notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Study Evolution Using Scientific Python \n",
    "## by Yoav Ram\n",
    "\n",
    "This notebook was produced for [PyCon Israel 2016](http://il.pycon.org/2016/) by [Yoav Ram](http://www.yoavram.com).\n",
    "\n",
    "The most recent version of this notebook, as well as some additional material and info, can be found at <https://github.com/yoavram/PyConIL2016>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versions and setup\n",
    "\n",
    "The notebook was written using Python 3.5 but should also work with 3.4.\n",
    "\n",
    "You can to interact with the notebook in the Cloud by launching binder:\n",
    "\n",
    "[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/yoavram/PyConIL2016)\n",
    "\n",
    "To run the notebook on your own machine, first [download the repository](https://github.com/yoavram/PyConIL2016/archive/master.zip) or clone it with `git`:\n",
    "\n",
    "```sh\n",
    "git clone https://github.com/yoavram/PyConIL2016.git\n",
    "```\n",
    "\n",
    "It is recommended that you download [Anaconda](https://store.continuum.io/cshop/), download this [environment.yml](https://raw.githubusercontent.com/yoavram/PyConIL2016/master/environment.yml), and setup a new environment:\n",
    "\n",
    "```sh\n",
    "conda create -n PyConIL2016 python=3.5\n",
    "activate PyConIL2016\n",
    "conda install --file environment.yml\n",
    "python -m ipykernel install --name PyConIL2016\n",
    "```\n",
    "\n",
    "You might need to install additional dependencies manually:\n",
    "\n",
    "- [LLVM](http://llvm.org/) for Numba\n",
    "- C compiler for Cython (see [cython-hello-world.ipynb](cython-hello-world.ipynb))\n",
    "\n",
    "Then start the notebook with:\n",
    "\n",
    "```sh\n",
    "jupyter notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixation probability\n",
    "\n",
    "We are interested in the probability that a single copy of allele **1** fixes in a population of $N-1$ **0** alleles, where allele **1** is favored by natural selection such that its relative fitness compared to allele **0** is $1+s$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate? False\n"
     ]
    }
   ],
   "source": [
    "def simulation(N, s):\n",
    "    n1 = 1\n",
    "\n",
    "    while 0 < n1 < N:\n",
    "        n0 = N - n1\n",
    "        p1 = n1 * (1 + s) / (n0 + n1 * (1 + s))\n",
    "        n1 = sum(\n",
    "            1 \n",
    "            for _ in range(N) \n",
    "            if random.random() < p1\n",
    "        )              \n",
    "\n",
    "    return n1 == N\n",
    "\n",
    "print(\"Fixate?\", simulation(N=1000, s=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 6.42 ms per loop\n",
      "1 loop, best of 3: 528 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=1000, s=0.1)\n",
    "%timeit simulation(N=1000000, s=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy 1.11.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(\"NumPy\", np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate? False\n"
     ]
    }
   ],
   "source": [
    "def simulation(N, s):\n",
    "    n1 = 1\n",
    "\n",
    "    while 0 < n1 < N:\n",
    "        n0 = N - n1\n",
    "        p1 = n1 * (1 + s) / (n0 + n1 * (1 + s))\n",
    "        n1 = np.random.binomial(N, p1)        \n",
    "\n",
    "    return n1 == N\n",
    "\n",
    "print(\"Fixate?\", simulation(N=1000, s=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 150 µs per loop\n",
      "1000 loops, best of 3: 313 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=1000, s=0.1)\n",
    "%timeit simulation(N=1000000, s=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cython\n",
    "\n",
    "See [cython-hello-world.ipynb](cython-hello-world.ipynb) for setup instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cython 0.24\n"
     ]
    }
   ],
   "source": [
    "import Cython\n",
    "print(\"Cython\", Cython.__version__)\n",
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "def simulation(np.uint64_t N, np.float64_t s):\n",
    "    cdef np.uint64_t n1 = 1\n",
    "    cdef np.uint64_t n0    \n",
    "    cdef np.float64_t p\n",
    "    \n",
    "    while 0 < n1 < N:\n",
    "        n0 = N - n1\n",
    "        p1 = n1 * (1 + s) / (n0 + n1 * (1 + s))\n",
    "        n1 = np.random.binomial(N, p1)               \n",
    "\n",
    "    return n1 == N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate? True\n"
     ]
    }
   ],
   "source": [
    "print(\"Fixate?\", simulation(N=1000, s=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 87.8 µs per loop\n",
      "1000 loops, best of 3: 177 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=1000, s=0.1)\n",
    "%timeit simulation(N=1000000, s=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Numba](http://numba.pydata.org/)\n",
    "\n",
    "**Note**: Numba is not installed on the *binder* app, so the following cell will fail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numba 0.25.0\n"
     ]
    }
   ],
   "source": [
    "import numba\n",
    "print(\"Numba\", numba.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate? False\n"
     ]
    }
   ],
   "source": [
