{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temperature-Dependent Power Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We implement Temperature-Dependent Power Flow (TDPF) in pandapower based on the following publications:\n",
    "\n",
    "S. Frank, J. Sexauer and S. Mohagheghi, \"Temperature-dependent power flow\", IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4007-4018, Nov 2013.\n",
    "\n",
    "B. Ngoko, H. Sugihara and T. Funaki, \"A Temperature Dependent Power Flow Model Considering Overhead Transmission Line Conductor Thermal Inertia Characteristics,\" 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019, pp. 1-6, doi: 10.1109/EEEIC.2019.8783234.\n",
    "\n",
    "This tutorial demonstrates how to use this feature in pandapower in order to calculate temperature of overhead lines and also obtain the results of the TDPF with the adjusted line parameters. Furthermore, thermal inertia of overhead lines can be considered by specifying the time delay after which the temperature should be calculated. This implementation also supports the distributed slack power flow calculation.\n",
    "\n",
    "We use the approach from Frank et al. to calculate the thermal resistance of the lines and to define the Jacobian matrix and the mismatch eqiation. The approach of Ngoko et al. is used to update the thermal resistance based on the line current and the weather parameters, and to introduce the thermal delay term in the Jacobian matrix. This allows using both methods. Furthermore, the approach to use thermal resistance is more versatile because it allows expanding the implementation for underground cables and transformers in the future.\n",
    "\n",
    "This implementation will be of benefit for grid studies of Dynamic Line Rating (DLR). In addition, a calculation of a grid state at a specified time after an N-1 event provides additional capabilities for contingency studies and for obtaining emergency line ratings for a given grid. \n",
    "\n",
    "In this tutorial, we demonstrate the use of this feature with a small example.\n",
    "\n",
    "After importing the necessary libraries, we create a 5-bus power system (inspired by Ngoko et al.) that we use to demonstrate the calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pandapower as pp\n",
    "import pandapower.plotting\n",
    "from pandapower.test.loadflow.test_tdpf import simple_test_grid\n",
    "from pandapower.pf.create_jacobian_tdpf import *\n",
    "from pandapower.pypower.idx_brch import BR_R, BR_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = simple_test_grid(load_scaling=0.25, sgen_scaling=0.5, with_gen=True, distributed_slack=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.plotting.simple_plot(net, plot_loads=True, plot_sgens=True, plot_gens=True, load_size=3, sgen_size=3, gen_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>p_from_mw</th>\n",
       "      <th>q_from_mvar</th>\n",
       "      <th>p_to_mw</th>\n",
       "      <th>q_to_mvar</th>\n",
       "      <th>pl_mw</th>\n",
       "      <th>ql_mvar</th>\n",
       "      <th>i_from_ka</th>\n",
       "      <th>i_to_ka</th>\n",
       "      <th>i_ka</th>\n",
       "      <th>vm_from_pu</th>\n",
       "      <th>va_from_degree</th>\n",
       "      <th>vm_to_pu</th>\n",
       "      <th>va_to_degree</th>\n",
       "      <th>loading_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.520700</td>\n",
       "      <td>56.677782</td>\n",
       "      <td>-2.264601</td>\n",
       "      <td>-55.071738</td>\n",
       "      <td>0.256099</td>\n",
       "      <td>1.606044</td>\n",
       "      <td>0.241507</td>\n",
       "      <td>0.241507</td>\n",
       "      <td>0.241507</td>\n",
       "      <td>1.027490</td>\n",
       "      <td>2.981261</td>\n",
       "      <td>0.998232</td>\n",
       "      <td>3.173039</td>\n",
       "      <td>25.156976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97.479300</td>\n",
       "      <td>-56.677782</td>\n",
       "      <td>-96.467660</td>\n",
       "      <td>63.021966</td>\n",
       "      <td>1.011640</td>\n",
       "      <td>6.344184</td>\n",
       "      <td>0.479997</td>\n",
       "      <td>0.479997</td>\n",
       "      <td>0.479997</td>\n",
       "      <td>1.027490</td>\n",
       "      <td>2.981261</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.999724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-88.710098</td>\n",
       "      <td>11.671945</td>\n",
       "      <td>89.248383</td>\n",
       "      <td>-8.296260</td>\n",
       "      <td>0.538285</td>\n",
       "      <td>3.375684</td>\n",
       "      <td>0.392043</td>\n",
       "      <td>0.392043</td>\n",
       "      <td>0.392043</td>\n",
       "      <td>0.998232</td>\n",
       "      <td>3.173039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.357838</td>\n",
       "      <td>40.837850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.974699</td>\n",
       "      <td>-16.600207</td>\n",
       "      <td>-65.779624</td>\n",
       "      <td>17.823557</td>\n",
       "      <td>0.195075</td>\n",
       "      <td>1.223350</td>\n",
       "      <td>0.298086</td>\n",
       "      <td>0.298086</td>\n",
       "      <td>0.298086</td>\n",
       "      <td>0.998232</td>\n",
       "      <td>3.173039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.135568</td>\n",
       "      <td>31.050611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210.751617</td>\n",
       "      <td>-141.687706</td>\n",
       "      <td>-206.430677</td>\n",
       "      <td>168.785130</td>\n",
       "      <td>4.320941</td>\n",
       "      <td>27.097424</td>\n",
       "      <td>1.110752</td>\n",
       "      <td>1.110752</td>\n",
       "      <td>1.110752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.357838</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>115.703355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-113.442497</td>\n",
       "      <td>220.182604</td>\n",
       "      <td>115.779624</td>\n",
       "      <td>-205.526046</td>\n",
       "      <td>2.337127</td>\n",
       "      <td>14.656558</td>\n",
       "      <td>1.031768</td>\n",
       "      <td>1.031768</td>\n",
       "      <td>1.031768</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.135568</td>\n",
       "      <td>107.475804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p_from_mw  q_from_mvar     p_to_mw   q_to_mvar     pl_mw    ql_mvar  \\\n",
       "0    2.520700    56.677782   -2.264601  -55.071738  0.256099   1.606044   \n",
       "1   97.479300   -56.677782  -96.467660   63.021966  1.011640   6.344184   \n",
       "2  -88.710098    11.671945   89.248383   -8.296260  0.538285   3.375684   \n",
       "3   65.974699   -16.600207  -65.779624   17.823557  0.195075   1.223350   \n",
       "4  210.751617  -141.687706 -206.430677  168.785130  4.320941  27.097424   \n",
       "5 -113.442497   220.182604  115.779624 -205.526046  2.337127  14.656558   \n",
       "\n",
       "   i_from_ka   i_to_ka      i_ka  vm_from_pu  va_from_degree  vm_to_pu  \\\n",
       "0   0.241507  0.241507  0.241507    1.027490        2.981261  0.998232   \n",
       "1   0.479997  0.479997  0.479997    1.027490        2.981261  1.050000   \n",
       "2   0.392043  0.392043  0.392043    0.998232        3.173039  1.000000   \n",
       "3   0.298086  0.298086  0.298086    0.998232        3.173039  1.000000   \n",
       "4   1.110752  1.110752  1.110752    1.000000        5.357838  1.050000   \n",
       "5   1.031768  1.031768  1.031768    1.050000        0.000000  1.000000   \n",
       "\n",
       "   va_to_degree  loading_percent  \n",
       "0      3.173039        25.156976  \n",
       "1      0.000000        49.999724  \n",
       "2      5.357838        40.837850  \n",
       "3      2.135568        31.050611  \n",
       "4      0.000000       115.703355  \n",
       "5      2.135568       107.475804  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.runpp(net)\n",
    "net.res_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>vm_pu</th>\n",
       "      <th>va_degree</th>\n",
       "      <th>p_mw</th>\n",
       "      <th>q_mvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.027490</td>\n",
       "      <td>2.981261</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.998232</td>\n",
       "      <td>3.173039</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.357838</td>\n",
       "      <td>-300.000000</td>\n",
       "      <td>149.983966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>416.340834</td>\n",
       "      <td>-451.989700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.135568</td>\n",
       "      <td>-50.000000</td>\n",
       "      <td>187.702489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      vm_pu  va_degree        p_mw      q_mvar\n",
       "0  1.027490   2.981261 -100.000000   -0.000000\n",
       "1  0.998232   3.173039   25.000000   60.000000\n",
       "2  1.000000   5.357838 -300.000000  149.983966\n",
       "3  1.050000   0.000000  416.340834 -451.989700\n",
       "4  1.000000   2.135568  -50.000000  187.702489"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.res_bus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation of the overhead line temperature\n",
    "\n",
    "It is possible to use the functions from the implementation separately, in order to calculate the overhead line temperature.\n",
    "\n",
    "The implementation in Ngoko et al. defines three terms to represent a simplified thermal model of an overhead line. The parameters $a_0$, $a_1$, and $a_2$ specify a constant, linear, and quadratic terms of the dependence of the line temperature from the square of the current $I$ that is flowing through the overhead line:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "T_{ss} = a_0 + a_1 \\cdot I^2 + a_2 \\cdot I^4\n",
    "$$\n",
    "\n",
    "$$\n",
    "T = T_{ss} - (T_{ss} - T_0) \\cdot \\exp(-t/\\tau)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the equations above, $T_{ss}$ stands for the steady-state temperature, $T_0$ is the initial temperature, and $T$ stands for the temperature at the time $t$ after a step change of current. The parameter $\\tau$ is a time constant, used in the caluclation of line temperature with thermal inertia. After $\\tau$ seconds, the temperature reaches approximately 63.2 % of the steady-state value.\n",
    "\n",
    "This approach requires the following inputs (default values are in parentheses):\n",
    "\n",
    "* air temperature (20 °C)\n",
    "* initial line temperature (20 °C)\n",
    "* reference temperature (20 °C)\n",
    "* resistance at reference temperature (r_ohm_per_km)\n",
    "* conductor outer diameter\n",
