ETE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETE usage for phylogenetic or other tree analyses\n",
    "\n",
    "Link: http://blog.genesino.com/2016/07/ete/\n",
    "\n",
    "Mail: <chentong_biology@163.com>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A virtual X-server XVFB is used in case you do not have X-server\n",
    "from xvfbwrapper import Xvfb\n",
    "\n",
    "vdisplay = Xvfb()\n",
    "vdisplay.start()\n",
    "\n",
    "# launch stuff inside virtual display here\n",
    "\n",
    "#vdisplay.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ete3 import Tree, faces, TreeStyle, NodeStyle\n",
    "from ete3 import ClusterTree, RectFace, AttrFace, ProfileFace, TextFace\n",
    "from ete3.treeview.faces import add_face_to_node\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import colorsys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most simple way of showing a tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQkAAACkCAYAAACEndvgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAN1wAADdcBQiibeAAADBFJREFUeJzt3X9M1fUex/HXOUAELhHxakYiBMeVk81wu4atSTONJdTO\nyhA9ublyZLJw/JXTftBys7nbmnVzGuOqY61T1Fn5449WE222pals0ajLzuCiXrKkwxBLPQfO/YN7\nDhwOfOWHAvJ5Pjb+4Pv9fNn3y+Tl5/P5fj7vYwsGg0EBwCDs430DACY2QgKAJUICgCVCAjCQzWaL\n+EpKStITTzyhxsbGqLaEBGCoYDCoYDCorq4uNTU1KTs7W6tWrYpqZ+PtBmAem82m/n/6f/31l2bP\nnq329vaI4/QkAAPs+Nd53bfypP6x/1zUuWAwqPb2du3cuVNbt26NOh87FjcITDQVFRXjfQtj5t+/\npqjm9EJd93dr6z//o/lZUyT19Cb6stls2r9/f9T19CRgnIqKCh09ejQ8Jp/sX1LPvENIKBtC57u7\nu+Xz+bRr1y7t2LEj6vfFnASMU1FRoWAwqDfeeGO8b2XMvHPggv756QW9XJyqsjWpA85JXLlyRdOn\nT9e1a9cijjPcAAxQvi5V5etSLdt0dnYqJSUl6jjDDQC6dOmSdu3apaKioqhzhARgqNBCKrvdrvnz\n5+vPP//U9u3bo9ox3AAMNJypSHoSMEZorcB3jXPG+1ZuK/QkDGbqWoGWXzP1t6lXxvuWbhv0JAxl\n8loBW1CSePM/VPQkDJaXl2fUWoFF/18rMC+5Xlkz/xjv27lt0JOAMcrXpcp78O96KDN6/wIGR0gA\nBjt8+LBsNpsOHz48aBtCAjDYnj179MILL2jv3r2DtmFOAjDU+fPndebMGXm9XjkcDl24cEGpqdFL\nt+lJAAYYqJ5EZWWlXC6X4uPj5XK5VFlZOeC19CRgpGPHjhmzTmSgehIrHkpSVVWVvvnmG0nS+vXr\ntWzZMm3btk0xMTER1xMSMM7rr7/eZ/2ECUJrRHoKSdhs0pEjR5SRkSGHwyFJcjgcysjI0JEjR1RY\nWBhxNSEBI5m0PkTqXSPycnGq8pdMV0HBHh0/fjyqOtXUqVMJCcBEfetJtLS0qK6uTlevXlV8fHy4\nzdWrV5WVlaVz585pzpze/S1MXAKGqaysVFlZWURASNKdd96p0tLSqAlMQgIwSFdXlz7//HO9+OKL\nA57fuHGjampqImpiMtwADBITE6P6+vpBzyclJemnn36KOEZPwjDUVMBwGd+TMOVduURNBYyM0T0J\naiqYsk4Ao2F8T4KaCoA1o3sSJqKmAoaLkAAM5fF4dMcdd4RL69tsNiUkJIT3c4QQEoCBPB6PXC6X\n3G53xLxVZWWlnE6njh8/Hm5r/JwEYKKioiK53W45nc6I42vXrlV3d7dKSkrU0NAgiZ4EYIT+9ST8\nfn9UQISsXbtWp0+fDn9PTwJGMnV9TKiehBW73a7ExMTe72/1DQITjcnrY6SeehID6TuB2XcLOT0J\nGMnU9TGhehID6VuEp29I0JMADBBaH1O2pqemRFxcnDwez4BtPR6P4uLiwt8TEoCB3G63XC5XVFD0\nfTUawnADMJDT6VR1dbWKiork9/vDx+Pi4qJejRISgKGcTqeuX79+w3YMN2AMammMDD0Jg5n62RPU\n0hgeQsJQJn/2BLU0hoeQMJhJ6wQkammMFHMSMAa1NEaGngRgoP6f3NVX/yEoIQEYaqjzUQw3AFgi\nJAAD9K8nMRwMN2AkU9eI9K0nMdC8xEBDECNDYse+89r76X/lSJ6j3KyW8b4djDGT14hIvfUkhvr8\nsZK5VXpYeWcuU9eIWNWTGEysNPREmRxYeQfzlK9LVfm61BFdGyuZm6qsvANuzMi3G6y8A4bOyJAA\nTDecKQZCAoAlQgKAJUICgCVCAoAlQgKAJSOXZQPS8GoqmIyQgNEIgxtjuAHAEiEBY4ympoLJGG4Y\nzNTdvzeqqSAxDOmLnoShKioqdPTo0XBdhcn+1bv7t0ffmgoDt0cIPQmD5eXlGbUDeDQ1FUxGSMAY\no6mpYDKGGwAs0ZOA0Zi4vDFCAsYiCIaG4QYAS4QEAEuEBABLhAQAS4SEYUL7F75rnDPet4LbhPFv\nN0z9TEg+vcxsA736jYuLk9vtltPpjDhudEiY/JmQfHoZ+v+793g8crlcqq6ujggKW9CcvxBIeqfP\np5ctvq/FqL0b6GWz2Qb8z9Hj8aioqEjXr18PH2NOwjB8epmZhlpLw+l0yu/3RxwzergBc5kyDyUN\nXktjqOhJwDjU0hje74ueBIxELY2hIyQAAwy1lobH41FcXFzEMYYbACT1vgJ1u90Rx+lJAIbqv6CK\nxVQAwoazPIrhBozBvpWRoSdhMPatYCgICUOxb8WU5x49QsJgJq0TkHrXCsxLrlfWzD/G+3ZuG8xJ\nwBjsWxkZQgIwVHt7uzZv3qy0tDQlJiYqJydHNTU1Ue0YbgAG6uzs1NKlS7VhwwadPHlSSUlJOnHi\nhNavXy+/36/i4uJwW3oSgIF27typZ599VqWlpbr77ruVkJCgxx57TPv27dN7770X0ZaQAAzQv57E\nZ599pmeeeSaq3bJly3TixImIYww3YCRT14iE6kk0NTUpIyNjwPb9l2sTEjCOyWtEpJ56Enb70AcR\n1LgEDPBOn3oSZWtS9eCDD8rj8Sg9PT2iXWdnpw4dOqTVq1eHjxESgIFee+01zZo1S5s2bYo4/uGH\nH8rtduvrr78OH2O4ARiovLxcixcvDr/uTEhI0MGDB7VlyxZ9+eWXEW15uwEYaNq0afrqq6/0/fff\nKzs7W/fcc492796tmpoaLVmyJKItww0AluhJALBESACwZD927Jhqa2vH+z5GbbI8BzDR2GtrayfF\nH9dkeQ5gomG4AcASIQHAEiEBwFJsc3Ozmpubb/sdcbW1tVHr0AGMXqykSbMjbjI8AzDRxKanp2vu\n3Lm3feXkiooKQgK4BZiTAGCJkABgiZAAYCk2Ly9vUozlJ8tzABMNW8UBWGK4AcBSVEgEAoFh/YCu\nri7Lbv5kWYMBmCocEm1tbXryySflcDg0e/ZsvfLKK+ru7h70wh9++EFLly7VggULlJOTo/z8fDU2\nNobPNzc36+mnn1ZGRoYyMzNVUFCglpaWW/s0AG66cEhs2LBBixcvVlNTk5qamnTmzBm9/fbbA17U\n1dWlgoIClZWVqaGhQWfPntXmzZvlcrkkSX6/X0899ZTy8/PV3Nwsr9ertLQ0Pf/882PzVABunmAw\nGPzll1+CKSkpQb/fHww5ffp0cNasWcFAIBDsr7OzM2iz2YJerzd87Ny5c8F77703/H1jY2PENbW1\ntcG5c+dG/SwAE5tdkk6dOqXs7GzFxvZW2F+4cKE6Ojr0888/RwXLlClTVFJSosLCQtXU1KiqqkqP\nP/643nzzzXCbrKwsBQIB+Xw+1dfX66233tJLL700BrEH4GayS1Jra6tSUlIiT9jtSk5O1u+//z7g\nhVu2bFFMTIw2btyokpISpaWlKT8/P6LNqVOn9Oijj2r58uXq6OhQYWHhLXoMALeKXZKSk5N1+fLl\nqJM+n08zZ86MOu71erVkyRK9+uqr+u2339Ta2qr09HTl5ubK5/OF2+Xm5qqurk6tra0qLS3VI488\nora2tlv4OABuNrskPfzwwzp79mzE68/m5mYlJCQoMzMz6qLq6motX75cq1atks1m04wZM7R7924l\nJibq22+/1eXLl1VXVxdxzXPPPadAIKAff/zxFj8SgJvJLkn333+/cnJy9O6770rqWduwfft2bdq0\nSfHx8ZKkTz75RB9//LEkad68eaqrq1NHR0f4B3m9Xl28eFEPPPCAvF6vVqxYoYaGhvD5L774Qn6/\nXwsWLBizhwMweuGZygMHDmjNmjX66KOPdO3aNS1atEjbtm0LN6yqqlIgENDq1atVXFysS5cuKTc3\nVykpKQoEAmpra9MHH3wgh8MhSXr//fe1cuVKJSUlqaOjQ3fddZcOHTqkGTNmjP1TAhixqL0bPp9P\nMTExmjp16pB+gM/nU1dX16B//BcvXlR8fLymTZs2+rsFMObY4AXAEhu8AFgiJABYIiQAWCIkAFgi\nJABYIiQAWCIkAFgiJABYIiQAWCIkAFgiJABYIiQAWCIkAFgiJABYIiQAWCIkAFgiJABYIiQAWCIk\nAFgiJABYIiQAWCIkAFgiJABYIiQAWCIkAFj6H4fKANvL89XzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = Tree()\n",
    "t.populate(7,names_library=['A','B','C','D','E','F','G'])\n",
    "## %%liline is used for showing plots in ipythonnotebook.\n",
    "## t.render(file_name=\"tree.pdf\") # will save tree into pdf file\n",
    "t.render(file_name=\"%%inline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the randomly generated tree in newick format and save to a string variable which can be read using `Tree()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'(((B:1,A:1)1:1,(G:1,F:1)1:1)1:1,(E:1,(D:1,C:1)1:1)1:1);'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_str = t.write(outfile=None, format=0)\n",
