Jaccard_Sim_Dist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### getting files imported and sorted  \n",
    "getting the sequences in and making sure that we get rid of duplicates and then separate genes (e.g. HA) to look at them separately. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from Bio.SeqIO.FastaIO import SimpleFastaParser\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools as it\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('influenza.fna') as fasta_file: \n",
    "    cgi_titles = []\n",
    "    cgi_sequences = []\n",
    "    for title, sequence in SimpleFastaParser(fasta_file):\n",
    "        cgi_titles.append(title)\n",
    "        cgi_sequences.append(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "influenza_na = pd.DataFrame(list(zip(cgi_titles, cgi_sequences)), columns=['titles','sequences'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>titles</th>\n",
       "      <th>sequences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gi|58576|gb|X52226|Influenza A virus (A/FPV/Ro...</td>\n",
       "      <td>AGCAAAAGCAGGAGTTCAAAATGAATCCAAATCAGAAAATAATAAC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gi|59292|gb|X53029|Influenza A virus (A/USSR/9...</td>\n",
       "      <td>AGCAAAAGCAGGTAGATGTTGAAAGATGAGTCTTCTAACCGAGGTC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gi|59294|gb|X59778|Influenza A virus (A/NIB/4/...</td>\n",
       "      <td>GGATCCTGTTATGTGCATTTACAGCTACAGATGCAGACACAATATG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gi|59296|gb|X66320|Influenza A virus (A/Chile/...</td>\n",
       "      <td>ACACCAAATGGAACGAAAATCAGAACCCTAGAATGTTTTTGGCCAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gi|60457|gb|X08088|Influenza A virus (A/WSN/33...</td>\n",
       "      <td>ATGAGTCTTCTAACCGAGGTCGAAACGTACGTTCTCTCTATCGTCC...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              titles  \\\n",
       "0  gi|58576|gb|X52226|Influenza A virus (A/FPV/Ro...   \n",
       "1  gi|59292|gb|X53029|Influenza A virus (A/USSR/9...   \n",
       "2  gi|59294|gb|X59778|Influenza A virus (A/NIB/4/...   \n",
       "3  gi|59296|gb|X66320|Influenza A virus (A/Chile/...   \n",
       "4  gi|60457|gb|X08088|Influenza A virus (A/WSN/33...   \n",
       "\n",
       "                                           sequences  \n",
       "0  AGCAAAAGCAGGAGTTCAAAATGAATCCAAATCAGAAAATAATAAC...  \n",
       "1  AGCAAAAGCAGGTAGATGTTGAAAGATGAGTCTTCTAACCGAGGTC...  \n",
       "2  GGATCCTGTTATGTGCATTTACAGCTACAGATGCAGACACAATATG...  \n",
       "3  ACACCAAATGGAACGAAAATCAGAACCCTAGAATGTTTTTGGCCAT...  \n",
       "4  ATGAGTCTTCTAACCGAGGTCGAAACGTACGTTCTCTCTATCGTCC...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "influenza_na.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(655764, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "influenza_na.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "remove duplicate sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniflna = influenza_na.drop_duplicates('sequences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>titles</th>\n",
       "      <th>sequences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gi|58576|gb|X52226|Influenza A virus (A/FPV/Ro...</td>\n",
       "      <td>AGCAAAAGCAGGAGTTCAAAATGAATCCAAATCAGAAAATAATAAC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gi|59292|gb|X53029|Influenza A virus (A/USSR/9...</td>\n",
       "      <td>AGCAAAAGCAGGTAGATGTTGAAAGATGAGTCTTCTAACCGAGGTC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gi|59294|gb|X59778|Influenza A virus (A/NIB/4/...</td>\n",
       "      <td>GGATCCTGTTATGTGCATTTACAGCTACAGATGCAGACACAATATG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gi|59296|gb|X66320|Influenza A virus (A/Chile/...</td>\n",
       "      <td>ACACCAAATGGAACGAAAATCAGAACCCTAGAATGTTTTTGGCCAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gi|60457|gb|X08088|Influenza A virus (A/WSN/33...</td>\n",
       "      <td>ATGAGTCTTCTAACCGAGGTCGAAACGTACGTTCTCTCTATCGTCC...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              titles  \\\n",
       "0  gi|58576|gb|X52226|Influenza A virus (A/FPV/Ro...   \n",
       "1  gi|59292|gb|X53029|Influenza A virus (A/USSR/9...   \n",
       "2  gi|59294|gb|X59778|Influenza A virus (A/NIB/4/...   \n",
       "3  gi|59296|gb|X66320|Influenza A virus (A/Chile/...   \n",
       "4  gi|60457|gb|X08088|Influenza A virus (A/WSN/33...   \n",
       "\n",
       "                                           sequences  \n",
       "0  AGCAAAAGCAGGAGTTCAAAATGAATCCAAATCAGAAAATAATAAC...  \n",
       "1  AGCAAAAGCAGGTAGATGTTGAAAGATGAGTCTTCTAACCGAGGTC...  \n",
       "2  GGATCCTGTTATGTGCATTTACAGCTACAGATGCAGACACAATATG...  \n",
       "3  ACACCAAATGGAACGAAAATCAGAACCCTAGAATGTTTTTGGCCAT...  \n",
