tests/workshop-abba-baba.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## (&#x1F4D7;) ipyrad Cookbook: `abba-baba` admixture tests\n",
    "\n",
    "The `ipyrad.analysis` Python module includes functions to calculate abba-baba admixture statistics (including several variants of these measures), to perform signifance tests, and to produce plots of results. All code in this notebook is written in Python, which you can copy/paste into an IPython terminal to execute, or, preferably, run in a Jupyter notebook like this one. See the other analysis cookbooks for [instructions](http://ipyrad.readthedocs.io/analysis.html) on using Jupyter notebooks. All of the software required for this tutorial is included with `ipyrad` (v.6.12+). Finally, we've written functions to generate plots for summarizing and interpreting results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ipyrad.analysis as ipa\n",
    "import ipyparallel as ipp\n",
    "import toytree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to cluster\n",
    "The code can be easily parallelized across cores on your machine, or many nodes of an HPC cluster using the `ipyparallel` library (see our [ipyparallel tutorial]()). An `ipcluster` instance must be started for you to connect to, which can be started by running `'ipcluster start'` in a terminal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipyclient = ipp.Client()\n",
    "len(ipyclient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in your .loci data file and a tree hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "locifile = \"./analysis-ipyrad/pedic_outfiles/pedic.loci\"\n",
    "newick = \"./analysis-raxml/RAxML_bestTree.pedic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:rgb(40%,76.1%,64.7%);stroke-opacity:1.0\"><circle cx=\"157.91743970315397\" cy=\"286.76470588235293\" r=\"0.0\"></circle></g></g></g><g class=\"toyplot-mark-Scatterplot\" id=\"t95f7baeffffc4f63942dd4162dbf2d54\" style=\"\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 0\n",
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       "dist: 0.00223\n",
       "support: 100</title><circle cx=\"140.00066260270341\" cy=\"214.27696078431373\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 10\n",
       "name: 10\n",
       "dist: 0.0007389\n",
       "support: 100</title><circle cx=\"122.08388550225284\" cy=\"260.87622549019608\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 11\n",
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       "dist: 0.00617527\n",
       "support: 100</title><circle cx=\"140.00066260270341\" cy=\"276.40931372549016\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 12\n",
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       "support: 100</title><circle cx=\"157.91743970315397\" cy=\"38.235294117647037\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 13\n",
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       "support: 100</title><circle cx=\"157.91743970315397\" cy=\"58.946078431372541\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 14\n",
       "name: 41478_cyathophylloides\n",
       "dist: 5.28218e-05\n",
       "support: 100</title><circle cx=\"157.91743970315397\" cy=\"79.65686274509801\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 15\n",
       "name: 41954_cyathophylloides\n",
       "dist: 8.88803e-05\n",
       "support: 100</title><circle cx=\"157.91743970315397\" cy=\"100.36764705882352\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 16\n",
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       "support: 100</title><circle cx=\"157.91743970315397\" cy=\"121.07843137254901\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 17\n",
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       "support: 100</title><circle cx=\"157.91743970315397\" cy=\"141.7892156862745\" r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:1.0;stroke:none\"><title>idx: 18\n",
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     "metadata": {},
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   ],
   "source": [
    "## parse the newick tree, re-root it, and plot it.\n",
    "tre = toytree.tree(newick=newick)\n",
    "tre.root(wildcard=\"prz\")\n",
    "tre.draw(node_labels=True, node_size=10);\n",
    "\n",
    "## store rooted tree back into a newick string.\n",
    "newick = tre.tree.write()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Short tutorial: calculating abba-baba statistics\n",
    "To give a gist of what this code can do, here is a quick tutorial version, each step of which we explain in greater detail below. We first create a `'baba'` analysis object that is linked to our data file, in this example we name the variable **bb**. Then we tell it which tests to perform, here by automatically generating a number of tests using the `generate_tests_from_tree()` function. And finally, we calculate the results and plot them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44 tests generated from tree\n",
      "[####################] 100%  calculating D-stats  | 0:02:35 |  \n"
     ]
    }
   ],
