My_sample_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Uncomment the following lines (by deleting the leading `#`) if you are running this in Colab. (Thanks to Jake VanderPlas for the tip!)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install biopython\n",
    "# !pip install folium\n",
    "# !curl -O https://raw.githubusercontent.com/jperkel/example_notebook/master/NC_005816.gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple notebook demonstrates how users can interleave text, code, and results in a single document. We start with a simple calculation -- computing the first 25 numbers in the Fibonacci sequence, where each value equals the sum of the two previous values. The Jupyter notebook allows us to express that mathematically, using the typesetting language $\\LaTeX{}$: $$F_n = F_{n-1} + F_{n-2}$$\n",
    "Thus, the sequence is: 0, 1, 1, 2, 3, 5, 8, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first cell contains an IPython 'magic' code, '%matplotlib', which allows the notebook to display plots inline, in the body of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368]\n"
     ]
    }
   ],
   "source": [
    "# calculate the first 25 Fibonacci numbers\n",
    "f1 = 0\n",
    "f2 = 1\n",
    "ar = [f1, f2] # a list to hold the computed values. We know the first two numbers\n",
    "\n",
    "# we only need to run our calculation 23 times, because positions 1 and 2 are known\n",
    "for i in range (23):\n",
    "    f3 = f1 + f2\n",
    "    ar.append (f3)\n",
    "    f1 = f2\n",
    "    f2 = f3\n",
    "    \n",
    "print (ar) # below, you see the output of the code itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot (range(25), ar, \"ro\")\n",
    "## uncomment the following call to ax.plot() (by removing the leading '#') and select \n",
    "## 'Cell > Run All' (in Binder) or 'Run > Run All Cells' (Jupyter) to change the graph below\n",
    "# ax.plot (range(25), ar)\n",
    "ax.set (xlabel = \"Sequence No.\", ylabel = \"Fibonacci No.\", \n",
    "       title = \"The First 25 Fibonacci Numbers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anything you can do programmatically, can be documented in a notebook. Here we'll do some simple sequence analysis with Biopython. The following example is adapted from the [Biopython tutorial](http://biopython.org/DIST/docs/tutorial/Tutorial.html). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll read in a Genbank-formatted file, which represents a circular DNA, called a plasmid, from the bacterium, *Yersinia pestis*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA())"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from Bio import SeqIO\n",
    "\n",
    "record = SeqIO.read(\"NC_005816.gb\", \"genbank\")\n",
    "record.seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How long is the DNA, and how which genes does it encode?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length: 9609 bp\n",
      "No. features: 41 \n",
      "\n",
      "Feat. 3: putative transposase\n",
      "Feat. 8: transposase/IS protein\n",
      "Feat. 15: putative replication regulatory protein\n",
      "Feat. 18: hypothetical protein\n",
      "Feat. 21: pesticin immunity protein\n",
      "Feat. 23: pesticin\n",
      "Feat. 29: hypothetical protein\n",
      "Feat. 32: outer membrane protease\n",
      "Feat. 35: putative transcriptional regulator\n",
      "Feat. 38: hypothetical protein\n"
     ]
    }
   ],
   "source": [
    "print(\"Length:\", len(record.seq), \"bp\")\n",
    "print(\"No. features:\", len(record.features),\"\\n\")\n",
    "\n",
    "for i,feat in enumerate (record.features):\n",
    "    if (feat.type == \"CDS\"):\n",
    "        product = feat.qualifiers['product'][0]\n",
    "        print (\"Feat. %d: %s\" % (i, product))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plasmid encodes 41 features, including 10 genes. Let's focus on feature #18, which encodes a 'hypothetical protein'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene location\n",
      "Start: 3485 \n",
      "End: 3857\n",
      "GeneID: ['GI:45478715', 'GeneID:2767720']\n"
     ]