    "@numba.jit(locals=dict(s=numba.float64, N=numba.uint64, n0=numba.uint64, n1=numba.uint64, p=numba.float64))\n",
    "def simulation(N, s):\n",
    "    n1 = 1\n",
    "\n",
    "    while N > n1 > 0:\n",
    "        n0 = N - n1\n",
    "        p = n1 * (1 + s) / (n0 + n1 * (1 + s))\n",
    "        n1 = np.random.binomial(N, p)        \n",
    "\n",
    "    return n1 == N\n",
    "\n",
    "print(\"Fixate?\", simulation(N=1000, s=0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 1: 50.1 µs per loop\n",
      "1000 loops, best of 1: 70.3 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 1000 -r 1 simulation(N=1000, s=0.1)\n",
    "%timeit -n 1000 -r 1 simulation(N=1000000, s=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate % 0.188\n"
     ]
    }
   ],
   "source": [
    "def one_simulation(N, s):\n",
    "    n1 = 1\n",
    "\n",
    "    while N > n1 > 0:\n",
    "        p = n1 * (1 + s) / (N + n1 * s)\n",
    "        n1 = sum(1 for _ in range(N) if random.random() < p)              \n",
    "\n",
    "    return n1 == N\n",
    "\n",
    "def simulation(N, s, repetitions=1000):\n",
    "    return [one_simulation(N, s) for _ in range(repetitions)]\n",
    "\n",
    "fixations = simulation(N=1000, s=0.1, repetitions=1000)\n",
    "print(\"Fixate %\", sum(fixations)/len(fixations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 8.11 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=1000, s=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate % 0.171\n"
     ]
    }
   ],
   "source": [
    "def simulation(N, s, repetitions=1000):\n",
    "    n1 = np.ones(repetitions, dtype=np.uint64)\n",
    "    update = (n1 > 0) & (n1 < N)\n",
    "    \n",
    "    while update.any():\n",
    "        p = n1 * (1 + s) / (N + n1 * s) \n",
    "        n1[update] = np.random.binomial(N, p[update])\n",
    "        update = (n1 > 0) & (n1 < N)\n",
    "\n",
    "    return n1 == N\n",
    "\n",
    "print(\"Fixate %\", simulation(N=1000, s=0.1).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 20 ms per loop\n",
      "1 loop, best of 3: 183 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=1000, s=0.1)\n",
    "%timeit simulation(N=1000000, s=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "cimport cython\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "def simulation(np.uint64_t N, np.float64_t s, np.uint32_t repetitions=1000):\n",
    "    cdef np.ndarray[np.uint64_t] n1 = np.ones(repetitions, dtype=np.uint64)\n",
    "    cdef np.ndarray[np.uint8_t, cast=True] update = (n1 > 0) & (n1 < N)\n",
    "    cdef np.ndarray[np.float64_t] p\n",
    "    \n",
    "    while update.any():\n",
    "        p = n1 * (1 + s) / (N + n1 * s) \n",
    "        n1[update] = np.random.binomial(N, p[update])\n",
    "        update = (n1 > 0) & (n1 < N)\n",
    "\n",
    "    return n1 == N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate % 0.182\n"
     ]
    }
   ],
   "source": [
    "print(\"Fixate %\", simulation(N=1000, s=0.1).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 37.1 ms per loop\n",
      "1 loop, best of 3: 353 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=1000, s=0.1)\n",
    "%timeit simulation(N=1000000, s=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixation probability as function of population size\n",
    "\n",
    "Kimura's equation:\n",
    "\n",
    "$$\n",
    "\\frac{1 - e^{-2s}}{1 - e^{-2Ns}}\n",
    "$$\n",
    "\n",
    "For large $N$, the denominator is roughly 1, and if $s$ is small than we can approximate this by $2s$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kimura(N, s):\n",
    "    return np.expm1(-2 * s) / np.expm1(-2 * N * s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Nrange_kimura = np.logspace(1, 6, 50000, dtype=np.int64)\n",
    "s = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 634 ms per loop\n",
      "1000 loops, best of 3: 3.85 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit [kimura(N=N, s=s) for N in Nrange_kimura]\n",
    "%timeit -n 1000 kimura(N=Nrange_kimura, s=s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Numexpr](https://github.com/pydata/numexpr/wiki/Numexpr-Users-Guide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numexpr 2.5.2\n",
      "Intel(R) Math Kernel Library Version 11.3.1 Product Build 20151021 for Intel(R) 64 architecture applications\n"
     ]
    }
   ],
   "source": [
    "import numexpr as ne\n",
    "print(\"Numexpr\", ne.__version__)\n",
    "print(ne.get_vml_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kimura(N, s):\n",
    "    return ne.evaluate(\"expm1(-2 * s) / expm1(-2 * N * s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 796 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 1000 kimura(N=Nrange_kimura, s=s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot fixation probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "repetitions = 1000\n",
    "s = 0.01\n",
    "Nrange = np.logspace(1, 6, 20, dtype=np.uint64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fix_prob_sim = np.array([\n",
    "    simulation(N=N, s=s, repetitions=repetitions)\n",
    "    for N in Nrange\n",
    "])\n",
    "\n",
    "fix_prob_sim_mean = fix_prob_sim.mean(axis=1)\n",
