{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics: Chapter 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This line configures matplotlib to show figures embedded in the notebook, \n",
    "# instead of opening a new window for each figure. More about that later. \n",
    "# If you are using an old version of IPython, try using '%pylab inline' instead.\n",
    "%matplotlib inline\n",
    "\n",
    "from pysolve.model import Model\n",
    "from pysolve.utils import is_close,round_solution\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model DISINF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_disinf2_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('C', desc='Consumption at current prices')\n",
    "    model.var('Ck', desc='Real consumption')\n",
    "    model.var('F', desc='Realized firm profits')\n",
    "    model.var('Fb', desc='Realized bank profits')\n",
    "    model.var('IN', desc='Stock of inventories at current costs')\n",
    "    model.var('INk', desc='Real inventories')\n",
    "    model.var('INke', desc='Expected real inventories')\n",
    "    model.var('INkt', desc='Target level of real inventories')\n",
    "    model.var('Ld', desc='Demand for loans')\n",
    "    model.var('Ls', desc='Supply of loans')\n",
    "    model.var('Mh', desc='Deposits held by households')\n",
    "    model.var('Mhk', desc='Real value of deposits held by households')\n",
    "    model.var('Ms', desc='Supply of deposits')\n",
    "    model.var('N', desc='Employment level')\n",
    "    model.var('omegat', desc='Target real wage rate')\n",
    "    model.var('P', desc='Price level')\n",
    "    model.var('PIC', desc='Inflation rate of unit costs')\n",
    "    model.var('Rl', desc='Interest rate on loans')\n",
    "    model.var('Rm', desc='Interest rate on deposits')\n",
    "    model.var('RRc', desc='Real interest rate on bank loans')\n",
    "    model.var('S', desc='Sales at current prices')\n",
    "    model.var('Sk', desc='Real sales')\n",
    "    model.var('Ske', desc='Expected real sales')\n",
    "    model.var('UC', desc='Unit costs')\n",
    "    model.var('W', desc='Wage rate')\n",
    "    model.var('WB', desc='The wage bill')\n",
    "    model.var('YD', desc='Disposable income')\n",
    "    model.var('YDk', desc='Real disposable income')\n",
    "    model.var('YDkhs', desc='Haig-Simons measure of real disposable income')\n",
    "    model.var('YDkhse', desc='Expected HS real disposable income')    \n",
    "    model.var('Yk', desc='Real output')\n",
    "\n",
    "    model.set_param_default(0)\n",
    "    model.param('alpha0', desc='Autonomous consumption')\n",
    "    model.param('alpha1', desc='Propensity to consume out of income')\n",
    "    model.param('alpha2', desc='Propensity to consume out of wealth')\n",
    "    model.param('beta', desc='Parameter in expectation formations on real sales')\n",
    "    model.param('eps', desc='Parameter in expectation formations on real disposable income')\n",
    "    model.param('gamma', desc='Speed of adjustment of inventories to the target level')\n",
    "    model.param('phi', desc='Mark-up on unit costs')\n",
    "    model.param('sigmat', desc='Target inventories to sales ratio')\n",
    "    model.param('omega0', desc='Exogenous component of the target real wage rate')\n",
    "    model.param('omega1', desc='Relation between the target real wage rate and productivity')\n",
    "    model.param('omega2', desc='Relation between the target real rate and the unemploment gap')\n",
    "    model.param('omega3', desc='Speed of adjustment of the wage rate')\n",