   "source": [
    "## create a baba object linked to a data file and newick tree\n",
    "bb = ipa.baba(data=locifile, newick=newick)\n",
    "\n",
    "## generate all possible abba-baba tests meeting a set of constraints\n",
    "bb.generate_tests_from_tree(\n",
    "    constraint_dict={\n",
    "        \"p4\": [\"32082_przewalskii\", \"33588_przewalskii\"],\n",
    "        \"p3\": [\"33413_thamno\"],\n",
    "    })\n",
    "\n",
    "## run all tests linked to bb \n",
    "bb.run(ipyclient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dstat</th>\n",
       "      <th>bootmean</th>\n",
       "      <th>bootstd</th>\n",
       "      <th>Z</th>\n",
       "      <th>ABBA</th>\n",
       "      <th>BABA</th>\n",
       "      <th>nloci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.099</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.043</td>\n",
       "      <td>2.311</td>\n",
       "      <td>258.844</td>\n",
       "      <td>315.438</td>\n",
       "      <td>6718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.114</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.039</td>\n",
       "      <td>2.886</td>\n",
       "      <td>303.938</td>\n",
       "      <td>381.906</td>\n",
       "      <td>8221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.089</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.042</td>\n",
       "      <td>2.131</td>\n",
       "      <td>258.750</td>\n",
       "      <td>309.500</td>\n",
       "      <td>6694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.101</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>0.040</td>\n",
       "      <td>2.544</td>\n",
       "      <td>302.188</td>\n",
       "      <td>370.188</td>\n",
       "      <td>8173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.098</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.042</td>\n",
       "      <td>2.331</td>\n",
       "      <td>238.688</td>\n",
       "      <td>290.750</td>\n",
       "      <td>6347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>0.040</td>\n",
       "      <td>3.079</td>\n",
       "      <td>279.375</td>\n",
       "      <td>356.812</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.068</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1.907</td>\n",
       "      <td>408.969</td>\n",
       "      <td>356.719</td>\n",
       "      <td>8899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.080</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.037</td>\n",
       "      <td>2.178</td>\n",
       "      <td>390.312</td>\n",
       "      <td>332.312</td>\n",
       "      <td>8390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.089</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.044</td>\n",
       "      <td>2.022</td>\n",
       "      <td>309.500</td>\n",
       "      <td>258.750</td>\n",
       "      <td>6694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.098</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.045</td>\n",
       "      <td>2.173</td>\n",
       "      <td>290.750</td>\n",
       "      <td>238.688</td>\n",
       "      <td>6347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.101</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.039</td>\n",
       "      <td>2.610</td>\n",
       "      <td>370.188</td>\n",
       "      <td>302.188</td>\n",
       "      <td>8173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.122</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.039</td>\n",
       "      <td>3.112</td>\n",
       "      <td>356.812</td>\n",
       "      <td>279.375</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.129</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.031</td>\n",
       "      <td>4.129</td>\n",
       "      <td>474.500</td>\n",
       "      <td>366.031</td>\n",
       "      <td>9390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.142</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.035</td>\n",
       "      <td>4.073</td>\n",
       "      <td>457.250</td>\n",
       "      <td>343.625</td>\n",
       "      <td>9151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.119</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.033</td>\n",
       "      <td>3.554</td>\n",
       "      <td>434.500</td>\n",
       "      <td>342.062</td>\n",
       "      <td>9048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.218</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.028</td>\n",
       "      <td>7.666</td>\n",
       "      <td>560.750</td>\n",
       "      <td>359.958</td>\n",
       "      <td>9455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.278</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.032</td>\n",
       "      <td>8.650</td>\n",
       "      <td>567.500</td>\n",
       "      <td>320.750</td>\n",
       "      <td>9113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.178</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.033</td>\n",
       "      <td>5.322</td>\n",
       "      <td>501.938</td>\n",
       "      <td>350.500</td>\n",
