ipynb/effect-of-blooms-on-bray-curtis-distance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effect of blooming bacteria on Bray-Curtis distance\n",
    "### Supplementary figure 1\n",
    "\n",
    "In this Notebook, we test the effect of removing bloom sequences and the impact in beta diversity as measured by Bray Curtis distance. To do so, we're going to compare samples from four studies, the American Gut Project (AGP; Qiita ID [10317](https://qiita.ucsd.edu/study/description/10317)), the Personal Genome Project microbiome samples (PGP; Qiita ID [1189](https://qiita.ucsd.edu/study/description/1189)), the Human Genetics Shape the Gut Microbiome dataset (Twins UK; Qiita ID [2014](https://qiita.ucsd.edu/study/description/2014)) and samples from a whole grain diet study (ERC; Qiita ID [1481](https://qiita.ucsd.edu/study/description/1481)).\n",
    "\n",
    "Samples in the AGP study are shipped at room temperature. The other three studies were fresh-frozen. Our expectation is that the filtering will reduce distances between the American Gut vs. fresh-frozen studies, while the distances between fresh-frozen studies will change comparably less. The expectation is based on the assumption that the blooms have a higher impact on the American Gut data stemming from room temperature shipping.\n",
    "\n",
    "Specifically, we:\n",
    "\n",
    "1. Load the study datasets\n",
    "2. For an increasing number of bloom sequences (starting with zero blooms, and sorted by decreasing severity):\n",
    "    1. Filter out the bloom sequences\n",
    "    2. Rarefy to 1000 sequences per sample\n",
    "    3. Randomly select 1000 samples from each study (with replacement)\n",
    "    4. Calculate the Bray-Curtis distance for:\n",
    "        - AGP vs. PGP\n",
    "        - AGP vs. Twins UK\n",
    "        - AGP vs. ERC\n",
    "        - Twins UK vs. PGP\n",
    "        - ERC vs. Twins UK\n",
    "        - ERC vs. PGP\n",
    "\n",
    "3. Plot the result."
   ]
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  {
   "cell_type": "code",
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    "# plots inside the notebook\n",
    "%matplotlib notebook\n",
    "\n",
    "# load modules used in the analysis\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import biom\n",
    "import pandas as pd\n",
    "import skbio\n",
    "from skbio.diversity import beta_diversity\n",
    "from skbio.stats.ordination import pcoa\n",
    "from scipy.spatial.distance import braycurtis\n",
    "from emperor import Emperor, nbinstall\n",
    "\n",
    "nbinstall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "source": [
    "def sniff_category(metadata_path):\n",
    "    \"\"\"Determine which category describes fecal\n",
    "    \n",
    "    The metadata are not 100% consistent on category and value used\n",
    "    to describe a fecal sample.\n",
    "    \"\"\"\n",
    "    sniff = open(metadata_path).read().strip().split('\\t')\n",
    "    if 'ENV_MATTER' in sniff:\n",
    "        key = 'ENV_MATTER'\n",
    "        value = 'ENVO:feces'\n",
    "    elif 'env_matter' in sniff:\n",
    "        key = 'env_matter'\n",
    "        value = 'ENVO:feces'\n",
    "    elif 'env_material' in sniff:\n",
    "        key = 'env_material'\n",
    "        value = 'feces'\n",
    "    else:\n",
    "        raise KeyError(\"No interpretable column for denoting fecal\")\n",
    "        \n",
    "    return key, value\n",
    "\n",
    "\n",
    "def load(table_path, metadata_path, study_acronym):\n",
    "    \"\"\"Load a study, filter to fecal, drop samples with < 1000 reads\"\"\"\n",
    "    key, value = sniff_category(metadata_path)\n",
    "    \n",
    "    # load the biom table and remove any sample that does not contain reads\n",
    "    table = biom.load_table(table_path)\n",
    "    table.filter(lambda v, i, md: v.sum() > 0)  # drop samples without data\n",
    "    \n",
    "    # load metadata, set the index to the samples\n",
    "    metadata = pd.read_csv(metadata_path, sep='\\t', dtype=object, usecols=['#SampleID', key])\n",
    "    metadata.set_index('#SampleID', inplace=True)\n",
    "   \n",
    "    # verify we have information about all samples\n",
    "    assert set(table.ids()).issubset(metadata.index)\n",
    "    \n",
    "    # reduce to the fecal subset\n",
    "    fecal_metadata = metadata[metadata[key] == value]\n",
    "    fecal_metadata = fecal_metadata.loc[set(fecal_metadata.index) & set(table.ids())]\n",
    "    \n",
    "    # subset to fecal, keep samples with >= 1000 reads, and remove any empty OTUs\n",
    "    table.filter(fecal_metadata.index)\n",
    "    table.filter(lambda v, i, md: v.sum() >= 1000)\n",
    "    table.filter(lambda v, i, md: v.sum() > 0, axis='observation')\n",
    "    \n",
    "    # reduce the metadata to only the samples represented\n",
    "    fecal_metadata = fecal_metadata.loc[table.ids()]\n",
    "    \n",
    "    # normalize metadata (we already filtered to fecal based on study specific values)\n",
    "    fecal_metadata.columns = ['ENV_MATTER']\n",
    "    fecal_metadata['ENV_MATTER'] = 'feces'\n",
    "    fecal_metadata['study_acronym'] = study_acronym\n",
    "    \n",
    "    print(\"Fecal samples: %d\" % len(table.ids()))\n",
    "    \n",
    "    return table, fecal_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading all experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fecal samples: 7073\n",
      "Fecal samples: 1017\n",
      "Fecal samples: 82\n",
      "Fecal samples: 88\n"
     ]
    }
   ],
   "source": [
    "ag_table, ag_metadata       = load('../data/ag.qiita-10317.biom',   '../data/ag.qiita-10317.txt', 'AGP')\n",
    "twins_table, twins_metadata = load('../data/twins.qiita-2014.biom', '../data/twins.qiita-2014.txt', 'Twins')\n",
    "pgp_table, pgp_metadata     = load('../data/pgp.qiita-1189.biom',   '../data/pgp.qiita-1189.txt', 'PGP')\n",
    "erc_table, erc_metadata     = load('../data/erc.qiita-1481.biom',   '../data/erc.qiita-1481.txt', 'ERC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Join all data into a single table and data frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of sOTUs: 17357\n",
      "Total number of samples: 8260\n"
     ]
    }
   ],
   "source": [
    "table = pgp_table.merge(erc_table).merge(twins_table).merge(ag_table)\n",
    "metadata = pd.concat([pgp_metadata, erc_metadata, twins_metadata, ag_metadata])\n",
    "\n",
    "assert set(table.ids()) == set(metadata.index)\n",
    "\n",
    "print(\"Total number of sOTUs: %d\\nTotal number of samples: %d\" % table.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the mean Bray-Curtis distance for filtering levels\n",
    "\n",
    "Remove incrementally more blooms, starting from the most severe to least severe, and at each increment, compute the Bray Curtis distance between random sets of samples within each study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Operating on 0 bloom(s)...\n",
      "Operating on 1 bloom(s)...\n",
      "  AG - 3 sample(s) did not pass filtering\n",
      "Operating on 2 bloom(s)...\n",
      "  AG - 4 sample(s) did not pass filtering\n",
      "Operating on 3 bloom(s)...\n",
      "  AG - 8 sample(s) did not pass filtering\n",
      "Operating on 4 bloom(s)...\n",
      "  AG - 67 sample(s) did not pass filtering\n",
      "Operating on 5 bloom(s)...\n",
      "  AG - 70 sample(s) did not pass filtering\n",
      "Operating on 6 bloom(s)...\n",
      "  AG - 77 sample(s) did not pass filtering\n",
      "Operating on 7 bloom(s)...\n",
      "  AG - 85 sample(s) did not pass filtering\n",
      "Operating on 8 bloom(s)...\n",
      "  AG - 85 sample(s) did not pass filtering\n",
