{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot divergence trees with baltic\n",
    "\n",
    "In this notebook, I am using baltic to plot divergence trees generated by iqTree within the nextstrain pipeline. Those tree jsons are available in the `../auspice/` folder in this repository [here](https://github.com/blab/mumps-wa-phylodynamics/tree/master/auspice). This notebook also contains code for plotting the root to tip regression from TempEst. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, subprocess, glob, os, shutil, re, importlib\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 json\n",
    "import pandas as pd\n",
    "from scipy.special import binom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_tree(tree_path):\n",
    "    tree = bt.loadNewick(tree_path)\n",
    "    return(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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": 7,
   "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",
    "                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}\n",
    "    metadata[\"KM597072.1\"] = {\"division\":\"reference\", \"date\":\"2013-XX-XX\"}\n",
    "    return(metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_full_genome_divergence_tree(tree,metadata,colors,division_order,output_name, width, height):\n",
    "    fig,ax = plt.subplots(figsize=(width, height),facecolor='w')\n",
    "\n",
    "    divergence = [0,0.002,0.004,0.006,0.008,0.01,0.012]\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=1.75 ## default branch width\n",
    "    tipSize = 20\n",
    "\n",
    "\n",
    "    # k objects are tips, nodes, branches\n",
    "    for k in tree.Objects: ## iterate over objects in tree\n",
    "        y=k.y ## or use absolute time instead\n",
    "        x=k.traits['node_attrs']['div']\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",
    "        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 = k.traits['division'].lower().replace(\" \",\"_\")\n",
    "            #division = metadata[k.numName]['division']['value'].lower().replace(\" \",\"_\")\n",
    "            region = regions[division]\n",
    "            c=colors[region]\n",
    "            \n",
    "            if division.lower() == \"reference\":\n",
    "                s = 0\n",
    "            else:\n",
    "                s=tipSize ## tip size can be fixed\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",
    "\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(0,0.0125)\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=20,size=5, width=2,color='grey')\n",
    "    ax.set_yticklabels([])\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",
    "    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": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_SH_divergence_tree(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=1.75 ## default branch width\n",
    "    tipSize = 20\n",
    "\n",
    "    # k objects are tips, nodes, branches\n",
    "    for k in tree.Objects: ## iterate over objects in tree\n",
    "        y=k.y ## or use absolute time instead\n",
    "        x=k.traits['node_attrs']['div']\n",
    "\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",
    "        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 = k.traits['division'].lower().replace(\" \",\"_\")\n",
    "            region = regions[division]\n",
    "            c=colors[region]\n",
    "            if division.lower() == \"reference\":\n",
    "                s = 0\n",
    "            else:\n",
    "                s=tipSize ## tip size can be fixed\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",
    "\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(-0.005,0.05)\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=20,size=5, width=2,color='grey')\n",
    "    ax.set_yticklabels([])\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=[0.5, 0.1], loc=3,prop={'size': 20}, 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": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output_treefile_with_dates_in_name(tree_path,metadata):\n",
    "    with open(tree_path, \"r\") as infile: \n",
    "        for line in infile: \n",
    "            for strain in metadata:\n",
    "                new_strain = strain + \"|\" + str(metadata[strain]['date'])\n",
    "                line = line.replace(strain, new_strain)\n",
    "            with open(tree_path.replace(\".nwk\",\".with-dates.nwk\"), \"w\") as outfile: \n",
    "                outfile.write(line)"