    "fix_prob_sim_sem = fix_prob_sim.std(axis=1, ddof=1) / np.sqrt(repetitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Nrange_kimura = np.logspace(1, 6, 500)\n",
    "fix_prob_kimura = kimura(N=Nrange_kimura, s=s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib 1.5.1\n",
      "Seaborn 0.7.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "print(\"Matplotlib\", mpl.__version__)\n",
    "import matplotlib.pyplot as plt\n",
    "try:\n",
    "    import seaborn as sns\n",
    "    print(\"Seaborn\", sns.__version__)\n",
    "    sns.set_style('ticks')\n",
    "    sns.set_context('talk', font_scale=1.5)\n",
    "    sns.set_palette('colorblind')\n",
    "except ImportError:\n",
    "    print(\"Install seaborn for plot aesthetics\")\n",
    "    plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RvZGVK1fi0KFDcHBwwODBg2FqaorU1FRcvHgR4eHhWLp0KaZPnw4AGDduHHr3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\nBy5EKzFPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98ff7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax.plot(Nrange_kimura, fix_prob_kimura, ls='-', label='Approximation')\n",
    "ax.axhline(2*s, color='gray', ls='--', label='$2s$')\n",
    "ax.errorbar(x=Nrange, y=fix_prob_sim_mean, yerr=fix_prob_sim_sem, \n",
    "            fmt='o', capsize=5, label='Simulation')\n",
    "\n",
    "\n",
    "ax.set(\n",
    "    xlabel='Population size (N)',\n",
    "    ylabel='Fixation probability',\n",
    "    xscale='log', \n",
    "    xlim=(0.5 * Nrange.min(), 1.5 * Nrange.max()),\n",
    ")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixation time\n",
    "\n",
    "Now we will focus on the time it takes for the fixation of one of the alleles to occur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate % 0.156\n"
     ]
    }
   ],
   "source": [
    "def simulation(N, s, repetitions=1000):\n",
    "    N = int(N) # very important! if this is a float than the while loop will be ~infinite\n",
    "    n1 = np.ones(repetitions, dtype=np.uint64)\n",
    "    T = np.empty_like(n1)\n",
    "    update = (n1 > 0) & (n1 < N)\n",
    "    t = 0\n",
    "    \n",
    "    while update.any():\n",
    "        t += 1\n",
    "        p = n1 * (1 + s) / (N + n1 * s) \n",
    "        n1[update] = np.random.binomial(N, p[update])\n",
    "        T[update] = t\n",
    "        update = (n1 > 0) & (n1 < N)\n",
    "\n",
    "    return n1 == N, T\n",
    "\n",
    "print(\"Fixate %\", simulation(N=1000, s=0.1)[0].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 11.8 ms per loop\n",
      "1 loop, best of 3: 418 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=10, s=0.01, repetitions=1000)\n",
    "%timeit simulation(N=1000000, s=0.01, repetitions=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Fixation: 0.18\n",
      "Avg. time: 168\n"
     ]
    }
   ],
   "source": [
    "fixations, times = simulation(N=10000, s=0.1, repetitions=1000)\n",
    "fixation_prob = fixations.mean()\n",
    "fixation_time = times[fixations].mean()\n",
    "print(\"% Fixation: {:.2g}\".format(fixation_prob))\n",
    "print(\"Avg. time: {:.0f}\".format(fixation_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0JKlTp06aO3euHn74YV133XW68sorlZGRoY8//ljDhw/X+PHjJUler1eLFi2S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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb600ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w, h = plt.rcParams['figure.figsize']\n",
    "fig, ax = plt.subplots(1, 2, figsize=(2 * w, h))\n",
    "\n",
    "sns.distplot(times[fixations], ax=ax[0])\n",
    "ax[0].axvline(times[fixations].mean(), color='k', ls='--')\n",
    "ax[0].set(xlabel='Fixation time', ylabel='Frequency')\n",
    "\n",
    "sns.distplot(times[~fixations], ax=ax[1])\n",
    "ax[1].axvline(times[~fixations].mean(), color='k', ls='--')\n",
    "ax[1].set(xlabel='Extinction time', ylabel='Frequency', \n",
    "          xlim=(0, times[~fixations].mean()*10))\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "cimport cython\n",
    "import numpy as np\n",
    "cimport numpy as np\n",
    "\n",
    "def simulation(np.uint64_t N, np.float64_t s, np.uint32_t repetitions=1000):\n",
    "    cdef np.ndarray[np.uint64_t] n1 = np.ones(repetitions, dtype=np.uint64)\n",
    "    cdef np.ndarray[np.uint64_t] T = np.empty_like(n1)\n",
    "    cdef np.ndarray[np.uint8_t, cast=True] update = (n1 > 0) & (n1 < N)\n",
    "    cdef np.ndarray[np.float64_t] p\n",
    "    cdef np.uint64_t t = 0\n",
    "    \n",
    "    while update.any():\n",
    "        t += 1\n",
    "        p = n1 * (1 + s) / (N + n1 * s) \n",
    "        n1[update] = np.random.binomial(N, p[update])\n",
    "        T[update] = t\n",
    "        update = (n1 > 0) & (n1 < N)\n",
    "\n",
    "    return n1 == N, T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 12.6 ms per loop\n",
      "1 loop, best of 3: 443 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=10, s=0.01, repetitions=1000)\n",
    "%timeit simulation(N=1000000, s=0.01, repetitions=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixation time as function of population size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "repetitions = 1000\n",
    "s = 0.1\n",
