{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "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 }, "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": { "collapsed": false }, "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", " -0.0024666647861060209,\n", " -0.0058258251130863803,\n", " -0.010739622617965183,\n", " -0.0046867791898770328,\n", " -0.0065940756391070776]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "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 }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }