Repository: wind-python/windpowerlib

Issue:
Delete example csv files
Example csv files in example/data are unnecessary now as the oedb csv files are provided along with the windpowerlib. The examples and documentation need to be adapted accordingly.

Discussion:

Patch:
diff --git a/README.rst b/README.rst
index edc8d25..019d454 100644
--- a/README.rst
+++ b/README.rst
@@ -64,18 +64,15 @@ The basic usage of the windpowerlib is shown in the `ModelChain example <http://
 * `ModelChain example (Python script) <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/modelchain_example.py>`_
 * `ModelChain example (Jupyter notebook) <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/modelchain_example.ipynb>`_
 
-To run the example you need the example weather and turbine data used:
+To run the example you need example weather that is downloaded automatically and can also be downloaded here:
 
 * `Example weather data file <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/weather.csv>`_
-* `Example power curve data file <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/data/example_power_curves.csv>`_
-* `Example power coefficient curve data file <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/data/example_power_coefficient_curves.csv>`_
-* `Example nominal power data file <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/data/example_turbine_data.csv>`_
 
-To run the examples locally you have to install windpowerlib. To run the notebook you also need to install `notebook` using pip3. To launch jupyter notebook type ``jupyter notebook`` in the terminal.
+To run the examples locally you have to install the windpowerlib. To run the notebook you also need to install `notebook` using pip3. To launch jupyter notebook type ``jupyter notebook`` in the terminal.
 This will open a browser window. Navigate to the directory containing the notebook to open it. See the jupyter notebook quick start guide for more information on `how to install <http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/install.html>`_ and
 `how to run <http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html>`_ jupyter notebooks. In order to reproduce the figures in a notebook you need to install `matplotlib`.
 
-Further functionalities, like the modelling of wind farms and wind turbine clusters, are shown in the `TurbineClusterModelChain example <http://windpowerlib.readthedocs.io/en/stable/turbine_cluster_modelchain_example_notebook.html>`_. As the ModelChain example it is available as jupyter notebook and as python script. The weather and turbine datadata used in this example is the same as in the example above.
+Further functionalities, like the modelling of wind farms and wind turbine clusters, are shown in the `TurbineClusterModelChain example <http://windpowerlib.readthedocs.io/en/stable/turbine_cluster_modelchain_example_notebook.html>`_. As the ModelChain example it is available as jupyter notebook and as python script. The weather used in this example is the same as in the ModelChain example.
 
 * `TurbineClusterModelChain example (Python script) <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/turbine_cluster_modelchain_example.py>`_
 * `TurbineClusterModelChain example (Jupyter notebook) <https://raw.githubusercontent.com/wind-python/windpowerlib/master/example/turbine_cluster_modelchain_example.ipynb>`_
diff --git a/example/modelchain_example.ipynb b/example/modelchain_example.ipynb
index 1b4dfa9..7d80f76 100644
--- a/example/modelchain_example.ipynb
+++ b/example/modelchain_example.ipynb
@@ -38,9 +38,9 @@
    "source": [
     "import os\n",
     "import pandas as pd\n",
+    "import requests\n",
     "\n",
-    "from windpowerlib.modelchain import ModelChain\n",
-    "from windpowerlib.wind_turbine import WindTurbine\n",
+    "from windpowerlib import ModelChain, WindTurbine, create_power_curve\n",
     "from windpowerlib import wind_turbine as wt"
    ]
   },
@@ -83,7 +83,7 @@
      "output_type": "stream",
      "text": [
       "variable_name             wind_speed          temperature         pressure\n",
-      "height                            10       80          2       10       0 \n",
+      "height                            10       80           2      10        0\n",
       "2010-01-01 00:00:00+01:00    5.32697  7.80697      267.60  267.57  98405.7\n",
       "2010-01-01 01:00:00+01:00    5.46199  7.86199      267.60  267.55  98382.7\n",
       "2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9\n"
@@ -97,9 +97,10 @@
     "\n",
     "    The data include wind speed at two different heights in m/s, air\n",
     "    temperature in two different heights in K, surface roughness length in m\n",
-    "    and air pressure in Pa. The file is located in the example folder of the\n",
-    "    windpowerlib. The height in m for which the data applies is specified in\n",
-    "    the second row.\n",
+    "    and air pressure in Pa. The height in m for which the data applies is\n",
+    "    specified in the second row.\n",
+    "    In case no weather data file exists, an example weather data file is \n",
+    "    automatically downloaded and stored in the same directory as this example.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -110,33 +111,43 @@
     "    ----------------\n",
     "    datapath : str, optional\n",
     "        Path where the weather data file is stored.\n",
-    "        Default: 'windpowerlib/example'.\n",
+    "        Default is the same directory this example is stored in.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    weather_df : :pandas:`pandas.DataFrame<frame>`\n",
+    "    :pandas:`pandas.DataFrame<frame>`\n",
     "        DataFrame with time series for wind speed `wind_speed` in m/s,\n",
     "        temperature `temperature` in K, roughness length `roughness_length`\n",
     "        in m, and pressure `pressure` in Pa.\n",
     "        The columns of the DataFrame are a MultiIndex where the first level\n",
-    "        contains the variable name (e.g. wind_speed) and the second level\n",
-    "        contains the height at which it applies (e.g. 10, if it was\n",
-    "        measured at a height of 10 m).\n",
+    "        contains the variable name as string (e.g. 'wind_speed') and the\n",
+    "        second level contains the height as integer at which it applies\n",
+    "        (e.g. 10, if it was measured at a height of 10 m). The index is a \n",
+    "        DateTimeIndex.\n",
     "\n",
     "    \"\"\"\n",
     "\n",
     "    if 'datapath' not in kwargs:\n",
-    "        kwargs['datapath'] = os.path.join(os.path.split(\n",
-    "            os.path.dirname(__file__))[0], 'example')\n",
+    "        kwargs['datapath'] = os.path.dirname(__file__)\n",
+    "    \n",
     "    file = os.path.join(kwargs['datapath'], filename)\n",
     "    \n",
+    "    # download example weather data file in case it does not yet exist\n",
+    "    if not os.path.isfile(file):\n",
+    "        logging.debug(\"Download weather data for example.\")\n",
+    "        req = requests.get(\"https://osf.io/59bqn/download\")\n",
+    "        with open(file, \"wb\") as fout:\n",
+    "            fout.write(req.content)\n",
+    "            \n",
     "    # read csv file \n",
     "    weather_df = pd.read_csv(\n",
-    "        file, index_col=0, header=[0, 1],\n",
+    "        file,\n",
+    "        index_col=0,\n",
+    "        header=[0, 1],\n",
     "        date_parser=lambda idx: pd.to_datetime(idx, utc=True))\n",
     "    \n",
-    "    # change type of index to datetime and set time zone\n",
-    "    weather_df.index = pd.to_datetime(weather_df.index).tz_convert(\n",
+    "    # change time zone\n",
+    "    weather_df.index = weather_df.index.tz_convert(\n",
     "        'Europe/Berlin')\n",
     "    \n",
     "    return weather_df\n",
@@ -158,9 +169,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To initialize a specific turbine you need a dictionary that contains the basic parameters. A turbine is defined by its nominal power, hub height, rotor diameter, and power or power coefficient curve.\n",
-    "\n",
-    "There are three ways to initialize a WindTurbine object in the windpowerlib. You can either use turbine data from the OpenEnergy Database (oedb) turbine library that is provided along with the windpowerlib, as done for the 'enercon_e126', or specify your own turbine by directly providing a power (coefficient) curve, as done below for 'my_turbine', or provide your own turbine data in csv files, as done for 'dummy_turbine'.\n",
+    "There are three ways to initialize a WindTurbine object in the windpowerlib. You can either use turbine data from the OpenEnergy Database (oedb) turbine library that is provided along with the windpowerlib, as done for the 'enercon_e126', or specify your own turbine by directly providing a power (coefficient) curve, as done below for 'my_turbine', or provide your own turbine data in csv files, as done for 'my_turbine2'.\n",
     "\n",
     "You can execute the following to get a table of all wind turbines for which power and/or power coefficient curves are provided."
    ]
@@ -170,13 +179,6 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:Data base connection successful.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -187,17 +189,18 @@
       "3       Enercon   E-115/3000             True          True\n",
       "4       Enercon   E-115/3200             True          True\n",
       "5       Enercon   E-126/4200             True          True\n",
-      "6       Enercon   E-141/4200             True          True\n",
-      "7       Enercon     E-53/800             True          True\n",
-      "8       Enercon    E-70/2000             True          True\n",
-      "9       Enercon    E-70/2300             True          True\n",
-      "10      Enercon    E-82/2000             True          True\n",
-      "11      Enercon    E-82/2300             True          True\n",
-      "12      Enercon    E-82/2350             True          True\n",
-      "13      Enercon    E-82/3000             True          True\n",
-      "14      Enercon    E-92/2350             True          True\n",
-      "15      Enercon   E/126/7500             True         False\n",
-      "16      Enercon      E48/800             True          True\n"
+      "6       Enercon   E-126/7500             True         False\n",
+      "7       Enercon   E-126/7580             True         False\n",
+      "8       Enercon   E-141/4200             True          True\n",
+      "9       Enercon     E-53/800             True          True\n",
+      "10      Enercon    E-70/2000             True         False\n",
+      "11      Enercon    E-70/2300             True          True\n",
+      "12      Enercon    E-82/2000             True          True\n",
+      "13      Enercon    E-82/2300             True          True\n",
+      "14      Enercon    E-82/2350             True          True\n",
+      "15      Enercon    E-82/3000             True          True\n",
+      "16      Enercon    E-92/2350             True          True\n",
+      "17      Enercon      E48/800             True          True\n"
      ]
     }
    ],
@@ -213,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -235,19 +238,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/birgit/virtualenvs/windpowerlib/git_repos/windpowerlib/windpowerlib/wind_turbine.py:322: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  wpp_df.dropna(axis=1, inplace=True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# specification of wind turbine where power curve is provided in the \n",
     "# oedb turbine library\n",
@@ -262,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,15 +279,27 @@
    "source": [
     "# specification of wind turbine where power coefficient curve and nominal\n",
     "# power is provided in an own csv file\n",
-    "csv_path = ''\n",
-    "dummy_turbine = {\n",
-    "    'turbine_type': 'DUMMY 1',  # turbine type as in file\n",
-    "    'hub_height': 100,  # in m\n",
-    "    'rotor_diameter': 70,  # in m\n",
-    "    'path': csv_path\n",
+    "\n",
+    "# Read your turbine data from your data file using functions like\n",
+    "# pandas.read_csv().\n",
+    "# >>> import pandas as pd\n",
+    "# >>> my_data = pd.read_csv(\"path/to/my/data/file\")\n",
+    "# >>> my_power = my_data[\"my_power\"]\n",
+    "# >>> my_wind_speed = my_data[\"my_wind_speed\"]\n",
+    "    \n",
+    "my_power = pd.Series(\n",
+    "    [0.0, 39000.0, 270000.0, 2250000.0, 4500000.0, 4500000.0]\n",
+    ")\n",
+    "my_wind_speed = (0.0, 3.0, 5.0, 10.0, 15.0, 25.0)\n",
+    "\n",
+    "my_turbine2 = {\n",
+    "    \"nominal_power\": 6e6,  # in W\n",
+    "    \"hub_height\": 115,  # in m\n",
+    "    \"power_curve\": create_power_curve(\n",
+    "        wind_speed=my_wind_speed, power=my_power\n",
+    "    ),\n",
     "}\n",
-    "# initialize WindTurbine object\n",
-    "dummy_turbine = WindTurbine(**dummy_turbine)"
+    "my_turbine2 = WindTurbine(**my_turbine2)"
    ]
   },
   {
@@ -305,37 +308,13 @@
    "source": [
     "### Use the ModelChain to calculate turbine power output\n",
     "\n",
-    "The ModelChain is a class that provides all necessary steps to calculate the power output of a wind turbine. When calling the 'run_model' method, first the wind speed and density (if necessary) at hub height are calculated and then used to calculate the power output. You can either use the default methods for the calculation steps, as done for 'my_turbine', or choose different methods, as done for the 'e126'. Of course, you can also use the default methods while only changing one or two of them, as done for 'dummy_turbine'."
+    "The ModelChain is a class that provides all necessary steps to calculate the power output of a wind turbine. When calling the 'run_model' method, first the wind speed and density (if necessary) at hub height are calculated and then used to calculate the power output. You can either use the default methods for the calculation steps, as done for 'my_turbine', or choose different methods, as done for the 'e126'. Of course, you can also use the default methods while only changing one or two of them, as done for 'my_turbine2'."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
-      "DEBUG:root:Calculating power output using power curve.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# power output calculation for my_turbine\n",
-    "\n",
-    "# initialize ModelChain with default parameters and use run_model\n",
-    "# method to calculate power output\n",
-    "mc_my_turbine = ModelChain(my_turbine).run_model(weather)\n",
-    "# write power output time series to WindTurbine object\n",
-    "my_turbine.power_output = mc_my_turbine.power_output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -344,7 +323,7 @@
       "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
       "DEBUG:root:Calculating temperature using temperature gradient.\n",
       "DEBUG:root:Calculating density using ideal gas equation.\n",
-      "DEBUG:root:Calculating power output using power curve.\n"
+      "DEBUG:root:Calculating power output using power coefficient curve.\n"
      ]
     }
    ],
@@ -360,7 +339,8 @@
     "                                            #  or 'interpolation_extrapolation'\n",
     "    'temperature_model': 'linear_gradient', # 'linear_gradient' (def.) or\n",
     "                                            # 'interpolation_extrapolation'\n",
-    "    'power_output_model': 'power_curve',    # 'power_curve' (default) or\n",
+    "    'power_output_model':\n",
+    "        'power_coefficient_curve',          # 'power_curve' (default) or\n",
     "                                            # 'power_coefficient_curve'\n",
     "    'density_correction': True,             # False (default) or True\n",
     "    'obstacle_height': 0,                   # default: 0\n",
@@ -376,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -384,9 +364,31 @@
      "output_type": "stream",
      "text": [
       "DEBUG:root:Calculating wind speed using logarithmic wind profile.\n",
-      "DEBUG:root:Calculating temperature using temperature gradient.\n",
-      "DEBUG:root:Calculating density using barometric height equation.\n",
-      "DEBUG:root:Calculating power output using power coefficient curve.\n"
+      "DEBUG:root:Calculating power output using power curve.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# power output calculation for my_turbine\n",
+    "\n",
+    "# initialize ModelChain with default parameters and use run_model\n",
+    "# method to calculate power output\n",
+    "mc_my_turbine = ModelChain(my_turbine).run_model(weather)\n",
+    "# write power output time series to WindTurbine object\n",
+    "my_turbine.power_output = mc_my_turbine.power_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:root:Calculating wind speed using hellman equation.\n",
+      "DEBUG:root:Calculating power output using power curve.\n"
      ]
     }
    ],
@@ -394,9 +396,9 @@
     "# power output calculation for example_turbine\n",
     "# own specification for 'power_output_model'\n",
     "mc_example_turbine = ModelChain(\n",
-    "    dummy_turbine,\n",
-    "    power_output_model='power_coefficient_curve').run_model(weather)\n",
-    "dummy_turbine.power_output = mc_example_turbine.power_output"
+    "    my_turbine2,\n",
+    "    wind_speed_model='hellman').run_model(weather)\n",
+    "my_turbine2.power_output = mc_example_turbine.power_output"
    ]
   },
   {
@@ -429,20 +431,9 @@
    "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/sabine/virtualenvs/windpowerlib/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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AoCSGIpXunj1hQ3UTUhyHOhn4IWnuVRwDIeQ7AGoppasJITNF5SiljwB4BACmT5/OHSk/ho+30T7+UZUPNfPx+np1o06mFUTAd7Pn9ckr/XIbSGxbTRu21bR1T0hsiZN9UNDCawNAKBTK/Q6FQkin0xg5ciSGDh2K999/HytXrsTTTz/NrScSiUBhxrCry2hubA7LbdYd8XRJbN+0cNyUUlx22WW4/fbbJd/w8wevm+W+hk6U9S0Foi2w31X808/tqmvDeQ8sw+VfHo1bzrNfaweaOtGnNAoCAhJuQ2zwfKSV/IMf9jaO4SsAziOEVAF4DsAZhJCnvFTkh5kqrwYtNkr3QO8ByXk3G3uVUajQjLWmpcvWKSbfXBWfZ/zsZz/DpZdeiu9///sIh/mOiEOHDkVtbS3q6+uRSCRyegMRXnjhBSiKgp07d2LXrl2WHA8inHnmmXjxxRdzeZobGhqwZ88edy/kgOrGDjT0sIK1N8BOZ2knYaCU4uHFO1Er4RSr6TFW7WlwLPvlO97Hdx9YCkKA+NC3EOu/Ahsa84/I0KsIA6X0d5TSEZTS0QB+COB9Suml3irzpT+5vxd+VoODzZ35VwqgtqULD32ww1FUxVolhQjfUsluc//inxbi7HuW5NHTwsKBpk7fvFfPO++8XB5nEaLRKG6++WbMmDEDZ511FiZOnGhb56hRozBjxgx861vfwj/+8Q+UlJRI9eX444/HbbfdhrPPPhsnnngizjrrLBw8mH9MfhYN7UlUN3Y4F+y18HainzS8j2RJasu5b69tw+3vbMF/PWP1QzDvA5pVkezBTQu3He2r5QLR3/W8B5bixFvmSdXDoleJkvyE35LAnz6xCgPLY77Udc1za/HxrgZ8fcJgnHBU39x188RiJ4ymW+hMZtCnRM8UVfR6lodGFAZVxIVlRo8ejU8//TT3+/HHH+feW79+PaZOncrd7NmQ2ddccw3XD0EL281rh8XMmTMxc+ZMAMhZNGlg+3nRRRdZdBZ+oRhqRYP9d7C7q+kL2JhGGvyOcaZxNZ8dbMGGam9GJb2KY2BBKf3Aqw+D+rwPfTD99stWuT07OcSKZHHso0+qnNnLQkVLZwrba1sDMwqgVC7Z0R133IHvfe97nxuZ/rZDYnFkocMwH4j3fSWXY8WpDUms2FWP0bPeYrtmwStr97uuV0OvJQz5wg//haAOStop36l+nh+DpYyhjsI+2VU3dqAzmQksH0ZNawIb9zdzzYRZzJo1C3v27MFXv/rVQPohwqGWrh7xAUh6tI7pTfCHs6a29chs8LznRdON2Ii/nlwu1iHZPSeLwiUMvnAMwW60TrXL5P74fLH53iY8L44+Dw1tKkfI8zjvaVBKUdvShd09HPe/cOB+jMNlu723lm1u04EWTk+cRFQu++pD6I6CJQy+WCUFxTGI2rP8luAY2L9d9vdITezjptcdyTQ2H2yRSqqUO8314s9ypB4EGtqT+PO7Vr+M7oK+5tx/PxJSo6qWDH3LtpxdzfbWTK67ZItoOP9tvah87kGYN2brRm19i5KI0TRSe4aEW0HRz9f+9TZo75pRKKJyqSrQmU156CaUwZEwd3ozFmyuQUtXChecPCJ37abXPsVbG/y1lOo+yHGqdhu83T0nYm8WDe1v6sSS7UbHXlZEFfJBblawhKFXr27JgeO9wrFDKqxlQh2omDAbmdbTAJyTb+8ckcoovpxK3EIT8Wi+GSeO8JcQ5nOqDBq9r0di/OxJNcwHSxgSqV6ip+AsPefAlAGLlE3Vp7NhfDRLJjO38c17lkgddLx6ewMFLErqzcrnXP2O7euLSbNttnAZCnJBvjIln4KH1RKOMm7wk8dW+lqfV3gVhQmV15JGATz0xrDbHR0dOPfcczFx4kSccMIJmDVrlqd6AABEwZFFnuTh1zq323PsuAKFUpQc9TziQ94AADy70j6PtBNR0Poxf7N3Z9zCJQwUAEnCLlSuYx0eF8L+pk6s2FUvvC86m5hPLTLts2VEG+X3/r7csR43WG7zboBKiDoDzIGrQRMTucVnB60KQEBn2b2M+g033IB169Zh3bp1KC0tzf0ts6k7hd2WJS48/Pa3v8WWLVuwdu1aLFu2DO+8846nesLxgwjFaz33o/fAy+jqe4hXIuIUFTnady1iA5eBUmqxPHNtZZRtK59IsAVLGLrSXaiceDPiQ7wtBECdBCTcyk3pZ4fT//IBLnrkY+F93VzVvcOMRQvBXugFzm7tiTS+9/fl+MVTq3u6K0LYnd7279uLqZNP6JVht/9+9x24+Tf/ja9+7etSYbfLyspw+umnAwBisRhOPvlkVFdXW/p5oKkT22udfRUI6Z0hp/OF417PrCvRmo1UbPOsR2AEA6hpsYp/ZA6IhCMIrYx71xQUrI6hK6O670f68lPhyYACqJgwG+n2sejce5X0c0523znjF8q/rmHLwWYk0hnEI2Hh7KUAQL2Z0wQhGNDkouv2uo99z+LPK/+MLQ1GKxbzSao0FnZUtKUyihqUMBzC0WXH4scTnE/wO3fuwIsv9p6w22mFoja7YVTt3IZ/v/4uBsQUV2G3m5qa8MYbb+BXv/qVpR89keQ+iIRTXqFu9nI6Bq8uNLZRkTkx0fzAOZOH4YXV1oOADAqWY/Bj09NOB5Fyf0MYa5uZzEnlX1lHFtGpwXlS9wx6y6L3gtG9LOx2RzKN2lZVj3TaGWe7DrudTqdx8cUX45prrsHYsWOl+kYpRUtnKkAv80CqdYdQB0i03tVe4VW8bOc0yd7irRr3oqT8P27Bcgy9WU+Wi5TqEBIDoJbQ2OYxz8cJ+LmV+3DTd473XgEHfi3462dcb7m2odoYy3/ckAqUxeyncH17AvsbOzGgPCYRHZQCUBCP9d6w27GY+7DbV111FcaPH49rr72We5+H5s4U9jZ0YHjfUuln7EFtfnUn9JbLj70boUgbnON0yvXWXpRkd88UdsPSujdJQD5ns4LlGPyYesIaSArhcu/OOkSWY4Aeu0c06YyTxt07JzMK/vHBTlfPOMGPSRkIJD6NEmpEOF4rtRCDDLv96WdbfQu7feONN6K5uRn33nuvYz2NjBNgOruTBRWavbudKw80WSMjhyJtnJI8sAYe3tq386Y30gXiqQ2/11vBEgZK8/9SogGKD3sNZaMeRyjuLgOaBq1nZoWUPrh2E5Fyfnp/18YOd4p1Wbjt0W1vbnZvRSGxgNz0QwnJh5UOMuz2ueeei5tvvzvvsNvV1dWYPXs2Nm/ejJNPPhnTpk3DnDlzhPXUMopPnWc1fmTZ8CJWmC3uuhelMY14W1t2PAgwISbsrYvEN/uVRoX3zESyssTIBfsR+8gtClaU5EucNUEdoahqrqn5D7hFTsfg2Efns2tvk5hpoSc6XJqrzlm6G188b7irZ2Te3e33OXrU0fhkjW6w0N1ht2taulDDJHOZOXMmHhg3DQDwy18b/RBkwm67OZkblaD8OZqP05SxX75UI40Vu8y+PG64AO9cuYZIWP2efTkEwmBYSIBvTxmOp1fsxeSj1VwQLV3OhzeWdGj15eMBXbgcQ+7zeJ+Bwm05zyBVoexXF5uwUWE7Vh1D7yIN9y7YDsA+bWhXKuNPTm7T77rWLnSJfBt8PHT1dNjtnhxypwPXAqFTlVnH0L0vsWK3P06eXsNe5MJuc6Or6g9GQjp/oEUXqG9zGe4/W90px/R39xyDAiYMPtQR0Nx15BgMdtPZfwVFexldcPzurV0pTLzpXdyTJSD5IBJibLcpxcHmLuyslZUbi+H0DkGG3e5R1YzoHBRKOBXJQQuH0dvw06+OEd5zwzHkm4+BB5bYUs7fXg/+WrQELyhYwqCjt2lBdThyDKC5k5XohN3L6IIjJ9CU1Wm8xNhXa9ZZFHKJcjSEHE5fMlA4yXl674wJFgaRRvbfps4UQrHD6n1KfTvpd/eBJpydLKGo6vVOQvKiTsJw7rZRUm3qsHVwM4e5ke4ZA4Z6+CFFKFjCoJkJhiLe49e7Ej26gMiPQSaIm8WUzTAJ3HfIb5beqTZtgbKT95lsbJg9TSnU19d3m8VKOqPg0/3NFgev3kZsexK6VRxFsjWJPU3ejBX0PBLpbKiankFsgDUHunNIbEnls93mb9MnA5cg+UzQKFjl876O/GO/B7VByYbEUM1Vtb8EJfK0SmLxhdkL8MkN38ivEodX0ojiwWZdwaopNO9f0YhvTGrlhpcAgJa2BDqZKJ2kKY5IWA8uWNOk1qk0xnNsdHsijcaOFNri4VxKVQD4rLUUqYyCmpYEGsIEQ/uU4FDbQQAUqRIF8UjPLI3WrhSaO9O5PgJATaPR1LIlGkJnnThvtVto9YcJgCa1zY5kGg3tKhEIRRtBQbGvcx/uXzEMP/+2+zY0z9+ysX9TTYLpeb703S1IhJMopxtESfYcg/1v9yIhWeMWMQqWMPjBTuVbR1cqgxJO4gCRjqE6twGoNwihmLfpEP7fWRNyZZwmkVuwpnB+WJw4fTOe+Ed7oiWhYMwYsSz450+uMkSMXHLd6Rg1sAyAGvri2zfocbGq7jgXgBqp8nevb8QPvzASz31y0HB/66FW/PypJRg/pALzf30SfvjPK6CE2vHI19/EtNHHOL1qILh/4Xb8df42wzt8a5YxQcw3Jg3FnMum+damVn+/sijW3Xw2AODlNdX49evrAQCVk3RrqNbEHXm1Fc4G4utu5XPuEGYz/2Rgm9rTtn1h84Y1YzgsZv8O8xaNXVvgh+t2g4IVJfkB3iZHwm2IlFVJPf/h9sPc69o4m+On1HM8c7dkE7FLObj1AuG4E6Hi5nHwSN3y3Vy06Kzm2Fa87szfXCOMylookJ0+n+5vzrutnjOasDYstGTjPONd+awd9qxf2bzPeJnXvLHL5xsXLGEoCweTzSxUsl+6rFhUxF+COUKRU3ZxzFXNZn+9TCAumzjei8WEV1NBER5cpAap21Pv7Nj28ydX4Vv3fei+EZfoyeFkNy27k/F37l+ad1uXze2pnB7WLzxv0yH7R2SVz3bzU7JHlKnH61zQtpF8fLkKljCUhCsBAEq6wqGkGIpDLofY4AW2992Oi85BiAmD5Zk8KQMFBYk2gIT9STL//ha5mP0ZhWJfQwceX7bbRe3+EkXvXrzBQe6detlpQALmHq/ak1/0XU8gSW5ATMdvzoYb96pjyK5tHr1lOTCeuaoMOjL699SjNxdFSRaw2c88g0ty9aF1irqacrnxaCylnXWSeayf+KjKVRs8VIy7E+Xj/HHWcrK51rqfUSgumbMCt7yxGU2d+Yfl8MJ+u5XdFuEdvYGzjQ16n3vd/jRPTeaqtoW93MKf3v5MuqwIG9teZZ6nnuvRULCEwRflc57Pt3a5i/2TNnMMEvvWrjp/Tvok5E8SlonD+kiX1Vz97WLV2yHfERaFBu/JTay7lbIsiOFvf4lmbwiqSEKCA4jNgKdDuse0khzgYJIqvpc79DkenOTEVg6VGP/1gAImDJpCyfvX4RMX+RnudoM5c+IQcw3ObahSSUv57TXOGbmCwIBycbAwM/RggnLlrRZZ+SkFw6JV2oObWE8SpSA3797AMYiWk13XGiv+nvs7FGuA4lUS4eL9/TKTL1olceCPuSpvEsjX61b+f1S/bOx7O+WzrbmqvrK//7BcnufujtzIn/Q9s2uEBLOf+hKBMTikMkH1T0757AU9yQnpcM8hUpMzXrUi1ivKJeOx9kHko6pdb3MpedCjJbh6zICCJQzaxyF5Tcg8FbsSI5POKDlRyt1Z+3V3bQDeTPCyz/ek6EI6yiwfgYmS8qw3H8i0fbDZmlugCO9wc0LvVPjOlwBw8m3vYV8D38LN1sHN4fd2l/G//Eg5ULCEYekO8QDKIl+uw+4EoWHcDe/gvAfM5n/y5qqGPvaGQ5kDeo5MW2ERJWke6cylK/65Ete9sN7nlm0gMeeC4vKCFCX1RE6BnsBbGw9yr9squAVL2M32w37fEAk5tumEgvV8XrO3AeViJ1op5CtRkH1+0wGB4xQn7HazKbFO/vYH3pDOKIiEQ6ht7UI8Es7FmfdCS4OIWCkDkVESW++irfkfMNxA5pWC2sCD3LqD5Ew7kmnHFK8q+Odg+575k2DITvls0ZXR/NryIyRGwZdhO78AACAASURBVHIMumWP96/DN3mVr8/7Aha38cc3Nxt+r9zd4Pkdw+XuRVcaxmXDT8yYvRAzZtv7c4ggymTnBU5V8O6LE5n4v4lRSrFmr7Ptvt17VE6ahfgQ+/Sg+eBIVD6v29eE42+eZwiVIgKJ8nMy2PfNzUeh4rps2hDNf7OIK1SyX/gOLDTuoaCUz4SQEkLISkLIekLIJkLIH7zV5Ify2VsdmlfvlKP72paTsTkyw96zmM1P4Fg5wmW7nQvZYGs2XIddUh4zeP2S/cpWWXB+YxyysAz5K+1EeOKjKlzw0EdYtFXOAVCE2MD8vY5lINK/9DasyxLbpdvtObuaxE5E+3zKvedmnfcpsedM/vzuFpx192JhG/xYSfrfFOLQ5uVj7kfFuDv5DRPrj0LjGBIAzqCUTgUwDcA5hJBTvVeXB8fAfda5vsEV8fxatuMAuLd4uggZKGjOw7nsm/dyQhh3oyjJUIfgje22t+7jF3QFYnWjveJY5pRn55j3l3lb8P4W59Pz5w3NKfE3sXeGNW6RMiIrnrI4F+3GQw4REqtD6TF/ty/j2Ct36HU6BqoeC7UvG83+536t5pl+M9sZTr3Oj+VSd3pUUsSRRBpAhKSRAJBIu8ufLAtCMp6dy3oD8iUo1g02OI5BNlSITNt2h/kHF+0EoEdmdYOgFMSr9zQ4EkSv6O7Za89J2VgeOYTdJszf5qLxwe8iUrZHvpOOvXFGb+QYQAgJE0LWAagFMJ9SusJ0/ypCyCpCyCpR7H7WTNWrw4jXPVOTXXt9fghpAgD0h6qUPu7Gdx2e8NgQUbrdVsSYcN56zf5Z8W+ZIS4bfT9Khv+bad/49gpRN6+6zgNS/XEDNv+EHWSMGoOS17Ofw895MeuljdzrsibVdsiFs85D9GV3YjcTS9tvb9OFHMfAKWSJruppfLlabS8VAeilhIFSmqGUTgMwAsAMQshk0/1HKKXTKaXTBw8ezK3j2CFlefeDH0RPxpwwW9L1wFCEYrWIQF0whMP1uKmRRJoRivPN54KCJ4VXNx37wqX7Ee23JvdbtJc0Jvnh0rsD4ikTPGE42NyFf33s/mTqFYlU9wUxtD/nd8cEFLfBcu3mw47sHsINuy3ZMx56JWHQQCltArAIwDlun502Ug+7nW8MdbfIOW65fG7ypA0oP/Zu7I2rT4Zka+CJzShQMf52lI+9z2Uv/MXB5k48l03dCcD0UaxpPt0gXxcOoY6hV0rX+JuH33hqefcRhnwjA/sF+3DubvpoLy6Suafm1dZqo/h0v/scILmw3YWkfCaEDCaE9Mv+XQrgLACu83Sy4Sy8fh/+hiXBMRC755lypt+Z6F7TFeZEFUogFKv1Pd1o0NYnl85ZgVkvb0RThzjPb3dsD+v2NUmX7UlvcGHbbITPADdUzVLrCDFK8gVKN4x3Z7oNlZNmIVVmDVUjGnNK85uLRv8Id/X4ThgIIfWEkLcJITcQQk4nhLiV6QwHsIgQsgHAJ1B1DK6Nt1lLA68LyeuQOAeHk2UP9XKlI+ei/Ni7PfbIro1gIZMuVHZ4LHGiXOiRtroJKuhDSAH/YRQl1bR0YfwNb2Njtfdsaou31eHa59YarslGIn9n40GhjiDafzlig+Z77lcQsJ8dwROGhqRqFZWqsCZ7MpirZsVHkcoNUJDwZBDAexu31odBcAxjANwL1ZrodwD2ZRXF9xFCfuD0MKV0A6X0JErpiZTSyZTSP3rphK4fyIfiWmWgMrGXCLEXkShIIVRS7XhaZ9vSrBJ4NfL65FWha4eS4f9GtL99cD5RkD/tJMre1jYhmT7UtyXw0eGX1PzDRD6omEIziA5YAhArxyJycOtRAQen8ZLhz6Ny4k1MEYrFW+uQylA8sbzKc1OXzV2JV9cZFe2yOSp++fQa/P5lvlK5ZNhriA9e6FiHH+RXfqzErXlVKHNqsrlDhUXM+8SBjl0oHfEMWiqekefcHHJZuw286Lu5KqW0BcB72f9ACCkHcAWAawH8N4B/i5/2D4YE2z7UEancgEzX0VLP7dDsmAUN78EzKB+zGPVdM0x3jA/wlc++zWLXiPZbgyjWINX4JelnNDkybxPOWW9JmG9V1XcgPmgRAIBEmkBTg6Q4jU0tS1Ay9G2EIizHoBIW8aLzlzTk4ysCANF+xlM9K2Lwe8TF3uBWVNXL5QLRqoxUbvDSJVdteMGQyph/HRFCm1MO0VUBZLIpA9Jhh5SjBlidW9l604o7RX8QoqSjCCEXEkLuJoR8COBdAOMA3AhgrN/tiZBLvh1OeFbCsIShdMQzKB/jTpEr4hjaaBUAoCNjdoQhpl/WwbSKTJRAREx+oSOpTnItYB3bfe1wKudLQUHCqjlpfOhbnLt8pJSsmWhINxctH/dnAOJNlSAs0R95bKh2o99wRgp6fX7rArQxCcKnIVLhWlXIxaYDzTiUNf+Vt9oRv89R/UrkG7dtTnzTjYFFmESyteVntcW2mO5pjgFANYA1AO4BMItSKtY6BgjW1FR0ylYUCkLsFLCmE3w4idKR/5Lvg+NYUJtf/CuWay7EKjz4vfxFr1zT0oXRg8qNbTuI3ETQMnHJPcbhVKKthvZzJWkElKQxqESOMwwCMhtdW8VLoPQk39qM9FmLUKwOycNn50RJfhMcEm41mAoD3vmyc/+2FIQAu2/XnfjyIWTdITrsTGnr1H6zV7lBHfmMQ2/L+fwVAM8A+D8AlhNCXiKE/JYQ8hVCSDyA9rhw+iiN7UmM/f3bmLusSlgm77DbeT7P12eYJ5b75CM9gU6OolK33nJbm1qXUfnssWMMIrSvf5UFCWbz8ONkX3r084gPVvMha8TycNd+VE6ahVBJdd71A0B82KuWa/lsXH4OkZu6bEW5NkPxt4U71Odj9n5F5vqdxjc2+D2Ey7cayvnxbXwnDJTS5ZTSuymlF1JKTwHwG6jxj54A4N2EQoC2BP/EbLRKst7XPFFfWLVPWHe+ZoFB7C9W9tLYSDIb0M6g5I179+Rt7Up5Du3BIpxTPut15Tyf3XIMJCjHqCwHE1DtMmA/xbIdh/FfT6+xliF6FE8/T/bRvp/kRElbWlZmr1nb9wQPY7Z0+2EcbnO2assXfkT3VWFXj7wFnZvkYvFB76Ns1D9N+RhUQdDBri0qcfew/gPxYyCETCSEXEkImQPgHQC/B7ARqp7BV+w+zFeA+WGbnK89u2vCYi7OXUzmQsbfp9xqNRMsH/s3cZs2G0tHMo0pt7yHP739mbiQU/ey4Bm7hFyIkgxFQgmQaIMwJaJUh6QbM6I7s6ddNnclN/ELO6/8JAyxgYtzYxIimp5FvKHL+sCITr1OI3PpYytwkWSK2vzQvRyi2XT68i+P1ntCjdPPy/hGiJobZUe7+u3C5dtdH1KDUD4fhmp59EUASwD8B6V0OKX0/1BK7/K7PRGcEsXLbPpBJ+rxclKx9Ns0cVoFHJQszn9wGU67UxUrtCdUkc0ra/fnVSfA30S0TchtCuNwySFx+GFxDyxXBleaJJsaobLZDM+512qH7ie6lBaUjpoDEm613RS0efDsyn3YVecu9aMQjBVcSNsa/AhGKYCMx/HOOjnLp7z64YIweD8s6s+ZFcFDzPOQeYYQuB4DPeez+m+4dL/rfgfBMRyb9UH4BaX0SUrpjgDacISM8tkJ+VoFiDd+2ZOW9flUdJfpikFVJVWvHdbta8K+BvVUrIl/MpQanJnszA6/M3U4AGDUAKNfIxs90nzNq6jqQLvuKe5ljMcNqQAAfG3CYFN/xM94MT11Q/93di1EpHwHogOWCZ+jMCaEeU8iSY1bhIm2NQQnWNvXyM+PvPVQK2590wWXinyVtLZ33dQkvmWzuSvQ1xYFQJl1LK1DYoppBEFrMdpng+tDbhB+DL7rEbzAj7AB+Vbh9Li9UAj8yURUmes/l+3OJsjht+LH+2viH0WhhnhHkYotSLeeyH1GS/E5on+p4TqPY5ANHQLw37IzbTpNhroAJQq4NDeNmuRcgYbEcJIvZzdk4sLaLAhdlt6PYEK+A8AFD33EDQ/+48dWoFbCYx7Q372hPQ/jR48fMG1JmiWuh2d6rsGzjiPEj9jLtWXs6ZAYvQVOymcNdnLSfJVS1zy71r4AU/2+hg60dZlPo+L2//DGZtzxzhZDGRLyV/7NnlbSzJEjXC5mAimliA99FR0wx33K3mfr96pj4NSVURRUHncL4sNfMZQ51CIX7jrbo2x/elD9nH2p2MCltg547Lvn60BnaRwAEW0NxP+2zJAlCixeWbsfnx10H3DOvif2qHOjGLcVBxkV0xlFs7pLC+dApO8qVB53i169gbPQOAa9XrccQ+ESBvZje67Egw7AIzE57c5FqG93v+hY72gSdr+gvJg7hqLiBdiebkZswMeoit5jakdFMpNAxcTfI9Jnnc6ReBwgtu9K1lvUbEWzfGe9Yz3m5v2zUlHBLm6nmlmxghAUhrnpd3/tUHL0s/YFQlbxEH9z87/PuzzqI7xyiLGwm+1TfFDVCIHaF2BFverAqUQOC1dnpEKcr72ydadeWRYPvL/dRV8DJAyEkDgh5EeEkN8TQm7W/guiLV6GM8WHIHpBR1101mFY2yfU7KUpECVJtB8b8BEaM1ttWnf//prXcFoxjom2OTQmGkCIgviQd12FxHDqny6XdaFIpECoZB8UpLJ917yz/R13Y1hlx9LO9Vnq96u/rLUT/3tGysUb0sLPalAx/naf+tJ9sP9+xntsWUtcKTuugLEwNHN4ZsLeldEJnBfdSUlCPQy1Zxpy18wxsZwQJMfwGoDvQg1M08785ztuf9vqau90ipJZS54XXKgLJNrgWGwHJzesqQdMX9QNl6QHCst4Qa3yibj1bNUtXWnc9pacMtCJ2OmnfL1cWqGqvJQk0S70S7G+p/GS++/QmKxF+ZgHUYUnc71Ta+q+E7gZRuW82CM/mB5Sy0YULjOLBMUt//SJVTmvdBa8HMh+cQx+fAmvVknutge98HubjTGQMhaxt71BSWzwe4j2MRqAGLjn7PN7Ole76aABQeZ8HkEpdZ1gxwv2N1ll69RgleQNXidd2egHEY7XofWzO2zLvbh6P4CRwvuGKaGEgXAGMMu/gzQn9PDMzjZVlKPFNdKgTdywZh9PlNypNKNQVB53C5RUHzR3noPyuNy0zND8THMT2ZNZO3YbrnN1DCQJ0DDcKrbNcDpsDKyIAg5qEZMkKVAv4FDMJIrzcILNKBRR712Sht16tdMbufl8dnleiE1dNFNuKGe4Z+qbkx9DfND7nBY4wfnyIJpBcgwfEUKmBFi/LfwIl+DJz4AC4Tg/D7XrunjRVSUXptccB/li6X6+nb82wUM5M0iaUxZu3K8astnpLnjQolAC+ljxItKKkMh0ZntiPOVqc4c10a2ceDPKRv/DVf+8oDTqnvD45uRG2D/l4ofJV+1ogxcoOjPiubX5gBtDSjsRtd07ZcWmaWt6GiPRMtbhbWjVOsaWnerpaSBYwvBVAKsJIVsJIRsIIRuzyXe6BYZB884yBAxq+/NQeSNILEtkAgoRbetE5YFq7G2wlxbqoRz0xRAuc3Z1oQCoYjx37mtoxz8W78zW676vCw89AwBIEI2QGyPAfv8fRq/bcKkaPqUjaeRUGtuTqJW0fnLqpeG+nVVSIApnXZTkd0jyUMyUR9snYib7GTa0vCO8d99CecUsFfzt5knzc3YpTmXDoBtFSVpD3j9ykIThWwDGAzgbwH8A+E72X9+xek+j5ZqR5fOqfA7IbNHFgEUrP9UeUv/v6pSSH7yqhO3vavdZwrCbuS9Gpn2c4feDCw7jjne2IJHOGJ+TNNs1s/Da4tLmjsbJmHH8zfPQwpgWn3TrfMz4k3NiGrVN82+Kj3YcZhTwznPOHIGzO0EIRSiuysjz29u79w1O6HOWL/UoDJfqVcdgESUxlmipTBoD4sNzv6XzY3CK9SpREiGkT/bPVsF/vqORk09Y2o/Bpt4gc+vKQ/6MWeE2toRD/Z5eX/BB9YB52gX/iK5ZYVd2zCPsXZsn+dNfRoTY0OZPNPl5m2rwozkrcpnYvCxmXneX76zH/AA8ogGgfOy9PtTSvWsrZLfVedTTWcfKWz1sPpKmZCPGlKvOo6HkMd7EhH44t+ZdgxXPZP9dDWBV9t/VzO9uQUzR7antWDU7eDFX9X+6m8zlzC0EpHxOZxSPhJH/THVjJ9bubWTu8wmD/ToQL0R2Mw+XHMRf39tqONXzatbDPhjL8FK6mpH2IeIsABzIGk7sqe/Iti1TrzEkBu+Jix/9GD9/0u1yY0+1/ifqYREfPC/Q+oOCYf1R4KiUrAFE7kTE8ZvR5xsxtyEJdrQUpi2vCCIkxney/47xu24ReK/PLm7PdvI+UN79TZ04up8xPISrWnMvR03/eqrNFpE+65FumQoAGHfDO5g4rNK3un/4yMcAgFevmaxeIHyFm9Cqw+Vr3v/+DtQZPGitFYi8e+UCLAYhaJNrm0rUkz+cNxVXp1nTASY2cBkStflLlv34Dm5ew+w4Ky0VJvr65YkT7XojFx7F+lyUuMhMZ8LnwvPZ6+HOD4/SbTWBSM8Y6H1sC+d3yis1ebVuOeSl7/bfzI/cDjkQdoyt9aqxpMTfhJg4hj6ZrE4h4ywmkktHaoXTYUOWYzA+46krjvV+bkDkxYK2Imob7t0uxwJrXQcQpl71DxJ1Tg1rmOXZ5wdGvWdSLljCwIqBvIqS/FgobrbqUAkvu1NQmwCv9vxBBIG9rC0STJm4FRUTf494NL9eeI2OaeYY+ihZs9k0P+onC6+EwQlSYgRqJCC+ubux5qp+5/bsYaJj940ilZul6/mUMUjwHrXZvKbNY2mSDri0LtLr9769B+ng1m3gTWL2BGl3SrUbWj/WvpsFxvMa1SEQJQXo4OYFkQp701OWYO/Dv0GIAhrqciSgFNTyriS3jGTMN3ksOuGWYOdOqHQPoFiXiV8E2sv+axEm+sgxaLqFYDUM/sHp3e+evw0njernVIt8g8wctFqH2e4mhucMd1j9GAlzdJsyG7w+Yn5Mh6AyuIUJIdY4FQGBq2MQKCZzz0jNfPeWM+wGFYrVultgnBDHsf4fZe9l6zeHZ+ll7D/PgYcFNSjazKZK3mANIyAHkaUKO3fKR/8d5WPv99gzTt0W+bLxt1RkV0euLH/IzNtPqhoxetZbOQV6b8TfFm7HFf8Uh31hMXrWW/jtC+sdSukD1mXKY14x/naEYqolWDqj4EePfoxPqrKhcXIEhVgOnBmLCayR+IC6c3rMHSHzWFeBEAZKaQbAVkLIqCDqlwFhwiXw2H4t6qZdqN58dQyhWJ09AbKc9q2bAoloYg217JiB5o2X30cSlszq5TNdURJHOTRntZgweqm775BXZaxZx6DV0rNWynrjwqkT7ujhPhrx1Md7eroL+YFZhy+urjbdFH/o//vsWsvdSIV6Ht7f1ImPdtbj1/9eBwAYNUA3QEmZ8jiY5791Dbg0BMg97t0kPEgdQ38AmwghCwkhr2v/BdEQb/Ptl9ZtuHkioTV7rU5xZvhhlcQ3++MPtJLq71jfl441BtEjMX6wvtJRjznWFQwclM8cAwyp8CXc6zRbpzdPYLOOQdsfZE7truXLJInYgMUGBylAn7uvrz+AKbfMMxxiRC2Ux8JCYhrtv1wVf3mCV5m54LphTLyvJSeDBTU6QHdRSr2dTQc4h8rsJNIj9aqXj+6vWwiZiY8o/lK+aUTz0T0FqWO4KcC6DeDNG/YaV5QkQYXztkoKdzqKrOLDXoGSHIBUw9cRKXNe0OYNMDZgCbecJQSBCL7rKOzry01WCp0wsMo3D4rkN9cfxKnj7EVYPJjngPvA3fKIDVqI+KDF2NI2DmpAACO0DGRdaWfTxGjYKFpg+1sy7DUAyAVwfOKjKlzGJJuXhg/KZ0r9ieOkUIpQdnQWfmZ02tvXuR4Vx/4VnQcuREY5SdwX21H1pmOwe1J77+pGLR6Xtvnz9KGK4e989x1dlOS9jsA4BkrpYgBVAKLZvz8BsMb2Ie9tWa61hypyf3M/tMyEtUv+K+oL2wRJ2zZDQBHrvwIlQ8VxXOxbgM3GLjcrktSbOW3pyH8CAMLl2xEqMbPfYiiKVcfg+WSTffemzqSnRSBUPkuGpXDVVljVC6QUe9NImU2BkJB0dFWRmGfxNmugx1CkHc1EzTroyrY/4MM6e8j76RNGp73GlBq/KlyyH796bh0ONXvTvyzdLnmQosQhjWiWYyDmq+KPZDRXpcwcoNJrgxjMaNW/zfP4r++Jc6+YEWSinp8DeBHAw9lLRwN4NYi2eJ8uw0xtno7BLgZJKqOgqSNpYftdg2R8NO+gzP91RMqq/GrAFSIV6iQrG/UYysc8IP2cUcdgPaNHGF+Mj3YeRmM+uXxZcHMXCzgGiZ3O7V4Y679CqpysSIwCIGFvRH3zgRZcNnclIpUbEB/6huFeTegtT3XyoL1JuGwXQjFn0a0IdsTSvHHON+U6kMWlj8mNT6ZzFE6+db54nEgKIEl0JI3zzS5PyZbEC7m/FarnV9fpg/MmclSTNY2wuY/3v+8crFJDkDqG/wLwFQAtAEAp3Q5gSBAN8cdIHwgt3AALc/IlFr96bi2m/XE+LLkP3MIxHpAHRavf2cXyEJxUTprFrZEFCbchUrkx95svLtYvatmtMgrFjx5dgZNunc+p1fQ0ddYLxPpZw0OIxIlBZnx2+tpSgRspRU1iOyomzEak7yrbMeSdf5o6VWJbOuIZxAYsM5bPbgluzKxF7WtzNT5McB6UdCyzm/L6etBze7iH+JnKLEddaVIYi0KixAe/j8qJN6PeEktLLEpqyugbNgXQkQ2zoRHEUNw55lWY4UT18fC+toMkDAlKaa63hJAIAtIQ8eyUKXPaf2CRu3ynb29UTx35JoWn6Uo+Z5Kby94scI4klI58AqUjns7lAjaenKzf5lv38fM5KFSP6qlDsyLy9lVEm5/fopFkRl68IcetUNQnVRFRpGy3bX/NJ1cA+O9nrKdLDZ4c2wTt5y4LTrwk7OxICLhzUD3h6L7SZXP9sLlXSo2muCeH1NSmZssiM8zjmDPT5rxKJ2P22thuzdvuPpd7/srnIAnDYkLI7wGUEkLOAvACgDccngEhZCQhZBEhZDMhZBMh5FdOz5xwVB/OVVahY/1AcpEHvPgx6Lb8NN3HZVA4MbQENJtbeNmbvNcddLA0ElVFCCQryqHMScte/2JETUc1J5GPXpc3hZ3JKin773Of7PHVs3ln66e5vx05BtbPI9KAaL+VljIUCg4lVLNIEm20rVNTfrKwlZFTuS2hdORchOL7bcs4D4nc3HMztqJER7YE1/aApjn8qWUGE9XzOZXmi3Y1mKdPbpPmvrJe+Nrn1xmekX911vzbaBHlBUEShlkA6gBsBPALAG8DuFHiuTSA31BKjwdwKoD/IoQcb/cAX7fMWrpwrJIk5qRT/mIxtM/Kf140XslGfsalaD9d/rmrw+8AtQHzINppMbv4+IYAzuPTmLRXDuYbkZL9TSnFun32MnE3XAoV/gBeWWvcXNl6IyMeRMnwlzn1UWxpUw8IkfJdPnM42kZov0AiFdtyFlDem5cjDObzWekx/0Bs0AJ+jcKw7/4cgLR3deJizPPcnvSwdynzW94Mm309Jbv/5JNPJkhz1dMBPEUpfdTNQ5TSgwAOZv9uJYR8BlVxLQxowvt0O8M6u8wTCckkwPAuzzduhjYtOPxWUTL8FabEkSZM0m191P/rMmFNnh0KcRz7mPGhlArEepooyXQ1IxdV0uLHkPuDwmnT2lnXjpNGOfudmGEevw3VxmRABkFbhJ8Nzzwv09Q/z+PcN5HaSLWTqUDH4DRXJWMAmTfZSFkVx+jCvq5oWpxZ0JwZ0HBPdJ1qHINbgsM7GIk38F0t21zWDyjZsUsp9tkU7RAkx/ATAOsJIR8TQv5CCPkPQoirlUQIGQ3gJAArTNevIoSsIoSsAvgcQxfRPwpPKjCgPObcAQlRkuZBbXxO3wx938YlKjzQ1GlZJ02cZEYqfBYlORBDdhPRPI/jEQeBGwX/vZnHjJsQGw1O3J/SsDisuGhfLB2lJgF6/KPdwmctdbE/HMZPTq9lLFOVVE/P/jhkut8SRM1q18WHMIfNvN8KkOhhV6IkEadT2SE2qVYSQ6Xrl1Xrmu8rOXUr70lTCHo2HpMnkbD694aO56We5SFIP4bLKKUTAFwAYB+AB6GKlqRACKkA8BKAaymlBuEypfQRSul0Sun07G/busrjVrnjV8cNcuzD9lpnc8CLH/0Y95tzxmrx04lIRijiKPwhI1++431LXWmFoqaly8EG24rogA9BonzvaieosWSMC5Vd5NomxKY25EGlC+JvQ+FNnjqsVA1LXKGokspw1mChlIiD+kXKd7lviBVrOhaVUz6zsfadzKpTGQVLOH4LfMgH0ZPdJONRL9sMRcnwV1A+5kFXymcvNEh0K50RH+y0Lsn2bIfyJADVV+Tnp43JXb9r3lYjx+CDw6kfB4Qg/RguJYQ8DNWX4RsAHgBwmuSzUahE4WlKqVXIakI8Yv8al33JGrKJgoJEWmBnnPjhNrnUiFpaRq3eUC6+kTUL2oLNNUwyeeO9GOyiq+r1e0GYEHzxTwtxctb8k6nQgPiQtxAf/iIAgITbUTL0LZR5DK9xy+ubLJYnbP91wuBs4SESVrBleLBaMllBEM3+q+JoctjXsNMGCbKTbFpifCkoTqj8du53irZl6+aXv3fBNvxk7kp8vIvD3Vogp2Mw9MeBY4hH+AphzTDBer0B0axHPwl32purwrhDi4ZNXsavI20IT6I5Y3pDBrpl2siBuujqgUU7bERJVFpU5bdkIkhR0r0ApgF4FMA1lNI7KaXLnR4i6op8DMBnlNK7HVshSRxlypBmRojzlk2JelSM/xNig99zbMIrCFEsYqyfPblKONGnhdyZ1fIQLs+vjtjAD3M2fICCVAAAIABJREFU/zQrEiNheVllpuOY3N/VjZ2WUOI6x0ByoiQnwpBIK44HaWPKRX0x2TuWGc+8rNLPaTl6PZQ5PSYXpymDMKLMb/tad9Wp42e1rbdrwx1ue9OqAtQOQKJvWT7mQe71inF3GqIB2IuS1Huxgcs8R50lNvMvkm06LfBkFqlJ7A4AhxN2gfp0uWk4RKQHwhhDj2PM4dIZMkhR0iAAVwIoATCbELKSEPIviUe/AuDHAM4ghKzL/vdtUeFQvFZisaklGtuTSGftj1tTalYkLRoiD7UtcnbWYlCpSa0h5OgQZ33GDP10bywn7If0wVAut62SGKaWbh/HPb0Z8ilTTXlpLwpZtuOws/LZw0ZNbeQBvuep0dp06GiMVNjeB8QclNMnCOqdCAHmLN2NSB+jf8T5Dy11fPbBRTvQlrCfW9rhav0++0xmkfJtYk7H5uPYfRZtg7Q8TgGQJBrCAvNY5u9E2n5+E5P46NvHq4Eyy0IpF8ptq7mqASF3vhBBipL6ABgF4BgAowH0hYRTKaV0KaWUUEpPpJROy/73tv0zTnWqg3PSrfNx02uqTbnMPuKU62BwLuCZaPD4goFBWVvoCugn8UjlBqzpky8hEkNoli+5oUb6ip2iRBWrqTXF/WjpzPo2cMJVsLqQj3c56zjMuXgdy3MDK7J/2y9IdpMl4TaQiHP6RVG7YHQsfcMjAQDptnEQzytqekkJ5X0gMLZberRR2bmvQZ3fLco+YQ1/mbcVs9+yz6CmRVf97oPLbMsB6rjcM38b/usZY1g2ewMogaCSWr8sy2MSu82WqfKVNSaTZNsZStE3qYo/Byj1uO2Nz2zKBocgRUlLAfwHgA0ALqKUHkcpvSyIhpxOYQpVcpvUG+uz6TM1d32B/BNwJgy8jJSZjH6xdMSz3JP6YKibyEDSwpR9xrYtM2IDFiM2aKF0eU8KKeYkE4pI5nfIfTMFK3dbN/Sc2IhdqUxoBC2PRG2rLhaYu2y3gOPJcgwwBxuTMUW29lhDJ2IgBIgPE6u32OcrJtyGivF3OLapPmfiEkuqUTnpBoTLtxrrpeJAASGa5BJCg8VXuDVHrDZnc44E68rIg9yce+4TMeFQ7++VbrGmpQv3LdyOtzYY0+R2Mh7g57YZRaNEglM3vwmlALXZPtn5alaetySNhgBKcoDhdzrrRUBAUd3oXjzmtB8u3laH0bPsY2IFKUo6kVJ6NYDXAcgdpzwirViVtlHKyGCplbnSzvL23rf2H5h3Ctl12LiB2m3IjUMcVS7WNjWCNvQdxAfPdyitw4sjr/Z6JNyF+JB5Us+UHv1vAECkfLegH3pHwiWqcp+UMBsDEYgVOP2P9V+BiuNuxIrdkpExhVAr1xZkE8qz9Vu9jgEgNvgdz9Yj5kUbLq0CoAcl5CUyMiOsGDeLEVkdG1tzxYTZOWK1t8E9JxpJyh4EbHoqSYkoBa56chXuW8DXjy3bYaM0N7Xx48f4Y7b1kC5jP71d7ntQhjMz7yBOamG79VbVsc7wO9X0Bf0H0Q85zjsU85hBlMThhhni98b6A471BSlKmkwIWQtgE4DNhJDVhJDJQbS1osmquhiS1m2TeR6AyZyYQ/zh7ZRSAP88ZJYX2kXuTsebxTddtSrRD0/yBD9lENTUD3fnV15Pov1Wg4TSWL6r1nWEdFUtYayVZpXhTj2LD1qMDPFGjMSnOeJwn63DWC4Ukp0TUsUAAH0Pr5Yue7hNJFKRnz/vba7BPQv4zlzrHHQLMmB7Yt70QvGDkAZh9Fo27+duvVGbX3LYx4Q+UXh+y9zowmIEKUp6BMCvKaXHUEpHAfhN9prvaE7xTBIZVpsVG1CKjEJx57uq0lkUJVEGXOGGC1d4/1p1hmiiBiZ+NiFccgCRik1SIi2eM57Tcw0d+ik6FGlH5aRZaFLEXqP22cWo4ybK04uYkVGoRZxmeQ0TAY9k1AUesVH2m3XtuROmT4P55PIqLN4u77uyrUbEXXTD7PLQhHnTCw/lc972ZrL6/ys4pxKDWM/CbZjAnOaJ6Wggba7KPKRYUtYC0f4fM204I0jCUE4pXaT9oJR+AGR5dJ/Rkrb6G1BDED2jEvi1dfuZe+LRd7IS4j1ppjP87HHeEXZI9CKC600j3A6E8s+FoIUbKB35L5SO/JfUSWraH60L1ekpcwIXAKhO2+tgrF3RA6Y5KZ9jEWcW5eElO/GDh5cbUkCKTXMVkEgzRreqJ/VjSK2w3gwoVjA+CX6HYn9mxV5k4JyAXlO+8szBezPCkp+LQl+ruthYI8I0R9SjLoN0WvRMJgdPg7GcZFBD3kGBBWu2LcM5BjmkuwghNxFCRmf/uxGAF7dRRzSlee7u+sdRFKMHMhvm1h5yM4hlpc0iHP/zJ3gLjCXekPnXKyfciopxd3pqyx56e6rljR6NloX1lGX3Hamj2I/fC36dRIJj+Mbxgx3b2FGrnqQbmFDKomGIDViOivG3I5l1chxDDgr7p74pq3ymhn8NMPigEOn5qEgcX8IlB1E5aRYoRArS7uBH3bcRknyG962WDarN3tPb5n0pVuG8rcboQ2AeJ6Pym4Jk50DIQJpcINv2zOQw989mESRhuBLAYAAvQ/Vi1vwaugesVQAURokkLGZByIOOwSwaePTjjy1Fukt8w0IkMTOfVoIGSaibZQmSQM5AwJ0VEeeu41jZ1WdZqBKEIeywcjIKxctrrGGpWT+O1Xus4ppkNqaOXfOD0ubTvPjjlB71nFQ5FlsOuXOGao6LnUSTHJPl7kTpyMcQ7b8M7BcV8UIk0gQQhogz9+y+nNgOQQFA8fhHVcbL1DzfjGtwdJOqQI+4WJs8jiGah2zCd8JACCkhhFwL4FaoiucvUkpPoZReSyn1nt/PNViOIW1Y/MbTqHjIHeXMnA9vPmWsO7jbvhKXUCRYfO5zgp11aMLf/jkiK0OPIqPb37DyeoH4zpYw2IQUsEM4pYp4hmU0Kw11PGWIjNPJ+/X1/FwFbM3f+7vVKi0jYe10Qmec2xduRPNoM1MuW4YkUDb2r7ZtUMnIp4CN9R4BJtzoJp+5sDe5v+JD3jbdsf9ekYrtKBlmTAMTEoxdxfg7UHaMHgzaTsFMKRAu2ylsl4QSqJz0e8QGLrbcS1OjFWXIFCsplVSJk5sDJKHWPW1ggm/mK6NWDYJjeALAdKh5GL4F4C8BtGEA70XZCZNBWpfb5Xaj7GYkMo/M1mIHrvLZ9olskxJlhG16fFgRzIZQvulLHWFs16AYozy/Uv3vaP9lILG67FXxl432XetoWmztFUU0pZ6M+2WyyYTYuw7VOS2uzqQssVJMv9Tfi8qt4jWmd4Z6bDdHahbJAeHyXQjHBUH1vMwv4TN+8cb6N4oNXKLXbvIAV32B+KfsKKPMt9v0wqWMWJrauRjSnGl2ilOoNaUS5PiQd9V/O3SxzqZWYy4JsxhUS72a64QEDKWykzdF+OHEX1wtjjSrIQjCcDyl9FJK6cMALgTwtQDaMIE3fAxhUPTTqTaVYv3V01pItEAgYa7KabYiHmSKiyMfGueibuTZfAwMcY4NUMMopJU0Soa9gYpj1ZOt3QmdhBLuCQMFhtSqnrQl2XwGWg2pkjoJZb3ToUF00jRejw82bhIyynlqEj3bPaH5irDlykY+4dyGKwoR8OFCwEXxPlW07xrrRQAnhvTTveymJxpDEm0wtM2rz8xxhTl6tFx9Bgs36vnwx9QAAGiNDHAoKUYQhCHHJ1FK5QLs5A37s3uGZgwLMpVRgJBz19xuNgAQkhpU7yM/KTbN03NaKBAzul3fkSMMyHEMrOlnbADf6c/phM4bq0Mt9l6jsSTfj6Rl4AYcaLf3tnX2tpd7zhxkUM6PwViGSnJ9qijJ/xEXz2b/OQZL7ab3UTL8HBs76FG5v3lWSeEyq12MyDu+YtydyDh8c3Oeb66CWtHWgl5XGbqwW1G5C3cEQqawPAEP4ng7lZBcrAcCNedzS/ZvSinlJWjOE/aEIU1TOSuBrpSCXXVtUl/d0fOZe0388f1YlAPCznkkeJCJN9QdWF/Nim20vBTWcmb9jlPOAd5opDIKxLm5ACWkRXe1dkMTBYjwacMqAGfZdIc5iDAKWKdNXCYdo9AqSTi9KEi0wWUQPReFgxYlCXRICrUGqaQpPmFgrax4Vkllx1hdrEyMmQHJjH1QutaUUZ3K20vunq96u7NWSSVIohb9AAAJajd7baDpnEydD8Xl0ggAAXAMlNIwpbRP9r9KSmmE+TsAogDQVD/rNWYgOtNtWLrd7KkqQRicspFxrpll+YQRk/hzWPP3xBcUxzA0zefI3tukOiMS0BzHEFZ4SnzDL4eTNPUkSrITlzj5MXx06AO8uUEcWoANILh6r+6562RunJE61ZnfNbsRCL5BtP8yVIz7C6o7tgUy3kMqJbIh5gU3Bgn8cSMGwmCP0bPewuJtdbYHuQRDGHgtHmg2h92w1qWF+mCtkiiA1gpVB5BxQZuNczl7CA4Z3cbKx94nXd8R5prCR7rtOM5VfSA+qHscaSa4nVMSFhJtQKRyPZy2TS5hMCtbM3oYZW9hKUxteq4jg9JRjyA+7KW8+yADUS9ZC4xjiRqKoDJt3VjM/p/27+2eMAAAJeHsv9oVfV7IpLhcVSU2sttTz4/H4yQqSjOboOhgYlFfO8yJcNkeAEBDQj70g5uvqZ6FOE9k+59qOsVFbZxqRJZqjgcGHWwyLxkHt8eX7QYFsDtu5Co1GDMRWrFsh9lB0VjDw4t35kJ9pPtvMJRrrlRjh3WErM99vcM5v7e2VrrCYr2GEwqCMPBGhp0wKaWLM4HEr14+5m8oHfGs42bTHA4jFBN7qKpd0+vII/qGDgnCEOmzFlQxSglJuBOR8l2I9f/E3EHfUN2ob4a1kQgifT+xlGHlqXv7qhsVL7yEcVOk9glsiHgTHdk8nHudgnJDBxgqtQMltqIZUTpKp0Q8MhncQln7eKYz6v8Fj0b7bMyVkzlXfGPSUFeUYdOBJpQY/CVUlB79lNqqIs9RbDnUYr2oEQZTwqic+a2xMLfe4X31ZF5hKT0OkM6I9ZCsaFOgohb+Kk9+Cbe/w88Dkxz+rt4G5/7AjAu/ozz2m8IgDNxPyMpgjfcJAdLtxwprI2FVcSTjTVt+7N2GWPyWhc+cdigo4kNfw9ZS7yMm4/lcevTzICblOhW41vslWth6qBVf/fMiw7VoHyvXxSO2vDdiv2Ok71rHDVO0R4cF762K/IynQVffglCbJPdmkSIzFx12ZpkTcCmM8u0xFSc6PuOm/gRqsWOImzwABNG+6y1XRRF2WUT7fgIQXQF/zr0fckqpc6Fywq1SfdHAfmv2vWVDYqQZ2xkrx2D/rPk7d5Tp8dzq6xmPZGIKO1Oql+NIWLExLkNkFdO/7lEQhIFm7EMw9YkMNfwmIKCMiOe1dfsx5ZZ5qrWSoZzcDCplciJb/QVYxaPY6kYWXnM+y8SczwdV9db0n5GKHSAh4wlHO9mzc17hnPb3tOhWIqVHveigtBWHxBBni7bnGBxzPlNia4FGKRCu+AyVk2YhFNNNovslD2Tvi/vlBHOJWEgVGTiJlDoyEh7NlGI7HkJnTD6daz4oOeollI+9B2XHPIT40Df4hUL8XOiUAmVJa5TbaL+PbR3hZDgGtX5xuQzDMfCmgSG8fPlWZCJ8ZXXlxJuFbSiAxVRXGEiX0TR7OuiYUCCEwZrzmYLiqJRK8SdVnm5gOaNhAvaz3frmZrR2pdHUYZyAqVI5mawx4b1pOJiBdaMeGCZQ3oqGe3LCKXUf/7lM0lueXK/gcgwcwpAxOSrZmQfGBy8Aqdzqqh8KpaBEC3Wt2UixzgH5helQKM35C4RL9XwTJWmVu0xmFJQc/bTludYu/ibI4nCEID7oA2t/HLaCpmSt4xwcmj6ABGzyH/DgcOiIwf6dQrEGhMv2IjaAn6EtNmAJ9zoFRd+E0cO8fOy9KBn+KmIDlxjedQ9zcJHhGD7YWmfgCqjpoGDQWXLqY4lKKGoOGy63EaRCQLRyk+FaXGoT0cp8zjkGkeGodjJ4c8MBKJQiPuRthOIHEIuEBM8Y0TrIbTpLXq1slFd5ynBRSxvW7rba0ouqiDhVLVi8Ilm4W8hWoxEGtnjG9NXiw16CYgpl7Kh0H8qL10OFMiaFZmDOwGUU/jgThvmfic3/2uP8fMdsG7rsn73v/CH3m6QJH27XArs5iKkkBqlCaUYa8kl6ACBcusf2/mkh63vaQUkbJQAWvVgWq6oabd+JvbNpsM6Bym56duuVFXVyOYY8NuWBzcfk/tY8pzVEhPODPdRQ478WZLLGNWIUBGHgKR4paO5k0JVOobwkjdjAJSg75mFMHdEvR8UzXd4jELKtabBMJmJzzwZEsDXFMnwW37lm0UT1UfssAZ7IJ2k6wsX6f4KMiTAQTpa+fGAeitGz3jKMj53+AABSLVMRtYmk1xXSN6IyonNljspliVhJZlHItppWpDOKhP7CuUwQ86GcuOVKrd+V1++fzF3JFWHynqHMd5UVEdvp85yMCGxFnw5jXJISWxOJuB3jocZ6+GIRG7wQpSOete1DQRAGgOKLf1qAejb8NXMeJKAY3k8PPFYaC6Nk2OsAgFA0/7h+Bgtiy4Rglc/u6uQNzpj6D1zUwlZo4z3qC+Rq0jkG+w3o+pc2GH6vWsM/NXrtVQaKpQ8G0YHTJpuuwPnTjrIto6GcCUutLdraFr7oT+Yr8rjDjlRGwqJJRn+RP2Fgg9RVTpqFD/g+ZzadsPahupFvpmnXW9HbSnMMNhrmtMJyDBw5AbMPWDkq74cDUd/rSq1cnogAhiLOmSMLhjDUtCSweJuu5OuLppyHIzGY6RHjgJs2zIwHm1IS0XUMZq6AhPUJ7SZeXUggBRErWZ3gHwng1i5bvXSuZNN39KI8J9Z6crUr1CI3ZoOhOYp0CEXIRvssel57i9PuXCR80glh08c+hhxCJuPk66FuVs5ca/5R9Mw1dMnFibHtg/h7iSF6VdkpaEdo2X2CH6lNvx/tZ47f5J2ARwQvVVXJRjZQywxMOgfLE6EwCANXlKSfrAhRDAO1vnGZoeThNtVkrCOZtqRidAvzZCo96gX9nitREh/mGgil+EWj8wmAREQst086BslyHQNXyZU3fQAvgcWifTbgYAU/SCIFRVu5KsvtzAbO6GBWQ3vCQWEaabX1jmbnW0tFNfc6DzwTRTPMGcMmkr3Sc8tZ3OQFxk5n3MXecKzPK0TfWtaj2E5c5BQryexHZLor1wEOeNnirNWrZSrS3qUhBUEYIn1URYo2H2tbukChx0QJQTGcHpqT+ubPnkQ/O9jiMp6MjvMfzBKbbDtf7LTKVRMZ+VSZ+6L8iUUpRXOnvmmZT70ilI/+u3TbXiBL81Jl4jAShvpMCki3Gdpy7YX51l37GzuQyXo+d9ASAMaAzTcKgg5qiA95F9vb+QpmwLgBK4zJbknUaclJaItMQ15GEnhl7X7HMSBERilKQDomO/aBBVXizoVcoL8L5bfd6V/0PYam7Z3EIpUbAZLCOxvFVomK4l35XAm+V7wO8UvJZGPJEUTB1kCizoffgiAM4bjR+/g3L6wHBWFksZQxAzR+LTYpy4j+ZYi4ZntVaO7t2imDZz76WcMmyzURnu/DF8xSApz25/cN1/LK75DHs8EiWKX4Bf/4CGaLb7eLoSbB9161w95GjXPjbxwtEraUFq4RCj7e1eDIDRDinNqTwr2eQenie5d7QWzg+2iOBheU+bXqA45vVzriacSHvon11WYzUx1pB6skO+VzObEPa7F/4DbhPbMY0Q6ikjKOhwVBGMxIpFV6nVM+k0wu7DQJdxq+mEKAkQNUP4jKkghKot4ypOXqy04I/vr2Z7Nr6fJv4bRLJLWXAQVFuGKzi/LuSnh17HPTg2MSIdv7ZhAbB7kIEXmoavXyv/uhUudYOGaOQdvInbiBNXsasaGaE3KCgY1aRojYQJ63sjfEh4jThPJAbViGlk6rOHBIOiO16YWiTfZWSQ4cQ6ZMbOoezoaAifZbKdET07MSZRLpDG57c3NeK6YgCYM2UKzy+XBKT9Rx2BRMTJMVKxQoi/lEGHg3/fAZEHrMekN1iZNjnBy2NG5A2cgnpcu3OiVNNi94aaW1LPT6amIZVE6ahR0l7ohka6dYJDGhz3THdr1iR9w4u9b3q5eqt7ZuGH78mDUHeaHidy9b/ScqKAX59l1Sz9txV2mF8XzmFEuHxDHU6oesROWkWSgZ/rJUP1g4+isB2FffjjlL1SCAbjgMFgVJGEKEoA3x3KmdgCIU1U9KliQaWUoiUuD9sMVFcvTsSYJnKpbxgy5wrrnlQwYLvaqd8eEevqVDUyLodN4BEIbseLdmV9vhKDXed8D2lvWoaxURVvtRKRvzgEwnpXCwtANpmnb2UaARqclCiT+HhaARH/YK7Mapvp2v0yOVMr5LGWO8LtO3rW3TObsYN9l2MCFo4hKmjR1xVVyp6lm9oSAJw/Jdqkt/OMcxKAgzStqUaYfWQiSbc8hquKilTSrcrVqHepLgiZIqohXWiy4hIl5urHbK8uBc+inBTHgzSMj4vanfC80HDkRJ9cXhNoE/gqj6bLvhkkOCAh77gi7JkOwSZWLyCV16ErH+K7C7j/hk3tIlMPYIOecni1TsMIgvXzxojMn04CJdD/DjNt5BKxjtnUxmtX399uZ6YOYYZMP2FxRhYM0HKdE351AoZQh4tmSbMRZMuHw7yo75O1KZNHdBE1AQaXNAlTDwPmyI5J8wL8nZmBucxDLdAL9TRpaNftjw+8DQVb7Wr0VJEkPCOqhrpDH9o+Eb8Cm1/7qSbF9oPkEYdDh/l54BCfPNrdtjYq/qXXUC6yYJwgAYD2HHmiyZWG4izpMoBsQxfJlj7SjCTnqUZRbKLtOe31FMIITMJYTUEkLs7QUdQXNyfmXoAqQYmaCiGGW0JUc9j3DZHrQkW8BbFATy4hptc+ClD/QjUQ8Pz/ep7PGlHNSGFxhIypaYGQMjisG+tzFHML/uqCAMeL5IodU5TIP0LO4ertANysbe419lITk9orZeL2luteyoFGx0Vev3CsFF3gQXGJDJ4K81fN8cMzIIWTb497fY54/R0OsIA4DHAZzj9qFov5Wm+OtG13wlMUS/l+ZnGLXbuLUlJUpZmatDEVslNXXI+zG4RXduy5UZ60IIiOYFBl6OX8P9UY871kFN8juDhkLwPfZWyi1qt9irvIi75onNHHXI2IP1PsIQirgL7KdC8K4huVzKGaqKM0enUpa62G/EI7dhTvIpPyAMu81gdOOo7F/UUr4tIadf7HWEgVK6BIBr9+OS4S9jS4tucUFhlMdpWaRoJg4lYczPoEGh/AxXLMcQAlBmI2fXJgzvw97xjnzyE1EbuyhfcebG9jxf8bq6UMztH1mUIRRrwM46F0YFXBC0drE5vdmDCX/8EgKHu3zR1JnEa+v325YpG/kk4oPtzUFTGcUx17nfsPcSDgCSoqSa8JsAgJcrKyyU/oTheqA7XjBFtw6ZsjpMmQ07wnxPc8BFWalFryMM+SCh6Ow/JUaOwXSeMzynnUYyVEFrymrnTShDGCiweK91AYYrss5OOR2DdQDccAyz6/gx8WtpP+k6gkIfDtHK0OCckkT4S601SYsbiC2K5FAy9G388FHdht/IMXQ3oaSQ4QZiA8Xe2sa6ug/mbIO+1WvJg5CFJGHIUHW98s7+JYzlFi8SbyZm7y9iRoukY61MZFi9jDmwvHx64SOSMBBCriKErCKErDJez/4bbUCT2eCXVQYJTkQ7mjehscvKrLAcAwFFCaUYZFJGlY18HIAekZHH8snk89VQKeJKCEXpqIf597oJ2luwZq+dMtnBfEZNOD+fEz8QiunEiaUFQemTipBHyfAX+TckdQxG7t84nq0d+iE0nHdsKGBPVE68RUsGOJY5VFGLykmz0BRLWvahgrZKopQ+QimdTik1eRGpA1Ry1L8BABtK9BgusUFqGAkSFp8Sjy4bg+0N1uQ47LBrH+wHrfyNMGeuyu+3sG1ZEMi5tHc3jsR90K9QbRp2Hdbl4E6K4N4Mr3Gpeh1ElkERudhOJMsrqN7gxgneFnOTE9sZshqJVJ8xjmX+f3vnHeZGdfX/z5G2e93LundcMMY2LtgYY4MNmBoCxvROCBBKIA1CDYQkgEMo6RDe8AuBBELgNaGEECCBAC+9N0OMy9q79tpee3uRzu+PO5JG0kgarcoWz/d59EiauTNnbpl77jn3lDrLj6GqpDlOc6EKgWZnVbod3ZIxJELYXNXaFLRzy8Le9o6MnChQRQNmoKjCU+86xTWPqKVSbf488+Fm65r4gg2t7kXmZHE7Y7GkwZ0FTbZR74sMn2c+yq5dvhtkzouyy83WbjUv5Otfbuf+V5NnNcs6/M0UD346dbkU2FYYBH/ujCTc4rSdu7h3c2b+FAVl0X0wvtXaG/MnClcSjVC8Iye18I6SSDiLD1IGRnRBy2W5gtbatEZt7AI1qAqauv5djjGIyIPAK8BkEdkoIuckK9/bpjQL6fpCOsugrXpqMxMUiUzQ7SJhKSKgyroap0nWnvQnOT6rMrpFu1VSc1v6Fgpf9t+P4NyvOT5LLA5paCRYmDjrU9y9i9yJrYkQMsZp8tmHT/5FhoGB9Nt1rs0OvDVBgvZ0UNBrTfh3qAUef3cT48RdFNlsoaDsS4oGZB7uorooubQwqzk/OcJ3+XzMbs6uB3Z4sZYkxpUdIYMKsxiMHd+R/5uz4Efk1njEV+D+PYf4hWxQ3e1TdDnGoKonqeowVS1U1ZGq+rtk5e3xyUOMIZR8vdG+oaMR3llc8TfHe1384JvscojDb8+m5rR6iEa8VdJdz1mTh7hPT7mruAKW3xx3fKODuaMv9JAO8HVHHY8LnLirjhkt6a1sz9uxk6PrI45S/x2cfnTUWBRXPBX+HWpqnwgL/O4j6XYn5CsRbCavVrIbAAAgAElEQVThHBIh/Owu9xhC77JTnQuIrLrzOYmWFLmTdkKIna9ag81UyG4SdjuE2I39dtt/8UUmZV9Rorg+zpYdRpUU+W3/jkc8Y9jVZCSUkgQMyQmGycVTqS+KN2vzkXi9nr+MzvllQFdt29GhuuVywIesziRFiNK1NYnzFHsweLK8V+pCaSI8XsTl5rPF6WNNPgGKpDT8e0lrFpSaLgezz6UPRgix/lSf1r1Cc5J91jCdtKh0QdjbMz4MstA/EGBW+x4ub5aEMUSJlcmeJ1QuvqCvxH2qvVp/I26zBn1WOqsTzCOjkThDXC6RXp0FeL0ku0ll7LhqtZWdTgO81ydxexy46oWcPUOu4SJdRFbQngVLn1iE312/28k1WmJY0GQLnEeEMczMgrXtni6lX7/L/ZEQYtXGQQ1SV5haBdvtGYMdsfbEQcxgSJY0IxpBHBmD2lVJydFmmbF+VjIjfExR/ru1Pi3HofLtr7rOJtfsSxwSI18SQ/Gg5/NEqeMQ4JM0RfF0UFBmQrt/1vI4X5TlRxefbxxV332lHQGYeSqUDXJVPmSuGmKGU2xqZrsD45AshDlJraI2cOvvkAhBl0EwezRjCK303bv4q6OPg5MqyQnrtjVQWWs2r5t8kQxsqnDTEx+TzgrXF3DnCQkmM1fCc67v4h7ddddCyM+zNwdT5+DujrhvUzUHuPTQ7YoQgMNvBfGxT3OzY2gXO4otPzjnSdI2krIg3bi9Q0GaEkMs3EYo6FGMwWmCNPp3l9OBaMId+0j+aPPt1JFrqusJ9v4UgCp/5AVS4J+fbMlBspkQhKEJcjtlneTiK7J8w44jf/snLiGJx4Ydhf1fzjrpYJtz/K9sQtIKvJJfuAksJygUlYHPx32bt/Dy+o30SWLZ1qfUvFOhXAz2VX1vtTmaud7MTvJsbj2SC0oyoqMuPSa6PWOIGqgOjWsYQxoSg0PZhuMfpio40NwvSQcGVQmUG3VCpS3oV10gcVLxhPciuSRgh1+D/KStnEu3x4cAaMv2m3zglV1meihz699vQXIc0alk+EPc+c81KfutZOjqrNO2J6LKJbpGz8djvgszWnVYPJ21M7HHfo2VjMcH8PUXo+peopE9BhZ9y+VTZg4pS+35nPR6t5vcGVHpYoidJ4a0+/EpjG52Z5ZY3usD/OXxESqloCjsE5GsXWvqIxtI9snz44Yn4sqOcghEF/UsaSTE6dvyMn0RljfE6381B5t4XQUDOpA0KJ2ERulCfK3c9g83EU67J9IJP58pStLsWzeb4uLw9MOSREtuajfvqPiLYdjeUVdX77L5OxVnLq25bdc+Ew6htv/0DtNxuzDqUYyhqDVat+tXMeKfSzlNh/yL4kEvxB0X8bGhxAyg95JYtXz/0ffDqig7Y3Dyf5nQ6swYxlrHS9OwMvq4qBC0q6zjuy7ytcfQddfVmSPTmh3o0kv/nJ3pSUBunsupzOFJnqcMSwrpFR/RWOwqmTwsvl7/cgN/2biZIaWD2DZ43w7fx224k27PGOpsu/Q1/307+qQIPjJPO+Lz+VhfYltZnPhgymvsE9CWungxd00C65iQzXTLhINdP18o61auh+e3tln+H6WdH+EVgBknpVU8L4zB1+y4Mu05yKwFb3cZETcds9i9m1scfQ3cQEjszT00aFRJQxuNQ5h9femLisOUeX+3FPZNer5YlcltbeDLbMpev92dVVm3ZwwtNm79+ZboSvs1gE8zZwxxOuMph9NQNMS5cOi52pWBlulqY2s7Bb3fjzqfSPc/2NoMKxgy2f3zhb5zOOsdUt/AmbtC+tguMvEN2TOt4n5NzT6HpEjElAq9JvyU9zb2TKskyLzn3U446aSwXFFX7yqBTSKGnWhM7FtntAMr24qt6yMoxuZ3kAWJoTiQXJJK1zkvEd7ekMi5NxrdnjFEI7qDCrUNH0oww47zOVgd7Cgd41CwkUCpCfylSJQVQ+nIPyZ50ghGWHGV0tGFq78456ok+70LugpjSBMfFxWmXFcWZshcfQV1dBnGmQNkY/FxXF3qbGyj+oyBA77j6n4BcTeRpdsrTw4wC02/NRnbr5/u/yLyZ2vmoVVcw+fHl5Evw25orqpIOB8CGCuUkCppdlPHA3L5bR7VA9tDewgO5WzRHIPYubxTTth4DG1vj+xRpLHH0DZkrwRPBJPSjCeUCPaheKZ0AVVSYS+oSE9ieKa8F21FvZOW6ahKYneAADpybsb3ub4mdayePsNnw4FXcYsL1VMAcckYEkkMzqgNrRJ88Yzh2XLbXmPNGjKF3V4uqX+FryAu9E8qXOOivePIpH1FV4YKL30eGUhCMKxKchNRMBHEQXxznrcjB3sVF9oisjp7U8fCZ/OwDmo6kUPF+OY5nCnO0kRnv/d8Snl/bXzeCif06oDlkCuc/yIMmJD2Ze3+5CExAj14tZ8MK3a5S7QUH3YmR/jsaRB3uzVKxzefQ9cDfCWBJBOqc8I5ZFTHN4OdUJcsWqv4GdG/NPF5B6y01WuSJE//GkKPYgwKfL4l0ggh87oNOiij6VGiWHRoq9ehnD+iJywtKrBtoDl7U8fiwtqd4dyv09vcT6iL+kwBDcbd87i6er6/PRf27e5bszBXMZwGOjOFfilCcZ/ZmHzIVxZmnn+4rPc7Gd8j35jpQrIUgF6Dc/4sADQbn5wWBzWwP2ZMBVzz8uQFY5M+Ri4zC8O6khHO56cf7/YBEiKYQpKNFGzLKGNcqbYxsbWVZSmsw3oUYwDhhr99ZPunKH5UnHX2NyTIqxwL+x5DaOyUFsZLEaXDI6kE+5YWhfcYnHwjnNA/EOCApmbeWrueaQXubaOnlI60GEP0yL6+ZjuD03QCS4Tqgo5teuV7gKV6ZSpac5/UqD3NfL/dBTJuMYEl1+SV5r1949+DQAcnxqixWB5vgpooXlEoomljcQKDk5I+/MDlXJII+2t0noXDEsWkCnbMOGJ/a8FZHCxwJWH1KMawtXe8mCRJjBQHuEz04rcxhtA6vqJPctf0yobKsMTgK0i92QbQag34QoB0UkOKD/ZeGaWeCuVj9mfJo+utElt9u0qOB4cJIlVtd09FUWq4aZdpCy5H0kgIlQ18vdalhdepj7goZKvlpe/AlZUw/sDwoUSTofgLrKsTt9KxGQYX3C8Y3a6LmhLEpBJ/h6ygLtxh2rFQ3QU26VGMYUvf+LDWPgUV51AIbu14/Dbb4VpL05BK+9kSbEtaIuDQ9FHli8pdPRtAQe2XsP9lNJwS8bB+boPJIOYLZCcL1lQHVcOpLpyQ3JgRdhj94i3DalPYeacT4dZDNHzix5ehHX0sEgblsxhQbJa+o+vqnSV9F3r+qHe2sBSKy2HWqbbzCa4TizHkaH/lZ9Vb4yf7RMO0tF+H/GRC0tCbI96i3idIisVdj2IMsWiUUkBo9Ad4uyR+he/WAkXEzyUxcYiaNLlIF0hx/3aHDW0fRDZUi9wnKimpXWMGVkH8ppRbBdA+KWLNzHewK98jgfe2HW7DCXcIDiunVKbJRX5PZsgEvkwnx6XXAlBsGSXMb2pmhlMKz/0vM/RiDt9Us52vOq3Ofan3hhyffK/jUo5QDeVvyJGH87LGprhniHtvr9wIX38R+gynsAO+DPa6byko4JkUiZB6HmPwxwwahapi5wnMbU5Z8floixkUj9QlN1Hri4+CJB3oc4hy6FOFviPNnzSskoosKwZxWM25Te15epJgYgBbGADf+jTqWLJNv9CKpCtNw7N8FRmpwa6tyUyP3JXhympPJPNVsxVw7ivW5N4vEHBePIxeALiTODcMXuKKMTiutEV43wpz85fezlJ684wzw3fIGWLqGdfKhWUwbG8ASjsQejvdXuv2jCE2wqa/ZEP4d7JNlpFtbZSpstyFblDwUR9jPFySItTuKdIfX5KBVOawhSAAA8aZP0H3jMEXNqdzYAx9Rrm6RyrroQZKoLe1YTfUBPFqT1I/ifnOFk7duYvLt7vz3ozF2dVr0oi0G4/eWdrI75JIUbWzao3a0MnZM20c9zsu3VHLaTt3cWhDo/MYGTUPgPbC1NY6UlTqyiM4lQqmNKhc58T8S4zfTkNLOibkmSFuQWdjyPv2mcAFO9Lzrk9Xg9rtGcPgGB2kSEznJdh8TZWRzW7X7eTHcEgvB89nG3qLH3+Sgeg0Pfn6jIwM8HQ2ny06Tvpf3yB3aU1LUjCGXQW251l2PZy+OulcEqp5tvcYvre9Nmmo5GQoDDSjGcaz76loLkke4sVnLbOyomefvoI+QeW722spIsEYKTCr+A/HnOJ4iz1aI3teI/oUgc+X0rcm2UINjPXRirr4hWLovcqlzUXKgPA2jYVP/FzodlMe4Ir1u5/EEIvSUX+I/JHEqwSfAuMWh5NwxN3Hdlx88YL2pKLkcdEPlt6MDyYeiE5UffucBosuhzELYfqKpPePwpyzrOeM6c7lN+M/9EeubpFqzJdiU8f5C2H84qTlQzXPdWi/kSnCl9shSnrtuhthy7ADk54/1powc7EBm+yOu+pecTgq2E073I6wVBLDHL/zO+0LLwxzOF2WV0T9zTSMTxRK+jruPyZDt2cMyZpPUVqTFZh/QcIGsK+g/eJnYsxG65G9xiV/rtr1fKclsS5Qgduqt7JnS2SfoyC0x3DWk1DaP+n9o+5lxYOPEvPFD/PPR0qSR20MIZV8MlTj1TeJLGF/XrUlnMzdrSfx+WmKxiEkdEpyggA+t4ng49GDFUlJX6SfVW9ldHs7DN0rxtnTGalSZqZBmj4VE+MPFpXT1IF4QYkSKPWz7EiW6zDH8z5fYnPVi3ZYRilXuIsEkAi9Fp4X9X/NqGPTvsetScKHrJ93bVr36vaMISkUmhPU8MuiQpINyTm2jWm/r5BZhdHxgSSVqqe1nhJ/4klIgYMbmzi4IWKu17fQvYmqHSHBJErldZ2Jj+JP8gx2BHzJN7Q+KxoYd6xk0uGOZe2tuqXQ3RAb4SKq6WUOewtj0oiG2oowp0/6YTRC6MmMoTiBA+M/11eyLGRSWliKTxJv8o5rbePWLTWcn46aY/ZZNCexnSvoHeOIdtitcN7zNHVgRf1ykbOxSahfEwWHDDEEpz28UKQCXC7AHPG159GYvZtWSfLeJqj78iTezFqSXua3bs8Ykg4PgfXFSV7n0fP5sNh5QrSbZ/p9BQSmHBld4K0/kBT9x1GQZKOussg0vd2kdVK5u43iWAwvDYmhDr4RLlbIX62rZ+3E05KW+bIwnhF+ZegCx7IdEYL/XZZY1J1g6ZMPaIw3mf2xy/j+ADUDplPWAYuOmaFFwtSj0742GQa1p7+ZOSeNcNTpYERfZ8e1IeE9PGtyTOLHcPPWGpY3NLozA9/vEvM9aTnvliYeo35bf/2yagvsex4M2oNG23OMKspgUgZ81r7k0M3/cjwvfvMe75D4tk93rDtKUyP2iVt1JDPsoGyQa3ohJ94ClwvEELo9Y0jWSClzJpf244tC54nCN2Ra5D4+P0ErlEIoxwLDZiS/98yT8TuswifHOIrZN97EhcmdHSfvrOPlLzcgFgMKOuh2pCD5RPiLqi3cULOd8gHJY+CUBuOHSkECy6l0X5a31q7niCTWYZH9CuCUR+Ccf4TP9bap/FJ5sn9YNs/VDmJsusej643n+ujB05yKJ0Sq9KwzW9J3Ply1pYYfba1hSksrD1RWscRlRrRUSOmfsPBSIFoqfXpDdKSB/takF1IfJnWAXHotHHs3TDo0KdmhtpXuPJuvTUhV+YuqLRwQMgSZdRrPrY93cg1h73rnyXGHNfEHCwvhrKcojem3kgLzXjYWxDt5OsVySoZfVG9xPN4WjFZVB5OZEY2cDSc+EHXocOv9iWU837T8r/xpzi3dnzEUJI6WWejCmSlh80+wbcaJj7H+Esa2tnHzVmuFavOYfHTj5vjr9z4Bxh0Qd/iUmCiWUR2QBlevaG/n7J27oibG0mKH620D4mwHEf8AawXapyy5ud/wgAODUWV4W7wqpyHNl6UQmJTEWe5oa9APDgRgj2VhU0YA+kakrFQOdzsa23CjELIz61nNzRxf18BTGyrZq2x4ymvteGXdRn5e5TwRAGz1uzf9vHdzNS9/uYGBwSBH1Tfy8KYqpre2cmcaEpMT5ln9P7XMWb8exsyTgejN5xExEk8UAyfF5OIvhL1XpnQam98cUbUW2fplvEV6kV2C+srPGRwI8tK6iMk6EB6jQ9uT9331mPkwZj8erYx+n4utwIo+h2dNxhjuqN4ad2xqgjE6rnwEE22WViE/pn2am7l/U1X8BVOOCP/8+4ZKfmh5g8euDY+x3p3agLuwPCF0e8ZQ4i+OW4WH4Cpkr0Oh0mDQDNoQ/MUU73U8j1duZt+QWmFKRL8+0ckyxl8AS66IOxzlL3DBy9TbE3sPGJ/0We1u7M9u2ERFaIVsmWAWWIwwKtS1bYV3WZIN3t6+5OGoS4IOA2vwZI6pjz/+4piV8WVTYNSsszgpxgy1KKjcs7maM3fW8dba9fRz2tOYcVLYdLEsxQp9m68Shs9imrVS388hHMOyhka+URvxcn+7pASKejOyA2ofgLIEEkqBalqrzT1bWqMWASE43WFhojATwAJbDJ4lDY3cVb2V99euZ3RxZGVeZPlr9A0EYNxi49g42MooGCNZXLgj0lZ+FIr7sqmPkax8Gi19/W5zdeIKJkCBTeq21/Xerbu4Z3O1ORajso01SNhkTexv9E4uoY2ddgIQz/CKrfer0GG6HHXwT8K/YyXEWQ4OtLH+CSdYC0UR4dHKKn63uZq3165HLcYwvrWNGQnmtx9s3cbCxiaGtwdMfLXzX4ozIgm12egB0f4gqaIQd3vG4BMff3HgqF+tq2dVW7Tb9/UuIyDesmVbtKrIX5AyREXI7PWpDZW8/qW1YomxmX937XoOs0R/nypUTKNsiPF0HtXWBv3i9xiOtsVS3yd2oI2aD7PPhF5mY7jIyjVwin2CdRPb5huvM6/PeO6o3srN1gp0QUwQr6CT3dL4xUx2WAG1xXh1P1y5OU7tEIW550LFnly6o5aVNonqsIYG9m1uQQZPNQP/wKvir11yJfdvqmbfpmYu21EbDrXghKLezVA+JCwRONmC/2xLDUfVN0WrExaZ8Az0GcFjGzfxwrrE6opYJHIcvKZmO02S2qdiYmsrqzduolfoPmc9HTk56TBHM8S+Sdpgqc3Y4bzaXRHGZZMsL9lRy1tr1/Pi+ko4Y3XEsRFAhIWNTdxevRWO+RUrbOOz7rRn4cr1tJUa9a4PjZIE56WINHC4gzqxqO8oFjQ1sap6K3x3bfj4wKZas0ibdJiRzsM3WZVwK3t7CgltQlmF4/HSArP/MrQ1fhE0tCIyTzy1YRMP2aSNYoe+Lzw0wkjeX7ueq0N51C217LzmFgoAsRhDsqXDsfUN/DoklQzZE4ZOj39L97/cPEuMZuWmFHNht2cMsauFEG6o2c7ooI+DrYl4UHuA4+obONQafIsSrKr+sb6SJU6RDYdMhX1Oh2+8Fj706MbNPFBZBZd9xHPrK7m9eisj2wMRU9eYFaEP8B/5M+7ZXM3TVpC74/pN55D6Bu7f5LyauqlmO++tXc9l23dw+5YafrN5ixl8U46Ek/8MR90RLlvkL+adteu5KGbCm97cYia6uV+LJ3D9Thg8CdqbOKixicMbGs2ArYm2APqw2LmdG/qODf+ebYn1pe3R7TeltS1uFRaFI34KU79CL1Wu2RahOyq02jz4B8bqo//Y+Gt9PspUuadqC+NGL45c44CTB4cyvsXn1BhvZ3DjFzPNWqVNbGuHhZfB2c/AmAVMaGtnoIvkQ++tXQ9XVjLjoJvCx+w696PqGyhzIdP+fnM149raTfTQi96AMQtMn12/E07+E1yxLu6aiiRtfVhDZPIdHAgYZ8WyQUYysHDGrjoS2uyJ8OvqrSxtbIKZJzPYptP2WRvF0/sbU+uKUQu5ctt2jqur56216yFF9rdjHBLlyMSl/LZqK4c2NkWbcM82vjuc9CCU2EJzz/saJTYG8qfKKsamUDFeur0WUcXXZI29S9+LfgaLocRqF/ZubolyRO0XDDK1tY1V1VuZ2NpKyRlPEAuZc6bzQwwYD3seY35X7EWZxaebigbA6audrwnlxjjvX3DuswD8cltDdMhuKzbVJH/E4vG6mm1hFXIidH/GUFACpz0adegPIQli0eWs2lLDP9ZXsnrjJphyJHtYap+DGg3DiN1oGhoIwIFXx9Px+eHouyIiNUaFNL21FfqOoH8waF6WY++GlfEWS2fV7oIzHocpR7JvcwvDLFGuvLCUn27dxoAkk40AZ++so9/IfdmvudnoKU/8I5TGpNj0FeAHZOapUYcf2FzNa+s2whGrEtJgj0Ng6lHhTcb2oug4+Hu3O29eTdzndMBs/O5pqXT8sYnND7oajvsd05tbGJpo4u41EC4wzkyhCbSpfLyxD590qPkuShDy+exnzPfcc5PuVSzdaibRIWLu07zkek7duYvl9Q08UrnZTF4X/h+s+B9+U7WFo+vqeWjjJiN1jbaidy77AZz056j7nrCrjpNj1GACUFyODJ8FwMpddVxqU+UVzP8GU1qdJ3D7RNY3qCb38YSl4OTFXlDMvZurucqWvrG11+T4cqFzi7/P7dVbWdDUxNCv3g0Lvwnf/SIsdQIw7+uw9Do4++8J78PCb5rvr78YPuS3LOCOKR/Hz6q3cmL5REZMOJTra7Ybie/Uvya+H7CgZCj3bq4276qtfoCJFWRfaB1xG1xV7bxHYUnq41vbmNbamnKSO3fnLt75cgNMXGYO9I9ENTh5Zx0UG6unFn+01mD/piYYOSdy4Mwn4YDvcGhjE49WVuGzYhuFMLa1DfzFLG1oNKE3phwJ33jdnBSBlfcZhn/Bf5g10EhdUweMTexMev5/4NznYPjMsEZjbkC4xS4NWO0jxZFnX1HXAEfdmbRNMk9XlQOIyHLgDkyQwXtU9SdJL5hwELwU+TuzpdU0MOBr3M7QJy6H6SvhuLs5+5FzKV/3FF+dZPTgx9Q18GDf3nxn2w4mtbaaGO1WI/+iaktCHTFgJpE2izt/dy2sfyVqUwiMvvc/ZaVcsqM2shl9+urIfsKs02DHl477EYCphyq8fo/JFLXuZeg32rlsYYnRB5fF+BwceBXUpnDAKSyFE+43tPY9n6q178I73w2fPi7BInRq+WieW7+RQYEgN1SMB9qNBYRV/jdVW+AMk9T9gUfOAeCaQQN4rHc5R9Q38IQ9ymPFnnD9Tg68ZQT394WlY+a7sw8fvW+4v69/fihPEpmkJ7a28nlRUaQdgEtLR1Gx4z1mD5vB/FDU3P0vg22fw5ApABQdchM3PXMVLI7pl/2tCfHVSKL6kDpgaWMj5wyz1BFXW5vOI+dEQjUceze8fSMAcvANSNV/ADOhH1LfwMj2dprEx3e372DWuNGMaWsz46osuQ363OYW5ja3sK6wgPv79iFY2BdwMIgABs09l6XP32QWMROXRU2s85uaqPf5YPmPE0riQLitgXBgNwC/Zb0j4xax7LkbzGJjwUVmrC/6VvTK3gmnPcrcu/aJlix8/mh64eM+8CVQxRWW8viGTQwIBuDCV7nwzyv5dhHRm7t2HH8fvg//6mj8sbyhIaE69qB+U6MZ09iF5jPlCHjv4ajQ+U9uqDRWWz4ft4cMBk78o/PzAAf0mcC9m6uZvdfBCcvQu8J87DjzCfj4ce5/ZRW77M89xJYfffnNZt7hzIS37nKMQYw93C+Ag4GNwOsislpVP0p+pQ3X2UJkzz3HfCwULr2OUx76PDxJXDHiEL75wUOU9R8PY+eYGO0WDph2cnKbYWsSAczLG8MUAH4hFQTXfkDB5R9HDtpXAIUlcOhNcddFQQTmWWqgKc5OZWHEOgQBLI5M8D/aWsOw9gBnDXPWpyJiQvv6PgBgWksLf9xUjf/I253LTz6MwTNOg7nnMuI/N0LDJ0wZ2JfLP9nBrOYWZs65IFL2vBegrYkfvHQH1655msKxi/jJ2hfjbjnv6Lt5708nIYtS1NUBpSv/wPyHD+XV0lIu2V7L13buYvo4i5Fa0t64hd/iyie+BRXT4OK3INBqVIV27HeR2fsodJ58fr+pmjOHV/DtbTvMPcoGMu+RcyBgZesLrXRF4OSHzWp/wDhmv3wVb5aWgL+Abw7bj6bP/8x1Ndvp1X8clPaFTW/Duc/x0r1LKSodnJIpAHDNNmitp+j+Q4FGxvQdCA5z6aH1DVDcB773JdRVxTHdu6ssfXWagfLGtLWxrrCQfmUWkx81D67dHrlPivH90OYa3irymXStV29xFRAvFcYuvQFGzoMhUxm257Gw8a+McjC5BmDaMebjgMq5VzHL+l1oM8Od0NbG5ON+7Xy/4bPMx4ZRy35k/BXASFnlCd6/EGacxNzN7xppOx1U7AkVezKjoAhqbdZZdgY2//zU91HVLvUBFgB/t/2/ErgyUfnZs2erqmrLn0/XvX6/l35w50xNC20tqrs2p3dNOmhtVG3Ylrv7p4tPn1bd9I7++1f76+O3DE1YLNiwXX932wj97zM3qO6qUg0GU966bd2r+titwzRQ9YHqOw+qVn+UoGCLan2N+W7a6Vymboub2jji6Qe+rz+8c4xWvfqgajCoG1d/Q1+7dVSH7+eI9f+n+pdzVNe+GHX48VtG6uO37ZX4sudv1Gd+bp1vrlN99gdmjATaowvu3KTaVJvWIzV/8Zze/9Ph2l71gX5yx15a+fDp+uGPhug9t43Q7T+bpq2v3ZPW/dyi4YnLdc0PB6q2t3bsBlvXqL73cHYfyoZg3VZ9aNUw3fnG711f89qPh+iRv52sjdvWhY9Vv7taV90+Wre/9WfV9x9xdZ/VPz9M775tZNrPnAs8cctQ/eKRs8L/gTc0wbwq2lXSNFoQkRXAclU91/p/GrCvql5kK3MecB7A6NGjZ69bZ23AtTUZ/WKOEjrm3ikAAA8zSURBVGr0KASDgCZfHQYDxjyxm7WnBoO0NNRS0ju9MABZQXuraTN/EmFcNX9tGgwYiagwvSBquz1Uob0lXmJsa04oRToi0A6BlrQSb+UMMUYxIvKmqs5xKtotN59V9beqOkdV5wwebPPYLSztdpNYp8HnS60y8HUsv2xnQ3y+zmEKAAVFyZkC5LdNfX6PKXQEIs4MIB2mAK5M3fMGEddjrysyhkrAbtA/0jrmwYMHDx7ygK7IGF4H9hCRcSJSBJwIJDDk9eDBgwcP2UaXs0pS1XYRuQj4O8Zc9V5V/bCTH8uDBw8edht0OcYAoKpPAk929nN48ODBw+6IrqhK8uDBgwcPnQiPMXjw4MGDhyh0OT+GdCEidcCneSY7CMgsEH7XoNGZNL36dX+aHr3uTW+Mqjpm6OqSewxp4tNEThq5goi8kWua+aDRmTS9+nV/mh697k0vGTxVkgcPHjx4iILHGDx48ODBQxR6AmP4bQ+l2VPr1Rm0OoNmT6+fR6/700uIbr/57MGDBw8esoueIDF48ODBg4cswmMMHjx48OAhCh5jcIBIfuIi54tOZ9P0kD3ku//ySa+nj83uVL9uwxhEJG/Pqj1746UcwilUcwoRGZFrGjH05olIisTCWaV3tIhMyBc9C+HkCt1ponGJ8JjsgXUD6DZ16tKMwXrxLs8jvSNE5AERuU5EJuaQznIR+V/gRhHJuUOLGAwRkReAewBUNZBDestE5E3ARXLZrNBbLCIfYbL65ZwxWPV7BfgdMCzX9CyaR4jIs8CdInIK5HYBIyJHiciDwBUiMiZXdCxaobrdJiIHQO4XZyJyjIjcmEsaNlqHW+/7rSKyJB80M0WXZAwiUiAi3wPuBFaJyExVDeZqlSsiJSLya+Ba4EFgPHC+iIzLIg2x6PweuBozqZQD54jIoGzRcYL1kjVbn71F5DDrmbLW/1b9ikTkl8Aq4EZVvcZ+Plu0YuiWAJcCN6jquaq6MRf0rPqVi8jjmP67GngVGGOdz9m7JCKHANcDdwCvAQeJyPAc0lsGXAPch4mOcLGIHGGdy2o9RWQscBNwF/AxcJ6IhNL6Zr1NRcRn3X8VhuktyjYNG61CEfkppu9+DewEThKRfXNFM1vokoxBVdsx8Y+mAJcDv7GO52SVq6rNmEG5QlUfB34M7IOZSLNFQy06/wssVtXVwF8xJsM5jY9ivWAjgXeAKzAMEFUNZouGVb9WoAx4TFUfs17CGaHz2aIVgxHANlX9k4iUisixIjIYSy2RLQZh1a8euF9Vl6jqPzE5Q75inc9aWzpgMfB3a2y+ARSq6qYc0lsG/E1Vn8a8e72Bs0WkVw7qOQF4SVX/F/gfjER7sYj0txaDWWXw1vOvAWYBFwI5kxpUtQ0zj52kqk9h6tYPyJm0ni10GcYgIpeIyE9EZKV16AlVbVbV24EhInKyVa4wy/SOtw79FtgoIsWq+gmm8zJWE8TWS1UfVdWA9f8RYIqI3Cgi+2dKy4HmcRbNILAJmAT8B9gsIueLyB5ZpHWCdehGYJGIrALeAn4oIr8VkUMzpRVDb4V1qA040Gq/x4DTgdsxq7Rs0jseQFX/bB33ATuADSJSnA1aDjTD7wLwTRG5GZOnZIyI3C0i51nlM5o8Hei9DCwUkRJV3YJZIPmBszOhY9FaEbNi3ggcZ713zar6gkX/2kxpJaH5sqrWqerdQC8ROccql/F86EDr98BaESmymHlvYGCmdHIOVe3UD2ZD5jLMhLUCs3I/ExhiK/NVoDLH9AbbyoyyzvfJAZ0K6/wSYDpGVL8As5oYnKO6DQDmANdZ5b4NNACPW/8LskTrHOvcxcDfgMmYF+ESjCg9KMt1O9c691PMyuxg6/9U4D1gzxyPk/2AT3L8LpxrjZGJwL3A/lbZw4GngLFZpncGZgHxP5iUus9bv88Cvg/4OkhrCPAvzALlMft9gP8H3G57phnAX0LvSgb1c6Rp0Qj9Pgz4EOifI1r2evYH/gkMzdaYydWn0x/AarDVwIHW7+XAbcBpMWWeB75t/V6WZXo/s9MDjsCkFAUYDszMIp0zHMrtDzwAlOegLW8HTsCokp7FrDg/BJ4B7rDKSZZo3QmstP6X28odYNWvLMt1u8Oq2yigFVhuK/trYEYux4l1/Fng6Ez7LUX/nWL9fx6YYP0ehVmNjskivcOsOh6PkRBmAUdY504B7s6Q1uXA3sCvgAtsxycAnwPTrP+TMcyoVxbaM44mNgZhff8F+B5mEXN8lmmJ7fxM4K/W75HAQdkaN9n+dKoqySa6vQEsAlCj11wDTBORybbiFwC3iEgVRq+cTXqfWfSmWecHAc0icjFGjzwqi3SmisikmEsOAZqsT4eQhOanmBXYLIzY/rqqTgNOBJaIyAi1RmoWaH0MzBaRyWr08SEcDDTSwT2bJPQ+wUhCuzCbwZeLyDQRuQbYC1PfbNILjZMpVrk+1jO0dYSOS5qfArMstd8/gVuscmdh3oMdWaT3FKaOc4GJqvq2qj5hlZsN/F+GtO4CPsIsSo4QkWEW3S8wxhi/tFSCp2JW4B3ez0hGU83ehY+IKv17mH3FNcDQLNNSEQmlNxgB+K155YmO0MoX8soYxLIqCulENbKR9TnQW0SmW///BfTFcHBEZCZwN0Ynv4+q3pcjeiEb8WMwppYTMavQx7NMp48YC57TROQ9YCxwpaaxuZ4GzX9j2nELcL6qXmeV3w4sVNXKLNL6F8ZcNNRvJ4rIBxjLne+ry43LDtAbp6q3APcD38D02/Gqui0H9Ppi+YKo6i7Myq/CDZ0MaJZZdH8JFIgxO56GkV525YBebyJ9eLiIvIbpw0cyoaWqbWoMS17GMNRLQ9eo6o8xzOEcjMRwjqq6XiilQfOS0Hk1e30TMCv8xzBzy105oNVuXXowcBRmfB6uqg+4rV++kRfGICILReQ+4GoRGRBaoUpkI/k1oB04REQKVPUjDHcN2fhvAy5U1ePVhTVGBvTmWef/ACxV1UuTTZwZ0JmtxoJnA0bkPF3NJl9KdIDmh5iXepaqNouI3zag651oZKF+oX5bl079OkhvGEbXj6r+P+BSVT1DVTfniJ69fgAnqurvU9HKkOYoYJ7FzE/CqOtOUNWqHNZxrnV+DWZBcZyqJpVOktAKjzkLNRgV1iQRGSnGx6a/1X9fV9WVburWQZqTLZqDxEh8NcBFqnpsqrklA1qhhcOfgENSzStdATlnDCIyHrPSeR4zQd0oIodD2JwLVf0cI9JOwJhTArRgJhZUdYOqvp8Hev+1zv9VVZ/PQ71eUNX/uKlXFmh+aZ0PuFEdZal+r6jqi3mo239D9wmVzTG9L230XKvHMqDZTGRsNqaxiMjGeFmjqm9lSCugqioixWKsjwKq+m/MXtcHGCllkFW21U3dskDzRczm9k5V/SzHtF4QkT1U9VVVfdZt/ToVmuNNDGAl8Cfr9wDgaxjRbZh17IcYEXIsxm9hNfAmxn46bQuIfNHLd73yTbOn9tvu0n9dcLzcgJHEx1r/z8eoN2/G+GXkoj2zRrMz6teZn+zfEOYDk2z/xwIvAaOt/3sCP8GYyYWscSbaypcD/boavXzXK980e2q/7S791w3HyzL7/65GszPq15U+2QyJ0E9EngD+AawUkXLrVLPVoCFHsk8xIlYf4H1VPVlVPxdrZ19V61W1tqvQy3e98k2zp/ZbZ9Hr6XXMAi2/RetZNWqslMgnzc6oX1dENvcYemFMOy+2fh9gHd+KiSkzXUT2VWN5UwkcoKo7wZh7afqu9vmil+965ZtmT+23zqLX0+uYKa2OhIPIJ83OqF+XQ0aMQUROFxPZso+aXfbfAg9huOs8MTbyAeAV4G1M9MRyjKndOhEpA/dxZvJFL9/1yjfNntpvnUWvp9exp7dnZ9SvqyPtnM8iIhjHjAcwDihfYDjrpWoFgxORhZjNmjdU9Q+2a2/D2H2PAU5X1U+7Cr181yvfNHtqv3UWvZ5ex57enp1Rv24FTW/zx299T8JEmQTjOn8Xlqu3rexlGKuHvkBvW9neXY1evuuVb5o9td92l/7r6eNld6hfd/u47jTgRxjTq8UY7737bOd9QBUmnHToWDkmzstrQDUwPJ1Bkg96+a5Xvmn21H7bXfqvp4+X3aF+3fXjpjEXY+L4/wpju/tvTHCv9RhvzFC584Hnbf9PwAQ2uxtbpNSuQi/f9co3zZ7ab7tL//X08bI71K87f9w06CKiI4/+EhPQ7kzgTeuYD6Ove4iIg8dXMDv26T1Qnujlu175ptlT+2136b+ePl52h/p154+bBi0Diono5U4Bfmz9fge42Po9B3gw4wfKE7181yvfNHtqv+0u/dfTx8vuUL/u/ElprqomNkuLRuxzD8bY9IIJ/TtVRP6GyZX8FkSiDnYE+aKX73rlm2ZP7bfOotcZNHvyeMk3zc6oX7dGGhzXjxG1nsJy9caEj+2HcQkfkU2OlS96+a5Xvmn21H7bXfqvp4+X3aF+3fGTjoNbECjEhJTd2+Ku1wBBVX1Jsx9GNl/08l2vfNPsqf3WWfQ6g2ZPHi/5ptkZ9et+SJPbzsc07EtY+X1z+ckXvXzXK980e2q/7S7919PHy+5Qv+72SbdBRwJXAsV5Gix5oZfveuWbZk/tt92l/3r6eNkd6tfdPmmHxPDgwYMHDz0bec357MGDBw8euj48xuDBgwcPHqLgMQYPHjx48BAFjzF48ODBg4coeIzBgwcPHjxEwWMMHjy4hIgMFJF3rE+ViFRav+tF5Jed/XwePGQLnrmqBw8dgIhcD9Sr6qrOfhYPHrINT2Lw4CFDiMgSK7QCInK9iNwnIi+KyDoROVZEbhGR90XkaREptMrNFpF/icibIvJ3ERnWubXw4CECjzF48JB9TAAOAo4G7sckfpkONAFHWMzhLmCFqs4G7gVu6qyH9eAhFgWd/QAePPRAPKWqbSLyPiaa59PW8feBscBkYC/gH1ZkZz+wuROe04MHR3iMwYOH7KMFQFWDItKmkY28IOadE+BDVV3QWQ/owUMyeKokDx7yj0+BwSKyAEBECkVkWic/kwcPYXiMwYOHPENVW4EVwM0i8i4mteR+nftUHjxE4JmrevDgwYOHKHgSgwcPHjx4iILHGDx48ODBQxQ8xuDBgwcPHqLgMQYPHjx48BAFjzF48ODBg4coeIzBgwcPHjxEwWMMHjx48OAhCv8f9Uew4OzK7cwAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -458,7 +449,7 @@
     "if plt:\n",
     "    e126.power_output.plot(legend=True, label='Enercon E126')\n",
     "    my_turbine.power_output.plot(legend=True, label='myTurbine')\n",
-    "    dummy_turbine.power_output.plot(legend=True, label='dummyTurbine')\n",
+    "    my_turbine2.power_output.plot(legend=True, label='myTurbine2')\n",
     "    plt.xlabel('Time')\n",
     "    plt.ylabel('Power in W')\n",
     "    plt.show()"
@@ -470,19 +461,20 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.font_manager:findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('/home/sabine/virtualenvs/windpowerlib/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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zXDnxmJlZrpx4zHqwdvNWpt24gLVvbq10KGZVw4nHrAcz5y5j0YoNzHx4WaVDMasa9ZUOwGwgOvDqB2lr3/72+1kLVzJr4Uoa6+tYOmNKBSMz2/G5xWPWhfmXT+KUCSNoasj+iTQ11DF1wgjmXzGpwpGZ7ficeMy6MGxIE4Mb62lr305jfR1t7dsZ3FjPsMFNlQ7NbIfnrjazbqzb0sb0o8Zy9sQxzH5qJa0eYGDWLxQR5Tmw1AQ8BjSSJbi7I+JzkvYF7gL2BBYDH4uI30lqBG4HjgTWA2dExIp0rKuA84G3gL+NiDmpfDLwb8Ag4FsRcW0qL7mO7jQ3N0dLS0v//FLMzGqEpMUR0dzVunJ2tbUBJ0bE4cAEYLKko4EvATdExP7AG2QJhfTzjVR+Q9oOSYcAZwLjgcnANyQNkjQI+DowBTgEOCttS6l1mJlZfsqWeCKzJb1tSK8ATgTuTuW3Aaem5anpPWn9n0hSKr8rItoi4lfAcmBiei2PiJci4ndkLZypaZ9S6zAzs5yUdXBBapk8A6wFHgJ+CWyMiPa0yWpgZFoeCawCSOs3kXWVvV3eaZ/uyvfsQx2d475AUoukltbW1r6dvJmZdamsiSci3oqICcAoshbKQeWsr79ExE0R0RwRzUOHDq10OGZmVSWX4dQRsRGYBxwD7CapYzTdKGBNWl4DjAZI63clGwDwdnmnfborX9+HOszMLCdlSzyShkraLS3vDHwIeJEsAZ2WNjsXuDct35fek9b/Z2RD7u4DzpTUmEarHQA8BSwCDpC0r6SdyAYg3Jf2KbUOMzPLSTnv4xkO3JZGn9UB34uI+yUtAe6SNAN4Grg5bX8z8B1Jy4ENZImEiHhB0veAJUA7cGFEvAUg6SJgDtlw6lsi4oV0rCtKqcPMzPJTtvt4qoXv4zEzK12l7uMxMzN7ByceMzPLlROPmZnlyonHzMxy5cRjZma5cuIxM7NcOfGYmVmunHjMzCxXTjxmZpYrJx4zM8tVt4lH0qmShuUZjJmZVb+eWjx/CTwtaZmk29LkaIfmFZiZmVWnbhNPRJwWESPJpjOYA/wR2dOmWyU9kFeAZmZWXXqdFiEiVkhqAnZOr45lMzOzknWbeCR9hmzG0KHAUuBJ4GvABR3z4ZiZmZWqpxbPOcCvgR8DTwALI2JTLlGZmVnV6jbxRMRBkvYAjgVOAK6UtAvwC+CJiPh2PiGamVk16fE+nojYEBH3A/8IXAV8H5gEfCuH2Mz63drNW5l24wLWvrm10qGY1aye7uM5RdK1kuYDa4HrgD2BvwP2ySk+s341c+4yFq3YwMyHl1U6FLOapYjoeoV0D/A42fWdxRHxuzwDGyiam5ujpaWl0mHYu3Tg1Q/S1r79HeWN9XUsnTGlAhGZVTdJiyOiuat1Pd3H8xcR8ZWIWFCrSceqx/zLJ3HKhBE0NWR/8k0NdUydMIL5V0yqcGRmtcfParOaMGxIE4Mb62lr305jfR1t7dsZ3FjPsMFNlQ7NrOb0egOpWbVYt6WN6UeN5eyJY5j91EpaPcDArCK6vcZjGV/jMTMrXU/XeHpt8Ug6DrgGGJu2FxAR8d7+DNLMzGpDMV1tNwOXAIsBPyrHzMzelWISz6aIeLDskZiZWU0oJvHMk/QvwD1AW0dhRPy8bFGZmVnVKibxHJV+Fl4kCuDE/g/HzMyqXTHz8fgOOzMz6zc9zcfzlxExS9KlXa2PiOvLF5aZmVWrnlo870k/B+cRiJmZ1Yae5uO5Mf38fH7hmJlZtfOz2szMLFdOPGZmlqseE4+kOknT8grGzMyqX29TX28HLs8pFjMzqwHFdLU9LOkySaMl7dHxKntkZmZWlYpJPGcAFwKPkT0odDHQ6zwBKVHNk7RE0guSPp3K95D0kKRl6efuqVySZkpaLulZSe8rONa5aftlks4tKD9S0nNpn5mS1Nc6zMwsH70mnojYt4tXMVMitAN/FxGHAEcDF0o6BLgSmBsRBwBz03uAKcAB6XUB8O+QJRHgc2SP7pkIfK4jkaRtPlmw3+RUXlIdZmaWn14Tj6Q/kHS1pJvS+wMkfaS3/SLi1Y4HiUbEm8CLwEhgKnBb2uw24NS0PBW4PTJPArtJGg6cDDwUERsi4g3gIWByWjckIp6MbDa72zsdq5Q6zMwsJ8V0tX0b+B1wbHq/BphRSiWSxgFHAAuBvSPi1bTqNWDvtDwSWFWw2+pU1lP56i7K6UMdneO9QFKLpJbW1tbiTtLMzIpSTOLZLyK+DGwDiIjfkM1CWhRJuwA/AC6OiM2F61JLpaxzb/eljoi4KSKaI6J56NChZYrMzKw2FZN4fidpZ9KXt6T9KJiXpyeSGsiSzh0RcU8qfr2jeyv9XJvK1wCjC3Yflcp6Kh/VRXlf6jAzs5wUk3iuAX4KjJZ0B9nF+l7v7UkjzG4GXuz0JOv7gI6RaecC9xaUn5NGnh1NNvPpq8Ac4CRJu6dBBScBc9K6zZKOTnWd0+lYpdRhZmY5KWY+np9JWkw2Mk3ApyNiXRHHPg74GPCcpGdS2WeAa4HvSTofeBnoeDLCA8CHgeXAb4CPp/o3SPonYFHa7gsRsSEt/w1wK7Az8GB6UWodZmaWH2WXQHrYQJoFPArMj4j/ziWqAaS5uTlaWnq9bcnMzApIWhwRzV2tK6ar7WZgOPBVSS9J+kHHzaBmZmalKuYG0nnAF4F/AL4JNAP/t8xxmfVq7eatTLtxAWvf3FrpUMysBMXcQDoXeJzs0TlLgT+OiIPKHZhZb2bOXcaiFRuY+fCySodiZiXodXAB8CxwJHAosAnYKGlBRPy2rJGZdePAqx+krX372+9nLVzJrIUraayvY+mMKRWMzMyKUUxX2yUR8UHgL4D1ZE8y2FjuwMy6M//ySZwyYQRNDdmfb1NDHVMnjGD+FZMqHJmZFaPXFo+ki4APkLV6VgC3APPLG5ZZ94YNaWJwYz1t7dtprK+jrX07gxvrGTa4qdKhmVkRiulqawKuBxZHRHuZ4zEryrotbUw/aixnTxzD7KdW0uoBBmY7jF7v4wGQdDhZqwey+3l+UdaoBhDfx2NmVrp3dR+PpL8F7gCGpdcsSZ/q3xDNzKxWFNPV9gngqIj4NYCkLwELgK+WMzAzM6tOxTy5QMBbBe/fooRpEczMzAoV0+L5NrBQ0g/T+1PJHqNjZmZWsmKeTn29pEeA96eij0fE02WNyszMqla3iUdSE/DXwP7Ac8A3PJzazMzerZ6u8dxG9kDQ54ApwHW5RGRmZlWtp662QyLiMABJNwNP5ROSmZlVs55aPNs6FtzFZmZm/aWnFs/hkjanZQE7p/cCIiKGlD06MzOrOt0mnogYlGcgZmZWG4q5gdTMzKzfOPGYmVmunHjMzCxXTjxmZpYrJx4zM8uVE4+ZmeXKicfMzHLlxGNmZrly4jEzs1w58diAsHbzVqbduIC1b26tdChmVmZOPDYgzJy7jEUrNjDz4WWVDsXMyqyYqa/NyubAqx+krX372+9nLVzJrIUraayvY+mMKRWMzMzKxS0eq6j5l0/ilAkjaGrI/hSbGuqYOmEE86+YVOHIzKxcnHisooYNaWJwYz1t7dtprK+jrX07gxvrGTa4qdKhmVmZuKvNKm7dljamHzWWsyeOYfZTK2n1AAOzqqaIqHQMA1pzc3O0tLRUOgwzsx2KpMUR0dzVOne1mZlZrpx4zMwsV048ZmaWq7IlHkm3SFor6fmCsj0kPSRpWfq5eyqXpJmSlkt6VtL7CvY5N22/TNK5BeVHSnou7TNTkvpah5mZ5aecLZ5bgcmdyq4E5kbEAcDc9B5gCnBAel0A/DtkSQT4HHAUMBH4XEciSdt8smC/yX2pw8zM8lW2xBMRjwEbOhVPBW5Ly7cBpxaU3x6ZJ4HdJA0HTgYeiogNEfEG8BAwOa0bEhFPRjYs7/ZOxyqlDjMzy1He13j2johX0/JrwN5peSSwqmC71amsp/LVXZT3pY53kHSBpBZJLa2trUWempmZFaNigwtSS6WsNxH1tY6IuCkimiOieejQoWWIzMysduWdeF7v6N5KP9em8jXA6ILtRqWynspHdVHelzrMzCxHeSee+4COkWnnAvcWlJ+TRp4dDWxK3WVzgJMk7Z4GFZwEzEnrNks6Oo1mO6fTsUqpw8zMclS2Z7VJuhM4AdhL0mqy0WnXAt+TdD7wMjAtbf4A8GFgOfAb4OMAEbFB0j8Bi9J2X4iIjgELf0M2cm5n4MH0otQ6zMwsX35WWy/8rDYzs9L5WW1mZjZgOPGYmVmunHjMzCxXTjxWFms3b2XajQtY60ndzKwTJx4ri5lzl7FoxQZmPrys0qGY2QDjqa+tXx149YO0tW9/+/2shSuZtXAljfV1LJ0xpYKRmdlA4RaP9av5l0/ilAkjaGrI/rSaGuqYOmEE86+YVOHIzGygcOKxfjVsSBODG+tpa99OY30dbe3bGdxYz7DBTZUOzcwGCHe1Wb9bt6WN6UeN5eyJY5j91EpaPcDAzAr4yQW98JMLzMxK5ycXmJnZgOHEY2ZmuXLiMTOzXDnxmJlZrpx4zMwsV048ZmaWKyceMzPLlROPmZnlyonHzMxy5cRjvfLcOmbWn5x4rFeeW8fM+pMfEmrd8tw6ZlYObvFYtzy3jpmVgxOPdctz65hZObirzXrkuXXMrL95Pp5eeD4eM7PSeT4eMzMbMJx4zMwsV048ZmaWKyeeGuOnEJhZpTnx1Bg/hcDMKs3DqWuEn0JgZgOFWzw1wk8hMLOBwomnRvgpBGY2ULirrYb4KQRmNhD4yQW9GOhPLli7eSsX3fk0Xzv7CLdezGzA8JMLqphHqZnZjsZdbTsoj1Izsx1VzbV4JE2WtFTScklXlquevtyoWco+HqVmZjuqmko8kgYBXwemAIcAZ0k6pBx19aULrJR9PErNzHZUtdbVNhFYHhEvAUi6C5gKLOmvCvrSBdbXbjOPUjOzHVFNjWqTdBowOSI+kd5/DDgqIi7qtN0FwAUAY8aMOfLll18uuo61m7cy44EX+dkLr7F123aaGuo4efw+fPbPDu62NdKXfczMBjKPaitRRNwUEc0R0Tx06NCS9u1LF5i7zcysltRaV9saYHTB+1GprF/1pQvM3WZmVitqrautHvgf4E/IEs4i4OyIeKG7fQb6DaRmZgNRT11tNdXiiYh2SRcBc4BBwC09JR0zM+t/NZV4ACLiAeCBSsdhZlarPLjAzMxy5cRjZma5cuIxM7NcOfGYmVmuamo4dV9IagU6Hl2wF7CuguFUks+9dtXy+dfyucO7O/+xEdHlHfhOPCWQ1NLduPRq53OvzXOH2j7/Wj53KN/5u6vNzMxy5cRjZma5cuIpzU2VDqCCfO61q5bPv5bPHcp0/r7GY2ZmuXKLx8zMcuXEY2ZmuXLiKYKkyZKWSlou6cpKx5M3SSskPSfpGUlVPUeEpFskrZX0fEHZHpIekrQs/dy9kjGWUzfnf42kNenzf0bShysZY7lIGi1pnqQlkl6Q9OlUXvWffw/nXpbP3td4eiFpENkcPh8CVpPN4XNWRCypaGA5krQCaI6Iqr+RTtIHgS3A7RFxaCr7MrAhIq5N//HYPSKuqGSc5dLN+V8DbImI6yoZW7lJGg4Mj4ifSxoMLAZOBc6jyj//Hs59GmX47N3i6d1EYHlEvBQRvwPuAqZWOCYrk4h4DNjQqXgqcFtavo3sH2RV6ub8a0JEvBoRP0/LbwIvAiOpgc+/h3MvCyee3o0EVhW8X00ZP5ABKoCfSVos6YJKB1MBe0fEq2n5NWDvSgZTIRdJejZ1xVVdV1NnksYBRwALqbHPv9O5Qxk+eyceK8b7I+J9wBTgwtQdU5Mi65uutf7pfwf2AyYArwJfqWw45SVpF+AHwMURsblwXbV//l2ce1k+eyee3q0BRhe8H5XKakZErEk/1wI/JOt+rCWvpz7wjr7wtRWOJ1cR8XpEvBUR24FvUsWfv6QGsi/eOyLinlRcE59/V+ders/eiad3i4ADJO0raSfgTOC+CseUG0nvSRcbkfQe4CTg+Z73qjr3Aeem5XOBeysYS+46vnSTP6dKP39JAm4GXoyI6wtWVf3n3925l+uz96i2IqQhhP8KDAJuiYgvVjik3Eh6L1krB8Nl+EsAAAQhSURBVKAemF3N5y/pTuAEssfBvw58DvgR8D1gDNkUGdMioiovwHdz/ieQdbUEsAL4q4JrHlVD0vuB+cBzwPZU/Bmyax1V/fn3cO5nUYbP3onHzMxy5a42MzPLlROPmZnlyonHzMxy5cRjZma5cuIxM7NcOfFYzZN0g6SLC97PkfStgvdfkXSppBGS7i7x2OdJ+lp/xlsqSeMKnzZdUF7y+ZRY74OSRpXr+LbjcuIxg8eBYwEk1ZHdwzK+YP2xwBMR8UpEnFaB+MqinOcjaWdgz4hYXY7j247NiccMngCOScvjye7OflPS7pIagYOBnxe2HFJL5h5JP03ztHy542CSPi7pfyQ9BRzXVYWSji+Y4+RpSYMlnSDpMUk/UTb/03+kRIikkyQtkPRzSd9Pz9RC0pGSHk0PcJ1T8GiXIyX9QtIvgAu7iaGo8+m0zwpJ/5zibpH0vlTvLyX9dcGmJwCPpH2uVTbPy7OSqnpqBSuOE4/VvIh4BWiXNIasdbOA7G71Y4Bm4Lk0JUZnE4AzgMOAM5RNpjUc+DxZwnk/cEg31V4GXBgRE4APAL9N5ROBT6X99gP+QtJewNXAn6aHtbYAl6Zna30VOC0ijgRuATqeKvFt4FMRcXgJv4p3nE83261Mcc8HbgVOA45O591hCvBTSXuSPWplfET8ETCjhHisStVXOgCzAeIJsqRzLHA92dQXxwKbyLriujI3IjYBSFoCjCXrpnskIlpT+XeBP+xi38eB6yXdAdwTEauzx2XxVES8lPa9kyx5bSVLRI+nbXYiS44HAocCD6XyQcCrknYDdktz6wB8hywR9Kar81nVxXYdzyp8Dtglzd/ypqQ2SbtFxEayxHsZ2eNXtgI3S7ofuL+IOKzKOfGYZTqu8xxG1tW2Cvg7YDNZ66ErbQXLb1HCv6c0m+VPgA+TJZSTO1Z13hQQ8FBEnFW4QtJhwAsRcUyn8t2KjaOTYs+nY7vtnfbZDtSn5/ut6mglSpoI/AlZy+gi4MQ+xmdVwl1tZpkngI+QTXH8VnoI5G5k3W1PlHCchcDxkvZMXWGnd7WRpP0i4rmI+BLZE9APSqsmpieh15F1e/0X8CRwnKT9077vkfSHwFJgqKRjUnmDpPGpxbExPfgRYHoJ8feHKcBPU0y7ALtGxAPAJUApXX9WpZx4zDLPkXWTPdmpbFNErCv2IOnJvdeQdYU9TjaFcFculvS8pGeBbcCDqXwR8LW036+AH6Zuu/OAO9P2C4CDUoviNOBLaRDBM6TRecDHga9LeoasxZSnyaTEAwwG7k9x/xdwac6x2ADkp1ObDRCSTgAui4iPVDqWvkqjAB+PiOZKx2IDl6/xmFm/iYg2spGAZt1yi8fMzHLlazxmZpYrJx4zM8uVE4+ZmeXKicfMzHLlxGNmZrn6/5W5/5ZKJ6dsAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -493,18 +485,20 @@
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n",
-      "DEBUG:matplotlib.axes._base:update_title_pos\n"
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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FHxEvAb9Of5C0C/DXwBzgLOCGShRoZmb9q1RXzxjgkPTnPenkZcD5wOLsSzMzsyyUOp1zHfAA8H3g3Ih4rTIlmZlZlkoF/6HA+4CPAl+StJqkpb8YaI2I9uzLMzOz/laqj78Q8t8DkNQMHA9cRTLoWlP25ZmZWX8reeWupHfR2c9/KLArcB/wg+xLMzOzLJQ6uLsRWE/S6r8b+HZEPFmpwszMLBulWvzvjIgXK1aJmZlVRI8XcDn0zczqk8fqMTPLGQe/mVnO9Doev6QhwF8BzcXzR8T/ya4sMzPLSjk3YrkJeJFkuAZftGVmNsCVE/zjIsI3STczqxPl9PHfK2la5pWYmVlFlNPiPww4TdIfSLp6BERE7J9pZWZmlolygv+YzKswM7OKKTVkw/D0ZiybK1iPmZllrFSL/1rgOJKzeYKki6cggD/LsC4zM8tIqWGZj0v/nVS5cszMLGu+ctdq1oaXtjDr0sVs2Lyl2qWY1RUHv9WseXc8wdLVLzDv9ieqXYpZXSnnrB6zipp8/q20b+vY/nz+kjXMX7KGIQ2DWDXXJ5mZ7aySLX5JgyU9XqlizAAWfe0IZk4fQ1Njsns2NQ7ihOljWHTOEVWuzKw+lAz+iHgdWCVpQoXqMWPk8CaGDWmgfVsHQxoG0b6tg2FDGhg5zLd5NusP5XT17AYsl3Q/8EphYkTMzKwqy72NL7dzykETOfm9E7j2/jW0+QCvWb9RRJSeQTq8u+kRcVd/F9PS0hKtra39vVgzs7olaVlEtPTlPb22+CPiLkkTgb0j4nZJbwMG72iRZmZWXb2ezinpdGABcGk6aSxwY5ZFmZlZdso5j/9M4FDgJYCIeAIYmWVRZmaWnXKCvz0iXis8kdRAMlaPmZkNQOUE/12SvgG8VdKRwE+B/+ztTZLGS1ooaYWk5ZK+sLPFmpnZzisn+M8F2oBHgc8CtwDnl/G+bcCXI2I/4GDgTEn77WihZmbWP8o5j/8IYH5E/LAvC46IZ4Bn0sebJa0kOTC8os9VmplZvymnxf9J4GFJ90n6jqTjJe3Wl5VIagZmAEu6ee0MSa2SWtva2vqyWDMz2wG9Bn9EfCoi9gH+F7AWuISk66cskoYCPwPmpHf06rr8yyKiJSJaRowYUX7lZma2Q3rt6pF0KvB+YBqwEbgYWFTOwiU1koT+NRHx852o08zM+kk5ffwXAf8N/ABYGBGry1mwJAGXAysj4ns7XKGZmfWrcrp69gQ+DTQBF0q6X9LVZSz7UOATwAclPZT+fHjnyjUzs51VTlfPcGACMBFoBt4OdJR6D0BE3MMbb9BuZmY1oJyunnuKfi6OiHXZlmRmZlkqZ3TO/WH72TlmZjbAlTM651RJDwLLgRWSlkmamn1pZmaWhXIu4LoM+FJETIyICcCX02lmZjYAlRP8u0TEwsKTiLgT2CWziszMLFPlHNz9vaT/DRRO4TwV+H12JZmZWZbKafF/GhgB/JzkKtzCef1mZjYA9djil9QEfA7Yi2RI5i9HxNZKFWZmZtko1eK/CmghCf1jgO9UpCIzM8tUqT7+/SJiGoCky4H7K1OSmZllqVSLf3u3TkRsq0AtZmZWAaVa/O+WVBg/XyT33H0pfRwRMTzz6szMrN/1GPwRMbiShZiZWWWUczqnmZnVEQe/mVnOOPjNzHLGwW9mljMOfjOznHHwm5nljIPfzCxnHPxmZjnj4DczyxkHv5lZzjj4zcxyxsFvZpYzDn4zs5xx8JuZ5YyD38wsZxz8ZmY54+A3M8sZB7+ZWc44+M3McsbBb2aWMw5+M7OccfCbmeVMZsEv6QpJGyQ9ltU6zMys77Js8V8JHJ3h8s3MbAdkFvwRcTfwQlbLNzOzHVP1Pn5JZ0hqldTa1tZW7XLMzOpe1YM/Ii6LiJaIaBkxYkS1yzEzq3tVD34zM6ssB7+ZWc5keTrnT4DFwGRJ6yR9Jqt1mZlZ+RqyWnBEnJTVss3MbMe5q8fMLGcc/GZmOePgNzPLGQe/mVnOOPjNzHLGwW9mljMOfjOznHHwm5nljIPfzCxnHPxmZjnj4DczyxkHv5lZzjj4zcxyxsFvZpYzDn4zs5xx8JuZ5YyD38wsZxz8ZmY54+A3M8sZB7+ZWc44+M3McsbBb2aWMw5+M7OccfDXoA0vbWHWpYvZsHlLtUsxszrk4K9B8+54gqWrX2De7U9UuxQzq0MN1S7AOk0+/1bat3Vsfz5/yRrmL1nDkIZBrJp7TBUrM7N64hZ/DVn0tSOYOX0MTY3Jr6WpcRAnTB/DonOOqHJlZlZPHPw1ZOTwJoYNaaB9WwdDGgbRvq2DYUMaGDmsqdqlmVkdcVdPjdn4cjunHDSRk987gWvvX0ObD/CaWT9TRFS7hu1aWlqitbW12mWYmQ0YkpZFREtf3uOuHjOznHHwm5nljIPfzCxnHPxl8tW0ZlYvHPxl8tW0ZlYvMj2rR9LRwL8Cg4EfRcS3S82/+8R94/HHHqyp89a7Xk1b4KtpzawW1NRZPZIGA5cAxwD7ASdJ2q/Ue155bVvNtah9Na2Z1ZssL+B6L/BkRPweQNJ1wAnAilJvqrXxaXw1rZnVmyyDfyywtuj5OuCgrjNJOgM4A+Ato/aiqXEQR00ZxXnH7pthaX3jq2nNrJ5UfciGiLgMuAygafTeUYst6ks/0dl9NvcjU6tYiZnZzssy+J8Gxhc9H5dO69FeI4fykYMmukVtZpahLIN/KbC3pEkkgf9x4ORSb2hqHOwWtZlZxjIL/ojYJuks4DaS0zmviIjlWa3PzMzKk2kff0TcAtyS5TrMzKxvfOWumVnOOPjNzHLGwW9mljMOfjOznKmpWy9K2gysqnYdNWJPYGO1i6gB3g6dvC06eVt0mhwRw/ryhqpfudvFqr6OMlevJLV6W3g7FPO26ORt0UlSn29U7q4eM7OccfCbmeVMrQX/ZdUuoIZ4WyS8HTp5W3TytujU521RUwd3zcwse7XW4jczs4w5+M3McqYmgl/S0ZJWSXpS0rnVrqeaJK2W9Kikh3bkNK2BTNIVkjZIeqxo2u6SfiPpifTf3apZY6X0sC0ukPR0um88JOnD1ayxUiSNl7RQ0gpJyyV9IZ2eu32jxLbo075R9T7+9KbsvwOOJLk941LgpIgoeW/eeiVpNdASEbm7OEXSnwMvA/8REVPTaf8MvBAR304bBbtFxDnVrLMSetgWFwAvR8S/VLO2SpM0GhgdEQ9IGgYsAz4CnEbO9o0S22IWfdg3aqHFv/2m7BHxGlC4KbvlTETcDbzQZfIJwFXp46tIdvK618O2yKWIeCYiHkgfbwZWktzTO3f7Rolt0Se1EPzd3ZS9zx+kjgTwa0nL0hvR5907IuKZ9PGzwDuqWUwNOEvSI2lXUN13bXQlqRmYASwh5/tGl20Bfdg3aiH47Y0Oi4gDgGOAM9Ov/AZE0i+Z5/OP/x14JzAdeAb4bnXLqSxJQ4GfAXMi4qXi1/K2b3SzLfq0b9RC8Pf5puz1LCKeTv/dAPyCpCssz55L+zUL/ZsbqlxP1UTEcxHxekR0AD8kR/uGpEaSoLsmIn6eTs7lvtHdtujrvlELwb/9puyS3kJyU/abq1xTVUjaJT1gg6RdgL8EHiv9rrp3M/Cp9PGngJuqWEtVFUIu9VFysm9IEnA5sDIivlf0Uu72jZ62RV/3jaqf1QOQnnp0EZ03Zb+wyiVVhaQ/I2nlQzJy6rV52haSfgJ8gGTI3eeAbwI3AjcAE4CngFkRUfcHPXvYFh8g+SofwGrgs0V93HVL0mHAIuBRoCOd/A2Svu1c7RsltsVJ9GHfqIngNzOzyqmFrh4zM6sgB7+ZWc44+M3McsbBb2aWMw5+M7OccfBbRUn6vqQ5Rc9vk/SjoufflfQlSTP7OlKrpCslfaw/6+0rSadJurib6X3+PH1YZ6OkB7JYttUnB79V2m+BQwAkDSI5T31K0euHAPdGxM0R8e0q1JeJjD/PYSTb1awsDn6rtHuB96WPp5BcYbhZ0m6ShgD7Ag8Ut5zTlvw8SfdK+n2hVa/Exem9HG4HRna3Qklnp+OXPyLpunTaBZKulrQ4Hc/99KL5vyppaTr/t4qmnyrp/nS880vTIcWR9NeSfifpfuDQHmro9fN0mb9Z0uPpvL+TdI2kv5D027Te4kvyjwZuTa/8/pWkhyU9Jml2Ob8Qy5+Gahdg+RIR6yVtkzSBpHW/mGQ01vcBLwKPRsRryZXpbzCapGX7LpJL9ReQXJo+GdiPZGTGFcAV3az2XGBSRLRL2rVo+v7AwcAuwIOSfgVMBfYmGetEwM3pQHltwGzg0IjYKunfgFMk/Qb4FnBgWv9C4MEyNkV3n6ervYATgU+TDG1ycvqemSRXaxaGIT4ireEYYH1EHAsg6e1l1GE55OC3ariXJPQPAb5HEvyHkARnT10WN6YDUK2QVBh+98+Bn0TE68B6Sf/Vw3sfAa6RdCPJEBAFN0XEn4A/SVpIEvaHkYyRVAjvoSR/CPYnCfel6R+lt5IMCnYQcGdEtAFIuh7Yp4xt0N3n6eoPEfFoutzlwB0REZIeBZrT6WNJbkbyajr9u5L+L/DLiFhURh2WQ+7qsWoo9PNPI+nquY+kxX8IyR+F7rQXPX7T14FeHAtcAhxAEtyFBk/X8UoiXfY/RcT09GeviLg8nX5V0fTJEXFBH+soVs7nKZ6no+h5B52NtqOB2wAi4nckn/FRYK6kv9+J+qyOOfitGu4FjiNpqb6eDqy1K0n49xT83bkbmC1pcDo64RFdZ0gPII+PiIXAOcDbSVrxACdIapK0B8kAaEtJQvTTSsY7R9JYSSOBO4CPpY8L93udSDJQ2OGS9lAyXO6JfdoSO+9o4Na0pjHAqxExH/gOyR8BszdxV49Vw6MkZ/Nc22Xa0D7ea/gXwAdJ+vbXkBwv6GowMD/t7xYwLyI2pd01j5D0ye8J/ENErCfpMtoXWJzO8zJwakSskHQ+yd3RBgFbgTMj4j4l98JdDGwCHupD/TslPbi8V0Q8nk6aBnxHUkda399WqhYbWDw6p+WS6uDG5ekQvadGxOeqXYsNLG7xmw1QEXEPcE+167CBxy1+M7Oc8cFdM7OccfCbmeWMg9/MLGcc/GZmOePgNzPLmf8B6ecstLRtZXEAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -543,6 +537,19 @@
     "                                    title='myTurbine power curve')\n",
     "        plt.xlabel('Wind speed in m/s')\n",
     "        plt.ylabel('Power in W')\n",
+    "        plt.show()\n",
+    "    if my_turbine2.power_coefficient_curve is not None:\n",
+    "        my_turbine2.power_coefficient_curve.plot(\n",
+    "            x='wind_speed', y='value', style='*',\n",
+    "            title='myTurbine2 power coefficient curve')\n",
+    "        plt.xlabel('Wind speed in m/s')\n",
+    "        plt.ylabel('Power in W')\n",
+    "        plt.show()\n",
+    "    if my_turbine2.power_curve is not None:\n",
+    "        my_turbine2.power_curve.plot(x='wind_speed', y='value', style='*',\n",
+    "                                    title='myTurbine2 power curve')\n",
+    "        plt.xlabel('Wind speed in m/s')\n",
+    "        plt.ylabel('Power in W')\n",
     "        plt.show()"
    ]
   },
@@ -570,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/example/modelchain_example.py b/example/modelchain_example.py
index 5619a66..5ed87d7 100644
--- a/example/modelchain_example.py
+++ b/example/modelchain_example.py
@@ -38,9 +38,10 @@ def get_weather_data(filename="weather.csv", **kwargs):
 
