12_spatial_dynamics.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pysal as ps\n",
      "import numpy as np\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = ps.open(\"data/usjoin.csv\",'r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To determine what is in the file, check the `header` attribute on the file object:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f.header"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "['Name',\n",
        " 'STATE_FIPS',\n",
        " '1929',\n",
        " '1930',\n",
        " '1931',\n",
        " '1932',\n",
        " '1933',\n",
        " '1934',\n",
        " '1935',\n",
        " '1936',\n",
        " '1937',\n",
        " '1938',\n",
        " '1939',\n",
        " '1940',\n",
        " '1941',\n",
        " '1942',\n",
        " '1943',\n",
        " '1944',\n",
        " '1945',\n",
        " '1946',\n",
        " '1947',\n",
        " '1948',\n",
        " '1949',\n",
        " '1950',\n",
        " '1951',\n",
        " '1952',\n",
        " '1953',\n",
        " '1954',\n",
        " '1955',\n",
        " '1956',\n",
        " '1957',\n",
        " '1958',\n",
        " '1959',\n",
        " '1960',\n",
        " '1961',\n",
        " '1962',\n",
        " '1963',\n",
        " '1964',\n",
        " '1965',\n",
        " '1966',\n",
        " '1967',\n",
        " '1968',\n",
        " '1969',\n",
        " '1970',\n",
        " '1971',\n",
        " '1972',\n",
        " '1973',\n",
        " '1974',\n",
        " '1975',\n",
        " '1976',\n",
        " '1977',\n",
        " '1978',\n",
        " '1979',\n",
        " '1980',\n",
        " '1981',\n",
        " '1982',\n",
        " '1983',\n",
        " '1984',\n",
        " '1985',\n",
        " '1986',\n",
        " '1987',\n",
        " '1988',\n",
        " '1989',\n",
        " '1990',\n",
        " '1991',\n",
        " '1992',\n",
        " '1993',\n",
        " '1994',\n",
        " '1995',\n",
        " '1996',\n",
        " '1997',\n",
        " '1998',\n",
        " '1999',\n",
        " '2000',\n",
        " '2001',\n",
        " '2002',\n",
        " '2003',\n",
        " '2004',\n",
        " '2005',\n",
        " '2006',\n",
        " '2007',\n",
        " '2008',\n",
        " '2009']"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ok, lets pull in the name variable to see what we have"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "name = f.by_col('Name')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "name"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "['Alabama',\n",
        " 'Arizona',\n",
        " 'Arkansas',\n",
        " 'California',\n",
        " 'Colorado',\n",
        " 'Connecticut',\n",
        " 'Delaware',\n",
        " 'Florida',\n",
        " 'Georgia',\n",
        " 'Idaho',\n",
        " 'Illinois',\n",
        " 'Indiana',\n",
        " 'Iowa',\n",
        " 'Kansas',\n",
        " 'Kentucky',\n",
        " 'Louisiana',\n",
        " 'Maine',\n",
        " 'Maryland',\n",
        " 'Massachusetts',\n",
        " 'Michigan',\n",
        " 'Minnesota',\n",
        " 'Mississippi',\n",
        " 'Missouri',\n",
        " 'Montana',\n",
        " 'Nebraska',\n",
        " 'Nevada',\n",
        " 'New Hampshire',\n",
        " 'New Jersey',\n",
        " 'New Mexico',\n",
        " 'New York',\n",
        " 'North Carolina',\n",
        " 'North Dakota',\n",
        " 'Ohio',\n",
        " 'Oklahoma',\n",
        " 'Oregon',\n",
        " 'Pennsylvania',\n",
        " 'Rhode Island',\n",
        " 'South Carolina',\n",
        " 'South Dakota',\n",
        " 'Tennessee',\n",
        " 'Texas',\n",
        " 'Utah',\n",
        " 'Vermont',\n",
        " 'Virginia',\n",
        " 'Washington',\n",
        " 'West Virginia',\n",
        " 'Wisconsin',\n",
        " 'Wyoming']"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now obtain per capital incomes in 1929 which is in the column associated with `1929`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y1929 = f.by_col('1929')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y1929"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[323,\n",
        " 600,\n",
        " 310,\n",
        " 991,\n",
        " 634,\n",
        " 1024,\n",
        " 1032,\n",
        " 518,\n",
        " 347,\n",
        " 507,\n",
        " 948,\n",
        " 607,\n",
