distribution_of_returns.ipynb

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   "cell_type": "code",
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      "text/plain": [
       "interactive(children=(Dropdown(description='sim_type', options=('garch', 't', 'norm', 'draw'), value='garch'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/plain": [
       "<function __main__.plot_sims(y, sim_type, random_seeds, BEGINNING_SAVINGS=9041, YEARLY_INFL_ADJUSTMENT=0.03, PAY_TAXES=True, YEARS=60, GAMMA=2.5, YEARLY_RF=0.02, YEARLY_MR=0.04, COST=0.002, DEBT_PCT_OFFSET=0.0, SIGMA=0.02837, MR=0.076)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "from ipywidgets import fixed\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import calculate_return\n",
    "\n",
    "\n",
    "def plot_sims(y, sim_type, random_seeds, BEGINNING_SAVINGS = 9041,\n",
    "                   YEARLY_INFL_ADJUSTMENT = 0.03, PAY_TAXES = True, YEARS = 60, GAMMA = 2.5,\n",
    "                   YEARLY_RF = 0.02, YEARLY_MR = 0.04, COST = 0.002, DEBT_PCT_OFFSET = 0.0,\n",
    "                   SIGMA = 0.02837, MR = 0.076):\n",
    "    \n",
    "    ports = calculate_return.fetch_returns(sim_type, random_seeds, BEGINNING_SAVINGS,\n",
    "                   YEARLY_INFL_ADJUSTMENT, PAY_TAXES, YEARS, GAMMA,\n",
    "                   YEARLY_RF, YEARLY_MR, COST, DEBT_PCT_OFFSET,\n",
    "                   SIGMA, MR)\n",
    "    \n",
    "    ports = ports.groupby(level=1).mean()\n",
    "    fig, ax= plt.subplots(1, 1, figsize=(12,9))\n",
    "    ax.plot(ports.loc[:,[*y]])\n",
    "    ax.legend([*y])\n",
    "    plt.show()\n",
    "\n",
    "interactive_plot = interact(plot_sims, y=fixed(['dual_phase', '100', '9050']), sim_type = ['garch', 't', 'norm', 'draw'],\n",
    "                                                               random_seeds = fixed(range(300)), \n",
    "                           BEGINNING_SAVINGS = fixed(9000),\n",
    "                   YEARLY_INFL_ADJUSTMENT = fixed(0.03), PAY_TAXES = True, YEARS = (20, 50, 10), GAMMA = fixed(2.0),\n",
    "                   YEARLY_RF = (0.0, 0.06, 0.01), YEARLY_MR = (0.023, 0.063, 0.01), COST=(0, 0.03, 0.005),\n",
    "                   DEBT_PCT_OFFSET = (0, 0.06, 0.01), SIGMA = fixed(0.02837), MR = fixed(0.076))\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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    {
     "data": {
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      "text/plain": [
       "interactive(children=(Dropdown(description='sim_type', options=('garch', 't', 'norm', 'draw'), value='garch'),…"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<function __main__.plot_dist(y, sim_type, random_seeds, BEGINNING_SAVINGS=9041, YEARLY_INFL_ADJUSTMENT=0.0, PAY_TAXES=True, YEARS=60, GAMMA=2.5, YEARLY_RF=0.02, YEARLY_MR=0.04, COST=0.002, SIGMA=0.02837, MR=0.076)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_dist(y, sim_type, random_seeds, BEGINNING_SAVINGS = 9041,\n",
    "                   YEARLY_INFL_ADJUSTMENT = 0.0, PAY_TAXES = True, YEARS = 60, GAMMA = 2.5,\n",
    "                   YEARLY_RF = 0.02, YEARLY_MR = 0.04, COST = 0.002,\n",
    "                   SIGMA = 0.02837, MR = 0.076):\n",
    "    \n",
    "    ports = calculate_return.fetch_returns(sim_type, random_seeds, BEGINNING_SAVINGS,\n",
    "                   YEARLY_INFL_ADJUSTMENT, PAY_TAXES, YEARS, GAMMA,\n",
    "                   YEARLY_RF, YEARLY_MR, COST,\n",
    "                   SIGMA, MR)\n",
    "    \n",
    "    data = ports.loc[[slice(None), YEARS*12], :].melt(value_vars=['tv_u', '100', '9050'], \n",
    "                       value_name='end_wealth', \n",
    "                       var_name = 'strategy')\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(15, 10))\n",
    "    sns.kdeplot(data = data, x='end_wealth', \n",
    "                hue='strategy',\n",
    "                cumulative=True,\n",
    "               linewidth = 3)\n",
    "    plt.show()\n",
    "\n",
    "interactive_plot = interact(plot_dist, y=fixed(['tv_u', '100', '9050']), sim_type = ['garch', 't', 'norm', 'draw'],\n",
    "                                                               random_seeds = fixed(range(400)), \n",
    "                           BEGINNING_SAVINGS = range(6000, 20000, 1000),\n",
    "                   YEARLY_INFL_ADJUSTMENT = (0, 0.05, 0.01), PAY_TAXES = True, YEARS = (20, 60, 10), GAMMA = (1, 2.5, 0.1),\n",
    "                   YEARLY_RF = (0, 0.04, 0.005), YEARLY_MR = (0.03, 0.08, 0.005), COST=(0, 0.01, 0.001),\n",
    "                   SIGMA = fixed(0.02837), MR = fixed(0.076))\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    " ports = calculate_return.fetch_returns('norm', range(10), BEGINNING_SAVINGS = 9041,\n",
    "                   YEARLY_INFL_ADJUSTMENT = 0.0, PAY_TAXES = True, YEARS = 60, GAMMA = 2.5,\n",
    "                   YEARLY_RF = 0.02, YEARLY_MR = 0.04, COST = 0.002,\n",
    "                   SIGMA = 0.02837, MR = 0.076)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pi_rf:   98.70%\n",
      "pi_rm:   58.16%\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nielseriksen/applied_finance/calculate_return.py:244: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  pp[i, pi_hat] = min(pp[i, pv_u] / ses_val, pp[i, pv_u] / pp[i, tv_u])\n",
      "/Users/nielseriksen/applied_finance/calculate_return.py:260: RuntimeWarning: invalid value encountered in true_divide\n",
      "  pp[:, g_hat] = pp[:, total_debt] / pp[:, equity]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nielseriksen/applied_finance/calculate_return.py:244: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  pp[i, pi_hat] = min(pp[i, pv_u] / ses_val, pp[i, pv_u] / pp[i, tv_u])\n",
      "/Users/nielseriksen/applied_finance/calculate_return.py:260: RuntimeWarning: invalid value encountered in true_divide\n",
      "  pp[:, g_hat] = pp[:, total_debt] / pp[:, equity]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n"
     ]
    }
   ],
   "source": [
    "ports = calculate_return.fetch_returns('garch', range(5000), YEARLY_MR=0.023+0.02, DEBT_PCT_OFFSET=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_wipeout = lambda x: 1 if (x['tv_u'] == 0 and x['savings'] == 0 and x['pi_hat'] == 0) else 0\n",
    "ports['wipeout'] = ports.loc[(slice(None), 600), :].apply(check_wipeout, axis=1)\n",
    "ports['wipeout'].fillna(method='bfill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ports['dual_return'] = ports['dual_phase'].pct_change()\n",
    "ports['single_return'] = ports['single_phase'].pct_change()\n",
    "ports['100_return'] = ports['100'].pct_change()\n",
    "ports['9050_return'] = ports['9050'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ports_no_wipeout = ports[ports['wipeout'] == 0]\n",
    "cols = ['dual_return', 'single_return', '100_return', '9050_return']\n",
    "sharpe_no_wipeout = (ports_no_wipeout.groupby('random_state', dropna=True).mean().loc[:, cols]/ports_no_wipeout.groupby('random_state', dropna=True).std().loc[:, cols]).melt()\n",
    "sharpe = (ports.groupby('random_state', dropna=True).mean().loc[:, cols]/ports.groupby('random_state', dropna=True).std().loc[:, cols]).melt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dual_return</th>\n",
       "      <th>single_return</th>\n",
       "      <th>100_return</th>\n",
       "      <th>9050_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4998.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>4951.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.108555</td>\n",
       "      <td>0.107795</td>\n",
       "      <td>0.165212</td>\n",
       "      <td>0.171880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.067508</td>\n",
       "      <td>0.067345</td>\n",
       "      <td>0.021479</td>\n",
       "      <td>0.015454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.091972</td>\n",
       "      <td>-0.093208</td>\n",
       "      <td>0.040812</td>\n",
       "      <td>0.058483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.054579</td>\n",
       "      <td>0.053068</td>\n",
       "      <td>0.154124</td>\n",
       "      <td>0.161690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.140792</td>\n",
       "      <td>0.142791</td>\n",
       "      <td>0.167221</td>\n",
       "      <td>0.172127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.160730</td>\n",
       "      <td>0.160115</td>\n",
       "      <td>0.178934</td>\n",
       "      <td>0.182066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.211734</td>\n",
       "      <td>0.199225</td>\n",
       "      <td>0.230352</td>\n",
       "      <td>0.230539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dual_return  single_return   100_return  9050_return\n",
       "count  4998.000000    5000.000000  4951.000000  5000.000000\n",
       "mean      0.108555       0.107795     0.165212     0.171880\n",
       "std       0.067508       0.067345     0.021479     0.015454\n",
       "min      -0.091972      -0.093208     0.040812     0.058483\n",
       "25%       0.054579       0.053068     0.154124     0.161690\n",
       "50%       0.140792       0.142791     0.167221     0.172127\n",
       "75%       0.160730       0.160115     0.178934     0.182066\n",
