notebooks/training_notebooks/cosmopower_NN_CMB_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g1V4CZrYEqXm"
   },
   "source": [
    "| | |\n",
    "| --- | --- |\n",
    "| **Author** | Alessio Spurio Mancini |\n",
    "| **email** | a.spuriomancini@ucl.ac.uk |\n",
    "\n",
    "<div align=\"center\"><img src='https://drive.google.com/uc?id=1KTafFIJzWQTBdtdd6xrI7OszfhpZYkVM' width=\"400\" height=\"300\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFVu43WWssWj"
   },
   "source": [
    "---\n",
    "---\n",
    "# **INTRODUCTION**\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook can also be run on Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eiDX_P0fxcuxv530xr2iceaPbY4CA5pD?usp=sharing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CDUW-8l2Enlf"
   },
   "source": [
    "In this notebook we will train a neural network emulator of Cosmic Microwave Background (CMB) power spectra, using the ``CosmoPower`` class ``cosmopower_NN``. As an example we will consider here emulation of **TT** temperature power spectra. Note that this just an example and the procedure is analogous for other CMB power spectra, as well as for the matter power spectrum.\n",
    "\n",
    "<img src='https://drive.google.com/uc?id=1ldao4kWmzsbIRoGAXXRu65UYDSC8bLqQ'>\n",
    "\n",
    "We denote with $\\boldsymbol{w}$ the collection of weights and biases of the $n$ nodes in our NN, \n",
    "$\\boldsymbol{w}= \\{ \\boldsymbol{W}_1, \\boldsymbol{b}_1, \\boldsymbol{W}_2, \\boldsymbol{b}_2, \\dots \\boldsymbol{W}_n, \\boldsymbol{b}_n \\}$. \n",
    "\n",
    "$\\boldsymbol{w}$ parameterizes the mapping between cosmological parameters $\\boldsymbol{\\theta}$ and power spectra $\\boldsymbol{P}_\\lambda$ ($= \\boldsymbol{C}_\\ell$ in this CMB case). \n",
    "\n",
    "**NOTE**: Whenever possible, we recommend taking the logarithm of the power spectra to reduce their dynamic range. The mapping learnt by the NN becomes then effectively a function between cosmological parameters and log-power spectra. \n",
    "\n",
    "If (as in this notebook!) log-spectra are used as training samples, ``cosmopower_NN`` has built-in methods that can be called to output either directly the log-spectra (sometimes this may be preferrable for e.g. interpolation), or the spectra obtained by exponentiating 10 to the output of the network (see an example of this in the \"Testing\" section of this notebook).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8NQyktNjPrHg"
   },
   "source": [
    "---\n",
    "---\n",
    "# **PRELIMINARY OPERATIONS** \n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ncuT-7G1EiMv"
   },
   "source": [
    "We will start with a few imports, as well as with checking that the notebook is running on a GPU - this is strongly recommended to speed up training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YYsBo-3R1Nl-"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking that we are using a GPU\n",
    "device = 'gpu:0' if tf.test.is_gpu_available() else 'cpu'\n",
    "print('using', device, 'device \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GyJVlJhaRpze"
   },
   "source": [
    "We will also set the random seed, for reproducibility of results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vzU9F5rSRaTr"
   },
   "outputs": [],
   "source": [
    "# setting the seed for reproducibility\n",
    "np.random.seed(1)\n",
    "tf.random.set_seed(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7GIndU8g2qTk"
   },
   "source": [
    "---\n",
    "---\n",
    "# **TRAINING/TESTING DATA DOWNLOAD**\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oScHqekIEVdw"
   },
   "source": [
    "Here we will download the datasets for training and testing our emulator.\n",
    "\n",
    "For the illustrative purposes of this notebook, we will use datasets of modest size. These are publicly shared via Google Drive links. Due to the size limits to file sharing via Google Drive, we uploaded the training and testing datasets used in [the CosmoPower release paper](https://arxiv.org/abs/2106.03846) on Zenodo. The user who wants to e.g. reproduce the results of the ``CosmoPower`` release paper is invited to download these files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q_S2i8BQl7TG",
    "outputId": "72fafafa-7e2f-45bb-95ac-b06f03f5ae14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1FERzMIinJjIPvITKFv6wzjZgm5Y5VeFv\n",
      "To: /home/camb_tt_testing_params.npz\n",
      "100% 459k/459k [00:00<00:00, 68.0MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=16MSqQOxKqqmH37fV2e2vsVGx1rJO5YuQ\n",
      "To: /home/camb_tt_testing_log_spectra.npz\n",
      "100% 191M/191M [00:01<00:00, 114MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1KF5NLYs9DJ1W7yfE48BHaouHgj71Fd1i\n",
      "To: /home/camb_tt_training_params.npz\n",
      "100% 2.05M/2.05M [00:00<00:00, 63.3MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1QmvUnrOoT2roQAPUNgoRCTq46HJTwkR7\n",
      "To: /home/camb_tt_training_log_spectra.npz\n",
      "100% 855M/855M [00:08<00:00, 99.0MB/s]\n"
     ]
    }
   ],
   "source": [
    "# Download data\n",
    "!gdown --id 1FERzMIinJjIPvITKFv6wzjZgm5Y5VeFv -O ./camb_tt_testing_params.npz\n",
    "!gdown --id 16MSqQOxKqqmH37fV2e2vsVGx1rJO5YuQ -O ./camb_tt_testing_log_spectra.npz\n",
    "!gdown --id 1KF5NLYs9DJ1W7yfE48BHaouHgj71Fd1i -O ./camb_tt_training_params.npz\n",
    "!gdown --id 1QmvUnrOoT2roQAPUNgoRCTq46HJTwkR7 -O ./camb_tt_training_log_spectra.npz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8fQx49RhHnPn"
   },
   "source": [
    "### **PARAMETER FILES**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "abt4A5RT2FzR"
   },
   "source": [
    "Let's take a look at the content of these files, starting with the parameter samples of the training set.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dXflAMoIiYYR"
   },
   "outputs": [],
   "source": [
    "# training parameters\n",
    "training_parameters = np.load('./camb_tt_training_params.npz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R8uNnzEC5Jpx"
   },
   "source": [
    "`training_parameters` is a `dict` of `np.arrays`. There is a `dict` key for each of the parameters the emulator is going to be trained on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nXGpfJyn0472",
    "outputId": "e547869e-5c7b-47fb-d034-303078a5a017"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['omega_b', 'omega_cdm', 'h', 'tau_reio', 'n_s', 'ln10^{10}A_s']\n"
     ]
    }
   ],
   "source": [
    "print(training_parameters.files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g1_0l-Jx2hCU"
   },
   "source": [
    "Each of these keys has a `np.array` of values: the number of elements in the array is the number of training samples in our set. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2L6IwbA43Gt7",
    "outputId": "ce21eb4e-f2fe-4b25-ccd8-7dd92e223942"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.03959524 0.03194429 0.03467569 ... 0.03958164 0.02816863 0.01942146]\n",
      "number of training samples:  42614\n"
     ]
    }
   ],
   "source": [
    "print(training_parameters['omega_b'])\n",
    "print('number of training samples: ', len(training_parameters['omega_b'])) # same for all of the other parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AV6FCD1THvw7"
   },
   "source": [
    "### **FEATURE FILES**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VrRUihe83qcv"
   },
   "source": [
    "Now let's take a look at the \"features\". With features here we refer to the predictions of the neural network: these may be spectra or log-spectra values. In this case we are emulating values of the TT log-power spectra. These are sampled at each multipole $\\ell$ in the range $[2, \\dots 2508]$, hence our training sample for each log-spectrum will be a 2507-dimensional array. The corresponding `.npz` files contain a `dict` with two keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IHy-ZM4o46e-",
    "outputId": "7569a66c-7d80-41e8-d161-2ff63034cbae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['modes', 'features']\n"
     ]
    }
   ],
   "source": [
    "# training features (= log-spectra, in this case)\n",
