jupyter-notebook-slides2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open Science Workshop: Jupyter notebooks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"files/bioconda-badge-tall.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's a Jupyter notebook?\n",
    "\n",
    "* It was formerly called Ipython notebook. Now **JUlia**, **PYThon**, and **R** making up Jupyter.\n",
    "* It enables you to write live code and execute it in \"cells\", add markdown, visualizations, run applications and more in one place.\n",
    "* The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. [Jupyter site](https://jupyter.org/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Navigating Jupyter Notebooks\n",
    "\n",
    "First steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a cell above\n",
    "# Shortcut Esc + A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a cell below\n",
    "# Shortcut Esc + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# executing in a cell \n",
    "# a shortcut command to run the cell below is presenting Shift + Enter\n",
    "2 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing a string and storing it for later\n",
    "# notice something strange about these two sequences\n",
    "string = \"ATGCGGGCCG\"\n",
    "string2 = \"TAA\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show the contents of the string\n",
    "print(string); print(string2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine the two strings together\n",
    "string + string2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting a substring: prefix and suffix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how to convert DNA to RNA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleting a cell\n",
    "# Use the menu above with the scissors icon/ use the Esc + D\n",
    "# make sure the cursor is in the cell below\n",
    "\"MAMAPRTEINSTRING\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How to save your work\n",
    "# same old cmd + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Searching for text in a long document\n",
    "# find and replace Esc + F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# See other keyboard shortcuts\n",
    "# Esc + h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coverage\n",
    "\n",
    "1. Styling\n",
    "2. Extensions\n",
    "3. Magics\n",
    "4. Interactive visualizations and data exploration tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Styling\n",
    "\n",
    "Changing the appearance of the notebook to suit your style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world!\n"
     ]
    }
   ],
   "source": [
    "# how to print output to screen\n",
    "# split cells\n",
    "print (\"Hello world!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cell_style": "split"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is bioinformatics tutorial\n"
     ]
    }
   ],
   "source": [
    "print(\"This is bioinformatics tutorial\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your DNA sequence, please :tcggctttttttttaaaa\n",
      "22.0\n"
     ]
    }
   ],
   "source": [
    "# Python2 to Python 3 conversion\n",
    "\n",
    "'''\n",
    "this is a python program that computes the gc content from the pseudocode and a function to automates the whole process\n",
    "'''\n",
    "\n",
    "# read DNA sequence from the user or use raw_input\n",
    "dna = \"atcggcgcacgctcgcgcggcgatagctgatcggctttttttttaaaag\"\n",
    "\n",
    "# count the number of C's in the dna sequence\n",
    "no_g = dna.count(\"c\")\n",
    "\n",
    "# count the number of G's in the dna sequence\n",
    "no_c = dna.count(\"g\")\n",
    "\n",
    "# Determine the length of the dna sequence\n",
    "dna_length = len(dna)\n",
    "\n",
    "# Compute the GC%\n",
    "gc_percent = (no_c+no_g) * 100.0 /dna_length\n",
    "\n",
    "def gc(dna):\n",
    "\t'''this is a function that computes the gc percentage\n",
    "    return the a floating point number with the percentage'''\n",
    "\tns = dna.count('n') + dna.count('N')\n",
    "\tgcpercent = float(dna.count('c') + dna.count('C') + dna.count('g') + dna.count('G'))* 100// (len(dna) - ns)\n",
    "\treturn gcpercent\n",
    "\n",
    "# making the output understandable\n",
    "dna = input(\"Enter your DNA sequence, please :\") # don't include the brackets it will throw an error\n",
    "print(gc(dna)) # see your results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCCTTAATCGGTCGTCTGGCTAGCGCTCGGCTAGCGGTATCCGTCAAGGGTAGAATGCAAGGCGTATTCGAATGAATCCGAATTATGTTAAGAAGTACCTGGGATCAGGGCTCAGCACTGATGGCCCCCCGAAAGCTCGCAGTTTGCCACCCGTTTACCGCGACAGGGCCCCGAAGTTAAGGGACTCGCCATGGCAAAATGAACAAGGTCCGTGCGGAGTTCAAGCACCACTCGATGGATAGAAGACCTTCCGTAATCACATGTGGACAGTCTGCGTGGTATCAGTACAAGAGAAGTGCTGTTAGAGCGTCACCACGCACTCGGTTTGGTTTCTTGACCCTACGAATACGTGTCCCCAATGACTCCGCTGGAGAATTGTGCAGACTCTTTACCTAAAGGACGTAGCTCTCGCGGCCATCGGAGGGGCGAAAGCTGTTTTGCCATCCACGTAAATTGCTGATACGTGAGCCGACGATCAGTCGTTAGGCGGAGCTTATCGTTCAAGGGTCCTGGTTTGCGGCTGAGTAAGTGCTCTATGGTTGCTACGGCCGCCCGTTCATGATTTCGTACTGCATACCTGCCAAGATTAGCCCAGCGCCAAGCAACGAGGAAGCAGATCCTACGTTAGACCTTAATTAGCGTAAATAAACATCACGACGGCCTCTGACCTTTCTAAGCGAAAGAATGGACCTATAGGCACTGTACTGCGAGTGTCGGAACACACGTTGTGGAGGCGTCTGGATAGCTCCTCGTCAAACGTGTCCTTTGCTATAGAGGCCCGCTCAGGTTGGAGGTGCACACCGTCCGTGTACGCCAGAATATGGCAAATTCTGGTCGTTCATTTAATGAAAACATGCTTATCGTTTCAGAGTGACTTGATATGGTGCCCCCACAGCTCGGGCGTGCGATTAGATCTATATAAGAGCACCGGGTTTCGGGAGTGTCGGCGAGCATTGTGTGATAGCGTAGATAACAGGCCAGGATAAACCGGCAACCTGCGGGGGTCTCCGAGTTTCGGAGTGACTTAAGACTTCTTGAGCAGCCACGTCCAAAGTAGGCTTTGCCTTTGAATGGGCTTCTTAATCTAGGGGCTAACTATGTTAAGGCAGTGGCACCCGTGTAGGCTTAATGGTCCAATGCAAGTACGTACAAGCCAAAGTAGAAACGTAGCAAGGCTATCTCCGGAAATTGCGACGCAAGCCCGTGTTCCTCAGCGGCTCCTCTGTGTTGCCTGTGCGTTCCTAGACCCACAGTGATCGTACATGGATCGACGAGCGACAGAACGGGGCACGGGGGTGAGCGTGGGGAGGGGACGAACATTTAACGGATCGTATCGGGACCTCGCCTGACACATGTAGTTCCATCCCCTAATTCTAATTCGGTCCCTCGGATTATACCACTTTAGGGATATCTTCTCGCAGCATGCGCTAAAGGCGCTACTTGTAAGGATCAATTTGCTAGTTATCTAAGTAGTCGGAAATCATAGGTACGCCCAAGGGACCCGCCTTTTGGCTGGAGATCTTAATGACTAAATTTGGCTCCTGAATATCACCGAGCAATGTGCATGAGCTTTGGCAGCATTCAGAGGTGGGCTGCAGCCGACTAGTTCCGCGACACAAAACTTTTGCGTATGATATCTCTTCGATACTCAAGCATTGTGTCTTAAGTCACATACAAATGAGAGGTTGCTAGCGTACTTTGGATCGAGTAATTGAGGATTTTTCTTTAGCGGTGGCACGCTCATGCGTGAAAATTTAATTCAGCAACCCACTTCAGAAGAGACCAGCCACCGCATTTTTACATGTGACGTCTTTCCTATTAATATTGCCAAATCGCCCGGTCTGTTAACATCCGGAATATGATAACAAGTGGCGGGATTACAGGTCGGCATGTAAGCCCAGTGTATCACGTGTAGTTGGCTAGGCACCACGGGTTATCACATATATTCAGAGGGGGGCACAGCTCCATCTATACCGACACGCAAGATACCGCGGCCTATAGTTCTTGCGTATCCGGATGTCTCCCCCCTCAGCTCATCGTATACATAGATGTTTTTGTAACCAAGGCGACGGATACCAAACCTCTTTTGCGCATCGCGCATGGAACCCAGACAGCTGGGGACGGTGACTAGGGCGGACGCGTTACCTTACTGGTTGAGCATCACCTTTCGGGTAACTTGATAGAACACCGACTGTATGTTGCCTAGTGGTGTAATGAGGTACACAATCGAGCGACAATGATCGTTGGCATGAACGAGCTACACACGTATTAGCTAAACAAGCTCTAGACTCAGGTTTATTCGCGGGTACTCTGTTCAAGCATCACATGTGTCATATCTTAGCGTTGGGTTCCTCACTACCCACTTCGTAGGACGTGTGCAAACTTGCTCCAACCGTCTTGTGGCGCGCTAGATTAGCAGTGGCAGGACCCATCTCGCTGATTAGTGTCACTTGTTTATTCGGGGTATCCGAATCTACGCATCTTAGAAGGGCACTATAATTCACCAGTCGCCGTATTGCCGCGTACTGATGATGTTGAATTAACGCCTCAAAGGTATCAGGTCGCCAACCACACGATGTTCGGGTAAATTGCTTCTCTGCCGCACGGTAGTAAATTCGGCAGCAAGTCCTTACCCGTGCTTGTAAGCACGTTGCGTGGGCGAAAGACTATCGTGCTCATACACATTACTCTCAGGCTCGAACGCGCTTTACGTAGGTGTCCGCGTCTGCAGGTACGACGTGTTAGACGGCTGGATCAAGAATCAAGAAACCATGAAATATGATGCCGCCCCGGCTATGAACACGCATCGAGGAAGGTTCACCGGGACTCACTCCCCTTGTGTGCTCCGCTCCAGCGGTTCGCCCTCACTGCGTGTAGGTTATCTCAGGCGGACCCCGAATCCGTCCGTCATGTGTGCCATAGGGTGTTGAAGTCACGATAGCGCGTTCTTGCAGGTAGAATTGTGCTCGAGGGCACTAACACGCTCGGTCATCACGATATCTGTGCGTTCGGAATCTCTCTCCGCGGTGGTCTGGTTACGGTAAAAATGAGCTAGTCCCGGTTTATCGCTATGCACGCGCCTAGGACCATTGCGGAACGTTCTTCCTGGAGTCAAGTAAATGTCTACGAGGAGTGACGGAAGCCATGTTCGATAAGAGGAGTGAGTTTTCCCTTGCCACGGGCAAAAGATACTTTCCAATCGTTTCTGCGGGATCCCTCTATTCCGGAAATTTTAACGGACGGGTAGACCTCCCGTTCTGGATTCAATTACAGATGGACCCTGGACCTCGGATCTGTATGCGGTATCTATCCCACAATTACAGCAAAGTTTCTCCAGCTTCCCTTCAGTCCTGCGTGGGACTGCTGTTAAATTGCTTCAAGACCGTCACCTCTCTCCCATGCTCCTCGAACGGGATCGGGCTGCCGATTTAATCGAAGGTACTGATACAGACACCTAGGAGAAAGATAACGGGACATGTGCTTCACGTATTAACTGGTTACGCCGCAATGAATGGGACGTCCGCTCACAGGTAATGCAAGGTTTGACGGGTTGGCTACTTATCGACCATCGTGAAATCGGGGGCATTCACGAGCAGGAGAGAGTCACTCCGTTCCCGAAATTACAACCAAGCATTTTGCCTGTGGGACTTGGAAAGGTAATGCAGCTAGGGTAAAGTTTGTAACCACGTTTGACTCAAAGAGTTGGACCCCCGCTCTCGACGATGGTCCGGACACCAGTCCGATTTTTATACTCGGTACGCTTGTCCGGAGAGTAACCGGGAGACAGCTGAGCGCCGGGTACCCGCCCTGGTCCGTTAGGCGTTCGAGAACTCCTATCAGCGGGCACTGCTAAGGTCGGGAGCTCACCTACAACACCGACCAACTACGTTGTTCGTCCGAATATAGGTCGAGGTGTACAACGCGGCCTCATGTTTCGCCTAAACGAGTTGGATCGTACTAGGTAATTTGAGGAAGTCATTATGTGTCCATGGGATTAACGGCTCGGTGTCGCTGATTGTTATTCGTTAAATGGCGCGTGCGGTTTACATAGAAGGCTTTAAGCCGATAGGGTTAAGCGATACACTTTTGCGTAATTCAGCCTGGCTGAAACCTCGCAGCCAAAAGGACCTATCCATTAGCCCGTTCATCTAGAATCGCTCTGGCGTGCACAGGGTATAGGTGTTTCGCTCAATAAGGGACCCTTGATACACTTTTGACGACAAAAGTGCCGACGTCTGGTGCGAGTTCGCGGTAGAGGTTGCAGGTTGATGAGTCTCCACAGATTTGGGGATGAACTAGACTTAGGTACGGGCTACCTCAAGGGGAAACATCAAGTATGCGATTCTTGGATATAAGCTGGGACGATGACACAGTCGAAGAGTATAAGATTCTGATAACCCTGAAATATACTCCGGAGGCCGGGAAAGTTCTGGTTCTGGGCCTGAGAACTATACGGGCCAAAGCAGAGCGGGACTTGTTGTTGGCAGGGTGCGGTGCGTACCCTGTTAGCCTAGGACCATTCAGCGCTTCCCATTCCCGAGCGTCTAACTGTCGAGAACCGCGTAAACGACGGGAATAAGCGGACATAATATCGTATGCCCAATACTGGGGAATCATAGCTCGACGCGAGAATCCCGCGGCAAAGATTCCGGTACGCCAAGTACTAATCGCAATGTGTGTTTTAACGGGGATCCCCACGAGAGAGCGCGAAGGTCTACCATGGCTTGAACCTAAGTAGCCGTAACAACGGCAACATAGACAATCTGACAACGACTCGTTATTCTGATGTTAGAGATTATAATTGACCCGGAGAGACTTCGTGGAGCGTACTATAGTCGCGTAGAGACTTGTCACCCGAGGATCCCTGGGACACAAATTTGCCGACCCCGCGACAAAACTGTGGTTTCGTGCGGTGTGGAGTTCTCGGAGCTGGTAACGGCCGCCTTGACACACTATGCCAGCCAACACGGAACTGCCTCCATCTGCAACATAGTTTAGATATGTAGCATGCGCACGAACGGACACAGATGACGAAGGCTCCCAGAAAGTTGTGTGGTATTTTAGCGACTGGTAATGTGATGTACAATATGGTCATGTAGCCCACGGATACATCGTCCCCGAATCCTGTAACGAAGGACTGAACTGATGAAAAACCCGGCTTAAAGGTTTTATTACCTTATGCAAGCAGCGGGCTCGGCAACTCACTGGCCCGGGTGTTCCGCAGTATTGACGATTTTCACTACGGTTGAATCCTTGCAGGTTTCAACTCGAACTCGCATCATGTATAGTAAACTGCTAGATCATAAGTCACAGGAAGCTACCTACAGTATTTGTGCCCTAGGGTACGATTCGAAGCGTCTGAGGCAACCTTACATGTTGTGATCTAACACATATCGGTCTAAGCCACCATCTCCCCCCTTACACAGATAGGTAACTCTATAACTGCATACCGTCCCCGGACACAGGTGCGGCGGGGATATGACCCAGTGAAAGGGATTCTTGGCACGAATTGCCGATGCCATGCACGCCACGTCTGAAGTATGACAAGTATTTTGAAGTCGTTGCATAGCCCGTTAGGCAGTCAGCTCGAGGGTCCAATAGCCAAAGCGTACGTACCACTCATGTGTATTCAGCTCCAGATCCGTAACTTTGCGCGGGAAGATCCAACTCTTGGTCTGTGACAATCACCGGGACCTTACTTGCGTGCGTTTCCGCCTGTTTATGACTCCTCGCTCCGGTAATGGGCTCTAAGCGGAAAGATCCACCGACTTCGGGTCAAATCCGGTCTCGGTACTAGTAATATAGTTGCTGAACACAGTTTCTGTCGACCGCAGAACGTCCCTGACGACAACTAACACAAAACGCTATGTCTCTCACCGGCCATAAGCCGAAGCTATTGAAAACCTCAATCGTTGGACCTCGACGCCATGAATGTCTTTACTCTACGCGGAGAAGGATTGGACACTCTTCGTCACAACCACTGCTATTAGGCGACCGTGTCATCCAATCTACTTAAAAAGAGTAGCTGCCGTCCAGACCCGCGATCTAACAACGTAATGCAAACACTGGAGAGGACCGTCGAGAGCCGGCGATGTCAACTACAAAGGAGCATGCCGCGTAATCTGAAGAGGACGTATTCAGAAGAGGCTTACCTGCCGCTCTACACCATTCTGTAAACATTGATACATGGCGAAAGGCGAGCTAGCACGACGCCGATATTAATGGAATGTTGTAGTCCCGGGTTCACAGTACTGGGGCTCTGGTAACAATACCATAATGAGGCTAGTTCTGAGTCAAAGTGGGTGCCTCCCATAGTATTATCTTATTATATAACGCCTTTGGCGTCGGTGTGGGGCAGGCAGAATCTCGTCAGCCGTGGAGACGACCCCGGAAGGCCGTGCGAGTTATAGTTTTGGCGTTGAGGCACTGGATCGTATTGGTACCAACTATCCTGAGAACGTTGAACATCTGGAAGTCGGATCAAGATGATAATCCCCCAGCTGGGAGATCGCCCTGATTTAATTTCACCTTACATTGCCTAGTGCCGGCTATGTTCGTGGAGGACGCAAACAGTCGTCTCCACGGCTGCCAGTGTCTGTTCGTGACAACAGTTCGGAAGGAGAGTTGAGATGTGTCCCCTAGTCGCTCAGTCTTTTATTCCATGCCAATACACCGTTTCACTTGATGGTTCAGCTAACACAAAATCCTTCCTGCATTAGTTATTCTAATCCGCTGTGATGTCAGAATGCATATGCGCCGGACAGCTTCGATTCTCCGCAGCTGCCTCCCCTCAGAATCGGAGGGTGCACAACAGAGCCTTTTGGGAACGAAAGTGTAGAGATGCTAATCTCCATTGGGTCGTACATTGACCCATATCCGAGACTTGGGGTAATGGTAACTTCCTTTCGAACCGGGGACTTTAACGGCTAGTATTATTACTAGTTCAAATTTCCCCCACTATTCTGGAATAATTTTCAACAGATAGACCACCACGCCGTAGAGGTCTTCTGTCACTGGTGCCCATGTGTCTTATATAGAAAGTGGTGCACAACCTGGTCCTATTGCAGCTCCCGCAGGTAACTACCGCAACACACTGTGCTTATCTACCAGGTGCGCAAGCGTCTTACACGGTACCCTAAACAACCTGGGGTGAACCGGGACGGTTTACTGAGACGCTCGGTGGGTGGCTTGGGACGCGCGCTCAGACTCCACTGACTTTGAGCCCCCCTTTTTAACTGACCTATGAACAGTGCGCCAACTATTGTTACAGTTATATCGTTGCACGTCTCAGCTCATGGGGTTGGACATAAAGGCCCTCTATATTCGGAGCTGGTCCGTTATGGAAACTTCGTAAGCTGGTAGGTTCCACAGGGTTACAGCTGGCATAATACGCAAGCATATGGACCCCACCATCGATGCACGCCTACGTAGGTTAAACTGGATTAAGCGGGAGTGGCAGGGGTGCTCCCTTGGGCGGCCTTATTAAGGGGCCCCACAGTCGATCGTAGTACCATGATTGTGTACCCGTGCAGCGCCGAGAATACGTTTCTGGGGTTACTGCCCTCTAAATGGAACGCGTAAGTCCCCGGGCCATGTTTCGCCCATTTGACGCGCGTATTGTTAATCGTTTAGAGCTCACTGGACCGGGTCCGAGCGTCTGGTCAAGACTTGTGGAAAGGCGTGATGATACCCTAAACAATATGATCCTACTAGGTCCCTCCAAGCGACGAGTGTTTGTGGACAAGTCTAAAGTGACGCGTTGGAAGCTCTATGCCATTACGCTAAGGATTGTAGCCTGTAGATCGAGGTGGACGGCTGGCACGATACCTAACCAACCGGGTATCTAGAAGGGCCAACTGAGTCCGGTATGGACCTAGCGTATCCGCCCCAACTAGGTGTCTATTGCATCAAAGCCATTACCTTCCAGAGTATCTAGGACTCGACAAAAAGCCGAATCTTGTTGTACAGTTTCGTTAAATCTCTCAATCTGCCTCTACTTAGCTAATCTTAATCTTGGACGGAGTATACGAACTTGGTACACCAGGTGTAGCTCGGACCGTGGCCTATAGGAGTAATATCCCCTCCTGTCGTAAAAGCTTCCGTGCCCGCTGAATAAATGAATATGTCACACTATCCGGCTTCATAGTCTGTTTCAGCCTGATCGCAAATAGCCAGCTCCAGAGGTGCCAACAAATACGTCCTTAACCAGTACAAGAGGAAGGGCAATATAAGTTTGCGGATTATTCTTGTATTGAGTAAGGCTTTGGGCCGGCCCAAGTACTGCGCTTTTCCCGGCAGGGCGCCAACATTTAGTAAGTCGAGGATACTTCGAGGGTGTTAACACCGTTACTGATTACTGACCGCACTTGAAAAAACCCTGATCCCCTTCTGTCTGCTAGTCACTACATGAAGTGTTTTACTATGCTCGTGTTTATTAGGTACTCGCTTCCGCACCACTATTTCAAGAATTCGCGTTATCTTGGGTGAGAGATGGATCTCCGGACAGTTCGATGGTCCCTAAAGGAGATTCTTTACAATCGCATCCGTAGATCCCCCATGCAGAAGACTGGGCCTCCTTGGTGATCCTTCTGCCCGGACAATGAGACTCAGACTTGTGGGAAGAATGTAATGACATTCTGTTATGGTATGACATCCGAGGTGCGAGCTAAGCCCGCAACAAGCCCCCGGGGATATGGCGCGGTCTCGGCAGGGTCTGTGGACGCAGGTTAGGCGTTGTGAGCCATTCGTAGCGGTACTGTTACTTAGATTAGCACTGACAGTGGCGCAACGGTATCGAACCACTGCCTAGGCCATCTGACGTTATCTCACTCAGCCCGTTAATCGCAAATACTCTTCTGGTTGACTTGATTTAAGCCTTGGGGTATTATCAATTGCTGGTCTGTAGAGTGTGACATGTCATGGGTGTACGACCCCCTTGGTGAATTGAGGTCCCATCTAGGGCGAGCGAACCAAATCTATCAGTTGAGAACACGGTGTAAGTGGATCGAACGTGCACCTCTAAATTATTCGAGAAGGCGAGTACGGGGGGATACGCCTCCCAAGAGTTACTTTCCGGGCGGCGGGCGGAGACAAGGTCAGTTTGGTAGACTACGAAAAGAAACGTATCGTAAGTACGACGATGGTCCCGCTCCGACCTTATACTCGTAGTGTTGAACAGTAGGATCTGTGTAGCTCTCATACCTATAGTGAGCAACGAGACCATGCACTAGGTCTTCTTACTGATTATTGTCTTATAATTTAACATGAGAAGCCCGTTTTAGGTCAACTTAGAGTCATTACATCCAATTTCGCCGTCTAGTCTTTGTACACGATCTGCTAACAATGAATCGCTACCTAAGGGGGGAAGCGACGAGTATAGCACCCGCCGAGCAGCGACTACAAAGCTCTGAGGTGCCCCGGGTGTGTATTATCGATATGTTGCCCTTTGGCACCCTGGGGCCGCGCTATCTAGGGGCGGGAGATGCATCCGCCCTCACAACCGCGGAGCTAAATGTCTTTGAGGTTTGAAAAATTAACTAGGTTCGGCGCAGTTTTATGATATCATCCCAGTGGTCGAGTAGCTCGGAGCCGCCCCATTGCCACGGTCACTCTGAACGCTTGTAAACGGCATCGTACGCAGGATAGCGGCCTAGGTTGTAGGACACGGATCAAATCCCGAGATGCAGTAATGTCTACCTGCACCGCTGACCAGGCAAGGCGTCTGATAAAGGTGTACCTGTGACTCGTTGTATTTTTGTTAGACATATGTTACAGCCTTAGCTGGCCCGTCCCGGTAATAAGTCTCAAGTTTGGATCAGATCTGCTATTCTTGGAACATAAGGATATCTAAAGTATTCCAGCTTCCAACTAACTAACCGCCAGATGCTAAAGTATCACCTACGTATAAGTTTGCTTTATTGAACGAGTATACAGTTGTATAGCCAGGTGCGCAGAAGTTCCAGCAGGCTGGACTGGATGCTCCCACTGTACTTTATTTCAACTGTGGGGGATTCGATTAAATCACGGATCTATGGGAGGAACTCCAATTTTTACTACCGACTCGACCTCCAGTAGTTTTCACTGGGGACCCAACAAACTTCCCGATCTGCTGGGCAAGTATGTGACTGAGGCCAGTTCCAGCTCTAAGTTACATTGTATCGGACTGTTAGGGTCTGCTAGTAGGATGCCCAATCAGAATGATGAAGCGCGACTCAGAACAGAGCTGGTCGAAACGTGACCTGCCCCAGCTAGGTTGCAACACATTGAAGCGGGCGCCTCTTAATCCTGATTGCTCTTCCAACTTCACCATTTATGTGAGGGAACTGGTTCCTTGCTGCCCCTGTCTACCAAAAATGTACGAAAGTCACACCTCCTGGAGCACCACCAATACCCCGGTGCTCCCGGGTTGCCTTAGACCCTCCCTCCTCCAAACGAGTTCCTGTTGCGTTGTAGATGATCAACTCCTAATCATTGCGTCGGCAACCAGTGATGGATGAGCTTGCGGGCCTAAGCGAAAAACCTTCAAGTGATTCTGCCAAGCCGGATCCGTTTATTCAAACCGGACTCATGTTCATCAATGAATCTACGACACGCATTCGGTGCCCGCCTTACTGATATCTCTACGACTTTACGCAGTAGACAGGACGGAGTGTTCAGCAAGACAGACATTGGGCTCGGCTGCTCCCGCGAACCAAACGAGATAAATTTCTATCCTCGCTCTTGTTTGTCCGGGTGAAGGATAAAGATAAAATAATTGTCGCCGGCATCGAGAAGAGTGTAGAAAGGTAGTATGCTCGCCTCCTGCAAAACCCAGACCGGATCAGGGTCCATGTCAAGGTCGACCGTATTTCGATCTTTCGTTGTCCGATGATAATGCCGTTAGATCCAGCTAGTGCGGTTGCCTTTTATCACGTTCTCTTCGTTTTCGGGGGTGAATCATCCACAGATGTTGTAAGACTTTACCAAACCATTGTAATAGCCTCGCCGCAAACACAACATGTCGAATAAAGTCCCCCTCGCCAGTAAGGTATGGAGGTGGTGCGTGGCTCAGTAGACCTCGGACACTACGTTAGGTGAACAGTTAGTCTTCAGGAATCACGGCTTCAGTCTCACTAATGACAGAGTCGAATTCGTAAATCTGTTTCTAGGTGGTGGGCACCTTTGCGCGCGGCGGATGTTTTCGCGTTGGATGGTCCTGGCGCCACTTATAGCATCAGGATTCGGGGAAACGGTAACAAAGCTCAGAAATGTAACACCTCCTGTTGCGGGAGGTTCAATTCTGACTGTTATAATCTAAATACCCCAAAAGGTATTCGCTACCTTCGACGTGAAGTCCCTTAGCGCGCTCCCAGGGTGCCGTTTCTCATGTACCGTGATTTACAATTATCTGTCGCGACATTGGGTAGTCAGGCGGGGGTGCATTATTTACCGCCGCCGCAAATCTGACTACTCAGTGCTGCCGCCTTCCGTCTGTTCAGACGTTAGCTCCTCTTCACGGGAGGAGGGTCTCAGCAGAAAATGCGGGCTTTCCCTTCAAAAGGTCGGACAAAGGGACTCATCGCTTCGTTTTCCAAACGAGCAAGCGTAATCCAGAAGGTCAAGAGTAACCGGCGACTATAACCTATAGCCACCCGTGTTCAATAATCGTACAAATTTGTGTTCTTTATATAACGACTTCGACGTACGCTCGTACAGCGCAGACATGAACGTGTATTCACAAGTAGGCCCCCGCATATGACTCTCAGGAGACGATGGATTCGTGTTTCGCATATAGAACGGACGCATTAGTCCTAAAGTTCGGGCTATCTCAGTGGTAGGCACGATATATCGTACTAAGGAATTCCCTGGTGCTCTGAGGCTTTGTAGAATAGGAGTGTGGTGATAGTTGTCTACTACATTGTAAAGTATAGAGCCTAACTTATCGCCTCCTGTACTGACCTGACGTGATCCTTTCGTGCCAATATTCAACTCTATGGTCCTGTGCCCCCCTGAACTCCTTCGGTGCGACTTAATACGAATACGAGGCGCAGCCTTGTTCAGATCTTTATGTAACGCCTGTTTGGTCCGGAACTTCGTGACTATACTACATGGCATCACAGACTAGGGTCTACGCAGTCTGGGGACCAGTCGATTGATTGTGTGCCTATTTGATGGTCCTTGCACTATGAGGCGTTGGCTAGGTACGACACGTAACATATGCAAGCAAGACAGATCGCAGAGAATACGATGGACGATAGCAGCGGAATTCTGATATGTCGGAATGCTGTTGAGGACTCTCTGGTCGACCAAGTTAATTAACGAAGGTGGATCTTTCGCCTCTATGGGCGACTCACTGCCACGGATTATAGTGCGCCCAACGATCCTTAAGCATGCGGCGGTGTTGACTAGAAGGCACTTAAAAATGATACGTTACTGGGTTGATTAACTTCCTAACGTATGAAAGGATAGTCACATTTGAAACCCACACCTAGAGTTTGGGCGCCGTGGATGAAACACCGATTGGCGTTGCCTAGCTTATAAAGGTGCAAAGATCCGAGCGCAAGAGGCGCACGGGAAAGTTCATCGAGAGTTCATTCGTCTCCCCATAAGATGCTCCTGCGCAGTTCACTTCTACAACGTTTGCACTCACTTCCGCGTGCATCATGTATTTGGCGGGGTTAGTGCTGCATAATATAGCGACAATGGCAACGTCTACCCTTTTTCAGTTGCAAGTGTTGGAAGCGGTACCGAATCGACACATGAAACTGAGCGCTTTAATTCGTTAAGCACTATTAATCTTATGTCTCACGAGAGGTTAAGCTATCTCTCTGTTGTAGCTGCATCTAACGAAAGTTCGCGAACTTAGGAATAGCCAGTTCTCTCTCGAGGCCCAATCCTCGAGCAGCCAGTAATATCTGCTCGGATTAGACAGGAAGAGTCCCGGTAACGATCAGCGTGGCGCATCAGACTATCCTTAATAAGTGCAGTAGGAACAGCGTCGAGAACGGTAACCACGCTCTTGCTCCAGCGGGTCGGTCGCAACCAAACCTCGGGGAGACACGGCCCAATTCGTACAGCTGCAAATCATCCAGGGGGGTCCCTAACATTTGGTTTGGGGAGTCCGAACGGCTTCAAGTGTATCGCGGCGGGTACTCCGACCATCGTACATCAGCCAATCTCATTGGCACGACCTGAGGTAGAGGTTCGAATACAGGGTGACGTCACGGCGAAGCGCCGTCCGTCTCAACTCCCTCTGTGAGCGGCAGTAGCCCACCCACGGATAATTGTACGAGGAAGTTCGACTCACTTTCCGTCGATTTTTGTAGCGGTTGGTGGTCGTCATCTAATGGACGATCCTCCGACCAGGCGGTGTGATAGAAGACTTCAAACTTTGATTAGATGGGAGCCCATTTTAATAGACGAGGACGTTCGATCCGCTGCTTCTTCGACAAAAGTGACTTAACTTGCAAAACCGAGAAACCCTCTGACTCCGTCAGACAGTCAATGCCTTGTGCAGGGCACGCTAGATATAACCATCTATTAGTGATCTTTTTAAGGCCATTTGGCCCCTTGCGGACTTCATCATGTCGCGGTGGCAGGAGGGGGGGCATAACGAGAATCGCTGACCATAAATCATAACCCCCCTCGAGTGCCCCGAGATGATCAGAATAAAGAGTAGATGGCCTCAGGGTCGCCTAGATGGCTTAGTAATTCAAAGGGTAAATTCCTATGATACCACATGCTACCTGGATGACATGACTTCGATGCAATCACCTCTAACTTTACAAAACCCGAGGTATGGGGGGTTATGGTGATGGCGCTAGTCCACCTACAACCTGCATACTGGGACCTTTTCTTTCCAGGGTGAGGCACTACCAGTCCGAGAAATAACCTGCTAGGCAGATTGCTCGGGAATGTCTAGGACTCATCCTGATCACCCAATGAAGATGGGATGATAGGAGGATATGCAACCTGCTATTCCGACTCATGATAAGGAAAATACTGTGGAATCCTCGAAAAGCTAAGGGTGTTCGACGACGCACATGATGGTAATACTCGGTAGACCCTCAGTCGCACGCTAATAACGGTGCCATGTACGTATACGTGAATATGCCTTCCCTCCCAGCAGGTTGACACCCACAAATTAGACTATCCAGCGGATAGAAATGCACGAAAATATGGTACCTGTTAGTGCTTAACAGTACCTCTCATGTGTATCATTTTTCGGGCGGCTGCGCTTTCGCGACCTTAAATTTGGACCCGAGTGCGGGTGGCTTAGGATCAAGAGTTTTAAACCTTGAACATCTTGCTGATGCGGTTTCTATCGCGATAGGAAAGGGGGGTTCGGACGGTGGATTGTTTCTATCAAACGCTCTGTGCGGCCTTGTGCTCTCGTGGCCTGACGTAAAATTTAAGCACCTAGCCTGGAGACGGCGACCCGACGCGCACAATTGGGAGATCTGTAATAATCGATTTTCTCCCTTCGTCCCAGAAGGCGGACGTCTCATATAAGTCCGAAGGGTTCTTACGGCGATAACTTTTACGTTGCTAAAAAAACGCACACCTAATGACCCCGAGTATAAGTTAGGAGCGGACTCCAGTTCTCGGTAGAATCGCTAATACTCAATGGTTTCGTCATGGAGGCCCAACATTTTTCAACTCTCACTAGAGGTGCGGTCAGGAATACCGCGTTCACGGATATTCCAAAGACACATTAAAGAAGCTCAAACGGCCGGAGTCTGGATTATAAGGCGGTGCAAAGTCATCACTTCAACAGCGAGCCAAGAGTGCGGAGTGACATTAGCTAAGCGCCTATTCATATAAGCTGGATAGATCCAGGCCTTTCGCGGACTCACTCCCCGGACGCCGCAGTATCTGCGTTAGCGCCTCCTCCCACTTTCGACACCCACTAGCTTAGGTCTGGAACATCTGTGTGAAATGCGTGTTGCGGGGGCTGACCATTATATGCAGGGGGCAGCGGTAGTCCATTTACTTTAGACAAAGCGCCAGCAGCGCACTACCTACGTTACTGAGCCGTGGGCCAGTAAAGAATACCCATTACCGGACTGCACCTCTACGAATATCGGGTTATCGCAGACCCTATCCTGTCCCTCTTATGCTAGAGTATGCGCCGCGACGGTCGCAAGCCTTTACGGTCCCGTGGCCGCGGCATATCTCTAAGGCAACTCTATTAAGCAGACAGCTGCGAAATTTCTTTATGTGAGCAAGCCTTTGATACAGCAGGTTTGAGGGTTGGTTAGAAACAGAAGTGTGTCAGGCGTATAGTACTCTGATTCATGAAAACTCGTTTCTTTAGGGCAGTCACCTAGAAAGGCGTACCTTGGGTGTCACGTAACATATTCCATTAACGTTAGATGGAGTTTGTAAGTATGATCTAAATCTATGCGTCGAGTGCCTTTCACACCTGCGTAACCGCTCGGCAGGGTTTCCTTCATGCCAACAGTGTTTGACCACTAGTTAACAGCGCCTCGCGGCTTACAGTCAGATTAATTGGGGACGGAGTGTCCAGCCCTGATGACGCTGGAGTCTGAAAATATGCGTCTTTTGATTATACACCGGACGTTGCGAGACGTACGCTCTTACCCAGGATTAGACAACCCGTGAATCATGCTAGCGTGCAAGCCAAGGCTTGAAAAAAATAACGAGTCCATGGGCGAAATGCTGGTTGTTTAGTCGTTGCGGATACTTGTTAATTTGCTTATTCCGATCCTCATCAGGTCGCATTTACAAGAATAGCGATCCACTACCTACACGGGACTAATTCAAGGAGTACGTTTCCGTCACTGCGCTTAATGGGGACCCGAATTGTTGGGCCGTCATCGTTAGACATGACAAGTTCAGCAACGAGGAACCAGACTGATAACGAGTCCCCCTCCATTCTGGATTGTTCAGAGTGATCACGCAGCCTTCGAACCTGTTCTTCGCTGGTCCGGGTGTGCCGCCACCATTGAGGGTACAAAGTACGCACTAGGATACATATTGAAGGCCCGACACGGACTCTGAACAGGGAGGGTAGCGGACGCTTCGGATAAGTACTCACGTGGGTCATCAGTTTGGGGTAAAAAGTGACAAGGATAGCAAAACGGACGGGCTAAAAACATAGTAAAGGGCACGATCCGCCGTTGCTGGCCCGGGCTCCCGTGCCCGCAGACCGCCCATACGAAGACTGTCAAGCTCAACGGGAGGCTTTCGCAGCAGGCACTTCGTGGTTTATTATTGGGGAACGTATGGTCGTGTCGTGTCACTCTCGTTGCAGGTTTAAGCCTGATCCGGGTAGCACATCTCGCTCAACCACGGACTTCGTCATCGGTTTCTATAAACGTAACAATCAGATGCTAGAAGCAATTTGCGCGTTACGCTGAATTTCTTCTTCGTTGTACACACCATCACTCAAACTGGCCCCTACTGGCTAAAAAACTTTCGCATAATTTGAAGAGTACTCTCGAGCACCGGTTCGCCCATAAGCCCTTCATTGAGGGCGTTAACAGCGTAAAGTGGTGAGAGCCAAGCCAGCGGGTTCTAATTGTAGGTTCCGCTATATTCACGTCGGCCGTCGCCCCAGGACGCGTAGAGCCAAATGCCATGAGTCGAGATTATTACCCTATCCAAAAGTAGCGTTGAGATTGTATGTTCCGGGTTAATCATATGCACATAGTAGATGTTACACGAAATCAGGATAGAGGGAGACTGACATGAAGGTATGTAGGCGGAAACATTTGCACTTTACGCAATGCCGCCGCAGAACTCCGCCCACTGAAAACTGCAGACAAAACTTGTACTGACCGGCCTTTAAGAAGAACCTTTCAGGGGTACGCTGCCGTTGGAGCACTCCTCTCGCTGCGGGGCCACGATTGTTGTGCTACACCAGAGGGGTACTAACCCGGGCGCGTTTAGTACTTAAAGAAGTGTTTGCGCGAGGGTCGTGAGTGTGTGGTCACCTGCGCGAAGGAAGCTGGCTGACCGTGGAAAATGGCCACCTTACAGGCCTTGCATCAGGTACCCGCGCAACAGATAGCCAGTATCCAACCATTGTACAGGCGAGAAGTGCTCGTGAATTGCTAGGACGAGGACGCAGCTCGCACCCTAGCATAGTCCTGTAAAGGATATACGTCGTAGTGCGGATCGATGAACATCTAGCTCGGTGATTCATTCCGCCAAACTCTGCGTCAGGCATTCAAAGCTTATTTATAAACACGTAGGACAATAATATAAATACATGTTTCGTTGAGTCATTAGAACCATTGCTACAGAATATATGCTGTGTTCGTATGACTACGCGCAGGACAACTACGGGCCCGGGAACGAATTTCAGATCCTCGACATCAGCTCTAACCCATAAAAACTAAAGAATCACTCTTCGAATCTGTAAGAGCTCTTGAGCGCCGCTACGTAGGGGAGGATGATCGCAATCCCATACACATTCCGTCAAATAGCACCCGGTCGAATGCCCATGGGAACAGTCGTTGACCTCCGGCGGGTACCTCTATATGAATGTTCACAAATCCCTCGCAGGCCATGGAACTGCGACTGCTTTAAGACGTCGGCAACTCATCCTCTATAGTAGTGCAGTATGTTCAAGCGTGAACTACATCATGGTCCAGACGGGGTATTTCGTTATTGCTTGCACCATTAGCTGATTTCAAGTCGACCCATCATTAGGCCGAGGGCGACACGTAATTCTACGTAACACCTGTCCTGCCGACATAGGCCGACATCGTGCAGTCGAGCTCAACTCAAGCTTTTCTATGACGGTAATAGCTCGACCCAGCGGTGTGTCGTACCTGTAGGGGGGAACGTTCTTTATGCTATCAGCCACGAGGAATGACTACTCGGATGCATTCGCCGGGATTTCGGCATAACGGTAGTTTGTGCCAAGTTACGCTGGAACCTTTTAGTAAGTATAAATTCGGCGATCCAGTATCGGAGTCCGGGTCGTGTAGTGAAGTTGGTTTGCCGTCGTAGCATATCCCGCCACGTCCTCTGGTTTGGGTCTCAACTTGGCTCAAGATCACACACGCTCTAGACCGTGTGATTATGGTTACCTAGGTGGTATCATTCGCCGCACCACAGTATATAGATATGTAACCGTGTAAGGGGCCTCGCGCTCCAGTCACATGTTCGTGTTATGATGAGACGAATCATTTTTCATCATAGAATTAAAGATCTTATTCGTTCGCATCGACGGGCTGACAGATCCGCAACGTCGGAACCCGAACCGATCGTATTTTGCAAGAGGTTTGACCTCAGGGTATTCCGTTAATTGCGGGCACTACTCAGGTAATATAAACAGTTCTAGTATGTTTTCTTCGCCTCCCTTACTGGACAGCCCGTTCCGGAGTCTCTCTCCTAAAACAGAGCGGGGCAGAACGGTGTTCTGATGGGATGAGAACCTCCTTCACAACGACAGTCATTCACTAACATCGCACCTTGAAAATCATAATTGGTCTTTTGATTTCTTCAGTGCACACGCTGCTGCTGAGTGCGAGTCTAACGATGTTTCTAATGGCCATGGACGTATATTAACGATGGCCACCTTTAGCCCTAGCAAAATCTTGACGAACGCCTCGGAACGGCCTTGAACGGCGCGTTCGTGTTACACTCATCTGACATTTTTAACGGCAGCGCACATACCACGCGATGAAGGCAGCACAACTTCCAAAGCCTATTTATTAAATTACCCGGGTTTAATGTCCGATGAATGCACGAGCCTGGCGTAAACCAAGCGGCTAATGAATGACGCTATGGAACTCGATACGGTAGAATACGATCGCAAGCATCTTGATCGACATTATACGGCCCCAGAAAGAGAACGCGGACAACAACAATTCGCCTGTGTTCACCTGCCACTTCACCCAAGACTTTTTGCGCTAGGCTTGGATAGAGGGTTAGGCTATTCATCTGTACAATGAATGCACTTGCCATTGCAAAACAACGATCAAAATGTGTGGAAGACAATCTAGAGACGGGGCTACTTACAGGATTATGAGCGATGCATAAAGTTAACCTAGTAGCCATGTATTGAATGGCCTCCCACAACCCGATCCATTGCCCACACAAGATCCCGATGGACAAATTCCTATCACATTATGTTGCCCTCCGTGCATCGTCCTCTAGAATGAGGTGTAAGTTAGGCATAAATAGAGATTCCTCGCACCGATAAGTCTCGGGCGACGCGCCCTTGAGGAGGTGGGTCTTAGTTTTAAATGAATTCCTCCTTGCGGAGGGACACCTTTCATAATGTTCACATAGGTAGGAACAAACACTCAATAAGTACTGGTCTGTACATACCGTCGCTAGCGAGTGCGTGGATTGCTGGGTAACGACTCCCCAGTATACCGCACAGGAGTTGCAACCCATTGCCCTCTCAGGAACACATTGGCACTCGTCGTGTAAAGACGGCTAATATTGATAAAAAGTTGCCTAGCATTCAATAATGTCTTACTGCGTGATGCAAGAACATTATTAGACACACCCCTGCTTACAGAGGACTGCATTAAGTCATTCAGTGTAGAGATCCGCTTACGACAGGACTGTTGTGTTGTGTACGCACCATGGCAGGGCTCGCGGTCTGTTACTCATTGTACGTTCATACAATATCAGGATTGGAACAGGATCCGGGTGCCCTCCAACTTCCCTTCGCTTGACTCCTTCAATCGATTCGATCCATTCATGTATGTCCCGAGTCTGGGGACTTACAAGAGCAATACGTTACTCTGGCCTGAAGTTAGCGCAACACAGAGGGTTTTCCGGCATCGCATCTACTGTAACACTCGTGCCTAGATAGATTAGATCTTATTGCATTGCTTGGCACGGATCAAATTGTATAAGAAGCGCGCCCCCCTCTCTGGGACCAACTAAGCCAGGTCAAGCGCTCTCTATTCCAATTGCTAATGGCGCATCCCACTCGTTTGTATTATCCGTTGGTTACTATACTTTCCTGCAACCAAACAGGGCGAGAGCACTGCTCTATTAGCATTATATGCCGGGACAGGATCCCGGAC\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"It's definately 20000\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limiting output\n",
    "\n",
    "# This is a made up DNA sequence using a pseudorandom algorithm\n",
    "import random  # come forth variables and functions from the module\n",
    "'''This just allows me to make up an artifical dna sequence with 1000 reads'''\n",
    "dna = \"\"  # an empty string, which will be populated by a long string\n",
    "for _ in range(20000):  # for nothing in the range 0 - 20000, this is a loop\n",
    "    dna += random.choice(\n",
    "        \"ACGT\")  # storing the results back into variable for future use\n",
    "print((\"\".join(dna)))\n",
    "\"It's definately \" + str(\n",
    "    len(dna))  # concatenating the string and confirming the no of characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boyer Moore algorithm\n"
     ]
    }
   ],
   "source": [
    "# Hiding\n",
    "# this is a very long function to do string matching which is very common\n",
    "# credit: Ben Langmead\n",
    "# credit: Gusfield, Dan. Algorithms on strings, trees and sequences: computer science and computational biology. Cambridge university press, 1997.\n",
    "import string\n",
    "\n",
    "def z_array(s):\n",
    "    \"\"\" Use Z algorithm (Gusfield theorem 1.4.1) to preprocess s \"\"\"\n",
    "    assert len(s) > 1\n",
    "    z = [len(s)] + [0] * (len(s)-1)\n",
    "    # Initial comparison of s[1:] with prefix\n",
    "    for i in range(1, len(s)):\n",
    "        if s[i] == s[i-1]:\n",
    "            z[1] += 1\n",
    "        else:\n",
    "            break\n",
    "    r, l = 0, 0\n",
    "    if z[1] > 0:\n",
    "        r, l = z[1], 1\n",
    "    for k in range(2, len(s)):\n",
    "        assert z[k] == 0\n",
    "        if k > r:\n",
    "            # Case 1\n",
    "            for i in range(k, len(s)):\n",
    "                if s[i] == s[i-k]:\n",
    "                    z[k] += 1\n",
    "                else:\n",
    "                    break\n",
    "            r, l = k + z[k] - 1, k\n",
    "        else:\n",
    "            # Case 2\n",
    "            # Calculate length of beta\n",
    "            nbeta = r - k + 1\n",
    "            zkp = z[k - l]\n",
    "            if nbeta > zkp:\n",
    "                # Case 2a: Zkp wins\n",
    "                z[k] = zkp\n",
    "            else:\n",
    "                # Case 2b: Compare characters just past r\n",
    "                nmatch = 0\n",
    "                for i in range(r+1, len(s)):\n",
    "                    if s[i] == s[i - k]:\n",
    "                        nmatch += 1\n",
    "                    else:\n",
    "                        break\n",
    "                l, r = k, r + nmatch\n",
    "                z[k] = r - k + 1\n",
    "    return z\n",
    "\n",
    "\n",
    "def n_array(s):\n",
    "    \"\"\" Compile the N array (Gusfield theorem 2.2.2) from the Z array \"\"\"\n",
    "    return z_array(s[::-1])[::-1]\n",
    "\n",
    "\n",
    "def big_l_prime_array(p, n):\n",
    "    \"\"\" Compile L' array (Gusfield theorem 2.2.2) using p and N array.\n",
    "        L'[i] = largest index j less than n such that N[j] = |P[i:]| \"\"\"\n",