       "max       0.211734       0.199225     0.230352     0.230539"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ports.groupby('random_state', dropna=True).mean().loc[:, cols]/ports.groupby('random_state', dropna=True).std().loc[:, cols]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='value', ylabel='Density'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1ZN/0nnF/1lZQCIQqVzPFli1buOOOOzBNk+zsbB544IHpntK0ULgSEREZQf/Ak2OOsnnj/qy9IB8MA1+CPjE4ERdeeCE7duwY9+d27drFBz/4wUGvpaSk8Oabb0ZralNK4UpERGQEfUeOACc7ro+HYbdj83jwzaDK1UStWLGC7du3T/c0oiamTwsahpFtGMYjhmHsMwxjr2EYYzuUSUREJA70HzkKhoGjtGRCn7fNmpWwva5k4mLdiuH7wDOmaS4BVgF7Y3w/ERGRqOk/egR7cTGWlJQJfd5eWIivTuFqpolZuDIMIwu4CLgfwDTNftM022J1PxERkWjrO1qJY974lwTDbAUF+KNwVp0kllhWrsqARuCXhmFsMwzjF4ZhpJ/pQyIiIvHADAbpP3qUlAlsZg+zud0Eu7sJ9vVFcWYS72IZrmzAWuBe0zTXAN3AnadfZBjGxwzD2GwYxubGxsYYTkdERGTs/HV1mL29OEpLJzyG1Z0LQKCpKUqzmhof/vCHyc/PZ/kp5ym2tLRw5ZVXsnDhQq688kpaW1uB0JE0n/nMZ1iwYAErV64cczf30VRWVvLb3/520uNMl1iGqyqgyjTN8HOUjxAKW4OYpnmfaZrrTdNcn5eXF8PpiIiIjF1/VRUAjrlzJjyGze0BwN/SEpU5TZXbb7+dZ555ZtBrd999N5dffjkHDx7k8ssv5+67Q+ce/uUvf+HgwYMcPHiQ++67j0984hNjusdoZ/hNNFwFAoFxfyYWYhauTNOsA04YhrF44KXLgT2xup+IiEg0+WpqALAXFU14DJvHDYA/wSpXF110Ebm5uYNee/LJJ/nQhz4EwIc+9KHIkTVPPvkkt912G4ZhsGHDBtra2qitHb79xEsvvcSFF17IDTfcwLJlywgEAnzxi1/krLPOYuXKlfzsZz8D4M477+SVV15h9erVfPe73+XBBx/kjjvuiIxz/fXXRw5rzsjI4Atf+AKrVq1i48aNZGRk8NWvfpVVq1axYcMG6qdhz1us+1x9GviNYRgO4AjwDzG+n4iISFT4qquBUDuFibLmhsJVYIKVq29t+hb7WvZN+P7DWZK7hC+f/eVxf66+vp5ZA38XhYWFkdBSXV3NnDknq3vFxcVUV1dHrj3d1q1b2b17N2VlZdx3331kZWXx1ltv0dfXx/nnn89VV13F3XffzT333MNTTz0FwIMPPjjivLq7uznnnHP47//+78jXGzZs4Jvf/CZf+tKX+PnPf85dd9017u93MmIarkzT3A4MOXNHREQk3vmqa7Dl52NxOCY8hm1gz5W/qTla04oLhmFgGMaEPnv22WdTNtCU9W9/+xs7d+7kkUceAaC9vZ2DBw/iGMffudVq5cYbb4x87XA4uP766wFYt24dzz777ITmORnq0C4iIjIMX3X1pJYEASxOJ5a0NAItEwtXE6kwxUpBQQG1tbXMmjWL2tpa8vPzASgqKuLEiROR66qqqiga5e8tPf1k4wDTNPnhD3/I1VdfPeia8JJfmM1mIxgMRr7u7e2N/Dk1NRWr1Rr52m63R4Kf1WoddW9XrMS6iaiIiEhCika4ArB6PElRubrhhht46KGHAHjooYd4xzveEXn9V7/6FaZp8sYbb5CVlTXikuDprr76au699158Ph8ABw4coLu7G5fLRWdnZ+S60tJStm/fTjAY5MSJE2zatCnK3110qXIlIiJyGjMQwFdXR2YUwpUtNxf/BCtX0+WWW27hpZdeoqmpieLiYr7+9a9z55138t73vpf777+fkpIS/vCHPwBw3XXX8fTTT7NgwQLS0tL45S9/Oeb7fOQjH6GyspK1a9dimiZ5eXk88cQTrFy5EqvVyqpVq7j99tv53Oc+R1lZGcuWLWPp0qWsXTuk+UBcMUzTnO45RKxfv97cvHnzdE9DRERmOF9tLYcuvYzC//g6Oe9976TGOnHHHfiOHWfen/44puv37t3L0qVLJ3VPib7h/rkYhrHFNM0he8u1LCgiInKa8JOC0VgWtOW68TcnVuVKJkfLgiIiIqeJhKvZsyc9ls3jJtDaihkIYJyy8TqZ7dq1iw9+8IODXktJSeHNN98c4RPJReFKRETkNL66UA8ne2HhpMey5rrBNAm0tmLzeCY9XiJYsWIF27dvn+5pTBstC4qIiJzG39CAJTMTi9M56bEiXdq1NDhjKFyJiIicxt9Qj70gPypjWbNzAAi0tkVlPIl/ClciIiKn8TU0YMuLVrjKAiDQ3h6V8ST+KVyJiIicxl/fgK2gICpjWbOzAQi0tUVlPIl/ClciIiKnMINB/I2N2PKjVbnKBhIvXH3/+99n+fLllJeX873vfQ+AlpYWrrzyShYuXMiVV15Ja2srEDquJisri9WrV7N69Wr+4z/+IzLOM888w+LFi1mwYAF33313VOb2ve99D6/XG5WxYkHhSkRE5BSB5mYIBLBFac+VJTUVIzU1oZYFd+/ezc9//nM2bdrEjh07eOqppzh06BB33303l19+OQcPHuTyyy8fFJYuvPBCtm/fzvbt2/m3f/s3AAKBAJ/61Kf4y1/+wp49e/jd737Hnj17xjSHQCAw4nsTCVdTecagWjGIiIicwtfQAIA9SpUrAGtW1oQqV3X/9V/07d0XtXkApCxdQuFXvjLqNXv37uWcc84hLS0NgIsvvpjHHnuMJ598MnKo8oc+9CEuueQSvvWtb404zqZNm1iwYAHz5s0D4H3vex9PPvkky5YtG/b60tJSbr75Zp599lm+9KUvkZuby7//+7/T19fH/Pnz+eUvf8kDDzxATU0Nl156KR6PhxdffJGMjAy6uroAeOSRR3jqqad48MEHuf3220lNTWXbtm2cf/75tLS0kJmZyebNm6mrq+Pb3/4273nPe8b7V3hGqlyJiIicwl8fClfR2nMFoaXBRFoWXL58Oa+88grNzc14vV6efvppTpw4QX19feRQ5sLCQurr6yOf2bhxI6tWreLaa6+loqICgOrqaubMmRO5pri4mOqBBq0jcbvdbN26lSuuuIJvfOMbPPfcc2zdupX169fzne98h8985jPMnj2bF198kRdffPGM30tVVRWvv/463/nOdwCora3l1Vdf5amnnuLOO+8c99/NWKhyJSIicgp/QygwRGvPFUw8XJ2pwhQrS5cu5ctf/jJXXXUV6enprF69Gutp3eUNw8AwDADWrl3LsWPHyMjI4Omnn+ad73wnBw8enNC9b775ZgDeeOMN9uzZw/nnnw9Af38/55577rjHu+mmmwbN/Z3vfCcWi4Vly5YNCofRpMqViIjIKfwNDWCxYHO7ozamNSsrofZcAfzjP/4jW7Zs4eWXXyYnJ4dFixZRUFBAbW0tEKoA5Q8E0MzMTDIyMgC47rrr8Pl8NDU1UVRUxIkTJyJjVlVVUXSG8xrT09MBME2TK6+8MrKPa8+ePdx///3DfiYc8gB6e3uHHS8sJSUl8mfTNEedy0QpXImIiJzCV1+Pze3GsEVvcSfRlgUBGgb2nh0/fpzHHnuM97///dxwww089NBDADz00EO84x3vAKCuri4SVDZt2kQwGMTtdnPWWWdx8OBBjh49Sn9/Pw8//DA33HDDmO6/YcMGXnvtNQ4dOgRAd3c3Bw4cAMDlctHZ2Rm5tqCggL179xIMBnn88cej8xcwCVoWFBEROYW/qQlbXl5Ux7RmZxNob8c0zUFVlnh244030tzcjN1u58c//jHZ2dnceeedvPe97+X++++npKSEP/zhD0BoE/m9996LzWbD6XTy8MMPYxgGNpuNH/3oR1x99dUEAgE+/OEPU15ePqb75+Xl8eCDD3LLLbfQ19cHwDe+8Q0WLVrExz72Ma655prI3qu7776b66+/nry8PNavXx/Z3D5djFiVxCZi/fr15ubNm6d7GiIiMoMdvfE9WN25zL3vvqiN2fzAL2n49rdZtPktrAPLZyPZu3cvS5cujdq9JTqG++diGMYW0zTXn36tlgVFRERO4W9uxub2RHVMa9bAETgJtjQoE6NlQRERkQGmaRJobsbmid5mdgBrTjYwcHhzcXFUx05E73rXuzh69Oig1771rW9x9dVXT9OMokvhSkREZECwowPT58MaxScFIXGPwImVeNh0HktaFhQRERngb24GiP6yYDhcjbEdQzzth5bx//NQuBIRERngb2oCiP6y4Dj2XKWmptLc3KyAFSdM06S5uZnU1NQxf0bLgiIiIgMCA5WrqC8LZmaGxm9vO+O1xcXFVFVV0djYGNU5yMSlpqZSPI69cgpXIiIiA/xNA8uCnuguCxp2O0ZaGsHOM/dfstvtlJWVRfX+MrW0LCgiIjLA39wEVmtkj1Q0WV0uAp0dUR9X4o/ClYiIyIBAczPW3BwMS/R/PFozXQQ7Os98oSQ8hSsREZEB/uaWqD8pGGZxZRLoVLiaCRSuREREBvibm7BFeTN7mNXlIqhwNSMoXImIiAwINDVjdefGZGxLpipXM4XClYiIyAB/S+yWBa0uF8EObWifCRSuREREgGBPD2ZPD9bcnJiMb3G5CHR2qjnoDKBwJSIiAgRaWwGw5cQmXFkzXRAIYHq9MRlf4ofClYiICOAfCFfWGIUri8sFoH1XM4DClYiICBBoCYer2GxojxyBo31XSU/hSkREBAi0hcNVdkzGD1eu1I4h+SlciYiIAIGWFgBsuTGuXClcJT2FKxEREQb2XFmtkQpTtFlVuZoxFK5EREQI7bmyZmfH5FxBCDURBe25mgkUrkRERAi1YrDFqMcVgDUjA1DlaiZQuBIRESEUrqzZsQtXhsOBkZpKoEPhKtkpXImIiBDacxWrHldhocObtSyY7BSuREREGKhcxXBZEAYOb1blKukpXImIyIxnBoME2tpUuZKoULgSEZEZL9DeDsFgzM4VDLNkugh0dsX0HjL9FK5ERGTGC7S2AbE7+ibM6sokqFYMSU/hSkREZrzI0TfZ2TG9T6hypT1XyU7hSkREZrxAWztw5nDVH+jn9ZrXqWiqIBAMjPs+1oxQuDJNcyLTlARhm+4JiIiITLdAezhcZY14TV+gj8+++Fleq34NgGtKr+HbF30bwzDGfB9Lpgt8PszeXgync3KTlrilypWIiMx4wY6BcDVwRM1w7nr1Ll6rfo0vrv8iH1r2IZ6pfIZnKp8Z132srvAROFoaTGaqXImIyIwXaG8Hwxjx0ObtDdt5pvIZPrHqE9xWfhv+oJ9tDdv4rzf/i8vmXkaKNWVM97Fmhg9v7oCC/KjNX+KLKlciIjLjBdrasWZmDntos2mafH/r93Gnurm9/HYAbBYbn1r9Kdr62ni9+vUx38eiytWMoHAlIiIzXqC9HcsI+622Nmxlc/1mPrryo6TZ0yKvnzXrLLJSsnj22LNjvk+kctWlcJXMFK5ERGTGC7S3Y83KHva9xw4+Rro9nXcvfPeg1+0WO5fNuYwXT7xIf6B/TPdR5WpmULgSEZEZLxSuhlauun3dPHvsWa4pvQanbejTfVeWXEmXr4s3at8Y030G7bmSpKVwJSIiM95I4eqvlX+lx9/Duxa+a9jPbZi1AYfFwZu1b47pPuEN86pcJTeFKxERmfEC7e3DtmH485E/U5pZykrPymE/Z7faWepeyu6m3WO6jyUlBcPhUOUqySlciYjIjGYGgwQ7OoY0EG3rbWNL/RauLLly1EahKzwr2NO8B3/QP6b7WTIzVblKcgpXIiIyowU7O8E0hywLvlz9MgEzwGVzLxv188s9y+kN9HK47fCY7md1uQiocpXUFK5ERGRGCx99YzktXL1w/AXy0/JZ5l426ufDS4Y7m3aO6X6WTBdBVa6SmsKViIjMaJFzBU8JV73+Xl6veZ1L51yKxRj9R2Wxq5islKwx77uyujIJqM9VUlO4EhGRGS3QFg5X2ZHXttZvpcffw8XFF5/x84ZhsNyznF1Nu8Z0P6sqV0lP4UpERGa0SOXqlA3tG2s3YrfYWVewbkxjLM5ZzNH2o2Pa1G5xZRLoVLhKZgpXIiIyowXa2wAGtWJ4veZ11uSvGXTczWjKssrwB/1Ud1Wf8VqrK4Nghza0JzOFKxERmdHCQSccrpp6mjjQeoBzZ5875jHKssoAONp+9IzXWlyZmP39BPv6JjBbSQQKVyIiMqMF2tqxpKVhOBwAkaNsxhOuSjNLgbGGqwxgoAWEJCWFKxERmdEC7e1YTtlvtal2E1kpWSzNXTrmMbJSsnCnuscUrqw6AifpKVyJiMiMFjpXMDvy9Zb6LazJX3PGFgynK8sqG2PlauDwZrVjSFoKVyIiMqOdemhzU08TxzuPs75g/bjHKcsq40j7EUzTHPW6SOVKy4JJS+FKRERmtFPD1Zb6LQCszV877nHKssro6O+gta911OssGQOVK4WrpGWL5eCGYVQCnUAA8JumOf7/KyAiIhJDp4crp83JEveScY8TfmLwSNsRcgtzR7zOmqnKVbKbisrVpaZprlawEhGReGOaJsH2dqxZoTYMW+u3sipvFXaLfdxjlbhKADjReWLU6yJ7rjq7xn0PSQxaFhQRkRnL7OnB9PmwZmXR1d/FgdYDE1oSBChML8RiWKjprhn1OktaGhgGgU41Ek1WsQ5XJvA3wzC2GIbxsRjfS0REZFzCR99YsrKoaK7AxGRl3soJjWW32slPy6e6c/Qu7YbFgsXlUuUqicV0zxVwgWma1YZh5APPGoaxzzTNl0+9YCB0fQxg7ty5MZ6OiIjISZFzBbOy2Nm4E4DlnuUTHq8oo2hsR+BkZGhDexKLaeXKNM3qgd8bgMeBs4e55j7TNNebprk+Ly8vltMREREZJNAWDlfZ7GzaSWlmKVkpWWf41MiKMorOuCwIoX1X2tCevGIWrgzDSDcMwxX+M3AVsDtW9xMRERmvk8uCmexq3MUKz4pJjVeUUUR9dz2+gG/U66wulypXSSyWlasC4FXDMHYAm4A/m6b5TAzvJyIiMi6B9jYAGlL6aO5tZkXe5MLV7IzZmJjUddeNep3F5SLQpT1XySpme65M0zwCrIrV+CIiIpMVrlzt6T8GwErPxDazhxVlFAFQ1VXFnMw5I15ncWUQPKCnBZOVWjGIiMiMFezowLDb2dt5CJvFxqKcRZMaLxyuarpG33dldWWqcpXEFK5ERGTGCrS1Y8nOYm/LXhZmL8RuHX/z0FPlp+VjNaxnfGLQ4go9LXimcwglMSlciYjIjBVob8eSlcm+ln0sdS+d9Hg2i43C9MIzhiurywXBIMFu76TvKfFH4UpERGasQHs7rfnptPW1sSR3/OcJDmd2xuwzLgtGjsDp0hODyUjhSkREZqxAeztHZxkALM2dfOUKoDCtkAZvw6jXWCPnCypcJSOFKxERmbEC7e0czfVjYEx6M3tYQXoBDd4GgmZwxGssGaFwpUaiyUnhSkREZqxAezuHM7yUZpWSZk+LypgFaQX4TT8tvS0jXmPNVOUqmSlciYjIjGT292N6vRxxtLMkJzr7rSAUrgDqu+tHvCa85yqgw5uTksKViIjMSIGODrwOqLd0sig3OkuCEFoWBKjzjtyl3ZKRAUCwU41Ek5HClYiIzEiB9naqPKE/z8+aH7Vxx1K5smZmhuagylVSUrgSEZEZKdDezom80JOCC3IWRG3cnNQc7BY79d6Rw5WRkgJ2u/ZcJSmFKxERmZECbaFwlWpJiRxbEw0Ww0J+Wv6ohzcbhoE1I4OAlgWTksKViIjMSIH2dk54YH56CRYjuj8OC9IKRq1cAVgyXQS1LJiUFK5ERGRGCrS3cSLPYEHOwqiPXZBeMOqeKwBrhouAOrQnJYUrERGZkVo66mnLMFiQtzjqYxemFVLvrR/1YGaLy0WwQ+EqGSlciYjIjHSkpwogZpUrX9BHa1/riNdYXS6dLZikFK5ERGRGOu4Pnf83L2te1MceayNRtWJITgpXIiIyI52gFUfAoDC9MOpje5yhBlqNPY0jXmN1ZagVQ5JSuBIRkRnphL2Tol5n1J8UBMhLywOgqadpxGssrkyC3d2YgUDU7y/TS+FKRERmpGpnD8W+zJiMHalceUevXAEEu7Q0mGwUrkREZMbxBXzUZwSYa+TGZPwUawqZjszRK1cZOrw5WSlciYjIjHO84xhBC8y158fsHnnOvNHDVWYoXOnw5uSjcCUiIjPOkbq9AJSkRe/Ym9N50jxn2NAerlxpU3uyUbgSEZEZ52jTAQBKMktjdg+P0zOmZUHtuUo+ClciIjLjVLZXktVlkpVdELN75DnzaPQ2jtil3TqwLBjo0LJgslG4EhGRGedEdxWzW8CalR2ze3icHvqD/XT0Dx+eLK7wnitVrpKNwpWIiMw41f0NFLSZWLOzYnaPPOfova6sGeFWDNpzlWwUrkREZEbp9ffSGOwgv83EmhXDcDXQSHSkTe2G3Y7hdBLQ4c1JR+FKRERmlJquGgAKW8GaGZsmojDGRqIZGapcJSGFKxERmVFOdJ4AoLA3BcNuj9l9wsuCzT3NI16jw5uTk8KViIjMKFVdVQAUErslQYB0ezpOm/OMva6Celow6ShciYjIjHKi8wTOgJXclJyY3scwDDzO0RuJWlwuAupzlXQUrkREZEap6qyiwGvHFsM2DGFnPALHlUFQHdqTjsKViIjMKCc6T1DYbsT0ScEwt9M9+oZ2V6aOv0lCClciIjJjBM0g1V3V5LcEpyRcqXI1MylciYjIjNHU00RfoI+8hr6pCVdpeXT5uujx9wz7vtXlwuzrI9jfH/O5yNRRuBIRkRkj3OMqvzmANSt2Pa7Cwr2umrzDV69OHoGj6lUyUbgSEZEZIxyuPB0mlilaFoSRu7RbFa6SksKViIjMGDXdoXCV186ULAtGKlcj7LuyZITClRqJJheFKxERmTHquuvItKST6gPrVLRiOMP5gtbMcOVKjUSTicKViIjMGDVdNRRasgGwZse+cpWdko3NsI1cuXKpcpWMFK5ERGTGqO2upSCYAYA1Ozvm