    "training_features = np.load('./camb_tt_training_log_spectra.npz')\n",
    "print(training_features.files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IF4-OxwP50wy"
   },
   "source": [
    "The first key, `modes`, contains a `np.array` of the sampled Fourier modes (multipoles, in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h0_vHiL86Bfl",
    "outputId": "56e967f5-e698-40c6-b50c-e35475f487d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   2    3    4 ... 2506 2507 2508]\n",
      "number of multipoles:  2507\n"
     ]
    }
   ],
   "source": [
    "print(training_features['modes'])\n",
    "print('number of multipoles: ', len(training_features['modes']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_19lIZop6TQA"
   },
   "source": [
    "The second key, `features`, has values equal to the actual values of the log-spectra. These are collected in a `np.array` of shape (number of training samples, number of Fourier modes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lQAQRoQs6nGy",
    "outputId": "f16682cc-51e0-43c2-89d4-ae27028c28e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(number of training samples, number of ell modes):  (42614, 2507)\n"
     ]
    }
   ],
   "source": [
    "training_log_spectra = training_features['features']\n",
    "print('(number of training samples, number of ell modes): ', training_log_spectra.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UGnS47Dj7Fs4"
   },
   "source": [
    "The files for the testing samples have the same type of content, hence here we simply load them directly. Note that since we will be comparing the performance of our trained emulator with the testing set spectra (not the log-values), we store them in memory here from their log10 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Aa5P5zCsaNf"
   },
   "outputs": [],
   "source": [
    "# testing parameters\n",
    "testing_params = np.load('./camb_tt_testing_params.npz')\n",
    "# testing features\n",
    "testing_spectra = 10.**(np.load('./camb_tt_testing_log_spectra.npz')['features'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "83cCKejC81q5"
   },
   "source": [
    "---\n",
    "---\n",
    "# **`cosmopower_NN` INSTANTIATION**\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XqBB7NpoEaUS"
   },
   "source": [
    "We will now create an instance of the `cosmopower_NN class`, which we will subsequently train. \n",
    "\n",
    "In order to instantiate the class, we need to first define some of the key aspects of our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2oIaJjVkh4Yo"
   },
   "source": [
    "### **PARAMETERS** \n",
    "\n",
    "Let's start by defining the parameters of our model. `cosmopower_NN` will take in input a set of these parameters for each prediction. \n",
    "\n",
    "If, for example, we want to emulate over a set of 6 standard $\\Lambda$CDM parameters,\n",
    "\n",
    "$\\omega_{\\mathrm{b}}, \\omega_{\\mathrm{cdm}}, h, \\tau, n_s, \\ln10^{10}A_s$\n",
    "\n",
    "we need to create a `list` with the names of all of these parameters, in _arbitrary_ order:     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QUfBM7jYh3bG"
   },
   "outputs": [],
   "source": [
    "# list of parameter names, in arbitrary order\n",
    "model_parameters = ['h', \n",
    "                    'tau_reio', \n",
    "                    'omega_b', \n",
    "                    'n_s', \n",
    "                    'ln10^{10}A_s', \n",
    "                    'omega_cdm', \n",
    "                    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UaUjbzHLOMgA"
   },
   "source": [
    "This `list` will be sent in input to the `cosmopower_NN` class, which will use this information: \n",
    "\n",
    "- to derive the *number* of parameters in our model, equal to the number of elements in `model_parameters`. This number also corresponds to the number of nodes in the input layer of the neural network;\n",
    "\n",
    "- to free the user from the burden of having to manually perform any ordering of the input parameters.\n",
    "\n",
    "The latter point guarantees flexibility and simplicity while using `cosmopower_NN`: to obtain predictions for a set of parameters, the user simply needs to feed a Python `dict` to `cosmopower_NN`, without having to worry about the ordering of the input parameters. \n",
    "\n",
    "For example, if I wanted to know the neural network prediction for a single set of parameters, I would collect them in the following `dict`:\n",
    "\n",
    "```python\n",
    "example_single_set_input_parameters = {'n_s': [0.96], \n",
    "                                       'h': [0.7], \n",
    "                                       'omega_b': [0.0225], \n",
    "                                       'omega_cdm': [0.13], \n",
    "                                       'tau_reio': [0.06], \n",
    "                                       'ln10^{10}A_s': [3.07], \n",
    "                                        }\n",
    "```\n",
    "\n",
    "Similarly, if I wanted to ask `cosmopower_NN` for e.g. 3 predictions, for 3 parameter sets, I would use:\n",
    "\n",
    "```python\n",
    "example_multiple_sets_input_parameters = {'n_s': np.array([0.96, 0.95, 0.97]), \n",
    "                                          'h': np.array([0.7, 0.64, 0.72]), \n",
    "                                          'omega_b': np.array([0.0225, 0.0226, 0.0213]), \n",
    "                                          'omega_cdm': np.array([0.11, 0.13, 0.12]), \n",
    "                                          'tau_reio': np.array([0.07,0.06, 0.08]), \n",
    "                                          'ln10^{10}A_s': np.array([2.97, 3.07, 3.04]), \n",
    "                                           }\n",
    "```\n",
    "The possibility of asking `cosmopower_NN` for batch predictions, in particular, makes it a particularly useful tool to integrate within samples that allow for batch evaluations of the likelihood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "apR42ZRas9AB"
   },
   "source": [
    "### **MODES**\n",
    "\n",
    "A second, important piece of information for the `cosmopower_NN` class is the number of its output nodes, which corresponds to the number of sampled Fourier modes in our (log)-spectra, i.e. the number of multipoles $\\ell$ for the CMB spectra, or the number of $k$-modes for the matter power spectrum.\n",
    "\n",
    "In this example, we will emulate all of the $\\ell$ multipoles between 2 and 2508. Note that we exclude $\\ell=0,1$ as these are always 0. We can read the sampled $\\ell$ range from the `modes` entry of our `training_features` dict (previously loaded):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UgEmvoj6fa4Z",
    "outputId": "0577ed84-7108-49dd-ccd1-01bad665bab2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ell range:  [   2    3    4 ... 2506 2507 2508]\n"
     ]
    }
   ],
   "source": [
    "ell_range = training_features['modes']\n",
    "print('ell range: ', ell_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UxlR_xPXtlfQ"
   },
   "source": [
    "### **CLASS INSTANTIATION**\n",
    "Finally, let's feed the information on model parameters and number of outputs to the `cosmopower_NN`class, which we instantiate with the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L4BmLowjiRau",
    "outputId": "f1c3edfe-96ed-451f-9c5b-2fb37d3a5315"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Initialized cosmopower_NN model, \n",
      "mapping 6 input parameters to 2507 output modes, \n",
      "using 4 hidden layers, \n",
      "with [512, 512, 512, 512] nodes, respectively. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from cosmopower import cosmopower_NN\n",
    "\n",
    "# instantiate NN class\n",
    "cp_nn = cosmopower_NN(parameters=model_parameters, \n",
    "                      modes=ell_range, \n",
    "                      n_hidden = [512, 512, 512, 512], # 4 hidden layers, each with 512 nodes\n",
    "                      verbose=True, # useful to understand the different steps in initialisation and training\n",
