{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import anndata\n",
    "from nero import Harmonia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "demux = Harmonia.MuxOut(\"/covid/demux/JYCVPBMC5/merged/freemux_merged.clust1.samples.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['path', 'label', 'raw', 'muxtype', 'type', 'usefulcols', 'useful_cols_run', 'store_meta_run', 'data', 'num_drops', 'num_sng', 'num_dbl', 'num_amb', 'sngrate', 'dblrate', 'ambrate', 'sngrate_nonamb', 'dblrate_nonamb', 'inds'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demux.__dict__.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>NUM.SNPS</th>\n",
       "      <th>NUM.READS</th>\n",
       "      <th>DROPLET.TYPE</th>\n",
       "      <th>BEST.GUESS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BARCODE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GAACCTACATAGAAAC-0</th>\n",
       "      <td>907</td>\n",
       "      <td>1930</td>\n",
       "      <td>SNG</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GTGCGGTTCCCTAACC-0</th>\n",
       "      <td>1170</td>\n",
       "      <td>2267</td>\n",
       "      <td>SNG</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACATACGAGGACCACA-0</th>\n",
       "      <td>2301</td>\n",
       "      <td>6278</td>\n",
       "      <td>SNG</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AACACGTAGAGTGAGA-0</th>\n",
       "      <td>1721</td>\n",
       "      <td>3991</td>\n",
       "      <td>SNG</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGCTGCTCAGTACACT-0</th>\n",
       "      <td>247</td>\n",
       "      <td>309</td>\n",
       "      <td>SNG</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    NUM.SNPS  NUM.READS DROPLET.TYPE BEST.GUESS\n",
       "BARCODE                                                        \n",
       "GAACCTACATAGAAAC-0       907       1930          SNG         12\n",
       "GTGCGGTTCCCTAACC-0      1170       2267          SNG          9\n",
       "ACATACGAGGACCACA-0      2301       6278          SNG         10\n",
       "AACACGTAGAGTGAGA-0      1721       3991          SNG          9\n",
