{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time series example with advanced output writer usage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial shows how specific results are extracted with the time series module in pandapower.\n", "\n", "In this example we define the output writer to log the following:\n", "* The maximum voltage of each medium voltage bus (vn_kv > 1.0 kV and vn_kv < 70.0) in an example grid\n", "* The sum of all p_kw values for every high voltage bus (vn_kv > 70.0 and vn_kv < 380.0)\n", "\n", "This time series calculation requires the minimum following inputs:\n", "* pandapower net\n", "* the time series (a Dataframe for example)\n", "* a pre defined output writer\n", "\n", "If you have just read the simple time_series jupyter notebook example. You can directly proceed to the section of create_output_writer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need some imports. Specific for this example are:\n", "* ConstControl -> \"constant\" controllers, which change the P and Q values of sgens and loads\n", "* DFData -> The Dataframe Datasource. This Dataframe holds the time series to be calculated\n", "* OutputWriter -> The output writer, which is required to write the outputs to the hard disk\n", "* run_timeseries -> the \"main\" time series function, which basically calls the controller functions (to update the P, Q of the ConstControllers) and runpp." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import tempfile\n", "import random\n", "import pandapower as pp\n", "from pandapower.control import ConstControl\n", "from pandapower.timeseries import DFData\n", "from pandapower.timeseries import OutputWriter\n", "from pandapower.timeseries.run_time_series import run_timeseries\n", "random.seed(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we look at the time series example function. It follows these steps:\n", "1. create a simple test net\n", "2. create the datasource (which contains the time series P values)\n", "3. create the controllers to update the P values of the load and the sgen\n", "4. define the output writer and desired variables to be saved\n", "5. call the main time series function to calculate the desired results" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def timeseries_example(output_dir):\n", " # 1. create test net\n", " net = simple_test_net()\n", "\n", " # 2. create (random) data source\n", " n_timesteps = 10\n", " profiles, ds = create_data_source(n_timesteps)\n", " # 3. create controllers (to control P values of the load and the sgen)\n", " net = create_controllers(net, ds)\n", "\n", " # time steps to be calculated. Could also be a list with non-consecutive time steps\n", " time_steps = range(0, n_timesteps)\n", "\n", " # 4. the output writer with the desired results to be stored to files.\n", " ow = create_output_writer(net, time_steps, output_dir)\n", "\n", " # 5. the main time series function\n", " run_timeseries(net, time_steps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by creating a simple example pandapower net consisting of five buses, a transformer, three lines, a load and a sgen. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def simple_test_net():\n", " \"\"\"\n", " simple net that looks like:\n", "\n", " ext_grid b0---b1 trafo(110/20) b2----b3 load\n", " |\n", " |\n", " b4 sgen\n", " \"\"\"\n", " net = pp.create_empty_network()\n", " pp.set_user_pf_options(net, init_vm_pu = \"flat\", init_va_degree = \"dc\", calculate_voltage_angles=True)\n", "\n", " b0 = pp.create_bus(net, 110)\n", " b1 = pp.create_bus(net, 110)\n", " b2 = pp.create_bus(net, 20)\n", " b3 = pp.create_bus(net, 20)\n", " b4 = pp.create_bus(net, 20)\n", "\n", " pp.create_ext_grid(net, b0)\n", " pp.create_line(net, b0, b1, 10, \"149-AL1/24-ST1A 110.0\")\n", " pp.create_transformer(net, b1, b2, \"25 MVA 110/20 kV\", name='tr1')\n", " pp.create_line(net, b2, b3, 10, \"184-AL1/30-ST1A 20.0\")\n", " pp.create_line(net, b2, b4, 10, \"184-AL1/30-ST1A 20.0\")\n", "\n", " pp.create_load(net, b2, p_mw=15., q_mvar=10., name='load1')\n", " pp.create_sgen(net, b4, p_mw=20., q_mvar=0.15, name='sgen1')\n", "\n", " return net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data source is a simple pandas DataFrame. It contains random values for the load and the sgen P values (\"profiles\"). Of course your time series values should be loaded from a file later on.\n", "Note that the profiles are identified by their column name (\"load1_p\", \"sgen1_p\"). You can choose here whatever you prefer.\n", "The DFData(profiles) converts the Dataframe to the required format for the controllers. Note that the controller" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def create_data_source(n_timesteps=10):\n", " profiles = pd.DataFrame()\n", " profiles['load1_p'] = np.random.random(n_timesteps) * 15.\n", " profiles['sgen1_p'] = np.random.random(n_timesteps) * 20.\n", "\n", " ds = DFData(profiles)\n", "\n", " return profiles, ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "create the controllers by telling the function which element_index belongs to which profile. In this case we map:\n", "* first load in dataframe (element_index=[0]) to the profile_name \"load1_p\"\n", "* first sgen in dataframe (element_index=[0]) to the profile_name \"sgen1_p\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def create_controllers(net, ds):\n", " ConstControl(net, element='load', variable='p_mw', element_index=[0],\n", " data_source=ds, profile_name=[\"load1_p\"])\n", " ConstControl(net, element='sgen', variable='p_mw', element_index=[0],\n", " data_source=ds, profile_name=[\"sgen1_p\"])\n", " return net" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "create the output writer. Instead of saving the whole net (which takes a lot of time), we extract only pre defined outputs.