       "      <td>9211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.160</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.036</td>\n",
       "      <td>4.387</td>\n",
       "      <td>403.750</td>\n",
       "      <td>292.438</td>\n",
       "      <td>7909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.176</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.035</td>\n",
       "      <td>5.043</td>\n",
       "      <td>491.625</td>\n",
       "      <td>344.312</td>\n",
       "      <td>9105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.164</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.033</td>\n",
       "      <td>4.987</td>\n",
       "      <td>477.984</td>\n",
       "      <td>343.266</td>\n",
       "      <td>9526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.057</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.033</td>\n",
       "      <td>1.737</td>\n",
       "      <td>438.625</td>\n",
       "      <td>391.469</td>\n",
       "      <td>9671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.034</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.939</td>\n",
       "      <td>357.750</td>\n",
       "      <td>334.094</td>\n",
       "      <td>8268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.050</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.033</td>\n",
       "      <td>1.528</td>\n",
       "      <td>418.812</td>\n",
       "      <td>379.156</td>\n",
       "      <td>9533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.173</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.034</td>\n",
       "      <td>5.070</td>\n",
       "      <td>436.375</td>\n",
       "      <td>307.625</td>\n",
       "      <td>9282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.044</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.035</td>\n",
       "      <td>1.263</td>\n",
       "      <td>401.969</td>\n",
       "      <td>368.031</td>\n",
       "      <td>9394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.014</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.373</td>\n",
       "      <td>324.750</td>\n",
       "      <td>315.688</td>\n",
       "      <td>8051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.039</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.035</td>\n",
       "      <td>1.114</td>\n",
       "      <td>389.625</td>\n",
       "      <td>360.062</td>\n",
       "      <td>9272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.188</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.036</td>\n",
       "      <td>5.257</td>\n",
       "      <td>460.688</td>\n",
       "      <td>314.875</td>\n",
       "      <td>9172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.080</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.036</td>\n",
       "      <td>2.252</td>\n",
       "      <td>420.906</td>\n",
       "      <td>358.469</td>\n",
       "      <td>9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.074</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.037</td>\n",
       "      <td>2.003</td>\n",
       "      <td>349.438</td>\n",
       "      <td>301.250</td>\n",
       "      <td>7964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.067</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1.866</td>\n",
       "      <td>395.875</td>\n",
       "      <td>346.438</td>\n",
       "      <td>9171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.086</td>\n",
       "      <td>-0.087</td>\n",
       "      <td>0.030</td>\n",
       "      <td>2.923</td>\n",
       "      <td>373.188</td>\n",
       "      <td>443.708</td>\n",
       "      <td>9652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.032</td>\n",
       "      <td>3.738</td>\n",
       "      <td>367.552</td>\n",
       "      <td>465.927</td>\n",
       "      <td>9537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-0.173</td>\n",
       "      <td>-0.174</td>\n",
       "      <td>0.034</td>\n",
       "      <td>5.129</td>\n",
       "      <td>307.625</td>\n",
       "      <td>436.375</td>\n",
       "      <td>9282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.188</td>\n",
       "      <td>-0.190</td>\n",
       "      <td>0.035</td>\n",
       "      <td>5.326</td>\n",
       "      <td>314.875</td>\n",
       "      <td>460.688</td>\n",
       "      <td>9172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-0.044</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.035</td>\n",
       "      <td>1.263</td>\n",
       "      <td>368.031</td>\n",
       "      <td>401.969</td>\n",
       "      <td>9394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>-0.080</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>0.037</td>\n",
       "      <td>2.182</td>\n",
       "      <td>358.469</td>\n",
       "      <td>420.906</td>\n",
       "      <td>9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>-0.014</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.382</td>\n",
       "      <td>315.688</td>\n",
       "      <td>324.750</td>\n",
