RFAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import dendropy\n",
    "import glob\n",
    "from dendropy.calculate import treecompare\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reference_tree = dendropy.Tree.get_from_path(\"barden.tre\", schema = \"nexus\", preserve_underscores=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_trees = glob.glob(\"Tri.sum.trees/mcc/*.tree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_untrees = glob.glob(\"Tri.sum.untrees/mcc/*.tree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'Tri.sum.trees/mcc\\\\98.nex.trees.mcc.tree',\n",
       " 'Tri.sum.trees/mcc\\\\99.nex.trees.mcc.tree']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'Tri.sum.untrees/mcc\\\\99.nex.trees.mcc.tree']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_untrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "rf_unlist = []\n",
    "for tree in list_untrees:\n",
    "    est_tree = dendropy.Tree.get_from_path(tree, schema = \"nexus\", preserve_underscores = True, taxon_namespace = reference_tree.taxon_namespace)\n",
    "#    unrf = treecompare.robinson_foulds_distance(reference_tree, est_tree)\n",
    "    unrf = treecompare.symmetric_difference(reference_tree, est_tree)\n",
    "\n",
    "    rf_unlist.append(unrf)\n",
    "    print(unrf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "rf_list = []\n",
    "for tree in list_trees:\n",
    "    est_tree = dendropy.Tree.get_from_path(tree, schema = \"nexus\", preserve_underscores = True, taxon_namespace = reference_tree.taxon_namespace)\n",
    "#    rf = treecompare.robinson_foulds_distance(reference_tree, est_tree)\n",
    "    rf = treecompare.symmetric_difference(reference_tree, est_tree)\n",
    "\n",
    "    rf_list.append(rf)\n",
    "    print(rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'RF': rf_list, 'unRF': rf_unlist})\n",
    "df[\"Magnitude\"] = df[\"unRF\"] - df[\"RF\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000019DABD40DD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000019DABAF14E0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000019DABEC4748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000019DABBB2F98>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNcJAl6RGGOiS1AgDXZIa8f8APJjsSpHd48YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19dab83efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Magnitude\"], kde = True, color = \"blue\")\n",
    "plt.xlabel(\"Change in Distance to True Tree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.savefig(\"Figure 1.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jywfN+OZZ82M9ruc1YnXXU1j9me/RatMq+k67n75T7+X0+67mlInXsWrwWJYV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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19dab9ccf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"RF\"], kde = True, label = \"Partitioned\", color = \"red\")\n",
    "sns.distplot(df[\"unRF\"], kde = True, label = \"Unpartitioned\", color = \"blue\")\n",
    "plt.xlabel(\"Distance to True Tree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.ylim(0,10)\n",
    "plt.savefig(\"Figure 2.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rf_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"symmetric_difference.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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