    "t_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ew6+//or+/n5kZmaisLAQVqvVtV9hYSE6OzvR39+P6upqPProoxgcHAzj4RBFjg4AHnnk\nEZw+fdrjtnhvby8SEhKQnZ3t9aaWlhasWLECq1evhqIoSE1Nxe7du5GYmIjvv/8ely5dQmdnp8d7\nysvLYbfb8eOPP4b5kIgiQwcA9957L/Lz8/Hee+8BGPtuaPv27di0aRPi4+MBAF988QX27t0LAFiw\nYAE6OzsxPDzs+iCz2Yzz589j4cKFMJvNeOKJJ9DV1eV6fd++fbDZbFi8ePGkHRzRZHLdcfjss8+w\ndu1a7NmzB9euXcODDz6Ibdu2uXZsbm6G3W5HWVkZ1qxZg4sXL6KwsBApKSmw2+0YHBzEhx9+iNzc\nXADABx98gKeffhrJyckYHh5GUlISDhw4gNTU1Mk/SqJJ4LU2z2q1IiYmBjNmzAjpA6xWKxwOh9+Q\nnD9/HvHx8Zg5c+bER0t0B+NCVyJBuNCVSBCGiUgQholIEIaJSBCGiUgQholIEIaJSBCGiUgQholI\nEIaJSBCGiUgQholIEIaJSBCGiUgQholIEIaJSBCGiUgQholIEIaJSBCGiUgQholIEIaJSBCGiUgQ\nholIEIaJSJD/B1RKyBgX3MnzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = Tree(t_str)\n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = True\n",
    "ts.show_branch_length = True\n",
    "ts.show_branch_support = True\n",
    "t.render(file_name=\"%%inline\", tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the randomly generated tree in newick format and save to file which can also be read using `Tree()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PzM3NwePxWMcgfESFLsbU1dXh6emJUaNGsY4iMWhhXFUhISHIzc2Fn58fcnJy8PHjR0RF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\nQNABIICgA0CA/wHNs5E6bp3wBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.write(outfile=\"tree.nw\", format=0)\n",
    "t = Tree(\"tree.nw\")\n",
    "ts.mode = \"c\"\n",
    "ts.arc_start = -180 # 0 degrees = 3 o'clock\n",
    "ts.arc_span = 180\n",
    "t.render(file_name=\"%%inline\", w=500, tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设置根节点、叶节点和中间节点的属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MdCJylAUVFbi8thaTFi+Odyk0CASDQSxcuBDr1q2LdymHjYFORI6SV1oKl9cb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\nsABBBwDAAgQdAAAL/A9EigQZunbXAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = True\n",
    "ts.show_branch_length = True\n",
    "ts.show_branch_support = True\n",
    "\n",
    "# Draws nodes as small red spheres of diameter equal to 10 pixels\n",
    "for n in t.traverse():  # Traverse each node and set attribute for each type of nodes\n",
    "    if n.is_leaf(): # Decide if leaf node\n",
    "        nstyle = NodeStyle()\n",
    "        nstyle[\"shape\"] = \"sphere\"\n",
    "        nstyle[\"size\"] = 10\n",
    "        nstyle[\"fgcolor\"] = \"darkred\"\n",
    "        n.set_style(nstyle)\n",
    "    else:\n",
    "        nstyle = NodeStyle()\n",
    "        nstyle[\"shape\"] = \"square\"\n",
    "        nstyle[\"size\"] = 15\n",
    "        nstyle[\"fgcolor\"] = \"orange\"\n",
    "        n.set_style(nstyle)        \n",
    "\n",
    "t.img_style[\"size\"] = 30\n",
    "t.img_style[\"fgcolor\"] = \"blue\"\n",
    "    \n",
    "t.render(file_name=\"%%inline\", w=500, tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "修改节点的名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z28sg5eiof/6gssmocpegRiMPrVaeQ+luKYFLuXHPzk4GNEV6ejquXr0KBwcH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\nAMwECwBgJlgAADPBAgCYCRYAwCxg2DxW6gaH3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = Tree(t_str)\n",
    "\n",
    "nameMap = {'A': 'American', 'B': 'Britain', 'C':'China', \n",
    "           'D':'Dutch', 'E':'Egypt','F':'France','G':'German'}\n",