\n", "In this case we:\n", "* save the results to the folder output_dir\n", "* write the results to \".xlsx\" Excel files. (Possible are: .json, .p, .csv - You should avoid Excel since it is slow)\n", "* log sum of real power (\"p_mw\") for each high voltage (hv) bus\n", "* log the maximum voltage magnitude (\"vm_pu\") of each medium voltage (mv) bus" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def create_output_writer(net, time_steps, output_dir):\n", " ow = OutputWriter(net, time_steps, output_path=output_dir, output_file_type=\".xlsx\", log_variables=list())\n", " \n", " # create a mask to get the indices of all the hv buses in the grid \n", " mask_hv_buses = (net.bus.vn_kv > 70.0) & (net.bus.vn_kv < 380.0)\n", " hv_busses_index = net.bus.loc[mask_hv_buses].index\n", " # create a mask to get the indices of all the mv buses in the grid\n", " mask_mv_buses = (net.bus.vn_kv > 1.0) & (net.bus.vn_kv < 70.0)\n", " mv_busses_index = net.bus.loc[mask_mv_buses].index\n", " # now define the output writer, so that it gets the indices and specify the evaluation functions\n", " # since we want the maximum voltage of all mv buses, we provide the indices of the mv buses and the maximum \n", " # function np.max. The variable \"eval_name\" is free to chose and contains the name of the column in\n", " # which the results are saved. \n", " ow.log_variable('res_bus', 'p_mw', index=hv_busses_index, eval_function=np.sum, eval_name=\"hv_bus_sum_p\")\n", " ow.log_variable('res_bus', 'vm_pu', index=mv_busses_index, eval_function=np.max, eval_name=\"mv_bus_max\")\n", " return ow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets execute the code." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results can be found in your local temp folder: C:\\Users\\mmilovic\\AppData\\Local\\Temp\\time_series_example\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.83it/s]\n" ] } ], "source": [ "output_dir = os.path.join(tempfile.gettempdir(), \"time_series_example\")\n", "print(\"Results can be found in your local temp folder: {}\".format(output_dir))\n", "if not os.path.exists(output_dir):\n", " os.mkdir(output_dir)\n", "timeseries_example(output_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything works you should have the desired results the temporary folder of your os (see print statement above).\n", "In this folder two excel files should have appeared containing the desired output for each of the ten time steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot results\n", "Now let us plot the results" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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2lfhUV1ejR48eeOihh3DHHXc0+Zzx48dj5cqVxo+vnF5titzcXOOAPiIiIrIt2dnZiIiIaPbzNpX4TJgwARMmTLjuc5RKJUJCQtr8Hp6engD0XzgvL682X+dqarUamzdvNh6jT9Li/bAuvB/Wh/fEuvB+tKyiogKRkZHG3+PNsanExxR//vkngoKC4Ovri5tvvhmvvPIK/P39TX69YXvLy8vL7ImPm5sbvLy8+E1rBXg/rAvvh/XhPbEuvB+ma6lMxa4Sn/Hjx+OOO+5AbGwszp8/j+effx4TJkzAnj17jBOWr1ZfX99oynBFRQUA/TeZWq02W2yGa5nzmtR2vB/WhffD+vCeWBfej5aZ+rWx2VldgiBcU9x8tQsXLiA+Ph5btmzBqFGjmnzO4sWLsWTJkmse//rrr+Hm5maucImIiMiCampqMH36dJSXl193x8auVnyuFhcXh4CAAJw7d67ZxGfRokVYsGCB8WPDHuHYsWPNvtWVnJyMMWPGcJnSCvB+WBfeD+vDe2JdeD9aZtixaYldJz4XL15ESUkJQkNDm32OUqlssvNLoVBY5JvLUteltuH9sC68H9bnRu6JTqeDSqUyc0SOSavVwsnJCVqt9rqt2vZMoVA0W7Zi+LwpbCrxqaqqwrlz54wfp6en48iRI/Dz84Ofnx+WLFmCO++8EyEhITh//jyeffZZdOjQAePGjZMwaiIix6NSqZCeng6dTid1KHZBFEWEhIQgOzvboc+Y8/HxQUhIyA19DWwq8Tl48CBGjhxp/NiwRTVjxgx8+OGHOHbsGFavXo2ysjKEhYVh7NixePnll1t9lg8REbWdKIrIy8uDXC5HZGSkw65QmJNOp0NVVRU8PDwc8uspiiJqampQWFgIANfdyWmJTSU+I0aMwPVqsX///fd2jIaIiJqi0WhQU1ODsLAwNomYiWHb0MXFxSETHwBwdXUFABQWFiIoKOi6217X45hfPSIishitVgsAcHZ2ljgSsjeGRPpG2vqZ+BARkUU4ci0KWYY5vqeY+BAREZHDYOJDREQEfR3p/Pnzbe7a1DpMfIiIiMhhMPEhIjIjlYbn1hBZMyY+RERmknyqAJ1f+A2r/8qQOhRqI51Oh2effRZ+fn4ICQnB4sWLAQDTp0/HtGnTGj1XrVYjICAAX3zxhUnX1mg0mDdvHry9vREQEIAXXnih0REtgiDgxx9/bPQaHx8frFq1CoD+UMh//OMfCA0NhYuLC6Kjo/H666+3+L6iKGLx4sWIioqCUqlEWFgYHn/8cZPfNyMjA4Ig4JtvvsHQoUPh6uqKfv364ezZszhw4AD69u0LDw8PTJgwAUVFRSZ9LWbOnIkpU6ZgyZIlCAwMhJeXFx599NF2Oenbps7xISKyVqIo4t0tZ6ETgY3H8zBjUIzUIVkNURRRq9ZK8t6uCnmrOoFWr16NBQsWYN++fdizZw9mzpyJwYMH47777sPUqVONhwgC+rPjampqcPvtt5t87Ycffhj79+/HwYMH8cgjjyAqKgqzZ8826fUff/wxfvnlF3zzzTeIiopCdnY2srOzW3zdd999h3feeQdr165FYmIi8vPzcfToUZPe80ovvfQS3n33XURFReGhhx7C9OnT4enpiffeew9ubm64++678eKLL+LDDz806Xpbt26Fi4sL/vzzT2RkZGDWrFnw9/fHq6++2urYWoOJDxGRGaRkluJkrn5I4qncCuh0ImQytnMDQK1ai64vSnPA7Kml4+DmbPqvuu7du+Oll14CAHTs2BHLly/H1q1b8corr8Dd3R0//PADHnjgAQDA119/jVtvvRWenp4mXTsyMhLvvPMOBEFAQkICjh8/jnfeecfkxOfixYvo2LEjhgwZAkEQEB0dbdLrsrKyEBISgtGjR0OhUCAqKgr9+/c36bVXevrpp40joJ544gnce++92Lp1KwYPHgwAePjhh42rRKZwdnbG559/Djc3NyQmJmLp0qV45pln8PLLL1v0kEZudRERmcHKK7a3Kus1yLpUI10w1Gbdu3dv9HFoaCgKCwvh5OSEu+++G1999RUAoLq6Gj/99BPuu+8+k6990003NVp9GjhwINLS0owHPrZk+vTpOHLkCBISEvD4449j8+bNJr1u6tSpqK2tRVxcHGbPno0ffvgBGo3G5LgNrvzaBAcHAwCSkpIaPWYYKWGKHj16NDrZe+DAgaiqqjJpFetGcMWHiOgG5ZXXYtOJfABAgIcziqtUOJFbjpgAd4kjsw6uCjlOLZVmWLSronVjDa6e8C0IgnHQ6n333Yfhw4ejsLAQycnJcHV1xfjx480WqyAI14xluvKE4h49euD8+fP4/fffsWXLFtx9990YPXo0vv322+teNzIyEmfOnMGWLVuQnJyMv//97/j3v/+N7du3Q6FQtPi+Bld+bQwJ3NWP2cJQWiY+REQ36Ku9WdDqRPSP9UOHIA98vS8LJ3IqMLl7mNShWQVBEFq13WStBg0ahMjISKxbtw6//fYbpk6dek2idD379u1r9PHevXvRsWNH48ypwMBA5OXlGT+flpaGmprGK4deXl6YNm0apk2bhrvuugvjx4/HpUuX4Ofnd933dnV1xS233IJbbrkFc+fORefOnXH8+HH07t3bpPe1hKNHj6K2ttY4g2vv3r3w8PBAZGSkRd/X9r8TiYgkVKfW4n