     The data include wind speed at two different heights in m/s, air
     temperature in two different heights in K, surface roughness length in m
-    and air pressure in Pa. The file is located in the example folder of the
-    windpowerlib. The height in m for which the data applies is specified in
-    the second row.
+    and air pressure in Pa. The height in m for which the data applies is
+    specified in the second row.
+    In case no weather data file exists, an example weather data file is
+    automatically downloaded and stored in the same directory as this example.
 
     Parameters
     ----------
@@ -51,18 +52,19 @@ def get_weather_data(filename="weather.csv", **kwargs):
     ----------------
     datapath : str, optional
         Path where the weather data file is stored.
-        Default: 'windpowerlib/example'.
+        Default is the same directory this example is stored in.
 
     Returns
     -------
     :pandas:`pandas.DataFrame<frame>`
-            DataFrame with time series for wind speed `wind_speed` in m/s,
-            temperature `temperature` in K, roughness length `roughness_length`
-            in m, and pressure `pressure` in Pa.
-            The columns of the DataFrame are a MultiIndex where the first level
-            contains the variable name as string (e.g. 'wind_speed') and the
-            second level contains the height as integer at which it applies
-            (e.g. 10, if it was measured at a height of 10 m).
+        DataFrame with time series for wind speed `wind_speed` in m/s,
+        temperature `temperature` in K, roughness length `roughness_length`
+        in m, and pressure `pressure` in Pa.
+        The columns of the DataFrame are a MultiIndex where the first level
+        contains the variable name as string (e.g. 'wind_speed') and the
+        second level contains the height as integer at which it applies
+        (e.g. 10, if it was measured at a height of 10 m). The index is a
+        DateTimeIndex.
 
     """
 
@@ -71,6 +73,7 @@ def get_weather_data(filename="weather.csv", **kwargs):
 
     file = os.path.join(kwargs["datapath"], filename)
 
+    # download example weather data file in case it does not yet exist
     if not os.path.isfile(file):
         logging.debug("Download weather data for example.")
         req = requests.get("https://osf.io/59bqn/download")
@@ -85,10 +88,8 @@ def get_weather_data(filename="weather.csv", **kwargs):
         date_parser=lambda idx: pd.to_datetime(idx, utc=True),
     )
 
-    # change type of index to datetime and set time zone
-    weather_df.index = pd.to_datetime(weather_df.index).tz_convert(
-        "Europe/Berlin"
-    )
+    # change time zone
+    weather_df.index = weather_df.index.tz_convert("Europe/Berlin")
 
     return weather_df
 
@@ -197,8 +198,7 @@ def calculate_power_output(weather, my_turbine, e126, my_turbine2):
     """
 
     # ************************************************************************
-    # **** Data is provided in the oedb turbine library **********************
-    # **** ModelChain with non-default specifications
+    # **** ModelChain with non-default specifications ************************
     modelchain_data = {
         "wind_speed_model": "logarithmic",  # 'logarithmic' (default),
         # 'hellman' or
@@ -207,8 +207,8 @@ def calculate_power_output(weather, my_turbine, e126, my_turbine2):
         # 'interpolation_extrapolation'
         "temperature_model": "linear_gradient",  # 'linear_gradient' (def.) or
         # 'interpolation_extrapolation'
-        "power_output_model": "power_curve",  # 'power_curve' (default) or
-        # 'power_coefficient_curve'
+        "power_output_model": "power_coefficient_curve",  # 'power_curve'
+        # (default) or 'power_coefficient_curve'
         "density_correction": True,  # False (default) or True
         "obstacle_height": 0,  # default: 0
         "hellman_exp": None,
@@ -220,17 +220,15 @@ def calculate_power_output(weather, my_turbine, e126, my_turbine2):
     e126.power_output = mc_e126.power_output
 
     # ************************************************************************
-    # **** Specification of wind turbine with your own data ******************
-    # **** ModelChain with default parameter
+    # **** ModelChain with default parameter *********************************
     mc_my_turbine = ModelChain(my_turbine).run_model(weather)
     # write power output time series to WindTurbine object
     my_turbine.power_output = mc_my_turbine.power_output
 
     # ************************************************************************
-    # **** Specification of wind turbine with data in own file ***************
-    # **** Using "power_coefficient_curve" as "power_output_model".
+    # **** ModelChain with non-default value for "wind_speed_model" **********
     mc_example_turbine = ModelChain(
-        my_turbine2, power_output_model="power_curve"
+        my_turbine2, wind_speed_model="hellman"
     ).run_model(weather)
     my_turbine2.power_output = mc_example_turbine.power_output
 
diff --git a/example/power_coefficient_curves.csv b/example/power_coefficient_curves.csv
deleted file mode 100755
index a3c70cc..0000000
--- a/example/power_coefficient_curves.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-turbine_type,0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5,12,12.5,13,13.5,14,14.5,15,15.5,16,16.5,17,17.5,18,18.5,19,19.5,20,20.5,21,21.5,22,22.5,23,23.5,24,24.5,25,25.5,26
-DUMMY 1,0,,0,,0,,0,0,0.13,,0.38,,0.46,,0.48,,0.47,,0.44,,0.4,,0.36,,0.31,,0.26,,0.23,,0.2,,0.18,,0.14,,0.11,,0.1,,0.09,,0.07,,0.05,,0.04,,0.04,,0.03,,0
-DUMMY 2,0,,0,,0,,0.16,0.29,0.35,0.38,0.4,0.41,0.42,0.43,0.43,0.44,0.44,0.44,0.44,0.43,0.42,0.39,0.36,0.32,0.29,0.26,0.23,0.2,0.18,0.16,0.15,0.14,0.12,0.11,0.1,0.09,0.09,0.08,0.07,0.07,0.06,0.06,0.05,0.05,0.05,0.04,0.04,0.04,0.04,0.03,0.03,0,0
diff --git a/example/power_curves.csv b/example/power_curves.csv
deleted file mode 100755
index 85890d5..0000000
--- a/example/power_curves.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-turbine_type,0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10,10.5,11,11.5,12,12.5,13,13.5,14,14.5,15,15.5,16,16.5,17,17.5,18,18.5,19,19.5,20,20.5,21,21.5,22,22.5,23,23.5,24,24.5,25,25.5,26
-DUMMY 3,0,0,0,0,0,0,0,18000,34000,70000,10000,150000,190000,260000,330000,420000,510000,620000,740000,880000,1020000,1180000,1330000,1420000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,1500000,0,
-DUMMY 4,0,,0,,0,,0,,4000,,22000,,46000,,76000,,111000,,147000,,184000,,219000,,249000,,274000,,290000,,297000,,302000,,307000,,307000,,305000,,295000,,280000,,260000,,240000,,230000,,225000,0,
diff --git a/example/turbine_cluster_modelchain_example.ipynb b/example/turbine_cluster_modelchain_example.ipynb
index 12f8050..634744a 100644
--- a/example/turbine_cluster_modelchain_example.ipynb
+++ b/example/turbine_cluster_modelchain_example.ipynb
@@ -55,25 +55,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                          wind_speed          temperature         pressure\n",
-      "                                  10       80          2       10       0 \n",
+      "variable_name             wind_speed          temperature         pressure\n",
+      "height                            10       80           2      10        0\n",
       "2010-01-01 00:00:00+01:00    5.32697  7.80697      267.60  267.57  98405.7\n",
       "2010-01-01 01:00:00+01:00    5.46199  7.86199      267.60  267.55  98382.7\n",
       "2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9\n",
       "\n",
       "nominal power of my_turbine: 3000000.0\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/sabine/virtualenvs/windpowerlib/windpowerlib/windpowerlib/wind_turbine.py:324: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  wpp_df.dropna(axis=1, inplace=True)\n"
-     ]
     }
    ],
    "source": [
@@ -82,7 +71,7 @@
     "print(weather[['wind_speed', 'temperature', 'pressure']][0:3])\n",
     "\n",
     "# Initialize wind turbines\n",
-    "my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()\n",
+    "my_turbine, e126, my_turbine2 = mc_e.initialize_wind_turbines()\n",
     "print()\n",
     "print('nominal power of my_turbine: {}'.format(my_turbine.nominal_power))"
    ]
@@ -305,7 +294,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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bHea3hYwgS64xrNOPZ67LW9tFK24KY15FJ3SWSOzLEydh+DSBbWxo37491q1bh61bi9cipNiweWcVAGDx7iaue6WlpWjfvr1WO29/W4aVW/fg8atOCnV8uggiwo1MYIsUYYibRGujPuJ4+TWBbWxIJBIOb+BCwfRVOzBowgoMurk7DKNxcGX1hUv7DgMAlD1zWU7tWDkPGoJIpNMUU1ZuD1Sv0JHPfan4iUTIp4D6OFRY8617gsl1TDv37kMybqBZSdEuBy38+r2Z2LF3H3ZW7kOb5iXeFfYThJUPuqHx3rTVgd4VP5ZQxYji1UlwfwO1IahcH0ri7Nh1iUSO/Z36xEhc8I/xObYSoVjx5uSyhh5CKCjbVum7TkVVrTJt6f6A4iUSoZjAutuoD07CGruu6V0Yv3XTruqc24gQoZARRIJ49wezHN8bQolNKUV55T6k0lQq+sqncLRoiYTtTBe8jYbSV1nd1pe4KUKE/QFB9EwrtzrzRX8ya31Yw9HGR9+tRbd+I3H0Q1/hmle+FZZhdRLxkPVpBUkkCCExQshsQsjQoG1YOolcWEXR3lsf+3HQuFNFnBwrQoScYYTwgrD+CPWF8UttK0CdoKJhG10UJJEAcA+Axbk0oBNvpVAhEjdV16awsaJKUj63fiJE2B+QL+fXfMOvHjRsw7yCIxKEkPYALgPwei7thKFgFm2i9bGxWj2wNt13vjsTZz09Rl3P59CGzMifA06ECIWE77fswRuTVuW1j4Y0lWXzk4TtVFtwRALA8wDuByAl+4SQOwghMwghM2QOWeE40+ldCxvW2NlFN36Z3PEsKEFcvWOvd6EIEYoAyxtJfmoRGjqSQkERCULI5QC2UEqV8S4opYMopd0ppd3btm0rLBNKgL96PhhQSrF44y7MX18BQOxMJ+ZugvUXJI5NhAiNEfWhryuUtyns31pQRALAOQCuJISUAfgIwPmEkPeCNBTK/lfPrMQbk1bh0hcmZr+LrJuEhCNgf7V1/mqu2V6JnXvFQd4iRGisUImQ+Q1XtQE3pI5vvzGBpZQ+SCltTyntCOBnAMZQSm8K0lY4ocIFp/Y8UgmLg8j2JehK9Ltki3PB+gqMzMTPF8HvM+rVfyx+2H+srzqFhE9mrlOL7QpckT9s3kas2e7fISwCoNpG/Ux7LpvxnLXlqNawtty6uwYd+w7DkBlmUqSGjvJcUEQiTISikxC08eTQnIyu/I7AdUXkYPf5bNN2mz/lXP7SJNz+zoxQR7SrOj8pW+sD9w2Zi1sHT2/oYQTGbz+YhctenOhdMEKDQLXlrNtZiatfnoy/fO7OAsgfTtaXm1aM705d7dnn+vIq7K6udVCvsLmKgiUSlNJxlNLLc6if+xiYz08MXYSZq3dg5bbclb2fzV6Hjn2HoaKyVllOZMbL/6zq2hReHPN9zmOK0Diwu6bxEulChWqn8CPfV205FVXmu75A4GfB1ytNmNuyDtdxzjNjcNXLk/UHGQAFSyRyRdg62TcmrcK1r0wJrS0AWLPDKTrg16OIzdyfwxa/PPZ7dOw7DDV1+18snUIXhTVm+Hm2QWfB6kJEc3IVja/cutdhAWURturaFI595Gs8/Nn8nNovYiKRHz+J+oSI0G2q2H9jLP174koAwN4af0RizfZKrbkkBeyyHhmi5Q9+Hq1qhah0B1kiIWhANre5mr6+Mm4F9tWl8f60NTm1U7REIl9+EvUJ0W9Yuslp770/5cG2Xhk/xPu7sh3o1X8s/vPdWs+yewtYlNOY8ixPXL4VX8/f6LhGKcXaHQ2ndA9K//mNWjULOstyoUDcJHuHVURnxMJN6JjJ8SFDdUgcdxETicbnJ+HqX4NMNfQY6xM7MzqcbxbKLbZ4fL9lDwC9mDc1daYSSPQiNzQa02Hg5jem4zfvO6Onvj5xFc79e2FaxtVLZGcNLsMP/jd3g+M7SwRLEjEA4cSqAoqYSIRx8Kpv0zNe3KGTz6IxbR5hYfDkVUrTXhaix+PlfVu5r/B0HqtCMJhoSExbtaNB+w8qJgoLqtc07HfYigIbVgynIiYSjV/eJOKGDmySdHxniaFsTbwyboV2+zqYv67Cu1Ae8f2WPb5Ne1n6+6PnJoQ8ovxi+54abNtTk/0etkNjRWXtfq3rUr0GYVk3qfYj/p5XFOiFGyowbN5G8U2mXsRJeCAcZ7qGhYgbOuzAUucFpswGyYv+7PAlIY4KuGLgpFDbC4p8xeMpNP31pS9MxM1v2P4d/YYuCrX9Hzw7Bmc+PTrUNosVqm1FxZGoJBv8rU9nmYE3l2dEpTwue9H9/omWbFjLuGiJRChROQI2UlFZqxQP6LarU6zQxE11qTR+2H+s8qQTFvIlFiowGoEtu2sc3/fVhRvyWuZ7sXBDRYNb+BULVO8p5aZzKzfffmH1tCok7/ziJRJ5Csuhg8sHTsR5A8b5rsdvTsIQHB7f/SAfr/+emjqs3l6JBz+dl4fWncjXib+QTWGB+pGhj126BZe9OAn/neFtFaaDQn6kSnFTSO2orNPY9zyoDxD7fK29L+IkPNCQSYfW7hAnB7Kg+8IMm7fR89RYaJyEhfrYaHXsyINsqB1aNwkynHpDvqb8zcmrsp9XZzjhQrT0ChthEV1VK7pEoryyNrQjQKS49kBjDBUuwjtTyhzf+THlMsZ3pqwO3Xa9EJ6ZGN5vTMc2TQEAyVjDvRY1dSn86J/jlYH88vWM35lixwqKZ55BbSqkDbShjUBU9+phbGoiYX8Ouq+zByaruUhx7YFQdBIhtJEr3PFbeEuI3Eb55uSynOrL4Hd9rttZ6bDg0UG+uKiGnPc3J5dh+ZY96KWItpsvcRO7lhIxcwJTHEv+7ffb8tJ3vrGhXM7dh/U0/YQcl9Yj4RGtsHJdFy+RaIxhOQLMaaE54loOaX5TOf7g2bHo/uQoX3X4pEmvT1yZdZ4LAktE1pCn3loNpXS+xsc+TusUyj/jPm99p2yjvLIw8400S8bz3odqWlo3S0rv8a+KFeAv14FEOgkPhOJMVwAbsJd4qaFjzfN4eawZkVYWUrxqXwod+w7Du1PKcu6LZeHrUmk8OWwxfvKv4BExg4T9CBthnf6CgOXMsjoln5YShWpK+8Lo5dJ7fuZbaaWkMnPN3DuwaULZJgHBFV0PAwAcf0gLAHohWRyK68wkxSJOQo1CVej6hdevyJUYhk1kdnqcJHdk7ssc/PygSSb8AAveLNZrGSxYX2FblEj2xUJDvsbHcn/W9sJ7Snu9V9W1DWgxosDVpx4mvaf6Re4oCMGevq6jXcwgWS6uadJc33y0aF10OaxloHo8ipZITFyWu+y0EOiMm3Pg7xfAIBl4jcYKGcCKMYKKiA5o4j6VyZ6HSCa8obwKl780yZUIpiEfaUPOpyjn+XpOlh90dA0thmrXolR6z88jVx7KNAiB6GzPqSRczfmNBmtzLXIRlx8ULZEYOm+DdyEPFIIohx9DmNZNgHMBhvIie4zHOiWxLLTuXJ1+ZCvPbtPUDGOhg90ZkZgV/M8aWyHMuwr5oiM6Yo2gRGxHA+dGD8siW5XPRe1xrXePwjsshwiRx3UA3H/J8Q09BCn8TL5X2TA3jG79RubchtcGa4lJRadWL7TiTkayvq595Vut9qxN0fprvVQiH5vxy7bmLQwIi0JhDGWbalDxZkNzwMruFPdcicACDlsZloOyn6lrXfvVLWSJjK9achQtkQhDAZjrOt5drU5Pyr+IfK4IAPh45jrnmEKOAhv2qdnLiTF74mfeGv2fIOeq2M9lmuEIXp9kJjFasdUZQkX0TG4dPL1eAgPqPIr8eZrr9XHX+zN9t80ThY0FFFDQT7ZH/newjpfqZvS5DH6TT8Q1tmlmwqx6YRHi0IkEIeRqQki7sNv1i7BzXAfB1JX+wiOLYhHxMmEehaagr/OgEtZwa0NwiaeOz+LnoHo6vAgka9DToDqJcMrkE1/N35RzG2c/MyaEkYSDsUu2aJdVWjcp65l/RZEIWC7D0UbI4TWCIh+cxE0AZhNClhNC3iaE3EEIOSkP/Sjh105fhFwJjVd9/nZtyr+NfGGRCGC05gtXl6L4z3dr0LHvME/CYiGX6RC9aDHuhbX0Mw29CTccmDzJDb411R+UHD/3GPhtRXetqPYjdv1T6tvq2AVr3wnNKS+cZmxQSn9KKT0cwI8AfAPgFABvE0K2EkK+Crs/GcJJOpQbZIpAqbxXS3Ho/P7NwtxPdWGiNO42S2Vhnfjr0hRPf22GMN8j8anwAkuEg7wQvKzXdg1oOCrRkH0XchC+fMLPE1f7QqhESvJ6z41cxoyF2pt85prOtIisosJaS3nTSVBKywDMAjAbwBwAWwDUW+S0QojdVF6l1knwOKhFie8+ZKa+FT77DgunHXmguoDgmeo+5lxPWDxkCsGG5CT0Djf5GeB+SiOUcCuuFXoxRTtWPdGSm7VGnFrXajso8S5YToIQ8hAh5EtCyFQADwJIAhgI4BRK6Xlh9ydDOA8ot0a8CBVP6c8/3luV4zKJlYzxtrfV4RPyBU9rLOZz1pqogXZlPgCaJS8uND0Pjz2S/A+5QldxHQQN/URVp3xf1oaKe2MWb5H2oy3ZEIibKmu8w4c7Q4X77NMD+QhocguAvQC+BPAtgGmU0nrPd1kInISX3TmlpvI0GTfQvCSO50fJQweo2hBh/vqGTTEqA3s6yjWcuNb8KArJLODYGgPHLEdLgdNe3qDxo8or88Ml7k96CBZ+XnPVvnL/J/OQjBu4+tTDFfXcz9jFjXBdLA1oeh2WdVPoRIJSejwhpDWAswH0BtCXENIcwFwA31JK3wy7TxEKIfCdJ5EAcNoTI3Fg0wTmPHqRVps1XAC4Aj/0uiDifIL/Bn2dhIgexbhrduwm+9qAEctQn2jI6cynTqKh16nqQKKO3sqH5VD3s2mX2LRX25mO2u9IcJ2CU6eRK/Kik6CU7qCUDgXwKEyR0xAA5wF43asuIaSUEDKdEDKXELKQEPJ4kDEUQo5rb07CvM+fDFtiL44l4oxgf+fyVU8v82dmCwAHYjcOQm6cxhdzTS/pQRNWZD/rQBSCQJegq+XB/mfLLW4S9xMG0mmKKSu2e5Zr6M3UQqFn57OweVc1uj85yjO0S1hz6vZpkK9J0XXRY3V6XAczxnDkk7A116EgHzqJKwkhzxBCJsJUVg8A0AbAfQAO0WiiBsD5lNKuALoBuIQQcqbfcYQTKjy3+icffoC6fcn1JxODMaLkAXQhZa57W3bphZxQjX1O6Z2YUfobrXZk+P2HszFi4SY89dUS/P7D2Vr9ArLfrPegw94/+Y1QFvg0DAyevAo///dUjF68WVkuV4uUjRVVgXUWjYMsOPH1/I3YtqcmlKjCOuBFj/xsPTt8CS56bryrnurQyh8mwzoohGXdlA+dRB8AkwHcD2AmpdRX0BZq7u7WsSCR+ef717LJdCilgU5GQR9y62ZJZawar9gspxumiKMV2Q1QYPFGO4VkgRw0AQB3vJub522uh1X2WQQzgXV+z6efxOKNplx5g4ensU7fqjANZz09Bke3bYbR9/X2MzwA+eMeqmtTgSOZeiGMqVI9c+uJxAyCVJriuINbeLa3bLObq8k60wnK89EHgsRuYpFlJApVcU0pvSbXNgghMQAzARwD4GVK6TS/bWxnNmlKg21IQR+yvhhFXKCWxgECJGCeCC99YaLvMRQSMWFBBS+LrvO1y5FQ40eqisjETfmwbvpk1jrvQtCbN6+1zIcZCYIwycVDn80PsTUxciFwOl7UyZiBqrTbykh3qWTjgwmGmXKIm9x960Bk3VTQOolcQSlNUUq7AWgPoCfvsZ3x4p5BCJmxdetW7/YCjyNYPV3Ztux2KjMtSbjFBoUWGpyHK7ZUmqJKEG7ERG5RVx3y2wD1eesmkeK6vqHTdwgRTYRYX16FITNMXViYTMX8dWL9l06EAS/U91yJ3r8rjG9RCrUYWNfRzgzwZ2NTDjGuCtZPIkxQSssBjAVwCXd9EKW0O6W0e9u2bXXaCdZ/wM3LOtV4BQ7j717fvQMAoDbD4ImIhDYKhJY8O3wJTnh0eJZQiB6J7vTk8pNEL5trH2wkocLz6cfx+sRVobcpIzhbdvvLaZ4vqJ5m9uAgKdU1vRAvJQdoxAtGAAAgAElEQVTiL/H39PoSrn+xMpxSis0SaynZONmxFrzHdVAQQtoSQg7MfG4CM7zHEnUtNeqdk8j89RI38e0n4hnikpmWhIiTCDYk7TGE3Z4lZtldY1pwsQvXFu/otT1hmZNrpNwJTIVRi90xpfjNq144CZ8OljJs3lWNox4chnnrxN66OhixcBOue3WK45puWOp7/zMHK7cGzycOiL2PGwK5ONM1g6lrOYSorQxVXTg5ifDe8Y3lNoFZm4NOKG9EghByDiFkJCFkGSFkJSFkFSFkpUbVQwGMJYTMA/AdgJEZc9rAqG+W1DohyWIxyWTftizRLEBC9SloGFhcFa+MI4RkNwkdTm/Jpl2ua7KQCM1L9FRtMuex8AlnDruQAGlKMX7ZVqQp8M6U1YHHdce7M10m1BaR8HKs+2z2ejzwybzAfQNunVB94XiyBk1hb6DNS9Xr5aex8Zhq/AoG0q61cXZ6lq++RdOrk+zJCyKdxMBMvvlc+8iHdZOFNwDcC1MB7e1XngGldB6AU8McSEOJDzzDcvCKWO6vQYKPu6F+M9+rTQjc960orDoJiPxkNmNPw0rLFZ6TyJPi2pEn2mNj1Ok5TW3CE/Y2myUSGg3nurkFpRFDZqzFUW2b4fQjW/te5UnUYnhJX4xPnYJba/sCAI5t11w5xsfib6M5qXYQFgs/TX8DwHvedC2WeOumIE9YVCeXmcqnuKmCUvo1pXQLpXS79S+P/Ukhmphd1bXo2HcYPpkptzrJda/wFDfxcZh0OImQN/98K8KtE6m18TpNYPV0N2ZF0SVGcc3c1918+GK2/Dlc+FHQ6swHpTRr4j35+9xzubPwmwUtFwTlJP788Txc+4pTTKbbVCxzXu1p2BJsryfenJjE4ViyTlqWepDrQRNNIcqeGp2QKuG/k7m85/kkEmMJIf0JIWcRQk6z/oXdyaptwcz9LBndvyfKJWC5bsieJy3XbecFI4C4aeLyrVrlpEPgMHTeBmzTzBktAv/ysv0ZmdUX2LAgx3fJ7UxnicbCJsTByk5ZsR2/YxwV2TLLNuv5XfiFxd1phafW3Jlloiuv2q+OXyHM1sjC71yJNnOvJmqo6UCXymG7nJvJo15dm0Z1rVOwctRBzZjx0UDrmhCCo8gGfJl8CLF9AtGs/yazyCeROANAdwBPAfhH5t+AsDvR8S4N7JSS417haQIr6c9ayAb8mwje/MZ0Ydsy8KKV/81Zj4+mrwEAlFfuw90fzMYv3/IRUdaHst46SYYhkw3j8KWjuH51/IrcO1KA7frmN6bhS0HIkzTnHBqGKamF+uQkVFNGKcUzXy/BFQMnabXlpUPh+xJx6bJ2l1E7YJ/8ndZ/bnwIkf/LWDWa7dvX/R5Sx5T8CScbZThk0zjXvYLkJCil5wn+nZ+v/nh0ZuSMwe3wA9RhJkMmRpHJJ3m5vYqT2LanBlt2y0+RQRfFPR/NQd9P5zv6WrOjEls0TfF+0PkgAEBSmpfXlqXHQiQSQea4Z6fWAIBjMmtFJ+nQM1/7N7QLWz/LP64PM0Q9DMT5qIcK6JaU/X6ZqGzayu3o86Z5MNlXlyenEAa6a6c1CRaN1U//FIxuMkSGtjZVQESCEHJT5u8fRf/C7k+GDq2bZj8H5yTUFQ8QhJBmq/x3htrLll0ce2rqmERBcp2Ehe5PjkLPv43Ofv+BMR/NEW7oA/akrxvE78g25nM//YhWAOyE96KFbzmz6RAJoTJOY15VqSmtU/ORmbWSr7AcQcVNMqQpdXCAFSGGDrfmXIew5fqY7vlojvD69YOmYvwybydZFoMnr0JNnUbeBbFyS4lERo/xZrK/4/ouZm2lczAh4E25c5dguK/lchDLBydhCdhaSP7VCxypLRXllCGEPfoQmVqydXi7fldZpnD3J0diOJeKVFfcdBAq8F7yaQxMvKRVnoXO2vGz/K3ftH2vWI/h0ElkGtaxbhK3JVZcs/AV6tun30YghOAnQSnw855HZL/vDjEJkf06OGf9EmM6TiDBzW3rAyMXqYMnAvavcjqfKcoTIC4xzmS5HC/FNQu3BIHimfgg9I1/6Lyu3aKrB9eVy1/SE9uJkI/YTa9l/gYK8R0EC9ZX4CQu4mosvQ9lpTegf+11oFQvVwMPSoFbY99gRvo4LKQdQxgp1z7zubrWveDE4ib3tVJimoceY6wXtq0cg6KgtWFV16aFsnEVREHOeOSqk5CNXfd1ldXPp8WXV8tahl6U4tCWpY7v+caryecBAB2rP8he45/zw5IYTfXl26OTNMnmJLwPGBZiDJFgy8YZ/c0lse8ATYZu2ebdOLm9vV+l0sDP4uMAACvwqu0xXSBOUQXncR0EooxuJWnT6ukX8eE5scWPJ97GsJKHhPdEk+hnYm8dPF3cbuaviEiIx2H+1VXGiXuTt7svlcZcSfwdd2seJ2XmNhH4Sch0H5725VqjU9dN0H3oQDbn1cPE63ewoahlTG6aOu8VQoItAHh/mlg3EjSzmg78Knrtd4TgYmM67o9/5FknDvYAZ/cR9LnzWSNdSYcCtCtypgsLRUEkhC9T1tlILOPTe5DiQmcbC7Ck5FY0o27z2zDnhwjETaL205lpJD5ORzrlgvyWL+duVPfHtGqFQJ/OOJv5ET1J7Ux8aootwn5nxfOYWHIvjFpzXnWV9UH6kqHLoS0BAN06HCgtw1uk5cMeSecRNpK8RA4wbpZ4Lfk87op/gakr1e5bBhGLfYObblPFdyrgdXz3ELimCHkhEoQQgxByXT7aFmGrIFCYwyFN8cxU61y2Bu6Jf4pSUovOtEy7jh+oxE3i8vBVXnscAX7MmCXuGEnONoOOxo03J69Cx77DkE7TgC+ss063GjM/RqzOJA7nDRgnrPXGpFWO78MXbMJ/vxNnElT3KC9RmjCkzyrNnTbzsVmzTZZA7O1eINKQQGAPVGz4ChFiLCfh4FyCga8nPRdR/fhWDlFbY+AkKKVpmEmH6gXCIGdM2AItj14BgsiPtVhej4lX6yQE7XH1/CBsTkLejyVnDa/N/80x9SS16XQoY7UtVMxNYa8kxPkTQxc5Aqb9+r2ZuD9gHKOlm3ajY99hnqdZFnw4aRGWbd6NOWuDB/9j8V2JOIthY+QkjiSmcUiSaEcKcugkWIQVvsUtbrK/6+hZ8o18iptGEUL+RAjpQAhpbf3LY38cbOoftmLU7sF7E5cF+VP2m/kbNmcg7kvcx9glW7DMw9vVD74r24H/frc2B58Vb92Jhap9Kdz57gxsKK9St+lqUj8wR01A+32+T8tXYPiCTZIxCdrwaBMALnpuAq5+eXKAEZpgRXYtifg5BtnABiX+gTkltwceFw/d9WQ9o5tjo3y1TwhxchLOzkOBLOmQ+V2vEyJgJP7v9Pa5DSyDfAb4uz7z97fMNQrgqDz2aSOTmYWABqb4IhFGW+zEGZm4L0Sj3W9XbM86mPmFyARWtGhyUtpKKv/Cj5e1AqUJA9W1adz7n7kAgKG/+wGA8E+h7O+oqk3hm4Wb0TSpt7xdj0CDsAc+eASq5USas6VvTFKfi2L+U96GiUOJ//BxcYkpemBxE5V/5+c1yDKz9ohWzZL+KwuQT4/rToJ/eSEQIkWlpfQ1QJXKUNUciO51MWwLjsdS3n4JdQHSiFmKaCGRUIibgoACuCX2Dc4z3DGCwoAqiNtZR7UBALRqalv0NMSGZz3TtA9OImgoDJfSkr+v0UY6TbV8RPKOhpeEeHP7jL7qaLIevWL+U6k2YbLOsR73gQ+f3Cw7clwzhjamY51eH+0q7N9l6VvCMqHNZz6JpoSQRwghgzLfOxNCLs9Xf67+M/loDaSFIh8tj1JBPdaz8hC4wwrw8yLaJHXnzq+4KZDqlgL9Em+7vEnDgurU1DQZAwDEDIO57/9XpCnVZssdY3FdIZLrgrohb8x2ylumD1nfXLkwQ5vb5tTemLOmHNNWbi8Ye34RjnroK/z6PZN7uTE2Wln2P9+tyVrcWehYtwoJif7C62ePW7rF4ZUtq8dvM0HMbI/Z8g1TXxw5IIjoG8ivTuJNAPsAnJ35vh7Ak/noSGwBYD6QGNJCTmLhBnMx8ItC1AYLL/d7frPKRawiyifRsU0z17Vg/hEm8p13whUOnfkuygUeZL/J1SkuiLipIVOc1seerLNu96XSuH7QVAyRhNsvFOLxzULTE9vLsOOBT+bj0hcmOq49tref43sPYnMSL41x+2dZ2LKrGn3e/A6/+8DNofPxqGR+EpT53xtuRwm+ZtDDRD6JxNGU0r8j44dIKa1EPTKoBrXFTSL58bilalNNAKDULVLw+gH8PARR7lmbvshP4opuh0nLB0I9v8f2SZUInemCoLo2FcrvoD7ETf7aZT67OCvzwjcLNuGcZ8ZoiSdPP7KVsA0R/AbIC3KokYXrD4tGhEVsgsRX4mtcErN1dR9OX8uVtZ+1FUHBOoy2YEL48ESVJxLs52Cvhvh3BrXyzCeR2JfJUU0BgBByNIC8ZD4Xy+mtCRMrrnUcroiQSKgfNH83yEsXY/Qp7g6Cic5EkNm/hwnXpsh8tjhAlojLuQJ5Hx/PlCeD8TW27IP0No8Mum/JOJANFdVYX16FnYoMfJcbU3AE2YwTD2up/Xsf/3Kh8LpOMLxcERapZdfHj7mTPtvH3R/MDixSWbNdHBzTj1k5W9JaSlYulvOObyet5yASoK6c1xZaoFL+zjKbQMqIu+oCwNSV6jzcMuSTSDwGYDiADoSQ9wGMRp58J4RsFDVfAgKKOkGYXJVCtbxyH+asLReeYDyJBO8NG2ADP90w2VhdnYSI49DBbbGv6l1owj4fKworqwRm1BOYtnI7yiu9CZk0JLvPMQXxM9GBI5icx6Asrmrqyh2utTQw+RK+TvbVd8aC05vdwobyKhz3yHDlOPysW7nTXziri53fRUrxMPC/ueuF11tUrsHt8a+k9Xr1H6s9no59hwXaG2Tg52/AN0sBmIQ8TSkI0jiarMf80tswKvlnYRvs2qXEyFxzNiwLA+SFfFo3jQBwDYA+AD4E0J1SOi4ffYleEosLIADGLXOLllSejNe/NhVXvzw5FDY3F2cYoTOdsI9gaEEq611u7OQk3OKmCctMY4BUmuL6QVPRrd9I7zap3sn+AOxBM9g2/7JTvYCBdJfxLiIs63XQ9dpYm5Ea87dyp08ZRJv9rDU7peUD6YRkzzGkpaWSwPF97K4WR8RtvdtHNGC2fcnbJcrPwBIJVUZGHtuYiBGUApsy4WAqa0wicUdsGEaXmMShgyGOLO08iIQb8j6f1k3vwSQSKyilQyml4SbjZXDNqYe7rtlEgmLYPHc8IdXGmg1IFkQnwZf3tYNTHE3sk5AwdpNQtCZeDW1QkfUwFaEEtXkLDncIE6WUBTv+mCCfxEOySKIBxsA/+rmld2BSyT3S8taGMLPM25Y+X8SVtayV9cDbcqmGIorGe7dAmcrD1+FGJiIMiU/1I0s/tUMr4XX1WPTbt0ruE5hAs0+Ml1So1ssIJsT534Ytzn5OUwpQoJuhDhuSb+RT3PQGgEMBvEQIWUkI+YQQIn9Dc0DTkpj7YjYsh1hxrbMsxIpr9THTrbjWR5/YN9kTA+DkJGJIoRmqfLG5U0ruxviSesvz5EA23LHgDgCAqEV+PKenOmFTiQmsqEYr4t40+aaHzt+A5R6RS4Nuf/w4p6xwEqSUjl+NQs8TGnKnEaGdZP04LkqXFJFvdX7ERNZBolZgEKDiJADgpthItIZaXDaFCc+S9qO4zqNJUD7FTWMB/A3AXwD8G2a+a3EQmJz7cl8zYOkkJPc1jvg6p0VXGZ5IiBz9JF13NZz5k1ki8Y/EK1hY+ivhcjayG7KzYTY+jey3+HmPO5DNOBB6oTqy3fGPh/luPQfWmc6+x8hYMyeqltiDcwz/OQtkGfvcU2f36ZXIZ0ZZMCUgr1gdzQVEdHASmaKHYrvD9JKTNoVqEhtEh8avrT/G/4tn4oNCGlFw+34WKkMVPz+ZSA8/amLTrmY1nky8iZc0EoMRpPFg/H20pdtAtTVl7Pti/Q1nYeRT3DQawGSY4TmWAuhBKT0+H32J1pCRUVwbhArZ1RMO9U6Sl0rZm+xZxkK0Rblrwjo9+BU2VdghpV3elIK+e9WMR1npDWgJp+kgv8hYj+urY99Kx8nX6/flIleZbXtkkTz1F9LEknsxoeQPWmX3pdLCttkrVo7rVk3V4QNMU0CK1xLP4/3k02iLctd9GU4iK7Gg9Db82JiaveZl1UXgvXHopnPl4bXfiTiJMSX3YUiJba/v5py857BKEqwwDPDP//fxz/Gz+Ljsdcv8kxWl+oEfcZP08KdoQra5U0pRU+e8Z23ZXrojfp4T1FxzB5I9aMOEy1i93W0+fBIpw53xYfin8WKwsBxE3ylUB/kUN82D6Ux3EoBTAJyUMYkNHV7Jfy4QmJ91budNJN6cvCr7+cPk3/B5yV/QgbiV4KzVgNub0nlh8cZduGLPJwCAI4k63aJo8bo8upF2iKgAM98vD4MAvf4+Fr9402nh4HchyYK98SivrMVHgoB+Ip2El5+EdXI+K2YSv4cS7wvLuK5R4CSjDABwLsOBfFvyO0kdO7y835wUuvDa70QbYhPiJGoH71nqq81PZ63DCY8Ox/dbxFxgU1RnOUQ/HtcWZNNnzf1lpxwKgOLB+Afigl7tK8PqOO8FoBFSIqEK4uilG+THbOlIKQj+cGHn7PUf9h/namdfJqReC+z1cYhjM2WEu3bzKW66l1LaC6byejtMD+xwYhdz6HSQ2wvZYGzdT2nvTuCio1Tbssu5IR5OtqNf4m1XOcsWWgT+YHjpCxNRq3k80LFuSkAvvzEhBGt2VGLsUs46Ij/6VwDAV/PdBgPWoiewRQBeMue9++ocxPZgOK1zVLWbJd1LvA1xbpa8/sTImzGs9wlUJyTU2ZvecXz32kdGLTYPI0skUX1Hl/wJc0rvdFwjqRo8En/XYQ2mgsgfY0O5yWHHYwQ3xMbgwliw+GB+OIkgtF2ZU4a7a4ub1LpBfk1brZxklDnC0KhgGMRzbnsbs9GJiBN9NQbrprsJIf8BMBvAVQAGA7g0X/25wDwh68X835z12JwxL9PRD27YKZZlK7vlvqs2Bf4Ewy/IC49v624/QMDA841Z0nHUd3gJtjcrb7aXl/GEZVsd9ZLESRhNE1jx75BZu/D1WZichL86uvCqpqe45qPAqlu1LJVkYz6UmPqV/4uNy147ZOXHuC3+Nf4Q/8RzOIQAb04uc12/8J/js5/PM+YI65779zEeoXHsDbf/N0tc90S/acfefa4w8aqnGsi/QchJ2GAJ2/ryKkz+3j6cuefL+f2np3cAAJTEDGHsJxZvJftjbMl9jCMokCYmJ3L43gUoK70BxxFxWlld5FPcVArgnwCOp5ReSCl9nFI6xqtSJv/EWELIIkLIQh2LKKEyl7FMogD21tThno/m4KbXp0nruMaiuXgc0mFu1Yo2Z91z6iEt3QrdZJ3TOiem4Ug3ODlAyrLn001i0vfbXPbkbH97MsphkbPjyq3277z7g9mO59rdWIb3En/TGoM6DwV/jxE3ecXoCkokPCrSdBpXG5OEEYCZUkITWFnbKk6XxW9iX7iulebZK3/tjir85fMFyjJW2I+Xx65QlgNMgtjjb6Nw9jPOrUZFe1XvueyO0DdLIm4at3SL455bZOy8kIiZ23J1rbln6YH1uDZ1HieWjwMAXB1z5hWpqKzFE0PdeksZ8iluGgCgGsCvM1xFV82qdQDuo5R2AXAmgN8SQrqoKgjDbjhSDtrKa0vJbL1Qxx8i100ESfqTCyfhbsx9P5FyEgn1ZsKOQ7sLT5xtqF9qnbbZWyKdBG+Hzrf1g9hCHALTXHD++grP36G6HYST0JQYZNp3c7UydN02FM8n/4VfxOQe0Xwek1PaH6Bsc1rG69prqh0bWeY0GtcIUeIFr7mZsXonBnyzFJ/NFgcKfGGUPJAej4ufnyAUX34yS9y2Cur16yFuYioTKBIXwf0OBzl/ON6h7AHZvPab+JeOss8MX+JKwatCPsVNvwfwPoB2mX/vEUJ+51WPUrqRUjor83k3gMUA3N5yjjqC/mlKeX9sJsCfaiFocxKs9ytXRXSCsYp48ROi2FEp4rQEYgmZikORh67wvyRvi8nDGwDAaWQZmqBaeE/UX52GIF5E5NoRU8VlydzviH2JY4j/zcAem8VJeMu2g3rSe6mjmtSZopdzJaa+JpziprYtTMdFr814jcCShoW1lvbU1GHRJlPUKguTzUKUY94vBo79PpuYiseM1XIP8TAgtW5SGKCKFdc2eELl4Pip4h70iMQB2IP74x9lv091hF9Rt6DzvrHIp7jpNgBnUEofpZQ+CpMr8JW3kBDSEcCpAKZx1+8ghMwghMyQ1mU2WGcALeDbFduyERzXK1JcBuMkvMVN8rr8ghQRCeeUsQu8PZE7tYvETQeRCu2xsTg/JmeBm6MSn5Y8hlcSL4gLpOrwTuJpdIctX+ZzSX82e51rExY7y9llPpu5Gg8lPsTnyUeZ+/44JUuuqyNu0klQ//2W3Th/wDhUVNlyZa/xEGqK4HrHxBsmYP6uNyatFF5XYU+NesO31tKfh8zF5BU7HNcs3BNz6yhWMKLBgxHMf4TFQc1Lcm7DD2TvuS4nbMEpbnLei7M+S4p6ANBpp2nufqwhNxl+NPEu7oq7xYNsBwWvuIZJWNlVmYIPyzpCSHMAnwD4A6XUodmilA6ilHanlHYHxBug4RA3OR8YG9pYnWFM7ynvrGSUSzwnkcNMCdOjcr9VX9zkbuuK2FScuntCoLHJYIknZKEEElVb0Cs2HwNicqci0YlSdAJnL70w2hRJNHUFGla96Wk8Fn8Lh9TxIZ+9xU1nZrLqqfCvcSuwcttejF5sm017mjRqrJede2s4XY+Pg4hS/Gm2xL4fpdzzvDv+ubse86ymld6tPRbpOOotoUCmPyknIbJuMiHW8YnFTSu37nGI7fgp4DmJdnvVcaYOwzZcaDjTwDrHaXZwcE2ZsL7f55vvpEPTCCGPEUIeAzAVZqgOTxBCEjAJxPuU0k+9ygupOiNusmKgAOZLwp4SVa9X0LX61/jbWFpyCwDg1+/NCt6yQNzEn6h1uR2ZmenR1Xr6BV10ysSKOpCIRRtpiaz7w8STeCI+WNquaHNjXy7rRecTNSk9sXetQJ/4CPxxxxOO64aASLTGLocI7XgPZ8xUmuLTWe6TIDucscKcJt7zyc+lroctmxpTBELMrWbJpt3ZUVwWc/rViMRPqugF01ftUPobiKDDpVnQ9SNQvScqZzo/kImbXp+0ClXUFhPzBzZ+bGkPC7dvSh7AAURheZlpv/PuafIyPpBPxfU/AfwCwI7Mv19QSp/3qkdM4/k3ACzOtOGJf450U16DUxRZE2OeDlglj7xd3VM6CwrgF/FvUEK8/RfcJrAclg53X3UtsNwU12GjT/wb5X0rjHGCIRJx1OGs2CLcHB8lrycYf5LxERHtK+ZLLv/hLfaYIps2KdM80dZJuOvMKv01hiYfVo6HxZcSj2z2BPqLN79z3dfZlwInmcrTGlDt6de9NgUfz1wn0FGpNu3wWYkTjNWK/sQQjdB63zyd6bgCVTD1RrtoE6FDLIuVkiROFloIHFodnETmcDn3gAuF9UUBT1UInUgQQkoJIX8ghAwE0APAvyilL1JKdT1pzgFwM4DzCSFzMv9+7HscrAkstckCP0EqR50gS1X1klsbRFqxGTlQU4FrDGeSFTeR8K9cd1zXqq0P73wb5l/WCfDW2AjPdtOUYkb6WMe1vbAjzbL9OmI1UbuEcxwUR6425etNqfNURiA2ODjasF8ur828UhIGw5tYa8wI1znl/sqQpmozBYd1k2L1d+AjBWjILw4jzkCGqnWiy0lQqu8Z34GIQ2yrxiIKGW9xr/6d6cx6KcRcNZ3iJopqqg5To4sUiQuv8zpAL+SDk3gbZjC/+TCd5wb4qUwpnUQpJZTSUyil3TL/1OY0AhicDNBOLENRXskqEhWLNRAnIW/POl2kM49dp33L0YlpxfFNl0j832tTtMrlCq/xWM87zhCJlgLRlCsePwV2UqeIZw/sKC/sC/pe8mkAwNB5G1ElycBGqc3VWLDea4J0zglzZPXDSMQjJfgeTaeper3rEomJJfc6vvsRD1loKQm6CKiD8bHw8yjnpI9WtSS56rZuiik5CRu8zsImLvYcWP4r7DvTDuX4Mn0WAGBZ+vBMu94/VKyhCucImA8i0YVSehOl9DUAPwXQKw99eMJwWDcBZZn0hNW1aWzZVe24J0OwiJiKe5m/qcxjd58pBGPwIW7aReWhsVjCKBoTEDwAGwuvUOp9PzYto1hOQofQpQWiI9nL042Jpjt/ndiCiwKgxBli3tIF60z7i2OWK4PmsZuxl0exs6L3wUEVDysXiHQxOohrUAl+ruaW3uG/Iw5hZb7jRz903gZU16bMg4RLcZ129L0m3Za5Z4+H9/MRrfE//ndu5h4nGs/0WYWAVl7ZA3E4Yrt8EInsbkQp1QsslAc4xE2gDjM93ZNKkLSgqmWb5SRohpMg3u27Fxe3UTKKWt3ge7Lm+ECBvpui1HPDr661N1aCNJqi2vdJya4ffJOglCINQR4SzXZXb6/MWlSJsJWJurt8i732vDY2kW8MD1kT3uE5vAw1bMW2nyd7bmd3+JhcoOuoyJ7Kc0EzTl9y9wez0W/oIolOwtqA3WB3lfemruHu2Rs3pcA3CzdhaiaXiFOiQLJlrb+HK0zbLbAEoWqfue2ubRJO0O18EImuhJBdmX+7AZxifSaE+DhS5Qb2wae5Q6juaSls9Zm1nq2TAi9uOpG4lWuuDSsdTNwkHVNOtZ1YvmUPjhBEyWWRVfyB4OH4+1hU+kuB2aobplUaz0mwn/39kjSFayHQ7LzYm6WBtFQsWCMRZQHyUBjep1+d38HrJDIbkEdVQjGw5/MAACAASURBVNRlrKfRo6N3vCsWIsMRWdteWLVtL9bu0DvspKnaWovFaR3kXukXxma6rm0orxLqcHhxk2wN7uRys1v1CCg+m70ed747M8ttOL2x3Wp7v17vX2byfFcbTX3VkyF0IkEpjVFKW2b+taCUxpnPLcPuTwYHF0Cd8ff5aaiuTQnDKAfSSTCrtiX2cPcyf7PjcII/0QDACYb4RGJBJ3ZTfWDhhgpc9NwEnOARTIz9zdfGTKV8U4l3NgtK3c/LkQnMSxcicM7jxU0xxpnOwpKSWzEq+SdhmyorHNkJ11NxrbHrxWjwUBnKfNgZcdOBTZOhiSr84rwB47TLUqp+nuwcxBQiMdk7JJoKi/vPRjNmOPnnEy9njSZ4xTV7mFvEiR/ZNupgZPcdax1aocPVIMynMI9++fWTaFAYDj8J54Tz6+VPQ+biwn9OwG4u4mKu1k09DWfcf/4U6TqlCGzQL4k5zST5zUdGyNpBL5SBbthyL6zbaZ7+pnvklWI5CZq95h6Dy/dcqChkiYQYcht4IM0prhNxI1vH2kyTJIWjDHGecJUoXmbqnuUmcwimZcWs8lEFAFCaEIvXmJYAhM9Bm22Gu3FZUAdwtD/v2CsPVCj6vVX7UkrFtcgq8pzYQtwYG23e54kEsdc9D5ZIsWHqs457PrfpbH0N0aUOipdIMA9+8cZdjqVUknD+7KmZvLLVtc6HmuvC5k8oFpFgl0lzVGYzpVVRDUUVN/GnG2JW/4OkXpRUNqteLrDemRRVLylWwpMV7xD3c3ZrYtziJke7AeaKcjoJ1k9CZ+NdvUM/lPzB2IG3E8+gJGVyrLUSKrJkk/9QKVbsJEqBNxL9cVNspLDcqUcc6CluIrCc4xqGk/CD8spa0HQKN8dGIAm3YQb7U1W+B6K1M23VDqHkz3oqMudU68DDB62MQY9IEGads3oML4iPnxGRUIK1bvrou7VowpyiOh3UXKuNQBsPU6U1p4LhWyMAFpTehjEl9wFw2uHLO3B+/YkxWVhMR9kFQPhy6aALKQMAxJDKEGTnwpaBZF8WVgcg8CznVTHU3bYfcROPNKUOitXvy0UMEdcLe7hgvXxDP4/LBfK7+Gf4YWweTtkxMtOH+MXX2ZrTXKl+QxdlOZMLYrPxZOJNcdseyriDyC6UpPdq/vr6hUh8d91rU7Bp4tt4IvEWfhv/n1YdSevSq/wGba1Vi8bL1iTfN2sCy8PNEfvn6Ci1S5933EHmWEOy/ioaInHT69Oy+QkAOMJyAMCxB9s29oceUAodBDud2nV4hRPP/dnWC07xgRrONlsRcbYxXTQjwSJ4flXyEABgRenNGJZ8MHvdM7Jt5q9T3OQmEhc/74wpJco5QCSfdcCbNw6evAq7q1O+2rqqmzw4sZUTwMJNcVMMYf1mXrFpQccQISWwyqqsTWlt7uy+cTRZj9OJUyTaZ/vzWF9eFSqZaI1dONJQGzR4YdMuN8e7ZkclmqRNDuEATv8HODdk1ZzKnrnIyGAtNVMhp5gDhahXt07CWuPukZxt2Fn9iEPcZHES/tDMOhBHRMKJSd9vw4iFtuyY33gckWAVz461fQ8UBZapkuA2dK+YLZo9OL4db6yVlNNsLYd19FbiWQDACcZa29qDEx21xF6cw4S9Fr0susr3XBTXN2Y2aQsUcDnTsW3pnEIPaOJOCpVt30PlcMZTo4X3dcyuCdI4gmxGWekNOCuzwdTWpX3P5eiSP+OTkscd1w6q24ytu2tCVVy/nHhReL0l1OEnWJw/YLzwuh+RjAwqvVX2TswUBW+mpuWXRQT4Xu9P/Bf/SfaThgoXjfP38c8cY7HHY/5VRXfOjtXRrjW2SNykhMoyiT9xbcvYtG/fW+M4sfjhJESy/e2ch7Bb3OR/hyYB0peqkMvLpQpnbeHfyX/g/eTTaJGx+hDF1NS1IuP7cxJZf89SRARYHQlvgSKCSnrDtu7ME+0ljvNGDGmcYSwGAPzEmATA7bwla9uL26CEaMX58rMBtZGEpI/5MO2sqvVv0aVLNGXPXBQh2npnVabMZxhLFDoJN9h30KmTMGHlTdFG1owyIhIuWC/tf75bg8pqlp2nWpzExvJqTF9li378nPTPfNoWJ1jEYTNaO8qkXSyqfyJx+vr3HByTDPqpV8M5MVq98f2ekPH94JVxbN86nuedBcmEcjEs+GDaGtdvZ3/Dw595R8dVPTmWCJ3KhE1Pxj0U+8xvusj4Dp8y+TEsGLCdFq3f8NKY5Z5PgxDiHboDsUysM/W6WFV6E6419MLMSxP3BFx7HcjmrHhJOQea60O2jl4dv9LVg1XW5iTEdd2xm+R6BpZYisRNOnAel/T0g7ooLiKRebyfzFyPI5kgZAQUgybYSVr4R9c0acrwjmrbDMcd0tJRzy8opVmZMX9S4l9QfsG8U/cjYZtlpTdkPx+2aw7ueNft/MOjvm1TZJuP5QXO235TsOFJvE88LQSxfp6+5qTsZ/b37qNepp7A018vQa5PSRUiW3Yan75KnZSHXXMvJ17EaYK8HDGksuWsvB2bd9V4isjmrCl3JEASIQ1DarnD44a4WGTmF008TXOdmFhyb9bYQyVu0mW6ZYdBkUNk1izVg0jwfhmiQ5KFuINIOMu2hn//43SaYtLybZG4SQTrnSUEuD4+LnvdAMVQJjwuy1UQArRpbkZdpNQZhyaY4tq2PnGlJaTi04Wf/lpXq53V/CIsBeW+lKX0Fbd4MNnpum8RCTbYnwyidlcy4S7Y+3shj2HFgtdJqKytLPRhck9XK0UgwZ6sW7LsRgzp7O89zliHS4zpWjqU50Ytw42vq3MMUEKQStePfZO1OctESWcbC9AGYlFVm6zBhpxIvDTGDpuiereW0A7C606OylnfVlyLsYYzj7YJgXscLJEymLkl0LdSZH//6MWbcdMb01C21a3MB4CuHQ7UatMeUxFCJPtnCTv7QpmU27zJyxmDmsC2yVB/t5+Ee1wscg2xwY1Es5SaYb879hmOJN7irV1V5kYve2Z1Ge6KFZOkM2Z7fsMOZEfHyFydYiw9bGt9KgBgWvp4Rz3VE3ks8U728z9GLkO5xEopqI+izpoziDNG1lHEzF2h0+Uqj1wFuu2EAfVvpfgg+RQ+Sj6p1ZaopW9X6FkNVkJs7ejYDjhR8QZF2mMR+iY+BAC0ElhhscEuCbHXHwF1mTvL4NRrmNhbE8y8nUejJxJtmtmx1y07cJ6lN0AdJonfZ06gP42NxwHYnaX6vGd2MGEEzWbvcpnAehAFnQ1ig3FooFHpYmjyIUwt+S0AoBV240+JIXg/+ZRnPa+TrOU1yooHbE5CR+nqbl/mcc2+MNfEuHwcgnJbqPNk5edwsKFc7IzoXEf67WkRCea0CZhZACm3doODAFTXgSs3cR0fTcDCD4252QRPnRV5nllYnCqLOWv1FL6yZy5WTpvX/vL5QmVdHpaZe5ykXea6zn2CZvsw/XV0iYT7Wy6GHSwaP5Go8Ra/mJyE/bCf+moJjiIbMCDxGp6Pv5y9TqlTwR0kdhMLGSchV0x5T2StIqgcAHyZfEjSthh8qZOMMhySeeGsTVwV+9/Vf8qMhb8i7SRmdlBDVidhchdxrSx+XvfFv1ck08/WcYVJcY4xF4QhsJFtEPy6MpBuAB7UjbauUDByURAA/C0hTlf7dvJZnGSUafVptXxFbGrWSMIv5ERC3p81v20lllsq8CatceLkiGUHHl2I9B/tM0mXgkRYaPREoiRlb2Cq+D283LM0EwqDneQ05fQVAcbD7js8J8HH62HbP5msxNnGIs/2vTb/kzMvVxiKa1Ya24MsUZa1RrWbmpEnN9A23H075IUFiwjx/iS6aNXEDnwWlOsTwZ9VCUUpalxBCmXiJq+2F6Q7AYAjJzIP07rJGTmUUqqMSusHOtZNIkwu+b3ju5HdSH2acPoA+zw7aohFvdpgIYoCa5vAQqovyQVm7CZ7k9cVN7FvgPWJXSPWwWLaqu1YtsmfA26jJxIsZMYm13WXe8ayC4RfFF6cBH9aBpxil4HJlxz3Xh2/IjNOq4xd9suSR9DR4NJCCmDFObol9g1uiw3zLJ8LWAVaD8ODSGS5JPMDn4fXlvfbp0trE2Gj3/KRc/mxsDjukBZZCw6Raa0X9lQ79Qm8kvL++EeebVAKfFvyOywq/aVj7mXiN55LOYasQ1npDeieIcKVmUQzs9PHQEb6zPDlzt87dulW9PybbW3UnmwJlESqNpXG3n2pQNxEkgtQab0/bXKMCqAL1YbqWD/J5tw9OdxJh8x2DmqezB5y/IInmlup06LSSST0+uB9Lcy/cF1LU/eB2QtFQSQsis7Gw2GztOka2Jnx6XNj3KnC7u7rBc6TzmvJ5323b714/RJv45HE+9Jy+qdh7xerJanE/Yn/Kluxevtn8lUAzuxw5n23TsKSN7Ny52aS3BKiUbae/ARWld7kIua6v3z4AmesLKuetQHfFf9CWG9i8p5szKtHPl+A1sQkbOzSkS0jfl7OzXijXxabJrwvQowjEqI6k0r+ECiJ1O7q8PKEhWuIIYbzt2ueuq8aqGjDhlMn4Sxz5lFtAhm2AEBvY47j+1t1lzBjcf4KfU7ChjUu9vlbnEQQX9yiIBL9MgHNWE5iWvqE7Geq8DxkKTAfn96LkxAGplPUsRYdG4zLL/SZT12dhLxFPy+5F3FNohbNUanM7KUCH+4DAEo3TAVgWYc47/80Nj4bXdezbdd39eg6GFuzpomsctS5pQQTZenML6+4zs9mrKO4ViNXnZ4OdEyGXTCc+Rmeir8hLOYIy8H1t3TT7sDPXWX6zia5Mj3k/SuubZ2n/fw7ZHQSQUZcFETiwEwMGPZxOthxAfkUPfo0pQ69gdf0iBYJVdg+Zh1wBBueLoLGOfILVUY2Ebwyrn2e/AsWlN6msUkGffFstCW7MCDxGu6LD/HZF2H+V6NEED13O+N8Jeck5GOJo44bk0pkFZ6BRVB4bWBhefzqQjvvApdsqoOxVVjM4VTImcBSyrz/zfylb+WfGv9OBzGBddQnbk7ireTfAQTLC14UROIsgcKXeBCJ7D3mc5o7OXidFEQvwdfz5XLgXSGw8rpelKIcDX6wsvQmjC4RZ2QT4amv1DoL2/PaHtei9JHa7as2HGdQNBteClMZV6OzuYkcAGdnuIpNFdXZJPfusYrnr098BL4vvQWlZF+mnBw64iYLQTx2Ab0TZ09jKWaV3JHNxsajPrz+HTpF3R4NPQE0HykYAPomTD1V2pFON7dfyiYbI8wBgIBm/Ys8xyrUSbhnUZrsSoGiIBLZqWLmyvESMWHDjzqoGVfLRppSLGECu3md+EUb8aTlTvO248kaV6CwXE5Y9RluoyXx5zCkA/bF2kDN2FbVNCG4rw9DQiT0x2LCjwmsaB6sQ9rLY+Vmt3zb/CakY268lLZ3jF312wdKIrAeDHV4EF20JntwZ3yo8F4+OAmeIHmZi5aV3oBn4oOcdyRE4lJjGjoRW08lyj5nIU2p7QQniSZ8prFImJqXfy6sn5AzLIf9fxAIJR2B2ikCuBP+OSeCpG3RAB+dkcU1//oWj31pcyWBNiziJAjtyE68Mm6FpLR/hC1aqG+RAAtLPOD0OLXH0xY7Hfdk6EQ2BnqV+FXD8J4adQUvYGZTUeVT9oL1e48x3AENLUxLn8BZrsjRhrg5iSPIZkwrvVtY3nqX/JjAyjzmw9KVsNzXgtLbuHt2HydmEmHx+BkTosesJCYSryRfwNhMTCgAmfAk4rAc7Vs1xVOJjC5DoPNsi3J8lHwSLyRMJflWekD2Hp+3no3xZjD8kPku6OoWbWQV16KMjwGmpKiIBMtJyIiEJWfslbEqOdGQO+B4baDCDZurkoaBLbvDSRFq9tlwm3oYsHNc26E6Ysxi7m3MzZb7rvS3+CATlkE1Fz805gnvB9V/HEqCnbJFHC0PA9SRS51NOAPYG25bsgslEidDNgqs+T2NQ1qKQ0scJyA2o5L6YkQdqPQsYUDXj+ZPiSFZU2IlOMW1DGmJuVoLVOKwA5vgh7F51k1X3dJMMq8fxWYBcBrSdOdSDss4iaAQKa4t7Lc6Ca8NwqD2S2nF2jnRWBWoXed97zpBballmJLuEmp79Q37XEaEz8ZKvWkRkx6ZF0qtsA9X3PRY4h0cDrEy04KIWN/1/qxMu2qLsetfm5r9flHMGdE3oeV97vy9fg8OvD8DiyCchPy0Gw6R8JN34iAB5wRwcy0gEuca81zXZIEO55feBqTZMbmfVanLsEH+LAwHkXD6SeRipSgWiRYBkSCEDCaEbCGEeAf0z8Bya5dZNxkMJ7E3k3lOxxLC6+UTcRKyUA+7qsMJtjUzfWwo7VgIMwOZDtgAf7XQO9Gx9UQgEJ/edUN5WC2zz8Ir0ctPY+Pl7XId8yaOqoRGOuI13rqJgGaSZYmf0QHYg/vi//UlqvSzlcg2srC4XjUn4ezDSi+qhEAn8W7yGdc1Z8h0Zz8kxUgHBIuvHRdLSrXZx7l8Es1ItWcdFWw/Ced83xgbVTQ6ibcAXOJViMXI1Gmua+wD7lnxjeu+anNsgUp0MVb75iQ2VVRj8Uanq36aElAKPPa/hQgDuegkrjC+xQ+YVKJA/nQS67mwHNn+GNFSiprLr5w2c5fjvqt+d7Df4M4AxsLLUqarIdczeVkmqaATEZe4xE1yaxYAeDTxLn4X/xw/MmZ4th0Est9kjWdpun1O7aueCf+sZfPmuK5p3dQkGUNriL3FYyyREPQpM2sVwRKxmuUonki8BQBojmrtI5zIuokn0n9LDC4O6yZK6QTAn+nFlkzeWcJQdFZpc3zlDFzU5WBHHZUY6IPkk/gq+aDnSagdKUcXRlm2YH2FSw5ohsQGPp3tP0SCCDqnMzbngYXW2IWXkgPxXvLpUMbhhcPJdlxsTHddZ0/v/xc3M5uJ5oL3F1BtrvzJmsXI1OnSOvwLyLagbXPvE7xhAw8dPxi3uMnNSbO4NhMJN6nBpVjww2ESAI/G7RDqVgj4JzMB/KZnQrEHhSVu0glZLxu307pJj4O9qcfhOELiQ+Ewq1cpoawumfmanDrRcY/103g4bkdREDqDnnqzZ1+sn4XrnsZYeRQckdABIeQOQsgMQsgMQPySsA8oRlOugGsTUydL2z85G4HSe0P+quQhXGyYIY9N22knYiTtoN4rS27EGR5xkFTQIRJszgMLQZxycsUv425iJVq4IhELm4nuucS/PDko2Sl6LRU7OvFey4Bzg/F6XrK7E5dvze4ZJdiH62NjtX0azHHpWVax5b5Nn5ip6/WM9HC5MQWDkwM0S5tvCTvX1rO7LOY+JPCYWXInTiRl6ELKpHogS9w0vuSPnu2lQdAau1xtOZ57rMSzHQAYtUB+sFNFceBRgn2OsOhD02dmP19uTHGUPTtmW1fGRA6tG5whPbLjYT6r/CSMAJZ3jZJIUEoHUUq7U0q7A8BqanIJMuumac3OcylsKuAWcfDQDQP8ZMYUbubqna6JiSGF7XttT9xcndyCiod0cjaEDREx5HMzA0CJgEhYoY0B4CexycrN89rYBHQh7pDxKsWfM0+0G14nadlobn5jeva09npiAJ5N/BsXMWIellO4ypgkHLMXSlGLexOfZL9bQQG9dTB6a+De+Mda5WTgCazqN7UhuzGs5CF8VfIQJpfeIyzzw5jYMRFwz0O/xFuYVfprQVtqxbUIY5fKA26OdOSZdz95dnPnc5qwa5kPAsoiTtI4gV/XsYS4sIa4CQAMAnQ6qBmu7HqYtF9XHe2SjQD76pxWAtsNUy6+KX4Y0uk0/h5/DacSM6Whzomtf2KQr/5fm7DS1W4caSTj/h/z/E6/An7ujkIqPS12OEPZnh8LkXyCCIgEz0n8Pf6a6zmqxDBHGlvwWvI54T3Z83JakcA1Ji9xk4qIEAA/Nqbi3Jhpe9GC2FwR+zteSP5Le7wszok5bTpU4gV3Oe91f7Sx0bMMi5M5S8GwRXVPJ8SxlQA7cKSFM43FwnK/j39mfzH0xqd6nl7zxJu16rYLAAuZSASvJF9w3kzopeYFxHtcImagJF2F83d4Rzi22ykCWBP20GfzmWvUlnVTipL0XlwXH4+3M1YMV8RMNm9l+pAQRiB24rPG9tV8/3Huj+/UATjuUtd1KXHzMG0LmiI0bIhO7zx3dV18vPA55tKf+3oaqg3Tayv9ONVLef/8mFgsEJa4SfRdx68nH4YKTbjIvSwBvdaYgAOId8pUFnymQBV0f80BDKGWOdPxUHFmjnkSyPnZ960J53XtNQej06cqBiXestk5kFk3AcD9H8/DLdXv4eqtryrHwKLgiAQh5EMAUwAcRwhZRwj5lVcda8J2VtpmpgQ0m/mM0BQjQzQn9KrYtwCQzcKWC1ixFP+St0Cw0BaJmHghy8VVHkRCYRtfn9DdpMIiEipxk8qCykt0s4fKT3SvTVjp6JftyUsxrUMkgqS9NcvlxxmTFy+x3/+RfBVXxKbyVZTw41sUSNMm2Wh5qNac8z1UE4m/cCH9vU3rFfdj4mRUf0rYwSy9Dg3xfY086RCl9OeU0kMppQlKaXtKqZzXzEAoewNFipiyx6qafZiaSYpO4XSc0Y2qqg/nWF5MDpSU84DECkEkV957/E89OYnTyTLJnfyYwMrwd00Rnlu343+eroxNwY8zeRp4iOI9sUnqvfq7ODbDg3DZbV8Zs5WT3kTC+3fy4jldcZNB8sNJ8OKlZkScE0S/PT+GnwHeX00LH9Vm7ZgnQXvVUGUWzMHCTUIkWOiuB10UHJEIgj8mbEWb5bTWzViB9ikzJMGG8kqHLLwDoxQNPxaSiW+JgmXUwWJx0DTRwk21PMKzOSsZUEPDy0nNwnYc4PgeVOFvJQTiQbhIqgCvt1H3d3FsBn4V+0pRQrwReeco8f6dvCmrrrjpcLJNa/uVmQ3LsE5iQRYER5DNOJxs1yrLR8PVRnO1iHlSye9xClmh5iRy2Oi96nZSmfpKFdc2VIprwBTn+kFREAkWM8rcLhbshB3IyUcTIYthsua4h4sc/HwQpLViFl3+UgTbRNtKwhgEwaWG+NQugtdmxb9IYRNznpNogwqspvbmoXMKU4kqa6lEXJhpVxRqHACu54PRCcDnslA5BfL1vH6XLFyKCiynlCsmlNyrXZaPYaWFB8o8xU3tyTb8If6Jsm0nEXA/+VMNdSRgA2n0JGIlO2su6+44dyLhF0VHJMq2uUMtx5B2TNoRZEve+reWS4um7oBrYYi2hBNPSLDwjgB+xMUOCoqeZLHbEkMBWYwdC2GIm1RgdRKnGssxs/Q3ONtY4LjvhaQg8ZCFMRLlo/U7TstY2QVBCXH2+1JyIHTiVy1Id8qLuKmh0IlsRMzDOdHC8FQP80OTVlrWTbwvCg/HehRQ5/OM2dK6Dyc+wMrSm/Dfkic8x+FCXBzIkYV1iAgkihOg6IhEv6HiBEQHMcpltycjVZ5U665+DVWxFlr9W+0cc4jbQiOMjS6MiV+d1ohvI8MjYgLbSiLWCYrOxOnIdLoRfFMVgeUkLPFG0pX8RY2TjZXSe7JkMdb8BdogFGiBKk/CVomSoiISI0oewD3xTxUl7N96gTHLl2nuebG5znftzN867jviSXW+2FU/fF2n1XFTzyJWYiQCOUfrB0VHJFhsMEyHka5khePl4Nnpe+OfYGXpTSiFROF2aDdsatVdq09Lh1WadCuY8sVJlK4cAT/iJt1sV0LEZd6q4W4+PFfy09iEUNsnHiawOnM1IuVcE+ymIqsfy9MmrSNKAvIXqyvfsKIa+AH7riRIyrfI0vGuXfKU455DXNjxB666eTM5P+YC7aIG9DPbqdspEvwm9oVjEWykrVEW7wjAjBG0lbG93kadStEbYqMAmAG1RDCIgYNaeLN5JjILS+DVmS8ikdw6X1CyflH/QT9yQ//EIOXJ+9OSxzzb4E+m7PzK2r5EI1RFENwRHwovQn0k2YymsoNQBhSkIAmJzFlShVz1WC0zvhW7z37Adc9BBATWTToh3wOhWVvgOnfYHRaWM56BNGojImHjgcRHuIQJKBdDGnVMKGorCOAeWop59ChHXaci0P2CEMPILoSt9ADgiLNcZToQ04XfUBIJH6eL9j2El6ULP6BOIhAOU0fdrTfE9b1PefSOzc15zCZ3Y7fBEglZ2w6nrhBxFNngSagfS7yDfyf/oSwTxjzmKziiX3gRRC/cEzdDn5QuH+a6l3QQAfeT17XQslB92JnehQAtH49RafP9NEjESbhQmtE1dCbr0I6UOzZUKzQCBXGd8qxczscY63EycYYYAMzIiZb7fwoG8MvhwDl/cJSZmLHKyL5kgnDEvjiJ8x6S3hqYECmI63GTTppxr+qa2ZF1DwxZJ6GF8x/JqXqu3M8xxgYcTTZkvxsa4qZ8QTej2WkKq5vwxlIYnMhfBYEu/cDaT0ScgsMMOUBkVR6JnZrzohF36ofGPJSV3oD2ZGvWoTgXFBWRsGCFKD66zo75/1T8dQBAC1IlfYE30Vbi5U2M7I7iZR6YJUACUzVf7K9kMRhI43KJg1i9IcO1xBkrEZ1kOeGPI7eNOIzNjN0s4g5xU/0SiQTq8OMQRFklpBYXx3LLOxEakUjqGYvIcC0XWM8vsvMpCOPxh7gdYBHVIZiRU00pgxHzlBp0y+Q6OcNYAiLYh/zm92j0RIIKJvBYYjrRsZ6brNkrnzXKggGKPRCIMBhOwvBIWJJ9QQTjCiMSq1DWfWl/X+ImvwHc3Mj0tdtup0F0Es0DWGkdckr24/kxuZmiLljrGj4NZX2iV2w+nlIEwtOFLECehfThegYcOaP7L4E/LQ21ya1NjvIuxCA7n4J3/hTCJJ1aOS6HUWWgu1w0405ZSLvKU1RCV79qotETiboSt6mpZY7ZgtoikDhjTy2L7ir14CS2TsL90LmiCp3Ej/3EsJFs+jfHR7kvNm0N6SrTjFOTK+r75IyffQB06OmreppCNAAAIABJREFUSlmnnwFn3Z393lVhwqoL1vEp1oDipnqDjyikOWH9zKxYMyxQn+9CTMFJ7GU32gO9Ix54Q5NKaGbVs5Amzn3oFLISBxNfOd0aP5Fg5YGyTGPDUnqbSUwaJZNkW/dSyqmIxF3xL7TGAQDY4WMDM/6/vfMOl6I6//jnvffS76UX6SgoKCIgCCKC2FBEYwNEwRYVsWLUFP3ZEpNYooZoNEZjxCSWGGM3mkQjGhELGhUbwYIVjRWwoHDv+/vjnLk7u3dmd7bN3d17vs+zz+7OnJnvOXPOnPeUt1RDQ/N2SrOqF8dLOGw62c5fvm3TFd54uDj5IaGY0JPP+GXr3xSNpzkhVZktfguC1eExJHKFZrk+32ioZzvmL6sSy19+bUnGHZN33jb22DLvewRhk43J9kabyWr6tDghkQZVNPCBdmFtQAzlIFRTHywkpIratsbuoVtdemOWxjXqAInfTbLwvvh1Ft5ppTp8fT7PdfvgezZ9RltWvVN4noJD4MMXMyfLEd7Ic37NPWnT+aPulRW6bkbDHvGEvy0GNMsNZk/NVe26/uOd9gpOWJd/uIE27TtlTgSwITev0h5y8QZdAUIiXfAXowIW1T9TDfVs7YtZnbiRUG03adu394RE8PTwhJo7zY8VCedvXclhYyubzr2qGkbPzZ4jZ5SG9koUPzZJECnqjOusVn8CMke1W9726KLloajY9yqktlfmdM2BgABdqUjav/zBm3Dqq7DpTqHpG5UxrOdVf4Cj5bppbvkMQ9S9hohR9cKQyzJo2QuJ6jQxWz+o2oSNWh3ZPuGuNuewMCBaGAh8YUMZevFx1weHNu3leTn9IrFRnqQJYbG8YVD6zLSLHniFt5fChBP48rj8N2IjIU6bjHTolJ2WhvFxVTwh4WmdNUc88VggguQQIzkndB+aXfpBkzImSVoqbt8VOvaGnX4Qmj6xKuAJiUTZN2h+nXUTRJ3l1G1CPoO0XCzBy15IVH31ceNvSXEnvUFaUU9Vsp+VXCBVibXstVYv/plFgUk7t2/qjkNQOpFsR5DqybMR3ssRwW98I9auNhpYxd6k3nIf853tCL5EYEaCxRVwu1Y9U7lCAkHybWPHRlRLHfvd6Pds2zlSm/y2IaBeBu3IKw39A9O/rUZ7rsHuFzSoL+RuoS2qMw28zv3c+E1r3zUvmlzCGJe9kEg3MmzbqoYN1OQf39kv5evDrTh78BlD1r+YuKZ9d8CorT7fdl5S2nZNnAxaeJbWDVnk2U5Bi2np+lT7KXCQWU4ptNZJXOiw+smMM4m8nB8C17W+tGQsjouCfIVE722i2T9sfWD0e049P9IyzIdfBA/MAtXegclecLKRBwPQplViSWj3qsJ4T25EQwahI5LGb1p0tMjlpnQY2LUd9VRRQz0P1ucRBMj/YqR5STwjlsZ0Nm2QemioUV6NnUFkajRJ+ZPkbz8CXIjkgqRNv30uh/HHFeS+caLfZ0+QSSOqX13+r0SmPYmyhUhhVKp/9HbmNLUmkFH9ARFsP+o3RMqXfybgh3dUe49MOt5OzECuVSsjgFq3SgiifKPvNc1cFu97tsu9vv2abGOFQIUJidT1tioRNlBNDfXBS04Ddoh2Y/8oJU1jPL/V9b5/0rhktGuA0VbgkkTfsTB+vpmBDNs7Wt4AxhwJQMd2KSONCSfC3tk7RgtCzw3vJv7U9YJpF0a6Lpug9oXAqoYMG6s7n5H2dPWXaaKCRURWqs6lgv2iqOxKQVxQRInn4KH6tX9kTtRQHylfGTvIPsER+aob480XUfh37FO8ew+dRkNb47vua9qwsqEvbLVv5MsrSkj8X82NKUeU2nZtqaE+eMNmh5Oi3dgvJNaFWysnRSr79A2oNtcFhezs1yXA6nHiAugxFH7wutlUiwovrV+A9dsO9vgZFEgbZdNvs4vn4I3achm55IP2mUZ4bTrGk5GyQ4QOsM/o2IwzGzE4wDV2z61yulXYMmC3DmY/Q6pDlqxsmdMa0v5wVU55akRKX7R0+Dn53S8FVbMWAXBqzW0Maf8l1EefuVSUkKiTr5MDhagyuFdnJlW/yMTql5pe0KF7tBtX1cCImdllZsNXaTefWwfFbI4QdSoQ3ovrf4GPtpbZUTeZN9s57em7q3dvenCvS0LTNzQaH8a79JIxhnapaGaVCrwBUJTOv7qGgo2mPd5dz03vS6hzyqby0Q/BMbkZRIYNWAa2sSGNP23q3BNofDYbJc2+R7suOeUJgNObDsA21qTZ98vF6r19NwB6y6fI+s9hRVPPtmEofyGRMjJ8s63PXkAbkHRm7P3H8Y2GdKL+CFBVNdmPyKf+NL2KZlBF56w1lGZPIqqF7PbHpz3dtUOAwEszxffcoBQqzm4jOvSEuhyn5vv/Nj8V2D0vyv3aUoW3Fp5puWZHG3u6UDOJY/4Fdb1h9KGsJY2B6pcfJ//vNxZa5TaY6hpizFr1+Srz45OVgW7wEzOJAqu9eqjt2URRZWD3NDPeAROgNksDvjzsK8pfSKR18qawKr3K3audmkaVApJV9aqqYfgByefPTmm8qdhmlumUwrB+Leyf4kOqR5a64R4aBU7Aix5V8GTwCbPlNy80PdhzeGDaZwcljMV6R3UB0D+CP/0JJ8L3V8JpPid0u/042v0BWtfCgPHR03voZEezWfrNKS+ECInRc81+xS52+SONMNnYa6QJ5bnDyZnpeo+E016F2h4MkzTW+mtCzlX79t+8PY7zgm2XPCQplvjR2QTpYfqlMC9glpJOMO56blrOyEjZuB7QJY0mU7vOwc4PD/lL+DUtWkikGwFFWFoYuTZk6tp9SDJHqvpZlM63Q4/wc+veh5EHwRRf3Ii6LPYhwGx0jzkyoTsd1JijdGwnPJVRu6JrqwD1wYHBmlMfdhoZeDwU2x0DgyaGn/dsR4KsyicuiM6j9dA2ovsDP0bNMd9Rlycj4r+dQwYo6XDoHYnfhfTIGtZO9r0SRh2S6Ij9bWyP5JCeX+37e5hza9btuE7SuJpo61N8+IFvOchTRe8zOnkAl2bQ8LWEzEA+f6splx+2zH2+DPCQOzj9Mm0ThO2Dpr5/G0NU5MPQritsMbXps7dKLfkMcMpfSKRbIy2kdW0XO9qYcX3Tc4ffG3xNkACb9ovwNNlqjuz7a9hnoe96W53ZGOKdt8bMYDam3/CV3iEdf8C0t3N1cBjYUEy/BEYeEn7e05kPErr+ZzZ0enqeqPsR3sgSzDLjpNPMrHCr/aJd7+GU5TDmiNDTm9WGGFQGYfefwNzbYfAups7OWwPHPJTcceYCz1K519bR0vuf94QTkk61bpOivu3zuGuuzaGj8gw4IbkT7zXCfB/zcLKB2Y6nhD6Th9pNS8/lzVqO+Vfy8XQziW+/DD8XtGc3JUS7LvX5e0Kwpm3TPiMVs/5oBnrQ9D3ey16bjS+4FJS/kGjdPvxFDAhQHglBm7ht6syLufUBTc9t6nMJMHS6mXIHoc9o6JuiZpeNgYy/Qz5vDfRM8RzpvcCpx/3XhKGXXTrq2Df4/MaQ0d6k05ocGt8l4MUZmGamAGbmlvoytOsCR/0TJn/f6NaHjeSn/sx8hwkyDyv/kZxuyG5N0+x5UfIy4RcfGtuVkbMTdgJ9ItrcdB4AI2YFnxs0iZqvswhxOX4+DAnQ9AmwwN3Qa1T4ffxGauPnwyG3wpnvQ48tEse9GYq/g05Ff7ts51OxbtvatmUvgE5VNXT33ffYR8PvFwb/3p1fdfawu+DIB4IHViGDpL2/uiPweCM8lfjUd9QzHg2ydt5kROL38P2Tz20TUPfVKe/73naQV9PavJ9zbzeW1Z6gHTkbxicb4jZin1+ZQcNW32m0K2kiJBpXPFKeU7pBWQpKUkiIyJ4iskJEXhORH2VIbR5WKo5/0mwee9MtgP/7INEQ+oW4D59zG8xOVaUNwaF3wMxF5vex/zbLErNvNFPuIMxbDP3GmLCb8xabY6MPhd6j4ISng69Z8Dwc/S8YN8+MHIdON0sAQWjdAWbfDHNTXoaRB4fGzG5Et8Fwzmdw0rNw4HVNNUjWfRh8XYAv/apUVcLz1sCRf2uSrgnGzzP8HsYda2JGVFWlXyba4UTTAU0+Pf2IeNvDzbfnhtrfgW99oFnW2n6+WUbb2YZG3T55tMy5nyXqLh28Pa3+vj0QfzudfWM0Vc4Tl5nnl8VgotWwkEEKJAQqmBC5rds3taCfe5vh9CzsU3HcUphr/ZH53Wd4697eu7XpZNMeNxlhOvVNMsxWPCHSy9fxVrcyz+DMFNXzDt1ClzuTynPEfeHLSB72vdIIs7oQ5RT77IcNSFnK6blVcrucucg8t9k3GUETZFnufzfOWwNjj0w+P2RXw+cJunRx3Mcckbz8CHDwzcE2Vv4BVOeBMC26EkaRtutzh4hUA1cCuwPvAk+LyN2q+nLkm5z9cUKC7rMweUlm9o3wwBlmiQPMnsDin5ulhB5DYXOfquceF6RXbRu8S+J3721gvwDngCNmwfJb4fu+TbPJ30/8bt8Vjn0knKPLIPPpZ0c3B98UnhZgWIA74/2vTn+Nh6oqqGoLI2bAx1Ytr6oVNGwwrg+CsPlU05GOngv/+RM8kSLA/MZ88x+DDevhlbvg8Stgu6Ph6d8lp+822Kg5/m5XGzMiIryX4OBbYKGvM6rrnbBt8TatZ94AfzncPKuTn4P6b5sqDUw+HUbPiWbktOB5ow33r5/CszfY/NgIeNU1cOid0G2I0Xa7x+6htO1ktFResYZ3W88ws9U3HzH5++0ksxTUffPM/Ge+b0aQNx0E7z6VvlOs7WV0+td90FTw1rQzM8ZM6py9QoSbN9IfNBHOeNeUB0y9p0OPLeGjV0wdnvFe0/2+KM/ADxH4zhVmxrfJCLN3ctfx0DUkMt3oueFelA9JDPiqUtXWg5aewbTb1La775WJWcf8xzJrJ20zGz55PXCmnhab7WQ+y29LcjKatCdxSoASSjqoakl9gAnA333/zwDOCEs/ZswYVVXVF/6iem5H1VuPUIc0+Gil6toPVG+Zo/rzfuHp1q81z3PZouj3XvOe6nV7qn7xser7z6l+8VH++c0FS39j8v7OMvP/pTtV7zu9sBxrV6s+f6vqp28mH794iOo1O4df98p9qncvML/rN6q+er9qQ0PTdPX15pMN/veq6i82N/Xw8IWqy29TXbS3eRZLLlf94MXs7hcVry9Wvfvk4HJEwVefqq5eXtg8+bF+nerCkaqrlkS/5pqdzXNb92Hi2Kol5tiqJaofvhLtPrcdZa4pBVw9WfWxhY1/gWUaoU8WLTHjIhGZAeypqkfb/4cC41X1RF+aecA8gAEDBox56y2rnaBaGLcBDuUNVePKPRt364XkLqU2qGoUOCpafbeE0VBvfEvlaNtRTIjIM6qaUUWuJPckMkFVr1HVsao6tkcPn8ZLKb2cDs0HkeYREB53KUHECYjmRFV1SQqIbFCKQuI9wG+L388ec3BwcHCIGaUoJJ4GNheRTUWkNTAbKEO3mg4ODg7lj5LTblLVjSJyIvB3oBr4vaoGeOdzcHBwcCg2Sk5IAKjq34AIivUODg4ODsVEKS43OTg4ODiUCJyQcHBwcHAIRcnZSWQLEVkHBPjNLSq6Axl8hZcFR3NyuvKVP6fjK2++gaqaxlW1QUnuSWSJFVEMQgoJEVlWbM44OJqT05Wv/DkdX3nzRYVbbnJwcHBwCIUTEg4ODg4OoagEIXFN5iRlyVmp5WoOrubgrPTyOb7y54uEst+4dnBwcHAoHiphJuHg4ODgUCQ4IeHg4ODgEAonJAIgEo+/57h4mpvToXCIu/7i5Kv0tlmu5SsbISEiseVVK3ujphYaw8QWFSLSt9gcKXzjRKRjjHzfEZHBcfFZNAY9LtdOJw0a22QFlg2gLMtU0kLCvoSnxsg3XURuEpFzRWRIEXn2FJG7gPNFpOjGM2LQU0QWA78DUNX6IvLtJiLPAPOLxZHCt5OIvIyJVlh0IWHLtxS4DuhdbD7LOV1EHgQuF5E5UNzBjIjsIyI3Az8SkYHF4rFcXtkuE5HJUPyBmojsJyIhgdsLzrWXfd9/ISJT4uAsJEpSSIhIjYj8ELgcuERERqlqQ7FGvyLSVkSuBs4BbgY2A+aLyKYF5BDLswg4C9PB1AJHiUj3QvEEwb5w6+1nGxGZZvNUsPq35WstIlcBlwDnq+rZ/vOF4krhbQssAH6iqker6rvF4LPlqxWRezD1dxbwBDDQni/auyQiU4HzgF8BTwG7iEifIvLtBpwN3IDxynCSiEy35wpaThEZBPwMuAJ4BZgnIl7o4oI/UxGpsve/BCMAJxWaw8fVSkQuxdTd1cAa4GARGV8szmKgJIWEqm7E+GMaBpwK/NYeL8roV1XXYxroDFW9B7gA2BbTqRaKQy3PXcBOqno3cDtGDbmo/lrsy9YPeA74EUYYoqoNheKw5fsWaA/cqap32hdypHe+UFwp6At8oqq3iEg7ETlARHpgly4KJSxs+b4A/qSqU1T1IUzMk33t+YI9ywDsBPzdts1lQCtVfb+IfLsB96rqA5h3rw74roh0KEI5BwOPqepdwPWYme5JItLFDgwLKuxt/lcCo4HjgaLNJlR1A6YfO1hV78eUrTNQtFl8MVAyQkJEThaRC0Vklj10n6quV9WFQE8ROcSma1Vgvpn20DXAuyLSRlVfxVRk3ksJqeVS1TtUtd7+/yswTETOF5Ed8+UK4DzQcjYA7wNbAEuA1SIyX0Q2LyDXQfbQ+cAkEbkEeBb4qYhcIyJ75MuVwjfDHtoA7Gyf353AYcBCzOitkHwzAVT1z/Z4FfAZ8I6ItCkEVwBn47sAnCIiF2HirAwUkWtFZJ5Nn1dHGsD3ODBRRNqq6v8wg6Vq4Lv58FiuGSkj6XeBA+17t15VF1v+c/LlSsP5uKquU9VrgQ4icpRNl3d/GMC1CHhTRFpbwV4HdMuXJ1aoarN+MJs538N0XjMwI/ojgJ6+NPsD7xWZr4cvTX97vmMReHrZ81OAEZjp/HGYUUaPIpWtKzAWONemOx34ErjH/q8pENdR9txJwL3AUMxLcTJmut29wGU72p67FDNi293+3xJ4AdiqyO1kB+DVIr8LR9s2MgT4PbCjTbsXcD8wqMB8h2MGE9djwgY/bH8fCZwJVOXI1RN4BDNYudN/H+APwEJfnkYCt3nvSh7lC+S0HN7vacBLQJcicfnL2QV4CNikUG0mjk+zZ8A+vLuBne3vPYHLgENT0jwMnG5/71Zgvl/6+YDpmLCpAH2AUQXkOTwg3Y7ATUBtEZ7lQuAgzHLTg5iR6EvAP4Bf2XRSIK7LgVn2f60v3WRbvvYFLtuvbNn6A98Ce/rSXg2MLGY7sccfBL6Tb71lqL859v/DwGD7uz9mlDqwgHzTbBlnYmYOo4Hp9twc4No8uU4FtgF+AxznOz4YeA0Ybv8PxQimDgV4nk048QkL+30b8EPMgGZmgbnEd34UcLv93Q/YpVDtppifZl1u8k3vlgGTANSsg64EhovIUF/y44CLReQDzDp0Ifn+a/mG2/PdgfUichJm3bl/AXm2FJEtUi6ZCnxtPzkhDecKzMhsNGZq/7SqDgdmA1NEpK/aVlsArleAMSIyVM36vYfdga/IcY8nDd+rmBnSWsxG8qkiMlxEzga2xpS3kHxeOxlm03W0ediQC09EzhXAaLs0+BBwsU13JOY9+KyAfPdjyrgdMERV/6Oq99l0Y4An8+S6AngZM0CZLiK9Le/rGEWOq+yy4VzMyDzn/Y90nGr2OqpILLf/ELMPuRLYpMBcKiJeSIa+QLXtV+7Lhas5EKuQEKud5K2hamIT7DWgTkRG2P+PAJ0wkh0RGQVci1nD31ZVbygSn6eDvh9GfXMIZnR6T4F5OorRBDpURF4ABgFnaBYb81lwPop5jv8D5qvquTb9p8BEVX2vgFyPYFRQvXqbLSIvYjSAztSIm5458G2qqhcDfwJOwNTbTFX9pAh8nbC2Jqq6FjMi7BWFJw/O9pb3KqBGjCrzcMysZm0R+OpI1OFeIvIUpg7/mg+Xqm5Qo5TyOEa4LvCuUdULMILiKMxM4ihVjTxoyoLzZO+8mr3BwZiR/52YvuWKInBttJfuDuyDaZ97qepNUcvXnIhFSIjIRBG5AThLRLp6I1dJbEI/BWwEpopIjaq+jJG6ng3BJ8DxqjpTI2h15ME3zp7/I7Crqi5I14nmwTNGjSbQO5hp6WFqNggzIgfOlzAv+GhVXS8i1b7G/UUQRwHK59XbW9mUL0e+3pi9AVT1D8ACVT1cVVcXic9fPoDZqrooE1eenP2BcVawH4xZ0jtIVT8oYhm3s+dXYgYXB6pq2llLGq7GNmfxMWaZawsR6SfGhqeLrb9jVXVWlLLlyDnUcnYXMxP8GDhRVQ/I1LfkweUNIm4BpmbqV0oNRRcSIrIZZgT0MKazOl9E9oJGFTFU9TXMtHcwRkUT4BtMJ4OqvqOqy2Pge8Oev11VH46hXItVdUmUchWAc5U9Xx9lealA5Vuqqv+OoWxvePfx0haZb5WPL/ISWh6c60m0za+yGFAUor2sVNVn8+SqV1UVkTZitJjqVfVRzN7Yi5jZS3eb9tsoZSsA578xG+NrVPW/ReZaLCKbq+oTqvpg1PKVDLTImx7ALOAW+7srcAxmetfbHvspZpo5CGMXcTfwDEY/O2tNirj44i5X3JyVWm8tpf5KsL38BDNDH2T/z8csgV6EsfsoxvMsGGdzlK9UPoW/IWwPbOH7Pwh4DBhg/28FXIhRvfO0eob40tcCnUuNL+5yxc1ZqfXWUuqvDNvLbv7/pcbZHOUr1U8h3TJ0FpH7gH8Cs0Sk1p5abx+uZ7S2AjMN6wgsV9VDVPU1sRoCqvqFqn5eKnxxlytuzkqtt+biq/QyFoCr2nI9qGapKyPi5GyO8pU6Crkn0QGjLnqS/T3ZHv8I4+NmhIiMV6PB8x4wWVXXgFEh0+zN/ePii7tccXNWar01F1+llzFfrlxcUsTJ2RzlK2nkJSRE5DAxHjg7qtmtvwa4FSN1x4nRwa8HlgL/wXh5rMWo770lIu0hut+buPjiLlfcnJVab83FV+llrPTn2RzlKydkHeNaRARjBHITxtjldYzEXaDWUZ2ITMRs9CxT1T/6rr0Mo1c+EDhMVVeUCl/c5Yqbs1Lrrbn4Kr2Mlf48m6N8ZQvNbuOo2n5vgfGGCcZ8/wqsubkv7fcw2hOdgDpf2rpS44u7XHFzVmq9tZT6q/T20hLKV86fyBUI/ByjzrUTxmrwBt/5KuADjAts71gtxu/MU8CHQJ9sGkwcfHGXK27OSq23llJ/ld5eWkL5KuET5cHuhIlD8BuMbvCjGMdjb2OsQL1084GHff8PwjhduxafR9dS4Yu7XHFzVmq9tZT6q/T20hLKVymfKA93EskeUq/CONs7AnjGHqvCrO/dSsKYZF/Mzn92GYqJL+5yxc1ZqfXWUuqv0ttLSyhfpXyiPNz2QBsS63hzgAvs7+eAk+zvscDNeWcoJr64yxU3Z6XWW0upv0pvLy2hfJXyyagCq8ZXzDea0P/dHaMzDMZd8ZYici8mNvSzkPCOmAvi4ou7XHFzVmq9NRdfc3BWcnuJm7M5ylcxyEISV2OmY/djzc0xLm87Y8zS+xZSesXFF3e54uas1HprKfVX6e2lJZSv3D/ZGNM1AK0wbnC3sVL3bKBBVR/Twru+jYsv7nLFzVmp9dZcfM3BWcntJW7O5ihfeSNLKbw95iE/ho1nXMxPXHxxlytuzkqtt5ZSf5XeXlpC+cr5k+3D7QecAbSJJXMx8cVdrrg5K7XeWkr9VXp7aQnlK+dP1m45HBwcHBxaDmKNce3g4ODgUF5wQsLBwcHBIRROSDg4ODg4hMIJCQcHBweHUDgh4eDg4OAQCickHEoeIvJLETnF9//vIvI73/9LReRUEekjIrdlee8jROTXhcxvthCRQSLyYsDxrMuTJe/9ItKvWPd3qAw4IeFQDlgC7AAmjjDQHRM60sMOwOOq+r6qzmiG/BUFxSyPiLQDuqnqu8W4v0PlwAkJh3LA48AE+3s48CKwTkS6iEgbYEvgWf+I3M4QbheRB0RkpYhc7N1MRI4Ukf+KyFPAxCBCMTGPn7Of/4hInYhMEZFHReQ+EVkhIldboYWITBWRpSLyrIj8RUwMZERkjIg8IiLP2BlQb9/x50XkeeCEkDxEKk/KNatE5AKb72Uisq3lfV1E5vuSTgEW22suFJGXReQFEbkkc3U4tCQ4IeFQ8lDV94GNIjIAM2tYCjyJERxjgeWq+m3ApaMwQWNGAAeJSH/bSf8YIxx2BLYKoT0dOEFVR2FiEXxtj48DTrLXDQYOEJHuwFnAbqq6LbAMOFVEWmFCYs5Q1THA74Gf2ftcj3FPPTKLR9GkPCHp3rb5/jewCJiBcUXxY1+aacADItIN2B8YrqrbYEJ1Ojg0oqa5M+DgEBGPYwTEDsBlQF/7ew1mOSoID6nqGgAReRkTuL47sFhVP7LH/4yJdZyKJcBlInIjJu7xu9Zz9FOq+oa99maMoFmPERpLbJrWGEE2FNga+Kc9Xg2sFpHOQGdVfdRy/RHTaWdCUHneCUh3t/1eDtSq6jrMzOsbEemsqp9jhOTpGB9G64HrrLO7eyPkw6EFwQkJh3KBty8xArPc9A5wGrAWMyoPwje+3/Vk0d5V9UIRuQ/YC9P57+GdSk0KCPBPVT3Yf0JERgAvqeqElOOdo+YjBVHL46VrSLmmAagRkc2Ad7zZl4iMA3bFzDhOBHbJMX8OFQi33ORQLngc2Bv4VFXrVfVTTAyACfZcVDwJ7CQi3exy0MygRCIyWFWXq+pFwNPAMHtqnIhsavciDsJ4En0CmCgiQ+y1HURkC2AF0ENEJtjjrURkuB3Jfy4iO9p7zski/4XANOABm6daoJOq/g34HpDN8pdDC4ATEg7lguWYpaInUo6tUdX1kue2AAAAwklEQVSPo95EVVcD52GWg5YAr4QkPUVEXhSRF4ANmCA1YATGr+11bwJ32KWrI4CbbfqlwDA7Up8BXGQ3qJ/DamlhoqFdKSLPYWYicWJPrJAA6oB7bb4fA06NOS8OJQ7nBdbBISJEZApwuqru3dx5yRVWG2yJqo5t7rw4lAfcnoSDQwuCqn6D0QhzcIgEN5NwcHBwcAiF25NwcHBwcAiFExIODg4ODqFwQsLBwcHBIRROSDg4ODg4hMIJCQcHBweHUPw/q2WygpcqA1wAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -333,7 +322,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -378,7 +367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/example/turbine_cluster_modelchain_example.py b/example/turbine_cluster_modelchain_example.py
index 1d6cb95..fe00321 100644
--- a/example/turbine_cluster_modelchain_example.py
+++ b/example/turbine_cluster_modelchain_example.py
@@ -219,7 +219,7 @@ def run_example():
 
