docs/tutorials/darkgrey_poc_demo_04.ipynb

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     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/compat/pandas.py:65: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import Int64Index as NumericIndex\n"
     ]
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     "name": "stdout",
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     "text": [
      "2022-01-28 15:26:47,654 darkgreybox  INFO     Logging enabled...\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "\n",
    "from darkgreybox.models import DarkGreyModel, Ti, TiTe, TiTh, TiTeTh, TiTeThRia\n",
    "from darkgreybox.fit import darkgreyfit\n",
    "from docs.tutorials.util.plot import plot\n",
    "from docs.tutorials.util.error import rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo Notebook 04 - DarkGreyFit\n",
    "\n",
    "This notebook demonstrates the usage of the `DarkGreyBox` models via fitting them with `DarkGreyFit`, setting up and evaluating multiple pipelines at once.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our temporal resolution is 1 hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the duration of a record\n",
    "rec_duration = 1 # hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read some demo data.  \n",
    "* Ph: Heating system power output\n",
    "* Ta: Ambient temperature\n",
    "* Ti: Internal temperature\n",
    "\n",
    "We are also setting a set of custom fields in our input data: `Ti0`, `Th0` and `Te0`. These fields define the initial conditions for `Ti`, `Th` and `Te` for each record of the input data respectively. E.g. if a sub-model is trained based on the 10-20 records of the training data, the initial conditions for the above params will be set by the 10. record of the input data. \n",
    "\n",
    "Note: This demo data is intentionally far from ideal and particularly challenging to model with low errors. It is taken from a building where there are many factors influencing the heat dynamics of the building that are not accounted for in the modelling (solar gains, passive gains, hot water demand, gas use in the canteen etc.). The time period is relatively short to maintain a reasonable solution time for the demo. However, it includes an also challenging holiday period when the heating system is shut down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input X shape: (792, 5), input y shape: (792,)\n"
     ]
    }
   ],
   "source": [
    "input_df = pd.read_csv('./data/demo_data.csv', index_col=0, parse_dates=True)\n",
    "\n",
    "input_df['Ti0'] = input_df['Ti']\n",
    "input_df['Th0'] = input_df['Ti']\n",
    "input_df['Te0'] = input_df['Ti'] - 2\n",
    "\n",
    "input_X = input_df[['Ph', 'Ta', 'Ti0', 'Te0', 'Th0']]\n",
    "input_y = input_df['Ti']\n",
    "\n",
    "print(f'Input X shape: {input_X.shape}, input y shape: {input_y.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: X shape: (672, 5), y shape: (672,)\n",
      "Test: X shape: (120, 5), y shape: (120,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(input_X, input_y, test_size=5 / 33, shuffle=False)\n",
    "\n",
    "print(f'Train: X shape: {X_train.shape}, y shape: {y_train.shape}')\n",
    "print(f'Test: X shape: {X_test.shape}, y shape: {y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the model training parameters for each model instance that we want to spin up in the pipeline.  \n",
    "\n",
    "The `Ti0` param is the initial condition for the internal temperature at t=0 - this is set to the first record of `X_train['Ti0']` as described above and is fixed, hence `vary: False`.  \n",
    "\n",
    "The `Te0` param is the initial condition for the building envelope temperature at t=0 - - this is set to the first record of `X_train['Te0']` as described above and is NOT fixed, hence `vary: True`.  \n",
    "\n",
    "The `Th0` param is the initial condition for the heating system temperature at t=0 - - this is set to the first record of `X_train['Th0']` as described above and is NOT fixed, hence `vary: True`.  \n",
    "\n",
    "`Ci`, `Ce`, `Rie` and `Ria` params are the initial conditions for these thermal parameters. As these will be fit by the model training their default is `vary: True`. The values for these params' initial conditions are set arbitrarily to `1` it is assumed that no estimates have been calculated for them (e.g. based on building physical properties).\n",
    "\n",
    "Note that we've added the `Ch` and `Rih` params with set initial conditions not allowed to vary. These are the RC params for the heating system and their values have been precalculated from the heating system's time constant to make this example simpler.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_params_Ti = {\n",
    "    'Ti0': {'value': X_train.iloc[0]['Ti0'], 'vary': False},\n",
    "    'Ci': {'value': 1},\n",
    "    'Ria': {'value': 1},\n",
    "}\n",
    "\n",
    "train_params_TiTe = {\n",
    "    'Ti0': {'value': X_train.iloc[0]['Ti0'], 'vary': False},\n",
    "    'Te0': {'value': X_train.iloc[0]['Ti0'], 'vary': True},\n",
    "    'Ci': {'value': 1},\n",
    "    'Ce': {'value': 1},\n",
    "    'Rie': {'value': 1},\n",
    "    'Rea': {'value': 1},\n",
    "}\n",
    "\n",
    "train_params_TiTh = {\n",
    "    'Ti0': {'value': X_train.iloc[0]['Ti0'], 'vary': False},\n",
    "    'Th0': {'value': X_train.iloc[0]['Th0'], 'vary': False},    \n",
    "    'Ci': {'value': 1},\n",
    "    'Ch': {'value': 2.55, 'vary': False},\n",
    "    'Ria': {'value': 1},\n",
    "    'Rih': {'value': 0.65, 'vary': False}\n",
    "}\n",
    "\n",
    "train_params_TiTeTh = {\n",
    "    'Ti0': {'value': X_train.iloc[0]['Ti0'], 'vary': False},\n",
    "    'Te0': {'value': X_train.iloc[0]['Te0'], 'vary': True, 'min': 10, 'max': 25},\n",
    "    'Th0': {'value': X_train.iloc[0]['Th0'], 'vary': False},    \n",
    "    'Ci': {'value': 1},\n",
    "    'Ch': {'value': 2.55, 'vary': False},\n",
    "    'Ce': {'value': 1},\n",
    "    'Rie': {'value': 1},\n",
    "    'Rea': {'value': 1},\n",
    "    'Rih': {'value': 0.65, 'vary': False}\n",
    "}\n",
    "\n",
    "train_params_TiTeThRia = {\n",
    "    'Ti0': {'value': X_train.iloc[0]['Ti0'], 'vary': False},\n",
    "    'Te0': {'value': X_train.iloc[0]['Te0'], 'vary': True, 'min': 10, 'max': 25},\n",
    "    'Th0': {'value': X_train.iloc[0]['Th0'], 'vary': False},    \n",
    "    'Ci': {'value': 1},\n",
    "    'Ch': {'value': 2.55, 'vary': False},\n",
    "    'Ce': {'value': 1},\n",
    "    'Rie': {'value': 1},\n",
    "    'Rea': {'value': 1},\n",
    "    'Ria': {'value': 1},\n",
    "    'Rih': {'value': 0.65, 'vary': False}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up our initial conditions parameters map for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ic_params_map = {\n",
    "    'Ti0': lambda X_test, y_test, train_result: y_test.iloc[0],\n",
    "    'Th0': lambda X_test, y_test, train_result: y_test.iloc[0],\n",
    "    'Te0': lambda X_test, y_test, train_result: train_result.var['Te'][-1],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can instantiate a range of models to be evaluated - in this case we are setting up 5 different models starting from the simplest `Ti` to `TiTeThRia` with medium complexity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [Ti(train_params_Ti, rec_duration),\n",
    "          TiTe(train_params_TiTe, rec_duration),\n",
    "          TiTh(train_params_TiTh, rec_duration),\n",
    "          TiTeTh(train_params_TiTeTh, rec_duration),\n",
    "          TiTeThRia(train_params_TiTeThRia, rec_duration)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split our training data set into managable chunks for the prefit stage. `Sklearn` is quite helpful here again, in case we wanted to define the prefit sets to be 1 day (24 hours) long:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefit_splits = KFold(n_splits=int(len(X_train) / 24), shuffle=False).split(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the performance of our models we will need to define an error metric. Again, there is a wide range of options available from `sklearn`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_metric = rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define a prefit \"survival threshold\" to trim the number of model fits during the training process. Any models achieving a higher error during the prefit stage will not make it into the training stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefit_filter = lambda error: abs(error) < 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Under the hood, `DarkGreyBox` uses `lmfit.minimize` to fit the thermal model parameters to the training data by passing it a custom objective function defined by the model class. The fit method used by `lmfit.minimize` can be customised as well, and while the standard Nelder-Mead and Levenberg-Marquardt methods work well in general, it is worth experimenting with others (see here for a list of supported methods: https://lmfit.github.io/lmfit-py/fitting.html#fit-methods-table)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "method = 'nelder'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up is ready! We only need to call the `darkgreyfit` convenience pipeline function that delegates the work to the prefit, train and test low-level procedures running on multiple processes and reducing / cleaning the interim results. The returned `pandas.DataFrame` contains the fit models and their train/test performance evaluation.   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:26:47,786 darkgreybox  INFO     Training models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:26:48,429 darkgreybox  INFO     Logging enabled...\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    2.2s\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    2.9s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:27:08,711 darkgreybox  INFO     Training models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed:   20.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed:   20.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-28 15:28:38,253 darkgreybox  INFO     Generating predictions for 18 models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 114 out of 114 | elapsed:  1.5min finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0368s.) Setting batch_size=2.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  18 | elapsed:    0.1s remaining:    0.3s\n",
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      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "df = darkgreyfit(models, X_train, y_train, X_test, y_test, ic_params_map, error_metric,\n",
    "                 prefit_splits=prefit_splits, prefit_filter=prefit_filter, reduce_train_results=True, \n",
    "                 method=method, n_jobs=-1, verbose=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this specific example:\n",
    "* *Initial Population:* 5 model structures were prefit to 28x 24-hour data chunks resulting in 140 pipelines\n",
    "* *Fitness Function:* The RMSE of a prefit had to be < 2˚C to survive\n",
    "* *Selection:* 110 pipelines survived the prefit selection and made it into training on the entire training set \n",
    "* (No *Crossover* or *Mutation*)\n",
    "* *Termination:* Out of the 110 pipelines trained, 11 distinct model results were found (i.e. many pipelines were arriving at the same parameter set results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>2019-12-23 00:00:00+00:00</td>\n",
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       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129c...</td>\n",
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       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129c...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>16.902711</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.371886</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129c...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.001259</td>\n",
       "      <td>0.340657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129c...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>17.496720</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.442067</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x12a7...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.9...</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.450844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129db8790&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>4.506215</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.449866</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129cc8d30&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.484442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeTh object at 0x129cd0d00&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>4.603980</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.450455</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeTh object at 0x129ec2e50&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.9...</td>\n",
       "      <td>0.000792</td>\n",
       "      <td>0.461968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeTh object at 0x129d44880&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>11.295815</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.719033</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeTh object at 0x12a7026d0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>0.573920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129b...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>14.698442</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.788181</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129d...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>0.807009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129cc8970&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 19.0...</td>\n",
       "      <td>6.201254</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.834821</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129bb1b80&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.981928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.Ti object at 0x129bbcd30&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>0.229748</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.864778</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.Ti object at 0x12a0107c0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.973313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129c...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.1...</td>\n",
       "      <td>14.468851</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.925101</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x12a0...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000973</td>\n",
       "      <td>1.088848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129d...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.1...</td>\n",
       "      <td>12.982735</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.925717</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTeThRia object at 0x129d...</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>1.085810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTh object at 0x129bb1940&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.0...</td>\n",