    "    lp = [0] * len(p)\n",
    "    for j in range(len(p)-1):\n",
    "        i = len(p) - n[j]\n",
    "        if i < len(p):\n",
    "            lp[i] = j + 1\n",
    "    return lp\n",
    "\n",
    "\n",
    "def big_l_array(p, lp):\n",
    "    \"\"\" Compile L array (Gusfield theorem 2.2.2) using p and L' array.\n",
    "        L[i] = largest index j less than n such that N[j] >= |P[i:]| \"\"\"\n",
    "    l = [0] * len(p)\n",
    "    l[1] = lp[1]\n",
    "    for i in range(2, len(p)):\n",
    "        l[i] = max(l[i-1], lp[i])\n",
    "    return l\n",
    "\n",
    "\n",
    "def small_l_prime_array(n):\n",
    "    \"\"\" Compile lp' array (Gusfield theorem 2.2.4) using N array. \"\"\"\n",
    "    small_lp = [0] * len(n)\n",
    "    for i in range(len(n)):\n",
    "        if n[i] == i+1:  # prefix matching a suffix\n",
    "            small_lp[len(n)-i-1] = i+1\n",
    "    for i in range(len(n)-2, -1, -1):  # \"smear\" them out to the left\n",
    "        if small_lp[i] == 0:\n",
    "            small_lp[i] = small_lp[i+1]\n",
    "    return small_lp\n",
    "\n",
    "\n",
    "def good_suffix_table(p):\n",
    "    \"\"\" Return tables needed to apply good suffix rule. \"\"\"\n",
    "    n = n_array(p)\n",
    "    lp = big_l_prime_array(p, n)\n",
    "    return lp, big_l_array(p, lp), small_l_prime_array(n)\n",
    "\n",
    "\n",
    "def good_suffix_mismatch(i, big_l_prime, small_l_prime):\n",
    "    \"\"\" Given a mismatch at offset i, and given L/L' and l' arrays,\n",
    "        return amount to shift as determined by good suffix rule. \"\"\"\n",
    "    length = len(big_l_prime)\n",
    "    assert i < length\n",
    "    if i == length - 1:\n",
    "        return 0\n",
    "    i += 1  # i points to leftmost matching position of P\n",
    "    if big_l_prime[i] > 0:\n",
    "        return length - big_l_prime[i]\n",
    "    return length - small_l_prime[i]\n",
    "\n",
    "\n",
    "def good_suffix_match(small_l_prime):\n",
    "    \"\"\" Given a full match of P to T, return amount to shift as\n",
    "        determined by good suffix rule. \"\"\"\n",
    "    return len(small_l_prime) - small_l_prime[1]\n",
    "\n",
    "\n",
    "def dense_bad_char_tab(p, amap):\n",
    "    \"\"\" Given pattern string and list with ordered alphabet characters, create\n",
    "        and return a dense bad character table.  Table is indexed by offset\n",
    "        then by character. \"\"\"\n",
    "    tab = []\n",
    "    nxt = [0] * len(amap)\n",
    "    for i in range(0, len(p)):\n",
    "        c = p[i]\n",
    "        assert c in amap\n",
    "        tab.append(nxt[:])\n",
    "        nxt[amap[c]] = i+1\n",
    "    return tab\n",
    "\n",
    "\n",
    "class BoyerMoore(object):\n",
    "    \"\"\" Encapsulates pattern and associated Boyer-Moore preprocessing. \"\"\"\n",
    "    \n",
    "    def __init__(self, p, alphabet='ACGT'):\n",
    "        self.p = p\n",
    "        self.alphabet = alphabet\n",
    "        # Create map from alphabet characters to integers\n",
    "        self.amap = {}\n",
    "        for i in range(len(self.alphabet)):\n",
    "            self.amap[self.alphabet[i]] = i\n",
    "        # Make bad character rule table\n",
    "        self.bad_char = dense_bad_char_tab(p, self.amap)\n",
    "        # Create good suffix rule table\n",
    "        _, self.big_l, self.small_l_prime = good_suffix_table(p)\n",
    "    \n",
    "    def bad_character_rule(self, i, c):\n",
    "        \"\"\" Return # skips given by bad character rule at offset i \"\"\"\n",
    "        assert c in self.amap\n",
    "        ci = self.amap[c]\n",
    "        assert i > (self.bad_char[i][ci]-1)\n",
    "        return i - (self.bad_char[i][ci]-1)\n",
    "    \n",
    "    def good_suffix_rule(self, i):\n",
    "        \"\"\" Given a mismatch at offset i, return amount to shift\n",
    "            as determined by (weak) good suffix rule. \"\"\"\n",
    "        length = len(self.big_l)\n",
    "        assert i < length\n",
    "        if i == length - 1:\n",
    "            return 0\n",
    "        i += 1  # i points to leftmost matching position of P\n",
    "        if self.big_l[i] > 0:\n",
    "            return length - self.big_l[i]\n",
    "        return length - self.small_l_prime[i]\n",
    "    \n",
    "    def match_skip(self):\n",
    "        \"\"\" Return amount to shift in case where P matches T \"\"\"\n",
    "        return len(self.small_l_prime) - self.small_l_prime[1]\n",
    "\n",
    "def boyer_moore(p, p_bm, t):\n",
    "    \"\"\" Do Boyer-Moore matching \"\"\"\n",
    "    i = 0\n",
    "    occurrences = []\n",
    "    while i < len(t) - len(p) + 1:\n",
    "        shift = 1\n",
    "        mismatched = False\n",
    "        for j in range(len(p)-1, -1, -1):\n",
    "            if p[j] != t[i+j]:\n",
    "                skip_bc = p_bm.bad_character_rule(j, t[i+j])\n",
    "                skip_gs = p_bm.good_suffix_rule(j)\n",
    "                shift = max(shift, skip_bc, skip_gs)\n",
    "                mismatched = True\n",
    "                break\n",
    "        if not mismatched:\n",
    "            occurrences.append(i)\n",
    "            skip_gs = p_bm.match_skip()\n",
    "            shift = max(shift, skip_gs)\n",
    "        i += shift\n",
    "    return occurrences\n",
    "\n",
    "print(\"Boyer Moore algorithm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = 'GCTAGCTCTACGAGTCTA'\n",
    "p = 'TCTA'\n",
