Thesis.ipynb

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   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Background"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## 1.3 Study question"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## 1.4 Hypothesis"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "# 2. Methodology"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## 2.1 Genome graph reference based"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## 2.2 Reports"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Packages\n",
    "#from Bio import SeqIO\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'DataFrame' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-651825c1e96a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"summarise.csv\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gene'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"read_count\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"length\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"coverage\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'DataFrame' object is not callable"
     ]
    }
   ],
   "source": [
    "os.getcwd()\n",
    "report = pd.read_csv(\"summarise.csv\",sep=\"\\t\",names=['gene',\"read_count\",\"length\",\"coverage\"])()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Conclusion and Discussion"
   ]
  }
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