mamba notebook.ipynb

{
 "metadata": {
  "name": "mamba notebook"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd d:\\workspace\\mamba"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d:\\workspace\\mamba\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import simulation\n",
      "reload(simulation);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simulation.mu*=100\n",
      "simulation.mu"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "0.3"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p,g = simulation.run(debug=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Starting simulation with  10 ticks\n",
        "Drift\n",
        "Selection\n",
        "Mutation\n",
        "Update"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-12-2caebf423e16>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msimulation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32md:\\workspace\\mamba\\simulation.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(ticks, tick_interval, debug)\u001b[0m\n\u001b[0;32m     35\u001b[0m                 \u001b[0mselection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfitness\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mdebug\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Mutation\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m                 \u001b[0mpopulation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgenomes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmutation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgenomes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmutation_rates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_loci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_genome\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     38\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mdebug\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Update\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m                 \u001b[0mfitness\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_fitness\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgenomes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_genome\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32md:\\workspace\\mamba\\model.pyc\u001b[0m in \u001b[0;36mmutation_implicit_genomes\u001b[1;34m(population, genomes, mutation_rates, num_loci, target_genome)\u001b[0m\n\u001b[0;32m    101\u001b[0m                                 \u001b[0mnew_counts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m                         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m                                 \u001b[0mnew_genome\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenomes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    104\u001b[0m                                 \u001b[0mnew_genome\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlocus\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_allele\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m                                 \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfind_row\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgenomes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_genome\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Tick 0\n",
        "Drift\n",
        "Selection\n",
        "Mutation\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import model\n",
      "reload(model);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p=model.create_mutation_free_population()\n",
      "p[0]-=100\n",
      "p[1]+=100\n",
      "f=model.create_fitness()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print p[-1]\n",
      "model.selection(p,f)\n",
      "print p[-1]\n",
      "model.selection(p,f)\n",
      "print p[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n",
        "0\n",
        "0\n"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.get_include()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "'C:\\\\Python27\\\\lib\\\\site-packages\\\\numpy\\\\core\\\\include'"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time.clock()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "24.026697858339496"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t=array(zeros(1000), dtype=np.int)\n",
      "g=zeros((100,1000), dtype=np.int)\n",
      "for i in range(100):\n",
      "    g[i, randint(0,999)] = 1\n",
      "g[1,5] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g.sum(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1])"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ham1(g,t):\n",
      "    a = zeros(100, dtype=np.int)\n",
      "    for i in range(a.shape[0]):\n",
      "        a[i] = (t!=g[i,:]).sum()\n",
      "    return a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g.shape[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "100L"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ham1(g,t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
        "       1, 1, 1, 1, 1, 1, 1, 1])"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "import numpy as np\n",
      "cimport numpy as np\n",
      "cimport cython\n",
      "\n",
      "@cython.boundscheck(False)\n",
      "def ham2(np.ndarray[int, ndim=2] g, np.ndarray[int, ndim=1] t):\n",
      "    cdef np.ndarray[int, ndim=1] a \n",
      "    a = np.zeros(g.shape[0], dtype=np.int)\n",
      "    cdef Py_ssize_t i\n",
      "    for i in range(g.shape[0]):\n",
      "        a[i] = (t!=g[i,:]).sum()\n",
      "    return a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def hamming_fitness(genome, target_genome):\n",
      "\treturn (genome != target_genome).sum()\n",
      "\n",
      "def ham3(genomes, t):\n",
      "    return np.apply_along_axis(hamming_fitness, 1, g, t) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n 10000 ham1(g,t)\n",
      "%timeit -n 10000 ham2(g,t)\n",
      "%timeit -n 10000 ham3(g,t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 2.58 ms per loop\n",
        "1000 loops, best of 3: 2.31 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 5.11 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 179
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=rand(1000)\n",
      "a[1:990]=0\n",
      "a.nonzero()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(array([  0, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999], dtype=int64),)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit 2**a\n",
      "%timeit 2.**a\n",
      "%timeit np.power(2,a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 18.4 us per loop\n",
        "100000 loops, best of 3: 17 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "100000 loops, best of 3: 18.2 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "env=np.random.binomial(1,0.2,10)\n",
      "env"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0])"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n 100000 int(1.3)\n",
      "%timeit -n 100000 math.floor(1.3)\n",
      "%timeit -n 100000 np.floor(1.3) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 217 ns per loop\n",
        "100000 loops, best of 3: 145 ns per loop\n",
        "100000 loops, best of 3: 1.64 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.random.rand(1000)\n",
      "%timeit -n 1000 [int(x) for x in a]\n",
      "%timeit -n 1000 [math.floor(x) for x in a]\n",
      "%timeit -n 1000 [np.floor(x) for x in a]\n",
      "%timeit -n 1000 np.floor(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 298 us per loop\n",
        "1000 loops, best of 3: 316 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 2.67 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 12.7 us per loop\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(np.arange(10.0)[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "numpy.float64"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(xrange(10)[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "int"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import cython_load\n",
      "import model_c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nums=model_c.genomes_to_nums(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(nums[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "numpy.float64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import random\n",
      "import numpy as np\n",
      "def choose_no_rep_python(n,k):\n",
      "    return random.sample(xrange(n), k)    \n",
      "def choose_no_rep_numpy(n, k):\n",
      "    return np.random.permutation(n)[:k]\n",
      "def choose_no_rep_numpy_take1(n, k):\n",
      "    return np.random.permutation(n).take(range(k))\n",
      "def choose_no_rep_numpy_take2(n, k):\n",
      "    return np.random.permutation(n).take(arange(k))\n",
      "def choose_no_rep_numpy_take3(n, k):\n",
      "    return np.random.permutation(n).take(xrange(k))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n 10000 choose_no_rep_python(1000, 4)\n",
      "%timeit -n 10000 choose_no_rep_numpy(1000, 4)\n",
      "%timeit -n 10000 choose_no_rep_numpy_take1(1000, 4)\n",
      "%timeit -n 10000 choose_no_rep_numpy_take2(1000, 4)\n",
      "%timeit -n 10000 choose_no_rep_numpy_take3(1000, 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 9.11 us per loop\n",