   ]
  },
  {
   "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": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try instead, clustering into regions and plotting it that way; we could do: west, 2 midwests, 2 souths, northeast\n",
    "\n",
    "regions = {\"california\":\"non-washington_west,_USA\",\n",
    "          \"montana\":\"non-washington_west,_USA\",\n",
    "           \n",
    "           \"washington\":\"washington,_USA\",\n",
    "           \"reference\":\"reference\",\n",
    "           \n",
    "           \"north_dakota\":\"midwest,_USA\",\n",
    "          \"kansas\":\"midwest,_USA\",\n",
    "           \"missouri\":\"midwest,_USA\",\n",
    "           \"iowa\":\"midwest,_USA\",\n",
    "           \"wisconsin\":\"midwest,_USA\",\n",
    "          \"indiana\":\"midwest,_USA\",\n",
    "          \"michigan\":\"midwest,_USA\",          \n",
    "          \"ohio\":\"midwest,_USA\",\n",
    "          \"illinois\":\"midwest,_USA\",\n",
    "          \n",
    "          \"north_carolina\":\"south,_USA\",\n",
    "          \"alabama\":\"south,_USA\",\n",
    "          \"virginia\":\"south,_USA\",\n",
    "          \"georgia\":\"south,_USA\",\n",
    "          \"texas\":\"south,_USA\",\n",
    "          \"arkansas\":\"south,_USA\",\n",
    "          \"louisiana\":\"south,_USA\",\n",
    "          \n",
    "          \"new_york\":\"northeast,_USA\",\n",
    "          \"massachusetts\":\"northeast,_USA\",\n",
    "          \"pennsylvania\":\"northeast,_USA\",\n",
    "          \"new_hampshire\":\"northeast,_USA\",          \n",
    "          \"new_jersey\":\"northeast,_USA\",\n",
    "          \n",
    "          \"manitoba\":\"manitoba_and_ontario,_Canada\",\n",
    "          \"ontario\":\"manitoba_and_ontario,_Canada\",\n",
    "          \"british_columbia\":\"british_columbia,_Canada\"}\n",
    "\n",
    "colors = {\"washington,_USA\":\"#2664A5\",\n",
    "          \"non-washington_west,_USA\":\"#93B2D2\",\n",
    "          \"midwest,_USA\":\"#5CA7A4\",\n",
    "          \"south,_USA\":\"#EEA160\",\n",
    "          \"northeast,_USA\":\"#544370\",\n",
    "          \"british_columbia,_Canada\":\"#CF7E86\",\n",
    "         \"manitoba_and_ontario,_Canada\":\"#B2313D\", \n",
    "         \"reference\":\"#000000\"}\n",
    "\n",
    "uncertainty_color = \"#B9B9B9\"\n",
    "\n",
    "\n",
    "division_order = [\"washington,_USA\",\"non-washington_west,_USA\",\"south,_USA\",\"northeast,_USA\",\"midwest,_USA\",\n",
    "                  \"manitoba_and_ontario,_Canada\",\"british_columbia,_Canada\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tree height: 0.010037\n",
      "Tree length: 0.086198\n",
      "annotations present\n",
      "\n",
      "Numbers of objects in tree: 828 (361 nodes and 467 leaves)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run for the full genome tree first \n",
    "tree_path = \"../auspice/mumps_north-america-full-genomes.json\"\n",
    "metadata_path = \"../auspice/metadata.tsv\"\n",
    "output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/full-genome-divergence-tree-\"+current_date+\".pdf\"\n",
    "\n",
    "width = 12\n",
    "height = 10\n",
    "tree = load_tree_json(tree_path)\n",
    "metadata = generate_metadata_dictionary(metadata_path)\n",
    "plot_full_genome_divergence_tree(tree,metadata,colors,division_order,output_name, width, height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tree height: 0.046611\n",
      "Tree length: 0.209742\n",
      "annotations present\n",
      "\n",
      "Numbers of objects in tree: 883 (434 nodes and 449 leaves)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run for SH only tree\n",
    "tree_path = \"../auspice/mumps_north-america-SH-only.json\"\n",
    "metadata_path = \"../auspice/metadata.tsv\"\n",
    "output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/SH-divergence-tree-\"+current_date+\".pdf\"\n",
    "\n",
    "width = 12\n",
    "height = 10\n",
    "\n",
    "tree = load_tree_json(tree_path)\n",
    "metadata = generate_metadata_dictionary(metadata_path)\n",
    "plot_SH_divergence_tree(tree,metadata,colors,division_order,output_name, width, height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot root to tip plot \n",
    "\n",
    "I ran `output_treefile_with_dates_in_name` on `/Users/lmoncla/src/mumps-no-dropped-na/results/tree-raw_na_rooted.nwk` and generated the output file `tree-raw_na_rooted.with-dates.nwk`. I read this file into TempEst version 1.5.1. and selected `heuristic residual mean squared` and `best-fitting root`. The correlation coefficient was 0.8637, and the R2 value was 0.7459. The slop was 3.749 x 10^-4. \n",
