From 3231cf38881e7b7a0aae196bc5622408d5cec839 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Wed, 16 Sep 2015 22:22:32 +0900 Subject: [PATCH 01/51] Fix optgrowth solution notebook --- solutions/optgrowth_solutions.ipynb | 927 +++++++--------------------- 1 file changed, 211 insertions(+), 716 deletions(-) diff --git a/solutions/optgrowth_solutions.ipynb b/solutions/optgrowth_solutions.ipynb index a5258d251..bc6b9cbfd 100644 --- a/solutions/optgrowth_solutions.ipynb +++ b/solutions/optgrowth_solutions.ipynb @@ -1,731 +1,226 @@ { - "metadata": { - "name": "", - "signature": "sha256:89ab78e36bdc736c251923085e90e94893f9cca4f6d752519c6a21410f45c597" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Infinite Horizon Dynamic Programming" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/dp_intro.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import compute_fixed_point\n", - "from quantecon.models import GrowthModel" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# quant-econ Solutions: Infinite Horizon Dynamic Programming" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solutions for http://quant-econ.net/py/dp_intro.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from quantecon import compute_fixed_point\n", + "from quantecon.models import GrowthModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "1 4.297e+00 6.331e-02 \n", + "2 4.080e+00 1.270e-01 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "1 4.297e+00 6.849e-02 \n", + "2 4.080e+00 1.322e-01 \n", + "3 3.875e+00 1.967e-01 \n", + "4 3.680e+00 2.656e-01 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "1 4.297e+00 6.273e-02 \n", + "2 4.080e+00 1.269e-01 \n", + "3 3.875e+00 1.882e-01 \n", + "4 3.680e+00 2.572e-01 \n", + "5 3.496e+00 3.295e-01 \n", + "6 3.327e+00 4.016e-01 \n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "alpha, beta = 0.65, 0.95\n", - "gm = GrowthModel() \n", - "true_sigma = (1 - alpha * beta) * gm.grid**alpha\n", - "w = 5 * gm.u(gm.grid) - 25 # Initial condition\n", - "\n", - "fig, ax = plt.subplots(3, 1, figsize=(8, 10))\n", - "\n", - "for i, n in enumerate((2, 4, 6)):\n", - " ax[i].set_ylim(0, 1)\n", - " ax[i].set_xlim(0, 2)\n", - " ax[i].set_yticks((0, 1))\n", - " ax[i].set_xticks((0, 2))\n", - "\n", - " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=n)\n", - " sigma = gm.compute_greedy(v_star)\n", - "\n", - " ax[i].plot(gm.grid, sigma, 'b-', lw=2, alpha=0.8, label='approximate optimal policy')\n", - " ax[i].plot(gm.grid, true_sigma, 'k-', lw=2, alpha=0.8, label='true optimal policy')\n", - " ax[i].legend(loc='upper left')\n", - " ax[i].set_title('{} value function iterations'.format(n))" - ], - "language": "python", + "data": { + "image/png": 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YDCgoKIDRaHT4Pg8PD8TExCAuLg5xcXHQ6XTaY3BwMIhaPAXa6SRghRBCtIva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\ng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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 4.296600\n", - "Computed iterate 2 with error 4.080228" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 4.296600" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 4.080228" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 3.875034" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 3.680327" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 4.296600" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 4.080228" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 3.875034" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 3.680327" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 3.495502" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 3.327466" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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o9+7dnYKn/TU+Ph4/P79WvoKuQwKsEEK0M3V1dZw4ccKpONf+evr0abf7RUdH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TkZPj4+8j4Wi1gyrb6+6/G+fcBXX4lZjlzRasXY0YQE0S4aHS0mZQgLE1W9nJSBaPCwBEvUB21t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\nSUVORK5CYII=\n", - "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, + "output_type": "display_data" + } + ], + "source": [ + "alpha, beta = 0.65, 0.95\n", + "gm = GrowthModel() \n", + "true_sigma = (1 - alpha * beta) * gm.grid**alpha\n", + "\n", + "fig, ax = plt.subplots(3, 1, figsize=(8, 10))\n", + "\n", + "for i, n in enumerate((2, 4, 6)):\n", + " ax[i].set_ylim(0, 1)\n", + " ax[i].set_xlim(0, 2)\n", + " ax[i].set_yticks((0, 1))\n", + " ax[i].set_xticks((0, 2))\n", + "\n", + " w = 5 * gm.u(gm.grid) - 25 # Initial condition\n", + " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=n, print_skip=1)\n", + " sigma = gm.compute_greedy(v_star)\n", + "\n", + " ax[i].plot(gm.grid, sigma, 'b-', lw=2, alpha=0.8, label='approximate optimal policy')\n", + " ax[i].plot(gm.grid, true_sigma, 'k-', lw=2, alpha=0.8, label='true optimal policy')\n", + " ax[i].legend(loc='upper left')\n", + " ax[i].set_title('{} value function iterations'.format(n))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "5 1.303e+00 3.270e-01 \n", + "10 5.142e-01 6.708e-01 \n", + "15 2.883e-01 1.032e+00 \n", + "20 1.690e-01 1.404e+00 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "5 2.792e+00 3.760e-01 \n", + "10 2.046e+00 7.315e-01 \n", + "15 1.497e+00 1.080e+00 \n", + "20 1.099e+00 1.426e+00 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "5 5.839e+00 3.705e-01 \n", + "10 5.247e+00 7.293e-01 \n", + "15 4.695e+00 1.084e+00 \n", + "20 4.189e+00 1.445e+00 \n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy import interp\n", - "\n", - "gm = GrowthModel() \n", - "w = 5 * gm.u(gm.grid) - 25 # To be used as an initial condition\n", - "discount_factors = (0.9, 0.94, 0.98)\n", - "series_length = 25\n", - "\n", - "fig, ax = plt.subplots(figsize=(8,5))\n", - "ax.set_xlabel(\"time\")\n", - "ax.set_ylabel(\"capital\")\n", - "\n", - "for beta in discount_factors:\n", - "\n", - " # Compute the optimal policy given the discount factor\n", - " gm.beta = beta\n", - " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=20)\n", - " sigma = gm.compute_greedy(v_star)\n", - "\n", - " # Compute the corresponding time series for capital\n", - " k = np.empty(series_length)\n", - " k[0] = 0.1\n", - " sigma_function = lambda x: interp(x, gm.grid, sigma)\n", - " for t in range(1, series_length):\n", - " k[t] = gm.f(k[t-1]) - sigma_function(k[t-1])\n", - " ax.plot(k, 'o-', lw=2, alpha=0.75, label=r'$\\beta = {}$'.format(beta))\n", - "\n", - "ax.legend(loc='lower right')\n", - "plt.show()" - ], - "language": "python", + "data": { + "image/png": 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O82Pej3gKq4iTV8S6I+vqNU5jyqXZxClXmj2++rsUqkIvtm3b4JFH7FXofvpToh9+mOgg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\nvsG3dSuOfDf7Wjlod24flo2fzG3Ff2o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SKxg3NoGZM2McoIhIB3ClRN8erFy5kvHjx8c6\njLBFKtH7Zuj+6Jo3wcFHne8hwRK4BpMNiYhIHEjo4Iuc+CPRNzSQ8FEJZxpgd9IkvjsEbr451kGJ\niEh78EgHf/3KF3/mHP73O7jaWvZ06Ub9uRF6pU5ERMTjj0T/3krONcC2brlk9e106ZscIiIiHVb8\nJ/pAACvezNkGqEgez8MPg7X4EQURERF/i/tE/9kH/8Kd+JIjyV2wbj/ggQdiHZGIiEj7EfeJ/r/v\nrqDhLJR2H8xDY5K4BusDiIiIxI24T/TnijZy7jzs6fow3gyFIiIi4on71+sSDh/nZFJn+g9/FG/i\nIRERucZMD0e1W1G9ojezUWa228wqzey5y3z+HTPbbGb1ZvbzcOo2amiA0vQBTJxwfTROQURErsI5\np68ofUVC1BK9mSUCC4FRQDYwycwuXRn+BDAD+E0r6japve0hMjMjGLw0KSoqinUIHYLaOfrUxtGn\nNm6fonlFPxTY45w74JxrAF4HLrqL7pw75pwrARrCrdvo/WNfctTti3z0AugX91pRO0ef2jj61Mbt\nUzQTfU/gfyH7VV5ZROv+pHMCp9/7Ay89/0KrghQREfGzaCb6ttxcaHHdrzpfx1Mpndi4/E9t+HEi\nIiL+FLVlas0sF3jJOTfK238eCDjn5l3m2BeBGufcq+HUNbP2uy6iiIhIlLSXZWpLgCwz+zZwGJgA\nTGrm2EsDblHdcE5URESkI4paonfOnTOznwH/BBKBAudcuZk96X2+yMxuBD4CugEBM3sayHbO1Vyu\nbrRiFRER8auoDd2LiIhI7MXtFLgtnVBHWs/MDpjZdjP7xMw+jHU8fmBmhWZ21MzKQsrSzWytmVWY\n2ftmlhbLGP2gmXZ+ycyqvP78iZmNimWM8c7MepnZejPbaWY7zGymV67+HCFXaOOw+nJcXtF7E+p8\nCowADhEc/p+k4f3IMrP9wF3OuZOxjsUvzGwYUAP82TmX45W9Ahx3zr3i/dHa3Tk3K5Zxxrtm2vlF\n4JRz7rcxDc4nvFuvNzrntplZV2Ar8BDwY9SfI+IKbTyeMPpyvF7Rt3hCHWkzPfAYQc65/wBfXFL8\nILDU215K8BdZ2qCZdgb154hxzn3mnNvmbdcA5QTnO1F/jpArtDGE0ZfjNdG3ZTIeaTkHrDOzEjN7\nPNbB+FgP59xRb/so0COWwfjcDDMrNbMCDSlHjveG1J3AFtSfoyKkjT/wilrcl+M10cff/Yb4dI9z\n7k5gNDDdGw6VKHLBe2nq39Hxe6APMBA4Arwa23D8wRtS/jvwtHPuVOhn6s+R4bXxmwTbuIYw+3K8\nJvpDQK+Q/V4Er+olgpxzR7x/jwH/IHjLRCLvqHcvDjO7Cfg8xvH4knPuc+cBlqD+3GZmdh3BJL/M\nOfeWV6z+HEEhbby8sY3D7cvxmuibJtQxsySCE+qsinFMvmJmXcws1dtOAUYCZVeuJa20CpjmbU8D\n3rrCsdJKXtJpNBb15zax4AL0BcAu59z8kI/UnyOkuTYOty/H5VP3AGY2GpjPhQl15sY4JF8xsz4E\nr+IhOLHSX9TGbWdmfwXuB75F8P7lbOBtYCVwC3AAGO+cq45VjH5wmXZ+EfgewaFOB+wHngy5lyxh\nMrN7gQ3Adi4Mzz8PfIj6c0Q008a/JDhTbIv7ctwmehEREbm6eB26FxERkRZQohcREfExJXoREREf\nU6IXERHxMSV6ERERH1OiFxER8TElehFpYmbfMLOfets3mdkbsY5JRNpG79GLSBNv4YzVjUu7ikj8\n6xTrAESkXfk1kGlmnwCVwO3OuRwz+xHB5Ua7AFkEF9FIBiYDZ4DvO+e+MLNMYCFwA3AaeNw59+m1\nPw0RaaShexEJ9Ryw11u18BeXfNaP4LzaQ4BfAV855wYBm4Efesf8EZjhnBvs1f/dNYlaRJqlK3oR\nCWXNbAOsd87VArVmVg2s9srLgDu8xY/uBt4IrsUBQFI0gxWRq1OiF5GWOhOyHQjZDxD8vyQB+MIb\nDRCRdkJD9yIS6hSQGmYdA3DOnQL2m9kjEFxi08zuiHB8IhImJXoRaeKcOwFsMrMy4BUuLI3pQra5\nzHbj/hQgz8y2ATuAB6MbsYhcjV6vExER8TFd0YuIiPiYEr2IiIiPKdGLiIj4mBK9iIiIjynRi4iI\n+JgSvYiIiI8p0YuIiPiYEr2IiIiP/R9Bxk/Ac39XjAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 5.999396\n", - "Computed iterate 2 with error 3.791226" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 2.531863" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 1.767013" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 1.303066" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 1.011176" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 0.816201" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 0.689199" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 0.590297" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 0.514201" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 0.454587" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 0.402620" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 0.359115" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 0.321714" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 0.288297" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 0.258360" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 17 with error 0.232150" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 0.208786" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 19 with error 0.187838" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 0.169022" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 4.401010" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 3.153136" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 2.962891" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 2.784289" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 2.616550" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 2.458976" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 2.318565" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 2.171183" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 2.042677" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 1.921553" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 1.806962" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 1.698904" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 1.597220" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 1.494743" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 1.405415" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 1.321367" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 17 with error 1.242295" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 1.167909" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 19 with error 1.097938" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 1.032124" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 7.118648" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 6.974372" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 6.833300" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 6.702746" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 6.564332" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 6.427286" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 6.299795" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 6.173511" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 6.048887" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 5.924557" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 5.805184" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 5.687164" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 5.570272" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 5.453828" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 5.338835" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 5.225067" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 17 with error 5.112733" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 5.001816" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 19 with error 4.892653" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 4.784860" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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thaVLkzh2LKFGaHbokExc3J24XOb7a3ut2N+0KYnCwoQa9W7TJpnBg+/0+Wez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\nJCamUVSUUHksMTGJWbPwKazre/3JNm3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- "text": [ - "" - ] - } - ], - "prompt_number": 4 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "from scipy import interp\n", + "\n", + "gm = GrowthModel() \n", + "w = 5 * gm.u(gm.grid) - 25 # To be used as an initial condition\n", + "discount_factors = (0.9, 0.94, 0.98)\n", + "series_length = 25\n", + "\n", + "fig, ax = plt.subplots(figsize=(8,5))\n", + "ax.set_xlabel(\"time\")\n", + "ax.set_ylabel(\"capital\")\n", + "ax.set_ylim(0.10, 0.30)\n", + "\n", + "for beta in discount_factors:\n", + "\n", + " # Compute the optimal policy given the discount factor\n", + " gm.beta = beta\n", + " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=20)\n", + " sigma = gm.compute_greedy(v_star)\n", + "\n", + " # Compute the corresponding time series for capital\n", + " k = np.empty(series_length)\n", + " k[0] = 0.1\n", + " sigma_function = lambda x: interp(x, gm.grid, sigma)\n", + " for t in range(1, series_length):\n", + " k[t] = gm.f(k[t-1]) - sigma_function(k[t-1])\n", + " ax.plot(k, 'o-', lw=2, alpha=0.75, label=r'$\\beta = {}$'.format(beta))\n", + "\n", + "ax.legend(loc='lower right')\n", + "plt.show()" + ] + }, + { + "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": "2.7.10" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 54de56bec5db51ffdd4dd0db7d7c9ce4c8a14edb Mon Sep 17 00:00:00 2001 From: John Stachurski Date: Wed, 23 Sep 2015 16:38:51 -0400 Subject: [PATCH 02/51] added Aiyagari examples --- examples/aiyagari_compute_equilibrium.py | 72 ++++++++++++++++++ examples/aiyagari_compute_policy.py | 42 +++++++++++ examples/aiyagari_household.py | 95 ++++++++++++++++++++++++ 3 files changed, 209 insertions(+) create mode 100644 examples/aiyagari_compute_equilibrium.py create mode 100644 examples/aiyagari_compute_policy.py create mode 100644 examples/aiyagari_household.py diff --git a/examples/aiyagari_compute_equilibrium.py b/examples/aiyagari_compute_equilibrium.py new file mode 100644 index 000000000..49c395724 --- /dev/null +++ b/examples/aiyagari_compute_equilibrium.py @@ -0,0 +1,72 @@ + +import numpy as np +import quantecon as qe +import matplotlib.pyplot as plt +from numba import jit +from aiyagari_household import Household, asset_marginal +from quantecon.markov import DiscreteDP + + +A = 2.5 +N = 0.05 +alpha = 0.33 +beta = 0.96 + + +def r_to_w(r): + return A * (1 - alpha) * (alpha / (1 + r))**(alpha / (1 - alpha)) + +def rd(K): + return A * alpha * (N / K)**(1 - alpha) + + +def prices_to_capital_stock(am, r): + """ + Map prices to the induced level of capital stock. + + Paramters: + ---------- + + am : AiyagariModel + An instance of an Aiyagari economy + r : float + The interest rate + """ + w = r_to_w(r) + am.set_prices(r, w) + aiyagari_ddp = DiscreteDP(am.R, am.Q, beta) + # Compute the optimal policy + results = aiyagari_ddp.solve(method='policy_iteration') + # Compute the stationary distribution + stationary_probs = results.mc.stationary_distributions[0] + # Extract the marginal distribution for assets + asset_probs = asset_marginal(stationary_probs, am.a_size, am.z_size) + # Return K + return np.sum(asset_probs * am.a_vals) + + +# Create an instance of Household +am = Household(a_max=20) + +# Use the instance to build a discrete dynamic program +am_ddp = DiscreteDP(am.R, am.Q, am.beta) + +# Create a grid of r values at which to compute demand and supply of capital +num_points = 20 +r_vals = np.linspace(0.0, 0.04, num_points) + +# Compute supply of capital +k_vals = np.empty(num_points) +for i, r in enumerate(r_vals): + k_vals[i] = prices_to_capital_stock(am, r) + +# Plot against demand for capital by firms +fig, ax = plt.subplots(figsize=(11, 8)) +ax.plot(k_vals, r_vals, lw=2, alpha=0.6, label='supply of capital') +ax.plot(k_vals, rd(k_vals), lw=2, alpha=0.6, label='demand for capital') +ax.grid() +ax.set_xlabel('capital') +ax.set_ylabel('interest rate') +ax.legend(loc='upper right') + +plt.show() diff --git a/examples/aiyagari_compute_policy.py b/examples/aiyagari_compute_policy.py new file mode 100644 index 000000000..bc4c2f79b --- /dev/null +++ b/examples/aiyagari_compute_policy.py @@ -0,0 +1,42 @@ + +import numpy as np +import quantecon as qe +import matplotlib.pyplot as plt +from aiyagari_household import Household +from quantecon.markov import DiscreteDP + +# Example prices +r = 0.03 +w = 0.956 + +# Create an instance of Household +am = Household(a_max=20, r=r, w=w) + +# Use the instance to build a discrete dynamic program +am_ddp = DiscreteDP(am.R, am.Q, am.beta) + +# Solve using policy function iteration +results = am_ddp.solve(method='policy_iteration') + +# Simplify names +z_size, a_size = am.z_size, am.a_size +z_vals, a_vals = am.z_vals, am.a_vals +n = a_size * z_size + +# Get all optimal actions across the set of a indices with z fixed in each row +a_star = np.empty((z_size, a_size)) +for s_i in range(n): + a_i = s_i // z_size + z_i = s_i % z_size + a_star[z_i, a_i] = a_vals[results.sigma[s_i]] + +fig, ax = plt.subplots(figsize=(9, 9)) +ax.plot(a_vals, a_vals, 'k--')# 45 degrees +for i in range(z_size): + lb = r'$z = {}$'.format(z_vals[i], '.2f') + ax.plot(a_vals, a_star[i, :], lw=2, alpha=0.6, label=lb) + ax.set_xlabel('current assets') + ax.set_ylabel('next period assets') +ax.legend(loc='upper left') + +plt.show() diff --git a/examples/aiyagari_household.py b/examples/aiyagari_household.py new file mode 100644 index 000000000..108f8d62f --- /dev/null +++ b/examples/aiyagari_household.py @@ -0,0 +1,95 @@ + +import numpy as np +from numba import jit + + +class Household(object): + """ + This class takes the parameters that define a household asset accumulation + problem and computes the corresponding reward and transition matrices R + and Q required to generate an instance of DiscreteDP, and thereby solve + for the optimal policy. + + Comments on indexing: We need to enumerate the state space S as a sequence + S = {0, ..., n}. To this end, (a_i, z_i) index pairs are mapped to s_i + indices according to the rule + + s_i = a_i * z_size + z_i + + To invert this map, use + + a_i = s_i // z_size (integer division) + z_i = s_i % z_size + + """ + + + def __init__(self, + r=0.01, # interest rate + w=1.0, # wages + beta=0.96, # discount factor + a_min=1e-10, + Pi = [[0.9, 0.1], [0.1, 0.9]], # Markov chain + z_vals=[0.1, 1.0], # exogenous states + a_max=18, + a_size=200): + + self.r, self.w, self.beta = r, w, beta + self.a_min, self.a_max, self.a_size = a_min, a_max, a_size + + self.Pi = np.asarray(Pi) + self.z_vals = np.asarray(z_vals) + self.z_size = len(z_vals) + + self.a_vals = np.linspace(a_min, a_max, a_size) + self.n = a_size * self.z_size + + self.Q = np.zeros((self.n, a_size, self.n)) + self.build_Q() + + self.R = np.empty((self.n, a_size)) + self.build_R() + + def set_prices(self, r, w): + self.r, self.w = r, w + self.build_R() + + def build_Q(self): + populate_Q(self.Q, self.a_size, self.z_size, self.Pi) + + def build_R(self): + self.R.fill(-np.inf) + populate_R(self.R, self.a_size, self.z_size, self.a_vals, self.z_vals, self.r, self.w) + + +@jit(nopython=True) +def populate_R(R, a_size, z_size, a_vals, z_vals, r, w): + n = a_size * z_size + for s_i in range(n): + a_i = s_i // z_size + z_i = s_i % z_size + a = a_vals[a_i] + z = z_vals[z_i] + for new_a_i in range(a_size): + a_new = a_vals[new_a_i] + c = w * z + (1 + r) * a - a_new + if c > 0: + R[s_i, new_a_i] = np.log(c) # Utility + +@jit(nopython=True) +def populate_Q(Q, a_size, z_size, Pi): + n = a_size * z_size + for s_i in range(n): + z_i = s_i % z_size + for a_i in range(a_size): + for next_z_i in range(z_size): + Q[s_i, a_i, a_i * z_size + next_z_i] = Pi[z_i, next_z_i] + + +@jit(nopython=True) +def asset_marginal(s_probs, a_size, z_size): + a_probs = np.zeros(a_size) + for a_i in range(a_size): + for z_i in range(z_size): + a_probs[a_i] += s_probs[a_i * z_size + z_i] + return a_probs From 0b3f08f68736d1a56cc58e446b5651cb0230310b Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Fri, 25 Sep 2015 22:44:21 +0900 Subject: [PATCH 03/51] ddp: Add "r" at the top of the file and make some minor edits in docstring --- quantecon/markov/ddp.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index c4b30daad..25fde858f 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -1,4 +1,4 @@ -""" +r""" Filename: ddp.py Author: Daisuke Oyama @@ -141,7 +141,7 @@ class DiscreteDP(object): with parameters: - * length L reward vector R, + * length L reward vector `R`, * L x n transition probability array `Q`, * discount factor `beta`, * length L array `s_indices`, and @@ -451,8 +451,8 @@ def _check_action_feasibility(self): def RQ_sigma(self, sigma): """ - Given a policy `sigma`, return the reward vector R_sigma and the - transition probability matrix Q_sigma. + Given a policy `sigma`, return the reward vector `R_sigma` and + the transition probability matrix `Q_sigma`. Parameters ---------- @@ -483,7 +483,7 @@ def RQ_sigma(self, sigma): def bellman_operator(self, v, Tv=None, sigma=None): """ The Bellman operator, which computes and returns the updated - value function Tv for a value function v. + value function `Tv` for a value function `v`. Parameters ---------- @@ -538,7 +538,7 @@ def compute_greedy(self, v, sigma=None): Value function vector, of length n. sigma : ndarray(int, ndim=1), optional(default=None) - Optional output array for sigma. + Optional output array for `sigma`. Returns ------- @@ -820,7 +820,7 @@ def controlled_mc(self, sigma): Returns ------- mc : MarkovChain - Controlled Markov Chain. + Controlled Markov chain. """ _, Q_sigma = self.RQ_sigma(sigma) From 826c3ebd0a25888b907b961ef3a7bd3fea80ee6f Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Fri, 25 Sep 2015 22:45:21 +0900 Subject: [PATCH 04/51] ddp: Fix wrong indentations --- quantecon/markov/ddp.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index 25fde858f..e0f07de72 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -708,12 +708,12 @@ def value_iteration(self, v_init=None, epsilon=None, max_iter=None): sigma = self.compute_greedy(v) res = DPSolveResult(v=v, - sigma=sigma, - num_iter=num_iter, - mc=self.controlled_mc(sigma), - method='value iteration', - epsilon=epsilon, - max_iter=max_iter) + sigma=sigma, + num_iter=num_iter, + mc=self.controlled_mc(sigma), + method='value iteration', + epsilon=epsilon, + max_iter=max_iter) return res @@ -745,11 +745,11 @@ def policy_iteration(self, v_init=None, max_iter=None): num_iter = i + 1 res = DPSolveResult(v=v_sigma, - sigma=sigma, - num_iter=num_iter, - mc=self.controlled_mc(sigma), - method='policy iteration', - max_iter=max_iter) + sigma=sigma, + num_iter=num_iter, + mc=self.controlled_mc(sigma), + method='policy iteration', + max_iter=max_iter) return res @@ -798,13 +798,13 @@ def midrange(z): num_iter = i + 1 res = DPSolveResult(v=v, - sigma=sigma, - num_iter=num_iter, - mc=self.controlled_mc(sigma), - method='modified policy iteration', - epsilon=epsilon, - max_iter=max_iter, - k=k) + sigma=sigma, + num_iter=num_iter, + mc=self.controlled_mc(sigma), + method='modified policy iteration', + epsilon=epsilon, + max_iter=max_iter, + k=k) return res From b5b0e2641f08b2c92fc4694dff3786aae5bf287d Mon Sep 17 00:00:00 2001 From: Thomas Sargent Date: Fri, 25 Sep 2015 17:12:39 -0400 Subject: [PATCH 05/51] Fix P update in stationary coefficients. --- quantecon/kalman.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/quantecon/kalman.py b/quantecon/kalman.py index 023e91b9c..5602b481e 100644 --- a/quantecon/kalman.py +++ b/quantecon/kalman.py @@ -192,17 +192,21 @@ def stationary_coefficients(self, j, coeff_type='ma'): if K_infinity is None: S, K_infinity = self.stationary_values() # == compute and return coefficients == # - coeffs = [np.identity(self.ss.k)] + coeffs = [] i = 1 if coeff_type == 'ma': - P = A + coeffs.append(np.identity(self.ss.k)) + P_mat = A + P = np.copy(P_mat) # Create a copy elif coeff_type == 'var': - P = A - dot(K_infinity, G) + coeffs.append(dot(G, K_infinity)) + P_mat = A - dot(K_infinity, G) + P = np.copy(P_mat) # Create a copy else: raise ValueError("Unknown coefficient type") while i <= j: coeffs.append(dot(dot(G, P), K_infinity)) - P = dot(P, P) + P = dot(P, P_mat) i += 1 return coeffs From 8caec7ced82b73388f55985ba7890920c54ee485 Mon Sep 17 00:00:00 2001 From: Thomas Sargent Date: Mon, 28 Sep 2015 17:19:16 -0400 Subject: [PATCH 06/51] Fix Kalman stationary MA representation. --- quantecon/kalman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantecon/kalman.py b/quantecon/kalman.py index 5602b481e..1e02883af 100644 --- a/quantecon/kalman.py +++ b/quantecon/kalman.py @@ -197,7 +197,7 @@ def stationary_coefficients(self, j, coeff_type='ma'): if coeff_type == 'ma': coeffs.append(np.identity(self.ss.k)) P_mat = A - P = np.copy(P_mat) # Create a copy + P = np.identity(self.ss.n) # Create a copy elif coeff_type == 'var': coeffs.append(dot(G, K_infinity)) P_mat = A - dot(K_infinity, G) From e1b83494f06217bd0e58e3c8ce474cc5b3a69b78 Mon Sep 17 00:00:00 2001 From: Thomas Sargent Date: Mon, 5 Oct 2015 17:07:47 -0400 Subject: [PATCH 07/51] Add impulse response method to LSS. --- quantecon/lss.py | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/quantecon/lss.py b/quantecon/lss.py index 4df6ec464..35766a0d4 100644 --- a/quantecon/lss.py +++ b/quantecon/lss.py @@ -336,3 +336,42 @@ def geometric_sums(self, beta, x_t): S_y = self.G.dot(S_x) return S_x, S_y + + def impulse_response(self, j=5): + """ + Pulls off the imuplse response coefficients to a shock + in w_{t} for x and y + + Important to note: We are uninterested in the shocks to + v for this method + + * x coefficients are C, AC, A^2 C... + * y coefficients are GC, GAC, GA^2C... + + Parameters + ---------- + j : Scalar(int) + Number of coefficients that we want + + Returns + ------- + xcoef : list(array_like(float, 2)) + The coefficients for x + ycoef : list(array_like(float, 2)) + The coefficients for y + """ + # Pull out matrices + A, C, G, H = self.A, self.C, self.G, self.H + Apower = np.copy(A) + + # Create room for coefficients + xcoef = [C] + ycoef = [np.dot(G, C)] + + for i in range(j): + xcoef.append(np.dot(Apower, C)) + ycoef.append(np.dot(G, np.dot(Apower, C))) + Apower = np.dot(Apower, A) + + return xcoef, ycoef + From 718cd5aad52f3e06f5ad8d4052911207dd9592fa Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Tue, 6 Oct 2015 10:03:24 -0400 Subject: [PATCH 08/51] Updates in version number to release new features for lss --- scripts/example-tests.log | 10 +- scripts/solutions-tests.log | 1662 +++++++++++++++++++++++------------ setup.py | 2 +- 3 files changed, 1098 insertions(+), 576 deletions(-) diff --git a/scripts/example-tests.log b/scripts/example-tests.log index d2d250fd8..9e19c9318 100644 --- a/scripts/example-tests.log +++ b/scripts/example-tests.log @@ -2,6 +2,12 @@ ---END '3dplot.py'--- ---Executing '3dvec.py'--- ---END '3dvec.py'--- +---Executing 'aiyagari_compute_equilibrium.py'--- +---END 'aiyagari_compute_equilibrium.py'--- +---Executing 'aiyagari_compute_policy.py'--- +---END 'aiyagari_compute_policy.py'--- +---Executing 'aiyagari_household.py'--- +---END 'aiyagari_household.py'--- ---Executing 'amss.py'--- ---END 'amss.py'--- ---Executing 'amss_figures.py'--- @@ -58,6 +64,8 @@ ---END 'evans_sargent_plot1.py'--- ---Executing 'evans_sargent_plot2.py'--- ---END 'evans_sargent_plot2.py'--- +---Executing 'finite_dp_og_example.py'--- +---END 'finite_dp_og_example.py'--- ---Executing 'gaussian_contours.py'--- ---END 'gaussian_contours.py'--- ---Executing 'ifp_savings_plots.py'--- @@ -148,7 +156,7 @@ Traceback (most recent call last): P = solve_discrete_riccati(A0, B0, R, Q, N) File "/home/matthewmckay/anaconda/lib/python2.7/site-packages/quantecon/matrix_eqn.py", line 197, in solve_discrete_riccati raise ValueError(fail_msg.format(i)) -ValueError: Convergence failed after 5001 iterations. +ValueError: Convergence failed after 501 iterations. ---END 'robust_monopolist.py'--- ---Executing 'sine2.py'--- ---END 'sine2.py'--- diff --git a/scripts/solutions-tests.log b/scripts/solutions-tests.log index fb0e79e89..88ccdbba0 100644 --- a/scripts/solutions-tests.log +++ b/scripts/solutions-tests.log @@ -1,24 +1,32 @@ ---> Executing 'arellano_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:42:10 PM INFO: Reading notebook arellano_solutions.ipynb -09/11/2015 03:42:11 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:43:53 AM INFO: Reading notebook arellano_solutions.ipynb +10/06/2015 09:43:55 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:42:12 PM INFO: Cell returned -09/11/2015 03:42:12 PM INFO: Running cell: +10/06/2015 09:43:55 AM INFO: Cell returned +10/06/2015 09:43:55 AM INFO: Running cell: from __future__ import division import numpy as np import matplotlib.pyplot as plt import quantecon as qe from quantecon.models import Arellano_Economy -09/11/2015 03:42:15 PM INFO: Cell returned -09/11/2015 03:42:15 PM INFO: Running cell: +10/06/2015 09:43:57 AM INFO: Cell returned +10/06/2015 09:43:57 AM INFO: Running cell: ae = Arellano_Economy(beta=.953, # time discount rate gamma=2., # risk aversion r=0.017, # international interest rate @@ -30,8 +38,8 @@ ae = Arellano_Economy(beta=.953, # time discount rate tol=1e-8, # error tolerance in iteration maxit=10000) -09/11/2015 03:42:30 PM INFO: Cell returned -09/11/2015 03:42:30 PM INFO: Running cell: +10/06/2015 09:44:13 AM INFO: Cell returned +10/06/2015 09:44:13 AM INFO: Running cell: # Create "Y High" and "Y Low" values as 5% devs from mean high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95 @@ -56,8 +64,8 @@ ax.set_xlabel(r"$B'$") ax.legend(loc='upper left', frameon=False) plt.show() -09/11/2015 03:42:31 PM INFO: Cell returned -09/11/2015 03:42:31 PM INFO: Running cell: +10/06/2015 09:44:15 AM INFO: Cell returned +10/06/2015 09:44:15 AM INFO: Running cell: # Create "Y High" and "Y Low" values as 5% devs from mean high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95 @@ -73,8 +81,8 @@ ax.set_ylabel(r"$V(y, B)$") ax.set_xlim(ae.Bgrid.min(), ae.Bgrid.max()) plt.show() -09/11/2015 03:42:32 PM INFO: Cell returned -09/11/2015 03:42:32 PM INFO: Running cell: +10/06/2015 09:44:16 AM INFO: Cell returned +10/06/2015 09:44:16 AM INFO: Running cell: xx, yy = ae.Bgrid, ae.ygrid zz = ae.default_prob @@ -90,8 +98,8 @@ ax.set_xlabel(r"$B'$") ax.set_ylabel(r"$y$") plt.show() -09/11/2015 03:42:32 PM INFO: Cell returned -09/11/2015 03:42:32 PM INFO: Running cell: +10/06/2015 09:44:17 AM INFO: Cell returned +10/06/2015 09:44:17 AM INFO: Running cell: T = 250 y_vec, B_vec, q_vec, default_vec = ae.simulate(T) @@ -133,34 +141,42 @@ for ax, series, title in zip(axes, plot_series, titles): plt.show() -09/11/2015 03:42:34 PM INFO: Cell returned -09/11/2015 03:42:34 PM INFO: Running cell: +10/06/2015 09:44:20 AM INFO: Cell returned +10/06/2015 09:44:20 AM INFO: Running cell: -09/11/2015 03:42:34 PM INFO: Cell returned -09/11/2015 03:42:34 PM INFO: Shutdown kernel +10/06/2015 09:44:20 AM INFO: Cell returned +10/06/2015 09:44:20 AM INFO: Shutdown kernel ---> END 'arellano_solutions.ipynb' <--- ---> Executing 'asset_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:42:35 PM INFO: Reading notebook asset_solutions.ipynb -09/11/2015 03:42:36 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:44:22 AM INFO: Reading notebook asset_solutions.ipynb +10/06/2015 09:44:23 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:42:36 PM INFO: Cell returned -09/11/2015 03:42:36 PM INFO: Running cell: +10/06/2015 09:44:24 AM INFO: Cell returned +10/06/2015 09:44:24 AM INFO: Running cell: from __future__ import division # Omit for Python 3.x import numpy as np import matplotlib.pyplot as plt from quantecon.models import AssetPrices -09/11/2015 03:42:38 PM INFO: Cell returned -09/11/2015 03:42:38 PM INFO: Running cell: +10/06/2015 09:44:26 AM INFO: Cell returned +10/06/2015 09:44:26 AM INFO: Running cell: # == Define primitives == # n = 5 P = 0.0125 * np.ones((n, n)) @@ -186,34 +202,42 @@ p_s = 150.0 w_bar, w_bars = ap.call_option(zeta, p_s, T = [10,20,30]) -09/11/2015 03:42:38 PM INFO: Cell returned -09/11/2015 03:42:38 PM INFO: Running cell: +10/06/2015 09:44:27 AM INFO: Cell returned +10/06/2015 09:44:27 AM INFO: Running cell: -09/11/2015 03:42:38 PM INFO: Cell returned -09/11/2015 03:42:38 PM INFO: Shutdown kernel +10/06/2015 09:44:27 AM INFO: Cell returned +10/06/2015 09:44:27 AM INFO: Shutdown kernel ---> END 'asset_solutions.ipynb' <--- ---> Executing 'career_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:42:39 PM INFO: Reading notebook career_solutions.ipynb -09/11/2015 03:42:40 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:44:29 AM INFO: Reading notebook career_solutions.ipynb +10/06/2015 09:44:30 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:42:40 PM INFO: Cell returned -09/11/2015 03:42:40 PM INFO: Running cell: +10/06/2015 09:44:31 AM INFO: Cell returned +10/06/2015 09:44:31 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import DiscreteRV, compute_fixed_point from quantecon.models import CareerWorkerProblem -09/11/2015 03:42:42 PM INFO: Cell returned -09/11/2015 03:42:42 PM INFO: Running cell: +10/06/2015 09:44:33 AM INFO: Cell returned +10/06/2015 09:44:33 AM INFO: Running cell: wp = CareerWorkerProblem() v_init = np.ones((wp.N, wp.N))*100 v = compute_fixed_point(wp.bellman_operator, v_init, verbose=False) @@ -248,8 +272,8 @@ plt.show() -09/11/2015 03:42:45 PM INFO: Cell returned -09/11/2015 03:42:45 PM INFO: Running cell: +10/06/2015 09:44:38 AM INFO: Cell returned +10/06/2015 09:44:38 AM INFO: Running cell: wp = CareerWorkerProblem() v_init = np.ones((wp.N, wp.N))*100 @@ -277,8 +301,8 @@ for i in range(M): print(np.median(samples)) -09/11/2015 03:42:53 PM INFO: Cell returned -09/11/2015 03:42:53 PM INFO: Running cell: +10/06/2015 09:44:42 AM INFO: Cell returned +10/06/2015 09:44:42 AM INFO: Running cell: from matplotlib import cm wp = CareerWorkerProblem() @@ -299,29 +323,310 @@ ax.text(4.0, 4.5, 'stay put', fontsize=14) -09/11/2015 03:42:57 PM INFO: Cell returned -09/11/2015 03:42:57 PM INFO: Shutdown kernel +10/06/2015 09:44:44 AM INFO: Cell returned +10/06/2015 09:44:44 AM INFO: Shutdown kernel ---> END 'career_solutions.ipynb' <--- +---> Executing 'discrete_dp_solutions.ipynb' <--- +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. + +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version + + """) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:44:47 AM INFO: Reading notebook discrete_dp_solutions.ipynb +10/06/2015 09:44:48 AM INFO: Running cell: +%matplotlib inline + +10/06/2015 09:44:49 AM INFO: Cell returned +10/06/2015 09:44:49 AM INFO: Running cell: +from __future__ import division, print_function +import numpy as np +import scipy.sparse as sparse +import matplotlib.pyplot as plt +from quantecon import compute_fixed_point +from quantecon.markov import DiscreteDP + +10/06/2015 09:44:50 AM INFO: Cell returned +10/06/2015 09:44:50 AM INFO: Running cell: +alpha = 0.65 +f = lambda k: k**alpha +u = np.log +beta = 0.95 + +10/06/2015 09:44:50 AM INFO: Cell returned +10/06/2015 09:44:50 AM INFO: Running cell: +grid_max = 2 +grid_size = 1500 +grid = np.linspace(1e-6, grid_max, grid_size) + +10/06/2015 09:44:50 AM INFO: Cell returned +10/06/2015 09:44:50 AM INFO: Running cell: +print(grid) + +10/06/2015 09:44:50 AM INFO: Cell returned +10/06/2015 09:44:50 AM INFO: Running cell: +# Consumption matrix, with nonpositive consumption included +C = f(grid).reshape(grid_size, 1) - grid.reshape(1, grid_size) + +# State-action indices +s_indices, a_indices = np.where(C > 0) + +# Number of state-action pairs +L = len(s_indices) + +10/06/2015 09:44:50 AM INFO: Cell returned +10/06/2015 09:44:50 AM INFO: Running cell: +print(L) +print(s_indices) +print(a_indices) + +10/06/2015 09:44:50 AM INFO: Cell returned +10/06/2015 09:44:50 AM INFO: Running cell: +R = u(C[s_indices, a_indices]) + +10/06/2015 09:44:51 AM INFO: Cell returned +10/06/2015 09:44:51 AM INFO: Running cell: +Q = sparse.lil_matrix((L, grid_size)) +Q[np.arange(L), a_indices] = 1 + +10/06/2015 09:44:54 AM INFO: Cell returned +10/06/2015 09:44:54 AM INFO: Running cell: +# data = np.ones(L) +# indptr = np.arange(L+1) +# Q = sparse.csr_matrix((data, a_indices, indptr), shape=(L, grid_size)) + +10/06/2015 09:44:54 AM INFO: Cell returned +10/06/2015 09:44:54 AM INFO: Running cell: +ddp = DiscreteDP(R, Q, beta, s_indices, a_indices) + +10/06/2015 09:44:56 AM INFO: Cell returned +10/06/2015 09:44:56 AM INFO: Running cell: +res = ddp.solve(method='policy_iteration') +v, sigma, num_iter = res.v, res.sigma, res.num_iter + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +num_iter + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +# Optimal consumption in the discrete version +c = f(grid) - grid[sigma] + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +# Exact solution of the continuous version +ab = alpha * beta +c1 = (np.log(1 - ab) + np.log(ab) * ab / (1 - ab)) / (1 - beta) +c2 = alpha / (1 - ab) +def v_star(k): + return c1 + c2 * np.log(k) + +def c_star(k): + return (1 - ab) * k**alpha + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +fig, ax = plt.subplots(1, 2, figsize=(14, 4)) +ax[0].set_ylim(-40, -32) +ax[0].set_xlim(grid[0], grid[-1]) +ax[1].set_xlim(grid[0], grid[-1]) + +lb0 = 'discrete value function' +ax[0].plot(grid, v, lw=2, alpha=0.6, label=lb0) + +lb0 = 'continuous value function' +ax[0].plot(grid, v_star(grid), 'k-', lw=1.5, alpha=0.8, label=lb0) +ax[0].legend(loc='upper left') + +lb1 = 'discrete optimal consumption' +ax[1].plot(grid, c, 'b-', lw=2, alpha=0.6, label=lb1) + +lb1 = 'continuous optimal consumption' +ax[1].plot(grid, c_star(grid), 'k-', lw=1.5, alpha=0.8, label=lb1) +ax[1].legend(loc='upper left') +plt.show() + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +np.abs(v - v_star(grid)).max() + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +np.abs(v - v_star(grid))[1:].max() + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +np.abs(c - c_star(grid)).max() + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +diff = np.diff(c) +(diff >= 0).all() + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:57 AM INFO: Running cell: +dec_ind = np.where(diff < 0)[0] + +10/06/2015 09:44:57 AM INFO: Cell returned +10/06/2015 09:44:58 AM INFO: Running cell: +len(dec_ind) + +10/06/2015 09:44:58 AM INFO: Cell returned +10/06/2015 09:44:58 AM INFO: Running cell: +np.abs(diff[dec_ind]).max() + +10/06/2015 09:44:58 AM INFO: Cell returned +10/06/2015 09:44:58 AM INFO: Running cell: +(np.diff(v) > 0).all() + +10/06/2015 09:44:58 AM INFO: Cell returned +10/06/2015 09:44:58 AM INFO: Running cell: +ddp.epsilon = 1e-4 +ddp.max_iter = 500 +res1 = ddp.solve(method='value_iteration') + +10/06/2015 09:45:02 AM INFO: Cell returned +10/06/2015 09:45:02 AM INFO: Running cell: +res1.num_iter + +10/06/2015 09:45:02 AM INFO: Cell returned +10/06/2015 09:45:02 AM INFO: Running cell: +np.array_equal(sigma, res1.sigma) + +10/06/2015 09:45:02 AM INFO: Cell returned +10/06/2015 09:45:02 AM INFO: Running cell: +res2 = ddp.solve(method='modified_policy_iteration') + +10/06/2015 09:45:03 AM INFO: Cell returned +10/06/2015 09:45:03 AM INFO: Running cell: +res2.num_iter + +10/06/2015 09:45:03 AM INFO: Cell returned +10/06/2015 09:45:03 AM INFO: Running cell: +np.array_equal(sigma, res2.sigma) + +10/06/2015 09:45:03 AM INFO: Cell returned +10/06/2015 09:45:03 AM INFO: Running cell: +%timeit ddp.solve(method='value_iteration') +%timeit ddp.solve(method='policy_iteration') +%timeit ddp.solve(method='modified_policy_iteration') + +10/06/2015 09:45:24 AM INFO: Cell returned +10/06/2015 09:45:24 AM INFO: Running cell: +w = 5 * np.log(grid) - 25 # Initial condition +n = 35 +fig, ax = plt.subplots(figsize=(8,5)) +ax.set_ylim(-40, -20) +ax.set_xlim(np.min(grid), np.max(grid)) +lb = 'initial condition' +ax.plot(grid, w, color=plt.cm.jet(0), lw=2, alpha=0.6, label=lb) +for i in range(n): + w = ddp.bellman_operator(w) + ax.plot(grid, w, color=plt.cm.jet(i / n), lw=2, alpha=0.6) +lb = 'true value function' +ax.plot(grid, v_star(grid), 'k-', lw=2, alpha=0.8, label=lb) +ax.legend(loc='upper left') + +plt.show() + +10/06/2015 09:45:24 AM INFO: Cell returned +10/06/2015 09:45:24 AM INFO: Running cell: +w = 5 * u(grid) - 25 # Initial condition + +fig, ax = plt.subplots(3, 1, figsize=(8, 10)) +true_c = c_star(grid) + +for i, n in enumerate((2, 4, 6)): + ax[i].set_ylim(0, 1) + ax[i].set_xlim(0, 2) + ax[i].set_yticks((0, 1)) + ax[i].set_xticks((0, 2)) + + w = 5 * u(grid) - 25 # Initial condition + compute_fixed_point(ddp.bellman_operator, w, max_iter=n, print_skip=1) + sigma = ddp.compute_greedy(w) # Policy indices + c_policy = f(grid) - grid[sigma] + + ax[i].plot(grid, c_policy, 'b-', lw=2, alpha=0.8, + label='approximate optimal consumption policy') + ax[i].plot(grid, true_c, 'k-', lw=2, alpha=0.8, + label='true optimal consumption policy') + ax[i].legend(loc='upper left') + ax[i].set_title('{} value function iterations'.format(n)) + +10/06/2015 09:45:25 AM INFO: Cell returned +10/06/2015 09:45:25 AM INFO: Running cell: +discount_factors = (0.9, 0.94, 0.98) +k_init = 0.1 + +# Search for the index corresponding to k_init +k_init_ind = np.searchsorted(grid, k_init) + +sample_size = 25 + +fig, ax = plt.subplots(figsize=(8,5)) +ax.set_xlabel("time") +ax.set_ylabel("capital") +ax.set_ylim(0.10, 0.30) + +# Create a new instance, not to modify the one used above +ddp0 = DiscreteDP(R, Q, beta, s_indices, a_indices) + +for beta in discount_factors: + ddp0.beta = beta + res0 = ddp0.solve() + k_path_ind = res0.mc.simulate(init=k_init_ind, ts_length=sample_size) + k_path = grid[k_path_ind] + ax.plot(k_path, 'o-', lw=2, alpha=0.75, label=r'$\beta = {}$'.format(beta)) + +ax.legend(loc='lower right') +plt.show() + +10/06/2015 09:45:29 AM INFO: Cell returned +10/06/2015 09:45:29 AM INFO: Running cell: + + +10/06/2015 09:45:29 AM INFO: Cell returned +10/06/2015 09:45:29 AM INFO: Shutdown kernel +---> END 'discrete_dp_solutions.ipynb' <--- + ---> Executing 'estspec_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:42:59 PM INFO: Reading notebook estspec_solutions.ipynb -09/11/2015 03:43:00 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:45:30 AM INFO: Reading notebook estspec_solutions.ipynb +10/06/2015 09:45:31 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:43:00 PM INFO: Cell returned -09/11/2015 03:43:00 PM INFO: Running cell: +10/06/2015 09:45:32 AM INFO: Cell returned +10/06/2015 09:45:32 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import ARMA, periodogram, ar_periodogram -09/11/2015 03:43:02 PM INFO: Cell returned -09/11/2015 03:43:02 PM INFO: Running cell: +10/06/2015 09:45:33 AM INFO: Cell returned +10/06/2015 09:45:33 AM INFO: Running cell: ## Data n = 400 @@ -347,8 +652,8 @@ for i, wl in enumerate((15, 55, 175)): # window lengths ax[i].set_title('window length = {}'.format(wl)) -09/11/2015 03:43:05 PM INFO: Cell returned -09/11/2015 03:43:05 PM INFO: Running cell: +10/06/2015 09:45:34 AM INFO: Cell returned +10/06/2015 09:45:34 AM INFO: Running cell: lp = ARMA(-0.9) wl = 65 @@ -373,31 +678,39 @@ for i in range(3): -09/11/2015 03:43:15 PM INFO: Cell returned -09/11/2015 03:43:15 PM INFO: Shutdown kernel +10/06/2015 09:45:37 AM INFO: Cell returned +10/06/2015 09:45:37 AM INFO: Shutdown kernel ---> END 'estspec_solutions.ipynb' <--- ---> Executing 'finite_mc_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:43:17 PM INFO: Reading notebook finite_mc_solutions.ipynb -09/11/2015 03:43:19 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:45:38 AM INFO: Reading notebook finite_mc_solutions.ipynb +10/06/2015 09:45:39 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:43:19 PM INFO: Cell returned -09/11/2015 03:43:19 PM INFO: Running cell: +10/06/2015 09:45:39 AM INFO: Cell returned +10/06/2015 09:45:39 AM INFO: Running cell: from __future__ import print_function, division # Omit for Python 3.x import numpy as np import matplotlib.pyplot as plt from quantecon import mc_compute_stationary, mc_sample_path -09/11/2015 03:43:22 PM INFO: Cell returned -09/11/2015 03:43:22 PM INFO: Running cell: +10/06/2015 09:45:41 AM INFO: Cell returned +10/06/2015 09:45:41 AM INFO: Running cell: alpha = beta = 0.1 N = 10000 @@ -426,8 +739,8 @@ ax.legend(loc='upper right') -09/11/2015 03:43:23 PM INFO: Cell returned -09/11/2015 03:43:23 PM INFO: Running cell: +10/06/2015 09:45:42 AM INFO: Cell returned +10/06/2015 09:45:42 AM INFO: Running cell: %%file web_graph_data.txt a -> d; a -> f; @@ -468,8 +781,8 @@ n -> j; n -> m; -09/11/2015 03:43:23 PM INFO: Cell returned -09/11/2015 03:43:23 PM INFO: Running cell: +10/06/2015 09:45:42 AM INFO: Cell returned +10/06/2015 09:45:42 AM INFO: Running cell: """ Return list of pages, ordered by rank """ @@ -507,42 +820,50 @@ for name, rank in sorted(ranked_pages.items(), key=itemgetter(1), reverse=1): -09/11/2015 03:43:24 PM INFO: Cell returned -09/11/2015 03:43:24 PM INFO: Running cell: +10/06/2015 09:45:43 AM INFO: Cell returned +10/06/2015 09:45:43 AM INFO: Running cell: -09/11/2015 03:43:24 PM INFO: Cell returned -09/11/2015 03:43:24 PM INFO: Running cell: +10/06/2015 09:45:43 AM INFO: Cell returned +10/06/2015 09:45:43 AM INFO: Running cell: -09/11/2015 03:43:24 PM INFO: Cell returned -09/11/2015 03:43:24 PM INFO: Running cell: +10/06/2015 09:45:43 AM INFO: Cell returned +10/06/2015 09:45:43 AM INFO: Running cell: -09/11/2015 03:43:24 PM INFO: Cell returned -09/11/2015 03:43:24 PM INFO: Shutdown kernel +10/06/2015 09:45:43 AM INFO: Cell returned +10/06/2015 09:45:43 AM INFO: Shutdown kernel ---> END 'finite_mc_solutions.ipynb' <--- ---> Executing 'ifp_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:43:26 PM INFO: Reading notebook ifp_solutions.ipynb -09/11/2015 03:43:27 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:45:44 AM INFO: Reading notebook ifp_solutions.ipynb +10/06/2015 09:45:45 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:43:27 PM INFO: Cell returned -09/11/2015 03:43:27 PM INFO: Running cell: +10/06/2015 09:45:45 AM INFO: Cell returned +10/06/2015 09:45:45 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import compute_fixed_point from quantecon.models import ConsumerProblem -09/11/2015 03:43:29 PM INFO: Cell returned -09/11/2015 03:43:29 PM INFO: Running cell: +10/06/2015 09:45:47 AM INFO: Cell returned +10/06/2015 09:45:47 AM INFO: Running cell: cp = ConsumerProblem() K = 80 @@ -569,8 +890,8 @@ ax.set_ylabel('consumption (low income)') ax.legend(loc='upper left') plt.show() -09/11/2015 03:43:42 PM INFO: Cell returned -09/11/2015 03:43:42 PM INFO: Running cell: +10/06/2015 09:45:57 AM INFO: Cell returned +10/06/2015 09:45:57 AM INFO: Running cell: r_vals = np.linspace(0, 0.04, 4) @@ -586,8 +907,8 @@ ax.set_ylabel('consumption (low income)') ax.legend(loc='upper left') plt.show() -09/11/2015 03:43:52 PM INFO: Cell returned -09/11/2015 03:43:52 PM INFO: Running cell: +10/06/2015 09:46:03 AM INFO: Cell returned +10/06/2015 09:46:03 AM INFO: Running cell: from scipy import interp from quantecon import mc_sample_path @@ -617,8 +938,8 @@ ax.set_xlabel('assets') ax.set_xlim(-0.05, 0.75) plt.show() -09/11/2015 03:44:00 PM INFO: Cell returned -09/11/2015 03:44:00 PM INFO: Running cell: +10/06/2015 09:46:07 AM INFO: Cell returned +10/06/2015 09:46:07 AM INFO: Running cell: M = 25 r_vals = np.linspace(0, 0.04, M) @@ -641,31 +962,39 @@ ax.grid(True) ax.legend(loc='upper left') plt.show() -09/11/2015 03:47:07 PM INFO: Cell returned -09/11/2015 03:47:07 PM INFO: Shutdown kernel +10/06/2015 09:47:51 AM INFO: Cell returned +10/06/2015 09:47:51 AM INFO: Shutdown kernel ---> END 'ifp_solutions.ipynb' <--- ---> Executing 'jv_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:47:09 PM INFO: Reading notebook jv_solutions.ipynb -09/11/2015 03:47:10 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:47:52 AM INFO: Reading notebook jv_solutions.ipynb +10/06/2015 09:47:53 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:47:10 PM INFO: Cell returned -09/11/2015 03:47:10 PM INFO: Running cell: +10/06/2015 09:47:53 AM INFO: Cell returned +10/06/2015 09:47:53 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt import random from quantecon import compute_fixed_point from quantecon.models import JvWorker -09/11/2015 03:47:12 PM INFO: Cell returned -09/11/2015 03:47:12 PM INFO: Running cell: +10/06/2015 09:47:54 AM INFO: Cell returned +10/06/2015 09:47:54 AM INFO: Running cell: wp = JvWorker(grid_size=25) G, pi, F = wp.G, wp.pi, wp.F # Simplify names @@ -704,8 +1033,8 @@ for x in plot_grid: plt.show() -09/11/2015 03:47:46 PM INFO: Cell returned -09/11/2015 03:47:46 PM INFO: Running cell: +10/06/2015 09:48:10 AM INFO: Cell returned +10/06/2015 09:48:10 AM INFO: Running cell: wp = JvWorker(grid_size=25) @@ -720,31 +1049,39 @@ ax.legend(loc='upper left') plt.show() -09/11/2015 03:47:47 PM INFO: Cell returned -09/11/2015 03:47:47 PM INFO: Shutdown kernel +10/06/2015 09:48:10 AM INFO: Cell returned +10/06/2015 09:48:10 AM INFO: Shutdown kernel ---> END 'jv_solutions.ipynb' <--- ---> Executing 'kalman_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:47:49 PM INFO: Reading notebook kalman_solutions.ipynb -09/11/2015 03:47:50 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:11 AM INFO: Reading notebook kalman_solutions.ipynb +10/06/2015 09:48:12 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:47:50 PM INFO: Cell returned -09/11/2015 03:47:50 PM INFO: Running cell: +10/06/2015 09:48:12 AM INFO: Cell returned +10/06/2015 09:48:12 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import Kalman from quantecon import LinearStateSpace from scipy.stats import norm -09/11/2015 03:47:51 PM INFO: Cell returned -09/11/2015 03:47:51 PM INFO: Running cell: +10/06/2015 09:48:13 AM INFO: Cell returned +10/06/2015 09:48:13 AM INFO: Running cell: # == parameters == # theta = 10 # Constant value of state x_t A, C, G, H = 1, 0, 1, 1 @@ -773,8 +1110,8 @@ for i in range(N): ax.set_title(r'First %d densities when $\theta = %.1f$' % (N, theta)) ax.legend(loc='upper left') -09/11/2015 03:47:53 PM INFO: Cell returned -09/11/2015 03:47:53 PM INFO: Running cell: +10/06/2015 09:48:14 AM INFO: Cell returned +10/06/2015 09:48:14 AM INFO: Running cell: from scipy.integrate import quad epsilon = 0.1 @@ -806,8 +1143,8 @@ ax.set_xlim(0, T) ax.plot(range(T), z) ax.fill_between(range(T), np.zeros(T), z, color="blue", alpha=0.2) -09/11/2015 03:47:55 PM INFO: Cell returned -09/11/2015 03:47:55 PM INFO: Running cell: +10/06/2015 09:48:16 AM INFO: Cell returned +10/06/2015 09:48:16 AM INFO: Running cell: from __future__ import print_function # Remove for Python 3.x from numpy.random import multivariate_normal from scipy.linalg import eigvals @@ -861,24 +1198,35 @@ ax.legend() -09/11/2015 03:47:56 PM INFO: Cell returned -09/11/2015 03:47:56 PM INFO: Running cell: +10/06/2015 09:48:16 AM INFO: Cell returned +10/06/2015 09:48:16 AM INFO: Running cell: -09/11/2015 03:47:56 PM INFO: Cell returned -09/11/2015 03:47:56 PM INFO: Shutdown kernel +10/06/2015 09:48:16 AM INFO: Cell returned +10/06/2015 09:48:16 AM INFO: Shutdown kernel ---> END 'kalman_solutions.ipynb' <--- ---> Executing 'lakemodel_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:47:57 PM INFO: Reading notebook lakemodel_solutions.ipynb -09/11/2015 03:47:57 PM INFO: Running cell: -%pylab inline +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:17 AM INFO: Reading notebook lakemodel_solutions.ipynb +10/06/2015 09:48:18 AM INFO: Running cell: +%matplotlib inline + +import numpy as np +import matplotlib.pyplot as plt from quantecon.models import LakeModel alpha = 0.012 @@ -891,121 +1239,129 @@ e0 = 0.92 u0 = 1-e0 T = 50 -09/11/2015 03:47:59 PM INFO: Cell returned -09/11/2015 03:47:59 PM INFO: Running cell: +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: LM0 = LakeModel(lamb,alpha,b,d) x0 = LM0.find_steady_state()# initial conditions -print "Initial Steady State: ", x0 +print("Initial Steady State: %s" % x0) -09/11/2015 03:47:59 PM INFO: Cell returned -09/11/2015 03:47:59 PM INFO: Running cell: +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: LM1 = LakeModel(0.2,alpha,b,d) -09/11/2015 03:47:59 PM INFO: Cell returned -09/11/2015 03:47:59 PM INFO: Running cell: +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: xbar = LM1.find_steady_state() # new steady state -X_path = vstack(LM1.simulate_stock_path(x0*N0,T)) # simulate stocks -x_path = vstack(LM1.simulate_rate_path(x0,T)) # simulate rates -print "New Steady State: ", xbar - -09/11/2015 03:47:59 PM INFO: Cell returned -09/11/2015 03:47:59 PM INFO: Running cell: -figure(figsize=[10,9]) -subplot(3,1,1) -plot(X_path[:,0]) -title(r'Employment') -subplot(3,1,2) -plot(X_path[:,1]) -title(r'Unemployment') -subplot(3,1,3) -plot(X_path.sum(1)) -title(r'Labor Force') - -09/11/2015 03:48:00 PM INFO: Cell returned -09/11/2015 03:48:00 PM INFO: Running cell: -figure(figsize=[10,6]) -subplot(2,1,1) -plot(x_path[:,0]) -hlines(xbar[0],0,T,'r','--') -title(r'Employment Rate') -subplot(2,1,2) -plot(x_path[:,1]) -hlines(xbar[1],0,T,'r','--') -title(r'Unemployment Rate') - -09/11/2015 03:48:01 PM INFO: Cell returned -09/11/2015 03:48:01 PM INFO: Running cell: +X_path = np.vstack(LM1.simulate_stock_path(x0*N0,T)) # simulate stocks +x_path = np.vstack(LM1.simulate_rate_path(x0,T)) # simulate rates +print("New Steady State: %s" % xbar) + +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: +plt.figure(figsize=[10,9]) +plt.subplot(3,1,1) +plt.plot(X_path[:,0]) +plt.title(r'Employment') +plt.subplot(3,1,2) +plt.plot(X_path[:,1]) +plt.title(r'Unemployment') +plt.subplot(3,1,3) +plt.plot(X_path.sum(1)) +plt.title(r'Labor Force') + +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: +plt.figure(figsize=[10,6]) +plt.subplot(2,1,1) +plt.plot(x_path[:,0]) +plt.hlines(xbar[0],0,T,'r','--') +plt.title(r'Employment Rate') +plt.subplot(2,1,2) +plt.plot(x_path[:,1]) +plt.hlines(xbar[1],0,T,'r','--') +plt.title(r'Unemployment Rate') + +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: bhat = 0.003 T_hat = 20 LM1 = LakeModel(lamb,alpha,bhat,d) -09/11/2015 03:48:01 PM INFO: Cell returned -09/11/2015 03:48:01 PM INFO: Running cell: -X_path1 = vstack(LM1.simulate_stock_path(x0*N0,T_hat)) # simulate stocks -x_path1 = vstack(LM1.simulate_rate_path(x0,T_hat)) # simulate rates - -09/11/2015 03:48:01 PM INFO: Cell returned -09/11/2015 03:48:01 PM INFO: Running cell: -X_path2 = vstack(LM0.simulate_stock_path(X_path1[-1,:2],T-T_hat+1)) # simulate stocks -x_path2 = vstack(LM0.simulate_rate_path(x_path1[-1,:2],T-T_hat+1)) # simulate rates - -09/11/2015 03:48:01 PM INFO: Cell returned -09/11/2015 03:48:01 PM INFO: Running cell: -x_path = vstack([x_path1,x_path2[1:]]) # note [1:] to avoid doubling period 20 -X_path = vstack([X_path1,X_path2[1:]]) # note [1:] to avoid doubling period 20 - -09/11/2015 03:48:01 PM INFO: Cell returned -09/11/2015 03:48:01 PM INFO: Running cell: -figure(figsize=[10,9]) -subplot(3,1,1) -plot(X_path[:,0]) -title(r'Employment') -subplot(3,1,2) -plot(X_path[:,1]) -title(r'Unemployment') -subplot(3,1,3) -plot(X_path.sum(1)) -title(r'Labor Force') - -09/11/2015 03:48:02 PM INFO: Cell returned -09/11/2015 03:48:02 PM INFO: Running cell: -figure(figsize=[10,6]) -subplot(2,1,1) -plot(x_path[:,0]) -hlines(x0[0],0,T,'r','--') -title(r'Employment Rate') -subplot(2,1,2) -plot(x_path[:,1]) -hlines(x0[1],0,T,'r','--') -title(r'Unemployment Rate') - -09/11/2015 03:48:02 PM INFO: Cell returned -09/11/2015 03:48:02 PM INFO: Running cell: - - -09/11/2015 03:48:02 PM INFO: Cell returned -09/11/2015 03:48:02 PM INFO: Shutdown kernel +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: +X_path1 = np.vstack(LM1.simulate_stock_path(x0*N0,T_hat)) # simulate stocks +x_path1 = np.vstack(LM1.simulate_rate_path(x0,T_hat)) # simulate rates + +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: +X_path2 = np.vstack(LM0.simulate_stock_path(X_path1[-1,:2],T-T_hat+1)) # simulate stocks +x_path2 = np.vstack(LM0.simulate_rate_path(x_path1[-1,:2],T-T_hat+1)) # simulate rates + +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: +x_path = np.vstack([x_path1,x_path2[1:]]) # note [1:] to avoid doubling period 20 +X_path = np.vstack([X_path1,X_path2[1:]]) # note [1:] to avoid doubling period 20 + +10/06/2015 09:48:19 AM INFO: Cell returned +10/06/2015 09:48:19 AM INFO: Running cell: +plt.figure(figsize=[10,9]) +plt.subplot(3,1,1) +plt.plot(X_path[:,0]) +plt.title(r'Employment') +plt.subplot(3,1,2) +plt.plot(X_path[:,1]) +plt.title(r'Unemployment') +plt.subplot(3,1,3) +plt.plot(X_path.sum(1)) +plt.title(r'Labor Force') + +10/06/2015 09:48:20 AM INFO: Cell returned +10/06/2015 09:48:20 AM INFO: Running cell: +plt.figure(figsize=[10,6]) +plt.subplot(2,1,1) +plt.plot(x_path[:,0]) +plt.hlines(x0[0],0,T,'r','--') +plt.title(r'Employment Rate') +plt.subplot(2,1,2) +plt.plot(x_path[:,1]) +plt.hlines(x0[1],0,T,'r','--') +plt.title(r'Unemployment Rate') + +10/06/2015 09:48:20 AM INFO: Cell returned +10/06/2015 09:48:20 AM INFO: Running cell: + + +10/06/2015 09:48:20 AM INFO: Cell returned +10/06/2015 09:48:20 AM INFO: Shutdown kernel ---> END 'lakemodel_solutions.ipynb' <--- ---> Executing 'lln_clt_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:48:04 PM INFO: Reading notebook lln_clt_solutions.ipynb -09/11/2015 03:48:04 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:22 AM INFO: Reading notebook lln_clt_solutions.ipynb +10/06/2015 09:48:22 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:48:05 PM INFO: Cell returned -09/11/2015 03:48:05 PM INFO: Running cell: +10/06/2015 09:48:22 AM INFO: Cell returned +10/06/2015 09:48:22 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt -09/11/2015 03:48:05 PM INFO: Cell returned -09/11/2015 03:48:05 PM INFO: Running cell: +10/06/2015 09:48:22 AM INFO: Cell returned +10/06/2015 09:48:22 AM INFO: Running cell: """ Illustrates the delta method, a consequence of the central limit theorem. """ @@ -1044,8 +1400,8 @@ ax.plot(xgrid, norm.pdf(xgrid, scale=asymptotic_sd), 'k-', lw=2, label=lb) ax.legend() plt.show() -09/11/2015 03:50:33 PM INFO: Cell returned -09/11/2015 03:50:33 PM INFO: Running cell: +10/06/2015 09:48:26 AM INFO: Cell returned +10/06/2015 09:48:26 AM INFO: Running cell: from scipy.stats import uniform, chi2 from scipy.linalg import inv, sqrtm @@ -1090,30 +1446,38 @@ ax.legend() ax.hist(chisq_obs, bins=50, normed=True) plt.show() -09/11/2015 03:50:44 PM INFO: Cell returned -09/11/2015 03:50:44 PM INFO: Shutdown kernel +10/06/2015 09:48:32 AM INFO: Cell returned +10/06/2015 09:48:32 AM INFO: Shutdown kernel ---> END 'lln_clt_solutions.ipynb' <--- ---> Executing 'lqcontrol_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:50:45 PM INFO: Reading notebook lqcontrol_solutions.ipynb -09/11/2015 03:50:46 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:33 AM INFO: Reading notebook lqcontrol_solutions.ipynb +10/06/2015 09:48:34 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:50:47 PM INFO: Cell returned -09/11/2015 03:50:47 PM INFO: Running cell: +10/06/2015 09:48:34 AM INFO: Cell returned +10/06/2015 09:48:34 AM INFO: Running cell: from __future__ import division import numpy as np import matplotlib.pyplot as plt from quantecon import LQ -09/11/2015 03:50:49 PM INFO: Cell returned -09/11/2015 03:50:49 PM INFO: Running cell: +10/06/2015 09:48:35 AM INFO: Cell returned +10/06/2015 09:48:35 AM INFO: Running cell: # == Model parameters == # r = 0.05 beta = 1 / (1 + r) @@ -1177,8 +1541,8 @@ axes[1].legend(ncol=1, **legend_args) plt.show() -09/11/2015 03:50:50 PM INFO: Cell returned -09/11/2015 03:50:50 PM INFO: Running cell: +10/06/2015 09:48:35 AM INFO: Cell returned +10/06/2015 09:48:35 AM INFO: Running cell: # == Model parameters == # r = 0.05 beta = 1 / (1 + r) @@ -1275,8 +1639,8 @@ axes[1].legend(ncol=1, **legend_args) plt.show() -09/11/2015 03:50:51 PM INFO: Cell returned -09/11/2015 03:50:51 PM INFO: Running cell: +10/06/2015 09:48:36 AM INFO: Cell returned +10/06/2015 09:48:36 AM INFO: Running cell: # == Model parameters == # a0 = 5 a1 = 0.5 @@ -1333,23 +1697,31 @@ s = r'dynamics with $\gamma = {}$'.format(gamma) ax.text(max(time) * 0.6, 1 * q_bar.max(), s, fontsize=14) plt.show() -09/11/2015 03:50:52 PM INFO: Cell returned -09/11/2015 03:50:52 PM INFO: Shutdown kernel +10/06/2015 09:48:36 AM INFO: Cell returned +10/06/2015 09:48:36 AM INFO: Shutdown kernel ---> END 'lqcontrol_solutions.ipynb' <--- ---> Executing 'lqramsey_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:50:53 PM INFO: Reading notebook lqramsey_solutions.ipynb -09/11/2015 03:50:54 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:37 AM INFO: Reading notebook lqramsey_solutions.ipynb +10/06/2015 09:48:38 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:50:55 PM INFO: Cell returned -09/11/2015 03:50:55 PM INFO: Running cell: +10/06/2015 09:48:38 AM INFO: Cell returned +10/06/2015 09:48:38 AM INFO: Running cell: import sys import os import numpy as np @@ -1359,8 +1731,8 @@ import matplotlib.pyplot as plt # to append it to the path so we can import it below sys.path.append(os.path.abspath("../examples")) -09/11/2015 03:50:55 PM INFO: Cell returned -09/11/2015 03:50:55 PM INFO: Running cell: +10/06/2015 09:48:38 AM INFO: Cell returned +10/06/2015 09:48:38 AM INFO: Running cell: from numpy import array from lqramsey import * @@ -1391,29 +1763,37 @@ T = 50 path = compute_paths(T, economy) gen_fig_1(path) -09/11/2015 03:50:59 PM INFO: Cell returned -09/11/2015 03:50:59 PM INFO: Shutdown kernel +10/06/2015 09:48:40 AM INFO: Cell returned +10/06/2015 09:48:40 AM INFO: Shutdown kernel ---> END 'lqramsey_solutions.ipynb' <--- ---> Executing 'lss_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:51:00 PM INFO: Reading notebook lss_solutions.ipynb -09/11/2015 03:51:01 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:41 AM INFO: Reading notebook lss_solutions.ipynb +10/06/2015 09:48:42 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:51:02 PM INFO: Cell returned -09/11/2015 03:51:02 PM INFO: Running cell: +10/06/2015 09:48:42 AM INFO: Cell returned +10/06/2015 09:48:42 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import LinearStateSpace -09/11/2015 03:51:04 PM INFO: Cell returned -09/11/2015 03:51:04 PM INFO: Running cell: +10/06/2015 09:48:43 AM INFO: Cell returned +10/06/2015 09:48:43 AM INFO: Running cell: phi_0, phi_1, phi_2 = 1.1, 0.8, -0.8 A = [[1, 0, 0], @@ -1433,8 +1813,8 @@ ax.set_xlabel('time') ax.set_ylabel(r'$y_t$', fontsize=16) plt.show() -09/11/2015 03:51:05 PM INFO: Cell returned -09/11/2015 03:51:05 PM INFO: Running cell: +10/06/2015 09:48:43 AM INFO: Cell returned +10/06/2015 09:48:43 AM INFO: Running cell: phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 sigma = 0.2 @@ -1460,8 +1840,8 @@ ax.set_ylabel(r'$y_t$', fontsize=16) plt.show() -09/11/2015 03:51:05 PM INFO: Cell returned -09/11/2015 03:51:05 PM INFO: Running cell: +10/06/2015 09:48:43 AM INFO: Cell returned +10/06/2015 09:48:43 AM INFO: Running cell: from __future__ import division from scipy.stats import norm import random @@ -1509,8 +1889,8 @@ ax.plot(population_means, color='g', lw=2, alpha=0.8, label=r'$G\mu_t$') ax.legend(ncol=2) plt.show() -09/11/2015 03:51:06 PM INFO: Cell returned -09/11/2015 03:51:06 PM INFO: Running cell: +10/06/2015 09:48:44 AM INFO: Cell returned +10/06/2015 09:48:44 AM INFO: Running cell: phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 sigma = 0.1 @@ -1554,34 +1934,42 @@ for i in range(80): ax.plot((T0, T1, T2), (y[T0], y[T1], y[T2],), 'ko', alpha=0.5) -09/11/2015 03:51:08 PM INFO: Cell returned -09/11/2015 03:51:08 PM INFO: Running cell: +10/06/2015 09:48:45 AM INFO: Cell returned +10/06/2015 09:48:45 AM INFO: Running cell: -09/11/2015 03:51:08 PM INFO: Cell returned -09/11/2015 03:51:08 PM INFO: Shutdown kernel +10/06/2015 09:48:45 AM INFO: Cell returned +10/06/2015 09:48:45 AM INFO: Shutdown kernel ---> END 'lss_solutions.ipynb' <--- ---> Executing 'lucas_asset_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:51:10 PM INFO: Reading notebook lucas_asset_solutions.ipynb -09/11/2015 03:51:10 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:46 AM INFO: Reading notebook lucas_asset_solutions.ipynb +10/06/2015 09:48:46 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:51:11 PM INFO: Cell returned -09/11/2015 03:51:11 PM INFO: Running cell: +10/06/2015 09:48:46 AM INFO: Cell returned +10/06/2015 09:48:46 AM INFO: Running cell: from __future__ import division # Omit for Python 3.x import numpy as np import matplotlib.pyplot as plt from quantecon.models import LucasTree -09/11/2015 03:51:13 PM INFO: Cell returned -09/11/2015 03:51:13 PM INFO: Running cell: +10/06/2015 09:48:47 AM INFO: Cell returned +10/06/2015 09:48:47 AM INFO: Running cell: fig, ax = plt.subplots(figsize=(10,7)) ax.set_xlabel(r'$y$', fontsize=16) @@ -1597,34 +1985,42 @@ for beta in (.95, 0.98): ax.legend(loc='upper left') ax.set_xlim(min(grid), max(grid)) -09/11/2015 03:51:17 PM INFO: Cell returned -09/11/2015 03:51:17 PM INFO: Running cell: +10/06/2015 09:48:49 AM INFO: Cell returned +10/06/2015 09:48:49 AM INFO: Running cell: -09/11/2015 03:51:17 PM INFO: Cell returned -09/11/2015 03:51:17 PM INFO: Shutdown kernel +10/06/2015 09:48:49 AM INFO: Cell returned +10/06/2015 09:48:49 AM INFO: Shutdown kernel ---> END 'lucas_asset_solutions.ipynb' <--- ---> Executing 'mpe_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:51:17 PM INFO: Reading notebook mpe_solutions.ipynb -09/11/2015 03:51:18 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:50 AM INFO: Reading notebook mpe_solutions.ipynb +10/06/2015 09:48:51 AM INFO: Running cell: import numpy as np import quantecon as qe import matplotlib.pyplot as plt from numpy import dot -09/11/2015 03:51:21 PM INFO: Cell returned -09/11/2015 03:51:21 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:51:21 PM INFO: Cell returned -09/11/2015 03:51:21 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: # == Parameters == # a0 = 10.0 a1 = 2.0 @@ -1655,8 +2051,8 @@ S1 = S2 = W1 = W2 = M1 = M2 = 0.0 F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2, beta=beta) -09/11/2015 03:51:21 PM INFO: Cell returned -09/11/2015 03:51:21 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: AF = A - B1.dot(F1) - B2.dot(F2) n = 20 x = np.empty((3, n)) @@ -1668,8 +2064,8 @@ q2 = x[2, :] q = q1 + q2 # Total output, MPE p = a0 - a1 * q # Price, MPE -09/11/2015 03:51:21 PM INFO: Cell returned -09/11/2015 03:51:21 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: R = a1 Q = gamma A = B = 1 @@ -1685,8 +2081,8 @@ for i in range(1, n): qm[i] = float(x) + q_bar pm = a0 - a1 * qm -09/11/2015 03:51:21 PM INFO: Cell returned -09/11/2015 03:51:21 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: fig, axes = plt.subplots(2, 1, figsize=(9, 9)) ax = axes[0] @@ -1705,8 +2101,8 @@ ax.set_ylabel("price") ax.set_xlabel("time") ax.legend(loc='upper right', frameon=0) -09/11/2015 03:51:22 PM INFO: Cell returned -09/11/2015 03:51:22 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: delta = 0.02 D = np.array([[-1, 0.5], [0.5, -1]]) b = np.array([25, 25]) @@ -1715,8 +2111,8 @@ e1 = e2 = np.array([10, 10, 3]) delta_1 = 1 - delta -09/11/2015 03:51:22 PM INFO: Cell returned -09/11/2015 03:51:22 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: # == Create matrices needed to compute the Nash feedback equilibrium == # A = np.array([[delta_1, 0, -delta_1*b[0]], @@ -1753,8 +2149,8 @@ W2 = np.array([[0, 0], M1 = np.array([[0, 0], [0, D[0, 1] / 2.]]) M2 = np.copy(M1) -09/11/2015 03:51:22 PM INFO: Cell returned -09/11/2015 03:51:22 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2) print("\nFirm 1's feedback rule:\n") @@ -1763,8 +2159,8 @@ print(F1) print("\nFirm 2's feedback rule:\n") print(F2) -09/11/2015 03:51:22 PM INFO: Cell returned -09/11/2015 03:51:22 PM INFO: Running cell: +10/06/2015 09:48:52 AM INFO: Cell returned +10/06/2015 09:48:52 AM INFO: Running cell: AF = A - B1.dot(F1) - B2.dot(F2) n = 25 x = np.empty((3, n)) @@ -1779,32 +2175,40 @@ ax.plot(I2, 'g-', lw=2, alpha=0.75, label='inventories, firm 2') ax.set_title(r'$\delta = {}$'.format(delta)) ax.legend() -09/11/2015 03:51:23 PM INFO: Cell returned -09/11/2015 03:51:23 PM INFO: Running cell: +10/06/2015 09:48:53 AM INFO: Cell returned +10/06/2015 09:48:53 AM INFO: Running cell: -09/11/2015 03:51:23 PM INFO: Cell returned -09/11/2015 03:51:23 PM INFO: Shutdown kernel +10/06/2015 09:48:53 AM INFO: Cell returned +10/06/2015 09:48:53 AM INFO: Shutdown kernel ---> END 'mpe_solutions.ipynb' <--- ---> Executing 'numpy_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:51:24 PM INFO: Reading notebook numpy_solutions.ipynb -09/11/2015 03:51:25 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:54 AM INFO: Reading notebook numpy_solutions.ipynb +10/06/2015 09:48:54 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: def p(x, coef): X = np.empty(len(coef)) X[0] = 1 @@ -1812,8 +2216,8 @@ def p(x, coef): y = np.cumprod(X) # y = [1, x, x**2,...] return np.dot(coef, y) -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: coef = np.ones(3) print(coef) print(p(1, coef)) @@ -1821,8 +2225,8 @@ print(p(1, coef)) q = np.poly1d(coef) print(q(1)) -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: from numpy import cumsum from numpy.random import uniform @@ -1847,14 +2251,14 @@ class discreteRV: """ return self.Q.searchsorted(uniform(0, 1, size=k)) -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: q = (0.1, 0.9) d = discreteRV(q) d.q = (0.5, 0.5) -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: """ Modifies ecdf.py from QuantEcon to add in a plot method @@ -1926,40 +2330,48 @@ class ECDF(object): plt.plot(x_vals, f(x_vals)) plt.show() -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: X = np.random.randn(1000) F = ECDF(X) F.plot() -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Running cell: +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Running cell: -09/11/2015 03:51:26 PM INFO: Cell returned -09/11/2015 03:51:26 PM INFO: Shutdown kernel +10/06/2015 09:48:55 AM INFO: Cell returned +10/06/2015 09:48:55 AM INFO: Shutdown kernel ---> END 'numpy_solutions.ipynb' <--- ---> Executing 'odu_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:51:27 PM INFO: Reading notebook odu_solutions.ipynb -09/11/2015 03:51:28 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:48:56 AM INFO: Reading notebook odu_solutions.ipynb +10/06/2015 09:48:57 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:51:29 PM INFO: Cell returned -09/11/2015 03:51:29 PM INFO: Running cell: +10/06/2015 09:48:57 AM INFO: Cell returned +10/06/2015 09:48:57 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import compute_fixed_point from quantecon.models import SearchProblem -09/11/2015 03:51:30 PM INFO: Cell returned -09/11/2015 03:51:30 PM INFO: Running cell: +10/06/2015 09:48:58 AM INFO: Cell returned +10/06/2015 09:48:58 AM INFO: Running cell: sp = SearchProblem(pi_grid_size=50) phi_init = np.ones(len(sp.pi_grid)) @@ -1976,8 +2388,8 @@ ax.text(0.42, 1.2, 'reject') ax.text(0.7, 1.8, 'accept') plt.show() -09/11/2015 03:51:31 PM INFO: Cell returned -09/11/2015 03:51:31 PM INFO: Running cell: +10/06/2015 09:48:58 AM INFO: Cell returned +10/06/2015 09:48:58 AM INFO: Running cell: from scipy import interp # Set up model and compute the function w_bar sp = SearchProblem(pi_grid_size=50, F_a=1, F_b=1) @@ -2038,19 +2450,27 @@ ax.axvline(change_date, color="red") ax.legend() plt.show() -09/11/2015 03:53:47 PM INFO: Cell returned -09/11/2015 03:53:47 PM INFO: Shutdown kernel +10/06/2015 09:50:10 AM INFO: Cell returned +10/06/2015 09:50:10 AM INFO: Shutdown kernel ---> END 'odu_solutions.ipynb' <--- ---> Executing 'oop_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:53:48 PM INFO: Reading notebook oop_solutions.ipynb -09/11/2015 03:53:48 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:11 AM INFO: Reading notebook oop_solutions.ipynb +10/06/2015 09:50:11 AM INFO: Running cell: class ECDF(object): def __init__(self, observations): @@ -2063,8 +2483,8 @@ class ECDF(object): counter += 1 return counter / len(self.observations) -09/11/2015 03:53:48 PM INFO: Cell returned -09/11/2015 03:53:48 PM INFO: Running cell: +10/06/2015 09:50:11 AM INFO: Cell returned +10/06/2015 09:50:11 AM INFO: Running cell: # == test == # from random import uniform @@ -2077,8 +2497,8 @@ F.observations = [uniform(0, 1) for i in range(1000)] print(F(0.5)) -09/11/2015 03:53:49 PM INFO: Cell returned -09/11/2015 03:53:49 PM INFO: Running cell: +10/06/2015 09:50:11 AM INFO: Cell returned +10/06/2015 09:50:11 AM INFO: Running cell: class Polynomial(object): def __init__(self, coefficients): @@ -2109,30 +2529,38 @@ class Polynomial(object): self.coefficients = new_coefficients -09/11/2015 03:53:49 PM INFO: Cell returned -09/11/2015 03:53:49 PM INFO: Shutdown kernel +10/06/2015 09:50:11 AM INFO: Cell returned +10/06/2015 09:50:11 AM INFO: Shutdown kernel ---> END 'oop_solutions.ipynb' <--- ---> Executing 'optgrowth_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:53:50 PM INFO: Reading notebook optgrowth_solutions.ipynb -09/11/2015 03:53:50 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:12 AM INFO: Reading notebook optgrowth_solutions.ipynb +10/06/2015 09:50:13 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:53:51 PM INFO: Cell returned -09/11/2015 03:53:51 PM INFO: Running cell: +10/06/2015 09:50:13 AM INFO: Cell returned +10/06/2015 09:50:13 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import compute_fixed_point from quantecon.models import GrowthModel -09/11/2015 03:53:53 PM INFO: Cell returned -09/11/2015 03:53:53 PM INFO: Running cell: +10/06/2015 09:50:14 AM INFO: Cell returned +10/06/2015 09:50:14 AM INFO: Running cell: alpha, beta = 0.65, 0.95 gm = GrowthModel() true_sigma = (1 - alpha * beta) * gm.grid**alpha @@ -2154,8 +2582,8 @@ for i, n in enumerate((2, 4, 6)): ax[i].legend(loc='upper left') ax[i].set_title('{} value function iterations'.format(n)) -09/11/2015 03:53:55 PM INFO: Cell returned -09/11/2015 03:53:55 PM INFO: Running cell: +10/06/2015 09:50:15 AM INFO: Cell returned +10/06/2015 09:50:15 AM INFO: Running cell: from scipy import interp gm = GrowthModel() @@ -2185,31 +2613,39 @@ for beta in discount_factors: ax.legend(loc='lower right') plt.show() -09/11/2015 03:54:03 PM INFO: Cell returned -09/11/2015 03:54:03 PM INFO: Shutdown kernel +10/06/2015 09:50:20 AM INFO: Cell returned +10/06/2015 09:50:20 AM INFO: Shutdown kernel ---> END 'optgrowth_solutions.ipynb' <--- ---> Executing 'pandas_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:05 PM INFO: Reading notebook pandas_solutions.ipynb -09/11/2015 03:54:06 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:21 AM INFO: Reading notebook pandas_solutions.ipynb +10/06/2015 09:50:21 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:54:07 PM INFO: Cell returned -09/11/2015 03:54:07 PM INFO: Running cell: +10/06/2015 09:50:22 AM INFO: Cell returned +10/06/2015 09:50:22 AM INFO: Running cell: import numpy as np import pandas as pd import datetime as dt import pandas.io.data as web import matplotlib.pyplot as plt -09/11/2015 03:54:07 PM INFO: Cell returned -09/11/2015 03:54:07 PM INFO: Running cell: +10/06/2015 09:50:22 AM INFO: Cell returned +10/06/2015 09:50:22 AM INFO: Running cell: ticker_list = {'INTC': 'Intel', 'MSFT': 'Microsoft', 'IBM': 'IBM', @@ -2242,19 +2678,27 @@ pc.sort() fig, ax = plt.subplots(figsize=(10,8)) pc.plot(kind='bar', ax=ax) -09/11/2015 03:54:10 PM INFO: Cell returned -09/11/2015 03:54:10 PM INFO: Shutdown kernel +10/06/2015 09:50:24 AM INFO: Cell returned +10/06/2015 09:50:24 AM INFO: Shutdown kernel ---> END 'pandas_solutions.ipynb' <--- ---> Executing 'pbe_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:11 PM INFO: Reading notebook pbe_solutions.ipynb -09/11/2015 03:54:12 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:25 AM INFO: Reading notebook pbe_solutions.ipynb +10/06/2015 09:50:26 AM INFO: Running cell: def factorial(n): k = 1 for i in range(n): @@ -2263,8 +2707,8 @@ def factorial(n): factorial(4) -09/11/2015 03:54:12 PM INFO: Cell returned -09/11/2015 03:54:12 PM INFO: Running cell: +10/06/2015 09:50:26 AM INFO: Cell returned +10/06/2015 09:50:26 AM INFO: Running cell: from random import uniform def binomial_rv(n, p): @@ -2277,8 +2721,8 @@ def binomial_rv(n, p): binomial_rv(10, 0.5) -09/11/2015 03:54:12 PM INFO: Cell returned -09/11/2015 03:54:12 PM INFO: Running cell: +10/06/2015 09:50:26 AM INFO: Cell returned +10/06/2015 09:50:26 AM INFO: Running cell: from __future__ import division # Omit if using Python 3.x from math import sqrt @@ -2295,8 +2739,8 @@ area_estimate = count / n print(area_estimate * 4) # dividing by radius**2 -09/11/2015 03:54:12 PM INFO: Cell returned -09/11/2015 03:54:12 PM INFO: Running cell: +10/06/2015 09:50:26 AM INFO: Cell returned +10/06/2015 09:50:26 AM INFO: Running cell: payoff = 0 count = 0 @@ -2308,12 +2752,12 @@ for i in range(10): print(payoff) -09/11/2015 03:54:12 PM INFO: Cell returned -09/11/2015 03:54:12 PM INFO: Running cell: +10/06/2015 09:50:26 AM INFO: Cell returned +10/06/2015 09:50:26 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:54:13 PM INFO: Cell returned -09/11/2015 03:54:13 PM INFO: Running cell: +10/06/2015 09:50:26 AM INFO: Cell returned +10/06/2015 09:50:26 AM INFO: Running cell: import matplotlib.pyplot as plt from random import normalvariate @@ -2328,8 +2772,8 @@ for i in range(ts_length + 1): plt.plot(x_values, 'b-') -09/11/2015 03:54:13 PM INFO: Cell returned -09/11/2015 03:54:13 PM INFO: Running cell: +10/06/2015 09:50:27 AM INFO: Cell returned +10/06/2015 09:50:27 AM INFO: Running cell: alphas = [0.0, 0.8, 0.98] ts_length = 200 @@ -2342,23 +2786,31 @@ for alpha in alphas: plt.plot(x_values, label='alpha = ' + str(alpha)) plt.legend() -09/11/2015 03:54:13 PM INFO: Cell returned -09/11/2015 03:54:13 PM INFO: Running cell: +10/06/2015 09:50:27 AM INFO: Cell returned +10/06/2015 09:50:27 AM INFO: Running cell: -09/11/2015 03:54:13 PM INFO: Cell returned -09/11/2015 03:54:13 PM INFO: Shutdown kernel +10/06/2015 09:50:27 AM INFO: Cell returned +10/06/2015 09:50:27 AM INFO: Shutdown kernel ---> END 'pbe_solutions.ipynb' <--- ---> Executing 'py_adv_feat_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:15 PM INFO: Reading notebook py_adv_feat_solutions.ipynb -09/11/2015 03:54:16 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:28 AM INFO: Reading notebook py_adv_feat_solutions.ipynb +10/06/2015 09:50:28 AM INFO: Running cell: def x(t): if t == 0: return 0 @@ -2368,12 +2820,12 @@ def x(t): return x(t-1) + x(t-2) -09/11/2015 03:54:16 PM INFO: Cell returned -09/11/2015 03:54:16 PM INFO: Running cell: +10/06/2015 09:50:28 AM INFO: Cell returned +10/06/2015 09:50:28 AM INFO: Running cell: print([x(i) for i in range(10)]) -09/11/2015 03:54:16 PM INFO: Cell returned -09/11/2015 03:54:16 PM INFO: Running cell: +10/06/2015 09:50:28 AM INFO: Cell returned +10/06/2015 09:50:28 AM INFO: Running cell: def column_iterator(target_file, column_number): """A generator function for CSV files. When called with a file name target_file (string) and column number @@ -2395,8 +2847,8 @@ for date in dates: break i += 1 -09/11/2015 03:54:16 PM INFO: Cell returned -09/11/2015 03:54:16 PM INFO: Running cell: +10/06/2015 09:50:28 AM INFO: Cell returned +10/06/2015 09:50:28 AM INFO: Running cell: %%file numbers.txt prices 3 @@ -2405,8 +2857,8 @@ prices 7 21 -09/11/2015 03:54:16 PM INFO: Cell returned -09/11/2015 03:54:16 PM INFO: Running cell: +10/06/2015 09:50:28 AM INFO: Cell returned +10/06/2015 09:50:28 AM INFO: Running cell: f = open('numbers.txt') total = 0.0 @@ -2421,64 +2873,72 @@ f.close() print(total) -09/11/2015 03:54:16 PM INFO: Cell returned -09/11/2015 03:54:16 PM INFO: Shutdown kernel +10/06/2015 09:50:28 AM INFO: Cell returned +10/06/2015 09:50:28 AM INFO: Shutdown kernel ---> END 'py_adv_feat_solutions.ipynb' <--- ---> Executing 'pyess_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:17 PM INFO: Reading notebook pyess_solutions.ipynb -09/11/2015 03:54:18 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:29 AM INFO: Reading notebook pyess_solutions.ipynb +10/06/2015 09:50:30 AM INFO: Running cell: from __future__ import division # Omit for Python 3.x -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: x_vals = [1, 2, 3] y_vals = [1, 1, 1] sum([x * y for x, y in zip(x_vals, y_vals)]) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: sum(x * y for x, y in zip(x_vals, y_vals)) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: sum([x % 2 == 0 for x in range(100)]) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: sum(x % 2 == 0 for x in range(100)) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: len([x for x in range(100) if x % 2 == 0]) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: sum([1 for x in range(100) if x % 2 == 0]) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: pairs = ((2, 5), (4, 2), (9, 8), (12, 10)) sum([x % 2 == 0 and y % 2 == 0 for x, y in pairs]) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: def p(x, coeff): return sum(a * x**i for i, a in enumerate(coeff)) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: p(1, (2, 4)) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: def f(string): count = 0 for letter in string: @@ -2487,8 +2947,8 @@ def f(string): return count f('The Rain in Spain') -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: def f(seq_a, seq_b): is_subset = True for a in seq_a: @@ -2501,13 +2961,13 @@ def f(seq_a, seq_b): print(f([1, 2], [1, 2, 3])) print(f([1, 2, 3], [1, 2])) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: def f(seq_a, seq_b): return set(seq_a).issubset(set(seq_b)) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Running cell: +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Running cell: def linapprox(f, a, b, n, x): """ Evaluates the piecewise linear interpolant of f at x on the interval @@ -2544,33 +3004,41 @@ def linapprox(f, a, b, n, x): return f(u) + (x - u) * (f(v) - f(u)) / (v - u) -09/11/2015 03:54:18 PM INFO: Cell returned -09/11/2015 03:54:18 PM INFO: Shutdown kernel +10/06/2015 09:50:30 AM INFO: Cell returned +10/06/2015 09:50:30 AM INFO: Shutdown kernel ---> END 'pyess_solutions.ipynb' <--- ---> Executing 'ree_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:19 PM INFO: Reading notebook ree_solutions.ipynb -09/11/2015 03:54:20 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:31 AM INFO: Reading notebook ree_solutions.ipynb +10/06/2015 09:50:31 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:54:20 PM INFO: Cell returned -09/11/2015 03:54:20 PM INFO: Running cell: +10/06/2015 09:50:32 AM INFO: Cell returned +10/06/2015 09:50:32 AM INFO: Running cell: from __future__ import print_function import numpy as np import matplotlib.pyplot as plt -09/11/2015 03:54:20 PM INFO: Cell returned -09/11/2015 03:54:20 PM INFO: Running cell: +10/06/2015 09:50:32 AM INFO: Cell returned +10/06/2015 09:50:32 AM INFO: Running cell: from quantecon import LQ -09/11/2015 03:54:22 PM INFO: Cell returned -09/11/2015 03:54:22 PM INFO: Running cell: +10/06/2015 09:50:32 AM INFO: Cell returned +10/06/2015 09:50:32 AM INFO: Running cell: # == Model parameters == # @@ -2605,8 +3073,8 @@ print(out1) print(out2) -09/11/2015 03:54:22 PM INFO: Cell returned -09/11/2015 03:54:22 PM INFO: Running cell: +10/06/2015 09:50:32 AM INFO: Cell returned +10/06/2015 09:50:32 AM INFO: Running cell: candidates = ( (94.0886298678, 0.923409232937), @@ -2634,8 +3102,8 @@ for kappa0, kappa1 in candidates: -09/11/2015 03:54:22 PM INFO: Cell returned -09/11/2015 03:54:22 PM INFO: Running cell: +10/06/2015 09:50:32 AM INFO: Cell returned +10/06/2015 09:50:32 AM INFO: Running cell: # == Formulate the planner's LQ problem == # @@ -2656,8 +3124,8 @@ kappa0, kappa1 = -F[1], 1 - F[0] print(kappa0, kappa1) -09/11/2015 03:54:22 PM INFO: Cell returned -09/11/2015 03:54:22 PM INFO: Running cell: +10/06/2015 09:50:32 AM INFO: Cell returned +10/06/2015 09:50:32 AM INFO: Running cell: A = np.array([[1, 0], [0, 1]]) B = np.array([[1], [0]]) @@ -2672,23 +3140,31 @@ m0, m1 = -F[1], 1 - F[0] print(m0, m1) -09/11/2015 03:54:22 PM INFO: Cell returned -09/11/2015 03:54:22 PM INFO: Shutdown kernel +10/06/2015 09:50:32 AM INFO: Cell returned +10/06/2015 09:50:32 AM INFO: Shutdown kernel ---> END 'ree_solutions.ipynb' <--- ---> Executing 'schelling_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:23 PM INFO: Reading notebook schelling_solutions.ipynb -09/11/2015 03:54:23 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:33 AM INFO: Reading notebook schelling_solutions.ipynb +10/06/2015 09:50:34 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:54:24 PM INFO: Cell returned -09/11/2015 03:54:24 PM INFO: Running cell: +10/06/2015 09:50:34 AM INFO: Cell returned +10/06/2015 09:50:34 AM INFO: Running cell: from random import uniform, seed from math import sqrt import matplotlib.pyplot as plt @@ -2784,19 +3260,27 @@ while 1: print('Converged, terminating.') -09/11/2015 03:54:34 PM INFO: Cell returned -09/11/2015 03:54:34 PM INFO: Shutdown kernel +10/06/2015 09:50:40 AM INFO: Cell returned +10/06/2015 09:50:40 AM INFO: Shutdown kernel ---> END 'schelling_solutions.ipynb' <--- ---> Executing 'scipy_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:35 PM INFO: Reading notebook scipy_solutions.ipynb -09/11/2015 03:54:35 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:41 AM INFO: Reading notebook scipy_solutions.ipynb +10/06/2015 09:50:41 AM INFO: Running cell: def bisect(f, a, b, tol=10e-5): """ Implements the bisection root finding algorithm, assuming that f is a @@ -2814,26 +3298,34 @@ def bisect(f, a, b, tol=10e-5): bisect(f, middle, upper) -09/11/2015 03:54:35 PM INFO: Cell returned -09/11/2015 03:54:35 PM INFO: Running cell: +10/06/2015 09:50:41 AM INFO: Cell returned +10/06/2015 09:50:41 AM INFO: Running cell: import numpy as np f = lambda x: np.sin(4 * (x - 0.25)) + x + x**20 - 1 bisect(f, 0, 1) -09/11/2015 03:54:36 PM INFO: Cell returned -09/11/2015 03:54:36 PM INFO: Shutdown kernel +10/06/2015 09:50:41 AM INFO: Cell returned +10/06/2015 09:50:41 AM INFO: Shutdown kernel ---> END 'scipy_solutions.ipynb' <--- ---> Executing 'short_path_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:37 PM INFO: Reading notebook short_path_solutions.ipynb -09/11/2015 03:54:38 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:42 AM INFO: Reading notebook short_path_solutions.ipynb +10/06/2015 09:50:43 AM INFO: Running cell: %%file graph.txt node0, node1 0.04, node8 11.11, node14 72.21 node1, node46 1247.25, node6 20.59, node13 64.94 @@ -2936,8 +3428,8 @@ node97, node98 0.30 node98, node99 0.33 node99, -09/11/2015 03:54:38 PM INFO: Cell returned -09/11/2015 03:54:38 PM INFO: Running cell: +10/06/2015 09:50:43 AM INFO: Cell returned +10/06/2015 09:50:43 AM INFO: Running cell: def read_graph(in_file): """ Read in the graph from the data file. The graph is stored @@ -3010,29 +3502,37 @@ while 1: J = next_J print_best_path(J, graph) -09/11/2015 03:54:38 PM INFO: Cell returned -09/11/2015 03:54:38 PM INFO: Shutdown kernel +10/06/2015 09:50:43 AM INFO: Cell returned +10/06/2015 09:50:43 AM INFO: Shutdown kernel ---> END 'short_path_solutions.ipynb' <--- ---> Executing 'speed_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:40 PM INFO: Reading notebook speed_solutions.ipynb -09/11/2015 03:54:40 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:44 AM INFO: Reading notebook speed_solutions.ipynb +10/06/2015 09:50:45 AM INFO: Running cell: import matplotlib.pyplot as plt import numpy as np from numba import jit -09/11/2015 03:54:41 PM INFO: Cell returned -09/11/2015 03:54:41 PM INFO: Running cell: +10/06/2015 09:50:45 AM INFO: Cell returned +10/06/2015 09:50:45 AM INFO: Running cell: p, q = 0.1, 0.2 # Prob of leaving low and high state respectively -09/11/2015 03:54:41 PM INFO: Cell returned -09/11/2015 03:54:41 PM INFO: Running cell: +10/06/2015 09:50:45 AM INFO: Cell returned +10/06/2015 09:50:45 AM INFO: Running cell: def compute_series(n): x = np.empty(n, dtype=int) x[0] = 1 # Start in state 1 @@ -3045,35 +3545,35 @@ def compute_series(n): x[t] = U[t] > q return x -09/11/2015 03:54:41 PM INFO: Cell returned -09/11/2015 03:54:41 PM INFO: Running cell: +10/06/2015 09:50:45 AM INFO: Cell returned +10/06/2015 09:50:45 AM INFO: Running cell: n = 100000 x = compute_series(n) print(np.mean(x == 0)) # Fraction of time x is in state 0 -09/11/2015 03:54:41 PM INFO: Cell returned -09/11/2015 03:54:41 PM INFO: Running cell: +10/06/2015 09:50:45 AM INFO: Cell returned +10/06/2015 09:50:45 AM INFO: Running cell: %timeit compute_series(n) -09/11/2015 03:54:47 PM INFO: Cell returned -09/11/2015 03:54:47 PM INFO: Running cell: +10/06/2015 09:50:48 AM INFO: Cell returned +10/06/2015 09:50:48 AM INFO: Running cell: compute_series_numba = jit(compute_series) -09/11/2015 03:54:47 PM INFO: Cell returned -09/11/2015 03:54:47 PM INFO: Running cell: +10/06/2015 09:50:48 AM INFO: Cell returned +10/06/2015 09:50:48 AM INFO: Running cell: x = compute_series_numba(n) print(np.mean(x == 0)) -09/11/2015 03:54:47 PM INFO: Cell returned -09/11/2015 03:54:47 PM INFO: Running cell: +10/06/2015 09:50:49 AM INFO: Cell returned +10/06/2015 09:50:49 AM INFO: Running cell: %timeit compute_series_numba(n) -09/11/2015 03:54:49 PM INFO: Cell returned -09/11/2015 03:54:49 PM INFO: Running cell: +10/06/2015 09:50:57 AM INFO: Cell returned +10/06/2015 09:50:57 AM INFO: Running cell: %load_ext cythonmagic -09/11/2015 03:54:50 PM INFO: Cell returned -09/11/2015 03:54:50 PM INFO: Running cell: +10/06/2015 09:50:57 AM INFO: Cell returned +10/06/2015 09:50:57 AM INFO: Running cell: %%cython import numpy as np from numpy cimport int_t, float_t @@ -3099,41 +3599,47 @@ def compute_series_cy(int n): x[t] = U[t] > q return np.asarray(x) -09/11/2015 03:54:50 PM INFO: Cell returned -09/11/2015 03:54:50 PM INFO: Running cell: +10/06/2015 09:50:57 AM INFO: Cell returned +10/06/2015 09:50:57 AM INFO: Running cell: compute_series_cy(10) -09/11/2015 03:54:50 PM INFO: Cell returned -09/11/2015 03:54:50 PM INFO: Running cell: -x = compute_series_cy(n) -print(np.mean(x == 0)) - -09/11/2015 03:54:50 PM INFO: Cell returned -09/11/2015 03:54:50 PM INFO: Running cell: -%timeit compute_series_cy(n) - -09/11/2015 03:54:53 PM INFO: Cell returned -09/11/2015 03:54:53 PM INFO: Shutdown kernel +10/06/2015 09:50:57 AM INFO: Cell raised uncaught exception: +--------------------------------------------------------------------------- +NameError Traceback (most recent call last) + in () +----> 1 compute_series_cy(10) + +NameError: name 'compute_series_cy' is not defined +10/06/2015 09:50:57 AM INFO: Shutdown kernel +10/06/2015 09:50:57 AM WARNING: Exiting with nonzero exit status ---> END 'speed_solutions.ipynb' <--- ---> Executing 'statd_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:54:53 PM INFO: Reading notebook statd_solutions.ipynb -09/11/2015 03:54:54 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:50:57 AM INFO: Reading notebook statd_solutions.ipynb +10/06/2015 09:50:58 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:54:55 PM INFO: Cell returned -09/11/2015 03:54:55 PM INFO: Running cell: +10/06/2015 09:50:58 AM INFO: Cell returned +10/06/2015 09:50:58 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt -09/11/2015 03:54:55 PM INFO: Cell returned -09/11/2015 03:54:55 PM INFO: Running cell: +10/06/2015 09:50:58 AM INFO: Cell returned +10/06/2015 09:50:58 AM INFO: Running cell: from scipy.stats import norm, gaussian_kde from quantecon import LAE @@ -3167,8 +3673,8 @@ ax.plot(ys, k_est(ys), 'k-', lw=2, alpha=0.6, label='kernel based estimate') ax.legend(loc='upper left') plt.show() -09/11/2015 03:54:57 PM INFO: Cell returned -09/11/2015 03:54:57 PM INFO: Running cell: +10/06/2015 09:50:59 AM INFO: Cell returned +10/06/2015 09:50:59 AM INFO: Running cell: from scipy.stats import lognorm, beta # == Define parameters == # @@ -3213,8 +3719,8 @@ for i in range(4): ax.plot(ygrid, psi(ygrid), color=g, lw=2, alpha=0.6) ax.set_xlabel('capital') -09/11/2015 03:55:05 PM INFO: Cell returned -09/11/2015 03:55:05 PM INFO: Running cell: +10/06/2015 09:51:04 AM INFO: Cell returned +10/06/2015 09:51:04 AM INFO: Running cell: n = 20 k = 5000 J = 6 @@ -3241,23 +3747,31 @@ for j in range(J): plt.show() -09/11/2015 03:55:10 PM INFO: Cell returned -09/11/2015 03:55:10 PM INFO: Shutdown kernel +10/06/2015 09:51:07 AM INFO: Cell returned +10/06/2015 09:51:07 AM INFO: Shutdown kernel ---> END 'statd_solutions.ipynb' <--- ---> Executing 'uncertainty_traps_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. + "You should import from nbformat instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. -- use IPython.nbformat for read/write/validate public API -- use IPython.nbformat.vX directly to composing notebooks of a particular version +- use nbformat for read/write/validate public API +- use nbformat.vX directly to composing notebooks of a particular version """) -09/11/2015 03:55:11 PM INFO: Reading notebook uncertainty_traps_solutions.ipynb -09/11/2015 03:55:12 PM INFO: Running cell: +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. + "You should import from ipykernel or jupyter_client instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. + "You should import from traitlets.config instead.", ShimWarning) +/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. + "You should import from ipython_nbconvert instead.", ShimWarning) +10/06/2015 09:51:08 AM INFO: Reading notebook uncertainty_traps_solutions.ipynb +10/06/2015 09:51:08 AM INFO: Running cell: %matplotlib inline -09/11/2015 03:55:13 PM INFO: Cell returned -09/11/2015 03:55:13 PM INFO: Running cell: +10/06/2015 09:51:09 AM INFO: Cell returned +10/06/2015 09:51:09 AM INFO: Running cell: from __future__ import division import matplotlib.pyplot as plt import numpy as np @@ -3265,8 +3779,8 @@ import quantecon as qe import seaborn as sns import itertools -09/11/2015 03:55:14 PM INFO: Cell returned -09/11/2015 03:55:14 PM INFO: Running cell: +10/06/2015 09:51:09 AM INFO: Cell returned +10/06/2015 09:51:09 AM INFO: Running cell: palette = itertools.cycle(sns.color_palette()) econ = qe.models.UncertaintyTrapEcon() rho, sig_theta, gx = econ.rho, econ.sig_theta, econ.gx # simplify names @@ -3283,8 +3797,8 @@ ax.set_ylabel(r"$\gamma'$", fontsize=16) ax.grid() plt.show() -09/11/2015 03:55:16 PM INFO: Cell returned -09/11/2015 03:55:16 PM INFO: Running cell: +10/06/2015 09:51:10 AM INFO: Cell returned +10/06/2015 09:51:10 AM INFO: Running cell: sim_length=2000 mu_vec = np.empty(sim_length) @@ -3316,16 +3830,16 @@ X, M = econ.gen_aggregates() X_vec[-1] = X M_vec[-1] = M -09/11/2015 03:55:16 PM INFO: Cell returned -09/11/2015 03:55:16 PM INFO: Running cell: +10/06/2015 09:51:10 AM INFO: Cell returned +10/06/2015 09:51:10 AM INFO: Running cell: fig, ax = plt.subplots(figsize=(9, 6)) ax.plot(range(sim_length), theta_vec, alpha=0.6, lw=2, label=r"$\theta$") ax.plot(range(sim_length), mu_vec, alpha=0.6, lw=2, label=r"$\mu$") ax.legend(fontsize=16) plt.show() -09/11/2015 03:55:17 PM INFO: Cell returned -09/11/2015 03:55:17 PM INFO: Running cell: +10/06/2015 09:51:11 AM INFO: Cell returned +10/06/2015 09:51:11 AM INFO: Running cell: fig, axes = plt.subplots(4, 1, figsize=(12, 20)) # Add some spacing fig.subplots_adjust(hspace=0.3) @@ -3347,11 +3861,11 @@ for ax, vals, name in zip(axes, series, names): plt.show() -09/11/2015 03:55:19 PM INFO: Cell returned -09/11/2015 03:55:19 PM INFO: Running cell: +10/06/2015 09:51:11 AM INFO: Cell returned +10/06/2015 09:51:11 AM INFO: Running cell: -09/11/2015 03:55:19 PM INFO: Cell returned -09/11/2015 03:55:19 PM INFO: Shutdown kernel +10/06/2015 09:51:11 AM INFO: Cell returned +10/06/2015 09:51:11 AM INFO: Shutdown kernel ---> END 'uncertainty_traps_solutions.ipynb' <--- diff --git a/setup.py b/setup.py index 477efcab2..dd517a84b 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ #-Write Versions File-# #~~~~~~~~~~~~~~~~~~~~~# -VERSION = '0.2.0' +VERSION = '0.2.1' def write_version_py(filename=None): """ From 16cd4f2fdef58cb70b85184139e0e8761487a09b Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Tue, 6 Oct 2015 10:30:04 -0400 Subject: [PATCH 09/51] Updating version number due to invalid upload to PyPI --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index dd517a84b..d1af07263 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ #-Write Versions File-# #~~~~~~~~~~~~~~~~~~~~~# -VERSION = '0.2.1' +VERSION = '0.2.2' def write_version_py(filename=None): """ From 2ee8b0c57b19dd22b3efea86fb881457b137d653 Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Tue, 6 Oct 2015 10:32:26 -0400 Subject: [PATCH 10/51] Updating build files from push to PYPI --- MANIFEST | 1 + quantecon/version.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/MANIFEST b/MANIFEST index c398408bc..2c72ca4bd 100644 --- a/MANIFEST +++ b/MANIFEST @@ -38,6 +38,7 @@ quantecon/models/asset_pricing.py quantecon/models/career.py quantecon/models/ifp.py quantecon/models/jv.py +quantecon/models/lake.py quantecon/models/lucastree.py quantecon/models/odu.py quantecon/models/optgrowth.py diff --git a/quantecon/version.py b/quantecon/version.py index 61f299333..90893e462 100644 --- a/quantecon/version.py +++ b/quantecon/version.py @@ -1,4 +1,4 @@ """ This is a VERSION file and should NOT be manually altered """ -version = '0.2.0' \ No newline at end of file +version = '0.2.2' \ No newline at end of file From 2c3b081b3373183be5b4c5393e176ed66feec1e3 Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Tue, 6 Oct 2015 11:13:16 -0400 Subject: [PATCH 11/51] Updating log from latest run --- scripts/solutions-tests.log | 978 ++++++++++++++++++------------------ 1 file changed, 489 insertions(+), 489 deletions(-) diff --git a/scripts/solutions-tests.log b/scripts/solutions-tests.log index 88ccdbba0..183ccb9be 100644 --- a/scripts/solutions-tests.log +++ b/scripts/solutions-tests.log @@ -13,20 +13,20 @@ "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:43:53 AM INFO: Reading notebook arellano_solutions.ipynb -10/06/2015 09:43:55 AM INFO: Running cell: +10/06/2015 10:37:01 AM INFO: Reading notebook arellano_solutions.ipynb +10/06/2015 10:37:02 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:43:55 AM INFO: Cell returned -10/06/2015 09:43:55 AM INFO: Running cell: +10/06/2015 10:37:02 AM INFO: Cell returned +10/06/2015 10:37:02 AM INFO: Running cell: from __future__ import division import numpy as np import matplotlib.pyplot as plt import quantecon as qe from quantecon.models import Arellano_Economy -10/06/2015 09:43:57 AM INFO: Cell returned -10/06/2015 09:43:57 AM INFO: Running cell: +10/06/2015 10:37:03 AM INFO: Cell returned +10/06/2015 10:37:03 AM INFO: Running cell: ae = Arellano_Economy(beta=.953, # time discount rate gamma=2., # risk aversion r=0.017, # international interest rate @@ -38,8 +38,8 @@ ae = Arellano_Economy(beta=.953, # time discount rate tol=1e-8, # error tolerance in iteration maxit=10000) -10/06/2015 09:44:13 AM INFO: Cell returned -10/06/2015 09:44:13 AM INFO: Running cell: +10/06/2015 10:37:12 AM INFO: Cell returned +10/06/2015 10:37:12 AM INFO: Running cell: # Create "Y High" and "Y Low" values as 5% devs from mean high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95 @@ -64,8 +64,8 @@ ax.set_xlabel(r"$B'$") ax.legend(loc='upper left', frameon=False) plt.show() -10/06/2015 09:44:15 AM INFO: Cell returned -10/06/2015 09:44:15 AM INFO: Running cell: +10/06/2015 10:37:12 AM INFO: Cell returned +10/06/2015 10:37:12 AM INFO: Running cell: # Create "Y High" and "Y Low" values as 5% devs from mean high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95 @@ -81,8 +81,8 @@ ax.set_ylabel(r"$V(y, B)$") ax.set_xlim(ae.Bgrid.min(), ae.Bgrid.max()) plt.show() -10/06/2015 09:44:16 AM INFO: Cell returned -10/06/2015 09:44:16 AM INFO: Running cell: +10/06/2015 10:37:12 AM INFO: Cell returned +10/06/2015 10:37:12 AM INFO: Running cell: xx, yy = ae.Bgrid, ae.ygrid zz = ae.default_prob @@ -98,8 +98,8 @@ ax.set_xlabel(r"$B'$") ax.set_ylabel(r"$y$") plt.show() -10/06/2015 09:44:17 AM INFO: Cell returned -10/06/2015 09:44:17 AM INFO: Running cell: +10/06/2015 10:37:13 AM INFO: Cell returned +10/06/2015 10:37:13 AM INFO: Running cell: T = 250 y_vec, B_vec, q_vec, default_vec = ae.simulate(T) @@ -141,12 +141,12 @@ for ax, series, title in zip(axes, plot_series, titles): plt.show() -10/06/2015 09:44:20 AM INFO: Cell returned -10/06/2015 09:44:20 AM INFO: Running cell: +10/06/2015 10:37:14 AM INFO: Cell returned +10/06/2015 10:37:14 AM INFO: Running cell: -10/06/2015 09:44:20 AM INFO: Cell returned -10/06/2015 09:44:20 AM INFO: Shutdown kernel +10/06/2015 10:37:14 AM INFO: Cell returned +10/06/2015 10:37:14 AM INFO: Shutdown kernel ---> END 'arellano_solutions.ipynb' <--- ---> Executing 'asset_solutions.ipynb' <--- @@ -164,19 +164,19 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:44:22 AM INFO: Reading notebook asset_solutions.ipynb -10/06/2015 09:44:23 AM INFO: Running cell: +10/06/2015 10:37:15 AM INFO: Reading notebook asset_solutions.ipynb +10/06/2015 10:37:16 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:44:24 AM INFO: Cell returned -10/06/2015 09:44:24 AM INFO: Running cell: +10/06/2015 10:37:16 AM INFO: Cell returned +10/06/2015 10:37:16 AM INFO: Running cell: from __future__ import division # Omit for Python 3.x import numpy as np import matplotlib.pyplot as plt from quantecon.models import AssetPrices -10/06/2015 09:44:26 AM INFO: Cell returned -10/06/2015 09:44:26 AM INFO: Running cell: +10/06/2015 10:37:17 AM INFO: Cell returned +10/06/2015 10:37:17 AM INFO: Running cell: # == Define primitives == # n = 5 P = 0.0125 * np.ones((n, n)) @@ -202,12 +202,12 @@ p_s = 150.0 w_bar, w_bars = ap.call_option(zeta, p_s, T = [10,20,30]) -10/06/2015 09:44:27 AM INFO: Cell returned -10/06/2015 09:44:27 AM INFO: Running cell: +10/06/2015 10:37:17 AM INFO: Cell returned +10/06/2015 10:37:17 AM INFO: Running cell: -10/06/2015 09:44:27 AM INFO: Cell returned -10/06/2015 09:44:27 AM INFO: Shutdown kernel +10/06/2015 10:37:17 AM INFO: Cell returned +10/06/2015 10:37:17 AM INFO: Shutdown kernel ---> END 'asset_solutions.ipynb' <--- ---> Executing 'career_solutions.ipynb' <--- @@ -225,19 +225,19 @@ w_bar, w_bars = ap.call_option(zeta, p_s, T = [10,20,30]) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:44:29 AM INFO: Reading notebook career_solutions.ipynb -10/06/2015 09:44:30 AM INFO: Running cell: +10/06/2015 10:37:18 AM INFO: Reading notebook career_solutions.ipynb +10/06/2015 10:37:18 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:44:31 AM INFO: Cell returned -10/06/2015 09:44:31 AM INFO: Running cell: +10/06/2015 10:37:18 AM INFO: Cell returned +10/06/2015 10:37:18 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import DiscreteRV, compute_fixed_point from quantecon.models import CareerWorkerProblem -10/06/2015 09:44:33 AM INFO: Cell returned -10/06/2015 09:44:33 AM INFO: Running cell: +10/06/2015 10:37:19 AM INFO: Cell returned +10/06/2015 10:37:19 AM INFO: Running cell: wp = CareerWorkerProblem() v_init = np.ones((wp.N, wp.N))*100 v = compute_fixed_point(wp.bellman_operator, v_init, verbose=False) @@ -272,8 +272,8 @@ plt.show() -10/06/2015 09:44:38 AM INFO: Cell returned -10/06/2015 09:44:38 AM INFO: Running cell: +10/06/2015 10:37:20 AM INFO: Cell returned +10/06/2015 10:37:20 AM INFO: Running cell: wp = CareerWorkerProblem() v_init = np.ones((wp.N, wp.N))*100 @@ -301,8 +301,8 @@ for i in range(M): print(np.median(samples)) -10/06/2015 09:44:42 AM INFO: Cell returned -10/06/2015 09:44:42 AM INFO: Running cell: +10/06/2015 10:37:22 AM INFO: Cell returned +10/06/2015 10:37:22 AM INFO: Running cell: from matplotlib import cm wp = CareerWorkerProblem() @@ -323,8 +323,8 @@ ax.text(4.0, 4.5, 'stay put', fontsize=14) -10/06/2015 09:44:44 AM INFO: Cell returned -10/06/2015 09:44:44 AM INFO: Shutdown kernel +10/06/2015 10:37:23 AM INFO: Cell returned +10/06/2015 10:37:23 AM INFO: Shutdown kernel ---> END 'career_solutions.ipynb' <--- ---> Executing 'discrete_dp_solutions.ipynb' <--- @@ -342,12 +342,12 @@ ax.text(4.0, 4.5, 'stay put', fontsize=14) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:44:47 AM INFO: Reading notebook discrete_dp_solutions.ipynb -10/06/2015 09:44:48 AM INFO: Running cell: +10/06/2015 10:37:24 AM INFO: Reading notebook discrete_dp_solutions.ipynb +10/06/2015 10:37:25 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:44:49 AM INFO: Cell returned -10/06/2015 09:44:49 AM INFO: Running cell: +10/06/2015 10:37:25 AM INFO: Cell returned +10/06/2015 10:37:25 AM INFO: Running cell: from __future__ import division, print_function import numpy as np import scipy.sparse as sparse @@ -355,25 +355,25 @@ import matplotlib.pyplot as plt from quantecon import compute_fixed_point from quantecon.markov import DiscreteDP -10/06/2015 09:44:50 AM INFO: Cell returned -10/06/2015 09:44:50 AM INFO: Running cell: +10/06/2015 10:37:26 AM INFO: Cell returned +10/06/2015 10:37:26 AM INFO: Running cell: alpha = 0.65 f = lambda k: k**alpha u = np.log beta = 0.95 -10/06/2015 09:44:50 AM INFO: Cell returned -10/06/2015 09:44:50 AM INFO: Running cell: +10/06/2015 10:37:26 AM INFO: Cell returned +10/06/2015 10:37:26 AM INFO: Running cell: grid_max = 2 grid_size = 1500 grid = np.linspace(1e-6, grid_max, grid_size) -10/06/2015 09:44:50 AM INFO: Cell returned -10/06/2015 09:44:50 AM INFO: Running cell: +10/06/2015 10:37:26 AM INFO: Cell returned +10/06/2015 10:37:26 AM INFO: Running cell: print(grid) -10/06/2015 09:44:50 AM INFO: Cell returned -10/06/2015 09:44:50 AM INFO: Running cell: +10/06/2015 10:37:26 AM INFO: Cell returned +10/06/2015 10:37:26 AM INFO: Running cell: # Consumption matrix, with nonpositive consumption included C = f(grid).reshape(grid_size, 1) - grid.reshape(1, grid_size) @@ -383,47 +383,47 @@ s_indices, a_indices = np.where(C > 0) # Number of state-action pairs L = len(s_indices) -10/06/2015 09:44:50 AM INFO: Cell returned -10/06/2015 09:44:50 AM INFO: Running cell: +10/06/2015 10:37:26 AM INFO: Cell returned +10/06/2015 10:37:26 AM INFO: Running cell: print(L) print(s_indices) print(a_indices) -10/06/2015 09:44:50 AM INFO: Cell returned -10/06/2015 09:44:50 AM INFO: Running cell: +10/06/2015 10:37:26 AM INFO: Cell returned +10/06/2015 10:37:26 AM INFO: Running cell: R = u(C[s_indices, a_indices]) -10/06/2015 09:44:51 AM INFO: Cell returned -10/06/2015 09:44:51 AM INFO: Running cell: +10/06/2015 10:37:26 AM INFO: Cell returned +10/06/2015 10:37:26 AM INFO: Running cell: Q = sparse.lil_matrix((L, grid_size)) Q[np.arange(L), a_indices] = 1 -10/06/2015 09:44:54 AM INFO: Cell returned -10/06/2015 09:44:54 AM INFO: Running cell: +10/06/2015 10:37:28 AM INFO: Cell returned +10/06/2015 10:37:28 AM INFO: Running cell: # data = np.ones(L) # indptr = np.arange(L+1) # Q = sparse.csr_matrix((data, a_indices, indptr), shape=(L, grid_size)) -10/06/2015 09:44:54 AM INFO: Cell returned -10/06/2015 09:44:54 AM INFO: Running cell: +10/06/2015 10:37:28 AM INFO: Cell returned +10/06/2015 10:37:28 AM INFO: Running cell: ddp = DiscreteDP(R, Q, beta, s_indices, a_indices) -10/06/2015 09:44:56 AM INFO: Cell returned -10/06/2015 09:44:56 AM INFO: Running cell: +10/06/2015 10:37:29 AM INFO: Cell returned +10/06/2015 10:37:29 AM INFO: Running cell: res = ddp.solve(method='policy_iteration') v, sigma, num_iter = res.v, res.sigma, res.num_iter -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:29 AM INFO: Cell returned +10/06/2015 10:37:29 AM INFO: Running cell: num_iter -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:29 AM INFO: Cell returned +10/06/2015 10:37:29 AM INFO: Running cell: # Optimal consumption in the discrete version c = f(grid) - grid[sigma] -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:29 AM INFO: Cell returned +10/06/2015 10:37:29 AM INFO: Running cell: # Exact solution of the continuous version ab = alpha * beta c1 = (np.log(1 - ab) + np.log(ab) * ab / (1 - ab)) / (1 - beta) @@ -434,8 +434,8 @@ def v_star(k): def c_star(k): return (1 - ab) * k**alpha -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:29 AM INFO: Cell returned +10/06/2015 10:37:29 AM INFO: Running cell: fig, ax = plt.subplots(1, 2, figsize=(14, 4)) ax[0].set_ylim(-40, -32) ax[0].set_xlim(grid[0], grid[-1]) @@ -456,73 +456,73 @@ ax[1].plot(grid, c_star(grid), 'k-', lw=1.5, alpha=0.8, label=lb1) ax[1].legend(loc='upper left') plt.show() -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: np.abs(v - v_star(grid)).max() -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: np.abs(v - v_star(grid))[1:].max() -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: np.abs(c - c_star(grid)).max() -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: diff = np.diff(c) (diff >= 0).all() -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:57 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: dec_ind = np.where(diff < 0)[0] -10/06/2015 09:44:57 AM INFO: Cell returned -10/06/2015 09:44:58 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: len(dec_ind) -10/06/2015 09:44:58 AM INFO: Cell returned -10/06/2015 09:44:58 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: np.abs(diff[dec_ind]).max() -10/06/2015 09:44:58 AM INFO: Cell returned -10/06/2015 09:44:58 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: (np.diff(v) > 0).all() -10/06/2015 09:44:58 AM INFO: Cell returned -10/06/2015 09:44:58 AM INFO: Running cell: +10/06/2015 10:37:30 AM INFO: Cell returned +10/06/2015 10:37:30 AM INFO: Running cell: ddp.epsilon = 1e-4 ddp.max_iter = 500 res1 = ddp.solve(method='value_iteration') -10/06/2015 09:45:02 AM INFO: Cell returned -10/06/2015 09:45:02 AM INFO: Running cell: +10/06/2015 10:37:33 AM INFO: Cell returned +10/06/2015 10:37:33 AM INFO: Running cell: res1.num_iter -10/06/2015 09:45:02 AM INFO: Cell returned -10/06/2015 09:45:02 AM INFO: Running cell: +10/06/2015 10:37:33 AM INFO: Cell returned +10/06/2015 10:37:33 AM INFO: Running cell: np.array_equal(sigma, res1.sigma) -10/06/2015 09:45:02 AM INFO: Cell returned -10/06/2015 09:45:02 AM INFO: Running cell: +10/06/2015 10:37:33 AM INFO: Cell returned +10/06/2015 10:37:33 AM INFO: Running cell: res2 = ddp.solve(method='modified_policy_iteration') -10/06/2015 09:45:03 AM INFO: Cell returned -10/06/2015 09:45:03 AM INFO: Running cell: +10/06/2015 10:37:33 AM INFO: Cell returned +10/06/2015 10:37:33 AM INFO: Running cell: res2.num_iter -10/06/2015 09:45:03 AM INFO: Cell returned -10/06/2015 09:45:03 AM INFO: Running cell: +10/06/2015 10:37:33 AM INFO: Cell returned +10/06/2015 10:37:33 AM INFO: Running cell: np.array_equal(sigma, res2.sigma) -10/06/2015 09:45:03 AM INFO: Cell returned -10/06/2015 09:45:03 AM INFO: Running cell: +10/06/2015 10:37:33 AM INFO: Cell returned +10/06/2015 10:37:33 AM INFO: Running cell: %timeit ddp.solve(method='value_iteration') %timeit ddp.solve(method='policy_iteration') %timeit ddp.solve(method='modified_policy_iteration') -10/06/2015 09:45:24 AM INFO: Cell returned -10/06/2015 09:45:24 AM INFO: Running cell: +10/06/2015 10:37:55 AM INFO: Cell returned +10/06/2015 10:37:55 AM INFO: Running cell: w = 5 * np.log(grid) - 25 # Initial condition n = 35 fig, ax = plt.subplots(figsize=(8,5)) @@ -539,8 +539,8 @@ ax.legend(loc='upper left') plt.show() -10/06/2015 09:45:24 AM INFO: Cell returned -10/06/2015 09:45:24 AM INFO: Running cell: +10/06/2015 10:37:55 AM INFO: Cell returned +10/06/2015 10:37:55 AM INFO: Running cell: w = 5 * u(grid) - 25 # Initial condition fig, ax = plt.subplots(3, 1, figsize=(8, 10)) @@ -564,8 +564,8 @@ for i, n in enumerate((2, 4, 6)): ax[i].legend(loc='upper left') ax[i].set_title('{} value function iterations'.format(n)) -10/06/2015 09:45:25 AM INFO: Cell returned -10/06/2015 09:45:25 AM INFO: Running cell: +10/06/2015 10:37:56 AM INFO: Cell returned +10/06/2015 10:37:56 AM INFO: Running cell: discount_factors = (0.9, 0.94, 0.98) k_init = 0.1 @@ -592,12 +592,12 @@ for beta in discount_factors: ax.legend(loc='lower right') plt.show() -10/06/2015 09:45:29 AM INFO: Cell returned -10/06/2015 09:45:29 AM INFO: Running cell: +10/06/2015 10:37:58 AM INFO: Cell returned +10/06/2015 10:37:58 AM INFO: Running cell: -10/06/2015 09:45:29 AM INFO: Cell returned -10/06/2015 09:45:29 AM INFO: Shutdown kernel +10/06/2015 10:37:58 AM INFO: Cell returned +10/06/2015 10:37:58 AM INFO: Shutdown kernel ---> END 'discrete_dp_solutions.ipynb' <--- ---> Executing 'estspec_solutions.ipynb' <--- @@ -615,18 +615,18 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:45:30 AM INFO: Reading notebook estspec_solutions.ipynb -10/06/2015 09:45:31 AM INFO: Running cell: +10/06/2015 10:37:59 AM INFO: Reading notebook estspec_solutions.ipynb +10/06/2015 10:38:00 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:45:32 AM INFO: Cell returned -10/06/2015 09:45:32 AM INFO: Running cell: +10/06/2015 10:38:00 AM INFO: Cell returned +10/06/2015 10:38:00 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import ARMA, periodogram, ar_periodogram -10/06/2015 09:45:33 AM INFO: Cell returned -10/06/2015 09:45:33 AM INFO: Running cell: +10/06/2015 10:38:01 AM INFO: Cell returned +10/06/2015 10:38:01 AM INFO: Running cell: ## Data n = 400 @@ -652,8 +652,8 @@ for i, wl in enumerate((15, 55, 175)): # window lengths ax[i].set_title('window length = {}'.format(wl)) -10/06/2015 09:45:34 AM INFO: Cell returned -10/06/2015 09:45:34 AM INFO: Running cell: +10/06/2015 10:38:01 AM INFO: Cell returned +10/06/2015 10:38:01 AM INFO: Running cell: lp = ARMA(-0.9) wl = 65 @@ -678,8 +678,8 @@ for i in range(3): -10/06/2015 09:45:37 AM INFO: Cell returned -10/06/2015 09:45:37 AM INFO: Shutdown kernel +10/06/2015 10:38:03 AM INFO: Cell returned +10/06/2015 10:38:03 AM INFO: Shutdown kernel ---> END 'estspec_solutions.ipynb' <--- ---> Executing 'finite_mc_solutions.ipynb' <--- @@ -697,20 +697,20 @@ for i in range(3): "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:45:38 AM INFO: Reading notebook finite_mc_solutions.ipynb -10/06/2015 09:45:39 AM INFO: Running cell: +10/06/2015 10:38:04 AM INFO: Reading notebook finite_mc_solutions.ipynb +10/06/2015 10:38:05 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:45:39 AM INFO: Cell returned -10/06/2015 09:45:39 AM INFO: Running cell: +10/06/2015 10:38:05 AM INFO: Cell returned +10/06/2015 10:38:05 AM INFO: Running cell: from __future__ import print_function, division # Omit for Python 3.x import numpy as np import matplotlib.pyplot as plt from quantecon import mc_compute_stationary, mc_sample_path -10/06/2015 09:45:41 AM INFO: Cell returned -10/06/2015 09:45:41 AM INFO: Running cell: +10/06/2015 10:38:06 AM INFO: Cell returned +10/06/2015 10:38:06 AM INFO: Running cell: alpha = beta = 0.1 N = 10000 @@ -739,8 +739,8 @@ ax.legend(loc='upper right') -10/06/2015 09:45:42 AM INFO: Cell returned -10/06/2015 09:45:42 AM INFO: Running cell: +10/06/2015 10:38:07 AM INFO: Cell returned +10/06/2015 10:38:07 AM INFO: Running cell: %%file web_graph_data.txt a -> d; a -> f; @@ -781,8 +781,8 @@ n -> j; n -> m; -10/06/2015 09:45:42 AM INFO: Cell returned -10/06/2015 09:45:42 AM INFO: Running cell: +10/06/2015 10:38:07 AM INFO: Cell returned +10/06/2015 10:38:07 AM INFO: Running cell: """ Return list of pages, ordered by rank """ @@ -820,20 +820,20 @@ for name, rank in sorted(ranked_pages.items(), key=itemgetter(1), reverse=1): -10/06/2015 09:45:43 AM INFO: Cell returned -10/06/2015 09:45:43 AM INFO: Running cell: +10/06/2015 10:38:07 AM INFO: Cell returned +10/06/2015 10:38:07 AM INFO: Running cell: -10/06/2015 09:45:43 AM INFO: Cell returned -10/06/2015 09:45:43 AM INFO: Running cell: +10/06/2015 10:38:07 AM INFO: Cell returned +10/06/2015 10:38:07 AM INFO: Running cell: -10/06/2015 09:45:43 AM INFO: Cell returned -10/06/2015 09:45:43 AM INFO: Running cell: +10/06/2015 10:38:07 AM INFO: Cell returned +10/06/2015 10:38:07 AM INFO: Running cell: -10/06/2015 09:45:43 AM INFO: Cell returned -10/06/2015 09:45:43 AM INFO: Shutdown kernel +10/06/2015 10:38:07 AM INFO: Cell returned +10/06/2015 10:38:07 AM INFO: Shutdown kernel ---> END 'finite_mc_solutions.ipynb' <--- ---> Executing 'ifp_solutions.ipynb' <--- @@ -851,19 +851,19 @@ for name, rank in sorted(ranked_pages.items(), key=itemgetter(1), reverse=1): "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:45:44 AM INFO: Reading notebook ifp_solutions.ipynb -10/06/2015 09:45:45 AM INFO: Running cell: +10/06/2015 10:38:08 AM INFO: Reading notebook ifp_solutions.ipynb +10/06/2015 10:38:09 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:45:45 AM INFO: Cell returned -10/06/2015 09:45:45 AM INFO: Running cell: +10/06/2015 10:38:09 AM INFO: Cell returned +10/06/2015 10:38:09 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import compute_fixed_point from quantecon.models import ConsumerProblem -10/06/2015 09:45:47 AM INFO: Cell returned -10/06/2015 09:45:47 AM INFO: Running cell: +10/06/2015 10:38:10 AM INFO: Cell returned +10/06/2015 10:38:10 AM INFO: Running cell: cp = ConsumerProblem() K = 80 @@ -890,8 +890,8 @@ ax.set_ylabel('consumption (low income)') ax.legend(loc='upper left') plt.show() -10/06/2015 09:45:57 AM INFO: Cell returned -10/06/2015 09:45:57 AM INFO: Running cell: +10/06/2015 10:38:17 AM INFO: Cell returned +10/06/2015 10:38:17 AM INFO: Running cell: r_vals = np.linspace(0, 0.04, 4) @@ -907,8 +907,8 @@ ax.set_ylabel('consumption (low income)') ax.legend(loc='upper left') plt.show() -10/06/2015 09:46:03 AM INFO: Cell returned -10/06/2015 09:46:03 AM INFO: Running cell: +10/06/2015 10:38:21 AM INFO: Cell returned +10/06/2015 10:38:21 AM INFO: Running cell: from scipy import interp from quantecon import mc_sample_path @@ -938,8 +938,8 @@ ax.set_xlabel('assets') ax.set_xlim(-0.05, 0.75) plt.show() -10/06/2015 09:46:07 AM INFO: Cell returned -10/06/2015 09:46:07 AM INFO: Running cell: +10/06/2015 10:38:24 AM INFO: Cell returned +10/06/2015 10:38:24 AM INFO: Running cell: M = 25 r_vals = np.linspace(0, 0.04, M) @@ -962,8 +962,8 @@ ax.grid(True) ax.legend(loc='upper left') plt.show() -10/06/2015 09:47:51 AM INFO: Cell returned -10/06/2015 09:47:51 AM INFO: Shutdown kernel +10/06/2015 10:39:58 AM INFO: Cell returned +10/06/2015 10:39:58 AM INFO: Shutdown kernel ---> END 'ifp_solutions.ipynb' <--- ---> Executing 'jv_solutions.ipynb' <--- @@ -981,20 +981,20 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:47:52 AM INFO: Reading notebook jv_solutions.ipynb -10/06/2015 09:47:53 AM INFO: Running cell: +10/06/2015 10:40:00 AM INFO: Reading notebook jv_solutions.ipynb +10/06/2015 10:40:00 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:47:53 AM INFO: Cell returned -10/06/2015 09:47:53 AM INFO: Running cell: +10/06/2015 10:40:00 AM INFO: Cell returned +10/06/2015 10:40:00 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt import random from quantecon import compute_fixed_point from quantecon.models import JvWorker -10/06/2015 09:47:54 AM INFO: Cell returned -10/06/2015 09:47:54 AM INFO: Running cell: +10/06/2015 10:40:02 AM INFO: Cell returned +10/06/2015 10:40:02 AM INFO: Running cell: wp = JvWorker(grid_size=25) G, pi, F = wp.G, wp.pi, wp.F # Simplify names @@ -1033,8 +1033,8 @@ for x in plot_grid: plt.show() -10/06/2015 09:48:10 AM INFO: Cell returned -10/06/2015 09:48:10 AM INFO: Running cell: +10/06/2015 10:40:20 AM INFO: Cell returned +10/06/2015 10:40:20 AM INFO: Running cell: wp = JvWorker(grid_size=25) @@ -1049,8 +1049,8 @@ ax.legend(loc='upper left') plt.show() -10/06/2015 09:48:10 AM INFO: Cell returned -10/06/2015 09:48:10 AM INFO: Shutdown kernel +10/06/2015 10:40:20 AM INFO: Cell returned +10/06/2015 10:40:20 AM INFO: Shutdown kernel ---> END 'jv_solutions.ipynb' <--- ---> Executing 'kalman_solutions.ipynb' <--- @@ -1068,20 +1068,20 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:11 AM INFO: Reading notebook kalman_solutions.ipynb -10/06/2015 09:48:12 AM INFO: Running cell: +10/06/2015 10:40:21 AM INFO: Reading notebook kalman_solutions.ipynb +10/06/2015 10:40:22 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:12 AM INFO: Cell returned -10/06/2015 09:48:12 AM INFO: Running cell: +10/06/2015 10:40:22 AM INFO: Cell returned +10/06/2015 10:40:22 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import Kalman from quantecon import LinearStateSpace from scipy.stats import norm -10/06/2015 09:48:13 AM INFO: Cell returned -10/06/2015 09:48:13 AM INFO: Running cell: +10/06/2015 10:40:23 AM INFO: Cell returned +10/06/2015 10:40:23 AM INFO: Running cell: # == parameters == # theta = 10 # Constant value of state x_t A, C, G, H = 1, 0, 1, 1 @@ -1110,8 +1110,8 @@ for i in range(N): ax.set_title(r'First %d densities when $\theta = %.1f$' % (N, theta)) ax.legend(loc='upper left') -10/06/2015 09:48:14 AM INFO: Cell returned -10/06/2015 09:48:14 AM INFO: Running cell: +10/06/2015 10:40:24 AM INFO: Cell returned +10/06/2015 10:40:24 AM INFO: Running cell: from scipy.integrate import quad epsilon = 0.1 @@ -1143,8 +1143,8 @@ ax.set_xlim(0, T) ax.plot(range(T), z) ax.fill_between(range(T), np.zeros(T), z, color="blue", alpha=0.2) -10/06/2015 09:48:16 AM INFO: Cell returned -10/06/2015 09:48:16 AM INFO: Running cell: +10/06/2015 10:40:25 AM INFO: Cell returned +10/06/2015 10:40:25 AM INFO: Running cell: from __future__ import print_function # Remove for Python 3.x from numpy.random import multivariate_normal from scipy.linalg import eigvals @@ -1198,12 +1198,12 @@ ax.legend() -10/06/2015 09:48:16 AM INFO: Cell returned -10/06/2015 09:48:16 AM INFO: Running cell: +10/06/2015 10:40:25 AM INFO: Cell returned +10/06/2015 10:40:25 AM INFO: Running cell: -10/06/2015 09:48:16 AM INFO: Cell returned -10/06/2015 09:48:16 AM INFO: Shutdown kernel +10/06/2015 10:40:25 AM INFO: Cell returned +10/06/2015 10:40:25 AM INFO: Shutdown kernel ---> END 'kalman_solutions.ipynb' <--- ---> Executing 'lakemodel_solutions.ipynb' <--- @@ -1221,8 +1221,8 @@ ax.legend() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:17 AM INFO: Reading notebook lakemodel_solutions.ipynb -10/06/2015 09:48:18 AM INFO: Running cell: +10/06/2015 10:40:26 AM INFO: Reading notebook lakemodel_solutions.ipynb +10/06/2015 10:40:26 AM INFO: Running cell: %matplotlib inline import numpy as np @@ -1239,26 +1239,26 @@ e0 = 0.92 u0 = 1-e0 T = 50 -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:27 AM INFO: Cell returned +10/06/2015 10:40:27 AM INFO: Running cell: LM0 = LakeModel(lamb,alpha,b,d) x0 = LM0.find_steady_state()# initial conditions print("Initial Steady State: %s" % x0) -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:27 AM INFO: Cell returned +10/06/2015 10:40:27 AM INFO: Running cell: LM1 = LakeModel(0.2,alpha,b,d) -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:27 AM INFO: Cell returned +10/06/2015 10:40:27 AM INFO: Running cell: xbar = LM1.find_steady_state() # new steady state X_path = np.vstack(LM1.simulate_stock_path(x0*N0,T)) # simulate stocks x_path = np.vstack(LM1.simulate_rate_path(x0,T)) # simulate rates print("New Steady State: %s" % xbar) -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:27 AM INFO: Cell returned +10/06/2015 10:40:27 AM INFO: Running cell: plt.figure(figsize=[10,9]) plt.subplot(3,1,1) plt.plot(X_path[:,0]) @@ -1270,8 +1270,8 @@ plt.subplot(3,1,3) plt.plot(X_path.sum(1)) plt.title(r'Labor Force') -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:28 AM INFO: Cell returned +10/06/2015 10:40:28 AM INFO: Running cell: plt.figure(figsize=[10,6]) plt.subplot(2,1,1) plt.plot(x_path[:,0]) @@ -1282,29 +1282,29 @@ plt.plot(x_path[:,1]) plt.hlines(xbar[1],0,T,'r','--') plt.title(r'Unemployment Rate') -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:28 AM INFO: Cell returned +10/06/2015 10:40:28 AM INFO: Running cell: bhat = 0.003 T_hat = 20 LM1 = LakeModel(lamb,alpha,bhat,d) -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:28 AM INFO: Cell returned +10/06/2015 10:40:28 AM INFO: Running cell: X_path1 = np.vstack(LM1.simulate_stock_path(x0*N0,T_hat)) # simulate stocks x_path1 = np.vstack(LM1.simulate_rate_path(x0,T_hat)) # simulate rates -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:28 AM INFO: Cell returned +10/06/2015 10:40:28 AM INFO: Running cell: X_path2 = np.vstack(LM0.simulate_stock_path(X_path1[-1,:2],T-T_hat+1)) # simulate stocks x_path2 = np.vstack(LM0.simulate_rate_path(x_path1[-1,:2],T-T_hat+1)) # simulate rates -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:28 AM INFO: Cell returned +10/06/2015 10:40:28 AM INFO: Running cell: x_path = np.vstack([x_path1,x_path2[1:]]) # note [1:] to avoid doubling period 20 X_path = np.vstack([X_path1,X_path2[1:]]) # note [1:] to avoid doubling period 20 -10/06/2015 09:48:19 AM INFO: Cell returned -10/06/2015 09:48:19 AM INFO: Running cell: +10/06/2015 10:40:28 AM INFO: Cell returned +10/06/2015 10:40:28 AM INFO: Running cell: plt.figure(figsize=[10,9]) plt.subplot(3,1,1) plt.plot(X_path[:,0]) @@ -1316,8 +1316,8 @@ plt.subplot(3,1,3) plt.plot(X_path.sum(1)) plt.title(r'Labor Force') -10/06/2015 09:48:20 AM INFO: Cell returned -10/06/2015 09:48:20 AM INFO: Running cell: +10/06/2015 10:40:29 AM INFO: Cell returned +10/06/2015 10:40:29 AM INFO: Running cell: plt.figure(figsize=[10,6]) plt.subplot(2,1,1) plt.plot(x_path[:,0]) @@ -1328,12 +1328,12 @@ plt.plot(x_path[:,1]) plt.hlines(x0[1],0,T,'r','--') plt.title(r'Unemployment Rate') -10/06/2015 09:48:20 AM INFO: Cell returned -10/06/2015 09:48:20 AM INFO: Running cell: +10/06/2015 10:40:29 AM INFO: Cell returned +10/06/2015 10:40:29 AM INFO: Running cell: -10/06/2015 09:48:20 AM INFO: Cell returned -10/06/2015 09:48:20 AM INFO: Shutdown kernel +10/06/2015 10:40:29 AM INFO: Cell returned +10/06/2015 10:40:29 AM INFO: Shutdown kernel ---> END 'lakemodel_solutions.ipynb' <--- ---> Executing 'lln_clt_solutions.ipynb' <--- @@ -1351,17 +1351,17 @@ plt.title(r'Unemployment Rate') "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:22 AM INFO: Reading notebook lln_clt_solutions.ipynb -10/06/2015 09:48:22 AM INFO: Running cell: +10/06/2015 10:40:30 AM INFO: Reading notebook lln_clt_solutions.ipynb +10/06/2015 10:40:31 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:22 AM INFO: Cell returned -10/06/2015 09:48:22 AM INFO: Running cell: +10/06/2015 10:40:31 AM INFO: Cell returned +10/06/2015 10:40:31 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt -10/06/2015 09:48:22 AM INFO: Cell returned -10/06/2015 09:48:22 AM INFO: Running cell: +10/06/2015 10:40:31 AM INFO: Cell returned +10/06/2015 10:40:31 AM INFO: Running cell: """ Illustrates the delta method, a consequence of the central limit theorem. """ @@ -1400,8 +1400,8 @@ ax.plot(xgrid, norm.pdf(xgrid, scale=asymptotic_sd), 'k-', lw=2, label=lb) ax.legend() plt.show() -10/06/2015 09:48:26 AM INFO: Cell returned -10/06/2015 09:48:26 AM INFO: Running cell: +10/06/2015 10:40:35 AM INFO: Cell returned +10/06/2015 10:40:35 AM INFO: Running cell: from scipy.stats import uniform, chi2 from scipy.linalg import inv, sqrtm @@ -1446,8 +1446,8 @@ ax.legend() ax.hist(chisq_obs, bins=50, normed=True) plt.show() -10/06/2015 09:48:32 AM INFO: Cell returned -10/06/2015 09:48:32 AM INFO: Shutdown kernel +10/06/2015 10:40:40 AM INFO: Cell returned +10/06/2015 10:40:40 AM INFO: Shutdown kernel ---> END 'lln_clt_solutions.ipynb' <--- ---> Executing 'lqcontrol_solutions.ipynb' <--- @@ -1465,19 +1465,19 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:33 AM INFO: Reading notebook lqcontrol_solutions.ipynb -10/06/2015 09:48:34 AM INFO: Running cell: +10/06/2015 10:40:41 AM INFO: Reading notebook lqcontrol_solutions.ipynb +10/06/2015 10:40:42 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:34 AM INFO: Cell returned -10/06/2015 09:48:34 AM INFO: Running cell: +10/06/2015 10:40:42 AM INFO: Cell returned +10/06/2015 10:40:42 AM INFO: Running cell: from __future__ import division import numpy as np import matplotlib.pyplot as plt from quantecon import LQ -10/06/2015 09:48:35 AM INFO: Cell returned -10/06/2015 09:48:35 AM INFO: Running cell: +10/06/2015 10:40:44 AM INFO: Cell returned +10/06/2015 10:40:44 AM INFO: Running cell: # == Model parameters == # r = 0.05 beta = 1 / (1 + r) @@ -1541,8 +1541,8 @@ axes[1].legend(ncol=1, **legend_args) plt.show() -10/06/2015 09:48:35 AM INFO: Cell returned -10/06/2015 09:48:35 AM INFO: Running cell: +10/06/2015 10:40:44 AM INFO: Cell returned +10/06/2015 10:40:44 AM INFO: Running cell: # == Model parameters == # r = 0.05 beta = 1 / (1 + r) @@ -1639,8 +1639,8 @@ axes[1].legend(ncol=1, **legend_args) plt.show() -10/06/2015 09:48:36 AM INFO: Cell returned -10/06/2015 09:48:36 AM INFO: Running cell: +10/06/2015 10:40:45 AM INFO: Cell returned +10/06/2015 10:40:45 AM INFO: Running cell: # == Model parameters == # a0 = 5 a1 = 0.5 @@ -1697,8 +1697,8 @@ s = r'dynamics with $\gamma = {}$'.format(gamma) ax.text(max(time) * 0.6, 1 * q_bar.max(), s, fontsize=14) plt.show() -10/06/2015 09:48:36 AM INFO: Cell returned -10/06/2015 09:48:36 AM INFO: Shutdown kernel +10/06/2015 10:40:46 AM INFO: Cell returned +10/06/2015 10:40:46 AM INFO: Shutdown kernel ---> END 'lqcontrol_solutions.ipynb' <--- ---> Executing 'lqramsey_solutions.ipynb' <--- @@ -1716,12 +1716,12 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:37 AM INFO: Reading notebook lqramsey_solutions.ipynb -10/06/2015 09:48:38 AM INFO: Running cell: +10/06/2015 10:40:47 AM INFO: Reading notebook lqramsey_solutions.ipynb +10/06/2015 10:40:47 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:38 AM INFO: Cell returned -10/06/2015 09:48:38 AM INFO: Running cell: +10/06/2015 10:40:48 AM INFO: Cell returned +10/06/2015 10:40:48 AM INFO: Running cell: import sys import os import numpy as np @@ -1731,8 +1731,8 @@ import matplotlib.pyplot as plt # to append it to the path so we can import it below sys.path.append(os.path.abspath("../examples")) -10/06/2015 09:48:38 AM INFO: Cell returned -10/06/2015 09:48:38 AM INFO: Running cell: +10/06/2015 10:40:48 AM INFO: Cell returned +10/06/2015 10:40:48 AM INFO: Running cell: from numpy import array from lqramsey import * @@ -1763,8 +1763,8 @@ T = 50 path = compute_paths(T, economy) gen_fig_1(path) -10/06/2015 09:48:40 AM INFO: Cell returned -10/06/2015 09:48:40 AM INFO: Shutdown kernel +10/06/2015 10:40:50 AM INFO: Cell returned +10/06/2015 10:40:50 AM INFO: Shutdown kernel ---> END 'lqramsey_solutions.ipynb' <--- ---> Executing 'lss_solutions.ipynb' <--- @@ -1782,18 +1782,18 @@ gen_fig_1(path) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:41 AM INFO: Reading notebook lss_solutions.ipynb -10/06/2015 09:48:42 AM INFO: Running cell: +10/06/2015 10:40:51 AM INFO: Reading notebook lss_solutions.ipynb +10/06/2015 10:40:52 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:42 AM INFO: Cell returned -10/06/2015 09:48:42 AM INFO: Running cell: +10/06/2015 10:40:53 AM INFO: Cell returned +10/06/2015 10:40:53 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import LinearStateSpace -10/06/2015 09:48:43 AM INFO: Cell returned -10/06/2015 09:48:43 AM INFO: Running cell: +10/06/2015 10:40:54 AM INFO: Cell returned +10/06/2015 10:40:54 AM INFO: Running cell: phi_0, phi_1, phi_2 = 1.1, 0.8, -0.8 A = [[1, 0, 0], @@ -1813,8 +1813,8 @@ ax.set_xlabel('time') ax.set_ylabel(r'$y_t$', fontsize=16) plt.show() -10/06/2015 09:48:43 AM INFO: Cell returned -10/06/2015 09:48:43 AM INFO: Running cell: +10/06/2015 10:40:54 AM INFO: Cell returned +10/06/2015 10:40:54 AM INFO: Running cell: phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 sigma = 0.2 @@ -1840,8 +1840,8 @@ ax.set_ylabel(r'$y_t$', fontsize=16) plt.show() -10/06/2015 09:48:43 AM INFO: Cell returned -10/06/2015 09:48:43 AM INFO: Running cell: +10/06/2015 10:40:54 AM INFO: Cell returned +10/06/2015 10:40:54 AM INFO: Running cell: from __future__ import division from scipy.stats import norm import random @@ -1889,8 +1889,8 @@ ax.plot(population_means, color='g', lw=2, alpha=0.8, label=r'$G\mu_t$') ax.legend(ncol=2) plt.show() -10/06/2015 09:48:44 AM INFO: Cell returned -10/06/2015 09:48:44 AM INFO: Running cell: +10/06/2015 10:40:55 AM INFO: Cell returned +10/06/2015 10:40:55 AM INFO: Running cell: phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 sigma = 0.1 @@ -1934,12 +1934,12 @@ for i in range(80): ax.plot((T0, T1, T2), (y[T0], y[T1], y[T2],), 'ko', alpha=0.5) -10/06/2015 09:48:45 AM INFO: Cell returned -10/06/2015 09:48:45 AM INFO: Running cell: +10/06/2015 10:40:56 AM INFO: Cell returned +10/06/2015 10:40:56 AM INFO: Running cell: -10/06/2015 09:48:45 AM INFO: Cell returned -10/06/2015 09:48:45 AM INFO: Shutdown kernel +10/06/2015 10:40:56 AM INFO: Cell returned +10/06/2015 10:40:56 AM INFO: Shutdown kernel ---> END 'lss_solutions.ipynb' <--- ---> Executing 'lucas_asset_solutions.ipynb' <--- @@ -1957,19 +1957,19 @@ for i in range(80): "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:46 AM INFO: Reading notebook lucas_asset_solutions.ipynb -10/06/2015 09:48:46 AM INFO: Running cell: +10/06/2015 10:40:57 AM INFO: Reading notebook lucas_asset_solutions.ipynb +10/06/2015 10:40:58 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:46 AM INFO: Cell returned -10/06/2015 09:48:46 AM INFO: Running cell: +10/06/2015 10:40:58 AM INFO: Cell returned +10/06/2015 10:40:58 AM INFO: Running cell: from __future__ import division # Omit for Python 3.x import numpy as np import matplotlib.pyplot as plt from quantecon.models import LucasTree -10/06/2015 09:48:47 AM INFO: Cell returned -10/06/2015 09:48:47 AM INFO: Running cell: +10/06/2015 10:40:59 AM INFO: Cell returned +10/06/2015 10:40:59 AM INFO: Running cell: fig, ax = plt.subplots(figsize=(10,7)) ax.set_xlabel(r'$y$', fontsize=16) @@ -1985,12 +1985,12 @@ for beta in (.95, 0.98): ax.legend(loc='upper left') ax.set_xlim(min(grid), max(grid)) -10/06/2015 09:48:49 AM INFO: Cell returned -10/06/2015 09:48:49 AM INFO: Running cell: +10/06/2015 10:41:01 AM INFO: Cell returned +10/06/2015 10:41:01 AM INFO: Running cell: -10/06/2015 09:48:49 AM INFO: Cell returned -10/06/2015 09:48:49 AM INFO: Shutdown kernel +10/06/2015 10:41:01 AM INFO: Cell returned +10/06/2015 10:41:01 AM INFO: Shutdown kernel ---> END 'lucas_asset_solutions.ipynb' <--- ---> Executing 'mpe_solutions.ipynb' <--- @@ -2008,19 +2008,19 @@ ax.set_xlim(min(grid), max(grid)) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:50 AM INFO: Reading notebook mpe_solutions.ipynb -10/06/2015 09:48:51 AM INFO: Running cell: +10/06/2015 10:41:03 AM INFO: Reading notebook mpe_solutions.ipynb +10/06/2015 10:41:03 AM INFO: Running cell: import numpy as np import quantecon as qe import matplotlib.pyplot as plt from numpy import dot -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:04 AM INFO: Cell returned +10/06/2015 10:41:04 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:04 AM INFO: Cell returned +10/06/2015 10:41:04 AM INFO: Running cell: # == Parameters == # a0 = 10.0 a1 = 2.0 @@ -2051,8 +2051,8 @@ S1 = S2 = W1 = W2 = M1 = M2 = 0.0 F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2, beta=beta) -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:04 AM INFO: Cell returned +10/06/2015 10:41:04 AM INFO: Running cell: AF = A - B1.dot(F1) - B2.dot(F2) n = 20 x = np.empty((3, n)) @@ -2064,8 +2064,8 @@ q2 = x[2, :] q = q1 + q2 # Total output, MPE p = a0 - a1 * q # Price, MPE -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:04 AM INFO: Cell returned +10/06/2015 10:41:04 AM INFO: Running cell: R = a1 Q = gamma A = B = 1 @@ -2081,8 +2081,8 @@ for i in range(1, n): qm[i] = float(x) + q_bar pm = a0 - a1 * qm -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:04 AM INFO: Cell returned +10/06/2015 10:41:04 AM INFO: Running cell: fig, axes = plt.subplots(2, 1, figsize=(9, 9)) ax = axes[0] @@ -2101,8 +2101,8 @@ ax.set_ylabel("price") ax.set_xlabel("time") ax.legend(loc='upper right', frameon=0) -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:05 AM INFO: Cell returned +10/06/2015 10:41:05 AM INFO: Running cell: delta = 0.02 D = np.array([[-1, 0.5], [0.5, -1]]) b = np.array([25, 25]) @@ -2111,8 +2111,8 @@ e1 = e2 = np.array([10, 10, 3]) delta_1 = 1 - delta -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:05 AM INFO: Cell returned +10/06/2015 10:41:05 AM INFO: Running cell: # == Create matrices needed to compute the Nash feedback equilibrium == # A = np.array([[delta_1, 0, -delta_1*b[0]], @@ -2149,8 +2149,8 @@ W2 = np.array([[0, 0], M1 = np.array([[0, 0], [0, D[0, 1] / 2.]]) M2 = np.copy(M1) -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:05 AM INFO: Cell returned +10/06/2015 10:41:05 AM INFO: Running cell: F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2) print("\nFirm 1's feedback rule:\n") @@ -2159,8 +2159,8 @@ print(F1) print("\nFirm 2's feedback rule:\n") print(F2) -10/06/2015 09:48:52 AM INFO: Cell returned -10/06/2015 09:48:52 AM INFO: Running cell: +10/06/2015 10:41:05 AM INFO: Cell returned +10/06/2015 10:41:05 AM INFO: Running cell: AF = A - B1.dot(F1) - B2.dot(F2) n = 25 x = np.empty((3, n)) @@ -2175,12 +2175,12 @@ ax.plot(I2, 'g-', lw=2, alpha=0.75, label='inventories, firm 2') ax.set_title(r'$\delta = {}$'.format(delta)) ax.legend() -10/06/2015 09:48:53 AM INFO: Cell returned -10/06/2015 09:48:53 AM INFO: Running cell: +10/06/2015 10:41:05 AM INFO: Cell returned +10/06/2015 10:41:05 AM INFO: Running cell: -10/06/2015 09:48:53 AM INFO: Cell returned -10/06/2015 09:48:53 AM INFO: Shutdown kernel +10/06/2015 10:41:05 AM INFO: Cell returned +10/06/2015 10:41:05 AM INFO: Shutdown kernel ---> END 'mpe_solutions.ipynb' <--- ---> Executing 'numpy_solutions.ipynb' <--- @@ -2198,17 +2198,17 @@ ax.legend() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:54 AM INFO: Reading notebook numpy_solutions.ipynb -10/06/2015 09:48:54 AM INFO: Running cell: +10/06/2015 10:41:06 AM INFO: Reading notebook numpy_solutions.ipynb +10/06/2015 10:41:07 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: def p(x, coef): X = np.empty(len(coef)) X[0] = 1 @@ -2216,8 +2216,8 @@ def p(x, coef): y = np.cumprod(X) # y = [1, x, x**2,...] return np.dot(coef, y) -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: coef = np.ones(3) print(coef) print(p(1, coef)) @@ -2225,8 +2225,8 @@ print(p(1, coef)) q = np.poly1d(coef) print(q(1)) -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: from numpy import cumsum from numpy.random import uniform @@ -2251,14 +2251,14 @@ class discreteRV: """ return self.Q.searchsorted(uniform(0, 1, size=k)) -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: q = (0.1, 0.9) d = discreteRV(q) d.q = (0.5, 0.5) -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: """ Modifies ecdf.py from QuantEcon to add in a plot method @@ -2330,18 +2330,18 @@ class ECDF(object): plt.plot(x_vals, f(x_vals)) plt.show() -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: X = np.random.randn(1000) F = ECDF(X) F.plot() -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Running cell: +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Running cell: -10/06/2015 09:48:55 AM INFO: Cell returned -10/06/2015 09:48:55 AM INFO: Shutdown kernel +10/06/2015 10:41:07 AM INFO: Cell returned +10/06/2015 10:41:07 AM INFO: Shutdown kernel ---> END 'numpy_solutions.ipynb' <--- ---> Executing 'odu_solutions.ipynb' <--- @@ -2359,19 +2359,19 @@ F.plot() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:48:56 AM INFO: Reading notebook odu_solutions.ipynb -10/06/2015 09:48:57 AM INFO: Running cell: +10/06/2015 10:41:09 AM INFO: Reading notebook odu_solutions.ipynb +10/06/2015 10:41:09 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:48:57 AM INFO: Cell returned -10/06/2015 09:48:57 AM INFO: Running cell: +10/06/2015 10:41:09 AM INFO: Cell returned +10/06/2015 10:41:09 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import compute_fixed_point from quantecon.models import SearchProblem -10/06/2015 09:48:58 AM INFO: Cell returned -10/06/2015 09:48:58 AM INFO: Running cell: +10/06/2015 10:41:10 AM INFO: Cell returned +10/06/2015 10:41:10 AM INFO: Running cell: sp = SearchProblem(pi_grid_size=50) phi_init = np.ones(len(sp.pi_grid)) @@ -2388,8 +2388,8 @@ ax.text(0.42, 1.2, 'reject') ax.text(0.7, 1.8, 'accept') plt.show() -10/06/2015 09:48:58 AM INFO: Cell returned -10/06/2015 09:48:58 AM INFO: Running cell: +10/06/2015 10:41:11 AM INFO: Cell returned +10/06/2015 10:41:11 AM INFO: Running cell: from scipy import interp # Set up model and compute the function w_bar sp = SearchProblem(pi_grid_size=50, F_a=1, F_b=1) @@ -2450,8 +2450,8 @@ ax.axvline(change_date, color="red") ax.legend() plt.show() -10/06/2015 09:50:10 AM INFO: Cell returned -10/06/2015 09:50:10 AM INFO: Shutdown kernel +10/06/2015 10:42:22 AM INFO: Cell returned +10/06/2015 10:42:22 AM INFO: Shutdown kernel ---> END 'odu_solutions.ipynb' <--- ---> Executing 'oop_solutions.ipynb' <--- @@ -2469,8 +2469,8 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:11 AM INFO: Reading notebook oop_solutions.ipynb -10/06/2015 09:50:11 AM INFO: Running cell: +10/06/2015 10:42:23 AM INFO: Reading notebook oop_solutions.ipynb +10/06/2015 10:42:24 AM INFO: Running cell: class ECDF(object): def __init__(self, observations): @@ -2483,8 +2483,8 @@ class ECDF(object): counter += 1 return counter / len(self.observations) -10/06/2015 09:50:11 AM INFO: Cell returned -10/06/2015 09:50:11 AM INFO: Running cell: +10/06/2015 10:42:24 AM INFO: Cell returned +10/06/2015 10:42:24 AM INFO: Running cell: # == test == # from random import uniform @@ -2497,8 +2497,8 @@ F.observations = [uniform(0, 1) for i in range(1000)] print(F(0.5)) -10/06/2015 09:50:11 AM INFO: Cell returned -10/06/2015 09:50:11 AM INFO: Running cell: +10/06/2015 10:42:24 AM INFO: Cell returned +10/06/2015 10:42:24 AM INFO: Running cell: class Polynomial(object): def __init__(self, coefficients): @@ -2529,8 +2529,8 @@ class Polynomial(object): self.coefficients = new_coefficients -10/06/2015 09:50:11 AM INFO: Cell returned -10/06/2015 09:50:11 AM INFO: Shutdown kernel +10/06/2015 10:42:24 AM INFO: Cell returned +10/06/2015 10:42:24 AM INFO: Shutdown kernel ---> END 'oop_solutions.ipynb' <--- ---> Executing 'optgrowth_solutions.ipynb' <--- @@ -2548,19 +2548,19 @@ class Polynomial(object): "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:12 AM INFO: Reading notebook optgrowth_solutions.ipynb -10/06/2015 09:50:13 AM INFO: Running cell: +10/06/2015 10:42:25 AM INFO: Reading notebook optgrowth_solutions.ipynb +10/06/2015 10:42:25 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:50:13 AM INFO: Cell returned -10/06/2015 09:50:13 AM INFO: Running cell: +10/06/2015 10:42:25 AM INFO: Cell returned +10/06/2015 10:42:25 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt from quantecon import compute_fixed_point from quantecon.models import GrowthModel -10/06/2015 09:50:14 AM INFO: Cell returned -10/06/2015 09:50:14 AM INFO: Running cell: +10/06/2015 10:42:26 AM INFO: Cell returned +10/06/2015 10:42:26 AM INFO: Running cell: alpha, beta = 0.65, 0.95 gm = GrowthModel() true_sigma = (1 - alpha * beta) * gm.grid**alpha @@ -2582,8 +2582,8 @@ for i, n in enumerate((2, 4, 6)): ax[i].legend(loc='upper left') ax[i].set_title('{} value function iterations'.format(n)) -10/06/2015 09:50:15 AM INFO: Cell returned -10/06/2015 09:50:15 AM INFO: Running cell: +10/06/2015 10:42:27 AM INFO: Cell returned +10/06/2015 10:42:27 AM INFO: Running cell: from scipy import interp gm = GrowthModel() @@ -2613,8 +2613,8 @@ for beta in discount_factors: ax.legend(loc='lower right') plt.show() -10/06/2015 09:50:20 AM INFO: Cell returned -10/06/2015 09:50:20 AM INFO: Shutdown kernel +10/06/2015 10:42:32 AM INFO: Cell returned +10/06/2015 10:42:32 AM INFO: Shutdown kernel ---> END 'optgrowth_solutions.ipynb' <--- ---> Executing 'pandas_solutions.ipynb' <--- @@ -2632,20 +2632,20 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:21 AM INFO: Reading notebook pandas_solutions.ipynb -10/06/2015 09:50:21 AM INFO: Running cell: +10/06/2015 10:42:33 AM INFO: Reading notebook pandas_solutions.ipynb +10/06/2015 10:42:34 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:50:22 AM INFO: Cell returned -10/06/2015 09:50:22 AM INFO: Running cell: +10/06/2015 10:42:34 AM INFO: Cell returned +10/06/2015 10:42:34 AM INFO: Running cell: import numpy as np import pandas as pd import datetime as dt import pandas.io.data as web import matplotlib.pyplot as plt -10/06/2015 09:50:22 AM INFO: Cell returned -10/06/2015 09:50:22 AM INFO: Running cell: +10/06/2015 10:42:34 AM INFO: Cell returned +10/06/2015 10:42:34 AM INFO: Running cell: ticker_list = {'INTC': 'Intel', 'MSFT': 'Microsoft', 'IBM': 'IBM', @@ -2678,8 +2678,8 @@ pc.sort() fig, ax = plt.subplots(figsize=(10,8)) pc.plot(kind='bar', ax=ax) -10/06/2015 09:50:24 AM INFO: Cell returned -10/06/2015 09:50:24 AM INFO: Shutdown kernel +10/06/2015 10:42:37 AM INFO: Cell returned +10/06/2015 10:42:37 AM INFO: Shutdown kernel ---> END 'pandas_solutions.ipynb' <--- ---> Executing 'pbe_solutions.ipynb' <--- @@ -2697,8 +2697,8 @@ pc.plot(kind='bar', ax=ax) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:25 AM INFO: Reading notebook pbe_solutions.ipynb -10/06/2015 09:50:26 AM INFO: Running cell: +10/06/2015 10:42:39 AM INFO: Reading notebook pbe_solutions.ipynb +10/06/2015 10:42:39 AM INFO: Running cell: def factorial(n): k = 1 for i in range(n): @@ -2707,8 +2707,8 @@ def factorial(n): factorial(4) -10/06/2015 09:50:26 AM INFO: Cell returned -10/06/2015 09:50:26 AM INFO: Running cell: +10/06/2015 10:42:39 AM INFO: Cell returned +10/06/2015 10:42:39 AM INFO: Running cell: from random import uniform def binomial_rv(n, p): @@ -2721,8 +2721,8 @@ def binomial_rv(n, p): binomial_rv(10, 0.5) -10/06/2015 09:50:26 AM INFO: Cell returned -10/06/2015 09:50:26 AM INFO: Running cell: +10/06/2015 10:42:39 AM INFO: Cell returned +10/06/2015 10:42:39 AM INFO: Running cell: from __future__ import division # Omit if using Python 3.x from math import sqrt @@ -2739,8 +2739,8 @@ area_estimate = count / n print(area_estimate * 4) # dividing by radius**2 -10/06/2015 09:50:26 AM INFO: Cell returned -10/06/2015 09:50:26 AM INFO: Running cell: +10/06/2015 10:42:39 AM INFO: Cell returned +10/06/2015 10:42:39 AM INFO: Running cell: payoff = 0 count = 0 @@ -2752,12 +2752,12 @@ for i in range(10): print(payoff) -10/06/2015 09:50:26 AM INFO: Cell returned -10/06/2015 09:50:26 AM INFO: Running cell: +10/06/2015 10:42:40 AM INFO: Cell returned +10/06/2015 10:42:40 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:50:26 AM INFO: Cell returned -10/06/2015 09:50:26 AM INFO: Running cell: +10/06/2015 10:42:40 AM INFO: Cell returned +10/06/2015 10:42:40 AM INFO: Running cell: import matplotlib.pyplot as plt from random import normalvariate @@ -2772,8 +2772,8 @@ for i in range(ts_length + 1): plt.plot(x_values, 'b-') -10/06/2015 09:50:27 AM INFO: Cell returned -10/06/2015 09:50:27 AM INFO: Running cell: +10/06/2015 10:42:40 AM INFO: Cell returned +10/06/2015 10:42:40 AM INFO: Running cell: alphas = [0.0, 0.8, 0.98] ts_length = 200 @@ -2786,12 +2786,12 @@ for alpha in alphas: plt.plot(x_values, label='alpha = ' + str(alpha)) plt.legend() -10/06/2015 09:50:27 AM INFO: Cell returned -10/06/2015 09:50:27 AM INFO: Running cell: +10/06/2015 10:42:40 AM INFO: Cell returned +10/06/2015 10:42:40 AM INFO: Running cell: -10/06/2015 09:50:27 AM INFO: Cell returned -10/06/2015 09:50:27 AM INFO: Shutdown kernel +10/06/2015 10:42:40 AM INFO: Cell returned +10/06/2015 10:42:40 AM INFO: Shutdown kernel ---> END 'pbe_solutions.ipynb' <--- ---> Executing 'py_adv_feat_solutions.ipynb' <--- @@ -2809,8 +2809,8 @@ plt.legend() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:28 AM INFO: Reading notebook py_adv_feat_solutions.ipynb -10/06/2015 09:50:28 AM INFO: Running cell: +10/06/2015 10:42:41 AM INFO: Reading notebook py_adv_feat_solutions.ipynb +10/06/2015 10:42:42 AM INFO: Running cell: def x(t): if t == 0: return 0 @@ -2820,12 +2820,12 @@ def x(t): return x(t-1) + x(t-2) -10/06/2015 09:50:28 AM INFO: Cell returned -10/06/2015 09:50:28 AM INFO: Running cell: +10/06/2015 10:42:42 AM INFO: Cell returned +10/06/2015 10:42:42 AM INFO: Running cell: print([x(i) for i in range(10)]) -10/06/2015 09:50:28 AM INFO: Cell returned -10/06/2015 09:50:28 AM INFO: Running cell: +10/06/2015 10:42:42 AM INFO: Cell returned +10/06/2015 10:42:42 AM INFO: Running cell: def column_iterator(target_file, column_number): """A generator function for CSV files. When called with a file name target_file (string) and column number @@ -2847,8 +2847,8 @@ for date in dates: break i += 1 -10/06/2015 09:50:28 AM INFO: Cell returned -10/06/2015 09:50:28 AM INFO: Running cell: +10/06/2015 10:42:42 AM INFO: Cell returned +10/06/2015 10:42:42 AM INFO: Running cell: %%file numbers.txt prices 3 @@ -2857,8 +2857,8 @@ prices 7 21 -10/06/2015 09:50:28 AM INFO: Cell returned -10/06/2015 09:50:28 AM INFO: Running cell: +10/06/2015 10:42:42 AM INFO: Cell returned +10/06/2015 10:42:42 AM INFO: Running cell: f = open('numbers.txt') total = 0.0 @@ -2873,8 +2873,8 @@ f.close() print(total) -10/06/2015 09:50:28 AM INFO: Cell returned -10/06/2015 09:50:28 AM INFO: Shutdown kernel +10/06/2015 10:42:42 AM INFO: Cell returned +10/06/2015 10:42:42 AM INFO: Shutdown kernel ---> END 'py_adv_feat_solutions.ipynb' <--- ---> Executing 'pyess_solutions.ipynb' <--- @@ -2892,53 +2892,53 @@ print(total) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:29 AM INFO: Reading notebook pyess_solutions.ipynb -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Reading notebook pyess_solutions.ipynb +10/06/2015 10:42:43 AM INFO: Running cell: from __future__ import division # Omit for Python 3.x -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: x_vals = [1, 2, 3] y_vals = [1, 1, 1] sum([x * y for x, y in zip(x_vals, y_vals)]) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: sum(x * y for x, y in zip(x_vals, y_vals)) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: sum([x % 2 == 0 for x in range(100)]) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: sum(x % 2 == 0 for x in range(100)) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: len([x for x in range(100) if x % 2 == 0]) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: sum([1 for x in range(100) if x % 2 == 0]) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: pairs = ((2, 5), (4, 2), (9, 8), (12, 10)) sum([x % 2 == 0 and y % 2 == 0 for x, y in pairs]) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: def p(x, coeff): return sum(a * x**i for i, a in enumerate(coeff)) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: p(1, (2, 4)) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: def f(string): count = 0 for letter in string: @@ -2947,8 +2947,8 @@ def f(string): return count f('The Rain in Spain') -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: def f(seq_a, seq_b): is_subset = True for a in seq_a: @@ -2961,13 +2961,13 @@ def f(seq_a, seq_b): print(f([1, 2], [1, 2, 3])) print(f([1, 2, 3], [1, 2])) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: def f(seq_a, seq_b): return set(seq_a).issubset(set(seq_b)) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Running cell: +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Running cell: def linapprox(f, a, b, n, x): """ Evaluates the piecewise linear interpolant of f at x on the interval @@ -3004,8 +3004,8 @@ def linapprox(f, a, b, n, x): return f(u) + (x - u) * (f(v) - f(u)) / (v - u) -10/06/2015 09:50:30 AM INFO: Cell returned -10/06/2015 09:50:30 AM INFO: Shutdown kernel +10/06/2015 10:42:43 AM INFO: Cell returned +10/06/2015 10:42:43 AM INFO: Shutdown kernel ---> END 'pyess_solutions.ipynb' <--- ---> Executing 'ree_solutions.ipynb' <--- @@ -3023,22 +3023,22 @@ def linapprox(f, a, b, n, x): "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:31 AM INFO: Reading notebook ree_solutions.ipynb -10/06/2015 09:50:31 AM INFO: Running cell: +10/06/2015 10:42:44 AM INFO: Reading notebook ree_solutions.ipynb +10/06/2015 10:42:45 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:50:32 AM INFO: Cell returned -10/06/2015 09:50:32 AM INFO: Running cell: +10/06/2015 10:42:45 AM INFO: Cell returned +10/06/2015 10:42:45 AM INFO: Running cell: from __future__ import print_function import numpy as np import matplotlib.pyplot as plt -10/06/2015 09:50:32 AM INFO: Cell returned -10/06/2015 09:50:32 AM INFO: Running cell: +10/06/2015 10:42:45 AM INFO: Cell returned +10/06/2015 10:42:45 AM INFO: Running cell: from quantecon import LQ -10/06/2015 09:50:32 AM INFO: Cell returned -10/06/2015 09:50:32 AM INFO: Running cell: +10/06/2015 10:42:46 AM INFO: Cell returned +10/06/2015 10:42:46 AM INFO: Running cell: # == Model parameters == # @@ -3073,8 +3073,8 @@ print(out1) print(out2) -10/06/2015 09:50:32 AM INFO: Cell returned -10/06/2015 09:50:32 AM INFO: Running cell: +10/06/2015 10:42:46 AM INFO: Cell returned +10/06/2015 10:42:46 AM INFO: Running cell: candidates = ( (94.0886298678, 0.923409232937), @@ -3102,8 +3102,8 @@ for kappa0, kappa1 in candidates: -10/06/2015 09:50:32 AM INFO: Cell returned -10/06/2015 09:50:32 AM INFO: Running cell: +10/06/2015 10:42:46 AM INFO: Cell returned +10/06/2015 10:42:46 AM INFO: Running cell: # == Formulate the planner's LQ problem == # @@ -3124,8 +3124,8 @@ kappa0, kappa1 = -F[1], 1 - F[0] print(kappa0, kappa1) -10/06/2015 09:50:32 AM INFO: Cell returned -10/06/2015 09:50:32 AM INFO: Running cell: +10/06/2015 10:42:46 AM INFO: Cell returned +10/06/2015 10:42:46 AM INFO: Running cell: A = np.array([[1, 0], [0, 1]]) B = np.array([[1], [0]]) @@ -3140,8 +3140,8 @@ m0, m1 = -F[1], 1 - F[0] print(m0, m1) -10/06/2015 09:50:32 AM INFO: Cell returned -10/06/2015 09:50:32 AM INFO: Shutdown kernel +10/06/2015 10:42:46 AM INFO: Cell returned +10/06/2015 10:42:46 AM INFO: Shutdown kernel ---> END 'ree_solutions.ipynb' <--- ---> Executing 'schelling_solutions.ipynb' <--- @@ -3159,12 +3159,12 @@ print(m0, m1) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:33 AM INFO: Reading notebook schelling_solutions.ipynb -10/06/2015 09:50:34 AM INFO: Running cell: +10/06/2015 10:42:47 AM INFO: Reading notebook schelling_solutions.ipynb +10/06/2015 10:42:47 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:50:34 AM INFO: Cell returned -10/06/2015 09:50:34 AM INFO: Running cell: +10/06/2015 10:42:47 AM INFO: Cell returned +10/06/2015 10:42:47 AM INFO: Running cell: from random import uniform, seed from math import sqrt import matplotlib.pyplot as plt @@ -3260,8 +3260,8 @@ while 1: print('Converged, terminating.') -10/06/2015 09:50:40 AM INFO: Cell returned -10/06/2015 09:50:40 AM INFO: Shutdown kernel +10/06/2015 10:42:53 AM INFO: Cell returned +10/06/2015 10:42:53 AM INFO: Shutdown kernel ---> END 'schelling_solutions.ipynb' <--- ---> Executing 'scipy_solutions.ipynb' <--- @@ -3279,8 +3279,8 @@ print('Converged, terminating.') "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:41 AM INFO: Reading notebook scipy_solutions.ipynb -10/06/2015 09:50:41 AM INFO: Running cell: +10/06/2015 10:42:54 AM INFO: Reading notebook scipy_solutions.ipynb +10/06/2015 10:42:55 AM INFO: Running cell: def bisect(f, a, b, tol=10e-5): """ Implements the bisection root finding algorithm, assuming that f is a @@ -3298,15 +3298,15 @@ def bisect(f, a, b, tol=10e-5): bisect(f, middle, upper) -10/06/2015 09:50:41 AM INFO: Cell returned -10/06/2015 09:50:41 AM INFO: Running cell: +10/06/2015 10:42:55 AM INFO: Cell returned +10/06/2015 10:42:55 AM INFO: Running cell: import numpy as np f = lambda x: np.sin(4 * (x - 0.25)) + x + x**20 - 1 bisect(f, 0, 1) -10/06/2015 09:50:41 AM INFO: Cell returned -10/06/2015 09:50:41 AM INFO: Shutdown kernel +10/06/2015 10:42:55 AM INFO: Cell returned +10/06/2015 10:42:55 AM INFO: Shutdown kernel ---> END 'scipy_solutions.ipynb' <--- ---> Executing 'short_path_solutions.ipynb' <--- @@ -3324,8 +3324,8 @@ bisect(f, 0, 1) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:42 AM INFO: Reading notebook short_path_solutions.ipynb -10/06/2015 09:50:43 AM INFO: Running cell: +10/06/2015 10:42:56 AM INFO: Reading notebook short_path_solutions.ipynb +10/06/2015 10:42:56 AM INFO: Running cell: %%file graph.txt node0, node1 0.04, node8 11.11, node14 72.21 node1, node46 1247.25, node6 20.59, node13 64.94 @@ -3428,8 +3428,8 @@ node97, node98 0.30 node98, node99 0.33 node99, -10/06/2015 09:50:43 AM INFO: Cell returned -10/06/2015 09:50:43 AM INFO: Running cell: +10/06/2015 10:42:56 AM INFO: Cell returned +10/06/2015 10:42:56 AM INFO: Running cell: def read_graph(in_file): """ Read in the graph from the data file. The graph is stored @@ -3502,8 +3502,8 @@ while 1: J = next_J print_best_path(J, graph) -10/06/2015 09:50:43 AM INFO: Cell returned -10/06/2015 09:50:43 AM INFO: Shutdown kernel +10/06/2015 10:42:56 AM INFO: Cell returned +10/06/2015 10:42:56 AM INFO: Shutdown kernel ---> END 'short_path_solutions.ipynb' <--- ---> Executing 'speed_solutions.ipynb' <--- @@ -3521,18 +3521,18 @@ print_best_path(J, graph) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:44 AM INFO: Reading notebook speed_solutions.ipynb -10/06/2015 09:50:45 AM INFO: Running cell: +10/06/2015 10:42:57 AM INFO: Reading notebook speed_solutions.ipynb +10/06/2015 10:42:58 AM INFO: Running cell: import matplotlib.pyplot as plt import numpy as np from numba import jit -10/06/2015 09:50:45 AM INFO: Cell returned -10/06/2015 09:50:45 AM INFO: Running cell: +10/06/2015 10:42:58 AM INFO: Cell returned +10/06/2015 10:42:58 AM INFO: Running cell: p, q = 0.1, 0.2 # Prob of leaving low and high state respectively -10/06/2015 09:50:45 AM INFO: Cell returned -10/06/2015 09:50:45 AM INFO: Running cell: +10/06/2015 10:42:58 AM INFO: Cell returned +10/06/2015 10:42:58 AM INFO: Running cell: def compute_series(n): x = np.empty(n, dtype=int) x[0] = 1 # Start in state 1 @@ -3545,35 +3545,35 @@ def compute_series(n): x[t] = U[t] > q return x -10/06/2015 09:50:45 AM INFO: Cell returned -10/06/2015 09:50:45 AM INFO: Running cell: +10/06/2015 10:42:58 AM INFO: Cell returned +10/06/2015 10:42:58 AM INFO: Running cell: n = 100000 x = compute_series(n) print(np.mean(x == 0)) # Fraction of time x is in state 0 -10/06/2015 09:50:45 AM INFO: Cell returned -10/06/2015 09:50:45 AM INFO: Running cell: +10/06/2015 10:42:58 AM INFO: Cell returned +10/06/2015 10:42:58 AM INFO: Running cell: %timeit compute_series(n) -10/06/2015 09:50:48 AM INFO: Cell returned -10/06/2015 09:50:48 AM INFO: Running cell: +10/06/2015 10:43:02 AM INFO: Cell returned +10/06/2015 10:43:02 AM INFO: Running cell: compute_series_numba = jit(compute_series) -10/06/2015 09:50:48 AM INFO: Cell returned -10/06/2015 09:50:48 AM INFO: Running cell: +10/06/2015 10:43:02 AM INFO: Cell returned +10/06/2015 10:43:02 AM INFO: Running cell: x = compute_series_numba(n) print(np.mean(x == 0)) -10/06/2015 09:50:49 AM INFO: Cell returned -10/06/2015 09:50:49 AM INFO: Running cell: +10/06/2015 10:43:03 AM INFO: Cell returned +10/06/2015 10:43:03 AM INFO: Running cell: %timeit compute_series_numba(n) -10/06/2015 09:50:57 AM INFO: Cell returned -10/06/2015 09:50:57 AM INFO: Running cell: +10/06/2015 10:43:03 AM INFO: Cell returned +10/06/2015 10:43:03 AM INFO: Running cell: %load_ext cythonmagic -10/06/2015 09:50:57 AM INFO: Cell returned -10/06/2015 09:50:57 AM INFO: Running cell: +10/06/2015 10:43:03 AM INFO: Cell returned +10/06/2015 10:43:03 AM INFO: Running cell: %%cython import numpy as np from numpy cimport int_t, float_t @@ -3599,19 +3599,19 @@ def compute_series_cy(int n): x[t] = U[t] > q return np.asarray(x) -10/06/2015 09:50:57 AM INFO: Cell returned -10/06/2015 09:50:57 AM INFO: Running cell: +10/06/2015 10:43:03 AM INFO: Cell returned +10/06/2015 10:43:03 AM INFO: Running cell: compute_series_cy(10) -10/06/2015 09:50:57 AM INFO: Cell raised uncaught exception: +10/06/2015 10:43:03 AM INFO: Cell raised uncaught exception: --------------------------------------------------------------------------- NameError Traceback (most recent call last)  in () ----> 1 compute_series_cy(10)  NameError: name 'compute_series_cy' is not defined -10/06/2015 09:50:57 AM INFO: Shutdown kernel -10/06/2015 09:50:57 AM WARNING: Exiting with nonzero exit status +10/06/2015 10:43:03 AM INFO: Shutdown kernel +10/06/2015 10:43:04 AM WARNING: Exiting with nonzero exit status ---> END 'speed_solutions.ipynb' <--- ---> Executing 'statd_solutions.ipynb' <--- @@ -3629,17 +3629,17 @@ compute_series_cy(10) "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:50:57 AM INFO: Reading notebook statd_solutions.ipynb -10/06/2015 09:50:58 AM INFO: Running cell: +10/06/2015 10:43:05 AM INFO: Reading notebook statd_solutions.ipynb +10/06/2015 10:43:06 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:50:58 AM INFO: Cell returned -10/06/2015 09:50:58 AM INFO: Running cell: +10/06/2015 10:43:06 AM INFO: Cell returned +10/06/2015 10:43:06 AM INFO: Running cell: import numpy as np import matplotlib.pyplot as plt -10/06/2015 09:50:58 AM INFO: Cell returned -10/06/2015 09:50:58 AM INFO: Running cell: +10/06/2015 10:43:06 AM INFO: Cell returned +10/06/2015 10:43:06 AM INFO: Running cell: from scipy.stats import norm, gaussian_kde from quantecon import LAE @@ -3673,8 +3673,8 @@ ax.plot(ys, k_est(ys), 'k-', lw=2, alpha=0.6, label='kernel based estimate') ax.legend(loc='upper left') plt.show() -10/06/2015 09:50:59 AM INFO: Cell returned -10/06/2015 09:50:59 AM INFO: Running cell: +10/06/2015 10:43:07 AM INFO: Cell returned +10/06/2015 10:43:07 AM INFO: Running cell: from scipy.stats import lognorm, beta # == Define parameters == # @@ -3719,8 +3719,8 @@ for i in range(4): ax.plot(ygrid, psi(ygrid), color=g, lw=2, alpha=0.6) ax.set_xlabel('capital') -10/06/2015 09:51:04 AM INFO: Cell returned -10/06/2015 09:51:04 AM INFO: Running cell: +10/06/2015 10:43:13 AM INFO: Cell returned +10/06/2015 10:43:13 AM INFO: Running cell: n = 20 k = 5000 J = 6 @@ -3747,8 +3747,8 @@ for j in range(J): plt.show() -10/06/2015 09:51:07 AM INFO: Cell returned -10/06/2015 09:51:07 AM INFO: Shutdown kernel +10/06/2015 10:43:16 AM INFO: Cell returned +10/06/2015 10:43:16 AM INFO: Shutdown kernel ---> END 'statd_solutions.ipynb' <--- ---> Executing 'uncertainty_traps_solutions.ipynb' <--- @@ -3766,12 +3766,12 @@ plt.show() "You should import from traitlets.config instead.", ShimWarning) /home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 09:51:08 AM INFO: Reading notebook uncertainty_traps_solutions.ipynb -10/06/2015 09:51:08 AM INFO: Running cell: +10/06/2015 10:43:17 AM INFO: Reading notebook uncertainty_traps_solutions.ipynb +10/06/2015 10:43:18 AM INFO: Running cell: %matplotlib inline -10/06/2015 09:51:09 AM INFO: Cell returned -10/06/2015 09:51:09 AM INFO: Running cell: +10/06/2015 10:43:19 AM INFO: Cell returned +10/06/2015 10:43:19 AM INFO: Running cell: from __future__ import division import matplotlib.pyplot as plt import numpy as np @@ -3779,8 +3779,8 @@ import quantecon as qe import seaborn as sns import itertools -10/06/2015 09:51:09 AM INFO: Cell returned -10/06/2015 09:51:09 AM INFO: Running cell: +10/06/2015 10:43:19 AM INFO: Cell returned +10/06/2015 10:43:19 AM INFO: Running cell: palette = itertools.cycle(sns.color_palette()) econ = qe.models.UncertaintyTrapEcon() rho, sig_theta, gx = econ.rho, econ.sig_theta, econ.gx # simplify names @@ -3797,8 +3797,8 @@ ax.set_ylabel(r"$\gamma'$", fontsize=16) ax.grid() plt.show() -10/06/2015 09:51:10 AM INFO: Cell returned -10/06/2015 09:51:10 AM INFO: Running cell: +10/06/2015 10:43:20 AM INFO: Cell returned +10/06/2015 10:43:20 AM INFO: Running cell: sim_length=2000 mu_vec = np.empty(sim_length) @@ -3830,16 +3830,16 @@ X, M = econ.gen_aggregates() X_vec[-1] = X M_vec[-1] = M -10/06/2015 09:51:10 AM INFO: Cell returned -10/06/2015 09:51:10 AM INFO: Running cell: +10/06/2015 10:43:20 AM INFO: Cell returned +10/06/2015 10:43:20 AM INFO: Running cell: fig, ax = plt.subplots(figsize=(9, 6)) ax.plot(range(sim_length), theta_vec, alpha=0.6, lw=2, label=r"$\theta$") ax.plot(range(sim_length), mu_vec, alpha=0.6, lw=2, label=r"$\mu$") ax.legend(fontsize=16) plt.show() -10/06/2015 09:51:11 AM INFO: Cell returned -10/06/2015 09:51:11 AM INFO: Running cell: +10/06/2015 10:43:21 AM INFO: Cell returned +10/06/2015 10:43:21 AM INFO: Running cell: fig, axes = plt.subplots(4, 1, figsize=(12, 20)) # Add some spacing fig.subplots_adjust(hspace=0.3) @@ -3861,11 +3861,11 @@ for ax, vals, name in zip(axes, series, names): plt.show() -10/06/2015 09:51:11 AM INFO: Cell returned -10/06/2015 09:51:11 AM INFO: Running cell: +10/06/2015 10:43:22 AM INFO: Cell returned +10/06/2015 10:43:22 AM INFO: Running cell: -10/06/2015 09:51:11 AM INFO: Cell returned -10/06/2015 09:51:11 AM INFO: Shutdown kernel +10/06/2015 10:43:22 AM INFO: Cell returned +10/06/2015 10:43:22 AM INFO: Shutdown kernel ---> END 'uncertainty_traps_solutions.ipynb' <--- From 60de40b603a0bc8e41a3601ba2d5f2c906a1242b Mon Sep 17 00:00:00 2001 From: Thomas Sargent Date: Fri, 9 Oct 2015 14:31:11 -0400 Subject: [PATCH 12/51] Add a whitener method to Kalman. Add LSS. --- quantecon/kalman.py | 55 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/quantecon/kalman.py b/quantecon/kalman.py index 1e02883af..6f9dd546e 100644 --- a/quantecon/kalman.py +++ b/quantecon/kalman.py @@ -9,6 +9,7 @@ import numpy as np from numpy import dot from scipy.linalg import inv +from quantecon.lss import LinearStateSpace from quantecon.matrix_eqn import solve_discrete_riccati @@ -83,6 +84,60 @@ def __str__(self): """ return dedent(m.format(n=self.ss.n, k=self.ss.k)) + def whitener_lss(self): + r""" + This function takes the linear state space system + that is an input to the Kalman class and it converts + that system to the time-invariant whitener represenation + given by (NOTE: FIX NOTATION LATER) + + x_{t+1}^* = A x + C v + a = G x + + where + + x_t = [x+{t}, \hat{x}_{t}, v_{t}] + + and + + A = [A 0 0 + KG A-KG KH + 0 0 0] + + C = [C 0 + 0 0 + 0 I] + + G = [G -G H] + """ + # Check for steady state Sigma and K + if self.K_infinity is None: + Sig, K = self.stationary_values() + self.Sigma_infinity = Sig + self.K_infinity = K + else: + K = self.K_infinity + + # Get the matrix sizes + n, k, m, l = self.ss.n, self.ss.k, self.ss.m, self.ss.l + A, C, G, H = self.ss.A, self.ss.C, self.ss.G, self.ss.H + + Atil = np.vstack([np.hstack([A, np.zeros((n, n)), np.zeros((n, l))]), + np.hstack([dot(K, G), A-dot(K, G), dot(K, H)]), + np.zeros((l, 2*n + l))]) + + Ctil = np.vstack([np.hstack([C, np.zeros((n, l))]), + np.zeros((n, m+l)), + np.hstack([np.zeros((l, m)), np.eye(l)])]) + + Gtil = np.hstack([G, -G, H]) + + whitened_lss = LinearStateSpace(Atil, Ctil, Gtil) + self.whitened_lss = whitened_lss + + return whitened_lss + + def prior_to_filtered(self, y): r""" Updates the moments (x_hat, Sigma) of the time t prior to the From cd5ea9f229f5e9c37f3da8b6f94afc5777b34f42 Mon Sep 17 00:00:00 2001 From: Thomas Sargent Date: Thu, 15 Oct 2015 18:08:07 -0400 Subject: [PATCH 13/51] Add documentation. --- quantecon/kalman.py | 32 +++++++++++++++++++++----------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/quantecon/kalman.py b/quantecon/kalman.py index 6f9dd546e..dd3e64396 100644 --- a/quantecon/kalman.py +++ b/quantecon/kalman.py @@ -89,26 +89,36 @@ def whitener_lss(self): This function takes the linear state space system that is an input to the Kalman class and it converts that system to the time-invariant whitener represenation - given by (NOTE: FIX NOTATION LATER) + given by - x_{t+1}^* = A x + C v - a = G x + \tilde{x}_{t+1}^* = \tilde{A} \tilde{x} + \tilde{C} v + a = \tilde{G} \tilde{x} where - x_t = [x+{t}, \hat{x}_{t}, v_{t}] + \tilde{x}_t = [x+{t}, \hat{x}_{t}, v_{t}] and - A = [A 0 0 - KG A-KG KH - 0 0 0] + \tilde{A} = [A 0 0 + KG A-KG KH + 0 0 0] - C = [C 0 - 0 0 - 0 I] + \tilde{C} = [C 0 + 0 0 + 0 I] - G = [G -G H] + \tilde{G} = [G -G H] + + with A, C, G, H coming from the linear state space system + that defines the Kalman instance + + + Returns + ------- + whitened_lss : LinearStateSpace + This is the linear state space system that represents + the whitened system """ # Check for steady state Sigma and K if self.K_infinity is None: From 5d45953c4b31c45f42aa5bf9d299c3fe7e30fb52 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Mon, 2 Nov 2015 21:58:43 +0900 Subject: [PATCH 14/51] ddp: Fix remaining mistakes from #198 --- quantecon/markov/ddp.