       "      <td>0.450825</td>\n",
       "      <td>nelder</td>\n",
       "      <td>0.925845</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTh object at 0x129e55b20&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.8...</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>1.088328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129dedf70&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.1...</td>\n",
       "      <td>5.389837</td>\n",
       "      <td>nelder</td>\n",
       "      <td>1.396299</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129e23130&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.9...</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>1.712312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129bb1220&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.1...</td>\n",
       "      <td>24.303944</td>\n",
       "      <td>nelder</td>\n",
       "      <td>1.531778</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x12a010ac0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.9...</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>2.057167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129be9400&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.1...</td>\n",
       "      <td>7.379673</td>\n",
       "      <td>nelder</td>\n",
       "      <td>1.563267</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTe object at 0x129e59190&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.9...</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>2.073670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-12-23 00:00:00+00:00</td>\n",
       "      <td>2020-01-19 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTh object at 0x129e3b490&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([18.1375    , 18.1...</td>\n",
       "      <td>29.029964</td>\n",
       "      <td>nelder</td>\n",
       "      <td>1.565403</td>\n",
       "      <td>2020-01-20 00:00:00+00:00</td>\n",
       "      <td>2020-01-24 23:00:00+00:00</td>\n",
       "      <td>&lt;darkgreybox.models.TiTh object at 0x129bc7ee0&gt;</td>\n",
       "      <td>DarkGreyModelResult(Z=array([17.9125    , 17.9...</td>\n",
       "      <td>0.000557</td>\n",
       "      <td>2.118035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       train                            \\\n",
       "                  start_date                  end_date   \n",
       "0  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "1  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "2  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "3  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "4  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "5  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "6  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "7  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "8  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "9  2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "10 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "11 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "12 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "13 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "14 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "15 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "16 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "17 2019-12-23 00:00:00+00:00 2020-01-19 23:00:00+00:00   \n",
       "\n",
       "                                                       \\\n",
       "                                                model   \n",
       "0   <darkgreybox.models.TiTeThRia object at 0x129c...   \n",
       "1   <darkgreybox.models.TiTeThRia object at 0x129b...   \n",
       "2   <darkgreybox.models.TiTeThRia object at 0x129e...   \n",
       "3   <darkgreybox.models.TiTeThRia object at 0x129c...   \n",
       "4   <darkgreybox.models.TiTeThRia object at 0x129c...   \n",
       "5     <darkgreybox.models.TiTe object at 0x129db8790>   \n",
       "6   <darkgreybox.models.TiTeTh object at 0x129cd0d00>   \n",
       "7   <darkgreybox.models.TiTeTh object at 0x129d44880>   \n",
       "8   <darkgreybox.models.TiTeThRia object at 0x129b...   \n",
       "9     <darkgreybox.models.TiTe object at 0x129cc8970>   \n",
       "10      <darkgreybox.models.Ti object at 0x129bbcd30>   \n",
       "11  <darkgreybox.models.TiTeThRia object at 0x129c...   \n",
       "12  <darkgreybox.models.TiTeThRia object at 0x129d...   \n",
       "13    <darkgreybox.models.TiTh object at 0x129bb1940>   \n",
       "14    <darkgreybox.models.TiTe object at 0x129dedf70>   \n",
       "15    <darkgreybox.models.TiTe object at 0x129bb1220>   \n",
       "16    <darkgreybox.models.TiTe object at 0x129be9400>   \n",
       "17    <darkgreybox.models.TiTh object at 0x129e3b490>   \n",
       "\n",
       "                                                                          \\\n",
       "                                         model_result       time  method   \n",
       "0   DarkGreyModelResult(Z=array([18.1375    , 18.0...  45.769822  nelder   \n",
       "1   DarkGreyModelResult(Z=array([18.1375    , 18.0...  26.679206  nelder   \n",
       "2   DarkGreyModelResult(Z=array([18.1375    , 18.0...  16.466620  nelder   \n",
       "3   DarkGreyModelResult(Z=array([18.1375    , 18.0...  16.902711  nelder   \n",
       "4   DarkGreyModelResult(Z=array([18.1375    , 18.0...  17.496720  nelder   \n",
       "5   DarkGreyModelResult(Z=array([18.1375    , 18.0...   4.506215  nelder   \n",
       "6   DarkGreyModelResult(Z=array([18.1375    , 18.0...   4.603980  nelder   \n",
       "7   DarkGreyModelResult(Z=array([18.1375    , 18.0...  11.295815  nelder   \n",
       "8   DarkGreyModelResult(Z=array([18.1375    , 18.0...  14.698442  nelder   \n",
       "9   DarkGreyModelResult(Z=array([18.1375    , 19.0...   6.201254  nelder   \n",
       "10  DarkGreyModelResult(Z=array([18.1375    , 18.0...   0.229748  nelder   \n",
       "11  DarkGreyModelResult(Z=array([18.1375    , 18.1...  14.468851  nelder   \n",
       "12  DarkGreyModelResult(Z=array([18.1375    , 18.1...  12.982735  nelder   \n",
       "13  DarkGreyModelResult(Z=array([18.1375    , 18.0...   0.450825  nelder   \n",
       "14  DarkGreyModelResult(Z=array([18.1375    , 18.1...   5.389837  nelder   \n",
       "15  DarkGreyModelResult(Z=array([18.1375    , 18.1...  24.303944  nelder   \n",
       "16  DarkGreyModelResult(Z=array([18.1375    , 18.1...   7.379673  nelder   \n",
       "17  DarkGreyModelResult(Z=array([18.1375    , 18.1...  29.029964  nelder   \n",
       "\n",
       "                                  test                            \\\n",
       "       error                start_date                  end_date   \n",
       "0   0.371866 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "1   0.371872 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "2   0.371873 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "3   0.371886 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "4   0.442067 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "5   0.449866 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "6   0.450455 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "7   0.719033 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "8   0.788181 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "9   0.834821 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "10  0.864778 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "11  0.925101 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "12  0.925717 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "13  0.925845 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "14  1.396299 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "15  1.531778 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "16  1.563267 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "17  1.565403 2020-01-20 00:00:00+00:00 2020-01-24 23:00:00+00:00   \n",
       "\n",
       "                                                       \\\n",
       "                                                model   \n",
       "0   <darkgreybox.models.TiTeThRia object at 0x129d...   \n",
       "1   <darkgreybox.models.TiTeThRia object at 0x129e...   \n",
       "2   <darkgreybox.models.TiTeThRia object at 0x129b...   \n",
       "3   <darkgreybox.models.TiTeThRia object at 0x129c...   \n",
       "4   <darkgreybox.models.TiTeThRia object at 0x12a7...   \n",
       "5     <darkgreybox.models.TiTe object at 0x129cc8d30>   \n",
       "6   <darkgreybox.models.TiTeTh object at 0x129ec2e50>   \n",
       "7   <darkgreybox.models.TiTeTh object at 0x12a7026d0>   \n",
       "8   <darkgreybox.models.TiTeThRia object at 0x129d...   \n",
       "9     <darkgreybox.models.TiTe object at 0x129bb1b80>   \n",
       "10      <darkgreybox.models.Ti object at 0x12a0107c0>   \n",
       "11  <darkgreybox.models.TiTeThRia object at 0x12a0...   \n",
       "12  <darkgreybox.models.TiTeThRia object at 0x129d...   \n",
       "13    <darkgreybox.models.TiTh object at 0x129e55b20>   \n",
       "14    <darkgreybox.models.TiTe object at 0x129e23130>   \n",
       "15    <darkgreybox.models.TiTe object at 0x12a010ac0>   \n",
       "16    <darkgreybox.models.TiTe object at 0x129e59190>   \n",
       "17    <darkgreybox.models.TiTh object at 0x129bc7ee0>   \n",
       "\n",
       "                                                                           \n",
       "                                         model_result      time     error  \n",
       "0   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000704  0.340593  \n",
       "1   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000758  0.338288  \n",
       "2   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000744  0.340617  \n",
       "3   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.001259  0.340657  \n",
       "4   DarkGreyModelResult(Z=array([17.9125    , 17.9...  0.000800  0.450844  \n",
       "5   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000696  0.484442  \n",
       "6   DarkGreyModelResult(Z=array([17.9125    , 17.9...  0.000792  0.461968  \n",
       "7   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000695  0.573920  \n",
       "8   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000685  0.807009  \n",
       "9   DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000542  0.981928  \n",
       "10  DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000456  0.973313  \n",
       "11  DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000973  1.088848  \n",
       "12  DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000695  1.085810  \n",
       "13  DarkGreyModelResult(Z=array([17.9125    , 17.8...  0.000558  1.088328  \n",
       "14  DarkGreyModelResult(Z=array([17.9125    , 17.9...  0.000519  1.712312  \n",
       "15  DarkGreyModelResult(Z=array([17.9125    , 17.9...  0.000699  2.057167  \n",
       "16  DarkGreyModelResult(Z=array([17.9125    , 17.9...  0.000622  2.073670  \n",
       "17  DarkGreyModelResult(Z=array([17.9125    , 17.9...  0.000557  2.118035  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select the model with the lowest error and display its params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th> name </th><th> value </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> Ti0 </td><td>  18.1375000 </td><td> 18.1375 </td><td>        -inf </td><td>         inf </td><td> False </td></tr><tr><td> Te0 </td><td>  18.8064057 </td><td> 16.1375 </td><td>  10.0000000 </td><td>  25.0000000 </td><td> True </td></tr><tr><td> Th0 </td><td>  18.1375000 </td><td> 18.1375 </td><td>        -inf </td><td>         inf </td><td> False </td></tr><tr><td> Ci </td><td>  156.988750 </td><td> 140.31495334559543 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Ch </td><td>  2.55000000 </td><td> 2.55 </td><td>        -inf </td><td>         inf </td><td> False </td></tr><tr><td> Ce </td><td>  389.155679 </td><td> 30.9844968286637 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Rie </td><td>  0.11065592 </td><td> 0.21214648000429043 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Rea </td><td>  6.8361e+11 </td><td> 0.2886086408479051 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Ria </td><td>  0.63738670 </td><td> 11.325200607900307 </td><td>        -inf </td><td>         inf </td><td> True </td></tr><tr><td> Rih </td><td>  0.65000000 </td><td> 0.65 </td><td>        -inf </td><td>         inf </td><td> False </td></tr></table>"
      ],
      "text/plain": [
       "Parameters([('Ti0', <Parameter 'Ti0', value=18.1375 (fixed), bounds=[-inf:inf]>), ('Te0', <Parameter 'Te0', value=18.806405697482944, bounds=[10:25]>), ('Th0', <Parameter 'Th0', value=18.1375 (fixed), bounds=[-inf:inf]>), ('Ci', <Parameter 'Ci', value=156.98875036219442, bounds=[-inf:inf]>), ('Ch', <Parameter 'Ch', value=2.55 (fixed), bounds=[-inf:inf]>), ('Ce', <Parameter 'Ce', value=389.1556789569189, bounds=[-inf:inf]>), ('Rie', <Parameter 'Rie', value=0.11065592383636488, bounds=[-inf:inf]>), ('Rea', <Parameter 'Rea', value=683609400583.3799, bounds=[-inf:inf]>), ('Ria', <Parameter 'Ria', value=0.6373867020486088, bounds=[-inf:inf]>), ('Rih', <Parameter 'Rih', value=0.65 (fixed), bounds=[-inf:inf]>)])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_idx = df[('train', 'error')].argmin()\n",
    "\n",
    "model = df.loc[select_idx, ('train', 'model')]\n",
    "train_results = df.loc[select_idx, ('train', 'model_result')]\n",
    "test_results = df.loc[select_idx, ('test', 'model_result')]\n",
    "\n",
