{ "cells": [ { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import *\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from dolo import *" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filename = '../models/rbc_taxes.yaml'" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = yaml_import(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model defined in `rbc_taxes.yaml` is the `rbc` model, with an agregate tax `g` that is proportional to income. " ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'states': array([1. , 9.35497829, 0. ]),\n", " 'controls': array([0.23387446, 0.33 ]),\n", " 'exogenous': array([0.]),\n", " 'parameters': array([0.99 , 1. , 1. , 8.04277482, 0.025 ,\n", " 0.33 , 0.8 , 1. ]),\n", " 'auxiliaries': array([0.03510101, 2.02026956, 0.99505814, 0.76118369])}" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.calibration" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'transition': array([0., 0., 0.]),\n", " 'arbitrage': array([ 0.0000000e+00, -8.8817842e-16])}" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.residuals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to compute the adjustment of the economy when this tax, goes back progressively from 10% to 0%, over 10 periods." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10, 1)\n" ] } ], "source": [ "exo_g = linspace(0.1,0,10) # this is a vector of size 10\n", "exo_g = atleast_2d(exo_g).T # the solver expects a 1x10 vector\n", "print(exo_g.shape)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0.1 ],\n", " [0.08888889],\n", " [0.07777778],\n", " [0.06666667],\n", " [0.05555556],\n", " [0.04444444],\n", " [0.03333333],\n", " [0.02222222],\n", " [0.01111111],\n", " [0. ]])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exo_g" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\t> 1 | 0.03282645604591261 | 0\n", "\t> 2 | 0.0013560969381939403 | 0\n", "\t> 3 | 2.8228642349192867e-06 | 0\n", "\t> 4 | 1.2459810960763207e-11 | 0\n", "> System was solved after iteration 4. Residual=1.2459810960763207e-11\n" ] }, { "data": { "text/html": [ "
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0.329524 0.034876 2.026689 \n", "28 1.0 9.428016 9.104032e-25 0.232152 0.329545 0.034886 2.026384 \n", "29 1.0 9.424467 3.843539e-24 0.232222 0.329564 0.034896 2.026093 \n", "30 1.0 9.421077 -1.857197e-24 0.232288 0.329583 0.034906 2.025815 \n", "31 1.0 9.417838 -6.793508e-24 0.232351 0.329600 0.034915 2.025550 \n", "32 1.0 9.414743 -5.467372e-24 0.232410 0.329617 0.034924 2.025297 \n", "33 1.0 9.411784 2.024085e-24 0.232465 0.329632 0.034933 2.025055 \n", "34 1.0 9.408955 1.058702e-24 0.232517 0.329647 0.034941 2.024824 \n", "35 1.0 9.406248 -4.566884e-26 0.232566 0.329661 0.034949 2.024604 \n", "36 1.0 9.403657 -4.728241e-24 0.232611 0.329674 0.034956 2.024393 \n", "37 1.0 9.401177 -1.553064e-24 0.232654 0.329686 0.034963 2.024192 \n", "38 1.0 9.398801 3.405749e-24 0.232693 0.329698 0.034970 2.024000 \n", "39 1.0 9.396524 2.583974e-24 0.232730 0.329708 0.034976 2.023816 \n", "40 1.0 9.394341 -3.028504e-24 0.232763 0.329718 0.034982 2.023641 \n", "41 1.0 9.392246 -3.214116e-24 0.232794 0.329728 0.034988 2.023473 \n", "42 1.0 9.390234 5.476225e-24 0.232822 0.329737 0.034994 2.023312 \n", "43 1.0 9.388300 2.264607e-24 0.232848 0.329745 0.034999 2.023158 \n", "44 1.0 9.386441 2.304774e-24 0.232871 0.329752 0.035005 2.023011 \n", "45 1.0 9.384651 -8.279587e-25 0.232891 0.329759 0.035009 