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index e0f07de72..504f8f81f 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -30,9 +30,9 @@ matrix for :math:`\sigma`, which are defined by :math:`r_{\sigma}(s) = r(s, \sigma(s))` and :math:`Q_{\sigma}(s, s') = q(s'|s, \sigma(s))`, respectively. The policy value function :math:`v_{\sigma}` for -:math`\sigma` is defined by +:math:`\sigma` is defined by -..math:: +.. math:: v_{\sigma}(s) = \sum_{t=0}^{\infty} \beta^t (Q_{\sigma}^t r_{\sigma})(s) @@ -45,7 +45,7 @@ The *Bellman equation* is written as -..math:: +.. math:: v(s) = \max_{a \in A(s)} r(s, a) + \beta \sum_{s' \in S} q(s'|s, a) v(s') \quad (s \in S). @@ -53,7 +53,7 @@ The *Bellman operator* :math:`T` is defined by the right hand side of the Bellman equation: -..math:: +.. math:: (T v)(s) = \max_{a \in A(s)} r(s, a) + \beta \sum_{s' \in S} q(s'|s, a) v(s') \quad (s \in S). @@ -61,7 +61,7 @@ For a policy function :math:`\sigma`, the operator :math:`T_{\sigma}` is defined by -..math:: +.. math:: (T_{\sigma} v)(s) = r(s, \sigma(s)) + \beta \sum_{s' \in S} q(s'|s, \sigma(s)) v(s') @@ -117,7 +117,7 @@ class DiscreteDP(object): - """ + r""" Class for dealing with a discrete dynamic program. There are two ways to represent the data for instantiating a From 3dd01260e50653a43b6106b8e9f16089e276282f Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Mon, 2 Nov 2015 21:59:43 +0900 Subject: [PATCH 15/51] ddp: Avoid "[0, 1)" --- quantecon/markov/ddp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index 504f8f81f..d6745ddba 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -23,7 +23,7 @@ \Delta(S)`, where :math:`q(s'|s, a)` is the probability that the state in the next period is :math:`s'` when the current state is :math:`s` and the action chosen is :math:`a`; and -* discount factor :math:`\beta \in [0, 1)`. +* discount factor :math:`0 \leq \beta < 1`. For a policy function :math:`\sigma`, let :math:`r_{\sigma}` and :math:`Q_{\sigma}` be the reward vector and the transition probability @@ -165,7 +165,7 @@ class DiscreteDP(object): Transition probability array. beta : scalar(float) - Discount factor. Must be in [0, 1). + Discount factor. Must be 0 <= beta < 1. s_indices : array_like(int, ndim=1), optional(default=None) Array containing the indices of the states. From 1c0bea9d5af5e28c304d0ec9285af3f42205cdea Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Fri, 6 Nov 2015 00:06:39 +0900 Subject: [PATCH 16/51] ddp: Fix typo --- quantecon/markov/ddp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index d6745ddba..20aefaa6e 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -297,7 +297,7 @@ def __init__(self, R, Q, beta, s_indices=None, a_indices=None): raise ValueError('R must be 1- or 2-dimensional') msg_dimension = 'dimensions of R and Q must be either 1 and 2, ' \ - 'of 2 and 3' + 'or 2 and 3' msg_shape = 'shapes of R and Q must be either (n, m) and (n, m, n), ' \ 'or (L,) and (L, n)' From eb2cae60086f9b52955c8f7ae97e91e0cc8df32f Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Fri, 6 Nov 2015 11:14:54 +0900 Subject: [PATCH 17/51] TRAVIS: set destination path for miniconda Resolve "conda: command not found" --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 4fa4e5430..d31fcc095 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,7 +14,7 @@ branches: before_install: - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh - chmod +x miniconda.sh - - ./miniconda.sh -b + - ./miniconda.sh -b -p /home/travis/miniconda - export PATH=/home/travis/miniconda/bin:$PATH - conda update --yes conda - sudo rm -rf /dev/shm From e1601163503414c25f83e4d733950a7c7dd27e00 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Mon, 2 Nov 2015 21:58:43 +0900 Subject: [PATCH 18/51] ddp: Fix remaining mistakes from #198 --- quantecon/markov/ddp.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index e0f07de72..504f8f81f 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -30,9 +30,9 @@ matrix for :math:`\sigma`, which are defined by :math:`r_{\sigma}(s) = r(s, \sigma(s))` and :math:`Q_{\sigma}(s, s') = q(s'|s, \sigma(s))`, respectively. The policy value function :math:`v_{\sigma}` for -:math`\sigma` is defined by +:math:`\sigma` is defined by -..math:: +.. math:: v_{\sigma}(s) = \sum_{t=0}^{\infty} \beta^t (Q_{\sigma}^t r_{\sigma})(s) @@ -45,7 +45,7 @@ The *Bellman equation* is written as -..math:: +.. math:: v(s) = \max_{a \in A(s)} r(s, a) + \beta \sum_{s' \in S} q(s'|s, a) v(s') \quad (s \in S). @@ -53,7 +53,7 @@ The *Bellman operator* :math:`T` is defined by the right hand side of the Bellman equation: -..math:: +.. math:: (T v)(s) = \max_{a \in A(s)} r(s, a) + \beta \sum_{s' \in S} q(s'|s, a) v(s') \quad (s \in S). @@ -61,7 +61,7 @@ For a policy function :math:`\sigma`, the operator :math:`T_{\sigma}` is defined by -..math:: +.. math:: (T_{\sigma} v)(s) = r(s, \sigma(s)) + \beta \sum_{s' \in S} q(s'|s, \sigma(s)) v(s') @@ -117,7 +117,7 @@ class DiscreteDP(object): - """ + r""" Class for dealing with a discrete dynamic program. There are two ways to represent the data for instantiating a From dd0174f8a487f92df634e76027d9e14567c0d4ae Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Mon, 2 Nov 2015 21:59:43 +0900 Subject: [PATCH 19/51] ddp: Avoid "[0, 1)" --- quantecon/markov/ddp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index 504f8f81f..d6745ddba 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -23,7 +23,7 @@ \Delta(S)`, where :math:`q(s'|s, a)` is the probability that the state in the next period is :math:`s'` when the current state is :math:`s` and the action chosen is :math:`a`; and -* discount factor :math:`\beta \in [0, 1)`. +* discount factor :math:`0 \leq \beta < 1`. For a policy function :math:`\sigma`, let :math:`r_{\sigma}` and :math:`Q_{\sigma}` be the reward vector and the transition probability @@ -165,7 +165,7 @@ class DiscreteDP(object): Transition probability array. beta : scalar(float) - Discount factor. Must be in [0, 1). + Discount factor. Must be 0 <= beta < 1. s_indices : array_like(int, ndim=1), optional(default=None) Array containing the indices of the states. From cf6c34019f1e4e287ea9ae409e26ef0dfbf7f209 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Fri, 6 Nov 2015 00:06:39 +0900 Subject: [PATCH 20/51] ddp: Fix typo --- quantecon/markov/ddp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantecon/markov/ddp.py b/quantecon/markov/ddp.py index d6745ddba..20aefaa6e 100644 --- a/quantecon/markov/ddp.py +++ b/quantecon/markov/ddp.py @@ -297,7 +297,7 @@ def __init__(self, R, Q, beta, s_indices=None, a_indices=None): raise ValueError('R must be 1- or 2-dimensional') msg_dimension = 'dimensions of R and Q must be either 1 and 2, ' \ - 'of 2 and 3' + 'or 2 and 3' msg_shape = 'shapes of R and Q must be either (n, m) and (n, m, n), ' \ 'or (L,) and (L, n)' From 0198d452032fe7934b5b29f7aaef64ea9e930f35 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Wed, 16 Sep 2015 22:22:32 +0900 Subject: [PATCH 21/51] Fix optgrowth solution notebook --- solutions/optgrowth_solutions.ipynb | 927 +++++++--------------------- 1 file changed, 211 insertions(+), 716 deletions(-) diff --git a/solutions/optgrowth_solutions.ipynb b/solutions/optgrowth_solutions.ipynb index a5258d251..bc6b9cbfd 100644 --- a/solutions/optgrowth_solutions.ipynb +++ b/solutions/optgrowth_solutions.ipynb @@ -1,731 +1,226 @@ { - "metadata": { - "name": "", - "signature": "sha256:89ab78e36bdc736c251923085e90e94893f9cca4f6d752519c6a21410f45c597" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Infinite Horizon Dynamic Programming" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/dp_intro.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import compute_fixed_point\n", - "from quantecon.models import GrowthModel" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# quant-econ Solutions: Infinite Horizon Dynamic Programming" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solutions for http://quant-econ.net/py/dp_intro.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from quantecon import compute_fixed_point\n", + "from quantecon.models import GrowthModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "1 4.297e+00 6.331e-02 \n", + "2 4.080e+00 1.270e-01 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "1 4.297e+00 6.849e-02 \n", + "2 4.080e+00 1.322e-01 \n", + "3 3.875e+00 1.967e-01 \n", + "4 3.680e+00 2.656e-01 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "1 4.297e+00 6.273e-02 \n", + "2 4.080e+00 1.269e-01 \n", + "3 3.875e+00 1.882e-01 \n", + "4 3.680e+00 2.572e-01 \n", + "5 3.496e+00 3.295e-01 \n", + "6 3.327e+00 4.016e-01 \n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "alpha, beta = 0.65, 0.95\n", - "gm = GrowthModel() \n", - "true_sigma = (1 - alpha * beta) * gm.grid**alpha\n", - "w = 5 * gm.u(gm.grid) - 25 # Initial condition\n", - "\n", - "fig, ax = plt.subplots(3, 1, figsize=(8, 10))\n", - "\n", - "for i, n in enumerate((2, 4, 6)):\n", - " ax[i].set_ylim(0, 1)\n", - " ax[i].set_xlim(0, 2)\n", - " ax[i].set_yticks((0, 1))\n", - " ax[i].set_xticks((0, 2))\n", - "\n", - " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=n)\n", - " sigma = gm.compute_greedy(v_star)\n", - "\n", - " ax[i].plot(gm.grid, sigma, 'b-', lw=2, alpha=0.8, label='approximate optimal policy')\n", - " ax[i].plot(gm.grid, true_sigma, 'k-', lw=2, alpha=0.8, label='true optimal policy')\n", - " ax[i].legend(loc='upper left')\n", - " ax[i].set_title('{} value function iterations'.format(n))" - ], - "language": "python", + "data": { + "image/png": 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YDCgoKIDRaHT4Pg8PD8TExCAuLg5xcXHQ6XTaY3BwMIhaPAXa6SRghRBCtIva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\ng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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 4.296600\n", - "Computed iterate 2 with error 4.080228" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 4.296600" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 4.080228" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 3.875034" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 3.680327" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 4.296600" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 4.080228" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 3.875034" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 3.680327" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 3.495502" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 3.327466" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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o9+7dnYKn/TU+Ph4/P79WvoKuQwKsEEK0M3V1dZw4ccKpONf+evr0abf7RUdH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TkZPj4+8j4Wi1gyrb6+6/G+fcBXX4lZjlzRasXY0YQE0S4aHS0mZQgLE1W9nJSBaPCwBEvUB21t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\nSUVORK5CYII=\n", - "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, + "output_type": "display_data" + } + ], + "source": [ + "alpha, beta = 0.65, 0.95\n", + "gm = GrowthModel() \n", + "true_sigma = (1 - alpha * beta) * gm.grid**alpha\n", + "\n", + "fig, ax = plt.subplots(3, 1, figsize=(8, 10))\n", + "\n", + "for i, n in enumerate((2, 4, 6)):\n", + " ax[i].set_ylim(0, 1)\n", + " ax[i].set_xlim(0, 2)\n", + " ax[i].set_yticks((0, 1))\n", + " ax[i].set_xticks((0, 2))\n", + "\n", + " w = 5 * gm.u(gm.grid) - 25 # Initial condition\n", + " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=n, print_skip=1)\n", + " sigma = gm.compute_greedy(v_star)\n", + "\n", + " ax[i].plot(gm.grid, sigma, 'b-', lw=2, alpha=0.8, label='approximate optimal policy')\n", + " ax[i].plot(gm.grid, true_sigma, 'k-', lw=2, alpha=0.8, label='true optimal policy')\n", + " ax[i].legend(loc='upper left')\n", + " ax[i].set_title('{} value function iterations'.format(n))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "5 1.303e+00 3.270e-01 \n", + "10 5.142e-01 6.708e-01 \n", + "15 2.883e-01 1.032e+00 \n", + "20 1.690e-01 1.404e+00 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "5 2.792e+00 3.760e-01 \n", + "10 2.046e+00 7.315e-01 \n", + "15 1.497e+00 1.080e+00 \n", + "20 1.099e+00 1.426e+00 \n", + "Iteration Distance Elapsed (seconds)\n", + "---------------------------------------------\n", + "5 5.839e+00 3.705e-01 \n", + "10 5.247e+00 7.293e-01 \n", + "15 4.695e+00 1.084e+00 \n", + "20 4.189e+00 1.445e+00 \n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy import interp\n", - "\n", - "gm = GrowthModel() \n", - "w = 5 * gm.u(gm.grid) - 25 # To be used as an initial condition\n", - "discount_factors = (0.9, 0.94, 0.98)\n", - "series_length = 25\n", - "\n", - "fig, ax = plt.subplots(figsize=(8,5))\n", - "ax.set_xlabel(\"time\")\n", - "ax.set_ylabel(\"capital\")\n", - "\n", - "for beta in discount_factors:\n", - "\n", - " # Compute the optimal policy given the discount factor\n", - " gm.beta = beta\n", - " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=20)\n", - " sigma = gm.compute_greedy(v_star)\n", - "\n", - " # Compute the corresponding time series for capital\n", - " k = np.empty(series_length)\n", - " k[0] = 0.1\n", - " sigma_function = lambda x: interp(x, gm.grid, sigma)\n", - " for t in range(1, series_length):\n", - " k[t] = gm.f(k[t-1]) - sigma_function(k[t-1])\n", - " ax.plot(k, 'o-', lw=2, alpha=0.75, label=r'$\\beta = {}$'.format(beta))\n", - "\n", - "ax.legend(loc='lower right')\n", - "plt.show()" - ], - "language": "python", + "data": { + "image/png": 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O82Pej3gKq4iTV8S6I+vqNU5jyqXZxClXmj2++rsUqkIvtm3b4JFH7FXofvpToh9+mOgg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\nvsG3dSuOfDf7Wjlod24flo2fzG3Ff2o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SKxg3NoGZM2McoIhIB3ClRN8erFy5kvHjx8c6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\n+JgSvYiIiI8p0YuIiPiYEr2IiIiP/R9Bxk/Ac39XjAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 5.999396\n", - "Computed iterate 2 with error 3.791226" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 2.531863" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 1.767013" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 1.303066" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 1.011176" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 0.816201" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 0.689199" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 0.590297" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 0.514201" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 0.454587" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 0.402620" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 0.359115" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 0.321714" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 0.288297" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 0.258360" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 17 with error 0.232150" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 0.208786" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 19 with error 0.187838" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 0.169022" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 4.401010" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 3.153136" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 2.962891" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 2.784289" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 2.616550" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 2.458976" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 2.318565" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 2.171183" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 2.042677" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 1.921553" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 1.806962" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 1.698904" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 1.597220" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 1.494743" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 1.405415" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 1.321367" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 17 with error 1.242295" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 1.167909" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 19 with error 1.097938" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 1.032124" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 1 with error 7.118648" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 2 with error 6.974372" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 6.833300" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 6.702746" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 6.564332" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 6.427286" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 6.299795" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 6.173511" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 6.048887" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 5.924557" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 5.805184" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 5.687164" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 5.570272" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 5.453828" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 5.338835" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 5.225067" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 17 with error 5.112733" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 5.001816" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 19 with error 4.892653" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 4.784860" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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thaVLkzh2LKFGaHbokExc3J24XOb7a3ut2N+0KYnCwoQa9W7TJpnBg+/0+Wez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\nJCamUVSUUHksMTGJWbPwKazre/3JNm3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IzMzE4/FQXV1NUVERY8eOBeDNN99k+fLlLFy4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\nIiIiIiIiIiIW8X/IZvgwaFDunAAAAABJRU5ErkJggg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 4 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "from scipy import interp\n", + "\n", + "gm = GrowthModel() \n", + "w = 5 * gm.u(gm.grid) - 25 # To be used as an initial condition\n", + "discount_factors = (0.9, 0.94, 0.98)\n", + "series_length = 25\n", + "\n", + "fig, ax = plt.subplots(figsize=(8,5))\n", + "ax.set_xlabel(\"time\")\n", + "ax.set_ylabel(\"capital\")\n", + "ax.set_ylim(0.10, 0.30)\n", + "\n", + "for beta in discount_factors:\n", + "\n", + " # Compute the optimal policy given the discount factor\n", + " gm.beta = beta\n", + " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=20)\n", + " sigma = gm.compute_greedy(v_star)\n", + "\n", + " # Compute the corresponding time series for capital\n", + " k = np.empty(series_length)\n", + " k[0] = 0.1\n", + " sigma_function = lambda x: interp(x, gm.grid, sigma)\n", + " for t in range(1, series_length):\n", + " k[t] = gm.f(k[t-1]) - sigma_function(k[t-1])\n", + " ax.plot(k, 'o-', lw=2, alpha=0.75, label=r'$\\beta = {}$'.format(beta))\n", + "\n", + "ax.legend(loc='lower right')\n", + "plt.show()" + ] + }, + { + "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": "2.7.10" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 147d0bf6fdccadd3f68fa17f8f1846124fb80999 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Wed, 11 Nov 2015 22:18:57 +0900 Subject: [PATCH 22/51] BUG: Fix bug in probvec --- quantecon/markov/tests/test_random.py | 10 ++++++++++ quantecon/random/utilities.py | 4 ++++ 2 files changed, 14 insertions(+) diff --git a/quantecon/markov/tests/test_random.py b/quantecon/markov/tests/test_random.py index dee4c7013..62cddb630 100644 --- a/quantecon/markov/tests/test_random.py +++ b/quantecon/markov/tests/test_random.py @@ -81,6 +81,16 @@ def test_random_stochastic_matrix_dense_vs_sparse(): assert_array_equal(P_dense, P_sparse.toarray()) +def test_random_stochastic_matrix_k_1(): + n, k = 3, 1 + P_dense = random_stochastic_matrix(n, k, sparse=False) + P_sparse = random_stochastic_matrix(n, k, sparse=True) + assert_array_equal(P_dense[P_dense != 0], np.ones(n)) + assert_array_equal(P_sparse.data, np.ones(n)) + for P in [P_dense, P_sparse]: + assert_array_almost_equal_nulp(P.sum(axis=1), np.ones(n)) + + class TestRandomDiscreteDP: def setUp(self): self.num_states, self.num_actions = 5, 4 diff --git a/quantecon/random/utilities.py b/quantecon/random/utilities.py index 67dc4e8a1..98944f01f 100644 --- a/quantecon/random/utilities.py +++ b/quantecon/random/utilities.py @@ -38,6 +38,10 @@ def probvec(m, k, random_state=None): [ 0.43772774, 0.34763084, 0.21464142]]) """ + if k == 1: + return np.ones((m, k)) + + # if k >= 2 random_state = check_random_state(random_state) r = random_state.random_sample(size=(m, k-1)) From 2f03c2806f766400d99df0b39025e19114065f87 Mon Sep 17 00:00:00 2001 From: John Stachurski Date: Mon, 23 Nov 2015 12:25:13 -0500 Subject: [PATCH 23/51] minor edits --- examples/aiyagari_compute_equilibrium.py | 10 +- examples/aiyagari_compute_policy.py | 5 + examples/aiyagari_household.py | 14 +- examples/first_notebook.ipynb | 230 +++++++++++++++-------- 4 files changed, 174 insertions(+), 85 deletions(-) diff --git a/examples/aiyagari_compute_equilibrium.py b/examples/aiyagari_compute_equilibrium.py index 49c395724..28b59319c 100644 --- a/examples/aiyagari_compute_equilibrium.py +++ b/examples/aiyagari_compute_equilibrium.py @@ -1,3 +1,7 @@ +""" +Created on Wed Sep 23 17:00:17 EDT 2015 +@authors: John Stachurski, Thomas Sargent +""" import numpy as np import quantecon as qe @@ -24,11 +28,11 @@ def prices_to_capital_stock(am, r): """ Map prices to the induced level of capital stock. - Paramters: + Parameters: ---------- - am : AiyagariModel - An instance of an Aiyagari economy + am : Household + An instance of an aiyagari_household.Household r : float The interest rate """ diff --git a/examples/aiyagari_compute_policy.py b/examples/aiyagari_compute_policy.py index bc4c2f79b..7af0f090a 100644 --- a/examples/aiyagari_compute_policy.py +++ b/examples/aiyagari_compute_policy.py @@ -1,3 +1,8 @@ +""" +Created on Wed Sep 23 17:00:17 EDT 2015 +@authors: John Stachurski, Thomas Sargent +""" + import numpy as np import quantecon as qe diff --git a/examples/aiyagari_household.py b/examples/aiyagari_household.py index 108f8d62f..9c7471ff4 100644 --- a/examples/aiyagari_household.py +++ b/examples/aiyagari_household.py @@ -1,8 +1,11 @@ +""" +Created on Wed Sep 23 17:00:17 EDT 2015 +@authors: John Stachurski, Thomas Sargent +""" import numpy as np from numba import jit - class Household(object): """ This class takes the parameters that define a household asset accumulation @@ -34,6 +37,7 @@ def __init__(self, a_max=18, a_size=200): + # Store values, set up grids over a and z self.r, self.w, self.beta = r, w, beta self.a_min, self.a_max, self.a_size = a_min, a_max, a_size @@ -44,13 +48,19 @@ def __init__(self, self.a_vals = np.linspace(a_min, a_max, a_size) self.n = a_size * self.z_size + # Build the array Q self.Q = np.zeros((self.n, a_size, self.n)) self.build_Q() + # Build the array R self.R = np.empty((self.n, a_size)) self.build_R() def set_prices(self, r, w): + """ + Use this method to reset prices. Calling the method will trigger a + re-build of R. + """ self.r, self.w = r, w self.build_R() @@ -62,6 +72,8 @@ def build_R(self): populate_R(self.R, self.a_size, self.z_size, self.a_vals, self.z_vals, self.r, self.w) +# Do the hard work using JIT-ed functions + @jit(nopython=True) def populate_R(R, a_size, z_size, a_vals, z_vals, r, w): n = a_size * z_size diff --git a/examples/first_notebook.ipynb b/examples/first_notebook.ipynb index c1e8e4eea..7362e38f1 100644 --- a/examples/first_notebook.ipynb +++ b/examples/first_notebook.ipynb @@ -1,93 +1,161 @@ { - "metadata": { - "name": "", - "signature": "sha256:1c9b10f6aa5899245b1bdbc936804f20f2b6e72d639e8fb2d0495120f94e3c49" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'foobar'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "foobar\n" - ] - } - ], - "prompt_number": 1 - }, + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "N = 50\n", - "x = np.random.rand(N)\n", - "y = np.random.rand(N)\n", - "area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses\n", - "\n", - "plt.scatter(x, y, s=area, alpha=0.5)\n", - "plt.show()" - ], - "language": "python", + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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SE5PAxIkLiIyc0u34pqYi4uO/4tFH7x6kyEc3MbgrDLn9+w9w/HgwaWkDb40l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\ncXH7+eUvH/D5ZJcLBgYGaG1t/bIMQ1BQEPHx8ej1I1t1rKG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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, + "output_type": "execute_result" + } + ], + "source": [ + "1 + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "np.rank?" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "foobar\n" + ] + } + ], + "source": [ + "print('foobar')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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gQAzgiT6/mIXcN8aEEMIUiURffvfdd1xrCzN1hFKK3Zs2IE5TjzBv926fB3m4\n4LlR1kv7nblTYvGSh8dmYV6IG3b/8C1aWlowZ84cxMXZlpKfkjgaBem9+2IspaPPRC6Xg8+2/CHK\nLNMhJj7ZdMM2tFotjuz/BdNGuRgVYRIK2LiVY305X5lcBblChdHR9fh59WcW6wHPmTPnnuj6EkLw\n1VdfCfl8/peEkAEl3Xc/GZNnIiIiPOfMmWOX/m5cu4aGK6cwOdz4liQhxCblsFixF6pbZKYbdmHn\n9WwM1tRjx/q1dvmmi4qKQlO5DrIWhc19GcMa+YGmllbUKwUIDg42+5xLF8/Dg1uKsKDuhh/QO1Qf\nmRhuODaVXNiV6loZYiO8MDzJDynhNVj3w387JTOaS1VVFbRa25Zeppg8eTKio6NFhJDf9+mFLOS+\nMCaEEC6Px/vPV199JbSH9W9qasKR9WswL9ynWxbwqtNXIVP2vFVoLu5CHiJMZBIb4w8TU/BwTAi0\nty/hrB0q0rFYLCTFjERuhnHlOGvo6DNRyKQWz0yyCmsRlTjG7NKfcrkcacc2YdoY85YeWq0O//j6\ntEVjCgl0g7enPuhu9FB/xIjLsGHtN71uGxujrq7OKmFyS1m1ahWPx+P9YyAJKd0XxoTJZP7f4MGD\n+WPG2F6YiVKKvVs2YgSnFT4uwm6fpw6J7VH4yFp+uXCzx61lQO+faZ+FsJ2YenX7KD+c37auR/lI\nSxiSkIKidOsyZ00hlzaCx7EsoCurQovYhKFmtz91/DAGB8rg5WHec8NkMvDBGxNMtpPJVfj5V+N+\nscmjB8Gbk4ltm36yKCEzNjYW48b1vZTrkCFDMG3aNDabzV7W8X1CyBpCSBUhJKPDe+6EkCOEkFxC\nyOG+ilUZ8MaEEOLCZrM//OGHH+wSMn8rIwNN189hbJjxQCljUoy2MiM+oluOT0dO5RSh64rGhc/F\nLB8ufl21wqaauwAQHBwMRqsQtVXW1QXqSiefiQVJfgAglatQJeN2ylrujXatkokjxKYbG4FSitMX\ni41+xmYxMXNyhNHPCCGYNTkQmsY0HD6wx+pr9yWffvopH8C7hJCOU+AfAXTVQXkXwBFKaSSAY23H\ndmfAGxMej/enuXPnOsXHW5eh25HW1lYc3vATZod4dNsGPpVTZHP/PeEm4PWaFLhoTJJR/0yMnxfC\npBXYu22LTTcmIQTD4sci72bPRaGsRSFrtsiYZBXWICJulNlZyx21SqyBEAKNRmf078diMeHh1vMW\nMJPJwOP/yX2RAAAgAElEQVSPBOJO+jZcv2bZ1m9TUxO++uori8drCREREXjmmWeIQCD4sP09Smka\ngK7T0DkA1rb9vBbAo30xngFtTAghPlqtduknn3xil+nCicMHEamu7xYuTymF1kKHnbX8c/8ZaLQ6\nbLp0Cxqt6enzw5EBqD53FOnXLddO6Uhy4lAU32yyi4ZKpzgTqYXGpFyD2MQUs9oWFhaiqri7Voml\nTB4TYthp0ekoPv5fmtnn8rgsPDndF0f3ruy1Ql9XXFxc8Nprr1k8Vkv5+9//zqWU/p4Q0lvAjg+l\ntKrt5yoA1qlem2BAGxMej/fOs88+Syzx+vdEdXU1bh3eg4eMLG8IIZgc03tour1YNm0UnJgMDA0S\nG5WA7Io+3N4Hh3/+HnV1dVZf18vLC56iQJQV2lchXy5rNjsvR65Qo7yZjfDwcJNt27VKpo40rlVi\nDWq1Fqs3XcMfX7asYqCXhwCzxrCx9Zf/QaEwf1fMHiEMphCLxXjxxRedBALB++a0p/opWp98cw5Y\nY0II4QJ46Z133rE5bJ5SikPbt2K8KwP8Ls7CVvW9VYtntflOInw8oDVzluDtLMAkZ4pta3oOtzeH\nlIQxKLhZZbqhCdp9Ju1JfubOTLKLahEWM9ysDFxztUrMRavVgcViYu7UKLBYlj/kMRFeCHQrxa9b\nfrZoyUkpxe7duy2+niW89dZbLLVa/QwhpPuOgp4qQogvoK8pBcD6gJxeGLDGBMD8pKQkREZG2txR\nfn4+GjMuYVhQZyeeWqPF18cu2ty/uWy6dAuSRr3+qUarwz/3nzH73GGBvnApy8Kxg9ZnoMfHJUCS\nI7dbeL1arQZ0arMfzqxyFWKTRphsp1KpcOLwBjw8xtNugWD//v4clEoNfLx6et56p7CkAfnlDHh5\n+1lkTAghPcor2Ivg4GBMnDhRSwjpKTR8N4BFbT8vArCzL8YxYI2Js7PzX5YsWSKytR9KKY5u24Qp\nvsJuTleWExN/fMT27WZzeSQuHGJX/a/kxGRYJLJECMGcqABk7t+GOx3EoC3BkvD647uuoLnxbtDW\nui8OoKlBf5x+Ix1ffPEFKioqDJX8vt50sZNodFdalRqUNDh10yUxxtm0Uwhyr7VI9MgUf/rDWHA6\nLMdyC+qw+0iOyfO0Wh2OnS3F9tNMzHvmr3h4+myLUgAA/TZuX7Ns2TKhSCT6EyFkI4BzAKIIIaWE\nkN8B+CeAqYSQXACT247tzoA0JoSQZBaLFfT444/b3NftW7fALMtFtNiyesN9gSvfeMq8SqPtNQ6l\nHT6HhXnBbti1eoXVdWF6Cq/ftuo4JKV3fTLRSUHgC++O99k3p8PF7W6cx5tvvgkOh2PIGH45dRgE\nvLs7Lv9YfRoq9d1I0NziOgRHDTWpnarXKtmGh0ZZp1XSEZlchdZW47OwyFAPTBwZ3Ov5DU0K/Li9\nEBLlMLzyxkcIM8PX0+t4rIioNZcpU6aAxWKJAXxNKfWjlLIppQGU0h8ppfWU0imU0khK6TRKaaPJ\nDq1gQBoTkUi09PXXX+fYKnqs0+lwcsdWTPZ37TRdppRi6xXrBY0soapJis2XbvXaRq5SY+d183RF\ngzxcMZTRYnW4fXt4/aGtF5F9o8jw/vyXJkMccDdcwS/IC049xMa0+0z0ofT699gsZqft7fd+Px7s\ntuWPolWNT36+hJgE00uc40f2YVikGq4u1muVtLP7SE6vsyVnUc+G7VZONVZvr8PgUYvx9KJXYGst\nJkopvv32W6Offf7554iLi0N8fDyeeuqpTgXOzIXBYODNN9/kiESit20aqA0MOGNCCHFVKpVPLF68\n2GZXeObt2+DVFCPUq7NcgUarQ4IREaS+wJnHwazE3v0+rnwunhttflLhhLBB0Ny+hHOnzQ8Zp5RC\nIpEYwuvdvZ0RnRRs9vnG0Cf5mW7HZDKQkJCEqOhoAEB2dna3chmAXu8175Zeq8QeLJwbb1bU7I9b\nrkOp1M9gVCotdh0pwokMDzz90t8warR1JTi6QgjB2293f87Ly8vx9ddf4+rVq8jIyIBWq8WmTZus\nusarr77KUKvVMwgh3raO1xoGojFZNGbMGF27Wrm1UEpxes92jBc7d7sZWE5MRPnem2UPj82yKDy/\noKbBZLYxg0GQGuWHc1t/Njv2ITMz06AKNiQhBeVZUrPH1JX2OBO9/IDpHak7JXUICE82KORHRUV1\nc6zrtUq2YNIwdiffhqXI5CrkFli2hT5zciQYDAJJVQu+31oMnes0LP6/9+HnZx+jZgqNRgO5XG74\ntzcZy95wd3dHamqqzsnJqV8SAAeUMSGEEKFQ+Mfly5fbHKR2584dMCoKEN5FXsCcQDF7cOhWHqSt\nlicMuvK4uFFaabKdC5+LmT5cbFu1osdp8c6dO/U7LgAGDx5syBkJDg4GUQhsDq+Xy2TgsUz/PTNL\n5IhJHGk4JoR0elB/+uknHD1yBIr6awatEmtJz6yCq4Vyjl4efFzNqMS6AwpMmPk2Hkt9qs/q4jQ0\nNHRKBPT398eyZcsQGBgIPz8/uLq62lS7eMmSJTwWi7WMENL3QS5dGFDGBMAkDw8PF3skSp0/fACj\nPfndZiX/PHDmnojY+LoIIbRCZ9VdyMOEqGCz2sb6eSG0pRz7ft1q9POwsDCjMR3t4fV30q1LImz3\nmShkTd3idrqiVmuRX8NEdIciYF159tlnkZl+Eg+PdrVJ9gEARg8LMGT/moNMrsLGvYXIqIjAhIcX\nwc3duMSBvXB1de0UzNbQ0IDdu3ejqKgIFRUVkEqlnaoHWsqIESMQEhLCAnDPC3cNKGPi7Oy8ZNGi\nRQJb16iVlZWozbyOwf7dA57em2mfNbApEgNs3404kV1ocsnzSGQAJGcOIb1NKb3jt15v+UzJiUNR\nktFsU3i9vMV0kl9+WQN8gwf36sC8dPE8PHllCAtyR5mkGas3WqbUL5OrcCQt33TDLhSUNOC7rRXw\njngSL7y8DAkJCaisND0rtAVCCEaMuOuIPnr0KEJCQuDh4QEnJyfMmzfPZgmDt956S+Tq6vqGrWO1\nlAFjTAghTkqlcuqLL75o85N++cxppLgwu8WVtF3H1u57pbDGfqn+iYN80WDCmLCcmJgf7oNDa79H\nVlaW2ZqqtoTXt/tM5DLTxiSrVIbYpJ5jeeRyOc4c32zQKhkkdsaLT5qvwAYAjc2tSIwx33hrtToc\nPVOCHWlOePTpv2LKtBlgMpng8XgYNmyYRde2lvbZcVBQEC5cuACFQqGPiTp6FLGxsTb1PXfuXMhk\nsnFtUeT3jAFjTACMCQ4O1gYEGFeIN5fW1lbcTjuGIV12a8rqm1HRaF1shrlQSnE613i6uzW4C3kI\n9uxdeoJSCjcBDxNFWlw9cQRisfmp+ikJY5B/0/pvYrm0GTxuz85SrVaH3CrS6xLn1PHDiA2Qdtp1\naTf4lFJs2Jlhclnq7+ts9tKmvlGBNb8Woko9HK+8/jejUghardagVN9XbN++HVlZWRg+fDjmz5+P\nIUOGGJTuFy9ebFPfnp6eCAoK0kAfoHbPGDDGhMfjpT755JM2q0Zl3LyJUIaym7+ioLYBAnbfVmUj\nhGDRGPNLN1jCT2dvoKW1u6N13807yKyoQUqQGKLSTBw7YH64vbXq9Xd9Ji29zkwKyhvg6R8FZ2fj\nkax1dXW9apUQQpCSaHxHRa5QW7wcysiuwuoddYgf8wqeeu7lHpdeCoWix+3ZxsZGzJ8/HzExMYiN\njcWFCxc6fV5RUYG9e3aZlG6cO3cuotu2ypcvX46srCxkZGRg7dq1dqkeuHjxYr5QKLynotO2RYXZ\nCUII4fP5Cx999FGbPdDXTxzBZK/uN+/4yCBbu+5XHk2OBtdIEF/HGJa5UQFYuX8rQqOiEG5G2Hp7\neH1+VjmiEy3/+8hlzeBze07EyyppQcyQnpc4h/eb1iqJCLkbSNexBCqHzcS86T3PeDqiUmmx/2QJ\nSpuD8OziP5icvQmFwh5nB2+88QZmzJiBbdu2QaPRQCaTgVKKoqIinDmxDzUlVyCXNiAgIAiJST1/\nsdgakGmKuXPnkuXLl88lhDAopfdkC3OgzEyiuVwuPzHRejV4QF8rWFqS1y1Ira+hlOLfh/pW99OV\nzzVkHNdJ5ciq6O7r4HNYmBfkhl0/fAup1Lw4kpTE0Si8aVlcRvqNdGg0GmjUSkOUa1d0OopsCUHs\nYOPq+nqtkjMWaZX87ctTBqFoJpMBd1fTEQSSqhZ8t6UYxGM6Xn7tfYuWgV1pampCWloaXnjhhbYx\nMCGRSLB6xafY+8tyxHlk4I0nA7Hg4UCcP73X5PKMUorS0lKrx9MbkZGREIlELAB9nxjUxoAwJkwm\nc25qairTVufozWtXES/oriq/7rxl9W8shRCClyeYr2lqK3/ZfgxiF+M5kMGerkimjdix3rxUeWvV\n6xUKBfgcRo8O7WJJI1x9wuHq2t3nY61WyQevj8ffvz5t1u9FKcX5a+VYf7AVk2b/EXMfe9JQ5N1c\nmpubsWvXLsNxYWEhvLy88PzzzyMmJgZjRw3F0S0fYUxoCf7v8WAkx4rBZDIQHuQOTUseiot7958R\nQnDw4EGLxmQJ06dPZ7NYrMf67AJdGBDGRCQSPZ2ammpTlBClFLfOnEScj1u390eF2ubUNYeuRbz6\nkhXPzISroGdH/cTwAKhuncf5NNOKYtao1ycmJbaF0vds/DOLmxGbPNboZ9ZqlTAYDLz3mj4GSa5Q\n99hOJldhw55C3JZE4fev/R1xVkp+Ojs7Y/DgwYbj1tZWXL16FW4CLd79/XBE+lNUVZUiNtyr0xcY\nIQQpUSxcPn/C5DVeeuklq8ZmDi+88AJLIBBYVgbSBvrdmBBCvORyeeTEiRNt6qeiogKMhir4dlGc\nJ4Qg3KfvApEKaxosrtFiDSqNFodu6cPhO255qzXdHX36cHt/nN3yMyoqTMsNWKNery8Lavz3ppQi\nq4IixsgSxxqtEnWH7GMmkwG5Qo01m43LWOYX12Pllgr4Ri3E7xYvha1lZMPDw6FQKHD65Ans374a\n7i48vP2kFxbNCcGL85NxPdP4blhitA/yM8/2aaawKUaNGgWNRuNnQtLRbvS7MQEwc+rUqSpbw5ez\nb2Ught/3cSRdSbtTgntxyUZ5a7d8IpVGi/8eOW+0vSufixne7F7D7duxVL0+/UZ6W16O8V+8tLIZ\nfPcgeHh0rxt07sxpi7VKvlxzsdNMRMBn47Xnh3dqo9XqcCStBLvOsjHv2Q/x0NTpNssmtrS04PCB\nvfjPP15HbeZPWJLqhuhQd8jaxnL0XAEG9zC74nKcED1Ii/QbprV7z5wxXyTLEpycnDBmzBgKYHaf\nXKAL/W5M3NzcFj7xxBPWyV91IPvSWUR3cbyW1DVh/fmbtnbdK8+NTrwnBszbWdAt5oTtxMSfphtf\nSgDAYH9vBDeVYf+OX3vtmxCClIRxFoXX6+UHjG8SZBY3Ija5u/BTc3MzLqZttVir5O2XR4PPM75d\nqtXq2mJHilCjHYlXXv8bQkJs0/Otr6/Hnp1bseKzt6CV/IqUUAW8XBnwchfg6w+m4+ll25E4eyVu\n5lThL6/0nPqRFOmC9MvHTF6PUmpzOZOeWLBgAc/Nze2ZPum8C/2+Ndza2jpywgTTBZN6o6GhAXJJ\nKfyHdN7eHOTmjMeH2RZN2N9kVtQg0N3FZJ6PXKmGWquFSxcBpkeiAvD96QO4GTMYCb3sliUlDEHa\nz3sxYorOpJJYYlIi5DKZ0Up+7Uucp1O7+yks0SqRyVXQ6ShEwt5nrJ+uPIvGVk/MW/gGho8YZZNh\nr6ysRNqJgyjMOo2USGDJfHE3I5YY44vL283zcwT5u6D1dCGqqqp6LYTel0W7Zs2ahVdeeSXpXmwR\n9+vMhBDiB4Bjbgh4T9zJzUU4t/sSh8Eg4PRSr8YWSuqacKnA/nVoulJY2wC+GcF2So0G+zPudHuf\n3RZuf/DHlaivr+/xfC8vL3gKA8wOr1fIWsDndF9GVNS0wEnkDy+vztN/iURikVbJgRN5UPSgkgYA\nSqUGOw4XQuQzFkv//CVGjBxt1JBotVokJydj9mzjM31KKYqLi7H+xxX4ZeW78GecwRsL/DBpRGCP\nsyFzIYQgwk+HWzdv2NSPLXh5ecHZ2VkLIKyvr9Xfy5yhQ4cObbV1mZCfcR3hLp1jDlQaLVRGnJP2\nJNLX8lrCljIzIdKsTFo3AQ8LRxjftfB1EWKCUItta1b1GpmZkjjWrPD69BvpbWVBuxvqrKIGxCaP\n76ZsZ6lWyfyZsT2GyFe0xY4wPWdi8WvvoTftmy+//BKxsbHdDA2lFDk5OViz8t/Y9fNfEeN6A28s\nCMToIYPAYRsf45UM085sQK93m55ViU0Hi3HlDkGrwrQT9vz5830WcxIaGkoA9HnsQr8uc5ycnFLG\njh1rk79Ep9Oh+NZNzI7o7C85mVMELxEfyYHWByn1RqCHi+lGNqDWaA1BapaSU1kLX2dhpyXP8GAx\nCjJu4/jBg5g6c6bR8+LjErDv5CaoVRqwenig2lHImsB37fzNTSlFZgUwf3Zno5aTnQ153VUkT+nd\nlyGTq1BU1ojBkcaFwvSxIxU4e4uDGY/9CYPj7u4WZWVloa6uDmPH3vUhlZWVYf/+/Xjvvffw3//+\nF4B+pnL71i2cOb4TTGUBxsQLEDs52CyDLanpObdLrlAju6AWmUVKlNaxERwxBLHjR2FuZKRBFKo3\nwsLC+kwaY8qUKbz09PSRAKyTcDOTfjUmIpFockpKik0u98rKSoi0rd18CtMG9/msrk/59OBZ/MVK\nuQQvoQCZkhqMCrsbX0MI0Yfb79OH2xsTR+6oXh+V0H3p+cvXhxAa44fEpEQUZV7Boax8hPi7GaJg\nq+pk0HG8OkWZarVaHN63HjPN0CrJvFODQD/jRloqU2Hn0TIoWYl4acmL3YLhoqOju22DL126FJ99\n9hmam/VSC5cuXsC5EzvhypJgWqILwgKDLfr7zp4c1em4RarUG5BiDSoa2AiLGYGkKcPxeESExeJK\n3t59p7Q4btw4snLlyj6vpt6vyxyFQhE/dKhts6/ioiIEce5Nac92Pjt4ts8Flt6bNd5qZ6K7kNfJ\nkLTD57DwWKALdq5e0WP8Q0f1+p/+sw8tTXLDZ4/9boJhxiKXNWNccqChKqFGo8N7K44jJqmzAbx8\n6QI8uKUICzId65OS6G+0rk1+cT2+2yqBX8zT+N3ipUajagkhneQO9+7dC29vb8TExCD9+nUU5mch\n/+K3SB2txPNzQhAe5G7V37eppRUXbpRhza5ifLOzESWacRg+/R28/df/4YmnXkBcXFyfqbRZy9Ch\nQyGTyWIIIX36vPfbzIQQ4icUCtm2Ol9LczIRLey8OyBTqqDSaOEmsEuJ4m78YWLKPY9nsZZDt/Iw\nMnSQYckT4uWGpIZS7PjlZzz90iudfo9du3YhKCjIEF7//LLOyyG+kAtCiD43R9aCqCBPw2zDyYmB\npIR4xMbrk9uam5uh1WqRdmwTFs3oeSdDJlfh1IVizJjcPTFRq9Xh2Lky3Cr1wLxnl5q15VtTUwNn\nZ2ecPHkSW7ZswcYN60B1GihVahw4fgMLp1seXV7fqEBmXg2ySinOpddixuzHMW7uKISEhNg1Ye/g\nwYMYPHgwbJXh6IqXlxc4HA5RKpVhALp76e1Efy5zhiYmJqpsFXApv5ONKT6dA6AyyqrBZ7P6zJhY\nI8doLiezizA6PABsK/0lXUkJ9odMpe7kP5kY7o8fb5zDhTNxGNVhW/Lhhx8Gl8tFfnEucjOykDza\neOaxjlIoW+Xgcu4G0dU2yKFiehiq17W0tGDliv9hZKQU3p49O6plcjWGxnf3a9U1yPHrYQlE4gl4\n5fWnwOfzzfp98/Pzcer4EXgJmrH2n7MwOskH6dmV+PcP5/HzZ+Ybkpp6GTLz6pBZooNM64ro+Bl4\naP4QTJqng5eXF1xc7O8zGzlyZK+BdjqdDrW1tZBIJKiSSDD14YfN/lIbNmyY+vjx40PxIBoTJpM5\nPDk52Sb9EqlUCmVDLdxCO8eXjAzru3KMrWoNuH203QzoK/3Zy5AA+iVPV5gMBlKj/LDilx9w+Phx\nfPjhhwAALldvcIYkpOCXg+eQ3EN975joGNyuz+rkA8ksrEXskDmGm5vL5ULErjFolcgVaqNbrcZ2\nbNIzq3DoogYTH3kVKcNHmvXAVFVV4czJQ8i/fRLDwoGRib6dpA1MdUEpRWWNFFkFDcgs0UFFPBGT\nMBcznkpCQECAxVX8rKHj8k2r1aK6uhoSiQSSoiJIcrNRVVQEkVYNPwbBLakcw4YPh7uZmrWTJk0S\nnjt3rk+dsP1mTJydnSfZw/nK1ypRVNsIH2ehSXFje/Dfw+fxl5l958saG9F3aRSrTl/FwuHxEHLZ\ncBPwMEfMwxFNK1QqVaeM2o7h9Z4+3b+BlSol+OzOD1dmBcUjD93V7+iqVfLN2kt466VRYDIZaG3V\n4KdtN/DKM3clEksrmvDMmztQUNIILWXj/17TB6GZoqSkBGdO7Iek8CJGRjMxa4Fft63dCcODMWF4\ncLdzKaUor2pBVn4DMksBsH0QmzQfj05KgL+//z1byqrValRXV6OiogKSggJI7uSitqQYrlQHMaEQ\nOzEQ5yyCb4A3OG3LKlVBCaqqqsw2JsOGDSN8Pr9PnbD9ZkxUKlWMrZF/QqEQ4TMex4mCO6jKLQJH\n3QpvFqBpacSwIDF8nYVwF/BsVjzvSF8akr5mQUoc+GwWqpqk8HERIn6QDwpuF2H/jl/x6IKFhnaE\nECREDcfRbbswfeEIuLh3dopev3YdIR0mFA3NCrRonQ36s+1aJY8/dXfG+MdX7ookcThMLJzTOQmw\ntkGOEcPi8Pa7izB2whSMHDkS8+bNQ4wRyUdKKfLy8pB2fDdaqm9izGAOnlgQ0EnOYN+JXMyc1L34\nmU5HUSppQmZBI7JKALZoEGITH8ETD8fD19fXLANy8OBBREdHIzg42GTbrqhUKlRWVkJSUQFJQT4k\nd3JRX1YGD0IhJhSnM7Px+pgR8AsWg9XLkscHOlRXVhr9+xhjyJAhaGlp6dNw8H4xJoQQJoPBcLW1\nOryvry9mPDoPgP4Ga2xsREFBAQ7s2weevy+OFxegJacEXiwCXxaFD5cJXxchfJyFfbpUsYaKxhak\n5RZjwXDjYkL2wJnHQUurEocz8/HsKH1o/fSoAHx/6gAyYgYjPiEBLS0tOHo0DTv3XMX5q00IjCzG\n6KmDO/WjUqvBY93dzcosrEV04iNgMBgmtUoopTh1oRgTRwUbjs9dLce5TC6e/8O/ENuW8h8TE4OK\niopOD4tOp9PHiJzYBcjyMDaBj8ETjceIeHsIDMpsWq0OxeVNyCxoQnYZgcAtCLGJc/DMrDirtmRH\njRplVhJha2urfplSUQFJ3h1I8u6gSVIBbyaBmFIEcFgY7iyCd4i/YVdsgtgHzhyOyS9ALx4PucVF\nZo/Zy8sLOp3OiRDCp5TKTZ9hOf31RHnzeDwVi8Wym3o2IQRubm4YOnQoOm43K5VKVFVVoaqqCpXF\nhcgozEN1ZjH4OjV82ICvE4WPiA9fZyHcBNxev5nav9HtzaHb+fBzEeKRONsKY5uDiMvBMyMT8Lc9\np/DBrPFgOzGRGuaFn9d8i9Jpj2LP/nS06hLhF/o2/GqP4OS+nd2MSWhICPiqu8XPs8p1mDROL+h1\nMz29Tauk884LpRQffXUaH7w+HpRSUEoNsSMqThJeeu1u7EhRURGuX79uKAmh0Whw4/o1nD2+A87M\nCkxJdEZ4UO8xIsmxYtwpqkdWYQtyygncfCIQk5CKF1LjzF4a9IQx56tMJtMbjvJyVObnQZJ3B9Lq\nKvgwCcTQIYzLxViREJ5hAUarJrTjyjPvkfDg81BbUmL2mAkh8PDwaK2urhYDsLwuiBn0lzHxc3Nz\ns0zF2Eo4HA4CAwP1U/CUFAD6b7iGhga9gSkvR3phHg4V5UPRUAIfDoGPE4UP18kwi2l3iP56LQuv\nTkqx6/hqW+TYUFsPEccJjCulGOYsxDBvD4R7u9vVEVsnlcOFx4UTU6+O9v7Mu3EsnkIuPEuu4d8f\nrcaQad/AR6T/tg4NGYbbmVvQUNsCN8+7ym6tSqWhLGhTSyvqWgUIDg6GSqXCsYPrsWByd60SQgje\nX6K/5qTRIbhTWIddJ6UYOvZZTJg0xeDglEqlmD9/Pr788kuwWCycSTuFi6d2Qexcj8dGuiHQr+ft\nYbVai7ziemQVyZBbQeDtH4vYxDGYsCDWaGyKtbS0tBgMhyQ3F5L8O1DW18GXSSAGRRSPi4kiITzC\nAu26xO6IB5+P+gpJJ11cU/j4+Girq6v98IAZE3FISEifJM5kZGT0WnwK0Ct2eXh4wMPDo61GyVQA\neinCqqoqVFVWoqK4ENcL81CTUQIR0cKXrU/pz5bUwtdFCBcexy4OupzqOrjEhSHmoRSoWpXIKqzA\n5ewS0HPXkSwSIsXTDZG+HjYvy3bdyMGY8ABkVzZhRlywIVSfguLytZvgyXgYxueiuvwKhNEzAOiX\nkVevRCL7RilGTbm73M68nYFhI/UPf1ZhLaISJoPJZCLt1AkEe9RhkDjY0Fap1IDNZoIQvZymRqPD\nsXOlyCz3wvBJj2Ps2LEGQ6JWq5GamoonnngCIgEXX/5zGcK8pHjmIU/4eN7tsyNKlQZ3iuqRWShH\nfiUDfkHxiB06BlOfiUFaWhr8/AdZbUgopWhqatIbjrIySHJzIMnPg665Cbezs7EgKhzxLiJME4ng\nFh5oN4ftp6fO4p0JPQtxAwCX5QSmSgm5XN5rgbNO53C5TgD6Jr8E/WhMAgMD+2TrJScnx6Qx6Qke\nj4fg4GC9Y22kvjauTqdDXV0dKisrUVVRjiv5uagqLoS6uaptFqODL58DH2cBvEUCi/Np0ptaUKVV\nwyWvDP4RAQiKDQViQ6FqVaGgWIIbOcXQXUhHPJ+H4V5uiPbxtGrX6pmRCfhwfyaymrxxszILL48O\ngQTV/GEAACAASURBVLezAEq1FqeqWpAgYmE0wxnb72xBg1c03DxCwWax4eUzDMd2b+5kTNTKVvA4\n+hs4q0KLManDDFolL8/rnHT3/YareP7xJIiEHNQ1yLHtkAQu/hPw8pKncPv2beTk5CAmJgaUUjz7\n7LPgcdmgsnwoijLx0kwfuLl0LzDfqtQgp6AWmUWtKKpxQkBoEmJHjcLM6OhOD1ZHQ2UKSikaGhr0\nhqO01GA4GDIpxEwCMaEYIuBD7CyCi7cLZIFi8FhOvS5ZrOW1UcNNNwLgwiBoamoy25gkJyc7Xb58\nuc+qsfeXA9bf09OzT6qNzZ8/3679MRgMeHl5QaPRICoqCuyHHwGgXyNXVVWhUiJBcXEhLhXlo/Zm\nCVwZOviy9Uul9mWSiMs2+q2l01Fktcgx5qmHweoSCMfmshEQFQREBUGtUqO8uBI/5pZAc+kmBvO4\nGOHphhhfT7MD6I5nl6JCF4vEuMkor72D9w8ew+9TXJASLMafZ09EfUMDzpzNxEOuQdh95RsIJv0N\nbLYAEWHDceXqejQ1yODipr9p/X09wedqIJWrUCXjIjQ0FHt3bcXQiO5aJUt+NwKUUty4XYnDl3SY\nNP3/MCxlhF6QqW3ZWV1djVUrv8aWzZsREuiOwhwWGAyCT5Y9hEfG6/1IMrkKOYV1yCxSobSOhZDI\nYYidMAKPRUUZ4mO60lO9nvYvCIlEAklJCSS52ZDk54OrUkLMAMSEYoRQCLGHECI/4wF3Qk7fBS6a\nIzkBAC5Eb0w6FoHvjeDgYA6bze6zIKx+MSZCoTB00KBB/S1/YBHHjx/vZKgEAgFCQ0P1FeHG6Kek\nWq0WtbW1qKysRGVZKc4X3EFVcRGorBI+HKJ39vI58HURwkvER02LHDpXIdi83nM5WGwW/CMC4B8R\nAI1ag5rSKqy/UwLV1VuIZLMxysMVsWIvOBvpZ+PFDEyKDsGOTBn8gvUBYL5ekZALffD15YOYJLmD\nhcNC4O7mhsEJQTh/OR/DiB/Sb6xDVMrL8PTygkoZiVuXCzBmmn7G19oqA48rRHZRLSLixqKmpgZ5\ntw5jyVP63BiZXAWVWgs3Fx5aWzXYd7IUlfJQLHrllU4iQaWlpThz8gDK885jfDQD8oz3wO0gUdAi\nVeJSejmySvSJdOGxI5E0JcWiRDqtVouampoOwV85qCoqhECjgh+DQMwAxomEEPu6m/0QDxRE0Jld\n0gQA/Pz8IBAI+iwDtl+MCZvNDggLs//vpFAoUFxcbKiUZk+efvppk22YTCZ8fHzg4+OD9hpAlFJI\npVLDLCa/KB9nC/LQkFcGWX0tSgPcwC2rgoebM4RcNtgmthydWE4Qh/pDHOoP7UPDUVNWjY15pVBe\nz0SYkxNGurkgzs/LkEowLiIIN0proeImgcO+Ox3m81wQEj4faeWXkHPgCl4dGwA3dw/IREUIl9eh\ntOoUyovjMSh4DPz9x+DY7rUGY1JQUAg+dwgyy9QYOmsoDu3bgolDWQatkmNnCzEyeRDKJM349Wgt\nwuLmYPGMuWCxWKCUIj8/H2nH96Cp8gZGx7IhY9didHIymEwGGptbkZVfi6wSLapbeIgcPB4jZqQg\nLCzMZKU7jUZzN2q0sBCS3BzsP3QII4MGIZzHhZhJECMSQuzvZZfQgE9OpOHPk+wfd7Tm8nXMiI6A\nr6j3nUMhKKQWlDH19fUFpdQ2Tcte6BdjotPp/M2dmlmCVCpFbW2t6Yb3EEIIRCIRRCIRwsPDgbZA\nPY1Gg182b0J2QyEKq1WQebtAWtcAhlYNAZNAyKAQsp0g5LDBY7PAMLJMYjox4Rsshm+wGLpJw1BX\nUYNf80rxS0YuAkEwyt0FsT4eOJDbAi9x9/gVBoOJoIBRqGsYhOVHDuKpBA5emjIGhYVFUFyrxN6b\nq9HiHoqw8BScTVsFabM+PEGlUoIQoLyZjSSNBvK6qxjSQatk9pRInL1SjvNZPMya9y5iYmMNMSJp\nx3dCJ72DsfF8xE3Qx4gI+CykXS5FbgXQ2CpEVPwUjJs7tNdEOpVKhaqqKn0MR2EBJLm5qC0tMQR/\niZ2YSHAW4YlJoyHksPvEt/Hm2JF27xMAnklOgJMZ4+WzWKhuML+qgL+/P3Q6XZ8pevWLMWltbfWy\npbJaT3h5eXWTC7QX+fn5sOdsysnJCTKqQdK08fCJ1BfPplQfFyOVSiGTSlHbUI+ixnoo65v/P3vn\nHR1Xde7t50xTmSKNerOqJfdu3I1tsDHBwRRjXIDQW8AJJDcFQnITkpAP7g1JCLkhIUBopptmA6a4\nylXuVrNs2eq9Tq9nf39IliWrjMqMLXPvs5bX0pk5Z+8ja+Y9e7/l9xKqoM3AqBRog9TogjRdMiQV\nSgXRI2KJHhGLvGAqzTWNfHSyjH8dKeREs4uxQaeJCU9BG2rs5r+JNI5Ap72F1/K/IbemgDtnpWGI\nqWPymWIO7P8bYxf+EhjHwewitLpgooxaSqtbSR01nffe+geXTxDYHW5OnmkiI8XIh19X4Ameyn3r\n7kKr1XLwwAF2bf0QLRUsmqgnKy2VhmYbOw+UdxTSjZm4mMVzJ7el8p/3RXI4HOeyRotPUX2yiJbq\nKqIliEeQGKRmul5HTFpCn1mj/ibEDz2Be6K/KQEhahUOc/9XJvHx8Tidzm+dMTH0JbA7HNm1a5df\njQlATXMjeuO5ehZJguDgIIKDg4iKioTUtnR0r9eL1WrFYrFgaTVR39KIpbEFldeDTiWhVQh0mjYD\nE6JWtYW+E6LZuzGbUWuWUJLXQEFtDcdPHsdQG8KIoHRiwpLRa8/lgwRpQknP+C55Ncf45ec7WTMx\nHqujlMaybEpyN5CUNJ9tn/yL76zORCnBiSoXrkgF+/YcICY0npjIJhxON/94r4bp87/HzNnzOHzo\nAHu2fUistpHl040EaaLIL27iqwNW3IpIxky8nmvmTepSSGe327tmjZ4swlxbS4yyzXCkBmmYrdcR\nk54UkNXGpUSwSoVjAD6TiIgI3G53kCRJGiGEy9/3IwVa5KfbhG0CLV5Zlv1eSHXq1CliYmJ69eIP\nJ4QQrHvqP0n43jIiRgx8yydE2xPbYrFgNZuxtDRhaWnGbbcSqpDQKQWhgL5deqDV4aSq1U15ZTOW\nShOOoga0VjWpoVnEhadh0MV0/D1MlnoaKj7n2kwX0Y4WXjhiQ3PZbzl2/Bnu/FkaXz23k5nTJpJ9\n8CQpsUr++MQcdh2soaAqhquX30F1ZTkHdn1CWpSZtPggmkxuCspBCopjzKT5jB0/iYSEhC5Zo++9\n8TpGlZIQt5s4lUS8kIkPCSbeoCcqNHRIyV9Pbd3J4wHwbfx9bw43TxhHpLZ/8gj9pdFq493jeTw4\nq+8EybKWVr4Ki+Hunz/W77HVarXX4/GECyH6b4X6SZ/GRJKkl4FlQJ0QYkL7a5OBF4AgwAN8XwiR\n0/7eY8BdgBf4gRDiy/bXrwV+B+wHvq9QKBxer9fvj5Xs7GxGjx5NVFT33IThhsvlYt1/PUlreDAL\nH7rdb+N6PJ5Oq5hWLM2N2EytqIUXnVIiBBkhBFaXl8pqEzVn6nAUNaJuUZAamkVS5CjC9bF4ZQ/l\n5TtJVefRUnmCPFcazdqxTJ6fT3l2KWkZoykvP82imZHow2MJipyJMTyawqNb0ClqMRqCqDWFoNGP\nYMzEeYxIScPr9VJTWdmRw+FpaSFe1ZY1GhWkIdGgJ1qn9ftDxunxdFTb+hO314tK0Xu/5cEihMAj\nyz63bNUmMx8H6XngV7/u99ghISEuh8MRK4Ro6el9SZKuBv4MKIF/CSGeliQpnTbpAjOwotdrfRiT\n+YAFeK2TMfkS+KMQYrMkSd8BfiqEWCRJ0lhgPXAZkAh8DWQKIYQkSW8Da4FfAx8rlcq9Ho9neFXa\n9YHdbqexsZGhFiZ2prW1lcf+9RwTf3yH38bsjMflQqlWI0kSQgjs9rZVjMVsxtrciKWlGY/DThAy\nsstJc6OZmtN1OIrqoF6QGJROWvQE3B4n1tpN6M2n2GfPQJPQRFqIzMmKVuZMDiPYkER0wnisDSeR\n3A0IdTjhUWlExGWhC9Vir6uh+lQxWExtyV/IxGu1xOt1hIf0XQv1f/RNvcXKO0LNw797qt/X6HQ6\nh9VqHSGE6BapkCRJCZwAFgOVQA6whrYFwvO0tcsYI4T4W09j9/mFFkLslCQp9byXZeBspVN4+6QA\n1wFvCSHcQIkkSaeAmcBe2rRmg4BQwKtUKgPaDMjftLa2UlBQ4Fdj4na7kQKY17Dt+Ve5/MHb0LR/\nYUNDQwgNDSEmJhpIb78HD1arBYvZQnhrC1GjGmmZ2YitsZmG0zWczn0H6lyEiThCRAphrhOcKJBo\n1jRgDNdT3RRBvNpNZfF+JGUYOk0GilYHorUGzZnatsiKTku8UY8+Lvz/DIefUUgS8gDbuSiVSkHv\n3/sZwCkhRAlA+yLgOtp2ILr2f712jB/M6uARYLMkSf9Nm5E4q2CTQJvhOEsFbSsUgH8CO4FvgDKV\nShUQR82hQ4eYMmWK3z+0cXFxffZmGQxerxdJqcBUW48h1v8RqMU/8t11Tq1WER4e3la7MiIJS2Mz\n1fknceqiSYgZgWXcaOpKy6jKPUlxbhlOuw1LdSU1wAinwGsvx1ljJ1KlwiA1oAkNJUYXilajQZYk\nqiWJmlYTR6Wattqc9r+Lov1nSWr7WaLt+O1judwyeULbuXR9v+PnQfxt/33wCHdMm+z7xAGSU15J\nqEbDuAD8/fpzz812B9awgQVn2t0LvX3vE4HOzXsqaFsQPA28AbTQtsPokcEYk+8DjwghPpQkaSXw\nMmcr5bojAIQQXwPTASRJ6vLbb9u2DYCFCxcO+bi8vJzW1lYkSfLLeIE8PqvTsfkPf2PS9UvJWthm\nk4u2tTUiD+Sx1+0medpEnBYrJ7buxm13Ejcqg8YTp/ny2S/xOJUEBxtRKFx47NVIkpNYlQqL2YwF\nQAKrVokYF0OdUkWZ2Y06KJS4jESCQkNorWlAoVQQlz4CZJnqU+UgBLEpCQhZUHO6AgTEJMWDLKgt\nrUTIAu3U0fxLp6a2rApkQVRCLEL2Ul9Zi5AFUXHRSJJEY3UdkqQgNikehSRRX1WLQpKIG5GAhERd\nVQ0gkZichEKSyNdpebqpmaSUtireqrJKFJJEctII5ky5jJLSUgAmTZwIwNFjx/p1rBs3FVVQEHva\nOyUO9Pq+jmvK67AtX9nn+RMnTGCeVjugz6AkSYLeu1L0+JAXQlQAC3u5pgOf0Zz2bc6nnXwmLUKI\n8PafJaBFCBEmSdLP2yf+f+3vfQH8pxBi33njGdRqdYPL5bpkcpcD4TNpaGjgP9/6FxPW+c6s7Q8e\nlwun1YbTYsNpsWJvacXtcIPVjmyxI1sdyBY7HosVNQoMWh3hOj1GnZ4InZ5wrR6300V29iFiYyNR\nSG4sNTW0lpYS5/VSUFjAlopyGnRRuCobUSemEzM2lLQJcQSrvCiUTiSFjD7SiNMiA2oM0dEYoqIx\nREcTFh2BIdpIiEE36JXjWR0UIcsIWSDL8nk/d35fRpb7Olfm2Dvb+c2dP+7SIuPbjsFgsJvN5gwh\nRPX570mSNAv4tRDi6vbjxwBZCPF0f8YezMqkSpKkBUKI7cAVQFH7658A6yVJepa25VImbdGb8/EI\nIS6pzbPVauX48eN+NSZqtRrh7lvSxet247BYcVntOC3W9n82hNWBsNjxWuzIVjseiw2VDGE6PWE6\nPQlaHUd27+OKBQtJSR6DLl2HTqdDq9Wi0+nQaLoXHlqtVgoKCsiMM1B6eD9Jssyk4GDQavkwZy+n\n6moIzRxJVtxYQpz7yLOFYyrVUmI1kX7dOEZmxiNMJupKijEmBhORHIo+SonLXoupvoTKIhemegde\nl9TJyMQQFh2BPiocrdHgs8JXOrvN8VN+SZkx3GeK/rcNWZYlevd7HAAy2xcQVcAq2hyw/aJPYyJJ\n0lvAAiBKkqRy4FfAvcBfJElSAXbgPgAhRL4kSe8C+ZwLGfe07PHIshyQbKOioiLi4+PR6/W+Tx4A\nUVFRfOc73/HrmMHBwThNFvK/3I4qKAjZYoeO1YMN2WpH6RXotTrCdTridQaMWj1GXSRh0Xq0qW2G\n4ayRCAo6T19lzW0+78FkMlFQUEDurl1U5uaSIgSZISFcnZCAQpLYWlTEkYJjHK1roDolhdlTZ1Gl\nzyTsyC4UyjhyHQ6aqyzUftqAZZqb9MtTmb56BQqbh8rCIk7vLcQQqyZhjIHMuSmEhoXgsrswN1gx\n1Tdhaaig9oALc72TmlMNBIVqyZwxCUN0LGHRRvRRRvSRYSiGmNV69Ms9TLqquzi1x+HqteK4P5w+\nfZr4+Ph+tf8cCF6vF5vN5vfPMUD7d6/Hp5gQwiNJ0sPAZtpCwy8JIQr6O7avaE5vVml6Ty8KIZ4C\nfMWpPLIsKwaiENVfGhsbCQ8PD8gfwd9oNBrmj5rAzg93sOqmlYRFJaNLPbd60Ol03Q2EH2hpaSE/\nL4/c7GxqT5wgVZYZr9NxbWJiR15Dg9XKxiMHUTXVIISgMS6ey2bPRq2NJSIhA5MsE6FrYrRtMqeM\nJuyqCHTHHJyoPYmpsYXlK5cwYlwGXs8Sak9XUpF/gq07CtEaIWFsGAljYkk7r9GUucGCtcWO02rG\nUl/N6WNuzPVObK1utOFGwqJjMETHYIhq2y7po8JR9XNVEdRD/yQhBG6bs9/9eHqiqKiIyMhIvxuT\n+vp6vvnmm34Vlw4Ut9vdqzEBEEJ8Dnw+mLEveAYsgCRJXqfTqejcXmG4c/LkSTIze25KNVxpbm7G\naDTS2NhI3vHj5O3aReOpU2QIQaZeT6rR2CUlXQjBsepqdh49xHicbLM4OKMz4pk+iR8sX8E7Bysg\nYRStTz3A6IQMysyLONxyBveksYimk+itJWTNDmfSrYvIuGxcF0Moe73UlVRRkV9EZWE+QXq53bDE\nYIju3fh7PV4sTTbM9RbM9VbM9U7M9U4sTS5C9AYMUVEYouMIi47sMDKaYN/yBE6bnSPPfcZ///y3\nQ/tPvoQQQqBSqWRZlkOFEE5/j39REsdCQkIsdXV1Bn/6IALN/v37LxljIoSgvr6e3/z614yOjsZc\nVsZIYLbBwIjk5B4rkB0eD1/k5dJaeZobDSH8o8qBzRBJy+g0bppyGa1WF4oRY5G9XpLCoxkXqeZU\nSxmXRUxla94OmDwJr7mBa2YuoeFwHQeKv2bidfMICm17aiuUSuIyRhCXMYKpyxbRWF5Def5J9rye\nhzLIRcIYAwljYwmL1XcxQkqVkrAYPWExXQ2OLMtYm+2Y6y2Y6k9SfeY4RftdWBqdaIK16KOi2rdL\nURiijRiijR33AmBrtRAR5j9d2EuBlpYWVCqVy+l0+t2QwEUyJsHBwXXV1dV+NyZms5mqqipGjRrl\n++QBEoglJ7TV15hMpkG1XOiMEIKamhryjh0jd8cOnJWVLJIkRplMJCb3rU9a0drKxkM5jHJbuDrK\nwPp6M6mpI/lQCRkjUrjlhhU8+deXiVo8n7pTuaAKIi44BK98ErvbQLzrFK5mG4se/RNf5nzGNaOT\nyDIE89ULm8i6YTYxaV2jJQqFguiUBKJTEphy9eXkfLSVkgMlVByvBMlKwhg9CWNjMSaE9XrfCoUC\nfaQWfaSWhNHnikaFENha24yMuaGUgj27cdkAoUJSBBEWHY0hOhaH2Um8S43JZEKv1w9qO5mbm8v4\n8f5vTWKxWAgKCvK7c7i6uprg4OAmvw7aiYtiTBQKRXVVVVVA+joEypgECrfbzZYtW1i9evWArxVC\nUFFRQf7RoxzfsQNRX89IYInRSHxKis8viCwEe0vOcKTgOMt0aozBofzyZAXzJ03jA5sV/fgx3Hrl\nUkwmEyaVAaPBCECVxYTsiWB2aggh6pPMSpnOC/mFnPrmbSZc9yAv/esJbpo/gQeX3crrG96lYUoV\noxdO6zFaI0kS05cvABYgKRS01DRQnn+KQx/m4XGfbDcsMUQkhfdLz1WSJLThoWjDQ4nLBG1EKGGx\nekLDQnBYnO0rmUqaaiqosqh5+h+/RPKoiI1OID56BPHRiURHtUlZhIf3nbVbWFgYEGOyceNGFixY\ngL9lOiorK1EoFAET/LkoxsTtdpefOnXK7+Pq9XoWLVrk93GhzWeSmprq96eFXq8fkCGRZZmysjLy\njhwhb+dOVE1NZALfjYwkpocVSLXZjFeWSTqv14vJ6WTT0SMoG6q4J0qPSpL4e2UjM8ZPYbfNhm1M\nOrNiE1k4bz6ffP4liqRzgtIKtQqzzcHaaZM6XrsrayRPF+6gPHUcU9f8lG92vI9CeZD/uGsd72/6\niH2vfM7EFZejDe/uH+kcrTHGR2OMj2bCFbMw1TdTnn+KY5vycFhPdRiWqBRjv4WiE0adW7WE6IMJ\n0QcTkx6FTisYH51ObGwsdpuT5noTLQ1nOFZ/HHOxG3O9A48dIsKiWTzvGqZMntJtbH/rDZ9lMA+W\n/lBdXY0kSaUBGZyLZExMJtOp6upqAVwy+SYVFRUYDAYuhg6L1+ulpKSE3EOHyM/OJtRkYqQkcUNk\nJFEpKX1eGxYczKHKyi7G5GR9PV8ePsBMhZs5ceG4ZMEbtS2MGTuZYoeDspQEYo0R3L7sOpRKJdlH\nCoi8/Fxlc1pMEhZbV4WvlIgIvtvUxMe73yM8KZOx197Prl0f4Xz3Y+67fQ05hw6w4cUvGHHNFEaM\nO6cLU3OqjLiR3fsrS5JEWEwEYTEzGL9wBubGFioKisn/Kh9ryyniRulJHBtFTHoUCuXAMw3cZjv6\n9DbDFhIaREhKNAkpXdPiXU43X7yxB+8A61+GK9XV1djt9oD0zIGL1+qiqr6+3k5b4Z9f2b9/PzNm\n9K9VwEAI1IoH2rJhZVnu4jfxeDycPn2a3AMHKNyzB4PFwkiFgtVRUYQbjf0eO1StZl57T1y318u2\nk0WcKS5klTGEpBADrS4PP8g/w8o581Go1ezAg37iGBZHJJKZmUlZWRnNhJBiPCfroFBpsJmc3RpA\nLU5LoyA3l+Kv/s24VT8jbd4NvPXsOswWCz/6/t1kpKbz0vtvcKS4ivFXzwIJig/k92hMzkcfGc6Y\nedMYM28a1hYzFQXFFO3I5+CGHGIzdSSMjSImIwqV+twqp7GimYaSJkbN6ypq5XG6UHolnyFdTZAa\npTe4xwfIyZMniY2NDYh2TmNjI5GR/hdEKy8vdzkcjnLfZw6OiyVVVV1WVtZr9eFQqK+vD8SwAcXl\nclFYWIjL5SI/P5/3XnuNP6xbxzd/+AP6LVu4RavllpQUZo4YQfggcxoarFZe252Nq6SQ+2LDSAoJ\nxiXLbGho5eqpM0iPiuYdUyvhS+YRY3Zww9K2JL2juQVIiV37XZc11SKUalznBQU0SiUrk5MJqyvk\nzM4PQJJY8MBTnNKM5I8v/BuDwcBP7/8hE7xx7P3nRiyNrcxdffWAfxdtuJ5Rsyez+O61XP39B4lO\nmsuZfTJf/PEA+9/LpSKvGo/LgyZYTfyo7o5tW4uZqPAInz4lj8eLud7ZozEpLS3tVZ92qKxfvz4g\n4x48eNBDW2ZrQLhYK5PqU6dOBUSsc9myZYEYFoDDhw8zZUr3vfNQcDqdNDY2UnXyJE+/+SYxLheZ\najV3REWh80MejhCCo1VVfH5gH8LSwvfHpCFJEm5Z5p26FsJSRzE3PYP/KiyAOdMIVqq4afblhIeH\nI4Rg55F8ImZ3LxSVNCHYHQ6CzssgTTYaWdzSwubi3VQnjiJh7AySZyzlzJHtPPM/r/Dj+7/H6htW\nknU4g/WvfUjkglFkzBg36OS8EL2WzBkTyJwxAYfVTmXhaSoOF3L4k0NEp4UQPyaCYH0wmuBzvi57\ns4UR4b7rcRprWoiNTKCnfKjFixcP6n77w7p16wIyrtVqdQPdanL8xUXb5rS0tFwy4khnKS0t9Ysx\nsdvtFBYWkr9vH8UHD5Lg8ZCp0TA/KopQPzp47W43m/NyMVWW8HCsjqgRbWLSbq/MrUdOsGLGHJaM\nHsMrJ05Qk5pEwpypRB0tZv6ctj5AVVVV1LtVJEd0fbqnRMWBy47D4ehx3vnJyRQVFXHqwEdYYlPQ\nRcaSNHkBG596A5f7ZRZMHY1KpeLxex7l1Q/Wc6D4KyZcN5/gHjJVB0KwNoSMaePImDYOl91B1YlS\nKvILOPbZESKTg4kfE07CqBhcTVYiU3xvI6pL6xmZPHFI9zScqK+vVxLAlcnFyoBVKRQKp9PpVPh7\nqeh0Ojly5AgzZ87067hD5WwhXe7u3ZQdPUqSLJMVHExGZCTBKhVeWSa7pIQF6el+me9s7shot4XF\nkWGo2jVUvULwTk0TJKRz/aTJfHnmDJ9olMTeeROWnOM8vvzmtsZiwOebv+bdMkiZee4pXHpoJ/Ef\nvsQIIZhklEhNS+1x/vKWFt5oaqYxaSoZ3/0+SpUaIcvUnDyC7sxWfvbAbcTExOD1etm85Ss2H99F\n1g2zuuWkDJadb37GuEXTiUiIwe10UX2yjIr8AkqOHiNUtnLNjfNJHZ2IVt+7AfvqjQMsm3p7ez/q\nc1RXV2M2m8nKyvLLvXamtraWyMhIv2+hhBAEBQV53G63MRD6r3CRViZCCI9WqzVXVVWFJSf7dr4N\nBI1GM6AuZ4HEZDKRn59P3u7dVOXlkeL1khUayncSEro121IqFH7RKZWFYE/JGY7mH+dag5rMTk27\nbR4vGxtaOSiCWR0bR2FdHd/IHrQL5+J1upifmNZhSIQQZB8tIGLqim5zlDbUkBqfgtnWoxQoACPC\nw5nV2so+axnl+78gdc61OMwtxI+aSq1SzW/+8hKPf/97JCYmcs2Sq8lKH8krG96iYXIVoxdOHXJx\n3/TlCwjRtzUdUwdpSB4/kuTxIwnVGkmvD0VVFsTn3xxGG6MgcWwYaWOS0Iediwd4PF6ayh2kreje\ns8psNg+ppqcvPv30U+68806/j9vU1IQQwhsoQwIXb5uDRqMpzM7Onrl2ba/CTYNCkiSuvPJKdXVJ\nhAAAIABJREFUv47Zma1bt/YZ2WlpaSEvN5e8XbuoLSwkDRiv1bI8MdFnY6VZQzSsJqeTjUcPo26s\n4t4YA/pOxkkWgkfzTjNtzHievGwmTXY7fy8rwzFxFBkLZ9G0/lNueOAHHefX1dVRZfWSHN1z4pQq\nKBhzS8/bnLPMTUri9OnTuDQ5FO8NwuNyMOry5UQkjWT7ZhO/f2E9P7vnZlJSUhiZMZLHH/gR6z98\nl32vfM6EFZejMw4+UnLWkHRGlmWshTUs/94PiY6OZoVnFadPn+Z4wRG+2nGAIKMgcayBtDFJtDZZ\nGBGb3mPEJxArkrPcc889ARn38OHD6PX6EwEZvJ2LZkysVuu2Q4cOXbZ27dpLqvlJT47CzoV0TadO\nkS4E0wwGUkaMuGC9XYrac0dmK93MjjV2qb+RheCTuhbmjJ/MiqnT8Mgy75WX05icQNDELHJffY9H\nFn2nS5jzeG4+JI7p8fdNiYpDFRSMxd63MdEolSyNjmZDaw12xUFSrnoQAHVwCIsf+gPNlaf5/T/f\n4Sd33Ehm5ki0Wi333HIHu/bu5v0XP2PENVNIHj/wROnWuibCYiK6vV5zqpzksLiORm0qlYqsrCyy\nsrK43ruCkpISjhccZdvLOTRb6rhpyR0Dnnu4cuDAAeF0OrMDOcdFMyZut3vfgQMHLIDfA/UnTpxA\nrVZ3LNn9ycKFCxFCUFdXR35uLrk7dmDpRyFdf9lcVESK0cjofnYmdHu9bC0qovR0IauNoSSFdP3v\ntLg9fFLXjDMmmZumTEUpSWwqK6M8LobwaxYQFGUk4lgxc2Z2bXW580gB4ROu7XVelSYYk9OFkGWk\nXgzm5sJC4g0GJrjdhKjtFO/eQNqSOzrONyam43Yu48m/r+fnd9/EuHFjkSSJebPnds1J+c5sVP0U\n33ZY7RzatJNFd17X7b2qA0WsmtbzqlWpVJKRkUFGRgbLr7me8vJyIiK6G6Tc3FzUanVASjaampqQ\nZTkgrVp27dplsdlse/w+cCcu5qrg4JEjR9SBcABHRkbi8fStYjZQhBBUVVXx9ebN/PmJJ/j3z35G\n0xtvsNBk4v7kZBanpJBiNA7JkAAsysggrZ9JafVWK6/vzsZTWsi9sWEkhXQtvRdC8OzpSuoM0ayY\nNh21UsmBqiqO6XS4x48kafY0Wr7ezUO33I6y3Ufx7LPPUlpaSmmLHUNsz4WYpQ1tAtGognB0yjUR\nQlDZ2tpxvHT0aCYmJDA3KQl16RlSFQ1UHNneZSx9ZDytIYn87sV3ycvL63g9Pj6en97/QyaJBPb+\nYyPN1f3LHwrWhvRoSMyNLYgKCxPGT/A5hkKhICUlpUddnODgYL8q7nUmPz8fs9kckLF37typAg4G\nZPB2LmZ4ttxut8tVVVV+1+CMioryi3U/W0iXd/QouTt2IOrqyJQkPLW13D19ekC2MOc7Znu7ryNV\nVew6dojFITAppntBmhCCLxtNxKdmcvP0GWiUSspbWtji8dCSEs/Ilcso27mPhSmZpLZnyAL84Ac/\nYMfOXZAwBq/LScE3G5hwTc9+La9KQ15lJdNHtm1FqkwmTtbXk3heHZBGqWRJVBSbqssItntpiUsj\nPKFtzpCwCBJGTUZ3prlbYZtGo+Hm629i9PEsXnv9fSIuz2TkzPGDykk5vSePJdPnDbm2auTIgNSn\nAjBv3ryAjNvc3IzNZlNwTmI1IFy0lYkQQmi12uMHDwbUWA4YWZYpKSlh44cf8syPf8z7v/oV7g8+\n4FpZ5u6UFBakpLAkMxNvgEPqBXV19LRqs7vdfHT0CMeP7OMOYzCTw7qXz1vcHv5ZWsMZbSQrp88g\nSKXC4nLxSWMj5hEJRN94FV6XC8XhQq67qmsGqkqlYtexQsJSxqDUBJE85dwH3GkxkfvF2215JoBb\nqaaksbHj/cSwMBb28mVLDAsjy2oly6CiOedD3A4bQgjKD2/HWLGTx9fdRUVFBa2dVjZnmThhIr+4\n91FCj7dyYP1XOKz2bucIIche37NAmM1kwZZXzZyZc3p8/9vOwYMH0ev1J4QQAS0yuqjOT6vVui0n\nJycgDbkOHTpEUVH/DLHX66W4uJiP332Xpx99lE9+8xukTz9lhUrFHSkpzEtJIUZ3TlU9xWjs1wpi\nKJidTipNXTvcl7e08O+d24ioL+OuOCNRQT0/Zd+vbqA1LJqbL5tJsEqFLASflpVhz0hHXDaW2DFZ\nlH+2ldULFqPT6bpc29zczOl6E+EJbRIGYfHnIkxBOgPjrz5X0RoUomdqVP97xsxNSsKee5wZI3SU\n7HifMzveJ8t5gsfX3U1ERARJSUn0Vk1uNBpZd9eDXBE7iZwXNlF7uqLL+0KWybhsbI/XFm0/wpKp\n89Bqu0d4BsJzzz2H1xuY72NtbS2lpYEp6M3JyREOhyOgzle4uNscXC7X/uzs7IA4YdPT07Hbuz/B\nzuLxeCguLibv4EEKdu0izGYjc4CFdIHQsT3LjE4aqbIQ7D59mmOFuW25I8awXq/LbjRhjYjj7plz\nOrJpd5SXU5+cTE2UjjFXL6I6t4A0u2DWZd0LIvPyCyB+dK9OVWjzmUxISEUVFILZ3tjreeejViq5\nMjKSLwpySY9JYmzqSG66/s6OrUdERESPTs+zKJVKrl68lKz0TF768E0aJlUxZtE0FEolCqWS+Mzu\nFdSmhmZcBXUsXHd3v++zN2677bYO35K/qampwTiAAs6BsGHDBkegna9wkY0JcDAnJycoEF/Kjk51\nnXC5XJw6dYrcnBxO7N1LlMPBSKWSW6PbersMhCqTiS3Fxdzq51qd82mx29l49DDBTbXcG6PvkjvS\nGavHy/+criQ4bgRrZs5B215PUtTQwDG9nmqthpSV1yBkmZYvd/GDm2/rURMk+3A++tSF/bo3lSYY\nc1Pf4eHzSQwLY2RpKWJSJGtW3tDreZ9++imXX345YWHdDWd6ejqPP/Aj3v7oPfa9/DkxU9LJmN59\nVSKEoPCL/Vw37yq/iD4H6ssOMGnSJN8nDZKysjIvAXa+wsU3JuUej8ddVVUVFKhGSE6nk6KiInL3\n7eNUTk5HId3sIRbSJRgMrJoY2LqNE3V1vJq9nSSvnbszRvRpcPc1m3AbY7h91pyO36vJZuMrmw33\nmCxCF0xFHxvDqS+2snjkGEacpw4PbRm7J6oaGTG3e9ZnZ876TFRBwVhsAzMmAHOSknhr61byZs5k\n3LhxPZ4zd+5c3O7eC8u1Wi13rb2djZ9t5OUX3kL9QzXJE7pq9FYWniGiVcXsmd3bXAyU1tbWHg3b\ncKe5uZnm5mY1AXa+wkX2mbQ7YQ/s3LnTr+Pa7XYOHz7M3555hrVLl7L/2WdJyMnhrqgobk5JYUpC\ngl8qctUBWvK6vV425+exI2cXP4oz8KgPQ3KwxcwxtZ67L1+IISioY4xPamoInTyJuqRIki6bjLm2\nDvXxU3x38VU9jpOfX4CIz+p3KrtSrcHp9iAP0I+gViq5MiqKj198EavV2uM5ERERPiNykiRx7bJr\n+Z/f/hHHthIOf7Qdj6vNALkcTko+P8yaZSuGvDWpqKjgiy++GNIYfbFt2zZM5/nH/MWWLVvQ6XS5\ngXa+wkU2JgBNTU1vvf/++z1/ogaAxWIhJyeHfz/3HP/18MMcee45Jp05w++mTePGlBQmxsf7tSL3\nLMWN/fcZ9Ic6i4XXdu9ELi/qyB3pzZC4ZJnH8orZIYJZPWsuYe1yAEIIviwrQzVpEvluKxnXtRmP\n8k1bWLtoSa+OyF1H8tGl9OzE7ExpQw3Q3mFPHdRr9XBfJIaFkW4ysfH9932e+/zzz2Oz2bq8Zjab\nkeU23318fDw/uf+HTJGS2Neek5L3xV4WZk3vEvYeLElJSaxatWrI4/SGTqcLWK+nF154wdnS0hIY\ngZTzuNjbHICNGzdufN7tdg84B6BLIV1uLimyzCittsdCukBR3NhIRGgoxiHuyYUQHK6sZPfxwywJ\nkZgY3T135JmiUn6cmYyy/fUTZhth0QmsmjW3i2jS0ZoaakaMoMrrJP7mpWhCQ6k8cpxMj4rpU6f1\nOL/FYiGvrJakGRk9vt8rmmAcDgehg4iUzE5M5O1vviFv+vRetzsAd955Zzefx/r167nllls6olEa\njYaV161gdG4W//jHa0Towlj2A/92YQwU06f32NNuyMiyTE5OjiyE+DggE5zHRTcmQoiq8PDwM9nZ\n2Vn9kUZsbm7u6EhXd+IEaUIwQadjeVJSr4V0To/HLxW5PXGVH4q+bG43X+Qex1pdwp2ReiJ7SR3/\n0cg2QyKEoMBs4yuPmlvnzSOiUwVrlcnEbqWSoMR43KMSiEhNxmW3Y/p6Dz9ae2evQsxnzpzB43JQ\ntudzQuPTCYsbgUbbcwuIsz4TAKEOGdTKBLpud1J///teV0ydXz/rrL///vt7PHfC+An8NvFxnE5n\nj6JGA2Xz5s0sXbp0yONcDHJychBCNAohAqb72pmLbkwAbDbb+g8//PDxRYsW9fnXf+f11zn22WeM\nDwpi+gAK6f62Zw8/mj/fb/frT8paWth0KIfxXis3xxo7dEd64ux7/5F7ipjYRNbMnUtUpy+aze1m\nU2MjyVddxXZHI2MWtiVplW7ZxdLRE0hISOh17AkTJvDsz+MpKjrJkaKjFBzdhMUroTBEI4eEIwfr\nUGpCqC/Ox9DcQKukQHg92JrrsRkHvwpMMBjIKCvjk3ffZY2P0nun08kTTzzBM88806cPyV9RFyFE\nQDReO/P888/z8MMPB2Ts9evXe5xO5zsBGbwHLoo4UrebkKRJsbGx2dXV1bq+PiRfb95My1tvcaUf\n9sH+5tWDB/ne1Kn9DnHLQrDr9GmOFx5nuUHDSF3/9DFOWmy8Z/Fi04TyRCepBVkINpw+Tejixeys\nKyf53tWEhIfRWlWDef1GfvfQIwMKjwohMJvNNDQ00NLSgtlswWJzcPjoESrefYc1Y8aiUavQaNTo\nDYZuyW8DwSPLvHXmDMt+9rM++9B8/fXXZGRkkJbWd7TpUqKhoSEghX0AiYmJtqqqqiVCiN0BmeA8\nhsXKBDjW2trqKSgo6KZq1ZmJU6bwyltvcUUAk8UGy9KsLPrbu6PV4WjPHanmvpgwdCrfT3a710ul\n3cnHNsGquQtIPO+JuaeiAu+kSRS2NBB2zQJCwsPaihM/28r9Vy4dcJ6FJEkYDIZuT2aDLoSvt20h\nM3OAvpU+UCkUXBkdzScvvkjaU0/1ut3prLsqhMButwdMpGgwPrzBEChDcubMGZqbm73AvoBM0AMX\nPZoDbSFipVL53scff9xnan1MTAyhKSlU9FC/4YszTU1YXa5B36Mv4vT6flUMF9bV8fr2LYy11LM2\n1tgvQwLw1+IK3m51c/2seV0MidPj4auTJ8k3GlHGRlMdF0bcuNEAVB46zhgpmKk9NJAaCuPi4nyf\nNEASDAZGms18/M47XWqS8vLy2L9/f7fzrVYrr7zyit/vA9oM1dNPPx2Qsc/idrsJUMtfAD755BOh\nVqs3XYiQ8FmGhTEBsFqt77/11ls+JeWmXHkleS29ywX2hlcISpubfZ84RHoLFbu9Xr7IzyM7Zxdr\n9EpmRxj6vboqtzlQGCK4ed6Cbp35bG43n9TUcPPDDxMbHY1cUkn9ydO4bDbMW/awZtnyYbeK641Z\nSUlUbN1Kbm5ux2s6nY5p07pHoHQ6HQ899FBA7kOSJJ544omAjH2WHTt2dPk9/c1LL71kNZlM7wZs\ngh4YLtscgO0nTpzQ1NXV9dnEe+LkyWxVKHB5vQMK/44MQFOjnjheU0NiWBjBnaJHdRYLnx4+QKK1\nmXtjwwjqZwc6q8dLkcXGFodg2Yy5JJ9XHuCRZTZWVfH9X/+a1NRUUlNTGTsyi1c2fsS+1gZunzGf\nuACsIvJqajoae/mTs9udd//2NxJ+/3siIyNJ8dGxENr8Dmq1+pLKUA2ktGhrayuFhYUa4KuATdID\nw2ZlIoRwhoSEbHvzzTf7PM9gMJA6fTonhmmzrevHjeswJEIIDpaX8+7Orcz3Wrg+1thvQwLwSU0D\nn5icXH3ZHNJ6MIbflJURc+WVzO6kg7FhwwZ+csc9rFuwlKWLAveBDRTxBgMtBQV88OabPUow9IRC\noWDHjh1DntvlcgWsAdaFZOPGjeh0un2BFI/uiWFjTABaW1v/+uKLL/qUmrrsiis4NsjchjcPH+73\nh3QoWF0uNhw5RMGxHO6MCGZi2MCiHTUOF9XKYG6Ys4CMHpx0uTU11CQnc8OaNV22MT/96U8JDw9n\n0cJFBAUFdbvOHwTCZ2Lp5D/4yYIFtB44wPHjx/t1bUREBNde27vEZH+RZZmFCxcOeRxfbN++3fdJ\nQ+DPf/6zubm5+a8BnaQHhpUxATaXl5fbc3Jy+jwpKysLe3Q0NYOQuFuQnk6gTUlpczM/+2gDlpIT\n3Blv7DUJrSesHi+baxtZ32Rj0bRZZPVQzVxrsbBDCNY+/HCfBiMnJ4dNmzYN6ne4kDRYrXxeWNhx\nfHa78+mLLw64bcnBgwd7FFjqD8HBwX3m4vgDh8MR0CjRsWPHyM3NFcBHAZukF4aVMRFCeO12+59+\n9atf9bnsUCgUzFy2jMODqItJCgsbsk5rb3hlmR2nTvHZ7u08Fh/GfSnxHanv/eVYq4X9DpmF02Yx\npocetw6Ph0/r6rj2oYf69C0BzJgxo0u71MrKygHdS2/k1dT4ZYyWdr2ZKK2WleeV4Mfp9WRarXz8\n9tsDWkmmpaVRVlY2oHupqanpVZTJ3wQHBzNnTuAU3/7yl784ZVn+qxAiIL28+2JYGRMAr9f7r61b\nt0qNPgzF9BkzKNZosAwi3CuEwOTnsFyrw8Fb+/fScPI498boydCFDDiK0uRys88ps3TGXMb2sJUQ\nQvB5WRljbriBiYOQP9i3bx++/l8vFFaXi1Af6e6zkpKo2rGDY8eO9XvciIgIJkzwLRrdmeLi4m76\ns5ciZrOZt99+W7hcrr9fjPmHnTERQjQEBQV9+sorr/SZcxIaGsqkq67iyCCfkq/5UXu2oLaW17dv\nYbytgTWx4V1yRzyy4M+nyvu83urx8mpZNW80WJg1eQYTe1lq76usxD1xIt9ZvnxQ93njjTcS2e7I\ndTgc/OEPfxjUOIPxmewoLmZ/pxXDjORkn9G4s9udjf/616BU2z/44IN+bXnmzp07ZEnH/vD73/8+\noOO/9tprQq1WbxNC+GcJOkCGRTr9+UiSNCs+Pv7riooKbW+FadDW/Orv//Ef3JOYGLBCvr5web18\nU1hIZUkRN0aEkhDcs//C5vES2kdyWrXdyb/rTMyfNovpPYgWQZsf5gtJ4sHf/CYgIdDKyko2bdrE\nfffdB7Q5IyVJ6ra62rlzJ9UvvdQtNCyEwOn1dkSyjldXU9XaytLRo4d8b7vKynDOns0t99wzoNVe\nU1MTKpWq1/qa3NzcPtP3/Y3T6QyYU1wIQVxcnKOurm6ZEGJLQCbxwbBbmbSzz2Kx1PlyHkZGRpK5\nYAFH/bCHHyi1Fguv7dqBovwk98aG9WpIgD4NidnjYUOzlTlTZvRqSExOJ5+bTKxcty5guRSJiYkd\nhgSgpKSEN954o+O4qKiIs2H7vJoaihsb+bBTtOVUQwPbOvkdJsTH+8WQQNt2p3rnTo4ePTqg6yIi\nIno1JGclPC8kgTIkANnZ2dhstkZga8Am8cGwNCZCCGE2m5967rnnfLryF37nOxz0enEPQjVcCMG7\nA9iPn73mQHk57+3YwgLZynWx4f3OHdnT2Ep2Y1v2rkcWPF1Uyht1JsaOm8LMXpKzvLLMxooK5tx2\nGxkZ/quH8UV6ejq33XZbx3FWVha33HJLx3FGZCQ3dPJNZEZHc7WfjMf5KBUKFkdHs+mllwbdpOrZ\nZ5/tkr6u0Wi4/vrr/XWLfbJnT8C1nPnTn/5ktdls/yUu4lZjWBqTdt7Kzs5W+JL/j42NJW3ePA5X\nVw94AkmSmD6A7mw2t5sNRw5ReCyHuyJDmDDA3JHZkWFMC29T1HLJMqGhWkaOnsTs1N6rYLeVlxM2\nfz6X90Pr5UIRiDwTX8Tq9Yyy2fho/fpB5Qk9+OCDBAUFcfz48W6qbYFmsKHq/lJdXc1nn32mlGX5\n1YBO5INha0yEEFaFQvHq008/7TNcc+W113JAlnEOoiVoeh+tFTpT2tzMqzu2EtdYwZ3xRiIGkDvS\nmWCFArvXyxt1LWSOnsS8PvohF9TVURofz4rbbrtk6msCyczERGqyszly+PCArz1bNV1eXk5wu7zl\nheLqq6/2fdIQeOaZZzwqleoDIcTAi9b8yLA1JgA2m+3/vfzyy6Lax6ojJiaG0VdeSc4gVidnOdPU\n1OPrXllm+6mTfLZ7O9cFebkyMmzAuSOd+d2JUl6vaeG0SkeS0dirkWiwWtnm8bB23Tq/tGnwJ/7I\nMxkMSoWCJbGxfPbyy4MWYL766qv53e9+5+c765m+1PX9RWNjIy+++KLHarX+JuCT+WBYGxMhRJlS\nqXzRVxIbwJXLlnFUocA8yPyRfeXl3ZbPLXY76/fvpfFkLvfF6EnXDu1L7ZJlEg164jLG8p9XXUVG\nL8WHLq+XT2tqWHrffd+K/Ad/EqPTMdpuH9B2Z/PmzVRUtHUAVCgU/PKXvwzkLQJtkZs//elPAZ/n\nt7/9rUuSpLeEECcDPpkPhmVouDOSJEWGhISUHjt2TOurafSXmzbR8N57fMcPFa35tbV8c/gAl2u8\nzAjvWQu1v9g8XiTgvfpW9CmjuHrs2C7jnWlqIrV9lSKEYOOZM+ivuYYbVq/ufdCLRG+h4QuJV5Z5\n58wZrnz0UaZMnerz/JKSkh5V6mVZxmazDUkl7mJSVlZGVlaWw+l0pgshBr8s9xPDemUCIIRo9Hq9\n/3X33Xf7XJ0sWLyYsvBwqobQg8Tp8fBZXi57Duzm1jAVM4391x3pjbcranm1upHQ5MxuhgTawswl\n7Vorh6qrMY8ezXdXrBjSnN9mlAoFi2Nj2dTHdsfr9XasXHprd2GxWC7pKuHHH3/cLknS88PBkMAl\nYEwAXC7Xf+fk5DgPHTrU53lBQUFc9b3vsbWhYVAe/xqzmQfffQfL6XzujQsjvo/ckf7ikQUhwcEY\nU0dxzbjxPRqmWcnJpEVEUNHayn6NhrUPPnhBJAMHy8XymXQmRqdjrN3Oh71IFbzwwgs0+xDDMhgM\nXXJr/EFJSQkbN27065g9kZ+fz4YNG7wOhyOwabUD4JIwJkIIq9vt/sUjjzzis1nXlClT0EyaxLEB\nfOCFEOSUlfFB9lYeSzDwvaQYNP1Qve8Lq8dLidXOhvoWRHwa3x0/oc8CQ6vLxaamJqImTuT06dND\nmvt/C5clJtKwZw+He3jIPPTQQ302QT+fqqoqv4RwIyIiuOqqnjsm+pN169ZZPR7Pby92BKczl4Qx\nAfB4PC8eOnTIvHnz5j7PkySJ6269lV0eT7+KAK0uF+8fOkhR7gHuighhQph/OqvtbWrlm2Yzrphk\nrps4qc+WHLIQbCovZ/rq1dx3330DLlS70FyMPJOeUCoULI6JYdPLL9PS0sLf//73ji5/AyU4OJi9\ne/cO+Z4MBoNf+vX0xd69e9m9e7fb7XZfcM2SvrhkjIkQwmW1Wn90//33231tYWJjY5mxYgXf+Ci5\nL2lq4tUdW0lsquSOOCPGTrkjTS43r5QObisqC4EJCXVCGtdPnuKzt092RQVBM2dyRXuzp7MfxrKy\nsgGnkP9vI1qnY5zDwcdvvcUNN9zQa5MxX0RERAyp2dZHH310QULBQgh++MMfWpxO538IIewBn3AA\nXDLGpJ13mpqaKjZs2ODzxIVLltCSkkJhD/KOXllm28mTfL5nBzcEy1wR1T13JEKjZmVi33oh52P1\neNnV0MLG+hZaIxK4YfLUXrsMnuVkQwNFkZGsvLN7t73ExES8gygTCDTDwWcCYHe72V9W1rbd2b2b\nyvbw71DZs2fPgLc8I0eOvCB+ri+++IKCgoJmIcRFzXbtiUvKmAghZLPZ/NDDDz9s86XApVarWXHv\nvWy12bq1uPjyxAmOHz/AfTF60vrIHelvG4qzVDuclDrdNBjjuXHqdNQ+Suyb7Xa+sttZvW5djyXw\nSqWSqZ1Cn4cvkOTkpUKTzUaCwdCWzBYXx2cvv+wXv8eoUaPwlSh5Phei+tjhcHD77bc7zGbzD4QQ\nA0/3DjCXlDEBEEJ8ZbFYNq1du9anQ2TEiBFctnIlmysqunwJZ6akIIXqMXv699R/p6KWBmffS1gh\nBCftLqxRiayYOt2nVofb6+WTqiquvPtuRvRSLXw+dXV1g+7r608ups/kWFUVTe21NYlhYSS1K/ZH\nabWMd7nY8MYbQza4ERERjO5H0WJzczO7du0a0lwD4Re/+IXL4XBsEUJccEnG/nDJGRMAi8Vy/9df\nf23dtm2bz3MXXXUVjtGjOdrpSRMRGsoVk6fzfpMVh9e3w25ZXFSvqxSrx8tb5TV809hKmS6KldNn\n+NRWEULwVVkZCUuWMHP2bJ/zn2Xp0nOd+U6cOEF+fn6/r/224PJ6CeultuayxESa9+7l4IEDfpvv\n7bff7nW1U19fz7hx4/w2V1/s3buXF154wWE2m/tuyHwRuSSNiRCi2W63f2/NmjU+tzsqlYpV993H\nboWCeuu5yPLYuDiS00fzSUOrzyeZTqUkuBeZASUQolZzMjSSmy+b2aVfTm8cr6mhMT2d61evHnRC\nXFpa2qAjF0PlQvpMSpub+aKT2PT0PprVKySJxXFxfP7KK7QMolFbTyxdurRXX0hWVhbh5/UyCgQO\nh4MVK1bYbDbbvUKIuoBPOEguSWMCIITYaLFYNt5///0+i3GioqK45r772FhTg6uTQ/PKUaMwhcey\ns6l/GbNCCP5a3NXJl2OyUKWLYtWMWYT0wwFXbTaTrVCw9qGHhhRC1Gg0Xfbp//73vzlz5sygxxtO\ndDbuUVoti7Oy+n1tlFbLBKfTL9sdAKPR2KWfsRCCjz66sLuMxx57zGU2m7cLIS5oh77DnUs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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "If $\\{A_n\\}$ is pairwise disjoint, then\n", - "\n", - "$$ \\mu(\\cup_n A_n) = \\sum_n \\mu(A_n) $$" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "Demo of bar plot on a polar axis.\n", + "\"\"\"\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "N = 20\n", + "theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)\n", + "radii = 10 * np.random.rand(N)\n", + "width = np.pi / 4 * np.random.rand(N)\n", + "\n", + "ax = plt.subplot(111, polar=True)\n", + "bars = ax.bar(theta, radii, width=width, bottom=0.0)\n", + "\n", + "# Use custom colors and opacity\n", + "for r, bar in zip(radii, bars):\n", + " bar.set_facecolor(plt.cm.jet(r / 10.))\n", + " bar.set_alpha(0.5)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.rank?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If $\\{A_n\\}$ is pairwise disjoint, then\n", + "\n", + "$$ \\mu(\\cup_n A_n) = \\sum_m \\mu(A_n) $$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", + "data": { + "text/plain": [ + "array([ 0.59530311, 0.68947945, 0.92696972])" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "np.random.randn(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 5635cf59db8128046ec8af1c379996d40e79a54b Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 15:42:40 -0500 Subject: [PATCH 24/51] Adjust Travis-CI to run in python 3.5 and adjust pip classifiers --- .travis.yml | 2 +- setup.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index d31fcc095..3d9c46b08 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,7 +2,7 @@ language: python python: - 2.7 - - 3.3 + - 3.5 notifications: email: false diff --git a/setup.py b/setup.py index d1af07263..28f0a361c 100644 --- a/setup.py +++ b/setup.py @@ -84,8 +84,7 @@ def write_version_py(filename=None): 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3.3', - 'Programming Language :: Python :: 3.4', + 'Programming Language :: Python :: 3.5', 'Topic :: Scientific/Engineering', ] From e90f36592ecde26452ce849fb42f2e20adb04387 Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 16:28:30 -0500 Subject: [PATCH 25/51] Relocating examples/ to QuantEcon.applications repo --- examples/3dplot.py | 24 -- examples/3dvec.py | 63 ---- examples/aiyagari_compute_equilibrium.py | 76 ---- examples/aiyagari_compute_policy.py | 47 --- examples/aiyagari_household.py | 107 ------ examples/amss.py | 295 --------------- examples/amss_figures.py | 210 ----------- examples/ar1_acov.py | 22 -- examples/ar1_cycles.py | 42 --- examples/ar1_sd.py | 25 -- examples/beta-binomial.py | 25 -- examples/bifurcation_diagram.py | 18 - examples/binom_df.py | 19 - examples/bisection.py | 20 -- examples/boxplot_example.py | 15 - examples/calibrations/BGP.py | 60 ---- examples/calibrations/CES.py | 68 ---- examples/calibrations/__init__.py | 7 - examples/career_vf_plot.py | 35 -- examples/cauchy_samples.py | 29 -- examples/chaos_class.py | 25 -- examples/chaotic_ts.py | 16 - examples/clt3d.py | 80 ----- examples/consumer.py | 17 - examples/dice.py | 16 - examples/duopoly_lqnash.py | 48 --- examples/duopoly_mpe.py | 48 --- examples/duopoly_mpe_dynamics.py | 20 -- examples/eigenvec.py | 57 --- examples/evans_sargent.py | 179 ---------- examples/evans_sargent_plot1.py | 44 --- examples/evans_sargent_plot2.py | 60 ---- examples/finite_dp_og_example.py | 49 --- examples/first_notebook.ipynb | 161 --------- examples/gaussian_contours.py | 101 ------ examples/gd.xls | Bin 56320 -> 0 bytes examples/ifp_savings_plots.py | 30 -- examples/illustrates_clt.py | 42 --- examples/illustrates_lln.py | 57 --- examples/jv_test.py | 28 -- examples/lakemodel_example.py | 151 -------- examples/lin_interp_3d_plot.py | 56 --- examples/linapprox.py | 28 -- examples/lq_permanent_1.py | 66 ---- examples/lqramsey.py | 296 --------------- examples/lqramsey_ar1.py | 35 -- examples/lqramsey_discrete.py | 38 -- examples/lucas_stokey.py | 437 ----------------------- examples/lucas_tree_price1.py | 22 -- examples/main_LS.py | 162 --------- examples/market.py | 58 --- examples/market.py~ | 58 --- examples/market_deadweight.py | 11 - examples/mc_convergence_plot.py | 40 --- examples/nds.py | 14 - examples/nx_demo.py | 22 -- examples/odu_plot_densities.py | 16 - examples/odu_vfi_plots.py | 55 --- examples/oligopoly.py | 172 --------- examples/oligopoly.py~ | 181 ---------- examples/optgrowth_v0.py | 71 ---- examples/paths_and_hist.py | 49 --- examples/paths_and_stationarity.py | 43 --- examples/perm_inc_figs.py | 65 ---- examples/perm_inc_ir.py | 58 --- examples/plot_example_1.py | 7 - examples/plot_example_2.py | 8 - examples/plot_example_3.py | 8 - examples/plot_example_4.py | 13 - examples/plot_example_5.py | 15 - examples/plot_market.py | 24 -- examples/preim1.py | 53 --- examples/pylab_eg.py | 5 - examples/pylab_eg2.py | 6 - examples/qm_plot.py | 16 - examples/qs.py | 47 --- examples/quadmap_class.py | 26 -- examples/robust_monopolist.py | 193 ---------- examples/sine2.py | 8 - examples/sine3.py | 8 - examples/sine4.py | 8 - examples/sine5.py | 10 - examples/six_hists.py | 15 - examples/solow.py | 47 --- examples/stochasticgrowth.py | 59 --- examples/subplots.py | 25 -- examples/temp.py | 1 - examples/test_program_1.py | 9 - examples/test_program_2.py | 11 - examples/test_program_3.py | 14 - examples/test_program_4.py | 17 - examples/test_program_5.py | 14 - examples/test_program_5_short.py | 19 - examples/test_program_6.py | 14 - examples/tests/__init__.py | 0 examples/tests/test_directory_pyfiles.py | 27 -- examples/tsh_hg.py | 37 -- examples/us_cities.py | 7 - examples/us_cities.txt | 9 - examples/utilities.py | 108 ------ examples/vecs.py | 26 -- examples/vecs2.py | 38 -- examples/wb_download.py | 37 -- examples/web_network.py | 51 --- examples/white_noise_plot.py | 7 - 105 files changed, 5536 deletions(-) delete mode 100644 examples/3dplot.py delete mode 100644 examples/3dvec.py delete mode 100644 examples/aiyagari_compute_equilibrium.py delete mode 100644 examples/aiyagari_compute_policy.py delete mode 100644 examples/aiyagari_household.py delete mode 100644 examples/amss.py delete mode 100644 examples/amss_figures.py delete mode 100644 examples/ar1_acov.py delete mode 100644 examples/ar1_cycles.py delete mode 100644 examples/ar1_sd.py delete mode 100644 examples/beta-binomial.py delete mode 100644 examples/bifurcation_diagram.py delete mode 100644 examples/binom_df.py delete mode 100644 examples/bisection.py delete mode 100644 examples/boxplot_example.py delete mode 100644 examples/calibrations/BGP.py delete mode 100644 examples/calibrations/CES.py delete mode 100644 examples/calibrations/__init__.py delete mode 100644 examples/career_vf_plot.py delete mode 100644 examples/cauchy_samples.py delete mode 100644 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mode 100644 examples/test_program_3.py delete mode 100644 examples/test_program_4.py delete mode 100644 examples/test_program_5.py delete mode 100644 examples/test_program_5_short.py delete mode 100644 examples/test_program_6.py delete mode 100644 examples/tests/__init__.py delete mode 100644 examples/tests/test_directory_pyfiles.py delete mode 100644 examples/tsh_hg.py delete mode 100644 examples/us_cities.py delete mode 100644 examples/us_cities.txt delete mode 100644 examples/utilities.py delete mode 100644 examples/vecs.py delete mode 100644 examples/vecs2.py delete mode 100644 examples/wb_download.py delete mode 100644 examples/web_network.py delete mode 100644 examples/white_noise_plot.py diff --git a/examples/3dplot.py b/examples/3dplot.py deleted file mode 100644 index 753f721d0..000000000 --- a/examples/3dplot.py +++ /dev/null @@ -1,24 +0,0 @@ -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d.axes3d import Axes3D -import numpy as np -from matplotlib import cm - - -def f(x, y): - return np.cos(x**2 + y**2) / (1 + x**2 + y**2) - -xgrid = np.linspace(-3, 3, 50) -ygrid = xgrid -x, y = np.meshgrid(xgrid, ygrid) - -fig = plt.figure(figsize=(8, 6)) -ax = fig.add_subplot(111, projection='3d') -ax.plot_surface(x, - y, - f(x, y), - rstride=2, cstride=2, - cmap=cm.jet, - alpha=0.7, - linewidth=0.25) -ax.set_zlim(-0.5, 1.0) -plt.show() diff --git a/examples/3dvec.py b/examples/3dvec.py deleted file mode 100644 index af9d7cfe7..000000000 --- a/examples/3dvec.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -QE by Tom Sargent and John Stachurski. -Illustrates the span of two vectors in R^3. -""" -import numpy as np -import matplotlib.pyplot as plt -from matplotlib import cm -from mpl_toolkits.mplot3d import Axes3D -from scipy.interpolate import interp2d - -fig = plt.figure() -ax = fig.gca(projection='3d') - -x_min, x_max = -5, 5 -y_min, y_max = -5, 5 - -alpha, beta = 0.2, 0.1 - -ax.set_xlim((x_min, x_max)) -ax.set_ylim((x_min, x_max)) -ax.set_zlim((x_min, x_max)) - -# Axes -ax.set_xticks((0,)) -ax.set_yticks((0,)) -ax.set_zticks((0,)) -gs = 3 -z = np.linspace(x_min, x_max, gs) -x = np.zeros(gs) -y = np.zeros(gs) -ax.plot(x, y, z, 'k-', lw=2, alpha=0.5) -ax.plot(z, x, y, 'k-', lw=2, alpha=0.5) -ax.plot(y, z, x, 'k-', lw=2, alpha=0.5) - - -# Fixed linear function, to generate a plane -def f(x, y): - return alpha * x + beta * y - -# Vector locations, by coordinate -x_coords = np.array((3, 3)) -y_coords = np.array((4, -4)) -z = f(x_coords, y_coords) -for i in (0, 1): - ax.text(x_coords[i], y_coords[i], z[i], r'$a_{}$'.format(i+1), fontsize=14) - -# Lines to vectors -for i in (0, 1): - x = (0, x_coords[i]) - y = (0, y_coords[i]) - z = (0, f(x_coords[i], y_coords[i])) - ax.plot(x, y, z, 'b-', lw=1.5, alpha=0.6) - - -# Draw the plane -grid_size = 20 -xr2 = np.linspace(x_min, x_max, grid_size) -yr2 = np.linspace(y_min, y_max, grid_size) -x2, y2 = np.meshgrid(xr2, yr2) -z2 = f(x2, y2) -ax.plot_surface(x2, y2, z2, rstride=1, cstride=1, cmap=cm.jet, - linewidth=0, antialiased=True, alpha=0.2) -plt.show() diff --git a/examples/aiyagari_compute_equilibrium.py b/examples/aiyagari_compute_equilibrium.py deleted file mode 100644 index 28b59319c..000000000 --- a/examples/aiyagari_compute_equilibrium.py +++ /dev/null @@ -1,76 +0,0 @@ -""" -Created on Wed Sep 23 17:00:17 EDT 2015 -@authors: John Stachurski, Thomas Sargent -""" - -import numpy as np -import quantecon as qe -import matplotlib.pyplot as plt -from numba import jit -from aiyagari_household import Household, asset_marginal -from quantecon.markov import DiscreteDP - - -A = 2.5 -N = 0.05 -alpha = 0.33 -beta = 0.96 - - -def r_to_w(r): - return A * (1 - alpha) * (alpha / (1 + r))**(alpha / (1 - alpha)) - -def rd(K): - return A * alpha * (N / K)**(1 - alpha) - - -def prices_to_capital_stock(am, r): - """ - Map prices to the induced level of capital stock. - - Parameters: - ---------- - - am : Household - An instance of an aiyagari_household.Household - r : float - The interest rate - """ - w = r_to_w(r) - am.set_prices(r, w) - aiyagari_ddp = DiscreteDP(am.R, am.Q, beta) - # Compute the optimal policy - results = aiyagari_ddp.solve(method='policy_iteration') - # Compute the stationary distribution - stationary_probs = results.mc.stationary_distributions[0] - # Extract the marginal distribution for assets - asset_probs = asset_marginal(stationary_probs, am.a_size, am.z_size) - # Return K - return np.sum(asset_probs * am.a_vals) - - -# Create an instance of Household -am = Household(a_max=20) - -# Use the instance to build a discrete dynamic program -am_ddp = DiscreteDP(am.R, am.Q, am.beta) - -# Create a grid of r values at which to compute demand and supply of capital -num_points = 20 -r_vals = np.linspace(0.0, 0.04, num_points) - -# Compute supply of capital -k_vals = np.empty(num_points) -for i, r in enumerate(r_vals): - k_vals[i] = prices_to_capital_stock(am, r) - -# Plot against demand for capital by firms -fig, ax = plt.subplots(figsize=(11, 8)) -ax.plot(k_vals, r_vals, lw=2, alpha=0.6, label='supply of capital') -ax.plot(k_vals, rd(k_vals), lw=2, alpha=0.6, label='demand for capital') -ax.grid() -ax.set_xlabel('capital') -ax.set_ylabel('interest rate') -ax.legend(loc='upper right') - -plt.show() diff --git a/examples/aiyagari_compute_policy.py b/examples/aiyagari_compute_policy.py deleted file mode 100644 index 7af0f090a..000000000 --- a/examples/aiyagari_compute_policy.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Created on Wed Sep 23 17:00:17 EDT 2015 -@authors: John Stachurski, Thomas Sargent -""" - - -import numpy as np -import quantecon as qe -import matplotlib.pyplot as plt -from aiyagari_household import Household -from quantecon.markov import DiscreteDP - -# Example prices -r = 0.03 -w = 0.956 - -# Create an instance of Household -am = Household(a_max=20, r=r, w=w) - -# Use the instance to build a discrete dynamic program -am_ddp = DiscreteDP(am.R, am.Q, am.beta) - -# Solve using policy function iteration -results = am_ddp.solve(method='policy_iteration') - -# Simplify names -z_size, a_size = am.z_size, am.a_size -z_vals, a_vals = am.z_vals, am.a_vals -n = a_size * z_size - -# Get all optimal actions across the set of a indices with z fixed in each row -a_star = np.empty((z_size, a_size)) -for s_i in range(n): - a_i = s_i // z_size - z_i = s_i % z_size - a_star[z_i, a_i] = a_vals[results.sigma[s_i]] - -fig, ax = plt.subplots(figsize=(9, 9)) -ax.plot(a_vals, a_vals, 'k--')# 45 degrees -for i in range(z_size): - lb = r'$z = {}$'.format(z_vals[i], '.2f') - ax.plot(a_vals, a_star[i, :], lw=2, alpha=0.6, label=lb) - ax.set_xlabel('current assets') - ax.set_ylabel('next period assets') -ax.legend(loc='upper left') - -plt.show() diff --git a/examples/aiyagari_household.py b/examples/aiyagari_household.py deleted file mode 100644 index 9c7471ff4..000000000 --- a/examples/aiyagari_household.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -Created on Wed Sep 23 17:00:17 EDT 2015 -@authors: John Stachurski, Thomas Sargent -""" - -import numpy as np -from numba import jit - -class Household(object): - """ - This class takes the parameters that define a household asset accumulation - problem and computes the corresponding reward and transition matrices R - and Q required to generate an instance of DiscreteDP, and thereby solve - for the optimal policy. - - Comments on indexing: We need to enumerate the state space S as a sequence - S = {0, ..., n}. To this end, (a_i, z_i) index pairs are mapped to s_i - indices according to the rule - - s_i = a_i * z_size + z_i - - To invert this map, use - - a_i = s_i // z_size (integer division) - z_i = s_i % z_size - - """ - - - def __init__(self, - r=0.01, # interest rate - w=1.0, # wages - beta=0.96, # discount factor - a_min=1e-10, - Pi = [[0.9, 0.1], [0.1, 0.9]], # Markov chain - z_vals=[0.1, 1.0], # exogenous states - a_max=18, - a_size=200): - - # Store values, set up grids over a and z - self.r, self.w, self.beta = r, w, beta - self.a_min, self.a_max, self.a_size = a_min, a_max, a_size - - self.Pi = np.asarray(Pi) - self.z_vals = np.asarray(z_vals) - self.z_size = len(z_vals) - - self.a_vals = np.linspace(a_min, a_max, a_size) - self.n = a_size * self.z_size - - # Build the array Q - self.Q = np.zeros((self.n, a_size, self.n)) - self.build_Q() - - # Build the array R - self.R = np.empty((self.n, a_size)) - self.build_R() - - def set_prices(self, r, w): - """ - Use this method to reset prices. Calling the method will trigger a - re-build of R. - """ - self.r, self.w = r, w - self.build_R() - - def build_Q(self): - populate_Q(self.Q, self.a_size, self.z_size, self.Pi) - - def build_R(self): - self.R.fill(-np.inf) - populate_R(self.R, self.a_size, self.z_size, self.a_vals, self.z_vals, self.r, self.w) - - -# Do the hard work using JIT-ed functions - -@jit(nopython=True) -def populate_R(R, a_size, z_size, a_vals, z_vals, r, w): - n = a_size * z_size - for s_i in range(n): - a_i = s_i // z_size - z_i = s_i % z_size - a = a_vals[a_i] - z = z_vals[z_i] - for new_a_i in range(a_size): - a_new = a_vals[new_a_i] - c = w * z + (1 + r) * a - a_new - if c > 0: - R[s_i, new_a_i] = np.log(c) # Utility - -@jit(nopython=True) -def populate_Q(Q, a_size, z_size, Pi): - n = a_size * z_size - for s_i in range(n): - z_i = s_i % z_size - for a_i in range(a_size): - for next_z_i in range(z_size): - Q[s_i, a_i, a_i * z_size + next_z_i] = Pi[z_i, next_z_i] - - -@jit(nopython=True) -def asset_marginal(s_probs, a_size, z_size): - a_probs = np.zeros(a_size) - for a_i in range(a_size): - for z_i in range(z_size): - a_probs[a_i] += s_probs[a_i * z_size + z_i] - return a_probs diff --git a/examples/amss.py b/examples/amss.py deleted file mode 100644 index cd94ac8f2..000000000 --- a/examples/amss.py +++ /dev/null @@ -1,295 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Sun Feb 22 10:26:11 2015 - -@author: dgevans -""" -import numpy as np -from scipy.optimize import root -from scipy.optimize import fmin_slsqp -import utilities -import lucas_stokey as LS -from utilities import simulate_markov - -class Planners_Allocation_Bellman(object): - ''' - Compute the planner's allocation by solving Bellman - equation. - ''' - def __init__(self,Para,mugrid): - ''' - Initializes the class from the calibration Para - ''' - self.beta = Para.beta - self.Pi = Para.Pi - self.G = Para.G - self.S = len(Para.Pi) # number of states - self.Theta = Para.Theta - self.Para = Para - self.mugrid = mugrid - - #now find the first best allocation - self.solve_time1_bellman() - self.T.time_0 = True #Bellman equation now solves time 0 problem - - def solve_time1_bellman(self): - ''' - Solve the time 1 Bellman equation for calibration Para and initial grid mugrid0 - ''' - Para,mugrid0 = self.Para,self.mugrid - Pi = Para.Pi - S = len(Para.Pi) - - #First get initial fit from lucas stockey solution. - #Need to change things to be ex_ante - PP = LS.Planners_Allocation_Sequential(Para) - interp = utilities.interpolator_factory(2,None) - - def incomplete_allocation(mu_,s_): - c,n,x,V = PP.time1_value(mu_) - return c,n,Pi[s_].dot(x),Pi[s_].dot(V) - cf,nf,xgrid,Vf,xprimef = [],[],[],[],[] - for s_ in range(S): - c,n,x,V = zip(*map(lambda mu: incomplete_allocation(mu,s_),mugrid0)) - c,n = np.vstack(c).T,np.vstack(n).T - x,V = np.hstack(x),np.hstack(V) - xprimes = np.vstack([x]*S) - cf.append(interp(x,c)) - nf.append(interp(x,n)) - Vf.append(interp(x,V)) - xgrid.append(x) - xprimef.append(interp(x,xprimes)) - cf,nf,xprimef = utilities.fun_vstack(cf), utilities.fun_vstack(nf),utilities.fun_vstack(xprimef) - Vf = utilities.fun_hstack(Vf) - policies = [cf,nf,xprimef] - - - #create xgrid - x = np.vstack(xgrid).T - xbar = [x.min(0).max(),x.max(0).min()] - xgrid = np.linspace(xbar[0],xbar[1],len(mugrid0)) - self.xgrid = xgrid - - #Now iterate on Bellman equation - T = BellmanEquation(Para,xgrid,policies) - diff = 1. - while diff > 1e-6: - PF = T(Vf) - - Vfnew,policies = self.fit_policy_function(PF) - diff = np.abs((Vf(xgrid)-Vfnew(xgrid))/Vf(xgrid)).max() - - print(diff) - Vf = Vfnew - - #store value function policies and Bellman Equations - self.Vf = Vf - self.policies = policies - self.T = T - - def fit_policy_function(self,PF): - ''' - Fits the policy functions - ''' - S,xgrid = len(self.Pi),self.xgrid - interp = utilities.interpolator_factory(3,0) - cf,nf,xprimef,Tf,Vf = [],[],[],[],[] - for s_ in range(S): - PFvec = np.vstack([PF(x,s_) for x in self.xgrid]).T - Vf.append(interp(xgrid,PFvec[0,:])) - cf.append(interp(xgrid,PFvec[1:1+S])) - nf.append(interp(xgrid,PFvec[1+S:1+2*S])) - xprimef.append(interp(xgrid,PFvec[1+2*S:1+3*S])) - Tf.append(interp(xgrid,PFvec[1+3*S:])) - policies = utilities.fun_vstack(cf), utilities.fun_vstack(nf),utilities.fun_vstack(xprimef),utilities.fun_vstack(Tf) - Vf = utilities.fun_hstack(Vf) - return Vf,policies - - def Tau(self,c,n): - ''' - Computes Tau given c,n - ''' - Para = self.Para - Uc,Un = Para.Uc(c,n),Para.Un(c,n) - - return 1+Un/(self.Theta * Uc) - - def time0_allocation(self,B_,s0): - ''' - Finds the optimal allocation given initial government debt B_ and state s_0 - ''' - PF = self.T(self.Vf) - - z0 = PF(B_,s0) - c0,n0,xprime0,T0 = z0[1:] - return c0,n0,xprime0,T0 - - def simulate(self,B_,s_0,T,sHist=None): - ''' - Simulates planners policies for T periods - ''' - Para,Pi = self.Para,self.Pi - Uc = Para.Uc - cf,nf,xprimef,Tf = self.policies - - if sHist == None: - sHist = simulate_markov(Pi,s_0,T) - - cHist,nHist,Bhist,xHist,TauHist,THist,muHist = np.zeros((7,T)) - #time0 - cHist[0],nHist[0],xHist[0],THist[0] = self.time0_allocation(B_,s_0) - TauHist[0] = self.Tau(cHist[0],nHist[0])[s_0] - Bhist[0] = B_ - muHist[0] = self.Vf[s_0](xHist[0]) - - #time 1 onward - for t in range(1,T): - s_,x,s = sHist[t-1],xHist[t-1],sHist[t] - c,n,xprime,T = cf[s_,:](x),nf[s_,:](x),xprimef[s_,:](x),Tf[s_,:](x) - - Tau = self.Tau(c,n)[s] - u_c = Uc(c,n) - Eu_c = Pi[s_,:].dot(u_c) - - muHist[t] = self.Vf[s](xprime[s]) - - cHist[t],nHist[t],Bhist[t],TauHist[t] = c[s],n[s],x/Eu_c,Tau - xHist[t],THist[t] = xprime[s],T[s] - return cHist,nHist,Bhist,xHist,TauHist,THist,muHist,sHist - - -class BellmanEquation(object): - ''' - Bellman equation for the continuation of the Lucas-Stokey Problem - ''' - def __init__(self,Para,xgrid,policies0): - ''' - Initializes the class from the calibration Para - ''' - self.beta = Para.beta - self.Pi = Para.Pi - self.G = Para.G - self.S = len(Para.Pi) # number of states - self.Theta = Para.Theta - self.Para = Para - - self.xbar = [min(xgrid),max(xgrid)] - self.time_0 = False - - self.z0 = {} - cf,nf,xprimef = policies0 - - for s_ in range(self.S): - for x in xgrid: - self.z0[x,s_] = np.hstack([cf[s_,:](x),nf[s_,:](x),xprimef[s_,:](x),np.zeros(self.S)]) - - self.find_first_best() - - def find_first_best(self): - ''' - Find the first best allocation - ''' - Para = self.Para - S,Theta,Uc,Un,G = self.S,self.Theta,Para.Uc,Para.Un,self.G - - def res(z): - c = z[:S] - n = z[S:] - return np.hstack( - [Theta*Uc(c,n)+Un(c,n), Theta*n - c - G] - ) - res = root(res,0.5*np.ones(2*S)) - if not res.success: - raise Exception('Could not find first best') - - self.cFB = res.x[:S] - self.nFB = res.x[S:] - IFB = Uc(self.cFB,self.nFB)*self.cFB + Un(self.cFB,self.nFB)*self.nFB - - self.xFB = np.linalg.solve(np.eye(S) - self.beta*self.Pi, IFB) - - self.zFB = {} - for s in range(S): - self.zFB[s] = np.hstack([self.cFB[s],self.nFB[s],self.Pi[s].dot(self.xFB),0.]) - - - - def __call__(self,Vf): - ''' - Given continuation value function next period return value function this - period return T(V) and optimal policies - ''' - if not self.time_0: - PF = lambda x,s: self.get_policies_time1(x,s,Vf) - else: - PF = lambda B_,s0: self.get_policies_time0(B_,s0,Vf) - return PF - - def get_policies_time1(self,x,s_,Vf): - ''' - Finds the optimal policies - ''' - Para,beta,Theta,G,S,Pi = self.Para,self.beta,self.Theta,self.G,self.S,self.Pi - U,Uc,Un = Para.U,Para.Uc,Para.Un - - def objf(z): - c,n,xprime = z[:S],z[S:2*S],z[2*S:3*S] - - Vprime = np.empty(S) - for s in range(S): - Vprime[s] = Vf[s](xprime[s]) - - return -Pi[s_].dot(U(c,n)+beta*Vprime) - - def cons(z): - c,n,xprime,T = z[:S],z[S:2*S],z[2*S:3*S],z[3*S:] - u_c = Uc(c,n) - Eu_c = Pi[s_].dot(u_c) - return np.hstack([ - x*u_c/Eu_c - u_c*(c-T)-Un(c,n)*n - beta*xprime, - Theta*n - c - G - ]) - - if Para.transfers: - bounds = [(0.,100)]*S+[(0.,100)]*S+[self.xbar]*S+[(0.,100.)]*S - else: - bounds = [(0.,100)]*S+[(0.,100)]*S+[self.xbar]*S+[(0.,0.)]*S - out,fx,_,imode,smode = fmin_slsqp(objf,self.z0[x,s_],f_eqcons=cons, - bounds=bounds,full_output=True,iprint=0) - - if imode >0: - raise Exception(smode) - - self.z0[x,s_] = out - return np.hstack([-fx,out]) - - def get_policies_time0(self,B_,s0,Vf): - ''' - Finds the optimal policies - ''' - Para,beta,Theta,G = self.Para,self.beta,self.Theta,self.G - U,Uc,Un = Para.U,Para.Uc,Para.Un - - def objf(z): - c,n,xprime = z[:-1] - - return -(U(c,n)+beta*Vf[s0](xprime)) - - def cons(z): - c,n,xprime,T = z - return np.hstack([ - -Uc(c,n)*(c-B_-T)-Un(c,n)*n - beta*xprime, - (Theta*n - c - G)[s0] - ]) - - if Para.transfers: - bounds=[(0.,100),(0.,100),self.xbar,(0.,100.)] - else: - bounds=[(0.,100),(0.,100),self.xbar,(0.,0.)] - out,fx,_,imode,smode = fmin_slsqp(objf,self.zFB[s0],f_eqcons=cons, - bounds=bounds,full_output=True,iprint=0) - - if imode >0: - raise Exception(smode) - - return np.hstack([-fx,out]) diff --git a/examples/amss_figures.py b/examples/amss_figures.py deleted file mode 100644 index 1aa42a821..000000000 --- a/examples/amss_figures.py +++ /dev/null @@ -1,210 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Feb 20 14:07:56 2015 - -@author: dgevans -""" -import matplotlib.pyplot as plt -import numpy as np -import lucas_stokey as LS -import amss -from calibrations.BGP import M1 -from calibrations.CES import M1 as M_convergence -from calibrations.CES import M_time_example -import utilities - -#initialize mugrid for value function iteration -muvec = np.linspace(-0.7,0.01,200) - - -''' -Time Varying Example -''' - -M_time_example.transfers = True #Government can use transfers -PP_seq_time = LS.Planners_Allocation_Sequential(M_time_example) #solve sequential problem -PP_im_time = amss.Planners_Allocation_Bellman(M_time_example,muvec) - -sHist_h = np.array([0,1,2,3,5,5,5]) -sHist_l = np.array([0,1,2,4,5,5,5]) - -sim_seq_h = PP_seq_time.simulate(1.,0,7,sHist_h) -sim_im_h = PP_im_time.simulate(1.,0,7,sHist_h) -sim_seq_l = PP_seq_time.simulate(1.,0,7,sHist_l) -sim_im_l = PP_im_time.simulate(1.,0,7,sHist_l) - -plt.figure(figsize=[14,10]) -plt.subplot(3,2,1) -plt.title('Consumption') -plt.plot(sim_seq_l[0],'-ok') -plt.plot(sim_im_l[0],'-or') -plt.plot(sim_seq_h[0],'-^k') -plt.plot(sim_im_h[0],'-^r') -plt.subplot(3,2,2) -plt.title('Labor') -plt.plot(sim_seq_l[1],'-ok') -plt.plot(sim_im_l[1],'-or') -plt.plot(sim_seq_h[1],'-^k') -plt.plot(sim_im_h[1],'-^r') -plt.legend(('Complete Markets','Incomplete Markets'),loc='best') -plt.subplot(3,2,3) -plt.title('Government Debt') -plt.plot(sim_seq_l[2],'-ok') -plt.plot(sim_im_l[2],'-or') -plt.plot(sim_seq_h[2],'-^k') -plt.plot(sim_im_h[2],'-^r') -plt.subplot(3,2,4) -plt.title('Tax Rate') -plt.plot(sim_seq_l[3],'-ok') -plt.plot(sim_im_l[4],'-or') -plt.plot(sim_seq_h[3],'-^k') -plt.plot(sim_im_h[4],'-^r') -plt.subplot(3,2,5) -plt.title('Government Spending') -plt.plot(M_time_example.G[sHist_l],'-ok') -plt.plot(M_time_example.G[sHist_l],'-or') -plt.plot(M_time_example.G[sHist_h],'-^k') -plt.plot(M_time_example.G[sHist_h],'-^r') -plt.ylim([0.05,0.25]) -plt.subplot(3,2,6) -plt.title('Output') -plt.plot(M_time_example.Theta[sHist_l]*sim_seq_l[1],'-ok') -plt.plot(M_time_example.Theta[sHist_l]*sim_im_l[1],'-or') -plt.plot(M_time_example.Theta[sHist_h]*sim_seq_h[1],'-^k') -plt.plot(M_time_example.Theta[sHist_h]*sim_im_h[1],'-^r') -plt.tight_layout() -plt.savefig('TaxSequence_time_varying_AMSS.png') - - - - -''' -BGP Example -''' - -M1.transfers = False #Government can use transfers -PP_seq = LS.Planners_Allocation_Sequential(M1) #solve sequential problem -PP_bel = LS.Planners_Allocation_Bellman(M1,muvec) #solve recursive problem -PP_im = amss.Planners_Allocation_Bellman(M1,muvec) - -T = 20 -#sHist = utilities.simulate_markov(M1.Pi,0,T) -sHist = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],dtype=int) - -#simulate -sim_seq = PP_seq.simulate(0.5,0,T,sHist) -#sim_bel = PP_bel.simulate(0.5,0,T,sHist) -sim_im = PP_im.simulate(0.5,0,T,sHist) - -#plot policies -plt.figure(figsize=[14,10]) -plt.subplot(3,2,1) -plt.title('Consumption') -plt.plot(sim_seq[0],'-ok') -#plt.plot(sim_bel[0],'-xk') -plt.plot(sim_im[0],'-^k') -plt.legend(('Complete Markets','Incomplete Markets'),loc='best') -plt.subplot(3,2,2) -plt.title('Labor') -plt.plot(sim_seq[1],'-ok') -#plt.plot(sim_bel[1],'-xk') -plt.plot(sim_im[1],'-^k') -plt.subplot(3,2,3) -plt.title('Government Debt') -plt.plot(sim_seq[2],'-ok') -#plt.plot(sim_bel[2],'-xk') -plt.plot(sim_im[2],'-^k') -plt.subplot(3,2,4) -plt.title('Tax Rate') -plt.plot(sim_seq[3],'-ok') -#plt.plot(sim_bel[3],'-xk') -plt.plot(sim_im[4],'-^k') -plt.subplot(3,2,5) -plt.title('Government Spending') -plt.plot(M1.G[sHist],'-ok') -#plt.plot(M1.G[sHist],'-^k') -plt.ylim([0.05,0.25]) -plt.subplot(3,2,6) -plt.title('Output') -plt.plot(M1.Theta[sHist]*sim_seq[1],'-ok') -#plt.plot(M1.Theta[sHist]*sim_bel[1],'-xk') -plt.plot(M1.Theta[sHist]*sim_im[1],'-^k') -plt.savefig('TaxSequence_AMSS.png') -plt.tight_layout() - - -#Now long simulations -T_long = 200 -sim_seq_long = PP_seq.simulate(0.5,0.,T_long) -sHist_long = sim_seq_long[-3] -sim_im_long = PP_im.simulate(0.5,0.,T_long,sHist_long) - -plt.figure(figsize=[14,10]) -plt.subplot(3,2,1) -plt.title('Consumption') -plt.plot(sim_seq_long[0],'-k') -plt.plot(sim_im_long[0],'-.k') -plt.legend(('Complete Markets','Incomplete Markets'),loc='best') -plt.subplot(3,2,2) -plt.title('Labor') -plt.plot(sim_seq_long[1],'-k') -plt.plot(sim_im_long[1],'-.k') -plt.subplot(3,2,3) -plt.title('Government Debt') -plt.plot(sim_seq_long[2],'-k') -plt.plot(sim_im_long[2],'-.k') -plt.subplot(3,2,4) -plt.title('Tax Rate') -plt.plot(sim_seq_long[3],'-k') -plt.plot(sim_im_long[4],'-.k') -plt.subplot(3,2,5) -plt.title('Government Spending') -plt.plot(M1.G[sHist_long],'-k') -plt.plot(M1.G[sHist_long],'-.k') -plt.ylim([0.05,0.25]) -plt.subplot(3,2,6) -plt.title('Output') -plt.plot(M1.Theta[sHist_long]*sim_seq_long[1],'-k') -plt.plot(M1.Theta[sHist_long]*sim_im_long[1],'-.k') -plt.tight_layout() -plt.savefig('Long_SimulationAMSS.png') - -''' -Show Convergence example -''' -muvec = np.linspace(-0.15,0.0,100) #change -PP_C = amss.Planners_Allocation_Bellman(M_convergence,muvec) -xgrid = PP_C.xgrid -xf = PP_C.policies[-2] #get x policies -plt.figure() -for s in range(2): - plt.plot(xgrid,xf[0,s](xgrid)-xgrid) - -sim_seq_convergence = PP_C.simulate(0.5,0.,2000) -sHist_long = sim_seq_convergence[-1] - -plt.figure(figsize=[14,10]) -plt.subplot(3,2,1) -plt.title('Consumption') -plt.plot(sim_seq_convergence[0],'-k') -plt.legend(('Complete Markets','Incomplete Markets'),loc='best') -plt.subplot(3,2,2) -plt.title('Labor') -plt.plot(sim_seq_convergence[1],'-k') -plt.subplot(3,2,3) -plt.title('Government Debt') -plt.plot(sim_seq_convergence[2],'-k') -plt.subplot(3,2,4) -plt.title('Tax Rate') -plt.plot(sim_seq_convergence[3],'-k') -plt.subplot(3,2,5) -plt.title('Government Spending') -plt.plot(M_convergence.G[sHist_long],'-k') -plt.ylim([0.05,0.25]) -plt.subplot(3,2,6) -plt.title('Output') -plt.plot(M_convergence.Theta[sHist_long]*sim_seq_convergence[1],'-k') -plt.tight_layout() -plt.savefig('Convergence_SimulationAMSS.png') - - diff --git a/examples/ar1_acov.py b/examples/ar1_acov.py deleted file mode 100644 index 7aa1370f6..000000000 --- a/examples/ar1_acov.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -Plots autocovariance function for AR(1) X' = phi X + epsilon -""" -import numpy as np -import matplotlib.pyplot as plt - -num_rows, num_cols = 2, 1 -fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 8)) -plt.subplots_adjust(hspace=0.4) - -# Autocovariance when phi = 0.8 -temp = r'autocovariance, $\phi = {0:.2}$' -for i, phi in enumerate((0.8, -0.8)): - ax = axes[i] - times = list(range(16)) - acov = [phi**k / (1 - phi**2) for k in times] - ax.plot(times, acov, 'bo-', alpha=0.6, label=temp.format(phi)) - ax.legend(loc='upper right') - ax.set_xlabel('time') - ax.set_xlim((0, 15)) - ax.hlines(0, 0, 15, linestyle='--', alpha=0.5) -plt.show() diff --git a/examples/ar1_cycles.py b/examples/ar1_cycles.py deleted file mode 100644 index 83dd1941b..000000000 --- a/examples/ar1_cycles.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Helps to illustrate the spectral density for AR(1) X' = phi X + epsilon -""" -import numpy as np -import matplotlib.pyplot as plt - -phi = -0.8 -times = list(range(16)) -y1 = [phi**k / (1 - phi**2) for k in times] -y2 = [np.cos(np.pi * k) for k in times] -y3 = [a * b for a, b in zip(y1, y2)] - -num_rows, num_cols = 3, 1 -fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 8)) -plt.subplots_adjust(hspace=0.25) - -# Autocovariance when phi = -0.8 -ax = axes[0] -ax.plot(times, y1, 'bo-', alpha=0.6, label=r'$\gamma(k)$') -ax.legend(loc='upper right') -ax.set_xlim(0, 15) -ax.set_yticks((-2, 0, 2)) -ax.hlines(0, 0, 15, linestyle='--', alpha=0.5) - -# Cycles at frequence pi -ax = axes[1] -ax.plot(times, y2, 'bo-', alpha=0.6, label=r'$\cos(\pi k)$') -ax.legend(loc='upper right') -ax.set_xlim(0, 15) -ax.set_yticks((-1, 0, 1)) -ax.hlines(0, 0, 15, linestyle='--', alpha=0.5) - -# Product -ax = axes[2] -ax.stem(times, y3, label=r'$\gamma(k) \cos(\pi k)$') -ax.legend(loc='upper right') -ax.set_xlim((0, 15)) -ax.set_ylim(-3, 3) -ax.set_yticks((-1, 0, 1, 2, 3)) -ax.hlines(0, 0, 15, linestyle='--', alpha=0.5) - -plt.show() diff --git a/examples/ar1_sd.py b/examples/ar1_sd.py deleted file mode 100644 index 2f5e49c9c..000000000 --- a/examples/ar1_sd.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Plots spectral density for AR(1) X' = phi X + epsilon -""" -import numpy as np -import matplotlib.pyplot as plt - - -def ar1_sd(phi, omega): - return 1 / (1 - 2 * phi * np.cos(omega) + phi**2) - -omegas = np.linspace(0, np.pi, 180) -num_rows, num_cols = 2, 1 -fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 8)) -plt.subplots_adjust(hspace=0.4) - -# Autocovariance when phi = 0.8 -temp = r'spectral density, $\phi = {0:.2}$' -for i, phi in enumerate((0.8, -0.8)): - ax = axes[i] - sd = ar1_sd(phi, omegas) - ax.plot(omegas, sd, 'b-', alpha=0.6, lw=2, label=temp.format(phi)) - ax.legend(loc='upper center') - ax.set_xlabel('frequency') - ax.set_xlim((0, np.pi)) -plt.show() diff --git a/examples/beta-binomial.py b/examples/beta-binomial.py deleted file mode 100644 index 09c9bdc59..000000000 --- a/examples/beta-binomial.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Filename: beta-binomial.py -Authors: John Stachurski, Thomas J. Sargent - -""" -from scipy.special import binom, beta -import matplotlib.pyplot as plt -import numpy as np - - -def gen_probs(n, a, b): - probs = np.zeros(n+1) - for k in range(n+1): - probs[k] = binom(n, k) * beta(k + a, n - k + b) / beta(a, b) - return probs - -n = 50 -a_vals = [0.5, 1, 100] -b_vals = [0.5, 1, 100] -fig, ax = plt.subplots() -for a, b in zip(a_vals, b_vals): - ab_label = r'$a = %.1f$, $b = %.1f$' % (a, b) - ax.plot(list(range(0, n+1)), gen_probs(n, a, b), '-o', label=ab_label) -ax.legend() -plt.show() diff --git a/examples/bifurcation_diagram.py b/examples/bifurcation_diagram.py deleted file mode 100644 index ccb5c0fa6..000000000 --- a/examples/bifurcation_diagram.py +++ /dev/null @@ -1,18 +0,0 @@ -""" -Filename: bifurcation_diagram.py -Reference: http://quant-econ.net/py/python_oop.html -""" -from chaos_class import Chaos -import matplotlib.pyplot as plt - -fig, ax = plt.subplots() -ch = Chaos(0.1, 4) -r = 2.5 -while r < 4: - ch.r = r - t = ch.generate_sequence(1000)[950:] - ax.plot([r] * len(t), t, 'b.', ms=0.6) - r = r + 0.005 - -ax.set_xlabel(r'$r$', fontsize=16) -plt.show() diff --git a/examples/binom_df.py b/examples/binom_df.py deleted file mode 100644 index c41e48ea9..000000000 --- a/examples/binom_df.py +++ /dev/null @@ -1,19 +0,0 @@ -import matplotlib.pyplot as plt -from scipy.stats import binom - -fig, axes = plt.subplots(2, 2) -plt.subplots_adjust(hspace=0.4) -axes = axes.flatten() -ns = [1, 2, 4, 8] -dom = list(range(9)) - -for ax, n in zip(axes, ns): - b = binom(n, 0.5) - ax.bar(dom, b.pmf(dom), alpha=0.6, align='center') - ax.set_xlim(-0.5, 8.5) - ax.set_ylim(0, 0.55) - ax.set_xticks(list(range(9))) - ax.set_yticks((0, 0.2, 0.4)) - ax.set_title(r'$n = {}$'.format(n)) - -fig.show() diff --git a/examples/bisection.py b/examples/bisection.py deleted file mode 100644 index dd7a688a9..000000000 --- a/examples/bisection.py +++ /dev/null @@ -1,20 +0,0 @@ - -def bisect(f, a, b, tol=10e-5): - """ - Implements the bisection root finding algorithm, assuming that f is a - real-valued function on [a, b] satisfying f(a) < 0 < f(b). - """ - lower, upper = a, b - - while upper - lower > tol: - middle = 0.5 * (upper + lower) - # === if root is between lower and middle === # - if f(middle) > 0: - lower, upper = lower, middle - # === if root is between middle and upper === # - else: - lower, upper = middle, upper - - return 0.5 * (upper + lower) - - diff --git a/examples/boxplot_example.py b/examples/boxplot_example.py deleted file mode 100644 index d3c797986..000000000 --- a/examples/boxplot_example.py +++ /dev/null @@ -1,15 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -n = 500 -x = np.random.randn(n) # N(0, 1) -x = np.exp(x) # Map x to lognormal -y = np.random.randn(n) + 2.0 # N(2, 1) -z = np.random.randn(n) + 4.0 # N(4, 1) - -fig, ax = plt.subplots(figsize=(10, 6.6)) -ax.boxplot([x, y, z]) -ax.set_xticks((1, 2, 3)) -ax.set_ylim(-2, 14) -ax.set_xticklabels((r'$X$', r'$Y$', r'$Z$'), fontsize=16) -plt.show() diff --git a/examples/calibrations/BGP.py b/examples/calibrations/BGP.py deleted file mode 100644 index 44c30cdac..000000000 --- a/examples/calibrations/BGP.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Feb 18 15:32:07 2015 - -@author: dgevans -""" -import numpy as np - -class baseline(object): - beta = 0.9 - psi = 0.69 - - Pi = 0.5 *np.ones((2,2)) - - G = np.array([0.1,0.2]) - - Theta = np.ones(2) - - transfers = False - - #derivatives of utiltiy function - def U(self,c,n): - return np.log(c) + self.psi*np.log(1-n) - - def Uc(self,c,n): - return 1./c - - def Ucc(self,c,n): - return -c**(-2) - - def Un(self,c,n): - return -self.psi/(1-n) - - def Unn(self,c,n): - return -self.psi/(1-n)**2 - - -#Model 1 -M1 = baseline() - -#Model 2 - -M2 = baseline() -M2.G = np.array([0.15]) -M2.Pi = np.ones((1,1)) -M2.Theta = np.ones(1) - -#Model 3 with time varying - -M_time_example = baseline() - -M_time_example.Pi = np.array([[0., 1., 0., 0., 0., 0.], - [0., 0., 1., 0., 0., 0.], - [0., 0., 0., 0.5, 0.5, 0.], - [0., 0., 0., 0., 0., 1.], - [0., 0., 0., 0., 0., 1.], - [0., 0., 0., 0., 0., 1.]]) - -M_time_example.G = np.array([0.1, 0.1, 0.1, 0.2, 0.1, 0.1]) -M_time_example.Theta = np.ones(6) # Theta can in principle be random \ No newline at end of file diff --git a/examples/calibrations/CES.py b/examples/calibrations/CES.py deleted file mode 100644 index 6c8144ef8..000000000 --- a/examples/calibrations/CES.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Mar 10 08:55:03 2015 - -@author: dgevans -""" - -import numpy as np - -class baseline(object): - beta = 0.9 - - sigma = 2. - - gamma = 2. - - Pi = 0.5 *np.ones((2,2)) - - G = np.array([0.1,0.2]) - - Theta = np.ones(2) - - transfers = False - - #derivatives of utiltiy function - def U(self,c,n): - sigma = self.sigma - if sigma == 1.: - U = np.log(c) - else: - U = (c**(1-sigma)-1)/(1-sigma) - return U - n**(1+self.gamma)/(1+self.gamma) - - def Uc(self,c,n): - return c**(-self.sigma) - - def Ucc(self,c,n): - return -self.sigma*c**(-self.sigma-1.) - - def Un(self,c,n): - return -n**self.gamma - - def Unn(self,c,n): - return -self.gamma * n**(self.gamma-1.) - -#Model 1 -M1 = baseline() - -#Model 2 - -M2 = baseline() -M2.G = np.array([0.15]) -M2.Pi = np.ones((1,1)) -M2.Theta = np.ones(1) - -#Model 3 with time varying - -M_time_example = baseline() - -M_time_example.Pi = np.array([[0., 1., 0., 0., 0., 0.], - [0., 0., 1., 0., 0., 0.], - [0., 0., 0., 0.5, 0.5, 0.], - [0., 0., 0., 0., 0., 1.], - [0., 0., 0., 0., 0., 1.], - [0., 0., 0., 0., 0., 1.]]) - -M_time_example.G = np.array([0.1, 0.1, 0.1, 0.2, 0.1, 0.1]) -M_time_example.Theta = np.ones(6) # Theta can in principle be random \ No newline at end of file diff --git a/examples/calibrations/__init__.py b/examples/calibrations/__init__.py deleted file mode 100644 index a0a54464b..000000000 --- a/examples/calibrations/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Feb 18 16:13:07 2015 - -@author: dgevans -""" - diff --git a/examples/career_vf_plot.py b/examples/career_vf_plot.py deleted file mode 100644 index 31c96b60b..000000000 --- a/examples/career_vf_plot.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -Origin: QE by John Stachurski and Thomas J. Sargent -Filename: career_vf_plot.py -Authors: John Stachurski and Thomas Sargent -LastModified: 11/08/2013 - -""" - -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d.axes3d import Axes3D -import numpy as np -from matplotlib import cm -import quantecon as qe -from quantecon.models import CareerWorkerProblem - -# === solve for the value function === # -wp = CareerWorkerProblem() -v_init = np.ones((wp.N, wp.N))*100 -v = qe.compute_fixed_point(wp.bellman_operator, v_init) - -# === plot value function === # -fig = plt.figure(figsize=(8, 6)) -ax = fig.add_subplot(111, projection='3d') -tg, eg = np.meshgrid(wp.theta, wp.epsilon) -ax.plot_surface(tg, - eg, - v.T, - rstride=2, cstride=2, - cmap=cm.jet, - alpha=0.5, - linewidth=0.25) -ax.set_zlim(150, 200) -ax.set_xlabel('theta', fontsize=14) -ax.set_ylabel('epsilon', fontsize=14) -plt.show() diff --git a/examples/cauchy_samples.py b/examples/cauchy_samples.py deleted file mode 100644 index c0a3e0d94..000000000 --- a/examples/cauchy_samples.py +++ /dev/null @@ -1,29 +0,0 @@ - -import numpy as np -from scipy.stats import cauchy -import matplotlib.pyplot as plt - -n = 1000 -distribution = cauchy() - -fig, ax = plt.subplots() -data = distribution.rvs(n) - -if 0: - ax.plot(list(range(n)), data, 'bo', alpha=0.5) - ax.vlines(list(range(n)), 0, data, lw=0.2) - ax.set_title("{} observations from the Cauchy distribution".format(n)) - -if 1: - # == Compute sample mean at each n == # - sample_mean = np.empty(n) - for i in range(n): - sample_mean[i] = np.mean(data[:i]) - - # == Plot == # - ax.plot(list(range(n)), sample_mean, 'r-', lw=3, alpha=0.6, - label=r'$\bar X_n$') - ax.plot(list(range(n)), [0] * n, 'k--', lw=0.5) - ax.legend() - -fig.show() diff --git a/examples/chaos_class.py b/examples/chaos_class.py deleted file mode 100644 index e29da6de4..000000000 --- a/examples/chaos_class.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -Filename: chaos_class.py -Reference: http://quant-econ.net/py/python_oop.html -""" -class Chaos: - """ - Models the dynamical system with :math:`x_{t+1} = r x_t (1 - x_t)` - """ - def __init__(self, x0, r): - """ - Initialize with state x0 and parameter r - """ - self.x, self.r = x0, r - - def update(self): - "Apply the map to update state." - self.x = self.r * self.x *(1 - self.x) - - def generate_sequence(self, n): - "Generate and return a sequence of length n." - path = [] - for i in range(n): - path.append(self.x) - self.update() - return path diff --git a/examples/chaotic_ts.py b/examples/chaotic_ts.py deleted file mode 100644 index fef5876f3..000000000 --- a/examples/chaotic_ts.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Filename: choatic_ts.py -Reference: http://quant-econ.net/py/python_oop.html -""" -from chaos_class import Chaos -import matplotlib.pyplot as plt - -ch = Chaos(0.1, 4.0) -ts_length = 250 - -fig, ax = plt.subplots() -ax.set_xlabel(r'$t$', fontsize=14) -ax.set_ylabel(r'$x_t$', fontsize=14) -x = ch.generate_sequence(ts_length) -ax.plot(range(ts_length), x, 'bo-', alpha=0.5, lw=2, label=r'$x_t$') -plt.show() diff --git a/examples/clt3d.py b/examples/clt3d.py deleted file mode 100644 index 0834c3787..000000000 --- a/examples/clt3d.py +++ /dev/null @@ -1,80 +0,0 @@ -""" -Origin: QE by John Stachurski and Thomas J. Sargent -Filename: clt3d.py - -Visual illustration of the central limit theorem. Produces a 3D figure -showing the density of the scaled sample mean \sqrt{n} \bar X_n plotted -against n. -""" - -import numpy as np -from scipy.stats import beta, gaussian_kde -from mpl_toolkits.mplot3d import Axes3D -from matplotlib.collections import PolyCollection -import matplotlib.pyplot as plt - -beta_dist = beta(2, 2) - - -def gen_x_draws(k): - """ - Returns a flat array containing k independent draws from the - distribution of X, the underlying random variable. This distribution is - itself a convex combination of three beta distributions. - """ - bdraws = beta_dist.rvs((3, k)) - # == Transform rows, so each represents a different distribution == # - bdraws[0, :] -= 0.5 - bdraws[1, :] += 0.6 - bdraws[2, :] -= 1.1 - # == Set X[i] = bdraws[j, i], where j is a random draw from {0, 1, 2} == # - js = np.random.random_integers(0, 2, size=k) - X = bdraws[js, np.arange(k)] - # == Rescale, so that the random variable is zero mean == # - m, sigma = X.mean(), X.std() - return (X - m) / sigma - -nmax = 5 -reps = 100000 -ns = list(range(1, nmax + 1)) - -# == Form a matrix Z such that each column is reps independent draws of X == # -Z = np.empty((reps, nmax)) -for i in range(nmax): - Z[:, i] = gen_x_draws(reps) -# == Take cumulative sum across columns -S = Z.cumsum(axis=1) -# == Multiply j-th column by sqrt j == # -Y = (1 / np.sqrt(ns)) * S - -# == Plot == # - -fig = plt.figure() -ax = fig.gca(projection='3d') - -a, b = -3, 3 -gs = 100 -xs = np.linspace(a, b, gs) - -# == Build verts == # -greys = np.linspace(0.3, 0.7, nmax) -verts = [] -for n in ns: - density = gaussian_kde(Y[:, n-1]) - ys = density(xs) - verts.append(list(zip(xs, ys))) - -poly = PolyCollection(verts, facecolors=[str(g) for g in greys]) -poly.set_alpha(0.85) -ax.add_collection3d(poly, zs=ns, zdir='x') - -# ax.text(np.mean(rhos), a-1.4, -0.02, r'$\beta$', fontsize=16) -# ax.text(np.max(rhos)+0.016, (a+b)/2, -0.02, r'$\log(y)$', fontsize=16) -ax.set_xlim3d(1, nmax) -ax.set_xticks(ns) -ax.set_xlabel("n") -ax.set_yticks((-3, 0, 3)) -ax.set_ylim3d(a, b) -ax.set_zlim3d(0, 0.4) -ax.set_zticks((0.2, 0.4)) -plt.show() diff --git a/examples/consumer.py b/examples/consumer.py deleted file mode 100644 index b6de2ccf6..000000000 --- a/examples/consumer.py +++ /dev/null @@ -1,17 +0,0 @@ -class Consumer: - - def __init__(self, w): - "Initialize consumer with w dollars of wealth" - self.wealth = w - - def earn(self, y): - "The consumer earns y dollars" - self.wealth += y - - def spend(self, x): - "The consumer spends x dollars if feasible" - new_wealth = self.wealth - x - if new_wealth < 0: - print("Insufficent funds") - else: - self.wealth = new_wealth diff --git a/examples/dice.py b/examples/dice.py deleted file mode 100644 index 115be4940..000000000 --- a/examples/dice.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Filename: dice.py -""" - -import random - - -class Dice: - - faces = (1, 2, 3, 4, 5, 6) - - def __init__(self): - self.current_face = 1 - - def roll(self): - self.current_face = random.choice(Dice.faces) diff --git a/examples/duopoly_lqnash.py b/examples/duopoly_lqnash.py deleted file mode 100644 index 4702fa6b4..000000000 --- a/examples/duopoly_lqnash.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Filename: lqnash.py -Authors: Chase Coleman, Thomas Sargent - -This file provides an example of a Markov Perfect Equilibrium for a -simple duopoly example. - -See the lecture at http://quant-econ.net/py/markov_perf.html for a -description of the model. - -""" -from __future__ import division -from numpy import array, eye -from quantecon.lqnash import nnash - - -# ---------------------------------------------------------------------# -# Set up parameter values and LQ matrices -# Remember state is x_t = [1, y_{1, t}, y_{2, t}] and -# control is u_{i, t} = [y_{i, t+1} - y_{i, t}] -# ---------------------------------------------------------------------# -a0 = 10. -a1 = 1. -beta = 1. -d = .5 - -a = eye(3) -b1 = array([[0.], [1.], [0.]]) -b2 = array([[0.], [0.], [1.]]) - -r1 = array([[a0, 0., 0.], - [0., -a1, -a1/2.], - [0, -a1/2., 0.]]) - -r2 = array([[a0, 0., 0.], - [0., 0., -a1/2.], - [0, -a1/2., -a1]]) - -q1 = array([[-.5*d]]) -q2 = array([[-.5*d]]) - - -# ---------------------------------------------------------------------# -# Solve using QE's nnash function -# ---------------------------------------------------------------------# - -f1, f2, p1, p2 = nnash(a, b1, b2, r1, r2, q1, q2, 0., 0., 0., 0., 0., 0., - tol=1e-8, max_iter=1000) diff --git a/examples/duopoly_mpe.py b/examples/duopoly_mpe.py deleted file mode 100644 index 124b64927..000000000 --- a/examples/duopoly_mpe.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -@authors: Chase Coleman, Thomas Sargent, John Stachurski - -Markov Perfect Equilibrium for the simple duopoly example. - -See the lecture at http://quant-econ.net/py/markov_perf.html for a -description of the model. -""" - -from __future__ import division -import numpy as np -import quantecon as qe - -# == Parameters == # -a0 = 10.0 -a1 = 2.0 -beta = 0.96 -gamma = 12.0 - -# == In LQ form == # - -A = np.eye(3) - -B1 = np.array([[0.], [1.], [0.]]) -B2 = np.array([[0.], [0.], [1.]]) - - -R1 = [[0., -a0/2, 0.], - [-a0/2., a1, a1/2.], - [0, a1/2., 0.]] - -R2 = [[0., 0., -a0/2], - [0., 0., a1/2.], - [-a0/2, a1/2., a1]] - -Q1 = Q2 = gamma - -S1 = S2 = W1 = W2 = M1 = M2 = 0.0 - -# == Solve using QE's nnash function == # -F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2, - beta=beta) - -# == Display policies == # -print("Computed policies for firm 1 and firm 2:\n") -print("F1 = {}".format(F1)) -print("F2 = {}".format(F2)) -print("\n") diff --git a/examples/duopoly_mpe_dynamics.py b/examples/duopoly_mpe_dynamics.py deleted file mode 100644 index e52f12109..000000000 --- a/examples/duopoly_mpe_dynamics.py +++ /dev/null @@ -1,20 +0,0 @@ -import matplotlib.pyplot as plt -from duopoly_mpe import * - -AF = A - B1.dot(F1) - B2.dot(F2) -n = 20 -x = np.empty((3, n)) -x[:, 0] = 1, 1, 1 -for t in range(n-1): - x[:, t+1] = np.dot(AF, x[:, t]) -q1 = x[1, :] -q2 = x[2, :] -q = q1 + q2 # Total output, MPE -p = a0 - a1 * q # Price, MPE - -fig, ax = plt.subplots(figsize=(9, 5.8)) -ax.plot(q, 'b-', lw=2, alpha=0.75, label='total output') -ax.plot(p, 'g-', lw=2, alpha=0.75, label='price') -ax.set_title('Output and prices, duopoly MPE') -ax.legend(frameon=False) -plt.show() diff --git a/examples/eigenvec.py b/examples/eigenvec.py deleted file mode 100644 index 3af24e498..000000000 --- a/examples/eigenvec.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Filename: eigenvec.py -Authors: Tom Sargent and John Stachurski. - -Illustrates eigenvectors. -""" - -import matplotlib.pyplot as plt -import numpy as np -from scipy.linalg import eig - -A = ((1, 2), - (2, 1)) -A = np.array(A) -evals, evecs = eig(A) -evecs = evecs[:, 0], evecs[:, 1] - -fig, ax = plt.subplots() -# Set the axes through the origin -for spine in ['left', 'bottom']: - ax.spines[spine].set_position('zero') -for spine in ['right', 'top']: - ax.spines[spine].set_color('none') -ax.grid(alpha=0.4) - -xmin, xmax = -3, 3 -ymin, ymax = -3, 3 -ax.set_xlim(xmin, xmax) -ax.set_ylim(ymin, ymax) -# ax.set_xticks(()) -# ax.set_yticks(()) - -# Plot each eigenvector -for v in evecs: - ax.annotate('', xy=v, xytext=(0, 0), - arrowprops=dict(facecolor='blue', - shrink=0, - alpha=0.6, - width=0.5)) - -# Plot the image of each eigenvector -for v in evecs: - v = np.dot(A, v) - ax.annotate('', xy=v, xytext=(0, 0), - arrowprops=dict(facecolor='red', - shrink=0, - alpha=0.6, - width=0.5)) - -# Plot the lines they run through -x = np.linspace(xmin, xmax, 3) -for v in evecs: - a = v[1] / v[0] - ax.plot(x, a * x, 'b-', lw=0.4) - - -plt.show() diff --git a/examples/evans_sargent.py b/examples/evans_sargent.py deleted file mode 100644 index 8b537cb0d..000000000 --- a/examples/evans_sargent.py +++ /dev/null @@ -1,179 +0,0 @@ -""" -Created on Mon Dec 16 19:12:17 2013 -@author: dgevans -Edited by: Chase Coleman, John Stachurski - -This file corresponds to the Ramsey model from the QE lecture on -history dependent policies: - - http://quant-econ.net/py/hist_dep_policies.html - -In the following, ``uhat`` and ``tauhat`` are what the planner would choose if -he could reset at time t, ``uhatdif`` and ``tauhatdif`` are the difference -between those and what the planner is constrained to choose. The variable -``mu`` is the Lagrange multiplier associated with the constraint at time t. - -For more complete description of inputs and outputs see the website. - -""" - -import numpy as np -from quantecon import LQ -from quantecon.matrix_eqn import solve_discrete_lyapunov -from scipy.optimize import root - - -def computeG(A0, A1, d, Q0, tau0, beta, mu): - """ - Compute government income given mu and return tax revenues and - policy matrixes for the planner. - - Parameters - ---------- - A0 : float - A constant parameter for the inverse demand function - A1 : float - A constant parameter for the inverse demand function - d : float - A constant parameter for quadratic adjustment cost of production - Q0 : float - An initial condition for production - tau0 : float - An initial condition for taxes - beta : float - A constant parameter for discounting - mu : float - Lagrange multiplier - - Returns - ------- - T0 : array(float) - Present discounted value of government spending - A : array(float) - One of the transition matrices for the states - B : array(float) - Another transition matrix for the states - F : array(float) - Policy rule matrix - P : array(float) - Value function matrix - """ - # Create Matrices for solving Ramsey problem - R = np.array([[0, -A0/2, 0, 0], - [-A0/2, A1/2, -mu/2, 0], - [0, -mu/2, 0, 0], - [0, 0, 0, d/2]]) - - A = np.array([[1, 0, 0, 0], - [0, 1, 0, 1], - [0, 0, 0, 0], - [-A0/d, A1/d, 0, A1/d+1/beta]]) - - B = np.array([0, 0, 1, 1/d]).reshape(-1, 1) - - Q = 0 - - # Use LQ to solve the Ramsey Problem. - lq = LQ(Q, -R, A, B, beta=beta) - P, F, d = lq.stationary_values() - - # Need y_0 to compute government tax revenue. - P21 = P[3, :3] - P22 = P[3, 3] - z0 = np.array([1, Q0, tau0]).reshape(-1, 1) - u0 = -P22**(-1) * P21.dot(z0) - y0 = np.vstack([z0, u0]) - - # Define A_F and S matricies - AF = A - B.dot(F) - S = np.array([0, 1, 0, 0]).reshape(-1, 1).dot(np.array([[0, 0, 1, 0]])) - - # Solves equation (25) - temp = beta * AF.T.dot(S).dot(AF) - Omega = solve_discrete_lyapunov(np.sqrt(beta) * AF.T, temp) - T0 = y0.T.dot(Omega).dot(y0) - - return T0, A, B, F, P - - -# == Primitives == # -T = 20 -A0 = 100.0 -A1 = 0.05 -d = 0.20 -beta = 0.95 - -# == Initial conditions == # -mu0 = 0.0025 -Q0 = 1000.0 -tau0 = 0.0 - - -def gg(mu): - """ - Computes the tax revenues for the government given Lagrangian - multiplier mu. - """ - return computeG(A0, A1, d, Q0, tau0, beta, mu) - -# == Solve the Ramsey problem and associated government revenue == # -G0, A, B, F, P = gg(mu0) - -# == Compute the optimal u0 == # -P21 = P[3, :3] -P22 = P[3, 3] -z0 = np.array([1, Q0, tau0]).reshape(-1, 1) -u0 = -P22**(-1) * P21.dot(z0) - - -# == Initialize vectors == # -y = np.zeros((4, T)) -uhat = np.zeros(T) -uhatdif = np.zeros(T) -tauhat = np.zeros(T) -tauhatdif = np.zeros(T-1) -mu = np.zeros(T) -G = np.zeros(T) -GPay = np.zeros(T) - -# == Initial conditions == # -G[0] = G0 -mu[0] = mu0 -uhatdif[0] = 0 -uhat[0] = u0 -y[:, 0] = np.vstack([z0, u0]).flatten() - -for t in range(1, T): - # Iterate government policy - y[:, t] = (A-B.dot(F)).dot(y[:, t-1]) - - # update G - G[t] = (G[t-1] - beta*y[1, t]*y[2, t])/beta - GPay[t] = beta*y[1, t]*y[2, t] - - # Compute the mu if the government were able to reset its plan - # ff is the tax revenues the government would receive if they reset the - # plan with Lagrange multiplier mu minus current G - - ff = lambda mu: (gg(mu)[0]-G[t]).flatten() - - # find ff = 0 - mu[t] = root(ff, mu[t-1]).x - temp, Atemp, Btemp, Ftemp, Ptemp = gg(mu[t]) - - # Compute alternative decisions - P21temp = Ptemp[3, :3] - P22temp = P[3, 3] - uhat[t] = -P22temp**(-1)*P21temp.dot(y[:3, t]) - - yhat = (Atemp-Btemp.dot(Ftemp)).dot(np.hstack([y[0:3, t-1], uhat[t-1]])) - tauhat[t] = yhat[3] - tauhatdif[t-1] = tauhat[t]-y[3, t] - uhatdif[t] = uhat[t]-y[3, t] - - -if __name__ == '__main__': - print("1 Q tau u") - print(y) - print("-F") - print(-F) diff --git a/examples/evans_sargent_plot1.py b/examples/evans_sargent_plot1.py deleted file mode 100644 index a9b34bc9d..000000000 --- a/examples/evans_sargent_plot1.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Plot 1 from the Evans Sargent model. - -@author: David Evans -Edited by: John Stachurski - -""" -import numpy as np -import matplotlib.pyplot as plt -from evans_sargent import T, y - -tt = np.arange(T) # tt is used to make the plot time index correct. - -n_rows = 3 -fig, axes = plt.subplots(n_rows, 1, figsize=(10, 12)) - -plt.subplots_adjust(hspace=0.5) -for ax in axes: - ax.grid() - ax.set_xlim(0, 15) - -bbox = (0., 1.02, 1., .102) -legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'} -p_args = {'lw': 2, 'alpha': 0.7} - -ax = axes[0] -ax.plot(tt, y[1, :], 'b-', label="output", **p_args) -ax.set_ylabel(r"$Q$", fontsize=16) -ax.legend(ncol=1, **legend_args) - -ax = axes[1] -ax.plot(tt, y[2, :], 'b-', label="tax rate", **p_args) -ax.set_ylabel(r"$\tau$", fontsize=16) -ax.set_yticks((0.0, 0.2, 0.4, 0.6, 0.8)) -ax.legend(ncol=1, **legend_args) - -ax = axes[2] -ax.plot(tt, y[3, :], 'b-', label="first difference in output", **p_args) -ax.set_ylabel(r"$u$", fontsize=16) -ax.set_yticks((0, 100, 200, 300, 400)) -ax.legend(ncol=1, **legend_args) -ax.set_xlabel(r'time', fontsize=16) - -plt.show() diff --git a/examples/evans_sargent_plot2.py b/examples/evans_sargent_plot2.py deleted file mode 100644 index dcf9923b0..000000000 --- a/examples/evans_sargent_plot2.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -Plot 2 from the Evans Sargent model. - -@author: David Evans -Edited by: John Stachurski - -""" -import numpy as np -import matplotlib.pyplot as plt -from evans_sargent import T, uhatdif, tauhatdif, mu, G - -tt = np.arange(T) # tt is used to make the plot time index correct. -tt2 = np.arange(T-1) - -n_rows = 4 -fig, axes = plt.subplots(n_rows, 1, figsize=(10, 16)) - -plt.subplots_adjust(hspace=0.5) -for ax in axes: - ax.grid(alpha=.5) - ax.set_xlim(-0.5, 15) - -bbox = (0., 1.02, 1., .102) -legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'} -p_args = {'lw': 2, 'alpha': 0.7} - -ax = axes[0] -ax.plot(tt2, tauhatdif, label=r'time inconsistency differential for tax rate', - **p_args) -ax.set_ylabel(r"$\Delta\tau$", fontsize=16) -ax.set_ylim(-0.1, 1.4) -ax.set_yticks((0.0, 0.4, 0.8, 1.2)) -ax.legend(ncol=1, **legend_args) - -ax = axes[1] -ax.plot(tt, uhatdif, label=r'time inconsistency differential for $u$', - **p_args) -ax.set_ylabel(r"$\Delta u$", fontsize=16) -ax.set_ylim(-3, .1) -ax.set_yticks((-3.0, -2.0, -1.0, 0.0)) -ax.legend(ncol=1, **legend_args) - -ax = axes[2] -ax.plot(tt, mu, label='Lagrange multiplier', **p_args) -ax.set_ylabel(r"$\mu$", fontsize=16) -ax.set_ylim(2.34e-3, 2.52e-3) -ax.set_yticks((2.34e-3, 2.43e-3, 2.52e-3)) -ax.legend(ncol=1, **legend_args) - -ax = axes[3] -ax.plot(tt, G, label='government revenue', **p_args) -ax.set_ylabel(r"$G$", fontsize=16) -ax.set_ylim(9100, 9800) -ax.set_yticks((9200, 9400, 9600, 9800)) -ax.legend(ncol=1, **legend_args) - -ax.set_xlabel(r'time', fontsize=16) - -plt.show() -# lines = plt.plot(tt, GPay, "o") diff --git a/examples/finite_dp_og_example.py b/examples/finite_dp_og_example.py deleted file mode 100644 index ab13eb810..000000000 --- a/examples/finite_dp_og_example.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -A simple optimal growth model, for testing the DiscreteDP class. - -Filename: finite_dp_og_example.py -""" -import numpy as np - -class SimpleOG(object): - - def __init__(self, B=10, M=5, alpha=0.5, beta=0.9): - """ - Set up R, Q and beta, the three elements that define an instance of - the DiscreteDP class. - """ - - self.B, self.M, self.alpha, self.beta = B, M, alpha, beta - self.n = B + M + 1 - self.m = M + 1 - - self.R = np.empty((self.n, self.m)) - self.Q = np.zeros((self.n, self.m, self.n)) - - self.populate_Q() - self.populate_R() - - def u(self, c): - return c**self.alpha - - def populate_R(self): - """ - Populate the R matrix, with R[s, a] = -np.inf for infeasible - state-action pairs. - """ - for s in range(self.n): - for a in range(self.m): - self.R[s, a] = self.u(s - a) if a <= s else -np.inf - - def populate_Q(self): - """ - Populate the Q matrix by setting - - Q[s, a, s'] = 1 / (1 + B) if a <= s' <= a + B - - and zero otherwise. - """ - - for a in range(self.m): - self.Q[:, a, a:(a + self.B + 1)] = 1.0 / (self.B + 1) - diff --git a/examples/first_notebook.ipynb b/examples/first_notebook.ipynb deleted file mode 100644 index 7362e38f1..000000000 --- a/examples/first_notebook.ipynb +++ /dev/null @@ -1,161 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1 + 1" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "foobar\n" - ] - } - ], - "source": [ - "print('foobar')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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seh1KqUXGgBCC999/f0AbEFPIpY3w5truvPZw5cPbTYDg8FCb/l9Gjx7d6bihoQFubm62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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\"\"\"\n", - "Demo of bar plot on a polar axis.\n", - "\"\"\"\n", - "%matplotlib inline\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "N = 20\n", - "theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)\n", - "radii = 10 * np.random.rand(N)\n", - "width = np.pi / 4 * np.random.rand(N)\n", - "\n", - "ax = plt.subplot(111, polar=True)\n", - "bars = ax.bar(theta, radii, width=width, bottom=0.0)\n", - "\n", - "# Use custom colors and opacity\n", - "for r, bar in zip(radii, bars):\n", - " bar.set_facecolor(plt.cm.jet(r / 10.))\n", - " bar.set_alpha(0.5)\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "np.rank?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If $\\{A_n\\}$ is pairwise disjoint, then\n", - "\n", - "$$ \\mu(\\cup_n A_n) = \\sum_m \\mu(A_n) $$" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.59530311, 0.68947945, 0.92696972])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.randn(3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/gaussian_contours.py b/examples/gaussian_contours.py deleted file mode 100644 index 9b24abbb9..000000000 --- a/examples/gaussian_contours.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -Filename: gaussian_contours.py -Authors: John Stachurski and Thomas Sargent - -Plots of bivariate Gaussians to illustrate the Kalman filter. -""" - -from scipy import linalg -import numpy as np -import matplotlib.cm as cm -from matplotlib.mlab import bivariate_normal -import matplotlib.pyplot as plt - -# == Set up the Gaussian prior density p == # -Sigma = [[0.4, 0.3], [0.3, 0.45]] -Sigma = np.matrix(Sigma) -x_hat = np.matrix([0.2, -0.2]).T -# == Define the matrices G and R from the equation y = G x + N(0, R) == # -G = [[1, 0], [0, 1]] -G = np.matrix(G) -R = 0.5 * Sigma -# == The matrices A and Q == # -A = [[1.2, 0], [0, -0.2]] -A = np.matrix(A) -Q = 0.3 * Sigma -# == The observed value of y == # -y = np.matrix([2.3, -1.9]).T - -# == Set up grid for plotting == # -x_grid = np.linspace(-1.5, 2.9, 100) -y_grid = np.linspace(-3.1, 1.7, 100) -X, Y = np.meshgrid(x_grid, y_grid) - - -def gen_gaussian_plot_vals(mu, C): - "Z values for plotting the bivariate Gaussian N(mu, C)" - m_x, m_y = float(mu[0]), float(mu[1]) - s_x, s_y = np.sqrt(C[0, 0]), np.sqrt(C[1, 1]) - s_xy = C[0, 1] - return bivariate_normal(X, Y, s_x, s_y, m_x, m_y, s_xy) - -fig, ax = plt.subplots() -ax.xaxis.grid(True, zorder=0) -ax.yaxis.grid(True, zorder=0) - -# == Code for the 4 plots, choose one below == # - - -def plot1(): - Z = gen_gaussian_plot_vals(x_hat, Sigma) - ax.contourf(X, Y, Z, 6, alpha=0.6, cmap=cm.jet) - cs = ax.contour(X, Y, Z, 6, colors="black") - ax.clabel(cs, inline=1, fontsize=10) - - -def plot2(): - Z = gen_gaussian_plot_vals(x_hat, Sigma) - ax.contourf(X, Y, Z, 6, alpha=0.6, cmap=cm.jet) - cs = ax.contour(X, Y, Z, 6, colors="black") - ax.clabel(cs, inline=1, fontsize=10) - ax.text(float(y[0]), float(y[1]), r"$y$", fontsize=20, color="black") - - -def plot3(): - Z = gen_gaussian_plot_vals(x_hat, Sigma) - cs1 = ax.contour(X, Y, Z, 6, colors="black") - ax.clabel(cs1, inline=1, fontsize=10) - M = Sigma * G.T * linalg.inv(G * Sigma * G.T + R) - x_hat_F = x_hat + M * (y - G * x_hat) - Sigma_F = Sigma - M * G * Sigma - new_Z = gen_gaussian_plot_vals(x_hat_F, Sigma_F) - cs2 = ax.contour(X, Y, new_Z, 6, colors="black") - ax.clabel(cs2, inline=1, fontsize=10) - ax.contourf(X, Y, new_Z, 6, alpha=0.6, cmap=cm.jet) - ax.text(float(y[0]), float(y[1]), r"$y$", fontsize=20, color="black") - - -def plot4(): - # Density 1 - Z = gen_gaussian_plot_vals(x_hat, Sigma) - cs1 = ax.contour(X, Y, Z, 6, colors="black") - ax.clabel(cs1, inline=1, fontsize=10) - # Density 2 - M = Sigma * G.T * linalg.inv(G * Sigma * G.T + R) - x_hat_F = x_hat + M * (y - G * x_hat) - Sigma_F = Sigma - M * G * Sigma - Z_F = gen_gaussian_plot_vals(x_hat_F, Sigma_F) - cs2 = ax.contour(X, Y, Z_F, 6, colors="black") - ax.clabel(cs2, inline=1, fontsize=10) - # Density 3 - new_x_hat = A * x_hat_F - new_Sigma = A * Sigma_F * A.T + Q - new_Z = 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Sargent -Filename: ifp_savings_plots.py -Authors: John Stachurski, Thomas J. Sargent -LastModified: 11/08/2013 - -""" - -from matplotlib import pyplot as plt -from quantecon import compute_fixed_point -from quantecon.models import ConsumerProblem - -# === solve for optimal consumption === # -m = ConsumerProblem(r=0.03, grid_max=4) -v_init, c_init = m.initialize() - -# Coleman Operator takes in (c)? -c = compute_fixed_point(m.coleman_operator, c_init) -a = m.asset_grid -R, z_vals = m.R, m.z_vals - -# === generate savings plot === # -fig, ax = plt.subplots() -ax.plot(a, R * a + z_vals[0] - c[:, 0], label='low income') -ax.plot(a, R * a + z_vals[1] - c[:, 1], label='high income') -ax.plot(a, a, 'k--') -ax.set_xlabel('current assets') -ax.set_ylabel('next period assets') -ax.legend(loc='upper left') -plt.show() diff --git a/examples/illustrates_clt.py b/examples/illustrates_clt.py deleted file mode 100644 index 53c427b87..000000000 --- a/examples/illustrates_clt.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Filename: illustrates_clt.py -Authors: John Stachurski and Thomas J. Sargent - -Visual illustration of the central limit theorem. Histograms draws of - - Y_n := \sqrt{n} (\bar X_n - \mu) - -for a given distribution of X_i, and a given choice of n. -""" -import numpy as np -from scipy.stats import expon, norm -import matplotlib.pyplot as plt -from matplotlib import rc - -# == Specifying font, needs LaTeX integration == # -rc('font', **{'family': 'serif', 'serif': ['Palatino']}) -rc('text', usetex=True) - -# == Set parameters == # -n = 250 # Choice of n -k = 100000 # Number of draws of Y_n -distribution = expon(2) # Exponential distribution, lambda = 1/2 -mu, s = distribution.mean(), distribution.std() - -# == Draw underlying RVs. Each row contains a draw of X_1,..,X_n == # -data = distribution.rvs((k, n)) -# == Compute mean of each row, producing k draws of \bar X_n == # -sample_means = data.mean(axis=1) -# == Generate observations of Y_n == # -Y = np.sqrt(n) * (sample_means - mu) - -# == Plot == # -fig, ax = plt.subplots() -xmin, xmax = -3 * s, 3 * s -ax.set_xlim(xmin, xmax) -ax.hist(Y, bins=60, alpha=0.5, normed=True) -xgrid = np.linspace(xmin, xmax, 200) -ax.plot(xgrid, norm.pdf(xgrid, scale=s), 'k-', lw=2, label=r'$N(0, \sigma^2)$') -ax.legend() - -plt.show() diff --git a/examples/illustrates_lln.py b/examples/illustrates_lln.py deleted file mode 100644 index 49ff2e880..000000000 --- a/examples/illustrates_lln.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Filename: illustrates_lln.py -Authors: John Stachurski and Thomas J. Sargent - -Visual illustration of the law of large numbers. -""" - -import random -import numpy as np -from scipy.stats import t, beta, lognorm, expon, gamma, poisson -import matplotlib.pyplot as plt - -n = 100 - -# == Arbitrary collection of distributions == # -distributions = {"student's t with 10 degrees of freedom": t(10), - "beta(2, 2)": beta(2, 2), - "lognormal LN(0, 1/2)": lognorm(0.5), - "gamma(5, 1/2)": gamma(5, scale=2), - "poisson(4)": poisson(4), - "exponential with lambda = 1": expon(1)} - -# == Create a figure and some axes == # -num_plots = 3 -fig, axes = plt.subplots(num_plots, 1, figsize=(10, 10)) - -# == Set some plotting parameters to improve layout == # -bbox = (0., 1.02, 1., .102) -legend_args = {'ncol': 2, - 'bbox_to_anchor': bbox, - 'loc': 3, - 'mode': 'expand'} -plt.subplots_adjust(hspace=0.5) - -for ax in axes: - # == Choose a randomly selected distribution == # - name = random.choice(list(distributions.keys())) - distribution = distributions.pop(name) - - # == Generate n draws from the distribution == # - data = distribution.rvs(n) - - # == Compute sample mean at each n == # - sample_mean = np.empty(n) - for i in range(n): - sample_mean[i] = np.mean(data[:i+1]) - - # == Plot == # - ax.plot(list(range(n)), data, 'o', color='grey', alpha=0.5) - axlabel = r'$\bar X_n$' + ' for ' + r'$X_i \sim$' + ' ' + name - ax.plot(list(range(n)), sample_mean, 'g-', lw=3, alpha=0.6, label=axlabel) - m = distribution.mean() - ax.plot(list(range(n)), [m] * n, 'k--', lw=1.5, label=r'$\mu$') - ax.vlines(list(range(n)), m, data, lw=0.2) - ax.legend(**legend_args) - -plt.show() diff --git a/examples/jv_test.py b/examples/jv_test.py deleted file mode 100644 index 3e1bdf116..000000000 --- a/examples/jv_test.py +++ /dev/null @@ -1,28 +0,0 @@ -""" -Origin: QE by John Stachurski and Thomas J. Sargent -Filename: jv_test.py -Authors: John Stachurski and Thomas Sargent -LastModified: 11/08/2013 - -Tests jv.py with a particular parameterization. - -""" -import matplotlib.pyplot as plt -from quantecon import compute_fixed_point -from quantecon.models import JvWorker - -# === solve for optimal policy === # -wp = JvWorker(grid_size=25) -v_init = wp.x_grid * 0.5 -V = compute_fixed_point(wp.bellman_operator, v_init, max_iter=40) -s_policy, phi_policy = wp.bellman_operator(V, return_policies=True) - -# === plot policies === # -fig, ax = plt.subplots() -ax.set_xlim(0, max(wp.x_grid)) -ax.set_ylim(-0.1, 1.1) -ax.plot(wp.x_grid, phi_policy, 'b-', label='phi') -ax.plot(wp.x_grid, s_policy, 'g-', label='s') -ax.set_xlabel("x") -ax.legend() -plt.show() diff --git a/examples/lakemodel_example.py b/examples/lakemodel_example.py deleted file mode 100644 index 75a19d0e8..000000000 --- a/examples/lakemodel_example.py +++ /dev/null @@ -1,151 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Feb 27 18:08:44 2015 - -Author: David Evans - -Example Usage of LakeModel in quantecon.models -""" -import numpy as np -import matplotlib.pyplot as plt -from quantecon.models import LakeModel, LakeModelAgent, LakeModel_Equilibrium - -import pandas as pd -pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier - -#Initialize Parameters -alpha = 0.013 -lamb = 0.283#0.2486 -b = 0.0124 -d = 0.00822 -g = b-d -N0 = 150. -e0 = 0.92 -u0 = 1-e0 -T = 50 - -LM = LakeModel(lamb,alpha,b,d) - -#Find steady state -xbar = LM.find_steady_state() - -#simulate stocks for T periods -E0 = e0*N0 -U0 = u0*N0 -X_path = np.vstack( LM.simulate_stock_path([E0,U0],T) ) -plt.figure(figsize=[10,9]) -plt.subplot(3,1,1) -plt.plot(X_path[:,0]) -plt.title(r'Employment') -plt.subplot(3,1,2) -plt.plot(X_path[:,1]) -plt.title(r'Unemployment') -plt.subplot(3,1,3) -plt.plot(X_path.sum(1)) -plt.title(r'Labor Force') -plt.tight_layout() -plt.savefig('example_stock_path.png') - -#simulate rates for T periods - -x_path = np.vstack( LM.simulate_rate_path([e0,u0],T) ) -plt.figure(figsize=[10,6]) -plt.subplot(2,1,1) -plt.plot(x_path[:,0]) -plt.hlines(xbar[0],0,T,'r','--') -plt.title(r'Employment Rate') -plt.subplot(2,1,2) -plt.plot(x_path[:,1]) -plt.hlines(xbar[1],0,T,'r','--') -plt.title(r'Unemployment Rate') -plt.tight_layout() -plt.savefig('example_rate_path.png') - - -#Simulate a single agent -T = 5000 - -A = LakeModelAgent(lamb,alpha) -pi_bar = A.compute_ergodic().flatten() - -sHist = np.hstack(A.simulate(1,T)) - -pi_u = np.cumsum(sHist)/(np.arange(T) + 1.) # time spent in unemployment after T periods -pi_e = 1- pi_u #time spent employed - -plt.figure(figsize=[10,6]) -plt.subplot(2,1,1) -plt.plot(range(50,T),pi_e[50:]) -plt.hlines(pi_bar[0],0,T,'r','--') -plt.title('Percent of Time Employed') -plt.subplot(2,1,2) -plt.plot(range(50,T),pi_u[50:]) -plt.hlines(pi_bar[1],0,T,'r','--') -plt.xlabel('Time') -plt.title('Percent of Time Unemployed') -plt.tight_layout() -plt.savefig('example_averages.png') - - -#============================================================================== -# Now add McCall Search Model -#============================================================================== -from scipy.stats import norm - -#using quaterly data -alpha_q = (1-(1-alpha)**3) -gamma = 1. - -logw_dist = norm(np.log(20.),1) -w = np.linspace(0.,175,201)# wage grid - -#compute probability of each wage level -cdf = logw_dist.cdf(np.log(w)) -pdf = cdf[1:]-cdf[:-1] -pdf /= pdf.sum() -w = (w[1:] + w[:1])/2 - -#Find the quilibirum -LME = LakeModel_Equilibrium(alpha_q,gamma,0.99,2.00,pdf,w) - -#possible levels of unemployment insurance -cvec = np.linspace(1.,75,25) -T,W,U,EV,pi = map(np.vstack,zip(* [LME.find_steady_state_tax(c) for c in cvec])) -W= W[:] -T = T[:] -U = U[:] -EV = EV[:] -i_max = np.argmax(W) - -plt.figure(figsize=[10,6]) -plt.subplot(221) -plt.plot(cvec,W) -plt.xlabel(r'$c$') -plt.title(r'Welfare' ) -axes = plt.gca() -plt.vlines(cvec[i_max],axes.get_ylim()[0],max(W),'k','-.') - -plt.subplot(222) -plt.plot(cvec,T) -axes = plt.gca() -plt.vlines(cvec[i_max],axes.get_ylim()[0],T[i_max],'k','-.') -plt.xlabel(r'$c$') -plt.title(r'Taxes' ) - - -plt.subplot(223) -plt.plot(cvec,pi[:,0]) -axes = plt.gca() -plt.vlines(cvec[i_max],axes.get_ylim()[0],pi[i_max,0],'k','-.') -plt.xlabel(r'$c$') -plt.title(r'Employment Rate' ) - - -plt.subplot(224) -plt.plot(cvec,pi[:,1]) -axes = plt.gca() -plt.vlines(cvec[i_max],axes.get_ylim()[0],pi[i_max,1],'k','-.') -plt.xlabel(r'$c$') -plt.title(r'Unemployment Rate' ) -plt.tight_layout() -plt.savefig('welfare_plot.png') \ No newline at end of file diff --git a/examples/lin_interp_3d_plot.py b/examples/lin_interp_3d_plot.py deleted file mode 100644 index 50413e770..000000000 --- a/examples/lin_interp_3d_plot.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Origin: QE by John Stachurski and Thomas J. Sargent -Filename: lin_inter_3d_plot.py -Authors: John Stachurski, Thomas J. Sargent -LastModified: 21/08/2013 -""" -from scipy.interpolate import LinearNDInterpolator -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d.axes3d import Axes3D -import numpy as np - -alpha = 0.7 -phi_ext = 2 * 3.14 * 0.5 - - -def f(a, b): - # return 2 + alpha - 2 * np.cos(b)*np.cos(a) - alpha*np.cos(phi_ext - 2*b) - return a + np.sqrt(b) - -x_max = 3 -y_max = 2.5 - -# === the approximation grid === # -Nx0, Ny0 = 25, 25 -x0 = np.linspace(0, x_max, Nx0) -y0 = np.linspace(0, y_max, Ny0) -X0, Y0 = np.meshgrid(x0, y0) -points = np.column_stack((X0.ravel(1), Y0.ravel(1))) - -# === generate the function values on the grid === # -Z0 = np.empty(Nx0 * Ny0) -for i in range(len(Z0)): - a, b = points[i, :] - Z0[i] = f(a, b) - -g = LinearNDInterpolator(points, Z0) - -# === a grid for plotting === # -Nx1, Ny1 = 100, 100 -x1 = np.linspace(0, x_max, Nx1) -y1 = np.linspace(0, y_max, Ny1) -X1, Y1 = np.meshgrid(x1, y1) - -# === the approximating function, as a matrix, for plotting === # -# ZA = np.empty((Ny1, Nx1)) -# for i in range(Ny1): -# for j in range(Nx1): -# ZA[i, j] = g(x1[j], y1[i]) -ZA = g(X1, Y1) -ZF = f(X1, Y1) - -# === plot === # -fig = plt.figure(figsize=(8, 6)) -ax = fig.add_subplot(1, 1, 1, projection='3d') -p = ax.plot_wireframe(X1, Y1, ZF, rstride=4, cstride=4) -plt.show() diff --git a/examples/linapprox.py b/examples/linapprox.py deleted file mode 100644 index 5fd147702..000000000 --- a/examples/linapprox.py +++ /dev/null @@ -1,28 +0,0 @@ -import numpy as np -import scipy as sp -import matplotlib.pyplot as plt - - -def f(x): - y1 = 2 * np.cos(6 * x) + np.sin(14 * x) - return y1 + 2.5 - -c_grid = np.linspace(0, 1, 6) - - -def Af(x): - return sp.interp(x, c_grid, f(c_grid)) - -f_grid = np.linspace(0, 1, 150) - -fig, ax = plt.subplots() -ax.set_xlim(0, 1) - -ax.plot(f_grid, f(f_grid), 'b-', lw=2, alpha=0.8, label='true function') -ax.plot(f_grid, Af(f_grid), 'g-', lw=2, alpha=0.8, - label='linear approximation') - -ax.vlines(c_grid, c_grid * 0, f(c_grid), linestyle='dashed', alpha=0.5) -ax.legend(loc='upper center') - -plt.show() diff --git a/examples/lq_permanent_1.py b/examples/lq_permanent_1.py deleted file mode 100644 index 9fe72be4e..000000000 --- a/examples/lq_permanent_1.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Filename: lq_permanent_1.py -Authors: John Stachurski and Thomas J. Sargent - -A permanent income / life-cycle model with iid income -""" - -import numpy as np -import matplotlib.pyplot as plt -from quantecon import LQ - -# == Model parameters == # -r = 0.05 -beta = 1 / (1 + r) -T = 45 -c_bar = 2 -sigma = 0.25 -mu = 1 -q = 1e6 - -# == Formulate as an LQ problem == # -Q = 1 -R = np.zeros((2, 2)) -Rf = np.zeros((2, 2)) -Rf[0, 0] = q -A = [[1 + r, -c_bar + mu], - [0, 1]] -B = [[-1], - [0]] -C = [[sigma], - [0]] - -# == Compute solutions and simulate == # -lq = LQ(Q, R, A, B, C, beta=beta, T=T, Rf=Rf) -x0 = (0, 1) -xp, up, wp = lq.compute_sequence(x0) - -# == Convert back to assets, consumption and income == # -assets = xp[0, :] # a_t -c = up.flatten() + c_bar # c_t -income = wp[0, 1:] + mu # y_t - -# == Plot results == # -n_rows = 2 -fig, axes = plt.subplots(n_rows, 1, figsize=(12, 10)) - -plt.subplots_adjust(hspace=0.5) -for i in range(n_rows): - axes[i].grid() - axes[i].set_xlabel(r'Time') -bbox = (0., 1.02, 1., .102) -legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'} -p_args = {'lw': 2, 'alpha': 0.7} - -axes[0].plot(list(range(1, T+1)), income, 'g-', label="non-financial income", - **p_args) -axes[0].plot(list(range(T)), c, 'k-', label="consumption", **p_args) -axes[0].legend(ncol=2, **legend_args) - -axes[1].plot(list(range(1, T+1)), np.cumsum(income - mu), 'r-', - label="cumulative unanticipated income", **p_args) -axes[1].plot(list(range(T+1)), assets, 'b-', label="assets", **p_args) -axes[1].plot(list(range(T)), np.zeros(T), 'k-') -axes[1].legend(ncol=2, **legend_args) - -plt.show() diff --git a/examples/lqramsey.py b/examples/lqramsey.py deleted file mode 100644 index 72ba777c3..000000000 --- a/examples/lqramsey.py +++ /dev/null @@ -1,296 +0,0 @@ -""" -Filename: lqramsey.py -Authors: Thomas Sargent, Doc-Jin Jang, Jeong-hun Choi, John Stachurski - -This module provides code to compute Ramsey equilibria in a LQ economy with -distortionary taxation. The program computes allocations (consumption, -leisure), tax rates, revenues, the net present value of the debt and other -related quantities. - -Functions for plotting the results are also provided below. - -See the lecture at http://quant-econ.net/py/lqramsey.html for a description of -the model. - -""" - -import sys -import numpy as np -from numpy import sqrt, eye, dot, zeros, cumsum -from numpy.random import randn -import scipy.linalg -import matplotlib.pyplot as plt -from collections import namedtuple -from quantecon import nullspace, mc_sample_path, var_quadratic_sum - - -# == Set up a namedtuple to store data on the model economy == # -Economy = namedtuple('economy', - ('beta', # Discount factor - 'Sg', # Govt spending selector matrix - 'Sd', # Exogenous endowment selector matrix - 'Sb', # Utility parameter selector matrix - 'Ss', # Coupon payments selector matrix - 'discrete', # Discrete or continuous -- boolean - 'proc')) # Stochastic process parameters - -# == Set up a namedtuple to store return values for compute_paths() == # -Path = namedtuple('path', - ('g', # Govt spending - 'd', # Endowment - 'b', # Utility shift parameter - 's', # Coupon payment on existing debt - 'c', # Consumption - 'l', # Labor - 'p', # Price - 'tau', # Tax rate - 'rvn', # Revenue - 'B', # Govt debt - 'R', # Risk free gross return - 'pi', # One-period risk-free interest rate - 'Pi', # Cumulative rate of return, adjusted - 'xi')) # Adjustment factor for Pi - - -def compute_paths(T, econ): - """ - Compute simulated time paths for exogenous and endogenous variables. - - Parameters - =========== - T: int - Length of the simulation - - econ: a namedtuple of type 'Economy', containing - beta - Discount factor - Sg - Govt spending selector matrix - Sd - Exogenous endowment selector matrix - Sb - Utility parameter selector matrix - Ss - Coupon payments selector matrix - discrete - Discrete exogenous process (True or False) - proc - Stochastic process parameters - - Returns - ======== - path: a namedtuple of type 'Path', containing - g - Govt spending - d - Endowment - b - Utility shift parameter - s - Coupon payment on existing debt - c - Consumption - l - Labor - p - Price - tau - Tax rate - rvn - Revenue - B - Govt debt - R - Risk free gross return - pi - One-period risk-free interest rate - Pi - Cumulative rate of return, adjusted - xi - Adjustment factor for Pi - - The corresponding values are flat numpy ndarrays. - - """ - - # == Simplify names == # - beta, Sg, Sd, Sb, Ss = econ.beta, econ.Sg, econ.Sd, econ.Sb, econ.Ss - - if econ.discrete: - P, x_vals = econ.proc - else: - A, C = econ.proc - - # == Simulate the exogenous process x == # - if econ.discrete: - state = mc_sample_path(P, init=0, sample_size=T) - x = x_vals[:, state] - else: - # == Generate an initial condition x0 satisfying x0 = A x0 == # - nx, nx = A.shape - x0 = nullspace((eye(nx) - A)) - x0 = -x0 if (x0[nx-1] < 0) else x0 - x0 = x0 / x0[nx-1] - - # == Generate a time series x of length T starting from x0 == # - nx, nw = C.shape - x = zeros((nx, T)) - w = randn(nw, T) - x[:, 0] = x0.T - for t in range(1, T): - x[:, t] = dot(A, x[:, t-1]) + dot(C, w[:, t]) - - # == Compute exogenous variable sequences == # - g, d, b, s = (dot(S, x).flatten() for S in (Sg, Sd, Sb, Ss)) - - # == Solve for Lagrange multiplier in the govt budget constraint == # - # In fact we solve for nu = lambda / (1 + 2*lambda). Here nu is the - # solution to a quadratic equation a(nu**2 - nu) + b = 0 where - # a and b are expected discounted sums of quadratic forms of the state. - Sm = Sb - Sd - Ss - # == Compute a and b == # - if econ.discrete: - ns = P.shape[0] - F = scipy.linalg.inv(np.identity(ns) - beta * P) - a0 = 0.5 * dot(F, dot(Sm, x_vals).T**2)[0] - H = dot(Sb - Sd + Sg, x_vals) * dot(Sg - Ss, x_vals) - b0 = 0.5 * dot(F, H.T)[0] - a0, b0 = float(a0), float(b0) - else: - H = dot(Sm.T, Sm) - a0 = 0.5 * var_quadratic_sum(A, C, H, beta, x0) - H = dot((Sb - Sd + Sg).T, (Sg + Ss)) - b0 = 0.5 * var_quadratic_sum(A, C, H, beta, x0) - - # == Test that nu has a real solution before assigning == # - warning_msg = """ - Hint: you probably set government spending too {}. Elect a {} - Congress and start over. - """ - disc = a0**2 - 4 * a0 * b0 - if disc >= 0: - nu = 0.5 * (a0 - sqrt(disc)) / a0 - else: - print("There is no Ramsey equilibrium for these parameters.") - print(warning_msg.format('high', 'Republican')) - sys.exit(0) - - # == Test that the Lagrange multiplier has the right sign == # - if nu * (0.5 - nu) < 0: - print("Negative multiplier on the government budget constraint.") - print(warning_msg.format('low', 'Democratic')) - sys.exit(0) - - # == Solve for the allocation given nu and x == # - Sc = 0.5 * (Sb + Sd - Sg - nu * Sm) - Sl = 0.5 * (Sb - Sd + Sg - nu * Sm) - c = dot(Sc, x).flatten() - l = dot(Sl, x).flatten() - p = dot(Sb - Sc, x).flatten() # Price without normalization - tau = 1 - l / (b - c) - rvn = l * tau - - # == Compute remaining variables == # - if econ.discrete: - H = dot(Sb - Sc, x_vals) * dot(Sl - Sg, x_vals) - dot(Sl, x_vals)**2 - temp = dot(F, H.T).flatten() - B = temp[state] / p - H = dot(P[state, :], dot(Sb - Sc, x_vals).T).flatten() - R = p / (beta * H) - temp = dot(P[state, :], dot(Sb - Sc, x_vals).T).flatten() - xi = p[1:] / temp[:T-1] - else: - H = dot(Sl.T, Sl) - dot((Sb - Sc).T, Sl - Sg) - L = np.empty(T) - for t in range(T): - L[t] = var_quadratic_sum(A, C, H, beta, x[:, t]) - B = L / p - Rinv = (beta * dot(dot(Sb - Sc, A), x)).flatten() / p - R = 1 / Rinv - AF1 = dot(Sb - Sc, x[:, 1:]) - AF2 = dot(dot(Sb - Sc, A), x[:, :T-1]) - xi = AF1 / AF2 - xi = xi.flatten() - - pi = B[1:] - R[:T-1] * B[:T-1] - rvn[:T-1] + g[:T-1] - Pi = cumsum(pi * xi) - - # == Prepare return values == # - path = Path(g=g, - d=d, - b=b, - s=s, - c=c, - l=l, - p=p, - tau=tau, - rvn=rvn, - B=B, - R=R, - pi=pi, - Pi=Pi, - xi=xi) - - return path - - -def gen_fig_1(path): - """ - The parameter is the path namedtuple returned by compute_paths(). See - the docstring of that function for details. - """ - - T = len(path.c) - - # == Prepare axes == # - num_rows, num_cols = 2, 2 - fig, axes = plt.subplots(num_rows, num_cols, figsize=(14, 10)) - plt.subplots_adjust(hspace=0.4) - for i in range(num_rows): - for j in range(num_cols): - axes[i, j].grid() - axes[i, j].set_xlabel(r'Time') - bbox = (0., 1.02, 1., .102) - legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'} - p_args = {'lw': 2, 'alpha': 0.7} - - # == Plot consumption, govt expenditure and revenue == # - ax = axes[0, 0] - ax.plot(path.rvn, label=r'$\tau_t \ell_t$', **p_args) - ax.plot(path.g, label=r'$g_t$', **p_args) - ax.plot(path.c, label=r'$c_t$', **p_args) - ax.legend(ncol=3, **legend_args) - - # == Plot govt expenditure and debt == # - ax = axes[0, 1] - ax.plot(list(range(1, T+1)), path.rvn, label=r'$\tau_t \ell_t$', **p_args) - ax.plot(list(range(1, T+1)), path.g, label=r'$g_t$', **p_args) - ax.plot(list(range(1, T)), path.B[1:T], label=r'$B_{t+1}$', **p_args) - ax.legend(ncol=3, **legend_args) - - # == Plot risk free return == # - ax = axes[1, 0] - ax.plot(list(range(1, T+1)), path.R - 1, label=r'$R_t - 1$', **p_args) - ax.legend(ncol=1, **legend_args) - - # == Plot revenue, expenditure and risk free rate == # - ax = axes[1, 1] - ax.plot(list(range(1, T+1)), path.rvn, label=r'$\tau_t \ell_t$', **p_args) - ax.plot(list(range(1, T+1)), path.g, label=r'$g_t$', **p_args) - axes[1, 1].plot(list(range(1, T)), path.pi, label=r'$\pi_{t+1}$', **p_args) - ax.legend(ncol=3, **legend_args) - - plt.show() - - -def gen_fig_2(path): - """ - The parameter is the path namedtuple returned by compute_paths(). See - the docstring of that function for details. - """ - - T = len(path.c) - - # == Prepare axes == # - num_rows, num_cols = 2, 1 - fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 10)) - plt.subplots_adjust(hspace=0.5) - bbox = (0., 1.02, 1., .102) - bbox = (0., 1.02, 1., .102) - legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'} - p_args = {'lw': 2, 'alpha': 0.7} - - # == Plot adjustment factor == # - ax = axes[0] - ax.plot(list(range(2, T+1)), path.xi, label=r'$\xi_t$', **p_args) - ax.grid() - ax.set_xlabel(r'Time') - ax.legend(ncol=1, **legend_args) - - # == Plot adjusted cumulative return == # - ax = axes[1] - ax.plot(list(range(2, T+1)), path.Pi, label=r'$\Pi_t$', **p_args) - ax.grid() - ax.set_xlabel(r'Time') - ax.legend(ncol=1, **legend_args) - - plt.show() diff --git a/examples/lqramsey_ar1.py b/examples/lqramsey_ar1.py deleted file mode 100644 index 0eea50061..000000000 --- a/examples/lqramsey_ar1.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -Filename: lqramsey_ar1.py -Authors: Thomas Sargent, Doc-Jin Jang, Jeong-hun Choi, John Stachurski - -Example 1: Govt spending is AR(1) and state is (g, 1). - -""" - -import numpy as np -from numpy import array -import lqramsey - -# == Parameters == # -beta = 1 / 1.05 -rho, mg = .7, .35 -A = np.identity(2) -A[0, :] = rho, mg * (1-rho) -C = np.zeros((2, 1)) -C[0, 0] = np.sqrt(1 - rho**2) * mg / 10 -Sg = array((1, 0)).reshape(1, 2) -Sd = array((0, 0)).reshape(1, 2) -Sb = array((0, 2.135)).reshape(1, 2) -Ss = array((0, 0)).reshape(1, 2) - -economy = lqramsey.Economy(beta=beta, - Sg=Sg, - Sd=Sd, - Sb=Sb, - Ss=Ss, - discrete=False, - proc=(A, C)) - -T = 50 -path = lqramsey.compute_paths(T, economy) -lqramsey.gen_fig_1(path) diff --git a/examples/lqramsey_discrete.py b/examples/lqramsey_discrete.py deleted file mode 100644 index 5db8c157d..000000000 --- a/examples/lqramsey_discrete.