_doc/notebooks/2016/pydata/im_ete3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# ete3\n",
        "\n",
        "[ete3](http://etetoolkit.org/) draws nice trees."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "[documentation](http://etetoolkit.org/docs/latest/index.html) [source](https://github.com/etetoolkit/ete/tree/master/ete3) [installation](https://pypi.python.org/pypi/ete3/) [tutorial](http://etetoolkit.org/docs/latest/tutorial/index.html) [gallery](http://etetoolkit.org/gallery/) [notebook](https://www.mrnoutahi.com/2016/01/09/Tree-manipulation-with-ETE/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n",
              "<script>\n",
              "function repeat_indent_string(n){\n",
              "    var a = \"\" ;\n",
              "    for ( ; n > 0 ; --n) {\n",
              "        a += \"    \";\n",
              "    }\n",
              "    return a;\n",
              "}\n",
              "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item) {\n",
              "    var anchors = document.getElementsByClassName(\"section\");\n",
              "    if (anchors.length == 0) {\n",
              "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n",
              "    }\n",
              "    var i,t;\n",
              "    var text_menu = begin;\n",
              "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n",
              "    var ind = \"\";\n",
              "    var memo_level = 1;\n",
              "    var href;\n",
              "    var tags = [];\n",
              "    var main_item = 0;\n",
              "    for (i = 0; i <= llast; i++) {\n",
              "        tags.push(\"h\" + i);\n",
              "    }\n",
              "\n",
              "    for (i = 0; i < anchors.length; i++) {\n",
              "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n",
              "\n",
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              "        for(t = 0; t < tags.length; t++) {\n",
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              "break;\n",
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              "        if (child == null){\n",
              "            text_memo += \"null\\n\";\n",
              "            continue;\n",
              "        }\n",
              "        if (anchors[i].hasAttribute(\"id\")) {\n",
              "            // when converted in RST\n",
              "            href = anchors[i].id;\n",
              "            text_memo += \"#1-\" + href;\n",
              "            // passer \u00e0 child suivant (le chercher)\n",
              "        }\n",
              "        else if (child.hasAttribute(\"id\")) {\n",
              "            // in a notebook\n",
              "            href = child.id;\n",