2.022869 \n", "46 1.0 9.382926 3.825730e-24 0.232909 0.329765 0.035014 2.022734 \n", "47 1.0 9.381261 -1.385624e-24 0.232924 0.329771 0.035019 2.022604 \n", "48 1.0 9.379654 2.241539e-26 0.232937 0.329776 0.035023 2.022480 \n", "49 1.0 9.378100 -4.563827e-25 0.232947 0.329781 0.035027 2.022360 \n", "50 1.0 9.376595 -7.297820e-25 0.232947 0.329781 0.035031 2.022253 \n", "\n", " y c e_g \n", "0 1.054853 0.718036 0.088889 \n", "1 1.015603 0.774170 0.077778 \n", "2 1.011887 0.773295 0.066667 \n", "3 1.008607 0.772533 0.055556 \n", "4 1.005782 0.771870 0.044444 \n", "5 1.003435 0.771289 0.033333 \n", "6 1.001587 0.770775 0.022222 \n", "7 1.000261 0.770313 0.011111 \n", "8 0.999481 0.769887 0.000000 \n", "9 0.999273 0.769481 0.000000 \n", "10 0.999074 0.769094 0.000000 \n", "11 0.998885 0.768725 0.000000 \n", "12 0.998703 0.768373 0.000000 \n", "13 0.998531 0.768038 0.000000 \n", "14 0.998365 0.767718 0.000000 \n", "15 0.998208 0.767414 0.000000 \n", "16 0.998057 0.767123 0.000000 \n", "17 0.997913 0.766847 0.000000 \n", "18 0.997776 0.766583 0.000000 \n", "19 0.997645 0.766332 0.000000 \n", "20 0.997519 0.766092 0.000000 \n", "21 0.997399 0.765864 0.000000 \n", "22 0.997284 0.765647 0.000000 \n", "23 0.997175 0.765439 0.000000 \n", "24 0.997070 0.765242 0.000000 \n", "25 0.996970 0.765054 0.000000 \n", "26 0.996873 0.764875 0.000000 \n", "27 0.996781 0.764704 0.000000 \n", "28 0.996693 0.764542 0.000000 \n", "29 0.996609 0.764387 0.000000 \n", "30 0.996528 0.764239 0.000000 \n", "31 0.996450 0.764099 0.000000 \n", "32 0.996375 0.763965 0.000000 \n", "33 0.996303 0.763838 0.000000 \n", "34 0.996234 0.763717 0.000000 \n", "35 0.996168 0.763602 0.000000 \n", "36 0.996103 0.763492 0.000000 \n", "37 0.996042 0.763388 0.000000 \n", "38 0.995982 0.763289 0.000000 \n", "39 0.995924 0.763195 0.000000 \n", "40 0.995868 0.763105 0.000000 \n", "41 0.995814 0.763020 0.000000 \n", "42 0.995761 0.762939 0.000000 \n", "43 0.995710 0.762862 0.000000 \n", "44 0.995660 0.762789 0.000000 \n", "45 0.995611 0.762720 0.000000 \n", "46 0.995564 0.762655 0.000000 \n", "47 0.995517 0.762592 0.000000 \n", "48 0.995471 0.762534 0.000000 \n", "49 0.995425 0.762478 0.000000 \n", "50 0.995373 0.762425 0.000000 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's solve for the optimal adjustment by assuming that the\n", "# economy returns to steady-state after T=50 periods.\n", "from dolo.algos.perfect_foresight import deterministic_solve\n", "sim = deterministic_solve(model, shocks=exo_g, T=50)\n", "display(sim) # it returns a timeseries object" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Model
nametaxes
typedtcc
filename../models/rbc_taxes.yaml
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
TypeEquationResidual
arbitrage$1 - \\beta \\; \\left(\\frac{c_{t}}{c_{t+1}}\\right)^{\\sigma} \\; \\left(1 - \\delta + rk_{t+1}\\right)$0.000
$\\chi \\; n_{t}^{\\eta} \\; c_{t}^{\\sigma} - w_{t}$-0.000
transition$z_{t} = \\left(1 - \\rho\\right) \\; \\overline{z} + \\rho \\; z_{t-1}$0.000
$k_{t} = \\left(1 - \\delta\\right) \\; k_{t-1} + i_{t-1}$0.000
$g_{t} = e_{g,t}$0.000
controls_lb$0$
$0$
controls_ub$inf$
$inf$
definitions$rk_{t} = \\alpha \\; z_{t} \\; \\left(\\frac{n_{t}}{k_{t}}\\right)^{1 - \\alpha}$
$w_{t} = \\left(1 - \\alpha\\right) \\; z_{t} \\; \\left(\\frac{k_{t}}{n_{t}}\\right)^{\\alpha}$
$y_{t} = z_{t} \\; k_{t}^{\\alpha} \\; n_{t}^{1 - \\alpha}$
$c_{t} = k_{t} \\; rk_{t} + w_{t} \\; n_{t} - i_{t} - g_{t}$