      "Operating on 9 bloom(s)...\n",
      "  AG - 90 sample(s) did not pass filtering\n",
      "Operating on 10 bloom(s)...\n",
      "  AG - 114 sample(s) did not pass filtering\n",
      "Operating on 11 bloom(s)...\n",
      "  AG - 122 sample(s) did not pass filtering\n",
      "Operating on 12 bloom(s)...\n",
      "  AG - 123 sample(s) did not pass filtering\n",
      "Operating on 13 bloom(s)...\n",
      "  AG - 126 sample(s) did not pass filtering\n",
      "Operating on 14 bloom(s)...\n",
      "  AG - 126 sample(s) did not pass filtering\n",
      "Operating on 15 bloom(s)...\n",
      "  AG - 128 sample(s) did not pass filtering\n",
      "Operating on 16 bloom(s)...\n",
      "  AG - 128 sample(s) did not pass filtering\n",
      "Operating on 17 bloom(s)...\n",
      "  AG - 128 sample(s) did not pass filtering\n",
      "Operating on 18 bloom(s)...\n",
      "  AG - 129 sample(s) did not pass filtering\n",
      "Operating on 19 bloom(s)...\n",
      "  AG - 129 sample(s) did not pass filtering\n",
      "Operating on 20 bloom(s)...\n",
      "  AG - 132 sample(s) did not pass filtering\n"
     ]
    }
   ],
   "source": [
    "def sample_and_remap(df, iterations, index, tag):\n",
    "    \"\"\"Sample with replacement from df, return the observed index positions\"\"\"\n",
    "    # determine what IDs are still in the table\n",
    "    valid = set(index).intersection(df.index)\n",
    "    \n",
    "    # randomly sample with replacement over the IDs\n",
    "    ids = df.loc[valid].sample(iterations, replace=True).index.values\n",
    "    \n",
    "    # determine their index position in the matrix\n",
    "    indices = [index[i] for i in ids]\n",
    "    \n",
    "    if len(df) - len(valid):\n",
    "        print(\"  %s - %d sample(s) did not pass filtering\" % (tag, len(df) - len(valid)))\n",
    "\n",
    "    return indices \n",
    "\n",
    "# initial conditions\n",
    "iterations = 1000\n",
    "sequences = list(skbio.read('../data/newbloom.all.fna', format='fasta'))\n",
    "\n",
    "# datasets to store the resulting bray curtis distances\n",
    "ag_vs_twin  = np.zeros((len(sequences) + 1, iterations))\n",
    "ag_vs_pgp   = np.zeros((len(sequences) + 1, iterations))\n",
    "ag_vs_erc   = np.zeros((len(sequences) + 1, iterations))\n",
    "pgp_vs_twin = np.zeros((len(sequences) + 1, iterations))\n",
    "erc_vs_twin = np.zeros((len(sequences) + 1, iterations))\n",
    "erc_vs_pgp  = np.zeros((len(sequences) + 1, iterations))\n",
    "\n",
    "blooms = set()\n",
    "for bloom_idx in range(len(sequences) + 1):\n",
    "    # make sure we run once without any blooms\n",
    "    if bloom_idx > 0:\n",
    "        blooms.add(str(sequences[bloom_idx - 1]))\n",
    "        \n",
    "    print(\"Operating on %d bloom(s)...\" % len(blooms))\n",
    "    \n",
    "    # remove bloom sequences, rarefy, normalize to [0, 1], and fetch a dense representation\n",
    "    table_wo_blooms = table.filter(blooms, invert=True, inplace=False, axis='observation')\n",
    "    table_wo_blooms = table_wo_blooms.subsample(1000).norm()\n",
    "    table_wo_blooms_mat = table_wo_blooms.matrix_data.T.toarray()\n",
    "    \n",
    "    # compute an index mapping each ID to a row in the matrix\n",
    "    index = {i: idx for idx, i in enumerate(table_wo_blooms.ids())}\n",
    "    \n",
    "    # get a bunch of random positions in the matrix for each study\n",
    "    ag_samples    = sample_and_remap(ag_metadata, iterations, index, 'AG') \n",
    "    pgp_samples   = sample_and_remap(pgp_metadata, iterations, index, 'PGP')\n",
    "    erc_samples   = sample_and_remap(erc_metadata, iterations, index, 'ERC')\n",
    "    twins_samples = sample_and_remap(twins_metadata, iterations, index, 'Twins')\n",
    "    \n",
    "    # pull out the matrix of counts corresponding to the random positions\n",
    "    ag_counts    = table_wo_blooms_mat[ag_samples]\n",
    "    pgp_counts   = table_wo_blooms_mat[pgp_samples]\n",
    "    erc_counts   = table_wo_blooms_mat[erc_samples]\n",
    "    twins_counts = table_wo_blooms_mat[twins_samples]\n",
    "    \n",
    "    # compute bray curtis\n",
    "    ag_vs_twin[bloom_idx]  = np.array([braycurtis(u, v) for u, v in zip(ag_counts, twins_counts)])\n",
    "    ag_vs_pgp[bloom_idx]   = np.array([braycurtis(u, v) for u, v in zip(ag_counts, pgp_counts)])\n",
    "    ag_vs_erc[bloom_idx]   = np.array([braycurtis(u, v) for u, v in zip(ag_counts, erc_counts)])\n",
    "    pgp_vs_twin[bloom_idx] = np.array([braycurtis(u, v) for u, v in zip(pgp_counts, twins_counts)])\n",
    "    erc_vs_twin[bloom_idx] = np.array([braycurtis(u, v) for u, v in zip(erc_counts, twins_counts)])\n",
    "    erc_vs_pgp[bloom_idx]  = np.array([braycurtis(u, v) for u, v in zip(erc_counts, pgp_counts)])"
   ]
  },
  {
   "cell_type": "code",
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       "\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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 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     "metadata": {},
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    }
   ],
   "source": [
    "def plot_comparison(results, color, against_ag):\n",
    "    \"\"\"Plot mean and stderr for the values in results\"\"\"\n",
    "    n_blooms, iterations = results.shape\n",
    "    x_axis = np.arange(n_blooms)\n",
    "    \n",
    "    results_mean = results.mean(axis=1)\n",
    "    results_stderr = results.std(axis=1) / np.sqrt(iterations)\n",
    "    \n",
    "    plt.fill_between(x_axis, \n",
    "                     results_mean + results_stderr, \n",
    "                     results_mean - results_stderr, \n",
    "                     alpha=0.25, color=color)\n",
    "    \n",
    "    linestyle = '-' if against_ag else '--'\n",
    "    plt.plot(x_axis, results_mean, color=color, lw=2, linestyle=linestyle)\n",
    "\n",
    "plt.figure()\n",
    "plot_comparison(ag_vs_twin, 'r', True)\n",
    "plot_comparison(ag_vs_pgp, 'b', True)\n",
    "plot_comparison(ag_vs_erc, 'y', True)\n",
    "plot_comparison(pgp_vs_twin, 'g', False)\n",
    "plot_comparison(erc_vs_twin, 'k', False)\n",
    "plot_comparison(erc_vs_pgp, 'c', False)\n",
    "\n",
    "plt.legend(['AG - UK Twins',\n",
    "            'AG - PGP',\n",
    "            'AG - ERC',\n",
    "            'PGP - UK Twins',\n",
    "            'ERC - UK Twins',\n",
    "            'ERC - PGP'], loc='center right')\n",
    "plt.xlabel('Number of candidate blooming bacteria', fontsize=15)\n",
    "plt.ylabel('Mean Bray-Curtis distance', fontsize=15)\n",
    "plt.xlim(0, len(ag_vs_twin) - 1)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='both', which='major', labelsize=14)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=14)\n",
    "\n",
    "ax.figure.savefig('../results/figure_s1.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Compare in principal coordinates space\n",
    "\n",
    "Next, we're going to produce principal coordinates plots highlighting the difference in beta diversity (via Bray Curtis) prior to and following the removal of blooms. For this set of figures, we're going to randomly subsample the larger studies down to a smaller number of samples to avoid a heavy study bias. \n",
    "\n",
    "The subsequent plots which are displayed in the manuscript use these EMPeror plots, colored by \"study_acronym\", with the samples scaled by the \"bloom_counts\" key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jdebelius/miniconda2/envs/bloom/lib/python3.5/site-packages/skbio/stats/ordination/_principal_coordinate_analysis.py:111: RuntimeWarning: The result contains negative eigenvalues. Please compare their magnitude with the magnitude of some of the largest positive eigenvalues. If the negative ones are smaller, it's probably safe to ignore them, but if they are large in magnitude, the results won't be useful. See the Notes section for more details. The smallest eigenvalue is -0.5075625180588856 and the largest is 22.212379187905388.\n",