    "* specific mass of the conductor multiplied by the specific thermal capacity of the material\n",
    "* wind speed (0.6 m/s)\n",
    "* wind angle of attack (45 °)\n",
    "* solar radiation (900 W/m²)\n",
    "* solar emissivity and solar absorptivity (0.5)\n",
    "* thermal coefficient of resistivity (4.03e-3)\n",
    "\n",
    "The parameters are specified in the net.line element table. If any parameters are missing, they will be substitutet by default assumptions. The parameter \"tdpf\" is always required. If the option to not update $R_\\Theta$ is used, then \"r_theta_kelvin_per_mw\" is required. Otherwise, \"conductor_outer_diameter_m\" is required. Furthermore, if thermal inertia is considered by setting the option \"tdpf_delay_s\", the parameter \"mc_joule_per_m_k\" is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 46.06044226,  53.12839992,  49.94224359,  47.29338662,\n",
       "       100.09505835,  91.48530994])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_air_pu = 35\n",
    "alpha_pu = 4.03e-3\n",
    "r_ref = net.line.r_ohm_per_km.values / 1e3\n",
    "a0, a1, a2, tau = calc_a0_a1_a2_tau(t_air_pu, 80, 20, r_ref, 30.6e-3,\n",
    "                                    1490, 0.6, 45, 900, alpha_pu, 0.5, 0.5)\n",
    "calc_T_ngoko(np.square(net.res_line.i_ka.values * 1e3), a0, a1, a2, None, None, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the approach of Frank et al., the parameters specifying the thermal model according to Ngoko et al. are used to calculate the thermal resistamce $R_{\\Theta}$: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "R_{\\Theta} = \\frac{T_{Rise}}{P_{Loss}} = \\frac{a_0 - T_{air} + a_1 \\cdot I^2 + a_2 \\cdot I^4}{P_{Loss}}\n",
    "$$\n",
    "\n",
    "$$\n",
    "T = T_{air} + R_\\Theta \\cdot P_{Loss}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, the thermal resistance can be approximated using an assumption for the rated temperature rise, as described in Frank et al. To this end, the air temperature $T_{air}$ and the temperature rise are added to calculate the line temperature. The temperature rise is calculated by multiplying the thermal resistance $R_\\Theta$ and the active power losses $P_{Loss}$. The thermal resistance is included in the line results table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 46.06044226,  53.12839992,  49.94224359,  47.29338662,\n",
       "       100.09505835,  91.48530994])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch = net._ppc[\"branch\"]\n",
    "tdpf_lines = np.ones(len(branch)).astype(bool)\n",
    "r = branch[tdpf_lines, BR_R].real\n",
    "#r = r * (1 + alpha_pu * (T - 20))\n",
    "x = branch[tdpf_lines, BR_X].real\n",
    "g, b = calc_g_b(r, x)\n",
    "Vm = abs(net._ppc[\"internal\"][\"V\"])\n",
    "Va = np.angle(net._ppc[\"internal\"][\"V\"])\n",
    "i_square_pu, p_loss_pu = calc_i_square_p_loss(branch, tdpf_lines, g, b, Vm, Va)\n",
    "#i_square_pu = np.square(net.res_line.i_ka.values*1e3)\n",
    "r_theta_pu = calc_r_theta(t_air_pu, a0, a1, a2, np.square(net.res_line.i_ka.values*1e3), p_loss_pu)\n",
    "calc_T_frank(p_loss_pu, t_air_pu, r_theta_pu, None, None, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36.43834877, 40.68175543, 38.79029548, 37.19122826, 65.42560553,\n",
       "       61.25238291])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using an approximation for the rated temperature rise:\n",
    "r_theta_kelvin_per_mw = calc_r_theta_from_t_rise(net, 25)\n",
    "calc_T_frank(p_loss_pu, t_air_pu, r_theta_kelvin_per_mw.values * net.sn_mva / 1, None, None, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By providing the option tdpf=True, the overhead line temperature can be obtained from the power flow caluclation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp.runpp(net, tdpf=True, max_iteration=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the results of the power flow calculation for the line current and using them as the input for the introduced functions, we can make sure that the temperature calculation delivers the same results. However, if we repeat the power flow calculation without the TDPF, the results will be different because the effect of the temperature on the resitance and the current is ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>p_from_mw</th>\n",
       "      <th>q_from_mvar</th>\n",
       "      <th>p_to_mw</th>\n",
       "      <th>q_to_mvar</th>\n",
       "      <th>pl_mw</th>\n",
       "      <th>ql_mvar</th>\n",
       "      <th>i_from_ka</th>\n",
       "      <th>i_to_ka</th>\n",
       "      <th>i_ka</th>\n",
       "      <th>vm_from_pu</th>\n",
       "      <th>va_from_degree</th>\n",
       "      <th>vm_to_pu</th>\n",
       "      <th>va_to_degree</th>\n",
       "      <th>loading_percent</th>\n",
       "      <th>r_ohm_per_km</th>\n",
       "      <th>r_theta_kelvin_per_mw</th>\n",
       "      <th>temperature_degree_celsius</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.145704</td>\n",
       "      <td>57.860539</td>\n",
       "      <td>-0.851651</td>\n",
       "      <td>-56.191113</td>\n",
       "      <td>0.294053</td>\n",
       "      <td>1.669426</td>\n",
       "      <td>0.246226</td>\n",
       "      <td>0.246226</td>\n",
       "      <td>0.246226</td>\n",
       "      <td>1.028013</td>\n",
       "      <td>3.058060</td>\n",
       "      <td>0.998271</td>\n",
       "      <td>3.324106</td>\n",
       "      <td>25.648576</td>\n",
       "      <td>0.065172</td>\n",
       "      <td>37.924674</td>\n",
       "      <td>46.151857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.854296</td>\n",
       "      <td>-57.860539</td>\n",
       "      <td>-97.672056</td>\n",
       "      <td>64.400365</td>\n",
       "      <td>1.182241</td>\n",
       "      <td>6.539826</td>\n",
       "      <td>0.487342</td>\n",
       "      <td>0.487342</td>\n",
       "      <td>0.487342</td>\n",
       "      <td>1.028013</td>\n",
       "      <td>3.058060</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.764815</td>\n",
       "      <td>0.066887</td>\n",
       "      <td>15.579987</td>\n",
       "      <td>53.419294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-89.838066</td>\n",
       "      <td>13.737926</td>\n",
       "      <td>90.460324</td>\n",
       "      <td>-10.255459</td>\n",
       "      <td>0.622257</td>\n",
       "      <td>3.482468</td>\n",
       "      <td>0.398196</td>\n",
       "      <td>0.398196</td>\n",
       "      <td>0.398196</td>\n",
       "      <td>0.998271</td>\n",
       "      <td>3.324106</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.550365</td>\n",
       "      <td>41.478734</td>\n",
       "      <td>0.066113</td>\n",
       "      <td>24.328248</td>\n",
       "      <td>50.138432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.689717</td>\n",
       "      <td>-17.546813</td>\n",
       "      <td>-65.473610</td>\n",
       "      <td>18.768694</td>\n",
       "      <td>0.216107</td>\n",
       "      <td>1.221881</td>\n",
       "      <td>0.297907</td>\n",
       "      <td>0.297907</td>\n",
       "      <td>0.297907</td>\n",
       "      <td>0.998271</td>\n",
       "      <td>3.324106</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.284085</td>\n",
       "      <td>31.031951</td>\n",
       "      <td>0.065440</td>\n",
       "      <td>56.856682</td>\n",
       "      <td>47.287124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>209.539677</td>\n",
       "      <td>-151.708806</td>\n",
       "      <td>-203.578073</td>\n",
       "      <td>179.827572</td>\n",
       "      <td>5.961603</td>\n",
       "      <td>28.118767</td>\n",
       "      <td>1.131492</td>\n",
       "      <td>1.131492</td>\n",
       "      <td>1.131492</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.550365</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117.863704</td>\n",
       "      <td>0.078446</td>\n",
       "      <td>11.305108</td>\n",
       "      <td>102.396570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-112.343080</td>\n",
       "      <td>225.658937</td>\n",
       "      <td>115.473611</td>\n",
       "      <td>-210.478384</td>\n",
       "      <td>3.130530</td>\n",
       "      <td>15.180553</td>\n",
       "      <td>1.050049</td>\n",
       "      <td>1.050049</td>\n",
       "      <td>1.050049</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.284085</td>\n",
       "      <td>109.380148</td>\n",
       "      <td>0.076301</td>\n",
       "      <td>18.626449</td>\n",
       "      <td>93.310661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p_from_mw  q_from_mvar     p_to_mw   q_to_mvar     pl_mw    ql_mvar  \\\n",
       "0    1.145704    57.860539   -0.851651  -56.191113  0.294053   1.669426   \n",
       "1   98.854296   -57.860539  -97.672056   64.400365  1.182241   6.539826   \n",
       "2  -89.838066    13.737926   90.460324  -10.255459  0.622257   3.482468   \n",
       "3   65.689717   -17.546813  -65.473610   18.768694  0.216107   1.221881   \n",
       "4  209.539677  -151.708806 -203.578073  179.827572  5.961603  28.118767   \n",
       "5 -112.343080   225.658937  115.473611 -210.478384  3.130530  15.180553   \n",
       "\n",
       "   i_from_ka   i_to_ka      i_ka  vm_from_pu  va_from_degree  vm_to_pu  \\\n",
       "0   0.246226  0.246226  0.246226    1.028013        3.058060  0.998271   \n",
       "1   0.487342  0.487342  0.487342    1.028013        3.058060  1.050000   \n",
       "2   0.398196  0.398196  0.398196    0.998271        3.324106  1.000000   \n",
       "3   0.297907  0.297907  0.297907    0.998271        3.324106  1.000000   \n",
       "4   1.131492  1.131492  1.131492    1.000000        5.550365  1.050000   \n",
       "5   1.050049  1.050049  1.050049    1.050000        0.000000  1.000000   \n",
       "\n",
       "   va_to_degree  loading_percent  r_ohm_per_km  r_theta_kelvin_per_mw  \\\n",
       "0      3.324106        25.648576      0.065172              37.924674   \n",
       "1      0.000000        50.764815      0.066887              15.579987   \n",
       "2      5.550365        41.478734      0.066113              24.328248   \n",
       "3      2.284085        31.031951      0.065440              56.856682   \n",
       "4      0.000000       117.863704      0.078446              11.305108   \n",
       "5      2.284085       109.380148      0.076301              18.626449   \n",
       "\n",
       "   temperature_degree_celsius  \n",
       "0                   46.151857  \n",
       "1                   53.419294  \n",
       "2                   50.138432  \n",
       "3                   47.287124  \n",
       "4                  102.396570  \n",
       "5                   93.310661  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.res_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 46.15308366,  53.42645102,  50.1425085 ,  47.28905922,\n",
       "       102.50991728,  93.39730687])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_T_ngoko(np.square(net.res_line.i_ka.values * 1e3), a0, a1, a2, None, None, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 46.06044226,  53.12839992,  49.94224359,  47.29338662,\n",
       "       100.09505835,  91.48530994])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.runpp(net)\n",
    "calc_T_ngoko(np.square(net.res_line.i_ka.values * 1e3), a0, a1, a2, None, None, None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calculation works for distributed slack power flow, too. However, the parameter net.sn_mva should be increased in order to avoid numerical instability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.sn_mva = 1000\n",
    "pp.runpp(net, tdpf=True, tdpf_delay_s=5 * 60, distributed_slack=True, max_iteration=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we demonstrate the calculation with a time delay of 5 minutes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pp.runpp(net, tdpf=True, tdpf_delay_s=5 * 60, max_iteration=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>p_from_mw</th>\n",
       "      <th>q_from_mvar</th>\n",
       "      <th>p_to_mw</th>\n",
       "      <th>q_to_mvar</th>\n",
       "      <th>pl_mw</th>\n",
       "      <th>ql_mvar</th>\n",
       "      <th>i_from_ka</th>\n",
       "      <th>i_to_ka</th>\n",
       "      <th>i_ka</th>\n",
       "      <th>vm_from_pu</th>\n",
       "      <th>va_from_degree</th>\n",
       "      <th>vm_to_pu</th>\n",
       "      <th>va_to_degree</th>\n",
       "      <th>loading_percent</th>\n",
       "      <th>r_ohm_per_km</th>\n",
       "      <th>r_theta_kelvin_per_mw</th>\n",
       "      <th>temperature_degree_celsius</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.169795</td>\n",
       "      <td>56.988937</td>\n",
       "      <td>-1.903741</td>\n",
       "      <td>-55.366515</td>\n",
       "      <td>0.266055</td>\n",
       "      <td>1.622422</td>\n",
       "      <td>0.242735</td>\n",
       "      <td>0.242735</td>\n",
       "      <td>0.242735</td>\n",
       "      <td>1.027632</td>\n",
       "      <td>3.001264</td>\n",
       "      <td>0.998243</td>\n",
       "      <td>3.212388</td>\n",
       "      <td>25.284924</td>\n",
       "      <td>0.060675</td>\n",
       "      <td>41.657558</td>\n",
       "      <td>27.096882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97.830205</td>\n",
       "      <td>-56.988937</td>\n",
       "      <td>-96.773739</td>\n",
       "      <td>63.383200</td>\n",
       "      <td>1.056465</td>\n",
       "      <td>6.394263</td>\n",
       "      <td>0.481888</td>\n",
       "      <td>0.481888</td>\n",
       "      <td>0.481888</td>\n",
       "      <td>1.027632</td>\n",
       "      <td>3.001264</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.196674</td>\n",
       "      <td>0.061132</td>\n",
       "      <td>17.225095</td>\n",
       "      <td>29.032648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-88.984503</td>\n",
       "      <td>12.221760</td>\n",
       "      <td>89.544640</td>\n",
       "      <td>-8.820053</td>\n",
       "      <td>0.560137</td>\n",
       "      <td>3.401707</td>\n",
       "      <td>0.393552</td>\n",
       "      <td>0.393552</td>\n",
       "      <td>0.393552</td>\n",
       "      <td>0.998243</td>\n",
       "      <td>3.212388</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.407436</td>\n",
       "      <td>40.994956</td>\n",
       "      <td>0.060926</td>\n",
       "      <td>26.756068</td>\n",
       "      <td>28.159076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.888243</td>\n",
       "      <td>-16.855245</td>\n",
       "      <td>-65.687504</td>\n",
       "      <td>18.077810</td>\n",
       "      <td>0.200740</td>\n",
       "      <td>1.222565</td>\n",
       "      <td>0.297990</td>\n",
       "      <td>0.297990</td>\n",
       "      <td>0.297990</td>\n",
       "      <td>0.998243</td>\n",
       "      <td>3.212388</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.174414</td>\n",
       "      <td>31.040639</td>\n",
       "      <td>0.060752</td>\n",
       "      <td>61.219320</td>\n",
       "      <td>27.425004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210.455359</td>\n",
       "      <td>-144.382347</td>\n",
       "      <td>-205.708171</td>\n",
       "      <td>171.751230</td>\n",
       "      <td>4.747188</td>\n",
       "      <td>27.368883</td>\n",
       "      <td>1.116302</td>\n",
       "      <td>1.116302</td>\n",
       "      <td>1.116302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.407436</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>116.281463</td>\n",
       "      <td>0.064177</td>\n",
       "      <td>13.824373</td>\n",
       "      <td>41.937483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-113.143651</td>\n",
       "      <td>221.648526</td>\n",
       "      <td>115.687503</td>\n",
       "      <td>-206.853411</td>\n",
       "      <td>2.543852</td>\n",
       "      <td>14.795116</td>\n",
       "      <td>1.036633</td>\n",
       "      <td>1.036633</td>\n",
       "      <td>1.036633</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.174414</td>\n",
       "      <td>107.982627</td>\n",
       "      <td>0.063617</td>\n",
       "      <td>22.370305</td>\n",
       "      <td>39.564852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p_from_mw  q_from_mvar     p_to_mw   q_to_mvar     pl_mw    ql_mvar  \\\n",
       "0    2.169795    56.988937   -1.903741  -55.366515  0.266055   1.622422   \n",
       "1   97.830205   -56.988937  -96.773739   63.383200  1.056465   6.394263   \n",
       "2  -88.984503    12.221760   89.544640   -8.820053  0.560137   3.401707   \n",
       "3   65.888243   -16.855245  -65.687504   18.077810  0.200740   1.222565   \n",
       "4  210.455359  -144.382347 -205.708171  171.751230  4.747188  27.368883   \n",
       "5 -113.143651   221.648526  115.687503 -206.853411  2.543852  14.795116   \n",
       "\n",
       "   i_from_ka   i_to_ka      i_ka  vm_from_pu  va_from_degree  vm_to_pu  \\\n",
       "0   0.242735  0.242735  0.242735    1.027632        3.001264  0.998243   \n",
       "1   0.481888  0.481888  0.481888    1.027632        3.001264  1.050000   \n",
       "2   0.393552  0.393552  0.393552    0.998243        3.212388  1.000000   \n",
       "3   0.297990  0.297990  0.297990    0.998243        3.212388  1.000000   \n",
       "4   1.116302  1.116302  1.116302    1.000000        5.407436  1.050000   \n",
       "5   1.036633  1.036633  1.036633    1.050000        0.000000  1.000000   \n",
       "\n",
       "   va_to_degree  loading_percent  r_ohm_per_km  r_theta_kelvin_per_mw  \\\n",
       "0      3.212388        25.284924      0.060675              41.657558   \n",
       "1      0.000000        50.196674      0.061132              17.225095   \n",
       "2      5.407436        40.994956      0.060926              26.756068   \n",
       "3      2.174414        31.040639      0.060752              61.219320   \n",
       "4      0.000000       116.281463      0.064177              13.824373   \n",
       "5      2.174414       107.982627      0.063617              22.370305   \n",
       "\n",
       "   temperature_degree_celsius  \n",
       "0                   27.096882  \n",
       "1                   29.032648  \n",
       "2                   28.159076  \n",
       "3                   27.425004  \n",
       "4                   41.937483  \n",
       "5                   39.564852  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.res_line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the temperature rise is substantially lower due to thermal inertia. The thermal inertia effect can be illustrated in the following figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZNgcfHx/IZDKbQHbj/bf7vN7e3ti5c2eD99dtUXOEm32mNft8fHzA4/Fw4MCBBv/TcOO+hn53du/ejevXr2Pv3r3W1hwAKCsra3KtUqkUQP3fjZpQ1pAba7H3vRD7UNghrdLgwYPh4eGB1NTU25oobuTIkVi+fDlOnz5tcyprw4YNDnudmj9WN/4R+vLLLxs8PiAgAPfffz8+//xzGAwGTJkyBaGhoU16nRtf48yZMzh8+DBCQkLsqhkAoqOjERAQgI0bN2L+/PnW95GZmYlDhw4hMDDwpo+vOQ2wfv16m9aY5ORkXLhwAQsXLrQ5fvbs2fjpp5+wY8cOrF+/HtOmTYOHh4f1/nHjxkEoFOLKlSuNnp5yFLlcjpEjRyIlJQU9evRotJUC4D53sVhs86WkUqnqjcaq+V37/vvv8a9//cu6/8bftZYyefJkLF26FN7e3g22bt7KjS1FdZ/3hx9+gNlsRnx8vCNKvaXGPtMRI0ZYa3rvvfeQm5uLBx544LZew55/x3VbSWUymXW/UqmEVCrFmTNnbI5vaOReYxzxXkjjKOyQVsnFxQWffPIJZs6ciZKSEtx3333w8/NDYWEhTp8+jcLCwnotHXXNmzcP3377LSZNmoQlS5ZAqVTi+++/x8WLFx32Ol27dkXnzp3x73//G4wxeHl5Ydu2bUhMTGy0rhdeeMH6RbF69eomfRaTJ0/GO++8g7feegvDhw9HWloa3n77bURERMBkMjXpOeri8/l455138NRTT2HatGmYM2cOysrKsGjRoiadeomOjsbTTz+NTz75BHw+HxMmTMC1a9fwxhtvICQkBC+++KLN8WPHjkVwcDDmzp0LlUqF2bNn29wfHh6Ot99+GwsXLsTVq1cxfvx4eHp6Ij8/H8eOHYNCocDixYvtfp+N+fjjjzFkyBAMHToUzz33HMLDw1FRUYH09HRs27bNOmnm5MmTsWnTJsydOxf33XcfsrOz8c477yAgIACXL1+2eX/Dhg3DggULUFlZib59++Lvv//Gd99957Ca7TFv3jz8+uuvGDZsGF588UX06NEDFosFWVlZ2LVrF1566aWbhpXu3btj06ZNWLlyJe666y7w+Xz07dsXDz30EL7//ntMnDgRL7zwAvr37w+RSIScnBzs2bMHU6dOxbRp0xz2PsRiMf73v/9Bo9GgX79+OHToEJYsWYIJEyZgyJAhALj/rDz99NOYPXs2jh8/jmHDhkGhUCAvLw8HDx5E9+7d8dxzz930dQYNGgRPT088++yzeOuttyASifD999/j9OnTDX42APD+++9jwoQJEAgE1tD86KOP4ttvv0Xnzp3Rs2dPHDt2zK7A64j3Qm7CyR2kSRtyO6Oxnn/+eZt9NaMWPvjgA5v9e/bsYQDYzz//bLN/3759bNKkSczLy4uJRCIWFBTEJk2aVO+4hqSmprKEhAQmlUqZl5cXe/LJJ9lvv/1Wb+h5U1+noREyNa/h6urKPD092f3338+ysrJuOgonPDycxcTE3LL+Gnq9nr388sssKCiISaVS1qdPH7Zly5Z6n3ljny1j9UeEMMbY119/zSIjI5lYLGZRUVHs22+/bfDn2BCz2czef/99FhUVxUQiEfPx8WGPPvooy87ObvD4//znPwwACwkJYWazucFjtmzZwkaOHMnc3NyYRCJhYWFh7L777rMZ/n+z38GGft8Y40YGzZw502ZfRkYGe+KJJ1hQUBATiUTM19eXDRo0iC1ZssTmuPfee4+Fh4cziUTCYmJi2FdffWUdTVNXWVkZe+KJJ5iHhweTy+UsISGBXbx40eGjseLi4uo9vqGfmUajYa+//jqLjo5mYrHYOtT+xRdfvOUQ5pKSEnbfffcxDw8PxuPxbN6r0Whk//3vf1nPnj2ZVCplLi4urGvXruyZZ55hly9fth4XFhbGJk2aVO+5m/o3oebnfObMGTZixAgmk8mYl5cXe+6552xGd9b49ttvWXx8PFMoFEwmk7HOnTuzxx9/nB0/fvyWnx9jjB06dIgNHDiQyeVy5uvry5566il28uTJej8XvV7PnnrqKebr62v9bGp+RuXl5eypp55iSqWSKRQKNmXKFHbt2rVGR2PVTNFwO++F2I/HGGMtmq4I6cDOnDmDnj174rPPPsPcuXOdXQ4hrdKsWbPwyy+/QKPROLsU0k7QaSxCWsCVK1eQmZmJ//znPwgICKg3bJsQQkjzoXl2CGkB77zzDhISEqDRaPDzzz/TOjiEENKC6DQWIYQQQto1atkhhBBCSLtGYYcQQggh7RqFHUIIIYS0azQaC9waR9evX4erq+sdTUVPCCGEkJbDGENFRQUCAwPrraNXF4UdcOv53M60+4QQQghxvuzs7Hpr9dVFYQe1C9hlZ2c3upoyIYQQQloXtVqNkJCQWy5ES2EHtQvBubm5UdghhBBC2phbdUGhDsqEEEIIadco7BBCCCGkXaOwQwghhJB2jfrs2MFsNsNoNDq7jGYhEokgEAicXQYhhBDicBR2moAxBpVKhbKyMmeX0qw8PDzg7+9Pcw0RQghpVyjsNEFN0PHz84NcLm93YYAxhqqqKhQUFAAAAgICnFwRIYQQ4jgUdm7BbDZbg463t7ezy2k2MpkMAFBQUAA/Pz86pUUIIaTdoA7Kt1DTR0culzu5kuZX8x7ba78kQgghHROFnSZqb6euGtIR3iMhhJCOh8IOIYQQQto1CjuEEEIIadco7LRzn3/+OSIiIiCVSnHXXXfhwIEDzi6JEEIIaVFODTv79+/HlClTEBgYCB6Phy1bttjczxjDokWLEBgYCJlMhhEjRuD8+fM2x+j1evzzn/+Ej48PFAoF7r77buTk5LTgu2i9fvzxR8ybNw8LFy5ESkoKhg4digkTJiArK8vZpRFCCOkAGGMwaE0oL9RCX+W8wS9OHXpeWVmJnj17Yvbs2bj33nvr3b98+XJ8+OGHWLNmDaKiorBkyRIkJCQgLS3Nupz7vHnzsG3bNvzwww/w9vbGSy+9hMmTJ+PEiRMdfvj0hx9+iCeffBJPPfUUAGDFihX4888/sXLlSixbtszJ1RFCCGlLGGMw6s3QaYzQVRqh0xih1Ritt7nrhnr7LWYGABg9KwZdBzhnHjenhp0JEyZgwoQJDd7HGMOKFSuwcOFCTJ8+HQCwdu1aKJVKbNiwAc888wzKy8vxzTff4LvvvsOYMWMAAOvXr0dISAiSkpIwbtw4h9fMGIPWaHb48zaFTCRo8ogpg8GAEydO4N///rfN/rFjx+LQoUPNUR4hhJA2xGQw14aShgLLjYFGY4TZZLmt1xJKBDAbb++xjtBqJxXMyMiASqXC2LFjrfskEgmGDx+OQ4cO4ZlnnsGJEydgNBptjgkMDES3bt1w6NChRsOOXq+HXq+33lar1U2uS2s0I/bNP2/jHd251LfHQS5u2o+sqKgIZrMZSqXSZr9SqYRKpWqO8gghhDgJYwz6KhMqy/TQVhhsWlasgcV6nQszJsPthQ+BkA+ZqwhSFxGkCm4rq95KXcSQudRcF3HXFSIIxc4909Jqw07NF3JDX9aZmZnWY8RiMTw9Pesdc7Mv9GXLlmHx4sUOrrh1urEliDFG8+kQQkgbYrEwVJUbUFmmh6ZMh8oyPXe91HZruo2WEz6fZxtM6gYWxY37RZC5iCEU89vc90irDTs1bufL+lbHvPbaa5g/f771tlqtRkhISJPqkYkESH3b8afHmvraTeXj4wOBQFAv9BUUFNQLkIQQQpzDZDBDU6ZHZame25bV2VaHmKpyPRhr2vNJFELIXcXWYFIvyChq9gshdRFDLG1694i2rNWGHX9/fwBc603dhSnrfln7+/vDYDCgtLTUpnWnoKAAgwYNavS5JRIJJBLJbdXF4/GafCrJmcRiMe666y4kJiZi2rRp1v2JiYmYOnWqEysjhJD2jzEGfaWpTnCp0yJTJ8zoq0xNej4enweFuxgKDwkUHhK4VG8VHhK4eNbuc/bpotaq1X5rR0REwN/fH4mJiejduzcArtPtvn378P777wMA7rrrLohEIiQmJuKBBx4AAOTl5eHcuXNYvny502pvLebPn4/HHnsMffv2xcCBA7Fq1SpkZWXh2WefdXZphBDSZjHGoK0wQl2srW2RaaBlpqkdcoVi/g2hRVobaDy5rcxNDD6//bfANBenhh2NRoP09HTr7YyMDJw6dQpeXl4IDQ3FvHnzsHTpUkRGRiIyMhJLly6FXC7HjBkzAADu7u548skn8dJLL8Hb2xteXl54+eWX0b17d+vorI7swQcfRHFxMd5++23k5eWhW7du2LFjB8LCwpxdGiGEtHr6KiPKCrQoL6hCWX6VzXWDrmmjcqUuonqtLzZbTwnEMmGHOJXkTE4NO8ePH8fIkSOtt2v60cycORNr1qzBggULoNVqMXfuXJSWliI+Ph67du2yzrEDAB999BGEQiEeeOABaLVajB49GmvWrOnwc+zUmDt3LubOnevsMgghpFUyGsworwkx1UGmvECLsoIqaCtuMgkeD3BpoDVG4Smuve4hhtCOvpak+fAYa2q3p/ZLrVbD3d0d5eXlcHNzs7lPp9MhIyPDuuRCe9aR3ishpOMwmyxQF2kbbKXRlOpv+li5uxgefnJ4+Mng7ieHh1IOdz8Z3H1lFGRagZt9f9fVavvsEEIIIU1lsTBoSnQoK6humakONGUFVago1oFZGv9/vUQuhIdSDg8/LsjUvS6W0tdke0A/RUIIIW0CYwxVakPtqab86lNPBVqUF1bBYmo80AglAnj4ybhWmurWGa7FRg6pi6gF3wVxBgo7hBBCWh19lRGFWRUoyKxAUXYFSqsDjlHfeMdgvpAHd1+5NdTUbaWRu4upE3AHRmGHEEKIU+m1JhRmVaAwswIFWWoUZlagvFDb4LE8HuDqI2uwlcbFS0rDs0mDKOwQQghpMQatCYXZFdZWm8KsCpTlVzV4rJuPFL6hbvALc4VngAIefjK4+cggEPJbuGrS1lHYIYQQ0iwMOhOKsjUoyFRbw01ZQRXQQNcaV28p/EJd4RvmCr9QN/iGulJfGuIwFHYIIYTcMaPejKLs2taagkw1SvMbDjYuXhIu0IS5WgOOzEXc8kWTDoPCDiGEELsYDWYUZWtQmKW2hpvSvMoGF6t08ZTAN9QVfmGu8A1zg1+oK2SuFGxIy6KwQwghpFEmgxlFORpra01BZuPBRuEu5gJNmGt1wHGD3I2CDXE+Cjvt2P79+/HBBx/gxIkTyMvLw+bNm3HPPfc4uyxCSCtlNlpQlGPbx6Ykr7LBCfnkbmKb1hrfMFco3CVOqJqQW6Ow045VVlaiZ8+emD17Nu69915nl0MIaWUYYyhVVSE7tQRZqSW4fqkUpgZW6pa5iuAXVtvHxi/MDQoPCjak7aCw045NmDABEyZMcHYZhJBWRFdpRM7FUmSnFiMrtaTe2lBSFxH8wrhAU9PXRuEhoQn5SJtGYcdejAHGhueEaHYiOTejFiGENJHFbEH+tQpkpRYjO7UEBdfUNv1tBEI+Arq4IzTWG6FxXvAKVFCwIe0OhR17GauApYHOee3/XAfECue8NiGkzVAXa62npnIulsKgNdnc7xmgQGiMF0LivBAY6QGRmFbvJu0bhR1CCGnjjHozci+VIiu1BNmpJfVmJJbIhQiJ8UJIrBdCYrzg6iV1UqWEOAeFHXuJ5FwLi7NemxDS4TELQ1Guxtp6k3elzGbFbx6fB/8INy7cxHrBL8yN1owiHRqFHXvxeHQqiRDS4qrUBmRfKOH63lwohVZtsLnf1VuK0OpwExztCYmcllogpAaFnXZMo9EgPT3dejsjIwOnTp2Cl5cXQkNDnVgZIeRWzCYL8q6UW0dNFWVrbO4XSgQIjvJASKw3QmO94O4no47FhDSCwk47dvz4cYwcOdJ6e/78+QCAmTNnYs2aNU6qihDSEMYYygu0yKoON7mXymDSm22O8Qlx4UZNxXrBv5M7BCJa/ZuQpqCw046NGDECrKE53QkhrYK+yoictOqOxedLUFGis7lf5ibmRk1VdyympRcIuT0UdgghpAXpNEZcSlYh/XgBVBlqm6UY+EIeAjp7IDTWC6FxXvAOdAGPOhYTcsco7BBCSDOzmC3ISi3BxcN5yDhTZDNyykMpt3YsDoryhEhCc94Q4mgUdgghpJmUqipx4VAe0o6qUFVeO3rKJ8QFXQcGIKKnD9y8ZU6skJCOgcIOIYQ4kF5rQvrxfFw4lIf8DLV1v9RFhKj+SsQMCoBPsKsTKySk46GwQwghd4hZGHIuleLioTxcTSm0rhzO4/MQ1s0bMQMDENbdGwIhjZ4ixBko7BBCyG1SF2lx4XAe0g6rbEZSeQYoEDMwAFHxSijcJU6skBACUNghhBC7GPVmXEkpwMVDeci9VGbdL5YJEdlPiZiBAfALd6UJ/gipZjToodNUQCKTQyxzzrJHFHYIIeQWGGNQXSnHhcN5SD9RAKOuerI/HhDS1RNdBwWgU09fCGn1cNJOWSxm6KuqoNNUQK/RQKepgLZSY72uq6yATlNZva2ATqOBrpK7z2w0AgDGz30RccNHO6V+CjuEENIITakOF4+ocPFwHsoLtNb9br4yxAz0R/SAAFpBnLQpRoO+NqDUCSRcYKmsc11TfT93W19VBdzBJLU8Ph9Gvd6B78Q+FHYIIaQOk9GMjNNFuHgoD9kXSqx/34USAbr08UXMoAAEdPGg01TE6SxmM6rU5agsK0VVWSk0ZSWoKiuDtkJdL8joq8OLyWi49RPfhEgqg1ThAqmLC6QurjbXJQoXyFxcIXVxgUTB7au5LZI6d+02Cjvt1LJly7Bp0yZcvHgRMpkMgwYNwvvvv4/o6Ghnl0ZIq8MYQ2FWBS4cysPl5Hzoq0zW+wIjPdB1YAA69/GFWEp/MknzYozBoNXeEGBKUVlWisqyMlSWlVRfL4VWrQZjFrtfg8fjVwcUF0gVrjZhhQsotWHFNtC4QCAUNcO7bn6t/l9uRUUF3njjDWzevBkFBQXo3bs3Pv74Y/Tr1w8A94uxePFirFq1CqWlpYiPj8dnn32GuLg4J1fuXPv27cPzzz+Pfv36wWQyYeHChRg7dixSU1OhUCicXR4hrUKV2oBLx1S4cCgPJdcrrftdPCXoOjAAXQf6w93XOR0qSftiNplQpS5DVVkZNKUl1jBTWV6KytLqMFPObU12nO7h8fiQu7tD7uEJhYcnFO6ekLm5VQeUG8MKF2zEUhl4/I41DUKrDztPPfUUzp07h++++w6BgYFYv349xowZg9TUVAQFBWH58uX48MMPsWbNGkRFRWHJkiVISEhAWloaXF077sRdO3futLm9evVq+Pn54cSJExg2bJiTqiLE+cxmCzLPFuPi4Txkni2GpXptKoGIj069fBEzMABBXT3BpzWpyC0wxqCvqqzTClNa2wpTWoLK8rLaVpgKtV19XkRSGVw8PSF3rw4xnlyQUdSEGk8vKDy4YMPnU8f4W2nVYUer1eLXX3/Fb7/9Zv2CXrRoEbZs2YKVK1finXfewYoVK7Bw4UJMnz4dALB27VoolUps2LABzzzzjMNrYoxBa9Le+sBmIBPe/jnP8vJyAICXl5cjSyKkzSjO1eDC4TxcOqqCtsJo3e8X7oaYQQGI7OsHibxtNtGT5sEYg65Sg/J8FcoL8lFeoKq+5ENdmI+KoiK7+sDw+Hwo3D1qW2E8PKHw8ILCw6POdU/IPTwgltIyIo7UqsOOyWSC2WyGVGo72kEmk+HgwYPIyMiASqXC2LFjrfdJJBIMHz4chw4dajTs6PV66Os0E6rV6gaPa4jWpEX8hng734ljHJ1xFHKR/U3qjDHMnz8fQ4YMQbdu3ZqhMkJaJ7PRgguH85B68DoKsyqs+2VuYkTH+yNmYAC8Aum0bkdmNOihLihAeaGqTqjJt4Yag7bqls8hlsmrW1o8uNaX6lYX7rSSh/W21NWVWmGcpFWHHVdXVwwcOBDvvPMOYmJioFQqsXHjRhw9ehSRkZFQqVQAAKVSafM4pVKJzMzMRp932bJlWLx4cbPW3pr84x//wJkzZ3Dw4EFnl0JIi7CYLbh4RIXk3zOgKeH+Y8MX8BDewwcxAwMQEucFgaBj9VnoqCwWMzQlxVyAyVehvDDf5nplacktn0Pu7gF3pT/cfZVw9/OHu1IJd19/uPn6QeHhAZGEph9o7Vp12AGA7777Dk888QSCgoIgEAjQp08fzJgxAydPnrQec+OpHcbYTU/3vPbaa5g/f771tlqtRkhISJPqkQllODrjqJ3vwjFkQvubNf/5z39i69at2L9/P4KDg5uhKkJaD2ZhuHwiH8e2ZVjnxVG4i9ErIRTR8f6QuYqdXCFxNMYYdJqKBoNMeYEK6sJCWMymmz6HWCbjgozSH+5+Srj5clsPJRdoKMy0fa0+7HTu3Bn79u1DZWUl1Go1AgIC8OCDDyIiIgL+/v4AAJVKhYCAAOtjCgoK6rX21CWRSCCR3N56NTwe77ZOJbU0xhj++c9/YvPmzdi7dy8iIiKcXRIhzYYxhozTRTi69ap1VJXURYS7xoeh27Agmtm4jeNONXFBpixfBXWhyibUGLQ370fJFwjh5uvLtcrUCTU116UutLxHe9fqw04NhUIBhUKB0tJS/Pnnn1i+fLk18CQmJqJ3794AAIPBgH379uH99993csXO9fzzz2PDhg347bff4Orqaj3l5+7uDpmMOr6R9oExhuwLJTj621UUZHJ9csQyIXonhKDHqBCaF6eNYRYLygpUKMq8hsKsDBRmXkNR9jWU5atuOZJJ4enFhRk/ZfWl9rqLlzf1lengWv1fgj///BOMMURHRyM9PR2vvPIKoqOjMXv2bPB4PMybNw9Lly5FZGQkIiMjsXTpUsjlcsyYMcPZpTvVypUrAQAjRoyw2b969WrMmjWr5QsixMGuXy7Dkd+uIC+dG2kolAjQc2QweiWEQqqgUVWtnbZCjaKsayjMykRRVgYKs66hKDuz0TlmJHIF3Oq0xtQNNW6+fhCJaXV50rhWH3bKy8vx2muvIScnB15eXrj33nvx7rvvQiTi/pgtWLAAWq0Wc+fOtU4quGvXrg49xw7A/Y+XkPaoIFONo79dRVYq17FUIOSj27Ag9BkfBrkb9clpbcwmI0qu56IoszrQZF1DYdY1aEqKGzxeKBLDOyQUPqHh8A2NgE9oGHxDwyF392jZwkm7wmP0rQi1Wg13d3eUl5fDzc3N5j6dToeMjAxERETUGwLf3nSk90ranuJcDY5ty8DVU4UAAD6fh66DA9BvYjhcPOn31dkYY9CUFlefgqoNNSW5OY12EHb3U1aHmnD4hEbANywcHv4BdMqJNNnNvr/ravUtO4SQjq2soArHtmXg8vF8gAHgAdH9/dFvcjgt5eAkRp0ORTmZXJ+aOsFGp6lo8HixTA7fsOpAExoGn9AI+ISEQSKnnx9pGRR2CCGtUkWJDsd/z8CFwyqw6iUdOvfxRf/JnWgiwBZi22G4JtRkNNphmMfjwzMwCL6h4fANi7C22rj6+NJoJ+JUFHYIIa1KZbkeJ3Zm4vyBXFhM3BdqWDdvxN/dCb6hHbsvXnMym4zIv5oO1ZV0FGVloCgrE0XZmTDqdQ0eL3f3sIaZmmDjHRQCoZj6TZHWh8IOIaRV0FUakbIrE2f25MBksAAAgqI8EH93JwR08XBuce2QyWBAXnoaci6cQ07qOVy/dBEmQ/2RUAKRCN7BoXU6C3NbhYenE6om5PZQ2CGEOJVBa8Kpv7JxOikLBp0ZAKCMcEP81E4Ijvak0x8OYtTpcP3SReRc5MJNXnoazEajzTFSVzcERkbDN6yTNdh4BgSCL6AOw6Rto7BDCHEKo8GMs3tzkPJnFnSV3Jeud5AL4qd2Qnh3bwo5d0hfVYXrly4gJ/Ussi+cQ/6Vy7CYzTbHyN09EBzbHcExcQiJ6Qbv4FDw+LRmGGl/KOwQQlqU2WjB+YPXceKPa6hSGwAAHko5+k+JQJc+fuDxKeTcDp1Gg5yL562npQoyroAxi80xLt4+CInphuCYbgiO7QbPgCAKlaRDoLBDCGkRDa1E7uotRb9JEYiOV4JPq5DbpUpdzgWb6nBTmHWt3ggpdz8lgmO4lpvg2O5w91NSuCEdEoUdQkizYhaG9BMFOLY9A2X5VQAAubsYfSeEI3ZIIARCCjlNoSktsQabnAvnUJyTVe8Yz4AgBMd2Q0hMNwTFdIObj68TKiWk9aGw006tXLkSK1euxLVr1wAAcXFxePPNNzFhwgTnFkY6jJqVyI9tu4ri3NqVyPuMC0P34bQS+a2oiwqQc+E8clLPIufCOZTmXa93jHdwKIJjuyMkthuCusbBxdPLCZUS0vpR2GmngoOD8d5776FLly4AgLVr12Lq1KlISUlBXFyck6sj7RljDDkXSnFk61UUXFMDAMRSAXolhKLnaFqJvCGMMZTnq6ynpbJTz0FdmG97EI8H37AIrs9NdbiRu7k7p2BC2hj6q9NOTZkyxeb2u+++i5UrV+LIkSMUdkizuZ5ehqO/XcX1y2UAAKGYjx6jQtCbViK3wRhDaV4uclLPIbu65ebGhTF5fD6UEZ2rR0t1Q1B0LKQuLk6qmJC2jcKOnRhjYFqtU16bJ5PdVudCs9mMn3/+GZWVlRg4cGAzVEY6Ol2lEfs3puHy8QIAtBJ5QxhjKMrOxKXDB5B2+CBK83Jt7ucLhPDvHGntcxMYHQOxjNaOIsQRKOzYiWm1SOtzl1NeO/rkCfDsWDjv7NmzGDhwIHQ6HVxcXLB582bExsY2Y4WkI8pNK0XSmlRoSvXg8XmIGRyAvhPC4epFK5EDQHFOFtKqA05JbrZ1v0AoREBUV+toqcCorhBJ6DMjpDlQ2GnHoqOjcerUKZSVleHXX3/FzJkzsW/fPgo8xCHMJguObcvAyV2ZAAPc/WRIeCIOynA3Z5fmdMW52bh0+CDSDh+wGTUlEAoR3usuRA8Ygk53xdOq34S0EAo7duLJZIg+ecJpr20PsVhs7aDct29fJCcn4+OPP8aXX37ZHOWRDqRUVYnEb1NRmFUBAIgdHIDB90d26M7HJddzuVNURw6iKOuadT9fIER4z96IHjgUnfvGQyKnFdsJaWkd9y/TbeLxeHadSmpNGGPQ6+sv9EdIUzHGcP7Adfz982WYjBZIFEKMejQGnXp3zPlcSlXXrS04hZkZ1v18gQBhPWoDjlRBHYsJcSYKO+3Uf/7zH0yYMAEhISGoqKjADz/8gL1792Lnzp3OLo20UdoKA3Z/dxHXzhQBAIK7emLMrFgoPCROrqxllanykHbkIC4dPoiCa1es+/kCAUK790L0gCHo3G8AZC6uTqySEFIXhZ12Kj8/H4899hjy8vLg7u6OHj16YOfOnUhISHB2aaQNyjpfjL/WXkCV2gC+kIeB93RGz1EhHWYdq/ICFdIOH8SlIweRfzXdup/H5yO0W09EDxyKLv0GQOZK/ZUIaY0o7LRT33zzjbNLIO2AyWjG4c1XcGZ3DgDAM0CBsU/Gwie4/bdaqAsLcOnIQaQdOQhV+iXrfh6Pj5BuPRA9cAi69BtIE/sR0gZQ2CGENKg4V4Nd35xHyXVuqYfuI4IxaHrndr3Mg7qoEJeP/o20wweQdznNup/H4yMkrhuiBgxFZP+BkLt7OK9IQojdKOwQQmwwC8OZPTk4vPkKzCYLZK4ijHo8BuHdfZxdWrOoKCnC5SN/I+3wQVy/dKH2Dh4PwTFxiB4wFJHxg6Dw8HRekYSQO0JhhxBiVVmux+61F5CVWgIACOvujVGPxbS7WZA1JcW4dPQQLh05gNyLqbV38HgIio5F9MAhiIwfTAtrEtJOUNghhAAArp4qxJ71F6HTGCEQ8THkvi6IGxZ0W0uUtEaVZaW4dPRvXDp8EDkXzwOMWe8LtAacQXD1ap8tWIR0ZBR2COngjHoz/v7lMs4fuA4A8AlxQcITcfAKaPuT3xkNelw4sAcXD+5D9oVzNgEnIKorogcMRdSAwXD1poBDSHtGYYeQDqwgU43Eb1NRll8FAOiVEIoBd3eCQMR3cmV3RluhxqldvyNl53Zo1eXW/QFdohE1cAiiBgyGm4+fEyskhLQkCjuEdEAWC8OpxCwc/e0qLBYGhYcEo2fFIKRr2+6jUl6QjxM7tuDs7l0wVc8W7ubrh54JExE9cCjc/ZROrpAQ4gwUdgjpYCpKdEhanYrrl8sAAJ17+2LEo10hVYicW9gdKLh2Fclbf0Xa4QNgFgsAwDe8E/rdfS+iBwwBX9B+h8sTQm6Nwg4hHcjl4/nYtyEN+ioThBIBhj0Yia4DA9pkJ2TGGLLOnUby1l+ReSbFuj+0ey/0u/tehHXv1SbfFyHE8SjsENIBGLQm7P/xEtKOqAAAfuFuSHgiFh5+bW9RW4vZjEtHDiJ52yYUZHBrU/F4fEQNHIJ+U6ZD2amLkyskhLQ2rboXoslkwuuvv46IiAjIZDJ06tQJb7/9NizVzdQA97+7RYsWITAwEDKZDCNGjMD58+edWHXrtGzZMvB4PMybN8/ZpZAWprpajh/fPYa0IyrweEDfieGY/kqfNhd0jDodUnZuwzcvPI3f/+8DFGRcgVAsQe/xU/Dk/63C5BcWUNAhhDSoVbfsvP/++/jiiy+wdu1axMXF4fjx45g9ezbc3d3xwgsvAACWL1+ODz/8EGvWrEFUVBSWLFmChIQEpKWlwdW1/a/f0xTJyclYtWoVevTo4exSSAuymC04vuMajv+RCWZhcPWWYszsWAR28XB2aXapUpfj1J/bkfLn79BVqAEAMlc39B4/BT3HTqS1qQght9Sqw87hw4cxdepUTJo0CQAQHh6OjRs34vjx4wC4Vp0VK1Zg4cKFmD59OgBg7dq1UCqV2LBhA5555hmn1d5aaDQaPPLII/jqq6+wZMkSZ5dDWkh5oRZJq89DdZULB1HxSgx7KBoSWav+J2+jLF+FE79vxrk9STAZuJFV7kp/9J00DXEjRkMkkTq5QkJIW9Gq//INGTIEX3zxBS5duoSoqCicPn0aBw8exIoVKwAAGRkZUKlUGDt2rPUxEokEw4cPx6FDh5ol7DDGYDJYbn1gMxCK+XZ3uHz++ecxadIkjBkzhsJOB8AYQ9pRFfZvvASj3gyxVIDhM6IR1d/f2aU1Wf7VdBzb+isuH/kbjHH/1pSduqDf3fcisv8gGllFCLFbqw47r776KsrLy9G1a1cIBAKYzWa8++67ePjhhwEAKhXX2VKptJ07Q6lUIjMzs9Hn1ev10FfPwQEAarW6yTWZDBasemGfPW/DYZ7+eDhEkqb/of/hhx9w8uRJJCcnN2NVpLXQVRqxb0Ma0k8UAAACurhjzOxYuHnLnFzZrTHGkHn6JJK3/Yqsc2es+8N73YV+U+5FSFx3GllFCLltrTrs/Pjjj1i/fj02bNiAuLg4nDp1CvPmzUNgYCBmzpxpPe7GP4KMsZv+YVy2bBkWL17cbHW3BtnZ2XjhhRewa9cuSKXU3N/e5aaVImlNKjSlevD5PPSbEoE+48LA57fugGA2mXDp8AEkb/0VhVnXAAA8Ph9dBw9H38nT4BfeybkFEkLaBR5jdRaLaWVCQkLw73//G88//7x135IlS7