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -Filename: lqramsey_discrete.py -Authors: Thomas Sargent, Doc-Jin Jang, Jeong-hun Choi, John Stachurski - -LQ Ramsey model with discrete exogenous process. - -""" -from numpy import array -import lqramsey - -# == Parameters == # -beta = 1 / 1.05 -P = array([[0.8, 0.2, 0.0], - [0.0, 0.5, 0.5], - [0.0, 0.0, 1.0]]) -# == Possible states of the world == # -# Each column is a state of the world. The rows are [g d b s 1] -x_vals = array([[0.5, 0.5, 0.25], - [0.0, 0.0, 0.0], - [2.2, 2.2, 2.2], - [0.0, 0.0, 0.0], - [1.0, 1.0, 1.0]]) -Sg = array((1, 0, 0, 0, 0)).reshape(1, 5) -Sd = array((0, 1, 0, 0, 0)).reshape(1, 5) -Sb = array((0, 0, 1, 0, 0)).reshape(1, 5) -Ss = array((0, 0, 0, 1, 0)).reshape(1, 5) - -economy = lqramsey.Economy(beta=beta, - Sg=Sg, - Sd=Sd, - Sb=Sb, - Ss=Ss, - discrete=True, - proc=(P, x_vals)) - -T = 15 -path = lqramsey.compute_paths(T, economy) -lqramsey.gen_fig_1(path) diff --git a/examples/lucas_stokey.py b/examples/lucas_stokey.py deleted file mode 100644 index 8a74ce84b..000000000 --- a/examples/lucas_stokey.py +++ /dev/null @@ -1,437 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Feb 18 15:43:37 2015 - -@author: dgevans -""" -import numpy as np -from scipy.optimize import root -from scipy.optimize import fmin_slsqp -from scipy.interpolate import UnivariateSpline -from quantecon import compute_fixed_point -from quantecon.markov import mc_sample_path - - -class Planners_Allocation_Sequential(object): - ''' - Class returns planner's allocation as a function of the multiplier on the - implementability constraint mu - ''' - def __init__(self,Para): - ''' - Initializes the class from the calibration Para - ''' - self.beta = Para.beta - self.Pi = Para.Pi - self.G = Para.G - self.S = len(Para.Pi) # number of states - self.Theta = Para.Theta - self.Para = Para - #now find the first best allocation - self.find_first_best() - - def find_first_best(self): - ''' - Find the first best allocation - ''' - Para = self.Para - S,Theta,Uc,Un,G = self.S,self.Theta,Para.Uc,Para.Un,self.G - - def res(z): - c = z[:S] - n = z[S:] - return np.hstack( - [Theta*Uc(c,n)+Un(c,n), Theta*n - c - G] - ) - res = root(res,0.5*np.ones(2*S)) - - if not res.success: - raise Exception('Could not find first best') - - self.cFB = res.x[:S] - self.nFB = res.x[S:] - self.XiFB = Uc(self.cFB,self.nFB) #multiplier on the resource constraint. - self.zFB = np.hstack([self.cFB,self.nFB,self.XiFB]) - - def time1_allocation(self,mu): - ''' - Computes optimal allocation for time t\geq 1 for a given \mu - ''' - Para = self.Para - S,Theta,G,Uc,Ucc,Un,Unn = self.S,self.Theta,self.G,Para.Uc,Para.Ucc,Para.Un,Para.Unn - def FOC(z): - c = z[:S] - n = z[S:2*S] - Xi = z[2*S:] - return np.hstack([ - Uc(c,n) - mu*(Ucc(c,n)*c+Uc(c,n)) -Xi, #foc c - Un(c,n) - mu*(Unn(c,n)*n+Un(c,n)) + Theta*Xi, #foc n - Theta*n - c - G #resource constraint - ]) - - #find the root of the FOC - res = root(FOC,self.zFB) - if not res.success: - raise Exception('Could not find LS allocation.') - z = res.x - c,n,Xi = z[:S],z[S:2*S],z[2*S:] - - #now compute x - I = Uc(c,n)*c + Un(c,n)*n - x = np.linalg.solve(np.eye(S) - self.beta*self.Pi, I ) - - return c,n,x,Xi - - def time0_allocation(self,B_,s_0): - ''' - Finds the optimal allocation given initial government debt B_ and state s_0 - ''' - Para,Pi,Theta,G,beta = self.Para,self.Pi,self.Theta,self.G,self.beta - Uc,Ucc,Un,Unn = Para.Uc,Para.Ucc,Para.Un,Para.Unn - - #first order conditions of planner's problem - def FOC(z): - mu,c,n,Xi = z - xprime = self.time1_allocation(mu)[2] - return np.hstack([ - Uc(c,n)*(c-B_) + Un(c,n)*n + beta*Pi[s_0].dot(xprime), - Uc(c,n) - mu*(Ucc(c,n)*(c-B_) + Uc(c,n)) - Xi, - Un(c,n) - mu*(Unn(c,n)*n+Un(c,n)) + Theta[s_0]*Xi, - (Theta*n - c - G)[s_0] - ]) - - #find root - res = root(FOC,np.array([0.,self.cFB[s_0],self.nFB[s_0],self.XiFB[s_0]])) - if not res.success: - raise Exception('Could not find time 0 LS allocation.') - - return res.x - - def time1_value(self,mu): - ''' - Find the value associated with multiplier mu - ''' - c,n,x,Xi = self.time1_allocation(mu) - U = self.Para.U(c,n) - V = np.linalg.solve(np.eye(self.S) - self.beta*self.Pi, U ) - return c,n,x,V - - def Tau(self,c,n): - ''' - Computes Tau given c,n - ''' - Para = self.Para - Uc,Un = Para.Uc(c,n),Para.Un(c,n) - - return 1+Un/(self.Theta * Uc) - - def simulate(self,B_,s_0,T,sHist=None): - ''' - Simulates planners policies for T periods - ''' - Para,Pi,beta = self.Para,self.Pi,self.beta - Uc = Para.Uc - - - if sHist == None: - sHist = mc_sample_path(Pi,s_0,T) - - cHist,nHist,Bhist,TauHist,muHist = np.zeros((5,T)) - RHist = np.zeros(T-1) - #time0 - mu,cHist[0],nHist[0],_ = self.time0_allocation(B_,s_0) - TauHist[0] = self.Tau(cHist[0],nHist[0])[s_0] - Bhist[0] = B_ - muHist[0] = mu - - #time 1 onward - for t in range(1,T): - c,n,x,Xi = self.time1_allocation(mu) - Tau = self.Tau(c,n) - u_c = Uc(c,n) - s = sHist[t] - Eu_c = Pi[sHist[t-1]].dot(u_c) - - cHist[t],nHist[t],Bhist[t],TauHist[t] = c[s],n[s],x[s]/u_c[s],Tau[s] - - RHist[t-1] = Uc(cHist[t-1],nHist[t-1])/(beta*Eu_c) - muHist[t] = mu - - return cHist,nHist,Bhist,TauHist,sHist,muHist,RHist - - - - -class Planners_Allocation_Bellman(object): - ''' - Compute the planner's allocation by solving Bellman - equation. - ''' - def __init__(self,Para,mugrid): - ''' - Initializes the class from the calibration Para - ''' - self.beta = Para.beta - self.Pi = Para.Pi - self.G = Para.G - self.S = len(Para.Pi) # number of states - self.Theta = Para.Theta - self.Para = Para - self.mugrid = mugrid - - #now find the first best allocation - self.solve_time1_bellman() - self.T.time_0 = True #Bellman equation now solves time 0 problem - - def solve_time1_bellman(self): - ''' - Solve the time 1 Bellman equation for calibration Para and initial grid mugrid0 - ''' - Para,mugrid0 = self.Para,self.mugrid - S = len(Para.Pi) - - #First get initial fit - PP = Planners_Allocation_Sequential(Para) - c,n,x,V = map(np.vstack, zip(*map(lambda mu: PP.time1_value(mu),mugrid0)) ) - - Vf,cf,nf,xprimef = {},{},{},{} - for s in range(2): - cf[s] = UnivariateSpline(x[:,s],c[:,s]) - nf[s] = UnivariateSpline(x[:,s],n[:,s]) - Vf[s] = UnivariateSpline(x[:,s],V[:,s]) - for sprime in range(S): - xprimef[s,sprime] = UnivariateSpline(x[:,s],x[:,s]) - policies = [cf,nf,xprimef] - - - #create xgrid - xbar = [x.min(0).max(),x.max(0).min()] - xgrid = np.linspace(xbar[0],xbar[1],len(mugrid0)) - self.xgrid = xgrid - - #Now iterate on bellman equation - T = BellmanEquation(Para,xgrid,policies) - diff = 1. - while diff > 1e-5: - PF = T(Vf) - - Vfnew,policies = self.fit_policy_function(PF) - - diff = 0. - for s in range(S): - diff = max(diff, np.abs((Vf[s](xgrid)-Vfnew[s](xgrid))/Vf[s](xgrid)).max() ) - - print(diff) - Vf = Vfnew - - #store value function policies and Bellman Equations - self.Vf = Vf - self.policies = policies - self.T = T - - def fit_policy_function(self,PF): - ''' - Fits the policy functions PF using the points xgrid using UnivariateSpline - ''' - xgrid,S = self.xgrid,self.S - - Vf,cf,nf,xprimef = {},{},{},{} - for s in range(S): - PFvec = np.vstack(map(lambda x:PF(x,s),xgrid)) - Vf[s] = UnivariateSpline(xgrid,PFvec[:,0],s=0) - cf[s] = UnivariateSpline(xgrid,PFvec[:,1],s=0,k=1) - nf[s] = UnivariateSpline(xgrid,PFvec[:,2],s=0,k=1) - for sprime in range(S): - xprimef[s,sprime] = UnivariateSpline(xgrid,PFvec[:,3+sprime],s=0,k=1) - - return Vf,[cf,nf,xprimef] - - def Tau(self,c,n): - ''' - Computes Tau given c,n - ''' - Para = self.Para - Uc,Un = Para.Uc(c,n),Para.Un(c,n) - - return 1+Un/(self.Theta * Uc) - - def time0_allocation(self,B_,s0): - ''' - Finds the optimal allocation given initial government debt B_ and state s_0 - ''' - PF = self.T(self.Vf) - - z0 = PF(B_,s0) - c0,n0,xprime0 = z0[1],z0[2],z0[3:] - return c0,n0,xprime0 - - def simulate(self,B_,s_0,T,sHist=None): - ''' - Simulates Ramsey plan for T periods - ''' - Para,Pi = self.Para,self.Pi - Uc = Para.Uc - cf,nf,xprimef = self.policies - - if sHist == None: - sHist = mc_sample_path(Pi,s_0,T) - - cHist,nHist,Bhist,TauHist,muHist = np.zeros((5,T)) - RHist = np.zeros(T-1) - #time0 - cHist[0],nHist[0],xprime = self.time0_allocation(B_,s_0) - TauHist[0] = self.Tau(cHist[0],nHist[0])[s_0] - Bhist[0] = B_ - muHist[0] = 0. - - #time 1 onward - for t in range(1,T): - s,x = sHist[t],xprime[sHist[t]] - c,n,xprime = np.empty(self.S),nf[s](x),np.empty(self.S) - for shat in range(self.S): - c[shat] = cf[shat](x) - for sprime in range(self.S): - xprime[sprime] = xprimef[s,sprime](x) - - Tau = self.Tau(c,n)[s] - u_c = Uc(c,n) - Eu_c = Pi[sHist[t-1]].dot(u_c) - muHist[t] = self.Vf[s](x,1) - - RHist[t-1] = Uc(cHist[t-1],nHist[t-1])/(self.beta*Eu_c) - - cHist[t],nHist[t],Bhist[t],TauHist[t] = c[s],n,x/u_c[s],Tau - - return cHist,nHist,Bhist,TauHist,sHist,muHist,RHist - -class BellmanEquation(object): - ''' - Bellman equation for the continuation of the Lucas-Stokey Problem - ''' - def __init__(self,Para,xgrid,policies0): - ''' - Initializes the class from the calibration Para - ''' - self.beta = Para.beta - self.Pi = Para.Pi - self.G = Para.G - self.S = len(Para.Pi) # number of states - self.Theta = Para.Theta - self.Para = Para - - self.xbar = [min(xgrid),max(xgrid)] - self.time_0 = False - - self.z0 = {} - cf,nf,xprimef = policies0 - for s in range(self.S): - for x in xgrid: - xprime0 = np.empty(self.S) - for sprime in range(self.S): - xprime0[sprime] = xprimef[s,sprime](x) - self.z0[x,s] = np.hstack([cf[s](x),nf[s](x),xprime0]) - - self.find_first_best() - - def find_first_best(self): - ''' - Find the first best allocation - ''' - Para = self.Para - S,Theta,Uc,Un,G = self.S,self.Theta,Para.Uc,Para.Un,self.G - - def res(z): - c = z[:S] - n = z[S:] - return np.hstack( - [Theta*Uc(c,n)+Un(c,n), Theta*n - c - G] - ) - res = root(res,0.5*np.ones(2*S)) - if not res.success: - raise Exception('Could not find first best') - - self.cFB = res.x[:S] - self.nFB = res.x[S:] - IFB = Uc(self.cFB,self.nFB)*self.cFB + Un(self.cFB,self.nFB)*self.nFB - - self.xFB = np.linalg.solve(np.eye(S) - self.beta*self.Pi, IFB) - - self.zFB = {} - for s in range(S): - self.zFB[s] = np.hstack([self.cFB[s],self.nFB[s],self.xFB]) - - - - def __call__(self,Vf): - ''' - Given continuation value function next period return value function this - period return T(V) and optimal policies - ''' - if not self.time_0: - PF = lambda x,s: self.get_policies_time1(x,s,Vf) - else: - PF = lambda B_,s0: self.get_policies_time0(B_,s0,Vf) - return PF - - def get_policies_time1(self,x,s,Vf): - ''' - Finds the optimal policies - ''' - Para,beta,Theta,G,S,Pi = self.Para,self.beta,self.Theta,self.G,self.S,self.Pi - U,Uc,Un = Para.U,Para.Uc,Para.Un - - def objf(z): - c,n,xprime = z[0],z[1],z[2:] - Vprime = np.empty(S) - for sprime in range(S): - Vprime[sprime] = Vf[sprime](xprime[sprime]) - - return -(U(c,n)+beta*Pi[s].dot(Vprime)) - - def cons(z): - c,n,xprime = z[0],z[1],z[2:] - return np.hstack([ - x - Uc(c,n)*c-Un(c,n)*n - beta*Pi[s].dot(xprime), - (Theta*n - c - G)[s] - ]) - - - out,fx,_,imode,smode = fmin_slsqp(objf,self.z0[x,s],f_eqcons=cons, - bounds=[(0.,100),(0.,100)]+[self.xbar]*S,full_output=True,iprint=0) - - if imode >0: - raise Exception(smode) - - self.z0[x,s] = out - return np.hstack([-fx,out]) - - def get_policies_time0(self,B_,s0,Vf): - ''' - Finds the optimal policies - ''' - Para,beta,Theta,G,S,Pi = self.Para,self.beta,self.Theta,self.G,self.S,self.Pi - U,Uc,Un = Para.U,Para.Uc,Para.Un - - def objf(z): - c,n,xprime = z[0],z[1],z[2:] - Vprime = np.empty(S) - for sprime in range(S): - Vprime[sprime] = Vf[sprime](xprime[sprime]) - - return -(U(c,n)+beta*Pi[s0].dot(Vprime)) - - def cons(z): - c,n,xprime = z[0],z[1],z[2:] - return np.hstack([ - -Uc(c,n)*(c-B_)-Un(c,n)*n - beta*Pi[s0].dot(xprime), - (Theta*n - c - G)[s0] - ]) - - - out,fx,_,imode,smode = fmin_slsqp(objf,self.zFB[s0],f_eqcons=cons, - bounds=[(0.,100),(0.,100)]+[self.xbar]*S,full_output=True,iprint=0) - - if imode >0: - raise Exception(smode) - - return np.hstack([-fx,out]) \ No newline at end of file diff --git a/examples/lucas_tree_price1.py b/examples/lucas_tree_price1.py deleted file mode 100644 index 99ce92269..000000000 --- a/examples/lucas_tree_price1.py +++ /dev/null @@ -1,22 +0,0 @@ - -from __future__ import division # Omit for Python 3.x -import matplotlib.pyplot as plt -from quantecon.models import LucasTree - -fig, ax = plt.subplots() - -tree = LucasTree(gamma=2, beta=0.95, alpha=0.90, sigma=0.1) -grid, price_vals = tree.grid, tree.compute_lt_price() -ax.plot(grid, price_vals, lw=2, alpha=0.7, label=r'$p^*(y)$') -ax.set_xlim(min(grid), max(grid)) - -# tree = LucasTree(gamma=3, beta=0.95, alpha=0.90, sigma=0.1) -# grid, price_vals = tree.grid, tree.compute_lt_price() -# ax.plot(grid, price_vals, lw=2, alpha=0.7, label='more patient') -# ax.set_xlim(min(grid), max(grid)) - -ax.set_xlabel(r'$y$', fontsize=16) -ax.set_ylabel(r'price', fontsize=16) -ax.legend(loc='upper left') - -plt.show() diff --git a/examples/main_LS.py b/examples/main_LS.py deleted file mode 100644 index 95bbe16f2..000000000 --- a/examples/main_LS.py +++ /dev/null @@ -1,162 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Feb 20 14:07:56 2015 - -@author: dgevans -""" -import matplotlib.pyplot as plt -import numpy as np -import lucas_stokey as LS -from calibrations.BGP import M1 -from calibrations.CES import M2 -from calibrations.CES import M_time_example - - -''' -Time Varying Example -''' - -PP_seq_time = LS.Planners_Allocation_Sequential(M_time_example) #solve sequential problem - -sHist_h = np.array([0,1,2,3,5,5,5]) -sHist_l = np.array([0,1,2,4,5,5,5]) - -sim_seq_h = PP_seq_time.simulate(1.,0,7,sHist_h) -sim_seq_l = PP_seq_time.simulate(1.,0,7,sHist_l) - -plt.figure(figsize=[14,10]) -plt.subplot(3,2,1) -plt.title('Consumption') -plt.plot(sim_seq_l[0],'-ok') -plt.plot(sim_seq_h[0],'-or') -plt.subplot(3,2,2) -plt.title('Labor Supply') -plt.plot(sim_seq_l[1],'-ok') -plt.plot(sim_seq_h[1],'-or') -plt.subplot(3,2,3) -plt.title('Government Debt') -plt.plot(sim_seq_l[2],'-ok') -plt.plot(sim_seq_h[2],'-or') -plt.subplot(3,2,4) -plt.title('Taxe Rate') -plt.plot(sim_seq_l[3],'-ok') -plt.plot(sim_seq_h[3],'-or') -plt.subplot(3,2,5) -plt.title('Government Spending') -plt.plot(M_time_example.G[sHist_l],'-ok') -plt.plot(M_time_example.G[sHist_h],'-or') -plt.subplot(3,2,6) -plt.title('Output') -plt.plot(M_time_example.Theta[sHist_l]*sim_seq_l[1],'-ok') -plt.plot(M_time_example.Theta[sHist_h]*sim_seq_h[1],'-or') - -plt.tight_layout() -plt.savefig('TaxSequence_time_varying.png') - -plt.figure(figsize=[8,5]) -plt.title('Gross Interest Rate') -plt.plot(sim_seq_l[-1],'-ok') -plt.plot(sim_seq_h[-1],'-or') -plt.tight_layout() -plt.savefig('InterestRate_time_varying.png') - -''' -Time 0 example -''' -PP_seq_time0 = LS.Planners_Allocation_Sequential(M2) #solve sequential problem - -B_vec = np.linspace(-1.5,1.,100) -taxpolicy = np.vstack([PP_seq_time0.simulate(B_,0,2)[3] for B_ in B_vec]) -interest_rate = np.vstack([PP_seq_time0.simulate(B_,0,3)[-1] for B_ in B_vec]) - -plt.figure(figsize=[14,6]) -plt.subplot(211) -plt.plot(B_vec,taxpolicy[:,0],linewidth=2.) -plt.plot(B_vec,taxpolicy[:,1],linewidth=2.) - -plt.title('Tax Rate') -plt.legend((r'Time $t=0$', 'Time $t\geq1$'),loc=2,shadow=True) -plt.subplot(212) -plt.title('Gross Interest Rate') -plt.plot(B_vec,interest_rate[:,0],linewidth=2.) -plt.plot(B_vec,interest_rate[:,1],linewidth=2.) -plt.xlabel('Initial Government Debt') -plt.tight_layout() - -plt.savefig('Time0_taxpolicy.png') - - - - -#compute the debt entered with at time 1 -B1_vec = np.hstack([PP_seq_time0.simulate(B_,0,2)[2][1] for B_ in B_vec]) -#now compute the optimal policy if the government could reset -tau1_reset = np.hstack([PP_seq_time0.simulate(B1,0,1)[3] for B1 in B1_vec]) - -plt.figure(figsize=[10,6]) -plt.plot(B_vec,taxpolicy[:,1],linewidth=2.) -plt.plot(B_vec,tau1_reset,linewidth=2.) -plt.xlabel('Initial Government Debt') -plt.title('Tax Rate') -plt.legend((r'$\tau_1$', r'$\tau_1^R$'),loc=2,shadow=True) -plt.tight_layout() - -plt.savefig('Time0_inconsistent.png') - - -''' -BGP Example -''' -#initialize mugrid for value function iteration -muvec = np.linspace(-0.6,0.0,200) - - -PP_seq = LS.Planners_Allocation_Sequential(M1) #solve sequential problem -PP_bel = LS.Planners_Allocation_Bellman(M1,muvec) #solve recursive problem - -T = 20 -#sHist = utilities.simulate_markov(M1.Pi,0,T) -sHist = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0],dtype=int) - -#simulate -sim_seq = PP_seq.simulate(0.5,0,T,sHist) -sim_bel = PP_bel.simulate(0.5,0,T,sHist) - -#plot policies -plt.figure(figsize=[14,10]) -plt.subplot(3,2,1) -plt.title('Consumption') -plt.plot(sim_seq[0],'-ok') -plt.plot(sim_bel[0],'-xk') -plt.legend(('Sequential','Recursive'),loc='best') -plt.subplot(3,2,2) -plt.title('Labor Supply') -plt.plot(sim_seq[1],'-ok') -plt.plot(sim_bel[1],'-xk') -plt.subplot(3,2,3) -plt.title('Government Debt') -plt.plot(sim_seq[2],'-ok') -plt.plot(sim_bel[2],'-xk') -plt.subplot(3,2,4) -plt.title('Tax Rate') -plt.plot(sim_seq[3],'-ok') -plt.plot(sim_bel[3],'-xk') -plt.subplot(3,2,5) -plt.title('Government Spending') -plt.plot(M1.G[sHist],'-ok') -plt.plot(M1.G[sHist],'-xk') -plt.plot(M1.G[sHist],'-^k') -plt.subplot(3,2,6) -plt.title('Output') -plt.plot(M1.Theta[sHist]*sim_seq[1],'-ok') -plt.plot(M1.Theta[sHist]*sim_bel[1],'-xk') - -plt.tight_layout() -plt.savefig('TaxSequence_LS.png') - -plt.figure(figsize=[8,5]) -plt.title('Gross Interest Rate') -plt.plot(sim_seq[-1],'-ok') -plt.plot(sim_bel[-1],'-xk') -plt.legend(('Sequential','Recursive'),loc='best') -plt.tight_layout() diff --git a/examples/market.py b/examples/market.py deleted file mode 100644 index 335e4f2ef..000000000 --- a/examples/market.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -Filename: market.py -Reference: http://quant-econ.net/py/python_oop.html -""" - -from __future__ import division -from scipy.integrate import quad - -class Market: - - def __init__(self, ad, bd, az, bz, tax): - """ - Set up market parameters. All parameters are scalars. See - http://quant-econ.net/py/python_oop.html for interpretation. - - """ - self.ad, self.bd, self.az, self.bz, self.tax = ad, bd, az, bz, tax - if ad < az: - raise ValueError('Insufficient demand.') - - def price(self): - "Return equilibrium price" - return (self.ad - self.az + self.bz*self.tax)/(self.bd + self.bz) - - def quantity(self): - "Compute equilibrium quantity" - return self.ad - self.bd * self.price() - - def consumer_surp(self): - "Compute consumer surplus" - # == Compute area under inverse demand function == # - integrand = lambda x: (self.ad/self.bd) - (1/self.bd)* x - area, error = quad(integrand, 0, self.quantity()) - return area - self.price() * self.quantity() - - def producer_surp(self): - "Compute producer surplus" - # == Compute area above inverse supply curve, excluding tax == # - integrand = lambda x: -(self.az/self.bz) + (1/self.bz) * x - area, error = quad(integrand, 0, self.quantity()) - return (self.price() - self.tax) * self.quantity() - area - - def taxrev(self): - "Compute tax revenue" - return self.tax * self.quantity() - - def inverse_demand(self,x): - "Compute inverse demand" - return self.ad/self.bd - (1/self.bd)* x - - def inverse_supply(self,x): - "Compute inverse supply curve" - return -(self.az/self.bz) + (1/self.bz) * x + self.tax - - def inverse_supply_no_tax(self,x): - "Compute inverse supply curve without tax" - return -(self.az/self.bz) + (1/self.bz) * x - diff --git a/examples/market.py~ b/examples/market.py~ deleted file mode 100644 index 335e4f2ef..000000000 --- a/examples/market.py~ +++ /dev/null @@ -1,58 +0,0 @@ -""" -Filename: market.py -Reference: http://quant-econ.net/py/python_oop.html -""" - -from __future__ import division -from scipy.integrate import quad - -class Market: - - def __init__(self, ad, bd, az, bz, tax): - """ - Set up market parameters. All parameters are scalars. See - http://quant-econ.net/py/python_oop.html for interpretation. - - """ - self.ad, self.bd, self.az, self.bz, self.tax = ad, bd, az, bz, tax - if ad < az: - raise ValueError('Insufficient demand.') - - def price(self): - "Return equilibrium price" - return (self.ad - self.az + self.bz*self.tax)/(self.bd + self.bz) - - def quantity(self): - "Compute equilibrium quantity" - return self.ad - self.bd * self.price() - - def consumer_surp(self): - "Compute consumer surplus" - # == Compute area under inverse demand function == # - integrand = lambda x: (self.ad/self.bd) - (1/self.bd)* x - area, error = quad(integrand, 0, self.quantity()) - return area - self.price() * self.quantity() - - def producer_surp(self): - "Compute producer surplus" - # == Compute area above inverse supply curve, excluding tax == # - integrand = lambda x: -(self.az/self.bz) + (1/self.bz) * x - area, error = quad(integrand, 0, self.quantity()) - return (self.price() - self.tax) * self.quantity() - area - - def taxrev(self): - "Compute tax revenue" - return self.tax * self.quantity() - - def inverse_demand(self,x): - "Compute inverse demand" - return self.ad/self.bd - (1/self.bd)* x - - def inverse_supply(self,x): - "Compute inverse supply curve" - return -(self.az/self.bz) + (1/self.bz) * x + self.tax - - def inverse_supply_no_tax(self,x): - "Compute inverse supply curve without tax" - return -(self.az/self.bz) + (1/self.bz) * x - diff --git a/examples/market_deadweight.py b/examples/market_deadweight.py deleted file mode 100644 index 0eb8a6e9b..000000000 --- a/examples/market_deadweight.py +++ /dev/null @@ -1,11 +0,0 @@ - -from market import Market - -def deadw(m): - "Computes deadweight loss for market m." - # == Create analogous market with no tax == # - m_no_tax = Market(m.ad, m.bd, m.az, m.bz, 0) - # == Compare surplus, return difference == # - surp1 = m_no_tax.consumer_surp() + m_no_tax.producer_surp() - surp2 = m.consumer_surp() + m.producer_surp() + m.taxrev() - return surp1 - surp2 diff --git a/examples/mc_convergence_plot.py b/examples/mc_convergence_plot.py deleted file mode 100644 index 2bc034075..000000000 --- a/examples/mc_convergence_plot.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Filename: mc_convergence_plot.py -Authors: John Stachurski, Thomas J. Sargent - -""" -import numpy as np -from mpl_toolkits.mplot3d import Axes3D -import matplotlib.pyplot as plt -from quantecon import mc_compute_stationary - -P = ((0.971, 0.029, 0.000), - (0.145, 0.778, 0.077), - (0.000, 0.508, 0.492)) -P = np.array(P) - -psi = (0.0, 0.2, 0.8) # Initial condition - -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') - -ax.set_xlim(0, 1) -ax.set_ylim(0, 1) -ax.set_zlim(0, 1) -ax.set_xticks((0.25, 0.5, 0.75)) -ax.set_yticks((0.25, 0.5, 0.75)) -ax.set_zticks((0.25, 0.5, 0.75)) - -x_vals, y_vals, z_vals = [], [], [] -for t in range(20): - x_vals.append(psi[0]) - y_vals.append(psi[1]) - z_vals.append(psi[2]) - psi = np.dot(psi, P) - -ax.scatter(x_vals, y_vals, z_vals, c='r', s=60) - -psi_star = mc_compute_stationary(P)[0] -ax.scatter(psi_star[0], psi_star[1], psi_star[2], c='k', s=60) - -plt.show() diff --git a/examples/nds.py b/examples/nds.py deleted file mode 100644 index 58adaa30f..000000000 --- a/examples/nds.py +++ /dev/null @@ -1,14 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import norm -from random import uniform - -fig, ax = plt.subplots() -x = np.linspace(-4, 4, 150) -for i in range(3): - m, s = uniform(-1, 1), uniform(1, 2) - y = norm.pdf(x, loc=m, scale=s) - current_label = r'$\mu = {0:.2f}$'.format(m) - ax.plot(x, y, linewidth=2, alpha=0.6, label=current_label) -ax.legend() -plt.show() diff --git a/examples/nx_demo.py b/examples/nx_demo.py deleted file mode 100644 index df40a0645..000000000 --- a/examples/nx_demo.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -Filename: nx_demo.py -Authors: John Stachurski and Thomas J. Sargent -""" - -import networkx as nx -import matplotlib.pyplot as plt -import numpy as np - -G = nx.random_geometric_graph(200, 0.12) # Generate random graph -pos = nx.get_node_attributes(G, 'pos') # Get positions of nodes -# find node nearest the center point (0.5,0.5) -dists = [(x - 0.5)**2 + (y - 0.5)**2 for x, y in list(pos.values())] -ncenter = np.argmin(dists) -# Plot graph, coloring by path length from central node -p = nx.single_source_shortest_path_length(G, ncenter) -plt.figure() -nx.draw_networkx_edges(G, pos, alpha=0.4) -nx.draw_networkx_nodes(G, pos, nodelist=list(p.keys()), - node_size=120, alpha=0.5, - node_color=list(p.values()), cmap=plt.cm.jet_r) -plt.show() diff --git a/examples/odu_plot_densities.py b/examples/odu_plot_densities.py deleted file mode 100644 index 281efd605..000000000 --- a/examples/odu_plot_densities.py +++ /dev/null @@ -1,16 +0,0 @@ -""" -Filename: odu_plot_densities.py -Authors: John Stachurski, Thomas J. Sargent - -""" -import numpy as np -import matplotlib.pyplot as plt -from quantecon.models import SearchProblem - -sp = SearchProblem(F_a=1, F_b=1, G_a=3, G_b=1.2) -grid = np.linspace(0, 2, 150) -fig, ax = plt.subplots() -ax.plot(grid, sp.f(grid), label=r'$f$', lw=2) -ax.plot(grid, sp.g(grid), label=r'$g$', lw=2) -ax.legend(loc=0) -plt.show() diff --git a/examples/odu_vfi_plots.py b/examples/odu_vfi_plots.py deleted file mode 100644 index e4fc74430..000000000 --- a/examples/odu_vfi_plots.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -Filename: odu_vfi_plots.py -Authors: John Stachurski and Thomas Sargent -""" - -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d.axes3d import Axes3D -from matplotlib import cm -from scipy.interpolate import LinearNDInterpolator -import numpy as np -from quantecon import compute_fixed_point -from quantecon.models import SearchProblem - - -sp = SearchProblem(w_grid_size=100, pi_grid_size=100) -v_init = np.zeros(len(sp.grid_points)) + sp.c / (1 - sp.beta) -v = compute_fixed_point(sp.bellman_operator, v_init) -policy = sp.get_greedy(v) - -# Make functions from these arrays by interpolation -vf = LinearNDInterpolator(sp.grid_points, v) -pf = LinearNDInterpolator(sp.grid_points, policy) - -pi_plot_grid_size, w_plot_grid_size = 100, 100 -pi_plot_grid = np.linspace(0.001, 0.99, pi_plot_grid_size) -w_plot_grid = np.linspace(0, sp.w_max, w_plot_grid_size) - -# plot_choice = 'value_function' -plot_choice = 'policy_function' - -if plot_choice == 'value_function': - Z = np.empty((w_plot_grid_size, pi_plot_grid_size)) - for i in range(w_plot_grid_size): - for j in range(pi_plot_grid_size): - Z[i, j] = vf(w_plot_grid[i], pi_plot_grid[j]) - fig, ax = plt.subplots() - ax.contourf(pi_plot_grid, w_plot_grid, Z, 12, alpha=0.6, cmap=cm.jet) - cs = ax.contour(pi_plot_grid, w_plot_grid, Z, 12, colors="black") - ax.clabel(cs, inline=1, fontsize=10) - ax.set_xlabel('pi', fontsize=14) - ax.set_ylabel('wage', fontsize=14) -else: - Z = np.empty((w_plot_grid_size, pi_plot_grid_size)) - for i in range(w_plot_grid_size): - for j in range(pi_plot_grid_size): - Z[i, j] = pf(w_plot_grid[i], pi_plot_grid[j]) - fig, ax = plt.subplots() - ax.contourf(pi_plot_grid, w_plot_grid, Z, 1, alpha=0.6, cmap=cm.jet) - ax.contour(pi_plot_grid, w_plot_grid, Z, 1, colors="black") - ax.set_xlabel('pi', fontsize=14) - ax.set_ylabel('wage', fontsize=14) - ax.text(0.4, 1.0, 'reject') - ax.text(0.7, 1.8, 'accept') - -plt.show() diff --git a/examples/oligopoly.py b/examples/oligopoly.py deleted file mode 100644 index 74c97e40e..000000000 --- a/examples/oligopoly.py +++ /dev/null @@ -1,172 +0,0 @@ -""" -Filename: oligopoly.py -Authors: Chase Coleman, Tom Sargent, Balint Szoke -This is an example for the lecture dyn_stack.rst from the QuantEcon -series of lectures by Tom Sargent and John Stachurski. -We deal with a large monopolistic firm who faces costs: -C_t = e Q_t + .5 g Q_t^2 + .5 c (Q_{t+1} - Q_t)^2 -where the fringe firms face: -sigma_t = d q_t + .5 h q_t^2 + .5 c (q_{t+1} - q_t)^2 -Additionally, there is a linear inverse demand curve of the form: -p_t = A_0 - A_1 (Q_t + \bar{q_t}) + \eta_t, -where: -.. math - \eta_{t+1} = \rho \eta_t + C_{\varepsilon} \varepsilon_{t+1}; - \varepsilon_{t+1} \sim N(0, 1) -For more details, see the lecture. -""" -import numpy as np -import scipy.linalg as la -from quantecon import LQ -from quantecon.matrix_eqn import solve_discrete_lyapunov -from scipy.optimize import root - - -def setup_matrices(params): - """ - This function sets up the A, B, R, Q for the oligopoly problem - described in the lecture. - Parameters - ---------- - params : Array(Float, ndim=1) - Contains the parameters that describe the problem in the order - [a0, a1, rho, c_eps, c, d, e, g, h, beta] - Returns - ------- - (A, B, Q, R) : Array(Float, ndim=2) - These matrices describe the oligopoly problem. - """ - - # Left hand side of (37) - Alhs = np.eye(5) - Alhs[4, :] = np.array([a0-d, 1., -a1, -a1-h, c]) - Alhsinv = la.inv(Alhs) - - # Right hand side of (37) - Brhs = np.array([[0., 0., 1., 0., 0.]]).T - Arhs = np.eye(5) - Arhs[1, 1] = rho - Arhs[3, 4] = 1. - Arhs[4, 4] = c / beta - - # R from equation (40) - R = np.array([[0., 0., (a0-e)/2., 0., 0.], - [0., 0., 1./2., 0., 0.], - [(a0-e)/2., 1./2, -a1 - .5*g, -a1/2, 0.], - [0., 0., -a1/2, 0., 0.], - [0., 0., 0., 0., 0.]]) - - Rf = np.array([[0., 0., 0., 0., 0., (a0-d)/2.], - [0., 0., 0., 0., 0., 1./2.], - [0., 0., 0., 0., 0., -a1/2.], - [0., 0., 0., 0., 0., -a1/2.], - [0., 0., 0., 0., 0., 0.], - [(a0-d)/2., 1./2., -a1/2., -a1/2., 0., -h/2.]]) - - Q = np.array([[c/2]]) - - A = Alhsinv.dot(Arhs) - B = Alhsinv.dot(Brhs) - - return A, B, Q, R, Rf - - -def find_PFd(A, B, Q, R, Rf, beta=.95): - """ - Taking the parameters A, B, Q, R as found in the `setup_matrices`, - we find the value function of the optimal linear regulator problem. - This is steps 2 and 3 in the lecture notes. - Parameters - ---------- - (A, B, Q, R) : Array(Float, ndim=2) - The matrices that describe the oligopoly problem - Returns - ------- - (P, F, d) : Array(Float, ndim=2) - The matrix that describes the value function of the optimal - linear regulator problem. - """ - - lq = LQ(Q, -R, A, B, beta=beta) - P, F, d = lq.stationary_values() - - Af = np.vstack((np.hstack([A-np.dot(B,F), np.array([[0., 0., 0., 0., 0.]]).T]),np.array([[0., 0., 0., 0., 0., 1.]]))) - Bf = np.array([[0., 0., 0., 0., 0., 1.]]).T - - lqf = LQ(Q, -Rf, Af, Bf, beta=beta) - Pf, Ff, df = lqf.stationary_values() - - return P, F, d, Pf, Ff, df - - -def solve_for_opt_policy(params, eta0=0., Q0=0., q0=0.): - """ - Taking the parameters as given, solve for the optimal decision rules - for the firm. - Parameters - ---------- - params : Array(Float, ndim=1) - This holds all of the model parameters in an array - Returns - ------- - out : - """ - # Step 1/2: Formulate/Solve the optimal linear regulator - (A, B, Q, R, Rf) = setup_matrices(params) - (P, F, d, Pf, Ff, df) = find_PFd(A, B, Q, R, Rf, beta=beta) - - # Step 3: Convert implementation into state variables (Find coeffs) - P22 = P[-1, -1] - P21 = P[-1, :-1] - P22inv = P22**(-1) - - # Step 4: Find optimal x_0 and \mu_{x, 0} - z0 = np.array([1., eta0, Q0, q0]) - x0 = -P22inv*np.dot(P21, z0) - D0 = -np.dot(P22inv, P21) - - # Return -F and -Ff because we use u_t = -F y_t - return P, -F, D0, Pf, -Ff - - -# Parameter values -a0 = 100. -a1 = 1. -rho = .8 -c_eps = .2 -c = 1. -d = 20. -e = 20. -g = .2 -h = .2 -beta = .95 -params = np.array([a0, a1, rho, c_eps, c, d, e, g, h, beta]) - - -P, F, D0, Pf,Ff = solve_for_opt_policy(params) - - -# Checking time-inconsistency: -A, B, Q, R, Rf = setup_matrices(params) -# arbitrary initial z_0 -y0 = np.array([[1, 1, 1, 1]]).T -# optimal x_0 = i_0 -i0 = np.dot(D0,y0) -# iterate one period using the closed-loop system -y1 = np.dot( A + np.dot(B,F) , np.vstack([y0, i0]) ) -# the last element of y_1 is x_1 = i_1 -i1_0 = y1[-1,0] - -# compare this to the case when the leader solves a Stackelberg problem -# in period 1. if in period 1 the leader could choose i1 given -# (1, v_1, Q_1, \bar{q}_1) -i1_1 = np.dot(D0, y1[0:-1,0]) - - -print("P = {}".format(P)) -print("-F = {}".format(F)) -print("D0 = {}".format(D0)) -print("Pf = {}".format(Pf)) -print("-Ff = {}".format(Ff)) -print("i1_0 = {}".format(i1_0)) -print("i1_1 = {}".format(i1_1)) diff --git a/examples/oligopoly.py~ b/examples/oligopoly.py~ deleted file mode 100644 index a596f0b33..000000000 --- a/examples/oligopoly.py~ +++ /dev/null @@ -1,181 +0,0 @@ -""" -Filename: oligopoly.py -Authors: Chase Coleman - -This is an example for the lecture dyn_stack.rst from the QuantEcon -series of lectures by Tom Sargent and John Stachurski. - -We deal with a large monopolistic firm who faces costs: - -C_t = e Q_t + .5 g Q_t^2 + .5 c (Q_{t+1} - Q_t)^2 - -where the fringe firms face: - -sigma_t = d q_t + .5 h q_t^2 + .5 c (q_{t+1} - q_t)^2 - -Additionally, there is a linear inverse demand curve of the form: - -p_t = A_0 - A_1 (Q_t + \bar{q_t}) + \eta_t, - -where: - -.. math - \eta_{t+1} = \rho \eta_t + C_{\varepsilon} \varepsilon_{t+1}; - \varepsilon_{t+1} \sim N(0, 1) - -For more details, see the lecture. -""" -import numpy as np -import scipy.linalg as la -from quantecon import LQ -from quantecon.matrix_eqn import solve_discrete_lyapunov -from scipy.optimize import root - - -def setup_matrices(params): - """ - This function sets up the A, B, R, Q for the oligopoly problem - described in the lecture. - - Parameters - ---------- - params : Array(Float, ndim=1) - Contains the parameters that describe the problem in the order - [a0, a1, rho, c_eps, c, d, e, g, h, beta] - - Returns - ------- - (A, B, Q, R) : Array(Float, ndim=2) - These matrices describe the oligopoly problem. - """ - - # Left hand side of (37) - Alhs = np.eye(5) - Alhs[4, :] = np.array([a0-d, 1., -a1, -a1-h, c]) - Alhsinv = la.inv(Alhs) - - # Right hand side of (37) - Brhs = np.array([[0., 0., 1., 0., 0.]]).T - Arhs = np.eye(5) - Arhs[1, 1] = rho - Arhs[3, 4] = 1. - Arhs[4, 4] = c / beta - - # R from equation (40) - R = np.array([[0., 0., (a0-e)/2., 0., 0.], - [0., 0., 1./2., 0., 0.], - [(a0-e)/2., 1./2, -a1 - .5*g, -a1/2, 0.], - [0., 0., -a1/2, 0., 0.], - [0., 0., 0., 0., 0.]]) - Q = np.array([[c/2]]) - - A = Alhsinv.dot(Arhs) - B = Alhsinv.dot(Brhs) - - return A, B, Q, R - - -def find_PFd(A, B, Q, R, beta=.95): - """ - Taking the parameters A, B, Q, R as found in the `setup_matrices`, - we find the value function of the optimal linear regulator problem. - This is steps 2 and 3 in the lecture notes. - - Parameters - ---------- - (A, B, Q, R) : Array(Float, ndim=2) - The matrices that describe the oligopoly problem - - Returns - ------- - (P, F, d) : Array(Float, ndim=2) - The matrix that describes the value function of the optimal - linear regulator problem. - - """ - - lq = LQ(Q, -R, A, B, beta=beta) - P, F, d = lq.stationary_values() - - return P, F, d - - -def solve_for_opt_policy(params, eta0=0., Q0=0., q0=0.): - """ - Taking the parameters as given, solve for the optimal decision rules - for the firm. - - Parameters - ---------- - params : Array(Float, ndim=1) - This holds all of the model parameters in an array - - Returns - ------- - out : - - """ - # Step 1/2: Formulate/Solve the optimal linear regulator - (A, B, Q, R) = setup_matrices(params) - (P, F, d) = find_PFd(A, B, Q, R, beta=beta) - - # Step 3: Convert implementation into state variables (Find coeffs) - P22 = P[-1, -1] - P21 = P[-1, :-1] - P22inv = P22**(-1) - dotmat = np.empty((5, 5)) - upper = np.eye(4, 5) # Gives me 4x4 identity with a column of 0s - lower = np.hstack([-P22inv*P21, P22inv]) - dotmat[:-1, :] = upper - dotmat[-1, :] = lower - - coeffs = np.dot(-F, dotmat) - - # Step 4: Find optimal x_0 and \mu_{x, 0} - z0 = np.array([1., eta0, Q0, q0]) - x0 = -P22inv*np.dot(P21, z0) - - # Do some rearranging for convenient representation of policy - # TODO: Finish getting the equations into the from - # u_t = rho u_{t-1} + gamma_1 z_t + gamma_2 z_{t-1} - - part1 = np.vstack([np.eye(4, 5), P[-1, :]]) - part2 = A - np.dot(B, F) - part3 = dotmat - m = np.dot(part1, part2).dot(part3) - m12 = m[-1, :-1] - m22 = m[-1, -1] - - f = np.dot(-F, dotmat) - f11 = f[-1, :-1] - f12 = f[-1, -1] - - coeff_utm1 = f12*m22*f12**(-1) - coeff_zt = coeffs[0, :-1] - coeff_ztm1 = f12*(m12 - f12**(-1)*m22*f11) - - return coeffs, x0, (coeff_utm1, coeff_zt, coeff_ztm1) - - -# Parameter values -a0 = 100. -a1 = 1. -rho = .8 -c_eps = .2 -c = 1. -d = 20. -e = 20. -g = .2 -h = .2 -beta = .95 -params = np.array([a0, a1, rho, c_eps, c, d, e, g, h, beta]) - - -coefficients, x0, alt_coeffs = solve_for_opt_policy(params) - -print("The original coefficients are") -print("u_t = {} [z_t mu_[x, t]]'".format(coefficients)) -print("or in other terms") -print("u_t = {} u_[t-1] \n + {} z_t \n + {} z_[t-1]".format(alt_coeffs[0], - alt_coeffs[1], - alt_coeffs[2])) diff --git a/examples/optgrowth_v0.py b/examples/optgrowth_v0.py deleted file mode 100644 index 39b52dc13..000000000 --- a/examples/optgrowth_v0.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -Filename: optgrowth_v0.py -Authors: John Stachurski and Thomas Sargent - -A first pass at solving the optimal growth problem via value function -iteration. A more general version is provided in optgrowth.py. - -""" -from __future__ import division # Omit for Python 3.x -import matplotlib.pyplot as plt -import numpy as np -from numpy import log -from scipy.optimize import fminbound -from scipy import interp - -# Primitives and grid -alpha = 0.65 -beta = 0.95 -grid_max = 2 -grid_size = 150 -grid = np.linspace(1e-6, grid_max, grid_size) -# Exact solution -ab = alpha * beta -c1 = (log(1 - ab) + log(ab) * ab / (1 - ab)) / (1 - beta) -c2 = alpha / (1 - ab) - - -def v_star(k): - return c1 + c2 * log(k) - - -def bellman_operator(w): - """ - The approximate Bellman operator, which computes and returns the updated - value function Tw on the grid points. - - * w is a flat NumPy array with len(w) = len(grid) - - The vector w represents the value of the input function on the grid - points. - """ - # === Apply linear interpolation to w === # - Aw = lambda x: interp(x, grid, w) - - # === set Tw[i] equal to max_c { log(c) + beta w(f(k_i) - c)} === # - Tw = np.empty(grid_size) - for i, k in enumerate(grid): - objective = lambda c: - log(c) - beta * Aw(k**alpha - c) - c_star = fminbound(objective, 1e-6, k**alpha) - Tw[i] = - objective(c_star) - - return Tw - -# === If file is run directly, not imported, produce figure === # -if __name__ == '__main__': - - w = 5 * log(grid) - 25 # An initial condition -- fairly arbitrary - n = 35 - fig, ax = plt.subplots() - ax.set_ylim(-40, -20) - ax.set_xlim(np.min(grid), np.max(grid)) - lb = 'initial condition' - ax.plot(grid, w, color=plt.cm.jet(0), lw=2, alpha=0.6, label=lb) - for i in range(n): - w = bellman_operator(w) - ax.plot(grid, w, color=plt.cm.jet(i / n), lw=2, alpha=0.6) - lb = 'true value function' - ax.plot(grid, v_star(grid), 'k-', lw=2, alpha=0.8, label=lb) - ax.legend(loc='upper left') - - plt.show() diff --git a/examples/paths_and_hist.py b/examples/paths_and_hist.py deleted file mode 100644 index 6fc891e29..000000000 --- a/examples/paths_and_hist.py +++ /dev/null @@ -1,49 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from quantecon import LinearStateSpace -import random - -phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 -sigma = 0.1 - -A = [[phi_1, phi_2, phi_3, phi_4], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0]] -C = [[sigma], [0], [0], [0]] -G = [1, 0, 0, 0] - -T = 30 -ar = LinearStateSpace(A, C, G, mu_0=np.ones(4)) - -ymin, ymax = -0.8, 1.25 - -fig, axes = plt.subplots(1, 2, figsize=(8, 3)) - -for ax in axes: - ax.grid(alpha=0.4) - -ax = axes[0] - -ax.set_ylim(ymin, ymax) -ax.set_ylabel(r'$y_t$', fontsize=16) -ax.vlines((T,), -1.5, 1.5) - -ax.set_xticks((T,)) -ax.set_xticklabels((r'$T$',)) - -sample = [] -for i in range(20): - rcolor = random.choice(('c', 'g', 'b', 'k')) - x, y = ar.simulate(ts_length=T+15) - y = y.flatten() - ax.plot(y, color=rcolor, lw=1, alpha=0.5) - ax.plot((T,), (y[T],), 'ko', alpha=0.5) - sample.append(y[T]) - -y = y.flatten() -axes[1].set_ylim(ymin, ymax) -axes[1].hist(sample, bins=16, normed=True, orientation='horizontal', alpha=0.5) - -plt.show() diff --git a/examples/paths_and_stationarity.py b/examples/paths_and_stationarity.py deleted file mode 100644 index 97bcdce7b..000000000 --- a/examples/paths_and_stationarity.py +++ /dev/null @@ -1,43 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from quantecon import LinearStateSpace -import random - -phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 -sigma = 0.1 - -A = [[phi_1, phi_2, phi_3, phi_4], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0]] -C = [[sigma], [0], [0], [0]] -G = [1, 0, 0, 0] - -T0 = 10 -T1 = 50 -T2 = 75 -T4 = 100 - -ar = LinearStateSpace(A, C, G, mu_0=np.ones(4)) -ymin, ymax = -0.8, 1.25 - -fig, ax = plt.subplots(figsize=(8, 5)) - -ax.grid(alpha=0.4) -ax.set_ylim(ymin, ymax) -ax.set_ylabel(r'$y_t$', fontsize=16) -ax.vlines((T0, T1, T2), -1.5, 1.5) - -ax.set_xticks((T0, T1, T2)) -ax.set_xticklabels((r"$T$", r"$T'$", r"$T''$"), fontsize=14) - -sample = [] -for i in range(80): - rcolor = random.choice(('c', 'g', 'b')) - x, y = ar.simulate(ts_length=T4) - y = y.flatten() - ax.plot(y, color=rcolor, lw=0.8, alpha=0.5) - ax.plot((T0, T1, T2), (y[T0], y[T1], y[T2],), 'ko', alpha=0.5) - -plt.show() diff --git a/examples/perm_inc_figs.py b/examples/perm_inc_figs.py deleted file mode 100644 index 7786f9bf5..000000000 --- a/examples/perm_inc_figs.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -Plots consumption, income and debt for the simple infinite horizon LQ -permanent income model with Gaussian iid income. -""" - - -import random -import numpy as np -import matplotlib.pyplot as plt - -r = 0.05 -beta = 1 / (1 + r) -T = 60 -sigma = 0.15 -mu = 1 - - -def time_path(): - w = np.random.randn(T+1) # w_0, w_1, ..., w_T - w[0] = 0 - b = np.zeros(T+1) - for t in range(1, T+1): - b[t] = w[1:t].sum() - b = - sigma * b - c = mu + (1 - beta) * (sigma * w - b) - return w, b, c - - -# == Figure showing a typical realization == # - -if 1: - fig, ax = plt.subplots() - - p_args = {'lw': 2, 'alpha': 0.7} - ax.grid() - ax.set_xlabel(r'Time') - bbox = (0., 1.02, 1., .102) - legend_args = {'bbox_to_anchor': bbox, 'loc': 'upper left', - 'mode': 'expand'} - - w, b, c = time_path() - ax.plot(list(range(T+1)), mu + sigma * w, 'g-', - label="non-financial income", **p_args) - ax.plot(list(range(T+1)), c, 'k-', label="consumption", **p_args) - ax.plot(list(range(T+1)), b, 'b-', label="debt", **p_args) - ax.legend(ncol=3, **legend_args) - - plt.show() - -# == Figure showing multiple consumption paths == # - -if 0: - fig, ax = plt.subplots() - - p_args = {'lw': 0.8, 'alpha': 0.7} - ax.grid() - ax.set_xlabel(r'Time') - ax.set_ylabel(r'Consumption') - b_sum = np.zeros(T+1) - for i in range(250): - rcolor = random.choice(('c', 'g', 'b', 'k')) - w, b, c = time_path() - ax.plot(list(range(T+1)), c, color=rcolor, **p_args) - - plt.show() diff --git a/examples/perm_inc_ir.py b/examples/perm_inc_ir.py deleted file mode 100644 index a63cd1b8e..000000000 --- a/examples/perm_inc_ir.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -Impulse response functions for the LQ permanent income model permanent and -transitory shocks. -""" - - -import numpy as np -import matplotlib.pyplot as plt - -r = 0.05 -beta = 1 / (1 + r) -T = 20 # Time horizon -S = 5 # Impulse date -sigma1 = sigma2 = 0.15 - - -def time_path(permanent=False): - "Time path of consumption and debt given shock sequence" - w1 = np.zeros(T+1) - w2 = np.zeros(T+1) - b = np.zeros(T+1) - c = np.zeros(T+1) - if permanent: - w1[S+1] = 1.0 - else: - w2[S+1] = 1.0 - for t in range(1, T): - b[t+1] = b[t] - sigma2 * w2[t] - c[t+1] = c[t] + sigma1 * w1[t+1] + (1 - beta) * sigma2 * w2[t+1] - return b, c - - -fig, axes = plt.subplots(2, 1) -plt.subplots_adjust(hspace=0.5) -p_args = {'lw': 2, 'alpha': 0.7} - -L = 0.175 - -for ax in axes: - ax.grid(alpha=0.5) - ax.set_xlabel(r'Time') - ax.set_ylim(-L, L) - ax.plot((S, S), (-L, L), 'k-', lw=0.5) - -ax = axes[0] -b, c = time_path(permanent=0) -ax.set_title('impulse-response, transitory income shock') -ax.plot(list(range(T+1)), c, 'g-', label="consumption", **p_args) -ax.plot(list(range(T+1)), b, 'b-', label="debt", **p_args) -ax.legend(loc='upper right') - -ax = axes[1] -b, c = time_path(permanent=1) -ax.set_title('impulse-response, permanent income shock') -ax.plot(list(range(T+1)), c, 'g-', label="consumption", **p_args) -ax.plot(list(range(T+1)), b, 'b-', label="debt", **p_args) -ax.legend(loc='lower right') -plt.show() diff --git a/examples/plot_example_1.py b/examples/plot_example_1.py deleted file mode 100644 index 441bfb4d6..000000000 --- a/examples/plot_example_1.py +++ /dev/null @@ -1,7 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -fig, ax = plt.subplots() -x = np.linspace(0, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'b-', linewidth=2) -plt.show() diff --git a/examples/plot_example_2.py b/examples/plot_example_2.py deleted file mode 100644 index e7c4915c0..000000000 --- a/examples/plot_example_2.py +++ /dev/null @@ -1,8 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -fig, ax = plt.subplots() -x = np.linspace(0, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'r-', lw=2, label='sine function', alpha=0.6) -ax.legend(loc='upper center') -plt.show() diff --git a/examples/plot_example_3.py b/examples/plot_example_3.py deleted file mode 100644 index 2b8a8ab16..000000000 --- a/examples/plot_example_3.py +++ /dev/null @@ -1,8 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -fig, ax = plt.subplots() -x = np.linspace(0, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'r-', lw=2, label=r'$y=\sin(x)$', alpha=0.6) -ax.legend(loc='upper center') -plt.show() diff --git a/examples/plot_example_4.py b/examples/plot_example_4.py deleted file mode 100644 index 4bb052f5b..000000000 --- a/examples/plot_example_4.py +++ /dev/null @@ -1,13 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import norm -from random import uniform -fig, ax = plt.subplots() -x = np.linspace(-4, 4, 150) -for i in range(3): - m, s = uniform(-1, 1), uniform(1, 2) - y = norm.pdf(x, loc=m, scale=s) - current_label = r'$\mu = {0:.2f}$'.format(m) - ax.plot(x, y, lw=2, alpha=0.6, label=current_label) -ax.legend() -plt.show() diff --git a/examples/plot_example_5.py b/examples/plot_example_5.py deleted file mode 100644 index b924d9731..000000000 --- a/examples/plot_example_5.py +++ /dev/null @@ -1,15 +0,0 @@ -import matplotlib.pyplot as plt -from scipy.stats import norm -from random import uniform -num_rows, num_cols = 2, 3 -fig, axes = plt.subplots(num_rows, num_cols, figsize=(12, 8)) -for i in range(num_rows): - for j in range(num_cols): - m, s = uniform(-1, 1), uniform(1, 2) - x = norm.rvs(loc=m, scale=s, size=100) - axes[i, j].hist(x, alpha=0.6, bins=20) - t = r'$\mu = {0:.1f},\; \sigma = {1:.1f}$'.format(m, s) - axes[i, j].set_title(t) - axes[i, j].set_xticks([-4, 0, 4]) - axes[i, j].set_yticks([]) -plt.show() diff --git a/examples/plot_market.py b/examples/plot_market.py deleted file mode 100644 index 8e35443a5..000000000 --- a/examples/plot_market.py +++ /dev/null @@ -1,24 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -from market import Market - -# Baseline ad, bd, az, bz, tax -baseline_params = 15, .5, -2, .5, 3 -m = Market(*baseline_params) - -q_max = m.quantity() * 2 -q_grid = np.linspace(0.0, q_max, 100) -pd = m.inverse_demand(q_grid) -ps = m.inverse_supply(q_grid) -psno = m.inverse_supply_no_tax(q_grid) - -fig, ax = plt.subplots() -ax.plot(q_grid, pd, lw=2, alpha=0.6, label='demand') -ax.plot(q_grid, ps, lw=2, alpha=0.6, label='supply') -ax.plot(q_grid, psno, '--k', lw=2, alpha=0.6, label='supply without tax') -ax.set_xlabel('quantity', fontsize=14) -ax.set_xlim(0, q_max) -ax.set_ylabel('price', fontsize=14) -ax.legend(loc='lower right', frameon=False, fontsize=14) -plt.show() - diff --git a/examples/preim1.py b/examples/preim1.py deleted file mode 100644 index aed9b6144..000000000 --- a/examples/preim1.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -QE by Tom Sargent and John Stachurski. -Illustrates preimages of functions -""" -import matplotlib.pyplot as plt -import numpy as np - - -def f(x): - return 0.6 * np.cos(4 * x) + 1.4 - - -xmin, xmax = -1, 1 -x = np.linspace(xmin, xmax, 160) -y = f(x) -ya, yb = np.min(y), np.max(y) - -fig, axes = plt.subplots(2, 1, figsize=(8, 8)) - -for ax in axes: - # Set the axes through the origin - for spine in ['left', 'bottom']: - ax.spines[spine].set_position('zero') - for spine in ['right', 'top']: - ax.spines[spine].set_color('none') - - ax.set_ylim(-0.6, 3.2) - ax.set_xlim(xmin, xmax) - ax.set_yticks(()) - ax.set_xticks(()) - - ax.plot(x, y, 'k-', lw=2, label=r'$f$') - ax.fill_between(x, ya, yb, facecolor='blue', alpha=0.05) - ax.vlines([0], ya, yb, lw=3, color='blue', label=r'range of $f$') - ax.text(0.04, -0.3, '$0$', fontsize=16) - -ax = axes[0] - -ax.legend(loc='upper right', frameon=False) -ybar = 1.5 -ax.plot(x, x * 0 + ybar, 'k--', alpha=0.5) -ax.text(0.05, 0.8 * ybar, r'$y$', fontsize=16) -for i, z in enumerate((-0.35, 0.35)): - ax.vlines(z, 0, f(z), linestyle='--', alpha=0.5) - ax.text(z, -0.2, r'$x_{}$'.format(i), fontsize=16) - -ax = axes[1] - -ybar = 2.6 -ax.plot(x, x * 0 + ybar, 'k--', alpha=0.5) -ax.text(0.04, 0.91 * ybar, r'$y$', fontsize=16) - -plt.show() diff --git a/examples/pylab_eg.py b/examples/pylab_eg.py deleted file mode 100644 index 27a1e9f1e..000000000 --- a/examples/pylab_eg.py +++ /dev/null @@ -1,5 +0,0 @@ -from pylab import * # Depreciated -x = linspace(0, 10, 200) -y = sin(x) -plot(x, y, 'b-', linewidth=2) -show() diff --git a/examples/pylab_eg2.py b/examples/pylab_eg2.py deleted file mode 100644 index 202142ba1..000000000 --- a/examples/pylab_eg2.py +++ /dev/null @@ -1,6 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -x = np.linspace(0, 10, 200) -y = np.sin(x) -plt.plot(x, y, 'b-', linewidth=2) -plt.show() diff --git a/examples/qm_plot.py b/examples/qm_plot.py deleted file mode 100644 index 7ee08f850..000000000 --- a/examples/qm_plot.py +++ /dev/null @@ -1,16 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np - - -def qm(x0, n): - x = np.empty(n+1) - x[0] = x0 - for t in range(n): - x[t+1] = 4 * x[t] * (1 - x[t]) - return x - -x = qm(0.1, 250) -fig, ax = plt.subplots(figsize=(10, 6.5)) -ax.plot(x, 'b-', lw=2, alpha=0.8) -ax.set_xlabel('time', fontsize=16) -plt.show() diff --git a/examples/qs.py b/examples/qs.py deleted file mode 100644 index d7d93c44c..000000000 --- a/examples/qs.py +++ /dev/null @@ -1,47 +0,0 @@ - -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import norm -from matplotlib import cm - -xmin, xmax = -4, 12 -x = 10 -alpha = 0.5 - -m, v = x, 10 - -xgrid = np.linspace(xmin, xmax, 200) - -fig, ax = plt.subplots() - -ax.spines['right'].set_color('none') -ax.spines['top'].set_color('none') -ax.spines['left'].set_color('none') -ax.xaxis.set_ticks_position('bottom') -ax.spines['bottom'].set_position(('data', 0)) - -ax.set_ylim(-0.05, 0.5) -ax.set_xticks((x,)) -ax.set_xticklabels((r'$x$',), fontsize=18) -ax.set_yticks(()) - -K = 3 -for i in range(K): - m = alpha * m - v = alpha * alpha * v + 1 - f = norm(loc=m, scale=np.sqrt(v)) - k = (i + 0.5) / K - ax.plot(xgrid, f.pdf(xgrid), lw=1, color='black', alpha=0.4) - ax.fill_between(xgrid, 0 * xgrid, f.pdf(xgrid), color=cm.jet(k), alpha=0.4) - - -ax.annotate(r'$Q(x,\cdot)$', xy=(6.6, 0.2), xycoords='data', - xytext=(20, 90), textcoords='offset points', fontsize=16, - arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.2")) -ax.annotate(r'$Q^2(x,\cdot)$', xy=(3.6, 0.24), xycoords='data', - xytext=(20, 90), textcoords='offset points', fontsize=16, - arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.2")) -ax.annotate(r'$Q^3(x,\cdot)$', xy=(-0.2, 0.28), xycoords='data', - xytext=(-90, 90), textcoords='offset points', fontsize=16, - arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0.2")) -fig.show() diff --git a/examples/quadmap_class.py b/examples/quadmap_class.py deleted file mode 100644 index ba32d8059..000000000 --- a/examples/quadmap_class.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -Filename: quadmap_class.py -Authors: John Stachurski, Thomas J. Sargent - -""" - - -class QuadMap(object): - - def __init__(self, initial_state): - self.x = initial_state - - def update(self): - "Apply the quadratic map to update the state." - self.x = 4 * self.x * (1 - self.x) - - def generate_series(self, n): - """ - Generate and return a trajectory of length n, starting at the - current state. - """ - trajectory = [] - for i in range(n): - trajectory.append(self.x) - self.update() - return trajectory diff --git a/examples/robust_monopolist.py b/examples/robust_monopolist.py deleted file mode 100644 index b4431ddf6..000000000 --- a/examples/robust_monopolist.py +++ /dev/null @@ -1,193 +0,0 @@ -""" -Filename: robust_monopolist.py -Authors: Chase Coleman, Spencer Lyon, Thomas Sargent, John Stachurski - -The robust control problem for a monopolist with adjustment costs. The -inverse demand curve is: - - p_t = a_0 - a_1 y_t + d_t - -where d_{t+1} = \rho d_t + \sigma_d w_{t+1} for w_t ~ N(0, 1) and iid. -The period return function for the monopolist is - - r_t = p_t y_t - gamma (y_{t+1} - y_t)^2 / 2 - c y_t - -The objective of the firm is E_t \sum_{t=0}^\infty \beta^t r_t - -For the linear regulator, we take the state and control to be - - x_t = (1, y_t, d_t) and u_t = y_{t+1} - y_t - -""" -import pandas as pd -import numpy as np -from scipy.linalg import eig -from scipy import interp -import matplotlib.pyplot as plt - -import quantecon as qe - -# == model parameters == # - -a_0 = 100 -a_1 = 0.5 -rho = 0.9 -sigma_d = 0.05 -beta = 0.95 -c = 2 -gamma = 50.0 - -theta = 0.002 -ac = (a_0 - c) / 2.0 - -# == Define LQ matrices == # - -R = np.array([[0., ac, 0.], - [ac, -a_1, 0.5], - [0., 0.5, 0.]]) - -R = -R # For minimization -Q = gamma / 2 - -A = np.array([[1., 0., 0.], - [0., 1., 0.], - [0., 0., rho]]) -B = np.array([[0.], - [1.], - [0.]]) -C = np.array([[0.], - [0.], - [sigma_d]]) - -# -------------------------------------------------------------------------- # -# Functions -# -------------------------------------------------------------------------- # - - -def evaluate_policy(theta, F): - """ - Given theta (scalar, dtype=float) and policy F (array_like), returns the - value associated with that policy under the worst case path for {w_t}, as - well as the entropy level. - """ - rlq = qe.robustlq.RBLQ(Q, R, A, B, C, beta, theta) - K_F, P_F, d_F, O_F, o_F = rlq.evaluate_F(F) - x0 = np.array([[1.], [0.], [0.]]) - value = - x0.T.dot(P_F.dot(x0)) - d_F - entropy = x0.T.dot(O_F.dot(x0)) + o_F - return list(map(float, (value, entropy))) - - -def value_and_entropy(emax, F, bw, grid_size=1000): - """ - Compute the value function and entropy levels for a theta path - increasing until it reaches the specified target entropy value. - - Parameters - ========== - emax: scalar - The target entropy value - - F: array_like - The policy function to be evaluated - - bw: str - A string specifying whether the implied shock path follows best - or worst assumptions. The only acceptable values are 'best' and - 'worst'. - - Returns - ======= - df: pd.DataFrame - A pandas DataFrame containing the value function and entropy - values up to the emax parameter. The columns are 'value' and - 'entropy'. - - """ - if bw == 'worst': - thetas = 1 / np.linspace(1e-8, 1000, grid_size) - else: - thetas = -1 / np.linspace(1e-8, 1000, grid_size) - - df = pd.DataFrame(index=thetas, columns=('value', 'entropy')) - - for theta in thetas: - df.ix[theta] = evaluate_policy(theta, F) - if df.ix[theta, 'entropy'] >= emax: - break - - df = df.dropna(how='any') - return df - - -# -------------------------------------------------------------------------- # -# Main -# -------------------------------------------------------------------------- # - - -# == Compute the optimal rule == # -optimal_lq = qe.lqcontrol.LQ(Q, R, A, B, C, beta) -Po, Fo, do = optimal_lq.stationary_values() - -# == Compute a robust rule given theta == # -baseline_robust = qe.robustlq.RBLQ(Q, R, A, B, C, beta, theta) -Fb, Kb, Pb = baseline_robust.robust_rule() - -# == Check the positive definiteness of worst-case covariance matrix to == # -# == ensure that theta exceeds the breakdown point == # -test_matrix = np.identity(Pb.shape[0]) - np.dot(C.T, Pb.dot(C)) / theta -eigenvals, eigenvecs = eig(test_matrix) -assert (eigenvals >= 0).all(), 'theta below breakdown point.' - - -emax = 1.6e6 - -optimal_best_case = value_and_entropy(emax, Fo, 'best') -robust_best_case = value_and_entropy(emax, Fb, 'best') -optimal_worst_case = value_and_entropy(emax, Fo, 'worst') -robust_worst_case = value_and_entropy(emax, Fb, 'worst') - -fig, ax = plt.subplots() - -ax.set_xlim(0, emax) -ax.set_ylabel("Value") -ax.set_xlabel("Entropy") -ax.grid() - -for axis in 'x', 'y': - plt.ticklabel_format(style='sci', axis=axis, scilimits=(0, 0)) - -plot_args = {'lw': 2, 'alpha': 0.7} - -colors = 'r', 'b' - -df_pairs = ((optimal_best_case, optimal_worst_case), - (robust_best_case, robust_worst_case)) - - -class Curve(object): - - def __init__(self, x, y): - self.x, self.y = x, y - - def __call__(self, z): - return interp(z, self.x, self.y) - - -for c, df_pair in zip(colors, df_pairs): - curves = [] - for df in df_pair: - # == Plot curves == # - x, y = df['entropy'], df['value'] - x, y = (np.asarray(a, dtype='float') for a in (x, y)) - egrid = np.linspace(0, emax, 100) - curve = Curve(x, y) - print(ax.plot(egrid, curve(egrid), color=c, **plot_args)) - curves.append(curve) - # == Color fill between curves == # - ax.fill_between(egrid, - curves[0](egrid), - curves[1](egrid), - color=c, alpha=0.1) - -plt.show() diff --git a/examples/sine2.py b/examples/sine2.py deleted file mode 100644 index e4be028a2..000000000 --- a/examples/sine2.py +++ /dev/null @@ -1,8 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -fig, ax = plt.subplots() -x = np.linspace(0, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'r-', linewidth=2, label='sine function', alpha=0.6) -ax.legend() -plt.show() diff --git a/examples/sine3.py b/examples/sine3.py deleted file mode 100644 index de2b8a466..000000000 --- a/examples/sine3.py +++ /dev/null @@ -1,8 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -fig, ax = plt.subplots() -x = np.linspace(0, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'r-', linewidth=2, label='sine function', alpha=0.6) -ax.legend(loc='upper center') -plt.show() diff --git a/examples/sine4.py b/examples/sine4.py deleted file mode 100644 index 4e5cac4c6..000000000 --- a/examples/sine4.py +++ /dev/null @@ -1,8 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -fig, ax = plt.subplots() -x = np.linspace(0, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'r-', linewidth=2, label=r'$y=\sin(x)$', alpha=0.6) -ax.legend(loc='upper center') -plt.show() diff --git a/examples/sine5.py b/examples/sine5.py deleted file mode 100644 index 9cb6e2100..000000000 --- a/examples/sine5.py +++ /dev/null @@ -1,10 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -fig, ax = plt.subplots() -x = np.linspace(0, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'r-', linewidth=2, label=r'$y=\sin(x)$', alpha=0.6) -ax.legend(loc='upper center') -ax.set_yticks([-1, 0, 1]) -ax.set_title('Test plot') -plt.show() diff --git a/examples/six_hists.py b/examples/six_hists.py deleted file mode 100644 index 21386e33a..000000000 --- a/examples/six_hists.py +++ /dev/null @@ -1,15 +0,0 @@ -import matplotlib.pyplot as plt -from scipy.stats import norm -from random import uniform -num_rows, num_cols = 3, 2 -fig, axes = plt.subplots(num_rows, num_cols, figsize=(8, 12)) -for i in range(num_rows): - for j in range(num_cols): - m, s = uniform(-1, 1), uniform(1, 2) - x = norm.rvs(loc=m, scale=s, size=100) - axes[i, j].hist(x, alpha=0.6, bins=20) - t = r'$\mu = {0:.1f}, \quad \sigma = {1:.1f}$'.format(m, s) - axes[i, j].set_title(t) - axes[i, j].set_xticks([-4, 0, 4]) - axes[i, j].set_yticks([]) -plt.show() diff --git a/examples/solow.py b/examples/solow.py deleted file mode 100644 index 11156cb85..000000000 --- a/examples/solow.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Filename: solow.py -Reference: http://quant-econ.net/py/python_oop.html -""" -from __future__ import division # Omit for Python 3.x -import numpy as np - -class Solow: - """ - Implements the Solow growth model with update rule - - .. math:: - k_{t+1} = \frac{s z k^{\alpha}_t}{1 + n} + k_t \frac{1 + d}{1 + n} - - """ - - def __init__(self, n, s, d, alpha, z, k): - """ - Solow growth model with Cobb Douglas production function. All - parameters are scalars. See http://quant-econ.net/py/python_oop.html - for interpretation. - """ - self.n, self.s, self.d, self.alpha, self.z = n, s, d, alpha, z - self.k = k - - - def h(self,x): - "Evaluate the h function" - temp = self.s * self.z * self.k**self.alpha + self.k * (1 - self.d) - return temp / (1 + self.n) - - def update(self): - "Update the current state (i.e., the capital stock)." - self.k = self.h(self.k) - - def steady_state(self): - "Compute the steady state value of capital." - return ((self.s * self.z) / (self.n + self.d))**(1 / (1 - self.alpha)) - - def generate_sequence(self, t): - "Generate and return a time series of length t" - path = [] - for i in range(t): - path.append(self.k) - self.update() - return path - diff --git a/examples/stochasticgrowth.py b/examples/stochasticgrowth.py deleted file mode 100644 index 79db69b0b..000000000 --- a/examples/stochasticgrowth.py +++ /dev/null @@ -1,59 +0,0 @@ -""" -Neoclassical growth model with constant savings rate, where the dynamics are -given by - - k_{t+1} = s A_{t+1} f(k_t) + (1 - delta) k_t - -Marginal densities are computed using the look-ahead estimator. Thus, the -estimate of the density psi_t of k_t is - - (1/n) sum_{i=0}^n p(k_{t-1}^i, y) - -This is a density in y. -""" -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import lognorm, beta -from quantecon import LAE - -# == Define parameters == # -s = 0.2 -delta = 0.1 -a_sigma = 0.4 # A = exp(B) where B ~ N(0, a_sigma) -alpha = 0.4 # We set f(k) = k**alpha -psi_0 = beta(5, 5, scale=0.5) # Initial distribution -phi = lognorm(a_sigma) - - -def p(x, y): - """ - Stochastic kernel for the growth model with Cobb-Douglas production. - Both x and y must be strictly positive. - """ - d = s * x**alpha - return phi.pdf((y - (1 - delta) * x) / d) / d - -n = 10000 # Number of observations at each date t -T = 30 # Compute density of k_t at 1,...,T+1 - -# == Generate matrix s.t. t-th column is n observations of k_t == # -k = np.empty((n, T)) -A = phi.rvs((n, T)) -k[:, 0] = psi_0.rvs(n) # Draw first column from initial distribution -for t in range(T-1): - k[:, t+1] = s * A[:, t] * k[:, t]**alpha + (1 - delta) * k[:, t] - -# == Generate T instances of LAE using this data, one for each date t == # -laes = [LAE(p, k[:, t]) for t in range(T)] - -# == Plot == # -fig, ax = plt.subplots() -ygrid = np.linspace(0.01, 4.0, 200) -greys = [str(g) for g in np.linspace(0.0, 0.8, T)] -greys.reverse() -for psi, g in zip(laes, greys): - ax.plot(ygrid, psi(ygrid), color=g, lw=2, alpha=0.6) -ax.set_xlabel('capital') -title = r'Density of $k_1$ (lighter) to $k_T$ (darker) for $T={}$' -ax.set_title(title.format(T)) -plt.show() diff --git a/examples/subplots.py b/examples/subplots.py deleted file mode 100644 index d955a4936..000000000 --- a/examples/subplots.py +++ /dev/null @@ -1,25 +0,0 @@ - -import matplotlib.pyplot as plt -import numpy as np - - -def subplots(): - "Custom subplots with axes throught the origin" - fig, ax = plt.subplots() - - # Set the axes through the origin - for spine in ['left', 'bottom']: - ax.spines[spine].set_position('zero') - for spine in ['right', 'top']: - ax.spines[spine].set_color('none') - - ax.grid() - return fig, ax - - -fig, ax = subplots() # Call the local version, not plt.subplots() -x = np.linspace(-2, 10, 200) -y = np.sin(x) -ax.plot(x, y, 'r-', linewidth=2, label='sine function', alpha=0.6) -ax.legend(loc='lower right') -plt.show() diff --git a/examples/temp.py b/examples/temp.py deleted file mode 100644 index 8b1378917..000000000 --- a/examples/temp.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/examples/test_program_1.py b/examples/test_program_1.py deleted file mode 100644 index f08e3e735..000000000 --- a/examples/test_program_1.py +++ /dev/null @@ -1,9 +0,0 @@ -from random import normalvariate -import matplotlib.pyplot as plt -ts_length = 100 -epsilon_values = [] # An empty list -for i in range(ts_length): - e = normalvariate(0, 1) - epsilon_values.append(e) -plt.plot(epsilon_values, 'b-') -plt.show() diff --git a/examples/test_program_2.py b/examples/test_program_2.py deleted file mode 100644 index 0282bb875..000000000 --- a/examples/test_program_2.py +++ /dev/null @@ -1,11 +0,0 @@ -from random import normalvariate -import matplotlib.pyplot as plt -ts_length = 100 -epsilon_values = [] -i = 0 -while i < ts_length: - e = normalvariate(0, 1) - epsilon_values.append(e) - i = i + 1 -plt.plot(epsilon_values, 'b-') -plt.show() diff --git a/examples/test_program_3.py b/examples/test_program_3.py deleted file mode 100644 index 80bf134ec..000000000 --- a/examples/test_program_3.py +++ /dev/null @@ -1,14 +0,0 @@ -from random import normalvariate -import matplotlib.pyplot as plt - - -def generate_data(n): - epsilon_values = [] - for i in range(n): - e = normalvariate(0, 1) - epsilon_values.append(e) - return epsilon_values - -data = generate_data(100) -plt.plot(data, 'b-') -plt.show() diff --git a/examples/test_program_4.py b/examples/test_program_4.py deleted file mode 100644 index 0b68e7d35..000000000 --- a/examples/test_program_4.py +++ /dev/null @@ -1,17 +0,0 @@ -from random import normalvariate, uniform -import matplotlib.pyplot as plt - - -def generate_data(n, generator_type): - epsilon_values = [] - for i in range(n): - if generator_type == 'U': - e = uniform(0, 1) - else: - e = normalvariate(0, 1) - epsilon_values.append(e) - return epsilon_values - -data = generate_data(100, 'U') -plt.plot(data, 'b-') -plt.show() diff --git a/examples/test_program_5.py b/examples/test_program_5.py deleted file mode 100644 index 516714670..000000000 --- a/examples/test_program_5.py +++ /dev/null @@ -1,14 +0,0 @@ -from random import normalvariate, uniform -import matplotlib.pyplot as plt - - -def generate_data(n, generator_type): - epsilon_values = [] - for i in range(n): - e = uniform(0, 1) if generator_type == 'U' else normalvariate(0, 1) - epsilon_values.append(e) - return epsilon_values - -data = generate_data(100, 'U') -plt.plot(data, 'b-') -plt.show() diff --git a/examples/test_program_5_short.py b/examples/test_program_5_short.py deleted file mode 100644 index 6744c2c39..000000000 --- a/examples/test_program_5_short.py +++ /dev/null @@ -1,19 +0,0 @@ -import pylab -from random import normalvariate, uniform - - -def generate_data(n, generator_type): - epsilon_values = [] - for i in range(n): - if generator_type == "U": - e = uniform(0, 1) - else: - e = normalvariate(0, 1) - - epsilon_values.append(e) - return epsilon_values - -ts_length = 100 -data = generate_data(ts_length, 'U') -pylab.plot(data, 'b-') -pylab.show() diff --git a/examples/test_program_6.py b/examples/test_program_6.py deleted file mode 100644 index ed20f96e9..000000000 --- a/examples/test_program_6.py +++ /dev/null @@ -1,14 +0,0 @@ -from random import uniform -import matplotlib.pyplot as plt - - -def generate_data(n, generator_type): - epsilon_values = [] - for i in range(n): - e = generator_type(0, 1) - epsilon_values.append(e) - return epsilon_values - -data = generate_data(100, uniform) -plt.plot(data, 'b-') -plt.show() diff --git a/examples/tests/__init__.py b/examples/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/tests/test_directory_pyfiles.py b/examples/tests/test_directory_pyfiles.py deleted file mode 100644 index 532271b82..000000000 --- a/examples/tests/test_directory_pyfiles.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Simple Test Script which can be used to run a directoy of py files - -Just run this file using `python $filename` - -""" -#-Subprocess Recipe-# -from subprocess import call -import glob -files = glob.glob("*.py") -for fl in files: - print "Testing File: %s" % fl - call(["python", fl]) - print "------------ END (%s) -----------------" % fl - -#-IPYTHON NOTEBOOK Recipe-# -#-Instructions-# -#--------------# -#-1. Open an IPython Notebook in quantecon.py/examples/ folder -#-2. Copy the following code recipe into the notebook and run -import glob -files = glob.glob("*.py") -%pylab inline -for fl in files: - print "----RUNNING (%s)----"%fl - %run $fl - print "----END (%s)-----"%fl \ No newline at end of file diff --git a/examples/tsh_hg.py b/examples/tsh_hg.py deleted file mode 100644 index 0ffc2caa4..000000000 --- a/examples/tsh_hg.py +++ /dev/null @@ -1,37 +0,0 @@ - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import norm -from quantecon import LinearStateSpace - -phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 -sigma = 0.1 - -A = [[phi_1, phi_2, phi_3, phi_4], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0]] -C = [[sigma], [0], [0], [0]] -G = [1, 0, 0, 0] - -T = 30 -ar = LinearStateSpace(A, C, G) - -ymin, ymax = -0.8, 1.25 - -fig, ax = plt.subplots(figsize=(8, 4)) - -ax.set_xlim(ymin, ymax) -ax.set_xlabel(r'$y_t$', fontsize=16) - -x, y = ar.replicate(T=T, num_reps=100000) -mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions() -f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y))) - -y = y.flatten() -ax.hist(y, bins=50, normed=True, alpha=0.4) - -ygrid = np.linspace(ymin, ymax, 150) -ax.plot(ygrid, f_y.pdf(ygrid), 'k-', lw=2, alpha=0.8, label='true density') -ax.legend() -plt.show() diff --git a/examples/us_cities.py b/examples/us_cities.py deleted file mode 100644 index 7c6f41dcb..000000000 --- a/examples/us_cities.py +++ /dev/null @@ -1,7 +0,0 @@ -data_file = open('us_cities.txt', 'r') -for line in data_file: - city, population = line.split(':') # Tuple unpacking - city = city.title() # Capitalize city names - population = '{0:,}'.format(int(population)) # Add commas to numbers - print(city.ljust(15) + population) -data_file.close() diff --git a/examples/us_cities.txt b/examples/us_cities.txt deleted file mode 100644 index 9be9260fc..000000000 --- a/examples/us_cities.txt +++ /dev/null @@ -1,9 +0,0 @@ -new york: 8244910 -los angeles: 3819702 -chicago: 2707120 -houston: 2145146 -philadelphia: 1536471 -phoenix: 1469471 -san antonio: 1359758 -san diego: 1326179 -dallas: 1223229 diff --git a/examples/utilities.py b/examples/utilities.py deleted file mode 100644 index 3d941c9f4..000000000 --- a/examples/utilities.py +++ /dev/null @@ -1,108 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Sun Feb 22 10:47:42 2015 - -@author: dgevans -""" -import numpy as np -from scipy.interpolate import UnivariateSpline - -class interpolate_wrapper(object): - ''' - Wrapper to interpolate vector function - ''' - def __init__(self,F): - ''' - Inits with array of interpolated functions - ''' - self.F = F - - def __getitem__(self,index): - ''' - Uses square brakets operator - ''' - return interpolate_wrapper(np.asarray(self.F[index])) - - - def reshape(self,*args): - ''' - Reshapes F - ''' - self.F = self.F.reshape(*args) - return self - - def transpose(self): - ''' - Transpose F - ''' - self.F = self.F.transpose() - - def __len__(self): - ''' - return length - ''' - return len(self.F) - - def __call__(self,xvec): - ''' - Evaluates F at X for each element of F, keeping track of the shape of F - ''' - x = np.atleast_1d(xvec) - shape = self.F.shape - if len(x) == 1: - fhat = np.hstack([f(x) for f in self.F.flatten()]) - return fhat.reshape(shape) - else: - fhat = np.vstack([f(x) for f in self.F.flatten()]) - return fhat.reshape( np.hstack((shape,len(x))) ) - -class interpolator_factory(object): - ''' - Generates an interpolator factory which will interpolate vector functions - ''' - def __init__(self,k,s): - ''' - Inits with types, orders and k - ''' - self.k = k - self.s = s - - def __call__(self,xgrid,Fs): - ''' - Interpolates function given function values Fs at domain X - ''' - shape,m = Fs.shape[:-1],Fs.shape[-1] - Fs = Fs.reshape((-1,m)) - F = [] - for Fhat in Fs: - #F.append(interpolate(X,Fs[:,i],self.INFO)) - F.append(UnivariateSpline(xgrid,Fhat,k=self.k,s=self.s)) - return interpolate_wrapper(np.array(F).reshape(shape)) - - -def fun_vstack(fun_list): - ''' - Performs vstack on interpolator wrapper - ''' - Fs = [IW.F for IW in fun_list] - return interpolate_wrapper(np.vstack(Fs)) - -def fun_hstack(fun_list): - ''' - Performs vstack on interpolator wrapper - ''' - Fs = [IW.F for IW in fun_list] - return interpolate_wrapper(np.hstack(Fs)) - -def simulate_markov(Pi,s_0,T): - ''' - Simulates markov chain Pi for T periods starting at s_0 - ''' - - sHist = np.empty(T,dtype = int) - sHist[0] = s_0 - S = len(Pi) - for t in range(1,T): - sHist[t] = np.random.choice(np.arange(S),p=Pi[sHist[t-1]]) - - return sHist \ No newline at end of file diff --git a/examples/vecs.py b/examples/vecs.py deleted file mode 100644 index 326f30c2e..000000000 --- a/examples/vecs.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -QE by Tom Sargent and John Stachurski. -Illustrates vectors in the plane. -""" -import matplotlib.pyplot as plt - -fig, ax = plt.subplots() -# Set the axes through the origin -for spine in ['left', 'bottom']: - ax.spines[spine].set_position('zero') -for spine in ['right', 'top']: - ax.spines[spine].set_color('none') - - -ax.set_xlim(-5, 5) -ax.set_ylim(-5, 5) -ax.grid() -vecs = ((2, 4), (-3, 3), (-4, -3.5)) -for v in vecs: - ax.annotate('', xy=v, xytext=(0, 0), - arrowprops=dict(facecolor='blue', - shrink=0, - alpha=0.7, - width=0.5)) - ax.text(1.1 * v[0], 1.1 * v[1], str(v)) -plt.show() diff --git a/examples/vecs2.py b/examples/vecs2.py deleted file mode 100644 index fe3c85d42..000000000 --- a/examples/vecs2.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -QE by Tom Sargent and John Stachurski. -Illustrates scalar multiplication. -""" -import matplotlib.pyplot as plt -import numpy as np - -fig, ax = plt.subplots() -# Set the axes through the origin -for spine in ['left', 'bottom']: - ax.spines[spine].set_position('zero') -for spine in ['right', 'top']: - ax.spines[spine].set_color('none') - -ax.set_xlim(-5, 5) -ax.set_ylim(-5, 5) - -x = (2, 2) -ax.annotate('', xy=x, xytext=(0, 0), - arrowprops=dict(facecolor='blue', - shrink=0, - alpha=1, - width=0.5)) -ax.text(x[0] + 0.4, x[1] - 0.2, r'$x$', fontsize='16') - - -scalars = (-2, 2) -x = np.array(x) - -for s in scalars: - v = s * x - ax.annotate('', xy=v, xytext=(0, 0), - arrowprops=dict(facecolor='red', - shrink=0, - alpha=0.5, - width=0.5)) - ax.text(v[0] + 0.4, v[1] - 0.2, r'${} x$'.format(s), fontsize='16') -plt.show() diff --git a/examples/wb_download.py b/examples/wb_download.py deleted file mode 100644 index 40f61e3c9..000000000 --- a/examples/wb_download.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -Origin: QE by John Stachurski and Thomas J. Sargent -Filename: wb_download.py -Authors: John Stachurski, Tomohito Okabe -LastModified: 29/08/2013 - -Dowloads data from the World Bank site on GDP per capita and plots result for -a subset of countries. - -NOTE: This is not dually compatible with Python 3. Python 2 and Python -3 call the urllib package differently. -""" -import sys -import matplotlib.pyplot as plt -from pandas.io.excel import ExcelFile - -if sys.version_info[0] == 2: - from urllib import urlretrieve -elif sys.version_info[0] == 3: - from urllib.request import urlretrieve - -# == Get data and read into file gd.xls == # -wb_data_file_dir = "http://api.worldbank.org/datafiles/" -file_name = "GC.DOD.TOTL.GD.ZS_Indicator_MetaData_en_EXCEL.xls" -url = wb_data_file_dir + file_name -urlretrieve(url, "gd.xls") - -# == Parse data into a DataFrame == # -gov_debt_xls = ExcelFile('gd.xls') -govt_debt = gov_debt_xls.parse('Sheet1', index_col=1, na_values=['NA']) - -# == Take desired values and plot == # -govt_debt = govt_debt.transpose() -govt_debt = govt_debt[['AUS', 'DEU', 'FRA', 'USA']] -govt_debt = govt_debt[36:] -govt_debt.plot(lw=2) -plt.show() diff --git a/examples/web_network.py b/examples/web_network.py deleted file mode 100644 index 01e6ba724..000000000 --- a/examples/web_network.py +++ /dev/null @@ -1,51 +0,0 @@ -import numpy as np -import re - -alphabet = 'abcdefghijklmnopqrstuvwxyz' - - -def gen_rw_mat(n): - "Generate an n x n matrix of zeros and ones." - Q = np.random.randn(n, n) - 0.8 - Q = np.where(Q > 0, 1, 0) - # Make sure that no row contains only zeros - for i in range(n): - if Q[i, :].sum() == 0: - Q[i, np.random.randint(0, n, 1)] = 1 - return Q - - -def adj_matrix_to_dot(Q, outfile='/tmp/foo_out.dot'): - """ - Convert an adjacency matrix to a dot file. - """ - n = Q.shape[0] - f = open(outfile, 'w') - f.write('digraph {\n') - for i in range(n): - for j in range(n): - if Q[i, j]: - f.write(' {0} -> {1};\n'.format(alphabet[i], alphabet[j])) - f.write('}\n') - f.close() - - -def dot_to_adj_matrix(node_num, infile='/tmp/foo_out.dot'): - Q = np.zeros((node_num, node_num), dtype=int) - f = open(infile, 'r') - lines = f.readlines() - f.close() - edges = lines[1:-1] # Drop first and last lines - for edge in edges: - from_node, to_node = re.findall('\w', edge) - i, j = alphabet.index(from_node), alphabet.index(to_node) - Q[i, j] = 1 - return Q - - -def adj_matrix_to_markov(Q): - n = Q.shape[0] - P = np.empty((n, n)) - for i in range(n): - P[i, :] = Q[i, :] / float(Q[i, :].sum()) - return P diff --git a/examples/white_noise_plot.py b/examples/white_noise_plot.py deleted file mode 100644 index c1e3109f6..000000000 --- a/examples/white_noise_plot.py +++ /dev/null @@ -1,7 +0,0 @@ -from pylab import plot, show, legend -from random import normalvariate - -x = [normalvariate(0, 1) for i in range(100)] -plot(x, 'b-', label="white noise") -legend() -show() From 927eca9ab78d34e30b855fe81823c299fe660b00 Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 16:34:34 -0500 Subject: [PATCH 26/51] Removing models subpackage for migration to QuantEcon.applications repository --- quantecon/models/__init__.py | 22 - quantecon/models/arellano_vfi.py | 251 ----- quantecon/models/asset_pricing.py | 216 ---- quantecon/models/career.py | 166 --- quantecon/models/ifp.py | 221 ---- quantecon/models/jv.py | 198 ---- quantecon/models/lake.py | 394 ------- quantecon/models/lucastree.py | 278 ----- quantecon/models/odu.py | 233 ---- quantecon/models/optgrowth.py | 110 -- quantecon/models/solow/__init__.py | 14 - quantecon/models/solow/ces.py | 134 --- quantecon/models/solow/cobb_douglas.py | 114 -- quantecon/models/solow/impulse_response.py | 343 ------ quantecon/models/solow/model.py | 1132 -------------------- quantecon/models/uncertainty_traps.py | 62 -- 16 files changed, 3888 deletions(-) delete mode 100644 quantecon/models/__init__.py delete mode 100644 quantecon/models/arellano_vfi.py delete mode 100644 quantecon/models/asset_pricing.py delete mode 100644 quantecon/models/career.py delete mode 100644 quantecon/models/ifp.py delete mode 100644 quantecon/models/jv.py delete mode 100644 quantecon/models/lake.py delete mode 100644 quantecon/models/lucastree.py delete mode 100644 quantecon/models/odu.py delete mode 100644 quantecon/models/optgrowth.py delete mode 100644 quantecon/models/solow/__init__.py delete mode 100644 quantecon/models/solow/ces.py delete mode 100644 quantecon/models/solow/cobb_douglas.py delete mode 100644 quantecon/models/solow/impulse_response.py delete mode 100644 quantecon/models/solow/model.py delete mode 100644 quantecon/models/uncertainty_traps.py diff --git a/quantecon/models/__init__.py b/quantecon/models/__init__.py deleted file mode 100644 index 5b76969b5..000000000 --- a/quantecon/models/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -models directory imports - -objects imported here will live in the `quantecon.models` namespace - -""" - -__all__ = ["AssetPrices", "CareerWorkerProblem", "ConsumerProblem", - "JvWorker", "LakeModel", "LakeModelAgent", "LakeModel_Equilibrium", "LucasTree", - "SearchProblem", "GrowthModel", "solow"] - -from . import solow as solow -from .asset_pricing import AssetPrices -from .career import CareerWorkerProblem -from .ifp import ConsumerProblem -from .jv import JvWorker -from .lake import LakeModel, LakeModelAgent, LakeModel_Equilibrium -from .lucastree import LucasTree -from .odu import SearchProblem -from .optgrowth import GrowthModel -from .arellano_vfi import Arellano_Economy -from .uncertainty_traps import UncertaintyTrapEcon diff --git a/quantecon/models/arellano_vfi.py b/quantecon/models/arellano_vfi.py deleted file mode 100644 index 56a82d423..000000000 --- a/quantecon/models/arellano_vfi.py +++ /dev/null @@ -1,251 +0,0 @@ -""" -Filename: arellano_vfi.py - -Authors: Chase Coleman, John Stachurski - -Solve the Arellano default risk model - -References ----------- - -http://quant-econ.net/py/arellano.html - -.. Arellano, Cristina. "Default risk and income fluctuations in emerging - economies." The American Economic Review (2008): 690-712. - -""" -from __future__ import division -import numpy as np -import random -import quantecon as qe -from numba import jit - - -class Arellano_Economy(object): - """ - Arellano 2008 deals with a small open economy whose government - invests in foreign assets in order to smooth the consumption of - domestic households. Domestic households receive a stochastic - path of income. - - Parameters - ---------- - beta : float - Time discounting parameter - gamma : float - Risk-aversion parameter - r : float - int lending rate - rho : float - Persistence in the income process - eta : float - Standard deviation of the income process - theta : float - Probability of re-entering financial markets in each period - ny : int - Number of points in y grid - nB : int - Number of points in B grid - tol : float - Error tolerance in iteration - maxit : int - Maximum number of iterations - """ - - def __init__(self, - beta=.953, # time discount rate - gamma=2., # risk aversion - r=0.017, # international interest rate - rho=.945, # persistence in output - eta=0.025, # st dev of output shock - theta=0.282, # prob of regaining access - ny=21, # number of points in y grid - nB=251, # number of points in B grid - tol=1e-8, # error tolerance in iteration - maxit=10000): - - # Save parameters - self.beta, self.gamma, self.r = beta, gamma, r - self.rho, self.eta, self.theta = rho, eta, theta - self.ny, self.nB = ny, nB - - # Create grids and discretize Markov process - self.Bgrid = np.linspace(-.45, .45, nB) - log_ygrid, Py = qe.markov.tauchen(rho, eta, 3, ny) - self.ygrid = np.exp(log_ygrid) - self.Py = Py - - # Output when in default - ymean = np.mean(self.ygrid) - self.def_y = np.minimum(0.969 * ymean, self.ygrid) - - # Allocate memory - self.Vd = np.zeros(ny) - self.Vc = np.zeros((ny, nB)) - self.V = np.zeros((ny, nB)) - self.Q = np.ones((ny, nB)) * .95 # Initial guess for prices - self.default_prob = np.empty((ny, nB)) - - # Compute the value functions, prices, and default prob - self.solve(tol=tol, maxit=maxit) - # Compute the optimal savings policy conditional on no default - self.compute_savings_policy() - - - def solve(self, tol=1e-8, maxit=10000): - # Iteration Stuff - it = 0 - dist = 10. - - # Alloc memory to store next iterate of value function - V_upd = np.zeros((self.ny, self.nB)) - - # == Main loop == # - while dist > tol and maxit > it: - - # Compute expectations for this iteration - Vs = self.V, self.Vd, self.Vc - EV, EVd, EVc = (np.dot(self.Py, v) for v in Vs) - - # Run inner loop to update value functions Vc and Vd. - # Note that Vc and Vd are updated in place. Other objects - # are not modified. - _inner_loop(self.ygrid, self.def_y, self.Bgrid, self.Vd, self.Vc, - EVc, EVd, EV, self.Q, - self.beta, self.theta, self.gamma) - - # Update prices - Vd_compat = np.repeat(self.Vd, self.nB).reshape(self.ny, self.nB) - default_states = Vd_compat > self.Vc - self.default_prob[:, :] = np.dot(self.Py, default_states) - self.Q[:, :] = (1 - self.default_prob)/(1 + self.r) - - # Update main value function and distance - V_upd[:, :] = np.maximum(self.Vc, Vd_compat) - dist = np.max(np.abs(V_upd - self.V)) - self.V[:, :] = V_upd[:, :] - - it += 1 - if it % 25 == 0: - print("Running iteration {} with dist of {}".format(it, dist)) - - return None - - - def compute_savings_policy(self): - """ - Compute optimal savings B' conditional on not defaulting. - The policy is recorded as an index value in Bgrid. - """ - - # Allocate memory - self.next_B_index = np.empty((self.ny, self.nB)) - EV = np.dot(self.Py, self.V) - - _compute_savings_policy(self.ygrid, self.Bgrid, self.Q, EV, - self.gamma, self.beta, self.next_B_index) - - - def simulate(self, T, y_init=None, B_init=None): - """ - Simulate time series for output, consumption, B'. - """ - # Find index i such that Bgrid[i] is near 0 - zero_B_index = np.searchsorted(self.Bgrid, 0) - - if y_init is None: - # Set to index near the mean of the ygrid - y_init = np.searchsorted(self.ygrid, self.ygrid.mean()) - if B_init is None: - B_init = zero_B_index - # Start off not in default - in_default = False - # Initialize Markov chain for output - mc = qe.markov.MarkovChain(self.Py) - - y_sim_indices = mc.simulate(T, init=y_init) - B_sim_indices = np.empty(T, dtype=np.int64) - B_sim_indices[0] = B_init - q_sim = np.empty(T) - in_default_series = np.zeros(T, dtype=np.int64) - - for t in range(T-1): - yi, Bi = y_sim_indices[t], B_sim_indices[t] - if not in_default: - if self.Vc[yi, Bi] < self.Vd[yi]: - in_default = True - Bi_next = zero_B_index - else: - new_index = self.next_B_index[yi, Bi] - Bi_next = new_index - else: - in_default_series[t] = 1 - Bi_next = zero_B_index - if random.uniform(0, 1) < self.theta: - in_default = False - B_sim_indices[t+1] = Bi_next - q_sim[t] = self.Q[yi, Bi_next] - - q_sim[-1] = q_sim[-2] # Extrapolate for the last price - return_vecs = (self.ygrid[y_sim_indices], - self.Bgrid[B_sim_indices], - q_sim, - in_default_series) - - return return_vecs - - -@jit(nopython=True) -def u(c, gamma): - return c**(1-gamma)/(1-gamma) - - -@jit(nopython=True) -def _inner_loop(ygrid, def_y, Bgrid, Vd, Vc, EVc, - EVd, EV, qq, beta, theta, gamma): - """ - This is a numba version of the inner loop of the solve in the - Arellano class. It updates Vd and Vc in place. - """ - ny, nB = len(ygrid), len(Bgrid) - zero_ind = nB // 2 # Integer division - for iy in range(ny): - y = ygrid[iy] # Pull out current y - - # Compute Vd - Vd[iy] = u(def_y[iy], gamma) + \ - beta * (theta * EVc[iy, zero_ind] + (1 - theta) * EVd[iy]) - - # Compute Vc - for ib in range(nB): - B = Bgrid[ib] # Pull out current B - - current_max = -1e14 - for ib_next in range(nB): - c = max(y - qq[iy, ib_next] * Bgrid[ib_next] + B, 1e-14) - m = u(c, gamma) + beta * EV[iy, ib_next] - if m > current_max: - current_max = m - Vc[iy, ib] = current_max - - return None - - -@jit(nopython=True) -def _compute_savings_policy(ygrid, Bgrid, Q, EV, gamma, beta, next_B_index): - # Compute best index in Bgrid given iy, ib - ny, nB = len(ygrid), len(Bgrid) - for iy in range(ny): - y = ygrid[iy] - for ib in range(nB): - B = Bgrid[ib] - current_max = -1e10 - for ib_next in range(nB): - c = max(y - Q[iy, ib_next] * Bgrid[ib_next] + B, 1e-14) - m = u(c, gamma) + beta * EV[iy, ib_next] - if m > current_max: - current_max = m - current_max_index = ib_next - next_B_index[iy, ib] = current_max_index - return None - diff --git a/quantecon/models/asset_pricing.py b/quantecon/models/asset_pricing.py deleted file mode 100644 index 312da91ef..000000000 --- a/quantecon/models/asset_pricing.py +++ /dev/null @@ -1,216 +0,0 @@ -""" -Filename: asset_pricing.py - -Authors: David Evans, John Stachurski and Thomas J. Sargent - -Computes asset prices in an endowment economy when the endowment obeys -geometric growth driven by a finite state Markov chain. The transition -matrix of the Markov chain is P, and the set of states is s. The -discount factor is beta, and gamma is the coefficient of relative risk -aversion in the household's utility function. - -References ----------- - - http://quant-econ.net/py/markov_asset.html - -""" -from textwrap import dedent -import numpy as np -from numpy.linalg import solve - - -class AssetPrices(object): - r""" - A class to compute asset prices when the endowment follows a finite - Markov chain. - - Parameters - ---------- - beta : scalar, float - Discount factor - P : array_like(float) - Transition matrix - s : array_like(float) - Growth rate of consumption - gamma : scalar(float) - Coefficient of risk aversion - - Attributes - ---------- - beta, P, s, gamma : see Parameters - n : scalar(int) - The number of rows in P - - Examples - -------- - - >>> n = 5 - >>> P = 0.0125 * np.ones((n, n)) - >>> P += np.diag(0.95 - 0.0125 * np.ones(5)) - >>> s = np.array([1.05, 1.025, 1.0, 0.975, 0.95]) - >>> gamma = 2.0 - >>> beta = 0.94 - >>> ap = AssetPrices(beta, P, s, gamma) - >>> zeta = 1.0 - >>> v = ap.tree_price() - >>> print("Lucas Tree Prices: %s" % v) - Lucas Tree Prices: [ 12.72221763 14.72515002 17.57142236 - 21.93570661 29.47401578] - - >>> v_consol = ap.consol_price(zeta) - >>> print("Consol Bond Prices: %s" % v_consol) - Consol Bond Prices: [ 87.56860139 109.25108965 148.67554548 - 242.55144082 753.87100476] - - >>> p_s = 150.0 - >>> w_bar, w_bars = ap.call_option(zeta, p_s, T = [10,20,30]) - >>> w_bar - array([ 64.30843769, 80.05179282, 108.67734545, 176.83933585, - 603.87100476]) - >>> w_bars - {10: array([ 44.79815889, 50.81409953, 58.61386544, - 115.69837047, 603.87100476]), - 20: array([ 56.73357192, 68.51905592, 86.69038119, - 138.45961867, 603.87100476]), - 30: array([ 60.62653565, 74.67608505, 98.38386204, - 153.80497466, 603.87100476])} - - """ - def __init__(self, beta, P, s, gamma): - self.beta, self.gamma = beta, gamma - self.P, self.s = P, s - self.n = self.P.shape[0] - - def __repr__(self): - m = "AssetPrices(beta={b:g}, P='{n:g} by {n:g}', s={s}, gamma={g:g})" - return m.format(b=self.beta, n=self.P.shape[0], s=self.s, g=self.gamma) - - def __str__(self): - m = """\ - AssetPrices (Merha and Prescott, 1985): - - beta (discount factor) : {b:g} - - P (Transition matrix) : {n:g} by {n:g} - - s (growth rate of consumption) : {s:s} - - gamma (Coefficient of risk aversion) : {g:g} - """ - - return dedent(m.format(b=self.beta, n=self.P.shape[0], s=repr(self.s), - g=self.gamma)) - - @property - def P_tilde(self): - P, s, gamma = self.P, self.s, self.gamma - return P * s**(1.0-gamma) # using broadcasting - - @property - def P_check(self): - P, s, gamma = self.P, self.s, self.gamma - return P * s**(-gamma) # using broadcasting - - def tree_price(self): - """ - Computes the function v such that the price of the lucas tree is - v(lambda)C_t - - Returns - ------- - v : array_like(float) - Lucas tree prices - - """ - # == Simplify names == # - beta = self.beta - - # == Compute v == # - P_tilde = self.P_tilde - I = np.identity(self.n) - O = np.ones(self.n) - v = beta * solve(I - beta * P_tilde, P_tilde.dot(O)) - - return v - - def consol_price(self, zeta): - """ - Computes price of a consol bond with payoff zeta - - Parameters - ---------- - zeta : scalar(float) - Coupon of the console - - Returns - ------- - p_bar : array_like(float) - Console bond prices - - """ - # == Simplify names == # - beta = self.beta - - # == Compute price == # - P_check = self.P_check - I = np.identity(self.n) - O = np.ones(self.n) - p_bar = beta * solve(I - beta * P_check, P_check.dot(zeta * O)) - - return p_bar - - def call_option(self, zeta, p_s, T=[], epsilon=1e-8): - """ - Computes price of a call option on a consol bond, both finite - and infinite horizon - - Parameters - ---------- - zeta : scalar(float) - Coupon of the console - - p_s : scalar(float) - Strike price - - T : iterable(integers) - Length of option in the finite horizon case - - epsilon : scalar(float), optional(default=1e-8) - Tolerance for infinite horizon problem - - Returns - ------- - w_bar : array_like(float) - Infinite horizon call option prices - - w_bars : dict - A dictionary of key-value pairs {t: vec}, where t is one of - the dates in the list T and vec is the option prices at that - date - - """ - # == Simplify names, initialize variables == # - beta = self.beta - P_check = self.P_check - - # == Compute consol price == # - v_bar = self.consol_price(zeta) - - # == Compute option price == # - w_bar = np.zeros(self.n) - error = epsilon + 1 - t = 0 - w_bars = {} - while error > epsilon: - if t in T: - w_bars[t] = w_bar - - # == Maximize across columns == # - to_stack = (beta*P_check.dot(w_bar), v_bar-p_s) - w_bar_new = np.amax(np.vstack(to_stack), axis=0) - - # == Find maximal difference of each component == # - error = np.amax(np.abs(w_bar-w_bar_new)) - - # == Update == # - w_bar = w_bar_new - t += 1 - - return w_bar, w_bars diff --git a/quantecon/models/career.py b/quantecon/models/career.py deleted file mode 100644 index fe5fbca7a..000000000 --- a/quantecon/models/career.py +++ /dev/null @@ -1,166 +0,0 @@ -""" -Filename: career.py - -Authors: Thomas Sargent, John Stachurski - -A class to solve the career / job choice model due to Derek Neal. - -References ----------- - -http://quant-econ.net/py/career.html - -.. [Neal1999] Neal, D. (1999). The Complexity of Job Mobility among - Young Men, Journal of Labor Economics, 17(2), 237-261. - -""" -from textwrap import dedent -import numpy as np -from quantecon.distributions import BetaBinomial - - -class CareerWorkerProblem(object): - """ - An instance of the class is an object with data on a particular - problem of this type, including probabilites, discount factor and - sample space for the variables. - - Parameters - ---------- - beta : scalar(float), optional(default=5.0) - Discount factor - B : scalar(float), optional(default=0.95) - Upper bound of for both epsilon and theta - N : scalar(int), optional(default=50) - Number of possible realizations for both epsilon and theta - F_a : scalar(int or float), optional(default=1) - Parameter `a` from the career distribution - F_b : scalar(int or float), optional(default=1) - Parameter `b` from the career distribution - G_a : scalar(int or float), optional(default=1) - Parameter `a` from the job distribution - G_b : scalar(int or float), optional(default=1) - Parameter `b` from the job distribution - - Attributes - ---------- - beta, B, N : see Parameters - theta : array_like(float, ndim=1) - A grid of values from 0 to B - epsilon : array_like(float, ndim=1) - A grid of values from 0 to B - F_probs : array_like(float, ndim=1) - The probabilities of different values for F - G_probs : array_like(float, ndim=1) - The probabilities of different values for G - F_mean : scalar(float) - The mean of the distribution for F - G_mean : scalar(float) - The mean of the distribution for G - - """ - - def __init__(self, B=5.0, beta=0.95, N=50, F_a=1, F_b=1, G_a=1, - G_b=1): - self.beta, self.N, self.B = beta, N, B - self.theta = np.linspace(0, B, N) # set of theta values - self.epsilon = np.linspace(0, B, N) # set of epsilon values - self.F_probs = BetaBinomial(N-1, F_a, F_b).pdf() - self.G_probs = BetaBinomial(N-1, G_a, G_b).pdf() - self.F_mean = np.sum(self.theta * self.F_probs) - self.G_mean = np.sum(self.epsilon * self.G_probs) - - # Store these parameters for str and repr methods - self._F_a, self._F_b = F_a, F_b - self._G_a, self._G_b = G_a, G_b - - def __repr__(self): - m = "CareerWorkerProblem(beta={b:g}, B={B:g}, N={n:g}, F_a={fa:g}, " - m += "F_b={fb:g}, G_a={ga:g}, G_b={gb:g})" - return m.format(b=self.beta, B=self.B, n=self.N, fa=self._F_a, - fb=self._F_b, ga=self._G_a, gb=self._G_b) - - def __str__(self): - m = """\ - CareerWorkerProblem (Neal, 1999) - - beta (discount factor) : {b:g} - - B (upper bound for epsilon and theta) : {B:g} - - N (number of realizations of epsilon and theta) : {n:g} - - F_a (parameter a from career distribution) : {fa:g} - - F_b (parameter b from career distribution) : {fb:g} - - G_a (parameter a from job distribution) : {ga:g} - - G_b (parameter b from job distribution) : {gb:g} - """ - return dedent(m.format(b=self.beta, B=self.B, n=self.N, fa=self._F_a, - fb=self._F_b, ga=self._G_a, gb=self._G_b)) - - def bellman_operator(self, v): - """ - The Bellman operator for the career / job choice model of Neal. - - Parameters - ---------- - v : array_like(float) - A 2D NumPy array representing the value function - Interpretation: :math:`v[i, j] = v(\theta_i, \epsilon_j)` - - Returns - ------- - new_v : array_like(float) - The updated value function Tv as an array of shape v.shape - - """ - new_v = np.empty(v.shape) - for i in range(self.N): - for j in range(self.N): - # stay put - v1 = self.theta[i] + self.epsilon[j] + self.beta * v[i, j] - - # new job - v2 = (self.theta[i] + self.G_mean + self.beta * - np.dot(v[i, :], self.G_probs)) - - # new life - v3 = (self.G_mean + self.F_mean + self.beta * - np.dot(self.F_probs, np.dot(v, self.G_probs))) - new_v[i, j] = max(v1, v2, v3) - return new_v - - def get_greedy(self, v): - """ - Compute optimal actions taking v as the value function. - - Parameters - ---------- - v : array_like(float) - A 2D NumPy array representing the value function - Interpretation: :math:`v[i, j] = v(\theta_i, \epsilon_j)` - - Returns - ------- - policy : array_like(float) - A 2D NumPy array, where policy[i, j] is the optimal action - at :math:`(\theta_i, \epsilon_j)`. - - The optimal action is represented as an integer in the set - 1, 2, 3, where 1 = 'stay put', 2 = 'new job' and 3 = 'new - life' - - """ - policy = np.empty(v.shape, dtype=int) - for i in range(self.N): - for j in range(self.N): - v1 = self.theta[i] + self.epsilon[j] + self.beta * v[i, j] - v2 = (self.theta[i] + self.G_mean + self.beta * - np.dot(v[i, :], self.G_probs)) - v3 = (self.G_mean + self.F_mean + self.beta * - np.dot(self.F_probs, np.dot(v, self.G_probs))) - if v1 > max(v2, v3): - action = 1 - elif v2 > max(v1, v3): - action = 2 - else: - action = 3 - policy[i, j] = action - - return policy diff --git a/quantecon/models/ifp.py b/quantecon/models/ifp.py deleted file mode 100644 index f6baea40d..000000000 --- a/quantecon/models/ifp.py +++ /dev/null @@ -1,221 +0,0 @@ -""" -Filename: ifp.py - -Authors: Thomas Sargent, John Stachurski - -Tools for solving the standard optimal savings / income fluctuation -problem for an infinitely lived consumer facing an exogenous income -process that evolves according to a Markov chain. - -References ----------- - -http://quant-econ.net/py/ifp.html - -""" -from textwrap import dedent -import numpy as np -from scipy.optimize import fminbound, brentq -from scipy import interp - - -class ConsumerProblem(object): - """ - A class for solving the income fluctuation problem. Iteration with - either the Coleman or Bellman operators from appropriate initial - conditions leads to convergence to the optimal consumption policy. - The income process is a finite state Markov chain. Note that the - Coleman operator is the preferred method, as it is almost always - faster and more accurate. The Bellman operator is only provided for - comparison. - - Parameters - ---------- - r : scalar(float), optional(default=0.01) - A strictly positive scalar giving the interest rate - beta : scalar(float), optional(default=0.96) - The discount factor, must satisfy (1 + r) * beta < 1 - Pi : array_like(float), optional(default=((0.60, 0.40),(0.05, 0.95)) - A 2D NumPy array giving the Markov matrix for {z_t} - z_vals : array_like(float), optional(default=(0.5, 0.95)) - The state space of {z_t} - b : scalar(float), optional(default=0) - The borrowing constraint - grid_max : scalar(float), optional(default=16) - Max of the grid used to solve the problem - grid_size : scalar(int), optional(default=50) - Number of grid points to solve problem, a grid on [-b, grid_max] - u : callable, optional(default=np.log) - The utility function - du : callable, optional(default=lambda x: 1/x) - The derivative of u - - Attributes - ---------- - r, beta, Pi, z_vals, b, u, du : see Parameters - asset_grid : np.ndarray - One dimensional grid for assets - - """ - - def __init__(self, r=0.01, beta=0.96, Pi=((0.6, 0.4), (0.05, 0.95)), - z_vals=(0.5, 1.0), b=0, grid_max=16, grid_size=50, - u=np.log, du=lambda x: 1/x): - self.u, self.du = u, du - self.r, self.R = r, 1 + r - self.beta, self.b = beta, b - self.Pi, self.z_vals = np.array(Pi), tuple(z_vals) - self.asset_grid = np.linspace(-b, grid_max, grid_size) - - def __repr__(self): - m = "ConsumerProblem(r={r:g}, beta={be:g}, Pi='{n:g} by {n:g}', " - m += "z_vals={z}, b={b:g}, grid_max={gm:g}, grid_size={gs:g}, " - m += "u={u}, du={du})" - return m.format(r=self.r, be=self.beta, n=self.Pi.shape[0], - z=self.z_vals, b=self.b, - gm=self.asset_grid.max(), gs=self.asset_grid.size, - u=self.u, du=self.du) - - def __str__(self): - m = """ - Consumer Problem (optimal savings): - - r (interest rate) : {r:g} - - beta (discount rate) : {be:g} - - Pi (transition matrix) : {n} by {n} - - z_vals (state space of shocks) : {z} - - b (borrowing constraint) : {b:g} - - grid_max (maximum of asset grid) : {gm:g} - - grid_size (number of points in asset grid) : {gs:g} - - u (utility function) : {u} - - du (marginal utility function) : {du} - """ - return dedent(m.format(r=self.r, be=self.beta, n=self.Pi.shape[0], - z=self.z_vals, b=self.b, - gm=self.asset_grid.max(), - gs=self.asset_grid.size, u=self.u, - du=self.du)) - - def bellman_operator(self, V, return_policy=False): - """ - The approximate Bellman operator, which computes and returns the - updated value function TV (or the V-greedy policy c if - return_policy is True). - - Parameters - ---------- - V : array_like(float) - A NumPy array of dim len(cp.asset_grid) times len(cp.z_vals) - return_policy : bool, optional(default=False) - Indicates whether to return the greed policy given V or the - updated value function TV. Default is TV. - - Returns - ------- - array_like(float) - Returns either the greed policy given V or the updated value - function TV. - - """ - # === Simplify names, set up arrays === # - R, Pi, beta, u, b = self.R, self.Pi, self.beta, self.u, self.b - asset_grid, z_vals = self.asset_grid, self.z_vals - new_V = np.empty(V.shape) - new_c = np.empty(V.shape) - z_idx = list(range(len(z_vals))) - - # === Linear interpolation of V along the asset grid === # - vf = lambda a, i_z: interp(a, asset_grid, V[:, i_z]) - - # === Solve r.h.s. of Bellman equation === # - for i_a, a in enumerate(asset_grid): - for i_z, z in enumerate(z_vals): - def obj(c): # objective function to be *minimized* - y = sum(vf(R * a + z - c, j) * Pi[i_z, j] for j in z_idx) - return - u(c) - beta * y - c_star = fminbound(obj, np.min(z_vals), R * a + z + b) - new_c[i_a, i_z], new_V[i_a, i_z] = c_star, -obj(c_star) - - if return_policy: - return new_c - else: - return new_V - - def coleman_operator(self, c): - """ - The approximate Coleman operator. - - Iteration with this operator corresponds to policy function - iteration. Computes and returns the updated consumption policy - c. The array c is replaced with a function cf that implements - univariate linear interpolation over the asset grid for each - possible value of z. - - Parameters - ---------- - c : array_like(float) - A NumPy array of dim len(cp.asset_grid) times len(cp.z_vals) - - Returns - ------- - array_like(float) - The updated policy, where updating is by the Coleman - operator. function TV. - - """ - # === simplify names, set up arrays === # - R, Pi, beta, du, b = self.R, self.Pi, self.beta, self.du, self.b - asset_grid, z_vals = self.asset_grid, self.z_vals - z_size = len(z_vals) - gamma = R * beta - vals = np.empty(z_size) - - # === linear interpolation to get consumption function === # - def cf(a): - """ - The call cf(a) returns an array containing the values c(a, - z) for each z in z_vals. For each such z, the value c(a, z) - is constructed by univariate linear approximation over asset - space, based on the values in the array c - """ - for i in range(z_size): - vals[i] = interp(a, asset_grid, c[:, i]) - return vals - - # === solve for root to get Kc === # - Kc = np.empty(c.shape) - for i_a, a in enumerate(asset_grid): - for i_z, z in enumerate(z_vals): - def h(t): - expectation = np.dot(du(cf(R * a + z - t)), Pi[i_z, :]) - return du(t) - max(gamma * expectation, du(R * a + z + b)) - Kc[i_a, i_z] = brentq(h, np.min(z_vals), R * a + z + b) - - return Kc - - def initialize(self): - """ - Creates a suitable initial conditions V and c for value function - and policy function iteration respectively. - - Returns - ------- - V : array_like(float) - Initial condition for value function iteration - c : array_like(float) - Initial condition for Coleman operator iteration - - """ - # === Simplify names, set up arrays === # - R, beta, u, b = self.R, self.beta, self.u, self.b - asset_grid, z_vals = self.asset_grid, self.z_vals - shape = len(asset_grid), len(z_vals) - V, c = np.empty(shape), np.empty(shape) - - # === Populate V and c === # - for i_a, a in enumerate(asset_grid): - for i_z, z in enumerate(z_vals): - c_max = R * a + z + b - c[i_a, i_z] = c_max - V[i_a, i_z] = u(c_max) / (1 - beta) - - return V, c diff --git a/quantecon/models/jv.py b/quantecon/models/jv.py deleted file mode 100644 index 50cebc7ed..000000000 --- a/quantecon/models/jv.py +++ /dev/null @@ -1,198 +0,0 @@ -""" -Filename: jv.py - -Authors: Thomas Sargent, John Stachurski - -References ------------ - -http://quant-econ.net/py/jv.html - -""" -from textwrap import dedent -import sys -import numpy as np -from scipy.integrate import fixed_quad as integrate -from scipy.optimize import minimize -import scipy.stats as stats -from scipy import interp - -# The SLSQP method is faster and more stable, but it didn't give the -# correct answer in python 3. So, if we are in python 2, use SLSQP, otherwise -# use the only other option (to handle constraints): COBYLA -if sys.version_info[0] == 2: - method = "SLSQP" -else: - # python 3 - method = "COBYLA" - -epsilon = 1e-4 # A small number, used in the optimization routine - - -class JvWorker(object): - r""" - A Jovanovic-type model of employment with on-the-job search. The - value function is given by - - .. math:: - - V(x) = \max_{\phi, s} w(x, \phi, s) - - for - - .. math:: - - w(x, \phi, s) := x(1 - \phi - s) - + \beta (1 - \pi(s)) V(G(x, \phi)) - + \beta \pi(s) E V[ \max(G(x, \phi), U)] - - Here - - * x = human capital - * s = search effort - * :math:`\phi` = investment in human capital - * :math:`\pi(s)` = probability of new offer given search level s - * :math:`x(1 - \phi - s)` = wage - * :math:`G(x, \phi)` = new human capital when current job retained - * U = RV with distribution F -- new draw of human capital - - Parameters - ---------- - A : scalar(float), optional(default=1.4) - Parameter in human capital transition function - alpha : scalar(float), optional(default=0.6) - Parameter in human capital transition function - beta : scalar(float), optional(default=0.96) - Discount factor - grid_size : scalar(int), optional(default=50) - Grid size for discretization - G : function, optional(default=lambda x, phi: A * (x * phi)**alpha) - Transition function for human captial - pi : function, optional(default=sqrt) - Function mapping search effort (:math:`s \in (0,1)`) to - probability of getting new job offer - F : distribution, optional(default=Beta(2,2)) - Distribution from which the value of new job offers is drawn - - Attributes - ---------- - A, alpha, beta : see Parameters - x_grid : array_like(float) - The grid over the human capital - - """ - - def __init__(self, A=1.4, alpha=0.6, beta=0.96, grid_size=50, - G=None, pi=np.sqrt, F=stats.beta(2, 2)): - self.A, self.alpha, self.beta = A, alpha, beta - - # === set defaults for G, pi and F === # - self.G = G if G is not None else lambda x, phi: A * (x * phi)**alpha - self.pi = pi - self.F = F - - # === Set up grid over the state space for DP === # - # Max of grid is the max of a large quantile value for F and the - # fixed point y = G(y, 1). - grid_max = max(A**(1 / (1 - alpha)), self.F.ppf(1 - epsilon)) - self.x_grid = np.linspace(epsilon, grid_max, grid_size) - - def __repr__(self): - m = "JvWorker(A={a:g}, alpha={al:g}, beta={b:g}, grid_size={gs})" - return m.format(a=self.A, al=self.alpha, b=self.beta, - gs=self.x_grid.size) - - def __str__(self): - m = """\ - Jovanovic worker (on the job search): - - A (parameter in human capital transition function) : {a:g} - - alpha (parameter in human capital transition function) : {al:g} - - beta (parameter in human capital transition function) : {b:g} - - grid_size (number of grid points for human capital) : {gs} - - grid_max (maximum of grid for human capital) : {gm:g} - """ - return dedent(m.format(a=self.A, al=self.alpha, b=self.beta, - gs=self.x_grid.size, gm=self.x_grid.max())) - - def bellman_operator(self, V, brute_force=False, return_policies=False): - """ - Returns the approximate value function TV by applying the - Bellman operator associated with the model to the function V. - - Returns TV, or the V-greedy policies s_policy and phi_policy when - return_policies=True. In the function, the array V is replaced below - with a function Vf that implements linear interpolation over the - points (V(x), x) for x in x_grid. - - - Parameters - ---------- - V : array_like(float) - Array representing an approximate value function - brute_force : bool, optional(default=False) - Default is False. If the brute_force flag is True, then grid - search is performed at each maximization step. - return_policies : bool, optional(default=False) - Indicates whether to return just the updated value function - TV or both the greedy policy computed from V and TV - - - Returns - ------- - s_policy : array_like(float) - The greedy policy computed from V. Only returned if - return_policies == True - new_V : array_like(float) - The updated value function Tv, as an array representing the - values TV(x) over x in x_grid. - - """ - # === simplify names, set up arrays, etc. === # - G, pi, F, beta = self.G, self.pi, self.F, self.beta - Vf = lambda x: interp(x, self.x_grid, V) - N = len(self.x_grid) - new_V, s_policy, phi_policy = np.empty(N), np.empty(N), np.empty(N) - a, b = F.ppf(0.005), F.ppf(0.995) # Quantiles, for integration - c1 = lambda z: 1.0 - sum(z) # used to enforce s + phi <= 1 - c2 = lambda z: z[0] - epsilon # used to enforce s >= epsilon - c3 = lambda z: z[1] - epsilon # used to enforce phi >= epsilon - guess = (0.2, 0.2) - constraints = [{"type": "ineq", "fun": i} for i in [c1, c2, c3]] - - # === solve r.h.s. of Bellman equation === # - for i, x in enumerate(self.x_grid): - - # === set up objective function === # - def w(z): - s, phi = z - h = lambda u: Vf(np.maximum(G(x, phi), u)) * F.pdf(u) - integral, err = integrate(h, a, b) - q = pi(s) * integral + (1.0 - pi(s)) * Vf(G(x, phi)) - # == minus because we minimize == # - return - x * (1.0 - phi - s) - beta * q - - # === either use SciPy solver === # - if not brute_force: - max_s, max_phi = minimize(w, guess, constraints=constraints, - options={"disp": 0}, - method=method)["x"] - max_val = -w((max_s, max_phi)) - - # === or search on a grid === # - else: - search_grid = np.linspace(epsilon, 1.0, 15) - max_val = -1.0 - for s in search_grid: - for phi in search_grid: - current_val = -w((s, phi)) if s + phi <= 1.0 else -1.0 - if current_val > max_val: - max_val, max_s, max_phi = current_val, s, phi - - # === store results === # - new_V[i] = max_val - s_policy[i], phi_policy[i] = max_s, max_phi - - if return_policies: - return s_policy, phi_policy - else: - return new_V diff --git a/quantecon/models/lake.py b/quantecon/models/lake.py deleted file mode 100644 index e3901b992..000000000 --- a/quantecon/models/lake.py +++ /dev/null @@ -1,394 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Feb 27 18:08:44 2015 - -Author: David Evans - -Provides a class call LakeModel that simulates the dynamics of unemployment and -employment. -""" - -import numpy as np -from scipy.stats import norm -from scipy.optimize import brentq -import quantecon as qe - -class LakeModel(object): - r""" - This class solves the lake model and simulates :math:`E_t` and :math:`U_t` - with the following parameters - - Parameters: - ------------ - lamb: The job finding rate for currently unemployed workers. - - alpha: The dismissal rate for currently employed workers. - - b : Entry rate into the labor force. - - d : Exit rate from the labor force. - - Attributes - ------------ - - In solving the lake model the program computes - - A : Matrix governing law of motion for :math:`E_t` and :math:`U_t` - - hatA : Matrix governing the law of motion for the rates :math:`e_t` - and :math:`u_t` - - """ - def __init__(self,lamb,alpha,b,d): - self.lamb = lamb - self.alpha= alpha - self.b = b - self.d = d - self.g = b-d - - self.A = self.ConstructA_matrix() - self.Ahat = self.Construct_ScaledA_matrix() - - def ConstructA_matrix(self): - r''' - Constructs the A matrix for :math:`X_{t+1} = A X_{t}` where :math:`X_t = (E_t,U_t)` - - Returns - -------- - A : (2x2) matrix governing state dynamics - ''' - lamb,alpha,b,d = self.lamb,self.alpha,self.b,self.d - - return np.array([ [(1-d)*(1-alpha), (1-d)*lamb], - [(1-d)*alpha + b, (1-lamb)*(1-d) + b ]]) - - def Construct_ScaledA_matrix(self): - r''' - Constructs the scaled A matrix for :math:`x_{t+1} = Ahat x_{t}` where - :math:`x_t = (E_t/N_t,U_t/N_t) = (e_t,u_t)` - - Returns - -------- - Ahat : (2x2) matrix governing state dynamics for employment rates. - ''' - A = self.ConstructA_matrix() - return A/(1+self.g) - - def find_steady_state(self): - r""" - Finds the steady state of the system :math:`x_{t+1} = \hat A x_{t}` - - Returns - -------- - - xbar : steady state vector of employment and unemployment rates - """ - x = np.ones(2)/2. - diff = 1. - while diff > 1e-6: - xprime = self.Ahat.dot(x) - diff = np.abs(xprime-x).max() - x = xprime - - return x - - def simulate_stock_path(self,X0,T): - r''' - Simulates the the sequence of Employment and Unemployent stocks - - Parameters - ------------ - - X0 : array containing initial values (E0,U0) - - T : Number of periods to simulate - - Returns - --------- - - X : iterator containing sequence of Employment and Unemployment stocks - ''' - X = np.atleast_1d(X0) # recast as array just in case - for t in range(T): - yield X - X = self.A.dot(X) - - def simulate_rate_path(self,x0,T): - r''' - Simulates the the sequence of Employment and Unemployent rates - - Parameters - ------------ - - x0 : array containing initial values (e0,u0) - - T : Number of periods to simulate - - Returns - --------- - - x : iterator containing sequence of Employment and Unemployment rates - ''' - x = np.atleast_1d(x0) # recast as array just in case - for t in range(T): - yield x - x = self.Ahat.dot(x) - - - -class LakeModelAgent(object): - r''' - This class holds methods necessary to simulate the life course of an agent - who lives in the lake model economy with the following parameters - - Parameters: - ------------ - lamb: The job finding rate if agent is currently unemployed. - - alpha: The dismissal rate if agent is currently employed. - ''' - def __init__(self,lamb,alpha): - self.lamb = lamb - self.alpha = alpha - - self.P = self.compute_P() - - def compute_P(self): - r''' - This method computes the transition matrix for the agent. - - Return - --------- - - P(2d-array) : Transition matrix for the agent - ''' - alpha,lamb = self.alpha,self.lamb - return np.array([ - [(1-alpha), alpha], - [lamb, 1-lamb] - ]) - - def compute_ergodic(self): - r''' - Computes the ergodic distribution over the unemployment and employment - states - - Returns - --------- - - pibar(1d-array) : the ergodic distribution of P - ''' - return qe.mc_compute_stationary(self.P) - - def simulate(self,s0,T): - r''' - Simulates the life of an agent for T periods - - Parameters - ------------- - - s0(int) : initial state - - T(int) : number of periods to simulate - - Returns - ----------- - - sHist(iterator) : history of employment(s==0) and unemployment(s==1) - ''' - pi0 = np.arange(2) == s0 - return qe.mc_sample_path(self.P,pi0,T) - - -class LakeModel_Equilibrium(object): - r''' - Solves for the steady state General Equilibirium of a Lake Model economy - using the McCall Search Model to model the behavior - - Parameters - ------------- - - alpha - (float) Exogenous firing rate. - - beta - (float) The discount factor. - - gamma - (float) Arrival rate of wage offer - - sigma - (float) Degree of risk aversion. - - pdf - (1d - array) pdf[s] Probability of receiving a wage wstar[s] - - wstar - (1d -array) Distribution of wages. - ''' - - def __init__(self,alpha,gamma,beta,sigma,pdf,wstar): - self.alpha = alpha - self.beta = beta - self.gamma = gamma - self.sigma = sigma - self.pdf = pdf - self.wstar = wstar - - def U(self,c): - r''' - Utility function of the agent - - Parameters - ------------- - - c - (array or float) consumption of the agent - - Returns - ---------- - - U - (array or float) Utility of the agent for each level of consumption - ''' - sigma = self.sigma - negative_c = c < 0. - if sigma == 1.: - U = np.log(c) - else: - U= (c**(1-sigma) - 1)/(1-sigma) - U[negative_c] = -9999999. - return U - - - - def solve_for_steadystate(self,c,T): - r''' - Solve for workers steady state policies given tax policy - - Paramaters - ------------ - - c - (float) Level of unemployment benefit - - T - (float) Lump sum tax - - Results - --------- - - V - (array) Value function of the worker - - C - (array) optimal policy function of the worker - - pi - (array) distribution between employed and unemployed - - W - (float) Welfare at steady state - ''' - - V,C = self.solveMcCallModel(c-T,self.wstar-T) - - U = self.U(np.array([c-T])) + self.beta*self.pdf.dot(V) # value of unemployment - - lamb = self.gamma * self.pdf.dot(C) # probability of accepting a job - - LM = LakeModel(lamb,self.alpha,0,0) #no birth or death - - pi = LM.find_steady_state() - - #Expected value of being employed - EV = (C*V).dot(self.pdf)/(C.dot(self.pdf)) - - W = pi[0]*EV + pi[1] * U - - return V,C,pi,W,U,EV - - def find_steady_state_tax(self,c): - r''' - Finds the lump sum tax that balances budget at steady state - - Parameters - ----------- - - c - (float) Level of unemployment benefit - - Results - ---------- - - T - (float) Lump sum tax that balances budget - - W - (float) Steady State Wealfare at that balanced budget tax - ''' - - #budget at steady state - def SS_budget(T): - V,C,pi,W,U,EV= self.solve_for_steadystate(c,T) - #return inflows minus outflows - return T - pi[1]*c - - T = brentq(SS_budget, 0., 0.9*c) - - V,C,pi,W,U,EV = self.solve_for_steadystate(c,T) - - return T,W,U,EV,pi - - - def iterateValueFunction(self,c,w,V): - r''' - Iterates McCall search value function v - - Parameters - ---------- - - c - (float) Level of unemployment insurance - - w - (float) Vector of possible wages - - V - (n array) continuation value function if offered w[s] next period - - Returns - -------- - - V_new - (n array) current value function if offered w[s] this period - - Choice - (n array) do we accept or reject wage w[s] - ''' - p,beta,alpha,gamma = self.pdf,self.beta,self.alpha,self.gamma - S = len(p) - Q = p.dot(V)# value before wage offer - V_U = (self.U(c*np.ones(S)) + beta*gamma*Q)/( 1-beta*(1-gamma) ) - #stack value of accepting and rejecting offer on top of each other - stacked_values = np.vstack([ V_U, - self.U(w) + (1-alpha)*beta*V + alpha*beta*V_U ]) - - #find whether it is optimal to accept or reject offer - V_new = np.amax(stacked_values, axis = 0) - Choice = np.argmax(stacked_values, axis = 0) - return V_new,Choice - - def solveMcCallModel(self,c,w,eps = 1e-6): - r''' - Solves the infinite horizon McCall search model - - Parameters - ----------- - - c - (float) Level of unemployment insurance - - w - (float) Vector of possible wages - - eps - (float) convergence criterion for infinite horizon - - Returns - -------- - - V - (1d-array) Value function that solves the infinite horizon problem - - Choice - (1d-array) Optimal policy of workers - ''' - - S = len(self.pdf) - v = np.zeros(S) #intialize with zero - diff = 1 #holds difference v_{t+1}-v_t - while diff > eps: - v_new,choice = self.iterateValueFunction(c,w,v) - diff = np.amax( np.abs(v-v_new) )#compute difference between value - v = v_new #copy v_new into v #add in infinte horizon solution - - return v,choice - - - - - \ No newline at end of file diff --git a/quantecon/models/lucastree.py b/quantecon/models/lucastree.py deleted file mode 100644 index afbd17884..000000000 --- a/quantecon/models/lucastree.py +++ /dev/null @@ -1,278 +0,0 @@ -r""" -Filename: lucastree.py - -Authors: Thomas Sargent, John Stachurski, Spencer Lyon - -Solves the price function for the Lucas tree in a continuous state -setting, using piecewise linear approximation for the sequence of -candidate price functions. The consumption endownment follows the log -linear AR(1) process - -.. math:: - - log y' = \alpha log y + \sigma \epsilon - -where y' is a next period y and epsilon is an iid standard normal shock. -Hence - -.. math:: - - y' = y^{\alpha} * \xi, - -where - -.. math:: - - \xi = e^(\sigma * \epsilon) - -The distribution phi of xi is - -.. math:: - - \phi = LN(0, \sigma^2), - -where LN means lognormal. - -""" -from __future__ import division # == Omit for Python 3.x == # -from textwrap import dedent -import numpy as np -from scipy import interp -from scipy.stats import lognorm -from scipy.integrate import fixed_quad -from ..compute_fp import compute_fixed_point - - -class LucasTree(object): - """ - Class to solve for the price of a the Lucas tree in the Lucas - asset pricing model - - Parameters - ---------- - gamma : scalar(float) - The coefficient of risk aversion in the household's CRRA utility - function - beta : scalar(float) - The household's discount factor - alpha : scalar(float) - The correlation coefficient in the shock process - sigma : scalar(float) - The volatility of the shock process - grid : array_like(float), optional(default=None) - The grid points on which to evaluate the asset prices. Grid - points should be nonnegative. If None is passed, we will create - a reasonable one for you - - Attributes - ---------- - gamma, beta, alpha, sigma, grid : see Parameters - grid_min, grid_max, grid_size : scalar(int) - Properties for grid upon which prices are evaluated - phi : scipy.stats.lognorm - The distribution for the shock process - - Examples - -------- - >>> tree = LucasTree(gamma=2, beta=0.95, alpha=0.90, sigma=0.1) - >>> grid, price_vals = tree.grid, tree.compute_lt_price() - - """ - - def __init__(self, gamma, beta, alpha, sigma, grid=None): - self.gamma = gamma - self.beta = beta - self.alpha = alpha - self.sigma = sigma - - # == set up grid == # - if grid is None: - (self.grid, self.grid_min, - self.grid_max, self.grid_size) = self._new_grid() - else: - self.grid = np.asarray(grid) - self.grid_min = min(grid) - self.grid_max = max(grid) - self.grid_size = len(grid) - - # == set up distribution for shocks == # - self.phi = lognorm(sigma) - - # == set up integration bounds. 4 Standard deviations. Make them - # private attributes b/c users don't need to see them, but we - # only want to compute them once. == # - self._int_min = np.exp(-4.0 * sigma) - self._int_max = np.exp(4.0 * sigma) - - # == Set up h from the Lucas Operator == # - self.h = self._init_h() - - def __repr__(self): - m = "LucasTree(gamma={g}, beta={b}, alpha={a}, sigma={s})" - return m.format(g=self.gamma, b=self.beta, a=self.alpha, s=self.sigma) - - def __str__(self): - m = """\ - Lucas Pricing Model (Lucas, 1978): - - gamma (coefficient of risk aversion) : {g} - - beta (discount parameter) : {b} - - alpha (correlation coefficient in shock process) : {a} - - sigma (volatility of shock process) : {s} - - grid bounds (bounds for where to compute prices) : ({gl:g}, {gu:g}) - - grid points (number of grid points) : {gs} - """ - return dedent(m.format(g=self.gamma, b=self.beta, a=self.alpha, - s=self.sigma, gl=self.grid_min, - gu=self.grid_max, gs=self.grid_size)) - - def _init_h(self): - """ - Compute the function h in the Lucas operator as a vector of - values on the grid - - Recall that h(y) = beta * int u'(G(y,z)) G(y,z) phi(dz) - """ - alpha, gamma, beta = self.alpha, self.gamma, self.beta - grid, grid_size = self.grid, self.grid_size - - h = np.empty(grid_size) - - for i, y in enumerate(grid): - # == u'(G(y,z)) G(y,z) == # - integrand = lambda z: (y**alpha * z)**(1 - gamma) - h[i] = beta * self.integrate(integrand) - - return h - - def _new_grid(self): - """ - Construct the default grid for the problem - - This is defined to be np.linspace(0, 10, 100) when alpha > 1 - and 100 evenly spaced points covering 4 standard deviations - when alpha < 1 - """ - grid_size = 100 - if abs(self.alpha) >= 1.0: - grid_min, grid_max = 0.0, 10.0 - else: - # == Set the grid interval to contain most of the mass of the - # stationary distribution of the consumption endowment == # - ssd = self.sigma / np.sqrt(1 - self.alpha**2) - grid_min, grid_max = np.exp(-4 * ssd), np.exp(4 * ssd) - - grid = np.linspace(grid_min, grid_max, grid_size) - - return grid, grid_min, grid_max, grid_size - - def integrate(self, g, int_min=None, int_max=None): - """ - Integrate the function g(z) * self.phi(z) from int_min to - int_max. - - Parameters - ---------- - g : function - The function which to integrate - - int_min, int_max : scalar(float), optional - The bounds of integration. If either of these parameters are - `None` (the default), they will be set to 4 standard - deviations above and below the mean. - - Returns - ------- - result : scalar(float) - The result of the integration - - """ - # == Simplify notation == # - phi = self.phi - if int_min is None: - int_min = self._int_min - if int_max is None: - int_max = self._int_max - - # == set up integrand and integrate == # - integrand = lambda z: g(z) * phi.pdf(z) - result, error = fixed_quad(integrand, int_min, int_max) - return result - - def lucas_operator(self, f, Tf=None): - """ - The approximate Lucas operator, which computes and returns the - updated function Tf on the grid points. - - Parameters - ---------- - f : array_like(float) - A candidate function on R_+ represented as points on a grid - and should be flat NumPy array with len(f) = len(grid) - - Tf : array_like(float) - Optional storage array for Tf - - Returns - ------- - Tf : array_like(float) - The updated function Tf - - Notes - ----- - The argument `Tf` is optional, but recommended. If it is passed - into this function, then we do not have to allocate any memory - for the array here. As this function is often called many times - in an iterative algorithm, this can save significant computation - time. - - """ - grid, h = self.grid, self.h - alpha, beta = self.alpha, self.beta - - # == set up storage if needed == # - if Tf is None: - Tf = np.empty_like(f) - - # == Apply the T operator to f == # - Af = lambda x: interp(x, grid, f) # Piecewise linear interpolation - - for i, y in enumerate(grid): - Tf[i] = h[i] + beta * self.integrate(lambda z: Af(y**alpha * z)) - - return Tf - - def compute_lt_price(self, error_tol=1e-3, max_iter=50, verbose=0): - """ - Compute the equilibrium price function associated with Lucas - tree lt - - Parameters - ---------- - error_tol, max_iter, verbose - Arguments to be passed directly to - `quantecon.compute_fixed_point`. See that docstring for more - information - - Returns - ------- - price : array_like(float) - The prices at the grid points in the attribute `grid` of the - object - - """ - # == simplify notation == # - grid, grid_size = self.grid, self.grid_size - lucas_operator, gamma = self.lucas_operator, self.gamma - - # == Create storage array for compute_fixed_point. Reduces memory - # allocation and speeds code up == # - Tf = np.empty(grid_size) - - # == Initial guess, just a vector of zeros == # - f_init = np.zeros(grid_size) - f = compute_fixed_point(lucas_operator, f_init, error_tol, - max_iter, verbose, Tf=Tf) - - price = f * grid**gamma - - return price diff --git a/quantecon/models/odu.py b/quantecon/models/odu.py deleted file mode 100644 index 1aafc8e1b..000000000 --- a/quantecon/models/odu.py +++ /dev/null @@ -1,233 +0,0 @@ -""" -Filename: odu.py - -Authors: Thomas Sargent, John Stachurski - -Solves the "Offer Distribution Unknown" Model by value function -iteration and a second faster method discussed in the corresponding -quantecon lecture. - -""" -from textwrap import dedent -from scipy.interpolate import LinearNDInterpolator -from scipy.integrate import fixed_quad -from scipy.stats import beta as beta_distribution -from scipy import interp -from numpy import maximum as npmax -import numpy as np - - -class SearchProblem(object): - """ - A class to store a given parameterization of the "offer distribution - unknown" model. - - Parameters - ---------- - beta : scalar(float), optional(default=0.95) - The discount parameter - c : scalar(float), optional(default=0.6) - The unemployment compensation - F_a : scalar(float), optional(default=1) - First parameter of beta distribution on F - F_b : scalar(float), optional(default=1) - Second parameter of beta distribution on F - G_a : scalar(float), optional(default=3) - First parameter of beta distribution on G - G_b : scalar(float), optional(default=1.2) - Second parameter of beta distribution on G - w_max : scalar(float), optional(default=2) - Maximum wage possible - w_grid_size : scalar(int), optional(default=40) - Size of the grid on wages - pi_grid_size : scalar(int), optional(default=40) - Size of the grid on probabilities - - Attributes - ---------- - beta, c, w_max : see Parameters - w_grid : np.ndarray - Grid points over wages, ndim=1 - pi_grid : np.ndarray - Grid points over pi, ndim=1 - grid_points : np.ndarray - Combined grid points, ndim=2 - F : scipy.stats._distn_infrastructure.rv_frozen - Beta distribution with params (F_a, F_b), scaled by w_max - G : scipy.stats._distn_infrastructure.rv_frozen - Beta distribution with params (G_a, G_b), scaled by w_max - f : function - Density of F - g : function - Density of G - pi_min : scalar(float) - Minimum of grid over pi - pi_max : scalar(float) - Maximum of grid over pi - """ - - def __init__(self, beta=0.95, c=0.6, F_a=1, F_b=1, G_a=3, G_b=1.2, - w_max=2, w_grid_size=40, pi_grid_size=40): - - self.beta, self.c, self.w_max = beta, c, w_max - self.F = beta_distribution(F_a, F_b, scale=w_max) - self.G = beta_distribution(G_a, G_b, scale=w_max) - self.f, self.g = self.F.pdf, self.G.pdf # Density functions - self.pi_min, self.pi_max = 1e-3, 1 - 1e-3 # Avoids instability - self.w_grid = np.linspace(0, w_max, w_grid_size) - self.pi_grid = np.linspace(self.pi_min, self.pi_max, pi_grid_size) - x, y = np.meshgrid(self.w_grid, self.pi_grid) - self.grid_points = np.column_stack((x.ravel(1), y.ravel(1))) - - def __repr__(self): - m = "SearchProblem(beta={b}, c={c}, F_a={fa}, F_b={fb}, G_a={ga}, " - m += "G_b={gb}, w_max={wu}, w_grid_size={wgs}, pi_grid_size={pgs})" - fa, fb = self.F.args - ga, gb = self.G.args - return m.format(b=self.beta, c=self.c, fa=fa, fb=fb, ga=ga, - gb=gb, wu=self.w_grid.max(), - wgs=self.w_grid.size, pgs=self.pi_grid.size) - - def __str__(self): - m = """\ - SearchProblem (offer distribution unknown): - - beta (discount factor) : {b:g} - - c (unemployment compensation) : {c} - - F (distribution F) : Beta({fa}, {fb:g}) - - G (distribution G) : Beta({ga}, {gb:g}) - - w bounds (bounds for wage offers) : ({wl:g}, {wu:g}) - - w grid size (number of points in grid for wage) : {wgs} - - pi bounds (bounds for probability of dist f) : ({pl:g}, {pu:g}) - - pi grid size (number of points in grid for pi) : {pgs} - """ - fa, fb = self.F.args - ga, gb = self.G.args - return dedent(m.format(b=self.beta, c=self.c, fa=fa, fb=fb, ga=ga, - gb=gb, - wl=self.w_grid.min(), wu=self.w_grid.max(), - wgs=self.w_grid.size, - pl=self.pi_grid.min(), pu=self.pi_grid.max(), - pgs=self.pi_grid.size)) - - def q(self, w, pi): - """ - Updates pi using Bayes' rule and the current wage observation w. - - Returns - ------- - - new_pi : scalar(float) - The updated probability - - """ - - new_pi = 1.0 / (1 + ((1 - pi) * self.g(w)) / (pi * self.f(w))) - - # Return new_pi when in [pi_min, pi_max] and else end points - new_pi = np.maximum(np.minimum(new_pi, self.pi_max), self.pi_min) - - return new_pi - - def bellman_operator(self, v): - """ - - The Bellman operator. Including for comparison. Value function - iteration is not recommended for this problem. See the - reservation wage operator below. - - Parameters - ---------- - v : array_like(float, ndim=1, length=len(pi_grid)) - An approximate value function represented as a - one-dimensional array. - - Returns - ------- - new_v : array_like(float, ndim=1, length=len(pi_grid)) - The updated value function - - """ - # == Simplify names == # - f, g, beta, c, q = self.f, self.g, self.beta, self.c, self.q - - vf = LinearNDInterpolator(self.grid_points, v) - N = len(v) - new_v = np.empty(N) - - for i in range(N): - w, pi = self.grid_points[i, :] - v1 = w / (1 - beta) - integrand = lambda m: vf(m, q(m, pi)) * (pi * f(m) - + (1 - pi) * g(m)) - integral, error = fixed_quad(integrand, 0, self.w_max) - v2 = c + beta * integral - new_v[i] = max(v1, v2) - - return new_v - - def get_greedy(self, v): - """ - Compute optimal actions taking v as the value function. - - Parameters - ---------- - v : array_like(float, ndim=1, length=len(pi_grid)) - An approximate value function represented as a - one-dimensional array. - - Returns - ------- - policy : array_like(float, ndim=1, length=len(pi_grid)) - The decision to accept or reject an offer where 1 indicates - accept and 0 indicates reject - - """ - # == Simplify names == # - f, g, beta, c, q = self.f, self.g, self.beta, self.c, self.q - - vf = LinearNDInterpolator(self.grid_points, v) - N = len(v) - policy = np.zeros(N, dtype=int) - - for i in range(N): - w, pi = self.grid_points[i, :] - v1 = w / (1 - beta) - integrand = lambda m: vf(m, q(m, pi)) * (pi * f(m) + - (1 - pi) * g(m)) - integral, error = fixed_quad(integrand, 0, self.w_max) - v2 = c + beta * integral - policy[i] = v1 > v2 # Evaluates to 1 or 0 - - return policy - - def res_wage_operator(self, phi): - """ - - Updates the reservation wage function guess phi via the operator - Q. - - Parameters - ---------- - phi : array_like(float, ndim=1, length=len(pi_grid)) - This is reservation wage guess - - Returns - ------- - new_phi : array_like(float, ndim=1, length=len(pi_grid)) - The updated reservation wage guess. - - """ - # == Simplify names == # - beta, c, f, g, q = self.beta, self.c, self.f, self.g, self.q - # == Turn phi into a function == # - phi_f = lambda p: interp(p, self.pi_grid, phi) - - new_phi = np.empty(len(phi)) - for i, pi in enumerate(self.pi_grid): - def integrand(x): - "Integral expression on right-hand side of operator" - return npmax(x, phi_f(q(x, pi))) * (pi*f(x) + (1 - pi)*g(x)) - integral, error = fixed_quad(integrand, 0, self.w_max) - new_phi[i] = (1 - beta) * c + beta * integral - - return new_phi diff --git a/quantecon/models/optgrowth.py b/quantecon/models/optgrowth.py deleted file mode 100644 index f5516bfb3..000000000 --- a/quantecon/models/optgrowth.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -Filename: optgrowth.py - -Authors: John Stachurski and Thomas Sargent - -Solving the optimal growth problem via value function iteration. - -""" -from __future__ import division # Omit for Python 3.x -from textwrap import dedent -import numpy as np -from scipy.optimize import fminbound -from scipy import interp - - -class GrowthModel(object): - """ - - This class defines the primitives representing the growth model. - - Parameters - ---------- - f : function, optional(default=k**.65) - The production function; the default is the Cobb-Douglas - production function with power of .65 - beta : scalar(int), optional(default=.95) - The utility discounting parameter - u : function, optional(default=np.log) - The utility function. Default is log utility - grid_max : scalar(int), optional(default=2) - The maximum grid value - grid_size : scalar(int), optional(default=150) - The size of grid to use. - - Attributes - ---------- - f, beta, u : see Parameters - grid : array_like(float, ndim=1) - The grid over savings. - - """ - def __init__(self, f=lambda k: k**0.65, beta=0.95, u=np.log, - grid_max=2, grid_size=150): - - self.u, self.f, self.beta = u, f, beta - self.grid = np.linspace(1e-6, grid_max, grid_size) - - def __repr__(self): - m = "GrowthModel(beta={b}, grid_max={gm}, grid_size={gs})" - return m.format(b=self.beta, gm=self.grid.max(), gs=self.grid.size) - - def __str__(self): - m = """\ - GrowthModel: - - beta (discount factor) : {b} - - u (utility function) : {u} - - f (production function) : {f} - - grid bounds (bounds for grid over savings values) : ({gl}, {gm}) - - grid points (number of points in grid for savings) : {gs} - """ - return dedent(m.format(b=self.beta, u=self.u, f=self.f, - gl=self.grid.min(), gm=self.grid.max(), - gs=self.grid.size)) - - def bellman_operator(self, w, compute_policy=False): - """ - The approximate Bellman operator, which computes and returns the - updated value function Tw on the grid points. - - Parameters - ---------- - w : array_like(float, ndim=1) - The value of the input function on different grid points - compute_policy : Boolean, optional(default=False) - Whether or not to compute policy function - - """ - # === Apply linear interpolation to w === # - Aw = lambda x: interp(x, self.grid, w) - - if compute_policy: - sigma = np.empty(len(w)) - - # == set Tw[i] equal to max_c { u(c) + beta w(f(k_i) - c)} == # - Tw = np.empty(len(w)) - for i, k in enumerate(self.grid): - objective = lambda c: - self.u(c) - self.beta * Aw(self.f(k) - c) - c_star = fminbound(objective, 1e-6, self.f(k)) - if compute_policy: - # sigma[i] = argmax_c { u(c) + beta w(f(k_i) - c)} - sigma[i] = c_star - Tw[i] = - objective(c_star) - - if compute_policy: - return Tw, sigma - else: - return Tw - - def compute_greedy(self, w): - """ - Compute the w-greedy policy on the grid points. - - Parameters - ---------- - w : array_like(float, ndim=1) - The value of the input function on different grid points - - """ - Tw, sigma = self.bellman_operator(w, compute_policy=True) - return sigma diff --git a/quantecon/models/solow/__init__.py b/quantecon/models/solow/__init__.py deleted file mode 100644 index d4aeccd96..000000000 --- a/quantecon/models/solow/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -models directory imports - -objects imported here will live in the `quantecon.models.solow` namespace - -""" -__all__ = ['Model', 'CobbDouglasModel', 'CESModel'] - -from . model import Model -from . import model -from . cobb_douglas import CobbDouglasModel -from . import cobb_douglas -from . ces import CESModel -from . import ces diff --git a/quantecon/models/solow/ces.py b/quantecon/models/solow/ces.py deleted file mode 100644 index c46d90f2a..000000000 --- a/quantecon/models/solow/ces.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -Solow model with constant elasticity of substitution (CES) production. - -@author : David R. Pugh -@date : 2014-12-11 - -""" -from __future__ import division -from textwrap import dedent - -import sympy as sym - -from . import model - -# declare key variables for the model -A, k, K, L, Y = sym.symbols('A, k, K, L, Y') - -# declare required model parameters -g, n, s, alpha, delta, sigma = sym.symbols('g, n, s, alpha, delta, sigma') - - -class CESModel(model.Model): - - _required_params = ['g', 'n', 's', 'alpha', 'delta', 'sigma', 'A0', 'L0'] - - def __init__(self, params): - """ - Create an instance of the Solow growth model with constant elasticity - of subsitution (CES) aggregate production. - - Parameters - ---------- - params : dict - Dictionary of model parameters. - - """ - rho = (sigma - 1) / sigma - ces_output = (alpha * K**rho + (1 - alpha) * (A * L)**rho)**(1 / rho) - super(CESModel, self).__init__(ces_output, params) - - def __str__(self): - """Human readable summary of a CESModel instance.""" - m = super(CESModel, self).__str__() - m += " - alpha (capital's weight in output) : {alpha:g}\n" - m += " - sigma (elasticity of substitution) : {sigma:g}" - formatted_str = dedent(m.format(alpha=self.params['alpha'], - sigma=self.params['sigma'])) - return formatted_str - - @property - def solow_residual(self): - """ - Symbolic expression for the Solow residual which is used as a measure - of technology. - - :getter: Return the symbolic expression. - :type: sym.Basic - - """ - rho = (sigma - 1) / sigma - residual = (((1 / (1 - alpha)) * (Y / L)**rho - - (alpha / (1 - alpha)) * (K / L)**rho)**(1 / rho)) - return residual - - @property - def steady_state(self): - r""" - Steady state value of capital stock (per unit effective labor). - - :getter: Return the current steady state value. - :type: float - - Notes - ----- - The steady state value of capital stock (per unit effective labor) - with CES production is defined as - - .. math:: - - k^* = \left[\frac{1-\alpha}{\bigg(\frac{g+n+\delta}{s}\bigg)^{\rho}-\alpha}\right]^{\frac{1}{rho}} - - where `s` is the savings rate, :math:`g + n + \delta` is the effective - depreciation rate, and :math:`\alpha` controls the importance of - capital stock relative to effective labor in the production of output. - Finally, - - ..math:: - - \rho=\frac{\sigma-1}{\sigma} - - where `:math:`sigma` is the elasticity of substitution between capital - and effective labor in production. - - """ - g = self.params['g'] - n = self.params['n'] - s = self.params['s'] - alpha = self.params['alpha'] - delta = self.params['delta'] - sigma = self.params['sigma'] - - ratio_investment_rates = (g + n + delta) / s - rho = (sigma - 1) / sigma - k_star = ((1 - alpha) / (ratio_investment_rates**rho - alpha))**(1 / rho) - - return k_star - - def _isdeterminate_steady_state(self, params): - """Check that parameters are consistent with determinate steady state.""" - g = params['g'] - n = params['n'] - s = params['s'] - alpha = params['alpha'] - delta = params['delta'] - sigma = params['sigma'] - - ratio_investment_rates = (g + n + delta) / s - rho = (sigma - 1) / sigma - - return ratio_investment_rates**rho - alpha > 0 - - def _validate_params(self, params): - """Validate the model parameters.""" - params = super(CESModel, self)._validate_params(params) - if params['alpha'] < 0.0 or params['alpha'] > 1.0: - raise AttributeError('Capital weight must be in (0, 1).') - elif params['sigma'] <= 0.0: - mesg = 'Elasticity of substitution must be strictly positive.' - raise AttributeError(mesg) - elif not self._isdeterminate_steady_state(params): - mesg = 'Steady state is indeterminate.' - raise AttributeError(mesg) - else: - return params diff --git a/quantecon/models/solow/cobb_douglas.py b/quantecon/models/solow/cobb_douglas.py deleted file mode 100644 index 8c612780c..000000000 --- a/quantecon/models/solow/cobb_douglas.py +++ /dev/null @@ -1,114 +0,0 @@ -""" -Solow growth model with Cobb-Douglas aggregate production. - -@author : David R. Pugh -@date : 2014-11-27 - -""" -from __future__ import division -from textwrap import dedent - -import numpy as np -import sympy as sym - -from . import model - -# declare key variables for the model -t, X = sym.symbols('t'), sym.DeferredVector('X') -A, k, K, L = sym.symbols('A, k, K, L') - -# declare required model parameters -g, n, s, alpha, delta = sym.symbols('g, n, s, alpha, delta') - - -class CobbDouglasModel(model.Model): - - _required_params = ['g', 'n', 's', 'alpha', 'delta', 'A0', 'L0'] - - def __init__(self, params): - """ - Create an instance of the Solow growth model with Cobb-Douglas - aggregate production. - - Parameters - ---------- - params : dict - Dictionary of model parameters. - - """ - cobb_douglas_output = K**alpha * (A * L)**(1 - alpha) - super(CobbDouglasModel, self).__init__(cobb_douglas_output, params) - - def __str__(self): - """Human readable summary of a CESModel instance.""" - m = super(CobbDouglasModel, self).__str__() - m += " - alpha (output elasticity) : {alpha:g}\n" - formatted_str = dedent(m.format(alpha=self.params['alpha'])) - return formatted_str - - @property - def steady_state(self): - r""" - Steady state value of capital stock (per unit effective labor). - - :getter: Return the current steady state value. - :type: float - - Notes - ----- - The steady state value of capital stock (per unit effective labor) - with Cobb-Douglas production is defined as - - .. math:: - - k^* = \bigg(\frac{s}{g + n + \delta}\bigg)^\frac{1}{1-\alpha} - - where `s` is the savings rate, :math:`g + n + \delta` is the effective - depreciation rate, and :math:`\alpha` is the elasticity of output with - respect to capital (i.e., capital's share). - - """ - s = self.params['s'] - alpha = self.params['alpha'] - return (s / self.effective_depreciation_rate)**(1 / (1 - alpha)) - - def _validate_params(self, params): - """Validate the model parameters.""" - params = super(CobbDouglasModel, self)._validate_params(params) - if params['alpha'] <= 0.0 or params['alpha'] >= 1.0: - raise AttributeError('Output elasticity must be in (0, 1).') - else: - return params - - def analytic_solution(self, t, k0): - """ - Compute the analytic solution for the Solow model with Cobb-Douglas - production technology. - - Parameters - ---------- - t : numpy.ndarray (shape=(T,)) - Array of points at which the solution is desired. - k0 : (float) - Initial condition for capital stock (per unit of effective labor) - - Returns - ------- - analytic_traj : ndarray (shape=t.size, 2) - Array representing the analytic solution trajectory. - - """ - s = self.params['s'] - alpha = self.params['alpha'] - - # lambda governs the speed of convergence - lmbda = self.effective_depreciation_rate * (1 - alpha) - - # analytic solution for Solow model at time t - k_t = (((s / (self.effective_depreciation_rate)) * (1 - np.exp(-lmbda * t)) + - k0**(1 - alpha) * np.exp(-lmbda * t))**(1 / (1 - alpha))) - - # combine into a (T, 2) array - analytic_traj = np.hstack((t[:, np.newaxis], k_t[:, np.newaxis])) - - return analytic_traj diff --git a/quantecon/models/solow/impulse_response.py b/quantecon/models/solow/impulse_response.py deleted file mode 100644 index e9df439e1..000000000 --- a/quantecon/models/solow/impulse_response.py +++ /dev/null @@ -1,343 +0,0 @@ -""" -Classes for generating and plotting impulse response functions. - -@author : David R. Pugh -@date : 2014-10-06 - -""" -from __future__ import division -from textwrap import dedent - -import matplotlib.pyplot as plt -import numpy as np - - -class ImpulseResponse(object): - """Base class representing an impulse response function for a Model.""" - - # number of points to use for "padding" - N = 10 - - # length of impulse response - T = 100 - - def __init__(self, model): - """ - Create an instance of the ImpulseResponse class. - - Parameters - ---------- - model : model.Model - Instance of the model.Model class representing a Solow model. - - """ - self.model = model - - def __repr__(self): - """Machine readable summary of a ImpulseResponse instance.""" - return self.__str__() - - def __str__(self): - """Human readable summary of a ImpulseResponse instance.""" - m = """ - Impulse response function (IRF): - - N (number of points used for padding) : {N:d} - - T (length of the impulse response) : {T:d} - """ - formatted_str = dedent(m.format(N=self.N, T=self.T)) - return formatted_str - - @property - def _padding(self): - """ - Impulse response functions are "padded" for pretty plotting. - - :getter: Return the current "padding" values. - :type: numpy.ndarray - - """ - return np.hstack((self._padding_time, self._padding_variables)) - - @property - def _padding_scaling_factor(self): - """ - Scaling factor used in constructing the impulse response function - "padding". - - :getter: Return the current scaling factor. - :type: numpy.ndarray - - """ - # extract the relevant parameters - A0 = self.model.params['A0'] - L0 = self.model.params['L0'] - g = self.model.params['g'] - n = self.model.params['n'] - - if self.kind == 'per_capita': - factor = A0 * np.exp(g * self._padding_time) - elif self.kind == 'levels': - factor = A0 * L0 * np.exp((g + n) * self._padding_time) - else: - factor = np.ones(self.N) - - return factor.reshape((self.N, 1)) - - @property - def _padding_time(self): - """ - The independent variable, time, is "padded" using values from -N to -1. - - :getter: Return the current "padding" values. - :type: numpy.ndarray - - """ - return np.linspace(-self.N, -1, self.N).reshape((self.N, 1)) - - @property - def _padding_variables(self): - """ - Impulse response functions for endogenous variables are "padded" with - N periods of steady state values. - - :getter: Return current "padding" values. - :kind: numpy.ndarray - - """ - # economy is initial in steady state - k0 = self.model.steady_state - y0 = self.model.evaluate_intensive_output(k0) - c0 = self.model.evaluate_consumption(k0) - i0 = self.model.evaluate_actual_investment(k0) - intitial_condition = np.array([[k0, y0, c0, i0]]) - - return self._padding_scaling_factor * intitial_condition - - @property - def _response(self): - """ - Response functions combined independent and endogenous variables. - - :getter: Return the current response values. - :type: numpy.ndarray - - """ - return np.hstack((self._response_time, self._response_variables)) - - @property - def _response_time(self): - """ - The independent variable, time, for the response ranges from 0 to T. - - :getter: Return the current resonse time values. - :type: numpy.ndarray - - """ - return np.linspace(0, self.T, self.T + 1).reshape((self.T + 1, 1)) - - @property - def _response_variables(self): - """ - Response of endogenous variables to exogenous impulse. - - :getter: Return the current response. - :type: numpy.ndarray - - """ - # economy is initial in steady state - k0 = self.model.steady_state - - # apply the impulse...force validate params! - tmp_params = self.model.params.copy() - tmp_params.update(self.impulse) - self.model.params = tmp_params - - # ...and generate the response - soln = self.model.ivp.solve(t0=0.0, y0=k0, h=1.0, T=self.T, - integrator='dop853') - # gather the results - k = soln[:, 1][:, np.newaxis] - y = self.model.evaluate_intensive_output(k) - c = self.model.evaluate_consumption(k) - i = self.model.evaluate_actual_investment(k) - - return self._response_scaling_factor * np.hstack((k, y, c, i)) - - @property - def _response_scaling_factor(self): - """ - Scaling factor used in constructing the impulse response. - - :getter: Return the current scaling factor. - :type: numpy.ndarray - - """ - # extract the relevant parameters - A0 = self.model.params['A0'] - L0 = self.model.params['L0'] - g = self.model.params['g'] - n = self.model.params['n'] - - if self.kind == 'per_capita': - factor = A0 * np.exp(g * self._response_time) - elif self.kind == 'levels': - factor = A0 * L0 * np.exp((g + n) * self._response_time) - else: - factor = np.ones(self.T + 1) - - return factor.reshape((self.T + 1, 1)) - - @property - def impulse(self): - """ - Dictionary of new parameter values representing an impulse. - - :getter: Return the current impulse dictionary. - :setter: Set a new impulse dictionary. - :type: dictionary - - """ - return self._impulse - - @property - def kind(self): - """ - The kind of impulse response function to generate. Must be one of: - 'levels', 'per_capita', 'efficiency_units'. - - :getter: Return the current kind of impulse responses. - :setter: Set a new value for the kind of impulse responses. - :type: str - - """ - return self._kind - - @property - def impulse_response(self): - """ - Impulse response functions generated by a shock to model parameter(s). - - :getter: Return the current impulse response functions. - :type: numpy.ndarray - - """ - orig_params = self.model.params.copy() - - # create the irf - tmp_irf = np.vstack((self._padding, self._response)) - - # reset the model parameters - self.model.params = orig_params - - return tmp_irf - - @impulse.setter - def impulse(self, params): - """Set a new impulse dictionary.""" - self._impulse = self._validate_impulse(params) - - @kind.setter - def kind(self, value): - """Set a new value for the kind attribute.""" - self._kind = self._validate_kind(value) - - def _validate_impulse(self, params): - """Validates the impulse attribute.""" - if not isinstance(params, dict): - mesg = "ImpulseResponse.impulse must have type dict, not {}." - raise AttributeError(mesg.format(params.__class__)) - elif not set(params.keys()) <= set(self.model.params.keys()): - mesg = "Invalid parameter included in the impulse dictionary.""" - raise AttributeError(mesg) - else: - return params - - @staticmethod - def _validate_kind(value): - """Validates the kind attribute.""" - valid_kinds = ['levels', 'per_capita', 'efficiency_units'] - - if value not in valid_kinds: - mesg = "The 'kind' attribute must be in {}." - raise AttributeError(mesg.format(valid_kinds)) - else: - return value - - def plot_impulse_response(self, ax, variable, log=False): - """ - Plot an impulse response function. - - Parameters - ---------- - ax : `matplotlib.axes.AxesSubplot` - An instance of `matplotlib.axes.AxesSubplot`. - variable : str - Variable whose impulse response functions you wish to plot. - impulse : dict - Dictionary of new parameter values representing the impulse whose - model response you wish to plot. - kind : str (default='efficiency_units') - Whether you want impulse response functions in 'levels', - 'per_capita', or 'efficiency_units'. - log : boolean (default=False) - Whether or not to have logarithmic scales on the vertical axes. - Useful when plotting impulse response functions with - kind='per_capita' or kind='levels'. - - Returns - ------- - A list containing: - - irf_line : maplotlib.lines.Line2D - A Line2D object representing the impulse response for the requested - variable. - bgp_line : maplotlib.lines.Line2D - A Line2D object representing the pre-impulse balanced growth path - for the model. - - """ - # create a mapping from variables to column indices - irf = self.impulse_response - irf_dict = {'capital': irf[:, [0, 1]], - 'output': irf[:, [0, 2]], - 'consumption': irf[:, [0, 3]], - 'investment': irf[:, [0, 4]], - } - - # create the plot - traj = irf_dict[variable] - irf_line = ax.plot(traj[:, 0], traj[:, 1]) - - # add the old balanced growth path - g = self.model.params['g'] - n = self.model.params['n'] - t = self.N + traj[:, 0] - - if self.kind == 'per_capita': - bgp_line = ax.plot(traj[:, 0], traj[0, 1] * np.exp(g * t), 'k--', - label='Original BGP') - ax.set_ylabel(variable.title() + ' (per capita)', fontsize=15, - family='serif') - elif self.kind == 'levels': - bgp_line = ax.plot(traj[:, 0], traj[0, 1] * np.exp((g + n) * t), - 'k--', label='Original BGP') - ax.set_ylabel(variable.title(), fontsize=15, family='serif') - else: - bgp_line = ax.axhline(traj[0, 1], linestyle='dashed', color='k', - label='Original BGP') - ax.set_ylabel(variable.title() + ' (per unit effective labor)', - fontsize=15, family='serif') - - # format axes, labels, title, legend, etc - ax.set_xlabel('Time', fontsize=15, family='serif') - ax.set_ylim(0.95 * traj[:, 1].min(), 1.05 * traj[:, 1].max()) - - if log is True: - ax.set_yscale('log') - - ax.set_title('Impulse response function', fontsize=20, family='serif') - ax.grid('on') - ax.legend(loc=0, frameon=False, bbox_to_anchor=(1.0, 1.0), - prop={'family': 'serif'}) - - return [irf_line, bgp_line] diff --git a/quantecon/models/solow/model.py b/quantecon/models/solow/model.py deleted file mode 100644 index 3ca98868f..000000000 --- a/quantecon/models/solow/model.py +++ /dev/null @@ -1,1132 +0,0 @@ -r""" -====================== -The Solow Growth Model -====================== - -The following summary of the [solow1956] model of economic growth -largely follows [romer2011]. - -Assumptions -=========== - -The production function ----------------------------------------------- - -The [solow1956] model of economic growth focuses on the behavior of four -variables: output, `Y`, capital, `K`, labor, `L`, and knowledge (or -technology or the ``effectiveness of labor''), `A`. At each point in -time the economy has some amounts of capital, labor, and knowledge that -can be combined to produce output according to some production function, -`F`. - -.. math:: - - Y(t) = F(K(t), A(t)L(t)) - -where `t` denotes time. - -The evolution of the inputs to production ------------------------------------------ - -The initial levels of capital, :math:`K_0`, labor, :math:`L_0`, and -technology, :math:`A_0`, are taken as given. Labor and technology are -assumed to grow at constant rates: - -.. math:: - - \dot{A}(t) = gA(t) - \dot{L}(t) = nL(t) - -where the rate of technological progrss, `g`, and the population growth -rate, `n`, are exogenous parameters. - -Output is divided between consumption and investment. The fraction of -output devoted to investment, :math:`0 < s < 1`, is exogenous and -constant. One unit of output devoted to investment yields one unit of -new capital. Capital is assumed to decpreciate at a rate :math:`0\le -\delta`. Thus aggregate capital stock evolves according to - -.. math:: - - \dot{K}(t) = sY(t) - \delta K(t). - -Although no restrictions are placed on the rates of technological -progress and population growth, the sum of `g`, `n`, and :math:`delta` -is assumed to be positive. - -The dynamics of the model -========================= - -Because the economy is growing over time (due to exogenous technological -progress and population growth) it is useful to focus on the behavior of -capital stock per unit of effective labor, :math:`k\equiv K/AL`. -Applying the chain rule to the equation of motion for capital stock -yields (after a bit of algebra!) an equation of motion for capital stock -per unit of effective labor. - -.. math:: - - \dot{k}(t) = s f(k) - (g + n + \delta)k(t) - -References -========== -.. [romer2011] D. Romer. *Advanced Macroeconomics, 4th edition*, MacGraw Hill, 2011. -.. [solow1956] R. Solow. *A contribution to the theory of economic growth*, Quarterly Journal of Economics, 70(1):64-95, 1956. - -@author : David R. Pugh -@date : 2014-11-27 - -""" -from __future__ import division -import collections -from textwrap import dedent - -import matplotlib.pyplot as plt -import numpy as np -from scipy import optimize -import sympy as sym - -from ... import ivp -from . import impulse_response - -# declare key variables for the model -t, X = sym.symbols('t'), sym.DeferredVector('X') -A, k, K, L, Y = sym.symbols('A, k, K, L, Y') - -# declare required model parameters -g, n, s, delta = sym.symbols('g, n, s, delta') - - -class Model(object): - - __intensive_output = None - - __mpk = None - - __numeric_jacobian = None - - __numeric_solow_residual = None - - __numeric_system = None - - _modules = [{'ImmutableMatrix': np.array}, "numpy"] - - _required_params = ['g', 'n', 's', 'delta', 'A0', 'L0'] - - def __init__(self, output, params): - """ - Create an instance of the Solow growth model. - - Parameters - ---------- - output : sym.Basic - Symbolic expression defining the aggregate production - function. - params : dict - Dictionary of model parameters. - - """ - self.irf = impulse_response.ImpulseResponse(self) - self.output = output - self.params = params - - def __repr__(self): - """Machine readable summary of a Model instance.""" - return self.__str__() - - def __str__(self): - """Human readable summary of a Model instance.""" - m = """ - Solow (1956) model of economic growth: - - Output : {Y} - - A0 (initial level of technology) : {A0:g} - - L0 (initial amount of available labor) : {L0:g} - - g (growth rate of technology) : {g:g} - - n (growth rate of the labor force) : {n:g} - - s (savings rate) : {s:g} - - delta (depreciation rate of physical capital) : {delta:g} - """ - formatted_str = dedent(m.format(Y=self.output, - A0=self.params['A0'], - L0=self.params['L0'], - g=self.params['g'], - n=self.params['n'], - s=self.params['s'], - delta=self.params['delta'])) - return formatted_str - - @property - def _intensive_output(self): - """ - :getter: Return vectorized symbolic intensive aggregate production. - :type: function - - """ - if self.__intensive_output is None: - args = [k] + sym.symbols(list(self.params.keys())) - self.__intensive_output = sym.lambdify(args, self.intensive_output, - self._modules) - return self.__intensive_output - - @property - def _mpk(self): - """ - :getter: Return vectorized symbolic marginal product capital. - :type: function - - """ - if self.__mpk is None: - args = [k] + sym.symbols(list(self.params.keys())) - self.__mpk = sym.lambdify(args, self.marginal_product_capital, - self._modules) - return self.__mpk - - @property - def _numeric_jacobian(self): - """ - Vectorized, numpy-aware function defining the Jacobian matrix of - partial derivatives. - - :getter: Return vectorized Jacobian matrix of partial derivatives. - :type: function - - """ - if self.__numeric_jacobian is None: - self.__numeric_jacobian = sym.lambdify(self._symbolic_args, - self._symbolic_jacobian, - self._modules) - return self.__numeric_jacobian - - @property - def _numeric_solow_residual(self): - """ - Vectorized, numpy-aware function defining the Solow residual. - - :getter: Return vectorized symbolic Solow residual. - :type: function - - """ - if self.__numeric_solow_residual is None: - tmp_args = [Y, K, L] + sym.symbols(list(self.params.keys())) - self.__numeric_solow_residual = sym.lambdify(tmp_args, - self.solow_residual, - self._modules) - return self.__numeric_solow_residual - - @property - def _numeric_system(self): - """ - Vectorized, numpy-aware function defining the system of ODEs. - - :getter: Return vectorized symbolic system of ODEs. - :type: function - - """ - if self.