              "            text_memo += \"#2-\" + href;\n",
              "        }\n",
              "        else {\n",
              "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n",
              "            continue;\n",
              "        }\n",
              "        var title = child.textContent;\n",
              "        var level = parseInt(child.tagName.substring(1,2));\n",
              "\n",
              "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n",
              "\n",
              "        if ((level < lfirst) || (level > llast)) {\n",
              "            continue ;\n",
              "        }\n",
              "        if (title.endsWith('\u00b6')) {\n",
              "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\").replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\")\n",
              "        }\n",
              "\n",
              "        if (title.length == 0) {\n",
              "            continue;\n",
              "        }\n",
              "\n",
              "        while (level < memo_level) {\n",
              "            text_menu += \"</ul>\\n\";\n",
              "            memo_level -= 1;\n",
              "        }\n",
              "        if (level == lfirst) {\n",
              "            main_item += 1;\n",
              "        }\n",
              "        if (keep_item != -1 && main_item != keep_item + 1) {\n",
              "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n",
              "            continue;\n",
              "        }\n",
              "        while (level > memo_level) {\n",
              "            text_menu += \"<ul>\\n\";\n",
              "            memo_level += 1;\n",
              "        }\n",
              "        text_menu += repeat_indent_string(level-2) + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n",
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              "    while (1 < memo_level) {\n",
              "        text_menu += \"</ul>\\n\";\n",
              "        memo_level -= 1;\n",
              "    }\n",
              "    text_menu += send;\n",
              "    //text_menu += \"\\n\" + text_memo;\n",
              "    return text_menu;\n",
              "};\n",
              "var update_menu = function() {\n",
              "    var sbegin = \"\";\n",
              "    var sformat = '<li><a href=\"#__HREF__\">__TITLE__</a></li>';\n",
              "    var send = \"\";\n",
              "    var keep_item = -1;\n",
              "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item);\n",
              "    var menu = document.getElementById(\"my_id_menu_nb\");\n",
              "    menu.innerHTML=text_menu;\n",
              "};\n",
              "window.setTimeout(update_menu,2000);\n",