      "  RuntimeWarning\n",
      "/Users/jdebelius/miniconda2/envs/bloom/lib/python3.5/site-packages/skbio/stats/ordination/_principal_coordinate_analysis.py:111: RuntimeWarning: The result contains negative eigenvalues. Please compare their magnitude with the magnitude of some of the largest positive eigenvalues. If the negative ones are smaller, it's probably safe to ignore them, but if they are large in magnitude, the results won't be useful. See the Notes section for more details. The smallest eigenvalue is -0.2826589606309191 and the largest is 19.657805591714595.\n",
      "  RuntimeWarning\n"
     ]
    }
   ],
   "source": [
    "max_samples = 200\n",
    "top_n_blooms = 10\n",
    "blooms = [str(s) for s in sequences[:top_n_blooms]]\n",
    "\n",
    "ag_table_subset = ag_table.subsample(max_samples, by_id=True)\n",
    "twins_table_subset = twins_table.subsample(max_samples, by_id=True)\n",
    "pgp_table_subset = pgp_table.subsample(max_samples, by_id=True)\n",
    "erc_table_subset = erc_table.subsample(max_samples, by_id=True)\n",
    "\n",
    "with_bloom = pgp_table_subset.merge(erc_table_subset).merge(twins_table_subset).merge(ag_table_subset)\n",
    "without_bloom = with_bloom.filter(set(blooms), axis='observation', inplace=False, invert=True)\n",
    "\n",
    "metadata_subset = metadata.loc[with_bloom.ids()].copy()\n",
    "metadata_subset['bloom_counts'] = (with_bloom.sum(axis='sample') - without_bloom.sum(axis='sample')).astype(int)\n",
    "\n",
    "with_bloom = with_bloom.subsample(1000)\n",
    "without_bloom = without_bloom.subsample(1000)\n",
    "\n",
    "with_bloom_counts = with_bloom.matrix_data.T.astype(int).toarray()\n",
    "without_bloom_counts = without_bloom.matrix_data.T.astype(int).toarray()\n",
    "\n",
    "with_bloom_dm = beta_diversity('braycurtis', with_bloom_counts, with_bloom.ids(), validate=False)\n",
    "with_bloom_pcoa = pcoa(with_bloom_dm)\n",
    "\n",
    "without_bloom_dm = beta_diversity('braycurtis', without_bloom_counts, without_bloom.ids(), validate=False)\n",
    "without_bloom_pcoa = pcoa(without_bloom_dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "\n",
       "  /* jQuery plugins */\n",
       "  'chosen': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/chosen.jquery.min',\n",
       "  'spectrum': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/spectrum.min',\n",
       "  'position': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/jquery.ui.position.min',\n",
       "  'contextmenu': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/jquery.contextMenu.min',\n",
       "\n",
       "  /* other libraries */\n",
       "  'underscore': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/underscore-min',\n",
       "  'chroma': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/chroma.min',\n",
       "  'filesaver': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/FileSaver.min',\n",
       "  'blob': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/Blob',\n",
       "  'd3': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/d3.min',\n",
       "\n",
       "  /* THREE.js and plugins */\n",
       "  'three': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.min',\n",
       "  'orbitcontrols': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.js-plugins/OrbitControls',\n",
       "  'projector': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.js-plugins/Projector',\n",
       "  'svgrenderer': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.js-plugins/SVGRenderer',\n",
       "\n",
       "  /* SlickGrid */\n",
       "  'slickcore': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.core.min',\n",
       "  'slickgrid': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.grid.min',\n",
       "  'slickformatters': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.editors.min',\n",
       "  'slickeditors': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.formatters.min',\n",
       "\n",
       "  /* Emperor's objects */\n",
       "  'util': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/util',\n",
       "  'model': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/model',\n",
       "  'view': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/view',\n",
       "  'controller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/controller',\n",
       "  'draw': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/draw',\n",
       "  'scene3d': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/sceneplotview3d',\n",
       "  'abcviewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/abc-view-controller',\n",
       "  'viewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/view-controller',\n",
       "  'colorviewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/color-view-controller',\n",
       "  'visibilitycontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/visibility-controller',\n",
       "  'scaleviewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/scale-view-controller',\n",
       "  'shapecontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/shape-controller',\n",
       "  'axescontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/axes-controller',\n",
       "  'shape-editor': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/shape-editor',\n",
       "  'color-editor': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/color-editor',\n",
       "  'scale-editor': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/scale-editor',\n",
       "  'shapes': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/shapes'\n",
       "},\n",
       "/*\n",
       "   Libraries that are not AMD compatible need shim to declare their\n",
       "   dependencies.\n",
       " */\n",
       "'shim': {\n",
       "  'jquery_drag': {\n",
       "    'deps': ['jquery', 'jqueryui']\n",
       "  },\n",
       "  'chosen': {\n",
       "    'deps': ['jquery'],\n",
       "    'exports': 'jQuery.fn.chosen'\n",
       "  },\n",
       "  'contextmenu' : {\n",
       "    'deps': ['jquery', 'jqueryui', 'position']\n",
       "  },\n",
       "  'filesaver' : {\n",
       "    'deps': ['blob']\n",
       "  },\n",
       "  'orbitcontrols': {\n",
       "    'deps': ['three']\n",
       "  },\n",
       "  'projector': {\n",
       "    'deps': ['three']\n",
       "  },\n",
       "  'svgrenderer': {\n",
       "    'deps': ['orbitcontrols', 'projector']\n",
       "  },\n",
       "'slickcore': ['jqueryui'],\n",
       "'slickgrid': ['slickcore', 'jquery_drag', 'slickformatters',\n",
       "              'slickeditors']\n",
       "}\n",
       "});\n",
       "\n",
       "requirejs(\n",
       "[\"jquery\", \"model\", \"controller\"],\n",
       "function($, model, EmperorController) {\n",
       "  var DecompositionModel = model.DecompositionModel;\n",
       "\n",
       "  var div = $('#emperor-notebook-0x4efe2aa9');\n",
       "\n",
