{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from genomics.popgen.admix import cluster, plot\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "k_range = range(2, 10)  # 2..9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The next cell is very slow. Example outputs are provided (so you can avoid running it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#for k in k_range:\n",
    "#    os.system('admixture --cv=10 hapmap10_auto_noofs_ld.bed %d > admix.%d' % (k, k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individual order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = open('hapmap10_auto_noofs_ld.fam')\n",
    "ind_order = []\n",
    "for l in f:\n",
    "    toks = l.rstrip().replace(' ', '\\t').split('\\t')\n",
    "    fam_id = toks[0]\n",
    "    ind_id = toks[1]\n",
    "    ind_order.append((fam_id, ind_id))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CV-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7e51e4c2d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BtzH8qrAkSZIkaZpFxPz2fjfu141xT+sbgDMyc0H9+DPA+sw8s+2ce2kV1Z2o7ms9Gbge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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e451d0650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CVs = []\n",
    "for k in k_range:\n",
    "    f = open('admix.%d' % k)\n",
    "    for l in f:\n",
    "        if l.find('CV error') > -1:\n",
    "            CVs.append(float(l.rstrip().split(' ')[-1]))\n",
    "            break\n",
    "    f.close()\n",
    "fig = plt.figure(figsize=(16, 9))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(k_range, CVs)\n",
    "ax.set_title('Cross-Validation error')\n",
    "ax.set_xlabel('K')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load meta-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = open('relationships_w_pops_121708.txt')\n",
    "pop_ind = defaultdict(list)\n",
    "f.readline()  # header\n",
    "for l in f:\n",
    "    toks = l.rstrip().split('\\t')\n",
    "    fam_id = toks[0]\n",
    "    ind_id = toks[1]\n",
    "    if (fam_id, ind_id) not in ind_order:\n",
    "        continue\n",
    "    mom = toks[2]\n",
    "    dad = toks[3]\n",
    "    if mom != '0' or dad != '0':\n",
    "        continue\n",
    "    pop = toks[-1]\n",
    "    pop_ind[pop].append((fam_id, ind_id))\n",
    "#ind_pop[('2469', 'NA20281')] = ind_pop[('2805', 'NA20281')]\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_Q(fname, ind_order):\n",
    "    ind_comps = {}\n",
    "    f = open(fname)\n",
    "    for i, l in enumerate(f):\n",
    "        comps = [float(x) for x in l.rstrip().split(' ')]\n",
    "        ind_comps[ind_order[i]] = comps\n",
    "    f.close()\n",
    "    return ind_comps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "comps = {}\n",
    "for k in k_range:\n",
    "    comps[k] = load_Q('hapmap10_auto_noofs_ld.%d.Q' % k, ind_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ordering = {}\n",
    "for k in k_range:\n",
    "    ordering[k] = cluster(comps[k], pop_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QIcuU02qGCqNme6PeRo9VTAEAAMtopojKyvJZDwAAoJn0np0qdhUb/jOB/DAz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e2211c990>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QIcuU02qGCqNme6PeRo9VTAEAAMtopojKyvJZDwAAoJn0np0qdhUb/jOB/DAz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e2211c990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.single(comps[4], ordering[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gAQAAANg4AQ8AAADAxv3TtfJwOBTPaggAAAAA/YqiONSXdQY8ERGvmPAcsogiW/4c2d+f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\nERERERFRwv0fdyoSiUWxg0YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e1e114710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 9))\n",
    "plot.stacked(comps, ordering[7], fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q files?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log-likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}