              "            </script>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from jyquickhelper import add_notebook_menu\n",
        "add_notebook_menu()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Example numerical matrix\n",
            "\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",
            "H\t-1.53\t-1.25\t0.59\t-0.30\t0.32\t1.41\t0.77\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from ete3 import ClusterTree\n",
        "# Example of a minimalistic numerical matrix. It is encoded as a text\n",
        "# string for convenience, but it usally be loaded from a text file.\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",
        "H\\t-1.53\\t-1.25\\t0.59\\t-0.30\\t0.32\\t1.41\\t0.77\n",
        "\"\"\"\n",
        "print(\"Example numerical matrix\")\n",
        "print(matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "         /-A\n",
            "      /-|\n",
            "     |   \\-B\n",
            "   /-|\n",
            "  |  |   /-C\n",
            "  |   \\-|\n",
            "  |     |   /-D\n",
            "--|      \\-|\n",
            "  |         \\-E\n",
            "  |\n",
            "  |   /-F\n",
            "   \\-|\n",
            "     |   /-G\n",
            "      \\-|\n",
            "         \\-H\n"
          ]
        }
      ],
      "source": [
        "t = ClusterTree(\"(((A,B),(C,(D,E))),(F,(G,H)));\", text_array=matrix)\n",
        "print(t)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
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          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "t.render(\"%%inline\", h=300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "from ete3 import TreeStyle\n",
        "ts = TreeStyle()\n",
        "ts.show_branch_length = True # show branch length\n",
        "ts.show_branch_support = True # show support\n",
        "ts.show_leaf_name = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMUAAAEkCAYAAACfV/VjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "t.render(\"%%inline\",tree_style=ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAEkCAYAAAA4rxSxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXlORQaWX5wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ts.layout_fn = \"heatmap\"\n",
        "t.render(\"%%inline\",tree_style=ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Ek5e3ZOUHvVjny7Vz2naW6vegUnOaV1ErQRlvRUXNa26A4Bqb0XoEE+KKHj1ixXe1vCpJ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\nSUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ts.layout_fn = \"cluster_cbars\"\n",
        "t.render(\"%%inline\",tree_style=ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": 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g+ZL8xxFzTgDQiV/91XBuXdWiwkPDIfUVHTZvtemnCKtBBCE5AUAfc2xzZ8u8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\njtDAoAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ts.layout_fn = \"cluster_bars\"\n",