       "      <td>8051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.074</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1.854</td>\n",
       "      <td>301.250</td>\n",
       "      <td>349.438</td>\n",
       "      <td>7964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-0.039</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.035</td>\n",
       "      <td>1.114</td>\n",
       "      <td>360.062</td>\n",
       "      <td>389.625</td>\n",
       "      <td>9272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-0.067</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1.858</td>\n",
       "      <td>346.438</td>\n",
       "      <td>395.875</td>\n",
       "      <td>9171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>-0.130</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>0.040</td>\n",
       "      <td>3.215</td>\n",
       "      <td>296.938</td>\n",
       "      <td>385.750</td>\n",
       "      <td>8044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>-0.123</td>\n",
       "      <td>-0.124</td>\n",
       "      <td>0.036</td>\n",
       "      <td>3.434</td>\n",
       "      <td>338.938</td>\n",
       "      <td>434.375</td>\n",
       "      <td>9235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dstat  bootmean  bootstd      Z     ABBA     BABA  nloci\n",
       "0  -0.099    -0.097    0.043  2.311  258.844  315.438   6718\n",
       "1  -0.114    -0.115    0.039  2.886  303.938  381.906   8221\n",
       "2  -0.089    -0.090    0.042  2.131  258.750  309.500   6694\n",
       "3  -0.101    -0.102    0.040  2.544  302.188  370.188   8173\n",
       "4  -0.098    -0.097    0.042  2.331  238.688  290.750   6347\n",
       "5  -0.122    -0.123    0.040  3.079  279.375  356.812   7725\n",
       "6   0.068     0.068    0.036  1.907  408.969  356.719   8899\n",
       "7   0.080     0.082    0.037  2.178  390.312  332.312   8390\n",
       "8   0.089     0.091    0.044  2.022  309.500  258.750   6694\n",
       "9   0.098     0.098    0.045  2.173  290.750  238.688   6347\n",
       "10  0.101     0.100    0.039  2.610  370.188  302.188   8173\n",
       "11  0.122     0.122    0.039  3.112  356.812  279.375   7725\n",
       "12  0.129     0.128    0.031  4.129  474.500  366.031   9390\n",
       "13  0.142     0.144    0.035  4.073  457.250  343.625   9151\n",
       "14  0.119     0.118    0.033  3.554  434.500  342.062   9048\n",
       "15  0.218     0.217    0.028  7.666  560.750  359.958   9455\n",
       "16  0.278     0.277    0.032  8.650  567.500  320.750   9113\n",
       "17  0.178     0.177    0.033  5.322  501.938  350.500   9211\n",
       "18  0.160     0.159    0.036  4.387  403.750  292.438   7909\n",
       "19  0.176     0.177    0.035  5.043  491.625  344.312   9105\n",
       "20  0.164     0.163    0.033  4.987  477.984  343.266   9526\n",
       "21  0.057     0.057    0.033  1.737  438.625  391.469   9671\n",
       "22  0.034     0.034    0.036  0.939  357.750  334.094   8268\n",
       "23  0.050     0.051    0.033  1.528  418.812  379.156   9533\n",
       "24  0.173     0.173    0.034  5.070  436.375  307.625   9282\n",
       "25  0.044     0.043    0.035  1.263  401.969  368.031   9394\n",
       "26  0.014     0.015    0.038  0.373  324.750  315.688   8051\n",
       "27  0.039     0.040    0.035  1.114  389.625  360.062   9272\n",
       "28  0.188     0.189    0.036  5.257  460.688  314.875   9172\n",
       "29  0.080     0.079    0.036  2.252  420.906  358.469   9292\n",
       "30  0.074     0.074    0.037  2.003  349.438  301.250   7964\n",
       "31  0.067     0.066    0.036  1.866  395.875  346.438   9171\n",
       "32 -0.086    -0.087    0.030  2.923  373.188  443.708   9652\n",
       "33 -0.118    -0.118    0.032  3.738  367.552  465.927   9537\n",
       "34 -0.173    -0.174    0.034  5.129  307.625  436.375   9282\n",
       "35 -0.188    -0.190    0.035  5.326  314.875  460.688   9172\n",
       "36 -0.044    -0.045    0.035  1.263  368.031  401.969   9394\n",
       "37 -0.080    -0.082    0.037  2.182  358.469  420.906   9292\n",
       "38 -0.014    -0.014    0.037  0.382  315.688  324.750   8051\n",
       "39 -0.074    -0.077    0.040  1.854  301.250  349.438   7964\n",
       "40 -0.039    -0.039    0.035  1.114  360.062  389.625   9272\n",
       "41 -0.067    -0.067    0.036  1.858  346.438  395.875   9171\n",
       "42 -0.130    -0.131    0.040  3.215  296.938  385.750   8044\n",
       "43 -0.123    -0.124    0.036  3.434  338.938  434.375   9235"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## save the results table to a csv file\n",
    "bb.results_table.to_csv(\"bb.abba-baba.csv\", sep=\"\\t\")\n",
    "\n",
    "## show the results table in notebook\n",
    "bb.results_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting and interpreting results\n",