    "\n",
    "I exported the data to a text file in `/Users/lmoncla/src/mumps-no-dropped-na/results/tree-raw_na.with-dates.root-to-tip-2020-01-24.txt`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in output file \n",
    "tempest_file = \"full-genome-tempest-root-to-tip-2020-01-24.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lmoncla/anaconda/envs/LHM-basics/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip</th>\n",
       "      <th>date</th>\n",
       "      <th>distance</th>\n",
       "      <th>residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington.USA/14.17/FH98/G|2017-04-06</td>\n",
       "      <td>2017.260274</td>\n",
       "      <td>0.010272</td>\n",
       "      <td>-0.000446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Washington.USA/5.17/FH104/G|2017-02-08</td>\n",
       "      <td>2017.104110</td>\n",
       "      <td>0.010191</td>\n",
       "      <td>-0.000468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Washington.USA/11.17/FH95/G|2017-03-15</td>\n",
       "      <td>2017.200000</td>\n",
       "      <td>0.010194</td>\n",
       "      <td>-0.000501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Washington.USA/8.17/FH142/G|2017-02-22</td>\n",
       "      <td>2017.142466</td>\n",
       "      <td>0.010271</td>\n",
       "      <td>-0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington.USA/19.17/FH100/G|2017-05-11</td>\n",
       "      <td>2017.356164</td>\n",
       "      <td>0.011022</td>\n",
       "      <td>0.000268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       tip         date  distance  residual\n",
       "0   Washington.USA/14.17/FH98/G|2017-04-06  2017.260274  0.010272 -0.000446\n",
       "1   Washington.USA/5.17/FH104/G|2017-02-08  2017.104110  0.010191 -0.000468\n",
       "2   Washington.USA/11.17/FH95/G|2017-03-15  2017.200000  0.010194 -0.000501\n",
       "3   Washington.USA/8.17/FH142/G|2017-02-22  2017.142466  0.010271 -0.000402\n",
       "4  Washington.USA/19.17/FH100/G|2017-05-11  2017.356164  0.011022  0.000268"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_csv(tempest_file, sep=\"\\t\")\n",
    "df.reset_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip</th>\n",
       "      <th>date</th>\n",
       "      <th>distance</th>\n",
       "      <th>residual</th>\n",
       "      <th>tip_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington.USA/14.17/FH98/G|2017-04-06</td>\n",
       "      <td>2017.260274</td>\n",
       "      <td>0.010272</td>\n",
       "      <td>-0.000446</td>\n",
       "      <td>Washington.USA/14.17/FH98/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Washington.USA/5.17/FH104/G|2017-02-08</td>\n",
       "      <td>2017.104110</td>\n",
       "      <td>0.010191</td>\n",
       "      <td>-0.000468</td>\n",
       "      <td>Washington.USA/5.17/FH104/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Washington.USA/11.17/FH95/G|2017-03-15</td>\n",
       "      <td>2017.200000</td>\n",
       "      <td>0.010194</td>\n",
       "      <td>-0.000501</td>\n",
       "      <td>Washington.USA/11.17/FH95/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Washington.USA/8.17/FH142/G|2017-02-22</td>\n",
       "      <td>2017.142466</td>\n",
       "      <td>0.010271</td>\n",
       "      <td>-0.000402</td>\n",
       "      <td>Washington.USA/8.17/FH142/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington.USA/19.17/FH100/G|2017-05-11</td>\n",
       "      <td>2017.356164</td>\n",
       "      <td>0.011022</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>Washington.USA/19.17/FH100/G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       tip         date  distance  residual  \\\n",
       "0   Washington.USA/14.17/FH98/G|2017-04-06  2017.260274  0.010272 -0.000446   \n",
       "1   Washington.USA/5.17/FH104/G|2017-02-08  2017.104110  0.010191 -0.000468   \n",
       "2   Washington.USA/11.17/FH95/G|2017-03-15  2017.200000  0.010194 -0.000501   \n",
       "3   Washington.USA/8.17/FH142/G|2017-02-22  2017.142466  0.010271 -0.000402   \n",
       "4  Washington.USA/19.17/FH100/G|2017-05-11  2017.356164  0.011022  0.000268   \n",
       "\n",
       "                       tip_name  \n",
       "0   Washington.USA/14.17/FH98/G  \n",
       "1   Washington.USA/5.17/FH104/G  \n",
       "2   Washington.USA/11.17/FH95/G  \n",