       "  var ids = ['Cowan.Stool', 'Shadyside.Stool', 'Gigi.Stool', 'Zambom.Stool', 'Dias.Stool', 'Trimmer.Stool', 'Fermanagh.Stool', 'Caryll.Stool', 'Azalea.Stool', 'Lassell.Stool', 'Turkey.Stool', 'Pasho.Stool', 'Nobscot.Stool', 'Elmira.Stool', 'Erlandson.Stool', 'Innitou.Stool', 'Pinrock.Stool', 'Overton.Stool', 'Salti.Stool', 'Brae.Stool', 'Hallets.Stool', 'Stannes.Stool', 'Eisenhower.Stool', 'Balcom.Stool', 'Lonergan.Stool', 'Hager.Stool', 'Bows.Stool', 'Tewmac.Stool', 'Northwind.Stool', 'Watoguadoc.Stool', 'Mariposa.Stool.1', 'Digregorio.Stool', 'Noel.Stool', 'Maryjane.Stool', 'Goddu.Stool', 'Milne.Stool', 'Coachmen.Stool', 'Purchade.Stool', 'Buckinghan.Stool', 'Innkeeper.Stool', 'Mariposa.Stool.2', 'Foothill.Stool', 'Lippokd.Stool', 'Mariposa.Stool.3', 'Sylvanus.Stool', 'Witch.Stool', 'Westbora.Stool', 'Bjorkland.Stool', 'Slades.Stool', 'Elmavenue.Stool', 'January.Stool', 'Devenwood.Stool', 'Caravel.Stool', 'Sandale.Stool', 'Winifreds.Stool', 'Claudett.Stool', 'Nyack.Stool', 'Larcridge.Stool', 'Janis.Stool', 'Vinedale.Stool', 'Wilbraview.Stool', 'Bulls.Stool', 'Gibbon.Stool', 'Trafford.Stool', 'Willowbe.Stool', 'Menekish.Stool', 'Misty.Stool', 'Malfa.Stool', 'Patmos.Stool', 'Ronald.Stool', 'Delmore.Stool', 'Netta.Stool', 'Starratt.Stool', 'Woodfin.Stool', 'Cornstalk.Stool', 'Meadowlands.Stool', 'Charmark.Stool', 'Cedarhurst.Stool', 'Padanaram.Stool', 'Burne.Stool', 'Postscript.Stool', 'Edenfield.Stool', '1481.PO1.1.T0', '1481.NA.12.T8', '1481.PO6.2.T0', '1481.NA.20.T0', '1481.NA.01.T0', '1481.NA.23.T8', '1481.PO5.5.T0', '1481.PO4.9.T0', '1481.NA.02.T8', '1481.PO1.15.T0', '1481.PO1.2.T8', '1481.PO4.4.T8', '1481.PO6.1.T8', '1481.PO1.17.T8', '1481.PO2.4.T8', '1481.NA.21.T0', '1481.PO7.2.T8', '1481.PO6.3.T0', '1481.NA.13.T8', '1481.PO1.3.T8', '1481.NA.10.T0', '1481.PO4.8.T0', '1481.PO5.10.T4', '1481.PO5.8.T4', '1481.PO4.6.T8', '1481.PO6.3.T8', '1481.PO7.2.T0', '1481.NA.21.T8', '1481.PO1.17.T0', '1481.PO4.8.T8', '1481.PO2.5.T4', '1481.NA.10.T8', '1481.PO1.3.T0', '1481.NA.01.T8', '1481.NA.20.T8', '1481.PO6.2.T8', '1481.PO4.7.T8', '1481.NA.12.T0', '1481.PO1.1.T8', '1481.PO6.1.T0', '1481.PO4.4.T0', '1481.PO1.2.T0', '1481.NA.13.T4', '1481.NA.02.T0', '1481.PO4.9.T8', '1481.PO1.15.T8', '1481.NA.23.T0', '1481.PO5.5.T8', '1481.NA.14.T0', '1481.PO3.2.T8', '1481.PO4.3.T4', '1481.NA.07.T8', '1481.PO2.3.T0', '1481.PO5.3.T8', '1481.NA.04.T0', '1481.NA.17.T8', '1481.PO1.11.T0', '1481.NA.06.T8', '1481.PO6.5.T8', '1481.NA.15.T0', '1481.PO3.3.T8', '1481.PO4.3.T0', '1481.NA.16.T8', '1481.PO1.5.T0', '1481.PO2.1.T8', '1481.PO2.3.T4', '1481.NA.24.T0', '1481.PO5.2.T8', '1481.PO3.3.T0', '1481.NA.15.T8', '1481.PO6.5.T0', '1481.NA.18.T0', '1481.PO1.11.T8', '1481.NA.06.T0', '1481.NA.24.T8', '1481.PO5.2.T0', '1481.NA.07.T4', '1481.PO2.1.T0', '1481.PO1.5.T8', '1481.NA.16.T0', '1481.PO6.4.T4', '1481.PO6.4.T0', '1481.PO3.2.T0', '1481.NA.14.T8', '1481.NA.17.T0', '1481.NA.04.T8', '1481.PO5.3.T0', '1481.NA.18.T4', '2014.Plate10.11', '2014.Plate8.67', '2014.Plate9.33', '2014.Plate14.24', '2014.Plate10.10', '2014.Plate9.9', '2014.Plate12.65', '2014.High1.15', '2014.Low2.83', '2014.Plate14.26', '2014.Plate11.47', '2014.Plate6.67', '2014.Plate13.33', '2014.Plate6.66', '2014.Low1.88', '2014.Plate5.1', '2014.Plate13.95', '2014.Plate8.36', '2014.Plate7.65', '2014.Plate7.36', '2014.Plate5.43', '2014.Low2.58', '2014.Plate10.4', '2014.Plate10.16', '2014.High1.9', '2014.Plate15.25', '2014.Plate10.17', '2014.High1.10', '2014.Plate13.64', '2014.Low1.8', '2014.Plate11.68', '2014.Low2.73', '2014.High1.69', '2014.Plate5.68', '2014.High2.30', '2014.Plate13.34', '2014.Low2.70', '2014.Plate12.32', '2014.Low1.29', '2014.High1.39', '2014.Plate15.8', '2014.Plate5.38', '2014.Plate14.86', '2014.Plate7.92', '2014.Plate14.73', '2014.Plate15.75', '2014.Plate14.20', '2014.Plate9.37', '2014.Low2.85', '2014.High1.40', '2014.Plate8.10', '2014.Plate6.39', '2014.Plate15.54', '2014.Low1.71', '2014.Plate10.35', '2014.Plate6.40', '2014.Plate14.78', '2014.Plate13.15', 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0.20398799627835162, 0.17630455540998055], [0.06871774984172303, 0.035208341490283915, 0.2325782096859259, 0.12693500110286385, -0.02715993429257761], [-0.046204195306599485, -0.09176053037973866, 0.20388848494541592, -0.004540468399406423, 0.08083033414519167]];\n",
       "  var pct_var = [9.857793312340691, 7.3426324067665245, 3.939871487974314, 3.220578593471221, 3.0230977614972656];\n",
       "  var md_headers = ['SampleID', 'ENV_MATTER', 'study_acronym', 'bloom_counts'];\n",
       "  var metadata = [['Cowan.Stool', 'feces', 'PGP', '0'], ['Shadyside.Stool', 'feces', 'PGP', '3445'], ['Gigi.Stool', 'feces', 'PGP', '780'], ['Zambom.Stool', 'feces', 'PGP', '54'], ['Dias.Stool', 'feces', 'PGP', '22'], ['Trimmer.Stool', 'feces', 'PGP', '2'], ['Fermanagh.Stool', 'feces', 'PGP', '37'], ['Caryll.Stool', 'feces', 'PGP', '0'], ['Azalea.Stool', 'feces', 'PGP', '2'], ['Lassell.Stool', 'feces', 'PGP', '0'], ['Turkey.Stool', 'feces', 'PGP', '0'], ['Pasho.Stool', 'feces', 'PGP', '16'], ['Nobscot.Stool', 'feces', 'PGP', '53'], ['Elmira.Stool', 'feces', 'PGP', '0'], ['Erlandson.Stool', 'feces', 'PGP', '6'], ['Innitou.Stool', 'feces', 'PGP', '53'], ['Pinrock.Stool', 'feces', 'PGP', '579'], ['Overton.Stool', 'feces', 'PGP', '0'], ['Salti.Stool', 'feces', 'PGP', '0'], ['Brae.Stool', 'feces', 'PGP', '175'], ['Hallets.Stool', 'feces', 'PGP', '2'], ['Stannes.Stool', 'feces', 'PGP', '0'], ['Eisenhower.Stool', 'feces', 'PGP', '13'], ['Balcom.Stool', 'feces', 'PGP', '580'], ['Lonergan.Stool', 'feces', 'PGP', '2027'], ['Hager.Stool', 'feces', 'PGP', '0'], ['Bows.Stool', 'feces', 'PGP', '694'], ['Tewmac.Stool', 'feces', 'PGP', '0'], ['Northwind.Stool', 'feces', 'PGP', '3'], ['Watoguadoc.Stool', 'feces', 'PGP', '4'], ['Mariposa.Stool.1', 'feces', 'PGP', '0'], ['Digregorio.Stool', 'feces', 'PGP', '13'], ['Noel.Stool', 'feces', 'PGP', '0'], ['Maryjane.Stool', 'feces', 'PGP', '0'], ['Goddu.Stool', 'feces', 'PGP', '1440'], ['Milne.Stool', 'feces', 'PGP', '5'], ['Coachmen.Stool', 'feces', 'PGP', '1873'], ['Purchade.Stool', 'feces', 'PGP', '2955'], ['Buckinghan.Stool', 'feces', 'PGP', '7'], ['Innkeeper.Stool', 'feces', 'PGP', '0'], ['Mariposa.Stool.2', 'feces', 'PGP', '15'], ['Foothill.Stool', 'feces', 'PGP', '0'], ['Lippokd.Stool', 'feces', 'PGP', '0'], ['Mariposa.Stool.3', 'feces', 'PGP', '0'], ['Sylvanus.Stool', 'feces', 'PGP', '9'], ['Witch.Stool', 'feces', 'PGP', '91'], ['Westbora.Stool', 'feces', 'PGP', '245'], ['Bjorkland.Stool', 'feces', 'PGP', '12'], ['Slades.Stool', 'feces', 'PGP', '2'], ['Elmavenue.Stool', 'feces', 'PGP', '0'], ['January.Stool', 'feces', 'PGP', '0'], ['Devenwood.Stool', 'feces', 'PGP', '6'], ['Caravel.Stool', 'feces', 'PGP', '0'], ['Sandale.Stool', 'feces', 'PGP', '186'], ['Winifreds.Stool', 'feces', 'PGP', '0'], ['Claudett.Stool', 'feces', 'PGP', '13'], ['Nyack.Stool', 'feces', 'PGP', '97'], ['Larcridge.Stool', 'feces', 'PGP', '2'], ['Janis.Stool', 'feces', 'PGP', '4'], ['Vinedale.Stool', 'feces', 'PGP', '867'], ['Wilbraview.Stool', 'feces', 'PGP', '169'], ['Bulls.Stool', 'feces', 'PGP', '55'], ['Gibbon.Stool', 'feces', 'PGP', '0'], ['Trafford.Stool', 'feces', 'PGP', '18'], ['Willowbe.Stool', 'feces', 'PGP', '440'], ['Menekish.Stool', 'feces', 'PGP', '0'], ['Misty.Stool', 'feces', 'PGP', '6'], ['Malfa.Stool', 'feces', 'PGP', '27'], ['Patmos.Stool', 'feces', 'PGP', '10'], ['Ronald.Stool', 'feces', 'PGP', '159'], ['Delmore.Stool', 'feces', 'PGP', '919'], ['Netta.Stool', 'feces', 'PGP', '14'], ['Starratt.Stool', 'feces', 'PGP', '9'], ['Woodfin.Stool', 'feces', 'PGP', '528'], ['Cornstalk.Stool', 'feces', 'PGP', '14'], ['Meadowlands.Stool', 'feces', 'PGP', '1095'], ['Charmark.Stool', 'feces', 'PGP', '0'], ['Cedarhurst.Stool', 'feces', 'PGP', '0'], ['Padanaram.Stool', 'feces', 'PGP', '0'], ['Burne.Stool', 'feces', 'PGP', '0'], ['Postscript.Stool', 'feces', 'PGP', '12'], ['Edenfield.Stool', 'feces', 'PGP', '0'], ['1481.PO1.1.T0', 'feces', 'ERC', '4'], ['1481.NA.12.T8', 'feces', 'ERC', '11'], ['1481.PO6.2.T0', 'feces', 'ERC', '7'], ['1481.NA.20.T0', 'feces', 'ERC', '7'], ['1481.NA.01.T0', 'feces', 'ERC', '5'], ['1481.NA.23.T8', 'feces', 'ERC', '0'], ['1481.PO5.5.T0', 'feces', 'ERC', '3'], ['1481.PO4.9.T0', 'feces', 'ERC', '31'], ['1481.NA.02.T8', 'feces', 'ERC', '5'], ['1481.PO1.15.T0', 'feces', 'ERC', '7'], ['1481.PO1.2.T8', 