     """
     weather = mc_e.get_weather_data("weather.csv")
-    my_turbine, e126, dummy_turbine = mc_e.initialize_wind_turbines()
+    my_turbine, e126, my_turbine2 = mc_e.initialize_wind_turbines()
     example_farm, example_farm_2 = initialize_wind_farms(my_turbine, e126)
     example_cluster = initialize_wind_turbine_cluster(
         example_farm, example_farm_2
diff --git a/example/turbine_data.csv b/example/turbine_data.csv
deleted file mode 100644
index 0e9b9f9..0000000
--- a/example/turbine_data.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-turbine_type,nominal_power
-DUMMY 1,4200000
-DUMMY 2,4200000
-DUMMY 3,1500000
-DUMMY 4,225000


Test patch:
diff --git a/example/test_examples.py b/example/test_examples.py
index afa612f..8be5e9d 100644
--- a/example/test_examples.py
+++ b/example/test_examples.py
@@ -22,7 +22,7 @@ class TestExamples:
         mc_e.calculate_power_output(weather, my_turbine, e126, dummy_turbine)
 
         assert_allclose(
-            2764.194772, (e126.power_output.sum() / e126.nominal_power), 0.01
+            2730.142, (e126.power_output.sum() / e126.nominal_power), 0.01
         )
         assert_allclose(
             1882.7567,