experiment_Negative Binomial Test.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from bx.bbi.bigwig_file import BigWigFile\n",
      "from clipper.src.AS_Structure_tools import parse_AS_STRUCTURE_dict\n",
      "import random\n",
      "from collections import Counter\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/nas3/yeolab/Software/Python_dependencies-2.7/matplotlib-1.1.0-py2.7-linux-x86_64.egg/pytz/__init__.py:35: UserWarning: Module pytz was already imported from /nas3/yeolab/Software/Python_dependencies-2.7/matplotlib-1.1.0-py2.7-linux-x86_64.egg/pytz/__init__.pyc, but /nas/nas0/yeolab/Software/Python_dependencies-2.7/pytz-2012f-py2.7.egg is being added to sys.path\n",
        "  from pkg_resources import resource_stream\n",
        "/nas3/yeolab/Software/Python_dependencies-2.7/matplotlib-1.1.0-py2.7-linux-x86_64.egg/pytz/__init__.py:35: UserWarning: Module dateutil was already imported from /nas3/yeolab/Software/Python_dependencies-2.7/matplotlib-1.1.0-py2.7-linux-x86_64.egg/dateutil/__init__.pyc, but /nas/nas0/yeolab/Software/Python_dependencies-2.7/python_dateutil-1.5-py2.7.egg is being added to sys.path\n",
        "  from pkg_resources import resource_stream\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "info, genes = parse_AS_STRUCTURE_dict(\"mm9\", \"/nas3/yeolab/Genome/ensembl/AS_STRUCTURE/mm9data4/\")\n",
      "FOX2_full = BigWigFile(open(\"/nas3/gpratt/projects/FOX2Brain.all.rmdup.read.bw\"))\n",
      "TAF15_195_full = BigWigFile(open(\"/nas3/gpratt/projects/structure/TAF15/TAF15_195.all.sort.bw\"))\n",
      "TAF15_196_full = BigWigFile(open(\"/nas3/gpratt/projects/structure/TAF15/TAF15_196.all.sort.bw\"))\n",
      "TDB43Brain_MP41_1_full = BigWigFile(open(\"/nas3/gpratt/projects/structure/TDP43/TDB43brain_MP41_1.all.sort.bw\"))\n",
      "TDB43Brain_MP42_1_full = BigWigFile(open(\"/nas3/gpratt/projects/structure/TDP43/TDB43brain_MP42_1.all.sort.bw\"))\n",
      "genome_sizes = \"/nas3/yeolab/Genome/ucsc/mm9/mm9.chrom.sizes\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mm9\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "big_wigs = {\"TAF15_195\" : TAF15_195_full, \"TAF15_196\" : TAF15_196_full, \"TDB43_1\" : TDB43Brain_MP41_1_full, \"TDB43_1\" : TDB43Brain_MP42_1_full}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(genes)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "36400\n"
       ]
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_result = {}\n",
      "\n",
      "for name, bw in big_wigs.items():\n",
      "    total_result[name] = []\n",
      "    for interval in genes:\n",
      "        coverage = bw.get_as_array(interval.chrom, interval.start, interval.stop)\n",
      "        if not all(isnan(coverage)):\n",
      "            total_result[name].append(coverage)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 214
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intermediate_results = {}\n",
      "for rbp in total_result:\n",
      "    intermediate_results[rbp] = []\n",
      "    for item in total_result[rbp]:\n",
      "        item[isnan(item)] = 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for rbp in total_result:\n",
      "    print rbp, len(total_result[rbp])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "TAF15_195 19302\n",
        "TDB43_1 19040\n",
        "TAF15_196 18989\n"
       ]
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_result['TAF15_195'][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 223,
       "text": [
        "array([ 0.,  0.,  0., ...,  0.,  0.,  0.], dtype=float32)"
       ]
      }
     ],
     "prompt_number": 223
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "from scipy.stats import nbinom\n",
      "from scipy.stats import poisson"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(20,100)\n",
      "count = 0\n",
      "x = 0\n",
      "total = 0 \n",
      "nb_better = 0\n",
      "for gene in total_result['TAF15_195']:\n",
      "    #if sum(gene) / 25 > 1000:\n",
      "        count += 1\n",
      "        #subplot(50,2, count)\n",
      "        \n",
      "        #hist(gene, bins = max(gene), normed=True)\n",
      "        #gamma_model = sm.GLM(total_result['TAF15_195'][x], total_result['TAF15_195'][x],\n",
      "        #                 family=sm.families.NegativeBinomial())\n",
      "        y, x = histogram(gene, bins=max(gene), normed=True)\n",
      "        \n",
      "        k = pow(mean(gene),2) / (var(gene) - pow(mean(gene),2))\n",
      "        p = (var(gene) / mean(gene)) - 1\n",
      "        nb_rv = nbinom(k,p)\n",
      "        nb_dist= nb_rv.pmf(arange(max(gene)))\n",
      "        #plot(nb_dist, label=\"nb\")\n",
      "        nb_chi_sq = sum(pow((y - nb_dist), 2) / nb_dist)\n",
      "        #print \"nb chisq\", nb_chi_sq\n",
      "        \n",
      "        poisson_rv = poisson(mean(gene))\n",
      "        poisson_dist = poisson_rv.pmf(arange(max(gene)))\n",
      "        #plot(poisson_dist, label=\"poisson\")\n",
      "        \n",