__numeric_system is None: - self.__numeric_system = sym.lambdify(self._symbolic_args, - self._symbolic_system, - self._modules) - return self.__numeric_system - - @property - def _symbolic_args(self): - """ - List of symbolic arguments used in constructing vectorized - versions of _symbolic_system and _symbolic_jacobian. - - :getter: Return list of symbolic arguments. - :type: list - - """ - args = [t, X] + sym.symbols(list(self.params.keys())) - return args - - @property - def _symbolic_jacobian(self): - """ - Symbolic Jacobian matrix for the system of ODEs. - - :getter: Return the symbolic Jacobian matrix. - :type: sym.MutableDenseMatrix - - """ - N = self._symbolic_system.shape[0] - return self._symbolic_system.jacobian([X[i] for i in range(N)]) - - @property - def _symbolic_system(self): - """ - Symbolic matrix defining the system of ODEs. - - :getter: Return the matrix defining the system of ODEs. - :type: sym.MutableDenseMatrix - - """ - change_of_vars = {k: X[0]} - return sym.Matrix([self.k_dot]).subs(change_of_vars) - - @property - def effective_depreciation_rate(self): - """ - Effective depreciation rate for capital stock (per unit - effective labor). - - :getter: Return the current effective depreciation rate. - :type: float - - Notes - ----- - The effective depreciation rate of physical capital takes into - account both technological progress and population growth, as - well as physical depreciation. - - """ - return sum(self.params[key] for key in ['g', 'n', 'delta']) - - @property - def intensive_output(self): - r""" - Symbolic expression for the intensive form of aggregate - production. - - :getter: Return the current intensive production function. - :type: sym.Basic - - Notes - ----- - The assumption of constant returns to scale allows us to work - the the intensive form of the aggregate production function, - `F`. Defining :math:`c=1/AL` one can write - - ..math:: - - F\bigg(\frac{K}{AL}, 1\bigg) = \frac{1}{AL}F(A, K, L) - - Defining :math:`k=K/AL` and :math:`y=Y/AL` to be capital per - unit effective labor and output per unit effective labor, - respectively, the intensive form of the production function can - be written as - - .. math:: - - y = f(k). - - Additional assumptions are that `f` satisfies :math:`f(0)=0`, is - concave (i.e., :math:`f'(k) > 0, f''(k) < 0`), and satisfies the - Inada conditions: - - .. math:: - :type: eqnarray - - \lim_{k \rightarrow 0} &=& \infty \\ - \lim_{k \rightarrow \infty} &=& 0 - - The [inada1964]_ conditions are sufficient (but not necessary!) - to ensure that the time path of capital per effective worker - does not explode. - - .. [inada1964] K. Inda. *Some structural characteristics of Turnpike Theorems*, Review of Economic Studies, 31(1):43-58, 1964. - - """ - return self.output.subs({'A': 1.0, 'K': k, 'L': 1.0}) - - @property - def ivp(self): - r""" - Initial value problem - - :getter: Return an instance of the ivp.IVP class representing - the Solow model. - :type: ivp.IVP - - Notes - ----- - The Solow model with can be formulated as an initial value - problem (IVP) as follows. - - .. math:: - - \dot{k}(t) = sf(k(t)) - (g + n + \delta)k(t),\ t\ge t_0,\ k(t_0) = k_0 - - The solution to this IVP is a function :math:`k(t)` describing - the time path of capital stock (per unit effective labor). - - """ - tmp_ivp = ivp.IVP(self._numeric_system, self._numeric_jacobian) - tmp_ivp.f_params = tuple(self.params.values()) - tmp_ivp.jac_params = tuple(self.params.values()) - return tmp_ivp - - @property - def k_dot(self): - r""" - Symbolic expression for the equation of motion for capital (per - unit effective labor). - - :getter: Return the current equation of motion for capital (per - unit effective labor). - :type: sym.Basic - - Notes - ----- - Because the economy is growing over time due to technological - progress, `g`, and population growth, `n`, it makes sense to - focus on the capital stock per unit effective labor, `k`, rather - than aggregate physical capital, `K`. Since, by definition, - :math:`k=K/AL`, we can apply the chain rule to the time derative - of `k`. - - .. math:: - :type: eqnarray - - \dot{k}(t) =& \frac{\dot{K}(t)}{A(t)L(t)} - \frac{K(t)}{[A(t)L(t)]^2}\bigg[\dot{A}(t)L(t) + \dot{L}(t)A(t)\bigg] \\ - =& \frac{\dot{K}(t)}{A(t)L(t)} - \bigg(\frac{\dot{A}(t)}{A(t)} + \frac{\dot{L}(t)}{L(t)}\bigg)\frac{K(t)}{A(t)L(t)} - - By definition, math:`k=K/AL`, and by assumption - :math:`\dot{A}/A` and :math:`\dot{L}/L` are `g` and `n` - respectively. Aggregate capital stock evolves according to - - .. math:: - - \dot{K}(t) = sF(K(t), A(t)L(t)) - \delta K(t). - - Substituting these facts into the above equation yields the - equation of motion for capital stock (per unit effective labor). - - .. math:: - :type: eqnarray - - \dot{k}(t) =& \frac{sF(K(t), A(t)L(t)) - \delta K(t)}{A(t)L(t)} - (g + n)k(t) \\ - =& \frac{sY(t)}{A(t)L(t)} - (g + n + \delta)k(t) \\ - =& sf(k(t)) - (g + n + \delta)k(t) - - """ - return s * self.intensive_output - (g + n + delta) * k - - @property - def marginal_product_capital(self): - r""" - Symbolic expression for the marginal product of capital (per - unit effective labor). - - :getter: Return the current marginal product of capital (per - unit effective labor). - :type: sym.Basic - - Notes - ----- - The marginal product of capital is defined as follows: - - .. math:: - - \frac{\partial F(K, AL)}{\partial K} \equiv f'(k) - - where :math:`k=K/AL` is capital stock (per unit effective labor) - - """ - return sym.diff(self.intensive_output, k) - - @property - def output(self): - r""" - Symbolic expression for the aggregate production function. - - :getter: Return the current aggregate production function. - :setter: Set a new aggregate production function - :type: sym.Basic - - Notes - ----- - At each point in time the economy has some amounts of capital, - `K`, labor, `L`, and knowledge (or technology), `A`, that can be - combined to produce output, `Y`, according to some function, - `F`. - - .. math:: - - Y(t) = F(K(t), A(t)L(t)) - - where `t` denotes time. Note that `A` and `L` are assumed to - enter multiplicatively. Typically `A(t)L(t)` denotes "effective - labor", and technology that enters in this fashion is known as - labor-augmenting or "Harrod neutral." - - A key assumption of the model is that the function `F` exhibits - constant returns to scale in capital and labor inputs. - Specifically, - - .. math:: - - F(cK(t), cA(t)L(t)) = cF(K(t), A(t)L(t)) = cY(t) - - for any :math:`c \ge 0`. - - """ - return self._output - - @property - def params(self): - """ - Dictionary of model parameters. - - :getter: Return the current dictionary of model parameters. - :setter: Set a new dictionary of model parameters. - :type: dict - - Notes - ----- - The following parameters are required: - - A0: float - Initial level of technology. Must satisfy :math:`A_0 > 0 `. - L0: float - Initial amount of available labor. Must satisfy - :math:`L_0 > 0 `. - g : float - Growth rate of technology. - n : float - Growth rate of the labor force. - s : float - Savings rate. Must satisfy `0 < s < 1`. - delta : float - Depreciation rate of physical capital. Must satisfy - :math:`0 < \delta`. - - Although no restrictions are placed on the rates of - technological progress and population growth, the sum of `g`, - `n`, and :math:`delta` is assumed to be positive. The user mus - also specify any additional model parameters specific to the - chosen aggregate production function. - - """ - return self._params - - @property - def solow_residual(self): - """ - Symbolic expression for the Solow residual which is used as a - measure of technology. - - :getter: Return the symbolic expression. - :type: sym.Basic - - """ - return sym.solve(Y - self.output, A)[0] - - @property - def speed_of_convergence(self): - r""" - The speed of convergence for the Solow model. - - :getter: Return the current speed of convergence. - :type: float - - Notes - ----- - The following is a derivation for the speed of convergence - :math:`\lambda`: - - .. :math:: - :type: eqnarray - - \lambda \equiv -\frac{\partial \dot{k}(k(t))}{\partial k(t)}\bigg|_{k(t)=k^*} =& -[sf'(k^*) - (g + n+ \delta)] \\ - =& (g + n+ \delta) - sf'(k^*) \\ - =& (g + n + \delta) - (g + n + \delta)\frac{k^*f'(k^*)}{f(k^*)} \\ - =& (1 - \alpha_K(k^*))(g + n + \delta) - - where the elasticity of output with respect to capital, - $\alpha_K(k)$, is defined as - - .. :math:: - - \alpha_K(k) = \frac{k'(k)}{f(k)}. - - """ - alpha_K = self.evaluate_output_elasticity(self.steady_state) - return (1 - alpha_K) * self.effective_depreciation_rate - - @property - def steady_state(self): - r""" - Steady state value of capital stock (per unit effective labor). - - :getter: Return the current steady state value. - :type: float - - Notes - ----- - The steady state value of capital stock (per unit effective - labor), `k`, is defined as the value of `k` that solves - - .. math:: - - 0 = sf(k) - (g + n + \delta)k - - where `s` is the savings rate, `f(k)` is intensive output, and - :math:`g + n + \delta` is the effective depreciation rate. - - """ - lower, upper = 1e-12, 1e12 - return self.find_steady_state(lower, upper) - - @output.setter - def output(self, value): - """Set a new production function.""" - self._output = self._validate_output(value) - self._clear_cache() - - @params.setter - def params(self, value): - """Set a new parameter dictionary.""" - valid_params = self._validate_params(value) - self._params = self._order_params(valid_params) - - def _clear_cache(self): - """Clear cached values.""" - self.__intensive_output = None - self.__mpk = None - self.__numeric_jacobian = None - self.__numeric_solow_residual = None - self.__numeric_system = None - - @staticmethod - def _order_params(params): - """Cast a dictionary to an order dictionary.""" - return collections.OrderedDict(sorted(params.items())) - - def _validate_output(self, output): - """Validate the production function.""" - if not isinstance(output, sym.Basic): - mesg = ("Output must be an instance of {}.".format(sym.Basic)) - raise AttributeError(mesg) - elif not ({A, K, L} < output.atoms()): - mesg = ("Output must be an expression of technology, 'A', " + - "capital, 'K', and labor, 'L'.") - raise AttributeError(mesg) - else: - return output - - def _validate_params(self, params): - """Validate the model parameters.""" - if not isinstance(params, dict): - mesg = "SolowModel.params must be a dict, not a {}." - raise AttributeError(mesg.format(params.__class__)) - elif not set(self._required_params) <= set(params.keys()): - mesg = "One of the required params in {} has not been specified." - raise AttributeError(mesg.format(self._required_params)) - elif params['s'] <= 0.0 or params['s'] >= 1.0: - raise AttributeError('Savings rate must be in (0, 1).') - elif params['delta'] <= 0.0 or params['delta'] >= 1.0: - raise AttributeError('Depreciation rate must be in (0, 1).') - elif params['g'] + params['n'] + params['delta'] <= 0.0: - raise AttributeError("Sum of g, n, and delta must be positive.") - elif params['A0'] <= 0.0: - mesg = "Initial value for technology must be strictly positive." - raise AttributeError(mesg) - elif params['L0'] <= 0.0: - mesg = "Initial value for labor supply must be strictly positive." - raise AttributeError(mesg) - else: - return params - - def evaluate_actual_investment(self, k): - """ - Return the amount of output (per unit of effective labor) - invested in the production of new capital. - - Parameters - ---------- - k : array_like (float) - Capital stock (per unit of effective labor) - - Returns - ------- - actual_inv : array_like (float) - Investment (per unit of effective labor) - - """ - actual_inv = self.params['s'] * self.evaluate_intensive_output(k) - return actual_inv - - def evaluate_consumption(self, k): - """ - Return the amount of consumption (per unit of effective labor). - - Parameters - ---------- - k : ndarray (float) - Capital stock (per unit of effective labor) - - Returns - ------- - c : ndarray (float) - Consumption (per unit of effective labor) - - """ - c = (self.evaluate_intensive_output(k) - - self.evaluate_actual_investment(k)) - return c - - def evaluate_effective_depreciation(self, k): - """ - Return amount of Capital stock (per unit of effective labor) - that depreciaties due to technological progress, population - growth, and physical depreciation. - - Parameters - ---------- - k : array_like (float) - Capital stock (per unit of effective labor) - - Returns - ------- - effective_depreciation : array_like (float) - Amount of depreciated Capital stock (per unit of effective - labor) - - """ - effective_depreciation = self.effective_depreciation_rate * k - return effective_depreciation - - def evaluate_intensive_output(self, k): - """ - Return the amount of output (per unit of effective labor). - - Parameters - ---------- - k : ndarray (float) - Capital stock (per unit of effective labor) - - Returns - ------- - y : ndarray (float) - Output (per unit of effective labor) - - """ - y = self._intensive_output(k, *self.params.values()) - return y - - def evaluate_k_dot(self, k): - """ - Return time derivative of capital stock (per unit of effective - labor). - - Parameters - ---------- - k : ndarray (float) - Capital stock (per unit of effective labor) - - Returns - ------- - k_dot : ndarray (float) - Time derivative of capital stock (per unit of effective - labor). - - """ - k_dot = (self.evaluate_actual_investment(k) - - self.evaluate_effective_depreciation(k)) - return k_dot - - def evaluate_mpk(self, k): - """ - Return marginal product of capital stock (per unit of effective - labor). - - Parameters - ---------- - k : ndarray (float) - Capital stock (per unit of effective labor) - - Returns - ------- - mpk : ndarray (float) - Marginal product of capital stock (per unit of effective - labor). - - """ - mpk = self._mpk(k, *self.params.values()) - return mpk - - def evaluate_output_elasticity(self, k): - """ - Return elasticity of output with respect to capital stock (per - unit effective labor). - - Parameters - ---------- - k : array_like (float) - Capital stock (per unit of effective labor) - - Returns - ------- - alpha_k : array_like (float) - Elasticity of output with respect to capital stock (per unit - effective labor). - - Notes - ----- - Under the additional assumption that markets are perfectly - competitive, the elasticity of output with respect to capital - stock is equivalent to capital's share of income. Since, under - perfect competition, firms earn zero profits it must be true - capital's share and labor's share must sum to one. - - """ - alpha_k = (k*self.evaluate_mpk(k)) / self.evaluate_intensive_output(k) - return alpha_k - - def evaluate_solow_residual(self, Y, K, L): - """ - Return Solow residual. - - Parameters - ---------- - k : array_like (float) - Capital stock (per unit of effective labor) - - Returns - ------- - residual : array_like (float) - Solow residual - - """ - residual = self._numeric_solow_residual(Y, K, L, *self.params.values()) - assert residual.all() > 0, "Solow residual show always be positive!" - return residual - - def find_steady_state(self, a, b, method='brentq', **kwargs): - """ - Compute the equilibrium value of capital stock (per unit - effective labor). - - Parameters - ---------- - a : float - One end of the bracketing interval [a,b]. - b : float - The other end of the bracketing interval [a,b] - method : str (default=`brentq`) - Method to use when computing the steady state. Supported - methods are `bisect`, `brenth`, `brentq`, `ridder`. See - `scipy.optimize` for more details (including references). - kwargs : optional - Additional keyword arguments. Keyword arguments are method - specific see `scipy.optimize` for details. - - Returns - ------- - x0 : float - Zero of `f` between `a` and `b`. - r : RootResults (present if ``full_output = True``) - Object containing information about the convergence. In - particular, ``r.converged`` is True if the routine - converged. - - """ - if method == 'bisect': - result = optimize.bisect(self.evaluate_k_dot, a, b, **kwargs) - elif method == 'brenth': - result = optimize.brenth(self.evaluate_k_dot, a, b, **kwargs) - elif method == 'brentq': - result = optimize.brentq(self.evaluate_k_dot, a, b, **kwargs) - elif method == 'ridder': - result = optimize.ridder(self.evaluate_k_dot, a, b, **kwargs) - else: - mesg = ("Method must be one of : 'bisect', 'brenth', 'brentq', " + - "or 'ridder'.") - raise ValueError(mesg) - - return result - - def linearized_solution(self, t, k0): - """ - Compute the linearized solution for the Solow model. - - Parameters - ---------- - t : ndarray (shape=(T,)) - Array of points at which the solution is desired. - k0 : (float) - Initial condition for capital stock (per unit of effective - labor) - - Returns - ------- - linearized_traj : ndarray (shape=t.size, 2) - Array representing the linearized solution trajectory. - - """ - kt = (self.steady_state + np.exp(-self.speed_of_convergence * t) * - (k0 - self.steady_state)) - linearized_traj = np.hstack((t[:, np.newaxis], kt[:, np.newaxis])) - - return linearized_traj - - def plot_factor_shares(self, ax, Nk=1e3, **new_params): - """ - Plot income/output shares of capital and labor inputs to - production. - - Parameters - ---------- - ax : `matplotlib.axes.AxesSubplot` - An instance of `matplotlib.axes.AxesSubplot`. - Nk : float (default=1e3) - Number of capital stock (per unit of effective labor) grid - points. - new_params : dict (optional) - Optional dictionary of parameter values to change. - - Returns - ------- - A list containing... - - capitals_share_line : maplotlib.lines.Line2D - A Line2D object representing the time path for capital's - share of income. - labors_share_line : maplotlib.lines.Line2D - A Line2D object representing the time path for labor's - share of income. - - """ - # create tmp_params dict to force check for valid params - tmp_params = self.params.copy() - tmp_params.update(new_params) - self.params = tmp_params # forces check for valid params! - - # create the plot - k_grid = np.linspace(0, 2 * self.steady_state, Nk) - capitals_share = self.evaluate_output_elasticity(k_grid) - labors_share = 1 - capitals_share - - capitals_share_line, = ax.plot(k_grid, capitals_share, 'r-', - label=r'$\alpha_K(k(t))$') - labors_share_line, = ax.plot(k_grid, labors_share, 'b-', - label=r'$1 - \alpha_K(k(t))$') - ax.set_xlabel('Capital (per unit effective labor), $k(t)$', - family='serif', fontsize=15) - ax.set_title('Factor shares', family='serif', fontsize=20) - ax.grid(True) - ax.legend(loc=0, frameon=False, prop={'family': 'serif'}, - bbox_to_anchor=(1.0, 1.0)) - - return [capitals_share_line, labors_share_line] - - def plot_intensive_output(self, ax, Nk=1e3, **new_params): - """ - Plot intensive form of the aggregate production function. - - Parameters - ---------- - ax : `matplotlib.axes.AxesSubplot` - An instance of `matplotlib.axes.AxesSubplot`. - Nk : float (default=1e3) - Number of capital stock (per unit of effective labor) grid - points. - new_params : dict (optional) - Optional dictionary of parameter values to change. - - Returns - ------- - A list containing... - - intensive_output : maplotlib.lines.Line2D - A Line2D object representing intensive output as a function - of capital stock (per unit effective labor). - - """ - # create tmp_params dict to force check for valid params - tmp_params = self.params.copy() - tmp_params.update(new_params) - self.params = tmp_params # forces check for valid params! - - # create the plot - k_grid = np.linspace(0, 2 * self.steady_state, Nk) - y_grid = self.evaluate_intensive_output(k_grid) - intensive_output_line, = ax.plot(k_grid, y_grid, 'r-') - ax.set_xlabel('Capital (per unit effective labor), $k(t)$', - family='serif', fontsize=15) - ax.set_ylabel('$f(k(t))$', family='serif', fontsize=20, - rotation='horizontal') - ax.yaxis.set_label_coords(-0.1, 0.5) - ax.set_title('Output (per unit effective labor)', - family='serif', fontsize=20) - ax.grid(True) - - return [intensive_output_line] - - def plot_intensive_investment(self, ax, Nk=1e3, **new_params): - """ - Plot actual investment (per unit effective labor) and effective - depreciation. The steady state value of capital stock (per unit - effective labor) balance acual investment and effective - depreciation. - - Parameters - ---------- - ax : `matplotlib.axes.AxesSubplot` - An instance of `matplotlib.axes.AxesSubplot`. - Nk : float (default=1e3) - Number of capital stock (per unit of effective labor) grid - points. - new_params : dict (optional) - Optional dictionary of parameter values to change. - - Returns - ------- - A list containing... - - actual_investment_line : maplotlib.lines.Line2D - A Line2D object representing the level of actual investment - as a function of capital stock (per unit effective labor). - breakeven_investment_line : maplotlib.lines.Line2D - A Line2D object representing the "break-even" level of - investment as a function of capital stock (per unit - effective labor). - ss_line : maplotlib.lines.Line2D - A Line2D object representing the steady state level of - investment. - - """ - # create tmp_params dict to force check for valid params - tmp_params = self.params.copy() - tmp_params.update(new_params) - self.params = tmp_params # forces check for valid params! - - # create the plot - k_grid = np.linspace(0, 2 * self.steady_state, Nk) - actual_investment_grid = self.evaluate_actual_investment(k_grid) - breakeven_investment_grid = self.evaluate_effective_depreciation(k_grid) - ss_investment = self.evaluate_actual_investment(self.steady_state) - - actual_investment_line, = ax.plot(k_grid, actual_investment_grid, 'g-', - label='$sf(k(t))$') - breakeven_investment_line, = ax.plot(k_grid, breakeven_investment_grid, - 'b-', label='$(g + n + \delta)k(t)$') - ss_line, = ax.plot(self.steady_state, ss_investment, 'ko', - label='$k^*={0:.4f}$'.format(self.steady_state)) - ax.set_xlabel('Capital (per unit effective labor), $k(t)$', - family='serif', fontsize=15) - ax.set_ylabel('Investment (per unit effective labor)', family='serif', - fontsize=15) - ax.set_title('Output (per unit effective labor)', - family='serif', fontsize=20) - ax.grid(True) - ax.legend(loc=0, frameon=False, prop={'family': 'serif'}, - bbox_to_anchor=(1.0, 1.0)) - - return [actual_investment_line, breakeven_investment_line, ss_line] - - def plot_phase_diagram(self, ax, Nk=1e3, **new_params): - """ - Plot the model's phase diagram. - - Parameters - ---------- - ax : `matplotlib.axes.AxesSubplot` - An instance of `matplotlib.axes.AxesSubplot`. - Nk : float (default=1e3) - Number of capital stock (per unit of effective labor) grid - points. - new_params : dict (optional) - Optional dictionary of parameter values to change. - - Returns - ------- - A list containing... - - k_dot_line : maplotlib.lines.Line2D - A Line2D object representing the rate of change of capital - stock (per unit effective labor) as a function of its level. - origin_line : maplotlib.lines.Line2D - A Line2D object representing the origin (i.e., locus of - points where k_dot is zero). - ss_line : maplotlib.lines.Line2D - A Line2D object representing the steady state level of - capital stock (per unit effective labor). - - """ - # create tmp_params dict to force check for valid params - tmp_params = self.params.copy() - tmp_params.update(new_params) - self.params = tmp_params # forces check for valid params! - - # create the plot - k_grid = np.linspace(0, 2 * self.steady_state, Nk) - k_dot_line, = ax.plot(k_grid, self.evaluate_k_dot(k_grid), - color='orange') - origin_line = ax.axhline(0, color='k') - ss_line, = ax.plot(self.steady_state, 0.0, 'ko', - label='$k^*={0:.4f}$'.format(self.steady_state)) - ax.set_xlabel('Capital (per unit effective labor), $k(t)$', - family='serif', fontsize=15) - ax.set_ylabel('$\dot{k}(t)$', family='serif', fontsize=25, - rotation='horizontal') - ax.yaxis.set_label_coords(-0.1, 0.5) - ax.set_title('Phase diagram', family='serif', fontsize=20) - ax.grid(True) - - return [k_dot_line, origin_line, ss_line] - - def plot_solow_diagram(self, ax, Nk=1e3, **new_params): - """ - Plot the classic Solow diagram. - - Parameters - ---------- - ax : `matplotlib.axes.AxesSubplot` - An instance of `matplotlib.axes.AxesSubplot`. - Nk : float (default=1e3) - Number of capital stock (per unit of effective labor) grid - points. - new_params : dict (optional) - Optional dictionary of parameter values to change. - - Returns - ------- - A list containing... - - actual_investment_line : maplotlib.lines.Line2D - A Line2D object representing the level of actual investment - as a function of capital stock (per unit effective labor). - breakeven_investment_line : maplotlib.lines.Line2D - A Line2D object representing the "break-even" level of - investment as a function of capital stock (per unit - effective labor). - ss_line : maplotlib.lines.Line2D - A Line2D object representing the steady state level of - investment. - - """ - # create tmp_params dict to force check for valid params - tmp_params = self.params.copy() - tmp_params.update(new_params) - self.params = tmp_params # forces check for valid params! - - # create the plot - k_grid = np.linspace(0, 2 * self.steady_state, Nk) - intensive_output_grid = self.evaluate_intensive_output(k_grid) - actual_investment_grid = self.evaluate_actual_investment(k_grid) - breakeven_investment_grid = self.evaluate_effective_depreciation(k_grid) - ss_investment = self.evaluate_actual_investment(self.steady_state) - - intensive_output_line, = ax.plot(k_grid, intensive_output_grid, 'r-', - label='$f(k(t)$') - actual_investment_line, = ax.plot(k_grid, actual_investment_grid, 'g-', - label='$sf(k(t))$') - breakeven_investment_line, = ax.plot(k_grid, breakeven_investment_grid, - 'b-', label='$(g + n + \delta)k(t)$') - ss_line, = ax.plot(self.steady_state, ss_investment, 'ko', - label='$k^*={0:.4f}$'.format(self.steady_state)) - ax.set_xlabel('Capital (per unit effective labor), $k(t)$', - family='serif', fontsize=15) - ax.set_title('Solow diagram', - family='serif', fontsize=20) - ax.grid(True) - ax.legend(loc=0, frameon=False, prop={'family': 'serif'}, - bbox_to_anchor=(1, 1)) - - lines = [intensive_output_line, actual_investment_line, - breakeven_investment_line, ss_line] - - return lines diff --git a/quantecon/models/uncertainty_traps.py b/quantecon/models/uncertainty_traps.py deleted file mode 100644 index 878150234..000000000 --- a/quantecon/models/uncertainty_traps.py +++ /dev/null @@ -1,62 +0,0 @@ -from __future__ import division -import numpy as np - -class UncertaintyTrapEcon(object): - - def __init__(self, - a=1.5, # Risk aversion - gx=0.5, # Production shock precision - rho=0.99, # Correlation coefficient for theta - sig_theta=0.5, # Std dev of theta shock - num_firms=100, # Number of firms - sig_F=1.5, # Std dev of fixed costs - c=-420, # External opportunity cost - mu_init=0, # Initial value for mu - gamma_init=4, # Initial value for gamma - theta_init=0): # Initial value for theta - - # == Record values == # - self.a, self.gx, self.rho, self.sig_theta = a, gx, rho, sig_theta - self.num_firms, self.sig_F, self.c, = num_firms, sig_F, c - self.sd_x = np.sqrt(1/ gx) - - # == Initialize states == # - self.gamma, self.mu, self.theta = gamma_init, mu_init, theta_init - - def psi(self, F): - temp1 = -self.a * (self.mu - F) - temp2 = self.a**2 * (1/self.gamma + 1/self.gx) / 2 - return (1 / self.a) * (1 - np.exp(temp1 + temp2)) - self.c - - def update_beliefs(self, X, M): - """ - Update beliefs (mu, gamma) based on aggregates X and M. - """ - # Simplify names - gx, rho, sig_theta = self.gx, self.rho, self.sig_theta - # Update mu - temp1 = rho * (self.gamma * self.mu + M * gx * X) - temp2 = self.gamma + M * gx - self.mu = temp1 / temp2 - # Update gamma - self.gamma = 1 / (rho**2 / (self.gamma + M * gx) + sig_theta**2) - - def update_theta(self, w): - """ - Update the fundamental state theta given shock w. - """ - self.theta = self.rho * self.theta + self.sig_theta * w - - def gen_aggregates(self): - """ - Generate aggregates based on current beliefs (mu, gamma). This - is a simulation step that depends on the draws for F. - """ - F_vals = self.sig_F * np.random.randn(self.num_firms) - M = np.sum(self.psi(F_vals) > 0) # Counts number of active firms - if M > 0: - x_vals = self.theta + self.sd_x * np.random.randn(M) - X = x_vals.mean() - else: - X = 0 - return X, M From 59287ff9e32dcf7e53e4a3c063c4be284002b5ea Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 16:38:11 -0500 Subject: [PATCH 27/51] Relocating tests/test_models/ to QuantEcon.applications repo --- quantecon/tests/tests_models/__init__.py | 0 .../tests/tests_models/test_asset_pricing.py | 94 ----------- quantecon/tests/tests_models/test_career.py | 48 ------ quantecon/tests/tests_models/test_ifp.py | 157 ------------------ quantecon/tests/tests_models/test_jv.py | 113 ------------- .../tests/tests_models/test_lucastree.py | 108 ------------ quantecon/tests/tests_models/test_odu.py | 138 --------------- .../tests/tests_models/test_optgrowth.py | 114 ------------- .../tests_models/tests_solow/__init__.py | 0 .../tests_models/tests_solow/test_ces.py | 68 -------- .../tests_solow/test_cobb_douglas.py | 84 ---------- .../tests_solow/test_impulse_response.py | 150 ----------------- .../tests_models/tests_solow/test_model.py | 147 ---------------- 13 files changed, 1221 deletions(-) delete mode 100644 quantecon/tests/tests_models/__init__.py delete mode 100644 quantecon/tests/tests_models/test_asset_pricing.py delete mode 100644 quantecon/tests/tests_models/test_career.py delete mode 100644 quantecon/tests/tests_models/test_ifp.py delete mode 100644 quantecon/tests/tests_models/test_jv.py delete mode 100644 quantecon/tests/tests_models/test_lucastree.py delete mode 100644 quantecon/tests/tests_models/test_odu.py delete mode 100644 quantecon/tests/tests_models/test_optgrowth.py delete mode 100644 quantecon/tests/tests_models/tests_solow/__init__.py delete mode 100644 quantecon/tests/tests_models/tests_solow/test_ces.py delete mode 100644 quantecon/tests/tests_models/tests_solow/test_cobb_douglas.py delete mode 100644 quantecon/tests/tests_models/tests_solow/test_impulse_response.py delete mode 100644 quantecon/tests/tests_models/tests_solow/test_model.py diff --git a/quantecon/tests/tests_models/__init__.py b/quantecon/tests/tests_models/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/quantecon/tests/tests_models/test_asset_pricing.py b/quantecon/tests/tests_models/test_asset_pricing.py deleted file mode 100644 index c6d6c0e91..000000000 --- a/quantecon/tests/tests_models/test_asset_pricing.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -Filename: test_asset_pricing.py -Authors: Spencer Lyon -Date: 2014-07-30 - -Tests for quantecon.asset_pricing module - -TODO: come up with some simple examples we can check by hand for price - methods. - -""" -from __future__ import division -import unittest -import numpy as np -from numpy.testing import assert_allclose -from quantecon.models import AssetPrices - -# parameters for object -n = 5 -P = 0.0125 * np.ones((n, n)) -P += np.diag(0.95 - 0.0125 * np.ones(5)) -s = np.array([1.05, 1.025, 1.0, 0.975, 0.95]) # state values -gamma = 2.0 -bet = 0.94 -zeta = 1.0 -p_s = 150.0 - - -class TestAssetPrices(unittest.TestCase): - - @classmethod - def setUpClass(cls): - cls.ap = AssetPrices(bet, P, s, gamma) - - def test_P_shape(self): - "asset_pricing: is P square" - shp = self.ap.P.shape - assert shp[0] == shp[1] - - def test_n(self): - "asset_pricing: n computed correctly" - assert self.ap.n == self.ap.P.shape[0] - - def test_P_tilde(self): - "asset_pricing: test P_tilde by hand using nested loops" - # unpack variables and allocate memory for new P_tilde - n, s, P, gam = (self.ap.n, self.ap.s, self.ap.P, self.ap.gamma) - p_tilde_2 = np.empty_like(self.ap.P) - - # fill in new p_tilde by hand - for i in range(n): - for k in range(n): - p_tilde_2[i, k] = P[i, k] * s[k] ** (1.0 - gam) - - assert_allclose(self.ap.P_tilde, p_tilde_2) - - def test_P_check(self): - "asset_pricing: test P_check by hand using nested loops" - # unpack variables and allocate memory for new P_tilde - n, s, P, gam = (self.ap.n, self.ap.s, self.ap.P, self.ap.gamma) - p_check_2 = np.empty_like(self.ap.P) - - # fill in new p_check by hand - for i in range(n): - for k in range(n): - p_check_2[i, k] = P[i, k] * s[k] ** (-gam) - - assert_allclose(self.ap.P_check, p_check_2) - - def test_tree_price_size(self): - "asset_pricing: test lucas_tree price size" - assert self.ap.tree_price().size == self.ap.n - - def test_consol_price_size(self): - "asset_pricing: test consol_price price size" - assert self.ap.consol_price(zeta).size == self.ap.n - - def test_call_option_size(self): - "asset_pricing: test call_option price size" - assert self.ap.call_option(zeta, p_s)[0].size == self.ap.n - - def test_tree_price(self): - pass - - def test_consol_price(self): - pass - - def test_call_option_price(self): - pass - - def test_multiple_periods_call_option(self): - "asset_pricing: T option works to return multiple periods" - w_bars = self.ap.call_option(zeta, p_s, T=[5, 7])[1] - self.assertEqual(len(w_bars), 2) diff --git a/quantecon/tests/tests_models/test_career.py b/quantecon/tests/tests_models/test_career.py deleted file mode 100644 index 97c9aab1b..000000000 --- a/quantecon/tests/tests_models/test_career.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Tests for quantecon.carrer module - -@author : Spencer Lyon -@date : 2014-07-31 - -""" -from __future__ import division -import unittest -import numpy as np -from quantecon.models import CareerWorkerProblem - - -class TestCareerWorkerProblem(unittest.TestCase): - - @classmethod - def setUpClass(cls): - cls.cp = CareerWorkerProblem() - cls.v_init = np.random.rand(cls.cp.N, cls.cp.N) - cls.v_prime = cls.cp.bellman_operator(cls.v_init) - cls.greedy = cls.cp.get_greedy(cls.v_init) - - def test_bellman_shape(self): - "career: bellman shape" - assert self.v_init.shape == self.v_prime.shape - - def test_greedy_shape(self): - "career: greedy shape" - assert self.v_init.shape == self.greedy.shape - - def test_greedy_new_life(self): - "career: want new life with worst job/career?" - if (self.greedy == 3).any(): - # if we ever want a new life, it will be with worst possible - # theta and worst epsilon - assert self.greedy[0, 0] == 3 - - def test_greedy_new_job(self): - "career: want new job with best carrer/worst job?" - # we should want a new job with best career and worst job - assert self.greedy[-1, 0] == 2 - - def test_greedy_stay_put(self): - "career: want to stayw with best career/job?" - if (self.greedy == 1).any(): - # if we ever want to stay put, it will be with best possible - # theta and best epsilon - assert self.greedy[-1, -1] == 1 diff --git a/quantecon/tests/tests_models/test_ifp.py b/quantecon/tests/tests_models/test_ifp.py deleted file mode 100644 index a29bb71b4..000000000 --- a/quantecon/tests/tests_models/test_ifp.py +++ /dev/null @@ -1,157 +0,0 @@ -""" -tests for quantecon.ifp - -@author : Spencer Lyon -@date : 2014-08-01 12:09:17 - -""" -from __future__ import division -import unittest -import numpy as np -from quantecon.models import ConsumerProblem -from quantecon import compute_fixed_point -from quantecon.tests import get_h5_data_file, write_array, max_abs_diff - - -def _solve_via_vfi(cp, v_init, return_both=False): - "compute policy rule using value function iteration" - v = compute_fixed_point(cp.bellman_operator, v_init, verbose=False, - error_tol=1e-5, - max_iter=1000) - - # Run one more time to get the policy - p = cp.bellman_operator(v, return_policy=True) - - if return_both: - return v, p - else: - return p - - -def _solve_via_pfi(cp, c_init): - "compute policy rule using policy function iteration" - p = compute_fixed_point(cp.coleman_operator, c_init, verbose=False, - error_tol=1e-5, - max_iter=1000) - - return p - - -def _get_vfi_pfi_guesses(cp, force_new=False): - """ - load precomputed vfi/pfi solutions, or compute them if requested - or we can't find old ones - """ - # open the data file - with get_h5_data_file() as f: - - # See if the ifp group already exists - group_existed = True - try: - ifp_group = f.getNode("/ifp") - except: - # doesn't exist - group_existed = False - ifp_group = f.create_group("/", "ifp", "data for ifp.py tests") - - if force_new or not group_existed: - # group doesn't exist, or forced to create new data. - # This function updates f in place and returns v_vfi, c_vfi, c_pfi - v_vfi, c_vfi, c_pfi = _new_solutions(cp, f, ifp_group) - - # We have what we need, so return - return v_vfi, c_pfi - - # if we made it here, the group exists and we should try to read - # existing solutions - try: # read in vfi - # Try reading vfi - c_vfi = ifp_group.c_vfi[:] - v_vfi = ifp_group.v_vfi[:] - - except: - # doesn't exist. Let's create it - v_vfi, c_vfi = _new_solutions(cp, f, ifp_group, which="vfi") - - try: # read in pfi - # Try reading pfi - c_pfi = ifp_group.c_pfi[:] - - except: - # doesn't exist. Let's create it - c_pfi = _new_solutions(cp, f, ifp_group, which="pfi") - - return v_vfi, c_pfi - - -def _new_solutions(cp, f, grp, which="both"): - v_init, c_init = cp.initialize() - if which == "both": - - v_vfi, c_vfi = _solve_via_vfi(cp, v_init, return_both=True) - c_pfi = _solve_via_pfi(cp, c_init) - - # Store solutions in chunked arrays... - write_array(f, grp, c_vfi, "c_vfi") - write_array(f, grp, v_vfi, "v_vfi") - write_array(f, grp, c_pfi, "c_pfi") - - return v_vfi, c_vfi, c_pfi - - elif which == "vfi": - v_vfi, c_vfi = _solve_via_vfi(cp, v_init, return_both=True) - write_array(f, grp, c_vfi, "c_vfi") - write_array(f, grp, v_vfi, "v_vfi") - - return v_vfi, c_vfi - - elif which == "pfi": - c_pfi = _solve_via_pfi(cp, c_init) - write_array(f, grp, c_pfi, "c_pfi") - - return c_pfi - - -class TestConsumerProblem(unittest.TestCase): - - @classmethod - def setUpClass(cls): - cls.cp = ConsumerProblem() - - # get precomputed answer for each method - print("reading old solutions") - old_vfi_sol, old_pfi_sol = _get_vfi_pfi_guesses(cls.cp) - cls.v_vfi = old_vfi_sol - - # compute answers again, using something close to old answer as - # initial value so it goes really fast - print("computing new vfi") - cls.c_vfi = _solve_via_vfi(cls.cp, old_vfi_sol * 0.99999) - print("computing new pfi") - cls.c_pfi = _solve_via_pfi(cls.cp, old_pfi_sol * 0.99999) - - def test_bellman_coleman_solutions_agree(self): - "ifp: bellman and coleman solutions agree" - self.assertLessEqual(max_abs_diff(self.c_vfi, self.c_pfi), 0.2) - - def test_bellman_fp(self): - "ifp: solution to bellman is a fixed point" - new_v = self.cp.bellman_operator(self.v_vfi) - self.assertLessEqual(max_abs_diff(self.v_vfi, new_v), 1e-3) - - def test_coleman_fp(self): - "ifp: solution to coleman is a fixed point" - new_c = self.cp.coleman_operator(self.c_pfi) - self.assertLessEqual(max_abs_diff(self.c_pfi, new_c), 1e-3) - - def test_initialize(self): - "ifp: initialize function works" - i = self.cp.initialize() - - # returned two things? - self.assertEqual(len(i), 2) - - shapes = (len(self.cp.asset_grid), len(self.cp.z_vals)) - self.assertEqual(i[0].shape, shapes) - self.assertEqual(i[1].shape, shapes) - diff --git a/quantecon/tests/tests_models/test_jv.py b/quantecon/tests/tests_models/test_jv.py deleted file mode 100644 index 2772b3e0d..000000000 --- a/quantecon/tests/tests_models/test_jv.py +++ /dev/null @@ -1,113 +0,0 @@ -""" -tests for quantecon.jv - -@author : Spencer Lyon -@date : 2014-08-01 13:53:29 - -""" -from __future__ import division -import sys -import unittest -from nose.plugins.skip import SkipTest -from quantecon.models import JvWorker -from quantecon import compute_fixed_point -from quantecon.tests import get_h5_data_file, write_array, max_abs_diff - -# specify params -- use defaults -A = 1.4 -alpha = 0.6 -beta = 0.96 -grid_size = 50 - -if sys.version_info[0] == 2: - v_nm = "V" -else: # python 3 - raise SkipTest("Python 3 tests aren't ready.") - v_nm = "V_py3" - - -def _new_solution(jv, f, grp): - "gets new solution and updates data file" - V = _solve_via_vfi(jv) - write_array(f, grp, V, v_nm) - - return V - - -def _solve_via_vfi(jv): - "compute policy rules via value function iteration" - v_init = jv.x_grid * 0.6 - V = compute_fixed_point(jv.bellman_operator, v_init, - max_iter=3000, - error_tol=1e-5) - return V - - -def _get_vf_guess(jv, force_new=False): - with get_h5_data_file() as f: - - # See if the jv group already exists - group_existed = True - try: - jv_group = f.getNode("/jv") - except: - # doesn't exist - group_existed = False - jv_group = f.create_group("/", "jv", "data for jv.py tests") - - if force_new or not group_existed: - # group doesn't exist, or forced to create new data. - # This function updates f in place and returns v_vfi, c_vfi, c_pfi - V = _new_solution(jv, f, jv_group) - - return V - - # if we made it here, the group exists and we should try to read - # existing solutions - try: - # Try reading vfi - if sys.version_info[0] == 2: - V = jv_group.V[:] - else: # python 3 - V = jv_group.V_py3[:] - - except: - # doesn't exist. Let's create it - V = _new_solution(jv, f, jv_group) - - return V - - -class TestJvWorkder(unittest.TestCase): - - @classmethod - def setUpClass(cls): - jv = JvWorker(A=A, alpha=alpha, beta=beta, grid_size=grid_size) - cls.jv = jv - - # compute solution - v_init = _get_vf_guess(jv) - cls.V = compute_fixed_point(jv.bellman_operator, v_init) - cls.s_pol, cls.phi_pol = jv.bellman_operator(cls.V * 0.999, - return_policies=True) - - def test_low_x_prefer_s(self): - "jv: s preferred to phi with low x?" - # low x is an early index - self.assertGreaterEqual(self.s_pol[0], self.phi_pol[0]) - - def test_high_x_prefer_phi(self): - "jv: phi preferred to s with high x?" - # low x is an early index - self.assertGreaterEqual(self.phi_pol[-1], self.s_pol[-1]) - - def test_policy_sizes(self): - "jv: policies correct size" - n = self.jv.x_grid.size - self.assertEqual(self.s_pol.size, n) - self.assertEqual(self.phi_pol.size, n) - - def test_bellman_sol_fixed_point(self): - "jv: solution to bellman is fixed point" - new_V = self.jv.bellman_operator(self.V) - self.assertLessEqual(max_abs_diff(new_V, self.V), 1e-4) diff --git a/quantecon/tests/tests_models/test_lucastree.py b/quantecon/tests/tests_models/test_lucastree.py deleted file mode 100644 index f20148e2f..000000000 --- a/quantecon/tests/tests_models/test_lucastree.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -Tests for quantecon.models.lucastree - -@author : Spencer Lyon -@date : 2014-08-05 09:15:45 - -""" -from __future__ import division -from nose.tools import (assert_equal, assert_true, assert_less_equal) -import numpy as np -from quantecon.models import LucasTree -from quantecon.tests import (get_h5_data_file, get_h5_data_group, write_array, - max_abs_diff) - -# helper parameters -_tol = 1e-6 - - -# helper functions -def _new_solution(tree, f, grp): - "gets a new set of prices and updates the file" - prices = tree.compute_lt_price(error_tol=_tol, max_iter=5000) - write_array(f, grp, prices, "prices") - return prices - - -def _get_price_data(tree, force_new=False): - "get price data from file, or create if necessary" - with get_h5_data_file() as f: - existed, grp = get_h5_data_group("lucastree") - - if force_new or not existed: - if existed: - grp.prices._f_remove() - prices = _new_solution(tree, f, grp) - - return prices - - # if we made it here, the group exists and we should try to read - # existing solutions - try: - # Try reading vfi - prices = grp.prices[:] - - except: - # doesn't exist. Let's create it - prices = _new_solution(tree, f, grp) - - return prices - - -# model parameters -gamma = 2.0 -beta = 0.95 -alpha = 0.90 -sigma = 0.1 - -# model object -tree = LucasTree(gamma, beta, alpha, sigma) -grid = tree.grid -prices = _get_price_data(tree) - - -def test_h5_access(): - "lucastree: test access to data file" - assert_true(prices is not None) - - -def test_prices_shape(): - "lucastree: test access shape of computed prices" - assert_equal(prices.shape, grid.shape) - - -def test_integrate(): - "lucastree: integrate function" - # just have it be a 1. Then integrate should give cdf - g = lambda x: x*0.0 + 1.0 - - # estimate using integrate function - est = tree.integrate(g) - - # compute exact solution - exact = tree.phi.cdf(tree._int_max) - tree.phi.cdf(tree._int_min) - - assert_less_equal(est - exact, .1) - - -def test_lucas_op_fixed_point(): - "lucastree: are prices a fixed point of lucas_operator" - # transform from p to f - old_f = prices / (grid ** gamma) - - # compute new f - new_f = tree.lucas_operator(old_f) - - # transform from f to p - new_p = new_f * grid**gamma - - # test if close. Make it one order of magnitude less than tol used - # to compute prices - assert_less_equal(max_abs_diff(new_p, prices), _tol*10) - - -def test_lucas_prices_increasing(): - "lucastree: test prices are increasing in y" - # sort the array and test that it is the same - sorted = np.sort(np.copy(prices)) - np.testing.assert_array_equal(sorted, prices) diff --git a/quantecon/tests/tests_models/test_odu.py b/quantecon/tests/tests_models/test_odu.py deleted file mode 100644 index 983f3d6a7..000000000 --- a/quantecon/tests/tests_models/test_odu.py +++ /dev/null @@ -1,138 +0,0 @@ -""" -tests for quantecon.models.odu - -@author : Spencer Lyon -@date : 2014-08-05 10:20:53 - -""" -from __future__ import division -import numpy as np -from nose.tools import (assert_equal, assert_true, assert_less_equal) -from quantecon import compute_fixed_point -from quantecon.models import SearchProblem -from quantecon.tests import (get_h5_data_file, get_h5_data_group, write_array, - max_abs_diff) - -# helper parameters -_tol = 1e-6 - - -# helper functions -def _new_solution(sp, f, grp): - "gets a new set of solution objects and updates the data file" - - # compute value function and policy rule using vfi - v_init = np.zeros(len(sp.grid_points)) + sp.c / (1 - sp.beta) - v = compute_fixed_point(sp.bellman_operator, v_init, error_tol=_tol, - max_iter=5000) - phi_vfi = sp.get_greedy(v) - - # also run v through bellman so I can test if it is a fixed point - # bellman_operator takes a long time, so store result instead of compute - new_v = sp.bellman_operator(v) - - # compute policy rule using pfi - - phi_init = np.ones(len(sp.pi_grid)) - phi_pfi = compute_fixed_point(sp.res_wage_operator, phi_init, - error_tol=_tol, max_iter=5000) - - # write all arrays to file - write_array(f, grp, v, "v") - write_array(f, grp, phi_vfi, "phi_vfi") - write_array(f, grp, phi_pfi, "phi_pfi") - write_array(f, grp, new_v, "new_v") - - # return data - return v, phi_vfi, phi_pfi, new_v - - -def _get_data(sp, force_new=False): - "get solution data from file, or create if necessary" - with get_h5_data_file() as f: - existed, grp = get_h5_data_group("odu") - - if force_new or not existed: - if existed: - grp.v._f_remove() - grp.phi_vfi._f_remove() - grp.phi_pfi._f_remove() - grp.new_v._f_remove() - v, phi_vfi, phi_pfi, new_v = _new_solution(sp, f, grp) - - return v, phi_vfi, phi_pfi, new_v - - # if we made it here, the group exists and we should try to read - # existing solutions - try: - # Try reading data - v = grp.v[:] - phi_vfi = grp.phi_vfi[:] - phi_pfi = grp.phi_pfi[:] - new_v = grp.new_v[:] - - except: - # doesn't exist. Let's create it - v, phi_vfi, phi_pfi, new_v = _new_solution(sp, f, grp) - - return v, phi_vfi, phi_pfi, new_v - -# model parameters -beta = 0.95 -c = 0.6 -F_a = 1 -F_b = 1 -G_a = 3 -G_b = 1.2 -w_max = 2 -w_grid_size = 40 -pi_grid_size = 40 - -sp = SearchProblem(beta, c, F_a, F_b, G_a, G_b, w_max, w_grid_size, - pi_grid_size) - -v, phi_vfi, phi_pfi, new_v = _get_data(sp) - - -def test_h5_access(): - "odu: test access to data file" - assert_true(v is not None) - assert_true(phi_vfi is not None) - assert_true(phi_pfi is not None) - - -def test_vfi_v_phi_same_shape(): - "odu: vfi value and policy same shape" - assert_equal(v.shape, phi_vfi.shape) - - -def test_phi_vfi_increasing(): - "odu: phi from vfi is increasing" - phi_mat = phi_vfi.reshape(w_grid_size, pi_grid_size) - sorted = np.sort(np.copy(phi_mat)) - np.testing.assert_array_equal(sorted, phi_mat) - - -def test_phi_pfi_increasing(): - "odu: phi from pfi is increasing" - sorted = np.sort(np.copy(phi_pfi))[::-1] # ascending - np.testing.assert_array_equal(sorted, phi_pfi) - - -def test_v_vfi_increasing(): - "odu: v from vfi is increasing" - # order so it sorts along the correct dimension (ascending) - v_mat = v[::-1].reshape(w_grid_size, pi_grid_size) - sorted = np.sort(np.copy(v_mat)) - np.testing.assert_array_equal(sorted, v_mat) - - -def test_v_vfi_fixed_point(): - "odu: v from vfi is fixed point" - assert_less_equal(max_abs_diff(v, new_v), _tol*10) - - -def test_phi_pfi_fixed_point(): - "odu: phi from pfi is fixed point" - new_phi = sp.res_wage_operator(phi_pfi) - assert_less_equal(max_abs_diff(new_phi, phi_pfi), _tol*10) diff --git a/quantecon/tests/tests_models/test_optgrowth.py b/quantecon/tests/tests_models/test_optgrowth.py deleted file mode 100644 index e76b0c1ce..000000000 --- a/quantecon/tests/tests_models/test_optgrowth.py +++ /dev/null @@ -1,114 +0,0 @@ -""" -tests for quantecon.models.optgrowth - -@author : Spencer Lyon -@date : 2014-08-05 10:20:53 - -TODO: I'd really like to see why the solutions only match analytical - counter part up to 1e-2. Seems like we should be able to do better - than that. -""" -from __future__ import division -from math import log -import numpy as np -from nose.tools import (assert_equal, assert_true, assert_less_equal) -from quantecon import compute_fixed_point -from quantecon.models import GrowthModel -from quantecon.tests import (get_h5_data_file, get_h5_data_group, write_array, - max_abs_diff) - - -# helper parameters -_tol = 1e-6 - - -# helper functions -def _new_solution(gm, f, grp): - "gets a new set of solution objects and updates the data file" - - # compute value function and policy rule using vfi - v_init = 5 * gm.u(gm.grid) - 25 - v = compute_fixed_point(gm.bellman_operator, v_init, error_tol=_tol, - max_iter=5000) - # sigma = gm.get_greedy(v) - - # write all arrays to file - write_array(f, grp, v, "v") - - # return data - return v - - -def _get_data(gm, force_new=False): - "get solution data from file, or create if necessary" - with get_h5_data_file() as f: - existed, grp = get_h5_data_group("optgrowth") - - if force_new or not existed: - if existed: - grp.w._f_remove() - v = _new_solution(gm, f, grp) - - return v - - # if we made it here, the group exists and we should try to read - # existing solutions - try: - # Try reading data - v = grp.v[:] - - except: - # doesn't exist. Let's create it - v = _new_solution(gm, f, grp) - - return v - -# model parameters -alpha = 0.65 -f = lambda k: k ** alpha -beta = 0.95 -u = np.log -grid_max = 2 -grid_size = 150 - -gm = GrowthModel(f, beta, u, grid_max, grid_size) - -v = _get_data(gm) - -# compute analytical policy function -true_sigma = (1 - alpha * beta) * gm.grid**alpha - -# compute analytical value function -ab = alpha * beta -c1 = (log(1 - ab) + log(ab) * ab / (1 - ab)) / (1 - beta) -c2 = alpha / (1 - ab) -def v_star(k): - return c1 + c2 * np.log(k) - - -def test_h5_access(): - "optgrowth: test access to data file" - assert_true(v is not None) - - -def test_bellman_return_both(): - "optgrowth: bellman_operator compute_policy option works" - assert_equal(len(gm.bellman_operator(v, compute_policy=True)), 2) - - -def test_analytical_policy(): - "optgrowth: approx sigma matches analytical" - sigma = gm.compute_greedy(v) - assert_less_equal(max_abs_diff(sigma, true_sigma), 1e-2) - - -def test_analytical_vf(): - "optgrowth: approx v matches analytical" - true_v = v_star(gm.grid) - assert_less_equal(max_abs_diff(v[1:-1], true_v[1:-1]), 5e-2) - - -def test_vf_fixed_point(): - "optgrowth: solution is fixed point of bellman" - new_v = gm.bellman_operator(v) - assert_less_equal(max_abs_diff(v[1:-1], new_v[1:-1]), 5e-2) diff --git a/quantecon/tests/tests_models/tests_solow/__init__.py b/quantecon/tests/tests_models/tests_solow/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/quantecon/tests/tests_models/tests_solow/test_ces.py b/quantecon/tests/tests_models/tests_solow/test_ces.py deleted file mode 100644 index b39266ad5..000000000 --- a/quantecon/tests/tests_models/tests_solow/test_ces.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -Test suite for ces.py module. - -@author : David R. Pugh -@date : 2014-12-08 - -""" -import nose - -import numpy as np - -from .... models.solow import ces - -params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'sigma': 1.1, 'delta': 0.05} -model = ces.CESModel(params) - - -def test_steady_state(): - """Compare analytic steady state with numerical steady state.""" - eps = 1e-1 - for g in np.linspace(eps, 0.05, 4): - for n in np.linspace(eps, 0.05, 4): - for s in np.linspace(eps, 1-eps, 4): - for alpha in np.linspace(eps, 1-eps, 4): - for delta in np.linspace(eps, 1-eps, 4): - for sigma in np.linspace(eps, 2.0, 4): - - tmp_params = {'A0': 1.0, 'g': g, 'L0': 1.0, 'n': n, - 's': s, 'alpha': alpha, 'delta': delta, - 'sigma': sigma} - try: - model.params = tmp_params - - # use root finder to compute the steady state - actual_ss = model.steady_state - expected_ss = model.find_steady_state(1e-12, 1e9) - - # conduct the test (numerical precision limits!) - nose.tools.assert_almost_equals(actual_ss, - expected_ss, - places=6) - - # handles params with non finite steady state - except AttributeError: - continue - - -def test_validate_params(): - """Testing validation of params attribute.""" - invalid_params_0 = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 1.33, 'delta': 0.03, 'sigma': 1.2} - invalid_params_1 = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.03, 'sigma': 0.0} - invalid_params_2 = {'A0': 1.0, 'g': 0.01, 'L0': 1.0, 'n': 0.01, 's': 0.12, - 'alpha': 0.75, 'delta': 0.01, 'sigma': 2.0} - - # alpha must be in (0, 1) - with nose.tools.assert_raises(AttributeError): - ces.CESModel(invalid_params_0) - - # sigma must be strictly positive - with nose.tools.assert_raises(AttributeError): - ces.CESModel(invalid_params_1) - - # parameters inconsistent with finite steady state - with nose.tools.assert_raises(AttributeError): - ces.CESModel(invalid_params_2) diff --git a/quantecon/tests/tests_models/tests_solow/test_cobb_douglas.py b/quantecon/tests/tests_models/tests_solow/test_cobb_douglas.py deleted file mode 100644 index ddd15a092..000000000 --- a/quantecon/tests/tests_models/tests_solow/test_cobb_douglas.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -Test suite for solow.cobb_douglas.py module. - -@author : David R. Pugh - -""" -import nose - -import numpy as np - -from .... models.solow import cobb_douglas - -params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} -model = cobb_douglas.CobbDouglasModel(params) - - -def test_ivp_solve(): - """Testing computation of solution to the initial value problem.""" - eps = 1e-1 - for g in np.linspace(eps, 0.05, 4): - for n in np.linspace(eps, 0.05, 4): - for s in np.linspace(eps, 1-eps, 4): - for alpha in np.linspace(eps, 1-eps, 4): - for delta in np.linspace(eps, 1-eps, 4): - - tmp_params = {'A0': 1.0, 'g': g, 'L0': 1.0, 'n': n, - 's': s, 'alpha': alpha, 'delta': delta} - model.params = tmp_params - - # solve the initial value problem - t0, k0 = 0, 0.5 * model.steady_state - numeric_soln = model.ivp.solve(t0, k0, T=100) - - # compute the analytic solution - tmp_ti = numeric_soln[:, 0] - analytic_soln = model.analytic_solution(tmp_ti, k0) - - # conduct the test - np.testing.assert_allclose(numeric_soln, analytic_soln) - - -def test_root_finders(): - """Testing conditional logic in find_steady_state.""" - valid_methods = ['brenth', 'brentq', 'ridder', 'bisect'] - for method in valid_methods: - actual_ss = model.find_steady_state(1e-6, 1e6, method=method) - expected_ss = model.steady_state - nose.tools.assert_almost_equals(actual_ss, expected_ss) - - -def test_steady_state(): - """Compare analytic steady state with numerical steady state.""" - eps = 1e-1 - for g in np.linspace(eps, 0.05, 4): - for n in np.linspace(eps, 0.05, 4): - for s in np.linspace(eps, 1-eps, 4): - for alpha in np.linspace(eps, 1-eps, 4): - for delta in np.linspace(eps, 1-eps, 4): - - tmp_params = {'A0': 1.0, 'g': g, 'L0': 1.0, 'n': n, - 's': s, 'alpha': alpha, 'delta': delta} - model.params = tmp_params - - # use root finder to compute the steady state - actual_ss = model.steady_state - expected_ss = model.find_steady_state(1e-12, 1e12) - - # conduct the test - nose.tools.assert_almost_equals(actual_ss, expected_ss) - - -def test_valid_methods(): - """Testing invalid method passed to find_steady_state.""" - with nose.tools.assert_raises(ValueError): - model.find_steady_state(1e-12, 1e12, method='invalid_method') - - -def test_valid_parameters(): - """Testing invalid value for output elasticity.""" - with nose.tools.assert_raises(AttributeError): - invalid_params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, - 's': 0.15, 'alpha': 1.1, 'delta': 0.03} - cobb_douglas.CobbDouglasModel(invalid_params) diff --git a/quantecon/tests/tests_models/tests_solow/test_impulse_response.py b/quantecon/tests/tests_models/tests_solow/test_impulse_response.py deleted file mode 100644 index 0e6d46836..000000000 --- a/quantecon/tests/tests_models/tests_solow/test_impulse_response.py +++ /dev/null @@ -1,150 +0,0 @@ -""" -Test suite for the impulse_response.py module. - -@author : David R. Pugh - -""" -from __future__ import division -import nose - -import matplotlib.pyplot as plt -import numpy as np - -from .... models.solow import cobb_douglas - -params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} -model = cobb_douglas.CobbDouglasModel(params) - - -def test_valid_impulse(): - """Testing validation of impulse attribute.""" - # impulse attribute must be a dict - with nose.tools.assert_raises(AttributeError): - model.irf.impulse = (('alpha', 0.75), ('g', 0.04)) - - # impulse sttribute must have valid keys - with nose.tools.assert_raises(AttributeError): - model.irf.impulse = {'alpha': 0.56, 'bad_key': 0.55} - - -def test_impulse_response(): - """Testing computation of impulse response.""" - original_params = {'A0': 1.0, 'g': 0.01, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} - model = cobb_douglas.CobbDouglasModel(original_params) - - # generate the impulse response - impulse = {'s': 0.30} - model.irf.impulse = impulse - model.irf.kind = 'efficiency_units' - model.irf.T = 500 # need to get "close" to new BGP - actual_ss = model.irf.impulse_response[-1, 1] - - # compute steady state following the impulse - model.params.update(impulse) - expected_ss = model.steady_state - - nose.tools.assert_almost_equals(actual_ss, expected_ss) - - -def test_per_capita_impulse_response(): - """Testing computation of per capita impulse response.""" - original_params = {'A0': 1.0, 'g': 0.01, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} - model = cobb_douglas.CobbDouglasModel(original_params) - - # generate the per capita impulse response - impulse = {'alpha': 0.15} - model.irf.impulse = impulse - model.irf.kind = 'per_capita' - model.irf.T = 500 # need to get "close" to new BGP - actual_c = model.irf.impulse_response[-1, 3] - - # compute steady state following the impulse - model.params.update(impulse) - A0, g = model.params['A0'], model.params['g'] - scaling_factor = A0 * np.exp(g * model.irf.T) - c_ss = model.evaluate_consumption(model.steady_state) - expected_c = c_ss * scaling_factor - - nose.tools.assert_almost_equals(actual_c, expected_c) - - -def test_levels_impulse_response(): - """Testing computation of levels impulse response.""" - original_params = {'A0': 1.0, 'g': 0.01, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} - model = cobb_douglas.CobbDouglasModel(original_params) - - # generate the per capita impulse response - impulse = {'delta': 0.15} - model.irf.impulse = impulse - model.irf.kind = 'levels' - model.irf.T = 500 # need to get "close" to new BGP - actual_y = model.irf.impulse_response[-1, 2] - - # compute steady state following the impulse - model.params.update(impulse) - A0, g = model.params['A0'], model.params['g'] - L0, n = model.params['L0'], model.params['n'] - scaling_factor = A0 * L0 * np.exp((g + n) * model.irf.T) - y_ss = model.evaluate_intensive_output(model.steady_state) - expected_y = y_ss * scaling_factor - - nose.tools.assert_almost_equals(actual_y, expected_y) - - -def test_plot_efficiency_units_impulse_response(): - """Testing return type for plot_impulse_response.""" - original_params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} - model = cobb_douglas.CobbDouglasModel(original_params) - - # initialize the impulse - model.irf.impulse = {'delta': 0.25} - model.irf.kind = 'efficiency_units' - - fig, ax = plt.subplots(1, 1) - tmp_lines = model.irf.plot_impulse_response(ax, variable='output') - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_plot_levels_impulse_response(): - """Testing return type for plot_impulse_response.""" - original_params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} - model = cobb_douglas.CobbDouglasModel(original_params) - - # initialize the impulse - model.irf.impulse = {'alpha': 0.25} - model.irf.kind = 'levels' - - fig, ax = plt.subplots(1, 1) - tmp_lines = model.irf.plot_impulse_response(ax, variable='output', - log=False) - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_plot_per_capita_impulse_response(): - """Testing return type for plot_impulse_response.""" - original_params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} - model = cobb_douglas.CobbDouglasModel(original_params) - - # initialize the impulse - model.irf.impulse = {'g': 0.05} - model.irf.kind = 'per_capita' - - fig, ax = plt.subplots(1, 1) - tmp_lines = model.irf.plot_impulse_response(ax, variable='output', - log=True) - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_valid_kind(): - """Testing validation of the kind attribute.""" - - # kind sttribute must be a valid string - with nose.tools.assert_raises(AttributeError): - model.irf.kind = 'invalid_kind' diff --git a/quantecon/tests/tests_models/tests_solow/test_model.py b/quantecon/tests/tests_models/tests_solow/test_model.py deleted file mode 100644 index f9488fc48..000000000 --- a/quantecon/tests/tests_models/tests_solow/test_model.py +++ /dev/null @@ -1,147 +0,0 @@ -""" -Test suite for solow module. - -@author : David R. Pugh -@date : 2014-11-27 - -""" -from __future__ import division -import nose - -import matplotlib.pyplot as plt -import numpy as np -import sympy as sym - -from .... models import solow - -# declare key variables for the model -A, E, k, K, L = sym.symbols('A, E, k, K, L') - -# declare required model parameters -g, n, s, alpha, delta = sym.symbols('g, n, s, alpha, delta') - - -# two different ways in which output can fail -def invalid_output_1(A, K, L, alpha): - """Output must be of type sym.basic, not function.""" - return K**alpha * (A * L)**(1 - alpha) - -invalid_output_2 = K**alpha * (A * E)**(1 - alpha) - -valid_output = K**alpha * (A * L)**(1 - alpha) - -valid_params = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.05} - - -# testing functions -def test_plot_factor_shares(): - """Testing return type for plot_factor_shares.""" - tmp_mod = solow.Model(output=valid_output, params=valid_params) - fig, ax = plt.subplots(1, 1) - tmp_lines = tmp_mod.plot_factor_shares(ax) - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_plot_intensive_investment(): - """Testing return type for plot_intensive_investment.""" - tmp_mod = solow.Model(output=valid_output, params=valid_params) - fig, ax = plt.subplots(1, 1) - tmp_lines = tmp_mod.plot_intensive_investment(ax) - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_plot_intensive_output(): - """Testing return type for plot_intensive_output.""" - tmp_mod = solow.Model(output=valid_output, params=valid_params) - fig, ax = plt.subplots(1, 1) - tmp_lines = tmp_mod.plot_intensive_output(ax) - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_plot_phase_diagram(): - """Testing return type for plot_phase_diagram.""" - tmp_mod = solow.Model(output=valid_output, params=valid_params) - fig, ax = plt.subplots(1, 1) - tmp_lines = tmp_mod.plot_phase_diagram(ax) - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_plot_solow_diagram(): - """Testing return type for plot_solow_diagram.""" - tmp_mod = solow.Model(output=valid_output, params=valid_params) - fig, ax = plt.subplots(1, 1) - tmp_lines = tmp_mod.plot_solow_diagram(ax) - nose.tools.assert_is_instance(tmp_lines, list) - - -def test_validate_output(): - """Testing validation of output attribute.""" - # output must have type sym.Basic - with nose.tools.assert_raises(AttributeError): - solow.Model(output=invalid_output_1, params=valid_params) - - # output must be function of K, A, L - with nose.tools.assert_raises(AttributeError): - solow.Model(output=invalid_output_2, params=valid_params) - - -def test_validate_params(): - """Testing validation of params attribute.""" - # four different ways in which params can fail - invalid_params_0 = (1.0, 1.0, 0.02, 0.02, 0.15, 0.33, 0.03) - invalid_params_1 = {'A0': 1.0, 'g': -0.02, 'L0': 1.0, 'n': -0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.03} - invalid_params_2 = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': -0.03} - invalid_params_3 = {'A0': 1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': -0.15, - 'alpha': 0.33, 'delta': 0.03} - invalid_params_4 = {'A0': -1.0, 'g': 0.02, 'L0': 1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.03} - invalid_params_3 = {'A0': 1.0, 'g': 0.02, 'L0': -1.0, 'n': 0.02, 's': 0.15, - 'alpha': 0.33, 'delta': 0.03} - - # params must be a dict - with nose.tools.assert_raises(AttributeError): - solow.Model(output=valid_output, params=invalid_params_0) - - # effective depreciation rate must be positive - with nose.tools.assert_raises(AttributeError): - solow.Model(output=valid_output, params=invalid_params_1) - - # physical depreciation rate must be positive - with nose.tools.assert_raises(AttributeError): - solow.Model(output=valid_output, params=invalid_params_2) - - # savings rate must be positive - with nose.tools.assert_raises(AttributeError): - solow.Model(output=valid_output, params=invalid_params_3) - - # initial condition for A must be positive - with nose.tools.assert_raises(AttributeError): - solow.Model(output=valid_output, params=invalid_params_4) - - -def test_evaluate_output_elasticity(): - """Testing computation of elasticity of output with respect to capital.""" - eps = 1e-1 - for g in np.linspace(eps, 0.05, 4): - for n in np.linspace(eps, 0.05, 4): - for s in np.linspace(eps, 1-eps, 4): - for alpha in np.linspace(eps, 1-eps, 4): - for delta in np.linspace(eps, 1-eps, 4): - - tmp_params = {'A0': 1.0, 'g': g, 'L0': 1.0, 'n': n, - 's': s, 'alpha': alpha, 'delta': delta} - tmp_mod = solow.Model(output=valid_output, - params=tmp_params) - - # use root finder to compute the steady state - tmp_k_star = tmp_mod.steady_state - - actual_elasticity = tmp_mod.evaluate_output_elasticity(tmp_k_star) - expected_elasticity = tmp_params['alpha'] - - # conduct the test - nose.tools.assert_almost_equals(actual_elasticity, - expected_elasticity) From 91e36a855f45ef9572bd61eacdba962cb8d375ec Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 16:38:44 -0500 Subject: [PATCH 28/51] Removing models sub-package from setup as this has been migrated to QuantEcon.applications repo --- setup.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/setup.py b/setup.py index 28f0a361c..da594b062 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ #-Write Versions File-# #~~~~~~~~~~~~~~~~~~~~~# -VERSION = '0.2.2' +VERSION = '0.3.0' def write_version_py(filename=None): """ @@ -94,8 +94,6 @@ def write_version_py(filename=None): setup(name='quantecon', packages=['quantecon', 'quantecon.markov', - 'quantecon.models', - 'quantecon.models.solow', 'quantecon.random', 'quantecon.tests', 'quantecon.util', From c4275d1bc920042910c62fb3fa88fb0bb8cd2155 Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 16:48:41 -0500 Subject: [PATCH 29/51] Migrating solutions/ notebooks to QuantEcon.applications --- solutions/arellano_solutions.ipynb | 326 - solutions/asset_solutions.ipynb | 116 - solutions/career_solutions.ipynb | 723 --- solutions/discrete_dp_solutions.ipynb | 1071 ---- solutions/estspec_solutions.ipynb | 145 - solutions/finite_mc_solutions.ipynb | 305 - solutions/graph.txt | 100 - solutions/ifp_solutions.ipynb | 288 - solutions/jv_solutions.ipynb | 222 - solutions/kalman_solutions.ipynb | 1626 ----- solutions/lakemodel_solutions.ipynb | 423 -- solutions/lln_clt_solutions.ipynb | 263 - solutions/lqcontrol_solutions.ipynb | 434 -- solutions/lqramsey_solutions.ipynb | 115 - solutions/lss_solutions.ipynb | 5297 ---------------- solutions/lucas_asset_solutions.ipynb | 134 - solutions/mpe_solutions.ipynb | 448 -- solutions/numbers.txt | 6 - solutions/numpy_solutions.ipynb | 353 -- solutions/odu_solutions.ipynb | 667 -- solutions/oop_solutions.ipynb | 135 - solutions/optgrowth_solutions.ipynb | 226 - solutions/pandas_solutions.ipynb | 143 - solutions/pbe_solutions.ipynb | 316 - solutions/py_adv_feat_solutions.ipynb | 220 - solutions/pyess_solutions.ipynb | 462 -- solutions/ree_solutions.ipynb | 490 -- solutions/schelling_solutions.ipynb | 212 - solutions/scipy_solutions.ipynb | 112 - solutions/short_path_solutions.ipynb | 285 - solutions/speed_solutions.ipynb | 352 -- solutions/statd_solutions.ipynb | 263 - solutions/test_table.csv | 6243 ------------------- solutions/uncertainty_traps_solutions.ipynb | 312 - solutions/web_graph_data.txt | 37 - 35 files changed, 22870 deletions(-) delete mode 100644 solutions/arellano_solutions.ipynb delete mode 100644 solutions/asset_solutions.ipynb delete mode 100644 solutions/career_solutions.ipynb delete mode 100644 solutions/discrete_dp_solutions.ipynb delete mode 100644 solutions/estspec_solutions.ipynb delete mode 100644 solutions/finite_mc_solutions.ipynb delete mode 100644 solutions/graph.txt delete mode 100644 solutions/ifp_solutions.ipynb delete mode 100644 solutions/jv_solutions.ipynb delete mode 100644 solutions/kalman_solutions.ipynb delete mode 100644 solutions/lakemodel_solutions.ipynb delete mode 100644 solutions/lln_clt_solutions.ipynb delete mode 100644 solutions/lqcontrol_solutions.ipynb delete mode 100644 solutions/lqramsey_solutions.ipynb delete mode 100644 solutions/lss_solutions.ipynb delete mode 100644 solutions/lucas_asset_solutions.ipynb delete mode 100644 solutions/mpe_solutions.ipynb delete mode 100644 solutions/numbers.txt delete mode 100644 solutions/numpy_solutions.ipynb delete mode 100644 solutions/odu_solutions.ipynb delete mode 100644 solutions/oop_solutions.ipynb delete mode 100644 solutions/optgrowth_solutions.ipynb delete mode 100644 solutions/pandas_solutions.ipynb delete mode 100644 solutions/pbe_solutions.ipynb delete mode 100644 solutions/py_adv_feat_solutions.ipynb delete mode 100644 solutions/pyess_solutions.ipynb delete mode 100644 solutions/ree_solutions.ipynb delete mode 100644 solutions/schelling_solutions.ipynb delete mode 100644 solutions/scipy_solutions.ipynb delete mode 100644 solutions/short_path_solutions.ipynb delete mode 100644 solutions/speed_solutions.ipynb delete mode 100644 solutions/statd_solutions.ipynb delete mode 100644 solutions/test_table.csv delete mode 100644 solutions/uncertainty_traps_solutions.ipynb delete mode 100644 solutions/web_graph_data.txt diff --git a/solutions/arellano_solutions.ipynb b/solutions/arellano_solutions.ipynb deleted file mode 100644 index bb5eb505a..000000000 --- a/solutions/arellano_solutions.ipynb +++ /dev/null @@ -1,326 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# quant-econ Solutions: Default Risk and Income Fluctuations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/arellano.html" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from __future__ import division\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import quantecon as qe\n", - "from quantecon.models import Arellano_Economy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the value function, policy and equilibrium prices" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running iteration 25 with dist of 0.34324232989\n", - "Running iteration 50 with dist of 0.0983915577985\n", - "Running iteration 75 with dist of 0.0292120955917\n", - "Running iteration 100 with dist of 0.00874510696905\n", - "Running iteration 125 with dist of 0.00262314121558\n", - "Running iteration 150 with dist of 0.000787192669915\n", - "Running iteration 175 with dist of 0.000236259111634\n", - "Running iteration 200 with dist of 7.09100062899e-05\n", - "Running iteration 225 with dist of 2.1282821141e-05\n", - "Running iteration 250 with dist of 6.38780295859e-06\n", - "Running iteration 275 with dist of 1.91722896759e-06\n", - "Running iteration 300 with dist of 5.75435290529e-07\n", - "Running iteration 325 with dist of 1.72710617363e-07\n", - "Running iteration 350 with dist of 5.1837215409e-08\n", - "Running iteration 375 with dist of 1.555838125e-08\n" - ] - } - ], - "source": [ - "ae = Arellano_Economy(beta=.953, # time discount rate\n", - " gamma=2., # risk aversion\n", - " r=0.017, # international interest rate\n", - " rho=.945, # persistence in output \n", - " eta=0.025, # st dev of output shock\n", - " theta=0.282, # prob of regaining access \n", - " ny=21, # number of points in y grid\n", - " nB=251, # number of points in B grid\n", - " tol=1e-8, # error tolerance in iteration\n", - " maxit=10000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the bond price schedule as seen in figure 3 of Arellano (2008)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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BIyMjOumkk9Tb2xvbuAA0Fv1YyIqdgzslSdPapyU8EtSKKBxhzZo1WrhwoTaGfwqvXLlS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\nH37bEb7Y4kWRsnYW4kSPSU2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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Create \"Y High\" and \"Y Low\" values as 5% devs from mean\n", - "high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95\n", - "iy_high, iy_low = (np.searchsorted(ae.ygrid, x) for x in (high, low))\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 6.5))\n", - "ax.set_title(\"Bond price schedule $q(y, B')$\")\n", - "\n", - "# Extract a suitable plot grid\n", - "x = []\n", - "q_low = []\n", - "q_high = []\n", - "for i in range(ae.nB):\n", - " b = ae.Bgrid[i]\n", - " if -0.35 <= b <= 0: # To match fig 3 of Arellano\n", - " x.append(b)\n", - " q_low.append(ae.Q[iy_low, i])\n", - " q_high.append(ae.Q[iy_high, i])\n", - "ax.plot(x, q_high, label=r\"$y_H$\", lw=2, alpha=0.7)\n", - "ax.plot(x, q_low, label=r\"$y_L$\", lw=2, alpha=0.7)\n", - "ax.set_xlabel(r\"$B'$\")\n", - "ax.legend(loc='upper left', frameon=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Draw a plot of the value functions" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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iIpIazN2jrqGBmRUARe4eM7PnAdz9GTO7B3gRmAmMBlYBk9091uT5pcB33P1t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\nACalyAEATOoPX53Pm74ffOwAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Create \"Y High\" and \"Y Low\" values as 5% devs from mean\n", - "high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95\n", - "iy_high, iy_low = (np.searchsorted(ae.ygrid, x) for x in (high, low))\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 6.5))\n", - "ax.set_title(\"Value Functions\")\n", - "ax.plot(ae.Bgrid, ae.V[iy_high], label=r\"$y_H$\", lw=2, alpha=0.7)\n", - "ax.plot(ae.Bgrid, ae.V[iy_low], label=r\"$y_L$\", lw=2, alpha=0.7)\n", - "ax.legend(loc='upper left')\n", - "ax.set_xlabel(r\"$B$\")\n", - "ax.set_ylabel(r\"$V(y, B)$\")\n", - "ax.set_xlim(ae.Bgrid.min(), ae.Bgrid.max())\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Draw a heat map for default probability" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "xx, yy = ae.Bgrid, ae.ygrid\n", - "zz = ae.default_prob\n", - "\n", - "# Create figure\n", - "fig, ax = plt.subplots(figsize=(10, 6.5))\n", - "fig.suptitle(\"Probability of Default\")\n", - "hm = ax.pcolormesh(xx, yy, zz)\n", - "cax = fig.add_axes([.92, .1, .02, .8])\n", - "fig.colorbar(hm, cax=cax)\n", - "ax.axis([xx.min(), 0.05, yy.min(), yy.max()])\n", - "ax.set_xlabel(r\"$B'$\")\n", - "ax.set_ylabel(r\"$y$\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot a time series of major variables simulated from the model." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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f+hRw003AU0/lv60wxpg1N2tm7xMnMi9r+6+Q3GX5kk6YPf209o3NSJ9448yG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AF8+SVQXQ1kZyfu2loMjh3L1rKvvgKef563LV8O/PGPbNUT/zLBa4jFTLAV8VNyL5J37iaS/GvV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\nh2w2W9PracSxxd5vR0dHRZ9nn2v0/VRzvGEDAOjxh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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "T = 250\n", - "y_vec, B_vec, q_vec, default_vec = ae.simulate(T)\n", - "\n", - "# Pick up default start and end dates\n", - "start_end_pairs = []\n", - "i = 0\n", - "while i < len(default_vec):\n", - " if default_vec[i] == 0:\n", - " i += 1\n", - " else:\n", - " # If we get to here we're in default\n", - " start_default = i\n", - " while i < len(default_vec) and default_vec[i] == 1:\n", - " i += 1\n", - " end_default = i - 1\n", - " start_end_pairs.append((start_default, end_default))\n", - " \n", - "plot_series = y_vec, B_vec, q_vec\n", - "titles = 'output', 'foreign assets', 'bond price'\n", - "\n", - "fig, axes = plt.subplots(len(plot_series), 1, figsize=(10, 12))\n", - "p_args = {'lw': 2, 'alpha': 0.7}\n", - "fig.subplots_adjust(hspace=0.3)\n", - "\n", - "for ax, series, title in zip(axes, plot_series, titles):\n", - " # determine suitable y limits\n", - " s_max, s_min = max(series), min(series)\n", - " s_range = s_max - s_min\n", - " y_max = s_max + s_range * 0.1\n", - " y_min = s_min - s_range * 0.1\n", - " ax.set_ylim(y_min, y_max)\n", - " for pair in start_end_pairs:\n", - " ax.fill_between(pair, (y_min, y_min), (y_max, y_max), color='k', alpha=0.3)\n", - " \n", - " ax.grid()\n", - " ax.set_title(title)\n", - " ax.plot(range(T), series, **p_args)\n", - " ax.set_xlabel(r\"time\")\n", - "\n", - "plt.show()" - ] - }, - { - "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": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/solutions/asset_solutions.ipynb b/solutions/asset_solutions.ipynb deleted file mode 100644 index 53da01cb2..000000000 --- a/solutions/asset_solutions.ipynb +++ /dev/null @@ -1,116 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:f95d50b3abd5dc694309bb15b01ad17d5b2269d03da61295961b5e7daef40de5" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: The Lucas Asset Pricing Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/markov_asset.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import division # Omit for Python 3.x\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon.models import AssetPrices" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# == Define primitives == #\n", - "n = 5\n", - "P = 0.0125 * np.ones((n, n))\n", - "P += np.diag(0.95 - 0.0125 * np.ones(5))\n", - "s = np.array([1.05, 1.025, 1.0, 0.975, 0.95])\n", - "gamma = 2.0\n", - "beta = 0.94\n", - "zeta = 1.0\n", - "\n", - "ap = AssetPrices(beta, P, s, gamma)\n", - "\n", - "v = ap.tree_price()\n", - "print(\"Lucas Tree Prices: \", v)\n", - "\n", - "v_consol = ap.consol_price(zeta)\n", - "print(\"Consol Bond Prices: \", v_consol)\n", - "\n", - "P_tilde = P * s**(1-gamma)\n", - "temp = beta * P_tilde.dot(v) + beta * P_tilde.dot(np.ones(n))\n", - "print(\"Should be 0: \", v - temp)\n", - "\n", - "p_s = 150.0\n", - "w_bar, w_bars = ap.call_option(zeta, p_s, T = [10,20,30])\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "('Lucas Tree Prices: ', array([ 12.72221763, 14.72515002, 17.57142236, 21.93570661, 29.47401578]))\n", - "('Consol Bond Prices: ', array([ 87.56860139, 109.25108965, 148.67554548, 242.55144082,\n", - " 753.87100476]))\n", - "('Should be 0: ', array([ -1.77635684e-15, -1.77635684e-15, 0.00000000e+00,\n", - " 0.00000000e+00, 0.00000000e+00]))\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/career_solutions.ipynb b/solutions/career_solutions.ipynb deleted file mode 100644 index f613c9228..000000000 --- a/solutions/career_solutions.ipynb +++ /dev/null @@ -1,723 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:b9dea1cbbf559fd5acb0661f63a244effc42bfd147016309292bad583956912a" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Modeling Career Choice" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/career.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import DiscreteRV, compute_fixed_point\n", - "from quantecon.models import CareerWorkerProblem" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Simulate job / career paths. \n", - "\n", - "In reading the code, recall that `optimal_policy[i, j]` = policy at\n", - "$(\\theta_i, \\epsilon_j)$ = either 1, 2 or 3; meaning 'stay put', 'new job' and\n", - "'new life'.\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "wp = CareerWorkerProblem()\n", - "v_init = np.ones((wp.N, wp.N))*100\n", - "v = compute_fixed_point(wp.bellman_operator, v_init, verbose=False)\n", - "optimal_policy = wp.get_greedy(v)\n", - "F = DiscreteRV(wp.F_probs)\n", - "G = DiscreteRV(wp.G_probs)\n", - "\n", - "def gen_path(T=20):\n", - " i = j = 0 \n", - " theta_index = []\n", - " epsilon_index = []\n", - " for t in range(T):\n", - " if optimal_policy[i, j] == 1: # Stay put\n", - " pass\n", - " elif optimal_policy[i, j] == 2: # New job\n", - " j = int(G.draw())\n", - " else: # New life\n", - " i, j = int(F.draw()), int(G.draw())\n", - " theta_index.append(i)\n", - " epsilon_index.append(j)\n", - " return wp.theta[theta_index], wp.epsilon[epsilon_index]\n", - "\n", - "theta_path, epsilon_path = gen_path()\n", - "\n", - "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n", - "for ax in axes:\n", - " ax.plot(epsilon_path, label='epsilon')\n", - " ax.plot(theta_path, label='theta')\n", - " ax.legend(loc='lower right')\n", - "\n", - "plt.show()\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ZZ9yMqHKffmqSNy8SJTC9Uvv3w7Zt3lw/3s2bB1dfrURJRKQ6ieRPfg9gONALWFPy6O9k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\nJEmSJEkS8C9yUPOCOjPh9AAAAABJRU5ErkJggg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The median for the original parameterization can be computed as follows" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "wp = CareerWorkerProblem()\n", - "v_init = np.ones((wp.N, wp.N))*100\n", - "v = compute_fixed_point(wp.bellman_operator, v_init)\n", - "optimal_policy = wp.get_greedy(v)\n", - "F = DiscreteRV(wp.F_probs)\n", - "G = DiscreteRV(wp.G_probs)\n", - "\n", - "def gen_first_passage_time():\n", - " t = 0\n", - " i = j = 0\n", - " while 1:\n", - " if optimal_policy[i, j] == 1: # Stay put\n", - " return t\n", - " elif optimal_policy[i, j] == 2: # New job\n", - " j = int(G.draw())\n", - " else: # New life\n", - " i, j = int(F.draw()), int(G.draw())\n", - " t += 1\n", - "\n", - "M = 25000 # Number of samples\n", - "samples = np.empty(M)\n", - "for i in range(M): \n", - " samples[i] = gen_first_passage_time()\n", - "print(np.median(samples))\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 5.000000\n", - "Computed iterate 2 with error 4.750000\n", - "Computed iterate 3 with error 4.512500" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 4.286875\n", - "Computed iterate 5 with error 4.072531" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 3.868905\n", - "Computed iterate 7 with error 3.675459" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 3.491686\n", - "Computed iterate 9 with error 3.317102" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 3.151247\n", - "Computed iterate 11 with error 2.993685" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 2.844000\n", - "Computed iterate 13 with error 2.701800" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 2.566710\n", - "Computed iterate 15 with error 2.438375" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 2.316456\n", - "Computed iterate 17 with error 2.200633" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 2.090602\n", - "Computed iterate 19 with error 1.986072" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 1.886768\n", - "Computed iterate 21 with error 1.792430" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 22 with error 1.702808\n", - "Computed iterate 23 with error 1.617668" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 24 with error 1.536784\n", - "Computed iterate 25 with error 1.459945" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 26 with error 1.386948\n", - "Computed iterate 27 with error 1.317600" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 28 with error 1.251720\n", - "Computed iterate 29 with error 1.189134" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 30 with error 1.129678\n", - "Computed iterate 31 with error 1.073194" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 32 with error 1.019534\n", - "Computed iterate 33 with error 0.968557" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 34 with error 0.920130\n", - "Computed iterate 35 with error 0.874123" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 36 with error 0.830417\n", - "Computed iterate 37 with error 0.788896" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 38 with error 0.749451\n", - "Computed iterate 39 with error 0.711979" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 40 with error 0.676380\n", - "Computed iterate 41 with error 0.642561" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 42 with error 0.610433\n", - "Computed iterate 43 with error 0.579911" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 44 with error 0.550916\n", - "Computed iterate 45 with error 0.523370" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 46 with error 0.497201\n", - "Computed iterate 47 with error 0.472341" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 48 with error 0.448724\n", - "Computed iterate 49 with error 0.426288" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 50 with error 0.404974\n", - "7.0" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To compute the median with $\\beta=0.99$ instead of the default value $\\beta=0.95$,\n", - "replace `wp = CareerWorkerProblem()` with `wp = CareerWorkerProblem(beta=0.99)`\n", - "\n", - "The medians are subject to randomness, but should be about 7 and 11\n", - "respectively. Not surprisingly, more patient workers will wait longer to settle down to their final job\n", - "\n" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here\u2019s the code to reproduce the original figure" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from matplotlib import cm\n", - "\n", - "wp = CareerWorkerProblem()\n", - "v_init = np.ones((wp.N, wp.N))*100\n", - "v = compute_fixed_point(wp.bellman_operator, v_init)\n", - "optimal_policy = wp.get_greedy(v)\n", - "\n", - "fig, ax = plt.subplots(figsize=(6,6))\n", - "tg, eg = np.meshgrid(wp.theta, wp.epsilon)\n", - "lvls=(0.5, 1.5, 2.5, 3.5)\n", - "ax.contourf(tg, eg, optimal_policy.T, levels=lvls, cmap=cm.winter, alpha=0.5)\n", - "ax.contour(tg, eg, optimal_policy.T, colors='k', levels=lvls, linewidths=2)\n", - "ax.set_xlabel('theta', fontsize=14)\n", - "ax.set_ylabel('epsilon', fontsize=14)\n", - "ax.text(1.8, 2.5, 'new life', fontsize=14)\n", - "ax.text(4.5, 2.5, 'new job', fontsize=14, rotation='vertical')\n", - "ax.text(4.0, 4.5, 'stay put', fontsize=14)\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 5.000000\n", - "Computed iterate 2 with error 4.750000\n", - "Computed iterate 3 with error 4.512500" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 4.286875\n", - "Computed iterate 5 with error 4.072531" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 3.868905\n", - "Computed iterate 7 with error 3.675459" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 3.491686\n", - "Computed iterate 9 with error 3.317102" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 3.151247\n", - "Computed iterate 11 with error 2.993685" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 2.844000\n", - "Computed iterate 13 with error 2.701800" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 2.566710\n", - "Computed iterate 15 with error 2.438375" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 16 with error 2.316456\n", - "Computed iterate 17 with error 2.200633" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 18 with error 2.090602\n", - "Computed iterate 19 with error 1.986072" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 20 with error 1.886768\n", - "Computed iterate 21 with error 1.792430" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 22 with error 1.702808\n", - "Computed iterate 23 with error 1.617668" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 24 with error 1.536784\n", - "Computed iterate 25 with error 1.459945" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 26 with error 1.386948\n", - "Computed iterate 27 with error 1.317600" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 28 with error 1.251720\n", - "Computed iterate 29 with error 1.189134" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 30 with error 1.129678\n", - "Computed iterate 31 with error 1.073194" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 32 with error 1.019534\n", - "Computed iterate 33 with error 0.968557" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 34 with error 0.920130\n", - "Computed iterate 35 with error 0.874123" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 36 with error 0.830417\n", - "Computed iterate 37 with error 0.788896" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 38 with error 0.749451\n", - "Computed iterate 39 with error 0.711979" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 40 with error 0.676380\n", - "Computed iterate 41 with error 0.642561" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 42 with error 0.610433\n", - "Computed iterate 43 with error 0.579911" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 44 with error 0.550916\n", - "Computed iterate 45 with error 0.523370" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 46 with error 0.497201\n", - "Computed iterate 47 with error 0.472341" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 48 with error 0.448724\n", - "Computed iterate 49 with error 0.426288" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 50 with error 0.404974\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we want to set `G_a = G_b = 100` and generate a new figure with these parameters. \n", - "\n", - "To do this replace:\n", - "\n", - " wp = CareerWorkerProblem()\n", - "\n", - "with:\n", - "\n", - " wp = CareerWorkerProblem(G_a=100, G_b=100)\n", - "\n", - "In the new figure, you will see that the region for which the worker will stay put has grown because the distribution for $\\epsilon$ has become more concentrated around the mean, making high-paying jobs less realistic\n" - ] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/discrete_dp_solutions.ipynb b/solutions/discrete_dp_solutions.ipynb deleted file mode 100644 index ca15c0d3f..000000000 --- a/solutions/discrete_dp_solutions.ipynb +++ /dev/null @@ -1,1071 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## quant-econ Solutions: Discrete Dynamic Programming" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/discrete_dp.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Prepared by **Daisuke Oyama**, Faculty of Economics, University of Tokyo" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The exercise is to replicate numerically the analytical solution for the benchmark model in [this lecture](http://quant-econ.net/py/dp_intro.html) of quant-econ, using the `DiscreteDP` class. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from __future__ import division, print_function\n", - "import numpy as np\n", - "import scipy.sparse as sparse\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import compute_fixed_point\n", - "from quantecon.markov import DiscreteDP" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To recall, we consider the following problem:\n", - "$$\n", - "\\begin{aligned}\n", - "&\\max_{\\{c_t\\}_{t=0}^{\\infty}} \\sum_{t=0}^{\\infty} \\beta^t u(c_t) \\\\\n", - "&\\ \\text{ s.t. }\\ k_{t+1} = f(k_t) - c_t,\n", - " \\quad \\text{$k_0$: given},\n", - "\\end{aligned}\n", - "$$\n", - "where\n", - "$k_t$ and $c_t$ are the capital stock and consumption at time $t$, respectively,\n", - "$u$ is the utility function,\n", - "$f$ is the production function, and\n", - "$\\beta \\in (0, 1)$ is the discount factor." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As in the lecture,\n", - "we let $f(k) = k^{\\alpha}$ with $\\alpha = 0.65$, $u(c) = \\log c$, and $\\beta = 0.95$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "alpha = 0.65\n", - "f = lambda k: k**alpha\n", - "u = np.log\n", - "beta = 0.95" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we want to solve a finite state version of the continuous state model above.\n", - "We discretize the state space into a grid of size `grid_size=1500`,\n", - "from $10^{-6}$ to `grid_max=2`.\n", - "\n", - "The grid size in [the lecture](http://quant-econ.net/py/dp_intro.html#computation) is 150,\n", - "where the value functions are approximated by linear interpolation,\n", - "while we choose a finer grid since we fill the gaps with discrete points." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "grid_max = 2\n", - "grid_size = 1500\n", - "grid = np.linspace(1e-6, grid_max, grid_size)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1.00000000e-06 1.33522215e-03 2.66944430e-03 ..., 1.99733156e+00\n", - " 1.99866578e+00 2.00000000e+00]\n" - ] - } - ], - "source": [ - "print(grid)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We choose the action to be the amount of capital to save for the next period\n", - "(the state is the capical stock at the beginning of the period).\n", - "Thus the state indices and the action indices are both `0`, ..., `grid_size-1`.\n", - "Action (indexed by) `a` is feasible at state (indexed by) `s` if and only if\n", - "`grid[a] < f([grid[s])`\n", - "(zero consumption is not allowed because of the log utility).\n", - "\n", - "Thus the Bellman equation is:\n", - "$$\n", - "v(k) = \\max_{0 < k' < f(k)} u(f(k) - k') + \\beta v(k'),\n", - "$$\n", - "where $k'$ is the capital stock in the next period." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The transition probability array `Q` will be highly sparse\n", - "(in fact it is degenerate as the model is deterministic),\n", - "so we formulate the problem with state-action pairs, to represent `Q` in\n", - "[scipy sparse matrix format](http://docs.scipy.org/doc/scipy/reference/sparse.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first construct indices for state-action pairs:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Consumption matrix, with nonpositive consumption included\n", - "C = f(grid).reshape(grid_size, 1) - grid.reshape(1, grid_size)\n", - "\n", - "# State-action indices\n", - "s_indices, a_indices = np.where(C > 0)\n", - "\n", - "# Number of state-action pairs\n", - "L = len(s_indices)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1069790\n", - "[ 0 1 1 ..., 1499 1499 1499]\n", - "[ 0 0 1 ..., 1174 1175 1176]\n" - ] - } - ], - "source": [ - "print(L)\n", - "print(s_indices)\n", - "print(a_indices)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Reward vector `R` (of length `L`):" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "R = u(C[s_indices, a_indices])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(Degenerate) transition probability matrix `Q` (of shape `(L, grid_size)`),\n", - "where we choose the [scipy.sparse.lil_matrix](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.lil_matrix.html)\n", - "format,\n", - "while any format will do\n", - "(internally it will be converted to the csr format):" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Q = sparse.lil_matrix((L, grid_size))\n", - "Q[np.arange(L), a_indices] = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(If you are familar with the data structure of\n", - "[scipy.sparse.csr_matrix](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html),\n", - "the following is the most efficient way to create the `Q` matrix in the current case.)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# data = np.ones(L)\n", - "# indptr = np.arange(L+1)\n", - "# Q = sparse.csr_matrix((data, a_indices, indptr), shape=(L, grid_size))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Discrete growth model:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ddp = DiscreteDP(R, Q, beta, s_indices, a_indices)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Notes**\n", - "\n", - "Here we intensively vectorized the operations on arrays to simplify the code.\n", - "As [noted](http://quant-econ.net/py/need_for_speed.html#pros-and-cons-of-vectorization),\n", - "however, vectorization is memory consumptive,\n", - "and it can be prohibitively so for grids with large size." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solving the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve the dynamic optimization problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "res = ddp.solve(method='policy_iteration')\n", - "v, sigma, num_iter = res.v, res.sigma, res.num_iter" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "14" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "num_iter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that `sigma` contains the *indices* of the optimal *capital stocks*\n", - "to save for the next period.\n", - "The following translates `sigma` to the corresponding consumption vector." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Optimal consumption in the discrete version\n", - "c = f(grid) - grid[sigma]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Exact solution of the continuous version\n", - "ab = alpha * beta\n", - "c1 = (np.log(1 - ab) + np.log(ab) * ab / (1 - ab)) / (1 - beta)\n", - "c2 = alpha / (1 - ab)\n", - "def v_star(k):\n", - " return c1 + c2 * np.log(k)\n", - "\n", - "def c_star(k):\n", - " return (1 - ab) * k**alpha" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us compare the solution of the discrete model with that of the original continuous model." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mTp06PPbYY9jY2DB8+HCcnJxo1KgRXbp0MWw3QIMGDZgwYQKmpqYMGTKEgIAA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\nHEjmrbcmU6hQOQYObJndlxcREfFMcjJMmrSP1q2f4aefplC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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n", - "ax[0].set_ylim(-40, -32)\n", - "ax[0].set_xlim(grid[0], grid[-1])\n", - "ax[1].set_xlim(grid[0], grid[-1])\n", - "\n", - "lb0 = 'discrete value function'\n", - "ax[0].plot(grid, v, lw=2, alpha=0.6, label=lb0)\n", - "\n", - "lb0 = 'continuous value function'\n", - "ax[0].plot(grid, v_star(grid), 'k-', lw=1.5, alpha=0.8, label=lb0)\n", - "ax[0].legend(loc='upper left')\n", - "\n", - "lb1 = 'discrete optimal consumption'\n", - "ax[1].plot(grid, c, 'b-', lw=2, alpha=0.6, label=lb1)\n", - "\n", - "lb1 = 'continuous optimal consumption'\n", - "ax[1].plot(grid, c_star(grid), 'k-', lw=1.5, alpha=0.8, label=lb1)\n", - "ax[1].legend(loc='upper left')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The outcomes appear very close to those of the continuous version." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Except for the \"boundary\" point, the value functions are very close:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "121.49819147053378" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.abs(v - v_star(grid)).max()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0038595076780651993" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.abs(v - v_star(grid))[1:].max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The optimal consumption functions are close as well:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0013020872868430011" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.abs(c - c_star(grid)).max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In fact, the optimal consumption obtained in the discrete version is not really monotone,\n", - "but the decrements are quit small:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "diff = np.diff(c)\n", - "(diff >= 0).all()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "dec_ind = np.where(diff < 0)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "521" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(dec_ind)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.00065355751082307734" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.abs(diff[dec_ind]).max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The value function is monotone:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(np.diff(v) > 0).all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Comparison of the solution methods" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us solve the problem by the other two methods." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Value iteration" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ddp.epsilon = 1e-4\n", - "ddp.max_iter = 500\n", - "res1 = ddp.solve(method='value_iteration')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "294" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res1.num_iter" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array_equal(sigma, res1.sigma)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Modified policy iteration" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "res2 = ddp.solve(method='modified_policy_iteration')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "16" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res2.num_iter" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array_equal(sigma, res2.sigma)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Speed comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loops, best of 3: 2.83 s per loop\n", - "1 loops, best of 3: 201 ms per loop\n", - "1 loops, best of 3: 204 ms per loop\n" - ] - } - ], - "source": [ - "%timeit ddp.solve(method='value_iteration')\n", - "%timeit ddp.solve(method='policy_iteration')\n", - "%timeit ddp.solve(method='modified_policy_iteration')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As is often the case, policy iteration and modified policy iteration are much faster\n", - "than value iteration." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Replication of the figures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using `DiscreteDP` we replicate the figures shown in the lecture." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convergence of value iteration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us first visualize the convergence of the value iteration algorithm as in the lecture,\n", - "where we use `ddp.bellman_operator` implemented as a method of `DiscreteDP`." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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vvDOEt98elI2DNps1uOYaGkE3NVmZoLOIi1Lki3lgIIhAoLhtWYyWa2rMqK21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\n5UjUchijGvpgKAdaGbStWEFw4vH2wECjZb1z55947rnniMVi3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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w = 5 * np.log(grid) - 25 # Initial condition\n", - "n = 35\n", - "fig, ax = plt.subplots(figsize=(8,5))\n", - "ax.set_ylim(-40, -20)\n", - "ax.set_xlim(np.min(grid), np.max(grid))\n", - "lb = 'initial condition'\n", - "ax.plot(grid, w, color=plt.cm.jet(0), lw=2, alpha=0.6, label=lb)\n", - "for i in range(n):\n", - " w = ddp.bellman_operator(w)\n", - " ax.plot(grid, w, color=plt.cm.jet(i / n), lw=2, alpha=0.6)\n", - "lb = 'true value function'\n", - "ax.plot(grid, v_star(grid), 'k-', lw=2, alpha=0.8, label=lb)\n", - "ax.legend(loc='upper left')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We next plot the consumption policies along the value iteration." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "1 6.924e+00 9.756e-03 \n", - "2 4.107e+00 1.951e-02 \n", - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "1 6.924e+00 9.754e-03 \n", - "2 4.107e+00 1.950e-02 \n", - "3 3.866e+00 3.034e-02 \n", - "4 3.673e+00 4.010e-02 \n", - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "1 6.924e+00 9.911e-03 \n", - "2 4.107e+00 1.975e-02 \n", - "3 3.866e+00 3.082e-02 \n", - "4 3.673e+00 4.225e-02 \n", - "5 3.489e+00 5.175e-02 \n", - "6 3.315e+00 6.115e-02 \n" - ] - }, - { - "data": { - "image/png": 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8cN1118HT01Or6W7evBk33ngj/P3921ynEOLyddnVdA4ePNhVm27VpVxbMiMj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ZNPPplwjx4R2UzLgM3IyMDPf/5z5OTkICoqCgUFBViyZIndOldddRXKysoQHh6Obdu24a9//ava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\nmtQsFguqqqpQWFiIg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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w = 5 * u(grid) - 25 # Initial condition\n", - "\n", - "fig, ax = plt.subplots(3, 1, figsize=(8, 10))\n", - "true_c = c_star(grid)\n", - "\n", - "for i, n in enumerate((2, 4, 6)):\n", - " ax[i].set_ylim(0, 1)\n", - " ax[i].set_xlim(0, 2)\n", - " ax[i].set_yticks((0, 1))\n", - " ax[i].set_xticks((0, 2))\n", - "\n", - " w = 5 * u(grid) - 25 # Initial condition\n", - " compute_fixed_point(ddp.bellman_operator, w, max_iter=n, print_skip=1)\n", - " sigma = ddp.compute_greedy(w) # Policy indices\n", - " c_policy = f(grid) - grid[sigma]\n", - "\n", - " ax[i].plot(grid, c_policy, 'b-', lw=2, alpha=0.8,\n", - " label='approximate optimal consumption policy')\n", - " ax[i].plot(grid, true_c, 'k-', lw=2, alpha=0.8,\n", - " label='true optimal consumption policy')\n", - " ax[i].legend(loc='upper left')\n", - " ax[i].set_title('{} value function iterations'.format(n))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dynamics of the capital stock" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let us work on [Exercise 2](http://quant-econ.net/py/dp_intro.html#exercise-2),\n", - "where we plot the trajectories of the capital stock for three different discount factors,\n", - "$0.9$, $0.94$, and $0.98$, with initial condition $k_0 = 0.1$." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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vt9lg0CBITIThw63nAQPgiy/UVS4iHc9ZE71pmitaMI4OzxsWZiV6gPh4/3Vf\n7a2UzsM0TbYc20JFVAVRYVH+JA/gsAVeT1tSWQl/+EMWO3akUlRkjatbUikstMbWExJg2DAroQ8b\nBkOG1N2Bqpq6ykWkIzrvGL1hGEOBJ4GRQPU/n6ZpmgODGVhHkzxkCOk+H6lOp7UhMrDY6WR8SkrA\ndby7/V0qe1UStjGM/tf09yd5Z46TlDsDr6e1lZXBunWwejWsWQNZWTVj62Fh1qz1qCgYMsTGK6/4\nOz8Copa4iHQ0gUzGWwL8AvgdcD3wbaDhTYDlwpgmSVu3Qr9+ZPTtiy0+Hp/DwfiUlIDH53ed3MWf\n1v2J2Itjue/S+9i2aRtunxuHzUHKnSlMGh/4OH9rKCiwkvrq1ZCdTZ3u+C5dvHTpAl26WAm+Wt++\nvkYleRGRjiiQRO80TfNjw9qdZi/whGEY64GfBzm2jiMnB3btImnAAJL+8Y+6p4oEwF3pZv7K+VR4\nK/j64K/z8BUPQzuYKnnokJXYV6+GL7+0tocFayb8qFFwxRXWY//+5htbFxHpaAJJ9G7DMOzATsMw\n/hs4BEQHN6wO5p//tJ5nzGh0kgd4bu1z7Cvax8WxF/PwZQ83c3BNc+Zs+cmTkyktTWL1amt2fLXw\ncBg3zkrskyZBt241n/Xtq7F1EZELdd4tcA3DGA/kAbHAAqAz8GvTNNcGP7xzC4ktcA8dsk6mCwuD\nf/wDujZu17qPdn3Ek6ufJMIewV9u+AsDug4IUqCNVz1b/uTJVE6csLaR9XrT6dcvmdjYJKKjYeJE\nK7lPmFC3W15ERBoWjC1wB5imuQ4oBu6t+pLbgVZP9CHhX/+y+qynTm10kj9w6gC/X/t7AB6e8HCb\nSvIAL7yQRV5eKgUFNdfCw1OJjs7gN79JIinpgjowRESkEQJJ9I8DrwVwTRqrpATee896fcstjSrq\n8XqY/+l8yirLuOriq7hhyA1BCPDCHDsGL78MH31kp6zMGnPv3t3qjo+KgkGDbCQnt3aUIiIdw7l2\nxpsOfB1IMAzjj0B1N0EM4DlbuTPquB74A9Ys/cWmaT5zxud3AY9W1V0MfN80zU2BlA0Jy5ZZu78k\nJVmLvxvhL1l/YcfJHVwUcxE/mfgT/1K61lRQYJ2Z/s471np3w/DSrRv06mXtQlfN4fC1XpAiIh2M\n7RyfHQKyAXfVc/XjHWDa+SqumsC3EGtJ3gjgTsMwhp9x225gsmmaY7DG///aiLLtm88Hb75pvW5k\na371vtXK4JioAAAgAElEQVS8mfcmYbYw5k2eR3RE686NLC6G9HS46y544w0ryU+dCgsXJpOYmF4n\nyVuz5ce1XrAiIh3MuXbGywFyDMP4m2maAbXgzzAB2Gma5h4AwzBeBWYA/oM5TdNcU+v+z4E+gZZt\n99autU5a6dXLmo0WoKOnj/Lrz34NwANjHyAxLjFYEZ5XWZn1t8qrr8Lp09a1SZPgvvusLWghiZ49\nNVteRKQ1navr/nXTNG8D1jfQLWxWtcLPJQHYX+v9AeCyc9yfCrx3gWXbnzfesJ5nzrQ2Zw9Apa+S\ntJVpFFcUM7HPRG4dcWsQAzy7igp4912rm756ot3YsZCaCiNG1L1XO9GJiLSuc03G+2HV8zcusO6A\n170ZhnEVcB9Q3bQNuOwTTzzhfz1lyhSmTJkSaNHWs2sXrF8PTid8/esBF3tx44t8efxLekT1YM4V\nc1p8XN7rhQ8+sCbaHTtmXRsxwkrwY8e2aCgiIh3GihUrWLFixQWXP1fX/aGq5z2GYfTCalH7gHWm\naR4JoO6DQN9a7/titczrMAxjDLAIuN40zYLGlIW6ib7dqB6bnz7d2rg9AFmHsliauxSbYeNnX/sZ\nXRxdghhg3Y1uwsO9DB+ezLp1SRyo+q8wcKCV4CdOtGbVi4hIcJzZiP3lL3/ZqPKBHGpzPzAPWF51\naaFhGPNN00w/T9EsYIhhGBdjTey7A7jzjLr7AW8Cs03T3NmYsu1WYSF8/LGVHW++OaAiJ8tO8uSq\nJzExuTfpXpJ6BbcrvPaxsEVFcOQIvPZaOv36wahRSXz72zBlSsAjDiIi0ooCWUf/KHCpaZonAAzD\n6A6sAc6Z6E3TrKzaMncZ1hK5dNM0txqG8d2qz1/A+gOiK/B8VTe0xzTNCWcre0E/YVvz7rvWIPfE\nidCnz3lv95k+nlr1FAXuAi7tdSmzx8wOeoiZmVmcPp3Kvn3WbnZgbXSTkJDBiy8mYdeRRiIi7UYg\niT4fOF3r/emqa+dlmub7wPtnXHuh1uv7gfsDLdvueTzw9tvW61sDm0j399y/k3U4i1hHLD/92k+x\nGcFvRh8+bCcvzwrXbrcWBsTFQa9eNiV5EZF2JpBEvwtYaxhGVYZiBrDJMIyfYM2+/13Qogs1n34K\nJ07AgAFw6aXnvf3LY1+yZOMSAB6/8nHiouKCGp7XC6+8Al984cXjsaYP9O9fs9mNNroREWl/Ak30\nu6iZCf921etOwQoqJJlmzSl1t9xy3hlsp8pPsWDlAryml1kjZzEhYUJQwzt2DNLSIDcXevZMxuNJ\np3v3VH+YOhZWRKR9Om+iN03ziRaII/Rt3gzbtkGXLnDNNee81TRNfvPZbzhWcowRcSNIHZt6zvub\natUq+M1vrB3uuneH3/0uibIybXQjIhIKApl1H481IW8E4Ky6bJqmOTWYgYWc6g1yvvENiIxs8BbX\nOheZyzLZdmIbecfzuHjExcydOZcwWyAdL41XXg7PP18zbeDyy2HOHIiNBdBGNyIioSCQDPI34B/A\njcB3sY6qPR7EmELP0aNWs9luhxkzGrzFtc5F2qtpnBh+gh3hOzB7mXj3evkq7yt6j+/d7CHt2QML\nFsDu3dZRsd/9rrVJn9bEi4iElkCmcHc3TXMxUGGa5qemaX4bUGu+Md5+25rpNmWKNX29AZnLMikZ\nXcLewr2YmHR3dsd5mZOly5Y2ayimCf/v/8H3vmcl+T59YOHCgKYNiIhIOxRIi76i6vmIYRg3Ym1g\n0zV4IYUYt9taOw/nPKWuwlfB8ZLjlHvLcYQ5SOicYBX3uZstlNOn4dlnoXonxWnT4Ic/tHbiFRGR\n0BRIok8zDCMW+Anwf0Bn4H+CGlUoWbbMyrAjR8Lwc5y064OjJUcB6NO5j3+9vMPmaJYwvvwSfvUr\na5c7pxN+/OPzzgkUEZEQEEjX/e2AYZpmrmmaU4BrgMD2bu3oGnHmfOzAWFgPMRExdIqwVi46c5yk\nTEtpcgiZmfCjH1lJPjERFi1SkhcR6SgCadGPqXXYDKZpnjQM4/y7vQhkZcG+fdCjB3zta2e97ejp\no+Tacuk3vB8jT4zEUezAYXOQcmcKk8ZPavTXVh9Ic+qUndxcLzZbMrGxSdxxh3UQTXh4U34oERFp\nTwJJ9IZhGN1M0zxZ9aYb1v7zcj7VS+puvhnCzv6rfinnJSq8FcycMpOf/9fPm/SV1QfSHD1q7VVf\nWQmRkek89BDcf7+Wy4mIdDSBJPpngTWGYbwGGMBtwK+CGlUo2LsXvvjCWjN/ww1nvW1P4R6W7VqG\n3bBz36X3NflrMzOz2L8/1X+cbEwM9O+fysaNGYASvYhIRxPIzngvG4aRjbWkzgRuNk1zS9Aja++q\nx+avuw46dz7rbenr0/GZPmYkzvDPtG+KXbvs/iTfuzf07Gm9drt1pqyISEcU0JZrpmluBjYHOZbQ\nceoUfPih9XrmzLPetuX4FlbvX40jzMHdY+5u8td+8gl8+aUXgIQEa2pANR1IIyLSMamZFwzvvWet\nnx8/Hi6+uMFbTNNkUfYiAG4dfivdo7o36StXrYKnnoL4+GT69Uuvk+StA2nGNal+ERFpn4KziXpH\n5vXCv/5lvT7Hkrp1h9ax8ehGYiJiuGPUHU36yi++gPnzra9+6KEkRozQgTQiImJRom9uq1ZZZ772\n62e16BvgM33+1vxdo+/yr5u/EBs3ws9/bs2uv+UWa/mcYehAGhERsajrvrlVL6mbORNsDf96l3+1\nnJ0FO+kR1YObht10wV+1eTP89KdQUWEdivfQQ9qvXkRE6lKib055edZes506WbPtG+DxesjYmAHA\nvZfcS2RYw0fWns/27fDYY1BWBtdea+18pyQvIiJnUqJvTtWt+RtvPOtJMe/teI9DxYfo16Uf0wZN\nu6Cv+eorePRRawv9yZOtM+TP0nkgIiIdnMbom0GOy0XW4sXYP/gAr2GQnJDQ4NY07ko3L296GYD7\nL70fu63xGwzu3w+PPAJFRXD55TB3rnXMvYiISEOU6Jsox+UiKy2N1N27rX702FjS//xn6NaNpEl1\n96n/55Z/crLsJMO6D+PKflc2+ruOHIGf/AROnoSxY+GXv9S+9SIicm7q8G2irMxMUktL4cQJ60KP\nHqSWlZG9dGmd+06Vn+LVL18F4IFxD2A0ckA9P986Wvb4cRg92jpyNiKiWX4EEREJYUr0TWSvqLB2\nwqustMblo6MBsLndde5bmruUEk8J4y8az6W9G3f4X0GB1ZI/fNg6Zvapp8DRPMfUi4hIiFOibyJv\nRAQUFlpvunb1X/fVysTHSo7xrzxrE537x97fqPpPnbLG5Pftg0GD4Ne/9v8tISIicl5K9E2UfNtt\npBcXW29iYwFY7HQyLiXFf89LG61jaK+6+CqGdh8acN0lJdbs+t27rf13fvObc56PIyIiUo8m4zVR\nkmFAnz5kVFZiGz4cn8PB+JQU/0S8vYV7+WDXB40+htbthscfh23b4KKL4Nln63QYiIiIBESJvqmW\nLycpNpakhx6CW2+t93HGhgx8po9vDv0mfTr3OWdVLlcOmZlZlJXZ2bDBi2EkM2RIEs8+C3FxwfoB\nREQklCnRN0VJCaxda21JN2VKvY+3Ht/Kyn0ribRHcnfSuY+hdblySEvLorQ0la++ssbmIyPTmTcP\nevXSvvUiInJhNEbfFJ99Zm00n5RUr8ltmiZ/zf4rALeOuJW4qHM3ya2WfCp791pJPiwMBgxI5ZNP\nsoMWvoiIhD4l+qZYvtx6vuqqeh9lH872H0M7a9Ss81ZVUWHn2DFrAr/dDgMHWkvo3G79JxIRkQun\nLHKhTp2CdeusrDx5cp2PfKbP35pPGZ0S0DG0breXw4et1/37Q1SU9drh8DVr2CIi0rEo0V+oVavA\n67X2oq1aVlft0z2fsuPkDuKi4rh52M3nrcrjgaKiZAwjne7da5bQOZ2LSUkZF4zoRUSkg9BkvAv1\nn/9Yz2d021f6KknfkA7At5K+FdAxtC+/DKdPJzFuHAwYkIHXa8Ph8JGSMp5JkzQRT0RELpwS/YUo\nKICNG60Zc1fWPZzmvR3vcbD4IH0792X64OnnrWrzZli61Jq4/+yzSYwercQuIiLNR133F+LTT8Hn\ngwkTICbGf9ld6ealnJcASL009bzH0Lrd8PTTVlWzZlmH1YiIiDQnJfoLUd1tP3VqnctvbHmDk2Un\nSeyeyOT+kxsoWNcLL8CBAzBgAHz728EIVEREOjp13TfWsWOQmwuRkVC1za1rnYuMf2fw4Vcf4vV6\nueeue857DG1WFrz1ljVp//HHda68iIgEh1r0jfXpp9bz5ZeD04lrnYu0V9NY1X0VxSOKsY+z8/rH\nr+Na5zprFadPW6fQAdx7LwwZEvywRUSkY1Kib6wzuu0zl2VSPLKY46XHAegd05uyMWUsXbb0rFX8\n3//B8eMwYgTceWfQIxYRkQ5Mib4xDh2CvDxwOuGyywCo8FVwouwEJiadIzsTFW7tdOP2uRusYtUq\n+PBDq+f/scesrnsREZFgUaJvjOotb6+4wsrUQLgRTn5pPgA9onr4b3XYHPWKFxRYx80CPPAA9O0b\n3HBFRESU6BujOtHXmm2fdGkS3mwvkfZIYiKspXbOHCcp01LqFDVNK8kXFVmb6d10U4tFLSIiHZhm\n3Qdqzx7YtQs6dYLkZP/lXRG76De8Hz0P96RPaR8cNgcpd6YwafykOsWXLbMOu4uOhjlzwKY/sURE\npAUo0QdqxQrrefJk/1q4/UX7yTqcRc9BPXn9tteJiYxpsOjRo7BwofX6Bz+A+PgWiFdERAR13QfG\nNBs8kvadbe8AcPWAq8+a5H0+ayldSYm1W+611wY9WhERET8l+kDs2gX79lmn1F16KWBtd/vBrg8A\nmDFsxlmLvvUWrF9vFf3xj6097UVERFqKEn0gqtfOT5niXw/38e6POV1xmhFxIxjafWiDxfbts7a5\nBSvJd+3aArGKiIjUokR/PrW77adMqbpk8nbe2wDcNKzh6fNer3VgTUUFTJsGX/taSwQrIiJSlxL9\n+eTlwZEjEBfnP15u8/HN7CzYSawjlikXT2mw2NKlsHWrNfHuv/+7BeMVERGpRYn+fKq77a+6yr8m\nrro1f8OQGwi31z+NZvt2eMk6rZY5c6wVeSIiIq1Bif5cfL6aZXVVs+0LygpYsXcFNsPGN4Z+o16R\nigqry97rhZkzrc1xREREWosS/bnk5kJ+PvTqBcOGAfDvHf+m0lfJxD4T6dmpZ70iS5bAV19Z29t+\n5zstHbCIiEhdSvTnUnvLW8PA6/P61843NAlv0yb4xz+sHv7HHwdH/e3uRUREWpR2xjsbr7fm7Pmq\nve3XHFjD8dLj9Onch7G9a/rkXa4cXnwxi08+seN2e7nnnmSGD09qjahFRETqUKI/mw0boLAQ+vWD\ngQMBeCvvLQBmJM7AZlidIS5XDmlpWWzfnsrJk9YJths2pONywaRJSvYiItK61HV/NrW3vDUM9hft\nJ/twNpH2SKYNmua/LTMzixMnUjlxwtr1rn9/KC9PZenS7FYKXEREpIYSfUM8Hli50npdNdv+7W3W\nkrprBl5TZ1/7igo7R45Yr3v0qBmXd7v1qxURkdanbNSQrCw4fRoGDYL+/SnzlPHBTmtf+zMn4ZWU\neDl1ypqAV/tUOofD15IRi4iINEiJviG1N8kBPvnqE0o8JYzqMYrB3QbXudVuT8ZmSycuDsKqZjw4\nnYtJSRnXkhGLiIg0KKiT8QzDuB74A2AHFpum+cwZnw8DlgCXAj8zTfPZWp/tAU4BXsBjmuaEYMbq\n53bDZ59Zr6+6CtM0aybhnXFK3ZYtcPBgEkOHwqhRGZimDYfDR0rKeE3EExGRNiFoid4wDDuwELgG\nOAisMwzjHdM0t9a67QTwMNDQyTAmMMU0zZPBirFBn38OZWUwfDhcdBFfHs1lV8EuYh2x/Ff//6pz\n64svWs+pqUncf78Su4iItD3BbNFPAHaaprkHwDCMV4EZgD/Rm6Z5HDhuGMYNZ6mj5U9vrz3bnpol\ndTcOubHOvvZffgnr1kFUFNx+e4tHKSLSZhhGy/9T3VGYptnkOoKZ6BOA/bXeHwAua0R5E/jYMAwv\n8IJpmouaM7gGlZbCmjXW6ylTOFl2kpX7VmIzbNw49MY6t1a35m+5BTp3DnpkIiJtWnMkJKmruf6A\nCmaib+p/9StM0zxsGEYP4CPDMPJM01zVHIGdlctlnUozZgz06MG/c16h0lfJlX2vrLOvfW4uZGdD\ndDTcdltQIxIREWmSYCb6g0DfWu/7YrXqA2Ka5uGq5+OGYfwLayigXqJ/4okn/K+nTJnClClTLixa\nqNNt7/V5eXf7u0D9SXjVrflbb4WYGERERIJmxYoVrKg+SfUCGMHqbjEMIwzYBlwNHAK+AO48YzJe\n9b1PAMXVs+4Nw4gC7KZpFhuGEQ18CPzSNM0PzyhnNlv8xcXWubI+H/zzn6w69SXzVsyjb+e+vHjT\ni/4tb3Ny4Ec/ss6Y//vfdda8iIhhGOq6D4Kz/V6rrgfcrx+0Fr1pmpWGYfw3sAxreV26aZpbDcP4\nbtXnLxiG0QtYB3QGfIZh/BAYAcQDb1aNT4QBfzszyTe71auhshLGjYOuXXnri/r72kNNa/6225Tk\nRUSk7QvqOnrTNN8H3j/j2gu1Xh+hbvd+tdPAJcGMrZ7qTXKmTmVv4V7WH1mPI8zBtME1+9pv2AAb\nN1oJ/pZbWjQ6ERGRC6LT6wAKCqwsbrfDlVfyztaXALhmwDV0irCa7aZZ05q//XZrIp6IiHQ8b731\nFlu2bMFms5GQkMDdd99d756MjAwOHTpEeHg4iYmJ3HRTQ9vFtAwlerAOsPF64fLLKXOGs2zXMqDu\nvvYbNsCmTdZSupkzWytQERFpio0bN7J7924AduzYwZw5cxpVvqioiAULFpCdbZ1QOnHiRKZPn05c\nXJz/ntzcXJYsWcKqVdb88WuvvZbrr78eR/WpZy1MiR5qZttPncpHuz+ixFPC6PjRDOo2CLBa80uW\nWLeoNS8iEjiXK4fMzCwqKuxERHiZPTu5UVuEN7V8bbm5uRQWFjKzqrU2derURif6lStXMmLECP/7\npKQkli9fzm211lp/8MEHDBgwwP8+Pj6ezz77jKuvvvqC4m6qDp3oc1wushYvxv7++3jDwhhnGLyd\nZx1HOyOxZklddra1E16XLnDzza0VrYhI++Jy5ZCWlkVZWar/WlpaOnPnElCybmr5M23ZsoU77rgD\ngOzsbEaNGgXA7t27WbTo7HuyXX755cyYYeWEAwcOEBsb6/8sNjaWHTt21Lk/JiYGj8fjf+92u9m6\ndasSfUvLcbnISksjdd8+6yCbLl34/VO/ZNvE08SPHsDk/pOBuq35O+6wtrwVEZHzy8y0kvTGjbWv\npnLbbRkMHXr+RL19exalpTVJ/pJLoKwslaVLMxqd6A8fPkxCQgK5ubksXryYr776ihdesOaGDxw4\nkKeeeiqgegoLC+t0wUdERHD69Ok698ycOZOMjAxM0+T06dNs27aN8ePHNyre5tRhj6nNyswktazM\nmogH0LUrN588QNe1x7hhyA3+fe3XrbNOqevSBVpxLoWISLtTUWFv8LrPF1jq8fkaLu92Nz51ff75\n51x++eWMHj2a5557junTp5ORkdHoemJiYuqsbS8rK6Nbt2517omPj2fJkiUsWrSIFStWMHr0aOLj\n4xv9Xc2lw7bo7RUV4PFY+9sbBp5OTgrz9+D0OPlG4jeAuq35O+8Ep7MVAxYRaWciIryA1RKvbeRI\nHwsXnr/8gw962VpvizVwOHyNjsXtdhMWVpPytmzZwpAhQ4DGdd0PGjSIrKws/2f5+fmMHTu2XpkR\nI0YwcuRIAObPn8+CBQsaHXNz6bCJ3hsRAadOWW9iYjjhLsAEunZNID7a+svr888hLw+6doUZM85e\nl4iI1Dd7djJpael1xtidzsWkpATWjd3U8rWtXLmSWbNmAVZyXrNmDU8++STQuK77yZMn8+ijj/rf\nr1+/nmeeeQaAXbt2MXDgQPbu3cuMGTPIyclh69at9O/fn8GDBzc65uYStC1wW0JTtsDNcbnIuvtu\nUk+exOzTh83mUf5g83Jt2m+545vfxjTh+9+HbdvgwQd1eI2IyNmcawtclyuHpUuzcbttOBw+UlLG\nNXrWfVPKA2zevJmdO3dSXFxMVFQUmzZtIjU1lb59G9qv7fxeeeUV9u7di8/nY9CgQdx1110AjB07\nlvT0dEaNGkVaWho9e/Zkx44dzJs3j65duzb6e5prC9wOm+gpLydn8mSyDxzg+ISRrC7eTOQ1Y3h9\nznsYhoHLBT/7GXTrBn/7G7TS8kcRkTavre91/9prr3H77be3dhiN1ub3um/z1q8nKSqKpG98gx/f\nHMXpIx6+PeE7Vb/Aml3wUlKU5EVE2jObrcPOOwc6cqJfs4ai4iJePbSJv7/yFRG2CLoM6QLAZ5/B\njh3QvTvceGMrxykiIk1y6623tnYIrap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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "discount_factors = (0.9, 0.94, 0.98)\n", - "k_init = 0.1\n", - "\n", - "# Search for the index corresponding to k_init\n", - "k_init_ind = np.searchsorted(grid, k_init)\n", - "\n", - "sample_size = 25\n", - "\n", - "fig, ax = plt.subplots(figsize=(8,5))\n", - "ax.set_xlabel(\"time\")\n", - "ax.set_ylabel(\"capital\")\n", - "ax.set_ylim(0.10, 0.30)\n", - "\n", - "# Create a new instance, not to modify the one used above\n", - "ddp0 = DiscreteDP(R, Q, beta, s_indices, a_indices)\n", - "\n", - "for beta in discount_factors:\n", - " ddp0.beta = beta\n", - " res0 = ddp0.solve()\n", - " k_path_ind = res0.mc.simulate(init=k_init_ind, ts_length=sample_size)\n", - " k_path = grid[k_path_ind]\n", - " ax.plot(k_path, 'o-', lw=2, alpha=0.75, label=r'$\\beta = {}$'.format(beta))\n", - "\n", - "ax.legend(loc='lower right')\n", - "plt.show()" - ] - }, - { - "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": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/solutions/estspec_solutions.ipynb b/solutions/estspec_solutions.ipynb deleted file mode 100644 index 4b889f662..000000000 --- a/solutions/estspec_solutions.ipynb +++ /dev/null @@ -1,145 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:ced859d6b4e0d7b96eb89edb3dc7dd9f5eb939faca76ce076900f1f0d1671a25" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Estimation of Spectra" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/estspec.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import ARMA, periodogram, ar_periodogram" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "## Data\n", - "n = 400\n", - "phi = 0.5\n", - "theta = 0, -0.8\n", - "lp = ARMA(phi, theta)\n", - "X = lp.simulation(ts_length=n)\n", - "\n", - "fig, ax = plt.subplots(3, 1, figsize=(10, 12))\n", - "\n", - "for i, wl in enumerate((15, 55, 175)): # window lengths\n", - " \n", - " x, y = periodogram(X)\n", - " ax[i].plot(x, y, 'b-', lw=2, alpha=0.5, label='periodogram')\n", - "\n", - " x_sd, y_sd = lp.spectral_density(two_pi=False, res=120)\n", - " ax[i].plot(x_sd, y_sd, 'r-', lw=2, alpha=0.8, label='spectral density')\n", - "\n", - " x, y_smoothed = periodogram(X, window='hamming', window_len=wl)\n", - " ax[i].plot(x, y_smoothed, 'k-', lw=2, label='smoothed periodogram')\n", - "\n", - " ax[i].legend()\n", - " ax[i].set_title('window length = {}'.format(wl))\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Em266yRNuveqqq1i2bBk9e/akfv36NGnSxHN+Zff23HPPZfjw4TRo0AClFE2a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JM07yZqjMn9HWUMUzaKkVrLW0DyE6Go/Hg81mY+zYsfzsZz9j7dq1Ua03ZcoUXn75ZVwuFwBP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\neMgqChkgYxhkTJO0rpPVdTLFYpmEFQoFdF2nWCyWXxcKBYrFYtX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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "lp = ARMA(-0.9)\n", - "wl = 65\n", - "\n", - "\n", - "fig, ax = plt.subplots(3, 1, figsize=(10,12))\n", - "\n", - "for i in range(3):\n", - " X = lp.simulation(ts_length=150)\n", - " ax[i].set_xlim(0, np.pi)\n", - "\n", - " x_sd, y_sd = lp.spectral_density(two_pi=False, res=180)\n", - " ax[i].semilogy(x_sd, y_sd, 'r-', lw=2, alpha=0.75, label='spectral density')\n", - "\n", - " x, y_smoothed = periodogram(X, window='hamming', window_len=wl)\n", - " ax[i].semilogy(x, y_smoothed, 'k-', lw=2, alpha=0.75, label='standard smoothed periodogram')\n", - "\n", - " x, y_ar = ar_periodogram(X, window='hamming', window_len=wl)\n", - " ax[i].semilogy(x, y_ar, 'b-', lw=2, alpha=0.75, label='AR smoothed periodogram')\n", - "\n", - " ax[i].legend(loc='upper left')\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Gjx5NXFwct912W6PnrPbII4/UCVeNKSoqIj09nSlTpgDg6+vL2LFjOXjwIFOn\nTuXAgQOsX78esD1oOiQkxL5vY/dWo9Hg4OBAcXExGo2Gbt264eDgUOvc/fr1a3D2dJ1Ox7fffktq\naioODg7k5ubi4eEBwJAhQzh9+jRDhgzB1dXV/jkQQoiOoKKigoKCAn7++WcuXvyZ6OgubN0aQEmh\niv5iMsMd/06s7gg7lWAKn3sOq7Vl81w5OTnh5OREly5d6NKli/13R0dHHBwccHBwqPNar9djMBjQ\n6/X2xWAwoNPp0Ov1aLXaBpea34VX6vQBqzm1Tq3pm2++ISoqCldXV8D2yJSazYSqquLv788DDzzA\nn/70J9asWdOk4z700EPMnz+fqKgoHnnkEZYsWWIPE1ejKJcffKmqqj3caDSaWtsURam3D1FD/Yoa\nCl56vZ7ly5dz11131dmm0+lq7Xetnfyv3Lc6mDR2zuaeq759at6jK6+lpqvd2+eff57bb7+dQYMG\n8e677za5PCdPnuTRRx/loYceIiwsjF69etVbho4+eEIIcWOxWCxcvHiR/Pz8Wj+rl+pQVf0/1BZL\nNzIy7qWwcCgo+bhYovHQ/51sbRmKZwBU1T5VVx5069aNbt261Xrt7OxMt27d6Nq1K87OzrV+du3a\nFUdHR/v3XVuJjY1tcFunD1jt6cyZM3Tr1s0ergBuv/12vvnmG+6///5a733iiSeIiIhg//79RERc\nfU4Pq9VK165dmTVrFufPnyc2NrbBgKWqqv0LdtKkSXz44Yf85S9/obS0lE8//ZTnnnsOgMmTJ7Nq\n1Srmz5+Po6Mj69ats9fWVHNyciIvLw+whYSmfFhnzJjBW2+9xYQJE+jWrRuqqtrDRWRkJOvWreP+\n+++npKSEqKioJl1/teo+Uunp6Rw5coShQ4de9ZzN1b17d/z9/dmxYwfTpk0jOTmZgwcP2pvcJkyY\nwJdffsmSJUtITEwkLi7Ovm9j97ayspLXXnuN6OhoevXqdU1l2rNnD1OnTuXBBx/k1KlTpKamSpgS\nQlx3xcXF5OXlkZubS25uLnl5eeTk5JCXl0d+fn6dPqgN0el0WK3jyMj4f6hqd/q6lDKVd4nouhfX\n/kNwevRRenh50aNHD1xcXOytADeCNglYRqPxFUBvMpn+ty3O11a++uor7rzzzlrr7rzzTlavXm0P\nWNVf+l26dOGFF15g6dKl/PDDD/bOzg155plnOHLkCIqi4O7uzt///nf78aqPWfNn9ev//d//ZcWK\nFUydOhVTHSyQAAAgAElEQVSr1cr8+fPtnbQnTpzIqVOnmDFjhr0v0BNPPFHrvL/4xS9YvHgxX331\nFYMHD641pUJDAWbevHlkZ2cze/Zse6duk8lEt27duPPOO/n++++57bbb6N27N97e3tcUhAwGA3Pm\nzCE/P59XX32VblWPTm/snFcrb2Peffddli5dyptvvomiKKxdu5YePXoAsHTpUh555BEmT55MYGAg\ngYGB9v0au7cGg4E+ffrwi1/8AicnJ3Q6HSNHjuT555+/alnnzp3LggUL7H3sIiIiyM3NrfM+Gc0o\nhLgWVquVvLw8srOzycnJISsri+zsbPvvV+vKodVq6dmzJ7169bIvvXv3xs3NDTc3N3r27Ile35ON\nG3vzww8G/P1ghONJ/lj8J/o6/Izl1rmYf/c7cHRsoytue5q2+L9ho9GoBV4zmUxPNva+qKgodeTI\nkXXWZ2Zm4uXldb2KJzqo2bNn8+c//5lhw4a1d1FaJCMjgyVLlvCPf/wDFxcXMjMziYyM5Pjx43Tt\n2rW9i9chyb95IVpOURTy8vLIzMy0L1lZWWRkZJCbm9tofydHR0fc3d3p06cP7u7u9tfVv/fs2bPR\nioIDB3S8+64TFy9qcNRb+U2Pz5iT/yFaLZT/6ldUzp0LN8D/GMbGxjJp0qR6L6RNarBMJpNiNBrb\n4lRCdDiurq44ODhgNBrtHSnfe+89CVdCiFZRWlpKRkYG6enptZasrCwsFkuD+/Xq1Yu+ffvi6elp\n/+nh4YGHhwcuLi7Nqhm/cEHDe+85cuCALV6E+BXyVOmz+OYfQ+3mTNnSpVhHjWr2tXYmzQpYRqMx\nEngN2GMymZbVWD8ZeLbq12dNJtOulhdR3Ky+/vrr9i5Cq3B2drbPeSWEEM1VUlJCampqrSU9PZ0L\nFy40uE/Pnj3x8vKqs3h4eODYis1zigLffmvg448dKC3V4OQEv55wmnn7nkJXWozi60vZypWo3t6t\nds6Orrk1WI7AKmBc9YqqZsDngeq57b8FdhmNRk31e41G42STydSxpzIXQggh2lFlZSXp6ekkJSXZ\ng1RKSop9ANKV9Ho9Xl5e+Pr64uPjg4+PD97e3nh5ebVJTXlqqpY1axw5dcrWZDj2lkoW99qAz7fr\nQVWxjBmD+cknwdn5upelI2lWwDKZTDuNRuPEK1YHA+dMJlMZgNFoPG80GoNNJlM8sLyF5RRCCCFu\nOAUFBSQlJZGUlERKSgpJSUmkpaXV2z/KYDDg4+ODv78//v7++Pj44Ovri4eHR5tPTwBQWQlffOHA\nF184YLWCq6vKo/fmcMcPf0H/0ynQaqm47z4qjEZoh/K1t9bsg+UGFBiNxjeqfr8E9AKa/zRdIYQQ\n4gagqioXL14kISGB8+fPk5iYSEJCAhcvXqzzXo1Gg6enJwEBAQQEBODv74+fnx+enp5XHYHeFlQV\nYmJ0fPihI1lZtuB0552VPDTkR3qvewNNURGqmxvmpUuxhoa2c2nbT2sGrAuAK/AYoAHWAPmtcWCd\nTkdpaal0ChbiBlf9JXTlTPdCdDaXLl3i7NmzxMfHc/78eRISEigoKKjzvi5duhAYGIi/vz+BgYH2\nQNWlS5d2KPXVpaRoef99R44dswU9X1+F3z5SwqjD/8Twuu0ZqtZRozA/8QRq1RQ3N6uWBKwrhxec\nBwbU+D3YZDIltOD4du7u7uTm5tb74RRC3DhUVaVHjx615jMToqMrLy/n/PnznDt3jnPnzhEfH09O\nTk6d9zk7O9O/f3/70q9fP7y8vNqlee9aFRbCxo2O/Oc/BhQFunVTue++CmaEJtJt9RtoExJAp6N8\nwQIq77nnpmwSvFJzRxE+BUwDPIxGo4vJZFpkMpmsRqPxeeC/VW97rpXKiEajoW/fvq11OCGEEKJZ\nVFUlOzubs2fP2pekpKQ6faYcHR0JCgpi4MCBBAUF0b9/f/tD2jsTiwW2bzfw2WcOlJRo0OlgxoxK\n7ptvptfuLTguXQ+Vlaju7pQtXYoyaFB7F7nDaJOJRpuqoYlGhRBCiPZQXl5OfHw8p0+f5ty5c5w9\ne7ZOa4pWq8XPz4/g4GAGDhxIcHAwfn5+HaK/VHNV97P65BNH0tNttVHDh1tZuLAcf0MGTm++ie7U\nKQAqJ0+m/OGHb7pRgtABJhoVQgghOoPCwkJOnz7N6dOnOXXqFPHx8XVqp1xcXBg0aBADBw60B6qO\n2meqOU6d0vLxx46cPm0LiF5eCg8+WM6YWyw4fPsfHD/8EMxmVFdXzI8/jnXMmHYuccckAUsIIcRN\n6+LFi5w4cYKTJ09y8uRJUlNTa23XaDQEBgYyePBgBg4cyKBBgzplU19TpKZqWb/egZgYWzTo0UPl\n3nsruPPOShwu5uD0/Bp0R44AYImMxLxoEbi4tGeROzQJWEIIIW4aFy5c4MSJExw/fpyTJ0+SkZFR\na7vBYCA4OJiQkBBCQkIYPHgwzjd409eFCxo2bHBg1y5bB3YnJ7j77grmzKnA2dGCYcsWHD/7DCoq\nUF1cKH/0USwTJrR3sTs8CVhCCCFuWBcuXOD48eMcP36cEydOkJWVVWu7k5MTgwcPJjQ0lCFDhhAU\nFHTTTBNSWAibNzvwzTcOVFaCTgfTplUyf34FPXuqaM+exWnNGrRJSYCt1qp84ULUnj3bueSdgwQs\nIYQQN4yCggKOHz9OXFwcJ06cqFND1aVLF0JCQggNDSU0NJT+/fuj199cX4WlpfDNNw78+98GSktt\nTZ3jx1u4//5yfHxUKCnB8b1PMWzfDqqK6u6O+dFHsY4e3c4l71xurk+VEEKIG0phYSEnT54kLi6O\nuLg40tLSam13cnJiyJAhhIaGEhYWRv/+/Tv16L6WqKiwTbmwaZMDhYW2YDV8uJUFC8oZMEABVUW/\nNxrHf/4TzYULoNNRMWcOFfPn29oNxTWRgCWEEKLTKCsr4+TJk/ZaqsTERGpON+To6MjgwYMJCwsj\nLCyMoKCgm66G6koWC0RFGfj8cwcuXLAFq0GDrDzwQAVhYbYRktrz53Fct84+9YJ14EDKH3sMJTCw\n3crd2d3cnzohhBAdmsVi4dy5cxw7doxjx45x9uzZWtMm6PV6Bg0axNChQwkLC2PAgAEYDIZ2LHHH\nYTbDrl0Gtmwx2J8ZGBiosGBBOaNHW9FoQHPpEg6fforhu+9szYE9elD+wANYJk+W2dhbSAKWEEKI\nDkNVVTIyMjhy5Ii92a+srMy+XavVMnDgQMLCwhg6dCiDBg3CSZqvaiko0LB9u4Ht2w32pkAvL4X7\n769g/HiLLTdZLBi2bcPh88/RlJSATkflzJmU33svyKOqWoUELCGEEO2qsrKS48ePc/DgQQ4ePEh+\nfn6t7T4+PgwbNoxhw4YRFhZ2w0+b0Fzp6Rq2bLFNt1BZaVsXHKxwzz0VRERY0Omw9bP6cR8O69ej\nzcwEwDpiBOULF6L4+rZf4W9AErCEEEK0ucLCQg4dOsTBgweJjY3FbDbbt/Xo0cMeqIYPH06fPn3a\nsaQdm6rCiRM6tmwxcPDg5a/0sWMtzJlTyZAhtqZAAN2JEzh89BG6c+cAULy8KH/wQdtM7DfgxKnt\nTQKWEEKINpGdnc2BAwc4cOAAZ86cQVEU+7bAwEDGjBnDmDFj6N+/P1rp/9OoigrYs0fP1q0OJCXZ\n7pXBAHfcUcmcORW26RaqaJOTcfjkE/SHDgGgurpSMX8+lVOnwk0+AOB6kjsrhBDiulBVlZSUFA4c\nOMD+/ftJqpqwEkCn0zF8+HB7qHJ3d2/HknYeFy9q2LHDwH/+Y+DSJVutk6uryl13VTJ9eiWurpeD\nlSYzEweTCcPu3baqLicnKubOpWLOHLiBnp3YUUnAEkII0WoUReHs2bPExMSwf//+WjOnOzk5MXr0\naCIiIhg5cqT0pboG8fFavvnGgb179VQPouzXT2HWrApuvdVCzYGTmqwsHL74whasrFZbB/Zp06gw\nGlFdXdvnAm5CErCEEEK0SEVFBXFxccTExBATE0NBQYF9m4uLC2PGjCEiIoJhw4bdNI+haQ0lJbBn\nj4HvvjOQmGhrBtRqISLCwqxZtftXAWhycmw1Vrt2XQ5WkydT8YtfoHp6ttNV3LwkYAkhhLhmRUVF\nHD58mAMHDtTppN6nTx/Cw8OJiIhg8ODBN+3M6c2hqnDmjJbvvjMQHW2gvNy2vnt3lUmTKpkxo5K+\nfdVa+2iysnDYvBnDzp22YKXVUnnHHbYaKy+vdrgKARKwhBBCNFFJSQkHDhxg7969HDt2rNaEn4GB\ngYwdO5bw8HACAwPRyKi0a1JYCN9/b6utSk293ME/LMzK1KmVRERYuLLyT5uUhMOXX6KPjgZFAa0W\ny223UW40ovr4tPEViCtJwBJCCNGgsrIyDh48SHR0NIcPH8ZisQC2TuphYWGEh4czZswY+vbt284l\n7XwsFoiN1REVZZtioTqvurqq3HFHJVOmVOLtrdbZT3vqFA6bNtlHBaLTUTlpEhXz5kmw6kAkYAkh\nhKjl0qVLHDt2jH379nHo0CEqKioA0Gg0hIWFERkZSUREBD169GjnknZOKSlaoqL0fP+9gYICW02f\nVgsjR9pqq265pXandQAUBd1PP+Hw1VfoTp60rXNwoPLOO6mYMwdVRmF2OBKwhBDiJldRUcHp06c5\ncuQIx44d4/z587W2h4SEMH78eMaPH4+bm1s7lbJzKyjQsHevnt27DSQkXG4C9PFRmDSpkttus9Cr\nV93aKsxmDLt2Yfj6a/vM66qzM5XTp1M5ezaqhNwOSwKWEELcZBRFITk5mWPHjnH06FFOnjxpr6UC\nMBgMhISEMGrUKMaPHy8zqTdTSQns36/nhx8MxMXpqJ5X1dlZJTLSwqRJlQwYoNQ7ibrm4kUM27Zh\n+M9/0BQVAaD26UPFrFlUTpkCMsVFhycBSwghbnCqqpKWlsbx48eJi4vjxIkTFFV9aVcLDAxk+PDh\nDB8+nJCQEBwdHduptJ1beTkcOqTnhx/0HDqktz8TUKeDW26xMHGihfDwuh3WAVBVtKdP47Bjh63j\nelWnLOvAgVTOmYMlIsJ2INEpSMASQogbkKqqxMTEEB0dTVxcXK25qcA2lcLQoUPtz/tzlQkom628\nHI4c0fHjj7bO6mVltvUaDYSGWpk40UJERCUuLg0coLQUw549GLZvR5uSYlun1WKJiKBizhyUkJA2\nuQ7RuiRgCSHEDURVVX766Sc2btxIYmKifb2rqytDhw5l6NChhIWF4eHhIVMptIDZDEeO6ImO1vPT\nT3pqTANGUJDCrbdWEhnZQL+qKtrUVAzbt9tmXK9KZaqrK5VTplB5553Scb2Tk4AlhBA3AFVVOXLk\nCBs3buTcuXMA9OzZkzlz5nDLLbfg4+MjgaqFzGZb89+PP9qa/6onAQXo319h/HjbfFX1Ta1gV1mJ\nPiYGw/bt6E6csK+2hoRQOX26rRmwzhBC0RlJwBJCiE4uLi6OjRs3curUKcBWWzV37lymTZsmfala\nqKzMFqqio/UcPqynxlgAgoMVxo2rZNw4C56ejYQqQJuSgj4qCsPu3WguXbKtdHKi8vbbqZw2DSUg\n4PpdhGgXErCEEKITKigoICYmhu+//56TVfMiubi4cM899zBjxgycnJzauYSdV1kZ/PSTrabqylA1\ncKCVceMsjBtnqfPImjqKizH88AOGqCi08fH21YqfH5XTplF5220yGvAGJgFLCCE6iaysLA4cOEBM\nTAynT59GVW1f8M7Oztx9993MnDkTZ/nCbpbcXA2HD+s5dEjH0aOXR/8BDBp0OVS5u18lVCkKurg4\nDFFR6Pfto/pAqrMzlshIKidPRgkOpt65GcQNRQKWEEJ0UIqikJiYyMGDBzlw4ADJycn2bXq9nuHD\nhxMeHs64cePo1q1b+xW0E7Ja4exZLT/9ZOtPlZKirbV98ODLoapPn6uEKkCTnm4bCbh7N5rc3KqV\nGqzDhlE5eTKW8HCQ5tqbigQsIYToQIqLizl69CiHDx/m8OHDtaZX6NKlC6NHjyY8PJyRI0dKbdU1\n+vlnDUeO6IiN1RMbq6O4+HItkpMTDB9u4ZZbLIwcaW109F81zYUL6H/4AcMPP6CtMfu96u5O5eTJ\nVN5xh4wEvIlJwBJCiHakqirJycn2QHXmzBms1U/9BXr16sXo0aMZO3Ysw4YNwyAjzJrMYoEzZ3TE\nxtpCVWJi7VoqLy+F0aOtjB5tYcgQa9MG7xUVod+/H8OePbZRgFXNtGrXrljGjcNy221YQ0NtDxcU\nNzUJWEII0cYKCgo4cuQIR48e5ciRI7VqqXQ6HaGhoYwaNYpRo0bh7+8v0ys0kapCRoaGuDg9R4/q\nOHbs8qSfAA4OMHSohREjrIwceZXpFGoym9EfOoR+zx70hw/bkhuAwYBl9Ggst92GZdQo6p+eXdys\nJGAJIcR1VvNhykeOHCEpKanWdjc3N0aOHMmoUaMYNmyY9Ke6BhcuaDh2TEdcnC1QXbhQO4z6+iqM\nGmULVUOGWJuegUpL0f/0E/p9+2yhqnoooVZr61d16622OavkbyUaIAFLCCFamaqqZGZmEhsby5Ej\nRzh+/DjlNWaldHBwIDQ0lOHDhzNy5Eh8fX2llqqJCgo0nDih48QJW6hKT6/dFNejh0pYmJVhw6yM\nGNGEUX81FRejP3gQ/f796GNjqTmU0DpwIJYJE7BERqK6ubXW5YgbmAQsIYRoBWVlZcTFxREbG0ts\nbCw5OTm1tgcGBjJixAhGjBjB4MGDcZDmpCa5cOFyoDp5sm6gcnKC0FALQ4faQpW/v3JN3Z80ly6h\nO3gQw7596I4etT9gGY0Ga0iIrV9VRARqnz6teFXiZiABSwghWkBVVXbu3MlHH31EUVGRfX337t3t\nNVQjRozATWo9rkpVIStLw8mTOk6f1nHqlI7MzNppydHRNi9VaKiVsDALAwYo6K/xm0yTno4+Jgb9\nTz+hO3MGFMW2QavFGhaGZfx4LOHhUlMlWkQClhBCNFN6ejpr167l+PHjAAQFBTFmzBhGjBhBcHAw\nWhlJ1iirFZKStJw6pbOHqoKC2k2lTk62OanCwmwj/YKClGt/VJ+ioD1zxhaqDh5Em5FxeZtOh3XE\nCFtN1dixqK6uLb8wIZCAJYQQ16yyspLNmzdjMpmorKzExcWFhQsXMnHiROlL1YjiYjh71hakzpzR\nce6cDrO59nt69FAZPNhKSIiVwYNtgUqna97J9MeOofvpJ/SHDqEpLLRvUrt1wzp6NJYxY7CMGCGP\nqxHXhQQsIYS4BqdOneKdd94hLS0NgEmTJvHggw/i4uLSziXrWFQVsrM1nDplC1OnT+tIS9NWTxtl\n5+mpEBJyOVB5e6vNe4qMoqBNTEQfG4suNhbd2bOX+1MBiqcnlrFjsY4Zg3XwYJqX2oRoOglYQghx\nFcXFxSQnJ7Nnzx6+/fZbALy8vHjssccYOnRoO5euY6ieg+rkSb29U/qVUybo9RAcbGXQIFuYGjRI\nwdX1Gkb5XUFz6RK6I0dsoerIETSXLl3eqNNhDQ3FMmoU1jFjUHx85Pl/ok1JwBJCiCqqqpKXl0dS\nUhJJSUkkJiaSmJhIbvWz5bBNBDpv3jyMRuNNPRLQbIbERC2JiTp7H6qff64dYLp3V+01U9XNfS2a\niN5qRXv2LPqqUKVNSKBmlZjau7ctUI0ciWXoUGn6E+1KApYQ4qajqioXL14kNTWVlJQU0tLS7EtJ\nSUmd9xsMBgICAujXrx8zZ87E39+/HUrdfoqKIClJR0KClqQkHefPa8nIqNvc16OHLVCFhdlG+fn5\nXduUCXWoKtq0NHTHjqGLi0N34gSamn8fvd5WSzVyJNZRo6SWSnQoErCEEDeFvLw8oqOjiYmJISUl\npd4gBeDi4kJgYCD9+vWjX79+BAYG4u3tje4m6bNTVATnz9tCVHy8jvPndeTk1A0tOp1tlvT+/a0E\nByuEhlrx9VVanG802dnojh+3dVCPi0NT4zFCYOtLZR01yhaqQkNtwwyF6IAkYAkhblg///wzP/74\nI9HR0Zw6darWNhcXF3x9ffH19cXPz8++9OjR46YYCaiqcPGihqQkLcnJWnuoys6uW+Xk4ACBgVb6\n9VOqFtuEnq3RQqq5eBHd8ePojh1DHxeHpkZzLIDq6op12DAsQ4diDQtD9fBo+UmFaAPXPWAZjUYX\n4G9Vv75sMpnOX+9zCiFuXgUFBcTExLB3715OnDiBUjWJpMFgYMyYMURGRhISEnLTBCmwPfElPV1r\nD1OJiTqSk7UUFta9fgcH6NfPSv/+ttqpoCAFX99mTpVQD01+PrqTJ9GdOoXuxAm0VaMxq6nOzljD\nwrAOHYp16FAUX19p9hOdUlvUYN0NvAXEA88A/9cG5xRC3CTMZjMnTpwgLi6Oo0ePkpycbN+m0+ns\noeqWW26ha9eu7VfQNqCqkJenISVFS3Kyruqnrb9UjRkL7JydVQIDlaql9cMUqoomIwN9daA6ebJO\nDRWOjliHDMESFoZ12DCUfv1oWcctITqGtghY3lXnmQ50aYPzCSFuYIqiEB8fz9GjRzl27BinT5/G\nWiM9GAwGQkNDmTBhAuHh4XTv3r0dS3v9mM3Yg1R1zVRKipaSkrq1PRoNeHkpBATYwlRAgJXAQIU+\nfZo551RDrFa0iYmXa6hOn649dQKgdu2KMmiQLVQNGYISHEzLhhYK0TE1K2AZjcZI4DVgj8lkWlZj\n/WTg2apfnzWZTLuATOAYkAIsbVlxhRA3I0VROHXqFPv27WP//v1cuHDBvk2r1TJgwACGDh3K8OHD\nGTRo0A01fYKiQE6Oxl4jVR2msrPrjuIDcHFRCQhQ8Pe3BSl/fwU/P+X69AUvLER37pxtOXPG9ly/\nK6ZmV11dsYaEYB0yBGtICEpgoNRQiZtCc2uwHIFVwLjqFUajUQs8D0yuWvUtsAv4N/ASoAVeb3ZJ\nhRA3FYvFwvHjx9m3bx8xMTEU1BhN1rt3b2655RaGDx9OaGjoDVNLVVhIraa9lBQtqal1HycDl0fx\n2cKUrQO6v79Cz56tXCtVTVHQpqSgO3sW7dmztp/p6XXf5ulpC1RVi+rlJX2oxE2pWQHLZDLtNBqN\nE69YHQycM5lMZQBGo/G80WgMNplM8cBjLSynEOImUFFRwdGjR9m/fz8HDx6kqKjIvs3T05OIiAjG\njRtHcHBwp+2grqpQUKAhLU1LaqqWtLTLy6VL9V+Tm5tqr5EKCLCFKh+fFk7aeRWaS5fQnjljq506\nexbduXN1aqcwGLAGB2MdMMDW7DdwIGqvXtevUEJ0Iq3ZB8sNKDAajW9U/X4J6IWtc7sQQtSrtLSU\n2NhY9u3bx6FDhzDX+BL39fVl3LhxjBs3joCAgE4Vqqqb9tLStKSn116Ki+u/Dicn8Pe3TdBZHaT8\n/a1c98cclpSgO38ebXw8uoQEdPHxdTujA2rfvlgHDrQvSmCg9J8SogGtGbAuAK7Yaqs0wBogvxWP\nL4S4ARQWFpKWlkZqaiqxsbHExsZSWVlp396/f38iIiIIDw/Hz8+vHUvaNJWVkJFhC06pqZdDVEaG\nlhqXVYuzs61GysfHNmqveund+zo179VkNts6oleFKW1CAtqMjLrvc3S0105ZBw1CGTgQtWfP61w4\nIW4cLQlYV/5n4DwwoMbvwSaTKaEFxxdCdFLl5eXk5uaSlZVFeno66enpZGZmkp6eTmFhYa33ajQa\nQkJCCA8PJyIigr59+7ZTqRumqrYZzquDU3WgSkvTkpOjpWqqrTp69VLtIcrH53KgcnVtgyAFUFJi\n6zeVmIj2/Hl08fG2flNXFlivx9qvH0pQENb+/VEGDLA9duYmmb1eiOuhuaMInwKmAR5Go9HFZDIt\nMplMVqPR+Dzw36q3PddKZRRCdDDVz/LLysoiKyuL3NxcsrOz7aGq4IrHm9TUpUsXfH198fHxYcCA\nAYSHh+Pm5taGpW9YWRlkZWnJytKSmaklI0NjD1QNNetptbYpEKpDVM0w1WbTbqkqmosXbTVTSUlo\nz59Hm5yMNiur7nt1OpTAQKzBwSjBwbZA5e8vTX1CtDKNWt8433YSFRWljhw5sr2LIYSoUlBQQGpq\nqj1IZWZmkpWVRXZ2NuXl5Q3up9PpcHd3p2/fvnh7e9sDlbe3N25ubu3Wl6q6JionxzbNQXa2lsxM\njT1QFRQ0XC4nJ/DxUfD2VuyBytfX9rpNs4nVijYjA21iItqkJFvtVFISmitqBgHQ61H8/bEGBqL0\n62cLVQEB4OjYhgUW4sYVGxvLpEmT6v0PhzyLUAhBSUkJqamp9iU5OZnU1FQuXTFJZE0uLi54enri\n4eGBh4cH7u7ueHh40LdvX3r37o22neY6UhTIz7fVPGVna8nJsQWonBzb6/om4qym14Onp4Knpy04\neXurVT+v4/QHjdBcuoQ2JcW2JCejS05Gm5xMfZ271G7dbDVT/fujBASg9Otna+bTy3/mhWgP8i9P\niJuIoihkZmaSnJxMUlISKSkpJCcnk1vPiDGwNef5+fnh7e2Np6cnnp6eeHl54eHhQbdu3dq49Jcp\nCly4oCEnx1YDZWvOs9VCZWc33LkcbDVRHh4KffvaQpSXl4qnp4KHh62TebvkwpIStKmp6FJT0aam\n2gJVaiqaBppa1b59L9dKBQaiBAai9ukj800J0YFIwBLiBqOqKpcuXSI7O5ucnBxycnLIysoiJSWF\nlJSUWiP2qhkMBnx9ffH398fPz8/+s0+fPu3WnFdeDpmZ2qo+URpycy/XSOXmarFYGt7X1dVW82QL\nTioeHkrVouLi0vY1UXbl5WjT0+0BSpuaii4lBU1eXv3v79IFq78/ip+fbenXD2tAALRjuBVCNI0E\nLCE6KVVVycnJ4fz58yQkJJCWlmYPVY31j+rTpw8BAQEEBAQQGBhIQEAAnp6e6NphxNiVo/NqzhmV\nk1P/o2CqubqqVbVQalVNlGJv3nN2brtrqFdRkS1Ipafb+ktVv87OrjuCD8BgQPH1tfWX8vNDqQpV\nUlj3aUAAACAASURBVCslROclAUuITsBisZCdnU1SUpI9UCUmJlJcXFzv+7t161anX5Svry8BAQFt\n3rRXVAS5uVpyc221T3l5WnJzbc17ubkaSkvrDxA6na0pz8vLVvPUt6+tWc/TU8Xd/To9W+9aKAqa\n/HxbcEpLqxWmGmraQ6ezBanqEOXvj9XXF9XTU6ZEEOIGIwFLiA5CURR+/vlnMjMzycjIqPUzOzsb\nq9VaZx9XV1f69+9P//79CQwMtIeptgxRJSXVAao6NF1uxsvNbbxTOdj6RHl7154nysfHVhPVIWYO\nKClBm5lpW7KyLgeqjAyoqKh/H0dHFB8f2+LtbQtVPj4oXl4yHYIQNwkJWEK0EUVRyM3NtS95eXnk\n5eXZX+fn59fbPwpsk3G6u7vj7+9vD1RBQUFtMuVBzQCVm6slL+9ykMrN1VBUdPUA5e5uq31yd1er\nXtt+ursrdO/eAVrBSksvB6jMTDRZWZdfNzKSUnV1vRykqkOUtzdq7960T295IURHIQFLiFakKAr5\n+flkZmba54yq/pmVlVVvLVRNLi4ueHt74+XlhZeXFz4+PvZRe47XYe6imvNCVTfZ5eXZQlR1mLpa\nDZTBQFXz3eXQVP3aw6ODBKj/z96dx0dRH/4ff80eyea+E+5DARVFLPoF9AuicihCEO3XLdajLWq1\n4q/VL1AB64G2nl8PPFBrrXig7aoUFaUqp2IFqqioIIdCQ4AQkhCSbLI5duf3x2SXhNzJkgR4Px+P\neezszM7MZz8M7JvPfOYzYLVE5eRYwWnvXitEVbdMNXhJDyAigkDXrgS6dbNeg4GqRw91NheRBilg\nibSAaZoUFRXVukMvOAVbphoLUcnJyXTp0oW0tDTS09NJS0urNe8Kc8ci04SiIoP9+w/1fQpewguG\nqhrPVq5XZKTVApWaeqgfVM2WqISEDrwrr6bKSoz9+7Ht24ctJwej+tWWm4uRk4PRQH81wOpk3qWL\nFaK6dcMMhqlu3TCTk9UaJSItpoAlchifz8fu3bvrhKfga2N36IHVLyrYAhUcNyo4fyQC1MGDBnv3\nWoNp5uTYyMuzQlRenhWqGhsTCiAqilrBKS3NCk5paZ3oEh5Yj4MpLMTIyTkUonJzrRC1bx9Gfn79\nd+gFRUQQyMg4FJxqtErpkp6IhJsClhy3qqqq2LNnD1lZWaExooKPhWnsEVIxMTGhx8AE79TLyMgg\nIyOD9PT0sIaoQMBqgcrLM8jPNygosFqfcnJsoUBVVtb4PmJiTNLSTFJTA6SlBS/dmdWBqhMFKL8f\nIz8f2/79VktUzdfcXGz79jXcqRzAZsNMS7NCVJcumBkZ1nxGBmaXLpiJiZ3ki4rI8UABS455wQcT\n79y5k507d4ZGL8/OzqaqntEq7XZ7qNUpeOkuGKTS09PDdodesP9TXp4tdAkv2PpUUGCQl2cjP9+g\niW5bxMSYdO16aDDNjIxDYSo1tR0fONwUr7f+8JSXZ13GKyhovAUKMOPi6gSn0Hxamu7QE5FOQwFL\njgl+v5/8/Hzy8vLIy8sL3aEXbJ0qLi6ud7uMjAx69+5dawTz7t274wzDD3VlpfU4l7y82p3Hg0Fq\n//6m+z8BxMWZJCebpKaaJCdbwSn4aJeuXTtJC5TXiy0/H6N6shUUWPM1QpTh9Ta+D8PATE4mkJZm\ntUQd/pqRQcePICoi0jwKWNLpBceHysvLIz8/n/3799cJUwcOHCDQSOtHbGwsvXv3Do1gHgxU0a1s\n3qnZ+hQMT8HQFAxRBw4YjY5EDoeGMAi2NqWnm6SkWO9TUgIkJ5sdO6BmIIBx4IAVlgoKsOXl1Q5Q\n1cualRQjIgikp1thKTXVeq35PjVVLVAicsxQwJJOwTRNDhw4QHZ2Nrt37w697t69m7y8vCaHNwCr\nc3l6ejopKSmkpaWRmppKjx496NOnDykpKS0aL6qszApPeXlGKDgFX/Pzrdcm+rpjs0FKihm6Ay8Y\noIKX79LSrEe6dEjrk99vdRg/cMCaCguxHTiAUVAQClS2/Hxr+IJm1D0REVZISk7GTEkhkJJivaal\nYaamEkhPp3M0tYmItA8FLGk3Xq+3ziCb+/fvJycnh927d1PWSG/thIQEUlNTSU1NJSUlJTRfc1lz\nL+uZJhw4YJCTYw1VEOwDZYUpK0iVlDQdBKKiCN1pl5pqhkJT8DUlxWzfp5+YJpSWYhw4gK1meCoo\nOBSgqsOUcfAgTTavBXebkGBduqsOTTUDlJmSQiA52RoPSuFJRCREAUvCxuv11hnSIDi/f/9+vE30\nwYmNjaVHjx5079499Nq9e3cyMjKIiIhodjmCl+/y862wtG+fdbfdvn2HhjJo7GY0sK5UpaYGg5MV\nlmreiZea2k4PFK6sxCgqskJR8PXgwUNTUVFo3nbgAE02qwUZBmZiojUlJRFISgrNh8JUdWuULtuJ\niLScApY0i2maHDx4kH379pGXl1fnUS+5ublNBqjIyEjS09NJTU0N3ZGXmppKRkYG3bp1IyEhocnL\neOXlUFBgcOCAwYED1t12BQVGKEzl51vzTQWo+HiTjAxrqIK0tENBKtgSFR9/hAbPrKqqFYrqnWoG\nqqY6hh8uIoJAcrIVjhISDgWmmgEqKQkzIQEc+usvInKk6F9YCY1OnpubW6vzeH5+fmhq7Dl5QcEA\nFRwP6vDxoeLi4hoMUBUVsG+fUR2YDgWnmvMHDjT92Jag6GiTlBRrysgIVA9hcGgogza3PgUC4PVi\nFBfXnUpKrHBUz/smB606nN1uDU2QkGBNiYmH5hMSCCQkYMbHW1NysnXdUpfqREQ6nALWcaCqqip0\n993hDxgOzlc01eSDdQkv+FiX4PhQNV/j4+PrBCif79BI45s2WYEpP98KS/n5h+abemBwkN0Oyckm\nSUkBkpJMkpKsIQxSUoIdya2hDJoVoEwTyssxvF6rpaikBKO0NPTeKCmxQlFxsRWQqt8HQ1NTYzbV\ny2Y7FJji463AFHytJzgRG6sRxkVEjkIKWEcxn89HUVERRUVFHDx4MPRaUFDA/v37aw1h0NjI5GCN\nTh688+7wzuTB1+AI5cHn21mX4wx+/NHG+vVWK1NRkUFhocHBgwaFhc0b5wms4JSUdGhogkPTofdJ\nSYeN+WSa4PNZoai01ApI+7wYP3oPhaaaYameZc26Q64BZnQ0xMVZgSk4xcZagSk4H1weH48ZG2uN\n46TAJCJyzFPA6kRM06SwsJDdu3dz4MCBUHgqKiqiuLi41vuioqJmtToB2Gw2kpOTQwGqvocMx8TE\n4PNZd9fVDEibNxu1lgVbneoZAL1eDgckJloPBE5KsjqLJ8dVkBzjIzXaS4rLS3JkCQlGEXZfKUZZ\nGZSVWaHpQBnsqZ6vuby01Jr3+VrXilST04kZE2OFoZgYiImx3genmuEpPv5QoIqNVR8mERFpkH4h\n2pnf76e4uJiCggL27NkTGuspOJWWljZ7X06nk/j4+NCUkJBAfHx8KEylpKQRHZ2OYSTj9TopLDRC\n086dBl99FXxv4+BBo3ZrU8C0wosZsF4DAYzAofm4iApSYspIifKSFlVCamQRKc4ikuxFJBgHSTIK\nSaSQmKoijHIfxgEf5JRboagNrUZ1RERgRkeHAlGdgFTdatTQMlpwd6KIiEhzKWC1gmmalJeX4/V6\nKS0tpaSkBK/XG3rv9XopKSkJtToVFxeHLuE19MiWoJiYGHr06EFKSgrxMTHEx8YSFxWNyxGHw4jD\nYcZgC0Rj+KMo9zooKYaSYpPiIoPiXIPsEhubvTYKS5wcLHHi94MRKLGCkhkMTXVfjUAAF5Uk2Q+S\nZCsi0V5EsqOIJEcxiY5ikh1FJDqKSYkoItV5EJetAgKAt3pqCafTCkXR0eByWfNRUVD9akZHQ1RU\n3fmoKCsYBeejotSKJCIinVKn+3Uy8vOtGdOsf4JD4SC4TUOfNU38VVWU+nxUVVZSUVFBZUUFlZWV\nVJWXU1FeTmVlJWWlpZSWlVFWVoa3+rU0OO/zUerz4fX5KCsvp6T6NVCzDDXK1lhZCASwAzF2FzH2\nBFKcqcTb04ixJRNpJmM3EynfG0fBdzEUVEWT5Y+ixB+F1x9NgMM7gQeApi8RxtnKqkNSCYnVr8HQ\nVPM1yVFMtM13qH+T3Y4ZGQmRkZgulxWEoqIgsgdm5IlUuVyYkZHWsur5Wq/BbYLzkZHWuqgojask\nIiLHvE4XsN6bOBG/aeIHAqYZmvfX00m7ZuQoDQQo8fsprp6Kqqoo8fvxtrWPTjXTBJNI/LgImNE4\niMFpiyXSFoPTiMFuROMwYrATjWHEAFHYiMYkBr8ZS5UZQ6UZQ3kgGq8ZgRfIbfKoBtgMsBu47JXE\nOsuJiaggxllBrKuCuKhK4iIriYuuIi7GT3yMn9iYAHGxARLi/SQmgiPKYV0Gi4jAjEiAiDTMiAgr\n8Dgch4KP00lpZCSm02l93uHQ7f4iIiKt1OkC1vxcPyZ2TMMBpg0TGyZ2wG6NPh1sNMKoEQAMTJwE\nTCcBIjFxETAjCZhOTFw4bVHYbU5shh0DJ3abE8NwYDOcGDYHNpsLu+HCqJ4wXJhGJCaRBMxIqgIu\nqkwXhs2G3WbDYbNhGAZVhkEVhpX0DANqzhu26nlb9fvqyWZgs1vjNCXEmyQlBkhMNElMhsRkg8Rk\nSEq1kZBkEBtvEBNjEh3duithJtD4yFUiIiJyJHS6gLUveTlgYBhGdSYxmvWQXpvNht1ux2534LDb\nsdvtOBx2bDZ7kw0xJtDYTXE2IALrylZ0tElUlInLZc27XBAVZVZP4HKZ1Z+x5mNjrcnqf20SE2Nt\nY5XJABp7WF3znhUnIiIinUunC1innNIFu53qyXpYrs1mTYcHpeB7wwCHw8TqMmRiXQEziYwMEBnp\nx+m0WoBq7rPm5HRa20ZEWK9OpxWCgsuD4aldH9wrIiIiR61OF7Befrmlt6SJiIiIdC4aUlpEREQk\nzBSwRERERMJMAUtEREQkzBSwRERERMJMAUtEREQkzBSwRERERMJMAUtEREQkzBSwRERERMJMAUtE\nREQkzBSwRERERMJMAUtEREQkzBSwRERERMJMAUtEREQkzBSwRERERMJMAUtEREQkzNolYLnd7ofc\nbvej7XEsERERkY7WXi1YswCjnY4lIiIi0qGOSMByu93/53a7n3S73dEAHo8ncCSOIyIiItIZOZrz\nIbfbPRJ4BFjt8Xhm1lg+Brir+u1dHo9nBYDH45kR7oKKiIiIHC2a24IVCdxfc4Hb7bYBc4Fx1dPd\nbre7zmVAt9ttuN3uB4BzqgOZiIiIyDGtWS1YHo9nmdvtHnXY4v7AVo/HUwbgdrt/APoB2w7b1sTq\ngyUiIiJyXGhWwGpAMlDodrsfq35/EEjhsIDVUhs2bGjL5iIiIiIdri0BKx9IBG7CukNwPpDXlsKM\nHj1adxqKiIjIUa8ldxEeHn5+AAbUeN/f4/Fsb3uRRERERI5uzQpYbrf7NuBuINPtdj8H4PF4/Fid\n3D8CPqxeLyIiInLcM0zT7OgyiIiIiBxT9CxCERERkTBrSyf3VmlocNK2flYOaWEdLwBOAnzAAo/H\n89KRL+HRraGBdxv4rM7hVmhhHS9A53CLuN3uZ7HqzAb8yuPx/NjIZ3UOt0IL63gBOodbzO12/xE4\nBwgAv+5s53G7tmA1d3DSln5WDmlFvZnAzzwez/n6S91sdQberY/O4TZpVh1X0zncQh6P50aPx3M+\n1vnZYIDVOdx6za3jajqHW8Hj8fzB4/FcgBWcbmvocx11Hrf3JcLQ4KTVA5QGBydt62flkNbUm/7B\nbAGPx7MMKGjGR3UOt1IL6jhI53DrFAMVjazXOdx2TdVxkM7h1hsObG5kfYecx+19ibAlg5MekYFM\njwMtrbdi4DW3210A3KqhNsJK53D70DncelOBeY2s1zncdk3VMegcbjW32/0xkAqMbORjHXIet3fA\nasngpGEfyPQ40aJ683g8vwVwu91nAA8Dl7ZDGY8XOofbgc7h1nG73ZnAFo/H830jH9M53AbNrGOd\nw23g8XjOdbvdQ4GXgQkNfKxDzuP2vkTYksFJNZBp67S23nxA5ZEp0jGpOc35OofbpqWXTHQON5Pb\n7T4TGOXxeB5v4qM6h1upBXVck87h1smh8QajDjmP27UFy+Px+N1ud3BwUqgxOKnb7b4cKPV4PO81\n9VlpWEvquHrZ34CuWE3U09qxqEet6oF3xwNd3G53vMfjuaF6uc7hMGluHVcv0znccm8Au9xu90rg\nmxotKDqHw6dZdVy9TOdwK7jd7r9jXR6sAG6usbxTnMcaaFREREQkzDTQqIiIiEiYKWCJiIiIhJkC\nloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiI\nhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJ\niIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiY\nKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiI\niEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkC\nloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiI\nhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJ\niIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiYKWCJiIiIhJkCloiIiEiY\nKWCJiIiIhJmjPQ7idrvTgN8DScBNHo+noj2OKyIiItIR2qUFy+Px7Pd4PDOBrUBGexxTREREpKO0\n2yVCt9t9FXA2kN1exxQRERHpCIZpmq3a0O12jwQeAVZXt04Fl48B7qp+e5fH41lRY93lQK7H41nd\n+iKLiIiIdG5t6YMVCdwPnBNc4Ha7bcBcYEz1og/cbvdK4BTguuptZiIiIiJyDGt1wPJ4PMvcbveo\nwxb3B7Z6PJ4yALfb/QPQz+PxbAL+t6l9Ll++vHXNaSIiIiIdYPTo0UZ9y8N9F2EyUOh2ux+rfn8Q\nSAG2NXcHQ4YMCXORRERERMJvw4YNDa4Ld8DKBxKBmwADmA/khfkYIiIiIp1aW+8iPLxZ7AdgQI33\n/T0ez/Y2HkNERETkqNLqgOV2u28D7gYy3W73cwAej8eP1cn9I+DD6vUiIiIix5VWD9NwJCxfvtxU\nHywRERE5GmzYsKHdOrkfMXl5eVRU6Ak7Ise61NRUIiIiOroYIiJtclQErJKSEgzDoFu3bh1dFBE5\nggKBALt37yYjI0MhS0SOau32qJy2OHjwIMnJyR1dDBE5wmw2G927dycvTzcfi8jR7agIWIZhYBj1\nXuIUkWOMzXZU/LMkItIo/UsmIiIiEmYKWCIiIiJhpoB1lPr222/56KOPwrrPG264gREjRnDFFVe0\naT9Tpkzh008/DVOpGrd9+3buv//+etc988wzlJWVtUs5REREalLAOkpt3LiRZcuWhXWfzz33HA8+\n+GCb99Oefeb69evH7Nmz61333HPPKWCJiEiHOCqGaWhM7KRJYdtXyTvvtHib//znP9x66634fD5K\nS0uZPn06mZmZADzwwAPs2rWL3NxccnJyOOecc2oFGI/Hw1//+lcMw2DIkCH86U9/Cq3btWsXt99+\nO/v378c0TX7+859zzTXXAPCXv/yFP//5z3i9Xr755htGjRrFbbfdFtp28ODBTJ8+nVdeeQWfz8fC\nhQvp1asXAPfffz+ff/45eXl5dOnShZdeegmXyxXatjUDzxYUFHDDDTdQVFREnz59OHjwYK39NPY9\ne/bsyb333suSJUvYuXMnTz/9NMOGDau3bmfMmMHEiRMB8Pl8XHbZZRQVFdGzZ09ef/310D59Ph+X\nXnopubm5TJkyBYfDwfPPP0/37t355JNPeOKJJ3jjjTcAK6jOmDGDDz/8sMXfW0REpCFHfcDqaH/+\n858ZM2YMN910U511hmGQn5/P3/72NwAmTZrEhx9+yLhx49i8eTOvvPIKS5YsweFwcNttt/H3v/+d\nn/3sZ/j9fq688kruuusuRo8eXWe/1113HTExMXz99dc88MAD9R5369at9V5CvP7660MtPldddRXv\nvfceP/3pT9tUBw888ABDhgxh9uzZ7Nu3j3HjxoVasBr7ngDl5eWkpaXx5ptv8tprr/Hiiy+GAlZj\ndetyuXj//ff59NNPeeqpp+qsW7p0KWeccQZ///vfSUpKCq0bOXIkM2fOZM+ePXTr1o3XXnuNqVOn\ntun7i4iIHO6oD1itaXUKp0suuYQZM2aQlZXFxIkTGTFiRK31I0eOxG63A1bAWr9+PePGjePjjz8m\nOzubyy67DIDS0lISExMB2LZtGy6Xq95wFWSaZqOtTdOnT693eWJiImvWrGH79u14vV5ycnJa9H3r\ns3btWl555RUAMjIyGDhwYGhdY98TrDA0YcIEAHr16sXBgwdD65qqW2hdi9tVV13F3//+d6ZNm8ay\nZcuYO3dui/chIiLSmKM+YHW0oUOHsmrVKtatW8czzzzDkiVLarUq1QwAgUAgNDq10+nk4osvrnW5\nrKZAINDocVvTx8nr9ZKZmcn48eMZOnQoJ554YqsCyuHsdnuD+2nqezamqbptrZ///OdkZmZywgkn\nMHbsWCIjI9u8TxERkZrUyb2NAoEANpuNs88+m5tvvpnPP/88tM40TZYuXUpFRQUVFRW89dZbnHvu\nuQCMHj2at99+mx07dtT6PED//v0pLy/n3XffbfC4kZGR7N+/P1SG5ti+fTtOp5OZM2dyxhlnsHHj\nxrAErBEjRvDWW28B8OOPP7Jx48bQusa+Z1Maq9vmiIyMJDc3t84xk5OTGThwIHfddRe//OUvW7RP\nERGR5lALVhu9+eabvPDCC6HLgA899FBonWEY9O/fn6uuuoo9e/YwYcIEhg8fDkDv3r2ZN28eN9xw\nQ6gF6O6772b48OHY7XYWLlzInDlzePrpp7HZbFxyySXccMMNoX2fd955zJs3j4suuoi4uDheeukl\noqOjQ8etz6BBg+jZsycjR46ke/fujBgxIhTSapZ5/fr1TJgwgXvuuYczzzyzyTqYMWMG119/PWPG\njKFv37707ds3tK6x73m4w+8+bKxuG9qmpqlTp3LllVfSs2dPLr300tBNAgBut5u9e/dy0kknNfn9\nREREWsoIRwtGuCxfvtwcMmRIneXBDslHmwcffJCYmBhuvvnmji6KHGbWrFmcd955XHTRRR1dFKnH\n0fp3XkSOLxs2bGD06NH1/i9flwiPMD1DsXN56623GD9+PIDClYiIHDG6RHgE1RybSjqHn/70p20e\nlkJERKQpasESERERCTMFLBEREZEwU8ASERERCTMFLBEREZEwU8ASERERaYJpgs/X/M8rYB1hRUVF\n/PWvfz2ix+jZs2fY9/nll18yadKksO83nBqq2zVr1nDFFVeE5RhHom5bY/v27dx///2t3v5o+PMU\nEenMXnopgjlzoikqat7nFbCOsMLCQl544YUjeozjdayt46lu+/Xrx+zZszu6GCIix6VFi5wsWhTB\njh02fvzR3qxtNA5WG/3nP//h1ltvxefzUVpayvTp08nMzARg/fr1zJkzh6ysLC6++GKSk5N59dVX\nQ9vef//9fP755+Tl5dGlSxdeeuklXC4XYLWc3HvvvSxZsoSdO3fy9NNPM2zYMAC+/vprfve73xEX\nF8fw4cNrPWevuLiYWbNmsXfvXrKzs5k0aRJ/+MMfQuunTZvGCSecwMqVK/H5fPzmN78JjQv1+uuv\n88QTT9ClSxcGDx7c7Drw+Xz8/ve/Z/PmzZimyahRo7jjjjsAyMzMZPjw4SxatIg5c+awYMECBgwY\nwCOPPALA6tWruf/++zEMg/j4eB555BF69OgRqtuZM2dSXFxMIBDgjjvuYMSIEc2qW5/Pxx133MHX\nX39NSUkJb775JsnJyQB89dVX3HXXXfj9fpKSknj88cdJSUlpsm4bs2bNGu677z769OnD1q1bSUhI\n4C9/+QtJSUlNHjMrK4spU6YwceJEVqxYQUxMDG+//Xboe1x22WUUFRXRs2dPXn/99VrHnT9/PosW\nLcJms3Haaadx3333hc6hxv48G6vb4uJirrvuOkpKSigrKyMxMZFrrrmGyZMnA42fQ4sXL2bhwoWh\nbZ9//nn69+/PmjVrePTRR4mOjsbv93PBBRfw9NNP88Ybb9C/f/9m1bGISEdYtszBggWRAPz2tz7O\nOMPfrO2O+kflhPOyxzvvvNPibW6//Xa6d+/OTTfdVO/6Xbt2MWXKFD799NM66/Ly8khNTQXgqquu\n4tJLLw39UKWnp/Piiy8yYcIEXnvtNT7++GOeffZZAM455xwefPBBRo4cydq1a5k0aVLoocYABw4c\nICkpibKyMs466yyWL19Oly5dAOvHcdeuXSxcuJC4uLjQNnv27GHs2LGsXr2a1NRUHnvsMVauXNms\nOnn//fdZuHAhCxcurLNu0qRJ/OxnP6O4uJj33nuPV155hWHDhrFlyxby8/MZPXo0S5cupWvXrrz3\n3nvMnz+f9957D7BGWp8+fTpjx45l165dTJw4kVWrVoVCS0N1u2bNGm688UYWLVrEgAEDmDZtGsOH\nD+fqq6+moqKC0aNH88Ybb9ClSxfefvttli1bxpNPPtmsum3ImjVrmDZtGh9++CEZGRncc889VFVV\ncc899zR5zKysLIYNG8YzzzwTCjGH+/TTT3nqqadqBayVK1fy8MMP8/bbb+N0Opk9ezZxcXHMqII6\nQgAAIABJREFUmTOnyT/P+up29erVJCYmMn/+fIqKipg1axYPP/wwFRUV3H777aHjNnQOARQUFISC\n7DPPPMPWrVt57LHHWLNmDbfeeiuffPIJAwcO5KWXXmLp0qX069ePqVOn1vm+elSOiHQGa9faefDB\nKPx+uP76cjIzK2ut16NyjqBLLrmEv/3tb8yaNYs1a9bUWd9YgE1MTGTNmjUsWLAAr9dLTk5OaJ3L\n5WLChAkA9OrVi4MHDwLWZbHi4mJGjhwJwPDhw0MtFkF2u50PPviAV199lYiIiDoB4frrr6/zw7hh\nwwZGjRoVCnznn39+c6uAYcOGUVBQwA033MCiRYsoLy+vtf7UU08lISGBU089lcTERMrKygD497//\nzfDhw+natSsAEyZMYOfOnXi9XoqLi8nOzmbs2LGA1aI3bNgw/v3vf4f221jdDho0iAEDBgC162/b\ntm3s3r2bX//610yaNInnn3+ePXv2AM2r28YMHDiQjIwMwBoxfv369U0eM+iEE05oMFw19F1XrFjB\nFVdcgdPpBOC6665j+fLlQON/ng3VbbC80dHRofo6cOBA6DvVVN85BJCcnMw333zD3/72N7Zv386+\nfftC6/r164fL5SI+Pj50TpSWljb4nUVEOtK339p5+GErXP3sZxV1wlVTjvpLhK1pdQqnoUOHsmrV\nKtatW8czzzzDkiVLeOCBB5rczuv1kpmZyfjx4xk6dCgnnnhisy5H2WyNZ+LvvvuOG2+8kalTpzJo\n0CBSUlLq7Le+4zgcjlrLW9KymZKSwtKlS9myZQtvvPEG8+bNY/Xq1XU+d/g+DcMgEAjU+Vyw31N9\n5W5rnyi73U6vXr3qPW+aqtuWCAQCRERENHnMcByn5nywfpr682ysbq+++mrGjBnD2LFjOeOMM/jl\nL39Z57gNnR/Tpk0DYPLkyQwePLhOkBQRORr88IONP/7RRWUlXHRRJT//eUWL96EWrDYKBALYbDbO\nPvtsbr75Zj7//PNa6yMjIzlw4EDohzD4w7R9+3acTiczZ87kjDPOYOPGjc0KNfHx8aSnp7N27VoA\nPvjgg1qtAKtXr2bcuHH86le/Ij4+nqysrGbt96yzzuKzzz6jsLAQ0zRDfYCawzRNTNPkpJNO4pZb\nbiEnJwev19vkdv/1X//FunXryM7OBqz+OyeeeCLR0dHExcXRu3dvli5dCsDOnTtZt24dQ4cODW3f\nUN02pn///pSXl7NkyZJa5Yem67YpGzZsYNeuXQAsXLiQc889t8ljtsWYMWN4/fXXQy2Gzz//fKhV\nqrE/z6bq9pVXXmHs2LF89NFHPPzwwzgczf9/2NKlS3nkkUcYPXo0X3/9dVi+p4hIe9q922Du3ChK\nSw1GjKjixhvLac3/7Y/6FqyO9uabb/LCCy9gt1t3FTz00EO11mdkZHDOOecwatQo0tLSuP322znz\nzDMZNGgQPXv2ZOTIkXTv3p0RI0awf//+eo9hGEatlpt58+bx29/+loiICEaOHEl0dHRo3WWXXcZV\nV13FJ598Qv/+/Tn77LPrXCKsrxUoNTWVOXPmMGHCBJKSkjjrrLOa3Vq0detWbr75ZpxOJxUVFcyd\nO5eYmJh6v0dNycnJPPnkk1x77bUYhkFCQgLz588PrX/22WeZMWMG8+bNIxAI8Mwzz5CQkBBa31Dd\nHl5fNY9tt9tZuHAhs2bN4sknn8Rms3HppZfy61//usm6bYxhGJx00kncf//9bN26le7du3PnnXc2\n65j11U19+z/8M6NGjWLTpk1MmDABwzAYNGgQt9xyC9D0n2djddunTx8ef/zx0CXv+Ph4Zs6cyZln\nntlkeadPnx46p8ePH89XX31VZ5ua23aWuzRFRADy8w3uuiuawkKDn/zEz623+mjtxY2jvpO7SGew\nZs0ann766Tp3+R2N7rvvPvr164fb7QbgjjvuIDIystbdqEea/s6LSHsrKYFZs6LJyrJx0kl+7rmn\njKioxrdprJN7u7Rgud3uZOBOoCtwk8fjyW+P44q0l/pamI5WAwcO5KmnnuLll1/G7/dz+umnc9tt\nt3V0sUREjhjThHnzXGRl2ejZM8AddzQdrprSLgHL4/EUALe43e6JwGlA3R7QIkex//7v/+a///u/\nO7oYYTF58uRG72gUETnWLF7sZN06BzExJn/4Qxnx8W3fZ3t3cv8JsK6djykiIiJSr02bbLz8sjWQ\n6O9+56Nr1/B0nWp1C5bb7R4JPAKs9ng8M2ssHwPcVf32Lo/Hs6J6+cXAco/H04JHJYqIiIgcGQcP\nGqGxri69tILhw5s3SntztOUSYSRwP3BOcIHb7bYBc4Ex1Ys+cLvdK4F+wGzgE7fb7fN4PBvacFwR\nERGRNgkE4NFHXeTnGwwc6Ofqq5sY6yoQwLFuHabdjr/GkEENaXXA8ng8y9xu96jDFvcHtno8njIA\nt9v9A9DP4/FsA0a29lgiIiIi4eTxRPDll3bi401mzPDR4JB/Ph/OFStwvv02tr17CfToQelZZ9HU\n+A3h7uSeDBS63e7Hqt8fBFKAbWE+joiIiEirfPWVnddfj8AwYPp0H6mp9fe7cnzyCZF//jNG9ePD\nzPR0KsePt5q/2jlg5QOJwE2AAcwH8sJ8DBEREZFWyc83eOQRF6YJU6ZU8JOf1NPvqqgI17PP4qge\ncDnQvz8Vl15K1dlnQ/XA4k1p612Ehw/88wMwoMb7/h6PZ3sbj9Hp7dixg5SUFN58881ayx944AF+\n8pOfcPHFF3P++ecze/bsTv/okPfff58tW7bUWZ6ZmVlrVO7WevLJJ3nwwQfbvJ9wePDBB9m2rfWN\nq1OmTOHTTz8NY4lERORI8vvh4YddHDxoMHiwnylT6va7sn/3HTE332yFK5eL8ptuovT//o+qESOa\nHa6gDQHL7XbfBtwNZLrd7ucAPB6PH6uT+0fAh9Xrj3n/+Mc/mDx5MosXL6613DAMrrvuOt5//31W\nrlzJtm3bWLZsWQeVsnnee++9egNWuAbR7EyDcd52223079+/1dsfS4OLiogcD15+OYJNm+wkJZlM\nn173MTiOZcuI+sMfMAoL8Q8ciHfePCovuojWPIywLZ3cHwTqNEV4PJ4PscJVu5g0KTZs+3rnnZJW\nbbdkyRJee+01Jk+eTFFREfE1RigLtlgVFhZSUFBAjx49mrXPFStW8NBDD2Gz2fB6vSxcuJAePXqw\nZs0aHn30UaKjo/H7/VxwwQU8/fTTvPHGG/Tv35/S0lJmz57N999/j9/vx+1213rm3fz581m0aBE2\nm43TTjuN++67D5fLBcBvf/tbli9fzhdffMGzzz7L//t//4/x48eHtl27di0PPfQQ27dv57rrrgvt\n1+/3M3fuXD7//HOqqqq49tpr+dnPfhbabtasWfzrX/+ia9eupKam0qtXr2bVQWZmJsOGDWPdunXs\n37+f3/3ud1xxxRXNOua0adM44YQTWLlyJT6fj9/85jf89Kc/BeCFF17grbfeYtOmTSxevJgzzjgj\ntN1//vMfZs6cSXFxMYFAgDvuuIMRI0YAUFBQwA033EBRURF9+vTh4MGDtVokG6vbRYsW8dRTT4We\nWdm1a1defvllALKyspgyZQoTJ05kxYoVxMTEhB7OXFxczKxZs9i7dy/Z2dlMmjQp9MiazMxMhg8f\nzqJFi5gzZw4LFixgwIABPPLII82qXxGR48mHHzr4xz8isNvh97/3kZhY44pSIEDEyy8TsWgRAJWT\nJlH+q1+1qMXqcHrYcxtt27aNhIQEunTpwsSJE1m6dGnoh940TV588UXefPNNAoEAf/rTnzjllFOa\ntd977rmHJ598kkGDBtVZt2vXLj755BMGDhzITTfdxMUXXxx6uPOjjz5KYmIiH3zwAT6fj0mTJnHy\nySdz7rnnsnLlSpYsWcLSpUtxOp3Mnj2bRx99lDlz5gDwxBNPMG3aNC666CIyMzPrHHfPnj289tpr\nZGVlcfHFF4cC1ssvv4zNZuP999+nvLw89MPfu3dv3n77bTZv3syqVaswTZMrr7yS3r17N6sODMMg\nOjqad999l/379zNq1CguvPBCkpOTGz1m0OrVq3n99deJi4urtd9rr72Wa6+9lkmTJtVpgbrhhhuY\nPn06Y8eOZdeuXUycOJHVq1eTmJjIAw88wJAhQ5g9ezb79u1j3Lhxoe0bq1vTNLnzzjtZu3Zt6KHM\n7733Xq3j7tixg4EDB4b+LILi4uL44x//SFJSEmVlZZx11llcd911dOnSBcMw6NOnD9dffz0LFizg\nlVdeYdiwYQpYIiKH+eorO888Y/2H98Ybyzn11Br9rqqqcD3+OI6PPwa7nfIbbrBardroqA9YrW11\nCpfFixeTlZXFuHHj8Pl8fPvtt6GAZRgGU6dOZeLEiYwfP77Z4Qrgmmuu4ZZbbmHcuHFceumlDBhw\nqGtbv379cLlcxMfHc+qpp/Kvf/2LsrIywGr5euGFFwBwuVxceeWVLFu2jHPPPZfly5dzxRVX4HQ6\nAUKtUIf/qDfUTyzYAtSrVy+KiopCy1euXElWVhaTJk0CwOfzsXXrVnr37s3atWtxu93YqtthR4wY\ngdfrbXY9jB49GoC0tDTOOussNm7cyHnnndfoMYOuv/76OuGqMcXFxWRnZzN27FgAevbsybBhw1i/\nfj3jxo1j7dq1vPLKKwBkZGQwcODA0LaN1a1hGERERFBSUoJhGMTGxhIREVHr2CeccEKDj6ex2+18\n8MEHZGVlERERQW5uLl26dAHg1FNPZfPmzZx66qkkJiaGzgMREbFkZdl48EEXfj9cdlkFF15YeWhl\nRQWuhx/GsW4dREVRNmcO/sGDw3Lcoz5gdbR3332X5cuXk5iYCFjPpKt5mdA0TXr37s3VV1/NnXfe\nyfz585u136lTpzJlyhSWL1/O9ddfz/Tp00NhoimBQCA0b5pmKNwYhlFrXSAQqLcPUUP9ihoKXg6H\ng1mzZnFRPYnfbrfX2q6lnfwP3zYYTBo7ZmuPVd82Nevo8O9SU1N1O3fuXM4//3xOPvlknn322WaX\n57vvvuPGG29k6tSpDBo0iJSUlHrL0NlvnhAR6QiFhQb33BOF12tw9tlVXHNNjU7tPh9Rf/oT9q+/\nxoyNpezuuwkMGNDwzlqovZ9FeEz5/vvviY2NDYUrgPPPP5933323zmdvueUW/vWvf/HZZ581a99+\nv5/o6GgyMzO59NJL2bCh4cHvTdMM/cCOHj2aF198EYDS0lJeffVVxoyxBtYfM2YMr7/+OuXl5QA8\n//zzodaaIJfLxf79+4HaQa0xEyZM4IknnqCkpCRUnqCRI0eyePFiTNOkpKSE5cuXN2ufQcEbB7Kz\ns/nyyy85/fTTmzxma8XFxdG7d2+WLl0KwM6dO1m/fj1Dq0fsHTFiBG+99RYAP/74Ixs3bgxt21jd\nVlZW8sgjj7BmzRr+8Y9/cM4559Bcq1evZty4cfzqV78iPj6erKwshSkRkWYoL4c//clFbq5B//4B\nbr21Rqd2r5eou++2wlViImX33RfWcAVqwWqTxYsXc+GFF9ZaduGFF/Lkk09y5ZVXAodag6Kiorjn\nnnuYMWMGH3/8caizc0PuuOMOvvzySwKBAOnp6Tz++OOh/QX3WfM1OP+///u/zJ49m3HjxuH3+5ky\nZUqok/aoUaPYtGkTEyZMCPUFuuWWW2od9/LLL2fatGksXryYU045pdaQCg21bP30pz8lJyeHSZMm\nhTp1ezweYmNjufDCC1m1ahXnnXceqampdO/evUV33jmdTi655BLy8vJ4+OGHiY2NbfKYTZW3Mc8+\n+ywzZsxg3rx5BAIBnnnmGRISEgCYMWMG119/PWPGjKFv37707ds3tF1jdet0OklLS+Pyyy/H5XJh\nt9sZMmQIc+fObbKsl112GVdddVWoj93ZZ59Nbm5unc/pbkYRkUMCAXjsMRdbtthJSzP5wx/KqP6p\ngKIiou++G9v27ZipqZTecw9mM29AawmjM/1vePny5eaQIUPqLN+zZw/dunXrgBJJR5o0aRL33nsv\ng8N0Pbyj7N69m+nTp/PnP/+Z+Ph49uzZw8iRI/nmm2+Ijo7u6OJ1Svo7LyJt8dJLEbz1VgTR0SYP\nPlhG797WFRmjoICoO+/ElpVFoGtXyu69FzM9vdXH2bBhA6NHj673f7hqwRI5whITE4mIiMDtduN0\nOnE4HDz33HMKVyIiR8BHHzl46y1rOIbbbvMdCle5uUTdcYf1PMGePa1wlZx8xMqhgCWd1jvvvNPR\nRQiLmJiY0JhXIiJy5KxbZ2f+/EPDMQQfg2NkZxN9550YeXkETjyR0rlzocaYlUeCApaIiIgc9dau\ntfPgg1F1hmOw7dxJ1J13hkZnL7vjDoiJOeLlUcASERGRo9pnnzl46CFrrKvJkyv4xS+s4RhsW7cS\nNXcuRnEx/sGDKbv9dg71dj+yjoqAZbfbKS0tVZ8VkWOcaZoUFBTUGYhVRKQhn37q4P/+79BAor/4\nRQWGAfZvvyXq3nuhrIyqYcPwzZwJ7fhvy1ERsNLT08nNzaWwsLCjiyIiR5BpmiQkJNQabkNEpCFr\n1jh45BErXP3P/1Rw9dXV4WrDBqLuuw8qKqgaORLfrbeCo30jz1ERsAzDICMjo6OLISIiIp3Exx87\neOwxK1xdfnkFV11lhSvHqlW45s0Dv5/KsWMpnzaNQyOMtp+jImCJiIiIBK1ebYWrQACmTKngiisq\nMDBxLvoHkQsWAFAxeTIVv/oVdNBAzApYIiIictRYscLBE09Y4eqKK6xwRSBA5F//irN6eJ/yqVOp\nnDy5Q8upgCUiIiKdnmmCxxPBwoVWR/Wf/7yCKVMqoLIS12OP4VizBux2fLfeStW553ZwaRWwRERE\npJOrrIT58yNZvtyJzQbXXltOZmal9dDm++7D/s03EBVF2e234z/99I4uLqCAJSIiIp1YSQk88EAU\nGzfaiYyEGTPKGDbMbz365o9/xLZzJ2ZSEmV3302gb9+OLm6IApaIiIh0Srm5BvfcE0VWlo3ERJM7\n7iijf/8A9m+/xfXAAxhFRQR69KDsrrswGxltwO/3U1lZSUVFBVVVVVRWVoamqqoqqqqq8Pv9BAKB\n0PvgvGmaBAIBAoEApmni91uP3wkEAqSlpTV4TAUsERER6RRM06SsrIyysjK++66Kxx5Lo7DQS2pq\nCZmZX7F58wE2LlhH1fLllFZW4u3SheKMDMqfeYby8vJaU0VFReg1GIrC7e67725wnQKWiIiIhJXf\n76e4uJiioiKKiopC88XFxZSUlNSavF4vxcXFeL1eysrKCAQCFBWdxq5dvyAQKCUmZivR0X/llZe8\n2PbuxSgoACCQmopps8EXXzRZHsMwcDgcRERE4HQ6Q5PD4Qi92u12bDYbDoej1nu73Y5hGNhsNmw2\nW2jeaGL4BwUsERERaZRpmni9Xg4cOEBhYSEHDx6ksLCw1nxRUVHo1ev1tvI4NgoKMtm//0KcTgc9\ne25m6NDPiI8YSNzatcQaBlFdumC78EIizjyTqKgooqKiiIyMxOVyERERQWRkJBEREaH3EREROByO\nJgNRa2zYsKHBdQpYIiIix7GysjLy8/PJz88nLy+PgoKC0FRYWBiar6ysbPY+DcMgLi6O+Ph44uPj\niYuLC72PiYkhNja2zlRWFsdzz6WwebOT1FRrGAa3uxv2rT2IevhhDKcT89RTKZszh8BJJx3BGgkP\nBSwREZFjVGVlJfv37ycvL4/c3NzQazBM5efnU1pa2qx9uVwukpKSSEpKIiEhgcTExHpf4+PjiY2N\nxdaCx9OsW2fniSdcFBcbJCWZTJ/u4/TTKnH+YzGRr7wCfj/+AQPwzZ6NmZLS2upoVwpYIiIiR6lg\ngNq3bx+5ubnk5OSE5nNzcyksLGxyH06nk9TUVFJSUkJTUlISycnJoUCVnJxMVFRU2MtfUQELFkSy\nZIkTgDPP9HPLLT4SKcR17+PYq/tXVWZmUv7LX4LTGfYyHCkKWCIiIp1YaWkpOTk57N27t9aUk5ND\nfn4+pmk2uK3dbiclJYXU1FTS0tJIS0sjPT09tCwlJYW4uLgj0j+pKdnZBg8/HMWOHTbsdvjFL8qZ\nNKkS53ff4HrkEYyCAsy4OHy//S3+YcPavXxtpYAlIiLSwSorK9m3bx/Z2dns3r2b3bt3s2fPHvbs\n2dNoK5TNZiM1NZWMjIzQlJ6eTpcuXUhPTyc5OblFl+rag2nCsmUOnn/ehc8HXboEmDHDx4B+VUT8\n7W9EeDwQCOAfOBDf9OmYjYw11ZkpYImIiLQTr9dLdnY2WVlZ7N69m127drF792727dvX4FhNTqeT\nLl260LVr11pTRkYGaWlpOI+iy2Z79xo8/bSLjRvtAIwcWcVNN/mIPZCN67Z52LdsAcOg4vLLqfj5\nz8Fu7+ASt54CloiISJh5vV7+85//kJWVxa5du8jKyiI7O5v8/Px6P28YBunp6XTv3p0ePXrQvXt3\nunfvTrdu3UhJSel0rVAtFQjAO+84efXVSCoqID7e5Npryznv3Aoi3l5M5MKFUFmJmZKC73e/w3/G\nGR1d5DZTwBIREWmlyspKsrOz2blzZyhQ7dy5k7y8vHo/73Q66dGjBz169KBnz5707NmT7t2707Vr\nVyIjI9u59O1j504bTz0VydatVmvUuedWcf315SQW78I1+wns338PQOWYMZRPnQqxsR1Z3LBRwBIR\nEWmGwsJCduzYwY4dO9i5cyc7duwgOzu73kt7TqeTXr160bt371CQ6tmzJxkZGUd9a1RzVVbCG29E\n8MYbEfj9kJJi8pvf+Bh6ViXOt98m8tVXrVar5GR8N9+M/6yzOrrIYdUuAcvtdj8EODwez/+2x/FE\nRERayzRN9u3bx48//sgPP/zADz/8wI8//lhvZ3PDMOjWrRu9e/emd+/e9OnTh169etGtW7fjJkjV\n58sv7fzlL5Hs2mXVwfjxlVxzTTlxe7cT+ftnsW/dCkDlBRdQfu21EBfXkcU9ItqrBWsW8Eg7HUtE\nRKRZTNNkz549bN++vVagqu9RL1FRUfTt25c+ffqEXnv37o3L5eqAkndO2dkGCxZEsn69FS+6dw8w\nbVo5p/U5SOQrr+JcuhRM02q1uukm/EOHdnCJj5x2CVgejyfgdrvb41AiIiINKigoYOvWrWzbto1t\n27axfft2SkpK6nwuMTGRE044gRNPPJETTzyRvn37HleX91qquBg8HmvAUL8fXC5wu8uZlFlB9Kcr\niXxoAUZhIdjtVGRmUjFlCkRHd3Sxj6hWBSy32z0Sq0VqtcfjmVlj+Rjgruq3d3k8nhVtL6KIiEjL\nlZWVsX37drZu3Rqa6ruLLzExkQEDBtCvX79QqEpOTu6QwTePNn4/fPCBk9dei6CoyMAwYMyYSq6+\nuoKUop1E3v0s9u++sz47cCDlN95IoE+fji10O2ltC1YkcD9wTnCB2+22AXOBMdWLPgBWuN1uI/hZ\nt9s9xuPxLGtDeUVEROoIBAJkZWWxZcuWUJjatWsXgUCg1ueio6Pp168f/fv3Z8CAAfTv35+UlBSF\nqRYyTfjiCzsLFkSSlWW16p12mp9rry2nX3I+Ea+9hvOjjyAQwExIoPyXv6TqggvgOKrnVgUsj8ez\nzO12jzpscX9gq8fjKQNwu90/uN3u/h6PZxtWHywREZGwKCoqYsuWLXz//fds2bKFbdu2UVZWVusz\ndrudfv36MWDAgNB0vHc+byvThK++svPaaxFs2WINu5CRYTJ1ajnDzygh8p23iXjrLfD5wG6n8uKL\nKb/yymOyE3tTwtkHKxkodLvdj1W/PwikANvCeAwRETnOBAIBdu3axffff8/mzZvZsmULu3fvrvO5\n9PR0TjrppFDr1IknnnjMji3VEb75xs7ChRFs2mQFq4QEk8suq2DixeVEfbKCyN+8ilFQAEDVsGGU\n/+IXmD16dGSRO1Q4A1Y+kAjcBBjAfKD+kdZEREQa4PP52LZtG5s3b2bz5s18//33de7qi4iIoF+/\nfpx88smcfPLJnHTSSSQlJXVQiY9tmzbZWLgwkm++sYJVXJwVrC4eX0HsxnVEznwN286dAAT69aP8\nV7/CP2hQB5a4c2hLwDr8QuoPwIAa7/t7PJ7tbdi/iIgcBwoKCkJhatOmTezYsaPO4J2pqamccsop\noUDVt29fHA6NlX2kmKbVYvXmmxF89ZUVrGJiTCZPrmTihHLiv/+CyD+8hm279TNvpqZSfs01VJ17\nLugSLND6uwhvA8YDXdxud7zH47nB4/H43W73XOCj6o/dHaYyiojIMSJ4uW/Tpk2hULVv375an7HZ\nbJxwwgmccsopoSktLa2DSnx88fthzRoHixdH8MMPVlCKioJLLqlgUmY5Cdu/ImLua9ZDmQEzMZGK\nyy+n8sILISKiI4ve6RimaXZ0GUKWL19uDhkypKOLISIiYZSTk8PGjRv56quv2LhxI0VFRbXWR0VF\ncdJJJ4XC1IABA4g+xsdI6mzKyuCjj5y8804EubnWBaqEBJMJEyq5eHw5Sds3EPHGG9g3bQLATEig\n4rLLqBw/3hr06ji1YcMGRo8eXe+tkWpfFRGRsCoqKmLjxo18/fXXfP311+Tk5NRan5KSwsCBAxk4\ncCCnnHIKffr00Z19HSQ/3+C995wsXerE67VyQvfuASZPruS8kT5iPv+UiDvfwrZjBwBmXBwVl15K\n5YQJVtOWNEgBS0RE2sTr9fLtt9/yzTff8M0337Cj+sc4KCYmhtNPP53Bgwdz+umn0717d4071YGC\n/avef9/JunUOgt3dBg70M3lyBUPPKCNy1Qqct/4D29691jaJiVRccgmVF10EMTEdWPqjhwKWiIi0\nSGlpKVu2bOHrr7/m22+/Zfv27bUG9HQ6nZxyyikMHjyYwYMH069fP7VQdQJeL6xYYbVWZWdbfx52\nO5x9dhWTJ1cwsNsBnB98gHP+EuuxNkCgSxcqL7uMygsuUB+rFlLAEhGRRuXn59e5y68V7ho9AAAg\nAElEQVRmoLLb7QwcOJBBgwYxaNAgTj75ZCL0Y9xp/PijjaVLnaxa5aS83FqWnGxy4YWVjBtXSVrx\nDpzvvINz9WqorAQg0LcvFf/zP1Sdc46VwqTFFLBERKSWiooKPvvsMzZs2MCmTZvq3OUXHCH99NNP\nZ9CgQQwcOJAo9cfpVLxe+OQTJx9+6GT79kOth6ef7ufiiysZelYFkV/+m4jH3sW+cWNofdVZZ1GZ\nmYn/jDOOq8faHAkKWCIiAlh3+/3zn/9k2bJlte70c7lcoTv8Bg4cyIABA3Adx3eOdVamCZs321i2\nzMknnxxqrYqJMTn//CrGj6+kV1wBzo8+wvnXDzByc60PuFxUjh5NxYQJx/XI6+GmgCUichwLBAJs\n2LCB999/ny+++ILg0D19+/blggsu4LTTTqNPnz7YdZmo0yosNFi1ysFHHznZtetQa9Vpp/kZN66S\ns4dXEr31G5yv/xPHZ58R7NVupqdTMXEilWPGQGxsRxX/mKWAJSJyHMrJyeHTTz/ln//8Z+gSoMPh\nYMSIEYwfP56TTz5Zd/p1YiUl8NlnDj75xMnGjXaCXeISE01Gj65kzJhKesQcwLFiBc5bP8IWfHaj\nzUbVsGFUXnQR/p/8RKOuH0EKWCIixwHTNPnhhx9Yt24d69atY2f1s+PAekjy+PHjGTNmDAkJCR1X\nSGmUzwf//reDjz928MUXDqqqrOV2OwwdWsWYMZWcNdiH66vPcby4HMcXXxxqrUpOpnLcOCrHjcNM\nTe3Ab3H8UMASETlGVVVV8e2334ZCVV5eXmhdVFQUZ555JhdccAFDhgzRMAqdlM8HX37p4NNPHaxf\n78Dns5YbhtVh/dxzKxk+rJLEgp04ly/H8dQqjGD/Obvdaq0aPRr/f/2X7gZsZwpYIiLHkGBL1cqV\nK1m9enWtzupJSUkMGzaMYcOGcfrpp+N0OjuwpNIQr9dqqfrsM6ulqqLi0LqTTvIzcmQVI0ZUkVK+\nB+cnn+CYsxrbrl2hzwR69aJy9Giqzj8fMzGxA76BgAKWiMgxIT8/n1WrVrFixQp21fix7dGjB8OH\nD2fYsGH0799fLVWd1MGDBuvX2/nXv5x89ZU9NLo6WKFq+PAqzjmnim6uAhxr1uC4/+PQA5fBeoRN\n1YgRVI4ezf9n777jo6ry/4+/7p2ZZNInPZRQQ5FiAZbigogUC8X22xG/lt8uylpwd3WBr6Brwd0V\n1BXFAqjrWlh0HZHVFeWnK01RQREUFSlBIEAS0ttkJpmZe39/3MwkIT0MKfB5Ph73ce/cPjeX5M05\n556r9esnXSx0ABKwhBCikyouLuabb75h8+bNfPfdd4EnAKOjoxk/fjwTJkygb9++0li9g8rKUti+\n3cy2bWb27q1uqK6qxhOAY8Z4GT3aS2JYKeYvv8Sy/FNM339PYEWr1agCvOgio98qKZHsUCRgCSFE\nJ+H1etm7dy+7du1i165dHDx4MBCqzGYzI0eOZMKECQwfPhyzWX69dzS6DgcPqmzbZmb7djNHjlSX\nJppMMGyYEapGjfJiU4sxb9+OecWXmL/9lpot2r2jRuEdNw7vyJEg/ZF1WPIvUAghOihN0zh+/Dg/\n/PADO3fuZPfu3bhcrsByi8XC4MGDGT16NGPHjiU6Orodz1bUx+2G774zsWOHmR07zOTnV5cmhofr\njBjhY9QoL8OGeYksz8W8bRvmx7/EtGdPdUmVouAbOhTvRRfhGTMG5OfcKUjAEkKIDqKkpIT9+/ez\nf/9+9u3bx/79+3E6nbXW6d69OxdccAHDhg1j8ODB0qN6B5SVpQQC1Q8/mPyv9wOMdwCOGmVU/Q0Z\n4iPkxDGjpOqhLzHt31+9osmE74IL8F54Id5Ro6SxeickAUsIIdpBWVkZhw4d4uDBg/z888/s37+f\nzMzMOuvFx8czcOBAzj//fC644AKSkpLa4WxFYwoLFX780cQPP5jYvdvEsWPVVX+KYjRSHz7cx4gR\nXvr0qMSydw+mHTswv7QD9dix6h2FhOAdPhzv6NF4f/EL6V29k5OAJYQQp5m/ZOrQoUOkp6dz6NAh\nsrOz66wXEhJCWloaAwYMYMCAAfTv358E6RSyw8nJMQKVfzh+vPaTmREROhdcYASqYcN8xFJoBKo1\nOzB9+y1KeXlgXT0iAt8vfoF3zBi8F1wgbarOIBKwhBAiiDRN48iRI+zbt4+9e/eyb98+jvtfU1KD\nxWKhd+/e9O7dm7S0NPr27UuvXr2kcXoHlJur8P33/hIqMzk5tZ/KtFph4EAfgwf7GDLEx4B+HkKO\nHMT8zTeY/rID04EDRgv3KlpqKt5f/ALfiBH4Bg4E+ZmfkeSnKoQQp+jEiRN8+umn7N69m/3799dq\niA7VJVNpaWn06dOHvn370r17d3mBcgeVn28Eqt27jVCVnV23hGrQIB+DBhmBqm9fDUtBDqZvv8X8\nwbeYdu+u7k0dwGIxGqmPGIF3xAj0lJQ2/kaiPUjAEkKIVnA6nXzxxRds3LiRH3/8sdaypKQkBg4c\nyIABAxg4cCC9e/eWkqkO7MSJ6jZUP/5oIiurdqAKD9cZPNjH0KFGoOrTR0N1OTH98APmzd9ievrb\n6pcpV9GTkvBecAHeESPwnXeeVP2dheRfvBBCNJPP52PXrl1s3LiR7du346l6PCwkJITRo0dz4YUX\nMnDgQOLi4tr5TEVDdB2OH1fYs8fEDz+Y+fFHE7m5tav8wsJg8GAvgwf7OPdco4RKrXRj2rsX07Yf\nMP39B6MX9Rrdrevh4fiGDjWe/Dv/fPQuXaQ39bOcBCwhhGiE0+lk9+7dfP311+zYsYOioqLAsiFD\nhjBhwgQuvPBCIiIi2vEsRUPKy+HAARN795rYt09l3z4TpaW1g09EhFFC5W9D1aePhqnSVTtQ7d9f\nK1BhMuEbNAjfeefhPe88tAED5GXKohYJWEIIUYOu6xw9epQdO3bwzTffsGfPHnw1/rB2796dCRMm\nMH78eOkyoYPRNDh+XA0EqX37TGRkqIH+Ov1sNp1zzjHC1JAhPnr21FArXJh++gnTF99jeulHo2F6\nzUClqmhpaXiHDME3eDC+IUNAQrVohAQsIYQAPB4Pa9asYcOGDeTk5ATmq6rKoEGDGD58OCNGjKBX\nr17ybr8OoqQE9u0zsX+/EaYOHFBxOmv/bEwm6NdPY8AAHwMG+Bg40EdSko5aVIhp717UTT8ZwSo9\nvf5ANXQoviFD8A0aJIFKtIgELCHEWe/IkSMsXbqUQ4cOARATE8Pw4cMZPnw45513nryCpgMoKYGD\nB038/LNKerqJgwfVOk/3AcTH6/Tv76N/fx/nnGO0nwo1+1CPHDGq/P75E6a9e1FOnKi9ocmEr39/\nI0wNGYLvnHMkUIlTIgFLCHHW0jSN//znP6xatQqPx0NKSgp33nkn5557Lqpa94+3OP103egm4dAh\nlUOHjCB18KCpTt9TAKGhkJbmqyqd0ujf30d8vA6lpZj27cO0ax+mN34y2k+53bU3tlqNQHXOOfgG\nDJBAJYJOApYQ4qyUk5PDsmXL+P777wGYMmUKs2bNIjw8vJ3P7OxRUQEZGSqHD6scPmzi0CGVI0fU\nOo3QwQhTffoYDdD79vWRlqaRmqph8lWiHjqE6cAB1FXpxvjo0Trb68nJ+AYONALVwIFoPXtKo3Rx\nWknAEkKcVXRdZ/PmzbzwwguUl5djs9mYM2cOo0aNau9TO2P5fMYLkA8fNhqdHzmikpGhkpVVtwE6\nQFSUTq9eGr16VYep7t01VN2HmpGBmp6O6YP9mNLTUQ8frt12CsBsxtevnxGkBg7EN3Agemxsm3xX\nIfwkYAkhzmiappGXl0d2djZZWVns2LGD7du3AzBq1CjmzJmDzWZr57M8M2ia8Z6+I0dUjh5VOXLE\nCFRHj6p4vXXXN5kgNdUIUr17+6rGGnFxOoquoR4/jvrzz5g+PmCEqvR0qKysvRNFQUtNxdevH1q/\nfvjS0tD69AGLpW2+tBANOO0By263RwN/q/r4mMPhOHi6jymEOPvous7x48fZu3cvhw8fJisri+zs\nbLKzswMdgvpZrVZmz57NpEmT5InAVtA0yMtTAuHJH6aOHlXrNHXyS0rS6dnTR48eGj17GkP37pqR