        " 581,\n",
        " 532,\n",
        " 393,\n",
        " 414,\n",
        " 601,\n",
        " 768,\n",
        " 906,\n",
        " 790,\n",
        " 599,\n",
        " 286,\n",
        " 621,\n",
        " 592,\n",
        " 596,\n",
        " 868,\n",
        " 686,\n",
        " 918,\n",
        " 410,\n",
        " 1152,\n",
        " 332,\n",
        " 382,\n",
        " 771,\n",
        " 455,\n",
        " 668,\n",
        " 772,\n",
        " 874,\n",
        " 271,\n",
        " 426,\n",
        " 378,\n",
        " 479,\n",
        " 551,\n",
        " 634,\n",
        " 434,\n",
        " 741,\n",
        " 460,\n",
        " 673,\n",
        " 675]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now 2009"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y2009 = f.by_col(\"2009\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y2009"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[32274,\n",
        " 32077,\n",
        " 31493,\n",
        " 40902,\n",
        " 40093,\n",
        " 52736,\n",
        " 40135,\n",
        " 36565,\n",
        " 33086,\n",
        " 30987,\n",
        " 40933,\n",
        " 33174,\n",
        " 35983,\n",
        " 37036,\n",
        " 31250,\n",
        " 35151,\n",
        " 35268,\n",
        " 47159,\n",
        " 49590,\n",
        " 34280,\n",
        " 40920,\n",
        " 29318,\n",
        " 35106,\n",
        " 32699,\n",
        " 37057,\n",
        " 38009,\n",
        " 41882,\n",
        " 48123,\n",
        " 32197,\n",
        " 46844,\n",
        " 33564,\n",
        " 38672,\n",
        " 35018,\n",
        " 33708,\n",
        " 35210,\n",
        " 38827,\n",
        " 41283,\n",
        " 30835,\n",
        " 36499,\n",
        " 33512,\n",
        " 35674,\n",
        " 30107,\n",
        " 36752,\n",
        " 43211,\n",
        " 40619,\n",
        " 31843,\n",
        " 35676,\n",
        " 42504]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These are read into regular Python lists which are not particularly well suited to efficient data analysis. So let's convert them to numpy arrays"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y2009 = np.array(y2009)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y2009"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([32274, 32077, 31493, 40902, 40093, 52736, 40135, 36565, 33086,\n",
        "       30987, 40933, 33174, 35983, 37036, 31250, 35151, 35268, 47159,\n",
        "       49590, 34280, 40920, 29318, 35106, 32699, 37057, 38009, 41882,\n",
        "       48123, 32197, 46844, 33564, 38672, 35018, 33708, 35210, 38827,\n",
        "       41283, 30835, 36499, 33512, 35674, 30107, 36752, 43211, 40619,\n",
        "       31843, 35676, 42504])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Much better. But pulling these in and converting them a column at a time is tedious and error prone. So we will do all of this in a list comprehension."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Y = np.array( [ f.by_col(str(year)) for year in range(1929,2010) ] ) * 1.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(81, 48)"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Y = Y.transpose()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(48, 81)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = np.arange(1929,2010)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(years,Y[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109a5b550>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8+SUcP25F3LIym/e/f78thw7Zr/qjR2273FybHdSggT1+KGTDPuXldvuhQ/br\n/9AhmDcvSOPGAQoLrabQsydcdpmdovPii+OLL9mCwSCBQCD9T3wSfowJ/BmXYoqPH2NKJr8ki1bA\n9qjrxUBPj2KpVK9eNnYfnrnz4YewaROMHx8Z59+507Zp1QpOO82+1EMhSwqHDkW2Ky+34Z9TTrEl\nN9fm/jdpYkvDhvalXreuFY7Ly+2L//BhSzS1akXuV7v21/cXuP9+O7Jpq1aaNSQiyeGXZOH7YkTL\nll/fizc/35aw48ctGZSUWIE4J8e+2HNy7Ms8XA9o0CB1X+L5+XDFFal5bBGpufzyu/O7QD5W5AYY\nBRznxCL3ZqBDesMSEcloW4CzvQ4imXKxF9UOqEPlBW4RERGuAt7HehCjPI5FREREREQyxVRgF7Ah\nqu07wNvAemAh0Ni1XwGsdu2rge9H3aebe4xNwMQ0xtQDWOOW9cBAH8QU1gY4BDzgg5jaAV8Qea+e\nSlFMicYFcJG77V13e50UxJVITD8m8j6tAcpdjF7GVA+Y49oLgZFR9/EqpjrANNe+FrgsRTGdCSwH\nNmKfkXtcezNgGfABsBRoGnWfUe65i4ArUxBXojE1c9sfBCZVeKxk//+lzPeALpz44fiHawf4CTDG\nrXcGznDr52NTa8MKsC9ugMVEiuSpjqk+EN5/+QxgD7ZzoZcxhb0IvMCJycKrmNpV2C5aMmNKNK5c\nYB1wobv+LSJ/T6//fgAXYEOyYV7FdBuWLMA+8x9hP0a8jOnnwBS3fjr2AzIsmTGdgX33ADTChsnP\nBX4HjHDtvwYed+vnYcmrNva530