    "model.result.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that the `TiTeThRia` model has a good fit on the training set (R2=0.9612) and the errors are low for both the training and test sets (RMSE 0.3719 and 0.3383 respectively) indicating that the model generalises well for data it hasn't seen before (no overfitting problem)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(y_train, train_results, 'TiTeThRia (Train)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(y_test, test_results, 'TiTeThRia (Test)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In contrast, the `Ti` model has a poor fit on the training set (R2=0.79) which is clear from the temperature plots as well. Due to low model complexity there is a definite underfitting problem going in here, which of course also results in poor test set performance. This is hardly surprising, as the `Ti` model only keeps internal states of the internal temperatures (`Ti`) and it is unaware of the internal states of the heating system / building envelope temperatures  (`Th` and `Te` respectively) - both of which are important for describing the system components' thermal inertia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(y_train, df.loc[10, ('train', 'model_result')], 'Ti (Train)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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BBx4gOTmZ2bNnd4m3n/70pzz22GMA7Nu3j5SUFFJSUnjyySe7xn/uuef49re/3fX1mjVr2Lx5MwDf/OY3SUtLY8aMGTzyyCN2+75taIGlcR3KTkBzDUQuUpGr+Mvgit+DxQs+fBAK9hu9Qo3ZOfIqCDeYeztYPNW22TfBlNkqilU9xm6CDv5X/V4tvgeu/Qdc8E1oqoLSY0avzKkIIZ4VQpQKIY72sf9+IcRB6+uoEKJDCBFs3XdaCHHEum/keSUalycrKwuAyy67jMcff5zOzk7+8Ic/IHVKu0ZzDlVVVWRlZbF8+XLKy8t59NFH+fjjj9m/fz9paWn88Y9/pKKigjfffJNjx45x+PBhHnroofPG+cpXvsL/+3//j0OHDg167l/+8pfs3buXw4cP89lnn3H48GF7fms6RVDjQuRuBzd3CJ9/dltgGFzxGHz0E/jsd7Dub+ATZNgSTU1nJ3z+OEReAFGLjV6N86kvheyPYfoq8JtwdrubGyz/Abx5Fxx9A5bea9gSnUrBfjjyGky/XL0AfFbAnmfg1BaYMsvY9TmX54C/AOcn9ANSyt8DvwcQQlwFfE9KWdntkIullOWOXqTGNcjKyiI4OJgJEyYwYcIEvvKVr/D444+zc+dOFi1aZPTyNKOYv/zlL10RVHsRFxd3ThTIHmzdupWUlBSysrK49957mTJlCu+++y7p6eldqYKtra0sWrSIwMBAvL29+drXvsaaNWvOi4pVV1dTXV3N8uXLAbj11lvZsGHDgGt45ZVXePrpp2lvb6eoqIj09HRmz55tt+9RR7A0roGUcGYHhKaCp++5+3yD4aIfQXsT7PmHIcszHVWn4cz2c7cdXw8nP4GD/zNkSYZz5FX1PvP68/f5BsPUOVC4f2wYp9QWwdY/QFAkLLjz7HYPb4hYCKc/P1uTNQaQUm4BKgc8UHEL8KIDl6NxcbKysoiPj+/6+sorryQ8PJynn36aTp1+q9GwbNkyDh06xLFjx3jmmWc4ePAgUkouu+wyDh48yMGDB0lPT+eZZ57BYrGwe/durr/+et59911WrVo16HksFss5v3M2m/VTp07x2GOPsWnTJg4fPsyVV15p90bNOoKlcQ2qTkF9Ccy6off9QZEw60Y49CLEXgJh85y7PjPR2QmbfwM1+apOLf4yFb058B/w9FfiqzJHmRmMFRrKIesjiLsM/Cf1fszUuUqUVufC+Cjnrs9ZSAkZG2DvMyDc4cIfqRTb7kxbBqe3QtEhCJtrzDpNihDCF1gFdH+cK4GPhBAS+LuU8mlDFmcSmpqaePTRR7n11ltJTEw0ejlOp7W1ldOnT7N48dksAYvFwte//nUeeeQRPvzwQ1avXm3gCjWjGXtHmhzNtGnTeOCBB/jtb3/LE088wd133012djZxcXE0NDRQUFDA1KlTaWxs5IorrmDJkiXExJx77xIUFERQUBDbtm1j6dKlvPDCC137oqOjeeqpp+js7KSgoIDdu3cDUFtbi5+fH4GBgZSUlLBhwwYuuugiu35vOoKlcQ3O7FDvkQv7Pmb2jRAYDjuehDb7PolwKc5sU+LKfzJsfwLy96rPBODyX6o0y7Fk6FCSDh88qP7dl0CHs6K8cJTW8jXXwMafqFqzkCS45snehWRYGnj4qDRBTU+uAj7vkR64VEo5F1gN3C2EWN7biUKIO4UQe4UQe8vKypyxVkPYsWMH27dv59e//jVtbW1GL8fpnDp1is7OznMiWKCe2CcnJ/Pss8/S0tJi0Oo0GvPxjW98gy1bttDQ0MBzzz3HLbfcwuzZs1m0aBEnTpygrq6ONWvWMHv2bJYuXcof//jH88b417/+xd13301qauo5tY5Llixh2rRpJCcn853vfIe5c9VDw5SUFObMmUNiYiJf+MIXhuxgOBjEaCq6TEtLk/bwrteYkLfvBg8/uOJ3/R9XfBQ+eEAZGQgB7p6w6NsQc6Fz1mk0UqrPSkq48jElLKpOg+yE+XfAjLXwyS+hNB1ufF6JrdFKa6OKaB57E/xDYOn3Bq4revObqj5r5aPOWaOzqCtR7oANpTD/65Cwun9L+q1/gLzdcNML4G5MooMQYp+UMs2J80UD70op+2ygIoR4E3hVStlrnq0Q4qdAvZTysf7mGs3Xqp/97Gfs2LGDlpYWbrvtNm6//Xajl+RU3n33Xf7whz/wv//9j9DQ0HP2HT58mO9+97vcf//9XHHFFQatUDPaOH78OElJSUYvY0zQ22fd17VKR7A05qe2CKrOQNQgioOnzISLHoSZ18KMdRAwRT2xb6py/DrNwJnPVYpbyk3g6QeX/hT8JsGkREi6Wh0Tt0JFM0ar62J9mTJqePU2Ja6mr4Kr/zI404awuVByDNpH0RPmyhx4/wfQXK2EY+IVA/f7mnah6o81WqN5w0AIEQhcCLzdbZufECLA9m9gJdCrE+FYoKWlhZ07d7Jq1SpWrFjBCy+8wJkzZ4xellPJysrCz8+PKVOmnLdv1qxZTJ48mc8//9yAlWk0Gmeia7A05if7Y/UetXRwx0cvUS+A2BWw/tuw62llhDGakVJFbMaFQbQ1S8k3GNb+Vd1Qu1mfp4Slgdc4OLkJIub3PZ6rUXFSuQCe3gpI9fMyYx1Mmj74MabOhfS3oeSo69fxSanqznb/QxnDrP7d4GvLQlNVvd6pLRCxwKHLNANCiBeBi4CJQoh84BHAA0BK+TfrYeuAj6SUDd1OnQy8KZRgtQD/k1J264Q+tti7dy/Nzc0sW7aM2NhYdu/ezR/+8Af+/Oc/I8ZIE+/MzEzi4+N7/X6FECxZsoT33nuPlpYWvLy8ehlBo9GMBrTA0pibjnZ1kxie1rc5QX8ERcDsm+HgC5B3yegSFDaqc1WdUdFBFelb9v2zYgrO9nuy4W5RKZMZG6ClDrwCnLpcuyKlqjE79iYUHwaLNyRdBcnXqLTAoTJlJrhZVHTPlQVWfRns+AsU7FM9vpZ+b2i/P+4W9buSt0eZpriN7mQHKeUtgzjmOZSde/dtOUCKY1blemzZsoWAgABSUlKwWCzceeed/OEPf+DIkSN2tT82Kx0dHeTk5HDNNdf0eczixYt544032L9/v7Zs12hGMQ69agohIoQQnwoh0oUQx4QQ37Vuv8H6dacQos8ceyHEKiFEhhAiWwjxgCPXqjEp+btVet/0EbguzboBAiNg55PQ1mS/tRmNlCoV7q1vqZvposMQd6lK7xqIuEuhs13ZcbsqubvgrW/Cpp9BbQHMux1ueA4WfH144gqUo96Uma6ZPlmeBVv/CG/cCa99BYqPwMK7lLHJcB5OhC+A1nrViFijGYD29na2b9/O4sWLsVjUs1tb4XhGRoaRS3MaeXl5tLa2nmdw0Z2UlBR8fX3Zvn17n8doNENlNPkpmJWhfsaOfizZDtwnpUwGLkA5LCWjctSvBfq0qRJCuANPopyZkoFbrOdqxhIZ74PfxHObCw8Vdwss+pay6s7ZbLelGUpHO2x97GyN0bq/w80vqCa5gzGuCI5RqYSu6hTXUgef/RYQsOw+uO4ZmHU9ePmPfOypc6EmT0WBXIXODvj0l5C3Szlpzrsd1j6lonnDTc2aOkeZxeTvsetSNaOTgwcPUl9f39XsE2D8+PFMmjRpzAiszMxMgH4FlsViYcGCBWzfvl3fFGvsgre3NxUVFfrnyYFIKamoqMDb23vQ5zg0RVBKWQQUWf9dJ4Q4DoRJKTcCA+VkLwCyrSkYCCFeAq4B0h25Zo2JqC2CwoOQ+sWRpyhNnql6ZWVvUg5qrkxzDWz5vfps5n5ZReiGehMthOp3dOglaKxUtVquRMYH0NEKF/4QgqfZd+ywubD3WZVelzD4hoaGcma7eoBwyU/6b2UwFLz81e9N/m6Yd5t9xtSMWrZs2YK3tzfz5p2bWjt9+vQxI7Cys7Px8vIiIiKi3+MWL17M5s2bycjIGJO9wjT2JTw8nPz8fEZz+wcz4O3tTXh4+KCPd1oNltUCdw6wa5CnhAF53b7OB+x056BxCTI/UEIgfuXIxxJCNSDe9xzUFEBg2MjHNIIzO1Q6YGv92SbCw2XaciWwznyuIh2uQkc7HF8PU1PtL64AgqJU1DR/j2sILCnh2BsQEGp/Q4qI+SoNtb50+GmXmlFPe3s7W7du5YILLjjPuCEhIYHPP/+choYG/Pz8DFqhc8jIyCA2NhZ39/6zCC644ALc3NzYvn27FliaEePh4cG0aQ64FmpGhFMql4UQ/sDrwL1Sylo7jz0mmjeOOTralXtgxELVl8gexF6i3k+6YJNdKeHzJ1QamO8EWPP4yMQVqIje+CjXSxM8vUXV5SWvdcz4Qqj6o8ID0N7qmDnsSdkJVX+VfM3w0wH7wpaaq9MENf3w2WefUV1dzeWXX37evunTlYtnVlaWs5flVNrb28nIyBhUP6KAgABmzZql67A0mlGMwwWWEMIDJa5ekFK+MYRTC4DucfZw67ZzkFI+LaVMk1KmTZo0jEJujTkpOaJS4eJGKCK64xus6kpOfqIEiyuRu0O5KSZfA1f+0X6Rm+jlUHrcdeqNpIRjbynTEke6/EUsUCmIJUccN4e9OPaWslSPu9T+Y48LU5GxvN32H1szKpBS8uqrrxIREcHChecnmSQkJACj3+ji1KlTtLS0kJw8uFLxxYsXc/LkSX72s5+xYcMGqqrGSK9GjWaM4GgXQQE8AxyXUv5xiKfvAeKFENOEEJ7AzcB6e69RY1Ly9oC7h0oDsydxK1StSvFh+47rSDraYe+/lKhI+6oy7bAX05ap99Nb7TemIyk5qhrnOiJa050ps8DdU/0cmpm6YpXiOf1y8Bh88e2gEUKJzaJD0NZs//E1Ls/Ro0fJyMjguuuu67WuOigoiJCQkC4DCIDTp0+PuujN8ePHAQYVwQJYs2YNa9as4ciRI/zud7/jq1/9Ki0to6jBuUYzxnF0BGsJcCtwiRDioPV1hRBinbWZ4yLgPSHEhwBCiKlCiPcBpJTtwLeBD4HjwCtSymMOXq/GDEip3NCmzFa22fYkchF4+CizC1ch432oK4L5XxucQ+BQGDcVJsS6jsDK/FBFa2Ivduw8Fi8l7vN3mzvamf2xEkGOrKELn68s/YsOOW4Ojcvy2muvERAQ0Gt6oI2EhISuCJaUkl/96lc89NBDoypt8Pjx4wQFBTFlypRBHe/r68t9993Hq6++yoMPPkh1dTUnTuiWCBrNaMGhAktKuU1KKaSUs6WUqdbX+1LKN6WU4VJKLynlZCnl5dbjC6WUV3Q7/30p5XQpZayU8peOXKvGRNTkQ32J/Qv2Qd04Ry9VT/1b6u0/vr1pqYdDL0JoiuNS4qYtVzU8dcWOGd+e1Jcoi3l7C+/eCF+gzB2qcx0/13CpKwa/ScqUw1FMnqkeSuTrNEENnDlzho8++oi6ujqKiorYunUra9as6de+OCEhgYKCAhoaGjh06FCXsPrjH/9IZ2ens5buUNLT00lOTh7IHfk8hBBdDYePHj3qiKVpNBoDcIrJhcZOFB6A3f9Qrz3/hMpTRq/IMdgK6sMdILAAEtdAewscedUx49uTI6+qnk9pX3VcSlyUagZK7k7HjG9PmqrAZ7xz5nIFg4fGSvBxsMW+u0XVLuaZPJqncQq//vWv+fWvf826deu47777cHNzY926df2eYzO6yMzM5OWXXyYoKIgf/OAHnDhxgnfffdcZy3Yo9fX15ObmDjo9sCcBAQFERUVx5IgL1HxqNJpBoQWWq9DZAVv/CCfeVWlS6evhk0eho83oldmf/D3K3c7fQaYlE2JViln62ypCYWZOb1ORvAmxjpsjYIqq7zKzkLDRVAU+Qc6Zy2+CipaZ+XNxluAMX6Dmqsxx/Fwa05KVlUVGRgY33ngjN9xwA0IIrrrqKgYymLIJrI0bN7Jz507WrVvH6tWrmTNnDk8//bTLGzzYUvuGK7AAZs2axbFjx3SzWI1mlKAFlquQt1vd4Fz0IHzpNbj0pypd6kS3p3/NtXDgv8p9z1VpqVdGBo6KXtmY82UVEdr/b8fOMxI62qGhFMZHO36uiPnqc29tdPxcw6W1UUUenRXBAhXFKjmmfrfMSFOlsu13NOHW9FTtJjimWb9+PV5eXtx6663cddddvPDCC3z3u98d8LzAwEAmT57Mhg0b8PT05JprrkEIwfe+9z1aWlp49tlnnbB6x5Geno4QYkQ9rWbOnEl9fT1nzpyx48o0Go1RaIHlKmS8r2zGbWlLYXNV2s6hl1UKWXsLbPq5ahy77zlDlzoiCverNCRH1F91x3+S6qOU85mqPzIjjeXqswgIdfxc4QtUlLTwgOPnGi5N1qfczhRYtp/Dwv3Om3OwtLdAa4P6u+BofMbDxOnmjuZpHEpjYyObNm3i4osvxt/ff8jn2+zaV61aRWBgIECXtburp8YdP36cyMjIETVSnjlzJqDrsDSa0YIWWK5AXbG68Y2//FwXubSvQms9HHwRtvxeNRydPAOyNrpufVbebvAKgIkJjp9r1g3gHags0M1IbaF6DxicK9WICEkCTz9zGxkYIbAmTlc/I2a0a3f25xE+H8ozz86rGVN88sknNDU1sWbNmmGdP3PmTNzc3LjhhhvO2R4REUFBQYHLml1IKTl+/Pig+1/1xdSpUwkKCnJ5sanRaBRaYLkCmR+o9+k9bHCDp0H8ZXB8vTIoWHAnXPKQsrHe64IpF1JCwX4VnXNzwo+mp6+yty4+DE3Vjp9vqNSXqHd/JwgsN3cIS4P8veY1MrDd2DsjYmNDCAhPg4J9KsJnJhor1bujTS5s2KJ5+fucM5/GVLzzzjvExMQMW0isXbuWf//734SHh5+zPTIykvb2dkpKSuyxTKdTXFxMTU3NiOqvQLkJzpo1S0ewNJpRghZYZqejXUWkIhb0bsU851b1BHvWDZB8tYr+pNykIl4FLnYj1FQFLbUwafh57EMmNEW9l5iwxVpdkRI+jrTg7k7EfFW/V5458LFGYEQEC1T6ZGs9lB537rwD0WQVWM4SnMEx6rM3c5RzmAghnhVClAoher27FUJcJISo6dbP8eFu+1YJITKEENlCiAect2rnkZmZSWZmJmvWrBmyDbkNDw+P88QV0LUtN9fE7RD6IT09HWDEESxQUb7CwkIqKytHPJZGozEWLbDMTu4OddObcEXv+32D4cbnYd5tZ7clrgH/ySqK5UppF1Wn1XtQlPPmnBAP7p7K4MFs1BWr6JWj7Nl7YuuzZdY6m6YqEG7gNc65806do4Su2YRFVwTLSYJTCPWgp/CAevAzungOWDXAMVu79XP8OYAQwh14ElgNJAO3CCFGfqdtMjZt2oTFYuGyyy6z+9gREREA5Ofn231sZ3D06FG8vb2Jjo4e8Vi6DkujGT1ogWV2jr8D/iEwdW7fx/S8AXf3gNRboOoMVGQ7dn32pNrqnhQU6bw53S2q/qj4sPPmHCx1Rc6pv7LhFaBq+MzqFGezaHeW4LTh6aua7ZqtDqupUglO70DnzRm+ANqazPn7MgKklFuA4YQNFgDZUsocKWUr8BJwjV0XZwL27dvHrFmzhmVuMRCBgYH4+/uTl5dn97GdwYEDB0hJScHd3X3ggwdg+vTpeHp6aoGl0YwCtMAyM2WZUJqu6oSGWpMUmmod44Tdl+Uwqs6om0Vn9TmyMWWWmttMVtxSqgiWMxwEuxM+X/U6MmN/MGc2Ge5J+HyoyVP/J2ahsVJFsJ0pOKemKjOUrI3Om9M8LBJCHBJCbBBCzLBuCwO6K4N867ZRQ3V1NSdPnmTevHkOGV8IQXh4uEtGsCoqKjhz5gxz5syxy3gWi4XExERtdKHRjAK0wDIz6W+Dh49yDxwqfhNVfxxXEljVZ5ybHmhjskrLMFUdVkudihQ4M4IFELVEvZ/e6tx5B0NTlfMMHXrSZfBgoihWU6XzBafFC2JXQO52cxrDOI79QJSUMgX4f8BbQx1ACHGnEGKvEGJvWVmZvdfnMPbvVy0KHCWwQKUJumIE6+DBgwDMndtPhskQSUxMJCcnh44Ok5nqaDSaIaEFllmpL1M3ufErVYrScAhJMl9hfl9ICdW5MN4AgTUpQaVVmqkOyxYpcXYEa1woTIiDU1ucO+9gMEJQ2Bg3FcaFmSt9stEgwZmwSjkqZn/s/LkNQkpZK6Wst/77fcBDCDERKAAiuh0abt3W2xhPSynTpJRpkyZNcvia7cW+ffvw8/Nj+vTpDpsjIiKC0tJSWlpaHDaHI9i/fz8BAQHExsbabczY2FhaW1spKOj1x0ij0bgIWmCZlRPvgOyE5BGk809KgIays8XwZqa+RDVONSKC5e4Bk5Kg2ERpGXVF6t3ZESyAacuh4uTZPlxmoLNTRUyMEligXBaLD6vmvmagqdK5lvU2giJVrV7GBvNa+tsZIcQUYbXPE0IsQF07K4A9QLwQYpoQwhO4GVhv3Erti5SSvXv3MnfuXNwc2DrDZnThaqLiwIEDpKam2vWziYmJASAnJ8duY2o0GuejBZYZaWuCzA8heqkyuBguNrtzV0gTrLZa9BoRwQKYMlPVHrXUGzN/T7oiWAYIrOhl6v2UidIEW2rUu5ECK3q5ityc3mbcGmx0tKk0UqNSJhNWq4cihQeMmd/OCCFeBHYACUKIfCHE14QQ3xBCfMN6yPXAUSHEIeAJ4GapaAe+DXwIHAdekVKaKNd4ZBQWFlJaWurQ9EBwTav2oqIiioqK7FZ/ZSMqKgo3NzctsDQaF8di9AI0vXD0dfWUfCTRK1CpXm7uKk0warF91uYoqgxwEOxO9zqsyIXGrKE7dUVKTFi8nD+3/yQlzk9vUT3VzIBRPbC6MzEeAsMhe9P5Tb+djRFNl7sTtQS8/q6aoIfZr/7EKKSUtwyw/y/AX/rY9z7wviPWZTTOqL+CswLLlYwuHFF/BapfWEREBCdPnrTruBqNxrnoCJbZKDoEh16C2ItVDdVIcPdQIqsswz5rcyTVp5Uxh6efMfNPSlBitMQkaYJ1xcZEr2xMW65Eb7VJniibQWAJoQweStOhtsi4dUC3HlgGCSx3D4i7VPXpc4UUZM2w2LdvHyEhIYSFOdYY0dvbm0mTJrmU0cX+/fsZP348kZH2fygYGxurI1gajYvjUIElhIgQQnwqhEgXQhwTQnzXuj1YCLFRCJFlfe/1rkkI0SGEOGh9jZq89j5pqoYtj6li+gu+ZZ8xJyVCRZb5G4NWGeQgaMPipT4rs9Rh1RsssKKXqnezmF00GhyxsRF7sXo/+Ymx62iyihojP4/pl6sarJxPjVuDxmF0dnayf/9+5s2bh3BCKwBXsmqXUnLgwAHmzJnjkM8mJiaG4uJiGhpMUu+p0WiGjKMjWO3AfVLKZOAC4G5rl/sHgE1Synhgk/Xr3miSUqZaX1c7eK3GIiVs/YOqq7joR8qe3R5MSlT1GpUmfhrW2QE1+calB9qYMluZO7TUGbuOjjZoKHe+g2B3fIPV53FqizmMDGwRLO8gQ5eB30QITVECy8jPpSuCZWBELzAcJk6HbIM/C41DyM7Opq6uzuHpgTYiIyPJy8tDusDPUl5eHhUVFXavv7JhcyU8deqUQ8bXaDSOx6ECS0pZJKXcb/13HaoIOAzV6f7f1sP+Dax15DpcgrzdqmB8/h0QHGO/cV3B6KKuCDrbjTO4sBGaot6NjmLVl6h3/8nGriN6iXISrDFB2k5TlXro4OFt9EogboX6PzKyb5otgmW04IxbofrXmfkBjmZYnDmj6mITEhKcMl94eDh1dXXU1pqo4XsfHDumfvdTUlIcMr7NSVDXYWk0rovTarCEENHAHGAXMFlKaStiKAb6upP0tjZm3CmEWOv4VRrIqS3g6Q/TV9l3XP9JKhphZoHVZXARbegymJQI7p6qDs5IbA6C46Yau47IRer9zHZj1wHWJsMGRmu6E7kYLN7G9oFqtPYEc6B19qCYtlzVLmZvMnYdGrtTXV0NQFBQkFPms1m1u0KaYG5uLhaLxWG1aZMmTcLf31/XYWk0LoxTrs5CCH/gdeBeKeU5j6ekygfoKycgSkqZBnwBeFwIcV43PyHEnVYRtresrMzeS3cO7S2Qt1NFDNwdYOw4yeQNh6ttAiui/+McjbtF2bUbLrCszx6MjmD5BivzDy2wzsXDW/2unvkc2luNWYNZPg+vAIhYCKc+M3+dp2ZIVFdXY7FY8PNzjvGQTWC5glV7bm4u4eHhDusNJoQgJiZGCyyNxoVxuMASQnigxNULUso3rJtLhBCh1v2hQGlv50opC6zvOcBmVASs5zFPSynTpJRpkyZNcsB34ATy9yiRNW25Y8afMlM1HK4xaRPH6lxl6GCEJXlPQlNUPVhDhXFrqCtWkTQz3EBHLlLpX/W9/oo6j6ZKc3weNqZdqPrVFewzZv7GCvCdYMzcPYldAc01xn0WGodQXV1NYGCgUwwuACZPnozFYnEJgZWXl9clCB2FzUnQFWrSNBrN+TjaRVAAzwDHpZR/7LZrPXCb9d+3AW/3cu54IYSX9d8TgSVAuiPXaxintoB3IEye5Zjxw+er9/zdjhl/pNTkq4J5MxCaqt6LDhq3hvpSFb1y0o1Nv9jSBHN3GLsOs0RsbISmgNc4OG1QM+amKuMdFW2EzVN/v4xMmdTYHZvAchbu7u5MmzaNjAxztxVpb2+nsLDQIfbs3YmJiaGxsZGSkhKHzqPRaByDoyNYS4BbgUu62a1fAfwGuEwIkQVcav0aIUSaEOKf1nOTgL1CiEPAp8BvpJSjT2C1NqoIVvQyx9VTBEyBwAjI3+uY8UdKU5V5nsYHx6i0JyPTBJsqzXPzHBim7PPPGCiw2ltUtMhMAsvNXaUJ5u6Etmbnzt3RriJGZvk83C0Qc5F6gGNk5FdjV2pqahg/3rk/YzNmzODEiRN0dnY6dd6hUFRUREdHh8MFls1JUKcJajSuiaNdBLdJKYWUcnY3u/X3pZQVUsoVUsp4KeWlUspK6/F7pZR3WP+9XUo5S0qZYn1/xpFrNYy8XcqWe9oyx84TMV+547U2OnaeodLZqW4WjXZDsyGEsicvPmyc9XSjiQQWQNQiKDmq+rQZgRmaDPdG9DLoaFUPSJxJc7V6N8tDCYCkq0B2wtHXjF6Jxk5UVVU5zeDCxowZM2hqauL06dNOnXco2FIYHS2woqOjgbHpJHjmzBl+9KMf6T5gGpfGYAsqDae3qhulkGTHzhO+QN0AFe537DxDpaVGvZvp5jk0RfWhqi10/txSmi8dzpYmmGdQiqlZBdbkmWpNzm7G3NUDy0QiPGCKqsXK/ODs+jQujbNTBAGSk9V10GaDbkby8lTbCkfXYPn4+DB16lQOHDgwquuw0tPTaWw898Hv+vXr2b17Nzt37jRoVRrNyNECy0haG1VhePRSx9fbhCQpG3ijbpL7wow3z7Z+WEakCbbWq55gZvo8gmNUTZiR9UZgrs8EVEpv9FIVwXJmZNisn8fsm9TP7tHXjV6JZoS0tbXR2Njo9AhWaGgoQUFBphZYubm5BAcHO8Vd8eqrr+bAgQNs3LjR4XMZQUVFBd/+9rf55z//2bVNSsmWLeqh1e7dJrtf0WiGgBZYRlKRBZ0dEDbX8XO5uati9IJ9xqW+9YYt7cwnyMhVnMu4qeA30RhXtK6bZxNFJ4RQDWULDyhDEmdjVkEByvmzs121WXAWjeXq3Q4pgjWNbby6N48nP83mVPkI03HGhaparIwNxqWTauxCTY3KLHC2wBJCkJycbHqB5ejolY0bbriBWbNm8cQTT1BaarCTqwPYtm0bUko2btxIa6tqeZGRkUF5eTn+/v7s2bNnVEfvNKMbLbCMpCxTvU+c7pz5IuareqfyTOfMNxjMePMsBERcoNIpnW1g0JX+ZaLPAyDhCnCzQPp5hp+Ox/aZeDs3XWlQTEpUYvzkJ86bs75EPTAZQZ1eTVMbf918krv/t5/X9+ez53QlD75xmMc/zqSopmn4a5t9s6pLO/bGwMdqTIuzmwx3Jzk5mfz8fGprawc+2ACcYdFuw83NjQcffJCOjg5+97vfjTqxsXXrVry8vKivr2frVpUhsWXLFtzd3bn99tupqqoiOzvb4FVqNMNDCywjKc9U0RKvAOfMFzZPvZspTdCMAguUsUNHm/Nr1myfh5lMLkBFGGMvhuxNSqQ7k/oSJWLc3J0772AQAuIvh8KDqp+bM6grHpGNf0NLO79+/zjbT5ZzcWIIf7oplSe/MJdr54RzILeaB984wv7cquGtLTAMYi6EU1t142EXxkiBNWPGDACOHz/u9LkHoqamhtraWocbXHQnNDSUu+++m3379vHBBx84bV5HU1dXx4EDB7j22muZMmUKGzZs6EoPnDNnDhdffDEAe/Y42URIo7ETWmAZSXkGTIx33nxeATB5hrn6YTVVqQbDHj5Gr+RcJs9UNWtntjt33iYTGhjYSF6rohMZTr7I15eA/xTnzjkUElaDu4fzont1xcpUYhi0tHfw+w8zyK9q4gcrE/ja0mmEBvrg52XhxvkR/OmmVEIDffj9BxlsOFI0vCfm8++AtU8p+3YXQQjxrBCiVAhxtI/9XxRCHBZCHBFCbBdCpHTbd9q6/aAQwqS9MIaGkQIrMTERIYQp0wRtBhfOFFgAV155JZMnTx5VYmPHjh10dnayfPlyVq9ezb59+9ixYwcFBQUsX76c4OBg4uLidB2WxmXRAssoGipU6tPEBOfOGzYPKk+djZQYTVO1eSzau+PmDpELlYGBM5/EN1aCu6f5BCfA+CiYOgdOvKOie85iBILCKfgEQewlKk3QGbVHQxScDS3tZJfWseNkBX/4KJOM4jruvjiOlIig844N9vPkkauSmR89nn/vOM2zn5+mo3OIIstnvDl/fvvnOWBVP/tPARdKKWcBvwCe7rH/YmsbkjQHrc+pGCmwvL29iY2NNaXAcpZFe0+EEMTGxo6qnlhbt25l0qRJJCQksGrVKoQQ/P73v0cIwdKlSwFYsGABR48ePc9lUKNxBbTAMopya7d6Z9Vf2ZhqNdQoMIldu9ksybsTuRhaG1RPLGfRVK0+D0e7Sg6XGWvVGk995pz52lvUz4j/ZOfMN1ySr1GiM2ODY+dpqVc/kwGD+zx25lTwzRf289BbR/nzpkyOFtRwx7JpLIrt2yDD28Od7102natSprIxvZjffnCCxtbRne4npdwC9Okvb+3LaHsqtRMId8rCDKK6uho3Nzf8/f0Nmd+sDYfz8vLw8PBg8mTn/z2KiYkhLy+PtjYnPtyyIy0tLV19rZqbm9mzZw9Lly5FCEFISAjz5s2jurqaWbNmdTW4XrBgAR0dHezfb5L7FY1mCGiBZRTlmSDclAW2M5kQq8wCzNIPq7navAJraqpKX8x1okNcU5X56q+6M3WuimQdeV01iXY09SXqfZCCwjCCIlV0+MS7jo3u2T6PASJYUkre2J/P4x9nEj3Bl/svT+C3183mn7elsSJp4M9SCMEXF0Zx1/JYjhXW8JO3jlFa52TDF/PyNaC7kpbAR0KIfUKIOw1ak12pqakhMDAQYdCDnuTkZBobG03XcDg3N5fw8HDc3Jx/6xQTE0NnZydnzpxx+twjpbi4mDvuuIMbb7yRt956i127dtHS0sKyZcu6jrnyyisBztk2Y8YMfH19dZqgxiXRAssoyjIheBpYPJ07rxAQmqqK8s3gSGTmCJbFS9005+5w3mfVVGnO+isbQiinuJo8OPO54+ert1oTB4Q6fq6RMmOtMgDJ2ey4OeqK1Xs/KZNNrR08sSmbV/bmsSx+Eg9dmcy8qGCiJvjh6zm0uqiLE0N4cHUS1Y2tPPTmUbJL60ayepdHCHExSmD9qNvmpVLKucBq4G4hxPI+zr1TCLFXCLG3rKzMCasdPtXV1V1RBCOwNRxOT083bA294UyL9p7ExKiHsWZPE5RSUlpa2lW/eebMGe655x6qq6uJj4/nz3/+M48++ijjxo1j9uzZXectW7aM++67jzVr1nRts1gszJ07l927d486B0XN6EcLLCOQUvXAcnb9lY2wuepGsNLgP9Qd7dBSZ16BBRC5SInAshPOma+x0tyfB0DUEgiMgEMvOl542gSF2VMEQT24GB8NR151XHSvvv/PI6ukjh+9fphdpyq4ZUEk37ooFk/LyP7MzwwL5OdrZ+Lt4c7P3klnx8mKEY3nqgghZgP/BK6RUnZ9CFLKAut7KfAmsKC386WUT0sp06SUaZMmTXLGkodNVVUVgYHGtUUICwsjICCAjIwMw9bQk/b2doqKigwTWGFhYVgsFk6dOmXI/IPl5Zdf5qabbuKqq67i/vvv55577qGzs5M///nP/OlPf+Khhx4iMDCQlStX4u5+1hnW3d2dNWvW4O3tfc54aWlplJSUUFRU5OxvRaMZEVpgGUFNHrQ1Ob/+ysbUOerdiEa63WmuVu9mFhTh85XhRe4Ox8/V3gJtjeZOEQRwc4OUm5QtuaNdFuuKlEOfmX9GbAgBKbdAbSGc3uKYOepKwNMPvM6tjZFS8t7hIh5Zf4xOKfnp1TO4JjXMbileYUE+PLpuJjET/fjzpkzeOlAwpp4oCyEigTeAW6WUmd22+wkhAmz/BlYCvToRuhI1NTWGGFzYEEKQkJDAiRNOerA1CIqKiujo6HC6wYUNi8VCVFSUqSNYeXl5PPvss6SmpnLxxRdTWVnJ1KlT+ctf/kJMTAxCCFasWMGrr77Kt771rUGNmZqaCsDBgwcdt3CTk5uby7333suBAweMXopmCGiBZQS2Rr+TDBJYvsHqSbvRRhc2xzUz3zx7+cOU2XDGCWmCZu0J1hvRy1UPt0P/c+znUl+q6o3MavrRk6jFEBQFh152TBSrvvi86JWUkv/uyuU/O0+TFjWe3143m+mT7d9bb5y3B/93ZTJLYify0p5c/r4lh/YOc5kQDBchxIvADiBBCJEvhPiaEOIbQohvWA95GJgAPNXDjn0ysE0IcQjYDbwnpXT5ZkXV1dWGCiyAhIQETp06RUtLi6HrsFFVpf4+T5jQt0GMo4mJiTGtwJJS8vvf/x4vLy9+8pOfcN999/HMM8/wt7/9jdDQc1O8hRCDfvgTGRlJUFDQmBVYLS0t/OxnP+PQoUM8+OCDHD7sRNMtzYjQAssIyjKVjXGgMakGgEoTLE2HVgPtT7sERZBxaxgMUYtVJKXqtGPn6fo8TB7BAhXFmn0TVJ2BvF2Om6euyPwGF90RAlIcWKPWw7K+vaOTv352kvcOF3L5jCl877Lp+Hk5rv+Up8WNb18Sx7Vzw9mcUcpvNpygocX1HQallLdIKUOllB5SynAp5TNSyr9JKf9m3X+HlHK81Yq9y45dSpkjpUyxvmZIKX9p7Hcyctrb26mvrzeFwOro6ODkyZOGrsNGfX09AAEB9n94MVhiYmIoLy+nrs58tZBvv/02R44c4e677yY42H7XMCEEqampHDx4cExFzW089dRT5OTk8KMf/YiQkBAeeOAB09UmanpHCywjKM+ACfHGPpUPmweyE4qPGLcGV4nYRCxU7452E2y0NRkOcuw89iLmIvAPgaNvOGZ8Kc3fZLg3HFWjZvs8ugmsNw4UsCWzjBvmRXD74minuL4JIbgxLYJvXRTHieI6Hn77GKW12mFwtFBTUwMY0wOrOwkJqkbZLHVYNlFjlHU9mNfoorq6mqeffpq0tDQuv/xyu48/Z84cysrKKCwstPvYZmbz5s2sX7+em266iVWrVvGnP/2J4OBgfvjDH5pSZGvORQssZyOlql0JnmbsOkKSVUNbI+uwbDVYZmw03B3fYAhJglwH1xvZBKfZa7BsuLlD0tUqElqWOfDxQ6WlTtUq2jGCJaVkc0Ypz+84zd7TlY7p7+SoGrXGSujs6EoRlFLyWWYZKRFBXDcv3OmW2sunT+LHVyRR09TKQ28dJbNEX/BHA0Y2Ge7OpEmTGD9+vGnqsGw3tEZHsMB8AiszM5Ompia+9KUvOeTv0Fisw2poaOCxxx4jMTGRO+64A1Dpqd///vdpaGgwze+Fpm+0wHI2zTWqT45/iLHrcPeAyclQesy4NTRVgYev863qh0PkIqg8ddbVzhE0Vaqoppdx7l1DJn6lSndNf9v+Yw+y59NgqW5s5XcfZvC3z07y4bFiHvsog689t5cnP82mua3DLnN0Eb0cxoXZt0atzuqiZY1gZZXWU1HfwpLYifYZfxgkTx3HL9bOxNfTnV+8m872k+WGrUVjH8wisGxGF2aJYNlSBP38/Axbw4QJEwgICDCdwCovV7/3jmrAHBERwfjx48eUwPr4449paGjg3nvvxWI5m/YdHx8PQHZ2tlFL0wwShwosIUSEEOJTIUS6EOKYEOK71u3BQoiNQogs63uvOWJCiNusx2QJIW5z5FqdRqPV3dfPBDa9ITNUDU2LQU+ezdwDqydRi9W7I90EGytVNM+AJpbDxtNXiazTW6Dezr19uno+jeyiXdfcxvtHirj/tcMcLajh9sXR/Ov2BTy8ZgarZ01hW1Y5j7xt50a6tihW1Rn7pZb2EJzbs8vxcHdjfrSxEc/QQB9+sXYmsZP8eWJT1phzGBxt2ASWkTbtNhITE8nNzaWx0cBaYSt1dXX4+fkZ0mTYhhCCadOmmc6q3SawJk50zMOesVaHJaVk/fr1xMfHd6XK2ggICGDKlClkZWUZtDrNYHH0X4p24D4pZTJwAaoJYzLwALBJShkPbLJ+fQ5CiGDgEWAhqq/II30JMZeiwXoT6mvcU+cuJqtmjpQZ9ITQlQRWwBQYH6XcBB2FK30e3Um+RkVpTrxj33GHGcFqbG3neFEtHx0r5vGPM/nmf/fz/I7ThAZ68+trZ7FqZiieFjeSp47jy4ui+dHqRMrrW/jxG0c5WlBjv/VPu1A1SLZXLVZXT7AQOjslO3MqmRMRhI+ne//nOYEAbw/+78oklsYph8G/fTZ6HAbHGmapwQJVhyWlNMXNZH19vaH1VzZiYmI4deqUqYRGWVkZQUFB50Ra7M2cOXMoLy8fE3VYGRkZ5OTkcNVVV/W6Pz4+3hS/E5r+cajAklIWSSn3W/9dBxwHwoBrgH9bD/s3sLaX0y8HNkopK6WUVcBGYJUj1+sUbALLzwQCa2ICCDcoMShNsKnKdQwdQJkXlKafrZWyN01VrlN/1R3/EIheChkf2NeVsq4YvAJUlGwQFNU08dfNJ/n683v52TvHePbzUxwrrOXSpBB+d10KP79mJuHjzx8rNSKIR9fNJNDHg1+9f5wPjhbb5+bFzV05LVbmQN7ukY9XXwy+E8Ddg/SiWqqbWllkYHpgTzzc3bj74jiumxtOQ0s7bq5ira85h+rqaoQQjBs3zuilkJiYCJjD6KKurs7Q+isbMTExNDY2UlJSYvRSuigrK8PRzbNtdVhjoRfUO++8g7e3NytWrOh1f1xcHAUFBTQ1NTl5ZZqh4LRYtxAiGpgD7AImSyltbbmLUb1EehIG5HX7Ot+6zbVpKFM3XmaIVHh4w4RYJRqMwNUiNlFL1Hv2JseM31TpGhbtvTHjWtUk+fh6+41Zf64leV/UNLXx5KfZfP/lQ2w/Wc5lSZP50apEnvzCXJ6+dR63L5lG5IT+RVpooA+Prp3JnMggntt+in9stVMEJuYiZUphjyhW3VkHwZ05FXhZ3JkTGTTiJdoTIQQ3pEXw/cum4+amBZYrUlVVRWBgoNNNU3ojKCiIkJAQUxT019fXm0ZggbmMLsrLyx2WHmgjPDyc4ODgUV+H1djYyKZNm1ixYgW+vr1ft+Li4pBSmupnQHM+ThFYQgh/4HXgXillbfd9Uj0qHvadhxDiTiHEXiHE3rIyO9eAOIKGCpUeaIKLF6DcBMszlfGGM2lvUQ5xriSwxkfBlFlw4l3osLP7XGenarzsSp9HdyZNV0YgR149azc/UupKzmuq25N9Zyq5/9VD7DhZwZqUUP5yy1xuXzKNOZHjmeDvNaSbRB9Pd36wMoF1c8L45EQpj753nJqmEf5euFtUFKsie+SOndYmw+0dnezMqSQtajzeHsanB/aGFleuS01NjSnSA22Yxeiirq7OFCmC06ZNQwhhKpMDZwgsIQQpKSkcOWJgaxknsHHjRlpaWvpMD4SzRhc6TdDcOFxgCSE8UOLqBSmlrWFOiRAi1Lo/FCjt5dQCoHsn3nDrtnOQUj4tpUyTUqY5OkRtFxrKzGFwYWPyDCWuKpz8x7qpWr27mqBIXgsN5fZvImuzrHfFFEEb825XP0sHXxj5WJ2d0FDaZwRLSskz207x+w8zCPbz5FfrZvHFhVEE+nqMaFohBDfNj+Q7K+LJKW/gx28e4XR5w4jGJPYS9Tt/cASOgu2tSrgGTOFIQQ31LW0sjpswsnVpNL1QXV1tKoGVmJhIYWGh4X1/zCKwfH19CQsLM43Aam1tpaamxuECCyApKYnS0lKqqhyUpm8gUkr27NnD//73P+Lj45k+fXqfx06cOJFx48aZ5mdA0zuOdhEUwDPAcSnlH7vtWg/YXAFvA3rzeP4QWCmEGG81t1hp3ebaNJSao/7KRojV6MLZdViu0mS4JxELYNxUSH/Lvk1kXfXz6E5gGCReCZkfKve8kdBYYe35dL7AklLy7+2n2ZhezJWzp/Lo2pkDpgAOlcWxE/npVckg4eH1x9iVUzH8wWxRrPJMKBxm/UCD9RmU/2Q2HC0myMeT2eFBw1+TRtMHZhNYthtNo5/WmyVFEFSKWGamA3oPDoOKCvW30RkPuG2OemZIGR0sNuHUnxPmiRMnuO+++/jhD3+Iu7s799xzT7/ZF0IIbXThAjg6grUEuBW4RAhx0Pq6AvgNcJkQIgu41Po1Qog0IcQ/AaSUlcAvgD3W18+t21yXzk5142imCJZPkBIMzq7DclVBIYRyzSvPgtLj9hvXVT+PnqTcrHqb7X12ZOPUn3XM68mbBwr44FgxV8wK5UsLI7G4O+bPWMwkf365biZRwb786eNMXt+XP3zzi7gV6sHKcKNY1p+1AjGZw/nVrJo5BQ8Hfd+asU11dbUpLNptTJs2DYDTp08btoa2tjZaWlpMI7CmT59OSUmJ4VE9UAYX4ByBNX36dIQQLiWwPvroI374wx9yww038NRTT5GXl0dLSwsAJ0+e5P/+7//45je/SU5ODvfccw/PP/88s2bNGnDc+Ph4cnJyaG+3c7mCxm44zlMTkFJuA/qS4efZo0gp9wJ3dPv6WWCEd2omoqlK3VyZKYIFKoqVt0utzVm1YbaUOFcUFLErYP9/4NibZ63uR4qtbslVTS5seAfC7Bth33OQuwsiFw5vnEprn5fA8HM2b84o5ZW9eSyLn8StF0Q5vBA/yNeTn6xJ5h9bc3h1Xx75VU1846IYvCxDrH1y94BZN8DOv0LRIZiaOrTzz2wHv0m8edoTL4s7lyY7pqGnZmzT0dFBXV0d48eb5+9ycHAw/v7+nDkzwqj4CLA1GTaLwOrebHbOnDmGrsXRPbC64+3tTXR0tClq8gaDlJKXX36ZyMhI4uPjef3113n11VcB8PDwoK2tDT8/P77yla9w/fXX92lq0RtxcXG0t7eTm5vbZXyiMRcOFViaHnRZtJsoggWqDiv7Y6jJh6CIgY+3B7aIjZfxVsBDxsMbElYrQ4e6wTndDYitAbWdBWdnp+RMZSMNLe0EeFsI8PZgvK+HY4VJ8lo4+QnsfEqZggzSZv0cCvaqyGq3CJaUkjcPFBAX4s9dy2Oc5nLmaXHjWxfFEhHsy4u7cimpa+b+lQmM9/Mc2kBxl8Ghl5SjYGjK4B9mtDZC4QHqoleyPaOS1TOn4O+l/3Rr7I+ZemDZEEIQFRVlaATLFikyQw0WqJtrUGmTRgssWwTLGQILVE3e9u3bkVKawumyP/bs2cOpU6d48MEHWblyJXfeeSe7du2itra2K+X0qquuGpZwt/0MZGdna4FlUvRV2pmYqQdWd7rXYTlTYHmNU/UprkjiGjj6OqSvh4V3jny82gL1c2EZ4k17L9S3tLPzZAUH8qo4XlRHY+u5KQQp4UHcsyLecTfp7hZYfA+8fz8c+A8svGto57c1Q9FhVc/VjdMVjZTUNnNNaozD0gL7QgjB1SlTCQvy5olN2fz4zSP84PIEYicN4YbL4qmiWLufhoL9ED5vcOcV7IPOdj5tVhfU1bNCh/EdaDQDU1urTH7N0AOrO9HR0Xz+uZ2NhYaA2QRWUFAQEydONEUNTnl5Od7e3vj5+TllvoSEBDZs2EBJSQlTptjh4aYDefnll5k4cSKXXHIJACEhIf26Aw6FiIgIvLy8yMrKYuXKlXYZU2NfdBK/M2lUoXTTRbDGTVU39/l7nDdnvcnMPoaK3wSYthyyPoLWEbrMAVTnQuDIxO2J4loe+zCDb/xnH//clkN+VROLYiZwzyXxPHRlMt+7dDo3pkVwrLCWh948Qn6VHZsC9yQkSQmk4+9A2RDTOYoOQWe7MhTpxs6cCtyEYH60cWmU86KC+cU1M3FzE/zsnfShm18kXKEinnufUSYegyF3J20eAbyRN47FsROY6O819IVrNIOgoUH9LTOLkLARFRVFdXU11dXVhsxvthRBwDQmBzaLdmdFk2zNp81eh5WVlcX+/fu57rrrsFjs/zDTzc2N2NhYU/wMaHpHCyxnUl8GFi/wNNfFCyFUD6PC/Sp64AxqC5Wwc2VmrIP2ZuWaNxI6O6EmD4Iih3V6YXUTf/gog5+uP0ZWaR2Xz5jMr6+dzeM3pfL15TEsiZvIzLBAFsZM4Nq54Tx8VTJNbR089NZR9p1xoG/M3NuU7fz2JwYvJgDyd4OHD4TM6NokpWTHyQpmhQUS4D0yK/aREjnBl1+tndVlfvHWgYLBm1+4W5SdfXWuSssdiI422nN38VFtFB0Irkl1/V7rZkQI8awQolQIcbSP/UII8YQQIlsIcVgIMbfbvtuEEFnW1229ne8q2ATWUGpBnEF0dDQAubm5hsxvVoGVm5vbZZhgFGVlZU4xuLARGxuLxWIxfR3Wyy+/jK+vr90iVr0RFxdHdnb28M2XNA5FCyxnYuuBZca84cgLVA+jwv2On6ujXbnEubrAmhCraoyOrx+agOhJQ6n67IcgsKSUHC2o4Y8bM/nBq4c4WlDDzfMjeeKWOdy6KJppE/36fKI4fXIAv1o3i9BAH37/YQZvHhiBO15/ePrCgjuVZftgxAQoo5W83TB17jnpo6fKGyita+aCGHP0fgr09eAna5JZEjuRl/bk8tTmk7R1dA7u5KglMClRpU+2NfV7aHPufnKLy9ndmcgPViYQEWyuG99RxHPAqn72rwbira87gb8CCCGCgUeAhcAC4BFrWxGXxGYl7ax0r8ESFRUFGOckaLYUQVACS0rJyZMnDV2HM5oMd8disRAXF2fqCFZjYyOffvopV155pUN/lxISEmhoaKCg4LwWsRoToAWWM2ksN29a3OSZ4BVg/wa6vVFfom6kA1xcYIF9Gg9XW5/KDlJg5ZTV84NXD/Poe+mkF9Zy5exQHr95DmvnhA3a3W6Cvxc/vVoJhJf35PHnTVk0t41AJPbFEMQEAJU5qj4vYv45m3fkVODuJkiLNs+9q6fFjW9fEscN8yLYmlXGL987Tm1z28AnCgHzv6aabR98oc+ocVNrB59tXE9th4VrVq8iJSLIruvXnEVKuQXoL5x7DfC8VOwEgoQQocDlwEYpZaWUsgrYSP9CzdSYNYI1adIkfHx8DHMStAkss0WwwNj+YFJKpwssUMIiIyODzs5BPtRyMpWVlXR2dvbbLNge2NIlzR7NG6togeVMGsrA16QCy80dIhZC3h4VYXIkdUXq3dUjWKDqhAJClWX7cKNANoE1iBqszRmlPLL+GM3tHXzrojie+uJcvrgwikCfoafNeVnc+fYlcXxhYRS7cir56fpjlNfbOd1ECJh/hxITR98Y+Pi83eo9LK1rk5SSXTmVzDRBemBPhBBcNy+c76yI52RZPQ+9eXRwtW0hSaqG79hb8L8bYP13YOffIGcz1BVTV3CC1174KxMr9zJh+mLmxpi7mHsMEAbkdfs637qtr+0uiVkjWEY7CdbV1eHl5eWQWprhEhISQkBAgKECq7q6mo6ODqemCIISFk1NTeTl5Q18sAE4S5BHRUXh5eVl6mjeWEYLLGfR0a5uMs1mcNGdyEXQ1gjFhx07T22heh8NAksImLFWNR4uG+YfuepcZc/u1Xf6SWen5Nltp/jbZye7UvyWT5+Ep2Vkv8I2d7wfrkqgpLaZH79xhBPFtSMa8zxCEiF6qXJdbByg5it/N0ycrhpgW8kxWXpgbyyOncjDa5Jpbuvg4bePcbSgZuCTln4fVjyinAU9/SF7I2x5jNZXvkrxC3cxr/gVYqYEE7H0C47/BjQORwhxpxBirxBir83a2myYNYIF6mbSqAiWzVLbTAghiIuLM1RgObMHVnfMbnRhc+N09M+Mu7s78fHxpv0cxjpaYDkLszoIdmfqHGXCkbvDsfPUFioTA+9Ax87jLGJXqBvkY28O7/wBDC46OyV//ewkH6UXc+WsUH58RdKwIlb9MSdyPI+um4Wfl4VH3z3OpuMldh2febeD7IAPHoAPHlSvvc+q9EpQDyBOfqqEag/3wE9PlGJxczPUPXAwxE8O4NF1Mwn28+TXGwbxGbpbVCrk3C/Dql+Rt+o5Xp/6A/7WcjlvB3wBz5ueY/xt/1MCVWM0BUD3EHO4dVtf289DSvm0lDJNSpnm7Cf+g6WxsRFfX19T9heKjo6moqKiy3DCmdTX15uq/srG9OnTycnJob3dwVknfWB7UODsn+fIyEi8vb1NKyxsESxntDtITEwkKyuLjg4HpPhrRoQWWM7CdiNp1hosUH16wtKUwHKkK01doUqrM+FFfFh4eEPCKjizXTUeHgpSqghWHwKro1Py5KfZbM0q48a0CG5dFI27m2M+t7AgH36xdiYzpo7jH1tz+Nfnp2gfrHHDQARMgUXfVpE62alMPY69Ca99FT75JbxxB2z9g0qTjLu067SC6iY+OVHKiqQQl2iuGxLgzS+umcnMsED+sTWH/+48MygDked3nOb+N47yVp4PbolXcPPNtxIXEzN6fkdcn/XAl61ughcANVLKIuBDYKUQYrzV3GKldZtLUl9fb8roFZw1ujAiilVXV2e6CBaoOqz29nbDUied3WTYhpubG0lJSRw92qvpp+E4K4IFSmC1tLQY2ohb0zvmv2MZLTSUqnczR7AAohYpw4ayE6pOxBHUFsKEeMeMbRSJV6kao6E2Hm4oh/aWPgXWs9tO8fnJcm6eH8naOY4v7fD3svDDVYn8b3cu7x0uJL+qie9eGs84e9Q+xV+mXjbqSpQDY/YmGB8NF3wLwuefIype2p2Ll8WNa+eGj3x+J+Hj6c4PL0/k39tP8+7hQkpqm7n74ji8PXo3IKlpbOPDY8Usjp3IV5ZEm67ObCwghHgRuAiYKITIRzkDegBIKf8GvA9cAWQDjcBXrPsqhRC/AGxNBH8upXRg7wPH0tjYaLr6Kxs2q/YzZ84wY8aM/g+2M3V1dUyePNmpcw4G2+dw5MgR4uLinD5/eXk5bm5uBAc7P7sgNTWV5557zpTi1xZldUbUMyEhAVDpkrGxsQ6fTzN4dATLWTS4QIogqBtcN3cVjXEEHe3KRXBcqGPGN4rhNh7ux+CivL6FT06UsmrGFKeIKxvuboJbL4jiWxfFkVlSx0NvHiW3wgFNiQMmw4KvwxdegtW/UamB3cTV8aJa9pyu5OrUqXZPiXQ07m6CryyJ5rZF0ew9XcXP3kmnqqG112M/yyqjo1Ny3bxwLa4MQkp5i5QyVErpIaUMl1I+I6X8m1VcYXUPvFtKGSulnCWl3Nvt3GellHHW17+M+y5GTkNDg2kjWJMnT8bLy8uQJ/VmTRGcMmUKkydP5uDBg4bMX15eTnBwMG5uzr+VTE1NRUrJoUOHnD73QNTW1uLn54e7++BcfUdCWFgY/v7+pk2XHMtogeUsGspVnY6Ht9Er6R9PPwhNcVyaoM2ifTQYXPRkOI2Ha/q2aP/khIp6XjHbGDG6fPokHrlqBm0dnfzk7aPsyqlw2txSSl7YdYbxvp5cMcs1xbgQgtWzQvnB5dMprG7iobfOF6pSSj45XkLilHGEBfkYtFKNRmHmCJabmxuRkZGGpQiaUWABpKSkcOjQIUOazRph0W4jKSkJLy8vw8Rlf9TV1Tml/grUdcZmWz9WOHPmDN/61rdMKa67owWWs2goU1EOVyBysaolqjpt/7FtFu2joQdWTybEqn5iQ2k8XJ2rzD68z/1j3N7RyacnSkmJCCIkwDhRHhfiz6/WzSIy2Jc/fZzJK3vznHIh/+REKdml9dyYFjHo3l5mZV5UMD+9egadUvLw+qMczKvu2nessJbi2mZWJIUYt0CNxkpDQ4NpBRZgiFV7Z2cnjY2NpktDs5GamkpNTY0hkb2ysjKnG1zY8PDwYMaMGaYUWLW1tU4V5ImJieTk5NDa2nuWxGjizJkz3HvvvRw/fpwPPvjA6OX0y4ACSwhRO8CrTgiR6YzFujSNFeDrKgJroXp3hJtgrdVgazRGsEBFsYbSeLi6dwfBfWeqqGps5bJk4/P+x/t58vBVyVyUEMIb+/N57KMMmlod51h0rLCGZz8/xezwIC6cbvKU2kEybaIfv1w3iynjvPnthhN8dEyZoXx6ohQ/TwsLppnbIdHs6OuUfbC5CJqVqKgoSktLu/p1OQNbPY2ZBRZgiNAoKyszLIIF6ns/efJkl6mEWXBmBAtUHVZHRwfZ2dlOm9MITp8+zb333oubmxvJyckcOHDA6CX1y2AiWCellOP6eQUAQyg6GaO01LqOLbnPeGVw4Yg6rNqi0WXR3pOhNB62OQj2Un/18fESJvh7MSciyDHrHCIe7m7ctTyG2xdHcyC3mofeOkpRTZPd5ymuaeZPGzOZPM6b766Ix81BjolGEOznyU+vnsHcqCCe/fwU/9iSw65TlSyLn+jyUToToK9TdsDsESxb/yNnusc5q2nscJkyZQohISFOT5dqbm6msbHRUIE1Z84cANOlitXW1jr158XsfcHsQWNjI/fffz/u7u48/vjjXHrppZSUlFBUVGT00vpkMALrOjsdM3aREpprwDvI6JUMnqjFKkVwqLbjA1FbYKhFe2VDK1uzyvjgaLH9LMi7IwQkXzO4xsONlaqxc48IVlFNE0cKarg0KcRUAkMIwaqZqg9XTVMbD715brrbSMmvauR3H5wABD9alYifC9iyDxVvD3fuuyyBK2aFsulECe2dnVySaHyUchSgr1MjREpp+gjWrFmzsFgsTn1y7UxHuOEghCA1NZWDBw86tQ7LFjUKDDTuYWliYqIp67CcHcGaOHEiwcHBo7oO67///S/l5eX84he/ICIigrlz5wKYOoo1GIHlJoRY0nOjEGKpECIWQEqZ09uJQohnhRClQoij3balCCF2CCGOCCHeEUL0+lMohDhtPeagEGJvb8e4DG1Nqu+PK0VtIhepd3unCdYWwjjnOeLZOJRXzfdfOci3XtjHk59m89z2UzzxSTYdnQ64IMVdam08/NbZbY2VKlpVnQsVJyHzI9j1N7Uv6NwI1sfHS3F3E1ycYM66nJlhgfxq3Uwm+Hvy2w0nWH+ocEQX9pqmNp7ZdoofvnaY6qY27ls5ncnjTG4GMwLc3ARfXhTNXRfGsm5OGJETzHtD60IM+zqlUTQ1qYi0mSNYXl5eJCUlOfWG2hbBMqvAgrN1WM40ADFDZM9isTBz5kxTCSwppdMjWEIIEhMTOX78uNPmdCTV1dXniMWCggJeffVVLr/8cpKSVPugyMhIxo8f7/IC63GgtwTXGuu+/ngOWNVj2z+BB6SUs4A3gfv7Of9iKWWqlDJtEOs0Ly3Wj8+VIlgBU1RvInumCXZZtDuv/qqto5P/7DjNrzccRyC49YJofn3tbL68KJrdpyp48tNsOu0tsjy8Yfrlqg7r1FbVSPeVL8Nb31Kvd74L25+A4iMqUjgpsevUlvYOPssoZX50MEG+nvZdlx0JGefNz6+ZycKYYP636wx/+SSblvbB12W1dXSy93Qlf/44i3tePMCm4yVcljyZx29OJSnUeU/+jOTihBBumt97/zPNkHmc4V+nNKj0QMDUESxQaWEZGRld63U0Zq/BAmPqsMwgsED9POTk5FBTU2PoOmw0NTXR2dnp1AgWqGheXl6e034vhktbWxt//etf+0xnbG9v5/777+cb3/gGf//732lvb+epp57Cw8ODr3/9613H2SK3Bw4cMMRBczAMJgdnspTySM+NUsojQojo/k6UUm7p5ZjpwBbrvzeiut7/ZBDrcF2aqtW7K0WwQN38H/yfir742qEIv75EvTuoB1Znp6S0roW8qkYKq5soqmkms7iOwpomViZP4YsXRHbVukyb6Ed7p+R/u87g7ib41kWxCHumLSZdpeqwPvutimbNugGCp6l9wk2J13Fh56VK7sqppL6lnUuTzJ825u3hzndXxBM9wY+X9+RRWNPEfSsTmOjv1e95Ukp+uv4YJ8vqCfD2YHn8RFbNnEL4eHPf2GlMzbCvUxqFzTjCzJEaUGLi+eef5/DhwyxatMjh85lFSPSHrQ7r4MGDrF271ilzmiWyZxOXhw8fZtmyZYauBYz7ebHVYWVmZnbVppmRLVu28Morr/D222/z6KOPkpZ2bvzkP//5D9nZ2SxcuJCXXnqJnTt3cvr0ae68804mTDjXKG7u3Ll8+umnFBQUEB4e7sxvY1AMRmAF9bNvOI1bjgHXAG8BNwDnV/grJPCREEICf5dSPj2MucxBs/XJiqsJrOhlSmCd+ky5442U2kL1PsIUQSklJ8saOJBbxZmKRupb2qlrbqesvoXWblGUQB8PQgN9+OIFkcyLOl8gXp0ylfaOTl7Zm4eXxY2vLZ1mP5HlNxGWfAfaWyB2xaD7n21ML2FqkA8zprpGFEcIwdo5YUQE+/KXT7L48RtH+N5l0/uNQp0oruNkWT23LIjkylmhWNx1twjNiAnqZ59uMDYIXCWClZyc3FWH5QyBZfYaLDj7NH/Xrl1IKe37sLAPzBLZS0hI6KrDMoPAstWmOftzSUhIAJTRhZkF1nvvvUdISAj+/v48+OCDPPzww13/bxkZGfz3v/9l5cqVPPjgg3zyySc89thjhIWFcf311583lk1c79+/32UF1l4hxNellP/ovlEIcQewbxhzfhV4QgjxE2A90Jdx/1IpZYEQIgTYKIQ4IaXc0vMgIcSdwJ2gcjJNiasKrKAImBgP2ZvsJLCsFu0Bw4tg5Vc18smJUrZllVPb3IZAEDbeh0AfD8LH+5ASEUhEsC8R430JC/LBx3NgZ7Z1c8Joae/k7YMFeHm486WFkfa7OMVdOqTDT5U3kFVax22Lop1ygbQn86LG8+i6WTz2YQaPvpfObYuiWTljSq/HfnKiFB8Pdy6fMUWLK429sPd1asxhE1hmrsECVYflzP5HdXV1WCwWvLz6j8wbTWpqKh999BG5ublERUU5fD6jhERPLBYLSUlJpKenG7oOG7YIlrNTBMeNG0doaKipnQTz8/M5cOAAd9xxB1dffTUPPvggjzzyCMnJySxZsoQPP/yQ4OBg7rnnHgAuueQSUlJScHNzw8PD47zxwsLCmDhxIgcOHODqq6929rczIIMRWPcCbwohvsjZC1Ua4AkM+a5bSnkCWAkghJgOXNnHcQXW91IhxJvAAs6mFnY/7mngaYC0tDRzJmK6qsACFX3Z9TeozIHgmJGNVVug0uUG8Tm0dXTy+r58KhpaaevopLKhlcySOixubsyLGs/86GBSIgIJ8D7/l24oCCG4eX4EzW0dvHe4EG+LGzek9RVUdSwfp5fgaXFnuYv2fgoL8uHRtTP5f59k8+znpzhT0cjtS6Lx6CaiGlvb2XWqkuXxE/H20NbkGrtxL3a8To1FXCWCBaru5t///jd1dXUOv8Gvq6vD39/f9A+9UlJSAFWH5QyBVVdXh5ubmyl+XmbMmMFLL71ES0uL4ULYSOGZlJTk1BYGQ+W9997Dzc2NVatWERAQwGOPPcYrr7zCtm3bePpplaT2u9/97pxocc+0wO4IIZgzZw579uxxWuR2KAz4+FhKWSKlXAz8DDhtff1MSrlISjlkD29rRAohhBvwEPC3Xo7xE0IE2P6NEmTm/akZiOZqcPccdJqYqZi2HNzcVRRrpFTnQWD4oCza/7PjDG8dLCCjuI7cykY6OiVfWBjFk1+cy/cum87S+IkjFlc2hBDcvjiaixJCeH1/Pu8fcX5fhcbWdrZml7M4doJL25P7eVn44eUJrE0NY9OJEn7xbjpVDWeD1J9nV9Da3sHFieZ0SNS4Jva+To1FbDVYZo9ggRJYUkqn9D+qr683PEozGEJDQ5k4caLTInv19fWmEZ4zZ86ko6PDFDblRkWwQNVhlZaWUlVV5fS5B6K9vZ0PPviAxYsXd4kmb29vvvzlL/P000/z8ssv89e//pX58+cPadw5c+ZQXV3N6dOnHbDqkTHoOzkp5afAp0MZXAjxInARMFEIkQ88AvgLIe62HvIG8C/rsVOBf0oprwAmo55G2tb4PynlB0OZ21Q017hm9ArAexyEL4CczTDvK+A+gpv/mjwIH/iXZ/vJcj5KL2bN7Kl86QLHP4kDJbK+viyGxpZ2nt9xGh9Pd6fapG/JLKO1vYPLks1vbjEQbm6CmxdEEjXBj79+dpIfv3mE7182nfjJAXxyopTIYF9iJpr/Jk7jegznOqVRuFIEKykpCU9PTw4cOMDSpUsdOpdNSJgdWx3Wvn37nPI03xnRw8GSnJwMqAbUs2fPNnQttgiWET8z3euwnFGfOBS2bdtGdXU1a9as6XV/SEgIISFDv+ey/X+np6czbdq0Ea3R3vQbwRJC3C6EeFcI8ZXhDC6lvEVKGSql9JBShkspn5FS/llKOd36ekBa/RWllIVWcYWUMkdKmWJ9zZBS/nI485uG5hrwCTJ6FcMnboX6HgpH0G+gpU6NEdh/IWJhdRNPb8lh+uQAbp7v3FQ9dzfBPSvimR0exNOf5bArp8Ip87Z1dPLOoSISp4wjdpL5L+SDZVHsBB69ZiYe7m787J10/rcrl1Pl9VySGGKKp56a0cFIr1MahS2C5QoCy8PDw2n9j8wkJAYiJSWFqqoq8vPzHT6XmT6XcePGERkZybFjx4xeCnV1dXh5eRmSqjh9+nSEEKasw7KZWww1QjUQU6dOJSAgwJTf80Apgt8HrgXuc8JaRi/NNeDlGq5wvRKWptZ/cgRpgjXWP/hBfRuRSCl5YlMWFjc3vrsi3hADBA93N2u0xZ//90k2+844PtT+6YlSKhpauG6u+VxwRkrkBF9+de0skqeOY/2hAjzc3Vga75o1ZhrToq9TdqChoQEfHx/c3FzDeMbW/6i6utqh85hJSAyEM/th2WrTzMKMGTM4evSo4T2RjPxcvL29mTZtmilSJbsjpeTgwYMsX77c7n9fhBAkJCSYssnyQN/pE6g0vj87YS2jF1ePYLlbIOZCyN0BrcNsYmcTWIF9R6WOFNRwuqKBLy+KYsIAvZQcibeHOw+sTiRygi9/2pjJ4fxqh83V1tHJ24cKmT45gJlhLizC+8Hfy8KPViVyw7wIblkQib8L15hpTMmIr1NCiFVCiAwhRLYQ4oFe9v9JCHHQ+soUQlR329fRbd/64a7BaBoaGlwiemXDZkXt6DosswmJ/ggLCyM4OHhM1qbNnDmT2tpap0Tv+qOurs6Q+isbNrFhtNDsTnNzM+3t7f0aVoyEpKQkTp06RUtLi0PGHy79Ciwp5T+llGt6Wt9qhoCUyuTCVWuwbEy7EDo7lMgaDtW54GYB/75rjDamlxDg7cGiWMf8Eg4FX08LP74iifDxPvz+wwyOFjimS/xnGWVU1Ldw/bzwUZ025+4muG5eOFfMckyTac3YZaTXKSGEO/AksBpIBm4RQiT3mON7UspUKWUq8P9Qgs5Gk22flNJ8XsGDpLGx0SUMLmwkJCTg7e3NgQMjSF0fACmly9Rgwdk6rIMHDzr8Bru2ttZUAmvGjBkAhrvoGf25JCYmUltbS3Gxebx9bHVpjhKeiYmJdHZ2kpWV5ZDxh8uAsTohxH57HDNmaWtSwsTVBdakBPCbBKe2Du/8mnwIDIM+wsPl9S3sPV3FJYkh59h6G4m/l4UfX5nElHHe/O7DDI4V2ldktXd08ubBAuJC/JkV5uI/HxqNgYzwOrUAyLbW/rYCLwHX9DPULcCLQ1+luXG1CJbFYiElJYX9+x13+1FbW4uUkqCgIIfNYW9SUlKoqKigsLDQYXPYhKeZBFZkZCQBAQGG12EZHcFKTEwEMFWaoKMFls3cw2xpgoO5k00SQhzu53UEmOjohboszdXq3dUFlhAwbZkyumiuHfr5NXn9pgd+crwUgBVJ5rLvHuftwUNrkgkJ8OK3H2SQXjiM770P3jxQQEW9qr0azdErjcYJjOQ6FQbkdfs637rtPIQQUcA04JNum72FEHuFEDuFEGvt8L0YgqtFsEClCebl5VFR4RhDotJSdV2aPNl13F1t/bAcmSbY1NREZ2enqQSWEILk5OQxH8GKiYnBw8PDVGKjpkY9nHaUwJowYQIhISGmEpUwOJv2xEEc0zHShYxaupoMBxm6DLsw7UI4+gac2Q4JqwZ/Xnsr1BVDzEW97+7oZNOJEuZEBhESYL5eYYE+HvxkTTI/fyed33xwggdXJ5IUOrI/FAdyq3hjfwHL4ieRGhFkn4VqNGMXZ12nbgZek1J2HytKSlkghIgBPhFCHJFSnux5ohDiTuBOUE/bzUZDQ4PDaiQcxdy5cwE4cOAAl156qd3Ht6VZuZLAioyMJCgoiIMHD3LFFVc4ZA5bryczCSxQdVi7du0y1JjE6AiWxWIhKSnJaf3QBoOjI1iAKY0uBtNo+MwgXsZWFZoZW7TH1SNYAMExMG4qnNoytPNqC9R7HxGs3acrqWlqY+WMKSNcoOMI9PHg4TXJTPT35DcbTpBZUjfssUprm/nLJ9lEBvtyx7JpOnql0YyQEV6nCoDuf5zCrdt642Z6pAdKKQus7znAZmBOH2t8WkqZJqVMmzTJfE6aDQ0NLhfBio2Nxd/f32FpgrYI1nD68xhF935YHR2OefZtVoFldB1Wa2srLS0thn8uCxYsIDMz0zQNh53RfDkpKYnCwsIuMWcGzFHsMprpShEMMnIV9kEImLYcig9DY+Xgz+tyEOzdhvzj9FJCArxJCTe3CA309eAnVyYT7OfJr98/TnZp/ZDOb+/o5HB+NY99pMLY962cjpfF3RFL1Wg0g2cPEC+EmCaE8ESJqPPcAIUQicB4YEe3beOFEF7Wf08ElgDpTlm1nXG1GiwANze3LlMHR1BSUoKXlxeBgea+NvXksssuo7Kykm3btjlkfNsNs9nMP2bMmIGvry+bN282ZH6zCM8FCxYAsGfPHkPXYcPRKYJgztozLbAcTVeKoGN+sHIrGnnwjcPc8+IB7n/1ED9/J33IN/5DInqZej/z+eDP6UdgldY2k15Uw8WJk1wikjPez5OHrkwiwNuDX71/nJNlA3/WUkr+u/MMd/1nH796/zildS3csyKekHHmS4fUaMYaUsp24NvAh8Bx4BUp5TEhxM+FEN1dAW8GXpLn2rMlAXuFEIeAT4HfSCldTmBJKWlqanK5CBaoOqyioiKHuKaVlJQQEuJ6jdEvuOACQkNDef311x0yfn29uu4ZLSR64unpycUXX8yWLVtoampy+vzOiNQMhri4OMaPH8/u3bsNXYeN2tpafH19sVgc16LF1mTZTGmCWmA5muYasHipl53Ze7qSn7x9lOrGNhKnBBAa6E1RTRO/fC/drmYM5zA+CoKiIOezwZ9Tkwf+Ib1+BluzygFY5kLNZyf4e/HwVcn4e1n45XsDi6xT5Q28e7iQxNAAfrAygb/fOk/XXWk0JkJK+b6UcrqUMlZK+UvrtoellOu7HfNTKeUDPc7bLqWcJaVMsb4/4+y124OmpiaklC4XwYKz/bAcYddeWlrqUvVXNtzc3Fi3bh1HjhxxiHW1LQ3LbAIL4PLLL6e5uZmtW4fpeDwCzPK5CCGYP38+u3fvprOz09C1gPpcHC06/fz8iIyM5MSJEw6dZyhogeVomqvtnh4opeTtgwX84aNMwsf78Kt1s7j74ji+vzKBX66bRbCfF7/ecJwDuQ7Kv429GMpOqN5Wg6E6t9f6KyklW7PKmDE1kIkGNhYeDhP9vfjJmsGJrM+zy7G4ufHNi+JIiw7WaYEajcZUNDY2ArhkBCs6OpqgoCCH1GGVlJS4pMACWL16Nd7e3rzxxhsDHzxEzJIK1xszZ84kNDSUjz76yOlzmyWCBbBw4ULq6upMITic5ayYkJDAiRMnTNNkWQssR9NcY9f0wNb2Tp7afJIXd+eyKHYCD1+VzHg/z679wX6ePHxVMuHjfXjsowx25jjAvjbuUnBzh4wNAx8rpTK56CU9MKu0nuLaZpZPd53oVXcmBZwrsrJLzze+6OyUbD9ZwZzIIPy9HBce12g0muHS0NAAuKbAEkKQlpbGrl27aG9vt9u4ra2tVFVVuZTBRXf8/f1ZtWoVH3/8MdXV1XYdu76+Hjc3N3x8fOw6rj0QQrBy5Ur2799PWVmZU+c2SwQLIC0tDSGEKdIEa2trnVLHmJSURFVVVZc5jdFogeVommvsFsGqaWzj0ffS2ZpVxg3zIrjnkrheoyE2W/G4Sf78+eMstlnT8OyGTxBELYGTn0B7S//H1pdAR1uvEawtmWV4WtxZEB1s3/U5kUkBKl0wwFuJrIzic0VWelEtVY2tLI7VreI0Go05sQksV0wRBLjwwgupq6uzq9mF7SZtyhTzutsOxLp162hvb2f9+vM8W0aEzQbdrLVpK1euRErJxx9/7NR5zRTBGjduHElJSezatcvopTglRRDOGl2YIWoHWmA5nuYau1i0n6lo4MdvHeF0RSPfXTGd6+b135zW19PCg1ckkTx1HE9+ms2nJ+ys6BNWQ2sDnB4gz9mWRhh0rsBqbe9kx8kKFk4LxsfTtVPmJvp78chVMwjy9eRX7x/nWGFN177Ps8vx9nBnblSQcQvUaDSafnDlFEGA+fPn4+Pjw2efDaE2eABc0aK9J5GRkcyfP5/169fbNbpnZJ+pwTB16lRmzpzJhx9+6NR0sdraWlNF9hYuXEhGRobdI5hDxVkCKzY2FovFogXWmEBKuwis3acq+cnbx5BS8shVySyKHVwzSG8Pd364KoHZEYH8fctJNqaXjGgd5zB5pkr7GyhNsOQYCDcIjj1n874zVTS0trMsfnREdoL9PHnkqmQm+nvx2w8yOJxfTVtHJ7tOVTJf111pNBoT4+oRLC8vLxYtWsS2bdvs1vuppERdL121BsvGtddeS0VFhV0t2+vq6kxn0d6Tyy+/nDNnzjjVVc5skb0FCxYgpWTv3r2GraGzs5P6+nqnCCwPDw/i4uK0wBoTtDVCZ8eIBNb7R4r448YMIoN9+OXaWcROGtofNS+LOz9YmcDcyPE8sy2HD47aycpWCJi+CsoyoDKn7+NKjsLE6eBxriX5Z5llBPt5MnOqa/UX6Y8gX1X/NmWcF7//MIPnd5yhsbWdpXGjQ0RqNJrRiatHsEClCVZXV3P48GG7jFdSUoIQAjM2hR4KCxYsIDQ0lDfffNNuY9bX15s6ggVwySWX4Ovr6xCTj74wW2QvISGBgIAAhzXiHgx1dXVIKZ3WSy4xMZGMjAxTuCc6VGAJIZ4VQpQKIY5225YihNghhDgihHhHCNGrrBVCrBJCZAghsoUQD/R2jOlpqlbvwxRYHx0r5vkdp1kwbQIPr5lxjpnFUPBwd+P7l00nLSqY57afYsORomGNcx5xK8DNAhkf9L6/rRnKs2DKrHM2l9W1cCivmosSQnBzM8eTHnsR6OPBw1fNIHy8LxvTixnn7cHMsNEjIjUazejD1SNYoNKhvLy87JYmWFpaSnBwsEN79zgDNzc31q5dy+HDhzl58qRdxnSWK9xI8PX15corr2Tz5s2Ul9u5Dr0P6urqTFF/ZUMIQWpqquECC5xn/JGUlERTUxO5uYN0uXYgjo5gPQes6rHtn8ADUspZwJvA/T1PEkK4A08Cq4Fk4BYhRLJjl+oAWqy9qIZhcrEls4xnPz/F3MjxfOeSODwtI/uvsri7ce+l8SyIDubfO07bJ5LlFQBRi+H0NhWp60lpOshOmDLznM2fZqjc9osTXDe3vT/8vSz8ZE0S86ODuSY1DPdRJiI1Gs3oYjREsGxpglu2bLHL0+vi4mKXTw+0sXr1ary8vHjrrbfsMp7ZIjV9sW7dOjo7O+32fQ+EGYVnamoqJSUlFBXZ6cH6EKmpUTXpzhKeNqMLMzQcdqjAklJuASp7bJ4ObLH+eyNwXS+nLgCypZQ5UspW4CXgGoct1FHYIlg+QUM67VBeNX/dfJKZUwO599LpWNzt899kcXfjOyviuyJZH9ujJit6mRKSRYfO31d8RKUSTkrq2tTRKfn0RCkpEUFMCnCt3ldDwdfTwn0rE7hydqjRS9FoNJp+qa+vx9vbGzc3164auPDCC6mqquLo0aMDHzwArtpkuDcCAgK47LLL2LhxY1dEYbhIKamvrzd9DRZAaGgoS5Ys4Z133qGlZQDHYztgtggWnG3EbU+HzaFgs653VopgREQEvr6+ZGRkOGW+/jDir+kxzoqlG4Dz/bshDMjr9nW+dZtr0Wx1k/Ma/C9cY2s7f99ykrDxPvzg8oQRR656YnF347uXxjMncjz/3JbTFU0aNmHzwMMHTm05f1/JUZgQB55n004O5lVR1djKiqTRGb3SaDQaV6OxsdGlo1c2LrjgArukCUopR5XAAli7di0tLS1s2DCI/pX90NjYiJTSdEKiL6677jpqa2udYtleU1NjugiWrRH3gQMHDJnfJrCc9fMihCAhIWH0R7D64KvAt4QQ+4AAoHUkgwkh7hRC7BVC7HV2U7kBsQmsIdRgvbAzl6qGNr5xYSzeHo5xnvNwd+N7l04nJTyIpz/LGVmfLIsnRF4AuTtUvysb7S1Qnnle/dXHx0sZ7+vJ3Mjxw59To9FoNHajoaHBpeuvbHh7ezN37lx27NgxInvuqqoq2traXNqivSexsbHMmjWLt99+e0SfjbNrakZKSkoKsbGxvP766w61bG9paaGxsZHgYHP19bTVYR04cMCplvU2nC2wQNVhnTx5ktbWEcmLEeN0gSWlPCGlXCmlnAe8CPRWdVnAuZGtcOu23sZ7WkqZJqVMM53bT3ONiu5YBmdOcbSghk0nSrhydihxIY4Nv3ta3Pj+yukkhY7jqc3Z7MqpGP5g0y5UPbEKuhVSlp1QdVlTZndtKq9v4WBuNRcnhOi6JI1GozEJoyWCBbBo0SKKiopGVORu64E1miJYoKJYhYWF7NmzZ9hj2ASWK6QIghIY69at49SpUw61766qqgJg/HjzPTyeM2cO5eXlFBT0ehvtUGpraxFCOPXvS2JiIh0dHWRnZzttzt5wusASQoRY392Ah4C/9XLYHiBeCDFNCOEJ3AzYtxW5M2goA9/B9axqbuvg6S05TBnnzQ1p4Q5emMLLovpkxYX488QnWew7UzW8gUJTwdMfTndLEyw+ot5DznqTbM5QEcaLEk0mhDUajWYMM1oiWKDcBAF27tw57DFGSw+snixfvpzx48fz9ttvD3uM+vp6wHUiWAAXX3wxnp6efPjhhw6bwyawzBbBAmPrsGxNhp3ZGywpSdX9G90Py9E27S8CO4AEIUS+EOJrKEfATOAEUAj8y3rsVCHE+wBSynbg28CHwHHgFSnlMUeu1SHUFULA4EwOXt+fT2ldM3cuj3VqU1pvD3ceWJ1IZLAfj3+cybHCmqEP4m5RboK5u1RqICiBNSG2q/5KSsnmjFJmho0jJMC7n8E0Go1G40xGUwQrJCSEmJgYduzYMewxbAJrNKUIAlgsFtasWcOOHTsoLh6ek7At5cuVBJavry/Lli3jk08+oa2tbeAThkFlpfJzM2MEKzw8nAkTJhhSh2UTWM5k4sSJTJgwYXQLLCnlLVLKUCmlh5QyXEr5jJTyz1LK6dbXA9KaFCqlLJRSXtHt3Petx8RKKX/pyHU6BCmhrhjGTR3w0DMVDbx3uIiLE0JInur8wlFfTwsPXpFISIA3v/8wg+zS+qEPMm05tDfDtj/BrqdVA+LJZ+3ZjxXWUl7fwkWj1Jpdo9FoXJWGhoZRI7BApQkeOXKkK9oyVEpKSvD19XWZNLihsGbNGgDeeeedYZ3vajVYNlauXEldXd2IIpv9YeYUQSPrsGpqagwxRElMTDTc6MK1PVnNTFOViuYETOn3MCkl/9iag7+XhS9eEOWkxZ3POG8PHroyiXHeHvxmw3HyKhuHNsCU2TAxXtVhZX8Mnn4QvbRr92eZZfh6WkiLNt8fH41GM7YZqLG9EOJ2IUSZEOKg9XVHt323CSGyrK/bnLty+zCaUgRBuQl2dnayd+/eYZ1fWlpKSEiIU9OanEVISAhLlizhvffeG5YJgE20upr4nDdvHsHBwQ5LEzRzBAtUmmBVVZXTG/DW1tY6zaK9O6mpqeTn5xtSd2ZDCyxHUWdt6hbQfwRrY3oJ2aX13LooCn8vYzvGj/fz5KErk/Bwd+NX7x+ntK558Ce7ucGaP8EXX1Gvm1+AEJUH29jazq5TlSyOneDU9EeNRqMZiCE0tn9ZSplqff3Tem4w8AiwENW/8REhhDnvsPpASjmqUgQBkpOTCQgIGHaaYElJyairv+rO2rVrqampYfPmzUM+t66uDnd3d3x8fOy/MAfi7u7OZZddxs6dO7ua39qTqqoqAgIC8PDwsPvY9iAtLQ2Abdu2OXVeo5ovL1++HIAtW3ppIeQktMByFF0Cq+8IVmNrOy/uzmXm1ECWxk100sL6J2ScNw+uTqK1vZNfv3+CmsaR5yvvOFlBa3sHFyVocwuNRmM6RtLY/nJgo5SyUkpZBWwEVjlonQ6hqqoKKaUpi/OHi5ubGwsWLGD37t10dnYO6VwpJYWFhUyZ0n/2iSszd+5cwsPDWb9+6N5hdXV1BAQEuGR0b+XKlXR0dPDJJ5/YfezKykrTRq9AGbakpKTwwQcfODVN0IgaLFCR2sTExBH3xBsJWmA5itoiEAL8+34KtjOngqa2Dm5eEGmqP1aRE3z50epEKhpa+c0Hx2lsbR/ReJ9llhEW5EvsJNdKKdBoNGOCwTa2v04IcVgI8ZoQwtZGZLDnmpbyctUHceJEczzksxeLFi2iurp6yIXuNTU1NDQ0EBbmUv+NQ0IIwdVXX82xY8eGbGVdV1fncumBNmJiYoiLi+P999+3u8ioqqoytcACWLVqFfn5+Rw75hzPuNbWVlpaWgxJEQS48MILycjI6DKtcTZaYDmKumLwC1EOe32wJbOcqUE+xE4yX2rG9MkBfO/SeHIrG3nsw0xa24f2FNBGXmUjmSV1XJgwyVQiUqPRaIbAO0C0lHI2Kkr176EOIIS4UwixVwixt6yszO4LHC62tZiuj+QIWbBgAe7u7kNOg7PVbERERAxwpGuzatUqvLy8hmzZXl9f73IGF9259tpryc7OtnuqXFVVlemjwBdeeCHe3t588MEHTpnPiCbD3TE6TVALLEdRVwjj+rZoL61t5kRxLcvjzSs85kSO55sXxpFeVMNfPsmis3NoT3zaOzr562cn8feycOH00XXx1mg0o4YBG9tLKSuklNYeFPwTmDfYc7uN8bSUMk1KmWYmMTNaI1gBAQEsXryYjz76iPb2wWdh5OfnA8raejQTEBDAJZdcwscff0xDQ8Ogz7OlCLoqK1euJCoqin/84x90dHTYbVyzpwgC+Pj4cNFFF/Hpp5/S3DyEGvthYrTAmjp1KvHx8YalCWqB5Shqi/rtgbU1S13Ulsab+6K2NH4ity2KZvfpSp7ZdmpIYfWX9uSRU1bPnctjCfQxZ+GnRqMZ8wzY2F4I0f2P+dWo/oygejWuFEKMt5pbrLRucxnKyspwc3Mz/c3hcFizZg01NTV8/vnngz4nPz8fNze3UV2DZeOaa66hubmZjz76aNDnGGVaYC/c3d254447yMvLs5ujYEtLC42NjaaPYIGKXDY2NrJ161aHz2UzEzFKYIGK2h07dgwjsga0wHIELXXQWt+nwJJSsjWrjBlTA5no7+XkxQ2d1bNCWZsaxqYTJby2L39Q5xzKq+bdw4VcljyFBdPM/0dHo9GMTfpqbC+E+LkQ4mrrYd8RQhwTQhwCvgPcbj23EvgFSqTtAX5u3eYylJWVMXHiRNzcRt/tQFpaGiEhIbz33nuDPic/P58pU6ZgsRjr6usMEhISSExM5O233x7Uw1MppUtEagZiyZIlJCcn869//YuWlpaBTxgAM/fA6sns2bMJDQ1lw4YNDp/L1jPNSIFlSxN0hqDsyej7i2oGam0Ogr0LrOzSeoprm1lm8uhVd26aH8GF00N4fX8+m473XzDY2NrOk59mEz7ely9dEOmkFWo0Gs3w6K2xvZTyYSnleuu/H5RSzpBSpkgpL5ZSnuh27rNSyjjr619GfQ/Dpby8fNSlB9pwc3Nj9erV7N27l+Li4kGdU1BQMOrTA7tz9dVXc+bMmUE1Za2urqalpYXQ0L6zc1wBIQR33nkn5eXlvPnmmyMezyawXCGCJYRg1apVHDhwgKKiIofOZXSKIKhaymnTphlSh6UFliOwWbT3UYO1JascT4s7C6dNcOKiRoYQgq8vm0ZqxHj+ufUU+870/ZB2x8kKapvbuHN5jO57pdFoNCZmNAssgNWrVwMM6om9lJL8/PwxJbAWLVoEwOHDhwc8trCwEMDlBRZASkoKixcv5rnnnuuquxsurhTBArjiiitwd3fntddec+g8ZkgRBFi8eDFHjhzpapLtLLTAcgR1fUewWto72HGynPlR4/HxdC3xYXF3495L45k2yY8/b8omq6Su1+O2ZpUzNdCH+BDXtHLVaDSasUJZWdmocxDszuTJk5k/fz4bNmwYsCdWVVUVTU1No9qivSdBQUFMnTqV9PT0AY+1RQGnTp3q6GU5he9973t4enry61//ekSGF5WV6oGzqwisiRMnsmLFCt5///2uND5HUFtbi5eXF15expbCLFy4kM7OTvbt2+fUebXAcgR1ReAzHizn/1Btz66gvqWdS5Nds0u8t4c7P1qVyHhfD373YQZFNU3n7C+tU+6Iy6ZPNK07okaj0WigsbGRpqamUR3BArjyyispKytj//79/R43VhwEe5KcnEx6evqAdVi2CNZoMQCZOHEi9957L+np6bz44ovDHsfVBBbAjTfeSHNzM++8847D5jCLIUpycjL+/v7s2rXLqfNqgeUIaotg3PlPeKSUfHismMhgXxKnGP9DN1wCfTx4cHUSAL9+/wQ1jW1d+z7PVu6IS+JG9wVbo9FoXJ3R2gOrJwsXLsTLy4sdO3b0e5ytB9ZYE1gzZsygoqKC0tLSfo8rKioiODjY8IiEPbnkkku45JJLeO6558jKyhrWGFVVVQQEBODh4TpuybGxscybN4833nhjSG0MhkJtba3h6YGgnCPT0tLYvXu33RtM94cWWI6grneL9qzSek5XNHD5jCkuH92ZEujNj1YlUt3Uxm8+OEFzW4fVHbGcxCnjCAnwNnqJGo1Go+kHm8Aa7REsLy8v5syZw44dO/q9wcrPz8fd3Z3Jk10zw2S4zJgxA2DANMHi4uJRUX/Vk3vvvZdx48bxxBNPDOsGvKqqyqWiVzZuuukmKioq2LRpk0PGr62tJTAw0CFjD5WFCxdSUVHByZMnnTanFlj2pq0Zmqp6jWB9eLQYX0/LqInuxIX4c++l8ZypaOBPH2eSXVpPYXWTS7kjajQazVjF1mR4tEewQJk5FBUVkZeX1+cx+fn5hIaG4u7uWvXRI2XatGl4eXlx7Nixfo8rLCwcNemB3QkICOD222/n6NGjw0ojc1Xr+rS0NKZNm8Yrr7zikMhOVVUVQUFBdh93OCxYsADAqWmCWmDZmy6Di3P/CFU3trLrVCUXJUzC22P0/PGeGzmeO5ZN41BeNb/9IAOLmxsLY1zHHVGj0WjGKjaBNWHC6P+bfcEFFwD0myY41hwEbVgsFhISEvoVWO3t7ZSWlo4ag4ueXHHFFUydOpV//OMfQxYbVVVVLmHR3hMhBDfeeCM5OTkOMYCoqKgwzd+W4OBg4uPjtcByaboE1rl/hDYdL6W9s5PLXNTcoj8uSZzMtXPDqW9pY27UePy9Rn+DRo1Go3F1ysrKCAgIGFU1NX0REhJCTEwMO3fu7HW/lHLM9cDqzowZM8jOzqa1tbXX/WVlZUgpR2WKICiR+dWvfpWcnBw++eSTIZ3rqhEsgBUrVhAcHMzLL79s13EbGxtpbm42jcAC9ZDl6NGjDnVO7I5DBZYQ4lkhRKkQ4mi3balCiJ1CiINCiL1CiAV9nNthPeagEGK9I9dpV2qs/RS69cBq7+jk4+MlpIQHERroY9DCHMsN88L5xoWxurGwRqPRuAjl5eVjIj3QxgUXXNBnP5yKigpaWlrGrMBKTk6mvb29T6OH0dQDqy8uueQSYmNjeeaZZwZt/NDS0kJjY6NLRrAAPDw8uPbaa9m7dy85OTl2G9fmrGimz2XhwoVIKZ1m1+7oCNZzwKoe234H/ExKmQo8bP26N5qklKnW19WOW6KdKTkGgRHg6de1ac/pKqoaW7l85ujLXbYhhOCihBBtbqHRaDQuwmjvgdWTRYsW0dHRwd69e8/bZ7NoH0s9sLpjM7roK02wqEhl54xmgSWE4Otf/zpFRUV89tlngzrH1ZoM98bVV1+Nl5cXr7zyit3GNKPASkpKIiAgYND/tyPFoQJLSrkFqOy5GbD5NgYChY5cg1OREkqPw+TkczZ/eKyYkABv5kQEGbMujUaj0Wh6UFZWNuodBLuTnJxMQEBAr3VYY7UHlo3x48cTGhrap5NgUVER7u7uo16Qz58/Hx8fH44cOTKo420Cy0xCYqgEBARw5ZVXsmnTJioqKuwypm0cM6UIurm5ceWVV7Jly5auptkOnc/hM5zPvcDvhRB5wGPAg30c521NIdwphFjrrMWNiKrT0NYIIWcF1unyBk4U144Ka3aNRqPRjA7a29uprq4e9TfM3XFzc2PBggXs3r2bzs7Oc/YVFBRgsVgICQkxaHXGk5yc3G8Ea/Lkybi5je7SfTc3N6ZPn05GRsagjh8NESyA66+/no6ODt544w27jGdGgQVw7bXXIoTg9ddfd/hcRvymfBP4npQyAvge8Ewfx0VJKdOALwCPCyFieztICHGnVYjttfX0MIzS4+p98oyuTR8eK8bT4s6FCWPnIqbRaDQac2O7ARpLESxQds3V1dWcPn36nO25ubmEhYWNegHRH6mpqZSXl/N///d/XSmBNoqKikZ1emB3EhMTyc7OHlQdli0VztUFVmhoKMuXL2f9+vU0NjaOeLyKigosFgsBAQF2WJ39mDRpEitWrODdd991uNmFEX9JbgNsEvlVoFeTCyllgfU9B9gMzOnjuKellGlSyjTDn8SVHgOf8eCvnALrmtvYll3OsriJ2llPo9FoNKZhrDQZ7klCQgIA2dnZ52zPzs4mJibGiCWZhtWrV3PXXXexf/9+br/9dl577bWufWNNYLW3tw/K9GG0CCxQjYfr6+vZsGHDiMeqrKwkODjYlJlbN954I83NzbzzzjsOnccIgVUIXGj99yXAeZY1QojxQggv678nAkuA/luMm4GSdBW9sv5AfZpRRltHJ5fPGL3mFhqNRqNxPcZSk+HuRERE4OXldY5bXk1NDaWlpUyfPt3AlRmPu7s7N998M88//zxz587lySefJCcnh6amJmpqasaMwLKJ8BMnTgx4bFVVFQEBAXh4eDh6WQ4nKSmJWbNm8eqrr9LR0TGisWwCy4zExsaSlpbGG2+8QVtbm8PmcbRN+4vADiBBCJEvhPga8HXgD0KIQ8CvgDutx6YJIf5pPTUJ2Gs95lPgN1JKcwus+jJoKIOQJEBZs394tJgZUwOJnOBr8OI0Go1GozmLLYI11gSWm5sbMTEx5wgs27/HusCyMWnSJH784x/j5+fHc88915UuOFqbDPdkypQpjBs3blB1WFVVVaMiemXjpptuoqSkhC1btoxoHDM1Ge6Nm266iYqKCjZt2uSwORztIniLlDJUSukhpQyXUj4jpdwmpZwnpUyRUi6UUu6zHrtXSnmH9d/bpZSzrMfMklL2VadlHkqt+i9E1V/tOlVJRUMLV8waG098NBqNxlURQqwSQmQIIbKFEA/0sv/7Qoh0IcRhIcQmIURUt30u2bOxrKwMT09P/P39jV6K04mPjyc7OxspJQCZmZld2zWKgIAArr/+erZu3cq2bdsAJTzGAkIIEhISBhXBKikpMbWQGCqLFy8mPDycl156qev3YziYXWDNmzeP1atXM3nyZIfNMXarOe1NaTpYvCB4GlJK3j1cxNRAH+ZGBhm9Mo1Go9H0gRDCHXgSWA0kA7cIIZJ7HHYASJNSzgZe49z+jS7Zs9HWZNiMNRKOJj4+noaGhi6r5szMTEJDQ01XkG80119/PX5+fvznP/8BRncPrJ4kJCRw+vRpWlpa+jympaWF7OxsEhMTnbgyxyKE4MYbbyQzM5PDhw8Pa4z29nZqa2tNLbCEEPzwhz9kzpxe7R3sghZY9qLkGExKBDd3ThTXcaq8nitmhY7Ji5dGo9G4EAuAbClljpSyFXgJuKb7AVLKT6WUNmutnYDLN0sqLy8fcwYXNuLi4oCzkavMzEydHtgL/v7+3HjjjbS3t+Pr68u4ceMGPmmUkJiYSGdn53lmKN1JT0+nvb2dlJQUJ67M8axcuZKgoKAuYT1UzNhk2Ai0wLIHrQ2qB5bVnv3dw4X4e3mwbPrYvHhpNBqNCxEG5HX7Ot+6rS++BnS32RpUz0ZTtRQBCgsLHZoeY2ZiYmJwc3MjKyuLuro6ioqKtMDqg+uuuw5/f3+mTBlbvTxtUan+0gQPHz6MEIKZM2c6a1lOwcvLiy9+8Yvs27eP3bt3D/l8LbAU2jvcHpSrp2BMSqSopon9Z6pZNzcML4u7sevSaDQajd0QQnwJSOOsEy6ono0FQogY4BMhxBEp5cme50opnwaeBkhLSxt+cYMdqK6upqKioiuSM9bw9PQkKiqK7OzsrgiFFli94+fnxyOPPGL0MpzOhAkTmDBhQr8C69ChQ8TFxeHn5+fElTmHtWvX8tZbb/HUU08xb9483N0Hfz9r1ibDzkZHsOxBTQEAMiiKf28/g4fFjcuTx0YxqEaj0bg4BUBEt6/DrdvOQQhxKfB/wNVSyq7CjMH2bDQTNlExVgUWqDqsrKysLqc4LbD6Ji0tjbS0NKOX4XQSEhL6dBJsb28nPT2d2bNnO3lVzsFisXDXXXdx5swZ3n///SGdqwWWQgsse1BbCBYvPs1t42BeFV9YEEGgr+v3RNBoNJoxwB4gXggxTQjhCdwMnOMGKISYA/wdJa5Ku213yZ6NWmCp772yspJdu3YxefLkMVVfpBkciYmJ5OXl0dDQcN6+jIwMWlpaRl39VXeWLl3K7NmzefbZZ2lsbBz4BCsVFRUIIQgKCnLc4lwALbDsQW0BTd4hPL8zlxlTA3VjYY1Go3ERpJTtwLeBD4HjwCtSymNCiJ8LIWyugL8H/IFXe9ixu17PRpTACgkJGdOueTZL9oMHD+rolaZXbLVVGzZsOG+fzWFv1qxZTl2TMxFC8M1vfpPq6mrWrx98B4rKykoCAwOxWMZ2FdLY/u7thKwrZHeFP8ITvnFh7JgqBNVoNBpXR0r5PvB+j20Pd/v3pX2ctx1wuTusrKysMR29gnOjd1pgaXojNTWVCy64gH/+858sWrSIsLCz3jeHDh0iMjJy1EdpEhMTiYiI4OjRo4M+p7KycswbXIAWWCOns4O6sgIyGtO49cIoJgV4Gb0ijUYzBNra2sjPz6e5udnopYxJvL29CQ8Px8NDp1U7g+bmZvLy8rjooouMXoqh+Pv7Exoaqh0ENX0ihOC+++7j9ttv53e/+x2PP/44Qgg6Ozs5cuQIl1xyidFLdArx8fFDElhmbzLsLLTAGin1pdQ1tdDgNZnl8ZOMXo1Goxki+fn5BAQEEB0draPPTkZKSUVFBfn5+UybNs3o5YwJTp06hZSyK0VuLBMXF6cFlqZf/n97dx4fV13vf/z1yb41TbrTvWylLKUtFdyQCsiiXlGvbFcRFeQniopeF7yu4HLRi14VVy6rXEVAQYsiiEhBL4K0UJa2FEr3FpouCWmbffL5/XHOpDPJJJkks+f9fDzy6MyZc85859NJznzm+/1+vhMmTOCyyy7j29/+NnfffTfvfve7eemll2hpaSno+VexDjvsMP7617/S3Nyc1FzF3bt36+85SrBGzJu3s6+tk0lz5lBSrCltIvmmra1NyVWWmBnjx48nF9aFGi1U4OKAM844g5qamoIf5iUjc/rpp7Ns2TJ+9KMfsXTpUiorKwEKtoJgb9EvY1588UWOO+64Afd1dxobG9WDhYpcjNgrW9fT1e0cfIguViL5SslV9ij2mfXiiy9SXV09ahcZjvX617+ez33uc9luhuQ4M+OLX/wiH/rQh5g6dSqvvPIKRxxxBJMmTcp20zIiNsEazKuvvkokEtEcLJRgjdjLW16i08o4+pDZ2W6KiOSh3bt3s2DBAhYsWMCUKVOYNm0aCxYsoKamho9+9KN99l+2bBlmxvXXX9+zbeXKlZgZ11xzTdLPu3Hjxp4qWcnss2zZMt7+9rcnfX6AJUuWsHz58iEdI+m1bt06Dj30UCW2IkMwZswY3ve+9/Gtb32Lu+66i5/85CfZblLG1NbWMmnSpKQSrD179gAowUJDBEesuWEztTUHMaayLNtNEZE8NH78eFauXAnA1772NWpqavjMZz4z4DFHH300d9xxBxdffDEAt91226iZDyDD193dzfr16/mXf/mXbDdFJG+Nxi8nogtzD0aLDB+gHqwR2L2vneJ9L1M9cWa2myIiBWagHqNZs2bR1tbGjh07cHfuu+8+zjzzzJ7HV65cyWtf+1rmz5/Pu971LhobGwFYsWIFxx57LMceeyw//vGPe/aPRCJ89rOf5TWveQ3z58/n5z//+YBt279/Px/60Ic4/vjjWbhwIb///e8BaG1t5bzzzmPevHm8613vorW1daRhkBTaunUr7e3tmn8lIkNy+OGHs3Xr1kH/pivBOkAJ1gg8uXE3dZE9TJymaikiklnvec97uPPOO3n00UdZtGgR5eUHloh4//vfz7e//W2eeeYZjjnmGK688koAPvjBD3Lttdfy9NNPx53rhhtuYOzYsTzxxBM88cQT/M///A8bNmzo97m/+c1vcvLJJ/PPf/6Thx56iM9+9rPs37+fn/70p1RVVbFmzRquvPJKVqxYkZ4XL8MS/QZaCZaIDMWhhx6Ku/PSSy8NuJ+GCB6Q1iGCZnYj8Hagwd2PDrctAH4GVABdwEfd/Z8Jjr0Q+FJ49xvufks62zocL6x/idklUD95VrabIiIpcMujG9m4e39Kzzl7fDUXvn52Ss8JcM4553Duuefy/PPPc/755/Poo48CwSTjpqYmTjrpJAAuvPBCzj77bJqammhqauJNb3oTABdccAF/+tOfAPjzn//MM888w29+85uec7z44ov9lq/+85//zNKlS3vmfLW1tbF582YeeeQRPvGJTwBBha3RUmUrX6xbt46SkhJmzdI1S0SSF1voYqC5u7t376aqqoqKiopMNS1npXsO1s3Aj4BfxGz7DnClu//JzN4a3l8Se5CZjQO+CiwGHFhhZkvdvTHN7U1ae1eEhq3rqSkvwWqnZrs5IjLKTJkyhdLSUh544AF+8IMf9CRYw+HuXHvttZx++ulx2zdu3Njv/r/97W+ZO3fusJ9TMm/Dhg3MmjWLkhJNvxaR5E2YMIG6urpB52Ft2rSJgw46KEOtym1p/Svr7o+Y2ezem4HoSmVjge0JDj0deMDd9wCY2QPAGcBtaWrqkG3a3UJtZwM15SWgBEukIKSjpymdrrrqKhoaGiguLu7ZNnbsWOrr6/nb3/7GiSeeyK233spJJ51EXV0ddXV1/P3vf+eNb3wjv/zlL3uOOf300/npT3/KySefTGlpKS+88ALTpk3r93lPP/10rr32Wq699lrMjKeeeoqFCxfypje9iV/96lecfPLJPPfcczzzzDNpff0yNFu2bNGiuiIyZGbGoYceOmCC1dXVxXPPPcfb3va2DLYsd2Xja6zLgfvN7BqCOWCvT7DPNGBLzP2t4bacsXHXfsZFdlNeWQ2V9dlujoiMQq9/faI/n3DLLbfwkY98hJaWFg4++GBuuukmAG666SY+9KEPYWacdtppPftffPHFbNy4kUWLFuHuTJw4kd/97nf9Pu+Xv/xlLr/8cubPn093dzdz5szhD3/4A5deeikf/OAHmTdvHvPmzRt0UUrJnK6uLl555RVOPvnkbDdFRPLQ4Ycfzu23305nZyelpaV9Hn/++edpb29n4cKFWWhd7jF3T+8TBD1Yf4iZg/VD4GF3/62ZnQNc4u6n9jrmM0CFu38jvP9loNXd+yzyYmaXAJcAzJw587hNmzal9fVE/c8j65m+4mrOOLgcO+vajDyniKTemjVrmDdvXrabMaol+j8wsxXuvjhLTUqrxYsXe6bXB9u6dSsXXHABV1xxRZ+hoCIig1m2bBlXXnkl1113Xc+crFi33norN910E7///e8ZM2ZMFlqYHf1dq7JRRfBC4K7w9p3A8Qn22QbMiLk/PdzWh7tf5+6L3X3xxIkTU9rQgWzcvZ/pxU3Y2JzqWBMREelj69atAEyfPj3LLRGRfDRv3jzMjF/96lck6pxZuXIlBx988KhKrgaSjQRrO3BSePtkINGAzvuB08ys3szqgdPCbTkh0u1s3b2XCTTBGE3mExGR3LZlSzDqXgmWiAzH5MmTueSSS1i2bBk333xz3GOdnZ2sWrWKBQsWZKVtuSitCZaZ3Qb8A5hrZlvN7CLgw8B3zexp4FuEw/vMbLGZXQ8QFrf4OvBE+HNVtOBFOnl3Nw3bNg6638uvtjKmo4HKEqBWPVgiIvnOzM4ws7Vmts7MrkjweLmZ3R4+/nhsAScz+0K4fa2Z5eT4u23btlFTU0Ntbe3gO4uIJHDuuedy5pln8otf/IK//OUvPduj86+UYB2Q7iqC5/fzUJ+Zz+6+HLg45v6NwI1palpCy3//I8peuId9513PwQOsE7JpdwtTu7ZSUVkME/qOQxURkfxhZsXAj4G3EBRVeiJcGmR1zG4XAY3ufqiZnQd8GzjXzI4EzgOOAqYCfzGzw909ktlXMbCtW7cyffp0zCzbTRGRPGVmfPrTn2b79u18+9vfZvr06RxxxBGsXLkSM+PYY4/NdhNzRjaGCOasQ153FmVFzqq7rmbdjr397rdx935mRLZSVlkFY2f0u5+IiOSF44F17r7e3TuAXwNn9drnLCC64P1vgFMsyFbOAn7t7u3uvgFYR+K5xSmzdetW9u3bN6RjtmzZouGBIjJiJSUlXHXVVYwbN44rr7ySvXv3snLlSg455BDNv4qhBCvGuCmzmPbmizmiYzW/uftO1u9MfAHbtLuFw4q3UzThcChSCEVE8lwyS4P07OPuXcCrwPgkj02Z9vZ2PvWpT/GFL3yBtra2pI7p6Ohg586dSrBEJCVqa2v52te+xq5du/jmN7+p+VcJKDvopXbR2Uw/9GhO33c319zzZJ8ky93ZsvNVpvsrGh4oIiO2e/duFixYwIIFC5gyZQrTpk1jwYIF1NTU8NGPfrTP/suWLcPMuP7663u2RYdnXHNNn5UscsayZct4+9vfnu1mZI2ZXWJmy81s+c6dO4d9nvLyci677DJWrVrFl7/8ZTo7Owc9Zvv27bi7EiwRSZl58+Zx6aWX8vjjj2v+VQJKsHorKqbypMs5qj7CW1rv5Zt/XMNLMUlWU0snlfs2U1niMGFuFhsqIoVg/PjxrFy5kpUrV/KRj3yET33qU6xcuZJ9+/bxk5/8JOExRx99NHfccUfP/dtuuy1rY98jkZyaajRcySwN0rOPmZUAY4HdSR6b0iVFTjrpJD772c+yfPlyvvGNbwz6f6AS7SKSDu9617tYsmQJJSUlzJ8/P9vNySlKsBKZcBhl89/N2yqe4ejIar71xzWsawiSrI279zOtaxMVpcUw4fAsN1RECtVAPT6zZs2ira2NHTt24O7cd999nHnmmT2Pv/TSS5xxxhkcd9xxnHjiiTz//PMA3HPPPZxwwgksXLiQU089lR07dgDw8MMP9/SiLVy4kL179/Z5/ssuu6ynNO/s2bP5/Oc/z6JFi7jzzjv585//zOte9zoWLVrE2Wef3TM/6L777uOII45g0aJF3HXXXeSwJ4DDzGyOmZURFK1Y2mufpQTrOAK8B/irB4vBLAXOC6sMzgEOA/6Z7gafeeaZfOxjH+ORRx7hs5/9LK+88kq/+yrBEpF0MDO++MUvctNNN2n+VS9KsPqz6ELKJh7KR0v/yOTivXzr3jWsa9jLpt0tTOvaSvmYCVA9IdutFJFR6j3veQ933nknjz76KIsWLaK8vLznsUsuuYRrr72WFStWcM011/QMNXzjG9/IY489xlNPPcV5553Hd77zHQCuueYafvzjH7Ny5Ur+9re/UVlZOejzjx8/nieffJJTTz2Vb3zjG/zlL3/hySefZPHixXzve9+jra2ND3/4w9xzzz2sWLFiwAQg28I5VZcRrLe4BrjD3VeZ2VVm9o5wtxuA8Wa2Dvg0cEV47CrgDmA1cB/wsUxVEHzPe97D5z73OdauXcuHPvQh7rnnnoQLgG7dupW6ujqqq6sz0SwRGUVKSkr05U0CaS3TnteKS2HJFZTf80m+XH0P/9H6b3zzj2uYXFvBv9o2SiYdCyp3K1JYHr8O9qxP7TnHHQwnXJLacwLnnHMO5557Ls8//zznn38+jz76KAD79u3j0Ucf5eyzz+7Zt729HQg+aJ977rm8/PLLdHR0MGfOHADe8IY38OlPf5r3vve9vPvd707qYnnuuecC8Nhjj7F69Wre8IY3AEFBhde97nU8//zzzJkzh8MOC+aqvu997+O6665LXQBSzN3vBe7tte0rMbfbgLN7Hxc+9k3gm2ltYD/OPPNMFi1axHe+8x2+973v8be//Y3Pf/7zjB8/vmefrVu3Mm2a1mwUEckU9WANpHYqvO4yKpte5OuznqGuqoxXdu5kCntgouZfiUj2TJkyhdLSUh544AFOOeWUnu3d3d3U1dX1zOtauXIla9asAeDjH/84l112Gc8++yw///nPe6rQXXHFFVx//fW0trbyhje8geeff56SkhK6u7t7ztu7Yl20N8Tdectb3tLzXKtXr+aGG25I98uXGJMnT+aaa67h8ssv55lnnuGDH/wgDz30UM/jW7duZcYMLSkiIpIp6sEazMEnwSvPUPPC3Vx54jHcsbyJWi9VBUGRQpSGnqZ0uuqqq2hoaKC4uLhnW21tLXPmzOHOO+/k7LPPxt155plnOPbYY3n11Vd7ejJuueWWnmNeeukljjnmGI455hieeOIJnn/+eY477jhWr15Ne3s7ra2tPPjgg7zxjW/s04bXvva1fOxjH2PdunUceuih7N+/n23btnHEEUewceNGXnrpJQ455BBuu+229AdkFDMzzjrrLI477ji+9a1vcdVVV9HW1saSJUvYvXu3hvCIiGSQerCScfwlUDeT2id+wMWzdjK2shTGK8ESkex6/etfzzvf+c4+23/5y19yww03cOyxx3LUUUfx+9//HoCvfe1rnH322Rx33HFMmHBgDun3v/99jj76aObPn09paSlnnnkmM2bM4JxzzuHoo4/mnHPOYeHChQnbMHHiRG6++WbOP/985s+f3zM8sKKiguuuu463ve1tLFq0iEmTJqUlBhJv+vTp/PCHP2Tx4sV897vf5Q9/+AOAhgiKiGSQJZoQm68WL17sy5cvT8/JmzbDPZdDpANqp8G7f56e5xGRjFqzZg3z5s3LdjNGtUT/B2a2wt0XZ6lJaZXWa1Vo//79XHbZZWzcuBGA66+/nkMOOSStzykiMtr0d61SD1ay6mbCay8Nbk9UeXYREcld1dXV/Od//id1dXWAerBERDJJc7CG4tBTIdIJk4/MdktEREQGNGXKFL773e+yatUqKioqst0cEZFRQwnWUJjBEW/NditERESScvDBB3PwwQdnuxkiIqOKhgiKyKhXSHNR841iLyIihUYJloiMahUVFezevVsf9LPA3dm9e7eGr4mISEHREEERGdWmT5/O1q1b2blzZ7abMipVVFRojSYRESkoaU2wzOxG4O1Ag7sfHW67HZgb7lIHNLn7ggTHbgT2AhGgq1DL9YpIdpWWljJnzpxsN0NEREQKRLp7sG4GfgT8IrrB3c+N3jaz7wKvDnD8m919V9paJyIiIiIikkJpTbDc/REzm53oMTMz4Bzg5HS2QUREREREJFOyWeTiRGCHu7/Yz+MO/NnMVpjZJRlsl4iIiIiIyLBks8jF+cBtAzz+RnffZmaTgAfM7Hl3f6T3TmHyFU3A9pnZ2hG2awKgYYkHKB59KSbxFI94ike84cZjVqobkitWrFixy8w2jfA0ep/FUzziKR7xFI94ikdfKb1WWbpLE4dDBP8QLXIRbisBtgHHufvWJM7xNWCfu1+TrnbGPNdyFdQ4QPHoSzGJp3jEUzziKR7pobjGUzziKR7xFI94ikdfqY5JtoYIngo8319yZWbVZjYmehs4DXgug+0TEREREREZsrQmWGZ2G/APYK6ZbTWzi8KHzqPX8EAzm2pm94Z3JwN/N7OngX8Cf3T3+9LZVhERERERkZFKdxXB8/vZ/oEE27YDbw1vrweOTWfbBnBdlp43VykefSkm8RSPeIpHPMUjPRTXeIpHPMUjnuIRT/HoK6UxSfscLBERERERkdEim2XaRURERERECsqoTLDCRY5FRERylq5VIiL5aVQmWEB59IYuYNKbmZXF3B717w8zq4m5rXgEDs52O3KJmZ0cVnyV1NK1Svqla1U8XasO0HWqr0xfp0ZVgmVm55nZ88D3zezTAD6KJ6GZ2YfN7Cdmdki225ILzOwCM/sHwfvjUzDq3x/vNbPlwH+Z2VUwuuMBYGbFwP3AjWY2MdvtybbwPbICeDPQme32FApdq+LpWhVP16p4ulbF03UqXrauU2mtIphLzGwW8AngQ0Aj8Bsz2+Xuv8huyzIv/OV7D/A54GXgBDPb5u5t2W1Z5oXfcpUDVxD88n0WKAWuNLOn3f2v2WxfpoXxqAA+A5wMfBrYDdxsZne4+2hfj64YKCP4cuqNZnaPu3dluU0ZFb5HSoBPAl8EznT3x7LbqsKha9UBulYdoGtVPF2rBqTrVA5cpwq6B8vMSmPuVgEvAKvcfQ1wOfDvZjYuG23Lhmg83D0CPAUcD/wUeBMwL4tNywozK/VAG/As8C53/zvwd+D/CNZjGzVi4tEK3O3ub3b3Rwj+UL8IbMtuCzMv9m+ImRW5ewdwD3AXcBEwKVtty4aY90gnwd/TXwKbzKzMzP7VzKZmuYl5SdeqeLpWxdO1Kp6uVfF0nYqXK9epgk2wzOwLwA1m9oFwXG4nwZusCsDdHyAI/OfC/Qs2FtAnHuPc/QV3bwR+AxhwopnVZ7eVmRMTjw+a2RiCP0SN4R+nTmA+sDerjcygXu+P8dFv/8zsFOB/CX53vmdmnwm3F/TvC8TF5EIzm+ju3eEf5lOBHxJ8o36Omb0zfA8VtAS/Mw8Bm4E/AU8C7wJuMbMvhvsX/HskFXStiqdrVTxdq+LpWhVP16l4uXSdKrg3npkdYWaPAkcBdxIML/iAu68DmoH/F7P7FcB5Zlbn7t2Zb2369ROP8y2cHBv+gf4tcBywqNexBTdJNEE8/hV4PxD9xqPbzCqBLmBl9lqaGf28P84zs+jk+q3Aie5+KnA18DUzm1Covy+QMCZnE1ygKoAdwBPh699CEJPLgEi22ptu/fzOXOjue4G/EVy4znD39wGfAj4TfvAp2PdIKuhaFU/Xqni6VsXTtSqerlPxcvE6VYhzsPYCd7j79wEsmOB3GvAj4CrgVxaMR13h7i+Z2V+AGqApS+1Nt0TxONndfxx+A9bt7n82szcAx4S/nLPd/ccFOkl00HgAtUCNu281s2OBo9z9V9lrclr1Gw8Ad18b3dHd15rZPQTfEO7KQlszZaD3yHTgvWZ2EsH8h6XAOqCQ54QkisepBH9TVwBPuns7gLs/Z2b3ARMI5kNI/3StiqdrVTxdq+LpWhVP16l4OXedKrgeLHffBvxPzKbHgWozq3b31cAvgI+Z2efN7KfAIRTwB4F+4jHWzMrDb8Ci74H7gP8I9y2jQA0Wj3DbcUCFmX0NuJHgD1RBGigesfuZWYmZ/ZDggr4xcy3MvAFiUuXuW4E7gKXu/nrgQmABMCPjDc2QfuIxxswq3L0jetEys1Izu5bgPbIpC03NK7pWxdO1Kp6uVfF0rYqn61S8XLxO5X2ClWj8pLvvj7l7MrAlZtsPwp+pwD7g7eFEyYIwhHhEM/nuMNP/DsGkyEPd/b8z0tgMGGo8QvOAYwkqNp3o7rekt5WZM5x4mNn7gH8SDC84291b0t7QDBpCTFrCx77g7t8Nb7cC73D3gkkohhCPtphjzgIe5cB7pJC/KR0WXavi6VoVT9eqeLpWxdN1Kl4+XKfycoigmb2D4I/r93ptNwjWPzCzEg/KUh4CPBI+fhyw092fNrNPe1ChKO+NIB6LgB3uvs3M3uXuBdGVPoJ4LAaeBx4GFrn7C5lteXqM8PflJYI/SP/n7hsy2/L0GUFMFgK73X2zmZkH8r787QjfI+sJ5oCc7e4bM9nuXKdrVTxdq+LpWhVP16p4uk7Fy7frVF4lWGZWAvw7cCkw08z+6u4rzazY3SPuwTjs8FuuFoLJn9XARDO7iWA87mXQU/41r6U4Hnl/wUpBPCYDH3X35Vl6CSmVwnisz9JLSLkU/87k/byPFL5HNmbnFeQmXavi6VoVT9eqeLpWxdN1Kl7eXqfcPa9+CEosVhCsDfJYr8eKCcpS/g44LAxqK/AccHm22654KB6KR/Z/FBPFQ3FVPLL9o3goHopHYccj60FLIqifICgxeU54vzTmsQ3Av8XcPxa4CaiP2fYpYFy2X4fioXgoHopJLvwoHoqr4qF4ZPtH8VA8Cj0eWQ/iAMG1MED/R7DewRrgA8CkmH3eBWzr5/jibL8GxUPxUDwUk1z5UTwUV8VD8cj2j+KheIyWeORsFUEPIvVm4Evu/huCgM8HTo/Z527gBTuwYvdbwn+LvADGrcdSPOIpHvEUj74Uk3iKR3oorvEUj3iKRzzFI57iEa+Q4pGTCZYdKL+4HDgRwN3vA14EjjKzuTG7Xwp8x8xeIShnixfYyt2KRzzFI57i0ZdiEk/xSA/FNZ7iEU/xiKd4xFM84hVaPHIiwTKz4vDfaKnFaJDWESwUdkx4/2FgLDAm3H8BwcJivyUoVVoQa0AoHvEUj3iKR1+KSTzFIz0U13iKRzzFI57iEU/xiFfo8chqgmVmbzCzW4Avmdm4sGsQM4uuRv5PgnKLp1lQ2341MA1YHD6+m6D04tnuvj3T7U81xSOe4hFP8ehLMYmneKSH4hpP8YineMRTPOIpHvFGSzyylmCZ2cHAT4CHgFnA183srQDu3hn+u46gq/AQ4Irw0HZgU/j4Fnd/NsNNTwvFI57iEU/x6Esxiad4pIfiGk/xiKd4xFM84ike8UZTPLLZg3U8sMbdbwY+Q7DC8r+Y2UEAZvYNM7sBWEFQ3/54M1sB7AHuz0qL00vxiKd4xFM8+lJM4ike6aG4xlM84ike8RSPeIpHvFETDwt75tL/RGb/QpCtLnf3x8Is9lbgfHffbGZHAu8HdgBPAB8FvhJmsphZDVDi7k0ZaXCaKR7xFI94ikdfikk8xSM9FNd4ikc8xSOe4hFP8Yg3muOR9h4sMzvIzO4BPgfUAzeZ2enuvh74B3B2uOtaYBVQCzzr7v/m7ussrCri7vvyMcC9KR7xFI94ikdfikk8xSM9FNd4ikc8xSOe4hFP8YineGRmiOBi4G/ufqK7fx34AXBJ+NjfgGPM7AQPatdvA97k7q9CULLRc6zsYgooHvEUj3iKR1+KSTzFIz0U13iKRzzFI57iEU/xiDfq45GWBMvM3m9mS8ysHHiQoDswajfwQnj7ceAp4HthN+BRwCYzq4Lcq2k/XIpHPMUjnuLRl2IST/FID8U1nuIRT/GIp3jEUzziKR7xSlJ1IjMzYArwK6AbeAn4MPBJd3/ZzEo9qBByEEF3Ie7+CvADM5sF3EgwTvP97t6SqnZli+IRT/GIp3j0pZjEUzzSQ3GNp3jEUzziKR7xFI94iscA3H3EP0Bx+O/hwP9GtwHXAnf12uce4NTw9qTw3xJgTCrakgs/iofioXgoJopH7v0oroqH4qF4KB6KRyZ+RtSDZcEqzF8His3sXoJJahEAd4+Y2SeB7WZ2krs/bGZlwE7gBTP7JvB2M1vi7o3A3pG0JRcoHvEUj3iKR1+KSTzFIz0U13iKRzzFI57iEU/xiKd4JGfYc7DM7CSCOvX1wDqCYHcCbzaz46FnHOXXgCvDwyqADxCMzRxDkM02DrcNuUTxiKd4xFM8+lJM4ike6aG4xlM84ike8RSPeIpHPMVjCIbb9QWcCFwQc/8nwKUEQVwRbisiGJt5BzCdYIGxXwALst11l+ofxUPxUDwUE8Uj934UV8VD8VA8FA/FI+OxGkGQq4ByDoyvfC/wn+HtlcDHw9uLgV9n+4WmPZCKh+KheCgmikfO/SiuiofioXgoHopHpn+GPUTQ3Vvcvd2DGvYAbyEYYwnwQWCemf0BuI2gOzFabaQgKR7xFI94ikdfikk8xSM9FNd4ikc8xSOe4hFP8YineCRvxGXaw8luDkwGloab9wL/ARwNbHD3bQAeprWFTPGIp3jEUzz6UkziKR7pobjGUzziKR7xFI94ikc8xWNwqVhouBsoBXYB88PM9ctAt7v/PRrgUUTxiKd4xFM8+lJM4ike6aG4xlM84ike8RSPeIpHPMVjEJaKxNLMXgs8Gv7c5O43jPikeUzxiKd4xFM8+lJM4ike6aG4xlM84ike8RSPeIpHPMVjYKlKsKYDFwDfc/f2EZ8wzyke8RSPeIpHX4pJPMUjPRTXeIpHPMUjnuIRT/GIp3gMLCUJloiIiIiIiKRmDpaIiIiIiIigBEtERERERCRllGCJiIiIiIikiBIsERERERGRFFGCJSIiIiIikiJKsERERERERFJECZaIiIiIiEiKKMESERERERFJESVYIiIiIiIiKaIES0REREREJEWUYImIiIiIiKSIEiwREREREZEUUYIlkkJmdryZ/cPMHjGz28ysNNttEhEREZHMUYIlklpbgJPd/U3ARuCs7DZHRERERDKpJNsNECkk7v5yzN0OoDtbbRERERGRzFMPlsgQmFm9mbmZ7TOzFjPbZGYXJdhvFnAacM8Inmucmd1tZvvD5/m3Afb9XzN72cyazewFM7s42XOZ2Wwzu9fMGs3sFTP7kZmVxDy+zMzawte8z8zWDvc1iYjIyIR/hw9OYr/Z4fVqVH+ZbmYfMLO/j+D4P5nZhalskxQ+JVgiQ7MA2OXuNe5eBXwB+LmZTYjuYGa1wK3AB9y9cwTP9WOCXrDJwHuBn5rZUf3s+5/AbHevBd4BfMPMjkvyXD8BGoCDwtd3EvDRXue/LHzNNe4+dwSvSUSkoJnZRjNrDROhHWZ2s5nVDPNcy3p/YRb+HV6fmtb2PEejmZUP8Tg3s0NT1Y5cYGZfM7P/jd3m7me6+y3ZapPkJyVYIkOzAHgy5v7DQDFQDxB+U/hr4Ep3H3ZPj5lVA/8KfNnd97n734GlwAWJ9nf3Ve7eHr0b/hyS5LnmAHe4e5u7vwLcB/SXyImIyOD+xd1rgEXAYuBLQznYAmn/jGZms4ETCa4Z70j3841Uot640d5DJ7lJCZbI0CwEVgCYWR1Bz9EKYF34+PnACcCXw28Fzw33/YmZ/WQIz3M40OXuL8Rse5oBEp/wOVqA54GXgXuTPNf3gfPMrMrMpgFnEiRZsf7TzHaZ2f+Z2ZIhvA4RkVHL3bcBfwKODoeY/8HMdoY9Rn8ws+nRfcNrxjfN7P+AFoKRECcCPwp7w34U7tfTc2RmbzOzp8Lh4VvM7GtDbOL7gceAm4G4YXC9e89ih9qZ2SPh5qfDtkWvdR82s3VmtsfMlprZ1JjjjzKzB8LHdpjZf4Tby83s+2a2Pfz5frQ3zcyWmNlWM/u8mb0C3BT2Mv0mHBrfDHzAzMaa2Q3hUPltZvYNMytO9ILN7AdhrJrNbIWZnRhuPwP4D+Dc8DU93TsOZlZkZl+yYKh9g5n9wszGho9Fh2ReaGabw2vmF4f4/yEFQgmWyNAsAD4Z/lFvBCYBZ7i7A7j7re4+3t2XhD+3h9s/6u69h90NpAZo7rXtVWBMfweE5x9DcEG+C4j2aA12rkcIkq1mYCuwHPhdzL6fBw4GpgHXAfeY2SFDeC0iIqOSmc0A3go8RfCZ6yZgFjATaAV+1OuQC4BLCP4+fwD4GweGaF+W4Cn2EyRJdcDbgEvN7J1DaOL7gV+GP6eb2eRkDgor5QIcG7btdjM7meBLx3MIhpxvIhjRgZmNAf5C8OXdVOBQ4MHwHF8EXktwfT0WOJ74Hr8pwDiCuF0SbjsL+A3B6/4lQYLYFZ53IcEc6LihlTGeCJ9rHPAr4E4zq3D3+4BvAbeHr+nYBMd+IPx5M8F1sYa+/4dvBOYCpwBfMbN5/bRDCpgSLJEkhd+ozQPmh3Od3kNwURjJPCvM7L12oIDEn8LN+4DaXrvWAnsHOpe7R8IhgNOBSwc7VzgE5T6ChKwamEAw3PHbMed83N33unt7OA79/wg+MIiISGK/M7Mm4O8EQ8m/5e673f237t7i7nuBbxLMeY11czjkuyuZObzuvszdn3X3bnd/BrgtwTkTMrM3EiQtd7j7CuAloN9iSkl4L3Cjuz8ZDln/AvC6cBji24FX3P274XD0ve7+eMxxV7l7g7vvBK4kfjh8N/DV8BrUGm77h7v/zt27Ca5nbwUud/f97t4A/DdwXqJGuvv/hv8XXe7+XaCcICFK9jV+z93Xu/u+8DWe12uY4pXu3uruTxOMFkmUqEmBU4IlkryjgTZgPYC7/xbYTDC/adjc/ZcxBSTODDe/AJSY2WExux4LrErytCWEc7AGOdc4gm9SfxRevHYTfMM6UALlgCXZDhGR0eid7l7n7rPCEQyt4TDsn4fDy5oJRg/U9RrKtmUoT2JmJ5jZQ+Gww1eBjxB8UZaMC4E/u/uu8P6v6DVMcIimEvRaARAmILsJRj/MIEjgBj0uvD015v5Od2/rdUxsnGYBpcDLZtYUJrY/Jxhh0oeZfcbM1pjZq+G+Y0k+ZonaWkJQQCrqlZjbLQS9XDLKKMESSd5CYFV0OGDoXtIwMdjd9xP0Kl1lZtVm9gaCIRG39t7XzCaZ2XlmVmNmxWZ2OsFcsAcHO1d4Yd1AMKykxIJ5ZRcCz4TnrjOz082sInz8vcCb6DtHS0REBvbvBD0lJ4SjIKLD7GK/sPJex/S+39uvCIoWzXD3scDPSOILMDOrJBjKd5IFy3O8AnwKONbMoj0u+4GqmMOmDHLa7QTJTvQ5qoHxwDaChKi/0vJxxxF86bc95n6iGMRu20IwJH5CmNTWuXutu/eZsxzOt/ocwWuvd/c6giHz0ZgNFu9Ebe0CdgxynIwySrBEkreAMPGIcR/wFjOrGOhAM/uZmf1siM/3UaCSoIT6bcCl7r4qPN+fohOECS4IlxLMn2oEriEYKrE0mXMB7wbOAHYSFOvoJLjQQvCt4DfCx3YBHyf4Zja2YIaIiAxuDMG8qyYzGwd8NYljdtB/YhI95x53bzOz40l+iN87gQhwJMG1bQHBEPi/EczLAlgJvDvseTsU6L3mY++23QZ80MwWhEPqvwU87u4bgT8AB5nZ5WFRizFmdkLMcV8ys4kWLHnyFSCuVPpA3P1l4M/Ad82sNixEcYiZJRoqOYYgIdpJMLLjK8QPod8BzLb+KzjeBnzKzOZYUHo/OmerK9n2yuig0pYiSUo0wdjdlxHMXRrs2I8M4/n2EFwEEz12ZsztnQwy5n6Qc60ElvTz2E7gNUk0V0REBvZ9gh6nXQQ9Id+ln7/LMX4A3GJmlxKMOvhEr8c/SpBY/IhgrtcdBIUfBnMhcJO7b47dGJ7nh2b2eYJ5TK8hSDqeISgmcWrM7l8L21YJXOLud5jZl4HfEszlfZRwHpS77zWzt4Sv56sEPU7fBx4n+BKvlgNfYN4ZbhuK9wNXA6sJkqj1xMwljnE/wRejLxD00P038cMN7wTeB+w2sw3uvqjX8TcSDBN8BKgIz/fxIbZVRgGLH+0kIiIiIiIiw6UhgiIiIiIiIimiBEtEREY1M7sxXDT0uX4eNzP7oQULqD5jZotiHrvQzF4Mf0ZSgU1ERAqEEiwRERntbiYo9NKfM4HDwp9LgJ8CxBQqOIFgcdSvmll9WlsqIiI5TwmWiIiMau7+CLBngF3OAn7hgccI1i46CDgdeMDd97h7I/AAAydqIiIyCijBEhERGdg04iuNbQ239bddRERGsYIq0z5hwgSfPXt2tpshIiIjtGLFil3uPjHb7UgVM7uEYHgh1dXVxx1xxBFDPkfD3nZ2NLf12T65toJJY8pH3EYRGT3W79wPwMET+640M9BjEq+/a1VBJVizZ89m+fLl2W6GiIiMkJltynYbYmwDZsTcnx5u20b8GnLTgWWJTuDu1wHXASxevNiHc616cM0OPn7bU7R0RHq2VZUVc+35Czll3uQhn09ERq9zf/4PAG7/f68b0mMSr79rlYYIioiIDGwp8P6wmuBrgVfd/WWCRUZPM7P6sLjFaeG2tFgydxILZtRRZMH9qrJiFsyoY8ncSQBEup0H1+zghw++yINrdhDp1jqXIiLZUFA9WCIiIkNlZrcR9ERNMLOtBJUBSwHc/WfAvcBbgXVAC/DB8LE9ZvZ14InwVFe5+0DFMkakuMi49aITOPMHj9DSHuHKs45iydxJFBcZkW7nghseZ+WWJlo7IlSGydetF51AcTQjExGRjFCCJSIio5q7nz/I4w58rJ/HbgRuTEe7EikuMuqryqivIm5Y4LK1Dazc0tQzfLClI8LKLU0sW9ug4YMiIhmmIYIiIiJ5btX2Zlpj5mYBtHZEWL29OUstEhEZvdSDJSKSx5paOtiwaz/NrZ3UVpYyZ0I1dVVl2W6WZNhRU2upLCuOK4BRWVbMkVNrs9gqEZHRST1YIpJ1TS0dPLW5kYfXNvDU5kaaWjqy3aS8EI1bR1c39VVldHR1K36j1GAFMEREJHPUgyUiWRVNEqrKSigpMlZtf5W/vdDAgpn1zJ9ep96YAWzYtZ+qshKqykrY29bJ9qZWdu9vp6G5ndOPnpL22PXuPRtXXcae/R3qTcuCgQpgiIhIZqkHS0SyKpokRLqdF3bso9iKmFBTzubdLeqNGURzayeVpcXsbetk7SvNdEa6mVBdTlNrZ9pj17v3bM++Dn735Fb27OtQb1qWRAtgTKuv5JR5k5VciYhkiRIsEcmqaJKwvamVitJiIt3OjuZ2nn+lmW1NrTyztSnbTcwJiYZR1laW0toZ6YldRWkJHRFnfHUZVWUlbNi1P23tie09MzMaWzoYW1lKY0sHZtbzWDrbICIikouUYIlIVkWThH3tEbq6u9m0Zz+tHRHGV5dRhLFyc1Pe9YKkek5Zf3OtxlWX0dLRxe797ZQVF9HWGaGtM8LUukoqS4tpbu1M0SvqK5oYR+1r72JMRSn72mOKLKS5DSIiIrlICZaIZNWcCdW0dHRRUgzbG1sxB8eZWFuJGYyrLsubXpCmlg4eeaGBXzy6kVXbXqWkyFIyVK53b1H09p79HSycWU9dZRm79ndQWmzMnTKGMRVB0lpbWZrCVxcvmhhHhydubWxh9cvNFJn37JPuNoiIiOQiJVgiklV1VWUsnFnPzHFVvNLcRlERzBpfTbFBW2eEOROq86IXJNrLtHlPCxNqyikuMl7YsZdIt494qFzv3qK9bZ1s3r2f/3txJxt27ee1h4zn8Mk1zBhXRU15CS0dXbR0dDFnQnUqXlpCcyZUs3NvG89sbaSjK8JBtRW82tbJ7n3tNLd2ZKQNIiIiuUhVBEUk6+qqynjT4UE56c27W+jqdipKi4NEq8ioKs/974KivUxdEaitKMYsKDCwvamVwyePoXEEPVjR3qJotcC1rzRjwOTaCjq6ulm/cx8HT6xhz/4OGsO5WXOn1Kekgl9/62zVVZVRW1lKTWspHREYW1XGafOm0LCvjed37GXRzPqUtUFERCSfKMESkZwxf3odkW6nuxu2Nu7nvuea6Ih0c9Lhk2hq6cjpD+vNrZ3UV5VRU15MY0sHe9s6aWmP0I1TX1XGuJrht33OhGr+/uJOmlo62bBrH2CMrynj8Cm1VJUFf8ajwwVTKbaEfn1VGa2dEZ7a3BgMS6wqwx3mT6vrSSYBpoytoLEl9W0RERHJF7n/tbCIFKREhSDqqso4eGINq19uZtX2ZqrLSjh0Yg2bdu8LE4zcLXYR7WWqrSjlpYZ9tLR3U1IM7vDctibGVY8sOXQAg/bObsqKLdwQSFcxif7mfkWHO0ZfcyzNuxIRkdFOPVgiknED9Yzs2d9BfXUpE2rGURHOO2rr7Ap7b/azcGZu9mLNmVDNU5sbadjXxsETq9mzv4NXWzuZO6WWaXVV7Nnfwazxw5uP9MzWJl5t6aSr26mvDobnVZUFpe3nTklfQYvtTa20tHexc18HrR1dVJWXMKG6jKryEhbOrO95zRAkea2dEVo6upg7Rb1XIiIyeinBEpGMi+0ZAXr+jc716epyqioPdLCXlxTzamtHzha7iM5TaumIsL5hPxPGBD1xU+sqGVNRirsPaQ5W7LwnM3hs/W6m11VSW1FKpLublxr2csjEGoqKrKeYRKqTmqaWDl5+tZWOzm72tHRSBLR2tOHulLV19fQ4LpxZz4Zd+1M+90tERCRfKcESkYyLzleKVVla3PMhvaTEaO/q7unBau+KUFpchBk8tbmxT8GFTIsmQNubWtnR3MqufR1MratkzvhqDp5Uzb62zp7kCgYeNte7iMS46jLW79zX07v3zLYm9rZ20lpdTmWZMa66AiZBQ3M7k8ZWUFZSNKSkJlHRCqDPtg279jNnfA2PvrSLYiuiuqKY/W2d7N7fwesPntDTmxgkWUqoREREopRgiUjGxVbFi4omIXMmVLO1sYWtja2M9VLAaW4LErLm1k7KS4oTFlzIlOjwxu5u2NHcxramNrq6IrR3lvPCjn1Mq6tkb1sX63fuY/70ugGHzSUaKrl05TbKS4opKymmpryY5tYuZo2vYntTK1VlJZSXFFFZWkJ1eYR/XTR9SK890fP97cWdGDBxTEVcXFs6Isyor2LCmHLaOrrY3x6hsqyEirJiJo4pH1FVRBERkUKW1iIXZjbDzB4ys9VmtsrMPhluPzu8321miwc4/gwzW2tm68zsinS2VUQyJ7q4cEtHF+4et2ZSXVUZJx42kaOm1tLW1UVrVzdHTh3LQXWVTBxT0W/BhXSLJid3PbmFbY2tbGtqobK0hGIroqailL1tnVSUFtPc1sn8aWPpiERobOmgrKSo3ySwdxGJSLezZU8rjfs7qK0ooTPi7N7XTlA1sJzSYqO5rYtunAUz64acWCYqWvFqaydNLZ194rqvrYvWzggTa8qZMraKeQfVMmVsBRNrylXIQkREZADp7sHqAv7d3Z80szHACjN7AHgOeDfw8/4ONLNi4MfAW4CtwBNmttTdV6e5zSKSZoPN3YmuixVdGwvg4bUNcYvtwoFhhekW2/NTRBFFBmtebuaIg8ZSVVZEZ8Rp6YhQXlJEc1sXJcVFLJw5btBS5b2HSm5vamV8dRmtXRHMjIrSYqbWVbJx137mThnD4ZPH9PSIzZ9eN+TXkWhoZleX47ElCQniWlMRLFhcX1XGpj37ae/sotudSeNrCq6QhZmdAfwAKAaud/erez3+38Cbw7tVwCR3rwsfiwDPho9tdvd3ZKTRIiKSs9KaYLn7y8DL4e29ZrYGmObuDwBxa6ckcDywzt3Xh/v+GjgLUIIlMgoNNKwwlRLNUYrt+akJe5bGVpbxclMrU+ureHHHXqrKimnrjFBSTNIJSO/XtK89wviaMl5ubqOts4vykmIqS4uoqSxh5viqEReSSBTDkhIDj/9b3NoZCeaUha+9rSvCvrYuaipKGFdTlrW5b+mQzJd57v6pmP0/DiyMOUWruy/IUHNFRCQPZGwOlpnNJrgoPZ7kIdOALTH3twInpLhZIpIFgy1gm0gmSoL3167ofCSAqXWVrH1lb08xikljypk4poyykiJ27Wtnwcw65k9Pbvhe79dUUgxtnd2cMGc8za2dNLd1UlJkvO7g8XG9ecOVKIZjK0sxgqSwd1yjBSwKfNHgoX6Zdz7w1Qy1TURE8lBGEiwzqwF+C1zu7s0pPvclwCUAM2fOTOWpRSRNBirT3l9FukyUBO+vXQ3N7T09P2MqSpk7ZQzrd+1jan0VlWUlTKyt6OnxGUp7er+mmeOqaG7tZGxlKVNqK0Y0HDCZ56utLOXEwyb2vPZRWmo96S/zzGwWMAf4a8zmCjNbTjAk/mp3/12a2ikiInki7QmWmZUSJFe/dPe7hnDoNmBGzP3p4bY47n4dcB3A4sWLvffjIpJ7BirTPpB0lwTvr13R+UjR+8VFxrS6St4+f2pSiUiiYYex881iX1N035EmO/09Z38xXDizrOeYp7c0ZbUMfg47D/iNu0dits1y921mdjDwVzN71t1f6n2gvgwUERk90l1F0IAbgDXu/r0hHv4EcJiZzTGzMoIL29JUt1FEMi86FyjWUOZTRYfyPby2gac2N9KUokIX/bVrTEUJxUXGc9tfZfmm3bR3RZIuDx9ta0dXN/VVZXR0dQ/Y5mgv00lzJw27BP1Qn3O4xxSIpL7MC50H3Ba7wd23hf+uB5YRPz8rdr/r3H2xuy+eOHHiSNssIiI5LK0JFvAG4ALgZDNbGf681czeZWZbgdcBfzSz+wHMbKqZ3Qvg7l3AZcD9wBrgDndfleb2iuS9/pKPdCUlwzFQmfbBpCIR6C8WidrVsLetZ/2t18wax1FT64h0J99Znqg0errLyw/nObPRzhyR1Jd5ZnYEUA/8I2ZbvZmVh7cnEFzzVIhJRGSUS3cVwb8D/ZUKvDvB/tuBt8bcvxe4Nz2tEyk8/RVpOHhiDet37htSUYl0Gsl8quHM34o1WIGN3u0aW1lKeUnxsJ9vuMMhR2I4z5mNduYCd+8ys+iXecXAje6+ysyuApa7ezTZOg/4tbvHZtfzgJ+bWTfBF5ZXaykRERHJWBVBEUm//pKPx17azewJ1QmThDkT6Hd+UDoNdz7VSBOBwRK03u0a6fpb/ZWXN4OnNjemJe7DKWmfqTL4uSjRl3nu/pVe97+W4LhHgWPS2jgREck76R4iKCIZ1NzamTAZ2Lm3LeH27U2teTfvZqTzt/qLUXNrZ1qeb6Bhh+mK+3CGYI5k2KaIiIgcoARLpID0lwxMHFORcPu+tq6cnXczlHlSQ0kEhpowjfT5osMOy0qKaGzpoKykiLGVpUwcU5G2uCd6zsGGgw7nGBEREelLQwRFCkh/i/G+9pDxrN+5r8/2mooSuiLdrH2lmX3tXdSUl3DQ2AraeiUgQzVQWfJkjx/KPKmhlDIf6oLFqVh/K9XDDofznOk6RkREROIpwRIpIAMlA2MrS/tsf2ZrE89ua2JsZRm1FaW0d0V4dlsTR04dO+w2DJYcJWOo86SGYjgJU6oTj9E830lERPJTpNtZtraBVdubOWpqLUvmTqK4qL9adqObEiyRPDWUhWQH6lFyjAPFPi28P3wjrfIH6a9ol+2emqH2oomIiGRTpNu54IbHWbmlidaOCJVlxSyYUcetF52gJCsBzcESyUNDWQtqoH3dYf60sZQWG81tXZQWG/OnjcWTX+apj6EWkUhkpIUlhitTa4Vle75TLq2JJiIiuW/Z2gZWbmmipSOCAy0dEVZuaWLZ2oZsNy0nKcESyUNDWRR2oH1rK0spKS5i7pRajptVz9wptZQUF40okUlFcpSNinapWMB4KKJJ1klzJ2UlucqnypEiIpJdq7Y309rR69reEWH19uYstSi3KcESyUND6SUaaN90JDKpOGc2eniGkrTms9HyOkVEJHWOmlpLZVmvzxJlxRw5tTZLLcptSrBE8tBQeokG2jcdiUyqzpnpHp5UDG3MB6PldYqISOosmTuJBTPqiE63qgrnYC2ZOym7DctRKnIhkoeGUiRhsH3TUfAh20UkhiNdlf1GWrI+1VTBUEREhqq4yLj1ohM48weP0NIe4cqzjlIVwQGoB0skDw2llyjbBRXyRTqGS+bifKdszG8TEZH8V1xk1FeVMa2+klPmTVZyNQD1YInksIF6P4bSS5SPPUqZlooFhXtLRcn6VEvH6xQREZEDlGCJ5KhULNiby3Jt6BykPhFN93pegxnKWmkiIiKSGhoiKJKjCrnaWy4OnUuHbK3nBaMnxiIiIrlGPVgiGZZsz022ez/SKdtD5zLVezaUYiSplu0Yi4iIjFZp7cEysxlm9pCZrTazVWb2yXD7ODN7wMxeDP9N+GnDzCJmtjL8WZrOtopkwlB6FbLZ+5Fu2SwVnsmenWwWGFE5dhERkexI9xDBLuDf3f1I4LXAx8zsSOAK4EF3Pwx4MLyfSKu7Lwh/3pHmtoqk3VCG/RVytbdsJo/pGnoZTdweXtsQl7Blej2vqEJO0FPNzM4ws7Vmts7M+lyPzOwDZrYz5gu/i2MeuzD8svBFM7swsy0XEZFclNYEy91fdvcnw9t7gTXANOAs4JZwt1uAd6azHSK5Yii9CkPt/ejvA34uymbymI6enVyc71TICXoqmVkx8GPgTOBI4Pzwi8Debo/5wu/68NhxwFeBE4Djga/2NyJDRERGj4zNwTKz2cBC4HFgsru/HD70CjC5n8MqzGw5QU/Y1e7+u3S3UySdhrrIa7LV3lJVcTBTc5OGWyo8Fe1Lx0K7uTjfSeXYk3Y8sM7d1wOY2a8JvgRcncSxpwMPuPue8NgHgDOA29LUVhERyQMZSbDMrAb4LXC5uzebHViYzN3dzLyfQ2e5+zYzOxj4q5k96+4v9Tr3JcAlADNnzkzPCxBJkXQVPUjFB/xMl4UfaqnwVLUvHf8H2S5IonLsIzIN2BJzfytBj1Rv/2pmbwJeAD7l7lv6OXZauhoqIiL5Ie1l2s2slCC5+qW73xVu3mFmB4WPHwQ0JDrW3beF/64HlhH0gPXe5zp3X+zuiydOnJiGVyCSOukqepCKYW+5XhY+Ve1Lx/+ByrEXvHuA2e4+H3iAA0Pck2Zml5jZcjNbvnPnzpQ3UEREcke6qwgacAOwxt2/F/PQUiA6GfhC4PcJjq03s/Lw9gTgDSQ3ZEMkp6Wj6EEqPuDnetW5VLYv1f8H2ZzvlOuJcR7YBsyIuT893NbD3Xe7e3t493rguGSPjTmHvgwUERkl0t2D9QbgAuDkmOpLbwWuBt5iZi8Cp4b3MbPFZnZ9eOw8YLmZPQ08RDAHSwmWSAKp+ICf61Xncrl9Ksee154ADjOzOWZWBpxH8CVgj+iIi9A7CAo2AdwPnBZ+IVgPnBZuExGRUSytc7Dc/e+A9fPwKQn2Xw5cHN5+FDgmfa0TSY1MFYYYSCoKGqR6blKq4zKc9mXy/2Yk851G0s50FO0YTdy9y8wuI0iMioEb3X2VmV0FLHf3pcAnzOwdBAWX9gAfCI/dY2ZfJ0jSAK6KFrwQEZHRK2NVBEUKUaYLQwxkpAUNUll1Lh1xGWr7cun/ZiAjbWe6CqeMJu5+L3Bvr21fibn9BeAL/Rx7I3BjWhsoIiJ5RQmWyAjkYnnukUhV1bl0xWUo7RtOG5LpSUp1r1iy7Ry4UqDKsYuIiOSKtFcRFClkmZ7/ki+LCefCvKChtiGZanzpqNiXTDsHe950FE4RERGR4VGCJTICmSy8kE/luHOhIMVQ25BMNb50VOxLpp2qFCgiIpI/lGCJjEAmy3Pn04fsbJYtH24bkulJSkfPXDLtzIUeQREREUmOEiyREchkee58+pCd6bLliYZODrUNyfQkpaNnLpl25kKPoIiIiCRHRS5ERihVhSEGk2/luDMVl8Gq8CXbhmSq8aWrYt9g7VSlQBERkfwxYIJlZksHejy0x90/kJrmiEh/9CE7sVRVLOxdjc8MiouMp7c0xVXty0bFPlUKFBEpDJFuZ9naBlZtb+aoqbUsmTuJ4qL+loyVfDVYD9Y8woV/+2HAj1PXHBHpTy5+yM6FRZabWzup7/WclaXFNA6j+Ee0Jym2VyyazA6nVyyVsvW8IiKSGpFu54IbHmflliZaOyJUlhWzYEYdt150gpKsAjNYgvVFd394oB3M7MoUtkdEBpBLH7JzZSHfdAydLLT1zUREJPuWrW1g5ZYmWjqCObUtHRFWbmli2doGTpk3Ocutk1QarMjFUjOb2HujmU00swoAd78jLS0TkZyWK1UN01GxMJ8KioiISH5Ytb2Z1o5eBYs6Iqze3pylFkm6DJZg/RA4McH2NwL/nfrmiEi+yEYSkopqgclQ1T4REUm1o6bWUlnW67pZVsyRU2uz1CJJl8GGCB7n7pf03ujud5vZN9LUJpG0y4W5Q/ku01UNU1UtMBkqKCIiIqm2ZO4kFsyo47H1u+l2qArnYC2ZOynbTZMUG6wHq2oEx4rkpOgH9Y6ubuqryujo6u7pDckFiXppclGmFxNO15DETPWKiYjI6FZcZNx60QkcOqmG6XWVXHv+QhW4KFCDJUkNZnZ8741m9hpgZ3qaJJJeuTJ3KJFcT/5iZToJSceQxIHiHX19J82dpORKRERSorjIqK8qY1p9JafMm6zkKoFIt/Pgmh388MEXeXDNDiLdnu0mDdlgQwQ/C9xhZjcDK8Jti4H3A+elsV0iaZPKst6plm/V6zJZ1VDVAkVERApboZSyH7AHy93/CRxPsN7VB8IfA05w98fT3TiRdMjlAgaqXtc/VQsUkUJXCN/ci4xEbCl7J76UfT4ZrAcLd28Avjqck5vZDOAXwGTAgevc/QdmNg64HZgNbATOcffGBMdfCHwpvPsNd79lOO0QiZXLBQwyXTgiGdkqCJLoeVO90HIuxltERqd0fHMf6XaWrW1g1fZmjppay5K5k/KqFyCVFIv8MFAp+3xaK6zfBMvMFhL0WP3C3Vf0t98guoB/d/cnzWwMsMLMHgjP+6C7X21mVwBXAJ/v9fzjCBK7xQTJ2QozW5ooERMZiujcmlR+UE+VXEv+srWY8EDPu3Dm8GKRKGHLtXhLdpjZGcAPgGLgene/utfjnwYuJrim7QQ+5O6bwsciwLPhrpvd/R0Za7gUlFQvQlsoQ61SQbHIH9FS9i0xSVY+lrIfaIjgdcC1wM+He3J3f9ndnwxv7wXWANOAs4Bob9QtwDsTHH468IC77wmTqgeAM4bbFpFYuVrAINeq12WrIEiqn7e/YhZATsVbMs/MioEfA2cCRwLnm9mRvXZ7Cljs7vOB3wDfiXms1d0XhD9KrmTYUr0IbaEMtUoFxSJ/REvZR/PefC1lP9AQwRUEPUhPpuKJzGw2sBB4HJjs7i+HD71CMISwt2nAlpj7W8NtIgUtk4UjBpOtgiCpft6Bi1nU50y8JSuOB9a5+3oAM/s1wZeAq6M7uPtDMfs/Brwvoy2UUSHV39wXylCrVFAs8ke0lP2ZP3iElvYIV551VF4O5+w3wXL3j5jZeHffPdInMbMa4LfA5e7ebHYgSO7uZjbsWZxmdglwCcDMmTNH2lQRiZGtOUqpft50J4pauDqvJfoy74QB9r8I+FPM/QozW04wfPBqd/9dylsoo0KqF6EtlKFWqaBY5JdoKfv6KvI2AR6simAqkqtSguTql+5+V7h5h5kdFD5+EJCoj3YbMCPm/vRwW+82Xufui9198cSJE0faXBGJkYnFhBMt9JuK54097yuvtrFzb3vc46lKFPNp7TIZGTN7H8G84P+K2TzL3RcD/wZ838wO6efYS8xsuZkt37lTy0hKX6lehLZQhlqlgmIhmTZggmVm1w12goH2saCr6gZgjbt/L+ahpcCF4e0Lgd8nOPx+4DQzqzezeuC0cJuIZEi654Sla25U7/NOqCnnuW1NNDS3pTxRzOWFqyUpSX2ZZ2anAl8E3uHuPdm6u28L/10PLCMYCt+HvgyUZKRyEdpUJ2z5TLGQTBusTPs7zaxtgMcNePMAj78BuAB41sxWhtv+A7iaYAHji4BNwDkAZrYY+Ii7X+zue8zs68AT4XFXufueQdorIkM02PC2VM0JS/Q86Zgb1dTSwf3PvUJTayfjq8uYWlfJpNoKjqaOXfvaKSm2lFaOzOWFqyUpTwCHmdkcgsTqPILeqB5hVd2fA2eES5dEt9cDLe7ebmYTCK55sQUwRLJqoKFWo61seSEMO5P8MViC9dkkzvG3/h5w978TJGGJnJJg/+UEpXCj928EbkyiDSJ5I9XzdUZyvkyVYe/veVo6Isyor4rbdyTJSfR5mlo7mFBdTkfEWfvKXuZOGcPEMeWUFBsnpXhIiNbSym/u3mVmlxGMkCgGbnT3VWZ2FbDc3ZcSDAmsAe4M5xBHy7HPA35uZt0EI0KudvfVCZ9IJIeobLlIeg2YYGlhX5HUSnVCM9LzDdyDlLoEq7/naWhuT2lyEn2e8dXldES6qSgNzru9qZUZ46rSkvRoLa385+73Avf22vaVmNun9nPco8Ax6W2dSOqles0tEYk34BwsEUmtVM/XGen5mls7qSwtjttWWVpMc2vnsNoz0PN0RbpZ+0ozKzY1svaVZroi3dRUlKS0iEb09Uytq6StM0JbZxdlxcbu/R0pL84RlWtrl4mIDCbVa27lu0i38+CaHfzwwRd5cM0OIt3DLm4tAgw+RFBEUijV83VGer5MDW8zg2e2vUpdZRm1FSW0d3XzzLZXOWpqLfOn17Fh134aWzpGPDcq+nrGVJQyd0ot25ta2bW/nbrKsrQmPbm0dpmIyGBUtvwADZeUdFCCJZJBqU5oRnq+TA5vMxyIfivo4f3hJyeJ5p7Fvp6a8hJmjKtifE16kyuRbPvHSwdWVGlu6+yzTfJLqv8PE52voqSYOROqWf1yM+5QXlLEnAnVVJQUF/R7J1EsntzUyIpNjbR3dQPBcMkVmxr52bKXWDQrPUO9c+H3dKA2DPexVMrE87zukPFpO/eACZaZ3cOBT0R9hJN8RSRJqU5okj1ff4UwosPbUtWD1B93OGZaHS+/2kZzWyc15SUcM62OrmEOwxho7tlQX08yRUK0kLCI5LLubmflliY27t7P7PHVwZpPA/S+FBUZ/3HmPD5/1zO0d0b4wOvnDHpModq4ez8dYXIV1dHVzcbd+9OWYEnhG6wH65rw33cDU4D/De+fD+xIV6NEClWqE5pkzjdYIYxMDG+rrSylo6ubuVMODD9p6eiiqnx400BTVd69d2x27m1nxaY9HDS2kql1lT1ztjJRaVFEZDi6u51v/WkN6xr20dHVTVlJEYdOquE/zpw3aJI1pqKEMRUlfRKJoSZs+Wz2+GrKSop6erAAykqKmD0+9XN2ZfQYrIrgwwBm9t1wpfqoe8xseVpbJlKgUp3QDHa+TFUKjBps6N5Qeu766zna3tRKS3sX+zsi1JSXMLWukprykiHPZYuNzd62TjbvaaHEjJb2rp5Fj4uLLKPxExEZipVbmljXsK8nQWjv6mZdwz5WbmkaVg/McBO2fLVgRh2HTqqJGy556KQaFsyoG/Y5R1OCKoklOwer2swODleqJ1yQUam9SB7I5EK4qR66Fz1XSZGxavur/O2FBg6dPIYNO/dRU15CbWUZ7V0R1r7SzMxx1YyrGVrCExub7U2tVJQWU15SRHNbZ08i9dz2V3nNrHFxx2khYZHRJZc/MKd6iFuqE7Zcl+rhkqMtQZXEkk2wPgUsM7P1BAsHzwL+X9paJZLncmnOTiYXwk3V0L3Yc0W6nRd27KOitJgJNeU8vbmJitIiWjojlJd2U15STHtnhA2793Pc7KFd/GNjs689ElY4DHrFIEikDNdCwiKjWK5/YE71ELdMz0nKheR1oOGSQzXaElRJLKkEy93vM7PDgCPCTc+7e3v6miWSv1K9mPBIZbJSYCp7y6LnemHHXipKi6koLca9iP0dXcwaX0tXt1NabDS3dVFdXkJVecmQ4xsbm+qyIppbO3BgVvjBpLUzwqGTxtDS0dXzWrSQsMjokisfmPtLRFI9xC2Tc5JyPXkdDhXNSK9cSMiTkVSCZWZVwKeBWe7+YTM7zMzmuvsf0ts8kfyT6TlPUdmuFAip7S2LnivaswTQ3hUJEzij272naEZLRxdlJUMvmBEbm6ryEl5t62LO+Gpqyg8sgLxwZnBBzET8RCS9hvPhLBc+MA+WiKRyiFs65iT1Z6DkdcGMurz4IN2bimakTz4l5MkOEbwJWAG8Lry/DbgTUIIlGZdLw+8SyeScp6hcqBQIqe0ti56rpBjaOiOYBf8eM62Ol3btY0x5Ce4+4h6laGwWzqzveW8lSqRU0EIk81L5bfVwP5zlwgfmwXrRUjnELZMl3PtLXjfs2se9z72cFx+ke8tkgjra5EpvcjKS/cr3EHf/DtAJ4O4tBHOxRDIqmkh0dHVTX1XWU+mtKYcKDkR7XmKle85ObK+Z2YGqdxt27U/bcyYS7REqKymisaWDspKiYQ+NjJ5r5rgqdu1rJ9LtHD55DGOrSpleX8nM8VUjfo7+nvOkuZNUhl0ky6IJ0Q//+iK/WbGVH/71Rb71pzV0D3P9vNgPZ078h7OBRD8wW/ipJxsfmAfqRUuHaMI2YUx5TwKXDtHkNVZZSRHdzrD+r3JBNEGdVlfJxJoyPnHyYXmRGOaDTP8ejESyPVgdZlZJuOiwmR0CaA6WZFy2ht8NRSbnPEVlo9esP6nsLaurKuNNh09i/vS6nl7LqvIiTjxsopIfkQKX6m+rBxvq119vWS4sypsLvWjp0F9vT5FZ1odljkQqexTlgHz6PUg2wfoqcB8ww8x+CbwB+EC6GiXSn1xKJPqTyTlPUZmsFJgNmRriKCK5I9Vznwb6cDbY8MFUf2Ae6tDHQh121l/yunJLU958kJbMyaffg0ETLDMrAuqBdwOvJRga+El335Xmton0kS+JRKYTgpH0mkXnHW1vamVfWxc1FcHCvYnmtuX6/DcRKRyp/rZ6oA9nmZzbMZy5YLnQi5YuiZLXfPogLZmTT78Hg87Bcvdu4HPuvtvd/+juf0g2uTKzG82swcyei9l2rJn9w8yeNbN7zKy2n2M3hvusNLPlSb8iKWhzJlT3VHdz957bcyaM7m+1hjv3KTqnbc++DnY0t9Ha0cWOV1vZs6+jz9y2fJj/JiKFI9VznwaaG5PJuR3DnQuWqXlRuUDzmKQ/+fJ7kGyRi7+Y2WfMbIaZjYv+JHHczcAZvbZdD1zh7scAdwOfHeD4N7v7AndfnGQ7pcClsojCSESTjYfXNmQ0yRjoeYdToCE6p62xpYPK0hLGVpVTGd7vXSQjVwppiKSDmZ1hZmvNbJ2ZXZHg8XIzuz18/HEzmx3z2BfC7WvN7PSMNryApeNDdn8fzvortpCOIWn5NFE/m/Llg7RIIskmWOcCHwMeISjXvgIYtFfJ3R8B9vTafHh4HoAHgH9Nsg0iQPYrvWWrJycdz9vc2kllaTH72iOUhx8uykuK2dfeRWVpMc2tnX32jdV7H5F8ZGbFwI+BM4EjgfPN7Mheu10ENLr7ocB/A98Ojz0SOA84iuALxZ+E55MUyNSH7OH2lnV3O09uauSuJ7fy5KbGpCocZjKZE5HsSKrIhbvPSeFzrgLOAn4HnA3M6O9pgT+bmQM/d/frUtgGkWFLVSXDoc5nSkcFxeictpryYtq7uqkoLaa9K0JNeUmfuW35Mv9NZBiOB9a5+3oAM/s1wXVqdcw+ZwFfC2//BviRmVm4/dfu3g5sMLN14fn+kaG2SwoMZ27HcNfV0vwikcJn7oN/22Jm70+03d1/kcSxs4E/uPvR4f0jgB8C44GlwCfcfXyC46a5+zYzm0TQ0/XxsEes936XAJcAzJw587hNmzYN+npERuLhtQ3UV5VhduAC6u40tnRw0txJSZ0jdmHg2KIUA/XIpeJ5+2tHdzds2rOfIqDbnVnjaygqIq49w2mzyHCZ2YpMDQ83s/cAZ7j7xeH9C4AT3P2ymH2eC/fZGt5/CTiBIOl6zN3/N9x+A/And/9Nf883btY8f8t/3Djs9q5+uRmAIw8KpjA3tx3oRd60uwWAWeOrhn3+kXB39rVHaOuMUFFaTE15cdzfrOEY6msarA0DnW8oj+1t62JbUyuxH6PMYFpdJWMqBv7+2t3ZsKuFbncm11bEtTFV7RvJMYM9lmrDaXsqnyffn2s4cv19kYk21FaM/AviOz7y+oTXqmTLtL8m5nYFcArwJDBogtWbuz8PnAZgZocDb+tnv23hvw1mdjfBN4J9EqywZ+s6gMWLFw9v9UGRIUhFT85weqPS0YMUW1K+rSvSU0VwXE1Znx61bJSfFykUsV8G1hx0yIjOFU2sEunvA0cmPrS7O5v3tNLSESy0bhYMI545rnLQ5GGgNgz0Iar3ccm0YaDzDeWxts4Ivb+jdof2zghjKkoGTKLMjIMnJh4SmKr2pet86fhgPpy2D+e5hhuLVL9vs/l7OpL2DfRYqtueyf+rdEh2iODHY++bWR3w6+E8oZlNCpOmIuBLwM8S7FMNFLn73vD2acBVw3k+kVRLxULCw1nPK10LGEdLyi+cOfh5tB6VFKhtxA9Xnx5uS7TPVjMrAcYCu5M8ts+Xgbf/v9elrPH/eGn3oPtc9YdVAHzl7Uel5LFE25/c1MgP//piz313iHQ7/zJ/Wk/57aGcbzDd3c7n73qGts4Ibz9mak+59cHakCrR1xtbRr68pKhneOG3/rSGjkg37rBzbztjKwcfPpgPUv1eynQ7UvlcufKaMtW+geT6+yLR9tcd0mcA3ZDd8ZHE25MtctHbfmDQeVlmdhvBOPS5ZrbVzC4imDz8AvA8sB24Kdx3qpndGx46Gfi7mT0N/BP4o7vfN8y2iqRUKioZRnujYg3WG5UrFRRFCtATwGFmNsfMygiKVizttc9S4MLw9nuAv3owxn4pcF5YZXAOcBjBdWvUyWR1vOj8p21Nreza18EP//oi3/rTGjbsylwbonOpykuKMBKvqxXt4Uq2FLuIFIakerDM7B6CohMQJGVHAncMdpy7n9/PQz9IsO924K3h7fXAscm0TSQdBitAMdKenOH2RqkHSST13L3LzC4D7geKgRvdfZWZXQUsd/elwA3ArWERiz0ESRjhfncQFMToAj7m7pGET1TgUr0w8ED6S2DmTh6TsTZEC2Os3NLExt37mT2+uqcwxkDJZqp70kQk9yQ7B+uamNtdwKboRF+RQhNbzKG+qozWzghPbW5MaW+R5jOJ5BZ3vxe4t9e2r8TcbiOofJvo2G8C30xrA/NAtEend1W9dFTH6y+BKTIy1gYIkqxFs+r7JE2ZTDZFJPckm2AtB1rdvTssTLHIzHa4uxbAkYKTjnLoiag3SkQKyUA9OgPp7nb2tnXR1hnhyU2NSR3TXwIzZ0IN71o4fchtSLVMJpsiknuSTbAeAU40s3rgzwTj1c8F3puuholky3AKUIiIjBYDJUT99egMdK7oXCp3+OFfXxzSWlKJEpihtiEdhptsSnKGk5SLZFKyCZa5e0tYpOIn7v4dM1uZxnaJZI0W1BURSWy4CVF/BioGMVCClA8JTC4keoUo1e9BkXRItoqgmdnrCHqs/hhuK05Pk0Sya86Ealo6umjp6MLde27PmaCx8yIyuqW6Ot5IKg9GE5h3L5rOoln1+nA9SqhCo+SDZBOsTwJfAO4OKyYdDDyUvmaJZI/KoYuIJJbqUuzRuVSxVAyi8ESH9O3c286Tmxrp7vbBD+pHJpcDEBmuZBcafoRgHlb0/nrgE+lqlEi2qQCFiEhfqa6Op2IQhS/VQ/pUoVHyQbLrYE0EPgccBVREt7v7yWlql4iIiKTZUIsFpDohyoe5VDIyw51n1x8l5ZIPki1y8UvgduDtwEcIVrPfma5GiaTbYAsJi4gUusF6FvpLvlKdEKkYRGFL9aLLg70HVWFQckGyc7DGu/sNQKe7P+zuHwLUeyV5KbqQcEdXN/VVZXR0dfPU5kaaVIZdREaRgXoWYpOvXfs6+OFfX+Rbf1pDd7eruIQMSTrm2fX3HhzofSuSSckmWNEFhV82s7eZ2UJgXJraJJJWsQsJm1nP7Q27NEFWREaPgXoWVKlNUiU6pK+8pAgDytM4pE/vW8kVyQ4R/IaZjQX+HbgWqAU+lbZWiaSRFhIWERm4WECqh3XJ6JXJeXaZfN9qKKIMJNkqgn8Ib74KvDl9zRFJv5EsJKy5WyJSKAYrFqBKbZIqmZpnl6kKg+lY7FgJW2FJaoigmR1uZg+a2XPh/flm9qX0Nk0kPYa7kLDmbolIIYn2LHzi5MN4z3HT+cTJh/V8QMzksC6RVMnU+zbVQxE1d6zwJDtE8H+AzwI/B3D3Z8zsV8A30tUwkXSJLiS8Ydd+Gls6qK0sZe6UwRcSjp27BfT8u2HXfq2ZJSJZ87pDxg+6T21Fab/7vuGwCQmPWXrIG1m2toHV25s5cmotS+ZOoniE36gP1A7JfZFuJ9LttLRHaOno6vOeyIX/30y8b5/YuCfhUMRu92G99gfX7GDDrv1xCduGXftp64pwyrzJCdswUPsG255qI3lfpLqN2Y5FVLIJVpW7/9Ms7g3alYb2iKTEYEP5hrOQsOZuichoUlxknDJvcs8HPBndIt3OBTc8zrqGfXQ7fPy2p1gwo45bLzphxAlMKmXifXvU1Foqy4pp6Yj0bKssK+bIqbXDOt+q7c20xpwLoLUjwurtzTn/+5cv74tMS7aK4C4zOwRwADN7D/By2lolMgLpGsoXnbsVK9m5WyIiIvls2dqGoIR/2MvS0hFh5ZYmlq1tyG7DsmDJ3EksmFFHVVkxBlSVFbNgRh1L5k4a1vmiCVuskSRsmaT3RWLJJlgfIxgeeISZbQMuBy4d7CAzu9HMGqJzt8Jtx5rZP8zsWTO7x8wSvnvM7AwzW2tm68zsiiTbKZK2MuzDnbslIiKS7wbqZRltiouMWy86gWvPX8in33I4156/cEQ9NqlO2DJpJO+LSLfT2NLBtsZWHlyzg0gBzTlLtorgeuBUM6sGitx9b5Lnvxn4EfCLmG3XA59x94fN7EMEc7u+HHuQmRUDPwbeAmwFnjCzpe6+OsnnlVEsXUP5hjt3S0Ryk5mNA24HZgMbgXPcvbHXPguAnxIsTxIBvunut4eP3QycRFBhF+AD7r4y/S0XybxUD4vLd6kcihhN2FI9dywThvu+KPShhQMmWGb26X62A+Du3xvoeHd/xMxm99p8OPBIePsB4H56JVjA8cC6MLHDzH4NnAUowZJBjaQM+2CGM3dLRHLWFcCD7n51OFLiCuDzvfZpAd7v7i+a2VRghZnd7+5N4eOfdfffZK7JItkR7WVZuaWJ1o4IlXnUy5IP8nXO43DfFwMNLcy3GCQyWA/WNcBK4E9AO5CKlHIVQbL0O+BsYEaCfaYBW2LubwVOSMFzS5ZlYh2pOROqeWpz8CV0ZWkxrZ1BVZu5U7Q4pojEOQtYEt6+BVhGrwTL3V+Iub3dzBqAiUBTRlookiPyuZdF0me474t8LuyRjMESrIXA+cDbgBXAbQTf9o1kkOSHgB+a2ZeBpcCIxm2Z2SXAJQAzZ84cyakkzaLFJ6rKSqivKqO1M8JTmxtZODO1w+w0lE9EkjTZ3aMFm14BBryqm9nxQBnwUszmb5rZV4AHgSvcvT0tLS0A0fkWLe0RHlyzQx/O81C+9rJIeg3nfVHoQ04HLHLh7k+7+xXuvgC4gXCYnpm9Y7hP6O7Pu/tp7n4cQcL2UoLdthHfszU93JbofNe5+2J3Xzxx4sThNksyIF3FJxKJJlknzZ2U8gRORPKHmf3FzJ5L8HNW7H7hF4f9fnloZgcBtwIfdPfoAjhfAI4AXgOMo+/wwtjjLzGz5Wa2fOfOnSN9WXkndr7F1qZWPn7bU1xww+MFNaldRPrqr5BFPhf2SEZSRS7MbCJBb9YxBMP1hl170cwmuXuDmRUBXwJ+lmC3J4DDzGwOQWJ1HvBvw31OyQ2pLD6R6qGGmRi6KCKZ5+6n9veYme0ws4Pc/eUwgUp4bQur3f4R+KK7PxZz7mjvV7uZ3QR8ZoB2XAdcB7B48eJRl1UU+nwLEelrsEIWhTzkdMAeLDP7kJndB9xJMP/qHHd/S+wFZpDjbwP+Acw1s61mdhFwvpm9ADwPbAduCvedamb3Arh7F3AZQQGMNcAd7r5qWK