\ngyoqjEbohw6hfnXQqPI7fNioQzz52CkpaGlpRpDq3x9fnz4g1bqiA2qLEqyrgGeAA8ADwJ/a4JhC\niDOcy+XiwIED7N27N/BS5dLS0nrXtdlsdO3alZSUFLp27cr48eNJTk5u4zPufLxeyM5WOH7cH6SM\nEHXsWMNBymbT6dlTqwpSPlJTjelA+/GSEkyHD6N+exDTvw+h/vyz8ZqZk6v5AD0xEV+fPkagqiqd\nQp7oFJ1EWwSsblXHuQIIa4PjCSHOQJqmkZ6eHuhR/dChQ2gnNeCJjY1l4MCB9OvXj27dupGSkkJK\nSgphYfKrpzElJXDsmMrx47WHrCy1vtwDGEEqNVULBChj8FXnn8pK1KNHUY8cQd2egenIEdQjR1Dy\n8uruTFXRevRA693bCFR9++Lr1UvClOjUWhWw7Hb7OOBJYIvD4ZhfY/4k4KGqjw85HI6NQCbwHXAE\nmHdqpyuEOJtUVFTw3Xff8fXXX/PVV19RWFgYWGYymUhLSwu8VPmcc84hMTFRqvwaUFICWVkqmZlG\ncKqeVigra/iaJSbqdOtmBCgjUPno3l2rzj6ahpKZienoUdQPDhuBKiMDNTOTeluwh4Tg690brXdv\ntD59jOlevYzHBIU4g7S2BCsUWAxc6J9ht9tVYBEwqWrWR8BG4N/AEkAFlrb6TIUQZyRd1ykpKSE/\nP7/WcPjwYXbt2kVljUbN8fHxjBw5kpEjRzJ48GCsVms7nnnHomlG/1HZ2SrZ2QpZWWrVtBGiTu7h\nvCarFbp2NdpEdetWPe7aVSNwiTUNJS/PKJX6xAhRpqowxUlt3ACjVKp7d7TUVLRevdB69sTXowd6\n164gfYyJs0CrApbD4fjEbrePP2l2P2C/w+FwAdjt9oN2u72fw+E4ANx5iucphOikvF4veXl55OTk\ncOLECXJzcwPTeXl5FBQU1GmEXlNaWhojR47kF7/4BX369DmrS6jcbjhxojpAnTihcuKEEghS9T2p\n52e1QpcuRmjq0sUYunXT6NJFx2bTCVzWykrUzEzUY8dQvzpmjI8dM9pJ1dPoHECPj0fr1Qtfz55G\nVV/PnmipqRASEvyLIEQnEcw2WHFAkd1uf6rqczEQj9G4XQhxhtF1HafTSUFBQZ0hPz+fwsLCQIA6\nua3UySIiIoiPj681JCUlMWzYMOLj49voG7U/XYeSEoWsrOrQlJ1tTGdmqhQVNR4ubTadLl00UlJ0\nUlK0wNCli05MTI0QBVBSYgSnHcerQ9SxY6gnTtRftQfoNhtat25GgOrZMxCoiIwM4lUQ4swQzICV\nD9gwSqsUYDlQT2tGIURn4HK5OH78OFlZWeTl5VFYWEh+fn6tEFV5cp9E9VAUhfj4eJKTk0lKSiIp\nKSkwnZCQQHx8/FlT1adpUFSkkJOjkJur1hrn5Kjk5DT8dB4Y/UYlJ9cNUP7PdS6jy4WalYX6YxZq\nZiZKljFWMzNRiorqP4iqonXtalTvde9uBKqqsTQ6F6L5TiVgnfxfqYNA/xqf+zkcjvRT2L8Q4jRz\nu93k5eVx4sQJjh8/XmvIz89vcvuwsDDi4uICQ3x8PDabjfj4+MC8hIQELGdJp48ej9E/VM3wlJtr\nhKfcXONzY9V4AOHhOikpOsnJ/qq86jCVmKjXbb7kdhshamcWSlV4UquClFLjoYA6rNbaASo11Rh3\n6SKddAoRBK19ivBe4HIgxW63RzscjtscDofPbrcvAv5btdrDQTpHIUQL6bpOaWkphYWFgdKm/Px8\n8vLyao0b6jcKwGw207VrV7p27UpiYmKtEOWfPpve2+evvsvL8w+1w1NOjlGFp+uN7ycqSicpSScx\nUQuMk5ON8JSYqBEVRe2qPF1HKS5GOXECdW82ak6OURKVnW2EqIKChg9mNqN16WKUSHXpgl5zOiFB\nGpsLcRopelO/DdrQhg0b9GHDhrX3aQjRoXk8nkD7ppphyd/uyV+N11jDcT+TyURCQgKJiYl069aN\nbt260b17d7p160ZycjLqWfIHWNOgsNAITgUFKnl5Cvn5RojKz1eqBrXeh+VqMpkgLs4ISomJOklJ\n1cEpKUknIUGj3i65XC7UEyeMEJVdFaJqTDdab2g2G72bd+1qBCh/iOraVUKUEKfZzp07mThxYr2N\nI+VdhEJ0IOXl5RQUFARCU83SJ3+YKi4upjn/MQoPDw+UNMXGxpKQkBBo8+Sfjo6OPqNDlK5DaSkU\nFqoUFCgUFhpDQYHxuaDAqLYrLFQa7FCzpogInfh4Y0hKqg5N/pKo+Hgdk6meDSsrjS4O9uUYQSo7\n2xjn5KBmZ6OUlDT+PSIi0FNS0JKS0FJS0JOTjSDVpQt6UpKEKCE6IAlYQrQxp9NJZmYmmZmZZGVl\nBYbMzExKmvhDC6CqaqBtk39c8+m72NhY4uLizujey30+KCgwSpyKioyAVFSk1BoXFhrzm2rz5Gez\nGcEpIUELhKj4eI2EBJ24OGNcb1t8fxVebi7qwTwjNOXVGOfmNtyg3M9iQUtONoJTcjJaUpJRGpWU\nhJacLE/pCdEJScASIki8Xm+gzVN93Rb4p8vKyhrch8ViqROY/IM/TMXGxmKqt5ik8/P5jHZORUU1\ng5JRPWdU2xlVdkVFSkM9CdQRHq4TF6cTG+sfa4HphAQjRMXH6w23666oMMLTXiMsqbm5RmlUTo4x\nzs2tv6PNmkwmo6+oxEQjSJ1UGqXHxkoplBBnGAlYQjTB7XZTVFREQUEBxcXFtUJUzeni4uJm7S8k\nJIQuXbrQtWvXwNg/HRcXd8Z1pOn1QnFxdelSUZFCcbFR8lSz1Km4WKGkpOlG4mA0ArfZ9EDpks1m\nBCj/EBOjBaYb7AFC11FKSlDy81GO5qMWFBjTeXnV0wUFKI08CBDYVVQUekICWmJi9TgxES0hAT0p\nCT0uTgKUEGcZCVjirOXvKDM3N5e8vLzA2D8UFhZSWFhIeXl5s/anqio2m61Wu6eaT9z5hzOh3ZPH\nQ1VIqhuaalbT+UNTcykKREcbPYv7h9jYulV1cXE65sZ+e1VWouTnox6sCkr5+bVCk5qXZzx915z6\nQ5OpOjglJaEnJBjhqUaIqr/luhDibCYBS5xx3G534Im6oqIiiouLKS4urjVdXFxMfn4+7saezqpi\nNpsD7ZpsNhs2my0Qnvzz/cs6c3DyeKgTjgoLa5cylZQY04291+5kqlo3NNlsGjExRniqOT8mpoFG\n4mC8C6+kBKWwEOVYoTEuKkItrJqu+qwUFqI4nc06Nz0yEj0uzqi+i4+vnq4a6wkJ6NHRUvokhGgx\nCVii06jZPUHN17H42zf5q+uaW+IEYLVaSUxMDDxV55/29/tks9mIjIzslNV2Hg+1Spj8AclfqlRz\nKC5Wcbmav2+TyejPyaiOqw5NNUud/OPo6Ho6x/TTdSgvRyksRM0sQtlTVCss1QpPJSU061G/qhPU\n4+LqhqaEhOrpuDgarj8UQohTIwFLtCt/aCoqKqKkpCRQ0lRaWhooafKHqOZ2T2CxWGqVKsXExATG\n/mn/584UntxuAoGotLR2WKoZmvztnVpSygRGaIqOrhmO6gYmf5CKjGygUEfXweUynqorKUHZV2SM\nS0pQiotRi4uNZTWGJhuI19x9dDS6zYYeG4seG4sWG1vrsx4bi2az0fAJCiFE25CAJU4bXdcpKyuj\noKCA3NxccnJyyMnJITc3NzAUFBQ0KzRBdRunk5+uq9nWKTY2lqioqE4RmtxuajXwLi5Wa1XF+UOU\nf7oZr/2rpWYpkz8wxcQYJUr+ISpKD8yLiKgnk2gaOJ0opaXGkFWEsi94gQmA0FCjRCk2Fj0mpuHw\nFBMjr3ARQnQaErBEi2maRllZWa12Tf5SJv/g7yyzqZcB+/t0stlsREdHB0qZ/NPR0dG12jt15O4J\nPB7qlC7VbADuH4x5jb/Utz4WC4FQFB2t1wlLMTHGMn9JU1RUjao5XTe6G/AHJf9wuASlrMx4Uq60\n1AhOVZ+V0lKUsjKa3R+CX0iIEYyio9GqxnpMTPX4pEGq6YQQZyIJWGc5n89HeXl5YCgtLaWkpKTW\nUFxcHJj2V+X5mtkWxmq1Bvp1SkxMJCkpKTAkJiYSHx+PudHHwdpHVU1XrWDkD03FxWqddkylpUqL\n2jCBEZjqa/QdE1MdnvxhKjpaJzQUFK/HCD1OpxGEyspQnE5jyHJCurN2QKoxtLhkyX8tIiIgMhI9\nKkoCkxBCNFPH+8smWsXj8dQKQqWlpYGwVHPsdDoDg8vlatZTdPWJiIggNjY2UNJU82k6f6CKi4sj\nIiIiyN+0dXw+AiVL1WMoLlZrBKfaYaqlecRfJVez6s3/ZFzgKbkoHzGWMmLNpURoZajlRjiiZljK\ndKIccFYHKX8VndPZ6pAEgMVi9NfU2BAdjR4VBf7PkZE03h+CEEKI+shvzg7K336pqKgo0B+TvzG4\nvxSpZumSq6XFJ1VUVSUsLIzw8HDCw8OJiooiOjq6wcHfONzSjm1hqh48o6xMoazMCEwnByR/SPK3\nZ2ppg2+A0FCM0qRojZhILzHhlcSEuogJdWMLLSfGVGYMainRFBPhLUF1lYPbbYSiMhdKTrnR6Nvp\nRHG5oKLi1L68yWR0LRARYYSfiAhj2j/459UTnqRkSQgh2o4ErDag6zrl5eV1+mQqKyujrKyM0tLS\nwLT/c0lJCZ4WlFaYTKZa4ejkaf8QGRlJZGRkIFCFhYW1a4Pw+p6MKy2tDk4lJUpVkCIw3+ls4sW8\nmm60G6oxKLqPaGsl0dYKokIqiAlxEWMpJ9rsrApKpdjUEmIpIkYrwOYrIKyi2AhH+eXN7x6gKYqC\nHh5uhCB/UPJPR0ZCeHj1Z39gqrGeUU/Y8RvwCyHE2U4C1kk0TaOyspLKykq8Xi9ut7tWGyWn0xmY\ndrlceDyewLoejwePxxOYdjqdgQbgLQlLfmFhYcTGxgY6tvQP/gbhNRuCR0REtFtQ8pco+UuKyssV\nyssJTBvjqiBVrFBc4KO4EIqLocIF6BpoOormq5o2Ptea1jQUvTowhasVRKnlRJnKiVLKiFWLiKaY\nWL2QGLWUWHMp0aYybOYybCFlRJnKURUdPBhD8/qhrGaxoIeHG+EoLKz2dNVnwsNrT4eHo1utRjDy\nz7daJSAJIcRZoMMFrFdffRWz2YzZbMZkMqGqKmazGVVV8Xq9dQZ/qPH5fHWW+Xw+PB4PmqYFPnu9\n3lqf/etUVlYG9nM6hIaGBvpfqtkHU81SpaioKCIiIoiMjCQmJgbr6ajS0TSjHY/Hg1I1prIyMK1X\neHCWapSV6DhLq4eyMoUyp0pJmUpRqZniMgvFTjOFZaEUlYfg9SnVJUcnhSSlRkiC2l0yWBUvMeYy\nYkxOok1Oos3GOMpUTpTZSYzZmI40lRvzzeVEqi4saiM/J5MJ3WoFqxU9LAysXdCtVrz+eTXHYWHo\noaFV61nRQ0ON0qWaYSksTLoHEEII0SIdLmCtXbu2vU8Bi9lMiMWCxWIh1GIhPCyM8LAwIsLCCLda\nA+OwkBBCTCZjfZMJs6piVtXAdFhICHGRkdgiIgizWIz3nvl8KD6fEWx8PuOz1wsFBZCTU/3Z56te\n3+s1pr1eoyTH46merrnM58NXqVFWYaHUZabMbaHMZaaswkKZ20JpZQhOj5UyXxjlWihOXxiuqrFT\ns+LyWSnXwtBpbgmLBrgAF+FqBdEmJ5EmFxEmF+FmNxEmNxGqi0iTi3CTm2iTE5vFSYzVTUyEh5hw\nD2ERCkqIxQg7ISFGwPGPQ8PRQ2xGtVjVPD0sDF9oqBGWqsIRYWFGSVHVtIQhIYQQ7a3DBaxbfD58\nuo5X0/BqGpqu49M0fLqORVEwKwohVWOzomBRFExVY7OiYAZjHmBRVcxV02ZArVpuqhrMioJZ17Fo\nGiFACGDW9erQ0sqG46dC16FCD6HcF0qZL4wyLZwyX7gxHRiiKK2aV+qLqBqHU+oLx6WFNn0QRQFF\nBVUx2gT5p83G/IiQSiJDPUSEeoi0eogM8xEZ5iEyTCM6wostyktMtEZ0lI/YGKN7gdAIE4SGooda\nIST6pKAUih4SYgQli6VWFVnbX2EhhBDi9OtwAeuGYHckWbOX8Ob0GK4oRnfW/rHJhK6qxnTVoCkm\n3Hoo5XoYFUoolYTgxYIXM5W6BY8SggczlVVByaWF4vKFUq5Zq4ZQyn2huH3GuNwbgttnweUNodxr\nQdOrjq8ooFBjWgGLAiFK7Xn4g5KCaoLICJ3ICJ2ISJ3IaIiMUoiIVoiMVomIVjHaUuuEhemEhxs9\nePunw8Nb94YRb8s3EUIIIc5YHS5gHf/L8kAVlY4RGsDIRj5dxacpxuCrGteYpwN61Wej2Y+Cplev\n7/EqeDUFj0fB61Pw+BS8XhWPzxgqPSqVXhWPx3gticcDFRUKLpfRiWR5uTHtdjcvqzVKrRrqqc2y\nWIwAFBmpExFhBCBjXD0vKgoiI/WTBo2wsJoBqWZVnw74qgYhhBBCnE4dLmDd9KeB7X0KzWI099Gr\nar10LBZjbDYb/TKGhBjzwsJ0wsJOHhslRVZr3emwMOnXUQghhOjsOtyfcputdtFQoBYMI3iYTDom\nU6D2rmow5vnX9S8zavSM97H5g48/BFksYDYb24WEGPNDQmpP+8f+EOQPR2Fhxv6FEEIIIerT4QLW\n66+3tIMiIYQQQoiOpRXNmYUQQgghRGMkYAkhhBBCBJkELCGEEEKIIJOAJYQQQggRZBKwhBBCCCGC\nTAKWEEIIIUSQScASQgghhAgyCVhCCCGEEEHWJgHLbrc/brfbl7bFsYQQQggh2ltblWAtoPabh4UQ\nQgghzlinJWDZ7fa/2e32Z+12eziAw+HQTsdxhBBCCCE6oma9i9But48DngS2OBIILf0AACAASURB\nVByO+TXmTwIeqvr4kMPh2AjgcDjmBftEhRBCCCE6i+aWYIUCi2vOsNvtKrAImFI1PGy32+tUA9rt\ndsVuty8BLqwKZEIIIYQQZ7RmlWA5HI5P7Hb7+JNm9wP2OxwOF4Ddbj8IpAEHTtpWx2iDJYQQQghx\nVmhWwGpAHFBkt9ufqvpcDMRzUsBqqZ07d57K5kIIIYQQ7e5UAlY+YAPuxHhCcDmQdyonM3HiRHnS\nUAghhBCdXkueIjw5/BwE+tf43M/hcKSf+ikJIYQQQnRuzQpYdrv9XuBhYLrdbn8BwOFw+DAauf8X\n+LhquRBCCCHEWU/Rdb29z0EIIYQQ4owi7yIUQgghhAiyU2nk3ioNdU56quuKai28xq8CAwA38KrD\n4Xjt9J9h59ZQx7sNrCv3cCu08Bq/itzDLWK321diXDMV+I3D4fi5kXXlHm6FFl7jV5F7uMXsdvtf\ngAsBDfhtR7uP27QEq7mdk7Z0XVGtFddNB65zOBwT5B91s9XpeLc+cg+fkmZd4ypyD7eQw+G43eFw\nTMC4PxsMsHIPt15zr3EVuYdbweFw/MnhcFyCEZzubWi99rqP27qKMNA5aVUHpf7OSU91XVGtNddN\nfmG2gMPh+AQoaMaqcg+3UguusZ/cw61TClQ2slzu4VPX1DX2k3u49UYDPzWyvF3u47auImxJ56Sn\npSPTs0BLr1sp8Ibdbi8A7pGuNoJK7uG2Ifdw680CljWyXO7hU9fUNQa5h1vNbrd/CiQA4xpZrV3u\n47YOWC3pnDToHZmeJVp03RwOx+8B7Hb7+cATwNVtcI5nC7mH24Dcw61jt9unA/scDsfeRlaTe/gU\nNPMayz18ChwOx0V2u30k8DowtYHV2uU+busqwpZ0TiodmbZOa6+bG/CcnlM6IzWnOF/u4VPT0ioT\nuYebyW63DwfGOxyOp5tYVe7hVmrBNa5J7uHWyabxAqN2uY/btATL4XD47Ha7v3NSqNE5qd1u/xVQ\n7nA4PmhqXdGwllzjqnn/ArpgFFHPacNT7bSqOt69HEix2+3RDofjtqr5cg8HSXOvcdU8uYdb7m3g\nqN1u3wR8X6MERe7h4GnWNa6aJ/dwK9jt9rcwqgcrgbtqzO8Q97F0NCqEEEIIEWTS0agQQgghRJBJ\nwBJCCCGECDIJWEIIIYQQQSYBSwghhBAiyCRgCSGEEEIEmQQsIYQQQoggk4AlhBBCCBFkErCEEEII\nIYJMApYQQgghRJBJwBJCCCGECDIJWEIIIYQQQSYBSwghhBAiyCRgCSGEEEIEmQQsIYQQQoggk4Al\nhBBCCBFkErCEEEIIIYJMApYQQgghRJBJwBJCCCGECDIJWEIIIYQQQSYBSwghhBAiyCRgCSGEEEIE\nmQQsIYQQQoggk4AlhBBCCBFkErCEEEIIIYJMApYQQgghRJBJwBJCCCGECDIJWEIIIYQQQSYBSwgh\nhBAiyCRgCSGEEEIEmQQsIYQQQoggk4AlhBBCCBFkErCEEEIIIYJMApYQQgghRJBJwBJCCCGECDIJ\nWEIIIYQQQSYBSwghhBAiyCRgCSGEEEIEmQQsIYQQQoggk4AlhBBCCBFkErCEEEIIIYJMApYQQggh\nRJBJwBJCCCGECDIJWEIIIYQQQSYBSwghhBAiyCRgCSGEEEIEmQQsIYQQQoggk4AlhBBCCBFkErCE\nEEIIIYJMApYQQgghRJBJwBJCCCGECDIJWEIIIYQQQSYBSwghhBAiyCRgCSGEEEIEmQQsIYQQQogg\nk4AlhBBCCBFkErCEEEIIIYJMApYQQgghRJBJwBJCCCGECDIJWEIIIYQQQSYBSwghhBAiyCRgCSGE\nEEIEmQQsIYQQQoggk4AlhBBCCBFkErCEEEIIIYLM3BYHsdvtccCDQBfgTofDkd8WxxVCCCGEaA+K\nruttdjC73T4NKHU4HFva7KBCCCGEEG2srasILwC2t/ExhRBCCCHaVKurCO12+zjgSWCLw+GYX2P+\nJOChqo8PORyOjVXzrwA2OBwO9ymcrxBCCCFEh3cqbbBCgcXAhf4ZdrtdBRYBk6pmfWS32zcBacBC\n4DO73e52OBw7T+G4QgghhBAdWqsDlsPh+MRut48/aXY/YL/D4XAB2O32g0Caw+E4AIxrap8bNmxo\nuwZhQgghhBCnaOLEiUp984P9FGEcUGS325+q+lwMxAMHmruDYcOGBfmUhBBCCCFaRzl+HOuLL2La\ntQsArW9fyhcvBquVnTsbrpALdsDKB2zAnYACLAfygnwMIYQQQojTzrx1K9anngKPBz0igsqbbsJz\n2WWgNv2M4KkGrJOLxQ4C/Wt87udwONJP8RhCCCGEEG3K8uGHhL7wAug63gkTqJg1Cz0mptnbt7qb\nBrvdfi/wMDDdbre/AOBwOHwYjdz/C3xctVwIIYQQonPQdULeeIPQlStB16m4+Wbcd9/donAFbdzR\naFM2bNigSxssIYQQQrSXkNWrCXnrLVBV3HPm4J08ucF1d+7c2WaN3E+bvLw8Kisr2/s0hBCnWUJC\nAiEhIe19GkKIs5Bl/frqcHXvvXjHjGn1vjpFwCorK0NRFLp27drepyKEOI00TeP48eMkJydLyBJC\ntCnTtm1GmyvAfeedpxSuoO1fldMqxcXFxMXFtfdpCCFOM1VV6datG3l58vCxEKLtqHv3EvbEE6Bp\nVF5/Pd4pU059n0E4r9NOURQUpd4qTiHEGUZtxuPPQggRLEphIWGLF4PHg2fKFCpnzgzKfuU3mRBC\nCCHOTl4v1sceQyksxDdoEBW33w5BKtCRgCWEEEKIs1LoK69g2rMHPTYW9733gjl4TdMlYHVSP/zw\nA//973+Dus/bbruNsWPHcv3115/SfmbOnMnnn38epLNqXHp6OosXL6532YoVK3C5XG1yHkIIIToX\n85YtWN5/H0wmXAsWoMfGBnX/ErA6qd27d/PJJ58EdZ8vvPACjz322Cnvpy3bzKWlpbFw4cJ6l73w\nwgsSsIQQQtShHjqE9dlnAaiYPRvtnHOCfoxO0U1DYyJnzAjavsr+858Wb3PkyBHuuece3G435eXl\nzJ07l+nTpwOwZMkSjh49Sk5ODtnZ2Vx44YW1AozD4eAf//gHiqIwbNgw/vrXvwaWHT16lPvvv5/c\n3Fx0Xed//ud/uPnmmwH4+9//zosvvojT6eT7779n/Pjx3HvvvYFtzzvvPObOncuqVatwu92sXr2a\nHj16ALB48WJ27NhBXl4eKSkpvPbaa1it1sC2rel4tqCggNtuu42SkhJ69epFcXFxrf009j1TU1P5\n85//zLp16zh8+DDPP/88o0aNqvfazps3j2nTpgHgdru55pprKCkpITU1lTfffDOwT7fbzdVXX01O\nTg4zZ87EbDbz0ksv0a1bNz777DOeeeYZ3n77bcAIqvPmzePjjz9u8fcWQgjRCZWWYl2yBCor8Vxy\nCZ7LLz8th+n0Aau9vfjii0yaNIk777yzzjJFUcjPz+df//oXADNmzODjjz9mypQp/PTTT6xatYp1\n69ZhNpu59957eeutt7juuuvw+XzccMMNPPTQQ0ycOLHOfm+99VYiIiL47rvvWLJkSb3H3b9/f71V\niLNnzw6U+Nx444188MEHXHvttad0DZYsWcKwYcNYuHAhJ06cYMqUKYESrMa+J0BFRQWJiYmsWbOG\nN954g1deeSUQsBq7tlarlQ8//JDPP/+c5557rs6y9evXc/755/PWW28RW6PYd9y4ccyfP5/MzEy6\ndu3KG2+8waxZs07p+wshhOgkNA3rU0+hZmWh9elDxR13BK1R+8k6fcBqTalTMF155ZXMmzePjIwM\npk2bxtixY2stHzduHCaTCTAC1ldffcWUKVP49NNPOXbsGNdccw0A5eXl2Gw2AA4cOIDVaq03XPnp\nut5oadPcuXPrnW+z2di6dSvp6ek4nU6ys7Nb9H3rs23bNlatWgVAcnIygwYNCixr7HuCEYamTp0K\nQI8ePSguLg4sa+raQutK3G688Ubeeust5syZwyeffMKiRYtavA8hhBCdT8hbb2HesQM9KgrXggUQ\nGnrajtXpA1Z7GzlyJJs3b2b79u2sWLGCdevW1SpVqhkANE0L9E5tsVi44ooralWX1aRpWqPHbU0b\nJ6fTyfTp07n88ssZOXIkffv2bVVAOZnJZGpwP019z8Y0dW1b63/+53+YPn06ffr0YfLkyYSexn9g\nQgghOgbT118T8uaboCi4581DT0k5rceTRu6nSNM0VFVlzJgx3HXXXezYsSOwTNd11q9fT2VlJZWV\nlbzzzjtcdNFFAEycOJH33nuPQ4cO1VofoF+/flRUVPD+++83eNzQ0FByc3MD59Ac6enpWCwW5s+f\nz/nnn8/u3buDErDGjh3LO++8A8DPP//M7t27A8sa+55NaezaNkdoaCg5OTl1jhkXF8egQYN46KGH\n+PWvf92ifQohhOh8lMxMrEuXAlB54434LrjgtB9TSrBO0Zo1a3j55ZcD1YCPP/54YJmiKPTr148b\nb7yRzMxMpk6dyujRowHo2bMny5Yt47bbbguUAD388MOMHj0ak8nE6tWrue+++3j++edRVZUrr7yS\n2267LbDviy++mGXLlnHZZZcRFRXFa6+9Rnh4eOC49Rk6dCipqamMGzeObt26MXbs2EBIq3nOX331\nFVOnTuWRRx5h+PDhTV6DefPmMXv2bCZNmkTv3r3p3bt3YFlj3/NkJz992Ni1bWibmmbNmsUNN9xA\namoqV199deAhAQC73U5WVhYDBgxo8vsJIYToxNxuwhYvRnE68Y4aReUptjtuLiUYJRjBsmHDBn3Y\nsGF15vsbJHc2jz32GBEREdx1113tfSriJAsWLODiiy/msssua+9TEfXorP/mhRAdjK5jffJJzJ9+\nitatG+V/+xtERARt9zt37mTixIn1/i9fqghPM3mHYsfyzjvvcHnVI7kSroQQ4sxm+fe/MX/6KVit\nuBcuDGq4aopUEZ5GNfumEh3Dtddee8rdUgghhOj4TDt3Evr66wC477kHrao/yLYiJVhCCCGEOKMo\nmZlYn3gCNI3KmTPxjhnT5ucgAUsIIYQQZw6Xi7BHH61u1D5zZruchgQsIYQQQpwZNA3r00+jZmSg\npabivvtuUNsn6kjAEkIIIcQZIeTttzF/+SV6RASuNm7UfjIJWEIIIYTo9EzbtxOyerXRU/vcuejd\nu7fr+UjAOs1KSkr4xz/+cVqPkZqaGvR97tq1ixkzZgR9v8HU0LXdunUr119/fVCOcTqubWukp6ez\nePHiVm/fGX6eQgjRWurPPxNWs6f2ESOCfgyfD/bvb35skoB1mhUVFfHyyy+f1mOcrX1tnU3XNi0t\njYULF7b3aQghRIej5OYS9sgj4HLhvegiKv/P/wnavjUNdu82sXx5KL/+dQTz54eTn9+8vwvSD9Yp\nOnLkCPfccw9ut5vy8nLmzp3L9OnTAfjqq6+47777yMjI4IorriAuLo5//vOfgW0XL17Mjh07yMvL\nIyUlhddeew2r1QoYJSd//vOfWbduHYcPH+b5559n1KhRAHz33Xf84Q9/ICoqitGjR9d6z15paSkL\nFiwgKyuLY8eOMWPGDP70pz8Fls+ZM4c+ffqwadMm3G43d9xxR6BfqDfffJNnnnmGlJQUzjvvvGZf\nA7fbzf/+7//y008/oes648eP54EHHgBg+vTpjB49mrVr13Lffffx6quv0r9/f5588kkAtmzZwuLF\ni1EUhejoaJ588km6VxXrHjlyhPnz51NaWoqmaTzwwAOMHTu2WdfW7XbzwAMP8N1331FWVsaaNWuI\ni4sD4Ntvv+Whhx7C5/MRGxvL008/TXx8fJPXtjFbt27l0UcfpVevXuzfv5+YmBj+/ve/Exsb2+Qx\nMzIymDlzJtOmTWPjxo1ERETw3nvvBb7HNddcQ0lJCampqbz55pu1jrt8+XLWrl2LqqoMGTKERx99\nNHAPNfbzbOzalpaWcuutt1JWVobL5cJms3HzzTdz1VVXAY3fQ++++y6rV68ObPvSSy/Rr18/tm7d\nytKlSwkPD8fn83HJJZfw/PPP8/bbb9OvX79mXWMhhKijrIywRYtQCgrwDRmC+w9/gFP8j7GmwZ49\nJrZuNfPFF2aKiqr317WrRk6OQnx8038bOv2rcoJZ7fGf//ynxdvcf//9dOvWjTvvvLPe5UePHmXm\nzJl8/vnndZbl5eWRkJAAwI033sjVV18d+EOVlJTEK6+8wtSpU3njjTf49NNPWblyJQAXXnghjz32\nGOPGjWPbtm3MmDEj8FJjgMLCQmJjY3G5XIwYMYINGzaQUvXW8Dlz5nD06FFWr15NVFRUYJvMzEwm\nT57Mli1bSEhI4KmnnmLTpk3NuiYffvghq1evZvXq1XWWzZgxg+uuu47S0lI++OADVq1axahRo9i3\nbx/5+flMnDiR9evX06VLFz744AOWL1/OBx98ABg9rc+dO5fJkydz9OhRpk2bxubNmwOhpaFru3Xr\nVm6//XbWrl1L//79mTNnDqNHj+amm26isrKSiRMn8vbbb5OSksJ7773HJ598wrPPPtusa9uQrVu3\nMmfOHD7++GOSk5N55JFH8Hq9PPLII00eMyMjg1GjRrFixYpAiDnZ559/znPPPVcrYG3atIknnniC\n9957D4vFwsKFC4mKiuK+++5r8udZ37XdsmULNpuN5cuXU1JSwoIFC3jiiSeorKzk/vvvDxy3oXsI\noKCgIBBkV6xYwf79+3nqqafYunUr99xzD5999hmDBg3itddeY/369aSlpTFr1qw631delSOEaJLH\nQ9iiRZh270ZLTaV8yRI46XdSSxQUKLzzTghbt5opLKwOVV26aPzyl17GjvXSu7dWK7/Jq3JOoyuv\nvJJ//etfLFiwgK1bt9ZZ3liAtdlsbN26lVdffRWn00l2dnZgmdVqZerUqQD06NGD4uJiwKgWKy0t\nZdy4cQCMHj06UGLhZzKZ+Oijj/jnP/9JSEhInYAwe/bsOn8Yd+7cyfjx4wOBb8KECc29BIwaNYqC\nggJuu+021q5dS0VFRa3lgwcPJiYmhsGDB2Oz2XC5XAB8/fXXjB49mi5dugAwdepUDh8+jNPppLS0\nlGPHjjF58mTAKNEbNWoUX3/9dWC/jV3boUOH0r9/f6D29Ttw4ADHjx/nt7/9LTNmzOCll14iMzMT\naN61bcygQYNITk4GjB7jv/rqqyaP6denT58Gw1VD33Xjxo1cf/31WCwWAG699VY2bNgANP7zbOja\n+s83PDw8cL0KCwsD36mm+u4hgLi4OL7//nv+9a9/kZ6ezokTJwLL0tLSsFqtREdHB+6J8vLyBr+z\nEEI0SNexPvccpt270W02XA8+2Opw5fHA2rUW7rgjgvfft1BYqJCcrHPNNZUsXVrOypXl3HxzJX36\naC0qHOv0VYStKXUKppEjR7J582a2b9/OihUrWLduHUuWLGlyO6fTyfTp07n88ssZOXIkffv2bVZ1\nlNpEfx4//vgjt99+O7NmzWLo0KHEx8fX2W99xzGbzbXmt6RkMz4+nvXr17Nv3z7efvttli1bxpYt\nW+qsd/I+FUVB07Q66/nbPdV33qfaJspkMtGjR49675umrm1LaJpGSEhIk8cMxnFqTvuvT1M/z8au\n7U033cSkSZOYPHky559/Pr/+9a/rHLeh+2POnDkAXHXVVZx33nl1gqQQQgRDyOrVmDdtAqsV14MP\notfzH8Hm2LXLxEsvhXLsmPH7f+RIL3Z7Jf36tSxM1UdKsE6RpmmoqsqYMWO466672LFjR63loaGh\nFBYWBv4Q+v8wpaenY7FYmD9/Pueffz67d+9uVqiJjo4mKSmJbdu2AfDRRx/VKgXYsmULU6ZM4Te/\n+Q3R0dFkZGQ0a78jRozgyy+/pKioCF3XA22AmkPXdXRdZ8CAAdx9991kZ2fjdDqb3O4Xv/gF27dv\n59ixY4DRfqdv376Eh4cTFRVFz549Wb9+PQCHDx9m+/btjBw5MrB9Q9e2Mf369aOiooJ169bVOn9o\n+to2ZefOnRw9ehSA1atXc9FFFzV5zFMxadIk3nzzzUCJ4UsvvRQolWrs59nUtV21ahWTJ0/mv//9\nL0888QRmc/P/H7Z+/XqefPJJJk6cyHfffReU7ymEEDWZP/6YEIcDVBXX//4vWlpai/eRk6OweLGV\nhx4K49gxlS5dNB580MWf/uSmf/9TD1dwBpRgtbc1a9bw8ssvYzKZAHj88cdrLU9OTubCCy9k/Pjx\nJCYmcv/99zN8+HCGDh1Kamoq48aNo1u3bowdO5bc3Nx6j6EoSq2Sm2XLlvH73/+ekJAQxo0bR3h4\neGDZNddcw4033shnn31Gv379GDNmTJ0qwvpKgRISErjvvvuYOnUqsbGxjBgxotmlRfv37+euu+7C\nYrFQWVnJokWLiKinc7eT9xcXF8ezzz7LLbfcgqIoxMTEsHz58sDylStXMm/ePJYtW4amaaxYsYKY\nmJjA8oau7cnXq+axTSYTq1evZsGCBTz77LOoqsrVV1/Nb3/72yavbWMURWHAgAEsXryY/fv3061b\nNx588MFmHbO+a1Pf/k9eZ/z48ezZs4epU6eiKApDhw7l7rvvBpr+eTZ2bXv16sXTTz8dqPKOjo5m\n/vz5DB8+vMnznTt3buCevvzyy/n222/rbFNz247ylKYQonMwbduGdcUKACruuKPF3TFUVMC//x3C\n22+H4PFAaChcd10FV17poaq1RdB0+kbuQnQEW7du5fnnn6/zlF9n9Oijj5KWlobdbgfggQceIDQ0\ntNbTqKeb/JsXQpzM9N13hC1aBF4vlb/6FZU33dSi7dPTVZYutQaqA8eN8/Kb31SQkND6HNRYI3cp\nwRIiCOorYeqsBg0axHPPPcfrr7+Oz+fj3HPP5d57723v0xJCnMXUn34i7K9/Ba8Xz9SpVN54Y7O3\n1TR4550Q3ngjBJ8PunfXuOOOCoYO9Z3GM5aAJURQ/PKXv+SXv/xle59GUFx11VWNPtEohBBtSU1P\nNzoSdbvxXHIJFbNnN7uvqxMnFJ56ysqePUYznmnTPPzf/1tBaOjpPGODBCwhhBBCdEjq/v2EPfQQ\nitOJd/RoKn73O2jGE9+6Dps3m3nhhVDKyxVsNp0//MHN8OGnt9SqJglYQgghhOhw1L17CXv4YZTy\ncryjR+OePx+qHihrTGkprFxp5bPPjIgzapSX3/3OTXT06T7j2iRgCSGEEKJDUffsIXzRIuP9ghde\niHvePGhGlzF79qj87W9h5OUpWK0we7abSZO8Qel2oaUkYAkhhBCiwzD9+KPxtKDbjXfcONx//GOT\nJVe6Du+9Z+G110Lx+WDAAB/33OOma9f26ylBApYQQgghOgTT998bDdorKvCOH4/77rubDFdlZbBs\nmZXt241Ic/XVldx0U2VzCrxOKwlYQgghhGh3pl27jK4YKiuNpwV///smG7Snp6s8/riV7GyViAij\nIfvo0W3XkL0x8qqcIDh06BDx8fGsWbOm1vwlS5ZwwQUXcMUVVzBhwgQWLlzY4V8d8uGHH7Jv3746\n86dPn16rV+7WevbZZ3nsscdOeT/B8Nhjj3HgwIFWbz9z5kw+//zzIJ6REEKcncwbNxolV5WVeCZN\najJc6Tr8v/9n4d57w8nOVunTR2Pp0vIOE65AAlZQ/Pvf/+aqq67i3XffrTVfURRuvfVWPvzwQzZt\n2sSBAwf45JNP2uksm+eDDz6oN2AFqxPNjtQZ57333ku/fv1avf2Z1LmoEEK0C13HsmYN1qefBp+P\nymuuoeKuuxoNVy4XLF1qZfnyUDweuPRSD48/Xk6XLh2rAKPTVxHOmBEZtH395z9lrdpu3bp1vPHG\nG1x11VWUlJQQXeNZUH+JVVFREQUFBXTv3r1Z+9y4cSOPP/44qqridDpZvXo13bt3Z+vWrSxdupTw\n8HB8Ph+XXHIJzz//PG+//Tb9+vWjvLychQsXsnfvXnw+H3a7vdY775YvX87atWtRVZUhQ4bw6KOP\nYrVaAfj973/Phg0b+Oabb1i5ciW/+93vuPzyywPbbtu2jccff5z09HRuvfXWwH59Ph+LFi1ix44d\neL1ebrnlFq677rrAdgsWLOCLL76gS5cuJCQk0KNHj2Zdg+nTpzNq1Ci2b99Obm4uf/jDH7j++uub\ndcw5c+bQp08fNm3ahNvt5o477uDaa68F4OWXX+add95hz549vPvuu5x//vmB7Y4cOcL8+fMpLS1F\n0zQeeOABxo4dC0BBQQG33XYbJSUl9OrVi+Li4lolko1d27Vr1/Lcc88F3lnZpUsXXn/9dQAyMjKY\nOXMm06ZNY+PGjURERARezlxaWsqCBQvIysri2LFjzJgxI/DKmunTpzN69GjWrl3Lfffdx6uvvkr/\n/v158sknm3V9hRCiXWkaoS++iOXDD0FRqLjlFjwzZjS6SUaGymOPWTl6VMVqhTvvdHPxxd42OuGW\n6fQBq70dOHCAmJgYUlJSmDZtGuvXrw/8odd1nVdeeYU1a9agaRp//etfOeecc5q130ceeYRnn32W\noUOH1ll29OhRPvvsMwYNGsSdd97JFVdcEXi589KlS7HZbHz00Ue43W5mzJjBwIEDueiii9i0aRPr\n1q1j/fr1WCwWFi5cyNKlS7nvvvsAeOaZZ5gzZw6XXXYZ06dPr3PczMxM3njjDTIyMrjiiisCAev1\n119HVVU+/PBDKioqAn/4e/bsyXvvvcdPP/3E5s2b0XWdG264gZ49ezbrGiiKQnh4OO+//z65ubmM\nHz+eSy+9lLi4uEaP6bdlyxbefPNNoqKiau33lltu4ZZbbmHGjBl1SqBuu+025s6dy+TJkzl69CjT\npk1jy5Yt2Gw2lixZwrBhw1i4cCEnTpxgypQpge0bu7a6rvPggw+ybdu2wEuZP/jgg1rHPXToEIMG\nDQr8LPyioqL4y1/+QmxsLC6XixEjRnDrrbeSkpKCoij06tWL2bNn8+qrSgAjvAAAIABJREFUr7Jq\n1SpGjRolAUsI0fFVVGBduhTzl1+C2Yz7j3/EW/Wf2YZ88omZlSutVFZCaqrGvfe66dFDa6MTbrlO\nH7BaW+oULO+++y4ZGRlMmTIFt9vNDz/8EAhYiqIwa9Yspk2bxuWXX97scAVw8803c/fddzNlyhSu\nvvpq+vfvH1iWlpaG1WolOjqawYMH88UXX+ByuQCj5Ovll18GwGq1csMNN/DJJ59w0UUXsWHDBq6/\n/nosVa8M95dCnfxHvaF2Yv4SoB49elBSUhKYv2nTJjIyMphR9T8Pt9vN/v376dmzJ9u2bcNut6NW\nFfeOHTsWp9PZ7OswceJEABITExkxYgS7d+/m4osvbvSYfrNnz64TrhpTWlrKsWPHmDx5MgCpqamM\nGjWKr776iilTprBt2zZWrVoFQHJyMoMGDQps29i1VRSFkJAQysrKUBSFyMhIQkJCah27T58+Db6e\nxmQy8dFHH5GRkUFISAg5OTmkpKQAMHjwYH766ScGDx6MzWYL3AdCCNFhlZQQ9uijmPbsQY+IwH3/\n/fiGDGlwdbcbVq4MZeNG4/frJZd4uP32CqoqCDqsTh+w2tv777/Phg0bsNlsgPFOuprVhLqu07Nn\nT2666SYefPBBli9f3qz9zpo1i5kzZ7JhwwZmz57N3LlzA2GiKZpWneh1XQ+EG0VRai3TNK3eNkQN\ntStqKHiZzWYWLFjAZZddVmeZyWSqtV1LG/mfvK0/mDR2zNYeq75tal6jk79LTU1d20WLFjFhwgQG\nDhzIypUrm30+P/74I7fffjuzZs1i6NChxMfH13sOHf3hCSGEAFAzMrD+9a+oWVno8fG4Hn4YrZFa\njZpVgiEhcPvtRsehjfH5fLhcLlwuF263m/LyciorK6moqKCyshK3243H48HtdlNZWUllZSUejwev\n14vH4wlM+z9rmoamafh8vsC0f5g5c2aD59EmActutz8OmB0Oxx/b4nhtZe/evURGRgbCFcCECRN4\n//33ueGGG2qte/fddzNmzBi+/PJLxowZ0+S+fT4f4eHhTJ8+nYMHD7Jz584GA5au64E/sBMnTuSV\nV17hL3/5C+Xl5fzzn//k4YcfBmDSpEksXryYmTNnEhoayksvvRQorfGzWq3k5uYCRkhQm/HOp6lT\np/LMM88wduxYIiMj0XU9EC7GjRvHSy+9xA033IDT6WTDhg3N+v5+/jZSx44dY9euXZx77rlNHrO1\noqKi6NmzJ+vXr+fyyy/n8OHDfPXVV4Eqt7Fjx/LOO+8wd+5cfv75Z3bv3h3YtrFr6/F4ePLJJ9m6\ndSvx8fEtOqctW7YwZcoUfvOb37Bnzx4yMjIkTAkhOiXT9u2ELV0KLhda7964HngAPSGhznq6rlNe\nXs769T7+8Y8YXC4nNlspV1zxDXl5J/jHP8opKyujrKwMp9OJ0+mkvLw8EKoqKyvb4dvV1VYlWAuA\nM65hyLvvvsull15aa96ll17Ks88+GwhY/j/6YWFhPPLII8ybN49PP/000Ni5IQ888AC7du1C0zSS\nkpJ4+umnA/vz77Pm2D/9xz/+kYULFzJlyhR8Ph8zZ84MNNIeP348e/bsYerUqYG2QHfffXet4/7q\nV79izpw5vPvuu5xzzjm1ulRoKMBce+21ZGdnM2PGjECjbofDQWRkJJdeeimbN2/m4osvJiEhgW7d\nurUoCFksFq688kry8vJ44okniIyMbPKYTZ1vY1auXMm8efNYtmwZmqaxYsUKYmJiAJg3bx6zZ89m\n0qRJ9O7dm969ewe2a+zaWiwWEhMT+dWvfoXVasVkMjFs2DAWLVrU5Llec8013HjjjYE2dmPGjCEn\nJ6fOevI0oxCiw9I0lDffpGTVKg57veQPGkTO5MkUbdxIUVFRYCguLqa0tJTi4gqOHr2KwsLRQD42\n2w7Cw9/i3XebF5wURSEsLCwwWK1WrFYrISEhhIaGEhoaWmfaYrFgMpmwWCxYLBbMZnNgrKoqJpMJ\nVVXrDG63u+HzaKv/Ddvt9qccDsc9ja2zYcMGfdiwYXXmZ2Zm0rVr19N2bqJjmjFjBn/+858577zz\n2vtUTsnx48eZO3cuL774ItHR0WRmZjJu3Di+//57wsPD2/v0OiT5Ny9E56DrOmVlZeTl5ZGbm0te\nXh4FBQXVQ24uRV9/Tam/ZiQ5GT0pqcH9lZf35OjRm/B6k7FY4Nz/396dB8dx3nf+f3f3XLgB4j55\nggd4ArwpShRFOhZleyMpcttOnNI6ydpee5OKvXblV5X9leOqX1V2N+sjWa/WrvWutXG8csYby0ri\n2DIpSqIk3gTBE8TBEyDuc4ABBjPT3b8/eqaBAUAKAEEc5PdV1dU93T2D5qhFfPh9nn6eTW+zdu1N\n0tPTSE1NJSUlhZSUFFJTUxNeJyUlkZKSgs/nw+v1ztk/Oqurqzlw4MCkP2xGFSxd15/Erki96/f7\nvz5m/0HgG7GX3/D7/Udn8vlCPEoyMzPxeDzouu78i+gHP/iBhCshxIJnWRZ9fX20tbXR0dFBe3s7\nnZ2dCcs9qzgjI6h37qCEQqiaRvr69WStXElmZiaZmZmkp6eTmZlJVlYWaWmZfPDBMt58M4/ychfL\nl1v8+38fYtmyqT11vhDNtInQC/wlsCe+Q9d1FfgmcDC2603gqK7rSvxcXdcP+v3+hT3Splgw/vEf\n/3G+L2FWpKSkOGNeCSHEQhOJRGhvb6elpYXW1lba2tpob293lg/r0+Tz+cjNzSUnJ4ecnByys7PJ\n7eyk4Ne/JrukhCVlZXi/8Q2Ue4yB2NKi8J3v+Kir01BVeP75MJ/9bJhxD1svOjMKWH6//4iu6/vG\n7S4H6v1+/zCAruvXdV0v9/v9Ddh9sIQQQggxD0zTpLOzk6amJpqbm50w1draSldXV8JT0OOlpaWR\nl5dHXl4eBQUF5ObmkpubS15eHjk5OaSmpo42yYXDeH/0I9xvvQVuN9G9e+0Jm1NSJnyuZcHhwy5+\n+EMfoRBkZ1t85SshNm1aONPdPIjZ7OS+BOjTdf07sdf9QDYw88nehBBCCDFl0WiUlpYW7ty5Q3Nz\nsxOo7t69e89KlKqq5OfnU1hYSFFREQUFBRQUFJCfn09eXh4pk4SjySjNzST91V+h3rwJmsbIv/7X\n9sjsk/SH6u9X+N73vJw6ZceQJ5+M8sUvhpjG0IUL3mwGrG4gE/gSoACvAF2z8cGapjE0NCR9VoR4\nxFmWRU9Pz4SBWIUQiUzTpK2tjTt37nD79m1nfffuXQxj8gpQZmYmJSUllJaWUlxc7ISp/Px8Z5Dk\nmXK98w6+V16BUAizoIDQ17+OeY+5Xk+d0vhv/81HX59CSorFF784wr59C3O6mwfxIAFrfCS9Dqwe\n87rc7/c3PsDnO/Ly8ujo6KCvr282Pk4IsUBZlkVGRkbCcBtCPO6Ghoa4desWt27d4ubNm872yMjI\npOfn5+dTVlbmBKn4ejozW0xZMIj3f/5P3Efs7tXRvXsJffnLkzYJDg3BD3/o5cgRO8xt2GDwp38a\nIi/v0Rzbb6ZPEf4ZcAgo0HU93e/3f8Hv9xu6rn8TOBw77S9m6RpRFIX8/PzZ+jghhBBiQQoEAjQ2\nNnL9+nUaGxu5desWra2tk567ZMkSli5dytKlSyktLaWsrIyysjKSkpLm5Fq1ixfx/c3foHR0gNvN\nyOc/T+S3fmvSJsErVzS++10f7e0Kbjf8/u+P8K/+VYQpjGW9aM3ZOFhTca9xsIQQQohHTSAQoKGh\nwQlT169fd2bSGMvlclFaWsry5ctZtmwZK1asYNmyZc6UbHMuFML74x/j/qd/AsBcuZLQV76COclT\ngpEI/OQnHl5/3YNlwYoVJl/96sKepHk6Zn0cLCGEEEJMXTgc5vr16zQ0NFBfX099fT1tbW0TzvP5\nfKxYsYKVK1eycuVKVqxYQUlJCS7Xwvh1rdbW4vvrv0ZtaQFNI6zrhD/5SZjk+m7eVPn2t33cvq2i\nqvDSS2E+/ekwD9jda9FYGP/FhBBCiEeEZVl0dnZSW1tLbW0t9fX13Lx5c0Lnc6/Xy8qVK1m1apWz\nLi4untIcsHMuHMbz2mt4Xn8dTBOzrIzQn/4p5qpVk53Kz37m4f/+Xw+GAYWFJl/5Soi1ax+NqtVU\nScASQgghHkAkEuHGjRtcu3aN2tparl27Rk9PT8I5iqJQVlbG6tWrWbNmDatXr6asrOxD56VdCLSL\nF/G+8opdtVIUwi++SPj3fo/JSlEXLmj89//upaXFDomHDkX43OdGiE0Z+1iRgCWEEEJMQygUoq6u\njitXrnD58mXq6+snjDGVmprK2rVrWbduHWvWrGHVqlWLbqghpb8fz49+hPuoPeudWVpK6Mtfxqyo\nmHBuf7/Cj37k4ehRO3SVlpp8+cshKioer6rVWBKwhBBCiPsIBoNcvXqVK1eucOXKFRobGyc095WU\nlDiBat26dRQVFS3Mpr6psCxcb72F99VXUQIBcLsJf+pThF94YULVyrLgrbdcvPqql0DAfkLw058O\n8/zzj09fq3uRgCWEEEKMYRgGdXV11NTUUFNTQ0NDQ0KgUlWVVatWsX79etavX09FRcX8PdE3y9Sm\nJrzf/z7apUsAGJs2EfrSl7CKiiac29ys8MorPi5ftps5N282+Lf/NkRR0cIZnWA+ScASQgjx2Gtt\nbeX8+fOcP3+eS5cuMTQ05BzTNI1169Y5gWrt2rVTnj5m0RgcxPvaa7j/5V/AMLDS0xn5wz8k+vTT\nE8a1GhoCv9/DG2/YndgzMiz+8A/t0dgnGQLrsSUBSwghxGMnGAxy6dIlqqurqampmTBkQklJCZs3\nb2bLli1s3Lhx0fWfmjLTxH34MJ4f/9huDlRVIs8+y8hnPwvjqnKWBe+95+J//S8vPT0KigIf+UiE\nl18eGX+qQAKWEEKIx4BpmjQ0NHD+/Hlqamqoq6tLaPZLTU1ly5YtzpKXlzePVzs3tMuX8f6P/2FP\nzgwY69cz8m/+DeaKFRPOvX1b5Qc/8DrNgatXG3z+8yOsXv34dmL/MBKwhBBCPJJ6e3uprq52qlQD\nAwPOMU3TqKiooLKyki1btlBeXr54O6VPk9LcjPfHP8Z14gQAVk4OI3/wB0SfeGJCc2AwCK+95uWX\nv3RjGJCebvHyyyMcOBB9pKe5mQ0SsIQQQjwSotEodXV1VFdXc+7cOW7cuJFwvKCggMrKSiorK9m4\nceOj14/qQyi9vXheew334cNgGPbTgb/zO4RffJHxA1VFIvCb37j5+7/30NenoKrw3HMRfvd3pTlw\nqiRgCSGEWLRCoRDnz5/nxIkTnDlzhmAw6Bxzu91s2rSJqqoqqqqqKC4unscrnUdDQ3hefx3PL34B\nIyN2P6uDBwn/3u9hZWcnnGqacOyYi5/8xEt7u13NWrfO4AtfGGHFCmkOnA4JWEIIIRaVYDDI2bNn\nOX78ONXV1YyMjDjHSkpKqKqqYuvWrVRUVOD1eufxSudZOIz7zTfx+P0o/f0ARHfuJPz7vz9hYmbL\ngrNnNX78Yy+3btltf6WlJp/9bJhdu+TpwJmQgCWEEGLB6+/v5/Tp05w4cYKamhqi0ahzbPXq1eze\nvZtdu3Y9vlWqsSIR3L/5DZ6f/QwlNmWPsW4dIy+/POko7Fevqvzt33q5etXuwJ6TY/GZz4zwzDNR\nFsFMPguWBCwhhBALUkdHBydPnuTkyZNcvXoV07SbqFRVZcOGDU6oys3NnecrXSAiEdxHjtgVq+5u\nAMzlyxn5zGcwdu6c0IG9rk7lpz/1cu6cnaLS0y1eeinMc89F8Hjm/OofORKwhBBCLBjNzc2cOHGC\nEydO0NjY6OzXNI2qqip2797Nzp07yczMnMerXGAiEdxHj9rBqrMTAHPpUsK/+7tEd+5k/ON+DQ0q\nr73m4exZOwL4fPD882F++7fDPGb9/h8qCVhCCCHmjWVZNDQ0OJWq5uZm55jX62Xr1q3s3r2bbdu2\nPXZP/X2o4WHcv/41njfecJoCzbIywp/+NNE9eyYEq8ZGO1idOTMarJ57LsyLL4blycCHQAKWEEKI\nOWUYBleuXOHEiROcPHmS7lhzFtgDfu7YsYPdu3ezZcuWx7uT+r0EAnj++Z9x//M/owwOAmAuW0b4\nk5+0x7IaF6yuX1f56U89nDpl/8r3euFjHwvzwgsRMjJk3sCHRQKWEEKIhy4cDlNTU8OJEyc4ffp0\nwqCf2dnZ7Ny5k927d7N+/XpcLvnVNBmlowPPP/0T7jffhFAIsDuvh196CWPbtoQ+VpYFly5p/MM/\neDh/3u5j5fGMBqvMTAlWD5vcxUIIIR6K4eFhzp0754xRFYqFArCHU9i5cye7du16rEZRnwm1rg7P\nG2/YI6/HpvcxqqrsYLV+fUKwMgw4ccLFz3/uobHR/k59Pnj22TAvvijBai5JwBJCCDFrBgcHneEU\nqquriUQizrEVK1awe/dudu/eTdm4cZjEOKaJ69Qp3G+8gXb1qr1P04ju20f4t38bc9WqhNNDITh6\n1M0vfuGmrc0OVhkZFp/4RIRnn5U+VvNBApYQQogH0t/fz6lTpzh+/DgXLlxImER57dq1znAKhYWF\n83iVi0QggPvoUdz/8i+obW0AWCkpRD76USIf+xjWuCEpAgH45S89/PKXbgIBu5JVWGjywgsR9u+P\nIF3Y5o8ELCGEENPW3d3NiRMnOH78+IQxqjZu3OhUqrLHTcUiJqfW1+P+1a9wHztmTwQImAUFRD7x\nCSIHD0JSUsL5LS0Kb7zh4a233ITD9r7ycpPf+R175HVpcZ1/ErCEEEJ8KMuyaG5u5uTJk5w+fZq6\nujrnmKZpznAKO3fuJCMjYx6vdBEZGcF17BieX/0KdcyYX0ZVFeFDhzC2b5/wROC1ayqvv+7h5EkX\nVqw71bZtUV54IcKGDYZMabOASMASQggxKdM0uXbtGqdPn+bkyZO0tLQ4x9xuN1VVVezZs4ft27eT\nmpo6j1e6uCh37+J+803cR444wyxYaWlEDh4k8tGPYhUVJZxvmnD6tMYvfuFxprNxueDppyM8/3yE\nsjKZhHkhkoAlhBDCYVkWtbW1vP3225w6dYq+vj7nWHp6Otu2bWPXrl1s2bIFn883j1e6yIyM4Dp5\nEvfhw2gXLzq7jdWriRw6RHTvXsZ3mOruVjh82M2bb7rp7rZLUykpFocORfj4xyMsWSJPBC5kErCE\nEELQ0tLCO++8wzvvvENbrHM1QH5+Prt27WLnzp2sW7cOTWb/nTrLsvtWvfUWrvfeQwkG7f1uN5F9\n+4gcOoRZXp7wFtOE8+c1fv1rN2fPuuKjMlBUZHLoUISPfCRCcvIc/znEjEjAEkKIx1QgEOD999/n\n7bffTuhTlZ2dzb59+9i3bx/Lli1DkY4906L09eF65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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/finite_mc_solutions.ipynb b/solutions/finite_mc_solutions.ipynb deleted file mode 100644 index 2b06cd0df..000000000 --- a/solutions/finite_mc_solutions.ipynb +++ /dev/null @@ -1,305 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:ef11aa46317b28348e59461a81a9d8dc6f5fd73d218983f81faa0af0fbcb9f6a" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Finite Markov Chains" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/finite_markov.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import print_function, division # Omit for Python 3.x\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import mc_compute_stationary, mc_sample_path\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Compute the fraction of time that the worker spends unemployed,\n", - "and compare it to the stationary probability.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "alpha = beta = 0.1\n", - "N = 10000\n", - "p = beta / (alpha + beta)\n", - "\n", - "P = ((1 - alpha, alpha), # Careful: P and p are distinct\n", - " (beta, 1 - beta))\n", - "P = np.array(P)\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 6))\n", - "ax.set_ylim(-0.25, 0.25)\n", - "ax.grid()\n", - "ax.hlines(0, 0, N, lw=2, alpha=0.6) # Horizonal line at zero\n", - "\n", - "for x0, col in ((0, 'blue'), (1, 'green')):\n", - " # == Generate time series for worker that starts at x0 == #\n", - " X = mc_sample_path(P, x0, N)\n", - " # == Compute fraction of time spent unemployed, for each n == #\n", - " X_bar = (X == 0).cumsum() / (1 + np.arange(N, dtype=float)) \n", - " # == Plot == #\n", - " ax.fill_between(range(N), np.zeros(N), X_bar - p, color=col, alpha=0.1)\n", - " ax.plot(X_bar - p, color=col, label=r'$X_0 = \\, {} $'.format(x0))\n", - " ax.plot(X_bar - p, 'k-', alpha=0.6) # Overlay in black--make lines clearer\n", - "\n", - "ax.legend(loc='upper right')\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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VaP5cfj6ZoamkBR2gqhneMBGgTMEKtG/TnRXrFgKQv1BBQqPvpSqTkGhuhQm7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\nSMaHzSJ5mEcyMYvkERrMK1psgRYtbiIiwhugRUIIIYQwgYFFS2A74ub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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First save the data into a file called `web_graph_data.txt` by executing the next cell" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%file web_graph_data.txt\n", - "a -> d;\n", - "a -> f;\n", - "b -> j;\n", - "b -> k;\n", - "b -> m;\n", - "c -> c;\n", - "c -> g;\n", - "c -> j;\n", - "c -> m;\n", - "d -> f;\n", - "d -> h;\n", - "d -> k;\n", - "e -> d;\n", - "e -> h;\n", - "e -> l;\n", - "f -> a;\n", - "f -> b;\n", - "f -> j;\n", - "f -> l;\n", - "g -> b;\n", - "g -> j;\n", - "h -> d;\n", - "h -> g;\n", - "h -> l;\n", - "h -> m;\n", - "i -> g;\n", - "i -> h;\n", - "i -> n;\n", - "j -> e;\n", - "j -> i;\n", - "j -> k;\n", - "k -> n;\n", - "l -> m;\n", - "m -> g;\n", - "n -> c;\n", - "n -> j;\n", - "n -> m;\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Overwriting web_graph_data.txt\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\"\"\"\n", - "Return list of pages, ordered by rank\n", - "\"\"\"\n", - "import numpy as np\n", - "from operator import itemgetter\n", - "import re\n", - "\n", - "infile = 'web_graph_data.txt'\n", - "alphabet = 'abcdefghijklmnopqrstuvwxyz'\n", - "\n", - "n = 14 # Total number of web pages (nodes)\n", - "\n", - "# == Create a matrix Q indicating existence of links == #\n", - "# * Q[i, j] = 1 if there is a link from i to j\n", - "# * Q[i, j] = 0 otherwise\n", - "Q = np.zeros((n, n), dtype=int)\n", - "f = open(infile, 'r')\n", - "edges = f.readlines()\n", - "f.close()\n", - "for edge in edges:\n", - " from_node, to_node = re.findall('\\w', edge)\n", - " i, j = alphabet.index(from_node), alphabet.index(to_node)\n", - " Q[i, j] = 1\n", - "# == Create the corresponding Markov matrix P == #\n", - "P = np.empty((n, n))\n", - "for i in range(n):\n", - " P[i,:] = Q[i,:] / Q[i,:].sum()\n", - "# == Compute the stationary distribution r == #\n", - "r = mc_compute_stationary(P)[0]\n", - "ranked_pages = {alphabet[i] : r[i] for i in range(n)}\n", - "# == Print solution, sorted from highest to lowest rank == #\n", - "print('Rankings\\n ***')\n", - "for name, rank in sorted(ranked_pages.items(), key=itemgetter(1), reverse=1):\n", - " print('{0}: {1:.4}'.format(name, rank))\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Rankings\n", - " ***\n", - "g: 0.1607\n", - "j: 0.1594\n", - "m: 0.1195\n", - "n: 0.1088\n", - "k: 0.09106\n", - "b: 0.08326\n", - "e: 0.05312\n", - "i: 0.05312\n", - "c: 0.04834\n", - "h: 0.0456\n", - "l: 0.03202\n", - "d: 0.03056\n", - "f: 0.01164\n", - "a: 0.002911\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A solution from the [quantecon library](https://github.com/jstac/quant-econ/tree/master/quantecon) can be found [here](https://github.com/jstac/quant-econ/blob/master/quantecon/markov/approximation.py)\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/graph.txt b/solutions/graph.txt deleted file mode 100644 index ae50eb228..000000000 --- a/solutions/graph.txt +++ /dev/null @@ -1,100 +0,0 @@ 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node91 22.35 -node80, node92 121.87, node88 28.78, node98 264.34 -node81, node94 99.78, node89 39.52, node92 99.89 -node82, node91 47.44, node88 28.05, node93 11.99 -node83, node94 114.95, node86 8.75, node88 5.78 -node84, node89 19.14, node94 30.41, node98 121.05 -node85, node97 94.51, node87 2.66, node89 4.90 -node86, node97 85.09 -node87, node88 0.21, node91 11.14, node92 21.23 -node88, node93 1.31, node91 6.83, node98 6.12 -node89, node97 36.97, node99 82.12 -node90, node96 23.53, node94 10.47, node99 50.99 -node91, node97 22.17 -node92, node96 10.83, node97 11.24, node99 34.68 -node93, node94 0.19, node97 6.71, node99 32.77 -node94, node98 5.91, node96 2.03 -node95, node98 6.17, node99 0.27 -node96, node98 3.32, node97 0.43, node99 5.87 -node97, node98 0.30 -node98, node99 0.33 -node99, \ No newline at end of file diff --git a/solutions/ifp_solutions.ipynb b/solutions/ifp_solutions.ipynb deleted file mode 100644 index 67979ddd4..000000000 --- a/solutions/ifp_solutions.ipynb +++ /dev/null @@ -1,288 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:93efd2c39b0b56525d85fa0248677e2a0739e27ff07ee120a92797b0261694bc" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Optimal Savings" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/ifp.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import compute_fixed_point\n", - "from quantecon.models import ConsumerProblem" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "cp = ConsumerProblem()\n", - "K = 80\n", - "\n", - "# Bellman iteration \n", - "V, c = cp.initialize()\n", - "print(\"Starting value function iteration\")\n", - "for i in range(K):\n", - " # print \"Current iterate = \" + str(i)\n", - " V = cp.bellman_operator(V) \n", - "c1 = cp.bellman_operator(V, return_policy=True) \n", - "\n", - "# Policy iteration \n", - "print(\"Starting policy function iteration\")\n", - "V, c2 = cp.initialize()\n", - "for i in range(K):\n", - " # print \"Current iterate = \" + str(i)\n", - " c2 = cp.coleman_operator(c2)\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "ax.plot(cp.asset_grid, c1[:, 0], label='value function iteration')\n", - "ax.plot(cp.asset_grid, c2[:, 0], label='policy function iteration')\n", - "ax.set_xlabel('asset level')\n", - "ax.set_ylabel('consumption (low income)')\n", - "ax.legend(loc='upper left')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Starting value function iteration\n", - "Starting policy function iteration" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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HZAYu2zCXiIjIS2eMIeh0EGP/Gs/vx1aR6mRD3I/+zID3SxEwANzc7J1Q7OVl\nDeD3BYpjKbge5Q2cfeT1OSA7KsZERCSRuHHvBjP2zGDsXxP451Zy7m3qQFmXCXz8QTpq1gRnZ3sn\nFHt7GcVYGmAB0A1LC9mTnuwRj3Wk/sCBA2Oe+/v74+/vHz/pRERE4pkxhm3ntzF+x3h+ObSITLfq\ncHnZZN6rVJFuI50oXNjeCSU+BQUFERQU9MI/b+uhgSmApcBy4PtY3h8PBAHzol8fAary75Yx3U0p\nIiIJXmh4KHP2z2Hc9nFcuvkPbkc6ELqpJR+0yUC7dpAhg70TysuQkO6mdAKmAIeIvRADWAx0wVKM\nlQNuoS5KERFxMAevHGTcjnHM3jeH7JFVuLbiS7KH1aB7N2caTQIXrVQkz2DLYqwi0BzYx/+mq+gD\n+EQ/nwD8juWOyhPAHaClDfOIiIjEm7CIMH45/Avjdozj6NUT5LzeBjN3L0Uq5qDbN1BOcwOIlRxl\nBhN1U4qISIJw+tZpJuyYwLQ908iavAhOOzpydk092rVOQadO4O1t74Ribwmpm1JERCRReDg569jt\nY/nz3FaKO72P24INRN57hW7doOkkSJ3a3inFUakYExEReYqrd64ydfdUJuycgEfyDGQ93xGm/kSa\ncq5M/hL8/TVLvvx3KsZEREQeYYxh67mtjN0xlqXHllLB821y7ZzPnmWlea0FjNkCuXPbO6UkJo5S\nz2vMmIiI2NSd8DvM2T+HsTvGcjsslLLOHTk0J5DbV7zo1g0CA8Hd3d4pxRHEdcyYijEREUnSDl89\nbJmWYv9symapQsbTnVg1vjqFCznTvTvUrq1Z8iVuNIBfRETkOSKiIvjtyG+M2T6Gw9cOU9e7Df93\nYg+rhuWgYUNYtRKKFrV3SkkqVIyJiEiScfH2RSbtmsTEnRPJlS4X5ZJ1xixvwLLDLnTuDGOOa5Z8\nefnUTSkiIomaMYbNZzYzZvsYVgavpEH+d8kY0pGFY14lXTro3h0aNdIs+RJ/1E0pIiKCZZ3IH/f9\nyNjtYwmPDOe9vJ3IfH0Cs9umxd8fpk+HChU0NYXYn6N8BdUyJiIiVjly7Qhjt49l9v7ZVMlZhWpu\nndk8qzpr1zgRGAhdukCuXPZOKYmZ7qYUEZEkJzIqkqXHljJ6+2j2X95Py1fbkP1ye2aNzsGVK9Ct\nG7RsCR4e9k4qSYGKMRERSTKu3rnKuL+mMH77ONKQjWL3u5DieEM2B6Ukd27o0QPq1oVkyeydVJIS\nFWMiIpJK4FJLAAAgAElEQVToGAPnz8PRo5bHhuPb2RQ+mstpF+N09G18LnfGL1NJChSAV16BkiWh\nSBF7p5akSsWYiIgkCrduwerVsGwZLF8OJL+PZ6WfuJF3NJEpr/KWdyc6lW+FX/70JNftaJKAqBgT\nERGHZAwcPmwpvpYtg507oXJlKP/6GS56j2fBycmUyFqCzqU7UztfbZI5q+9REiYVYyIi4jDu3YP1\n6+H33y0FWFQUvPkmvPGGIUW+DUzaN4r1p9YTUCyAzmU6kz99fntHFnkuFWMiIpKgGQMrV8LYsRAU\nBH5+lgLszTfBN98dZu//kdHbRxMZFUmXMl0IKBaAe0qt0C2OQ8WYiIgkSGFhMGcOfPutZeHt7t2h\nfn3w9ISTN08y5q8xzNg7g4o+FelapivVc1V/+EtNxKFoBn4REUlQbtyA8eNh9GgoVsxSjP3f/4Eh\nitXBqxm1fBRbz22lVfFWbG+7nVyempFVkhYVYyIiYhPBwfD99zB7Nrz1lqVrsmhRuB12mzHbZzD6\nr9GkTJ6SrmW68lOjn3BN4WrvyCJ2oWJMRETi1Z9/wogRlvFg7drBgQOQLRucuHGC7itGM3PvTKrn\nrs7EuhOp7FNZXZGS5KkYExGR/ywyEhYvhuHD4eJFy8z306eDq5ulK7LdnFFsO7+NNsXbsKfDHnzS\n+tg7skiC4Sh/jmgAv4hIAnTvHsyYYRkH5ukJvXpBgwZwN+I2M/fOZNRfo0iZPCUflPmApkWbkjpF\nantHFrE5DeAXERGbu34dxoyxPMqUgcmTLRO0Bt88Qa/VY5i5bybVfKupK1LECs72DiAiIo7j5Eno\n0gXy5oWQEMuErYsXG8K8V1NvXl3KTylPyuQp2dVuFwsaL6BKzioqxESeQy1jIiLyXNu3wzffwLp1\n0LYtHDoEaTPc5cd9P9Jo3EiccKJb2W7Mbzhfd0WKxJGj/LmiMWMiIi9ZVJRlmaJvvoHTpy2D8lu3\nhltRZxmzfQxTdk+hfPbydCvbjddyvaYWMJFoGjMmIiL/SXi4Zab8b74BFxf46CNo2NCw/dIWWq8Y\nyZqTa3j/1ff5s/Wf5PXKa++4Ig7PUf6MUcuYiIiN/fMPTJxomai1YEH4+GOo7B/Gz4d+YuS2kfwd\n9jddy3Ql0C8Qj5Qe9o4rkmBpbUoREYmTCxfghx9g0iSoVcvSEuad/woTdkxg3I5xFM5UmG5lu1E7\nX22cnXTfl8jzqJtSRESscviwZZLWX3+F5s1hxw647bqPkVtH8suqX2hUqBGrA1ZTOFNhe0cVSdRU\njImIJDF//AFffw1bt1qmqTh6LIqt15fRZtP3HLl2hM6lO3O863EyuGawd1SRJEHFmIhIEvDwzsgv\nv7QsV9SrF0yeGcr8o9OpMHckaVOmpUe5HjQq3AiXZC72jiuSpKgYExFJxB48gPnz4auvIHly+PRT\nKPV/pxm3czQFJkzjtVyvMf2t6VTIUUFTU4jYiaP8P08D+EVE4uDuXZg61TImLFcuSxHmXvBPvtv2\nLetOraOlX0u6lOmCbzpfe0cVSXR0N6WISBJ28yaMHQujRkH58vDhRxGc91jId1u/4+rdq3Qr242W\nfi1xT+lu76giiZbuphQRSYIuXIDvvrO0htWrB7+tvMWmO5Np9tcocqbNyScVP6HeK/VI5pzM3lFF\n5AmaMEZExIEdP25ZK7JIEYiIgN82niTtu915Y0Vudl/azcLGC9nYciNvF3xbhZhIAqViTETEAe3d\nC02aQIUKkDWbYWbQZkLKNaD+sjKkSp6KfR33MbvBbEplK2XvqCLyHBozJiLiQDZvhmHDYM8e+KB7\nBJmrLWTs7hHcvH+T7mW708KvBWlc0tg7pkiSpgH8IiKJjDGwciUMHQrnz0PXXv8QVngyY3eNJGfa\nnHxY/kPq5K+jbkiRBEID+EVEEonISPjlF0tL2IMH0LbXGU5n/oHB+6ZR40oNFjRaQGnv0vaOKSL/\nkbXFmBuQAzDAOeCOzRKJiCRx4eHw44+WiVq9vKD5xzvYnvxbPj+5ksAsgexqt4uc6XLaO6aIxJNn\nNaG5A22BJkAG4HL0/pmB68BsYBIQauOMoG5KEUkC7t2zTE3x9deQL38U1dovZdXtEZy6dYpuZbvR\npkQb0qZKa++YIvIc8TlmbC0wD1iMpRB7VBagHvAuUD1uEV+IijERSbRCQ2H8eBgxAkqWvUeR5jP5\n9dK3eKT04MPyH/JOwXdIkSyFvWOKiJU0gF9ExEHcumWZKX/UKKhY4xpZ3xrLL2fGUsa7DB+W/5Aq\nOatovUgRBxTXYsyaecacgQCgf/RrH6BMnJOJiAgAV69Cnz6QJw/sOXOc//uuE0HF8vEg1VmCAoNY\n/N5iqvpWVSEmkkRYU4yNBcoDTaNfh0ZvExGRODh/Hnr0gFdegSN3/qT08AZszF+B3Fm9ONz5MJPq\nTaJAhgL2jikiL5k1d1OWBYoDu6Nf3wA0eEFExEqnT1vujJz/cySVWi8mz9Dh7Am7SM/CPVnoNws3\nFzd7RxQRO7KmGAsHHp1JMCMQZZs4IiKJx7Fjlolalyy/R9n2M/HsN4JLbun4uMJHNCjYQJO0ighg\nXTE2CvgVyAQMBRoC/WwZSkTEke3fD198AWv+uIFf23Ek7zWKZNlLM63CZCr7VNZYMBF5jLX/IhTk\nf1NYrAUO2ybOU+luShFJ8HbsgCFDYMvBEF4J/I4DyWZSv+Bb9Crfi8KZCts7noi8JLZaDukSsCl6\n/9RACWBXXMOJiCRGmzdbWsJ2XdiLT5NviCy/nPIlWjOv7H68PbztHU9EEjhrirHBQCBwksfHilWz\nRSAREUdgDKxbB4MGG46Fryd9va9JnmI/jcp1o33JMZopX0SsZk0T2jGgCJaB/PaibkoRSRAeFmED\nBkYSnHIhqap/TUr3O3xc8SOaFW1GyuQp7R1RROzMFt2UBwFP/r0kkohIkmEMrF8P/Qfd57jrDHj9\nG/JkycynlfpTJ38dnJ2smbZRROTfrKnaSgO/AQeAsOhtBsvalC+LWsZExG7Wr4d+Q25x1GMcESV/\noFKuUnxa+RMq+VSydzQRSYBs0TI2E/gSSzH2cMyYKiMRSfSCgqD30AscSfc9D/ynUL/wm3xaaTVF\nMhWxdzQRSUSsKcZCgR9sHUREJKHYsAE+/uoYR7y+IbLyQlqWDKBXhV3kTJfT3tFEJBGypgntWyzd\nk4v5XzclvNypLdRNKSI2t3Ej9By+gyMZvsQ51wa6VehEt/JdyeCawd7RRMSBxLWb0podg4i9W/Jl\nTm2hYkxEbOaPPwxdv13PkYxDSZ39GH1e60n7Um1I45LG3tFExAHZohhLCFSMiUi8+3NrFJ1GLuZQ\n+mF4Zf2bwbU+4f3izXBJ5mLvaCLiwGwxgD8dMACoEv06CBgE/B3HbCIiCcLWvx7QYfQ8Dnp9SbYi\nqZhZtw8Ni9TXwt0iYhfWVG2/APuBGdH7BwDFgAY2zPUktYyJyH/25457tB83jUOe35Dby5dvG/Tm\nzVdqaOFuEYlXtuim3Au8asU2W1IxJiIv7I+df9Nu0jiOeIykULrSjHq3N/55yts7logkUrboprwH\nVMayUDhAJeBunJOJiLxkm3ddo93U7zmSZjzFc9Ria8AqSvsUtXcsEZHHWFOMdcAy8evDVW9vAi1s\nlkhE5D/auPsC7aYP55jrdMpkbcTewG0U9c5j71giIrGKy0CJh8WYPQbuq5tSRJ5r/e7TtJ/1FSdS\nzqeCWwumtu5F/qze9o4lIklMXLsprVnZdhiWOyr/jn54AkNeJJyIiC2s2X2UvB8FUv2nkmRN58nx\nbkfY3O87FWIi4hCsqdr2AH5PbNsNFI//OE+lljER+Zffd+2ly/wvOE0Q/q5dmdq+C75ZPO0dS0SS\nOFsM4HcGUgH3o1+nBjQjoojYzeKd2+i6YAhnI3ZSPfWHbOg8lRyZNVu+iDgma4qx2cBaYCqWKq8l\nlgH9IiIv1eI9m+i6YDDn7h3l/1J/wpbuP+OdOZW9Y4mI/CfWNqG9AfwfljUqVwMrbZYoduqmFEmi\njDEsObCergsGce72Waol78O07gHkyKYGehFJmLQ2pYgkCsYYfju0ku4LB3PuxjUqRPVlWo+m5Mll\nTYO+iIj92GLM2DvAl0DmRw5sAI+4hhMReR5jDL8eWkrPRYM5f+UOJe98xrKejShcSOtGikjiZE3V\nFgzUAQ7bOMuzqGVMJJGLMlH8cmgRvRYP4cKlKApf+4xJPd+mVElrZuAREUk4bNEydgn7FmIikohF\nmSh+PriAj5cN5sqFVPieGcjq7nWpWtVRRlGIiPw31hRjO4D5wCIgPHqbAX6xVSgRSfwioyJZcGgB\nvVcM5sr5NGQ69BULPniD2rWdcFIdJiJJiDXFWFosi4XXfGK7ijERibOHRVjf1YO4cdGd5JuHM65j\nLZqNcMJZPZIikgQ5yt+fGjMm4uAioyL5+dDP9F83iH8up+XeioH0fbcmH3zgRCpNFSYiiUh8jhn7\nBPgKGBXLewb4IE7JRCRJioyK5KeDPzEwaBB3b6Tj70Xf07paDfqtciJ9enunExGxv2cVY4ei/3cn\nluLrIacnXouI/MvDImzQhkGE/+PJrUUjqZGnBkMXOpE7t73TiYgkHOqmFJF4Zbk78mcGbhgI9zy5\n+/vn+Eb9H8O/caJ0aXunExGxPVtMbSEi8lxRJopfD//KgKABEJ6GFBtG8uBoDcZ87cSbb6I7JEVE\nnkLFmIj8J8YYFh9dzICgAUQ+SI7Xnq85seINPh/oRMufIbn+lREReSZr/plMBdy3dRARcSzGGJaf\nWE7/9f25F/6AXKcG8eeMejTp4cTyY+DmZu+EIiKOwZpi7CBwGdgEbAQ2A3/bMpSIJFzGGFafXE3/\n9f35534ofjc/Z+XIt6ne1JlphyFjRnsnFBFxLNYUY3mAnEAlLGtUjgVuAn42zCUiCdD6U+vpH9Sf\nq3euUiVqIEu/akxkJWe2bYW8ee2dTkTEMVlTjGUHKgKVsRRgB7G0kolIEvHn2T/pt74fIbdCqOcx\nkOXj3uNYxmQs+hXKlLF3OhERx2bN/U1RwHZgGPAb9pljTFNbiNjBnkt76LeuH/su7yMgZ382/tCC\nm9dS8NVXULu27pAUEYlNXKe2sGbHV7G0ilUGfIDjWMaOTX6BfC9KxZjIS3Tk2hH6r+/PpjObaF+o\nN0dmt2PT+lQMGgSBgZAsmb0TiogkXLYoxgDcsXRVVgGaR2/ziVOy/0bFmMhLcOrmKT7f8DnLji+j\nc/EP+WdNV2ZMcqNrV+jVC9KksXdCEZGEzxaTvu7AMr3FFiwtYpWBkBcJJyIJ0/l/zjNk4xB+OvQT\nHUp05pPUxxnePB21a8O+feDtbe+EIiKJlzXFWG3giq2DiMjLd+3uNb7c/CVTd0+lVfHWjMx7hC+6\nZsTbG1asAD/dMy0iYnPOVuwTDnyHZcHwncAIIK0tQ4mIbd0Ou82gDYMoMLoAdx/cZV6VA+z68huG\n9svI8OGwerUKMRGRl8WaYmwq8A/QCGgM3Aam2TKUiNhGWEQYP2z7gXyj8nHg4jHGltjG3Z/H8v7b\n2Wjc2NIlqXUkRUReLmsnfW3wyOuBwF6bpBGReGUMXL4MBw5FMnP3bBb93Z8UtwrjtG4ly0Ne5XRB\nqFULjh0DDw97pxURSZqsKcbuYRm0/3Ci10rAXZslEpEXdusWzJsHO3fCoUNw6LAhMs8SIv374O6S\nlhaeM6lbqQoFP4Ls2dUCJiKSEFjzT7EfMJP/jRO7CbTg5baOaWoLkWfYuRPGjYOFC+H116FqVQjP\nupFZFz/lvrnN0NeGUid/nYe3W4uIiA3ZYmqLPUAx4GEnxj9xjyUi8e3ePZg/H8aOhStXoH17OHIE\nLpo99Fnbh8OnDzPIfxBNizYlmbNmaRURSaieVbV9+MjzR5ulnKJff2uTRLFTy5hItGPHYPx4mDkT\nypaFjh3hjTfg3O0Q+q3vx+rg1fSt3Jd2JduRMnlKe8cVEUly4rNlzB37rEMpIk+IiIAlSyytYPv2\nQcuWsH075MoFN+/d5NO1Q5m6ZyqdSnXiWNdjeKTUaHwREUdh6wEkU4E3sUwaWzSW9/2xLD5+Mvr1\nQmBILPupZUySpMhImDoVPv8cfH2hUyd45x1ImRLuR9xn9F+j+eqPr2hQoAED/QeS1T2rvSOLiCR5\n8dkyNhAYB1x+yvtZgQ7AgGccYxowCssNAE+zAaj3jPdFkqRt26BLF3BxgUWLoFQpy/YoE8WsvbPp\nt74fxbMUZ2PgRgpmLGjfsCIi8sKeVYztAOYBLsAu4CKWKi8LUAIIA4Y/5/ibAN/n7KPbu0Qecfky\n9O4NK1fCl19C8+b/m4JiVfAqPlnzCamSp2J2g9lU8qlk37AiIvKfPasYWxr9yAFUBHyit28GvgLO\nxcP5DVAByzQZ54FewKF4OK6Iw3nwwDImbMgQaNECDh/+30Ssuy/u5pM1n3D61mmGVR9Gg4INNE2F\niEgiYc3UFmextJDZwi4sxd5d4A1gEZDfRucSSbDWr4euXSFrVti4EQpG9zqe++ccfdf1ZeWJlfSv\n2p+2JdqSIlkK+4YVEZF4ZU0xZku3H3m+HBgLeAE3ntxx4MCBMc/9/f3x9/e3cTQR2zt7Fnr1sowP\n+/ZbePttS5dkaHgoX23+irE7xtKhZAfdISkikoAFBQURFBT0wj//Mvo5fIElxH43ZWYsd1oaoAzw\nE7GPMdPdlJKo3L8PI0ZYCrAuXeCTT8DVFSKjIpm2Zxr91/fntVyvMbT6UHzS+jz/gCIikmDYYgb+\n/2IuUBXIgKW7cwDwsI9lAtAQ6AhEYOmqbGLjPCJ2t3q1ZYqKQoUsc4Xlzh29PXg1H676kHSp0vFb\nk98o7V3avkFFROSlsKZqywS0xdJi9bB4M0ArG2WKjVrGxOFduAA9esBff8GoUVCnjmX7wSsH+Wj1\nRxy7foyva3zN2wXe1uB8EREHFteWMWcr9vkNy7qUq4FljzxExAoRETByJBQrBnnzwsGDlkLsyp0r\ndFzaEf8Z/tTMU5NDnQ/pLkkRkSTImm7K1MAntg4ikhht22ZZOzJdOti0yXKX5P2I+3y5+XuGbxlO\nQLEAjnY5ildqL3tHFRERO7GmGFuKZUkjtYaJWOnmTcvErb/9BsOHQ9OmAIZfDy+i1+peFMlUhD9b\n/0m+9PnsHVVEROzMmv6QUMAVCAceRG8zWLouXxaNGROHYAzMmmW5O7JBA8sErp6esP/yfrqv7M6l\n0Et8X+t7auSpYe+oIiJiI7a4mzLNC6cRSUIOHbLcJRkaCosXQ+nScO3uNTot68+CQwvoX7U/HUp1\nILmzvaf3ExGRhMSaAfwAbwEjsKxFWdd2cUQcz/378NlnULUqNGxoGSfmV+IBI7eOpOCYgiRzSsbh\nzofpUqaLCjEREfkXa34zfAmUBmZjaXL7AMt6kr1tmEvEIWzcCO3aQeHCsGcPeHvDihMr6LGyBzk8\nchDUIojCmQrbO6aIiCRg1vRn7gf8gMjo18mAPcQ+o76taMyYJCi3bsHHH8Pvv8Po0VC/Phy7foye\nK3ty9PpRRtQcQd38dTVNhYhIEmSLecYMkO6R1+mit4kkOcbAggWWlrDkyS1zhlV/4zafrP6EClMq\nUDVnVQ50PEC9V+qpEBMREatY0005DNgFBEW/rgp8aqtAIgnVuXPQuTMcPw4//QQVKhjmH5xPr1W9\neC3XaxzodIAsabLYO6aIiDgYa/90z4Zl3JgB/gIu2SxR7NRNKXYTFQXjxsHAgZZFvT/9FI7/fYCu\ny7ty895NRtceTSWfSvaOKSIiCUR8Tm1REDgMlMRShJ2L3p4t+rHrxSKKOI6DB6FtW3B2tgzWz5br\nbz5dP5Af9//IwKoDaV+qve6QFBGR/+RZv0V6YlkgfASxjxGrZpNEIglAeDgMG2YZnD94MLRpG8Wc\nAz/y6ZhPqZ2vNoc6HSKjW0Z7xxQRkUTgWcVY2+j/fR24/8R7qWwTR8T+9uyBwEDInt3y/GqyPVSd\n0ZnwyHAWNVlEGe8y9o4oIiKJiDV3U26xcpuIQwsPhwEDoGZN6NkTZv50k6F7OlPrx1q09GvJtjbb\nVIiJiEi8e1bLWFYsY8NcgRJYBqI9XJPS1fbRRF6e3bstrWE+PrB7t2H99dkUHvcRbxd4m8OdD+OV\n2sveEUVEJJF6VjFWEwgEvLGMG3voNtDHhplEXprwcMti3uPHw4gRUOr1IwT83olb92+xuMliSnuX\ntndEERFJ5Ky57bIhsMDWQZ5DU1tIvNu5E1q2BF9f+H7MPaYdH8q4HePoX7U/nUp30l2SIiLyQuJz\naouHgoBRQCUs3ZSbgEHA9bjHE7G/sDDLHZKTJsG334JXmRXU+LUzJbOWZG+HvXh7eNs7ooiIJCHW\nDOCfB1wBGmBpJbsKzLdlKBFb2bkTSpWC/fthxR8X+M2lMV2Wd2b0G6P5qdFPKsREROSls6YJ7QBQ\n5Ilt+9FC4eJAIiIs84aNGgUjvo3kRt4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DjydvzwAaAk0Io2mx9MEH8PzzsHAhrN2wlt6j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\nhaSxbtgzVmYK5VuMDSbmV/XsJIcSrnCqQ/h+epyw5mG2yue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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "r_vals = np.linspace(0, 0.04, 4) \n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "for r_val in r_vals:\n", - " cp = ConsumerProblem(r=r_val)\n", - " v_init, c_init = cp.initialize()\n", - " c = compute_fixed_point(cp.coleman_operator, c_init, verbose=False)\n", - " ax.plot(cp.asset_grid, c[:, 0], label=r'$r = %.3f$' % r_val)\n", - "\n", - "ax.set_xlabel('asset level')\n", - "ax.set_ylabel('consumption (low income)')\n", - "ax.legend(loc='upper left')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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LVXWAL0/2n9jPpKhJTImZQq1KtXiyyZPM7T6XMsU1A0pyTsmYiEghsXevadQa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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "from scipy import interp\n", - "from quantecon import mc_sample_path \n", - "\n", - "def compute_asset_series(cp, T=500000, verbose=False):\n", - " \"\"\"\n", - " Simulates a time series of length T for assets, given optimal savings\n", - " behavior. Parameter cp is an instance of consumerProblem\n", - " \"\"\"\n", - "\n", - " Pi, z_vals, R = cp.Pi, cp.z_vals, cp.R # Simplify names\n", - " v_init, c_init = cp.initialize()\n", - " c = compute_fixed_point(cp.coleman_operator, c_init, verbose=verbose)\n", - " cf = lambda a, i_z: interp(a, cp.asset_grid, c[:, i_z])\n", - " a = np.zeros(T+1)\n", - " z_seq = mc_sample_path(Pi, sample_size=T)\n", - " for t in range(T):\n", - " i_z = z_seq[t]\n", - " a[t+1] = R * a[t] + z_vals[i_z] - cf(a[t], i_z)\n", - " return a\n", - "\n", - "cp = ConsumerProblem(r=0.03, grid_max=4)\n", - "a = compute_asset_series(cp)\n", - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "ax.hist(a, bins=20, alpha=0.5, normed=True)\n", - "ax.set_xlabel('assets')\n", - "ax.set_xlim(-0.05, 0.75)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following code takes a few minutes to run" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "M = 25\n", - "r_vals = np.linspace(0, 0.04, M) \n", - "fig, ax = plt.subplots(figsize=(10,8))\n", - "\n", - "for b in (1, 3):\n", - " asset_mean = []\n", - " for r_val in r_vals:\n", - " cp = ConsumerProblem(r=r_val, b=b)\n", - " mean = np.mean(compute_asset_series(cp, T=250000))\n", - " asset_mean.append(mean)\n", - " ax.plot(asset_mean, r_vals, label=r'$b = %d$' % b)\n", - " print(\"Finished iteration b=%i\" % b)\n", - "\n", - "ax.set_yticks(np.arange(.0, 0.045, .01))\n", - "ax.set_xticks(np.arange(-3, 2, 1))\n", - "ax.set_xlabel('capital')\n", - "ax.set_ylabel('interest rate')\n", - "ax.grid(True)\n", - "ax.legend(loc='upper left')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Finished iteration b=1\n", - "Finished iteration b=3" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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77jzuPu7ugi3GJG3o1Vdh9Gj4xS9CRyIpG3J9Pc4xwD+J+sHuJ1q+4uKy791b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\nsgeJFoU9qOx4DnBjziIqUiX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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/jv_solutions.ipynb b/solutions/jv_solutions.ipynb deleted file mode 100644 index 8d22a3fcd..000000000 --- a/solutions/jv_solutions.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:508943d1ce2372490699818cedf35a0997172a9adcaf9c6de4b3eeb42efd275d" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: On-the-Job Search" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/jv.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "from quantecon import compute_fixed_point\n", - "from quantecon.models import JvWorker" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here\u2019s code to produce the 45 degree diagram" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "wp = JvWorker(grid_size=25)\n", - "G, pi, F = wp.G, wp.pi, wp.F # Simplify names\n", - "\n", - "v_init = wp.x_grid * 0.5\n", - "print(\"Computing value function\")\n", - "V = compute_fixed_point(wp.bellman_operator, v_init, max_iter=40, verbose=False)\n", - "print(\"Computing policy functions\")\n", - "s_policy, phi_policy = wp.bellman_operator(V, return_policies=True)\n", - "\n", - "# Turn the policy function arrays into actual functions\n", - "s = lambda y: np.interp(y, wp.x_grid, s_policy)\n", - "phi = lambda y: np.interp(y, wp.x_grid, phi_policy)\n", - "\n", - "def h(x, b, U):\n", - " return (1 - b) * G(x, phi(x)) + b * max(G(x, phi(x)), U)\n", - "\n", - "plot_grid_max, plot_grid_size = 1.2, 100\n", - "plot_grid = np.linspace(0, plot_grid_max, plot_grid_size)\n", - "fig, ax = plt.subplots(figsize=(8,8))\n", - "ax.set_xlim(0, plot_grid_max)\n", - "ax.set_ylim(0, plot_grid_max)\n", - "ticks = (0.25, 0.5, 0.75, 1.0)\n", - "ax.set_xticks(ticks)\n", - "ax.set_yticks(ticks)\n", - "ax.set_xlabel(r'$x_t$', fontsize=16)\n", - "ax.set_ylabel(r'$x_{t+1}$', fontsize=16, rotation='horizontal')\n", - "\n", - "ax.plot(plot_grid, plot_grid, 'k--') # 45 degree line\n", - "for x in plot_grid:\n", - " for i in range(50):\n", - " b = 1 if random.uniform(0, 1) < pi(s(x)) else 0\n", - " U = wp.F.rvs(1)\n", - " y = h(x, b, U)\n", - " ax.plot(x, y, 'go', alpha=0.25)\n", - "\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computing value function\n", - "Computing policy functions" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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1BkanRpHgE9hc30RzVzM2/BuS+1GaXy8vW2wwNuTMvSulKQxGAxpsDbjYf1Hy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\nT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UOG0alRVlgB2HEdeqkqALVGZTQwis1mI0OGGzduUNNWQyaTz/CvrKtEi2r89PM/nX+n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\n3kOGDA91PMSZs2eo+GRO5BVFIf2DNF/85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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looking at the dynamics, we can see that \n", - "\n", - "* If $x_t$ is below about 0.2 the dynamics are random, but $x_{t+1} > x_t$ is very likely\n", - "* As $x_t$ increases the dynamics become deterministic, and $x_t$ converges to a steady state value close to 1\n", - "\n", - "Referring back to the figure here\n", - "\n", - "http://quant-econ.net/py/jv.html#solving-for-policies\n", - "\n", - "we see that $x_t \\approx 1$ means that $s_t = s(x_t) \\approx 0$ and $\\phi_t = \\phi(x_t) \\approx 0.6$\n", - "\n" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The figure can be produced as follows" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "wp = JvWorker(grid_size=25)\n", - "\n", - "def xbar(phi):\n", - " return (wp.A * phi**wp.alpha)**(1 / (1 - wp.alpha))\n", - "\n", - "phi_grid = np.linspace(0, 1, 100)\n", - "fig, ax = plt.subplots(figsize=(9, 7))\n", - "ax.set_xlabel(r'$\\phi$', fontsize=16)\n", - "ax.plot(phi_grid, [xbar(phi) * (1 - phi) for phi in phi_grid], 'b-', label=r'$w^*(\\phi)$')\n", - "ax.legend(loc='upper left')\n", - "\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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Tp+qMnlSmgCJSBc8/7+XJJ/3sv3+MwYM1tSOSTtq1i3LssRHeeMPL8uUaRUlV\nCigiO+m33wwGDvxjaiegX8JE0oph/DGKMmWK/gOnqkqjo2ma+wH9gSgw27KsVZVcew7QcsubH1uW\ntXBn7yGSDoYPT0ztXH11OUccoakdkXTUoUOUtm2jvPaalzff9HDssWqumGqSjaD0sixrsGVZw4Gz\nKrvQsqwnLcuaaFnWRGDPqtxDJNUtWeLhscf8tGypqR2RdJYYRUn8H9ZalNSUbPKtqMLjpD+NTdM8\nDBgO3FLVe4ikqqIiuO66REO2adO0a0ck3XXuHKWgIMqyZV7eecfNUUdpRDSVJBtBMSo8Lk92M8uy\nPgD6Ar2qeg+RVDV2bJDvv3dx6aVqyCaSCSquRZk6Vb9xpJpkAcVb4fEOtbSxLKsY+Lk69xBJNcuX\ne3jggQB77RX7vV22iKS/k06K0KZNlCVLvHz0kU46TiXJAko+gGmaxtbHW97uZJpm+4oXmqa5x3bu\nu817iKSL0lK45pocAO66q5S8PIcLEpEaYxhw3XWJUZS77tJalFSSbA3KXNM0J5AIHDMrPH8OEAde\nrfDcuaZp5pCY0pm3A/cQSQsTJwb5+ms3ffqE6NBBK/1FMk2PHhH23TfGvHlehg1zse++cadLEv68\nPsRxixcvtgsKCpwuQ+R3K1a46do1n113tXnzzSLq19cspUgmmj3bx/XX59K3b4jbby91upyMUVhY\nSJcuXaqUNdSoTWQ7IhG49toc4nGDqVNLFU5EMti554bZffc4jz3mY/36lPrdPWspoIhsx4wZfj75\nxEOPHmFOPTXidDkiUov8frjiinLCYYOZM7UWJRUooIhsw5dfupg8OUi9enEmT9Zwr0g26Ns3RL16\ncR54wE9RUfLrpXYpoIj8RTwO112XQyhkMHZsGbvvrqkdkWxQrx707x9i82aDBx7wO11O1lNAEfmL\nRx7xsXy5l3btIlx0UdjpckSkDl12WYhAwObeewOUqeWRoxRQRCpYv95g5Mggfr/NHXeUYmitnEhW\n2XVXmwsuCPHjjy4ef9zndDlZTQFFpILBg3MoKnJx003ltGypXggi2ejKK0O4XDbTpgWI6VQLxyig\niGzx4ote5s/30bp1lKuu0rFRItmqRYs4vXpFWLPGzbPPepN/gNQKBRQRoLgYBg0KYhg2t99eilc/\nk0Sy2pVXJn5JuftubTl2igKKCDBpUpC1a91cckmItm01piuS7Q4/PMaxx0Z45x0P776rQwSdoIAi\nWe+jj9zYGkvUAAAgAElEQVTce6+f3XePM2KElu2LSMKVV4YAuOcejaI4QQFFslosBtdfn2hnP2lS\nKfXqOV2RiKSKbt0ShwjOn+/l22/1clnX9B2XrHb//X5WrPBw8slhevRQO3sR+YPbDVdcESIeN5g5\nU43b6poCimSttWsNbrklSG6uzZQp6nkiIn933nkhGjSI8/DDan9f1xRQJGsNHZpDcbHBkCFlNG+u\ndvYi8ne5udCvX4jiYoOHH9YoSl1SQJGs9MILXhYu9NGmTZTLLgs5XY6IpLD+/UN4vTYzZ/qJRp2u\nJnsooEjWKSmBm29O9Dy57bZSPB6nKxKRVNa0qc2ZZ4ZZu9bN/PlqklRXFFAk69x6a5DvvnPTr1+I\nI45QzxMRSW7AgMRI6913B7A1I1wnFFAkq3z+uYsZM/zsskucESPUzl5Edswhh8Ro3z5CYaEat9UV\nBRTJGvE4DByYQzRqMH58GfXr69cgEdlxW9er3X+/GrfVBQUUyRpz5vh46y0v7dtHOPvssNPliEia\n6dYtwl57xZg3z8v69epLUNsUUCQrbNxoMGpUEJ/PZupU9TwRkZ3ndsMll4SIRg1mzdKW49qmgCJZ\nYfToIBs3urj22nJatYo7XY6IpKmLLgoTDNrMmuUnrIHYWqWAIhnvnXfcPPKInxYtYlx3nRbGikjV\nNWxoc/bZYX780cW8eT6ny8loCiiS0aJRGDQoB4DJk0sJBh0uSETS3tbFsvfdp2me2qSAIhntgQf8\nfPyxh1NPDdO1q1pAikj1tW4d47jjIrz/vofCQm05ri0KKJKxNmwwmDAhSDBoc8stZU6XIyIZ5I8t\nxxpFqS0KKJKxRo8OsnmzwY03lrPnnloYKyI1p3v3CHvsEefpp3389JO2BdYGBRTJSG+84eGJJ/y0\nahXjyiu1MFZEapbHk9hyHA4bzJ6tUZTaoIAiGScSgRtvTCyMnTKlFJ8W2otILejTJ4Tfb/Pgg34i\nEaeryTwKKJJxZs70s3KlmzPOCNOhgxbGikjtaNw4ccrxunUunn9epxzXNAUUySjr1hlMmRIkL89m\n3LhSp8sRkQx3ySWJxbIPPqhpnpqmgCIZZdSoIMXFBoMGldGsmQ4DFJHaVVAQ49BDo7zyipfVq/WS\nWpP03ZSM8cYbHv73v8TC2MsvDzldjohkAcOAvn0TP2+0WLZmKaBIRohG4aabEm1iJ0/WwlgRqTtn\nnRUmL8/mscd8lGvTYI1RQJGM8N//+vnsMw+nnx6mY0ctjBWRupOXB717h/j1V53PU5M8lb3TNM39\ngP5AFJhtWdaqSq7tCBwPuIH/WZb12ZbnB1b4PO9ZlrWkBuoW+d2PPxrcckuQnByb8eO1MFZE6l6/\nfiH++98ADz7op3dvHXNcEyoNKEAvy7IGA5imORiYVMm1e1qWNWHLtdcAn215vtiyrJnVrlRkO8aM\nSXSMHTGijObNtTBWROreQQfFOfroKG+/7eHTT920bh1zuqS0l2yKp6jC40oPM7Es6+HtvMtjmuZQ\n0zSHm6Z56k5VJ5LE22+7mTPHT8uWMQYM0OSviDinX7/EYtlZszTNUxOSBZSKBwzs0E9/0zQvB57Z\n+rZlWTMsy7rFsqzxwL47X6LItsVicPPNiY6xEyeW4tcCehFx0Omnh2nUKM4TT/gpLna6mvSXLKBU\nbI2XdOzcNM0+wNuWZX27nUv0K67UmIcf9vHRRx5OOSVMly5aGCsizgoE4PzzwxQXG/zvfxpFqa5k\nASUfwDRNY+vjLW93Mk2zfcULTdO8APjKsqwP/vJ8m7/eT6S6fv3VYPz4IH6/zYQJlc4+iojUmYsv\n3jrN48fWkrhqSbZIdq5pmhNIBJmKC13PAeLAqwCmae4DnAu8bprm8UATy7IGbbm2jWmaZ2x5/EKN\nVS5ZbdKkABs3uhg4sIwWLeJOlyMiAkDLlnE6dIjwyiteCgvdHHGEFstWlZH8krqzePFiu6CgwOky\nJMV9+qmbDh3y2X13m7ff3kRurtMViYj8Yd48L/365dGnT4g778zu1geFhYV06dKlSllDjdokrdg2\nDB4cJB43GDu2VOFERFLOKadEaNw4zlNP+bRYthoUUCStPPOMl+XLvRx/fIQzzog4XY6IyN/4fNC7\nd2Kx7DPPaLFsVSmgSNooKYERI3JwuWwmTSrDSKkJShGRP1x4YWKx7MMPq/9BVSmgSNq4884AP/zg\n4pJLQurSKCIp7YAD4hx1VJR33/WwcqVeaqtC3zVJC99842L69ACNGsUZMkTtdEQk9V10UWIU5ZFH\nNIpSFQookhZGjAgSChkMG1ZGw4ZqLiAiqa9nzzB5eTZPPOEjFHK6mvSjgCIp79VXPSxc6KN16yh9\n+uiUUBFJD3l5cNZZYX75xcXzz3uTf4D8iQKKpLRoFIYMSZy3M2lSGW63wwWJiOyErdM8Wiy78xRQ\nJKXNmuXn88/d9OwZ5vjjdd6OiKSXww+P0bp1lGXLPHz7rV5yd4a+W5KyNm40uOWWAIGAzdixOm9H\nRNKPYcCFF4axbYNHH1VPlJ2hgCIpa9KkAL/95uLqq8vZc0+dtyMi6emcc8L4/TaPPeYnpg4JO0wB\nRVLSZ5+5eOABP82axbnmGm0rFpH01bChzWmnRfj+exdLlyY7o1e2UkCRlGPbMHRoDvG4wZgxOm9H\nRNLfBRckFsvOmaPFsjtKAUVSzrPPenn1VS/HHBPhzDN13o6IpL8TToiyxx5xnnvOy2+/6ZyOHaGA\nIiklFIKRI4MYhs0tt+i8HRHJDG43nHtuiFDI4Omn1RNlRyigSEq5914/a9a4Oe+8MIcdptVkIpI5\nevdONJrUNM+OUUCRlLFhg8FttwXJy7MZMULbikUks+y3X+IAwffe8/B//6eX32T0HZKUMX58kOJi\ngxtuKGO33XTejohknvPOSyyWffxx9URJRgFFUsKHH7p57DEfe+8d44ordKqWiGSmM84IEwjYPPGE\neqIko4AijrNtGDIkiG0bjB1bRiDgdEUiIrWjXj049dQI69a5eOUV9USpjAKKOO6ZZ7y89ZaXdu0i\nnHaathWLSGbbOs2jxbKVU0ARR5WVwejRQVwubSsWkezQoUOUpk3jPPusl02b9ENvexRQxFH33BPg\nu+/cXHhhmIMP1oSsiGQ+txt69w5RXq6eKJVRQBHHrF9vcMcdAfLybIYN07ZiEcke556b6Iny+OOa\n5tkeBRRxzIQJQUpKDG68sYxddtG2YhHJHvvvH+fII6O8846H1av1Urwt+q6II7ZuK27RIsbll2tb\nsYhkn62LZZ94Qj1RtkUBReqcbcPw4YltxWPGlOHXCKeIZKFevSJ4vTaW5cPWIPLfKKBInVu40Mvy\n5V6OP17bikUkezVsaHPSSRG+/dbN22+7nS4n5SigSJ0KhWDUqMRpxRMmaFuxiGQ300wslrUsDSX/\nlQKK1KmZMxOnFV9wQZg2bbStWESy20knRahXL87TT3sJh52uJrUooEid+emnP04r1rZiEREIBKBn\nzwi//eZi8WL1RKlIAUXqzMSJQTZvNrjuunKdViwissU55ySGTp58Urt5KlJAkTrx2WcuHnrIR/Pm\nMf71r3KnyxERSRnHHhtljz3ivPiiWt9XpIAitc62YcSIHOJxg1GjyggGna5IRCR1uFxgmiFCIYP5\n8zXNs5UCitS6xYs9LF3q5cgjo5x5prYVi4j81R+7eTTNs5Wnsneaprkf0B+IArMty1pVybUdgeMB\nN/A/y7I+29l7SOaJRGD48BwAJkwo1bZiEZFtOPDAOAcfHOX1172sXWvQvLnW6SUbQellWdZgy7KG\nA2cluXZPy7ImWJY1FuhSxXtIhpk928+qVW7OOitM27baViwisj1bR1GeekqjKJA8oBRVeFzpvlDL\nsh6u7j0ks/z2m8GkSQECAZuRI/VXLyJSmbPOCmMYtnbzbJEsoFQckN+hrRemaV4OPFOde0hmuO22\nABs3uhgwoJw994w7XY6ISEpr1symffson33m4dNP1fo+WUCpuJw46YSYaZp9gLcty/q2qveQzPD1\n1y7uu8/PrrvGufZa5VIRkR2xdZpHoyjJA0o+gGmaxtbHW97uZJpm+4oXmqZ5AfCVZVkf7Mg9JLON\nHh0kEjEYMqSMfP2ti4jskNNOC+P32zz9tJd4lg88V7qLB5hrmuYEEkFmZoXnzwHiwKsApmnuA5wL\nvG6a5vFAE8uyBiW5h2SoN9/0sGCBj4MOinLhhTpcQkRkR9WrB127Rli40Me777o5+ujs3VyQUps+\nFy9ebBcUFDhdhlRDPA5du+azYoWHuXM306lT1OmSRETSytNPe/nnP/O47LJyJk1K7w0GhYWFdOnS\npUpZQ43apEb9738+Vqzw0LVrROFERKQKunWLkJtr88wzPqJZ/GNUAUVqTGkpjB0bxO22GTOm1Oly\nRETSUk4OnHJKmB9/dLF8ebKVGJlLAUVqzD33BPjhBxd9+4Y44IAsX90lIlINW48FyeambQooUiM2\nbDC4884A+fk2N9+sbcUiItVx4okR6tePs2CBl3CW7jVQQJEaccstQUpKDAYOLKNJE7W7ERGpDp8P\nevSI8NtvLpYuzc4TjhVQpNo+/dTNI4/42GuvGJddFnK6HBGRjHDWWVvP5lFAEdlptg0jRgSxbYOR\nI8sIBJyuSEQkM7RrF2XXXeM895yP0izcd6CAItWyeLGHZcu8HHlklDPOiDhdjohIxnC7oWfPMCUl\nBi+9lH2jKAooUmXRKIwcmQPA+PGlGCnV9k9EJP2deebWaZ7s282jgCJV9vDDPr74wk2vXmGOOip7\n2zGLiNSWtm1jNG8eY9EiL0VFTldTtxRQpEqKimDixCA+n82oUendillEJFW5XImeKKGQwbPPZtco\nigKKVMlddwX4+WcXl18eYu+91ZRNRKS2bJ3meeYZBRSRSq1da3DPPQEaNYpzww1qyiYiUpsOOSTG\nPvvEWLbMw6ZN2bPYTwFFdtq4cUHKyw1uvrmc+vXVlE1EpDYZRmI3TyRi8Nxz2bObRwFFdkphoRvL\n8tOqVYy+fdWUTUSkLvTsmWjjMG+eAorI39g2DB8eBGD06DK82fP/RETEUW3axGjRIsbSpd6smeZR\nQJEdtnChl7fe8tKuXYSTT1ZTNhGRupKY5okQiRg8/3x2/HaogCI7JByGMWOCGIbNuHFlasomIlLH\nevZM7ObJlmkeBRTZIf/9r5+vvnLTu3eYQw9VUzYRkbp26KEx9t47e6Z5FFAkqV9/NZg6NUAwaDNs\nmJqyiYg4Yes0TzicHdM8CiiS1K23BvjtNxcDBpSzxx7aViwi4pRsmuZRQJFKff21i//8x8+uu8a5\n5ho1ZRMRcdJhh8XYa6/ENE+mn82jgCKVGjMmSCRiMHhwGfn5TlcjIpLd/jzNk9mt7xVQZLveesvN\n/Pk+DjggxoUXhp0uR0REyJ5pHgUU2SbbhhEjcgAYO7YUj8fhgkREBIDDD09M87z8cmZP8yigyDY9\n9ZSX99/30KlThC5dok6XIyIiWxgGnH56YprnhRcyd5pHAUX+prwcxo4N4nLZjBtX6nQ5IiLyF9kw\nzaOAIn9z331+vvvOzfnnhznooLjT5YiIyF8UFMRo1izO0qVeioudrqZ2KKDIn/zyi8HttwfIzbUZ\nOlRN2UREUpFhwGmnhSkvN1iyJDNHURRQ5E+mTAlQVOTi6qvL2X13NWUTEUlVPXokDm1duDAz16Eo\noMjvVq1y8eCDfpo2jXPllWrKJiKSyo45JkqTJnFeeslLKOR0NTVPAUV+N2ZMkGjUYNiwMnJzna5G\nREQq43bDKadE2LzZ4NVXM68XhAKKALB8uYfnnvNxyCFRevdWUzYRkXRw2mmJn9eZOM2jgCLE4zB8\neBCAsWPLcLsdLkhERHZI+/ZR8vNtnnvOSyzmdDU1SwFFsCwfH37o4aSTwnTooKZsIiLpwu+Hbt3C\n/PKLi7feyqxpnhoNKKZpukzTzKzvUIYrLYVx44K43TZjxmhbsYhIujnttMRungULMmu7caVhwjTN\n/YD+QBSYbVnWqkquvQooACYDX1R4fmCFz/OeZVlLqlu01Jy77w7www8u/vnPcv7xDzVlExFJN507\nRwgEbBYu9DFxYhmG4XRFNSPZaEcvy7IGA5imORiYtL0LLcuabppmh228q9iyrJnVqFFqyYYNBnfd\nFSA/3+bmm7WtWEQkHeXmJkLKs8/6WLHCTUFBZixGSRZQKp6TWNXxf49pmkNJTCetsCzr2SreR2rY\nxIlBSkoMRo0qpUkTNWUTEUlXp56aCCgLF3qzJqBUHCiq0q/YlmXN2PrYNM2rq3IPqXmffebikUd8\n7LlnjMsvz8AOPyIiWaRbtwgej82CBT5GjCjPiGmeZItkK664qYlfsTWPkCJGjswhHjcYObKMQMDp\nakREpDoaNrRp1y7Kl1+6WbkyMzboJvsq8gFM0zS2Pt7ydifTNNvvyCcwTbPNX+8nzlqyxMPLL3sp\nKIhy5pkRp8sREZEa0KNHZjVtSxZQ5pqmOQG4BZhb4flzgN4VLzRN81LgfOBS0zQvrPCuNqZpjjJN\ncxSwvAZqlmqIRmHEiBwAxo8vzYhhQBERge7dIxiGzbPPZsZ245R6eVq8eLFdUFDgdBkZbdYsHzfc\nkMvpp4eZNavE6XJERKQGnXRSPu+95+Gjj36jeXPnNz8UFhbSpUuXKmWNzJiokh1SVJTYueP12owa\npaZsIiKZpnv3xDTP88+n/zSPAkoW+fe/A/z0k4vLLguxzz5qyiYikmlOOSWxrvC559J/mkcBJUus\nXWtw990BGjWKc+ON2kwlIpKJ9t8/TsuWMZYv97BpU0qt4thpCihZYty4IOXlBjfdVE79+s7PS4qI\nSM0zjMQoSjRqsHhxeh+Np4CSBd5/341l+dlvvxj9+qkpm4hIJtu6DuW559J7HYoCSoazbRgxIgjA\nmDFleNN/WlJERCrRtm2MJk3iLF7sJRx2upqqU0DJcAsWeHnrLS8nnBDh5JPVlE1EJNO53XDSSRE2\nbzZ4/fX0neZRQMlgoRCMHh3EMGzGjcucI7hFRKRy3bsnfiF9/vn0HTZXQMlg99/vZ80aN+edF6ZN\nm8w43VJERJLr2DFCMGjz/PM+7DTdF6GAkqF+/tng1lsD5OTYDBumpmwiItkkJwc6dYrwww8uPvzQ\n7XQ5VaKAkqGmTAlQVOTimmvKado0TeOziIhUWbo3bVNAyUBffOHiwQf9NG0a56qr1JRNRCQbdesW\nweWy03YdigJKBho5ModYzGDkyDJycpyuRkREnNCkic1RR0X59FMP33yTfi/36VexVOrllz0sWuTl\n8MOjmGYab4AXEZFq2zrNk46jKAooGSQWgxEjEkMm48eX4dLfrohIVkvn7cZ6CcsgDz/s4/PP3fTo\nEebYY6NOlyMiIg5r2TJOq1Yx3nwz/Q4PVEDJEEVFcMstQXw+m9Gjta1YREQSunVLHB748svp1VVW\nASVD3H57kJ9/dnH55SH22SfudDkiIpIiunVLTPO8+GJ6TfMooGSANWtc3HuvnyZN4gwcqNETERH5\nw9FHR6lfP86iRV5iadRUXAElA4waFSQcNhg6tIx69ZyuRkREUonHA126RPn1Vxfvvps+XWUVUNLc\n8uUeFizw0bp1lIsu0rZiERH5u27dEq8PL72UPtM8CihpLBaDYcOCQGJbsTt9grGIiNShzp2juN02\nL7zgc7qUHaaAksbmzPHx0UceTjklTIcO2lYsIiLb1rChzdFHR1m50p02XWXTo0r5m82bYcKEIF6v\nzdixWhgrIiKVO+mkxG6edJnmUUBJU3fdFWDDBhf9+4do2VLbikVEpHInn5wIKC+8oIAiteSbb1zM\nmBGgUaM4gwbptGIREUmuVas4++wTY/lyD5s3O11NcgooaWjkyCChUGJbcYMGttPliIhIGjCMxDRP\nOGzwyiupP4qigJJmtm4rPvDAGH36aFuxiIjsuHSa5lFASSOxGAwZkthWfMstpXjS61gFERFx2LHH\nRsnLs1m0yEs8xZcvKqCkkUce8fHJJx5OPVXbikVEZOf5fHDiiRF++snFihWp3TxLASVNFBUlthX7\nfNpWLCIiVbf18MBUn+ZRQEkTU6cmTiu+4gqdViwiIlXXtWsEw7BTvh+KAkoa+PJLF/fd52fXXePc\ncINGT0REpOqaNLE54ogYH3/sYd06w+lytksBJQ2MGBEkEjEYNkynFYuISPV17ZqY5lmyJHVHUWo0\noJim6TJNU3tLatCSJR5eeMHHoYdGOf98bSsWEZHq69IlEVAWLUrdgFJpmDBNcz+gPxAFZluWtaqS\na68CCoDJwBdVuYf8WTgMQ4fmADBxYqlOKxYRkRpx6KExdtklztKlXiIR8KZgTkk2gtLLsqzBlmUN\nB86q7ELLsqYDs6tzD/mz++/3s2qVm7PPDnHMMTGnyxERkQzhciVGUYqLDd5+OzUnPpIFlKIKj6u6\nOrMm7pF1fvzRYMqUILm5NqNH69smIiI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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Observe that the maximizer is around 0.6\n", - "\n", - "This this is similar to the long run value for $\\phi$ obtained\n", - "in exercise 1\n", - "\n", - "Hence the behaviour of the infinitely patent worker is similar to that of the\n", - "worker with $\\beta = 0.96$\n", - "\n", - "This seems reasonable, and helps us confirm that our dynamic programming\n", - "solutions are probably correct\n" - ] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/kalman_solutions.ipynb b/solutions/kalman_solutions.ipynb deleted file mode 100644 index 7aa4e305b..000000000 --- a/solutions/kalman_solutions.ipynb +++ /dev/null @@ -1,1626 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# quant-econ Solutions: The Kalman Filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/kalman.html" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import Kalman\n", - "from quantecon import LinearStateSpace\n", - "from scipy.stats import norm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAlQAAAHqCAYAAADCsNCxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - 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"output_type": "display_data" - } - ], - "source": [ - "# == parameters == #\n", - "theta = 10 # Constant value of state x_t\n", - "A, C, G, H = 1, 0, 1, 1\n", - "ss = LinearStateSpace(A, C, G, H, mu_0=theta)\n", - "\n", - "# == set prior, initialize kalman filter == #\n", - "x_hat_0, Sigma_0 = 8, 1\n", - "kalman = Kalman(ss, x_hat_0, Sigma_0)\n", - "\n", - "# == draw observations of y from state space model == #\n", - "N = 5\n", - "x, y = ss.simulate(N)\n", - "y = y.flatten()\n", - "\n", - "# == set up plot == #\n", - "fig, ax = plt.subplots(figsize=(10,8))\n", - "xgrid = np.linspace(theta - 5, theta + 2, 200)\n", - "\n", - "for i in range(N):\n", - " # == record the current predicted mean and variance == #\n", - " m, v = [float(z) for z in (kalman.x_hat, kalman.Sigma)]\n", - " # == plot, update filter == #\n", - " ax.plot(xgrid, norm.pdf(xgrid, loc=m, scale=np.sqrt(v)), label=r'$t=%d$' % i)\n", - " kalman.update(y[i])\n", - "\n", - "ax.set_title(r'First %d densities when $\\theta = %.1f$' % (N, theta)) \n", - "ax.legend(loc='upper left')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAiAAAAGnCAYAAACO1OzhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xm83HV97/HXh4RASEIgCYQtEJAgi4rKKqKCS6VYpde2\n", - "Ki6tVnDX9tpb0S7q1bZqe6u21VrEtep1qV4V3FCruLSKguyLsu+ELQlZIYHv/ePzG87k5CxzzpmZ\n", - "3yyv5+NxHjPzm9/M75sfy3nnu3y+UUpBkiSpm7aruwGSJGn4GEAkSVLXGUAkSVLXGUAkSVLXGUAk\n", - "SVLXGUAkSVLXTRpAIuITEbEyIi6b4Jx/johrIuKSiHhCe5soSZIGTSs9IJ8EThrvzYg4GTiwlLIC\n", - "eBXwkTa1TZIkDahJA0gp5SfAqglOeR7w6erc84FdImJpe5onSZIGUTvmgOwN3NL0+lZgnzZ8ryRJ\n", - 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- "T = 600\n", - "z = np.empty(T)\n", - "x, y = ss.simulate(T)\n", - "y = y.flatten()\n", - "\n", - "for t in range(T):\n", - " # Record the current predicted mean and variance, and plot their densities\n", - " m, v = [float(temp) for temp in (kalman.x_hat, kalman.Sigma)]\n", - " \n", - " f = lambda x: norm.pdf(x, loc=m, scale=np.sqrt(v))\n", - " integral, error = quad(f, theta - epsilon, theta + epsilon)\n", - " z[t] = 1 - integral\n", - " \n", - " kalman.update(y[t])\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 7))\n", - "ax.set_ylim(0, 1)\n", - "ax.set_xlim(0, T)\n", - "ax.plot(range(T), z) \n", - "ax.fill_between(range(T), np.zeros(T), z, color=\"blue\", alpha=0.2) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues of A:\n", - "[ 0.9+0.j -0.1+0.j]\n", - "Stationary prediction error variance:\n", - "[[ 0.40329109 0.1050718 ]\n", - " [ 0.1050718 0.41061711]]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAhMAAAFwCAYAAAACK/lNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XecXFd9///Xe5tWWvXqLslyE7bBNtiADVh0CDaEFiAQ\n", - "QiAkIYSahBaS1YaW/PINJbRQElpIIZSEECCQYNkY0ww2tnHFVa6SLFl9Ja328/vjnDs7Wu3uzOzs\n", - "7hS9n4+HPTszd2bOjGbu/dzP+ZxzFBGYmZmZTVZHoxtgZmZmrc3BhJmZmdXFwYSZmZnVxcGEmZmZ\n", - "1cXBhJmZmdXFwYSZmZnVpWIwIekNkq6VdJ2kN8xEo8zMzKx1TBhMSDoD+F3gXOARwEWS1sxEw8zM\n", - "zKw1VMpMnAb8OCIGI+IgcCnwvOlvlpmZmbWKSsHEdcDjJS2WNAd4FnDc9DfLzMzMWkXXRHdGxI2S\n", - "/hr4DrAbuAoYnomGmZmZWWtQLWtzSHovcFdE/H3ZbV7cw8zMrM1EhKrddsLMBICk5RGxSdIJwHOB\n", - "R9fzgjb9JK2PiPWNbocl/vdoPv43aS7+92g+tSYKKgYTwJclLQEOAH8YETsm1TIzMzNrSxWDiYh4\n", - 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C, G, H, mu_0 = np.zeros(2))\n", - "\n", - "# === Define the prior density === #\n", - "Sigma = [[0.9, 0.3], \n", - " [0.3, 0.9]]\n", - "Sigma = np.array(Sigma)\n", - "x_hat = np.array([8, 8])\n", - "\n", - "# === Initialize the Kalman filter === #\n", - "kn = Kalman(ss, x_hat, Sigma)\n", - "\n", - "# == Print eigenvalues of A == #\n", - "print(\"Eigenvalues of A:\")\n", - "print(eigvals(A))\n", - "\n", - "# == Print stationary Sigma == #\n", - "S, K = kn.stationary_values()\n", - "print(\"Stationary prediction error variance:\")\n", - "print(S)\n", - "\n", - "# === Generate the plot === #\n", - "T = 50\n", - "x, y = ss.simulate(T)\n", - "\n", - "e1 = np.empty(T-1)\n", - "e2 = np.empty(T-1)\n", - "\n", - "for t in range(1, T):\n", - " kn.update(y[:,t])\n", - " e1[t-1] = np.sum((x[:,t] - kn.x_hat.flatten())**2)\n", - " e2[t-1] = np.sum((x[:,t] - np.dot(A, x[:,t-1]))**2)\n", - "\n", - "fig, ax = plt.subplots(figsize=(9,6))\n", - "ax.plot(range(1, T), e1, 'k-', lw=2, alpha=0.6, label='Kalman filter error') \n", - "ax.plot(range(1, T), e2, 'g-', lw=2, alpha=0.6, label='conditional expectation error') \n", - "ax.legend()\n", - "\n" - ] - }, - { - "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 -} diff --git a/solutions/lakemodel_solutions.ipynb b/solutions/lakemodel_solutions.ipynb deleted file mode 100644 index 930a10e28..000000000 --- a/solutions/lakemodel_solutions.ipynb +++ /dev/null @@ -1,423 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lake Model Solutions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Excercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We begin by initializing the variables and import the necessary modules" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon.models import LakeModel\n", - "\n", - "alpha = 0.012\n", - "lamb = 0.2486\n", - "b = 0.001808\n", - "d = 0.0008333\n", - "g = b-d\n", - "N0 = 100.\n", - "e0 = 0.92\n", - "u0 = 1-e0\n", - "T = 50" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now construct the class containing the initial conditions of the problem" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial Steady State: [ 0.94737184 0.05262816]\n" - ] - } - ], - "source": [ - "LM0 = LakeModel(lamb,alpha,b,d)\n", - "x0 = LM0.find_steady_state()# initial conditions\n", - "\n", - "print(\"Initial Steady State: %s\" % x0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "New legislation changes $\\lambda$ to $0.2$" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "LM1 = LakeModel(0.2,alpha,b,d)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New Steady State: [ 0.93540871 0.06459129]\n" - ] - } - ], - "source": [ - "xbar = LM1.find_steady_state() # new steady state\n", - "X_path = np.vstack(LM1.simulate_stock_path(x0*N0,T)) # simulate stocks\n", - "x_path = np.vstack(LM1.simulate_rate_path(x0,T)) # simulate rates\n", - "print(\"New Steady State: %s\" % xbar)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now plot stocks" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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MgJdeCuFq/nw46qh0qDrhBOjVK+6eiohIS1WSQesf/3BuuCGstbn6arjwQk39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\nXgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=[10,9])\n", - "plt.subplot(3,1,1)\n", - "plt.plot(X_path[:,0])\n", - "plt.title(r'Employment')\n", - "plt.subplot(3,1,2)\n", - "plt.plot(X_path[:,1])\n", - "plt.title(r'Unemployment')\n", - "plt.subplot(3,1,3)\n", - "plt.plot(X_path.sum(1))\n", - "plt.title(r'Labor Force')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And how the rates evolve:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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B+2ZmZmal5LcjW7DeevD978OvflXuSMzMzMy6UUsYwIIFsPnm8MQTsNlmJTutmZmZmVvC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\nTwObSxoKzAEOAcb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EnFmKrsOI+GtEfCoijitFXGZm7aFQEtam16vzjutJejPpjxGxPbAI\nqP88SU+gf0TsBJxMGnRqZmZm1m20OiaMNA4sd1boweRMpNhCnUFZmYBZETExK7+F9BYV2TluBYiI\nidm319aNiHdzzoMkf0TYzMzMOo2IKHpsa6GWsKeBzbMP3vYmvTZ+R16dO4BxAJJ2At6PiHnZN8xm\nSqp/VX0v0qvaALeTvtFGtr93fgKWczNeOuFyxhlnlD0GL35+3XXx8+u8i59d517aqtWWsIiolXQ8\ncA9piorLI2KqpPHZ/ksj4i5JoyVNJ3U5HpVzihOAa7ME7tWcfVcAV0h6HlhKlsSZmZmZdReFuiOJ\n9LHeu/PKLs3bPr6FYycDX2ymvAa/Nm5mZmbdmGfMt3ZRWVlZ7hBsJfj5dW5+fp2Xn133UnDG/HKS\nFB05PjMzM7N6kogSDsw3MzMzs3bgJMzMzMysDJyEmZmZmZWBkzAzMzOzMnASZmZmZlYGTsLMzMzM\nysBJmJmZmVkZOAkzMzMzK4OCny0yM1vVImDZsrTU1TVdzy9bkSWi6Xprv/VLoe0VXervu6Wy+vXW\nygr9trSvmPX8ebNb225L3ea2WyprrbwtddtyvZXVnvONd4S5zDtCDO3tl7+E3r3b59wdPwlTMxPP\nnnEGVFU1La+qgjPPdH3X79b16xCf0IclrM7HrMbHJ57CkmNP4pNP4JNPYOnS9PvJldfxyQ23spTe\nfEKfhmXp3l9laeWXqamBmppUv6YGav79FDVP/5caerGU3tTQixp6UbvFMGo23pLaWpZf3pxD7Vvz\nqaVnqkdPaunJsrX6s2zNtVm2LNWrT6aWfVKTtqkg6EEPllHBMip6ih69e1FRAT16QEVFWnos/oiK\nRR/Qgzp6UEcFy9L6Ov3pMWBdevRg+WXeW/SYOxsRDcf0oA4NHkyPoRshpXoNv6+/il57NdUhGo7T\nFpujLbdcrr4EmjoFTX2xoW7D8rnPoeHbNNarXyb/F02etFxdAG23HfrCDg31IKv/9ER4emJjvfpj\nvjgC7bRjQ72G3yeeQE8+3lC34Zidd4Zddlm+LsC//43+81jDf071x7Drl9BuX1q+LsCjj6LHHlnu\nP0ERsNvusPvuTf7q1iMPwyMPNz3/7iOhsrJp/Yer0cMPNf3vfGQl2nOPpuUPPQTVTeurshL23LNp\n+UMPwkMPNo1/jz1hr72anv+BB5rUB4qq33CvAHvu1XL9Bx9oWl6g/nLnrq+/995N699/f8vnX8n6\nUlb/gfub1t9r75bP30nqS83Ub+nv5zbyZ4vMymDpUvjwQ1i4MP3mri9aBIsXp9/6paXtJUvg44/T\nUr++dCn06QOrrw6rrZZ++/RJ6336pH/R9enT8nrv3mnp1avxN3fJL+vZs+lva0t9EpW7nr9dn9yY\nmXUmbf1sUcEkTNIo4AKgArgsIs5pps5FwFeAxcCRETEpK+8HXAZsDQRwdEQ8kXPcj4BzgfUi4r1m\nzuskzDqk2lpYsCAt773X+vr77zdNtJYtg7XXTstaazX+rrUWrLlm02WNNZrfzk20chMuJzBmZqte\nW5OwVrsjJVUAFwN7A7OBiZLuiIipOXVGA5tFxOaSdgT+BOyU7b4QuCsiDpLUE1gz57jBwD7AG8UG\na9Ze6upSwjR/fnHLwoXQrx/07w/rrLP8b//+MGgQbLNNWu/XD/r2bUy21l7biZKZmRUeEzYCmB4R\nMwAk3QAcAEzNqTMGuBogIp6U1E/SQOBjYLeIOCLbVwt8kHPc+cBPgH+U4D7MWrRsGcybB7NmNb/M\nnAlz5qSWpU9/uumyzTZNy/r1S11mZmZmK6pQErYhMDNnexawYxF1BgHLgLclXQlsCzwDnBQRiyUd\nAMyKiOfk5gArgQULYPr0tLz6auP6m2/C3LmplWrQoOWXbbdtXP/MZ1JXnpmZ2apSKAkrdkBWfiYV\n2bm3B46PiImSLgBOlXQWcDqpK7Kl4xtU5bwFVllZSWVlZZEhWVfzwQfw4ovwyitNk63aWthss8Zl\nt93gyCNh6NCUYLXX68VmZtZ9VVdXU11dvcLHtzowX9JOQFVEjMq2TwPqcgfnS/ozUB0RN2TbLwEj\nSYnV4xGxcVb+JeDUbHmANIgfUqvZbGBERMzPu74H5ndDtbXw8svw3HPw/PONv++8A1ttBVtumRKt\nTTdtTLrWW89jrMzMrLxKOjAfeBrYXNJQYA5wCDA2r84dwPHADVnS9n5EzMuCmSlpi4h4mTS4/8WI\neAEYmBPw68AOzb0daV3fggUwcWJKtOqTrWnTGge2Dx8ORx2V1jfZJE1fYGZm1hW0moRFRK2k44F7\nSFNUXB4RUyWNz/ZfGhF3SRotaTqwCDgq5xQnANdK6g28mrev4TKluBHr+OrqUgvXf/6TlscfT2O2\ndtgBPv95GDkSTjgBhg1Lg+TNzMy6Mk/Wau3mo4/gqadSslWfdPXrB9mE3eyyS2rh6tnxv9tgZmZW\nUMknay0nJ2Gdy9Kl8O9/w913py9ATJuWWrh22SUlXjvvDBtsUO4ozczM2oeTMFulZs1KSdfdd8OD\nD8IWW8BXvgL77pu6Gfv0KXeEZmZmq4aTMGtXNTWNrV13350mOf3yl2H06JR4DRhQ7gjNzMzKw0mY\nldzixXD77XDLLam1a7PNUmvX6NHwxS/6jUUzMzNwEmYlUlcH1dUwYUJKwHbaCQ49FEaNgoEDCx5u\nZmbW7TgJs5Xy4osp8br22jQB6rhxMHYsrL9+uSMzMzPr2Eo9Wat1A/PmwfXXp+Rr7lw47LA03utz\nnyt3ZGZmZl2XW8K6qbo6uOMO+Otf00D7Aw6Aww+HPfbwGC8zM7MV4ZYwa9Unn6QWr3PPhbXXhhNP\nhJtu8gz1ZmZmq5qTsG7igw/g0kvhwgvT9xj//GeorPRHr83MzMrFSVgX99ZbcMEFcNllaVqJu+6C\nbbctd1RmZmbWo9wBWPuYNg2++13Yemv4+GN45hn429+cgJmZmXUURSVhkkZJeknSK5JOaaHORdn+\nyZK2yynvJ+lmSVMlTZG0Y1Z+blY2WdKtkvqW5pa6t6efhq9/HXbfHTbaCF55JXVBDh1a7sjMzMws\nV8EkTFIFcDEwChgGjJW0VV6d0cBmEbE5cCzwp5zdFwJ3RcRWwHDgpaz8XmDriNgWeBk4bSXvpVub\nNw+OOiq95bjXXvD66/CLX8C665Y7MjMzM2tOMS1hI4DpETEjImqAG4AD8uqMAa4GiIgngX6SBmat\nW7tFxBXZvtqI+CBbvy8i6rLjnwQGrfztdD81NXD++anbcb31YOpUOP54WGONckdmZmZmrSlmYP6G\nwMyc7VnAjkXUGQQsA96WdCWwLfAMcFJELM47/mjg+jbEbcD996cpJjbaCB57DD772XJHZGZmZsUq\npiWs2NlS8yc7CFKStz3wx4jYHlgEnLrcQdJPgaURcV2R1+n2ZsyAb3wDjj0WzjorzW7vBMzMzKxz\nKaYlbDYwOGd7MKmlq7U6g7IyAbMiYmJWfjM5SZikI4HRwF4tXbyqqqphvbKyksrKyiJC7pqWLIFz\nzoE//AF++MP0tuPqq5c7KjMzs+6purqa6urqFT6+4GeLJPUEppESpTnAU8DYiJiaU2c0cHxEjJa0\nE3BBROyU7XsE+G5EvCypClg9Ik6RNAo4DxgZEe+0cG1/tgiIgNtugx/9CL74Rfjd71IXpJmZmXUc\nJf9sUUTUSjoeuAeoAC6PiKmSxmf7L42IuySNljSd1OV4VM4pTgCuldQbeDVn3x+A3sB9StO2Px4R\nxxUbeHfx1lvprcdZs+Dyy2HPPcsdkZmZmZWCP+Ddgd1/P4wbB8ccAz/7GfTqVe6IzMzMrCX+gHcX\nsGwZnHlm+tTQhAlp3i8zMzPrWpyEdTBz5sC3vgUVFfDss7D++uWOyMzMzNqDvx3Zgdx7L+ywQxr3\nde+9TsDMzMy6MreEdQC1tVBVBVdeCdddB3vsUe6IzMzMrL05CSuzOXNg7Fjo3Tt1Pw4cWO6IzMzM\nbFVwd2QZ1Xc/7r03/N//OQEzMzPrTtwSVgZ1dXDGGan78frroRt/BMDMzKzbchK2itXWwne/C9On\nwzPPuPXLzMysu3IStgotXQrf/jZ88AHccw+suWa5IzIzM7NycRK2iixZAgcdlGa9v/NO6NOn3BGZ\nmZlZOXlg/iqwcCF89avQrx/8/e9OwMzMzMxJWLtbsAC+/GXYdFO45hp//9HMzMwSJ2Ht6O230+z3\nO+0Ef/lL+hSRmZmZGRSRhEkaJeklSa9IOqWFOhdl+ydL2i6nvJ+kmyVNlTRF0k5Z+TqS7pP0sqR7\nJfUr3S11DLNnw+67w/77w/nng4r+prqZmZl1B60mYZIqgIuBUcAwYKykrfLqjAY2i4jNgWOBP+Xs\nvhC4KyK2AoYDU7PyU4H7ImIL4IFsu8t4/fWUgB11FPzyl07AzMzMrKlCLWEjgOkRMSMiaoAbgAPy\n6owBrgaIiCeBfpIGSuoL7BYRV2T7aiPig/xjst+vrfytdAwvvQQjR8KPfgQ/+Um5ozEzM7OOqlAS\ntiEwM2d7VlZWqM4gYGPgbUlXSnpW0l8lrZHVGRgR87L1eUCXmLJ08uQ0BuxXv4Ljjit3NGZmZtaR\nFUrCosjz5He4BWkOsu2BP0bE9sAimul2jIhow3U6rBdeSG9BXnghHHFEuaMxMzOzjq7QZK2zgcE5\n24NJLV2t1RmUlQmYFRETs/JbgPqB/fMkrR8RcyVtAMxvKYCqqqqG9crKSio74IcW334bxoyB886D\nb36z3NGYmZnZqlBdXU11dfUKH6/UENXCTqknMA3YC5gDPAWMjYipOXVGA8dHxOjs7ccLIqL+LchH\ngO9GxMuSqoDVI+IUSb8F3o2IcySdCvS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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=[10,6])\n", - "plt.subplot(2,1,1)\n", - "plt.plot(x_path[:,0])\n", - "plt.hlines(xbar[0],0,T,'r','--')\n", - "plt.title(r'Employment Rate')\n", - "plt.subplot(2,1,2)\n", - "plt.plot(x_path[:,1])\n", - "plt.hlines(xbar[1],0,T,'r','--')\n", - "plt.title(r'Unemployment Rate')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that it takes 20 periods for the economy to converge to it's new steady state levels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This next exercise has the economy expriencing a boom in entrances to the labor market and then later returning to the original levels. For 20 periods the economy has a new entry rate into the labor market " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "bhat = 0.003\n", - "T_hat = 20\n", - "LM1 = LakeModel(lamb,alpha,bhat,d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We simulate for 20 periods at the new parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "X_path1 = np.vstack(LM1.simulate_stock_path(x0*N0,T_hat)) # simulate stocks\n", - "x_path1 = np.vstack(LM1.simulate_rate_path(x0,T_hat)) # simulate rates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now using the state after 20 periods for the new initial conditions we simulate for the additional 30 periods" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "X_path2 = np.vstack(LM0.simulate_stock_path(X_path1[-1,:2],T-T_hat+1)) # simulate stocks\n", - "x_path2 = np.vstack(LM0.simulate_rate_path(x_path1[-1,:2],T-T_hat+1)) # simulate rates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally we combine these two paths and plot" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "x_path = np.vstack([x_path1,x_path2[1:]]) # note [1:] to avoid doubling period 20\n", - "X_path = np.vstack([X_path1,X_path2[1:]]) # note [1:] to avoid doubling period 20" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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fRUrh0L8gipvuX/HSvStuun/1S64tVwcD4919vbuXAmOA8xLf/Rn4f/koTkRE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\ntWZNGqFavNhV083MrHPKuqA9IlZKGtvs9HTSZs4AC4A8MDc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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=[10,9])\n", - "plt.subplot(3,1,1)\n", - "plt.plot(X_path[:,0])\n", - "plt.title(r'Employment')\n", - "plt.subplot(3,1,2)\n", - "plt.plot(X_path[:,1])\n", - "plt.title(r'Unemployment')\n", - "plt.subplot(3,1,3)\n", - "plt.plot(X_path.sum(1))\n", - "plt.title(r'Labor Force')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And the rates:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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icLbcl91vIGnC9IHZPX8jqWS1e506wU9/mmrQyrD116ykrr46NWnutVepIzEz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\npUve0ZiZtV3NPZXGDKBfNoqyC3A4cE9JnXuAY7K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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=[10,6])\n", - "plt.subplot(2,1,1)\n", - "plt.plot(x_path[:,0])\n", - "plt.hlines(x0[0],0,T,'r','--')\n", - "plt.title(r'Employment Rate')\n", - "plt.subplot(2,1,2)\n", - "plt.plot(x_path[:,1])\n", - "plt.hlines(x0[1],0,T,'r','--')\n", - "plt.title(r'Unemployment Rate')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.4.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/solutions/lln_clt_solutions.ipynb b/solutions/lln_clt_solutions.ipynb deleted file mode 100644 index f0374ffba..000000000 --- a/solutions/lln_clt_solutions.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:f143b3d9651965911f999068d8298ff7f16dc26102a931f2446fab57b788162b" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: LLN and CLT" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/lln_clt.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Standard imports" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is one solution\n", - "\n", - "You might have to modify or delete the lines starting with `rc`, depending on your configuration" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\"\"\"\n", - "Illustrates the delta method, a consequence of the central limit theorem.\n", - "\"\"\"\n", - "\n", - "from scipy.stats import uniform, norm\n", - "from matplotlib import rc\n", - "\n", - "# == Specifying font, needs LaTeX integration == #\n", - "rc('font',**{'family':'serif','serif':['Palatino']})\n", - "rc('text', usetex=True)\n", - "\n", - "# == Set parameters == #\n", - "n = 250\n", - "replications = 100000\n", - "distribution = uniform(loc=0, scale=(np.pi / 2))\n", - "mu, s = distribution.mean(), distribution.std()\n", - "\n", - "g = np.sin\n", - "g_prime = np.cos\n", - "\n", - "# == Generate obs of sqrt{n} (g(\\bar X_n) - g(\\mu)) == #\n", - "data = distribution.rvs((replications, n)) \n", - "sample_means = data.mean(axis=1) # Compute mean of each row\n", - "error_obs = np.sqrt(n) * (g(sample_means) - g(mu))\n", - "\n", - "# == Plot == #\n", - "asymptotic_sd = g_prime(mu) * s\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "xmin = -3 * g_prime(mu) * s\n", - "xmax = -xmin\n", - "ax.set_xlim(xmin, xmax)\n", - "ax.hist(error_obs, bins=60, alpha=0.5, normed=True)\n", - "xgrid = np.linspace(xmin, xmax, 200)\n", - "lb = r\"$N(0, g'(\\mu)^2 \\sigma^2)$\"\n", - "ax.plot(xgrid, norm.pdf(xgrid, scale=asymptotic_sd), 'k-', lw=2, label=lb)\n", - "ax.legend()\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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HzTl2bDcxMVvp3n1h1pp5cXFX7i8ijku35EQkR0uXLuW5554DYNq0aXTo0MFwIvtSsmRF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\nkmN8fDwICwsu5KSSX1SY5BI2m43vv/8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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What happens when you replace $[0, \\pi / 2]$ with $[0, \\pi]$?\n", - "\n", - "In this case, the mean $\\mu$ of this distribution is $\\pi/2$, and since $g' = \\cos$, we have $g'(\\mu) = 0$\n", - "\n", - "Hence the conditions of the delta theorem are not satisfied\n" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we want to verify the claim that\n", - "\n", - "$$\n", - " \\sqrt{n} \\mathbf Q ( \\bar{\\mathbf X}_n - \\boldsymbol \\mu )\n", - " \\stackrel{d}{\\to} \n", - " N(\\mathbf 0, \\mathbf I)\n", - "$$\n", - "\n", - "This is straightforward given the facts presented in the exercise\n", - "\n", - "Let\n", - "\n", - "$$\n", - " \\mathbf Y_n := \\sqrt{n} ( \\bar{\\mathbf X}_n - \\boldsymbol \\mu )\n", - " \\quad \\text{and} \\quad\n", - " \\mathbf Y \\sim N(\\mathbf 0, \\Sigma)\n", - "$$\n", - "\n", - "By the multivariate CLT and the continuous mapping theorem, we have\n", - "\n", - "$$\n", - " \\mathbf Q \\mathbf Y_n \n", - " \\stackrel{d}{\\to} \n", - " \\mathbf Q \\mathbf Y\n", - "$$\n", - "\n", - "Since linear combinations of normal random variables are normal, the vector\n", - "$\\mathbf Q \\mathbf Y$ is also normal\n", - "\n", - "Its mean is clearly $\\mathbf 0$, and its variance covariance matrix is\n", - "\n", - "$$\n", - " \\mathrm{Var}[\\mathbf Q \\mathbf Y]\n", - " = \\mathbf Q \\mathrm{Var}[\\mathbf Y] \\mathbf Q'\n", - " = \\mathbf Q \\Sigma \\mathbf Q'\n", - " = \\mathbf I\n", - "$$\n", - "\n", - "In conclusion, $\\mathbf Q \\mathbf Y_n \\stackrel{d}{\\to} \\mathbf Q \\mathbf Y \\sim N(\\mathbf 0, \\mathbf I)$, which is what we aimed to show\n", - "\n", - "Now we turn to the simulation exercise\n", - "\n", - "Our solution is as follows\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.stats import uniform, chi2\n", - "from scipy.linalg import inv, sqrtm\n", - "\n", - "# == Set parameters == #\n", - "n = 250\n", - "replications = 50000\n", - "dw = uniform(loc=-1, scale=2) # Uniform(-1, 1)\n", - "du = uniform(loc=-2, scale=4) # Uniform(-2, 2)\n", - "sw, su = dw.std(), du.std()\n", - "vw, vu = sw**2, su**2\n", - "Sigma = ((vw, vw), (vw, vw + vu))\n", - "Sigma = np.array(Sigma)\n", - "\n", - "# == Compute Sigma^{-1/2} == #\n", - "Q = inv(sqrtm(Sigma)) \n", - "\n", - "# == Generate observations of the normalized sample mean == #\n", - "error_obs = np.empty((2, replications))\n", - "for i in range(replications):\n", - " # == Generate one sequence of bivariate shocks == #\n", - " X = np.empty((2, n))\n", - " W = dw.rvs(n)\n", - " U = du.rvs(n)\n", - " # == Construct the n observations of the random vector == #\n", - " X[0, :] = W\n", - " X[1, :] = W + U\n", - " # == Construct the i-th observation of Y_n == #\n", - " error_obs[:, i] = np.sqrt(n) * X.mean(axis=1)\n", - "\n", - "# == Premultiply by Q and then take the squared norm == #\n", - "temp = np.dot(Q, error_obs)\n", - "chisq_obs = np.sum(temp**2, axis=0)\n", - "\n", - "# == Plot == #\n", - "fig, ax = plt.subplots(figsize=(10, 6))\n", - "xmax = 8\n", - "ax.set_xlim(0, xmax)\n", - "xgrid = np.linspace(0, xmax, 200)\n", - "lb = \"Chi-squared with 2 degrees of freedom\"\n", - "ax.plot(xgrid, chi2.pdf(xgrid, 2), 'k-', lw=2, label=lb)\n", - "ax.legend()\n", - "ax.hist(chisq_obs, bins=50, normed=True)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/lqcontrol_solutions.ipynb b/solutions/lqcontrol_solutions.ipynb deleted file mode 100644 index d0dc34921..000000000 --- a/solutions/lqcontrol_solutions.ipynb +++ /dev/null @@ -1,434 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:e48cf621b35f99f4171d6e351d4743b93d56b3b64a0395068394da284edb1648" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: LQ Control Problems" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/lqcontrol.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Common imports for the exercises" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import division\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import LQ" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here\u2019s one solution\n", - "\n", - "We use some fancy plot commands to get a certain style \u2014 feel free to use simpler ones" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "The model is an LQ permanent income / life-cycle model with hump-shaped income\n", - "\n", - "$$ y_t = m_1 t + m_2 t^2 + \\sigma w_{t+1} $$\n", - "\n", - "where $\\{w_t\\}$ is iid $N(0, 1)$ and the coefficients $m_1$ and $m_2$ are chosen so that\n", - "$p(t) = m_1 t + m_2 t^2$ has an inverted U shape with\n", - "\n", - "* $p(0) = 0, p(T/2) = \\mu$, and \n", - "* $p(T) = 0$.\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# == Model parameters == #\n", - "r = 0.05\n", - "beta = 1 / (1 + r)\n", - "T = 50\n", - "c_bar = 1.5\n", - "sigma = 0.15\n", - "mu = 2\n", - "q = 1e4\n", - "m1 = T * (mu / (T/2)**2)\n", - "m2 = - (mu / (T/2)**2)\n", - "\n", - "# == Formulate as an LQ problem == #\n", - "Q = 1\n", - "R = np.zeros((4, 4)) \n", - "Rf = np.zeros((4, 4))\n", - "Rf[0, 0] = q\n", - "A = [[1 + r, -c_bar, m1, m2], \n", - " [0, 1, 0, 0],\n", - " [0, 1, 1, 0],\n", - " [0, 1, 2, 1]]\n", - "B = [[-1],\n", - " [0],\n", - " [0],\n", - " [0]]\n", - "C = [[sigma],\n", - " [0],\n", - " [0],\n", - " [0]]\n", - "\n", - "# == Compute solutions and simulate == #\n", - "lq = LQ(Q, R, A, B, C, beta=beta, T=T, Rf=Rf)\n", - "x0 = (0, 1, 0, 0)\n", - "xp, up, wp = lq.compute_sequence(x0)\n", - "\n", - "# == Convert results back to assets, consumption and income == #\n", - "ap = xp[0, :] # Assets\n", - "c = up.flatten() + c_bar # Consumption\n", - "time = np.arange(1, T+1)\n", - "income = wp[0, 1:] + m1 * time + m2 * time**2 # Income\n", - "\n", - "\n", - "# == Plot results == #\n", - "n_rows = 2\n", - "fig, axes = plt.subplots(n_rows, 1, figsize=(12, 10))\n", - "\n", - "plt.subplots_adjust(hspace=0.5)\n", - "for i in range(n_rows):\n", - " axes[i].grid()\n", - " axes[i].set_xlabel(r'Time')\n", - "bbox = (0., 1.02, 1., .102)\n", - "legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'}\n", - "p_args = {'lw' : 2, 'alpha' : 0.7}\n", - "\n", - "axes[0].plot(range(1, T+1), income, 'g-', label=\"non-financial income\", **p_args)\n", - "axes[0].plot(range(T), c, 'k-', label=\"consumption\", **p_args)\n", - "axes[0].legend(ncol=2, **legend_args)\n", - "\n", - "axes[1].plot(range(T+1), ap.flatten(), 'b-', label=\"assets\", **p_args)\n", - "axes[1].plot(range(T+1), np.zeros(T+1), 'k-')\n", - "axes[1].legend(ncol=1, **legend_args)\n", - "\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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IIRqdypJdIerUs88+a+wQRAMk9UKUR+qFKI/UC2EI9Tl/sPQhFkIIIYQQdaomfYilhVgY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\nA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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a permanent income / life-cycle model with polynomial growth in income\n", - "over working life followed by a fixed retirement income. The model is solved\n", - "by combining two LQ programming problems as described in the lecture." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# == Model parameters == #\n", - "r = 0.05\n", - "beta = 1 / (1 + r)\n", - "T = 60\n", - "K = 40\n", - "c_bar = 4\n", - "sigma = 0.35\n", - "mu = 4\n", - "q = 1e4\n", - "s = 1\n", - "m1 = 2 * mu / K\n", - "m2 = - mu / K**2\n", - "\n", - "# == Formulate LQ problem 1 (retirement) == #\n", - "Q = 1\n", - "R = np.zeros((4, 4)) \n", - "Rf = np.zeros((4, 4))\n", - "Rf[0, 0] = q\n", - "A = [[1 + r, s - c_bar, 0, 0], \n", - " [0, 1, 0, 0],\n", - " [0, 1, 1, 0],\n", - " [0, 1, 2, 1]]\n", - "B = [[-1],\n", - " [0],\n", - " [0],\n", - " [0]]\n", - "C = [[0],\n", - " [0],\n", - " [0],\n", - " [0]]\n", - "\n", - "# == Initialize LQ instance for retired agent == #\n", - "lq_retired = LQ(Q, R, A, B, C, beta=beta, T=T-K, Rf=Rf)\n", - "# == Iterate back to start of retirement, record final value function == #\n", - "for i in range(T-K):\n", - " lq_retired.update_values()\n", - "Rf2 = lq_retired.P\n", - "\n", - "# == Formulate LQ problem 2 (working life) == #\n", - "R = np.zeros((4, 4)) \n", - "A = [[1 + r, -c_bar, m1, m2], \n", - " [0, 1, 0, 0],\n", - " [0, 1, 1, 0],\n", - " [0, 1, 2, 1]]\n", - "B = [[-1],\n", - " [0],\n", - " [0],\n", - " [0]]\n", - "C = [[sigma],\n", - " [0],\n", - " [0],\n", - " [0]]\n", - "\n", - "# == Set up working life LQ instance with terminal Rf from lq_retired == #\n", - "lq_working = LQ(Q, R, A, B, C, beta=beta, T=K, Rf=Rf2)\n", - "\n", - "# == Simulate working state / control paths == #\n", - "x0 = (0, 1, 0, 0)\n", - "xp_w, up_w, wp_w = lq_working.compute_sequence(x0)\n", - "# == Simulate retirement paths (note the initial condition) == #\n", - "xp_r, up_r, wp_r = lq_retired.compute_sequence(xp_w[:, K]) \n", - "\n", - "# == Convert results back to assets, consumption and income == #\n", - "xp = np.column_stack((xp_w, xp_r[:, 1:]))\n", - "assets = xp[0, :] # Assets\n", - "\n", - "up = np.column_stack((up_w, up_r))\n", - "c = up.flatten() + c_bar # Consumption\n", - "\n", - "time = np.arange(1, K+1)\n", - "income_w = wp_w[0, 1:K+1] + m1 * time + m2 * time**2 # Income\n", - "income_r = np.ones(T-K) * s\n", - "income = np.concatenate((income_w, income_r))\n", - "\n", - "# == Plot results == #\n", - "n_rows = 2\n", - "fig, axes = plt.subplots(n_rows, 1, figsize=(12, 10))\n", - "\n", - "plt.subplots_adjust(hspace=0.5)\n", - "for i in range(n_rows):\n", - " axes[i].grid()\n", - " axes[i].set_xlabel(r'Time')\n", - "bbox = (0., 1.02, 1., .102)\n", - "legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'}\n", - "p_args = {'lw' : 2, 'alpha' : 0.7}\n", - "\n", - "axes[0].plot(range(1, T+1), income, 'g-', label=\"non-financial income\", **p_args)\n", - "axes[0].plot(range(T), c, 'k-', label=\"consumption\", **p_args)\n", - "axes[0].legend(ncol=2, **legend_args)\n", - "\n", - "axes[1].plot(range(T+1), assets, 'b-', label=\"assets\", **p_args)\n", - "axes[1].plot(range(T+1), np.zeros(T+1), 'k-')\n", - "axes[1].legend(ncol=1, **legend_args)\n", - "\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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iya/KZ2v21naXuS9/H6crTxPuE86ImBHnvT60x1D+dvnfUKHi3YPvsv7E+naX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\n8xdfccpdTg4MGAB/+lP4FKR/f2jYEKZNg3vvhTPPDJNPp04NazDru+x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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first task is to find the matrices $A, B, C, Q, R$ that define the\n", - "LQ problem\n", - "\n", - "Recall that $x_t = (\\bar q_t \\;\\, q_t \\;\\, 1)'$, while $u_t = q_{t+1} - q_t$\n", - "\n", - "Letting $m_0 := (a_0 - c) / 2a_1$ and $m_1 := 1 / 2 a_1$, we can\n", - "write $\\bar q_t = m_0 + m_1 d_t$, and then, with some manipulation\n", - "\n", - "$$\n", - " \\bar q_{t+1} = m_0 (1 - \\rho) + \\rho \\bar q_t + m_1 \\sigma w_{t+1}\n", - "$$\n", - "\n", - "By our definition of $u_t$, the dynamics of $q_t$ are $q_{t+1} = q_t + u_t$\n", - "\n", - "Using these facts you should be able to build the correct $A, B, C$ matrices (and then\n", - "check them against those found in the solution code below)\n", - "\n", - "Suitable $R, Q$ matrices can be found by inspecting the objective\n", - "function, which we repeat here for convenience:\n", - " \n", - "$$\n", - " \\min\n", - " \\mathbb E \\,\n", - " \\left\\{ \n", - " \\sum_{t=0}^{\\infty} \\beta^t \n", - " a_1 ( q_t - \\bar q_t)^2 + \\gamma u_t^2\n", - " \\right\\}\n", - "$$\n", - "\n", - "Our solution code is\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# == Model parameters == #\n", - "a0 = 5\n", - "a1 = 0.5\n", - "sigma = 0.15\n", - "rho = 0.9\n", - "gamma = 1\n", - "beta = 0.95\n", - "c = 2\n", - "T = 120\n", - "\n", - "# == Useful constants == #\n", - "m0 = (a0 - c) / (2 * a1)\n", - "m1 = 1 / (2 * a1)\n", - "\n", - "# == Formulate LQ problem == #\n", - "Q = gamma\n", - "R = [[a1, -a1, 0],\n", - " [-a1, a1, 0],\n", - " [0, 0, 0]]\n", - "A = [[rho, 0, m0 * (1 - rho)],\n", - " [0, 1, 0],\n", - " [0, 0, 1]]\n", - "\n", - "B = [[0],\n", - " [1],\n", - " [0]]\n", - "C = [[m1 * sigma],\n", - " [0],\n", - " [0]]\n", - "\n", - "lq = LQ(Q, R, A, B, C=C, beta=beta)\n", - "\n", - "# == Simulate state / control paths == #\n", - "x0 = (m0, 2, 1)\n", - "xp, up, wp = lq.compute_sequence(x0, ts_length=150)\n", - "q_bar = xp[0, :] \n", - "q = xp[1, :]\n", - "\n", - "# == Plot simulation results == #\n", - "fig, ax = plt.subplots(figsize=(10, 6.5))\n", - "ax.set_xlabel('Time')\n", - "\n", - "# == Some fancy plotting stuff -- simplify if you prefer == #\n", - "bbox = (0., 1.01, 1., .101)\n", - "legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'}\n", - "p_args = {'lw' : 2, 'alpha' : 0.6}\n", - "\n", - "time = range(len(q))\n", - "ax.set_xlim(0, max(time))\n", - "ax.plot(time, q_bar, 'k-', lw=2, alpha=0.6, label=r'$\\bar q_t$')\n", - "ax.plot(time, q, 'b-', lw=2, alpha=0.6, label=r'$q_t$')\n", - "ax.legend(ncol=2, **legend_args)\n", - "s = r'dynamics with $\\gamma = {}$'.format(gamma)\n", - "ax.text(max(time) * 0.6, 1 * q_bar.max(), s, fontsize=14)\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - 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OirumpiZvb4jL5eKjjz6K6jmIsccwDJ566ik2b96M1Wrllltu4brrrvP2Eaal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\neX2iM1ku115ycq6lulrN9wlj145hoa6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JnsrKynA6neTm5vr1/3Xdp+rtUEFJxDcfdb6j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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/lqramsey_solutions.ipynb b/solutions/lqramsey_solutions.ipynb deleted file mode 100644 index 3ba4cac7b..000000000 --- a/solutions/lqramsey_solutions.ipynb +++ /dev/null @@ -1,115 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:8b871eeb53c0476bfcab412f6c1c759213122ffe4093af5762506c44a35b1d3e" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Optimal Taxation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/lqramsey.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import sys\n", - "import os\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# lqramsy.py lives in the examples folder. We need\n", - "# to append it to the path so we can import it below\n", - "sys.path.append(os.path.abspath(\"../examples\"))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from numpy import array\n", - "from lqramsey import *\n", - "\n", - "# == Parameters == #\n", - "beta = 1 / 1.05 \n", - "rho, mg = .95, .35\n", - "A = array([[0, 0, 0, rho, mg*(1-rho)],\n", - " [1, 0, 0, 0, 0],\n", - " [0, 1, 0, 0, 0],\n", - " [0, 0, 1, 0, 0],\n", - " [0, 0, 0, 0, 1]])\n", - "C = np.zeros((5, 1))\n", - "C[0, 0] = np.sqrt(1 - rho**2) * mg / 8\n", - "Sg = array((1, 0, 0, 0, 0)).reshape(1, 5) \n", - "Sd = array((0, 0, 0, 0, 0)).reshape(1, 5) \n", - "Sb = array((0, 0, 0, 0, 2.135)).reshape(1, 5) # Chosen st. (Sc + Sg) * x0 = 1\n", - "Ss = array((0, 0, 0, 0, 0)).reshape(1, 5)\n", - "\n", - "economy = Economy(beta=beta, \n", - " Sg=Sg, \n", - " Sd=Sd, \n", - " Sb=Sb, \n", - " Ss=Ss, \n", - " discrete=False, \n", - " proc=(A, C))\n", - "\n", - "T = 50\n", - "path = compute_paths(T, economy)\n", - "gen_fig_1(path)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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s2nTmgrNbt0rZXbq4T8kBcc+KiBArSvmMhHZ7maujM2tOeYxG15QckCQBXl6S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\n6LFjsHatPbdyZWO3qYKm7qeCIzr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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/lss_solutions.ipynb b/solutions/lss_solutions.ipynb deleted file mode 100644 index 9ac2d36d0..000000000 --- a/solutions/lss_solutions.ipynb +++ /dev/null @@ -1,5297 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# quant-econ Solutions: The Linear State Space Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/linear_models.html" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import LinearStateSpace" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAf4AAAExCAYAAACd0cBEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXUWZ//HPk4QkJOz7YgQEFFCQNYKiBFdEBRFnEHcd\n", - "5TfjMuiPUWZTxNEXw6gjOjiIMyq4wagRAQdkRxhHwCBhRzbDTghLIAuELM/8UXW6b3dud9/b99Q5\n", - "1fd8369Xv27f7dzqJ+l+TtVTVcfcHREREWmGSXU3QERERKqjxC8iItIgSvwiIiINosQvIiLSIEr8\n", - "IiIiDaLELyIi0iCVJn4z+56ZLTSzm0d4/j1mdqOZ3WRmvzWzPVqeO8TM7jCzu8zs+OpaLSIi0j+q\n", - "7vF/HzhklOfvBV7j7nsA/wR8B8DMJgOnxvfuBhxtZrsmbquIiEjfqTTxu/vVwFOjPP87d3863r0W\n", - "eEH8fjZwt7svcPeVwNnA4UkbKyIi0odyrvH/BXBB/H5b4IGW5x6Mj4mIiEgXptTdgHbM7GDgw8Cr\n", - "4kPaV1hERKQE2SX+OKHvP4BD3L0oCzwEzGp52SxCr7/d+3WSICIijeLu1ulrs0r8ZvZC4BfAe939\n", - "7pan5gE7m9n2wMPAUcDRIx2nmwBI98zsDHf/YN3t6GeKcTUU5/QU4/S67fBWmvjN7CzgIGAzM3sA\n", - 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mu_0=np.ones(3))\n", - "x, y = ar.simulate(ts_length=50)\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 4.6))\n", - "y = y.flatten()\n", - "ax.plot(y, 'b-', lw=2, alpha=0.7)\n", - "ax.grid()\n", - "ax.set_xlabel('time')\n", - "ax.set_ylabel(r'$y_t$', fontsize=16)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAgQAAAExCAYAAAADXboVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztvXn8ZEdV9/8+mZnMZJZksu/JQBZIQkgCJiIIBNkiYBBB\n", - "BEUWeQBZBRURHx5BQeFR4PHH+kPZIkoQzQ8eoiyiguwgyBKWQBIYmCRkMllmMntmJvX7o+p0V1fX\n", - "Xbr7dve93z7v1+v7un1v3+/t6q57qz51zqlT4pzDMAzDMIzF5qB5F8AwDMMwjPljgsAwDMMwDBME\n", - "hmEYhmGYIDAMwzAMAxMEhmEYhmFggsAwDMMwDFooCETk3SKyWUSuKnj/YhHZJiJfD3+vmHUZDcMw\n", - 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"text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5\n", - "sigma = 0.2\n", - "\n", - "A = [[phi_1, phi_2, phi_3, phi_4],\n", - " [1, 0, 0, 0],\n", - " [0, 1, 0, 0],\n", - " [0, 0, 1, 0]]\n", - "C = [[sigma], \n", - " [0], \n", - " [0], \n", - " [0]]\n", - "G = [1, 0, 0, 0]\n", - "\n", - "ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))\n", - "x, y = ar.simulate(ts_length=200)\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 4.6))\n", - "y = y.flatten()\n", - "ax.plot(y, 'b-', lw=2, alpha=0.7)\n", - "ax.grid()\n", - "ax.set_xlabel('time')\n", - "ax.set_ylabel(r'$y_t$', fontsize=16)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 3" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - 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range(I):\n", - " x, y = ar.simulate(ts_length=T)\n", - " y = y.flatten()\n", - " ax.plot(y, 'c-', lw=0.8, alpha=0.5)\n", - " ensemble_mean = ensemble_mean + y\n", - "\n", - "ensemble_mean = ensemble_mean / I\n", - "ax.plot(ensemble_mean, color='b', lw=2, alpha=0.8, label=r'$\\bar y_t$')\n", - "\n", - "m = ar.moment_sequence()\n", - "population_means = []\n", - "for t in range(T):\n", - " mu_x, mu_y, Sigma_x, Sigma_y = next(m)\n", - " population_means.append(float(mu_y))\n", - "ax.plot(population_means, color='g', lw=2, alpha=0.8, label=r'$G\\mu_t$')\n", - "ax.legend(ncol=2)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 4" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAfsAAAE/CAYAAABB8mpwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - 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"\n", - "ax.grid(alpha=0.4)\n", - "ax.set_ylim(ymin, ymax)\n", - "ax.set_ylabel(r'$y_t$', fontsize=16)\n", - "ax.vlines((T0, T1, T2), -1.5, 1.5)\n", - "\n", - "ax.set_xticks((T0, T1, T2))\n", - "ax.set_xticklabels((r\"$T$\", r\"$T'$\", r\"$T''$\"), fontsize=14)\n", - "\n", - "mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions()\n", - "ar.mu_0 = mu_x\n", - "ar.Sigma_0 = Sigma_x\n", - "\n", - "for i in range(80):\n", - " rcolor = random.choice(('c', 'g', 'b'))\n", - " x, y = ar.simulate(ts_length=T4)\n", - " y = y.flatten()\n", - " ax.plot(y, color=rcolor, lw=0.8, alpha=0.5)\n", - " ax.plot((T0, T1, T2), (y[T0], y[T1], y[T2],), 'ko', alpha=0.5)\n" - ] - }, - { - "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 -} diff --git a/solutions/lucas_asset_solutions.ipynb b/solutions/lucas_asset_solutions.ipynb deleted file mode 100644 index 485119e30..000000000 --- a/solutions/lucas_asset_solutions.ipynb +++ /dev/null @@ -1,134 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:5f292f065c543bb774ce6339e9df65c4c0355185a80ee7706e978871fff5d702" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: The Lucas Asset Pricing Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/lucas_model.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start with standard imports" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import division # Omit for Python 3.x\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon.models import LucasTree" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(figsize=(10,7))\n", - "\n", - "ax.set_xlabel(r'$y$', fontsize=16)\n", - "ax.set_ylabel(r'price', fontsize=16)\n", - "\n", - "for beta in (.95, 0.98):\n", - " print(\"Comuting at beta = {}\".format(beta))\n", - " tree = LucasTree(gamma=2, beta=beta, alpha=0.90, sigma=0.1)\n", - " grid, price_vals = tree.grid, tree.compute_lt_price()\n", - " label = r'$\\beta = {}$'.format(beta)\n", - " ax.plot(grid, price_vals, lw=2, alpha=0.7, label=label)\n", - "\n", - "ax.legend(loc='upper left')\n", - "ax.set_xlim(min(grid), max(grid))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Comuting at beta = 0.95\n", - "Comuting at beta = 0.98" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "(0.39945149497311855, 2.5034328637756031)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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RN/QarnCpwhggGXgMiABOANWA5WgpU0RERC7zfez3/Hfzf92iHUZBZswcRVPKdQUCQRe+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\nkHXooLMrRSTvFMxEShDLslhzeA2fb/2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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/mpe_solutions.ipynb b/solutions/mpe_solutions.ipynb deleted file mode 100644 index c0ef4f81d..000000000 --- a/solutions/mpe_solutions.ipynb +++ /dev/null @@ -1,448 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:fe34c3ed8ef6dc3c221131eb9cb17288d7b967994bfe193a90cb5a044cc5a030" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Markov Perfect Equilibria" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/markov_perf.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We begin with some standard imports" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import quantecon as qe\n", - "import matplotlib.pyplot as plt\n", - "from numpy import dot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First let's compute the duopoly MPE under the stated parameters \n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# == Parameters == #\n", - "a0 = 10.0\n", - "a1 = 2.0\n", - "beta = 0.96\n", - "gamma = 12.0\n", - "\n", - "# == In LQ form == #\n", - "\n", - "A = np.eye(3)\n", - "\n", - "B1 = np.array([[0.], [1.], [0.]])\n", - "B2 = np.array([[0.], [0.], [1.]])\n", - "\n", - "\n", - "R1 = [[0., -a0/2, 0.],\n", - " [-a0/2., a1, a1/2.],\n", - " [0, a1/2., 0.]]\n", - "\n", - "R2 = [[0., 0., -a0/2],\n", - " [0., 0., a1/2.],\n", - " [-a0/2, a1/2., a1]]\n", - "\n", - "Q1 = Q2 = gamma\n", - "\n", - "S1 = S2 = W1 = W2 = M1 = M2 = 0.0\n", - "\n", - "# == Solve using QE's nnash function == #\n", - "F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,\n", - " beta=beta)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we evaluate the time path of industry output and prices given initial condition $q_{10} = q_{20} = 1$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "AF = A - B1.dot(F1) - B2.dot(F2)\n", - "n = 20\n", - "x = np.empty((3, n))\n", - "x[:, 0] = 1, 1, 1 \n", - "for t in range(n-1):\n", - " x[:, t+1] = np.dot(AF, x[:, t])\n", - "q1 = x[1, :]\n", - "q2 = x[2, :]\n", - "q = q1 + q2 # Total output, MPE\n", - "p = a0 - a1 * q # Price, MPE" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next let's have a look at the monopoly solution\n", - "\n", - "For the state and control we take\n", - "\n", - "$$ \n", - " x_t = q_t - \\bar q \n", - " \\quad \\text{and} \\quad\n", - " u_t = q_{t+1} - q_t\n", - "$$\n", - "\n", - "To convert to an LQ problem we set\n", - "\n", - "$$\n", - " R = a_1\n", - " \\quad \\text{and} \\quad\n", - " Q = \\gamma \n", - "$$\n", - "\n", - "in the payoff function $x_t' R x_t + u_t' Q u_t$ and \n", - "\n", - "$$\n", - " A = B = 1\n", - "$$\n", - "\n", - "in the law of motion $x_{t+1} = A x_t + B u_t$\n", - "\n", - "We solve for the optimal policy $u_t = - Fx_t$ and track the resulting dynamics of $\\{q_t\\}$, starting at $q_0 = 2.0$\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "R = a1\n", - "Q = gamma\n", - "A = B = 1\n", - "lq_alt = qe.LQ(Q, R, A, B, beta=beta)\n", - "P, F, d = lq_alt.stationary_values()\n", - "q_bar = a0 / (2.0 * a1)\n", - "qm = np.empty(n)\n", - "qm[0] = 2\n", - "x0 = qm[0] - q_bar\n", - "x = x0\n", - "for i in range(1, n):\n", - " x = A * x - B * F * x\n", - " qm[i] = float(x) + q_bar\n", - "pm = a0 - a1 * qm" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's have a look at the different time paths" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, axes = plt.subplots(2, 1, figsize=(9, 9))\n", - "\n", - "ax = axes[0]\n", - "ax.plot(qm, 'b-', lw=2, alpha=0.75, label='monopolist output')\n", - "ax.plot(q, 'g-', lw=2, alpha=0.75, label='MPE total output')\n", - "ax.set_ylabel(\"output\")\n", - "ax.set_xlabel(\"time\")\n", - "ax.set_ylim(2, 4)\n", - "ax.legend(loc='upper left', frameon=0)\n", - "\n", - "\n", - "ax = axes[1]\n", - "ax.plot(pm, 'b-', lw=2, alpha=0.75, label='monopolist price')\n", - "ax.plot(p, 'g-', lw=2, alpha=0.75, label='MPE price')\n", - "ax.set_ylabel(\"price\")\n", - "ax.set_xlabel(\"time\")\n", - "ax.legend(loc='upper right', frameon=0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Bx31r2Vbz2OrnBhzWV31Pa4aE2rbX3CbN39JbltIutJ2ny3CgwCIibmersHGq/JTTqcRW\nQnFZsX2+vKKcMlsZZRVl9oBRZjPnyyrK7MGjzGbOV+1XZiuj3Di9rayizP5ZXlHu6W+BOGHBfErS\nguOTk9W3Vd+3anvN/atvq3nu6sc6u0bNdTXrc3beM/Y7y3WcqWtfZ9doyDldPc4ZP4tfvfZvCgos\nIuKgwqigoLSAgtIC8kvyyS/Np6C0gMLSwjPCxanyUxSXFdvna5vKKso8/WXhb/UnwBpAgF8A/lZ/\n/Cx++Fn9HD+rzftb/R2213qMk32sFit+VvPTarFiwWJfbsx5q8Vq/4VtsVjs66vmgTPWVR1n31Z5\nfG3nAhzWw5lBo67ttW0TqS8FFhEfZKuwmYGjNJ/8knzX5ssK7EGlsVmwEOwfTEhACEF+Qea8fwhB\n/uZ81RTkF0SAX4A9WNT1GegXaIYQJ8v2+Wrr9UtSxLspsIh4iZLyEo4UHbFPh4sOc6ToCEeLjnLi\n1InT4aM0n6KyorOfsBYWLIQFhhEeGE54YDitAlsRHhROWECYGSpqhAxXpgBrgAKDiJwTBRYRD6sw\nKsg7lecQRmqGkiNFR8gvzXf5nBYs9qBhDx2B4YQHnX0+LDDM3mUgItJcKLCIuFGprZTDhYedBpCq\n6WjxUZduCvW3+tMutB3tQtqZn9WmqJAoh/ARGhCq0CEiPkWBRaQRlNpKyc7LJutElsO0P3+/S49y\ntg5q7TSIVJ8igiMUQkSkxXJ3YAkGPgeCgEDgXeDhGvukVq7fXbn8H+BJN9cl0iCltlL25u11DCZ5\nZjCpGteiOj+LH9Fh0bWGkeiwaNqGtCXIP8gDX42IiPdwd2A5BQwBiiqv9QVwReVndZ8D17u5FhGX\nldnK2Hty7xktJjn5ObUGk4SIBJIikkiKTCIxMpGkyCTiW8cT4Bfgga9ARMS3NEWXUNXjCoGAH3DM\nyT56fEA8osxWxr6T+9iTt4esE1lkHs8kKy+LnJM52AzbGftbLVY6te5kDyZVU0JEgoKJiIgbNUVg\nsQIbgRTgeWBbje0GMADYAuQA05zsI9IoCksL2XJwCxv2b2DTgU1k52U7DSYWLMSFx5EUmUTnyM72\nVpOEiAQC/QI9ULmISMvWlC0bEcDHwENARrX14YANsyVmJPAP4Pwax+rlh9IgZbYyth3exsbcjWzI\n3cD2I9sdAooFC7HhsSRGJJ7RYqL7SkRE3KO5v/wwD/gAuBTHwFJ9cIn/Ac8BbajRdZSenm6fT01N\nJTU11T1VilczDIPME5ls2L+Bjbkb2XJwC8XlxfbtfhY/Lm5/Mb079qZ3x96c3/Z8gv2DPVixiIjv\ny8jIICMj45zO4e4WlnZAOXACCMFsYfkTsLLaPjHAIcyuob7AW0BSjfOohUVqdbjwMBtyN5gh5cBG\njhU73ibVObIzvTv2pk/HPlwScwlhgWEeqlRERKB5trB0BBZi3sdiBRZhhpW7Kre/CNwM/BYz2BQB\n49xck3i5wtJCNh/YzPr969l4YCPZedkO29uFtqNPxz72VpTm9op0ERGpP295OkctLC1Y1X0oVa0o\n249ud3i0ODQglJ4xPekT24c+HfuQEJGg99aIiDRjzbGFRaTeqt+HsiF3A1sObuFU+Sn7dj+LH5e0\nv4Q+sWYryoXtLsTfqj/KIiK+TP/KS7NRaitlVeYqlm1fxo6jOxy2dY7sTJ+OfegTa96HEhoQ6qEq\nRUTEExRYxOMOFhzkvR3v8cHPH5BXkgdARFAE/Tr1s9+L0ja0rYerFBERT1JgEY8wDIPNBzbz3+3/\nZe3etfZ7Us5vcz5juo5haOehGqBNRETsFFikSRWXFfPJrk9YtmMZWSeyAPC3+jM0aSg3XHgD3aK7\n6YZZERE5gwKLNIm9eXt5d8e7fLTzIwrLCgFoG9KW6y+4nmvPv5Y2IW08XKGIiDRnCiziNhVGBety\n1vHOj+/w7f5v7esvbn8xN3a9kSsSrtDTPSIi4hL9tpBGl1+Sz0c7P2LZjmXsz98PQKBfIFd1voox\nXcdwXpvzPFyhiIh4GwUWaTS7ju1i2fZlrNi9ghJbCQAdWnXghgtuYGSXkbQOau3hCkVExFspsMg5\nKa8o54vsL1i2fRlbDm6xr78s9jJuuPAG+nXqh9Vi9WCFIiLiCxRYpEGOFx9n+U/Lef+n9zlcdBgw\nh8i/JuUaRl84moSIBA9XKCIivkSBReplf/5+FmxeQEZWBmUVZQAkRCQw5sIxDE8erjchi4iIWyiw\niEsqjAre3f4uL218iVPlp7BarAyMH8iYC8fQu2NvjZ0iIiJupcAiZ3Wg4ABPr32aTQc2AXBV56u4\no/cddGjVwcOViYhIS6HAIrUyDIPlPy3n+fXPU1xeTGRwJL/v93sGJQ7ydGkiItLCKLCIU4cKD/HM\n2mdYn7segNTEVO7tdy+RwZEerkxERFoiBRZxYBgGH+38iH99+y8KywqJCIrgvn73kZqU6unSRESk\nBVNgEbsjRUeY/eVsvs75GoAr4q/g9/1/T1RIlIcrExGRlk6BRTAMgxW7VzB33VwKSgsIDwznnsvv\nYVjnYXr6R0REmgUFlhbuWPEx/vbV31i7dy0A/eL6cf+A+2kX2s7DlYmIiJymwNJCGYbBZ1mf8Y9v\n/sHJkpOEBYQxpe8Urk65Wq0qIiLS7CiwtEAnTp3g71//nc/3fA6Y7/15YMADRIdFe7gyERER5xRY\nWpjVe1bz7NfPcuLUCUL8Q7j7srv5f13+n1pVRESkWVNgaSFOlpxkzjdzWJm5EoDeHXrz4MAHiWkV\n4+HKREREzk6BpQX4cu+XzP5qNseKjxHsH8xv+vyG6y64DqvF6unSREREXKLA4sPyS/L557p/8snu\nTwDoEdODBwc+SGx4rIcrExERqR8FFh/1zb5vmPXVLI4UHSHIL4hf9f4VY7qOUauKiIh4JQUWH1NY\nWshz3z7Hhzs/BKB7dHemD5xOfES8hysTERFpOAUWH7L10FaeWP0EhwoPEegXyOSek7ml+y1qVRER\nEa/nLc+yGoZheLqGZm3HkR38/pPfU1RWxIVtL+ShKx4iMTLR02WJiIicoXIojXplELWw+ICsE1lM\n/3Q6RWVFDE0ayoxBM/Cz+nm6LBERkUajvgIvd6DgAA+ueJC8kjz6xfXj4UEPK6yIiIjPUWDxYseK\njzHtk2kcLjpMj5ge/DH1j/hb1WgmIiK+R4HFS+WX5PPAJw+Qk5/D+W3O589D/0ywf7CnyxIREXEL\nBRYvVFxWzEOfPsTuE7tJiEjgr8P/SlhgmKfLEhERcRsFFi9TZivjD5/9gW1HthETFsMzw58hMjjS\n02WJiIi4lQKLF7FV2Hhy9ZOsz11PVHAUs0bMon1Ye0+XJSIi4nYKLF6iwqhg1pezWJ29mlaBrXhm\n+DN0at3J02WJiIg0CVcCy70urhM3MQyD5799no92fUSwfzAzh80kpU2Kp8sSERFpMq4ElklO1t3e\nyHVIHRZ9t4i3f3wbf6s/j6c+zkXtL/J0SSIiIk2qrkE7xgO3Ap2B96utDweOurMoOe2dH99h/ub5\nWC1WHh30KJfFXebpkkRERJpcXYHlSyAXiAZmcXrM/3xgi5vrEuCTXZ8wd91cAKb1n8aVSVd6uCIR\nERHP0MsPm6kvsr8gPSMdm2Hj7kvv5pbut3i6JBERkUbhrpcf5lebDwQCgAKgdX0uJK7bmLuRxz9/\nHJthI+2SNIUVERFp8VwJLOHV5q3A9UA/95QjPx7+kUdXPUpZRRljLhzD7T11f7OIiEhDu4Q2Az0b\ns5CzaBFdQpnHM7n3o3vJL81nePJwHrriIawWDZUjIiK+xV1dQjdVm7cCfYDi+lxEzm5//n6mrZhG\nfmk+A+MH8uDABxVWREREKrkSWK4Dqpo3yoEsYLS7CmqJjhQdYdon0zhWfIxeHXrxhyv/gL/VlR+N\niIhIy+DOp4SCgc+BIMybdd8FHnay3xxgJFCEOUjdJif7+GyX0MmSk9z70b1kncjiwrYXMvvq2YQG\nhHq6LBEREbdpSJeQK30OKZgDxx0BDmMGj2QXjjsFDMG81+WSyvkrauwzCjgP6AL8Gnjepap9RFFZ\nEdNXTCfrRBZJkUn8dfhfFVZERESccCWw/Bt4C+gIxAJLgTddPH9R5Wcg4Accq7H9emBh5fw3QCQQ\n4+K5vVqprZRHVz3K9qPb6diqI88Mf4bWQXpSXERExBlXAksIsAgoq5xex+zucfX8m4GDwGfAthrb\n44C91Zb3AT7/CuLyinIe//xxNh3YRJuQNswaMYt2oe08XZaIiEiz5Upg+R/mvSdJldP0ynVtKqe6\nVGB2CXUCBgOpTvap2YflmzerVKowKnh67dOs3buW8MBwZg2fRWx4rKfLEhERadZceRRlLGaI+HUt\n6125nyUP+AC4FMiotj4HiK+23Kly3RnS09Pt86mpqaSmprpw2ebFMAz+ue6frNi9ghD/EP561V/p\nHNXZ02WJiIi4VUZGBhkZGed0Dlfu0A3GvIH2bOtqaof5GPQJzG6lj4E/ASur7TMKmFL52Q/4O85H\n0fWJp4Re3fQqi75bRIA1gL9c9Rd6d+zt6ZJERESanLsGjvsSqPmb1dm6mjpi3lBrrZwWYYaVuyq3\nvwh8iBlWdgKFgM+OQ//fH//Lou8W4Wfx47HBjymsiIiI1ENdgaXqqaBQzHBiwewCal257my+x3mo\nebHG8hQXzuXVDhYc5IUNLwDwwIAHGJQ4yMMViYiIeJe6AssIzIHc4oDZ1dbnAzPcWJPPeX7985Ta\nShmSNISrz7va0+WIiIh4nboCy8LK6SbgP01Tju/ZlLuJz/d8TrB/ML+59DeeLkdERMQruXIPy0VA\nd053CVV53C0V+ZDyinLmrpsLwC8v/iXtw9p7uCIRERHv5EpgKeR0UAkBruXMAeDEifd2vEfmiUxi\nw2P5RfdfeLocERERr+VKYJlVY/kZ4BM31OJTjhcfZ/7m+QD832X/R6BfoIcrEhER8V6ujHRbUxjm\njbhSh3mb5lFQWkDf2L7079Tf0+WIiIh4NVdaWL6vNm8F2qP7V+q048gOPvz5Q/yt/kzpO6VqgBwR\nERFpIFcCy3WYb1EeDERgvkdovTuL8mYVRgVzvpmDgcHNXW8mPiL+7AeJiIhInVzpEhqN+YbmdkAg\nMB+4x51FebMVu1aw7cg22oS04bZLbvN0OSIiIj7BlRaWO4HLMZ8WAvgL8DUwx11FeavC0kJe2vgS\nAHf1uYuwwDAPVyQiIuIbXL3ptqKWeanmtS2vcaz4GN2ju3NV8lWeLkdERMRnuNLCMh/4BngHc/C4\nG4BX3VmUN8rOy+Y/P/4HCxbuufwerJaGPIAlIiIizrgSWP4GfA5cgTmA3CRgkxtr8jqGYfDPdf/E\nZti4tsu1nN/2fE+XJCIi4lNcCSwAGyonceLLvV/y7f5vaRXYijt63+HpckRERHyO+i3OUamtlH99\n+y8Abu95O5HBkR6uSERExPcosJyjJVuXkFuQS+fIzoy+YLSnyxEREfFJCizn4FDhId74/g0Apvad\nip/Vz8MViYiI+CYFlnPwwvoXKLGVkJqYSq+OvTxdjoiIiM9SYGmgzQc281nWZwT5BfGbS3/j6XJE\nRER8mgJLA9gqbMz9Zi4At158KzGtYjxckYiIiG9TYGmA93a8x+4Tu+nYqiPjLhrn6XJERER8ngJL\nPZ04dYJXN5sD/d592d0E+gV6uCIRERHfp8BST69uepWC0gIui72MgfEDPV2OiIhIi6DAUg8/Hf2J\n5T8tx8/ix5S+U7BYLJ4uSUREpEVQYHGRYRjM+WYOBgY3db2JhIgET5ckIiLSYiiwuGjF7hX8cPgH\n2oS0YUKPCZ4uR0REpEVRYHFBUVkRL254EYBf9/41YYFhHq5IRESkZVFgccGiLYs4VnyMbu26MTxl\nuKfLERERaXEUWM5ib95e3v7xbSxYmHr5VKwWfctERESamn771sEwDP657p+UV5Qz8ryRXNjuQk+X\nJCIi0iIpsNThq31fsW7/OloFtuLO3nd6uhwREZEWS4GlFqW2Uv717b8AmNRjElEhUR6uSEREpOVS\nYKnF0h+Wsj9/P0mRSYy+cLSnyxEREWnRFFicOFx4mNe/fx2AqX2n4m/193BFIiIiLZsCixMvrH+B\nU+WnGJwawtLHAAAgAElEQVQwmN4de3u6HBERkRZPgaWGLQe2sCprFYF+gdx92d2eLkdERERQYHFg\nq7Axd91cAG696FZiWsV4uCIREREBBRYHy39azq7ju+jQqgPjLhrn6XJERESkkgJLpbxTeczbNA+A\nuy+9myD/IA9XJCIiIlUUWCq9uulV8kvz6dOxD1ckXOHpckRERKQaBRbg56M/8/5P7+Nn8WNK3ylY\nLBZPlyQiIiLVKLAAz69/HgODG7veSFJkkqfLERERkRpafGDZm7eXTQc2EeIfwoQeEzxdjoiIiDjR\n4gPLJ7s+AeDKxCtpFdjKw9WIiIiIMy06sFQYFazYvQKAESkjPFyNiIiI1KZFB5YtB7ZwsPAgHVp1\noEeHHp4uR0RERGrRogPLx7s+BmB48nCslhb9rRAREWnWWuxv6eKyYlbvWQ2YgUVERESaL3cHlnjg\nM+AHYCtwj5N9UoE8YFPl9KibawJgTfYaisuL6R7dnfiI+Ka4pIiIiDSQv5vPXwb8DtgMtAI2ACuA\nH2vs9zlwvZtrcfDxTrM76OqUq5vysiIiItIA7m5hOYAZVgAKMINKrJP9mnRo2UOFh9h0YBOBfoGk\nJqU25aVFRESkAdzdwlJdEtAL+KbGegMYAGwBcoBpwDZ3FrJi1woMDAZ0GkB4ULg7LyUiIk4YhjlV\nX3a2vua62o6pa1vN853t2Nr2cbZfbcc7O9/ZzlHf4+paX9d5XNkvNhb8mzIhuKCpymkFvA3ci9nS\nUt1GzHtdioCRwDLgfHcVYhgGn+w2B4u7+jx1B4k0ZxUVYLOd+1Re7rhsGOZnRYXzqWrb2far67ia\nn87W1bVvffarbar6HjqbrzpP1bwr56q5r7Pluvapmpfmb+lSaNfO01U4aorAEgD8B3gdM4zUlF9t\n/n/Ac0Ab4Fj1ndLT0+3zqamppKamNqiY7Ue2k52XTVRwFJfGXtqgc4j4CsOAsjIoKTGn0lJzcrZc\nVmZO5eWOnzab8/UN+XQWLMS3WSzmVDVffb2zz5rrar6r9mznq+2cru5zttpqO19d9TTWcWc7T33W\n+fm5fh1XZGRkkJGRcU7ncPe9IxZgIXAU8+ZbZ2KAQ5hdQ32BtzC7j6ozjEb6l+sfX/+DZTuWcUu3\nW7j7srsb5Zwi7lAVJoqKzKm4uPbPU6dqDxo1l2uub+78/c1/PKummssNmaxW8x/pqnlnU237uXKc\nxeK4f9WyK5+u7n+2CU7vC6ePdTbvyrmcBQFn+zjbt2awcBY0pGWxmH8A6vWnwN0tLAOB24DvMB9Z\nBpgBJFTOvwjcDPwWKMfsFhrnrmLKbGWszFwJ6OkgcR+bDfLz4eRJcyosNENFVcCoChnVl52tKy42\nz+VugYGnp6Cg01PNdf7+EBBgTlXzdX1WTa7uHxDgPFiIiID7A8sXnP1JpH9VTm731b6vyC/NJyUq\nhZQ2KU1xSfFihmGGjargcfKkGUTy8k7Pnzx55nJBzbu0zkFAAISGQkiI+Vk1X3M5ONh5yHC2XP0z\nIEChQES8QzO7B9i9qt7MrNaVlskwzEBx+DAcOWJOVWHD2ZSf37AWDosFwsOhdWtzCgtzDBihoWbA\nqL7sLISEhJiBQkREWlBgOXHqBF/v+xo/ix/Dkod5uhxpZOXlcPSoGUKqB5Kq+cOHze31vWcjNPR0\n8Gjd2gwiERGn51u3PnO5VSu1WoiINLYWE1hW7l6JzbDRL64fbULaeLocqYfi4jPDR83P48dde6Kk\nVSuIjjYf12vXDiIjHQNJzXCiFg4RkeahxQQWe3eQxl5pdgzDbP3Yuxeys81p797TgcSVe0IsFmjb\n1jGMVM1X/wwOdv/XIyIija9FBJbM45n8dOwnWgW2YkD8AE+X02KVlcH+/Y6hZM8ec76oqPbjAgPr\nDiLt2kGbNs1vVEYREWk8LeKf+KrWlSFJQwj0C/RwNb4vP9+xtaRq2r+/9ptYw8MhMRESEswpPh5i\nYsxQEh6uMRtERFo6nw8stgobK3avAGBEyggPV+M7KirMLpuqFpKqFpPsbDh2zPkxFov5forqoaRq\nPiJCoURERGrn84FlQ+4GjhYfJS48ju7R3T1djlcyDLN15IcfYOtW+PFHM5yUlDjfPzjYMYxUhZNO\nncyxP0REROrL5wNLVXfQiJQRVUMBy1mUlcFPP5nh5IcfzMlZq0mbNo6hJDHRDCbR0XqsV0REGpdP\nB5bC0kLWZK8B1B1Ul7y8060nW7fCjh1njlcSGQndu8NFF5mfnTubjwiLiIg0BZ8OLBlZGZTaSukZ\n05MOrTp4upxmwTDM7pyqcPLDD+Z9JzUlJZ0OJxddBHFxusdEREQ8x6cDS/XuoJaqpMRsMakeUE6e\ndNwnKAi6dj0dULp1MwdOExERaS58NrDsz9/Pd4e+I9g/mCuTrvR0OU3m+PHT4WTrVvNelPJyx33a\ntjXDSVVA6dJFY5iIiEjz5rO/pqpaVwYlDCI0INTD1bjX/v3w+eeQkWEGlOosFkhJOR1QLrrIHN9E\n3TsiIuJNfDKwGIbh891BBw/CZ5+ZIWXHjtPrQ0LM7p2LLzbDSdeu5tuCRUREvJlPBpbvD31PbkEu\n0aHR9O7Y29PlNJqDB2H1ajOkbNt2en1ICAwcCKmpcNll5lD2IiIivsQnA8vHOz8GYHjycKwW7x4Q\n5PDh0909P/xwen1ICPTvb4aUvn01IJuIiPg2nwssJeUlZOzJALy3O+jIkdMtKd9/f3p9cLBjSNGb\nh0VEpKXwucDyRfYXFJUVcWHbC0mMTPR0OS47etQxpBiGuT4wEPr1gyFDzE+FFBERaYl8LrB8vMvs\nDrr6vKs9XMnZHTsGa9aYIWXLFseQcvnlZktK//5m94+IiEhL5lOB5UjRETbkbsDf6s/QzkM9XY5T\nx487hpSKCnN9QIDZzTNkiBlSQn37SWwREZF68anA8unuT6kwKhgYP5DWQc1nqNaKCjOgfPABbN7s\nGFL69TNbUgYM0OPHIiIitfGZwFJ97JWrU5pHd5DNBqtWweuvn35fj7//6ZaUAQP0AkERERFX+Exg\n+fnYz2SeyCQiKIK+cX09Wkt5OaxYAW+8ATk55roOHWDcOBg6FMLDPVqeiIiI1/GZwFLVujKs8zAC\n/AI8UkNZGXzyiRlUcnPNdXFx8MtfwvDhel+PiIhIQ/nEr9AyWxmf7v4U8MzTQWVl8L//wb//bY5G\nCxAfD7fdBsOGgZ9fk5ckIiLiU3wisHy7/1vySvJIikyiS5suTXbd0lLzRto33zRHpAVITIS0NPMe\nFat3D7IrIiLSbPhEYKkaiv/qlKuxNMFriE+dguXLYfFic8A3gORkM6gMHqygIiIi0ti8PrCcLDnJ\nl/u+xGqxclXyVW69VnExvPceLFlijqcCcN55MGGC+fJBBRURERH38PrA8lnmZ5RXlHNZ7GW0C23n\nlmsUFcGyZfDWW5CXZ6674AIzqPTvD03QqCMiItKieX1gsQ/F74axVwoL4Z134O234eRJc123bmZQ\n6dtXQUVERKSpeHVgyc7L5scjPxIaEMrAhIGNdt78fPjPf8ypoMBcd/HFZlDp00dBRUREpKl5dWCp\nGnslNTGVYP9zf43xyZOwdKnZqlJUZK7r2dMMKj17KqiIiIh4itcGlgqjwh5YRqSMOKdznThh3p+y\nbJl5Yy2YLSlpadCjx7lWKiIiIufKawPL5gObOVx0mI6tOnJxzMUNPs9XX8Ff/3r6Ztq+fc2gctFF\njVSoiIiInDOvDSxVY6+MSBmB1VL/54lLSuDFF+G//zWXe/WCX/0KunZtzCpFRESkMXhlYCkqK2J1\n9mqgYd1BWVnw+OOQmWm+3+fOO+GWWzSOioiISHPllYFlzZ41nCo/xcXtLyY2PNbl4wzDHPjtuefM\nYfU7dYLHHoPzz3djsSIiInLOvDKwVI29Up/Wlbw8eOYZWLvWXB45EqZOhZAQd1QoIiIijcnrAsvB\ngoNsOrCJQL9AhiQNcemYTZvgqafgyBFo1Qp+/3vz5YQiIiLiHbwusKzYvQKAK+KvICwwrM59y8th\n/nzzbcqGYQ7+9sgjEBPTFJWKiIhIY/GqwGIYhsvdQTk58OSTsH27eTPtxIlw223g59cUlYqIiEhj\n8qrAsu3wNvad3EebkDZcGnup030MA1asgL//3RwELibGbFW5uOFDtYiIiIiHeVVgqRrZdnjycPys\nZzaVFBaaQeXTT83l1FTzfpXw8CYsUkRERBqd1wSWUlspq7JWAc67g7ZtM7uAcnMhOBjuuQeuuUbv\n/xEREfEFXhNYvtz7JQWlBXRp04XkqGT7+ooK+Pe/YcECsNnMMVUefRTi4z1Xq4iIiDQurwksVd1B\nV6dcbV936JD5uPKWLeby2LFwxx0QEOCJCkVERMRdvCawrMtZh5/Fj6GdhwKwZo05EFx+PrRpAw8/\nDJc6vw9XREREvJy7354TD3wG/ABsBe6pZb85wM/AFqCXsx1sho3L4y4nxBLF7Nnwhz+YYaVfP3jl\nFYUVERERX+buwFIG/A7oDvQD/g+o+T7kUcB5QBfg18DztZ2se/DV3HUXLF8OgYHm0PpPPQVRUe4p\nXjwrIyPD0yVIE9PPvGXSz11c4e7AcgDYXDlfAPwI1Hxb4fXAwsr5b4BI4IyxaPOPhvPq4/3JzobE\nRPMFhjfeqKeAfJn+EWt59DNvmfRzF1c05T0sSZjdPd/UWB8H7K22vA/oBBysvtOJTUMJLw3g+uvh\nt781H10WERGRlqGpAksr4G3gXsyWlppqtpMYNXdIKL2aPz0Ogwa5oToRERFp1pqiQyUAWA78D/i7\nk+0vABnA4srl7cCVOLaw7ARS3FeiiIiINKFdmPevNhsW4DXg2Tr2GQV8WDnfD/ja3UWJiIiIVHcF\nUIF54+2mymkkcFflVOWfmK0oW4DeTVyjiIiIiIiIiIjvuwbzvpafgekerkWaRhbwHWaL3DrPliJu\n9CrmvWrfV1vXBlgB/AR8gjnMgfgOZz/zdMynQ6ta4a9p+rLEzWobRNan/r77YXYVJWHevLuZMwee\nE9+TifkHWXzbIMyhDqr/8noaeLByfjrwl6YuStzK2c/8j8DvPVOONJEOQM/K+VbADszf5T71970/\n8FG15YcqJ/FtmUBbTxchTSIJx19e2zk9cGSHymXxLUmcGVju90wp4iHLgKuo5993d490e66cDSoX\n56FapOkYwKfAeuBXHq5FmlYMp4c0OIiTUa/FJ03FfOhiHl7eLSBnlcTpQWTr9fe9uQeWMwaQkxZh\nIOYf6JGY75/ScIEtk4H+DWgJngc6Y3YZ5AKzPVuOuFEr4D+Yg8jm19h21r/vzT2w5GDerFMlHrOV\nRXxbbuXnYeC/QF8P1iJN6yBm0zBAR+CQB2uRpnGI07+sXkF/331VAGZYWYTZJQT1/Pve3APLesy3\nOCcBgcBY4D1PFiRuFwqEV86HASNw7O8W3/YeMLFyfiKn/2ET39Wx2vwY9PfdF1kwu/u24Tjivc/9\nfR+JeUfxTuBhD9ci7tcZ82mwzZiPv+ln7rveBPYDpZj3qt2O+XTYp/jIY45yhpo/88mYo6F/h3kP\nyzJ035IvcjaI7DXo77uIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItCwRwG8r5zsCSz1Y\ni4iIiIhTSWj0UhEREWnmFgNFmKNbvsXp8DIJc2TTT4BMYAowDdgIfAVEVe6XAvwP81Udq4ELmqhu\nERERaUESOR1Sqs9PAn7GfGdUOyAP+HXltr9hvtEVYCVwXuX85ZXLItLC+Hu6ABHxeZZa5gE+Awor\npxPA+5XrvwcuwQwzA3C87yXQPWWKSHOmwCIinlRSbb6i2nIF5r9PVuA40KuJ6xKRZsbq6QJExOfl\nA+H1PKaqJSYf8/6Wm6utv6SR6hIRL6LAIiLudhRYi9nN8zRgVK43qs3jZL5q+ZfAHZivpt8KXO/O\nYkVERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\nREREREREvFMk8DbwI7AN6OdknznAz8AWoFfTlSYiIiJiWghMrpz3ByJqbB8FfFg5fznwdRPVJSIi\nIgKY4WT3WfZ5ARhbbXk7EOO2ikRERMTrWN18/s7AYWA+sBF4GQitsU8csLfa8j6gk5vrEhERES/i\n7sDiD/QGnqv8LAQecrKfpcay4ea6RERExIv4u/n8+yqnbyuX3+bMwJIDxFdb7lS5zs7SOtgwTp5y\nV40iIiLStHYB59XnAHcHlgOY3T3nAz8BVwE/1NjnPWAKsBjzCaITwMHqOxgnT9HlNw8Tt/0pEhLg\nz3+GTuo08nnp6emkp6d7ugxpQvqZt0z6ubc8Foslpb7HuLtLCGAq8AbmI8uXADOBuyonMJ8Q2g3s\nBF4E7nZ2ko591tMpuZDsbLj7btiwwe11i4iISDPRFIFlC3AZ0AO4EbMF5cXKqcoUzKahHpg3557B\nGlDGuGlfM3Ag5OfD9Onwzjtg6G4XERERn9cUgaXRrDuwmscfh9tuA5sN5s6Fv/0Nyso8XZm4Q2pq\nqqdLkCamn3nLpJ+7uKLm0znNlZG6IJVg/2D+O/a/BPsHs3IlPP00lJbCJZfAn/4EkZGeLlNERETO\nxmKxQD0ziNe0sHRt15VT5af4Nsd84GjYMPjHP6BdO/juO/jtb2HXLg8XKSIiIm7hNYFlUMIgAFbv\nWW1fd+GF8Pzz0LUrHDgAU6fCmjWeqlBERETcxWsCy+DEwQB8te8rymynb1pp1w6efRauugqKi+EP\nf4BFi3QzroiIiC/xmsAS1zqOlKgUCssK2Zjr+CBRUBDMmAF33QUWC7z6KjzxBJzSWHMiIiI+wWsC\nC5xuZaneLVTFYoFx48xB5UJD4bPP4L774PDhpq5SREREGptXBpa1e9diq7A53ad/f/jXvyA2Fnbs\ngN/8BrZta8oqRUREXJeVlYXVaqWiogKAUaNGsWjRIo/U4slrn43XPNZsGAaGYTDp3Ulk52XztxF/\no1fHXrUecPIkpKfDpk0QEADTpsGIEU1XsIiIiCuysrJITk6mvLwcq9X1dgSr1crOnTtJTk52Y3Xu\n4dOPNYP5BTp7WsiZ1q3NcVpuuMEcWG7mTHjhBagMsCIiIl7PaKQnTKoaBZozrwoscLpbaE32GiqM\nutOHvz/cey/87nfg5wdLlsAjj0BhYVNUKiIiTSEpKYlZs2ZxySWXEB4ezh133MHBgwcZOXIkERER\nDB8+nBMnTtj3f++99+jevTtRUVEMGTKE7du3O5xr9uzZ9OjRg8jISMaNG0dJSYl9+8svv0yXLl1o\n27Yto0ePJjc3177NarUyd+5cUlJSiI6O5sEHH7SHAMMwePLJJ0lKSiImJoaJEydy8uRJp19Pamoq\n8+bNA2Dnzp1ceeWVREZGEh0dzfjx4wEYPNj8XdijRw/Cw8NZunTpGedZsGABAwcOZOrUqURGRtK1\na1dWrVrlcJ1HH32UgQMH0qpVK3bv3u1w7aqvt1u3brRu3Zru3buzadMmAPbv389NN91E+/btSU5O\nZu7cuS7+tHyfUaWiosIY9/Y4I3VBqvH9we8NV23aZBjXX28YqamGMXGiYezb5/KhIiJSh9TUxpka\nKikpyejfv79x6NAhIycnx2jfvr3Rq1cvY/PmzcapU6eMoUOHGn/6058MwzCMHTt2GGFhYcann35q\nlJeXG08//bRx3nnnGWVlZfZzXX755UZubq5x7Ngxo2vXrsYLL7xgGIZhrFy50mjXrp2xadMmo6Sk\nxJg6daoxePBgex0Wi8UYOnSocfz4cSM7O9s4//zzjVdeecUwDMOYN2+ecd555xmZmZlGQUGBceON\nNxppaWmGYRhGZmamYbFYDJvNVvn9TDXmzZtnGIZhjBs3znjqqacMwzCMkpISY+3atQ7X27VrV63f\nl/nz5xv+/v7G3//+d6O8vNxYsmSJERERYRw/ftwwDMO48sorjcTERGPbtm2GzWYzysrKHK791ltv\nGXFxccb69esNwzCMnTt3Gnv27DFsNpvRu3dv44knnjDKysqM3bt3G8nJycbHH3/s8s8MqHdzjte1\nsFgsFgYn1P60UG169jQHmevcGfbsMUfG3ej0NYsiIuJtpk6dSnR0NLGxsQwaNIj+/fvTo0cPgoKC\nGDNmjL1lYMmSJVx77bUMGzYMPz8/pk2bRnFxMV9++aX9XPfccw8dOnQgKiqK6667js2bNwPwxhtv\ncMcdd9CzZ08CAwOZOXMmX331FdnZ2fZjp0+fTmRkJPHx8dx33328+eab9mPvv/9+kpKSCAsLY+bM\nmSxevNh+o21tAgMDycrKIicnh8DAQAYMGFCv70v79u2599578fPz4xe/+AUXXHABy5cvB8zfp5Mm\nTaJr165YrVb8/f0djn3llVeYPn06ffr0ASAlJYWEhAS+/fZbjhw5wqOPPoq/vz+dO3fmzjvvZPHi\nxfWqrb78z75L8zMocRBvbXuLNdlr+O2lv626eeesYmPNFyY+9RR8+SU8+CDcfTeMGWM+Fi0iIvX3\n2WeergBiYmLs8yEhIQ7LwcHBFBQUAGZXRkJCgn2bxWIhPj6enJwc+7oOHTo4nKuq2yc3N5dLL73U\nvi0sLIy2bduSk5NjP2d8fLx9e0JCAvv377cfm5iY6LCtvLycgwcP1vl1Pf300zz22GP07duXqKgo\n7r//fm6//XYXviOmuLg4h+XExESHbqzq9da0b98+UlJSzli/Z88e9u/fT1RUlH2dzWazd1O5i9e1\nsAB0i+5G25C2HCg4wM/Hfq7XsWFh5qByv/yl3vgsIuKrjFpuII2Li2PPnj0O++3du/eMX+zOxMbG\nkpWVZV8uLCzk6NGjDsdWb23Jzs62b6t5bHZ2Nv7+/g7BypmYmBheeuklcnJyePHFF7n77rvZvXv3\nWWutUj2IgRk2YmNj7ct1/Yc/Pj6enTt3nrE+ISGBzp07c/z4cft08uRJe8uNu3hlYLFarC4/LeT0\neCvceSc8+igEBsLy5fD730M9/gyIiIgXuuWWW/jggw9YtWoVZWVlzJ49m+Dg4Dq7WqrCz/jx45k/\nfz5btmyhpKSEGTNm0K9fP4cWm1mzZnHixAn27t3LnDlzGDt2rP3YZ599lqysLAoKCpgxYwbjxo07\n62PMS5cuZd++fQBERkZisVjsx8TExLDrLG/9PXToEHPmzKGsrIylS5eyfft2Ro0adcbX5sydd97J\nrFmz2LhxI4ZhsHPnTrKzs+nbty/h4eE8/fTTFBcXY7PZ2Lp1K+vXr6+zlnPllYEFHEe9resbXpfq\nb3zeuhV+9SuYPRuOHWvMSkVEpKlVbzmwWCz25QsuuIDXX3/dfs/LBx98wPvvv3/G/RvOjh02bBhP\nPPEEN910E7GxsWRmZp5x38bo0aPp06cPvXr14tprr2Xy5MkATJ48mbS0NAYPHkxycjKhoaEOT9bU\n1tKxfv16+vXrR3h4OKNHj2bOnDkkJSUBkJ6ezsSJE4mKiuLtt992evzll1/Ozz//THR0NI899hj/\n+c9/HLpy6mphufnmm3nkkUe49dZbad26NTfeeCPHjx/HarWyfPlyNm/eTHJyMtHR0fz617+u9amn\nxuItd24YNUOJrcLGTW/dRF5JHq9e/yqdozo3+OR5ebBwIbz3ntlNFBJidhndfLP5niIREZGzaW4D\nuS1YsIB58+axZs0aT5dyBp8fOK46P6sfA+MHAg3rFqouIgLuucd8aWL//uZbn195BSZOhJUr9eZn\nERERT/PawAJ1vwyxIRISzCeIZs+GlBQ4eBCefBL+7//MLiMREZHauPrEalOp3p3lC7zlKzmjSwig\nzFbGmCVjKCwrZNGYRXRq3anRLlhRAR99BPPmnb6nJTXVvM+l2g3WIiIiUk8tqksIIMAvgP6d+gOw\nZk/j9tFZrTBqFCxaBGlp5tNEGRkwaRK8+KKG9xcREWlKXh1YoPG7hWoKDYXJk83gMny4OV7L4sXm\nTbnvvmvepCsiIiLu5dVdQgCnyk8xZskYTpWfYvFNi4lpVfcgPOdq+3Z47jn4/ntzOTHRHOa/b1+N\nlisiIuKK5tollAV8B2wC1jnZngrkVW7fBDxan5MH+wdzedzlgPkGZ3e78EJz7JY//cm8l2XPHnjo\nIXOYfw08JyIi4h5NEVgMzFDSC+hbyz6fV27vBTxZ3wu4u1uoJosFBg+GBQvM1pVWrWD9eg08JyIi\n4i5NdQ/L2Zp9zqkzpV+nfgRYA9h6aCvHipsuLQQEwC9+Aa+/fvoFisuXw223wRtvQElJk5UiIiLN\n2MyZM/nVr37l6TK8WlO1sHwKrAec/bQMYACwBfgQ6FbfC4QGhHJp7KUYGHyR/cW51NogGnhORMRz\nkpKSCAoK4ujRow7re/XqhdVqtb+QcNKkSQQFBREeHk7btm0ZMWIEO3bsAMxh7gMCAggPD7dPbdq0\nabQaH374YV5++eVGO19L1BSBZSBmV89I4P+AQTW2bwTigR7AXGCZs5Okp6fbp4yMjDO2N3W3kDNV\nA8/NmuU48NyUKRp4TkTEXSwWC8nJybz55pv2dd9//z3FxcVnvFNo+vTp5Ofns2/fPtq3b8+kSZPs\n28ePH09+fr59OtZI/fs2PU5KRkaGw+/xhmiKwJJb+XkY+C9n3seSDxRVzv8PCADOiLXVv9DU1NQz\nLjIgfgB+Fj82H9jMyRL3voDpbPr0gZdeggcegDZtYNs2mDoV/vhH2LxZLS4iIo3ttttu47XXXrMv\nL1y4kAkTJtT6ctyQkBDGjx/P1mr/m3T1RbpZWVlYrVZefvll4uLiiI2NZfbs2fbt6enp3HzzzaSl\npREREcGCBQtIT08nLS3Nvs8XX3zBgAEDiIqKIiEhgYULFwJQUlLCtGnTSExMpEOHDvz2t7/l1KlT\n9fpeNEepqannHFicv56y8YQCfpihJAwYAfypxj4xwCHMrqG+mPez1DvWtg5qTa8OvVifu5612WsZ\n2UFuDFIAACAASURBVGXkORV+rqoGnktNNcdtWbIEVq82p7g4uOYauPpqiI72aJkiIudsyMIhjXKe\nzyZ+1uBj+/Xrx6JFi9i+fTtdunRhyZIlrF27lkcfdXzwtCqUFBQU8MYbb9C7d+8GXzMjI4OdO3ey\na9cuhg4dSs+ePRk2bBgA7733Hm+//TaLFi3i1KlT/PWvf7Uft2fPHkaNGsXLL7/MzTffTF5eHnv3\n7gXgoYceIjMzky1btuDv78+tt97K448/zlNPPdXgOn2Fu1tYYoA1wGbgG2A58AlwV+UEcDPwfeU+\nfwfGNfRizaFbqKaqgedefx0mTDADSk6OOeT/uHEwfTp8/rk5IJ2IiDRcWloar732GitWrKBbt27E\nxcU5bDcMg1mzZhEVFUWXLl0oKipiwYIF9u1vvfUWUVFR9qkqfNTmj3/8IyEhIVx00UXcfvvtDl1S\nAwYM4PrrrwcgODjYofXm3//+N8OHD2fs2LH4+fnRpk0bevTogWEYvPzyy/ztb38jMjKSVq1a8fDD\nD7N48eJG+O54P3e3sGQCPZ2sf7Ha/L8qp3N2RcIVPPv1s2zI3UBhaSFhgWGNcdpGER0Nt99u3oi7\nYQN8+CF88QWsW2dOERHmSLojR0IzeTO5iIhLzqVlpLFYLBbS0tIYNGgQmZmZTruDLBYLDzzwAI8/\n/rjTc4wdO9ahW+ls/n97dx4fVXn3ffwzM8lkJSEQCEsSIGyCggZQASvGR6tVW9tarRYtRVurtfWp\ntbW194O36G0X621bq32pba0KUlRKpWrFhWoEoSIqq+yrASFsWSD7LM8f10wymcyEBDJzZvm+X6/z\nOts1Z34aA1+vc53rFBUVtW4XFxez3j+jKFBYGP7ddhUVFZSE+IP+0KFD1NfXM3HixNZjXq8Xj8fT\n5ZoSWdxPzR8oLyOPcf3H0eJp4f2971tdTkh2O5x9thnP8ve/m7Etw4dDTY3Z//a34dZb4eWX4fhx\nq6sVEYkfxcXFlJSUsHjxYq666qqQbcKNU7HZbF0ew+Lnf/rIvx3Yo9PZW5KLi4vZsWNHh+P5+flk\nZGSwceNGqqqqqKqqorq6mtpaa8dlxoqECiwQm7eFwsnNhauugj//2bxQ8StfMZPQbdkCv/sdfO1r\n5qmj1avN26NFRKRzTz31FG+//TYZGRkdznUWSLobVgAeeOABGhoa+OSTT3jmmWe49tpru/S56dOn\ns2TJEhYsWIDL5eLIkSOsXbsWu93OzTffzB133MGhQ4cA2LdvH2+++Wa3a0tECRdYzh9inpr+4LMP\naHTFx8hqmw1GjYIf/tD0ssyaBRMmQHMzvPUW3HmneWP03Llw8KDV1YqIxK6SkpJ2A2mDH2sO1/Nh\ns9l44YUX2s3DkpOTw+HDh8N+1wUXXMCIESO4+OKLueuuu7j44ovDfk/gseLiYl577TUefvhh+vbt\nS2lpKevWrQPgwQcfZMSIEUyePJnc3Fw+//nPs3Xr1pP7l5Fg4uV1fWFffhjKbf+6jU2HN3F/2f2t\nASYe7d8Pb7wBixe3BRWbDSZNMk8gnXeemW1XRESiZ/fu3ZSUlOByubDbE+7/+6MiVl9+GHXxdFuo\nMwMHwsyZMH8+PPQQXHghpKTAqlXm5YvXXAOPPQYhboWKiIgklITsYdlXu48bXrqBrNQsXrr2JVId\nidMNUVsLS5aYXpft29uOjx5tnjC66CIzDkZERCJj9+7dDB8+nJaWFvWwnKST6WFJyMAC8J2Xv8OO\nqh38+qJfc27huREqy1pbt5rgsmRJ2xNFqalw+ulQWmrGwZx2mumVERERiRUKLAHmrJ3D02ue5vIR\nl3PXeXdFqKzY0NwMy5aZuV1Wr24/9X9mJpx5pgkvEybAsGFmHIyIiIhVFFgC7K7ezY3/vJHctFwW\nfn0hDrsjQqXFltpaWLsWPv7YLAHTBACQl9fW+zJhghknIyIiEk0KLO0/wMx/zuTTmk95+JKHmTDw\n5N8XEc8OHTK9Lh99ZAJM8BN6AwealzVOmGCCTO/e1tQpIiLJQ4ElyFMfP8Vz65/jy6O/zB2T74hA\nWfHF64WKirbel9WrO86mO3x4W+/L+PHmlpKIiEhPUmAJsvXIVm559Rb6ZPRhwTULsNs0mjuQx2MG\n7voDzPr1ZjyMn8MBY8aYHpjSUhg7VvO+iIjIqVNg6fghpv9jOgeOH+DRyx7ljP5nRKC0xNHcDJ98\n0hZgNm9u/0qA9HQYN870vowaZV7SqFtIIiLSXQosITy+6nFe3Pgi14y9htvOvq2Hy0psdXXtB/Du\n2tWxTZ8+JriUlJjbSSUlMGSIemJERCQ8BZYQNhzcwO2Lb2dA9gD+dtXfOn2DpnTu6FEz7mXtWhNe\nduyAhoaO7RwOKC5uCzL+pV8/PVItIiIKLCF5vB6u/fu1HK4/zBNXPMHo/NE9XFry8nigshJ27jTh\nZedOs+zd234uGL/s7LZeGH+PzNChEOKlqiIiksAUWMJ45P1HWLRlEdPPmM7NE2/uwbIklMZG2LOn\nY5CpqQndfvDgjreVBg4EzXgtIpKYFFjCWL1/NXe+eSeFOYXM+coc3RaygNdrbikFh5g9e8Dl6tg+\nPd2EloICswwY0H67d28FGhGReKXAEobb4+ZrL36NmqYa/nrlXxmWN6wHS5NT0dJibiEFB5lDhzr/\nnNMJ/fuHDjMFBZCfb8bSiIhI7FFg6cRDyx/ite2vMfPMmXzrrG/1UFkSKcePw4EDZoyMfx24He72\nkp/DYQb5hgozBQUm7OhJJhERayiwdGLl3pXc/e+7KeldwlNffqqHyhKrNDR0DDGB66NHO/+8zWYe\nye7bF3JzzS2mvDyz7V/37t22ZGToCScRkZ5yMoElJTKlxJ4JAyeQ7cxmZ/VO9tbupTCn0OqS5BRk\nZJgnjIYODX2+uRkOHgzfQ3PoEBw5YpaucDrbB5jgJTDo5OWZMTgKOCIiPSdpAkuqI5UphVN4a+db\nLN2zlOnjpltdkkSQ0wmFhWYJxeUyYeXoUXN7qbq686WpyQSggwe7/v3+XpvsbMjK6rhkZpp1dnbb\ntn9R4BERaS8afyTuBmoBN9ACnBOizR+Ay4B6YCawOuj8Kd8SAnjv0/e45517GN13NE988YlTvp4k\nj8ZGqKoy4aWmxmwHrgPDTVVV+3cynQy73YQYf9jpLNxkZZkep/R0SEszi3/bv3Y69VSViMSOWL0l\n5AXKgHCjCi4HRgAjgXOBx4HJkShk0qBJpKeks+XIFiqPV1KQXRCJr5EE5H/MeuDAE7f1ek3A8QeY\nujoziLiuDurrzTrUUl/f1q6pyWwHv037VIQKMunpJsyEOh4cfpxOs6Smhl+cTkhJab+tniIR6QnR\nuiXU2R9ZVwLP+rZXAr2BAqCyp4tIT0nn3MHn8u6ed1n26TKuHnt1T3+FCDab6fHIyOhawAnF5Qof\nbgKDjb9NQ4MJOU1NJiyFWvuXaEtNNcElOOwEhpvAfYfDrP3b/v1Q61Btg4+Fuo7dbhb/cf9+d4/Z\nbApkItESrR6WJZhbQk8Cfw46PxioCNjfCxQSgcACMG3INN7d8y5L9yxVYJGYlZICOTlm6Qkej7lN\n5Q8wzc3hg01gwPFvNzSYz7hcZu6c5mazbmkxxwL3wy2h3juVCEIFmcB9m63jdvC6K+fDnYO2/eAl\n1Pnufgbanz+V/a6c8zvR8eBjwZ/p7HioawYfC3W8u+dP9H2dfaa77cN9NtyxE11n0iTzPxGxJBqB\n5TxgP9APeAvYDCwLahP8r+7UB6yEMblwMk6Hkw0HN3C04Sh9MvpE6qtEYobdbm7tpKebJ5mixett\nCzShwk6oxe02i8vVtvZvBx8PPNad4x5Px/WJjoU67/W2XVMkkSxYYCbgjCXRCCz7fetDwEuYQbeB\ngWUfUBSwX+g71s7s2bNbt8vKyigrKzupYjJTM5k0cBIr9q5g2Z5lfPm0L5/UdUTkxGy2tls+icgf\nZEIFGq/XLP5z/u3gdbjzJ/pM4PX9tfiPBS6hznf3M9D+fHf2u9I2VPvg48HHgj/T1ePB1+9qm66e\nD7Uf7pmRcG260r4r3xtOVz7T070r5eXllJeXn9I1In33NRNwAMeALOBN4D7f2u9y4Ae+9WTg93Qc\ndNsjTwn5vb79dR5c/iATBkzg4Usf7rHrioiIyInF4lNCBZheFf93zcOElVt8x54EXsOEle1AHXBj\nhGtiatFUHDYHayvXUttUS05aDw0UEBERkYiIdGDZBZwV4viTQfs/iHAd7eSk5VA6oJQP93/I8k+X\nc9nIy6L59SIiItJNSTuV1LQh0wBYumepxZWIiIjIiSRtYPlc8eewYeOj/R9R11xndTkiIiLSiaQN\nLHkZeYwvGE+Lp4X3975vdTkiIiLSiaQNLADnF58P6LaQiIhIrEvuwDLEBJaV+1bS6Gq0uBoREREJ\nJ6kDS/+s/ozJH0OTu4lV+1ZZXY6IiIiEkdSBBfS0kIiISDxQYPEFlhV7V9DksuBVtiIiInJCSR9Y\nBvUaxOi+o6lvqefVra9aXY6IiIiEkPSBBWDGmTMAmLd+ngbfioiIxCAFFmBK4RRO63saVY1VLNq8\nyOpyREREJIgCC+atkd+e8G0A5m+Yr5lvRUREYowCi8/EgRMZ138ctU21LNy00OpyREREJIACi4/N\nZuPbpaaX5cVPXqS2qdbiikRERMRPgSXAmQPOZOLAidS11LHgkwVWlyMiIiI+CixBbiq9CYCFmxZS\n3VhtcTUiIiICCiwdjO03lsmDJ9PgamD++vlWlyMiIiIosIR0Y+mNACzasojD9YctrkZEREQUWEIY\n1XcU04qn0exuZt66eVaXIyIikvQUWMKYedZMbNh4ddurVB6vtLocERGRpKbAEsawvGFcNOwiXB4X\nc9fNtbocERGRpKbA0okZZ87AbrPz+vbX2Ve7z+pyREREkpYCSyeKcou4dPiluL1u5qydY3U5IiIi\nSUuB5QRmnDmDFHsKb+18i93Vu60uR0REJClFI7A4gNXAKyHOlQE1vvOrgVlRqKdbBmQP4PIRl+PF\ny7NrnrW6HBERkaQUjcDyQ2Aj4A1z/l2g1Lc8EIV6uu2G8TfgdDgp31POjqM7rC5HREQk6UQ6sBQC\nlwN/AWxh2oQ7HjP6ZfXjylFXAvD0mqctrkZERCT5RDqw/A64C/CEOe8FpgJrgdeAsRGu56RNHzed\n9JR0llcsZ9OhTVaXIyIiklRSInjtLwIHMWNTysK0+RgoAuqBy4BFwKhQDWfPnt26XVZWRllZuEtG\nRl5GHl897avM3zCfp9c8zW8+/5uofr+IiEi8Ki8vp7y8/JSuEcnbMb8Evgm4gHQgB1gIzOjkM7uA\nicDRoONerzfcEJjoqW2q5RsLv0F9Sz2PfOERxheMt7okERGRuGOz2aCbGSSSt4T+C9N7Mgy4Dnib\njmGlgLaCz/FtB4eVmJGTlsM1Y68B4OnVTxMLIUpERCQZRHMeFv/f7rf4FoCrgfXAGuD3mGAT064e\nezW9nL1YU7mG1QdWW12OiIhIUoj5J3R8YuKWkN+8dfP4y+q/MDZ/LI9d/pi/a0tERES6INZuCSWs\nq8ZcRe/03mw8vJGV+1ZaXY6IiEjC62pgGQpc7NvOxAygTVoZqRlMP2M6AH9d/VeNZREREYmwrgSW\n7wILgCd9+4XASxGrKE5cOfpK+mb0ZdvRbbz36XtWlyMiIpLQuhJYvg98Dqj17W8F+kesojiRlpLG\nDeNvAMzstx5vuLnxRERE5FR1JbA0+Ra/FMK/FyipXDHyCgqyCthVvYt3dr1jdTkiIiIJqyuB5V3g\n/2HGrnwec3so1JuXk06qI5UZZ5qpZZ5Z+wxuj9viikRERBJTVwLL3cAhzHwpt2De+TMrkkXFk0uG\nX8LgXoPZW7uXN3e8aXU5IiIiCakrz0BnAY2Av/vAAaRh3v8TLTE1D0uwt3a8xS/f+yUDsgcw5ytz\nSHWkWl2SiIhIzIrUPCxvAxkB+5nAku58SaK7qOQihuQO4cDxAyzevtjqckRERBJOVwJLGnA8YP8Y\nJrSIj91mZ+ZZMwF4bt1zNLubrS1IREQkwXQlsNRh3qDsNwloiEw58WvakGmMyBvBofpDvLJFY5JF\nRER6UlcCyx3Ai8B7vuUF4PZIFhWP7DY7N5beCMC89fNodDVaXJGIiEji6EpgWQWMAb4H3AqcBnwY\nyaLi1ZTCKYzJH0NVYxUvbUr6yYBFRER6TGeB5SLf+mvAF4FRwGjgS8BVEa4rLtlsNm4qvQmA5z95\nnrrmOosrEhERSQydBZZpvvWXfMsXfYt/X0KYOHAi4/uPp7aploWbFlpdjoiISEI40TPQduAazLgV\nK8X0PCzB1h5Yyx1v3EFWahZ/+9rfyElL6pdbi4iItBOJeVg8wE9PtqBkdeaAM5k4cCJ1LXUs+GSB\n1eWIiIjEva4Mun0L+AlQBPQJWKQT/rEsCzctpKqhyuJqRERE4ltXAst1wPeBpcBHAYt0Ymy/sUwp\nnEKDq4HnNzxvdTkiIiJxrSuBZQzwR2AtsBp4FBgbyaISxY1nmXlZFm1ZxOH6wxZXIyIiEr+6Eljm\nYELLI8BjmLAyJ5JFJYqRfUcyrXgaze5m5q2bZ3U5IiIicasrI3Q30rFHJdSxSIqrp4QC7a7ezU3/\nvAmH3cFzX32OguwCq0sSERGxVKTe1vwxMCVgfzIaw9JlQ3sP5aJhF+HyuJi7bq7V5YiIiMSlrgSW\nScByYA+wG1jhO7YeWBexyhLIjDNn4LA5eH3762w9stXqckREROJOV7pjhp7g/O4TnHdg3j20l9Az\n5P4BuAyoB2ZiBvYGi9tbQn6PvP8Ii7Yson9Wf5644gnyMvKsLklERMQSkboltPsEy4n8EDPmJVTi\nuBwYAYwEvgs83oXrxaXbzr6N0/udzsG6g9xbfi8t7harSxIREYkbXQksp6IQE0r+QugkdSXwrG97\nJdAbSMhRqamOVO6/8H76ZfZj/cH1PLLyEeK910hERCRaIh1YfgfchZniP5TBQEXA/l5MyElIfTL6\n8D8X/g9Oh5N/bfsXizYvsrokERGRuJASwWt/ETiIGZNS1km74J6XkN0Os2fPbt0uKyujrKyzS8au\n0fmj+enUn/LAsgf446o/MrT3UEoHllpdloiISMSUl5dTXl5+Stfo1oCXbvol8E3ABaQDOcBCYEZA\nmyeAcsA/d/1m4AKgMuhacT/oNtifPvoT8zfMJycth8eveJxBvQZZXZKIiEhURGrQ7cn6L8wLE4dh\n3kf0Nu3DCsDLAccmA9V0DCsJ6TsTvsPkwZOpbapl1tuzqG+pt7okERGRmBXpMSyB/F0kt/gWgNeA\nncB24EngtijWYym7zc6sabMozi1mV/Uufrnsl3i84Yb6iIiIJLdI3hLqSQl3S8ivoqaC2167jePN\nx5kxfgY3lt5odUkiIiIRFWu3hKQLinKLuPeCe7Hb7MxZN4fy3eVWlyQiIhJzFFhiwKRBk7h14q0A\nPLj8QbYf3W5xRSIiIrFFgSVGXD32ai4dfimNrkZmvT2LqoYqq0sSERGJGQosMcJms3HnlDsZmz+W\nyrpKZpfP1vT9IiIiPgosMcTpcHL/hfeTn5nPuoPrePSDRzV9v4iICAosMadvZt/W6ftf2foKL295\n2eqSRERELKfAEoNOyz+Nu6beBcCjHzzK6v2rLa5IRETEWgosMerikou57vTrcHvd3Pfufew/tt/q\nkkRERCyjwBLDbp54M+cOPpeaphpmvT2LhpYGq0sSERGxhAJLDLPb7Nwz7R6Kc4vZWb2TX733K03f\nLyIiSUmBJcZlObN44MIHyHZms+zTZcxZO8fqkkRERKJOgSUOFOUWcc+0e7Db7Dy79lmW7llqdUki\nIiJRpcASJ84ZfA63TDQvuf7Ve79ix9EdFlckIiISPQosceSasddwScklZvr+d2ZR3VhtdUkiIiJR\nocASR2w2Gz+e+mPG5I/hwPEDmr5fRESShgJLnPFP3983oy9rK9fy2AePWV2SiIhIxCmwxKH8zPzW\n6ftf3voy/9z8T6tLEhERiSgFljg1pt8Yfjzlx4CZvn/tgbUWVyQiIhI5Cixx7JLhl3Dt6dfi9rq5\nt/xeDhw/YHVJIiIiEaHAEue+O/G7nDPoHE3fLyIiCU2BJc7ZbXbuueAeinKK2FG1g1+/92tN3y8i\nIglHgSUBZDuz+cX/+QXZzmyWfrqUWW/P4ljTMavLEhER6TEKLAmiKLeI+8vup5ezF//Z+x9uefUW\nth7ZanVZIiIiPSLSgSUdWAmsATYCvwrRpgyoAVb7llkRrilhlQ4s5U9f+hOj+45m//H93L74dl7d\n+iper9fq0kRERE6JLQrfkQnUAynAe8BPfGu/MuBO4MpOruHVX7pd1+Ju4bEPHuPlrS8DcOnwS7lj\n8h2kp6RbXJmIiIiZuZ1uZpBo3BKq962dgAM4GqJNNIJT0kh1pPKjKT/i55/7OWmONN7Y8Qbf/9f3\nqaipsLo0ERGRkxKNwGLH3BKqBN7B3BoK5AWmAmuB14CxUagpKVwy/BIev+JxinKK2Fm9k1v/dSvL\n9iyzuiwREZFui2bPRi7wBnA3UB5wvBfgxvTEXAY8AowK+qxuCZ2CuuY6HlrxEO/ueReAr4/9OjdP\nvJkUe4rFlYmISDI6mVtC0b4Vcw/QAPxvJ212ARNpf+vIe++997bulJWVUVZWFon6EpbX6+Ufm/7B\n4x8+jtvrZlz/cfz3Bf9Nfma+1aWJiEiCKy8vp7y8vHX/vvvugxgLLPmAC6gGMjA9LPcB/w5oUwAc\nxNwaOgd4ERgadB31sPSQDQc3cN+793G4/jB56XncM+0eSgeWWl2WiIgkkVjsYRkHPIsZx2IH5gIP\nAbf4zj8JfB/4HibY1GOeGHo/6DoKLD2oqqGKB5Y+wMcHPsZus3PTWTfxjXHfwG7TtDwiIhJ5sRhY\neooCSw/zeD08s+YZ5q6bC8CUwin8/HM/p1daL4srExGRRKfAIt22cu9KfrHsFxxrPsbA7IHMLpvN\nqL7BY55FRER6jgKLnJQDxw8wu3w2W45swelwcvs5t3PFyCv8/0GJiIj0KAUWOWnN7mb++MEfNTuu\niIhEnAKLnLI3d7zJb//zW5rcTZT0LuG+C++jMKfQ6rJERCSBKLBIj9hVtYt7y++loraCzNRM7j7v\nbs4fcr7VZYmISIJQYJEeo9lxRUQkUhRYpEd5vV4WblrIEx8+odlxRUSkxyiwSERodlwREelJCiwS\nMcGz415ScgnTx02nKLfI6tJERCTOKLBIRPlnx/3b+r/h9rqx2+yUDSnj+vHXU5JXYnV5IiISJxRY\nJCo+O/YZ89fP5/Udr+PyuAA4r+g8bhh/A6fln2ZxdSIiEusUWCSqDtYd5IUNL/DqtldpdjcDcPag\ns/nm+G8yrmCcxdWJiEisUmARS1Q1VLFg4wIWbV5Eg6sBgLMKzuKG8TcwYeAETfEvIiLtKLCIpWqb\navnHpn+wcNNCjjcfB2Bs/liuH389UwqnKLiIiAigwCIxoq65jkWbF7Fg4wJqmmoAGJE3guvHX8+0\nIdOw2+wWVygiIlZSYJGY0uhq5JUtr/DCJy9wpOEIAMW5xVw/7nouGnYRDrvD4gpFRMQKCiwSk5rd\nzSzetpjnP3meA8cPADCo1yC+ccY3uHT4paQ6Ui2uUEREokmBRWKay+Niyc4lzFs/j721ewHol9mP\n6864jstHXk56SrrFFYqISDQosEhc8Hg9lO8u57l1z7GrehcAeel5fP30r3Pl6CvJTM20uEIREYkk\nBRaJKx6vhxUVK3hu3XNsObIFgF7OXlw99mq+etpX6ZXWy+IKRUQkEhRYJC55vV5WfbaK59Y9x/qD\n6wHITM3k/OLzmVo0lUmDJqnXRUQkgSiwSFzzer2sq1zH3HVz+Wj/R63HU+2plA4oZWrRVKYWTaVf\nVj8LqxQRkVOlwCIJ49OaT1lRsYIVFSvYcHADXtp+/qP6jGJq0VSmFE1hZJ+RmpBORCTOKLBIQqpq\nqGLlvpWsqFjBqs9W0ehqbD3XL7Nfa89L6YBSPSItIhIHYi2wpAPvAmmAE/gn8PMQ7f4AXAbUAzOB\n1SHaKLAIYOZ0Wb1/NcsrlrOiYkXrhHQAGSkZnD3obM4rPo9zB59LbnquhZWKiEg4sRZYADIxQSQF\neA/4iW/tdznwA9/6XOARYHKI6yiwSAcer4dtR7a13jraXrW99ZzdZueMfme09r4U5RZZWKmIiASK\nxcDil4npbfkWsDHg+BPAO8ALvv3NwAVAZdDnFVjkhCqPV7aGlzWVa3B5XK3ninOLmVpoxr2c0f8M\nvc9IRMRCsRhY7MDHwHDgceCnQedfAX4FrPDtLwF+BnwU1E6BRbqlrrmOVZ+tYkXFCt7f+z7Hmo+1\nnstNy2Vy4WSmFk1lXP9x5GXkWVipiEjyOZnAkhKZUlp5gLOAXOANoAwoD2oTXHDIZDJ79uzW7bKy\nMsrKynqmQklIWc4syoaWUTa0DLfHzYaDG1hRsYLlFcvZd2wfb+x4gzd2vAFAfmY+I/JGMLLvSEb2\nGcmIPiMYkD1ATx+JiPSQ8vJyysvLT+ka0fwT+R6gAfjfgGNPYALM87593RKSiPJ6va2PTK/ct5Kt\nR7bS4Gro0C7bmd0aYkb0GcHIPiMpzi3WG6ZFRHpArN0SygdcQDWQgelhuQ/4d0CbwEG3k4Hfo0G3\nEkUer4d9tfvYfnQ7245ua11XN1Z3aOt0OCnpXdIuxJTklZCWkmZB5SIi8SvWAss44FnMOBY7MBd4\nCLjFd/5J3/ox4AtAHXAjZsxLMAUWiRqv18uRhiNsO7KtXYg5cPxAh7Z2m53i3OIOt5T0HiQRkfBi\nLbD0JAUWsdyxpmNsP7q9XW/MpzWf4va6O7QdkD2gNbwU5RRRkF1AQVYBeRl5ekJJRJKeAotIlDW5\nmthZtbNdiNlRtYNmd3PI9qn2VPpn9acgq6A1xASu+2X202y9IpLwFFhEYoDb46aitoJtR0yAN3CZ\nOgAAB3BJREFU2X98P5XHK6msq6SmqabTz9qw0TezrwkwYUKN3lwtIvFOgUUkxjW6GlvDS4d1XSWH\n6w/j8Xo6vUYvZ68OIaZvRl9y03PJScshJy2H3LRc0lPS9Wi2iMQkBRaROOfyuDhcf7jTUBPudlOw\nVHuqCTHOnA5hJifNHAvczknLISs1SyFHRCJOgUUkwXm9XqobqzuEmarGKmoaa6htqqWmqYaappou\nB5tADpujLcA4c9qFmV7OXmSkZpCRkkFGagaZqZmkp6S37mekmGNOh1OhR0Q6pcAiIq0aXY3UNtWa\nEBMQZgKP+ff96/qW+lP+XrvN3iHEZKRkmHATFHgC22WkmjZOh5NUeypOh9NsO9q2/YuetBKJbwos\nInJKWtwtYYNNfUs99S31NLQ00OBqaLeud7UdP5mene5y2BwhA027oBMm9KTaU0mxp+CwO0ixp5ht\nm6Pdvv9Y67bvXKhjgccD2znsDuw2Ow6bWdttdvU8ifgosIiI5dweN42uRhNugoJNg6uhXehpdDW2\nO97oaqTF3UKzu9ksnuZ2+y2eFppcTXhDv3Is5tmwmRBjbwsxwaGmS+d9+zabuZ7/up3t22y2LrXz\n7/u3gdbz/n8G//nAdeA/n/8z4dqEOxd8vvVYwH7gdYLPBX9v8DUDP9fu5xLimsE/t1Dtwn0++Hgo\nnbUNVeOJrtnZP9eJhPrsxEETcTqcXfr8yVBgEZGE5/V6cXvdHYJMa8hxtw857Y772rk9btxeNy6P\nC5fHhdtjtgOP+Y8Ht+vQxrfvv4b/mNvjxuP1tC6hJhgUiVULrllAfmZ+xK4fi29rFhHpUTabjRSb\nuRWTkZphdTndEhhgPF5PyFAT6lhnn/Ff14vXrL3eTvdb25+gXeA+0LoP4MWL1+vtsPafa23nOxf4\nneE+72/vPx/8+cD9wP+BDf7u4OsEtw08H3gs8DvCtQ33HaGE+1yoc+HanajWzoT6jlCfD1dLJHtX\nTpZ6WERERCSqTqaHRUPtRUREJOYpsIiIiEjMU2ARERGRmKfAIiIiIjFPgUVERERingKLiIiIxDwF\nFhEREYl5CiwiIiIS8xRYREREJOYpsIiIiEjMU2ARERGRmKfAIiIiIjEv0oGlCHgH+ATYAPzfEG3K\ngBpgtW+ZFeGaREREJM5EOrC0AD8CTgcmA98HxoRo9y5Q6lseiHBNEif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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We treat the case $\\delta = 0.02$\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "delta = 0.02\n", - "D = np.array([[-1, 0.5], [0.5, -1]])\n", - "b = np.array([25, 25])\n", - "c1 = c2 = np.array([1, -2, 1])\n", - "e1 = e2 = np.array([10, 10, 3])\n", - "\n", - "delta_1 = 1 - delta" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Recalling that the control and state are\n", - "\n", - "$$\n", - " u_{it} =\n", - " \\begin{bmatrix} \n", - " p_{it} \\\\\n", - " q_{it} \n", - " \\end{bmatrix}\n", - " \\quad \\text{and} \\quad\n", - " x_t =\n", - " \\begin{bmatrix}\n", - " I_{1t} \\\\\n", - " I_{2t} \\\\\n", - " 1\n", - " \\end{bmatrix}\n", - "$$\n", - "\n", - "we set up the matrices as follows:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# == Create matrices needed to compute the Nash feedback equilibrium == #\n", - "\n", - "A = np.array([[delta_1, 0, -delta_1*b[0]],\n", - " [0, delta_1, -delta_1*b[1]],\n", - " [0, 0, 1]])\n", - "\n", - "B1 = delta_1 * np.array([[1, -D[0, 0]],\n", - " [0, -D[1, 0]],\n", - " [0, 0]])\n", - "B2 = delta_1 * np.array([[0, -D[0, 1]],\n", - " [1, -D[1, 1]],\n", - " [0, 0]])\n", - "\n", - "R1 = -np.array([[0.5*c1[2], 0, 0.5*c1[1]],\n", - " [0, 0, 0],\n", - " [0.5*c1[1], 0, c1[0]]])\n", - "R2 = -np.array([[0, 0, 0],\n", - " [0, 0.5*c2[2], 0.5*c2[1]],\n", - " [0, 0.5*c2[1], c2[0]]])\n", - "\n", - "Q1 = np.array([[-0.5*e1[2], 0], [0, D[0, 0]]])\n", - "Q2 = np.array([[-0.5*e2[2], 0], [0, D[1, 1]]])\n", - "\n", - "S1 = np.zeros((2, 2))\n", - "S2 = np.copy(S1)\n", - "\n", - "W1 = np.array([[0, 0],\n", - " [0, 0],\n", - " [-0.5*e1[1], b[0]/2.]])\n", - "W2 = np.array([[0, 0],\n", - " [0, 0],\n", - " [-0.5*e2[1], b[1]/2.]])\n", - "\n", - "M1 = np.array([[0, 0], [0, D[0, 1] / 2.]])\n", - "M2 = np.copy(M1)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now compute the equilibrium using `qe.nnash`" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2)\n", - "\n", - "print(\"\\nFirm 1's feedback rule:\\n\")\n", - "print(F1)\n", - "\n", - "print(\"\\nFirm 2's feedback rule:\\n\")\n", - "print(F2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Firm 1's feedback rule:\n", - "\n", - "[[ 2.43666582e-01 2.72360627e-02 -6.82788293e+00]\n", - " [ 3.92370734e-01 1.39696451e-01 -3.77341073e+01]]\n", - "\n", - "Firm 2's feedback rule:\n", - "\n", - "[[ 2.72360627e-02 2.43666582e-01 -6.82788293e+00]\n", - " [ 1.39696451e-01 3.92370734e-01 -3.77341073e+01]]\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's look at the dynamics of inventories, and reproduce the graph corresponding to $\\delta = 0.02$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "AF = A - B1.dot(F1) - B2.dot(F2)\n", - "n = 25\n", - "x = np.empty((3, n))\n", - "x[:, 0] = 2, 0, 1\n", - "for t in range(n-1):\n", - " x[:, t+1] = np.dot(AF, x[:, t])\n", - "I1 = x[0, :]\n", - "I2 = x[1, :]\n", - "fig, ax = plt.subplots(figsize=(9, 5))\n", - "ax.plot(I1, 'b-', lw=2, alpha=0.75, label='inventories, firm 1')\n", - "ax.plot(I2, 'g-', lw=2, alpha=0.75, label='inventories, firm 2')\n", - "ax.set_title(r'$\\delta = {}$'.format(delta))\n", - "ax.legend()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/numbers.txt b/solutions/numbers.txt deleted file mode 100644 index acd67b486..000000000 --- a/solutions/numbers.txt +++ /dev/null @@ -1,6 +0,0 @@ -prices -3 -8 - -7 -21 \ No newline at end of file diff --git a/solutions/numpy_solutions.ipynb b/solutions/numpy_solutions.ipynb deleted file mode 100644 index a30c69f12..000000000 --- a/solutions/numpy_solutions.ipynb +++ /dev/null @@ -1,353 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:5d40d5ac28199d1ce7bf6b873bf4f8ba1b3446df849b7677095b54f0e8b14ad0" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: NumPy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/numpy.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tell the notebook to display figures embedded in the browser:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import numpy and some plotting functionality:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code does the job" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def p(x, coef):\n", - " X = np.empty(len(coef))\n", - " X[0] = 1\n", - " X[1:] = x\n", - " y = np.cumprod(X) # y = [1, x, x**2,...]\n", - " return np.dot(coef, y)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's test it" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "coef = np.ones(3)\n", - "print(coef)\n", - "print(p(1, coef))\n", - "# For comparison\n", - "q = np.poly1d(coef)\n", - "print(q(1))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[ 1. 1. 1.]\n", - "3.0\n", - "3.0\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's our first pass at a solution:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from numpy import cumsum\n", - "from numpy.random import uniform\n", - "\n", - "class discreteRV:\n", - " \"\"\"\n", - " Generates an array of draws from a discrete random variable with vector of\n", - " probabilities given by q. \n", - " \"\"\"\n", - "\n", - " def __init__(self, q):\n", - " \"\"\"\n", - " The argument q is a NumPy array, or array like, nonnegative and sums\n", - " to 1\n", - " \"\"\"\n", - " self.q = q\n", - " self.Q = cumsum(q)\n", - "\n", - " def draw(self, k=1):\n", - " \"\"\"\n", - " Returns k draws from q. For each such draw, the value i is returned\n", - " with probability q[i].\n", - " \"\"\"\n", - " return self.Q.searchsorted(uniform(0, 1, size=k)) " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The logic is not obvious, but if you take your time and read it slowly, you will understand\n", - "\n", - "There is a problem here, however\n", - "\n", - "Suppose that `q` is altered after an instance of `discreteRV` is created, for example by" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "q = (0.1, 0.9)\n", - "d = discreteRV(q)\n", - "d.q = (0.5, 0.5)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The problem is that `Q` does not change accordingly, and `Q` is the data used in the `draw` method\n", - "\n", - "To deal with this, one option is to compute `Q` every time the draw method is called\n", - "\n", - "But this is inefficient relative to computing `Q` once off\n", - "\n", - "A better option is to use descriptors\n", - "\n", - "A solution from the [quantecon library](https://github.com/jstac/quant-econ/tree/master/quantecon) using descriptors that behaves as we desire can be found [here](https://github.com/jstac/quant-econ/blob/master/quantecon/discrete_rv.py)\n" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An example solution is given below.\n", - "\n", - "In essence we've just taken [this code](https://github.com/jstac/quant-econ/blob/master/quantecon/ecdf.py) from \n", - "[QuantEcon](https://github.com/jstac/quant-econ/tree/master/quantecon) and added in a plot method" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\"\"\"\n", - "Modifies ecdf.py from QuantEcon to add in a plot method\n", - "\n", - "\"\"\"\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "class ECDF(object):\n", - " \"\"\"\n", - " One-dimensional empirical distribution function given a vector of\n", - " observations.\n", - "\n", - " Parameters\n", - " ----------\n", - " observations : array_like\n", - " An array of observations\n", - "\n", - " Attributes\n", - " ----------\n", - " observations : array_like\n", - " An array of observations\n", - "\n", - " \"\"\"\n", - "\n", - " def __init__(self, observations):\n", - " self.observations = np.asarray(observations)\n", - "\n", - " def __call__(self, x):\n", - " \"\"\"\n", - " Evaluates the ecdf at x\n", - "\n", - " Parameters\n", - " ----------\n", - " x : scalar(float)\n", - " The x at which the ecdf is evaluated\n", - "\n", - " Returns\n", - " -------\n", - " scalar(float)\n", - " Fraction of the sample less than x\n", - "\n", - " \"\"\"\n", - " return np.mean(self.observations <= x)\n", - "\n", - " def plot(self, a=None, b=None):\n", - " \"\"\"\n", - " Plot the ecdf on the interval [a, b].\n", - "\n", - " Parameters\n", - " ----------\n", - " a : scalar(float), optional(default=None)\n", - " Lower end point of the plot interval\n", - " b : scalar(float), optional(default=None)\n", - " Upper end point of the plot interval\n", - "\n", - " \"\"\"\n", - "\n", - " # === choose reasonable interval if [a, b] not specified === #\n", - " if a is None:\n", - " a = self.observations.min() - self.observations.std()\n", - " if b is None:\n", - " b = self.observations.max() + self.observations.std()\n", - "\n", - " # === generate plot === #\n", - " x_vals = np.linspace(a, b, num=100)\n", - " f = np.vectorize(self.__call__)\n", - " plt.plot(x_vals, f(x_vals))\n", - " plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's an example of usage" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "X = np.random.randn(1000)\n", - "F = ECDF(X)\n", - "F.plot()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/odu_solutions.ipynb b/solutions/odu_solutions.ipynb deleted file mode 100644 index 924916dad..000000000 --- a/solutions/odu_solutions.ipynb +++ /dev/null @@ -1,667 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:3657e17830750f2d1264e69668c8c58421e2d2ede892e952f3999c1a6edf9559" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Search with Unknown Offer Distribution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/odu.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import compute_fixed_point\n", - "from quantecon.models import SearchProblem" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "This code solves the \"Offer Distribution Unknown\" model by iterating on a guess of the\n", - "reservation wage function. You should find that the run time is much shorter than that of the value function approach in `odu_vfi.py`\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sp = SearchProblem(pi_grid_size=50)\n", - "\n", - "phi_init = np.ones(len(sp.pi_grid)) \n", - "w_bar = compute_fixed_point(sp.res_wage_operator, phi_init)\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 7))\n", - "ax.plot(sp.pi_grid, w_bar, linewidth=2, color='black')\n", - "ax.set_ylim(0, 2)\n", - "ax.grid(axis='x', linewidth=0.25, linestyle='--', color='0.25')\n", - "ax.grid(axis='y', linewidth=0.25, linestyle='--', color='0.25')\n", - "ax.fill_between(sp.pi_grid, 0, w_bar, color='blue', alpha=0.15)\n", - "ax.fill_between(sp.pi_grid, w_bar, 2, color='green', alpha=0.15)\n", - "ax.text(0.42, 1.2, 'reject')\n", - "ax.text(0.7, 1.8, 'accept')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 0.426161\n", - "Computed iterate 2 with error 0.127050" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 0.076090" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 0.046400" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 0.028295" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 0.018182" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 0.013566" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 0.009611" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 0.007113" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 0.005174" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 0.003732" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 0.002657" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 0.001876" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 0.001348" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 0.000965" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next piece of code is not one of the exercises from quant-econ, it's just a fun simulation to see \n", - "what the effect of a change in the underlying distribution on the unemployment rate is.\n", - "\n", - "At a point in the simulation, the distribution becomes significantly worse. It takes a while for agents to learn this, and in the meantime they are too optimistic, and turn down too many jobs. As a result, the unemployment rate spikes.\n", - "\n", - "The code takes a few minutes to run." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy import interp\n", - "# Set up model and compute the function w_bar\n", - "sp = SearchProblem(pi_grid_size=50, F_a=1, F_b=1)\n", - "pi_grid, f, g, F, G = sp.pi_grid, sp.f, sp.g, sp.F, sp.G\n", - "phi_init = np.ones(len(sp.pi_grid)) \n", - "w_bar_vals = compute_fixed_point(sp.res_wage_operator, phi_init)\n", - "w_bar = lambda x: interp(x, pi_grid, w_bar_vals)\n", - "\n", - "\n", - "class Agent(object):\n", - " \"\"\"\n", - " Holds the employment state and beliefs of an individual agent.\n", - " \"\"\"\n", - "\n", - " def __init__(self, pi=1e-3):\n", - " self.pi = pi\n", - " self.employed = 1\n", - "\n", - " def update(self, H):\n", - " \"Update self by drawing wage offer from distribution H.\"\n", - " if self.employed == 0:\n", - " w = H.rvs()\n", - " if w >= w_bar(self.pi):\n", - " self.employed = 1\n", - " else:\n", - " self.pi = 1.0 / (1 + ((1 - self.pi) * g(w)) / (self.pi * f(w)))\n", - "\n", - "\n", - "num_agents = 5000\n", - "separation_rate = 0.025 # Fraction of jobs that end in each period \n", - "separation_num = int(num_agents * separation_rate)\n", - "agent_indices = list(range(num_agents))\n", - "agents = [Agent() for i in range(num_agents)]\n", - "sim_length = 600\n", - "H = G # Start with distribution G\n", - "change_date = 200 # Change to F after this many periods\n", - "\n", - "unempl_rate = []\n", - "for i in range(sim_length):\n", - " if i % 20 == 0:\n", - " print(\"date =\", i)\n", - " if i == change_date:\n", - " H = F\n", - " # Randomly select separation_num agents and set employment status to 0\n", - " np.random.shuffle(agent_indices)\n", - " separation_list = agent_indices[:separation_num]\n", - " for agent_index in separation_list:\n", - " agents[agent_index].employed = 0\n", - " # Update agents\n", - " for agent in agents:\n", - " agent.update(H)\n", - " employed = [agent.employed for agent in agents]\n", - " unempl_rate.append(1 - np.mean(employed))\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 7))\n", - "ax.plot(unempl_rate, lw=2, alpha=0.8, label='unemployment rate')\n", - "ax.axvline(change_date, color=\"red\")\n", - "ax.legend()\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Computed iterate 1 with error 0.426161\n", - "Computed iterate 2 with error 0.127050" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 3 with error 0.076090" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 4 with error 0.046400" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 5 with error 0.028295" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 6 with error 0.018182" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 7 with error 0.013566" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 8 with error 0.009611" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 9 with error 0.007113" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 10 with error 0.005174" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 11 with error 0.003732" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 12 with error 0.002657" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 13 with error 0.001876" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 14 with error 0.001348" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Computed iterate 15 with error 0.000965" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "date = 0\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 20\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 40\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 60\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 80\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 100\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 120\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 140\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 160\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 180\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 200\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 220\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 240\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 260\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 280\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 300\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 320\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 340\n", - "date = " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 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P+jEzdHzwgQ4mXbo4BxO/X47x+4GZM+WiOWMG8P770oz04IPW97afC0XOrJTZ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\niYuTpqoLLpAKzsSJ1oqJEirgKImJMmfNgAHVC2B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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/oop_solutions.ipynb b/solutions/oop_solutions.ipynb deleted file mode 100644 index 9b5c34d99..000000000 --- a/solutions/oop_solutions.ipynb +++ /dev/null @@ -1,135 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:39099b5d6503621c4076c7a6bd2ec02bc59c2747a07ebb54a8b27f1175ed52d6" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Object Oriented Programming" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/python_oop.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "class ECDF(object):\n", - "\n", - " def __init__(self, observations):\n", - " self.observations = observations\n", - "\n", - " def __call__(self, x):\n", - " counter = 0.0\n", - " for obs in self.observations:\n", - " if obs <= x:\n", - " counter += 1\n", - " return counter / len(self.observations)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# == test == #\n", - "\n", - "from random import uniform\n", - "samples = [uniform(0, 1) for i in range(10)]\n", - "F = ECDF(samples)\n", - "\n", - "print(F(0.5)) # Evaluate ecdf at x = 0.5\n", - "\n", - "F.observations = [uniform(0, 1) for i in range(1000)]\n", - "\n", - "print(F(0.5))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.5\n", - "0.486\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "class Polynomial(object):\n", - "\n", - " def __init__(self, coefficients):\n", - " \"\"\"\n", - " Creates an instance of the Polynomial class representing \n", - "\n", - " p(x) = a_0 x^0 + ... + a_N x^N, \n", - " \n", - " where a_i = coefficients[i].\n", - " \"\"\"\n", - " self.coefficients = coefficients\n", - "\n", - " def __call__(self, x):\n", - " \"Evaluate the polynomial at x.\"\n", - " y = 0\n", - " for i, a in enumerate(self.coefficients):\n", - " y += a * x**i \n", - " return y\n", - "\n", - " def differentiate(self):\n", - " \"Reset self.coefficients to those of p' instead of p.\"\n", - " new_coefficients = []\n", - " for i, a in enumerate(self.coefficients):\n", - " new_coefficients.append(i * a)\n", - " # Remove the first element, which is zero\n", - " del new_coefficients[0] \n", - " # And reset coefficients data to new values\n", - " self.coefficients = new_coefficients\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/optgrowth_solutions.ipynb b/solutions/optgrowth_solutions.ipynb deleted file mode 100644 index bc6b9cbfd..000000000 --- a/solutions/optgrowth_solutions.ipynb +++ /dev/null @@ -1,226 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# quant-econ Solutions: Infinite Horizon Dynamic Programming" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/dp_intro.html" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from quantecon import compute_fixed_point\n", - "from quantecon.models import GrowthModel" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 1" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "1 4.297e+00 6.331e-02 \n", - "2 4.080e+00 1.270e-01 \n", - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "1 4.297e+00 6.849e-02 \n", - "2 4.080e+00 1.322e-01 \n", - "3 3.875e+00 1.967e-01 \n", - "4 3.680e+00 2.656e-01 \n", - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "1 4.297e+00 6.273e-02 \n", - "2 4.080e+00 1.269e-01 \n", - "3 3.875e+00 1.882e-01 \n", - "4 3.680e+00 2.572e-01 \n", - "5 3.496e+00 3.295e-01 \n", - "6 3.327e+00 4.016e-01 \n" - ] - }, - { - "data": { - "image/png": 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YDCgoKIDRaHT4Pg8PD8TExCAuLg5xcXHQ6XTaY3BwMIhaPAXa6SRghRBCtIva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\ng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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alpha, beta = 0.65, 0.95\n", - "gm = GrowthModel() \n", - "true_sigma = (1 - alpha * beta) * gm.grid**alpha\n", - "\n", - "fig, ax = plt.subplots(3, 1, figsize=(8, 10))\n", - "\n", - "for i, n in enumerate((2, 4, 6)):\n", - " ax[i].set_ylim(0, 1)\n", - " ax[i].set_xlim(0, 2)\n", - " ax[i].set_yticks((0, 1))\n", - " ax[i].set_xticks((0, 2))\n", - "\n", - " w = 5 * gm.u(gm.grid) - 25 # Initial condition\n", - " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=n, print_skip=1)\n", - " sigma = gm.compute_greedy(v_star)\n", - "\n", - " ax[i].plot(gm.grid, sigma, 'b-', lw=2, alpha=0.8, label='approximate optimal policy')\n", - " ax[i].plot(gm.grid, true_sigma, 'k-', lw=2, alpha=0.8, label='true optimal policy')\n", - " ax[i].legend(loc='upper left')\n", - " ax[i].set_title('{} value function iterations'.format(n))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercise 2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "5 1.303e+00 3.270e-01 \n", - "10 5.142e-01 6.708e-01 \n", - "15 2.883e-01 1.032e+00 \n", - "20 1.690e-01 1.404e+00 \n", - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "5 2.792e+00 3.760e-01 \n", - "10 2.046e+00 7.315e-01 \n", - "15 1.497e+00 1.080e+00 \n", - "20 1.099e+00 1.426e+00 \n", - "Iteration Distance Elapsed (seconds)\n", - "---------------------------------------------\n", - "5 5.839e+00 3.705e-01 \n", - "10 5.247e+00 7.293e-01 \n", - "15 4.695e+00 1.084e+00 \n", - "20 4.189e+00 1.445e+00 \n" - ] - }, - { - "data": { - "image/png": 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1aFEqHo8Dl8tHbOyFDRJDcZpWnMaUi+L4T4PxgsW//mWPsI+KgoQEuyKdhXfS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\nvsG3dSuOfDf7Wjlod24flo2fzG3Ff2o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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy import interp\n", - "\n", - "gm = GrowthModel() \n", - "w = 5 * gm.u(gm.grid) - 25 # To be used as an initial condition\n", - "discount_factors = (0.9, 0.94, 0.98)\n", - "series_length = 25\n", - "\n", - "fig, ax = plt.subplots(figsize=(8,5))\n", - "ax.set_xlabel(\"time\")\n", - "ax.set_ylabel(\"capital\")\n", - "ax.set_ylim(0.10, 0.30)\n", - "\n", - "for beta in discount_factors:\n", - "\n", - " # Compute the optimal policy given the discount factor\n", - " gm.beta = beta\n", - " v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=20)\n", - " sigma = gm.compute_greedy(v_star)\n", - "\n", - " # Compute the corresponding time series for capital\n", - " k = np.empty(series_length)\n", - " k[0] = 0.1\n", - " sigma_function = lambda x: interp(x, gm.grid, sigma)\n", - " for t in range(1, series_length):\n", - " k[t] = gm.f(k[t-1]) - sigma_function(k[t-1])\n", - " ax.plot(k, 'o-', lw=2, alpha=0.75, label=r'$\\beta = {}$'.format(beta))\n", - "\n", - "ax.legend(loc='lower right')\n", - "plt.show()" - ] - }, - { - "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": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/solutions/pandas_solutions.ipynb b/solutions/pandas_solutions.ipynb deleted file mode 100644 index 09e3dd20a..000000000 --- a/solutions/pandas_solutions.ipynb +++ /dev/null @@ -1,143 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:64f4052833189e82a5e08a5f420c03f35541190523433e0cc861059c15e642b9" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/pandas.