    "\n",
    "    model.param('ADD', desc='Spread of loans rate over the deposit rate')\n",
    "    model.param('Nfe', desc='Full employment level')\n",
    "    model.param('PR', desc='Labor productivity')\n",
    "    model.param('Rlbar', desc='Rate of interest on bank loans, set exogenously')\n",
    "    model.param('RRcbar', desc='Real interest rate on bank loans, set exogenously')\n",
    "\n",
    "\n",
    "    # The production decision\n",
    "    model.add('Yk = Ske + INke - INk(-1)')\n",
    "    model.add('INkt = sigmat*Ske')\n",
    "    model.add('INke = INk(-1) + gamma*(INkt - INk(-1))')\n",
    "    model.add('INk - INk(-1) = Yk - Sk')\n",
    "    model.add('Ske = beta*Sk(-1) + (1-beta)*Ske(-1)')\n",
    "    model.add('Sk = Ck')\n",
    "    model.add('N = Yk / PR')\n",
    "    model.add('WB = N*W')\n",
    "    model.add('UC = WB/Yk')\n",
    "    model.add('IN = INk*UC')\n",
    "    \n",
    "    # The pricing decision\n",
    "    model.add('S = P*Sk')\n",
    "    model.add('F = S - WB + IN - IN(-1) - Rl(-1)*IN(-1)')\n",
    "    model.add('P = (1 + phi)*(1+RRc*sigmat)*UC')\n",
    "    \n",
    "    # The banking system\n",
    "    model.add('Ld = IN')\n",
    "    model.add('Ls = Ld')\n",
    "    model.add('Ms = Ls')\n",
    "    model.add('Rm = Rl - ADD')\n",
    "    model.add('Fb = Rl(-1)*Ld(-1) - Rm(-1)*Mh(-1)')\n",
    "    model.add('PIC = (UC/UC(-1)) - 1')\n",
    "    model.add('RRc = RRcbar')\n",
    "    model.add('Rl = (1 + RRc)*(1 + PIC) - 1')\n",
    "    \n",
    "    # The consumption decision\n",
    "    model.add('YD = WB + F + Fb + Rm(-1)*Mh(-1)')\n",
    "    model.add('Mh - Mh(-1) = YD - C')\n",
    "    model.add('YDkhs = Ck + (Mhk - Mhk(-1))')\n",
    "    model.add('YDk = YD/P')\n",
    "    model.add('C = Ck*P')\n",
    "    model.add('Mhk = Mh/P') \n",
    "    model.add('Ck = alpha0 + alpha1*YDk + alpha2*Mhk(-1)')\n",
    "    model.add('YDkhse = eps*YDkhs(-1) + (1 - eps)*YDkhse(-1)')\n",
    "    \n",
    "    # The inflation process\n",
    "    model.add('omegat = omega0 + omega1*PR + omega2*(N/Nfe)')\n",
    "    model.add('W = W(-1)*(1 + omega3*(omegat(-1)-(W(-1)/P(-1))))')\n",
    "\n",
    "    return model\n",
    "\n",
    "disinf2_parameters = {'alpha0': 15,\n",
    "                      'alpha1': 0.8,\n",
    "                      'alpha2': 0.1,\n",
    "                      'beta': 0.9,\n",
    "                      'eps': 0.8,\n",
    "                      'gamma': 0.25,\n",
    "                      'phi': 0.24,\n",
    "                      'sigmat': 0.2,\n",
    "                      'omega1': 1,\n",
    "                      'omega2': 1.2,\n",
    "                      'omega3': 0.3}\n",
    "disinf2_exogenous = {'ADD': 0.02,\n",
    "                     'PR': 1,\n",
    "                     'RRcbar': 0.04,\n",
    "                     'W': 1}\n",
    "disinf2_variables = [('omega0', '0.8 - omega1*PR - omega2'),\n",
    "                     ('UC', 'W/PR'),\n",
    "                     ('P', '(1+phi)*(1+RRcbar*sigmat)*UC'),\n",
    "                     ('YDkhs', 'alpha0/(1-alpha1-alpha2*sigmat*UC/P)'),\n",
    "                     ('Ck', 'YDkhs'),\n",
    "                     ('Sk', 'Ck'),\n",
    "                     ('INk', 'sigmat*Sk'),\n",
    "                     ('IN', 'INk*UC'),\n",
    "                     ('Ld', 'IN'),\n",
    "                     ('Mh', 'Ld'),\n",
    "                     ('Mhk', 'Mh/P'),\n",
    "                     ('Ms', 'Mh'),\n",
    "                     ('Ls', 'Ld'),\n",
    "                     ('Ske', 'Sk'),\n",