        "10000 loops, best of 3: 408 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 424 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 413 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 425 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ge=g[2,:].copy()\n",
      "sum(ge)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i= choose_no_rep_python(1000,4)\n",
      "ge[i] = (ge[i] + 1) % 2\n",
      "sum(ge)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "13"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def genome_to_num1(genome):\n",
      "\ti = array(range(genome.shape[0]))\n",
      "\treturn (2. ** i * genome).sum()\n",
      "def genome_to_num2(genome):\n",
      "\ti = np.arange(genome.shape[0])\n",
      "\treturn (2. ** i * genome).sum()\n",
      "def genome_to_num3(genome):\n",
      "\ti = array(xrange(genome.shape[0]))\n",
      "\treturn (2. ** i * genome).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n 1000 genome_to_num1(ge)\n",
      "%timeit -n 1000 genome_to_num2(ge)\n",
      "%timeit -n 1000 genome_to_num3(ge)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 409 us per loop\n",
        "1000 loops, best of 3: 172 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 399 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import model\n",
      "reload(model);\n",
      "import cython_load\n",
      "import model_c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=model.create_fitness_by_mutational_load()\n",
      "p=model.create_mutation_free_population()\n",
      "m=model.create_muation_rates()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.drift(p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.selection(p,f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_c.mutation_by_mutation_load(p,m);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "array([982566623,  17306161,    126779,       436,         1])"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "import scipy.stats as sps\n",
      "import simulation\n",
      "reload(simulation);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p=simulation.run(10**3,0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Starting simulation with  1000 ticks\n",
        "Simulation finished,"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000 ticks, time elapsed 0.163826916401 seconds\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lam = model.mu/model.s\n",
      "poi=sps.poisson(lam)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print [exp(-lam) * lam**k / math.factorial(k) for k in range(6)]\n",
      "print [poi.pmf(k) for k in range(6)]\n",
      "print [p[k]/float(p.sum()) for k in range(6)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0.74081822068171788, 0.22224546620451535, 0.033336819930677303, 0.0033336819930677298, 0.00025002614948007977, 1.5001568968804782e-05]\n",
        "[0.74081822068171788, 0.22224546620451532, 0.033336819930677296, 0.0033336819930677277, 0.00025002614948007934, 1.5001568968804773e-05]\n",
        "[0.74045606799999997, 0.222601676, 0.033354796999999999, 0.003323749, 0.00024877800000000001, 1.4267000000000001e-05]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "100 generations are enough to reach an MSB with 10^9 population size and no explicint genomes (just counting mutations). Typical running time: 0.15 seconds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sum([ i*x for i,x in enumerate(p) if x>0])/float(p.sum())\n",
      "print lam\n",
      "print sum([ ((1-model.s)**i)*x for i,x in enumerate(p) if x>0])/float(p.sum())\n",
      "print e**(-model.mu)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.300352987\n",
        "0.3\n",
        "0.997000962813\n",
        "0.997004495503\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import model\n",
      "reload(model);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tg = model.create_target_genome(5)\n",
      "tg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "array([0, 0, 0, 0, 0])"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g=tg.copy()\n",
      "g.resize( (1,g.shape[0]) )\n",
      "print tg.shape, g.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(5L,) (1L, 5L)\n"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n 100000 np.float(5)\n",
      "%timeit -n 100000 float(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 324 ns per loop\n",
        "100000 loops, best of 3: 280 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import simulation\n",
      "reload(simulation)\n",
      "import params\n",
      "reload(params);\n",
      "import model\n",
      "reload(model);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p=simulation.run(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Starting simulation with  0 ticks\n",
        "Tick 0\n",
        "Simulation finished, 0 ticks, time elapsed 0.00117361184493 seconds\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "muts=p*model.create_muation_rates(0.00003, 2)\n",
      "muts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "array([ 29.70537,   0.29463])"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "muts = np.random.poisson(muts)\n",
      "muts[0]=5\n",
      "muts[1]=2\n",
      "muts.cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "array([5, 7])"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loci = np.random.randint(0,5,muts.sum())\n",
      "loci"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "array([2, 3, 3, 3, 0, 1, 4])"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loci_split = np.split(loci, muts.cumsum())[:-1]\n",
      "type(loci_split)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 135,
       "text": [
        "list"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_allele = (tg[loci]+1)%2\n",
      "new_allele"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "array([0, 1, 1, 1, 1, 0, 1])"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g[0,:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 121,
       "text": [
        "array([0, 0, 0, 0, 0])"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d={}\n",
      "d[(0,0)]=(1,array([1,0,0,0,0]))\n",
      "d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "{(0, 0): (1, array([1, 0, 0, 0, 0]))}"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for (strain, locus),(count,genome) in d.items():\n",
      "    print strain, locus, count, genome\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 0 1 [1 0 0 0 0]\n"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d1,d2={},{}\n",
      "v = [randint(0,100) for _ in range(100)]\n",
      "for x in v:\n",
      "    d1[x]=str(x)\n",
      "    d2[x]=x\n",
      "v1= d1.values()\n",
      "v2= d2.values()\n",
      "for i in range(len(v1)):\n",
      "    print int(v1[i])==v2[i]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n",
        "True\n"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "?np.append"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import model\n",
      "reload(model);\n",
      "import cython_load\n",
      "import model_c\n",
      "import params\n",
      "reload(params);\n",
      "import simulation\n",
      "reload(simulation);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p,g=simulation.run(100,10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Starting simulation with  100 ticks\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100\n",
        "Simulation finished, 100 ticks, time elapsed 25.0971423397 seconds\n"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p.shape[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "985L"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(log(p));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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urEx9lo8fL2si+rwLv/mN/MzhWszQoTJ5dPS3ZqtWslW9rEyum+fG4okn7Psr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\nTkSuQmCC\n"
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ml = g.sum(1)\n",
      "hist(ml);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD9CAYAAABUS3cAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAP9H2fMhh1StzGNAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p1=p[0];p2=p.sum()-p1;p1=p1/float(p.sum());p2=p2/float(p.sum())\n",
      "print p1, p2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.996954 0.003046\n"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats as scs\n",
      "poi = scs.poisson(params.mu/params.s)\n",
      "[poi.pmf(k) for k in range(5)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "[0.74081822068171788,\n",
        " 0.22224546620451532,\n",
        " 0.033336819930677296,\n",
        " 0.0033336819930677277,\n",
        " 0.00025002614948007934]"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(ml==2).any()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nums=model_c.genomes_to_nums(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(np.unique(nums))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "960"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(nums)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 123,
       "text": [
        "985"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ge=g[22,:]\n",
      "len(ge)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "1000"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ge in g"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2=g[:,1]\n",