    "Nrange = np.logspace(1, 6, 10, dtype=np.uint64)\n",
    "\n",
    "def fix_time_simulation(N):\n",
    "    fixations, times = simulation(N=N, s=s, repetitions=repetitions)\n",
    "    fixation_time_mean = times[fixations].mean()\n",
    "    fixation_time_std =  times[fixations].std(ddof=1) / np.sqrt(repetitions)\n",
    "    return fixation_time_mean, fixation_time_std\n",
    "\n",
    "fix_time_sim = np.array([\n",
    "    fix_time_simulation(N=N)\n",
    "    for N in Nrange\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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avHmzWrRoodjYWKWlpV30y0IAAAC4fCZH5S7nqJPc3FzFx8dr27Zt1T6TAAAA0NRdLA81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\nDjvPSFdhQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb053240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fixation_time_plot(N, mean, sem):\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "    ax.errorbar(x=N, y=mean, yerr=sem, \n",
    "                fmt='o', capsize=5, label='Simulation')\n",
    "\n",
    "    ax.set(\n",
    "        xlabel='Population size (N)',\n",
    "        ylabel='Fixation time',\n",
    "        xscale='log', \n",
    "        xlim=(0.5 * Nrange.min(), 1.5 * Nrange.max()),\n",
    "    )\n",
    "    return fig, ax\n",
    "\n",
    "fixation_time_plot(Nrange, fix_time_sim[:,0], fix_time_sim[:,1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixation time approximations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deterministic approximation\n",
    "\n",
    "The first approximation is attributed to [Haldane 1924](http://link.springer.com/article/10.1007%2FBF02459574) (via [Hartfiled & Otto 2011](http://www.ncbi.nlm.nih.gov/pubmed/21884046)).\n",
    "\n",
    "The frequency of the mutant can be approximated by: \n",
    "\n",
    "$$\n",
    "f(t) = \\frac{f(0)}{f(0) + (1-f(0))(1-s)^t}\n",
    "$$\n",
    "or assuming $f(0)=1/N$ and $N-1 \\approx N$:\n",
    "$$\n",
    "f(t) = \\frac{1}{1 + N (1-s)^t}\n",
    "$$\n",
    "\n",
    "Inversing this equation and looking for time $T_{fix}$ in which $f(T_{fix})=\\frac{N-1}{N}$ we get:\n",
    "$$\n",
    "T_{fix} = -2 \\frac{\\log{N}}{\\log{(1-s)}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 8.54 ms per loop\n",
      "100 loops, best of 3: 7.64 ms per loop\n"
     ]
    }
   ],
   "source": [
    "def T_haldane(N, s):\n",
    "    return -2 * np.log(N) / np.log1p(-s)\n",
    "%timeit T_haldane(np.logspace(2, 9, 100000), 0.01)\n",
    "def T_haldane(N, s):\n",
    "    return ne.evaluate(\"-2 * log(N) / log1p(-s)\")\n",
    "%timeit T_haldane(np.logspace(2, 9, 100000), 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic approximation\n",
    "\n",
    "A more complex approximation is based on a *diffusion equation* and is taken from eq. (17) in [Kimura and Ohta 1969](http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1212239) (the reference assumes a population size of $2N$ gametes and selection advantage of $s/2$ rather then $N$ and $s$, therefore I'm changing $s=2s$ and $N=N/2$).\n",
    "\n",
    "$$\n",
    "I_1(x) = \\frac{1 - e^{-2Nsx} - e^{-2Ns(1-x)} + e^{-2Ns}}{x(1-x)} \\\\\n",
    "I_2(x) = \\frac{(e^{2Nsx} - 1) (1 - e^{-2Ns(1-x)})}{x(1-x)} \\\\\n",
    "J_1 = \\frac{1}{s(1-e^{-2Ns})} \\int_{x}^{1}{I_1(y) dy} \\\\\n",
    "J_2 = \\frac{1}{s(1-e^{-2Ns})} \\int_{0}^{x}{I_2(y) dt} \\\\\n",
    "u = \\frac{1 - e^{-2Nsx}}{1 - e^{-2Ns}} \\\\\n",
    "T_{fix} = J1 + \\frac{1-u}{u} J_2\n",
    "$$\n",
    "\n",
    "Here we need to integrate some functions, we'll do this using `scipy.integrate.quad`. \n",
    "\n",
    "`functools.partial` reduces the number of arguments a function expects, effectively freezing some of them - mathematically, it creates a projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.integrate import quad \n",
    "from functools import partial\n",
    "\n",
    "def integral(f, N, s, a, b):\n",
    "    f = partial(f, N, s)    \n",
    "    return quad(f, a, b)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def I1(N, s, x):\n",
    "    if x == 1:\n",
    "        return 0\n",
    "    return (1 - np.exp(-2*N*s*x) - np.exp(-2 * N * s * (1 - x)) + np.exp(-2 * N *s)) / (x*(1-x))\n",
    "\n",
    "def I2(N, s, x):\n",
    "    if x == 0:\n",
    "        return 0\n",
    "    return -np.expm1(2 * N * s * x) * np.expm1(-2 * N * s * x) / (x * (1 - x))\n",
    "\n",
    "@np.vectorize\n",
    "def T_kimura(N, s):\n",
    "    x = 1.0 / N\n",
    "    J1 = -1.0 / (s * np.expm1(-2 * N * s)) * integral(I1, N, s, x, 1)\n",
    "    u = np.expm1(-2 * N * s * x) / np.expm1(-2 * N * s)\n",
    "    J2 = -1.0 / (s * np.expm1(-2 * N *s)) * integral(I2, N, s, 0, x)\n",
    "    return J1 + ((1 - u) / u) * J2\n",
    "\n",
    "assert 254 < T_kimura(1e6, 0.1) < 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 9.02 s per loop\n",
      "1 loop, best of 3: 4.34 s per loop\n"
     ]
    }
   ],
   "source": [
    "Nrange_kimura = np.logspace(1, 6, 1000)\n",
    "%timeit x = np.array([T_kimura(N=N, s=s) for N in Nrange_kimura])\n",
    "%timeit y = T_kimura(N=Nrange_kimura, s=s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = 0.1\n",
    "Nrange_kimura = np.logspace(1, 6, 100)\n",
    "fix_time_kimura = T_kimura(N=Nrange_kimura, s=s)\n",