    "                     ('YDkhse', 'YDkhs'),\n",
    "                     ('Rl', '(1 + RRcbar) - 1'),\n",
    "                     ('Rm', 'Rl -ADD'),\n",
    "                     ('omegat', 'W/P'),\n",
    "                     ('Nfe', 'Sk/PR'),\n",
    "                     ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model DISINF2, increase in the target real wage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "omega0 = create_disinf2_model()\n",
    "omega0.set_values(disinf2_parameters)\n",
    "omega0.set_values(disinf2_exogenous)\n",
    "omega0.set_values(disinf2_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "from pysolve.model import SolutionNotFoundError\n",
    "\n",
    "for _ in range(15):\n",
    "    omega0.solve(iterations=100, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "omega0.set_values({'omega0': -1.35})\n",
    "\n",
    "for _ in range(40):\n",
    "    omega0.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 9.4  Evolution of (Haig-Simons) real disposable income and of real\n",
    "    consumption following an increase in the rate of inflation, in a variant\n",
    "    where households are blind to the capital losses inflicted by inflation.'''\n",
    "ydkhsdata = [s['YDkhs'] for s in omega0.solutions[5:]]\n",
    "ckdata = [s['Ck'] for s in omega0.solutions[5:]]\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "axes.set_ylim(80.2, 84)\n",
    "\n",
    "axes.plot(ydkhsdata, linestyle='-', color='r')\n",
    "axes.plot(ckdata, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(6, 82.21, 'Real')\n",
    "plt.text(6, 82.1, 'consumption')\n",
    "plt.text(16, 82, 'Haig-Simons real disposable income')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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88Y9/ZOzYsTRq1Ii5c+cyb948ttxyy0rXd/DBB9O6dWsAevXqxezZs+nSpUttNpE0YLmcShgGDEobdrO793H3vsBIIFt3J5nmFZEG6L334LLLQqnj6Dq62CSuMZg9ezarV6/mzjvvZP369bRp04aJEycmHx9//DEAF1xwAeeffz5TpkzhnnvuYeXKlZUuf7/99mPcuHGMGzeOAw44gA4dOjBixAj69+8PhJba22+/PbmeL774goEDBzJ8+HAWLFjA+++/z8SJE9liiy2qXBfAxhtvnHzeuHFj1q5dW4utIw1dlYmBu48FFqcN+z7lZQvAc51XRBqe77+HE0+ELbcMnSOZxR1R0Lp1a2677TZuueUWmjdvzrbbbsuTTz4JhB/vSZMmAeGIvlOnTgA89NBDVS63S5cuLFy4kE8//ZTtttuOfffdl1tuuSWZGBx66KHcddddrFmzBoAZM2awbNkyvvvuOzbffHOaNm3K6NGjmT17doVlt2rVih9++CEv718kkxpffGhmfzGz/wEnk73FQESEc8+F2bPh0UehXbu4o9nQrrvuyi677MJjjz3G8OHDuf/++9lll13Yaaedkhf/DRkyhOOPP57+/fvTvn37nJa755570j26D7N///7MnTuXfffdF4AzzzyTXr16sdtuu9G7d2/OPvts1q5dy8knn8yECRMoKytj+PDh9OzZs8Jy+/TpQ5MmTdhll12SFx+K5JO5ZzzY33Ais67AyEzXCZjZ5UAzd7+6uvOmTTcYGAyw9dZb98uUKYtI/bNwIey+O5x+OvzpT3FHIyIJZnY20e9uZKi7D81HYrAN8EK2H/5cE4NUZWVlPmHChFwnF5Eit2xZ6AuhadO4IxGRFBlP6tXoVIKZ7ZDy8khgek2WIyKlbcQIWLECWrRQUiBSX1SZGJjZo8DbQA8zm2NmZwA3mNlUM5sMDAQuiqbtaGYvVjGviDQAr70Gxx8Pt90WdyQiUh05nUqoazqVIFK/LVsGO+8MTZrApEnQvHncEYlIBhlPJVRZ4EhEpLr+9Cf44ovQJ4KSApH6RX0liEhejR8fKhyecw7st1/c0YhIdSkxEJG8at0ajj4abrwx7khEpCZ0KkFE8mrHHeHpp+OOQkRqSi0GIpIX06bBL38JixbFHYmI1IYSAxGptXXr4Iwz4MUXYf36uKMRkdrQqQQRqbXbb4d33oHhw6FDh7ijEZHaUIuBiNTKzJlwxRVw+OFw0klxRyMitaXEQERq5Q9/gMaN4a67iqc7ZRGpOZ1KEJFaue22UN2wS5e4IxGRfFBiICI1sm4dNGoEW20VHiJSGnQqQURq5NZboX9/+OGHuCMRkXxSYiAi1TZvHlx7bahy2KpV3NGISD4pMRCRavvjH2HlytBqICKlRYmBiFTLhAnw4INw0UXQvXvc0YhIvjWYiw/HjCk/F2oWHu3bwx57hGFvvAErVmw4zxZbwG67heejRsHq1RuO79wZdtklPH/xRXDfcPw220Dv3qES3EsvVYypWzfo2TMs99VXK47v3h223x6WLw/xpevVC7p2De/rv/+tOH7nnUOM334bis+k69sXttwSFi4MX/bpysrCNvrmm3DVebo99oC2bWHuXJg6teL4ffZRM3Mpuukm2Hzz0LWyiJQgdy+6R79+/TzfdtrJPfx0lz8GDiwfv802Fccfe2z5+HbtKo4/9dTy8U2bVhx//vlh3KpVFceB++WXh/ELF2Ye/5e/hPFffJF5/G23hfGTJ2ce/+CDYfybb2YeP2JEGP+f/2Qe/5//hPEjRmQe/+abYfyDD2YeP3my+w8/uL/zjvu6dXn5GKUILF/u/sEHcUchInmQ8TfYPP0wtwiUlZX5hEyHsLUwdWo4J5r42QLYdNNwxA7w4YcVWwTati1vKv3gA1i7dsMCLpttBtttF55PmFCxxWDzzUOrwfr18P77FWPaaqtwRL9mTVh/us6doWNHWLWq4hG7e1j2lluGFoUpUyrOv912oTztDz+EDm7S7bBDeA9LlsD06RXH9+wJbdqETnFmzKg4vnfv0CIwfz58/nnF8X36wKOPwllnwaxZIV6pv5YvD7cnNmsWdyQikicZS5I1mMRA4jF2LOy/P7zyChxySNzRSG388Y/w+OMwcaJOEYmUiIyJgS4+lIJKtLh88km8cUjtzJwJ//d/8KMfKSkQKXVKDKSgttgCWrbMfCpC6o9LLoGmTeGGG+KOREQKrcHclSDxMAutBkoM6q9XX4V//xuuvz5c8yIipU2JgRTcjTeGVgOpnx5+OFzIevHFcUciInVBiYEU3IABcUcgtfHAA6FWhe5GEGkYdI2BFNy338LTT4fbGqX+WL48FL9q1EhdKos0JEoMpOA+/xyOOw7efjvuSKQ6brstnEL46qu4IxGRuqTEQApuhx3CX92yWH8sXhzuQNh/f11wKNLQKDGQgmvdOty2qDsT6o8bb4Tvvw93IohIw6LEQOqEblmsP+bMCacRTjkldMQlIg2LEgOpE0oM6o8XXgj9e1xzTdyRiEgc1FeC1ImZM2HduvLrDaS4zZkTOvESkZJWs74SzOwBM5tvZlNThl1nZpPNbKKZvWJmGS9PMrNBZvaJmX1mZpfVPHap77bbTklBfTB3bvirpECk4crlVMIwYFDasJvdvY+79wVGAlelz2RmjYE7gR8DvYCTzKxXraKVemvZMvjHP+C99+KORLJ5773QNfazz8YdiYjEqcrEwN3HAovThn2f8rIFkOl8xB7AZ+4+091XA48BR9UiVqnHmjQJJXVfeCHuSCQTd7jsMmjXDg46KO5oRCRONS6JbGZ/AX4JfAccmGGSTsD/Ul7PAfasZHmDgcEAW2+9dU3DkiK18cbQtasuQCxWr74Kr78eWnXUrbJIw5D6uxsZ6u5Da3xXgrtf4e5dgOHA+ZnWmWm2SpY31N3L3L2sQ4cONQ1LipjuTChO69eH1oKuXeHss+OORkTqSurvbvQYCvm5XfER4LgMw+cAqRXWOwMqrtqAJRKDIrwRpkGbMgWmTYNrrw0tOyLSsNXoVIKZ7eDun0YvjwSmZ5jsPWAHM9sWmAucCPy8RlFKSejeHX74ARYsgM03jzsaSdhlF/jsM9hqq7gjEZFikMvtio8CbwM9zGyOmZ0B3GBmU81sMjAQuCiatqOZvQjg7msJpxj+A3wMPOHu0wr0PqQe+NWvwt0JSgqKxzffhL+dO0PjxvHGIiLFQQWORBqoFStg++3hpJPgllvijkZEYlCzAkci+XTllfDQQ3FHIQD33hu6VD7iiLgjEZFiosRA6tQzz4SHxGvFCvjrX+GAA8JDRCShxnUMRGqiRw+YnulSValT99wTri947LG4IxGRYqMWA6lT3buHK+DXrYs7kobLHR54AA48EPbfP+5oRKTYqMVA6lT37rBmDcyeHTpWkrpnBm+9BYsWxR2JiBQjtRhInereHdq3h3nz4o6kYVq9GtauhZYtQ4dJIiLplBhInfrRj0KBo733jjuShumOO2CnnWDx4qqnFZGGSYmB1CnLeNes1IXly+HGG0Mxo3bt4o5GRIqVEgOpc3/6E5x3XtxRNDx33QXz58M118QdiYgUMyUGUue++AJeeCHuKBqWZcvgpptgwADYd9+4oxGRYqbEQOpc9+7w5ZehyI7UjeHDQ2vBkCFxRyIixU6JgdS57t3DvfSffx53JA3HGWfAqFHh4k8RkcooMZA61717+DtjRrxxNBTr14eeEwcMiDsSEakPlBhIndthB9hlF2ikva/gli2D3r3hySfjjkRE6gtVPpQ616oVTJwYdxQNw513wscfh1sURURyoWM2kRK1dCncfDMceqgKSolI7pQYSCxuuAF69Yo7itL2z3/CwoW6E0FEqkeJgcSiSZPQxP3tt3FHUppWroT/+z8YOBD22ivuaESkPtE1BhKLxJ0Jn34Ke+wRbyylqFkzGDEiXM8hIlIdajGQWOiWxcLr3x/69o07ChGpb5QYSCy22y7crvjJJ3FHUnoeeAB+/etwOkFEpLp0KkFisdFGcPrp5S0Hkh9r1sC118JWW8HGG8cdjYjUR0oMJDb33ht3BKXn4Ydh9uxQv0BdXItITehUgsRqxYrQb4LU3rp1cP31sOuucNhhcUcjIvWVEgOJzbBh0KIFfP113JGUhscfh88+gyuvVGuBiNScEgOJTefOobVAdybkxx57wOWXw9FHxx2JiNRnSgwkNokLD3VnQn5sv304laDOqUSkNvQVIrHp3DkU4lGLQe24wyWXqGMqEckPJQYSm0aNQhfMSgxq58UX4W9/U2IgIvmh2xUlVhdcoPvta8MdrrsOttkGTj457mhEpBQoMZBYnXVW3BHUb6+/Du+8A3fdBU2bxh2NiJSCKk8lmNkDZjbfzKamDLvZzKab2WQze8bM2mSZ9yIzm2pm08zsN/kLW0rF2rXw+eewdGnckdRP110HHTvCr34VdyQiUipyucZgGDAobdgooLe79wFmAJenz2RmvYGzgD2AXYAjzGyHWkUrJefdd8PV9GPGxB1J/bN2begoacgQnY4RkfypMjFw97HA4rRhr7j72ujleKBzhll3BMa7+/Jo2jHAMbWMV0qMelmsuSZNQouBTseISD7l466E04GXMgyfCuxnZpuZ2SbAYUCXbAsxs8FmNsHMJixYsCAPYUl9sNlm0K4dfPRR3JHUL5MmwbPPqpy0iNRc6u9u9BgMtbz40MyuANYCw9PHufvHZnYj4bTDUmBSNG1G7j4UGApQVlamr7sGwixU7HvzzbgjqV+uvBLeeit0mNSyZdzRiEh9lPq7m6rGLQZmdipwBHCye+bjFne/3913c/f9CKcjPq3p+qR07bcffPwxzJ8fdyT1w4cfwsiRcPHFSgpEJP9q1GJgZoOAPwD7u/vySqbb3N3nm9nWwLHA3jULU0rZz34GO+6oH7lc/fnP0Lp1qAEhIpJvVSYGZvYocADQ3szmAFcT7kLYGBhloRu38e5+jpl1BO5z90Snr0+Z2WbAGuA8d/+2AO9B6rlu3cJDqjZ1Kjz9NPzpTyE5EBHJtyoTA3c/KcPg+7NM+xXhIsPE6/41D00akmnTQqGe00+PO5Li9s030Ls3/OY3cUciIqVKfSVIURgxAs48E5YsiTuS4jZgAEyeHO7kEBEpBCUGUhT23z/cevff/8YdSfF67TVYuTLcySEiUihKDKQo7LknbLQRjB0bdyTFaeZMOPTQUNBIRKSQlBhIUWjePNQzUGnkzG64IVQ6PO+8uCMRkVKnxECKxv77h6vuV66MO5Li8uWXMGwYnHFG6DBJRKSQlBhI0bjkEli4EJo1izuS4nLTTeHvH/4Qbxwi0jDUqiSySD61bRt3BMVn/XqYMgVOPRW23jruaESkIVBiIEXlnnvCD+Edd8QdSXFo1AjeeEOnV0Sk7uhUghSVzz6De+/VDyHAt9+GUytm4eJMEZG6oMRAisp++8Hq1aEKYkN3442w3XYhQRARqStKDKSo7LtvOEJu6