "target_leaves1 = [\"Dunedin.NZL/32.17/G\",\"NewPlymouth.NZL/30.17/G\",\"Washington.USA/19.17/FH100/G\",\"Washington.USA/9.17/FH16/G\",\"Washington.USA/14.17/FH145/G\",\"Washington.USA/5.17/FH104/G\",\"Washington.USA/8.17/FH142/G\",\"Washington.USA/11.17/FH95/G\",\"Washington.USA/14.17/FH98/G\"]\n",
    "target_leaves2 = [\"Ontario.CAN/13.10/G\",\"31170187\",\"Washington.USA/12.17/FH78/G\",\"BritishColumbia.CAN/28.16/3/G\",\"Washington.USA/28.17/FH151/G\",\"Massachusetts.USA/37.16/1/G\",\"Gabon/9.13/2/G\",\"KM597072.1\",\"Gabon/13/2/G\"]\n",
    "target_leaves3 = [\"Washington.USA/9.17/FH94/K\",\"Massachusetts.USA/24.17/5/K\"]\n",
    "\n",
    "subtrees = []\n",
    "subtree_y_values = []\n",
    "\n",
    "for k in tree.Objects: \n",
    "    if k.branchType == \"node\":\n",
    "        if k.leaves == set(target_leaves1):\n",
    "            subtree1 = return_subtree(k, tree)\n",
    "            subtrees.append(subtree1)\n",
    "            subtree_y_values.append(k.y)\n",
    "        if k.leaves == set(target_leaves2):\n",
    "            subtree2 = return_subtree(k,tree)\n",
    "            subtrees.append(subtree2)\n",
    "            subtree_y_values.append(k.y)\n",
    "            \n",
    "        if k.leaves == set(target_leaves3):\n",
    "            subtree3 = return_subtree(k,tree)\n",
    "            subtrees.append(subtree3)\n",
    "            subtree_y_values.append(k.y)\n",
    "            \n",
    "print(subtree1,subtree2, subtree3)            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/NZ-cluster-2021-03-05.pdf\"\n",
    "width = 5\n",
    "height = 2.5\n",
    "\n",
    "plot_subtree(subtree1,metadata,colors,division_order,output_name, width, height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/other-2G-2021-03-05.pdf\"\n",
    "width = 5\n",
    "height = 2.5\n",
    "\n",
    "plot_subtree(subtree2,metadata,colors,division_order,output_name, width, height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/K-cluster-2021-03-05.pdf\"\n",
    "width = 5\n",
    "height = 1\n",
    "\n",
    "plot_subtree(subtree3,metadata,colors,division_order,output_name, width, height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LHM-basics (python3)",
   "language": "python",
   "name": "lhm-basics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}