    "                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZH-y08FC54bH"
   },
   "source": [
    "---\n",
    "---\n",
    "# **TRAINING**\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dHFQuDIAExeE"
   },
   "source": [
    "In this section we will train our model. \n",
    "\n",
    "To do this, we will call the method `train()` from the `cosmopower_NN` class.\n",
    "Here are the input arguments for this function:\n",
    "\n",
    "* `training_parameters`: as explained above, this is a `dict` of `np.arrays` of input parameters. Each `dict` key has a `np.array` of values, for example\n",
    "\n",
    "```python\n",
    "training_parameters = {`omega_b` : np.array([0.0222, 0.0213, 0.0241, ...]),\n",
    "                       `omega_cdm` : np.array([0.114, 0.134, 0.124, ...]),\n",
    "                        ... etc ...\n",
    "                      }\n",
    "```\n",
    "(each `np.array` has the same number of elements, equal to the number of training samples);\n",
    "\n",
    "* `training_features`: a `np.array` of training (log)-power spectra. Its dimensions are (number of training samples, number of Fourier modes);\n",
    "\n",
    "* `filename_saved_model`: path (without suffix) to the `.pkl` file where the trained model will be saved;\n",
    "\n",
    "* `validation split`: `float` between 0 and 1, percentage of samples from the training set that will be used for validation.\n",
    "\n",
    "\n",
    "Some of the input arguments allow for the implementation of a learning schedule with different steps, each characterised by a different learning rate. In addition to the learning rate, the user can change other hyperparameters, such as the batch size, at each learning step:\n",
    "\n",
    "* `learning_rates`: `list` of `float` values, the optimizer learning rate at each learning step;\n",
    "\n",
    "* `batch_sizes`: `list` of `float` values, batch size for each learning step;\n",
    "\n",
    "* `gradient_accumulation_steps`: `list` of `int` values, the number of accumulation batches if using gradient accumulation, at each learning step. Set these numbers to > 1 to activate gradient accumulation - only worth doing if using very large batch sizes;\n",
    "\n",
    "* `patience_values`: `list` of `int` values, the number of epochs to wait for early stopping, at each learning step;\n",
    "\n",
    "* `max_epochs`: `list` of `int` values, the maximum number of epochs for each learning step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m7V4KKRfln4w",
    "outputId": "5d967041-f0ae-44c0-b1b7-3084a4322d57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting cosmopower_NN training, \n",
      "using 10 per cent of training samples for validation. \n",
      "Performing 5 learning steps, with \n",
      "[0.01, 0.001, 0.0001, 1e-05, 1e-06] learning rates \n",
      "[1024, 1024, 1024, 1024, 1024] batch sizes \n",
      "[1, 1, 1, 1, 1] gradient accumulation steps \n",
      "[100, 100, 100, 100, 100] patience values \n",
      "[1000, 1000, 1000, 1000, 1000] max epochs \n",
      "\n",
      "learning rate = 0.01, batch size = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 424/1000 [07:59<10:51,  1.13s/it, loss=0.0079]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss = 0.004728115\n",
      "Model saved.\n",
      "Reached max number of epochs. Validation loss = 0.004728115\n",
      "Model saved.\n",
      "learning rate = 0.001, batch size = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 133/1000 [02:21<15:24,  1.07s/it, loss=0.00206]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss = 0.0015296168\n",
      "Model saved.\n",
      "Reached max number of epochs. Validation loss = 0.0015296168\n",
      "Model saved.\n",
      "learning rate = 0.0001, batch size = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 101/1000 [01:51<16:35,  1.11s/it, loss=0.000792]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss = 0.0007253906\n",
      "Model saved.\n",
      "Reached max number of epochs. Validation loss = 0.0007253906\n",
      "Model saved.\n",
      "learning rate = 1e-05, batch size = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 100/1000 [01:47<16:05,  1.07s/it, loss=0.000697]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss = 0.0006648694\n",
      "Model saved.\n",
      "Reached max number of epochs. Validation loss = 0.0006648694\n",
      "Model saved.\n",
      "learning rate = 1e-06, batch size = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 100/1000 [01:53<17:03,  1.14s/it, loss=0.000675]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss = 0.00065838336\n",
      "Model saved.\n",
      "Reached max number of epochs. Validation loss = 0.00065838336\n",
      "Model saved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "with tf.device(device):\n",
    "    # train\n",
    "    cp_nn.train(training_parameters=training_parameters,\n",
    "                training_features=training_log_spectra,\n",
    "                filename_saved_model='TT_cp_NN_example',\n",
    "                # cooling schedule\n",
    "                validation_split=0.1,\n",
    "                learning_rates=[1e-2, 1e-3, 1e-4, 1e-5, 1e-6],\n",
    "                batch_sizes=[1024, 1024, 1024, 1024, 1024],\n",
    "                gradient_accumulation_steps = [1, 1, 1, 1, 1],\n",
    "                # early stopping set up\n",
    "                patience_values = [100,100,100,100,100],\n",
    "                max_epochs = [1000,1000,1000,1000,1000],\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BCYXFX2avky0"
   },
   "source": [
    "The model is trained! If you are satisfied with its performance on the testing set (see next section) you can download the `.pkl` file where the model was saved (given by the `filename_saved_model` argument of the `train` method of the `cosmopower_NN` class)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lJ3X82Zzvf4W"
   },
   "source": [
    "---\n",
    "---\n",
    "# **TESTING**\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qj2qOlqrWxjn"
   },
   "source": [
    "**N.B.: We remind the user that the purpose of this notebook is to merely provide an illustrative guide on how to train a `cosmopower_NN` model. Therefore, you may find the following predictions on the testing parameters to be not accurate enough for your purposes - this is due to the limited size of the training set used in this notebook, shared via Google Drive (which imposes limits on the size of shareable files). For higher accuracy results, such as the ones reported in [the CosmoPower release paper](https://arxiv.org/abs/2106.03846), the user is invited to increase the size of the training dataset, by e.g. downloading the datasets available on Zenodo, which were used to produce the high accuracy results of [the CosmoPower release paper](https://arxiv.org/abs/2106.03846).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "at7Q9jiMviTa"
   },
   "source": [
    "Let's load the trained model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GnNI4WwgHILn"
   },
   "outputs": [],
   "source": [
    "cp_nn = cosmopower_NN(restore=True, \n",
    "                      restore_filename='TT_cp_NN_example',\n",
    "                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R0IypLN3kI1H"
   },
   "source": [
    "Let's compute the predictions for the testing parameters. Note that we use the function `ten_to_predictions_np`, which, given input parameters,\n",
    "\n",
    "* first performs forward passes of the trained model \n",
    "* and then computes 10^ these predictions.\n",
    "\n",
    "Since we trained our model to emulate _log-spectra_ , the output of `ten_to_predictions_np` is then given by predicted _spectra_.\n",
    "\n",
    "**NOTE 1**: If we had trained our model to emulate spectra (without log), then to obtain predictions we would have used the function `predictions_np`.\n",
    "\n",
    "**NOTE 2**: the functions `ten_to_predictions_np` and `predictions_np` implement forward passes through the network using only Numpy. These are optimised to be used in pipelines developed in simple Python code (not pure-Tensorflow pipelines). \n",