    "Interpreting the results of D-statistic tests is actually *very* complicated. You cannot treat every test as if it were independent because introgression between one pair of species may cause one or both of those species to *appear* as if they have also introgressed with other taxa in your data set. This problem is described in great detail in [this paper (Eaton et al. 2015)](http://onlinelibrary.wiley.com/doi/10.1111/evo.12758/abstract). A good place to start, then, is to perform many tests and focus on those which have the strongest signal of admixture. Then, perform additional tests, such as `partitioned D-statistics` (described further below) to tease apart whether a single or multiple introgression events are likely to have occurred. \n",
    "\n",
    "In the example plot below we find evidence of admixture between the sample **33413_thamno** (black) with several other samples, but the signal is strongest with respect to **30556_thamno** (test 33). It also appears that admixture is consistently detected with samples of (**40578_rex** & **35855_rex**) when contrasted against **35236_rex** (tests 14, 20, 29, 32)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "          function save_csv(data_table)\n",
       "          {\n",
       "            var uri = \"data:text/csv;charset=utf-8,\";\n",
       "            uri += data_table.names.join(\",\") + \"\\n\";\n",
       "            for(var i = 0; i != data_table.columns[0].length; ++i)\n",
       "            {\n",
       "              for(var j = 0; j != data_table.columns.length; ++j)\n",
       "              {\n",
       "                if(j)\n",
       "                  uri += \",\";\n",
       "                uri += data_table.columns[j][i];\n",
       "              }\n",
       "              uri += \"\\n\";\n",
       "            }\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = data_table.filename + \".csv\";\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "          }\n",
       "\n",
       "          function open_popup(data_table)\n",
       "          {\n",
       "            return function(e)\n",
       "            {\n",
       "              var popup = document.querySelector(\"#te31350674c024344ab4fbad575f00834 .toyplot-mark-popup\");\n",
       "              popup.querySelector(\".toyplot-mark-popup-title\").innerHTML = data_table.title;\n",
       "              popup.querySelector(\".toyplot-mark-popup-save-csv\").onclick = function() { popup.style.visibility = \"hidden\"; save_csv(data_table); }\n",
       "              popup.style.left = (e.clientX - 50) + \"px\";\n",
       "              popup.style.top = (e.clientY - 20) + \"px\";\n",
       "              popup.style.visibility = \"visible\";\n",
       "              e.stopPropagation();\n",
       "              e.preventDefault();\n",
       "            }\n",
       "\n",
       "          }\n",
       "\n",
       "          for(var i = 0; i != data_tables.length; ++i)\n",
       "          {\n",
       "            var data_table = data_tables[i];\n",
       "            var event_target = document.querySelector(\"#\" + data_table.id);\n",
       "            event_target.oncontextmenu = open_popup(data_table);\n",
       "          }\n",
       "        })();\n",
       "        </script></div></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot the results, showing here some plotting options.\n",
    "bb.plot(height=900,\n",
    "        width=600,\n",
    "        pct_tree_y=0.1, \n",
    "        ewidth=2, \n",
    "        alpha=4.,\n",
    "        style_test_labels={\"font-size\":\"10px\"},\n",
    "        );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Tutorial\n",
    "\n",
    "### Creating a `baba` object\n",
    "\n",
    "The fundamental object for running abba-baba tests is the `ipa.baba()` object. This stores all of the information about the data, tests, and results of your analysis, and is used to generate plots. If you only have one data file that you want to run many tests on then you will only need to enter the path to your data once. The data file must be a `'.loci'` file from an ipyrad analysis. In general, you will probably want to use the largest data file possible for these tests (`min_samples_locus`=4), to maximize the amount of data available for any test. Once an initial `baba` object is created you create different copies of that object that will inherit its parameter setttings, and which you can use to perform different tests on, like below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ipyrad.analysis.baba.Baba object at 0x7fccb85601d0>\n",
      "<ipyrad.analysis.baba.Baba object at 0x7fccb8560250>\n",
      "<ipyrad.analysis.baba.Baba object at 0x7fccb8560410>\n"
     ]
    }
   ],
   "source": [
    "## create an initial object linked to your data in 'locifile'\n",
    "aa = ipa.baba(data=locifile)\n",
    "\n",
    "## create two other copies\n",
    "bb = aa.copy()\n",
    "cc = aa.copy()\n",
    "\n",
    "## print these objects\n",
    "print aa\n",
    "print bb\n",