       "4  ATGAGTCTTCTAACCGAGGTCGAAACGTACGTTCTCTCTATCGTCC...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(441221, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get just the HA gene sequences (for now, other genes later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brian/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "uniflna_HA = uniflna.loc[uniflna.iloc[:,0].str.contains(r'\\(HA\\) | hemagglutinin | (segment 4)')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102414, 2)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna_HA.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(83475)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# character kgram function, out is set\n",
    "def charkgram(text, k):\n",
    "    results=set()\n",
    "    for i in range(len(text)-k+1):\n",
    "        if text[i:i+k] not in results:\n",
    "            results.add(text[i:i+k])\n",
    "    return(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def JacSim(a,b):\n",
    "    top = len(a & b)\n",
    "    bottom = len(a | b)\n",
    "    return(top/bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniflna_HA_sub1 = uniflna_HA.sample(n=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "idl=list(it.combinations(list(uniflna_HA_sub1.index),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19900"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(idl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=4\n",
    "JSresults4=[]\n",
    "for i in idl:\n",
    "    JSresults4.append(JacSim(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],4),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],4)))\n",
    "plt.hist(JSresults4, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=5\n",
    "JSresults5=[]\n",
    "for i in idl:\n",
    "    JSresults5.append(JacSim(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],5),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],5)))\n",
    "plt.hist(JSresults5, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=6\n",
    "JSresults6=[]\n",
    "for i in idl:\n",
    "    JSresults6.append(JacSim(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],6),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],6)))\n",
    "plt.hist(JSresults6, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEnpJREFUeJzt3W+Qned51/HvL1b+QJtEcix7PJJA6VSFuJ1JYnZsdTJT2qjIitOJ/CIGZWitejRopphSoAMo8ELFbgYHBkw9Q11ELCpn2jrGUKypTY1GcSbAYMfrOnVju0GqY+xFJtpGsqB4kmD34sW51aydXe2z0tmzWt/fz8zOeZ7ruc8597W7Or99/pyjVBWSpP68ZaUnIElaGQaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVNrVnoC53LZZZfV5s2bV3oakrSqPPHEE39YVesXG3dRB8DmzZuZnp5e6WlI0qqS5H8MGechIEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6tRF/U7gi93mfQ/OW3/+9o9OeCaStHTuAUhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnq1KAASLI2yf1Jfj/Js0l+OMmlSY4kOdZu17WxSXJnkuNJnkpy9ZzH2d3GH0uye7makiQtbugewC8Bv11Vfx54P/AssA84WlVbgKNtHeAjwJb2tRe4CyDJpcB+4FrgGmD/2dCQJE3eogGQ5F3AjwB3A1TVt6vqZWAncKgNOwTc0JZ3AvfUyKPA2iRXAtcBR6rqVFWdBo4AO8bajSRpsCF7AN8HzAL/JsmTST6T5HuAK6rqJYB2e3kbvwF4cc79Z1ptofrrJNmbZDrJ9Ozs7JIbkiQNMyQA1gBXA3dV1QeB/8t3DvfMJ/PU6hz11xeqDlTVVFVNrV+/fsD0JEnnY0gAzAAzVfVYW7+fUSB8vR3aod2enDN+05z7bwROnKMuSVoBiwZAVf0v4MUkf66VtgHPAIeBs1fy7AYeaMuHgZva1UBbgTPtENHDwPYk69rJ3+2tJklaAUP/R7CfBX4tyduA54CbGYXHfUn2AC8AN7axDwHXA8eBV9pYqupUktuAx9u4W6vq1Fi6kCQt2aAAqKovA1PzbNo2z9gCblngcQ4CB5cyQUnS8vCdwJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1alAAJHk+ye8l+XKS6Va7NMmRJMfa7bpWT5I7kxxP8lSSq+c8zu42/liS3cvTkiRpiKXsAfxYVX2gqqba+j7gaFVtAY62dYCPAFva117gLhgFBrAfuBa4Bth/NjQkSZN3IYeAdgKH2vIh4IY59Xtq5FFgbZIrgeuAI1V1qqpOA0eAHRfw/JKkCzA0AAr4T0meSLK31a6oqpcA2u3lrb4BeHHOfWdabaH66yTZm2Q6yfTs7OzwTiRJS7Jm4LgPVdWJJJcDR5L8/jnGZp5anaP++kLVAeAAwNTU1HdtlySNx6A9gKo60W5PAr/J6Bj+19uhHdrtyTZ8Btg05+4bgRPnqEuSVsCiAZDke5K88+wysB34CnAYOHslz27ggbZ8GLipXQ20FTjTDhE9DGxPsq6d/N3eapKkFTDkENAVwG8mOTv+16vqt5M8DtyXZA/wAnBjG/8QcD1wHHgFuBmgqk4luQ14vI27tapOja0TSdKSLBoAVfUc8P556t8Ats1TL+CWBR7rIHBw6dOUJI2b7wSWpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVNrhg5McgkwDfzPqvqJJO8F7gUuBX4H+Kmq+naStwP3AH8B+AbwV6rq+fYYnwT2AK8Bf7OqHh5nM8tl874HV3oKkjR2S9kD+Dng2TnrnwbuqKotwGlGL+y029NV9f3AHW0cSa4CdgE/COwAfrmFiiRpBQwKgCQbgY8Cn2nrAT4M3N+GHAJuaMs72zpt+7Y2fidwb1V9q6q+BhwHrhlHE5KkpRu6B/AvgL8H/HFbfw/wclW92tZngA1teQPwIkDbfqaN/5P6PPf5E0n2JplOMj07O7uEViRJS7FoACT5CeBkVT0xtzzP0Fpk27nu851C1YGqmqqqqfXr1y82PUnSeRpyEvhDwMeSXA+8A3gXoz2CtUnWtL/yNwIn2vgZYBMwk2QN8G7g1Jz6WXPvI0masEX3AKrqk1W1sao2MzqJ+/mq+qvAI8DH27DdwANt+XBbp23/fFVVq+9K8vZ2BdEW4Etj60SStCSDLwOdx98H7k3yi8CTwN2tfjfw2STHGf3lvwugqp5Och/wDPAqcEtVvXYBzy9JugBLCoCq+gLwhbb8HPNcxVNV3wRuXOD+nwI+tdRJSpLGz3cCS1KnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROXcj/B6AFbN734Lz152//6IRnIkkLcw9AkjplAEhSpwwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1KlFAyDJO5J8KcnvJnk6yT9q9fcmeSzJsSSfS/K2Vn97Wz/etm+e81ifbPWvJrluuZqSJC1uyB7At4APV9X7gQ8AO5JsBT4N3FFVW4DTwJ42fg9wuqq+H7ijjSPJVcAu4AeBHcAvJ7lknM1IkoZbNABq5I/a6lvbVwEfBu5v9UPADW15Z1unbd+WJK1+b1V9q6q+BhwHrhlLF5KkJRt0DiDJJUm+DJwEjgB/ALxcVa+2ITPAhra8AXgRoG0/A7xnbn2e+8x9rr1JppNMz87OLr0jSdIggwKgql6rqg8AGxn91f6++Ya12yywbaH6G5/rQFVNVdXU+vXrh0xPknQelnQVUFW9DHwB2AqsTXL200Q3Aifa8gywCaBtfzdwam59nvtIkiZsyFVA65Osbct/Cvhx4FngEeDjbdhu4IG2fLit07Z/vqqq1Xe1q4TeC2wBvjSuRiRJSzPk/wO4EjjUrth5C3BfVf1WkmeAe5P8IvAkcHcbfzfw2STHGf3lvwugqp5Och/wDPAqcEtVvTbediRJQy0aAFX1FPDBeerPMc9VPFX1TeDGBR7rU8Cnlj5NSdK4+U5gSeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdGvJ/Andj874HV3oKkjQx7gFIUqcMAEnqlAEgSZ0yACSpU4sGQJJNSR5J8mySp5P8XKtfmuRIkmPtdl2rJ8mdSY4neSrJ1XMea3cbfyzJ7uVrS5K0mCF7AK8CP19V7wO2ArckuQrYBxytqi3A0bYO8BFgS/vaC9wFo8AA9gPXAtcA+8+GhiRp8hYNgKp6qap+py3/H+BZYAOwEzjUhh0CbmjLO4F7auRRYG2SK4HrgCNVdaqqTgNHgB1j7UaSNNiSzgEk2Qx8EHgMuKKqXoJRSACXt2EbgBfn3G2m1RaqS5JWwOAASPK9wL8D/lZV/e9zDZ2nVueov/F59iaZTjI9Ozs7dHqSpCUaFABJ3sroxf/Xqurft/LX26Ed2u3JVp8BNs25+0bgxDnqr1NVB6pqqqqm1q9fv5ReJElLMOQqoAB3A89W1T+fs+kwcPZKnt3AA3PqN7WrgbYCZ9ohooeB7UnWtZO/21tNkrQChnwW0IeAnwJ+L8mXW+0fALcD9yXZA7wA3Ni2PQRcDxwHXgFuBqiqU0luAx5v426tqlNj6UKStGSLBkBV/RfmP34PsG2e8QXcssBjHQQOLmWCkqTl4TuBJalTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6pQBIEmdMgAkqVMGgCR1ygCQpE4ZAJLUKQNAkjplAEhSpwwASerUogGQ5GCSk0m+Mqd2aZIjSY6123WtniR3Jjme5KkkV8+5z+42/liS3cvTjiRpqCF7AL8K7HhDbR9wtKq2AEfbOsBHgC3tay9wF4wCA9gPXAtcA+w/GxqSpJWxaABU1ReBU28o7wQOteVDwA1z6vfUyKPA2iRXAtcBR6rqVFWdBo7w3aEiSZqg8z0HcEVVvQTQbi9v9Q3Ai3PGzbTaQnVJ0goZ90ngzFOrc9S/+wGSvUmmk0zPzs6OdXKSpO843wD4eju0Q7s92eozwKY54zYCJ85R/y5VdaCqpqpqav369ec5PUnSYs43AA4DZ6/k2Q08MKd+U7saaCtwph0iehjYnmRdO/m7vdUkSStkzWIDkvwG8KPAZUlmGF3NcztwX5I9wAvAjW34Q8D1wHHgFeBmgKo6leQ24PE27taqeuOJZUnSBC0aAFX1iQU2bZtnbAG3LPA4B4GDS5qdJGnZ+E5gSeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkTi36aaAan837Hpy3/vztH53wTCTJPQBJ6pYBIEmdMgAkqVMGgCR1ypPAFwFPDktaCQbARcxgkLScPAQkSZ0yACSpUx4C0kXFw17S5Ew8AJLsAH4JuAT4TFXdPuk5rHZvhhfJhXoY1/jV9L2QVkqqanJPllwC/HfgLwEzwOPAJ6rqmfnGT01N1fT09NjnsdQXkx4t9AL6Zv7eLbXn1RIy45z/uH7+q+V7t1oleaKqphYdN+EA+GHgF6rqurb+SYCq+sfzjTcAtBot94vbm/n3d1x/eCz1Z7DaQ/6NhgbApA8BbQBenLM+A1w74TlIy+rN/AK93Mb1vbvYHudcVjJkJh0Amaf2ul2QJHuBvW31j5J89QKe7zLgDy/g/qtNb/2CPffiTdtzPr3gpgvp+c8OGTTpAJgBNs1Z3wicmDugqg4AB8bxZEmmh+wGvVn01i/Ycy/seXlM+n0AjwNbkrw3yduAXcDhCc9BksSE9wCq6tUkfwN4mNFloAer6ulJzkGSNDLx9wFU1UPAQxN6urEcSlpFeusX7LkX9rwMJnoZqCTp4uFnAUlSp1Z9ACTZkeSrSY4n2TfP9rcn+Vzb/liSzZOf5XgN6PnvJHkmyVNJjiYZdEnYxWyxnueM+3iSSrLqrxgZ0nOSv9x+1k8n+fVJz3HcBvxu/5kkjyR5sv1+X78S8xyXJAeTnEzylQW2J8md7fvxVJKrxzqBqlq1X4xOJP8B8H3A24DfBa56w5i/DvxKW94FfG6l5z2Bnn8M+NNt+Wd66LmNeyfwReBRYGql5z2Bn/MW4ElgXVu/fKXnPYGeDwA/05avAp5f6XlfYM8/AlwNfGWB7dcD/5HRe6i2Ao+N8/lX+x7ANcDxqnquqr4N3AvsfMOYncChtnw/sC3JfG9IWy0W7bmqHqmqV9rqo4zeb7GaDfk5A9wG/BPgm5Oc3DIZ0vNfA/5lVZ0GqKqTE57juA3puYB3teV384b3Ea02VfVF4NQ5huwE7qmRR4G1Sa4c1/Ov9gCY76MlNiw0pqpeBc4A75nI7JbHkJ7n2sPoL4jVbNGek3wQ2FRVvzXJiS2jIT/nHwB+IMl/TfJo+6Td1WxIz78A/GSSGUZXE/7sZKa2Ypb6731JVvv/B7DoR0sMHLOaDO4nyU8CU8BfXNYZLb9z9pzkLcAdwE9PakITMOTnvIbRYaAfZbSX95+T/FBVvbzMc1suQ3r+BPCrVfXP2odLfrb1/MfLP70VsayvX6t9D2DRj5aYOybJGka7jefa5brYDemZJD8O/EPgY1X1rQnNbbks1vM7gR8CvpDkeUbHSg+v8hPBQ3+3H6iq/1dVXwO+yigQVqshPe8B7gOoqv8GvIPRZ+a8WQ36936+VnsADPloicPA7rb8ceDz1c6urFKL9twOh/wrRi/+q/24MCzSc1WdqarLqmpzVW1mdN7jY1U1/s8Sn5whv9v/gdEJf5JcxuiQ0HMTneV4Den5BWAbQJL3MQqA2YnOcrIOAze1q4G2Ameq6qVxPfiqPgRUC3y0RJJbgemqOgzczWg38Tijv/x3rdyML9zAnv8p8L3Av23nu1+oqo+t2KQv0MCe31QG9vwwsD3JM8BrwN+tqm+s3KwvzMCefx7410n+NqNDIT+9mv+gS/IbjA7hXdbOa+wH3gpQVb/C6DzH9cBx4BXg5rE+/yr+3kmSLsBqPwQkSTpPBoAkdcoAkKROGQCS1CkDQJI6ZQBIUqcMAEnqlAEgSZ36/7MDe1Nh9K1IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=7\n",
    "JSresults7=[]\n",
    "for i in idl:\n",
    "    JSresults7.append(JacSim(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],7),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],7)))\n",
    "plt.hist(JSresults7, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(JSresults4, 50, range=(0,1), color='blue', alpha=0.35, label='k=4')\n",
    "plt.hist(JSresults5, 50, range=(0,1), color='red', alpha=0.35, label='k=5')\n",
    "plt.hist(JSresults6, 50, range=(0,1), color='green', alpha=0.35, label='k=6')\n",
    "plt.hist(JSresults7, 50, range=(0,1), color='orange', alpha=0.35, label='k=7')\n",
    "plt.xlabel(\"Jaccard Similarity\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def JacDist(a,b):\n",
    "    top = len(a & b)\n",
    "    bottom = len(a | b)\n",
    "    return(1-top/bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>titles</th>\n",
       "      <th>sequences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gi|60504|gb|X17224|Influenza A virus (STRAIN A...</td>\n",