      "        poisson_chi_sq = sum(pow((y - poisson_dist), 2) / poisson_dist)\n",
      "        #print \"poisson chisq\", poisson_chi_sq\n",
      "        total += 1\n",
      "        if nb_chi_sq < poisson_chi_sq:\n",
      "            nb_better += 1\n",
      "            #print \"better\"\n",
      "        #legend()\n",
      "        #gamma_results = gamma_model.fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-231-4679ee272bed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgene\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgene\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mnb_rv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnbinom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mnb_dist\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnb_rv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpmf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgene\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m         \u001b[1;31m#plot(nb_dist, label=\"nb\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[0mnb_chi_sq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mnb_dist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnb_dist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 231
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nb_better / (total * 1.0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 229,
       "text": [
        "0.23451593505712567"
       ]
      }
     ],
     "prompt_number": 229
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print nb_better\n",
      "print total"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3510\n",
        "14967\n"
       ]
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "poisson_rv."
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lets test out different binning stragaties..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(6,6)\n",
      "hist(gene)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 238,
       "text": [
        "(array([104568,      0,      0,   9206,      0,      0,   1363,      0,\n",
        "            0,    118]),\n",
        " array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8,  2.1,  2.4,  2.7,  3. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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FxaqoqFA0GlV2drYkKTs7W5FIRNKFF3+v1+sc7/F4FA6HlZGRoUmTJjnjPp9Pf/zjH7Vq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\nHV0AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 238
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print mean(gene) * 100\n",
      "print mean(gene * 100)\n",
      "print mean([sum(x) for x in grouper(100, gene, padvalue=0)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10.6598412216\n",
        "10.6598412216\n",
        "10.6556808326"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 255
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from itertools import islice\n",
      "from itertools import izip, chain"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 249
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 260
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = islice(gene, step=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "islice() does not take keyword arguments",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-240-af3f875ac582>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mislice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgene\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mTypeError\u001b[0m: islice() does not take keyword arguments"
       ]
      }
     ],
     "prompt_number": 240
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def grouper(n, iterable, padvalue=None):\n",
      "    \"grouper(3, 'abcdefg', 'x') --> ('a','b','c'), ('d','e','f'), ('g','x','x')\"\n",
      "    return izip(*[chain(iterable, repeat(padvalue, n-1))]*n)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 250
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for x in grouper(2, arange(10)):\n",
      "    print x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(0, 1)\n",
        "(2, 3)\n",
        "(4, 5)\n",
        "(6, 7)\n",
        "(8, 9)\n"
       ]
      }
     ],
     "prompt_number": 252
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print var(gene)\n",
      "print mean(pow(gene, 2)) - pow(mean(gene),2) \n",
      "print mean(pow(gene * 100, 2)) - pow(mean(gene * 100),2) \n",
      "print var([sum(x) for x in grouper(100, gene, padvalue=0)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.125181315005\n",
        "0.125029993558\n",
        "1250.29993558\n",
        "393.020212741"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 258
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(30,30)\n",
      "for x, rbp in enumerate(total_result):\n",
      "    subplot(2,2, x+1)\n",
      "    rbp_lst = np.concatenate(total_result[rbp])\n",
      "    rbp_lst = rbp_lst[rbp_lst != 0]\n",
      "    hist(rbp_lst)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": "*"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intermediate_results = []\n",