'feces', 'ERC', '13'], ['1481.PO4.4.T8', 'feces', 'ERC', '116'], ['1481.PO6.1.T8', 'feces', 'ERC', '5'], ['1481.PO1.17.T8', 'feces', 'ERC', '0'], ['1481.PO2.4.T8', 'feces', 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['1481.PO6.1.T0', 'feces', 'ERC', '1215'], ['1481.PO4.4.T0', 'feces', 'ERC', '98'], ['1481.PO1.2.T0', 'feces', 'ERC', '0'], ['1481.NA.13.T4', 'feces', 'ERC', '36'], ['1481.NA.02.T0', 'feces', 'ERC', '85'], ['1481.PO4.9.T8', 'feces', 'ERC', '17'], ['1481.PO1.15.T8', 'feces', 'ERC', '3'], ['1481.NA.23.T0', 'feces', 'ERC', '119'], ['1481.PO5.5.T8', 'feces', 'ERC', '0'], ['1481.NA.14.T0', 'feces', 'ERC', '28'], ['1481.PO3.2.T8', 'feces', 'ERC', '43'], ['1481.PO4.3.T4', 'feces', 'ERC', '0'], ['1481.NA.07.T8', 'feces', 'ERC', '9'], ['1481.PO2.3.T0', 'feces', 'ERC', '14'], ['1481.PO5.3.T8', 'feces', 'ERC', '7'], ['1481.NA.04.T0', 'feces', 'ERC', '97'], ['1481.NA.17.T8', 'feces', 'ERC', '2'], ['1481.PO1.11.T0', 'feces', 'ERC', '30'], ['1481.NA.06.T8', 'feces', 'ERC', '3'], ['1481.PO6.5.T8', 'feces', 'ERC', '26'], ['1481.NA.15.T0', 'feces', 'ERC', '7'], ['1481.PO3.3.T8', 'feces', 'ERC', '92'], ['1481.PO4.3.T0', 'feces', 'ERC', '3'], ['1481.NA.16.T8', 'feces', 'ERC', '0'], ['1481.PO1.5.T0', 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'0'], ['2014.Plate10.11', 'feces', 'Twins', '2945'], ['2014.Plate8.67', 'feces', 'Twins', '5'], ['2014.Plate9.33', 'feces', 'Twins', '71'], ['2014.Plate14.24', 'feces', 'Twins', '7'], ['2014.Plate10.10', 'feces', 'Twins', '4'], ['2014.Plate9.9', 'feces', 'Twins', '20'], ['2014.Plate12.65', 'feces', 'Twins', '99'], ['2014.High1.15', 'feces', 'Twins', '205'], ['2014.Low2.83', 'feces', 'Twins', '26'], ['2014.Plate14.26', 'feces', 'Twins', '0'], ['2014.Plate11.47', 'feces', 'Twins', '4017'], ['2014.Plate6.67', 'feces', 'Twins', '40'], ['2014.Plate13.33', 'feces', 'Twins', '1990'], ['2014.Plate6.66', 'feces', 'Twins', '36'], ['2014.Low1.88', 'feces', 'Twins', '11'], ['2014.Plate5.1', 'feces', 'Twins', '1546'], ['2014.Plate13.95', 'feces', 'Twins', '859'], ['2014.Plate8.36', 'feces', 'Twins', '626'], ['2014.Plate7.65', 'feces', 'Twins', '14'], ['2014.Plate7.36', 'feces', 'Twins', '594'], ['2014.Plate5.43', 'feces', 'Twins', '3'], ['2014.Low2.58', 'feces', 'Twins', '2'], ['2014.Plate10.4', 'feces', 'Twins', '11505'], ['2014.Plate10.16', 'feces', 'Twins', '65'], ['2014.High1.9', 'feces', 'Twins', '47'], ['2014.Plate15.25', 'feces', 'Twins', '177'], ['2014.Plate10.17', 'feces', 'Twins', '53'], ['2014.High1.10', 'feces', 'Twins', '706'], ['2014.Plate13.64', 'feces', 'Twins', '3'], ['2014.Low1.8', 'feces', 'Twins', '0'], ['2014.Plate11.68', 'feces', 'Twins', '35'], ['2014.Low2.73', 'feces', 'Twins', '3'], ['2014.High1.69', 'feces', 'Twins', '17886'], ['2014.Plate5.68', 'feces', 'Twins', '1640'], ['2014.High2.30', 'feces', 'Twins', '20'], ['2014.Plate13.34', 'feces', 'Twins', '2'], ['2014.Low2.70', 'feces', 'Twins', '8'], ['2014.Plate12.32', 'feces', 'Twins', '69'], ['2014.Low1.29', 'feces', 'Twins', '5'], ['2014.High1.39', 'feces', 'Twins', '3'], ['2014.Plate15.8', 'feces', 'Twins', '49'], ['2014.Plate5.38', 'feces', 'Twins', '415'], ['2014.Plate14.86', 'feces', 'Twins', '2966'], ['2014.Plate7.92', 'feces', 'Twins', '1261'], ['2014.Plate14.73', 'feces', 'Twins', '6702'], ['2014.Plate15.75', 'feces', 'Twins', '23'], ['2014.Plate14.20', 'feces', 'Twins', '524'], ['2014.Plate9.37', 'feces', 'Twins', '9'], ['2014.Low2.85', 'feces', 'Twins', '1155'], ['2014.High1.40', 'feces', 'Twins', '1219'], ['2014.Plate8.10', 'feces', 'Twins', '26'], ['2014.Plate6.39', 'feces', 'Twins', '100'], ['2014.Plate15.54', 'feces', 'Twins', '552'], ['2014.Low1.71', 'feces', 'Twins', '1160'], ['2014.Plate10.35', 'feces', 'Twins', '428'], ['2014.Plate6.40', 'feces', 'Twins', '20'], ['2014.Plate14.78', 'feces', 'Twins', '35'], ['2014.Plate13.15', 'feces', 'Twins', '43'], ['2014.High1.19', 'feces', 'Twins', '1777'], ['2014.Plate6.41', 'feces', 'Twins', '3046'], ['2014.Plate11.61', 'feces', 'Twins', '79'], ['2014.Plate7.1', 'feces', 'Twins', '19555'], ['2014.Low1.1', 'feces', 'Twins', '345'], ['2014.High1.48', 'feces', 'Twins', '2'], ['2014.Plate6.43', 'feces', 'Twins', '1440'], ['2014.Plate14.51', 'feces', 'Twins', '244'], ['2014.High1.62', 'feces', 'Twins', '4'], ['2014.Plate9.15', 'feces', 'Twins', '0'], ['2014.Plate15.1', 'feces', 'Twins', '4'], ['2014.Plate5.62', 'feces', 'Twins', '15'], ['2014.Plate13.44', 'feces', 'Twins', '85'], ['2014.High1.66', 'feces', 'Twins', '6'], ['2014.Plate7.41', 'feces', 'Twins', '12'], ['2014.Plate7.12', 'feces', 'Twins', '11'], ['2014.Plate12.14', 'feces', 'Twins', '125'], ['2014.High2.14', 'feces', 'Twins', '0'], ['2014.Plate7.13', 'feces', 'Twins', '17'], ['2014.Low1.6', 'feces', 'Twins', '68'], ['2014.Plate5.35', 'feces', 'Twins', '511'], ['2014.Plate13.11', 'feces', 'Twins', '14'], ['2014.High2.45', 'feces', 'Twins', '245'], ['2014.Plate5.9', 'feces', 'Twins', '4936'], ['2014.Plate10.9', 'feces', 'Twins', '15821'], ['2014.Plate7.4', 'feces', 'Twins', '28'], ['2014.Low1.5', 'feces', 'Twins', '530'], ['2014.Plate7.69', 'feces', 'Twins', '32144'], ['2014.Plate11.65', 'feces', 'Twins', '11'], ['2014.Plate10.63', 'feces', 'Twins', '97'], ['2014.Plate13.10', 'feces', 'Twins', '39'], ['2014.Low2.54', 'feces', 'Twins', '232'], ['2014.Plate7.10', 'feces', 'Twins', '2431'], ['2014.High1.80', 'feces', 'Twins', '3287'], ['2014.Plate9.28', 'feces', 'Twins', '46'], ['2014.Plate6.54', 'feces', 'Twins', '11'], ['2014.Plate6.7', 'feces', 'Twins', '1384'], ['2014.Plate11.74', 'feces', 'Twins', '3577'], ['2014.Plate15.13', 'feces', 'Twins', '20'], ['2014.Plate15.41', 'feces', 'Twins', '0'], ['2014.Low1.64', 'feces', 'Twins', '11'], ['2014.Plate10.20', 'feces', 'Twins', '549'], ['2014.Plate15.12', 'feces', 'Twins', '221'], ['2014.Plate10.59', 'feces', 'Twins', '5486'], ['2014.Plate5.80', 'feces', 'Twins', '60'], ['2014.Plate15.38', 'feces', 'Twins', '35'], ['2014.Plate13.6', 'feces', 'Twins', '181'], ['2014.Low1.35', 'feces', 'Twins', '22'], ['2014.Plate5.77', 'feces', 'Twins', '9'], ['2014.Plate9.52', 'feces', 'Twins', '6'], ['2014.Plate12.88', 'feces', 'Twins', '5358'], ['2014.Plate6.88', 'feces', 'Twins', '65'], ['2014.Plate13.51', 'feces', 'Twins', '69'], ['2014.Plate8.2', 'feces', 'Twins', '14607'], ['2014.Plate9.78', 'feces', 'Twins', '56'], ['2014.Plate10.84', 'feces', 'Twins', '11'], ['2014.Plate6.5', 'feces', 'Twins', '165'], ['2014.Plate15.68', 'feces', 'Twins', '26921'], ['2014.Plate12.89', 'feces', 'Twins', '5'], ['2014.Plate5.83', 'feces', 'Twins', '92'], ['2014.High1.82', 'feces', 'Twins', '8156'], ['2014.Plate11.76', 'feces', 'Twins', '654'], ['2014.Plate5.76', 'feces', 'Twins', '7445'], ['2014.Plate14.17', 'feces', 'Twins', '3'], ['2014.Plate14.44', 'feces', 'Twins', '87'], ['2014.Plate11.86', 'feces', 'Twins', '23'], ['2014.High1.58', 'feces', 'Twins', '3'], ['2014.Plate7.55', 'feces', 'Twins', '49'], ['2014.Low1.62', 'feces', 'Twins', '14'], ['2014.Plate15.14', 'feces', 'Twins', '13'], ['2014.Plate9.57', 'feces', 'Twins', '13'], ['2014.Plate6.78', 'feces', 'Twins', '500'], ['2014.Low1.30', 'feces', 'Twins', '1331'], ['2014.Plate11.72', 'feces', 'Twins', '6090'], ['2014.Plate8.28', 'feces', 'Twins', '54'], ['2014.Plate8.7', 'feces', 'Twins', '29'], ['2014.High2.53', 'feces', 'Twins', '771'], ['2014.Plate12.52', 'feces', 'Twins', '365'], ['2014.Plate14.2', 'feces', 'Twins', '33893'], ['2014.Plate6.52', 'feces', 'Twins', '94'], ['2014.Plate11.70', 'feces', 'Twins', '508'], ['2014.Plate6.29', 'feces', 'Twins', '38'], ['2014.Low1.61', 'feces', 'Twins', '229'], ['2014.High2.51', 'feces', 'Twins', '21'], ['2014.Plate13.3', 'feces', 'Twins', '25'], ['2014.Plate12.50', 'feces', 'Twins', '11'], ['2014.High1.84', 'feces', 'Twins', '121'], ['2014.Plate8.4', 'feces', 'Twins', '82'], ['2014.Plate6.2', 