       "TGCTGCTCAGTACACT-0       247        309          SNG         16"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demux.sng().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J7j7R3ScAJwC/BS4E/jPO4LKh5rmIxCFKxmhw9z8nF9z9fuBf3P0JoDq2yLKk5rmIxCFK0txsZl8xs+nhdCXwrpklgGiZKcfMbIGZLWxpaRlwGzXPRSQOUZLmp4FpwBLgv4H3hWUJ4LT4QhuYuy919/Nqa2vTbRPpWKppikgmoozcvhG4xMz2BLrdfVvK6pdii0xEpAgNWtM0s/eb2d+BVcBqM1thZofEH5qISPGJ0jy/Cbjc3ae7+3Tgy8DCeMMSESlOUZLmaHd/KLng7suA0bFFJCJSxAa9pgm8YmbfBG4Llz8DvBpfSCIixStKTfPzwCTgbuD34fw5cQYlIlKsovSevwt8MQ+xFITu0xSRTGTy3vNeCvnecxGRQinr956LiORaWb/3XEQk1zTEj4hIBpQ0RUQyoKQpIpKBKK+7OAC4Apieur27HxtjXCIiRSnKE0G/A34G/BzoijccEZHiFiVpdrr7T2OPRESkBES5prnUzC40sylmNj45xR6ZiEgRilLTPCv8vCKlzIFZuQ9HRKS4RXn2fGY+AsmEmS0AFsyePbvQoYjIMJPu2fNj3f1BM/tkf+vd/e74wkrP3ZcCSxsaGs4tVAwiMjylq2keAzwILOhnnRMMFSciMqyke/b82+Gnxs4UEQnpiSARkQwoaYqIZEBJU0QkA1Heez7KzL5pZj8Pl/c3s4/HH1p2fOBB50VEhixKTfMXwE7gyHC5CbgmtohERIpYlKS5n7tfC3QAuHsr6G1kIjI8RUma7WY2kvAla2a2H0HNU0Rk2Iny7Pm3gT8B+5rZr4EPA2fHGZSISLGK8uz5A2b2DPAhgmb5pe6+MfbIRESKUJTe81MIxtT8o7vfA3Sa2b/FH5qISPGJck3z2+7eklxw9y0ETXYRkWEnStLsb5so10JLgpluBBCR6KIkzeVm9kMz28/MZpnZ9cCKuAMTESlGUZLmJUA7cAfBS9bagIviDEpEpFhF6T3fDnw1D7GIiBS9qO89/9/ADPTecxEZ5jJ57/nN6L3nIjLM6b3nIiIZ0HvPRUQyoPeei4hkoCTfey4iUihRR27/hpktDJdLYuR2EZE4RB25vR04KlzWyO0iMmxp5HYRkQxo5HYRkQxEGhqO3iO3/wW4MtaoBmFmC8xsYUtLy+Abi4jkUNqkacG4ac8DnyR4xcVioMHdl8UeWRruvtTdz6utrS1kGCIyDKW95cjd3cyWuPs84I95iklEpGhFaZ4/YWaHxx6JiEgJiPJE0EeB883sNWA7Qc+5u/uhcQYmIlKMoiTNE2KPIgbuXugQRKQMDdo8d/f1wL7AseH8jij7iYiUoyiPUX4b+ArwtbCoEvhVnEGJiBSrKDXGU4CTCa5n4u5vAWPiDEpEpFhFeiLIgwuEySeCRscbkohI8YqSNH9rZjcBY83sXOB/gJ/HG5aISHEasPfczKrdfae7/8DMPga8BxwIfMvdH8hbhCIiRSTdLUePA3PN7DZ3/yxQlonSNGCTiGQgXdKsMrOzgKPM7JN9V7r73fGFJSJSnNIlzfOBfwfGAgv6rHNASVNEhp10SXOKu19gZn9394V5i0hEpIil6z1P3sx+fj4CEREpBelqmpvM7CFgppn9oe9Kdz85vrBERIpTuqR5EjAXuA24Lj/hiIgUtwGTpru3E4yleZS7N+cxJhGRopXu5vYfufuXgFvNbLdx1tQ8F5HhKF3z/Lbw8wf5CEREpBSka56vCD//amaTwnk100VkWBvwliMLXGVmGwneSPmCmTWb2bfyF56ISHFJd5/ml4APA4e7+wR3HwccAXzYzC7LS3QiIkUmXdL8HHCmu7+aLHD3V4DPhOtERIaddEmz0t039i0Mr2tWxheSiEjxSpc024e4TkSkbKW75egDZvZeP+UG1MQUj4hIUUt3y1Ein4GIiJQCvb9cRCQDZZs0nd2e/BQRyVrZJk0RkTgoaYqIZEBJU0QkA0qaIiIZUNIUEcmAkqaISAaUNEVEMjDsk6aZFToEESkhwz5piohkQklTRCQDSpoiIhlQ0hQRyYCSpohIBtINQpxXZvZvwEnAZOBGd7+/wCGJDGsdHR00NTXR1tZW6FBiU1NTw7Rp06isjP4Gn1iTppndCnwc2ODuh6SUzwd+DCSAm939++6+BFhiZuOAHwBKmiIF1NTUxJgxY5gxY0ZZ3prn7mzatImmpiZmzpwZeb+4m+eLgPmpBWaWAG4ETgAOBs40s4NTNvlGuF5ECqitrY0JEyaUZcKE4B7tCRMmZFyTjrWm6e4Pm9mMPsUfBF4KXweMmd0OfMLM1gLfB+5z92fijEtE0lv22jLWb1jPOyPfyev3Ns5ozOv3DeU/hEJ0BE0F3khZbgrLLgGOAz5lZucPtLOZnWdmy81seXNzc7yRikhBJRIJ6uvrqaur4wMf+AA//OEP6e7uBmDZsmXU1tZSX1/PoYceynHHHceGDRsAWLRoERdffHEsMRUiafaX2t3df+Lu89z9fHf/2UA7u/tCd29w94ZJkybFGKaIFNrIkSNZuXIlq1ev5oEHHuDee+/l6quv7ln/kY98hJUrV/Lss89y+OGHc+ON8V/ZK0TSbAL2TVmeBrxVgDhEpIRMnjyZhQsXcsMNN+De+x1g7s7WrVsZN25c7HEU4pajp4H9zWwm8CZwBvDpAsQhIiVm1qxZdHd39zTDH3nkEerr69m0aROjR4/me9/7XuwxxFrTNLPFwOPAgWbWZGZfcPdO4GLgz8Ba4LfuvjrOOESkfKTWMpPN8zfeeINzzjmHK6+8Mvbvj7v3/MwByu8F7o3zu0Wk/LzyyiskEgkmT57M2rVre607+eSTOfXUU2OPQY9RikhJaG5u5vzzz+fiiy/u91ahRx99lP322y/2OIrmMcpMmNkCYMHs2bMLHYqIxKi1tZX6+no6OjqoqKjgs5/9LJdffnnP+uQ1TXentraWm2++uWfdokWLWLJkSc/yE088wbRp07KOqSSTprsvBZY2NDScW+hYRCQ+XV1dA65rbGykpaWl33Vnn302Z599diwxlWTSjKLvLQkiEl3jjEbWtq5lzow5hQ6l6OiapohIBpQ0RUQyoKQpIpIBJU0RkQyUbUeQiAzdsmXLWL9+Pe+8k+eh4Rob8/p9Q1GSNU0zW2BmCwe63UBEysfvf/97zIznn38egNdeew0z45vf/GbPNhs3bqSysrJnOLirrrqKqVOnUl9fz0EHHcQFF1zQM6Rctkoyabr7Unc/r7a2NutjWb8j1YlIsVi8eDFHH300t99+e0/ZrFmzuOeee3qWf/e731FXV9drv8suu4yVK1eyZs0aVq1axV//+tecxFOSSVNEhodt27bxt7/9jVtuuaVX0hw5ciRz5sxh+fLlANxxxx2cdtpp/R6jvb2dtra2nA0bp6QpIkVryZIlzJ8/nwMOOIDx48fzzDO73oRzxhlncPvtt9PU1EQikWCfffbpte/1119PfX09U6ZM4YADDqC+vj4nMSlpikjRWrx4MWeccQYQJMnFixf3rJs/fz4PPPAAixcv5vTTT99t32TzfMOGDWzfvr1XTTUb6j0XkaK0adMmHnzwQZ577jnMjK6uLsyMCy+8EICqqirmzZvHddddx+rVq1m6dGm/x6msrGT+/Pk8/PDDPQk4G0qaIlKU7rzzTj73uc9x00039ZQdc8wxNDU19Sx/+ctf5phjjmHChAkDHsfdeeyxx9Q8F5HytnjxYk455ZReZaeeemqvV1rU1dVx1lln9bt/8prmIYccQmdnZ08NNVtWyqMBNTQ0eLL3rK/m7c2sbh78LRqTRk2ibnLdoNuJDCfJm9unT5+e1+8txM3ta9euZc6c3qM5mdkKd2/ob3s1z0VkN42Njf0mEynR5rmeCBKRQinJpJnLJ4JERDJRkklTRKRQlDRFRDKgjiAR2d2yZYxavx7yPDQcGhpORGTovvvd71JXV8ehhx5KfX09Tz75JI2NjTQ07LobaPny5b1uVXrqqadobGxk//33Z+7cuZx00kmsWrUqZzGppikiRenxxx/nnnvu4ZlnnqG6upqNGzfS3t4OwIYNG7jvvvs44YQTeu3zzjvvcNppp/Gb3/yGo446CoBHH32Ul19+mfe///05iUtJU0SK0ttvv83EiROprq4GYOLEiT3rrrjiCq655prdkuYNN9zAWWed1ZMwAY4++uicxqXmuYgUpeOPP5433niDAw44gAsvvLDXIMJHHnkk1dXVPPTQQ732Wb16NXPnzo01LiVNESlKe+yxBytWrGDhwoVMmjSJ008/nUWLFvWs/8Y3vsE111yT9hhHHHEEc+bM4dJLL81ZXGWbNJ3SfaZeRAKJRILGxkauvvpqbrjhBu66666edcceeyxtbW088cQTPWV1dXW9Bip+8skn+c53vkMunx4syaSpxyhFyt+6det48cUXe5ZXrly52wAiX//617n22mt7li+66CIWLVrEY4891lO2Y8eOnMZVkh1B7r4UWNrQ0HBuoWMRkXhs27aNSy65hC1btlBRUcHs2bNZuHAhn/rUp3q2OfHEE5k0aVLP8t57780dd9zBV77yFd58800mT57MxIkT+da3vpWzuEoyaYpI+Zs3b16vGmPSsmXLei2vWLGi1/KHPvShnL15sj9KmiKyu8ZGdqxdCxoabjcleU1TRKRQlDRFRDKgpCkiAyrl1+FEMZSfb9gnTTMrdAgiRammpoZNmzaVbeJ0dzZt2kRNTU1G+6kjSET6NW3aNJqammhubi50KLGpqalh2rRpGe2jpCki/aqsrGTmzJmFDqPoDPvmuYhIJpQ0RUQyUJJJU8+ei0ihWCn3jJlZM7A+w90mAhtjCCdupRo3lG7spRo3lG7sxRL3dHef1N+Kkk6aQ2Fmy929YfAti0upxg2lG3upxg2lG3spxF2SzXMRkUJR0hQRycBwTJoLCx3AEJVq3FC6sZdq3FC6sRd93MPumqaISDaGY01TRGTIhk3SNLP5ZrbOzF4ys68WKIZ9zewhM1trZqvN7NKwfLyZPWBmL