    }
   ],
   "source": [
    "feat = record.features[18]\n",
    "print (\"Gene location\\nStart:\", feat.location.start, \"\\nEnd:\", feat.location.end)\n",
    "print (\"GeneID:\", feat.qualifiers.get(\"db_xref\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These data show us that the gene is located between bases 3485 and 3857. Let's retrieve that segment and translate it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VSKKRRPQKRPRRRRFFHRLRPPDEHHKNRRSSQRWRNPTGLKDTRRFPPEAPSCALLFRPCRLPDTSPPFSLREAWRFLIAHAVGISVRCRSFAPSWAVCTNPPFSPTTAPYPVTIVLSPTR*\n"
     ]
    }
   ],
   "source": [
    "my_gene = record[3485:3857]\n",
    "print (my_gene.seq.translate(table=\"Bacterial\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's find out more about this gene, using the NCBI Entrez database. We learned the accession number above: `GeneID: ['GI:45478715', 'GeneID:2767720']`.\n",
    "\n",
    "**20 May 2020**: Updated the following two cells to reflect changes in NCBI's Entrez database. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Bio import Entrez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOCUS       AAS58761                 123 aa            linear   BCT 26-JUL-2016\n",
      "DEFINITION  conserved hypothetical protein (plasmid) [Yersinia pestis biovar\n",
      "            Microtus str. 91001].\n",
      "ACCESSION   AAS58761\n",
      "VERSION     AAS58761.1\n",
      "DBLINK      BioProject: PRJNA10638\n",
      "            BioSample: SAMN02602970\n",
      "DBSOURCE    accession AE017046.1\n",
      "KEYWORDS    .\n",
      "SOURCE      Yersinia pestis biovar Microtus str. 91001\n",
      "  ORGANISM  Yersinia pestis biovar Microtus str. 91001\n",
      "            Bacteria; Proteobacteria; Gammaproteobacteria; Enterobacterales;\n",
      "            Yersiniaceae; Yersinia.\n",
      "REFERENCE   1  (residues 1 to 123)\n",
      "  AUTHORS   Song,Y., Tong,Z., Wang,J., Wang,L., Guo,Z., Han,Y., Zhang,J.,\n",
      "            Pei,D., Zhou,D., Qin,H., Pang,X., Han,Y., Zhai,J., Li,M., Cui,B.,\n",
      "            Qi,Z., Jin,L., Dai,R., Chen,F., Li,S., Ye,C., Du,Z., Lin,W.,\n",
      "            Wang,J., Yu,J., Yang,H., Wang,J., Huang,P. and Yang,R.\n",
      "  TITLE     Complete genome sequence of Yersinia pestis strain 91001, an\n",
      "            isolate avirulent to humans\n",
      "  JOURNAL   DNA Res. 11 (3), 179-197 (2004)\n",
      "   PUBMED   15368893\n",
      "REFERENCE   2  (residues 1 to 123)\n",
      "  AUTHORS   Zhou,D., Tong,Z., Song,Y., Han,Y., Pei,D., Pang,X., Zhai,J., Li,M.,\n",
      "            Cui,B., Qi,Z., Jin,L., Dai,R., Du,Z., Wang,J., Guo,Z., Wang,J.,\n",
      "            Huang,P. and Yang,R.\n",
      "  TITLE     Genetics of metabolic variations between Yersinia pestis biovars\n",
      "            and the proposal of a new biovar, microtus\n",
      "  JOURNAL   J. Bacteriol. 186 (15), 5147-5152 (2004)\n",
      "   PUBMED   15262951\n",
      "REFERENCE   3  (residues 1 to 123)\n",
      "  AUTHORS   Song,Y., Tong,Z., Wang,L., Han,Y., Zhang,J., Pei,D., Wang,J.,\n",
      "            Zhou,D., Han,Y., Pang,X., Zhai,J., Chen,F., Qin,H., Wang,J., Li,S.,\n",
      "            Guo,Z., Ye,C., Du,Z., Lin,W., Wang,J., Yu,J., Yang,H., Wang,J.,\n",
      "            Huang,P. and Yang,R.\n",
      "  TITLE     Direct Submission\n",
      "  JOURNAL   Submitted (24-APR-2003) The Institute of Microbiology and\n",
      "            Epidemiology, Academy of Military Medical Sciences, No. 20,\n",
      "            Dongdajie Street, Fengtai District, Beijing 100071, People's\n",
      "            Republic of China\n",
      "COMMENT     Method: conceptual translation.\n",
      "FEATURES             Location/Qualifiers\n",
      "     source          1..123\n",
      "                     /organism=\"Yersinia pestis biovar Microtus str. 91001\"\n",
      "                     /strain=\"91001\"\n",
      "                     /sub_species=\"microtus\"\n",
      "                     /db_xref=\"taxon:229193\"\n",
      "                     /plasmid=\"pPCP1\"\n",
      "                     /note=\"biovar: Microtus\"\n",