    "p_bm = BoyerMoore(p, alphabet='ACGT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 14]\n",
      "Shows where your substring matches in the genome for example\n"
     ]
    }
   ],
   "source": [
    "print(boyer_moore(p, p_bm, t))\n",
    "\n",
    "print(\"Shows where your substring matches in the genome for example\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Magics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/user/OpenScience/TeachingJupyterNotebooks/files\n"
     ]
    }
   ],
   "source": [
    "# You can use command line commands can be done here\n",
    "%cd files/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/user/OpenScience/TeachingJupyterNotebooks/files'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see where i am the directory\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/user/OpenScience/TeachingJupyterNotebooks\n"
     ]
    }
   ],
   "source": [
    "# Navigate back to our working directory\n",
    "%cd ../"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 12.9 µs\n",
      "[6, 14]\n",
      "This is where the string matched from the long string\n"
     ]
    }
   ],
   "source": [
    "# allows us to time a function\n",
    "# try timing the gc content function and the boyermoore\n",
    "%time\n",
    "print(boyer_moore(p, p_bm, t))\n",
    "\n",
    "print (\"This is where the string matched from the long string\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting documentation for a certain function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%pdoc np.random.choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "# alternatively\n",
    "?np.random.choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$c = \\sqrt{a^2 + b^2}$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex \n",
    "$$c = \\sqrt{a^2 + b^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$y=mx+b$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "$$y=mx+b$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "learn more over here  https://data-blog.udacity.com/posts/2016/10/latex-primer/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting files/readfastq.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile files/readfastq.py\n",
    "\n",
    "def readFastq(filename):\n",
    "    '''Reading and parsing a fastq file\n",
    "    return the reads in a list\n",
    "    returns the quality score for the individual read'''\n",
    "    sequences = [] # stores a list sequences \n",
    "    qualities = [] # stores a list of quality scores\n",
    "    with open(filename) as fh: # open the file and store it in a file handle\n",
    "        while True: # loop through the file\n",
    "            fh.readline() # skip name line\n",
    "            seq = fh.readline().rstrip() # read base sequence and store it\n",
    "            fh.readline() # skip placeholder line\n",
    "            qual = fh.readline().rstrip() # read base quality scores and store them\n",
    "            if len(seq) == 0: # if the end of the file is reached \n",
    "                break # stop looping through the file\n",
    "            sequences.append(seq) # the elif we are still reading through the file append it to seq\n",
    "            qualities.append(qual) # continue appending the quality scores past the influence of the loop\n",
    "    return sequences, qualities # returns multiple values \n",
    "\n",
    "seqs, quals = readFastq('files/SRR835775_1.first1000.fastq')\n",
    "print (seqs[:5]) ; print (quals[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run files/readfasta.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAAACCCTAAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCAACCCCAACCCCAACCCCAACCCCAACCCCAACCCTAACCCCTAACCCTAACCCTAACCCTACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTCGCGGTACCCTCAGCCGGCCCGCCCGCCCGGGTCTGACCTGAGGAGAACTGTGCTCCGCCTTCAGAGTACCACCGAAATCTGTGCAGAGGACAACGCAGCTCCGCCCTCGCGGTGCTCTCCGGGTCTGTGCTGAGGAGAACGCACCTCCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGAGGCGCGCCGCGCCGGCGCAGGCGCAGAGACACATGCTAGCGCGTCCAGGGGGGTGGAGGCGTGGCGCAGGCGCAGAGACGCACGCCTACGGGCGGGGGGTGGGGGTGCGTGTGTTGCAGGAGCAAAGTCGCACGGCGCCGGGCTGGGGGCGGGGGGCCGTGCACGCGCAGAAACTCACGTCACGGCGGCGCGGCGCAGAGACGGGTGGAACCTCAGTAATCCGAAACGCCGGGATCGACAGCCCCTTGCTTGCAGCCGGGCACGACAGGACC'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTCACCCTAACCCTAACCCTAACCGTATCCGTCACCCTAACCCTAAC', 'TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACC', 'TAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGG', 'TAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAGGGTTAAGGGTTGGGGGTTAGGGGTAGGGGTAGGGTTA', 'CTAACCCTAACCCTAACCCTAACCCTACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCCAACCCTAACCCTAACCCTCACC']\n",
      "['???B1ADDD8??BB+C?B+:AA883CEE8?C3@DDD3)?D2;DC?8?=BAD=@C@(.6.6=A?=?@##################################', 'CCCFFFFFGHHGHJJJJJIJGIIJJJJJJJIJIJJJJJFJJFGIIIIH=CBFCF=CCEG)=>EHB2@@DEC>;;?=;(=?BBD?59?BA###########', '@@<DD?DDHHH<CBHII:CFGIGAGHIIG?CCGGE0BDHIIHIGICH8=FHGI=@EHGGGEEHH>);?CC@.;(=;?59,5<A599?CB>ABBCB><88A', '@CCFFDDFHHHDFHIJJCGGIJJHIIHJC?DHHIJ0?DGHI9BBFHICGGIGI=CDEGI=?AAEF7@?################################', '@<@FDFDDBBFHH@EBGGGEH@FFHE;EHIEGGBHG?)9?8BDGGBGGBCDGI=93=C6==C;CCD(?@>@#############################']\n"
     ]
    }
   ],
   "source": [
    "%run files/readfastq.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run the cells in storedf notebook before executing the following cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>A.baumanii</th>\n",
       "      <th>E.coli</th>\n",
