jupyter_notebooks/Data_Cleaning_NCBI_NR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application of BoaG in identifying misassigned taxonomies in the NCBI's NR database\n",
    "\n",
    "Here we use BoaG infrastructure to quickly get the frequency and proveance of annotations for the entire NR and all the clusters at 95% sequence similarity. Then, we use ETE3 library to generate tree from the BoaG output.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Boa query to get cluster ID, sequence ID, and tax id\n",
    "\n",
    "```\n",
    "s: Sequence = input;\n",
    "counts: output sum[string][string][string] of int;\n",
    "\n",
    "foreach(i:int; def(s.cluster[i])){\n",
    "  if (s.cluster[i].similarity==95){\n",
    "    foreach(j:int; def(s.annotation[j]))\n",
    "\t  counts [s.cluster[i].cid][s.annotation[j].keyID][s.annotation[j].tax_id] <<1;\n",
    "  }\n",
    "}\n",
    "\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from ete3 import NCBITaxa, Tree, faces, AttrFace, TreeStyle, NodeStyle, TextFace\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAD1CAYAAABqSP81AAAACXBIWXMAAA3XAAAN1wFCKJt4AAAg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\nWHJ0Dc6PGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ete3 import NCBITaxa, PhyloTree, Tree\n",
    "\n",
    "def layout(node):\n",
    "    # If node is a leaf, add the nodes name and a its scientific name\n",
    "    nameFace = faces.AttrFace(\"sci_name\", fsize=20, fgcolor=\"#009000\")\n",
    "\n",
    "    rank = 100\n",
    "    if node.is_leaf():\n",
    "        faces.add_face_to_node(nameFace, node, column=0)\n",
    "        node.add_face(TextFace(rank), column=2, position = \"branch-right\")\n",
    "\n",
    "        \n",
    "ncbi = NCBITaxa()\n",
    "\n",
    "setData=set()\n",
    "listData = list()\n",
    "\n",
    "\n",
    "# load the whole leaf name as species taxid\n",
    "tree = PhyloTree('((9606, 9598), 10090);', sp_naming_function=lambda name: name)\n",
    "tax2names, tax2lineages, tax2rank = tree.annotate_ncbi_taxa()\n",
    "\n",
    "# split names by '|' and return the first part as the species taxid\n",
    "tree = PhyloTree('((9606|protA, 9598|protA), 10090|protB);', sp_naming_function=lambda name: name.split('|')[0])\n",
    "tax2names, tax2lineages, tax2rank = tree.annotate_ncbi_taxa()\n",
    "\n",
    "# print (tree.get_ascii(attributes=[\"name\", \"sci_name\", \"taxid\"]))\n",
    "\n",
    "ts.layout_fn = layout\n",
    "\n",
    "\n",
    "tree.render(\"%%inline\", w=100, units=\"mm\", tree_style=ts)\n",
    "# tree.show(tree_style =ts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotate tree with sci_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAACXCAYAAAAvdFFkAAAACXBIWXMAAA3XAAAN1wFCKJt4AAAg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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### 1K9O:32630=3;7130=2;9913=2\n",
    "\n",
    "from ete3 import NCBITaxa, PhyloTree, Tree, faces, AttrFace, TreeStyle\n",
    "ncbi = NCBITaxa()\n",
    "\n",
    "def layout(node):\n",
    "    # If node is a leaf, add the nodes name and a its scientific name\n",
    "    nameFace = faces.AttrFace(\"sci_name\", fsize=20, fgcolor=\"#009000\")\n",
    "\n",
    "    rank = 100\n",
    "    if node.is_leaf():\n",
    "        faces.add_face_to_node(nameFace, node, column=0)\n",
    "        node.add_face(TextFace(rank), column=2, position = \"branch-right\")\n",
    "\n",
    "\n",
    "\n",
    "tree = ncbi.get_topology([32630, 7130,9913], intermediate_nodes=False)\n",