B+/XpcvHgRV69eRefOnXHy5En07t3beszUqVPh4eGBtWvXNvi8DbXshISEoLy8HG5ubjbH6nQ6ZGRkICIiAlKptM2cxtqyZQumTZsGQZ0mf7PZDB6PBz6fD71eb3MfUP+9ktbPbLLg2LarOLkrC2CAu58MCU/EQRnudusHO5FBp8XZv3bhxI4tqCgqBACIJFJ0Hz0Od02cCjdfWsqBEHJrarUa7u7uDX5/19WqW3aqqqrA59uOjhcIBNah5xEREfD390diYqI17BgMBuzbtw/vv/9+o88rkUggkUhuqyYej2fXqSRnGT16NM6ePWuzb/bs2ejatSteffXVekGHtD2lqkokfpuKwqwKAEDs4AAMvj8SYmnr/WddWVaKlJ3bcXrX79BVagAAcncP68gqmQuNoCSEOF7r/asIYMqUKXj33XcRGhqKuLg4pKSk4MMPP8QTTzwBANZ5Y5YuXYrIyEhERkZi6dKlkMvlmDFjhpOrdy5XV1d069bNZp9CoYC3t3e9/aRtYYwh9eB1HPzpMkxGCyQKIUY9GoNOvX2dXVqjSvNycXz7Zpzf9xfMRiMAwDMgEH0nT0fssFEQisVOrpAQ0p616rDzySef4I033sDcuXNRUFCAwMBAPPPMM3jzzTetxyxYsABarRZz585FaWkp4uPjsWvXLppjh7RLjDEc3nQFKYlZAIDgrp4YMysWCo/ba6lsbnnpaUje+isuHzsMVJ8x9+8Shf5334fO/eLB51MLIyGk+bXqPjst5Wbn/DpSP5aO9F7bIsYYDvx0GWf35AAABtzTCX3GhoHXyjohM8aQceo4krf+ipzUc9b9nfr0Q78p9yIoJo5GVhFCHKJd9NkhhHCYhWHvxjSkHrgO8IARM6IRNzTI2WXZMJtMuPj3PhzftglF2dzUD3yBEDFDRqDvlGnwCQlzcoWEkI6Kwg4hrZzFwrBn3QVcrF7batTjMeg6MMDZZdnIvZiKxK8+RXEOd3pNLJOhx5gJ6DPhbrh6+zi5OkJIR0dhh5BWzGy24K/Vqbh8vAA8Pg8Js2MR2U956we2EF2lBgc2rMGZpJ0AuDWr+k6Zjh5jxkOqcHFydYQQwqGwQ0grZTZZsOub87iaUgi+gIdxT3VrNSOuGGO4dORv7FnzJSrLSgEA3UaOxbBHZ9PwcUJIq0Nhh5BWyGQ0Y+eqc8g8WwyBkI/xz3RDePfWcTpIXViAv75diasnuWVIPAODkTDneYTEdndyZYQQ0jAKO4S0MkaDGX98cRbZqSUQiviY8Fx3hMZ6O7ssWMxmnPxjK/7+aT1Mej34AiHip92P/vc8AKFI5OzyCCGkURR2CGlFDDoTdnx+BrmXyiCUCDB5bg8ERXs6uyzkX03HrlWfoCDjCgAgqGscEub8A97BIU6ujBBCbo3CDiGthF5rwvZPTkN1tRxiqQCT/9kLAZ3dnVqTQafFoZ++x8kdW8GYBRKFAsMemY3uI8eCd8NSLoQQ0lpR2CGkFdBVGrHt/06hILMCErkQU/7Vy+mLeV49mYykbz63LtQZPWgYRs6cA4WH81uaCCHEHhR2CHEyrcaArR+fQlG2BlKFCHfP6wXfEOeNaKosK8XuNatw6fABAICbrx/GPDkXEb37Oq0mQgi5E9QO3U4tWrQIPB7P5uLv7+/sssgNqtQGbPkwBUXZGsjcxLhnfm+nBR1mseBM0k6sfvFZXDp8ADw+H32nTMes/35OQYcQ0qZRy047FhcXh6SkJOttgYAWXWxNNKV6/LYiBWX5VVC4izH1xd7w9Fc4pZbinCzsWvUprqelAgCUnbog4el/QhnR2Sn1EEKII1HYsRNjDCa93imvLZRI7FpAUSgUUmtOK6Uu1uK3j1KgLtLBxUuCe17sDXdfeYvXYTIYcHTLTzi25RdYzCaIJFIMeegx9Bo/mVYkJ4S0GxR27GTS6/F/M+9zymv/a+0vENmxGvnly5cRGBgIiUSC+Ph4LF26FJ06dWrGCklTlBdWYctHKdCU6OHmI8XUF3vDzVvW4nVknTuDpK8/Q2leLgBuVfLRTz4HNx+/Fq+FEEKaE4Wddio+Ph7r1q1DVFQU8vPzsWTJEgwaNAjnz5+Ht7fzJ6jrqEpVlfjtoxRUlhvgoZRj6rzecPGUtGgN2go19q3/Fuf3cqc4FZ5eGDXraUTGD7ar5ZAQQtoKCjt2Ekok+NfaX5z22k01YcIE6/Xu3btj4MCB6Ny5M9auXYv58+c3R3nkFopzNfjt41PQqg3wClTg7hd6QeHeckGHMYYLB/di79qvoK1QAwB6JkzE0BkzIZE7p68QIYS0BAo7duLxeHadSmotFAoFunfvjsuXLzu7lA6pMKsCWz8+BV2lET4hLrj7hV6QuYhb7PXLVHlI+uZzZJ5JAQB4B4ci4el/Iig6psVqIIQQZ7mjsKPX6yGxo7WBOI9er8eFCxcwdOhQZ5fS4eRfU2Pb/52CvsoEvzBXTPlXL0gVLbOWlNlkwonft+DwzxtgMhogEIkwYPpD6Hf3dAiEtJ4VIaRjsCvs/Pnnn9i4cSMOHDiArKwsWCwWyOVy9OnTB2PHjsXs2bMRGBjYXLUSO7z88suYMmUKQkNDUVBQgCVLlkCtVmPmzJnOLq1DyUsvw7ZPT8OoM8O/kzsm/7MnJLKWaVDNu5yGxFWfoDDrGgAgtFsPjHnqeXgGBLXI6xNCSGvRpL+6W7Zswauvvory8nJMnDgRr7zyCoKCgiCTyVBSUoJz584hKSkJ77zzDmbNmoV33nkHvr6+zV07uYmcnBw8/PDDKCoqgq+vLwYMGIAjR44gLCzM2aV1GLlppdj++RmY9GYERXlg4tweEEubP+joq6pw8Id1OLXrd4AxSF3dMOKxJxE7bBR1QCaEdEg8xhi71UH9+/fHG2+8gUmTJoF/k8X/cnNz8fHHH0OpVOKll15yaKHNSa1Ww93dHeXl5XBzs12PSKfTISMjAxEREZC2wb469uhI77W5ZaUWY8fKszAbLQiJ8cSE53pAJG7+eWsuJx/G7m+/gKakGAAQO2wUhj/2JORuzl1QlBBCmsPNvr/ratJ/M48dO9akFw0KCsLy5cubViEh7dS1M0X4Y9VZWEwMYd29Mf7pbhCKmjfoVBQXYffqL5CefAQA4KEMwJinnkdYj17N+rqEENIW2NWmrlar4eLiUq91x2w2o7Ky8qapipCO4EpKAXZ9fR4WM0OnXr4Y+1QcBMLmW4LOYjHj1J878PeP62DQasEXCNDv7nsRP/1BiMQ0eIAQQgA7FgLdvHkz+vbtC51OV+8+vV6Pfv36Ydu2bQ4tjpC25HJyPv78igs6kX39MHZO8wadwswMbHzjFexZ8yUMWi0CIqPx6HsfY8hDj1PQIYSQOprcsrNy5UosWLAAcnn99XvkcjleffVVfPrpp5gyZYpDCySkLbh4OA+7110AY0DXAf4Y+XgM+Pzm6QxsNplw6Kf1SN62CcxigVgmx9CHZ6JnwgTwbtKnjhBCOqom/2U8d+4cRowY0ej9w4YNw9mzZx1RU6vUhH7cbV5HeI/N4fyBXPxVHXRihwRiVDMGHV2lBpuWvYVjv/0CZrEgsv8gzPrwc/QaN4mCDiGENKLJLTulpaUwmUyN3m80GlFaWuqQoloTkYibeK2qqgoyWcsv1tiSqqqqANS+Z3JrZ/Zk48CP3KzU3UcGY+gDkc02vLu8IB+b31+M4pwsiKQyjJ87D1Hxg5vltQghpD1pctgJDw/H8ePH0bVr1wbvP378eLucw0UgEMDDwwMFBQUAuFN27W2uEsYYqqqqUFBQAA8PDwgEzT9Euj1I2ZWFQ5vSAQC9EkIxaHrnZvvdyEtPw5bl76CqvAwuXt6Y9upb8AunFewJIaQpmhx2pk+fjoULFyIhIQFKpdLmPpVKhddffx2PPvqowwtsDfz9/QHAGnjaKw8PD+t7JTd3fEcGjm7NAAD0nRiO/lMimi3oXD52CDs++R9MBj18wyIw7dW34Ort0yyvRQgh7VGTJhUEgIqKCgwcOBBZWVl49NFHER0dDR6PhwsXLuD7779HSEgIjhw5AldX1+au2eGaOimR2WyG0WhswcpajkgkohadJmCM4ejWqzjxRyYAIP7uCPSdGNFsr3Vi+2bs+341wBgievfF5BcWQCyrP0iAEEI6IodOKggArq6u+Pvvv/Haa6/hxx9/tPbP8fT0xKOPPoqlS5e2yaBjD4FAQIGgA2OM4dCmKziVmAUAGDS9C3qPDW2W17KYzdi9+kucTtwBAOg5dhJGzXoafPr9I4QQu9k1fMPd3R2ff/45ioqKkJ+fD5VKhaKiInz++efw8PBolgLDw8PB4/HqXZ5//nkA3BfQokWLEBgYCJlMhhEjRuD8+fPNUgvpuJiF4cCPl61BZ+iDUc0WdAzaKmxZ/jYXdHg8DH/sSYx+4lkKOoQQcptua6wqj8eDr68v/Pz8mr2zbnJyMvLy8qyXxMREAMD9998PAFi+fDk+/PBDfPrpp0hOToa/vz8SEhJQUVHRrHWRjoNZGPZuSMPZvTkADxjxSDR6jAxulteqKC7CD2+9ioxTJyAUS3D3/NfQd/K0dtcpnhBCWlKTw05BQQGefvppPPTQQy3acuLr6wt/f3/rZfv27ejcuTOGDx8OxhhWrFiBhQsXYvr06ejWrRvWrl2LqqoqbNiwocVqJO2XxcLw17oLSD14HTweMPrxGMQNDWqW1yq4dhUbFs5HYWYG5O4eeOCtpYjsP6hZXosQQjqSJoed2bNnw9/fH9OmTcOECROcMgGdwWDA+vXr8cQTT4DH4yEjIwMqlQpjx461HiORSDB8+HAcOnSo0efR6/VQq9U2F0JuxBjDnnUXkHZEBR6fh4Qn4tB1YECzvNbVlGT88OYCaEpL4B0cihlL/oeALtHN8lqEENLRNDnspKSk4MEHH8QDDzwAlUqFwsLC5qyrQVu2bEFZWRlmzZoFgBvyDqDeUHilUmm9ryHLli2Du7u79RISEtJsNZO26/z+XFw8ogKfz8O4OXGI7Ke89YNuw6k/f8eW99+BUa9DaLeeeOjt5XD3a57XIoSQjqjJo7HuuecevPbaawgLC0OPHj3g5+fXnHU16JtvvsGECRMQGBhos//G/gyMsZv2cXjttdcwf/586221Wk2Bh9gozKrAgZ+5mZEHTOuMzr0d//tusZixf/1qnPh9CwAgbsQYJMx5HgIhzWBNCCGO1OSw8+mnn2Ljxo0oKyvDu+++25w1NSgzMxNJSUnYtGmTdV/NBHgqlQoBAbWnFwoKCuq19tQlkUggkdCq0KRh+iojdq46C4uJIaKnD3qNcXwQNup12PHJ/5CefBgAMOShx9H/nvupIzIhhDSDJocdPp+PRx55pDlruanVq1fDz88PkyZNsu6LiIiAv78/EhMT0bt3bwBcv559+/bh/fffd1appA1jjGH3uotQF+ng6i3FqMdjHB5AKstKsWX521BduQyBUIhxc19EzODhDn0NQgghtZocdpzJYrFg9erVmDlzJoTC2pJ5PB7mzZuHpUuXIjIyEpGRkVi6dCnkcjlmzJjhxIpJW3Vmdw6unioEX8DDuDndIFU49pRSUXYmNr+/GOrCAkhd3TD15YUI7hrn0NcghBBiq0lhZ/z48XjzzTcxaNDNh8FWVFTg888/h4uLi3XSP0dISkpCVlYWnnjiiXr3LViwAFqtFnPnzkVpaSni4+Oxa9eudj+bM3E81dVyHPqVW9hz8H2RUIY3PvX47cg8ewrbPlwGfVUlPAMCMe3Vt+AZ0DzD2AkhhNRq0tpY33zzDd566y24urri7rvvRt++fREYGAipVIrS0lKkpqbi4MGD2LFjByZPnowPPvigTXX4beraGqT90lUa8eO7x6Ap0aNzHz+MmxPn0NNXZ/fsQtJXn8FiNiOoayymvvw6ZK70u0YIIXeiqd/fTV4I1GAw4JdffsGPP/6IAwcOoKysjHsCHg+xsbEYN24c5syZg+jotjc3CIWdjo1ZGHasPINrZ4vh7ivDA//pB7HMMWd4mcWCv39aj6ObfwIAdB08HOOefQFCsdghz08IIR2Zw8POjcrLy6HVauHt7Q2RqG0PlaWw07Gd/DMThzdfgUDIx72v3gXfEMecAjUZDNi5cgXSDu0HAAy49yEMuv8RGnFFCCEO4vBVz29UMyEfIW3Z9fQyHPntKgBg6IORDgs6Vepy/Pbfd3E9LRV8gQAJT/8T3UaMcchzE0IIsU+bGI1FSHPQVhiw66tzYBaGqP5KxA4JvPWDmqA0Lxeb3luEMlUeJHIF7n7pPwjt1tMhz00IIcR+FHZIh8QsDImrU1FZboCnvxzDZ0Q75PRSzoVz+O2/70KnqYCbrxLT/70I3sFtp7M+IYS0RxR2SId0Yuc1ZKeWQCjiY9ycbhBL7/yfwoWDe/HnyhUwm0zw7xKFe155AwoPTwdUSwgh5E5Q2CEdTk5aKY5tywAADJ8RDe8glzt6PsYYjm76EX//tB4AEBk/CBOenw+RRHrHtRJCCLlztxV2ysrK8Msvv+DKlSt45ZVX4OXlhZMnT0KpVCIoiCZJI61XZbkeu745D8aAmEEB6Dow4NYPugmzyYjEVZ/h/L4kAEDfKdMxbMYs8Ph8R5RLCCHEAewOO2fOnMGYMWPg7u6Oa9euYc6cOfDy8sLmzZuRmZmJdevWNUedhNwxi4Uh8Zvz0KoN8ApUYOhDUXf0fLpKDbZ9uBRZ586Ax+dj9BPPomfCRAdVSwghxFHs/u/n/PnzMWvWLFy+fBlSaW0z/YQJE7B//36HFkeIIyVvz0DupTKIJAKMf7obRGLBbT9XeYEKG994BVnnzkAklWHaq29R0CGEkFbK7pad5ORkfPnll/X2BwUFQaVSOaQoQhwt63wxjv9xDQAw4tFoePorbvu58i6nYfPyt6FVl8PFyxvTXn0LfuGdHFQpIYQQR7M77EilUqjV6nr709LS4Ovr65CiCHEkTakOiatTAQbEDQtCVD//236uS0f/xh+f/A8mowF+4Z1xz6tvwNXLx4HVEkIIcTS7T2NNnToVb7/9NoxGIwBubaysrCz8+9//xr333uvwAgm5E2azBbu+Pg+dxgifEBcMub/LbT0PYwzJ2zZh20fvwWQ0oFOffnhw8XsUdAghpA2wO+z897//RWFhIfz8/KDVajF8+HB06dIFrq6uePfdd5ujRkJu29HfriLvSjnEUq6fjlBkfz8di9mMpK8/w/713wKMode4SZj68usQS2XNUDEhhBBHs/s0lpubGw4ePIjdu3fj5MmTsFgs6NOnD8aMoXV/SOty7UwRUnZlAQBGPR4Dd1+53c+hr6rC9o/fx7VTJwAeDyMeewp9Jt5Ni3kSQkgbYlfYMZlMkEqlOHXqFEaNGoVRo0Y1V12E3BF1sRZJa1IBAD1GBaNzHz+7n8Oo1+GXd1+HKv0ShGIJJv7rZUT2G+joUgkhhDQzu8KOUChEWFgYzGZzc9VDyB0zmyz486vz0FeZ4BfuhkHT7e+nwywW/PHph1ClX4LU1Q33vrYY/p0jm6FaQgghzc3uPjuvv/46XnvtNZSUlDRHPYTcsUOb0lFwTQ2JXIhxT8VBILR/NuMDP6zD5WOHIBAKMfXlhRR0CCGkDbO7z87//d//IT09HYGBgQgLC4NCYTtfycmTJx1WHCH2upJSgDO7cwAAY2bFws3H/k7EZ/fsQvJvvwAAxj77AoK7xjm0RkIIIS3L7rBzzz33NEMZhNy58sIq7F57AQDQOyEU4T3sHxaede4Mkr76DAAw4N6HETt0pENrJIQQ0vLsDjtvvfVWc9RByB0xGc3YueocDDozAjq7I/4e+2c0Lrmeg60fvguL2YzoQcMw6P4ZzVApIYSQlkZLM5N24e+f01GUrYHURYSxT8VBILDvV7tKXY7N7y2GvrISAVFdMf65eTS8nBBC2gm7W3b4fP5NvwRopBZpaZeSVTi3PxfgAWNmx8LFU3rrB9VhMhqx9X9LUZafBzdfJe55+XUIxeJmqpYQQkhLszvsbN682ea20WhESkoK1q5di8WLFzusMEKaolRVib3r0wAAfSeEIyzO267HM8aQ+OX/IffieYhlckz/91uQu3s0Q6WEEEKcxe6wM3Xq1Hr77rvvPsTFxeHHH3/Ek08+6ZDCCLkVo8GMP786B6PejKAoD/SbHGH3cxzd/BNSD+wBj8/HlPmvwTs4tBkqJYQQ4kwO67MTHx+PpKQkRz0dIbd04IdLKM6thMxNjIQn48Dn29fH5uKh/fj7x+8AAKOfeA7hPXo3R5mEEEKczCFhR6vV4pNPPkFwcLAjno6QW7p4OA8XDuWBxwPGPhkHhbvErsdfv3QROz//CABw16R70DNhQnOUSQghpBWw+zSWp6enTQdlxhgqKiogl8uxfv16hxZHSEOKczXYt4Hrp9NvcgSCoz3tenx5QT5+++8SmI1GdO4bj2GPzm6OMgkhhLQSdoedjz76yCbs8Pl8+Pr6Ij4+Hp6e9n3pEGIvg86EP786B5PRgpBYL/SdEG7X4/VVldj8/mJUlZfBN7wTJv7zZfD5guYplhBCSKtgd9gZNWoUQkJCGhx+npWVhdBQ6uBJmgdjDPs2pKFUVQWFuxgJs2PBs6OfjsVsxraP3kNxThZcPL0wbcGbEEvtX06CEEJI22J3n52IiAgUFhbW219cXIyICPtHwxDSVKkHr+PSsXzw+DyMfaobZK5NnwuHMYbdq79E5pkUCCUS3LPgTbh627+cBCGEkLbH7rDDGGtwv0ajgVRq32RuTZGbm4tHH30U3t7ekMvl6NWrF06cOGFTz6JFixAYGAiZTIYRI0bg/PnzDq+DOFdhdgUO/HgZADBgaicERnrY9fiUP7bidOIOgMfDxH++DGWnLs1QJSGEkNaoyaex5s+fDwDg8Xh48803IZfLrfeZzWYcPXoUvXr1cmhxpaWlGDx4MEaOHIk//vgDfn5+uHLlCjw8PKzHLF++HB9++CHWrFmDqKgoLFmyBAkJCUhLS4Orq6tD6yHOYdCa8OeqczCbLAjr7o3eCfadKr1y4ij2rPsaADD8kdmI7DewOcokhBDSSjU57KSkpADgWlLOnj0LcZ3p9MViMXr27ImXX37ZocW9//77CAkJwerVq637wsPDrdcZY1ixYgUWLlyI6dOnAwDWrl0LpVKJDRs24JlnnnFoPaTlMcaw+7uLKC/UwsVLgjEz7eunU3DtKn7/+AOAMXQfPQ53TZ7WjNUSQghpjZocdvbs2QMAmD17Nj7++GO4ubk1W1E1tm7dinHjxuH+++/Hvn37EBQUhLlz52LOnDkAgIyMDKhUKowdO9b6GIlEguHDh+PQoUONhh29Xg+9Xm+9rVarm/eNkNt2bl8urpwsAF/Aw7g53SB1ETX5sZqSYmxe/jaMeh1Cu/XE6Ceeo8U9CSGkA7K7z87q1atbJOgAwNWrV7Fy5UpERkbizz//xLPPPot//etfWLduHQBApVIBAJRKpc3jlEql9b6GLFu2DO7u7tZLSEhI870JctsKMtU4+DPXT2fQ9C7wj3Bv8mONOh22fPAONMVF8AoMxpT5r0EgtHvwISGEkHbgtv76Jycn4+eff0ZWVhYMBoPNfZs2bXJIYQBgsVjQt29fLF26FADQu3dvnD9/HitXrsTjjz9uPe7G/60zxm76P/jXXnvN2gcJ4Fp2KPC0LrpKI3auOgeLmaFTL1/0GNX02bmZxYIdn/4X+VfTIXN1w7R/L4JU4dKM1RJCCGnN7G7Z+eGHHzB48GCkpqZi8+bNMBqNSE1Nxe7du+Hu3vT/eTdFQEAAYmNjbfbFxMQgKysLAODv7w8A9VpxCgoK6rX21CWRSODm5mZzIa0HYwy7111ARbEObj5SjHq8q12nnw5sXIv05CMQCIWY+vLr8FD6N2O1hBBCWju7w87SpUvx0UcfYfv27RCLxfj4449x4cIFPPDAAw6fUHDw4MFIS0uz2Xfp0iWEhYUB4Ob88ff3R2JiovV+g8GAffv2YdCgQQ6thbSc039lI+N0EfhCrp+ORN70fjpn/voTyVt/BQCMe24egrrG3uIRhBBC2ju7w86VK1cwadIkAFwLSWVlJXg8Hl588UWsWrXKocW9+OKLOHLkCJYuXYr09HRs2LABq1atwvPPPw+AO301b948LF26FJs3b8a5c+cwa9YsyOVyzJgxw6G1kJahulqOw5uuAACG3h8Jv7Cmt7plnj2Fv775HAAw8L4ZiBkyojlKJIQQ0sbY3WfHy8sLFRUVAICgoCCcO3cO3bt3R1lZGaqqqhxaXL9+/bB582a89tprePvttxEREYEVK1bgkUcesR6zYMECaLVazJ07F6WlpYiPj8euXbtojp02SKsx4M+vzsFiYejS1w9xw4Ka/Nji3Gxs+2gZLGYzug4ejoH3PdyMlRJCCGlLeKyxKZEbMWPGDPTt2xfz58/Hu+++i48//hhTp05FYmIi+vTp49AOyi1FrVbD3d0d5eXl1H/HSZiFYftnZ5B1vhgeSjnuf60vxNKmZfEqdTk2vP4SyvNVCIyKwf1vvAuhuOlLSRBCCGmbmvr9bXfLzqeffgqdTgeAG9UkEolw8OBBTJ8+HW+88cbtV0w6tHP7c5F1vhgCER/j5nRrctAxGY347b/vojxfBXc/Jaa+8joFHUIIITbsCjsmkwnbtm3DuHHjAAB8Ph8LFizAggULmqU40jHotSYc25YBABg0vTN8gps2TJwxhl1f/h+up6VCIldg2quLIHdz7IhAQgghbZ9dHZSFQiGee+45m9mHCblTJ/64Bl2lEZ7+cnSzo5/OkU0/4MKBPeDx+Zjy4mvwDqa5kgghhNRn92is+Ph46zpZhNwpdZEWZ3bnAOBmSeYLmvYreeHvfTj00/cAgDFPzUVYj17NVSIhhJA2zu4+O3PnzsVLL72EnJwc3HXXXVAoFDb39+jRw2HFkfbvyG9XYTZZEBTtibDu3k16TG7aBfy5cgUAoO+U6egxenwzVkgIIaStszvsPPjggwCAf/3rX9Z9PB7PukSD2Wx2XHWkXcvPUONycj7AAwbf26VJsySXF6jw23+XwGw0onPfARg6Y2YLVEoIIaQtszvsZGRkNEcdpINhjOHvX7lFPqPj/eEbeut5kXSVGmx6bzG06nL4hXfGpH++DD5f0NylEkIIaePsDjs1SzUQcicyThUhL70cAhEfA6Z2uuXxZpMJ21e8j5LcbLh4eeOeV9+ASCptgUoJIYS0dXZ3UAaA7777DoMHD0ZgYCAyMzMBACtWrMBvv/3m0OJI+2Q2WXBoUzoAoNeYELh43jy0MMawe/UXyDyTAqFEgnsWvAlXL5+WKJUQQkg7YHfYWblyJebPn4+JEyeirKzM2kfHw8MDK1ascHR9pB06fyAX5YVayFxF6DPu1i2FJ3f8hjNJOwEeD5P+tQDKiM4tUCUhhJD2wu6w88knn+Crr77CwoULIRDU9pfo27cvzp4969DiSPujrzIiefs1AED/KZ1uOVNy+vGj2PvdNwCAEY89iS5945u7REIIIe2M3WEnIyMDvXv3rre/ZgV0Qm7mxB+Z1gkEYwcH3PTY/Iwr2PF/HwCMoceY8egzcWoLVUkIIaQ9sTvsRERE4NSpU/X2//HHH4iNjXVETaSdUhdpcXpPNgBg0L03n0CwoqQIW5a/DaNeh7AevTFq9rNNGppOCCGE3Mju0VivvPIKnn/+eeh0OjDGcOzYMWzcuBHLli3D119/3Rw1knbiyJYrsJgYgrt6Iqxb4xMIGnU6bHn/HWhKiuEdHIrJ816FQGj3ryohhBAC4DbCzuzZs2EymbBgwQJUVVVhxowZCAoKwscff4yHHnqoOWok7YAqoxyXjxcAPK5Vp7FWGovFjN8/+S8Krl2BzM0d0159E1JF0xYGJYQQQhpyW/9dnjNnDubMmYOioiJYLBb4+fk5ui7SjjDGcOgXbqh51wH+8A1pfALBAxvW4srxIxCIRLjnldfh7uffUmUSQghpp2773EBBQQHS0tLA4/HA4/Hg6+vryLpIO3L1VCHyrpRDKOIj/u7GJxA8k7QTx7dtAgCMe24eAqNiWqpEQggh7ZjdHZTVajUee+wxBAYGYvjw4Rg2bBgCAwPx6KOPory8vDlqJG2Y2WTB4U1XAAC9EkIbnUAw58I5JH3zOQBg0AOPIGbw8BarkRBCSPtmd9h56qmncPToUfz+++8oKytDeXk5tm/fjuPHj2POnDnNUSNpw87tq55A0E2M3mNDGzzGZDRi15efgFks6Dp4OAZMp75fhBBCHMfu01i///47/vzzTwwZMsS6b9y4cfjqq68wfvx4hxZH2jZdpRHJO7iFY+OnRDQ6geCxLT+jNC8XCg9PjH7yORpiTgghxKHsbtnx9vaGu7t7vf3u7u7w9PR0SFGkfTixMxP6ShM8AxSIGdTwBIIl13NwbMtPAICRs56mkVeEEEIczu6w8/rrr2P+/PnIy8uz7lOpVHjllVfwxhtvOLQ40napi7Q4Uz2B4OBGJhBkjCHp689hNpkQ0esuRA0YUu8YQggh5E7ZfRpr5cqVSE9PR1hYGEJDuT4YWVlZkEgkKCwsxJdffmk99uTJk46rlLQph+tMIBga59XgMan7dyP7/BkIxRI6fUUIIa2dxQKY9YBJB5hqtoYbbtdsG9gXmQD4d3dK6XaHnXvuuacZyiDtiepqOdKrJxAcfF/DEwhWqcutC3wOvO9hmk+HEEKaijEuPBiqAGNl9bb6YqgCTNo6IaShINLI1txYcKlz/52Qe7WdsPPWW281Rx2knWCM4e+aCQQHBsAnuOEJBPd/vxq6CjV8QsJw16R7WrBCQghpARYzYKisDiCVtUHEWAkYtTcElTqB5ZbHVl+YxclvkAeIZIBQAgiltluBpIH9UsCr8XnWmtsdLTik0Whgsdh+4G5ubndUEGnbrqYUQnW1HEIxH/FTGv7Fzj5/Buf3JgEAEp7+B617RQhpHcxGQKcG9OXVW3X1tqLO9Tr36SsaDyVmfcvULJAAYjkgUlRvZYBIXj9sCMT1w0dDQaXetpH7+EKgDXU9sPtbJiMjA//4xz+wd+9e6HQ6637GGHg8Hsxms0MLJG2H2WTBoc11JxCU1DvGZDQi8Wtu8sCeCRNolmRCiGOY9HUCSvkNAUV9w30N7Vdzp38cjcfngohIdkMokQNiRfVW3sgxDRwrktW5LgcE9J/FprD7U3rkkUcAAN9++y2USiV1KiVW5/blQl0zgWBCwxMIJv/2C0qv50Du7oEhD89s4QoJIa2a2QhUFXOXyqLa61XFXEhpsNWleuvIlhSRApC6ARK3Rrbu3FbsckNgqRtgFLUtLPQ96XR2h50zZ87gxIkTiI6Obo56SBulqzQi+febTyBYcj0XRzf/CIDm1CGk3WOMa12pG1isAaZ6W3nDdb0DlhwSu94QTFwbDysNhRmJG7WWtEN2/0T79euH7OxsCjvExvE/rkFfZYJXoAIxgwPr3c/NqfMZzCYTwnvdheiBQ51QJSHktplNgLakTmipG1huDDDVt29n9A6PD8i8ALk3oPDhRvDIvQGpR51g4t5IUHEF+AKHv3XS9tkddr7++ms8++yzyM3NRbdu3SASiWzu79Gjh8OKW7RoERYvXmyzT6lUQqVSAeC+QBcvXoxVq1ahtLQU8fHx+OyzzxAXF+ewGsitlRdqcXZPDgBg0L1dwOfXb7KtO6fOGJpTh5DWwWIGNAWA+jqgzuG2mvzqMFNSJ8AUAbqy23sNkZwLKzUXhU8Dt31qr0vdKbAQh7M77BQWFuLKlSuYPXu2dR+Px2u2DspxcXFISkqy3hYIav8RLF++HB9++CHWrFmDqKgoLFmyBAkJCUhLS4Ora8NDnonjHdlyBRYzQ0iMJ0Jj608gqK1QY1/1nDoD7n2I5tQhpCWYTVxwqRtk1NcBdS5Qnstdr8gDmD1/s3mAzLN+aLFerwkt3rW3xfJme4uENJXdYeeJJ55A7969sXHjxhbpoCwUCuHvX//LkTGGFStWYOHChZg+fToAYO3atVAqldiwYQOeeeaZZq2LcFRXy5F+gptAcNC9kQ3+Puz/fjW01XPq9J08zQlVEtLOmI1AhYoLLurchoOMRtW0uVh4AsA1AHAL5C6u/tWhxat+q4vMk1pdSJtkd9jJzMzE1q1b0aVLl+aop57Lly8jMDAQEokE8fHxWLp0KTp16oSMjAyoVCqMHTvWeqxEIsHw4cNx6NAhCjstgJtA8DIAIGZgAHyC63c4zk49i3N7EgEAY+bQnDqE3JLJAFRctw0w6utAeZ3WGU0+AHbr5+ILAdfA2iDjFgi4B1dfD+K2Cj/qkEvaPbt/w0eNGoXTp0+3SNiJj4/HunXrEBUVhfz8fCxZsgSDBg3C+fPnrf12lEqlzWOUSiUyMzNv+rx6vR56fe0wRbVa7fjiO4ArJwuhuqrmJhC8u/4EgiajEUlffQYA6DFmPIKiaU4dQqAtA4qvACVXa08vlddpoaksaNrz8EW2ocUmyFTvV/hSSwwhuI2wM2XKFLz44os4e/YsunfvXq+D8t133+2w4iZMmGC93r17dwwcOBCdO3fG2rVrMWDAAACod9qkpu/QzSxbtqxex2diH7PRgsObuWUheieEQuFRfwLB5K2/oKR6Tp2hD89q4QoJcSKzESi9BhRdBoovA8XpQFE6d72y8NaPF0hsg4x7kG2ocQviTi/x+c3+Vgi5XRZmgdFihN6sh8FsgFwoh1zknD5cdoedZ599FgDw9ttv17uvuWdQVigU6N69Oy5fvmxdkFSlUiEgIMB6TEFBQb3Wnhu99tprmD9/vvW2Wq1GSEhIs9TcXp3dlwN1kQ5yNzF6NTCBIDenzk8AgJEz50DqQnPqkHaGMW4kU/Hl6lCTXh1qLnNB52Ydf138Ae8uXEuMe9ANLTTBXH8ZGrFI7MQYg8li4sKFxQCDuc6l+rbRYoTBbLAeYzQbbe6/8XrN8Q09n9FstO7Xm/U2tw0WA0wWk019iwctxvTI6U75bOwOOzeuhdWS9Ho9Lly4gKFDhyIiIgL+/v5ITExE7969AQAGgwH79u3D+++/f9PnkUgkkEjqt0SQptFVGnF8xzUAQPzUTvUmEGSM4a9vPoPZaER4zz6IHjTMCVUS4iCGquogc5k7/WRtrbnCzd7bGJEC8O7MhRqfSMA7EvDpAnh15uaFIR0GYwwGiwE6k467mLmt1qSF3qznrpu11vv1Zj20Jq3NsdbtDY/TmXXWY/VmPcx2ja5rWTeGn5Z0R73SdDodpFKpo2qp5+WXX8aUKVMQGhqKgoICLFmyBGq1GjNnzgSPx8O8efOwdOlSREZGIjIyEkuXLoVcLseMGTOarSYCHN9RO4Fg14EB9e6/cGAPss6dgVAkxugn59KcOqT1s5iB8mzb001F1YFGndP443h8wCOUCzQ1YcY7krvtFkitM20MYwx6sx4aowZVxipojBpUGisbvVSZqmyCRk0Q0Zl10Jv0NkGENaVDuYMJ+UKI+WJIBBKIBCKI+WKIBdUXvhgigQgSgcR6vWa/WCCGiF99n6D2dt37b7xec//Nns+Z3wV2hx2z2YylS5fiiy++QH5+Pi5duoROnTrhjTfeQHh4OJ588kmHFZeTk4OHH34YRUVF8PX1xYABA3DkyBGEhYUBABYsWACtVou5c+daJxXctWsXzbHTjMoLq3B2L/fHf3ADEwhqK9TYu+5rAMCA+x6Gh5Lm1CGtSFUJF2CsYaY60BRfufnaSjKv6taZLnVaaroAXp24tY+I0zDGoDPrbhpKKo2VTQ4wzd0yIuAJIBPKIBVKIRVIbbdCKWRCGSQCiXV/zbESgeTmjxNUHyeUcOGGLwKfR326atgddt59912sXbsWy5cvx5w5c6z7u3fvjo8++sihYeeHH3646f08Hg+LFi3CokWLHPaa5OYOb+YmEAyN9UJonHe9+/d/vwbaCjW8g0NpTh3iPGYjUHABuH4SuJ4CFFzkgk1VceOPEYi58FL3tFPNdXn9yTKJ4+hMOpTpy1CuL0e5vpy7bii33i7XlzcYUmoCTHMEFLlQDheRC+QibqsQKepd5CK5NZzcKsBIBVwQEfFFt35x4nB2h51169Zh1apVGD16tLWzMsAtE3Hx4kWHFkdal7wr5bhyshA8HrcsxI1yUs/h3J5dAIAEmlOHtBSLhRvGnXuCCze5JwHVGcCka/h418Da0011W2s8QmmY9h0ymA2NBpa6YabcYHtb76AVy5sSUFzELtxxYhcohIoGj5WL5NQq0s7Y/W2Um5vb4Bw7FosFRqPRIUWR1qfuBIJdBwXAO8h2dJXJaETiV58CAHqMHo+grrEtXiPpABjj5qPJPVkbbK6fani1bIk7ENgLCOoDKLtxwcarMyChkYG3YrKYrKGkwbDSSJjRmrS3/ZpCnhBuEje4S9zhIfGAu9gd7pLai4vIhQIKuW12h524uDgcOHDA2m+mxs8//2wdFUXan/QTBcjPaHwCweNbf62dU2fGrJYvkLRPVSV1gs0J7npDk+4JpYB/Dy7YBPYBgu7iTknRPDRWjDFUGCtQVFWEAm0BCqsKUaQtQkFVgc22RFcCjVFz26/D5/HhJnaDh8SDCy/i6vBSJ7hYw4zU3Xq/QqSgwQyk2TQ57DzxxBP4+OOP8dZbb+Gxxx5Dbm4uLBYLNm3ahLS0NKxbtw7bt29vzlqJk5iNFhzZcgUA0HtsGBTuth0yS/NycWTzjwCAETSnDrldeg2Qd8q21aasgdnQeQLALxYI6s2FmsA+gF8MIOiYfSEYY1Ab1CisKkSB1ja4FFYVolBbaA02OnMjp/Ya4Sp2bTCYNBRcasKNq9iVWlhIq9PksLN27Vq89957mDJlCn788UcsXboUPB4Pb775Jvr06YNt27YhISGhOWslTnJmb/UEgu5i9L5hAkHGGJK+/hxmoxFhPXqjK82pQ5rCpAfyz1UHmxRuW5TW8MKVXp25UFPTauPfvUOspM0YQ5m+zBpW6m5vDDQGi6HJz+sqdoWvzBe+cl/bbfV1L6kXF1zEbhBQHybSTjQ57DBWO0fAuHHjMG7cuGYpiLQuOo0RJ/64BgCIv7sTRBLbP34XDu5F1rnTEIrEGENz6pCGWMxA0SUu0NR0Is4/D5gb+IJ2CwICe9cGm8DegMyjxUtubhqDBrmaXJtTSDUhxhpstIV2TcLmLnG3CS01Wx+ZD/zkfvCR+cBX5gupsPnmRiOktbKrzw59kXU8NRMIege51JtAUFuhxt61XwEABtz7EDz8608wSDoYxrilEqydh1OAvNOAoYE+IDLP6v41fWq3ru1jXibGGAq1hciuyLa55FbkIrsiG6X60iY/l6fEEz5yH/jJqgPLjS0y1YFGIqD5fohjMcYAoxGs+mIxGIDqbc0+VnPdYAQzGqq3N97HbV2GDYU0xjkLQtsVdqKiom4ZeEpKSu6oINJ6lBVU4ey+xicQtJlTZwrNqdNhqa8D6UnA5UTg2kFA28DfAJECCOjJBZqacOMZ3qZnGDaajcjV5FqDTI4mh9tW5CCnIueW/WM8JZ5ci0udIOMn97MJMj4yH4g6aF+kjoxZLFxA0Olg0eu5rU4PZqhzXV+z1cOi14HV7NPrwfQNhZGb7LvJfY4k8PBoG2Fn8eLFcHd3b65aSCtzpGYCwTgvhMTaTqqWc+HGOXXoD3KHYTYC2Ue5cJOexPW9qYsvAvy72bba+Ea3yTlsKgwVtWGmIsdmq6pSwdJQH6NqfB4fAYoAhLiGINg1GCGuIdZLsEswXMTUkb8tYSYTLFotLFVVsFRWwVJZCYu2CkxfHTisAYQLHha9jgsdOp01jNju09fed8M+Zmh6H6wWxeOBJxZzF5Go/tZmnwg8kbh6y13E4eFOK92usPPQQw/Bz8+vuWohrcj19DJcSameQHC67bxKZpMRiV99BgDoPnoczanTEZTnAumJXMC5ug8wVNS5k8eFmi4JQJfRXAtOG1lCwcIsKKwqtGmZqRtoyvRlN328TCjjgoxL/UAT4BJAs+U6CTMauVBS91JZBUtVZfW25lJpvc7qHldZWe/xTO+YiQ/tJhSCLxaDJ5WCJ5WAL5GCJ5U2sK96K5GAJ2k8hPBvGlBusk8sBk/Q9v7DUqPJYYf663Qc3ASC6QCAmMGB9SYQTN66CSW52TSnTntmMgDZR2pbbwpSbe+XewOdRwORCUDnUYDCxzl1NoHRbESOJscaYGxaaTQ5t5y910vqVdsic0Og8ZZ6099GB2EWCywaDczqCljU5TCr1TCr1bCo1dy+BgKINaxU38eqg4yjT7/YEAjAVyjAl8vBl8m44CGR1G4lEttQIpWAJ5bYhhKptHaflAso3FYKvqQ6xEiqH0sz0TvEbY3GIu1b+okCFFxTQygRoP+UCJv7SlXXcWQTt2bZiMefgsyFFl1tN8pzasNNQ603wX251pvIMUBA71Y5YV+5vhxpJWm4WHIRaaVpSCtJw5XyKzcd1STgCaynm24MNcGuwVCIFC34Dto2ZjDAXFEBc7kalgourFivl1eHl7rXqwONuaIClooKbukPB+KJRODL5eAp5Fw4kVeHFLm8NrDUvShuvF17DK/6Nk/k3NW77WW2MBjNFhjMFhhNNVvGbasvhpr9ZgajyWI93mCq3me+cR+3v/Z67bH1n7f2OV9MiMLE7s4ZyNLksGNx8C8haZ1MRjMOb+YmEOwzNtRmAsF6c+oMHu6sMokjmAxA1uHq01NJQOEF2/vlPkCXMbWtN61oMUzGGHI0OUgrSUNaaXW4KUlDXmVeg8fLhLJ6fWZqrvu7+NPppmqMMbCqqtrAoi63va6usA0pN1xn2ttfLqIGTyKBwM0NfDe36q0rBK5u4LsouLCiuDG0yBsILQqu1UUsdsCncmcYYzBZGHRGM/Qmi3WrN1qgM5lttvobbtd7jMkMnbGhrQX6OsfoTbVhw9KK2ilKq5zXF4nax4iNs3tyUVGsg8JdjF5jbCcQvHhwL7LOnqI5ddqysizb1htjZe19PD4Q1JcLN13GAAG9WkXrjd6sx5WyK9YWm4slF3Gp9FKjSxoEuwSjq1dXRHlFoatnV3T16gp/hX+H/X1lFgvM5eUwFxXBVFwCU3ERzMXFMBUVc9eLimEqKbFed0TnWL6LCxdU3N0hcHWFwN0NfFcuvFivu1eHmbrX3dzAl7Rsfy+zhaHKYEKVwYxKPbetMphRaTBBe8O+KoMJlXoztEZuW2UwW8OF/oZgUnfbmgKHSMCDSMC3XsQCHsTC2tsiIbdPJOBb94sF/NrHCbnb3H28Os/Dr/M8ts9Zc2xnX+d1yqewQ6x0GiOO10wgONV2AkGtpgJ71n0NgObUaVNMeiDzUO3Q8KI02/sVftWtN2OATiOd3npTqiu1nn6qCTYZ5RkwM3O9Y0V8Ebp4dEFXr66I9ormAo5nFFzF7f/UKjOZYC4thak6tJiLi6rDS/X14hLuelERTKWlgKnpkxMCAIRCLpi4unKBxc0NAjdXrrWlOpzUtLxwIcUdAjfX6vDi2iwdWeuGEttgUhtUtEZzdQgx2YQT63WDGVrrPlN1WGnZsxZiIR9SIR8SkQBSER8S4a23kpqtkA+p6ObbmmNFAl51SOECSs3tjhr6KewQq+TfM2DQmuAd7ILoAbZh5sD3q6FVl9OcOm1BaWbtqamM/fVbb4L7c+GmSwK3eKYTWm8szIKcihxroKk5FVVQ1cAinwA8JB5coPGsDTbh7uHt6vSTxWCwbXGpCTIlxbZBpqgY5rIybgJHO/Dd3SH09obQ2xuC6q3Qp/q6j0/1fh8IPT3Ak8sd/qVoMFmg1hmh1hqh1pmqt0aotaY6+2+8XXuczti8oYTPAxRiIeQSARRiIWRigfW2XCyAXCyEQiyArHorlwghszOwiAX8evOVkZZBYYcAAMryq3BuXy6A+hMI5lw4h7O7uTl1xsx5nubUaW1MeiDzby7cpCdySzPU5aLkWm+6jAE6j+RmLm5BOpMO6WXpNqeg0krSUGWqavD4UNdQa6Cpaa1RypVt+n+k5ooKGK9fhzE3F8bc6q1KBVNRIXcaqbiY66BrDx4PAi8vCL28IPDxhtC7OrDUXPfxhsCLCzRCL6877r/SWsJKTSiRiQVQSITVQaQ6jEgEkIm4rVxce1/tcTWBxXafXMy1irTl3zFycxR2CADg8OYrsFgYQuO8ERJTeyrDbDIi6evPAQDdR41FcNc4Z5VI6irNBC7v4k5PZewHjHWCA08AhMTXab3p3mIzFRdri+t1Gs5QZzQ4+Z5EIEGkRySivaJtTkO1tdFPjDGYy8q4EHO9OszUBJvr3HWLWt20JxMK64WXuqHFphXG0/O2ThcxxlChN6FYY0CxRo8ijQHFlXrr7dIqY7OGFVeJEG4yEVyl3NZNKoKbTFi9FcGtof1SEVykFErI7aOwQ3D9chmunqqeQPDezjb3Hd+2GcU5WZC5uWPoI7OdVCEBABi1QOpW4OQ6IPOg7X0u/rXhptOIFls8s0hbhGRVMo7mHUWyKhlZFVkNHucl9UK0Z7RN/5owtzAI+a3/TxBjDOaiIpvwYqi5npsL4/U8sKqGW6nqEnh4QBQYCFFQELcNDIDQ15c7deTjDYGXFwTu7uDdxmlFndGM4kourBRrDCjS6FFcaUBJZfV1m0BjgMF8+8HldsKKu6w2sAjoNA5xgtb/l4Y0K2Zh+PuXywCAmCGB8A6s7S1fqrqOI79yc+qMpDl1nEd1lgs4Z34EdOXcPh4fCB1YOzRc2a1FWm/K9eU4rjqOoyou3KSXpdvczwMPYW5h1kAT7cm12vjKfFvt/8aZ2QxTQYFta0yubctMU0YoCXx9IAoMhLg6zAjrXBcFBoKvaHqLldnCUFplqG19qRNkiiurW2OqA02xxgCN3s4OyABcJEJ4u4jhrRDD20UCHxcxvBUSeMhFFFZIu0Nhp4O7fCIfBZkVEEkE6D+5dgJBxhj++mYlTEYDQrv3QtchI5xXZEekKwfO/gKkfMetHF7DPRTo8xjQawbgHtzsZVQaK3Ei/wSO5R3DMdUxXCy5CAbbjrFdvbqiv39/9Pfvjz7KPq1uNBQzGmHMz4cxJ7fe6aWavjO3HK3E50OoVFa3zARaA4yobphpwpBpjd6E62Va5JZpkVemswaWG1tfSqoM9vY/hkjAg7dCwgUYFwl8FGLrdW+FGD4uEpvbUlHbnfqfEHtR2OnATEYzjmy+CgDoM852AsGLf+9D5pkUCEQijHmK5tRpEYwBWUe4VpzzmwFT9QRtfBEQMxno8zgQMaJZR0/pTDqcKjyFY3nHcFR1FOeLztcb9t3JvRP6+/dHfEA8+ir7wkPq0Wz12IOZTDBkZUF/OR369MvQp6fDkJ4O/bVM4FbLBwiFEAUE1A8xQUFcuFEqwRPdvGO+2cJQUKGrDjPctuaSU8pt1Tr7WmA85aL6YaU60PjUCS7eLhK4SYX075SQRlDY6cDO7MlBRYkOCg8JetaZQFCrqcDemjl1pj8ET/9AZ5XYMWgKgTM/cCGn7kgq365cwOnxYLOtPWU0G3G26CyOqo7iWN4xnC48DaPFNhiEuIZYW276+feDr9y3WWppKmY2w5idDX16One5zG0NV682uiYSTyKpDTJ1Q0zNKSdf31t29q2s0yqTaw0yOut1VbkOpibMHucuEyHQQ4ZAdyl8XW0DTG3LjBhecjGEAudP6khIe0Bhp4PSagw48UcmAGDA1E4QiWv/