{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot coverage example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we plot genomic tracks using the plotGenomeTrack function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from pkg_resources import resource_filename\n",
    "\n",
    "from janggu.data import Cover\n",
    "from janggu.data import HeatTrack\n",
    "from janggu.data import LineTrack\n",
    "from janggu.data import plotGenomeTrack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load some sample data in bed and bigwig format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = resource_filename('janggu',\n",
    "                        'resources/sample.bed')\n",
    "\n",
    "bw_file = resource_filename('janggu',\n",
    "                            'resources/sample.bw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cover = Cover.create_from_bigwig('coverage1',\n",
    "                                 bigwigfiles=[bw_file] * 2,\n",
    "                                 conditions=['rep1', 'rep2'],\n",
    "                                 roi=roi,\n",
    "                                 binsize=200,\n",
    "                                 stepsize=200,\n",
    "                                 resolution=50)\n",
    "\n",
    "cover2 = Cover.create_from_bigwig('coverage2',\n",
    "                                  bigwigfiles=bw_file,\n",
    "                                  roi=roi,\n",
    "                                  binsize=200,\n",
    "                                  stepsize=200,\n",
    "                                  resolution=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can provide a list of the Cover objects directly that should be inspected along with the genomic coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/storage/wolfgang/wolfgang/src/janggu/src/janggu/data/coverage.py:1602: FutureWarning: Convert the Dataset object to proper Track objects. In the future, only Track objects will be supported.\n",
      "  FutureWarning)\n",