" ], "text/plain": [ "\n", " Model:\n", " ------\n", " name: \"taxes\"\n", " type: \"dtcc\"\n", " file: \"../models/rbc_taxes.yaml\n", "\n", "Equations:\n", "----------\n", "\n", "transition\n", " 1 : 0.0000 : z(0) == ((1) - (rho)) * (zbar) + (rho) * (z(-(1)))\n", " 2 : 0.0000 : k(0) == ((1) - (delta)) * (k(-(1))) + i(-(1))\n", " 3 : 0.0000 : g(0) == e_g(0)\n", "\n", "arbitrage\n", " 1 : 0.0000 : (1) - (((beta) * (((c(0)) / (c(1))) ** (sigma))) * ((1) - (delta) + rk(1)))\n", " 2 : 0.0000 : (((chi) * ((n(0)) ** (eta))) * ((c(0)) ** (sigma))) - (w(0))\n", "\n", "definitions\n", " 1 : rk = alpha*z*(n/k)**(1-alpha)\n", " 2 : w = (1-alpha)*z*(k/n)**(alpha)\n", " 3 : y = z*k**alpha*n**(1-alpha)\n", " 4 : c = k*rk + w*n - i - g\n" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lpTF+/HhsNhtg1OAfeughOnXqRPfu3V1/Lzt27KBHjx5kZGTwyCOPlCprhw4dXOcbMWIEQ4YMISUlhfvuu8+V76233uLiiy+mX79+/OlPf+LOO+/08idZmu6yWQc8/sU61u85Vq3HbNcsiseuaF9u+rp163jyySdZvnw5DRs2pKCgABFhxYoViAhvvvkmzzzzDM899xwAv/zyCytWrODkyZN07tyZyy+/3HWs4OBgJk+eTE5ODq+++ioAx44dY8mSJQQFBfHdd9/x97//nY8//rhaP6M3lFJWEbkT+Aajy+bbSql1IjIZyFFKzcVozokA/uO4eebsmtkWeENE7BgVp6eVUvU26Pvi+szJyeHjjz9mzZo1WK1WunTpQteuXWnbti3FxcVs376dVq1aMXv2bEaNGsXEiRN5/vnnWbhwIQ0bNjznuSdPnsw333xD8+bNOXLkiGv7ypUr+e2332jQoAEZGRlcfvnlhIeHM3v2bJYvX47FYuH222/n/fffZ8yYMZw8eZLu3bvz5JNPct999zF9+nQefvhh/vKXv3DbbbcxZswYXnvttXLLsXbtWtasWUNISAitW7fmz3/+M2azmX/84x+sXr2ayMhIBgwYQKdOZw184BUd9APU999/z8iRI11/IHFxcfz6669cc8017N27l+Li4lL9gIcPH05YWBhhYWH079+flStXkpaWVu7xjx49ytixY9myZQsiQklJSY1/pvOllJqHMT6U+7ZH3ZYvKWe/H4CONVu6wLZs2TLXNQdwxRVXuNJGjRrFRx99xAMPPMDs2bOZPXv2eR07KyuLcePGMWrUKEaMGOHaPnDgQOLj4wEYMWIEy5YtIygoiNzcXDIyMgA4ffo0jRs3BozKjvP+VNeuXZk/fz4Ay5cvd1VwbrzxRu6//36P5cjOziY6OhqAdu3asXPnTg4ePEjfvn2Ji4sD4Oqrr2bz5s3n9fkqooN+HXCuGk9NUUqd1fXrz3/+M3fffTfDhg1j0aJFTJo0yZVWNm9F3cYeeeQR+vfvz6effkpeXh79+vWrrqJrtcxX12d5rrnmGq6++mpGjBiBiJCSknJex546dSo//fQTX375JWlpaaxduxbwfI0rpRg7dixPPfXUWcexWCyufcxmM1artdS+FQkJCXEtO/c/1+euLrpNP0BlZ2fz0UcfcejQIQAKCgo4evQozZsbvRbfeeedUvk///xzCgsLOXToEIsWLXLVfJwiIyM5fvy4a939WDNnzqzBT6LVR7169eKLL76gsLCQEydO8OWXX7rSLrzwQlczyDXXXOPaXvYaLM+2bdvo1q0bkydPpmHDhuzebfTcnT9/PgUFBZw+fZrPPvuMrKwssrOzmTNnDvv3G7d0CgoK2Llz5zmPn5WVxaxZswB4//33z+tzZ2ZmsnjxYg4fPozVaq2RJlEd9ANU+/bteeihh+jbty+dOnXi7rvvZtKkSVx99dX07t37rHbRzMxMLr/8crp3784jjzxCs2bNSqX379+f9evXu27k3nfffTz44INkZWW5bnxpWmVlZGQwbNgwOnXqxIgRI0hPT3c1hYBR23/vvfcYNWqUa9utt97KpZde6rqRe8stt5CTk3PWse+99146duxIhw4d6NOnj6vNvFevXtx4442kpaVx1VVXkZ6eTrt27XjiiScYNGgQqampDBw4kL17zz0lyEsvvcRrr71GRkYGR48ePa/P3bx5c/7+97/TrVs3LrnkEtq1a1fqc1cLpVSdenXt2lUFgvXr1/u6CJX22GOPqWeffdbXxagSTz9njBu1+to+h7pwfR4/flwppdTJkydV165dVW5ubo2da8aMGeqOO+6oseOfD+fnLikpUUOHDlWffPLJWXm8ua51m76maXXSrbfeyvr16yksLGTs2LF06dLF10WqFZMmTeK7776jsLCQQYMGceWV1fvAd5WDvoi0AN4FmgJ2YJpS6qUyefoBnwM7HJs+UUpNruo5Nd9wv6GrabXlgw8+qLVzjRs3jnHjxtXa+c5lypQpNXp8b2r6VuBvSqnVjrlyc0Vkvjq7v/JSpVSdeO5e0zQt0FX5Rq5Saq9SarVj+TiwgXMPWKVpmqb5WLX03hGRJIyxR37ykNxDRH4Wka9EpPY7/GqapmkuXt/IFZEI4GPgr0qpss9qrwYuUEqdEJHLgM+As56kEJFbgVsBWrZs6W2RNE3TtHJ4VdMXEQtGwH9fKfVJ2XSl1DGl1AnH8jzA4higqmw+PRKhn3MfTOp8LVq0iB9++MG1PnXqVN59993qKpqm1YqZM2eyZ8+ZgVpvueUW1q+ve0MyedN7R4C3gA1KqefLydMU2KeUUiKSifElc6iq59Rqn81mw2w21+g5Fi1aREREBD179gRgwoQJNXo+TasJM2fOpEOHDq4HF998800fl8gzb2r6WcCNwAARWet4XSYiE0TE+Vc7EvhNRH4GXgaudTxEoNUBeXl5tGnThrFjx5KamsrIkSM5deoUSUlJTJ48mV69evGf//yHtWvX0r17d1JTU/nDH/7A4cOHAcjNzaVTp0706NGj1GiCM2fOLDUc7NChQ1m0aBEAX3/9NV26dKFTp05kZ2eTl5fH1KlTeeGFF0hLS2Pp0qVMmjTJ1W2tvHP369eP+++/n8zMTC6++GKWLl1aSz+16