/7swAAswbFoLRG/y/lk7nlUoZFFjJ9+nR89NFHOHv2LLZt29aq12ZlZWHBggWYM2cODh06hP/+9794++23jZ+/+eabsXz5cgwcOBBarRbPPfdco8RqxYoViImJQZ8+fSCTybB+/XqEhITAx8fnuu+7atUqaLVaDBgwAG5ublizZg1cXV2NNUItva+lqFQqPPzww/jXv/6FjIwMvPTSS5g3b57Fh7Ay8SEiugG/HstDSbUKYd4uGNM12FjgfDK3AqIocl6Vnbnvvvvw6quvIjo62ljUa6oHH3wQtbW16N+/P+RyOZ544gk88sgjxs+//fbbmDVrFoYOHYqwsDC89957SElJMX7ew8MD//nPf5CWlga5XI5+/fph48aNLSYKPj4+eOONN7BgwQJotVokJSXhl19+gb+/v0nvaymjRo1Cx44dMWzYMNTX1+Pee+81Hh9gSYJ49caeg6uoqIC3tzfKy8vh5eVltuuq1Wps3LgREydObJdMmq6P98O62Or9EEURty7fjeM55Xh2fAL+PqID6tRaJL70O7Q6EX8tvBlhPq5Sh9kmN3JP6urqkJ6ejtjYWLi4uFgoQsei0+lQUVEBLy8vi6+ItIeZM2eirKzsmvODWnK97y1Tf3/b/lePiEgih7JKcTynHM5OMtzTLwoA4KKQo2OQ/pyXEznc7iKyNkx8iIjaaNVfmQCAKT3D4OfubHy8W7g3AOBEw7YX2besrCx4eHg0+19WVpZF3/+rr75q9r0TExMt+t7Nud7XY+fOnZLEZMAaHyKiNiioqMNvx/WdMFef0twtzAvfpgAnueLjEMLCwnDkyJHrft6Sbr31VgwYMKDJz0m1dXy9r0d4eDiGDh3afsFchYkPEVEbfLU3ExqdiP4xfkgM8270OcOKz0mu+DgEJycndOjQQbL39/T0NPn06PYi5dejJdzqIiJqpXqNFl83tLA3NZOrS6gXBAHIr6hDUWV9O0dHRNfDxIeIqJU2HMtDcZUKod4uGJsYfM3n3ZVOiG04tdmRz/Nh0zCZmzlOhuZWFxFRK4iiiFUNc7nuvykaCnnT/37sFuaNC0XVOJlbgREJQe0YofQMYxCKiooQGBjIs4zMQKfTQaVSoa6uzi7a2VtLFEWoVCoUFRVBJpPB2dm55Rc1g4kPEVErHM4uw7GLhhb25o/W7xbuhZ+P5jpkS7tcLkdERAQuXryIjIwMqcOxC6IoGsc7OHIi6ebmhqioqBtK/pj4EBG1wqrdGQCAW3uEwd9D2ezzuoUZWtodL/EB9O3MHTt2bHLYJbWeWq3Gjh07MGzYMJs65NOc5HI5nJycbjjxY+JDRGSigoo6bGxoYZ/ZRFHzlQydXtmXalFeo4a3m+P9spLL5cYBnHRj5HI5NBoNXFxcHDbxMRfH2ygkImqjr/ZlQaMT0Tfa19iy3hxvNwUifPXjKk7mOeaqD5E1YuJDRGSCeo0WX+/Tt7DPHBxj0msM210nc3ieD5G1YOJDRGSCjcfzUFxVjxAvF4xLDDHpNd3C9YMSHbXOh8gaMfEhIjKBYS7XfQOimm1hv1qiYWaXA3Z2EVkrJj5ERC04nFWKo9llcJbLcO+AKJNfZ9jqulBcjRqVxlLhEVErMPEhImrB6oYDCyf3CEXAdVrYrxboqUSQpxKiCKTmsc6HyBow8SEiuo7CyjpsaGhhnzUottWv72bc7mLiQ2QNmPgQEV3H1/uyoNaK6B3lg6SI67ewN6VbWEOBM+t8iKwCEx8iomaoNDp8ZWxhb/1qD3BFgXMuV3yIrAETHyKiZvx2Ig9FlfUI8lRiQjfTWtivZtjqSiuoRL1Ga87wiKgNmPgQETXDlCnsLQnzdoGvmwIanYiz+VVmjI6I2oKJDxFRE45ml+FwVkMLe3/TW9ivJgiCcW4XDzIkkh4THyKiJhhb2LuHItDT9Bb2piSGs8CZyFow8SEiukpRZT1+OZYLAJjRwhR2U3QLY4EzkbVg4kNEdJX/7de3sPeK8kGPSJ8bvp6hwDk1rwJqre6Gr0dEbcfEh4joCiqNDmv26udyzTTDag8ARPu5wUPpBJVGh/NFLHAmkhITHyKiK2w6mY/CynoEeioxoVuoWa4pkwno2nCQ4Ume4EwkKSY+RERXWLU7HYB+Cruzk/n+ikw0nODMzi4iSTHxISJqcOxiGQ5llUEhFzC9FVPYTWEocOaKD5G0mPgQETUwHFg4KSkUQZ4uZr22ocD5ZG45dDrRrNcmItMx8SEiAlBcVY9fj+qnsLd1Ltf1xAe6Q+kkQ7VKi4ySarNfn4hMw8SHiAjA//ZlQaXVoUekD3qaoYX9ak5yGbqEGup8uN1FJBUmPkTk8NRaHdbs07ewzzJTC3tTDAXOJ1ngTCQZJj5E5PA2nchHQUU9AjyUmJhknhb2phjrfFjgTCQZJj5E5PAMc7nM3cJ+tW5XDCsVRRY4E0mBiQ8RObQTOeU4mFkKJ5mA+8zcwn61TiEecJIJKKtRI6es1qLvRURNY+JDRA7N2MLePRRBXuZtYb+a0kmOTsGeAIAT3O4ikgQTHyJyWCVV9fj5qPmmsJuiW7i+wPkUC5yJJMHEh4gc1toD2VBpdOgR4Y1eFmhhb0qisc6HKz5EUmDiQ0QOSa3V4cs9+hb2GYNiIAhCu7yvYcXnRA5XfIikwMSHiBzS5pMFyK+oQ4CHMyZ1t1wL+9W6hHpBEIDCynoUVtS12/sSkR4THyJySKv+0k9hn94/Ckonebu9r5uzE+IDPQAAJ7ndRdTumPgQkcM5kVOOAxkNLew3Rbf7+3cL43YXkVSY+BCRwzEcWDghKRTBFm5hb8rlSe1c8SFqb0x8iMihXKpW4aeGFvaZg9p/tQcAuhpWfNjSTtTubCrx2bFjB2655RaEhYVBEAT8+OOPjT4viiJefPFFhIaGwtXVFaNHj0ZaWpo0wRKRVVp7IAsqjQ5J4d7oHeUrSQyGlvaLpbUoq1FJEgORo7KpxKe6uho9evTAihUrmvz8W2+9hffffx8fffQR9u3bB3d3d4wbNw51deycICJAo9VhjQQt7FfzdlUgys8NALe7iNqbk9QBtMaECRMwYcKEJj8niiLeffdd/Otf/8Jtt90GAPjiiy8QHByMH3/8Effcc097hkpEVij5VAFyy+vg7+6Mye3Ywt6UbuFeyLpUgxM55RjcIUDSWIgciU2t+FxPeno68vPzMXr0aONj3t7eGDBgAPbs2SNhZERkLVY2FDXf2z8KLor2a2FvCk9wJpKGTa34XE9+fj4AIDg4uNHjwcHBxs81pb6+HvX19caPKyr0fwmp1Wqo1WqzxWe4ljmvSW3H+2Fd2uN+pOZVYn/6JchlAqb1DZP83ncJdgcAnMwpkzyWpvBnxLrwfrTM1K+N3SQ+bfX6669jyZIl1zy+efNmuLm5mf39kpOTzX5NajveD+tiyfvxv/MyADJ099Xi0K4/LPY+pqpUA4AT0our8f0vG+Ei7QJUs/gzYl14P5pXU1Nj0vPsJvEJCQkBABQUFCA09PLefUFBAXr27Nns6xYtWoQFCxYYP66oqEBkZCTGjh0LLy8vs8WnVquRnJyMMWPGQKFQmO261Da8H9bF0vejtEaFZw/sAKDDc7cPQJ9oabq5rvbfs9tRUFGPqO4D0ddKYjLgz4h14f1omWHHpiV2k/jExsYiJCQEW7duNSY6FRUV2LdvHx577LFmX6dUKqFUKq95XKFQWOSby1LXpbbh/bAulrof3x3OQr1Gh8QwLwyID5Ssm+tqSeHeKKgoxOmCagzsECR1OE3iz4h14f1onqlfF5tKfKqqqnDu3Dnjx+np6Thy5Aj8/PwQFRWF+fPn45VXXkHHjh0RGxuLF154AWFhYZgyZYp0QRORpDRaHdbs1bewz5Swhb0piWHe2JJaiBM5LHAmai82lfgcPHgQI0eONH5s2KKaMWMGVq1ahWeffRbV1dV45JFHUFZWhiFDhmDTpk1wcWn/I+mJyDpsSS1ATlkt/NydcUuPMKnDaeTy6Aqe4EzUXmwq8RkxYgREUWz284IgYOnSpVi6dGk7RkVE1myVsYU9UvIW9qslNoyuSCusQp1aa3XxEdkjuznHh4joaql5Fdh7Qd/Cfr8EU9hbEurtAj93Z2h1Is7kV0odDpFDYOJDRHbriz0ZAIDxiSEI9XaVNpgmCIJgXPXhwFKi9sHEh4jsUlmNCj8czgGgn8tlrQx1PixwJmofTHyIyC6tO5CNOrUOXUO90C/Gus7IuVK3MBY4E7UnJj5EZHe0OhFf7LHOFvardQvXb3Wdzq+EWquTOBoi+8fEh4jsjqGF3ddNgVt7WlcL+9Uifd3gqXSCSqPDucIqqcMhsntMfIjI7qzanQEAuMcKprC3RCYT0NVQ4JzD7S4iS2PiQ0R25Ux+JfZcKLHaFvamXD7IkAXORJbGxIeI7IrhwMKxXYMR7mN9LexNMdT5cMWHyPKY+BCR3SivUeOHwxcB6IuabYWhs+tUXgW0uuZPpyeiG8fEh4jsxrqDWahT69A5xBP9Y/2kDsdkcYEecFHIUKPSIqOkWupwiOwaEx8isgtXtrDPGmzdLexXk8sEdAnldhdRe2DiQ0R2YWtqAS6W1sLHTYHbeoZLHU6rXT7IkAXORJbExIeI7MLqhrlc0/pZ3xR2U7DAmah9MPEhIpt3tqASu8+VQCYAD9hIC/vVEsMMM7vKIYoscCayFCY+RGTzVje0sI/pGowIXzdpg2mjTsGeUMgFVNRpcLG0VupwiOwWEx8ismnltWp8f0g/hX3moFiJo2k7ZycZEkI8AXBgKZElMfEhIpu2/mA2atVaJAR74qY422lhb0piqGG7iwXORJbCxIeIbFajKew21sLeFGOBM1d8iCyGiQ8R2axtpwuRdakG3q4KTLHBFvarJYazwJnI0pj4EJHNMrSw39MvEq7OttfCfrUuIV6QCUBxlQqFlfVSh0Nkl5j4EJFNOldYiZ1pxZAJsJkp7C1xdZajQ5AHAJ7nQ2QpTHyIyCat/ktf2zO6SzAi/Wyzhb0piTzBmciimPgQkc2pqFPju0O2N4XdFIlhPMGZyJKY+BCRzVl/8CJqVFp0CvbAwHh/qcMxq27hXPEhsiQmPkRkU3Q6EV80FDXPGGT7LexX69qw4pNTVotL1SqJoyGyP0x8iMim/Hm2EJklNfByccLtvWy/hf1qXi4KxPjra5Z4gjOR+THxISKbsnJ3BgD9FHY3Zydpg7GQRG53EVkMEx8ishnnCquwM60YggA8ODBG6nAshgXORJbDxIeIbIahtmdUZ/tqYb9aN7a0E1kMEx8isgkVdWp8l6JvYZ81OEbaYCzMsOKTXlyNyjq1xNEQ2RcmPkRkE749eBHVKi06BnlgkJ21sF/N30OJMG8XAMAprvoQmRUTHyKyevbewt4U48BSJj5EZsXEh4is3vazRcgoqYGnnbawN+VynQ8LnInMiYkPEVm9VX9lAACm9Y2Eu9I+W9ivZqjzOZnDFR8ic2LiQ0RW7XxRFbafLbL7FvarGUZXpBVWolallTgaIvvBxIeIrNqXe/RT2Ed1DkKUv/22sF8t2EuJAA9n6ETgdD5XfYjMhYkPEVmtyjo11h/