4af48JyM7OfhDE/a2ZzU451Vrj9i2Z2Vkr5PDNbFe7zE8vhiCRmljCzv5vZPeHyTDN7MozhDjOrCsurw+WXwvUzUo7xtbB8nZn9a0p5bL8fMxtrZnea2fPhuT+yhM75ZeG/lefMbLGZ1RTreTezW81sg5k9l1IW+3ke6Dti4+5lPwEJ4GVgFlAF/AM4uABxTAHmhvNjgBeAg4Frga+G5V8F/l84fyJwH2DAh4Anw/LxwCvh57hwfly47ingyHCf+4ATchj/5cBvgHvC5d8CZ4TzPwMuCOcvBH4Wzp8B3BHOHxye+2pgZvg7ScT9+wF+CfyvcL4KGFsK5xyYCrwKjEw532cX63kH/gWYCzyXUhb7eR7oO2L7O47z4MUyhSf6zynLXwO+VgRx/TfwMWAdMCUsmwKsC+dvAs5M2X5duP5M4KaU8pvCsinA8ynlvbbLMtZpwF+AY4F7wn+4G4GKvucY+DNwZDhfEW5nfc97crs4fz/AnmHisT7lpXDOpwJvhAmkIjzv/1rM5x2YQe+kGft5Hug74pqGS/M8+Y8vqSksK5iw6XQY8CSwl7u/DRB+Tg43GyjudOVN/ZTnwo+AK4HucHkCsMXdO/v5rp74wvUt4faZ/jy5MAtoBn4RXlq42cxGUwLn3N3fBH4AvA68TXAeV1Aa5z0pH+d5oO+IxXBJmv1dYyrYbQNmtgdwF/Ald38v3ab9lPkQyrNiZh8HNrh76mv/0n1XUcQdqiBoMv7U3Q8DthM04QZSNLGH1+Y+QdCk3gcYDZyQ5vuKJvYISinWXoZL0mwC9k1Znga8VYhAzKySIGH+2t3vDovfMbMp4fopwIawfKC405VP66c8Wx8GTjaz14DbCZroPwLGmllyTNbU7+qJL1xfC2wews+TC01Ak7s/GS7fSZBEi/2cAxwHvOruze7eAdwNHEVpnPekfJzngb4jHnG2/YtlIqhtvELwP3bygnddAeIw4L+AH/Up/w96X8i+Npw/id4Xy58Ky8cTXKcbF06vAuPDdU+H2yYvlp+Y45+hkV0dQb+jd4fEheH8RfTukPhtOF9H7w6JVwg6I2L9/QCPAAeG81eF57vozzlwBLAaGBUe++XHQ1wAAAMZSURBVJfAJcV83tn9mmbs53mg74jt7zjOgxfTRNBb9wJBb+HXCxTD0QRNimeBleF0IsF1p78AL4afyX8kBtwYxrwKaEg51ueBl8LpnJTyBuC5cJ8b6NMBkoOfoZFdSXMWQY/mS+EfcnVYXhMuvxSun5Wy/9fD2NaR0ssc5+8HqAeWh+d9SfjHWBLnHLgaeD48/m0Eia8ozzuwmODaawdBzfAL+TjPA31HXJOeCBIRycBwuaYpIpITSpoiIhlQ0hQRyYCSpohIBpQ0RUQyoKQpeWdme5vZ7Wb2spmtMbN7zeyAQfbZFn7OSB1FJx/M7DUzm5jP75TipaQpeRUO5/V7YJm77+fuBwP/B9irsJHFI+XJHSkTSpqSbx8FOtz9Z8kCd1/p7o8AmNkVZvZ0OMbi1VEPamaNZrYsZdzMX6eMt9hTUzSzBjNbFs5fZWa/NLP7w20+aWbXhmM2/il85DXpCjN7Kpxmh/tPMrO7wnifNrMPpxx3oZndT/AEmJQRJU3Jt0MIRurZjZkdD+wPfJDgKZ55ZvYvGRz7MOBLBONHziJ4Zn4w+xE80vcJ4FfAQ+7+fqA1LE96z90/SPAkyo/Csh8D17v74cCpwM0p288DPuHun84gfikBajpIMTk+nP4eLu9BkEQfjrj/U+7eBGBmKwmeg350kH3uc/cOM1tF8Dz2n8LyVeH+SYtTPq8P548DDrZdA7XvaWZjwvk/uHtrxLilhChpSr6tBj41wDoD/q+73zTEY+9Mme9i17/vTna1qmr628fdu82sw3c9V9xN778P72d+BMGgv72SY5hEtw/lB5Dip+a55NuDQLWZnZssMLPDzewYghHFPx+ON4qZTTWzXAwo+xpBcxmCZvRQnJ7y+Xg4fz9wcXIDM6sf4rGlhChpSl6FNblTgI+FtxytJhiu7S13v5/gHUSPh83lOwnepZStq4Efm9kjBDXQoag2syeBS4HLwrIvAg1hp9Ua4PzsQ5Vip1GOREQyoJqmiEgGlDRFRDKgpCkikgElTRGRDChpiohkQElTRCQDSpoiIhlQ0hQRycD/B/HSVGmXl/KjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demux.llk_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uOMrm7UfY9NoRKkpDXH9eC7e2LeCaFbMIn6UnkpQ0ReSXlJWEWH9uC+vPbeFzN63i5f29fH9bJ5teO8ym144wp7aCmy9p5da2+SxrqSl0uNNKSVNEJhQKGWsXN7J2cSN/euNKfrTjKI+8fJD7n97HV3