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Show the plot inline in the browser:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run some imports:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import pandas as pd\n", - "import datetime as dt\n", - "import pandas.io.data as web\n", - "import matplotlib.pyplot as plt" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now the main code" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ticker_list = {'INTC': 'Intel',\n", - " 'MSFT': 'Microsoft',\n", - " 'IBM': 'IBM',\n", - " 'BHP': 'BHP',\n", - " 'RSH': 'RadioShack',\n", - " 'TM': 'Toyota',\n", - " 'AAPL': 'Apple',\n", - " 'AMZN': 'Amazon',\n", - " 'BA': 'Boeing',\n", - " 'QCOM': 'Qualcomm',\n", - " 'KO': 'Coca-Cola',\n", - " 'GOOG': 'Google',\n", - " 'SNE': 'Sony',\n", - " 'PTR': 'PetroChina'}\n", - "\n", - "start = dt.datetime(2013, 1, 1)\n", - "end = dt.datetime.today()\n", - "\n", - "price_change = {}\n", - "\n", - "for ticker in ticker_list:\n", - " prices = web.DataReader(ticker, 'yahoo', start, end)\n", - " closing_prices = prices['Close']\n", - " change = 100 * (closing_prices[-1] - closing_prices[0]) / closing_prices[0]\n", - " name = ticker_list[ticker]\n", - " price_change[name] = change\n", - "\n", - "pc = pd.Series(price_change)\n", - "pc.sort()\n", - "fig, ax = plt.subplots(figsize=(10,8))\n", - "pc.plot(kind='bar', ax=ax)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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kSVp2drQkSZIqcKA1tlQ7QItUO8BIiz4H36XI+cxWxmxlImeD2PnMVsaOliRJ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\nrfT/f99aLw5PIb+hfBjDO2Q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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/pbe_solutions.ipynb b/solutions/pbe_solutions.ipynb deleted file mode 100644 index 59bf723b7..000000000 --- a/solutions/pbe_solutions.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:146d44fe94502737f63b15030f98115c70f0a5640993ee9a6d598a28f6c87000" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Python by Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/python_by_example.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def factorial(n):\n", - " k = 1\n", - " for i in range(n):\n", - " k = k * (i + 1)\n", - " return k\n", - "\n", - "factorial(4)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "24" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from random import uniform\n", - "\n", - "def binomial_rv(n, p):\n", - " count = 0\n", - " for i in range(n):\n", - " U = uniform(0, 1)\n", - " if U < p:\n", - " count = count + 1 # Or count += 1\n", - " return count\n", - "\n", - "binomial_rv(10, 0.5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "6" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider the circle of diameter 1 embedded in the unit square\n", - "\n", - "Let $A$ be its area and let $r=1/2$ be its radius \n", - "\n", - "If we know $\\pi$ then we can compute $A$ via $A = \\pi r^2$\n", - "\n", - "But here the point is to compute $\\pi$, which we can do by $\\pi = A / r^2$\n", - "\n", - "Summary: If we can estimate the area of the unit circle, then dividing by $r^2 = (1/2)^2 = 1/4$\n", - "gives an estimate of $\\pi$\n", - "\n", - "We estimate the area by sampling bivariate uniforms and looking at the fraction that fall into the unit circle\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import division # Omit if using Python 3.x\n", - "from math import sqrt\n", - "\n", - "n = 100000\n", - "\n", - "count = 0\n", - "for i in range(n):\n", - " u, v = uniform(0, 1), uniform(0, 1)\n", - " d = sqrt((u - 0.5)**2 + (v - 0.5)**2)\n", - " if d < 0.5:\n", - " count += 1\n", - "\n", - "area_estimate = count / n\n", - "\n", - "print(area_estimate * 4) # dividing by radius**2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3.14008\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 4" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "payoff = 0\n", - "count = 0\n", - "\n", - "for i in range(10):\n", - " U = uniform(0, 1)\n", - " count = count + 1 if U < 0.5 else 0\n", - " if count == 3:\n", - " payoff = 1\n", - "\n", - "print(payoff)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The next line embeds all subsequent figures in the browser itself" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib.pyplot as plt\n", - "from random import normalvariate\n", - "\n", - "alpha = 0.9\n", - "ts_length = 200\n", - "current_x = 0\n", - "\n", - "x_values = []\n", - "for i in range(ts_length + 1):\n", - " x_values.append(current_x)\n", - " current_x = alpha * current_x + normalvariate(0, 1)\n", - "plt.plot(x_values, 'b-')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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heMXdGpbJlXMPdW0Zt7BMMuzOPVFnqhdqayW+mChuD3ICfOc7Ep7Zt0+u2E1N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\nO4ryctGmRBSSc38RMGX1XwM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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 6" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "alphas = [0.0, 0.8, 0.98]\n", - "ts_length = 200\n", - "\n", - "for alpha in alphas:\n", - " x_values = []\n", - " current_x = 0\n", - " for i in range(ts_length):\n", - " x_values.append(current_x)\n", - " current_x = alpha * current_x + normalvariate(0, 1)\n", - " plt.plot(x_values, label='alpha = ' + str(alpha))\n", - "plt.legend()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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2UDuFNYXkaAuM/mVrq/SvHzhg+fbO0aFGA/X1Xe2T9HRjfQhYRvqd7Z3fKvrJ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\n19tVfv/wU435eIzq80Of078NoFLddx+99uuvVao773Tteq2ltVWl8vF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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/py_adv_feat_solutions.ipynb b/solutions/py_adv_feat_solutions.ipynb deleted file mode 100644 index 3530509bf..000000000 --- a/solutions/py_adv_feat_solutions.ipynb +++ /dev/null @@ -1,220 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:00d3c4a4e3d630d4fdd4d251547c405d48259d94e963483ef895180aa5e81cbe" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: More Language Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/python_advanced_features.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's the standard solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def x(t):\n", - " if t == 0:\n", - " return 0\n", - " if t == 1:\n", - " return 1\n", - " else:\n", - " return x(t-1) + x(t-2)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's test it" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print([x(i) for i in range(10)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One solution is as follows" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def column_iterator(target_file, column_number):\n", - " \"\"\"A generator function for CSV files.\n", - " When called with a file name target_file (string) and column number \n", - " column_number (integer), the generator function returns a generator \n", - " which steps through the elements of column column_number in file\n", - " target_file.\n", - " \"\"\"\n", - " f = open(target_file, 'r')\n", - " for line in f:\n", - " yield line.split(',')[column_number - 1]\n", - " f.close()\n", - "\n", - "dates = column_iterator('test_table.csv', 1) \n", - "\n", - "i = 1\n", - "for date in dates:\n", - " print(date)\n", - " if i == 10:\n", - " break\n", - " i += 1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Date\n", - "2009-05-21\n", - "2009-05-20\n", - "2009-05-19\n", - "2009-05-18\n", - "2009-05-15\n", - "2009-05-14\n", - "2009-05-13\n", - "2009-05-12\n", - "2009-05-11\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's save the data first" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%file numbers.txt\n", - "prices\n", - "3\n", - "8\n", - "\n", - "7\n", - "21" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Overwriting numbers.txt\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "f = open('numbers.txt')\n", - "\n", - "total = 0.0 \n", - "for line in f:\n", - " try:\n", - " total += float(line)\n", - " except ValueError:\n", - " pass\n", - "\n", - "f.close()\n", - "\n", - "print(total)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "39.0\n" - ] - } - ], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/pyess_solutions.ipynb b/solutions/pyess_solutions.ipynb deleted file mode 100644 index cd3de969d..000000000 --- a/solutions/pyess_solutions.ipynb +++ /dev/null @@ -1,462 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:de49037570346c68172d121a401bf7fdc0293e786765a79b1e3ea6888656c8a3" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Python Essentials" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/python_essentials.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import division # Omit for Python 3.x" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "heading", - "level": 4, - "metadata": {}, - "source": [ - "Part 1 solution:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's one possible solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x_vals = [1, 2, 3]\n", - "y_vals = [1, 1, 1]\n", - "sum([x * y for x, y in zip(x_vals, y_vals)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 2, - "text": [ - "6" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This also works" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sum(x * y for x, y in zip(x_vals, y_vals))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "6" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 4, - "metadata": {}, - "source": [ - "Part 2 solution:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One solution is" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sum([x % 2 == 0 for x in range(100)])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "50" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This also works:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sum(x % 2 == 0 for x in range(100))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "50" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some less natural alternatives that nonetheless help to illustrate the flexibility of list comprehensions are" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "len([x for x in range(100) if x % 2 == 0])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 6, - "text": [ - "50" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sum([1 for x in range(100) if x % 2 == 0])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "50" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "heading", - "level": 4, - "metadata": {}, - "source": [ - "Part 3 solution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's one possibility" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pairs = ((2, 5), (4, 2), (9, 8), (12, 10))\n", - "sum([x % 2 == 0 and y % 2 == 0 for x, y in pairs])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "2" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def p(x, coeff):\n", - " return sum(a * x**i for i, a in enumerate(coeff))\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p(1, (2, 4))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "6" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's one solution:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(string):\n", - " count = 0\n", - " for letter in string:\n", - " if letter == letter.upper() and letter.isalpha():\n", - " count += 1\n", - " return count\n", - "f('The Rain in Spain')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "3" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's a solution:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(seq_a, seq_b):\n", - " is_subset = True\n", - " for a in seq_a:\n", - " if a not in seq_b:\n", - " is_subset = False\n", - " return is_subset\n", - "\n", - "# == test == #\n", - "\n", - "print(f([1, 2], [1, 2, 3]))\n", - "print(f([1, 2, 3], [1, 2]))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "True\n", - "False\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course if we use the `sets` data type then the solution is easier" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(seq_a, seq_b):\n", - " return set(seq_a).issubset(set(seq_b))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 5" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def linapprox(f, a, b, n, x):\n", - " \"\"\"\n", - " Evaluates the piecewise linear interpolant of f at x on the interval \n", - " [a, b], with n evenly spaced grid points.\n", - "\n", - " Parameters \n", - " ===========\n", - " f : function\n", - " The function to approximate\n", - "\n", - " x, a, b : scalars (floats or integers) \n", - " Evaluation point and endpoints, with a <= x <= b\n", - "\n", - " n : integer\n", - " Number of grid points\n", - "\n", - " Returns\n", - " =========\n", - " A float. The interpolant evaluated at x\n", - "\n", - " \"\"\"\n", - " length_of_interval = b - a\n", - " num_subintervals = n - 1\n", - " step = length_of_interval / num_subintervals \n", - "\n", - " # === find first grid point larger than x === #\n", - " point = a\n", - " while point <= x:\n", - " point += step\n", - "\n", - " # === x must lie between the gridpoints (point - step) and point === #\n", - " u, v = point - step, point \n", - "\n", - " return f(u) + (x - u) * (f(v) - f(u)) / (v - u)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 14 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/ree_solutions.ipynb b/solutions/ree_solutions.ipynb deleted file mode 100644 index d58bf5a40..000000000 --- a/solutions/ree_solutions.ipynb +++ /dev/null @@ -1,490 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:74697b84bde6bdaa59ce401c9a3df13db7f91c9e4deccbfa69ccf0b066b7a4df" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Rational Expectations Equilibrium" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/rational_expectations.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following solutions were put together by Chase Coleman, Spencer Lyon, Thomas Sargent and John Stachurski" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Common imports for the solutions" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from __future__ import print_function\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll use the LQ class from quantecon" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from quantecon import LQ" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To map a problem into a [discounted optimal linear control problem](http://quant-econ.net/py/lqcontrol.html), we need to define\n", - "\n", - "* state vector $x_t$ and control vector $u_t$\n", - "\n", - "* matrices $A, B, Q, R$ that define preferences and the law of motion for the state\n", - "\n", - "For the state and control vectors we choose\n", - "\n", - "$$\n", - " x_t = \\begin{bmatrix} y_t \\\\ Y_t \\\\ 1 \\end{bmatrix},\n", - " \\qquad\n", - " u_t = y_{t+1} - y_{t}\n", - "$$\n", - "\n", - "For $, B, Q, R$ we set\n", - "\n", - "$$\n", - " A = \n", - " \\begin{bmatrix} \n", - " 1 & 0 & 0 \\\\\n", - " 0 & \\kappa_1 & \\kappa_0 \\\\ \n", - " 0 & 0 & 1 \n", - " \\end{bmatrix},\n", - " \\quad\n", - " B = \\begin{bmatrix} 1 \\\\ 0 \\\\ 0 \\end{bmatrix} ,\n", - " \\quad\n", - " R = \n", - " \\begin{bmatrix} \n", - " 0 & a_1/2 & -a_0/2 \\\\ \n", - " a_1/2 & 0 & 0 \\\\ \n", - " -a_0/2 & 0 & 0 \n", - " \\end{bmatrix},\n", - " \\quad\n", - " Q = \\gamma / 2\n", - "$$\n", - "\n", - "By multiplying out you can confirm that\n", - "\n", - "* $x_t' R x_t + u_t' Q u_t = - r_t$\n", - "\n", - "* $x_{t+1} = A x_t + B u_t$\n", - "\n", - "We'll use the module ``lqcontrol.py`` to solve the firm's problem at the stated parameter values\n", - "\n", - "This will return an LQ policy $F$ with the interpretation $u_t = - F x_t$, or\n", - "\n", - "$$\n", - " y_{t+1} - y_t = - F_0 y_t - F_1 Y_t - F_2\n", - "$$\n", - "\n", - "Matching parameters with $y_{t+1} = h_0 + h_1 y_t + h_2 Y_t$ leads to\n", - "\n", - "$$\n", - " h_0 = -F_2, \\quad h_1 = 1 - F_0, \\quad h_2 = -F_1\n", - "$$\n", - "\n", - "Here's our solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "# == Model parameters == #\n", - "\n", - "a0 = 100\n", - "a1 = 0.05\n", - "beta = 0.95\n", - "gamma = 10.0\n", - "\n", - "# == Beliefs == #\n", - "\n", - "kappa0 = 95.5\n", - "kappa1 = 0.95\n", - "\n", - "# == Formulate the LQ problem == #\n", - "\n", - "A = np.array([[1, 0, 0], [0, kappa1, kappa0], [0, 0, 1]])\n", - "B = np.array([1, 0, 0])\n", - "B.shape = 3, 1\n", - "R = np.array([[0, a1/2, -a0/2], [a1/2, 0, 0], [-a0/2, 0, 0]])\n", - "Q = 0.5 * gamma\n", - "\n", - "# == Solve for the optimal policy == #\n", - "\n", - "lq = LQ(Q, R, A, B, beta=beta)\n", - "P, F, d = lq.stationary_values()\n", - "F = F.flatten()\n", - "out1 = \"F = [{0:.3f}, {1:.3f}, {2:.3f}]\".format(F[0], F[1], F[2])\n", - "h0, h1, h2 = -F[2], 1 - F[0], -F[1]\n", - "out2 = \"(h0, h1, h2) = ({0:.3f}, {1:.3f}, {2:.3f})\".format(h0, h1, h2)\n", - "\n", - "print(out1)\n", - "print(out2)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "F = [0.000, 0.046, -96.949]\n", - "(h0, h1, h2) = (96.949, 1.000, -0.046)\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The implication is that\n", - "\n", - "$$\n", - " y_{t+1} = 96.949 + y_t - 0.046 \\, Y_t\n", - "$$\n", - "\n", - "\n", - "For the case $n > 1$, recall that $Y_t = n y_t$, which, combined with the previous equation, yields\n", - "\n", - "$$\n", - " Y_{t+1} \n", - " = n \\left( 96.949 + y_t - 0.046 \\, Y_t \\right) \n", - " = n 96.949 + (1 - n 0.046) Y_t \n", - "$$" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To determine whether a $\\kappa_0, \\kappa_1$ pair forms the\n", - "aggregate law of motion component of a rational expectations equilibrium, we\n", - "can proceed as follows:\n", - "\n", - "* Determine the corresponding firm law of motion $y_{t+1} = h_0 + h_1 y_t + h_2 Y_t$\n", - "\n", - "* Test whether the associated aggregate law :$Y_{t+1} = n h(Y_t/n, Y_t)$ evaluates to $Y_{t+1} = \\kappa_0 + \\kappa_1 Y_t$\n", - "\n", - "In the second step we can use $Y_t = n y_t = y_t$, so that $Y_{t+1} = n h(Y_t/n, Y_t)$ becomes\n", - "\n", - "$$\n", - " Y_{t+1} = h(Y_t, Y_t) = h_0 + (h_1 + h_2) Y_t\n", - "$$\n", - "\n", - "Hence to test the second step we can test $\\kappa_0 = h_0$ and $\\kappa_1 = h_1 + h_2$\n", - "\n", - "The following code implements this test\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "candidates = (\n", - " (94.0886298678, 0.923409232937),\n", - " (93.2119845412, 0.984323478873),\n", - " (95.0818452486, 0.952459076301)\n", - " )\n", - "\n", - "for kappa0, kappa1 in candidates:\n", - "\n", - " # == Form the associated law of motion == #\n", - " A = np.array([[1, 0, 0], [0, kappa1, kappa0], [0, 0, 1]])\n", - "\n", - " # == Solve the LQ problem for the firm == #\n", - " lq = LQ(Q, R, A, B, beta=beta)\n", - " P, F, d = lq.stationary_values()\n", - " F = F.flatten()\n", - " h0, h1, h2 = -F[2], 1 - F[0], -F[1]\n", - "\n", - " # == Test the equilibrium condition == #\n", - " if np.allclose((kappa0, kappa1), (h0, h1 + h2)):\n", - " print('Equilibrium pair =', kappa0, kappa1)\n", - " print('(h0, h1, h2) = ', h0, h1, h2)\n", - " break\n", - "\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Equilibrium pair = 95.0818452486 0.952459076301\n", - "(h0, h1, h2) = 95.0818139011 1.0 -0.0475409397193\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output tells us that the answer is pair (iii), which implies $(h_0, h_1, h_2) = (95.0819, 1.0000, -.0475)$\n", - "\n", - "(Notice we use `np.allclose` to test equality of floating point numbers, since exact equality is too strict)\n", - "\n", - "Regarding the iterative algorithm, one could loop from a given\n", - "$(\\kappa_0, \\kappa_1)$ pair to the associated firm law and then to a new $(\\kappa_0, \\kappa_1)$ pair\n", - "\n", - "This amounts to implementing the operator $\\Phi$ described in the lecture\n", - "\n", - "(There is in general no guarantee that this iterative process will converge to\n", - "a rational expectations equilibrium)\n" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are asked to write the planner problem as an LQ problem \n", - "\n", - "For the state and control vectors we choose\n", - "\n", - "$$\n", - " x_t = \\begin{bmatrix} Y_t \\\\ 1 \\end{bmatrix},\n", - " \\quad\n", - " u_t = Y_{t+1} - Y_{t}\n", - "$$\n", - "\n", - "For the LQ matrices we set\n", - "\n", - "$$\n", - " A = \\begin{bmatrix} 1 & 0 \\\\ 0 & 1 \\end{bmatrix},\n", - " \\quad\n", - " B = \\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix},\n", - " \\quad\n", - " R = \\begin{bmatrix} a_1/2 & -a_0/2 \\\\ -a_0/2 & 0 \\end{bmatrix},\n", - " \\quad\n", - " Q = \\gamma / 2\n", - "$$\n", - "\n", - "By multiplying out you can confirm that\n", - "\n", - "* $x_t' R x_t + u_t' Q u_t = - s(Y_t, Y_{t+1})$\n", - "\n", - "* $x_{t+1} = A x_t + B u_t$\n", - "\n", - "By obtaining the optimal policy and using $u_t = - F x_t$ or\n", - "\n", - "$$\n", - " Y_{t+1} - Y_t = -F_0 Y_t - F_1 \n", - "$$\n", - "\n", - "we can obtain the implied aggregate law of motion via $\\kappa_0 = -F_1$\n", - "and $\\kappa_1 = 1-F_0$\n", - "\n", - "The Python code to solve this problem is below:\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "# == Formulate the planner's LQ problem == #\n", - "\n", - "A = np.array([[1, 0], [0, 1]])\n", - "B = np.array([[1], [0]])\n", - "R = np.array([[a1 / 2, -a0 / 2], [-a0 / 2, 0]])\n", - "Q = gamma / 2\n", - "\n", - "# == Solve for the optimal policy == #\n", - "\n", - "lq = LQ(Q, R, A, B, beta=beta)\n", - "P, F, d = lq.stationary_values()\n", - "\n", - "# == Print the results == #\n", - "\n", - "F = F.flatten()\n", - "kappa0, kappa1 = -F[1], 1 - F[0]\n", - "print(kappa0, kappa1)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "95.0818452486 0.952459076301\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output yields the same $(\\kappa_0, \\kappa_1)$ pair obtained as an equilibrium from the previous exercise\n" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The monopolist's LQ problem is almost identical to the planner's problem from\n", - "the previous exercise, except that\n", - "\n", - "$$\n", - " R = \\begin{bmatrix} \n", - " a_1 & -a_0/2 \\\\ \n", - " -a_0/2 & 0 \n", - " \\end{bmatrix} \n", - "$$\n", - "\n", - "The problem can be solved as follows\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "A = np.array([[1, 0], [0, 1]])\n", - "B = np.array([[1], [0]])\n", - "R = np.array([[a1, -a0 / 2], [-a0 / 2, 0]])\n", - "Q = gamma / 2\n", - "\n", - "lq = LQ(Q, R, A, B, beta=beta)\n", - "P, F, d = lq.stationary_values()\n", - "\n", - "F = F.flatten()\n", - "m0, m1 = -F[1], 1 - F[0]\n", - "print(m0, m1)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "73.472927865 0.926527070421\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that the law of motion for the monopolist is approximately $Y_{t+1} = 73.4729 + 0.9265 Y_t$\n", - "\n", - "In the rational expectations case the law of motion was approximately\n", - "$Y_{t+1} = 95.0818 + 0.9525 Y_t$\n", - "\n", - "One way to compare these two laws of motion is by their fixed points, which give long run equilibrium output in each case\n", - "\n", - "For laws of the form $Y_{t+1} = c_0 + c_1 Y_t$, the fixed point is $c_0 / (1 - c_1)$\n", - "\n", - "If you crunch the numbers, you will see that the monopolist adopts a lower long run\n", - "quantity than obtained by the competitive market, implying a higher market price\n", - "\n", - "This is analogous to the elementary static-case results\n" - ] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/schelling_solutions.ipynb b/solutions/schelling_solutions.ipynb deleted file mode 100644 index 1b6120ee2..000000000 --- a/solutions/schelling_solutions.ipynb +++ /dev/null @@ -1,212 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:5e1afd01c6098b2b6a7baba17451d4df9ed1533d55b3ed33ccdbcf380e3f1cd6" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Schelling's Segregation Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/schelling.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's one solution that does the job we want. If you feel like a further exercise you can probably speed up some of the computations and then increase the number of agents. \n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from random import uniform, seed\n", - "from math import sqrt\n", - "import matplotlib.pyplot as plt\n", - "\n", - "seed(10) # for reproducible random numbers\n", - "\n", - "class Agent:\n", - "\n", - " def __init__(self, type):\n", - " self.type = type\n", - " self.draw_location()\n", - "\n", - " def draw_location(self):\n", - " self.location = uniform(0, 1), uniform(0, 1)\n", - "\n", - " def get_distance(self, other):\n", - " \"Computes euclidean distance between self and other agent.\"\n", - " a = (self.location[0] - other.location[0])**2\n", - " b = (self.location[1] - other.location[1])**2\n", - " return sqrt(a + b)\n", - "\n", - " def happy(self, agents):\n", - " \"True if sufficient number of nearest neighbors are of the same type.\"\n", - " distances = []\n", - " # distances is a list of pairs (d, agent), where d is distance from\n", - " # agent to self\n", - " for agent in agents:\n", - " if self != agent:\n", - " distance = self.get_distance(agent)\n", - " distances.append((distance, agent))\n", - " # == Sort from smallest to largest, according to distance == #\n", - " distances.sort()\n", - " # == Extract the neighboring agents == #\n", - " neighbors = [agent for d, agent in distances[:num_neighbors]]\n", - " # == Count how many neighbors have the same type as self == #\n", - " num_same_type = sum(self.type == agent.type for agent in neighbors)\n", - " return num_same_type >= require_same_type\n", - "\n", - " def update(self, agents):\n", - " \"If not happy, then randomly choose new locations until happy.\"\n", - " while not self.happy(agents):\n", - " self.draw_location()\n", - "\n", - " \n", - "def plot_distribution(agents, cycle_num):\n", - " \"Plot the distribution of agents after cycle_num rounds of the loop.\"\n", - " x_values_0, y_values_0 = [], []\n", - " x_values_1, y_values_1 = [], []\n", - " # == Obtain locations of each type == #\n", - " for agent in agents:\n", - " x, y = agent.location\n", - " if agent.type == 0:\n", - " x_values_0.append(x)\n", - " y_values_0.append(y)\n", - " else:\n", - " x_values_1.append(x)\n", - " y_values_1.append(y)\n", - " fig, ax = plt.subplots(figsize=(8, 8))\n", - " plot_args = {'markersize' : 8, 'alpha' : 0.6}\n", - " ax.set_axis_bgcolor('azure')\n", - " ax.plot(x_values_0, y_values_0, 'o', markerfacecolor='orange', **plot_args)\n", - " ax.plot(x_values_1, y_values_1, 'o', markerfacecolor='green', **plot_args)\n", - " ax.set_title('Cycle {}'.format(cycle_num - 1))\n", - " plt.show()\n", - "\n", - "# == Main == #\n", - "\n", - "num_of_type_0 = 250\n", - "num_of_type_1 = 250\n", - "num_neighbors = 10 # Number of agents regarded as neighbors\n", - "require_same_type = 7 # Want at least this many neighbors to be same type\n", - "\n", - "# == Create a list of agents == #\n", - "agents = [Agent(0) for i in range(num_of_type_0)]\n", - "agents.extend(Agent(1) for i in range(num_of_type_1))\n", - "\n", - "\n", - "count = 1\n", - "# == Loop until none wishes to move == #\n", - "while 1:\n", - " print('Entering loop ', count)\n", - " plot_distribution(agents, count)\n", - " count += 1\n", - " no_one_moved = True\n", - " for agent in agents:\n", - " old_location = agent.location\n", - " agent.update(agents)\n", - " if agent.location != old_location:\n", - " no_one_moved = False\n", - " if no_one_moved:\n", - " break\n", - " \n", - "print('Converged, terminating.')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Entering loop 1\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Pc5NcOBMKiwl/R2CX9B3ZwuzH//ko7mq4FvzE55pG2fQaVqTWwNBDTrJ2T2MxVDGrXXD5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+/ZNZbxJyVVsc62WfPtOJpTJIZUUVjZuakIqivztGb2Ep0J5Mav6V9AdIh3iR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9RmManUxKuLQq94qObEVa+Y66LciTIf7jzXHuvhFqqoqY8E8yMNZHqWOEv39l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- "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Converged, terminating.\n" - ] - } - ], - "prompt_number": 2 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/scipy_solutions.ipynb b/solutions/scipy_solutions.ipynb deleted file mode 100644 index f8a0da152..000000000 --- a/solutions/scipy_solutions.ipynb +++ /dev/null @@ -1,112 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:da5a4e2755161c4b9d309d2697cd1a0f4c41ef40220265dd07daac825891c8ff" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: SciPy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/scipy.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's a reasonable solution:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def bisect(f, a, b, tol=10e-5):\n", - " \"\"\"\n", - " Implements the bisection root finding algorithm, assuming that f is a\n", - " real-valued function on [a, b] satisfying f(a) < 0 < f(b).\n", - " \"\"\"\n", - " lower, upper = a, b\n", - " if upper - lower < tol:\n", - " return 0.5 * (upper + lower)\n", - " else:\n", - " middle = 0.5 * (upper + lower)\n", - " print('Current mid point = {}'.format(middle))\n", - " if f(middle) > 0: # Implies root is between lower and middle\n", - " bisect(f, lower, middle)\n", - " else: # Implies root is between middle and upper\n", - " bisect(f, middle, upper)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can test it as follows" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "f = lambda x: np.sin(4 * (x - 0.25)) + x + x**20 - 1\n", - "\n", - "bisect(f, 0, 1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Current mid point = 0.5\n", - "Current mid point = 0.25\n", - "Current mid point = 0.375\n", - "Current mid point = 0.4375\n", - "Current mid point = 0.40625\n", - "Current mid point = 0.421875\n", - "Current mid point = 0.4140625\n", - "Current mid point = 0.41015625\n", - "Current mid point = 0.408203125\n", - "Current mid point = 0.4091796875\n", - "Current mid point = 0.40869140625\n", - "Current mid point = 0.408447265625\n", - "Current mid point = 0.4083251953125\n", - "Current mid point = 0.40826416015625\n" - ] - } - ], - "prompt_number": 2 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/short_path_solutions.ipynb b/solutions/short_path_solutions.ipynb deleted file mode 100644 index 2dc6b8c52..000000000 --- a/solutions/short_path_solutions.ipynb +++ /dev/null @@ -1,285 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:f97b7eff16bd2ce0f5f813b45826fad4a21fd16ae441ee3fe59c2e4abdd0aa9f" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Shortest Paths" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/short_path.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you Shift-Enter in the next cell you'll save the data we want to work with in the local directory --- then scroll down for the solution." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%file graph.txt\n", - "node0, node1 0.04, node8 11.11, node14 72.21\n", - "node1, node46 1247.25, node6 20.59, node13 64.94\n", - "node2, node66 54.18, node31 166.80, node45 1561.45\n", - "node3, node20 133.65, node6 2.06, node11 42.43\n", - "node4, node75 3706.67, node5 0.73, node7 1.02\n", - "node5, node45 1382.97, node7 3.33, node11 34.54\n", - "node6, node31 63.17, node9 0.72, node10 13.10\n", - "node7, node50 478.14, node9 3.15, node10 5.85\n", - "node8, node69 577.91, node11 7.45, node12 3.18\n", - "node9, node70 2454.28, node13 4.42, node20 16.53\n", - "node10, node89 5352.79, node12 1.87, node16 25.16\n", - "node11, node94 4961.32, node18 37.55, node20 65.08\n", - "node12, node84 3914.62, node24 34.32, node28 170.04\n", - "node13, node60 2135.95, node38 236.33, node40 475.33\n", - "node14, node67 1878.96, node16 2.70, node24 38.65\n", - "node15, node91 3597.11, node17 1.01, node18 2.57\n", - "node16, node36 392.92, node19 3.49, node38 278.71\n", - "node17, node76 783.29, node22 24.78, node23 26.45\n", - "node18, node91 3363.17, node23 16.23, node28 55.84\n", - "node19, node26 20.09, node20 0.24, node28 70.54\n", - "node20, node98 3523.33, node24 9.81, node33 145.80\n", - "node21, node56 626.04, node28 36.65, node31 27.06\n", - "node22, node72 1447.22, node39 136.32, node40 124.22\n", - "node23, node52 336.73, node26 2.66, node33 22.37\n", - "node24, node66 875.19, node26 1.80, node28 14.25\n", - "node25, node70 1343.63, node32 36.58, node35 45.55\n", - "node26, node47 135.78, node27 0.01, node42 122.00\n", - "node27, node65 480.55, node35 48.10, node43 246.24\n", - "node28, node82 2538.18, node34 21.79, node36 15.52\n", - "node29, node64 635.52, node32 4.22, node33 12.61\n", - "node30, node98 2616.03, node33 5.61, node35 13.95\n", - "node31, node98 3350.98, node36 20.44, node44 125.88\n", - "node32, node97 2613.92, node34 3.33, node35 1.46\n", - "node33, node81 1854.73, node41 3.23, node47 111.54\n", - "node34, node73 1075.38, node42 51.52, node48 129.45\n", - "node35, node52 17.57, node41 2.09, node50 78.81\n", - "node36, node71 1171.60, node54 101.08, node57 260.46\n", - "node37, node75 269.97, node38 0.36, node46 80.49\n", - "node38, node93 2767.85, node40 1.79, node42 8.78\n", - "node39, node50 39.88, node40 0.95, node41 1.34\n", - "node40, node75 548.68, node47 28.57, node54 53.46\n", - "node41, node53 18.23, node46 0.28, node54 162.24\n", - "node42, node59 141.86, node47 10.08, node72 437.49\n", - "node43, node98 2984.83, node54 95.06, node60 116.23\n", - "node44, node91 807.39, node46 1.56, node47 2.14\n", - "node45, node58 79.93, node47 3.68, node49 15.51\n", - "node46, node52 22.68, node57 27.50, node67 65.48\n", - "node47, node50 2.82, node56 49.31, node61 172.64\n", - "node48, node99 2564.12, node59 34.52, node60 66.44\n", - "node49, node78 53.79, node50 0.51, node56 10.89\n", - "node50, node85 251.76, node53 1.38, node55 20.10\n", - "node51, node98 2110.67, node59 23.67, node60 73.79\n", - "node52, node94 1471.80, node64 102.41, node66 123.03\n", - "node53, node72 22.85, node56 4.33, node67 88.35\n", - "node54, node88 967.59, node59 24.30, node73 238.61\n", - "node55, node84 86.09, node57 2.13, node64 60.80\n", - "node56, node76 197.03, node57 0.02, node61 11.06\n", - "node57, node86 701.09, node58 0.46, node60 7.01\n", - "node58, node83 556.70, node64 29.85, node65 34.32\n", - "node59, node90 820.66, node60 0.72, node71 0.67\n", - "node60, node76 48.03, node65 4.76, node67 1.63\n", - "node61, node98 1057.59, node63 0.95, node64 4.88\n", - "node62, node91 132.23, node64 2.94, node76 38.43\n", - "node63, node66 4.43, node72 70.08, node75 56.34\n", - "node64, node80 47.73, node65 0.30, node76 11.98\n", - "node65, node94 594.93, node66 0.64, node73 33.23\n", - "node66, node98 395.63, node68 2.66, node73 37.53\n", - "node67, node82 153.53, node68 0.09, node70 0.98\n", - "node68, node94 232.10, node70 3.35, node71 1.66\n", - "node69, node99 247.80, node70 0.06, node73 8.99\n", - "node70, node76 27.18, node72 1.50, node73 8.37\n", - "node71, node89 104.50, node74 8.86, node91 284.64\n", - "node72, node76 15.32, node84 102.77, node92 133.06\n", - "node73, node83 52.22, node76 1.40, node90 243.00\n", - "node74, node81 1.07, node76 0.52, node78 8.08\n", - "node75, node92 68.53, node76 0.81, node77 1.19\n", - "node76, node85 13.18, node77 0.45, node78 2.36\n", - "node77, node80 8.94, node78 0.98, node86 64.32\n", - "node78, node98 355.90, node81 2.59\n", - "node79, node81 0.09, node85 1.45, node91 22.35\n", - "node80, node92 121.87, node88 28.78, node98 264.34\n", - "node81, node94 99.78, node89 39.52, node92 99.89\n", - "node82, node91 47.44, node88 28.05, node93 11.99\n", - "node83, node94 114.95, node86 8.75, node88 5.78\n", - "node84, node89 19.14, node94 30.41, node98 121.05\n", - "node85, node97 94.51, node87 2.66, node89 4.90\n", - "node86, node97 85.09\n", - "node87, node88 0.21, node91 11.14, node92 21.23\n", - "node88, node93 1.31, node91 6.83, node98 6.12\n", - "node89, node97 36.97, node99 82.12\n", - "node90, node96 23.53, node94 10.47, node99 50.99\n", - "node91, node97 22.17\n", - "node92, node96 10.83, node97 11.24, node99 34.68\n", - "node93, node94 0.19, node97 6.71, node99 32.77\n", - "node94, node98 5.91, node96 2.03\n", - "node95, node98 6.17, node99 0.27\n", - "node96, node98 3.32, node97 0.43, node99 5.87\n", - "node97, node98 0.30\n", - "node98, node99 0.33\n", - "node99, " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Overwriting graph.txt\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's our solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "def read_graph(in_file):\n", - " \"\"\" Read in the graph from the data file. The graph is stored\n", - " as a dictionary, where the keys are the nodes, and the values\n", - " are a list of pairs (d, c), where d is a node and c is a number.\n", - " If (d, c) is in the list for node n, then d can be reached from\n", - " n at cost c.\n", - " \"\"\"\n", - " graph = {}\n", - " infile = open(in_file)\n", - " for line in infile:\n", - " elements = line.split(',')\n", - " node = elements.pop(0).strip()\n", - " graph[node] = []\n", - " if node != 'node99':\n", - " for element in elements:\n", - " destination, cost = element.split()\n", - " graph[node].append((destination.strip(), float(cost)))\n", - " infile.close()\n", - " return graph\n", - "\n", - "def update_J(J, graph):\n", - " \"The Bellman operator.\"\n", - " next_J = {}\n", - " for node in graph:\n", - " if node == 'node99':\n", - " next_J[node] = 0\n", - " else:\n", - " next_J[node] = min(cost + J[dest] for dest, cost in graph[node])\n", - " return next_J\n", - "\n", - "def print_best_path(J, graph):\n", - " \"\"\" Given a cost-to-go function, computes the best path. At each node n, \n", - " the function prints the current location, looks at all nodes that can be \n", - " reached from n, and moves to the node m which minimizes c + J[m], where c \n", - " is the cost of moving to m.\n", - " \"\"\"\n", - " sum_costs = 0\n", - " current_location = 'node0'\n", - " while current_location != 'node99':\n", - " print(current_location)\n", - " running_min = 1e100 # Any big number\n", - " for destination, cost in graph[current_location]:\n", - " cost_of_path = cost + J[destination]\n", - " if cost_of_path < running_min:\n", - " running_min = cost_of_path\n", - " minimizer_cost = cost\n", - " minimizer_dest = destination\n", - " current_location = minimizer_dest\n", - " sum_costs += minimizer_cost\n", - "\n", - " print('node99\\n')\n", - " print('Cost: ', sum_costs)\n", - "\n", - "\n", - "## Main loop\n", - "\n", - "graph = read_graph('graph.txt')\n", - "M = 1e10\n", - "J = {}\n", - "for node in graph:\n", - " J[node] = M\n", - "J['node99'] = 0\n", - "\n", - "while 1:\n", - " next_J = update_J(J, graph)\n", - " if next_J == J:\n", - " break\n", - " else:\n", - " J = next_J\n", - "print_best_path(J, graph)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "node0\n", - "node8\n", - "node11\n", - "node18\n", - "node23\n", - "node33\n", - "node41\n", - "node53\n", - "node56\n", - "node57\n", - "node60\n", - "node67\n", - "node70\n", - "node73\n", - "node76\n", - "node85\n", - "node87\n", - "node88\n", - "node93\n", - "node94\n", - "node96\n", - "node97\n", - "node98\n", - "node99\n", - "\n", - "Cost: 160.55000000000007\n" - ] - } - ], - "prompt_number": 2 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/speed_solutions.ipynb b/solutions/speed_solutions.ipynb deleted file mode 100644 index c31920a22..000000000 --- a/solutions/speed_solutions.ipynb +++ /dev/null @@ -1,352 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:728c30baab8bd9b2a010feb63e9e78bbf9dff69dade2e90e64e8118ed3ba9652" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Need for Speed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/need_for_speed.html" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start with some imports" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from numba import jit" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We let \n", - "\n", - "* 0 represent \"low\"\n", - "* 1 represent \"high\"" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "p, q = 0.1, 0.2 # Prob of leaving low and high state respectively" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's a pure Python version of the function" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def compute_series(n):\n", - " x = np.empty(n, dtype=int)\n", - " x[0] = 1 # Start in state 1\n", - " U = np.random.uniform(0, 1, size=n)\n", - " for t in range(1, n):\n", - " current_x = x[t-1]\n", - " if current_x == 0:\n", - " x[t] = U[t] < p\n", - " else:\n", - " x[t] = U[t] > q\n", - " return x" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's run this code and check that the fraction of time spent in the low state is about 0.666" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n = 100000\n", - "x = compute_series(n)\n", - "print(np.mean(x == 0)) # Fraction of time x is in state 0" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.66725\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's time it" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%timeit compute_series(n)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 loops, best of 3: 198 ms per loop\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next let's implement a Numba version, which is easy" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "compute_series_numba = jit(compute_series)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check we still get the right numbers" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = compute_series_numba(n)\n", - "print(np.mean(x == 0))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.66283\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see the time" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%timeit compute_series_numba(n)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1000 loops, best of 3: 1.77 ms per loop\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a nice speed improvement for one line of code\n", - "\n", - "Now let's implement a Cython version" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%load_ext cythonmagic" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%%cython\n", - "import numpy as np\n", - "from numpy cimport int_t, float_t\n", - "\n", - "def compute_series_cy(int n):\n", - " # == Create NumPy arrays first == #\n", - " x_np = np.empty(n, dtype=int)\n", - " U_np = np.random.uniform(0, 1, size=n)\n", - " # == Now create memoryviews of the arrays == #\n", - " cdef int_t [:] x = x_np\n", - " cdef float_t [:] U = U_np\n", - " # == Other variable declarations == #\n", - " cdef float p = 0.1\n", - " cdef float q = 0.2\n", - " cdef int t\n", - " # == Main loop == #\n", - " x[0] = 1 \n", - " for t in range(1, n):\n", - " current_x = x[t-1]\n", - " if current_x == 0:\n", - " x[t] = U[t] < p\n", - " else:\n", - " x[t] = U[t] > q\n", - " return np.asarray(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "compute_series_cy(10)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0])" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = compute_series_cy(n)\n", - "print(np.mean(x == 0))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.66842\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%timeit compute_series_cy(n)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "100 loops, best of 3: 3.39 ms per loop\n" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Cython implementation is fast, but not as fast as Numba" - ] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/solutions/statd_solutions.ipynb b/solutions/statd_solutions.ipynb deleted file mode 100644 index d631303ff..000000000 --- a/solutions/statd_solutions.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:eaaf9c03d6c37a8e46ed0c66d97861af8fafdbfef1ce39d7383cc2ab548ee7d5" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "quant-econ Solutions: Continuous State Markov Chains" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/stationary_densities.html" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Look ahead estimation of a TAR stationary density, where the TAR model is\n", - "\n", - "$$ X_{t+1} = \\theta |X_t| + (1 - \\theta^2)^{1/2} \\xi_{t+1} $$\n", - "\n", - "and $\\xi_t \\sim N(0,1)$. Try running at n = 10, 100, 1000, 10000 to get an\n", - "idea of the speed of convergence." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.stats import norm, gaussian_kde\n", - "from quantecon import LAE\n", - "\n", - "phi = norm()\n", - "n = 500\n", - "theta = 0.8\n", - "# == Frequently used constants == #\n", - "d = np.sqrt(1 - theta**2) \n", - "delta = theta / d\n", - "\n", - "def psi_star(y):\n", - " \"True stationary density of the TAR Model\"\n", - " return 2 * norm.pdf(y) * norm.cdf(delta * y) \n", - "\n", - "def p(x, y):\n", - " \"Stochastic kernel for the TAR model.\"\n", - " return phi.pdf((y - theta * np.abs(x)) / d) / d\n", - "\n", - "Z = phi.rvs(n)\n", - "X = np.empty(n)\n", - "for t in range(n-1):\n", - " X[t+1] = theta * np.abs(X[t]) + d * Z[t]\n", - "psi_est = LAE(p, X)\n", - "k_est = gaussian_kde(X)\n", - "\n", - "fig, ax = plt.subplots(figsize=(10,7))\n", - "ys = np.linspace(-3, 3, 200)\n", - "ax.plot(ys, psi_star(ys), 'b-', lw=2, alpha=0.6, label='true')\n", - "ax.plot(ys, psi_est(ys), 'g-', lw=2, alpha=0.6, label='look ahead estimate')\n", - "ax.plot(ys, k_est(ys), 'k-', lw=2, alpha=0.6, label='kernel based estimate')\n", - "ax.legend(loc='upper left')\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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pxdtvv01ubi5lZWX87ne/49ZbbwWgpqYGOzs7nJ2dKSsr47e//a3pvKKiIr76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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's one program that does the job" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.stats import lognorm, beta\n", - "\n", - "# == Define parameters == #\n", - "s = 0.2\n", - "delta = 0.1\n", - "a_sigma = 0.4 # A = exp(B) where B ~ N(0, a_sigma)\n", - "alpha = 0.4 # f(k) = k**alpha\n", - "\n", - "phi = lognorm(a_sigma) \n", - "\n", - "def p(x, y):\n", - " \"Stochastic kernel, vectorized in x. Both x and y must be positive.\"\n", - " d = s * x**alpha\n", - " return phi.pdf((y - (1 - delta) * x) / d) / d\n", - "\n", - "n = 1000 # Number of observations at each date t\n", - "T = 40 # Compute density of k_t at 1,...,T\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(11, 8))\n", - "axes = axes.flatten()\n", - "xmax = 6.5\n", - "\n", - "for i in range(4):\n", - " ax = axes[i] \n", - " ax.set_xlim(0, xmax)\n", - " psi_0 = beta(5, 5, scale=0.5, loc=i*2) # Initial distribution\n", - "\n", - " # == Generate matrix s.t. t-th column is n observations of k_t == #\n", - " k = np.empty((n, T))\n", - " A = phi.rvs((n, T))\n", - " k[:, 0] = psi_0.rvs(n)\n", - " for t in range(T-1):\n", - " k[:, t+1] = s * A[:,t] * k[:, t]**alpha + (1 - delta) * k[:, t]\n", - "\n", - " # == Generate T instances of lae using this data, one for each t == #\n", - " laes = [LAE(p, k[:, t]) for t in range(T)]\n", - "\n", - " ygrid = np.linspace(0.01, xmax, 150)\n", - " greys = [str(g) for g in np.linspace(0.0, 0.8, T)]\n", - " greys.reverse()\n", - " for psi, g in zip(laes, greys):\n", - " ax.plot(ygrid, psi(ygrid), color=g, lw=2, alpha=0.6)\n", - " ax.set_xlabel('capital')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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cSgTQtm1z4HptbLwD0qVmRIRfrVYjFAoZcQjXIoayz+LiIqFQiP7+flzXpVQq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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Exercise 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here's a possible solution.\n", - "\n", - "Note the way we use vectorized code to simulate the $k$ time series for one boxplot all at once." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n = 20\n", - "k = 5000\n", - "J = 6\n", - "\n", - "theta = 0.9\n", - "d = np.sqrt(1 - theta**2) \n", - "delta = theta / d\n", - "\n", - "fig, axes = plt.subplots(J, 1, figsize=(10, 4*J))\n", - "initial_conditions = np.linspace(8, 0, J)\n", - "X = np.empty((k, n))\n", - "\n", - "for j in range(J):\n", - " \n", - " axes[j].set_ylim(-4, 8)\n", - " title = 'time series from t = ' + str(initial_conditions[j])\n", - " axes[j].set_title(title)\n", - " \n", - " Z = np.random.randn(k, n)\n", - " X[:,0] = initial_conditions[j]\n", - " for t in range(1, n):\n", - " X[:, t] = theta * np.abs(X[:, t-1]) + d * Z[:, t]\n", - " axes[j].boxplot(X)\n", - "\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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GB/qMVZZRd51ObqnuOZGCi+dokmyE3AqTh9CfzxBatnwLuetRLU0iUsfHN91K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\n333D9PHHwze+UXm4rJsDy2Nsbd8OBx8cplOMsbViRfwhHEateW5k+et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dtne0lbflpJOq\nbxo3aZI0VlIM4VAWuzmu0WjMeYstt6Spff+df349q/056PdLbLP/dlOVb599miRlr9yn6dJLW4lT\nij5Go96HSRolsfsvmjRJyp6DaVYrdcf61FJvX+77M6XYn3WTJknSvOSehKbevtz3Z07s0yRprPjL\nXRoPMT7rJk2SxopJU7Vy35+pty/3/ZlSjH3pOE2SJEkFx2mSJEnqU79J0weBm4CNwOXAwX2vkSRJ\n0hDqN2n6KvCfgOXAj4Dz+l4jSZKkIdRv0nQVsKuY/g5wZJ/LkyRJGkpV9mk6E/hShcuTJEkaGr0M\nbnkVsKzD428H/rWYfgfwGHBZpwVMlUbqmpycZNJzKiVJ0hCo1+vUe7zoXxVDDpwB/AnwW8CjHZ53\nyAFJkjQS5hpyoN/LqLwMeAuwks4JkyRJUhb6rTT9B7APcF9x/9vAn7W9xkqTJEkaCXNVmhwRXJIk\nqeCI4JIkSX0yaZIkSeqBSZMkSVIPTJokSZJ6YNIkSZLUA5MmSZKkHpg0SZIk9cCkSZIkqQcmTZIk\nST0waZIkSeqBSZMkSVIPTJokSZJ6YNIkSZLUA5MmSZKkHpg0SZIk9cCkSZIkqQcmTZIkST0waZIk\nSeqBSZMkSVIPTJokSZJ6YNIkSZLUA5MmSZKkHpg0SZIk9aCKpOkvgF3AoRUs60n1er3KxRnPeCMb\nL+dtM57xjDe4eDlvW6x4/SZNTwdeAmypYF1myWHnGs94oxbLeMYz3vjEy3nbYsXrN2n6X8BfVrEi\nkiRJw6yfpOlVwO3A9ytaF0mSpKFV28PzVwHLOjz+DuDtwEuBB4BbgeOBezu8dgOwvI91lCRJSmUj\nsKLKBf4K8DNCsnQr8DiwGfjFKoNIkiTl5lYqPntOkiRpmFQ1TlOjouVIkiRJkiRJ1fkkob/U9Yni\nPR1YD/wAuAH488jx9gO+Q+ggfyPw/sjxABYB1wH/miDWZsIZldcB300QbymwFriJsD9fGDHWswnb\n1bxtJ/775TzCe/N64DJg38jxzili3VBMV63T5/tQwkknPwK+Svibxoz3+4R9uhN4foWxusX7IOH9\nuRG4HDg4crz3FLE2AP9G+I6LFaspxiDHneJNEc7abn4GXxY5HsDZhL/fDcAHIsf7LK1tu7X4P2a8\nFxC+p68DrgF+LXK85cC3CceILwAHVRiv27E85vfLUHgx8KukS5qW0eolvwT4IXBc5JgHFP8vBq4G\nTowc703AZwhv0thS92+7FDizmF5MtQekuewF/JTqDkidTAA/ppUo/TNwesR4v0L43O1HSLSvAp5R\ncYxOn++/ojXe21uBCyPHew7wLMIXbNVJU6d4L6HVFeJC4m9f+UB0NvDxiLEgfAaupPrPfqd45xO+\nz2LoFO8kwudg7+L+4ZHjlX0IeGfkeHXgt4vplxM+EzHjXVM8DvB64N0Vxut2LK/0+2UYrz33dWBb\nwnh3En6RATxE+EXxtMgxf178vw/h4HRfxFhHAr9D+OLc0xATVUkV52DCB/CTxf0nCNWfFE4GbgF+\nEjHGA4QzUw8gJIQHAFsjxnsOoQr6KKEKMwP8XsUxOn2+X0lIfin+XxU53s2EX50xdIp3FaEKA2H/\nHhk53oOl6SXAPRFjQbxBjrvFi/X90inenxJaAx4v7t8dOV5TDfgD4J8ix/sprR+aS6n2+6VTvGcW\njwP8X+C/Vhiv07H8CCr+fhnGpGmQJgiZ8Xcix9mL8Mf9GSGzvzFirA8Db6H1pR1bg/BhuBb4k8ix\njiF8iX0K+HfgY7SqeLG9mtBcFtN9wF8DtwF3APcT9m0sNxCS0EMJ+/EVVHuA7+aphM8Cxf9PTRBz\nUM4EvpQgznsJ75vTqbay1W4QgxyfTWh+/ATxm1qeCfwGoUWgThiPMIUXEz4Lt0SO8zZa3zEfJHQH\niOkHhPcMhGbyWJX6CVrH8kq/X0yaWpYQ+sacQ8hSY9pFKCMeSfhATkaKcwpwF6G9OlX15wTCm/Xl\nwBtplWJjWExoXvlo8f/DhC+B2PYBfhf4l8hxngGsJnwBPI3wHn1dxHg3E/psfBX4MuF9kyrZbmqQ\n79m47wAeI36y3Yx1FHAJ4YdTDAcQBjk+v/RY7O+ZvyX8WFpBqJL8deR4i4FDCH0l3wL8n8jxml5D\nmvfJJwh9f44CzqVVtY/lTODPCD+qlxA+D1VbAnyOcCx/sO25vr9fTJqCvQk7+R+BdQnjbge+SLxf\nLy8ilCZvJZR5fxP4dKRYTT8t/r8b+Dyho2Estxe3a4r7a6m+j0onLwe+R7Wl+k6OB75FGGn/CUIn\n4hdFjvnJIu5KQmXrh5HjQfj117zywC8REv3cnEFoJo+Z9HZyGdV27i17BiGh30j4jjmS8LmIOcjx\nXbQOfB8n7vcLhO+Xy4vpawg/In4hcszFwKmEPoyxvYDwPQ3h+zP2/vwhoQ/V8YRO71VX0prH8n+g\ndSyv9PvFpCn8MvoEoYlsTYJ4h9EqKe9P6CRa5RkSZW8nlD+PITQn/T/gjyPFgvDLs9kJ9UDCZXZi\ndui/k9Cn6FnF/ZMJ5d/YXkO1fQ26uZnwC3d/wvv0ZOI25ULrgHcU4Ys7xa/dL9Dq4H46aX+4pKjA\nvoxQpXgVob9YbM8sTb+KeN8v1xOaOo4pbrcTfrTETHp/qTR9KvFPGFpH+LEJ4XtmHzpfLqxKJxP6\n49wROQ7AJsIPJAjbGauvX1OzI/1ehE7uf1vhsrsdywf5/ZLEPxHeLDsIB8TXR453IuHXwwbinMba\n7nmE/jcbCP0A3hIxVtlK4p89dwxhuzYQ+sfEbh+HcArrNcQ5nbuTAwkda6s8VXYuf0lryIFLaZ3F\nE8vXingbCGcOVa35+X6M1uf7UEJfrRinBLfHO5PQEfQnwCOExPvLkeP9B7CF1vfLRyPHW0t4v2wg\n/OquqvKzp+/mH1Pt2XOdtu3ThO/NjYSDX5X93zpt396EqsX1hCraZOR4EPpovqHCOO3xyp+942kN\ngfNtQteKWPHOJDQF/rC4va/CWND9WB7z+0WSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJGlPjgIeJM3FZqv0IDBR8TJPIFzb7UHglRUvW5IkjZjNtK60rtn+DTh7QLEnCBcI3SvC\nss8FbgEeAH5GuIhrt4tEvxC4CrgXuAv4P8CyCOskjb0YH3ZJ1WowelWlssURl30UcGOX52qk2W8x\nYvx/hCvQPwV4DmE739HltUuBvwOOLm4PEpIsSZLGyj8AO4GfEw6Gb2b3CkcdeA/wzeI1XwAOAz4D\nbAe+SziYNj2HVmXiZuD354h/Bq2Kx4+B15aeO5OQsNwHXEk4sDftAv6M0HR2S+mxXy6m9wU+BGwB\n7gT+FtiveO4w4ApgW7GOX6NzYnILrX3zALBPsS8uKPbFz4t4LwKuAe4v9sWvl5ZRZ377ruy2Ypse\nLG7/pcvr+vULhL/XOT2+/vmE/SFJ0ti5ldnNcxPsnjT9CDiGUJn4ASFZ+U1gEXAp8MnitQcCPwFO\nL+ZfAdwNHNch7oGExOGZxf2nAs8tpl9VxHh2sZx3EBKPpl3AVwhVkH1LjzWTpg8D64rnlxCSlfcV\nz72fkEQtKm4ndFi3pvZ9Uyc0Zx5XrNdTCcnX64r7ryYkeYeUXt/rvmt3NHtunnttEb/T7T7gyD3M\nu72Icdkcr2u3GvjWPF4vSVI29pQ0rQfOKz3/IeCLpfunANcV039IqNyU/W/gXR3iHkg4uP8esH/b\nc18mVJqa9gIeBp5e3N8FTLbN00yaasBDtBIoCNWfHxfT04SE6hkd1qld+75ZD0yV7v834Oq2eb5F\nSBqbr+9137WbIF6fprJji3U4t4fX/mdCdW6uRFPSAtmnScrDz0rTjxI6BJfvLymmjyY0I5UrHq8l\nVGTaPUxIsv4HcAehyezZpeVcVFrGvcXjR5Tm/0mXdT0cOAD4Xmn+LxOaxQA+CGwCvkpogntrl+V0\nU477NEIzWtmW4vGmXvfdoGwCLgT+eA+vOxb4EvDnzK76SaqISZM0/BoVvv42YIbQPNW8HQS8scvr\nvwq8lHA21s3Ax0rLeUPbcg5kdlWn23rcAzxCaOprzruU0DwGoQr1ZkKl6ZXAm5jf2YPluFvZvU/S\n0cXje5p3PnG6eR2tPk/ttweYu3mubG9CH61ujib0e3o3oT+WpAhMmqTh9zP23FRV6zLd7ovAs4A/\nIhyI9wZ+jdA5vN0vEvouHQg8Tqg87Sye+zvg7bT6OB3M3B3Ky3YRkq81hKoThArVS4vpVxCqJjVC\nYrGzFLcX5e3/EmF7X0M4i+8PCdt6RZfXz+dMuLsJ2zLX3+YzhKS00+0pwO1d5vvvtPbNc4G3AZ/r\n8tojgP8HXAz8fe+rL2m+TJqk4fd+4J2EZqw3FY+1VzkabdPdnn+QkJy8mlBt+Wmx/H06xN2L0I9m\nK6H57cXAnxbPrQM+AHyW0Fn5euC3u6xPp8feSmh2urqY/ypCcgOh4/lVxbp+C/gbQnWsV+U49xH6\nJf0FocL15uL+fV1eP9e+a/dz4L2EprBtwAvmsY578iLCPn0Q+DzwaULn+aYbCIkghATrGEJfrnIV\nS9IQOo9wxsn1hDM89p375ZIkSeNngnDGSzNR+mdaZ6VIkiRlo9+Reh8g9HU4gNDn4AC6d7CUJEka\na28gtKHfRRi9WJIkKTv9XjPpGcC/EjqIbgf+BVhL6ZTX5cuXNzZu3NhnGEmSpCQ2Eq6WsJt+z547\nnnB2y73AE8DlhLM+WpE3bqTRaMz7dv755y9ovoXejGe8YY2X87YZz3jGG1y8nLetn3jA8m5JT79J\n083ACwmXWKgBJ9P9iuOSJEkjq9+kaSNh/JBrge8Xjzm4miRJys6iCpbxTeCjhKuSryP09sn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lW7zqw0KQtWttLwS7RaVrY0HykrablX7WLEs9JUyKHSlHsfqnE9OzD3X9aqlkNGGG8YY41SvNiV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\n04s+7t0LEydmPw/h1ltrL4KaFmm33hom7957GxeF9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-2009-05-01,8848.84,9017.32,8827.13,8977.37,154800,8977.37 -2009-04-30,8615.45,8844.77,8615.45,8828.26,174800,8828.26 -2009-04-28,8678.28,8808.64,8493.77,8493.77,169600,8493.77 -2009-04-27,8783.34,8840.53,8648.51,8726.34,152200,8726.34 -2009-04-24,8832.10,8852.83,8694.92,8707.99,212200,8707.99 -2009-04-23,8776.94,8860.55,8647.80,8847.01,186200,8847.01 -2009-04-22,8777.53,8802.90,8683.27,8727.30,195000,8727.30 -2009-04-21,8802.09,8802.09,8612.76,8711.33,175400,8711.33 -2009-04-20,8899.59,8933.80,8813.72,8924.75,167800,8924.75 -2009-04-17,8854.33,8953.34,8834.63,8907.58,184200,8907.58 -2009-04-16,8848.43,9030.00,8720.62,8755.26,173000,8755.26 -2009-04-15,8777.68,8800.52,8681.03,8742.96,158400,8742.96 -2009-04-14,8955.90,8961.73,8749.92,8842.68,181800,8842.68 -2009-04-13,8930.35,9024.45,8888.10,8924.43,167400,8924.43 -2009-04-10,9041.23,9068.80,8856.69,8964.11,228600,8964.11 -2009-04-09,8665.16,8920.86,8664.26,8916.06,192000,8916.06 -2009-04-08,8746.73,8765.64,8556.75,8595.01,168000,8595.01 -2009-04-07,8838.66,8884.45,8778.92,8832.85,150600,8832.85 -2009-04-06,8856.84,8992.06,8812.36,8857.93,173600,8857.93 -2009-04-03,8814.10,8884.63,8697.17,8749.84,217400,8749.84 -2009-04-02,8453.73,8741.67,8449.87,8719.78,213000,8719.78 -2009-04-01,8173.36,8351.91,8084.62,8351.91,157000,8351.91 -2009-03-31,8199.43,8383.74,8088.45,8109.53,178800,8109.53 -2009-03-30,8621.85,8651.06,8236.08,8236.08,164200,8236.08 -2009-03-27,8711.72,8843.18,8626.97,8626.97,157800,8626.97 -2009-03-26,8430.22,8640.28,8383.99,8636.33,135200,8636.33 -2009-03-25,8499.69,8553.01,8392.56,8479.99,162000,8479.99 -2009-03-24,8334.68,8504.41,8297.27,8488.30,195000,8488.30 -2009-03-23,7943.14,8229.13,7922.55,8215.53,178400,8215.53 -2009-03-19,8017.93,8034.09,7902.49,7945.96,138000,7945.96 -2009-03-18,8006.86,8054.35,7895.28,7972.17,176800,7972.17 -2009-03-17,7767.34,7967.03,7723.94,7949.13,171400,7949.13 -2009-03-16,7630.20,7754.75,7630.20,7704.15,150400,7704.15 -2009-03-13,7301.12,7571.45,7300.87,7569.28,217000,7569.28 -2009-03-12,7320.45,7345.02,7198.25,7198.25,153200,7198.25 -2009-03-11,7165.39,7393.81,7161.85,7376.12,155800,7376.12 -2009-03-10,7059.77,7100.77,7021.28,7054.98,133000,7054.98 -2009-03-09,7191.13,7241.02,7028.49,7086.03,132800,7086.03 -2009-03-06,7328.29,7328.29,7167.07,7173.10,159600,7173.10 -2009-03-05,7336.02,7532.87,7336.02,7433.49,185400,7433.49 -2009-03-04,7146.71,7320.65,7104.63,7290.96,159800,7290.96 -2009-03-03,7177.79,7288.14,7088.47,7229.72,146600,7229.72 -2009-03-02,7454.28,7454.28,7234.96,7280.15,125400,7280.15 -2009-02-27,7463.42,7589.77,7414.40,7568.42,140600,7568.42 -2009-02-26,7470.60,7599.81,7433.06,7457.93,143200,7457.93 -2009-02-25,7368.44,7471.03,7330.44,7461.22,165400,7461.22 -2009-02-24,7266.68,7270.90,7155.16,7268.56,146800,7268.56 -2009-02-23,7314.30,7417.18,7209.43,7376.16,164200,7376.16 -2009-02-20,7544.07,7554.70,7382.33,7416.38,140800,7416.38 -2009-02-19,7604.22,7642.69,7537.56,7557.65,138200,7557.65 -2009-02-18,7539.96,7565.79,7479.18,7534.44,149400,7534.44 -2009-02-17,7690.13,7710.43,7615.94,7645.51,120800,7645.51 -2009-02-16,7732.68,7804.24,7694.73,7750.17,110600,7750.17 -2009-02-13,7789.35,7887.74,7730.27,7779.40,149600,7779.40 -2009-02-12,7842.53,7862.52,7685.68,7705.36,146000,7705.36 -2009-02-10,8066.94,8124.79,7917.27,7945.94,138600,7945.94 -2009-02-09,8178.07,8257.71,7969.03,7969.03,140400,7969.03 -2009-02-06,8054.27,8169.04,8033.24,8076.62,147200,8076.62 -2009-02-05,7985.53,8093.96,7901.04,7949.65,169400,7949.65 -2009-02-04,7897.24,8084.97,7863.65,8038.94,156800,8038.94 -2009-02-03,7862.95,8084.41,7800.80,7825.51,181600,7825.51 -2009-02-02,7908.51,7955.75,7795.27,7873.98,159800,7873.98 -2009-01-30,8142.88,8142.88,7922.39,7994.05,148000,7994.05 -2009-01-29,8201.16,8305.38,8138.99,8251.24,160200,8251.24 -2009-01-28,8052.25,8171.63,7936.59,8106.29,140000,8106.29 -2009-01-27,7782.90,8115.15,7782.07,8061.07,152400,8061.07 -2009-01-26,7714.26,7807.16,7671.04,7682.14,115000,7682.14 -2009-01-23,7965.41,7965.41,7745.25,7745.25,119800,7745.25 -2009-01-22,7988.30,8051.74,7809.89,8051.74,142600,8051.74 -2009-01-21,7949.96,8009.22,7829.30,7901.64,147000,7901.64 -2009-01-20,8187.14,8190.42,7962.46,8065.79,128000,8065.79 -2009-01-19,8318.26,8351.68,8221.84,8256.85,102600,8256.85 -2009-01-16,8125.20,8283.91,8067.47,8230.15,140600,8230.15 -2009-01-15,8309.38,8309.38,7997.73,8023.31,158400,8023.31 -2009-01-14,8425.75,8516.07,8359.16,8438.45,132800,8438.45 -2009-01-13,8732.63,8732.93,8405.50,8413.91,135600,8413.91 -2009-01-09,8932.71,8956.85,8773.20,8836.80,137000,8836.80 -2009-01-08,9143.21,9148.83,8876.42,8876.42,150000,8876.42 -2009-01-07,9133.80,9325.35,9106.05,9239.24,205600,9239.24 -2009-01-06,9130.01,9171.03,9029.94,9080.84,154800,9080.84 -2009-01-05,8991.21,9127.38,8987.36,9043.12,85000,9043.12 -2008-12-30,8716.28,8859.56,8702.95,8859.56,60800,8859.56 -2008-12-29,8726.31,8763.67,8638.60,8747.17,83200,8747.17 -2008-12-26,8642.14,8740.76,8611.36,8739.52,75800,8739.52 -2008-12-25,8531.51,8599.50,8531.16,8599.50,61200,8599.50 -2008-12-24,8630.25,8631.83,8476.69,8517.10,100800,8517.10 -2008-12-22,8602.50,8751.18,8593.76,8723.78,109800,8723.78 -2008-12-19,8640.22,8743.22,8570.56,8588.52,138000,8588.52 -2008-12-18,8565.16,8728.36,8534.84,8667.23,138800,8667.23 -2008-12-17,8658.22,8741.24,8425.66,8612.52,148400,8612.52 -2008-12-16,8608.40,8634.26,8471.24,8568.02,132200,8568.02 -2008-12-15,8349.85,8700.17,8349.85,8664.66,129200,8664.66 -2008-12-12,8599.12,8610.73,8087.99,8235.87,185600,8235.87 -2008-12-11,8642.26,8720.55,8519.11,8720.55,162200,8720.55 -2008-12-10,8376.00,8704.92,8376.00,8660.24,155600,8660.24 -2008-12-09,8362.37,8499.60,8314.85,8395.87,150800,8395.87 -2008-12-08,7970.69,8358.27,7959.01,8329.05,139000,8329.05 -2008-12-05,7975.05,8024.33,7908.65,7917.51,139400,7917.51 -2008-12-04,8030.20,8107.69,7849.84,7924.24,148000,7924.24 -2008-12-03,7965.31,8056.38,7889.82,8004.10,121200,8004.10 -2008-12-02,8266.32,8266.32,7863.69,7863.69,136000,7863.69 -2008-12-01,8464.36,8464.36,8307.28,8397.22,107000,8397.22 -2008-11-28,8400.05,8518.13,8336.57,8512.27,145400,8512.27 -2008-11-27,8311.24,8458.68,8300.49,8373.39,116600,8373.39 -2008-11-26,8229.72,8317.83,8149.56,8213.22,118800,8213.22 -2008-11-25,8026.06,8356.83,8025.69,8323.93,155000,8323.93 -2008-11-21,7600.35,7994.68,7406.18,7910.79,185200,7910.79 -2008-11-20,8149.77,8149.79,7703.04,7703.04,154400,7703.04 -2008-11-19,8309.35,8370.09,8115.71,8273.22,143800,8273.22 -2008-11-18,8415.60,8440.41,8302.24,8328.41,144200,8328.41 -2008-11-17,8366.88,8767.98,8218.82,8522.58,146000,8522.58 -2008-11-14,8378.13,8689.85,8378.13,8462.39,155600,8462.39 -2008-11-13,8564.47,8564.47,8148.30,8238.64,166600,8238.64 -2008-11-12,8694.91,8782.48,8574.20,8695.51,153800,8695.51 -2008-11-11,8965.29,9056.31,8704.56,8809.30,153800,8809.30 -2008-11-10,8711.99,9106.29,8711.99,9081.43,155400,9081.43 -2008-11-07,8774.49,8868.10,8266.09,8583.00,206400,8583.00 -2008-11-06,9373.65,9380.30,8806.71,8899.14,176000,8899.14 -2008-11-05,9224.05,9521.24,9216.30,9521.24,208600,9521.24 -2008-11-04,8702.77,9142.29,8699.77,9114.60,164800,9114.60 -2008-10-31,8958.22,9012.31,8576.98,8576.98,206000,8576.98 -2008-10-30,8269.71,9030.85,8269.71,9029.76,220600,9029.76 -2008-10-29,7741.52,8211.90,7741.52,8211.90,222800,8211.90 -2008-10-28,7143.34,7626.42,6994.90,7621.92,240800,7621.92 -2008-10-27,7568.36,7878.97,7141.27,7162.90,237400,7162.90 -2008-10-24,8391.04,8391.04,7647.07,7649.08,194600,7649.08 -2008-10-23,8547.79,8547.79,8016.61,8460.98,216800,8460.98 -2008-10-22,9198.14,9198.14,8674.69,8674.69,160600,8674.69 -2008-10-21,9139.26,9358.51,9135.41,9306.25,154800,9306.25 -2008-10-20,8775.24,9038.45,8687.70,9005.59,165600,9005.59 -2008-10-17,8579.57,8763.71,8539.51,8693.82,171400,8693.82 -2008-10-16,9400.85,9400.85,8458.45,8458.45,186200,8458.45 -2008-10-15,9390.50,9601.30,9269.49,9547.47,185000,9547.47 -2008-10-14,8407.94,9455.62,8407.94,9447.57,169400,9447.57 -2008-10-10,9016.34,9016.34,8115.41,8276.43,247200,8276.43 -2008-10-09,9168.16,9443.45,9100.93,9157.49,213400,9157.49 -2008-10-08,10011.64,10011.64,9159.81,9203.32,205400,9203.32 -2008-10-07,10328.54,10363.14,9916.21,10155.90,210800,10155.90 -2008-10-06,10817.27,10839.50,10374.38,10473.09,185000,10473.09 -2008-10-03,11052.10,11099.73,10938.14,10938.14,174400,10938.14 -2008-10-02,11423.13,11452.39,11143.79,11154.76,156400,11154.76 -2008-10-01,11396.61,11456.64,11314.28,11368.26,142400,11368.26 -2008-09-30,11565.70,11565.70,11160.83,11259.86,165000,11259.86 -2008-09-29,11883.25,12062.67,11721.05,11743.61,122400,11743.61 -2008-09-26,12026.34,12082.64,11788.73,11893.16,131400,11893.16 -2008-09-25,11925.71,12025.41,11835.28,12006.53,113600,12006.53 -2008-09-24,12031.98,12115.03,11904.60,12115.03,152600,12115.03 -2008-09-22,12037.89,12263.95,12037.89,12090.59,150400,12090.59 -2008-09-19,11631.60,11920.86,11615.20,11920.86,184600,11920.86 -2008-09-18,11576.94,11577.88,11301.46,11489.30,163000,11489.30 -2008-09-17,11737.62,11880.03,11708.70,11749.79,158000,11749.79 -2008-09-16,12028.45,12028.45,11551.40,11609.72,184000,11609.72 -2008-09-12,12256.78,12277.57,12059.09,12214.76,194600,12214.76 -2008-09-11,12237.52,12259.02,12081.51,12102.50,131600,12102.50 -2008-09-10,12249.14,12404.67,12159.97,12346.63,155200,12346.63 -2008-09-09,12529.96,12529.96,12335.74,12400.65,119600,12400.65 -2008-09-08,12359.93,12671.76,12352.35,12624.46,135000,12624.46 -2008-09-05,12385.65,12385.65,12163.33,12212.23,155600,12212.23 -2008-09-04,12627.64,12660.57,12514.26,12557.66,145200,12557.66 -2008-09-03,12703.36,12767.50,12647.29,12689.59,129200,12689.59 -2008-09-02,12779.89,12920.52,12491.07,12609.47,126600,12609.47 -2008-09-01,12936.81,12940.55,12834.18,12834.18,87600,12834.18 -2008-08-29,12925.45,13079.37,12918.49,13072.87,120800,13072.87 -2008-08-28,12827.72,12847.46,12718.53,12768.25,89600,12768.25 -2008-08-27,12734.39,12783.63,12681.98,12752.96,87000,12752.96 -2008-08-26,12711.03,12801.21,12656.09,12778.71,87000,12778.71 -2008-08-25,12797.54,12949.33,12797.54,12878.66,86600,12878.66 -2008-08-22,12727.37,12732.69,12631.94,12666.04,87800,12666.04 -2008-08-21,12885.34,12885.34,12723.83,12752.21,105200,12752.21 -2008-08-20,12753.98,12923.66,12753.98,12851.69,110400,12851.69 -2008-08-19,13016.50,13016.50,12782.10,12865.05,103200,12865.05 -2008-08-18,12971.49,13270.37,12934.22,13165.45,114000,13165.45 -2008-08-15,12991.91,13029.58,12952.21,13019.41,94800,13019.41 -2008-08-14,12942.61,13090.68,12926.98,12956.80,116400,12956.80 -2008-08-13,13205.64,13205.64,12953.34,13023.05,125200,13023.05 -2008-08-12,13397.99,13420.10,13276.15,13303.60,125200,13303.60 -2008-08-11,13259.46,13468.81,13259.46,13430.91,113800,13430.91 -2008-08-08,13026.53,13259.73,12962.82,13168.41,162400,13168.41 -2008-08-07,13257.99,13257.99,13034.15,13124.99,136000,13124.99 -2008-08-06,13059.43,13295.51,13048.97,13254.89,156400,13254.89 -2008-08-05,12957.01,13049.58,12893.34,12914.66,144600,12914.66 -2008-08-04,13083.28,13113.94,12910.17,12933.18,142000,12933.18 -2008-08-01,13276.57,13294.17,13039.21,13094.59,135000,13094.59 -2008-07-31,13410.40,13467.67,13256.38,13376.81,137000,13376.81 -2008-07-30,13267.37,13372.28,13264.08,13367.79,109000,13367.79 -2008-07-29,13220.33,13220.33,13018.22,13159.45,106400,13159.45 -2008-07-28,13407.36,13468.94,13327.12,13353.78,93600,13353.78 -2008-07-25,13452.37,13469.83,13324.22,13334.76,109600,13334.76 -2008-07-24,13411.28,13603.31,13393.57,13603.31,126200,13603.31 -2008-07-23,13259.65,13388.63,13238.55,13312.93,127200,13312.93 -2008-07-22,12944.56,13184.96,12921.02,13184.96,112400,13184.96 -2008-07-18,12976.22,12999.64,12762.33,12803.70,113200,12803.70 -2008-07-17,12889.80,12929.74,12852.93,12887.95,111000,12887.95 -2008-07-16,12725.12,12815.40,12671.34,12760.80,122400,12760.80 -2008-07-15,12902.13,12902.13,12715.81,12754.56,118800,12754.56 -2008-07-14,13022.29,13185.90,12969.93,13010.16,123800,13010.16 -2008-07-11,13063.50,13164.10,12918.22,13039.69,146200,13039.69 -2008-07-10,12934.31,13139.85,12930.32,13067.21,120200,13067.21 -2008-07-09,13169.89,13284.65,13038.77,13052.13,123000,13052.13 -2008-07-08,13286.50,13294.97,12984.54,13033.10,121800,13033.10 -2008-07-07,13212.80,13409.30,13169.55,13360.04,107000,13360.04 -2008-07-04,13285.49,13288.55,13135.46,13237.89,117400,13237.89 -2008-07-03,13161.78,13326.95,13118.89,13265.40,154200,13265.40 -2008-07-02,13489.87,13489.87,13247.05,13286.37,136600,13286.37 -2008-07-01,13514.86,13576.41,13448.35,13463.20,121400,13463.20 -2008-06-30,13584.51,13598.48,13454.28,13481.38,123600,13481.38 -2008-06-27,13605.26,13605.56,13453.35,13544.36,127800,13544.36 -2008-06-26,13845.41,13950.56,13798.05,13822.32,114400,13822.32 -2008-06-25,13820.78,13833.23,13635.68,13829.92,123600,13829.92 -2008-06-24,13766.28,13877.49,13738.39,13849.56,100200,13849.56 -2008-06-23,13769.44,13920.75,13667.84,13857.47,117600,13857.47 -2008-06-20,14171.02,14190.00,13903.21,13942.08,129600,13942.08 -2008-06-19,14324.71,14324.71,14069.16,14130.17,130800,14130.17 -2008-06-18,14301.36,14469.99,14301.36,14452.82,110800,14452.82 -2008-06-17,14387.00,14387.00,14299.67,14348.37,110800,14348.37 -2008-06-16,14118.23,14369.09,14103.50,14354.37,120000,14354.37 -2008-06-13,14011.12,14041.34,13810.38,13973.73,230400,13973.73 -2008-06-12,14010.32,14010.32,13826.07,13888.60,133400,13888.60 -2008-06-11,14137.54,14194.48,13993.57,14183.48,131000,14183.48 -2008-06-10,14281.36,14308.89,13983.56,14021.17,120600,14021.17 -2008-06-09,14275.34,14278.84,14117.79,14181.38,122000,14181.38 -2008-06-06,14530.36,14601.27,14489.44,14489.44,150600,14489.44 -2008-06-05,14392.59,14392.59,14262.02,14341.12,152000,14341.12 -2008-06-04,14270.07,14435.57,14250.11,14435.57,168800,14435.57 -2008-06-03,14275.61,14289.47,14127.75,14209.17,152600,14209.17 -2008-06-02,14342.96,14461.03,14189.97,14440.14,000,14440.14 -2008-05-30,14195.66,14366.63,14192.17,14338.54,166600,14338.54 -2008-05-29,13832.65,14147.89,13832.65,14124.47,128800,14124.47 -2008-05-28,13937.14,13979.39,13665.57,13709.44,132400,13709.44 -2008-05-27,13750.82,13931.23,13750.82,13893.31,100200,13893.31 -2008-05-26,13875.98,13883.51,13670.92,13690.19,117800,13690.19 -2008-05-23,13945.10,14157.24,13925.38,14012.20,146400,14012.20 -2008-05-22,13772.65,13984.81,13658.02,13978.46,159200,13978.46 -2008-05-21,14002.52,14041.24,13847.18,13926.30,160200,13926.30 -2008-05-20,14220.12,14286.67,14121.92,14160.09,159800,14160.09 -2008-05-19,14294.52,14343.19,14219.08,14269.61,133800,14269.61 -2008-05-16,14363.62,14392.53,14194.91,14219.48,144200,14219.48 -2008-05-15,14167.02,14352.84,14167.02,14251.74,160200,14251.74 -2008-05-14,13961.96,14121.94,13877.40,14118.55,142400,14118.55 -2008-05-13,13814.39,13976.92,13734.50,13953.73,129200,13953.73 -2008-05-12,13565.91,13793.41,13540.68,13743.36,103000,13743.36 -2008-05-09,13941.30,13946.51,13639.99,13655.34,130400,13655.34 -2008-05-08,14008.19,14036.31,13930.28,13943.26,120400,13943.26 -2008-05-07,14147.57,14208.67,14022.79,14102.48,139800,14102.48 -2008-05-02,13944.26,14072.92,13944.26,14049.26,114000,14049.26 -2008-05-01,13802.59,13884.63,13727.07,13766.86,110800,13766.86 -2008-04-30,13802.94,13976.10,13766.24,13849.99,143200,13849.99 -2008-04-28,13907.97,14003.28,13745.61,13894.37,139400,13894.37 -2008-04-25,13614.53,13886.37,13614.53,13863.47,127200,13863.47 -2008-04-24,13613.83,13654.78,13497.16,13540.87,108800,13540.87 -2008-04-23,13455.56,13717.05,13449.04,13579.16,112200,13579.16 -2008-04-22,13587.51,13608.17,13519.12,13547.82,100800,13547.82 -2008-04-21,13639.85,13739.44,13639.77,13696.55,124600,13696.55 -2008-04-18,13426.26,13485.04,13323.74,13476.45,102400,13476.45 -2008-04-17,13315.83,13495.94,13313.06,13398.30,127200,13398.30 -2008-04-16,13130.66,13222.43,13112.07,13146.13,121200,13146.13 -2008-04-15,12952.65,13052.82,12875.92,12990.58,112400,12990.58 -2008-04-14,13132.67,13132.67,12858.63,12917.51,101000,12917.51 -2008-04-11,13061.77,13329.40,13040.35,13323.73,147600,13323.73 -2008-04-10,13029.81,13062.46,12898.49,12945.30,128000,12945.30 -2008-04-09,13295.16,13348.38,12998.54,13111.89,117200,13111.89 -2008-04-08,13373.96,13402.91,13225.76,13250.43,110600,13250.43 -2008-04-07,13240.56,13485.90,13228.86,13450.23,125200,13450.23 -2008-04-04,13286.35,13360.81,13220.00,13293.22,122000,13293.22 -2008-04-03,13190.16,13389.90,13137.10,13389.90,142600,13389.90 -2008-04-02,12836.41,13189.36,12836.41,13189.36,145000,13189.36 -2008-04-01,12539.80,12779.14,12521.84,12656.42,114600,12656.42 -2008-03-31,12709.28,12709.28,12430.63,12525.54,124000,12525.54 -2008-03-28,12594.34,12874.45,12507.68,12820.47,122600,12820.47 -2008-03-27,12618.42,12621.56,12475.88,12604.58,114200,12604.58 -2008-03-26,12648.97,12711.78,12591.01,12706.63,107400,12706.63 -2008-03-25,12639.74,12791.24,12572.77,12745.22,130200,12745.22 -2008-03-24,12473.06,12582.46,12438.20,12480.09,108400,12480.09 -2008-03-21,12331.64,12496.41,12308.03,12482.57,122000,12482.57 -2008-03-19,12142.33,12374.75,12142.33,12260.44,143800,12260.44 -2008-03-18,11828.99,11995.06,11793.60,11964.16,157800,11964.16 -2008-03-17,12089.03,12132.69,11691.00,11787.51,172600,11787.51 -2008-03-14,12509.81,12582.57,12167.09,12241.60,232000,12241.60 -2008-03-13,12741.26,12772.37,12351.72,12433.44,143400,12433.44 -2008-03-12,12841.88,13071.22,12799.42,12861.13,141000,12861.13 -2008-03-11,12392.31,12674.89,12352.79,12658.28,162200,12658.28 -2008-03-10,12716.84,12777.07,12527.07,12532.13,151200,12532.13 -2008-03-07,13024.61,13024.61,12744.52,12782.80,144200,12782.80 -2008-03-06,13068.89,13365.22,13050.55,13215.42,134200,13215.42 -2008-03-05,12973.03,13044.01,12919.81,12972.06,128800,12972.06 -2008-03-04,13080.29,13110.39,12883.07,12992.28,138600,12992.28 -2008-03-03,13412.87,13413.63,12992.18,12992.18,142400,12992.18 -2008-02-29,13735.44,13738.56,13533.25,13603.02,126600,13603.02 -2008-02-28,13877.88,13962.30,13794.71,13925.51,118400,13925.51 -2008-02-27,14007.32,14105.47,13956.44,14031.30,127400,14031.30 -2008-02-26,14036.89,14053.85,13803.46,13824.72,133800,13824.72 -2008-02-25,13612.31,13969.18,13612.31,13914.57,152000,13914.57 -2008-02-22,13530.19,13540.62,13378.72,13500.46,144200,13500.46 -2008-02-21,13462.72,13783.97,13439.59,13688.28,143000,13688.28 -2008-02-20,13729.75,13729.75,13310.37,13310.37,163600,13310.37 -2008-02-19,13761.76,13853.21,13691.88,13757.91,147400,13757.91 -2008-02-15,13508.53,13666.68,13356.39,13622.56,154800,13622.56 -2008-02-14,13254.59,13626.45,13251.86,13626.45,147200,13626.45 -2008-02-13,13162.95,13240.26,13036.62,13068.30,138800,13068.30 -2008-02-12,12998.87,13138.28,12923.42,13021.96,147600,13021.96 -2008-02-08,13143.08,13279.52,12997.88,13017.24,170600,13017.24 -2008-02-07,13077.25,13244.19,12972.55,13207.15,163000,13207.15 -2008-02-06,13548.53,13552.19,13099.24,13099.24,176800,13099.24 -2008-02-05,13806.17,13821.92,13665.64,13745.50,133600,13745.50 -2008-02-04,13642.60,13889.24,13642.60,13859.70,138200,13859.70 -2008-02-01,13517.74,13648.39,13444.08,13497.16,142600,13497.16 -2008-01-31,13227.48,13622.68,13154.77,13592.47,165200,13592.47 -2008-01-30,13500.52,13514.13,13271.13,13345.03,152400,13345.03 -2008-01-29,13246.69,13506.81,13224.66,13478.86,147000,13478.86 -2008-01-28,13482.84,13501.86,13087.91,13087.91,152800,13087.91 -2008-01-25,13258.77,13647.16,13248.89,13629.16,178800,13629.16 -2008-01-24,12952.50,13134.77,12952.50,13092.78,189600,13092.78 -2008-01-23,12756.31,13063.78,12619.78,12829.06,178000,12829.06 -2008-01-22,13125.23,13125.23,12572.68,12573.05,199000,12573.05 -2008-01-21,13701.43,13704.65,13320.51,13325.94,152600,13325.94 -2008-01-18,13577.50,13902.64,13365.32,13861.29,191000,13861.29 -2008-01-17,13596.38,13803.08,13472.45,13783.45,192800,13783.45 -2008-01-16,13796.73,13841.93,13500.59,13504.51,204600,13504.51 -2008-01-15,14134.91,14224.00,13915.15,13972.63,163800,13972.63 -2008-01-11,14419.43,14447.49,14096.54,14110.79,175400,14110.79 -2008-01-10,14546.32,14584.73,14388.11,14388.11,130200,14388.11 -2008-01-09,14364.71,14602.65,14271.57,14599.16,155400,14599.16 -2008-01-08,14429.30,14547.80,14365.86,14528.67,146800,14528.67 -2008-01-07,14549.38,14667.85,14438.61,14500.55,139800,14500.55 -2008-01-04,15155.73,15156.66,14542.58,14691.41,98800,14691.41 -2007-12-28,15413.37,15413.37,15240.96,15307.78,61200,15307.78 -2007-12-27,15616.41,15628.31,15535.51,15564.69,93000,15564.69 -2007-12-26,15613.96,15653.54,15559.47,15653.54,94000,15653.54 -2007-12-25,15441.33,15583.39,15441.33,15552.59,90600,15552.59 -2007-12-21,15044.35,15275.61,14998.01,15257.00,135400,15257.00 -2007-12-20,15151.37,15161.66,15017.97,15031.60,108600,15031.60 -2007-12-19,15165.09,15267.75,15030.51,15030.51,118200,15030.51 -2007-12-18,15099.20,15301.69,15004.41,15207.86,139800,15207.86 -2007-12-17,15433.30,15508.50,15219.07,15249.79,113200,15249.79 -2007-12-14,15547.19,15697.05,15433.77,15514.51,200200,15514.51 -2007-12-13,15818.48,15833.10,15532.53,15536.52,141000,15536.52 -2007-12-12,15828.65,15963.43,15700.15,15932.26,142800,15932.26 -2007-12-11,16003.09,16075.61,15972.45,16044.72,102800,16044.72 -2007-12-10,16007.33,16017.14,15826.25,15924.39,116200,15924.39 -2007-12-07,15992.24,16107.65,15948.54,15956.37,146000,15956.37 -2007-12-06,15782.40,15898.26,15740.94,15874.08,129200,15874.08 -2007-12-05,15418.95,15621.54,15365.39,15608.88,137000,15608.88 -2007-12-04,15613.89,15683.18,15446.47,15480.19,124200,15480.19 -2007-12-03,15747.47,15799.69,15577.69,15628.97,137600,15628.97 -2007-11-30,15520.53,15751.20,15520.53,15680.67,171200,15680.67 -2007-11-29,15339.61,15555.04,15339.61,15513.74,129800,15513.74 -2007-11-28,15271.14,15280.91,15089.35,15153.78,134200,15153.78 -2007-11-27,14953.93,15312.55,14801.87,15222.85,159200,15222.85 -2007-11-26,14921.57,15295.21,14912.83,15135.21,146000,15135.21 -2007-11-22,14726.62,15000.18,14669.85,14888.77,161200,14888.77 -2007-11-21,15113.83,15154.31,14770.22,14837.66,149000,14837.66 -2007-11-20,14868.98,15222.24,14751.27,15211.52,184600,15211.52 -2007-11-19,15177.17,15302.76,15040.10,15042.56,125400,15042.56 -2007-11-16,15238.73,15238.73,15030.02,15154.61,118800,15154.61 -2007-11-15,15536.35,15587.31,15396.30,15396.30,129800,15396.30 -2007-11-14,15311.38,15504.99,15287.26,15499.56,139000,15499.56 -2007-11-13,15148.16,15235.56,14988.77,15126.63,151000,15126.63 -2007-11-12,15377.37,15386.80,14998.51,15197.09,158600,15197.09 -2007-11-09,15675.48,15834.97,15566.12,15583.42,160400,15583.42 -2007-11-08,15889.03,15891.23,15626.06,15771.57,161200,15771.57 -2007-11-07,16325.18,16326.58,16081.03,16096.68,136000,16096.68 -2007-11-06,16152.58,16353.93,16144.40,16249.63,138800,16249.63 -2007-11-05,16458.14,16458.14,16211.79,16268.92,142800,16268.92 -2007-11-02,16647.40,16654.73,16484.54,16517.48,148200,16517.48 -2007-11-01,16812.90,16887.04,16795.55,16870.40,145000,16870.40 -2007-10-31,16620.59,16738.98,16552.54,16737.63,144800,16737.63 -2007-10-30,16655.31,16682.87,16492.56,16651.01,149200,16651.01 -2007-10-29,16613.30,16774.18,16613.30,16698.08,122000,16698.08 -2007-10-26,16358.61,16505.63,16348.86,16505.63,110200,16505.63 -2007-10-25,16368.02,16438.57,16199.02,16284.17,111600,16284.17 -2007-10-24,16518.00,16578.59,16330.33,16358.39,109200,16358.39 -2007-10-23,16486.71,16554.91,16416.92,16450.58,90400,16450.58 -2007-10-22,16563.57,16563.57,16264.70,16438.47,122000,16438.47 -2007-10-19,16965.00,16965.00,16711.57,16814.37,111600,16814.37 -2007-10-18,16974.69,17147.73,16974.69,17106.09,113800,17106.09 -2007-10-17,17114.20,17114.20,16795.74,16955.31,150600,16955.31 -2007-10-16,17267.07,17283.05,17104.20,17137.92,114400,17137.92 -2007-10-15,17399.67,17430.09,17292.13,17358.15,97000,17358.15 -2007-10-12,17417.94,17441.75,17280.60,17331.17,127000,17331.17 -2007-10-11,17188.81,17488.97,17154.75,17458.98,133000,17458.98 -2007-10-10,17231.14,17254.52,17146.39,17177.89,101800,17177.89 -2007-10-09,17166.17,17237.40,17133.52,17159.90,104600,17159.90 -2007-10-05,17070.61,17144.35,17032.75,17065.04,97200,17065.04 -2007-10-04,17085.32,17160.47,17043.76,17092.49,129200,17092.49 -2007-10-03,17066.14,17205.42,17017.77,17199.89,148200,17199.89 -2007-10-02,17027.92,17072.67,16986.38,17046.78,136000,17046.78 -2007-10-01,16773.10,16899.84,16685.80,16845.96,108600,16845.96 -2007-09-28,16903.64,16929.26,16755.21,16785.69,111600,16785.69 -2007-09-27,16551.94,16868.94,16551.94,16832.22,126800,16832.22 -2007-09-26,16388.51,16457.72,16388.51,16435.74,103800,16435.74 -2007-09-25,16317.19,16434.80,16240.26,16401.73,118000,16401.73 -2007-09-21,16284.43,16353.97,16245.94,16312.61,123000,16312.61 -2007-09-20,16474.66,16491.45,16344.28,16413.79,120800,16413.79 -2007-09-19,16038.12,16386.17,16038.12,16381.54,112200,16381.54 -2007-09-18,16037.49,16037.49,15780.90,15801.80,101000,15801.80 -2007-09-14,15895.05,16142.08,15877.09,16127.42,173000,16127.42 -2007-09-13,15886.76,15931.09,15802.36,15821.19,101600,15821.19 -2007-09-12,15978.78,16032.26,15731.32,15797.60,111000,15797.60 -2007-09-11,15787.86,15940.38,15610.65,15877.67,111000,15877.67 -2007-09-10,15906.52,15906.52,15651.83,15764.97,115400,15764.97 -2007-09-07,16179.78,16230.58,16027.93,16122.16,101000,16122.16 -2007-09-06,16003.88,16257.00,15840.05,16257.00,141200,16257.00 -2007-09-05,16506.11,16553.22,16154.90,16158.45,114200,16158.45 -2007-09-04,16445.73,16511.64,16392.21,16420.47,86600,16420.47 -2007-09-03,16511.07,16575.97,16452.74,16524.93,96600,16524.93 -2007-08-31,16270.99,16569.09,16266.23,16569.09,119400,16569.09 -2007-08-30,16182.09,16269.66,16091.28,16153.82,97200,16153.82 -2007-08-29,16068.10,16068.10,15830.28,16012.83,112000,16012.83 -2007-08-28,16214.09,16343.28,16192.84,16287.49,83800,16287.49 -2007-08-27,16429.01,16504.72,16263.95,16301.39,91600,16301.39 -2007-08-24,16286.01,16329.96,16188.08,16248.97,102800,16248.97 -2007-08-23,16093.82,16333.36,16093.82,16316.32,118200,16316.32 -2007-08-22,15866.60,15957.96,15787.96,15900.64,106400,15900.64 -2007-08-21,15773.86,16101.64,15754.51,15901.34,132600,15901.34 -2007-08-20,15477.26,15940.61,15477.26,15732.48,146600,15732.48 -2007-08-17,16035.38,16062.59,15262.10,15273.68,196800,15273.68 -2007-08-16,16296.40,16296.40,15859.46,16148.49,177000,16148.49 -2007-08-15,16659.07,16667.36,16433.30,16475.61,131400,16475.61 -2007-08-14,16824.63,16855.05,16747.94,16844.61,115400,16844.61 -2007-08-13,16791.80,16948.40,16725.55,16800.05,148200,16800.05 -2007-08-10,16923.21,16948.96,16651.71,16764.09,213200,16764.09 -2007-08-09,17170.36,17274.33,17148.95,17170.60,218800,17170.60 -2007-08-08,16930.39,17085.10,16911.68,17029.28,165000,17029.28 -2007-08-07,17009.63,17049.45,16863.46,16921.77,135000,16921.77 -2007-08-06,16781.14,16951.98,16675.39,16914.46,130600,16914.46 -2007-08-03,17019.80,17102.07,16913.27,16979.86,137200,16979.86 -2007-08-02,16956.33,16999.16,16652.80,16984.11,158000,16984.11 -2007-08-01,17169.20,17169.20,16845.54,16870.98,161400,16870.98 -2007-07-31,17318.01,17318.01,17195.29,17248.89,146400,17248.89 -2007-07-30,17138.53,17289.30,17042.66,17289.30,154000,17289.30 -2007-07-27,17454.59,17454.59,17196.16,17283.81,160400,17283.81 -2007-07-26,17807.23,17861.47,17678.98,17702.09,125200,17702.09 -2007-07-25,17810.97,17881.31,17733.96,17858.42,135200,17858.42 -2007-07-24,17998.76,18018.94,17906.11,18002.03,127400,18002.03 -2007-07-23,17995.71,18009.47,17892.75,17963.64,147200,17963.64 -2007-07-20,18148.60,18223.04,18124.74,18157.93,176600,18157.93 -2007-07-19,18095.95,18131.12,18037.33,18116.57,127600,18116.57 -2007-07-18,18136.06,18136.06,17964.28,18015.58,136600,18015.58 -2007-07-17,18269.36,18269.36,18167.82,18217.27,123400,18217.27 -2007-07-13,18161.02,18268.64,18150.99,18238.95,126200,18238.95 -2007-07-12,18105.89,18130.44,17919.17,17984.14,125600,17984.14 -2007-07-11,18116.66,18116.66,18028.87,18049.51,120600,18049.51 -2007-07-10,18244.69,18259.81,18204.04,18252.67,118800,18252.67 -2007-07-09,18226.07,18282.15,18213.59,18261.98,101200,18261.98 -2007-07-06,18184.64,18184.64,18086.01,18140.94,100600,18140.94 -2007-07-05,18191.89,18295.27,18191.89,18221.48,88800,18221.48 -2007-07-04,18158.77,18207.97,18143.58,18168.72,80400,18168.72 -2007-07-03,18206.49,18230.89,18146.74,18149.90,104800,18149.90 -2007-07-02,18139.04,18175.30,18062.49,18146.30,108600,18146.30 -2007-06-29,18010.50,18144.63,17973.50,18138.36,106000,18138.36 -2007-06-28,17915.63,17960.22,17893.37,17932.27,100000,17932.27 -2007-06-27,17981.77,17983.35,17848.05,17849.28,114200,17849.28 -2007-06-26,18098.33,18101.89,18008.60,18066.11,100400,18066.11 -2007-06-25,18107.68,18203.56,18079.85,18087.48,114400,18087.48 -2007-06-22,18177.89,18200.11,18092.36,18188.63,125600,18188.63 -2007-06-21,18117.30,18287.73,18107.81,18240.30,137200,18240.30 -2007-06-20,18173.07,18297.00,18141.95,18211.68,141400,18211.68 -2007-06-19,18131.59,18163.61,18103.56,18163.61,112000,18163.61 -2007-06-18,18127.41,18194.26,18112.93,18149.52,120600,18149.52 -2007-06-15,17945.84,18007.99,17930.34,17971.49,123800,17971.49 -2007-06-14,17834.78,17875.02,17815.30,17842.29,106800,17842.29 -2007-06-13,17631.87,17781.41,17591.93,17732.77,119200,17732.77 -2007-06-12,17845.22,17862.99,17735.56,17760.91,117200,17760.91 -2007-06-11,17899.02,17932.10,17801.63,17834.48,119600,17834.48 -2007-06-08,17904.68,17904.68,17696.51,17779.09,223600,17779.09 -2007-06-07,17879.01,18053.38,17866.52,18053.38,152400,18053.38 -2007-06-06,18001.20,18073.05,17991.19,18040.93,140800,18040.93 -2007-06-05,18019.79,18071.72,17950.45,18053.81,126200,18053.81 -2007-06-04,18067.90,18071.80,17973.42,17973.42,166600,17973.42 -2007-06-01,17949.92,18017.73,17943.68,17958.88,158600,17958.88 -2007-05-31,17715.77,17875.75,17701.57,17875.75,134200,17875.75 -2007-05-30,17664.37,17727.17,17484.30,17588.26,125800,17588.26 -2007-05-29,17524.82,17700.52,17521.64,17672.56,108000,17672.56 -2007-05-28,17544.98,17630.37,17544.67,17587.59,94200,17587.59 -2007-05-25,17529.27,17529.27,17370.18,17481.21,117800,17481.21 -2007-05-24,17680.45,17760.57,17606.56,17696.97,116400,17696.97 -2007-05-23,17763.15,17802.71,17699.12,17705.12,130600,17705.12 -2007-05-22,17578.31,17730.84,17545.26,17680.05,127800,17680.05 -2007-05-21,17455.55,17599.35,17411.51,17556.87,118400,17556.87 -2007-05-18,17563.53,17563.53,17320.81,17399.58,114800,17399.58 -2007-05-17,17586.33,17656.07,17482.44,17498.60,117000,17498.60 -2007-05-16,17487.00,17539.95,17430.70,17529.00,141000,17529.00 -2007-05-15,17577.14,17609.55,17491.59,17512.98,136000,17512.98 -2007-05-14,17682.74,17786.65,17673.86,17677.94,151400,17677.94 -2007-05-11,17616.02,17616.02,17455.28,17553.72,157600,17553.72 -2007-05-10,17793.49,17827.48,17712.89,17736.96,165400,17736.96 -2007-05-09,17616.58,17753.33,17616.58,17748.12,167000,17748.12 -2007-05-08,17651.47,17711.67,17587.92,17656.84,152400,17656.84 -2007-05-07,17564.17,17715.99,17558.24,17669.83,149400,17669.83 -2007-05-02,17310.75,17441.10,17227.09,17394.92,113400,17394.92 -2007-05-01,17396.30,17396.30,17203.03,17274.98,120200,17274.98 -2007-04-27,17377.04,17542.25,17299.37,17400.41,153800,17400.41 -2007-04-26,17359.84,17496.11,17321.05,17429.17,130000,17429.17 -2007-04-25,17379.52,17379.52,17221.55,17236.16,115200,17236.16 -2007-04-24,17363.84,17500.34,17305.78,17451.77,116000,17451.77 -2007-04-23,17589.61,17656.55,17413.64,17455.37,123600,17455.37 -2007-04-20,17471.56,17502.02,17404.62,17452.62,123400,17452.62 -2007-04-19,17530.00,17530.00,17219.73,17371.97,142600,17371.97 -2007-04-18,17557.40,17706.85,17538.34,17667.33,120000,17667.33 -2007-04-17,17750.67,17782.08,17452.12,17527.45,125200,17527.45 -2007-04-16,17507.18,17696.87,17507.18,17628.30,112000,17628.30 -2007-04-13,17629.02,17662.89,17327.37,17363.95,134000,17363.95 -2007-04-12,17601.65,17601.65,17455.18,17540.42,122200,17540.42 -2007-04-11,17699.39,17723.39,17618.19,17670.07,113800,17670.07 -2007-04-10,17631.02,17706.92,17613.15,17664.69,131400,17664.69 -2007-04-09,17606.03,17747.82,17606.03,17743.76,124600,17743.76 -2007-04-06,17502.90,17560.30,17422.55,17484.78,121000,17484.78 -2007-04-05,17507.46,17531.30,17430.38,17491.42,132800,17491.42 -2007-04-04,17400.68,17576.51,17394.14,17544.09,150400,17544.09 -2007-04-03,17154.78,17279.73,17095.92,17244.05,152000,17244.05 -2007-04-02,17346.25,17425.74,16999.05,17028.41,154000,17028.41 -2007-03-30,17318.85,17380.80,17267.10,17287.65,115200,17287.65 -2007-03-29,17119.95,17350.88,17036.22,17263.94,144600,17263.94 -2007-03-28,17328.12,17442.62,17141.65,17254.73,153000,17254.73 -2007-03-27,17357.26,17516.89,17315.91,17365.05,125800,17365.05 -2007-03-26,17517.92,17558.04,17425.00,17521.96,91800,17521.96 -2007-03-23,17519.51,17534.77,17407.97,17480.61,128400,17480.61 -2007-03-22,17383.62,17489.19,17379.40,17419.20,146400,17419.20 -2007-03-20,17155.35,17267.74,17146.53,17163.20,129400,17163.20 -2007-03-19,16713.99,17026.46,16713.99,17009.55,130800,17009.55 -2007-03-16,16779.48,16939.43,16643.76,16744.15,173200,16744.15 -2007-03-15,16803.99,16942.31,16760.68,16860.39,165000,16860.39 -2007-03-14,16936.19,16936.19,16628.85,16676.89,163600,16676.89 -2007-03-13,17268.71,17299.48,17153.20,17178.84,135200,17178.84 -2007-03-12,17312.14,17325.45,17206.94,17292.39,130000,17292.39 -2007-03-09,17224.67,17246.21,17100.74,17164.04,231600,17164.04 -2007-03-08,16729.79,17090.31,16685.95,17090.31,179200,17090.31 -2007-03-07,16982.30,16988.01,16731.77,16764.62,217400,16764.62 -2007-03-06,16654.85,16882.92,16649.10,16844.50,210000,16844.50 -2007-03-05,16992.44,16992.44,16532.91,16642.25,211000,16642.25 -2007-03-02,17351.40,17356.45,17160.43,17217.93,198000,17217.93 -2007-03-01,17542.23,17557.42,17261.60,17453.51,222400,17453.51 -2007-02-28,17843.61,17843.61,17382.79,17604.12,250200,17604.12 -2007-02-27,18239.30,18272.68,18073.22,18119.92,198000,18119.92 -2007-02-26,18219.75,18300.39,18145.42,18215.35,194800,18215.35 -2007-02-23,18113.56,18239.13,18046.42,18188.42,193400,18188.42 -2007-02-22,18033.23,18132.84,18024.48,18108.79,183200,18108.79 -2007-02-21,17896.60,17968.26,17850.09,17913.21,211200,17913.21 -2007-02-20,17919.33,17953.03,17828.95,17939.12,152200,17939.12 -2007-02-19,17835.13,17974.00,17810.05,17940.09,137000,17940.09 -2007-02-16,17828.78,17884.89,17793.35,17875.65,137600,17875.65 -2007-02-15,17891.24,17911.58,17815.17,17897.23,151400,17897.23 -2007-02-14,17662.29,17789.92,17648.81,17752.64,164800,17752.64 -2007-02-13,17481.77,17628.03,17440.43,17621.45,170000,17621.45 -2007-02-09,17339.56,17545.71,17274.89,17504.33,159600,17504.33 -2007-02-08,17368.35,17400.35,17212.78,17292.48,141800,17292.48 -2007-02-07,17367.91,17374.82,17199.66,17292.32,172200,17292.32 -2007-02-06,17384.50,17433.27,17345.10,17406.86,157000,17406.86 -2007-02-05,17531.41,17531.41,17294.98,17344.80,152400,17344.80 -2007-02-02,17569.16,17633.61,17532.64,17547.11,162000,17547.11 -2007-02-01,17377.03,17543.96,17361.01,17519.50,167800,17519.50 -2007-01-31,17493.83,17497.86,17275.84,17383.42,146000,17383.42 -2007-01-30,17510.00,17558.53,17452.63,17490.19,155000,17490.19 -2007-01-29,17393.19,17489.59,17319.37,17470.46,149200,17470.46 -2007-01-26,17368.05,17421.93,17300.84,17421.93,134400,17421.93 -2007-01-25,17604.60,17617.64,17427.54,17458.30,159000,17458.30 -2007-01-24,17505.31,17553.03,17498.36,17507.40,192200,17507.40 -2007-01-23,17350.31,17442.00,17321.29,17408.57,161200,17408.57 -2007-01-22,17429.90,17484.59,17401.32,17424.18,125600,17424.18 -2007-01-19,17340.38,17378.21,17242.77,17310.44,121400,17310.44 -2007-01-18,17248.14,17408.62,17220.42,17370.93,148800,17370.93 -2007-01-17,17153.25,17335.03,17002.67,17261.35,147000,17261.35 -2007-01-16,17190.90,17287.96,17175.85,17202.46,128400,17202.46 -2007-01-15,17160.25,17273.58,17144.44,17209.92,124400,17209.92 -2007-01-12,16979.73,17160.77,16941.39,17057.01,139400,17057.01 -2007-01-11,16958.57,17057.45,16758.46,16838.17,122000,16838.17 -2007-01-10,17192.42,17199.42,16847.57,16942.40,133400,16942.40 -2007-01-09,17018.89,17261.03,16983.97,17237.77,142400,17237.77 -2007-01-05,17315.54,17327.13,17011.10,17091.59,158600,17091.59 -2007-01-04,17322.50,17379.46,17315.76,17353.67,80200,17353.67 -2006-12-29,17228.49,17281.19,17225.83,17225.83,75400,17225.83 -2006-12-28,17290.11,17301.69,17163.75,17224.81,130600,17224.81 -2006-12-27,17207.12,17260.57,17207.12,17248.63,69200,17248.63 -2006-12-26,17070.32,17185.71,17056.59,17169.19,114000,17169.19 -2006-12-25,17104.80,17122.48,17056.69,17092.89,87000,17092.89 -2006-12-22,17011.42,17104.96,16992.77,17104.96,117000,17104.96 -2006-12-21,17040.93,17109.17,17010.04,17047.83,147200,17047.83 -2006-12-20,16829.58,17050.73,16829.58,17011.04,133200,17011.04 -2006-12-19,16883.86,16954.83,16754.21,16776.88,113000,16776.88 -2006-12-18,16962.65,16993.88,16930.55,16962.11,107400,16962.11 -2006-12-15,16927.94,16959.91,16858.35,16914.31,106200,16914.31 -2006-12-14,16714.34,16829.20,16714.34,16829.20,94600,16829.20 -2006-12-13,16609.25,16692.93,16589.73,16692.93,105400,16692.93 -2006-12-12,16621.69,16682.65,16583.78,16637.78,121200,16637.78 -2006-12-11,16487.30,16608.97,16470.40,16527.99,115000,16527.99 -2006-12-08,16429.80,16493.00,16387.80,16417.82,175600,16417.82 -2006-12-07,16461.91,16550.73,16416.30,16473.36,102200,16473.36 -2006-12-06,16309.80,16401.31,16254.45,16371.28,109800,16371.28 -2006-12-05,16370.56,16400.14,16239.28,16265.76,121400,16265.76 -2006-12-04,16263.98,16362.04,16185.92,16303.59,108600,16303.59 -2006-12-01,16313.02,16376.30,16242.01,16321.78,111800,16321.78 -2006-11-30,16183.30,16274.33,16152.85,16274.33,119200,16274.33 -2006-11-29,15948.69,16126.35,15945.07,16076.20,115200,16076.20 -2006-11-28,15711.72,15855.26,15653.69,15855.26,114000,15855.26 -2006-11-27,15615.56,15912.11,15615.56,15885.38,94800,15885.38 -2006-11-24,15784.26,15789.89,15639.19,15734.60,90600,15734.60 -2006-11-22,15680.53,15914.23,15675.48,15914.23,104200,15914.23 -2006-11-21,15766.43,15817.73,15696.22,15734.14,100200,15734.14 -2006-11-20,16004.34,16036.18,15725.94,15725.94,116200,15725.94 -2006-11-17,16182.31,16238.26,16067.27,16091.73,107000,16091.73 -2006-11-16,16292.48,16367.10,16143.70,16163.87,94400,16163.87 -2006-11-15,16348.74,16373.48,16243.47,16243.47,103200,16243.47 -2006-11-14,16178.81,16318.05,16176.02,16289.55,112800,16289.55 -2006-11-13,16016.42,16067.46,15913.86,16022.49,107600,16022.49 -2006-11-10,16133.91,16280.66,16104.74,16112.43,130000,16112.43 -2006-11-09,16218.86,16286.26,16094.49,16198.57,109400,16198.57 -2006-11-08,16404.05,16423.83,16199.43,16215.74,121600,16215.74 -2006-11-07,16509.79,16512.51,16378.72,16393.41,95800,16393.41 -2006-11-06,16278.78,16398.51,16204.13,16364.76,104200,16364.76 -2006-11-02,16281.95,16350.02,16209.14,16350.02,99000,16350.02 -2006-11-01,16338.72,16444.51,16246.24,16375.26,103800,16375.26 -2006-10-31,16389.45,16477.06,16314.29,16399.39,104400,16399.39 -2006-10-30,16544.50,16549.71,16329.89,16351.85,119000,16351.85 -2006-10-27,16879.33,16879.33,16643.91,16669.07,111800,16669.07 -2006-10-26,16794.41,16863.24,16772.31,16811.60,114400,16811.60 -2006-10-25,16837.80,16849.05,16697.01,16699.30,114600,16699.30 -2006-10-24,16853.73,16901.53,16759.98,16780.47,111400,16780.47 -2006-10-23,16641.32,16797.71,16598.53,16788.82,92400,16788.82 -2006-10-20,16556.09,16663.61,16552.36,16651.63,86400,16651.63 -2006-10-19,16673.26,16688.96,16506.22,16551.36,86400,16551.36 -2006-10-18,16519.91,16666.20,16466.74,16653.00,93600,16653.00 -2006-10-17,16704.86,16704.86,16561.26,16611.59,93600,16611.59 -2006-10-16,16663.22,16732.44,16648.43,16692.76,90200,16692.76 -2006-10-13,16494.49,16586.33,16493.64,16536.54,121600,16536.54 -2006-10-12,16386.37,16495.54,16343.49,16368.81,106400,16368.81 -2006-10-11,16497.27,16595.91,16399.74,16400.57,115400,16400.57 -2006-10-10,16325.48,16620.15,16325.48,16477.25,104400,16477.25 -2006-10-06,16444.71,16457.92,16360.64,16436.06,96800,16436.06 -2006-10-05,16291.79,16481.31,16286.64,16449.33,125800,16449.33 -2006-10-04,16288.92,16363.12,16028.32,16082.55,125000,16082.55 -2006-10-03,16198.55,16260.47,16148.89,16242.09,91000,16242.09 -2006-10-02,16169.00,16329.24,16157.98,16254.29,102600,16254.29 -2006-09-29,16097.08,16127.58,16007.37,16127.58,83400,16127.58 -2006-09-28,15970.39,16032.98,15911.22,16024.85,87000,16024.85 -2006-09-27,15692.98,15947.87,15681.44,15947.87,97600,15947.87 -2006-09-26,15594.48,15666.95,15517.90,15557.45,76400,15557.45 -2006-09-25,15550.56,15691.06,15513.87,15633.81,98200,15633.81 -2006-09-22,15706.85,15735.26,15580.19,15634.67,93000,15634.67 -2006-09-21,15820.11,15859.21,15674.84,15834.23,93400,15834.23 -2006-09-20,15757.97,15763.67,15622.28,15718.67,103000,15718.67 -2006-09-19,15948.47,16096.18,15867.05,15874.28,96600,15874.28 -2006-09-15,15872.29,15907.03,15763.90,15866.93,85400,15866.93 -2006-09-14,15831.72,15994.79,15801.88,15942.39,102000,15942.39 -2006-09-13,15889.50,15965.31,15730.67,15750.05,108600,15750.05 -2006-09-12,15843.80,15882.38,15675.37,15719.34,117000,15719.34 -2006-09-11,16053.13,16053.13,15772.07,15794.38,97800,15794.38 -2006-09-08,15907.97,16156.18,15831.75,16080.46,163000,16080.46 -2006-09-07,16141.94,16141.94,15944.03,16012.41,115600,16012.41 -2006-09-06,16350.27,16400.71,16245.16,16284.09,126000,16284.09 -2006-09-05,16357.29,16403.90,16280.68,16385.96,109400,16385.96 -2006-09-04,16280.18,16414.94,16280.18,16358.07,109000,16358.07 -2006-09-01,16072.81,16158.48,16029.56,16134.25,100200,16134.25 -2006-08-31,15884.61,16207.41,15882.26,16140.76,112200,16140.76 -2006-08-30,15929.90,15962.93,15769.16,15872.02,95600,15872.02 -2006-08-29,15881.92,15946.42,15811.73,15890.56,77400,15890.56 -2006-08-28,15953.08,16005.09,15745.01,15762.59,88000,15762.59 -2006-08-25,15955.76,16156.78,15874.63,15938.66,87000,15938.66 -2006-08-24,16088.54,16089.13,15910.62,15960.62,86800,15960.62 -2006-08-23,16161.82,16226.59,16118.12,16163.03,92600,16163.03 -2006-08-22,15994.99,16244.84,15994.99,16181.17,100400,16181.17 -2006-08-21,16104.50,16145.50,15936.61,15969.04,92000,15969.04 -2006-08-18,16052.58,16169.84,16022.12,16105.98,122400,16105.98 -2006-08-17,16142.56,16204.60,16008.44,16020.84,154600,16020.84 -2006-08-16,15970.60,16085.07,15962.98,16071.36,113400,16071.36 -2006-08-15,15831.69,15913.34,15807.72,15816.19,98400,15816.19 -2006-08-14,15550.96,15857.11,15549.67,15857.11,82800,15857.11 -2006-08-11,15622.24,15681.40,15555.60,15565.02,113600,15565.02 -2006-08-10,15585.09,15690.86,15536.15,15630.91,105800,15630.91 -2006-08-09,15417.02,15659.41,15240.30,15656.59,111400,15656.59 -2006-08-08,15234.39,15476.94,15189.10,15464.66,89200,15464.66 -2006-08-07,15494.47,15516.24,15154.06,15154.06,90000,15154.06 -2006-08-04,15502.56,15555.90,15435.33,15499.18,89200,15499.18 -2006-08-03,15527.27,15580.91,15441.67,15470.37,87000,15470.37 -2006-08-02,15341.53,15466.07,15287.81,15464.29,95600,15464.29 -2006-08-01,15387.52,15522.03,15365.71,15440.91,93000,15440.91 -2006-07-31,15462.39,15536.32,15433.20,15456.81,109400,15456.81 -2006-07-28,15217.44,15351.79,15150.53,15342.87,113000,15342.87 -2006-07-27,14883.50,15220.33,14839.49,15179.78,109600,15179.78 -2006-07-26,15065.57,15108.14,14882.67,14884.07,94600,14884.07 -2006-07-25,14970.60,15078.36,14948.36,15005.24,99400,15005.24 -2006-07-24,14700.58,14851.91,14560.67,14794.50,98400,14794.50 -2006-07-21,14825.16,14867.81,14784.24,14821.26,91200,14821.26 -2006-07-20,14713.56,14962.13,14705.43,14946.84,107000,14946.84 -2006-07-19,14503.83,14625.64,14456.43,14500.26,116200,14500.26 -2006-07-18,14714.18,14747.20,14437.24,14437.24,128000,14437.24 -2006-07-14,14914.01,14997.45,14815.90,14845.24,114200,14845.24 -2006-07-13,15127.74,15370.35,15053.61,15097.95,111800,15097.95 -2006-07-12,15405.42,15463.72,15169.15,15249.32,111400,15249.32 -2006-07-11,15485.26,15498.20,15333.59,15473.82,102000,15473.82 -2006-07-10,15149.91,15555.43,15079.74,15552.81,111200,15552.81 -2006-07-07,15428.32,15437.04,15276.15,15307.61,90400,15307.61 -2006-07-06,15455.18,15460.83,15278.18,15321.40,94600,15321.40 -2006-07-05,15504.17,15584.62,15479.93,15523.94,89600,15523.94 -2006-07-04,15677.04,15710.39,15617.81,15638.50,91000,15638.50 -2006-07-03,15573.35,15617.22,15513.29,15571.62,102600,15571.62 -2006-06-30,15333.10,15521.22,15333.10,15505.18,108800,15505.18 -2006-06-29,14981.70,15137.58,14975.78,15121.15,87600,15121.15 -2006-06-28,14998.01,14998.01,14824.80,14886.11,96400,14886.11 -2006-06-27,15165.63,15207.38,15095.11,15171.81,94000,15171.81 -2006-06-26,15080.23,15216.78,14987.77,15152.40,93200,15152.40 -2006-06-23,15001.77,15126.52,14865.57,15124.04,98200,15124.04 -2006-06-22,14812.17,15138.47,14812.17,15135.69,109000,15135.69 -2006-06-21,14712.86,14713.43,14482.96,14644.26,95000,14644.26 -2006-06-20,14811.11,14845.71,14621.87,14648.41,91800,14648.41 -2006-06-19,14815.86,14918.60,14772.14,14860.35,86400,14860.35 -2006-06-16,14679.35,14976.67,14679.35,14879.34,130200,14879.34 -2006-06-15,14453.33,14593.85,14417.70,14470.76,112600,14470.76 -2006-06-14,14084.52,14458.82,14045.53,14309.56,146000,14309.56 -2006-06-13,14650.59,14658.23,14218.60,14218.60,118600,14218.60 -2006-06-12,14685.46,14845.04,14580.87,14833.01,116000,14833.01 -2006-06-09,14530.50,14825.48,14389.31,14750.84,219200,14750.84 -2006-06-08,14990.04,14990.04,14496.96,14633.03,171400,14633.03 -2006-06-07,15285.05,15433.29,15095.15,15096.01,122200,15096.01 -2006-06-06,15500.92,15507.78,15340.93,15384.86,96200,15384.86 -2006-06-05,15719.31,15784.95,15622.75,15668.31,89600,15668.31 -2006-06-02,15600.28,15789.31,15266.97,15789.31,141200,15789.31 -2006-06-01,15603.25,15655.00,15417.51,15503.74,102600,15503.74 -2006-05-31,15660.87,15660.87,15442.53,15467.33,114400,15467.33 -2006-05-30,15920.79,15937.91,15814.83,15859.45,86400,15859.45 -2006-05-29,16111.54,16111.54,15885.07,15915.68,96800,15915.68 -2006-05-26,15827.87,15970.76,15819.32,15970.76,102000,15970.76 -2006-05-25,15809.33,15849.23,15644.62,15693.75,104600,15693.75 -2006-05-24,15676.64,15907.20,15508.51,15907.20,136200,15907.20 -2006-05-23,15722.05,15776.20,15582.86,15599.20,137200,15599.20 -2006-05-22,16254.56,16268.51,15837.26,15857.87,126400,15857.87 -2006-05-19,16041.18,16166.34,15925.69,16155.45,125000,16155.45 -2006-05-18,16089.25,16139.14,15914.39,16087.18,131800,16087.18 -2006-05-17,16259.02,16319.30,16033.66,16307.67,147800,16307.67 -2006-05-16,16508.89,16596.48,16116.73,16158.42,134600,16158.42 -2006-05-15,16395.88,16486.91,16317.20,16486.91,118800,16486.91 -2006-05-12,16655.98,16655.98,16422.49,16601.78,131600,16601.78 -2006-05-11,16887.37,17087.00,16840.85,16862.14,110200,16862.14 -2006-05-10,17161.47,17253.11,16883.39,16951.93,127600,16951.93 -2006-05-09,17253.81,17294.50,17178.95,17190.91,116600,17190.91 -2006-05-08,17334.19,17375.25,17248.59,17291.67,121000,17291.67 -2006-05-02,16920.13,17188.54,16900.33,17153.77,89200,17153.77 -2006-05-01,16929.83,16965.33,16868.69,16925.71,79600,16925.71 -2006-04-28,17039.37,17043.67,16750.50,16906.23,112400,16906.23 -2006-04-27,17118.92,17176.06,17094.72,17114.54,103600,17114.54 -2006-04-26,16993.23,17107.93,16944.54,17055.93,105200,17055.93 -2006-04-25,16929.36,17000.20,16787.44,16970.29,103400,16970.29 -2006-04-24,17245.62,17245.62,16892.15,16914.40,110600,16914.40 -2006-04-21,17332.35,17479.73,17258.49,17403.96,109000,17403.96 -2006-04-20,17391.94,17412.59,17283.97,17317.53,96200,17317.53 -2006-04-19,17405.92,17459.24,17350.12,17350.12,104600,17350.12 -2006-04-18,16971.76,17268.05,16945.30,17232.86,98400,17232.86 -2006-04-17,17233.73,17233.73,17000.36,17000.36,78200,17000.36 -2006-04-14,17319.33,17319.33,17149.08,17233.82,89600,17233.82 -2006-04-13,17232.28,17303.27,17068.96,17199.15,101200,17199.15 -2006-04-12,17297.18,17325.21,17162.00,17162.55,117400,17162.55 -2006-04-11,17460.68,17489.16,17295.42,17418.13,111600,17418.13 -2006-04-10,17455.03,17489.61,17385.25,17456.58,104000,17456.58 -2006-04-07,17498.88,17563.37,17418.94,17563.37,118000,17563.37 -2006-04-06,17365.57,17489.33,17347.01,17489.33,111400,17489.33 -2006-04-05,17340.14,17464.54,17187.35,17243.98,125800,17243.98 -2006-04-04,17295.58,17410.30,17266.39,17292.91,115400,17292.91 -2006-04-03,17127.61,17387.08,17105.50,17333.31,129600,17333.31 -2006-03-31,17088.76,17094.61,16995.77,17059.66,100600,17059.66 -2006-03-30,17011.13,17125.64,16974.47,17045.34,140400,17045.34 -2006-03-29,16670.38,16976.29,16613.72,16938.41,109000,16938.41 -2006-03-28,16550.47,16690.24,16463.95,16690.24,95600,16690.24 -2006-03-27,16599.92,16711.16,16599.92,16650.10,92600,16650.10 -2006-03-24,16501.50,16612.52,16462.47,16560.87,80800,16560.87 -2006-03-23,16605.48,16661.14,16464.47,16489.37,99000,16489.37 -2006-03-22,16578.50,16583.25,16477.26,16495.48,122600,16495.48 -2006-03-20,16299.35,16667.13,16299.35,16624.80,102200,16624.80 -2006-03-17,16172.74,16339.73,16106.38,16339.73,90600,16339.73 -2006-03-16,16354.71,16356.49,16032.55,16096.21,97400,16096.21 -2006-03-15,16342.52,16367.76,16291.77,16319.04,89600,16319.04 -2006-03-14,16401.11,16410.29,16238.36,16238.36,94600,16238.36 -2006-03-13,16264.59,16379.47,16242.16,16361.51,94800,16361.51 -2006-03-10,16007.41,16264.89,15982.02,16115.63,166800,16115.63 -2006-03-09,15645.30,16049.61,15645.30,16036.91,117800,16036.91 -2006-03-08,15657.79,15720.65,15553.14,15627.49,113400,15627.49 -2006-03-07,15864.57,15864.57,15678.12,15726.02,113000,15726.02 -2006-03-06,15668.63,15901.16,15609.80,15901.16,99800,15901.16 -2006-03-03,15835.43,15897.29,15658.64,15663.34,111200,15663.34 -2006-03-02,16068.76,16106.26,15879.71,15909.76,118200,15909.76 -2006-03-01,16026.82,16053.30,15910.65,15964.46,139200,15964.46 -2006-02-28,16218.62,16229.68,15953.32,16205.43,154400,16205.43 -2006-02-27,16156.15,16290.15,16123.29,16192.95,152600,16192.95 -2006-02-24,16034.67,16118.11,15947.04,16101.91,122800,16101.91 -2006-02-23,15908.87,16096.10,15892.51,16096.10,121400,16096.10 -2006-02-22,15882.64,15923.00,15679.91,15781.78,145600,15781.78 -2006-02-21,15602.83,15894.94,15573.71,15894.94,126800,15894.94 -2006-02-20,15620.58,15661.85,15389.58,15437.93,128800,15437.93 -2006-02-17,16078.51,16129.94,15702.67,15713.45,121600,15713.45 -2006-02-16,15901.38,16109.20,15842.00,16043.67,116200,16043.67 -2006-02-15,16302.94,16312.74,15932.83,15932.83,123200,15932.83 -2006-02-14,15845.19,16184.87,15691.86,16184.87,149200,16184.87 -2006-02-13,16191.93,16191.93,15877.66,15877.66,143800,15877.66 -2006-02-10,16525.50,16525.50,16090.93,16257.83,180200,16257.83 -2006-02-09,16444.74,16540.49,16351.43,16439.67,130800,16439.67 -2006-02-08,16609.96,16682.91,16272.68,16272.68,134400,16272.68 -2006-02-07,16768.16,16769.37,16681.04,16720.99,151400,16720.99 -2006-02-06,16736.23,16777.37,16578.19,16747.76,120200,16747.76 -2006-02-03,16596.21,16665.10,16567.87,16659.64,125000,16659.64 -2006-02-02,16632.60,16736.18,16611.53,16710.55,158600,16710.55 -2006-02-01,16594.90,16671.91,16480.09,16480.09,160400,16480.09 -2006-01-31,16603.90,16718.79,16561.31,16649.82,143200,16649.82 -2006-01-30,16615.91,16754.60,16538.72,16551.23,203400,16551.23 -2006-01-27,16079.94,16460.68,16079.92,16460.68,153800,16460.68 -2006-01-26,15783.70,15891.02,15764.89,15891.02,121800,15891.02 -2006-01-25,15725.76,15849.52,15651.00,15651.00,143800,15651.00 -2006-01-24,15470.91,15685.14,15470.39,15648.89,94600,15648.89 -2006-01-23,15497.61,15564.90,15312.71,15360.65,108400,15360.65 -2006-01-20,15847.17,15875.39,15597.77,15696.69,124000,15696.69 -2006-01-19,15396.60,15740.82,15396.60,15696.28,148000,15696.28 -2006-01-18,15725.64,15725.64,15059.52,15341.18,192400,15341.18 -2006-01-17,16152.07,16324.17,15805.95,15805.95,140200,15805.95 -2006-01-16,16360.04,16387.63,16221.59,16268.03,110000,16268.03 -2006-01-13,16454.32,16490.27,16383.23,16454.95,137400,16454.95 -2006-01-12,16426.69,16472.99,16310.45,16445.19,129800,16445.19 -2006-01-11,16164.92,16363.59,16005.24,16363.59,146200,16363.59 -2006-01-10,16487.05,16487.05,16124.35,16124.35,154600,16124.35 -2006-01-06,16408.31,16479.55,16320.43,16428.21,170400,16428.21 -2006-01-05,16441.27,16474.52,16368.51,16425.37,164600,16425.37 -2006-01-04,16294.65,16361.54,16250.76,16361.54,94200,16361.54 -2005-12-30,16412.75,16413.18,16111.43,16111.43,55600,16111.43 -2005-12-29,16247.54,16445.56,16246.67,16344.20,96200,16344.20 -2005-12-28,15920.67,16194.61,15911.23,16194.61,82000,16194.61 -2005-12-27,16033.94,16079.18,15962.73,15969.40,84400,15969.40 -2005-12-26,16027.66,16108.94,16026.18,16107.67,101800,16107.67 -2005-12-22,15975.99,15991.30,15759.73,15941.37,161800,15941.37 -2005-12-21,15713.07,16010.17,15711.91,15957.57,134800,15957.57 -2005-12-20,15388.71,15647.69,15365.39,15641.26,122200,15641.26 -2005-12-19,15252.15,15391.48,15196.00,15391.48,98200,15391.48 -2005-12-16,15222.00,15365.48,15095.56,15173.07,149000,15173.07 -2005-12-15,15376.27,15469.35,15254.44,15254.44,151400,15254.44 -2005-12-14,15817.93,15885.52,15447.15,15464.58,228800,15464.58 -2005-12-13,15754.31,15782.30,15666.09,15778.86,238400,15778.86 -2005-12-12,15549.65,15764.99,15548.46,15738.70,185600,15738.70 -2005-12-09,15127.80,15447.13,15117.15,15404.05,254400,15404.05 -2005-12-08,15470.65,15523.15,15183.36,15183.36,147800,15183.36 -2005-12-07,15520.34,15558.32,15467.75,15484.66,151200,15484.66 -2005-12-06,15518.67,15572.72,15423.38,15423.38,188600,15423.38 -2005-12-05,15413.52,15563.39,15379.64,15551.31,244600,15551.31 -2005-12-02,15272.62,15421.60,15245.36,15421.60,211800,15421.60 -2005-12-01,14914.75,15130.50,14880.18,15130.50,150800,15130.50 -2005-11-30,14981.34,15013.24,14872.15,14872.15,149000,14872.15 -2005-11-29,14900.64,14995.08,14868.03,14927.70,128200,14927.70 -2005-11-28,14847.58,14986.94,14821.75,14986.94,121400,14986.94 -2005-11-25,14693.87,14784.29,14613.18,14784.29,128000,14784.29 -2005-11-24,14816.71,14866.99,14721.60,14742.58,130400,14742.58 -2005-11-22,14726.04,14763.26,14650.06,14708.32,128400,14708.32 -2005-11-21,14719.28,14808.21,14590.56,14680.43,171000,14680.43 -2005-11-18,14542.78,14633.35,14542.78,14623.12,150800,14623.12 -2005-11-17,14192.78,14448.75,14169.47,14411.79,171600,14411.79 -2005-11-16,14035.74,14170.87,14015.62,14170.87,186800,14170.87 -2005-11-15,14069.87,14142.27,14043.45,14091.77,166600,14091.77 -2005-11-14,14218.72,14218.72,14105.45,14116.04,153600,14116.04 -2005-11-11,14170.33,14206.14,14133.89,14155.06,151200,14155.06 -2005-11-10,14058.29,14121.71,13981.99,14080.88,181400,14080.88 -2005-11-09,13989.31,14136.16,13951.40,14072.20,252400,14072.20 -2005-11-08,14067.78,14071.74,13982.85,14036.73,302000,14036.73 -2005-11-07,14084.11,14097.59,13982.75,14061.60,213800,14061.60 -2005-11-04,14040.63,14099.49,13978.96,14075.96,226800,14075.96 -2005-11-02,13865.10,13927.51,13807.81,13894.78,241000,13894.78 -2005-11-01,13718.21,13867.86,13706.33,13867.86,113800,13867.86 -2005-10-31,13459.99,13606.50,13456.07,13606.50,179000,13606.50 -2005-10-28,13344.93,13373.43,13272.84,13346.54,158200,13346.54 -2005-10-27,13440.72,13501.24,13387.66,13417.08,166000,13417.08 -2005-10-26,13291.46,13406.19,13285.60,13395.02,143400,13395.02 -2005-10-25,13227.92,13336.64,13219.05,13280.62,149200,13280.62 -2005-10-24,13232.41,13244.24,13083.04,13106.18,115800,13106.18 -2005-10-21,13065.96,13243.08,12996.29,13199.95,142800,13199.95 -2005-10-20,13221.91,13264.40,13175.93,13190.46,170400,13190.46 -2005-10-19,13297.65,13304.98,13073.46,13129.49,179200,13129.49 -2005-10-18,13376.19,13441.78,13321.97,13352.24,187600,13352.24 -2005-10-17,13486.18,13510.63,13341.64,13400.29,119400,13400.29 -2005-10-14,13581.32,13581.32,13361.96,13420.54,136400,13420.54 -2005-10-13,13388.02,13475.86,13266.98,13449.24,134600,13449.24 -2005-10-12,13565.87,13704.09,13463.74,13463.74,202200,13463.74 -2005-10-11,13280.33,13556.71,13241.84,13556.71,170600,13556.71 -2005-10-07,13279.30,13332.12,13221.33,13227.74,163000,13227.74 -2005-10-06,13554.56,13554.56,13285.83,13359.51,180200,13359.51 -2005-10-05,13761.81,13783.60,13655.94,13689.89,181200,13689.89 -2005-10-04,13597.38,13738.84,13593.01,13738.84,201600,13738.84 -2005-10-03,13566.20,13584.61,13454.67,13525.28,203400,13525.28 -2005-09-30,13677.45,13678.44,13539.15,13574.30,198400,13574.30 -2005-09-29,13515.73,13617.24,13440.90,13617.24,245800,13617.24 -2005-09-28,13307.53,13487.85,13306.52,13435.91,226600,13435.91 -2005-09-27,13370.09,13373.00,13282.30,13310.04,237400,13310.04 -2005-09-26,13229.27,13392.63,13229.27,13392.63,195000,13392.63 -2005-09-22,13120.89,13170.36,13090.28,13159.36,177800,13159.36 -2005-09-21,13181.71,13235.42,13108.65,13196.57,230600,13196.57 -2005-09-20,12991.63,13159.39,12991.63,13148.57,185200,13148.57 -2005-09-16,12992.18,12992.99,12888.74,12958.68,155200,12958.68 -2005-09-15,12817.78,12986.78,12806.71,12986.78,158800,12986.78 -2005-09-14,12847.62,12871.54,12830.88,12834.25,129000,12834.25 -2005-09-13,12896.22,12940.68,12847.19,12901.95,130600,12901.95 -2005-09-12,12841.02,12926.57,12813.97,12896.43,126200,12896.43 -2005-09-09,12561.84,12692.04,12556.43,12692.04,201400,12692.04 -2005-09-08,12601.02,12601.02,12498.40,12533.89,113600,12533.89 -2005-09-07,12682.85,12682.85,12574.90,12607.59,111400,12607.59 -2005-09-06,12686.68,12730.21,12581.28,12599.43,149200,12599.43 -2005-09-05,12616.30,12655.15,12580.37,12634.88,98800,12634.88 -2005-09-02,12571.88,12600.00,12544.37,12600.00,89800,12600.00 -2005-09-01,12501.43,12573.01,12501.43,12506.97,111200,12506.97 -2005-08-31,12428.60,12443.94,12393.68,12413.60,87200,12413.60 -2005-08-30,12411.41,12457.04,12396.10,12453.14,101000,12453.14 -2005-08-29,12386.76,12386.76,12274.81,12309.83,86400,12309.83 -2005-08-26,12458.08,12482.63,12385.03,12439.48,89800,12439.48 -2005-08-25,12443.21,12466.84,12401.34,12405.16,95800,12405.16 -2005-08-24,12421.53,12515.66,12416.51,12502.26,96200,12502.26 -2005-08-23,12511.81,12612.16,12472.93,12472.93,137600,12472.93 -2005-08-22,12330.70,12478.82,12330.70,12452.51,116200,12452.51 -2005-08-19,12276.80,12291.73,12219.52,12291.73,91000,12291.73 -2005-08-18,12322.80,12369.53,12292.82,12307.37,99800,12307.37 -2005-08-17,12286.83,12369.74,12270.85,12273.12,135000,12273.12 -2005-08-16,12324.76,12336.85,12277.41,12315.67,116600,12315.67 -2005-08-15,12254.53,12308.61,12236.61,12256.55,103600,12256.55 -2005-08-12,12276.24,12324.43,12228.13,12261.68,107800,12261.68 -2005-08-11,12178.08,12284.76,12167.48,12263.32,127400,12263.32 -2005-08-10,11996.29,12138.71,11991.69,12098.08,132800,12098.08 -2005-08-09,11797.33,11958.07,11797.33,11900.32,89800,11900.32 -2005-08-08,11670.71,11794.84,11614.71,11778.98,83800,11778.98 -2005-08-05,11842.16,11863.39,11724.61,11766.48,76600,11766.48 -2005-08-04,11945.14,11945.14,11823.20,11883.31,87600,11883.31 -2005-08-03,11987.98,12009.56,11950.31,11981.80,93600,11981.80 -2005-08-02,11954.23,11982.20,11920.88,11940.20,92800,11940.20 -2005-08-01,11907.42,11972.84,11906.04,11946.92,96200,11946.92 -2005-07-29,11900.56,11913.50,11826.86,11899.60,83800,11899.60 -2005-07-28,11881.86,11889.86,11853.62,11858.31,82000,11858.31 -2005-07-27,11770.57,11848.66,11770.57,11835.08,74400,11835.08 -2005-07-26,11762.69,11772.68,11719.02,11737.96,65600,11737.96 -2005-07-25,11721.70,11782.21,11718.66,11762.65,68000,11762.65 -2005-07-22,11751.92,11753.01,11650.37,11695.05,67000,11695.05 -2005-07-21,11808.53,11867.23,11786.73,11786.73,75800,11786.73 -2005-07-20,11780.73,11816.99,11761.21,11789.35,84800,11789.35 -2005-07-19,11761.61,11770.56,11731.99,11764.84,78800,11764.84 -2005-07-15,11825.67,11828.31,11758.68,11758.68,71000,11758.68 -2005-07-14,11715.46,11784.51,11715.46,11764.26,64600,11764.26 -2005-07-13,11705.83,11707.96,11659.65,11659.84,63000,11659.84 -2005-07-12,11737.32,11738.30,11672.94,11692.14,63200,11692.14 -2005-07-11,11676.97,11713.12,11668.77,11674.79,64200,11674.79 -2005-07-08,11563.84,11653.26,11563.84,11565.99,93000,11565.99 -2005-07-07,11586.12,11602.81,11567.51,11590.14,68200,11590.14 -2005-07-06,11648.04,11676.20,11603.53,11603.53,66800,11603.53 -2005-07-05,11645.23,11658.26,11606.76,11616.70,64800,11616.70 -2005-07-04,11664.22,11664.22,11629.16,11651.55,61200,11651.55 -2005-07-01,11573.37,11663.66,11540.93,11630.13,72000,11630.13 -2005-06-30,11573.78,11589.63,11542.45,11584.01,000,11584.01 -2005-06-29,11567.82,11594.57,11547.13,11577.44,66600,11577.44 -2005-06-28,11421.48,11519.48,11413.84,11513.83,70000,11513.83 -2005-06-27,11445.64,11445.64,11378.99,11414.28,58000,11414.28 -2005-06-24,11480.33,11537.03,11472.61,11537.03,64600,11537.03 -2005-06-23,11539.42,11576.75,11530.78,11576.75,61400,11576.75 -2005-06-22,11487.06,11560.60,11445.43,11547.28,70800,11547.28 -2005-06-21,11474.23,11511.23,11464.25,11488.74,57800,11488.74 -2005-06-20,11539.18,11539.18,11455.23,11483.35,80200,11483.35 -2005-06-17,11472.36,11514.03,11463.36,11514.03,91000,11514.03 -2005-06-16,11419.94,11462.52,11386.01,11416.38,91800,11416.38 -2005-06-15,11365.62,11429.93,11355.85,11415.88,82800,11415.88 -2005-06-14,11348.53,11363.48,11326.51,11335.92,64000,11335.92 -2005-06-13,11308.65,11371.82,11299.78,11311.51,65200,11311.51 -2005-06-10,11192.99,11331.37,11173.93,11304.23,145400,11304.23 -2005-06-09,11289.03,11294.43,11148.36,11160.88,67000,11160.88 -2005-06-08,11235.27,11322.43,11229.86,11281.03,73200,11281.03 -2005-06-07,11233.08,11258.28,11179.13,11217.45,59200,11217.45 -2005-06-06,11232.65,11270.62,11184.60,11270.62,60600,11270.62 -2005-06-03,11302.96,11317.94,11233.65,11300.05,67200,11300.05 -2005-06-02,11341.89,11374.69,11280.05,11280.05,82000,11280.05 -2005-06-01,11220.94,11329.67,11220.55,11329.67,76400,11329.67 -2005-05-31,11273.81,11297.33,11221.46,11276.59,80600,11276.59 -2005-05-30,11201.32,11302.52,11197.79,11266.33,78600,11266.33 -2005-05-27,11098.26,11192.33,11089.23,11192.33,69600,11192.33 -2005-05-26,11024.36,11047.37,10978.85,11027.94,79400,11027.94 -2005-05-25,11127.31,11127.90,10988.37,11014.43,83400,11014.43 -2005-05-24,11184.64,11199.23,11101.93,11133.65,75000,11133.65 -2005-05-23,11073.01,11163.54,11056.86,11158.65,66600,11158.65 -2005-05-20,11104.33,11110.45,11034.82,11037.29,70800,11037.29 -2005-05-19,10973.28,11102.43,10953.08,11077.16,87400,11077.16 -2005-05-18,10848.63,10891.69,10821.41,10835.41,77400,10835.41 -2005-05-17,11046.30,11066.76,10788.59,10825.39,86400,10825.39 -2005-05-16,11045.29,11048.85,10935.53,10947.22,68400,10947.22 -2005-05-13,11044.71,11103.08,11017.79,11049.11,79000,11049.11 -2005-05-12,11118.13,11135.50,11069.69,11077.94,70600,11077.94 -2005-05-11,11091.26,11120.70,11038.26,11120.70,71600,11120.70 -2005-05-10,11193.22,11211.36,11125.00,11159.46,85200,11159.46 -2005-05-09,11199.10,11199.10,11119.48,11171.32,81200,11171.32 -2005-05-06,11119.78,11192.17,11109.33,11192.17,75600,11192.17 -2005-05-02,10954.21,11035.95,10913.97,11002.11,59600,11002.11 -2005-04-28,10979.05,11008.90,10892.71,11008.90,105600,11008.90 -2005-04-27,10968.77,11022.44,10968.58,11005.42,82600,11005.42 -2005-04-26,11085.75,11085.75,11019.94,11035.83,65200,11035.83 -2005-04-25,11064.95,11114.45,11020.59,11073.77,58200,11073.77 -2005-04-22,11116.61,11134.99,11045.95,11045.95,82600,11045.95 -2005-04-21,10950.73,11001.31,10770.58,10984.39,102200,10984.39 -2005-04-20,11173.84,11199.38,11052.38,11088.58,85000,11088.58 -2005-04-19,11019.76,11082.84,10966.46,11065.86,100200,11065.86 -2005-04-18,11223.65,11223.65,10920.66,10938.44,130800,10938.44 -2005-04-15,11463.15,11463.15,11343.73,11370.69,95400,11370.69 -2005-04-14,11578.78,11580.55,11474.82,11563.17,81800,11563.17 -2005-04-13,11688.22,11718.78,11602.94,11637.52,72000,11637.52 -2005-04-12,11739.05,11764.01,11657.66,11670.30,62200,11670.30 -2005-04-11,11847.92,11847.92,11744.86,11745.64,65600,11745.64 -2005-04-08,11867.15,11911.90,11839.66,11874.75,90800,11874.75 -2005-04-07,11848.49,11848.49,11757.87,11810.99,83000,11810.99 -2005-04-06,11783.74,11841.25,11760.71,11827.16,72400,11827.16 -2005-04-05,11695.97,11786.65,11695.97,11774.31,80400,11774.31 -2005-04-04,11666.31,11701.19,11652.66,11667.54,79600,11667.54 -2005-04-01,11590.45,11723.63,11557.13,11723.63,79400,11723.63 -2005-03-31,11623.10,11668.95,11590.72,11668.95,73200,11668.95 -2005-03-30,11548.93,11608.45,11506.85,11565.88,94400,11565.88 -2005-03-29,11809.66,11809.66,11563.12,11599.82,83600,11599.82 -2005-03-28,11709.79,11816.72,11709.79,11792.30,57400,11792.30 -2005-03-25,11788.68,11802.70,11732.80,11761.10,66400,11761.10 -2005-03-24,11741.17,11819.37,11706.27,11745.97,88200,11745.97 -2005-03-23,11823.97,11823.97,11681.16,11739.12,94600,11739.12 -2005-03-22,11867.34,11889.13,11830.96,11841.97,89000,11841.97 -2005-03-18,11792.29,11922.54,11790.96,11879.81,80000,11879.81 -2005-03-17,11785.95,11808.04,11754.98,11775.50,86600,11775.50 -2005-03-16,11822.18,11873.18,11793.03,11873.18,72000,11873.18 -2005-03-15,11899.20,11912.81,11785.16,11821.09,87600,11821.09 -2005-03-14,11947.29,11955.29,11850.25,11850.25,84400,11850.25 -2005-03-11,11838.02,11964.21,11838.02,11923.89,177800,11923.89 -2005-03-10,11892.38,11959.18,11863.54,11864.91,99000,11864.91 -2005-03-09,11882.25,11966.69,11882.25,11966.69,105800,11966.69 -2005-03-08,11936.84,11936.84,11878.89,11886.91,86800,11886.91 -2005-03-07,11935.80,11975.46,11917.77,11925.36,103800,11925.36 -2005-03-04,11815.79,11881.98,11769.67,11873.05,99200,11873.05 -2005-03-03,11790.91,11856.46,11790.91,11856.46,98400,11856.46 -2005-03-02,11804.84,11831.69,11780.61,11813.71,100600,11813.71 -2005-03-01,11734.14,11780.53,11719.77,11780.53,103200,11780.53 -2005-02-28,11742.01,11754.90,11704.32,11740.60,93200,11740.60 -2005-02-25,11585.84,11677.20,11585.46,11658.25,74600,11658.25 -2005-02-24,11513.84,11557.86,11507.20,11531.15,65200,11531.15 -2005-02-23,11510.76,11510.76,11452.42,11500.18,75600,11500.18 -2005-02-22,11636.47,11651.51,11592.04,11597.71,69000,11597.71 -2005-02-21,11682.19,11690.49,11651.02,11651.02,70200,11651.02 -2005-02-18,11562.93,11660.12,11562.93,11660.12,75600,11660.12 -2005-02-17,11583.84,11638.18,11574.06,11582.72,73000,11582.72 -2005-02-16,11629.69,11684.91,11585.46,11601.68,82800,11601.68 -2005-02-15,11648.68,11676.26,11635.57,11646.49,75200,11646.49 -2005-02-14,11644.40,11677.57,11626.42,11632.20,110400,11632.20 -2005-02-10,11434.89,11553.73,11414.99,11553.56,112200,11553.56 -2005-02-09,11519.90,11538.13,11457.96,11473.35,111200,11473.35 -2005-02-08,11504.12,11519.03,11464.34,11490.43,105400,11490.43 -2005-02-07,11393.04,11531.25,11381.78,11499.86,72600,11499.86 -2005-02-04,11381.50,11383.05,11271.04,11360.40,89000,11360.40 -2005-02-03,11430.90,11444.18,11344.41,11389.35,102800,11389.35 -2005-02-02,11432.34,11447.28,11399.62,11407.14,103800,11407.14 -2005-02-01,11422.02,11422.02,11330.06,11384.40,104000,11384.40 -2005-01-31,11296.99,11467.50,11266.09,11387.59,88200,11387.59 -2005-01-28,11334.41,11340.28,11218.88,11320.58,87800,11320.58 -2005-01-27,11390.09,11390.09,11316.30,11341.31,85800,11341.31 -2005-01-26,11346.81,11379.57,11329.42,11376.57,108200,11376.57 -2005-01-25,11261.62,11276.91,11214.60,11276.91,81600,11276.91 -2005-01-24,11213.03,11303.21,11212.63,11289.49,75400,11289.49 -2005-01-21,11226.07,11290.14,11222.24,11238.37,74600,11238.37 -2005-01-20,11335.12,11335.12,11259.27,11284.77,86200,11284.77 -2005-01-19,11467.74,11486.93,11396.43,11405.34,89600,11405.34 -2005-01-18,11504.16,11509.40,11401.32,11423.26,106400,11423.26 -2005-01-17,11475.20,11535.86,11453.86,11487.10,100600,11487.10 -2005-01-14,11341.80,11491.18,11320.49,11438.39,111400,11438.39 -2005-01-13,11398.94,11424.68,11355.05,11358.22,69400,11358.22 -2005-01-12,11537.60,11548.89,11449.49,11453.39,85000,11453.39 -2005-01-11,11495.46,11580.69,11495.46,11539.99,87800,11539.99 -2005-01-07,11528.69,11528.69,11432.19,11433.24,72200,11433.24 -2005-01-06,11372.35,11492.26,11372.21,11492.26,87000,11492.26 -2005-01-05,11458.92,11461.10,11416.97,11437.52,77600,11437.52 -2005-01-04,11458.27,11547.02,11431.57,11517.75,41000,11517.75 -2004-12-30,11462.31,11489.28,11454.94,11488.76,29800,11488.76 -2004-12-29,11481.32,11500.95,11381.56,11381.56,60200,11381.56 -2004-12-28,11314.40,11424.13,11314.40,11424.13,64600,11424.13 -2004-12-27,11374.52,11383.10,11325.46,11362.35,46400,11362.35 -2004-12-24,11302.46,11369.98,11302.46,11365.48,72000,11365.48 -2004-12-22,11205.92,11240.18,11193.67,11209.44,79600,11209.44 -2004-12-21,11125.22,11186.71,11125.22,11125.92,67600,11125.92 -2004-12-20,11073.55,11129.13,11036.80,11103.42,59200,11103.42 -2004-12-17,10939.20,11130.82,10921.81,11078.32,68000,11078.32 -2004-12-16,10909.29,10980.21,10871.69,10924.37,63200,10924.37 -2004-12-15,10952.71,10999.92,10921.65,10956.46,69000,10956.46 -2004-12-14,10842.80,10941.70,10821.36,10915.58,68800,10915.58 -2004-12-13,10825.06,10854.76,10785.59,10789.25,62000,10789.25 -2004-12-10,10730.38,10828.67,10730.38,10756.80,147800,10756.80 -2004-12-09,10930.87,10930.87,10742.73,10776.63,73800,10776.63 -2004-12-08,10808.44,10948.97,10808.44,10941.37,65800,10941.37 -2004-12-07,10971.21,11001.68,10863.81,10873.63,58000,10873.63 -2004-12-06,11021.16,11026.86,10959.49,10981.96,74200,10981.96 -2004-12-03,11064.25,11107.10,11059.56,11074.89,76400,11074.89 -2004-12-02,10922.57,10995.38,10912.87,10973.07,67800,10973.07 -2004-12-01,10790.45,10800.33,10721.59,10784.25,64000,10784.25 -2004-11-30,10909.25,10923.56,10841.27,10899.25,67600,10899.25 -2004-11-29,10844.36,11013.30,10844.31,10977.89,74000,10977.89 -2004-11-26,10924.45,10927.44,10816.38,10833.75,67000,10833.75 -2004-11-25,10853.10,10900.34,10818.24,10900.34,62200,10900.34 -2004-11-24,10832.02,10915.20,10828.11,10872.33,60800,10872.33 -2004-11-22,10956.41,10956.41,10769.52,10849.39,67000,10849.39 -2004-11-19,11120.94,11158.45,11077.09,11082.84,61400,11082.84 -2004-11-18,11182.09,11235.32,11062.73,11082.42,73800,11082.42 -2004-11-17,11132.05,11192.33,11127.20,11131.29,69400,11131.29 -2004-11-16,11234.72,11268.81,11143.56,11161.75,76000,11161.75 -2004-11-15,11079.17,11231.14,11073.77,11227.57,87600,11227.57 -2004-11-12,10841.43,11026.93,10841.43,11019.98,80800,11019.98 -2004-11-11,11021.88,11048.29,10845.07,10846.92,68200,10846.92 -2004-11-10,10972.63,11030.22,10965.91,10994.96,63400,10994.96 -2004-11-09,10967.17,11040.44,10945.10,10964.87,62400,10964.87 -2004-11-08,11095.50,11096.45,10974.33,10983.83,59600,10983.83 -2004-11-05,11040.06,11089.60,11023.16,11061.77,75200,11061.77 -2004-11-04,10990.70,11005.20,10946.27,10946.27,88000,10946.27 -2004-11-02,10775.00,10895.70,10775.00,10887.81,75600,10887.81 -2004-11-01,10731.02,10735.19,10690.95,10734.71,60400,10734.71 -2004-10-29,10805.12,10805.12,10719.10,10771.42,89000,10771.42 -2004-10-28,10809.10,10895.09,10798.13,10853.12,93000,10853.12 -2004-10-27,10740.65,10777.43,10657.15,10691.95,70600,10691.95 -2004-10-26,10652.37,10683.94,10626.60,10672.46,62600,10672.46 -2004-10-25,10718.93,10718.93,10575.23,10659.15,72600,10659.15 -2004-10-22,10848.75,10892.27,10811.15,10857.13,78800,10857.13 -2004-10-21,10882.05,10901.34,10753.07,10789.23,78000,10789.23 -2004-10-20,10992.50,10992.50,10853.89,10882.18,88000,10882.18 -2004-10-19,11028.38,11106.82,11024.38,11064.86,65200,11064.86 -2004-10-18,11022.11,11022.11,10914.47,10965.62,59800,10965.62 -2004-10-15,10960.25,11015.45,10913.21,10982.95,83600,10982.95 -2004-10-14,11150.59,11150.59,11033.31,11034.29,99200,11034.29 -2004-10-13,11235.46,11306.87,11195.99,11195.99,63600,11195.99 -2004-10-12,11295.05,11320.79,11182.50,11201.81,74200,11201.81 -2004-10-08,11311.41,11370.34,11302.75,11349.35,80200,11349.35 -2004-10-07,11399.42,11410.40,11337.19,11354.59,80000,11354.59 -2004-10-06,11221.54,11408.10,11218.38,11385.38,93600,11385.38 -2004-10-05,11257.20,11304.72,11242.12,11281.83,78000,11281.83 -2004-10-04,11111.45,11282.65,11105.00,11279.63,94400,11279.63 -2004-10-01,10893.19,10987.18,10893.19,10985.17,84400,10985.17 -2004-09-30,10870.21,10928.19,10823.57,10823.57,73800,10823.57 -2004-09-29,10873.88,10873.89,10770.23,10786.10,66800,10786.10 -2004-09-28,10805.66,10821.84,10737.78,10815.57,66000,10815.57 -2004-09-27,10863.35,10888.04,10782.56,10859.32,56400,10859.32 -2004-09-24,10934.02,10934.02,10826.40,10895.16,75400,10895.16 -2004-09-22,11111.30,11135.46,10963.82,11019.41,73000,11019.41 -2004-09-21,11148.21,11151.11,11059.95,11080.87,78800,11080.87 -2004-09-17,11132.06,11145.49,11046.10,11082.49,75800,11082.49 -2004-09-16,11092.70,11177.66,11089.82,11139.36,76000,11139.36 -2004-09-15,11278.66,11285.80,11158.58,11158.58,96800,11158.58 -2004-09-14,11312.72,11352.42,11274.43,11295.58,92200,11295.58 -2004-09-13,11139.97,11257.85,11131.03,11253.11,86200,11253.11 -2004-09-10,11089.90,11089.96,10960.03,11083.23,168400,11083.23 -2004-09-09,11276.49,11330.33,11145.79,11170.96,104000,11170.96 -2004-09-08,11345.12,11357.85,11270.53,11279.19,89200,11279.19 -2004-09-07,11275.28,11312.06,11226.78,11298.94,3200,11298.94 -2004-09-06,11086.69,11270.36,11057.32,11244.37,94400,11244.37 -2004-09-03,11182.10,11186.82,11013.36,11022.49,75000,11022.49 -2004-09-02,11179.32,11190.51,11097.81,11152.75,62000,11152.75 -2004-09-01,11104.85,11169.43,11102.41,11127.35,66000,11127.35 -2004-08-31,11119.15,11154.28,11042.29,11081.79,66200,11081.79 -2004-08-30,11181.30,11226.30,11128.71,11184.53,61000,11184.53 -2004-08-27,11148.21,11209.59,11107.94,11209.59,58200,11209.59 -2004-08-26,11196.29,11225.95,11101.63,11129.33,105200,11129.33 -2004-08-25,10959.52,11143.75,10934.45,11130.02,84800,11130.02 -2004-08-24,10978.62,11016.12,10897.11,10985.33,60800,10985.33 -2004-08-23,10961.91,11007.64,10947.75,10960.97,61600,10960.97 -2004-08-20,10859.08,10938.94,10837.01,10889.14,67000,10889.14 -2004-08-19,10861.92,10908.74,10830.65,10903.53,63400,10903.53 -2004-08-18,10724.82,10774.26,10659.21,10774.26,51600,10774.26 -2004-08-17,10772.24,10802.64,10705.74,10725.97,52600,10725.97 -2004-08-16,10728.98,10730.53,10545.89,10687.81,59800,10687.81 -2004-08-13,10901.56,10903.61,10757.20,10757.20,65600,10757.20 -2004-08-12,11005.13,11091.64,11005.13,11028.07,54200,11028.07 -2004-08-11,11049.80,11076.38,10997.07,11049.46,66600,11049.46 -2004-08-10,10849.70,10972.07,10849.70,10953.55,67200,10953.55 -2004-08-09,10827.60,10916.46,10737.42,10908.70,70200,10908.70 -2004-08-06,10944.61,10974.45,10894.65,10972.57,78000,10972.57 -2004-08-05,11060.18,11104.69,10981.32,11060.89,74200,11060.89 -2004-08-04,11077.52,11084.98,10888.78,11010.02,78000,11010.02 -2004-08-03,11230.29,11258.88,11102.50,11140.57,70600,11140.57 -2004-08-02,11274.45,11279.71,11161.84,11222.24,64600,11222.24 -2004-07-30,11211.20,11325.99,11210.32,11325.78,71600,11325.78 -2004-07-29,11187.31,11189.28,11018.79,11116.84,67800,11116.84 -2004-07-28,11133.48,11236.03,11132.92,11204.37,66600,11204.37 -2004-07-27,11126.27,11180.80,11028.35,11031.54,69800,11031.54 -2004-07-26,11099.87,11159.55,11065.72,11159.55,52400,11159.55 -2004-07-23,11263.50,11264.25,11170.54,11187.33,61800,11187.33 -2004-07-22,11310.57,11310.57,11227.95,11285.04,51400,11285.04 -2004-07-21,11335.24,11433.86,11326.68,11433.86,60400,11433.86 -2004-07-20,11318.14,11318.14,11191.76,11258.37,64400,11258.37 -2004-07-16,11327.75,11475.12,11243.28,11436.00,79200,11436.00 -2004-07-15,11413.33,11440.02,11315.97,11409.14,75800,11409.14 -2004-07-14,11663.40,11664.00,11356.65,11356.65,91600,11356.65 -2004-07-13,11544.02,11608.62,11501.22,11608.62,65800,11608.62 -2004-07-12,11532.64,11598.90,11474.08,11582.28,62600,11582.28 -2004-07-09,11283.28,11449.45,11283.08,11423.53,68800,11423.53 -2004-07-08,11372.63,11411.31,11279.55,11322.23,61600,11322.23 -2004-07-07,11359.71,11429.24,11251.35,11384.86,70400,11384.86 -2004-07-06,11536.45,11606.60,11475.27,11475.27,56200,11475.27 -2004-07-05,11622.85,11648.85,11511.49,11541.71,52400,11541.71 -2004-07-02,11781.54,11781.54,11693.00,11721.49,57800,11721.49 -2004-07-01,11933.33,11988.12,11889.70,11896.01,64600,11896.01 -2004-06-30,11869.86,11887.95,11808.13,11858.87,68000,11858.87 -2004-06-29,11816.89,11885.50,11788.15,11860.81,83000,11860.81 -2004-06-28,11801.88,11884.06,11774.67,11884.06,67800,11884.06 -2004-06-25,11742.94,11780.40,11661.11,11780.40,77600,11780.40 -2004-06-24,11652.54,11744.15,11649.29,11744.15,84200,11744.15 -2004-06-23,11642.23,11678.84,11547.13,11580.56,83400,11580.56 -2004-06-22,11546.93,11581.27,11472.08,11581.27,70400,11581.27 -2004-06-21,11474.39,11729.13,11474.39,11600.16,68600,11600.16 -2004-06-18,11552.72,11555.20,11310.16,11382.08,61000,11382.08 -2004-06-17,11630.21,11649.33,11510.18,11607.90,61200,11607.90 -2004-06-16,11492.43,11673.48,11492.43,11641.72,79400,11641.72 -2004-06-15,11457.11,11482.92,11349.43,11387.70,72200,11387.70 -2004-06-14,11495.80,11622.80,11475.31,11491.66,68200,11491.66 -2004-06-11,11595.00,11637.73,11486.55,11526.82,134000,11526.82 -2004-06-10,11378.45,11624.71,11367.40,11575.97,81400,11575.97 -2004-06-09,11514.72,11521.06,11419.13,11449.74,73600,11449.74 -2004-06-08,11533.93,11542.59,11452.99,11521.93,79000,11521.93 -2004-06-07,11212.64,11485.63,11212.64,11439.92,80000,11439.92 -2004-06-04,11057.19,11128.05,11017.25,11128.05,69400,11128.05 -2004-06-03,11275.95,11358.51,10963.56,11027.05,97600,11027.05 -2004-06-02,11283.21,11283.21,11184.73,11242.34,69600,11242.34 -2004-06-01,11204.69,11338.47,11170.09,11296.76,75000,11296.76 -2004-05-31,11288.79,11297.15,11100.85,11236.37,76200,11236.37 -2004-05-28,11267.23,11345.40,11255.99,11309.57,97800,11309.57 -2004-05-27,11163.82,11219.46,11119.15,11166.03,66600,11166.03 -2004-05-26,11095.37,11213.79,11093.83,11152.09,71600,11152.09 -2004-05-25,11071.51,11071.51,10927.44,10962.93,68800,10962.93 -2004-05-24,11094.71,11170.27,11043.22,11101.64,83600,11101.64 -2004-05-21,10889.89,11076.43,10872.01,11070.25,77600,11070.25 -2004-05-20,10949.07,11045.66,10760.69,10862.04,98000,10862.04 -2004-05-19,10777.38,10993.08,10716.23,10967.74,99800,10967.74 -2004-05-18,10533.17,10711.09,10524.50,10711.09,94600,10711.09 -2004-05-17,10790.79,10790.79,10489.84,10505.05,102200,10505.05 -2004-05-14,10846.61,10939.09,10739.49,10849.63,107600,10849.63 -2004-05-13,11081.75,11081.75,10825.10,10825.10,96200,10825.10 -2004-05-12,11017.65,11157.34,10984.79,11153.58,111200,11153.58 -2004-05-11,10850.48,10970.47,10790.13,10907.18,123000,10907.18 -2004-05-10,11384.03,11392.79,10838.93,10884.70,126400,10884.70 -2004-05-07,11497.40,11582.07,11438.82,11438.82,102200,11438.82 -2004-05-06,11777.44,11785.26,11554.02,11571.34,93000,11571.34 -2004-04-30,11862.35,11862.35,11652.17,11761.79,115200,11761.79 -2004-04-28,12077.21,12085.39,11969.33,12004.29,108800,12004.29 -2004-04-27,12115.05,12115.05,12025.21,12044.88,93400,12044.88 -2004-04-26,12135.31,12195.66,12095.70,12163.89,107200,12163.89 -2004-04-23,12050.70,12120.66,12015.22,12120.66,126200,12120.66 -2004-04-22,12026.09,12074.14,11952.78,11980.10,98600,11980.10 -2004-04-21,11943.90,12001.27,11881.42,11944.30,107800,11944.30 -2004-04-20,11821.46,12037.95,11768.08,11952.26,111200,11952.26 -2004-04-19,11854.64,11861.17,11623.65,11764.21,113800,11764.21 -2004-04-16,11817.97,11864.41,11690.24,11824.56,109600,11824.56 -2004-04-15,12118.03,12189.98,11770.40,11800.40,188600,11800.40 -2004-04-14,12064.49,12134.27,12034.46,12098.18,169600,12098.18 -2004-04-13,12140.12,12170.96,12096.96,12127.82,125200,12127.82 -2004-04-12,11926.48,12085.07,11926.48,12042.70,83800,12042.70 -2004-04-09,12012.63,12012.63,11862.34,11897.51,105400,11897.51 -2004-04-08,12013.16,12119.31,11961.56,12092.59,109800,12092.59 -2004-04-07,12017.35,12097.70,12001.00,12019.62,119000,12019.62 -2004-04-06,12042.99,12095.64,11937.52,12079.70,144000,12079.70 -2004-04-05,11949.31,12003.92,11934.58,11958.32,104400,11958.32 -2004-04-02,11726.87,11843.93,11715.41,11815.95,89000,11815.95 -2004-04-01,11756.16,11813.70,11648.48,11683.42,100000,11683.42 -2004-03-31,11716.55,11783.83,11592.83,11715.39,72600,11715.39 -2004-03-30,11824.83,11869.00,11677.20,11693.68,70800,11693.68 -2004-03-29,11775.46,11843.34,11672.92,11718.24,82200,11718.24 -2004-03-26,11619.48,11782.18,11611.54,11770.65,107600,11770.65 -2004-03-25,11431.61,11530.91,11425.58,11530.91,123800,11530.91 -2004-03-24,11295.97,11384.69,11234.70,11364.99,122400,11364.99 -2004-03-23,11200.83,11327.62,11071.66,11281.09,93000,11281.09 -2004-03-22,11332.24,11351.71,11284.84,11318.51,74800,11318.51 -2004-03-19,11416.04,11487.73,11364.36,11418.51,73800,11418.51 -2004-03-18,11571.36,11647.71,11452.39,11484.28,121200,11484.28 -2004-03-17,11297.46,11478.35,11297.46,11436.86,92800,11436.86 -2004-03-16,11253.43,11311.07,11236.35,11242.29,97800,11242.29 -2004-03-15,11284.35,11348.40,11278.05,11317.90,97800,11317.90 -2004-03-12,11163.58,11191.45,11045.94,11162.75,169400,11162.75 -2004-03-11,11310.15,11354.96,11237.04,11297.04,118000,11297.04 -2004-03-10,11490.26,11492.57,11353.90,11433.24,98200,11433.24 -2004-03-09,11460.60,11532.04,11439.04,11532.04,90800,11532.04 -2004-03-08,11578.02,11643.37,11502.86,11502.86,129000,11502.86 -2004-03-05,11476.49,11537.29,11411.66,11537.29,121600,11537.29 -2004-03-04,11341.98,11480.84,11336.97,11401.79,141600,11401.79 -2004-03-03,11365.72,11430.01,11320.00,11351.92,119800,11351.92 -2004-03-02,11344.83,11386.48,11282.49,11361.51,103200,11361.51 -2004-03-01,11112.68,11329.00,11096.68,11271.12,106800,11271.12 -2004-02-27,10852.75,11069.28,10851.77,11041.92,86600,11041.92 -2004-02-26,10734.33,10815.29,10690.95,10815.29,53600,10815.29 -2004-02-25,10626.64,10727.47,10617.86,10658.73,55400,10658.73 -2004-02-24,10821.97,10856.56,10629.72,10644.13,61800,10644.13 -2004-02-23,10745.05,10893.43,10736.44,10868.96,65000,10868.96 -2004-02-20,10743.80,10765.79,10676.21,10720.69,55800,10720.69 -2004-02-19,10745.40,10813.06,10723.43,10753.80,63600,10753.80 -2004-02-18,10759.48,10798.15,10676.81,10676.81,67800,10676.81 -2004-02-17,10556.18,10721.31,10529.26,10701.13,81400,10701.13 -2004-02-16,10566.02,10617.70,10534.41,10548.72,66000,10548.72 -2004-02-13,10436.69,10572.62,10413.06,10557.69,70400,10557.69 -2004-02-12,10450.37,10557.01,10449.70,10459.26,70000,10459.26 -2004-02-10,10418.18,10460.40,10299.43,10365.40,61400,10365.40 -2004-02-09,10533.40,10595.94,10359.17,10402.61,67200,10402.61 -2004-02-06,10484.43,10502.35,10399.40,10460.92,57600,10460.92 -2004-02-05,10377.90,10478.41,10374.90,10464.60,67800,10464.60 -2004-02-04,10614.73,10627.26,10418.77,10447.25,82200,10447.25 -2004-02-03,10787.32,10800.78,10507.93,10641.92,80400,10641.92 -2004-02-02,10784.93,10861.97,10765.38,10776.73,67800,10776.73 -2004-01-30,10761.83,10838.40,10729.38,10783.61,62200,10783.61 -2004-01-29,10755.37,10786.21,10666.55,10779.44,68000,10779.44 -2004-01-28,10844.58,10901.45,10799.54,10852.47,64200,10852.47 -2004-01-27,11049.12,11075.30,10916.51,10928.03,63200,10928.03 -2004-01-26,11004.44,11013.37,10872.79,10972.60,74800,10972.60 -2004-01-23,11001.87,11139.39,10937.48,11069.01,77600,11069.01 -2004-01-22,11060.31,11115.13,10997.45,11000.70,75400,11000.70 -2004-01-21,11056.14,11163.62,11002.39,11002.39,83200,11002.39 -2004-01-20,11055.01,11193.64,10979.25,11103.10,88200,11103.10 -2004-01-19,10937.46,11044.31,10916.99,11036.33,90400,11036.33 -2004-01-16,10719.19,10857.20,10715.25,10857.20,72600,10857.20 -2004-01-15,10855.20,10881.77,10665.15,10665.15,85600,10665.15 -2004-01-14,10797.70,10883.24,10730.45,10863.00,89200,10863.00 -2004-01-13,10965.61,10965.61,10790.41,10849.68,80400,10849.68 -2004-01-09,10932.34,11008.56,10861.28,10965.05,95000,10965.05 -2004-01-08,10760.98,10888.61,10727.80,10837.65,80400,10837.65 -2004-01-07,10835.90,10852.06,10710.30,10757.82,64800,10757.82 -2004-01-06,10918.48,10945.30,10790.60,10813.99,76800,10813.99 -2004-01-05,10787.83,10862.35,10785.87,10825.17,44000,10825.17 -2003-12-30,10617.62,10681.28,10609.58,10676.64,39200,10676.64 -2003-12-29,10454.69,10574.94,10454.69,10500.62,52400,10500.62 -2003-12-26,10367.13,10417.41,10336.08,10417.41,55400,10417.41 -2003-12-25,10347.75,10367.68,10311.60,10365.35,44800,10365.35 -2003-12-24,10397.86,10400.25,10326.17,10371.27,54800,10371.27 -2003-12-22,10248.81,10386.44,10248.62,10372.51,54000,10372.51 -2003-12-19,10215.71,10305.81,10213.21,10284.54,62200,10284.54 -2003-12-18,10096.80,10173.68,10073.26,10104.00,57600,10104.00 -2003-12-17,10276.19,10280.08,10058.34,10092.64,62000,10092.64 -2003-12-16,10352.04,10352.04,10222.88,10271.60,58200,10271.60 -2003-12-15,10308.34,10490.77,10308.34,10490.77,76000,10490.77 -2003-12-12,10139.69,10228.22,10085.85,10169.66,98000,10169.66 -2003-12-11,9972.58,10088.11,9951.34,10075.14,59000,10075.14 -2003-12-10,10053.17,10053.17,9859.00,9910.56,60000,9910.56 -2003-12-09,10107.26,10158.65,10004.71,10124.28,60200,10124.28 -2003-12-08,10281.25,10293.74,10013.59,10045.34,60200,10045.34 -2003-12-05,10406.47,10456.78,10333.64,10373.46,57800,10373.46 -2003-12-04,10341.71,10449.54,10339.89,10429.99,68400,10429.99 -2003-12-03,10412.24,10485.47,10326.39,10326.39,58400,10326.39 -2003-12-02,10452.64,10552.35,10373.95,10410.15,68600,10410.15 -2003-12-01,10006.13,10439.00,9911.92,10403.27,66600,10403.27 -2003-11-28,10144.43,10144.43,10033.47,10100.57,47600,10100.57 -2003-11-27,10155.28,10173.60,10075.23,10163.38,51000,10163.38 -2003-11-26,9965.54,10161.79,9965.54,10144.83,58800,10144.83 -2003-11-25,9976.13,10064.45,9959.51,9960.20,64400,9960.20 -2003-11-21,9804.44,9888.93,9758.34,9852.83,68600,9852.83 -2003-11-20,9722.07,9883.89,9653.76,9865.70,74600,9865.70 -2003-11-19,9804.09,9804.70,9614.60,9614.60,71200,9614.60 -2003-11-18,9790.92,9906.88,9678.78,9897.05,80600,9897.05 -2003-11-17,10077.88,10077.88,9756.50,9786.83,75800,9786.83 -2003-11-14,10338.20,10354.84,10164.12,10167.06,79800,10167.06 -2003-11-13,10345.21,10431.41,10277.54,10337.67,63800,10337.67 -2003-11-12,10252.57,10328.91,10155.64,10226.22,69600,10226.22 -2003-11-11,10407.80,10407.80,10112.10,10207.04,90600,10207.04 -2003-11-10,10592.35,10617.98,10481.07,10504.54,63800,10504.54 -2003-11-07,10583.12,10641.50,10479.25,10628.98,67600,10628.98 -2003-11-06,10825.14,10825.14,10537.13,10552.30,87600,10552.30 -2003-11-05,10816.23,10837.54,10668.39,10837.54,79400,10837.54 -2003-11-04,10693.66,10869.35,10693.66,10847.97,78000,10847.97 -2003-10-31,10708.04,10774.11,10521.40,10559.59,64200,10559.59 -2003-10-30,10702.94,10761.62,10638.89,10695.56,74600,10695.56 -2003-10-29,10653.54,10791.75,10653.54,10739.22,81800,10739.22 -2003-10-28,10490.29,10592.05,10456.85,10561.01,61200,10561.01 -2003-10-27,10360.86,10483.34,10349.76,10454.12,59200,10454.12 -2003-10-24,10402.67,10480.53,10186.77,10335.70,80200,10335.70 -2003-10-23,10754.01,10754.01,10304.29,10335.16,97000,10335.16 -2003-10-22,11060.55,11060.85,10883.77,10889.62,82600,10889.62 -2003-10-21,11234.31,11238.63,10996.54,11031.52,105800,11031.52 -2003-10-20,10988.48,11210.94,10875.27,11161.71,109400,11161.71 -2003-10-17,11057.22,11112.29,10977.90,11037.89,96000,11037.89 -2003-10-16,10917.47,11025.15,10831.76,11025.15,114200,11025.15 -2003-10-15,10997.61,10997.61,10856.42,10899.95,107800,10899.95 -2003-10-14,10873.30,11031.73,10863.06,10966.43,96400,10966.43 -2003-10-10,10569.72,10852.42,10569.72,10786.04,104000,10786.04 -2003-10-09,10550.41,10598.96,10485.60,10531.44,66400,10531.44 -2003-10-08,10757.62,10797.80,10511.42,10542.20,75000,10542.20 -2003-10-07,10761.71,10820.33,10697.83,10820.33,66200,10820.33 -2003-10-06,10813.56,10905.19,10719.91,10740.14,106400,10740.14 -2003-10-03,10618.08,10726.92,10583.51,10709.29,114600,10709.29 -2003-10-02,10476.58,10620.62,10465.89,10593.53,116800,10593.53 -2003-10-01,10232.57,10361.24,10173.54,10361.24,80800,10361.24 -2003-09-30,10294.75,10420.76,10219.05,10219.05,59400,10219.05 -2003-09-29,10303.26,10309.17,10148.36,10229.57,59600,10229.57 -2003-09-26,10217.23,10366.21,10213.75,10318.44,73800,10318.44 -2003-09-25,10350.03,10371.69,10225.48,10310.04,84200,10310.04 -2003-09-24,10526.89,10672.54,10367.17,10502.29,116000,10502.29 -2003-09-22,10858.09,10858.09,10412.20,10475.10,121400,10475.10 -2003-09-19,11123.73,11160.19,10938.42,10938.42,115600,10938.42 -2003-09-18,10936.53,11067.62,10869.69,11033.32,87400,11033.32 -2003-09-17,11008.63,11099.06,10964.99,10990.11,115800,10990.11 -2003-09-16,10786.33,10887.03,10758.33,10887.03,93000,10887.03 -2003-09-12,10656.93,10751.43,10613.70,10712.81,126200,10712.81 -2003-09-11,10742.49,10749.01,10540.28,10546.33,71200,10546.33 -2003-09-10,10872.43,10938.30,10839.21,10856.32,89600,10856.32 -2003-09-09,10766.47,10927.62,10765.04,10922.04,93400,10922.04 -2003-09-08,10584.36,10725.54,10562.78,10683.76,61800,10683.76 -2003-09-05,10700.83,10707.75,10592.93,10650.77,73800,10650.77 -2003-09-04,10751.53,10783.68,10646.95,10646.95,90600,10646.95 -2003-09-03,10782.10,10813.59,10601.48,10715.69,94000,10715.69 -2003-09-02,10668.58,10748.76,10616.61,10690.08,103000,10690.08 -2003-09-01,10399.53,10670.18,10381.98,10670.18,100400,10670.18 -2003-08-29,10315.26,10362.85,10282.51,10343.55,85800,10343.55 -2003-08-28,10326.58,10356.42,10189.80,10225.22,85000,10225.22 -2003-08-27,10349.64,10415.53,10272.96,10308.99,101600,10308.99 -2003-08-26,10208.56,10356.76,10171.17,10332.57,86400,10332.57 -2003-08-25,10264.61,10331.27,10205.25,10276.64,62400,10276.64 -2003-08-22,10374.22,10378.13,10262.27,10281.17,90600,10281.17 -2003-08-21,10245.96,10377.39,10202.72,10362.69,108600,10362.69 -2003-08-20,10201.21,10334.29,10164.42,10292.06,107400,10292.06 -2003-08-19,10143.31,10241.94,10131.21,10174.10,117800,10174.10 -2003-08-18,9933.55,10048.92,9933.55,10032.97,85000,10032.97 -2003-08-15,9945.30,10038.09,9854.27,9863.47,95400,9863.47 -2003-08-14,9730.63,9924.69,9678.99,9913.47,94800,9913.47 -2003-08-13,9637.93,9762.16,9637.93,9752.75,78400,9752.75 -2003-08-12,9547.58,9620.21,9517.14,9564.81,60400,9564.81 -2003-08-11,9349.05,9498.15,9332.47,9487.80,50000,9487.80 -2003-08-08,9256.69,9367.64,9251.73,9327.53,74000,9327.53 -2003-08-07,9324.58,9334.56,9224.05,9265.56,64600,9265.56 -2003-08-06,9296.65,9375.43,9287.39,9323.91,68200,9323.91 -2003-08-05,9456.82,9459.16,9304.71,9382.58,78000,9382.58 -2003-08-04,9538.94,9538.94,9452.79,9452.79,60800,9452.79 -2003-08-01,9646.66,9652.19,9520.14,9611.67,80600,9611.67 -2003-07-31,9616.33,9646.21,9507.39,9563.21,73400,9563.21 -2003-07-30,9811.60,9821.28,9632.66,9632.66,79200,9632.66 -2003-07-29,9895.71,9932.18,9822.72,9834.31,76400,9834.31 -2003-07-28,9750.20,9846.19,9737.15,9839.91,67600,9839.91 -2003-07-25,9652.44,9692.40,9569.87,9648.01,69400,9648.01 -2003-07-24,9635.06,9718.38,9606.10,9671.00,75200,9671.00 -2003-07-23,9574.55,9631.26,9565.68,9615.34,88000,9615.34 -2003-07-22,9501.59,9541.05,9406.49,9485.97,56000,9485.97 -2003-07-18,9468.12,9591.62,9460.28,9527.73,67000,9527.73 -2003-07-17,9659.30,9659.30,9495.99,9498.86,67800,9498.86 -2003-07-16,9806.57,9823.59,9639.31,9735.97,69200,9735.97 -2003-07-15,9839.21,9909.65,9741.39,9751.00,82000,9751.00 -2003-07-14,9713.96,9798.44,9660.54,9755.63,64600,9755.63 -2003-07-11,9849.15,9851.68,9600.96,9635.35,95400,9635.35 -2003-07-10,9959.42,10070.11,9926.04,9955.62,101000,9955.62 -2003-07-09,9894.79,9990.95,9813.29,9990.95,89200,9990.95 -2003-07-08,9899.87,10027.60,9856.04,9898.72,117600,9898.72 -2003-07-07,9589.19,9839.90,9589.19,9795.16,97200,9795.16 -2003-07-04,9530.57,9637.66,9483.01,9547.73,78800,9547.73 -2003-07-03,9702.69,9896.64,9503.14,9624.80,145800,9624.80 -2003-07-02,9353.77,9592.24,9353.77,9592.24,126000,9592.24 -2003-07-01,9097.61,9285.53,9078.74,9278.49,107200,9278.49 -2003-06-30,9119.59,9140.71,9076.62,9083.11,97400,9083.11 -2003-06-27,9014.59,9132.95,9014.59,9104.06,91800,9104.06 -2003-06-26,8931.42,8941.46,8846.75,8923.41,68400,8923.41 -2003-06-25,8899.81,8977.69,8899.81,8932.26,73600,8932.26 -2003-06-24,9060.43,9074.66,8895.82,8919.26,74800,8919.26 -2003-06-23,9112.56,9172.53,9074.37,9137.14,95200,9137.14 -2003-06-20,9034.12,9122.90,9032.29,9120.39,81400,9120.39 -2003-06-19,9118.18,9140.76,9001.95,9110.51,69600,9110.51 -2003-06-18,9089.70,9188.95,9081.31,9092.97,75000,9092.97 -2003-06-17,8944.35,9062.38,8944.35,9033.00,77800,9033.00 -2003-06-16,8923.79,8924.46,8809.47,8839.83,53200,8839.83 -2003-06-13,8941.79,9020.89,8891.18,8980.64,126000,8980.64 -2003-06-12,8979.86,9002.29,8893.31,8918.60,78600,8918.60 -2003-06-11,8834.46,9007.29,8834.46,8890.30,98600,8890.30 -2003-06-10,8752.64,8807.26,8716.62,8789.09,69800,8789.09 -2003-06-09,8790.03,8882.51,8747.67,8822.73,94000,8822.73 -2003-06-06,8685.40,8814.04,8633.30,8785.87,92400,8785.87 -2003-06-05,8651.97,8685.55,8606.95,8657.23,89600,8657.23 -2003-06-04,8617.73,8671.56,8557.86,8557.86,67800,8557.86 -2003-06-03,8547.65,8605.54,8492.24,8564.49,65000,8564.49 -2003-06-02,8490.22,8602.22,8488.89,8547.17,77000,8547.17 -2003-05-30,8389.25,8461.73,8379.76,8424.51,77200,8424.51 -2003-05-29,8275.05,8383.93,8264.39,8375.36,62000,8375.36 -2003-05-28,8216.48,8311.84,8216.48,8234.18,48800,8234.18 -2003-05-27,8195.85,8205.58,8106.90,8120.24,46200,8120.24 -2003-05-26,8192.12,8262.81,8192.12,8227.32,47600,8227.32 -2003-05-23,8120.45,8219.87,8110.30,8184.76,64200,8184.76 -2003-05-22,8008.01,8072.76,7998.27,8051.66,61400,8051.66 -2003-05-21,8060.90,8120.91,7983.08,8018.51,59400,8018.51 -2003-05-20,7986.81,8100.84,7962.37,8059.48,58800,8059.48 -2003-05-19,8091.39,8098.74,7974.17,8039.13,63600,8039.13 -2003-05-16,8127.13,8151.50,8087.24,8117.29,54000,8117.29 -2003-05-15,8217.66,8217.66,8080.53,8123.40,67200,8123.40 -2003-05-14,8210.30,8271.47,8187.94,8244.91,55600,8244.91 -2003-05-13,8253.67,8339.07,8190.26,8190.26,63400,8190.26 -2003-05-12,8193.73,8236.83,8152.38,8221.12,58600,8221.12 -2003-05-09,8083.60,8152.16,8009.37,8152.16,65000,8152.16 -2003-05-08,8072.67,8072.67,8019.33,8031.55,53600,8031.55 -2003-05-07,8126.86,8156.00,8061.81,8109.77,57600,8109.77 -2003-05-06,7995.49,8133.34,7995.49,8083.56,61400,8083.56 -2003-05-02,7862.62,7907.19,7792.20,7907.19,53400,7907.19 -2003-05-01,7804.03,7896.16,7745.69,7863.29,55800,7863.29 -2003-04-30,7694.93,7831.42,7694.93,7831.42,63200,7831.42 -2003-04-28,7679.11,7685.36,7603.76,7607.88,46000,7607.88 -2003-04-25,7806.21,7806.21,7660.62,7699.50,52200,7699.50 -2003-04-24,7844.14,7937.86,7806.13,7854.57,51400,7854.57 -2003-04-23,7829.01,7895.60,7756.75,7793.38,52000,7793.38 -2003-04-22,7946.95,7946.95,7747.86,7790.46,47800,7790.46 -2003-04-21,7889.42,7996.54,7852.95,7969.08,49400,7969.08 -2003-04-18,7866.07,7899.35,7863.92,7874.51,47600,7874.51 -2003-04-17,7828.67,7851.09,7807.96,7821.90,48400,7821.90 -2003-04-16,7898.41,7934.69,7856.94,7879.49,63400,7879.49 -2003-04-15,7805.17,7906.19,7805.17,7838.83,60800,7838.83 -2003-04-14,7830.94,7885.52,7693.46,7752.10,64600,7752.10 -2003-04-11,7960.89,7974.22,7807.99,7816.49,65400,7816.49 -2003-04-10,8028.80,8031.00,7941.05,7980.12,51400,7980.12 -2003-04-09,8091.26,8159.50,8024.81,8057.61,57600,8057.61 -2003-04-08,8200.25,8200.25,8071.39,8131.41,50400,8131.41 -2003-04-07,8123.94,8249.98,8077.46,8249.98,51000,8249.98 -2003-04-04,7999.90,8101.37,7966.39,8074.12,51200,8074.12 -2003-04-03,8160.66,8178.24,7999.12,8017.75,53400,8017.75 -2003-04-02,8052.95,8069.85,7917.54,8069.85,51800,8069.85 -2003-04-01,7907.13,8019.47,7866.67,7986.72,54400,7986.72 -2003-03-31,8240.10,8240.10,7950.96,7972.71,45600,7972.71 -2003-03-28,8360.72,8360.72,8247.98,8280.16,43800,8280.16 -2003-03-27,8351.94,8381.93,8323.74,8368.67,43800,8368.67 -2003-03-26,8257.34,8375.79,8257.34,8351.92,39400,8351.92 -2003-03-25,8351.54,8377.17,8231.95,8238.76,48200,8238.76 -2003-03-24,8300.32,8451.05,8299.64,8435.07,58000,8435.07 -2003-03-20,8127.77,8287.09,8121.91,8195.05,54600,8195.05 -2003-03-19,7956.44,8051.04,7824.82,8051.04,48400,8051.04 -2003-03-18,7975.25,8081.17,7954.46,7954.46,56000,7954.46 -2003-03-17,8010.39,8018.32,7870.78,7871.64,41200,7871.64 -2003-03-14,7912.34,8038.46,7912.34,8002.69,1037455600,8002.69 -2003-03-13,7968.78,8003.18,7868.56,7868.56,408692800,7868.56 -2003-03-12,7908.23,7998.01,7888.31,7943.04,505407600,7943.04 -2003-03-11,7970.86,8062.12,7862.43,7862.43,678628400,7862.43 -2003-03-10,8097.27,8112.87,7975.36,8042.26,539741000,8042.26 -2003-03-07,8296.54,8336.30,8144.12,8144.12,564456800,8144.12 -2003-03-06,8461.71,8509.43,8369.15,8369.15,593320600,8369.15 -2003-03-05,8401.72,8493.61,8370.59,8472.62,569636600,8472.62 -2003-03-04,8474.84,8499.77,8414.51,8480.22,468741800,8480.22 -2003-03-03,8397.15,8490.40,8357.42,8490.40,465136400,8490.40 -2003-02-28,8429.98,8448.78,8332.41,8363.04,496882000,8363.04 -2003-02-27,8344.08,8377.61,8266.97,8359.38,502866000,8359.38 -2003-02-26,8327.46,8429.17,8327.46,8356.81,408184400,8356.81 -2003-02-25,8475.55,8481.23,8324.70,8360.49,495549600,8360.49 -2003-02-24,8503.98,8606.58,8489.03,8564.95,374840800,8564.95 -2003-02-21,8652.68,8685.42,8506.83,8513.54,456682200,8513.54 -2003-02-20,8626.44,8650.92,8575.66,8650.92,441357000,8650.92 -2003-02-19,8756.61,8772.95,8667.17,8678.44,540626400,8678.44 -2003-02-18,8775.38,8794.40,8674.38,8692.97,611585200,8692.97 -2003-02-17,8765.69,8821.31,8731.96,8771.89,628536200,8771.89 -2003-02-14,8627.00,8771.63,8613.73,8701.92,714486200,8701.92 -2003-02-13,8644.85,8672.99,8550.48,8599.66,540752800,8599.66 -2003-02-12,8514.78,8676.53,8514.78,8664.17,658504600,8664.17 -2003-02-10,8427.30,8502.36,8427.30,8484.93,376913800,8484.93 -2003-02-07,8482.74,8517.45,8422.67,8448.16,383372800,8448.16 -2003-02-06,8562.28,8595.23,8451.20,8484.19,484900600,8484.19 -2003-02-05,8425.19,8574.42,8423.93,8549.85,553701000,8549.85 -2003-02-04,8555.73,8579.41,8484.90,8484.90,596242200,8484.90 -2003-02-03,8285.55,8511.87,8253.76,8500.79,506553400,8500.79 -2003-01-31,8291.95,8348.07,8237.03,8339.94,583234200,8339.94 -2003-01-30,8364.90,8407.15,8312.41,8316.81,536095600,8316.81 -2003-01-29,8529.67,8529.67,8304.05,8331.08,540654000,8331.08 -2003-01-28,8531.53,8582.93,8511.14,8525.39,508021200,8525.39 -2003-01-27,8656.50,8690.13,8588.63,8609.47,504887600,8609.47 -2003-01-24,8779.44,8825.67,8701.32,8731.65,773407200,8731.65 -2003-01-23,8648.60,8794.97,8562.19,8790.92,796891800,8790.92 -2003-01-22,8680.57,8708.54,8568.66,8611.04,696631200,8611.04 -2003-01-21,8562.11,8755.35,8528.81,8708.58,641889600,8708.58 -2003-01-20,8639.04,8657.79,8495.00,8558.82,645672800,8558.82 -2003-01-17,8566.81,8732.85,8562.13,8690.25,648807800,8690.25 -2003-01-16,8572.66,8619.84,8536.11,8609.17,591225600,8609.17 -2003-01-15,8564.17,8612.13,8475.04,8611.75,597795400,8611.75 -2003-01-14,8512.82,8569.80,8451.70,8553.06,462942600,8553.06 -2003-01-10,8563.38,8569.85,8378.18,8470.45,495221600,8470.45 -2003-01-09,8440.32,8498.03,8400.59,8497.93,394982400,8497.93 -2003-01-08,8614.41,8614.41,8491.49,8517.80,380133400,8517.80 -2003-01-07,8810.74,8829.06,8654.52,8656.50,480549800,8656.50 -2003-01-06,8669.89,8761.78,8669.89,8713.33,232615000,8713.33 -2002-12-30,8617.65,8617.65,8543.70,8578.95,168062800,8578.95 -2002-12-27,8686.50,8714.05,8630.82,8714.05,326003800,8714.05 -2002-12-26,8575.58,8706.48,8572.72,8700.10,292922400,8700.10 -2002-12-25,8502.11,8527.73,8455.63,8501.14,328678000,8501.14 -2002-12-24,8434.26,8556.58,8389.49,8512.37,510317800,8512.37 -2002-12-20,8408.38,8421.91,8305.80,8406.88,493461400,8406.88 -2002-12-19,8312.30,8411.76,8256.52,8387.57,483763400,8387.57 -2002-12-18,8468.43,8489.53,8310.42,8344.01,437482000,8344.01 -2002-12-17,8525.15,8583.68,8487.20,8510.73,464156000,8510.73 -2002-12-16,8499.09,8570.43,8416.20,8450.94,445362800,8450.94 -2002-12-13,8694.23,8694.23,8496.29,8516.07,885691800,8516.07 -2002-12-12,8742.63,8754.81,8682.98,8708.69,363620600,8708.69 -2002-12-11,8855.87,8876.03,8725.32,8727.66,470391200,8727.66 -2002-12-10,8756.10,8869.26,8753.50,8804.52,481268200,8804.52 -2002-12-09,8836.68,8942.21,8798.56,8828.05,439849200,8828.05 -2002-12-06,8897.67,8907.15,8805.04,8863.26,489670400,8863.26 -2002-12-05,8968.63,9035.42,8907.35,8917.57,492858600,8917.57 -2002-12-04,9126.63,9126.63,8960.62,9006.73,528189000,9006.73 -2002-12-03,9244.17,9320.11,9183.58,9205.11,536443000,9205.11 -2002-12-02,9208.61,9251.82,9112.46,9174.47,477585200,9174.47 -2002-11-29,9171.76,9294.10,9125.35,9215.56,651403400,9215.56 -2002-11-28,8968.49,9185.68,8967.64,9176.78,657135600,9176.78 -2002-11-27,8761.17,8927.04,8761.17,8875.88,472613800,8875.88 -2002-11-26,8943.57,8983.24,8749.88,8823.99,520090600,8823.99 -2002-11-25,8818.40,8956.48,8751.62,8944.44,656192800,8944.44 -2002-11-22,8758.22,8819.60,8715.94,8772.56,682103600,8772.56 -2002-11-21,8537.97,8683.30,8530.93,8668.06,733791400,8668.06 -2002-11-20,8383.56,8533.01,8355.41,8459.62,687147600,8459.62 -2002-11-19,8329.63,8413.59,8246.53,8365.26,650037600,8365.26 -2002-11-18,8477.66,8479.78,8292.35,8346.01,550172000,8346.01 -2002-11-15,8402.26,8517.25,8399.68,8503.59,525206200,8503.59 -2002-11-14,8428.59,8501.36,8303.39,8303.39,517190000,8303.39 -2002-11-13,8505.93,8505.93,8389.36,8438.52,473855800,8438.52 -2002-11-12,8402.56,8526.88,8380.46,8464.77,498522400,8464.77 -2002-11-11,8619.74,8619.74,8430.50,8460.37,459883600,8460.37 -2002-11-08,8824.46,8824.46,8657.18,8690.77,464859000,8690.77 -2002-11-07,8911.67,8955.70,8854.44,8920.44,523936200,8920.44 -2002-11-06,8955.83,9100.68,8914.09,8953.29,523289800,8953.29 -2002-11-05,8790.66,8995.51,8790.57,8937.56,528042800,8937.56 -2002-11-01,8651.67,8698.19,8571.43,8685.72,416409200,8685.72 -2002-10-31,8830.71,8830.71,8576.70,8640.48,483240000,8640.48 -2002-10-30,8631.41,8842.78,8615.30,8756.59,444388200,8756.59 -2002-10-29,8715.74,8785.44,8678.25,8708.76,390968400,8708.76 -2002-10-28,8680.06,8757.51,8557.93,8757.51,366290400,8757.51 -2002-10-25,8612.57,8758.75,8612.57,8726.29,404263000,8726.29 -2002-10-24,8724.70,8735.62,8549.48,8614.30,448206200,8614.30 -2002-10-23,8623.87,8758.67,8499.49,8714.52,515509600,8714.52 -2002-10-22,8969.51,8969.51,8689.39,8689.39,444839200,8689.39 -2002-10-21,9108.51,9116.52,8948.33,8978.41,360508800,8978.41 -2002-10-18,9055.93,9134.80,9055.93,9086.13,475214800,9086.13 -2002-10-17,8894.13,9038.43,8894.13,8959.88,361571000,8959.88 -2002-10-16,8934.07,8974.79,8826.42,8884.87,485844600,8884.87 -2002-10-15,8641.66,8871.44,8641.66,8836.73,466605600,8836.73 -2002-10-11,8512.57,8611.40,8483.49,8529.61,564830200,8529.61 -2002-10-10,8468.03,8487.59,8197.22,8439.62,619958000,8439.62 -2002-10-09,8649.06,8651.66,8498.46,8539.34,538685200,8539.34 -2002-10-08,8712.22,8798.94,8674.48,8708.90,543959400,8708.90 -2002-10-07,8920.74,8920.74,8650.36,8688.00,608972800,8688.00 -2002-10-04,8899.77,9027.55,8860.65,9027.55,621473800,9027.55 -2002-10-03,9057.98,9087.96,8927.57,8936.43,574866000,8936.43 -2002-10-02,9252.29,9293.86,9049.33,9049.33,415330400,9049.33 -2002-10-01,9289.53,9289.53,9143.28,9162.26,442816800,9162.26 -2002-09-30,9421.25,9470.71,9315.25,9383.29,407879400,9383.29 -2002-09-27,9415.74,9572.37,9415.74,9530.44,582292200,9530.44 -2002-09-26,9264.97,9386.96,9264.97,9320.92,415439200,9320.92 -2002-09-25,9212.63,9349.24,9106.45,9165.41,437237000,9165.41 -2002-09-24,9396.42,9396.42,9188.29,9321.64,546891800,9321.64 -2002-09-20,9566.16,9672.82,9448.32,9481.08,478701200,9481.08 -2002-09-19,9606.78,9884.60,9606.78,9669.62,827962000,9669.62 -2002-09-18,9431.88,9521.58,9257.86,9472.06,547574400,9472.06 -2002-09-17,9349.38,9577.42,9349.38,9543.94,486859200,9543.94 -2002-09-13,9298.90,9305.69,9156.79,9241.93,1118312800,9241.93 -2002-09-12,9359.13,9440.54,9251.46,9415.23,398403600,9415.23 -2002-09-11,9384.18,9432.44,9353.18,9400.08,404387600,9400.08 -2002-09-10,9354.89,9455.85,9274.46,9309.31,455690200,9309.31 -2002-09-09,9221.50,9353.44,9221.50,9306.26,417316000,9306.26 -2002-09-06,9108.60,9150.45,8969.26,9129.07,510023400,9129.07 -2002-09-05,9147.66,9290.40,9075.70,9222.12,517238400,9222.12 -2002-09-04,9122.70,9159.06,8995.20,9075.09,624809200,9075.09 -2002-09-03,9449.20,9472.56,9217.04,9217.04,510067400,9217.04 -2002-09-02,9564.90,9565.08,9487.89,9521.63,326847800,9521.63 -2002-08-30,9646.78,9677.57,9524.78,9619.30,366890800,9619.30 -2002-08-29,9681.41,9697.55,9558.68,9620.14,390558800,9620.14 -2002-08-28,9921.08,9953.40,9745.31,9766.73,387014200,9766.73 -2002-08-27,10001.44,10067.25,9899.24,9907.30,384928800,9907.30 -2002-08-26,9811.84,10162.30,9797.04,10067.74,512197600,10067.74 -2002-08-23,9894.80,9979.88,9863.84,9867.45,544396800,9867.45 -2002-08-22,9650.54,9851.89,9551.14,9814.02,529763000,9814.02 -2002-08-21,9540.71,9706.69,9523.86,9642.61,412661600,9642.61 -2002-08-20,9696.43,9743.10,9585.67,9620.69,415972400,9620.69 -2002-08-19,9773.38,9773.38,9499.51,9599.10,383241400,9599.10 -2002-08-16,9859.17,9883.65,9727.88,9788.13,341109400,9788.13 -2002-08-15,9740.20,9851.68,9740.20,9795.57,404492400,9795.57 -2002-08-14,9642.03,9681.84,9618.87,9638.41,355714000,9638.41 -2002-08-13,9671.33,9795.95,9644.14,9688.61,332842800,9688.61 -2002-08-12,9931.82,9931.82,9747.82,9747.82,346130000,9747.82 -2002-08-09,9871.77,10043.30,9856.86,9999.79,531142800,9999.79 -2002-08-08,9844.70,9941.01,9740.43,9799.57,473644200,9799.57 -2002-08-07,9637.24,9875.33,9636.54,9834.40,539977600,9834.40 -2002-08-06,9622.52,9623.16,9439.41,9501.02,630054400,9501.02 -2002-08-05,9637.31,9775.93,9636.92,9704.93,557791200,9704.93 -2002-08-02,9716.71,9791.94,9633.83,9709.66,539051000,9709.66 -2002-08-01,9912.59,9912.59,9738.23,9793.51,493524600,9793.51 -2002-07-31,9964.75,9965.02,9831.59,9877.94,480490400,9877.94 -2002-07-30,9801.09,10013.99,9791.45,10003.72,544350600,10003.72 -2002-07-29,9654.13,9852.80,9636.69,9666.67,498883000,9666.67 -2002-07-26,9868.33,9868.33,9547.85,9591.03,578064000,9591.03 -2002-07-25,10079.98,10166.68,9911.13,9929.91,514695200,9929.91 -2002-07-24,10134.88,10142.90,9901.09,9947.72,538825600,9947.72 -2002-07-23,10102.46,10268.05,10003.81,10215.63,495785200,10215.63 -2002-07-22,10076.30,10295.71,9982.24,10189.01,447048200,10189.01 -2002-07-19,10421.53,10421.53,10174.68,10202.36,435193400,10202.36 -2002-07-18,10366.95,10513.99,10340.31,10498.26,514417200,10498.26 -2002-07-17,10256.39,10328.01,10113.61,10296.02,528231600,10296.02 -2002-07-16,10313.21,10503.04,10250.42,10250.42,503632200,10250.42 -2002-07-15,10548.21,10548.21,10373.29,10375.15,374059000,10375.15 -2002-07-12,10608.63,10694.41,10570.00,10601.45,530861200,10601.45 -2002-07-11,10647.59,10647.59,10457.07,10485.74,455178000,10485.74 -2002-07-10,10865.93,10975.72,10752.66,10752.66,448201800,10752.66 -2002-07-09,10836.19,10960.25,10770.99,10960.25,480981600,10960.25 -2002-07-08,10969.21,11050.69,10760.10,10769.20,558547800,10769.20 -2002-07-05,10701.37,10885.83,10701.37,10826.09,502188000,10826.09 -2002-07-04,10752.34,10791.22,10625.92,10632.81,493688400,10632.81 -2002-07-03,10521.64,10862.69,10495.67,10812.30,594448800,10812.30 -2002-07-02,10516.66,10622.32,10371.26,10622.32,449126600,10622.32 -2002-07-01,10655.00,10677.10,10541.22,10595.44,438658400,10595.44 -2002-06-28,10390.53,10621.84,10366.47,10621.84,489978400,10621.84 -2002-06-27,10182.12,10335.97,10176.18,10261.60,424463800,10261.60 -2002-06-26,10376.29,10376.29,10060.72,10074.56,513651000,10074.56 -2002-06-25,10463.97,10580.49,10404.22,10496.67,522084600,10496.67 -2002-06-24,10255.99,10490.87,10169.07,10471.32,526313200,10471.32 -2002-06-21,10489.78,10489.78,10327.91,10354.35,457947200,10354.35 -2002-06-20,10467.22,10629.44,10325.55,10612.98,601852000,10612.98 -2002-06-19,10757.03,10771.60,10448.70,10476.18,579618400,10476.18 -2002-06-18,10799.43,10884.26,10747.68,10839.93,481418000,10839.93 -2002-06-17,10858.05,10888.04,10577.89,10664.11,564320000,10664.11 -2002-06-14,11121.89,11127.16,10911.07,10920.63,1128034000,10920.63 -2002-06-13,11366.06,11396.28,11132.59,11144.84,464889200,11144.84 -2002-06-12,11392.32,11405.29,11261.93,11327.06,427608000,11327.06 -2002-06-11,11390.41,11514.53,11390.41,11449.44,395610200,11449.44 -2002-06-10,11470.92,11522.04,11370.21,11370.21,387837600,11370.21 -2002-06-07,11467.03,11467.03,11365.61,11438.53,000,11438.53 -2002-06-06,11700.13,11743.89,11540.32,11574.94,000,11574.94 -2002-06-05,11703.82,11769.40,11654.12,11663.87,000,11663.87 -2002-06-04,11854.51,11874.30,11624.39,11653.07,000,11653.07 -2002-06-03,11804.04,11905.16,11796.45,11901.39,000,11901.39 -2002-05-31,11780.49,11911.91,11743.99,11763.70,000,11763.70 -2002-05-30,11804.46,11812.59,11680.58,11770.03,000,11770.03 -2002-05-29,11837.27,11887.73,11796.59,11853.00,000,11853.00 -2002-05-28,11941.97,11950.28,11889.56,11936.08,000,11936.08 -2002-05-27,11975.33,12081.43,11952.68,11976.35,000,11976.35 -2002-05-24,12013.35,12023.42,11842.80,11976.28,000,11976.28 -2002-05-23,12001.28,12019.86,11936.87,11979.85,000,11979.85 -2002-05-22,11767.57,11963.23,11767.00,11961.98,000,11961.98 -2002-05-21,11803.74,11824.73,11765.21,11801.16,000,11801.16 -2002-05-20,11887.80,11942.91,11836.22,11856.54,000,11856.54 -2002-05-17,11820.33,11926.90,11817.65,11847.32,000,11847.32 -2002-05-16,11671.42,11747.35,11579.12,11738.69,000,11738.69 -2002-05-15,11486.06,11693.91,11486.06,11642.97,000,11642.97 -2002-05-14,11451.60,11507.66,11336.81,11356.19,000,11356.19 -2002-05-13,11473.93,11473.93,11309.48,11336.95,000,11336.95 -2002-05-10,11534.73,11587.43,11523.84,11531.11,000,11531.11 -2002-05-09,11633.80,11727.52,11620.52,11633.30,000,11633.30 -2002-05-08,11356.54,11581.41,11356.54,11520.75,000,11520.75 -2002-05-07,11500.13,11508.58,11250.86,11316.04,000,11316.04 -2002-05-02,11609.69,11609.69,11518.57,11551.01,000,11551.01 -2002-05-01,11540.09,11591.49,11528.29,11552.79,000,11552.79 -2002-04-30,11532.57,11548.78,11440.66,11492.54,000,11492.54 -2002-04-26,11681.80,11685.08,11464.59,11541.39,000,11541.39 -2002-04-25,11691.73,11708.01,11583.07,11648.72,000,11648.72 -2002-04-24,11749.32,11808.25,11663.71,11672.88,000,11672.88 -2002-04-23,11633.18,11812.99,11576.71,11736.83,000,11736.83 -2002-04-22,11555.08,11765.05,11555.08,11721.64,000,11721.64 -2002-04-19,11488.50,11525.88,11386.72,11512.01,000,11512.01 -2002-04-18,11499.78,11635.98,11485.09,11575.73,000,11575.73 -2002-04-17,11422.75,11544.83,11403.79,11543.71,000,11543.71 -2002-04-16,11160.91,11346.66,11141.27,11346.66,000,11346.66 -2002-04-15,11013.67,11138.70,10941.81,11137.30,000,11137.30 -2002-04-12,11069.84,11122.77,10896.12,10962.98,000,10962.98 -2002-04-11,11291.38,11320.67,11147.27,11147.27,000,11147.27 -2002-04-10,11089.13,11293.17,11052.70,11218.58,000,11218.58 -2002-04-09,11337.49,11363.20,11113.08,11114.49,000,11114.49 -2002-04-08,11321.28,11430.03,11265.18,11352.89,000,11352.89 -2002-04-05,11366.40,11412.99,11301.95,11335.49,000,11335.49 -2002-04-04,11430.25,11537.13,11336.20,11379.20,000,11379.20 -2002-04-03,11103.72,11476.94,11042.25,11400.71,000,11400.71 -2002-04-02,11142.83,11217.24,11049.85,11204.49,000,11204.49 -2002-04-01,11106.07,11148.50,11007.63,11028.70,000,11028.70 -2002-03-29,11350.35,11389.60,11024.94,11024.94,000,11024.94 -2002-03-28,11313.56,11348.22,11240.99,11333.11,000,11333.11 -2002-03-27,11251.70,11421.01,11190.39,11323.68,000,11323.68 -2002-03-26,11214.25,11524.23,11165.00,11207.92,000,11207.92 -2002-03-25,11339.06,11378.78,11166.92,11261.09,000,11261.09 -2002-03-22,11460.95,11519.68,11326.22,11345.08,000,11345.08 -2002-03-20,11833.97,11833.97,11503.79,11526.78,000,11526.78 -2002-03-19,11597.86,11792.82,11597.86,11792.82,000,11792.82 -2002-03-18,11745.81,11789.46,11477.68,11498.38,000,11498.38 -2002-03-15,11594.87,11709.44,11537.89,11648.01,000,11648.01 -2002-03-14,11471.83,11568.82,11347.25,11568.82,000,11568.82 -2002-03-13,11548.24,11774.18,11415.31,11415.31,000,11415.31 -2002-03-12,11863.71,11912.29,11607.33,11607.33,000,11607.33 -2002-03-11,11941.92,12034.04,11772.86,11919.30,000,11919.30 -2002-03-08,11710.20,12010.25,11634.31,11885.79,000,11885.79 -2002-03-07,11473.63,11690.36,11472.88,11648.34,000,11648.34 -2002-03-06,11375.87,11648.38,11358.53,11358.53,000,11358.53 -2002-03-05,11529.21,11602.75,11348.45,11348.45,000,11348.45 -2002-03-04,10942.50,11450.22,10941.36,11450.22,000,11450.22 -2002-03-01,10641.36,10813.45,10540.31,10812.00,000,10812.00 -2002-02-28,10634.93,10798.67,10587.83,10587.83,000,10587.83 -2002-02-27,10268.87,10573.09,10268.87,10573.09,000,10573.09 -2002-02-26,10405.46,10458.87,10183.52,10202.63,000,10202.63 -2002-02-25,10394.73,10446.28,10289.83,10296.47,000,10296.47 -2002-02-22,10219.85,10418.64,10165.72,10356.78,000,10356.78 -2002-02-21,9913.85,10295.42,9895.45,10295.42,000,10295.42 -2002-02-20,9783.16,9901.24,9773.95,9834.13,000,9834.13 -2002-02-19,10113.69,10129.34,9847.16,9847.16,000,9847.16 -2002-02-18,10021.55,10119.21,9980.89,10093.25,000,10093.25 -2002-02-15,10098.23,10152.00,10025.55,10048.10,000,10048.10 -2002-02-14,10014.18,10235.38,10014.18,10081.09,000,10081.09 -2002-02-13,9901.89,10039.25,9865.75,9968.35,000,9968.35 -2002-02-12,9816.96,9949.73,9816.96,9877.99,000,9877.99 -2002-02-08,9564.75,9753.75,9538.45,9686.06,000,9686.06 -2002-02-07,9481.09,9634.79,9458.70,9583.27,000,9583.27 -2002-02-06,9494.62,9602.52,9420.85,9420.85,000,9420.85 -2002-02-05,9577.17,9683.93,9473.46,9475.60,000,9475.60 -2002-02-04,9808.82,9808.82,9623.99,9631.93,000,9631.93 -2002-02-01,10026.96,10032.25,9735.05,9791.43,000,9791.43 -2002-01-31,9962.35,10012.15,9896.84,9997.80,000,9997.80 -2002-01-30,9921.73,9938.33,9843.12,9919.48,000,9919.48 -2002-01-29,10191.75,10191.75,10026.03,10026.03,000,10026.03 -2002-01-28,10190.28,10303.74,10156.19,10220.85,000,10220.85 -2002-01-25,10133.59,10149.87,10017.48,10144.14,000,10144.14 -2002-01-24,10089.25,10240.39,10012.80,10074.05,000,10074.05 -2002-01-23,10063.92,10154.82,10040.91,10040.91,000,10040.91 -2002-01-22,10225.52,10280.30,10050.98,10050.98,000,10050.98 -2002-01-21,10252.43,10393.56,10169.80,10280.25,000,10280.25 -2002-01-18,10165.16,10296.79,10151.43,10293.32,000,10293.32 -2002-01-17,10184.95,10257.45,10074.45,10128.18,000,10128.18 -2002-01-16,10172.16,10269.03,10096.34,10177.58,000,10177.58 -2002-01-15,10359.44,10359.44,10208.05,10208.05,000,10208.05 -2002-01-11,10536.25,10571.50,10441.59,10441.59,000,10441.59 -2002-01-10,10652.17,10710.48,10494.35,10538.43,000,10538.43 -2002-01-09,10661.25,10747.60,10638.43,10663.98,000,10663.98 -2002-01-08,10841.97,10843.26,10662.25,10695.60,000,10695.60 -2002-01-07,10803.45,10979.92,10803.45,10942.36,000,10942.36 -2002-01-04,10631.00,10871.49,10617.08,10871.49,000,10871.49 -2001-12-28,10498.80,10571.75,10427.62,10542.62,000,10542.62 -2001-12-27,10213.32,10457.61,10176.31,10457.61,000,10457.61 -2001-12-26,10273.05,10300.63,10170.90,10192.57,000,10192.57 -2001-12-25,10359.22,10359.22,10178.87,10254.81,000,10254.81 -2001-12-21,10394.73,10418.59,10253.72,10335.45,000,10335.45 -2001-12-20,10485.80,10501.98,10346.40,10434.52,000,10434.52 -2001-12-19,10393.04,10500.44,10347.18,10471.93,000,10471.93 -2001-12-18,10422.16,10582.07,10330.58,10432.17,000,10432.17 -2001-12-17,10482.40,10484.38,10302.97,10323.35,000,10323.35 -2001-12-14,10465.23,10603.71,10379.59,10511.65,000,10511.65 -2001-12-13,10721.61,10731.81,10433.45,10433.45,000,10433.45 -2001-12-12,10490.07,10821.13,10490.07,10801.52,000,10801.52 -2001-12-11,10523.86,10606.92,10467.61,10473.91,000,10473.91 -2001-12-10,10736.12,10738.39,10571.01,10571.01,000,10571.01 -2001-12-07,10833.30,10918.19,10762.97,10796.89,000,10796.89 -2001-12-06,10830.86,11052.51,10813.79,10857.28,000,10857.28 -2001-12-05,10549.39,10724.64,10523.22,10713.81,000,10713.81 -2001-12-04,10414.94,10478.47,10326.52,10452.65,000,10452.65 -2001-12-03,10694.65,10695.04,10370.62,10370.62,000,10370.62 -2001-11-30,10659.87,10697.90,10550.66,10697.44,000,10697.44 -2001-11-29,10607.45,10668.73,10512.66,10655.96,000,10655.96 -2001-11-28,10862.24,10900.31,10624.81,10624.81,000,10624.81 -2001-11-27,11012.85,11186.75,10948.89,10948.89,000,10948.89 -2001-11-26,10797.12,11067.92,10797.12,11064.30,000,11064.30 -2001-11-22,10617.44,10702.34,10529.21,10696.82,000,10696.82 -2001-11-21,10530.29,10789.18,10490.60,10661.08,000,10661.08 -2001-11-20,10779.58,10779.58,10555.44,10575.62,000,10575.62 -2001-11-19,10643.33,10849.19,10618.69,10727.94,000,10727.94 -2001-11-16,10488.70,10850.48,10454.20,10649.09,000,10649.09 -2001-11-15,10159.38,10489.89,10142.42,10489.89,000,10489.89 -2001-11-14,10120.16,10231.16,10076.59,10086.76,000,10086.76 -2001-11-13,10035.65,10057.74,9955.09,10030.56,000,10030.56 -2001-11-12,10225.59,10260.70,10081.56,10081.56,000,10081.56 -2001-11-09,10412.62,10412.62,10213.01,10215.71,000,10215.71 -2001-11-08,10345.49,10431.79,10269.97,10431.79,000,10431.79 -2001-11-07,10607.35,10632.49,10284.98,10284.98,000,10284.98 -2001-11-06,10518.02,10633.72,10494.89,10633.72,000,10633.72 -2001-11-05,10427.35,10447.54,10345.28,10447.54,000,10447.54 -2001-11-02,10460.61,10538.45,10322.28,10383.78,000,10383.78 -2001-11-01,10430.59,10498.39,10318.17,10347.28,000,10347.28 -2001-10-31,10443.65,10478.51,10366.34,10366.34,000,10366.34 -2001-10-30,10522.82,10538.79,10416.44,10512.82,000,10512.82 -2001-10-29,10779.19,10798.08,10612.31,10612.31,000,10612.31 -2001-10-26,10951.88,11020.50,10774.29,10795.16,000,10795.16 -2001-10-25,10847.10,11052.01,10838.60,10880.10,000,10880.10 -2001-10-24,10811.68,10960.91,10771.63,10802.15,000,10802.15 -2001-10-23,10685.35,10861.56,10649.59,10861.56,000,10861.56 -2001-10-19,10472.44,10595.97,10437.80,10538.79,000,10538.79 -2001-10-18,10654.53,10668.69,10474.85,10474.85,000,10474.85 -2001-10-17,10672.00,10790.03,10563.52,10755.45,000,10755.45 -2001-10-16,10433.90,10694.06,10412.25,10637.82,000,10637.82 -2001-10-15,10545.25,10545.25,10447.99,10452.54,000,10452.54 -2001-10-12,10474.35,10632.35,10421.57,10632.35,000,10632.35 -2001-10-11,10074.51,10347.01,10052.08,10347.01,000,10347.01 -2001-10-10,9995.43,10030.22,9934.00,9964.88,000,9964.88 -2001-10-09,10143.39,10143.39,10011.77,10011.77,000,10011.77 -2001-10-05,10165.45,10261.86,10039.51,10205.87,000,10205.87 -2001-10-04,10038.61,10215.89,10038.61,10205.48,000,10205.48 -2001-10-03,10194.73,10221.82,9924.23,9924.23,000,9924.23 -2001-10-02,9937.81,10136.56,9871.94,10136.56,000,10136.56 -2001-10-01,9766.75,9972.28,9604.09,9972.28,000,9972.28 -2001-09-28,9783.80,9933.69,9737.42,9774.68,000,9774.68 -2001-09-27,9601.07,9727.30,9585.81,9696.53,000,9696.53 -2001-09-26,9687.51,9697.12,9551.73,9641.70,000,9641.70 -2001-09-25,9637.60,9867.86,9593.07,9693.97,000,9693.97 -2001-09-21,9658.19,9658.19,9382.95,9554.99,000,9554.99 -2001-09-20,9837.17,9842.16,9688.12,9785.16,000,9785.16 -2001-09-19,9682.52,10061.10,9681.79,9939.60,000,9939.60 -2001-09-18,9623.98,9945.80,9623.98,9679.88,000,9679.88 -2001-09-17,9881.06,9881.06,9447.76,9504.41,000,9504.41 -2001-09-14,9625.26,10009.45,9580.34,10008.89,000,10008.89 -2001-09-13,9663.93,9682.63,9476.87,9613.09,000,9613.09 -2001-09-12,10140.42,10140.42,9600.84,9610.10,000,9610.10 -2001-09-11,10244.55,10344.58,10207.03,10292.95,000,10292.95 -2001-09-10,10395.10,10457.09,10195.69,10195.69,000,10195.69 -2001-09-07,10541.86,10566.65,10405.80,10516.79,000,10516.79 -2001-09-06,10575.26,10812.89,10509.68,10650.33,000,10650.33 -2001-09-05,10679.53,10679.53,10452.98,10598.79,000,10598.79 -2001-09-04,10413.71,10772.59,10325.83,10772.59,000,10772.59 -2001-09-03,10729.60,10758.90,10409.68,10409.68,000,10409.68 -2001-08-31,10811.37,10860.70,10684.16,10713.51,000,10713.51 -2001-08-30,10918.54,10969.06,10807.75,10938.45,000,10938.45 -2001-08-29,11087.15,11142.31,10973.27,10979.76,000,10979.76 -2001-08-28,11255.68,11267.95,11049.86,11189.40,000,11189.40 -2001-08-27,11273.96,11366.18,11273.96,11275.01,000,11275.01 -2001-08-24,11190.54,11230.14,11075.42,11166.31,000,11166.31 -2001-08-23,11414.99,11414.99,11104.18,11126.92,000,11126.92 -2001-08-22,11220.25,11504.06,11202.67,11396.43,000,11396.43 -2001-08-21,11317.13,11370.61,11159.27,11280.38,000,11280.38 -2001-08-20,11347.24,11358.09,11239.45,11257.94,000,11257.94 -2001-08-17,11537.69,11581.25,11412.36,11445.54,000,11445.54 -2001-08-16,11645.30,11645.30,11450.77,11515.02,000,11515.02 -2001-08-15,11812.00,11823.92,11648.86,11755.40,000,11755.40 -2001-08-14,11587.04,11936.75,11587.04,11917.95,000,11917.95 -2001-08-13,11697.12,11697.12,11417.70,11477.56,000,11477.56 -2001-08-10,11683.22,11870.91,11683.22,11735.06,000,11735.06 -2001-08-09,12036.99,12043.05,11754.56,11754.56,000,11754.56 -2001-08-08,12265.24,12293.39,12129.37,12163.67,000,12163.67 -2001-08-07,12154.72,12388.61,12079.55,12319.46,000,12319.46 -2001-08-06,12170.11,12327.23,12095.11,12243.90,000,12243.90 -2001-08-03,12336.51,12365.88,12241.27,12241.97,000,12241.97 -2001-08-02,12072.11,12407.37,12060.50,12399.20,000,12399.20 -2001-08-01,11920.64,11972.25,11817.76,11959.33,000,11959.33 -2001-07-31,11656.65,11877.84,11656.65,11860.77,000,11860.77 -2001-07-30,11846.00,11868.14,11539.16,11579.27,000,11579.27 -2001-07-27,11862.41,11948.20,11706.47,11798.08,000,11798.08 -2001-07-26,11914.34,11962.73,11822.77,11858.56,000,11858.56 -2001-07-25,11823.10,12054.30,11760.97,11891.61,000,11891.61 -2001-07-24,11608.86,11883.25,11562.38,11883.25,000,11883.25 -2001-07-23,11902.23,11902.23,11531.68,11609.63,000,11609.63 -2001-07-19,11898.11,11980.25,11863.12,11908.39,000,11908.39 -2001-07-18,12121.41,12135.31,11847.73,11892.58,000,11892.58 -2001-07-17,12215.47,12224.71,12102.94,12128.57,000,12128.57 -2001-07-16,12408.39,12408.39,12263.45,12343.37,000,12343.37 -2001-07-13,12417.56,12444.56,12294.04,12355.15,000,12355.15 -2001-07-12,12133.05,12407.95,12133.05,12407.95,000,12407.95 -2001-07-11,12178.25,12178.25,12005.11,12005.11,000,12005.11 -2001-07-10,12247.94,12382.29,12143.94,12300.41,000,12300.41 -2001-07-09,12191.31,12239.68,12029.20,12239.68,000,12239.68 -2001-07-06,12499.30,12499.30,12289.61,12306.08,000,12306.08 -2001-07-05,12563.44,12676.83,12515.51,12607.30,000,12607.30 -2001-07-04,12801.46,12801.46,12584.99,12629.02,000,12629.02 -2001-07-03,12855.50,12922.15,12747.05,12817.41,000,12817.41 -2001-07-02,12929.66,12929.66,12629.51,12751.18,000,12751.18 -2001-06-29,12843.95,12985.21,12819.46,12969.05,000,12969.05 -2001-06-28,12853.65,12876.56,12567.26,12679.88,000,12679.88 -2001-06-27,12936.52,12987.85,12828.98,12828.98,000,12828.98 -2001-06-26,12856.15,13026.80,12837.80,12978.82,000,12978.82 -2001-06-25,13052.96,13073.49,12823.45,12896.47,000,12896.47 -2001-06-22,13041.89,13079.11,12940.58,13044.61,000,13044.61 -2001-06-21,12776.76,13005.46,12727.55,12962.43,000,12962.43 -2001-06-20,12575.35,12762.09,12512.13,12674.64,000,12674.64 -2001-06-19,12734.13,12912.92,12511.66,12574.26,000,12574.26 -2001-06-18,12766.38,12787.23,12656.58,12697.79,000,12697.79 -2001-06-15,12722.38,12797.87,12578.78,12790.38,000,12790.38 -2001-06-14,12826.18,12935.07,12804.03,12846.66,000,12846.66 -2001-06-13,12883.51,12970.12,12803.42,12823.45,000,12823.45 -2001-06-12,13111.64,13164.19,12840.10,12840.10,000,12840.10 -2001-06-11,13413.10,13447.39,13224.99,13226.48,000,13226.48 -2001-06-08,13324.25,13510.70,13320.37,13430.22,000,13430.22 -2001-06-07,13122.58,13300.50,13050.17,13277.51,000,13277.51 -2001-06-06,13289.59,13313.14,13127.62,13174.84,000,13174.84 -2001-06-05,13232.52,13256.40,12984.07,13182.00,000,13182.00 -2001-06-04,13294.21,13312.35,13213.65,13312.35,000,13312.35 -2001-06-01,13365.08,13394.40,13244.90,13261.84,000,13261.84 -2001-05-31,13394.75,13419.94,13216.57,13262.14,000,13262.14 -2001-05-30,13680.77,13680.77,13468.73,13493.35,000,13493.35 -2001-05-29,13697.61,13836.40,13697.61,13773.89,000,13773.89 -2001-05-28,13732.10,13820.42,13701.83,13737.77,000,13737.77 -2001-05-25,13869.53,13958.35,13758.66,13765.92,000,13765.92 -2001-05-24,13914.32,13941.44,13801.46,13895.79,000,13895.79 -2001-05-23,14012.19,14205.09,13990.32,14067.70,000,14067.70 -2001-05-22,14271.70,14345.42,14091.19,14091.19,000,14091.19 -2001-05-21,13938.78,14214.21,13938.78,14176.83,000,14176.83 -2001-05-18,13931.62,14067.73,13877.77,13877.77,000,13877.77 -2001-05-17,13845.49,13975.12,13725.25,13910.67,000,13910.67 -2001-05-16,14050.63,14050.63,13694.27,13694.27,000,13694.27 -2001-05-15,13829.30,14103.06,13806.05,14054.03,000,14054.03 -2001-05-14,14041.79,14041.79,13828.70,13873.02,000,13873.02 -2001-05-11,14055.09,14178.32,14043.92,14043.92,000,14043.92 -2001-05-10,14035.50,14197.09,14015.70,14017.79,000,14017.79 -2001-05-09,14235.30,14235.30,13957.81,14084.85,000,14084.85 -2001-05-08,14420.17,14420.17,14227.37,14289.05,000,14289.05 -2001-05-07,14384.14,14556.11,14184.85,14529.41,000,14529.41 -2001-05-02,14441.39,14444.84,14296.54,14421.64,000,14421.64 -2001-05-01,14096.32,14425.46,14096.32,14425.46,000,14425.46 -2001-04-27,14036.26,14065.48,13795.05,13934.32,000,13934.32 -2001-04-26,13968.93,14084.55,13957.63,13973.03,000,13973.03 -2001-04-25,13799.29,13920.67,13769.58,13827.50,000,13827.50 -2001-04-24,13628.10,13762.95,13403.27,13743.18,000,13743.18 -2001-04-23,13837.19,14051.97,13639.11,13715.60,000,13715.60 -2001-04-20,13855.03,13999.58,13686.66,13765.67,000,13765.67 -2001-04-19,13789.47,14099.49,13789.47,13868.28,000,13868.28 -2001-04-18,13170.09,13705.86,13170.09,13641.79,000,13641.79 -2001-04-17,13179.33,13203.61,13019.60,13067.09,000,13067.09 -2001-04-16,13338.92,13451.02,13217.64,13254.89,000,13254.89 -2001-04-13,13454.87,13578.64,13291.20,13385.72,000,13385.72 -2001-04-12,13205.06,13452.60,13126.21,13352.44,000,13352.44 -2001-04-11,12784.64,13208.65,12724.47,13174.93,000,13174.93 -2001-04-10,12847.89,12893.85,12579.56,12620.27,000,12620.27 -2001-04-09,13304.36,13304.36,12841.76,12841.76,000,12841.76 -2001-04-06,13517.70,13674.58,13284.79,13383.76,000,13383.76 -2001-04-05,13344.20,13555.46,13323.16,13381.38,000,13381.38 -2001-04-04,13043.18,13242.78,12874.76,13242.78,000,13242.78 -2001-04-03,12970.67,13357.96,12970.67,13124.47,000,13124.47 -2001-04-02,13057.65,13089.47,12781.34,12937.86,000,12937.86 -2001-03-30,13203.00,13457.90,12992.45,12999.70,000,12999.70 -2001-03-29,13620.07,13620.07,13072.36,13072.36,000,13072.36 -2001-03-28,13726.48,13867.58,13567.71,13765.51,000,13765.51 -2001-03-27,13766.90,13829.47,13536.53,13638.33,000,13638.33 -2001-03-26,13309.72,13862.31,13296.98,13862.31,000,13862.31 -2001-03-23,12866.02,13242.72,12866.02,13214.54,000,13214.54 -2001-03-22,12982.53,13237.33,12853.97,12853.97,000,12853.97 -2001-03-21,12184.32,13103.94,12100.97,13103.94,000,13103.94 -2001-03-19,12183.98,12544.68,12143.67,12190.97,000,12190.97 -2001-03-16,12169.65,12374.45,12071.49,12232.98,000,12232.98 -2001-03-15,11685.64,12152.83,11433.88,12152.83,000,12152.83 -2001-03-14,11912.61,12004.38,11793.27,11843.59,000,11843.59 -2001-03-13,12044.78,12044.78,11710.33,11819.70,000,11819.70 -2001-03-12,12509.67,12509.67,12171.37,12171.37,000,12171.37 -2001-03-09,12549.19,12667.21,12500.51,12627.90,000,12627.90 -2001-03-08,12694.22,12756.97,12584.10,12650.56,000,12650.56 -2001-03-07,12748.56,12824.19,12539.75,12723.89,000,12723.89 -2001-03-06,12402.89,12687.74,12351.14,12687.74,000,12687.74 -2001-03-05,12285.46,12389.09,12133.90,12322.16,000,12322.16 -2001-03-02,12594.46,12594.46,12261.80,12261.80,000,12261.80 -2001-03-01,12811.52,12844.35,12528.50,12681.66,000,12681.66 -2001-02-28,12987.50,13040.31,12784.17,12883.54,000,12883.54 -2001-02-27,13232.86,13262.22,13041.33,13059.86,000,13059.86 -2001-02-26,13255.29,13315.86,13170.99,13201.14,000,13201.14 -2001-02-23,13054.46,13272.81,13048.54,13246.00,000,13246.00 -2001-02-22,13041.72,13125.34,12861.33,13073.36,000,13073.36 -2001-02-21,13181.50,13185.23,13084.55,13100.08,000,13100.08 -2001-02-20,13092.36,13248.36,13073.24,13248.36,000,13248.36 -2001-02-19,13060.20,13137.10,12950.74,13119.59,000,13119.59 -2001-02-16,13348.72,13349.08,13166.46,13175.49,000,13175.49 -2001-02-15,13273.91,13416.55,13273.67,13327.39,000,13327.39 -2001-02-14,13179.27,13405.52,13119.34,13284.06,000,13284.06 -2001-02-13,13431.72,13461.11,13247.95,13274.70,000,13274.70 -2001-02-09,13140.61,13460.39,13135.02,13422.83,000,13422.83 -2001-02-08,13336.34,13336.34,12966.83,13138.23,000,13138.23 -2001-02-07,13273.93,13373.78,13268.61,13366.01,000,13366.01 -2001-02-06,13316.25,13379.44,13240.37,13269.85,000,13269.85 -2001-02-05,13588.64,13588.64,13367.91,13385.52,000,13385.52 -2001-02-02,13764.69,13862.29,13703.63,13703.63,000,13703.63 -2001-02-01,13740.92,13779.55,13667.93,13779.55,000,13779.55 -2001-01-31,13855.74,13855.74,13726.51,13843.55,000,13843.55 -2001-01-30,13885.27,13910.71,13713.55,13826.65,000,13826.65 -2001-01-29,13724.26,13908.40,13721.86,13845.28,000,13845.28 -2001-01-26,13726.98,13750.23,13626.05,13696.06,000,13696.06 -2001-01-25,13879.83,13879.83,13730.17,13803.38,000,13803.38 -2001-01-24,14020.61,14034.11,13857.83,13893.58,000,13893.58 -2001-01-23,13966.64,14060.23,13913.10,13984.66,000,13984.66 -2001-01-22,14010.07,14039.40,13841.03,14032.42,000,14032.42 -2001-01-19,13956.34,14186.62,13947.92,13989.12,000,13989.12 -2001-01-18,13734.79,13931.91,13723.21,13873.92,000,13873.92 -2001-01-17,13593.83,13688.90,13476.55,13667.63,000,13667.63 -2001-01-16,13561.73,13598.20,13442.09,13584.45,000,13584.45 -2001-01-15,13450.28,13573.55,13441.52,13506.23,000,13506.23 -2001-01-12,13246.20,13451.95,13246.20,13347.74,000,13347.74 -2001-01-11,13433.09,13436.61,13123.81,13201.07,000,13201.07 -2001-01-10,13593.16,13593.16,13349.15,13432.65,000,13432.65 -2001-01-09,13732.85,13732.85,13460.82,13610.51,000,13610.51 -2001-01-05,13763.22,13947.06,13725.46,13867.61,000,13867.61 -2001-01-04,13898.09,13990.57,13667.68,13691.49,000,13691.49 -2000-12-29,13899.49,13966.82,13781.12,13785.69,000,13785.69 -2000-12-28,13966.95,13990.27,13866.18,13946.96,000,13946.96 -2000-12-27,13935.26,13981.49,13797.74,13981.49,000,13981.49 -2000-12-26,13878.52,14019.73,13794.43,14007.85,000,14007.85 -2000-12-22,13469.99,13517.23,13338.11,13427.08,000,13427.08 -2000-12-21,13768.03,13780.71,13182.51,13423.21,000,13423.21 -2000-12-20,14052.62,14082.64,13801.98,13914.43,000,13914.43 -2000-12-19,14461.64,14461.64,14132.37,14132.37,000,14132.37 -2000-12-18,14462.35,14566.21,14384.60,14483.90,000,14483.90 -2000-12-15,14832.39,14832.39,14552.29,14552.29,000,14552.29 -2000-12-14,15097.00,15117.69,14883.35,14927.19,000,14927.19 -2000-12-13,15086.25,15273.40,14989.85,15168.68,000,15168.68 -2000-12-12,15097.29,15270.89,15071.70,15114.64,000,15114.64 -2000-12-11,14775.93,15051.34,14775.93,15015.70,000,15015.70 -2000-12-08,14663.86,14769.44,14622.83,14696.51,000,14696.51 -2000-12-07,14825.62,14834.42,14720.36,14720.36,000,14720.36 -2000-12-06,14842.95,15109.64,14842.95,14889.37,000,14889.37 -2000-12-05,15068.97,15068.97,14695.05,14695.05,000,14695.05 -2000-12-04,14922.67,15067.19,14899.09,14954.73,000,14954.73 -2000-12-01,14600.53,14984.37,14596.39,14835.33,000,14835.33 -2000-11-30,14449.74,14683.50,14385.16,14648.51,000,14648.51 -2000-11-29,14606.36,14606.36,14438.56,14507.64,000,14507.64 -2000-11-28,14664.88,14788.41,14573.27,14658.87,000,14658.87 -2000-11-27,14422.25,14746.88,14412.43,14720.39,000,14720.39 -2000-11-24,14247.31,14430.11,14229.65,14315.35,000,14315.35 -2000-11-22,14420.04,14464.00,14172.64,14301.31,000,14301.31 -2000-11-21,14413.13,14414.59,14211.03,14408.46,000,14408.46 -2000-11-20,14510.39,14577.73,14450.63,14531.65,000,14531.65 -2000-11-17,14510.22,14601.17,14420.36,14544.30,000,14544.30 -2000-11-16,14852.24,14857.48,14551.66,14587.03,000,14587.03 -2000-11-15,14812.39,14958.33,14769.26,14799.14,000,14799.14 -2000-11-14,14680.54,14685.02,14549.73,14660.04,000,14660.04 -2000-11-13,14824.71,14824.71,14461.16,14664.64,000,14664.64 -2000-11-10,14952.18,15015.19,14873.52,14988.54,000,14988.54 -2000-11-09,15270.16,15270.16,14997.91,15060.05,000,15060.05 -2000-11-08,15256.16,15602.39,15219.11,15399.64,000,15399.64 -2000-11-07,15356.94,15422.15,15259.78,15340.33,000,15340.33 -2000-11-06,14904.29,15371.44,14887.36,15371.44,000,15371.44 -2000-11-02,14857.36,14961.69,14767.96,14837.78,000,14837.78 -2000-11-01,14557.45,14888.03,14557.45,14872.39,000,14872.39 -2000-10-31,14475.98,14566.05,14333.16,14539.60,000,14539.60 -2000-10-30,14607.02,14715.66,14425.22,14464.56,000,14464.56 -2000-10-27,14852.79,14989.41,14582.20,14582.20,000,14582.20 -2000-10-26,14787.38,14858.43,14577.20,14858.43,000,14858.43 -2000-10-25,15108.46,15108.46,14840.47,14840.47,000,14840.47 -2000-10-24,15100.08,15229.46,15066.60,15148.19,000,15148.19 -2000-10-23,15197.93,15224.81,15035.24,15097.96,000,15097.96 -2000-10-20,14842.81,15314.97,14842.81,15198.73,000,15198.73 -2000-10-19,14901.11,15027.13,14707.88,14811.08,000,14811.08 -2000-10-18,15324.35,15324.35,14832.97,14872.48,000,14872.48 -2000-10-17,15526.99,15545.06,15340.22,15340.22,000,15340.22 -2000-10-16,15366.81,15688.17,15366.81,15512.32,000,15512.32 -2000-10-13,15516.15,15516.15,15101.64,15330.31,000,15330.31 -2000-10-12,15491.80,15580.32,15392.74,15550.64,000,15550.64 -2000-10-11,15795.15,15795.15,15424.71,15513.57,000,15513.57 -2000-10-10,15958.12,15958.12,15792.36,15827.72,000,15827.72 -2000-10-06,16084.05,16084.05,15884.65,15994.24,000,15994.24 -2000-10-05,16157.20,16192.78,16052.12,16099.26,000,16099.26 -2000-10-04,15906.18,16153.67,15808.66,16149.08,000,16149.08 -2000-10-03,15905.25,15956.45,15779.54,15912.09,000,15912.09 -2000-10-02,15735.71,15902.51,15514.04,15902.51,000,15902.51 -2000-09-29,15663.71,15898.07,15663.71,15747.26,000,15747.26 -2000-09-28,15642.84,15888.35,15625.87,15626.96,000,15626.96 -2000-09-27,15907.19,15907.19,15621.88,15639.95,000,15639.95 -2000-09-26,15992.86,16038.07,15905.37,15928.62,000,15928.62 -2000-09-25,15850.56,16144.19,15850.56,15992.90,000,15992.90 -2000-09-22,16268.94,16270.42,15785.63,15818.25,000,15818.25 -2000-09-21,16422.49,16460.49,16290.79,16311.05,000,16311.05 -2000-09-20,16146.04,16523.27,16146.04,16458.31,000,16458.31 -2000-09-19,16013.33,16124.19,15774.72,16124.19,000,16124.19 -2000-09-18,16174.92,16174.92,15965.71,16061.16,000,16061.16 -2000-09-14,16208.33,16311.04,16144.11,16213.28,000,16213.28 -2000-09-13,16080.57,16306.40,16080.57,16190.52,000,16190.52 -2000-09-12,16111.97,16133.19,15885.32,16040.23,000,16040.23 -2000-09-11,16467.69,16477.53,16089.00,16130.90,000,16130.90 -2000-09-08,16370.23,16540.92,16239.26,16501.55,000,16501.55 -2000-09-07,16394.39,16397.10,16243.18,16300.46,000,16300.46 -2000-09-06,16433.85,16531.81,16364.95,16399.87,000,16399.87 -2000-09-05,16677.79,16712.33,16401.28,16452.27,000,16452.27 -2000-09-04,16764.33,16883.31,16661.48,16688.21,000,16688.21 -2000-09-01,16915.04,17018.52,16700.36,16739.78,000,16739.78 -2000-08-31,16918.03,17056.54,16769.49,16861.26,000,16861.26 -2000-08-30,17131.36,17131.36,16895.36,16901.67,000,16901.67 -2000-08-29,17165.91,17210.80,17004.01,17141.75,000,17141.75 -2000-08-28,16895.55,17209.16,16840.51,17181.12,000,17181.12 -2000-08-25,16718.54,16926.22,16603.69,16911.33,000,16911.33 -2000-08-24,16433.36,16778.02,16433.36,16670.82,000,16670.82 -2000-08-23,16446.47,16544.80,16351.42,16436.65,000,16436.65 -2000-08-22,16075.52,16454.74,15985.20,16454.74,000,16454.74 -2000-08-21,16249.94,16257.34,15945.92,16040.18,000,16040.18 -2000-08-18,16156.64,16280.49,16118.89,16280.49,000,16280.49 -2000-08-17,16356.14,16356.14,16079.74,16161.03,000,16161.03 -2000-08-16,16300.49,16409.13,16249.94,16356.03,000,16356.03 -2000-08-15,16181.50,16309.50,16131.48,16298.29,000,16298.29 -2000-08-14,16109.95,16227.35,16055.07,16153.91,000,16153.91 -2000-08-11,16008.06,16125.66,15888.56,16117.50,000,16117.50 -2000-08-10,16050.87,16053.61,15946.60,15975.65,000,15975.65 -2000-08-09,15873.42,16034.60,15778.86,16034.60,000,16034.60 -2000-08-08,16044.81,16073.20,15755.22,15820.11,000,15820.11 -2000-08-07,15666.81,16039.40,15666.81,16002.71,000,16002.71 -2000-08-04,15799.99,15897.25,15557.33,15667.36,000,15667.36 -2000-08-03,16185.72,16185.72,15725.98,15814.44,000,15814.44 -2000-08-02,16118.80,16211.55,16054.66,16206.19,000,16206.19 -2000-08-01,15784.60,16099.67,15773.72,16099.67,000,16099.67 -2000-07-31,15821.31,15854.66,15394.71,15727.49,000,15727.49 -2000-07-28,16134.19,16134.19,15815.53,15838.57,000,15838.57 -2000-07-27,16457.70,16457.70,16027.34,16182.01,000,16182.01 -2000-07-26,16553.57,16563.71,16400.22,16502.61,000,16502.61 -2000-07-25,16506.15,16573.59,16341.52,16573.59,000,16573.59 -2000-07-24,16767.20,16767.20,16370.50,16547.12,000,16547.12 -2000-07-21,17025.66,17098.74,16800.59,16811.49,000,16811.49 -2000-07-19,16905.34,17015.47,16703.01,16983.57,000,16983.57 -2000-07-18,17261.08,17349.59,16834.02,16945.07,000,16945.07 -2000-07-17,17188.98,17476.86,17150.17,17286.83,000,17286.83 -2000-07-14,17136.70,17176.20,17019.50,17142.90,000,17142.90 -2000-07-13,17348.34,17348.34,17009.65,17036.90,000,17036.90 -2000-07-12,17519.04,17541.98,17210.18,17342.13,000,17342.13 -2000-07-11,17567.50,17573.69,17436.58,17504.36,000,17504.36 -2000-07-10,17421.56,17594.66,17421.56,17572.68,000,17572.68 -2000-07-07,17290.46,17483.47,17290.46,17398.24,000,17398.24 -2000-07-06,17402.71,17402.71,17154.58,17282.37,000,17282.37 -2000-07-05,17473.76,17576.28,17364.70,17435.95,000,17435.95 -2000-07-04,17655.57,17661.11,17434.07,17470.15,000,17470.15 -2000-07-03,17451.65,17636.67,17451.65,17614.66,000,17614.66 -2000-06-30,17482.09,17510.07,17305.81,17411.05,000,17411.05 -2000-06-29,17448.35,17511.32,17383.01,17475.90,000,17475.90 -2000-06-28,17289.31,17421.07,17230.04,17370.17,000,17370.17 -2000-06-27,16969.48,17285.47,16969.48,17279.06,000,17279.06 -2000-06-26,16927.27,16975.07,16761.59,16925.40,000,16925.40 -2000-06-23,17055.67,17214.47,16899.00,16963.21,000,16963.21 -2000-06-22,17228.85,17364.26,17092.44,17106.01,000,17106.01 -2000-06-21,16922.25,17212.38,16854.21,17210.08,000,17210.08 -2000-06-20,16645.99,16907.55,16645.99,16907.55,000,16907.55 -2000-06-19,16382.11,16627.54,16359.96,16591.35,000,16591.35 -2000-06-16,16358.71,16480.08,16289.92,16318.31,000,16318.31 -2000-06-15,16635.24,16635.24,16334.96,16338.70,000,16338.70 -2000-06-14,16920.21,16920.21,16477.98,16654.42,000,16654.42 -2000-06-13,16950.93,16950.93,16768.86,16914.95,000,16914.95 -2000-06-12,16876.68,17018.64,16791.87,16980.61,000,16980.61 -2000-06-09,17004.72,17004.72,16785.77,16861.91,000,16861.91 -2000-06-08,17177.45,17250.83,16979.30,17004.34,000,17004.34 -2000-06-07,17134.14,17206.98,17019.25,17144.96,000,17144.96 -2000-06-06,17163.39,17207.15,17064.92,17170.08,000,17170.08 -2000-06-05,16842.04,17261.87,16842.04,17201.79,000,17201.79 -2000-06-02,16682.25,16941.42,16682.25,16800.06,000,16800.06 -2000-06-01,16320.08,16694.30,16320.08,16694.30,000,16694.30 -2000-05-31,16274.14,16538.67,16224.06,16332.45,000,16332.45 -2000-05-30,16275.96,16485.54,16210.04,16228.90,000,16228.90 -2000-05-29,16028.76,16245.44,16028.76,16245.44,000,16245.44 -2000-05-26,16219.02,16219.02,15870.25,16008.14,000,16008.14 -2000-05-25,16090.36,16374.06,16090.36,16247.82,000,16247.82 -2000-05-24,16237.88,16261.33,15876.34,16044.44,000,16044.44 -2000-05-23,16345.26,16493.70,16169.93,16318.73,000,16318.73 -2000-05-22,16802.66,16802.66,16174.40,16386.01,000,16386.01 -2000-05-19,16960.29,16960.29,16572.05,16858.17,000,16858.17 -2000-05-18,17365.09,17365.09,16971.82,17031.63,000,17031.63 -2000-05-17,17588.81,17691.39,17348.88,17404.03,000,17404.03 -2000-05-16,17342.84,17558.51,17283.52,17551.25,000,17551.25 -2000-05-15,17396.45,17396.45,17192.71,17313.69,000,17313.69 -2000-05-12,16963.87,17362.20,16963.87,17357.86,000,17357.86 -2000-05-11,17605.65,17605.65,16779.42,16882.46,000,16882.46 -2000-05-10,17799.21,17803.44,17393.59,17701.47,000,17701.47 -2000-05-09,18152.37,18152.37,17804.04,17844.54,000,17844.54 -2000-05-08,18465.71,18475.45,18189.80,18199.96,000,18199.96 -2000-05-02,18497.52,18586.16,18428.92,18439.36,000,18439.36 -2000-05-01,17979.25,18403.08,17979.25,18403.08,000,18403.08 -2000-04-28,18035.54,18137.01,17926.43,17973.70,000,17973.70 -2000-04-27,18122.70,18245.44,18012.41,18019.17,000,18019.17 -2000-04-26,18326.90,18439.18,17948.36,18134.31,000,18134.31 -2000-04-25,18433.30,18630.30,18176.83,18272.33,000,18272.33 -2000-04-24,18247.48,18987.34,18247.48,18480.15,000,18480.15 -2000-04-21,19050.68,19269.06,18091.10,18252.68,000,18252.68 -2000-04-20,19041.53,19382.44,18959.32,18959.32,000,18959.32 -2000-04-19,18998.06,19192.63,18794.48,19086.62,000,19086.62 -2000-04-18,19089.00,19330.40,18547.38,18969.52,000,18969.52 -2000-04-17,20341.50,20341.50,18603.87,19008.64,000,19008.64 -2000-04-14,20456.48,20604.37,20330.89,20434.68,000,20434.68 -2000-04-13,20736.40,20736.40,20385.16,20526.42,000,20526.42 -2000-04-12,20475.96,20833.21,20436.74,20833.21,000,20833.21 -2000-04-11,20544.40,20656.87,20516.94,20522.52,000,20522.52 -2000-04-10,20368.81,20640.11,20368.81,20619.06,000,20619.06 -2000-04-07,20269.59,20463.67,20252.81,20252.81,000,20252.81 -2000-04-06,20476.23,20556.97,20171.62,20223.61,000,20223.61 -2000-04-05,20546.60,20654.63,20311.79,20462.77,000,20462.77 -2000-04-04,20747.82,20747.82,20536.33,20594.93,000,20594.93 -2000-04-03,20327.79,20726.99,20271.92,20726.99,000,20726.99 -2000-03-31,20371.07,20550.10,20259.35,20337.32,000,20337.32 -2000-03-30,20706.45,20809.79,20439.44,20441.50,000,20441.50 -2000-03-29,20406.56,20809.18,20406.56,20706.65,000,20706.65 -2000-03-28,20273.68,20388.18,20014.08,20374.34,000,20374.34 -2000-03-27,19976.14,20295.69,19880.11,20281.03,000,20281.03 -2000-03-24,19752.16,20012.41,19697.57,19958.08,000,19958.08 -2000-03-23,19749.51,19762.30,19568.77,19704.60,000,19704.60 -2000-03-22,19612.23,19734.43,19602.88,19733.59,000,19733.59 -2000-03-21,19598.53,19602.36,19455.11,19602.36,000,19602.36 -2000-03-17,19335.60,19573.48,19335.60,19566.32,000,19566.32 -2000-03-16,19095.63,19313.18,18892.26,19253.23,000,19253.23 -2000-03-15,19120.06,19120.06,18765.88,19078.60,000,19078.60 -2000-03-14,19139.57,19335.22,18956.25,19141.84,000,19141.84 -2000-03-13,19731.50,19760.35,19060.08,19189.93,000,19189.93 -2000-03-10,19734.84,19982.44,19686.50,19750.40,000,19750.40 -2000-03-09,19751.72,19885.54,19614.82,19662.33,000,19662.33 -2000-03-08,19856.37,19856.37,19692.04,19766.80,000,19766.80 -2000-03-07,19802.54,19944.24,19704.23,19944.24,000,19944.24 -2000-03-06,20041.48,20160.03,19742.58,19796.35,000,19796.35 -2000-03-03,20023.04,20034.60,19859.42,19927.54,000,19927.54 -2000-03-02,20097.59,20202.96,19903.29,20065.11,000,20065.11 -2000-03-01,20030.08,20165.51,20007.64,20081.67,000,20081.67 -2000-02-29,19761.52,19978.75,19747.02,19959.52,000,19959.52 -2000-02-28,19783.44,19904.86,19713.86,19720.10,000,19720.10 -2000-02-25,19623.38,19817.88,19539.00,19817.88,000,19817.88 -2000-02-24,19616.26,19698.95,19558.04,19571.44,000,19571.44 -2000-02-23,19439.17,19527.79,19373.04,19519.55,000,19519.55 -2000-02-22,19553.62,19712.37,19353.40,19390.58,000,19390.58 -2000-02-21,19736.15,19763.51,19543.75,19543.75,000,19543.75 -2000-02-18,19852.18,19862.94,19670.05,19789.03,000,19789.03 -2000-02-17,19646.37,19803.69,19518.08,19791.40,000,19791.40 -2000-02-16,19418.56,19612.60,19300.14,19599.18,000,19599.18 -2000-02-15,19587.82,19689.16,19331.05,19367.83,000,19367.83 -2000-02-14,19698.57,19748.42,19556.46,19556.46,000,19556.46 -2000-02-10,19915.53,19915.56,19710.02,19710.02,000,19710.02 -2000-02-09,19930.41,20046.14,19925.64,20007.77,000,20007.77 -2000-02-08,19955.30,19983.44,19834.81,19868.88,000,19868.88 -2000-02-07,19832.76,19948.60,19780.56,19945.43,000,19945.43 -2000-02-04,19866.19,20011.91,19752.12,19763.13,000,19763.13 -2000-02-03,19648.35,19878.83,19648.35,19786.42,000,19786.42 -2000-02-02,19522.33,19860.26,19522.33,19578.91,000,19578.91 -2000-02-01,19536.68,19553.68,19266.96,19423.38,000,19423.38 -2000-01-31,19375.11,19539.70,19224.47,19539.70,000,19539.70 -2000-01-28,19261.01,19595.83,19237.49,19434.78,000,19434.78 -2000-01-27,19125.62,19238.08,18971.68,19209.72,000,19209.72 -2000-01-26,18982.84,19145.90,18982.84,19111.19,000,19111.19 -2000-01-25,19004.39,19131.19,18815.37,18895.53,000,18895.53 -2000-01-24,18878.46,19124.57,18877.13,19056.71,000,19056.71 -2000-01-21,18994.89,18994.89,18713.17,18878.09,000,18878.09 -2000-01-20,18930.26,19167.03,18921.11,19008.01,000,19008.01 -2000-01-19,19181.87,19181.87,18897.75,18897.75,000,18897.75 -2000-01-18,19412.47,19412.47,19145.17,19196.57,000,19196.57 -2000-01-17,19025.62,19442.58,19025.62,19437.23,000,19437.23 -2000-01-14,18882.99,19058.02,18733.83,18956.55,000,18956.55 -2000-01-13,18667.18,18845.03,18667.18,18833.29,000,18833.29 -2000-01-12,18780.17,18811.87,18626.92,18677.42,000,18677.42 -2000-01-11,18246.10,18887.56,18246.10,18850.92,000,18850.92 -2000-01-07,18194.05,18285.73,18068.10,18193.41,000,18193.41 -2000-01-06,18574.01,18582.74,18168.27,18168.27,000,18168.27 -2000-01-05,19003.51,19003.51,18221.82,18542.55,000,18542.55 -2000-01-04,18937.45,19187.61,18937.45,19002.86,000,19002.86 -1999-12-30,18793.55,18960.33,18722.51,18934.34,000,18934.34 -1999-12-29,18777.85,18886.04,18729.08,18810.58,000,18810.58 -1999-12-28,18545.41,18816.70,18480.79,18783.52,000,18783.52 -1999-12-27,18596.49,18666.62,18472.60,18546.90,000,18546.90 -1999-12-24,18496.46,18777.04,18496.46,18584.95,000,18584.95 -1999-12-22,18164.34,18461.93,18164.34,18461.93,000,18461.93 -1999-12-21,18166.28,18168.10,18024.64,18080.38,000,18080.38 -1999-12-20,18137.94,18272.25,18060.35,18175.49,000,18175.49 -1999-12-17,18126.11,18255.92,18095.12,18095.12,000,18095.12 -1999-12-16,18126.54,18187.50,18006.32,18111.31,000,18111.31 -1999-12-15,18145.35,18302.83,18030.72,18138.36,000,18138.36 -1999-12-14,18204.52,18210.18,18039.61,18165.55,000,18165.55 -1999-12-13,18284.98,18284.98,18150.71,18205.08,000,18205.08 -1999-12-10,18264.54,18416.07,18212.99,18271.85,000,18271.85 -1999-12-09,18405.76,18405.76,18081.78,18260.72,000,18260.72 -1999-12-08,18556.63,18604.91,18383.89,18401.20,000,18401.20 -1999-12-07,18512.98,18657.44,18466.40,18593.96,000,18593.96 -1999-12-06,18380.70,18664.49,18380.70,18507.20,000,18507.20 -1999-12-03,18528.09,18610.77,18349.61,18368.14,000,18368.14 -1999-12-02,18535.88,18684.20,18339.85,18514.41,000,18514.41 -1999-12-01,18563.26,18718.92,18465.82,18495.95,000,18495.95 -1999-11-30,18853.44,18864.86,18477.72,18558.23,000,18558.23 -1999-11-29,18883.41,18909.89,18722.89,18850.27,000,18850.27 -1999-11-26,18776.98,19002.25,18675.05,18914.50,000,18914.50 -1999-11-25,18914.64,19020.90,18649.31,18721.78,000,18721.78 -1999-11-24,18796.02,19036.08,18664.02,18896.21,000,18896.21 -1999-11-22,18607.05,18848.73,18607.05,18822.12,000,18822.12 -1999-11-19,18576.42,18837.01,18570.84,18570.84,000,18570.84 -1999-11-18,18299.95,18686.87,18292.30,18532.81,000,18532.81 -1999-11-17,18218.72,18509.70,18071.25,18274.82,000,18274.82 -1999-11-16,18214.89,18289.41,18090.92,18155.14,000,18155.14 -1999-11-15,18295.50,18519.52,18198.09,18198.09,000,18198.09 -1999-11-12,18358.28,18483.50,18245.70,18258.55,000,18258.55 -1999-11-11,18551.86,18659.55,18310.26,18327.28,000,18327.28 -1999-11-10,18258.37,18622.78,18214.79,18567.87,000,18567.87 -1999-11-09,18259.41,18498.05,18259.41,18292.16,000,18292.16 -1999-11-08,18381.24,18481.52,18179.78,18240.98,000,18240.98 -1999-11-05,18340.18,18491.31,18173.97,18354.90,000,18354.90 -1999-11-04,18067.36,18377.73,18067.36,18348.13,000,18348.13 -1999-11-02,17976.91,18058.42,17857.11,17991.96,000,17991.96 -1999-11-01,17982.39,18119.94,17952.96,17996.92,000,17996.92 -1999-10-29,17467.89,17947.57,17467.89,17942.08,000,17942.08 -1999-10-28,17403.27,17525.88,17403.27,17413.71,000,17413.71 -1999-10-27,17655.78,17655.78,17360.83,17382.36,000,17382.36 -1999-10-26,17656.43,17751.83,17578.73,17671.79,000,17671.79 -1999-10-25,17494.93,17787.73,17494.93,17648.79,000,17648.79 -1999-10-22,17458.98,17607.66,17432.19,17438.80,000,17438.80 -1999-10-21,17543.83,17605.41,17325.77,17448.27,000,17448.27 -1999-10-20,17297.66,17560.25,17297.66,17534.71,000,17534.71 -1999-10-19,17325.04,17377.28,17178.47,17254.17,000,17254.17 -1999-10-18,17568.92,17568.92,17194.03,17275.33,000,17275.33 -1999-10-15,17779.78,17808.44,17511.11,17601.57,000,17601.57 -1999-10-14,17765.75,17941.51,17702.24,17780.26,000,17780.26 -1999-10-13,18030.40,18030.91,17754.49,17754.49,000,17754.49 -1999-10-12,18098.20,18221.69,18084.49,18090.81,000,18090.81 -1999-10-08,18164.67,18164.67,17915.15,18062.18,000,18062.18 -1999-10-07,17985.96,18228.39,17985.96,18136.55,000,18136.55 -1999-10-06,17822.64,17926.13,17734.30,17896.42,000,17896.42 -1999-10-05,17819.00,17985.47,17775.95,17784.15,000,17784.15 -1999-10-04,17747.56,17888.86,17675.28,17763.71,000,17763.71 -1999-10-01,17589.73,17886.39,17510.29,17712.56,000,17712.56 -1999-09-30,17307.22,17771.21,17307.22,17605.46,000,17605.46 -1999-09-29,17313.32,17313.32,17071.02,17282.28,000,17282.28 -1999-09-28,16892.46,17388.70,16892.46,17325.70,000,17325.70 -1999-09-27,16888.02,17039.42,16820.93,16821.06,000,16821.06 -1999-09-24,17290.01,17290.01,16652.04,16871.73,000,16871.73 -1999-09-22,17842.87,17842.87,17248.92,17325.76,000,17325.76 -1999-09-21,17585.78,17932.79,17558.74,17932.79,000,17932.79 -1999-09-20,17418.23,17662.82,17418.23,17575.26,000,17575.26 -1999-09-17,17273.99,17391.15,17118.31,17342.27,000,17342.27 -1999-09-16,17703.97,17703.97,17058.13,17291.59,000,17291.59 -1999-09-14,17880.99,17880.99,17610.31,17777.22,000,17777.22 -1999-09-13,17735.60,17988.59,17735.60,17909.29,000,17909.29 -1999-09-10,17648.89,17831.60,17589.05,17711.02,000,17711.02 -1999-09-09,17707.09,17859.71,17677.28,17677.56,000,17677.56 -1999-09-08,17710.02,17710.02,17509.73,17641.38,000,17641.38 -1999-09-07,17777.92,17858.13,17651.94,17707.50,000,17707.50 -1999-09-06,17664.06,17839.71,17664.06,17756.51,000,17756.51 -1999-09-03,17632.10,17716.74,17559.40,17629.99,000,17629.99 -1999-09-02,17795.40,17800.80,17630.32,17631.25,000,17631.25 -1999-09-01,17479.57,17819.60,17479.57,17802.48,000,17802.48 -1999-08-31,17886.20,17886.20,17425.49,17436.56,000,17436.56 -1999-08-30,17642.05,17925.07,17642.05,17918.97,000,17918.97 -1999-08-27,17710.97,17816.67,17567.86,17599.37,000,17599.37 -1999-08-26,17863.91,17995.49,17666.29,17666.29,000,17666.29 -1999-08-25,18099.23,18164.99,17795.08,17855.16,000,17855.16 -1999-08-24,18266.22,18397.93,18095.41,18095.41,000,18095.41 -1999-08-23,18133.20,18384.86,18133.20,18233.55,000,18233.55 -1999-08-20,17907.52,18123.29,17907.52,18098.11,000,18098.11 -1999-08-19,17843.11,17923.30,17665.97,17879.74,000,17879.74 -1999-08-18,17873.31,18082.47,17827.89,17892.26,000,17892.26 -1999-08-17,17842.97,17916.08,17766.69,17860.09,000,17860.09 -1999-08-16,17499.12,17937.21,17499.12,17826.03,000,17826.03 -1999-08-13,17430.05,17493.95,17284.70,17435.17,000,17435.17 -1999-08-12,17280.79,17476.68,17280.79,17422.97,000,17422.97 -1999-08-11,17185.70,17361.82,17097.41,17211.16,000,17211.16 -1999-08-10,17188.87,17203.39,17040.72,17202.09,000,17202.09 -1999-08-09,17105.93,17313.97,17095.50,17190.45,000,17190.45 -1999-08-06,17345.53,17345.53,17046.12,17084.24,000,17084.24 -1999-08-05,17638.93,17638.93,17210.04,17358.19,000,17358.19 -1999-08-04,17913.62,17913.62,17631.81,17685.38,000,17685.38 -1999-08-03,17824.68,17969.93,17597.74,17969.93,000,17969.93 -1999-08-02,17827.24,17909.89,17726.05,17825.70,000,17825.70 -1999-07-30,17857.91,17959.28,17677.14,17861.86,000,17861.86 -1999-07-29,17633.34,17957.37,17633.34,17869.92,000,17869.92 -1999-07-28,17508.66,17729.68,17508.66,17579.91,000,17579.91 -1999-07-27,17484.36,17613.24,17367.08,17462.72,000,17462.72 -1999-07-26,17568.32,17663.22,17491.34,17491.34,000,17491.34 -1999-07-23,17657.17,17657.17,17414.36,17534.44,000,17534.44 -1999-07-22,18221.07,18221.07,17665.04,17730.34,000,17730.34 -1999-07-21,18458.58,18458.58,18182.91,18257.52,000,18257.52 -1999-07-19,18288.37,18532.58,18288.37,18532.58,000,18532.58 -1999-07-16,18438.05,18623.15,18248.30,18248.30,000,18248.30 -1999-07-15,18402.63,18459.51,18269.52,18431.86,000,18431.86 -1999-07-14,18188.36,18455.60,18165.92,18357.86,000,18357.86 -1999-07-13,18231.83,18343.25,18172.25,18181.09,000,18181.09 -1999-07-12,17921.58,18274.18,17843.39,18274.18,000,18274.18 -1999-07-09,17965.70,17989.20,17813.46,17937.73,000,17937.73 -1999-07-08,17999.30,18045.56,17887.18,17967.65,000,17967.65 -1999-07-07,18075.21,18158.38,17949.73,17958.90,000,17958.90 -1999-07-06,18131.76,18159.55,17960.72,18050.73,000,18050.73 -1999-07-05,18001.16,18243.14,18001.16,18135.06,000,18135.06 -1999-07-02,17945.45,18066.28,17875.17,17932.47,000,17932.47 -1999-07-01,17607.60,17972.03,17607.60,17860.75,000,17860.75 -1999-06-30,17832.59,17958.34,17529.74,17529.74,000,17529.74 -1999-06-29,17675.84,17825.79,17649.31,17782.79,000,17782.79 -1999-06-28,17501.12,17688.03,17501.12,17610.58,000,17610.58 -1999-06-25,17597.78,17673.51,17430.42,17436.52,000,17436.52 -1999-06-24,17590.01,17700.46,17528.90,17628.32,000,17628.32 -1999-06-23,17742.53,17843.53,17580.05,17586.75,000,17586.75 -1999-06-22,17780.74,17828.21,17662.94,17777.62,000,17777.62 -1999-06-21,17485.90,17747.04,17485.90,17738.85,000,17738.85 -1999-06-18,17504.33,17650.38,17431.26,17431.26,000,17431.26 -1999-06-17,17268.45,17550.13,17268.45,17470.45,000,17470.45 -1999-06-16,17284.98,17346.23,17144.19,17210.18,000,17210.18 -1999-06-15,17216.28,17339.57,16990.55,17282.00,000,17282.00 -1999-06-14,17193.61,17384.90,17168.57,17188.82,000,17188.82 -1999-06-11,17084.57,17483.38,17070.60,17198.55,000,17198.55 -1999-06-10,16627.80,17130.36,16627.80,17102.62,000,17102.62 -1999-06-09,16499.16,16713.26,16450.76,16622.50,000,16622.50 -1999-06-08,16497.95,16571.12,16469.84,16562.92,000,16562.92 -1999-06-07,16318.44,16576.51,16318.44,16475.89,000,16475.89 -1999-06-04,16237.27,16368.15,16169.88,16300.75,000,16300.75 -1999-06-03,16418.92,16418.92,16127.90,16227.50,000,16227.50 -1999-06-02,16404.12,16458.34,16304.99,16417.99,000,16417.99 -1999-06-01,16090.15,16408.50,16026.95,16408.50,000,16408.50 -1999-05-31,15979.20,16111.65,15901.80,16111.65,000,16111.65 -1999-05-28,16118.26,16118.26,15886.62,15972.68,000,15972.68 -1999-05-27,16287.02,16330.77,16012.19,16177.19,000,16177.19 -1999-05-26,16172.72,16334.87,16070.88,16230.52,000,16230.52 -1999-05-25,16305.50,16305.50,16175.98,16214.23,000,16214.23 -1999-05-24,16261.15,16409.76,16157.22,16390.49,000,16390.49 -1999-05-21,16220.38,16303.83,16138.88,16292.98,000,16292.98 -1999-05-20,16150.42,16297.31,15946.66,16199.99,000,16199.99 -1999-05-19,16354.60,16354.60,16111.98,16128.18,000,16128.18 -1999-05-18,16440.38,16582.19,16291.12,16378.62,000,16378.62 -1999-05-17,16750.91,16750.91,16420.97,16421.02,000,16421.02 -1999-05-14,16899.84,16960.77,16750.67,16810.39,000,16810.39 -1999-05-13,16952.85,16958.02,16792.98,16851.25,000,16851.25 -1999-05-12,16782.56,17104.49,16782.56,16947.36,000,16947.36 -1999-05-11,17003.44,17003.44,16736.90,16743.18,000,16743.18 -1999-05-10,16983.29,17078.98,16931.63,16977.01,000,16977.01 -1999-05-07,17281.53,17281.53,16929.68,16946.52,000,16946.52 -1999-05-06,16762.78,17300.61,16762.78,17300.61,000,17300.61 -1999-04-30,16919.58,16953.13,16701.53,16701.53,000,16701.53 -1999-04-28,17013.87,17149.91,16881.60,16942.24,000,16942.24 -1999-04-27,16992.13,17069.72,16934.52,16957.27,000,16957.27 -1999-04-26,16919.76,17143.49,16915.15,16918.51,000,16918.51 -1999-04-23,16715.86,16923.25,16715.86,16923.25,000,16923.25 -1999-04-22,16563.72,16665.88,16484.08,16665.88,000,16665.88 -1999-04-21,16724.43,16736.06,16454.34,16495.02,000,16495.02 -1999-04-20,16587.17,16749.05,16526.53,16697.11,000,16697.11 -1999-04-19,16858.28,16858.28,16583.64,16674.21,000,16674.21 -1999-04-16,16754.31,16979.06,16754.31,16851.58,000,16851.58 -1999-04-15,16758.59,16844.97,16589.36,16727.08,000,16727.08 -1999-04-14,16698.18,16799.17,16436.19,16764.68,000,16764.68 -1999-04-13,16617.05,16855.67,16617.05,16715.16,000,16715.16 -1999-04-12,16797.08,16797.08,16507.40,16507.40,000,16507.40 -1999-04-09,16933.40,17166.06,16827.93,16855.63,000,16855.63 -1999-04-08,16552.50,16866.66,16483.52,16846.69,000,16846.69 -1999-04-07,16429.86,16563.20,16341.25,16554.50,000,16554.50 -1999-04-06,16360.98,16484.27,16084.71,16479.71,000,16479.71 -1999-04-05,16320.07,16634.79,16231.13,16334.78,000,16334.78 -1999-04-02,16351.72,16453.50,16243.46,16290.19,000,16290.19 -1999-04-01,15868.05,16449.97,15813.41,16327.56,000,16327.56 -1999-03-31,15865.77,16027.27,15651.35,15836.59,000,15836.59 -1999-03-30,16091.92,16184.54,15805.69,15859.12,000,15859.12 -1999-03-29,16031.32,16185.80,16002.28,16008.84,000,16008.84 -1999-03-26,16030.53,16181.09,15905.66,16016.99,000,16016.99 -1999-03-25,15591.41,16061.02,15591.41,15986.04,000,15986.04 -1999-03-24,15947.57,15947.57,15515.47,15515.47,000,15515.47 -1999-03-23,16401.96,16437.23,15988.41,16019.10,000,16019.10 -1999-03-19,15804.71,16421.81,15804.17,16378.78,000,16378.78 -1999-03-18,16238.91,16303.12,15717.92,15717.92,000,15717.92 -1999-03-17,16088.09,16269.20,15977.87,16268.11,000,16268.11 -1999-03-16,15749.55,16082.07,15591.62,16072.82,000,16072.82 -1999-03-15,15526.51,15790.24,15404.10,15779.60,000,15779.60 -1999-03-12,15588.89,15709.01,15408.38,15488.86,000,15488.86 -1999-03-11,15485.43,15840.23,15417.03,15502.14,000,15502.14 -1999-03-10,15134.05,15484.63,15134.05,15480.00,000,15480.00 -1999-03-09,14842.17,15096.70,14842.17,15096.70,000,15096.70 -1999-03-08,14925.59,15116.10,14779.05,14779.05,000,14779.05 -1999-03-05,14256.02,14895.39,14256.02,14894.00,000,14894.00 -1999-03-04,14202.20,14224.73,14121.02,14183.45,000,14183.45 -1999-03-03,13956.18,14170.46,13927.73,14170.36,000,14170.36 -1999-03-02,14260.55,14306.01,13921.06,13921.06,000,13921.06 -1999-03-01,14362.86,14468.86,14221.60,14221.75,000,14221.75 -1999-02-26,14456.33,14494.58,14363.31,14367.54,000,14367.54 -1999-02-25,14386.09,14470.45,14363.81,14470.45,000,14470.45 -1999-02-24,14483.59,14534.82,14326.15,14355.45,000,14355.45 -1999-02-23,14284.27,14500.65,14284.27,14500.65,000,14500.65 -1999-02-22,14149.67,14314.32,14096.70,14256.67,000,14256.67 -1999-02-19,14144.00,14179.77,14045.17,14098.04,000,14098.04 -1999-02-18,14155.89,14168.57,14041.93,14146.79,000,14146.79 -1999-02-17,14277.81,14407.33,14146.64,14158.67,000,14158.67 -1999-02-16,14095.25,14358.68,14095.25,14232.64,000,14232.64 -1999-02-15,14008.36,14101.22,14003.23,14054.72,000,14054.72 -1999-02-12,13925.24,14062.92,13925.24,13973.69,000,13973.69 -1999-02-10,13852.62,13971.15,13795.61,13952.40,000,13952.40 -1999-02-09,14008.56,14015.57,13885.79,13902.66,000,13902.66 -1999-02-08,13886.64,14024.92,13770.89,13992.49,000,13992.49 -1999-02-05,14049.69,14049.69,13769.25,13898.08,000,13898.08 -1999-02-04,14176.13,14255.67,13925.14,14086.85,000,14086.85 -1999-02-03,14250.55,14250.55,14089.09,14161.31,000,14161.31 -1999-02-02,14459.81,14459.81,14285.96,14349.83,000,14349.83 -1999-02-01,14544.12,14641.41,14356.30,14465.18,000,14465.18 -1999-01-29,14413.90,14628.88,14413.90,14499.25,000,14499.25 -1999-01-28,14472.05,14522.33,14331.77,14342.32,000,14342.32 -1999-01-27,14416.24,14526.21,14370.52,14450.06,000,14450.06 -1999-01-26,14251.29,14501.44,14251.29,14382.01,000,14382.01 -1999-01-25,14141.56,14251.09,14076.35,14208.81,000,14208.81 -1999-01-22,14235.67,14407.23,14154.40,14154.40,000,14154.40 -1999-01-21,14048.85,14331.23,14009.45,14245.42,000,14245.42 -1999-01-20,13794.92,14028.05,13738.96,14028.05,000,14028.05 -1999-01-19,13840.58,13883.01,13756.96,13770.44,000,13770.44 -1999-01-18,13762.34,13964.19,13754.58,13805.06,000,13805.06 -1999-01-14,13369.08,13738.86,13369.08,13738.86,000,13738.86 -1999-01-13,13353.81,13444.34,13313.77,13403.60,000,13403.60 -1999-01-12,13313.47,13535.66,13212.30,13360.97,000,13360.97 -1999-01-11,13354.41,13452.89,13224.63,13368.48,000,13368.48 -1999-01-08,13507.06,13507.06,13328.34,13391.81,000,13391.81 -1999-01-07,13576.00,13854.16,13516.41,13536.56,000,13536.56 -1999-01-06,13281.69,13475.63,13216.18,13468.46,000,13468.46 -1999-01-05,13437.03,13437.03,13122.61,13232.74,000,13232.74 -1999-01-04,13779.05,13779.05,13415.89,13415.89,000,13415.89 -1998-12-30,13832.32,13913.55,13812.87,13842.17,000,13842.17 -1998-12-29,13780.44,13846.90,13664.79,13846.90,000,13846.90 -1998-12-28,13876.54,13894.55,13703.29,13709.06,000,13709.06 -1998-12-24,13810.58,13815.21,13657.03,13706.73,000,13706.73 -1998-12-22,14183.74,14185.54,13722.10,13779.45,000,13779.45 -1998-12-21,14158.82,14158.82,14038.70,14152.95,000,14152.95 -1998-12-18,14161.46,14269.50,14078.14,14194.29,000,14194.29 -1998-12-17,14064.22,14169.32,13917.33,14126.99,000,14126.99 -1998-12-16,14092.32,14157.53,14016.81,14096.30,000,14096.30 -1998-12-15,14084.96,14192.60,13965.28,14011.19,000,14011.19 -1998-12-14,14349.18,14349.18,14103.86,14111.62,000,14111.62 -1998-12-11,14729.95,14729.95,14382.06,14405.64,000,14405.64 -1998-12-10,14890.92,15007.01,14807.40,14807.80,000,14807.80 -1998-12-09,14751.79,14931.90,14700.66,14931.90,000,14931.90 -1998-12-08,14775.02,14916.09,14775.02,14808.20,000,14808.20 -1998-12-07,14703.64,14748.91,14650.42,14723.49,000,14723.49 -1998-12-04,14641.41,14654.20,14536.01,14639.97,000,14639.97 -1998-12-03,14875.60,14875.60,14589.63,14697.08,000,14697.08 -1998-12-02,14863.71,15011.09,14781.78,14986.62,000,14986.62 -1998-12-01,14821.53,14931.16,14763.88,14835.41,000,14835.41 -1998-11-30,15107.09,15139.87,14883.11,14883.70,000,14883.70 -1998-11-27,15188.77,15320.23,15069.39,15069.39,000,15069.39 -1998-11-26,15101.62,15219.81,15043.72,15207.77,000,15207.77 -1998-11-25,15108.04,15124.60,14942.10,15073.47,000,15073.47 -1998-11-24,14904.20,15164.64,14904.20,15164.64,000,15164.64 -1998-11-20,14461.20,14779.94,14461.20,14779.94,000,14779.94 -1998-11-19,14557.10,14642.96,14354.41,14354.46,000,14354.46 -1998-11-18,14396.09,14701.45,14383.36,14599.23,000,14599.23 -1998-11-17,14475.88,14480.50,14276.81,14413.00,000,14413.00 -1998-11-16,14339.43,14450.56,14205.13,14428.27,000,14428.27 -1998-11-13,14119.38,14268.21,13984.68,14268.21,000,14268.21 -1998-11-12,14364.85,14418.08,14075.06,14075.06,000,14075.06 -1998-11-11,14137.63,14428.02,14065.51,14428.02,000,14428.02 -1998-11-10,14154.00,14270.05,14099.23,14108.09,000,14108.09 -1998-11-09,14172.80,14362.66,14063.27,14194.54,000,14194.54 -1998-11-06,14308.99,14308.99,14121.97,14121.97,000,14121.97 -1998-11-05,14624.45,14625.15,14180.71,14341.37,000,14341.37 -1998-11-04,14095.65,14527.81,14095.65,14527.81,000,14527.81 -1998-11-02,13648.28,13952.75,13648.28,13952.75,000,13952.75 -1998-10-30,13732.19,13835.56,13454.64,13564.51,000,13564.51 -1998-10-29,13566.80,13727.02,13432.10,13668.72,000,13668.72 -1998-10-28,13804.32,13920.81,13516.07,13516.07,000,13516.07 -1998-10-27,13855.05,14054.86,13762.44,13820.68,000,13820.68 -1998-10-26,14054.86,14054.86,13806.36,13843.46,000,13843.46 -1998-10-23,14327.65,14523.48,14042.63,14144.70,000,14144.70 -1998-10-22,14287.75,14742.44,14245.77,14295.56,000,14295.56 -1998-10-21,13921.36,14366.89,13921.36,14216.33,000,14216.33 -1998-10-20,13569.14,13808.84,13483.24,13808.05,000,13808.05 -1998-10-19,13263.18,13786.36,13263.18,13567.20,000,13567.20 -1998-10-16,13109.33,13309.99,13109.33,13280.54,000,13280.54 -1998-10-15,13113.36,13184.64,12892.96,12995.37,000,12995.37 -1998-10-14,13264.62,13454.04,13057.05,13070.73,000,13070.73 -1998-10-13,13538.80,13591.82,13241.20,13242.79,000,13242.79 -1998-10-12,12977.82,13572.87,12977.82,13555.01,000,13555.01 -1998-10-09,12923.45,13301.53,12787.90,12879.97,000,12879.97 -1998-10-08,13749.01,13749.01,12987.91,13026.06,000,13026.06 -1998-10-07,13095.90,13825.61,13095.90,13825.61,000,13825.61 -1998-10-06,12936.38,13216.28,12927.78,13021.64,000,13021.64 -1998-10-05,13185.98,13185.98,12910.27,12948.12,000,12948.12 -1998-10-02,13141.56,13320.23,12973.24,13223.69,000,13223.69 -1998-10-01,13377.24,13558.45,13018.75,13197.12,000,13197.12 -1998-09-30,13878.69,13966.77,13406.39,13406.39,000,13406.39 -1998-09-29,13937.82,13952.35,13553.02,13821.43,000,13821.43 -1998-09-28,13760.74,14121.47,13687.03,13909.37,000,13909.37 -1998-09-25,14057.85,14057.85,13678.47,13723.84,000,13723.84 -1998-09-24,13896.79,14297.50,13896.79,14205.78,000,14205.78 -1998-09-22,13640.56,13871.48,13521.13,13789.81,000,13789.81 -1998-09-21,13875.77,13875.77,13580.72,13597.30,000,13597.30 -1998-09-18,13799.75,14006.09,13697.75,13983.12,000,13983.12 -1998-09-17,14213.49,14279.77,13784.07,13859.14,000,13859.14 -1998-09-16,14229.32,14375.57,14176.02,14197.70,000,14197.70 -1998-09-14,13979.32,14329.92,13844.31,14227.37,000,14227.37 -1998-09-11,14551.25,14551.25,13725.62,13916.98,000,13916.98 -1998-09-10,14805.29,14902.00,14530.92,14666.03,000,14666.03 -1998-09-09,14968.18,15099.85,14629.62,14755.54,000,14755.54 -1998-09-08,14766.93,15294.26,14766.93,14913.49,000,14913.49 -1998-09-07,13984.47,14790.06,13912.69,14790.06,000,14790.06 -1998-09-04,14158.34,14185.86,14042.91,14042.91,000,14042.91 -1998-09-03,14362.54,14368.28,14207.84,14261.24,000,14261.24 -1998-09-02,14361.04,14589.41,14287.16,14376.62,000,14376.62 -1998-09-01,13979.82,14369.83,13664.74,14369.63,000,14369.63 -1998-08-31,13955.79,14224.18,13845.15,14107.89,000,14107.89 -1998-08-28,14289.21,14289.21,13792.76,13915.63,000,13915.63 -1998-08-27,14794.41,14794.41,14378.67,14413.79,000,14413.79 -1998-08-26,15113.04,15113.04,14866.03,14866.03,000,14866.03 -1998-08-25,15070.23,15226.97,15070.23,15072.93,000,15072.93 -1998-08-24,15145.25,15145.25,14859.34,14988.36,000,14988.36 -1998-08-21,15277.12,15416.88,15226.62,15298.20,000,15298.20 -1998-08-20,15445.75,15445.75,15236.26,15391.41,000,15391.41 -1998-08-19,15164.24,15416.48,15161.84,15406.34,000,15406.34 -1998-08-18,14896.70,15102.90,14845.75,15063.79,000,15063.79 -1998-08-17,15130.77,15137.66,14655.69,14794.66,000,14794.66 -1998-08-14,15305.09,15355.09,15049.45,15123.93,000,15123.93 -1998-08-13,15412.99,15476.32,15239.71,15382.02,000,15382.02 -1998-08-12,15287.31,15534.07,15269.83,15378.97,000,15378.97 -1998-08-11,15571.23,15630.07,15310.59,15406.99,000,15406.99 -1998-08-10,15800.35,15800.35,15595.70,15626.42,000,15626.42 -1998-08-07,15924.53,16037.26,15797.30,15829.17,000,15829.17 -1998-08-06,16048.15,16075.72,15834.77,15876.22,000,15876.22 -1998-08-05,16015.13,16019.33,15817.03,15992.16,000,15992.16 -1998-08-04,16061.44,16180.92,16004.25,16023.58,000,16023.58 -1998-08-03,16303.60,16303.60,16104.55,16165.08,000,16165.08 -1998-07-31,16286.61,16399.90,16286.61,16378.97,000,16378.97 -1998-07-30,16190.71,16311.54,16190.71,16201.60,000,16201.60 -1998-07-29,16074.18,16282.32,16047.25,16158.09,000,16158.09 -1998-07-28,16018.88,16180.27,15958.19,16114.54,000,16114.54 -1998-07-27,16281.97,16281.97,15944.26,15944.36,000,15944.36 -1998-07-24,16120.88,16418.38,16091.41,16361.89,000,16361.89 -1998-07-23,16268.48,16320.53,16158.44,16188.01,000,16188.01 -1998-07-22,16460.69,16460.69,16286.56,16293.06,000,16293.06 -1998-07-21,16619.78,16698.15,16407.74,16556.69,000,16556.69 -1998-07-17,16732.07,16732.07,16569.78,16570.78,000,16570.78 -1998-07-16,16605.24,16756.89,16498.25,16731.92,000,16731.92 -1998-07-15,16599.55,16614.14,16435.46,16614.14,000,16614.14 -1998-07-14,16379.92,16489.41,16331.37,16488.91,000,16488.91 -1998-07-13,15993.16,16360.39,15804.35,16360.39,000,16360.39 -1998-07-10,16464.79,16531.47,16066.03,16090.06,000,16090.06 -1998-07-09,16492.51,16492.51,16370.08,16446.95,000,16446.95 -1998-07-08,16528.12,16635.86,16497.30,16530.97,000,16530.97 -1998-07-07,16354.35,16531.22,16354.35,16416.28,000,16416.28 -1998-07-06,16433.67,16488.51,16350.45,16350.45,000,16350.45 -1998-07-03,16354.60,16625.42,16289.66,16511.24,000,16511.24 -1998-07-02,16433.47,16743.36,16432.62,16471.58,000,16471.58 -1998-07-01,15852.10,16362.89,15739.76,16362.89,000,16362.89 -1998-06-30,15471.43,15830.47,15462.24,15830.27,000,15830.27 -1998-06-29,15254.80,15424.53,15230.77,15365.73,000,15365.73 -1998-06-26,15123.03,15233.97,14977.67,15210.04,000,15210.04 -1998-06-25,15161.04,15205.04,15075.37,15132.22,000,15132.22 -1998-06-24,15129.02,15210.49,15000.15,15123.18,000,15123.18 -1998-06-23,15320.48,15320.48,15054.60,15054.60,000,15054.60 -1998-06-22,15285.01,15413.79,15246.25,15309.09,000,15309.09 -1998-06-19,15312.29,15351.45,15194.01,15267.98,000,15267.98 -1998-06-18,14825.17,15398.20,14825.17,15361.54,000,15361.54 -1998-06-17,14790.81,14899.85,14673.28,14715.38,000,14715.38 -1998-06-16,14743.21,14917.58,14614.74,14720.38,000,14720.38 -1998-06-15,14964.34,14964.34,14789.31,14825.17,000,14825.17 -1998-06-12,14978.32,15060.69,14784.52,15022.33,000,15022.33 -1998-06-11,15285.01,15285.01,15002.55,15014.04,000,15014.04 -1998-06-10,15520.38,15520.38,15298.80,15339.26,000,15339.26 -1998-06-09,15320.33,15530.17,15316.43,15530.17,000,15530.17 -1998-06-08,15283.52,15357.89,15254.00,15294.71,000,15294.71 -1998-06-05,15436.41,15440.86,15276.02,15323.43,000,15323.43 -1998-06-04,15314.24,15522.78,15292.91,15426.47,000,15426.47 -1998-06-03,15525.62,15525.62,15256.09,15347.00,000,15347.00 -1998-06-02,15404.15,15554.45,15353.20,15554.45,000,15554.45 -1998-06-01,15670.73,15702.05,15321.03,15321.03,000,15321.03 -1998-05-29,15740.41,15790.31,15604.70,15670.78,000,15670.78 -1998-05-28,15639.01,15891.71,15639.01,15796.55,000,15796.55 -1998-05-27,15824.53,15824.53,15549.90,15664.29,000,15664.29 -1998-05-26,15792.36,15942.16,15788.91,15884.82,000,15884.82 -1998-05-25,15784.42,15812.54,15733.12,15783.12,000,15783.12 -1998-05-22,15892.61,15915.23,15736.16,15801.65,000,15801.65 -1998-05-21,15695.25,15972.88,15695.25,15845.25,000,15845.25 -1998-05-20,15578.87,15788.01,15578.87,15652.95,000,15652.95 -1998-05-19,15397.95,15582.87,15309.19,15551.65,000,15551.65 -1998-05-18,15277.17,15405.00,15069.68,15384.47,000,15384.47 -1998-05-15,15270.63,15412.84,15213.29,15242.86,000,15242.86 -1998-05-14,15322.83,15448.45,15294.51,15307.69,000,15307.69 -1998-05-13,15281.77,15343.81,15162.89,15343.81,000,15343.81 -1998-05-12,15441.71,15446.20,15306.92,15322.48,000,15322.48 -1998-05-11,15213.34,15433.87,15213.34,15381.90,000,15381.90 -1998-05-08,15105.77,15209.81,15096.72,15149.00,000,15149.00 -1998-05-07,15187.79,15244.10,15020.05,15143.03,000,15143.03 -1998-05-06,15536.96,15536.96,15129.07,15243.84,000,15243.84 -1998-05-01,15656.14,15665.78,15463.60,15601.10,000,15601.10 -1998-04-30,15493.42,15647.65,15483.18,15641.26,000,15641.26 -1998-04-28,15526.47,15625.37,15334.47,15395.43,000,15395.43 -1998-04-27,15969.00,15969.00,15645.00,15650.00,000,15650.00 -1998-04-24,15836.00,16201.00,15834.00,16011.00,000,16011.00 -1998-04-23,15753.00,15906.00,15651.00,15762.00,000,15762.00 -1998-04-22,15832.00,15832.00,15601.00,15762.00,000,15762.00 -1998-04-21,15784.00,15927.00,15583.00,15826.00,000,15826.00 -1998-04-20,15706.00,15730.00,15599.00,15697.00,000,15697.00 -1998-04-17,15839.00,15854.00,15465.00,15704.00,000,15704.00 -1998-04-16,16372.00,16417.00,15875.00,15884.00,000,15884.00 -1998-04-15,16322.00,16404.00,16292.00,16299.00,000,16299.00 -1998-04-14,16309.00,16414.00,16188.00,16277.00,000,16277.00 -1998-04-13,16374.00,16401.00,16277.00,16318.00,000,16318.00 -1998-04-10,16500.00,16520.00,16266.00,16481.00,000,16481.00 -1998-04-09,16433.00,16624.00,16271.00,16537.00,000,16537.00 -1998-04-08,15988.00,16475.00,15962.00,16377.00,000,16377.00 -1998-04-07,15667.00,15979.00,15602.00,15979.00,000,15979.00 -1998-04-06,15568.00,15768.00,15488.00,15706.00,000,15706.00 -1998-04-03,15747.00,15955.00,15465.00,15518.00,000,15518.00 -1998-04-02,16215.00,16215.00,15634.00,15703.00,000,15703.00 -1998-04-01,16433.00,16441.00,16148.00,16242.00,000,16242.00 -1998-03-31,16295.00,16585.00,16178.00,16527.00,000,16527.00 -1998-03-30,16840.00,17010.00,16239.00,16263.00,000,16263.00 -1998-03-27,16974.00,17076.00,16737.00,16739.00,000,16739.00 -1998-03-26,16685.00,17112.00,16669.00,16981.00,000,16981.00 -1998-03-25,16659.00,16939.00,16575.00,16658.00,000,16658.00 -1998-03-24,16769.00,16769.00,16551.00,16606.00,000,16606.00 -1998-03-23,16884.00,17046.00,16764.00,16869.00,000,16869.00 -1998-03-20,16615.00,16879.00,16470.00,16830.00,000,16830.00 -1998-03-19,16612.00,16769.00,16560.00,16679.00,000,16679.00 -1998-03-18,17012.00,17022.00,16500.00,16620.00,000,16620.00 -1998-03-17,16893.00,17062.00,16844.00,16997.00,000,16997.00 -1998-03-16,17063.00,17063.00,16792.00,16861.00,000,16861.00 -1998-03-13,16564.00,17129.00,16555.00,17060.00,000,17060.00 -1998-03-12,16750.00,16750.00,16575.00,16575.00,000,16575.00 -1998-03-11,16949.00,16949.00,16746.00,16756.00,000,16756.00 -1998-03-10,17000.00,17063.00,16901.00,16983.00,000,16983.00 -1998-03-09,17205.00,17352.00,16976.00,16977.00,000,16977.00 -1998-03-06,16864.00,17191.00,16864.00,17132.00,000,17132.00 -1998-03-05,17011.00,17011.00,16845.00,16849.00,000,16849.00 -1998-03-04,17124.00,17213.00,17026.00,17096.00,000,17096.00 -1998-03-03,17237.00,17326.00,17063.00,17168.00,000,17168.00 -1998-03-02,16901.00,17276.00,16901.00,17264.00,000,17264.00 -1998-02-27,16561.00,16832.00,16561.00,16832.00,000,16832.00 -1998-02-26,16369.00,16549.00,16278.00,16502.00,000,16502.00 -1998-02-25,16156.00,16361.00,15932.00,16361.00,000,16361.00 -1998-02-24,16634.00,16634.00,16167.00,16198.00,000,16198.00 -1998-02-23,16717.00,16717.00,16610.00,16610.00,000,16610.00 -1998-02-20,16628.00,16799.00,16502.00,16756.00,000,16756.00 -1998-02-19,16582.00,16866.00,16548.00,16616.00,000,16616.00 -1998-02-18,16787.00,16854.00,16593.00,16614.00,000,16614.00 -1998-02-17,16738.00,16791.00,16588.00,16791.00,000,16791.00 -1998-02-16,16750.00,16776.00,16589.00,16776.00,000,16776.00 -1998-02-13,17161.00,17161.00,16711.00,16791.00,000,16791.00 -1998-02-12,17247.00,17252.00,17069.00,17175.00,000,17175.00 -1998-02-10,17215.00,17256.00,17162.00,17205.00,000,17205.00 -1998-02-09,17102.00,17224.00,17053.00,17205.00,000,17205.00 -1998-02-06,17022.00,17134.00,16981.00,17040.00,000,17040.00 -1998-02-05,16803.00,17051.00,16771.00,17003.00,000,17003.00 -1998-02-04,17050.00,17074.00,16796.00,16883.00,000,16883.00 -1998-02-03,16904.00,17144.00,16904.00,17023.00,000,17023.00 -1998-02-02,16685.00,16909.00,16641.00,16777.00,000,16777.00 -1998-01-30,17011.00,17011.00,16628.00,16628.00,000,16628.00 -1998-01-29,17045.00,17107.00,16926.00,17015.00,000,17015.00 -1998-01-28,17061.00,17259.00,16974.00,16974.00,000,16974.00 -1998-01-27,17101.00,17158.00,16908.00,16982.00,000,16982.00 -1998-01-26,16835.00,17353.00,16835.00,17073.00,000,17073.00 -1998-01-23,16402.00,16797.00,16402.00,16789.00,000,16789.00 -1998-01-22,16624.00,16712.00,16397.00,16406.00,000,16406.00 -1998-01-21,16459.00,16752.00,16459.00,16684.00,000,16684.00 -1998-01-20,16220.00,16430.00,16077.00,16367.00,000,16367.00 -1998-01-19,16155.00,16461.00,16155.00,16262.00,000,16262.00 -1998-01-16,15193.00,16063.00,15193.00,16046.00,000,16046.00 -1998-01-14,14866.00,15149.00,14866.00,15122.00,000,15122.00 -1998-01-13,14758.00,14901.00,14546.00,14756.00,000,14756.00 -1998-01-12,14877.00,14909.00,14629.00,14664.00,000,14664.00 -1998-01-09,14941.00,15066.00,14724.00,14995.00,000,14995.00 -1998-01-08,15061.00,15608.00,15019.00,15019.00,000,15019.00 -1998-01-07,14898.00,15038.00,14848.00,15028.00,000,15028.00 -1998-01-06,15008.00,15067.00,14714.00,14896.00,000,14896.00 -1998-01-05,15269.00,15307.00,14957.00,14957.00,000,14957.00 -1997-12-30,14838.00,15259.00,14838.00,15259.00,000,15259.00 -1997-12-29,14830.00,14853.00,14582.00,14775.00,000,14775.00 -1997-12-26,15312.00,15364.00,14779.00,14803.00,000,14803.00 -1997-12-25,14940.00,15730.00,14940.00,15300.00,000,15300.00 -1997-12-24,14792.00,15013.00,14682.00,14925.00,000,14925.00 -1997-12-22,15280.00,15280.00,14569.00,14799.00,000,14799.00 -1997-12-19,16105.00,16105.00,15171.00,15315.00,000,15315.00 -1997-12-18,16456.00,16456.00,16100.00,16162.00,000,16162.00 -1997-12-17,16014.00,16817.00,15795.00,16541.00,000,16541.00 -1997-12-16,15954.00,16130.00,15803.00,15985.00,000,15985.00 -1997-12-15,15866.00,15909.00,15643.00,15909.00,000,15909.00 -1997-12-12,16050.00,16153.00,15738.00,15904.00,000,15904.00 -1997-12-11,16397.00,16397.00,16025.00,16050.00,000,16050.00 -1997-12-10,16676.00,16676.00,16409.00,16478.00,000,16478.00 -1997-12-09,16185.00,16687.00,16185.00,16687.00,000,16687.00 -1997-12-08,16439.00,16516.00,16110.00,16132.00,000,16132.00 -1997-12-05,16351.00,16597.00,16348.00,16424.00,000,16424.00 -1997-12-04,16567.00,16589.00,16249.00,16307.00,000,16307.00 -1997-12-03,16861.00,16861.00,16583.00,16586.00,000,16586.00 -1997-12-02,17010.00,17074.00,16848.00,16910.00,000,16910.00 -1997-12-01,16594.00,17118.00,16486.00,17008.00,000,17008.00 -1997-11-28,16648.00,16785.00,16591.00,16633.00,000,16633.00 -1997-11-27,16117.00,16630.00,16117.00,16603.00,000,16603.00 -1997-11-26,15926.00,16379.00,15926.00,16046.00,000,16046.00 -1997-11-25,16608.00,16608.00,15774.00,15868.00,000,15868.00 -1997-11-21,16420.00,16809.00,16420.00,16722.00,000,16722.00 -1997-11-20,15875.00,16545.00,15831.00,16308.00,000,16308.00 -1997-11-19,16600.00,16600.00,15746.00,15842.00,000,15842.00 -1997-11-18,16242.00,17006.00,16090.00,16727.00,000,16727.00 -1997-11-17,15154.00,16283.00,15154.00,16283.00,000,16283.00 -1997-11-14,15355.00,15372.00,14966.00,15083.00,000,15083.00 -1997-11-13,15355.00,15603.00,15083.00,15427.00,000,15427.00 -1997-11-12,15820.00,15864.00,15359.00,15434.00,000,15434.00 -1997-11-11,15720.00,15867.00,15648.00,15867.00,000,15867.00 -1997-11-10,15722.00,15912.00,15565.00,15697.00,000,15697.00 -1997-11-07,16464.00,16464.00,15824.00,15836.00,000,15836.00 -1997-11-06,16466.00,16634.00,16422.00,16534.00,000,16534.00 -1997-11-05,16519.00,16525.00,16290.00,16448.00,000,16448.00 -1997-11-04,16494.00,16641.00,16400.00,16500.00,000,16500.00 -1997-10-31,16254.00,16635.00,16082.00,16459.00,000,16459.00 -1997-10-30,16828.00,16828.00,16302.00,16365.00,000,16365.00 -1997-10-29,16386.00,16920.00,16386.00,16857.00,000,16857.00 -1997-10-28,17019.00,17019.00,16218.00,16313.00,000,16313.00 -1997-10-27,17262.00,17262.00,16906.00,17039.00,000,17039.00 -1997-10-24,17040.00,17494.00,16864.00,17364.00,000,17364.00 -1997-10-23,17647.00,17647.00,17152.00,17152.00,000,17152.00 -1997-10-22,17267.00,17694.00,17267.00,17688.00,000,17688.00 -1997-10-21,17369.00,17555.00,17210.00,17210.00,000,17210.00 -1997-10-20,17391.00,17455.00,17231.00,17295.00,000,17295.00 -1997-10-17,17591.00,17591.00,17384.00,17478.00,000,17478.00 -1997-10-16,17335.00,17764.00,17184.00,17707.00,000,17707.00 -1997-10-15,17342.00,17427.00,17186.00,17331.00,000,17331.00 -1997-10-14,17220.00,17419.00,16968.00,17306.00,000,17306.00 -1997-10-13,17318.00,17318.00,17152.00,17205.00,000,17205.00 -1997-10-09,17624.00,17628.00,17331.00,17377.00,000,17377.00 -1997-10-08,17531.00,17718.00,17531.00,17619.00,000,17619.00 -1997-10-07,17843.00,17890.00,17479.00,17511.00,000,17511.00 -1997-10-06,17646.00,17854.00,17636.00,17825.00,000,17825.00 -1997-10-03,17468.00,17686.00,17402.00,17647.00,000,17647.00 -1997-10-02,17876.00,17876.00,17415.00,17455.00,000,17455.00 -1997-10-01,17820.00,17937.00,17522.00,17842.00,000,17842.00 -1997-09-30,18023.00,18054.00,17850.00,17888.00,000,17888.00 -1997-09-29,17992.00,17992.00,17681.00,17987.00,000,17987.00 -1997-09-26,18308.00,18354.00,17933.00,17995.00,000,17995.00 -1997-09-25,18370.00,18440.00,18184.00,18342.00,000,18342.00 -1997-09-24,18251.00,18420.00,18139.00,18420.00,000,18420.00 -1997-09-22,18077.00,18304.00,17960.00,18201.00,000,18201.00 -1997-09-19,17937.00,18076.00,17763.00,18058.00,000,18058.00 -1997-09-18,17682.00,17982.00,17661.00,17930.00,000,17930.00 -1997-09-17,18032.00,18176.00,17564.00,17683.00,000,17683.00 -1997-09-16,18005.00,18037.00,17873.00,17975.00,000,17975.00 -1997-09-12,18216.00,18216.00,17803.00,17966.00,000,17966.00 -1997-09-11,18626.00,18626.00,18189.00,18282.00,000,18282.00 -1997-09-10,18649.00,18724.00,18575.00,18705.00,000,18705.00 -1997-09-09,18637.00,18733.00,18514.00,18696.00,000,18696.00 -1997-09-08,18661.00,18775.00,18634.00,18634.00,000,18634.00 -1997-09-05,18577.00,18671.00,18455.00,18650.00,000,18650.00 -1997-09-04,18706.00,18713.00,18573.00,18615.00,000,18615.00 -1997-09-03,18349.00,18749.00,18349.00,18735.00,000,18735.00 -1997-09-02,18027.00,18245.00,17968.00,18233.00,000,18233.00 -1997-09-01,18215.00,18271.00,17885.00,17974.00,000,17974.00 -1997-08-29,18329.00,18329.00,17974.00,18229.00,000,18229.00 -1997-08-28,18473.00,18586.00,18386.00,18451.00,000,18451.00 -1997-08-27,18755.00,18755.00,18432.00,18442.00,000,18442.00 -1997-08-26,18666.00,18867.00,18538.00,18815.00,000,18815.00 -1997-08-25,18683.00,18741.00,18550.00,18656.00,000,18656.00 -1997-08-22,19074.00,19074.00,18576.00,18650.00,000,18650.00 -1997-08-21,19320.00,19394.00,19125.00,19157.00,000,19157.00 -1997-08-20,18953.00,19252.00,18906.00,19252.00,000,19252.00 -1997-08-19,19122.00,19246.00,18803.00,18961.00,000,18961.00 -1997-08-18,19213.00,19213.00,18835.00,19041.00,000,19041.00 -1997-08-15,19313.00,19466.00,19313.00,19326.00,000,19326.00 -1997-08-14,19056.00,19269.00,18989.00,19222.00,000,19222.00 -1997-08-13,19041.00,19154.00,18802.00,19009.00,000,19009.00 -1997-08-12,18946.00,19257.00,18917.00,19099.00,000,19099.00 -1997-08-11,19462.00,19462.00,18824.00,18824.00,000,18824.00 -1997-08-08,19395.00,19642.00,19256.00,19604.00,000,19604.00 -1997-08-07,19719.00,19772.00,19366.00,19476.00,000,19476.00 -1997-08-06,19537.00,19704.00,19233.00,19702.00,000,19702.00 -1997-08-05,19645.00,19768.00,19362.00,19514.00,000,19514.00 -1997-08-04,19839.00,19930.00,19457.00,19668.00,000,19668.00 -1997-08-01,20345.00,20399.00,19798.00,19804.00,000,19804.00 -1997-07-31,20241.00,20333.00,20041.00,20331.00,000,20331.00 -1997-07-30,20414.00,20419.00,20171.00,20213.00,000,20213.00 -1997-07-29,20628.00,20699.00,20403.00,20403.00,000,20403.00 -1997-07-28,20420.00,20601.00,20420.00,20575.00,000,20575.00 -1997-07-25,20330.00,20390.00,20304.00,20390.00,000,20390.00 -1997-07-24,20164.00,20290.00,20164.00,20286.00,000,20286.00 -1997-07-23,20244.00,20324.00,19999.00,20131.00,000,20131.00 -1997-07-22,20247.00,20280.00,20056.00,20157.00,000,20157.00 -1997-07-18,20444.00,20545.00,20248.00,20249.00,000,20249.00 -1997-07-17,20412.00,20585.00,20309.00,20519.00,000,20519.00 -1997-07-16,20139.00,20436.00,20139.00,20359.00,000,20359.00 -1997-07-15,20237.00,20243.00,20037.00,20069.00,000,20069.00 -1997-07-14,19909.00,20229.00,19909.00,20229.00,000,20229.00 -1997-07-11,19788.00,19893.00,19643.00,19875.00,000,19875.00 -1997-07-10,19710.00,19821.00,19609.00,19755.00,000,19755.00 -1997-07-09,19921.00,19964.00,19496.00,19697.00,000,19697.00 -1997-07-08,19732.00,19927.00,19732.00,19854.00,000,19854.00 -1997-07-07,19932.00,19932.00,19678.00,19705.00,000,19705.00 -1997-07-04,20123.00,20123.00,19907.00,19968.00,000,19968.00 -1997-07-03,20239.00,20252.00,20080.00,20121.00,000,20121.00 -1997-07-02,20204.00,20247.00,19976.00,20196.00,000,20196.00 -1997-07-01,20562.00,20562.00,20143.00,20176.00,000,20176.00 -1997-06-30,20586.00,20684.00,20493.00,20605.00,000,20605.00 -1997-06-27,20629.00,20743.00,20524.00,20524.00,000,20524.00 -1997-06-26,20715.00,20911.00,20625.00,20625.00,000,20625.00 -1997-06-25,20419.00,20736.00,20419.00,20679.00,000,20679.00 -1997-06-24,20383.00,20383.00,20215.00,20342.00,000,20342.00 -1997-06-23,20429.00,20462.00,20380.00,20436.00,000,20436.00 -1997-06-20,20536.00,20576.00,20357.00,20386.00,000,20386.00 -1997-06-19,20491.00,20591.00,20400.00,20508.00,000,20508.00 -1997-06-18,20598.00,20620.00,20429.00,20498.00,000,20498.00 -1997-06-17,20692.00,20721.00,20559.00,20594.00,000,20594.00 -1997-06-16,20595.00,20778.00,20549.00,20681.00,000,20681.00 -1997-06-13,20668.00,20815.00,20452.00,20528.00,000,20528.00 -1997-06-12,20340.00,20697.00,20340.00,20564.00,000,20564.00 -1997-06-11,20527.00,20634.00,20282.00,20290.00,000,20290.00 -1997-06-10,20228.00,20582.00,20228.00,20533.00,000,20533.00 -1997-06-09,20474.00,20519.00,20224.00,20224.00,000,20224.00 -1997-06-06,20474.00,20541.00,20363.00,20486.00,000,20486.00 -1997-06-05,20596.00,20606.00,20423.00,20488.00,000,20488.00 -1997-06-04,20592.00,20708.00,20512.00,20612.00,000,20612.00 -1997-06-03,20444.00,20672.00,20432.00,20563.00,000,20563.00 -1997-06-02,20084.00,20452.00,20041.00,20452.00,000,20452.00 -1997-05-30,20331.00,20390.00,20029.00,20069.00,000,20069.00 -1997-05-29,20367.00,20367.00,20125.00,20312.00,000,20312.00 -1997-05-28,19928.00,20352.00,19928.00,20351.00,000,20351.00 -1997-05-27,20068.00,20149.00,19848.00,19890.00,000,19890.00 -1997-05-26,20037.00,20155.00,19989.00,20044.00,000,20044.00 -1997-05-23,19932.00,20067.00,19907.00,20009.00,000,20009.00 -1997-05-22,19849.00,19939.00,19687.00,19877.00,000,19877.00 -1997-05-21,20322.00,20322.00,19764.00,19842.00,000,19842.00 -1997-05-20,20511.00,20612.00,20245.00,20333.00,000,20333.00 -1997-05-19,20290.00,20562.00,20206.00,20490.00,000,20490.00 -1997-05-16,20081.00,20348.00,20081.00,20325.00,000,20325.00 -1997-05-15,20162.00,20162.00,19855.00,20056.00,000,20056.00 -1997-05-14,20132.00,20210.00,20030.00,20210.00,000,20210.00 -1997-05-13,20207.00,20452.00,20120.00,20129.00,000,20129.00 -1997-05-12,19731.00,20148.00,19560.00,20144.00,000,20144.00 -1997-05-09,20097.00,20145.00,19757.00,19803.00,000,19803.00 -1997-05-08,19974.00,20107.00,19925.00,20062.00,000,20062.00 -1997-05-07,20147.00,20237.00,19952.00,20049.00,000,20049.00 -1997-05-06,19617.00,20223.00,19617.00,20181.00,000,20181.00 -1997-05-02,19244.00,19516.00,19188.00,19515.00,000,19515.00 -1997-05-01,19232.00,19588.00,19222.00,19275.00,000,19275.00 -1997-04-30,18764.00,19195.00,18764.00,19151.00,000,19151.00 -1997-04-28,18617.00,18684.00,18545.00,18670.00,000,18670.00 -1997-04-25,18648.00,18849.00,18571.00,18613.00,000,18613.00 -1997-04-24,18737.00,18983.00,18665.00,18698.00,000,18698.00 -1997-04-23,18618.00,18842.00,18618.00,18735.00,000,18735.00 -1997-04-22,18528.00,18732.00,18490.00,18544.00,000,18544.00 -1997-04-21,18400.00,18561.00,18400.00,18552.00,000,18552.00 -1997-04-18,18129.00,18370.00,18073.00,18352.00,000,18352.00 -1997-04-17,18016.00,18100.00,17970.00,18093.00,000,18093.00 -1997-04-16,17985.00,18094.00,17959.00,18031.00,000,18031.00 -1997-04-15,17712.00,18001.00,17712.00,17934.00,000,17934.00 -1997-04-14,17761.00,17827.00,17547.00,17692.00,000,17692.00 -1997-04-11,17493.00,17869.00,17448.00,17847.00,000,17847.00 -1997-04-10,17756.00,17934.00,17479.00,17486.00,000,17486.00 -1997-04-09,18007.00,18007.00,17703.00,17703.00,000,17703.00 -1997-04-08,17745.00,18035.00,17626.00,18022.00,000,18022.00 -1997-04-07,17902.00,17991.00,17664.00,17716.00,000,17716.00 -1997-04-04,18136.00,18136.00,17766.00,17861.00,000,17861.00 -1997-04-03,18022.00,18182.00,17973.00,18129.00,000,18129.00 -1997-04-02,17875.00,18048.00,17707.00,18037.00,000,18037.00 -1997-04-01,17935.00,17935.00,17529.00,17870.00,000,17870.00 -1997-03-31,18158.00,18205.00,17793.00,18003.00,000,18003.00 -1997-03-28,18184.00,18234.00,18058.00,18190.00,000,18190.00 -1997-03-27,18520.00,18594.00,18003.00,18210.00,000,18210.00 -1997-03-26,18461.00,18528.00,18189.00,18472.00,000,18472.00 -1997-03-25,18117.00,18549.00,18117.00,18440.00,000,18440.00 -1997-03-24,18683.00,18750.00,18044.00,18044.00,000,18044.00 -1997-03-21,18500.00,18634.00,18423.00,18633.00,000,18633.00 -1997-03-19,18490.00,18555.00,18379.00,18494.00,000,18494.00 -1997-03-18,18073.00,18445.00,18073.00,18445.00,000,18445.00 -1997-03-17,17962.00,18084.00,17864.00,18054.00,000,18054.00 -1997-03-14,17854.00,17940.00,17617.00,17924.00,000,17924.00 -1997-03-13,18144.00,18144.00,17900.00,17900.00,000,17900.00 -1997-03-12,18281.00,18326.00,18010.00,18183.00,000,18183.00 -1997-03-11,18130.00,18268.00,18089.00,18268.00,000,18268.00 -1997-03-10,18196.00,18196.00,17936.00,18114.00,000,18114.00 -1997-03-07,18001.00,18199.00,17837.00,18199.00,000,18199.00 -1997-03-06,18342.00,18435.00,17977.00,18041.00,000,18041.00 -1997-03-05,18618.00,18657.00,18208.00,18274.00,000,18274.00 -1997-03-04,18500.00,18689.00,18497.00,18565.00,000,18565.00 -1997-03-03,18517.00,18517.00,18346.00,18429.00,000,18429.00 -1997-02-28,19007.00,19007.00,18540.00,18557.00,000,18557.00 -1997-02-27,18936.00,19025.00,18855.00,19022.00,000,19022.00 -1997-02-26,19128.00,19217.00,18895.00,18991.00,000,18991.00 -1997-02-25,18854.00,19098.00,18776.00,19070.00,000,19070.00 -1997-02-24,19066.00,19229.00,18860.00,18897.00,000,18897.00 -1997-02-21,19031.00,19173.00,18965.00,19035.00,000,19035.00 -1997-02-20,18687.00,19101.00,18687.00,19052.00,000,19052.00 -1997-02-19,18483.00,18674.00,18329.00,18599.00,000,18599.00 -1997-02-18,18708.00,18728.00,18471.00,18471.00,000,18471.00 -1997-02-17,18731.00,18854.00,18654.00,18751.00,000,18751.00 -1997-02-14,18729.00,18881.00,18609.00,18722.00,000,18722.00 -1997-02-13,18505.00,18855.00,18505.00,18688.00,000,18688.00 -1997-02-12,18240.00,18521.00,18240.00,18410.00,000,18410.00 -1997-02-11,18158.00,18629.00,18158.00,18314.00,000,18314.00 -1997-02-10,17882.00,18268.00,17843.00,18181.00,000,18181.00 -1997-02-07,18072.00,18267.00,17792.00,17867.00,000,17867.00 -1997-02-06,18200.00,18257.00,17875.00,18038.00,000,18038.00 -1997-02-05,18303.00,18307.00,17901.00,18186.00,000,18186.00 -1997-02-04,18158.00,18629.00,18158.00,18314.00,000,18314.00 -1997-02-03,18308.00,18308.00,18077.00,18086.00,000,18086.00 -1997-01-31,17949.00,18610.00,17949.00,18330.00,000,18330.00 -1997-01-30,18305.00,18366.00,17782.00,17864.00,000,17864.00 -1997-01-29,17843.00,18335.00,17665.00,18335.00,000,18335.00 -1997-01-28,17301.00,17797.00,17195.00,17797.00,000,17797.00 -1997-01-27,17658.00,17665.00,17280.00,17335.00,000,17335.00 -1997-01-24,17894.00,17894.00,17541.00,17689.00,000,17689.00 -1997-01-23,17982.00,18129.00,17878.00,17909.00,000,17909.00 -1997-01-22,17441.00,18066.00,17441.00,18014.00,000,18014.00 -1997-01-21,17441.00,17572.00,17283.00,17358.00,000,17358.00 -1997-01-20,18104.00,18109.00,17237.00,17480.00,000,17480.00 -1997-01-17,18097.00,18447.00,17970.00,18090.00,000,18090.00 -1997-01-16,18126.00,18318.00,17971.00,18144.00,000,18144.00 -1997-01-14,18061.00,18182.00,17546.00,18093.00,000,18093.00 -1997-01-13,17338.00,18152.00,17020.00,18119.00,000,18119.00 -1997-01-10,18056.00,18058.00,17124.00,17304.00,000,17304.00 -1997-01-09,18632.00,18726.00,18072.00,18074.00,000,18074.00 -1997-01-08,18911.00,18998.00,18556.00,18680.00,000,18680.00 -1997-01-07,19444.00,19444.00,18896.00,18896.00,000,18896.00 -1997-01-06,19364.00,19501.00,19204.00,19446.00,000,19446.00 -1996-12-30,19391.00,19392.00,19109.00,19361.00,000,19361.00 -1996-12-27,19332.00,19424.00,19161.00,19369.00,000,19369.00 -1996-12-26,19543.00,19543.00,18820.00,19292.00,000,19292.00 -1996-12-25,19212.00,19556.00,19212.00,19549.00,000,19549.00 -1996-12-24,19698.00,19700.00,19162.00,19162.00,000,19162.00 -1996-12-20,19642.00,19823.00,19556.00,19690.00,000,19690.00 -1996-12-19,20070.00,20099.00,19560.00,19571.00,000,19571.00 -1996-12-18,20416.00,20416.00,20093.00,20093.00,000,20093.00 -1996-12-17,20370.00,20501.00,20231.00,20413.00,000,20413.00 -1996-12-16,20410.00,20472.00,20291.00,20422.00,000,20422.00 -1996-12-13,20452.00,20452.00,19952.00,20341.00,000,20341.00 -1996-12-12,20475.00,20504.00,20321.00,20501.00,000,20501.00 -1996-12-11,20756.00,20756.00,20467.00,20568.00,000,20568.00 -1996-12-10,20676.00,20854.00,20676.00,20822.00,000,20822.00 -1996-12-09,20397.00,20670.00,20397.00,20604.00,000,20604.00 -1996-12-06,20979.00,21002.00,20172.00,20277.00,000,20277.00 -1996-12-05,20672.00,20977.00,20669.00,20944.00,000,20944.00 -1996-12-04,20577.00,20677.00,20525.00,20660.00,000,20660.00 -1996-12-03,20682.00,20763.00,20479.00,20631.00,000,20631.00 -1996-12-02,21035.00,21068.00,20675.00,20675.00,000,20675.00 -1996-11-29,21014.00,21155.00,20978.00,21020.00,000,21020.00 -1996-11-28,21290.00,21290.00,21036.00,21036.00,000,21036.00 -1996-11-27,21398.00,21461.00,21259.00,21345.00,000,21345.00 -1996-11-26,21359.00,21460.00,21254.00,21418.00,000,21418.00 -1996-11-25,21276.00,21369.00,21197.00,21294.00,000,21294.00 -1996-11-22,21118.00,21216.00,21021.00,21216.00,000,21216.00 -1996-11-21,21169.00,21301.00,21105.00,21143.00,000,21143.00 -1996-11-20,20997.00,21218.00,20997.00,21190.00,000,21190.00 -1996-11-19,20792.00,20956.00,20733.00,20956.00,000,20956.00 -1996-11-18,20932.00,20940.00,20773.00,20796.00,000,20796.00 -1996-11-15,21058.00,21160.00,20920.00,20930.00,000,20930.00 -1996-11-14,21020.00,21084.00,20936.00,21031.00,000,21031.00 -1996-11-13,21201.00,21229.00,20923.00,20979.00,000,20979.00 -1996-11-12,21098.00,21251.00,21098.00,21206.00,000,21206.00 -1996-11-11,21153.00,21263.00,21033.00,21065.00,000,21065.00 -1996-11-08,20770.00,21227.00,20770.00,21201.00,000,21201.00 -1996-11-07,21064.00,21142.00,20757.00,20771.00,000,20771.00 -1996-11-06,20645.00,21095.00,20645.00,20992.00,000,20992.00 -1996-11-05,20655.00,20690.00,20494.00,20592.00,000,20592.00 -1996-11-01,20498.00,20695.00,20388.00,20633.00,000,20633.00 -1996-10-31,20676.00,20739.00,20450.00,20467.00,000,20467.00 -1996-10-30,20980.00,21001.00,20633.00,20682.00,000,20682.00 -1996-10-29,20920.00,21055.00,20920.00,20958.00,000,20958.00 -1996-10-28,20740.00,20904.00,20738.00,20885.00,000,20885.00 -1996-10-25,20943.00,20943.00,20700.00,20740.00,000,20740.00 -1996-10-24,21050.00,21115.00,20856.00,21003.00,000,21003.00 -1996-10-23,21046.00,21090.00,20791.00,21082.00,000,21082.00 -1996-10-22,21246.00,21246.00,21023.00,21124.00,000,21124.00 -1996-10-21,21607.00,21607.00,21300.00,21303.00,000,21303.00 -1996-10-18,21465.00,21789.00,21458.00,21612.00,000,21612.00 -1996-10-17,21403.00,21463.00,21330.00,21424.00,000,21424.00 -1996-10-16,21458.00,21478.00,21364.00,21397.00,000,21397.00 -1996-10-15,21095.00,21430.00,21095.00,21430.00,000,21430.00 -1996-10-14,20997.00,21058.00,20905.00,21029.00,000,21029.00 -1996-10-11,20891.00,21008.00,20806.00,20968.00,000,20968.00 -1996-10-09,20980.00,20983.00,20814.00,20871.00,000,20871.00 -1996-10-08,21100.00,21206.00,20969.00,21039.00,000,21039.00 -1996-10-07,21157.00,21161.00,21032.00,21161.00,000,21161.00 -1996-10-04,21267.00,21267.00,21060.00,21148.00,000,21148.00 -1996-10-03,21523.00,21548.00,21298.00,21332.00,000,21332.00 -1996-10-02,21469.00,21510.00,21413.00,21499.00,000,21499.00 -1996-10-01,21533.00,21564.00,21430.00,21463.00,000,21463.00 -1996-09-30,21538.00,21590.00,21494.00,21556.00,000,21556.00 -1996-09-27,21437.00,21604.00,21423.00,21547.00,000,21547.00 -1996-09-26,21369.00,21580.00,21369.00,21461.00,000,21461.00 -1996-09-25,21159.00,21351.00,21150.00,21351.00,000,21351.00 -1996-09-24,21105.00,21248.00,21041.00,21172.00,000,21172.00 -1996-09-20,21310.00,21310.00,21112.00,21112.00,000,21112.00 -1996-09-19,21109.00,21333.00,21023.00,21323.00,000,21323.00 -1996-09-18,21304.00,21312.00,21095.00,21157.00,000,21157.00 -1996-09-17,20929.00,21366.00,20929.00,21311.00,000,21311.00 -1996-09-13,20473.00,20924.00,20473.00,20843.00,000,20843.00 -1996-09-12,20514.00,20530.00,20375.00,20444.00,000,20444.00 -1996-09-11,20541.00,20580.00,20425.00,20571.00,000,20571.00 -1996-09-10,20269.00,20561.00,20269.00,20560.00,000,20560.00 -1996-09-09,20232.00,20373.00,20159.00,20202.00,000,20202.00 -1996-09-06,20367.00,20367.00,20123.00,20153.00,000,20153.00 -1996-09-05,20215.00,20487.00,20215.00,20380.00,000,20380.00 -1996-09-04,20218.00,20307.00,20066.00,20202.00,000,20202.00 -1996-09-03,20082.00,20290.00,19920.00,20198.00,000,20198.00 -1996-09-02,20187.00,20230.00,20082.00,20107.00,000,20107.00 -1996-08-30,20481.00,20481.00,20092.00,20167.00,000,20167.00 -1996-08-29,20687.00,20707.00,20504.00,20553.00,000,20553.00 -1996-08-28,20904.00,21040.00,20661.00,20710.00,000,20710.00 -1996-08-27,20862.00,21022.00,20828.00,20910.00,000,20910.00 -1996-08-26,21213.00,21213.00,20880.00,20884.00,000,20884.00 -1996-08-23,21378.00,21398.00,21188.00,21229.00,000,21229.00 -1996-08-22,21252.00,21386.00,21240.00,21363.00,000,21363.00 -1996-08-21,21215.00,21391.00,21215.00,21275.00,000,21275.00 -1996-08-20,21141.00,21163.00,20971.00,21127.00,000,21127.00 -1996-08-19,20848.00,21153.00,20848.00,21106.00,000,21106.00 -1996-08-16,20967.00,20967.00,20824.00,20834.00,000,20834.00 -1996-08-15,21008.00,21106.00,20913.00,20968.00,000,20968.00 -1996-08-14,20805.00,20981.00,20729.00,20981.00,000,20981.00 -1996-08-13,20653.00,20865.00,20643.00,20865.00,000,20865.00 -1996-08-12,20516.00,20667.00,20449.00,20667.00,000,20667.00 -1996-08-09,20735.00,20735.00,20492.00,20551.00,000,20551.00 -1996-08-08,20531.00,20772.00,20531.00,20731.00,000,20731.00 -1996-08-07,20717.00,20828.00,20453.00,20478.00,000,20478.00 -1996-08-06,20993.00,20993.00,20742.00,20745.00,000,20745.00 -1996-08-05,21020.00,21188.00,21020.00,21077.00,000,21077.00 -1996-08-02,21041.00,21149.00,20940.00,20940.00,000,20940.00 -1996-08-01,20665.00,21092.00,20525.00,20985.00,000,20985.00 -1996-07-31,20863.00,20863.00,20653.00,20693.00,000,20693.00 -1996-07-30,20907.00,20911.00,20790.00,20880.00,000,20880.00 -1996-07-29,21188.00,21285.00,20964.00,20968.00,000,20968.00 -1996-07-26,20938.00,21125.00,20877.00,21125.00,000,21125.00 -1996-07-25,20708.00,20979.00,20638.00,20884.00,000,20884.00 -1996-07-24,21114.00,21114.00,20628.00,20631.00,000,20631.00 -1996-07-23,20957.00,21164.00,20833.00,21164.00,000,21164.00 -1996-07-22,21476.00,21476.00,21006.00,21006.00,000,21006.00 -1996-07-19,21612.00,21702.00,21465.00,21476.00,000,21476.00 -1996-07-18,21432.00,21574.00,21428.00,21566.00,000,21566.00 -1996-07-17,21468.00,21567.00,21282.00,21413.00,000,21413.00 -1996-07-16,21637.00,21637.00,21363.00,21406.00,000,21406.00 -1996-07-15,21622.00,21753.00,21549.00,21753.00,000,21753.00 -1996-07-12,21807.00,21807.00,21558.00,21656.00,000,21656.00 -1996-07-11,21768.00,21900.00,21717.00,21893.00,000,21893.00 -1996-07-10,21955.00,22042.00,21758.00,21779.00,000,21779.00 -1996-07-09,21917.00,21977.00,21844.00,21920.00,000,21920.00 -1996-07-08,22148.00,22148.00,21802.00,21925.00,000,21925.00 -1996-07-05,22267.00,22374.00,22218.00,22232.00,000,22232.00 -1996-07-04,22354.00,22354.00,22204.00,22293.00,000,22293.00 -1996-07-03,22334.00,22390.00,22248.00,22379.00,000,22379.00 -1996-07-02,22467.00,22485.00,22270.00,22348.00,000,22348.00 -1996-07-01,22568.00,22600.00,22416.00,22456.00,000,22456.00 -1996-06-28,22545.00,22613.00,22461.00,22531.00,000,22531.00 -1996-06-27,22652.00,22657.00,22439.00,22502.00,000,22502.00 -1996-06-26,22590.00,22757.00,22573.00,22667.00,000,22667.00 -1996-06-25,22599.00,22659.00,22517.00,22597.00,000,22597.00 -1996-06-24,22566.00,22702.00,22504.00,22603.00,000,22603.00 -1996-06-21,22496.00,22599.00,22404.00,22531.00,000,22531.00 -1996-06-20,22361.00,22437.00,22133.00,22437.00,000,22437.00 -1996-06-19,22274.00,22504.00,22225.00,22367.00,000,22367.00 -1996-06-18,22306.00,22430.00,22264.00,22332.00,000,22332.00 -1996-06-17,22339.00,22519.00,22245.00,22245.00,000,22245.00 -1996-06-14,22127.00,22481.00,22127.00,22289.00,000,22289.00 -1996-06-13,22104.00,22205.00,22042.00,22082.00,000,22082.00 -1996-06-12,21883.00,22131.00,21883.00,22105.00,000,22105.00 -1996-06-11,21677.00,21863.00,21594.00,21818.00,000,21818.00 -1996-06-10,21730.00,21733.00,21641.00,21719.00,000,21719.00 -1996-06-07,21800.00,21800.00,21672.00,21752.00,000,21752.00 -1996-06-06,21912.00,22033.00,21804.00,21804.00,000,21804.00 -1996-06-05,21887.00,21986.00,21784.00,21881.00,000,21881.00 -1996-06-04,21641.00,21867.00,21641.00,21858.00,000,21858.00 -1996-06-03,21971.00,21972.00,21589.00,21589.00,000,21589.00 -1996-05-31,21905.00,22052.00,21887.00,21956.00,000,21956.00 -1996-05-30,21991.00,21991.00,21836.00,21886.00,000,21886.00 -1996-05-29,21953.00,22145.00,21877.00,22022.00,000,22022.00 -1996-05-28,21754.00,21993.00,21754.00,21945.00,000,21945.00 -1996-05-27,21840.00,21863.00,21557.00,21700.00,000,21700.00 -1996-05-24,21692.00,21805.00,21621.00,21798.00,000,21798.00 -1996-05-23,21934.00,22015.00,21632.00,21724.00,000,21724.00 -1996-05-22,22093.00,22196.00,21840.00,21958.00,000,21958.00 -1996-05-21,22001.00,22196.00,21893.00,22092.00,000,22092.00 -1996-05-20,21977.00,22311.00,21977.00,21979.00,000,21979.00 -1996-05-17,22102.00,22132.00,21815.00,21917.00,000,21917.00 -1996-05-16,22082.00,22251.00,22082.00,22147.00,000,22147.00 -1996-05-15,21371.00,22061.00,21371.00,22056.00,000,22056.00 -1996-05-14,21208.00,21311.00,21174.00,21301.00,000,21301.00 -1996-05-13,21459.00,21504.00,21171.00,21172.00,000,21172.00 -1996-05-10,21406.00,21477.00,21321.00,21420.00,000,21420.00 -1996-05-09,21742.00,21744.00,21298.00,21412.00,000,21412.00 -1996-05-08,21480.00,21729.00,21432.00,21729.00,000,21729.00 -1996-05-07,21624.00,21624.00,21431.00,21495.00,000,21495.00 -1996-05-02,21775.00,21783.00,21517.00,21662.00,000,21662.00 -1996-05-01,22031.00,22087.00,21773.00,21815.00,000,21815.00 -1996-04-30,22141.00,22163.00,21953.00,22041.00,000,22041.00 -1996-04-26,22250.00,22348.00,22231.00,22235.00,000,22235.00 -1996-04-25,22302.00,22345.00,22209.00,22230.00,000,22230.00 -1996-04-24,22151.00,22330.00,22151.00,22282.00,000,22282.00 -1996-04-23,22154.00,22217.00,22105.00,22120.00,000,22120.00 -1996-04-22,21913.00,22124.00,21913.00,22124.00,000,22124.00 -1996-04-19,21815.00,21931.00,21680.00,21884.00,000,21884.00 -1996-04-18,21764.00,21843.00,21695.00,21813.00,000,21813.00 -1996-04-17,21906.00,22008.00,21802.00,21816.00,000,21816.00 -1996-04-16,21927.00,22078.00,21866.00,21868.00,000,21868.00 -1996-04-15,21705.00,21923.00,21705.00,21883.00,000,21883.00 -1996-04-12,21698.00,21800.00,21579.00,21660.00,000,21660.00 -1996-04-11,21760.00,21782.00,21649.00,21694.00,000,21694.00 -1996-04-10,21774.00,21871.00,21773.00,21792.00,000,21792.00 -1996-04-09,21508.00,21818.00,21508.00,21744.00,000,21744.00 -1996-04-08,21636.00,21636.00,21398.00,21424.00,000,21424.00 -1996-04-05,21505.00,21728.00,21497.00,21696.00,000,21696.00 -1996-04-04,21452.00,21569.00,21397.00,21471.00,000,21471.00 -1996-04-03,21636.00,21755.00,21350.00,21465.00,000,21465.00 -1996-04-02,21569.00,21641.00,21465.00,21600.00,000,21600.00 -1996-04-01,21451.00,21758.00,21451.00,21560.00,000,21560.00 -1996-03-29,21301.00,21486.00,21250.00,21407.00,000,21407.00 -1996-03-28,21322.00,21433.00,21199.00,21296.00,000,21296.00 -1996-03-27,21047.00,21330.00,20992.00,21330.00,000,21330.00 -1996-03-26,20943.00,21290.00,20943.00,21015.00,000,21015.00 -1996-03-25,20746.00,20947.00,20746.00,20915.00,000,20915.00 -1996-03-22,20763.00,20811.00,20600.00,20701.00,000,20701.00 -1996-03-21,20471.00,20746.00,20471.00,20728.00,000,20728.00 -1996-03-19,20333.00,20615.00,20333.00,20443.00,000,20443.00 -1996-03-18,20243.00,20352.00,20224.00,20285.00,000,20285.00 -1996-03-15,19960.00,20247.00,19960.00,20191.00,000,20191.00 -1996-03-14,19735.00,19924.00,19709.00,19924.00,000,19924.00 -1996-03-13,19932.00,19932.00,19628.00,19735.00,000,19735.00 -1996-03-12,19882.00,20008.00,19854.00,19950.00,000,19950.00 -1996-03-11,20056.00,20056.00,19748.00,19796.00,000,19796.00 -1996-03-08,19919.00,20165.00,19823.00,20156.00,000,20156.00 -1996-03-07,20201.00,20201.00,19917.00,19957.00,000,19957.00 -1996-03-06,20167.00,20301.00,19946.00,20241.00,000,20241.00 -1996-03-05,20070.00,20304.00,20070.00,20184.00,000,20184.00 -1996-03-04,20166.00,20222.00,20062.00,20064.00,000,20064.00 -1996-03-01,20094.00,20250.00,19936.00,20169.00,000,20169.00 -1996-02-29,19974.00,20129.00,19903.00,20125.00,000,20125.00 -1996-02-28,20053.00,20211.00,19879.00,19920.00,000,19920.00 -1996-02-27,20427.00,20427.00,19977.00,20000.00,000,20000.00 -1996-02-26,20294.00,20480.00,20294.00,20480.00,000,20480.00 -1996-02-23,20393.00,20500.00,20267.00,20300.00,000,20300.00 -1996-02-22,20390.00,20437.00,20311.00,20341.00,000,20341.00 -1996-02-21,20655.00,20655.00,20322.00,20372.00,000,20372.00 -1996-02-20,20649.00,20668.00,20410.00,20656.00,000,20656.00 -1996-02-19,20760.00,20760.00,20632.00,20721.00,000,20721.00 -1996-02-16,20835.00,20835.00,20581.00,20803.00,000,20803.00 -1996-02-15,20921.00,21010.00,20751.00,20886.00,000,20886.00 -1996-02-14,20800.00,21042.00,20800.00,20944.00,000,20944.00 -1996-02-13,20964.00,21056.00,20783.00,20784.00,000,20784.00 -1996-02-09,21136.00,21157.00,20892.00,20935.00,000,20935.00 -1996-02-08,20956.00,21150.00,20911.00,21118.00,000,21118.00 -1996-02-07,20742.00,21039.00,20642.00,20943.00,000,20943.00 -1996-02-06,20606.00,20768.00,20556.00,20751.00,000,20751.00 -1996-02-05,20882.00,20882.00,20624.00,20653.00,000,20653.00 -1996-02-02,20947.00,21069.00,20874.00,20904.00,000,20904.00 -1996-02-01,20806.00,20943.00,20761.00,20935.00,000,20935.00 -1996-01-31,20787.00,21022.00,20787.00,20813.00,000,20813.00 -1996-01-30,20602.00,20797.00,20602.00,20722.00,000,20722.00 -1996-01-29,20658.00,20689.00,20532.00,20589.00,000,20589.00 -1996-01-26,20398.00,20693.00,20258.00,20664.00,000,20664.00 -1996-01-25,20359.00,20458.00,20289.00,20415.00,000,20415.00 -1996-01-24,20071.00,20313.00,19985.00,20313.00,000,20313.00 -1996-01-23,20209.00,20364.00,20041.00,20081.00,000,20081.00 -1996-01-22,20381.00,20392.00,20089.00,20197.00,000,20197.00 -1996-01-19,20376.00,20377.00,20156.00,20366.00,000,20366.00 -1996-01-18,20537.00,20537.00,20298.00,20370.00,000,20370.00 -1996-01-17,20656.00,20754.00,20570.00,20570.00,000,20570.00 -1996-01-16,20304.00,20567.00,20303.00,20567.00,000,20567.00 -1996-01-12,20423.00,20542.00,20208.00,20287.00,000,20287.00 -1996-01-11,20548.00,20548.00,20260.00,20378.00,000,20378.00 -1996-01-10,20592.00,20676.00,20459.00,20612.00,000,20612.00 -1996-01-09,20565.00,20653.00,20454.00,20652.00,000,20652.00 -1996-01-08,20617.00,20667.00,20471.00,20564.00,000,20564.00 -1996-01-05,20578.00,20670.00,20456.00,20669.00,000,20669.00 -1996-01-04,19946.00,20648.00,19946.00,20618.00,000,20618.00 -1995-12-29,19882.00,19940.00,19822.00,19868.00,000,19868.00 -1995-12-28,19990.00,20024.00,19868.00,19873.00,000,19873.00 -1995-12-27,19931.00,20012.00,19925.00,20012.00,000,20012.00 -1995-12-26,19792.00,19905.00,19691.00,19905.00,000,19905.00 -1995-12-25,19772.00,19831.00,19725.00,19775.00,000,19775.00 -1995-12-22,19728.00,19809.00,19670.00,19744.00,000,19744.00 -1995-12-21,19441.00,19658.00,19433.00,19653.00,000,19653.00 -1995-12-20,19201.00,19538.00,19201.00,19449.00,000,19449.00 -1995-12-19,19242.00,19242.00,19077.00,19140.00,000,19140.00 -1995-12-18,19365.00,19418.00,19299.00,19311.00,000,19311.00 -1995-12-15,19493.00,19500.00,19293.00,19347.00,000,19347.00 -1995-12-14,19318.00,19548.00,19268.00,19499.00,000,19499.00 -1995-12-13,19357.00,19439.00,19276.00,19283.00,000,19283.00 -1995-12-12,19236.00,19390.00,19234.00,19313.00,000,19313.00 -1995-12-11,19315.00,19378.00,19162.00,19227.00,000,19227.00 -1995-12-08,19421.00,19454.00,19159.00,19287.00,000,19287.00 -1995-12-07,19084.00,19443.00,19084.00,19412.00,000,19412.00 -1995-12-06,18893.00,19118.00,18889.00,19068.00,000,19068.00 -1995-12-05,18914.00,18978.00,18823.00,18880.00,000,18880.00 -1995-12-04,18889.00,19062.00,18889.00,18897.00,000,18897.00 -1995-12-01,18752.00,18986.00,18696.00,18833.00,000,18833.00 -1995-11-30,18578.00,18847.00,18578.00,18744.00,000,18744.00 -1995-11-29,18675.00,18738.00,18470.00,18534.00,000,18534.00 -1995-11-28,18560.00,18745.00,18540.00,18688.00,000,18688.00 -1995-11-27,18231.00,18690.00,18231.00,18543.00,000,18543.00 -1995-11-24,18246.00,18256.00,18147.00,18215.00,000,18215.00 -1995-11-22,18372.00,18372.00,18213.00,18240.00,000,18240.00 -1995-11-21,18376.00,18460.00,18251.00,18384.00,000,18384.00 -1995-11-20,18189.00,18445.00,18189.00,18384.00,000,18384.00 -1995-11-17,18008.00,18175.00,17987.00,18151.00,000,18151.00 -1995-11-16,17697.00,17942.00,17686.00,17940.00,000,17940.00 -1995-11-15,17805.00,17883.00,17655.00,17683.00,000,17683.00 -1995-11-14,17829.00,17889.00,17766.00,17803.00,000,17803.00 -1995-11-13,17856.00,17891.00,17692.00,17789.00,000,17789.00 -1995-11-10,17831.00,17893.00,17737.00,17844.00,000,17844.00 -1995-11-09,17877.00,18128.00,17821.00,17821.00,000,17821.00 -1995-11-08,17984.00,17984.00,17851.00,17863.00,000,17863.00 -1995-11-07,18034.00,18074.00,17936.00,18021.00,000,18021.00 -1995-11-06,18006.00,18252.00,17971.00,18037.00,000,18037.00 -1995-11-02,17533.00,18040.00,17533.00,18029.00,000,18029.00 -1995-11-01,17623.00,17623.00,17409.00,17474.00,000,17474.00 -1995-10-31,17502.00,17684.00,17358.00,17655.00,000,17655.00 -1995-10-30,17380.00,17516.00,17365.00,17509.00,000,17509.00 -1995-10-27,17699.00,17699.00,17337.00,17337.00,000,17337.00 -1995-10-26,17971.00,17976.00,17682.00,17727.00,000,17727.00 -1995-10-25,18017.00,18049.00,17945.00,17971.00,000,17971.00 -1995-10-24,18122.00,18261.00,18014.00,18014.00,000,18014.00 -1995-10-23,18122.00,18173.00,18025.00,18156.00,000,18156.00 -1995-10-20,17985.00,18219.00,17973.00,18157.00,000,18157.00 -1995-10-19,17908.00,18050.00,17908.00,17955.00,000,17955.00 -1995-10-18,17911.00,17918.00,17746.00,17896.00,000,17896.00 -1995-10-17,18006.00,18072.00,17841.00,17917.00,000,17917.00 -1995-10-16,17915.00,18141.00,17915.00,18016.00,000,18016.00 -1995-10-13,17924.00,17976.00,17777.00,17881.00,000,17881.00 -1995-10-12,17909.00,18052.00,17841.00,17971.00,000,17971.00 -1995-10-11,18156.00,18156.00,17891.00,17891.00,000,17891.00 -1995-10-09,18504.00,18504.00,18169.00,18176.00,000,18176.00 -1995-10-06,18201.00,18547.00,18183.00,18506.00,000,18506.00 -1995-10-05,18143.00,18267.00,18101.00,18220.00,000,18220.00 -1995-10-04,18167.00,18347.00,18056.00,18145.00,000,18145.00 -1995-10-03,17770.00,18160.00,17737.00,18143.00,000,18143.00 -1995-10-02,17921.00,17950.00,17684.00,17740.00,000,17740.00 -1995-09-29,18046.00,18139.00,17883.00,17913.00,000,17913.00 -1995-09-28,18219.00,18295.00,18023.00,18023.00,000,18023.00 -1995-09-27,17928.00,18262.00,17765.00,18262.00,000,18262.00 -1995-09-26,17595.00,17922.00,17595.00,17922.00,000,17922.00 -1995-09-25,17734.00,17855.00,17566.00,17566.00,000,17566.00 -1995-09-22,17958.00,17958.00,17666.00,17714.00,000,17714.00 -1995-09-21,18143.00,18143.00,17949.00,18035.00,000,18035.00 -1995-09-20,18561.00,18639.00,18142.00,18199.00,000,18199.00 -1995-09-19,18275.00,18481.00,18230.00,18474.00,000,18474.00 -1995-09-18,18781.00,18848.00,18319.00,18319.00,000,18319.00 -1995-09-14,18680.00,18791.00,18604.00,18759.00,000,18759.00 -1995-09-13,18470.00,18654.00,18421.00,18614.00,000,18614.00 -1995-09-12,18527.00,18674.00,18442.00,18472.00,000,18472.00 -1995-09-11,18271.00,18570.00,18195.00,18486.00,000,18486.00 -1995-09-08,17643.00,18501.00,17643.00,18280.00,000,18280.00 -1995-09-07,17656.00,17714.00,17512.00,17621.00,000,17621.00 -1995-09-06,17807.00,17899.00,17620.00,17620.00,000,17620.00 -1995-09-05,17731.00,17844.00,17503.00,17794.00,000,17794.00 -1995-09-04,18116.00,18116.00,17638.00,17749.00,000,17749.00 -1995-09-01,18053.00,18153.00,17909.00,18121.00,000,18121.00 -1995-08-31,17972.00,18160.00,17894.00,18117.00,000,18117.00 -1995-08-30,18171.00,18242.00,17956.00,17984.00,000,17984.00 -1995-08-29,17892.00,18185.00,17825.00,18135.00,000,18135.00 -1995-08-28,17742.00,17897.00,17581.00,17847.00,000,17847.00 -1995-08-25,17920.00,17920.00,17689.00,17771.00,000,17771.00 -1995-08-24,17700.00,17945.00,17600.00,17922.00,000,17922.00 -1995-08-23,17874.00,17933.00,17666.00,17732.00,000,17732.00 -1995-08-22,17858.00,18029.00,17818.00,17878.00,000,17878.00 -1995-08-21,18000.00,18000.00,17723.00,17871.00,000,17871.00 -1995-08-18,18095.00,18095.00,17913.00,18032.00,000,18032.00 -1995-08-17,18116.00,18268.00,18020.00,18150.00,000,18150.00 -1995-08-16,17522.00,18300.00,17522.00,18159.00,000,18159.00 -1995-08-15,16915.00,17453.00,16869.00,17453.00,000,17453.00 -1995-08-14,16819.00,17004.00,16819.00,16917.00,000,16917.00 -1995-08-11,16757.00,16856.00,16701.00,16792.00,000,16792.00 -1995-08-10,16771.00,16788.00,16546.00,16729.00,000,16729.00 -1995-08-09,16833.00,16931.00,16714.00,16789.00,000,16789.00 -1995-08-08,16645.00,16854.00,16512.00,16839.00,000,16839.00 -1995-08-07,16805.00,16873.00,16507.00,16615.00,000,16615.00 -1995-08-04,16881.00,16956.00,16642.00,16741.00,000,16741.00 -1995-08-03,16799.00,17160.00,16799.00,16894.00,000,16894.00 -1995-08-02,16319.00,16796.00,16274.00,16721.00,000,16721.00 -1995-08-01,16645.00,16645.00,16325.00,16359.00,000,16359.00 -1995-07-31,16652.00,16919.00,16594.00,16678.00,000,16678.00 -1995-07-28,16612.00,16726.00,16434.00,16649.00,000,16649.00 -1995-07-27,16367.00,16695.00,16308.00,16625.00,000,16625.00 -1995-07-26,16165.00,16387.00,16158.00,16387.00,000,16387.00 -1995-07-25,16572.00,16572.00,16148.00,16148.00,000,16148.00 -1995-07-24,16611.00,16644.00,16374.00,16592.00,000,16592.00 -1995-07-21,16509.00,16613.00,16482.00,16589.00,000,16589.00 -1995-07-20,16365.00,16453.00,16182.00,16453.00,000,16453.00 -1995-07-19,16518.00,16518.00,16240.00,16422.00,000,16422.00 -1995-07-18,16890.00,17018.00,16574.00,16574.00,000,16574.00 -1995-07-17,16574.00,16942.00,16574.00,16842.00,000,16842.00 -1995-07-14,16534.00,16591.00,16425.00,16518.00,000,16518.00 -1995-07-13,16632.00,16769.00,16436.00,16506.00,000,16506.00 -1995-07-12,16603.00,16834.00,16512.00,16606.00,000,16606.00 -1995-07-11,16217.00,16589.00,15955.00,16588.00,000,16588.00 -1995-07-10,16285.00,16703.00,16243.00,16243.00,000,16243.00 -1995-07-07,15309.00,16389.00,15309.00,16213.00,000,16213.00 -1995-07-06,14872.00,15257.00,14760.00,15257.00,000,15257.00 -1995-07-05,14759.00,14888.00,14675.00,14830.00,000,14830.00 -1995-07-04,14498.00,14769.00,14472.00,14756.00,000,14756.00 -1995-07-03,14519.00,14519.00,14296.00,14485.00,000,14485.00 -1995-06-30,14511.00,14624.00,14452.00,14517.00,000,14517.00 -1995-06-29,14678.00,14905.00,14445.00,14507.00,000,14507.00 -1995-06-28,14708.00,14755.00,14537.00,14618.00,000,14618.00 -1995-06-27,15155.00,15163.00,14745.00,14759.00,000,14759.00 -1995-06-26,15320.00,15378.00,15145.00,15145.00,000,15145.00 -1995-06-23,14987.00,15265.00,14987.00,15265.00,000,15265.00 -1995-06-22,14940.00,14954.00,14793.00,14926.00,000,14926.00 -1995-06-21,14685.00,14983.00,14665.00,14951.00,000,14951.00 -1995-06-20,14770.00,14866.00,14528.00,14666.00,000,14666.00 -1995-06-19,14705.00,14849.00,14700.00,14700.00,000,14700.00 -1995-06-16,14931.00,15021.00,14656.00,14703.00,000,14703.00 -1995-06-15,14667.00,14867.00,14376.00,14867.00,000,14867.00 -1995-06-14,14624.00,14801.00,14624.00,14660.00,000,14660.00 -1995-06-13,14818.00,14932.00,14582.00,14600.00,000,14600.00 -1995-06-12,15030.00,15030.00,14742.00,14813.00,000,14813.00 -1995-06-09,15415.00,15415.00,14978.00,15044.00,000,15044.00 -1995-06-08,15630.00,15630.00,15356.00,15442.00,000,15442.00 -1995-06-07,15643.00,15734.00,15557.00,15680.00,000,15680.00 -1995-06-06,15875.00,15923.00,15608.00,15661.00,000,15661.00 -1995-06-05,15854.00,15921.00,15740.00,15897.00,000,15897.00 -1995-06-02,15626.00,16016.00,15626.00,15849.00,000,15849.00 -1995-06-01,15481.00,15648.00,15420.00,15595.00,000,15595.00 -1995-05-31,15746.00,15746.00,15292.00,15437.00,000,15437.00 -1995-05-30,15573.00,15824.00,15573.00,15763.00,000,15763.00 -1995-05-29,15608.00,15608.00,15420.00,15574.00,000,15574.00 -1995-05-26,15540.00,15713.00,15406.00,15694.00,000,15694.00 -1995-05-25,16009.00,16033.00,15559.00,15579.00,000,15579.00 -1995-05-24,15885.00,16006.00,15808.00,15971.00,000,15971.00 -1995-05-23,15793.00,15916.00,15744.00,15916.00,000,15916.00 -1995-05-22,16102.00,16102.00,15712.00,15789.00,000,15789.00 -1995-05-19,16267.00,16267.00,16043.00,16141.00,000,16141.00 -1995-05-18,16501.00,16527.00,16205.00,16313.00,000,16313.00 -1995-05-17,16403.00,16516.00,16359.00,16471.00,000,16471.00 -1995-05-16,16606.00,16606.00,16385.00,16389.00,000,16389.00 -1995-05-15,16457.00,16636.00,16363.00,16610.00,000,16610.00 -1995-05-12,16506.00,16684.00,16387.00,16421.00,000,16421.00 -1995-05-11,16843.00,16873.00,16462.00,16462.00,000,16462.00 -1995-05-10,16908.00,16956.00,16800.00,16826.00,000,16826.00 -1995-05-09,17121.00,17167.00,16937.00,16958.00,000,16958.00 -1995-05-08,17111.00,17190.00,17072.00,17104.00,000,17104.00 -1995-05-02,16820.00,17116.00,16734.00,17089.00,000,17089.00 -1995-05-01,16820.00,16839.00,16708.00,16811.00,000,16811.00 -1995-04-28,16867.00,16869.00,16740.00,16807.00,000,16807.00 -1995-04-27,16902.00,16989.00,16809.00,16884.00,000,16884.00 -1995-04-26,16859.00,16932.00,16713.00,16826.00,000,16826.00 -1995-04-25,16837.00,17110.00,16837.00,16910.00,000,16910.00 -1995-04-24,16944.00,16981.00,16779.00,16804.00,000,16804.00 -1995-04-21,16700.00,16968.00,16700.00,16968.00,000,16968.00 -1995-04-20,16430.00,16665.00,16430.00,16643.00,000,16643.00 -1995-04-19,16181.00,16424.00,15977.00,16376.00,000,16376.00 -1995-04-18,16274.00,16323.00,16153.00,16225.00,000,16225.00 -1995-04-17,16046.00,16305.00,15893.00,16304.00,000,16304.00 -1995-04-14,16406.00,16530.00,16048.00,16048.00,000,16048.00 -1995-04-13,16336.00,16564.00,16275.00,16439.00,000,16439.00 -1995-04-12,16270.00,16434.00,16234.00,16345.00,000,16345.00 -1995-04-11,16205.00,16365.00,16109.00,16269.00,000,16269.00 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-1991-01-23,23173.00,23173.00,22952.00,23050.00,000,23050.00 -1991-01-22,23372.00,23497.00,23202.00,23254.00,000,23254.00 -1991-01-21,23755.00,23755.00,23352.00,23352.00,000,23352.00 -1991-01-18,23479.00,24050.00,23323.00,23808.00,000,23808.00 -1991-01-17,22403.00,23447.00,22100.00,23447.00,000,23447.00 -1991-01-16,23140.00,23140.00,22382.00,22443.00,000,22443.00 -1991-01-14,23198.00,23239.00,22911.00,23213.00,000,23213.00 -1991-01-11,23078.00,23241.00,22856.00,23241.00,000,23241.00 -1991-01-10,22925.00,23185.00,22712.00,23047.00,000,23047.00 -1991-01-09,22847.00,23099.00,22662.00,22969.00,000,22969.00 -1991-01-08,23708.00,23708.00,22859.00,22898.00,000,22898.00 -1991-01-07,24037.00,24037.00,23736.00,23737.00,000,23737.00 -1991-01-04,23827.00,24110.00,23796.00,24069.00,000,24069.00 -1990-12-28,23954.00,24055.00,23771.00,23849.00,000,23849.00 -1990-12-27,23938.00,24264.00,23866.00,23941.00,000,23941.00 -1990-12-26,23785.00,23976.00,23695.00,23888.00,000,23888.00 -1990-12-25,24090.00,24090.00,23768.00,23768.00,000,23768.00 -1990-12-21,24495.00,24495.00,23964.00,24120.00,000,24120.00 -1990-12-20,24853.00,24853.00,24516.00,24525.00,000,24525.00 -1990-12-19,24473.00,25064.00,24473.00,24877.00,000,24877.00 -1990-12-18,24094.00,24424.00,24094.00,24424.00,000,24424.00 -1990-12-17,24327.00,24327.00,24017.00,24088.00,000,24088.00 -1990-12-14,24637.00,24637.00,24174.00,24350.00,000,24350.00 -1990-12-13,24036.00,24643.00,24036.00,24643.00,000,24643.00 -1990-12-12,23956.00,24326.00,23893.00,23999.00,000,23999.00 -1990-12-11,23760.00,24006.00,23434.00,23957.00,000,23957.00 -1990-12-10,23565.00,23862.00,23351.00,23785.00,000,23785.00 -1990-12-07,22592.00,23538.00,22592.00,23522.00,000,23522.00 -1990-12-06,22239.00,22609.00,22239.00,22553.00,000,22553.00 -1990-12-05,21902.00,22251.00,21627.00,22194.00,000,22194.00 -1990-12-04,22679.00,22679.00,21862.00,21863.00,000,21863.00 -1990-12-03,22457.00,23034.00,22457.00,22726.00,000,22726.00 -1990-11-30,22684.00,22684.00,21934.00,22455.00,000,22455.00 -1990-11-29,23031.00,23031.00,22266.00,22713.00,000,22713.00 -1990-11-28,23610.00,23767.00,23046.00,23054.00,000,23054.00 -1990-11-27,23737.00,23737.00,23533.00,23624.00,000,23624.00 -1990-11-26,23417.00,23766.00,23417.00,23698.00,000,23698.00 -1990-11-22,22821.00,23400.00,22821.00,23400.00,000,23400.00 -1990-11-21,23158.00,23158.00,22615.00,22817.00,000,22817.00 -1990-11-20,23479.00,23479.00,23203.00,23205.00,000,23205.00 -1990-11-19,23193.00,23518.00,23177.00,23518.00,000,23518.00 -1990-11-16,23455.00,23455.00,22874.00,23172.00,000,23172.00 -1990-11-15,23931.00,23960.00,23453.00,23487.00,000,23487.00 -1990-11-14,23941.00,24046.00,23630.00,23937.00,000,23937.00 -1990-11-13,22939.00,23974.00,22939.00,23974.00,000,23974.00 -1990-11-09,22947.00,22947.00,22482.00,22932.00,000,22932.00 -1990-11-08,23444.00,23444.00,22834.00,22970.00,000,22970.00 -1990-11-07,23938.00,23938.00,23401.00,23500.00,000,23500.00 -1990-11-06,24416.00,24645.00,23868.00,23966.00,000,23966.00 -1990-11-05,24232.00,24575.00,24232.00,24385.00,000,24385.00 -1990-11-02,24244.00,24385.00,23672.00,24195.00,000,24195.00 -1990-11-01,25160.00,25160.00,24205.00,24295.00,000,24295.00 -1990-10-31,25253.00,25445.00,25145.00,25194.00,000,25194.00 -1990-10-30,25329.00,25329.00,24885.00,25242.00,000,25242.00 -1990-10-29,25004.00,25393.00,25004.00,25329.00,000,25329.00 -1990-10-26,25323.00,25323.00,24866.00,25006.00,000,25006.00 -1990-10-25,24908.00,25486.00,24908.00,25353.00,000,25353.00 -1990-10-24,25271.00,25271.00,24677.00,24877.00,000,24877.00 -1990-10-23,25099.00,25433.00,25084.00,25298.00,000,25298.00 -1990-10-22,24490.00,25232.00,24490.00,25071.00,000,25071.00 -1990-10-19,24364.00,25003.00,24364.00,24481.00,000,24481.00 -1990-10-18,23858.00,24367.00,23763.00,24367.00,000,24367.00 -1990-10-17,23615.00,24054.00,23551.00,23859.00,000,23859.00 -1990-10-16,23143.00,23819.00,23143.00,23606.00,000,23606.00 -1990-10-15,22404.00,23109.00,22404.00,23109.00,000,23109.00 -1990-10-12,22557.00,22557.00,22132.00,22390.00,000,22390.00 -1990-10-11,23480.00,23480.00,22521.00,22586.00,000,22586.00 -1990-10-09,23643.00,23971.00,23362.00,23495.00,000,23495.00 -1990-10-08,22858.00,23630.00,22858.00,23630.00,000,23630.00 -1990-10-05,22308.00,23140.00,22308.00,22828.00,000,22828.00 -1990-10-04,22829.00,22829.00,22260.00,22278.00,000,22278.00 -1990-10-03,22898.00,23463.00,22578.00,22849.00,000,22849.00 -1990-10-02,20222.00,22899.00,20222.00,22898.00,000,22898.00 -1990-10-01,20986.00,21076.00,19782.00,20222.00,000,20222.00 -1990-09-28,21756.00,21756.00,20671.00,20984.00,000,20984.00 -1990-09-27,22232.00,22312.00,21532.00,21772.00,000,21772.00 -1990-09-26,23371.00,23518.00,22251.00,22251.00,000,22251.00 -1990-09-25,23762.00,23762.00,23219.00,23359.00,000,23359.00 -1990-09-21,23570.00,23782.00,23050.00,23778.00,000,23778.00 -1990-09-20,23734.00,23836.00,23432.00,23603.00,000,23603.00 -1990-09-19,23879.00,24131.00,23726.00,23726.00,000,23726.00 -1990-09-18,24331.00,24331.00,23308.00,23885.00,000,23885.00 -1990-09-17,24889.00,24889.00,24286.00,24366.00,000,24366.00 -1990-09-14,25064.00,25064.00,24834.00,24897.00,000,24897.00 -1990-09-13,25254.00,25488.00,25012.00,25075.00,000,25075.00 -1990-09-12,24601.00,25288.00,24464.00,25216.00,000,25216.00 -1990-09-11,25064.00,25064.00,24471.00,24605.00,000,24605.00 -1990-09-10,23997.00,25081.00,23997.00,25081.00,000,25081.00 -1990-09-07,23787.00,24045.00,23406.00,23962.00,000,23962.00 -1990-09-06,24096.00,24268.00,23620.00,23812.00,000,23812.00 -1990-09-05,24888.00,24888.00,23641.00,24078.00,000,24078.00 -1990-09-04,25429.00,25460.00,24803.00,24908.00,000,24908.00 -1990-09-03,26014.00,26163.00,25418.00,25420.00,000,25420.00 -1990-08-31,25646.00,26183.00,25561.00,25978.00,000,25978.00 -1990-08-30,24916.00,25673.00,24751.00,25670.00,000,25670.00 -1990-08-29,25701.00,25701.00,24855.00,24895.00,000,24895.00 -1990-08-28,25146.00,25914.00,25146.00,25711.00,000,25711.00 -1990-08-27,24183.00,25142.00,24183.00,25142.00,000,25142.00 -1990-08-24,23731.00,24485.00,23547.00,24166.00,000,24166.00 -1990-08-23,25199.00,25199.00,23649.00,23738.00,000,23738.00 -1990-08-22,26254.00,26254.00,24845.00,25211.00,000,25211.00 -1990-08-21,26549.00,26956.00,26298.00,26298.00,000,26298.00 -1990-08-20,26760.00,26924.00,26456.00,26490.00,000,26490.00 -1990-08-17,27527.00,27527.00,26652.00,26787.00,000,26787.00 -1990-08-16,28097.00,28097.00,27436.00,27549.00,000,27549.00 -1990-08-15,26717.00,28159.00,26717.00,28112.00,000,28112.00 -1990-08-14,26194.00,26789.00,25949.00,26673.00,000,26673.00 -1990-08-13,27282.00,27282.00,25914.00,26176.00,000,26176.00 -1990-08-10,27646.00,27919.00,27168.00,27330.00,000,27330.00 -1990-08-09,28502.00,28502.00,27616.00,27616.00,000,27616.00 -1990-08-08,27657.00,28522.00,27573.00,28509.00,000,28509.00 -1990-08-07,28599.00,28599.00,27241.00,27653.00,000,27653.00 -1990-08-06,29486.00,29486.00,28273.00,28600.00,000,28600.00 -1990-08-03,30222.00,30222.00,29516.00,29516.00,000,29516.00 -1990-08-02,30800.00,30800.00,29929.00,30245.00,000,30245.00 -1990-08-01,31086.00,31372.00,30657.00,30838.00,000,30838.00 -1990-07-31,30504.00,31041.00,30504.00,31036.00,000,31036.00 -1990-07-30,30846.00,30846.00,30297.00,30443.00,000,30443.00 -1990-07-27,31341.00,31341.00,30378.00,30863.00,000,30863.00 -1990-07-26,31706.00,31795.00,31328.00,31370.00,000,31370.00 -1990-07-25,31739.00,31846.00,31651.00,31701.00,000,31701.00 -1990-07-24,31834.00,31924.00,31504.00,31702.00,000,31702.00 -1990-07-23,32421.00,32421.00,31782.00,31895.00,000,31895.00 -1990-07-20,33023.00,33023.00,32417.00,32422.00,000,32422.00 -1990-07-19,33051.00,33078.00,32848.00,33056.00,000,33056.00 -1990-07-18,33174.00,33187.00,32972.00,33048.00,000,33048.00 -1990-07-17,33065.00,33178.00,32969.00,33172.00,000,33172.00 -1990-07-16,32678.00,33022.00,32678.00,33022.00,000,33022.00 -1990-07-13,32617.00,32768.00,32524.00,32644.00,000,32644.00 -1990-07-12,32331.00,32578.00,32266.00,32575.00,000,32575.00 -1990-07-11,32151.00,32457.00,32144.00,32294.00,000,32294.00 -1990-07-10,32553.00,32553.00,32152.00,32152.00,000,32152.00 -1990-07-09,32458.00,32609.00,32418.00,32538.00,000,32538.00 -1990-07-06,32346.00,32482.00,32272.00,32445.00,000,32445.00 -1990-07-05,32449.00,32591.00,32352.00,32352.00,000,32352.00 -1990-07-04,32414.00,32627.00,32399.00,32446.00,000,32446.00 -1990-07-03,32180.00,32415.00,32134.00,32415.00,000,32415.00 -1990-07-02,31924.00,32160.00,31766.00,32160.00,000,32160.00 -1990-06-29,32145.00,32343.00,31934.00,31940.00,000,31940.00 -1990-06-28,32324.00,32339.00,31931.00,32106.00,000,32106.00 -1990-06-27,31600.00,32313.00,31600.00,32313.00,000,32313.00 -1990-06-26,31119.00,31573.00,31086.00,31572.00,000,31572.00 -1990-06-25,31638.00,31638.00,31124.00,31124.00,000,31124.00 -1990-06-22,32040.00,32040.00,31645.00,31695.00,000,31695.00 -1990-06-21,32100.00,32317.00,31923.00,32087.00,000,32087.00 -1990-06-20,32045.00,32182.00,32006.00,32088.00,000,32088.00 -1990-06-19,32324.00,32324.00,31914.00,32040.00,000,32040.00 -1990-06-18,32530.00,32634.00,32306.00,32377.00,000,32377.00 -1990-06-15,32659.00,32714.00,32537.00,32538.00,000,32538.00 -1990-06-14,32409.00,32756.00,32409.00,32668.00,000,32668.00 -1990-06-13,32349.00,32466.00,32191.00,32372.00,000,32372.00 -1990-06-12,32490.00,32594.00,32285.00,32322.00,000,32322.00 -1990-06-11,32964.00,32964.00,32488.00,32540.00,000,32540.00 -1990-06-08,33212.00,33345.00,32951.00,32993.00,000,32993.00 -1990-06-07,32939.00,33217.00,32937.00,33193.00,000,33193.00 -1990-06-06,32914.00,33052.00,32839.00,32954.00,000,32954.00 -1990-06-05,32945.00,33054.00,32856.00,32922.00,000,32922.00 -1990-06-04,32911.00,33082.00,32898.00,32925.00,000,32925.00 -1990-06-01,33110.00,33110.00,32831.00,32891.00,000,32891.00 -1990-05-31,32961.00,33228.00,32877.00,33131.00,000,33131.00 -1990-05-30,32805.00,33002.00,32465.00,32926.00,000,32926.00 -1990-05-29,33191.00,33204.00,32777.00,32818.00,000,32818.00 -1990-05-28,32839.00,33224.00,32839.00,33192.00,000,33192.00 -1990-05-25,32340.00,32839.00,32340.00,32794.00,000,32794.00 -1990-05-24,32184.00,32329.00,32070.00,32312.00,000,32312.00 -1990-05-23,31990.00,32317.00,31990.00,32177.00,000,32177.00 -1990-05-22,31721.00,31938.00,31600.00,31938.00,000,31938.00 -1990-05-21,31987.00,31987.00,31605.00,31765.00,000,31765.00 -1990-05-18,32093.00,32203.00,31870.00,32014.00,000,32014.00 -1990-05-17,31970.00,32179.00,31882.00,32062.00,000,32062.00 -1990-05-16,31983.00,32083.00,31906.00,31968.00,000,31968.00 -1990-05-15,32046.00,32323.00,31889.00,31997.00,000,31997.00 -1990-05-14,31542.00,32071.00,31542.00,32043.00,000,32043.00 -1990-05-11,31013.00,31513.00,31013.00,31512.00,000,31512.00 -1990-05-10,30957.00,31261.00,30957.00,30980.00,000,30980.00 -1990-05-09,30980.00,31111.00,30835.00,30946.00,000,30946.00 -1990-05-08,30950.00,31040.00,30730.00,30971.00,000,30971.00 -1990-05-07,30212.00,30957.00,30212.00,30956.00,000,30956.00 -1990-05-02,29736.00,30174.00,29736.00,30174.00,000,30174.00 -1990-05-01,29594.00,29691.00,29519.00,29690.00,000,29690.00 -1990-04-27,29437.00,29637.00,29437.00,29585.00,000,29585.00 -1990-04-26,29574.00,29680.00,29425.00,29425.00,000,29425.00 -1990-04-25,29507.00,29711.00,29433.00,29564.00,000,29564.00 -1990-04-24,29630.00,29630.00,29285.00,29501.00,000,29501.00 -1990-04-23,29834.00,29953.00,29521.00,29679.00,000,29679.00 -1990-04-20,29968.00,30200.00,29625.00,29835.00,000,29835.00 -1990-04-19,29296.00,30034.00,29296.00,29945.00,000,29945.00 -1990-04-18,28427.00,29250.00,28427.00,29249.00,000,29249.00 -1990-04-17,28407.00,28861.00,28336.00,28462.00,000,28462.00 -1990-04-16,29153.00,29153.00,28396.00,28463.00,000,28463.00 -1990-04-13,29586.00,29586.00,28954.00,29214.00,000,29214.00 -1990-04-12,29457.00,29688.00,29128.00,29623.00,000,29623.00 -1990-04-11,29654.00,30003.00,29340.00,29440.00,000,29440.00 -1990-04-10,30383.00,30383.00,29625.00,29625.00,000,29625.00 -1990-04-09,29298.00,30524.00,29298.00,30398.00,000,30398.00 -1990-04-06,28274.00,29279.00,28274.00,29279.00,000,29279.00 -1990-04-05,28423.00,28423.00,27251.00,28249.00,000,28249.00 -1990-04-04,28789.00,29143.00,28090.00,28443.00,000,28443.00 -1990-04-03,28005.00,28791.00,27678.00,28760.00,000,28760.00 -1990-04-02,29980.00,29980.00,28002.00,28002.00,000,28002.00 -1990-03-30,31002.00,31002.00,29828.00,29980.00,000,29980.00 -1990-03-29,31238.00,31438.00,30877.00,31026.00,000,31026.00 -1990-03-28,31800.00,31800.00,31107.00,31264.00,000,31264.00 -1990-03-27,31834.00,32164.00,31368.00,31826.00,000,31826.00 -1990-03-26,30378.00,31840.00,30378.00,31840.00,000,31840.00 -1990-03-23,29851.00,30372.00,29597.00,30372.00,000,30372.00 -1990-03-22,30776.00,30776.00,28830.00,29843.00,000,29843.00 -1990-03-20,31242.00,31551.00,30570.00,30807.00,000,30807.00 -1990-03-19,32609.00,32721.00,31198.00,31263.00,000,31263.00 -1990-03-16,32659.00,32918.00,32472.00,32616.00,000,32616.00 -1990-03-15,32365.00,32720.00,32365.00,32672.00,000,32672.00 -1990-03-14,32578.00,32762.00,32280.00,32352.00,000,32352.00 -1990-03-13,33318.00,33318.00,32621.00,32621.00,000,32621.00 -1990-03-12,33985.00,34006.00,33366.00,33368.00,000,33368.00 -1990-03-09,33724.00,34320.00,33724.00,33993.00,000,33993.00 -1990-03-08,33273.00,33939.00,32971.00,33691.00,000,33691.00 -1990-03-07,33798.00,33807.00,33180.00,33362.00,000,33362.00 -1990-03-06,33857.00,33989.00,33730.00,33791.00,000,33791.00 -1990-03-05,34072.00,34118.00,33752.00,33845.00,000,33845.00 -1990-03-02,33855.00,34092.00,33771.00,34058.00,000,34058.00 -1990-03-01,34587.00,34588.00,33830.00,33830.00,000,33830.00 -1990-02-28,33950.00,34756.00,33950.00,34592.00,000,34592.00 -1990-02-27,33346.00,34001.00,32793.00,33898.00,000,33898.00 -1990-02-26,34863.00,34863.00,32443.00,33322.00,000,33322.00 -1990-02-23,35803.00,35803.00,34841.00,34891.00,000,34891.00 -1990-02-22,35767.00,36148.00,35088.00,35827.00,000,35827.00 -1990-02-21,36866.00,36866.00,35695.00,35734.00,000,35734.00 -1990-02-20,37158.00,37158.00,36868.00,36896.00,000,36896.00 -1990-02-19,37496.00,37611.00,37097.00,37223.00,000,37223.00 -1990-02-16,37523.00,37674.00,37405.00,37460.00,000,37460.00 -1990-02-15,37186.00,37585.00,37186.00,37472.00,000,37472.00 -1990-02-14,37125.00,37183.00,37018.00,37156.00,000,37156.00 -1990-02-13,37320.00,37351.00,37094.00,37107.00,000,37107.00 -1990-02-09,37509.00,37509.00,37193.00,37288.00,000,37288.00 -1990-02-08,37346.00,37516.00,37181.00,37516.00,000,37516.00 -1990-02-07,37684.00,37693.00,37256.00,37302.00,000,37302.00 -1990-02-06,37680.00,37887.00,37643.00,37667.00,000,37667.00 -1990-02-05,37677.00,37732.00,37583.00,37631.00,000,37631.00 -1990-02-02,37256.00,37665.00,37256.00,37650.00,000,37650.00 -1990-02-01,37242.00,37332.00,37125.00,37206.00,000,37206.00 -1990-01-31,37201.00,37208.00,36957.00,37189.00,000,37189.00 -1990-01-30,37210.00,37336.00,37192.00,37216.00,000,37216.00 -1990-01-29,36913.00,37225.00,36913.00,37174.00,000,37174.00 -1990-01-26,36978.00,37088.00,36846.00,36874.00,000,36874.00 -1990-01-25,36822.00,37082.00,36766.00,36969.00,000,36969.00 -1990-01-24,37396.00,37463.00,36683.00,36779.00,000,36779.00 -1990-01-23,37213.00,37379.00,37018.00,37378.00,000,37378.00 -1990-01-22,36853.00,37257.00,36851.00,37257.00,000,37257.00 -1990-01-19,36704.00,36840.00,36365.00,36837.00,000,36837.00 -1990-01-18,36834.00,37003.00,36521.00,36729.00,000,36729.00 -1990-01-17,36894.00,37285.00,36821.00,36821.00,000,36821.00 -1990-01-16,37469.00,37469.00,36658.00,36850.00,000,36850.00 -1990-01-12,38130.00,38130.00,37517.00,37517.00,000,37517.00 -1990-01-11,37706.00,38170.00,37604.00,38170.00,000,38170.00 -1990-01-10,37928.00,37928.00,37460.00,37697.00,000,37697.00 -1990-01-09,38281.00,38297.00,37730.00,37951.00,000,37951.00 -1990-01-08,38332.00,38564.00,38121.00,38295.00,000,38295.00 -1990-01-05,38717.00,38787.00,38091.00,38275.00,000,38275.00 -1990-01-04,38922.00,38951.00,38705.00,38713.00,000,38713.00 -1989-12-29,38913.00,38957.00,38828.00,38916.00,000,38916.00 -1989-12-28,38835.00,38920.00,38678.00,38877.00,000,38877.00 -1989-12-27,38709.00,38884.00,38709.00,38802.00,000,38802.00 -1989-12-26,38470.00,38786.00,38470.00,38681.00,000,38681.00 -1989-12-25,38060.00,38467.00,37905.00,38424.00,000,38424.00 -1989-12-22,38268.00,38428.00,37863.00,38040.00,000,38040.00 -1989-12-21,38523.00,38540.00,38196.00,38215.00,000,38215.00 -1989-12-20,38442.00,38572.00,38311.00,38512.00,000,38512.00 -1989-12-19,38560.00,38560.00,38250.00,38439.00,000,38439.00 -1989-12-18,38304.00,38586.00,38304.00,38586.00,000,38586.00 -1989-12-15,38181.00,38273.00,38063.00,38271.00,000,38271.00 -1989-12-14,38058.00,38202.00,37994.00,38181.00,000,38181.00 -1989-12-13,37831.00,38062.00,37831.00,38062.00,000,38062.00 -1989-12-12,37764.00,37900.00,37676.00,37804.00,000,37804.00 -1989-12-11,37714.00,37840.00,37688.00,37753.00,000,37753.00 -1989-12-08,37864.00,37880.00,37625.00,37724.00,000,37724.00 -1989-12-07,37656.00,37858.00,37550.00,37858.00,000,37858.00 -1989-12-06,37453.00,37654.00,37299.00,37654.00,000,37654.00 -1989-12-05,37327.00,37548.00,37327.00,37494.00,000,37494.00 -1989-12-04,37135.00,37313.00,37124.00,37304.00,000,37304.00 -1989-12-01,37284.00,37333.00,37059.00,37133.00,000,37133.00 -1989-11-30,37034.00,37269.00,37019.00,37269.00,000,37269.00 -1989-11-29,36989.00,37129.00,36936.00,37021.00,000,37021.00 -1989-11-28,36893.00,36987.00,36826.00,36985.00,000,36985.00 -1989-11-27,36488.00,36883.00,36488.00,36882.00,000,36882.00 -1989-11-24,36305.00,36486.00,36305.00,36484.00,000,36484.00 -1989-11-22,36098.00,36388.00,36098.00,36287.00,000,36287.00 -1989-11-21,35920.00,36060.00,35920.00,36060.00,000,36060.00 -1989-11-20,35963.00,35975.00,35882.00,35894.00,000,35894.00 -1989-11-17,35903.00,36025.00,35900.00,35964.00,000,35964.00 -1989-11-16,35855.00,35953.00,35833.00,35876.00,000,35876.00 -1989-11-15,35806.00,35977.00,35805.00,35852.00,000,35852.00 -1989-11-14,35726.00,35776.00,35697.00,35769.00,000,35769.00 -1989-11-13,35714.00,35800.00,35678.00,35750.00,000,35750.00 -1989-11-10,35679.00,35742.00,35595.00,35663.00,000,35663.00 -1989-11-09,35626.00,35690.00,35467.00,35657.00,000,35657.00 -1989-11-08,35319.00,35629.00,35319.00,35596.00,000,35596.00 -1989-11-07,35371.00,35371.00,35099.00,35270.00,000,35270.00 -1989-11-06,35495.00,35573.00,35411.00,35434.00,000,35434.00 -1989-11-02,35546.00,35556.00,35361.00,35495.00,000,35495.00 -1989-11-01,35545.00,35640.00,35453.00,35564.00,000,35564.00 -1989-10-31,35413.00,35583.00,35413.00,35549.00,000,35549.00 -1989-10-30,35523.00,35523.00,35336.00,35417.00,000,35417.00 -1989-10-27,35694.00,35743.00,35375.00,35527.00,000,35527.00 -1989-10-26,35452.00,35697.00,35452.00,35678.00,000,35678.00 -1989-10-25,35536.00,35645.00,35442.00,35442.00,000,35442.00 -1989-10-24,35609.00,35651.00,35449.00,35527.00,000,35527.00 -1989-10-23,35514.00,35670.00,35514.00,35586.00,000,35586.00 -1989-10-20,35419.00,35611.00,35419.00,35486.00,000,35486.00 -1989-10-19,35129.00,35392.00,35129.00,35374.00,000,35374.00 -1989-10-18,35005.00,35152.00,34992.00,35108.00,000,35108.00 -1989-10-17,34508.00,35184.00,34508.00,34996.00,000,34996.00 -1989-10-16,35076.00,35076.00,34461.00,34469.00,000,34469.00 -1989-10-13,34844.00,35119.00,34844.00,35116.00,000,35116.00 -1989-10-12,35220.00,35220.00,34795.00,34795.00,000,34795.00 -1989-10-11,35381.00,35399.00,35006.00,35240.00,000,35240.00 -1989-10-09,35229.00,35405.00,35229.00,35376.00,000,35376.00 -1989-10-06,35508.00,35508.00,35051.00,35209.00,000,35209.00 -1989-10-05,35382.00,35537.00,35382.00,35523.00,000,35523.00 -1989-10-04,35390.00,35419.00,35338.00,35383.00,000,35383.00 -1989-10-03,35603.00,35603.00,35293.00,35366.00,000,35366.00 -1989-10-02,35672.00,35771.00,35613.00,35623.00,000,35623.00 -1989-09-29,35704.00,35778.00,35537.00,35637.00,000,35637.00 -1989-09-28,35398.00,35690.00,35398.00,35690.00,000,35690.00 -1989-09-27,35475.00,35654.00,35308.00,35371.00,000,35371.00 -1989-09-26,34978.00,35445.00,34975.00,35445.00,000,35445.00 -1989-09-25,34836.00,34990.00,34834.00,34961.00,000,34961.00 -1989-09-22,34783.00,34865.00,34733.00,34772.00,000,34772.00 -1989-09-21,34537.00,34759.00,34537.00,34745.00,000,34745.00 -1989-09-20,34499.00,34560.00,34448.00,34471.00,000,34471.00 -1989-09-19,34477.00,34581.00,34368.00,34471.00,000,34471.00 -1989-09-18,34394.00,34525.00,34385.00,34473.00,000,34473.00 -1989-09-14,34292.00,34410.00,34262.00,34402.00,000,34402.00 -1989-09-13,34345.00,34345.00,34186.00,34287.00,000,34287.00 -1989-09-12,34105.00,34333.00,34091.00,34333.00,000,34333.00 -1989-09-11,34129.00,34165.00,33955.00,34114.00,000,34114.00 -1989-09-08,34159.00,34316.00,34107.00,34116.00,000,34116.00 -1989-09-07,34228.00,34300.00,34111.00,34153.00,000,34153.00 -1989-09-06,34434.00,34436.00,34118.00,34271.00,000,34271.00 -1989-09-05,34513.00,34635.00,34338.00,34442.00,000,34442.00 -1989-09-04,34355.00,34559.00,34306.00,34484.00,000,34484.00 -1989-09-01,34439.00,34491.00,34220.00,34348.00,000,34348.00 -1989-08-31,34483.00,34516.00,34244.00,34431.00,000,34431.00 -1989-08-30,34700.00,34759.00,34423.00,34472.00,000,34472.00 -1989-08-29,34612.00,34688.00,34506.00,34688.00,000,34688.00 -1989-08-28,34740.00,34749.00,34493.00,34607.00,000,34607.00 -1989-08-25,34792.00,34892.00,34639.00,34740.00,000,34740.00 -1989-08-24,34903.00,34958.00,34663.00,34787.00,000,34787.00 -1989-08-23,35111.00,35179.00,34849.00,34893.00,000,34893.00 -1989-08-22,35115.00,35158.00,35011.00,35114.00,000,35114.00 -1989-08-21,35097.00,35191.00,35068.00,35141.00,000,35141.00 -1989-08-18,35104.00,35126.00,34951.00,35063.00,000,35063.00 -1989-08-17,35122.00,35192.00,34986.00,35090.00,000,35090.00 -1989-08-16,34819.00,35086.00,34819.00,35084.00,000,35084.00 -1989-08-15,34660.00,34813.00,34632.00,34811.00,000,34811.00 -1989-08-14,34693.00,34725.00,34590.00,34672.00,000,34672.00 -1989-08-11,34729.00,34832.00,34625.00,34713.00,000,34713.00 -1989-08-10,34843.00,34911.00,34709.00,34720.00,000,34720.00 -1989-08-09,34764.00,34861.00,34737.00,34859.00,000,34859.00 -1989-08-08,34616.00,34761.00,34531.00,34759.00,000,34759.00 -1989-08-07,34737.00,34759.00,34556.00,34630.00,000,34630.00 -1989-08-04,34747.00,34747.00,34660.00,34742.00,000,34742.00 -1989-08-03,34889.00,34930.00,34699.00,34780.00,000,34780.00 -1989-08-02,34894.00,35016.00,34851.00,34899.00,000,34899.00 -1989-08-01,34950.00,34964.00,34767.00,34898.00,000,34898.00 -1989-07-31,34723.00,34955.00,34723.00,34954.00,000,34954.00 -1989-07-28,34824.00,34946.00,34686.00,34706.00,000,34706.00 -1989-07-27,34539.00,34788.00,34539.00,34785.00,000,34785.00 -1989-07-26,34586.00,34771.00,34511.00,34516.00,000,34516.00 -1989-07-25,34156.00,34543.00,34156.00,34539.00,000,34539.00 -1989-07-24,33912.00,34095.00,33867.00,34093.00,000,34093.00 -1989-07-21,33689.00,33971.00,33619.00,33899.00,000,33899.00 -1989-07-20,33598.00,33701.00,33584.00,33665.00,000,33665.00 -1989-07-19,33344.00,33558.00,33344.00,33557.00,000,33557.00 -1989-07-18,33459.00,33465.00,33309.00,33344.00,000,33344.00 -1989-07-17,33585.00,33594.00,33415.00,33456.00,000,33456.00 -1989-07-14,33666.00,33699.00,33539.00,33575.00,000,33575.00 -1989-07-13,33733.00,33778.00,33628.00,33631.00,000,33631.00 -1989-07-12,33767.00,33816.00,33693.00,33702.00,000,33702.00 -1989-07-11,33689.00,33799.00,33665.00,33747.00,000,33747.00 -1989-07-10,33719.00,33843.00,33629.00,33676.00,000,33676.00 -1989-07-07,33480.00,33714.00,33480.00,33704.00,000,33704.00 -1989-07-06,33348.00,33516.00,33348.00,33423.00,000,33423.00 -1989-07-05,33221.00,33358.00,33221.00,33310.00,000,33310.00 -1989-07-04,33268.00,33280.00,33181.00,33190.00,000,33190.00 -1989-07-03,32895.00,33236.00,32697.00,33236.00,000,33236.00 -1989-06-30,32919.00,32950.00,32642.00,32949.00,000,32949.00 -1989-06-29,33198.00,33223.00,32923.00,32956.00,000,32956.00 -1989-06-28,33431.00,33431.00,32952.00,33246.00,000,33246.00 -1989-06-27,33622.00,33646.00,33445.00,33469.00,000,33469.00 -1989-06-26,33565.00,33693.00,33525.00,33626.00,000,33626.00 -1989-06-23,33398.00,33702.00,33398.00,33531.00,000,33531.00 -1989-06-22,33341.00,33427.00,33262.00,33325.00,000,33325.00 -1989-06-21,33241.00,33377.00,33177.00,33345.00,000,33345.00 -1989-06-20,33014.00,33283.00,33008.00,33233.00,000,33233.00 -1989-06-19,33039.00,33051.00,32858.00,33013.00,000,33013.00 -1989-06-16,32951.00,33195.00,32606.00,33055.00,000,33055.00 -1989-06-15,33467.00,33525.00,32913.00,32913.00,000,32913.00 -1989-06-14,33235.00,33408.00,33018.00,33403.00,000,33403.00 -1989-06-13,33464.00,33556.00,33135.00,33214.00,000,33214.00 -1989-06-12,33615.00,33615.00,33315.00,33398.00,000,33398.00 -1989-06-09,33756.00,33812.00,33581.00,33640.00,000,33640.00 -1989-06-08,33657.00,33838.00,33657.00,33718.00,000,33718.00 -1989-06-07,33497.00,33677.00,33399.00,33627.00,000,33627.00 -1989-06-06,33433.00,33574.00,33248.00,33452.00,000,33452.00 -1989-06-05,33650.00,33809.00,33408.00,33457.00,000,33457.00 -1989-06-02,33986.00,34065.00,33628.00,33667.00,000,33667.00 -1989-06-01,34294.00,34328.00,33932.00,33981.00,000,33981.00 -1989-05-31,34071.00,34269.00,34005.00,34267.00,000,34267.00 -1989-05-30,34142.00,34162.00,33990.00,34077.00,000,34077.00 -1989-05-29,34214.00,34338.00,34102.00,34161.00,000,34161.00 -1989-05-26,34054.00,34192.00,34024.00,34192.00,000,34192.00 -1989-05-25,33889.00,34006.00,33859.00,34005.00,000,34005.00 -1989-05-24,33809.00,33934.00,33707.00,33852.00,000,33852.00 -1989-05-23,34043.00,34043.00,33582.00,33817.00,000,33817.00 -1989-05-22,34007.00,34124.00,34003.00,34068.00,000,34068.00 -1989-05-19,33858.00,34007.00,33804.00,34001.00,000,34001.00 -1989-05-18,33983.00,33999.00,33768.00,33856.00,000,33856.00 -1989-05-17,33957.00,34040.00,33947.00,33992.00,000,33992.00 -1989-05-16,33796.00,33931.00,33740.00,33926.00,000,33926.00 -1989-05-15,33864.00,33864.00,33612.00,33716.00,000,33716.00 -1989-05-12,34078.00,34094.00,33839.00,33866.00,000,33866.00 -1989-05-11,34008.00,34082.00,33971.00,34081.00,000,34081.00 -1989-05-10,34047.00,34154.00,33988.00,33992.00,000,33992.00 -1989-05-09,34147.00,34175.00,33948.00,34032.00,000,34032.00 -1989-05-08,33980.00,34171.00,33980.00,34135.00,000,34135.00 -1989-05-02,33821.00,33978.00,33821.00,33955.00,000,33955.00 -1989-05-01,33722.00,33881.00,33722.00,33793.00,000,33793.00 -1989-04-28,33552.00,33738.00,33552.00,33713.00,000,33713.00 -1989-04-27,33458.00,33565.00,33425.00,33501.00,000,33501.00 -1989-04-26,33257.00,33446.00,33158.00,33435.00,000,33435.00 -1989-04-25,32854.00,33318.00,32854.00,33245.00,000,33245.00 -1989-04-24,33037.00,33115.00,32704.00,32806.00,000,32806.00 -1989-04-21,33153.00,33153.00,32781.00,33030.00,000,33030.00 -1989-04-20,33368.00,33397.00,33048.00,33185.00,000,33185.00 -1989-04-19,33320.00,33414.00,33286.00,33364.00,000,33364.00 -1989-04-18,33308.00,33362.00,33217.00,33322.00,000,33322.00 -1989-04-17,33189.00,33402.00,33189.00,33308.00,000,33308.00 -1989-04-14,33061.00,33151.00,32925.00,33150.00,000,33150.00 -1989-04-13,33268.00,33336.00,32951.00,33064.00,000,33064.00 -1989-04-12,33259.00,33435.00,33137.00,33256.00,000,33256.00 -1989-04-11,32998.00,33307.00,32940.00,33250.00,000,33250.00 -1989-04-10,33192.00,33224.00,32910.00,32999.00,000,32999.00 -1989-04-07,33027.00,33218.00,33025.00,33185.00,000,33185.00 -1989-04-06,33334.00,33334.00,32839.00,32996.00,000,32996.00 -1989-04-05,33338.00,33413.00,33240.00,33361.00,000,33361.00 -1989-04-04,33066.00,33340.00,33066.00,33312.00,000,33312.00 -1989-04-03,32863.00,33077.00,32847.00,33042.00,000,33042.00 -1989-03-31,32835.00,32964.00,32678.00,32839.00,000,32839.00 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-1984-01-09,9954.00,9954.00,9954.00,9954.00,000,9954.00 -1984-01-06,9961.00,9961.00,9961.00,9961.00,000,9961.00 -1984-01-05,9947.00,9947.00,9947.00,9947.00,000,9947.00 -1984-01-04,9927.00,9927.00,9927.00,9927.00,000,9927.00 diff --git a/solutions/uncertainty_traps_solutions.ipynb b/solutions/uncertainty_traps_solutions.ipynb deleted file mode 100644 index 2f6e43a3d..000000000 --- a/solutions/uncertainty_traps_solutions.ipynb +++ /dev/null @@ -1,312 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# quant-econ Solutions: Uncertainty Traps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solutions for http://quant-econ.net/py/uncertainty_traps.html" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from __future__ import division\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import quantecon as qe\n", - "import seaborn as sns\n", - "import itertools" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This exercise asked you to validate the laws of motion for $\\gamma$ and $\\mu$ given in the lecture, based on the stated result about Bayesian updating in a scalar Gaussian setting. The stated result tells us that after observing average output $X$ of the $M$ firms, our posterior beliefs will be\n", - "\n", - "$$\n", - " N(\\mu_0, 1/\\gamma_0)\n", - "$$\n", - "\n", - "where\n", - "\n", - "$$\n", - " \\mu_0 = \\frac{\\mu \\gamma + M X \\gamma_x}{\\gamma + M \\gamma_x}\n", - " \\quad \\text{and} \\quad\n", - " \\gamma_0 = \\gamma + M \\gamma_x\n", - "$$\n", - "\n", - "If we take a random variable $\\theta$ with this distribution and then evaluate the distribution of $\\rho \\theta + \\sigma_\\theta w$ where $w$ is independent and standard normal, we get the expressions for $\\mu'$ and $\\gamma'$ given in the lecture." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First let's replicate the plot that illustrates the law of motion for precision, which is\n", - "\n", - "$$\n", - " \\gamma_{t+1} = \n", - " \\left(\n", - " \\frac{\\rho^2}{\\gamma_t + M \\gamma_x} + \\sigma_\\theta^2\n", - " \\right)^{-1}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here $M$ is the number of active firms. The next figure plots $\\gamma_{t+1}$ against $\\gamma_t$ on a 45 degree diagram for different values of $M$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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5EMfqXhQre1FsHMTO9N902QyYGHBgUum/6XebO+IF+S7E80VsK9Wb7UQGR5m6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aCqsYaNOZ+SUXocgABXfxZEzSTSa4CZK7uRNYSdnIIqPkg/S4G2FoARkX4kJ0bg7e5s5sqEEKL7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\nds0XuwmLDDzADQORo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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "palette = itertools.cycle(sns.color_palette())\n", - "econ = qe.models.UncertaintyTrapEcon()\n", - "rho, sig_theta, gx = econ.rho, econ.sig_theta, econ.gx # simplify names\n", - "g = np.linspace(1e-10, 3, 200) # gamma grid\n", - "fig, ax = plt.subplots(figsize=(9, 9))\n", - "ax.plot(g, g, 'k-') # 45 degree line\n", - "for M in range(7):\n", - " g_next = 1 / (rho**2 / (g + M * gx) + sig_theta**2)\n", - " label_string = r\"$M = {}$\".format(M)\n", - " ax.plot(g, g_next, lw=2, label=label_string, color=next(palette))\n", - "ax.legend(loc='lower right', fontsize=14)\n", - "ax.set_xlabel(r'$\\gamma$', fontsize=16)\n", - "ax.set_ylabel(r\"$\\gamma'$\", fontsize=16)\n", - "ax.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The points where the curves hit the 45 degree lines are the long run steady states corresponding to each $M$, if that value of $M$ was to remain fixed. As the number of firms falls, so does the long run steady state of precision." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next let's generate time series for beliefs and the aggregates -- that is, the number\n", - "of active firms and average output." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "sim_length=2000\n", - "\n", - "mu_vec = np.empty(sim_length)\n", - "theta_vec = np.empty(sim_length)\n", - "gamma_vec = np.empty(sim_length)\n", - "X_vec = np.empty(sim_length)\n", - "M_vec = np.empty(sim_length)\n", - "\n", - "mu_vec[0] = econ.mu\n", - "gamma_vec[0] = econ.gamma\n", - "theta_vec[0] = 0\n", - "\n", - "w_shocks = np.random.randn(sim_length)\n", - "\n", - "for t in range(sim_length-1):\n", - " X, M = econ.gen_aggregates()\n", - " X_vec[t] = X\n", - " M_vec[t] = M\n", - "\n", - " econ.update_beliefs(X, M)\n", - " econ.update_theta(w_shocks[t])\n", - "\n", - " mu_vec[t+1] = econ.mu\n", - " gamma_vec[t+1] = econ.gamma\n", - " theta_vec[t+1] = econ.theta\n", - "\n", - "# Record final values of aggregates\n", - "X, M = econ.gen_aggregates()\n", - "X_vec[-1] = X\n", - "M_vec[-1] = M" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First let's see how well $\\mu$ tracks $\\theta$ in these simulations" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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f2IfYfNzz9WbRVTEmPoHD070vYEA9AoC5chqF8+Yz0hc0VknOScJZt8TKaL9p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NUxmPa2nRpe65Lb88VxQtf1XBufKwVA1/WXdF3/IP/ldwHcB77mPUfTtF8G8N8B+BVd17fQwmO9\nE4HJXwD4DQDQNO0sgJuuFBHtcZqmfVPTtN/P/3kawBSA/xv51xzA1wH8AMDPADysadpovsboCQCv\nduApU/OcuVkAfoTqr/WTsNas/wLAb+bv+1UAL7XtGVOznCteTdP+naZp9+W/tOek8TXvIZqmjcLa\nXfkVXdc38je37FjvSEt6TdP+EMAzsLYN/S1d1znwr0fk34x/AmAC1hLOP4CV3v2XAGIArsDaKmZo\nmvZ1AH8XVtr3j3Rd/1cdedLUEE3THgPwTwFMA8jB2i76ZVjbAqu+1pqmKQD+GYBjAFIAfkvXdWbN\nupjPa74G4PdhXUnvANiG9Zqv8jXvHZqm/aewXufP8jdJAL8F67UM/FjnrBwiIiLqGt1Q/EpEREQE\ngIEJERERdREGJkRERNQ1GJgQERFR12BgQkRERF2DgQkRERF1DQYmRERE1DUYmBAREVHX+P8BolHf\ntv8BhnAAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(9, 6))\n", - "ax.plot(range(sim_length), theta_vec, alpha=0.6, lw=2, label=r\"$\\theta$\")\n", - "ax.plot(range(sim_length), mu_vec, alpha=0.6, lw=2, label=r\"$\\mu$\")\n", - "ax.legend(fontsize=16)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's plot the whole thing together" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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tLLBrjuJ2zI4P0VjmWDPHFbkxs4DTV6cNTUjMAWOhsJ1XOyeJUo/v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\nlSvP8OubG9X6sXSy8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phPGGO2iXMTTZY7NxdyD98r6R8kDTVq3GyHiYCOCrd7gGY5JumD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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(4, 1, figsize=(12, 20))\n", - "# Add some spacing\n", - "fig.subplots_adjust(hspace=0.3)\n", - "\n", - "series = (theta_vec, mu_vec, gamma_vec, M_vec)\n", - "names = r'$\\theta$', r'$\\mu$', r'$\\gamma$', r'$M$'\n", - "\n", - "for ax, vals, name in zip(axes, series, names):\n", - " # determine suitable y limits\n", - " s_max, s_min = max(vals), min(vals)\n", - " s_range = s_max - s_min\n", - " y_max = s_max + s_range * 0.1\n", - " y_min = s_min - s_range * 0.1\n", - " ax.set_ylim(y_min, y_max)\n", - " # Plot series\n", - " ax.plot(range(sim_length), vals, alpha=0.6, lw=2)\n", - " ax.set_title(\"time series for {}\".format(name), fontsize=16)\n", - " ax.grid()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "If you run the code above you'll get different plots, of course. Try experimenting with different parameters to see the effects on the time series. (It would also be interesting to experiment with non-Gaussian distributions for the shocks, but this is a big exercise since it takes us outside the world of the standard Kalman filter.)" - ] - }, - { - "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": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/solutions/web_graph_data.txt b/solutions/web_graph_data.txt deleted file mode 100644 index acb184273..000000000 --- a/solutions/web_graph_data.txt +++ /dev/null @@ -1,37 +0,0 @@ -a -> d; -a -> f; -b -> j; -b -> k; -b -> m; -c -> c; -c -> g; -c -> j; -c -> m; -d -> f; -d -> h; -d -> k; -e -> d; -e -> h; -e -> l; -f -> a; -f -> b; -f -> j; -f -> l; -g -> b; -g -> j; -h -> d; -h -> g; -h -> l; -h -> m; -i -> g; -i -> h; -i -> n; -j -> e; -j -> i; -j -> k; -k -> n; -l -> m; -m -> g; -n -> c; -n -> j; -n -> m; \ No newline at end of file From 2ae1d62193404e27e3d57a8425907843e191f11f Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 16:52:19 -0500 Subject: [PATCH 30/51] Migrating scripts used for testing examples/ and solutions/ notebooks to QuantEcon.applications --- scripts/common.py | 20 - scripts/example-tests.log | 208 -- scripts/solutions-tests.log | 3871 ----------------------------------- scripts/test-examples.py | 90 - scripts/test-solutions.py | 58 - 5 files changed, 4247 deletions(-) delete mode 100644 scripts/common.py delete mode 100644 scripts/example-tests.log delete mode 100644 scripts/solutions-tests.log delete mode 100644 scripts/test-examples.py delete mode 100644 scripts/test-solutions.py diff --git a/scripts/common.py b/scripts/common.py deleted file mode 100644 index d95a97d74..000000000 --- a/scripts/common.