MBqAvanYkgiAgMYwFzkTmxsSHiKzWdyn6FvYOQR4Y0iFA6nDaXbdwQ50PC5yJzIWJDxFZJZ1OxOqGba4ZA6MdooX9ajzBmcj8mPgQkVXakVaE9OJqeCqdcEfvCKnDkYRhq+tMfiVUGp3E0RDZByY+RGSVDC3sUx2ohf1qkX6u8HRxgkqrQ1phpdThENkFJj5EZHXSi6vx5xlDC3u01OFIRhCEy9tdPM+HyCyY+BCR1TGMpxiZEISYAHdpg5GYocD5BE9wbrUalQbz1x7GugNZUodCVsQx14+JyGpV1Wuw/qB+CrujtbA3xXCQ4Ql2drXaj4dz8eORXPx8NBexAR7oH+sndUhkBbjiQ0RW5ftDF1FVr0FcoDuGOmAL+9UMoytS8yqh1YkSR2NbNhzPBQDoRODJdUdQXquWOCKyBkx8iMhq6HSisah5xsAYyGSO18J+tdgAD7gq5KhVa5FeXCV1ODajuKoee86XAABCvFyQU1aLF386IXFUZA2Y+BCR1dh9oQQXiqrhoXTCnX0cs4X9anKZgK4Nqz4nWOBssk0n8qETge4R3vjg/t6QywT8dCQXPx7OkTo0khgTHyKyGl/s0Reh3tUnAh4O2sLelG7GxId1PqbacCwPADC5eyh6R/niiVEdAQAv/HgC2ZdqpAyNJMbEh4isQlEtsD2tGACLmq+WaChwZmeXSQor67AvXb/NNTEpFADw9xHx6Bvti8p6DeavOwKNlgdCOiomPkRkFXYWyCCKwIiEQMQ6eAv71a4cXSGKLHBuye8N21w9I30Q4esGAHCSy/DOtJ7wVDohJbMUK7adlzhKkgoTHyKSXHW9BvsK9YXMM7nac42OwR5wlstQWadB9qVaqcOxer9csc11pUg/N7w8pRsA4P0/0pCSWdrusZH0mPgQkeR+OpqHOq2AGH83DOsYKHU4VkchlyEhxBMAt7taUlBRhwMZlwAAE5JCr/n8lF7huK1nGLQ6EfPXHUZlHVvcHQ0THyKS3I6G2p67eoezhb0ZxhOcWeB8Xb8dz4MoAr2jfBDu49rkc5be1g3hPq7IvlSLxT+faucISWpMfIhIUqIo4lBWGQCgf6yvtMFYMcOk9hO5bGm/ng3H9dtck7qHNfscb1cF3pnWEzIB+O7QRfxyNLe9wiMrwMSHiCR1obgapTVqOAkiEkO9pA7HahlGV5zMKWeBczPyy+twIENftzMxKeS6z+0f64e5IzsAAP75w3HklLF2ylEw8SEiSRkKTKM8AGcn/pXUnM4hnpDLBJRUq1BQUS91OFZpY8NqT99oX4R6N73NdaXHR3VEj0gfVNRpsGDdEY4EcRD8W4aIJJXS8C/0WE/+0rkeF4UcHQI9ALDOpzmXt7muLWpuikIuw3vTesLNWY596Zfw8Q62uDsCJj5EJKmULCY+pko0FDizs+sauWW1SMkshSAAE7qZlvgAQEyAOxbfmggAWLb5LI5dLLNQhAQAdWotdBKvrDHxISLJlNWocK5QP3iTiU/LDAcZcmbXtQzbXP2i/RDi7dKq107tE4GJSSHQ6EQ8sfYIalQaS4To8DRaHeZ9fQhPfnMEaglPzrarxGfx4sUQBKHRf507d5Y6LCJqhqG+Jy7ADR4KiYOxAcYCZ674XKO121xXEgQBr92ehBAvF6QXV+PlX9nibm46nYjnvjuOLamF2HQiH2cLKiWLxa4SHwBITExEXl6e8b9du3ZJHRIRNcOQ+PSK8pE2EBthmNKeV16HkioWOBtcLK3B4ayyhm2u63dzNcfHzRnLpvWAIAD/25+NTSfyzRyl4xJFEa9uTMV3hy5CLhOwYnpv4/EMUrC7xMfJyQkhISHG/wICAqQOiYiacbAh8enDxMckHkon4xyzkzzPx+i34/okpX+MH4K8WrfNdaVB8QF4ZFgcAGDh98dQUFFnlvgc3Qd/nsdnu9IBAG/d2R2juwZLGo/dJT5paWkICwtDXFwc7rvvPmRlZUkdEhE1QaXR4Wh2GQCgV6SPpLHYksQwFjhf7dfjTc/maounxiSgW7gXymrUeOqbo5IX4tq6/+3Pwr9/PwMA+NekLrizT4TEEQFOUgdgTgMGDMCqVauQkJCAvLw8LFmyBEOHDsWJEyfg6enZ5Gvq6+tRX395ybiiQv+vKLVaDbXafDNcDNcy5zWp7Xg/pHfsYjnqNTr4uCoQ6eOMs+D9MEWXEA/8egw4nl1m0a+XrfyMXCytxdHsMsgEYFRCwA3HKwB4+84k3PbhHuw6V4xPd5zDQ4NjzBLrjbCV+3GlTScL8M8fjgMAHh0Wixk3RbbL92xLBNGOjwAtKytDdHQ0li1bhocffrjJ5yxevBhLliy55vGvv/4abm5ulg6RyGFtyxXwY6Ycib46PNJZug4PW3OmTMAHqXIEKEW80FsrdTiS25oj4OcsOTp66TAv0XzfR38VCFh3QQ65IGJBkhYR7ma7tEM4Uy7g41QZtKKAgUE6TIvTQbDwGL6amhpMnz4d5eXl8PJq/hR4u1rxuZqPjw86deqEc+fONfucRYsWYcGCBcaPKyoqEBkZibFjx173C9daarUaycnJGDNmDBQKtq9IjfdDer+tPQqgAOP7dMKYQZG8HyYaWKPCB6l/orhewJCRY+Dlapmvl638jPzfR3sBVOD+4YmY2D/SbNedIIoo+foItpwuwvd53vjh0Zvg6iw32/Vby1buB6BfzX1+5UFoRS3GdQ3Ce9N6QN4Ow4cNOzYtsevEp6qqCufPn8cDDzzQ7HOUSiWUSuU1jysUCot8c1nqutQ2vB/SaDSYNC7AeA94P1oW5K1AuI8rcspqcbaoFgPjLbsybc33JKukBsdzKiATgEk9ws0e51tTe2Lcuztwvqga/9lyDktv62bW67eFNd8PADhXWIW/fXkI1SotBnfwx/vTe0Pp1D4Jo6lfF7sqbn766aexfft2ZGRk4K+//sLtt98OuVyOe++9V+rQiOgKF0trUVhZDyeZgO4RPlKHY3MMBc6Ofp6P4eyegfH+CPC49h+wN8rP3RlvT+0BAPhiTya2phaY/T3sSW5ZLR78bB9Ka9ToHuGNjx/o225JT2vYVeJz8eJF3HvvvUhISMDdd98Nf39/7N27F4GBgVKHRkRXMJzfkxjuLen2ga26fJChY7e0bzieCwCYlBRmsfcY1ikQDw+JBQA8++0xFFXy/KSmXKpW4YHP9iG3vA5xge5YNas/PJTWualknVG10dq1a6UOgYhMcDDzEgD9FG1qvW6GmV0OPKw0o7gaJ3IqIJcJGJdo2XNhnhmXgN3ninE6vxLPfHsUK2f2g2DpSl0bUlWvwayV+3G+qBqh3i748uEB8HN3ljqsZtnVig8R2YaUzDIAQB8mPm1imNl1vqjKYedKGba5BsX7w98C21xXclHI8f69vaB0kuHPM0X4Yk+mRd/PltRrtHj0yxQcvVgOXzcFvny4P8J9XKUO67qY+BBRu6qsU+NMvn6Lhis+bRPk5YJATyV0IpCaJ93MIyltONYwmyvpxg8tNEWnYE88P7ELAODVjamSzpqyFlqdiCfXHcGuc8Vwc5Zj5az+6BDU9Jl51oSJDxG1q8NZZdCJQKSf6w2NF3B03RoKnE85YIHzhaIqnMqrgJNMwLjEts3maosHB0ZjZEIgVBodHv/fYdSpHfccJVEU8cJPJ7DxeD4UcgGfPNAXPW3kBHYmPkTUrlKM87m42nMjDEMeT+Q4XoHzxoZtrsEdAuDbjrUkgiDgrbt6wN/dGafzK42jGBzR25vP4ut9WRAE4L17emFIR9uZi8nEh4jalTHxifGTOBLbZixwdsAVn18N21xmmM3VWoGeSvx7ancAwGe70rHjbFG7xyC1z3alY/k2/cHAr0zphonttN1oLkx8iKjdaHUiDmdxxcccDCs+ZwsqUa9xnC2Xc4VVOJ1fCYVcwLiu7bfNdaWbOwfjwYHRAICn1h/FpWqVJHFI4ftDF/Hyr6cA6Lvd7hsQLXFErcfEh4jazen8ClSrtPBUOiEhxPqLIK1ZhK8rvF0VUGtFpBVUSR1OuzFscw3pEABvN+lOMH5+Yhd0CPJAUWU9nvvuGOx47KXR1tQCPPPtMQDAQ4Nj8fcR8RJH1DZMfIio3Ri2uXpG+bTL7B57JgiCQ57n8+uxhkMLu1vu0EJTuCjkeO+ennCWy5B8qgD/258taTyWtj/9Ev7+1SFodSLu6BWOf03qYrNnGTHxIaJ2Y6zvYRu7WRjO83GUE5zPFlTibEEVFHIBY7pa9tBCUySGeePZ8QkAgKW/nsS5QvtceTuVW4GHVx9AvUaHUZ2D8OZd3SGz4X+4MPEhonZzMEOf+PSNZmGzOXQNc6wCZ8PZPcM6BsLbQlPpW+uhwbEY0iEAdWod5q87DJVGJ3VIZpVZUo0HP9+PyjoN+sX4YsV9vaGQ23bqYNLIip9//rnVFx4zZgxcXa379EYiaj/55XXIKauFTNBvddGNM8zsSs2rgEarg5ON/0K6HlEUjac1S9HN1RyZTMDbd/fAuHd34EROBZYln8XCCZ2lDsssCivq8MBn+1FcVY/OIZ74vxn94KKw/dl6JiU+U6ZMadVFBUFAWloa4uLi2hITEdkhwzZX5xAvqx1eaGti/d3h7ixHtUqLC8XV6BRsvwXjZwuqcK6wCs5yGUZbwTbXlYK9XPDGHd3x6JoUfLzjPIZ1CsCgeNs516Yp5TVqPPj5fmRdqkGUnxu+eLi/1ayy3SiT/3mQn58PnU5n0n9ubm6WjJmIbJBxMGkM63vMRSYTLm932XmB84aGouZhnQLh5WJ9v4DHdwvBvf0jIYrAgnVHUVZjuy3utSotHl59AKfzKxHoqcSahwcgyNN+Tlk3KfGZMWNGq7at7r//fnh5ebU5KCKyP4dY2GwRjnCCsyiK+LVhm2uyFW1zXe2FyV0RF+CO/Io6PP/DcZtscVdrdZj79SEczCyFp4sTvnioP6L87Wsxw6TEZ+XKlfD0NH0J9cMPP0RAgG0v8xGR+dSqtMbOIyY+5pXYsOJz0o4LnE/nV+JCUTWcnWQY1SVI6nCa5ebshHfv6QknmYCNx/PxbcpFqUNqFZ1OxLPfHsMfpwuhdJLh85n90CXU/hYxTN7qWrlyJTIzMy0ZCxHZqaMXy6DRiQj2UiLch00P5mQocD6VWwGdzvZWGExh6OYa0SkQnla4zXWl7hE+WDC2EwBg8c8nkVFcLXFEphFFES9vOIUfDudALhPw4f290c9Ox8qYnPj8/e9/R1xcHOLi4vDwww9jzZo1yMnJsWRsRGQnDIXNfaP9bPbQM2vVIcgDzk4yVNZrkHWpRupwzM5au7muZ86weAyI9UO1Sov5645ArbX+FvcV285h5e4MAMB/pnbHzZ2tq4DcnExOfMrKyrBlyxY8+OCDOHfuHGbPno2oqCgkJCTg0Ucfxbp161BQUGDJWInIRh3M0Bc2c5vL/BRyGbo0jP+wx/N8TuVVIL24GkonGUZ1sY1fxnKZgHem9YSXixOOZJfhv1vTpA7putbszcR/Np8FALw4uStu7xUhcUSWZXLio1QqMXLkSCxevBjbt29HaWkptmzZgmnTpuHUqVOYOXMmwsPDLRkrEdkgnU7EoawyAEx8LCUx3H4LnA3bXCMTgmzqGIQwH1e8dkcSAGD5tnM40JD8W5sNx/Lwwk8nAAD/uLkDHhoSK3FEltfm065kMhlkMhkEQYAgCBBFEVFRUeaMjYjswPmiKpTXquGikBlbr8m8Lo+usK8VH1vc5rrS5O5huLN3BHQiMH/tEVTUqaUOqZGdaUWYv+4wRBGYPiAKC8Z0kjqkdmFy4qNSqbBjxw4sXboUI0aMgLe3N+bMmYO8vDzMnj0baWlpuHDhgiVjJSIbZKjv6RHhY/NH3Vury51dFTbZQt2ck7kVyCypgYtChps7W2831/UsvrUrovzckFNWixd/PCF1OEZHsssw58sUqLUiJiWF4uXbujlM/Z3J64be3t4ICgrCLbfcgrlz52Lt2rUICQmxZGxEZAcOGgqbeXChxSSEeEIuE3CpWoW88jqE2Unn3K8N21w3dw6Cuw1tc13J00WBd6b1xN0f78GPR3IxIiEIU3pJWxZyrrASM1fuR41Ki6EdA7BsWg/IbXjoaGuZ/M+vHj16ID8/Hzt27MDOnTuxe/dulJSUWDI2IrIDV3Z0kWW4KOToGOQBwH5OcNZvc+lPa56UFCZxNDemT7QvHr+5IwDghR9PIFvC7rucslo88Nl+lNWo0SPSBx/d3wdKJ9ufv9UaJic+e/fuRUlJCd566y24urrirbfeQmhoKLp164Z58+Zh/fr1KCwstGSsRGRjSqrqkd5wjkkvDia1KMN5Pidy7aPA+XhOObIv1cJVIcfIzoFSh3PD5o6MR59oX1TWa/DkuiPQSNDiXlJVjwc+24e88jp0CPLAypn9bHYl7Ua0asPdw8MD48ePx5tvvol9+/YZEyGFQoHZs2cjLMy2s3IiMi/Dak/HIA/4uDlLHI1962ao87GTFR9DN9fNXYLg5mz7v5yd5DK8O60nPJROOJhZig/+PN+u719Vr8HMlQdwoagaYd4u+OKh/vBzd8yfyTZ9N+l0Ohw4cAB//vkntm3bht27d6O6uhrR0dHmjo+IbFhKFudztRfDis9JO1jxEUXRWN8zOcn2urmaE+nnhpenJOLJdUfx3tY0DOkYgN5Rlv/ZqFNr8cgXB3E8pxx+7s744uEBdlMH1hYmr/js378fb731FiZOnAgfHx8MHDgQK1asQFBQEN5//31cuHAB6enployViGxMSgYTn/bSJdQLggDkV9ShqLJe6nBuyNGL5cgpq4WbsxwjEmyzm6s5U3qG49YeYdDqRMxfewRV9RqLvp/hff46XwJ3ZzlWzeqHDg31YI7K5BWfm266CSEhIRg5ciSWLVuGkSNHIj4+3pKxEZENq9docaxh26Wvnc78sSbuSifEBrjjQlE1TuaW23TCsOGYvqh5VJdguDrbV+GtIAh4eUo3pGSWIutSDRb/fBL/mdrDIu8liiL++cNxbDqZD2e5DJ8+2BfdI3ws8l62xOTEJzU1FQkJCZaMhYjsyImcCqg0Ovi7OyPG303qcBxCtzDvhsSnwmYTH1EUjfU9k+xom+tK3q76Fvd7PtmDb1MuYkRCICZ3N3+N7L9/P4O1B7IhE4D37+2JQR0CzP4etsjkrS4mPUTUGimZ+iP6e0f7OszBaFLrFq4vcLbllvbD2WXILa+Du7McIxJsv5urOf1j/fD3ER0AAM9/fxy5ZbVmvf6nOy4YC6hfuz0J47vZZxLZFiav+MTFxZn0PJ7eTETAlef3sL6nvRhGV9jysFLDas+YrsFwUdjXNtfVnhjdETvTinD0YjmeXHcEX8++ySwHCX6bchGvbkwFADw7PgH39Oc4qSuZnPhkZGQgOjoa06dPR1CQbS6hElH7EEXRmPiwsLn9JDYkPtmXalFeo4a3m0LiiFpHpxOx0Tiby/6PR1HIZXj3nl6Y9P5O7Eu/hE92XMBjI26sdjb5VAGe++4YAGD20Fg8Npy1uFczOfFZt24dPv/8cyxbtgwTJkzAQw89hIkTJ0Im4+wdImoss6QGxVUqOMtlxjZrsjxvNwUifF1xsbQWJ/PKMSjetmo6DmeXIq+8Dp5KJwztaFuxt1VsgDsW35KIZ787hrc3n8GQDgFIimjbz8y+CyWY+/UhaHUi7uwdgecnduE2cxNMzlqmTp2K3377DefOnUOfPn3w5JNPIjIyEgsXLkRaWpolYyQiG2NY7ekW7mX32xXWxjipPcf2zvP51YG2ua40tW8EJiaFQKMT8cTaw6hRtb7F/WRuOf62+iBUGh1GdwnGm3cmMelpRquXa8LDw/HPf/4TaWlp+Prrr7Fv3z507twZpaWlloiPiGzQ5cGkbGNvb8YCZxur82m8zeVYhbiCIOC125MQ4uWCC8XVePnX1Fa9Pr24GjM+34/Keg36x/ph+fRecJJzN6Y5bfrK1NXVYc2aNViyZAn27duHqVOnws2N7apEpHeI9T2SSTTM7LKxzq6DmaUoqKiHp4sThjjINteVfNycsWxaDwgC8L/9Wfj9ZL5JryuoqMMDn+1DcZUKXUO98H8z+jrUallbtCrx2bdvHx555BGEhIRg2bJluOOOO5CTk4O1a9dCqVRaKkYisiHltWqcLawEgHY5jp8aM2x1XSiuRrWFTwU2J8OhhWO7hjjctHCDQfEBeGSYvoN64XfHUFBRd93nl9eo8eBn+3GxtBYx/m5Y/VB/eLnYVkG7FExOfBITEzF58mS4urpi+/btOHToEObNmwdfX/7FRkSXHcoqhSgCMf5uCPTkP4jaW6CnEkGeSogicDrfNup8tDoRG0/oVzgmO9g219WeGpOAbuFeKK1R4+n1R6HTiU0+r0alwUOrD+BMQSWCPJX48uEB/HkzkcmJT2pqKurq6vDFF19g5MiR8PPza/I/InJshm2u3tzmkkw343aXbSQ+BzIuoaiyHl4uThjs4KcLOzvJ8O60XnBRyLAzrRif7752BqZaq8PfvzqElMxSeLk44cuHByDSj+UmpjK5nX3lypWWjIOoXZXWqHDeNn4n2JyDGYaDC/kPIal0C/PCH6cLbabOx3Bo4bjEEDg7sSi3Q5AHXpjcFf/84QTe2nQGg+ID0DFQP01dpxPx7HdH8eeZIrgoZFg5qx8SQjwljti2mJz4zJgxw5JxELUbURTxyJrDOJLthJ5pxbi5q2MvrZuTRqvDkewyAEDfGK74SMVY4Jxr/dm9VifitxOO2c11PdP7R2Hb6SJsSS3AE2sP4/tHB0AUgVd+O4OfjuTCSSbgw/v7oA//gdFqTK3J4ey5UIIj2fp/Cf9y3LTOCTJNal4latVaeLk4oUOgh9ThOCzDVldaQSXqNVqJo7m+feklKK5SwdtV4fDbXFcSBAFv3pmEQE8l0gqr8NbvZ7E5R8CXe7MAAG/f3QMjbXQQrdRMSnz8/PxQXFxs8kWjoqKQmZnZ5qCILOmTHZfnyW07XQSNVidhNPbl4BWDSWVmmDlEbRPm7QJfNwU0OhFn86ukDue6DNtc4xNDoODZM434eyjxn6k9AABf7svGxmx9t9viW7ritp7hUoZm00za6iorK8Nvv/0Gb2/TjtEuKSmBVmvd/8ogx3Q6vwJ/nimCTAAUMhFltWocyCjFwHh/qUOzC8b5XGxjl5QgCEgM88auc8U4kVve5hEIlqbR6rCpoZuL21xNG94pEA8NjjUWOc8bEYeZg2Mljsq2scaHHIphtWdc12CUFOZhf5GAzafymfiYiTHxYX2P5BLDvfSJjxUXOO9Lv4SSahV83RT8GbyOZ8cnQK3RoCI/A4/fzKGjN8qkdUWdTtfq/+Li4iwdO1Gr5JXX4ucj+kPS/jYkBt399OdjbD5ZAFFs+qwMMl1uWS3yyusglwnoGekjdTgOz3CQoTUXOBtmc43vxm2u63FRyPHi5C64OUzk/C0z4HcaOYzPd6VDoxNxU5wfukd4I8FbhItChpyyWpzKs95fDrbCMJ+ra6gX3JxNXkwmCzEUOKfmVUBthXVs+m2uhm6upDCJoyFHwsSHHEJ5rRr/258NAJgzTL9U7CwHhjZ0kWw+WSBZbPYiJUNf2Mz5XNYh2s8NHkonqDQ6nC+yvgLnPRdKUFqjhp+7M26KY0s2tR8mPuQQvt6Xhap6DToFe2BEQqDx8dFd9P+/+RQTnxuVksXBpNZEJhPQNUw/qf2kFZ7gvOGKbS5OEqf2xO82snv1Gi1WNnREPDIsvtEe+ciEQMgE/XZA9qUaqUK0edX1GqTm6QeT8uBC65HYkPicyLWuAme1VodNDdPHJyexm4vaFxMfsns/HclFYWU9QrxccGuPxrUEvm7O6B+rX2bnqk/bHc0ug1YnItzHFaHerlKHQw0MBc7WtuLz1/kSlNWoEeBx+eePqL2YnPjodDq8+eabGDx4MPr164eFCxeitrbWkrER3TCdTjS2sD80JKbJOUBju4YAADaf5CnObXWQg0mtkqHA+WRuebNTvqWw4Zi+u5LbXCQFk7/jXn31VTz//PPw8PBAeHg43nvvPcydO9eSsRHdsG1nCnGusAqeSifc2z+qyeeM6RoMQD8h+lK1qj3DsxuGxKcvEx+rEh/oDqWTDNUqLTJKqqUOBwCg0ujwe0MzAbu5SAomJz5ffPEFPvjgA/z+++/48ccf8csvv+Crr76CTmd9bZJEBh9v16/2TB8QBU8XRZPPifRzQ9dQL+hEYGsqt7taS6cTcTiThc3WyEkuQ5dQQ52PdWx37T5fjPJaNQI8lNzmIkmYnPhkZWVh4sSJxo9Hjx4NQRCQm5trkcCIbtThrFLsz7gEhVzArBaOeB+bqF/1YZ1P650trERlvQZuznJ0DvGUOhy6Srfwhs4uKylwNnRzTUwKgZzz3EgCJic+Go0GLi4ujR5TKBRQq9VmD4rIHAy1Pbf1DEeIt8t1n2uo89mZVoRaFefMtYZhTEWvKB/Wa1ihRCsqcNZvczXM5mI3F0nE5ONVRVHEzJkzoVQqjY/V1dXh0Ucfhbu7u/Gx77//3rwRErVBenG1sV32kWEtj0/pEuqJCF9XXCytxY60IoxLDLF0iHYjJYODSa3Z5dEV5RBFaUce7DpXhMo6DYI8legXw20uksYNDSm9//77zRoMkbn8384LEEXg5s5B6BTc8vaLIAgY2zUEn+9Ox+aTBUx8WuGgcTApf5FZo04hHnCSCSirUSOnrBYRvm6SxfKrcZsrFDJuc5FETE58Vq5cack4iMymuKoe61MuAjBttcdgbGIwPt+djq2nC6DR6rhtY4LCyjpkXaqBIOi3usj6KJ3k6BTsiVN5FTiRUyFZ4lOv0SK5oZtrcnduc5F0+Dc72Z0v/sqASqNDj0gfDGhF10jfaF/4uilQVqPGgYbtG7q+Qw2rPQnBnvBqpmuOpGcNBc47zxajsl6DEC8X9Oa2KEmIiQ/ZlRqVBl/szQQAzBkW16p6Bie5DKO6GLq7eJihKVLYxm4TjAXOEra0/9pwaCG3uUhqdpn4rFixAjExMXBxccGAAQOwf/9+qUOidvLNgWyU1agR7e/WpjqdsQ2HGW4+WQBRtJ6Tbq3VQSY+NsGw4nMiR5oVnzq1FskNR0VM4jYXSczuEp9169ZhwYIFeOmll3Do0CH06NED48aNQ2FhodShkYVptDr83y79MNK/DY1r0xkhQzsGwkUhQ05ZLU7lSd/+a83q1FrjL9K+0SxstmZdQr0gCEBhZT0KK+ra/f23ny1CtUqLMG8X9Ir0aff3J7qS3SU+y5Ytw+zZszFr1ix07doVH330Edzc3PD5559LHRpZ2MYT+bhYWgs/d2dM7RPRpmu4OssxrGMgAP2qDzXveE451FoRgZ5KRPpxMKk1c3N2QnygBwBptrs2sJuLrIjJXV22QKVSISUlBYsWLTI+JpPJMHr0aOzZs6fJ19TX16O+vt74cUWF/i8FtVpt1sMZDdfigY+WIYoiPt5+DgBw/4BIyKGDWt38OJXr3Y9RnQOw+VQBfj+Zj3kjrn/isyPbd74YANAr0hsajeaGrsWfD8vrGuKJc4VVOJpdiiHxLW9Nmuue1Km12NIwCmZc10De4zbiz0jLTP3a2FXiU1xcDK1Wi+Dg4EaPBwcH4/Tp002+5vXXX8eSJUuueXzz5s1wczN/22dycrLZr0nA2XIBJ3PlUMhEBFecwcaNZ0x6XVP3Q6sGBMhxOr8SX36/Ef7XP/TZYW06LQMgg1t1HjZuNM/oGv58WI6sXAAgxx+HzyK2pum/D5tyo/fkaImAGpUcvs4ico79hdzjN3Q5h8efkebV1NSY9Dy7SnzaYtGiRViwYIHx44qKCkRGRmLs2LHw8vIy2/uo1WokJydjzJgxUCjY9mtu365OAVCCaf2icPfkLi0+v6X78VPJAexLL4UmJBETB0VbIGLbJooiFh/9E4Aa08cNvOG6Df58WJ7fhUv4ceVBXNK5YeLEYS0+31z3ZPO6YwDycUe/GEwan9Dm6zg6/oy0zLBj0xK7SnwCAgIgl8tRUNC4NqOgoAAhIU13+CiVykZjOAwUCoVFvrksdV1HlppXgZ3nSiATgEeGdWjV17e5+zEuMRT70kux5XQRHhnewZzh2oXzRVUorVFD6SRDzyh/KJzMUy7Inw/L6R6lL0C/WFaHarUIHzdnk153I/ekVqXFH2eKAAC39ozgvTUD/ow0z9Svi10VNzs7O6NPnz7YunWr8TGdToetW7di4MCBEkZGlmQYRjohKRRR/ubZnjRMaz+YcQklVfUtPNvxGM7v6RHhA2czJT1kWd6uCkT56X8+2qvAeduZQtSqtYjwdUX3CO92eU+iltjd31gLFizAp59+itWrVyM1NRWPPfYYqqurMWvWLKlDIwvILavFL0f19SVzWjGeoiURvm5IDPOCTgS2nuZRCFczDCbtzfN7bEp7n+dj6Oaa1D1U0uGoRFeyu8Rn2rRp+M9//oMXX3wRPXv2xJEjR7Bp06ZrCp7JPny+Kx0anYiBcf7oHuFj1muP7arfHmVb+7VSsvSJT18mPjYl0Tip3fIrPjUqDbaebpjNlRRm8fcjMpXdJT4AMG/ePGRmZqK+vh779u3DgAEDpA6JLKC8Vo3/7c8CADwy3HyrPQaG7a6daUWoUd1Yu7Y9KatR4VxhFQCu+NiabuGG0RWWX/H543Qh6tQ6RPm5GVeaiKyBXSY+5Bi+2peJapUWCcGeGNEp0OzX7xziiUg/V9RrdNhxttjs17dVhvqeuEB3+LmbViBL1iExTJ+ApBdXo6ressk8t7nIWjHxIZtUr9Fi5e4MAMAjrRxGaipBEC5vd3FoqZEh8eE2l+0J8FAixMsFoqjvhrSU6noN/miojZuUxNlcZF2Y+JBN+vFwDooq6xHq7YJbeliufsAwtHRraiE02uZPgnYkHExq29qjwHnr6ULUa3SI8XczrjIRWQsmPmRzdDrR2ML+0OBYi7ZT94n2hZ+7M8pr1difccli72MrVBodjmaXAQD6cDCpTTIWOOdYbsVnwzF9pyW3ucgaMfEhm7P1dCHOF1XDU+mEe/pHWvS9nOQyjOocBIDdXQBwKq8C9RodfNwUiAtwlzocagNLFzhX1WuwreHQwkns5iIrxMSHbM4nO84DAO67KRqeLpY/wXRsor7OJ/lUAURRtPj7WbODDatefaJ8OWXbRhm2utIKq1Cn1pr9+ltTC6DS6BAX4I4uoZ5mvz7RjWLiQzYlJbMUBzJKoZALmDU4pl3ec2jHALgq5Mgpq223E2+t1aGG83v6xLC+x1aFeLnAz90ZWp2IM/mVZr/+r+zmIivHxIdsimG15/Ze4Qj2ap+x6S4KOYZ1CgAAbD7luNtdoijiYMOJzX2imPjYKkEQjAXHJ8y83VVZp8Z2wzZXd3ZzkXVi4kM240JRlTHxeMSM4ylMcfkUZ8dta79YWovCyno4yQT0uMFp7CQtQ52PuQuct6QWQKXVIT7QHQnB3OYi68TEh2zGpzvTIYrAqM5B6BDUvn+p3tw5CHKZgNP5lcgqqWnX97YWhvN7EsO94aKQSxwN3YhuYZYpcL58aGEYt7nIajHxIZtQVFmP7w5dBADMGR7f7u/v6+6M/jH69m1HPczwYKa+sJkHF9o+Q4Hz6bxKqM10PlV5rdp4wvlkbnORFWPiQzbhiz0ZUGl06Bnpg34SFdYaZnc5ap1PSmYZACY+9iDS1w2eSieotDrj3LUbteWUfpurU7AHOnGbi6wYEx+yetX1GnyxJxMAMMdC4ylMMabhFOeDGZdQUlUvSQxSqaxT40y+vh6EJzbbPplMQNcw857gvOF4wzYXz+4hK8fEh6zeNwezUV6rRoy/m/FMHSlE+OqP39eJ+kMUHcnhrDLoRCDSzxVB7dRNR5Z1+SDDGy9wLq9RY2eaoZtLup9RIlMw8SGrptHq8H870wEAfxsaB7nEh+Zd7u5yrO2uy4NJOabCXphzZtfmU/lQa0V0DvFs98YDotZi4kNWbcPxPOSU1cLf3Rl39YmQOhxjnc/OtCLUqDQSR9N+DIlPb25z2Q1DZ9epvApodTd2Irnx0EJOYicbwMSHrJYoXh5GOmNQjFW0UHcO8USknyvqNTpjB4u90+pEHM4yrPgw8bEXcYEecFHIUKPSIqOkus3XKa1WYfc5/c/CRHZzkQ1g4kNWa/e5EpzMrYCrQo4HboqWOhwA+lNvjdtdDtLWfjq/AtUqLTyVTuzWsSNymYAuoTe+3bX5VD40OhFdQr0QH+hhrvCILIaJD1mtjxvGU0zrFwlfd2eJo7lsbEN319bUQmjMdAaKNTNsc/WM8pG8xorM6/JBhm0vcDZsc/HsHrIVTHzIKp3MLcfOtGLIBODhIbFSh9NIn2hf+Lk7o7xWjf3pl6QOx+JY2Gy/brTA+VK1Cn+dLwEATGR9D9kIJj5klT5tqO2Z1D0MkX5uEkfTmJNchlGdgwA4xmGGxsGkrO+xO4lhhpld5RDF1hc4/34yH1qdiMQwL8QGuJs7PCKLYOJDVudiaQ1+aVg+n9POw0hNNS7x8tDStvzCsBX55XXIKauFTNBvdZF96RTsCYVcQEWdBhdLa1v9+suzubjaQ7aDiQ9Znc93ZUCrEzEo3t94yJq1GdIxAK4KOXLL68xyAJy1MmxzdQ7xgofSSeJoyNycnWRICNEXrLd2YGlJVT3+Oq/v5mIbO9kSJj5kVcpr1Fh7IAuANMNITeWikGN4p0AA+lUfe2UcTCrRfDSyvMRQw3ZX6xL4TSfzoROBpHBvRPtzm4tsBxMfsipr9mWiRqVF5xBPDOsYIHU41+UIQ0sPZbK+x94ZC5xbueLDbS6yVUx8yGrUqbVYuTsDADBnuHTDSE11c+cgyGUCTudXIvMGDoCzVrUqrXEbj4mP/UoMb32Bc1FlPfZe0HdzcZuLbA0TH7IaPx7OQXFVPcK8XTC5u/VPePZxc8aAWH2Ld7IdrvocvVgGjU5EiJcLwn1cpQ6HLKRLiBdkAlBcpUJhZb1JrzFsc/WI8La6rkuiljDxIaug04n4ZKe+hf2hIbFQyG3jW9NwmKE9Di1NuWKby9pX36jtXJ3l6BCkP3HZ1PN8NhzLBcBtLrJNtvHbhezeltQCXCiqhqeLE+7pHyV1OCYb09DWfjDzEoqrTPvXsq04mKEvbOY2l/1LbMUJzoWVddjXcHAnDy0kW8TEh6yCYRjp/TdF21TbdLiPK7qFe0EnAn+kFkodjtnodCIOZZUBYEeXI0gMM/0E500n8iGKQM9IH0T4cpuLbA8TH5JcSuYlHMwshbNchlmDYqQOp9XscWjp+aIqlNeq4aqQGwdZkv0ynJdlyooPZ3ORrWPiQ5L7eLt+tef2XuEI8nKROJrWM7S170grRnW9RuJozMNQ39Mj0ttm6q2o7bo2rPjklNXiUrWq2ecVVNThQMMW6ARuc5GN4t9oJKnzRVVITtUXBs8eZl3DSE2VEOyJKD83qDQ67EwrkjocszjI83scipeLAjH++m2r653g/NvxPIgi0DvKh51+ZLOY+JCk/m/nBYgiMLpLMDoEeUodTpsIgmB33V2cyO54Lp/n0/x214bjhkMLrf+4CaLmMPEhyRRW1uG7QzkA9AcW2rKxDd1dW08XQq3VSRzNjSmpqkd6sf5Axt5RXPFxFIYC5+ZWfPLL63AgQ58QT0wKabe4iMyNiQ9JZvVfGVBpdOgV5YO+Nr6l0ifaF37uziivVeNAQ6uvrTKs9nQM8oC3m0LiaKi9dGuhpX1jw2pP32hfhHpzm4tsFxMfkkR1vQZf7skEAMwZFm/zB+TJZQJGdwkCYPuzu1KyGra52MbuUAwrPunF1aisU1/z+cvbXCxqJtvGxIcksfZANirqNIgNcMeYhvoYW2dsaz+Zb/LMI2uU0rCdwW0ux+LvoUSYt76r8tRVqz65ZbVIySyFIPDQQrJ9THyo3am1Ony+Kx0AMHtoHOQy217tMRjSMQCuCjlyy+tMOg/FGtVrtDjWcIhd3xgWNjsaY4HzVd+/hm2ufjF+CLbBIyeIrsTEh9rdxuN5yCmrRYCHM+7oHS51OGbjopBjeKdAAPpVH1t0IqcCKo0O/u7OxvZmchyX63waFzgbtrl4aCHZAyY+1K5EUcRHDQcWzhgYAxeFXOKIzMtwmKGt1vmkZOoLs3tzMKlDMnZ2XdHSnlNWi8NZZRAEYHw3dnOR7WPiQ+1q17lipOZVwFUhxwMDo6UOx+xu7hwEuUzA6fxKZJZUSx1Oq10+v4f1PY7IMLoirbAStSotAGBTw9lUA2L9EOTJbS6yfUx8qF0ZxlNM6xcJHzdniaMxPx83ZwyI1dfGJNvYqo8oipcTH3Z0OaRgLyUCPJyhE4EzBZUAgI3H9du2PLSQ7AUTH2o3J3LKsetcMeQyAQ8Psc3xFKYwnOL8u43V+WSW1KC4SgVnuQyJDbUe5FgEQTDe+5N5lSipA47lVEAmAOMTuc1F9oGJD7WbT3fqV3smJYUi0s9+C2fHNPyCOJhZiuKqeomjMZ1htScpwtvuaq/IdN3C9XU+p3IrcKREX+d1U5w/Aj2VUoZFZDZMfKhdXCytwa/H9J0hjwyz7fEULQn3cUW3cC+IIrA11Xa2uziYlIDLnV2n8ipxuET/K4KHFpI9YeJD7eKzXenQ6kQM6RBgLKC0Z5cPM7SdxOcQEx8CjFtdqfmVyK4WuM1FdoeJD1lcWY0Ka/dnA7D/1R6DcQ2/KHaeK0Z1vUbiaFpWXqvG2UJ9MSsTH8cW6ecKTxcnaHX608dvivODvwe