97MYllF7XVkzQnPR4c3JReFKRERmBNM0qemqIa8/FZiaPVcweq8rS2boicVAh8JVMlG4EhGRGaGjvwOv30u+14aRmoolNXVK7jva+YKW9HQwDD0tmGQUrkREZEao7a4FwNM5NUuCYZ40z4iVK8NiwZKeTkDLgklF4UpERGaESI+rlsCUhqs8Zx4tvS34gr5h37dkughqWTCpKFyJiMiMEK5cuRv7prZyNdDrqrW3ddj3rRkuVa6SjMKViIjMCLVdtaRYU8ho6JzScOV2uoFRGom6XATViiGpKFyJiMiMUOetY1b6LIJt7VPS4yrMnRoKV809zcO+b3W5CKiJaFJRuBIRkRmhvrue/LR8Au3tU9KdPSxcuWruHT5cWTJVuUo2ClciIjIj1HvrybfnQjA4tcuCZ6xcZRLsUOUqmShciYhI0gsEAzR6G8m3hJp2TmW4SrOn4bQ5R61cBTo7MU1zyuYksaVwJSIiSa+ltwW/6ccTSAem5lzBU3mcI/e6sroyIRgk2O2d0jlJ7ChciYhI0qv31gPg8aUAU1u5gtDSYEtPy7DvWTNDhzcHtak9aShciYhI0qvvHghXXiswDeHK6R55WdCl8wWTjcKViIgkvTpvHQDuTgOYnsrViBvaVblKOgpXIiKS9Oq99dgtdjLa+8AwsGZmTun93U43rX2twx6Bo8pV8lG4EhGRpFfXXUdBWgHBtnYsmZkYVuuU3n+0I3BUuUo+ClciIpL06rvrKUgvINDWNuVPCsLova4srlC4UuUqeShciYhI0qv31lOQFg5X2VN+/9G6tFszMgB0BE4SUbgSEZGkZpomDd6G6Q1Xo1SuDLsdIy2NoCpXSUPhSkREklpbXxu+oC+yLGibxsrVyI1EdXhzMlG4EhGRpNbgbQAgz5kXOrR5GsLVmY7AsWa6VLlKIgpXIiKS1Bp7GgHw2LMJdndjyZr6De0weq8riyuTQKfCVbJQuBIRkaTW6A2FK7c/FZj6BqJho3Vpt7pcBDu0LJgsFK5ERCSphStXOb2h3lbTsecKzlC5ylTlKpkoXImISFJr9DaS6cjE1uEFpq9y5XF6Rj4CR5WrpKJwJSIiSa2xp5H8tHz8bW3A9C4LtvW14Q/6h7xnyXQR6OzENM1pmJlEm8KViIgktUZvIx6nh8B0h6tUNybm8EfguDIhGCTY7Z2GmUm0KVyJiEhSC1eugu3tAFin62nBUbq0W3S+YFJRuBIRkaQVNIM09jSGely1tUW6oU+HSLgaZt+V1ZUJ6HzBZKFwJSIiSSu8xykvLQ//wNE3hmFMy1w8qR5g+C7tVlWukkrMw5VhGFbDMLYZhvFUrO8lIiJyqnCPq3Dlarr2W8EZlgVVuUoqU1G5+iywdwruIyIiMki4x1V+Wv60h6vIETjDLQuqcpVUYhquDMMoBt4G/CKW9xERERlOuHIVflpwOsMVQG5q7ggb2lW5Siaxrlx9D/gSEIzxfURERIYIV67y0vIItE3Poc2ncjuH79JuzcgAIKDKVVKIWbgyDON6oME0zS1nuO5jhmFsNgxjc2NjY6ymIyIiM1CDt4GslCwcFgeB9nas2dPThiHMk+oZdkN7+CnGoCpXSSGWlavzgRsMw6gEHgYuMwzjf06/yDTN+0zTXG+a5vq8vLwYTkdERGaapp4m8px5oeacPl9cVK5aeluGfc/qchHoUrhKBjELV6Zp/otpmsWmaZYC7wNeME3z1ljdT0RE5HSN3pM9rmD6urOHuZ1uWntbhz0Cx5rpUuUqSajPlYiIJK2GnoaB/VZtQByEq4EjcNr62oa8Z3Flas9VkpiScGWa5kumaV4/FfcSERGBUHf28LJg3ISrUbu0q3KVLFS5EhGRpHRqd/Z4CVce58hd2i2ZmQQ6Fa6SgcKViIgkpdO7s8P0hyt36shd2kOVKy0LJgOFKxERSUqDurO3twFgHWjWOV1GWxa0ZLoIdHZimuZUT0uiTOFKRESSUqRyNdBA1JKRgWG3T+uc0mxppFpTR9hzlQmBAKbXOw0zk2hSuBIRkaQUrlzFy9E3AIZhhLq0D3sETuh8Qe27SnwKVyIikpTC3dlTrClxE64gtDQ43IZ2qyt8vqD2XSU6hSsREUlK4QaiQHyFq9ThK1fWgcpVUJWrhKdwJSIiSSnc4wriLFyNcHizRZWrpKFwJSIiSSncnR0YOLQ5e3onNMCd6qatr41AMDDodVWukofClYiIJB3TNCOVK9PvJ9jRgTUra7qnBYQqV0EzSGtf66DXLZnhypXCVaJTuBIRkaQzqDv7wDJbvFSuwl3aT18atGZkABDU+YIJT+FKRESSzultGCB+wlWkS/tp4cqw2zHS0lS5SgIKVyIiknSavKFWB/F09E1YpEv7CEfgBFS5SngKVyIiknQaehqAOA1XI1SuILSpPajKVcJTuBIRkaQTbtLpSfMQaG0DwJqTPX0TOkW6PZ0Ua8rwXdozVLlKBgpXIiKSdBq9jWTYM3DanATa2wHi5mlBwzDwOD3Ddmm3qHKVFBSuREQk6TT2NEaeygu0tYHNhmXgabx44E4dvpGo1ZWpswWTgMKViIgknaaeppMNRNvasGZlYRjGNM/qpFxn7ohH4ATVoT3hKVyJiEjSafQOrlzFy2b2sJEqV5aBypVpmtMwK4kWhSsREUkqp3ZnhzgNV043rX2twx+BEwhger3TNDOJBoUrERFJKl2+LnoDvXEdrjxOz/BH4LhC5wtq31ViU7gSEZGkEunOnnbqsmB8PCkYNlKvK2vkfEHtu0pkClciIpJUTu3ODhBob8ealT2NMxpqpC7t4cpVUJWrhKZwJSIiSSVcucpLyyPY04PZ1xd3y4KqXCU3hSsREUkq4eacg4++ibNlwYHKVUtvy6DXrapcJQWFKxERSSqN3kZSralk2DPi7lzBsAx7BinWlCFd2i2RypXCVSJTuBIRkaQS7s5uGEbchivDMIbtdWUd6CIf1PmCCU3hSkREksrp3dkh/sIVhJYGT9/QbjgcGE6nKlcJTuFKRESSSoO3YfC5gsRpuBrxfEEXAVWuEprClYiIJJVB3dnb24E4DVdO95AN7QCWTBdBVa4SmsKViIgkjR5/D12+rpPLgq1tGGlpWByOaZ7ZULmpubT0thA0g4Net7oyVblKcGMKV4ZhPGYYxtsMw1AYExGRuBVuIDr40Ob4asMQ5na6CZgBOvoGBylVrhLfWMPST4D3AwcNw7jbMIzFMZyTiIjIhEQaiMbxuYJhuam5wNAu7aHKlcJVIhtTuDJN8znTND8ArAUqgecMw3jdMIx/MAzDHssJioiIjFXkXMFTKle2OA1XI3dpd6mJaIIb8zKfYRhu4HbgI8A24PuEwtazMZmZiIjIOEW6s6clTuXq9E3tloHKlWma0zEtiQLbWC4yDONxYDHwa+DtpmnWDrz1e8MwNsdqciIiIuPR6G3EZtjITskGBg5tjtNwNdLhzdZMF/j9mD09GGlp0zE1maQxhSvg56ZpPn3qC4ZhpJim2Wea5voYzEtERGTcGnsacTvdWAwLZjBIoKMDS1Z8bmjPSsnCaliHLAtaBs4XDHR2YlG4SkhjXRb8xjCvbYzmRERERCbr1B5XwY4OCAbjds+VxbCQk5oz9PDmgfMFgx1qx5CoRq1cGYZRCBQBTsMw1gDGwFuZgOK0iIjElcaeRooyioD47s4e5k4degTOqZUrSUxnWha8mtAm9mLgO6e83gl8JUZzEhERmZAmbxNr8tYAiRGuclNzaekZvnIVUOUqYY0arkzTfAh4yDCMG03TfHSK5iQiIjJuvoCP1r5WPGmhNgz+BAhXbqeb453HB71mHahcqR1D4jrTsuCtpmn+D1BqGMY/nf6+aZrfGeZjIiIiUy68vHZqA1GI73AVPgLnVBZVrhLemZYF0wd+z4j1RERERCaj0Tu4O3swfGhznD4tCKHKVY+/B6/PS5o9tJXZmhH6kavKVeI607LgzwZ+//rUTEdERGRiGnoaAAYvC1oskUpQPDr1CJxwuDIcDgynk4DOF0xYYz24+duGYWQahmE3DON5wzAaDcO4NdaTExERGavwoc2DzhXMzMSwjPkwkikXPgJnSDsGl4tgp5YFE9VY/427yjTNDuB6QmcLLgC+GKtJiYiIjFdjTyMGRqQaFM9H34TlOgcqV6c3Es10qXKVwMYarsLLh28D/tc0zfYYzUdERGRCmnqayE3NxWYJ/chKhHAVObz59CNwXJmqXCWwsYarpwzD2AesA543DCMP6I3dtERERMansacxcmAzQKAtfs8VDIsc3txz+hODqlwlsjGFK9M07wTOA9abpukDuoF3xHJiIiIi49HobcTj9ES+ToTKlcPqwOVwDVu5CqhylbDGenAzwBJC/a5O/cyvojwfERGRCWnqaWJJ7pLI14H29rhuwxDmTnUP7XXlyiCoylXCGlO4Mgzj18B8YDsQGHjZROFKRETiQCAYoLm3OVK5Cvb3Y3q9WHOyp3diY5CbmjtkQ3uoctWJaZoYhjHCJyVejbVytR5YZpqmGcvJiIiITERrXytBMxjZcxVobQPiuzt7mNvp5nDb4UGvWTNd4Pdj9vRgpKVN08xkosa6oX03UBjLiYiIiEzU6d3ZE+Hom7Dc1Nwhe64sroEjcNSlPSGNtXLlAfYYhrEJ6Au/aJrmDTGZlYiIyDg0eEPd2QvSCoDECldup5v2vnZ8QR92ix0YqFwBwY4OKCiYzunJBIw1XH0tlpMQERGZjHpvPQD5aflAgoWrgV5Xrb2tkfmrcpXYxtqK4e+EOrPbB/78FrA1hvMSEREZs3pvPRbDgtsZCiqJGK5OfWIwXLkKdKgdQyIa69mCHwUeAX428FIR8ESM5iQiIjIuDd4GPKmek93Z20MHiSRCK4bhjsCxuAaWBVW5Skhj3dD+KeB8oAPANM2DQH6sJiUiIjIeDd6GyJIahCpXRkoKFqcz9IJpQvNhaK+CvvgKLMNXrgaWBVW5Skhj3XPVZ5pmf7jXxkAjUbVlEBGRuNDgbaAksyTy9aDu7AE/PPkp2PnwyQ+svQ3e/gOIgx5S4SNwVLlKHmMNV383DOMrgNMwjCuBTwJ/it20RERExq6+u56zCs+KfB0JVwEfPPYxqHgMzr0D8hZD9RbY8iCkueGKr03XlCPS7emkWFMGtWOwOBwYqak6XzBBjTVc3Qn8I7AL+DjwNPCLWE1KRERkrLw+L52+ziHLgtbsbHjyjlCwuvI/4PzPht5c80EwLPDqd8E1C875+PRMfIBhGOSm5g45AsfqchHU+YIJaUzhyjTNoGEYTwBPmKbZGNspiYiIjN3pPa4gFK5cS7Jg5+Nw4RdOBisILQVedw901sMzd8KCK8A9f6qnPYg71T3kCBxLZqYqVwlq1A3tRsjXDMNoAvYD+w3DaDQM49+mZnoiIiKjGzZctbeT5dodWvq74J+Gfshiheu/CxYbbPzRVE11RLlOVa6SyZmeFvw8oacEzzJNM9c0zVzgHOB8wzA+H/PZiYiInMHpDURN08RhqyfFOAbnfw5SMob/oKsAVt0C234DXQ1TNNvhDV+5cqlylaDOFK4+CNximubR8AumaR4BbgVui+XERERExuL0cBXs6iJvWRtBiwvO+sjoHz7vMxDohzd/Nvp1MRbec2WaJx/Et7oyCahylZDOFK7spmk2nf7iwL4re2ymJCIiMnYN3gZcdhdp9jQAgvteIL2gn97Ct4MjbfQPexbA0uvhrZ9DX9cUzHZ4bqcbv+mno/9kmLJkugiqcpWQzhSu+if4noiIyJQ4vYGoUfG/BH0GgbIbxjbA+Z+D3nbY/pvYTHAMIr2uTmnHEKpcdQ6qZkliOFO4WmUYRscwvzqBFVMxQRERkdE0eBsoSB/YzO7vx1r1Ip3VqVjdhWMboHg9FCyH3Y/FbpJnED4T8dR9V9ZMF/j9mD090zUtmaBRw5VpmlbTNDOH+eUyTVPLgiIiMu3qvfUnK1eHn8fwd9FxzDm+Q5uXvh1OvDltG9vDlatTnxi0uAaOwFGX9oQz1rMFRURE4o4/6Kepp+lkuNr1CEFLOl11KVizx3Fo85LrARP2/Tkm8zyT8PmCQypXQFDnCyYchSsREUlYzT3NBM1gqMdVfzfsf5o++zIwjcjhx2NSUA45pbDvqZjNdTTZKdlYDMvgI3B0eHPCUrgSEZGENaiB6P6/gM+L1zcfS2Ymhm2sJ7wR6tq+5Ho48vfQ5vYpZrVYyU7JHrQsaM3KBkINUSWxxCxcGYaRahjGJsMwdhiGUWEYxtdjdS8REZmZwuEqPy0fdj8Krtn0tmeMb79V2NIbIOiDg89Gd5Jj5HYObiQaXtYMtClcJZpYVq76gMtM01wFrAauMQxjQwzvJyIiM0ykgagjC468BEveRqCtfWLhqvgsyCiAvX+M6hzH6vTDm61ZA+FKlauEE7NwZYaEO7LZB36pWYeIiERNvbcem8VGTtMh8Hlh/mUE2trGt5k9zGKBxdfBwecg4Iv+ZM/g9CNwLBkZYLUSaG+b8rnI5MR0z5VhGFbDMLYDDcCzpmm+Gcv7iYjIzNLgbSDfmY/l8IuhQ5hLLxgIV9kTG3DeJeDrhprtUZzl2JxeuTKM0KZ8Va4ST0zDlWmaAdM0VwPFwNmGYSw//RrDMD5mGMZmwzA2NzY2xnI6IiKSZCINRA+/AMVnQ2oojIQ3g49byfmh34+9GrU5jpXb6cbr99LjP9k01JqVRbBdTwsmmil5WtA0zTbgReCaYd67zzTN9aZprs/Ly5uK6YiISJJo8DZQasuE2h0w/1JMn49gV9fElgUBMvLAsxgqX4vuRMcg3Ovq9H1Xqlwlnlg+LZhnGEb2wJ+dwJXAvljdT0REZhbTNKn31rPe2wWYof1WA0FkwsuCAKXnw/E3IOCPyjzHargjcCzZCleJKJaVq1nAi4Zh7ATeIrTnanq6s4mISNLp9HXS4+9haWstpGbB7DUE2tqASYarkvOhvxPqdkZlnmM13BE41kyFq0Q0jg5r42Oa5k5gTazGFxGRma2uuw5Mk+LGg6GN6BZr9MIVwLHXoGjtZKc5ZsMegaNlwYSkDu0iIpKQ6rrrKPP5cXY3w7xLAfC3tgJgy82d+MCZsyB33pTvu8p1DlO5ysoi2NGBGQhM6VxkchSuREQkIdV117Guty/0RdlFAAQGwpU1J2fI9cGgSa9vjCGl5Hw4/joEpy7UpFhTyLBnDDpfMNJIVOcLJpSYLQuKiIjEUm13LWv7+jHTPBi58wAItLYBg5cF+/wBnthWzc9ePsKxZi8b5uVydXkh715bTEbKCD8GSy+Abb+G+gqYtTLG38lJuam5wx6BE2xvh2ECo8QnVa5ERCQh1XbXsq7fhzF3Q+jgZUKVKyMtDUtqKgDVbT1cds/f+fKju3DarfzDeaXUtffyb09W8MH738TbP8ITgeF9V8c3TsW3EuF2unUEThJQ5UpERBKSt7WS2f19MOecyGuB1lZsA1Urb7+fjz60mY4eHw99+GwuWujBMAzuun4ZT++q5Y7fbuWO327jZx9ch916Wq0hew5kFEL11in8jkKVq8r2ysjXCleJSZUrERFJSHlNh0N/mLsh8pq/rRVrTg6mafLF/93J3roOfvD+NVy8KA9joLoFcN2KWfznO5fzwr4G/uWxXZjmMEffzl4DNVMbrtypgytXlki40p6rRKJwJSIiCScQDFDaXo/fYoNZq06+3tqGNSeH+189yp931fIv1y7h0sX5w47xgXNK+PRlC3hkSxV/PzDM8WtFa6HpIPROXbBxO9209bXhD4aWK8N7x1S5SiwKVyIiknCae5tZ2dtDa24J2FIirwdaW+nOyeP7zx/ksiX5fPTCeaOO8+nLFjI3N41vPbOfYPC06tXsNYAZOlpniuSm5mJi0tbXBoDV5QIg0N42ZXOQyVO4EhGRhFPffoxlff30zl496PVAaysPOxfQ1efnS9csHrQUOByHzcIXrlrE3toO/rSzZvCbswcaiE7h0uDpR+AYNhsWl0uVqwSjcCUiIgnHe/w17IAx99zIa2Z/Py398AdfHtevnM2SwswxjfX2lbNZOiuT//7bAfr9wZNvpLshey7UbIvy7EcWPgLn9F5XQYWrhKJwJSIiCcdWtRmArAVXRl7zt7Xxh0WX0mcafP6KhWMey2Ix+NI1izne4uX3bx0f/ObsNVP6xOCwR+BkZhJoU7hKJApXIiKScDLr93HM7iAjqyTyWl11E0+VnccNhRbm5WWMa7xLFuWxek42D208NvjJwdlroe0YeFtG/nAUDXsETrbOF0w0ClciIpJYTJP8tiqOZOQM2lP12K56fFY7H1mePe4hDcPgfWfN4VBDF9tOtJ18Y/aa0O9TtO/KZXdht9gHLQtadHhzwlG4EhGRxNJZS1Z/Dw3ZRZGXTNPksaNeypuOsLAkb0LDXr9qNk67lf/dfOLki+EN81O078owDHJTc2npGdylXeEqsShciYhIYhnYA9WZd3Jf1eZjrRzrgauPbxr20OaxyEix8baVs/jTjtqTx+KkZoF7AVRP3aZ2t9N92ob2bAIdHcM3OpW4pHAlIiIJxV+9GT9gFKyIvPb7t06QbgS5sHpn5MiYibj5rDl09fl5elfdyRdnr53yJwYHbWjPygK/n2C3d8rmIJOjcCUiIgnFd2IThxx2PFlzAejs9fHnnbVcYW0hLT0Vw26f8NjrS3KY50nnD2+dsjRYuAI6a6ZsU/vpR+CEw2JQjUQThsKViIgkDtPEVreTCoeDWemzAHhqZy09vgBv6z2GNSd7UsMbhsFN6+ewqbKF480DlaKC8tDv9RWTGnuscp25tPS2RJYBrdk6vDnRKFyJiEjiaDuGva+TihQHhemFADy+rZqF+RksbjmGLXti+61O9bYVodD27N760AsFy0O/T1G4cqe68QV9dPo6gZOVK4WrxKFwJSIiiWNg71NFSgoF6QU0dfXxVmUL162YRaCtdcKb2U81153G4gIXz+4Z2HflKoD0PKjfNemxxyLSpX1g35XCVeJRuBIRkcRRvRW/YaXZlU+KNYXn9tRjmnBVeQGB1raohCuAK5bl81ZlK23e/tALBeVTV7kaOF8wvO/KEg5X6tKeMBSuREQkcdRsoyo9C48r1OPqrxV1FOc4WTYrk0BrdCpXAFcuKyQQNHlpf2PohYLl0LAXgoGojD+a04/AUeUq8ShciYhIYggGoXYHe1JSKUwvpLPXx2uHmrm6vBCztxezt3fSG9rDVhZlke9K4dk94X1X5eDvhZYjURl/NEMqVykpGKmpClcJROFKREQSQ8sR6OtgqzXIrPRZvLS/kf5AkKvLCwm0tgJgi1LlymIxuHxpAX8/0EifP3DKE4O7ozL+aLJTsjEwTmskmkVArRgShsKViIgkhrodAGy3QWF6IX+tqMOd7mBdSQ7+gXAVrWVBgCuX5dPV5+eNIy3gWQyGdUr2XdksNrJTsoccgRPs6Ij5vSU6FK5ERCQx1O3CtNg47LDjTi3gpf2NXLmsAKvFINDaBkQ3XJ0334PTbuW5PfVgTwXPIqiLfeUKhjkCJzs7EiAl/ilciYhIYqjdSXf2HPyGQXNLLl19fq4qLwCILAtao9DnKizVbuW8+W5ePdQUemEKnxjMTc0d3KU9N5dAi8JVolC4EhGRxFC3i8asUIPPA9UWHFYL587zAKeEqyhtaA87b4GHo03d1LT1hMJV+3Hojf3Gcneqe9D5grbcHAItU3P8jkyewpWIiMS/zjrobuBYWiY2i43NlV7OKsvB6bACEGhrBYtlUoc2D+e8+aEn9zYebj6lU/ueqN5jOLnO3MHLgjm5BNrbMf3+mN9bJk/hSkRE4l9dqDv6PocDt62M/XWdXLgwL/K2v7UVa3Y2hiW6P9YWF7jITXfw2uEmKFgWerEh9kuD7lQ33b5uev29AFhzc8A0CbS1xfzeMnkKVyIiEv9qB54UtASw9a0A4MKFnsjb0ezOfiqLxeDceW42Hm7GdM0Ghwsa90f9PqcLH4ET3ndlyw197dfSYEJQuBIRkfhXtwtySjna10xPRymeDAdLCzMjbwdaWrBmZ8fk1uctcFPb3ktlSw/kLYbGfTG5z6lObyRqzQmFK21qTwwKVyIiEv/qdmEWLKe+u5HG5lwuWODBYjEib/tbmrG53TG59XnzQxWy1w83Qd4SaIh9uBpyeHNuqCoXaFXlKhEoXImISHzr64SWw3S759Pf68HbZxu03wog0NSM1Z0bk9uXutOYlZXK64eaIX8JdDeAN7Yh5/TKlZYFE4vClYiIxLeB3lIN2bPwdy8EBu+3Mv1+Am1t2NyeYT8+WYZhcO58NxuPNBP0LA69GON9V5HK1cATg+ElTy0LJgaFKxERiW+1OwE4lpZJoGshpR4H+ZmpkbfD1RxbjCpXEFoabOnuZz8loRdivO/KaXOSZkuLLAsaNlvofEEtCyYEhSsREYlvdTvBmcshXx+BnhIuWHDakuBAuLLGaM8VwDlloeC2uckB9vQp2dSem3par6vcXPyqXCUEhSsREYlv9RVQuJxd1Z1gOrhgQeGgt/1NoQASqw3tAMU5TvJdKWw53gZ5i6bsicGhR+CocpUIFK5ERCR+BQOhIFOwnP01QQDOLhu8/BdoiX24MgyDdSU5bD7WGnpicAp6XQ09AidXy4IJQuFKRETiV2sl+LyQv4yapnRcGR3kpjsGXRKuXMVyWRBgXUkOVa091LvKobMWetpier9c59DDm/3NCleJQOFKRETi18CTgj7PUro685iT1zfkkkBLM4bDgSUjI6ZTWV8aqphtCSwIvdB0IKb3c6e6ae1tJRAMAKFeV4G2NsxgMKb3lclTuBIRkfhVXwEYvNmTixl0sKzYPuQSf1MzVrcbwzCGfj6Kymdnkmq3sLkjO/RCw96Y3i83NRcTk9a+0CZ2W04uBIME2ttjel+ZPIUrERGJXw0VkDuPF480AkP3W0Fsu7Ofym61sLI4my31AbA5Y77vasgROLnhI3C0NBjvFK5ERCR+1e+BgnLeOtqKxVHPIs+sIZfEsjv76daX5FBR00FP7tKYPzF4+hE4tvAROApXcU/hSkRE4lN/N7QcwZ9Xzr4aP9a0I8zOmD3kMn9zc8y6s59ufWkO/qDJDuc501a5Uq+r+KdwJSIi8alxH2Cy21ZOv9/AkXEMj3NwiDJNE39LS0y7s59q7dxQ9WiLuQg6qkIBMEbcqaFwFTm8OWdgWVDtGOKewpWIiMSngScFN3tDTUNnuXuwGIN/bAXa2sDnw+aZmspVdpqDBfkZbO4auF/z4ZjdK9ORic2wnTy8OScb0OHNiUDhSkRE4lP9HrCnsbnRICW1i5LcrCGX+BsaALAVFEzZtNbMyWZHqx3TBJoPxuw+hmEMOgLHcDiwuFw6vDkBKFyJiEh8qt+NmbeUzcfasKcdpzC9cMglkXCVP3XhatWcbFp6glSZedB0KKb3GnoETo42tCcAhSsREYk/pgkNezietZ6mrj78jv3Db2avrwfAlp8/ZVNbPScbgB3Os2NauYKBw5tPPQInJ1fLgglA4UpEROJPVwN4m9lsLAfA4jzG7PSh4co3ULmy5+dN2dQWF7pw2CzssK2IfZd2Hd6ckBSuREQk/tTvBmBzTyFOh4ElpX74ZcH6Bqy5uRgOx5D3YsVutVA+O5MdvrmhDe2mGbN7hStX5sA9rLk5+PW0YNxTuBIRkfjTsAeALU02SvIDGIY54rLgVC4Jhq0qzmZXdyb+Pm/oEOcYcae66Q/20+XrAkLLgoHWtkjYkvikcCUiIvGnvoL2tFIONPaQl9MJMOKGdlvB1Ier1XOy6QlYOGQWQVPs9l3lOkO9rQY1EvX7CXZ0xOyeMnkKVyIiEn/qK9iacREAzowa3KluUqwpQy7zNTRgn8InBcNWFofaQuwIzo/ppvbTG4mGj8DRpvb4pnAlIiLxJeCHxv1sMZZjtRj4HYeGXRI0fT4Czc3TsixY6k4nM9XGDmNRTNsxDD0CJ/R1oFW9ruKZwpWIiMSXlsMQ6GNzTyHLZmXS2FvNrPShBzb7m5rANKdlWdBiMVg1J5sdxpKYPjE45PDmgWN+/M3NI35Gpp/ClYiIxJf6CvymhR0tdtbOzaa2u3b4cDXQ48o+hd3ZT7WyOIt9/fn0Nh2N2T1yUkPLgJHK1cAB1QGFq7imcCUiIvGlvoIDlNDjN1k4y05foI9ZGUPDla8+3J196itXEHpiMICFilYb+Hpicg+7xU5WSlbkCJzInqvGppjcT6JD4UpEROJLwx62Oc8FoCDXCzBsA9HpOFfwVCuLswHYHSyFliMxu4879WQjUcNux5qdjb9Z4SqeKVyJiEh8qd/Ndms5uekOgtZQgBqucuVvqIeBsDEdCjJT8Dgt7DZLY9uO4bQjcKwet5YF45zClYiIxI++Tmg7zra+2ayek02dtw5g2D1Xvvp67Hl5GJbp+VFmGAbLirKoCJbGth2D001Tz8lKlc3twd+kcBXPFK5ERCR+NOyl3UzjUFcKa+aENrOn29PJdGQOudTf0DhtS4Jhy4tzOWDOoa8hdsuCec48GnsaI1/bPB49LRjnFK5ERCR+1O9mZ3AeAKvnZlPTVcOs9FkYhjHk0uk6+uZUy4uy8GPlQG3s+k7lpeXR4++h29cNgM3jDrWhkLilcCUiIvGjfg/bLeUYBqyak01dd92wS4IwfUffnKp8dqiiVtFCzA5wznPmAdDoDVWvrG4PptdL0OuNyf1k8hSuREQkftRXsM26kvl5GWSm2qnprhk2XAW6ugl2d09bj6uwublpuGxBdvcXQldDTO6RlzYQrgaWBm3uUJd2LQ3GL4UrERGJD6aJWV/B9v4i1szJxuvz0t7XPvKTgkxfj6swwzBY5rGG2jHEaFP76ZUrm2cgXGlpMG4pXImISHzoqOZ4TwotfgerBzqzwxl6XE3Doc2nWz4nl33mXPwNsQlXHmeoK3u4cmX1qEt7vFO4EhGR+FC/h+3mAgDWzMmhpqsGGKHHVeTom+mtXAEsLy2ilxSOVFXHZPxMRyYp1pRTKlehcKXKVfxSuBIRkfjQUMG24AKcdguLCjIilavhe1xN79E3pyoPd2qv6YzJ+IZh4HF6Tu65yh04vFm9ruKWwpWIiMSH+gq2GctYWZyNzWqhtrsWm2GL7Dk6lb+hAYvLhSUtbRomOtg8Tzqphp+K1tj9SM1z5kUaiRp2O9asLB2BE8cUrkREJC701u1nT6CI1XOzAajpqqEgvQCrxTrk2njocRVms1pYmtnH7h43+Ptico+8tDwavCefRrR6PARUuYpbClciIjL9/P3saejDZ1pZMycHgOquaooyioa93NdQHxf7rcLK8xzsCZYQbI5Np/ZTK1egLu3xLmbhyjCMOYZhvGgYxh7DMCoMw/hsrO4lIiIJrvkg2wJlAKwZqFyNFq78DY1x8aRg2PK5HjpJ40TloZiMn5eWR5evC68v1DjU5nZrWTCOxbJy5Qe+YJrmMmAD8CnDMJbF8H4iIpKo6vewPbiA2S4rBZmp9Ph7aOppothVPORSMxjE39gYN8uCAMsXhI7s2X2sNibjh/edhatXVo+bQKPCVbyKWbgyTbPWNM2tA3/uBPYCw/9fEBERmdnqd7PNXMjqklCbgXAbhuEqV4HmZvD7p/3om1MtnFuAjQAVdbE5kibSSDTSpd1D0Osl2NMTk/vJ5EzJnivDMEqBNcCbU3E/ERFJLI3VR6gy81gzN9RmoLor1DNquMqVb6CB6HQffXOqFJuVRSkt7G6zx2T8yBE4p3dp176ruBTzcGUYRgbwKPA50zQ7hnn/Y4ZhbDYMY3NjY2OspyMiInFoe22oAhN+UvBE5wlg+MqVP456XJ2qPKufih43ZgwOcB5SuQp3aVcj0bgU03BlGIadULD6jWmajw13jWma95mmud40zfV5eUN7mYiISJLraWV7Vw42w2T57CwgVLly2py4U91DLo+cKxhHlSuA5QUpNJsu6uvroj52VkoWdov95BE47oEu7apcxaVYPi1oAPcDe03T/E6s7iMiIgmubjfbzAUsybXgdIR6WlV3hp4UDP0oGczf0AAWCzb30OA1nZbPDYW93Qeif8agYRjkOfOGLgtqU3tcimXl6nzgg8BlhmFsH/h1XQzvJyIiCShQV8HO4DzWlHoir1V1VY3c46q+HpvbjWGzTdUUx2TJgvkYBNl9rOHMF0+AJ22YI3DUjiEuxezfTNM0XwWG/l8OERGRUxyqPEoXc1g9L3SGoGmaVHdVs75g/bDX+xsa425JECA9v4x5xktUNDhjMn6+M5+j7UcBMBwOLFlZoScnJe6oQ7uIiEyr7dXdAKyZG+rM3t7XTreve9gnBWHg6Js4DFdYbSxPbaKizRGT4T1ODw09J6tiNrdbhzfHKYUrERGZPgE/29ucZNl8lHnSgdCSIAz/pCCEzxWMzwegyrN91PjSaenuj/rYeWl5dPZ30uvvBXQETjxTuBIRkenTfIhtgXmszjMim9dHC1fBvj4C7e1x1ePqVMsLUgGoqGqN+thD2zG41YohTilciYjItOmq2s0Bs5jVA81DIfSkIAzfQNQ/0EDUlhdfPa7CyksKAdh95HjUxw43Eo0cgeP24Fe4iksKVyIiMm12HqwkiIU1i+dHXqvqqiInJYd0e/qQ6/318dnjKixr1nyKaGTPieiHnkjlKtyOwe0m2N1NsLc36veSyVG4EhGRabO9JrSZffUpbRiqO6tH3sweOfomPitXeBayzHKMPQ19UR86cgROz2m9rrSpPe4oXImIyLTZ1prCvNQustNOPmFX3VU9So+r+Dz6JiI9j2X2Oo502fD2+6M6dHZKNjbDFqlcWcNH4KjXVdxRuBIRkWlhdjWyvb+Y1Z6TZ/EFggFqumtGfVLQSE3Fkpk5VdMcH8OgPNuHicG+us6oDm0xLIMbieoInLilcCUiItOi+tBOGslhTcnJzewN3gb8Qf+oy4K2gvxhj8WJF8sK0gDYU9MR9bHznfk0eAeqdzoCJ24pXImIyLTYfjD0RN3qpYsjr52px5WvoR57nD4pGFY0u5gsuqioaon62PlpJ8OVdeBsRR2BE38UrkREZFpsre4iBR9LyuZEXqvqDIWr4oyRurM3xO2TgmGGZ35oU3sMnhg8NVxZHA4smZkEtKE97ihciYjItNjWksLKtGbs1pM/iqq7qrEYFgozCodcb5rmwLJgfIcr3AtZZhxjX1Mf/kAwqkMXpBfQ5eui2xd6ylJd2uOTwpWIiEy5vt5eKvrzWesZHD6Odxxndvps7Bb7kM8E29sx+/ri9uibCPd8yi2V9AUMjjZ1R3Xo/LTQkmi9d6Dfl9utZcE4pHAlIiJTbveeCvqxs6bEPej1Y53HKMksGfYzvnCPq3htwxDmSGeZqweAPbXR3dRekBaq2tV3h8KV1ePWsmAcUrgSEZEpt+1gJQBrly6MvGaaJsc6jjE3c+6wn/E3DrQgiPdwBczPz8Rh+KmI8hOD4XAVeWJQR+DEJYUrERGZcluruigymsgvWRp5rbm3mW5f94iVK39D4oQru2ceiy3V7Klpj+q44WXBU9sxBLu6CPZFvyO8TJzClYiITLltramsTWsAqy3y2vGOUGuGkcNV+NDmON9zBaFjcDjCnpp2TNM88/VjlGpLJSsl6+Seq3CXdlWv4orClYiITKnaNi+1/gzWegaHjmMdxwAocY0QrhobsbhcWJzOmM9x0twLKDcqafH6qeuI7sHKBWkFkXB1steV9l3FE4UrERGZUlsPhELU2tLTNrN3HMNmsTErY9awn/M3NCTEkiAA7gUss4S+z2h3as9Py49saA9XrnR4c3xRuBIRkSm19cAxUuhn6cIFg14/3nmc4oxibBbbsJ/zNzQkxpIgQPZcltjqMDBjsqn95Ib2gcpVU2NU7yGTo3AlIiJTamtVFyuMozhmrxj0+rGOkdswQGhZMO57XIVZrGS4Z1Oa0hn1ylVBWgHNvc34Ar7IsmBAy4JxReFKRESmTJ8/QEW7g7XOWkg7eWBz0AxyovPEiG0Ywt3Z477H1ancC1hmORH9XlfpoXYMjT2NWFJSsLhcWhaMMwpXIiIyZSpqOug3rUM2s9d119Hj76Esq2zYzwXa2jB9vsRZFoRQuPLv4XiLl45eX9SGHdKlXUfgxB2FKxERmTJbj4b2Cp3emf1I+xEA5mXNG/ZzidRANMK9gGWEvq+9UVwajHRpP+UIHLViiC8KVyIiMmW2HayiiEYKysoHvX6k7QzhKoEaiEZ4FlJuqQSI6qb2SCPR7lBQtapyFXcUrkREZMpsre5mjeUQzFo16PWjHUfJTskmJzVn2M9FKlcJtiyYb7TjSQlEdd9VpiOTVGvq4MObVbmKKwpXIiIyJWrbe6jtsbI2pQqyB29cP9J2ZMSqFSRYd/awNDekZrPM2RbVJwYNw6AgvWDwETidnToCJ44oXImIyJTYdrwNgLWFNjCMQe8dbT864mZ2CIWrhOnOHmYYA0uDxzjY0Em/Pxi1ofPT8k92aQ8fgaOlwbihcCUiIlNia2UTDvpZVlo86PXW3lZa+1pHD1eNjYm13yrMs4hlvgp8AZODDZ1RG3ZwI9GBLu0KV3FD4UpERKbE1sN1oeahRSsHvX60/Sgw8mZ2CB99k0BLgmGehZT3bQOiv6m93ltP0Axi84S7tGvfVbxQuBIRkZjr8wfY3dDHWstBmL160HvhNgxnWhZMqP1WYZ5FlBj1pNmNqO67KkgrwB/009rbesoROApX8ULhSkREYq6ipoP+oMHalBrIGRyiDrUdwmlzMjtj9rCfNU0Tf2NjYnVnD/MswmqYLMnyR/WJwXCvqwZvg/ZcxSGFKxERibnIZvZZKUM2sx9sPciC7AVYjOF/JEW6sydiuMopBYuN8tRm9tZ0EAyaZ/zIWISPwKn31usInDikcCUiIjG3pbKZIqOJgrkLh7x3qO0QC3OGvh4WaSCaiMuCVjvkzmOZcZTOPj9VrT1RGTbSSDSyqd2Nv1nLgvFC4UpERGLKNE02HWnkLGMfzF4z6L3mnmZaeltYkL1gxM8n5NE3p/IsorxvBwAVNe1RGdKd6sZqWKnrrgPA6nETUOUqbihciYhITB1r9tLkDbLesh8KBz8peKjtEMDo4SrcQDRhw9VCFnVuwmoxorbvymqxkpeWd0qXdh2BE08UrkREJKY2VbYAcHZqFbgHh6iDrQcBzrAsmIDd2U/lWUSq6WV+jj2qTwzOSp8VqVzpCJz4onAlIiIxtbmyhWxLDwuKC8Ey+MfOobZD5KTk4E51j/h5f2MjlsxMLKmpsZ5qbHgWAVCe1R/VXleF6YXUdtcCYMvzEOzoINjfH7XxZeIUrkREJKbeOtrMemMvluJ1Q9472HqQBTkLME57gvBUCdtANMwTqsotS2mkrqOX5q7onAFYmF5IXXcdQTOIdaDXldoxxAeFKxERiZmGzl6ONveENrMXrR30XiAY4GDbQRbnLB51jIRtIBqWmgUZhZQTapYarX1Xs9Jn4Qv6aOltwTbQ60rtGOKDwpWIiMTMlspWAM6y7IOiwZWr453H6fH3sDj3DOEqURuInsqzkKW9oWNworXvalb6LADquutOdmlXO4a4oHAlIiIxs6myhVSLn+WZvZA5uAP7vpZ9ACzNXTri58Pd2RO6cgXgWURO605mZ6VGbd9VOFzVdtdiHTi8OaBN7XFB4UpERGJmc2Urq23HcRSvHvLevpZ92Cy2UQ9sTuju7KfyLILeNpYVpEZtWbAwvRCA2q7aUw5v1rJgPFC4EhGRmOjq81NR087Zge1D9ltBKFwtyF6A3WofcYxId/ZED1d5oScGl2V4OdLYhbffP+khMx2ZOG1O6rx1WFJTsWRkqNdVnFC4EhGRmNhc2ULQHH6/lWma7GvZx5LcJaOOkfA9rsLyQt/nckcdQRP2RqF6ZRjGkF5XAe25igsKVyIiEhMbjzRjN0zWWw4MOfamsaeRlt6WM4erRD/6Jsw1C1IyWREM7TPbVRWdY3AK0wup7Qr1urJ6PFoWjBMKVyIiEhNvHGlhtbMBZ15pqB3BKcKb2WdM5cowIG8xhe078GQ42FUdvU3tkUai6tIeNxSuREQk6jp7feyubmeDuX3IkiDA/pb9ACzKWTTqOP6GhsTuzn6qvMUYTftYXpQVtQOcC9MLae5tpj/Qj83j1p6rOKFwJSIiUbe5spVA0ORc/1sw5+wh7+9t2UtxRjEuh2vUcfyNjYndnf1UeUuhu5EVeQ4ONnTR6wtMeshwO4b67nqsHg/B9nZMHYEz7RSuREQk6t440ozDYrLGcgjmbBjy/v6W/Sx1j9zfKszf0JD4DUTDwpvaM9oIBM2otGSItGPorsU20OvK39Iy6XFlchSuREQk6jYeaWZ1RhvOVGckVIR19XdxvPP4GY+9AfA1JvjRN6fKC32/KyyVAOyunvzSYKRLu7dOva7iiMKViIhEVUd4vxW7YM5ZYBn8o+ZA6wGAM1auzGAQf2NT4j8pGJZVDI4MZnXtxZ3uiMoTgwXpBcBAI9HI4c3a1D7dFK5ERCSqwv2tNvS8POyS4N6WvQBnrFwFWlrA58NWUBiTeU45wwDPIozGvSwvymJXFCpXKdYUclNzQ0fgRA5vVriabgpXIiISVRsPN+OwwlrLQZh7zpD397fsJzc1l/y00StSvvp6AGwFSVK5AshfCo37WVGUFdVN7YMOb9ay4LRTuBIRkah69VAzazM7SbUEh23DsLdlL0tyl2AYxqjj+AfClb0wSSpXENp31VXH8jwrgaAZlU7t4XBlcTqxpKfj17LgtFO4EhGRqGns7GNvbQcX2iqgcAU40ge93+vv5VDrIcrd5WccKxyubPkFMZnrtAg/MZgSao4ajU3themF1HbXYpomVo+bgCpX007hSkREoua1Q6GqyUXdz8LcofutDrQewG/6xxSufHX1YLVGnoJLCgNPDBb1HCQ33cHOKGxqL0wvxOv30tHfgc3tUSPROKBwJSIiUfPywUZyUg3KA/uGbR5a0VwBwDL3sjOO5a+vx5aXh2G1Rn2e0yZrLtjTMJr2sbI4KyrhKtKOYWDflTa0Tz+FKxERiQrTNHn1YBPnu7uwGOawTwruad5DbmpupPnlaPwN9dgLkmhJEEJtKfIWQ8MeVhVnc7Chk+4+/6SGHBSu8jwKV3FA4UpERKLiQH0XDZ19XGTsgNx5kFU05JqK5gqWuZedcTM7hJYFbckWrgDyy6F+D6vmZBE0J7/vKhxUa7prsOXnE2xvJ9jXF42ZygQpXImISFS8crARgAvan4LSC4e83+Pv4XDb4TEtCcLAsmBhEoargmXQ3cDKnFAbhskuDbqdbhwWR6iR6MDmf39Dw6SnKROncCUiIlHxysEm5ufYmO2rhLKLhry/v2U/QTM4ps3sga4ugt3dybcsCJAfCpee7gMUZTvZXtU2qeEshoXZGbOp6qqKdLNXuJpeClciIjJpvb4Abx5t5sLsgf0+pRcMuSa8mX3GtmEIK1ge+r1+D6vnZLPjRNukhyzKKKKmqwZbfugcRoWr6aVwJSIik/ZWZQu9viAXBTeDeyG4hm5Y39O8B4/Tc8bO7HBqA9EkDFcZeZCeBw0VrJqTRVVrD81dk9sjNTtjNtVd1dhVuYoLClciIjJpz+9tIMVm4dyWJ4atWgFUNI1vMzuQnBvaIbQ0WF/ByuJsYPL7rooyimjra6MnzYbhcOBTuJpWClciIjIppmny/L56Lii24/S1QNnQzexen5ejHUfHtCQIoTYMkMThqqAcGvaxYlYGFgN2THLfVVFG6MnM8BOD/obGKExSJkrhSkREJuVwYxcnWnq4zHU89MIwTwrua9k35s3sAL66OqzZ2VhSUqI51fhRUA7+HtK9VSzIz5j0vqtIuOoaCFcDy6oyPRSuRERkUp7fG1qCuqz/RfAshoyhe6rG05kdwF/fgC2ZDmw+3cATg9RXsKo4m51V7ZimOeHhZmfMBqC6q3qgcqVlwemkcCUiIpPy/N4GlhVmMKvmOZh38bDXVDRXkO/MJy8tb0xj+urrsBWceeN7wspbAhihcDUnm+bufqpaeyY8XG5qLk6bM7SpvUDharopXImIyIS1efvZfKyFy2f1gs8LC64Y9ro9zXtY5hlb1QpClSt7QRJXrhxpoS72DRWsmZsNwNbjrRMezjAMZqfPjiwLBr1eAl3dUZqsjJfClYiITNjfDzQSNOFyy1awpgz7pGBXfxeV7ZVj3m9l9vcTaG5O7soVhDq11+9hcYGLNIeVrccmHq4AilxFkWVBOPlQgEw9hSsREZmw5/Y24MlwsLL+SSg5DxzpQ67Z27IXE3PM+618A0+6JWV39lMVLIeWI9gCPawqzmbr8bZJDVeUUURVZxXWcLiqq4vCJGUiYhauDMN4wDCMBsMwdsfqHiIiMn16fQFe3NfA5fMzsDTtHXVJEMaxmT3ShiGJlwVhoFO7CfV7WFuSzZ7aDrz9/gkPN9c1ly5fF92eUMD11dZGaaIyXrGsXD0IXBPD8UVEZBq9crCJrj4/12UeCb0wQriqaK6gML0Qj9MzpnHDFZekXxactTL0e90O1pXkEAiak2omOsc1B4BaZ6jbu69WlavpErNwZZrmy0BLrMYXEZHp9fSuWrKcds7rfAYyiyFv8bDX7Wnew7LcsW9m99WHnnRL+mXBrDngzIHanayZkwNMblN7OFxV9dZizfPgq1Plarpoz5WIiIxbnz/Ac3vquWppHvajL8GCy2GYY23a+9o51nGMcs/YNrMD+GpqsKSnY8nMjOKM45BhQOFKqN1BTrqDeZ50th5rm/BwRa4iDAxOdJzAXjgLvypX02baw5VhGB8zDGOzYRibGxvVrl9EJBG8erCJzj4/1xW2Q19HKFwNY0fjDgBW560e89i+mhrss2eP6QzChDdrJTTsgYCPNXNz2Ha8dcLNRFOsKRSkF3Ci8wT2wkLtuZpG0x6uTNO8zzTN9aZprs/LG1tzORERmV5/3lVLZqqN87ueBasD5l067HU7GndgNaws9ywf89jhcDUjzFoNgX5o3M/aklAz0eMt3gkPN8c1JxSuZs/CV1c3qa7vMnHTHq5ERCSx9PuDPLunniuXFeA48MdQsEodfglvR8MOFuUsIs2eNubxfdXV2ItmSLgqDG9q38naudHZd3W88zi2wlmYXi/Bjo5ozFLGKZatGH4HbAQWG4ZRZRjGP8bqXiIiMnVeOdhIZ6+f64p6oe04LL1+2Ov8QT87m3ayOn/1mMcOdHUR7OjAXlQUpdnGOfd8sKdB7Q4WFbjISLGxZRLNROe45tDS20J/QTagdgzTxRargU3TvCVWY4uIyPR5dGsVuekOLux5AQwLLL5u2OsOtR2ix9/DqrxVYx7bV10DMHOWBS3WUL+r2p1YLQZr5mazuXLyTww25FpwEApXqUuWRGmyMlZaFhQRkTFr8/bz3J4Gblg1G8f+P8Lc8yB9+P5V2xu2A4yrcuWrrgZmULgCmLUK6nZBMMg5Zbnsq+ukzds/oaHmuuYCUBPpdaXK1XRQuBIRkTH7085a+gNB3rMAaNw74pIgwPbG7eQ585idPvag5KuZYZUrCD0x2N8JrUc5u8wNwFsTrF6VZJYAcJwWDLs9ElZlailciYjImD26pYolhS7Km58NvbDkbSNeu71hO6vzV4+rpYKvpgYjJQWrZ2zd3JNCeFN77Q5WFmfhsFnYdLR5QkOl2dMoSCvgWMcx7MXF+E5URXGiMlYKVyIiMiaHG7vYfqKNG9cWY+x9ItRGIHvusNc29TRR3VU9rv1WMPCk4KxZM6PHVVj+UrDYoXYHqXYrq4uz2XR04geclGWVcbT9KPY5xfRXnYjiRGWsFK5ERGRMHt1ShdVi8I65vVCzDVbcNOK1OxpCzUMnFK5m0pIggC0FCpdD9RYAzi7LZXdNB119EzvEuTSzlMqOylDl6vgJ9bqaBjF7WlBEZDJM06SquZM9xxto6eiiJ2AQtKXjzkylwJXKgvwM8jNTp3uaM4YvEOSRLVVcvCiP/MOPhJ4SXPGeEa/f3rgdu8XOMvfYzxQE6D9xgsxrr5nsdBNP0TrY8XsIBji7LJcfvXiIrcdauWjR+Jtrl2WV0eXronNuLsGuLoLt7Vizs6M/ZxmRwpWIxI2+jib+/tLf+NO+Dl5u9dBuOke9vthlYf38Qq4qL+TSxfk4HdYpmunM88zuOho6+/jgOXPgmT+EGoe6Cke8fnvDdsrd5TisjjHfI9DeTrC9Hcec4Zcak1rRenjrF9B0gLUlC7FaDDYdbZlQuCrNKgWgNs9GPtB/ogqnwtWUUrgSkellmnTsf5lf/eVl7q9fSCtZ5Bg2rs6pZlVBCuX5dgpcKaQZfdDdSFNTE/W1J9jTarC1exGv7Cjnie01OG0G16yYza0bSlg7N3tm7dmZAg+9XkmJO42LUw9C+wm4/N9GvLbH38Pu5t18cNkHx3WP/oHN1/Y5xZOaa0IqWhf6vXoLGflLWT47k02VE9t3NS9rHgBVLh/5gK/qBM4VYz9+SCZP4UpEpo3/8N/55WNP88PmdXSwlss9rXzwfA/nn3UNdtvwVagsYD5wXk8rHPgbgYo/8uaB4zzlW88fd1zE49uqWT47k09duoCrywuxWBSyJmt3dTubj7Vy19uWYtn5HbCnj/qU4LaGbfiDfs4pPGdc9/ENbL52zJ2BlSv3AkjJgqrNsOZWzirN5VdvHKPXFyDVPr6KbH5aPk6bkyprB2s5GVpl6ihcicjU66xnx2P/j3/ZV8Ye8yIuKejln999FstL8sc+hjMHVt2MddXNnNfVyHnbfsVX3/w6j7fN44HGd/KJ33SwpNDF569cxFXLClTJmoSHXq/Eabdy0yoP/PhJWHYDONJHvH5T7SZsho01+WvGdZ/+46FwZS+eM6n5JiSLBYrWRDa1nzPPzS9ePcq2422cO989vqEMC6WZpRz1nsDqduM7oScGp5qeFhSRKRXc+Qg/uudfedfei2l2FPHT9y3nl5979/iC1eky8uDCL5D++be49d3v4ln3d/ie/cf0NZ/g47/ewvU/fJXn9tTrqakJaOnu58kdNbx7bRFZR/8CfR2w6n2jfmZT3SZW5K0Y12HNAL4TJ7Dm5mLNGDm4JbWi9VBfAf1ezpmXi8WA1w83TWio+dnzOdR2CEdxMf3HjkV5onImClciMjX6umj8w+f40MMHuafvHVy/JItn/+VtXLO6JHpVJasd1n4Q66c3884bbuTZjK9xj/1eOptr+civNvOen25ky7GJ9w+aiX698Rj9/iAfOq8UNt0H7oVQetGI13f2d1LRXMHZhWeP+179VSdwzJmBVauwonVgBqB2B5mpdlbNyebVQxMLVwtzFlLvradvQTF9R49EeaJyJgpXIhJ7zYfZ/ZMPcMO2tbzFcr71rnK+/6ELyUy1x+Z+Vhuc9Y/YPruZ91y0juetn+X/pjzE8doGbrx3I5/4ny0cbeqOzb2TSEevj/tfPcKVywpY5D8I1ZvhrI+ElrBGsLluM0EzOKFw5Tt+AvtMD1cQWRo8f76HnVXtdPT6xj3UwuyFoaHmuQg0NhHo6IjaNOXMFK5EJLYOPsczP/48N9V/CCPNw6N3XMTN55ROzR6o1Cy48uvYP/0GtyxP5yXjY3w+/W/8fV8tV37n7/z7k7tp7uqL/TwS1K9er6Sj189nL18YahNgT4fVt4z6mddqXsNpc47rsGYAs78fX23tzHxSMMxVAFlzQiEWOH+Bh0DQ5M0j46+2LswJhavjA6cI9R0+HLVpypkpXIlI7Lx1P7/81c/5P96Ps3hWNk987jLKZ2dN/TxySuCmX5L+4Sf4rGcLL1k/yXszd/M/bxzjkv/3Ej9+8RC9vsDUzyuOdfX5+cWrR7l8ST7Ls/2w65HQXqvUkf/5mabJq9Wvcs6sc8bV3wpCzUMJBkkpK5vs1BNb0bpI5WptSTapdguvTWBpsCCtAJfDRaWzC4D+I1oanEoKVyISfaaJ+dx/8J0nXufrvtu4aqmHhz95Mfmuae6oXnIefPzv5L/tX/kvfsxfHV/mnIx6/t9f93PpPS/xv5tPEAhq0zuE9lq1eX185vKFsO1XEOiDsz866meOdhyluquaC4suHPf9+o8eBcAx08NV8VnQdhw6akmxWTm7zD2hcGUYBguzF3LYX4vhcNB3WOFqKilciUh0+fsJPP4J7nqxlR8E3s171xXxk1vPGnevnpixWEMh4dNbWbD+Cn7R/Rkedv2AfGsXX3xkJ9f/8FVePtA43bOcVu1eH/e9fJiLF+WxqjAF3rgXyi4KHTA8ilerXgXg/KLzx33PviMKVwCUnBv6/fhGAM6f7+ZgQxf1Hb3jHmphzkIOtR3GXlpCv5YFp5TClYhET28H/b95H5/Z7OE3gSv4+EXz+NZ7VmGzxuF/atLd8PbvwcdeYkOhyePdH+IH+U/R1d3NbQ9s4oP3v8mempm5Cfi7zx2gvcfHndcuga2/hq56uOhLZ/zcK9WvUJZVRlFG0bjv2X/0KLa8PKwZGROZcvIoXBXa23bsdSC07wqYUPVqUc4iunxdtC2dTZ+WBadUHP4XT0QSUkct3Q+8g3/cfxZ/Dp7Lv1y7hH+5bmn8N++cvRo+/Fcs7/4pNwSf57m+D3BXyR52nmjlbT98hS/8YQe17T3TPcsps6+ug1+/cYwPnFPC0rwUePW7MPc8KL1g1M+197WzuW4zl8y5ZEL37T96VFUrCD3pOuesSOVq2axMctLsvHpw/OFqaW6o0lhZlo6vqoqg1xvVqcrIFK5EZPIa99P683fwgap385q5km/fuJKPXzx/umc1doYR2qx9x2ZSzv8EH2m8m5cdn+Oj8zv4045qLvl/L/HtZ/ZN6JH4RGKaJl//4x4yUmz805WLYNuvobMGLv5S6O9oFC9XvYzf9HPF3CsmdN8+hauTSs4PNRPtacViMbhgYR4vH2wkOM79gItyF2EzbBzOD4Jp0rt/f4wmLKdTuBKRyTn2OrU/fy83tXycPcY87r11He89K0F7FaVmwlXfgE9sJGvOUr5S9X943n0P1xT7+clLh7nk/73Eg68dpc+fnE8WPrWzlo1HmvnCVYvISQFe+S7MOQfmXXLGzz5//Hnynfks94z/gOBAayvB9nZS5ilcATD3XMCE428CcNmSPJq6+tlV3T6uYVKsKSzMWcgBR6iVQ++ePdGeqYxA4UpEJm7H7zn4y//Djd1fos46mwc/fA5XlxdO96wmL28R3PoY3PJ75qR4+X7drfyp+NcszgrwtT/t4eJvh0JWMrVvqGvv5a4ndrOyOIv3nz0X3vgJdFTBJXeesWrV4+/hterXuGzuZViM8f9Y0ZOCpyleDxY7HA/tu7p4UT6GAc/vaxj3UMs9y9nTeRBLTja9e/dGe6YyAoUrERk/04QX/y9vPvodbuz/Gj5nHg9//DzOm++Z7plFj2HA4mvgE6/BO37Miv6d/Lb5Zn5d+jfmZgT52p/2cOG3X+QXrxyhpz+xQ1YwaPLP/7uDfn+Q7928Glt3Pbz8/2DxdTD/sjN+/vXq1+kN9HJ5yeUTun94s7XC1QC7E4rWRja156Y7WDs3hxcnEK7K3eV09nfSuna+KldTSOFKRMbH3wePf5w/v/AiH/R9FU9uDo998gKWF01Dc9CpYLHCmlvh01swrvw6F7Y9zh9a3sPD859lYRZ84897ufDbL/CjFw7S5u2f7tlOyC9fr+TVQ03cdf1S5uVlwHNfg0A/XP3NMX3++ePPk5WSxbqCdRO6f//RSgyHA/vs2RP6fFIqOQ9qtkF/aBP6ZUvy2VXdTsM4WzKEl2krl2TTd/AQZn9i/juaaBSuRGTsupvhV+/k/q3t3OH7DCvmenj0E+czJzdtumcWe3YnXPA5+NwuuPSrbGh+jN823cj/LniWcreFe/52gPPufoGv/bGCEy2J81TWG0ea+dZf9nHF0vzQcuDxN2Hnw3DepyF33hk/7wv6eKnqJS4uvhi7ZWJnRfYfPYqjpATDGie90OLB3PMg6I8chXPp4nwAXto/vh5s87Pnk2pN5WBBAHw+eg8ejPpUZSiFKxEZm6ot+H96Cf95ZCH/6b+Nq8oL+c1HziEnfXzHnCS81KzQ03Of3QmXfIWzGh/jofp388ziP3PN/NTQkTr3vMRnfreN7Sfapnu2ozrU0MnHfrWZue40/vum1Ri+HvjjHZBZBBf805jGeKvuLTr7Oyf0lGBY/5EjWhI83dxzwLBAZagx69JZLmZlpfLCOJcGbRYb5Z5yKuyhz/Vs3x7tmcowFK5EZHSmCZt+Ttv97+Yf2j/M/f6ruf28Un7ygXXx03V9Ojiz4ZIvw+d2wiX/wpK6p/jO0XfwyuJH+ceVTl7Y18A7f/wa7/jRqzy2tSrunjBs6OzlQw+8hcNm5Ze3n0VWmh3+dhc0HYB3/gRSxtbM87ljz+G0OTl39rkTmofZ309/VRUOPSk4WGoWzF4Lh18EQsfZXLI4n1cONtLvD45rqHUF69jXeRhfcT49W7bGYrZyGoUrERlZVyP87hZ2P/VD3u7/Fm8GFvOtG1fwtRvKsVrivDnoVHFmh56o+9xOuPhOZtU9z1f2vZM35v6Ur59ro7PPzz/9YQfn/d8XuOev+6lqnf4lw+PNXt770420dPfzwO3rQ8u6+5+BzfeHlgPH0HoBQkuCzx57lkuKLyHVNrFzI/urqiAQ0IHNw5l/WWhZsKcNgMuX5NPdH+CNI83jGmZdwToCZoDK80rxbtmCaer8zFhTuBKRoUwT9jxJ8Cfn84t9Nt7l+y98Tg+///i53HzW3OmeXXxyZsOl/wKf2w1X/18y2vfzoW3v5fm0u/j15f2smZPFj186xIXffpEP3v8mf9xRMy2tHHZXt/Pue1+nrcfH/3zkbFYWZ0PrMXjyU1C4Ai771zGP9UbNG7T1tXFt2bUTnk+/nhQc2fzLwAzC0ZcBuGChhzSHlb/srhvXMKvzVmM1rOxbkIq/vh5/TU0sZiunULgSkcHaq+Dh91P9+89zu/fTfKP/Fi5ZWshfPnsRa+bmTPfs4l9KBpz7SfjMdnjHTzACfVz42u38ov1jvHJNI5+5pIwjjd185nfbOOe/nuffn9zNtuOtMa8mmKbJb948xnt/tpEUm4VH/s+5rCvJBW8L/M+NEPTBe34JtpQxj/lM5TO4HK4JHdQc1qceVyMrXg8OFxx+AYBUu5XLluTzt4o6/IGxLw2m2dNYmruU3RmtAHi3amkw1hSuRCSktwOe/0+CP1jPQ3sNrvJ/n7cCC/nPd5Rz3wfXzbyN65Nlc8CaD8An34SbfwNpuRS/+Fk+v/MdvHL+Dn59azkXLcrjd5tO8K6fvM4F33qRbzy1h60xCFrHm73c9sAmvvr4btbMzebRT5zHgnwX+Hrh4fdD2zG45WHwLBzzmL3+Xp4//jxXzL0Ch3Xi/270HzocOrDZ5ZrwGEnLaoeyi+Dw86FqMvC2FbNo7u5n09GWcQ21rmAdu7sP4cvOoPvNN2MxWzmFbbonIBJX/P2hHzRd9aGw4fOGntix2sGRAekeSM+HjPwzdq1OGL0dsPkBeP2HbOzM45v2e9jdn8OFCz3817tWzIw2C7FkscDS62HJ20JPfr36HSzPf40LHfdw4eoP0P6pj/JcrZOnd9Xyq43H+MWrR5mdlcrlSwu4cKGHc+e7caVOrMXB/rpOfvr3w/xxRw0pNgvfeOdyPnDO3NBh2n2d8IfbQgcEv+eBUF+lcXil+hW6fd2TWhIE6D14gJRFiyY1RlKbfyns/zO0HAH3fC5ZnI/TbuXp3bWct2DsTXs3zN7AQ3se4sjlS3G+9jqmacb/oeoJTOFKZraOmtDTOJWvwok3CbQco87MosrMo8NMw0sqfaYdh+HHjp9suigwWslP8eHKnYXhmQ/uBeBZBAXlod+tE/tBOOVaK2HLg7D5AXZ43fzA8QWe95VQlO7k+zcs5oZVs/Uf32gyDCi7MPSrdmfoeJnND5C16T5uXHwdN178CTpuvpzn9jbw9K46Ht1axa/fOIbFgCWFmawtyaZ8