vffX/aw7cAJXxejzik79PfOnTvJzs4mNTWV7Oxsdu3aBRjdLSdOnEjPnj1p1aqVa8z6vXv30qdPH9LS0ujQoYPrGnFOagIwZ84cV1fNcePGcdttt9G/f39atWrF4sWLufnmm2nbtm2p7pwRERH87W9/o0uXLmRnZ3PgwAHmzJlDTk4O119/PWlpaZw+fZp+/fq5ngj+8MMPXU8Buw/AVt7wzDWpyjV9pdQyPM8+5J7nVeDVqp4jYHz1APz+a/Ues2lHuPTpCrNt2rSJt956i6ysLG6++Wb+9a9/ARAaGsqyZcsASE1N5ZVXXqFv3748+uijPP7447z44ovcdNNNru333ntvhec6cOAAf/rTn1iyZAnJyckUFBQQFxfHhAkTiIiI4J577gFgwYIFrn3GjBnj8dwAVquVlStXMm/ePB5//HG+++678/4x+VqR1cajn6/j8KliBrVrwoS+F9K5Zayvi1WaD65PT0N/jx07ljFjxjB27FjefvttJk6cyGeffQYYAX7ZsmVs3LiRYcOGMXLkSD744AMGDx7MQw89hM1mq9QEK4cPH+b7779n7ty5XHHFFSxfvpw333yTjIwM1q5dS1paGidPnqRLly4899xzTJ48mccff5xXX32VV199lSlTppCenl7qmHv27OH+++8nNzeX2NhYBg0axGeffcaVV15Z7vDMNUmPvRPgWrRoQVZWFgA33HCDK9A7B7I6evQoR44coW/fvgCMHTuWJUuWnLX9xhtvrPBcK1asoE+fPq4hm53Dx5anvHM7OYfF7dq1K3l5eZX9yHVKSJCZb+/qw539L2LF9gL+8K8fuOaNH1m0aX+tjLhYV3ka+vvHH3/kuuuuA4zrzXmtAlx55ZWYTCbatWvnqi1nZGQwY8YMJk2axK+//kpkZGSF573iiisQETp27EiTJk3o2LEjJpOJ9u3bu64xk8nk+vtw/5spz6pVq+jXrx+NGjUiKCiI66+/3nUdlx2euTauYz0MQ11QiRp5TSlvyOTw8PBz7qc8DM3sFBQUhN1ud60XFhZWuE9VOIemLTusrb9pGBHC3wa1ZnzfC5m1chdvLt3BuBmraJsQxYS+rbi8YwJBZh/Wz3xwfVbmWnFPdx+m2Pll2adPH5YsWcKXX37JjTfeyL333suYMWNK7ee8Nssex2QylTqmyWQq9xqrqJzn+vI+1/DMNUXX9APcrl27+PHHHwGj3bFXr16l0qOjo4mNjXW1h/773/+mb9++xMTEEB0d7arluA8hm5SUxNq1a7Hb7ezevZuVK1cC0KNHDxYvXsyOHcaoHAUFBUD5Q+KWd+76KiIkiFt6t2LJff15dmQqxVYbf5m1lv7PLeLdH/M4XRw4o5V6Gvq7Z8+epYYsLnutlrVz504aN27Mn/70J/74xz+yevVqAJo0acKGDRuw2+18+umn5102u93uum/wwQcfuMpR3nXcrVs3Fi9ezMGDB7HZbHz44Yc+vY51TT/AtW3blnfeeYfx48eTkpLCbbfdxiuvvFIqzzvvvMOECRM4deoUrVq1YsaMGQDMmDGDm2++mQYNGjB48GBX/qysLJKTk103rpwDZTVq1Ihp06YxYsQI7HY7jRs3Zv78+VxxxRWMHDmSzz//vNLnrs+Cg0xcnd6Cq7ok8t2Gfby+eBuPfr6Ol77bwrieSYzpkUR0A4uvi1mj3If+NpvNdO7cmZdffpmbb76ZZ599lkaNGlV4LSxatIhnn30Wi8VCRESEqxvw008/zdChQ2nRogUdOnTgxInzu4keHh7OunXr6Nq1K9HR0a6Zu8aNG8eECRMICwtzVaQAEhISeOqpp+jfvz9KKS677DKGDx9+nj+R6iN1rd0wPT1deRoDu77ZsGEDbdu29WkZ8vLyGDp0KL/99ptPy1GTPP2cRSRXKZVezi41pqrXtlKKlTsKmLp4Gws3HSA82MzozJb8sXcyCdFhNVDSunF91lURERHn/UVR3by5rnVNX9PqOBGhW6t4urWKZ8PeY7yxeBszfsjjnR/zuDKtOeP7tuKixhXfpNQ00G36AS0pKale1/IrQ0SGiMgmEdkqIg94SA8RkdmO9J8c40w50x50bN8kIoPL7lsT2iZE8eK1nVl0Tz+uy2zJF7/s4ZLnl/Cnd3PI3Xm4NooQ8Hxdy/eWDvo+VNea1uqbin6+ImIGXgMuBdoBo0WkXZlsfwQOK6UuAl4A/unYtx1wLdAeGAL8y3G8WtEirgGPD+/A8vsHMDE7hZU7Crjq9R8YNfVHFm6snu6e+vqsm7z9vfhP8876z2HztxCXDPEXQngjMAWBmMFkcrybjXcxOZZNIOJ4d7yQM9sRxzt4fM6s3K5YFXQ7rES3xFCLmUMH9hMfF3emy1f19Wb0Qp0ohNcUcKjgMKGhoefKlglsVUptBxCRWcBwwH3AlOHAJMfyHOBVxxAkw4FZSqkiYIeIbHUc70dqUXxECHcPvJjxfVoxa9Vu3lq6nZtmrqJN00jG923FJW2bYKpCN1lLcAgHDh4kLi6+WrvZat5RSnHkcEFF1/U5+U/QP/o/2PodnPjd1yWpFonBMeR3uZ8D0a2oL4G2TmkQT2hUPImJiefK1RzY7baeD3QrL49SyioiR4F4x/YVZfb12XwS4SFB/LFXMmN6XMDctXt4Y8k27pr9c5WPFxVi4s/dYrkgZheir8865aKE2Iqu63Pyn6Df43bjVXQCCrbD6cOgbGB3vJzLyu5YtgPKWLfbjGXnu3Jsx/Fv0vn8u1Rh3nLSy+xnAYznUvPOvV+lz1uJMlS4W3X8O1+NTQLelKfZEGiUXFEuT9Gs7EnLy1OZfWt92HCL2cRVXRP5Q+fmLN58gC37z+43XlkKyPPfZ97qJbPJxMDkCq/rc/KfoO8UEgEJqb4uhVY/5AMt3NYTgT3l5MkXkSAgGiio5L4opaYB08DoslltJa+AyST0b9OY/m0a19YpNT+hb+RqgWwVkCIiySISjHFjdm6ZPHOBsY7lkcD3jpFi5wLXOnr3JGNMDrSylsqtaVXmfzV9Tasmjjb6O4FvADPwtlJqnYhMBnKUUnMx5oz4t+NGbQHGFwOOfB9h3PS1AncopQJnnATNb9W5J3JF5ACws5zkhsDBWixObavPn68ufbYLlFK1PkVbAF/b9fmzQd35fJW6rutc0D8XEcnxxePztaU+f776/NmqQ33++dTnzwb+9/l0m76maVoA0UFf0zQtgPhb0J/m6wLUsPr8+erzZ6sO9fnnU58/G/jZ5/OrNn1N0zTNO/5W09c0TdO8oIO+pmlaAPGboF/RuOf+RERaiMhCEdkgIutE5C+O7XEiMl9EtjjeY31d1qoSEbOIrBGR/zrWkx3j0W9xjE8f7Osy1gX16boGfW37w7XtF0G/kuOe+xMr8DelVFugO3CH4/M8ACxQSqUACxzr/uovwAa39X8CLzg+22GMceoDWj28rkFf23