       "      <td>AAAACAACCAAAATGAAAGCAAAACTACTAGTCCTGTTATGTGCAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>gi|60723|gb|X13551|Influenza B virus HA gene f...</td>\n",
       "      <td>ACTCATGGTAGTAACATCCAATGCAGATCGAATCTGCACTGGGATA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gi|60728|gb|X13552|Influenza B virus (B/USSR/1...</td>\n",
       "      <td>ACTCATGGTAGTAACATCCAACGCAGATCGAATCTGCACTGGGATA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>gi|60733|gb|X13553|Influenza B virus HA gene f...</td>\n",
       "      <td>ACTCATGGTAGTAACATCCAATGCAGATCGAATCTGCACTGGGATA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>gi|60738|gb|X13550|Influenza B virus (B/Bonn/4...</td>\n",
       "      <td>ACTCATGGTAGTAACATCCAATGCAGATCGAATCTGCACTGGGATA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               titles  \\\n",
       "21  gi|60504|gb|X17224|Influenza A virus (STRAIN A...   \n",
       "28  gi|60723|gb|X13551|Influenza B virus HA gene f...   \n",
       "29  gi|60728|gb|X13552|Influenza B virus (B/USSR/1...   \n",
       "30  gi|60733|gb|X13553|Influenza B virus HA gene f...   \n",
       "31  gi|60738|gb|X13550|Influenza B virus (B/Bonn/4...   \n",
       "\n",
       "                                            sequences  \n",
       "21  AAAACAACCAAAATGAAAGCAAAACTACTAGTCCTGTTATGTGCAT...  \n",
       "28  ACTCATGGTAGTAACATCCAATGCAGATCGAATCTGCACTGGGATA...  \n",
       "29  ACTCATGGTAGTAACATCCAACGCAGATCGAATCTGCACTGGGATA...  \n",
       "30  ACTCATGGTAGTAACATCCAATGCAGATCGAATCTGCACTGGGATA...  \n",
       "31  ACTCATGGTAGTAACATCCAATGCAGATCGAATCTGCACTGGGATA...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna_HA.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "titles       gi|60504|gb|X17224|Influenza A virus (STRAIN A...\n",
       "sequences    AAAACAACCAAAATGAAAGCAAAACTACTAGTCCTGTTATGTGCAT...\n",
       "Name: 21, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna_HA.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "titles       gi|60723|gb|X13551|Influenza B virus HA gene f...\n",
       "sequences    ACTCATGGTAGTAACATCCAATGCAGATCGAATCTGCACTGGGATA...\n",
       "Name: 28, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna_HA.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ACTCATGGTAGTAACATCCAACGCAGATCGAATCTGCACTGGGATAACATCTTCAAACTCCCCCCATGTGGTCAAAACAGCTACTCAAGGGGAAGTTAATGTGACTGGTGTGATACCACTGACAACAACACCAACCAAATCTCATTTTGCAAATCTCAAAGGAACAAAGACCAGAGGGAAACTATGCCCAAACTGTCTCAACTGCACAGATCTGGACGTGGCCTTGGGCAGACCAAAGTGTATGGGGACCATACCTTCGGCAAAAGCTTCAATACTCCACGAAGTCAAACCTGTTACATCTGGGTGCTTTCCTATAATGCACGACAGAACAAAAATCAGACAGCTACCCAATCTTCTCAGAGGATATGAAAATATCAGGTTATCAACCCGTAACGTTATCAACGCAGAAAGGGCACCAGGAGGACCCTACATAATTGGAACCTCAGGATCTTGCCCTAACGTTACCAATGGAAACGGATTCTTCGCAACAATGGCTTGGGCTGTCCCAAAAGACAACAAAACAGCAACGGATCCATTAACAATAGAAGTACCATACATTTGTACAAAAGGAGAAGACCAAATTACTGTTTGGGGGTTCCATTCTGATAACAAAAACCAAATGGTAAAACTCTATGGAGACTCAAAGCCTCAAAAGTTCACCTCATCTGCCAATGGAGTAACCACACATTATGTTTCTCAGATTGGTGGTTTCCCAAATCAAACAGAAGACGGAGGGCTACCACAAAGCGGCAGAATTGTTGTTGATTACATGGTGCAGAAACCTGGAAAACCAGGAACAATTGTCTATCAAAGAGGTGTTTTATTGCCTCAAAAAGTGTGGTGCGCAAGTGGCAGGAGCAAGGTAATAAAAGGGTCCTTGCCTTTAATTGGTGAAGCAGATTGCCTCCACGAAAAATACGGTGGATTAAACAAAAGCAAGCCTTACTACACAGGAGAACATGCAAAAGCCATAGGAAATTGCCCAATATGGGTGAAAACACCCTTGAAGCTGGCCAATGGAACCAAATATAGACCTCCTGCAAAACTATTAAAGGAAAGGGGTTTCTTCGGAGCTATTGCTGGTTTCTTGGAAGGAGGATGGGAAGGAATGATTGCAGGTTGGCACGGATACACATCTCATGGAGCACATGGAGTGGCAGTGGCAGCAGACCTTAAGAGTACGCAAGAAGCCATAAACAAGATAACAAAAAATCTCAATTCTTTGAGTGAGCTAGAAGTAAAGAATCTTCAAAGACTAAGCGGTGCCATGGATGAACTCCACAATGAAATACTCGAGCTGGATGAGAAAGTGGATGATCTCAGAGCTGACACAATAAGCTCGCAAATAGAGCTTGCAGTCTTGCTTTCCAACGAAGGAATAATCAACAGTGAAGATGAGCATCTCTTGGCACTTGAAAGAAAACTAAAGAAAATGCTGGGCCCCTCTGCTGTAGACATAGGGAATGGATGCTTCGAAACCAAACACAAGTGCAACCAGACCTGCTTAGACAGGATAGCTGCTGGCACCTTTAATGCAGGAGAATTTTCTCTCCCCACCTTTGATTCACTAAATATTACTGCTGCATCTTTAAATGATGATGGATTGGATAATCATACTATACTGCTTTACTACTCAACTGCTGCTTCTAGTTTGGCTGTAACATTGATGATAGCTATCTTTATTGTTTATATGGTCTCCAGAGACAATGTTTCTTGCTCCATCTGTCTATAAGGAAAATTAAGCCCTGTATTTTCCTTTATTGTAGTGCTTGTTTGCTTGTTACCATTACAAAAAACGTTATTGAAAAATGCTCTTGTTACTACT'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna_HA.iloc[2].sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5594237695078031"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JacSim(charkgram(uniflna_HA.iloc[0].sequences,5), charkgram(uniflna_HA.iloc[1].sequences,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8986666666666666"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JacSim(charkgram(uniflna_HA.iloc[1].sequences,5), charkgram(uniflna_HA.iloc[2].sequences,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9014647137150466"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JacSim(charkgram(uniflna_HA.iloc[2].sequences,5), charkgram(uniflna_HA.iloc[3].sequences,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44057623049219685"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JacDist(charkgram(uniflna_HA.iloc[0].sequences,5), charkgram(uniflna_HA.iloc[1].sequences,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10133333333333339"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JacDist(charkgram(uniflna_HA.iloc[1].sequences,5), charkgram(uniflna_HA.iloc[2].sequences,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09853528628495345"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JacDist(charkgram(uniflna_HA.iloc[2].sequences,5), charkgram(uniflna_HA.iloc[3].sequences,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard Distances!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=4\n",
    "JDresults4=[]\n",
    "for i in idl:\n",
    "    JDresults4.append(JacDist(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],4),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],4)))\n",
    "plt.hist(JDresults4, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=5\n",
    "JDresults5=[]\n",
    "for i in idl:\n",
    "    JDresults5.append(JacDist(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],5),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],5)))\n",
    "plt.hist(JDresults5, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=6\n",
    "JDresults6=[]\n",
    "for i in idl:\n",
    "    JDresults6.append(JacDist(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],6),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],6)))\n",
    "plt.hist(JDresults6, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=7\n",
    "JDresults7=[]\n",
    "for i in idl:\n",
    "    JDresults7.append(JacDist(charkgram(uniflna_HA_sub1.loc[[i[0]]].sequences.values[0],7),charkgram(uniflna_HA_sub1.loc[[i[1]]].sequences.values[0],7)))\n",
    "plt.hist(JDresults7, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(JDresults4, 50, range=(0,1), color='blue', alpha=0.35, label='k=4')\n",
    "plt.hist(JDresults5, 50, range=(0,1), color='red', alpha=0.35, label='k=5')\n",
    "plt.hist(JDresults6, 50, range=(0,1), color='green', alpha=0.35, label='k=6')\n",
    "plt.hist(JDresults7, 50, range=(0,1), color='orange', alpha=0.35, label='k=7')\n",
    "plt.xlabel(\"Jaccard Distance\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make a dataframe with the indices and JDs with different values of k.  \n",
    "just to be clear, the values in the indices column are the index values from the uniflna_HA_sub1 dataframe which actually refer to the original indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "Jdistance_sub1 = pd.DataFrame(\n",
    "    {'indices': idl,\n",
    "     'JDk4': JDresults4,\n",
    "     'JDk5': JDresults5,\n",
    "     'JDk6': JDresults6,\n",
    "     'JDk7': JDresults7\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indices</th>\n",
       "      <th>JDk4</th>\n",
       "      <th>JDk5</th>\n",
       "      <th>JDk6</th>\n",
       "      <th>JDk7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(430939, 263646)</td>\n",
       "      <td>0.071146</td>\n",
       "      <td>0.355429</td>\n",
       "      <td>0.738653</td>\n",
       "      <td>0.918396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(430939, 9315)</td>\n",
       "      <td>0.157258</td>\n",
       "      <td>0.481338</td>\n",
       "      <td>0.724579</td>\n",
       "      <td>0.814076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(430939, 629402)</td>\n",
       "      <td>0.067460</td>\n",
       "      <td>0.348028</td>\n",
       "      <td>0.724190</td>\n",
       "      <td>0.909028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(430939, 62558)</td>\n",
       "      <td>0.091633</td>\n",
       "      <td>0.385528</td>\n",
       "      <td>0.762582</td>\n",
       "      <td>0.928880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(430939, 286026)</td>\n",
       "      <td>0.028112</td>\n",
       "      <td>0.102828</td>\n",
       "      <td>0.214865</td>\n",
       "      <td>0.286311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            indices      JDk4      JDk5      JDk6      JDk7\n",
       "0  (430939, 263646)  0.071146  0.355429  0.738653  0.918396\n",
       "1    (430939, 9315)  0.157258  0.481338  0.724579  0.814076\n",
       "2  (430939, 629402)  0.067460  0.348028  0.724190  0.909028\n",
       "3   (430939, 62558)  0.091633  0.385528  0.762582  0.928880\n",
       "4  (430939, 286026)  0.028112  0.102828  0.214865  0.286311"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jdistance_sub1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indices</th>\n",
       "      <th>JDk4</th>\n",
       "      <th>JDk5</th>\n",
       "      <th>JDk6</th>\n",
       "      <th>JDk7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19895</th>\n",