3uch+MPEhi1uzNxO1ai26hHphaMcAqcNpF52CPRDt7waVRocdZ4ukDqdFh7JKIYpAjL8bAvhLzqEJgmDc7gKAiTy7h+wMEx+yqDq1Fqv+ygAAzBkW5zCH4gmCYOzusoXDDC9vc3FMBV0ucJZBxJiG4btE9oKJD1nU94dyUFylQpi3i8MVSI5t2O7amloAtVYncTTXdzCD9T102U1x/gCArr4i/Nzt77wtcmxMfMhidDoR/9fQwv7w0Dgo5I717dY7yhf+7s6oqNNgf/olqcNplkarw5HsMgA8uJD0bu4chDUP9cV9Haw7YSdqC8f6TUTtKjm1ABeKq+Hl4oR7+kVKHU67k8sEjO7SsN1lxYcZpuZVolathZeLEzoEekgdDlkBQRAwINYPbk5SR0Jkfkx8yGI+3n4eAHD/TdFwVzrm36BXDi0VRVHiaJp28IrBpDKZY9RgEZHjYuJDFnEw4xIOZZXBWS7DzMExUocjmcEdAuDmLEdeeR1OXDHx2ppwMCkRORImPmQRHzUMI72jd7hDT3R2UcgxvFMgAGDzKevc7jIkPr2Z+BCRA2DiQ2Z3rrAKW1ILIAjAbAc5sPB6jNtdVniKc25ZLfLK6yCXCegZ6SN1OEREFsfEh8zO0Mk1uksw4lksi5sTgiGXCThTUImM4mqpw2nEMJ8rMcwLbs6OWYdFRI6FiQ+ZVWFFHb4/lANAf2AhAd5uCtwUpz8YMNnKDjNMyWgobI7iNhcROQYmPmRWq/7KgEqrQ59oX/SN4SnABsahpVZW55OS1VDYzPN7iMhB2FXiExMTA0EQGv33xhtvSB2Ww6iq1+DLvZkAHGcYqanGNIyvOJhZiuKqeomj0auu1yA1j4NJicix2FXiAwBLly5FXl6e8b9//OMfUofkMNbuz0JlnQZxAe4Y03BwH+mF+bgiKdwboqgfYWENjmaXQasTEe7jilBvV6nDISJqF3aX+Hh6eiIkJMT4n7u7u9QhOQS1VofPd6UD0Hdy8SC8axmHllpJd9fBTM7nIiLHY3dtHG+88QZefvllREVFYfr06XjyySfh5NT8H7O+vh719Ze3Hioq9IfMqdVqqNVqs8VluJY5r2lNfjqSi9zyOgR4OOOWbkFW/+eU4n7cnOCPt5OBneeKUVZVK/lp1gfSSwAAPSO8JL9f9v7zYYt4T6wL70fLTP3aCKK1nqPfBsuWLUPv3r3h5+eHv/76C4sWLcKsWbOwbNmyZl+zePFiLFmy5JrHv/76a7i5uVkyXLshisBbx+TIrREwKVKLsRF28y1lVqIIvHJYjuJ6AbM6adHTX7qvk04EFh2Qo04r4JnuGkRwYZSIbFxNTQ2mT5+O8vJyeHl5Nfs8q098Fi5ciDfffPO6z0lNTUXnzp2vefzzzz/HnDlzUFVVBaVS2eRrm1rxiYyMRHFx8XW/cK2lVquRnJyMMWPGQKFQmO261mBnWjEe+uIQ3Jzl2PH0MHi7Wv+fT6r78camM/hsdyZu6xGK/9yV1G7ve7Uz+ZWYvGIP3JzlSHl+JJzk0u562/PPh63iPbEuvB8tq6ioQEBAQIuJj9VvdT311FOYOXPmdZ8TF9d0B9GAAQOg0WiQkZGBhISEJp+jVCqbTIoUCoVFvrksdV0pffaXvpPrnn5RCPCyrVWy9r4f45PC8NnuTGw7UwTI5FBIlHAczdV3c/WK8oGrS9P/KJCCPf582DreE+vC+9E8U78uVp/4BAYGIjAwsE2vPXLkCGQyGYKCgswcFRmcyCnH7nMlkMsEPDQkRupwrF7vKF/4uzujpFqF/emXMLhDgCRxpGQYCpt51hIRORa76eras2cP3n33XRw9ehQXLlzAV199hSeffBL3338/fH3ZtWIpH+/Qj6eY3D0UEb62tdojBblMwOiGVv/fT0p3mCE7uojIUdlN4qNUKrF27VoMHz4ciYmJePXVV/Hkk0/ik08+kTo0u5V9qQYbj+cB4IGFrXHl0FIpSuwKK+uQdakGgqDf6iIiciRWv9Vlqt69e2Pv3r1Sh+FQPtuVDq1OxNCOAUgM85Y6HJsxuEMA3JzlyK+ow/GccnSP8GnX9z/UsNqTEOwJLxfWChCRY7GbFR9qX6XVKqw7kA0AmDMsXuJobIuLQo7hnfR1a1IcZpjCbS4icmBMfKhN1uzNRK1ai66hXhjcwV/qcGzOuETphpYa6ns4mJSIHBETH2q1OrUWq/7KAADMGR4HQeB4itYamRAEJ5mAswVVSC+ubrf3rVNrcSKnHADQJ4odXUTkeJj4UKt9d+giSqpVCPdxxcSkUKnDsUnebgrcFKdfKUtux1Wf4znlUGtFBHoqEenHwaRE5HiY+FCraHUi/m+nfhjpw0NiJTuAzx5c2d3VXg42nN/TN9qXK3VE5JD4W4taJflUPtKLq+HtqsC0fpFSh2PTDOf5pGSVoqiyvoVnmwcLm4nI0THxIZPtOV+Cl39NBQA8cFO05NPFbV2Yjyu6R3hDFIGtqZZf9RFFEYeymPgQkWNj4kMtKq9R47lvj+HeT/cip6wWYd4umDk4Ruqw7MLYrg3bXacsn/hcKK7GpWoVlE4ynrtERA6LiQ81SxRF/HI0F6OWbce6g/oze6YPiMJv84chwMN6BlvasrENbe27zhWjql5j0fcybHP1iPCBsxN/9InIMXGvgpqUU1aLF348gT9OFwIA4gPd8cad3dEvhi3Q5tQxyAMx/m7IKKnBjrNFFu2SMw4m5fk9ROTA+M8+akSrE7FydzrGLtuOP04XQiEX8MSojtj4xFAmPRYgCIJx1WezhYeWphjqe6KY+BCR4+KKDxmdzq/Ac98dx9HsMgD6lufX70hCx2BPaQOzc2O7BuOTHRew9XQh1FqdRY4IKKtR4VxhFQCgNwubiciBMfEh1Km1eH9rGj7ZcQEanQhPpROem9AZ0/tHQSbjWS+W1ivKFwEeziiuUmHfhUsY0jHA7O9hqO+JC3SHn7uz2a9PRGQruNXl4P46X4zx7+7AB3+eh0YnYlxiMJIXDMf9N0Uz6WkncplgPNPHUrO7DIlPX672EJGDY+LjoMpqVHj226OY/uk+ZJTUIMhTiY/u74OPH+iLEG8XqcNzOFee4iyKotmvbxxMGs06LSJybNzqcjCiKOKXY3lY+stJFFepAAD33xSFZ8d3hpeLQuLoHNeg+AC4OcuRX1GH4znl6B7hY7ZrqzQ6Y90W63uIyNEx8XEgV7eodwjywBt3JKEvu7Uk56KQY0RCIDYez8fmkwVmTXxO5VWgXqODj5sC8YHuZrsuEZEt4laXA9DqRHy+Kx1jGlrUneUyzB/dERseH8Kkx4qM7drQ1m7mOp+DGZcA6NvYOZiUiBwdV3zsXGpeBRZ+37hF/Y07k9AhiC3q1mZkQhCcZALOFlQhvbgasQHmWZ0xzufiwYVERFzxsVd1ai3e3HQat/x3F45ml8FT6YRXb++Gb+YMZNJjpbzdFLgpzh8AkGymVR9RFHEwg4XNREQGTHzs0F/n9C3qHza0qI9PDMGWp4bjvgFsUbd2V3Z3mcPF0loUVtZDIRfQPYKDSYmIuNVlR8pqVHh1QyrWp1wEAAR7KbH0tm4Y1zASgazf6C7BePGnk0jJKkVRZT0CPW9sGKzh/J7EMG+4KOTmCJGIyKZxxccOiKKIn4/mYvSy7VifchGCADxwUzSSFwxn0mNjwnxc0T3CG6IIbEm98VWfg5kNhc1sYyciAsAVH5t3sbQG//rxBP48UwRAP+37jTuT0If1HDZrbNdgHLtYjs0n83Fv/6gbulZKZhkAnthMRGTAxMdGaXUiVv2Vgbc3n0GNSgtnuQxzR3bAoyPioHTiloYtG5sYgv9sPovd50pQVa+Bh7JtP6aVdWqcya8AwBUfIiIDJj426FRuBRZ9fwxHL5YDAPrH+OG1O5LQIchD4sjIHDoGeSDG3w0ZJTXYfqYIk7qHtuk6h7PKoBOBSD9XBHlxDAkREcAaH5tibFFfvgtHL5bD08UJr92ehLWP3MSkx44IgoCxiTd+mGEK53MREV2DKz42Yve5Yjz/w3FkltQAACZ0C8GSWxP5L3k7NS4xGJ/suIA/ThdCpdHB2an1/0YxJD7c5iIiuoyJj5UrrVbh1Y2p+LahRT3EywVLb0s0rgiQfeoZ6YsADyWKq+qxL70EQzsGtur1Wp2Iw1lMfIiIrsatLisliiJ+OpKD0cu249uGFvUHB0YjecEwJj0OQC4TMKZrEIC2HWZ4Or8C1SotPJVO6BTMk7qJiAyY+Fih7Es1mLXqAJ5YewQl1Sp0CvbAt48OxNLbusHTRSF1eNRODENLk08VQKcTW/VawzZXzygfyHlaNxGREbe6rIhWJ2Ll7nS8vfksatX6FvV5N3fAo8Pj21TjQbZtYLw/3J3lyK+ow/GccvSI9DH5tSxsJiJqGhMfK3EytxyLvj+OY2xRpwYuCjlGJARhw/E8bD6V36rExziYlBPZiYga4TKCxGpVWrz+WypuXb4bxxpa1F+/gy3qpNeWoaX55XXIKauFTECrkiUiIkfAFR8J7Uorxj9/vNyiPjEpBItvYYs6XTYiIQhOMgFphVW4UFSFuMCWk2HDNleXUK82n/pMRGSv+LeiBEqrVXhlQyq+O6RvUQ/1dsHS27phTNdgiSMja+PtqsDAeH/sTCtG8qkCzBnecuLDwaRERM3jVlc7EkXg56N5GL1sO747pG9RnzEwGpufHMakh5o1tuF7Y/Mp07a7DvHgQiKiZnHFp51cLK3Fx6dlSN17HADQKdgDr9/Rnb+cqEWjuwbjhZ9O4lBWKQor6xDk2fxWaK1Ki5O5+sGkfWPY0UVEdDWu+LSDC0VVmPjf3Ugtk0EhF/DUmE749R9DmfSQSUK9XdEjwhuiCGxNLbzuc49eLINGJyLEywVh3qwVIyK6GhOfdhAb4I5+Mb7o4CXi17mD8I9RHXkuD7WKcWjpyesPLTXO54rxhSDw4EIioqvxt287EAQB797dA3O7ahEX6C51OGSDDHU+u8+VoKpe0+zzDmY0FDZHcTWRiKgpTHzaiaeLEzg5gNqqQ5AHYgPcodLqsP1MUZPP0elEHMoqA8CDC4mImsPEh8gGCIJwRXdX09td54uqUF6rhqtCji6hXu0ZHhGRzWDiQ2QjDKc4/3G6ECqN7prPG+p7ekR6QyHnjzYRUVP4tyORjegZ6YsADyUq6zTYl15yzecPcjApEVGLmPgQ2Qi5TMCYrkEAmp7dlcKDC4mIWsTEh8iGjO3a0NZ+Kh86nWh8vKSqHunF1QCA3uzoIiJqFhMfIhsyMN4f7s5yFFTU41hOufFxw2pPp2APeLsppAqPiMjqMfEhsiEuCjlGJBi2uy53d6VkcZuLiMgUTHyIbIyhu+vKoaUpGYbEh4XNRETXw8SHyMaMSAiCk0zAucIqnC+qQr1Ga9z24ooPEdH1MfEhsjHergoMjPcHACSfKsCJnAqoNDr4uzsjxt9N4uiIiKwbEx8iG3Tl0NKUzIb5XNEcTEpE1BImPkQ2aEwXfZ3P4ewybDqhL3LmNhcRUcuY+BDZoBBvF/SI9IEogoNJiYhagYkPkY0yDC0FAGe5DN3CvSWMhojINjDxIbJR4