      "     Protein         1..123\n",
      "                     /product=\"conserved hypothetical protein\"\n",
      "     CDS             1..123\n",
      "                     /locus_tag=\"YP_pPCP04\"\n",
      "                     /old_locus_tag=\"pPCP04\"\n",
      "                     /coded_by=\"AE017046.1:3486..3857\"\n",
      "                     /note=\"Best Blastp hit = gi|321919|pir||JQ1541\n",
      "                     hypothetical 16.9K protein - Salmonella typhi murium\n",
      "                     plasmid NTP16.\"\n",
      "                     /transl_table=11\n",
      "ORIGIN      \n",
      "        1 mskkrrpqkr prrrrffhrl rppdehhknr rssqrwrnpt glkdtrrfpp eapscallfr\n",
      "       61 pcrlpdtspp fslreawrfl iahavgisvr crsfapswav ctnppfsptt apypvtivls\n",
      "      121 ptr\n",
      "//\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Entrez.email = \"A.N.Other@example.com\"  # Always tell NCBI who you are\n",
    "with Entrez.efetch (db=\"protein\",rettype=\"gb\",retmode=\"text\",id=\"AAS58761\") as handle:\n",
    "   print (handle.read())\n",
    "# handle = Entrez.efetch (db=\"nucleotide\",id=\"45478715\",rettype=\"gb\",retmode=\"text\")\n",
    "# print (handle.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter also supports interactive data exploration. Here we'll add an interactive map, just as in our June [mapping feature](https://www.nature.com/articles/d41586-018-05331-6) -- something you might do when working with geospatial data. This requires the Python Leaflet library, folium. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a simple map: a few points in London, Oxford and Cambridge, overlaid on either a street map, or on a map of geological data provided by the [Macrostrat Project](https://macrostrat.org/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = { \n",
    "    0: { \"name\": \"Nature\", \"lat\": 51.533925, \"long\": -0.121553 },\n",
    "    1: { \"name\": \"Francis Crick Institite\", \"lat\": 51.531877, \"long\": -0.128767 },\n",
    "    2: { \"name\": \"MRC Laboratory for Molecular Cell Biology\", \"lat\": 51.524435, \"long\": -0.132495 },\n",
    "    3: { \"name\": \"Kings College London\", \"lat\": 51.511573, \"long\": -0.116083 },\n",
    "    4: { \"name\": \"Imperial College London\", \"lat\": 51.498780, \"long\": -0.174888 },\n",
    "    5: { \"name\": \"Cambridge University\", \"lat\": 52.206960, \"long\": 0.115034 },\n",
    "    6: { \"name\": \"Oxford University\", \"lat\": 51.754843, \"long\": -1.254302 },\n",
    "    7: { \"name\": \"Platform 9 3/4\", \"lat\": 51.532349, \"long\": -0.123806 }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<folium.features.LatLngPopup at 0x120acde50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map(location = [51.8561, -0.2966], tiles = 'CartoDB positron', zoom_start = 9)\n",
    "\n",
    "# add the locations to the map\n",
    "for key in coords.keys():\n",
    "    folium.CircleMarker(\n",
    "    location=[coords[key]['lat'], coords[key]['long']],\n",
    "    popup=coords[key]['name'],\n",
    "    color=('crimson' if coords[key]['name'] == 'Nature' else 'blue'),\n",
    "    fill=False,\n",
    "    ).add_to(m)\n",
    "\n",
    "# pull in the Macrostrat tile layer\n",
    "folium.TileLayer(tiles='https://tiles.macrostrat.org/carto/{z}/{x}/{y}.png', \n",
    "                 attr='Macrostrat', name='Macrostrat').add_to(m)\n",
    "folium.LayerControl().add_to(m) # allow user to switch between layers\n",
    "folium.LatLngPopup().add_to(m) # click on the map to get Lat/Long in a popup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw the map. **Note that this map is interactive**: you can zoom, pan, click the points of interest, and alternate between the two layers (by clicking on the tiles icon in the upper-right corner of the map). If you click anywhere on the map, a popup will appear showing the latitude and longitude of that position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_bbc772b675484f009f2f71100db367fb {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_bbc772b675484f009f2f71100db367fb" ></div>
        