'feces', 'Twins', '15'], ['2014.Plate13.2', 'feces', 'Twins', '3'], ['2014.High1.85', 'feces', 'Twins', '51'], ['2014.Plate11.22', 'feces', 'Twins', '6211'], ['2014.Plate13.9', 'feces', 'Twins', '12'], ['2014.Low1.41', 'feces', 'Twins', '67'], ['2014.Plate8.20', 'feces', 'Twins', '71'], ['2014.Low1.12', 'feces', 'Twins', '41'], ['2014.Plate7.25', 'feces', 'Twins', '1555'], ['2014.Plate6.70', 'feces', 'Twins', '4876'], ['2014.High2.71', 'feces', 'Twins', '809'], ['2014.High1.28', 'feces', 'Twins', '44'], ['2014.Plate8.59', 'feces', 'Twins', '4'], ['2014.High1.29', 'feces', 'Twins', '1'], ['2014.Plate11.5', 'feces', 'Twins', '19'], ['2014.Low2.33', 'feces', 'Twins', '73'], ['2014.Plate7.77', 'feces', 'Twins', '30'], ['2014.Low1.40', 'feces', 'Twins', '6'], ['2014.Plate6.8', 'feces', 'Twins', '2'], ['2014.Plate7.82', 'feces', 'Twins', '2'], ['2014.Plate14.18', 'feces', 'Twins', '1470'], ['2014.Low2.48', 'feces', 'Twins', '0'], ['2014.High2.6', 'feces', 'Twins', '52'], ['2014.Plate14.61', 'feces', 'Twins', '713'], ['2014.Low1.42', 'feces', 'Twins', '1055'], ['2014.Plate11.52', 'feces', 'Twins', '268'], ['2014.Plate13.81', 'feces', 'Twins', '151'], ['2014.Plate15.93', 'feces', 'Twins', '0'], ['2014.Plate7.27', 'feces', 'Twins', '1086'], ['2014.Low1.46', 'feces', 'Twins', '596'], ['2014.Plate8.8', 'feces', 'Twins', '119'], ['2014.Plate15.63', 'feces', 'Twins', '0'], ['2014.High2.2', 'feces', 'Twins', '27'], ['2014.High1.56', 'feces', 'Twins', '7'], ['2014.Plate9.87', 'feces', 'Twins', '6'], ['2014.Low2.35', 'feces', 'Twins', '6'], ['2014.Low2.34', 'feces', 'Twins', '6'], ['2014.Plate15.62', 'feces', 'Twins', '0'], ['2014.Plate8.9', 'feces', 'Twins', '75'], ['2014.Plate6.83', 'feces', 'Twins', '6'], ['2014.Plate8.26', 'feces', 'Twins', '19713'], ['2014.Plate11.56', 'feces', 'Twins', '61'], ['2014.Plate5.89', 'feces', 'Twins', '94'], ['2014.High2.3', 'feces', 'Twins', '23'], ['2014.Plate14.93', 'feces', 'Twins', '34'], ['2014.Plate7.72', 'feces', 'Twins', '15138'], ['2014.Plate6.27', 'feces', 'Twins', '505'], ['2014.Low2.90', 'feces', 'Twins', '247'], ['2014.Plate11.54', 'feces', 'Twins', '7'], ['2014.Low2.91', 'feces', 'Twins', '181'], ['2014.Plate12.80', 'feces', 'Twins', '335'], ['2014.Plate14.67', 'feces', 'Twins', '412'], ['2014.Plate7.73', 'feces', 'Twins', '365'], ['2014.Plate7.20', 'feces', 'Twins', '269'], ['10317.000017166', 'feces', 'AGP', '1497'], ['10317.000020350', 'feces', 'AGP', '2026'], ['10317.000008976', 'feces', 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['10317.000005918', 'feces', 'AGP', '2242'], ['10317.000032985', 'feces', 'AGP', '185'], ['10317.000029409', 'feces', 'AGP', '12290'], ['10317.000038480', 'feces', 'AGP', '0'], ['10317.000002893', 'feces', 'AGP', '127'], ['10317.000013411', 'feces', 'AGP', '19'], ['10317.000015568', 'feces', 'AGP', '737'], ['10317.000006940', 'feces', 'AGP', '13494'], ['10317.000014173', 'feces', 'AGP', '5504'], ['10317.000008952A', 'feces', 'AGP', '53'], ['10317.000014019', 'feces', 'AGP', '1791'], ['10317.000001130', 'feces', 'AGP', '13410'], ['10317.000016764', 'feces', 'AGP', '2'], ['10317.000035483', 'feces', 'AGP', '1524'], ['10317.000001196', 'feces', 'AGP', '5926'], ['10317.000009061', 'feces', 'AGP', '7611'], ['10317.000007171', 'feces', 'AGP', '1437'], ['10317.000014565', 'feces', 'AGP', '10'], ['10317.000009215', 'feces', 'AGP', '6495'], ['10317.000029175', 'feces', 'AGP', '1086'], ['10317.000003812', 'feces', 'AGP', '19'], ['10317.000030087', 'feces', 'AGP', '1236'], ['10317.000001008', 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['10317.000033394', 'feces', 'AGP', '2310'], ['10317.000027599', 'feces', 'AGP', '29203'], ['10317.000016355', 'feces', 'AGP', '173'], ['10317.000040057', 'feces', 'AGP', '9'], ['10317.000011985', 'feces', 'AGP', '4744'], ['10317.000003955', 'feces', 'AGP', '1883'], ['10317.000007037', 'feces', 'AGP', '51'], ['10317.000009127', 'feces', 'AGP', '32'], ['10317.000021149', 'feces', 'AGP', '7929'], ['10317.000014308', 'feces', 'AGP', '31'], ['10317.000001253', 'feces', 'AGP', '23891'], ['10317.000015264', 'feces', 'AGP', '360'], ['10317.000002114', 'feces', 'AGP', '25014'], ['10317.000028933', 'feces', 'AGP', '7495'], ['10317.000027355', 'feces', 'AGP', '8197'], ['10317.000012348', 'feces', 'AGP', '171'], ['10317.000028821', 'feces', 'AGP', '103']];\n",
       "  var axesNames = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5'];\n",
       "\n",
       "  var dm, ec;\n",
       "\n",
       "  function init() {\n",
       "    // Initialize the DecompositionModel\n",
       "    dm = new DecompositionModel(name, ids, coords, pct_var,\n",
       "                                md_headers, metadata, axesNames);\n",
       "    // Initialize the EmperorController\n",
       "    ec = new EmperorController(dm, 'emperor-notebook-0x4efe2aa9');\n",
       "  }\n",
       "\n",
       "  function animate() {\n",
       "    requestAnimationFrame(animate);\n",
       "    ec.render();\n",
       "  }\n",
       "  $(window).resize(function() {\n",
       "    ec.resize(div.innerWidth(), div.innerHeight());\n",
       "  });\n",
       "\n",
       "  $(function(){\n",
       "    init();\n",
       "    animate();\n",
       "\n",
       "  });\n",
       "\n",
       "}); // END REQUIRE.JS block\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<emperor.core.Emperor at 0x1149c36a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Emperor(with_bloom_pcoa, metadata_subset, remote=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "if ($(\"#emperor-css\").length == 0){{\n",
       "    $(\"head\").append([\n",
       "\n",
       "        '<link id=\"emperor-css\" rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/css/emperor.css\">',\n",
       "        '<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/css/jquery-ui.min.css\">',\n",
       "        '<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/css/slick.grid.min.css\">',\n",
       "        '<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/css/spectrum.min.css\">',\n",
       "        '<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/css/chosen.min.css\">',\n",
       "        '<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/css/jquery.contextMenu.min.css\">'\n",
       "    ]);\n",
       "}}\n",
       "</script>\n",
       "\n",
       "<div id='emperor-notebook-0x654ba585' style=\"position: relative; width:100%; height:500px;\">\n",
       "  <div class='loading' style=\"position: absolute;top: 50%;left: 50%;margin-left: -229px; margin-top: -59px; z-index: 10000;height:118px;width:458px;padding:0px\"><img src='https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/img/emperor.png' alt='Emperor resources missing. Expected them to be found in https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files'></div>\n",
       "</div>\n",
       "</div>\n",
       "\n",
       "<script type=\"text/javascript\">\n",
       "requirejs.config({\n",
       "// the left side is the module name, and the right side is the path\n",
       "// relative to the baseUrl attribute, do NOT include the .js extension\n",
       "'paths': {\n",
       "  /* jQuery */\n",
       "  'jquery': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/jquery-2.1.4.min',\n",
       "  'jqueryui': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/jquery-ui.min',\n",
       "  'jquery_drag': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/jquery.event.drag-2.2.min',\n",
       "\n",
       "  /* jQuery plugins */\n",
       "  'chosen': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/chosen.jquery.min',\n",
       "  'spectrum': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/spectrum.min',\n",
