ExtDataFig1d-f.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c57_UMIs = pd.read_pickle('data/SS3_c57_UMIs_concat_ML.pkl')\n",
    "c57_rpkms = pd.read_pickle('data/SS3_c57_rpkms_concat_ML.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c57_UMIs = c57_UMIs[c57_UMIs[1]][0]\n",
    "c57_rpkms = c57_rpkms[c57_rpkms[1]][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c57_rpkms = c57_rpkms[~c57_rpkms.index.duplicated(keep='first')]\n",
    "c57_UMIs = c57_UMIs[~c57_UMIs.index.duplicated(keep='first')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c57_df = pd.DataFrame([c57_UMIs, c57_rpkms], index=['UMI','rpkm']).T.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c57_bf_UMI = pd.Series([gene[0] for gene in c57_df['UMI']], index=c57_df.index)\n",
    "c57_bf_rpkm = pd.Series([gene[0] for gene in c57_df['rpkm']], index=c57_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cast_UMIs = cast_UMIs[cast_UMIs[1]][0]\n",
    "cast_rpkms = cast_rpkms[cast_rpkms[1]][0]\n",
    "cast_rpkms = cast_rpkms[~cast_rpkms.index.duplicated(keep='first')]\n",
    "cast_UMIs = cast_UMIs[~cast_UMIs.index.duplicated(keep='first')]\n",
    "cast_df = pd.DataFrame([cast_UMIs, cast_rpkms], index=['UMI','rpkm']).T.dropna(how='any')\n",
    "cast_bf_UMI = pd.Series([gene[0] for gene in cast_df['UMI']], index=cast_df.index)\n",
    "cast_bf_rpkm = pd.Series([gene[0] for gene in cast_df['rpkm']], index=cast_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.9330578992330714, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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DpWNTmTUunRumZTFrXK1eEPnIdZP1SZnRn9vLG8sGRJxjINEdcfGjxT56DCHE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\n4lpGKZcexN+lNpxe7+9/h29/G06fhgULYOlSiIqyGz82NZZff3SChdM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KhULhc5RZTKFQ\nKBQ+RykXhUKhUPgcpVwUCoVC4XOUclEoFAqFz1HKRaFQKBQ+RykXhcIJQohUIYQUQrzVxc9dJ4T4\nb73qcqs+x6geElOhCFiUclEofMsvgYVoOR1LgZfQ6m65RFdAbnMCdGUlreXgLQpQCiHqjcRQJ58V\nQohCy9hbHc5vc3ZcobgcVPkXhcK3zAaOSyn/zY/XbAUigK/ivAXuDLTE0VbU/3mFn1A7F4XCtyQB\nZz2O8i370XZHj7k4/xha5v8mv0mkuOZRykWh8IAQYrgQ4m9CiGohRIMQYqcQ4jaHMdt005YAbrGY\noLb5QcRW4E0gRwgx0kGueLQ+K38Bqv0gi0IBKOWiUHgiDfgEiAVWoNWgGgNsEELcbxn3Fpp/BaBE\nf/+SftwfvIHWo8Zx9zIPrZvh7/0kh0IBKOWiUHhiKloRxqlSysVSyoeAKUA7sFwIcR2AlPItKeWP\n9M8USyl/pP+85Q8hpZQngY+ArwshwiynHgVOSCm3+UMOhcJAKReFwj11aNVoTaSUuWhN06LRqhkH\nCr9Hk2kugBBiCjAcbVejUPgVpVwUCvcckFJedHJ8m/56kx9l8cRaoJIO09h8oAX/meYUChOlXBQK\n95S7OG7krkT54BoSQAjh7v+jca7d5SRS2oC3gclCiJvRGl+tl1I67RCpUPQkSrkoFO5JdHHc6Ctf\n54NrGHPEOTsphBBoAQUAtR7mMhz376I1wXKW96JQ9DhKuSgU7hntIvP9Vv31Mx9c45D+erOL8yPQ\nkiSLpZQX3E0kpTwGfIzWd70Yldui6CWUclEo3BMF/NB6QAiRA3wdbcex1gfXeEt/fVkIEe1wrb5o\nJWWs4zwxHy3Q4G6pWs0qeglVCkKhcM8O4FEhxHhgF3A9cD/ag9njnnYSXrIK+BLwAHBcCLEezacT\nh9ZLfTCwHfi5N5Ppu5djPpBLoeg2aueiULinCJgI1AALgPuAA8AdUsrVvriAvrv4GvAN4DBaRv33\n0JRNKfAkMEtK2eyL6ykU/kCoXbNCoVAofI3auSgUCoXC5yjlolAoFAqfo5SLQqFQKHyOUi4KhUKh\n8DlKuSgUCoXC5yjlolAoFAqfo5SLQqFQKHyOUi4KhUKh8DlKuSgUCoXC5yjlolAoFAqf8/8BEaF7\n2tfAygkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.log10(c57_bf_UMI), np.log10(c57_bf_rpkm), s=1)\n",
    "print(spearmanr(np.log10(c57_bf_UMI), np.log10(c57_bf_rpkm)))\n",
    "plt.plot([-2.5,0],[-2.5,0], color='red')\n",
    "plt.xlabel('bf UMI', fontsize=20)\n",
    "plt.ylabel('bf rpkm', fontsize=20)\n",
    "plt.savefig('figures/ExtDataFig1e.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c57_bs_UMI = pd.Series([gene[2]/gene[1] for gene in c57_df['UMI']], index=c57_df.index)\n",
    "c57_bs_rpkm = pd.Series([gene[2]/gene[1] for gene in c57_df['rpkm']], index=c57_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SpearmanrResult(correlation=0.4498724256326824, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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8uaqAeW7Pee2YhdOyMRsN1Fx0capJqTpePDtXEfbFNna+fRGA/Cwzz9y3hNJp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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.log10(c57_bs_UMI), np.log10(c57_bs_rpkm), s=1)\n",