    "\n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = False\n",
    "ts.show_branch_length = True\n",
    "ts.show_branch_support = True\n",
    "\n",
    "# Creates my own layout function. I will use all previously created\n",
    "# faces and will set different node styles depending on the type of\n",
    "# node.\n",
    "def mylayout(node):   \n",
    "    # If node is a leaf, add the nodes name and a its scientific\n",
    "    # name\n",
    "    if node.is_leaf():\n",
    "        # We can also create faces on the fly\n",
    "        newName = nameMap.get(node.name, node.name)\n",
    "        newNameFace = faces.TextFace(newName)\n",
    "        faces.add_face_to_node(newNameFace, node, column=1, aligned=True)\n",
    "\n",
    "        # Sets the style of leaf nodes\n",
    "        node.img_style[\"size\"] = 12\n",
    "        node.img_style[\"shape\"] = \"sphere\"\n",
    "        node.img_style[\"fgcolor\"] = \"blue\"\n",
    "    #If node is an internal node\n",
    "    else:\n",
    "        # Sets the style of internal nodes\n",
    "        node.img_style[\"size\"] = 1\n",
    "        node.img_style[\"shape\"] = \"circle\"\n",
    "        node.img_style[\"fgcolor\"] = \"darkred\"\n",
    "\n",
    "ts.layout_fn = mylayout\n",
    "\n",
    "t.img_style[\"size\"] = 30\n",
    "t.img_style[\"fgcolor\"] = \"black\"\n",
    "    \n",
    "t.render(file_name=\"%%inline\", w=600, tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不同的节点设置不同的背景色，树枝的颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cQnh5BVJXV9ot+wZSEASQlLSb9etDyMryv0zwaT3jxn3n6e561N6977F373tu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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = Tree(t_str)\n",
    "\n",
    "nameMap = {'A': 'American', 'B': 'Britain', 'C':'China', \n",
    "           'D':'Dutch', 'E':'Egypt','F':'France','G':'German'}\n",
    "\n",
    "colorMap = { 'American': '#ACFFFF',\n",
    "             'Britain': '#ACACFF',\n",
    "             'China': '#ACACAC',\n",
    "             'Dutch': '#59ACAC',\n",
    "             'Egypt': '#5959AC',\n",
    "             'France': '#595959',\n",
    "             'German': '#065959'}\n",
    "\n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = False\n",
    "ts.show_branch_length = True\n",
    "ts.show_branch_support = True\n",
    "\n",
    "# Creates my own layout function. I will use all previously created\n",
    "# faces and will set different node styles depending on the type of\n",
    "# node.\n",
    "def mylayout(node):   \n",
    "    #Change branch color\n",
    "    node.img_style[\"hz_line_color\"] = 'orange'  # change horizontal branch color \n",
    "    node.img_style[\"vt_line_color\"] = 'red' # Change vertical branch color\n",
    "    \n",
    "    # If node is a leaf, add the nodes name and a its scientific\n",
    "    # name\n",
    "    if node.is_leaf():\n",
    "        # We can also create faces on the fly\n",
    "        newName = nameMap.get(node.name, node.name)\n",
    "        newNameFace = faces.TextFace(newName)\n",