0B/YsAZV5WXwCgpBv7unO6vE9s1iBq7u4QLOxR1ATcAQyYFu04E+M4Hgfg7vh2OymHCh+AKOqbjTUikFKdCabKf495P7Id4/Hv0DuIAT6OKcsMssFhhzc61hxtpac+VqoytQ8+RySDp3hqRLF+4SyW2FAQE3bfWwWBjyy3V1QozW2kJTs69ce+vFJQV8HvzdpAjylCHIQ4ZADykXbDy42wHuUrhKaeoGQloahZ0OKvn3azBoTfAJcUFUvL91f87F8zj7158AgASaU8fxyrKBU98DKeuB8uza/UF3ca04cdMBqZvDXs7CLLhcetk6Wup4/vF6yyx4Sb2srTbxAfEIdQ1t0dMhjDGYrl+vbam5xIUa/dWrja61xJNKIenUCZLILhBbg00kRIGBDY5mqtSbkFdeHV5K64YZLa6Xc60yRvOtW2XcpEJrcAn0kCHIsybIcKHGz1VKHXgJaYUo7HRAZflVOF89geCgOhMImk1GJH31GQCg28ixCI7p5rQa2xWTAbj0B9eKk/4XUNPBV+oB9HwI6P0Y4O+Yz5oxhgx1hrVDcbIqGWX6MptjXMWu6KfsZ2256eLRpUXCDWMMpvx86C9frtNaw/WrsTQydJsnFkPcqZNtS01kJERBQfVOOzHGkK/WI71Ag8sFFdVbDa4UaFBc2YTRVDWtMg20yAR5UqsMIW0ZhZ0O6NCmdFgsDGHdvRHStXYCwbpz6gx7lObUuWOFl4CUdcCpjUBVUe3+iGHcaaqukwGR9I5fJqciB8dUx6ytN4XaQpv75UI5+ij7WE9NRXtGQ8BvvpE4jDGYCgqhT7/MdRCu06/Goml48U6IRJCEh9e21ERGQtKlC8QhIeAJbf9MmS0M2aVam0CTXh1qKm4yBNtVKuSCS3WICawONTW3lW7UKkNIe0Vhp4MpyatExukibgLBaV2s+8tUedY5dUbQnDq3z1AJpP7GteJkHa7d7+IP9H4E6P0o4NXpjl6i0liJvdl7cSTvCJJVycjV5NrcLxFI0Muvl7VTcZxPXLOuIWUqLETVyRRoT56E9tw5LtSUlzd8sFAIcVhYnZaaSC7ghIbWG+1kMFmQXlxpE2guF2hwtVDT6OKNAj4PYd5ydPF1QaTSBV38XBDp54pQbzncqFWGkA6Lwk4Hc34/98UY1t0HXoHcJGeMMSR98zk3p063noihOXXswxiQd4oLOGd/AfTVSwPwBEDUOK4vTpcEQHD7/9wMZgMO5h7Ejowd2Ju9F3pzbQddIU+IHr49rKelevj2gERw6zlfbgezWGC4csUabqpSUmDMamDWZD4f4tBQ62knSZfqFpvw8HprNGkNZpyvDjN1T0FlFlc1OrpJLOSjk48CkUpXm2AT7q2AWEgjmAghtijsdCBGvRkXj6gAAN2GB1n3Xzy0n+bUuR3aUi7cnFzLzXJcwzOcCzg9ZwBuAY0+/FbMFjNO5J/Ajowd2JW5CxWG2oUiw93CMTJ0JOL949HbrzfkIvkdvJHGWXQ66M6erQ03p07Vb7Xh8SCJioKsT2/Ie/WCJDoa4oiIepPslWuNOJ+nwZWCfGugSS/UIKdU2+gEegqxAF2qAw3XSsNtQ7zkdMqJENJkFHY6kMvH82HQmuDmI0Vo9WKfOo0Ge9d+BQAYMO1BeAYE3ewpCGPcCuMn13Gnq0w6br9AAsTezXU2Dh962xP/McZwoeQCdlzdgT+u/YGCqgLrfX4yP0yImICJnSYixiumWUKpqbgY2pQUVJ04yZ2WSk2tNyEfTyaDrEcPLtz06QNZr14QuLpa6y/SGJCeo0F6oQrp+RVIL9Tgcr4GBRUNDxcHuMnzIv1c0blOoIlUusDfTUrhmxByx9pU2Fm2bBn+85//4IUXXsCKFSsAcH9cFy9ejFWrVqG0tBTx8fH47LPPEBdHq3PXxRjDueoRWHFDg8Cr/l9x3Tl1+t59rzNLbN0q8oHTG4CT3wElV2r3+8UBd80Eut8PyL0af/wtZKozsSNjB3Zc3YFr6mvW/a5iV4wNG4tJnSahj18fh3YsZozBkJHBtdhUhxtDZma944S+vpD16QN5n96Q9ekDadeu1v415VVGHMguxcmsPKRkleJsbjnKqhqfj8bfTYou1WGmbkuNt0vznHYjhBCgDYWd5ORkrFq1Cj169LDZv3z5cnz44YdYs2YNoqKisGTJEiQkJCAtLQ2urtTJtkbBtQoUZlVAIOQjZjB3aiX3YirO/LUTAJDw1PMQ3mI6/A4pYz9w9Esg7Q+gZtkEsQvQ7V4u5AT2ue2J/wqrCrHz2k7suLoD54rPWfdLBVKMCBmBiRETMThoMMQC8U2epeksBgN0587VhpuUFJjLymwP4vEg6dLFJtyIgoPB4/FgsTBcLtDgZEoeTmaW4mRWKa4UVtZ7HR4PCPGUW4NMzaWznwt1EiaEOEWbCDsajQaPPPIIvvrqKyxZssS6nzGGFStWYOHChZg+nZvpd+3atVAqldiwYQOeeeYZZ5Xc6pzbnwMA6HyXL2QuYphNRiR+9SmA6jl1YmlOHRvXTwFJi7hZjmsE96+e+G8aIHFp7JE3VWGoQFJmEn7P+B3JqmRYGDeqSMATYEDgAEyKmIRRoaOgEDV9hezGmEpLoU2p7mtzMgW6s2frLafAk0oh6969Ntz06gWBuzsArtXmUHYpTiZdRkpWKU5llTU4tDvcW44+oZ7oHeaJ3iEe6OLnQotMEkJalTYRdp5//nlMmjQJY8aMsQk7GRkZUKlUGDt2rHWfRCLB8OHDcejQoUbDjl6vh77OdPNqtbr5im8FdJVGXD7O9f3oNoxbKds6p46rG4Y9MsuJ1bUyxVeA3UuA85u423wR14LT7ynAL+a2nlJv1mN/zn7suLoD+3P2w2CpneCul28vTOw0EWPDxsJb5n3bZTPGYLh2DdqTKahKOQntyRQYrl6td5zAxwfy3r2t4UYaEwOeWFzbapNWipOZ15CSXYb0gvpz4sjFAvQM9kDvUA8u4IR60CkoQkir1+rDzg8//ICTJ08iOTm53n0qFTeySKlU2uxXKpXIbKDvQY1ly5Zh8eLFji20Fbt4OA9mowXeQS7w7+SGsnyV7Zw6ro5bnqDNqsgH9r3PjayymADwuH44I/8DeEXY/XQmiwnHVMew4+oO/JX1l80SDV08umBSp0kYHz4ewa7Bt1UuMxigPX/eJtyYS0rqHSfu3JnrRFwdbkSh3FIQ5VVGHM0uxcl915rcatMn1APRSldanJIQ0ua06rCTnZ2NF154Abt27YJU2vhMszeO1mCM3XQEx2uvvYb58+dbb6vVaoSEhNx5wa0QYwznD1wHwA035/F4+OvbldVz6vRAzNCRTq7QyXTlwN//Bxz5HDBWL1nQZQww+i0goMfNH3sDxhjOFZ3D7xm/Y2fGThTriq33BSgCuJFUERMR5Rl1WyOMzGo1NPv2oSIxCZoDB+qtG8UTiyHt3r063HCnpISenrWtNlmlOHnsDLXaEEI6nFYddk6cOIGCggLcdddd1n1msxn79+/Hp59+irS0NABcC09AQO18JgUFBfVae+qSSCSQSDrGH/GctFKU5VdBJBEgqr8SuRdTce3UCQiEQox+8vmOO6zXqAOOfwPs/y+grW4RCeoLjFkERAy166mull/Fjqs7sCNjB7Irahf39JB4YFz4OEyMmIhefr3A59nfImIqLETFX7tRkZSEyqNHbYaBCzw9q1tsuHAjjYsDXyxGeZURJ7JLcfJ4IVKyLlGrDSGkw2vVYWf06NE4e/aszb7Zs2eja9euePXVV9GpUyf4+/sjMTERvXv3BgAYDAbs27cP77//vjNKbnVqFvyMjveHWCpE8jauL0rssFHwCuyAc+pYzMCZH4E9S2tXHfeJAka/ya1V1cTwp6pUYWfGTuzI2IELJRes+2VCGUaFjsLEiIkYGDjwtpZpMGRnoyIxCRVJSdCmpKDujHviLp3hmpAA1zFjII2NBWPA5QINUrJKcXLrBZzMarjVRiYSoGeIO/qEelKrDSGkw2nVYcfV1RXdutmOElIoFPD29rbunzdvHpYuXYrIyEhERkZi6dKlkMvlmDFjhjNKblUqy/S4eppbgLLb8CAU52bjyvEjAIC7Jk9zZmktjzHg0k7gr7eBglRun2sgMOLfQK9HmrSUQ7m+HLsyd2HH1R04kX8CrHr1ciFPiMFBgzExYiJGhIywezZjxhj0ly6jIikRFYlJ0F+8aHO/tEcPuCaMgeuYMRCHh+NcrhobLxbg+JFj1GpDCCFN0KrDTlMsWLAAWq0Wc+fOtU4quGvXLppjB0Dq39fBLAwBnd3hHeSCXV9+CwDo3HcAvIPaZx+lBmUdARLfArK5oAepOzBkPhD/DCCS3fShOpMOe7P34veM33Ew9yBMltpgcZfyLkyM4EZSeUg97CqJWSzQnj6NiqQkVCQm2a4vJRBA3q8fXMeMgeuY0bD4+OLQlWIkncrHXxt3Q6XW2TwXtdoQQsjN8RhrbFWajkOtVsPd3R3l5eVwc2sfI5MsZgvWLTyMyjI9xsyORVCUGF//4wmYTSY89PYHCIq+vWHUbUp+KteSc+kP7rZQCsQ/CwyZB8g8b/pQjUGDH9J+wLrz61CqL7Xuj/aMxsROEzEhfAICXOxb94oZjag8dgwVSUnQJP0FU2Gh9T6eWAzFkCFwHTMGLiNHQC1WYM/FAiRdyMf+S4WoNJitx8rFAgyL9MXgSB9qtSGEdGhN/f5u8y07pGHXzhajskwPqYsIXfr44dDP62A2mRAYHdv+g05ZFrBnGXB6IwDGrT7e+1HulJVb4M0fqivD+gvrseHiBuvCmwGKAEzuNBkTIyaii2cXu0qxaLXQHDwITVISKvbus1lEk+/iApcRI7iAM3QIrlUx/HQhH0k/XsTxzBLUXfBb6SbBmBglxsQqMbCTN03aRwghdqCw006d2891TI4dHACTUYfTiVzrRr/2vP5VZTFw4H9A8leAuXrivpi7uc7HPpE3fWiRtghrz6/Fj2k/QmvihnR3cu+Ep7o/hQkREyDkN/2filmthmbvXlQkJkJz4CCYrva0k8DbG66jRsF1bAIk/frjtKoS61LzkfhFMq7esPRCTIAbEmL8kBDrj25Bbh135BwhhNwhCjvtUFlBFbJTSwAet+jn2b/+gL6qEl6Bwejcp5+zy3M8QyVw+HPg0P8B+urZsMOHAmMWA8F33fSheZo8fHvuW2y6vMk6s3FXr654usfTGB06usnDxY0FBdDs3o2KXYmoPHYMMNX27REFBXH9bxLGwBLbHQevliLpQj52f7AfJZW1symLBDwM6OSNMTFKjI7xQ7CnfR2dCSGENIzCTjtUM4lgaKw3FB5CnPhjKwCg75Tp4PHbUd8Os5Gb8XjfckCTz+3z787NldN59E2HkWeps/DNuW+wNX0rTIwLJj18e+CZHs9gaNDQJrWiGLKyaoeInzplM0RcEtnFOkS8LCgCSRcLkHQ2H39v2Q2DyWI9zk0qxMiufkiIVWJYlC8tlEkIIc2Awk47YzKYceFQ7YzJF//eD01xERQenu1ntmSLBUjdzK1hVVK9/pNnODDqDSBuOnCTQJdemo6vzn6Fndd2WhfhjPePx5wec9Dfv/9NQw43RPwSKnYloiIpCfrqSS1rSHv2gFtCAlxGj8ZVqTd+T81H4u58nMnJsDkuxEuGhBh/jIn1Q79wL4ioczEhhDQrCjvtTPrJAugrTXDxkiA0zgvfLfgVANBn4lQIRe2g1eDKbm418rzT3G2FLzD8VaDPTEAobvRh54vP46szX+GvrL+s+4YFD8Oc7nPQy69Xo49jFgu0p2qGiCfCmF07QzIEAsj7c0PEpSNG4WSVCGsv5CPxxwzklqXaPE/vUA+MiVEiIVaJSD8X6n9DCCEtiMJOO3OuesbkuCFByDxzAsU5WRDLZOgxZryTK7tDuSe5kJOxj7stdgUG/wsYMBeQuDT6sJSCFHx55kv8nfs3AIAHHsaEjcGc7nMQ4934qDSzRoPyX39FyXfrYczJse7nSSTWIeIsfjD2FxiQmJqPfavO2EzuJxHyMTTSB2NilBgV4wc/18bXdiOEENK8KOy0I4XZFcjPUIPP5yFmcAC2r/gCANBjzARIFY0HglatKB3Y/Q6QuoW7LRAD/Z4Chr4EKHwafAhjDEfyjmDVmVU4nn8cAMDn8TExYiKe6v4UOnt0bvTlDFlZKFm/HuW/boKlkhsdZR0inpCA0tg+SMysQNKFfBz7NBmmOuPDfVzEGN2VGx4+pIsPZGIaHk4IIa0BhZ12pGa4eafevlAXXkNO6jnwBQL0mXC3kyu7DRUqYO97wMl1ADMD4AE9HwJGvAZ4hjX4EMYY9uXsw6ozq3C2iFtTTcgXYmrnqXiy25MIcWt41mjGGKqOHkPJunXQ7Nlj7Wgs7tQJno89hsw+w7D1mhpJqQVI23PU5rGRfi4YE6vEmBgleoV4QMCn01OEENLaUNhpJwxaEy4d40YkdRsWhONbuVadmCEj4OrdcAtIq6QtA/7+GDiyEqie7wZR47m5cpRxDT7EbDEjMSsRX535CpdKLwEAJAIJ7ou6D7PiZsFf4d/g4yx6PdTbf0fJunU2nY0VQ4dC8uDD+E0YjPXHcpB1OsV6n4DPQ79wT26Cvxglwn0UjnnfhBBCmg2FnXYi7agKJr0Znv5yyNyqcOnYIQBA37ay4KfJABz7kpsUUFu9PENwfyBhMRA2qMGHGC1G7Li6A1+f/RrX1NcAAHKhHA91fQiPxT4GH1nDIc9UWIjSjT+g9IcfYC4pAQDwZDK43zMVxWPvwaf/396dx0Vd7Y8ff82wDPumrLK4IYIKKrigiZqJuaWX23LdS/spqSW3ay51TcqSVlu07Npimlp23S3NKBU0s9TkKrhkSqYo4oKA7DNzfn+MjiGQ9E0cwPfz8ZjHo8/+nncI78c553NOFqzffoZSvalwcrS1oleIF/eEedE7xAs3h+oHQgshhKh7pNhpAJRS5i6sNjFN+OnLdaAUzTt2onFgU4vGViOXMmHVWDjzk2nbs7WpJSdkQJVz5ZQZylj3yzo+Sv+IrCum7+1i68KI0BGMCB2Bq861yscUZ2SQu3QpeZs2Q3k5ANY+PrgMH87u0O58fDCX/euvD0YO83VhdHQQ97X3w8FW/qkIIUR9Jb/BG4Czv+Rx6Uwh1rZaAkPt2PbRNwB0GlwPloY4uAo2JkBZAdi5QewL0H44aCsP7i0qL2LVz6tYkrGEnOIcADzsPBjTZgwPhTyEo03lLiVlMFDw7bdcWrqU4r37zPvtO3SAvz/EKocWfLrvLBdPmObCsbHS0L+tL6Ojg4gMcpdXxIUQogGQYqcBuNaq06qTN4dSv8JQXo5vyxCahFY9xqVOKCuEzdNh/yem7cBoiHsf3CoPIi4oK2Dl0ZUVViD3cvBibNuxxAXHYW9tX+kaQ0EBl1etJnfZMsqzTPnB2hqXe/txqvd9vHHRgW/2ncOoTgLg42LHiC6BPNQ5QF4TF0KIBkaKnXquKL+M4z+ZWjlCujZmbdKXAETdF1d3WyXOZcB/H4ELRwENxDxlmhjQquKPo3kF8sMrKCg3rUDu7+TPuHbjuK/FfdhaVR47U/brr1xatpy8NWswFhUBYOXmhv3f7yc1NIaPjhZyfEcBYLpfdPNGjI4Oom+YN9Yyk7EQQjRIUuzUc4d3ncFoUHg1dSH7l+8pKbyCm48vLTt1tXRolSkFez+Er54GQyk4+0LcImgWU+G0P7sCuVKKot27ubRkKVdSUsyvjuuCW1I25EGWuYSxKv08RTtNRaGjrRV/j/RnVNcggr2db8MXF0IIYUlS7NRjRqMyL/oZ1t2b7z57B4CoQXFoqxjzYlHFubDhcTi80bQdHAtDF1aYGPDPrkBuLCkh/4svuLRkKaXHjpn3O/TsyfEeg/jPFQ9++CUXyAagpZcTo6OD+FuHJjjLgptCCHHHkGKnHvst4yIFF0vQOVijDMcouHAeB1c3wnrebenQKvptN6x+FPJOgdbG9Dp514nmN63KDeX858B/+DD9Q/RG05ILEZ4RjA8fX+UK5OXncsj9dAWXV36OIdc0hkfj4IDtwMF807onH540cC69FMjFSqshNsybUdFBRDdvVHe79oQQQtQaKXbqsWsDk0O6+vDTpvkAdLh3MDa2OkuGdZ3RADvfgG1zTbMguzeDBxaDXwfzKRkXMvj3d//ml8u/ANDZpzMTwifQyadTpcKk+GA6l5YuJX/zZtCbiiJrPz+uDIjjE49wNvxSgP6gaZxOYycdwzoHMLxLIL6ulQcwCyGEuHNIsVNP5V8o5mT6RQDcvS7y48lMbHR2RMQOsHBkVxVkw5r/B5mppu12D8DAeWDnAkCpoZT3/vcei9MXY1AG3HXuPNP1Gfo17VfhNkqvp+Cbq6+O//STeb+uQ0cOdx/AO2V+HMopggv5AEQFuTMqOoj+bX2xtZYBx0IIIaTYqbcydp4BBf6t3Tm8czUA7e6Oxd6pDgy4PZYMa+Oh6ALYOMCA10xz51xtqTlw/gCzvpvFibwTANzb9F5mdpmJh52H+RaGvDwur1rFpeXL0Z85a9ppY4O29z1sad2T98/ZUXBaDxRhZ6NlaPsmjIoOoo1f1RMKCiGEuHNJsVMPGfRGDn9nGpjs30rP9iVpaLRaIgcOtWxg+jLY+jzsMnWp4d0O7v8IPFsBUKIv4d20d1lyaAlGZcTDzoNZXWdxT9A95luUnsgkd9knXF67DlVsehPLyt2dS30G8bFnJF+d1cNvAHqaNnJgZNcgHogMwNVBBhwLIYSomhQ79dCJ/ecpLijHwdWWMz9vBaB1txhcPL0sF9SlE7Bq3PUlHzpPgL7Pg41pgr60nDRmfTfLvIbVwOYDmdFpBm52bgCUHD5MzptvUpiSar6lVctg0qP785ZqzskrejirR6OBu0O8GBUdREywJ1pZZVwIIcRNSLFTD10bmNy8vY59678DIGpwnOUCunHJh6HvQuuBABTri5m/fz7LDi1DofC092RW11n0DuwNgCE/n/NvvU3up5+C0QgaDYau3fmqVU8WFXhQVqAAPW4ONjzUKYCRXYII8HCw2FcVQghR/0ixU89cPHOFM8cuo9FqKL78I0oZaRrREa+mzW9/MGWFsHka7F9m2g6Mhr9/AK7+AOzN3svsXbP5reA3AO5rcR/TOk3DVeeKMhrJW7eenNdeM688ntelJ/9p2ZdthXaQB6AI93dlVNcgBkf4YWdTx+YOEkIIUS9IsVPPZKSaxuoEtLbn6C5TF1an+yyw4Gd2Oqx6BC78DGig5zSImQZW1hSVF/HWT2+x4sgKwLSO1ezo2cT4m2ZKLjl8mOzn51C8fz8Aev9AFoYPZZNtIBSCrZWWQRG+jI5uSvsAt9v/3YQQQjQoUuzUI2Uleo7uNr2ZZGNzCH1ZKV7NWhDQJvz2BaEU7PkAtjxT5ZIPe7L38Ox3z3L6ymkA4oLjmBo1FWdbZ1OX1dvzyV2xwtRlZW/Pti6DmefSEb3WGg9HWx7t0YyHogJo5FRH5goSQghR70mxU48c23OOshIDLo2sObHvG8DUqnPbZgUuzoX1k+HIF6bt4H6m8TmOjSksL+SNfW+w8uhKAHwcfUiMTqR7k+4oo5HLa9eZuqwumuYGOhXRjVm+fThn54q1VsOj3ZryeJ9gXO3lrSohhBC3lhQ79YRSyjww2c3rJDm/5OPq5U2rLt1vTwCVlnx4Hro+BhoN35/5nsRdiZwpNHWxPdDqAZ6MfBInWydKjhwxdVldnRCw2MefN8LuY4eLaYxRn9ZePDMwlOaeTrfnewghhLjjSLFTT5z7NZ8Lp66gtYLsY9sBiBz0N7RWtTxo12iAnfNgW5JpyQeP5qa5c/w6cKXsCq/tfY3Vx0yTGjZxakJit0S6+nbFkJ9P9isvmruslJ09GyP68753Z/Raa4K9nJg1KIyYVp61G78QQog7nhQ79URGiqlVx9P/HCfTzmHn7ELbXvfc5Kq/KP8srB3/uyUfHoRB80DnzHdZ35H4fSLZhaYVxYe1HkZCxwTsrewqdVkdDe3CC4F9uWDvhpuDDf+8pxUjugRibSXLOQghhKh9dfqvzcKFCwkPD8fFxQUXFxeio6PZvHmz+bhSisTERPz8/LC3t6dXr15kZGRYMOLaUXKlnGP7clBKUXB+FwAd+g3CRmdXew89lgzvdTcVOjYOMHQhxC0iX6OY9d0s4r+JJ7swmwDnAD7q9xFPd3ka7fHfODlyFGdnzsRw8SIFXk149q54EkIeINfRnYe7NWX71F6M6dZUCh0hhBC3TZ1u2fH39+ell16iZcuWACxZsoQhQ4awf/9+2rRpwyuvvMK8efP4+OOPadWqFS+88AJ9+/bl6NGjODvXgTWibpHD35/FUG7E2eMCF05kYm2ro32/gbXzMH0ZfPscfL/AtO3dzrRSeeNgUk+n8tyu58gpzkGDhhGhI3i8w+PoivVkv3C9y8qgs+O/YbEsD+iGXmtNTCtPZg0MJdi74fw/EUIIUX/U6WJn8ODBFbZffPFFFi5cyO7duwkLC+PNN9/kmWeeIS7ONHvwkiVL8Pb2ZsWKFUyYMMESId9yyqjIuDowGb1pkG/b3vfg4FILC15eOgGrxsIZ0/w315Z8yDOW8vKOp9l4YiMAQS5BzOk+h/ae7clbv57Tr17vsvpfi0hea9GfCw5uNG/syL8HhdI7xOv2vTEmhBBC3KBOFzu/ZzAY+O9//0thYSHR0dFkZmaSnZ1NbGys+RydTkfPnj3ZtWvXHxY7paWllJaWmrfz8/NrNfa/4vSRXPLOF6O1usiF3zLQaLREDvzbrX/Q75d8sHeHIe9A64Fs/W0rc3bP4ULxBbQaLaPDRjOp/ST45VdOJow0v2WV28iXV0IGk+bVCmc7a/7dJ5jR0U2xtZbuKiGEEJZV54udgwcPEh0dTUlJCU5OTqxdu5awsDB27TKNXfH29q5wvre3NydPnvzDeyYlJfHcc8/VWsy30rXXze3t0ikCWnXtjpu3z617QFkhbJoGadeWfOgGf3+fXJ0jSanT2JxpGiPVzLUZc7rPoa2uGedfnkfu8uVgNKK31bG81T2sat4Do5U1wzsH8q++rWRSQCGEEHVGnS92QkJCSEtL4/Lly6xevZoxY8aQkpJiPn5j94hS6qZdJjNnzuTJJ580b+fn5xMQEHBrA78FruSWknngAsqYz6WrXUu3dGmI7IOmbqsblnxIPr2NF3a/wKWSS2g1Wh5p8wjxEfGUfPEVx1+dZO6y+jGoA/NDBnLBwY3o5o14dnAYob4uty4+IYQQ4hao88WOra2teYByVFQUe/bs4a233mL69OkAZGdn4+vraz4/JyenUmvPjXQ6HTpd3W95OLQzC2VU2NllUJpnJLBtON7NW/71G1e55MP7XPQJY+6O6Xx98msAWrq1ZE73ObS8YE32mEcp3rcPgBw3H94Iu480r1YEejjw3oBQ+rXxlnE5Qggh6qQ6X+zcSClFaWkpzZo1w8fHh+TkZDp06ABAWVkZKSkpvPzyyxaO8q8zGIwc2nkGZSzhyiXTuJhOg29Bq46+FNaMh0PrTNvB/VBD3mXL+b3MXf83cktzsdJYMa7dOB5tOoy8dxeRebXLqtxGxyfBfVjbMgadnY7pdwfzSPemshq5EEKIOq1OFztPP/00/fv3JyAggIKCAj777DO2b9/OV199hUajISEhgblz5xIcHExwcDBz587FwcGB4cOHWzr0v+zXAxcozCtDQzqG8lI8A5sSFNHxr91UXwqfj4GfN5uXfLgQ8SAv/vAi3/xmWmurlXsr5nR7Hr+dxzg1eai5y+o7/wjeCxvMRUc3Hoj0Z2q/ELyca3GeHyGEEOIWqdPFzrlz5xg1ahRnz57F1dWV8PBwvvrqK/r27QvAtGnTKC4uZuLEieTm5tKlSxe+/vrrBjHHTnpKFkrpMZZcbdX5qwt+6suuFzrWdqh/fMqX2iJe2jCUvNI8rDXWjA8fzyibHlyYksTZq11WZ128ebvtENK8WtGpqTuLB7WhnX8tvPYuhBBC1BKNUkpZOghLy8/Px9XVlby8PFxcLD/A9vK5IpbP3o2+9AD6om9wbuzJuLfex8r6/1ib6svgv2Pg6CawtuP83//D82e/Zfup7QCEeoTyfMQM3D75yvyWVZm1Lcta3cPaljF4ezgzo39rBoX7yrgcIYQQdUZN/37X6ZadO1X6jiyUMqJRpjewogYOvWWFTsagl5l44A0ulVzCWmtNfLsJ3J/pycXhU8i9cAGAHU3CWdTmPgpdG/FErxaMj2ku43KEEELUW1Ls1DH6MgNHdp3FWH6c8uKL2Dk60fbu2JtfWPXN4L8PmwodKx27+j1LQsa7FOuLCXEPYa7veGxfXUrO1S6rLGcvFrQbSppXK/7WoQnT7g3B19X+1n05IYQQwgKk2KljftmXQ0lhOUpvKkAiYgdia/d/KDj0ZbDqETj6JVjp+PKeqfz78AfolZ4Ytyhm/K8pV2Y+SbHBQKm1Lcuvdlm1DWrMmsFhdAx0v8XfTAghhLAMKXbqmPTULJQ+C33pGaxsbOhw76A/fxNDuanQOfIFWOn4pOcEXvn5EwBGEk3cGz9zJXs3AKl+4bzf9j6sfLx5tX9rhkQ0QauVcTlCCCEaDil26pDzvxVwLjMffeleANr07IOj259sYTGUm7qujnyBstLxZvQwPspcB8BTed3o/OEP6EtLyXJszDsRcRzya82EmObE92qBg638OAghhGh45K9bHZKechqj4QLG8hOg0RA16E8u+Pm7QqfcSsdzkYNZn7UVjVK89nMUAWtSUcAe79a8FDWC3pHNmd+/Nf7uDrXxdYQQQog6QYqdOqK0WM/Pe85hKDGN1QnuHI27b5Oa3+B3XVfF1rZMDe9N6vkfcSjXMn9nS5x3/wDAqpY9WRM1lIXDI+nZyrM2vooQQghRp0ixU0cc3X2W8pI8DOWHgT+5NISh3LSg5+GNXLbRMTm0C//LPUSTAhte2eSBzYlDlGuteLv9/WRH92HDiEgCPKQ1RwghxJ1Bip06QClFekoW+pL9oIz4h7bFNzikZhcbymH1ODi8gWxbOya0bMeJgl+JPGvPU2tBm5fFJZ0zc7qMoW3fu1gwtK3MmSOEEOKOIsVOHXDm2GUunc3FUHYAMC0NUSOGclj9KBxazy92DsQHteBc8TmGHnJi2JcFaPQGfnFtwtxujzDpobsY0SVQZkAWQghxx5Fipw5IT83CUHoAVBmN/ANp1j7y5heZC511pNk7MqmJP1dKLjNlhzPdd10GTK+VL+s5mgUPdyMySObNEUIIcWeSYsfCivLLOP7TWfS/X/BTq/3jiwx6c6Gz3cmZp7y9sLpSxNxNDjT/+TIAS1v34/i9D7J6REdZnVwIIcQdTYodCzv03RnKiw+DKsTJoxGtu8f88QUGPawxFTprnV14rrEH3ufLmL3WFvfzBZRY2fBax2GEPDiE5QNaY2N1k8JJCCGEaOCk2LEgo1GRnnoaQ4lpEsGOA4ZgZW1T/QUGPaz5f6iMtXzo5sZb7i5EHNczdaMWXXEx5+zdeOmucTw2rj9D2v+J19aFEEKIBkyKHQv6Lf0i+ecOo4yXsLV3ILzPvdWfbNDD2vEYM9bwaqNGLHN2YOCPRkZtVWiVngyPpizu9xhvjO9FqG/1y9wLIYQQdxopdiwoPTXLvDREROwAdA7VzH1ztdApT1/NM16eJOvseOxLI70PKgC2BHYm/YHxrBjRGVeHP2gZEkIIIe5AUuxYSP6FYjLTDqL0WWitrOl47+CqTzToYe0ECjPWkODjzWGjLbM/NRJyWmFAwwdtBxM0fiyL7mklC3gKIYQQVZBix0IydpxBX7IHgLCYu3HyaFT5JIMe1sVz8dAaJvp6U3jZhqRVRhrnK65Y2/FW9zE8kjCMPqHetzl6IYQQov6QYscCDOVGDm4/iLH8OABRg6tY8NNogHXxnD68hgl+Pvge1zLzCwO6cjjt2Jilgx5nzuMDadbY8TZHL4QQQtQvUuxYwPH9ORRdMi3M2TyyM42aBFQ8wWiAtfEcObqOiT7e9Nyt5cGdRgB+8mzF/kem8p+R0TjYyv8+IYQQ4mbkr6UFpH17BEPZIQA633d/xYNXC509x9YztZEXY76A6COmQmddyxg8p07l1Z4tZdkHIYQQooak2LnNLmZd4cyRVMCAd4sQmrQOu37QaIB1j5F8fCOv2HsybYWR5uegXGPFki4Pcv+sSUS3qGJsjxBCCCGqJcXObfa/b49jKP0fAF3/9sD1A0YDrJvI55lf8nlZI57/zIhbEVy2deSzwZOZMe0hfF3tLRS1EEIIUX9JsXMblZXoOZSaDKoU58a+tIjsbDpgNKDWTWThb5s5dMaNZ78yYmOAEy6+pMX/m1dG90RnbWXZ4IUQQoh6Soqd2+jI92covWKaRLBr3NUFP40GDOsm8eKpLdj95MSkH03jc773a4fzc3N4pkeIJUMWQggh6j0pdm4TpRR7v/ga1BVs7V0I63E3GA2UrpvIrJPfEJHsQMcTphmRN4TfS//XZhEe6GHhqIUQQoj6T4qd2yT7RB6Xz+wAoOOA+7C2tiJ//WPMPrqNAV/Y4n9RUWplxRf9x/NY4gQaOeksHLEQQgjRMEixc5t8v3oryngRrZUtkf0HkLNuAq/t28HwL61xKoHzDo6kxycyY9wArK20lg5XCCGEaDCk2LkNSq6Uc/JAMgAh3e7h7JYnWLH1B0Zv02Kl4GhjX+zmzmdiTBsLRyqEEEI0PFLs3AY/rP8eY/kpQIun406SP99P3AHTse+D23PXO/NpFdjYojEKIYQQDZUUO7VMGRUHt24EwNnDjdMr9tDjFBiBlLv/xojXE3G1t7VskEIIIUQDVqcHhyQlJdGpUyecnZ3x8vJi6NChHD16tMI5SikSExPx8/PD3t6eXr16kZGRYaGIKzu86zClV44A4J9xiNanoNhWw0+PzSB+wYtS6AghhBC1rE4XOykpKUyaNIndu3eTnJyMXq8nNjaWwsJC8zmvvPIK8+bNY8GCBezZswcfHx/69u1LQUGBBSO/7vvVqwCFvcGFZjmFnHe1puD1Dxg1ZQxaraxvJYQQQtQ2jVJKWTqImjp//jxeXl6kpKQQExODUgo/Pz8SEhKYPn06AKWlpXh7e/Pyyy8zYcKEGt03Pz8fV1dX8vLycHFxuWXxZp/IYvnMiYCBiNNFlNrn0WbRepo2979lzxBCCCHuVDX9+12nW3ZulJeXB4CHh2myvczMTLKzs4mNjTWfo9Pp6NmzJ7t27ar2PqWlpeTn51f43GoGvZ41z/wbMGCDGxcCDPRat1MKHSGEEOI2qzfFjlKKJ598krvuuou2bdsCkJ2dDYC3t3eFc729vc3HqpKUlISrq6v5ExAQcMvjtbK2xmBrC2ixd3PioU+34+gkC3kKIYQQt1u9KXYmT57MgQMH+PTTTysd02gqjn1RSlXa93szZ84kLy/P/Dl16tQtjxdg0uKFhA/uz8Pz5/9hPEIIIYSoPfXi1fPHH3+cDRs2kJqair//9W4gHx8fwNTC4+vra96fk5NTqbXn93Q6HTpd7S/HoNVq6TvysVp/jhBCCCGqV6dbdpRSTJ48mTVr1rB161aaNWtW4XizZs3w8fEhOTnZvK+srIyUlBS6det2u8MVQgghRB1Up1t2Jk2axIoVK1i/fj3Ozs7mcTiurq7Y29uj0WhISEhg7ty5BAcHExwczNy5c3FwcGD48OEWjl4IIYQQdUGdLnYWLlwIQK9evSrsX7x4MQ8//DAA06ZNo7i4mIkTJ5Kbm0uXLl34+uuvcXZ2vs3RCiGEEKIuqlfz7NSW2ppnRwghhBC1p0HOsyOEEEII8WdJsSOEEEKIBk2KHSGEEEI0aFLsCCGEEKJBk2JHCCGEEA2aFDtCCCGEaNCk2BFCCCFEgybFjhBCCCEaNCl2hBBCCNGg1enlIm6Xa5NI5+fnWzgSIYQQQtTUtb/bN1sMQoodoKCgAICAgAALRyKEEEKIP6ugoABXV9dqj8vaWIDRaOTMmTM4Ozuj0Whu2X3z8/MJCAjg1KlTsuZWDUi+ak5yVXOSq5qTXNWc5KrmajNXSikKCgrw8/NDq61+ZI607ABarRZ/f/9au7+Li4v8Y/gTJF81J7mqOclVzUmuak5yVXO1las/atG5RgYoCyGEEKJBk2JHCCGEEA2aFDu1SKfTMXv2bHQ6naVDqRckXzUnuao5yVXNSa5qTnJVc3UhVzJAWQghhBANmrTsCCGEEKJBk2JHCCGEEA2aFDtCCCGEaNCk2BFCCCFEgybFTi169913adasGXZ2dkRGRrJjxw5Lh2RxqampDB48GD8/PzQaDevWratwXClFYmIifn5+2Nvb06tXLzIyMiwTrIUlJSXRqVMnnJ2d8fLyYujQoRw9erTCOZIvk4ULFxIeHm6etCw6OprNmzebj0ueqpeUlIRGoyEhIcG8T/JlkpiYiEajqfDx8fExH5c8VZSVlcXIkSNp1KgRDg4OtG/fnn379pmPWzJfUuzUkpUrV5KQkMAzzzzD/v376dGjB/379+e3336zdGgWVVhYSEREBAsWLKjy+CuvvMK8efNYsGABe/bswcfHh759+5rXL7uTpKSkMGnSJHbv3k1ycjJ6vZ7Y2FgKCwvN50i+TPz9/XnppZfYu3cve/fu5e6772bIkCHmX6SSp6rt2bOHRYsWER4eXmG/5Ou6Nm3acPbsWfPn4MGD5mOSp+tyc3Pp3r07NjY2bN68mUOHDvH666/j5uZmPsei+VKiVnTu3FnFx8dX2Ne6dWs1Y8YMC0VU9wBq7dq15m2j0ah8fHzUSy+9ZN5XUlKiXF1d1XvvvWeBCOuWnJwcBaiUlBSllOTrZtzd3dUHH3wgeapGQUGBCg4OVsnJyapnz55qypQpSin5ufq92bNnq4iIiCqPSZ4qmj59urrrrruqPW7pfEnLTi0oKytj3759xMbGVtgfGxvLrl27LBRV3ZeZmUl2dnaFvOl0Onr27Cl5A/Ly8gDw8PAAJF/VMRgMfPbZZxQWFhIdHS15qsakSZMYOHAg99xzT4X9kq+Kjh07hp+fH82aNeMf//gHJ06cACRPN9qwYQNRUVE88MADeHl50aFDB95//33zcUvnS4qdWnDhwgUMBgPe3t4V9nt7e5OdnW2hqOq+a7mRvFWmlOLJJ5/krrvuom3btoDk60YHDx7EyckJnU5HfHw8a9euJSwsTPJUhc8++4yffvqJpKSkSsckX9d16dKFpUuXsmXLFt5//32ys7Pp1q0bFy9elDzd4MSJEyxcuJDg4GC2bNlCfHw8TzzxBEuXLgUs/3Mlq57XIo1GU2FbKVVpn6hM8lbZ5MmTOXDgADt37qx0TPJlEhISQlpaGpcvX2b16tWMGTOGlJQU83HJk8mpU6eYMmUKX3/9NXZ2dtWeJ/mC/v37m/+7Xbt2REdH06JFC5YsWULXrl0BydM1RqORqKgo5s6dC0CHDh3IyMhg4cKFjB492nyepfIlLTu1oHHjxlhZWVWqVnNycipVteK6a285SN4qevzxx9mwYQPbtm3D39/fvF/yVZGtrS0tW7YkKiqKpKQkIiIieOuttyRPN9i3bx85OTlERkZibW2NtbU1KSkpvP3221hbW5tzIvmqzNHRkXbt2nHs2DH5ubqBr68vYWFhFfaFhoaaX8qxdL6k2KkFtra2REZGkpycXGF/cnIy3bp1s1BUdV+zZs3w8fGpkLeysjJSUlLuyLwppZg8eTJr1qxh69atNGvWrMJxydcfU0pRWloqebpBnz59OHjwIGlpaeZPVFQUI0aMIC0tjebNm0u+qlFaWsrhw4fx9fWVn6sbdO/evdLUGD///DNBQUFAHfh9VetDoO9Qn332mbKxsVEffvihOnTokEpISFCOjo7q119/tXRoFlVQUKD279+v9u/frwA1b948tX//fnXy5EmllFIvvfSScnV1VWvWrFEHDx5Uw4YNU76+vio/P9/Ckd9+jz32mHJ1dVXbt29XZ8+eNX+KiorM50i+TGbOnKlSU1NVZmamOnDggHr66aeVVqtVX3/9tVJK8nQzv38bSynJ1zX/+te/1Pbt29WJEyfU7t271aBBg5Szs7P597jk6boff/xRWVtbqxdffFEdO3ZMLV++XDk4OKhly5aZz7FkvqTYqUXvvPOOCgoKUra2tqpjx47mV4bvZNu2bVNApc+YMWOUUqbXE2fPnq18fHyUTqdTMTEx6uDBg5YN2kKqyhOgFi9ebD5H8mUyduxY8781T09P1adPH3Oho5Tk6WZuLHYkXyYPPfSQ8vX1VTY2NsrPz0/FxcWpjIwM83HJU0UbN25Ubdu2VTqdTrVu3VotWrSownFL5kujlFK1334khBBCCGEZMmZHCCGEEA2aFDtCCCGEaNCk2BFCCCFEgybFjhBCCCEaNCl2hBBCCNGgSbEjhBBCiAZNih0hhBBCNGhS7AghqpWYmEj79u0tHQYA27dvR6PRcPny5Rpf07RpU958881ai+maixcv4uXlxa+//vqX7tOrVy8SEhJqfH5OTg6enp5kZWX9pecK0dBJsSPEHUqj0fzh5+GHH2bq1Kl8++23lg61zktKSmLw4ME0bdr0L91nzZo1zJkzp8bne3l5MWrUKGbPnv2XnitEQ2dt6QCEEJZx9uxZ83+vXLmSZ599tsJCfvb29jg5OeHk5GSJ8OqN4uJiPvzwQzZt2vSX7+Xh4fGnr3nkkUfo3Lkzr776Ku7u7n85BiEaImnZEeIO5ePjY/64urqi0Wgq7buxG+vhhx9m6NChzJ07F29vb9zc3HjuuefQ6/U89dRTeHh44O/vz0cffVThWVlZWTz00EO4u7vTqFEjhgwZctMun02bNtGqVSvs7e3p3bt3lefv2rWLmJgY7O3tCQgI4IknnqCwsLDae86bN4927drh6OhIQEAAEydO5MqVKwAUFhbi4uLCqlWrKlyzceNGHB0dKSgoqPKemzdvxtramujoaPO+a11uW7ZsoUOHDtjb23P33XeTk5PD5s2bCQ0NxcXFhWHDhlFUVGS+7sZurKZNmzJ37lzGjh2Ls7MzgYGBLFq0qMLz27Vrh4+PD2vXrq32ewtxp5NiRwjxp2zdupUzZ86QmprKvHnzSExMZNCgQbi7u/PDDz8QHx9PfHw8p06dAqCoqIjevXvj5OREamoqO3fuxMnJiXvvvZeysrIqn3Hq1Cni4uIYMGAAaWlpPProo8yYMaPCOQcPHqRfv37ExcVx4MABVq5cyc6dO5k8eXK1sWu1Wt5++23S09NZsmQJW7duZdq0aQA4Ojryj3/8g8WLF1e4ZvHixdx///04OztXec/U1FSioqKqPJaYmMiCBQvYtWsXp06d4sEHH+TNN99kxYoVfPnllyQnJzN//vxq4wV4/fXXiYqKYv/+/UycOJHHHnuMI0eOVDinc+fO7Nix4w/vI8Qd7bYsNyqEqNMWL16sXF1dK+2fPXu2ioiIMG+PGTNGBQUFKYPBYN4XEhKievToYd7W6/XK0dFRffrpp0oppT788EMVEhKijEaj+ZzS0lJlb2+vtmzZUmU8M2fOVKGhoRWumT59ugJUbm6uUkqpUaNGqfHjx1e4bseOHUqr1ari4mKllFJBQUHqjTfeqPZ7f/7556pRo0bm7R9++EFZWVmprKwspZRS58+fVzY2Nmr79u3V3mPIkCFq7NixFfZt27ZNAeqbb74x70tKSlKAOn78uHnfhAkTVL9+/czbN64+HhQUpEaOHGneNhqNysvLSy1cuLDC8/75z3+qXr16VRujEHc6adkRQvwpbdq0Qau9/qvD29ubdu3ambetrKxo1KgROTk5AOzbt49ffvkFZ2dn8xggDw8PSkpKOH78eJXPOHz4MF27dkWj0Zj3/b6b6Np9P/74Y/M9nZyc6NevH0ajkczMzCrvu23bNvr27UuTJk1wdnZm9OjRXLx40dz11blzZ9q0acPSpUsB+OSTTwgMDCQmJqbafBQXF2NnZ1flsfDw8Ap5cnBwoHnz5hX2XctTdX5/j2tdjTdeY29vX6E7TAhRkQxQFkL8KTY2NhW2NRpNlfuMRiMARqORyMhIli9fXulenp6eVT5DKXXTOIxGIxMmTOCJJ56odCwwMLDSvpMnTzJgwADi4+OZM2cOHh4e7Ny5k3HjxlFeXm4+79FHH2XBggXMmDGDxYsX88gjj1Qoum7UuHFjcnNzqzz2+7zcLE/Vqck1ly5dqjaXQggpdoQQtaxjx46sXLkSLy8vXFxcanRNWFgY69atq7Bv9+7dle6bkZFBy5Yta3TPvXv3otfref31180tU59//nml80aOHMm0adN4++23ycjIYMyYMX943w4dOrBs2bIaxVBb0tPT6dWrl0VjEKIuk24sIUStGjFiBI0bN2bIkCHs2LGDzMxMUlJSmDJlCqdPn67ymvj4eI4fP86TTz7J0aNHWbFiBR9//HGFc6ZPn87333/PpEmTSEtL49ixY2zYsIHHH3+8ynu2aNECvV7P/PnzOXHiBJ988gnvvfdepfPc3d2Ji4vjqaeeIjY2Fn9//z/8fv369SMjI6Pa1p3aVlRUxL59+4iNjbXI84WoD6TYEULUKgcHB1JTUwkMDCQuLo7Q0FDGjh1LcXFxtS09gYGBrF69mo0bNxIREcF7773H3LlzK5wTHh5OSkoKx44do0ePHnTo0IFZs2bh6+tb5T3bt2/PvHnzePnll2nbti3Lly8nKSmpynPHjRtHWVkZY8eOven3a9euHVFRUVW2Et0O69evJzAwkB49eljk+ULUBxpVk85xIYS4gyxfvpwpU6Zw5swZbG1tb3r+pk2bmDp1Kunp6RUGb98OnTt3JiEhgeHDh9/W5wpRn8iYHSGEuKqoqIjMzEySkpKYMGFCjQodgAEDBnDs2DGysrIICAio5Sivy8nJ4f7772fYsGG37ZlC1EfSsiOEEFclJiby4osvEhMTw/r162WpDCEaCCl2hBBCCNGgyQBlIYQQQjRoUuwIIYQQokGTYkcIIYQQDZoUO0IIIYRo0KTYEUIIIUSDJsWOEEIIIRo0KXaEEEII0aBJsSOEEEKIBk2KHSGEEEI0aP8f65/lBW172wkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delays = np.arange(0, 65, 5)\n",
    "delay_tab = pd.DataFrame(index=delays, columns=net.line.index.values)\n",
    "\n",
    "for d in delays:\n",
    "    pp.runpp(net, tdpf=True, tdpf_delay_s=d * 60, max_iteration=30)\n",
    "    delay_tab.loc[d, :] = net.res_line.temperature_degree_celsius.values\n",
    "    \n",
    "delay_tab.plot(ylabel=\"Temperature (°C)\", xlabel=\"Time delay (min)\", \n",
    "               title=\"Time delay and overhead line temperature\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}