    "print(spearmanr(np.log10(c57_bs_UMI), np.log10(c57_bs_rpkm)))\n",
    "plt.plot([0,3],[0,3], color='red')\n",
    "plt.xlabel('bs UMI', fontsize=20)\n",
    "plt.ylabel('bs rpkm', fontsize=20)\n",
    "plt.savefig('figures/ExtDataFig1f.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def MaximumLikelihood(vals, export_asymp_ci = False, fix = 0):\n",
    "    from inference.moment_inference import MomentInference\n",
    "    from scipy.interpolate import interp1d\n",
    "    from scipy.optimize import minimize\n",
    "    from scipy import special\n",
    "    from scipy.stats import poisson,norm\n",
    "    from scipy.special import j_roots\n",
    "    from scipy.special import beta as beta_fun    \n",
    "    import numpy as np\n",
    "    if len(vals) == 0:\n",
    "        return np.nan\n",
    "    def dBP(at, alpha, bet, lam):\n",
    "        at.shape = (len(at), 1)\n",
    "        np.repeat(at, 40, axis = 1)\n",
    "        def fun(at, m):\n",
    "            if(max(m) < 1e6):\n",
    "                return(poisson.pmf(at,m))\n",
    "            else:\n",
    "                return(norm.pdf(at,loc=m,scale=sqrt(m)))\n",
    "        \n",
    "        x,w = j_roots(40,alpha = bet - 1, beta = alpha - 1)\n",
    "        gs = np.sum(w*fun(at, m = lam*(1+x)/2), axis=1)\n",
    "        prob = 1/beta_fun(alpha, bet)*2**(-alpha-bet+1)*gs\n",
    "        return(prob)\n",
    "    def LogLikelihood(x, vals):\n",
    "        kon = x[0]\n",
    "        koff = x[1]\n",
    "        ksyn = x[2]\n",
    "        return(-np.sum(np.log( dBP(vals,kon,koff,ksyn) + 1e-10))/len(vals))\n",
    "    x0 = MomentInference(vals)\n",
    "    if np.isnan(x0).any():\n",
    "        x0 = np.array([10,10,10])\n",
    "    bnds = ((1e-3,1e3),(1e-3,1e3), (1, 1e10))\n",
    "    vals_ = np.copy(vals) # Otherwise the structure is violated.\n",
    "    try:\n",
    "        ll = minimize(LogLikelihood, x0, args = (vals_), method='L-BFGS-B', bounds = bnds)\n",
    "    except:\n",
    "        return np.nan\n",
    "    #se = ll.hess_inv.todense().diagonal()\n",
    "    estim = ll.x\n",
    "    return ll.fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def removeNAs(row):\n",
    "    arr = row[1:len(row)]\n",
    "    arr = arr[pd.notnull(arr)]\n",
    "    return(arr.astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SS3_c57_rpkms = pd.read_csv('data/SS3_c57_rpkms_concat.csv', index_col=0)\n",
    "SS3_c57_UMIs = pd.read_csv('data/SS3_c57_UMIs_concat.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SS3_c57_rpkms = SS3_c57_rpkms.loc[~SS3_c57_rpkms.index.duplicated(keep='first')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SS3_c57_UMIs = SS3_c57_UMIs.loc[~SS3_c57_UMIs.index.duplicated(keep='first')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from joblib import delayed,Parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "[Parallel(n_jobs=50)]: Done  28 tasks      | elapsed:    0.5s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "[Parallel(n_jobs=50)]: Done 188 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=50)]: Done 412 tasks      | elapsed:    3.2s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "[Parallel(n_jobs=50)]: Done 700 tasks      | elapsed:    5.2s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "[Parallel(n_jobs=50)]: Done 1052 tasks      | elapsed:    7.5s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:643: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  grad[k] = (f(*((xk + d,) + args)) - f0) / d[k]\n",
      "[Parallel(n_jobs=50)]: Done 1468 tasks      | elapsed:   10.1s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "[Parallel(n_jobs=50)]: Done 1948 tasks      | elapsed:   13.1s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:643: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  grad[k] = (f(*((xk + d,) + args)) - f0) / d[k]\n",