    "        faces.add_face_to_node(newNameFace, node, column=1, aligned=True)\n",
    "\n",
    "        # Sets the style of leaf nodes\n",
    "        node.img_style[\"size\"] = 12\n",
    "        node.img_style[\"shape\"] = \"sphere\"\n",
    "        node.img_style[\"fgcolor\"] = \"blue\"\n",
    "        node.img_style[\"bgcolor\"] = colorMap[newName]\n",
    "        \n",
    "        node.img_style[\"hz_line_color\"] = 'blue'  # change branch color \n",
    "    #If node is an internal node\n",
    "    else:\n",
    "        # Sets the style of internal nodes\n",
    "        node.img_style[\"size\"] = 1\n",
    "        node.img_style[\"shape\"] = \"circle\"\n",
    "        node.img_style[\"fgcolor\"] = \"darkred\"\n",
    "\n",
    "ts.layout_fn = mylayout\n",
    "\n",
    "t.img_style[\"size\"] = 30\n",
    "t.img_style[\"fgcolor\"] = \"black\"\n",
    "    \n",
    "t.render(file_name=\"%%inline\",tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 默认树+热图（自定义颜色+列名字）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自定义热图函数，同时该函数也支持替换或新增节点的名字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nameFace = AttrFace(\"name\", fsize=12)\n",
    "\n",
    "def setup_heatmap(tree, tree_style, header, center_value=0.0, nameMap ={}, nameLabel = '',\n",
    "                  color_up=0.7, color_down=0.2, color_center=\"white\"):\n",
    "    DEFAULT_COLOR_SATURATION = 0.5\n",
    "    BASE_LIGHTNESS = 0.7\n",
    "    def gradient_color(value, max_value, saturation=0.5, hue=0.1):    \n",
    "        def rgb2hex(rgb):\n",
    "            return '#%02x%02x%02x' % rgb\n",
    "        def hls2hex(h, l, s):\n",
    "            return rgb2hex( tuple(map(lambda x: int(x*255), \n",
    "                                      colorsys.hls_to_rgb(h, l, s))))\n",
    "    \n",
    "        lightness = 1 - (value * BASE_LIGHTNESS) / max_value\n",
    "        return hls2hex(hue, lightness, DEFAULT_COLOR_SATURATION)\n",
    "\n",
    "\n",
    "    # Calculate max gradient value from the ClusterTree matrix\n",
    "    maxv = abs(center_value - tree.arraytable._matrix_max)\n",
    "    minv = abs(center_value - tree.arraytable._matrix_min)\n",
    "    if center_value <= tree.arraytable._matrix_min:\n",
    "        MAX_VALUE = minv + maxv\n",
    "    else:\n",
    "        MAX_VALUE = max(maxv, minv)\n",
    "        \n",
    "    # Add heatmap colors to tree\n",
    "    cols_add_before_heat = 0\n",
    "    if nameMap:\n",
    "        cols_add_before_heat = 1\n",
    "    for lf in tree:\n",
    "        if nameMap:\n",
    "            longNameFace = faces.TextFace(nameMap.get(lf.name, lf.name))\n",
    "            lf.add_face(longNameFace, column=0, position=\"aligned\")\n",
    "            \n",
    "        for i, value in enumerate(getattr(lf, \"profile\", [])):\n",
    "            if value > center_value:\n",
    "                color = gradient_color(abs(center_value - value), MAX_VALUE, \n",
    "                                       hue=color_up)\n",
    "            elif value < center_value:\n",