        "t.render(\"%%inline\",tree_style=ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "data": {
            "image/png": 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2M9y8eROPHj0aLmdwyiAOjMimS5eAK1esJLybm2kfzbikshvMWtasASqt0Xpx\nBqb0R+sZ0DaaKHUGczN1Smhu+GUq13Grp6Jvem0btJ/py1haWsL58+dx8eJFHBwc4Je//CUANutR\nSvKU3xAAHj8u4sGD9/H0qfX83j2g2033mOaVdJNbHpr5sn58kTBuYx8q9uzs45UtqNsbuG00cHKm\njAraXUea8soF1LYfTd4PwpQx8vu///v4yU9+gosXLzI4UfJarRbu3LmD1dXVtA8llOfPnwF4EUC4\nEWlpJwD1Wj5KUgqETVRqr5ul3KMja5kQy74zw6b1/qysWMd0fHwM56osfF6zLI+jhmZo29gtXR1N\n3x6xX/mVXxn+zeBEqVhdXc1dEuHNTeD+fetX9blzQLk83ev8LhJRLvdKJuq3vX0RtoNS2HInXfym\nWT5ei4rmvGZZ7vyBYQfLuD8vv3VxlasIgUMAQBlqv4uZZ13S6yjur6Pf9Z8jd17379/H22+/DYDB\niWhqN25YTXlCTB+YsiZLnfxpDDn3m5DPCrhp9wXFo+t+cwMGP/gxNGUQmOaf4dbtk08+wWeffYZX\nXnkFKysrePPNNwEwOBGFoiiTt8niUPIs96XE2RflF4wWWmEN6yhiR2+gXQGg76CJdfQHUUevC1TR\ngdmujD9315gC9xNchtNHH33keZgMTkRzcg8bTyMwBTUhRV0ziaMvw68WFTbnnd9+J5Fxdl9/BTT2\nVChFAestK0Ptt71rRIaG7V0AqELsjhbXOibalaD9hCjDB4MT0RyyVkNyy1Iz3jTsWtQ0x8xa0RwK\nDXRN796nStsczZ0bsN3E9ZNeOwHvcyKSgNeIurgCU9y54Zz5+Zxl2cucee6cj3kkme+OufWmw5oT\nkYTyVmNyGzXzLZ9YRouBNSciicg2O+vR0TEn9VtQrDkRSUKmoETE4ESUQydHpi0nFphkHNkm4znl\nHYMTUcZMkxaJNSSSHYMTUcy8crgFGeW8s3jlUjs+Ti+Hm70u7dxxXJ58br0kMTgRxcrEyoprycRa\nT7y59eZdHkVuvWmXJ3Vezgu9bLn18orBiWKRxRQ+6eD7QDQLDiWnyOVtFlgiyh4GJ4oUa0zpkHW0\nGUfrLS4GJ4oUAxNR9ul1ASHshwJt4jQaBjTFYzu97thPHfrYSzQoocoYx+BEJAFZc8MlVZaM5+RP\nx62eir5pwjRNmJ0Smhua7zRPhqZAiCKahydWQKkCneF+gKpi78eAttFEqTNdGV4YnIiIFkoFbeek\ngZULqAX1Ou+lAAAgAElEQVRsXWh0YZp9qO4JNvsPcVi7gOEsT5UtqNjHbQOAcRv7ULFV8Vg3JQYn\nIqIFZmjb2C2dCT/DbfEsyru3xpvyIsTgREQkscB+H71uTb/erni+NlChgT21h+pw/x5Nf3PgfU4U\nC6+BEXfvpnAgC0LW0WYcrTe/rs8gJUNTrMDkbOILqdDoYjSfoI66uIUzBSBU55KPYXBy3pvCEVcU\ntUuXgAcPrLxxr776B/j+9/887UMiWlh6XaCKDszuyRrTcF2o2pQBTamip/bRBoDCGtZRxI7eQLsC\nQN9BE+voh4iCLwEn703hvSrZdnBwgFarlfZhTO3x4yIePHgfT59az7/88g08eVJM96ASxJuSKVMM\nDdu7AFCF2B0trnVMeMUjQ1NQtNvrigLNsjqsbTnXldU+ug07+hTQ2FOhFAWsb38Zar8dqobGZr2c\n+eCDD6yhmTn78fD8+TMALw6f83pNlJJCA91RW9wJlbYJc2xzZ9Ode1f+6yaVMwmDUw59+OGHaR9C\naJubwP37VtLTb33rGN/+dj/tQ0pMEj8kvBKksqzslZN0WXmutXO0HiXixg3g2jXghz8E3n77L9I+\nHKIF45PhYWwTZ0aH0aNujxWPMRuEFwYnisU1j19sigKU3TfyEVGsfDM8uBUa6NrZHkwTptlBDTVc\nqCD2bBBeGJwocl6BiYgsKyvLifa5+mZ4mMDQttFTt6wMEDFng/DyAmC1iTurcXnrbKfsuCYErvD7\nkzhZ79OR7T4nIax+V9O0/s7u7zgdO80Srtqj72LOBuFlOCCCAYmiwMBE5M0OTDb7b2eAysp/H73u\nuGcJGGSDUFAci6ZlqDEeQ2Cz3qefWv0EigI8fnwmxsMgonnImlVblqzkzsDkLsuuSWWmNmVo2N6t\njWpNA1bzoLM/qmRlg4jJCwDw1VdfDRfYf/+X/wL8u38H/K//ZT3u3VvH//yf/3t8R0JEJCF3jSmI\nO0jFHaj0uoCojzfW6TtNwO5r8mRngxhsU1jDOprYGY7qs7JBrM0ZuF4AgFdffXW44M/+7M8AAIeH\nwLNnow1NU7D2REQUQpjA5BRlbWo0Wu8QzaKAUAJG0ul1VHsq9honI4u1HyvB6/76