      "len(g2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "985"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2 in g"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.select?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 133,
       "text": [
        "array([[0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       ..., \n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0]])"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ge=g[444,:]\n",
      "ge.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_row_index(genomes, genome):\n",
      "\t'''looks for genome in the rows of genome, returns the index if found, -1 otherwise'''\n",
      "\tfor i, row in enumerate(genomes):\n",
      "\t\tif (row==genome).all():\n",
      "\t\t\treturn i\n",
      "\treturn -1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def find_row_index2(genomes, genome):\n",
      "\t'''looks for genome in the rows of genome, returns the index if found, -1 otherwise'''\n",
      "\tfor i in xrange(genomes.shape[0]):\n",
      "\t\tif (genomes[i]==genome).all():\n",
      "\t\t\treturn i\n",
      "\treturn -1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "import numpy as np\n",
      "cimport numpy as np\n",
      "cimport cython\n",
      "from cpython cimport bool\n",
      "\n",
      "@cython.boundscheck(False)\n",
      "@cython.wraparound(False)\n",
      "def find_row_index3(np.ndarray[int, ndim=2] genomes, np.ndarray[int, ndim=1] genome):\n",
      "    cdef np.ndarray[int, ndim=1] row \n",
      "    cdef Py_ssize_t i, j\n",
      "    cdef bool s\n",
      "    for i, row in enumerate(genomes):\n",
      "        s = True\n",
      "        for j in range(genome.shape[0]):\n",
      "            if genome[j]!=row[j]:\n",
      "                s = False\n",
      "                break\n",
      "        if s:\n",
      "            return i\n",
      "    return -1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 232
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "import numpy as np\n",
      "cimport numpy as np\n",
      "cimport cython\n",
      "\n",
      "@cython.boundscheck(False)\n",
      "@cython.wraparound(False)\n",
      "def find_row_index4(np.ndarray[int, ndim=2] genomes, np.ndarray[int, ndim=1] genome):\n",
      "    cdef np.ndarray[int, ndim=1] row \n",
      "    cdef Py_ssize_t i, j\n",
      "    cdef int s\n",
      "    for i, row in enumerate(genomes):\n",
      "        s = 0\n",
      "        for j in range(genome.shape[0]):\n",
      "            if genome[j]==row[j]:\n",
      "                s = s + 1\n",
      "            else:\n",
      "                break\n",
      "        if s == genome.shape[0]:\n",
      "            return i\n",
      "    return -1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n 1000 find_row_index(g,ge)\n",
      "%timeit -n 1000 find_row_index2(g,ge)\n",
      "%timeit -n 1000 find_row_index3(g,ge)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 4.59 ms per loop\n",
        "1000 loops, best of 3: 4.43 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 813 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit -n 5000 find_row_index3(g,ge)\n",
      "%timeit -n 5000 find_row_index4(g,ge)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5000 loops, best of 3: 803 us per loop\n",
        "5000 loops, best of 3: 795 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd d:\\workspace\\mamba"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d:\\workspace\\mamba\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import model\n",
      "reload(model);\n",
      "import cython_load\n",
      "import model_c\n",
      "import params\n",
      "reload(params);\n",
      "import simulation\n",
      "reload(simulation);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p,g = simulation.run(1000,10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Starting simulation with  1000 ticks\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 50\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 60\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 70\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 80\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 100\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 110\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 120\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 130\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 140\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 150\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 160\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 170\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 180\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 190\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 200\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 210\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 220\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 230\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 240\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 250\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 260\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 270\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 280\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 290\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 300\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 310\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 320\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 330\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 340\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 350\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 360\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 370\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 380\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 390\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 400\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 410\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 420\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 430\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 440\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 450\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 460\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 470\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 480\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 490\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 500\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 510\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 520\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 530\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 540\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 550\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 560\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 570\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 580\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 590\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 600\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 610\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 620\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 630\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 640\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 650\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 660\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 670\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 680\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 690\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 700\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 710\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 720\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 730\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 740\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 750\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 760\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 770\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 780\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 790\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 800\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 810\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 820\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 830\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 840\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 850\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 860\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 870\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 880\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 890\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 900\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 910\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 920\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 930\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 940\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 950\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 960\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 970\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 980\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 990\n",
        "Tick"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1000\n",