    "\n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = True\n",
    "ts.branch_vertical_margin = 10 # 10 pixels between adjacent branches\n",
    "ts.layout_fn = layout\n",
    "\n",
    "tree.render(\"%%inline\", w=100, units=\"mm\", tree_style=ts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example of misannotations\n",
    "#### Boa output\n",
    "\n",
    "```\n",
    "count[NP_001026909][AAH13172][9606] = 1\n",
    "count[NP_001026909][AEE60937][9606] = 1\n",
    "count[NP_001026909][AIC53124][32630] = 1\n",
    "count[NP_001026909][EAW98870][9606] = 1\n",
    "count[NP_001026909][EAW98876][9606] = 1\n",
    "count[NP_001026909][NP_001026909][9606] = 1\n",
    "count[NP_001026909][Q96DX5][9606] = 1\n",
    "count[NP_001026909][SJX33320][1958742] = 1\n",
    "count[NP_001026909][XP_005274503][9606] = 1\n",
    "\n",
    "\n",
    "\n",
    "                                          /-synthetic construct, species, 1\n",
    "             /artificial sequences, no rank\n",
    "-root, no rank                            \\-Human ORFeome Gateway entry vector, species, 1\n",
    "            |\n",
    "             \\-Homo sapiens, species, 7\n",
    "             \n",
    "             \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in tree ncbi\n",
      "Error in tree ncbi\n",
      "Error in tree ncbi\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAABICAYAAADMHP+eAAAACXBIWXMAAA3XAAAN1wFCKJt4AAAd\n60lEQVR4nO3df1zUVb7H8RcwAzMwIKAojooIKvgDVDSFFAW1kpLyB2XXtK22NGsr7W621a61tG3X\n9ofiriZmsZvhbkrp4t3QSvFXgiaQTMn4Y0QRSEUYhNEZZwa/948ZYIABtKuPEfc8H48eMfP9nnM+\n53zh8GVk3rhJkiQhCIIguIy7qwsQBEH4Tyc2YkEQBBcTG7EgCIKLiY1YuAmsGMxWVxchCF2W2IiF\nn6Tshyz26e2bryGP33yZ95PaGisycFu3FsOtKPIGa7kd+hH+M4mN2EUMukK0J75jW35l03NWoxUM\nOvZpqtBX6jtsbzR20G9V+xuCdtc+qmyjUXVOi/ZcGYYGMNRVYWhw6N+gxwgYTQasWKk8p0V7sbEm\nA0dLHibzZAl6gwEr4K3wwdpQhaZMi77BcUQjurJCCisqnbaVB83g5KzZqJrGLUNTpqPK5HwOzo4b\n9FoKSzUO4xoxmK1YTZWt6mk557bzMGIw29rr6/RYGwzoTc0LbTDoaR7VSFmFBu3FKqxO1kMQboTH\nW2+99Zari7gT5eyvwVL0dzJ2n8a9RkeVUsaRby5g/C6LLQc1VJ4uQufux5m8gxw/fhGF/hCf7trD\n8coLFB1TEB2ioP74N+zIP0S11wD6K8+S9cl2jpabMBzPQ1PyHdoTBWTvLuL4j+6c3/ExX+uu4lu3\nn0N6M1nr92Ct19Otl4Fdh86gyd3D0XILhuJD+I6L5pvPp5Fado0rZR8w+cAF7q1bzMLKRJ4I7QEN\nGh5JX8yIux7l/N459N32AZdrT/LkN48Q2Osloq/m8XpeJsWXrnH2bDkRYQP515ejWa2t41Tpkzxy\nOJhfjh6DZ0MZqesns/faAOpPvskzR7szL7CCXzu27e/B0x9v46HY8VQVpxL0eQbBluM88NUHTBv5\nKH1lzWta5uT4Nc0b9MzJZdi170nMyeTBkbPobS1gztr+fHDiskM9w9iR1XLOC/tcJtWxlr4yXvnb\nfDYUreLJb49zb7fvefiQF4si+wN6Vr4/C0Y+QX+3MlLXB5FRHcyPmgf43Wk1R8tzmvsZOI5AD5d9\n6gldkdTFzJ07VwJu7//kPST1PQekBUs+ljLfT5Oyvz8ipS1+Xdp7wSKdLDgjSVcKpHXr/ybtLd4v\nZRfXSCXZm6Vt7y+XsvfmSJmbs6TMvRekM5ozUt5Hm6WKK/XSmQtXJEvFTmlzwQWp/kyhlPFRgWSp\nz5PeX/uhVFxzQcrJ3C+V7M2RPkp7X9q/L1vaW7xf2lxQIxVkbpZOntkrffz5Z/a2Z6RvMjdLFdZi\nacbKKCmn6krzwtbvlaJWLpYqJEmqKFwoJeQUSJIkSXlfLZayqyySJElSTclvpahteZIkSVLxVzOk\n5afs7Y17pcVbc+wd1Ui/XZkg7TVK0skD86SorTnShfoaqeZSgbRw5Qyp4GrrtnnSjNVpUo21RJq3\nMkHKu2rvxnpFumJ1uPBOj1dIr6+c0fRcxZHFtrqd1WNwMuc288iTFm74SLrQuCSnlksJ9vlKUo2U\nlj7DPq8ZTesgSZJ05aqlZT+CcIO63EsTBw4coLS0FEmSbt//zFVMH5bHrx8fTGDoWEaH9UM9YBTD\ng2T0GhAI9GNyYjxygzdDg+V0HzqKCXNm41MLMVMmEsY5lL0CGTNjOAX/3o9FJkemjiPiQj6HLvYi\nZaKZnP2ePHT/JIIVSoaOG4yfjw+9R41nxOjRyA3ejApWEJ00nKNHLNydcI+9rYxBUX2prhnC6gd+\nRebn43BLc+PZ3flYVXG802snWSe1ZH17jFfvjrGvuDf+CtttqdxLQXf7sxYrmBpMtgdW8Pbytx+R\n46+0fXzpkgEsxXx2eAMbDu1nZMxTBLdu28hSRbk8iQhP+2MPJUqPTo5bdByQxzU9F+A/AKxm5/V4\nRLWdc5t5WOgePISgxjEbAJlnUz+NqqprSRkY0fRY6SlzPidBuE6yzk8Rfor0FS8D0A+wVmmJSHmA\nAIAAFaAiPCyI8DD7yUG2V0gnJ4fbHsc3bgWRJKdE2j+WETUt2f6xmsZTAQhXAUGo7Q9j49TN7ZPt\n7R3aBjXoqfR6hA0L5rLBtI/o9C+pT4hlavyv8