xkElGy4ko0pgZAL+AOvp4skv3/l1LtOPHDsS9q/Uwse1aUA3yK\n/UHyOHHHvUHA0x7E1B4r0OODmK7HPjijiSQLL2OquF1YKmJKJK6rgVlpiivemKKNBR71QUw/xH7V\nnwI0x76cmnoc05PALVG3/Q24OEUxRZsP/ADrNbRwbWe462C9il9Hbb8Em+mZyrhixRR2Gycmi4Rj\n8tuRfTZie24D/Dv2AanoRuCfQBm2M190L6PEtaUrph7u9o3A/a7Ny5gaYb8u8its7/X71B4bVgkC\nvdMY08ni6oTt37ME+zw9mMa44vmcDyDyi97LmF4DDgA7sCMs/B77EvcypnVAPyyBtceGU1qnOKZ2\nWM/nHexLeZdr30XkS7plhecvds9fsT1ZccUTU1jFfdkSfq/8lixuB4Zj3cpGwJcVbj8f617d4ZOY\nClxMXbGZbdGaAAACjElEQVQxvyYex5QPjAcOk/59aL4pplLsn7wLllBn8/Uaixdx5WKJ62Z3+SPg\n/5CeHURjfc57Yn/DwjTEEiumW7Dhpzzsi/lX7tLLmKZiX3Srsc/7W1h9J1V/u0bAPOBebOw/WlKP\nv5SAtMfklz24w97Hur1gv/yuibqtNfASMAQbNwXLhq0rbFOSxpjCiojs/FLsQUxXu/UeWM/rd9hQ\nwXGsuPySBzGF36cvifyT/wt7nzqSnr/dyeLaDqwA9rrri7Gk///SEFesz9QgLKmGefE5D3+mLgVe\nxr6MdwMrsV/yb3oQU/h9KifSk8fF9AGwPwUx1ca+lGdhQz5gv9zPAHZiSfQT117Cib3E1tj3QbL/\nfonE9E0SjslvPYvT3WUt4GFgsrveFHgVGw98O2r7HVgXuSf2S3oIkTcv1TG1I5Js22JfgJuwP1a6\nYwqPNfbBfvW1xw73/lts9pEXMYXfp+ZECv9nYe/Th6Tnb3eyuF7Ditv1sb/jZdiwh5fvVbjt37GD\naYZ58TkPf6aKsB4XQENsDL4Ib9+n+i4WsJmSZS6mZL9POVghvRD7fwpbCAx160OjnmMhlujrYP+D\nHbHRh2S+V4nGFH2/aOn6/0uKOdgQxZfYr7zbsWlg77tlbNS2D2NTQaOnFTZ3t4Wnf20GnkhjTLdg\nU9fWYB+I6JkEXsUUbTQn/vryKqYbiLxP/+TEX9HJjCnRuMCmqr7rYng8qt3Lv18AG1apyKuY6mK9\nrQ1YMq1sOna6Y2qHJYdCbJpo9K/5ZMbUG+udryXyvdMXm476NyqfOvuQe+4iIr2iZMZVlZi2YpOC\nDmLv7TlJjklERERERERERERERERERERERERERERERERERESi/X8llUfsjV4rXQAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1098743d0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RY = Y / Y.mean(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(years,RY[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109b52850>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zB77/Htq1K789UoPxli02PUS8fv7ZFqw/8cTqxSgiUhUlAuCEE2z20B074j93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\nERERERERERERERHJJP8ft2R/RcyY6UQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1098a8990>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "name = np.array(name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.nonzero(name=='Ohio')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(array([32]),)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(years, RY[32], label='Ohio')\n",
      "plot(years, RY[0], label='Alabama')\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.legend.Legend at 0x109d87750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PVHkANzc3QiJCeH3V6yQYCfSq1Qv/4v6UL1aeMt5luBF3g6iYKC7dvMSRi0eY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\nSimllFJKKaWUylv+H2uURm5GgPbjAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109ba2a10>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Kernel Density (univariate, aspatial)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats.kde import gaussian_kde"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "density = gaussian_kde(Y[:,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Y[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([  323.,   600.,   310.,   991.,   634.,  1024.,  1032.,   518.,\n",
        "         347.,   507.,   948.,   607.,   581.,   532.,   393.,   414.,\n",
        "         601.,   768.,   906.,   790.,   599.,   286.,   621.,   592.,\n",
        "         596.,   868.,   686.,   918.,   410.,  1152.,   332.,   382.,\n",
        "         771.,   455.,   668.,   772.,   874.,   271.,   426.,   378.,\n",
        "         479.,   551.,   634.,   434.,   741.,   460.,   673.,   675.])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "density = gaussian_kde(Y[:,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minY0 = Y[:,0].min()*.90\n",
      "maxY0 = Y[:,0].max()*1.10\n",
      "x = np.linspace(minY0, maxY0, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x,density(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109fdd7d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PPAL336+by+JF6dIwZox1W+Xk+B2NSGzTneMlICvLlrXYskWJI57k5kIgADfcAIMG+R2N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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109ddced0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d2009 = gaussian_kde(Y[:,-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minY0 = Y[:,-1].min()*.90\n",
      "maxY0 = Y[:,-1].max()*1.10\n",
      "x = np.linspace(minY0, maxY0, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x,d2009(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a1022d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YNMh3JCKlC7LXb9BpWUFmIGSV8n5Fca4T/VplrIXzGNbC2U1OTs6vx9nZ2WRn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\niIiIiIiIiIiIiIiIpI//D1Rs1Jp0pw1OAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109abd110>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minR0 = RY.min()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "maxR0 = RY.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(minR0, maxR0, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d1929 = gaussian_kde(RY[:,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d2009 = gaussian_kde(RY[:,-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x, d1929(x))\n",
      "plot(x, d2009(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10a13cd50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7k/avteeDrR+Q4yr8SmrlyqY1s3GjOaM/fdpcfO3d2ywDmJLi49+A+D13xb0H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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a13cd10>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x, d1929(x), label='1929')\n",
      "plot(x, d2009(x), label='2009')\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.legend.Legend at 0x10a2e4cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Kf0FReLzz99tLUqUK/OMfavjkzp1qDpuffy7+OS1rtcS7kjf74vbZJqQQFpLi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\nEEIIIYQQQgghhBBCCCGEKNr/B06aFYcocMrnAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109dc9050>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Markov Chains"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = np.array([['b','a','c'],\n",
      "['c','c','a'],\n",
      "['c','b','c'],\n",
      "['a','a','b'],\n",
      "['a','b','c']])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "array([['b', 'a', 'c'],\n",
        "       ['c', 'c', 'a'],\n",
        "       ['c', 'b', 'c'],\n",
        "       ['a', 'a', 'b'],\n",
        "       ['a', 'b', 'c']], \n",
        "      dtype='|S1')"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = ps.Markov(c)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.classes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "array(['a', 'b', 'c'], \n",
        "      dtype='|S1')"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.transitions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "array([[ 1.,  2.,  1.],\n",
        "       [ 1.,  0.,  2.],\n",
        "       [ 1.,  1.,  1.]])"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "matrix([[ 0.25      ,  0.5       ,  0.25      ],\n",
        "        [ 0.33333333,  0.        ,  0.66666667],\n",
        "        [ 0.33333333,  0.33333333,  0.33333333]])"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "State Per Capita Incomes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = ps.open(\"data/usjoin.csv\")\n",
      "pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])\n",
      "pci.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "(81, 48)"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Put series into cross-sectional quintiles (i.e., quintiles for each year)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q5 = np.array([ps.Quantiles(y).yb for y in pci]).transpose()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q5.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "(48, 81)"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "q5[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "array([0, 2, 0, 4, 2, 4, 4, 1, 0, 1, 4, 2, 2, 1, 0, 1, 2, 3, 4, 4, 2, 0, 2,\n",
        "       2, 2, 4, 3, 4, 0, 4, 0, 0, 3, 1, 3, 3, 4, 0, 1, 0, 1, 2, 2, 1, 3, 1,\n",
        "       3, 3])"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pci.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "(81, 48)"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pci[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "array([ 323,  600,  310,  991,  634, 1024, 1032,  518,  347,  507,  948,\n",
        "        607,  581,  532,  393,  414,  601,  768,  906,  790,  599,  286,\n",
        "        621,  592,  596,  868,  686,  918,  410, 1152,  332,  382,  771,\n",
        "        455,  668,  772,  874,  271,  426,  378,  479,  551,  634,  434,\n",