        "Simulation finished, 1000 ticks, time elapsed 3718.33720633 seconds\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm = g.sum(axis=1)\n",
      "lm.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(951L,)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simulation.mu=0.1\n",
      "print simulation.mu\n",
      "simulation.pop_size=10**6\n",
      "print simulation.pop_size"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.1\n",
        "1000000\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 9/12/12\n",
      "\n",
      "## testing serialization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd d:\\workspace\\mamba"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d:\\workspace\\mamba\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run simulation.py --ticks -1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,273 - mamba - INFO - Logging to log\\test_2012-Dec-09_16-27-51-269000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,273 - mamba - INFO - Logging to log\\test_2012-Dec-09_16-27-51-269000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,273 - simulation - INFO - Simulation ID: 2012-Dec-09_16-27-51-269000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,273 - simulation - INFO - Simulation ID: 2012-Dec-09_16-27-51-269000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,275 - simulation - INFO - Logging to log\\test_2012-Dec-09_16-27-51-269000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,275 - simulation - INFO - Logging to log\\test_2012-Dec-09_16-27-51-269000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,276 - simulation - INFO - Parametes from file and command line: {'params_dir': 'params', 'log_ext': '.log', 'ticks': -1, 'tick_interval': 0, 'output_ext': '.csv', 'console': True, 'output_dir': 'output', 'mu': 0.003, 'debug': True, 's': 0.01, 'r': 6e-05, 'num_loci': 1000, 'log_dir': 'log', 'params': 'params.py', 'ser_ext': '.ser', 'params_ext': '.py', 'stats_interval': 1, 'pop_size': 100000, 'job_name': 'test', 'ser_dir': 'serialization'}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,276 - simulation - INFO - Parametes from file and command line: {'params_dir': 'params', 'log_ext': '.log', 'ticks': -1, 'tick_interval': 0, 'output_ext': '.csv', 'console': True, 'output_dir': 'output', 'mu': 0.003, 'debug': True, 's': 0.01, 'r': 6e-05, 'num_loci': 1000, 'log_dir': 'log', 'params': 'params.py', 'ser_ext': '.ser', 'params_ext': '.py', 'stats_interval': 1, 'pop_size': 100000, 'job_name': 'test', 'ser_dir': 'serialization'}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,276 - simulation - INFO - Parameters saved to file params\\test_2012-Dec-09_16-27-51-269000.py\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,276 - simulation - INFO - Parameters saved to file params\\test_2012-Dec-09_16-27-51-269000.py\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,278 - simulation - INFO - Saving output to output\\test_2012-Dec-09_16-27-51-269000.csv\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,278 - simulation - INFO - Saving output to output\\test_2012-Dec-09_16-27-51-269000.csv\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,279 - simulation - INFO - Starting simulation with -1 ticks\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,279 - simulation - INFO - Starting simulation with -1 ticks\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,279 - simulation - INFO - Simulation finished, 0 ticks, time elapsed 0.001 seconds\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,279 - simulation - INFO - Simulation finished, 0 ticks, time elapsed 0.001 seconds\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,292 - simulation - INFO - Serialized population to serialization\\test_2012-Dec-09_16-27-51-269000.ser\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:27:51,292 - simulation - INFO - Serialized population to serialization\\test_2012-Dec-09_16-27-51-269000.ser\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls serialization"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Volume in drive D is Purple24\n",
        " Volume Serial Number is 12B2-607F\n",
        "\n",
        " Directory of d:\\workspace\\mamba\\serialization\n",
        "\n",
        "12/09/2012  04:27 PM    <DIR>          .\n",
        "12/09/2012  04:27 PM    <DIR>          ..\n",
        "12/09/2012  04:24 PM         4,260,575 test_2012-Dec-09_16-24-34-037000.ser\n",
        "12/09/2012  04:24 PM         4,276,591 test_2012-Dec-09_16-24-45-008000.ser\n",
        "12/09/2012  04:27 PM            32,350 test_2012-Dec-09_16-27-51-269000.ser\n",
        "               3 File(s)      8,569,516 bytes\n",
        "               2 Dir(s)  115,987,808,256 bytes free\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fname = 'serialization/test_2012-Dec-09_16-24-34-037000.ser'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = deserialize(fname)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:28:31,578 - simulation - INFO - Deserialized population from serialization/test_2012-Dec-09_16-24-34-037000.ser\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2012-12-09 16:28:31,578 - simulation - INFO - Deserialized population from serialization/test_2012-Dec-09_16-24-34-037000.ser\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p,g,tg=p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ml=g.sum(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p[ml>0].sum()/float(p.sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "0.0030200000000000001"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks OK"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Implementing recombination"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mutation_rates = array([0.003,0.003])\n",
      "recombination_rates = array([0.0006,0.0006])\n",
      "total_rates = mutation_rates + recombination_rates\n",
      "population = array([10**7, 10**5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events = np.random.poisson(population * total_rates, size=population.shape)\t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "array([35702,   359])"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events.cumsum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "array([35702, 36061])"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events.sum()==events.cumsum()[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prob_mu = mutation_rates/(mutation_rates+recombination_rates)\n",
      "prob_mu"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "array([ 0.83333333,  0.83333333])"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loci = np.random.randint(0, 1000, events.cumsum()[-1])\n",
      "loci[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "array([356, 997, 241, 911, 757,  85,  31, 863, 873, 278])"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loci_split = np.split(loci, events.cumsum())[:-1]\n",
      "len(loci_split[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "359"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mutations=np.random.binomial(events, prob_mu, size=population.shape)\n",
      "mutations"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "array([29706,   308])"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "events"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "array([35702,   359])"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "split2=[np.split(x,mutations[i:i+1]) for i,x in enumerate(loci_split)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[[len(x) for x in y ] for y in split2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "[[29769, 5933], [306, 53]]"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mutations, events-mutations"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "(array([29769,   306]), array([5933,   53]))"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(split2[1][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "306"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "split2[0][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "array([356, 997, 241, ..., 756, 116, 939])"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(split2[0][0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.binomial([0,10,11], [0.3,0.3,0.4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "n <= 0",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-114-1236cfd012b4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbinomial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\numpy\\random\\mtrand.pyd\u001b[0m in \u001b[0;36mmtrand.RandomState.binomial (numpy\\random\\mtrand\\mtrand.c:14633)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: n <= 0"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.int(array([0,10,11])*array([0.3,0.3,0.4]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "only length-1 arrays can be converted to Python scalars",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-120-98b108d60cee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0.3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mTypeError\u001b[0m: only length-1 arrays can be converted to Python scalars"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=np.round(array([0,10,11])*array([0.3,0.3,0.4]))\n",
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 134,
       "text": [
        "array([ 0.,  3.,  4.])"