xIvJz5JEd5QOskljIaIyDYw8SGyUfGBHogLcAcA9GV9DxGRSZj4ENkoQRAw7+YOiAtwx119IqQOh4jIJjhJHQARtd0dvSNwR28mPUREprKZFZ9XX30VgwYNgpubG3x8fJp8TlZWFiZNmgQ3NzcEBQXhmWeegUajad9AiYiIyGrZzIqPSqXC1KlTMXDgQHz22WfXfF6r1WLSpEkICQnBX3/9hby8PDz44INQKBR47bXXJIiYiIiIrI3NrPgsWbIETz75JJKSkpr8/ObNm3Hq1CmsWbMGPXv2xIQJE/Dyyy9jxYoVUKlU7RwtERERWSObWfFpyZ49e5CUlITg4MstvuPGjcNjjz2GkydPolevXk2+rr6+HvX19caPKyoqAABqtRpqtdps8RmuZc5rUtvxflgX3g/rw3tiXXg/Wmbq18ZuEp/8/PxGSQ8A48f5+fnNvu7111/HkiVLrnl88+bNcHMz/8DH5ORks1+T2o73w7rwflgf3hPrwvvRvJqaGpOeJ2nis3DhQrz55pvXfU5qaio6d+5ssRgWLVqEBQsWGD+uqKhAZGQkxo4dCy8vL7O9j1qtRnJyMsaMGQOFQmG261Lb8H5YF94P68N7Yl14P1pm2LFpiaSJz1NPPYWZM2de9zlxcXEmXSskJAT79+9v9FhBQYHxc81RKpVQKpXXPK5QKCzyzWWp61Lb8H5YF94P68N7Yl14P5pn6tdF0sQnMDAQgYGBZrnWwIED8eqrr6KwsBBBQUEA9EuCXl5e6Nq1q1neg4iIiGybzdT4ZGVl4dKlS8jKyoJWq8WRI0cAAB06dICHhwfGjh2Lrl274oEHHsBbb72F/Px8/Otf/8LcuXObXNEhIiIix2Mzic+LL76I1atXGz82dGlt27YNI0aMgFwux6+//orHHnsMAwcOhLu7O2bMmIGlS5dKFTIRERFZGZtJfFatWoVVq1Zd9znR0dHYuHFj+wRERERENsdmDjAkIiIiulFMfIiIiMhhMPEhIiIih2EzNT7tRRRFAKYfhGQqtVqNmpoaVFRU8AwGK8D7YV14P6wP74l14f1omeH3tuH3eHOY+FylsrISABAZGSlxJERERNRalZWV8Pb2bvbzgthSauRgdDodcnNz4enpCUEQzHZdwyiM7Oxss47CoLbh/bAuvB/Wh/fEuvB+tEwURVRWViIsLAwyWfOVPFzxuYpMJkNERITFru/l5cVvWivC+2FdeD+sD++JdeH9uL7rrfQYsLiZiIiIHAYTHyIiInIYTHzaiVKpxEsvvcS5YVaC98O68H5YH94T68L7YT4sbiYiIiKHwRUfIiIichhMfIiIiMhhMPEhIiIih8HEh4iIiBwGE592smLFCsTExMDFxQUDBgzA/v37pQ7JIb3++uvo168fPD09ERQUhClTpuDMmTNSh0UN3njjDQiCgPnz50sdisPKycnB/fffD39/f7i6uiIpKQkHDx6UOiyHpNVq8cILLyA2Nhaurq6Ij4/Hyy+/3OIsKro+Jj7tYN26dViwYAFeeuklHDp0CD169MC4ceNQWFgodWgOZ/v27Zg7dy727t2L5ORkqNVqjB07FtXV1VKH5vAOHDiAjz/+GN27d5c6FIdVWlqKwYMHQ6FQ4LfffsOpU6fw9ttvw9fXV+rQHNKbb76JDz/8EMuXL0dqairefPNNvPXWW/jvf/8rdWg2je3s7WDAgAHo168fli9fDkA/DywyMhL/+Mc/sHDhQomjc2xFRUUICgrC9u3bMWzYMKnDcVhVVVXo3bs3PvjgA7zyyivo2bMn3n33XanDcjgLFy7E7t27sXPnTqlDIQCTJ09GcHAwPvvsM+Njd955J1xdXbFmzRoJI7NtXPGxMJVKhZSUFIwePdr4mEwmw+jRo7Fnzx4JIyMAKC8vBwD4+flJHIljmzt3LiZNmtTo54Ta388//4y+ffti6tSpCAoKQq9evfDpp59KHZbDGjRoELZu3YqzZ88CAI4ePYpdu3ZhwoQJEkdm2zik1MKKi4uh1WoRHBzc6PHg4GCcPn1aoqgI0K+8zZ8/H4MHD0a3bt2kDsdhrV27FocOHcKBAwekDsXhXbhwAR9++CEWLFiA559/HgcOHMDjjz8OZ2dnzJgxQ+rwHM7ChQtRUVGBzp07Qy6XQ6vV4tVXX8V9990ndWg2jYkPOay5c+fixIkT2LVrl9ShOKzs7Gw88cQTSE5OhouLi9ThODydToe+ffvitddeAwD06tULJ06cwEcffcTERwLffPMNvvrqK3z99ddITEzEkSNHMH/+fISFhfF+3AAmPhYWEBAAuVyOgoKCRo8XFBQgJCREoqho3rx5+PXXX7Fjxw5ERERIHY7DSklJQWFhIXr37m18TKvVYseOHVi+fDnq6+shl8sljNCxhIaGomvXro0e69KlC7777juJInJszzzzDBYuXIh77rkHAJCUlITMzEy8/vrrTHxuAGt8LMzZ2Rl9+vTB1q1bjY/pdDps3boVAwcOlDAyxySKIubNm4cffvgBf/zxB2JjY6UOyaGNGjUKx48fx5EjR4z/9e3bF/fddx+OHDnCpKedDR48+JrjHc6ePYvo6GiJInJsNTU1kMka/5qWy+XQ6XQSRWQfuOLTDhYsWIAZM2agb9++6N+/P959911UV1dj1qxZUofmcObOnYuvv/4aP/30Ezw9PZGfnw8A8Pb2hqurq8TROR5PT89r6qvc3d3h7+/PuisJPPnkkxg0aBBee+013H333di/fz8++eQTfPLJJ1KH5pBuueUWvPrqq4iKikJiYiIOHz6MZcuW4aGHHpI6NJvGdvZ2snz5cvz73/9Gfn4+evbsiffffx8DBgyQOiyHIwhCk4+vXLkSM2fObN9gqEkjRoxgO7uEfv31VyxatAhpaWmIjY3FggULMHv2bKnDckiVlZV44YUX8MMPP6CwsBBhYWG499578eKLL8LZ2Vnq8GwWEx8iIiJyGKzxISIiIofBxIeIiIgcBhMfIiIichhMfIiIiMhhMPEhIiIih8HEh4iIiBwGEx8iIiJyGEx8iEhyf/75JwRBQFlZmdShEJGdY+JDRO1qxIgRmD9/fqPHBg0ahLy8PHh7e0sTVDNiYmJ4gjSRneGsLiKSnLOzM0JCQqQOg4gcAFd8iKjdzJw5E9u3b8d7770HQRAgCAIyMjKu2epatWoVfHx88OuvvyIhIQFubm646667UFNTg9WrVyMmJga+vr54/PHHodVqjdevr6/H008/jfDwcLi7u2PAgAH4888/m41HFEUsXrwYUVFRUCqVCAsLw+OPPw5AvzKVmZmJJ5980hirwa5duzB06FC4uroiMjISjz/+OKqrq42fj4mJwcsvv4x7770X7u7uCA8Px4oVK8z7xSSiNmHiQ0Tt5r333sPAgQMxe/Zs5OXlIS8vD5GRkU0+t6amBu+//z7Wrl2LTZs24c8//8Ttt9+OjRs3YuPGjfjyyy/x8ccf49tvvzW+Zt68edizZw/Wrl2LY8eOYerUqRg/fjzS0tKafI/vvvsO77zzDj7++GOkpaXhxx9/RFJSEgDg+++/R0REBJYuXWqMFQDOnz+P8ePH484778SxY8ewbt067Nq1C/PmzWt07X//+9/o0aMHDh8+jIULF+KJJ55AcnKyOb6MRHQjRCKidjR8+HDxiSeeaPTYtm3bRABiaWmpKIqiuHLlShGAeO7cOeNz5syZI7q5uYmVlZXGx8aNGyfOmTNHFEVRzMzMFOVyuZiTk9Po2qNGjRIXLVrUZCxvv/222KlTJ1GlUjX5+ejoaPGdd95p9NjDDz8sPvLII40e27lzpyiTycTa2lrj68aPH9/oOdOmTRMnTJjQ5PsQUfvhig8RWSU3NzfEx8cbPw4ODkZMTAw8PDwaPVZYWAgAOH78OLRaLTp16gQPDw/jf9u3b8f58+ebfI+pU6eitrYWcXFxmD17Nn744QdoNJrrxnX06FGsWrWq0XuMGzcOOp0O6enpxucNHDiw0esGDhyI1NTUVn8diMi8WNxMRFZJoVA0+lgQhCYf0+l0AICqqirI5XKkpKRALpc3et6VydKVIiMjcebMGWzZsgXJycn4+9//jn//+9/Yvn37Ne9lUFVVhTlz5hhrga4UFRVl8p+PiKTBxIeI2pWzs3OjgmRz6dWrF7RaLQoLCzF06FCTX+fq6opbbrkFt9xyC+bOnYvOnTvj+PHj6N27d5Ox9u7dG6dOnUKHDh2ue929e/de83GXLl1M/wMRkUUw8SGidhUTE4N9+/YhIyMDHh4e8PPzM8t1O3XqhPvuuw8PPvgg3n77bfTq1QtFRUXYunUrunfvjkmTJl3zmlWrVkGr1WLAgAFwc3PDmjVr4OrqiujoaGOsO3bswD333AOlUomAgAA899xzuOmmmzBv3jz87W9/g7u7O06dOoXk5GQsX77ceO3du3fjrbfewpQpU5CcnIz169djw4YNZvmzElHbscaHiNrV008/Dblcjq5duyIwMBBZWVlmu/bKlSvx4IMP4qmnnkJCQgKmTJmCAwcONLsF5ePjg08//RSDBw9G9+7dsWXLFvzyyy/w9/cHACxduhQZGRmIj49HYGAgAKB79+7Yvn07zp49i6FDh6JXr1548cUXERYW1ujaTz31FA4ePIhevXrhlVdewbJlyzBu3Diz/VmJqG0EURRFqYMgIrInMTExmD9//jUnVBOR9LjiQ0RERA6DiQ8RERE5DG51ERERkcPgig8RERE5DCY+RERE5DCY+BAREZHDYOJDREREDoOJDxERETkMJj5ERETkMJj4EBERkcNg4kNEREQOg4kPEREROYz/B/Fnr7LavTiMAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "\n", "# voltage results\n", "vm_pu_file = os.path.join(output_dir, \"res_bus\", \"vm_pu.xlsx\")\n", "vm_pu = pd.read_excel(vm_pu_file, index_col=0)\n", "vm_pu.plot(label=\"vm_pu\")\n", "plt.xlabel(\"time step\")\n", "plt.ylabel(\"voltage mag. [p.u.]\")\n", "plt.title(\"Voltage Magnitude\")\n", "plt.grid()\n", "plt.show()\n", "\n", "\n", "# p_mw results\n", "p_mw_file = os.path.join(output_dir, \"res_bus\", \"p_mw.xlsx\")\n", "p_mw = pd.read_excel(p_mw_file, index_col=0)\n", "p_mw.plot(label=\"p_mw\")\n", "plt.xlabel(\"time step\")\n", "plt.ylabel(\"P [MW]\")\n", "plt.title(\"Real Power at Buses\")\n", "plt.grid()\n", "plt.show()" ] } ], "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 2 }