PUMFi2CO++Eww/XtRciUreUGEhRadsW+vRRPYN//AOWLoUrrog7EhFpaJQYSNE54oiGfZS8eHFIDI49NnSYJCJSl3RXghSdP/857gji9dJLobVgyJC4IxGRhkgtBlK01qyJO4J4nHwyfPEF7Lxz3JGISEOkxECK0tFHwzHHxB1F3Vu0KPxVMSMRiYsSAylKW24J48bBunVxR1J35s6FbbaB++6LOxIRaciUGEhR2n9/+P57mDQp7kjqzvXXhxoOAwbEHYmINGRKDKQo7bdf+NtQblucNStUfDzjDOjaNe5oRKQhU2IgRalTJ+jWreEUOrruutAvguoWiEjcdLuiFK1LL4UWLeKOovCWLIEnnoBzz4XOneOORkQaOiUGUrQGD447grrRpg188glstFHckYiIKDGQIuYezr2vWgU9e8YdTWEsXx56TuzYMe5IREQCXWMgRW2//eDqq+OOonB++Us48siQBImIFAMlBlK0zEJiMGZMaf5wfvghPPUU9OsX3quISDFQYiBFbf/9Yd48+PTTuCPJv6uuCp1FXXxx3JGIiJRTYiBFLVHPoNRuWxw/HkaOhN//Hlq3jjsaEZFySgykqPXoAZtvXnqFjm69FTp0gAsuiDsSEZEN6a4EKWpm8PTTsN12cUeSXw88AB99BC1bxh2JiMiGlBhI0fvRj+KOIH/WrYO1a0Phpt13jzsaEZGKqjyVYGYPmNl8M5uaMuxmM5tuZpPN7Bkza5Nl3ovNbJqZTTWzR82sWR5jlwbCHe66Cx56KO5Iau/ee6F3b/jmm7gjERHJLJdrDIYBg9KGjQJ6u3sfYAZwefpMZtYJuBAoc/feQGPgxFpFKw2SGTz5JAwZAuvXxx1NzS1eHPpC6NQJttgi7mhERDKrMjFw97HA4rRhr7j72ujleCBbhfcmQHMzawJsAnxVi1ilATv77FAF8ZVX4o6k5q6+OvSLcNttqlsgIsUrH3clnA68lD7Q3ecCtwBfAl8D37l71q91MxtsZhPMbMKCBQvyEJaUkmOOCVfx33NP3JHUzJQp8M9/wjnnQJ8+cUcjIrLh7270GAy1TAzM7ApgLTA8w7i2wFHAtkBHoIWZnZJtWe4+1N3L3L2sQ4cOtQlLStBGG8Hpp8Pzz8PcuXFHU31Dh4bOkq69Nu5IRESC1N/d6DEUapEYmNmpwBHAye4ZC9YOAL5w9wXuvgZ4GtinpusTGTwY9t4bFi6MO5Lq+8c/4K23YLPN4o5ERKRyNbpd0cwGAX8A9nf35Vkm+xLYy8w2AVYABwMTahSlCKGWwbhxcUdRPStWwNKl4TRIjx5xRyMiUrVcbld8FHgb6GFmc8zsDOAOoBUwyswmmtnd0bQdzexFAHd/BxgBfABMidY1tDBvQxqSBQvqT98JN90E3buH/h5EROoDy3wWIF5lZWU+YYIaF6Qi99BysNNOoa+BYjZ7NvTsCUcdBY89Fnc0IiIVZLw/Sn0lSL1iBqecAi++GH54i9nvfhfivfnmuCMREcmdEgOpd848M/y9775446jM6NEwYgRcfjl06RJ3NCIiuVNiIPXONtvAj38cEoM1a+KOJrNRo6Br19BqICJSnygxkHrpnHNCfwNvvx13JJldfz188AE0bx53JCIi1aPeFaVeOuww+Pzz4uuOec6ccNfErrtC27ZxRyMiUn1qMZB6qXHj8qSgWG6sWbMGTjwRBg6EZcvijkZEpGaUGEi9tX49/PSncOWVcUcSXHEFvPlm6CSpRYu4oxERqRklBlJvNWoE69bBvffC6tXxxvL88+G2xHPPhZNOijcWEZHaUGIg9do554Rz+s88E18Mc+bAqaeG6wr+9rf44hARyQclBlKvHXIIbLst3H13fDFsuSWcfz48+SQ0axZfHCIi+aDEQOq1Ro3grLPgjTdg+vS6X/+yZdCkSehOuVu3ul+/iEi+KTGQeu/00+Gaa+q+S+Onngo9Js6YUbfrFREpJHWiJCVl3bpwK2OhffYZ9OsXOkkaNw422qjw6xQRyTN1oiSl7b33oHfvwh/Br1wJxx8fEpAnnlBSICKlRYmBlIy2bWHRIhg0KJRLLpSLL4aJE+Ff/wr9NoiIlBIlBlIytt8eXngB5s2Dww+HH37I/zpWrw7dPV96KRxxRP6XLyISN/WVICVl991Dd8c/+QkcdxyMHJmfpv5Vq2DhQujUKSxz/fraL1NEpBipxUBKTqJL5hYtwsWItfXZZ7DPPqHjprVrwy2STZRSi0iJUmIgJem00+Dpp0O3xytX1nw5TzwBu+0GM2eGWgVKCESk1CkxkJJlFi5G3GMPuP326s27cmUot3zCCbDTTuFiw6OOKkiYIiJFRYmBlLQ2bUJFwosuCiWLq+Pdd8NFhmPH6u4DEWk41DAqJa1xY3jkERgwAE45BUaPhoED4eijwwWETz4JrVuXPyZPDtcotG4Nb72lvg9EpOFRYiAlr3nz0C3yccfBo49Cq1YhMVi6FE48seL011wDV12lpEBEGiYlBtIgtGsXWgtStWgB06bBd9+VP5o00bUEItKwKTGQBqtxY+jVK+4oRESKiy4+FBERkSQlBiIiIpKkxEBERESSlBiIiIhIkhIDERERSVJiICIiIklVJgZm9oCZzTezqSnDbjaz6WY22cyeMbM2GebrYWYTUx7fm9lv8hu+iIiI5FMuLQbDgEFpw0YBvd29DzADuDx9Jnf/xN37untfoB+wHHimVtGKiIhIQVWZGLj7WGBx2rBX3H1t9HI80LmKxRwMfO7us2sUpYiIiNSJfFxjcDrwUhXTnAg8WtkEZjbYzCaY2YQFCxbkISwRERHJJvV3N3oMBjB3z2XmrsBId++dNvwKoAw41rMsyMw2Ar4CdnL3ebkEW1ZW5hMmTMhlUhEREakZyzSwxn0lmNmpwBHAwdmSgsiPgQ9yTQpEREQkPjVKDMxsEPAHYH93X17F5CdRxWkEERERKQ653K74KPA20MPM5pjZGcAdQCtgVHQr4t3RtB3N7MWUeTcBDgGeLkj0IiIiklc5XWNQ13SNgYiISMFlvMZAlQ9FREQkSYmBiIiIJCkxEBERkSQlBiIiIpKkxEBERESSlBiIiIhIkhIDERERSVJiICIiIklKDERERCRJiYGIiIgkKTEQERGRJCUGIiIikqTEQERERJKUGIiIiEiSEgMRERFJUmIgIiIiSUoMREREJEmJgYiIiCQpMRAREZEkJQYiIiKSpMRAREREkpQYiIiISJISAxEREUlSYiAiIiJJSgxEREQkSYmBiIiIJCkxEBERkSQlBiIiIpKkxEBERESSlBiIiIhIUpWJgZk9YGbzzWxqyrCbzWy6mU02s2fMrE2WeduY2Yho2o/NbO88xi4iIiJ5lkuLwTBgUNqwUUBvd+8DzAAuzzLvP4CX3b0nsAvwcQ3jFBERkTpQZWLg7mOBxWnDXnH3tdHL8UDn9PnMbFNgP+D+aJ7V7r6ktgGLiIhI4eTjGoPTgZcyDN8OWAA8aGYfmtl9ZtYiD+sTERGRAqlVYmBmVwBrgeEZRjcBdgPucvddgWXAZZUsa7CZTTCzCQsWLKhNWCIiIlKF1N/d6DEYwo93TRd4KnAEcLC7e4ZJ5gBz3P2d6PUIKkkM3H0oMBSgrKws0/JEREQkT1J