      "/mnt/storage/wolfgang/wolfgang/src/janggu/src/janggu/data/coverage.py:1626: FutureWarning: Convert the Dataset object to proper Track objects. In the future, only Track objects will be supported.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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TPVxbg258PAp5aaq38YhJ+spXkldy69IhpYidTB0qCkCZpPlRV9S0sHXYzgmH6CpN5XD//nDLDdnWoGpVcrNUqJJWcumQkhwdUoCw0my3ExPZA+Kg9FYmHKKrNOefzZoVbrkh2xpURVZy69IhhenzgOpJu91OTKSfE73zkmRa2Dps54RDdJVmJ756dZhlmoVra5AVWcnlsHJyVA6BsOiQkg/CIbpKUzlsNqWNG9P1cj1SoyFt2BC1hWyKrOSmPaxMh5Rq7zSAMiEc5oNwiK7SVlKazeg8tTQl+ltuiZ574YXR2IgEwzCKrOT2eli5bOccMn0eUD2Ew3wQDtFVL2PepTVjRnT9xBP5LWMQtSq5WSSt5DLOYXJ12GkAZVLGcNhalypv54RDdFXEOViEw/w0m9E0T5K0eXP6E6+TVnLpkJIclUMgrDKGw9b3FeMcopaoHFbf449H1yeckN8y6JCSHJVDIKwyhsMPfCC63rYt/UxhZr/+Ud9P5lWOtoEtXLjQd+zY0e/VmE6mTcBsoUvpX2OjIa1fH/6cwNNPzz4ROpJJMj5XWllnxknyuQox+05aebxXkjRzpnTwYPh2AeS33aYxOhpuDuek3LPlgqOhcohE8pjabnSUYFikLVvC/iItYsrEfgRDKfx7JUnz5hEMgTzlsd32sg51QOWwA5XD6c2eLe3dm2UNOtclTDtIJ9QmP2eOtG9fmLa6fa5CLqMXIb8e+bwDxehnrOnHdk7lEH3H1HZoKWKgbT5vAFA8wiFSYWo7tBQx0DafNwBVknXu5rKobTg0s/eZ2Q/MzM1suN/rUwehp7ary0ZUJSHf8yIG2u7nVIqhP58jI2HbA/B0/d6vTEz0fx1CKM05h2bWcPepgO29StLPJG2VtNDdpz0VfDDOORx2aU4Pz3SX9u+VDj6SZflP97JTpWedHLZNHN0vfyHd//2wbQ6fIs2a0/uZNkk+V1mX0Ys83itJmjcinfiM8O0CyG+7zVuW77hhud8W/LuxkeRBZvYOSaskuaT7JP25pE9KGpb0sKRLJP08/tuL3P1JMztJ0vckvVjSLEn/KGmmpEOS/sjdv2dm6yU9JulVku40s5slfUzSiZIelXSJu99vZs+UtF7SKyTdL+k3Jb3X3XeY2esl/ZWkEyQ9ED/nl+5+b7zuvb87NeR+kDcEAAB0NW04NLN5kv5M0iJ3P2hmp0i6UdKN7n6jmb1b0sfd/Q/M7NuSXivp65LeJGmzu0+a2TpJl7r7983sDElrJZ0bL+IFcduHzexkSee4+5SZjUq6StJbJK2U9DN3HzGzV0j6drxuw/G6jbr7r8zsTyR9UNJfJ30DzGy5pOWSNIsTnAAAwIBLUjk8V9Km1mFZd3/EzM6S9D/iv2+QdE18+xZJb1UUDi+WtNbMniVpkaRNHVW8zvkaNrl7azKpZ0u60cxOVVSlHIrvf42iiqLc/btmdl98/5mSRhRVHSVphqS7ErymNndfJ2mdFB1WTvNcAACAukl0WDmFL0q6Kq4uni7pa5JOkvQf7v7KLs/5Vcftj0j6uru/2czmKDpf8FhM0lfd/W1ZVhoAAACRJL2VvybpQjN7jiTFwW+bosqgJDUl3SFJ7v5LSf+mqMr3JXc/7O6/kLTHzC6Mn29mdlqXZT1b0o/i2+/quP9OSRfFzx+R9Nvx/dslnW1mL4n/dpKZvTTBaxpYS5cudUVVWS5cuHDhwqWyl/FxeaMhN+vtMjQUtdHv15Hxkotpw6G775S0WtLtZvbvkv5O0vslXRIf3h2T9McdT7lF0rL4uqUp6Q/j5++UdEGXxV0j6W/M7F49taq5VtJMM9sl6cq4jZ+7+8OKQuSn43W5S9LLJcnM/reZ/VDROY33mdn/ne61DoKDzN8FAKi41tSahw9P/9hu8pgWti5KM5TNsZjZ8ZKG3P0xM5sraULSy9z9iZDLGYShbCryGgEA6KqI6TsrIpcRSEKfc5iXZ0r6upkNKXojVoYOhgAAoBqKmL5zkFUiHLr7f0pa2O/1AAAA/TdrVrjKIaPYPV1tp88DAAD1VMT0nYOMcAgAACql2ZQ2bszWRqMhbdgQtYWnIhwCAIDKaTalRYui23fcIblPf7kmnrJj1SppcpJg2A3hEAAAVFJrwBVL2Ge39bgnn8xnfeqCcAgAACqp13BYgVH8+opwCAAAKiltODwuTj1UDo+NcAgAACqJymE+CIcABt74eNRz0az3y8qV/X4VwOBpVQCPS5hmqBwmU4lBsAEgL605WrO6/vroeu3a7G0BSIbKYT6oHAIYaFdcEa6tdevCtQVgepxzmA/CIYCBFnJe1cOHw7UFYHpUDvNBOAQw0ELOq3r88eHaAjC9XiuHhMNjIxwCGGgh51VdvjxcWwCmxyDY+SAcAhhoIeZolaQVK+iMAhSNymE+CIcABl6zKX34w9HtNWuSzdF6zz3R4087Lfo3wRAoHpXDfBAOAUDpx0trnV9IJxSgf6gc5oNwCABKv5MhHAL9R+UwH4RDAIUaHc02E8noaD7rRTgEqidtxZ+hbJIhHAIozOiotGVLtja2bMknIHJYGaievA8rZ51ac2goaqNqCIcldeDAAV188cU655xzdNVVV2lycrL9NzP7fB9XDehZ1mAYup1OaXcyjXjy0amp8OsCIJk8Dyu3ptbM8gNwakoaG6teQCQcltS73/1uve51r9N1112nH//4x3rta1+rn/70p60/z+7nugF11NrJUDkEqiPPymGoqTXdw07TWYRGv1cAR/fwww/r0ksvlSRdd9112rhxoxYvXqxdu3bNlcTZEkBgrUoC5xwC1ZFn5TDk1Joh2yoClcOSmpyc1GOPPdb+97Jly/Sxj31MkjZLen6/1gvIYsmScrXTiQ4pQPXkWTkMObVmyLaKQDgsqfe85z26++67n3LfaHQW/oWSvtuPdQKympiQFi/O1saSJVE7odEhBaiePCuHoabWNAs7TWcROKxcUpdddtlR73f3eyX992LXBghn0ybpec+TZs6UfvKTZM9ZvFi64w7p9tuzh8tu6JACVE+elcNmM7petiz9erU0GtL69b9uqyqoHJbc7t27df7552t4eFjPfe5zZWZfMLMX9Xu9gF6lPbev87F5jk1GhxSgevIeBLvZlM48M7p9553Jpta87rro8e99rzQ5Wb1gKBEOS+/tb3+7LrroIj300EN68MEHJWmTpJv7vFqoqSIGqE4bwjofm+esBnRIAaon7ekgvUyf19rGW9t80mVUeRYWwmHJHTp0SGNjY2o0Gmo0GnL3jZJO7Pd6oX6KGqA67Zd552Pz/LJNW4H44Aej60cf7S1Iz5uXz+sABkmvlUPC4bERDkvuvPPO05o1a7R3717t27dPZna5pC+b2Slmdkqeyy7rNGfIR1EDVJc9HCZZr5UrpXXrsi1v1y4CIpBVr+ccpvkuGcRwSIeUkrv11lslSTfccEPrrhXx9cWKxjt8cR7LDVlFyqNnKaqrrOEwzWHlrMGwZdeuMO0Ag4rKYT4IhyW3Z8+eI+8qpDNKmac5Q7XVoUMK5xkC5ZB3hxQpfTjsZRllw2Hlkjt06JCuvPJKLV++XJJkZqea2Zv6vFqooaIGqC5rh5Q0O5mkOwkA+cpzKJuWXiuHef6YzRvhsOQuueQSzZgxQ9u2bWvd9SNJV/ZxlVBTExPSokXZ2kgyQHUdDivHv9UyGxkJ0w4wqMpYOazDYWXCYck98MADuvzyyzU0NCRJcvdDklIckOtNmac5Q37Wr4+uX/KSZON5uT91DLAk55eWNRymqWiuXSutWDH9445lZETauTNbG8CgK3PlkHCI3MyYMUOPPvqoLP7km9lcSY/nvdyJCWnBgmxt5DXNGfLTmu2jkeJs5LRfhGU95zDteq1dmzxAty6///vRcz//eYIhEAIdUvJBOCwxd9ell16qpUuX6sCBA2pGw6xvkXR5Ectfsya6XrIk+c6vNbn4nj0EwypK+yXY+di04bDKlcNepX2vABwbQ9nkg97KJWZmuvbaa7V161Zt375d7q6bbrppobsfLGL5rQ92mqAQH/1mvtmKylI5TNqDtw4dUnqV9r0CcGxpf2xSOUyGcFhyCxYs0O7du