       "  'position': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/jquery.ui.position.min',\n",
       "  'contextmenu': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/jquery.contextMenu.min',\n",
       "\n",
       "  /* other libraries */\n",
       "  'underscore': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/underscore-min',\n",
       "  'chroma': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/chroma.min',\n",
       "  'filesaver': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/FileSaver.min',\n",
       "  'blob': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/Blob',\n",
       "  'd3': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/d3.min',\n",
       "\n",
       "  /* THREE.js and plugins */\n",
       "  'three': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.min',\n",
       "  'orbitcontrols': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.js-plugins/OrbitControls',\n",
       "  'projector': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.js-plugins/Projector',\n",
       "  'svgrenderer': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/three.js-plugins/SVGRenderer',\n",
       "\n",
       "  /* SlickGrid */\n",
       "  'slickcore': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.core.min',\n",
       "  'slickgrid': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.grid.min',\n",
       "  'slickformatters': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.editors.min',\n",
       "  'slickeditors': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/vendor/js/slick.formatters.min',\n",
       "\n",
       "  /* Emperor's objects */\n",
       "  'util': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/util',\n",
       "  'model': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/model',\n",
       "  'view': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/view',\n",
       "  'controller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/controller',\n",
       "  'draw': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/draw',\n",
       "  'scene3d': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/sceneplotview3d',\n",
       "  'abcviewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/abc-view-controller',\n",
       "  'viewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/view-controller',\n",
       "  'colorviewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/color-view-controller',\n",
       "  'visibilitycontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/visibility-controller',\n",
       "  'scaleviewcontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/scale-view-controller',\n",
       "  'shapecontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/shape-controller',\n",
       "  'axescontroller': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/axes-controller',\n",
       "  'shape-editor': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/shape-editor',\n",
       "  'color-editor': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/color-editor',\n",
       "  'scale-editor': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/scale-editor',\n",
       "  'shapes': 'https://cdn.rawgit.com/biocore/emperor/new-api/emperor/support_files/js/shapes'\n",
       "},\n",
       "/*\n",
       "   Libraries that are not AMD compatible need shim to declare their\n",
       "   dependencies.\n",
       " */\n",
       "'shim': {\n",
       "  'jquery_drag': {\n",
       "    'deps': ['jquery', 'jqueryui']\n",
       "  },\n",
       "  'chosen': {\n",
       "    'deps': ['jquery'],\n",
       "    'exports': 'jQuery.fn.chosen'\n",
       "  },\n",
       "  'contextmenu' : {\n",
       "    'deps': ['jquery', 'jqueryui', 'position']\n",
       "  },\n",
       "  'filesaver' : {\n",
       "    'deps': ['blob']\n",
       "  },\n",
       "  'orbitcontrols': {\n",
       "    'deps': ['three']\n",
       "  },\n",
       "  'projector': {\n",
       "    'deps': ['three']\n",
       "  },\n",
       "  'svgrenderer': {\n",
       "    'deps': ['orbitcontrols', 'projector']\n",
       "  },\n",
       "'slickcore': ['jqueryui'],\n",
       "'slickgrid': ['slickcore', 'jquery_drag', 'slickformatters',\n",
       "              'slickeditors']\n",
       "}\n",
       "});\n",
       "\n",
       "requirejs(\n",
       "[\"jquery\", \"model\", \"controller\"],\n",
       "function($, model, EmperorController) {\n",
       "  var DecompositionModel = model.DecompositionModel;\n",
       "\n",
       "  var div = $('#emperor-notebook-0x654ba585');\n",
       "\n",
       "  var ids = ['Cowan.Stool', 'Shadyside.Stool', 'Gigi.Stool', 'Zambom.Stool', 'Dias.Stool', 'Trimmer.Stool', 'Fermanagh.Stool', 'Caryll.Stool', 'Azalea.Stool', 'Lassell.Stool', 'Turkey.Stool', 'Pasho.Stool', 'Nobscot.Stool', 'Elmira.Stool', 'Erlandson.Stool', 'Innitou.Stool', 'Pinrock.Stool', 'Overton.Stool', 'Salti.Stool', 'Brae.Stool', 'Hallets.Stool', 'Stannes.Stool', 'Eisenhower.Stool', 'Balcom.Stool', 'Lonergan.Stool', 'Hager.Stool', 'Bows.Stool', 'Tewmac.Stool', 'Northwind.Stool', 'Watoguadoc.Stool', 'Mariposa.Stool.1', 'Digregorio.Stool', 'Noel.Stool', 'Maryjane.Stool', 'Goddu.Stool', 'Milne.Stool', 'Coachmen.Stool', 'Purchade.Stool', 'Buckinghan.Stool', 'Innkeeper.Stool', 'Mariposa.Stool.2', 'Foothill.Stool', 'Lippokd.Stool', 'Mariposa.Stool.3', 'Sylvanus.Stool', 'Witch.Stool', 'Westbora.Stool', 'Bjorkland.Stool', 'Slades.Stool', 'Elmavenue.Stool', 'January.Stool', 'Devenwood.Stool', 'Caravel.Stool', 'Sandale.Stool', 'Winifreds.Stool', 'Claudett.Stool', 'Nyack.Stool', 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'2014.Plate9.33', '2014.Plate14.24', '2014.Plate10.10', '2014.Plate9.9', '2014.Plate12.65', '2014.High1.15', '2014.Low2.83', '2014.Plate14.26', '2014.Plate11.47', '2014.Plate6.67', '2014.Plate13.33', '2014.Plate6.66', '2014.Low1.88', '2014.Plate5.1', '2014.Plate13.95', '2014.Plate8.36', '2014.Plate7.65', '2014.Plate7.36', '2014.Plate5.43', '2014.Low2.58', '2014.Plate10.4', '2014.Plate10.16', '2014.High1.9', '2014.Plate15.25', '2014.Plate10.17', '2014.High1.10', '2014.Plate13.64', '2014.Low1.8', '2014.Plate11.68', '2014.Low2.73', '2014.High1.69', '2014.Plate5.68', '2014.High2.30', '2014.Plate13.34', '2014.Low2.70', '2014.Plate12.32', '2014.Low1.29', '2014.High1.39', '2014.Plate15.8', '2014.Plate5.38', '2014.Plate14.86', '2014.Plate7.92', '2014.Plate14.73', '2014.Plate15.75', '2014.Plate14.20', '2014.Plate9.37', '2014.Low2.85', '2014.High1.40', '2014.Plate8.10', '2014.Plate6.39', '2014.Plate15.54', '2014.Low1.71', '2014.Plate10.35', '2014.Plate6.40', '2014.Plate14.78', '2014.Plate13.15', 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[-0.2025482567464848, 0.08567561703866218, -0.22215364073507124, -0.12069699955846949, 0.21299718877183013], [0.19251782123495562, 0.016868194250106196, -0.17604978479247027, 0.03622819725825064, -0.04744648650682389], [0.017060276317142423, -0.16603986038065657, -0.1491300255150548, 0.10886486521151077, -0.009397923136700152], [-0.04085414505037012, 0.02342821138503991, 0.061601208382788625, 0.014144376997632957, -0.11418993868811206], [-0.14426712690207028, -0.14786917131099644, -0.02277098785058098, 0.34502948640032055, 0.003650058197393093], [-0.02420774609825367, -0.27358598801736994, -0.11785744248279137, 0.03235461504650809, 0.07699581230417248], [-0.035402610368672545, -0.17206878182014354, 0.12707598318452995, 0.07940846084697295, -0.06608590918041961]];\n",
       "  var pct_var = [9.0332397116706, 4.514055967430541, 3.956176744945482, 3.5175520624016436, 2.923864183508601];\n",
       "  var md_headers = ['SampleID', 'ENV_MATTER', 'study_acronym', 'bloom_counts'];\n",