    "                color = gradient_color(abs(center_value - value), MAX_VALUE, \n",
    "                                       hue=color_down)\n",
    "            else:\n",
    "                color = color_center\n",
    "            lf.add_face(RectFace(20, 20, color, color), position=\"aligned\", \n",
    "                        column=i+cols_add_before_heat)\n",
    "            # Uncomment to add numeric values to the matrix\n",
    "            #lf.add_face(TextFace(\"%0.2f \"%value, fsize=5), position=\"aligned\", column=i)\n",
    "        lf.add_face(nameFace, column=i+cols_add_before_heat+1, position=\"aligned\")\n",
    "        \n",
    "    if nameMap and nameLabel:\n",
    "        nameF = TextFace(nameLabel, fsize=7)\n",
    "        #nameF.rotation = -90\n",
    "        tree_style.aligned_header.add_face(nameF, column=0)\n",
    "    # Add header \n",
    "    for i, name in enumerate(header):\n",
    "        nameF = TextFace(name, fsize=7)\n",
    "        nameF.rotation = -90\n",
    "        tree_style.aligned_header.add_face(nameF, column=i+cols_add_before_heat)\n",
    " #-------------END setup_heatmap----------------------------------------------        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读入矩阵 （可把文后的测试矩阵存储到文件中读入）\n",
    "\n",
    "矩阵需满足三个条件：\n",
    "* 矩阵为TAB键分割，第一行是每列的名字\n",
    "* 矩阵每一行第一列为行名字，与树的节点对应\n",
    "* 矩阵可以存储与一个文件中，也可以是如下的字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_table(\"matrix\", header=0, index_col=0)\n",
    "data.index.name = \"#Names\"  #修改第一行的名字使其符合ETE的要求\n",
    "data_mat = data.to_csv(None, sep=\"\\t\", float_format=\"%.2f\")\n",
    "header = list(data.columns.values)  #获取列的名字用于标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
       "      <th>col5</th>\n",
       "      <th>col6</th>\n",
       "      <th>col7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#Names</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-1.23</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.78</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.76</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.19</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-1.22</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>0.79</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-1.47</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-1.04</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>0.87</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.17</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        col1  col2  col3  col4  col5  col6  col7\n",
       "#Names                                          \n",
       "A      -1.23 -0.81  1.79  0.78 -0.42 -0.69  0.58\n",
       "B      -1.76 -0.94  1.16  0.36  0.41 -0.35  1.12\n",
       "C      -2.19  0.13  0.65 -0.51  0.52  1.04  0.36\n",
       "D      -1.22 -0.98  0.79 -0.76 -0.29  1.54  0.93\n",
       "E      -1.47 -0.83  0.85  0.07 -0.81  1.53  0.65\n",
       "F      -1.04 -1.11  0.87 -0.14 -0.80  1.74  0.48\n",