       ]
      }
     ],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b=array(a,dtype=np.int)\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 132,
       "text": [
        "array([         0,          0,          0, 1074266112,          0,\n",
        "       1074790400])"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "array([np.int(x) for x in a])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 135,
       "text": [
        "array([0, 3, 4])"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 137,
       "text": [
        "dtype('float64')"
       ]
      }
     ],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.in64(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AttributeError",
       "evalue": "'module' object has no attribute 'in64'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-138-603e7308b6b6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0min64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'in64'"
       ]
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 4/1/2013\n",
      "## Pandas and Gzip"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df=pd.DataFrame({'a':1,'b':3},index=[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv('tmp.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "?df.to_csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.version.version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "'0.9.0'"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Time to update *pandas*..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.version.version"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "'0.10.0'"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import gzip"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=gzip.open('tmp.csv.gz','wb')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame.__module__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "'pandas.core.frame'"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Modifiers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run simulation.py --ticks=5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:24,601 - mamba - INFO - Logging to log\\test_2013-Jan-04_10-42-24-594000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:24,602 - simulation - INFO - Simulation ID: 2013-Jan-04_10-42-24-594000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:24,605 - simulation - INFO - Logging to log\\test_2013-Jan-04_10-42-24-594000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:24,605 - simulation - INFO - Parametes from file and command line: {'params_dir': 'params', 'log_ext': '.log', 'ticks': 5, 'tick_interval': 0, 'output_ext': '.csv', 'console': True, 'output_dir': 'output', 'mu': 0.003, 'debug': True, 's': 0.01, 'r': 6e-05, 'num_loci': 1000, 'log_dir': 'log', 'params': 'params.py', 'ser_ext': '.ser', 'params_ext': '.py', 'stats_interval': 1, 'pop_size': 100000, 'job_name': 'test', 'ser_dir': 'serialization'}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:24,607 - simulation - INFO - Parameters saved to file params\\test_2013-Jan-04_10-42-24-594000.py\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:24,608 - simulation - INFO - Saving output to output\\test_2013-Jan-04_10-42-24-594000.csv.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:24,608 - simulation - INFO - Starting simulation with 5 ticks\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:25,714 - simulation - INFO - Simulation finished, 5 ticks, time elapsed 1.106 seconds\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 10:42:26,369 - simulation - INFO - Serialized population to serialization\\test_2013-Jan-04_10-42-24-594000.ser.gz\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(514L, 1000L)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mods = np.array([0,1,0,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print tg.shape, mods.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1000L,) (4L,)\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs = np.concatenate( (tg,mods)).copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs.resize((1,gs.shape[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "array([[0, 0, 0, ..., 1, 0, 1]])"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs[0][-4:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "array([0, 1, 0, 1])"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs[,-4:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-55-39f6b9c0b380>, line 1)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-55-39f6b9c0b380>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    gs[,-4:]\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "(1L, 1004L)"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs[:,-4:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "array([[0, 1, 0, 1]])"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs.nonzero()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "(array([0, 0], dtype=int64), array([1001, 1003], dtype=int64))"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "array([[0, 0, 0, ..., 1, 0, 1]])"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs.nonzero()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "(array([0, 0], dtype=int64), array([1001, 1003], dtype=int64))"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "array([[0, 0, 0, ..., 1, 0, 1]])"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "non_zero = gs[0,].nonzero()[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "non_zero=non_zero[non_zero<num_loci]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(2.**non_zero).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(gs[:,:-4]==gs[:,:num_loci]).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs[:,:num_loci].shape, tg.reshape(1, num_loci).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "((1L, 1000L), (1L, 1000L))"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Time methods of taking part of the `genomes` matrix"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs2=concatenate((gs,)*100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs2.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "(100L, 1004L)"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit gs2.take(range(num_loci),axis=1)\n",
      "%timeit gs2.take(np.arange(num_loci),axis=1)\n",
      "%timeit gs2[:, :num_loci]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 598 us per loop\n",
        "1000 loops, best of 3: 322 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000000 loops, best of 3: 1.89 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "slicing is the best!"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Testing the modifiers change\n",
      "Not functional yet, just added 4 columns to the end of the `genomes` matrix and want to make sure that nothing broke."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "target_genome = create_target_genome(num_loci)\n",
      "genomes = np.concatenate((target_genome, np.array([0, 1.0, 0, 1.0])))\n",
      "genomes.resize( (1, genomes.shape[0]) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "genomes[:, :num_loci].shape, target_genome.reshape(1, num_loci).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "((1L, 1000L), (1L, 1000L))"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.spatial.distance import cdist, hamming"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "load = cdist(genomes[:, :num_loci], target_genome.reshape(1, num_loci), 'hamming') * num_loci"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "((1 - s) ** load).reshape(genomes.shape[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "array([ 1.])"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd d:\\workspace\\mamba"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d:\\workspace\\mamba\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Creating mutation rates with modifiers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd d:\\workspace\\mamba"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d:\\workspace\\mamba\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run simulation.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,770 - mamba - INFO - Logging to log\\test_2013-Jan-04_11-49-43-767000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,772 - simulation - INFO - Simulation ID: 2013-Jan-04_11-49-43-767000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,773 - simulation - INFO - Logging to log\\test_2013-Jan-04_11-49-43-767000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,773 - simulation - INFO - Parametes from file and command line: {'params_dir': 'params', 'log_ext': '.log', 'ticks': 10, 'tick_interval': 10, 'output_ext': '.csv', 'console': True, 'output_dir': 'output', 'mu': 0.003, 'debug': True, 's': 0.01, 'r': 6e-05, 'num_loci': 1000, 'log_dir': 'log', 'params': 'params.py', 'ser_ext': '.ser', 'params_ext': '.py', 'stats_interval': 1, 'pop_size': 100000, 'job_name': 'test', 'ser_dir': 'serialization'}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,776 - simulation - INFO - Parameters saved to file params\\test_2013-Jan-04_11-49-43-767000.py\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,778 - simulation - INFO - Saving output to output\\test_2013-Jan-04_11-49-43-767000.csv.