M4aAn2ysfh1PD+ztbadI5amj4LVoWiKh/Bsgm1s\nq6GKeg9nbU2g6M9Iy0T2618mOUCGsa4Sk0JNQOM+6PT4cJIsz3OwbinT/ODkqVz8u90L9lfBW9TT\noIeebefcphaHF3gV3cOo3nUIIzEoTaUcNcJYIKRPBG9+d5BlkdMAI5V6UwfrIQid63J3xF2RLCiS\nKLXS1WU0aygnNUNO9LqZzPz4eabETCcAUPZJYjnwftxUh+/QXi2aNt5nhgxI4s1/B+K2Zgmaq86H\nUUe/TbbvR7ilRZO4Lhr5h7+nvKFVWxMgUwAhLJuZyZ8/jmFmRiLeGamUt+jN2fEAnpm5hKUZ0czM\niCb6eCKr4yOd1tLenDuahyzgbuaxCO+0aBL/8R4H7P+kqB71Lptly3FbN5PENG9Svy9vOydBuAFu\nktS1siYGDBhAbm4uoaGhri6lyzOaDDS4q1A13nXWbSf6k2L2PLfUdvfeGbMRo4ccpUfHP1hZzQZM\nKFB5OpzXblsrRpMFpaK9b1xOjjcY0BshQKVqp02zNnPudB5WDCYLKmf1mI0YHV9Cuc71EITWxGfM\nfzClouXGpf1hBymTXrm+TRjAU8n13OfLPFW02SLbbStDqejo09LJcQ+V7RWf69B6zh3XYhtP1V49\nrdtd53oIQmtiIxaaRMatYJmrixCE/0Bd6jXiigtmJPG9QxCEO4wMID09naVLl+Lufnvvy70nZXPB\n92VXlyEIgnBTyQC++uorVq1axUMPPeTqejr06lvrqS/+B5Dk6lIEQRBuGhmAm5sbPj4++Pv7d3a+\nS61d8UvC/rXa1WUIgiDcVLf3axGtuLm5ugJBEISbr0ttxIIgCHcisRELXYJm9xLWljanFhvK1vLs\n7kIXVgSGc9t5dk000WuicUtLZFWxznbArGHJumgS1yWSuG4mq4q0TW20eW8QvSbRdmyNG8/u07io\netAWryIxzY3oNdFEr0nk2e3baDd8tUHHxn27aCd99bbi0mzon7hOYiO+hfSa7WiMYNXuorCq43ON\nzj5vrGVkrc1gY8Zatmn0aLLWkrVrOxkZ2ynXZLFq4za279LYcoOv4/Ou+Rwj+zauZWPWRjZu17Y4\npzGv2NpOh9f7vF6zC03Hkcqtaup4fIvpNBccc44b6jhmCw/GYM9Otj1vQG/PKm7KUq7QoKuznaG/\nqEVzrvFiWNHry9CWadDpHaPp28szdmAuJPnTJMYl7aT4uWKkn/+Jb3MHsqpUD9cuc9qaQubPc/ni\n0d9wdO8QVpXZ+r9y6SjJkzLJ/fkXfPH0FVbcPcQ2Yp2OwpMaKlu8PdqAtrQQzUXHhXSsTWebt1mP\ntqx129Y50C3pvp3PkP0m1i+UKH6umMKn/s5UawmlBrCa9ZSd06Kp0DVlVBv0R/mfwp2cqdOjt/81\nltY1G036pvOtJgPGxrZN18fJepsNVBkctq0GA3qDs22s7XyuLyu7ZcZ0bTu5203M+g7qcb6mjhnZ\nztbJ2XU0mgxNa6Y3WcUv5d4qBSUGwiw15GzZhqa8hLCH/dl+rh+hFQVU/FjMCa8guOqFl1cYdweX\nk3v4KD2GhXDqrERQnzieTI6k6vAhes1+kvggIxtTt1A9EFTePtTXn8doMePpGUifgcEc2biW7+rM\nDL77bs59fxquqlBTjLZpjP4MRoOmzszge55hst8RzobNYe4YK5qSOuorC9mxYz+VvokMPlVEVcNZ\ninQXUal6M2hEd4oOw9jecnr1sbA79zDekRF0C7kL/xM6hifHU7NrI1uPX6RHWAQXtaX4eoYyyOsQ\n5yQF//zjMSaO6M2QCT05em4Ak7oVkUck3kXHMZq1HKszEzpsKDVna/HtNxb54SKqGs5z3gRePr7k\n76zk+d8+CYBeX0qVvjtG5FyqvWT/p2Y9az6cxZiFuUxWgKH0z0z6IZHih+Ip2T+f0T/UsrBPBAcq\nDhDu1x3kEVCdTq+xJawddYXXNq9AHXYXlaeiUY4qYcVdkWA6wvz0OGr9FhJBOumW96lf8GyLdwdW\nnfqM3X12kjvAHtCkimHVfa8TWFDEi9O9QeZPgAco/aIZKYc6hy/+K+Y6DEY5xmugVARR9kMq0/P1\nLBnSm//+92O8/NhBkgMqeSN9INUhaSgrR3NxcDEbEqKaa+u+kAjLAdIt4SQAEX61pJ+PoGTRWiI9\nykj9cDr6sCUMqP1vnuBlClOSHb7Yq8g8cITsJzcQrrA9I1OEkDJ9KQCa3a+x7KKaREUl0TolJYt+\nz+n8TWgoZ+WXrxEW/gqPemS2qTn0+1m857WeDXFq1qf7cmzqBVYMg3c/fITkhbnEUtx2vaNqmfLh\ncjb9YguRHqDdN59FvEtugkNmSIPz+The3/SKdNIerOEZ5UHSK0BXv5rUM0P5xb1jeeuT1zDIqtlq\nvJtPBh5gvc8mW/8NGuZ+uIzUX2whqvFt6teOMeXD1Wz6xQYiPUB3YD5PN6SSG9/NaQ2Vxan0zy3i\nt+GhvKnTsSK0W4t1WhqN0+tYsn8RT5wth7rdjBhR0PU24pEjRzJ+/Pjb/neeGbaJWUMvkfLaXCK1\n9WhNZgyX66itMWD0imZ2Uh/yy4MZdHoHxWfc6RMEmvJrJM9JpiLH9iOu0t+L82erIKCOqz2D8caT\nfiH+HD9kwctcw+BRY4lSG8k4CYMnqDGUlxM44QGGHtnCvmrHMbaRUwQjJqgxWxpA6UldyTEMEf58\n88/d+M4Pxezri+niZXr2VVOpO8WAYdHU1/lQ9FEungOusrf3QmZXHMK3h4KLyhF4Z6/m1NRfEgt8\nf7zcdn6lgQGJcxh0ej9Sn6FIcndGzZ7BlOAiXkndz5K/xqCUjaUm9Tecm/kcV7doGTxBTVVZGf3u\nfYRwo5FzZ9VUlsOkJyfx1apsRs2ehVoG1QoVK4vew6jzAeByXTr+YbMBUCj9m0MqPRR097A9MhNK\n9mN/ILmHDM3XM/mo52pWRKsxloZx/w+1oIhl7ROr0J4r5bTPYpK+/ZJld0USgIXQ0BxWPDQNeBd1\n2iyKTM8Sr2i+vGdPbyNKNbvFJZd7dYOKw+jdp4JxPX/Y7QnnPiWrZyZ7Bti2cblCxcq8RVz8zp9y\nCzw//U3+lZvLO3M2MUElY4xMw4vflTC65wYOhBWQOy0GSGFJ2vPkx24h1rG2hkKO/fUzMl96BzVG\nhq6bS9U1kBe8QVav99gZOw7ZtREczXibYnMyMY35GiYdWXRnugLAiqZ4E99cuoqXrDf3jZ1GVMJa\nPrqopbT2NK+XJZFdtoylSUuY8eF+VqS8iLJBy/y/tq35q+GPc2T3Cayjq/gGKC8pwDrIn6PKObym\nAIhxst4vsjpcx8riKtaOspB+BP60qGVwk+6Q8/k4Xt93tWom/XCMF6dPI3XYDHLCV7B0gBJM+fj4\nPc6f5z3JFgDDPpZ/mE5lwgooXk1txG+aN2EARSyrQxewQVvFO8Pgo8JaUhdGoTs0v20NxkGsyM0l\nb1EusZ6wrMGIsaGEPX+zrxNQWbzE