      "[Parallel(n_jobs=50)]: Done 2492 tasks      | elapsed:   16.8s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "[Parallel(n_jobs=50)]: Done 3100 tasks      | elapsed:   20.8s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "[Parallel(n_jobs=50)]: Done 3772 tasks      | elapsed:   25.5s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "[Parallel(n_jobs=50)]: Done 4508 tasks      | elapsed:   30.3s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: overflow encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "[Parallel(n_jobs=50)]: Done 5308 tasks      | elapsed:   35.5s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "[Parallel(n_jobs=50)]: Done 6172 tasks      | elapsed:   41.5s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "[Parallel(n_jobs=50)]: Done 7187 out of 7187 | elapsed:   47.8s finished\n"
     ]
    }
   ],
   "source": [
    "SS3_c57_ll = Parallel(n_jobs=50, verbose = 3)(delayed(MaximumLikelihood)(np.around(SS3_c57_rpkms.loc[gene][pd.notnull(SS3_c57_rpkms.loc[gene])])) for gene in c57_df.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=50)]: Done  28 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=50)]: Done 276 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=50)]: Done 724 tasks      | elapsed:    6.7s\n",
      "[Parallel(n_jobs=50)]: Done 1061 tasks      | elapsed:    9.4s\n",
      "[Parallel(n_jobs=50)]: Done 1413 tasks      | elapsed:   12.4s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/optimize/optimize.py:643: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  grad[k] = (f(*((xk + d,) + args)) - f0) / d[k]\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in multiply\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "[Parallel(n_jobs=50)]: Done 1829 tasks      | elapsed:   15.8s\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:1302: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: overflow encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/scipy/special/orthogonal.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1)\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/mnt/kauffman/nbserver/jupyter-run-software/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:23: RuntimeWarning: invalid value encountered in double_scalars\n",
      "[Parallel(n_jobs=50)]: Done 2309 tasks      | elapsed:   19.8s\n",
      "[Parallel(n_jobs=50)]: Done 2853 tasks      | elapsed:   24.2s\n",
      "[Parallel(n_jobs=50)]: Done 3461 tasks      | elapsed:   29.1s\n",
      "[Parallel(n_jobs=50)]: Done 4133 tasks      | elapsed:   34.4s\n",
      "[Parallel(n_jobs=50)]: Done 4869 tasks      | elapsed:   40.3s\n",
      "[Parallel(n_jobs=50)]: Done 5669 tasks      | elapsed:   46.7s\n",
      "[Parallel(n_jobs=50)]: Done 6533 tasks      | elapsed:   54.0s\n",
      "[Parallel(n_jobs=50)]: Done 7187 out of 7187 | elapsed:   59.6s finished\n"
     ]
    }
   ],
   "source": [
    "SS3_c57_ll_UMIs = Parallel(n_jobs=50, verbose = 3)(delayed(MaximumLikelihood)(np.around(SS3_c57_UMIs.loc[gene][pd.notnull(SS3_c57_UMIs.loc[gene])])) for gene in c57_df.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tb/a6FAAAUMIISQDyQqgnpKd3P60Vc1ZoeNlwr8sBAAAljJAEIC9s27dNp86f0sr6/D1A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ie9ozx6dxWtg2N81KVcd8obYsP2Vl470ovLE6TCbmF6kZn/9qWb+MXbLexxeVlSaWdhhR1n3wi7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\nSZKkAoYkSZIkSSrw/wEGm0ogifkI3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,14))\n",
    "plt.scatter(6 + 2*np.asarray(SS3_c57_ll), 6 + 2*np.asarray(SS3_c57_ll_UMIs), s=1)\n",
    "plt.title('AIC (lower is better)')\n",
    "plt.xlabel('AIC rpkm', fontsize=20)\n",
    "plt.ylabel('AIC UMI', fontsize=20)\n",
    "plt.plot([5,20],[5,20],color='green')\n",
    "plt.savefig('figures/ExtDataFig1d.pdf')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}