    "print cc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linking tests to the baba object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next thing we need to do is to link a `'test'` to each of these objects, or a list of tests. In the [Short tutorial](#Short-tutorial:-calculating-abba-baba-statistics) above we auto-generated a list of tests from an input tree, but to be more explicit about how things work we will write out each test by hand here. A test is described by a Python dictionary that tells it which samples (individuals) should represent the 'p1', 'p2', 'p3', and 'p4' taxa in the ABBA-BABA test. You can see in the example below that we set two samples to represent the outgroup taxon (p4). This means that the SNP frequency for those two samples combined will represent the p4 taxon. For the `baba` object named `'cc'` below we enter two tests using a list to show how multiple tests can be linked to a single `baba` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aa.tests = {\n",
    "    \"p4\": [\"32082_przewalskii\", \"33588_przewalskii\"],\n",
    "    \"p3\": [\"29154_superba\"], \n",
    "    \"p2\": [\"33413_thamno\"], \n",
    "    \"p1\": [\"40578_rex\"],\n",
    "}\n",
    "\n",
    "bb.tests = {\n",
    "    \"p4\": [\"32082_przewalskii\", \"33588_przewalskii\"],\n",
    "    \"p3\": [\"30686_cyathophylla\"], \n",
    "    \"p2\": [\"33413_thamno\"], \n",
    "    \"p1\": [\"40578_rex\"],\n",
    "}\n",
    "\n",
    "cc.tests = [\n",
    "    {\n",
    "     \"p4\": [\"32082_przewalskii\", \"33588_przewalskii\"],\n",
    "     \"p3\": [\"41954_cyathophylloides\"], \n",
    "     \"p2\": [\"33413_thamno\"], \n",
    "     \"p1\": [\"40578_rex\"],\n",
    "    },\n",
    "    {\n",
    "     \"p4\": [\"32082_przewalskii\", \"33588_przewalskii\"],\n",
    "     \"p3\": [\"41478_cyathophylloides\"], \n",
    "     \"p2\": [\"33413_thamno\"], \n",
    "     \"p1\": [\"40578_rex\"],\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other parameters\n",
    "Each `baba` object has a set of parameters associated with it that are used to filter the loci that will be used in the test and to set some other optional settings. If the `'mincov'` parameter is set to 1 (the default) then loci in the data set will only be used in a test if there is at least one sample from every tip of the tree that has data for that locus. For example, in the tests above where we entered two samples to represent \"p4\" only one of those two samples *needs* to be present for the locus to be included in our analysis. If you want to require that both samples have data at the locus in order for it to be included in the analysis then you could set `mincov=2`. However, for the test above setting `mincov=2` would filter out *all* of the data, since it is impossible to have a coverage of 2 for 'p3', 'p2', and 'p1', since they each have only one sample. Therefore, you can also enter the `mincov` parameter as a dictionary setting a different minimum for each tip taxon, which we demonstrate below for the `baba` object `'bb'`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "database   None                \n",
       "mincov     1                   \n",
       "nboots     1000                \n",
       "quiet      False               "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## print params for object aa\n",
    "aa.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "database   None                \n",
       "mincov     {'p2': 1, 'p3': 1, 'p1': 1, 'p4': 2}\n",
       "nboots     1000                \n",
       "quiet      False               "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## set the mincov value as a dictionary for object bb\n",
    "bb.params.mincov = {\"p4\":2, \"p3\":1, \"p2\":1, \"p1\":1}\n",
    "bb.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the tests\n",
    "When you execute the `'run()'` command all of the tests for the object will be distributed to run in parallel on your cluster (or the cores available on your machine) as connected to your `ipyclient` object. The results of the tests will be stored in your `baba` object under the attributes `'results_table'` and `'results_boots'`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[####################] 100%  calculating D-stats  | 0:00:06 |  \n",
      "[####################] 100%  calculating D-stats  | 0:00:05 |  \n",
      "[####################] 100%  calculating D-stats  | 0:00:07 |  \n"
     ]
    }
   ],
   "source": [
    "## run tests for each of our objects\n",
    "aa.run(ipyclient)\n",
    "bb.run(ipyclient)\n",
    "cc.run(ipyclient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The results table\n",
    "The results of the tests are stored as a data frame (pandas.DataFrame) in `results_table`, which can be easily accessed and manipulated. The tests are listed in order and can be reference by their `'index'` (the number in the left-most column). For example, below we see the results for object `'cc'` tests 0 and 1. You can see which taxa were used in each test by accessing them as `'cc.tests[0]'` or `'cc.tests[1]'`. An even better way to see which individuals were involved in each test, however, is to use our plotting functions, which we describe further below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dstat</th>\n",
       "      <th>bootmean</th>\n",
       "      <th>bootstd</th>\n",