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,779 - simulation - INFO - Starting simulation with 10 ticks\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:43,859 - simulation - DEBUG - Tick 0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:46,204 - simulation - DEBUG - Tick 10\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:46,207 - simulation - INFO - Simulation finished, 10 ticks, time elapsed 2.428 seconds\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-04 11:49:47,684 - simulation - INFO - Serialized population to serialization\\test_2013-Jan-04_11-49-43-767000.ser.gz\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "genomes[:, num_loci].shape==p.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = create_fitness(g,tg,0.01,num_loci)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pi = g[:, num_loci]\n",
      "tau = g[:, num_loci+1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pi = (1-s)**pi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tau[:10]=2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w[:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "array([ 1.  ,  0.99,  0.99,  0.99,  0.99,  0.99,  0.99,  0.99,  0.99,\n",
        "        0.99,  0.99,  0.99,  0.99,  0.99,  0.99])"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pi[:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
        "        1.,  1.])"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tau[:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "array([ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  1.,  1.,  1.,\n",
        "        1.,  1.])"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w<=pi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
        "        True,  True,  True,  True,  True,  True], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mus = create_muation_rates(mu, g.shape[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mus[w<=pi] *= tau[w<=pi] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.unique(mus)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "array([ 0.003,  0.006,  0.012])"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Assertion bug\n",
      "The bug is that `(population-events>0).all()!=False`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=array([1,2,3,4,5,6])\n",
      "b=array([0,0,2,5,5,3])\n",
      "a-b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "array([ 1,  2,  1, -1,  0,  3])"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "array([min(a[i],b[i]) for i in range(a.shape[0])])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "array([0, 0, 2, 4, 5, 3])"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M=array((a,b))\n",
      "M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "array([[1, 2, 3, 4, 5, 6],\n",
        "       [0, 0, 2, 5, 5, 3]])"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M.min(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "array([0, 0, 2, 4, 5, 3])"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "array((a,b)).min(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "array([0, 0, 2, 4, 5, 3])"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The fix is to set `events` to the minimum of `events` and `population`. This means that there is a limit of one functional mutation per individual per generation, and that if the Poisson distribution resulted in numerous mutations they are taken together to be a single mutation."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 5/1/13\n",
      "## Invasion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run simulation.py --ticks=3 --console"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p2 = p.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(p2==p).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p2 *= 0.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p2.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "dtype('int32')"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p[:5],p2[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(array([98743,     2,     4,    18,     6]),\n",
        " array([49371,     1,     2,     9,     3]))"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p -= p2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p[:5],p2[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "(array([49372,     1,     2,     9,     3]),\n",
        " array([49371,     1,     2,     9,     3]))"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2=g.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2[:,1000:]=array([0,0,0,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2[:,1000:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "array([[0, 0, 0, 0],\n",
        "       [0, 0, 0, 0],\n",
        "       [0, 0, 0, 0],\n",
        "       ..., \n",
        "       [0, 0, 0, 0],\n",
        "       [0, 0, 0, 0],\n",
        "       [0, 0, 0, 0]])"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=array([0,0,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(a), type(array(a))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "(numpy.ndarray, numpy.ndarray)"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.concatenate((g,g2),axis=0).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "(932L, 1004L)"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.concatenate((p,p2),axis=1).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "(932L,)"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 17/1/2013\n",
      "## Implementing `genome_to_num` with modifiers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = np.random.binomial(1,0.01,1000)\n",
      "g[:100]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2=np.concatenate((g,np.array([1,10,0,1])))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num = model.genome_to_num(g2,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "modifiers = g2[1000:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tuple(modifiers)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(1, 10, 0, 1)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(num,)+tuple(modifiers)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(3.1185004836933799e+290, 1, 10, 0, 1)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "geno=np.array([g2,g2])\n",
      "print geno.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2L, 1004L)\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num=model.genome_to_num(g[:1000],1000)\n",
      "print '%f' % num\n",
      "s = np.binary_repr(num,1000)\n",
      "print s\n",
      "print len(s),1000-len(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "311850048369337986039074908356580191286631485273518940205657389135257021249410708085175330220849964020600859463538984619999944452075825746690034490295331418766744034060298332147660495641766575175738981940279809888430447181726856422784384978737478515029032130478060163688790584926200496390144.000000\n",
        "0000000000000000000000000000000000100000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000\n",
        "1000 0\n"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r=''.join([str(x) for x in g[:1000]])\n",
      "print r\n",
      "print len(r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000100000100000000000000000000000000000000000000000000000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000010000000000000000000000000000000000010000000000000000000000000000000000\n",
        "1000\n"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sum([1 for x in s if x == '1' ])\n",
      "print sum([1 for x in r if x == '1' ])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n",
        "8\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tuple(g.nonzero()[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "(71, 335, 729, 733, 739, 801, 929, 965)"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "3.1185004836933799e+290"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2**4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "16"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2**5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "32"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "2**2+2**3+2**4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "28"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit tuple(g2[:1000].nonzero()[0])+tuple(g2[1000:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 17 us per loop\n"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit \n",