/XGN75RklRUMJR+vUGk2Ym3QsXkZPj1Qf56qRz2eli5bOGwl8PdadVhpwGUCZXDfHBYueTuvvtunXXWWZo7d67mz58vM/uOmd1XxLKzVA4Jh9XUS+Ww18PKZTvnsMjDylQOgTDKeM5hEd9XeaNyWHKbN29+yr/nzJlT2BiHWSqHHFaupiyVw6SBp6yVwyIPK1e5ogCUSdrtdtWq6Hrr1vTb+vBwso6WddjOCYclN3v27Kf82933FbVsKoeDp8jeymULh72sV1pUDoGw0oTD0VHpnnuyLS/JtLB1CIccVkZXVA4HT5bDynRImV4ddhpAmaTZbouaFrYO2znhEF2lPcQ4Pi7ddVd0+zWviTbWNJehoagN9E+RHVJ6Oeew6h1SqBwCYRXxoy4twiFqLc1htvFxadmybMubmpLGxgiI/VRkh5ReKodV75BSh50GUCaEw3wQDtFVmirSFVeEWaZ7uLYG3fh4FPLSVG/f8IboubfdlrySS4eU5KgcAmGl2W6LmhaWcIhaS9MhZf/+cMsN2dagalVys4SQpJVcOqQkV4edBlAmacLhxET2gDgovZUJh+gqTYeU1rR5IYRsa1AVWclNe1h5kDukUDkEwkr7o25iIv2c6J2XJNPC1mGcQ8IhukpTOVy9OswyzcK1NciKrOT2eli5rINg01sZqA7OOcwH4RBdpakcNpvSxo3perkeqdGQNmyI2kI2RVZyOaycHJVDICzCYT4Ih+gq7bAmzWZ0nlqaEv3NN0fPveiiaOBsgmEYRVZy0waesoZDKodA9RAO80E4RFdFVFJaM6o88UR+yxhErUpuFkkruWm/CMt6ziHjHALVQzjMB+EQXfUyIHJaM2ZE10y3F16zKb3+9dHtr3wl/YnXSSu5vY5zWNZzDumtDFQH4TAfhEN0VUTlsBUOqRzm4/HHo+sTTshvGYxzmByVQyCsMobDD3wgut62Lf1MYWbR3M39Zl7lvtaBLVy40Hfs2NHv1ZhOpk3AbKFL6V9joyGtXx/+nMDTT88+ETqSSTI+V1pZZ8ZJ8rkKMftOWnm8V5I0c6Z08GD4dgHkt92mMToabg7npNyz5YKjoXKIRPKY2m50lGBYpC1bwv4iLWLKxH4EQyn8eyVJ8+YRDIE85bHd9rIOdUDlsAOVw+nNni3t3ZtlDTrXJUw7SCfUJj9njrRvX5i2un2uQi6jFyG/Hvm8A8XoZ6zpx3ZO5RB9x9R2aClioG0+bwBQPMIhUmFqO7QUMdA2nzcAVZJ17uayIBwisdBT29VlI6qSkO95EQNt93MqxdCfz5GRsO0BeLp+71cmJvq/DiFwzmEHM7vN3Zf2ez3yZDbs0pwenuku7d8rHXwk7Bq97FTpWSeHbRNH98tfSPd/P2ybw6dIs+b0fqZNks9V1mX0Io/3SpLmjUgnPiN8uwDy227zluU7bljutwX/biQcAgAAoI3DygAAAGgjHAIAAKCNcAgAAIA2wiEAAADaCIcAAABoIxwCAACgjXAIAACANsIhAAAA2giHAAAAaCMcAgAAoI1wCAAAgDbCIQAAANoIhwAAAGgjHAIAAKCNcAgAAIA2wiEAAADaCIcAAABoIxwCAACgjXAIAACANsIhAAAA2giHAAAAaCMcAgAAoI1wCAAAgDbCIQAAANoIhwAAAGgjHAIAAKCNcNhh6dKlLqnsl0F4jVy4cOHChcsxL+Pj8kZDbtbbZWgoaqPfryPjJReNvBquooMHD/Z7FXI3CK8RAFBv4+PSsmXZ2piaksbGotvNZvZ1qhMqhwAAoFKuuCJMO+7h2qoTwiEAAKiU/fvL2VZdEA4BAEClzJpVzrbqgnAIAAAqZfXqMO2YhWurTgiHAACgUppNaePGbG00GtKGDXRGORrCIQAAqJxmU1q0KLp9xx1R55LpLldfHT1+1SppcpJg2A3hEAAAVJLHI/2ZJXt863Ge2wiB9UA4BAAAlZQ2HB4Xp54nn8xnfeqCcAgAACqJymE+CIcAAKCSqBzmg3AIYOCNj0c9F816v6xc2Unq8ecAAArvSURBVO9XAQweKof5YG5lAAMtxBytknT99dH12rXZ2wKQDJXDfFA5BDDQQs6rum5duLYATI/KYT4IhwAGWsh5VQ8fDtcWgOn1WjkkHB4b4RDAQAs5r+rxx4drC