6XX0xIfn4jfx7EDbN/IutxFnZmZSU1Pj6jI69dwbX/DKgsm4\nqyAgfBzhAb3wyculIjiCEf27oVQoGRqsxM97LBFV57lo6ccD/oEEK5R0G2fLuFRFJjMqfztZOZ7c\n97PJyM/oUKnDeeqBQnTmPkSqVSBT8fATY8k9cpkxU8dwbs/XnAm9h3uHGh3GmMR/jbjEt0cuM7q/\nCpQxzJmYz/adJxj51Bz6Bp4nuNKHsAg1/QB3RRQ12lIGTbgLeYiawGCokakJPN+XQJ++RKi7cdmv\nH4OG2FIpxsyZzd79Jxg0cSxymRw/76G4Ka9gNfmg7qZAFqAmYpw3ITIAFZNmzscyZBB+3erJPXKZ\n+PvvRrtzO2VjpxEe1Rd3mYqCbRomzE3CYrbtfhaTgeX3bLR9cQHWsqHcU2x7acJkdUwLdtgtHbKU\nLVbo7W1r23hzqi9dS2D2GpbHvENctwgSZLb+2stXdhSsngJl5rYXvlc0AdfMwBQSB40k9+gi7o77\ne1N+h8VkYHnSF03zwJTPaqBE+xmlVjMwnpdDgynTfkdiZOMbzv2IUNoDPR1rs5jxV/ay51s0vy7R\nnAN9HLOVphzo5iUKIZlqas2AJ8g9gxkcWMNfvl7OwFHj2PGPQFbIlvNOVBz9AhOos4+F1TaK0lJL\nuZOaZcHeTKnbxNcFVxn/wEn8Dv+VHG1veg1OQtXBesePX8bEzZ+x2LeOnaEvsMIxkKmD+ZR1mpWt\nbJsxrYrjnV7Pk3VSC98e49VHY2gtbtyvmJizh8Xd69nW62XeUUChsxqcZmQ7rBNQVtbOdUTNsOCm\nqrreRuzt7Y23t7ery+hU9t8XND9QhqMG1JOTcbzsKiUQNJig8MEtG4c3/xAcHjuNpujhSPtH4TGM\ncOwnJIbkENvH6qbcYccxbP01ngMQEB5LSlPHAUye3HgXEmL7pB0U0Xii/QwgIJbJkYC1Ck3CbCLt\ne4ksILw5S5nm8XrYH1Zpr3D/w7HNh6Psq+BYd0qK/WisbfwhtNH0xYVjCLsvQwJrOXTBQGyIivJz\nR0E2tm1jHLcqm2M/rGFx4k6WRgdhvZhFtdXZ66kWJ8+BOiwZchewS1/I5AAZUMmanFd5fdIFQAfK\nAYzqE0v80yXMf78/q3xreHFgQJt5oFAzkt2EDc8lpQeAlSq9CVlkInHfHWTZwGlgLmWHUcV4Zz8E\nWttmbraXA+1QPckRELd1IzXz5hIZOZlIDBzesxpM37Oi+nV2vrSUIKxs01TbNmIs1DauYDs1Q0/u\nC3yTpEMLKXkpHL966JP7KpkpL3S83gEPkOntzZB/Q/Zjbde78/m01FHGNMg6zd2WBSfxPpOY8iks\nmXmg/RqUxrYZ2Y7rBES0cx0v0zJzVQZwxerb/qwEoTVZEFHtxP46ExTZ9q7jp1C0emy7L5QxOiqJ\nB7f4sl6ewAhlNX37NX4TbM5SlstUze09FPjLYMTIZSz4tCfR+xMIVwLKOddfjGoyOx94jikfy+nu\nN4Pquq2MGLGXjGFBYLK9tGQB8Ixkw+M5RH88icGPF9KzzTxCeDslm+RMN1KVCWDczZSxJayIe4HM\nw5NwW5dOlHEr86ZWEOUJON6Eu3uikjX2JsdTbs9Ljn6b7NL5uKW9QYISdhunULxoBQEOm1fstD1k\n/+9TBKb9DzP8wtEZdYT3+RXzfMfwTp/n6Zm2jQRlOFhhpgcg70+SbCKBaa+yMKaYPzqtOZLowYuB\n+4gEGDSLqL1GYoJt33RGjGlvvZUkxf4WdvVjao+294btzaezrOw35YspnjOrTX+Nudt+LXK3HQXw\n4NhkFn3djekhHa/pspmZzPo4ho/8urO1LoLiBa+3WKe18c6vY36r2t3m/bpE2vblUda/co2UprsS\nQeharGYDJg8Vqhv9o50NRgwWnOcNX9/IGAz1oAy48bFb92MyoVCoWmwOBoMePANa5idfb4/OcqBb\nazBisDS0GddoMoBC1SbW02gyIlco7ec6r7lD7az3rqxo9g/fw7LI9kNYr2s+jTrKhr7R3O1Oa2ib\nkd1ynTq/ju6f/GEKQwMP3WA5gnB7kXn+hE0YwEP5/9iEAWSoVP/fTdjej5MNTaX6aZsw2Neks03L\nQ+l0XKWTTdj2vNLhXOc1dz5eq57NGrbVz+PpDjZhuM75NPJUthvQb8vdnnfDm3D7Ncja/CGDluvU\n+XV0kyRJSkl5hEcffUTcEQuCILiAO4CbW5f6a0mCIAh3lNv8l3EFQRDufGIjFgRBcDGxEQuCILiY\n2IgFQRBcTGzEgiAILiY24utlLmTJPzNwSMQl459LKGz7DtPbirEiA7d1azF0fqogCC7S5TZivWY7\nGgNg1LK9k5Df683ONZRpKOtsp7pm5nRNy/Hq605zubEPvZbCUk2L7NrO83Dbb9uiXlMV2jItZXWN\nRTrP0XUcT2t/Xhk0g5OzZjdFOLbNvW0/e7ftuIIg3ApdKvTnqlkCSw05m7dTQTE/9h/Pxo174KqK\niSNq2HjoKgrzVTzpw/jhVzl2GbyC7iY5NoDtq/7C6R5juC8KducexjfxGSJO53Dsqifefp4E+Few\nNf84nupExvMN39R7ERbqR/63Vdw772fEBgJcolRfSfcGkHtUc96e+lVW9Ab986pJC1MyOvsiBQs3\nEKOg8zzcuMh22zYynsvi/k8zSRo2irwf3iTxwRpe7F3qNEe3ebyRHKhYScrUCpaF63g6M4/PX1pK\nfbGT3FuV8+xdD2fjDvgp70USBKEzMoD+/fvz1ltv8ac//cnV9XTI2v+P9PK4wMt/eITJ+PC3XeUE\nTnyQoUe2oKv3JGnOPVQdPIu/pYIzZ+yZthk6iO1J6Khofiw6Tomuhy1Tt9bKcP8opkRe5tPPj0J/\nNzwHDKdHfRXukffz7JCLbPi6llFBFZRoq4id4AmW3/PejmrsibikWyCZStL3HiVv0RZiPSFFvYTH\ndheSOy2m8zzcDto2OqNdze7QV/li6jSWTm2M0wtwmqNrJpTsx1eQHADG0t6MO1jMsnB//OUKaNDy\nhpPc2+QJzrN3g5yOKwjCrSADePvtt7vE25v/8MFBFv5XApEBMiCc+MnDuVRsy98d270Og0JJt0HB\nyOhBmN8V8rdpmPDwZAD8fHzoPWo8w4Muc7yqLxFqGfIzp9mv6UNK8iTMSj8CDxbAuHH0D5QDSsYN\ndOcUw5k4Sg3XKkGZxrpHX7S/B9/IyHVzsZh0HJDH8Uv7+8gD/Ac45NR2nIdLh21tIuMzyfzfV/FO\nSwL5QvLm/ZWI6vXOc3Txxt8ezKsMGkP3mu/RY4+EbCdDFqvOafaus3Fj/brUD1CC0GXIAJRKJbGx\nsZ2d63Kbx8bS/Ic51LYoPXVj/m6QbYN0yPKdltyc1aiOiUdt/7iv/WlDr6mEq5rDOtTTpjmMpmLw\nkCAGN+bimgCrCYdEXFsioWI4SZbnOVi3lGl+cPJULv7d7nVaf5t/17uOtnq9lZkPbUBiA/v+Fc22\nSj2cbD9H9/I12//LTmyiOmwBAZhtI7ebIeuoOQvW2bixfkEIgnDzdalbnJv915FUqhtM3ZK1TsQF\nCOCZmUuYlBFNuh9stTxNxYLGbwAd5+F23Nbm/InfMyTzgC0z1pLM36YEMcTfea6rj6yMuE8SSZBV\ns5sUSp6IgWv59rrbyb2Ncj5VZ+MKgnBruEmSJBJ/boYGA3ojBKhUnZ97o23tmbEqharVcy1zXfO/\nfgNL7DvEeVqRtRsXeAMZss7GFQThputSd8S3NQ8VAT91v+qsrYeybd6tk+d8vHpxGTrYhKExQ/b6\n6nIyriAIN524IxYEQXCxLveGDkEQhDuN+9mzZ29Z57eyb0EQhDuF++eff35LOrZYLNyqvgVBEO4k\n4qUJQRAEF5Pt2bOHW/HvdQ0NDeTn5/PSSy/d9L4FQRDuJLL4+HheeOGFm96xxWLB/Wa/A0MQBOEO\nJHN3d8fD4+b/sui1a9fERiwIgnAd3E6cOCENHDjwpncsSRI6nY5b0bcgCMKdRDYw1JOM1FTOXoWx\nT73MNHtojkGbxbubKunXU839P0vB8vUqPqvwxM9vGD+bG0/ltg4ePxjEB3/OxrOnHz969iE+xETW\nex9wynSJvtN/ydwYkWsrCILQSFaVt5Mejy3jyfAy1q4tgvB4ACy1MOreREap+xKi1JOxq5a4px4h\ncmAkSvTs7eixqRDfoXHEjQonMkSJPv/vnBoylQf7mCFY7uIpC4Ig3F7c62ohKBDAF68LZ9HbDyjU\nvfCyQNX+v5BR2MDAu4bi432Fz363lkoUHT4uM5rx8pLDmWze2KgF4MeLZrzl5WzaUeqquQqCINyW\n3HuFwt6DZVirijjbM5LGFw30lbUERQ5heGQ/rl42gG8vBocPIbSbmTprQ4eP9aYr+PaJIDIqlCsX\na1H4+BHQQ03IgDAUVy93VI8gCMJ/HDdJskja7ZvY/WMgs+dPQ3VmG1sqY5kbZ2H7hmx+9B3Jwymx\nWHT72PrVSfonzGByZAD6Dh8rKNz2Tw5d7M2D86ehloFu10b+fdyTGfNSCBGpioIgCE1E+pogCIKL\niV/0FQRBcDGxEQuCILiY2IgFQRBcTGzEgiAILiY2YkEQBBcTG7EgCIKLiY1YEATBxcRGLAiC4GJi\nIxYEQXAxsRELgiC4mNiIBUEQXOz/AFWdjS0WN7HsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ete3 import NCBITaxa\n",
    "ncbi = NCBITaxa()\n",
    "\n",
    "\n",
    "def layout(node):\n",
    "\n",
    "    # If node is a leaf, add the nodes name and a its scientific name\n",
    "    nameFace = faces.AttrFace(\"name\", fsize=20, fgcolor=\"#009000\")\n",
    "\n",
    "    rank = 100\n",
    "    if node.is_leaf():\n",
    "        faces.add_face_to_node(nameFace, node, column=2)\n",
    "        try:\n",
    "            txt2name=ncbi.get_taxid_translator([node.name])\n",
    "\n",
    "            for item in txt2name:\n",
    "                sci_name =txt2name[item]\n",
    "\n",
    "            faces.add_face_to_node(TextFace(sci_name, fsize=30), node, column=0)\n",
    "\n",
    "        except:\n",
    "            print(\"Error in tree ncbi\")\n",
    "\n",
    "tax_list = [9606, 32630, 1958742]\n",
    "# tree = ncbi.get_topology(tax_list, intermediate_nodes=False)\n",
    "tree= Tree(\"((synthetic construct:1,Human ORFeome Gateway entry vector:1)1:1,Homo sapiens:1);\")\n",
    "\n",
    "for node in tree:\n",
    "    node.add_features(count=14) \n",
    "    \n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = True\n",
    "ts.branch_vertical_margin = 10 # 10 pixels between adjacent branches\n",
    "ts.layout_fn = layout\n",
    "\n",
    "\n",
    "tree.render(\"%%inline\", w=100, units=\"mm\", tree_style=ts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((synthetic construct:1,Human ORFeome Gateway entry vector:1)1:1,Homo sapiens:1);\n"
     ]
    }
   ],
   "source": [
    "print(tree.write())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      /-synthetic construct\n",
      "   /-|\n",
      "--|   \\-Human ORFeome Gateway entry vector\n",
      "  |\n",
      "   \\-Homo sapiens\n"
     ]
    }
   ],
   "source": [
    "print (tree.get_ascii(attributes=[\"name\", \"sci_name\"]))\n",
    "#((synthetic construct:1,Human ORFeome Gateway entry vector:1)root:1,Homo sapiens:1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      /-synthetic construct\n",
      "   /-|\n",
      "--|   \\-Human ORFeome Gateway entry vector\n",
      "  |\n",
      "   \\-Homo sapiens\n"
     ]
    }
   ],
   "source": [
    "print(Tree(\"((synthetic construct:1,Human ORFeome Gateway entry vector:1)1:1,Homo sapiens:1);\", format=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example numerical matrix\n",