    "fix_time_haldane = T_haldane(N=Nrange_kimura, s=s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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YQd26dWnatGm+ynkSoCcnJ+e6plQqycrKKlQgLYQQQoBsbyTKCB8fHxQKBZ07d8ba2hpd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c26fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = fixation_time_plot(Nrange, fix_time_sim[:,0], fix_time_sim[:,1]);\n",
    "ax.plot(Nrange_kimura, fix_time_haldane, ls='-', label='Deterministic')\n",
    "ax.plot(Nrange_kimura, fix_time_kimura, ls='-', label='Stochastic')\n",
    "ax.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parallelization with [IPyParallel](http://ipyparallel.readthedocs.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixate % 0.17\n"
     ]
    }
   ],
   "source": [
    "def simulation(N, s, repetitions=1000):\n",
    "    import numpy as np # needed for parallel runs\n",
    "    N = int(N)\n",
    "    \n",
    "    n1 = np.ones(repetitions, dtype=np.uint64)\n",
    "    T = np.empty_like(n1)\n",
    "    update = (n1 > 0) & (n1 < N)\n",
    "    t = 0\n",
    "    \n",
    "    while update.any():\n",
    "        t += 1\n",
    "        p = n1 * (1 + s) / (N + n1 * s) \n",
    "        n1[update] = np.random.binomial(N, p[update])\n",
    "        T[update] = t\n",
    "        update = (n1 > 0) & (n1 < N)\n",
    "\n",
    "    return n1 == N, T\n",
    "\n",
    "print(\"Fixate %\", simulation(N=1000, s=0.1)[0].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 364 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit simulation(N=1000000, s=0.01, repetitions=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean time: 2035.00 +- 5.94276 (sem)\n"
     ]
    }
   ],
   "source": [
    "def fix_time_aggregate(sim_results):\n",
    "    fixations, times = sim_results\n",
    "    fixation_time_mean = times[fixations].mean()\n",
    "    fixation_time_sem =  times[fixations].std(ddof=1) / np.sqrt(repetitions)\n",
    "    return fixation_time_mean, fixation_time_sem\n",
    "\n",
    "fixation_time_mean, fixation_time_sem = fix_time_aggregate(simulation(N=1000000, s=0.01, repetitions=1000))\n",
    "print(\"Mean time: {:.2f} +- {:.5f} (sem)\".format(fixation_time_mean, fixation_time_sem))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can't use `%timeit` with parallelized code, so this is a simple context manager to measure time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import contextlib\n",
    "import time\n",
    "\n",
    "@contextlib.contextmanager\n",
    "def tictoc():\n",
    "    tic = time.time()\n",
    "    yield tic\n",
    "    toc = time.time()\n",
    "    print(\"Elapsed: {:.4f} seconds\".format(toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed: 9.7886 seconds\n"
     ]
    }
   ],
   "source": [
    "repetitions = 1000\n",
    "s = 0.01\n",
    "Nrange = np.logspace(1, 6, 50)\n",
    "\n",
    "with tictoc():\n",
    "    fix_time_sim = [\n",
    "        simulation(N=N, s=s, repetitions=repetitions)\n",
    "        for N in Nrange\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to parallelize with IPyParallel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython Parallel 5.0.1\n"
     ]
    }
   ],
   "source": [
    "import ipyparallel\n",
    "print(\"IPython Parallel\", ipyparallel.__version__)\n",
    "from ipyparallel import Client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open a terminal and start the IPython cluster with the following command:\n",
    "```\n",
    "$ ipcluster start --n=4\n",
    "```\n",
    "Now you can connect to the cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Engines IDs [0, 1, 2]\n"
     ]
    }
   ],
   "source": [
    "rc = Client()\n",
    "print(\"Engines IDs\", rc.ids)\n",
    "dview = rc[:] # use all engines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed: 5.9733 seconds\n"
     ]
    }
   ],
   "source": [
    "with tictoc():\n",
    "    fix_time_sim = dview.map_sync(\n",
    "        simulation, \n",
    "        Nrange,\n",
    "        [s]*len(Nrange),\n",
    "        [repetitions]*len(Nrange)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T5rIuXbpQvXr1XIEUoFWrVgBcvHixUJ+fIzw8nNjYWEaMGGEOpAB2dnb861//4qeffmLD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd468160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = np.array([fix_time_aggregate(res) for res in fix_time_sim])\n",
    "fixation_time_mean = _[:,0]\n",
    "fixation_time_sem  = _[:,1]\n",
    "\n",
    "fig, ax = fixation_time_plot(Nrange, fixation_time_mean, fixation_time_sem)\n",
    "ax.plot(Nrange, T_kimura(Nrange, s), label='Approximation')\n",
    "ax.legend(loc=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# IPyWidgets\n",
    "\n",
    "IPyWidgets are great for interacting with plots.\n",
    "\n",
    "Here is a static plot of Kimura's approximation to the fixation time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AihUr5qvNXZaUlARAQkLOAahUqdJta69du3bb2sTExLvaFxEREZGSkJ1t8P73Z/jjl0dI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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd7336d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def approximation_plot(log10Nmax=6, log10s=-2):\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    \n",
    "    Nrange = np.logspace(1, log10Nmax, 1000)\n",
    "    s = 10**log10s\n",
    "    ax.plot(Nrange_kimura, T_kimura(Nrange_kimura, s))\n",
    "\n",
    "    ax.set(\n",
    "        xlabel='Population size (N)',\n",
    "        ylabel='Fixation time',\n",
    "        xscale='log', \n",
    "        xlim=(0.5 * Nrange_kimura.min(), 1.5 * Nrange_kimura.max()),\n",
    "    )\n",
    "            \n",
    "approximation_plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPyWidgets 4.1.1\n"
     ]
    }
   ],
   "source": [
    "import ipywidgets\n",
    "print(\"IPyWidgets\", ipywidgets.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the following command will create an interactive plot with sliders for the maximum of $\\log_{10}{N}$ and $\\log_{10}{s}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AihUr5qvNXZaUlARAQkLOAahUqdJta69du3bb2sTExLvaFxEREZGSkJ1t8P73Z/jjl0dI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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd48da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipywidgets.interact(approximation_plot, log10Nmax=(1.0, 12.0), log10s=(-6.0, -1.0));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-type model\n",
    "\n",
    "The above simulations are hard to extend for a multi-type model in which there are more than two types, because we used scalar arithmetics and the binomial distribution.\n",
    "\n",
    "The following is the extension that uses matrix multiplication and the multinomial distribution.\n",
    "\n",
    "There are now **L** types; the fitness of type **i** is $(1+s)^i$; type **i** can mutate to types **i-1** and **i+1** with probability $m$.\n",
    "\n",
    "Note the use of the matrix multiplication operator, `@`, which is new in Python 3.5 and NumPy 1.10. If you are using an earlier version, replace `M @ S` and `E @ p` with `M.dot(S)` and `E.dot(p)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def simulation(N, s, m, L, repetitions=1000):\n",
    "    assert N > 0\n",
    "    N = np.uint64(N)\n",
    "    \n",
    "    w = (1 + s) ** np.arange(L)\n",
    "    S = np.diag(w)\n",
    "    \n",
    "    M = np.diag(np.repeat(1 - 2 * m, L))\n",
    "    M += np.diag(np.repeat(m, L - 1), 1)\n",
    "    M += np.diag(np.repeat(m, L - 1), -1)\n",
    "    \n",
    "    E = M @ S\n",
    "\n",
    "    # rows are genotypes, cols are repretitions\n",
    "    n = np.zeros((L, repetitions))\n",
    "    n[0,:] = N    \n",
    "    # which columns to update\n",
    "    update = np.array([True] * repetitions)\n",
    "    # follow time\n",
    "    T = np.zeros(repetitions)\n",
    "    t = 0\n",
    "    # follow population mean fitness\n",
    "    W = []\n",
    "    \n",
    "    while update.any():\n",
    "        t += 1\n",
    "        T[update] = t        \n",
    "        p = n/N  # counts to frequencies\n",
    "        W.append(w.reshape(1, L) @ p)  # mean fitness\n",
    "        p = E @ p  # natural selection + mutation        \n",
    "        p /= p.sum(axis=0)  # mean fitness\n",
    "        for j in update.nonzero()[0]:\n",
    "            # random genetic drift\n",
    "            n[:,j] = np.random.multinomial(N, p[:,j])\n",
    "        update = (n[-1,:] < N)  # fixation of fittest genotype\n",
    "        \n",
    "    return T, W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaptation time: 441.34\n"
     ]
    }
   ],
   "source": [
    "N=10000\n",
    "s=0.1\n",
    "m=1e-4\n",
    "L=5\n",
    "\n",
    "times, fitness = simulation(N, s, m, L, repetitions=1000)\n",
    "print(\"Adaptation time: {:.2f}\".format(times.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas 0.18.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print(\"Pandas\", pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put fitness data in a pandas data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
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       "      <th>992</th>\n",
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       "      <th>994</th>\n",
       "      <th>995</th>\n",
       "      <th>996</th>\n",
       "      <th>997</th>\n",
       "      <th>998</th>\n",
       "      <th>999</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
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       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00000</td>\n",
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       "      <td>1.00002</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00003</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
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       "      <td>1.00002</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00004</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00004</td>\n",
       "      <td>1.00004</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00003</td>\n",
       "      <td>1.00007</td>\n",
       "      <td>1.00003</td>\n",
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       "      <td>1.00001</td>\n",
       "      <td>1.00002</td>\n",