8D0WoeHj0uYZlohksPKx0Y4BDDQQs6runx5uLYATI/KYT4IhwAGWog5WiVpxQo6owBF6/WcQyqHx0Y4BDDwmk3pwx+Obq9Zk2yO1h07ose/6lXRvwmGQPGoHOaDcAgASr+TacQDgU1N5bM+AKZH5TAfhEMAUPqdTKvzCT2Ugf5hKJt8EA4BFGp0NNtMJKOj+axX2l6PhEOg/zisnA/CIYDCjI5KW7Zka2PLlnwCIpVDoHryPqycdWrNoaGojaph+jwAhckaDEO306m1k6FyCFRHnpXDEFNrTk1JY2PR7WYzW1tFonIIAPp1JYEOKUB15Fk5DDW1pnvYaTqLQDgEAHFYGaiitOcKp6kchpxaM2RbRSAcAijMkiXlaqcTHVKA6smzchhyas2QbRWBcAigMBMT0uLF2dpYsiRqJzQqh0D15HnOYaipNc3CTtNZhNqGQzMbN7P7zey7ZvZJMxvq9zoBkD7zmeh6eDjZTCTu0mteEz3nG9/IJxhKdEgBqijPymGIqTUbDWnDhmp1RpFKFA7NLHTP6XFJL5f025KeIek9gdsH0IO0h287H5vnrAZ0SAGqJ+9xDptN6ayzotvf/GayH7Mf/3j0+Pe9T5qcrF4wlBKGQzN7h5ndZ2b/bmYbzGyOmX0tvm+Lmc0ys2eb2T4zOy5+zklmdsDMhsxsrpndZmb/z8zuMLOXx49Zb2b/ZGZ3S7rGzF5tZneZ2b1mts3MXhY/7plmdquZ7TKzz5nZ3Wa2MP7b6+Pn3GNmm8zsWZLk7l/2mKRvSXpBDu8fUCtFDFBd1nDIYWWgeoqYIaW1jbe2+ekU8X2Vt2m/ns1snqQ/k3Suu58m6Y8lXSfpRnefr6hC93F3/7mkb0t6bfzUN0na7O6TktZJer+7ny5plaTOKepfIGmRu39Q0vcknePur5L0F5Kuih+zUtLP3H1E0p9LOj1et+F43UbdfYGkHZI+eMT6D0kak3Rbl9e33Mx2mNmOhx9+eLq3A6itogaoTluh63xsnrMapD2s/MH4m+bQod6C9Lx5+bwOYJD0WjlME9wIh0d3rqRN7n5Qktz9EUlnSbop/vsGSfEZQbpF0lvj2xdLuiWu5C2StMnMvi3pBknP72h/k7u3fns/O37cdyX9vaTW1+drJN0cL/+7ku6L7z9T0oikO+O23ylp9hHrv1bSN9z9jqO9OHdf5+4L3X3hzJkzE7wdg6Os05whH0UNUJ02hHU+tiyHlVeulD7xiWzL27WLgAhkReUwH6HP8/uipKvM7BRF1b2vSTpJ0n+4+yu7POdXHbc/Iunr7v5mM5sjaes0yzNJX3X3tx31j2Z/KWmmpP+V9AUgErKKlFcHAlRT2Q8rJ1mvdevCLHPXrjDtAIMq7+nzpMEMh0m+nr8m6UIze44kxcFvm6LKoCQ1Jd0hSe7+S0n/Juljkr7k7ofd/ReS9pjZhfHzzcxO67KsZ0v6UXz7XR333ynpovj5I4o6mUjSdklnm9lL4r+dZGYvjW+/R9LvSXqbu1f4v6g/yjzNGaqtrOEwTeWQ8wyBckh7mkraDikS4fCo3H2npNWSbjezf5f0d5LeL+kSM7tP0fl8f9zxlFskLYuvW5qS/jB+/k5JF3RZ3DWS/sbM7tVTq5prJc00s12Srozb+Lm7P6woRH46Xpe7FPVQlqR/kvQ8SXeZ2bfN7C+me63AICtqgOqyn3OYZL2S7iQA5CvtaSpUDpNJdFjZ3W+UdOMRd5/b5bGfUXS4t/O+PZKWHuWx7zri33dJemnHXX8WXz8maZm7P2ZmcyVNSNoXP+drkn7nKG2HPmQO1NrEhHT22dK2bb23kWSA6rJWDtPsZJYvl66/PvsyR0aytwEMsryHspEGMxyWZpzDaTxT0jfjyuPnJK109yf6vE61VuZpzpCfG+OfgHPnJh+g+owzouds25bs/NI6dEhZu1ZasSLb8kZGpJ07s7UBDLoydkgp4khH3ioRDt39P+Mexae5+3x3/0q/16nuJiakBQuytZHXNGfIT9ovQSl9cKtD5VCKAmLSAN26nH9+9NwvfIFgCITAUDb5qEQ4RH9cfXV0fe65yXd+L3xh9Jx9+wiGVdSa7aOR4qSM1hdm2nBY5XMOe8XA2UBYZawcEg5Ra60PdpoqUitUTE6GXx/kL0vlMGngKWvlsJfQmlYddhpAmVA5zAfhEF21Nog0O/Ghoeia+WarqZfKYdovwrKec9jLeqWVtsoK4NioHOaDcIiuqBwOniyHlakcTi9tlRXAsVE5zAfhEF1RORw8dEjJt3JYh50GUCZpw+GqVdH11q3Jp4J95JHoOTNnJpsWtg7bOeEQXVE5HDxFHFamQwqVQyCUND82R0ele+7JtrzWtLDHQjhErWWpHBIOq6mXyiGHlZOrw04DKJM0P+qKmhaWcQ5Ra2mDwvi4tH17dPvss5OX7FuXoaGoDfQPHVKK6ZBC5RAIo4iKf1p1+BFIOERXaQ4rj49Ly5ZlW97UlDQ2RkDspyyVw7qcc0jlEKgOwmE+CIfoKs1h5SuuCLNM93BtDbrx8agCmKZ6uzSeAf1f/zV5JbfXcQ7Lds5hL6E1LSqHQFhpwmFR08ISDlFraSqH+/eHW27ItgZVq5KbJYQkreTWrbcylUOgOtJstxMT2QNikmlh67CdEw7RVZrK4axZ4ZYbsq1BVWQllw4pyVE5BMJK+6NuYiL9nOidlyTTwhIOUWtpKoerV4dZplm4tgZZkZVcOqQkV4edBlAmnHOYD8IhukpTOWw2pY0b03VkOFKjIW3YELWFbIqs5PbaIaVs5xwyziFQPYTDfBAO0VXanqvNZnSeWpoSfet8tosvjsZGJBiGUWQlt9cOKWWrHBbRIaUOOw2gTMoYDhnnELVWxM5yxozomkGzw2pVcrNIWsmlQ0pyVA6BsIrYT6VVhx+BJXo7UTa9jHmXViscPvFEfssYVM2mdN550e0vfSn9iddJK7mMc5hcHXYaQJmUsXJYh+2ccIiuiqwcEg7z8fjj0fUJJ+S3jLSHlXv5Mm89tuqHlakcAmGVMRxedll0feed6WcKM5t+7uYimFf5oHhgCxcu9B07dvR7NaaTaRMwW+hS+tfYaEjr14c/J/D007NPhI5kkozPlVbWmXGSfK5CzL6TVh7vlSTNnCkdPBi+XQD5bbdpjI6Gm8M5KfdsueBoqBwikTymthsdJRgWacuWsL9Ii5gysR/BUAr/XknSvHkEQyBPeWy3vaxDHVA57EDlcHqzZ0t792ZZg851CdMO0gm1yc+ZI+3bF6atbp+rkMvoRcivRz7vQDH6GWv6sZ1TOUTfMbUdWooYaJvPGwAUj3CIVJjaDi1FDLTN5w1AlWSdu7ksCIdILPTUdnXZiKok5HtexEDb/ZxKMfTnc2QkbHsAnq7f+5WJif6vQwicc9jBzG5z96X9Xo88mQ27NKeHZ7pL+/dKBx8Ju0YvO1V61slh28TR/fIX0v3fD9vm8CnSrDm9n2mT5HOVdRm9yOO9kqR5I9KJzwjfLoD8ttu8ZfmOG5b7bcG/GwmHAAAAaOOwMgAAANoIhwAAAGgjHAIAAKCNcAgAAIA2wiEAAADaCIcAAABoIxwCAACgjXAIAACANsIhAAAA2v4/wWzrwaQT+2EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotGenomeTrack([cover, cover2],\n",
    "                'chr1', 16000, 18000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the above expression plots the coverage tracks with default settings,\n",
    "the Track classes provide additional flexibility.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotGenomeTrack([HeatTrack(cover), LineTrack(cover2, marker=0, color='r')],\n",
    "                'chr1', 16000, 18000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If necessary, it is also possible to derive custom Track classes for plotting genome tracks."
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}