      "for item in total_result['FOX2']:\n",
      "    intermediate_results.append(Counter(item))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd_results = pd.DataFrame(intermediate_results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(10,10)\n",
      "\n",
      "plot((pd_results.mean() + pd_results.std())[1:], label=\"max\")\n",
      "plot((pd_results.mean() - pd_results.std())[1:], label=\"min\")\n",
      "plot((pd_results.mean())[1:], label=\"med\")\n",
      "#plot()\n",
      "legend()\n",
      "#pd_results.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-32-5dc72c9c61ba>, line 3)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-32-5dc72c9c61ba>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    plot((pd_results.mean() + pd_results.std())[1:], label=\"max\")\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Estimating negative binomial... need to estimate r and p\n",
      "\n",
      "display(Math(p = \\frac{ \\sum_{i=1}^N k_i / N } {r + \\sum_{i=1}^N k_i / N }))"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Equaltions got from here:  http://en.wikipedia.org/wiki/Negative_binomial_distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.misc import factorial\n",
      "from scipy.special import digamma"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 104
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "K = samples \n",
      "r = variance \n",
      "p = mean"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def p_estimate(k, r):   \n",
      "    return np.mean(k) / (r + np.mean(k)) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def log_likelihood_negative_binomial(k, r, p):\n",
      "    return (sum(log(gamma(k + r))) - \n",
      "           sum(log(factorial(k))) - \n",
      "           len(k) * log(gamma(r)) + \n",
      "           (len(k) * r * log(1-p)) + \n",
      "           sum(k * log(p)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def partial_wrt_r(k, r):\n",
      "    return (sum(digamma(k + r)) - \n",
      "            len(k) * digamma(r) + \n",
      "            len(k) * log(r / (r + np.mean(k))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This method is wrong, need to take the hessian, not this, need the second derivitive"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prev_r = -9999\n",
      "cur_r  = 500\n",
      "gene = total_result['TAF15_195'][0]\n",
      "while abs(prev_r - cur_r) > .05:\n",
      "    #print \"running\"\n",
      "    prev_r = cur_r\n",
      "    #print \"cur_r\", cur_r\n",
      "    p = p_estimate(gene, cur_r)\n",
      "    cur_r = max(cur_r - log_likelihood_negative_binomial(gene, cur_r, p) / partial_wrt_r(gene, cur_r),0)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "shape <= 0",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-209-30e7232aacd8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;31m#print \"cur_r\", cur_r\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp_estimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgene\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcur_r\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m     \u001b[0mcur_r\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcur_r\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlog_likelihood_negative_binomial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgene\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcur_r\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mpartial_wrt_r\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgene\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcur_r\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-107-a62fa0b6dc17>\u001b[0m in \u001b[0;36mlog_likelihood_negative_binomial\u001b[1;34m(k, r, p)\u001b[0m\n\u001b[0;32m      3\u001b[0m            \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfactorial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m            \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgamma\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m            \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mr\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m            sum(k * log(p)))\n",
        "\u001b[1;32m/nas3/yeolab/Software/Python-2.7.5/lib/python2.7/site-packages/numpy/random/mtrand.so\u001b[0m in \u001b[0;36mmtrand.RandomState.gamma (numpy/random/mtrand/mtrand.c:10572)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: shape <= 0"
       ]
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from clipper.src.peaks import shuffle, find_sections\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 261
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shuffle(163, 100, 0, .05, [26, 26, 26, 25, 26, 26, 26, 26, 25, 26, 26, 25, 40, 40, 39, 40, 40, 40, 39, 40, 40, 39]\n",
      ")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 263,
       "text": [
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