       "  var metadata = [['Cowan.Stool', 'feces', 'PGP', '0'], ['Shadyside.Stool', 'feces', 'PGP', '3445'], ['Gigi.Stool', 'feces', 'PGP', '780'], ['Zambom.Stool', 'feces', 'PGP', '54'], ['Dias.Stool', 'feces', 'PGP', '22'], ['Trimmer.Stool', 'feces', 'PGP', '2'], ['Fermanagh.Stool', 'feces', 'PGP', '37'], ['Caryll.Stool', 'feces', 'PGP', '0'], ['Azalea.Stool', 'feces', 'PGP', '2'], ['Lassell.Stool', 'feces', 'PGP', '0'], ['Turkey.Stool', 'feces', 'PGP', '0'], ['Pasho.Stool', 'feces', 'PGP', '16'], ['Nobscot.Stool', 'feces', 'PGP', '53'], ['Elmira.Stool', 'feces', 'PGP', '0'], ['Erlandson.Stool', 'feces', 'PGP', '6'], ['Innitou.Stool', 'feces', 'PGP', '53'], ['Pinrock.Stool', 'feces', 'PGP', '579'], ['Overton.Stool', 'feces', 'PGP', '0'], ['Salti.Stool', 'feces', 'PGP', '0'], ['Brae.Stool', 'feces', 'PGP', '175'], ['Hallets.Stool', 'feces', 'PGP', '2'], ['Stannes.Stool', 'feces', 'PGP', '0'], ['Eisenhower.Stool', 'feces', 'PGP', '13'], ['Balcom.Stool', 'feces', 'PGP', '580'], ['Lonergan.Stool', 'feces', 'PGP', '2027'], ['Hager.Stool', 'feces', 'PGP', '0'], ['Bows.Stool', 'feces', 'PGP', '694'], ['Tewmac.Stool', 'feces', 'PGP', '0'], ['Northwind.Stool', 'feces', 'PGP', '3'], ['Watoguadoc.Stool', 'feces', 'PGP', '4'], ['Mariposa.Stool.1', 'feces', 'PGP', '0'], ['Digregorio.Stool', 'feces', 'PGP', '13'], ['Noel.Stool', 'feces', 'PGP', '0'], ['Maryjane.Stool', 'feces', 'PGP', '0'], ['Goddu.Stool', 'feces', 'PGP', '1440'], ['Milne.Stool', 'feces', 'PGP', '5'], ['Coachmen.Stool', 'feces', 'PGP', '1873'], ['Purchade.Stool', 'feces', 'PGP', '2955'], ['Buckinghan.Stool', 'feces', 'PGP', '7'], ['Innkeeper.Stool', 'feces', 'PGP', '0'], ['Mariposa.Stool.2', 'feces', 'PGP', '15'], ['Foothill.Stool', 'feces', 'PGP', '0'], ['Lippokd.Stool', 'feces', 'PGP', '0'], ['Mariposa.Stool.3', 'feces', 'PGP', '0'], ['Sylvanus.Stool', 'feces', 'PGP', '9'], ['Witch.Stool', 'feces', 'PGP', '91'], ['Westbora.Stool', 'feces', 'PGP', '245'], ['Bjorkland.Stool', 'feces', 'PGP', '12'], ['Slades.Stool', 'feces', 'PGP', '2'], ['Elmavenue.Stool', 'feces', 'PGP', '0'], ['January.Stool', 'feces', 'PGP', '0'], ['Devenwood.Stool', 'feces', 'PGP', '6'], ['Caravel.Stool', 'feces', 'PGP', '0'], ['Sandale.Stool', 'feces', 'PGP', '186'], ['Winifreds.Stool', 'feces', 'PGP', '0'], ['Claudett.Stool', 'feces', 'PGP', '13'], ['Nyack.Stool', 'feces', 'PGP', '97'], ['Larcridge.Stool', 'feces', 'PGP', '2'], ['Janis.Stool', 'feces', 'PGP', '4'], ['Vinedale.Stool', 'feces', 'PGP', '867'], ['Wilbraview.Stool', 'feces', 'PGP', '169'], ['Bulls.Stool', 'feces', 'PGP', '55'], ['Gibbon.Stool', 'feces', 'PGP', '0'], ['Trafford.Stool', 'feces', 'PGP', '18'], ['Willowbe.Stool', 'feces', 'PGP', '440'], ['Menekish.Stool', 'feces', 'PGP', '0'], ['Misty.Stool', 'feces', 'PGP', '6'], ['Malfa.Stool', 'feces', 'PGP', '27'], ['Patmos.Stool', 'feces', 'PGP', '10'], ['Ronald.Stool', 'feces', 'PGP', '159'], ['Delmore.Stool', 'feces', 'PGP', '919'], ['Netta.Stool', 'feces', 'PGP', '14'], ['Starratt.Stool', 'feces', 'PGP', '9'], ['Woodfin.Stool', 'feces', 'PGP', '528'], ['Cornstalk.Stool', 'feces', 'PGP', '14'], ['Meadowlands.Stool', 'feces', 'PGP', '1095'], ['Charmark.Stool', 'feces', 'PGP', '0'], ['Cedarhurst.Stool', 'feces', 'PGP', '0'], ['Padanaram.Stool', 'feces', 'PGP', '0'], ['Burne.Stool', 'feces', 'PGP', '0'], ['Postscript.Stool', 'feces', 'PGP', '12'], ['Edenfield.Stool', 'feces', 'PGP', '0'], ['1481.PO1.1.T0', 'feces', 'ERC', '4'], ['1481.NA.12.T8', 'feces', 'ERC', '11'], ['1481.PO6.2.T0', 'feces', 'ERC', '7'], ['1481.NA.20.T0', 'feces', 'ERC', '7'], ['1481.NA.01.T0', 'feces', 'ERC', '5'], ['1481.NA.23.T8', 'feces', 'ERC', '0'], ['1481.PO5.5.T0', 'feces', 'ERC', '3'], ['1481.PO4.9.T0', 'feces', 'ERC', '31'], ['1481.NA.02.T8', 'feces', 'ERC', '5'], ['1481.PO1.15.T0', 'feces', 'ERC', '7'], ['1481.PO1.2.T8', 'feces', 'ERC', '13'], ['1481.PO4.4.T8', 'feces', 'ERC', '116'], ['1481.PO6.1.T8', 'feces', 'ERC', '5'], ['1481.PO1.17.T8', 'feces', 'ERC', '0'], ['1481.PO2.4.T8', 'feces', 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['10317.000029409', 'feces', 'AGP', '12290'], ['10317.000038480', 'feces', 'AGP', '0'], ['10317.000002893', 'feces', 'AGP', '127'], ['10317.000013411', 'feces', 'AGP', '19'], ['10317.000015568', 'feces', 'AGP', '737'], ['10317.000006940', 'feces', 'AGP', '13494'], ['10317.000014173', 'feces', 'AGP', '5504'], ['10317.000008952A', 'feces', 'AGP', '53'], ['10317.000014019', 'feces', 'AGP', '1791'], ['10317.000001130', 'feces', 'AGP', '13410'], ['10317.000016764', 'feces', 'AGP', '2'], ['10317.000035483', 'feces', 'AGP', '1524'], ['10317.000001196', 'feces', 'AGP', '5926'], ['10317.000009061', 'feces', 'AGP', '7611'], ['10317.000007171', 'feces', 'AGP', '1437'], ['10317.000014565', 'feces', 'AGP', '10'], ['10317.000009215', 'feces', 'AGP', '6495'], ['10317.000029175', 'feces', 'AGP', '1086'], ['10317.000003812', 'feces', 'AGP', '19'], ['10317.000030087', 'feces', 'AGP', '1236'], ['10317.000001008', 'feces', 'AGP', '9757'], ['10317.000002051', 'feces', 'AGP', '9993'], ['10317.000021692', 'feces', 'AGP', '2873'], ['10317.000010908', 'feces', 'AGP', '30'], ['10317.000029400', 'feces', 'AGP', '7925'], ['10317.000031441', 'feces', 'AGP', '9760'], ['10317.000001398', 'feces', 'AGP', '4556'], ['10317.000006808', 'feces', 'AGP', '14'], ['10317.000013419', 'feces', 'AGP', '3'], ['10317.000022395', 'feces', 'AGP', '12'], ['10317.000038095', 'feces', 'AGP', '4411'], ['10317.000005910', 'feces', 'AGP', '16501'], ['10317.000001589', 'feces', 'AGP', '8'], ['10317.000002952', 'feces', 'AGP', '8581'], ['10317.000023841', 'feces', 'AGP', '7'], ['10317.000014294', 'feces', 'AGP', '452'], ['10317.000014155', 'feces', 'AGP', '3998'], ['10317.000027673', 'feces', 'AGP', '765'], ['10317.000021162', 'feces', 'AGP', '4'], ['10317.000027598', 'feces', 'AGP', '3209'], ['10317.000006620', 'feces', 'AGP', '5181'], ['10317.000033394', 'feces', 'AGP', '2310'], ['10317.000027599', 'feces', 'AGP', '29203'], ['10317.000016355', 'feces', 'AGP', '173'], ['10317.000040057', 'feces', 'AGP', '9'], ['10317.000011985', 'feces', 'AGP', '4744'], ['10317.000003955', 'feces', 'AGP', '1883'], ['10317.000007037', 'feces', 'AGP', '51'], ['10317.000009127', 'feces', 'AGP', '32'], ['10317.000021149', 'feces', 'AGP', '7929'], ['10317.000014308', 'feces', 'AGP', '31'], ['10317.000001253', 'feces', 'AGP', '23891'], ['10317.000015264', 'feces', 'AGP', '360'], ['10317.000002114', 'feces', 'AGP', '25014'], ['10317.000028933', 'feces', 'AGP', '7495'], ['10317.000027355', 'feces', 'AGP', '8197'], ['10317.000012348', 'feces', 'AGP', '171'], ['10317.000028821', 'feces', 'AGP', '103']];\n",
       "  var axesNames = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5'];\n",
       "\n",
       "  var dm, ec;\n",
       "\n",
       "  function init() {\n",
       "    // Initialize the DecompositionModel\n",
       "    dm = new DecompositionModel(name, ids, coords, pct_var,\n",
       "                                md_headers, metadata, axesNames);\n",
       "    // Initialize the EmperorController\n",
       "    ec = new EmperorController(dm, 'emperor-notebook-0x654ba585');\n",
       "  }\n",
       "\n",
       "  function animate() {\n",
       "    requestAnimationFrame(animate);\n",
       "    ec.render();\n",
       "  }\n",
       "  $(window).resize(function() {\n",
       "    ec.resize(div.innerWidth(), div.innerHeight());\n",
       "  });\n",
       "\n",
       "  $(function(){\n",
       "    init();\n",
       "    animate();\n",
       "\n",
       "  });\n",
       "\n",
       "}); // END REQUIRE.JS block\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<emperor.core.Emperor at 0x1110cdc50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Emperor(without_bloom_pcoa, metadata_subset, remote=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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