</body>
<script>    
    
            var map_bbc772b675484f009f2f71100db367fb = L.map(
                "map_bbc772b675484f009f2f71100db367fb",
                {
                    center: [51.8561, -0.2966],
                    crs: L.CRS.EPSG3857,
                    zoom: 9,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_b99f96c3bee147db846e2f1b5e099f12 = L.tileLayer(
                "https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png",
                {"attribution": "\u0026copy; \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors \u0026copy; \u003ca href=\"http://cartodb.com/attributions\"\u003eCartoDB\u003c/a\u003e, CartoDB \u003ca href =\"http://cartodb.com/attributions\"\u003eattributions\u003c/a\u003e", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
            var circle_marker_fe90409abb844d5b8d9c61a898828940 = L.circleMarker(
                [51.533925, -0.121553],
                {"bubblingMouseEvents": true, "color": "crimson", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "crimson", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_c394eb19a03f4f08b171e6bd14ab9c7c = L.popup({"maxWidth": "100%"});

        
            var html_bcf245091d5447759e1f586f05a0ca9d = $(`<div id="html_bcf245091d5447759e1f586f05a0ca9d" style="width: 100.0%; height: 100.0%;">Nature</div>`)[0];
            popup_c394eb19a03f4f08b171e6bd14ab9c7c.setContent(html_bcf245091d5447759e1f586f05a0ca9d);
        

        circle_marker_fe90409abb844d5b8d9c61a898828940.bindPopup(popup_c394eb19a03f4f08b171e6bd14ab9c7c)
        ;

        
    
    
            var circle_marker_811b67f9b2af445099fc7f1202833f66 = L.circleMarker(
                [51.531877, -0.128767],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_0eacf99847d8468da75310d6fb687c42 = L.popup({"maxWidth": "100%"});

        
            var html_d84d9fec10d14a2bbdbbd7682110d41c = $(`<div id="html_d84d9fec10d14a2bbdbbd7682110d41c" style="width: 100.0%; height: 100.0%;">Francis Crick Institite</div>`)[0];
            popup_0eacf99847d8468da75310d6fb687c42.setContent(html_d84d9fec10d14a2bbdbbd7682110d41c);
        

        circle_marker_811b67f9b2af445099fc7f1202833f66.bindPopup(popup_0eacf99847d8468da75310d6fb687c42)
        ;

        
    
    
            var circle_marker_032b0c727e1942cb910078e287d0b189 = L.circleMarker(
                [51.524435, -0.132495],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_ef733d4264a242d7b26a7d6a305bf438 = L.popup({"maxWidth": "100%"});

        
            var html_9213813646d549bbb60661c41999a822 = $(`<div id="html_9213813646d549bbb60661c41999a822" style="width: 100.0%; height: 100.0%;">MRC Laboratory for Molecular Cell Biology</div>`)[0];
            popup_ef733d4264a242d7b26a7d6a305bf438.setContent(html_9213813646d549bbb60661c41999a822);
        

        circle_marker_032b0c727e1942cb910078e287d0b189.bindPopup(popup_ef733d4264a242d7b26a7d6a305bf438)
        ;

        
    
    
            var circle_marker_b093a291ca504ec2ada0268e5db38c27 = L.circleMarker(
                [51.511573, -0.116083],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_72c69e842177443cab9de2ed11562557 = L.popup({"maxWidth": "100%"});

        
            var html_09032f0eb21f453e8a1cc7684b875756 = $(`<div id="html_09032f0eb21f453e8a1cc7684b875756" style="width: 100.0%; height: 100.0%;">Kings College London</div>`)[0];
            popup_72c69e842177443cab9de2ed11562557.setContent(html_09032f0eb21f453e8a1cc7684b875756);
        

        circle_marker_b093a291ca504ec2ada0268e5db38c27.bindPopup(popup_72c69e842177443cab9de2ed11562557)
        ;

        
    
    
            var circle_marker_81947c29e5c54ca8b2d858e6c9f0f21f = L.circleMarker(
                [51.49878, -0.174888],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_6660d2ea09944bc793fe68e3a6170a0e = L.popup({"maxWidth": "100%"});

        
            var html_a2c41e7380644c4c991525393429e589 = $(`<div id="html_a2c41e7380644c4c991525393429e589" style="width: 100.0%; height: 100.0%;">Imperial College London</div>`)[0];
            popup_6660d2ea09944bc793fe68e3a6170a0e.setContent(html_a2c41e7380644c4c991525393429e589);
        

        circle_marker_81947c29e5c54ca8b2d858e6c9f0f21f.bindPopup(popup_6660d2ea09944bc793fe68e3a6170a0e)
        ;

        
    
    
            var circle_marker_12267dce62f84c6ab34e2333a5b8ee76 = L.circleMarker(
                [52.20696, 0.115034],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_0c9e587be82747848c5d2b2abceb7177 = L.popup({"maxWidth": "100%"});

        
            var html_682d58d5bca147ddbb7ed53fbd3011be = $(`<div id="html_682d58d5bca147ddbb7ed53fbd3011be" style="width: 100.0%; height: 100.0%;">Cambridge University</div>`)[0];
            popup_0c9e587be82747848c5d2b2abceb7177.setContent(html_682d58d5bca147ddbb7ed53fbd3011be);
        

        circle_marker_12267dce62f84c6ab34e2333a5b8ee76.bindPopup(popup_0c9e587be82747848c5d2b2abceb7177)
        ;