       "      <th>Z</th>\n",
       "      <th>ABBA</th>\n",
       "      <th>BABA</th>\n",
       "      <th>nloci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.022</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.466</td>\n",
       "      <td>225.875</td>\n",
       "      <td>236.188</td>\n",
       "      <td>8439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.020</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.432</td>\n",
       "      <td>245.000</td>\n",
       "      <td>235.625</td>\n",
       "      <td>8993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dstat  bootmean  bootstd      Z     ABBA     BABA  nloci\n",
       "0 -0.022    -0.019    0.048  0.466  225.875  236.188   8439\n",
       "1  0.020     0.019    0.045  0.432  245.000  235.625   8993"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## you can sort the results by Z-score\n",
    "cc.results_table.sort_values(by=\"Z\", ascending=False)\n",
    "\n",
    "## save the table to a file \n",
    "cc.results_table.to_csv(\"cc.abba-baba.csv\")\n",
    "\n",
    "## show the results in notebook\n",
    "cc.results_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auto-generating tests\n",
    "Entering all of the tests by hand can be pain, which is why we wrote functions to auto-generate tests given an input **rooted** tree, and a number of contraints on the tests to generate from that tree. It is important to add constraints on the tests otherwise the number that can be produced becomes very large very quickly. Calculating results runs pretty fast, but summarizing and interpreting thousands of results is pretty much impossible, so it is generally better to limit the tests to those which make some intuitive sense to run. You can see in this example that implementing a few contraints reduces the number of tests from 1608 to 13. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1608 tests generated from tree\n",
      "117 tests generated from tree\n",
      "13 tests generated from tree\n"
     ]
    }
   ],
   "source": [
    "## create a new 'copy' of your baba object and attach a treefile\n",
    "dd = bb.copy()\n",
    "dd.newick = newick\n",
    "\n",
    "## generate all possible tests\n",
    "dd.generate_tests_from_tree()\n",
    "\n",
    "## a dict of constraints\n",
    "constraint_dict={\n",
    "        \"p4\": [\"32082_przewalskii\", \"33588_przewalskii\"],\n",
    "        \"p3\": [\"40578_rex\", \"35855_rex\"],\n",
    "    }\n",
    "\n",
    "## generate tests with contraints\n",
    "dd.generate_tests_from_tree(\n",
    "    constraint_dict=constraint_dict, \n",
    "    constraint_exact=False,\n",
    ")\n",
    "\n",
    "## 'exact' contrainst are even more constrained\n",
    "dd.generate_tests_from_tree(\n",
    "    constraint_dict=constraint_dict,\n",
    "    constraint_exact=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[####################] 100%  calculating D-stats  | 0:00:12 |  \n"
     ]
    },
    {
     "data": {
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       "      <td>-0.113</td>\n",
       "      <td>0.041</td>\n",
       "      <td>2.725</td>\n",
       "      <td>266.414</td>\n",
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       "      <td>3.970</td>\n",
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       "      <td>8715</td>\n",
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       "      <td>8010</td>\n",
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       "      <td>3.459</td>\n",
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       "      <td>318.250</td>\n",
       "      <td>6597</td>\n",
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       "      <td>2.694</td>\n",
       "      <td>262.266</td>\n",
       "      <td>326.672</td>\n",
       "      <td>6961</td>\n",
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       "      <th>9</th>\n",
       "      <td>-0.021</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.397</td>\n",
       "      <td>171.125</td>\n",
       "      <td>178.406</td>\n",
       "      <td>6468</td>\n",
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       "      <td>0.055</td>\n",
       "      <td>2.569</td>\n",
       "      <td>39.234</td>\n",
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       "      <td>0.462</td>\n",
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      "text/plain": [
       "    dstat  bootmean  bootstd      Z     ABBA     BABA  nloci\n",
       "0  -0.107    -0.107    0.035  3.095  348.016  431.828   9298\n",
       "1  -0.130    -0.128    0.040  3.290  295.312  383.953   8537\n",
       "2  -0.111    -0.113    0.041  2.725  266.414  333.086   6975\n",
       "3  -0.138    -0.138    0.035  3.970  313.633  413.930   8715\n",
       "4  -0.157    -0.156    0.041  3.865  268.891  369.172   8010\n",
       "5  -0.141    -0.141    0.041  3.459  239.750  318.250   6597\n",
       "6  -0.105    -0.104    0.034  3.045  343.203  423.312   9275\n",
       "7  -0.129    -0.126    0.040  3.257  291.266  377.641   8517\n",