yWwQvepg3EK1\nB3Vv9nx9tpeA8f6mZrMJAPgX/wL4T/8J+Pu/t5a//PI/4Y03/j8gIJ4SEZFl1sDk5u6bCrvPoCwO\n7mwQqLRh+lzi48wG4cU3Q8Q/+2fAxx8D//7fW2/Kr/3aRxDieaSFk7w4MCJZMo6gS7KsqMsJCkyz\nlpXlARRxGAtO7mHA3/kO8OMfW3+3Wn8v9RtBFBd3B7h9ccrr8pUVa/nRUTaOZ9GWm+ZouRCjz8K9\nfdJTc0RNmIM2PfvGSb9fvK1WC6Zp5jKvG2XLIn2XkrpvMKl8bUKMLoRJ/ILPY269Sc15cZyT32eR\n5/tWXwB44ySRlzwnzYya3Snvvk8nU0OgMyCqfqawZPwsXgLYP0DkxsA0Ms0Fd9H6Q7ykFZjcxj+L\nDBzQjJhbj8glz00hUQt7wfX6Bb8IcT4rgcnJOp78vvmcz4nIJY+BKa4+DK+3Ytqyxvs+/PcXJA+j\n9cIGprwPVEgKgxMRjYmjFiBrs18Wa0yyYLMekQSiyg03zcV2nrLCNvtlObferIEpydyEeTZWc+LA\nCKJwggZO5K15MOlaQBTNfmlhjSl+bNYjmlPegpBbFgJDnpr9GJiSwWY9Ih9eQefuXeshC+fkd1ng\nbvZLetbYSaQJTIYGZTjBrAItYNZavT6aiPbEtkH7mbKM69evDx+ff/75cDmDE9GULl0CrlwBtraA\nzc20j2bcLCPAZr3QJjXaLMkbS6c5p6gCU/qj9QxoG02UOoO5mTolNDf8MpXruNVT0Te9tg3az/Rl\nvPXWW7h8+TIuX76MN998c7iczXqUioODA7RarbQPY2qPHxfx4MH7ePrUen7vHtDtWn/79Ttltbkv\nC814Yc2bmXte0tSYAMC4jX2o2LOzj1e2oG5v4LbRwMmZMipodx1pyisXUNt+NHk/mL6Mzz77bPh3\noVDA6dOnATA4UQo++OCD4YyaefL8+TMALwIY/yU/6TyykjDUzodnJ2y1V4fdj70ujeM3zfGEp4D/\nDLNRJrq1y43rvOJYHkcNzdC2sVu6Opq+PQKvv/46CgUrYp06dWq4nMGJUpHHpK+bm8D9+9bF8Nw5\noFye7nV+F4kol3slE3U/H/36n6/cSRe/JM7Le6Tf7OU6L/TO5c4aU1Tn5bcurvdTEQKHAIAy1H4X\nM8+6pNdR3F9HvxvtnH5LS0vD2pITgxPRlG7csJryhJg+MGWFVM1SLnka6ZeGrvvNCBj84MfQlEFg\nmn+GW7dPPvlk2LT3gx/8YNjvxOBEFIKipH0E4eSxf2lWUd83JW1AL6xhHUXs6A20KwD0HTSxjv4g\n6uh1gSo6MNuV8efuGlPgfoLLcProo488D5PBiWhOWRgQ4dXEE9fFNQ8z4YYdQOHfBBq99EfrFdDY\nU6EUxSAtbBlqv+1dIzI0bO8CQBVid7S41jHRrgTtJ0QZPoaTDU6ySBPEEUUlrQzn0v7qn1GYJj+Z\n3rs8Z9jnfU5EErA79L0mBYyrrCREVdake6ac71/c13Lm1psOm/WIJCHTL/44+TX58f3LFgYnopyz\nLrLLvLCGNB6k+P5lDZv1iHLIOd2EaY5urKXw+P5lE2tORDng7iNx/8rPwwi6LJcl4znlHYMTUUbx\nplJaZAxORDELm/vOZjc1ZSVXm3tdVo6Hy72X572GxvuciGI0zX0mUdSQvHLQxUXGsmQ8JyDf9zmx\n5kSUsEn9R0TE4ESUCPYfEYXD4EQUKzORmztlHW3G0XqLi/c5EcVKsKZEmaPXBYSwHwq0idNoGNAU\nj+30umM/dehjL9GghCpjHIMTkQTymO8uS2XJeE7+dNzqqegPZqM2OyU0NzTfaZ4MTYEQRTQPT6yA\nUgU6w/0AVcXejwFto4lSZ7oyvDA4EREtlArazkkDKxdQC9i60OjCNPtQ3RNs9h/isHYBw1meKltQ\nsY/bBgDjNvahYqvisW5KDE5ERAvM0LaxWzoTfobb4lmUd2+NN+VFiMGJiEhigf0+et2afr1d8Xxt\noEIDe2oP1eH+PZr+5sDgRCQBWUebcbTe/Lp2n5DZRcNRPTI0BWL7LPrOJr6QrCY/e/8d1FDCmVl3\n5sLgRES0YPS6QPHhVZgegUmvC4h62MY6A5pSRU/dsvqgCmtYRxM79m70HTSxjrUQgYv3ORHFTLhT\nQhClydCwvQsAVYjd0eJax4RX656hKSja7XVFgWZZHda2nOvKah/dYdWsgMaeCqUoYH37y1D77VA1\nNObWI4oRAxOljbn1iMhTEhcHWROXypj4Ncnp4PP844h9TkRECRHCmgrFnsU4OT4ZHsY2cWZ0GD2G\n3U8xZoPwwuBERJQAZ43JNK1HEkHKN8ODW6HhGNlnj76r4UIFsWeD8MLgREQUM7+mPGeQiotvhocJ\nDG17NPou5mwQXhiciCQg6306Mtzn5A5MXmUlVYuano6dZglX7dF3MWeD8MIBEUREGWAHMDtApTnI\nTq9X0VP7aNsLCg3sqQqKY9GzDDXGY2DNiUgCsmbVzntWcq/mvEllJdHUF8jQsL1bG9WaBuLMBuGF\nwYmIKAbzDhlPoqnPKxuEvtME7L4mT9Fng/DC4EREFLGo7mWKYlTfaLTeIZpFAaEEjKTT66j2VOw1\nTkYWaz9Wgtf99ZPZIHrVwVDyag/q3uz5+mzscyIiilAcN9nO0x9VaHRhNrzXVdomzPEFMH2qTEH7\nsYah+62cDWtORBKQcQRdkmVFVc40gWmeslLvj0oQa05EMXN3gNsXp7wuX1mxlh8dZeN4