       "      <th>H.pylori</th>\n",
       "      <th>K.pneumoniae</th>\n",
       "      <th>S.aureus</th>\n",
       "      <th>S.pneumoniae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>246</td>\n",
       "      <td>283</td>\n",
       "      <td>434</td>\n",
       "      <td>232</td>\n",
       "      <td>239</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>190</td>\n",
       "      <td>207</td>\n",
       "      <td>401</td>\n",
       "      <td>168</td>\n",
       "      <td>213</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>151</td>\n",
       "      <td>163</td>\n",
       "      <td>342</td>\n",
       "      <td>124</td>\n",
       "      <td>178</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>147</td>\n",
       "      <td>191</td>\n",
       "      <td>326</td>\n",
       "      <td>155</td>\n",
       "      <td>160</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A.baumanii  E.coli  H.pylori  K.pneumoniae  S.aureus  S.pneumoniae\n",
       "G         246     283       434           232       239           345\n",
       "A         190     207       401           168       213           334\n",
       "T         151     163       342           124       178           394\n",
       "C         147     191       326           155       160           481"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'qgrid'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-4cdca28c33dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# interactively go through the dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mqgrid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_toolbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'qgrid'"
     ]
    }
   ],
   "source": [
    "# interactively go through the dataframe\n",
    "import qgrid\n",
    "\n",
    "table = qgrid.show_grid(df2, show_toolbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57e33cc74fbc470f9ae080828dac59e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bokeh in another notebook for phylogenetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10e0184e0>]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interractive matplotlib plots\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.plot(np.random.rand(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/https://youtu.be/i-icXZ2tMRM\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1128b85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# embedding a video in presentation slide \n",
    "# try with another browser\n",
    "from IPython.display import YouTubeVideo\n",
    "yt = YouTubeVideo('https://youtu.be/i-icXZ2tMRM', width = 600, height = 400)\n",
    "display(yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=\"320\" height=\"240\" controls>\n",
       "  <source src=\"https://you.tube/i-icXZ2tMRM\" type=\"video/mp4\">\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<video width=\"320\" height=\"240\" controls>\n",
    "  <source src=\"https://you.tube/i-icXZ2tMRM\" type=\"video/mp4\">\n",
    "</video>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"files/ebiokit.research.paper.pdf\" width=800 height=400></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# embedding a research paper\n",
    "# you could use this in the references section of your paper \n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<iframe src=\"files/ebiokit.research.paper.pdf\" width=800 height=400></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![TED talk about the microbiome](https://yt3.ggpht.com/a-/ACSszfGphJaMd8qvzZQtiCeHboRT3iaKXkbNLksgqQ=s300-mo-c-c0xffffffff-rj-k-no)](https://www.youtube.com/watch?v=i-icXZ2tMRM&feature=youtu.be)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review \n",
    "\n",
    "1. Styling\n",
    "2. Extensions\n",
    "3. Magics\n",
    "4. Interactive visualizations and data exploration tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Jupyter has a lot of tools to make reports and makes sharing findings to be people easier. It also has support for many programmming languages empowering you not to slow down and always move forward."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/deepsense-ai/pydatawarsaw-notebooks/blob/master/presentation.ipynb\n",
    "\n",
    "https://www.youtube.com/watch?time_continue=1&v=b8g-8T0amuk\n",
    "\n",
    "https://github.com/Shuyib/Phylogenetic-tree-study\n",
    "\n",
    "https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where to learn more about Bioinformatics, how to code and making your work reproducible\n",
    "* rosalind.info\n",
    "* edx.org and coursera.org\n",
    "* Data analysis for life sciences by Rafael Irizzary (leanpub.com)\n",
    "* Finding Hidden Messages in DNA by Pavel Pevzner and Philip Compeau (leanpub.com)\n",
    "* Learn to code https://www.codecademy.com/\n",
    "* https://conda.io/docs/user-guide/tasks/manage-environments.html read about creating an environment and sharing an environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank you for coming!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Twitter: [Follow me on twitter](https://twitter.com/Shuyin_ben)\n",
    "\n",
    "Github: [Follow me on github](https://github.com/Shuyib)\n",
    "\n",
    "Our hash tag # OpenScienceKe"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Table of Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "624px",
    "left": "1275px",
    "top": "111px",
    "width": "186px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}