       "      <td>(96277, 126934)</td>\n",
       "      <td>0.008163</td>\n",
       "      <td>0.033708</td>\n",
       "      <td>0.059144</td>\n",
       "      <td>0.075965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19896</th>\n",
       "      <td>(96277, 215931)</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.347876</td>\n",
       "      <td>0.720537</td>\n",
       "      <td>0.896936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19897</th>\n",
       "      <td>(389645, 126934)</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.347271</td>\n",
       "      <td>0.713262</td>\n",
       "      <td>0.899429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19898</th>\n",
       "      <td>(389645, 215931)</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.334852</td>\n",
       "      <td>0.695521</td>\n",
       "      <td>0.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19899</th>\n",
       "      <td>(126934, 215931)</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>0.347876</td>\n",
       "      <td>0.718500</td>\n",
       "      <td>0.894200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                indices      JDk4      JDk5      JDk6      JDk7\n",
       "19895   (96277, 126934)  0.008163  0.033708  0.059144  0.075965\n",
       "19896   (96277, 215931)  0.055556  0.347876  0.720537  0.896936\n",
       "19897  (389645, 126934)  0.079365  0.347271  0.713262  0.899429\n",
       "19898  (389645, 215931)  0.071429  0.334852  0.695521  0.880000\n",
       "19899  (126934, 215931)  0.063492  0.347876  0.718500  0.894200"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jdistance_sub1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19900, 5)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jdistance_sub1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second random sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniflna_HA_sub2 = uniflna_HA.sample(n=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "idl2=list(it.combinations(list(uniflna_HA_sub2.index),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=4\n",
    "JDresults42=[]\n",
    "for i in idl2:\n",
    "    JDresults42.append(JacDist(charkgram(uniflna_HA_sub2.loc[[i[0]]].sequences.values[0],4),charkgram(uniflna_HA_sub2.loc[[i[1]]].sequences.values[0],4)))\n",
    "plt.hist(JDresults42, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=5\n",
    "JDresults52=[]\n",
    "for i in idl2:\n",
    "    JDresults52.append(JacDist(charkgram(uniflna_HA_sub2.loc[[i[0]]].sequences.values[0],5),charkgram(uniflna_HA_sub2.loc[[i[1]]].sequences.values[0],5)))\n",
    "plt.hist(JDresults52, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=6\n",
    "JDresults62=[]\n",
    "for i in idl2:\n",
    "    JDresults62.append(JacDist(charkgram(uniflna_HA_sub2.loc[[i[0]]].sequences.values[0],6),charkgram(uniflna_HA_sub2.loc[[i[1]]].sequences.values[0],6)))\n",
    "plt.hist(JDresults62, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=7\n",
    "JDresults72=[]\n",
    "for i in idl2:\n",
    "    JDresults72.append(JacDist(charkgram(uniflna_HA_sub2.loc[[i[0]]].sequences.values[0],7),charkgram(uniflna_HA_sub2.loc[[i[1]]].sequences.values[0],7)))\n",
    "plt.hist(JDresults72, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(JDresults42, 50, range=(0,1), color='blue', alpha=0.35, label='k=4')\n",
    "plt.hist(JDresults52, 50, range=(0,1), color='red', alpha=0.35, label='k=5')\n",
    "plt.hist(JDresults62, 50, range=(0,1), color='green', alpha=0.35, label='k=6')\n",
    "plt.hist(JDresults72, 50, range=(0,1), color='orange', alpha=0.35, label='k=7')\n",
    "plt.xlabel(\"Jaccard Distance\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe of distances for sub-sample2\n",
    "Jdistance_sub2 = pd.DataFrame(\n",
    "    {'indices': idl2,\n",
    "     'JDk4': JDresults42,\n",
    "     'JDk5': JDresults52,\n",
    "     'JDk6': JDresults62,\n",
    "     'JDk7': JDresults72\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indices</th>\n",
       "      <th>JDk4</th>\n",
       "      <th>JDk5</th>\n",
       "      <th>JDk6</th>\n",
       "      <th>JDk7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(46623, 53144)</td>\n",
       "      <td>0.071713</td>\n",
       "      <td>0.383803</td>\n",
       "      <td>0.726209</td>\n",
       "      <td>0.915049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(46623, 312008)</td>\n",
       "      <td>0.059524</td>\n",
       "      <td>0.358885</td>\n",
       "      <td>0.730061</td>\n",
       "      <td>0.906526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(46623, 146653)</td>\n",
       "      <td>0.051383</td>\n",
       "      <td>0.350694</td>\n",