       "3   Washington.USA/8.17/FH142/G  \n",
       "4  Washington.USA/19.17/FH100/G  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add in color information as a column\n",
    "splits = df['tip'].str.split(\"|\", n=1, expand=True)\n",
    "df['tip_name'] = splits[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip_name</th>\n",
       "      <th>division</th>\n",
       "      <th>date</th>\n",
       "      <th>colors_to_plot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02-49</td>\n",
       "      <td>Japan</td>\n",
       "      <td>XXXX-XX-XX</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31170132</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2017-XX-XX</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31170170</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2017-XX-XX</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31170186</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2017-XX-XX</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31170187</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2017-XX-XX</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tip_name division        date colors_to_plot\n",
       "0     02-49    Japan  XXXX-XX-XX          Japan\n",
       "1  31170132    Japan  2017-XX-XX          Japan\n",
       "2  31170170    Japan  2017-XX-XX          Japan\n",
       "3  31170186    Japan  2017-XX-XX          Japan\n",
       "4  31170187    Japan  2017-XX-XX          Japan"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = pd.DataFrame.from_dict(metadata, orient=\"index\")\n",
    "m.reset_index(inplace=True)\n",
    "m.columns = ['tip_name','division','date']\n",
    "\n",
    "# look up the color and add it as a column in the dataframe\n",
    "m[\"colors_to_plot\"] = m[\"division\"].replace(colors)\n",
    "m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge dataframes to plot \n",
    "combined = df.merge(m, how=\"left\", on='tip_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_region(division, regions_dict):\n",
    "    division = division.lower().replace(\" \",\"_\")\n",
    "    region = regions_dict[division]\n",
    "    region = region.replace(\",\",\"\").replace(\"-\",\"_\")\n",
    "    return(region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tip</th>\n",
       "      <th>date_x</th>\n",
       "      <th>distance</th>\n",
       "      <th>residual</th>\n",
       "      <th>tip_name</th>\n",
       "      <th>division</th>\n",
       "      <th>date_y</th>\n",
       "      <th>colors_to_plot</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington.USA/14.17/FH98/G|2017-04-06</td>\n",
       "      <td>2017.260274</td>\n",
       "      <td>0.010272</td>\n",
       "      <td>-0.000446</td>\n",
       "      <td>Washington.USA/14.17/FH98/G</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Washington</td>\n",
       "      <td>washington_USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Washington.USA/5.17/FH104/G|2017-02-08</td>\n",
       "      <td>2017.104110</td>\n",
       "      <td>0.010191</td>\n",
       "      <td>-0.000468</td>\n",
       "      <td>Washington.USA/5.17/FH104/G</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>Washington</td>\n",
       "      <td>washington_USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Washington.USA/11.17/FH95/G|2017-03-15</td>\n",
       "      <td>2017.200000</td>\n",
       "      <td>0.010194</td>\n",
       "      <td>-0.000501</td>\n",
       "      <td>Washington.USA/11.17/FH95/G</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2017-03-15</td>\n",
       "      <td>Washington</td>\n",
       "      <td>washington_USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Washington.USA/8.17/FH142/G|2017-02-22</td>\n",
       "      <td>2017.142466</td>\n",
       "      <td>0.010271</td>\n",
       "      <td>-0.000402</td>\n",
       "      <td>Washington.USA/8.17/FH142/G</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2017-02-22</td>\n",
       "      <td>Washington</td>\n",
       "      <td>washington_USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington.USA/19.17/FH100/G|2017-05-11</td>\n",
       "      <td>2017.356164</td>\n",
       "      <td>0.011022</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>Washington.USA/19.17/FH100/G</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>Washington</td>\n",
       "      <td>washington_USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       tip       date_x  distance  residual  \\\n",
