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   "source": [
    "This notebook creates a jackknifed PCoA plot based on multiple rarefactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   "outputs": [],
   "source": [
    "from emperor import Emperor, nbinstall\n",
    "\n",
    "nbinstall()\n",
    "\n",
    "from skbio.stats.ordination import pcoa\n",
    "from skbio.diversity import beta_diversity\n",
    "\n",
    "from biom import load_table\n",
    "\n",
    "# pydata/scipy\n",
    "import pandas as pd, numpy as np\n",
    "from scipy.spatial import procrustes\n",
    "\n",
    "def load_mf(fn, index='#SampleID'):\n",
    "    _df = pd.read_csv(fn, sep='\\t', dtype=str, keep_default_na=False, na_values=[])\n",
    "    _df.set_index(index, inplace=True)\n",
    "    return _df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We are going to load data from [Fierer et al. 2010](http://www.pnas.org/content/107/14/6477.full) (the data was retrieved from study [232](https://qiita.ucsd.edu/study/description/232) in [Qiita](https://qiita.ucsd.edu), remember you need to be logged in to access the study)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "mf = load_mf('keyboard/mapping-file.txt')\n",
    "bt = load_table('keyboard/otu-table.biom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We'll create five different distance matrices and compare them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ordinations = []\n",
    "distances = ['jaccard', 'dice', 'russellrao']\n",
    "rarefied = bt.subsample(1000)\n",
    "\n",
    "for r in range(len(distances)):\n",
    "    data = np.array([rarefied.data(i) for i in rarefied.ids()], dtype='int64')\n",
    "    \n",
    "    res = pcoa(beta_diversity(distances[r], data, rarefied.ids()))\n",
    "    \n",
    "    ordinations.append(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Procrustes plots need a *master* set of coordinates where there rest of the matrices will be fitted around."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "master = ordinations[0]\n",
    "fitted_ordinations = []\n",
    "\n",
    "for i in range(1, len(ordinations)):\n",
    "    reference, matrix, _ = procrustes(master.samples.values, ordinations[i].samples.values)\n",
    "    \n",
    "    if i == 0:\n",
    "        master.samples.iloc[:] = reference\n",
    "    \n",
    "    samples = pd.DataFrame(index=ordinations[i].samples.index,\n",
    "                           columns=ordinations[i].samples.columns,\n",
    "                           data=matrix)\n",
    "    ordinations[i].samples = samples\n",
    "    fitted_ordinations.append(ordinations[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "If you want to share your notebook via GitHub use `remote=True` and make sure you share your notebook using nbviewer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "viz = Emperor(master, mf, procrustes=fitted_ordinations, remote=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Lastly, we set the name of the distances as the `procrustes_names` attribute so we can differentiate them in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "viz.procrustes_names = distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "viz"
   ]
  }
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