dhbz8zIo9aSRm+bAZj25+BAImlS39nC4qYtNR1t4bk89Bxu6cNqt/MN5pXzkwnkUZg1sOu9qgN/cBHW74IYfwvIbx/3t/OXoX3Cnujm78OwJ/5WYfj/9hw6T/oENEx4j6YU75B9+AdzzcTpCS4PP7K7n6zcsH/ODJesL1pNiTWHH0lQWP1pL/9GjpMw7cx8zmRiFK5l5fD2w6xHY+Xvaj27lpeBKNltWscXyeQ72ZeMzx7Ba3g/OLh/5VR3MNatZYLzMQuN3LLTVszAvneyiRVC4HAqWh353xslepd522Pc07PpfAode5GVzJb9w/Duv9ReSabHxpWvm8+Hzy2Z2i4WpMGslvOuncMXX4K1fhCqH+/9MZt4S3r32Nt590y30Odaw9VgbGw83sfV4G09sq+F/3jg+aBhXqg2H1UKfP0ivL4A/GFo6slkMzi7L5X1nz+Xda4oGL+lWvgZP/B/oboJbfgeLrh739Lv6u3i56mVuXHgjVsvE/13pP34cs7+flMWqXI0odx5kl4T+T+DZHwXg2hWF/HlXLW9VtrBhnntMw6TaUllfuJ63Wit5L9D96qsKVzGkcCUzR287vPULfBt/xjOdZTxquYbX+j+Oz7SQkWJldVEO/1iURYk7jeIcJzlpDpwOKw6rBX/QxBcI0tLdT31HLw0dfdR39FLX0cuxplIebuiix2+CHzgBeVXtLDGOscT4LUssJ1js6mdBUR6ps5aFwlbhCsguDS0ZxVIwCE37Q/9hPvw8HH2ZQz4Pf7JfzSPGA1T3OchLSeFfri3jAxtKyEjRfxKmlKsQLrsLLvxCKPBveRD++hV47mukLH075669jXOvuAgsFoJBk5r2Hg43dlPZ1E2rt582rw9fIEiKzYrTYWFubhrz8jJYXOgi8/SlxL4ueOEb8OZPIacEbn8KiiZ2XM2LJ16kL9A36SXBvv37AUjVsuDIDCNUvdr1v6Gjp2wpXLo4n1S7hb/sqh1zuAK4sOhC7q5+jZZlRaS/+iq5t90Ww4nPbPovqSS/YAC2/Q/dz/1fft25jof4T2r9GRRlO/mHc2Zx7fJCVhZnT6pvUzBoUt3Ww6GGLg7Ud3Kgvot91bN4qGkl/T6gBawtQcp217LEeIsllkdZbG9iSUEaxcUlGOEqV1YRpOeNf2nRNKGnNbRfrPEANO6F2h1QtYWu3j7eCi5hY8r5PG++j8P9aRg+uGCBh6+ePZcrlhbgsOnZlmlld8LaD4Z+1VfAlodCR9DsfhQyCmHZDViWXE/x3A0U5+Rx8aK8sY/tbYFN94VCVU8rnP2xUMXMkT7h6T599Glmp89mVd6qCY8B0HvgAFitOObPn9Q4SW/xdbDll3Dk77DoKtJTbFy6OJ8/76rlruuXYbeO7X+/588OPdVZcfEc3Pe/SaCrG2vGxP89kJEpXElya9hL4PFP8siJTP7b/DoNgQzOX+DmmxeUccmifCxRaoRpsRjMyU1jTm4aly7Jj7zuDwSpbO5mX10n++s62VuTz46auTzVEQhVuSoho7KHRcYJSoxHKTKaKDKamJ3aT5HLQk56Ki6HBYfdGgpcVgcwsBm5ryO0Gb2vHX93G6390GDmcNzMp5LZHHCcQ4X5Pg71pxM0DRxBC+tLc7h9eSFXlRdSkDmxpo8SYwXlcN234cqvw74/w54nYOuvQgHJnhbaeF60DgpXhvb4uQohxRWqcJgmdDdC8+FQuD7wl9AyYNAX+gF94T9D8cSqVWGtva28UfMGt5XfNuk9eX0HDuIoKcGSMvb2DzPSvItDLRn2/QkWXQXAjWuL+cvuOv6+v5ErlhWMaZiSzBJKM0t5M93Lhf39dL/yMpnXTq76KMNTuJLkFAzCmz9l799+wRf7P8buwBzWzs3m3rctY13J1O1/slktLMh3sSDfxfUrT77e1edn/0Dg2lfbzv5qN5tavNR5TQKmAV2Efg1w4Mdl6SPD6MWCST82fNjxY8WHla6ADZPBP+gKnCmsKMri2tlZnF2Wy7qSHO2lSiR2J6x4T+hXX1fooYvDL4QaSh5+IdRcMizcCiHoH/y6ZxFs+ASsel8otEXBs8eexW/6ua7sukmP1XfgAKnl0ZlXUrOlwMIrQ/slr/8eWKxcvDgPT4aDR7ZUjTlcGYbBFSVX8Mvdv8Q7K5vOZ59VuIoRhStJPj2t+B/5GPfuT+MHga+RlZbCD25YwdtXzoqbp98yUmysK8kZEvT8gSANnX1Ut/VQ09ZDm9dHV5+fzl4/nb2hP5sm2KwGDqsFm9XAZrGQ6bTjyXCQl5HCnNw0StxpE358X+JQSgYsvib0C6DfC/W7Q098dtaBtynUMsSwhtqFuBdC3iLIjn43/b8c/QvzsuaxKGdy+6SC3d34Tpwg+93vitLMktzSt0PFY3DiTSg5D7vVwjtXF/HQxkpauvvJHWMfuitKruAXu37BrmsXkfHw3wn29alyGAMKV5Jc6vfQ8JuP8emmd/JmcCnXr5zFf7xj+Zj/wzPdbFYLs7OdzM52TvdUJJ450mDO2aFfU6iqs4rN9Zu5Y/Udk18SPHQIQD2uxmrhlWBNgb1PRXqS3biumF+8epQ/bq/m9vPH1uF+We4yZqfPZmNZP+d4vXRv3IjrkktiOPGZSbtYJXkceo5NP/sEb2v8FDssS/nvm1bxo/evTZhgJRLvnjz8JAYG71jwjkmP1XvgAKBwNWYprtCB2nv/FOnWvnRWJsuLMnlka9WYhzEMgytLrmRT7z66POl0PvtsjCY8sylcSXLY/lse+dUP+YD3C2Tk5PHEHRdy47ri6Z6VSNIIBAM8cegJzis6j8L0wkmP17f/AEZaGvaioijMboZYej20H4e6nZGX3rO2mN3VHeyt7RjzMG+f/3b8QT+br5tH1/MvYPr9sZjtjKZwJQnPfO1HfOd/n+ef+z/G2WVunvj0xSwpzJzuaYkklTdr36Suu453LYjOHqm+AwdIWbgAI9a93pLJ4utC++p2PxZ56R2ri3DYLPzPG8fGPkzuYhbnLObF0i4CbW14N2+OxWxnNP1bLQkt+PqP+crTR/hB4N28d+1sfvmP55Ll1EZukWh77NBjZKdkc+mcSyc9lmmaoScFtSQ4Pume0N6rnb8P9e8DctIdvHP1bB7bWk271zfmod4+/+3s9Z2gushJx5+fjtWMZyyFK0lYwY0/5c4/H+V3gcv5xEVlfOum1WqGKRIDbb1tvHD8Ba6fdz0O6+T3MPobGgm0tZGyUOFq3FbdAp21cOTFyEu3n1dGjy/A7zcfH+WDg71t3tuwGlY2XjuHjr/+lWB/fyxmO2PpJ5EkpMCbP+eLfzrCHwKX8plL5/Gla5fGTZsFkWTz56N/xhf08c4F74zKeL17KgBILV8WlfFmlMXXQmo2bP9d5KVlszM5pyyXh14/hj8QHPmzp/A4PZxfdD4vFrbg7+yg6+9/j9GEZyaFK0k4gU33889PHubR4MV87rJ5/NPVClYisWKaJo8ceIRl7mUszl0clTF7d1eAYZC6ZElUxptRbCmhxrL7ngqdlzrgH84vpbqth+f2Nox5qLfPfzuNgTb2rMii409PxWK2M5bClSQU/6Zf8vknDvN48EK+cPl8PnfV0umekkhS21i7kUNth7hlyS1RG7O3ogLH/HlY0nWu3YSsej/4e6HiichLVywtoCjbyQOvHR3zMJfOuRSX3cVrl+TR9eKLBDrG/sShjE7hShKGb/Ov+OwTh/nj/2/vzuOjKu/Fj3++M5OZyTpZ2UIgAQKIVBQoq4JLBaSIVQH3rbW2Wtt7295qW/Vqbxer/mztjl6tVa8WUHhVtC4ICooIAoJsmrAvYc++TGY7z++PGZOAQSKZJSHf94vzyjnPnPOc53znzPCdsz3WeO66eADfv1h/9SoVa89teY4cd05Uurv5TOPmzSRrtzenLn94uGujdf/XVOSw27hlfCEf7qxg1Y7yNlXjsruY2m8qy9PKqLUHqF20KFYt7nI0uVKdQmDt8/xgwXb+bY3l55MHcMdF0Tk9oZQ6sa2VW1letpxrBl8TlQvZAQKHDxM8ckT7FGwPERh+E+z7EPavayq+bnRfctNc/GHJ1jZXNXPgTHwmwPIJOVQvfCUWre2SNLlSHZ5/7T/53vytvG6N4t5LirntAk2slIqH2R/PJsWRwlWDropanY2bP7uYXZOrdhl+AzjT4YO/NhUlO+18d2I/Vmwvb/PRq0HZgxiWN4y3zhbqV68mcPBgrFrcpWhypTo037p53DF/K4usr/LA14u5daLeuq1UPJRWlrJo9yKuO+M6Mt2ZUavXu2ED2O24z9DrJdvF7QknWJsXQM3+puLrx/QlL93FY4vbfvRq1qBZ7LVXsaUAav7971i0tsvR5Ep1WI3rX+K7L25lsTWCX04byM3naWKlVLz86aM/kZaUxk1n3hTVer3r1uMeNAhbSkpU6+2SRn8HjAUfPtFU5E6y892J/flgRzkfbG/b0atJfSeR4czg7Quy9NRglGhypTqkxvUL+M68Ut6xzuY30wdyw7nFiW6SUl3G+2Xvs3TfUm79yq14XJ6o1WuCQRo3bCD5nHOiVmeXllUIg6fBmqfBX99UfN3oPvT0uPnlq1sIWeak1bgdbi4bcBkf9Krj0N4SGj/9NIaN7ho0uVIdjvfjf/HteaW8a53FQ9MHcu04TayUipdAKMBDqx+iT3ofbhhyQ1Tr9m3ditXQoMlVNI29ExqrYPVTTUXuJDv3fP0Mthyo4YUP2/bU9pkDZxLEYunwJKrmzYtRY7sOTa5Uh1K/Zi7fmlvKcmsoD39jEFdpYqVUXM3eMJud1Tu5e9TdUbtD8DPe9esBSD777KjW26X1GQ39L4LlvzvmoaJf/0pPxvbL4dFFJVTWn7xrmyJPEaN7jGbRGBflr7yMVV9/0mXUiWlypTqMyvf/wbXzD7LKOoNHrxjMzDGaWCkVT5vLN/PUxqeY3n86E3pPiHr9DWs/wpGXR1J+r6jX3aV97X7wVsL7f2gqEhEemH4mtY1B/t+ikjZVc/2Q6zma1MjKfC/Vr2lnzu2hyZXqEA68PZuZr/r4hCL+du0wrhg1INFNUqpLqfXXcteyu8hJzuGur94V9fqNMdSvXEnK6NHaXVW09RwGQ2eEH8tQ2/wohUE90rlxbF9e+HAPK7YfPWk1E3pPoE96H16fkELVXD012B6aXKnEMoYdrz3GjEVuDkp3nv3mKCad1SfRrVKqS7GMxf0r7qesroxHJjwS1YvYP+Mr3Uro6FFSx42Let0KuPAesAKw9MFjin8yeRCFOan8eN7HVDcEvrAKm9i47ozrKMnxsb5qE95Nm2PZ4tOaJlcqcSyLTfMfZOa73fA5Mphz+wTGFPdIdKuU6nL+uv6vvLX7LX444ocM7z48JuuoX7ECgNSxY2JSf5eX3Q9G3QZrn4HdHzQVpzgdPHbV2Ryu9XHfy5tOWs3lxZeT5cxkwblJVM75ZyxbfFrT5Eolhr+Bd568m6vXFON2uXjxBxcztCA70a1SqsuZXzqfxzc8zuUDLufGITfGbD31K1bgLCoiqWfPmK2jy7vgHsgsgIV3QsDbVDysIJP/vKiYhR/v56W1+76wimRHMjcOvYn1RYZ1K18mcOhwrFt9WtLkSsWdqS7j8cce4Js7JtDXY2f+D6dS1C090c1Sqst5Zfsr/OKDXzA+fzz3jbkvZtdCWfX1NKxeTer48TGpX0W40uDSP0L5ts+dHrz9/P6M6ZfNzxdsZM2uii+s5qpBV5HuSGPuOEPF00/HssWnLU2uVFzVlSzjPx59igcrJjK10MaLP55Oj8zkRDdLqS7nuS3P8fPlP2dUj1E8dv5jJNmTYrauumXLMI2NZEyeFLN1qIj+F8DwG2HFn2D3iqZih93G364bQa9MN995bi17KxpOWEW6M51bh32bdf2F997/px69OgWaXKn4sEJsfvXPXPqPrbzqP4f/Gp/Nn78zlRSnI9EtU6pLqQ/U87P3fsbDqx/m4r4X85ev/QW3wx3Tdda8/gaOvDySh8fmei51nEm/gqwimHsDVO1tKs5KdfLUzV8lELK45R+rKa/znbCKawdfS3dXHs+dG+LgY7+PR6tPK5pcqZgLlu9k9u/u4/Ll+TQ4MnnhlrO589Kxeju2UnEUsAIs3L6Q6f+azms7X+OOYXfwyIRHcNldMV1vqK6eunffJX3yZMRuj+m6VITbA9fMgZAf5lwL/uajVP3z0nj8hpHsq2zg6idWcri2sfUqHG5+NOq/2NED5u5b2PQAWNU2mlyp2AkFKXnzf7ny0X/z26PjOb+38Npd0xgzqCDRLVOqS2gINPDevvd4ePXDTJk/hXuW30Nech7PTHmG28++Hbst9slO9YIFGJ8Pz6XTYr4u1ULeQLjySTi4ERZ8G0LNj2EY2z+Hp28eRVmVl6sfX8nB6tYTrEuKLuG8HuOYM9HG2l/9BMvrbXU+9XlizMk7dYyXkSNHmjVr1iS6GSoKqktX8PsXF/Fc7XA8dj+/mDaQaWO+okerlIqBal81u2p2sat6F7tqdrG7Zjc7q3eyq2YXQSuI0+ZkVM9RzBo4i4kFE7FJfH5Xm2CQ7ZOn4OjencIXno/LOtVxVs6GN+4Od/A842lwNHdptGZXBTc/vZoUp52/XT+cEX0/f8f2wfqDXLHgMrIP1PPHqqkU/foh/R5vQUTWGmNGfq5ckysVTQ37NvF/8xfwtwMDqSaVawYafjxrMtlpsT31oFRXUNlYyebyzZRUlDQlU7trdlPpq2yaxyEOeqf3pjCjkH6Z/RjdYzTDuw+P+XVVral++WX23/1Tev/lz6RfdFHc168iVj0Br/8EBk6BGX8HZ2rTSyUHa7ntuTXsr/Ly39OGcP2Yvp9LnpaXLeeOt25nVInFrwpup8cdd8Z7CzosTa5UTFWWfsCc15fw5IF+lOPhvNx6fjZrIkP6dE9005TqtPwhPx8e/JC397zNiv0rKKsra3otNzmXwoxC+mb0pchT1DSen55Pki12d/61VbC8nB3TLiUpP5/CeXMRm16FklBr/g6v/gi6nQGznoXc5r5bq70B/nPOOt4pOcJ5xbn85vKvUJCdcsziz25+lkfWPMKYTyweyL+NXnf+QI9gocmVigHjreKjd19h3pp9/Kt2MD6cnJdTw39cOpaRgwsT3TylOqWqxiqW71/O0r1LWV62nPpAPcmOZMb1GsewvGEMzR3KoOxBZDgzEt3UE7J8PvZ9704aVq2iaMF8XMXaCXuHsP1tmH8rBH1wycNw9rUQSZAsy/Dcyt08/ManWAbuvHAAt4wvPOaO7mc3/YNH1j5K//2GeyvP5Zx7H8ae0XH3w3jQ5EpFhVVfzoZV77D44+0sPNyNPaYbyeLn8sIAN06dyOACPVKlvpyAFeBIwxEaAg2kJqWS6c4k2dF5n31mjKE+UE+tvxYAEcEudpx2Jy67C6fdiU1s+EN+6gJ1VHgrKK0spbSylHWH17H+yHosY5HtzuaCggu4sM+FjO45OuZ39UWLf88eDtxzLw2rV9Pjl/9D1syZiW6Saql6H7z0Ldi7EgrGwNRHoOdZTS/vr/Ly3y9vZvEnh8hJdfLdif25elQB6e7w0dDFuxdz7zt3Ewj6uHK9m1suvpse02d02aNYmlypUxKoq2DLhjV8VLKDtWX1rKzrwVEysWExLrOay0cWMvnc0aS5E38aQnUeB+oO8MqOV1i2dxmfVHxCwDq2Q9lkRzI57hzyUvLIdmfjtDlx2BzYbXYsYwHhzoYtY2GMwWl34nF58Lg8ZLoyyXBlkOFsHtKcadjF3rwMx37vpSalfumEzhfysb1qe1NiVFpZys6qnVT4KghawS9c1i52QiZ0TJnD5mBg1kDOyz+Pib0ncmbumXG78Ly9AocO07B6NbWLF1O3ZAkkJdHzgfvxTJ+e6Kap1lgWrH8eFt8PDeUwaCqM+wH0GdN0JGvt7kp+91YJ728rJ8VpZ/qwXswcWcA5BZkcajjIg0vu5Z2qD0lpNEzd342bzv8xfc+f2uWSrIQkVyIyBfgDYAeeNMb89ovm1+QqcazGOg7u286OHdvZsf8QO442sKXGxQZ/LxoJ/2LOd9QwMi/EhUP7MHH0KDLT4n+BrOq8LGOx6sAq5nw6h6X7lmIZi7Nyz2JE9xEUegpJSUqhIdBAZWMlFY0VHPUe5Yj3CJWNlQSsAEErSMiEEASb2Jr/iuAL+aj2VeMNnvqt4i67i0xXJnnJeXRP7U63lG50T+lOalIqIRMiZIU44j3SdCH53tq9TQmS2+5mQOYA+mf2Jzc5lyx3FmlJaYgIxhhCJoQ/5McX8uEP+fFbflIcKaQmpeJxeRiQOYB+nn4xfUp6exljCFVWEijbT2D/fnzbt9G4aTONmzYRPBx+grc9K4uMadPIufVWkrp3S3CL1Ul5K+GDv8Lq/w2PdxsCQ6+AM68IdwQtwsd7q3h+1W4WfryfxoBF9wwXk4b04NziXNLSDjDn3Qd5N/gJYBh+OI1pA7/BlKl3kupKS/TWxUXckysRsQOlwMXAPmA1cI0xZsuJltHkKoqMIeT3UlNdRXV1JTXVlVRXV1FdU0N5bQMHawMcqgty2Csc9Lsps7Lw0pwspYqP4pQ6RuTBiAH5DB92Nj3ychK4QaqzOuo9ysvbXmbB1gXsqd1DliuLK4qvYNagWfRK6xXVdflCPmp8NVT5qqj111Ljr2n6CxyTmH2W+FhY1AfqqfZVU9lYyVHvUQ41HOJQ/SFqA7XH1O+0OemT0Ye+GX0ZkDmAgVkDGZg1kIL0grg8M+p4xhhMQwOhujqs2lpCtbVYkXFxJ5PUOx9nfj621NSTV9ZCsLIS77p1eNetx7dzB4Hde/Dv24dp+ZwjEZxFRbiHnkny0KEkn3MO7iFD9EGhnZG/Hj7+J2x4MXy6EMBTAEUTIH8E9BxGTUYxS7bX8samgywrPUJjIHwEubhbGoW5NuoqP2BHcC216Udx2So5y+QxvvgiRg2ZxIDMAaQ5T89kKxHJ1VjgAWPM5Mj0zwCMMQ+eaJmOmFwZy8JYBoMJj2OOmabFtGWFT1EYwFgGTAhjhTDBAIHIEAwECASDBCNDeDxEIBQkEAyFx4Mhgk3zBmj0B2kMBGkMWnj9Ft6goTFo8AahMQTekA1vSGgM2Wi0hHoriRormVpSTrhdSQTpZq+jm9NP9xToleGgX24a/fr0pn//QXTLSu9yh3fVqTHGELSCNIYaqWqsCicmDYcorSxl7aG1bDy6EctYjOg+giuLr2RS4aR2Xz9kjIFAAMvvx/j9GK+XUE0NoeoaQjXVhKqrsWpqCNXVYXM6saWmYktLx56ZGRk84b9paeBwnPBOtoZAA96gF7vYsdlspDpSsSGYQCBcf2SwamoIVVcTqgqvG2MhLjc2twtbhge7xxNepycyZGQgTufntsk0Nh6TIAXLKwiWHyV09CjBo+UEy8vD4+XlhCoqCNXVQSjUattbSioowD14EK7Bg3ENKMaRm4M9K/xMI6u+nlBVJb6t2/CVluLdsAH/jh2RBZNw9u2Ds09fnAW9ScrvTVJ+L5J69SKpoA/2tC+XtKlOoGovlL4BO9+FXcvB26KT55Qc8PTGl96XDbbBrGzIZ31NOpuqXRzyHvv/hVNqEFcFdnsd2BtIFh/pEiLdFiJDIEMceBxJpLncZLiT8aSm4UnLIMuTSUZGLumeHDIy83AluUiy2bHZBLtNsIlgEzrM/0+JSK5mAFOMMbdGpm8ARhtjTviAjJgnV09ezHtlFrd5vxdOgBAM4TcoPN5cZnXQh9cnEcQtfpIlgFtCJNuCuG0hkm0WyQ5wO4TkJCHDZcPjtuNJTsKT4saTnorHk4knM4fsnG5ke9Kx2TrGzqk6r++//X2W7V32uWuYIHwN0RnZZzA+fzyXFF1CP0+/dq/v4K9/Q9XcuRi/v20LiEBbvuNEwG4PJ1l2e/iaFGPCSZwxTdNtqitGbOnpOHJzceTkYM/NxZGdhS09A3t6Gra0dGzpadgzMrClpWFPS8Pyegns24d/zx4aS0rwffIp/t27v3Ab7Hm5JA85k+Thw0kZMRz30KHY3Hr6v8syBqr2hJ/yfuTT8MXwTcNe8Nc1zVpu0tlturPX5LHXdIsMeVSYDCpMOlWk4Sd6p70FwgmXCIT/hcsjIylOBx/dd3HU1nfCdnTU5EpEbgNui0wOAkpi0qDOIxc4muhGdBAai2Yai2Yai2Yai2Yai2Yai2axjkVfY0ze8YWO1uaMkjKgZSdyvSNlxzDGPAE8EcN2dCoisqa1LLgr0lg001g001g001g001g001g0S1QsYnnuazVQLCJFIuIErgYWxnB9SimllFIJF7MjV8aYoIjcCbxJ+FEMfzfGbI7V+pRSSimlOoJYnhbEGPMa8Fos13Ea0lOkzTQWzTQWzTQWzTQWzTQWzTQWzRISiw71hHallFJKqc6uYz5vQCmllFKqk9LkKgFEJFtE3hKRrZG/WSeY7w0RqRKRV48rLxKRVSKyTUTmRm4Y6JS+RCxuisyzVURualG+VERKRGR9ZOh0fW6IyJTINmwTkZ+28ror8j5vi7zvhS1e+1mkvEREJse14TFwqrEQkUIR8bbYD2bHvfFR1oZYTBCRj0QkGHn0TcvXWv28dFbtjEWoxX7R6W+qakMsfiQiW0Rkg4gsEZG+LV7ravvFF8UitvuFiTwoT4f4DcDDwE8j4z8FHjrBfBcBlwKvHlc+D7g6Mj4buD3R2xTLWADZwI7I36zIeFbktaXAyERvRzu23w5sB/oBTuBjYMhx89wBzI6MXw3MjYwPiczvAooi9dgTvU0JikUhsCnR2xDnWBQCZwHPAjNalJ/w89IZh/bEIvJaXaK3Ic6xuABIiYzf3uIz0hX3i1ZjEY/9Qo9cJcZlwDOR8WeAb7Q2kzFmCXBM52YSfub/hcBLJ1u+k2hLLCYDbxljKowxlcBbwJT4NC/mRgHbjDE7jDF+YA7hmLTUMkYvARdF9oPLgDnGGJ8xZiewLVJfZ9WeWJxuThoLY8wuY8wGwDpu2dPt89KeWJxu2hKLd4wxDZHJlYSfMQldc784USxiTpOrxOhujDkQGT8IdP8Sy+YAVcaYYGR6H5AfzcbFWVtikQ/sbTF9/DY/HTm0e18n/I/2ZNt2zDyR972a8H7QlmU7k/bEAqBIRNaJyDIROS/WjY2x9ry3XXG/+CJuEVkjIitF5BtRbVn8fdlYfAt4/RSX7ejaEwuI8X4R00cxdGUishjo0cpL97ScMMYYETmtb9mMcSyuM8aUiUg6MB+4gfCpAdW1HAD6GGPKRWQE8C8ROdMYU5PohqmE6xv5jugHvC0iG40x2xPdqFgTkeuBkcDERLcl0U4Qi5juF5pcxYgx5msnek1EDolIT2PMARHpCRz+ElWXA5ki4oj8cm+1W6GOJAqxKAPObzHdm/C1VhhjyiJ/a0XkBcKHijtTctWWbqI+m2efiDgAD+H9oE1dTHUipxwLE76IwgdgjFkrItuBgUAMe4KPqfa8tyf8vHRS7drPW3xH7BCRpcA5hK/V6YzaFAsR+RrhH68TjTG+Fsuef9yyS2PSyvhoTyxivl/oacHEWAh8dqfGTcDLbV0w8p/IO8Bnd8R8qeU7oLbE4k1gkohkSfhuwknAmyLiEJFcABFJAqYBm+LQ5mhqSzdRLWM0A3g7sh8sBK6O3EFXBBQDH8ap3bFwyrEQkTwRsQNEfokWE75gt7NqT/dhrX5eYtTOeDjlWERi4IqM5wLjgS0xa2nsnTQWInIO8Dgw3RjT8sdql9svThSLuOwXsb6iX4dW73LIAZYAW4HFQHakfCTwZIv53gOOAF7C55MnR8r7Ef5PdBvwIuBK9DbFIRbfjGzvNuCWSFkqsBbYAGwG/kAnvFsOmAqUEv7VdE+k7H8IfyEAuCPv87bI+96vxbL3RJYrAS5J9LYkKhbAlZF9YD3wEXBporclDrH4auR7oZ7wkczNLZb93OelMw+nGgtgHLCR8J1kG4FvJXpb4hCLxcChyGdhPbCwC+8XrcYiHvuFPqFdKaWUUiqK9LSgUkoppVQUaXKllFJKKRVFmlwppZRSSkWRJldKKaWUUlGkyZVSSimlVBRpcqWUOm2JSF2i26CU6no0uVJKKaWUiiJNrpRSnYaI/FZEvtdi+gERuVdElojIRyKyUUQua2W580Xk1RbTfxaRmyPjIyKdPa8VkTcj3TAppdQp0+RKKdWZzAVmtZieBTwDXG6MGQ5cADwqItKWyiLdJv0JmGGMGQH8Hfh1dJuslOpqtONmpVSnYYxZJyLdRKQXkAdUAgeB34vIBMAC8oHukfKTGQQMBd6K5GN24EAs2q6U6jo0uVJKdTYvEu60uQfhI1nXEU60RhhjAiKyi3AfhC0FOfZI/WevC+F+6MbGtMVKqS5FTwsqpTqbucDVhBOsFwEPcDiSWF0A9G1lmd3AEBFxiUgmcFGkvATIE5GxED5NKCJnxnoDlFKnNz1ypZTqVIwxm0UkHSgzxhwQkeeBV0RkI7AG+LSVZfaKyDxgE7ATWBcp94vIDOCPIuIh/J34GLA5PlujlDodiTEm0W1QSimllDpt6GlBpZRSSqko0uRKKaWUUiqKNLlSSimllIoiTa6UUkoppaJIkyullFJKqSjS5EoppZRSKoo0uVJKKaWUiiJNrpRSSimlouj/A66IEYBnRX7MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "sns.kdeplot(data = sharpe, x='value', hue='variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='value', ylabel='Density'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "sns.kdeplot(data = sharpe_no_wipeout, x='value', hue='variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dual_return</th>\n",
       "      <th>single_return</th>\n",
       "      <th>100_return</th>\n",
       "      <th>9050_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4998.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>4951.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.108555</td>\n",
       "      <td>0.107795</td>\n",
       "      <td>0.165212</td>\n",
       "      <td>0.171880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.067508</td>\n",
       "      <td>0.067345</td>\n",
       "      <td>0.021479</td>\n",
       "      <td>0.015454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.091972</td>\n",
       "      <td>-0.093208</td>\n",
       "      <td>0.040812</td>\n",
       "      <td>0.058483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.054579</td>\n",
       "      <td>0.053068</td>\n",
       "      <td>0.154124</td>\n",
       "      <td>0.161690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.140792</td>\n",
       "      <td>0.142791</td>\n",
       "      <td>0.167221</td>\n",
       "      <td>0.172127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.160730</td>\n",
       "      <td>0.160115</td>\n",
       "      <td>0.178934</td>\n",
       "      <td>0.182066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.211734</td>\n",
       "      <td>0.199225</td>\n",
       "      <td>0.230352</td>\n",
       "      <td>0.230539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dual_return  single_return   100_return  9050_return\n",
       "count  4998.000000    5000.000000  4951.000000  5000.000000\n",
       "mean      0.108555       0.107795     0.165212     0.171880\n",
       "std       0.067508       0.067345     0.021479     0.015454\n",
       "min      -0.091972      -0.093208     0.040812     0.058483\n",
       "25%       0.054579       0.053068     0.154124     0.161690\n",
       "50%       0.140792       0.142791     0.167221     0.172127\n",
       "75%       0.160730       0.160115     0.178934     0.182066\n",
       "max       0.211734       0.199225     0.230352     0.230539"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ports.groupby('random_state', dropna=True).mean().loc[:, cols]/ports.groupby('random_state', dropna=True).std().loc[:, cols]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dual_return</th>\n",
       "      <th>single_return</th>\n",
       "      <th>100_return</th>\n",
       "      <th>9050_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4994.000000</td>\n",
       "      <td>4996.000000</td>\n",
       "      <td>4948.000000</td>\n",
       "      <td>4996.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.108592</td>\n",
       "      <td>0.107832</td>\n",
       "      <td>0.165229</td>\n",
       "      <td>0.171894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.067511</td>\n",
       "      <td>0.067349</td>\n",
       "      <td>0.021474</td>\n",
       "      <td>0.015449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.091972</td>\n",
       "      <td>-0.093208</td>\n",
       "      <td>0.040812</td>\n",
       "      <td>0.058483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.054579</td>\n",
       "      <td>0.053068</td>\n",
       "      <td>0.154161</td>\n",
       "      <td>0.161710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.140854</td>\n",
       "      <td>0.142800</td>\n",
       "      <td>0.167230</td>\n",
       "      <td>0.172135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.160748</td>\n",
       "      <td>0.160117</td>\n",
       "      <td>0.178945</td>\n",
       "      <td>0.182071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.211734</td>\n",
       "      <td>0.199225</td>\n",
       "      <td>0.230352</td>\n",
       "      <td>0.230539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dual_return  single_return   100_return  9050_return\n",
       "count  4994.000000    4996.000000  4948.000000  4996.000000\n",
       "mean      0.108592       0.107832     0.165229     0.171894\n",
       "std       0.067511       0.067349     0.021474     0.015449\n",
       "min      -0.091972      -0.093208     0.040812     0.058483\n",
       "25%       0.054579       0.053068     0.154161     0.161710\n",
       "50%       0.140854       0.142800     0.167230     0.172135\n",
       "75%       0.160748       0.160117     0.178945     0.182071\n",
       "max       0.211734       0.199225     0.230352     0.230539"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ports_no_wipeout.groupby('random_state', dropna=True).mean().loc[:, cols]/ports_no_wipeout.groupby('random_state', dropna=True).std().loc[:, cols]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-1:\n",
      "Process ForkPoolWorker-2:\n",
      "Process ForkPoolWorker-6:\n",
      "Process ForkPoolWorker-7:\n",
      "Process ForkPoolWorker-3:\n",
      "Process ForkPoolWorker-5:\n",
      "Process ForkPoolWorker-8:\n",
      "Process ForkPoolWorker-4:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 418, in main\n",
      "    pay_taxes, dual_phase = True)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 420, in main\n",
      "    pay_taxes, dual_phase=False)\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 420, in main\n",
      "    pay_taxes, dual_phase=False)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 223, in calculate_return\n",
      "    pp[i, SU_debt] = debt_available['SU'].debt_amount\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 420, in main\n",
      "    pay_taxes, dual_phase=False)\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 230, in calculate_return\n",
