{
 "metadata": {
  "name": "",
  "signature": "sha256:82e542fc52d1084d69fac76e25fdba1c03c32464161a5a633bd8a392a269df72"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "[``anhima.f2``](http://anhima.readthedocs.org/en/latest/f2.html) - Doubleton sharing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division, print_function\n",
      "import numpy as np\n",
      "np.random.seed(42)\n",
      "import scipy.stats\n",
      "import random\n",
      "random.seed(1)\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import sys\n",
      "# import anhima\n",
      "# # dev imports\n",
      "sys.path.insert(0, '..')\n",
      "%reload_ext autoreload\n",
      "%autoreload 1\n",
      "%aimport anhima.sim\n",
      "%aimport anhima.gt\n",
      "%aimport anhima.af\n",
      "%aimport anhima.f2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# simulate genotypes \n",
      "n_variants = 10000\n",
      "n_samples = 100\n",
      "ploidy = 2\n",
      "af_dist = scipy.stats.beta(a=.1, b=6)\n",
      "p_missing = 0\n",
      "genotypes = anhima.sim.simulate_biallelic_genotypes(n_variants, n_samples, af_dist, p_missing, ploidy)\n",
      "\n",
      "# simulate three sub-populations with relatedness\n",
      "pop1_genotypes = anhima.sim.simulate_relatedness(genotypes[:, :n_samples//3], relatedness=.5, n_iter=100)\n",
      "pop23_genotypes = anhima.sim.simulate_relatedness(genotypes[:, n_samples//3:], relatedness=.5, n_iter=100)\n",
      "pop2_genotypes = anhima.sim.simulate_relatedness(pop23_genotypes[:, n_samples//3:], relatedness=.5, n_iter=20)\n",
      "pop3_genotypes = anhima.sim.simulate_relatedness(pop23_genotypes[:, :n_samples//3], relatedness=.5, n_iter=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pop1_ac = anhima.af.allele_count(pop1_genotypes, allele=1)\n",
      "pop2_ac = anhima.af.allele_count(pop2_genotypes, allele=1)\n",
      "pop3_ac = anhima.af.allele_count(pop3_genotypes, allele=1)\n",
      "subpops_ac = np.column_stack([pop1_ac, pop2_ac, pop3_ac])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# raw counts of shared doubletons\n",
      "counts = anhima.f2.count_shared_doubletons(subpops_ac)\n",
      "counts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[53, 14, 13],\n",
        "       [14, 59, 79],\n",
        "       [13, 79, 44]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subpop_labels = ['pop1', 'pop2', 'pop3']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts, \n",
      "                                 subpop_labels=subpop_labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nISxDgb6+hwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts, \n",