srJ8ZWV5\n8J7EX64zQNmfg3v7pKfmiBoTvxLFSAghVW49O/3O8vJyIkOe85Rbb9rmvKjPKehzSOr7Fwc262VQ\nnpM1krzcF98kU/BkXZLJXN1kbepjs17GMDBRFgVdfLN082ga0gxMTs4AlYXjmReDU4bYVXAGKMqK\nMBc7d5Ca9nV5lpXAZPP6DPKKwSlD8to2TOmLo1/G78I7qSzna+b9JZ/l0XqzBqYkzmkUpPJ7TWFw\nIqIToryJ1N6f83neZa3G5E8AyMWBnsABEUQSiDI33KQL7yxlzTp4Iou59eYNTEnmJswz1pxyyq9f\nik2DNI+4awR575fKT40p/xiccoyBiKKU5IU3yn4pkhOb9TLIK+jcvWs9iOKQlft0sjzKTKpak6FB\nEYNErUKBFjBrrV63t/PYNmg/U5Zx/fr14ePzzz8fLmdwyoFLl4ArV4CtLWBzM+2joSyaZwRY2Itu\nXKPNvIJUVkbrRRmY0k8rZEDbaKLUGczN1CmhueGXqVzHrZ6Kvum1bdB+pi/jrbfewuXLl3H58mW8\n+eabw+UL16zXarXSPoRQHj8u4sGD9/H0qfX83j2g27X+dvc7sZmPwshqc1rW+qWkqjEBgHEb+1Cx\nZ2cfr2xB3d7AbaOBkzNlVNDuOtKUVy6gtv1o8n4wfRmfffbZ8O/z588P/16o4NRqtXDnzh2srq6m\nfSihPH/+DMCLAMZ/VTIY5UPaCUm9lo+SlAJhE5Xa6+I+TmdiWSFGtY0kE86urCwPA1MWP8eg5XHU\n0AxtG7ulq6Pp2yPw+uuvo1A4OfvTQgUnAFhdXc1d8trNTeD+fevX27lzQLmc9hFRGH4XiSiXeyUT\n9dt+dMGdbnv38kkXvzjOa3wAxfj29rp5ynVe6O3l7hpTVOflty6u91MRAocAgDLUfhczz7qk11Hc\nX0e/6z9H7iyWlpZw+vTpE8sXLjjl0Y0bVlOeEAxMNB8Zmqjcxx9H858M75Ot6z6RgMEPfgxNGQSm\n+We4nRYHROSEojAw0XxkuuA62QMpnIMp5hn1J+v7NFRYwzqa2NEHz/UdNLGOtUHU0esCoq4PN9fr\nAsWHV2G6A1PQfiaU4eTsZ3JizYlIArKONgtbltf9U+7lUZQzj/RH6xXQ2FOhFAWst6gMtd/2rhEZ\nGrZ3AaAKsTtaXOuYaFeC9hOiDB8MTjnFwRA0LelrAj78ApV7ndPCvFeFBrqmd+9TpW2OsvEFbDdx\n/aTXTsBmPSIJ+OVri+Nim2RuuKjKcjb9eTX/HR8fJxaYmFtvOqw5EUkoq/cwZcXJWtUy36uMYc2J\nSDJ2DYAX2+mY5sl7pyh9DE5EElmYPhOSHpv1iGJlzjSsOWyAWV5eTiwwyTiyTcZzyjsGJ6JYiZlG\nViYR0IiyjMGJKGaz5EY7Ogq3vfVvuO1nXW6vSzt3HJcnn1svScKc8mddq9WCaZq5y0vnJMM5UL4I\nMVvNKSyv3HosK3vlJF1WUt+/OHBABBERZQ6DExERZQ6DE5EEZB1txtF6i4vBiYhoweh1ASHshwJt\n4jQaBjTFYzu97thPHfrYSzQoocoYx+BEJIE85rvLUlkynpM/Hbd6KvqmCdM0YXZKaG5ovtM8GZoC\nIYpoHp5YAaUKdIb7AaqKvR8D2kYTpc50ZXhhcCIiWigVtJ1zM1UuoBawdaHRhWn2obrnk+s/xGHt\nAobz4la2oGIftw0Axm3sQ8VWxWPdlBiciIgWmKFtY7d0JvwMt8WzKO/eGm/KixCDExGRxAL7ffS6\nNf16u+L52kCFBvbUHqrD/Xs0/c2BwYlIArKONuNovfl17T4hs4uGo3pkaArE9ln03dOvh2A1+dn7\n76CGEs7MujMXBiciogWj1wWKD6/C9AhMel1A1MM21hnQlCp66pbVB1VYwzqa2LF3o++giXWshQhc\nzK1HFDMxSxZXorgYGrZ3AaAKsTtaXOuY8GrdMzQFRbu9rijQLKvD2pZzXVntozusmhXQ2FOhFAWs\nb38Zar8dqobG4EREtEgKDXTNhu/qStuEObZ5F36bB62bVM4k0jXr8VcqZc2oTT6+x9HRUSLlyFqW\njOdkmvlM+GqTKjgxMBERefHJ8DC2iTOjw+gx7H6KMRuEF2mCU55TwxMRxcU3w4NboeEY2WfCGn1X\nw4UKYs8G4UWa4MTARER0km+GhwkMbXs0+i7mbBBepAlORItM1vt0eJ9TWnTsNEu4ao++izkbhBeO\n1iMiojF6vYqe2kfbXlBoYE9VUBzr1y9DjfEYWHMikkCSma6FsB5JYFbyFBgatndro1rTQJzZILww\nOBHR1IQAjo6OYZrJBimZrKwsZ+Z988oGoe80AbuvyVP02SC8SBecODCCKB5CAM7/XqYJBqkQ7Pcp\n6eA+Gq13iGZRQCgBI+n0Oqo9FXuNk5HF2o+V4HV//WQ2iF51MJS82oO6N3u+Ppv0fU6/+AWwtWX9\nfebMa3jllf+e7gER5ZA7MDnZy+0LLX8fjnO/L3arnvt9cy6LUlAWB3c2CFTaMH2qTHFmg/AiXc3J\n6e/+DvijPwL+9m+tR7f7r/H1199M+7CIIhfnCDB3YPIrK46aVN5H69nvXdD7Z69nLXScMKdsBzs4\nOMDq6mrMhxOt73zn3+D0aQ3Pn1vPX3jhOf75P/9/sbf3f6d7YLQwhBA4OjoaW2ZfnNwd41lcbjdD\nzbKflRXr+ajGkJ3zinu5HWCc712Y/djvnXsfYfZjHUd+kxNMHZzy6KuvgN/93a/x7NnLAIAXX3yK\nH/zgOv7kT/5tykdGiyKpi8Px8XEsv/69mvPCljVPc19c5xVnOUFNoLOUNc/7l+fgJHWz3qlTwN7e\ny3jtNeC73wV+53du4uWX/yHtw1oIzHOYf5MustNalCYr+/yijgWL8v65SRecrrk+vd/8TeCnPwX+\n6q+AV155ktJRLRYGpvzjRTYcr76lqLnfP9neQzepRuu5AxMlz25GYIDKrzgCk5NMo/vSOAdnWTK8\nh36kqTldEwJXZPyEciav7dt5l1R/SZRlTVOTyupoPWcTXtivfJTnJHNtSpqaEwMT0XzirjH5yVtN\nKq33KYiMtSlpak5Ei2zefG1hLrhx5YbzqkllKbdeVAMe4j4n5/sI5DdCSVNzovxy90+xaTBZWasJ\njNeklk8sT0PW3qNpWAFKIK8BijUnSpU9gML54GCK5GT5omuaozx07n6VpPpX4hoeTpMxOFEsvGo/\nd+9aD5vfDYIMUMnI20XXmeYnKGBF8dWZZ8BDLhgaFDFI1CoUaAGz1up1ezuPbYP2M2UZjx49ws2b\nN3H9+nXcvHkTn3/+OQAJgxMHRmTTpUvAlStWEt7Nzcnbs2kvHFlncp1UllfAmqWW5Swn7qCU/ky4\nBrSNJkqdQWtFp4Tmhl+mch23eir6pte2QfuZroyvvvoKBwcHuHjxIi5fvoyLFy/iV3/1VwGwzymX\nWq1W2ocQyuPHRTx48D6ePrWe37sHdLvpHtOiy1utKSyvc/MKUDKOcpvIuI19qNizs49XtqBub+C2\n0cDJmTIqaHcdacorF1DbfjR5P5iujPv37+Odd94ZK/HMmTMAGJxyp9Vq4c6dO7lLwvv8+TMALwLw\n/yUr68CILCUkBUZzCtmrw+7HXpe185q03M6/61wuxMkBF2kfZ1TL46ihGdo2dktXR9O3R+Ab3/gG\nAOCnP/0pfv7zn+PUqVO4fPkyg1Mera6u4sMPP0z7MELZ3ATu37cuAOfOAeXyyW2cwUimPie/i0SU\ny72SiXptP6oxnVw3bbmTLn5pnNesy8cD0nLk+/fjtS6uchUhcAgAKEPtdzHzrEt6HcX9dfS7/nPk\nhlUoFPDXf/3XuHjxIt5991381m/9Fv7yL/8SAGtOlJAbN6ymPCG8AxPFT/amPPLWdX/oAYMf/Bia\nMghM889w63T69GmsrKzg5s2bWFlZwdHREd566y0ADE6UIEUZf26PynM33+U5zX9WMTDRUGEN6yhi\nR2+gXQGg76CJdfQHUUevC1TRgdmujD9315gC9xNchtP58+dx/vz54d82BidKldewcQam8IKakKIO\nTFkarZe3cpIuy1sBjT0VSlHA+p9Xhtpve9eIDA3buwBQhdgdLa51TLQrQfsJUYYPBidKHYNRfFhj\nIk+FBrqmd+9TpW2OckoEbDdx/aTXTiDdfU5Ei8hrRF1cgSmpfHdJliXjOeUdgxORhFhjorxjcCIi\nosxhcCKSDGtNJAMGJyIJOLM/xB2YZBzZJuM55R2DE5EkWGMimXAoOVHM4syl5rxFzM6XF3cON3td\n2rnjuDz53HpJEuYC3WTSarVgmmbu8tI5yXAOiyTKbBdBWbW9ctDFRcayZDwnIN/ZVlhzyqA8f6Fo\nfn45b/mVoEXC4JQxMmXjpskmzTFEtKgYnDLErjExQMkpzkAk62gzjtZbXBytlyFsypOROZxc0Ws6\ncaI06HUBIeyHAm3iNBoGNMVjO73u2E8d+thLNCihyhjH4EQUK5FIIJI1Nxxz68VBx62eir5pwjRN\nmJ0Smhua7zRPhqZAiCKahydWQKkCneF+gKpi78eAttFEqTNdGV4YnIiIFkoFbeekgZULqAVsXWh0\nYZp9qO5JQvsPcVi7gOEsT5UtqNjHbQOAcRv7ULFV8Vg3JQYnIqIFZmjb2C2dCT/DbfEsyru3xpvy\nIsTgREQkscB+H71uTb/erni+NlChgT21h+pw/x5Nf3PgaL0M8hoYcfduCgdCuSHraDOO1ptf16fD\n09AUKzA5m/hCKjS6GM0nqKMubuFMAQjVueSDwSkHLl0CHjywRny9+uof4Pvf//O0D4mIckyvC1TR\ngdk9WWMargtVmzKgKVX01D7aAFBYwzqK2NEbaFcA6DtoYh39EFFw4YLTwcEBWq1W2ocxtcePi3jw\n4H08fWo9//LLN/DkSTHdg6JQeN8aZYqhYXsXAKoQu6PFtY4Jr3hkaAqKdntdUaBZVoe1Lee6stpH\nt2FHnwIaeyqUooD17S9D7bdD1dAWKrcegNzlpHvy5Azu3fuXeP78RQDACy88w2//9sf40Y/+VcpH\nRtNgYKK05fUSv3A1p7wFJwDY3ATu37fulfnWt47x7W/30z4kCiGJi4OsiUuZ+HU+ef5xxNF6OXDj\nBnDtGvDDHwJvv/0XaR8OEeWOT4aHsU2cGR1Gj7o9VjzGbBBeGJwy6JrHrx1FAcrum+CIiCbwzfDg\nVmiga2d7ME2YZgc11HChgtizQXhhcMoYr8BERDQr3wwPExjaNnrqlpUBIuZsEF4YnDLkmhC4ktPO\nS0qXrPfpyHafkxDAysqyZ4b6bNGx0yzhqj36LuZsEF4WbkBEljEwEcnLzkzvfO6Upf/+et1xzxIw\nyAahoDh20GWoMR4Da05EEpA1q7YMWcmdU6Y4y3JPn2Jvl3qtytCwvVsb1ZoGrOZBZ39UycoGERMG\nJyKiGLjn8ZokjUCl1wVEfbyxTt9pAnZfkyc7G8Rgm8Ia1tHEznBUn5UNYm3OwMXgREQUobBByYtf\noJolWI1G6x2iWRQQSsBIOr2Oak/FXuNkZLH2YyV43V8/mQ2iVx0MJa/2oO7Nnq/Pxj4nIqII2IEj\n6r4j9/6cAWqassaTs46rtE2Y4wtg+lSZgvZjDUP3WzkbBqcM4sAICkvGEXRJljVPOWGD0rzn5Deo\nQrbLBoMTUczcne32xYnL8718ZcVafnR0PFif/PEcHY2WCzEe9PIerBYu8WvetVotmKaZyxyBi0gI\nwdx6OSgrTDnzNt8ldU5xNTMmhQMiKBZ5TjhJ5CWKgQ5Jso4xv/8P2axHkWNgkpvdnJWHC3QU8l4D\nySvWnChSSTVjUTqEsPpYnEOcZZW3mpJsWHOiSDEwpSOpPgzr4x2vOcU5YiyN0Xpx15SSHO2YZwxO\nRBRo0sXaa2hzHn+j5PnYZcRmPSIJxJUbzqtZK6isqNPvJJFbL+nmuyRzE+YZgxOlxm/gBAdUZIM7\ni3YYXul3ssKdDsg0R/cqUXawWY+ITpgnMLm5+6aSbjbL8tQU5I81J4qF18CIu3etB2VblIHJKamM\n2141I+eDYE27LgaJWoUCLWDWWr1ub+exbdB+pizj888/H/v7yZMnABicKCGXLgFXrgBbW8DmZtpH\nI58oRoC55x2Kq6wwTX5hsjbME4xkzBfoz4C20USpM5ibqVNCc8MvU7mOWz0VfdNr26D9TF/Gl19+\nOfb3P/7jPwJgs14uHRwcoNVqpX0YU3v8uIgHD97H06fW83v3gG7X+pv9S9kQV21pklmHo7Opbg7G\nbexDxZ6dfbyyBXV7A7eNBk7OlFFBu+tIU165gNr2o8n7wfRlfPbZZ2N/FwrWBgxOOfPBBx8MZ6PM\nk+fPnwF4EcD4r2Wv85AtYGUlUWlQAlPTnH57e13cCUydgxTsrBQ257qsvZ9ZWR5HDc3QtrFbujqa\nvj0Cr7/++jAg2U16AINTLuUx6evmJnD/vvUL99w5oFxO+4iS43eRiHK5VzLRafbjrDFNW+6ki9+8\ny0e1qeUTyxyvirRc54U+7s/Lb11c5SpC4BAAUIba72LmWZf0Oor76+h3/efIncXS0hJOnz4NADCM\nUcMfgxMl4sYNqylPiMUKTFmWVlPetOzaXPp9NPnWdX/IAYMf/BiaMghM889wOy0OiKDEKAoDU1Zk\nPTBRjAprWEcTO/rgub6DJtaxNog6el1A1PXh5npdoPjwKkx3YAraz4QynM6fPz/2t12LgkmUEr+v\nn0xfy6ydiz2GjRaD7/evr5plwARgAmVT7Y9WdWowUet4bDd62KuD9hO4bgqcbJAoRlnK0s7a0uLJ\n0vcvLDbrEUlgUr62KANTkrnhkipLxnPKOwYnIsmxxkR5xOBEJDEGJsorBiciSTEwUZ5xQARRjNLo\nkOakeWTL84AI3oRLJBHWlkgWDE5EMUsql9rKyvIg51y8OdzsdWnnjuPy5HPrJYnNekQxiqtZxSs3\nblL/k5NMKZRUWTKeE5DvZj0OiCDKGPdkeV4P95xFnGacZMNmPaKETZoRJKc/dIkixWY9ohil2fxG\nxGY9IvIhZpo2nChOel1ACPuhQJs4jYYBTfHYTq879lOHPvYSDUqoMsYxOBFJQNbccMytFwcdt3oq\n+kVje/sAAALbSURBVIMZtc1OCc0NzXeaJ0NTIEQRzcMTK6BUgc5wP0BVsfdjQNtootSZrgwvDE5E\nRAulgrZzbqbKBdQCti40ujDNPlT3XGz9hzisXcBwXtzKFlTs47YBwLiNfajYqnismxKDExHRAjO0\nbeyWzoSf4bZ4FuXdW+NNeRFicCIiklhgv49et6Zfb1c8Xxuo0MCe2kN1uH+Ppr85MDgRSSDJbAAy\nliXjOdm6dp+Q2UXDUT0yNAVi+yz67unXQ7Ca/Oz9d1BDCWdm3ZkLgxMR0YLR6wLFh1dhegQmvS4g\n6mEb6wxoShU9dcvqgyqsYR1N7Ni70XfQxDrWwgSucLO6E1EYAPjgI9XHCX3VLHtsV+tYqzs1mLCf\nmKbZV8vj25ZVs++xrqz2A8opm+7Vk4hPP/3UNE0Tq6uryLODgwPIcB5ERAS88Omnn+LTTz9N+zjm\nJst5EBER+5yIiCiDGJyIiChzGJyIiChzXvriiy/wxRdfoNVqpX0sc/n000/xxhtvpH0YREQUgZcA\nDG+iyjsZzoGIiICX3njjDfzGb/wGPvzww7SPZS6tVovBiYhIEuxzIiKizGFwIiKizGFwIiKizHlp\ndXVVir4aWc6DiIgAYfKKTkREGcNmPSIiypyx4CSEmOpFfttN+3oiIqIgw+DEwERERFkxDE7Tdj35\nbceuKyIiigr7nIiIKHMYnIiIKHMYnIiIKHMmBicOdCAioqSdGK03KRj5bTft64mIiCZhhggiIsoc\n9jkREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgR\nEVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHm\nMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHmMDgREVHm/P8M\nJ6VcbR7jzAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ts.layout_fn = \"cluster_lines\"\n",
        "t.render(\"%%inline\",tree_style=ts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}