      "nz=g2.nonzero()[0]\n",
      "nz=nz[nz<1000]\n",
      "tuple(nz)+tuple(g2[1000:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 24.2 us per loop\n"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit (model.genome_to_num(g2,1000),)+tuple(g2[1000:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 37.2 us per loop\n"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(model);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.genome_to_key(g2,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "(71, 335, 729, 733, 739, 801, 929, 965)"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.genome_to_key_w_modifiers(g2,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "((71, 335, 729, 733, 739, 801, 929, 965), (1, 10, 0, 1))"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.genomes_to_keys(geno,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "array([[ 71, 335, 729, 733, 739, 801, 929, 965],\n",
        "       [ 71, 335, 729, 733, 739, 801, 929, 965]], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "geno[:,1000:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "array([[ 1, 10,  0,  1],\n",
        "       [ 1, 10,  0,  1]])"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def genomes_to_keys_w_mod1(genomes,num_loci):\n",
      "    return np.array([model.genome_to_key_w_modifiers(g, num_loci) for g in genomes])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def genomes_to_keys_w_mod2(genomes,num_loci):\n",
      "    keys = np.array([np.concatenate((g[:num_loci].nonzero()[0],g[num_loci:])) for g in genomes])\n",
      "    return keys    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def genomes_to_keys_w_mod3(genomes,num_loci):\n",
      "    keys = np.array([g[:num_loci].nonzero()[0] for g in genomes])\n",
      "    mods = np.array([g[num_loci:] for g in genomes])\n",
      "    return np.concatenate((keys,mods),axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit genomes_to_keys_w_mod1(geno,1000)\n",
      "%timeit genomes_to_keys_w_mod2(geno,1000)\n",
      "%timeit genomes_to_keys_w_mod3(geno,1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 97 us per loop\n",
        "10000 loops, best of 3: 51.3 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 70.9 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(model);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keys = model.genomes_to_keys_w_modifiers(geno,1000)\n",
      "keys"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 156,
       "text": [
        "array([[ 71, 335, 729, 733, 739, 801, 929, 965,   1,  10,   0,   1],\n",
        "       [ 71, 335, 729, 733, 739, 801, 929, 965,   1,  10,   0,   1]], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 156
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.find_row_keys(keys,model.genome_to_key_w_modifiers(g2,1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 158,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k=keys[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 162,
       "text": [
        "array([ 71, 335, 729, 733, 739, 801, 929, 965,   1,  10,   0,   1], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "str(k)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 164,
       "text": [
        "'[ 71 335 729 733 739 801 929 965   1  10   0   1]'"
       ]
      }
     ],
     "prompt_number": 164
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "k"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 166,
       "text": [
        "array([ 71, 335, 729, 733, 739, 801, 929, 965,   1,  10,   0,   1], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit (k==k).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 4.45 us per loop\n"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit np.array_equal(k,k)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 11.8 us per loop\n"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit np.array_equiv(k,k)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 10.8 us per loop\n"
       ]
      }
     ],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(model);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(model.genome_to_num(g,1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "non_zero [ 71 335 729 733 739 801 929 965]\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 180,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(model.genomes_to_nums(geno,1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "non_zero [ 71 335 729 733 739 801 929 965]\n",
        "non_zero [ 71 335 729 733 739 801 929 965]\n",
        "nums: [[ 71 335 729 733 739 801 929 965]\n",
        " [ 71 335 729 733 739 801 929 965]]\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 182,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.find_row_nums(model.genomes_to_nums(geno,1000),model.genome_to_num(g,1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "non_zero [ 71 335 729 733 739 801 929 965]\n",
        "non_zero [ 71 335 729 733 739 801 929 965]\n",
        "nums: [[ 71 335 729 733 739 801 929 965]\n",
        " [ 71 335 729 733 739 801 929 965]]\n",
        "non_zero [ 71 335 729 733 739 801 929 965]\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 183,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 183
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run simulation --ticks=3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,071 - simulation - INFO - Simulation ID: 2013-Jan-17_14-39-38-063000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,071 - simulation - INFO - Simulation ID: 2013-Jan-17_14-39-38-063000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,071 - simulation - INFO - Simulation ID: 2013-Jan-17_14-39-38-063000\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,072 - simulation - INFO - Logging to log\\test\\test_2013-Jan-17_14-39-38-063000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,072 - simulation - INFO - Logging to log\\test\\test_2013-Jan-17_14-39-38-063000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,072 - simulation - INFO - Logging to log\\test\\test_2013-Jan-17_14-39-38-063000.log\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,073 - simulation - INFO - Parametes from file and command line: {u'tau': 1, u'params_dir': u'params', 'params_file': 'params.json', 'datetime': '2013-Jan-17_14-39-38-063000', u'envch_rate': 0.01, u'pop_size': 100000, 'console': True, u'log_ext': u'.log', 'envch_start': False, u'ticks': 3, u'tick_interval': 10, u'log_dir': u'log', u'rb': False, u'stats_interval': 1, u'pi': 0, u'job_name': u'test', u'output_dir': u'output', u'phi': 0, u'ser_ext': u'.ser', 'debug': True, u'ser_dir': u'serialization', u'rho': 1, u'output_ext': u'.csv', u'mu': 0.003, u's': 0.01, u'r': 6e-05, u'num_loci': 1000, u'params_ext': u'.json', u'envch_str': 0}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,073 - simulation - INFO - Parametes from file and command line: {u'tau': 1, u'params_dir': u'params', 'params_file': 'params.json', 'datetime': '2013-Jan-17_14-39-38-063000', u'envch_rate': 0.01, u'pop_size': 100000, 'console': True, u'log_ext': u'.log', 'envch_start': False, u'ticks': 3, u'tick_interval': 10, u'log_dir': u'log', u'rb': False, u'stats_interval': 1, u'pi': 0, u'job_name': u'test', u'output_dir': u'output', u'phi': 0, u'ser_ext': u'.ser', 'debug': True, u'ser_dir': u'serialization', u'rho': 1, u'output_ext': u'.csv', u'mu': 0.003, u's': 0.01, u'r': 6e-05, u'num_loci': 1000, u'params_ext': u'.json', u'envch_str': 0}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,073 - simulation - INFO - Parametes from file and command line: {u'tau': 1, u'params_dir': u'params', 'params_file': 'params.json', 'datetime': '2013-Jan-17_14-39-38-063000', u'envch_rate': 0.01, u'pop_size': 100000, 'console': True, u'log_ext': u'.log', 'envch_start': False, u'ticks': 3, u'tick_interval': 10, u'log_dir': u'log', u'rb': False, u'stats_interval': 1, u'pi': 0, u'job_name': u'test', u'output_dir': u'output', u'phi': 0, u'ser_ext': u'.ser', 'debug': True, u'ser_dir': u'serialization', u'rho': 1, u'output_ext': u'.csv', u'mu': 0.003, u's': 0.01, u'r': 6e-05, u'num_loci': 1000, u'params_ext': u'.json', u'envch_str': 0}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,075 - simulation - INFO - Parameters saved to file params\\test\\test_2013-Jan-17_14-39-38-063000.json\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,075 - simulation - INFO - Parameters saved to file params\\test\\test_2013-Jan-17_14-39-38-063000.json\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,075 - simulation - INFO - Parameters saved to file params\\test\\test_2013-Jan-17_14-39-38-063000.json\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,078 - simulation - INFO - Saving temporary output to output\\test\\tmp\\test_2013-Jan-17_14-39-38-063000.csv.