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -Provides Context Manager for Test Scripts -""" - -import sys - -class RedirectStdStreams(object): - def __init__(self, stdout=None, stderr=None): - self._stdout = stdout or sys.stdout - self._stderr = stderr or sys.stderr - - def __enter__(self): - self.old_stdout, self.old_stderr = sys.stdout, sys.stderr - self.old_stdout.flush(); self.old_stderr.flush() - sys.stdout, sys.stderr = self._stdout, self._stderr - - def __exit__(self, exc_type, exc_value, traceback): - self._stdout.flush(); self._stderr.flush() - sys.stdout = self.old_stdout - sys.stderr = self.old_stderr \ No newline at end of file diff --git a/scripts/example-tests.log b/scripts/example-tests.log deleted file mode 100644 index 9e19c9318..000000000 --- a/scripts/example-tests.log +++ /dev/null @@ -1,208 +0,0 @@ ----Executing '3dplot.py'--- ----END '3dplot.py'--- ----Executing '3dvec.py'--- ----END '3dvec.py'--- ----Executing 'aiyagari_compute_equilibrium.py'--- ----END 'aiyagari_compute_equilibrium.py'--- ----Executing 'aiyagari_compute_policy.py'--- ----END 'aiyagari_compute_policy.py'--- ----Executing 'aiyagari_household.py'--- ----END 'aiyagari_household.py'--- ----Executing 'amss.py'--- ----END 'amss.py'--- ----Executing 'amss_figures.py'--- ----END 'amss_figures.py'--- ----Executing 'ar1_acov.py'--- ----END 'ar1_acov.py'--- ----Executing 'ar1_cycles.py'--- ----END 'ar1_cycles.py'--- ----Executing 'ar1_sd.py'--- ----END 'ar1_sd.py'--- ----Executing 'beta-binomial.py'--- ----END 'beta-binomial.py'--- ----Executing 'bifurcation_diagram.py'--- ----END 'bifurcation_diagram.py'--- ----Executing 'binom_df.py'--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure - "matplotlib is currently using a non-GUI backend, " ----END 'binom_df.py'--- ----Executing 'bisection.py'--- ----END 'bisection.py'--- ----Executing 'boxplot_example.py'--- ----END 'boxplot_example.py'--- ----Executing 'career_vf_plot.py'--- ----END 'career_vf_plot.py'--- ----Executing 'cauchy_samples.py'--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice. - warnings.warn("Mean of empty slice.", RuntimeWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/numpy/core/_methods.py:71: RuntimeWarning: invalid value encountered in double_scalars - ret = ret.dtype.type(ret / rcount) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure - "matplotlib is currently using a non-GUI backend, " ----END 'cauchy_samples.py'--- ----Executing 'chaos_class.py'--- ----END 'chaos_class.py'--- ----Executing 'chaotic_ts.py'--- ----END 'chaotic_ts.py'--- ----Executing 'clt3d.py'--- ----END 'clt3d.py'--- ----Executing 'consumer.py'--- ----END 'consumer.py'--- ----Executing 'dice.py'--- ----END 'dice.py'--- ----Executing 'duopoly_lqnash.py'--- ----END 'duopoly_lqnash.py'--- ----Executing 'duopoly_mpe.py'--- ----END 'duopoly_mpe.py'--- ----Executing 'duopoly_mpe_dynamics.py'--- ----END 'duopoly_mpe_dynamics.py'--- ----Executing 'eigenvec.py'--- ----END 'eigenvec.py'--- ----Executing 'evans_sargent.py'--- ----END 'evans_sargent.py'--- ----Executing 'evans_sargent_plot1.py'--- ----END 'evans_sargent_plot1.py'--- ----Executing 'evans_sargent_plot2.py'--- ----END 'evans_sargent_plot2.py'--- ----Executing 'finite_dp_og_example.py'--- ----END 'finite_dp_og_example.py'--- ----Executing 'gaussian_contours.py'--- ----END 'gaussian_contours.py'--- ----Executing 'ifp_savings_plots.py'--- ----END 'ifp_savings_plots.py'--- ----Executing 'illustrates_clt.py'--- ----END 'illustrates_clt.py'--- ----Executing 'illustrates_lln.py'--- ----END 'illustrates_lln.py'--- ----Executing 'jv_test.py'--- ----END 'jv_test.py'--- ----Executing 'lakemodel_example.py'--- ----END 'lakemodel_example.py'--- ----Executing 'lin_interp_3d_plot.py'--- ----END 'lin_interp_3d_plot.py'--- ----Executing 'linapprox.py'--- ----END 'linapprox.py'--- ----Executing 'lq_permanent_1.py'--- ----END 'lq_permanent_1.py'--- ----Executing 'lqramsey.py'--- ----END 'lqramsey.py'--- ----Executing 'lqramsey_ar1.py'--- ----END 'lqramsey_ar1.py'--- ----Executing 'lqramsey_discrete.py'--- ----END 'lqramsey_discrete.py'--- ----Executing 'lucas_stokey.py'--- ----END 'lucas_stokey.py'--- ----Executing 'lucas_tree_price1.py'--- ----END 'lucas_tree_price1.py'--- ----Executing 'main_LS.py'--- ----END 'main_LS.py'--- ----Executing 'market.py'--- ----END 'market.py'--- ----Executing 'market_deadweight.py'--- ----END 'market_deadweight.py'--- ----Executing 'mc_convergence_plot.py'--- ----END 'mc_convergence_plot.py'--- ----Executing 'nds.py'--- ----END 'nds.py'--- ----Executing 'nx_demo.py'--- ----END 'nx_demo.py'--- ----Executing 'odu_plot_densities.py'--- ----END 'odu_plot_densities.py'--- ----Executing 'odu_vfi_plots.py'--- ----END 'odu_vfi_plots.py'--- ----Executing 'oligopoly.py'--- ----END 'oligopoly.py'--- ----Executing 'optgrowth_v0.py'--- ----END 'optgrowth_v0.py'--- ----Executing 'paths_and_hist.py'--- ----END 'paths_and_hist.py'--- ----Executing 'paths_and_stationarity.py'--- ----END 'paths_and_stationarity.py'--- ----Executing 'perm_inc_figs.py'--- ----END 'perm_inc_figs.py'--- ----Executing 'perm_inc_ir.py'--- ----END 'perm_inc_ir.py'--- ----Executing 'plot_example_1.py'--- ----END 'plot_example_1.py'--- ----Executing 'plot_example_2.py'--- ----END 'plot_example_2.py'--- ----Executing 'plot_example_3.py'--- ----END 'plot_example_3.py'--- ----Executing 'plot_example_4.py'--- ----END 'plot_example_4.py'--- ----Executing 'plot_example_5.py'--- ----END 'plot_example_5.py'--- ----Executing 'plot_market.py'--- ----END 'plot_market.py'--- ----Executing 'preim1.py'--- ----END 'preim1.py'--- ----Executing 'pylab_eg.py'--- ----END 'pylab_eg.py'--- ----Executing 'pylab_eg2.py'--- ----END 'pylab_eg2.py'--- ----Executing 'qm_plot.py'--- ----END 'qm_plot.py'--- ----Executing 'qs.py'--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure - "matplotlib is currently using a non-GUI backend, " ----END 'qs.py'--- ----Executing 'quadmap_class.py'--- ----END 'quadmap_class.py'--- ----Executing 'robust_monopolist.py'--- -Traceback (most recent call last): - File "_robust_monopolist.py", line 132, in - Po, Fo, do = optimal_lq.stationary_values() - File "/home/matthewmckay/anaconda/lib/python2.7/site-packages/quantecon/lqcontrol.py", line 209, in stationary_values - P = solve_discrete_riccati(A0, B0, R, Q, N) - File "/home/matthewmckay/anaconda/lib/python2.7/site-packages/quantecon/matrix_eqn.py", line 197, in solve_discrete_riccati - raise ValueError(fail_msg.format(i)) -ValueError: Convergence failed after 501 iterations. ----END 'robust_monopolist.py'--- ----Executing 'sine2.py'--- ----END 'sine2.py'--- ----Executing 'sine3.py'--- ----END 'sine3.py'--- ----Executing 'sine4.py'--- ----END 'sine4.py'--- ----Executing 'sine5.py'--- ----END 'sine5.py'--- ----Executing 'six_hists.py'--- ----END 'six_hists.py'--- ----Executing 'solow.py'--- ----END 'solow.py'--- ----Executing 'stochasticgrowth.py'--- ----END 'stochasticgrowth.py'--- ----Executing 'subplots.py'--- ----END 'subplots.py'--- ----Executing 'temp.py'--- ----END 'temp.py'--- ----Executing 'test_program_1.py'--- ----END 'test_program_1.py'--- ----Executing 'test_program_2.py'--- ----END 'test_program_2.py'--- ----Executing 'test_program_3.py'--- ----END 'test_program_3.py'--- ----Executing 'test_program_4.py'--- ----END 'test_program_4.py'--- ----Executing 'test_program_5.py'--- ----END 'test_program_5.py'--- ----Executing 'test_program_5_short.py'--- ----END 'test_program_5_short.py'--- ----Executing 'test_program_6.py'--- ----END 'test_program_6.py'--- ----Executing 'tsh_hg.py'--- ----END 'tsh_hg.py'--- ----Executing 'us_cities.py'--- ----END 'us_cities.py'--- ----Executing 'utilities.py'--- ----END 'utilities.py'--- ----Executing 'vecs.py'--- ----END 'vecs.py'--- ----Executing 'vecs2.py'--- ----END 'vecs2.py'--- ----Executing 'wb_download.py'--- ----END 'wb_download.py'--- ----Executing 'web_network.py'--- ----END 'web_network.py'--- ----Executing 'white_noise_plot.py'--- ----END 'white_noise_plot.py'--- diff --git a/scripts/solutions-tests.log b/scripts/solutions-tests.log deleted file mode 100644 index 183ccb9be..000000000 --- a/scripts/solutions-tests.log +++ /dev/null @@ -1,3871 +0,0 @@ ----> Executing 'arellano_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:37:01 AM INFO: Reading notebook arellano_solutions.ipynb -10/06/2015 10:37:02 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:37:02 AM INFO: Cell returned -10/06/2015 10:37:02 AM INFO: Running cell: -from __future__ import division -import numpy as np -import matplotlib.pyplot as plt -import quantecon as qe -from quantecon.models import Arellano_Economy - -10/06/2015 10:37:03 AM INFO: Cell returned -10/06/2015 10:37:03 AM INFO: Running cell: -ae = Arellano_Economy(beta=.953, # time discount rate - gamma=2., # risk aversion - r=0.017, # international interest rate - rho=.945, # persistence in output - eta=0.025, # st dev of output shock - theta=0.282, # prob of regaining access - ny=21, # number of points in y grid - nB=251, # number of points in B grid - tol=1e-8, # error tolerance in iteration - maxit=10000) - -10/06/2015 10:37:12 AM INFO: Cell returned -10/06/2015 10:37:12 AM INFO: Running cell: - -# Create "Y High" and "Y Low" values as 5% devs from mean -high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95 -iy_high, iy_low = (np.searchsorted(ae.ygrid, x) for x in (high, low)) - -fig, ax = plt.subplots(figsize=(10, 6.5)) -ax.set_title("Bond price schedule $q(y, B')$") - -# Extract a suitable plot grid -x = [] -q_low = [] -q_high = [] -for i in range(ae.nB): - b = ae.Bgrid[i] - if -0.35 <= b <= 0: # To match fig 3 of Arellano - x.append(b) - q_low.append(ae.Q[iy_low, i]) - q_high.append(ae.Q[iy_high, i]) -ax.plot(x, q_high, label=r"$y_H$", lw=2, alpha=0.7) -ax.plot(x, q_low, label=r"$y_L$", lw=2, alpha=0.7) -ax.set_xlabel(r"$B'$") -ax.legend(loc='upper left', frameon=False) -plt.show() - -10/06/2015 10:37:12 AM INFO: Cell returned -10/06/2015 10:37:12 AM INFO: Running cell: - -# Create "Y High" and "Y Low" values as 5% devs from mean -high, low = np.mean(ae.ygrid)*1.05, np.mean(ae.ygrid)*.95 -iy_high, iy_low = (np.searchsorted(ae.ygrid, x) for x in (high, low)) - -fig, ax = plt.subplots(figsize=(10, 6.5)) -ax.set_title("Value Functions") -ax.plot(ae.Bgrid, ae.V[iy_high], label=r"$y_H$", lw=2, alpha=0.7) -ax.plot(ae.Bgrid, ae.V[iy_low], label=r"$y_L$", lw=2, alpha=0.7) -ax.legend(loc='upper left') -ax.set_xlabel(r"$B$") -ax.set_ylabel(r"$V(y, B)$") -ax.set_xlim(ae.Bgrid.min(), ae.Bgrid.max()) -plt.show() - -10/06/2015 10:37:12 AM INFO: Cell returned -10/06/2015 10:37:12 AM INFO: Running cell: - -xx, yy = ae.Bgrid, ae.ygrid -zz = ae.default_prob - -# Create figure -fig, ax = plt.subplots(figsize=(10, 6.5)) -fig.suptitle("Probability of Default") -hm = ax.pcolormesh(xx, yy, zz) -cax = fig.add_axes([.92, .1, .02, .8]) -fig.colorbar(hm, cax=cax) -ax.axis([xx.min(), 0.05, yy.min(), yy.max()]) -ax.set_xlabel(r"$B'$") -ax.set_ylabel(r"$y$") -plt.show() - -10/06/2015 10:37:13 AM INFO: Cell returned -10/06/2015 10:37:13 AM INFO: Running cell: -T = 250 -y_vec, B_vec, q_vec, default_vec = ae.simulate(T) - -# Pick up default start and end dates -start_end_pairs = [] -i = 0 -while i < len(default_vec): - if default_vec[i] == 0: - i += 1 - else: - # If we get to here we're in default - start_default = i - while i < len(default_vec) and default_vec[i] == 1: - i += 1 - end_default = i - 1 - start_end_pairs.append((start_default, end_default)) - -plot_series = y_vec, B_vec, q_vec -titles = 'output', 'foreign assets', 'bond price' - -fig, axes = plt.subplots(len(plot_series), 1, figsize=(10, 12)) -p_args = {'lw': 2, 'alpha': 0.7} -fig.subplots_adjust(hspace=0.3) - -for ax, series, title in zip(axes, plot_series, titles): - # determine suitable y limits - s_max, s_min = max(series), min(series) - s_range = s_max - s_min - y_max = s_max + s_range * 0.1 - y_min = s_min - s_range * 0.1 - ax.set_ylim(y_min, y_max) - for pair in start_end_pairs: - ax.fill_between(pair, (y_min, y_min), (y_max, y_max), color='k', alpha=0.3) - - ax.grid() - ax.set_title(title) - ax.plot(range(T), series, **p_args) - ax.set_xlabel(r"time") - -plt.show() - -10/06/2015 10:37:14 AM INFO: Cell returned -10/06/2015 10:37:14 AM INFO: Running cell: - - -10/06/2015 10:37:14 AM INFO: Cell returned -10/06/2015 10:37:14 AM INFO: Shutdown kernel ----> END 'arellano_solutions.ipynb' <--- - ----> Executing 'asset_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:37:15 AM INFO: Reading notebook asset_solutions.ipynb -10/06/2015 10:37:16 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:37:16 AM INFO: Cell returned -10/06/2015 10:37:16 AM INFO: Running cell: -from __future__ import division # Omit for Python 3.x -import numpy as np -import matplotlib.pyplot as plt -from quantecon.models import AssetPrices - -10/06/2015 10:37:17 AM INFO: Cell returned -10/06/2015 10:37:17 AM INFO: Running cell: -# == Define primitives == # -n = 5 -P = 0.0125 * np.ones((n, n)) -P += np.diag(0.95 - 0.0125 * np.ones(5)) -s = np.array([1.05, 1.025, 1.0, 0.975, 0.95]) -gamma = 2.0 -beta = 0.94 -zeta = 1.0 - -ap = AssetPrices(beta, P, s, gamma) - -v = ap.tree_price() -print("Lucas Tree Prices: ", v) - -v_consol = ap.consol_price(zeta) -print("Consol Bond Prices: ", v_consol) - -P_tilde = P * s**(1-gamma) -temp = beta * P_tilde.dot(v) + beta * P_tilde.dot(np.ones(n)) -print("Should be 0: ", v - temp) - -p_s = 150.0 -w_bar, w_bars = ap.call_option(zeta, p_s, T = [10,20,30]) - - -10/06/2015 10:37:17 AM INFO: Cell returned -10/06/2015 10:37:17 AM INFO: Running cell: - - -10/06/2015 10:37:17 AM INFO: Cell returned -10/06/2015 10:37:17 AM INFO: Shutdown kernel ----> END 'asset_solutions.ipynb' <--- - ----> Executing 'career_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:37:18 AM INFO: Reading notebook career_solutions.ipynb -10/06/2015 10:37:18 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:37:18 AM INFO: Cell returned -10/06/2015 10:37:18 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -from quantecon import DiscreteRV, compute_fixed_point -from quantecon.models import CareerWorkerProblem - -10/06/2015 10:37:19 AM INFO: Cell returned -10/06/2015 10:37:19 AM INFO: Running cell: -wp = CareerWorkerProblem() -v_init = np.ones((wp.N, wp.N))*100 -v = compute_fixed_point(wp.bellman_operator, v_init, verbose=False) -optimal_policy = wp.get_greedy(v) -F = DiscreteRV(wp.F_probs) -G = DiscreteRV(wp.G_probs) - -def gen_path(T=20): - i = j = 0 - theta_index = [] - epsilon_index = [] - for t in range(T): - if optimal_policy[i, j] == 1: # Stay put - pass - elif optimal_policy[i, j] == 2: # New job - j = int(G.draw()) - else: # New life - i, j = int(F.draw()), int(G.draw()) - theta_index.append(i) - epsilon_index.append(j) - return wp.theta[theta_index], wp.epsilon[epsilon_index] - -theta_path, epsilon_path = gen_path() - -fig, axes = plt.subplots(2, 1, figsize=(10, 8)) -for ax in axes: - ax.plot(epsilon_path, label='epsilon') - ax.plot(theta_path, label='theta') - ax.legend(loc='lower right') - -plt.show() - - - -10/06/2015 10:37:20 AM INFO: Cell returned -10/06/2015 10:37:20 AM INFO: Running cell: - -wp = CareerWorkerProblem() -v_init = np.ones((wp.N, wp.N))*100 -v = compute_fixed_point(wp.bellman_operator, v_init) -optimal_policy = wp.get_greedy(v) -F = DiscreteRV(wp.F_probs) -G = DiscreteRV(wp.G_probs) - -def gen_first_passage_time(): - t = 0 - i = j = 0 - while 1: - if optimal_policy[i, j] == 1: # Stay put - return t - elif optimal_policy[i, j] == 2: # New job - j = int(G.draw()) - else: # New life - i, j = int(F.draw()), int(G.draw()) - t += 1 - -M = 25000 # Number of samples -samples = np.empty(M) -for i in range(M): - samples[i] = gen_first_passage_time() -print(np.median(samples)) - - -10/06/2015 10:37:22 AM INFO: Cell returned -10/06/2015 10:37:22 AM INFO: Running cell: -from matplotlib import cm - -wp = CareerWorkerProblem() -v_init = np.ones((wp.N, wp.N))*100 -v = compute_fixed_point(wp.bellman_operator, v_init) -optimal_policy = wp.get_greedy(v) - -fig, ax = plt.subplots(figsize=(6,6)) -tg, eg = np.meshgrid(wp.theta, wp.epsilon) -lvls=(0.5, 1.5, 2.5, 3.5) -ax.contourf(tg, eg, optimal_policy.T, levels=lvls, cmap=cm.winter, alpha=0.5) -ax.contour(tg, eg, optimal_policy.T, colors='k', levels=lvls, linewidths=2) -ax.set_xlabel('theta', fontsize=14) -ax.set_ylabel('epsilon', fontsize=14) -ax.text(1.8, 2.5, 'new life', fontsize=14) -ax.text(4.5, 2.5, 'new job', fontsize=14, rotation='vertical') -ax.text(4.0, 4.5, 'stay put', fontsize=14) - - - -10/06/2015 10:37:23 AM INFO: Cell returned -10/06/2015 10:37:23 AM INFO: Shutdown kernel ----> END 'career_solutions.ipynb' <--- - ----> Executing 'discrete_dp_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:37:24 AM INFO: Reading notebook discrete_dp_solutions.ipynb -10/06/2015 10:37:25 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:37:25 AM INFO: Cell returned -10/06/2015 10:37:25 AM INFO: Running cell: -from __future__ import division, print_function -import numpy as np -import scipy.sparse as sparse -import matplotlib.pyplot as plt -from quantecon import compute_fixed_point -from quantecon.markov import DiscreteDP - -10/06/2015 10:37:26 AM INFO: Cell returned -10/06/2015 10:37:26 AM INFO: Running cell: -alpha = 0.65 -f = lambda k: k**alpha -u = np.log -beta = 0.95 - -10/06/2015 10:37:26 AM INFO: Cell returned -10/06/2015 10:37:26 AM INFO: Running cell: -grid_max = 2 -grid_size = 1500 -grid = np.linspace(1e-6, grid_max, grid_size) - -10/06/2015 10:37:26 AM INFO: Cell returned -10/06/2015 10:37:26 AM INFO: Running cell: -print(grid) - -10/06/2015 10:37:26 AM INFO: Cell returned -10/06/2015 10:37:26 AM INFO: Running cell: -# Consumption matrix, with nonpositive consumption included -C = f(grid).reshape(grid_size, 1) - grid.reshape(1, grid_size) - -# State-action indices -s_indices, a_indices = np.where(C > 0) - -# Number of state-action pairs -L = len(s_indices) - -10/06/2015 10:37:26 AM INFO: Cell returned -10/06/2015 10:37:26 AM INFO: Running cell: -print(L) -print(s_indices) -print(a_indices) - -10/06/2015 10:37:26 AM INFO: Cell returned -10/06/2015 10:37:26 AM INFO: Running cell: -R = u(C[s_indices, a_indices]) - -10/06/2015 10:37:26 AM INFO: Cell returned -10/06/2015 10:37:26 AM INFO: Running cell: -Q = sparse.lil_matrix((L, grid_size)) -Q[np.arange(L), a_indices] = 1 - -10/06/2015 10:37:28 AM INFO: Cell returned -10/06/2015 10:37:28 AM INFO: Running cell: -# data = np.ones(L) -# indptr = np.arange(L+1) -# Q = sparse.csr_matrix((data, a_indices, indptr), shape=(L, grid_size)) - -10/06/2015 10:37:28 AM INFO: Cell returned -10/06/2015 10:37:28 AM INFO: Running cell: -ddp = DiscreteDP(R, Q, beta, s_indices, a_indices) - -10/06/2015 10:37:29 AM INFO: Cell returned -10/06/2015 10:37:29 AM INFO: Running cell: -res = ddp.solve(method='policy_iteration') -v, sigma, num_iter = res.v, res.sigma, res.num_iter - -10/06/2015 10:37:29 AM INFO: Cell returned -10/06/2015 10:37:29 AM INFO: Running cell: -num_iter - -10/06/2015 10:37:29 AM INFO: Cell returned -10/06/2015 10:37:29 AM INFO: Running cell: -# Optimal consumption in the discrete version -c = f(grid) - grid[sigma] - -10/06/2015 10:37:29 AM INFO: Cell returned -10/06/2015 10:37:29 AM INFO: Running cell: -# Exact solution of the continuous version -ab = alpha * beta -c1 = (np.log(1 - ab) + np.log(ab) * ab / (1 - ab)) / (1 - beta) -c2 = alpha / (1 - ab) -def v_star(k): - return c1 + c2 * np.log(k) - -def c_star(k): - return (1 - ab) * k**alpha - -10/06/2015 10:37:29 AM INFO: Cell returned -10/06/2015 10:37:29 AM INFO: Running cell: -fig, ax = plt.subplots(1, 2, figsize=(14, 4)) -ax[0].set_ylim(-40, -32) -ax[0].set_xlim(grid[0], grid[-1]) -ax[1].set_xlim(grid[0], grid[-1]) - -lb0 = 'discrete value function' -ax[0].plot(grid, v, lw=2, alpha=0.6, label=lb0) - -lb0 = 'continuous value function' -ax[0].plot(grid, v_star(grid), 'k-', lw=1.5, alpha=0.8, label=lb0) -ax[0].legend(loc='upper left') - -lb1 = 'discrete optimal consumption' -ax[1].plot(grid, c, 'b-', lw=2, alpha=0.6, label=lb1) - -lb1 = 'continuous optimal consumption' -ax[1].plot(grid, c_star(grid), 'k-', lw=1.5, alpha=0.8, label=lb1) -ax[1].legend(loc='upper left') -plt.show() - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -np.abs(v - v_star(grid)).max() - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -np.abs(v - v_star(grid))[1:].max() - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -np.abs(c - c_star(grid)).max() - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -diff = np.diff(c) -(diff >= 0).all() - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -dec_ind = np.where(diff < 0)[0] - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -len(dec_ind) - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -np.abs(diff[dec_ind]).max() - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -(np.diff(v) > 0).all() - -10/06/2015 10:37:30 AM INFO: Cell returned -10/06/2015 10:37:30 AM INFO: Running cell: -ddp.epsilon = 1e-4 -ddp.max_iter = 500 -res1 = ddp.solve(method='value_iteration') - -10/06/2015 10:37:33 AM INFO: Cell returned -10/06/2015 10:37:33 AM INFO: Running cell: -res1.num_iter - -10/06/2015 10:37:33 AM INFO: Cell returned -10/06/2015 10:37:33 AM INFO: Running cell: -np.array_equal(sigma, res1.sigma) - -10/06/2015 10:37:33 AM INFO: Cell returned -10/06/2015 10:37:33 AM INFO: Running cell: -res2 = ddp.solve(method='modified_policy_iteration') - -10/06/2015 10:37:33 AM INFO: Cell returned -10/06/2015 10:37:33 AM INFO: Running cell: -res2.num_iter - -10/06/2015 10:37:33 AM INFO: Cell returned -10/06/2015 10:37:33 AM INFO: Running cell: -np.array_equal(sigma, res2.sigma) - -10/06/2015 10:37:33 AM INFO: Cell returned -10/06/2015 10:37:33 AM INFO: Running cell: -%timeit ddp.solve(method='value_iteration') -%timeit ddp.solve(method='policy_iteration') -%timeit ddp.solve(method='modified_policy_iteration') - -10/06/2015 10:37:55 AM INFO: Cell returned -10/06/2015 10:37:55 AM INFO: Running cell: -w = 5 * np.log(grid) - 25 # Initial condition -n = 35 -fig, ax = plt.subplots(figsize=(8,5)) -ax.set_ylim(-40, -20) -ax.set_xlim(np.min(grid), np.max(grid)) -lb = 'initial condition' -ax.plot(grid, w, color=plt.cm.jet(0), lw=2, alpha=0.6, label=lb) -for i in range(n): - w = ddp.bellman_operator(w) - ax.plot(grid, w, color=plt.cm.jet(i / n), lw=2, alpha=0.6) -lb = 'true value function' -ax.plot(grid, v_star(grid), 'k-', lw=2, alpha=0.8, label=lb) -ax.legend(loc='upper left') - -plt.show() - -10/06/2015 10:37:55 AM INFO: Cell returned -10/06/2015 10:37:55 AM INFO: Running cell: -w = 5 * u(grid) - 25 # Initial condition - -fig, ax = plt.subplots(3, 1, figsize=(8, 10)) -true_c = c_star(grid) - -for i, n in enumerate((2, 4, 6)): - ax[i].set_ylim(0, 1) - ax[i].set_xlim(0, 2) - ax[i].set_yticks((0, 1)) - ax[i].set_xticks((0, 2)) - - w = 5 * u(grid) - 25 # Initial condition - compute_fixed_point(ddp.bellman_operator, w, max_iter=n, print_skip=1) - sigma = ddp.compute_greedy(w) # Policy indices - c_policy = f(grid) - grid[sigma] - - ax[i].plot(grid, c_policy, 'b-', lw=2, alpha=0.8, - label='approximate optimal consumption policy') - ax[i].plot(grid, true_c, 'k-', lw=2, alpha=0.8, - label='true optimal consumption policy') - ax[i].legend(loc='upper left') - ax[i].set_title('{} value function iterations'.format(n)) - -10/06/2015 10:37:56 AM INFO: Cell returned -10/06/2015 10:37:56 AM INFO: Running cell: -discount_factors = (0.9, 0.94, 0.98) -k_init = 0.1 - -# Search for the index corresponding to k_init -k_init_ind = np.searchsorted(grid, k_init) - -sample_size = 25 - -fig, ax = plt.subplots(figsize=(8,5)) -ax.set_xlabel("time") -ax.set_ylabel("capital") -ax.set_ylim(0.10, 0.30) - -# Create a new instance, not to modify the one used above -ddp0 = DiscreteDP(R, Q, beta, s_indices, a_indices) - -for beta in discount_factors: - ddp0.beta = beta - res0 = ddp0.solve() - k_path_ind = res0.mc.simulate(init=k_init_ind, ts_length=sample_size) - k_path = grid[k_path_ind] - ax.plot(k_path, 'o-', lw=2, alpha=0.75, label=r'$\beta = {}$'.format(beta)) - -ax.legend(loc='lower right') -plt.show() - -10/06/2015 10:37:58 AM INFO: Cell returned -10/06/2015 10:37:58 AM INFO: Running cell: - - -10/06/2015 10:37:58 AM INFO: Cell returned -10/06/2015 10:37:58 AM INFO: Shutdown kernel ----> END 'discrete_dp_solutions.ipynb' <--- - ----> Executing 'estspec_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:37:59 AM INFO: Reading notebook estspec_solutions.ipynb -10/06/2015 10:38:00 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:38:00 AM INFO: Cell returned -10/06/2015 10:38:00 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -from quantecon import ARMA, periodogram, ar_periodogram - -10/06/2015 10:38:01 AM INFO: Cell returned -10/06/2015 10:38:01 AM INFO: Running cell: - -## Data -n = 400 -phi = 0.5 -theta = 0, -0.8 -lp = ARMA(phi, theta) -X = lp.simulation(ts_length=n) - -fig, ax = plt.subplots(3, 1, figsize=(10, 12)) - -for i, wl in enumerate((15, 55, 175)): # window lengths - - x, y = periodogram(X) - ax[i].plot(x, y, 'b-', lw=2, alpha=0.5, label='periodogram') - - x_sd, y_sd = lp.spectral_density(two_pi=False, res=120) - ax[i].plot(x_sd, y_sd, 'r-', lw=2, alpha=0.8, label='spectral density') - - x, y_smoothed = periodogram(X, window='hamming', window_len=wl) - ax[i].plot(x, y_smoothed, 'k-', lw=2, label='smoothed periodogram') - - ax[i].legend() - ax[i].set_title('window length = {}'.format(wl)) - - -10/06/2015 10:38:01 AM INFO: Cell returned -10/06/2015 10:38:01 AM INFO: Running cell: -lp = ARMA(-0.9) -wl = 65 - - -fig, ax = plt.subplots(3, 1, figsize=(10,12)) - -for i in range(3): - X = lp.simulation(ts_length=150) - ax[i].set_xlim(0, np.pi) - - x_sd, y_sd = lp.spectral_density(two_pi=False, res=180) - ax[i].semilogy(x_sd, y_sd, 'r-', lw=2, alpha=0.75, label='spectral density') - - x, y_smoothed = periodogram(X, window='hamming', window_len=wl) - ax[i].semilogy(x, y_smoothed, 'k-', lw=2, alpha=0.75, label='standard smoothed periodogram') - - x, y_ar = ar_periodogram(X, window='hamming', window_len=wl) - ax[i].semilogy(x, y_ar, 'b-', lw=2, alpha=0.75, label='AR smoothed periodogram') - - ax[i].legend(loc='upper left') - - - - -10/06/2015 10:38:03 AM INFO: Cell returned -10/06/2015 10:38:03 AM INFO: Shutdown kernel ----> END 'estspec_solutions.ipynb' <--- - ----> Executing 'finite_mc_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:38:04 AM INFO: Reading notebook finite_mc_solutions.ipynb -10/06/2015 10:38:05 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:38:05 AM INFO: Cell returned -10/06/2015 10:38:05 AM INFO: Running cell: -from __future__ import print_function, division # Omit for Python 3.x -import numpy as np -import matplotlib.pyplot as plt -from quantecon import mc_compute_stationary, mc_sample_path - - -10/06/2015 10:38:06 AM INFO: Cell returned -10/06/2015 10:38:06 AM INFO: Running cell: - -alpha = beta = 0.1 -N = 10000 -p = beta / (alpha + beta) - -P = ((1 - alpha, alpha), # Careful: P and p are distinct - (beta, 1 - beta)) -P = np.array(P) - -fig, ax = plt.subplots(figsize=(9, 6)) -ax.set_ylim(-0.25, 0.25) -ax.grid() -ax.hlines(0, 0, N, lw=2, alpha=0.6) # Horizonal line at zero - -for x0, col in ((0, 'blue'), (1, 'green')): - # == Generate time series for worker that starts at x0 == # - X = mc_sample_path(P, x0, N) - # == Compute fraction of time spent unemployed, for each n == # - X_bar = (X == 0).cumsum() / (1 + np.arange(N, dtype=float)) - # == Plot == # - ax.fill_between(range(N), np.zeros(N), X_bar - p, color=col, alpha=0.1) - ax.plot(X_bar - p, color=col, label=r'$X_0 = \, {} $'.format(x0)) - ax.plot(X_bar - p, 'k-', alpha=0.6) # Overlay in black--make lines clearer - -ax.legend(loc='upper right') - - - -10/06/2015 10:38:07 AM INFO: Cell returned -10/06/2015 10:38:07 AM INFO: Running cell: -%%file web_graph_data.txt -a -> d; -a -> f; -b -> j; -b -> k; -b -> m; -c -> c; -c -> g; -c -> j; -c -> m; -d -> f; -d -> h; -d -> k; -e -> d; -e -> h; -e -> l; -f -> a; -f -> b; -f -> j; -f -> l; -g -> b; -g -> j; -h -> d; -h -> g; -h -> l; -h -> m; -i -> g; -i -> h; -i -> n; -j -> e; -j -> i; -j -> k; -k -> n; -l -> m; -m -> g; -n -> c; -n -> j; -n -> m; - - -10/06/2015 10:38:07 AM INFO: Cell returned -10/06/2015 10:38:07 AM INFO: Running cell: -""" -Return list of pages, ordered by rank -""" -import numpy as np -from operator import itemgetter -import re - -infile = 'web_graph_data.txt' -alphabet = 'abcdefghijklmnopqrstuvwxyz' - -n = 14 # Total number of web pages (nodes) - -# == Create a matrix Q indicating existence of links == # -# * Q[i, j] = 1 if there is a link from i to j -# * Q[i, j] = 0 otherwise -Q = np.zeros((n, n), dtype=int) -f = open(infile, 'r') -edges = f.readlines() -f.close() -for edge in edges: - from_node, to_node = re.findall('\w', edge) - i, j = alphabet.index(from_node), alphabet.index(to_node) - Q[i, j] = 1 -# == Create the corresponding Markov matrix P == # -P = np.empty((n, n)) -for i in range(n): - P[i,:] = Q[i,:] / Q[i,:].sum() -# == Compute the stationary distribution r == # -r = mc_compute_stationary(P)[0] -ranked_pages = {alphabet[i] : r[i] for i in range(n)} -# == Print solution, sorted from highest to lowest rank == # -print('Rankings\n ***') -for name, rank in sorted(ranked_pages.items(), key=itemgetter(1), reverse=1): - print('{0}: {1:.4}'.format(name, rank)) - - - -10/06/2015 10:38:07 AM INFO: Cell returned -10/06/2015 10:38:07 AM INFO: Running cell: - - -10/06/2015 10:38:07 AM INFO: Cell returned -10/06/2015 10:38:07 AM INFO: Running cell: - - -10/06/2015 10:38:07 AM INFO: Cell returned -10/06/2015 10:38:07 AM INFO: Running cell: - - -10/06/2015 10:38:07 AM INFO: Cell returned -10/06/2015 10:38:07 AM INFO: Shutdown kernel ----> END 'finite_mc_solutions.ipynb' <--- - ----> Executing 'ifp_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:38:08 AM INFO: Reading notebook ifp_solutions.ipynb -10/06/2015 10:38:09 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:38:09 AM INFO: Cell returned -10/06/2015 10:38:09 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -from quantecon import compute_fixed_point -from quantecon.models import ConsumerProblem - -10/06/2015 10:38:10 AM INFO: Cell returned -10/06/2015 10:38:10 AM INFO: Running cell: -cp = ConsumerProblem() -K = 80 - -# Bellman iteration -V, c = cp.initialize() -print("Starting value function iteration") -for i in range(K): - # print "Current iterate = " + str(i) - V = cp.bellman_operator(V) -c1 = cp.bellman_operator(V, return_policy=True) - -# Policy iteration -print("Starting policy function iteration") -V, c2 = cp.initialize() -for i in range(K): - # print "Current iterate = " + str(i) - c2 = cp.coleman_operator(c2) - -fig, ax = plt.subplots(figsize=(10, 8)) -ax.plot(cp.asset_grid, c1[:, 0], label='value function iteration') -ax.plot(cp.asset_grid, c2[:, 0], label='policy function iteration') -ax.set_xlabel('asset level') -ax.set_ylabel('consumption (low income)') -ax.legend(loc='upper left') -plt.show() - -10/06/2015 10:38:17 AM INFO: Cell returned -10/06/2015 10:38:17 AM INFO: Running cell: - -r_vals = np.linspace(0, 0.04, 4) - -fig, ax = plt.subplots(figsize=(10, 8)) -for r_val in r_vals: - cp = ConsumerProblem(r=r_val) - v_init, c_init = cp.initialize() - c = compute_fixed_point(cp.coleman_operator, c_init, verbose=False) - ax.plot(cp.asset_grid, c[:, 0], label=r'$r = %.3f$' % r_val) - -ax.set_xlabel('asset level') -ax.set_ylabel('consumption (low income)') -ax.legend(loc='upper left') -plt.show() - -10/06/2015 10:38:21 AM INFO: Cell returned -10/06/2015 10:38:21 AM INFO: Running cell: - -from scipy import interp -from quantecon import mc_sample_path - -def compute_asset_series(cp, T=500000, verbose=False): - """ - Simulates a time series of length T for assets, given optimal savings - behavior. Parameter cp is an instance of consumerProblem - """ - - Pi, z_vals, R = cp.Pi, cp.z_vals, cp.R # Simplify names - v_init, c_init = cp.initialize() - c = compute_fixed_point(cp.coleman_operator, c_init, verbose=verbose) - cf = lambda a, i_z: interp(a, cp.asset_grid, c[:, i_z]) - a = np.zeros(T+1) - z_seq = mc_sample_path(Pi, sample_size=T) - for t in range(T): - i_z = z_seq[t] - a[t+1] = R * a[t] + z_vals[i_z] - cf(a[t], i_z) - return a - -cp = ConsumerProblem(r=0.03, grid_max=4) -a = compute_asset_series(cp) -fig, ax = plt.subplots(figsize=(10, 8)) -ax.hist(a, bins=20, alpha=0.5, normed=True) -ax.set_xlabel('assets') -ax.set_xlim(-0.05, 0.75) -plt.show() - -10/06/2015 10:38:24 AM INFO: Cell returned -10/06/2015 10:38:24 AM INFO: Running cell: - -M = 25 -r_vals = np.linspace(0, 0.04, M) -fig, ax = plt.subplots(figsize=(10,8)) - -for b in (1, 3): - asset_mean = [] - for r_val in r_vals: - cp = ConsumerProblem(r=r_val, b=b) - mean = np.mean(compute_asset_series(cp, T=250000)) - asset_mean.append(mean) - ax.plot(asset_mean, r_vals, label=r'$b = %d$' % b) - print("Finished iteration b=%i" % b) - -ax.set_yticks(np.arange(.0, 0.045, .01)) -ax.set_xticks(np.arange(-3, 2, 1)) -ax.set_xlabel('capital') -ax.set_ylabel('interest rate') -ax.grid(True) -ax.legend(loc='upper left') -plt.show() - -10/06/2015 10:39:58 AM INFO: Cell returned -10/06/2015 10:39:58 AM INFO: Shutdown kernel ----> END 'ifp_solutions.ipynb' <--- - ----> Executing 'jv_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:00 AM INFO: Reading notebook jv_solutions.ipynb -10/06/2015 10:40:00 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:40:00 AM INFO: Cell returned -10/06/2015 10:40:00 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -import random -from quantecon import compute_fixed_point -from quantecon.models import JvWorker - -10/06/2015 10:40:02 AM INFO: Cell returned -10/06/2015 10:40:02 AM INFO: Running cell: - -wp = JvWorker(grid_size=25) -G, pi, F = wp.G, wp.pi, wp.F # Simplify names - -v_init = wp.x_grid * 0.5 -print("Computing value function") -V = compute_fixed_point(wp.bellman_operator, v_init, max_iter=40, verbose=False) -print("Computing policy functions") -s_policy, phi_policy = wp.bellman_operator(V, return_policies=True) - -# Turn the policy function arrays into actual functions -s = lambda y: np.interp(y, wp.x_grid, s_policy) -phi = lambda y: np.interp(y, wp.x_grid, phi_policy) - -def h(x, b, U): - return (1 - b) * G(x, phi(x)) + b * max(G(x, phi(x)), U) - -plot_grid_max, plot_grid_size = 1.2, 100 -plot_grid = np.linspace(0, plot_grid_max, plot_grid_size) -fig, ax = plt.subplots(figsize=(8,8)) -ax.set_xlim(0, plot_grid_max) -ax.set_ylim(0, plot_grid_max) -ticks = (0.25, 0.5, 0.75, 1.0) -ax.set_xticks(ticks) -ax.set_yticks(ticks) -ax.set_xlabel(r'$x_t$', fontsize=16) -ax.set_ylabel(r'$x_{t+1}$', fontsize=16, rotation='horizontal') - -ax.plot(plot_grid, plot_grid, 'k--') # 45 degree line -for x in plot_grid: - for i in range(50): - b = 1 if random.uniform(0, 1) < pi(s(x)) else 0 - U = wp.F.rvs(1) - y = h(x, b, U) - ax.plot(x, y, 'go', alpha=0.25) - -plt.show() - -10/06/2015 10:40:20 AM INFO: Cell returned -10/06/2015 10:40:20 AM INFO: Running cell: - -wp = JvWorker(grid_size=25) - -def xbar(phi): - return (wp.A * phi**wp.alpha)**(1 / (1 - wp.alpha)) - -phi_grid = np.linspace(0, 1, 100) -fig, ax = plt.subplots(figsize=(9, 7)) -ax.set_xlabel(r'$\phi$', fontsize=16) -ax.plot(phi_grid, [xbar(phi) * (1 - phi) for phi in phi_grid], 'b-', label=r'$w^*(\phi)$') -ax.legend(loc='upper left') - -plt.show() - -10/06/2015 10:40:20 AM INFO: Cell returned -10/06/2015 10:40:20 AM INFO: Shutdown kernel ----> END 'jv_solutions.ipynb' <--- - ----> Executing 'kalman_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:21 AM INFO: Reading notebook kalman_solutions.ipynb -10/06/2015 10:40:22 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:40:22 AM INFO: Cell returned -10/06/2015 10:40:22 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -from quantecon import Kalman -from quantecon import LinearStateSpace -from scipy.stats import norm - -10/06/2015 10:40:23 AM INFO: Cell returned -10/06/2015 10:40:23 AM INFO: Running cell: -# == parameters == # -theta = 10 # Constant value of state x_t -A, C, G, H = 1, 0, 1, 1 -ss = LinearStateSpace(A, C, G, H, mu_0=theta) - -# == set prior, initialize kalman filter == # -x_hat_0, Sigma_0 = 8, 1 -kalman = Kalman(ss, x_hat_0, Sigma_0) - -# == draw observations of y from state space model == # -N = 5 -x, y = ss.simulate(N) -y = y.flatten() - -# == set up plot == # -fig, ax = plt.subplots(figsize=(10,8)) -xgrid = np.linspace(theta - 5, theta + 2, 200) - -for i in range(N): - # == record the current predicted mean and variance == # - m, v = [float(z) for z in (kalman.x_hat, kalman.Sigma)] - # == plot, update filter == # - ax.plot(xgrid, norm.pdf(xgrid, loc=m, scale=np.sqrt(v)), label=r'$t=%d$' % i) - kalman.update(y[i]) - -ax.set_title(r'First %d densities when $\theta = %.1f$' % (N, theta)) -ax.legend(loc='upper left') - -10/06/2015 10:40:24 AM INFO: Cell returned -10/06/2015 10:40:24 AM INFO: Running cell: -from scipy.integrate import quad - -epsilon = 0.1 -theta = 10 # Constant value of state x_t -A, C, G, H = 1, 0, 1, 1 -ss = LinearStateSpace(A, C, G, H, mu_0=theta) - -x_hat_0, Sigma_0 = 8, 1 -kalman = Kalman(ss, x_hat_0, Sigma_0) - -T = 600 -z = np.empty(T) -x, y = ss.simulate(T) -y = y.flatten() - -for t in range(T): - # Record the current predicted mean and variance, and plot their densities - m, v = [float(temp) for temp in (kalman.x_hat, kalman.Sigma)] - - f = lambda x: norm.pdf(x, loc=m, scale=np.sqrt(v)) - integral, error = quad(f, theta - epsilon, theta + epsilon) - z[t] = 1 - integral - - kalman.update(y[t]) - -fig, ax = plt.subplots(figsize=(9, 7)) -ax.set_ylim(0, 1) -ax.set_xlim(0, T) -ax.plot(range(T), z) -ax.fill_between(range(T), np.zeros(T), z, color="blue", alpha=0.2) - -10/06/2015 10:40:25 AM INFO: Cell returned -10/06/2015 10:40:25 AM INFO: Running cell: -from __future__ import print_function # Remove for Python 3.x -from numpy.random import multivariate_normal -from scipy.linalg import eigvals - - -# === Define A, C, G, H === # -G = np.identity(2) -H = np.sqrt(0.5) * np.identity(2) - -A = [[0.5, 0.4], - [0.6, 0.3]] -C = np.sqrt(0.3) * np.identity(2) - -# === Set up state space mode, initial value x_0 set to zero === # -ss = LinearStateSpace(A, C, G, H, mu_0 = np.zeros(2)) - -# === Define the prior density === # -Sigma = [[0.9, 0.3], - [0.3, 0.9]] -Sigma = np.array(Sigma) -x_hat = np.array([8, 8]) - -# === Initialize the Kalman filter === # -kn = Kalman(ss, x_hat, Sigma) - -# == Print eigenvalues of A == # -print("Eigenvalues of A:") -print(eigvals(A)) - -# == Print stationary Sigma == # -S, K = kn.stationary_values() -print("Stationary prediction error variance:") -print(S) - -# === Generate the plot === # -T = 50 -x, y = ss.simulate(T) - -e1 = np.empty(T-1) -e2 = np.empty(T-1) - -for t in range(1, T): - kn.update(y[:,t]) - e1[t-1] = np.sum((x[:,t] - kn.x_hat.flatten())**2) - e2[t-1] = np.sum((x[:,t] - np.dot(A, x[:,t-1]))**2) - -fig, ax = plt.subplots(figsize=(9,6)) -ax.plot(range(1, T), e1, 'k-', lw=2, alpha=0.6, label='Kalman filter error') -ax.plot(range(1, T), e2, 'g-', lw=2, alpha=0.6, label='conditional expectation error') -ax.legend() - - - -10/06/2015 10:40:25 AM INFO: Cell returned -10/06/2015 10:40:25 AM INFO: Running cell: - - -10/06/2015 10:40:25 AM INFO: Cell returned -10/06/2015 10:40:25 AM INFO: Shutdown kernel ----> END 'kalman_solutions.ipynb' <--- - ----> Executing 'lakemodel_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:26 AM INFO: Reading notebook lakemodel_solutions.ipynb -10/06/2015 10:40:26 AM INFO: Running cell: -%matplotlib inline - -import numpy as np -import matplotlib.pyplot as plt -from quantecon.models import LakeModel - -alpha = 0.012 -lamb = 0.2486 -b = 0.001808 -d = 0.0008333 -g = b-d -N0 = 100. -e0 = 0.92 -u0 = 1-e0 -T = 50 - -10/06/2015 10:40:27 AM INFO: Cell returned -10/06/2015 10:40:27 AM INFO: Running cell: -LM0 = LakeModel(lamb,alpha,b,d) -x0 = LM0.find_steady_state()# initial conditions - -print("Initial Steady State: %s" % x0) - -10/06/2015 10:40:27 AM INFO: Cell returned -10/06/2015 10:40:27 AM INFO: Running cell: -LM1 = LakeModel(0.2,alpha,b,d) - -10/06/2015 10:40:27 AM INFO: Cell returned -10/06/2015 10:40:27 AM INFO: Running cell: -xbar = LM1.find_steady_state() # new steady state -X_path = np.vstack(LM1.simulate_stock_path(x0*N0,T)) # simulate stocks -x_path = np.vstack(LM1.simulate_rate_path(x0,T)) # simulate rates -print("New Steady State: %s" % xbar) - -10/06/2015 10:40:27 AM INFO: Cell returned -10/06/2015 10:40:27 AM INFO: Running cell: -plt.figure(figsize=[10,9]) -plt.subplot(3,1,1) -plt.plot(X_path[:,0]) -plt.title(r'Employment') -plt.subplot(3,1,2) -plt.plot(X_path[:,1]) -plt.title(r'Unemployment') -plt.subplot(3,1,3) -plt.plot(X_path.sum(1)) -plt.title(r'Labor Force') - -10/06/2015 10:40:28 AM INFO: Cell returned -10/06/2015 10:40:28 AM INFO: Running cell: -plt.figure(figsize=[10,6]) -plt.subplot(2,1,1) -plt.plot(x_path[:,0]) -plt.hlines(xbar[0],0,T,'r','--') -plt.title(r'Employment Rate') -plt.subplot(2,1,2) -plt.plot(x_path[:,1]) -plt.hlines(xbar[1],0,T,'r','--') -plt.title(r'Unemployment Rate') - -10/06/2015 10:40:28 AM INFO: Cell returned -10/06/2015 10:40:28 AM INFO: Running cell: -bhat = 0.003 -T_hat = 20 -LM1 = LakeModel(lamb,alpha,bhat,d) - -10/06/2015 10:40:28 AM INFO: Cell returned -10/06/2015 10:40:28 AM INFO: Running cell: -X_path1 = np.vstack(LM1.simulate_stock_path(x0*N0,T_hat)) # simulate stocks -x_path1 = np.vstack(LM1.simulate_rate_path(x0,T_hat)) # simulate rates - -10/06/2015 10:40:28 AM INFO: Cell returned -10/06/2015 10:40:28 AM INFO: Running cell: -X_path2 = np.vstack(LM0.simulate_stock_path(X_path1[-1,:2],T-T_hat+1)) # simulate stocks -x_path2 = np.vstack(LM0.simulate_rate_path(x_path1[-1,:2],T-T_hat+1)) # simulate rates - -10/06/2015 10:40:28 AM INFO: Cell returned -10/06/2015 10:40:28 AM INFO: Running cell: -x_path = np.vstack([x_path1,x_path2[1:]]) # note [1:] to avoid doubling period 20 -X_path = np.vstack([X_path1,X_path2[1:]]) # note [1:] to avoid doubling period 20 - -10/06/2015 10:40:28 AM INFO: Cell returned -10/06/2015 10:40:28 AM INFO: Running cell: -plt.figure(figsize=[10,9]) -plt.subplot(3,1,1) -plt.plot(X_path[:,0]) -plt.title(r'Employment') -plt.subplot(3,1,2) -plt.plot(X_path[:,1]) -plt.title(r'Unemployment') -plt.subplot(3,1,3) -plt.plot(X_path.sum(1)) -plt.title(r'Labor Force') - -10/06/2015 10:40:29 AM INFO: Cell returned -10/06/2015 10:40:29 AM INFO: Running cell: -plt.figure(figsize=[10,6]) -plt.subplot(2,1,1) -plt.plot(x_path[:,0]) -plt.hlines(x0[0],0,T,'r','--') -plt.title(r'Employment Rate') -plt.subplot(2,1,2) -plt.plot(x_path[:,1]) -plt.hlines(x0[1],0,T,'r','--') -plt.title(r'Unemployment Rate') - -10/06/2015 10:40:29 AM INFO: Cell returned -10/06/2015 10:40:29 AM INFO: Running cell: - - -10/06/2015 10:40:29 AM INFO: Cell returned -10/06/2015 10:40:29 AM INFO: Shutdown kernel ----> END 'lakemodel_solutions.ipynb' <--- - ----> Executing 'lln_clt_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:30 AM INFO: Reading notebook lln_clt_solutions.ipynb -10/06/2015 10:40:31 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:40:31 AM INFO: Cell returned -10/06/2015 10:40:31 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt - -10/06/2015 10:40:31 AM INFO: Cell returned -10/06/2015 10:40:31 AM INFO: Running cell: -""" -Illustrates the delta method, a consequence of the central limit theorem. -""" - -from scipy.stats import uniform, norm -from matplotlib import rc - -# == Specifying font, needs LaTeX integration == # -rc('font',**{'family':'serif','serif':['Palatino']}) -rc('text', usetex=True) - -# == Set parameters == # -n = 250 -replications = 100000 -distribution = uniform(loc=0, scale=(np.pi / 2)) -mu, s = distribution.mean(), distribution.std() - -g = np.sin -g_prime = np.cos - -# == Generate obs of sqrt{n} (g(\bar X_n) - g(\mu)) == # -data = distribution.rvs((replications, n)) -sample_means = data.mean(axis=1) # Compute mean of each row -error_obs = np.sqrt(n) * (g(sample_means) - g(mu)) - -# == Plot == # -asymptotic_sd = g_prime(mu) * s -fig, ax = plt.subplots(figsize=(10, 6)) -xmin = -3 * g_prime(mu) * s -xmax = -xmin -ax.set_xlim(xmin, xmax) -ax.hist(error_obs, bins=60, alpha=0.5, normed=True) -xgrid = np.linspace(xmin, xmax, 200) -lb = r"$N(0, g'(\mu)^2 \sigma^2)$" -ax.plot(xgrid, norm.pdf(xgrid, scale=asymptotic_sd), 'k-', lw=2, label=lb) -ax.legend() -plt.show() - -10/06/2015 10:40:35 AM INFO: Cell returned -10/06/2015 10:40:35 AM INFO: Running cell: -from scipy.stats import uniform, chi2 -from scipy.linalg import inv, sqrtm - -# == Set parameters == # -n = 250 -replications = 50000 -dw = uniform(loc=-1, scale=2) # Uniform(-1, 1) -du = uniform(loc=-2, scale=4) # Uniform(-2, 2) -sw, su = dw.std(), du.std() -vw, vu = sw**2, su**2 -Sigma = ((vw, vw), (vw, vw + vu)) -Sigma = np.array(Sigma) - -# == Compute Sigma^{-1/2} == # -Q = inv(sqrtm(Sigma)) - -# == Generate observations of the normalized sample mean == # -error_obs = np.empty((2, replications)) -for i in range(replications): - # == Generate one sequence of bivariate shocks == # - X = np.empty((2, n)) - W = dw.rvs(n) - U = du.rvs(n) - # == Construct the n observations of the random vector == # - X[0, :] = W - X[1, :] = W + U - # == Construct the i-th observation of Y_n == # - error_obs[:, i] = np.sqrt(n) * X.mean(axis=1) - -# == Premultiply by Q and then take the squared norm == # -temp = np.dot(Q, error_obs) -chisq_obs = np.sum(temp**2, axis=0) - -# == Plot == # -fig, ax = plt.subplots(figsize=(10, 6)) -xmax = 8 -ax.set_xlim(0, xmax) -xgrid = np.linspace(0, xmax, 200) -lb = "Chi-squared with 2 degrees of freedom" -ax.plot(xgrid, chi2.pdf(xgrid, 2), 'k-', lw=2, label=lb) -ax.legend() -ax.hist(chisq_obs, bins=50, normed=True) -plt.show() - -10/06/2015 10:40:40 AM INFO: Cell returned -10/06/2015 10:40:40 AM INFO: Shutdown kernel ----> END 'lln_clt_solutions.ipynb' <--- - ----> Executing 'lqcontrol_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:41 AM INFO: Reading notebook lqcontrol_solutions.ipynb -10/06/2015 10:40:42 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:40:42 AM INFO: Cell returned -10/06/2015 10:40:42 AM INFO: Running cell: -from __future__ import division -import numpy as np -import matplotlib.pyplot as plt -from quantecon import LQ - -10/06/2015 10:40:44 AM INFO: Cell returned -10/06/2015 10:40:44 AM INFO: Running cell: -# == Model parameters == # -r = 0.05 -beta = 1 / (1 + r) -T = 50 -c_bar = 1.5 -sigma = 0.15 -mu = 2 -q = 1e4 -m1 = T * (mu / (T/2)**2) -m2 = - (mu / (T/2)**2) - -# == Formulate as an LQ problem == # -Q = 1 -R = np.zeros((4, 4)) -Rf = np.zeros((4, 4)) -Rf[0, 0] = q -A = [[1 + r, -c_bar, m1, m2], - [0, 1, 0, 0], - [0, 1, 1, 0], - [0, 1, 2, 1]] -B = [[-1], - [0], - [0], - [0]] -C = [[sigma], - [0], - [0], - [0]] - -# == Compute solutions and simulate == # -lq = LQ(Q, R, A, B, C, beta=beta, T=T, Rf=Rf) -x0 = (0, 1, 0, 0) -xp, up, wp = lq.compute_sequence(x0) - -# == Convert results back to assets, consumption and income == # -ap = xp[0, :] # Assets -c = up.flatten() + c_bar # Consumption -time = np.arange(1, T+1) -income = wp[0, 1:] + m1 * time + m2 * time**2 # Income - - -# == Plot results == # -n_rows = 2 -fig, axes = plt.subplots(n_rows, 1, figsize=(12, 10)) - -plt.subplots_adjust(hspace=0.5) -for i in range(n_rows): - axes[i].grid() - axes[i].set_xlabel(r'Time') -bbox = (0., 1.02, 1., .102) -legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'} -p_args = {'lw' : 2, 'alpha' : 0.7} - -axes[0].plot(range(1, T+1), income, 'g-', label="non-financial income", **p_args) -axes[0].plot(range(T), c, 'k-', label="consumption", **p_args) -axes[0].legend(ncol=2, **legend_args) - -axes[1].plot(range(T+1), ap.flatten(), 'b-', label="assets", **p_args) -axes[1].plot(range(T+1), np.zeros(T+1), 'k-') -axes[1].legend(ncol=1, **legend_args) - -plt.show() - -10/06/2015 10:40:44 AM INFO: Cell returned -10/06/2015 10:40:44 AM INFO: Running cell: -# == Model parameters == # -r = 0.05 -beta = 1 / (1 + r) -T = 60 -K = 40 -c_bar = 4 -sigma = 0.35 -mu = 4 -q = 1e4 -s = 1 -m1 = 2 * mu / K -m2 = - mu / K**2 - -# == Formulate LQ problem 1 (retirement) == # -Q = 1 -R = np.zeros((4, 4)) -Rf = np.zeros((4, 4)) -Rf[0, 0] = q -A = [[1 + r, s - c_bar, 0, 0], - [0, 1, 0, 0], - [0, 1, 1, 0], - [0, 1, 2, 1]] -B = [[-1], - [0], - [0], - [0]] -C = [[0], - [0], - [0], - [0]] - -# == Initialize LQ instance for retired agent == # -lq_retired = LQ(Q, R, A, B, C, beta=beta, T=T-K, Rf=Rf) -# == Iterate back to start of retirement, record final value function == # -for i in range(T-K): - lq_retired.update_values() -Rf2 = lq_retired.P - -# == Formulate LQ problem 2 (working life) == # -R = np.zeros((4, 4)) -A = [[1 + r, -c_bar, m1, m2], - [0, 1, 0, 0], - [0, 1, 1, 0], - [0, 1, 2, 1]] -B = [[-1], - [0], - [0], - [0]] -C = [[sigma], - [0], - [0], - [0]] - -# == Set up working life LQ instance with terminal Rf from lq_retired == # -lq_working = LQ(Q, R, A, B, C, beta=beta, T=K, Rf=Rf2) - -# == Simulate working state / control paths == # -x0 = (0, 1, 0, 0) -xp_w, up_w, wp_w = lq_working.compute_sequence(x0) -# == Simulate retirement paths (note the initial condition) == # -xp_r, up_r, wp_r = lq_retired.compute_sequence(xp_w[:, K]) - -# == Convert results back to assets, consumption and income == # -xp = np.column_stack((xp_w, xp_r[:, 1:])) -assets = xp[0, :] # Assets - -up = np.column_stack((up_w, up_r)) -c = up.flatten() + c_bar # Consumption - -time = np.arange(1, K+1) -income_w = wp_w[0, 1:K+1] + m1 * time + m2 * time**2 # Income -income_r = np.ones(T-K) * s -income = np.concatenate((income_w, income_r)) - -# == Plot results == # -n_rows = 2 -fig, axes = plt.subplots(n_rows, 1, figsize=(12, 10)) - -plt.subplots_adjust(hspace=0.5) -for i in range(n_rows): - axes[i].grid() - axes[i].set_xlabel(r'Time') -bbox = (0., 1.02, 1., .102) -legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'} -p_args = {'lw' : 2, 'alpha' : 0.7} - -axes[0].plot(range(1, T+1), income, 'g-', label="non-financial income", **p_args) -axes[0].plot(range(T), c, 'k-', label="consumption", **p_args) -axes[0].legend(ncol=2, **legend_args) - -axes[1].plot(range(T+1), assets, 'b-', label="assets", **p_args) -axes[1].plot(range(T+1), np.zeros(T+1), 'k-') -axes[1].legend(ncol=1, **legend_args) - -plt.show() - -10/06/2015 10:40:45 AM INFO: Cell returned -10/06/2015 10:40:45 AM INFO: Running cell: -# == Model parameters == # -a0 = 5 -a1 = 0.5 -sigma = 0.15 -rho = 0.9 -gamma = 1 -beta = 0.95 -c = 2 -T = 120 - -# == Useful constants == # -m0 = (a0 - c) / (2 * a1) -m1 = 1 / (2 * a1) - -# == Formulate LQ problem == # -Q = gamma -R = [[a1, -a1, 0], - [-a1, a1, 0], - [0, 0, 0]] -A = [[rho, 0, m0 * (1 - rho)], - [0, 1, 0], - [0, 0, 1]] - -B = [[0], - [1], - [0]] -C = [[m1 * sigma], - [0], - [0]] - -lq = LQ(Q, R, A, B, C=C, beta=beta) - -# == Simulate state / control paths == # -x0 = (m0, 2, 1) -xp, up, wp = lq.compute_sequence(x0, ts_length=150) -q_bar = xp[0, :] -q = xp[1, :] - -# == Plot simulation results == # -fig, ax = plt.subplots(figsize=(10, 6.5)) -ax.set_xlabel('Time') - -# == Some fancy plotting stuff -- simplify if you prefer == # -bbox = (0., 1.01, 1., .101) -legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'} -p_args = {'lw' : 2, 'alpha' : 0.6} - -time = range(len(q)) -ax.set_xlim(0, max(time)) -ax.plot(time, q_bar, 'k-', lw=2, alpha=0.6, label=r'$\bar q_t$') -ax.plot(time, q, 'b-', lw=2, alpha=0.6, label=r'$q_t$') -ax.legend(ncol=2, **legend_args) -s = r'dynamics with $\gamma = {}$'.format(gamma) -ax.text(max(time) * 0.6, 1 * q_bar.max(), s, fontsize=14) -plt.show() - -10/06/2015 10:40:46 AM INFO: Cell returned -10/06/2015 10:40:46 AM INFO: Shutdown kernel ----> END 'lqcontrol_solutions.ipynb' <--- - ----> Executing 'lqramsey_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:47 AM INFO: Reading notebook lqramsey_solutions.ipynb -10/06/2015 10:40:47 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:40:48 AM INFO: Cell returned -10/06/2015 10:40:48 AM INFO: Running cell: -import sys -import os -import numpy as np -import matplotlib.pyplot as plt - -# lqramsy.py lives in the examples folder. We need -# to append it to the path so we can import it below -sys.path.append(os.path.abspath("../examples")) - -10/06/2015 10:40:48 AM INFO: Cell returned -10/06/2015 10:40:48 AM INFO: Running cell: -from numpy import array -from lqramsey import * - -# == Parameters == # -beta = 1 / 1.05 -rho, mg = .95, .35 -A = array([[0, 0, 0, rho, mg*(1-rho)], - [1, 0, 0, 0, 0], - [0, 1, 0, 0, 0], - [0, 0, 1, 0, 0], - [0, 0, 0, 0, 1]]) -C = np.zeros((5, 1)) -C[0, 0] = np.sqrt(1 - rho**2) * mg / 8 -Sg = array((1, 0, 0, 0, 0)).reshape(1, 5) -Sd = array((0, 0, 0, 0, 0)).reshape(1, 5) -Sb = array((0, 0, 0, 0, 2.135)).reshape(1, 5) # Chosen st. (Sc + Sg) * x0 = 1 -Ss = array((0, 0, 0, 0, 0)).reshape(1, 5) - -economy = Economy(beta=beta, - Sg=Sg, - Sd=Sd, - Sb=Sb, - Ss=Ss, - discrete=False, - proc=(A, C)) - -T = 50 -path = compute_paths(T, economy) -gen_fig_1(path) - -10/06/2015 10:40:50 AM INFO: Cell returned -10/06/2015 10:40:50 AM INFO: Shutdown kernel ----> END 'lqramsey_solutions.ipynb' <--- - ----> Executing 'lss_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:51 AM INFO: Reading notebook lss_solutions.ipynb -10/06/2015 10:40:52 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:40:53 AM INFO: Cell returned -10/06/2015 10:40:53 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -from quantecon import LinearStateSpace - -10/06/2015 10:40:54 AM INFO: Cell returned -10/06/2015 10:40:54 AM INFO: Running cell: -phi_0, phi_1, phi_2 = 1.1, 0.8, -0.8 - -A = [[1, 0, 0], - [phi_0, phi_1, phi_2], - [0, 1, 0]] -C = np.zeros((3, 1)) -G = [0, 1, 0] - -ar = LinearStateSpace(A, C, G, mu_0=np.ones(3)) -x, y = ar.simulate(ts_length=50) - -fig, ax = plt.subplots(figsize=(8, 4.6)) -y = y.flatten() -ax.plot(y, 'b-', lw=2, alpha=0.7) -ax.grid() -ax.set_xlabel('time') -ax.set_ylabel(r'$y_t$', fontsize=16) -plt.show() - -10/06/2015 10:40:54 AM INFO: Cell returned -10/06/2015 10:40:54 AM INFO: Running cell: -phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 -sigma = 0.2 - -A = [[phi_1, phi_2, phi_3, phi_4], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0]] -C = [[sigma], - [0], - [0], - [0]] -G = [1, 0, 0, 0] - -ar = LinearStateSpace(A, C, G, mu_0=np.ones(4)) -x, y = ar.simulate(ts_length=200) - -fig, ax = plt.subplots(figsize=(8, 4.6)) -y = y.flatten() -ax.plot(y, 'b-', lw=2, alpha=0.7) -ax.grid() -ax.set_xlabel('time') -ax.set_ylabel(r'$y_t$', fontsize=16) -plt.show() - - -10/06/2015 10:40:54 AM INFO: Cell returned -10/06/2015 10:40:54 AM INFO: Running cell: -from __future__ import division -from scipy.stats import norm -import random - -phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 -sigma = 0.1 - -A = [[phi_1, phi_2, phi_3, phi_4], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0]] -C = [[sigma], - [0], - [0], - [0]] -G = [1, 0, 0, 0] - -I = 20 -T = 50 -ar = LinearStateSpace(A, C, G, mu_0=np.ones(4)) -ymin, ymax = -0.5, 1.15 - -fig, ax = plt.subplots(figsize=(8, 5)) - -ax.set_ylim(ymin, ymax) -ax.set_xlabel(r'time', fontsize=16) -ax.set_ylabel(r'$y_t$', fontsize=16) - -ensemble_mean = np.zeros(T) -for i in range(I): - x, y = ar.simulate(ts_length=T) - y = y.flatten() - ax.plot(y, 'c-', lw=0.8, alpha=0.5) - ensemble_mean = ensemble_mean + y - -ensemble_mean = ensemble_mean / I -ax.plot(ensemble_mean, color='b', lw=2, alpha=0.8, label=r'$\bar y_t$') - -m = ar.moment_sequence() -population_means = [] -for t in range(T): - mu_x, mu_y, Sigma_x, Sigma_y = next(m) - population_means.append(float(mu_y)) -ax.plot(population_means, color='g', lw=2, alpha=0.8, label=r'$G\mu_t$') -ax.legend(ncol=2) -plt.show() - -10/06/2015 10:40:55 AM INFO: Cell returned -10/06/2015 10:40:55 AM INFO: Running cell: -phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5 -sigma = 0.1 - -A = [[phi_1, phi_2, phi_3, phi_4], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0]] -C = [[sigma], - [0], - [0], - [0]] -G = [1, 0, 0, 0] - -T0 = 10 -T1 = 50 -T2 = 75 -T4 = 100 - -ar = LinearStateSpace(A, C, G, mu_0=np.ones(4)) -ymin, ymax = -0.6, 0.6 - -fig, ax = plt.subplots(figsize=(8, 5)) - -ax.grid(alpha=0.4) -ax.set_ylim(ymin, ymax) -ax.set_ylabel(r'$y_t$', fontsize=16) -ax.vlines((T0, T1, T2), -1.5, 1.5) - -ax.set_xticks((T0, T1, T2)) -ax.set_xticklabels((r"$T$", r"$T'$", r"$T''$"), fontsize=14) - -mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions() -ar.mu_0 = mu_x -ar.Sigma_0 = Sigma_x - -for i in range(80): - rcolor = random.choice(('c', 'g', 'b')) - x, y = ar.simulate(ts_length=T4) - y = y.flatten() - ax.plot(y, color=rcolor, lw=0.8, alpha=0.5) - ax.plot((T0, T1, T2), (y[T0], y[T1], y[T2],), 'ko', alpha=0.5) - - -10/06/2015 10:40:56 AM INFO: Cell returned -10/06/2015 10:40:56 AM INFO: Running cell: - - -10/06/2015 10:40:56 AM INFO: Cell returned -10/06/2015 10:40:56 AM INFO: Shutdown kernel ----> END 'lss_solutions.ipynb' <--- - ----> Executing 'lucas_asset_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:40:57 AM INFO: Reading notebook lucas_asset_solutions.ipynb -10/06/2015 10:40:58 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:40:58 AM INFO: Cell returned -10/06/2015 10:40:58 AM INFO: Running cell: -from __future__ import division # Omit for Python 3.x -import numpy as np -import matplotlib.pyplot as plt -from quantecon.models import LucasTree - -10/06/2015 10:40:59 AM INFO: Cell returned -10/06/2015 10:40:59 AM INFO: Running cell: -fig, ax = plt.subplots(figsize=(10,7)) - -ax.set_xlabel(r'$y$', fontsize=16) -ax.set_ylabel(r'price', fontsize=16) - -for beta in (.95, 0.98): - print("Comuting at beta = {}".format(beta)) - tree = LucasTree(gamma=2, beta=beta, alpha=0.90, sigma=0.1) - grid, price_vals = tree.grid, tree.compute_lt_price() - label = r'$\beta = {}$'.format(beta) - ax.plot(grid, price_vals, lw=2, alpha=0.7, label=label) - -ax.legend(loc='upper left') -ax.set_xlim(min(grid), max(grid)) - -10/06/2015 10:41:01 AM INFO: Cell returned -10/06/2015 10:41:01 AM INFO: Running cell: - - -10/06/2015 10:41:01 AM INFO: Cell returned -10/06/2015 10:41:01 AM INFO: Shutdown kernel ----> END 'lucas_asset_solutions.ipynb' <--- - ----> Executing 'mpe_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:41:03 AM INFO: Reading notebook mpe_solutions.ipynb -10/06/2015 10:41:03 AM INFO: Running cell: -import numpy as np -import quantecon as qe -import matplotlib.pyplot as plt -from numpy import dot - -10/06/2015 10:41:04 AM INFO: Cell returned -10/06/2015 10:41:04 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:41:04 AM INFO: Cell returned -10/06/2015 10:41:04 AM INFO: Running cell: -# == Parameters == # -a0 = 10.0 -a1 = 2.0 -beta = 0.96 -gamma = 12.0 - -# == In LQ form == # - -A = np.eye(3) - -B1 = np.array([[0.], [1.], [0.]]) -B2 = np.array([[0.], [0.], [1.]]) - - -R1 = [[0., -a0/2, 0.], - [-a0/2., a1, a1/2.], - [0, a1/2., 0.]] - -R2 = [[0., 0., -a0/2], - [0., 0., a1/2.], - [-a0/2, a1/2., a1]] - -Q1 = Q2 = gamma - -S1 = S2 = W1 = W2 = M1 = M2 = 0.0 - -# == Solve using QE's nnash function == # -F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2, - beta=beta) - -10/06/2015 10:41:04 AM INFO: Cell returned -10/06/2015 10:41:04 AM INFO: Running cell: -AF = A - B1.dot(F1) - B2.dot(F2) -n = 20 -x = np.empty((3, n)) -x[:, 0] = 1, 1, 1 -for t in range(n-1): - x[:, t+1] = np.dot(AF, x[:, t]) -q1 = x[1, :] -q2 = x[2, :] -q = q1 + q2 # Total output, MPE -p = a0 - a1 * q # Price, MPE - -10/06/2015 10:41:04 AM INFO: Cell returned -10/06/2015 10:41:04 AM INFO: Running cell: -R = a1 -Q = gamma -A = B = 1 -lq_alt = qe.LQ(Q, R, A, B, beta=beta) -P, F, d = lq_alt.stationary_values() -q_bar = a0 / (2.0 * a1) -qm = np.empty(n) -qm[0] = 2 -x0 = qm[0] - q_bar -x = x0 -for i in range(1, n): - x = A * x - B * F * x - qm[i] = float(x) + q_bar -pm = a0 - a1 * qm - -10/06/2015 10:41:04 AM INFO: Cell returned -10/06/2015 10:41:04 AM INFO: Running cell: -fig, axes = plt.subplots(2, 1, figsize=(9, 9)) - -ax = axes[0] -ax.plot(qm, 'b-', lw=2, alpha=0.75, label='monopolist output') -ax.plot(q, 'g-', lw=2, alpha=0.75, label='MPE total output') -ax.set_ylabel("output") -ax.set_xlabel("time") -ax.set_ylim(2, 4) -ax.legend(loc='upper left', frameon=0) - - -ax = axes[1] -ax.plot(pm, 'b-', lw=2, alpha=0.75, label='monopolist price') -ax.plot(p, 'g-', lw=2, alpha=0.75, label='MPE price') -ax.set_ylabel("price") -ax.set_xlabel("time") -ax.legend(loc='upper right', frameon=0) - -10/06/2015 10:41:05 AM INFO: Cell returned -10/06/2015 10:41:05 AM INFO: Running cell: -delta = 0.02 -D = np.array([[-1, 0.5], [0.5, -1]]) -b = np.array([25, 25]) -c1 = c2 = np.array([1, -2, 1]) -e1 = e2 = np.array([10, 10, 3]) - -delta_1 = 1 - delta - -10/06/2015 10:41:05 AM INFO: Cell returned -10/06/2015 10:41:05 AM INFO: Running cell: -# == Create matrices needed to compute the Nash feedback equilibrium == # - -A = np.array([[delta_1, 0, -delta_1*b[0]], - [0, delta_1, -delta_1*b[1]], - [0, 0, 1]]) - -B1 = delta_1 * np.array([[1, -D[0, 0]], - [0, -D[1, 0]], - [0, 0]]) -B2 = delta_1 * np.array([[0, -D[0, 1]], - [1, -D[1, 1]], - [0, 0]]) - -R1 = -np.array([[0.5*c1[2], 0, 0.5*c1[1]], - [0, 0, 0], - [0.5*c1[1], 0, c1[0]]]) -R2 = -np.array([[0, 0, 0], - [0, 0.5*c2[2], 0.5*c2[1]], - [0, 0.5*c2[1], c2[0]]]) - -Q1 = np.array([[-0.5*e1[2], 0], [0, D[0, 0]]]) -Q2 = np.array([[-0.5*e2[2], 0], [0, D[1, 1]]]) - -S1 = np.zeros((2, 2)) -S2 = np.copy(S1) - -W1 = np.array([[0, 0], - [0, 0], - [-0.5*e1[1], b[0]/2.]]) -W2 = np.array([[0, 0], - [0, 0], - [-0.5*e2[1], b[1]/2.]]) - -M1 = np.array([[0, 0], [0, D[0, 1] / 2.]]) -M2 = np.copy(M1) - -10/06/2015 10:41:05 AM INFO: Cell returned -10/06/2015 10:41:05 AM INFO: Running cell: -F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2) - -print("\nFirm 1's feedback rule:\n") -print(F1) - -print("\nFirm 2's feedback rule:\n") -print(F2) - -10/06/2015 10:41:05 AM INFO: Cell returned -10/06/2015 10:41:05 AM INFO: Running cell: -AF = A - B1.dot(F1) - B2.dot(F2) -n = 25 -x = np.empty((3, n)) -x[:, 0] = 2, 0, 1 -for t in range(n-1): - x[:, t+1] = np.dot(AF, x[:, t]) -I1 = x[0, :] -I2 = x[1, :] -fig, ax = plt.subplots(figsize=(9, 5)) -ax.plot(I1, 'b-', lw=2, alpha=0.75, label='inventories, firm 1') -ax.plot(I2, 'g-', lw=2, alpha=0.75, label='inventories, firm 2') -ax.set_title(r'$\delta = {}$'.format(delta)) -ax.legend() - -10/06/2015 10:41:05 AM INFO: Cell returned -10/06/2015 10:41:05 AM INFO: Running cell: - - -10/06/2015 10:41:05 AM INFO: Cell returned -10/06/2015 10:41:05 AM INFO: Shutdown kernel ----> END 'mpe_solutions.ipynb' <--- - ----> Executing 'numpy_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:41:06 AM INFO: Reading notebook numpy_solutions.ipynb -10/06/2015 10:41:07 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: -def p(x, coef): - X = np.empty(len(coef)) - X[0] = 1 - X[1:] = x - y = np.cumprod(X) # y = [1, x, x**2,...] - return np.dot(coef, y) - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: -coef = np.ones(3) -print(coef) -print(p(1, coef)) -# For comparison -q = np.poly1d(coef) -print(q(1)) - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: -from numpy import cumsum -from numpy.random import uniform - -class discreteRV: - """ - Generates an array of draws from a discrete random variable with vector of - probabilities given by q. - """ - - def __init__(self, q): - """ - The argument q is a NumPy array, or array like, nonnegative and sums - to 1 - """ - self.q = q - self.Q = cumsum(q) - - def draw(self, k=1): - """ - Returns k draws from q. For each such draw, the value i is returned - with probability q[i]. - """ - return self.Q.searchsorted(uniform(0, 1, size=k)) - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: -q = (0.1, 0.9) -d = discreteRV(q) -d.q = (0.5, 0.5) - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: -""" -Modifies ecdf.py from QuantEcon to add in a plot method - -""" - -import numpy as np -import matplotlib.pyplot as plt - - -class ECDF(object): - """ - One-dimensional empirical distribution function given a vector of - observations. - - Parameters - ---------- - observations : array_like - An array of observations - - Attributes - ---------- - observations : array_like - An array of observations - - """ - - def __init__(self, observations): - self.observations = np.asarray(observations) - - def __call__(self, x): - """ - Evaluates the ecdf at x - - Parameters - ---------- - x : scalar(float) - The x at which the ecdf is evaluated - - Returns - ------- - scalar(float) - Fraction of the sample less than x - - """ - return np.mean(self.observations <= x) - - def plot(self, a=None, b=None): - """ - Plot the ecdf on the interval [a, b]. - - Parameters - ---------- - a : scalar(float), optional(default=None) - Lower end point of the plot interval - b : scalar(float), optional(default=None) - Upper end point of the plot interval - - """ - - # === choose reasonable interval if [a, b] not specified === # - if a is None: - a = self.observations.min() - self.observations.std() - if b is None: - b = self.observations.max() + self.observations.std() - - # === generate plot === # - x_vals = np.linspace(a, b, num=100) - f = np.vectorize(self.__call__) - plt.plot(x_vals, f(x_vals)) - plt.show() - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: -X = np.random.randn(1000) -F = ECDF(X) -F.plot() - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Running cell: - - -10/06/2015 10:41:07 AM INFO: Cell returned -10/06/2015 10:41:07 AM INFO: Shutdown kernel ----> END 'numpy_solutions.ipynb' <--- - ----> Executing 'odu_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:41:09 AM INFO: Reading notebook odu_solutions.ipynb -10/06/2015 10:41:09 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:41:09 AM INFO: Cell returned -10/06/2015 10:41:09 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -from quantecon import compute_fixed_point -from quantecon.models import SearchProblem - -10/06/2015 10:41:10 AM INFO: Cell returned -10/06/2015 10:41:10 AM INFO: Running cell: -sp = SearchProblem(pi_grid_size=50) - -phi_init = np.ones(len(sp.pi_grid)) -w_bar = compute_fixed_point(sp.res_wage_operator, phi_init) - -fig, ax = plt.subplots(figsize=(9, 7)) -ax.plot(sp.pi_grid, w_bar, linewidth=2, color='black') -ax.set_ylim(0, 2) -ax.grid(axis='x', linewidth=0.25, linestyle='--', color='0.25') -ax.grid(axis='y', linewidth=0.25, linestyle='--', color='0.25') -ax.fill_between(sp.pi_grid, 0, w_bar, color='blue', alpha=0.15) -ax.fill_between(sp.pi_grid, w_bar, 2, color='green', alpha=0.15) -ax.text(0.42, 1.2, 'reject') -ax.text(0.7, 1.8, 'accept') -plt.show() - -10/06/2015 10:41:11 AM INFO: Cell returned -10/06/2015 10:41:11 AM INFO: Running cell: -from scipy import interp -# Set up model and compute the function w_bar -sp = SearchProblem(pi_grid_size=50, F_a=1, F_b=1) -pi_grid, f, g, F, G = sp.pi_grid, sp.f, sp.g, sp.F, sp.G -phi_init = np.ones(len(sp.pi_grid)) -w_bar_vals = compute_fixed_point(sp.res_wage_operator, phi_init) -w_bar = lambda x: interp(x, pi_grid, w_bar_vals) - - -class Agent(object): - """ - Holds the employment state and beliefs of an individual agent. - """ - - def __init__(self, pi=1e-3): - self.pi = pi - self.employed = 1 - - def update(self, H): - "Update self by drawing wage offer from distribution H." - if self.employed == 0: - w = H.rvs() - if w >= w_bar(self.pi): - self.employed = 1 - else: - self.pi = 1.0 / (1 + ((1 - self.pi) * g(w)) / (self.pi * f(w))) - - -num_agents = 5000 -separation_rate = 0.025 # Fraction of jobs that end in each period -separation_num = int(num_agents * separation_rate) -agent_indices = list(range(num_agents)) -agents = [Agent() for i in range(num_agents)] -sim_length = 600 -H = G # Start with distribution G -change_date = 200 # Change to F after this many periods - -unempl_rate = [] -for i in range(sim_length): - if i % 20 == 0: - print("date =", i) - if i == change_date: - H = F - # Randomly select separation_num agents and set employment status to 0 - np.random.shuffle(agent_indices) - separation_list = agent_indices[:separation_num] - for agent_index in separation_list: - agents[agent_index].employed = 0 - # Update agents - for agent in agents: - agent.update(H) - employed = [agent.employed for agent in agents] - unempl_rate.append(1 - np.mean(employed)) - -fig, ax = plt.subplots(figsize=(9, 7)) -ax.plot(unempl_rate, lw=2, alpha=0.8, label='unemployment rate') -ax.axvline(change_date, color="red") -ax.legend() -plt.show() - -10/06/2015 10:42:22 AM INFO: Cell returned -10/06/2015 10:42:22 AM INFO: Shutdown kernel ----> END 'odu_solutions.ipynb' <--- - ----> Executing 'oop_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:23 AM INFO: Reading notebook oop_solutions.ipynb -10/06/2015 10:42:24 AM INFO: Running cell: -class ECDF(object): - - def __init__(self, observations): - self.observations = observations - - def __call__(self, x): - counter = 0.0 - for obs in self.observations: - if obs <= x: - counter += 1 - return counter / len(self.observations) - -10/06/2015 10:42:24 AM INFO: Cell returned -10/06/2015 10:42:24 AM INFO: Running cell: -# == test == # - -from random import uniform -samples = [uniform(0, 1) for i in range(10)] -F = ECDF(samples) - -print(F(0.5)) # Evaluate ecdf at x = 0.5 - -F.observations = [uniform(0, 1) for i in range(1000)] - -print(F(0.5)) - -10/06/2015 10:42:24 AM INFO: Cell returned -10/06/2015 10:42:24 AM INFO: Running cell: -class Polynomial(object): - - def __init__(self, coefficients): - """ - Creates an instance of the Polynomial class representing - - p(x) = a_0 x^0 + ... + a_N x^N, - - where a_i = coefficients[i]. - """ - self.coefficients = coefficients - - def __call__(self, x): - "Evaluate the polynomial at x." - y = 0 - for i, a in enumerate(self.coefficients): - y += a * x**i - return y - - def differentiate(self): - "Reset self.coefficients to those of p' instead of p." - new_coefficients = [] - for i, a in enumerate(self.coefficients): - new_coefficients.append(i * a) - # Remove the first element, which is zero - del new_coefficients[0] - # And reset coefficients data to new values - self.coefficients = new_coefficients - - -10/06/2015 10:42:24 AM INFO: Cell returned -10/06/2015 10:42:24 AM INFO: Shutdown kernel ----> END 'oop_solutions.ipynb' <--- - ----> Executing 'optgrowth_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:25 AM INFO: Reading notebook optgrowth_solutions.ipynb -10/06/2015 10:42:25 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:42:25 AM INFO: Cell returned -10/06/2015 10:42:25 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt -from quantecon import compute_fixed_point -from quantecon.models import GrowthModel - -10/06/2015 10:42:26 AM INFO: Cell returned -10/06/2015 10:42:26 AM INFO: Running cell: -alpha, beta = 0.65, 0.95 -gm = GrowthModel() -true_sigma = (1 - alpha * beta) * gm.grid**alpha -w = 5 * gm.u(gm.grid) - 25 # Initial condition - -fig, ax = plt.subplots(3, 1, figsize=(8, 10)) - -for i, n in enumerate((2, 4, 6)): - ax[i].set_ylim(0, 1) - ax[i].set_xlim(0, 2) - ax[i].set_yticks((0, 1)) - ax[i].set_xticks((0, 2)) - - v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=n) - sigma = gm.compute_greedy(v_star) - - ax[i].plot(gm.grid, sigma, 'b-', lw=2, alpha=0.8, label='approximate optimal policy') - ax[i].plot(gm.grid, true_sigma, 'k-', lw=2, alpha=0.8, label='true optimal policy') - ax[i].legend(loc='upper left') - ax[i].set_title('{} value function iterations'.format(n)) - -10/06/2015 10:42:27 AM INFO: Cell returned -10/06/2015 10:42:27 AM INFO: Running cell: -from scipy import interp - -gm = GrowthModel() -w = 5 * gm.u(gm.grid) - 25 # To be used as an initial condition -discount_factors = (0.9, 0.94, 0.98) -series_length = 25 - -fig, ax = plt.subplots(figsize=(8,5)) -ax.set_xlabel("time") -ax.set_ylabel("capital") - -for beta in discount_factors: - - # Compute the optimal policy given the discount factor - gm.beta = beta - v_star = compute_fixed_point(gm.bellman_operator, w, max_iter=20) - sigma = gm.compute_greedy(v_star) - - # Compute the corresponding time series for capital - k = np.empty(series_length) - k[0] = 0.1 - sigma_function = lambda x: interp(x, gm.grid, sigma) - for t in range(1, series_length): - k[t] = gm.f(k[t-1]) - sigma_function(k[t-1]) - ax.plot(k, 'o-', lw=2, alpha=0.75, label=r'$\beta = {}$'.format(beta)) - -ax.legend(loc='lower right') -plt.show() - -10/06/2015 10:42:32 AM INFO: Cell returned -10/06/2015 10:42:32 AM INFO: Shutdown kernel ----> END 'optgrowth_solutions.ipynb' <--- - ----> Executing 'pandas_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:33 AM INFO: Reading notebook pandas_solutions.ipynb -10/06/2015 10:42:34 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:42:34 AM INFO: Cell returned -10/06/2015 10:42:34 AM INFO: Running cell: -import numpy as np -import pandas as pd -import datetime as dt -import pandas.io.data as web -import matplotlib.pyplot as plt - -10/06/2015 10:42:34 AM INFO: Cell returned -10/06/2015 10:42:34 AM INFO: Running cell: -ticker_list = {'INTC': 'Intel', - 'MSFT': 'Microsoft', - 'IBM': 'IBM', - 'BHP': 'BHP', - 'RSH': 'RadioShack', - 'TM': 'Toyota', - 'AAPL': 'Apple', - 'AMZN': 'Amazon', - 'BA': 'Boeing', - 'QCOM': 'Qualcomm', - 'KO': 'Coca-Cola', - 'GOOG': 'Google', - 'SNE': 'Sony', - 'PTR': 'PetroChina'} - -start = dt.datetime(2013, 1, 1) -end = dt.datetime.today() - -price_change = {} - -for ticker in ticker_list: - prices = web.DataReader(ticker, 'yahoo', start, end) - closing_prices = prices['Close'] - change = 100 * (closing_prices[-1] - closing_prices[0]) / closing_prices[0] - name = ticker_list[ticker] - price_change[name] = change - -pc = pd.Series(price_change) -pc.sort() -fig, ax = plt.subplots(figsize=(10,8)) -pc.plot(kind='bar', ax=ax) - -10/06/2015 10:42:37 AM INFO: Cell returned -10/06/2015 10:42:37 AM INFO: Shutdown kernel ----> END 'pandas_solutions.ipynb' <--- - ----> Executing 'pbe_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:39 AM INFO: Reading notebook pbe_solutions.ipynb -10/06/2015 10:42:39 AM INFO: Running cell: -def factorial(n): - k = 1 - for i in range(n): - k = k * (i + 1) - return k - -factorial(4) - -10/06/2015 10:42:39 AM INFO: Cell returned -10/06/2015 10:42:39 AM INFO: Running cell: -from random import uniform - -def binomial_rv(n, p): - count = 0 - for i in range(n): - U = uniform(0, 1) - if U < p: - count = count + 1 # Or count += 1 - return count - -binomial_rv(10, 0.5) - -10/06/2015 10:42:39 AM INFO: Cell returned -10/06/2015 10:42:39 AM INFO: Running cell: -from __future__ import division # Omit if using Python 3.x -from math import sqrt - -n = 100000 - -count = 0 -for i in range(n): - u, v = uniform(0, 1), uniform(0, 1) - d = sqrt((u - 0.5)**2 + (v - 0.5)**2) - if d < 0.5: - count += 1 - -area_estimate = count / n - -print(area_estimate * 4) # dividing by radius**2 - -10/06/2015 10:42:39 AM INFO: Cell returned -10/06/2015 10:42:39 AM INFO: Running cell: -payoff = 0 -count = 0 - -for i in range(10): - U = uniform(0, 1) - count = count + 1 if U < 0.5 else 0 - if count == 3: - payoff = 1 - -print(payoff) - -10/06/2015 10:42:40 AM INFO: Cell returned -10/06/2015 10:42:40 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:42:40 AM INFO: Cell returned -10/06/2015 10:42:40 AM INFO: Running cell: -import matplotlib.pyplot as plt -from random import normalvariate - -alpha = 0.9 -ts_length = 200 -current_x = 0 - -x_values = [] -for i in range(ts_length + 1): - x_values.append(current_x) - current_x = alpha * current_x + normalvariate(0, 1) -plt.plot(x_values, 'b-') - - -10/06/2015 10:42:40 AM INFO: Cell returned -10/06/2015 10:42:40 AM INFO: Running cell: -alphas = [0.0, 0.8, 0.98] -ts_length = 200 - -for alpha in alphas: - x_values = [] - current_x = 0 - for i in range(ts_length): - x_values.append(current_x) - current_x = alpha * current_x + normalvariate(0, 1) - plt.plot(x_values, label='alpha = ' + str(alpha)) -plt.legend() - -10/06/2015 10:42:40 AM INFO: Cell returned -10/06/2015 10:42:40 AM INFO: Running cell: - - -10/06/2015 10:42:40 AM INFO: Cell returned -10/06/2015 10:42:40 AM INFO: Shutdown kernel ----> END 'pbe_solutions.ipynb' <--- - ----> Executing 'py_adv_feat_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:41 AM INFO: Reading notebook py_adv_feat_solutions.ipynb -10/06/2015 10:42:42 AM INFO: Running cell: -def x(t): - if t == 0: - return 0 - if t == 1: - return 1 - else: - return x(t-1) + x(t-2) - - -10/06/2015 10:42:42 AM INFO: Cell returned -10/06/2015 10:42:42 AM INFO: Running cell: -print([x(i) for i in range(10)]) - -10/06/2015 10:42:42 AM INFO: Cell returned -10/06/2015 10:42:42 AM INFO: Running cell: -def column_iterator(target_file, column_number): - """A generator function for CSV files. - When called with a file name target_file (string) and column number - column_number (integer), the generator function returns a generator - which steps through the elements of column column_number in file - target_file. - """ - f = open(target_file, 'r') - for line in f: - yield line.split(',')[column_number - 1] - f.close() - -dates = column_iterator('test_table.csv', 1) - -i = 1 -for date in dates: - print(date) - if i == 10: - break - i += 1 - -10/06/2015 10:42:42 AM INFO: Cell returned -10/06/2015 10:42:42 AM INFO: Running cell: -%%file numbers.txt -prices -3 -8 - -7 -21 - -10/06/2015 10:42:42 AM INFO: Cell returned -10/06/2015 10:42:42 AM INFO: Running cell: -f = open('numbers.txt') - -total = 0.0 -for line in f: - try: - total += float(line) - except ValueError: - pass - -f.close() - -print(total) - - -10/06/2015 10:42:42 AM INFO: Cell returned -10/06/2015 10:42:42 AM INFO: Shutdown kernel ----> END 'py_adv_feat_solutions.ipynb' <--- - ----> Executing 'pyess_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:43 AM INFO: Reading notebook pyess_solutions.ipynb -10/06/2015 10:42:43 AM INFO: Running cell: -from __future__ import division # Omit for Python 3.x - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -x_vals = [1, 2, 3] -y_vals = [1, 1, 1] -sum([x * y for x, y in zip(x_vals, y_vals)]) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -sum(x * y for x, y in zip(x_vals, y_vals)) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -sum([x % 2 == 0 for x in range(100)]) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -sum(x % 2 == 0 for x in range(100)) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -len([x for x in range(100) if x % 2 == 0]) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -sum([1 for x in range(100) if x % 2 == 0]) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -pairs = ((2, 5), (4, 2), (9, 8), (12, 10)) -sum([x % 2 == 0 and y % 2 == 0 for x, y in pairs]) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -def p(x, coeff): - return sum(a * x**i for i, a in enumerate(coeff)) - - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -p(1, (2, 4)) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -def f(string): - count = 0 - for letter in string: - if letter == letter.upper() and letter.isalpha(): - count += 1 - return count -f('The Rain in Spain') - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -def f(seq_a, seq_b): - is_subset = True - for a in seq_a: - if a not in seq_b: - is_subset = False - return is_subset - -# == test == # - -print(f([1, 2], [1, 2, 3])) -print(f([1, 2, 3], [1, 2])) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -def f(seq_a, seq_b): - return set(seq_a).issubset(set(seq_b)) - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Running cell: -def linapprox(f, a, b, n, x): - """ - Evaluates the piecewise linear interpolant of f at x on the interval - [a, b], with n evenly spaced grid points. - - Parameters - =========== - f : function - The function to approximate - - x, a, b : scalars (floats or integers) - Evaluation point and endpoints, with a <= x <= b - - n : integer - Number of grid points - - Returns - ========= - A float. The interpolant evaluated at x - - """ - length_of_interval = b - a - num_subintervals = n - 1 - step = length_of_interval / num_subintervals - - # === find first grid point larger than x === # - point = a - while point <= x: - point += step - - # === x must lie between the gridpoints (point - step) and point === # - u, v = point - step, point - - return f(u) + (x - u) * (f(v) - f(u)) / (v - u) - - -10/06/2015 10:42:43 AM INFO: Cell returned -10/06/2015 10:42:43 AM INFO: Shutdown kernel ----> END 'pyess_solutions.ipynb' <--- - ----> Executing 'ree_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:44 AM INFO: Reading notebook ree_solutions.ipynb -10/06/2015 10:42:45 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:42:45 AM INFO: Cell returned -10/06/2015 10:42:45 AM INFO: Running cell: -from __future__ import print_function -import numpy as np -import matplotlib.pyplot as plt - -10/06/2015 10:42:45 AM INFO: Cell returned -10/06/2015 10:42:45 AM INFO: Running cell: -from quantecon import LQ - -10/06/2015 10:42:46 AM INFO: Cell returned -10/06/2015 10:42:46 AM INFO: Running cell: - -# == Model parameters == # - -a0 = 100 -a1 = 0.05 -beta = 0.95 -gamma = 10.0 - -# == Beliefs == # - -kappa0 = 95.5 -kappa1 = 0.95 - -# == Formulate the LQ problem == # - -A = np.array([[1, 0, 0], [0, kappa1, kappa0], [0, 0, 1]]) -B = np.array([1, 0, 0]) -B.shape = 3, 1 -R = np.array([[0, a1/2, -a0/2], [a1/2, 0, 0], [-a0/2, 0, 0]]) -Q = 0.5 * gamma - -# == Solve for the optimal policy == # - -lq = LQ(Q, R, A, B, beta=beta) -P, F, d = lq.stationary_values() -F = F.flatten() -out1 = "F = [{0:.3f}, {1:.3f}, {2:.3f}]".format(F[0], F[1], F[2]) -h0, h1, h2 = -F[2], 1 - F[0], -F[1] -out2 = "(h0, h1, h2) = ({0:.3f}, {1:.3f}, {2:.3f})".format(h0, h1, h2) - -print(out1) -print(out2) - - -10/06/2015 10:42:46 AM INFO: Cell returned -10/06/2015 10:42:46 AM INFO: Running cell: - -candidates = ( - (94.0886298678, 0.923409232937), - (93.2119845412, 0.984323478873), - (95.0818452486, 0.952459076301) - ) - -for kappa0, kappa1 in candidates: - - # == Form the associated law of motion == # - A = np.array([[1, 0, 0], [0, kappa1, kappa0], [0, 0, 1]]) - - # == Solve the LQ problem for the firm == # - lq = LQ(Q, R, A, B, beta=beta) - P, F, d = lq.stationary_values() - F = F.flatten() - h0, h1, h2 = -F[2], 1 - F[0], -F[1] - - # == Test the equilibrium condition == # - if np.allclose((kappa0, kappa1), (h0, h1 + h2)): - print('Equilibrium pair =', kappa0, kappa1) - print('(h0, h1, h2) = ', h0, h1, h2) - break - - - - -10/06/2015 10:42:46 AM INFO: Cell returned -10/06/2015 10:42:46 AM INFO: Running cell: - -# == Formulate the planner's LQ problem == # - -A = np.array([[1, 0], [0, 1]]) -B = np.array([[1], [0]]) -R = np.array([[a1 / 2, -a0 / 2], [-a0 / 2, 0]]) -Q = gamma / 2 - -# == Solve for the optimal policy == # - -lq = LQ(Q, R, A, B, beta=beta) -P, F, d = lq.stationary_values() - -# == Print the results == # - -F = F.flatten() -kappa0, kappa1 = -F[1], 1 - F[0] -print(kappa0, kappa1) - - -10/06/2015 10:42:46 AM INFO: Cell returned -10/06/2015 10:42:46 AM INFO: Running cell: - -A = np.array([[1, 0], [0, 1]]) -B = np.array([[1], [0]]) -R = np.array([[a1, -a0 / 2], [-a0 / 2, 0]]) -Q = gamma / 2 - -lq = LQ(Q, R, A, B, beta=beta) -P, F, d = lq.stationary_values() - -F = F.flatten() -m0, m1 = -F[1], 1 - F[0] -print(m0, m1) - - -10/06/2015 10:42:46 AM INFO: Cell returned -10/06/2015 10:42:46 AM INFO: Shutdown kernel ----> END 'ree_solutions.ipynb' <--- - ----> Executing 'schelling_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:47 AM INFO: Reading notebook schelling_solutions.ipynb -10/06/2015 10:42:47 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:42:47 AM INFO: Cell returned -10/06/2015 10:42:47 AM INFO: Running cell: -from random import uniform, seed -from math import sqrt -import matplotlib.pyplot as plt - -seed(10) # for reproducible random numbers - -class Agent: - - def __init__(self, type): - self.type = type - self.draw_location() - - def draw_location(self): - self.location = uniform(0, 1), uniform(0, 1) - - def get_distance(self, other): - "Computes euclidean distance between self and other agent." - a = (self.location[0] - other.location[0])**2 - b = (self.location[1] - other.location[1])**2 - return sqrt(a + b) - - def happy(self, agents): - "True if sufficient number of nearest neighbors are of the same type." - distances = [] - # distances is a list of pairs (d, agent), where d is distance from - # agent to self - for agent in agents: - if self != agent: - distance = self.get_distance(agent) - distances.append((distance, agent)) - # == Sort from smallest to largest, according to distance == # - distances.sort() - # == Extract the neighboring agents == # - neighbors = [agent for d, agent in distances[:num_neighbors]] - # == Count how many neighbors have the same type as self == # - num_same_type = sum(self.type == agent.type for agent in neighbors) - return num_same_type >= require_same_type - - def update(self, agents): - "If not happy, then randomly choose new locations until happy." - while not self.happy(agents): - self.draw_location() - - -def plot_distribution(agents, cycle_num): - "Plot the distribution of agents after cycle_num rounds of the loop." - x_values_0, y_values_0 = [], [] - x_values_1, y_values_1 = [], [] - # == Obtain locations of each type == # - for agent in agents: - x, y = agent.location - if agent.type == 0: - x_values_0.append(x) - y_values_0.append(y) - else: - x_values_1.append(x) - y_values_1.append(y) - fig, ax = plt.subplots(figsize=(8, 8)) - plot_args = {'markersize' : 8, 'alpha' : 0.6} - ax.set_axis_bgcolor('azure') - ax.plot(x_values_0, y_values_0, 'o', markerfacecolor='orange', **plot_args) - ax.plot(x_values_1, y_values_1, 'o', markerfacecolor='green', **plot_args) - ax.set_title('Cycle {}'.format(cycle_num - 1)) - plt.show() - -# == Main == # - -num_of_type_0 = 250 -num_of_type_1 = 250 -num_neighbors = 10 # Number of agents regarded as neighbors -require_same_type = 7 # Want at least this many neighbors to be same type - -# == Create a list of agents == # -agents = [Agent(0) for i in range(num_of_type_0)] -agents.extend(Agent(1) for i in range(num_of_type_1)) - - -count = 1 -# == Loop until none wishes to move == # -while 1: - print('Entering loop ', count) - plot_distribution(agents, count) - count += 1 - no_one_moved = True - for agent in agents: - old_location = agent.location - agent.update(agents) - if agent.location != old_location: - no_one_moved = False - if no_one_moved: - break - -print('Converged, terminating.') - - -10/06/2015 10:42:53 AM INFO: Cell returned -10/06/2015 10:42:53 AM INFO: Shutdown kernel ----> END 'schelling_solutions.ipynb' <--- - ----> Executing 'scipy_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:54 AM INFO: Reading notebook scipy_solutions.ipynb -10/06/2015 10:42:55 AM INFO: Running cell: -def bisect(f, a, b, tol=10e-5): - """ - Implements the bisection root finding algorithm, assuming that f is a - real-valued function on [a, b] satisfying f(a) < 0 < f(b). - """ - lower, upper = a, b - if upper - lower < tol: - return 0.5 * (upper + lower) - else: - middle = 0.5 * (upper + lower) - print('Current mid point = {}'.format(middle)) - if f(middle) > 0: # Implies root is between lower and middle - bisect(f, lower, middle) - else: # Implies root is between middle and upper - bisect(f, middle, upper) - - -10/06/2015 10:42:55 AM INFO: Cell returned -10/06/2015 10:42:55 AM INFO: Running cell: -import numpy as np -f = lambda x: np.sin(4 * (x - 0.25)) + x + x**20 - 1 - -bisect(f, 0, 1) - -10/06/2015 10:42:55 AM INFO: Cell returned -10/06/2015 10:42:55 AM INFO: Shutdown kernel ----> END 'scipy_solutions.ipynb' <--- - ----> Executing 'short_path_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:56 AM INFO: Reading notebook short_path_solutions.ipynb -10/06/2015 10:42:56 AM INFO: Running cell: -%%file graph.txt -node0, node1 0.04, node8 11.11, node14 72.21 -node1, node46 1247.25, node6 20.59, node13 64.94 -node2, node66 54.18, node31 166.80, node45 1561.45 -node3, node20 133.65, node6 2.06, node11 42.43 -node4, node75 3706.67, node5 0.73, node7 1.02 -node5, node45 1382.97, node7 3.33, node11 34.54 -node6, node31 63.17, node9 0.72, node10 13.10 -node7, node50 478.14, node9 3.15, node10 5.85 -node8, node69 577.91, node11 7.45, node12 3.18 -node9, node70 2454.28, node13 4.42, node20 16.53 -node10, node89 5352.79, node12 1.87, node16 25.16 -node11, node94 4961.32, node18 37.55, node20 65.08 -node12, node84 3914.62, node24 34.32, node28 170.04 -node13, node60 2135.95, node38 236.33, node40 475.33 -node14, node67 1878.96, node16 2.70, node24 38.65 -node15, node91 3597.11, node17 1.01, node18 2.57 -node16, node36 392.92, node19 3.49, node38 278.71 -node17, node76 783.29, node22 24.78, node23 26.45 -node18, node91 3363.17, node23 16.23, node28 55.84 -node19, node26 20.09, node20 0.24, node28 70.54 -node20, node98 3523.33, node24 9.81, node33 145.80 -node21, node56 626.04, node28 36.65, node31 27.06 -node22, node72 1447.22, node39 136.32, node40 124.22 -node23, node52 336.73, node26 2.66, node33 22.37 -node24, node66 875.19, node26 1.80, node28 14.25 -node25, node70 1343.63, node32 36.58, node35 45.55 -node26, node47 135.78, node27 0.01, node42 122.00 -node27, node65 480.55, node35 48.10, node43 246.24 -node28, node82 2538.18, node34 21.79, node36 15.52 -node29, node64 635.52, node32 4.22, node33 12.61 -node30, node98 2616.03, node33 5.61, node35 13.95 -node31, node98 3350.98, node36 20.44, node44 125.88 -node32, node97 2613.92, node34 3.33, node35 1.46 -node33, node81 1854.73, node41 3.23, node47 111.54 -node34, node73 1075.38, node42 51.52, node48 129.45 -node35, node52 17.57, node41 2.09, node50 78.81 -node36, node71 1171.60, node54 101.08, node57 260.46 -node37, node75 269.97, node38 0.36, node46 80.49 -node38, node93 2767.85, node40 1.79, node42 8.78 -node39, node50 39.88, node40 0.95, node41 1.34 -node40, node75 548.68, node47 28.57, node54 53.46 -node41, node53 18.23, node46 0.28, node54 162.24 -node42, node59 141.86, node47 10.08, node72 437.49 -node43, node98 2984.83, node54 95.06, node60 116.23 -node44, node91 807.39, node46 1.56, node47 2.14 -node45, node58 79.93, node47 3.68, node49 15.51 -node46, node52 22.68, node57 27.50, node67 65.48 -node47, node50 2.82, node56 49.31, node61 172.64 -node48, node99 2564.12, node59 34.52, node60 66.44 -node49, node78 53.79, node50 0.51, node56 10.89 -node50, node85 251.76, node53 1.38, node55 20.10 -node51, node98 2110.67, node59 23.67, node60 73.79 -node52, node94 1471.80, node64 102.41, node66 123.03 -node53, node72 22.85, node56 4.33, node67 88.35 -node54, node88 967.59, node59 24.30, node73 238.61 -node55, node84 86.09, node57 2.13, node64 60.80 -node56, node76 197.03, node57 0.02, node61 11.06 -node57, node86 701.09, node58 0.46, node60 7.01 -node58, node83 556.70, node64 29.85, node65 34.32 -node59, node90 820.66, node60 0.72, node71 0.67 -node60, node76 48.03, node65 4.76, node67 1.63 -node61, node98 1057.59, node63 0.95, node64 4.88 -node62, node91 132.23, node64 2.94, node76 38.43 -node63, node66 4.43, node72 70.08, node75 56.34 -node64, node80 47.73, node65 0.30, node76 11.98 -node65, node94 594.93, node66 0.64, node73 33.23 -node66, node98 395.63, node68 2.66, node73 37.53 -node67, node82 153.53, node68 0.09, node70 0.98 -node68, node94 232.10, node70 3.35, node71 1.66 -node69, node99 247.80, node70 0.06, node73 8.99 -node70, node76 27.18, node72 1.50, node73 8.37 -node71, node89 104.50, node74 8.86, node91 284.64 -node72, node76 15.32, node84 102.77, node92 133.06 -node73, node83 52.22, node76 1.40, node90 243.00 -node74, node81 1.07, node76 0.52, node78 8.08 -node75, node92 68.53, node76 0.81, node77 1.19 -node76, node85 13.18, node77 0.45, node78 2.36 -node77, node80 8.94, node78 0.98, node86 64.32 -node78, node98 355.90, node81 2.59 -node79, node81 0.09, node85 1.45, node91 22.35 -node80, node92 121.87, node88 28.78, node98 264.34 -node81, node94 99.78, node89 39.52, node92 99.89 -node82, node91 47.44, node88 28.05, node93 11.99 -node83, node94 114.95, node86 8.75, node88 5.78 -node84, node89 19.14, node94 30.41, node98 121.05 -node85, node97 94.51, node87 2.66, node89 4.90 -node86, node97 85.09 -node87, node88 0.21, node91 11.14, node92 21.23 -node88, node93 1.31, node91 6.83, node98 6.12 -node89, node97 36.97, node99 82.12 -node90, node96 23.53, node94 10.47, node99 50.99 -node91, node97 22.17 -node92, node96 10.83, node97 11.24, node99 34.68 -node93, node94 0.19, node97 6.71, node99 32.77 -node94, node98 5.91, node96 2.03 -node95, node98 6.17, node99 0.27 -node96, node98 3.32, node97 0.43, node99 5.87 -node97, node98 0.30 -node98, node99 0.33 -node99, - -10/06/2015 10:42:56 AM INFO: Cell returned -10/06/2015 10:42:56 AM INFO: Running cell: - -def read_graph(in_file): - """ Read in the graph from the data file. The graph is stored - as a dictionary, where the keys are the nodes, and the values - are a list of pairs (d, c), where d is a node and c is a number. - If (d, c) is in the list for node n, then d can be reached from - n at cost c. - """ - graph = {} - infile = open(in_file) - for line in infile: - elements = line.split(',') - node = elements.pop(0).strip() - graph[node] = [] - if node != 'node99': - for element in elements: - destination, cost = element.split() - graph[node].append((destination.strip(), float(cost))) - infile.close() - return graph - -def update_J(J, graph): - "The Bellman operator." - next_J = {} - for node in graph: - if node == 'node99': - next_J[node] = 0 - else: - next_J[node] = min(cost + J[dest] for dest, cost in graph[node]) - return next_J - -def print_best_path(J, graph): - """ Given a cost-to-go function, computes the best path. At each node n, - the function prints the current location, looks at all nodes that can be - reached from n, and moves to the node m which minimizes c + J[m], where c - is the cost of moving to m. - """ - sum_costs = 0 - current_location = 'node0' - while current_location != 'node99': - print(current_location) - running_min = 1e100 # Any big number - for destination, cost in graph[current_location]: - cost_of_path = cost + J[destination] - if cost_of_path < running_min: - running_min = cost_of_path - minimizer_cost = cost - minimizer_dest = destination - current_location = minimizer_dest - sum_costs += minimizer_cost - - print('node99\n') - print('Cost: ', sum_costs) - - -## Main loop - -graph = read_graph('graph.txt') -M = 1e10 -J = {} -for node in graph: - J[node] = M -J['node99'] = 0 - -while 1: - next_J = update_J(J, graph) - if next_J == J: - break - else: - J = next_J -print_best_path(J, graph) - -10/06/2015 10:42:56 AM INFO: Cell returned -10/06/2015 10:42:56 AM INFO: Shutdown kernel ----> END 'short_path_solutions.ipynb' <--- - ----> Executing 'speed_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:42:57 AM INFO: Reading notebook speed_solutions.ipynb -10/06/2015 10:42:58 AM INFO: Running cell: -import matplotlib.pyplot as plt -import numpy as np -from numba import jit - -10/06/2015 10:42:58 AM INFO: Cell returned -10/06/2015 10:42:58 AM INFO: Running cell: -p, q = 0.1, 0.2 # Prob of leaving low and high state respectively - -10/06/2015 10:42:58 AM INFO: Cell returned -10/06/2015 10:42:58 AM INFO: Running cell: -def compute_series(n): - x = np.empty(n, dtype=int) - x[0] = 1 # Start in state 1 - U = np.random.uniform(0, 1, size=n) - for t in range(1, n): - current_x = x[t-1] - if current_x == 0: - x[t] = U[t] < p - else: - x[t] = U[t] > q - return x - -10/06/2015 10:42:58 AM INFO: Cell returned -10/06/2015 10:42:58 AM INFO: Running cell: -n = 100000 -x = compute_series(n) -print(np.mean(x == 0)) # Fraction of time x is in state 0 - -10/06/2015 10:42:58 AM INFO: Cell returned -10/06/2015 10:42:58 AM INFO: Running cell: -%timeit compute_series(n) - -10/06/2015 10:43:02 AM INFO: Cell returned -10/06/2015 10:43:02 AM INFO: Running cell: -compute_series_numba = jit(compute_series) - -10/06/2015 10:43:02 AM INFO: Cell returned -10/06/2015 10:43:02 AM INFO: Running cell: -x = compute_series_numba(n) -print(np.mean(x == 0)) - -10/06/2015 10:43:03 AM INFO: Cell returned -10/06/2015 10:43:03 AM INFO: Running cell: -%timeit compute_series_numba(n) - -10/06/2015 10:43:03 AM INFO: Cell returned -10/06/2015 10:43:03 AM INFO: Running cell: -%load_ext cythonmagic - -10/06/2015 10:43:03 AM INFO: Cell returned -10/06/2015 10:43:03 AM INFO: Running cell: -%%cython -import numpy as np -from numpy cimport int_t, float_t - -def compute_series_cy(int n): - # == Create NumPy arrays first == # - x_np = np.empty(n, dtype=int) - U_np = np.random.uniform(0, 1, size=n) - # == Now create memoryviews of the arrays == # - cdef int_t [:] x = x_np - cdef float_t [:] U = U_np - # == Other variable declarations == # - cdef float p = 0.1 - cdef float q = 0.2 - cdef int t - # == Main loop == # - x[0] = 1 - for t in range(1, n): - current_x = x[t-1] - if current_x == 0: - x[t] = U[t] < p - else: - x[t] = U[t] > q - return np.asarray(x) - -10/06/2015 10:43:03 AM INFO: Cell returned -10/06/2015 10:43:03 AM INFO: Running cell: -compute_series_cy(10) - -10/06/2015 10:43:03 AM INFO: Cell raised uncaught exception: ---------------------------------------------------------------------------- -NameError Traceback (most recent call last) - in () -----> 1 compute_series_cy(10) - -NameError: name 'compute_series_cy' is not defined -10/06/2015 10:43:03 AM INFO: Shutdown kernel -10/06/2015 10:43:04 AM WARNING: Exiting with nonzero exit status ----> END 'speed_solutions.ipynb' <--- - ----> Executing 'statd_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:43:05 AM INFO: Reading notebook statd_solutions.ipynb -10/06/2015 10:43:06 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:43:06 AM INFO: Cell returned -10/06/2015 10:43:06 AM INFO: Running cell: -import numpy as np -import matplotlib.pyplot as plt - -10/06/2015 10:43:06 AM INFO: Cell returned -10/06/2015 10:43:06 AM INFO: Running cell: -from scipy.stats import norm, gaussian_kde -from quantecon import LAE - -phi = norm() -n = 500 -theta = 0.8 -# == Frequently used constants == # -d = np.sqrt(1 - theta**2) -delta = theta / d - -def psi_star(y): - "True stationary density of the TAR Model" - return 2 * norm.pdf(y) * norm.cdf(delta * y) - -def p(x, y): - "Stochastic kernel for the TAR model." - return phi.pdf((y - theta * np.abs(x)) / d) / d - -Z = phi.rvs(n) -X = np.empty(n) -for t in range(n-1): - X[t+1] = theta * np.abs(X[t]) + d * Z[t] -psi_est = LAE(p, X) -k_est = gaussian_kde(X) - -fig, ax = plt.subplots(figsize=(10,7)) -ys = np.linspace(-3, 3, 200) -ax.plot(ys, psi_star(ys), 'b-', lw=2, alpha=0.6, label='true') -ax.plot(ys, psi_est(ys), 'g-', lw=2, alpha=0.6, label='look ahead estimate') -ax.plot(ys, k_est(ys), 'k-', lw=2, alpha=0.6, label='kernel based estimate') -ax.legend(loc='upper left') -plt.show() - -10/06/2015 10:43:07 AM INFO: Cell returned -10/06/2015 10:43:07 AM INFO: Running cell: -from scipy.stats import lognorm, beta - -# == Define parameters == # -s = 0.2 -delta = 0.1 -a_sigma = 0.4 # A = exp(B) where B ~ N(0, a_sigma) -alpha = 0.4 # f(k) = k**alpha - -phi = lognorm(a_sigma) - -def p(x, y): - "Stochastic kernel, vectorized in x. Both x and y must be positive." - d = s * x**alpha - return phi.pdf((y - (1 - delta) * x) / d) / d - -n = 1000 # Number of observations at each date t -T = 40 # Compute density of k_t at 1,...,T - -fig, axes = plt.subplots(2, 2, figsize=(11, 8)) -axes = axes.flatten() -xmax = 6.5 - -for i in range(4): - ax = axes[i] - ax.set_xlim(0, xmax) - psi_0 = beta(5, 5, scale=0.5, loc=i*2) # Initial distribution - - # == Generate matrix s.t. t-th column is n observations of k_t == # - k = np.empty((n, T)) - A = phi.rvs((n, T)) - k[:, 0] = psi_0.rvs(n) - for t in range(T-1): - k[:, t+1] = s * A[:,t] * k[:, t]**alpha + (1 - delta) * k[:, t] - - # == Generate T instances of lae using this data, one for each t == # - laes = [LAE(p, k[:, t]) for t in range(T)] - - ygrid = np.linspace(0.01, xmax, 150) - greys = [str(g) for g in np.linspace(0.0, 0.8, T)] - greys.reverse() - for psi, g in zip(laes, greys): - ax.plot(ygrid, psi(ygrid), color=g, lw=2, alpha=0.6) - ax.set_xlabel('capital') - -10/06/2015 10:43:13 AM INFO: Cell returned -10/06/2015 10:43:13 AM INFO: Running cell: -n = 20 -k = 5000 -J = 6 - -theta = 0.9 -d = np.sqrt(1 - theta**2) -delta = theta / d - -fig, axes = plt.subplots(J, 1, figsize=(10, 4*J)) -initial_conditions = np.linspace(8, 0, J) -X = np.empty((k, n)) - -for j in range(J): - - axes[j].set_ylim(-4, 8) - title = 'time series from t = ' + str(initial_conditions[j]) - axes[j].set_title(title) - - Z = np.random.randn(k, n) - X[:,0] = initial_conditions[j] - for t in range(1, n): - X[:, t] = theta * np.abs(X[:, t-1]) + d * Z[:, t] - axes[j].boxplot(X) - -plt.show() - -10/06/2015 10:43:16 AM INFO: Cell returned -10/06/2015 10:43:16 AM INFO: Shutdown kernel ----> END 'statd_solutions.ipynb' <--- - ----> Executing 'uncertainty_traps_solutions.ipynb' <--- -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbformat.py:13: ShimWarning: The `IPython.nbformat` package has been deprecated. You should import from nbformat instead. - "You should import from nbformat instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/nbformat/current.py:19: UserWarning: nbformat.current is deprecated. - -- use nbformat for read/write/validate public API -- use nbformat.vX directly to composing notebooks of a particular version - - """) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead. - "You should import from ipykernel or jupyter_client instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead. - "You should import from traitlets.config instead.", ShimWarning) -/home/matthewmckay/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead. - "You should import from ipython_nbconvert instead.", ShimWarning) -10/06/2015 10:43:17 AM INFO: Reading notebook uncertainty_traps_solutions.ipynb -10/06/2015 10:43:18 AM INFO: Running cell: -%matplotlib inline - -10/06/2015 10:43:19 AM INFO: Cell returned -10/06/2015 10:43:19 AM INFO: Running cell: -from __future__ import division -import matplotlib.pyplot as plt -import numpy as np -import quantecon as qe -import seaborn as sns -import itertools - -10/06/2015 10:43:19 AM INFO: Cell returned -10/06/2015 10:43:19 AM INFO: Running cell: -palette = itertools.cycle(sns.color_palette()) -econ = qe.models.UncertaintyTrapEcon() -rho, sig_theta, gx = econ.rho, econ.sig_theta, econ.gx # simplify names -g = np.linspace(1e-10, 3, 200) # gamma grid -fig, ax = plt.subplots(figsize=(9, 9)) -ax.plot(g, g, 'k-') # 45 degree line -for M in range(7): - g_next = 1 / (rho**2 / (g + M * gx) + sig_theta**2) - label_string = r"$M = {}$".format(M) - ax.plot(g, g_next, lw=2, label=label_string, color=next(palette)) -ax.legend(loc='lower right', fontsize=14) -ax.set_xlabel(r'$\gamma$', fontsize=16) -ax.set_ylabel(r"$\gamma'$", fontsize=16) -ax.grid() -plt.show() - -10/06/2015 10:43:20 AM INFO: Cell returned -10/06/2015 10:43:20 AM INFO: Running cell: -sim_length=2000 - -mu_vec = np.empty(sim_length) -theta_vec = np.empty(sim_length) -gamma_vec = np.empty(sim_length) -X_vec = np.empty(sim_length) -M_vec = np.empty(sim_length) - -mu_vec[0] = econ.mu -gamma_vec[0] = econ.gamma -theta_vec[0] = 0 - -w_shocks = np.random.randn(sim_length) - -for t in range(sim_length-1): - X, M = econ.gen_aggregates() - X_vec[t] = X - M_vec[t] = M - - econ.update_beliefs(X, M) - econ.update_theta(w_shocks[t]) - - mu_vec[t+1] = econ.mu - gamma_vec[t+1] = econ.gamma - theta_vec[t+1] = econ.theta - -# Record final values of aggregates -X, M = econ.gen_aggregates() -X_vec[-1] = X -M_vec[-1] = M - -10/06/2015 10:43:20 AM INFO: Cell returned -10/06/2015 10:43:20 AM INFO: Running cell: -fig, ax = plt.subplots(figsize=(9, 6)) -ax.plot(range(sim_length), theta_vec, alpha=0.6, lw=2, label=r"$\theta$") -ax.plot(range(sim_length), mu_vec, alpha=0.6, lw=2, label=r"$\mu$") -ax.legend(fontsize=16) -plt.show() - -10/06/2015 10:43:21 AM INFO: Cell returned -10/06/2015 10:43:21 AM INFO: Running cell: -fig, axes = plt.subplots(4, 1, figsize=(12, 20)) -# Add some spacing -fig.subplots_adjust(hspace=0.3) - -series = (theta_vec, mu_vec, gamma_vec, M_vec) -names = r'$\theta$', r'$\mu$', r'$\gamma$', r'$M$' - -for ax, vals, name in zip(axes, series, names): - # determine suitable y limits - s_max, s_min = max(vals), min(vals) - s_range = s_max - s_min - y_max = s_max + s_range * 0.1 - y_min = s_min - s_range * 0.1 - ax.set_ylim(y_min, y_max) - # Plot series - ax.plot(range(sim_length), vals, alpha=0.6, lw=2) - ax.set_title("time series for {}".format(name), fontsize=16) - ax.grid() - -plt.show() - -10/06/2015 10:43:22 AM INFO: Cell returned -10/06/2015 10:43:22 AM INFO: Running cell: - - -10/06/2015 10:43:22 AM INFO: Cell returned -10/06/2015 10:43:22 AM INFO: Shutdown kernel ----> END 'uncertainty_traps_solutions.ipynb' <--- - diff --git a/scripts/test-examples.py b/scripts/test-examples.py deleted file mode 100644 index 9dd187617..000000000 --- a/scripts/test-examples.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/python -""" -Test script for QuantEcon executables -===================================== - examples/*.py - solutions/*.ipynb - -This script uses a context manager to redirect stdout and stderr -to capture runtime errors for writing to the log file. It also -reports basic execution statistics on the command line (pass/fail) - -Usage ------ -python test.py - -Default Logs ------------- - examples/*.py => example-tests.log -""" - -import sys -import os -import glob -import subprocess -import re - -from common import RedirectStdStreams - -set_backend = "import matplotlib\nmatplotlib.use('Agg')\n" - -def generate_temp(fl): - """ - Modify file to supress matplotlib figures - Preserve __future__ imports at front of file for python intertpreter - """ - doc = open(fl).read() - doc = set_backend+doc - #-Adjust Future Imports-# - if re.search(r"from __future__ import division", doc): - doc = doc.replace("from __future__ import division", "") - doc = "from __future__ import division\n" + doc - return doc - -def example_tests(test_dir='examples/', log_path='../scripts/example-tests.log'): - """ - Execute each Python Example File and check exit status. - The stdout and stderr is also captured and added to the log file - """ - os.chdir(test_dir) - test_files = glob.glob('*.py') - test_files.sort() - passed = [] - failed = [] - with open(log_path, 'w') as f: - for i,fname in enumerate(test_files): - print("Checking program %s (%s/%s) ..."%(fname,i,len(test_files))) - with RedirectStdStreams(stdout=f, stderr=f): - print("---Executing '%s'---" % fname) - sys.stdout.flush() - #-Generate tmp File-# - tmpfl = "_" + fname - fl = open(tmpfl,'w') - fl.write(generate_temp(fname)) - fl.close() - #-Run Program-# - exit_code = subprocess.call(["python",tmpfl], stderr=f) - if exit_code == 0: - passed.append(fname) - else: - failed.append(fname) - #-Remove tmp file-# - os.remove(tmpfl) - print("---END '%s'---" % fname) - sys.stdout.flush() - #-Report-# - print("[examples/*.py] Passed %i/%i: " %(len(passed), len(test_files))) - if len(failed) == 0: - print("Failed Files:\n\tNone") - else: - print("Failed Files:\n\t" + '\n\t'.join(failed)) - print(">> See %s for details" % log_path) - os.chdir('../') - return passed, failed - - -if __name__ == '__main__': - print("-------------------------") - print("Running all examples/*.py") - print("-------------------------") - example_tests(*sys.argv[1:]) \ No newline at end of file diff --git a/scripts/test-solutions.py b/scripts/test-solutions.py deleted file mode 100644 index 0078a4cf4..000000000 --- a/scripts/test-solutions.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/python -""" -Test solutions/*.ipynb - -Notes ------ - 1. This script should be run from the root level "python scripts/test-solutions.py" - -""" - -import sys -import os -import glob -import subprocess - -from common import RedirectStdStreams - -def solutions_tests(test_dir='solutions/', log_path='../scripts/solutions-tests.log'): - """ - Execute each Jupyter Notebook - """ - os.chdir(test_dir) - test_files = glob.glob(os.path.join('*.ipynb')) - test_files.sort() - passed = [] - failed = [] - with open(log_path, 'w') as f: - for i,fname in enumerate(test_files): - print("Checking notebook %s (%s/%s) ..."%(fname,i,len(test_files))) - with RedirectStdStreams(stdout=f, stderr=f): - print("---> Executing '%s' <---" % fname) - sys.stdout.flush() - #-Run Program-# - exit_code = subprocess.call(["runipy",fname], stdout=open(os.devnull, 'wb'), stderr=f) - sys.stderr.flush() - if exit_code == 0: - passed.append(fname) - else: - failed.append(fname) - print("---> END '%s' <---" % fname) - print - sys.stdout.flush() - #-Report-# - print("[solutions/*.py] Passed %i/%i: " %(len(passed), len(test_files))) - if len(failed) == 0: - print("Failed Notebooks:\n\tNone") - else: - print("Failed Notebooks:\n\t" + '\n\t'.join(failed)) - print(">> See %s for details" % log_path) - os.chdir('../') - return passed, failed - - -if __name__ == '__main__': - print("-----------------------------") - print("Running all solutions/*.ipynb") - print("-----------------------------") - solutions_tests(*sys.argv[1:]) \ No newline at end of file From 1e9fb28b1263bb543191d3c44ba39d8311ad7cae Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 23 Nov 2015 16:57:30 -0500 Subject: [PATCH 31/51] Remove models/ subpackage from api due to migration to QuantEcon.applications --- quantecon/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/quantecon/__init__.py b/quantecon/__init__.py index 43742faf7..ffab5eb5e 100644 --- a/quantecon/__init__.py +++ b/quantecon/__init__.py @@ -2,7 +2,6 @@ Import the main names to top level. """ -from . import models as models from .compute_fp import compute_fixed_point from .discrete_rv import DiscreteRV from .ecdf import ECDF From 43dca4ad969e44bb753c152e8f7768febea6fb68 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Mon, 30 Nov 2015 11:08:35 -0500 Subject: [PATCH 32/51] Add Check for numba in base anaconda distribution. If not found issue meaningful warning message --- quantecon/__init__.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/quantecon/__init__.py b/quantecon/__init__.py index ffab5eb5e..a8dd7d6e2 100644 --- a/quantecon/__init__.py +++ b/quantecon/__init__.py @@ -2,6 +2,11 @@ Import the main names to top level. """ +try: + import numba +except: + raise ImportError("Cannot import numba from current anaconda distribution. Please run `conda install numba` to install the latest version.") + from .compute_fp import compute_fixed_point from .discrete_rv import DiscreteRV from .ecdf import ECDF From bd7dbcbdbe04cfaf808250de48c30c7bd9e457d1 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Mon, 30 Nov 2015 11:18:47 -0500 Subject: [PATCH 33/51] Add ImportError notification for quantecon.models --- quantecon/models/__init__.py | 1 + setup.py | 2 ++ 2 files changed, 3 insertions(+) create mode 100644 quantecon/models/__init__.py diff --git a/quantecon/models/__init__.py b/quantecon/models/__init__.py new file mode 100644 index 000000000..4deb9796c --- /dev/null +++ b/quantecon/models/__init__.py @@ -0,0 +1 @@ +raise ImportError("The quantecon.models subpackage has been migrated to the QuantEcon.applications (https://github.com/QuantEcon/QuantEcon.applications)") \ No newline at end of file diff --git a/setup.py b/setup.py index da594b062..9c8168769 100644 --- a/setup.py +++ b/setup.py @@ -97,6 +97,8 @@ def write_version_py(filename=None): 'quantecon.random', 'quantecon.tests', 'quantecon.util', + #-Deprecated-# + 'quantecon.models', ], version=VERSION, description=DESCRIPTION, From c532ec8237bb470e6fbc1f579413d6244ecd67d1 Mon Sep 17 00:00:00 2001 From: Matthew McKay Date: Mon, 30 Nov 2015 12:19:20 -0500 Subject: [PATCH 34/51] Add exclusion to models/ subpackage to prevent nosetests running __init__.py which triggers an ImportWarning to inform users of the move from quantecon.models to QuantEcon.applications --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 3d9c46b08..d84b2bcc6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -28,7 +28,7 @@ install: - cp quantecon/tests/matplotlibrc . script: - - nosetests --with-coverage --cover-package=quantecon + - nosetests --with-coverage --cover-package=quantecon --exclude=models #quantecon.models excluded from tests to prevent triggering the ImportWarning after_success: - coveralls From acb1ea57fb532640391973003d8d10b8ed4fa839 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Wed, 2 Dec 2015 12:40:47 -0500 Subject: [PATCH 35/51] Improved message --- quantecon/models/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/quantecon/models/__init__.py b/quantecon/models/__init__.py index 4deb9796c..1ebf46304 100644 --- a/quantecon/models/__init__.py +++ b/quantecon/models/__init__.py @@ -1 +1 @@ -raise ImportError("The quantecon.models subpackage has been migrated to the QuantEcon.applications (https://github.com/QuantEcon/QuantEcon.applications)") \ No newline at end of file +raise ImportError("The code previously contained in the quantecon.models subpackage has been migrated to the QuantEcon.applications (https://github.com/QuantEcon/QuantEcon.applications) repo") \ No newline at end of file From 1d0c097c8713bd282e01438a4b518b2e6327771c Mon Sep 17 00:00:00 2001 From: John Stachurski Date: Tue, 22 Dec 2015 13:55:25 -0500 Subject: [PATCH 36/51] added asarray in discrete_rv --- quantecon/discrete_rv.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/quantecon/discrete_rv.py b/quantecon/discrete_rv.py index fd27f0044..4b5567d1e 100644 --- a/quantecon/discrete_rv.py +++ b/quantecon/discrete_rv.py @@ -8,6 +8,7 @@ """ +import numpy as np from numpy import cumsum from numpy.random import uniform @@ -31,7 +32,7 @@ class DiscreteRV(object): """ def __init__(self, q): - self._q = q + self._q = np.asarray(q) self.Q = cumsum(q) def __repr__(self): @@ -54,7 +55,7 @@ def q(self, val): Setter method for q. """ - self._q = val + self._q = np.asarray(val) self.Q = cumsum(val) def draw(self, k=1): From 323cbc755dc234a2faab7518b0c1bf5571fabb42 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Tue, 5 Jan 2016 14:02:04 -0500 Subject: [PATCH 37/51] Adding a demo notebook fetching service --- quantecon/__init__.py | 2 +- quantecon/util/__init__.py | 1 + quantecon/util/notebooks.py | 65 +++++++++++++++++++++++++++++++++++++ 3 files changed, 67 insertions(+), 1 deletion(-) create mode 100644 quantecon/util/notebooks.py diff --git a/quantecon/__init__.py b/quantecon/__init__.py index a8dd7d6e2..f97383c1b 100644 --- a/quantecon/__init__.py +++ b/quantecon/__init__.py @@ -28,7 +28,7 @@ from .rank_nullspace import rank_est, nullspace from .robustlq import RBLQ from . import quad as quad -from .util import searchsorted +from .util import searchsorted, fetch_nb_dependancies #Add Version Attribute from .version import version as __version__ diff --git a/quantecon/util/__init__.py b/quantecon/util/__init__.py index 85ff46e77..5f67c2548 100644 --- a/quantecon/util/__init__.py +++ b/quantecon/util/__init__.py @@ -4,5 +4,6 @@ from .array import searchsorted from .external import jit, numba_installed +from .notebooks import fetch_nb_dependancies from .random import check_random_state from .timing import tic, tac, toc \ No newline at end of file diff --git a/quantecon/util/notebooks.py b/quantecon/util/notebooks.py new file mode 100644 index 000000000..efc1e574a --- /dev/null +++ b/quantecon/util/notebooks.py @@ -0,0 +1,65 @@ +""" +Support functions to Support QuantEcon.notebooks + +The purpose of these utilities is to implement simple support functions to allow for automatic downloading +of any support files (python modules, or data) that may be required to run demonstration notebooks. + +Note +---- +Files on the REMOTE Github Server can be organised into folders but they will end up at the root level of +when downloaded as a support File + +"https://github.com/QuantEcon/QuantEcon.notebooks/raw/master/dependancies/mpi/something.py" --> ./somthing.py + +TODO +---- +1. Write Style guide for QuantEcon.notebook contributions +2. Write an interface for Dat Server +3. Platform Agnostic (replace wget usage) + +""" + +from invoke import run, task +import os + +#-Remote Structure-# +REPO = "https://github.com/QuantEcon/QuantEcon.notebooks" +RAW = "raw" +BRANCH = "master" +DEPS = "dependancies" #Hard Coded Dependancies Folder on QuantEcon.notebooks + +def fetch_nb_dependancies(files, repo=REPO, raw=RAW, branch=BRANCH, deps=DEPS, verbose=True): + """ + Retrieve raw files from QuantEcon.notebooks Github repo + + Parameters + ---------- + file_list list or dict + A list of files to specify a collection of filenames + A dict of dir : list(files) to specify a directory + repo str, optional(default=REPO) + branch str, optional(default=BRANCH) + deps str, optional(default=DEPS) + verbose bool, optional(default=True) + + TODO + ---- + 1. Should we update this to allow people to specify their own folders on a different GitHub repo? + + """ + + #-Generate Common Data Structure-# + if type(files) == list: + files = {"" : files} + + #-Obtain each requested file-# + for directory in files.keys(): + if directory != "": + if verbose: print("Parsing directory: %s") + for fl in files[directory]: + if directory != "": + fl = directory+"/"+fl + if verbose: print("Fetching file: %s"%fl) + url = "/".join([repo,raw,branch,deps,fl]) + run("wget %s"%url) + From 2c2b06aa894e60b9c0d41ebabbf43550264b760f Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Tue, 5 Jan 2016 14:11:12 -0500 Subject: [PATCH 38/51] Removed use of wget and changed to useplatform independant requests library) --- quantecon/util/notebooks.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/quantecon/util/notebooks.py b/quantecon/util/notebooks.py index efc1e574a..bea038b16 100644 --- a/quantecon/util/notebooks.py +++ b/quantecon/util/notebooks.py @@ -19,8 +19,8 @@ """ -from invoke import run, task import os +import requests #-Remote Structure-# REPO = "https://github.com/QuantEcon/QuantEcon.notebooks" @@ -61,5 +61,7 @@ def fetch_nb_dependancies(files, repo=REPO, raw=RAW, branch=BRANCH, deps=DEPS, v fl = directory+"/"+fl if verbose: print("Fetching file: %s"%fl) url = "/".join([repo,raw,branch,deps,fl]) - run("wget %s"%url) + r = requests.get(url) + with open(fl, "wb") as fl: + fl.write(r.content) From ab010fac38b0cb302b0f03ef16f6d1f994a18edd Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Tue, 5 Jan 2016 14:57:25 -0500 Subject: [PATCH 39/51] FIX spelling mistake dependancies to dependencies --- quantecon/__init__.py | 2 +- quantecon/util/__init__.py | 2 +- quantecon/util/notebooks.py | 6 +++--- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/quantecon/__init__.py b/quantecon/__init__.py index f97383c1b..6203f3797 100644 --- a/quantecon/__init__.py +++ b/quantecon/__init__.py @@ -28,7 +28,7 @@ from .rank_nullspace import rank_est, nullspace from .robustlq import RBLQ from . import quad as quad -from .util import searchsorted, fetch_nb_dependancies +from .util import searchsorted, fetch_nb_dependencies #Add Version Attribute from .version import version as __version__ diff --git a/quantecon/util/__init__.py b/quantecon/util/__init__.py index 5f67c2548..39dcf0b0e 100644 --- a/quantecon/util/__init__.py +++ b/quantecon/util/__init__.py @@ -4,6 +4,6 @@ from .array import searchsorted from .external import jit, numba_installed -from .notebooks import fetch_nb_dependancies +from .notebooks import fetch_nb_dependencies from .random import check_random_state from .timing import tic, tac, toc \ No newline at end of file diff --git a/quantecon/util/notebooks.py b/quantecon/util/notebooks.py index bea038b16..7f5924d2a 100644 --- a/quantecon/util/notebooks.py +++ b/quantecon/util/notebooks.py @@ -9,7 +9,7 @@ Files on the REMOTE Github Server can be organised into folders but they will end up at the root level of when downloaded as a support File -"https://github.com/QuantEcon/QuantEcon.notebooks/raw/master/dependancies/mpi/something.py" --> ./somthing.py +"https://github.com/QuantEcon/QuantEcon.notebooks/raw/master/dependencies/mpi/something.py" --> ./somthing.py TODO ---- @@ -26,9 +26,9 @@ REPO = "https://github.com/QuantEcon/QuantEcon.notebooks" RAW = "raw" BRANCH = "master" -DEPS = "dependancies" #Hard Coded Dependancies Folder on QuantEcon.notebooks +DEPS = "dependencies" #Hard Coded Dependencies Folder on QuantEcon.notebooks -def fetch_nb_dependancies(files, repo=REPO, raw=RAW, branch=BRANCH, deps=DEPS, verbose=True): +def fetch_nb_dependencies(files, repo=REPO, raw=RAW, branch=BRANCH, deps=DEPS, verbose=True): """ Retrieve raw files from QuantEcon.notebooks Github repo From 9731125655a23f6f0dc0f766439ac7190de50d33 Mon Sep 17 00:00:00 2001 From: mmcky Date: Thu, 7 Jan 2016 11:37:05 -0500 Subject: [PATCH 40/51] Update README.md Added a section to track major changes. --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index be6e2b2ca..b4d59504f 100644 --- a/README.md +++ b/README.md @@ -67,3 +67,10 @@ modification, are permitted provided that the following conditions are met: LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +## Major Changes + +#### Ver. 0.3 + +1. Removes ``quantecon/models`` subpackage and the collection of code examples. Code has been migrated to the [QuantEcon.applications](https://github.com/QuantEcon/QuantEcon.applications) repository. +2. Adds a utility for fetching notebook dependencies from [QuantEcon.applications](https://github.com/QuantEcon/QuantEcon.applications) to support community contributed notebooks. From 9f4c32e394be01f43b9877e6904eef38cdf12716 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Thu, 7 Jan 2016 11:40:28 -0500 Subject: [PATCH 41/51] Updates to version files for 0.3 release --- MANIFEST | 16 +--------------- quantecon/version.py | 2 +- 2 files changed, 2 insertions(+), 16 deletions(-) diff --git a/MANIFEST b/MANIFEST index 2c72ca4bd..6473ad47a 100644 --- a/MANIFEST +++ b/MANIFEST @@ -33,21 +33,6 @@ quantecon/markov/ddp.py quantecon/markov/gth_solve.py quantecon/markov/random.py quantecon/models/__init__.py -quantecon/models/arellano_vfi.py -quantecon/models/asset_pricing.py -quantecon/models/career.py -quantecon/models/ifp.py -quantecon/models/jv.py -quantecon/models/lake.py -quantecon/models/lucastree.py -quantecon/models/odu.py -quantecon/models/optgrowth.py -quantecon/models/uncertainty_traps.py -quantecon/models/solow/__init__.py -quantecon/models/solow/ces.py -quantecon/models/solow/cobb_douglas.py -quantecon/models/solow/impulse_response.py -quantecon/models/solow/model.py quantecon/random/__init__.py quantecon/random/utilities.py quantecon/tests/__init__.py @@ -76,5 +61,6 @@ quantecon/util/__init__.py quantecon/util/array.py quantecon/util/common_messages.py quantecon/util/external.py +quantecon/util/notebooks.py quantecon/util/random.py quantecon/util/timing.py diff --git a/quantecon/version.py b/quantecon/version.py index 90893e462..de8a251b1 100644 --- a/quantecon/version.py +++ b/quantecon/version.py @@ -1,4 +1,4 @@ """ This is a VERSION file and should NOT be manually altered """ -version = '0.2.2' \ No newline at end of file +version = '0.3.0' \ No newline at end of file From b79fcc25b63e4525b3e0d3be02d647d3a6862d8f Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Tue, 12 Jan 2016 09:22:51 -0500 Subject: [PATCH 42/51] Update docs to remove models subpackage and update the qe_api.py script to python3 --- docs/qe_apidoc.py | 68 +++++----------------------------- docs/source/index.rst | 1 - docs/source/util.rst | 1 + docs/source/util/notebooks.rst | 7 ++++ 4 files changed, 18 insertions(+), 59 deletions(-) create mode 100644 docs/source/util/notebooks.rst diff --git a/docs/qe_apidoc.py b/docs/qe_apidoc.py index 197eb935f..520d962ed 100644 --- a/docs/qe_apidoc.py +++ b/docs/qe_apidoc.py @@ -58,24 +58,6 @@ :show-inheritance: """ -model_module_template = """{mod_name} -{equals} - -.. automodule:: quantecon.models.{mod_name} - :members: - :undoc-members: - :show-inheritance: -""" - -solow_model_module_template = """{mod_name} -{equals} - -.. automodule:: quantecon.models.solow.{mod_name} - :members: - :undoc-members: - :show-inheritance: -""" - random_module_template = """{mod_name} {equals} @@ -132,7 +114,6 @@ :maxdepth: 2 markov - models random tools util @@ -170,7 +151,7 @@ def source_join(f_name): def all_auto(): # Get list of module names mod_names = glob("../quantecon/[a-z0-9]*.py") - mod_names = map(lambda x: x.split('/')[-1], mod_names) + mod_names = list(map(lambda x: x.split('/')[-1], mod_names)) # Ensure source/modules directory exists if not os.path.exists(source_join("modules")): @@ -185,8 +166,8 @@ def all_auto(): # write index.rst file to include these autogenerated files with open(source_join("index.rst"), "w") as index: - generated = "\n ".join(map(lambda x: "modules/" + x.split(".")[0], - mod_names)) + generated = "\n ".join(list(map(lambda x: "modules/" + x.split(".")[0], + mod_names))) temp = all_index_template.format(generated=generated) index.write(temp) @@ -194,42 +175,30 @@ def all_auto(): def model_tool(): # list file names with markov markov_files = glob("../quantecon/markov/[a-z0-9]*.py") - markov = map(lambda x: x.split('/')[-1][:-3], markov_files) + markov = list(map(lambda x: x.split('/')[-1][:-3], markov_files)) # Alphabetize markov.sort() - # list file names with models - mod_files = glob("../quantecon/models/[a-z0-9]*.py") - models = map(lambda x: x.split('/')[-1][:-3], mod_files) - # Alphabetize - models.sort() - - # list file names with models.solow - solow_files = glob("../quantecon/models/solow/[a-z0-9]*.py") - solow = map(lambda x: x.split('/')[-1][:-3], solow_files) - # Alphabetize - solow.sort() - # list file names with random random_files = glob("../quantecon/random/[a-z0-9]*.py") - random = map(lambda x: x.split('/')[-1][:-3], random_files) + random = list(map(lambda x: x.split('/')[-1][:-3], random_files)) # Alphabetize random.sort() # list file names of tools (base level modules) tool_files = glob("../quantecon/[a-z0-9]*.py") - tools = map(lambda x: x.split('/')[-1][:-3], tool_files) + tools = list(map(lambda x: x.split('/')[-1][:-3], tool_files)) # Alphabetize tools.remove("version") tools.sort() # list file names of utilities util_files = glob("../quantecon/util/[a-z0-9]*.py") - util = map(lambda x: x.split('/')[-1][:-3], util_files) + util = list(map(lambda x: x.split('/')[-1][:-3], util_files)) # Alphabetize util.sort() - for folder in ["markov","models","models/solow","random","tools","util"]: + for folder in ["markov","random","tools","util"]: if not os.path.exists(source_join(folder)): os.makedirs(source_join(folder)) @@ -238,21 +207,7 @@ def model_tool(): new_path = os.path.join("source", "markov", mod + ".rst") with open(new_path, "w") as f: equals = "=" * len(mod) - f.write(markov_module_template.format(mod_name=mod, equals=equals)) - - # Write file for each model - for mod in models: - new_path = os.path.join("source", "models", mod + ".rst") - with open(new_path, "w") as f: - equals = "=" * len(mod) - f.write(model_module_template.format(mod_name=mod, equals=equals)) - - # Write file for each model.solow - for mod in solow: - new_path = os.path.join("source", "models", "solow", mod + ".rst") - with open(new_path, "w") as f: - equals = "=" * len(mod) - f.write(solow_model_module_template.format(mod_name=mod, equals=equals)) + f.write(markov_module_template.format(mod_name=mod, equals=equals)) # Write file for each random file for mod in random: @@ -280,20 +235,17 @@ def model_tool(): index.write(split_index_template) mark = "markov/" + "\n markov/".join(markov) - mods = "models/" + "\n models/".join(models) - mods = mods + "\n solow/" #Add solow sub directory to models rand = "random/" + "\n random/".join(random) tlz = "tools/" + "\n tools/".join(tools) utls = "util/" + "\n util/".join(util) #-TocTree-# toc_tree_list = {"markov":mark, - "models": mods, "tools": tlz, "random":rand, "util":utls, } - for f_name in ("markov","models","random","tools","util"): + for f_name in ("markov","random","tools","util"): with open(source_join(f_name + ".rst"), "w") as f: temp = split_file_template.format(name=f_name.capitalize(), equals="="*len(f_name), diff --git a/docs/source/index.rst b/docs/source/index.rst index 5d5c87bc1..d41452b68 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -16,7 +16,6 @@ econ.net `_. :maxdepth: 2 markov - models random tools util diff --git a/docs/source/util.rst b/docs/source/util.rst index 0d08cd3f2..b4a08b104 100644 --- a/docs/source/util.rst +++ b/docs/source/util.rst @@ -7,5 +7,6 @@ Util util/array util/common_messages util/external + util/notebooks util/random util/timing diff --git a/docs/source/util/notebooks.rst b/docs/source/util/notebooks.rst new file mode 100644 index 000000000..fb1c378d9 --- /dev/null +++ b/docs/source/util/notebooks.rst @@ -0,0 +1,7 @@ +notebooks +========= + +.. automodule:: quantecon.util.notebooks + :members: + :undoc-members: + :show-inheritance: From d7bb2582bd4de308964849866e491ae612ee4f33 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Wed, 13 Jan 2016 21:55:42 +0900 Subject: [PATCH 43/51] Add game_theory.normal_form_game --- quantecon/game_theory/__init__.py | 6 + quantecon/game_theory/normal_form_game.py | 668 ++++++++++++++++++ .../tests/test_normal_form_game.py | 372 ++++++++++ setup.py | 17 +- 4 files changed, 1055 insertions(+), 8 deletions(-) create mode 100644 quantecon/game_theory/__init__.py create mode 100644 quantecon/game_theory/normal_form_game.py create mode 100644 quantecon/game_theory/tests/test_normal_form_game.py diff --git a/quantecon/game_theory/__init__.py b/quantecon/game_theory/__init__.py new file mode 100644 index 000000000..b7beb74cf --- /dev/null +++ b/quantecon/game_theory/__init__.py @@ -0,0 +1,6 @@ +""" +Game Theory SubPackage + +""" +from .normal_form_game import Player, NormalFormGame +from .normal_form_game import pure2mixed, best_response_2p diff --git a/quantecon/game_theory/normal_form_game.py b/quantecon/game_theory/normal_form_game.py new file mode 100644 index 000000000..8d3f47f47 --- /dev/null +++ b/quantecon/game_theory/normal_form_game.py @@ -0,0 +1,668 @@ +r""" +Authors: Tomohiro Kusano, Daisuke Oyama + +Tools for normal form games. + +Definitions and Basic Concepts +------------------------------ + +An :math:`N`-player *normal form game* :math:`g = (I, (A_i)_{i \in I}, +(u_i)_{i \in I})` consists of + +- the set of *players* :math:`I = \{0, \ldots, N-1\}`, +- the set of *actions* :math:`A_i = \{0, \ldots, n_i-1\}` for each + player :math:`i \in I`, and +- the *payoff function* :math:`u_i \colon A_i \times A_{i+1} \times + \cdots \times A_{i+N-1} \to \mathbb{R}` for each player :math:`i \in + I`, + +where :math:`i+j` is understood modulo :math:`N`. Note that we adopt the +convention that the 0-th argument of the payoff function :math:`u_i` is +player :math:`i`'s own action and the :math:`j`-th argument is player +(:math:`i+j`)'s action (modulo :math:`N`). A mixed action for player +:math:`i` is a probability distribution on :math:`A_i` (while an element +of :math:`A_i` is referred to as a pure action). A pure action +:math:`a_i \in A_i` is identified with the mixed action that assigns +probability one to :math:`a_i`. Denote the set of mixed actions of +player :math:`i` by :math:`X_i`. We also denote :math:`A_{-i} = A_{i+1} +\times \cdots \times A_{i+N-1}` and :math:`X_{-i} = X_{i+1} \times +\cdots \times X_{i+N-1}`. + +The (pure-action) *best response correspondence* :math:`b_i \colon +X_{-i} \to A_i` for each player :math:`i` is defined by + +.. math:: + + b_i(x_{-i}) = \{a_i \in A_i \mid + u_i(a_i, x_{-i}) \geq u_i(a_i', x_{-i}) + \ \forall\,a_i' \in A_i\}, + +where :math:`u_i(a_i, x_{-i}) = \sum_{a_{-i} \in A_{-i}} u_i(a_i, +a_{-i}) \prod_{j=1}^{N-1} x_{i+j}(a_j)` is the expected payoff to action +:math:`a_i` against mixed actions :math:`x_{-i}`. A profile of mixed +actions :math:`x^* \in X_0 \times \cdots \times X_{N-1}` is a *Nash +equilibrium* if for all :math:`i \in I` and :math:`a_i \in A_i`, + +.. math:: + + x_i^*(a_i) > 0 \Rightarrow a_i \in b_i(x_{-i}^*), + +or equivalently, :math:`x_i^* \cdot v_i(x_{-i}^*) \geq x_i \cdot +v_i(x_{-i}^*)` for all :math:`x_i \in X_i`, where :math:`v_i(x_{-i})` is +the vector of player :math:`i`'s payoffs when the opponent players play +mixed actions :math:`x_{-i}`. + +Creating a NormalFormGame +------------------------- + +There are three ways to construct a `NormalFormGame` instance. + +The first is to pass an array of payoffs for all the players: + +>>> matching_pennies_bimatrix = [[(1, -1), (-1, 1)], [(-1, 1), (1, -1)]] +>>> g = NormalFormGame(matching_pennies_bimatrix) +>>> print(g.players[0]) +Player in a 2-player normal form game with payoff array: +[[ 1, -1], + [-1, 1]] +>>> print(g.players[1]) +Player in a 2-player normal form game with payoff array: +[[-1, 1], + [ 1, -1]] + +If a square matrix (2-dimensional array) is given, then it is considered +to be a symmetric two-player game: + +>>> coordination_game_matrix = [[4, 0], [3, 2]] +>>> g = NormalFormGame(coordination_game_matrix) +>>> print(g) +2-player NormalFormGame with payoff profile array: +[[[4, 4], [0, 3]], + [[3, 0], [2, 2]]] + +The second is to specify the sizes of the action sets of the players, +which gives a `NormalFormGame` instance filled with payoff zeros, and +then set the payoff values to each entry: + +>>> g = NormalFormGame((2, 2)) +>>> print(g) +2-player NormalFormGame with payoff profile array: +[[[ 0., 0.], [ 0., 0.]], + [[ 0., 0.], [ 0., 0.]]] +>>> g[0, 0] = 1, 1 +>>> g[0, 1] = -2, 3 +>>> g[1, 0] = 3, -2 +>>> print(g) +2-player NormalFormGame with payoff profile array: +[[[ 1., 1.], [-2., 3.]], + [[ 3., -2.], [ 0., 0.]]] + +The third is to pass an array of `Player` instances, as explained in the +next section. + +Creating a Player +----------------- + +A `Player` instance is created by passing a payoff array: + +>>> player0 = Player([[3, 1], [0, 2]]) +>>> player0.payoff_array +array([[3, 1], + [0, 2]]) + +Passing an array of `Player` instances is the third way to create a +`NormalFormGame` instance. + +>>> player1 = Player([[2, 0], [1, 3]]) +>>> player1.payoff_array +array([[2, 0], + [1, 3]]) +>>> g = NormalFormGame((player0, player1)) +>>> print(g) +2-player NormalFormGame with payoff profile array: +[[[3, 2], [1, 1]], + [[0, 0], [2, 3]]] + +Beware that in `payoff_array[h, k]`, `h` refers to the player's own +action, while `k` refers to the opponent player's action. + +""" +import re +import numbers +import numpy as np +from numba import jit + +from ..util import check_random_state + + +class Player(object): + """ + Class representing a player in an N-player normal form game. + + Parameters + ---------- + payoff_array : array_like(float) + Array representing the player's payoff function, where + `payoff_array[a_0, a_1, ..., a_{N-1}]` is the payoff to the + player when the player plays action `a_0` while his N-1 + opponents play actions `a_1`, ..., `a_{N-1}`, respectively. + + Attributes + ---------- + payoff_array : ndarray(float, ndim=N) + See Parameters. + + num_actions : scalar(int) + The number of actions available to the player. + + num_opponents : scalar(int) + The number of opponent players. + + """ + def __init__(self, payoff_array): + self.payoff_array = np.asarray(payoff_array) + + if self.payoff_array.ndim == 0: + raise ValueError('payoff_array must be an array_like') + + self.num_opponents = self.payoff_array.ndim - 1 + self.num_actions = self.payoff_array.shape[0] + + self.tol = 1e-8 + + def __repr__(self): + N = self.num_opponents + 1 + s = 'Player in a {N}-player normal form game'.format(N=N) + return s + + def __str__(self): + s = self.__repr__() + s += ' with payoff array:\n' + s += np.array2string(self.payoff_array, separator=', ') + return s + + def payoff_vector(self, opponents_actions): + """ + Return an array of payoff values, one for each own action, given + a profile of the opponents' actions. + + Parameters + ---------- + opponents_actions : see `best_response`. + + Returns + ------- + payoff_vector : ndarray(float, ndim=1) + An array representing the player's payoff vector given the + profile of the opponents' actions. + + """ + def reduce_last_player(payoff_array, action): + """ + Given `payoff_array` with ndim=M, return the payoff array + with ndim=M-1 fixing the last player's action to be `action`. + + """ + if isinstance(action, numbers.Integral): # pure action + return payoff_array.take(action, axis=-1) + else: # mixed action + return payoff_array.dot(action) + + if self.num_opponents == 1: + payoff_vector = \ + reduce_last_player(self.payoff_array, opponents_actions) + elif self.num_opponents >= 2: + payoff_vector = self.payoff_array + for i in reversed(range(self.num_opponents)): + payoff_vector = \ + reduce_last_player(payoff_vector, opponents_actions[i]) + else: # Trivial case with self.num_opponents == 0 + payoff_vector = self.payoff_array + + return payoff_vector + + def is_best_response(self, own_action, opponents_actions): + """ + Return True if `own_action` is a best response to + `opponents_actions`. + + Parameters + ---------- + own_action : scalar(int) or array_like(float, ndim=1) + An integer representing a pure action, or an array of floats + representing a mixed action. + + opponents_actions : see `best_response` + + Returns + ------- + bool + True if `own_action` is a best response to + `opponents_actions`; False otherwise. + + """ + payoff_vector = self.payoff_vector(opponents_actions) + payoff_max = payoff_vector.max() + + if isinstance(own_action, numbers.Integral): + return payoff_vector[own_action] >= payoff_max - self.tol + else: + return np.dot(own_action, payoff_vector) >= payoff_max - self.tol + + def best_response(self, opponents_actions, tie_breaking='smallest', + payoff_perturbation=None, random_state=None): + """ + Return the best response action(s) to `opponents_actions`. + + Parameters + ---------- + opponents_actions : array_like(int or array_like(float)) or + array_like(int, ndim=1) or scalar(int) + A profile of N-1 opponents' actions. If N=2, then it must be + a 1-dimensional array of floats (in which case it is treated + as the opponent's mixed action) or a scalar of integer (in + which case it is treated as the opponent's pure action). If + N>2, then it must be an array of N-1 objects, where each + object must be an integer (pure action) or an array of + floats (mixed action). + + tie_breaking : {'smallest', 'random', False}, + optional(default='smallest') + Control how, or whether, to break a tie (see Returns for + details). + + payoff_perturbation : array_like(float), optional(default=None) + Array of length equal to the number of actions of the player + containing the values ("noises") to be added to the payoffs + in determining the best response. + + random_state : scalar(int) or np.random.RandomState, + optional(default=None) + Random seed (integer) or np.random.RandomState instance to + set the initial state of the random number generator for + reproducibility. If None, a randomly initialized RandomState + is used. Relevant only when tie_breaking='random'. + + Returns + ------- + scalar(int) or ndarray(int, ndim=1) + If tie_breaking=False, returns an array containing all the + best response pure actions. If tie_breaking='smallest', + returns the best response action with the smallest index; if + tie_breaking='random', returns an action randomly chosen + from the best response actions. + + """ + payoff_vector = self.payoff_vector(opponents_actions) + if payoff_perturbation is not None: + payoff_vector += payoff_perturbation + + if tie_breaking == 'smallest': + best_response = np.argmax(payoff_vector) + return best_response + else: + best_responses = \ + np.where(payoff_vector >= payoff_vector.max() - self.tol)[0] + if tie_breaking == 'random': + return self.random_choice(best_responses, + random_state=random_state) + elif tie_breaking is False: + return best_responses + else: + msg = "tie_breaking must be one of 'smallest', 'random' " + \ + "or False" + raise ValueError(msg) + + def random_choice(self, actions=None, random_state=None): + """ + Return a pure action chosen randomly from `actions`. + + Parameters + ---------- + actions : array_like(int), optional(default=None) + An array of integers representing pure actions. + + random_state : scalar(int) or np.random.RandomState, + optional(default=None) + Random seed (integer) or np.random.RandomState instance to + set the initial state of the random number generator for + reproducibility. If None, a randomly initialized RandomState + is used. + + Returns + ------- + scalar(int) + If `actions` is given, returns an integer representing a + pure action chosen randomly from `actions`; if not, an + action is chosen randomly from the player's all actions. + + """ + random_state = check_random_state(random_state) + + if actions is not None: + n = len(actions) + else: + n = self.num_actions + + if n == 1: + idx = 0 + else: + idx = random_state.randint(n) + + if actions is not None: + return actions[idx] + else: + return idx + + +class NormalFormGame(object): + """ + Class representing an N-player normal form game. + + Parameters + ---------- + data : array_like(Player) or array_like(int, ndim=1) or + array_like(float, ndim=2 or N+1) + Data to initialize a NormalFormGame. `data` may be an array of + Players, in which case the shapes of the Players' payoff arrays + must be consistent. If `data` is an array of N integers, then + these integers are treated as the numbers of actions of the N + players and a NormalFormGame is created consisting of payoffs + all 0 with `data[i]` actions for each player `i`. `data` may + also be an (N+1)-dimensional array representing payoff profiles. + If `data` is a square matrix (2-dimensional array), then the + game will be a symmetric two-player game where the payoff matrix + of each player is given by the input matrix. + + Attributes + ---------- + players : tuple(Player) + Tuple of the Player instances of the game. + + N : scalar(int) + The number of players. + + nums_actions : tuple(int) + Tuple of the numbers of actions, one for each player. + + """ + def __init__(self, data): + # data represents an array_like of Players + if hasattr(data, '__getitem__') and isinstance(data[0], Player): + N = len(data) + + # Check that the shapes of the payoff arrays are consistent + shape_0 = data[0].payoff_array.shape + for i in range(1, N): + shape = data[i].payoff_array.shape + if not ( + len(shape) == N and + shape == shape_0[i:] + shape_0[:i] + ): + raise ValueError( + 'shapes of payoff arrays must be consistent' + ) + + self.players = tuple(data) + + # data represents action sizes or a payoff array + else: + data = np.asarray(data) + + if data.ndim == 0: # data represents action size + # Trivial game consisting of one player + N = 1 + self.players = (Player(np.zeros(data)),) + + elif data.ndim == 1: # data represents action sizes + N = data.size + # N instances of Player created + # with payoff_arrays filled with zeros + # Payoff values set via __setitem__ + self.players = tuple( + Player(np.zeros(tuple(data[i:]) + tuple(data[:i]))) + for i in range(N) + ) + + elif data.ndim == 2 and data.shape[1] >= 2: + # data represents a payoff array for symmetric two-player game + # Number of actions must be >= 2 + if data.shape[0] != data.shape[1]: + raise ValueError( + 'symmetric two-player game must be represented ' + + 'by a square matrix' + ) + N = 2 + self.players = tuple(Player(data) for i in range(N)) + + else: # data represents a payoff array + # data must be of shape (n_0, ..., n_{N-1}, N), + # where n_i is the number of actions available to player i, + # and the last axis contains the payoff profile + N = data.ndim - 1 + if data.shape[-1] != N: + raise ValueError( + 'size of innermost array must be equal to ' + + 'the number of players' + ) + self.players = tuple( + Player( + data.take(i, axis=-1).transpose(list(range(i, N)) + + list(range(i))) + ) for i in range(N) + ) + + self.N = N # Number of players + self.nums_actions = tuple( + player.num_actions for player in self.players + ) + + @property + def payoff_profile_array(self): + N = self.N + dtype = \ + np.result_type(*(player.payoff_array for player in self.players)) + payoff_profile_array = \ + np.empty(self.players[0].payoff_array.shape + (N,), dtype=dtype) + for i, player in enumerate(self.players): + payoff_profile_array[..., i] = \ + player.payoff_array.transpose(list(range(N-i, N)) + + list(range(N-i))) + return payoff_profile_array + + def __repr__(self): + s = '{N}-player NormalFormGame'.format(N=self.N) + return s + + def __str__(self): + s = self.__repr__() + s += ' with payoff profile array:\n' + s += _payoff_profile_array2string(self.payoff_profile_array) + return s + + def __getitem__(self, action_profile): + if self.N == 1: # Trivial game with 1 player + if not isinstance(action_profile, numbers.Integral): + raise TypeError('index must be an integer') + return self.players[0].payoff_array[action_profile] + + # Non-trivial game with 2 or more players + try: + if len(action_profile) != self.N: + raise IndexError('index must be of length {0}'.format(self.N)) + except TypeError: + raise TypeError('index must be a tuple') + + payoff_profile = [ + player.payoff_array[ + tuple(action_profile[i:]) + tuple(action_profile[:i]) + ] + for i, player in enumerate(self.players) + ] + + return payoff_profile + + def __setitem__(self, action_profile, payoff_profile): + if self.N == 1: # Trivial game with 1 player + if not isinstance(action_profile, numbers.Integral): + raise TypeError('index must be an integer') + self.players[0].payoff_array[action_profile] = payoff_profile + return None + + # Non-trivial game with 2 or more players + try: + if len(action_profile) != self.N: + raise IndexError('index must be of length {0}'.format(self.N)) + except TypeError: + raise TypeError('index must be a tuple') + + try: + if len(payoff_profile) != self.N: + raise ValueError( + 'value must be an array_like of length {0}'.format(self.N) + ) + except TypeError: + raise TypeError('value must be a tuple') + + for i, player in enumerate(self.players): + player.payoff_array[ + tuple(action_profile[i:]) + tuple(action_profile[:i]) + ] = payoff_profile[i] + + def is_nash(self, action_profile): + """ + Return True if `action_profile` is a Nash equilibrium. + + Parameters + ---------- + action_profile : array_like(int or array_like(float)) + An array of N objects, where each object must be an integer + (pure action) or an array of floats (mixed action). + + Returns + ------- + bool + True if `action_profile` is a Nash equilibrium; False + otherwise. + + """ + if self.N == 2: + for i, player in enumerate(self.players): + own_action, opponent_action = \ + action_profile[i], action_profile[1-i] + if not player.is_best_response(own_action, opponent_action): + return False + + elif self.N >= 3: + for i, player in enumerate(self.players): + own_action = action_profile[i] + opponents_actions = \ + tuple(action_profile[i+1:]) + tuple(action_profile[:i]) + + if not player.is_best_response(own_action, opponents_actions): + return False + + else: # Trivial case with self.N == 1 + if not self.players[0].is_best_response(action_profile[0], None): + return False + + return True + + +def _payoff_array2string(payoff_array, class_name=None): + prefix, suffix = '', '' + if class_name is not None: + prefix = class_name + '(' + suffix = ')' + s = np.array2string(payoff_array, separator=', ', prefix=prefix) + return prefix + s + suffix + + +def _payoff_profile_array2string(payoff_profile_array, class_name=None): + s = np.array2string(payoff_profile_array, separator=', ') + + # Remove one linebreak + s = re.sub(r'(\n+)', lambda x: x.group(0)[0:-1], s) + + if class_name is not None: + prefix = class_name + '(' + next_line_prefix = ' ' * len(prefix) + suffix = ')' + l = s.splitlines() + l[0] = prefix + l[0] + for i in range(1, len(l)): + if l[i]: + l[i] = next_line_prefix + l[i] + l[-1] += suffix + s = '\n'.join(l) + + return s + + +def pure2mixed(num_actions, action): + """ + Convert a pure action to the corresponding mixed action. + + Parameters + ---------- + num_actions : scalar(int) + The number of the pure actions (= the length of a mixed action). + + action : scalar(int) + The pure action to convert to the corresponding mixed action. + + Returns + ------- + ndarray(float, ndim=1) + The mixed action representation of the given pure action. + + """ + mixed_action = np.zeros(num_actions) + mixed_action[action] = 1 + return mixed_action + + +# Numba jitted functions # + +@jit(nopython=True) +def best_response_2p(payoff_matrix, opponent_mixed_action): + """ + Numba-optimized version of `Player.best_response` compilied in + nopython mode, specialized for 2-player games (where there is only + one opponent). + + Return the best response action (with the smallest index if more + than one) to `opponent_mixed_action` under `payoff_matrix`. + + Parameters + ---------- + payoff_matrix : ndarray(float, ndim=2) + Payoff matrix. + + opponent_mixed_action : ndarray(float, ndim=1) + Opponent's mixed action. Its length must be equal to + `payoff_matrix.shape[1]`. + + Return + ------ + scalar(int) + Best response action. + + """ + n, m = payoff_matrix.shape + + best_response = 0 + payoff_0 = 0 + for b in range(m): + payoff_0 += payoff_matrix[0, b] * opponent_mixed_action[b] + payoff_max = payoff_0 + + for a in range(1, n): + payoff = 0 + for b in range(m): + payoff += payoff_matrix[a, b] * opponent_mixed_action[b] + if payoff > payoff_max: + payoff_max = payoff + best_response = a + + return best_response diff --git a/quantecon/game_theory/tests/test_normal_form_game.py b/quantecon/game_theory/tests/test_normal_form_game.py new file mode 100644 index 000000000..479df40d9 --- /dev/null +++ b/quantecon/game_theory/tests/test_normal_form_game.py @@ -0,0 +1,372 @@ +""" +Author: Daisuke Oyama + +Tests for normal_form_game.py + +""" +from __future__ import division + +import numpy as np +from numpy.testing import assert_array_equal +from nose.tools import eq_, ok_, raises + +from quantecon.game_theory import ( + Player, NormalFormGame, pure2mixed, best_response_2p +) + + +# Player # + +class TestPlayer_1opponent: + """Test the methods of Player with one opponent player""" + + def setUp(self): + """Setup a Player instance""" + coordination_game_matrix = [[4, 0], [3, 2]] + self.player = Player(coordination_game_matrix) + + def test_best_response_against_pure(self): + eq_(self.player.best_response(1), 1) + + def test_best_response_against_mixed(self): + eq_(self.player.best_response([1/2, 1/2]), 1) + + def test_best_response_list_when_tie(self): + """best_response with tie_breaking=False""" + assert_array_equal( + sorted(self.player.best_response([2/3, 1/3], tie_breaking=False)), + sorted([0, 1]) + ) + + def test_best_response_with_random_tie_breaking(self): + """best_response with tie_breaking='random'""" + ok_(self.player.best_response([2/3, 1/3], tie_breaking='random') + in [0, 1]) + + seed = 1234 + br0 = self.player.best_response([2/3, 1/3], tie_breaking='random', + random_state=seed) + br1 = self.player.best_response([2/3, 1/3], tie_breaking='random', + random_state=seed) + eq_(br0, br1) + + def test_best_response_with_smallest_tie_breaking(self): + """best_response with tie_breaking='smallest' (default)""" + eq_(self.player.best_response([2/3, 1/3]), 0) + + def test_best_response_with_payoff_perturbation(self): + """best_response with payoff_perturbation""" + eq_(self.player.best_response([2/3, 1/3], + payoff_perturbation=[0, 0.1]), + 1) + + def test_is_best_response_against_pure(self): + ok_(self.player.is_best_response(0, 0)) + + def test_is_best_response_against_mixed(self): + ok_(self.player.is_best_response([1/2, 1/2], [2/3, 1/3])) + + +class TestPlayer_2opponents: + """Test the methods of Player with two opponent players""" + + def setUp(self): + """Setup a Player instance""" + payoffs_2opponents = [[[3, 6], + [4, 2]], + [[1, 0], + [5, 7]]] + self.player = Player(payoffs_2opponents) + + def test_payoff_vector_against_pure(self): + assert_array_equal(self.player.payoff_vector((0, 1)), [6, 0]) + + def test_is_best_response_against_pure(self): + ok_(not self.player.is_best_response(0, (1, 0))) + + def test_best_response_against_pure(self): + eq_(self.player.best_response((1, 1)), 1) + + def test_best_response_list_when_tie(self): + """ + best_response against a mixed action profile with + tie_breaking=False + """ + assert_array_equal( + sorted(self.player.best_response(([3/7, 4/7], [1/2, 1/2]), + tie_breaking=False)), + sorted([0, 1]) + ) + + +def test_random_choice(): + n, m = 5, 4 + payoff_matrix = np.zeros((n, m)) + player = Player(payoff_matrix) + + eq_(player.random_choice([0]), 0) + + actions = list(range(player.num_actions)) + ok_(player.random_choice() in actions) + + +# NormalFormGame # + +class TestNormalFormGame_Sym2p: + """Test the methods of NormalFormGame with symmetric two players""" + + def setUp(self): + """Setup a NormalFormGame instance""" + coordination_game_matrix = [[4, 0], [3, 2]] + self.g = NormalFormGame(coordination_game_matrix) + + def test_getitem(self): + assert_array_equal(self.g[0, 1], [0, 3]) + + def test_is_nash_pure(self): + ok_(self.g.is_nash((0, 0))) + + def test_is_nash_mixed(self): + ok_(self.g.is_nash(([2/3, 1/3], [2/3, 1/3]))) + + +class TestNormalFormGame_Asym2p: + """Test the methods of NormalFormGame with asymmetric two players""" + + def setUp(self): + """Setup a NormalFormGame instance""" + matching_pennies_bimatrix = [[(1, -1), (-1, 1)], + [(-1, 1), (1, -1)]] + self.g = NormalFormGame(matching_pennies_bimatrix) + + def test_getitem(self): + assert_array_equal(self.g[1, 0], [-1, 1]) + + def test_is_nash_against_pure(self): + ok_(not self.g.is_nash((0, 0))) + + def test_is_nash_against_mixed(self): + ok_(self.g.is_nash(([1/2, 1/2], [1/2, 1/2]))) + + +class TestNormalFormGame_3p: + """Test the methods of NormalFormGame with three players""" + + def setUp(self): + """Setup a NormalFormGame instance""" + payoffs_2opponents = [[[3, 6], + [4, 2]], + [[1, 0], + [5, 7]]] + player = Player(payoffs_2opponents) + self.g = NormalFormGame([player for i in range(3)]) + + def test_getitem(self): + assert_array_equal(self.g[0, 0, 1], [6, 4, 1]) + + def test_is_nash_pure(self): + ok_(self.g.is_nash((0, 0, 0))) + ok_(not self.g.is_nash((0, 0, 1))) + + def test_is_nash_mixed(self): + p = (1 + np.sqrt(65)) / 16 + ok_(self.g.is_nash(([1 - p, p], [1 - p, p], [1 - p, p]))) + + +def test_normalformgame_input_action_sizes(): + g = NormalFormGame((2, 3, 4)) + + eq_(g.N, 3) # Number of players + + assert_array_equal( + g.players[0].payoff_array, + np.zeros((2, 3, 4)) + ) + assert_array_equal( + g.players[1].payoff_array, + np.zeros((3, 4, 2)) + ) + assert_array_equal( + g.players[2].payoff_array, + np.zeros((4, 2, 3)) + ) + + +def test_normalformgame_setitem(): + g = NormalFormGame((2, 2)) + g[0, 0] = (0, 10) + g[0, 1] = (0, 10) + g[1, 0] = (3, 5) + g[1, 1] = (-2, 0) + + assert_array_equal( + g.players[0].payoff_array, + [[0, 0], [3, -2]] + ) + assert_array_equal( + g.players[1].payoff_array, + [[10, 5], [10, 0]] + ) + + +def test_normalformgame_constant_payoffs(): + g = NormalFormGame((2, 2)) + + ok_(g.is_nash((0, 0))) + ok_(g.is_nash((0, 1))) + ok_(g.is_nash((1, 0))) + ok_(g.is_nash((1, 1))) + + +def test_normalformgame_payoff_profile_array(): + nums_actions = (2, 3, 4) + for N in range(1, len(nums_actions)+1): + payoff_arrays = [ + np.arange(np.prod(nums_actions[0:N])).reshape(nums_actions[i:N] + + nums_actions[0:i]) + for i in range(N) + ] + players = [Player(payoff_array) for payoff_array in payoff_arrays] + g = NormalFormGame(players) + g_new = NormalFormGame(g.payoff_profile_array) + for player_new, payoff_array in zip(g_new.players, payoff_arrays): + assert_array_equal(player_new.payoff_array, payoff_array) + + +# Trivial cases with one player # + +class TestPlayer_0opponents: + """Test for trivial Player with no opponent player""" + + def setUp(self): + """Setup a Player instance""" + payoffs = [0, 1] + self.player = Player(payoffs) + + def test_payoff_vector(self): + """Trivial player: payoff_vector""" + assert_array_equal(self.player.payoff_vector(None), [0, 1]) + + def test_is_best_response(self): + """Trivial player: is_best_response""" + ok_(self.player.is_best_response(1, None)) + + def test_best_response(self): + """Trivial player: best_response""" + eq_(self.player.best_response(None), 1) + + +class TestNormalFormGame_1p: + """Test for trivial NormalFormGame with a single player""" + + def setUp(self): + """Setup a NormalFormGame instance""" + data = [[0], [1], [1]] + self.g = NormalFormGame(data) + + def test_construction(self): + """Trivial game: construction""" + ok_(self.g.N == 1) + assert_array_equal(self.g.players[0].payoff_array, [0, 1, 1]) + + def test_getitem(self): + """Trivial game: __getitem__""" + eq_(self.g[0], 0) + + def test_is_nash_pure(self): + """Trivial game: is_nash with pure action""" + ok_(self.g.is_nash((1,))) + ok_(not self.g.is_nash((0,))) + + def test_is_nash_mixed(self): + """Trivial game: is_nash with mixed action""" + ok_(self.g.is_nash(([0, 1/2, 1/2],))) + + +def test_normalformgame_input_action_sizes_1p(): + g = NormalFormGame(2) + + eq_(g.N, 1) # Number of players + + assert_array_equal( + g.players[0].payoff_array, + np.zeros(2) + ) + + +def test_normalformgame_setitem_1p(): + g = NormalFormGame(2) + + eq_(g.N, 1) # Number of players + + g[0] = 10 # Set payoff 10 for action 0 + eq_(g.players[0].payoff_array[0], 10) + + +# Invalid inputs # + +@raises(ValueError) +def test_normalformgame_invalid_input_players_shape_inconsistent(): + p0 = Player(np.zeros((2, 3))) + p1 = Player(np.zeros((2, 3))) + g = NormalFormGame([p0, p1]) + + +@raises(ValueError) +def test_normalformgame_invalid_input_players_num_inconsistent(): + p0 = Player(np.zeros((2, 2, 2))) + p1 = Player(np.zeros((2, 2, 2))) + g = NormalFormGame([p0, p1]) + + +@raises(ValueError) +def test_normalformgame_invalid_input_nosquare_matrix(): + g = NormalFormGame(np.zeros((2, 3))) + + +@raises(ValueError) +def test_normalformgame_invalid_input_payoff_profiles(): + g = NormalFormGame(np.zeros((2, 2, 1))) + + +# Utility functions # + +def test_pure2mixed(): + num_actions = 3 + pure_action = 0 + mixed_action = [1., 0., 0.] + + assert_array_equal(pure2mixed(num_actions, pure_action), mixed_action) + + +# Numba jitted functions # + +def test_best_response_2p(): + test_case0 = { + 'payoff_array': np.array([[4, 0], [3, 2], [0, 3]]), + 'mixed_actions': + [np.array([1, 0]), np.array([0.5, 0.5]), np.array([0, 1])], + 'brs_expected': [0, 1, 2] + } + test_case1 = { + 'payoff_array': np.zeros((2, 3)), + 'mixed_actions': [np.array([1, 0, 0]), np.array([1/3, 1/3, 1/3])], + 'brs_expected': [0, 0] + } + + for test_case in [test_case0, test_case1]: + for mixed_action, br_expected in zip(test_case['mixed_actions'], + test_case['brs_expected']): + br_computed = \ + best_response_2p(test_case['payoff_array'], mixed_action) + eq_(br_computed, br_expected) + + +if __name__ == '__main__': + import sys + import nose + + argv = sys.argv[:] + argv.append('--verbose') + argv.append('--nocapture') + nose.main(argv=argv, defaultTest=__file__) diff --git a/setup.py b/setup.py index 9c8168769..60b5832fd 100644 --- a/setup.py +++ b/setup.py @@ -12,10 +12,10 @@ def write_version_py(filename=None): """ doc = "\"\"\"\nThis is a VERSION file and should NOT be manually altered\n\"\"\"" doc += "\nversion = '%s'" % VERSION - + if not filename: filename = os.path.join(os.path.dirname(__file__), 'quantecon', 'version.py') - + fl = open(filename, 'w') try: fl.write(doc) @@ -30,12 +30,12 @@ def write_version_py(filename=None): DESCRIPTION = "QuantEcon is a package to support all forms of quantitative economic modelling." #'Core package of the QuantEcon library' LONG_DESCRIPTION = """ -**QuantEcon** is an organization run by economists for economists with the aim of coordinating -distributed development of high quality open source code for all forms of quantitative economic modelling. +**QuantEcon** is an organization run by economists for economists with the aim of coordinating +distributed development of high quality open source code for all forms of quantitative economic modelling. The project website is located at `http://quantecon.org/ `_. This website provides -more information with regards to the **quantecon** library, documentation, in addition to some resources -in regards to how you can use and/or contribute to the package. +more information with regards to the **quantecon** library, documentation, in addition to some resources +in regards to how you can use and/or contribute to the package. The **quantecon** Package ------------------------- @@ -68,7 +68,7 @@ def write_version_py(filename=None): Additional Links ---------------- -1. `QuantEcon Course Website `_ +1. `QuantEcon Course Website `_ """ @@ -93,7 +93,8 @@ def write_version_py(filename=None): setup(name='quantecon', packages=['quantecon', - 'quantecon.markov', + 'quantecon.game_theory', + 'quantecon.markov', 'quantecon.random', 'quantecon.tests', 'quantecon.util', From c7c9fdfa7c67fdcd377b06e848840c80ef36367c Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Wed, 13 Jan 2016 21:55:59 +0900 Subject: [PATCH 44/51] Add docs for game_theory --- docs/qe_apidoc.py | 49 +++++++++++++++----- docs/source/game_theory.rst | 7 +++ docs/source/game_theory/normal_form_game.rst | 7 +++ docs/source/index.rst | 9 ++-- 4 files changed, 56 insertions(+), 16 deletions(-) create mode 100644 docs/source/game_theory.rst create mode 100644 docs/source/game_theory/normal_form_game.rst diff --git a/docs/qe_apidoc.py b/docs/qe_apidoc.py index 520d962ed..4923ce60a 100644 --- a/docs/qe_apidoc.py +++ b/docs/qe_apidoc.py @@ -20,7 +20,7 @@ Examples -------- $ python qe_apidoc.py # generates the two separate directories -$ python qe_apidoc.py foo_bar # generates the two separate directories +$ python qe_apidoc.py foo_bar # generates the two separate directories $ python qe_apidoc.py single # generates the single directory @@ -49,6 +49,15 @@ :show-inheritance: """ +game_theory_module_template = """{mod_name} +{equals} + +.. automodule:: quantecon.game_theory.{mod_name} + :members: + :undoc-members: + :show-inheritance: +""" + markov_module_template = """{mod_name} {equals} @@ -101,10 +110,10 @@ ======================= The `quantecon` python library consists of a number of modules which -includes economic models (models), markov chains (markov), random -generation utilities (random), a collection of tools (tools), -and other utilities (util) which are -mainly used by developers internal to the package. +includes economic models (models), markov chains (markov), random +generation utilities (random), a collection of tools (tools), +and other utilities (util) which are +mainly used by developers internal to the package. The models section, for example, contains implementations of standard models, many of which are discussed in lectures on the website `quant- @@ -113,6 +122,7 @@ .. toctree:: :maxdepth: 2 + game_theory markov random tools @@ -173,6 +183,12 @@ def all_auto(): def model_tool(): + # list file names with game_theory + game_theory_files = glob("../quantecon/game_theory/[a-z0-9]*.py") + game_theory = list(map(lambda x: x.split('/')[-1][:-3], game_theory_files)) + # Alphabetize + game_theory.sort() + # list file names with markov markov_files = glob("../quantecon/markov/[a-z0-9]*.py") markov = list(map(lambda x: x.split('/')[-1][:-3], markov_files)) @@ -191,23 +207,30 @@ def model_tool(): # Alphabetize tools.remove("version") tools.sort() - + # list file names of utilities util_files = glob("../quantecon/util/[a-z0-9]*.py") util = list(map(lambda x: x.split('/')[-1][:-3], util_files)) # Alphabetize util.sort() - for folder in ["markov","random","tools","util"]: + for folder in ["game_theory", "markov", "random", "tools", "util"]: if not os.path.exists(source_join(folder)): os.makedirs(source_join(folder)) + # Write file for each game_theory file + for mod in game_theory: + new_path = os.path.join("source", "game_theory", mod + ".rst") + with open(new_path, "w") as f: + equals = "=" * len(mod) + f.write(game_theory_module_template.format(mod_name=mod, equals=equals)) + # Write file for each markov file for mod in markov: new_path = os.path.join("source", "markov", mod + ".rst") with open(new_path, "w") as f: equals = "=" * len(mod) - f.write(markov_module_template.format(mod_name=mod, equals=equals)) + f.write(markov_module_template.format(mod_name=mod, equals=equals)) # Write file for each random file for mod in random: @@ -234,18 +257,20 @@ def model_tool(): with open(source_join("index.rst"), "w") as index: index.write(split_index_template) + gt = "game_theory/" + "\n game_theory/".join(game_theory) mark = "markov/" + "\n markov/".join(markov) rand = "random/" + "\n random/".join(random) tlz = "tools/" + "\n tools/".join(tools) utls = "util/" + "\n util/".join(util) #-TocTree-# - toc_tree_list = {"markov":mark, + toc_tree_list = {"game_theory": gt, + "markov": mark, "tools": tlz, - "random":rand, - "util":utls, + "random": rand, + "util": utls, } - for f_name in ("markov","random","tools","util"): + for f_name in ("game_theory", "markov", "random", "tools", "util"): with open(source_join(f_name + ".rst"), "w") as f: temp = split_file_template.format(name=f_name.capitalize(), equals="="*len(f_name), diff --git a/docs/source/game_theory.rst b/docs/source/game_theory.rst new file mode 100644 index 000000000..dbd3f2541 --- /dev/null +++ b/docs/source/game_theory.rst @@ -0,0 +1,7 @@ +Game_theory +=========== + +.. toctree:: + :maxdepth: 2 + + game_theory/normal_form_game diff --git a/docs/source/game_theory/normal_form_game.rst b/docs/source/game_theory/normal_form_game.rst new file mode 100644 index 000000000..6f8d426f3 --- /dev/null +++ b/docs/source/game_theory/normal_form_game.rst @@ -0,0 +1,7 @@ +normal_form_game +================ + +.. automodule:: quantecon.game_theory.normal_form_game + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/index.rst b/docs/source/index.rst index d41452b68..6a9e03446 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,10 +3,10 @@ QuantEcon documentation ======================= The `quantecon` python library consists of a number of modules which -includes economic models (models), markov chains (markov), random -generation utilities (random), a collection of tools (tools), -and other utilities (util) which are -mainly used by developers internal to the package. +includes economic models (models), markov chains (markov), random +generation utilities (random), a collection of tools (tools), +and other utilities (util) which are +mainly used by developers internal to the package. The models section, for example, contains implementations of standard models, many of which are discussed in lectures on the website `quant- @@ -15,6 +15,7 @@ econ.net `_. .. toctree:: :maxdepth: 2 + game_theory markov random tools From d2d492338d34fe26b9516e5f55d1ef4a3d51236a Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Wed, 13 Jan 2016 10:15:30 -0500 Subject: [PATCH 45/51] Adjust api to include the game_theory sub-package --- quantecon/__init__.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/quantecon/__init__.py b/quantecon/__init__.py index 6203f3797..71198ddc4 100644 --- a/quantecon/__init__.py +++ b/quantecon/__init__.py @@ -7,10 +7,15 @@ except: raise ImportError("Cannot import numba from current anaconda distribution. Please run `conda install numba` to install the latest version.") +#-Modules-# +from . import game_theory + +#-Objects-# from .compute_fp import compute_fixed_point from .discrete_rv import DiscreteRV from .ecdf import ECDF from .estspec import smooth, periodogram, ar_periodogram +# from .game_theory import #Place Holder if we wish to promote any general objects to the qe namespace. from .graph_tools import DiGraph from .gridtools import cartesian, mlinspace from .kalman import Kalman @@ -22,8 +27,8 @@ from .matrix_eqn import solve_discrete_lyapunov, solve_discrete_riccati from .quadsums import var_quadratic_sum, m_quadratic_sum #->Propose Delete From Top Level -from .markov import MarkovChain, random_markov_chain, random_stochastic_matrix, gth_solve, tauchen #Promote to keep current examples working -from .markov import mc_compute_stationary, mc_sample_path #Imports that Should be Deprecated with markov package +from .markov import MarkovChain, random_markov_chain, random_stochastic_matrix, gth_solve, tauchen #Promote to keep current examples working +from .markov import mc_compute_stationary, mc_sample_path #Imports that Should be Deprecated with markov package #<- from .rank_nullspace import rank_est, nullspace from .robustlq import RBLQ From c5cd62969ebee36c99923ad7a5b9e7c527bff6cc Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Wed, 13 Jan 2016 11:34:48 -0500 Subject: [PATCH 46/51] Update base api to include module and object imports, fix missing import of random subpackage --- quantecon/__init__.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/quantecon/__init__.py b/quantecon/__init__.py index 6203f3797..a20742025 100644 --- a/quantecon/__init__.py +++ b/quantecon/__init__.py @@ -7,6 +7,11 @@ except: raise ImportError("Cannot import numba from current anaconda distribution. Please run `conda install numba` to install the latest version.") +#-Modules-# +from . import quad +from . import random + +#-Objects-# from .compute_fp import compute_fixed_point from .discrete_rv import DiscreteRV from .ecdf import ECDF @@ -27,8 +32,7 @@ #<- from .rank_nullspace import rank_est, nullspace from .robustlq import RBLQ -from . import quad as quad from .util import searchsorted, fetch_nb_dependencies -#Add Version Attribute +#-Add Version Attribute-# from .version import version as __version__ From 79e6fec1bc6145ba4059ac35962f54615895e293 Mon Sep 17 00:00:00 2001 From: Chase Date: Wed, 13 Jan 2016 21:19:45 -0500 Subject: [PATCH 47/51] Change converter functions to use default float --- quantecon/lqcontrol.py | 4 ++-- quantecon/lss.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/quantecon/lqcontrol.py b/quantecon/lqcontrol.py index e61712852..0690c3e89 100644 --- a/quantecon/lqcontrol.py +++ b/quantecon/lqcontrol.py @@ -100,7 +100,7 @@ class LQ(object): def __init__(self, Q, R, A, B, C=None, N=None, beta=1, T=None, Rf=None): # == Make sure all matrices can be treated as 2D arrays == # - converter = lambda X: np.atleast_2d(np.asarray(X, dtype='float32')) + converter = lambda X: np.atleast_2d(np.asarray(X, dtype='float')) self.A, self.B, self.Q, self.R, self.N = list(map(converter, (A, B, Q, R, N))) # == Record dimensions == # @@ -123,7 +123,7 @@ def __init__(self, Q, R, A, B, C=None, N=None, beta=1, T=None, Rf=None): if T: # == Model is finite horizon == # self.T = T - self.Rf = np.asarray(Rf, dtype='float32') + self.Rf = np.asarray(Rf, dtype='float') self.P = self.Rf self.d = 0 else: diff --git a/quantecon/lss.py b/quantecon/lss.py index 35766a0d4..dbfc2d029 100644 --- a/quantecon/lss.py +++ b/quantecon/lss.py @@ -141,7 +141,7 @@ def convert(self, x): well formed 2D NumPy arrays """ - return np.atleast_2d(np.asarray(x, dtype='float32')) + return np.atleast_2d(np.asarray(x, dtype='float')) def simulate(self, ts_length=100): """ From ed2e1b8e5a6b265ce872ef4a4184a9383f2e8570 Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Sun, 17 Jan 2016 12:59:02 +0900 Subject: [PATCH 48/51] Add `distributions` to quantecon/__init__.py --- quantecon/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/quantecon/__init__.py b/quantecon/__init__.py index a20742025..8394e936b 100644 --- a/quantecon/__init__.py +++ b/quantecon/__init__.py @@ -8,6 +8,7 @@ raise ImportError("Cannot import numba from current anaconda distribution. Please run `conda install numba` to install the latest version.") #-Modules-# +from . import distributions from . import quad from . import random From c0a7f803fa5b6268989a42045624053ed44b55cf Mon Sep 17 00:00:00 2001 From: Daisuke Oyama Date: Wed, 20 Jan 2016 21:59:18 +0900 Subject: [PATCH 49/51] Fix type error --- quantecon/game_theory/normal_form_game.py | 5 ++++- quantecon/game_theory/tests/test_normal_form_game.py | 3 +++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/quantecon/game_theory/normal_form_game.py b/quantecon/game_theory/normal_form_game.py index 8d3f47f47..2619bffa3 100644 --- a/quantecon/game_theory/normal_form_game.py +++ b/quantecon/game_theory/normal_form_game.py @@ -295,7 +295,10 @@ def best_response(self, opponents_actions, tie_breaking='smallest', """ payoff_vector = self.payoff_vector(opponents_actions) if payoff_perturbation is not None: - payoff_vector += payoff_perturbation + try: + payoff_vector += payoff_perturbation + except TypeError: # type mismatch + payoff_vector = payoff_vector + payoff_perturbation if tie_breaking == 'smallest': best_response = np.argmax(payoff_vector) diff --git a/quantecon/game_theory/tests/test_normal_form_game.py b/quantecon/game_theory/tests/test_normal_form_game.py index 479df40d9..bc0b30f45 100644 --- a/quantecon/game_theory/tests/test_normal_form_game.py +++ b/quantecon/game_theory/tests/test_normal_form_game.py @@ -59,6 +59,9 @@ def test_best_response_with_payoff_perturbation(self): eq_(self.player.best_response([2/3, 1/3], payoff_perturbation=[0, 0.1]), 1) + eq_(self.player.best_response([2, 1], # int + payoff_perturbation=[0, 0.1]), + 1) def test_is_best_response_against_pure(self): ok_(self.player.is_best_response(0, 0)) From 811793bce3ab8856809adcb2eea9cdb593d76a93 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Fri, 22 Jan 2016 11:16:23 -0500 Subject: [PATCH 50/51] Increment version number for new release --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 60b5832fd..4c1a46ca7 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ #-Write Versions File-# #~~~~~~~~~~~~~~~~~~~~~# -VERSION = '0.3.0' +VERSION = '0.3.1' def write_version_py(filename=None): """ From dbb3368e4ba2c183d99134c9076aec5e4bcee864 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Fri, 22 Jan 2016 11:21:56 -0500 Subject: [PATCH 51/51] Update release for 0.3.1 --- MANIFEST | 2 ++ README.md | 6 ++++++ quantecon/version.py | 2 +- 3 files changed, 9 insertions(+), 1 deletion(-) diff --git a/MANIFEST b/MANIFEST index 6473ad47a..7adf65608 100644 --- a/MANIFEST +++ b/MANIFEST @@ -26,6 +26,8 @@ quantecon/quadsums.py quantecon/rank_nullspace.py quantecon/robustlq.py quantecon/version.py +quantecon/game_theory/__init__.py +quantecon/game_theory/normal_form_game.py quantecon/markov/__init__.py quantecon/markov/approximation.py quantecon/markov/core.py diff --git a/README.md b/README.md index b4d59504f..cb6489b7e 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,13 @@ modification, are permitted provided that the following conditions are met: ## Major Changes +#### Ver. 0.3.1 (22-January-2016) + +1. Adds the ``quantecon/game_theory/`` sub package +2. Updates api for using ``distributions`` as a module ``qe.distributions`` + #### Ver. 0.3 1. Removes ``quantecon/models`` subpackage and the collection of code examples. Code has been migrated to the [QuantEcon.applications](https://github.com/QuantEcon/QuantEcon.applications) repository. 2. Adds a utility for fetching notebook dependencies from [QuantEcon.applications](https://github.com/QuantEcon/QuantEcon.applications) to support community contributed notebooks. + diff --git a/quantecon/version.py b/quantecon/version.py index de8a251b1..c9eab20eb 100644 --- a/quantecon/version.py +++ b/quantecon/version.py @@ -1,4 +1,4 @@ """ This is a VERSION file and should NOT be manually altered """ -version = '0.3.0' \ No newline at end of file +version = '0.3.1' \ No newline at end of file