        "        741,  460,  673,  675])"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "we are looping over the rows of y which is ordered Txn (rows are cross sections, row 0 is the cross-section for period 0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m5 = ps.Markov(q5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m5.classes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "array([0, 1, 2, 3, 4])"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m5.transitions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "array([[ 729.,   71.,    1.,    0.,    0.],\n",
        "       [  72.,  567.,   80.,    3.,    0.],\n",
        "       [   0.,   81.,  631.,   86.,    2.],\n",
        "       [   0.,    3.,   86.,  573.,   56.],\n",
        "       [   0.,    0.,    1.,   57.,  741.]])"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m5.p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "matrix([[ 0.91011236,  0.0886392 ,  0.00124844,  0.        ,  0.        ],\n",
        "        [ 0.09972299,  0.78531856,  0.11080332,  0.00415512,  0.        ],\n",
        "        [ 0.        ,  0.10125   ,  0.78875   ,  0.1075    ,  0.0025    ],\n",
        "        [ 0.        ,  0.00417827,  0.11977716,  0.79805014,  0.07799443],\n",
        "        [ 0.        ,  0.        ,  0.00125156,  0.07133917,  0.92740926]])"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m5.steady_state"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "matrix([[ 0.20774716],\n",
        "        [ 0.18725774],\n",
        "        [ 0.20740537],\n",
        "        [ 0.18821787],\n",
        "        [ 0.20937187]])"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmpt = ps.ergodic.fmpt(m5.p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmpt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "matrix([[   4.81354357,   11.50292712,   29.60921231,   53.38594954,\n",
        "          103.59816743],\n",
        "        [  42.04774505,    5.34023324,   18.74455332,   42.50023268,\n",
        "           92.71316899],\n",
        "        [  69.25849753,   27.21075248,    4.82147603,   25.27184624,\n",
        "           75.43305672],\n",
        "        [  84.90689329,   42.85914824,   17.18082642,    5.31299186,\n",
        "           51.60953369],\n",
        "        [  98.41295543,   56.36521038,   30.66046735,   14.21158356,\n",
        "            4.77619083]])"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For a state with income in the first quintile, it takes on average 11.5 years for it to first enter the second quintile, 29.6 to get to the third quintile, 53.4 years to enter the fourth, and 103.6 years to reach the richest quintile."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Spatial Markov"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pci.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(81, 48)"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pci = pci.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pci.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "(48, 81)"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rpci = pci / pci.mean(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rpci[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "array([ 0.5250254 ,  0.97527938,  0.50389434,  1.61083644,  1.03054521,\n",
        "        1.6644768 ,  1.67748053,  0.8419912 ,  0.56403657,  0.82411107,\n",
        "        1.54094142,  0.98665764,  0.94439553,  0.86474771,  0.63880799,\n",
        "        0.67294277,  0.97690484,  1.2483576 ,  1.47267186,  1.28411785,\n",
        "        0.97365391,  0.46488317,  1.00941416,  0.96227565,  0.96877751,\n",
        "        1.41090417,  1.11506942,  1.49217745,  0.66644091,  1.8725364 ,\n",
        "        0.53965459,  0.62092787,  1.253234  ,  0.73958686,  1.08581104,\n",
        "        1.25485946,  1.42065696,  0.44050119,  0.69244836,  0.61442601,\n",
        "        0.77859804,  0.89563156,  1.03054521,  0.70545208,  1.20447003,\n",
        "        0.74771419,  1.09393837,  1.0971893 ])"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rpci[:,0].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "0.99999999999999989"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read in a GAL file to construct our W"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w = ps.open(ps.examples.get_path(\"states48.gal\")).read()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "48"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w.transform = 'R'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm = ps.Spatial_Markov(rpci, w, fixed=True, k=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm.p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "matrix([[ 0.91461837,  0.07503234,  0.00905563,  0.00129366,  0.        ],\n",