      "                                 subpop_labels=subpop_labels,\n",
      "                                 flip=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts, \n",
      "                                 subpop_labels=subpop_labels,\n",
      "                                 relative=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0X8Bve4wNqz6eXYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_samples = [pop1_genotypes.shape[1], pop2_genotypes.shape[1], pop3_genotypes.shape[1]]\n",
      "n_samples"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[33, 34, 33]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# normalise doubleton counts by the number of distinct pairs of haplotypes in each comparison\n",
      "counts_normed = anhima.f2.normalise_doubleton_counts(counts, n_samples=n_samples, ploidy=2)\n",
      "counts_normed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([[ 0.02470862,  0.00311943,  0.00298439],\n",
        "       [ 0.00311943,  0.02589991,  0.0176025 ],\n",
        "       [ 0.00298439,  0.0176025 ,  0.02051282]])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_shared_doubletons(counts_normed, \n",
      "                                 subpop_labels=subpop_labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAANEAAADQCAYAAACZZoRKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACfZJREFUeJzt3U+sXGUZx/Hvo8VIwJgudVGv/FuQAIHGpLDoAtCEEIgR\noYFQjeLCysZATJTNvXdDQkHCAgiaACGp0NrGaEj8kxC00bQUEUIRAkktRRaaUEnFhcQ2PC7OFKft\n3OvQZ+6cc+d8P5uZ+87pzPP23t+858y85z2RmUg6fR9ruwBptTNEUpEhkooMkVRkiKQiQyQVGSKp\nqPchiohX2q6hKiLWRcT2iPhDRNwVEWcMPfbzNmubhIi4JCKeGfTx8xHx24j4Z0T8PiLOa7u+NW0X\nMA0RccOI5gQC+MyUy1kJjwG7gH3AbcDuiLg+Mw8Dn2u1ssl4BLgbOBvYA9wB7ACuBR4GvtReaRB9\nmLEQEUeBJ4EPTn4I+Gpmnj39qiYnIl7OzEuGfr4VuAu4DtiVmZe2VtwERMRLx/sQEQcy87xRj7Wl\nFyMR8ApwX2aesusWEVe1UM+krYmIT2bm+wCZuS0i/g78Bjir3dIm4uND9+8/6bEzaFlfjom+C7y3\nxGNfmWYhK+RRYMNwQ2Y+A9wI/LmViibr4Yj4FEBmPny8MSLOB55prarjdfRhd05aSX0ZiQCIiHMj\n4umIOBwR70TELyLinLbrmhT7145ehYjmw4Wf0nwi91lgJ/BUqxVNlv1rQa925yJif2ZefFLbCZ9s\nrWb2rx19C9E9wBH+9+61CVgLbAXIzHdbKm0i7F87/etbiA7RfMk6SmZm6/vXFfavnf71KkTSSujL\nl60ARMQngC3ARpp3tN3AI5l5tNXCJsT+tVRXn0aiiHiU5o3jCZopP5uBY5n5rVYLmxD711JdPQvR\nqE93TmlbrexfO/r2PdGx4anzEXEucKzFeibN/rWgV8dEwPeAZyPiIM3uwBzwjVYrmiz714K+jUR7\ngB/TnBLxD+BHg7ZZYf9a0Ldjop00s7m30byT3QJ8OjNvbLWwCbF/LdXVsxC9lpkX/r+21cr+taNv\nu3MvRsTlx3+IiA3An1qsZ9LsXwv6NhK9DlwAvE3zZd064A2aT3iy7Y9Kq+xfO/3rW4jmlns8Mw9N\npZAVYv/a6V+vQiSthL4dE0kT17UvWx0W1WUxqtGRSCoyRFKRIZKKDJFUZIikIkMkFRkiqcgQSUWG\nSCoyRFJR16b9aBWLGDkrZmYsNVnbEGmi+jj50d05qcgQSUWGSCrymGiKZv3Au68M0bQttF3AClpo\nu4B2uDsnFRkiqcgQSUXTCVHEt4nYT8RLROwlYiauZi3B9Eain5B5MZmXAncDP5zS60orbvkQRcwR\n8ToR24h4jYidRJxJxFVEvDgYXR6luZYmRBwi4p5B+z6aizBB5r+GnvVs4PDKdEeavnFGoguAh2hW\n3n8PuBN4HLiJZu3jNTQXo4Vm6tSRQfuDwAMfPkvEd4g4ANwP/GBiPZBaNk6I3iZz7+D+NuBK4CCZ\nBwZtT9Bczfm4pwa324EPV/An82EyzwPuAB6rFC11yTghGp6YG8ARTlwJMlh68u6o9h3AZWNVJ60C\n44RoHc11YKC5MtkLwNyHxzvNZdB3D22/aei2uRRgxPlDj18L7D/dgqWuGWfazxvA7UQ8BrxKc0zz\nHLCTiDXA88AjQ9uvJeJl4H3g5kHb7URcDRwF3qEDF6uVJmX5S6s014N5msyLxnu2eBNYT+a7p1nP\nTJ/TFRGzPb9sYcZ/gZmnvaD9R/l/men/Q2mU5XfnmiuPjX8Jv8xzauVIq49z56QiQyQVGSKpyBBJ\nRYZIKjJEUpEhkooMkVRkiKQiQyQVGSKpyBBJRZ1aRti1qrUadSpEjVk+m8I3iVnk7pxUZIikIkMk\nFRkiqWj5hUqmLCK6U4w0Qo5YrKRTIZJWI3fnpCJDJBUZIqnIEElFhkgqMkRSkSGSiro2i3umv7Tq\nw6keM/6948hfYNdCNPsW2i5gBS20XUA73J2TigyRVGSIpCJDJBUZIqnIEElFhkgqMkRSkSGSigyR\nVGSIpCJDJBUZIqnIEElFngqhiZrlc6aWOlfKEGmiZvqUvCW4OycVGSKpyBBJRVM5JorFuAO4DTgG\nvAN8M+fzr9N4bWmlTWskehFYn/N5CbAL2Dql15VW3LIjUSzGHPBr4AXgMuBV4GvAFcC9g3//R2BL\nzud/YjEOATuAa4B/A7fkfP4l5/N3Q0+7D7h1kp2Q2jTOSHQB8FDO54XAe8CdwOPATTmfF9MEactg\n2wSODNofBB4Y8Xy3Ab+sFi51xTghejvnc+/g/jbgSuBgzueBQdsTwMah7Z8a3G4HLh9+oliMW2lG\ntHtPu2KpY8YJ0fD3ZwEc4cSVIIOlv2P7sD0W42rgLuD6nM+jH7FOqbPGCdG6WIwNg/u30BwfzcVi\nnDto2wzsHtp+09DtHoBYjEuBR4Drcj4Pl6uWOmScj7jfAG6PxXiM5oOF+4HngJ2xGGuA52kCctza\nWIyXgfeBmwdtW4GzgF2xGABv5Xx+eTJdkNo1ToiO5XxuPqntWZpjm1G25nx+f7gh5/OLp1OctBp8\n1GOiSW4rzYTo2KUwOlXMpEXEbF85YWHGf4GZI8/zcO6cVGSIpCJDJBUZIqnIEElFhkgqMkRSkSGS\nigyRVGSIpCJDJBUZIqmoU8sIz/I6zppdnQpRY5bnAfsmMYvcnZOKDJFUZIikIkMkFXXq9PCI6E4x\n0gg54hTxToVIWo3cnZOKDJFUZIikIkMkFRkiqcgQSUWGSCoyRFJR106F8JtfdZkL2ksroVMjUR/O\nbHWa1ezpVIgas/xHNvtvEn3k7pxUZIikIkMkFRkiqcgQSUWGSCoyRFKRIZKKDJFUZIikIkMkFRki\nqcgQSUWGSCoyRFKRIZKKphKiCDZG8GIERyO4YRqvKU3LtEait4CvA09O6fWkqVk2RBHMRfB6BNsi\neC2CnRGcGcFVg5FlfwSPRvCJwfaHIrhn0L4vgnMBMnkrk1eAD6bQJ2mqxhmJLgAeyuRC4D3gTuBx\n4KZMLqZZp2HLYNsEjgzaHwQemHzJUreME6K3M9k7uL8NuBI4mMmBQdsTwMah7Z8a3G4HLp9IlVKH\njROi4eV3AjjCicvWBEsv0TOqfZaX81EPjROidRFsGNy/BXgBmDt+vANsBnYPbb9p6HbPSc8VuG6U\nZsyy12yNYA74FU1w1gOv0oTmCuA+muOh54EtmRyN4E1gB3AN8D5wcyYHI/gC8DNg7aD9b5lcdOrr\nRc72QBUu3ri6jRwAxgnR06P+4JfY/k1gfSbvnlaFhkjddtprcX+U37p/IeqdZUeiaXMkUsd5VQhp\nJRgiqcgQSUWGSCoyRFKRIZKKDJFUZIikIkMkFRkiqcgQSUWGSCpa03YBfRPhOYmr1VKThw3RtC20\nXcAKWpjtOfhLcXdOKjJEUpEhkooMkVTUwdPDpe7KzFM+Xu1UiKTVyN05qcgQSUWGSCoyRFKRIZKK\n/gsKYPUkkHsAiAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_total_doubletons(counts, subpop_labels=subpop_labels);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEICAYAAAB7+s71AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACwRJREFUeJzt3W+sZAdZx/HfU1rSpvypmhIxsWCLaDCB2NIEojX+7QtB\nVLDFmBJEjaHqC5RUxIhtY/yDpEhiYooRsLVUtKgoiQJaAmpraGyxBRpMCDYB3khR21ottvbxxdzN\n3rt79+6YnXPvPt3PJ9l05kwz8yRn8z1nz5xzpro7AJz8TjvoAQBYj2ADDCHYAEMINsAQgg0wxOkL\nvrfTTwD+/+pYL9jDBhhCsAGGEGyAIQQbYAjBBhhCsAGGEGyAIQQbYAjBBhhCsAGGEGyAIZa8lwin\niGuvvfagR3jCu/rqqw96BE4C9rABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEE\nG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhvCr6XCK86v3y9vUr97bwwYY\nQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AI\nwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEE\nG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBs\ngCEEG2AIwQYYQrABhhBsgCEEG2CI4wa7qt5SVU+rqjOq6taqur+qXrUfwwFw2Dp72Jd294NJXprk\nviQXJLlqyaEAONo6wT59678vTfLe7n4gSS83EgC7Of34/0veX1WfTvJIkiur6hlbjwHYR8fdw+7u\nX0jyLUku6u7/SfJwku9fejAAdlpnDztJvjHJs6rqjK3nneTGZUYCYDfHDXZV3ZTk/CT/lOR/t70k\n2AD7aJ097IuSPK+7fdEIcIDWOUvkk0meufQgAOxtnT3sc5PcW1V3JPny1rLu7pctNxYAR1on2Nds\n/ffQIZGK87AB9t1xg93dH6mqr05ycVahvqO7/3XxyQDYYZ17iVye5GNJLktyeZI7quqypQcDYKd1\nDon8UpKLD+1VV9W5SW5NcsuSgwGw0zpniVSSL257/qWtZQDso3X2sD+Q5INVdXNWoX5lkr9adCoA\njrLOl45XVdUrsrqfSJK8vbv/bNmxADjSOpemv7m735DkT3ZZBsA+WesHDHZZ9r2bHgSAvR1zD7uq\nrkzyU0kuqKpPbHvpqUluW3owAHba65DIzVl9ufgbSd6Qw2eGPNTdX1p6MAB2OuYhke5+oLvv6+4f\nTnJeku/o7vuSnFZVX7dfAwKwss6Vjtck+fkkb9xa9OQk715wJgB2sc6Xjj+Y1U+CPZwk3f2FJE9Z\ncigAjrZOsL/c3Y8felJVZy84DwDHsM6VjrdU1duTnFNVP5nkx5L83iaHuPbaazf5dhzD1VdffdAj\nACdgnSsd31JVlyZ5KMlzk7ypu/968ckA2GGtX03v7g8l+dDCswCwh70unPnPHPuXZbq7n7bMSADs\n5pjB7m5nggCcRNY6JFJVFya5JMnjSW7r7rsWnQqAo6xz4cwvJ7khyVdm9Qvq76qqNy09GAA7rbOH\nfUWS53f3I0lSVb+e5O4kv7LkYADstM6FM19Icta252cm+fwy4wBwLHudJfLbWw8fSPKpqjp0Wt/3\nJLlj6cEA2GmvQyJ3ZnVa351J3rdt+Udy7NP9AFjIXqf1/f4+zgHAcazzm47/ssvi7u7zF5gHgGNY\n5yyRi7c9PjPJDyX5qmXGAeBYjnuWSHffv+3P57v7bUlesg+zAbDNOodELsrhLxlPS/LCJE9acigA\njrbOIZHrcjjYjyW5L8nlSw0EwO7WuR/2t+/DHAAcxzr3Ejmnqn6rqu7c+nNdVT19P4YD4LB1Lk1/\nZ5IHk1yW1aGQh5K8a8mhADjaOsewL+jul297fk1V3b3UQADsbp097P+uqksOPamqb03yX8uNBMBu\n1tnDfm2SG6rqnK3n/57k1cuNBMBu9rpb3+u3Pb0xydlbjx9O8l1Z3RMbgH2y1x72U7M6//obsro8\n/S+2ll8Rt1cF2Hd73a3vmiSpqr9LcmF3P7T1/Jokf7kfwwFw2DpfOj4jyaPbnj+6tQyAfbTOl443\nJrmjqv40SSX5gax+lBeAfbTOpem/WlUfSHJJVse0f7S7P774ZADssM4edrr7zqx+KgyAA7LOMWwA\nTgKCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgA\nQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMM\nIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCE\nYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCC\nDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2\nwBCCDTCEYAMMIdgAQwg2wBCCDTBEdfcyb1y1zBsDPMF1d+22fLFgA7BZDokADCHYAEMINsAQiwa7\nqj6x5Ptz4qrqvKp6T1X9fVX9YlWdse219x3kbMBOp5/oG1TVK3ZZ3EkqyTNP9P1Z3DuTvDfJx5L8\neJKPVtXLuvv+JM860MnYU1W9IMl1Se5P8sas1uWFSe5J8pru/swBjscCTjjYSd6T5OYkjx+xvJKc\nuYH3Z1nndvf1W49/pqquSPK3VfV9BzkUa7k+ya8leUqS25P8XJI/SvKSJL+T5NKDG40lnPBpfVV1\nV5JXd/dRhz+q6nPd/bUn9AEsqqo+leSi7n5k27LvzioGZ3e3fyWdpKrq4939zVuPP9Pdz9ntNZ44\nNnEM+3VJHjzGay/fwPuzrHckedH2Bd39N0kuS/LJA5mIdT1p2+O3HvHaGeEJx4UzMFRVvTbJu7v7\noSOWf32Sn+7u1x3MZCxlY8GuqguSvC3Ji7P60vH2JD/b3Z/dyAewKOsPTn6bPK3v5iR/nNWZIV+T\n5JYkf7jB92dZ1t9QVXVBVb2/qu6vqi9W1Z9X1fkHPRebt8lgn9Xdf9Ddj279uSnOEpnE+pvLxvYU\nsclDIm9O8h85/BfllUm+IslvJkl3/9tGPohFWH9zVdU93f38I5bd3d0vOKiZWMYmg31fVsc+d9Pd\n7Z9oJzHrby4b21OHs0RgOBvbU8cm97CfnOTKJN+W1V+ejya5vrsf3cgHsCjrD05+mwz2O7K61P2G\nrC5Lf1WSx7r7JzbyASzK+pvLxvbUsclg7/bFx1HLODlZf3PZ2J46NnHzp0Meq6rnHLpD2NaFGI9t\n8P1ZlvU318VHbFhvrap7DmwaFrPJYF+V5MNV9dmstvLPTvKaDb4/y7L+5rKxPUVsMti3J/ndJN+Z\n1SlGH9xaxgzW31w2tqeITR7DviWru/bdlNVfmh9J8vTuvmwjH8CirL+5quqsJK/P4Y3tPyZ56/Zb\n5vLEsMlg39vdzzveMk5O1t9cNranjk0eErmrql7c3f+QJFX1oiR3bvD9WZb1N9c3HbFh/XBV3Xtg\n07CYTQb7hUluq6rPZXUu6HlJ/nnrh3jb6WEnPetvLhvbU8QmD4k8e6/Xu/u+jXwQi7D+5qqqTyd5\nbpIdG9uszhSxsX0CcS8RGM7G9tQh2ABDbPIHDABYkGADDCHYAEMINsAQ/wd/UcnE3ihKnQAAAABJ\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_total_doubletons(counts, subpop_labels=subpop_labels, orientation='horizontal');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD/CAYAAAD7X81yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_f2_fig(counts, subpop_labels=subpop_labels, figsize_factor=1.5);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_f2_fig(counts, subpop_labels=subpop_labels, figsize_factor=1.5, relative=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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es2JfYd83CEnSw2hMyN8BnJ5wG/BE4CzgtcDlCbcAe4Bzp7Y/NOFm4A3AmwASnpNwN/BK\n4EMJt87wOUiS1rBtxDZ7qjh1RduVwLPW2P6MKt4+3VDFl4CnbKA+SdJBONA5+VluK0l6mK07kq9i\nF7Bj7M6qOPxgC5IkzY5r10hSY4a8JDVmyEtSY4a8JDVmyEtSY4a8JDVmyEtSY4a8JDVmyEtSY4a8\nJDVmyEtSY2NWodQWlbh0/yxUue6eti5DvrOd8y6ggZ3zLkA6OIa8tB9+IpoNPxHNhyEv7YfRpK3M\nL14lqTFDXpIaM+QlqTFDXpIaM+QlqTFDXpIaM+QlqbEs0gkKScqjkmclnq05Czt9Rc5M1YbOKktS\ni5RTC2rNvnUkL0mNGfKS1JghL0mNGfKS1JghL0mNGfKS1JghL0mNGfKS1NgCngwlqavawAlRw8lQ\n/jzXBi1UyEuSZsvpGklqzJCXpMYMeUlqzJCXpMYMeUlq7P8BpUInigNqQfYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "anhima.f2.plot_f2_fig(counts, \n",
      "                      subpop_labels=subpop_labels, \n",
      "                      figsize_factor=1.5, \n",
      "                      relative=True, \n",
      "                      normed=True, \n",
      "                      n_samples=n_samples, \n",
      "                      ploidy=2);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xFFFFFFFFF>"
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}