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,078 - simulation - INFO - Saving temporary output to output\\test\\tmp\\test_2013-Jan-17_14-39-38-063000.csv.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,078 - simulation - INFO - Saving temporary output to output\\test\\tmp\\test_2013-Jan-17_14-39-38-063000.csv.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,082 - simulation - INFO - Starting simulation with 3 ticks\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,082 - simulation - INFO - Starting simulation with 3 ticks\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,082 - simulation - INFO - Starting simulation with 3 ticks\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,108 - simulation - DEBUG - Tick 0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,108 - simulation - DEBUG - Tick 0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,108 - simulation - DEBUG - Tick 0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,157 - simulation - INFO - Simulation finished, 3 ticks, time elapsed 0.080 seconds\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,157 - simulation - INFO - Simulation finished, 3 ticks, time elapsed 0.080 seconds\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,157 - simulation - INFO - Simulation finished, 3 ticks, time elapsed 0.080 seconds\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,168 - simulation - INFO - Serialized population to serialization\\test\\test_2013-Jan-17_14-39-38-063000.ser.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,168 - simulation - INFO - Serialized population to serialization\\test\\test_2013-Jan-17_14-39-38-063000.ser.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,168 - simulation - INFO - Serialized population to serialization\\test\\test_2013-Jan-17_14-39-38-063000.ser.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,173 - simulation - INFO - Saved output to output\\test\\test_2013-Jan-17_14-39-38-063000.csv.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,173 - simulation - INFO - Saved output to output\\test\\test_2013-Jan-17_14-39-38-063000.csv.gz\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "2013-01-17 14:39:38,173 - simulation - INFO - Saved output to output\\test\\test_2013-Jan-17_14-39-38-063000.csv.gz\n"
       ]
      }
     ],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nums=model.genomes_to_nums(geno,1000)\n",
      "nums"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 191,
       "text": [
        "array([[ 71, 335, 729, 733, 739, 801, 929, 965],\n",
        "       [ 71, 335, 729, 733, 739, 801, 929, 965]], dtype=int64)"
       ]
      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Series([ str(n) for n in nums])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 193,
       "text": [
        "0    [ 71 335 729 733 739 801 929 965]\n",
        "1    [ 71 335 729 733 739 801 929 965]"
       ]
      }
     ],
     "prompt_number": 193
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.Series([i for i in range(len(nums))])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 195,
       "text": [
        "0    0\n",
        "1    1"
       ]
      }
     ],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(data = { \"genome\":pd.Series([ str(n) for n in nums]), \"tick\":pd.Series([i for i in range(len(nums))])})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 196
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>genome</th>\n",
        "      <th>tick</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> [ 71 335 729 733 739 801 929 965]</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> [ 71 335 729 733 739 801 929 965]</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 197,
       "text": [
        "                              genome  tick\n",
        "0  [ 71 335 729 733 739 801 929 965]     0\n",
        "1  [ 71 335 729 733 739 801 929 965]     1"
       ]
      }
     ],
     "prompt_number": 197
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv(\"tmp.csv\",header=True,index_label=\"index\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 200
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The problem is with `(v1==v2).all()`, if one of the arrays is empty, it gives `True`.\n",
      "So I need to use `array_equal`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(np.array([189])==np.array([])).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 205,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.array_equal(np.array([189]),np.array([]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 206,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 206
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.array_equal.__module__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 209,
       "text": [
        "'numpy.core.numeric'"
       ]
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But I checked `array_euqal` [code](https://github.com/numpy/numpy/blob/master/numpy/core/numeric.py) and it checks all the elements, instead of aborting when it finds an element which is not equal. So I'm looking at my own implementation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = model.create_target_genome(1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 236
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def arr_eq(a1,a2):\n",
      "    try:\n",
      "        a1, a2 = asarray(a1), asarray(a2)\n",
      "    except:\n",
      "        return False\n",
      "    if a1.shape != a2.shape:\n",
      "        return False\n",
      "    for i in range(a1.shape[0]):\n",
      "        if a1[i] != a2[i]:\n",
      "            return False\n",
      "    return True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext cythonmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 225
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%cython\n",
      "import numpy as np\n",
      "cimport numpy as np\n",
      "cimport cython\n",
      "\n",
      "@cython.boundscheck(False)\n",
      "def arr_eq_cython(np.ndarray[int, ndim=1] a1, np.ndarray[int, ndim=1] a2):\n",
      "    try:\n",
      "        a1, a2 = np.asarray(a1), np.asarray(a2)\n",
      "    except:\n",
      "        return False\n",
      "    if a1.shape!=a2.shape:\n",
      "        return False\n",
      "    cdef Py_ssize_t i\n",
      "    for i in range(a1.shape[0]):\n",
      "        if a1[i] != a2[i]:\n",
      "            return False\n",
      "    return True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 244
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.array_equal(g,g),arr_eq(g,g),arr_eq_cython(g,g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 245,
       "text": [
        "(True, True, True)"
       ]
      }
     ],
     "prompt_number": 245
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g1 = g.copy()\n",
      "g1[randint(0,1000)] = 1\n",
      "np.array_equal(g1,g),arr_eq(g1,g),arr_eq_cython(g1,g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 246,
       "text": [
        "(False, False, False)"
       ]
      }
     ],
     "prompt_number": 246
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit np.array_equal(g,g)\n",
      "%timeit arr_eq(g,g)\n",
      "%timeit arr_eq_cython(g,g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 15 us per loop\n",
        "1000 loops, best of 3: 494 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "100000 loops, best of 3: 8.59 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 247
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g1 = g.copy()\n",
      "r = randint(0,1000)\n",
      "print r\n",
      "g1[r] = 1\n",
      "%timeit np.array_equal(g1,g)\n",
      "%timeit arr_eq(g1,g)\n",
      "%timeit arr_eq_cython(g1,g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "457\n",
        "100000 loops, best of 3: 14.1 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1000 loops, best of 3: 248 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "100000 loops, best of 3: 6.68 us per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 253
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g.dtype"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 255,
       "text": [
        "dtype('int32')"
       ]
      }
     ],
     "prompt_number": 255
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}