       "      <td>0.700052</td>\n",
       "      <td>0.881873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(46623, 358762)</td>\n",
       "      <td>0.059524</td>\n",
       "      <td>0.317857</td>\n",
       "      <td>0.634981</td>\n",
       "      <td>0.825534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(46623, 314943)</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>0.328671</td>\n",
       "      <td>0.720632</td>\n",
       "      <td>0.904091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           indices      JDk4      JDk5      JDk6      JDk7\n",
       "0   (46623, 53144)  0.071713  0.383803  0.726209  0.915049\n",
       "1  (46623, 312008)  0.059524  0.358885  0.730061  0.906526\n",
       "2  (46623, 146653)  0.051383  0.350694  0.700052  0.881873\n",
       "3  (46623, 358762)  0.059524  0.317857  0.634981  0.825534\n",
       "4  (46623, 314943)  0.063492  0.328671  0.720632  0.904091"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jdistance_sub2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indices</th>\n",
       "      <th>JDk4</th>\n",
       "      <th>JDk5</th>\n",
       "      <th>JDk6</th>\n",
       "      <th>JDk7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19895</th>\n",
       "      <td>(149231, 240946)</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.485612</td>\n",
       "      <td>0.800835</td>\n",
       "      <td>0.939407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19896</th>\n",
       "      <td>(149231, 46238)</td>\n",
       "      <td>0.102362</td>\n",
       "      <td>0.484262</td>\n",
       "      <td>0.806667</td>\n",
       "      <td>0.938914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19897</th>\n",
       "      <td>(63399, 240946)</td>\n",
       "      <td>0.141129</td>\n",
       "      <td>0.549125</td>\n",
       "      <td>0.830664</td>\n",
       "      <td>0.950333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19898</th>\n",
       "      <td>(63399, 46238)</td>\n",
       "      <td>0.140562</td>\n",
       "      <td>0.558108</td>\n",
       "      <td>0.847759</td>\n",
       "      <td>0.954574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19899</th>\n",
       "      <td>(240946, 46238)</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.365152</td>\n",
       "      <td>0.575526</td>\n",
       "      <td>0.694466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                indices      JDk4      JDk5      JDk6      JDk7\n",
       "19895  (149231, 240946)  0.117647  0.485612  0.800835  0.939407\n",
       "19896   (149231, 46238)  0.102362  0.484262  0.806667  0.938914\n",
       "19897   (63399, 240946)  0.141129  0.549125  0.830664  0.950333\n",
       "19898    (63399, 46238)  0.140562  0.558108  0.847759  0.954574\n",
       "19899   (240946, 46238)  0.105263  0.365152  0.575526  0.694466"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jdistance_sub2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>indices</th>\n",
       "      <th>JDk4</th>\n",
       "      <th>JDk5</th>\n",
       "      <th>JDk6</th>\n",
       "      <th>JDk7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5269</th>\n",
       "      <td>(60329, 92882)</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.481928</td>\n",
       "      <td>0.804062</td>\n",
       "      <td>0.937047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3053</th>\n",
       "      <td>(350602, 437802)</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.336867</td>\n",
       "      <td>0.695720</td>\n",
       "      <td>0.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10094</th>\n",
       "      <td>(69021, 484781)</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.360179</td>\n",
       "      <td>0.741239</td>\n",
       "      <td>0.913885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>(350602, 88848)</td>\n",
       "      <td>0.134694</td>\n",
       "      <td>0.459873</td>\n",
       "      <td>0.798539</td>\n",
       "      <td>0.923495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19120</th>\n",
       "      <td>(573870, 181615)</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.107792</td>\n",
       "      <td>0.201778</td>\n",
       "      <td>0.268177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13835</th>\n",
       "      <td>(429091, 16330)</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.377752</td>\n",
       "      <td>0.727177</td>\n",
       "      <td>0.905306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>(312008, 308500)</td>\n",
       "      <td>0.055336</td>\n",
       "      <td>0.359770</td>\n",
       "      <td>0.725077</td>\n",