      "\n",
      "#Names\tRefSeq\tGenBank\tPDB\tSwissProt\tTrEMBL\n",
      "Homo sapiens\t1\t5.0\t0\t1\t0  \n",
      "synthetic construct\t0\t1\t0\t0\t0  \n",
      "Human ORFeome Gateway entry vector\t1\t0\t0\t0\t0  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import Tree instance and faces module\n",
    "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",
    "\n",
    "#print(Tree(\"((synthetic construct:1,Human ORFeome Gateway entry vector:1)1:1,Homo sapiens:1);\", format=2))\n",
    "matrix = \"\"\"\n",
    "#Names\\tRefSeq\\tGenBank\\tPDB\\tSwissProt\\tTrEMBL\n",
    "Homo sapiens\\t1\\t5.0\\t0\\t1\\t0  \n",
    "synthetic construct\\t0\\t1\\t0\\t0\\t0  \n",
    "Human ORFeome Gateway entry vector\\t1\\t0\\t0\\t0\\t0  \n",
    "\"\"\"\n",
    "print (\"Example numerical matrix\")\n",
    "print (matrix)\n",
    "# We load a tree structure whose leaf nodes correspond to rows in the\n",
    "# numerical matrix. \n",
    "\n",
    "# We use the text_array argument to link the tree\n",
    "# with numerical matrix.\n",
    "t = ClusterTree(tree.write(),  text_array=matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster_bars\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAEtCAYAAABQwq40AAAACXBIWXMAAA3XAAAN1wFCKJt4AAAg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\nkiRJylD/P0976ivOsDfBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"cluster_bars\")\n",
    "t.render(\"%%inline\", \"cluster_bars\", w=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((A:1,B:1)Internal_1:0.7,(C:1,D:1)Internal_2:0.5);\n",
      "\n",
      "                        /-A, 1.0, custom Value, [[0, 1], [2, 3], [1, 11], [1, 0]]\n",
      "         /Internal_1, 0.7\n",
      "        |               \\-B, 1.0\n",
      "-root, 1.3\n",
      "        |               /-C, 1.0\n",
      "         \\Internal_2, 0.5\n",
      "                        \\-D, 1.0\n"
     ]
    }
   ],
   "source": [
    "from ete3 import Tree\n",
    "t = Tree(\"((A, B)Internal_1:0.7, (C, D)Internal_2:0.5)root:1.3;\", format=1)\n",
    "# we add a custom annotation to the node named A\n",
    "(t & \"A\").add_features(label=\"custom Value\")\n",
    "# we add a complex feature to the A node, consisting of a list of lists\n",
    "(t & \"A\").add_features(complex=[[0,1], [2,3], [1,11], [1,0]])\n",
    "print(t.write(format=1))\n",
    "print (t.get_ascii(attributes=[\"name\", \"dist\", \"label\", \"complex\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Cluster misannotations (cid =169919)\n",
    "\n",
    "### Boa query to get cluster ID, sequence ID, and tax id\n",
    "\n",
    "```\n",
    "s: Sequence = input;\n",
    "counts: output sum[string][string][string] of int;\n",
    "\n",
    "foreach(i:int; def(s.cluster[i])){\n",
    "  if (s.cluster[i].similarity==95){\n",
    "    foreach(j:int; def(s.annotation[j]))\n",
    "\t  counts [s.cluster[i].cid][s.annotation[j].keyID][s.annotation[j].tax_id] <<1;\n",
    "  }\n",
    "}\n",
    "\n",
    "```\n",
    "\n",
    "#### Outupt:\n",
    "\n",
    "```\n",
    "counts[4068274][ABY50073][490503] = 1\n",
    "counts[4695268][ABY50072][490503] = 1\n",
    "counts[490503][AAF97425][69577] = 1\n",
    "counts[490503][AAR86692][328670] = 1\n",
    "counts[490503][AAS48724][69577] = 1\n",
    "counts[490503][ABU53625][69577] = 1\n",
    "counts[490503][ABU53626][69577] = 1\n",
    "counts[490503][ABU53627][69577] = 1\n",
    "counts[490503][ABU53628][69577] = 1\n",
    "counts[490503][ABU53629][69577] = 1\n",