       "G      -1.57 -1.17  1.29  0.23 -0.20  1.17  0.26"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#Names\\tcol1\\tcol2\\tcol3\\tcol4\\tcol5\\tcol6\\tcol7\\nA\\t-1.23\\t-0.81\\t1.79\\t0.78\\t-0.42\\t-0.69\\t0.58\\nB\\t-1.76\\t-0.94\\t1.16\\t0.36\\t0.41\\t-0.35\\t1.12\\nC\\t-2.19\\t0.13\\t0.65\\t-0.51\\t0.52\\t1.04\\t0.36\\nD\\t-1.22\\t-0.98\\t0.79\\t-0.76\\t-0.29\\t1.54\\t0.93\\nE\\t-1.47\\t-0.83\\t0.85\\t0.07\\t-0.81\\t1.53\\t0.65\\nF\\t-1.04\\t-1.11\\t0.87\\t-0.14\\t-0.80\\t1.74\\t0.48\\nG\\t-1.57\\t-1.17\\t1.29\\t0.23\\t-0.20\\t1.17\\t0.26\\n'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['col1', 'col2', 'col3', 'col4', 'col5', 'col6', 'col7']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "调用函数绘制热图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9+vXDnDlzuudTERF1M4XVarWePXsWqampKCsrg7u7OwDg6NGjGD16NC5dugQ3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\n34p/iwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = ClusterTree(t_str, data_mat)\n",
    "\n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = False\n",
    "ts.show_branch_length = True\n",
    "ts.show_branch_support = True\n",
    "\n",
    "setup_heatmap(t, ts, header, center_value=0, color_up=0.9, color_down=0.3, color_center=\"white\")\n",
    "\n",
    "t.render(file_name=\"%%inline\", tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制热图时修改Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mylayout_only(node):   \n",
    "    #Change branch color\n",
    "    node.img_style[\"hz_line_color\"] = 'orange'  # change horizontal branch color \n",
    "    node.img_style[\"vt_line_color\"] = 'red' # Change vertical branch color\n",
    "    \n",
    "    # If node is a leaf, add the nodes name and a its scientific\n",
    "    # name\n",
    "    if node.is_leaf():\n",
    "        newName = nameMap.get(node.name)\n",
    "        node.img_style[\"size\"] = 12\n",
    "        node.img_style[\"shape\"] = \"sphere\"\n",
    "        node.img_style[\"fgcolor\"] = \"blue\"\n",
    "        node.img_style[\"bgcolor\"] = colorMap[newName]        \n",
    "        node.img_style[\"hz_line_color\"] = 'blue'  # change branch color \n",
    "    #If node is an internal node\n",
    "    else:\n",
    "        # Sets the style of internal nodes\n",
    "        node.img_style[\"size\"] = 1\n",
    "        node.img_style[\"shape\"] = \"circle\"\n",
    "        node.img_style[\"fgcolor\"] = \"darkred\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rUwJg8mQlAADHzp048/LQjRx5+v1rajDMnQsuF+zdiyYwEN3EieBwYPvvf0Gj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\nTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = ClusterTree(t_str, data_mat)\n",
    "\n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = False\n",
    "ts.show_branch_length = True\n",
    "ts.show_branch_support = True\n",
    "ts.layout_fn = mylayout_only\n",
    "\n",
    "setup_heatmap(t, ts, header, center_value=0, color_up=0.9, color_down=0.3, \n",
    "              color_center=\"white\", nameMap=nameMap, nameLabel=\"Full\")\n",
    "\n",
    "t.render(file_name=\"%%inline\", tree_style=ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## 矩阵为TAB键分割\n",
    "## 矩阵每一行第一列为行名字，与树的节点对应\n",