       "      <td>0.900036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10575</th>\n",
       "      <td>(282109, 63708)</td>\n",
       "      <td>0.123506</td>\n",
       "      <td>0.492806</td>\n",
       "      <td>0.820397</td>\n",
       "      <td>0.942689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>(78862, 230666)</td>\n",
       "      <td>0.074219</td>\n",
       "      <td>0.344470</td>\n",
       "      <td>0.695763</td>\n",
       "      <td>0.879244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17556</th>\n",
       "      <td>(13500, 511113)</td>\n",
       "      <td>0.112403</td>\n",
       "      <td>0.367698</td>\n",
       "      <td>0.722727</td>\n",
       "      <td>0.901714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12995</th>\n",
       "      <td>(285459, 240946)</td>\n",
       "      <td>0.092742</td>\n",
       "      <td>0.450372</td>\n",
       "      <td>0.802332</td>\n",
       "      <td>0.926893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6678</th>\n",
       "      <td>(574630, 1726)</td>\n",
       "      <td>0.099602</td>\n",
       "      <td>0.471210</td>\n",
       "      <td>0.810392</td>\n",
       "      <td>0.939886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                indices      JDk4      JDk5      JDk6      JDk7\n",
       "5269     (60329, 92882)  0.095238  0.481928  0.804062  0.937047\n",
       "3053   (350602, 437802)  0.080000  0.336867  0.695720  0.882353\n",
       "10094   (69021, 484781)  0.055556  0.360179  0.741239  0.913885\n",
       "2997    (350602, 88848)  0.134694  0.459873  0.798539  0.923495\n",
       "19120  (573870, 181615)  0.032258  0.107792  0.201778  0.268177\n",
       "13835   (429091, 16330)  0.055556  0.377752  0.727177  0.905306\n",
       "578    (312008, 308500)  0.055336  0.359770  0.725077  0.900036\n",
       "10575   (282109, 63708)  0.123506  0.492806  0.820397  0.942689\n",
       "7664    (78862, 230666)  0.074219  0.344470  0.695763  0.879244\n",
       "17556   (13500, 511113)  0.112403  0.367698  0.722727  0.901714\n",
       "12995  (285459, 240946)  0.092742  0.450372  0.802332  0.926893\n",
       "6678     (574630, 1726)  0.099602  0.471210  0.810392  0.939886"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jdistance_sub2.sample(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also checking NA gene to make sure pattern with increasing k is not just in HA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brian/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "uniflna_NA = uniflna.loc[uniflna.iloc[:,0].str.contains(r'\\(NA\\) | neuraminidase | (segment 6)')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66339, 2)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniflna_NA.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12478)\n",
    "uniflna_NA_sub1 = uniflna_NA.sample(n=200)\n",
    "uniflna_NA_sub2 = uniflna_NA.sample(n=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "idl3=list(it.combinations(list(uniflna_NA_sub1.index),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=4\n",
    "JDresults4n1=[]\n",
    "for i in idl3:\n",
    "    JDresults4n1.append(JacDist(charkgram(uniflna_NA_sub1.loc[[i[0]]].sequences.values[0],4),charkgram(uniflna_NA_sub1.loc[[i[1]]].sequences.values[0],4)))\n",
    "plt.hist(JDresults4n1, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=5\n",
    "JDresults5n1=[]\n",
    "for i in idl3:\n",
    "    JDresults5n1.append(JacDist(charkgram(uniflna_NA_sub1.loc[[i[0]]].sequences.values[0],5),charkgram(uniflna_NA_sub1.loc[[i[1]]].sequences.values[0],5)))\n",
    "plt.hist(JDresults5n1, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=6\n",
    "JDresults6n1=[]\n",
    "for i in idl3:\n",
    "    JDresults6n1.append(JacDist(charkgram(uniflna_NA_sub1.loc[[i[0]]].sequences.values[0],6),charkgram(uniflna_NA_sub1.loc[[i[1]]].sequences.values[0],6)))\n",
    "plt.hist(JDresults6n1, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# k=7\n",
    "JDresults7n1=[]\n",
    "for i in idl3:\n",
    "    JDresults7n1.append(JacDist(charkgram(uniflna_NA_sub1.loc[[i[0]]].sequences.values[0],7),charkgram(uniflna_NA_sub1.loc[[i[1]]].sequences.values[0],7)))\n",
    "plt.hist(JDresults7n1, 50, range=(0,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(JDresults4n1, 50, range=(0,1), color='blue', alpha=0.35, label='k=4')\n",
    "plt.hist(JDresults5n1, 50, range=(0,1), color='red', alpha=0.35, label='k=5')\n",
    "plt.hist(JDresults6n1, 50, range=(0,1), color='green', alpha=0.35, label='k=6')\n",
    "plt.hist(JDresults7n1, 50, range=(0,1), color='orange', alpha=0.35, label='k=7')\n",
    "plt.xlabel(\"Jaccard Distance\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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