X+2vaLoI/buOeiY96BAAAgAElEQVRKqWLAOe65X1JK7VVKrXYsH8e4gJpjfKZ3HNneAa70TQm9IyKJwOXAm451AQZgjEcPfvzZqlm9uq5BX9v4wWfzl6Dvadxzn41dXp0c0+91Bn4Cmiil9oLxxwP46xCJLwL3AXbHejxwRCnlHKi33vz+vFRvr2vQ13Zd5S9Bv1Jjl/sbEYkAPgb+qpQ65uvyVAcRGQrsV0rlum/2kNXvf3/VoN7+XPS1XXf5yyiblRq73J+IiAXjj+J9pdQnjs37RCRBKbVXRBKA/b4rYZVlAcNE5DIgFIjCqB3FiEiQo0bk97+/alLvrmvQ1zZ1/HfoLzX9yox77jcc7YBvARuUUs+7JbmP3T4W+Ly2y+YtpdSDSqlEpVQSxu/pe6XU9cBCjPHowU8/Ww2oV9c16GsbP/hsfhH0Hd+gznHPNwAfKaXW+bZUXskCbgQGiMhax+sy4GlgoIhsAQY61uuL+4G7HePSx2MEhoBWD69r0Nd2nb+29TAMmqZpAcQvavqaVt3Ke4ioTB4RkZcdD079IiJd3NJaisi3jv3XO3qqaFqd5y83cjWtujkfIlotIpFArojMV0qtd8tzKcbctylAN+B1xzvAu8CTSqn5jp4qdjTND+igrwUkR19xZ7/x4yLifIjIPegPB951TIS+QkRiHD1PYoEgpdR8x/4narf0mlZ1dS7oN2zYUCUlJfm6GFo9lpube9B9LtEyDxG5K+/hqUTgiIh8AiQD3wEPVDQxur62tZpU9rouT50L+klJSeTk5Pi6GFo9JiI73ZbP9RBReQ/eBAG9Mb4odgGzgXF46LUhIrcCtwK0bNlSX9tajXG/rs9F38jVAlY5DxG5K+/hqXxgjWPMHCvwGdDFw/4opaYppdKVUumNGlVYCdO0Guc3Qf/3o4V8u+53XxdDqyfO8RCRu7nAGEcvnu7AUce9gFVArIg4o/gASt8LqLJNvx/HbtfdqLWa4zdBf+YPedz+/mqOFZb4uiha/eDxISIRmSAiExx55gHbga3AdOB2AEfb/T3AAhH5FaMZaLq3BTpwvIhLX1rCnNx8bw+laeWqc2365clu25ipi7exZPMBhqY283VxND+nlFqG5zZ79zwKuKOctPlAanWW6ejpEuwKlm87yKiMFhXvEEBKSkrIz8+nsLDQ10XxudDQUBITE7FYLFXa32+CfpeWscQ0sPD9hv066Gv1UpHV6PyzakeBj0tS9+Tn5xMZGUlSUhJGy1xgUkpx6NAh8vPzSU5OrtIx/KZ5x2wS+rduzMJN+7HpNk+tHiqyGs937TlaSP7hUz4uTd1SWFhIfHx8QAd8ABEhPj7eq/94/CboAwxo05jDp0pYs+uwr4uiadWuqOTMQ72r8nRtv6xAD/hO3v4c/Cro97m4EUEmYcFGfxyKW9POzdm8A7Byh67YaDXDr4J+dJiFjKQ4vt+gg75W/xQ7mncaR4bomr5WY/wq6IPRi2fTvuPsLtBtnlr94mzT73VRQ7buP0HByWIfl0hz98wzz/Dyyy8DcNdddzFgwAAAFixYwA033MBtt91Geno67du357HHHnPtN2/ePNq0aUOvXr2YOHEiQ4cOBeDkyZPcfPPNZGRk0LlzZz7/3Jh7Zd26dWRmZpKWlkZqaipbtmyp1s/hN713nAa0acwTX27g+437GdszydfF0bRq4wr6KQ35ZM3/WJVXwOD2TX1cqrrn8S/WsX5P9U67265ZFI9d0f6cefr06cNzzz3HxIkTycnJoaioiJKSEpYtW0bv3r25+uqriYuLw2azkZ2dzS+//MLFF1/M+PHjWbJkCcnJyYwePdp1vCeffJIBAwbw9ttvc+TIETIzM7nkkkuYOnUqf/nLX7j++uspLi7GZjvnkE7nze+CfqtGESQ3DOcf/13Pqwu3EtvAQnSY8YoKsxAZEkRkqIWI0CDCg82EBQfRINhMSJCJUIsZi9lEkFmwmEyYTGASQQRsdoVSYFcKm11hV0b3KLtjm12dSXduU0pht5/ZBu5pxjs40h35HNlcy868yrHuvuwsA+75nHkw1sGR30OacS4jk6c01/4YG5x9otyP53YIV96y8+6oMudwz+u+f9l09/mjPc3l4+lY5eUt6/ruF5DWIqbijHWIs00/IymO4CATq3booF+XdO3aldzcXI4fP05ISAhdunQhJyeHpUuX8vLLL/PRRx8xbdo0rFYre/fuZf369djtdlq1auXqXjl69GimTZsGwLfffsvcuXOZMmUKYPRQ2rVrFz169ODJJ58kPz+fESNGkJKSUq2fw++CPsAzI1NZsGE/R04Vc/hUMUdPl7DnSCEb9h7nRJGV44Ul6F6dZzhv9gvGnX9xbBPE9XhSqW2u5TM9BUr1FyiT5ukcbllLpYPg3vnA/bieOiUIZ5/jrPJ44I/B0tl7JyrUQlpijG7XL0dFNfKaYrFYSEpKYsaMGfTs2ZPU1FQWLlzItm3bCAsLY8qUKaxatYrY2FjGjRtHYWGhq0LkiVKKjz/+mNatW5fa3rZtW7p168aXX37J4MGDefPNN11NSdXBL4N+RlIcGUlx5aYrpThdYuNUsY1TRTZOlVgpKrFTWGKjxKYosdux2tSZ2royavxmkxFizCYjMJlNYvwnAJicy2LkNYkR3MyObc7t7ulgvLtvh7PzOoOte6A1uQVl57ncA7MryHrY15VPd3HzK87mnRCLiYzkWKYu3s7JIivhIX75Z1ov9enThylTpvD222/TsWNH7r77brp27cqxY8cIDw8nOjqaffv28dVXX9GvXz/atGnD9u3bycvLIykpidmzZ7uONXjwYF555RVeeeUVRIQ1a9bQuXNntm/fTqtWrZg4cSLbt2/nl19+0UG/IiJCg+AgGgQHQYSvS6NpleNs3gk2m+iWHM9rC7exKq+Afq0b+7hkmlPv3r158skn6dGjB+Hh4YSGhtK7d286depE586dad++Pa1atSIrKwuAsLAw/vWvfzFkyBAaNmxIZmam61iPPPIIf/3rX0lNTUUpRVJSEv/973+ZPXs27733HhaLhaZNm/Loo49W62eol0Ff0/xRkdVOsNmEySRkJhvt+su2HNRBvw7Jzs6mpOTMoI+bN292Lc+cOdPjPv3792fjxo0opbjjjjtIT08HjC+EN95446z8Dz74IA8++GD1FtyN33XZ1LT6qqjETkiQ8ScZajGTkRTL0i0HfVwqzVvTp08nLS2N9u3bc/ToUcaPH+/T8uiavqbVEcU2G8FBZ+phvS5qxD+/3sj+Y4U0jgr1Yck0b9x1113cddddvi6Gi67pa1od4V7TB+id0hCAZVt1bV+rPjroa1odUWS1E2Ixu9bbJUQRHx7MMt3EA3DO7o+BxNufgw76mlZHFFltpWr6JpPQ86KGLN16MOADXmhoKIcOHQr4n4NzPP3Q0Ko39+k2fU2rI4qspZt3AHpf1