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       "      <td>1.00004</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00003</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00007</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00002</td>\n",
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       "      <td>1.00003</td>\n",
       "      <td>1.00004</td>\n",
       "      <td>1.00005</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00004</td>\n",
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       "      <td>1.00004</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00007</td>\n",
       "      <td>1.00008</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00010</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00007</td>\n",
       "      <td>1.00006</td>\n",
       "      <td>1.00005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1001 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0        1        2        3        4        5        6        7  \\\n",
       "0  1.00000  1.00000  1.00000  1.00000  1.00000  1.00000  1.00000  1.00000   \n",
       "1  1.00002  1.00001  1.00001  1.00000  1.00003  1.00000  1.00002  1.00002   \n",
       "2  1.00003  1.00002  1.00002  1.00000  1.00001  1.00001  1.00003  1.00005   \n",
       "3  1.00003  1.00007  1.00003  1.00004  1.00001  1.00002  1.00006  1.00004   \n",
       "4  1.00004  1.00012  1.00004  1.00002  1.00000  1.00001  1.00007  1.00008   \n",
       "\n",
       "         8        9  ...       991      992      993      994      995  \\\n",
       "0  1.00000  1.00000  ...   1.00000  1.00000  1.00000  1.00000  1.00000   \n",
       "1  1.00002  1.00000  ...   1.00002  1.00002  1.00001  1.00000  1.00001   \n",
       "2  1.00002  1.00000  ...   1.00001  1.00001  1.00004  1.00001  1.00001   \n",
       "3  1.00001  1.00001  ...   1.00003  1.00001  1.00007  1.00001  1.00002   \n",
       "4  1.00001  1.00002  ...   1.00001  1.00001  1.00010  1.00001  1.00002   \n",
       "\n",
       "       996      997      998      999  Time  \n",
       "0  1.00000  1.00000  1.00000  1.00000     0  \n",
       "1  1.00001  1.00002  1.00001  1.00002     1  \n",
       "2  1.00001  1.00004  1.00004  1.00002     2  \n",
       "3  1.00002  1.00003  1.00004  1.00005     3  \n",
       "4  1.00002  1.00007  1.00006  1.00005     4  \n",
       "\n",
       "[5 rows x 1001 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitness = np.array(fitness).squeeze()\n",
    "df = pd.DataFrame(data=fitness)\n",
    "df['Time'] = df.index\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melt the data frame so that each fitness value has its own row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Fitness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time Replicate  Fitness\n",
       "0     0         0  1.00000\n",
       "1     1         0  1.00002\n",
       "2     2         0  1.00003\n",
       "3     3         0  1.00003\n",
       "4     4         0  1.00004"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.melt(df, id_vars='Time', var_name='Replicate', value_name='Fitness')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now group by `Time` and calculate the average fitness across all replicates. `reset_index` recreates the `Time` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Fitness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.000035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.000047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time   Fitness\n",
       "0     0  1.000000\n",
       "1     1  1.000010\n",
       "2     2  1.000022\n",
       "3     3  1.000035\n",
       "4     4  1.000047"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df = df.groupby('Time')['Fitness'].mean().reset_index()\n",
    "mean_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now draw a random sample of 5% of simulations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grp = df.groupby('Replicate')\n",
    "filtered = grp.filter(lambda x: random.random() < 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pivot the table - this is the reverse of `melt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Replicate</th>\n",
       "      <th>63</th>\n",
       "      <th>65</th>\n",
       "      <th>86</th>\n",
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       "      <th>196</th>\n",
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       "      <th>244</th>\n",
       "      <th>277</th>\n",
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       "      <th>358</th>\n",
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       "      <th>941</th>\n",
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       "      <th>974</th>\n",
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       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>1.00000</td>\n",
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       "      <td>1.00000</td>\n",
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       "      <td>1.00000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1.00001</td>\n",
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       "      <td>1.00002</td>\n",
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       "      <td>1.00002</td>\n",
       "      <td>1.00003</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00004</td>\n",