       "0   Washington.USA/14.17/FH98/G|2017-04-06  2017.260274  0.010272 -0.000446   \n",
       "1   Washington.USA/5.17/FH104/G|2017-02-08  2017.104110  0.010191 -0.000468   \n",
       "2   Washington.USA/11.17/FH95/G|2017-03-15  2017.200000  0.010194 -0.000501   \n",
       "3   Washington.USA/8.17/FH142/G|2017-02-22  2017.142466  0.010271 -0.000402   \n",
       "4  Washington.USA/19.17/FH100/G|2017-05-11  2017.356164  0.011022  0.000268   \n",
       "\n",
       "                       tip_name    division      date_y colors_to_plot  \\\n",
       "0   Washington.USA/14.17/FH98/G  Washington  2017-04-06     Washington   \n",
       "1   Washington.USA/5.17/FH104/G  Washington  2017-02-08     Washington   \n",
       "2   Washington.USA/11.17/FH95/G  Washington  2017-03-15     Washington   \n",
       "3   Washington.USA/8.17/FH142/G  Washington  2017-02-22     Washington   \n",
       "4  Washington.USA/19.17/FH100/G  Washington  2017-05-11     Washington   \n",
       "\n",
       "           region  \n",
       "0  washington_USA  \n",
       "1  washington_USA  \n",
       "2  washington_USA  \n",
       "3  washington_USA  \n",
       "4  washington_USA  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined['region'] = combined['division'].apply(add_region, args=[regions])\n",
    "combined['division'] = combined['division'].str.replace(\"_\",\" \")\n",
    "combined['division'] = combined['division'].str.title()\n",
    "combined = combined[combined['division'] != \"Reference\"]\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'british_columbia_Canada',\n",
       " 'manitoba_and_ontario_Canada',\n",
       " 'midwest_USA',\n",
       " 'non_washington_west_USA',\n",
       " 'northeast_USA',\n",
       " 'south_USA',\n",
       " 'washington_USA'}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(combined['region'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 1000 -h 500 -u px -i combined  # this sets the size of the plot...otherwise, it will go off the page\n",
    "require(ggplot2)\n",
    "library(ggplot2)\n",
    "\n",
    "#combined$regionf = factor(combined$region, levels=c(\"California\",\"New York\",\"Massachusetts\",\"Virginia\",\"Georgia\",\"Manitoba\",\"Arkansas\",\"North Dakota\",\"Louisiana\",\"Ontario\",\"Texas\",\"Indiana\",\"Michigan\",\"Montana\",\"New Hampshire\",\"Ohio\",\"Washington\",\"Illinois\",\"Kansas\",\"Pennsylvania\",\"New Jersey\",\"British Columbia\", \"Wisconsin\",\"Iowa\",\"Alabama\",\"Missouri\",\"North Carolina\"))\n",
    "\n",
    "p <- ggplot(data=combined, aes(x=date_x, y=distance, color=region)) + \n",
    "    geom_point()+\n",
    "    geom_smooth(method='lm',formula=y~x, color=\"black\", se=FALSE)+\n",
    "    #scale_color_manual(values = setNames(combined$colors_to_plot, levels(combined$division))) +\n",
    "    scale_color_manual(values=c(washington_USA=\"#2664A5\",non_washington_west_USA=\"#93B2D2\",midwest_USA=\"#5CA7A4\",south_USA=\"#EEA160\",northeast_USA=\"#544370\",british_columbia_Canada=\"#CF7E86\",manitoba_and_ontario_Canada=\"#B2313D\"), guide=FALSE)+\n",
    "    labs(x=\"date\",y=\"root to tip distance\")+\n",
    "    theme(plot.title = element_text(size=20, hjust=0.5))+\n",
    "    scale_x_continuous(limits=c(2006,2018), breaks=seq(2006,2018,2))+\n",
    "    theme(panel.grid.major=element_line(colour=NA,size=NA))+\n",
    "    theme(panel.grid.minor=element_line(colour=NA,size=NA))+    \n",
    "    theme(strip.background = element_rect(colour=NA, fill=NA))+\n",
    "    theme(axis.line.x=element_line(colour=\"black\"))+\n",
    "    theme(axis.line.y=element_line(colour=\"black\"))+\n",
    "    theme(axis.title.y=element_text(size=20, vjust=8))+\n",
    "    theme(axis.title.x=element_text(size=20, vjust=-4))+\n",
    "    theme(axis.text=element_text(size=20, colour=\"black\"))+\n",
    "    theme(legend.title=element_blank())+\n",
    "    theme(panel.margin=unit(1, \"lines\"))+\n",
    "    theme(plot.margin=unit(c(1,1,1,1),\"cm\"))+\n",
    "    theme(legend.key.size=unit(0.7, \"cm\"))+\n",
    "    theme(panel.background=element_rect(fill=NA))+\n",
    "    theme(legend.key=element_rect(fill=NA))\n",
    "\n",
    "# add in annotation with the regression stuff\n",
    "p1 = p + annotate(\"text\",x=2009.75,y=0.011,label=expression(\"slope = 3.75 x 10\"^-4), size=7)\n",
    "ggsave(\"2020-03-04-root-to-tip-no-legend.pdf\", p1, width = 8, height = 4, path=\"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/individual-PDFs/\")\n",
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}