        
    
    
            var circle_marker_e7642c4aa90c4fe69e42d387982ce525 = L.circleMarker(
                [51.754843, -1.254302],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_95f136d6383b4d158d377829677c6be8 = L.popup({"maxWidth": "100%"});

        
            var html_474acb0e5a4441dea4eb1ee1fc2360a4 = $(`<div id="html_474acb0e5a4441dea4eb1ee1fc2360a4" style="width: 100.0%; height: 100.0%;">Oxford University</div>`)[0];
            popup_95f136d6383b4d158d377829677c6be8.setContent(html_474acb0e5a4441dea4eb1ee1fc2360a4);
        

        circle_marker_e7642c4aa90c4fe69e42d387982ce525.bindPopup(popup_95f136d6383b4d158d377829677c6be8)
        ;

        
    
    
            var circle_marker_561d2772624a4d9fa82a63ed4139850f = L.circleMarker(
                [51.532349, -0.123806],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 10, "stroke": true, "weight": 3}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
        var popup_e8ed1bfc0ea7419fb409be31ff81395c = L.popup({"maxWidth": "100%"});

        
            var html_0224aeb78a7f4b128c48b35581698490 = $(`<div id="html_0224aeb78a7f4b128c48b35581698490" style="width: 100.0%; height: 100.0%;">Platform 9 3/4</div>`)[0];
            popup_e8ed1bfc0ea7419fb409be31ff81395c.setContent(html_0224aeb78a7f4b128c48b35581698490);
        

        circle_marker_561d2772624a4d9fa82a63ed4139850f.bindPopup(popup_e8ed1bfc0ea7419fb409be31ff81395c)
        ;

        
    
    
            var tile_layer_9ad0b18472d04abc9a6e268780dd6c92 = L.tileLayer(
                "https://tiles.macrostrat.org/carto/{z}/{x}/{y}.png",
                {"attribution": "Macrostrat", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
        
    
            var layer_control_c237a13a288b425b907380c11e16f66c = {
                base_layers : {
                    "cartodbpositron" : tile_layer_b99f96c3bee147db846e2f1b5e099f12,
                    "Macrostrat" : tile_layer_9ad0b18472d04abc9a6e268780dd6c92,
                },
                overlays :  {
                },
            };
            L.control.layers(
                layer_control_c237a13a288b425b907380c11e16f66c.base_layers,
                layer_control_c237a13a288b425b907380c11e16f66c.overlays,
                {"autoZIndex": true, "collapsed": true, "position": "topright"}
            ).addTo(map_bbc772b675484f009f2f71100db367fb);
            tile_layer_9ad0b18472d04abc9a6e268780dd6c92.remove();
        
    
                var lat_lng_popup_7a5a42e82a524704be7f2e77209b4526 = L.popup();
                function latLngPop(e) {
                    lat_lng_popup_7a5a42e82a524704be7f2e77209b4526
                        .setLatLng(e.latlng)
                        .setContent("Latitude: " + e.latlng.lat.toFixed(4) +
                                    "<br>Longitude: " + e.latlng.lng.toFixed(4))
                        .openOn(map_bbc772b675484f009f2f71100db367fb);
                    }
                map_bbc772b675484f009f2f71100db367fb.on('click', latLngPop);
            
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x120ae1910>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Document our session, for [computational reproducibility](https://www.nature.com/articles/d41586-018-05990-5)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'commit_hash': '8bda98619',\n",
      " 'commit_source': 'installation',\n",
      " 'default_encoding': 'UTF-8',\n",
      " 'ipython_path': '/opt/anaconda3/lib/python3.7/site-packages/IPython',\n",
      " 'ipython_version': '7.12.0',\n",
      " 'os_name': 'posix',\n",
      " 'platform': 'Darwin-18.7.0-x86_64-i386-64bit',\n",
      " 'sys_executable': '/opt/anaconda3/bin/python',\n",
      " 'sys_platform': 'darwin',\n",
      " 'sys_version': '3.7.6 (default, Jan  8 2020, 13:42:34) \\n'\n",
      "                '[Clang 4.0.1 (tags/RELEASE_401/final)]'}\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "print(IPython.sys_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "biopython==1.76\n",
      "folium==0.11.0\n",
      "matplotlib==3.1.3\n"
     ]
    }
   ],
   "source": [
    "!pip freeze | grep -E 'folium|matplotlib|biopython'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}