9Qckaq1pFK9TpX6Vo3S0Ry3lLgwvD2hcDve+9gZmXA3cAvehezCJMyLKj89E7gud7HS0AlvkVGn8HWyIoOLfz4KYdxyrzJBZNcweA9WNcTXDA2AacDp0UrCAK4+4BDBd39/H4e+kGCfbcDb425fy9w7yDtkzySquITsUMNgZ5/N+zaP6wKf6k+n4jkjauBO8Iv/zYB5wCY2WLgI+5+cbjtTcB4M/tAeFy0HPsvwxEeRlAQ6iMZbX0eKfT5FiLSV6EXshjIYAnWmzPSChkVUlV8ItXrXKVr3SwRyW3uvhs4JcH25cDF4e3/Bf63n+NPTmsDC4hKfIuMPqP5i5UBEyx3fzhTDZHRIRXrSKV6nat0rpslIiIq8S0yGo3mL1aSKnIhkktSvc6V1s0SEUk/lfgWGV1G8xcrSrAk76R6nSutmyUiIiKSeqP1ixUlWJKXUjHUMJ3nExEREZHRacAEy8x+5O6Xmdk9JFiAcbAqgiIiIiIiIqPJYD1Y7ydYj+qaDLRFREREREQkrw2WYL0EqiYoIiIiIiKSjMESrIlm9un+HnT376W4PSIiIiIiInlrsASrGKghWKVeREREREREBjBYgvWyu1+VkZaIiIiIiIjkuaJBHlfPlYiIiIiISJIGS7BOyUgrRERERERyTKTbaWzpYFtjKw+u2UGku8+qRSJ9DJhgufueTDVERERERCRXRLqdC254nHUN+9ja1MrHb3uKC254XEmWDGqwOVgiPZpaOtiwaz/NrZ3UVpYyZ0I1dVVl2W6WiIiISMotW9vAyi1NRPOplo4IK7c0sWxtA6fMm5zdxklOG2yIoAgQJFdPbW6ko6ub+qoyOrq6eWpzI00tHdlumoiIiEjKrdreTGtHJG5ba0eE1dubs9QiyRdKsCQpG3btp6qshKqyEsys5/aGXfuz3TQRERGRlDtqai2VZcVx2yrLijlyam2WWiT5Iq0JlpndaGYNZvZczLYFZvaYma00s+Vmdnw/x0bCfVaa2dJ0tlMG19zaSWVprz8ypcU0t3ZmqUUiIiNnZuPM7AEzezH8t76f/RJek8xsjpk9bmbrzOx2M9O4aZECsWTuJBbMqKOqrBgDqsqKWTCjjiVzJ2W7aZLj0j0H62bgR8AvYrZ9B7jS3f9kZm8N7y9JcGyruy9Ic/skSbWVpbR2RqgqO/CWae2MUFtZmsVWiYiM2BXAg+5+tZldEd7/fIL9+rsmfRv4b3f/tZn9DLgI+GnaWisiGVNcZNx60QksW9vA6u3NHDm1liVzJ1FcpFWM8km0EmRLe4QH1+zIyP9hWnuw3P0RoHclQgeifatjge3pbIOkxpwJ1bR0dNHS0YW799yeM6E6200TERmJs4Bbwtu3AO9M9kAzM+Bk4DfDOV5Ecl9xkXHKvMl8/JTDOGXeZCVXeSZblSCzMQfrcuC/zGwLcA3whX72qwiHED5mZu/MVOMksbqqMhbOrKespIjGlg7KSopYOLNeVQRFJN9NdveXw9uvAP2VBkt0TRoPNLl7V3h/KzAtfU0VEZGhGKgSZDplo0z7pcCn3P23ZnYOcANwaoL9Zrn7NjM7GPirmT3r7i/13snMLgEuAZg5c2Y62z3qBUmWEioRyS9m9hdgSoKHvhh7x93dzPr7WrPPNQl4dQht0LVKRCTDBqoEmc5S+9nowboQuCu8fSeQsMiFu28L/10PLAMW9rPfde6+2N0XT5w4MfWtFRGRvObup7r70Ql+fg/sMLODAMJ/E36t2c81aTdQZ2bRLyunA9v6OV7XKilo0Xku2xpbeXDNDi3GKzkhW5Ugs5FgbQdOCm+fDLzYewczqzez8vD2BOANwOqMtVBEREaLpQRf/BH++/veO/R3TXJ3Bx4C3jPQ8SKFLlvzXEQGk61KkGkdImhmtxFUCJxgZluBrwIfBn4QfuPXRjhkwswWAx9x94uBecDPzaybIAm82t2VYImISKpdDdxhZhcBm4BzYEjXpM8DvzazbwBPEQx7FxlVBprnks5hWCKDyVYlyLQmWO5+fj8PHZdg3+XAxeHtR4Fj0tg0ERER3H03cEqC7Uldk8IhgwmHuouMFtma5yKSjGglyEy+F7NR5EJyTFNLBxt27ae5tZPaylLmTKhWdUARERFJSnSeS0tMkpWJeS4iuSobc7AkhzS1dPDU5kY6urqpryqjo6ubpzY30tTSke2miYiISB7I1jwXkVylHqxRbsOu/VSVlVBVFrwVov9u2LVfJdlFRERkUNma5yKSq5RgjXLNrZ3U9xoOWFlaTKN6sERERCRJ2ZjnIpKrNERwlKutLKW1s9fE1M4ItZWlWWqRiIiIiEj+UoI1ys2ZUE1LRxctHV24e8/tOROqs900EREREZG8owRrlKurKmPhzHrKSopobOmgrKSIhTPrVUVQRERERGQYNAdLwiRLCZWIiIiIyEipB0tERERERCRFlGCJiIgUsEi309jSwbbGVh5cs4NIt2e7SSIiKZdLf+uUYImIiBSoSLdzwQ2Ps65hH1ubWvn4bU9xwQ2PK8kSkYKSa3/rlGCJiIgUqGVrG1i5pYnoZ4yWjggrtzSxbG1DdhsmIpJCufa3TgmWiIhIgVq1vZnWjl5rHXZEWL29OUstEhFJvVz7W6cES0REpEAdNbWWyrLiuG2VZcUcObU2Sy0SEUm9XPtbpwRLRESkQC2ZO4kFM+qoKivGgKqyYhbMqGPJ3EnZbpqISMrk2t86rYMlIiJSoIqLjFsvOoFlaxtYvb2ZI6fWsmTuJIqLLNtNExFJmVz7W5fWBMvMbgTeDjS4+9HhtgXAz4AKoAv4qLv/M8GxFwJfCu9+w91vSWdbRUREClFxkXHKvMmcMm9ytpsiIpI2ufS3Lt1DBG8Gzui17TvAle6+APhKeD+OmY0DvgqcABwPfNXM6tPaUhERGVXMbJyZPWBmL4b/9rnOmNmbzWxlzE+bmb0zfOxmM9sQ89iCTL8GERHJPWlNsNz9EWBP781AdMbZWGB7gkNPBx5w9z3u3gg8QN9ETUREZCSuAB5098OAB8P7cdz9IXdfEH4peDLQAvw5ZpfPRh9395UZaLOIiOS4bMzBuhy438yuIUjwXp9gn2nAlpj7W8NtIiIiqXIWsCS8fQuwDPj8APu/B/iTu7ekt1kiIpLPslFF8FLgU+4+A/gUcMNITmZml5jZcjNbvnPnzpQ0MNc0tXTw1OZGHl7bwFObG2lq6ch2k0RECsFkd385vP0KMNjA/fOA23pt+6aZPWNm/21m5SlvoYiI5J1sJFgXAneFt+8kmGPV2zZgRsz96eG2Ptz9Ondf7O6LJ06cmNKG5oJoctXR1U19VRkdXd1KskREkmRmfzGz5xL8nBW7n7s7wRD2/s5zEHAMcH/M5i8ARwCvAcYxQO/XaPgyUEREAtkYIrgdOIlgKMbJwIsJ9rkf+FbMhOPTCC5keaeppYMNu/bT3NpJbWUpcyZUU1dVlvTxG3btp6qshKqy4L8q+u+GXftZODP584iIjEbufmp/j5nZDjM7yN1fDhOohgFOdQ5wt7t3xpw72vvVbmY3AZ8ZoB3XAdcBLF68uN9ETkRE8l9ae7DM7DbgH8BcM9tqZhcBHwa+a2ZPA98CLgn3XWxm1wO4+x7g68AT4c9V4ba8korep+bWTipLe61MXVpMc2tnP0eIiEiSlhKMqiD89/cD7Hs+vYYHhkkZZmbAO4HnUt9EERHJN2ntwXL38/t56LgE+y4HLo65fyNwY5qalhGp6H2qrSyltTPScyxAa2eE2srS1DdYRGR0uRq4I/zybxNBLxVmthj4iLtfHN6fTTBs/eFex//SzCYCBqwEPpKZZouISC7LxhDBUaO5tZP6XsMBK0uLaRxCD9acCdU8tbmx59jWzggtHV3MnaJlwURERsLddwOnJNje+wu/jSSoZOvuJ6ezfSIikp+UYKVAf/OsUtH7VFdVxsKZ9WzYtZ/Glg5qK0uZO6V+SPO4REREREQkM5RgjVB0nlVVWQn1VWW0dkZ4anMjC2fWp6z3KUiylFCJiIiIiOS6bJRpLyix86zMrOf2hl37e3qfykqKaGzpoKykiIUz1fskIiIiIlKo1IM1QoPNs1Lvk4iIiIgMV6TbaWzpoKU9woNrdrBk7iSKiyzbzZIBKMFKwkBrWanKn4iIiIikQ6TbueCGx1nXsI9uh4/f9hQLZtRx60UnKMnKYRoiOIjB1rKaM6Galo4uWjq6cPee23MmVGe55SIiIiKSz5atbWDllia6w+XJWzoirNzSxLK1A62LLtmmBGsQA82xAjTPSkRERETSYtX2Zlo7InHbWjsirN7enKUWSTI0RHAQyaxlpXlWIiIiIpJqR02tpbKsmJaYJKuyrJgjp9ZmsVUyGCVYoXSuZSUiIiIiMlRL5k5iwYw6Vm5porUjQmVZMQtm1LFk7qRsN00GoASLzKxlJSIiIiIyFMVFxq0XncCytQ2s3t7MkVNrVUUwDyjBIn6eFdDz74Zd+1k4s56FM+vZsGs/jS0d1FaWMneK5liJiIiISPoVFxmnzJvMKfMmZ7spkiQlWGgtKxERERERSQ1VEeTAWlaxNM9KRERERESGSgkWWstKRERERERSQwkWWstKRERERERSQ3OwQppnJSIiIiIiI5XWHiwzu9HMGszsuZhtt5vZyvBno5mt7OfYjWb2bLjf8nS2U0RERiczO9vMVplZt5ktHmC/M8xsrZmtM7MrYrbPMbPHw+23m5m+qRMRGeXSPUTwZuCM2A3ufq67L3D3BcBvgbsGOP7N4b79XvRERERG4Dng3cAj/e1gZsXAj4EzgSOB883syPDhbwP/7e6HAo3AReltroiI5Lq0Jlju/giwJ9FjZmbAOcBt6WyDiIhIf9x9jbuvHWS344F17r7e3TuAXwNnhdexk4HfhPvdArwzbY0VEZG8kM0iFycCO9z9xX4ed+DPZrbCzC7JYLtERERiTQO2xNzfGm4bDzS5e1ev7SIiMopls8jF+Qzce/VGd99mZpOAB8zs+bBHLE6YfEUTsH1mNtg3kflgArAr243IMsVAMQDFAEZvDGal6kRm9hdgSoKHvujuv0/V8wzShlRfq0br+yIRxeIAxeKAUReLOz6ScPOEOz4yuuIwgHS8JxJeq7KSYJlZCcGY9+P628fdt4X/NpjZ3QRDNPokWO5+HXBdmpqaFWa2fLTPO1MMFANQDEAxSAV3P3WEp9gGzIi5Pz3cthuoM7OSsBcruj1RG1J6rdL74gDF4gDF4gDFIqA4HJDJWGRriOCpwPPuvjXRg2ZWbWZjoreB0wgmIouIiGTaE8BhYcXAMuA8YKm7O/AQ8J5wvwuBjPSIiYhI7kp3mfbbgH8Ac81sq5lFqyudR6/hgWY21czuDe9OBv5uZk8D/wT+6O73pbOtIiIy+pjZu8xsK/A64I9mdn+4veeaFPZOXQbcD6wB7nD3VeEpPg982szWEczJuiHTr0FERHJLWocIuvv5/Wz/QIJt24G3hrfXA8ems205rqCGPA6TYqAYgGIAikFaufvdwN0Jtvdck8L79wL3JthvPcEQ9kzT++IAxeIAxeIAxSKgOByQsVhYMMJBRERERERERiqbZdpFREREREQKihKsDDKzG82swcyei9m2wMweM7OVZrbczBIONTGzC83sxfDnwsy1OrVGGINIuM9KM1uauVanVj8xONbM/mFmz5rZPWZW28+xZ5jZWjNbZ2ZXZK7VqTXCGGwM91lpZssz1+rUMbMZZvaQma02s1Vm9slw+zgzeyD8PX/AzOr7Ob4g/h7I0BXK34Dh6udvR1K/N4VkpH9DComZVZjZP83s6TAWV4bb55jZ4+Hvyu1hgZpRwcyKzewpM/tDeH9UxiLR54WM/Y64u34y9AO8CVgEPBez7c/AmeHttwLLEhw3Dlgf/lsf3q7P9uvJZAzCx/Zlu/1pjMETwEnh7Q8BX09wXDHwEnAwUAY8DRyZ7deTyRiEj20EJmT7NYzw9R8ELApvjwFeAI4EvgNcEW6/Avh2gmML5u+Bfob8vimYvwEjiEGivx2D/t4U2s9I/oYU2g9gQE14uxR4HHgtcAdwFV2owAAAB35JREFUXrj9Z8Cl2W5rBmPyaeBXwB/C+6MyFok+L2Tqd0Q9WBnkwULJe3pvBqLf1I8Ftic49HTgAXff4+6NwAPAGWlraBqNIAYFo58YHM6Bdd4eAP41waHHA+vcfb27dwC/Bs5KW0PTaAQxKAju/rK7Pxne3ktQmW4awf/nLeFutwDvTHB4wfw9kCErmL8Bw9XP345kfm8Kygj/hhQUD+wL75aGPw6cDPwm3D4qYgFgZtOBtwHXh/eNURqLfmTkd0QJVvZdDvyXmW0BrgG+kGCfacCWmPtbw22F4nIGjwFARTiE8DEze2emGpchqzjwQels4hc1jSr090EyMYDgwvlnM1thZpdkpGVpZGazgYUE37pOdveXw4deIViyordCfx9I//R/n1gyvzcFaxh/QwpOOCRuJdBA8KXTS0CTB0sswOj6Xfk+8DmgO7w/ntEbi0SfFzLyO6IEK/suBT7l7jOATzE611BJNgazPFiB+9+A75vZIZlqYAZ8CPioma0gGO7RkeX2ZEOyMXijuy8CzgQ+ZmZvylQDU83MaoDfApe7e3PsYx6MX1CZV5EhGG2/N/obEnD3iLsvAKYT9PQekd0WZYeZvR1ocPcV2W5Ljhjw80I6f0eUYGXfhcBd4e07Sbyeyjbiv82fHm4rFMnEAHffFv67HlhG8I1dQXD35939NHc/jmAR7pcS7FbQ74MkYxD7PmggWL8oG2sQjZiZlRJ8MPqlu0ff/zvM7KDw8YMIvo3traDfBzIg/d8nlszvTcEZwd+QguXuTcBDBAuH15lZdL3X0fK78gbgHWa2kWAI8cnADxidsejv80JGfkeUYGXfduCk8PbJwIsJ9rkfOM3M6sNqJ6eF2wrFoDEIX3t5eHsCwR+R1RlrYZqZ2aTw3yLgSwSTUHt7AjgsrAZUBpwH5G01xd6SiYGZVZvZmOhtgt+F53rvl+vCMfE3AGvc/XsxDy0l+MKB8N/fJzi80P8eSP8K+m/ACCTze1NQRvg3pKCY2UQzqwtvVwJvIZiT9hDwnnC3URELd/+Cu09399kEfx/+6u7vZRTGYoDPC5n5Hcl2hY/R9EPwrfzLQCfBGNiLgDcCKwiqQT0OHBfuuxi4PubYDwHrwp8PZvu1ZDoGwOuBZ8N9ngUuyvZrSXEMPklQBeoF4GoOLAI+Fbg35ti3hvu8BHwx268l0zEgqJ72dPizKl9jEL7nHXgGWBn+vJVgrPyDBF8y/AUYF+5fkH8P9DOs905B/A0YwetP9Lcj4e9NIf8M9W9IIf8A84Gnwlg8B3wl3H4w8M/w7+SdQHm225rhuCzhQBXBUReL/j4vZOp3JPoBRkREREREREZIQwRFRERERERSRAmWiIiIiIhIiijBEhERERERSRElWCIiIiIiIimiBEtERERERCRFlGCJSB9m9hoze9LMfprttoiIiIjkEyVYIr2Y2XgzWxn+vGJm28Lb+8zsJwn2X2JmbmYXx2xbEG77zBCed7aZDbhobuw+4fP+YYivbZmZLU6wfaOZPRvz2IXAuUCtmY0P9yk1s6vN7MUw+fqHmZ0ZPvZQGJ8+5xYREREZTUqy3QCRXOPuu4EFAGb2NWCfu18zyGHPAecA14f3zydY3C6fvNndd4W3bwVuB54I4wHwdeAg4Gh3bzezycBJAO7+ZjNblukGi4iIiOQa9WCJJGmQHqNNQIWZTTYzA84A/hRz7AIze8zMnjGzu82sPtx+nJk9bWZPAx+L2b/YzP7LzJ4Ij/l/g7St2sxuNLN/mtlTZnZWuL3SzH5tZmvM7G6gMpnX6u6Pu/sid/9/4XmqgA8DH3f39nCfHe5+RzLnExERERktlGCJpM5vgLOB1wNPAu0xj/0C+Ly7zweeBb4abr+JIGk5tte5LgJedffXAK8BPmxmcwZ47i8Cf3X344E3A/9lZtXApUCLu88Ln/O4Yb62Q4HN7t48zONFRERERgUlWCKpcwdBgnU+cFt0o5mNBerc/eFw0y3Am8ysLtz+SLj91phznQa838xWAo8D44HDBnju04Arwv2XARXATOBNwP8CuPszwDPDfnUiIiIiMijNwRJJEXd/xcw6gbcAnyToyRouI+jZuj9uo9nsAfb/V3df22v/ETQhzjpgppnVqhdLREREpH/qwRJJra8QDAWMRDe4+6tAo5mdGG66AHjY3ZuAJjN7Y7j9vTHnuR+41MxKAczs8HDIX3/uBz4ezv/CzBaG2x8B/i3cdjQwfzgvyt1bgBuAH5hZWXi+iWZ29nDOJyIiIlKo1IMlkkLu/mg/D10I/CwsFrEe+GC4/YPAjWbmwJ9j9r8emA08GSZNO4F3DvDUXwe+DzxjZkXABuDtwE+Bm8xsDbAGWDH0V9XjS8A3gNVm1gbsJ0goRURERCRk7p7tNohIlpnZRmBxTJn24ZxjGfAZd1+eqnaJiIiI5BsNERQRCHrIHhzuQsFm9hBwMNCZ0laJiIiI5Bn1YImIiIiIiKSIerBERERERERSRAmWiIiIiIhIiijBEhERERERSRElWCIiIiIiIimiBEtERERERCRFlGCJiIiIiIikyP8HfaaHG3AnwRoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:226: RuntimeWarning: overflow encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:323: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Ti[i] = Ti[i-1] + dTi\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:420: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:226: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:507: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Ph[i-1]) / (Ci)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:420: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:111: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:111: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:416: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Ph[i-1]) / (Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:221: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:225: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Ti[i] = Ti[i-1] + dTi\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:420: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:221: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:221: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:226: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:226: RuntimeWarning: overflow encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:111: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:115: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:420: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:226: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:420: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:111: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:115: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:111: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:109: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci) +\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:111: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:110: RuntimeWarning: overflow encountered in double_scalars\n",
      "  (Ta[i-1] - Ti[i-1]) / (Ria * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:114: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Ti[i] = Ti[i-1] + dTi\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:226: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:507: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Ph[i-1]) / (Ci)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:323: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Ti[i] = Ti[i-1] + dTi\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:221: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:221: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:420: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:114: RuntimeWarning: overflow encountered in double_scalars\n",
      "  Ti[i] = Ti[i-1] + dTi\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:109: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci) +\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:226: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:507: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Ph[i-1]) / (Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:323: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Ti[i] = Ti[i-1] + dTi\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:115: RuntimeWarning: overflow encountered in double_scalars\n",
      "  Te[i] = Te[i-1] + dTe\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:111: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:221: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:222: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:221: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/scipy/optimize/optimize.py:761: RuntimeWarning: invalid value encountered in subtract\n",
      "  np.max(np.abs(fsim[0] - fsim[1:])) <= fatol):\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:365: RuntimeWarning: overflow encountered in square\n",
      "  self.chisqr = (self.residual**2).sum()\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/sklearn/metrics/_regression.py:442: RuntimeWarning: overflow encountered in square\n",
      "  output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:847: RuntimeWarning: invalid value encountered in sqrt\n",
      "  par.stderr = np.sqrt(self.result.covar[ivar, ivar])\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:854: RuntimeWarning: invalid value encountered in sqrt\n",
      "  (par.stderr * np.sqrt(self.result.covar[jvar, jvar])))\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:417: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTe = ((Ti[i-1] - Te[i-1]) / (Rie * Ce) + (Ta[i-1] - Te[i-1]) / (Rea * Ce)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:320: RuntimeWarning: overflow encountered in double_scalars\n",
      "  dTi = ((Ta[i-1] - Ti[i-1]) / (Ria * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:323: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  Ti[i] = Ti[i-1] + dTi\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:110: RuntimeWarning: overflow encountered in double_scalars\n",
      "  (Ta[i-1] - Ti[i-1]) / (Ria * Ci)) * self.rec_duration\n",
      "/Users/csaba/dev/darkgreybox/darkgreybox/models.py:109: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dTi = ((Te[i-1] - Ti[i-1]) / (Rie * Ci) + (Th[i-1] - Ti[i-1]) / (Rih * Ci) +\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:853: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  par.correl[varn2] = (self.result.covar[ivar, jvar] /\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:184: RuntimeWarning: overflow encountered in multiply\n",
      "  return (r*r).sum()\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/numpy/core/_methods.py:48: RuntimeWarning: overflow encountered in reduce\n",
      "  return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/scipy/optimize/optimize.py:761: RuntimeWarning: invalid value encountered in subtract\n",
      "  np.max(np.abs(fsim[0] - fsim[1:])) <= fatol):\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/lmfit/minimizer.py:365: RuntimeWarning: overflow encountered in square\n",
      "  self.chisqr = (self.residual**2).sum()\n",
      "/Users/csaba/.pyenv/versions/3.8.7/envs/darkgreybox/lib/python3.8/site-packages/sklearn/metrics/_regression.py:442: RuntimeWarning: overflow encountered in square\n",
      "  output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight)\n"
     ]
    }
   ],
   "source": [
    "plot(y_test, df.loc[10, ('test', 'model_result')], 'Ti (Test)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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