        "        [ 0.06570302,  0.82654402,  0.10512484,  0.00131406,  0.00131406],\n",
        "        [ 0.00520833,  0.10286458,  0.79427083,  0.09505208,  0.00260417],\n",
        "        [ 0.        ,  0.00913838,  0.09399478,  0.84856397,  0.04830287],\n",
        "        [ 0.        ,  0.        ,  0.        ,  0.06217617,  0.93782383]])"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for p in sm.P:\n",
      "    print p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.96341463  0.0304878   0.00609756  0.          0.        ]\n",
        " [ 0.06040268  0.83221477  0.10738255  0.          0.        ]\n",
        " [ 0.          0.14        0.74        0.12        0.        ]\n",
        " [ 0.          0.03571429  0.32142857  0.57142857  0.07142857]\n",
        " [ 0.          0.          0.          0.16666667  0.83333333]]\n",
        "[[ 0.79831933  0.16806723  0.03361345  0.          0.        ]\n",
        " [ 0.0754717   0.88207547  0.04245283  0.          0.        ]\n",
        " [ 0.00537634  0.06989247  0.8655914   0.05913978  0.        ]\n",
        " [ 0.          0.          0.06372549  0.90196078  0.03431373]\n",
        " [ 0.          0.          0.          0.19444444  0.80555556]]\n",
        "[[ 0.84693878  0.15306122  0.          0.          0.        ]\n",
        " [ 0.08133971  0.78947368  0.1291866   0.          0.        ]\n",
        " [ 0.00518135  0.0984456   0.79274611  0.0984456   0.00518135]\n",
        " [ 0.          0.          0.09411765  0.87058824  0.03529412]\n",
        " [ 0.          0.          0.          0.10204082  0.89795918]]\n",
        "[[ 0.8852459   0.09836066  0.          0.01639344  0.        ]\n",
        " [ 0.03875969  0.81395349  0.13953488  0.          0.00775194]\n",
        " [ 0.0049505   0.09405941  0.77722772  0.11881188  0.0049505 ]\n",
        " [ 0.          0.02339181  0.12865497  0.75438596  0.09356725]\n",
        " [ 0.          0.          0.          0.09661836  0.90338164]]\n",
        "[[ 0.33333333  0.66666667  0.          0.          0.        ]\n",
        " [ 0.0483871   0.77419355  0.16129032  0.01612903  0.        ]\n",
        " [ 0.01149425  0.16091954  0.74712644  0.08045977  0.        ]\n",
        " [ 0.          0.01036269  0.06217617  0.89637306  0.03108808]\n",
        " [ 0.          0.          0.          0.02352941  0.97647059]]\n"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm.S"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "array([[ 0.43509425,  0.2635327 ,  0.20363044,  0.06841983,  0.02932278],\n",
        "       [ 0.13391287,  0.33993305,  0.25153036,  0.23343016,  0.04119356],\n",
        "       [ 0.12124869,  0.21137444,  0.2635101 ,  0.29013417,  0.1137326 ],\n",
        "       [ 0.0776413 ,  0.19748806,  0.25352636,  0.22480415,  0.24654013],\n",
        "       [ 0.01776781,  0.19964349,  0.19009833,  0.25524697,  0.3372434 ]])"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for f in sm.F:\n",
      "    print f"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[   2.29835259   28.95614035   46.14285714   80.80952381  279.42857143]\n",
        " [  33.86549708    3.79459555   22.57142857   57.23809524  255.85714286]\n",
        " [  43.60233918    9.73684211    4.91085714   34.66666667  233.28571429]\n",
        " [  46.62865497   12.76315789    6.25714286   14.61564626  198.61904762]\n",
        " [  52.62865497   18.76315789   12.25714286    6.           34.1031746 ]]\n",
        "[[   7.46754205    9.70574606   25.76785714   74.53116883  194.23446197]\n",
        " [  27.76691978    2.94175577   24.97142857   73.73474026  193.4380334 ]\n",
        " [  53.57477715   28.48447637    3.97566318   48.76331169  168.46660482]\n",
        " [  72.03631562   46.94601483   18.46153846    4.28393653  119.70329314]\n",
        " [  77.17917276   52.08887197   23.6043956     5.14285714   24.27564033]]\n",
        "[[   8.24751154    6.53333333   18.38765432   40.70864198  112.76732026]\n",
        " [  47.35040872    4.73094099   11.85432099   34.17530864  106.23398693]\n",