/d1PVqMXAzAYBfwCOdPflWVb4DfA/M+sRDToY+Kgm6xMREZG6kcvtio8CbwM9zGyOmZ0B3AG0AkaZ2UQzuzuatqOZvZgy+wXAcDObDPQFrs/3GxAREZH8scxnAeJVVlbmEyZMiDsMERGRUmaZBqryoYiIiCQpMRAREZEkJQYiIiKSpMRAREREkpQYiIiISJISAxEREUlSYiAiIiJJSgxEREQkSYmBiIiIJCkxEBERkSQlBiIiIpKkxEBERESSlBiIiIhIkhIDERERSVJiICIiIklKDERERCRJiYGIiIgkKTEQERGRJCUGIiIikqTEQERERJKUGIiIiEiSEgMRERFJUmIgIiIiSUoMREREJEmJgYiIiCQpMRAREZEkJQYiIiKSpMRAREREkpQYiIiISJISAxEREUlSYiAiIiJJVSYGZvaAmc03s6kpw242s+lmNtnMnjGzNlnmnWVmU8xsoplNyGPcIiIiUgC5tBgMAwalDRsF9Hb3PsAM4PJK5j/Q3fu6e1nNQhQREZG6UmVi4O5jgcVpw15x97XRy/FA5wLEJiIiInWsSR6WcTrweJZxDrxiZg7c4+5Dsy3EzAYDg6OXS83skzzElqo9sDDPy5RA27YwtF0LQ9u1cLRtC6NQ23UqsDLl9VB3H1qrxMDMrgDWAsOzTPIjd//KzDYHRpnZ9KgFooIoaciaONSWmU3Q6YzC0LYtDG3XwtB2LRxt28Ko6+1a47sSzOxU4AjgZHf3TNO4+1fR3/nAM8AeNV2fiIiIFF6NEgMzGwT8ATjS3ZdnmaaFmbVKPAcGEpotREREpEjlcrvio8DbQA8zm2NmZwB3AK0Ipwcmmtnd0bQdzezFaNYtgP+a2STgXeAFd3+5IO8iNwU7TSHatgWi7VoY2q6Fo21bGHW6XS3LWQARERFpgFT5UERERJKUGIiIiEhSg0gMzGyQmX1iZp+Z2WVxx1NfZSmP3c7MRpnZp9HftnHGWB+ZWRczG21mH5vZNDO7KBqubVtLZtbMzN41s0nRtr0mGq5tmwdm1tjMPjSzkdFrbdc8yNSdQF1u25JPDMysMXAn8GOgF3CSmfWKN6p6axgVy2NfBrzm7jsAr0WvpXrWApe4+47AXsB50T6qbVt7q4CD3H0XoC8wyMz2Qts2Xy4CPk55re2aP+ndCdTZti35xIBQO+Ezd5/p7quBx4CjYo6pXspUHpuwLR+Knj8EHF2XMZUCd//a3T+Inv9A+KLthLZtrXmwNHrZNHo42ra1ZmadgcOB+1IGa7sWTp1t24aQGHQC/pfyek40TPJjC3f/GsIPHLB5zPHUa2bWFdgVeAdt27yImrsnAvOBUe6ubZsffwcuBdanDNN2zY9EdwLvR90FQB1u23z0lVDsLMMw3aMpRcfMWgJPAb9x9+/NMu26Ul3uvg7oG3UP/4yZ9Y45pHrPzI4A5rv7+2Z2QMzhlKIK3QnU5cobQovBHKBLyuvOwFcxxVKK5pnZVgDR3/kxx1MvmVlTQlIw3N2fjgZr2+aRuy8B3iBcJ6NtWzs/Ao40s1mE07MHmdnDaLvmRZbuBOps2zaExOA9YAcz29bMNgJOBJ6LOaZS8hxwavT8VODZGGOplyw0DdwPfOzuf0sZpW1bS2bWIWopwMyaAwOA6Wjb1oq7X+7und29K+E79XV3PwVt11qrpDuBOtu2DaLyoZkdRjgf1hh4wN3/Em9E9VNUHvsAQheg84CrgX8DTwBbA18Cx7t7+gWKUgkz2xcYB0yh/HztHwnXGWjb1oKZ9SFcqNWYcCD0hLtfa2aboW2bF9GphN+5+xHarrVnZtsRWgkgnO5/xN3/UpfbtkEkBiIiIpKbhnAqQURERHKkxEBERESSlBiIiIhIkhIDERERSVJiICIiIklKDERERCRJiYGIiIgkKTEQERGRJCUGIiIikqTEQERERJJKJjEwsyFmNtfMJkaPG8zsHDP7ZR3HcZCZfWBmU83sITPL2LW1ma1LibXKTp0yvL+Jic5hqhnfaWZ2RxXTdDWzn6e8LjOz26q7rmrGdbyZfWxmowuw7KX5XmaW9Qwzs59Gz39jZpvUJgYze9TMJpvZxTmu8w0zK6tJ7CnLu8/MetVmGflgZkea2WVxx1FdZvZWNac/zcw6pryeZWbt8x9ZhfX+sdDrkPor449WPXaru99SiAWbWeOoX/fKpmlE6LDlYHefYWbXEnrBuj/D5CvcvW81wyjY+0vTFfg58AiAu08AJhR4nWcAv3b3rIlBLp9BEfkN8DCwvCYzm9mWwD7uvk0+g6qKu59Zl+vLxt2fIw+9oJpZE3dfm4eQcuLu+1RzltMIPefltSv4HP5X/ghcn891SukomRaDTKKj7N9Fz3ePjr7eNrObzWxqNHyDI2gzGxn1FoaZLTWza83sHWBvMzvFzN6NjtbvMbPGaavcDFjl7jOi16OA4wr8Ht8xs51SXr9hZv3MrJ2Z/Tt6z+OjXubS500ebUavE0e1NwD9o/d5sZkdYGYjo2kyLjfa1g9E659pZhdmifckM5sStajcGA27CtgXuNvMbk6b/gAzG21mjwBTzKxx9Pm9F8VwdjRdSzN7LWqtmWJmR1Wx3S5NxGhmt5rZ69Hzgy30K4+ZDYz2lw/M7Ekza5mIN1r/VDMbamaWtuwLgY7A6NQWEDP7i5lNirbbFpXFB7wCbB59Bv3NrG8032Qze8bM2lbx/jJt55+Z2d+i5xeZ2czoeTcz+2/0PNnqEO3/FWKOph8fbYNrLUtrSLSfvG9m08xscMrwjMtNmzf5fxntp7eZ2VvRvpW6z14avc9JZnZDynu43szGABdF/w9jolj+Y+V92p8VvYdJZvaURS08FlqvpkbDx0bDMu53GeJeGv09IIpjhJlNN7PhGfaTnwJlwPDoc24ejbogZT/uGU3bwsL/13tm9mGm/dvS/leyfQbRdmoerXN4NKyq7zZpSNy9JB7AEGAuMDF6HBoN+100firhCAzCD9/U6PlpwB0pyxkJHBA9d+Bn0fMdgeeBptHrfwK/TIvBgNlAWfT6H8CULPGuJRyFjweOrsH7Gx0Nvxi4Jnq+FTAjen47cHX0/CBgYvr7BYYBP01Zx9Lo7wHAyJThydeVLHcI8BawMaFb5kWJbZWynI6E7kI7EFqrXk+8d+CNxHZLm+cAYBmwbfR6MHBl9HzjaBtuGy1v02h4e+AzynsPXZphuXsBT0bPxwHvAk0JXUmfHS1jLNAimuYPwFXR83Ypy/l/wE/StycwC2ifMp2nTHdT4j1U8nl3JdpHo9eTgf2j59cCf8+wzjcIPzQZtzOwJfBeNO0I4D2gE6FV66/pn0O2mAn/IydFz8/JtH1TtxPQnPD/t1mu24KK++mThAOZXsBn0fAfE/a5TdLW9wbwz+h502iaDtHrEwhdr5OIJ3r+Z+CC6PkUoFP0vE1l+12GuFP/h74DOkdxvw3sm2H65PZO2W8ScfwauC96fj1wSiImYAbRvpntf6WKz2BpyjRVfrfp0bAepdZicKu7940e/0kMtHAuvpW7J87/PZLj8tYBT0XPDwb6Ae+Z2cTo9XapE7u7AycCt5rZu8APhAQgk63dvYzQZP93M+uWQzyp7+/AaNgTwPHR858RvkAhHIH/vyiu14HNzKx1DuuoSmXLfcHdV7n7QmA+kH4kuDvwhrsv8NC8OxzYL4d1vuvuX0TPBwK/jD6DdwitNDsQkrLrzWwy8CrhB6+yo/L3gX5m1gpYRfjiLgP6ExKFvQg/Qm9G6zoVSDTrH2ihpWYKITnaiaqtJvygJtbdNYd5AIi2bxt3HxMNeojKt1vG7ezu3wAto/fchfB/sB/l7znXmPemfD+r7H/pQjObREh+uxA+p8qWW5l/u/t6d/+I8s91APCguy8H8A37pn88+tsD6A2Mij7HKwk/1gC9zWxc9DmeTPnn+CYwzMzOAhJHztn2u8q86+5z3H09IZnP5X0CPB39Td02A4HLovW/ATQDts6yzi9SXmf7DFJV+d0mDUupXWOQjVUybi0bnlJplvJ8pZefpzPgIXe/vLIVufvbhC9azGwg0D3LdF9Ff2ea2RvArsDnlS07y3LmmtkiC036JxCOdhPxVpg87XXyvUfNnBvlsMrKlrsqZdg6Ku5flX0OlVmWtowLUhM/CE3PhCPkfu6+xsxmseFnuWHA5dP8inBEORk4EOgGfBz9HeXuJ6WtpxnhiKrM3f9nZkMqW0+KNVHiCJm3TT5Vtp3fJrznTwjJwOmEH/pLMkxb45gtnI4bAOzt7sujfTyxnWqy3NR9y1L+pu/TCctSppnm7ntnmGYYocVqUrT/HADg7ueY2Z7A4cBEM+tLlv2uGjFXZ/sl5kudx4Dj3P2TKuZN/q9U8Rmkyum7TRqOUmsxyMjdvwV+MLO9okEnpoyeBfQ1s0Zm1gXYI8tiXgN+amabQ/Jce4ULw1LGb0xofr47wzRto/FYuAL5R8BHNXlvkceAS4HW7j4lGjaWcBSU+IJY6O7fp803i3CkAHAUodkVQktHqyzrymW52bwD7G9m7aNzmCcBY6qYJ91/gHPNrGkUQ3czawG0BuZHP/gHUn50X5mxwO+iv+MIzeITox+t8cCPzGz7aD2bmFl3yr9YF1q45uCnFRcLVL4Nk8zsGDP7a2XTuPt3wLdm1j8a9Asq326VbefU9/whIRlaFa0jV+Mpv3bmxCzTtAa+jX6QehJaYPLtFeD0lGsD2mWY5hOgg5ntHU3T1MqvyWkFfB3tSycnZjCzbu7+jrtfBSwkHGln2+9qK6f9JFr/BYnrFMxs1xzmqewzWJN4L+T43SYNR0NpMYBw1fu9ZraM0BSX+CJ8E/iCcF5xKvBBppnd/SMzuxJ4xcLdB2uA8wjXFKT6vZkdQUi67oqa27FwQdc5Hq763hG4x8zWR9PdEDWRVuViMzsl5fXR7j6LcL74H8B1KeOGAA9GTevLCU3h6e4Fno1Oe7xG+dHGZGBt1AQ5jPADUp3lZuTuX5vZ5cBowlHKi+7+bK7zR+4jNK9+EH1JLiCcPx8OPG9mEwjNttNzWNY44ArgbXdfZmYro2G4+4LoKPLRRBJHOMc8w8zuJewvswjn6TMZCrxkZl+nnPbJpBuQS2J1KuHizE2AmYSj/oyq2M7jCD90Y919nZn9j9y2VarfAA+b2SXAC5T/L6V6GTgn2k8+ISQTeeXuL0dH8xPMbDXwIuFq+9RpVlu4yO+26JRME+DvwDTgT4Qkajbh80z8QN9sZonTU68Bkwj/E12puN/V1jDC57qC0HKTzXVR3JOj9c8Cjqhi2ZV9BkOjZX3g7ifn+N0mDUTi4qySZ2Yt3T1xxfBlwFbuflHMYUkDZ+EOiIvdfUHcseQqSk5WuLub2YmECxErvQtEROqPhpQYnABcTjhimA2cVp++jEWKRXRK4w7CEfUS4HR3/yzWoEQkbxpMYiAiIiJVaxAXH4qIiEhuSj4xMLMOFu45/zDlqu7EuCrrwqfPbznUMre0OuRWzfrpubAY+oHIRS7btJJ5DzCz6paUTcy7QQXLQrE89EdQw/VuUKUyh+m7WlTdM8O4uN5D1s/IyisGdjSzEflYbm32p9pK/f+0tP4QKpmnWp9xJcup9jYUSdUQ7ko4GJju7hWunvfc6sJvML9tWNU0mw3qkHv166dXyd0r3AYZNwv12WtTa/8AYCmhrkCdsvrVD0NRsTz2RxDV96j1j2PkAGLan9L+P0+jAP0hVLLufG5DaYBKpsXAzLaxUCt/cvR36+hWppuAw2zDWuSJeSqtC5/D/LnWIU8cDZlF/TRYqIN+QjS8yrrqGd7vECvvB+INM7vRQq3zGektI9E0VfYlYGbnmtlNKa9PM7Pbs73XlO2W2p9E6ja9y8wmRPNckzLPLDO7JiWWnmbWlVBH4OJo26W37kwxszbRNlyUcjT2/8xsQDRZRzN72cw+TXsf2fo8mGWh34P/Asdnmy6D49O3tZk1M7MHozg/tFBLocLRrEV9cViovT8sZV+4OBrfLXoP71uoytczZb37WVp/Adn2qbRt19zMHrPwv/E4oTwu2WJIm/cnVt5i9qqV95cwxEI/Ea8A/7LQsvaUhVr+75nZj7Jsuy7R+/vEzK7OsL5kS0e07Z7O8pn+Ktr+Ywh1QCosh7T9yTJ8R2SYr2XK5zjZzI6Lhle2Lyf+99618roXQ8zsd5ahPwSroq+NDDFl6+ela7SPfBA99sl1G+by2UsDlo+6ysXwINT6PjV6fjqhhCqk9YWQNs8bVF0XfoP5SamBTw51yFNfE4rCjCKUWd2CUM9+K3Ksq562zCGU9wPxBvB/0fPDgFczTJ+1L4GUaToQ1aGPXr+UiKOS9+pE/Ulk2KaJeRpHw/ukbMNM9eCT7ylD/HcTKtH1JtQOuDca/inQMvqcZhKKujQj3HnShcr7PJgFXJqyTTJOl2GfqbCtCZUDH4ye94w+22Zk6YuDUFhqVMrwNtHf14Adoud7Aq9Hz4eRub+AbPtUV8r7A/kt5f0D9CFUvCzLFkPa+21L+UXKZ6a89yGEkr3No9ePUL6vbA18nGFZpwFfE8oJJ/ajxL6S+B9JjTvbZ7oV5X1BbESoRVLhf5y0/Yks3xFp89xI1A9F4v3nsC9fET3/JeV9iiTXTcX+EKrsayMtpmz9vGwCNIue7wBMqMY2rPKz16PhPkrpVMLewLHR8/9H+HGvjvT67YfkMM+FZnZM9DxRh3xRJdPvCzzqocl6XnS0szuhwM277j4HwEK98q7Af6sRf6b66qkSfQnsB6ynvC+BbxITeCjqM9NChchPCXXm36zivab2J5HuZxZaF5oQvsx7EQrFpMd7bIZ5040j1PWfDdwFDDazTsBid18aHXS95lEFPzP7iFD9sA3lfR5A+CF5O2W5iZr6e1UxXapM23pfQgdTuPt0M5tNlnLYkZnAdhZaZF4gFJdpCewDPJlyELlxyjz/9lB3/yMr75Ew2z41OWW+/YDbotgmWyh4kzGGDHF2Bh630CPhRoRiYAnPufuK6PkAoFdK3JuaWSt3/yFteaPcfRGAmT0dxV9Zl96ZPtP2RH1BRMMfp/JtnZDLd8QAUqo5eqiaCpXvy4+m/L01hzgONLNLCT/s7QjFlp7PNKFl7uclUdioKXCHhZbNdWTfBpm24TSq/uylgSqlxCBdde/DrFb9dsu9DvkGs1UyrqZ11dPnzzbvyeTWl8DjhM6YpgPPuLtX8V5T+5NIMrNtCaV3d3f3b81sWNr6qoo33VhCNbatCdUKjyGcR03t/CfTNjQy9HmQIrWmfmXTpcpWyz6TjH1xRNtkF0IvoOcRtvlvgCXu3reK9aauL9f+Jyr8P2SJ4fS0yW4H/ubuz0X7wZCUcal9WDQi7B8rqFx6HFX9n2b7v8jHfdaZllGh/4Uc9mXP8rziwqvf10Zln+/FwDxgF8L2X5llugrbMMfPXhqokrnGgHCBUSLTP5nqHW3XRK51yFONBU6Izu91IBzJvVvZSszsrylH6rWNN5e+BJ4mlHo9ifKj6ZrUvd+U8MPxXXR0++Mc5slaN97d/0c4UtzB3WcSPt/fkblXwFTZ+jyo6XTZpPYh0Z2QwHxClr44LNzZ0sjdnyKU5t3NQ58TX5jZ8dE0Fn15V7Xeqvap1Nh6E04nZIwhw/JbE7r7hsrLX78CnJ94ER3FZnKIhVr8zQn72ZtZpqvMO8ABZrZZ9H92fJbp0venXL4j0t9HW6rel09I+ZuplSk1jlz72gCq7OelNfB11Ir0C8p7gqxSjp+9NFCllBhcCPwqaib9BVDocscvA02i9V1H5jrkw9PmeYbQ/DgJeJ1wfvsbKrczKc39tTAcKLPQl8DJZKmPH30RfQRs4+6JH5jK3mtG7j6J0MfCNOABcvsBeB44xjJcfBh5h9APPYSEoBNVJIBRc/NphD4PJkex96zpdJX4J9DYQhe+jxMqa65iw744bqG8L45OwBvRaaNhhKqcED6bMyz0UzGN0LlVZXLZp+4idLc8mdDZVuJzzRZDqiGEUxvjCB0KZXMhYf+aHDVXn5Nluv8SmvEnAk+5e2WnETJy96+juN4mdLGdsX8TKu5PuXxH/BloG12UNwk4MId9eWMLF99eRDiKTzeM0B/CRMLRe6KvjX+Tva+NVGcAQ83sbUILQqJvin8Cp5rZeMJphGVZ5s8kl89eGihVPixyZvYfdz807jhEpKLolFyZu1eWNNV2HernRepUKV9jUBKUFIg0eIdb6C0z2c9LvOFIqVOLgYiIiCSV0jUGIiIiUktKDCKWUkkwl+F1zUJ1wIkWKtB1K+B6jozOY1Y13c0WqsDdnMs2MrOjLaUPBQvVEgdUNo+IiNQ9XWNQfxwNPOvuG5SRtVBRxqJblmrN3Z8Dnsth0rOBDu6+KroXuypHEwpIfRSt56qaxigiIoVTMi0GVnlN9wcs1PCfaWYXpsxzhYWa7a8SqvxVtY6+FvpRmGxmz0T3OGNmF5rZR9Hwx6Jh+0dH+Imj/FbR8N9bqJM+2aKa62bWwsxesNBPw1RLq3dvZocRit+caWajLdRC/9jM/km4VauLZe+DYYyZPWGhrvwNZnayhZruUzK1PFhKbX8LtdRvs4r1+Z8DWgDvZIj1rOj9TbJQO38TCzXcjwRujrZHN0vpSc7MDo620ZTos9o4Gl6hT4WqPiMREamdkkkMCPdH7+XuuwKPEe7XTuhJqPC1B3C1mTU1s36EYiG7Esqk7p7DOv4F/MHd+xDuQ04cvV8G7BoNT9y//TvgvKiKXX9ghZkNJJQS3gPoC/SzUKJ4EPCVu+/i7r0JdQOS3P1FQl8Bt7r7gdHgHsC/ovdbFi1vF0KFwpstlLAlGnYRoR7CL4Du7r4HcB9wQQ7veStC2dojCHXacfcjgRXu3tfdH0+b/ml3393ddwE+Bs6Iyrk+B/w+mufzxMQWKsENA05w950JrVjnpixvobvvRrgXP/ZTOiIipa6UEoPOwH8sFJj5PbBTyrgX3H1VdK/xfEIfAf0JJX+XRxXnKm0+N7PWhI5GxkSDHiJUmYNQYGa4mZ1CKIELoQjK36IWijYeuqUdGD0+JBzp9yQkClOAARZ6aeufqGtehdnunig0lKyX7+7zgES9fID33P3rqNjO55TXRJ9C5j4V0v3b3de7+0eE7VaV3hZ6fJtCKNazUxXT9wC+cPdE4aLU7QpV9wEhIiJ5VEqJwe2EHtZ2Jpz/zlSXH/Jfbx1Cr393Enose99C//Q3EHqjaw6Mj5rBDfhrdNTc1923d/f7ox/FfoQf67+aWS7n31OrnOXaB8P6lNfrye0ak0z1+SszDDg/+hyuoXb9R6Suvyb9R4iISDWVUmKQa033hLGEcqnNo/P/P6ls4ugo/lsrL9X7C2CMmTUCurj7aMLpizaE8rPd3H2Ku99I6D2uJ/Af4HQLNdIxs05mtrmZdQSWu/vDhLK51a1bXu0+GAqoFfC1hRr2J6cMz9YPwnSgq0V9FBBt18KGKCIi2ZTSEdgQQk33uYQ699tWNrG7f2Chu9aJhGpiVXXGAyHhuNvMNiF0WfsrQsclD0enGoxwHcASM7vOQmdF6whX4r8UXcG/I/B2uJmApcApwPaE6wLWA2vY8Bx7Lp4hdCk7idAKcqm7fxPTxXp/IvRpMJvQApJIBh4D7o1OrSQ7jnH3lWb2K8Jn14RQO/7uylZgZmXAOe5+ZgHiFxFp0FT5UERERJJK6VSCiIiI1JISAxEREUlSYiAiIiJJSgxEREQkSYmBiIiIJCkxEBERkSQlBiIiIpL0/wFG5Jyqqg6FpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 9.5  Evolution of real wealth, following an increase in the rate\n",
    "    of inflation, in a variant where households are blind to capital gains\n",
    "    and losses from inflation.'''\n",
    "data = [s['Mhk'] for s in omega0.solutions[5:]]\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "axes.set_ylim(12.5, 13.3)\n",
    "\n",
    "axes.plot(data, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(30, 13.1, 'Real wealth')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 9.6  Evolution of the rate of price inflation, following a one-shot\n",
    "    increase in the target real wage of workers.'''\n",
    "data = list()\n",
    "\n",
    "for i in range(5, len(omega0.solutions)):\n",
    "    s = omega0.solutions[i]\n",
    "    s_1 = omega0.solutions[i-1]\n",
    "    \n",
    "    data.append((s['P']/s_1['P'])-1)\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "axes.set_ylim(-.005, .025)\n",
    "\n",
    "axes.plot(data, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, .018, 'Inflation rate')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}