local_SI.ipynb

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     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "os.chdir('../')\n",
    "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.api import families\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "austria = pd.read_csv('http://dl.dropbox.com/u/8649795/AT_Austria.csv')\n",
    "austria = austria[austria['Origin'] != austria['Destination']]\n",
    "f = austria['Data'].values\n",
    "o = austria['Origin'].values\n",
    "d = austria['Destination'].values\n",
    "dij = austria['Dij'].values\n",
    "o_vars = austria['Oi2007'].values\n",
    "d_vars = austria['Dj2007'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00976746026969\n"
     ]
    }
   ],
   "source": [
    "model = Gravity(f, o_vars, d_vars, dij, 'exp')\n",
    "print model.params[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.01699776161094757,\n",
       " -0.0053210259160796358,\n",
       " -0.0028594272276957211,\n",
       " -0.006533037784217155,\n",
       " -0.0024666647861060209,\n",
       " -0.0058258251130860472,\n",
       " -0.010739622617965516,\n",
       " -0.0046867791898773659,\n",
       " -0.0065940756391066335]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local = model.local(loc_index=o, locs=np.unique(o))\n",
    "local['param2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00727113391179\n"
     ]
    }
   ],
   "source": [
    "model = Production(f, o, d_vars, dij, 'exp')\n",
    "print model.params[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.016997761610949791,\n",
       " -0.005321025916080413,\n",
       " -0.0028594272276953325,\n",
       " -0.0065330377842177101,\n",
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       " -0.010739622617965183,\n",
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       " -0.0065940756391070776]"
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     "execution_count": 42,
     "metadata": {},
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   ],
   "source": [
    "local = model.local()\n",
    "local['param2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00693754909526\n"
     ]
    }
   ],
   "source": [
    "model = Attraction(f, d, o_vars, dij, 'exp')\n",
    "print model.params[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.010872636479707154,\n",
       " -0.0054690202130680543,\n",
       " -0.0025567421332022833,\n",
       " -0.0051439340488994012,\n",
       " -0.0036020461535491433,\n",
       " -0.010088935906795271,\n",
       " -0.012926843651020203,\n",
       " -0.0075750287063747201,\n",
       " -0.0081576735088411123]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local = model.local()\n",
    "local['param2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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