      "    pp[i, pv_p] = pp[i - 1, pv_u] + pp[i, nip]\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 418, in main\n",
      "    pay_taxes, dual_phase = True)\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 221, in calculate_return\n",
      "    pp[i - 1, pi_rf], pp[i - 1, dst], gearing_cap, pp[i, period])\n",
      "KeyboardInterrupt\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 221, in calculate_return\n",
      "    pp[i - 1, pi_rf], pp[i - 1, dst], gearing_cap, pp[i, period])\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 250, in calculate_return\n",
      "    pp[i, interest] = max(interest_all_debt(pp[i, period]), 0)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 101, in interest_all_debt\n",
      "    interest_bill += debt_available['Nordnet'].calculate_interest(deduction=0)\n",
      "KeyboardInterrupt\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\", line 47, in starmapstar\n",
      "    return list(itertools.starmap(args[0], args[1]))\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 443, in main\n",
      "    port.set_index(['random_state', 'period'], drop=True, inplace=True)\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 420, in main\n",
      "    pay_taxes, dual_phase=False)\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 438, in main\n",
      "    port.loc[:, flt_cols] = port.loc[:, flt_cols].astype(int)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py\", line 4611, in set_index\n",
      "    del frame[c]\n",
      "  File \"/Users/nielseriksen/applied_finance/calculate_return.py\", line 292, in calculate_return\n",
      "    pp = pd.DataFrame(pp, columns=columns)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\", line 873, in __getitem__\n",
      "    return self._getitem_tuple(key)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/frame.py\", line 497, in __init__\n",
      "    mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/generic.py\", line 3714, in __delitem__\n",
      "    self._mgr.idelete(loc)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\", line 1055, in _getitem_tuple\n",
      "    return self._getitem_tuple_same_dim(tup)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/managers.py\", line 1031, in idelete\n",
      "    for blkno, _ in _fast_count_smallints(self.blknos[affected_start:]):\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/construction.py\", line 173, in init_ndarray\n",
      "    elif is_extension_array_dtype(values) or is_extension_array_dtype(dtype):\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\", line 750, in _getitem_tuple_same_dim\n",
      "    retval = getattr(retval, self.name)._getitem_axis(key, axis=i)\n",
      "KeyboardInterrupt\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/dtypes/common.py\", line 1506, in is_extension_array_dtype\n",
      "    return isinstance(dtype, ExtensionDtype) or registry.find(dtype) is not None\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\", line 1099, in _getitem_axis\n",
      "    return self._getitem_iterable(key, axis=axis)\n",
      "KeyboardInterrupt\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\", line 1039, in _getitem_iterable\n",
      "    {axis: [keyarr, indexer]}, copy=True, allow_dups=True\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/generic.py\", line 4532, in _reindex_with_indexers\n",
      "    copy=copy,\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/managers.py\", line 1291, in reindex_indexer\n",
      "    new_blocks = self._slice_take_blocks_ax0(indexer, fill_value=fill_value)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/managers.py\", line 1419, in _slice_take_blocks_ax0\n",
      "    nb = blk.take_nd(taker, axis=0, new_mgr_locs=mgr_locs)\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/internals/blocks.py\", line 1256, in take_nd\n",
      "    values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/algorithms.py\", line 1735, in take_nd\n",
      "    arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info\n",
      "  File \"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/algorithms.py\", line 1520, in _get_take_nd_function\n",
      "    func = _take_2d_axis0_dict.get(tup, None)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-6d285f090256>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcalculate_return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mports\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalculate_return\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_returns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'garch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPAY_TAXES\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSEED_INDEX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/applied_finance/calculate_return.py\u001b[0m in \u001b[0;36mfetch_returns\u001b[0;34m(sim_type, random_seeds, BEGINNING_SAVINGS, YEARLY_INCOME_GROWTH, PAY_TAXES, YEARS, GAMMA, YEARLY_RF, YEARLY_RM, COST, SIGMA2, MR, SEED_INDEX)\u001b[0m\n\u001b[1;32m    552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstarmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomb_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[0mdfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mstarmap\u001b[0;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[1;32m    274\u001b[0m         \u001b[0;31m`\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mbecomes\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m         '''\n\u001b[0;32m--> 276\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstarmapstar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     def starmap_async(self, func, iterable, chunksize=None, callback=None,\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 651\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    652\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "from ipywidgets import fixed\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import calculate_return\n",
    "import pandas as pd\n",
    "ports = calculate_return.fetch_returns('garch', range(5000), PAY_TAXES=True, SEED_INDEX=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period', ylabel='value'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "cols = ['dual_phase', 'single_phase', '100', '9050']\n",
    "df = ports.loc[:, cols].reset_index().melt(value_vars=cols, id_vars=['random_state', 'period'])\n",
    "sns.lineplot(data = df, x = 'period', y = 'value', hue = 'variable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9c9b38fa58>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "parameter = '100'\n",
    "df = ports.loc[:, [parameter]].reset_index()\n",
    "plt.scatter(x=df['period'], y=df[parameter], alpha=0.002)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Illustrating temporal smoothing of market exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import simulate\n",
    "import seaborn as sns\n",
    "import calculate_return as cr\n",
    "import matplotlib\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "plt.style.use('seaborn')\n",
    "plt.rc('font', size=10)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=16)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=16)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=13)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=13)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=13)    # legend fontsize\n",
    "plt.rc('figure', titlesize=13)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n",
      "warning: catastrophic wipeout\n"
     ]
    }
   ],
   "source": [
    "ports = cr.fetch_returns('garch', range(10000), PAY_TAXES=False)\n",
    "grouped = ports.groupby('period', dropna=True).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delta version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot((grouped['new_debt'] + grouped['savings']), linewidth = 3)      # Levered investments\n",
    "ax.plot(grouped['savings'], linewidth = 3)\n",
    "#ax.plot(grouped['new_debt'], linewidth = 3)\n",
    "#ax.get_yaxis().set_visible(False)\n",
    "ax.set_ylabel('DKK', rotation=0, loc='top', labelpad=-71)\n",
    "ax.get_yaxis().set_major_formatter(\n",
    "    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "ax.legend(['Leveraged strategy', 'Unleveraged strategy'])\n",
    "\n",
    "fig.savefig('Empirical/Net_monthly_investments.png', bbox_inches = 'tight', pad_inches = 0.1, dpi=192)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "ax.plot((grouped['new_debt'] + grouped['savings']).cumsum()/1000, linewidth = 3)      # Levered investments\n",
    "ax.plot(grouped['savings'].cumsum()/1000, linewidth = 3)\n",
    "#ax.get_yaxis().set_visible(False)\n",
    "\n",
    "ax.set_ylabel('DKKt', rotation=0, loc='top', labelpad=-66)\n",
    "ax.legend(['Leveraged strategy', 'Unleveraged strategy'])\n",
    "fig.savefig('Empirical/Net_monthly_investments_cumsum.png', bbox_inches = 'tight', pad_inches = 0.1, dpi=192)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference between top 25% and bottom 25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "q75 = grouped.loc[slice(None), 600].quantile(0.75)\n",
    "q25 = grouped.loc[slice(None), 600].quantile(0.25)"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}