       "8  -0.109    -0.111    0.041  2.694  262.266  326.672   6961\n",
       "9  -0.021    -0.021    0.052  0.397  171.125  178.406   6468\n",
       "10  0.141     0.142    0.055  2.569   39.234   29.516   9218\n",
       "11  0.014     0.016    0.030  0.462  564.281  548.625   9456\n",
       "12 -0.036    -0.038    0.059  0.607  104.875  112.656   8480"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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       "\n",
       "          function save_csv(data_table)\n",
       "          {\n",
       "            var uri = \"data:text/csv;charset=utf-8,\";\n",
       "            uri += data_table.names.join(\",\") + \"\\n\";\n",
       "            for(var i = 0; i != data_table.columns[0].length; ++i)\n",
       "            {\n",
       "              for(var j = 0; j != data_table.columns.length; ++j)\n",
       "              {\n",
       "                if(j)\n",
       "                  uri += \",\";\n",
       "                uri += data_table.columns[j][i];\n",
       "              }\n",
       "              uri += \"\\n\";\n",
       "            }\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = data_table.filename + \".csv\";\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "          }\n",
       "\n",
       "          function open_popup(data_table)\n",
       "          {\n",
       "            return function(e)\n",
       "            {\n",
       "              var popup = document.querySelector(\"#tc48cf491494e4bb799d3f404934b0d97 .toyplot-mark-popup\");\n",
       "              popup.querySelector(\".toyplot-mark-popup-title\").innerHTML = data_table.title;\n",
       "              popup.querySelector(\".toyplot-mark-popup-save-csv\").onclick = function() { popup.style.visibility = \"hidden\"; save_csv(data_table); }\n",
       "              popup.style.left = (e.clientX - 50) + \"px\";\n",
       "              popup.style.top = (e.clientY - 20) + \"px\";\n",
       "              popup.style.visibility = \"visible\";\n",
       "              e.stopPropagation();\n",
       "              e.preventDefault();\n",
       "            }\n",
       "\n",
       "          }\n",
       "\n",
       "          for(var i = 0; i != data_tables.length; ++i)\n",
       "          {\n",
       "            var data_table = data_tables[i];\n",
       "            var event_target = document.querySelector(\"#\" + data_table.id);\n",
       "            event_target.oncontextmenu = open_popup(data_table);\n",
       "          }\n",
       "        })();\n",
       "        </script></div></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## run the dd tests\n",
    "dd.run(ipyclient)\n",
    "dd.plot(height=500, pct_tree_y=0.2, alpha=4., tree_style='c');\n",
    "dd.results_table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More about input file paths (i/o)\n",
    "The default (required) input data file is the `.loci` file produced by `ipyrad`. When performing D-statistic calculations this file will be parsed to retain the maximal amount of information useful for each test. \n",
    "\n",
    "An additional (*optional*) file to provide is a newick tree file. While you do not need a tree in order to run ABBA-BABA tests, you do need at least need *a hypothesis* for how your samples are related in order to setup meaningful tests. By loading in a tree for your data set we can use it to easily set up hypotheses to test, and to plot results on the tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## path to a locifile created by ipyrad\n",
    "locifile = \"./analysis-ipyrad/pedicularis_outfiles/pedicularis.loci\"\n",
    "\n",
    "## path to an unrooted tree inferred with tetrad\n",
    "newick = \"./analysis-tetrad/tutorial.tree\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see in the `results_table` below that the D-statistic ranges between 0.2 and 0.4 in these tests. These values are not too terribly informative, and so we instead generally focus on the Z-score representing how far the distribution of D-statistic values across bootstrap replicates deviates from its expected value of zero. The default number of bootstrap replicates to perform per test is 1000. Each replicate resamples nloci with replacement. \n",
    "\n",
    "In these tests ABBA and BABA occurred with pretty equal frequency. The values are calculated using SNP frequencies, which is why they are floats instead of integers, and this is also why we were able to combine multiple samples to represent a single tip in the tree (e.g., see the test we setup, above). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   dstat  bootmean  bootstd      Z     ABBA     BABA  nloci\n",
      "0 -0.022    -0.018    0.046  0.393  225.875  236.188   8439\n",
      "1  0.020     0.020    0.044  0.466  245.000  235.625   8993\n"
     ]
    }
   ],
   "source": [
    "print cc.results_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running 5-taxon (partitioned) D-statistics\n",
    "Coming soon.  "
   ]
  }
 ],
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