+6l7WLGrl97QLec+HcQoc6LZQ0RSRrZSUhbrhwLjdcOJdjb5/msVcO8dCLB/hvj2zjc4+/wa0XtbDx+hpm1RTvLZ1KmiIyKS01Fdx97VI+ec0Snt/TzYPP7uPrzx/in146zB1rF/Kp9UuZXVt8138qaYrIlJjZmac1vbzrII+09/Ctn+3noRcP8LGrFvNfrltKbUVpocPMmewvwBIR8bCoMcJf33IRP/6Da7lh1Ry+umUP6//mKR568QBjY8Vx0bySpojk3DlNVXzh9kv43u/8CstmVfPZx17jtq89z77oyUKHNmVKmiKSNxfOr+PhjZfz17esZueRt3nv3z/Nd9qP4QJ8q6aSpojkVShkfHDNAp78/Wu4dGEDn//hPv7o0XZOD48WOrRJUdIUkWkxty7CP/32Wu66Yh6Pbu3gtq8+T9fbg4UOyzclTRGZNqGQ8cmr5nPfR9rYdbSfW+97joM9pwodli9KmiIy7TasmsM/f2IdvaeGufW+59nT1V/okLI2Y5KmmZ1vZveZ2aNm9qlCxyMi+dV2TgMPb7yc4dExbvvqz9gTDcY3zrwmTTN70MyOmdn2lPYNZrbTzHab2WcAnHM7nHN3Ax8E1uQzLhGZGc6fW8u/ffJyzGDjwzt45UBvoUPylO9vmt8ANiQ2mFkY+ApwA7ASuMPMVsa33QQ8A/woz3GJyAyxrKWGb999JTUVYT5y/wts2dVV6JAmlNek6ZzbAvSkNK8Fdjvn9jrnhoCHgZvj/b/nnLsS+HA+4xKRmWVhUyX3376ShY2VfPybL/HYKx2FDikjy/dFpma2CHjcObcqXr8F2OCc+0S8fiewDngU+ABQDrQ7576SYX8bgY0Ara2tbZs3b2ZgYIBIJAKQtpzYFo1GJ/0058T9+O2Trj21zWscieWgjCNT/LkYRzZjmWh7Lj4Tza3cjmM0VM69T3fRfnSQ21bV8v7lZVRVVvoaVy7GsXr16q3OufTLhM65vP4Ai4DtCfVbgfsT6ncCX5rMvtva2pxzznV1dblx6cqJbe3t7W6yEvfjt0+69tQ2r3EkloMyjtR66mcylXFMFGc223PxmWhuZRdjNn3G2weHR90fPPKqO+fTj7s77nva9Z4czPh38zW3gJddhrxTiLPnHcCChPp8oLMAcYjIDFRWEuJvblnNX/7GKl7cf4L3ffkZ3ug8UeiwzijEo+FeApab2WLgEHA78CE/O9DbKIMzjtS63kbpP06vPsU6t25YVkX9jQv53I87ef8/PMNnf20xV88vy/h3iuJtlGb2ELAeaDazDuDPnHMPmNk9wJNAGHjQOfe6n/06vY0yUONIrettlP7j9OpTrHPrCuAHFy7j9x76OX/+xF7uaJvDX35gMSXhUNq/E/i3UTrn7sjQvgnYlM/fLSLFoaWmgm99fB2f/8EOvvHcW3SceImvfPjSgj3YOJBPbtfheXDGkVrX4bn/OL36nC1z654rZ9Nccpr/88xRPvDlp/nib55LxdjpX+ob6MPzfNHh+cw6hErXpsNz/zS3vMdx+1q4aPlCPvXPr7Dx33byf289l6XTfHg+Y+49FxHJxtXLZ/Hwxss5cXqYTz2yg2j/9D5eTklTRAJn1bw6vvGxtURPDnH3t7YyPDo2bb87kIfnWtMMzjhS61rT9B+nV5+zdW6dUwV/ePVcPv+TTu79/jY+fGFtUh+taSbQmubMXnfyqmtN03+cXn3O1rn1/jbY0Wd888VO3n1u05n1Ta1piohk8KfvXUlVWZgvbTk4Lb9PSVNEAq2hqoxPXruUZ/cdp73jeN5/XyAPz7WmGZxxpNa1puk/Tq8+mlvw3hXVfOlHxj9ueZN71jUCWtNMojXNYK07pda1puk/Tq8+Z/vcagbWL2/kJ7t7+fS7FmlNU0TEy/plDRw/Ncz2zv68/h4lTREpCuvOqSNk8MJ+7yWCqVDSFJGiUFNRwsrWWl499HZef08g1zR1Iig440it60SQ/zi9+mhuvVNe0VzBD3dE6erq0omgRDoRFMzF+tSyTgRlH6dXH82tmEsWhXls2zFGyqqJRCI6ESQiMpGls6oB2HPsZN5+h5KmiBSNRc1VALzVraQpIuJpTm0FJSGjo3cgb79DSVNEikYoZMyuKeNwn5KmiEhWWmrKONx32rvjJAXy7LkuOQrOOFLruuTIf5xefTS3ksv15fBm90kGBsK65GicLjkK9mUhuuTIf5xefTS33inPqa/ihYOndMmRiEg2mqtKOTU0ysBwfl6BoaQpIkWlsTL2PvTjp0fzsn8lTREpKg2R2KrjiUF90xQR8VQf/6appCkikoXxb5p9OjwXEfGW72+agbzkSNdpBmccqXVdp+k/Tq8+mlvJ5eFTb1MSMnpODuo6zXG6TjP419KN/6nrNLOL06uP5lZyubGqjFOjIV2nKSKSjcaqMp0IEhHJVmNVmU4EiYhkq6aihH29w3nZt5KmiBSd0nCIwVGXl30raYpI0WmsKgNgaCT365pKmiJSdObVRwDo6h/M+b6VNEWk6MxvqATgSB4eRqykKSJFpzZ+K+XRE0qaIiKeWuOH5/2DIznfdyDvCNJtlMEZR2pdt1H6j9Orj+ZWcrmvr49wRSxZvra/i+sXRTxj8iOQSVO3URbHrW66jTL7OL36aG4ll5uamgDoH8n9rZQ6PBeRomNmAITif+aSkqaIFKWFdaXsPHoi5/tV0hSRojQ65igvCed8v0qaIlKUZleXsKerP+f7VdIUkaJUFjYGhnL/pCMlTREpShUlxtCo7j0XEcnK7OoSnIOxsdw+7UhJU0SKUmVpLL2dHMrtXUFKmiJSlIbiz9PM9bqmkqaIFKWWqtjlRqeUNEVEshG7G6hvILevvVDSFJGiVF0WS28jhTgRZGZLzaw8Xl5vZr9nZvU5jUREJIcqS2PfNAeHC3N4/m1g1MyWAQ8Ai4F/zWkkgJn9hpl93cy+a2bvzvX+ReTsEQ7FkuaRHD+IONukOeacGwHeD3zBOff7wNxs/qKZPWhmx8xse0r7BjPbaWa7zewzAM65f3fO3QV8FLgt61GIiKQYPzwfT565km3SHDazO4DfAh6Pt5Vm+Xe/AWxIbDCzMPAV4AZgJXCHma1M6PI/4ttFRCaloiSWLI+fKsyJoI8BVwCfd87tM7PFwD9n8xedc1uAnpTmtcBu59xe59wQ8DBws8X8FfCEc+6VLGMTEfkl5fGkeSLHZ8/NuYnPLJnZJcBS4HXn3I5J/RKzRcDjzrlV8fotwAbn3Cfi9TuBdcAuYt9mXwJedc7dl2ZfG4GNAK2trW2bN29mYGCASCT2SPt05cS2aDQ66Sc5J+7Hb5907altXuNILAdlHJniz8U4shnLRNtz8ZlobhVuHJniH//z8LEuNv7HAHdeVMcHV9X5Gsfq1au3OufWpN3onMv4A/xPYonsIWAvcNdE/SfYzyJge0L9VuD+hPqdwJf87retrc0551xXV5cbl66c2Nbe3u4mK3E/fvuka09t8xpHYjko40itp34mUxnHRHFmsz0Xn4nmVnYxZtMn13Nr27Zt7pxPP+4+8+1tnjGlAl52GfKO1zuCbgMuds6dMrMmYDPwdV8pO70OYEFCfT7QmYP9iogA77zyojSc28vRvZLmaefcKQDnXLeZ5eq3vwQsj6+NHgJuBz6U7V/W2yiDM47Uut5G6T9Orz6aW8nlxHE0VpbQf2pqcyyVV9Jcambfi5ctpY5z7iavX2BmDwHrgWYz6wD+zDn3gJndAzwJhIEHnXOvZxu009soAzWO1LreRuk/Tq8+mlvJ5fFxRMr6OTFkOX0jpVfSvDml/rd+f4Fz7o4M7ZuATX73JyKSrVNDI5zO8R1BEyZN59xPc/rbckSH58EZR2pdh+f+4/Tqo7mVXE49PHejI9N3eG5mPwEyXZPknHO/mrNIfNDheXEdQunw3DtOrz6aW8nl8XE0Vo/gQqFpPTz/wzRtlwN/DBzLWRQiInlQXhLO+RspvQ7Pt46Xzexa4E+BcuBu59wTOY1ERCTHuk8O0VBZltN9en3TxMx+nViyPE3sNsqf5DSCSdCaZnDGkVrXmqb/OL36aG4llxPHMbc6zFs9p6d1TfMlYBbwN8Dz8bZLx7e7At0frjXN4lp30pqmd5xefTS3ksvj46iuHGGsZ3Ba1zRPAv3ALfGf1JNC1+csEhGRHCsNhziZ43cEeSXNPwYOOucOA5jZbwG/CbwF/HlOIxERybHh0TG63h7M6T69kuZ9wLsAzOwa4F7gd4GLga8R+/Y57bSmGZxxpNa1puk/Tq8+mlvJ5cRxDA+NUVdRMq23UYadc+PPwrwN+Jpz7tvAt83s1ZxF4ZPWNItr3Ulrmt5xevXR3Eouj49jdkMIt68vp2uaXg/gCJvZeGL9VeDHCds8z7yLiBRSOGTk+GWUnonvIeCnZhYFBoCnAeIvWPP+Hi4iUkDhkDEyNpbTfXpd3P55M/sRsZeo/TD+cE6IfUP93ZxGIiKSY+GQkeOc6X2I7Zz7WZq2XbkNQ0Qk90qm+5vmTKWz58EZR2pdZ8/9x+nVR3MruZw4jtMDo4w56OrqOvMk96kKZNLU2fPiOsOps+fecXr10dxKLo+Po6a6AoD6xqacvfYity/PEBGZQXpODgHg8dJdX5Q0RaRozaopz/k+lTRFRHxQ0hQR8SGQJ4J09jw440it6+y5/zi9+mhuJZcTx3HyZOxyo2g0SllJbr4jBjJp6ux5cZ3h1Nlz7zi9+mhuJZfHx1FVFQGgqbmJ8pKwZ1zZ0OG5iIgPSpoiIj4oaYqI+KCkKSJFTxe3i4hkIUe3mydR0hQR8SGQlxzpOs3gjCO1rus0/cfp1UdzK7mcOI5TJ2PH5d3d3ZTrOk1dpxmUcaTWdZ2m/zi9+mhuJZfHx1E5fp1mUxMVpbpOU0RkQkbuFzWVNEVEfFDSFBHxQUlTRMQHJU0RKXq6uF1EJAu6uF1EpMCUNEVEfFDSFBHxIZB3BOk2yuCMI7Wu2yj9x+nVR3MruZz8uovYGaBoNEqkLDd3BAUyaeo2yuK61U23UXrH6dVHcyu5PD6O6qpKIPa6i8qy3KQ7HZ6LiPigpCki4oOSpogUPV3cLiKSBV3cLiJSYEqaIiI+KGmKiPigpCkiRS+H54GUNEWkeOl1FyIiBaakKSLig5KmiIgPSpoiUvRcDm8JUtIUkaJV1HcEmdkSM3vAzB4tdCwiIpnkNWma2YNmdszMtqe0bzCznWa228w+A+Cc2+uc+3g+4xERmap8f9P8BrAhscHMwsBXgBuAlcAdZrYyz3GIyFkslxe3Wy4XSNP+ArNFwOPOuVXx+hXAnzvnfj1e/yyAc+7eeP1R59wtE+xvI7ARoLW1tW3z5s0MDAwQiUQA0pYT26LR6KSfSp24H7990rWntnmNI7EclHFkij8X48hmLBNtz8VnorlVuHFkij9xHM90lfHAK8d5+Nb5VJVl/x1x9erVW51za9JudM7l9QdYBGxPqN8C3J9QvxP4MtAE3AfsAT6bzb7b2tqcc851dXW5cenKiW3t7e1ushL347dPuvbUNq9xJJaDMo7UeupnMpVxTBRnNttz8ZlobmUXYzZ98jG3vr5ljzvn04+7voEhz7gSAS+7DHmnEO8ISnc+yznnuoG7pzsYERE/CpE0O4AFCfX5QKefHehtlMEZR2pdb6P0H6dXH82t5HLy2yhj23q6uxkqz026K0TSfAlYbmaLgUPA7cCH/OzA6W2UgRpHal1vo/Qfp1cfza3k8vg4qqqqAGhobKIuUuoZVzbyfcnRQ8DzwLlm1mFmH3fOjQD3AE8CO4BHnHOv5zMOETk7WR6ubs/rN03n3B0Z2jcBm/L5u0VE8qEQh+dTpjXN4Iwjta41Tf9xevXR3EouZ1rTHK4I7prmlGlNs7jWnbSm6R2nVx/NreTymTXNoWoAGpsCsqYpIjIj5PAenkB+09TheXDGkVrX4bn/OL36aG4llxPHcepk7ERQd083w0qApy8AAAm8SURBVKd0eK7D84CMI7Wuw3P/cXr10dxKLr9zeB675KipsYm6Sh2ei4hMOyVNESl6LoeLmkqaIlK08vHk9kCuaepEUHDGkVrXiSD/cXr10dxKLidfpxk/EdTdw4hOBOlEUFDGkVrXiSD/cXr10dxKLp85ETQcPxHU1Eh9ZZlnXNnQ4bmIiA9KmiJS9HL5ggolTREpWnk4DxTMNU2dCArOOFLrOhHkP06vPppbyeXEcfQn3BE0OpCbi9sDmTR1Iqi4Fut1Isg7Tq8+mlvJ5fFxVA+/c0dQQ5VOBImITDslTREperl8UbmSpogUrXy87kJJU0TEh0CeCNLZ8+CMI7Wus+f+4/Tqo7mVXE46e94f+6bZ093NmM6e6+x5UMaRWtfZc/9xevXR3Eounzl7PvLO6y4adfZcRCQ7Loe3BClpikjRysej4ZQ0RUR8UNIUEfFBSVNEip4ubhcRyYKechSn6zSDM47Uuq7T9B+nVx/NreRy8nWase+FPd09cFrXaeo6zYCMI7Wu6zT9x+nVR3MruXzmOs3R2FOOGpsaaa4u94wrGzo8FxHxQUlTRIqeXnchIpINPeVIRKSwlDRFRHxQ0hQR8UFJU0SKnsvhPUFKmiJStPJxR5CSpoiID4G8I0i3UQZnHKl13UbpP06vPppbyeW0t1H29BAazM2T2wOZNHUbZXHd6qbbKL3j9OqjuZVcPnMb5Vj8dRcNjTTXVnjGlQ0dnotI0dKT20VECkxJU0TEByVNEREflDRFpOjpdRciIlmwPFzerqQpIuKDkqaIiA9KmiIiPihpikjR0+suRESyoDuCREQKTElTRMQHJU0RKXq5fHL7jHk0nJlVAf8ADAFPOef+pcAhiUjABe7J7Wb2oJkdM7PtKe0bzGynme02s8/Emz8APOqcuwu4KZ9xiYhMVr4Pz78BbEhsMLMw8BXgBmAlcIeZrQTmAwfj3UbzHJeIyKTkNWk657YAPSnNa4Hdzrm9zrkh4GHgZqCDWOLMe1wiIpNlLpdXfab7BWaLgMedc6vi9VuADc65T8TrdwLrgE8DXwZOA89kWtM0s43ARoDW1ta2zZs3MzAwQCQSAUhbTmyLRqOTfpR/4n789knXntrmNY7EclDGkSn+XIwjm7FMtD0Xn4nmVuHGkSn+xHEMlNbxRtcg15xTSaQ0++9iq1ev3uqcW5N2o3Murz/AImB7Qv1W4P6E+p3Alyaz77a2Nuecc11dXW5cunJiW3t7u5usxP347ZOuPbXNaxyJ5aCMI7We+plMZRwTxZnN9lx8Jppb2cWYTZ+ZNLeAl12GvFOIw+AOYEFCfT7QWYA4RER8K8QlRy8By81sMXAIuB34kJ8d6BW+wRlHal2v8PUfp1cfza3kcq7mViZ5TZpm9hCwHmg2sw7gz5xzD5jZPcCTQBh40Dn3up/9Or3CN1DjSK3rFb7+4/Tqo7mVXM7V3Eonr0nTOXdHhvZNwKZ8/m4RkXyYMXcE+aHD8+CMI7Wuw3P/cXr10dxKLgf68DxfdHiuQ6hs4sxmuw7PNbf80kXkIiI+KGmKiPgQyMNzrWkGZxypda1p+o/Tq4/mVnJZa5ppaE1T607ZxJnNdq1pam75lfd7z/PJzLqA/UAdMP5PTrpyYlszMNl/fhL347dPuvbUNq9xJJaDMo7UeupnMpVxTBRnNttz8ZlobmUXYzZ9ZtLcOsc5Nyvtlkz3VwbpB/jaROWUtoz3lPr5PX77pGtPbfMaR8qYAjEOr89kKuPIZiwTbc/FZ6K5VbxzK9NPsZwI+r5HObEtV7/Hb5907altXuPINgYv0zmO1Pp0fyYTbc/FZ6K55R1Ltn1m2txKK9CH55NhZi+7TI98ChCNY+YplrFoHBMrlm+afnyt0AHkiMYx8xTLWDSOCZx13zRFRKbibPymKSIyaUqaIiI+KGmKiPhwVidNM6sys2+a2dfN7MOFjmcqzGyJmT1gZo8WOpapMLPfiH8e3zWzdxc6nskys/PN7D4ze9TMPlXoeKYq/v/KVjO7sdCxTJaZrTezp+Ofy/rJ7qfokqaZPWhmx8xse0r7BjPbaWa7zewz8eYPAI865+4Cbpr2YD34GYuLvRL544WJdGI+x/Hv8c/jo8BtBQg3I5/j2OGcuxv4IDDjLt/x+f8JxN4W+8j0RunN5zgc0A9UEHtX2eTk44r5Qv4A1wCXkvwGzDCwB1gClAHbgJXAZ4GL433+tdCxT2UsCdsfLXTcORrH3wGXFjr2qYyD2D/EzwEfKnTsUxkL8C5i7/L6KHBjoWOfwjhC8e2zgX+Z7O8sum+azrktQE9K81pgt4t9GxsCHgZuJvavzfx4nxn338LnWGYsP+OwmL8CnnDOvTLdsU7E7+fhnPuec+5KYMYt/fgcy3XA5cRegHiXmc2Y/1f8jMM5Nxbf3guUT/Z3BvIpR5MwDziYUO8A1gF/D3zZzN5Lnm+9yqG0YzGzJuDzwCVm9lnn3L0FiS57mT6T3yX2zabOzJY55+4rRHA+ZPo81hNb/iknOO/DSjsW59w9AGb2USCakHxmqkyfyQeAXwfqgS9PdudnS9K0NG3OOXcS+Nh0BzNFmcbSDdw93cFMQaZx/D2xf8yCItM4ngKemt5QpiztWM4UnPvG9IUyJZk+k8eAx6a68xnzNTvPOoAFCfX5QGeBYpmqYhmLxjHzFMtY8jqOsyVpvgQsN7PFZlZGbFH7ewWOabKKZSwax8xTLGPJ7zgKffYrD2fTHgIOA8PE/sX5eLz9PcAuYmfV/qTQcZ5NY9E4Zt5PsYylEOPQAztERHw4Ww7PRURyQklTRMQHJU0RER+UNEVEfFDSFBHxQUlTRMQHJU0JNDObY2YPm9keM3vDzDaZ2YoJ+vfH/1yU+jgxkWwoaUpgmZkB3wGecs4tdc6tBP47sUd/ieSFkqYE2XXAsEt4EpJz7lXn3NNm9kdm9pKZtZvZX0y0EzO7wMxeNLNX4/2X5z1yCSwlTQmyVcDW1Mb4azKWE3uu4sVAm5ldM8F+7ga+6Jy7mNhT1if/VG8pemfLo+Hk7PLu+M/P4/VqYkl0S4b+zwN/Ymbzgcecc2/mP0QJKn3TlCB7HWhL027Avc65i+M/y5xzD2TaiXPuX4m9mmIAeNLMrs9PuFIMlDQlyH4MlJvZXeMNZnYZcAL4bTOrjrfNM7OWTDsxsyXAXhd7APL3gNX5DVuCTIfnEljOOWdm7we+EH/j4GngLeC/AseB52Mn2OkHPgIcy7Cr24CPmNkwcAT4XJ5DlwDTo+FERHzQ4bmIiA9KmiIiPihpioj4oKQpIuKDkqaIiA9KmiIiPihpioj4oKQpIuLD/wdQtU2UtmDcHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demux.snp_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demux.ind_hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNG rate: 0.8071364378335748\n",
      "DBL rate: 0.18921372631733396\n",
      "AMB rate: 0.0036498358490912094\n"
     ]
    }
   ],
   "source": [
    "print(\"SNG rate: {}\\nDBL rate: {}\\nAMB rate: {}\".format(demux.sngrate,demux.dblrate,demux.ambrate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}