    "counts[490503][ABU53630][69577] = 1\n",
    "counts[490503][ABU53631][69577] = 1\n",
    "counts[490503][ABU53632][69577] = 1\n",
    "counts[490503][ACL37065][69577] = 1\n",
    "counts[490503][ACL37066][69577] = 1\n",
    "counts[490503][ACL37067][69577] = 1\n",
    "counts[490503][ADB24016][52838] = 1\n",
    "counts[490503][ADB24017][52838] = 1\n",
    "counts[490503][ADB24018][52838] = 1\n",
    "counts[490503][ADB24019][52838] = 1\n",
    "counts[490503][ADB24020][52838] = 1\n",
    "counts[490503][ADB24021][52838] = 1\n",
    "counts[490503][ADB24022][52838] = 1\n",
    "counts[490503][ADB24023][52838] = 1\n",
    "counts[490503][ADB24024][52838] = 1\n",
    "counts[490503][ADB24025][52838] = 1\n",
    "counts[490503][ADB24026][52838] = 1\n",
    "counts[490503][ADB24027][52838] = 1\n",
    "counts[490503][ADB24028][52838] = 1\n",
    "counts[490503][ADB24029][52838] = 1\n",
    "counts[490503][ADB24030][52838] = 1\n",
    "counts[490503][ADB24031][52838] = 1\n",
    "counts[490503][ADB24032][52838] = 1\n",
    "counts[490503][ADB24033][52838] = 1\n",
    "counts[490503][ADB24034][52838] = 1\n",
    "counts[490503][ADB24035][52838] = 1\n",
    "counts[490503][AEP82796][328670] = 1\n",
    "counts[490503][AEP82797][328670] = 1\n",
    "counts[490503][AEP82798][328670] = 1\n",
    "counts[490503][AEP82799][328670] = 1\n",
    "counts[490503][AEP82800][328670] = 1\n",
    "counts[490503][AEP82801][328670] = 1\n",
    "counts[490503][AEP82802][328670] = 1\n",
    "counts[490503][AEP82803][328670] = 1\n",
    "counts[490503][AEP82804][328670] = 1\n",
    "counts[490503][AEP82805][328670] = 1\n",
    "counts[490503][AEP82806][328670] = 1\n",
    "counts[490503][AEP82807][328670] = 1\n",
    "counts[490503][AHA62482][328670] = 1\n",
    "counts[490503][AHM92951][328670] = 1\n",
    "counts[490503][ANK58362][69577] = 1\n",
    "counts[490503][APW78128][328670] = 1\n",
    "counts[490503][APW78129][328670] = 1\n",
    "counts[490503][APW78131][328670] = 1\n",
    "counts[490503][AQL57118][328670] = 1\n",
    "counts[490503][AQL57119][328670] = 1\n",
    "counts[490503][ASB34187][69577] = 1\n",
    "counts[490503][ASB34188][69577] = 1\n",
    "counts[490503][ASB34189][69577] = 1\n",
    "counts[490503][CAI91424][328670] = 1\n",
    "counts[490503][CAI91425][328670] = 1\n",
    "counts[490503][CAI91426][328670] = 1\n",
    "counts[490503][CAI91427][328670] = 1\n",
    "counts[490503][CAI91428][328670] = 1\n",
    "counts[490503][CAI91429][328670] = 1\n",
    "counts[490503][CAI91430][328670] = 1\n",
    "counts[490503][CAI91431][328670] = 1\n",
    "counts[490503][CAI91432][328670] = 1\n",
    "counts[490503][CAP19917][69577] = 1\n",
    "counts[490503][YP_233107][328670] = 1\n",
    "\n",
    "```\n",
    "\n",
    "##### Tree\n",
    "\n",
    "```\n",
    " \n",
    "                              /-Banana streak GF virus, species, 30\n",
    "             /Badnavirus, genus\n",
    "-root, no rank                \\-Banana streak virus, species, 18\n",
    "            |\n",
    "             \\-Musa balbisiana, species, 20   \n",
    "             \n",
    "```    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAByCAYAAABp/dtVAAAACXBIWXMAAA3XAAAN1wFCKJt4AAAg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\nQeb/Az8XTEacNcbHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ete3 import NCBITaxa\n",
    "ncbi = NCBITaxa()\n",
    "\n",
    "\n",
    "def layout(node):\n",
    "\n",
    "    # If node is a leaf, add the nodes name and a its scientific name\n",
    "    nameFace = faces.AttrFace(\"name\", fsize=20, fgcolor=\"#009000\")\n",