JAvft7Dpn3HadM0ykcl873ExETy8/M5cOCAr4vic86Zs6pKB31NqyOMNn1zqW29nO36Ww4GdNC3WCxVnilKK82r5h0RGSIim0Rkq4g84CF9goj86ph0epmItPPmfJpWnxVZbYRYSv9JNosJo1WjcN11U6s2VQ76ImIGXgMuBdoBoz0E9Q+UUh2VUmnAM8DzVS6pptVznpp3APqkNGLF9kOcKrb6oFRafeNNTT8T2KqU2q6UKgZmAcPdMyil3Mc/Dcd9WEVN00oxgr75rO0D2zWhyGrXtX2tWngT9JsDu93W8x3bShGRO0RkG0ZNf6IX59O0eq3Yai/1cJZTZnIc0WEWvl23zwel0uobb4K+pyEcz6rJK6VeU0pdCNwPPOzxQCK3ikiOiOTou/NaoCrbZdPJYjaR3aYxCzbuw2qze9hT0yrPm6CfD7RwW08E9pwj/yzgSk8JSqlpSql0pVR6o0aNvCiSpvmv8tr0AQa1b8KRUyWsytMTpmve8SborwJSRCRZRIKBa4G57hlExH3Kl8uB6p3sUdPqkaKS0k/kuutzcSNCgkx8u/73Wi6VVt9UOegrpazAncA3wAbgI6XUOhGZLCLDHNnuFJF1IrIWuBsY63WJNa0eUkqV27wD0CA4iN4pDfl23b6AfypV845XD2cppeYB88pse9Rt+S/eHN+ppKSE/Px8CgsLq+NwWgWcT/xZLBZfFyVgWB3zMpcX9AEGtWvKdxv2s37vMdo3i67F0mn1iV88kZufn09kZCRJSUl6CsAa5hzbIz8/Xz8BWYtcUyV66LLplN22MSaBb9bt00FfqzK/GHCtsLCQ+Ph4HfBrgYgQHx+v/6uqZUUlxlSJZZ/IdRcfEUJGUhz//XmPbuLRqswvgj7oSb5rk/5Z1z5nTT/YfO4/yau6JLL94EnW7D5SG8XS6iG/Cfr1RVJSEgcP6icrtdJczTvnqOkDXNqxKaEWEx/n5tdGsbR6SAf9WmSz2XxdBK2OKq5Emz5AZKiFIe2b8sXPeygs0deTdv500K+kZ555hpdffhkw5rwcMGAAAAsWLOCGG27gww8/pGPHjnTo0IH777/ftV9ERASPPvoo3bp148cff3RtP336NEOGDGH69Om1+0G0OqnI6mjTP0fvHaeruiZyrNDKgg37a7pYWj3kF7133D3+xTrW7zlWccbz0K5ZFI9d0f6cefr06cNzzz3HxIkTycnJoaioiJKSEpYtW0ZKSgr3338/ubm5xMbGMmjQID777DOuvPJKTp48SYcOHZg8ebLrWCdOnODaa69lzJgxjBkzplo/i+afKtN7x6nnhQ1pGhXKx6vzuTw1oaaLptUzuqZfSV27diU3N5fjx48TEhJCjx49yMnJYenSpcTExNCvXz8aNWpEUFAQ119/PUuWLAHAbDZz1VVXlTrW8OHDuemmm3TA11yKSirXpg9gNgl/6NKcxZsPsP+47mWlnR+/q+lXVCOvKRaLhaSkJGbMmEHPnj1JTU1l4cKFbNu2jZYtW5Kbm+txv9DQUMzm0rW3rKwsvvrqK6677jrdU0YDzq95B4xePK8v2sac3Hxu73dRTRZNq2d0Tf889OnThylTptCnTx969+7N1KlTSUtLo3v37ixevJiDBw9is9n48MMP6du3b7nHmTx5MvHx8dx+++21WHqtLjuf5h2AixpHkHVRPO/8kOf6wtC0ytBB/zz07t2bvXv30qNHD5o0aUJoaCi9e/cmISGBp556iv79+9OpUye6dOnC8OHDz3msF198kcLCQu67775aKr1Wloi0EJGFIrLBMUbUWcOGiOFlx5Sgv4hIlzLpUSLyPxF51ZuynG9NH2B8nwvZd6yIz9eea3BbTSvN75p3fCk7O5uSkhLX+ubNm13L1113Hdddd91Z+5w4caLUel5enmt5xowZ1V9I7XxYgb8ppVaLSCSQKyLzlVLr3fJcCqQ4Xt2A1x3vTv8AFntbEGebvqdJVMrTO6UhbROimL5kOyO7JGIy6aZCrWK6pq8FLKXUXqXUasfycYzRYsvO/jYceFcZVgAxIpIAICJdgSbAt96W5UzzTuX/JEWE8X1asWX/CRZu0t03tcrRQV/TABFJAjoDP5VJ8jgtqIiYgOeAe6vj/K6Hs8oZT788l6cm0Cw6lDeWbK+OYmgBQAd9LeCJSATwMfBXpVTZh0DKmxb0dmCeUmq3h3T3Y1dqKtCqtOmDMZXizb2SWbmjgBXbD53Xvlpg0kFfC2giYsEI+O8rpT7xkKW8aUF7YEwSlAdMAcaIyNNld67sVKBFVjsmgaAqtMtf3+0CmseEMWnuOj2HrlYhHfS1gCXGQxJvARuUUs+Xk20uRkAXEekOHHXcC7heKdVSKZUE3IPR7v9AVctizI9rrtJzG2HBZh4Z2o6Nvx/n3R93VrUIWoDQvXe0QJYF3Aj86pjSE+DvQEsApdRUjJnhLgO2AqeAm2qiIEUltko9jVuewe2b0OfiRrwwfzNDOyXQODK0Gkun1Sf+U9O328Ba5LPT9+zZs1bO89lnn7F+/fqKM/rJeeoypdQypZQopVKVUmmO1zyl1FRHwMfRa+cOpdSFSqmOSqkcD8eZqZS605uyGDX9qv85igiTrmhHodXG0/M2elMUrZ7zn6B/Yh/sXw8+mjHohx9+qJXz6KAfmJzNO95o1SiCCX0v5JM1/+ODn3ZVU8m0+sZ/gr7J8QehfHOjKiIiAoBFixbRr18/Ro4cSZs2bbj++utRSvHVV18xatQoV/5FixZxxRVXAPDtt9/So0cPunTpwtVXX+16YOuBBx6gXbt2pKamcs899/DDDz8wd+5c7r33XtLS0ti2bRv9+vXjrrvuok+fPrRt25ZVq1YxYsQIUlJSePjhh13ne++998jMzCQtLY3x48e7xu6PiIjgoYceolOnTnTv3p19+/Z5PI/mW0VW23k9mFWev2Sn0L91Ix75/DcW6b77mgf+06YvjqD/9QOwr5prqE07wqVndbwo15o1a1i3bh3NmjUjKyuL5cuXM3DgQMaPH8/JkycJDw9n9uzZXHPNNRw8eJAnnniC7777jvDwcP75z3/y/PPPc+edd/Lpp5+yceNGRIQjR44QExPDsGHDGDp0KCNHjnSdLzg4mCVLlvDSSy8xfPhwcnNziYuL48ILL+Suu+5i//79zJ49m+XLl2OxWLj99tt5//33GTNmDCdPnqR79+48+eST3HfffUyfPp2HH37Y43k03ykq8a55xynIbOKV67owauqP3PH+av4zoSftmkVVQwm1+sKPavqO7ycf1fTdZWZmkpiYiMlkIi0tjby8PIKCghgyZAhffPEFVquVL7/8kuHDh7NixQrWr19PVlYWaWlpvPPOO+zcuZOoqChCQ0O55ZZb+OSTT2jQoEG55xs2bBgAHTt2pH379iQkJBASEkKrVq3YvXs3CxYsIDc3l4yMDNLS0liwYAHbtxsP6wQHBzN06FDAGB7afRgIre4otlVP0AeICAni7XEZRIVZGDn1B95atgObXU+krhn8p6bvbN65ZBKERPqyJISEhLiWzWYzVqsVgGuuuYbXXnuNuLg4MjIyiIyMRCnFwIED+fDDD886zsqVK1mwYAGzZs3i1Vdf5fvvvz/n+UwmU6lzm0wmrFYrSinGjh3LU089dda+FovF1Q3Qvaxa3WLU9L1r03fXNDqUObf15OFPf+Uf/13P3LX/46kRqbrWr/lTTd/xB2Gvu8PI9uvXj9WrVzN9+nSuueYaALp3787y5cvZunUrAKdOnWLz5s2cOHGCo0ePctlll/Hiiy+ydq3RYzAyMpLjx4+f13mzs7OZM2cO+/cbbbgFBQXs3Hnu/tpVOY9Wc4qs3nXZ9KR5TBhvj8vgpWvTyD98mqGvLGXyF+s5XlhS8c5aveU/QV8c/5TY625N1Ww2M3ToUL766itXk0qjRo2YOXMmo0ePJjU1le7du7Nx40aOHz/O0KFDSU1NpW/fvrzwwgsAXHvttTz77LN07ty50jdY27VrxxNPPMGgQYNITU1l4MCB7N2795z7VOU8Ws3xtstmeUSE4WnNWfC3vozObMmMH3aQ/dxiPl2Tj/JRTzjNt6Su/eLT09NVTk7prtAbNmygbeuL4fdfILIZRDbxUem8pJSjy2ll36l8mvP4rvfylh37oRybPaXBhu35tN023biHouyOsjuXbW7LzjS3dJSHfeylz+lMq/TPwu069VjuMi6fAq0v9ZgkIrlKqXTPO9YcT9e2U/8pi+jYPJqXR3eu0TKs3X2ERz//jV/yj9KlZQyThrUnNTGmRs+p1Y7KXtf+06YvJkBAVXNNXykoPun4D8I9SLkFLsqsuwcrVx5VJq/bNud2n3I83i9SZrlsmmPdWmw8FyFmY7vr3blscjS5uW0zmY3/yJy/q1L7uW1zpcnZ23DmpZx0t89Ttvzuwht7+wOrVUUlthqp6ZeV1iKGz27PYs7qfJ75ehPDXl3OHzo3557BrWkeE1bj59d8z4+CvhhBpbrb9E/sg+PnbgpxBaKyweusZdOZYHZeQa4y75TZ5lj3mE45wf08FAjcuer899OqpMhqr/Y2/fKYTMKo9BZc2qEpry/axpvLdvDlr3u5KSuJ2/teRHQDS62UQ/MN/wn6YNQaqzPo26xwYj+EREFkQunAflbg1rSaU2S1E2yuvt47lREZauG+IW24vvsFTPlmE9OWbOeDn3Yxoe+F3JSVRINg/woPWuX4zY1cpZTRV786b+Se2Ge0T0c1g+AGYAmFoBAwW4xzOYN/gKlr93kCQU303qms5jFhvHBNGvMm9iYzKY5nv9lE738uZPqS7Zwurru95bSq8YugHxoayqFDh1BiMoJ0dbAWw8kDEBYHFt2W6aSU4tChQ4SG6lEaa4vdriixqVpp0z+XtglRvDUug49vM57ifXLeBno/s5BpS7Zxsqju9prTzo9f/P+WmJhIfn4+Bwr2GsH6YDXURE8VGDdwo8zw+wbvj1ePhIaGkpiY6OtiBIxim3N+3Npt3ilP1wti+fcfu7Eqr4CXvtvC/83byL8WbeOmnsmM6XEBseHBvi6i5gW/CPoWi4Xk5GTY8Dr8PAse9HIEwf0b4KOh0O026PF/1VNITauiopLznxS9NmQkxfHeLd1Yu/sIr36/lRe+28zUxdsYlZ7ILb1b0SKu/KFDtLrLL4K+S1gMFB01buaavKgVffuIMZRDn3uqr2yaVkWu+XF91KZfkbQWMbw5Np3N+44bN3tX7uLfK3YysF0Tbs5KJjM5rkozfmm+4WdBP9Z4LzwKDeKqdoytC2DrfBj0RNWPoWnVqMhat5p3ynNxk0imXN2Jewa15t8r8nj/p118s24fbROiGNPjAoanNdM9fvxA3axalCfU8eTg6cNV299uM2r5sUmQeWu1FUvTvOGq6dex5p3yNI0O5d7BbfjxgWyeGtERpRQPfvIr3f5vAZPmrmPT73pMp7rMv76WnTX900eqtv+a92D/Orh6ptE1U9PqgEJHm351TKJSm8KCzYzObMm1GS3I2XmYf/+4kw9+2sXMH/Lo3DKGUektGJqaQGSoftirLvGzoO+o6RdWoaZfeAy+fwISM6HdldVbLk3zwpnmHf8K+k4iQkZSHBlJcRScLOaT1fnMWrWbBz/5lce/WMelHRL4Q+fmZF3UELNJt/37mldBX0SGAC8BZuBNpdTTZdLvBm4BrMAB4Gal1LnH/D0Xb2r6S56Bk/vhulkB+cCVVnedad6p2236lREXHswtvVvxx17JrN19hP/k5vPfn/fw6Zr/0SgyhCtSmzEsrRmdEqP1zV8fqXLQFxEz8BowEMgHVonIXKWU+1yGa4B0pdQpEbkNeAa4psqlrWqb/oHNsOJ16HwDNO9a5dNrWk0odtb062jvnaoQETq3jKVzy1geHdqOhRv388ma//HvFXm8vXwHLeMacHlqApd1SKBD8yj9BVCLvKnpZwJblVLbAURkFjAccAV9pdRCt/wrgBu8OJ9b88551PSVMubVtTSA7ElenV7TaoK/N+9UJNRi5tKOCVzaMYGjp0r4Zv3vfPHzHqYt2c7ri7aRGBvG4PZNGdy+KV0viNVNQDXMm6DfHNjttp4PdDtH/j8CX3lxPuPmq6XB+TXvbPoKti2AwU9BRCOvTq9pNcFfumxWh+gGFkalt2BUegsOnyxm/vp9zPttL//+cSdvLdtBXHgw/Vs3JrttY3qnNNQ3gWuAN0Hf09exx/ERROQGIB3oW076rcCtAC1btjz3WUNjKh/0i0/B1/dDw9aQ+afK7aNptayoxL+6bFaX2PBgRmW0YFRGC44XlrB48wHmr9/Hdxv28fHqfIJMQnpSLP1aN6bvxY1o0zRSNwNVA2+Cfj7Qwm09EdhTNpOIXAI8BPRVShV5OpBSahowDYzZhc551rDYyrfpL50CR3bB2P8aI2dqWh1UVA/b9M9XZKiFoanNGJraDKvNTu7OwyzcdIBFm/bz9FcbefqrjTSMCKF3SkN6XhhP1kUNaaYnfakSb4L+KiBFRJKB/wHXAte5ZxCRzsAbwBCl1H4vznVGWEzl2vQPbILlL0PqtZDcu1pOrWk1IZCadyojyGyiW6t4urWK54FL27D36GmWbTnI0i0HWbL5AJ+u+R8AyQ3D6ZYcR/dW8XRrFUdCtP4SqIwqB32llFVE7gS+weiy+bZSap2ITAZylFJzgWeBCOA/jn/LdimlhnlV4rBYKNhRUeHgy78ZY+QPesKr02laTfO3J3JrW0J0GFent+Dq9BbY7YpN+46zfOtBftx2iC9/3cusVcatxcTYMDKS4khPiqXrBbGkNI7UN4U98KqfvlJqHjCvzLZH3ZYv8eb4HoXGVNy888tHkLcULn9e37zV6jznKJvBZh30K2IyCW0TomibEMUtvVthsys27D3Gyh0FrMorYOmWM/8JRIYEkdoims4tYklrEUNqi2gaR+p5IvzriVyouHnn5EGji2ZiBnQdV2vF0rSqMqZKNGHStdLzZjYJHZpH06F5NDf3SkYpxa6CU+TuPEzuzsOs3X2E1xdvw2Y3bhU2jQqlY2I0HZtH06F5FB2aRdMoMiSgbhD7Z9AvOQXWIs/j53z9IBQdh2GveDf8sqbVkiKrTTftVBMR4YL4cC6ID2dEF2MioNPFNn7bc5Sfdx/h1/8d5df8o8xfv8+1T8OIYNomRNEuIYo2CZG0bhLFhY3D6+09Fv8L+q6nco9AZJPSaVvmw68fQd8HoHHb2i+bplVBsdUe0D13alpYsNk1NpDT8cISNuw9zro9R1m/5xjr9hxjxvI81yxmZpOQ3DCci5tEcFHjSFIaR5DSJIKk+HBCLf79ZeB/Qd81/s7h0kG/6AT89y6jT37vu31TNk2rgiKrvd7WKuuqyFALmclxZCaf+SIosdnJO3iSDb8fZ/Pvx9m07zjr9hzj699+x9E6hAi0iG1Aq0bhJDcMp1WjCJLiG5AUH06zmDC/uHHsh0G/nKEYvnsMjubDzV/rYZM1v2IEfV3T9zWL2URKk0hSmpdIxH4AAAZtSURBVERCpzPbC0tsbD9wkm0HTrDtwAm27j/BjoMnWbmjgFPFNle+YLOJxNgwWsY34IK4BrSIa0BLx3tibFidebrYD4O+h5E2ty+GVW9C9zugZXfflEvTqqioxOZ3Y+kHklCLmXbNomjXLKrUdqUUvx8rJO/gKXYeOsmOQyfZdegUOw+dIifvMCeKrKXyR4dZaB4TRvPYMJrHhNEsJpRmMWEkRIeREB1K48gQgmqhB5f/Bf2yI20WnYC5d0LchTDgYd+VS9OqqMhqJ8TP24kDkYg4AnYYPS6ML5WmlOLIqRJ2FZwi//Bp8g+fYvfhU+w5UsiuQ6f4cduhs74UTAKNIkNoGhVKk6hQmkYb740jQ2gYGUKjiBAaRYbQ2MveRv4X9F3z5Dpq+vMfhSO7jWad4Aa+K5emVVGR1UaI7qNfr4gIseHBxIYH06lFjMc8xwpL2HukkD1HTrP3aCF7j57m96OFxn8Ph07y044Cjp4uKbVPsNnEpieGeFU2/wv6odHG++nDxiTnOW/pZh3NrxVZ7USE+N+fouadqFALUU0ttG4aWW6ewhIb+48VceBEEQeOF3GiyOr1MwX+d6WZzBASDYfzYPW7Rm+d7Ed8XSrNz4hIC+BdoClgB6YppV4qk0cwZoa7DDgFjFNKrRaRNOB1IAqwAU8qpWZXtSxFJXbiw3VNXztbqMVMy/gGtIyvvlYM/wv6YPTg+WU2mIJg9Cyw6IGWtPNmBf7mCOKRQK6IzC8z89ulQIrj1Q0j0HfD+AIYo5TaIiLNHPt+o5SqwjyezoezdJu+Vjv8s3rh7LbZ7wFolubbsmh+SSm1Vym12rF8HNiAMTGQu+HAu8qwAogRkQSl1Gal1BbHvnuA/UCVB3kqtukum1rt8c+afpMOEBIFWXf5uiRaPSAiSUBn4KcySZ5mh2sO7HXbNxMIBrZV9fxFJfqJXK32+GfQH/6aMXyySf+haN4RkQjgY+CvSqljZZM97OKa5EdEEoB/A2OVUvZyjl/hrHAL7+lHAI33pfmYf0ZNER3wNa+JiAUj4L+vlPrEQ5ZyZ4cTkSjgy/9v7w5e4yjDOI5/f6RE0ZPG2Etr9ZCDOQcJBDwUCiVK66GIRTSRHhUCHjRK/wB7aXMRJJhDbtoK0uClSJqzVDEgIUiC4Ck0CurVy+Nh3gWR7Foym5153/l9ICQzGcLz7Dx5dpjdfV7gerr1c6SIWI2ImYiYmZw8+g7Qk4+d4onxPK+/LD/unNZJ6Z05a8BuRNzsc9gG8LYqs8BfEXEgaRz4mup+/50RhWw2FL68sK6aA94CfpK0nfZ9DDwHEBGfUS0QNA/sU71j55103OvAy8CEpMW0bzEien/HrLUUMXgd8lGT9Bvwa59fPwP8PsJwRq3k/NqU27mIGPmSah2u7ZJzg/bk90h13bqmP4ik7yNipuk4TkrJ+ZWc2zCU/PiUnBvkl5/v6ZuZdYibvplZh+TW9FebDuCElZxfybkNQ8mPT8m5QWb5ZXVP38zM6sntSt/MzGrIpulLuijpZ0n7kpabjqcOSWclbUnalbQjaSntf1rSt5L20venmo71uCSNSfpR0jdp+wVJ36XcvkwfcOq8kuoaXNs51HYWTV/SGPAp1ajbaeCqpOlmo6qlN9b3RWAWeDflswxsRsQUsJm2c7VENbmy5wZwK+X2B3CtkahapMC6Btd262s7i6YPvATsR8QvEfE38AXV2NssDRjrexlYT4etA681E2E9ks4ArwCfp20B54Gv0iHZ5jZkRdU1uLbJILdcmn6/EbfZ+89Y39MRcQDVPw/wbHOR1bICfEC1IhXABPBnRPRWgi7m/NVUbF2Da7utcmn6A0fc5up/xvpmSdKrwGFE/PDv3Uccmv35G4JiHxfXdnvlMnCt74jbXPUZ6/swrcx0kGa1HzYX4bHNAZckzQOPU60ju0K16tSpdEWU/fkbkuLqGlzbtPwc5nKl/wCYSq+SjwNvUI29zdKAsb4bwEL6eQG4O+rY6oqIjyLiTEQ8T3We7kfEm8AWcCUdlmVuJ6CougbXNhnklkXTT8+g7wH3qF4Yuh0RO81GVUtvrO95Sdvpax74BLggaQ+4kLZL8SHwvqR9qvugaw3H07gC6xpc262vbX8i18ysQ7K40jczs+Fw0zcz6xA3fTOzDnHTNzPrEDd9M7MOcdM3M+sQN30zsw5x0zcz65B/AKdXj0Wk6+usAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(figsize=(10,10))\n", "plt.subplot(221)\n", "plt.plot(sim['k'], label='capital')\n", "plt.plot(sim['y'], label='production')\n", "plt.legend()\n", "plt.subplot(222)\n", "plt.plot(sim['g'], label='gvt. spending')\n", "plt.plot(sim['c'], label='consumption')\n", "plt.legend()\n", "plt.subplot(223)\n", "plt.plot(sim['n'], label='work')\n", "plt.plot(sim['i'], label='investment')\n", "plt.legend()\n", "plt.subplot(224)\n", "plt.plot(sim['w'], label='wages')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_input": false, "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }