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 },
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#[Extrapolating weak selection in Evolutionary Games](dx.doi.org/10.1371/journal.pcbi.1003381)\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook assumes that *ews.py* and *visualization.py* are in the same folder of this notebook."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Estimating the number of ranking changes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We need to specify some parameters to call the estimation procedure."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python ews.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Traceback (most recent call last):\r\n",
        "  File \"ews.py\", line 298, in <module>\r\n",
        "    assert len(sys.argv) == 5, \"You must specify the SEED, the NUMBER_OF_STRATEGIES \" \\\r\n",
        "AssertionError: You must specify the SEED, the NUMBER_OF_STRATEGIES and the NUMBER_OF_REPETITIONS, and the DISTRIBUTION_TYPE 0 -- UNIFORM, 1 -- NORMAL\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us call the estimation procedure with **SEED=1234**, **3** strategies, **50** samples and uniformly distributed games. Fifty samples will not give a quality estimate, but it is fine for demonstration purposes. This will take a couple of minutes..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python ews.py 1234 3 50 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Files n_3_dist_0_seed_1234_count.pickle and ma_n_3_dist_0_seed_1234_count.pickle have been created\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can list the newly created files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -l *.pickle"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-rw-rw-  1 garcia  staff  54 Oct  4 10:46 ma_n_3_dist_0_seed_1234_count.pickle\r\n",
        "-rw-rw-rw-  1 garcia  staff  62 Oct  4 10:46 n_3_dist_0_seed_1234_count.pickle\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can inspect the results by [unpickling](http://docs.python.org/2/library/pickle.html) the new files."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle\n",
      "filename_ranking_counts = 'n_3_dist_0_seed_1234_count.pickle'\n",
      "filename_most_abundant_counts = 'ma_n_3_dist_0_seed_1234_count.pickle'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ranking_changes = pickle.load(open(filename_ranking_counts))\n",
      "most_abundant_changes = pickle.load(open(filename_most_abundant_counts))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ranking_changes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "Counter({0: 34, 1: 13, 2: 3})"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this paticular estimate, out of 50 samples, 34 have no ranking changes, 13 have one ranking change and 3 have two ranking changes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "most_abundant_changes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "Counter({0: 42, 1: 8})"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In 8 out of 50 cases, the most abundant strategy changes once."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Inside the sampling procedure"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We first import *ews.py* to load all the function definitions. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from ews import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first step in the estimation is to generate a random game. For this example, we use 3 strategies and uniformly distributed payoffs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "number_of_strategies = 3\n",
      "distribution_type = 0 # for uniform\n",
      "a_random_game = random_game(number_of_strategies, distribution_type)\n",
      "print a_random_game"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.89150299  0.18478255  0.43155487]\n",
        " [ 0.0920854   0.80987834  0.04519806]\n",
        " [ 0.88729132  0.56049353  0.20090459]]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let us create a vector of selection intensities, in order to compute the abundance curves. Inside this process, for every intensity of selection we will compute the transition matrix of the markov chain that describes the imitation process, and then compute the stationary distribution."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We use the default limits in variables LEFT_LIMIT and RIGHT_LIMIT:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'Intensity of selection interval = {}'.format((10**LEFT_LIMIT, 10**RIGHT_LIMIT))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "'Intensity of selection interval = (0.001, 4.466835921509632)'"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intensity_vector = np.logspace(LEFT_LIMIT, RIGHT_LIMIT, num=100,\n",
      "                                       endpoint=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We create an auxliary matrix that contains the abundances vector across the values of our intensity vector.\n",
      "\n",
      "Therefore, this matrix has as many columns as strategies in our game, and as many rows as values in *intensity_vector*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "population_size = 30\n",
      "matrix = get_curves_truncating(intensity_vector, a_random_game,\n",
      "                                           population_size,\n",
      "                                           truncate=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What are the dimensions of matrix?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matrix.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(96, 3)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that if *matrix* has lesss than 100 values, the graph has been truncated to avoid overflow. This happens when selection is strong enough and the stationary distribution becomes a singleton."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can now count how many intersections are there for this case:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_intersections(matrix)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And how many changes in the most abundant strategy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_most_abundant_changes(matrix)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we use the function in visualization.py to build a graph."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from visualization import plot_abundance"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_abundance(matrix, intensity_vector)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s2bMRGBiIAQMGwMrKCn5+fjh58iQAwNfXF1999RWCgoLg7OwMqVQKBwcHNGrU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\nRABYEImIiACwIBIREQFgQSQiIgIA/BfzdAoAAoeekQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1022c3890>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can re-run the cells up to here to generate another example. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Using the functions judiciously"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can use the functions in *ews.py*, for instance, to replicate **Figure 3A**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure_3a_game = np.array([[0.18,0.91,0.85],[0.11,0.59,0.78],[0.75,0.11,0.54]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "intensity_vector = np.logspace(LEFT_LIMIT, RIGHT_LIMIT, num=100,\n",
      "                                       endpoint=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "auxiliary_matrix = get_curves_truncating(intensity_vector, figure_3a_game, population_size=30, truncate=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_abundance(auxiliary_matrix, intensity_vector)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ciBs3btQ7LOPdCQ0NxdChQ0FE+PLLL9G1a1cZZXjjxg3ExMQgODhYjlIyGO+I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\n0hYZ3t6Ary9w+LCkYVhVdTUwdy7wwQcW21SE3SebhGg0GjF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58xEfH4/o6GiJopef1t7P\njz76CNnZ2fjss89Mc0hZ6+/ntWvX8Pvf/x7fffedMr6hN1NL7ufEiROxY8cOzJkzB+PGjZMoYnlr\nyf20579J2UzMt4TGFgw/evRovXMiIyMRGRlp7dBsUnPu57x58zBv3jxrh2aTmnM/PT09sW7dOmuH\nZpOaup8uLi5ISkqSMDLb1NT9tOe/SbupITaGV6wxL76f5sX307z4fpqXEu+nXSdEb29vlJWVmR6X\nlZVBq9VKGJFt4/tpXnw/zYvvp3kp8X7adUIMDw9HcXExSktLUVVVha1bt3IfQhvw/TQvvp/mxffT\nvBR5P6Ue1WMuOp1OdO/eXbRr105otVqRlJQkhBBiz5494sknnxR+fn5i2bJlEkdpO/h+mhffT/Pi\n+2lefD8JT8xnjDHGYOdNpowxxlhzcUJkjDHGwAmRMcYYA8AJkTHGGAPACZExxhgDwAmRMcYYA8AJ\nkTHGGAPACZExxhgDwAmRMcYYAwD8PxqDJhGtVTRHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105720fd0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Unit Tests"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also run the unit tests."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!python tests.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "......\r\n",
        "----------------------------------------------------------------------\r\n",
        "Ran 8 tests in 2.885s\r\n",
        "\r\n",
        "OK\r\n"
       ]
      }
     ],
     "prompt_number": 23
    }
   ],
   "metadata": {}
  }
 ]
}