    "\n",
    "    rank = 100\n",
    "    if node.is_leaf():\n",
    "        faces.add_face_to_node(nameFace, node, column=2)\n",
    "        try:\n",
    "            txt2name=ncbi.get_taxid_translator([node.name])\n",
    "\n",
    "            for item in txt2name:\n",
    "                sci_name =txt2name[item]\n",
    "\n",
    "            faces.add_face_to_node(TextFace(sci_name, fsize=30), node, column=0)\n",
    "\n",
    "        except:\n",
    "            print(\"Error in tree ncbi\")\n",
    "\n",
    "tax_list = [69577,328670, 52838, 328670 ]\n",
    "tree = ncbi.get_topology(tax_list, intermediate_nodes=False)\n",
    "# tree= Tree(\"((synthetic construct:1,Human ORFeome Gateway entry vector:1)1:1,Homo sapiens:1);\")\n",
    "\n",
    "for node in tree:\n",
    "    node.add_features(count=14) \n",
    "    \n",
    "ts = TreeStyle()\n",
    "ts.show_leaf_name = True\n",
    "ts.branch_vertical_margin = 10 # 10 pixels between adjacent branches\n",
    "ts.layout_fn = layout\n",
    "\n",
    "\n",
    "tree.render(\"%%inline\", w=100, units=\"mm\", tree_style=ts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((328670:1,69577:1)1:1,52838:1);\n"
     ]
    }
   ],
   "source": [
    "print(tree.write())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                        /-328670, Banana streak GF virus\n",
      "       /10652, Badnavirus\n",
      "-1, root                \\-69577, Banana streak virus\n",
      "      |\n",
      "       \\-52838, Musa balbisiana\n"
     ]
    }
   ],
   "source": [
    "print (tree.get_ascii(attributes=[\"name\", \"sci_name\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tree= Tree(\"((Banana streak GF virus:1,Banana streak virus:1)1:1,Musa balbisiana:1);\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example numerical matrix\n",
      "\n",
      "#Names\tRefSeq\tGenBank\tPDB\tSwissProt\tTrEMBL\n",
      "Banana streak GF virus\t2\t28\t0\t0\t0  \n",
      "Banana streak virus\t2\t16\t0\t0\t0  \n",
      "Musa balbisiana\t0\t20\t0\t0\t0  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import Tree instance and faces module\n",
    "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",
    "\n",
    "#print(Tree(\"((synthetic construct:1,Human ORFeome Gateway entry vector:1)1:1,Homo sapiens:1);\", format=2))\n",
    "matrix = \"\"\"\n",
    "#Names\\tRefSeq\\tGenBank\\tPDB\\tSwissProt\\tTrEMBL\n",
    "Banana streak GF virus\\t2\\t28\\t0\\t0\\t0  \n",
    "Banana streak virus\\t2\\t16\\t0\\t0\\t0  \n",
    "Musa balbisiana\\t0\\t20\\t0\\t0\\t0  \n",
    "\"\"\"\n",
    "print (\"Example numerical matrix\")\n",
    "print (matrix)\n",
    "# We load a tree structure whose leaf nodes correspond to rows in the\n",
    "# numerical matrix. \n",
    "\n",
    "# We use the text_array argument to link the tree\n",
    "# with numerical matrix.\n",
    "t = ClusterTree(tree.write(),  text_array=matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster_bars\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAFMCAYAAACTanLLAAAACXBIWXMAAA3XAAAN1wFCKJt4AAAg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\nYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"cluster_bars\")\n",
    "t.render(\"%%inline\", \"cluster_bars\", w=900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['count', '12E8]', '3JAB]', '9913] = 4']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str =  \"count[12E8][3JAB][9913] = 4\"\n",
    "words = str.split(\"[\")\n",
    "words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}