    "## 矩阵可以存储与一个文件中，也可以是如下的字符串\n",
    "matrix = \"\"\"\n",
    "#Names\\tcol1\\tcol2\\tcol3\\tcol4\\tcol5\\tcol6\\tcol7\n",
    "A\\t-1.23\\t-0.81\\t1.79\\t0.78\\t-0.42\\t-0.69\\t0.58\n",
    "B\\t-1.76\\t-0.94\\t1.16\\t0.36\\t0.41\\t-0.35\\t1.12\n",
    "C\\t-2.19\\t0.13\\t0.65\\t-0.51\\t0.52\\t1.04\\t0.36\n",
    "D\\t-1.22\\t-0.98\\t0.79\\t-0.76\\t-0.29\\t1.54\\t0.93\n",
    "E\\t-1.47\\t-0.83\\t0.85\\t0.07\\t-0.81\\t1.53\\t0.65\n",
    "F\\t-1.04\\t-1.11\\t0.87\\t-0.14\\t-0.80\\t1.74\\t0.48\n",
    "G\\t-1.57\\t-1.17\\t1.29\\t0.23\\t-0.20\\t1.17\\t0.26\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产生颜色的辅助函数，给定一个列表，这个函数会自动返回一个字典包含每个字段对应的颜色。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hex2rgb(hexcolor):\n",
    "    return [(hexcolor>>16) & 0xff, (hexcolor>>8) & 0xff, hexcolor & 0xff]\n",
    "\n",
    "def rgb2hex(rgbcolor):\n",
    "    r, g, b = rgbcolor\n",
    "    rgb = hex((r << 16) + (g << 8) +b)[2:].upper()\n",
    "    zero = '0'* (6-len(rgb))\n",
    "    return '#'+zero+rgb\n",
    "#----------------------------------\n",
    "def generateColor(labelL):\n",
    "    labelL = list(set(labelL))\n",
    "    labelL.sort()\n",
    "    colorD = {}\n",
    "    \n",
    "    r = 255\n",
    "    g = 255\n",
    "    b = 255\n",
    "    len_label = int(len(labelL) / 3 + 1)\n",
    "    step = int(250 / len_label)\n",
    "    \n",
    "    cnt = 1\n",
    "    for labels in labelL:\n",
    "        if cnt % 3 == 1:\n",
    "            r = r - step \n",
    "        elif cnt % 3 == 2:\n",
    "            g = g -step\n",
    "        else:\n",
    "            b = b - step\n",
    "        cnt += 1\n",
    "        color = rgb2hex([r, g, b])\n",
    "        colorD[labels] = color\n",
    "    return colorD\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'American': '#ACFFFF',\n",
       " 'Britain': '#ACACFF',\n",
       " 'China': '#ACACAC',\n",
       " 'Dutch': '#59ACAC',\n",
       " 'Egypt': '#5959AC',\n",
       " 'France': '#595959',\n",
       " 'German': '#065959'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clan_colorD = generateColor(nameMap.values())\n",
    "clan_colorD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "带有Support value的Newick树，供测试不同的属性使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nw = \"\"\"\n",
    "(((Dre:0.008339,Dme:0.300613)1.000000:0.596401,\n",
    "(Cfa:0.640858,Hsa:0.753230)1.000000:0.182035)1.000000:0.106234,\n",
    "((Dre:0.271621,Cfa:0.046042)1.000000:0.953250,\n",
    "(Hsa:0.061813,Mms:0.110769)1.000000:0.204419)1.000000:0.973467);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn off virtual X-server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vdisplay.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "* ETE tutorial <http://etetoolkit.org/docs/latest/tutorial/index.html>\n",
    "* ETE googlegroup <https://groups.google.com/d/topic/etetoolkit/pXr4B71Ozt0> \n",
    "* Vdisplay <http://stackoverflow.com/questions/6183276/how-do-i-run-selenium-in-xvfb> "
   ]
  }
 ],
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