        " [  69.42288828   24.76666667    3.794921     22.32098765   94.37966594]\n",
        " [  83.72288828   39.06666667   14.3           3.44668119   76.36702977]\n",
        " [  93.52288828   48.86666667   24.1           9.8           8.79255406]]\n",
        "[[  12.87974382   13.34847151   19.83446328   28.47257282   55.82395142]\n",
        " [  99.46114206    5.06359731   10.54545198   23.05133495   49.68944423]\n",
        " [ 117.76777159   23.03735526    3.94436301   15.0843986    43.57927247]\n",
        " [ 127.89752089   32.4393006    14.56853107    4.44831643   31.63099455]\n",
        " [ 138.24752089   42.7893006    24.91853107   10.35          4.05613474]]\n",
        "[[  56.2815534     1.5          10.57236842   27.02173913  110.54347826]\n",
        " [  82.9223301     5.00892857    9.07236842   25.52173913  109.04347826]\n",
        " [  97.17718447   19.53125       5.26043557   21.42391304  104.94565217]\n",
        " [ 127.1407767    48.74107143   33.29605263    3.91777427   83.52173913]\n",
        " [ 169.6407767    91.24107143   75.79605263   42.5           2.96521739]]\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "LISA Markov"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm = ps.LISA_Markov(pci,w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.classes "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "array([1, 2, 3, 4])"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.transitions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "array([[  1.08700000e+03,   4.40000000e+01,   4.00000000e+00,\n",
        "          3.40000000e+01],\n",
        "       [  4.10000000e+01,   4.70000000e+02,   3.60000000e+01,\n",
        "          1.00000000e+00],\n",
        "       [  5.00000000e+00,   3.40000000e+01,   1.42200000e+03,\n",
        "          3.90000000e+01],\n",
        "       [  3.00000000e+01,   1.00000000e+00,   4.00000000e+01,\n",
        "          5.52000000e+02]])"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "matrix([[ 0.92985458,  0.03763901,  0.00342173,  0.02908469],\n",
        "        [ 0.07481752,  0.85766423,  0.06569343,  0.00182482],\n",
        "        [ 0.00333333,  0.02266667,  0.948     ,  0.026     ],\n",
        "        [ 0.04815409,  0.00160514,  0.06420546,  0.88603531]])"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.steady_state"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "matrix([[ 0.28561505],\n",
        "        [ 0.14190226],\n",
        "        [ 0.40493672],\n",
        "        [ 0.16754598]])"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ps.ergodic.fmpt(lm.p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "matrix([[  3.50121609,  37.93025465,  40.55772829,  43.17412009],\n",
        "        [ 31.72800152,   7.04710419,  28.68182751,  49.91485137],\n",
        "        [ 52.44489385,  47.42097495,   2.46952168,  43.75609676],\n",
        "        [ 38.76794022,  51.51755827,  26.31568558,   5.96851095]])"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Test of independence of own chains and lag chains"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.transitions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "array([[  1.08700000e+03,   4.40000000e+01,   4.00000000e+00,\n",
        "          3.40000000e+01],\n",
        "       [  4.10000000e+01,   4.70000000e+02,   3.60000000e+01,\n",
        "          1.00000000e+00],\n",
        "       [  5.00000000e+00,   3.40000000e+01,   1.42200000e+03,\n",
        "          3.90000000e+01],\n",
        "       [  3.00000000e+01,   1.00000000e+00,   4.00000000e+01,\n",
        "          5.52000000e+02]])"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.expected_t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "array([[  1.12328098e+03,   1.15377356e+01,   3.47522158e-01,\n",
        "          3.38337644e+01],\n",
        "       [  3.50272664e+00,   5.28473882e+02,   1.59178880e+01,\n",
        "          1.05503814e-01],\n",
        "       [  1.53878082e-01,   2.32163556e+01,   1.46690710e+03,\n",
        "          9.72266513e+00],\n",
        "       [  9.60775143e+00,   9.86856346e-02,   6.23537392e+00,\n",
        "          6.07058189e+02]])"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm.chi_2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "(162.47505958346289, 0.0, 9)"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}