       "      <td>1.00000</td>\n",
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       "      <td>1.00002</td>\n",
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       "      <td>1.00004</td>\n",
       "      <td>1.00002</td>\n",
       "      <td>1.00004</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00001</td>\n",
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       "      <td>1.00004</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1.00002</td>\n",
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       "      <td>1.00004</td>\n",
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       "      <td>1.00004</td>\n",
       "      <td>1.00005</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.00002</td>\n",
       "      <td>1.00001</td>\n",
       "      <td>1.00008</td>\n",
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       "      <td>1.00004</td>\n",
       "      <td>1.00004</td>\n",
       "      <td>1.00005</td>\n",
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       "      <td>1.00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.00008</td>\n",
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       "      <td>1.00006</td>\n",
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       "      <td>1.00003</td>\n",
       "      <td>1.00007</td>\n",
       "      <td>1.00008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Replicate      63       65       86   180      196      202      244      277  \\\n",
       "Time                                                                            \n",
       "0          1.00000  1.00000  1.00000  1.0  1.00000  1.00000  1.00000  1.00000   \n",
       "1          1.00000  1.00001  1.00000  1.0  1.00000  1.00001  1.00001  1.00001   \n",
       "2          1.00001  1.00004  1.00000  1.0  1.00002  1.00002  1.00005  1.00004   \n",
       "3          1.00002  1.00009  1.00002  1.0  1.00004  1.00004  1.00006  1.00005   \n",
       "4          1.00003  1.00014  1.00002  1.0  1.00003  1.00007  1.00007  1.00008   \n",
       "\n",
       "Replicate      324      358   ...         840      861      867      886  \\\n",
       "Time                          ...                                          \n",
       "0          1.00000  1.00000   ...     1.00000  1.00000  1.00000  1.00000   \n",
       "1          1.00002  1.00001   ...     1.00001  1.00002  1.00000  1.00002   \n",
       "2          1.00002  1.00004   ...     1.00001  1.00001  1.00001  1.00005   \n",
       "3          1.00004  1.00005   ...     1.00002  1.00002  1.00001  1.00008   \n",
       "4          1.00005  1.00005   ...     1.00005  1.00005  1.00001  1.00012   \n",
       "\n",
       "Replicate      941      944      964      967      969      974  \n",
       "Time                                                             \n",
       "0          1.00000  1.00000  1.00000  1.00000  1.00000  1.00000  \n",
       "1          1.00000  1.00002  1.00002  1.00002  1.00002  1.00003  \n",
       "2          1.00003  1.00004  1.00004  1.00002  1.00004  1.00005  \n",
       "3          1.00005  1.00004  1.00004  1.00005  1.00007  1.00005  \n",
       "4          1.00006  1.00004  1.00009  1.00003  1.00007  1.00008  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted = filtered.pivot('Time', 'Replicate', 'Fitness')\n",
    "pivoted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to plot. \n",
    "\n",
    "The left plot is similar to the *Fixation time* plots from above.\n",
    "\n",
    "The right plot is new - we plot 5% of the simulations using colored solid lines and the average line using a thick black dashed line. The gray line marks the expected theoretical final population mean fitness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KE8MG7Hflolpr3jB9o0S7ObOinY7BPrY3d9M3EKF3IEpwMMapZ//LqOf//LxG/mfLTp58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qtYVv5U57TTTmPRokXMmzcPTfvgf3SPx8MNN9zAc889x6pVqw6e1Q5jTls0H8pRNo4LuwwjSRJN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1830c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "sns.distplot(times, ax=ax[0])\n",
    "ax[0].axvline(times.mean(), color='gray', ls='--')\n",
    "ax[0].set(xlabel='Adaptation time', ylabel='Frequency')\n",
    "\n",
    "pivoted.plot(ax=ax[1], alpha=0.5)\n",
    "mean_df.plot('Time', 'Fitness', color='k', alpha=0.85, ls='--', lw=3, ax=ax[1])\n",
    "ax[1].axhline((1 + s)**(L - 1) * (1 - 2 * m), color='gray', ls='--')\n",
    "ax[1].legend().set_visible(False)\n",
    "ax[1].set(ylabel='Population mean fitness', xlim=(0, df.Time.max()/2))\n",
    "\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Colophon\n",
    "\n",
    "This notebook was produced for [PyCon Israel 2016](http://il.pycon.org/2016/) by [Yoav Ram](http://www.yoavram.com).\n",
    "\n",
    "The most recent version of this notebook, as well as some additional material and info, can be found at <https://github.com/yoavram/PyConIL2016>.\n",
    "\n",
    "The notebook is under a CC-BY-SA 3.0 license."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyConIL2016",
   "language": "python",
   "name": "pyconil2016"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}