    "\n",
    "Conversely, the functions `ten_to_predictions_tf` and `predictions_tf` are pure-Tensorflow implementations, and as such are optimised to be used in pure-Tensorflow pipelines.\n",
    "\n",
    "**NOTE 3**: both pure-Tensorflow and Numpy implementations allow for batch evaluation of input parameters - here, in particular, we can obtain predictions for all of the `testing_parameters` in just a single batch evaluation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "g0F91_YAkK5l"
   },
   "outputs": [],
   "source": [
    "predicted_testing_spectra = cp_nn.ten_to_predictions_np(testing_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vpvz7S1Py_zw"
   },
   "source": [
    "Plot examples of power spectra predictions from the testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "id": "GUPRqDIKwhYS",
    "outputId": "17f8959c-f8b2-448f-a653-632d932638c4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3600x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import gridspec\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(50,10))\n",
    "for i in range(3):\n",
    "    pred = predicted_testing_spectra[i]*ell_range*(ell_range+1)/(2.*np.pi)\n",
    "    true = testing_spectra[i]*ell_range*(ell_range+1)/(2.*np.pi)\n",
    "    ax[i].semilogx(ell_range, true, 'blue', label = 'Original')\n",
    "    ax[i].semilogx(ell_range, pred, 'red', label = 'NN reconstructed', linestyle='--')\n",
    "    ax[i].set_xlabel('$\\ell$', fontsize='x-large')\n",
    "    ax[i].set_ylabel('$\\\\frac{\\ell(\\ell+1)}{2 \\pi} C_\\ell$', fontsize='x-large')\n",
    "    ax[i].legend(fontsize=15)\n",
    "plt.savefig('examples_reconstruction_TT.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zfYz7VnZJdaL"
   },
   "source": [
    "We want to plot accuracy in units of Simons Observatory (SO) noise curves. \n",
    "Start by loading SO noise curves from [the SO repository](https://github.com/simonsobs/so_noise_models) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CM0baRkTKgm1",
    "outputId": "65e319e7-8d55-48c6-8eb1-00f4b961ebf8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'so_noise_models'...\n",
      "remote: Enumerating objects: 179, done.\u001b[K\n",
      "remote: Counting objects: 100% (179/179), done.\u001b[K\n",
      "remote: Compressing objects: 100% (129/129), done.\u001b[K\n",
      "remote: Total 179 (delta 69), reused 147 (delta 47), pack-reused 0\u001b[K\n",
      "Receiving objects: 100% (179/179), 7.96 MiB | 16.56 MiB/s, done.\n",
      "Resolving deltas: 100% (69/69), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/simonsobs/so_noise_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2J1EZabvO1bQ"
   },
   "outputs": [],
   "source": [
    "# load noise models from the SO noise repo\n",
    "noise_levels_load = np.loadtxt('./so_noise_models/LAT_comp_sep_noise/v3.1.0/SO_LAT_Nell_T_atmv1_goal_fsky0p4_ILC_CMB.txt')\n",
    "conv_factor = (2.7255e6)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WELDc5ZSirzr"
   },
   "outputs": [],
   "source": [
    "ells = noise_levels_load[:, 0]\n",
    "SO_TT_noise = noise_levels_load[:, 1][:2509-40] / conv_factor\n",
    "new_ells = ells[:2509-40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KowIsngd4pwH"
   },
   "outputs": [],
   "source": [
    "f_sky = 0.4\n",
    "prefac = np.sqrt(2/(f_sky*(2*new_ells+1)))\n",
    "denominator = prefac*(testing_spectra[:, 38:]+SO_TT_noise)  # use all of them\n",
    "diff = np.abs((predicted_testing_spectra[:, 38:] - testing_spectra[:, 38:])/(denominator))\n",
    "\n",
    "# Compute percentiles\n",
    "percentiles = np.zeros((4, diff.shape[1]))\n",
    "\n",
    "percentiles[0] = np.percentile(diff, 68, axis = 0)\n",
    "percentiles[1] = np.percentile(diff, 95, axis = 0)\n",
    "percentiles[2] = np.percentile(diff, 99, axis = 0)\n",
    "percentiles[3] = np.percentile(diff, 99.9, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 657
    },
    "id": "60K7P6LF2s0Z",
    "outputId": "856c558c-67fa-4897-f37d-9888baade894"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAKACAYAAACBhdleAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxU1fnH8c+TBcKmARREQAWVCqhUG7SoCIrGBdqfIoJCBTcEUVurUjesFlFxAakKAgqCFAQUd6FSN3ZUwBYVBJVFRFlkJ+tM5vz+mMk4SSYhyyQzk3zfr9e85s495577ZETg4Zz7HHPOISIiIiIiIhWXEO0AREREREREqgslWCIiIiIiIhGiBEtERERERCRClGCJiIiIiIhEiBIsERERERGRCFGCJSIiIiIiEiFKsERERERERCJECVYNYmYNzOwhM/vSzA6a2T4z+9zM7jSzWuUcs7mZDTGzV83sOzPLCrw2mtkrZnZ+KcdpamajzGxd4PrdZrbIzG40MytPbCIiIiIiVc200XDNYGbHAp8AxwVOZQKJQO3A5y+Abs65PWUYsyWwGQhNgDIDn+uEnJsM3OScyytmnN8B7wONA6cOAilAUuDz+8AfnXO5pY1NRERERCQaNINVA5hZEvAO/uTqZ+BC51w9oC5wFXAAOA34VxmHTsSfTH0IDACaB8atD7QH3gr0ux54qJjYDgfexZ9cfQN0dM41AOoBtwIe4CJgTBljExERERGpcprBqgHM7AbgxcDHs5xzywq1Xw3MCHy8wDn3YSnHPRw43jm3qph2A+YCF+OflTrSOZddqM/DwDAgC2jvnNtYqP1e4FEgD2jnnFtfmthERERERKJBM1g1w4DA+8eFk6uAmUB+YtO/tIM65/YVl1wF2h3+5YHgn9VqG6Zb/v1mFk6uAp7Fn5wlAv1KG5uIiIiISDQowarmzKwucHbg47xwfQKJ0L8DH9MjHELojFViodh+AxxziNgOAosqKTYRERERkYhSglX9teXX/85fldAvv+0oM2sUwft3DbznAoWX950c5v7h5Le1i1BMIiIiIiKVQglW9Xd0yPHWEvqFth1dbK8yMLNWwODAx1nOuf0VjO0wM6sfidhERERERCpD0qG7SJxrEHKcWUK/0LYGxfYqJTOrA7yKv1LhL8A9EYztYJj73QTcBFCvXr3fnXTSSWUNWURERESk1FauXPmLc+7IwueVYEnEBcrCzwB+h7/Mej/n3E+VeU/n3ERgIkBaWppbsWJFZd5ORERERGo4M9sc7ryWCFZ/B0KO65bQL7TtQLG9DsHMEoHpwGWAF+jrnJsfC7GJiIiIiFQ2JVjVX+jMUfMS+oW2lWu2KZBc/QvojX/fqj85516LYGz7A1UFRURERERikhKs6m8t4Ascn1xCv/y2bc653WW9ScjM1VX8mlzNOsRloZUDSxPbmrLGJSIiIiJSlZRgVXPOuUxgSeDjxeH6mJkBFwU+Frecr1iB5GoG0Idfk6uZpbh0PfDDIWKrB3Qub2wiIiIiIlVJCVbNMDXwfp6ZnRmm/UqgdeD45bIMHDJz1Rv/M1f9Splc5W9wnH+/q8zsuDDdbgHq40/cppclNhERERGRqqYEq2aYCnwJGDDHzLoBmFmCmV0JvBDoN88592HohWb2kJm5wOu4Qm35z1z14deCFodaFljYU8A2/IUs3jOz3wXGrmVmNwMPB/pNdM4V3qhYRERERCSmqEx7DeCc85rZH4GPgeOAD8wsE3+CnRLo9gXQr4xDn43/mSsABzxrZs+W0P8vhRMw59w+M+sBvA+0A1aY2YFAXMmBbvOBv5YxNhERERGRKqcEq4Zwzm0ys1OBu4CeQCv8e1R9DbwCPOucyy3jsKEzoMlA00P0r1NMbCvNrD1wN9ADaAlk4C+CMRWY7JzzhbtWRERERCSWmP8xGJHqQxsNi4iIiEhlM7OVzrm0wuf1DJaIiIiIiEiEKMESERERERGJECVYIiIiIiIiEaIES0REREREJEKUYImIiIiIiESIEiwREREREZEIUYIlIiIiIiISIUqwREREREREIkQJloiIiIiISIQowRIREREREYkQJVgiIiIiIiIRogRLREREREQkQpRgiYiIiIiIRIgSLBERERERkQhRgiUiIiIiIhIhSrBEREREREQiRAmWiIiIiIhIhCjBEhERERERiZCkaAcgEs+8Lz2H27c32mFUGTs8laTrbq3y+65evZrx48ezYMECtmzZgsfjoVmzZnTq1IkBAwaQnp5eqnE2bNjAuHHj+PDDD9m0aRNZWVk0bdqU008/nX79+tGrV69DjpGXl8f48eOZPHkya9euJSkpiQ4dOnDrrbfSp0+fQ17/3Xffccopp5CTk8OSJUvo1KlTqWIXERGR+KAES6QC3L69WN160Q6jylR1Mun1ehk6dChjxowp0rZx40Y2btzIjBkz6NOnD1OmTCElJaXYsR5//HGGDRuG1+stcP6HH37ghx9+4M033+T8889n9uzZNG7cOOwYzjmuvPJK3njjjQLnFy9ezOLFi1mxYgVPPvlkiT/TzTffTHZ2NoMGDVJyJSIiUg0pwRKRmDV48GAmTZoEQHJyMn/6058499xzSUlJYe3atUyaNImtW7cya9YscnJyeP311zGzIuOMGDGCBx54AAAz44orriA9PZ3DDz+c77//nqlTp7Ju3To++ugjunfvzscff0ydOnWKjDNx4sRgctWpUyeuvfZasrOzGTt2LOvXr+epp54iPT2dCy+8MOzPM336dD744AOOOuooRo4cGamvSURERGKIOeeiHYNIRKWlpbkVK1ZUyb08Y0bUrBmszAySbx9WJfeaN28el156KQANGjTgww8/pGPHjgX6HDhwgO7du7No0SIApk6dSv/+/Qv0+frrrzn11FPx+XwkJSXxxhtv0KNHjwJ9PB4P/fr149VXXwXgH//4B3//+9+LxPTb3/6W//3vf3To0IHPP/+c5ORkAPbu3UubNm3YuXMnl112WZEZLoA9e/Zw0kknsWPHDl555RWuuuqqcn4zIiIiEgvMbKVzLq3weRW5EJGY9MwzzwSPH3300SLJFfgTrxkzZlCrVi0AHnjgAQr/o9Fzzz2Hz+cD4C9/+UuR5Ar8s2MvvfQSRx55JABPPPEE+/fvL9AnMzOT1atXAzBo0KBgcgWQmprKgAEDAFi+fHnYn+dvf/sbO3bs4OKLL1ZyJSIiUo0pwRKRmOPz+ViwYAHgX9LXr1+/Yvu2aNGC888/H/A/T7V48eIC7R999FHw+Jprril2nHr16nHFFVcAkJGRwVtvvVWgfc+ePcHkrVWrVkWub926NQC7du0q0rZkyRImTZpEnTp1GDt2bLExiIiISPxTgiUiMWfXrl1kZWUB0KRJExo2bFhi/zZt2gSP586dW6Dtxx9/DB7/5je/Kfc4devWLRBfuJgL9wP/8sNBgwbhnOOBBx4IJmIiIiJSPSnBEpGYU5FnQ7/88suIjFV4nIYNG9KsWTMA3nvvvSL93333XQDatWtX4PyTTz7J119/zcknn8xdd91VrlhEREQkfijBEpGY06hRo+AzTjt37mTv3pLLw69fvz54vG7dugJtRx11VNh+hxpn/fr1RZKz3r17AzBr1iwee+wxduzYwY8//shtt93Gp59+ClBgL6wNGzYwYsQIzIwJEyYUeG5LREREqiclWCISc5KSkjjzzDMB//NY06dPL7bv1q1bCzxnVTgZO/vss4PH06ZNK3aczMxMXn/99eBnj8dDZmZmgT7Dhg2jefPm+Hw+7rvvPpo2bUrLli157rnnADj99NMZPHhwsP+QIUPIyspi4MCBnHXWWSX9yCIiIlJNKMESkZg0cODA4PF9993HypUri/Q5ePAg/fr1Izc3N3iucPW/0HHGjBnDvHnziozj8Xi44YYb2LFjR4Hzhcc64ogjWLRoEV27di1w3szo3bs3H3zwAbVr1wZg5syZvP/++zRt2pTHH3882Pftt9+ma9euHHbYYdSrV4+OHTsyefLkCi2LFBERkdihfbCk2tE+WJWnKvfBysvLIz09PTg7VatWrSIbDU+ePJktW7bQunVrNmzYAEBKSkqwQEa+a6+9lqlTpwKQkJBAr169SE9Pp0GDBmzYsIGpU6fyzTffFBgHYNu2bTRt2jRsfJs3b2bdunUkJCRw6qmn0qRJk2Db3r17adu2Ldu2bWP69On07dsXgJEjR3LvvfcC/mIYSUlJwSRu8ODBPP/885H46kRERKQKFLcPlhIsqXaUYFWeqkywwJ+o9O3bN+ysU760tDRGjhzJBRdcAECzZs346aefCvTJyclh0KBBwSQrnBNOOIGZM2eSlpZW4Lr8PbbKYvDgwUyYMIH09HTef/99AFauXMkZZ5yBz+fj/vvv5+9//ztJSUmMHz+eW265BYA33niDyy67rMz3ExERkaqnjYZFJO6kpqYyd+5c3nnnHXr16kWLFi2oXbs2qampdOrUieeee45ly5aRkPDrb2WhRS3y1a5dmylTprBw4UKuueYaWrVqRZ06dWjQoAGnnXYajz76KP/9739p1KhR8JqGDRuWK7latmwZEydOpE6dOowbNy54Pn/D47Zt2/Lwww9Tq1YtEhISGDJkCOnp6UDBzZVFREQkPiVFOwARkUPp0aMHPXr0KLZ9zZo1weOOHTsW269z58507ty5wuMUx+v1Bve8GjZsGMcff3ywbeHChQBcfvnlmFmB63r16sX8+fNZunQpXq+XpCT91iwiIhKvNIMlInFvwYIFweOSEqjKHmfUqFF8+eWXtG/fnqFDhxZo27p1KwAtWrQocl3+uZycnLCbGIuIiEj8UIIlInHtl19+CW7ym5qaSs+ePcs1Tk5ODq+88goAiYmJDBgwoEzXb9q0ieHDh2NmjB8/vtg9rzIyMoqcO3jwYNkDFhERkZikBEtE4tqdd94ZrBp4yy23ULdu3XKN8+ijj/Ljjz8CcNVVV9GyZcsyXX/LLbeQmZnJDTfcwDnnnFOk/eijjwYKLkPMl3+udu3aNG7cuKyhi4iISAxRgiUiMWv58uXk5OSEbcvJyeGOO+7g5ZdfBuCkk05i2LDwFQ5XrVrFgQMHwrb5fD5GjRrFww8/DMCRRx7JmDFjyhTn7NmzmTt3Lk2aNOGJJ54I2yd/yeGsWbPYvn178HxmZiaTJk0CoFOnTnr+SkREJM7pT3IRiVkjRoxg6dKlXHrppZxxxhk0a9aMzMxM1qxZw+zZs9m0aRMAzZs35+233yYlJSXsOJMnT2bKlClcfPHFdOrUiRYtWuDxePj222957bXXgjNIhx9+OG+//TZHHHFEqWPcv38/t99+OwCjR4+mYcOGYfsNGTKEadOmkZmZSZcuXfjrX/9KSkoK48aNY8uWLQDcdtttpb6viIiIxCYlWCIS0/bs2cP06dOZPn162PbzzjuPSZMm0apVqxLHycjIYM6cOcyZMyds+2mnncbkyZP57W9/W6b47r33Xn7++WcuvPBC+vXrV2y/M888kwcffJCHHnqIdevWMXjw4ALtAwcOLPfzYyIiIhI7lGCJVIAdnorbtzfaYVQZOzy1Su83fPhwOnbsyIIFC9i4cSPbt28nMTGRZs2acdZZZ9GnTx8uueSSQ47z5z//mZYtW/LJJ5/w7bffsn37dnw+H02bNiUtLY1evXpxxRVXkJiYWKb4PvvsM8aPHx+ciTqUBx98kFNOOYUxY8bwxRdfkJeXR/v27Rk8eDDXX399me4tIiIiscmcc9GOQSSi0tLS3IoVK6IdhoiIiIhUY2a20jmXVvi8ilyIiIiIiIhEiBIsERERERGRCFGCJSIiIiIiccn342acxxPtMApQgiUiIiIiInEp773XcJu+i3YYBSjBEhERERGRuOOcD/J8UK9+tEMpQAmWiIiIiIjEH48HnA9LjK2dp5RgiYiIiIhI/PF6IQa3nFKCJSIiIiIi8cfrUYIlIiIiIiISCb5v1yrBEhERERERiQTfp4vB54t2GEUowRIRERERkfjjy/M/hxVjlGCJiIiIiEj8qZ0CTjNYIiIiIiIiFefLg+Ra0Y6iCCVYIiIiIiISfzweSIi9dCb2IhIRERERESmBcz7/81dKsERERERERCrI4wEzwKIdSRFKsEREREREJL5kZ8fk7BUowRIRERERkXiTkxWYwYo9SrBERERERCSuuOysaIdQLCVYIiIiIiISXw4eAOeiHUVYSrBERERERCSuuIMHIC8v2mGElRTtAETiWs+esGlTtKOoOscdB6+/XuW3Xb16NePHj2fBggVs2bIFj8dDs2bN6NSpEwMGDCA9Pb3E66+99lqmTp1a6vu5Ev5FLC8vj/HjxzN58mTWrl1LUlISHTp04NZbb6VPnz6HHPu7777jlFNOIScnhyVLltCpU6dSxyUiIiIBmRkx+wyWEiyRiti0CZo3j3YUVaeKk0mv18vQoUMZM2ZMkbaNGzeyceNGZsyYQZ8+fZgyZQopKSmVGo9zjiuvvJI33nijwPnFixezePFiVqxYwZNPPlniGDfffDPZ2dkMGjRIyZWIiEg5uYyDYLG5GE8JlojErMGDBzNp0iQAkpOT+dOf/sS5555LSkoKa9euZdKkSWzdupVZs2aRk5PD66+/jh3iX7MmTJhAkyZNyhXPxIkTg8lVp06duPbaa8nOzmbs2LGsX7+ep556ivT0dC688MKw10+fPp0PPviAo446ipEjR5YrBhEREQGyMiFBM1giIqU2b968YHLVoEEDPvzwQzp27Figz1133UX37t1ZtGgRb775JtOmTaN///4ljpuens5xxx1Xrpief/55ADp06MCCBQtITk4GoH///rRp04adO3cybty4sAnWnj17uOOOOwB4+umnSU1NLVcMIiIiAmRn+WewYrDQRWzOq4lIjffMM88Ejx999NEiyRX4E68ZM2ZQq1YtAB544IESn5+qiMzMTFavXg3AoEGDgskVQGpqKgMGDABg+fLlYa//29/+xo4dO7j44ou56qqrKiVGERGRmsLlZMfsM1hKsEQk5vh8PhYsWACAmdGvX79i+7Zo0YLzzz8fgB9++IHFixdXSkx79uwJJm+tWrUq0t66dWsAdu3aVaRtyZIlTJo0iTp16jB27NhKiU9ERKRGyc1VgiUiUlq7du0iK8u/gWCTJk1o2LBhif3btGkTPJ47d26lxFS3bt0C8RWWfy60H4DH42HQoEE453jggQeCiZiIiIhUgCcXEmIzlYnNqESkRqvIMr8vv/yyxPaBAwdyzDHHULt2bVJTU2nXrh0DBw5k4cKFJV7XsGFDmjVrBsB7771XpP3dd98FoF27dgXOP/nkk3z99decfPLJ3HXXXWX5UURERCQM58uL2T2wQAmWiMSgRo0aBZ9x2rlzJ3v37i2x//r164PH69atK7HvBx98wJYtW8jNzWXfvn2sXbuWF198kS5dutCjRw92795d7LW9e/cGYNasWTz22GPs2LGDH3/8kdtuu41PP/0UoMBeWBs2bGDEiBGYGRMmTCjw3JaIiIiUk8cDCXbIysHRogRLRGJOUlISZ555JuB/Hmv69OnF9t26dSsfffRR8HNxyViDBg3o2bMnI0eOZPr06cycOTNYVj3fe++9R5cuXdi/f3/YMYYNG0bz5s3x+Xzcd999NG3alJYtW/Lcc88BcPrppzN48OBg/yFDhpCVlcXAgQM566yzSv8FiIiISPFyc2N2DyxQgiUiMWrgwIHB4/vuu4+VK1cW6XPw4EH69etHbm5u8Fy45Oi2227j559/Zs6cOdx999307duXPn36cOedd/L++++zcOHC4N5YX331FXfeeWfYmI444ggWLVpE165dC5w3M3r37s0HH3xA7dq1AZg5cybvv/8+TZs25fHHHw/2ffvtt+natSuHHXYY9erVo2PHjkyePLnSqh+KiIhUO57YLXABYPpDXaqbtLQ0t2LFiqq52emnQ/PmVXOvWLB1K6xaVSW3ysvLIz09PTg7VatWrSIbDU+ePJktW7bQunVrNmzYAEBKSkqwQEZZLFmyhM6dO+OcIzExkc2bN9O8hP+2mzdvZt26dSQkJHDqqacW2Lx47969tG3blm3btjF9+nT69u0LwMiRI7n33nsBfzGMpKSkYEI4ePDg4D5bIiIiUjz3849450zHatXC5eaSdGV/rGmzKo/DzFY659IKn9cMlojEpMTERObMmcMll1wCQG5uLpMnT+baa6/lqquu4h//+AdbtmwhLS2NiRMnBq87VMXB4px99tnB5YJ5eXm8//77JfY/9thjSU9P54ILLiiQXAHcc889bNu2jfT09GBytXLlSu6//34A7r//fvbs2cOePXuCZdvHjx/Pm2++Wa7YRUREahLfTz+C1xPtMIqlBEtEYlZqaipz587lnXfeoVevXrRo0SJY/a9Tp04899xzLFu2jISQMq1HHXVUue8XuvTvm2++KdcYy5YtY+LEidSpU4dx48YFzz/33HP4fD7atm3Lww8/TK1atUhISGDIkCHBxC50c2UREREpRnZmtCMoUVK0AxAROZQePXrQo0ePYtvXrFkTPO7YsWO579O4cePg8aEqF4bj9XqDe14NGzaM448/PtiWXwb+8ssvL1L1qFevXsyfP5+lS5fi9XpJStJvzSIiIsXKyY7pZ7A0gyUicW/BggXB486dO5d7nNANhFNTU8t8/ahRo/jyyy9p3749Q4cOLdC2detWAFq0aFHkuvxzOTk5YTcxFhERkV85JVgiIpXnl19+CW7ym5qaSs+ePcs9Vmii1qZNmzJdu2nTJoYPH46ZMX78+GL3vMrIyChy7uDBg2ULVEREpCbLie0qgkqwRCSu3XnnncGqgbfccgt169Yt1zhLliwJFrZISEjgoosuKtP1t9xyC5mZmdxwww2cc845RdqPPvpooOByxnz552rXrl1gmaKIiIiE4clRgiUiUh7Lly8nJycnbFtOTg533HEHL7/8MgAnnXQSw4YNK9Lv5Zdf5j//+U+J+0wtXryYnj17Bvv079+fli1bljrO2bNnM3fuXJo0acITTzwRtk/+0sVZs2axffv24PnMzEwmTZoEQKdOnfT8lYiIyKHk5sT0RsP6k1xEYtaIESNYunQpl156KWeccQbNmjUjMzOTNWvWMHv2bDZt2gRA8+bNefvtt0lJSSkyxqpVq/jnP/9Jy5YtueiiizjllFM48sgjSUxM5Mcff2T+/PnMnz8/mFy1b9+ep59+utQx7t+/n9tvvx2A0aNHF1smfsiQIUybNo3MzEy6dOnCX//6V1JSUhg3bhxbtmwB/Bsii4iIyCF4PDE9g6UES0Ri2p49e5g+fTrTp08P237eeecxadIkWrVqVeI4W7Zs4cUXXyyxz+WXX84LL7xQpgIX9957Lz///DMXXngh/fr1K7bfmWeeyYMPPshDDz3EunXrGDx4cIH2gQMHVuj5MRERkZrCKcESqcaOOw4Csyg1wnHHVenthg8fTseOHVmwYAEbN25k+/btJCYm0qxZM8466yz69OkT3Ii4OEOHDiUtLY1ly5axatUqtm/fzi+//EJ2djaHH344rVq14qyzzqJ///6cfvrpZYrvs88+Y/z48cGZqEN58MEHOeWUUxgzZgxffPEFeXl5tG/fnsGDB3P99deX6d4iIiI1lscDCbG7RNBKei5BJB6lpaW5FStWRDsMEREREakEnmcfg1q1MTNcbi5JV/bHmjar8jjMbKVzLq3w+dhN/UREREREREI454O8vGiHUSIlWCIiIiIiEh+8eWCGxfAzWEqwREREREQkPnhju8AFKMGqUcysgZk9ZGZfmtlBM9tnZp+b2Z1mVqucY6aa2f+Z2XAze9fMfjYzF3hdW8axTjCzUWb2VSC2DDPbYGZvmtmQ8sQnIiIiItVInjfmEyxVEawhzOxY4BPguMCpTKA2kBZ49TOzbs65PWUc+jLgpQjEdzswMhBTfnw+oFXg1RU4dJk2EREREam+vLGfYGkGqwYwsyTgHfzJ1c/Ahc65ekBd4CrgAHAa8K9y3mIbMA94BCjzRj5mdgfwNJAIPAa0ds7Vc841ABoBFwMlb2AkIiIiItWf1wvEdoJV42awzCy/7IhzztWUn38AcErg+Arn3DIA55wPmGVmCcAM4NLALNaHZRh7mnNuSuiJsjx0aGan4J+5ArjKOTcntD0wo/Z+4CUiIiIiNZnXE+0IDqkmzmBZyKumGBB4/zg/uSpkJrAxcNy/LAM75ypaJ/M+IBl4s3ByJSIiIiISyuVkA7G9j29NTLBqFDOrC5wd+DgvXB/n323634GP6VURF4CZ1QOuCHycVlX3FREREZE4lZ0FTgmWRFdbfv3v/FUJ/fLbjjKzRpUbUtAZ+GevAFaa2Tlm9paZ7TSzbDPbaGYvmdnJVRSPiIiIiMSy7Gzw+aIdRYmUYFV/R4ccby2hX2jb0cX2iqw2Ice9gYXAH4EUwIO/KMe1wCozu66KYhIRERGRWOX1aAZLoq5ByHFmCf1C2xoU2yuyGoYcjwT+B5zpnGsQqCD4e2A1/lmuiWZ2RnEDmdlNZrbCzFbs3LmzUoMWERERkehwntxoh3BIZaqiZ2ZlKoBQyf7rnFsd7SCkQkIT/Gygu3Pup/wTzrlPzawHsA6oA9wP/F+4gZxzE4GJAGlpabH9zxoiIiIiUj65uTG/D1ZZy5RPIXbKdvwD/+yGlOxAyHHdEvqFth0otldkhd5nRmhylc85t8XMZgA3AN3MLDEClQtFREREJB5V0zLtFiMvKZ3QpKV5Cf1C24okOpUk9LmvtSX0WxN4rwc0rrxwRERERCSWuWo4g/WPSomifD6JdgBxYi3gw59Mn0wxpdoDbQDbnHO7qyIwSj8DGfp/UazMoIqIiIhIVfN6q1eC5ZyLpQRLSsE5l2lmS4DOwMXAk4X7mJkBFwU+zq/C2L4zsw1Aa/zl5IvTLvC+H9hV6YGJiIiISGzyeoj1xWyqIlgzTA28n2dmZ4ZpvxJ/kgPwctWEFDQl8N7XzIqUhzezlsDVgY9znXOxvfGBiIiIiFQejyfW8yslWDXEVOBL/L8c55hZNwAzSzCzK4EXAv3mOec+DL3QzB4yMxd4HRducDM7IkTPk9IAACAASURBVPQV0lS/UFu4Ihujgc34i2y8F1qKPXD8Lv4KglnA8LL/6CIiIiJSbXg8Mb9EUAlWDeCc8+LfwHcT/mIWH5hZBpABzAYOA74A+pXzFjsLvfI9W+j838LEloF/6eJW4LfAp2Z2wMwOAJ8CpwIHgd7OuZIKYYiIiIhIdeeN/SIXSrBqCOfcJvzJynDgK/zFIjzASuAu4PfOuT1Riu0boH0gtvzCF4n49796BjjZOfduNGITERERkdjh4mAGq6xVBCWOOecOAA8GXqW95iHgoUP0qfCvcufcPsoYWywYd9RRZG7fHu0wqkzdpk0Zsm1bVO69ZMkSXnnlFT755BN++uknsrKyaNKkCS1btuTcc8/l0ksv5Zxzzin2+u+++44XXniBTz75hG+//ZYDBw5Qp04djj76aDp27MjVV1/NpZdeWmIMWVlZjB49mhkzZrBhwwZSUlI444wzGDp0KBdccMEhf4ZFixbRpUsXUlJS+Prrr2nVqlWZvwcREZEazesl1h/CMudiq+q1mV0GXAgcC9RxznULaasHdACcc25ZOcfPL5LgnHOJFY1XYk9aWppbsWJFldzrqRj/F5TKcFcV/57xyy+/cPPNN/Paa6+V2K9Dhw7897//Dds2cuRI/v73v+PxlLw54fnnn8+rr75Ko0aNirRlZWXRrVs3li0r+luPmfHMM89w6623Fjt2bm4up512GmvWrOGxxx7jnnvuKTEWERERKcoz9nFITMIS/AvxXG4uSVf2x5o2q/JYzGylcy6t8PmYmcEys1bA6/iXsYE/NS38N7kcYAbQ0szOKW+SJSLxYfv27XTr1o2vv/4agLZt23LZZZfRpk0b6tevz65du/jqq6+YN6+47d1gzJgx3HvvvcHP5557Lt27d6dly5bs2bOHL774gmnTppGTk8NHH31E9+7dWbx4MYmJBf/9Zfjw4cHk6pJLLqFXr17s3r2bUaNGsW3bNv7617/SrVs32rYNv+PAk08+yZo1azj55JO58847K/rViIiI1DguL88/g5WUHO1QShQTCZaZHQZ8ALQCfsa/GW4f/JXlgpxzXjN7AXgY6AkowRKpppxz9O7dm6+//prExETGjBnDkCFDSEgI/+joli1bipzLzMzkgQceCH6eNGkS119/fZF+9913H507d2br1q0sX76cd955h8suuyzY7vP5mDBhAgDdu3fnnXfewQKzlz179qRdu3bk5OQwceJEnn766SLjf//994wYMQIzY+LEiSQnx/YfDCIiIjFp/15ISAz+GRyrYqXIxe34k6vPgfbOuRvxV44L563A+9lVEZiIRMeECRNYuHAhAE899RS33nprsckVQMuWLYucW7p0KQcP+n8r6dixY9jkCqBVq1YFluwtWrSoQPv69evZs8dfA+aWW24p8Bt769at+cMf/gDA8uXLw45/8803k52dzU033USnTp2K/RlERESkBNlZMV/gAmInwboc/3LAvzjn9h6i7xrAC5xY6VGJSFQ45xg1ahQAxx9/PH/+85/LNc6OHTuCxyeeWPJvGW3atAkeZ2RkFGjbtWtX8DhcYYrWrVsX6ZdvxowZ/Oc//6Fp06aMHDmydIGLiIhIEc5b8rPUsSJWEqzj8ZcM/+xQHZ1zPmAfcHhlByUi0bFo0SK+++47APr27VvizFVJmjZtGjz+9ttvS+wb2l74Oaq6dX9drRwuico/F9oPYM+ePdxxxx0APP3006SmppYychERESnC46FoiYbYEysJViLgCSRPJTL/2pz6+DfJFZFqKH9pIMAZZ5yBz+fjpZdeokuXLhxxxBGkpKRw7LHHcvXVVzN//vxixzn77LM54ogjAPj888956aWXwvbbtGkTjz32GACNGzfmT3/6U4H2Nm3akJTkf2T1vffeK9CWm5sbjKFdu3YF2u6++262b9/ORRddxNVXX12aH11ERESK4/VCjFVADycmilwAPwInmFlT59yhNhU6E6iNf6mgiFRDoWX269evT5cuXVi8eHGBPj/88AM//PADM2fOpFevXkydOrXIDFJKSgrPP/88V199NV6vl+uvv54pU6bQo0ePYBXBVatWBasINm/enNdff53GjRsXGKdevXp0796dt956i6effprWrVtzxRVXsHv3bu6+++5ggY0+ffoEr1m6dCkvvvgiderUYdy4cZH+ikRERGoej0cJVhl8BJwA3Ag8UlwnM0sARuCfG/x31YQmIlVtW8hmxoMGDWL9+vWkpqZy4403ctppp+HxeFi4cCHTpk3D4/Hw2muvkZuby1tvvVVkrF69etGwYUNuu+021q5dy8KFCwvMkIE/gXrkkUe47rrrwu6BBfDEE0/w0UcfceDAAQYOHMjAgQMLtHfv3p0//vGPAHg8HgYNGoRzjgceeCD4jJaIiIhUgDc+EqxYWSI4Cn/hinvNrGe4DmZ2AvA2cD6QBTxXdeGJSFXau/fXWjfr16/nhBNO4Msvv+TJJ5+kb9++DBgwgEmTJrF48WIOO+wwAN5++21mzZoVdrzzzjuPf/7zn0WW8OXLyMhg9OjRvPjiixS3+XqbNm345JNPOO200wqcT0pKCm6EnF9dcNSoUXz11Ve0b9+eu+66C/AX7pgyZQpnnnkm9evX57DDDqNLly5hk0IREREpynlylWCVlnPuO2AIUAd41cw2AA0BzGy+mX0DrAMuAXzADc65n6MVr4hULp+v4OOYU6ZMoUWLFkX6nXHGGTzyyK+T3v/85z+L9NmxYwddu3YlPT2dnTt3MnbsWDZv3kxubi47d+5kzpw5dOjQgZ9++om7776ba665psj9851++umsWrWKb775hnnz5vHxxx+zc+dOxo0bR0pKCgAbN25k+PDhmBkTJkwI7nl18803c9111/HZZ5+RkJBAXl4eCxcu5LLLLuPxxx8v93clIiJSY2RnRTuCUomJBAvAOTcJ+AOwCTgOqAUYcAHQJnD8A/AH51z4f6YWkWqhQYMGweN27dpx9tnFb3t33XXXBZOYzz77LLjvFfhnpjp37syiRYto3Lgxn376KUOGDOGYY44hOTmZI444gp49e7J8+fLg/lTTp0/n+eefLzG+3/zmN1x88cV07dq1SGXAIUOGkJWVxY033hiM+4033mDChAmYGc8//zx79+5l79693HvvvYB/o+NVq1aV4RsSERGpgXKywWImfSlWTEXonJuLf3+rbsADwDhgAjAcuBg40Tk3L3oRikhVCE1afve735XYt169evzmN78BIC8vj02bNgXbxo4dy/r16wEYOnRo2D2swF8MY/To0cHPzz77bLninjVrFv/+979p2rRpgVmpZ555BoCLLrqIwYMHk5CQQHJyMo888ggnnXQSPp+P557TqmcREZGSuJxsbTRcHs45n3PuY+fcI865W51zNzvnHnLOzXfOeaMdn4hUvvyECeDwww+95V1on3379gWPQ0uqX3DBBSWOkf9sFMC6devYv39/qePNv+/tt98OwOjRo2nYsCEAXq+XZcuWAdCzZ8FHTM0seK5w4Q0REREpJDdHCZaISHmceuqpwePQhKk4oX1Ck62ffvopeJxfDKM4ZlagT0ZG2bbau+eee9i2bRvp6en07ds3eP6XX34hJycHIOxzZPnntm7dWqb7iYiI1Di5uXGRYMVEmXYz61+e65xzL0c6FhGJvksuuSR4vHLlyhL7ZmRksG7dOgCSk5MLLAMMTZi2bNnCiSeeWOw4WVlZ7Ny5M/i5uHLt4SxfvpyJEyeSkpJS4p5X4ZK20GfGREREpAQej78qQ4yLiQQLmIJ/b6uycIASLJFq6Nhjj6VTp04sW7aMNWvWsGTJkmILXbz00kt4PB4AzjnnHOrVqxdsO/nkk4PFI2bOnMn5559f7D3nzJkTHOeUU06hdu3apYrV6/UyaNAgfD4fw4YN4/jjjy/Q3rhxY2rVqkVubi5r1hTdHz3/3NFHH12q+4mIiNRYXk9czGDFyhLBHw7x2oc/XzUgM3BuS1QiFZEqMWLEiODxtddeG3YJ3eeff879998f/Dx06NAC7VdffXXweNKkSfzrX/8Ke6/Vq1cHn58CuOaaa0od5+jRo1m9ejXt2rXjb3/7W5H25ORkfv/73wPw4osvkpmZGWzbtm0bs2fPBuDcc88t9T1FRERqIueJjwTLittUM9aY2fHAvUA/4Ebn3PRyjpO/wY1zziVGKj6JHWlpaW7FihVVcq+n4uB/8ki7qwp/zxgyZEiwZHpqaioDBw7ktNNOw+PxsHDhQl5++eXgrNPAgQOZOHFikTEuv/xy3nzzzeDn9PR0evToQbNmzdi/fz8LFixg1qxZweekOnTowLJly6hTp84h49u8eTPt2rUjKyuLhQsXcs4554Tt9+qrr9K7d2/Av3fXzTffTG5uLqNHj2bdunUkJCTw6aefkpaWVrYvSEREpAbxjB8FzmGJv/4V3uXmknRlf6xpsyqPx8xWOueK/OEdNwlWPjN7BhgMdHbOfVqO65VgVXNKsCpXVSZYPp+Pv/zlL4wdO5aSfq+67bbbePrpp0lMLPq/dFZWFjfddFOxs1ehzjvvPF555RWaNm1aqvh69OjBe++9x4033sgLL7xQYt/rrruOKVOmhG0bMWJEgZk4ERERKcrz3EhISsYSfl2EF4sJVqwsESyLh4FE4L5oByJSt5R/Ea8uqvrnTUhI4Nlnn2Xp0qXceOONnHDCCdStW5e6dety4okncuONN7Jy5UqeeeaZsMkVQJ06dZg2bVpwk+Hf/va3pKamkpiYSP369WnTpg3XXHMNc+fO5cMPPyx1cvXaa6/x3nvv0aRJE5544olD9p88eTIvvPACaWlp1K1bl/r169O5c2feeOMNJVciIiKH4JyDPK+WCFYWM9sNeJ1zTcpxrWawqrmqnMESERERkcrnvB68Y5/A6tQteD4GZ7BipYpgqZnZ4UAqkB3tWEREREREpArESYELiM8lgv8IvH8f1ShERERERKRqxFGCFRMzWKXYaDgFaAH8ETgF/x5YUys7LhERERERiQGeXCVYZTSF0m00nP+tzgaerrRoREREREQkdsTJJsMQOwnWD5ScYHmBPcCXwCzn3PwqiUpERERERKLOeb2lm46JATGRYDnnjot2DCIiIiIiEqM8HuIlw4qJBMvMDgscZjjn8qIajIiIiIiIxBavJ9oRlFqsVBHcC+wGjo52ICIiIiIiEmO8HoiT/XtjYgYLOIh/4+At0Q5ERERERERijNcbNwlWrMxgbQTqmlmsJHwiIiIiIhIrPLngfNGOolRiJcGaDSQDl0U7EBERERERiS0uNzdealzETIL1JLACmGBm3aIdjIiIiIiIxJCcbO2DVUb3AB8BbYH5ZrYaWAbsBIqtKuicG1414YmIiIiISNQowSqzh/BP+uV/ax2AU0txnRIsEREREZFqzuXkKMEqo4XEzapKERERERGpUrlKsMrEOdc12jGIiIiIiEiM8uTy62K32FZikQszO8bMmldVMCIiIiIiIkV4PNVmBmsT8DOgJEtERERERKLCxVGCVZoy7ZX+k5iZz8y2lqH/RjPzVmZMIiIiIiISI7zVK8GqKmX9xuLjGxYRERERkYpRglXpalPC/lgiIiIiIlKNeL1KsCqLmR0FNAF2RzsWERERERGpXM6XB3nxM7cSlTLtZnYu0LXQ6fpm9veSLgNSgYsDx59VTnQiIiIiIhIzPB5ISMDiZAarNAlWIzP7qAL3cM65boXOnQc8SMHNhesFzh2KATnAYxWISURERERE4kEcbTIMpUuwkik621QWLsy5TcCCkM9dAA+wrIRxfMB+4CtgmnNufQViEhERERGReJCbW+0SrGxgdiRv6pybCkzN/2xmPmC3c+68SN5HRERERETim8vOIp4KiJcmwdrnnLuukuO4Dsiq5HuIiIiIiEi8ycwg/KK42BSVIheFBWa0RERERERECsrKBJ8v2lGUWtyVaRcRERERkZrDZWcpwRIREREREYmIjANxVeRCCZaIiIiIiMQsl5kJCfGTtpQm0iMrPQoREREREZFwsrOq3QzWzkqPQkREREREJJw422g4fubaRERERESk5qmGGw3HFDPrHzhc6Zz7OqrBiIiIiIhIpXIeJViVbQr+ncacmc0BhivREhERERGppjy5YPGz8C5+Ii3I8Md+JfA/M3styvGIiIiIiEiEOee0RLAKtAq8Hw2cF3hdHL1wRERERESkUni9AFg1S7AamdlHFbiHc851q8D1hQfbHDjcDCwDHjWz5EiNLyIiIiIiMSI3O672wILSJVjJQNcK3MNV4NrS3cA5T2XfQ0REREREqlicLQ+E0iVY2cDsyg5ERERERESkAE/8zaOUJsHa55y7LpI3NbPrgN8D+/Ev9VuOv+z6IWe7zKyRc253JOMREREREZHY47we/PXt4keVF7kwsy7ApEKnHbDPzN4DJjvnPi5hiD+YWW/gCudcdmXFKSIiIiIiUeb1UAVPHEVUNJ4Yywm8Xwu0B/oAY4BNQF/gAzP73swGm1mtMNcvAk4Anq/8UEVEREREJGoCVQTjSTQSrJXA1/hnsR4CMoF7nXOnA8cB9wB5wDhgg5ndYmaJ+Rc75zYAlwDXmNnhVRu6iIiIiEjF5X00j7zVK6MdRuzzeODQTxHFlCpPsAIV/3oAH+HfKPhtYI+Z/Qe4EfgGuBDoDnwPPAOsNrMLQ4a5CP9iTJVnFxEREZG44nKy8a1eie/zJdEOJfZ54y/BispGw4G9rC4ys5OAq4E/AOcD3fh1kWUWsB7YDrQF/m1mS4CWwDHAPOfcL1Udu4iIiIhIReR9OBdyssDnw7fpOxKOOyHaIcUuTy74fNGOokxKnMFyziU4546urJs7575xzj0YWB54JNATGIX/Oasc4LfAUYHuBpwDHAusBa6qrLhERERERCrNgX1Q7zDIzsL3yfxoRxPTXG5utEMos6jMYIUTKL3+ZuAFgJk1BhoDdYADQAP8SwY7A+OBP1V9pCIiIiIiFZCdDQkGqQ0hIRolEeJIVma13Gg4LDNrD+xyzm2LYDwFOOd2AbsKne5iZrfgrzgoIiIiIhJXXG4OWAIYuIyD0Q4nprns7LhLsMqVMptZc/ybA281s5/NbHRkwyqZc24s8H9VeU8RERERkYjIzfEnDZYAuTm4OCxFXmXyv6s4Ut45yUeBevifizL8y/aqlApciIiIiEi8cb48/95OZpgZJCb6n8mS8Lwe/OlG/ChzgmVmR+DfHNgFXjc55zZFOC4RERERkernwH5ISPAnVwFu394oBhTjvJ4aMYPVG6gVOP7IOfd2RQIws8PM7H0ze8HMBpvZqRUZT0REREQkVrn9ewsWtsjLw7dqOc7FVynyKuPxxtsEVrkSrM4hx89XNADn3H5gCnADMBZYZGZ1KzquiIiIiEjM2bsH8vJ+/ZySgtuwHvf1/6IXUyzLqwFLBIHTAu85wLxIBOGcewX4FP+3Vx/oFYlxRURERERiiTt4oMDGuWYJkFwLt2tnFKOKXS7wvFo8KU+CdST+Z69+cM5lRTCW0EqEqhAoIiIiItVPxoGie18lJOJ2q35bWHl5NSLBOizw/lMkAwHmAL/gn8XqGuGxRURERESizmUcLJowJCbi9u6JTkCxzuOJtxWC5Uqw8metEiMZiPM/2fdJ4GOqmZ0YyfFFRERERKIuM6PoDFagVLsLfTZL/MsD8zdljiPliXYX/jzy6AjHArAo5PikShhfRERERCRqXJgEy8z85/ZpFquAjAOQmFigpH08KE+C9W3gvZWZNYpkMMDWkOPmER5bRERERCRq3L49/n2wipmRcRkHqjiiGOf1EnfrAylfgrU88G5Evtpf6NN9hxXbS0REREQkzrgd28DrwQovEQRwzr98UH7ljb/nr6B8CdbckOOhZhbJZ7GSIjiWiIiIiEjMcAf2Q3Jy+EafD/fjD1UbUIxzXm+0QyiXMidYzrnlwBr8+WRr4OEIxnNUyLEWoYqIiIhI9bF/L8VOySQm4X7ZXqXhxLw8r39zqDhT3pIc/wg5/puZ3RCJYICzQ44jXQZeRERERCRq3P69RSsI5ktI8Jdwl195vcRjhlWuBMs59yowD38KngBMMLN/lHxVycysHtAn/xbAsoqMJyIiIiISU3bv8pdkDycxEQ4ewPl8VRtTLIvTsvUVKSo/ANiIPxlKAIaZ2XIz+105x3scaBQYb5VzbncFYpMwzKyBmT1kZl+a2UEz22dmn5vZnWZWq5xjpprZ/5nZcDN718x+NjMXeF1biutPCNz/HTPbbGY5ZpZhZuvNbFIFfj2JiIiIxAzn8/lnsBLDlxwwM/8GxPv3VnFkMczr8Rf/iDPlLirhnPvFzNKBD4FjAqfPAD4zs38Do4CPnSv5WzGzZOAJ4OaQ02PKG5eEZ2bH4t/I+bjAqUygNpAWePUzs27OubI++3YZ8FI5YzobWFzo9IFAXCcGXtea2SPOub+X5x4iIiIiMeHAPjAreU8n8y8TtNRI74QUp3JzIQ5n9Cq0LbJz7nvg98AS/MsFXeD9YuA/wDYze8nMbjGzLmbW3syOMbO2ZnaRmT0CbAL+HHL9CmBmReKSgswsCXgHf3L1M3Chc64eUBe4Cn9Scxrwr3LeYhv+JaOPAD3LcF0ykAe8CVwJHOGcOywQ1xn4k68E4IEIPucnIiIiUuXc3t3+GaoSO/lg/76qCSgOuJzsmjWDlc85t83MzgXuwF9RMCXQZMCRQP/Aqzj5iRXATqCXcy7+UtXYNgA4JXB8hXNuGUDge55lZgnADODSwCzWh2UYe5pzbkroiTLstv0d0NY5923oSedcHvC5mXUDPgdOBe4FJpUhLhEREZHYsXfPoZ8pcg7fxm9JaHtKyf1qiqzMQyelMahCM1j5nN8o/H+JnwTk5jeFdLMwr/w+BnwF/N45tyUSMUkBAwLvH+cnV4XMxP88HZScDBcRSIbKxTn3Y+HkqlB7Lr/Oqh1vZg3Ley8RERGRaHK7dh66U0KifylhHMpbMJ+85QsiOqaryQlWPufc9865gcCxwIPAagomU4UZ/r/Y3wKkOec2RTIeATOry6/l7+eF6xN4Tu7fgY/pVRFXGWSHHEdyU2sRERGRKuPbsB6SitlkOF8cl2p3Wzbh+3wpEV2IlplRfFn7GFbhJYLhOOd24F8u+LCZNQJ+B5yAf8lgErAX2A4sDzzHJZWnLb8m0l+V0C+/7SgzaxRDVRy7Bt5/BnZFMQ4RERGRcnG5OZBxEFLqlNwxIQGys0vuE6vq1oNtP8HuXeTt3UNCo8ZYw8YVGzMzA0wJVhGBv6j/J/CSqnd0yPHWEvqFth0NRD3BMrNO+KsUArxYUkVKM7sJuAngmGOOKa6biIiISNU7eAASEw/9nLoZeHJxzpXlmfbYkJUJ+J8h8y39BF/tFJKuvxVLLtdOQAC47CxIiLPvgQgvEZSY1CDkOLOEfqFtDYrtVUXM7EjgFfy/Rr/FX8q/WM65ic65NOdc2pFHHlkVIYqIiIiUSmmfJQomVV5vJUcUee7APkhKxrfqU/8sXFYm7C3/v9c75wssEYy/J0SUYEnMMbP6wNv4n+U7AFzpnIvPBckiIiIiZSk3npAAuTmVGo5v8/d4XvxnxJ6XclmZ/qSqdoq/SEetWuDLw1VkuWNmBlCm6tQxQwlW9Xcg5LhuCf1C2w4U26uSmVk94D38+6sdBC51zv0vWvGIiIiIVFh2tn+Pq9Iwq9QEyx3YR968N2HfXtyPmyMypnfaBMjzYomJUL8B1KkLybX8e3+VN849u+OywAUowaoJfgo5bl5Cv9C2n4rtVYlCkqtzgQygu3NucTRiEREREYmYnCzwlXa2yMDjqZQwnPPhnf4CHNgPnlz/eyTk5QWXNZolBGed3Pfryj/m/r1l+M5iixKs6m8tkP+r8+QS+uW3bYtGBcGQ5KoL/ufBujvnFlZ1HCIiIiKR5jIzCu4Oe6j+ntxDdyqPPbvh4EFISoKUOrh9eyMzrvNBXqHnxpKScdu24nzl3DI1O1sJlsQm51wmsCTw8eJwfcz/zwwXBT7Or4q4Ct2/HjAXf3KVgX9ZYGR3qhMRERGJEpdxsAzL3RzkVk6C5XbthJQUrF59SEzE/bSFEoo0l5odlgqHNyp4LjERMg7iW/pJ+WLNPBiXmwyDEqyaYmrg/TwzOzNM+5VA68Dxy1UTkl9IcpW/LFDJlYiIiFQvmRllSBacf/leJXC7dvxaoTApGbf5e9hesSdDnPP5n7VKDFPtL7kWvlWflmtGzmUowZJSMLOTzOx3Ztaw0Pk6Zna3mX1hZvvNbJeZzTezSyN066nAl4ABc8ysW+C+CWZ2JfBCoN8859yHhWJ7yMxc4HVcMT/XEaGvkKb6hdrqFrquLvAu/uTqIHCJlgWKiIhItZOZUfoZrDxfYE+pyHPbfg4mQpaYCLVq4fbvq9iggaV8Fubns5Q6kOfFbfq+fOMqwZLimNkZZrYO+Br4DNhhZpPMLDEwg7MQeBToANQHGgLdgHfM7J6K3t855wX+CGzCX8ziAzPLwD9jNBs4DPgC6FfOW+ws9Mr3bKHzfyt0XS+ga+D4/9m78zhJz7Le/5+rqnqbNTNJJASBhEWWIAQBUQmHJeyLCoi7LD9Bj3g8gqIe1CM56sHzewkcwA3DIkEIJiBCIBAgCyHEQPZMMpNkMvtMz9p7d3Xtz3X+uKtnenqqq57an5r5vl+venVN1f3cz9VDZ+ir7vu+rgzwRTM7XOfxMy3GJyIiItI33kyCBfjUscaDWolj6hikMydeqFTwhTYLXeRz9b+3QoHK9V9vft5iAWwwU5VM4yHSDjM7D/gWIYlZSsPTwNuA/cBZwHOAWeA6YB/wo8DLgHOBvzKzb7n7Pe3E4e57zOyZwHuBNwIXAiVC0vcF4O/cvUsnKle1/L+a0eqjntZbgYuIiIj0Sz4HmaF4Y1MpfK5D1f2WcY9C1cDRgjINpgAAIABJREFUsWWvGsxMtzdxIc+JX3Fr2HAWlEr44gK2Zl1z8w7oClbPEiwz+3T16ay7v6dX902A/w5sBCaA9wD3AhcDHwbeSki87iNUzTu+CdbMNgNfAV4A/Ffgt9sNxN3ngfdXH3GvuQy4rMGYln763f0zwGdauVZERERkEHilHCrsDcX8nDiVgnwXtgguLoLZyY17U2l8aqKtaT27QN0Siek0zM9Q+e63ybzmjfHnLRWVYMXwNsLf/hFConGmeCXh+/5dd/9i9bWtZlYgbM9z4NeXJ1cA7j5lZu8G7iScURIRERGRQZPLQSp9cmJTj1m4ptMWa1QyHB4KlQRXOUMVh09NhMIZwyM13zczfGwtFPPNTVwsDGyC1euNjYP5t9SeJ1a/fnXF69cse34rNbj73UCOsGVQRERERAbNwnxziYKl8EKTyUgMnsux8ldxs1SIbXGh9YmzMb6/TAaa6Lnl7mGLYItJX7/1Our2C+0PnvXA1MrzTdU/T1Wf19toOwesqfO+iIiIiCSUTx5trmFuysLqTaflc9T8VdwMsq0nWJ7PNU6w0ml8bjacA4ujVAQn/qpfwgxmWjhYjFBMopbVXhcRERGR04DPzzWXYGGhul+l3NlAcotQqdS4neHtJHT5fMNqf7b0ftxEbnpyYFevQAmWiIiIiEjX+OxMU8mCmUFUgaOHOxvHsVXmc4dCGwlWIR9W3WKIHrg31jifnmw9ngRQgiUiIiIi0i2zU82vxhQK+OHxjobh01PHmwyfJIraTLBiFqPILhDde3u8OUsliLudMIHUB6s3NpvZjbVeB1jlvZPGiIiIiMjg8dmZ2olNPWNrOl+5IL9YeytfFOFtFLnwQuMtggAMD4fEKY5SqcltlcmiBKs3hoEX13m/3ntwZhYHERERERlo7h7OPo2MNn9tLtvZWPKrVOVLpYju/iHp572gtYmLBUjHSCnWrIudNHmxQfPihFOC1X1X9DsAEREREemDavGIpqvhpVKw2LkEy91DFcHhGs2OR0Ygu4DPzWAbzmpu3oW5UPEvM9R4sBmUY65gxd12mFBKsLrM3d/e7xhEREREpA/yudaq4VkKb6N0+ikKefDoRDW/5beyFJ5OE225i/QllzY1bXT/3RBF8RPIKMIrFazBlklfmI+37TChBjdyEREREZEka7VhcMo6uoLFwjyk6iQ16Qx+cH/T0/rsTLzVK6qreOlUvB5fk0ebP7eWIEqwRERERES6wIvF1i5MpfBObhHMLTa+XysrZtmF5lboLFVteFyf5xbVB0tERERERFZodQUrZiISW6O5Wj3z1XSCZfG+r1Ipdm+tJFKCJSIiIiLSDcViaOTbLDOoVPC4Zc0byeegUqeCnxmUSniTpdE9u1B/6+EpFzjeIMFyj6BcRlUEZVVmdk0HpnF3/7kOzCMiIiIivVIstNTPyczwdBqy83BW+y1RfW6mbuNeM8NT1fNRo2Px5owqYYVubE0TgXjjFaxiEVLWfOXFBFGC1X2vI/SxauenRH2wRERERAaMF/N1E5uG109PYp1IsKYmGxeNWNqWGDPBIhcqJDaVCJXL+K5H4GnPXH1MsTjQFQRBCVYv7QFu7ncQIiIiItIjuVzryUJUCdX/OsAP7m9c7c8s9LSKq5US9Ok00a7tpNxXT8yKg90DC5Rg9UKZ8Pf8eOBFwGeBK9x9d1+jEhEREZGu8txi68lC5KEfVCcU8jAy2nCYl0qxt1z5YvNVB210LJzBWlyAtetrDyoWGOTzV6AiF71wPvAHwBbgAuB/Ao+Y2U1m9lYzW9vP4ERERESkS/K51hOsVAqfmWo7BPco5jkwb24Fa2a6pfNlpNL4zPTqURQKDPrpGCVYXebuE+7+EXd/NvBs4O+AScJq1qeBw2b2GTN7ST/jFBEREZEOy+db3yKYSsP8bPsxlMtgMYtGNFG10OdmWkuwaFDooqgEq1mDvd7XJne/z93fDTwGeCPwNWAYeAtwvZntNrPLzOwJ/YxTRERERDqgkG+9n1Mq1ZktguVyvBjcm0uwFuZbawYcef3+YKUStF4XJBF6mWC9uvr41R7eM5HcvezuX3H3nyckW38A3E84p7W0hfCP+hmjiIiIiLTHi4U2VrBSUCi0H0S5RKw1jihqbovgYra1722pvPtqyqW2Ki8mQc8SLHf/VvXx3V7dcxAs20J4MfAeQlEMgHP7GJaIiIiItKudM1hmUCrgrTQqXq5UihdDjCbAJ4Y6Pnmscen31a5fzK7+XrHQWnPmBNEZrD4zswvM7C/MbAfwYWCIsDC6r7+RiYiIiEirvFyGSqXlBMvMwlGkcrnh2LpxLGZjxxDd/cN4k1aqJeRbSbBSKVhcXP39eqtbA0Jl2vvAzNYAbwbeBryQsG5rwAPAFcDn3P1I3wIUERERkfbkFptvxLtSKgWFHAw16GFVz2I2XjEKdygWcI+wRlv/CnlIp1v73szwfIMES42GJS4zezEhqXojsJaQVE0CVxJ6Y93dt+BEREREpHMKbWwPXGKpkKit29D6HIsLYcWpkTVrILsQVpfWrqs/tp1mwJYK1RVX4YW8Gg1LfdWKgG8lVAp8HCGpKhEqCH4GuNbd45dsEREREZHE80IbScgSC1v82pnFF+ZjxWGWwodHIZdtnGAV8rRcHDyVwuuVn+/E31ufKcHqvh2EHbQG3Av8C3Clu0/2NSoRERER6Z5Cvv1iDVFUv2dUDD4/21TC4oVCw9TJ5+douVdVOg3zc3iljKVrpCLtrI4lhBKs3tkDHABeDry8yT2r7u4/142gRERERKQLOpVgtVuqfW4uNC2OpUGPqqVRc7Oh+MbwSNPhmBmeSsH8HJy1+dQBxaISLInFgAsIfa5a+YkZ7FqVIiIiImeafC5ecYl6miidvuoU2SYaArvXPR913OJCe0mQGT4zjdVIsLykBEsau6LfAYiIiIhIb3l2of1JzEIVwFZjcA+FK4aH410QVUJxjkbzNlH6vaZKBZ84Ahc88dT34vbtSjAlWF3m7m/vdwwiIiIi0ltxi0vUZYbnWk+wKBYgTtn1JR4zMcwttv+9zc3Ufr1cglpnswbIYBeZFxERERFJosVs/K15q0nVL2neUHahifNX4X6+MN94XLXHV8tWuY+7hwRrwFewlGCJiIiIiHTaYrb9hrlmbVURDOe3mkhWMhl8fF9IdOrNuzDfXOK2UioFtRK5qAJYe82ZE0AJVg+Y2e+Z2Y1mdnnM8WZml1ev+e1uxyciIiIineX5HKQ60Gg4RlW/VeUWaapWWjoTEsOpiVWHuEcdWMFKV0u9r3AanL+CPpzBMrO3dGoud/9sp+bqFjM7F/gbYAT4wzjXuLub2T8BdwA/YWZXufsqG1VFREREJEnco5CojIy2N1Eq1V6xjHweovgJlpnhUUT0g++Rfu2bag/KLdL2KlMqBfkcXi5jmWXpSLuJW0L04wTZZ+hM2XEHEp9gAb8GrAG+4O73xL3I3e8xsy8Av1p9/GOX4hMRERGRTlpcBGh/q5sZlMt4IY+1kKx5brG67a450a7tpNxrx7/QRNn3VZgZXinjux/Bnvy0E29Uk7dB148UcV+dx9JGUQMqwJHqo7Ls9cXq2P29DrxFryAkg62Ua/8M4Xt+bScDEhEREZEuyi50ZCXGzCCdrn1eKY6Fuaa33NnYmtAPa5XqhZ7PdyYHqlTwQwdOnrvZLY0J1fMEy90vcPcLVz6ADwFDwI3Ay4B17n6+u58PrAUuBW6ojvlg9ZpB8Mzq15tbuPb7K+YQERERkYTzxQ70wFo+X4sJlre62pRKwewqp1MK+ZCAtWtktJpQLbOYhUqbzZkTIBGbHM3slcBHgKvd/WXufqO7F5fed/eSu9/k7i8HrgY+amav6Fe8TTobmHX3QrMXVq+ZAc7peFQiIiIi0hU+M93S1rzak3nrhS5a7Vfljs9M1X6v2lurbalUWOlbftuF+c7M3WeJSLCA9674Ws8fExYmYxWMiMvMHmdmj+nknFVlIGb77JpGqnOIiIiIyADw/bvp2FkidygVG4+rpZBrrVR8pYLPzdYOJ5/vzCpTKo2vqFbo05OnRZGLpHwHzwZm3P1Io4HufpiwqvOcDsewB7i9w3MCHAPGzOxRzV5YvWasOoeIiIiIJJx7hO/fC0PtfL5+0oRh1aiVS/P51krFp9P4vt2135ub6Uwp9XQasvN4Zdk6wux0eH3AJSXBGgU2mNm6RgOrYzZUr+m0bpQtubP6tZVCFa+rfr2rQ7GIiIiISDfNzkCljHUqUXAPiVIrCvnWVrCGhvEjB2uHMzfTuQIeqXS1cmB17nYbGCdEUhKsB4E08J4YY99THftQVyPqnGsJidv7zGxN3IvMbC3wPkIplWu7FJuIiIiIdFDlhm+01xx4JbOTkpC4vFJuvXGvWdgmWGvlbH6uc0mQGWSzROP78EK+Y9UX+y0p38EnCUnIZWb2QTP7kZUDzOxcM/tb4P2EpOPyHsfYqisJJeWfAHzZzDY2uqA65svVaw4An+9qhCIiIiLSGQvzMBb7M/XGUil8sXbJ9LryOUinW+rFdbw8fI0mx77YySTI8SMHqVxzNdEdt0Ih337vsARIRILl7v8MfImQZL0HGDez+83suupjCzAO/AEh5v9w94FIsNy9BLyd0Mvr5cA2M/tjM3vqyrFm9lQz+xNgK6FUfRn4/6pziIiIiEiCeRThs9OQGercpC2uYFEstn1WamV5eC+VIJ/vXIJVrlC57WYoFvD9e6CFZspJlIgEq+qXgT8F5glbAC8iNOl9BfAMIFN978+AX+pTjC1x9xuBXyc0Un408DfAVjNbNLOD1cciIbH6AHA+kAfe4u439CtuEREREWnC/CyYdXYVxlKQbzHBaqe8QBThk0dPfm16AlKpzn1/lgrbKQv5UFHwNChwASFpSQR3j4D/Y2YfJSRVzwHOrb59DLgb+La7t/ATFttmM7uxjevd3S9d5Y2rqytxHwB+jvATPwqct3Io8BXgz919WxuxiIiIiEgP+cxUZyrsLZdKtVTkwlst7b78vg9thYt/ctmcLZ7pWk06deK8WrEAwyOdm7uPEpNgLXH3HPDV6qPXhoAXt3F93bbW7v4Q8EYzOw94CfB0QiNigElgG3BTtRS9iIiIiAySXC6UVe8ks9aKZpRKNPjVtL7MEH7sMO4RtlSJsFxqa8pTDA2F7Y/rN4by72NrOzh5/yQuweqzPHB1t29STaC+0O37iIiIiEgPFQsQdaAJ73JmUCnjlTKWbuJX91KRdrIhS6XC1YtZWLs+vFgutzXnKfewFGzchLvD2BrsNKggCAlMsMzs5wnFIB4PjC3fclctXf4swla827pw+1l3f3sX5hURERGRhIj27MDOeww2OtbReb2Y7/gKlpnhloZCAdY0mWC1G4tZuO/xBKvU+RU6qlULO/y/RT8lJsEyswsJpcmfufQSp6bIBULZ88ea2SVdSrJERERE5DRW+eZ/kP7JS7Dn/HRnJ87lOn8GCyBlYXVsTRNb6AoFqLS7mmbVYhlV5XJXEqzTTSLW4cxsA3A9YXXqMPAvwCkF/929DHyCkHy9sZcxioiIiMjgi7beB4UCXql0fG6fnQmV8TpuRaITJ5ZWSrvXuK0Xl53/KhY7vwXyNJSIBAt4N3AhcAdwkbu/Azi1s1mwVPziBb0ITEREREROH9G9t0NuEd9yF36sw3XFJo9Cphulxh0vFpq7JJdtP9nzKKzKLf1x6lh3VuhOM0lJsN5A2A74++4+02DsNkID3id3PSoREREROb1s2Ahja/C5Wcr//rlQYKEDvFwOK1jNFKJoRpMrUp7Lha2F7ahUqFx/LdHOh3F3/ND4adOrqpuSkmA9ESgBtzcaWO2XNQts7EIc5zYeIiIiIiKDyKMIH98X+i1FEWQXYGaqM5PPhe2BHW0yvKRcDj22mpHvwHmwVBoW5ogevB8f34dPHlOCFUNSEqw0UKomT3VZ+KldR40zWh1wrAtztszM3lJ9XNTvWEREREQG3tFDUCphmQy2YSOMjOITRzsytc9OhyoB3ZBKheSmGYV8+1sEh0dgdAw/PB6KbORzIemSupKSYB0A1pjZo2KMfT4wAuzqbkiJ8BlCwY/7zOwqJVoiIiIirYseuOfkIg2VCn7oQEfm9vF9UCl3ZK5TpNMwM91cPMVC2ytYx8unZxfwwwdDryqdwWooKQnWjdWv76g3yEIb6b8mnNe6rttBJYQR/nd6MyHR+lKf4xEREREZOJVt9xE9vA0yQydeTGc6l2DNTHdvdSeVxudnm7umWGj/DBbVJKtUCsVBos5XXjwdJSXB+hChcMX7zKxm+XUzexJwDfBSIAf8fe/C65sLq48XAH8O3AS8qq8RiYiIiAwYn58luu6rsJg9+QxROoXPz3XmJtn5LpVoB1IpWMziMRMcdw99sDoVT6Uczqt1bQ/k6SURjYbdfYeZvQu4HPiime0FNgGY2beBx3GiamAE/Ka7H+pLsD3k7nurT/cCtwEfMLOhOpeIiIiIyDLuTvlzl4ckZWQUSy1LOlLpkLh4hLWRjPjsNH7kEAx159c0M8PNwhmoNesaX1AuHb+uI9auD3OOrenMfKe5pKxg4e6fAl4P7AEuAIYJafLLgB+rPt8HvN7dr+pPlP3n7qV+xyAiIiIyMHKL4TEyiq1IgMwsnFPKtlc7zfftDvfoZgGIVAry+cbjoBpL537Nt+FhbM1anb+KKRErWEvc/Rtmdh3wIuBngEcTKgweAf4TuNHdu3R6EIDNZnZj42Grcne/tGPRiIiIiEh7ZqdheASrU17cjxzE1j2lvft0vQCEhcqAMfikGgL3U6ISLDje5+qm6qPXhoAXt3H9KZ3qzOztwE8Bc4Stfj8A7vIYXe3MbLO7d6g5g4iIiMiZxT3CD+6vP2ipkuATW0+wfGqiBwUgHM/n4p2Cyi6cXC1ReioRCZaZRcAhd39MzPG7gce6e6fjzwNXd2oyM3sR8KkVLzswa2bXAp9293qJ5OvN7BeBN7l7zDVhEREREQHwHQ9TueWG+s1xMxmie++EfI70y17X0n2irfdC1PCz8/a4x98iWCwoweqjRCRYVc2uY3Zj3XPW3d/ewfkK1a9vA+4AnkFYzXoJ8KvAr5rZHuBvCclWccX1twB/CvwT0Mm4RERERE5r7k7lhmuh1KA4Q2YIZqeJdm4n/bIWbzY01L0KgksqFSjkYg31xfbOlEl7ElPkokkjwCAU4r8L2EpYxboMWATe5+4/QSjk8T8I38c/ArvM7HfN7PhHLO6+C3g18BtmtrG3oYuIiIgMsJmpUJZ93fq6Z6MslYINZ1HjpEd8xVJPzjx5diH+uA4WuZDmDNzfvJmdB/wIkPizSdWKf68jNFJ+M6GP17SZfYfQVPkh4OXAa4GdwMeALWb28mXTvJKwWqfy7CIiIiIxRdu3QSpdt7jFcdUKfaEUQHPcPZQw73aClU7jU5Pxxs5MdbeiodTVly2CZvZfOLWYxDoz+4t6lwFnERrtGnB7d6LrrGovq1ea2VOBXyGUon8pcCknPirJAdsJ1RKfBlxnZrcCjyX0APumu0/0OnYRERGRQeWHx+ufvVrGzPAogskJOOdHmrtRuXx8jq5KpUNFxBh8dib29y6d168zWC8B3s/Ja7Frq681YoSzTX/Thbi6xt0fInx/7zezzcB/IZSi/0ngx4GLlw034JLq823AL/cwVBEREZHBtzDf3Da5YoHK179E5m3vau4+iz3ajpcyPN/4DJaXS6EP1uhY92OSmvqVYO0Bbl725xcBJeC2OtdEhFLnDwD/6u7buxZdA2aW8lbWkKuqpde/Un0szXk2cDYwBswD6wlbBl8IfBz49XZiFhERETmThHNITazirNsAmeZXfXxupjc9pywFhULjcfNzkE6rKXAf9SXBcvcrgCuW/lwt0z7l7i/pRzzVmJr56GHezLYC9wF/5+5bOnD/SWDlxtoXmdnvEioOioiIiEgMLa3ipFL43BxeqcQ7t7V0r4MHQoW/bjODUhH3CKtTsdDnZrsfi9SVlDLtbyecQxoUY8Bzqo/bgLYTrNW4+z+Y2VXdml9ERETktFM9g9TMKo6l03ihAEcPwaN/NP69FuZaCLB5ZoanqqtY9RLH3GLomSV9k4gEq7qiJatQgQsRERGR+HymxWLTHlHZcheZJhIsL+S73wNrSSoF+Vz9BKtUVILVZ4lIsAbcqJm9mVC04inAKJAH9gL3Aze5+/19jE9ERETkjOJTE6G63/BIcxemUvgjD+LPeh523vnxrikUenMGCwCDfL7+kGIBopZLBUgHJCrBMrPHELYLXgKcT6gsuNpPrLv7E3sVWx0fY/UYATCzQ8AngU+4+3hPohIRERE5Q/nhg62VKbcUFHL49ESsBMs9wg/u72GC5XguW/cXzziVBqW7EpNgmdkvAZ8inG9aNala9l4S1j6Nk+NZLe7zgf8J/JGZ/R/g/3f3Yg/iExERETmjeKmI790J6RZ+za02HI62bSH1tGc2Hp/PhxWjsTXN36sVUQTZhfpjcrnebVmUmhKRYJnZM4HPAkPADcC1wIeBWeAPgUcBLyP0z5oA/hfQ4KerJ5aSPCNUAPwhcABIAY8BLgIev2zMGHAZ8Boz+1l3P9bTaEVEREROd7MzEEVYs9sDqRa62HwOfvAAXipiQ8P1L8jnYGgYa6YcfDuiCJ9eWXT6ZJ7LQkol2vspEQkW8B5CcvV5d/8NADP7MJBz909Xx/yNmb0C+BLwNuAF/Qh0BSMker9P6M1VPmWA2eOAXwTeCTy5+vLzge+b2U+5e7yW3CIiIiLSUEgwWk94LJXCDZibhbPPrT84n6PBSZHOGhrC9+4KXVJXs5jVClafJeVv/0WE1aC/rjfI3b8NvJtQHv0PexBXIw68w93/pVZyBeDu+9z9g8DTgd8jFMBw4EnAF3oWqYiIiMgZoPL1L0Gl5q9l8ZmFRsUNeH7p17oeSWfw+QZl4fM5rWD1WVISrEcDJXd/eNlrEaEi30pXAmXCqlC/HXb3q+MMdPeKu/8DoYDHDOHjjpeb2Zu6GaCIiIjIIPB2kyKqTXbz+eYaDNcSRWElqJFCrrcl0c2gWMBLqx/l9+xCWyt40r6kJFg5YH7FawvARjM7afOru+eBLPCEHsVWy1K77keavdDd7wF+YdlLf9aRiEREREQGkC9miXY8RPmTH8OPHGxvrvG9kMk01WC4pqgCuRgJVj7f05LoZhaqI07WbpHqhTyUSj2saii1JCXBGickU8vT7d3Vr89dPtDMzgM20t/zYzPVr82fngTc/SbgK4RVrGeZ2WM7FZiIiIjIIPF9u6hc+++QW6Ry351tzRXt39OhhMfwhZWf/Z/Kc4u9b+rrjs/P1n4vn4N0uv0EU9qSlATrQSANPGPZazcTEpC/MLNRgOpq1ker72/taYQnO0KI7alm1mqi9+Vlz3+m/ZBEREREBlA+B7lFyGXxbVvqbn+rxwt5fMdDzTcXriWVxo8cajwuOx9Ku/dSVFk9wSoW6GnRDakpKQnWtwk/Da9f9trfA0Xg5cB+M7uVUAL9FwinCf+h10Eu88Pq1w20fhZs37Ln57UXjoiIiMhg8tkZGFuDnf0jgFP+1N8Rje9reN0p8+zZAeUy1kqD4ZWGhvDD43iD1SnPZvuwHc9gldU1LxR6HIvUkpQE60uE3laHl15w9x3ArxHOYp0N/DRwTvXtD7n7Fb0Ocpnrlj3/YItb/DYue96Bj1pEREREBo9PHjtRlCGdgZkpfNf25ueZm+3YeShLpcLWv9VWipbkFntfEt0MX60AR7FAT6saSk2J6IPl7jOEBGvl6/9uZt8FXg08ltB4+Dvu3nRxiQ77MmEF6rGE1acbzewN7v5AE3O8Ztnzo50MTkRERGQQuDs+cQQyIcGykVHcUvj4/ubn2b0jFIDoZHyHxrENZ9V+L5+rxj7U0Xs2ZKmQ2NVSKPT+TJicIhEJVj3uPgl8rt9xLOfuFTN7J/DN6ktPBH5oZh8lrK7VbbFtZq8C3rHspfu6E6mIiIic6aL9u/GDB0g/v1532j6ZmYJ8AUaWbebJZPBjh/F8Dotbbj27gB8eh5FaHX5aVKngM1Orvh3de2fYktiJM1/NSBms1qOrWOhpVUOpLSlbBAeOu38HeBehX5cDY8CfAONm9lUz+30zu8TMHm1m68zsXDN7iZl9CvgaoaiHA7uqpdu7zszWm9llZna/mS2Y2ayZ3WFmf7iyHH4Lcz/KzD5kZg+bWc7MpszsFjN7hzUoZWNmGTP7TTP7jpkdNbOSmc1X4/yomT2xndhERETOZNHD24huvalustAvfuwIGCdVvbNUCiplKrdcH3+i+VlIpTpbPS+drls23ndv7/3qFYRmw3O1ty56vsd9uaSmxK9gJZm7X25me4FPE5olAwwDr6s+VrP8v/73dCm8k29o9njgu8AF1ZcWCWe/nlt9/JqZXeru0y3M/RzgW4SzchDOza0nNFW+BPgFM/tZdz+lLJCZbSKsBD5/2cvzhIT1GdXHb5vZb7j7F5uNTURE5Ezk1VUM3/kwHD0UmtMeOYidtbnPkZ3MpyehUjn1jeERfPs2/PkvXHWL3nLRti2hd1UnZTLHC13UStx8ehIyffhV2gxKRbxUxIZWfD6eXVAPrATo+U+Fmf1Fp+Zy97/s1FxtxPAtM3sKoWHwbwGbqF8fc+ljhQh4r7t/vcshUi0l/zVCcnUIeIu7X29mKeDNwCeAZxO2Yr62ybk3Al8nJFcPAb/h7ndWV8TeCfxf4JXARwgrfit9hBPJ1WXA37v7ZLUn2iWEapEXAVeY2X+6+3gz8YmIiJyJypd/OGwVyy1C5DA8QvTgA6Se8ozGF/fS3EzNMueWGcJnpih/7nKG3vXHdadwj4i23df5YhOpdDjTtDAP6zecfM/cIpTLMNL7BMvMQgI9cRQe/aMnx7XQh7Lxcop+rGBdRufKm/Q9wQIUDnHZAAAgAElEQVRw9wXgfWZ2GfAm4FXAiwhFMFYqE8rS/5W7396jEN8K/Hj1+Zvc/TYAd4+Aq6qJ1pXAa6qrWDc0Mfd7CYU+csBr3H13de4i8A9mtgH4APBbZvYRdz9eFsjMRoBfqv7xCnc/XujE3SvAzWb2c8AOworW64B/bvJ7FxEROaN4sRASg8UsrF0Xzjc5+IE9uEdYr6ve1eEL86snRus2QKlIZdt9pJ/+rFXniO69MySSGzd1NDYzwy0F+cVTE6zpSUj1saFvPkflzv8k8/oV3YL60ZdLTtGPBOt7nKb1I929QEhUrgQws83AhcCjCInVDLDF3fM9Du2t1a83LSVXK/wb8L8Jsb4FaCbBesvSHEvJ1Qp/B/wpsI5Qdv/9y97bxIkS9TVbt7v7TjObAjZX5xAREZE6KjddB2bY2eeeeNGq2wazCyFxSQCfm8EP7F218p9lMvjcDNG3v0bqgidha9bWmGOW6I5bYc26riU7ns+dujVpYZ6+/jq7dv2J0vbLeHZBCVYC9DzBcvcX9/qe/eLuU0BfT5Sa2RrgBdU/frPWGHd3M7sO+B3gFU3M/RTgcQ3mXjCzWwil9l/ByQnWESALrCWcA6t1jycSkitYJQkTERGRE/yRB6FWZbtUCp+ewhKQYHmlTPnzn4BCHtZvXH3gprOhVAzVBmskWOUrPwkLc/XnaCvQCBZPLYnuC3NQKdO3VqaZDL5nB16pHG+s7B6FVctOVlKUlijFPf09jRP/O9fr07X03nnVlbc4lm/kjjP305e/6KE9+tKWv7ea2fvN7GwAM0ub2YuAr1bf/6K73xwzLhERkTOST0+Gs0G1VnMqFXzvzt4HVYOP74NsFtZvqLvyZGYQRURHD682AJzubdWrVELSsoy74w/cC6n+1YqzTCZUC5ybOfFiPmyQ6tu2RTlOCdbp7/xlz+sViFj+3vmrjmpv7g1mtnKb358Bn60+vwyYMLNZIE+oerhU/v5X6gViZr9lZnea2Z3Hjh2LF72IiMhppnJb+Cyy5i/ZQ0NEd/8QL5V6HNXJvFwm+v6N1bLqMX4VTaXxLXetMlkE69Z3NsDlzIgOHTjppejuH+DHDsNwWx1u2mcWzrAtmZupuW1Qek8J1ulv+b86q7T9PuW9uP9StT139TzaO4A/Apb+xd/Aie2rawhbBOuuwbv75e7+XHd/7rnnnltvqIiIyOmrzi/+lhkK53Pma/dQ6hV/+AH84H4YWxPvgqEhfHoSL53o9uIeEW25C3K57p45GhnFt28jeuBEy1J/cAuQgJUi97DFcumPkxOdL1UvLUlEHywze0vjUady9882HiVJZmYXErYB/jih2MYHgYcJBTBeCvwNYQXrZWb24mrFRhEREVnB3UMD2norKx4RHdxPevM5vQvspNtHRPt2QWbo+NmhRswMz+eofPUq0m/6NZifg0I+FPMol7qa6FgqhRcKVG66Dnvy08I5tnwufnLYTVF0UoIVPXCPemAlRCISLOAzNF+KxTmxtUxWt2ztmHr/Gix/b37VUfXnnmtm7mqvq6Xk6rPu/tZl4xYIva/uAO4GnkNItP5nzNhERETOLItZcG+w7S6FP7wVnvHsnoW1nG+9F9/+4KqVA1e14Sz80AF82xYqN11H6qKLQ/GLWsU8Om3DRljM4seOhCRmbg7WJCDBcifavYPURReH5ProIVjZeFj6IilbBPc1eMwSmvcaYbvZPmB/XyIdPAeXPX9MnXHL3zu46qj25p5bsQL1Ck705/pgrQvdfRtwbfWPb4oZl4iIyBnHp4413i6XyeATR3sTUA3RA/eG6ntNJgKWToM7le99BxbmiR7eCkPDWA9WkiydhkqZyteuxnc8DPlc55sat2JoGD9S/VVsbiYk1yrRngiJ+F/B3S9w9wvrPDYDTwY+TVh1+3N3v7C/UQ+MB4Go+rxe+/al9w5Xy8vHsbxyYJy5t614fXlVwXpljR6pftX/5iIiIqtZmA/ncupJpaCQx2enY0/rUdR4UJx5KmV84gisXddaIjAyeqKiXz4HQ0MdiSuWtetDEpOdh01n9//8FYRVwNlpfHqS6L47w5ZBSYREJFhxuPtOd38H8AngX8zs+f2OaRC4+yJwa/WPr6o1xsK/Eq+s/vHbTUy/nbCaWG/utcALV5l7+b8Ej69zn0dVv8bduigiInLmyeVCWfE6wv/lO9GeeOXaS5/6GOWP19xk0rwjh0ID5BZXf8wMRsdCP6zcYija0SM2NARnnR3unZBVIjOD4RF84ii+dxdkknLyR5LxE9KcvwLSwJ/2O5ABckX160tWSUzfDDyh+jz2ubZqH6ul8b9sZhfUGPa7wDqgAnx+xXt3L3v+O7XuYWbnAW+o/vG2uLGJiIicaXxhLl6RAzc4eqjxsEIe5mYhnyfatgVf0Q+qWZWbv1Ntzts6GxoOK1l9KNJhZtjIaDJWr5ZUKkR7duDzs5BWgpUUA5dgufsxwpmsn+53LAPkCuB+whm2fzezSwHMLGVmbyasCgJ8091vWH6hmV1mZl59XFBj7g8ChwmFLK41s+dUrxs2s98hJMQAl7v79hXX3gLcV33+38zsw2Z2fvX6UTN7FfA9YCOhqMmHW/v2RURETn8+PxdvdSWTJtqzE29Q0tsPjYdteAaVb19DdM8PW4+tWMAnj0Kl/W1sZpasJKef0mn8wftDk2H9nSTGwCVYZrYROIuwKiIxuHsZ+FlgD6HgxPVmlgWywNWEvlP3AL/WwtyzwOuAScKZqjvNbI5QBfAfgWHC1sD31Lg2IhSu2EVI/t4DjJvZfDW2bxLO3lWAd7v7zc3GJyIicsZYmItVfMGGhsMv5Efqr2L5+N6w4jQ8AotZonvvxHP12l7WMTMdzk2NjrV2vdRkwyOhVHulrKQzQQYuwQL+V/VrvM3DAoC77wGeCfwloTiFExr73gW8F/gpd49/4vXkue8CLgL+L6EgxRAhQfo+8E7g1e5eWOXandW43gN8F5gARoE88BDwz8BPuPvHWolNRETkTOGL2fjng6IK0cED9ecb3wfpTEjIzj4X5mepfP4TeLn5bX6ey8LoGKZzQt2h5CpREvFTHqPR8Cjwo4RVmB8nJAdX1L1CTuHu88D7q4+411wGXBZj3BHgD6qPZuPKAh+pPkRERKQVuUVIxewvlU7jd91GefcjpF/3C9iKlSWfncYPjx/vM2Vm+Kaz8ewCvn0r9vRnNRfb4mLjCofSmg1n9TsCWSERCRbxGw0vpedXE1ZLRERERM54XqlAqQSjMX+1GxrGs/MwM4Xv2Yk99eRuK5UbroVsFhs90WfKzHAzKjd8A3vSU8P2tLjxzUw1rHAordHWwORJSoK1j/oJVhmYJhRquMrdmyklLiIiInJ6q24PjPvLtpnB2vV4Pke09V7sx56GVVe/PLeIH9gHG09dGbHRMbyQx/fuwp78tNjh+cH9KiMuZ4xE/KS7+wX9jkFERERkUPnsdKwCF6fIDOHje6l86xoyr34D7k50282hcMLIKlvPoiicz2omwZqeDI1xRc4AiUiwRERERKQNC3PgLZRAT6dhbhF/5EGiH/8JKtf+ezjLlRlafTUsncYnj8W+hVcqkF1QBUE5YwxiFUERERGRgeQeUb7iH6nc+M3OzpvNtnTGycywzedAIU/lmqtCopZbhDVrV78oncGPHsLjFq1YmGtq+6LIoFOCJSIiItIrC/P4xDGirfc2bPTblLnp9kp1r1sftgUW8rDp7LrJkKXTUCxCsWYHllP4sSMqIy5nlERtETSzpwC/QCjFvonQT2k17u6X9iQwERERkQ7wqUkYGYFymfInP0bmnb+PtXJ2auW801PxS7TXYOkMns7ApnOwOL200umw7W9ktOHQaPtWiFrYvigSR6mUuBYAiUiwLHxM8iHgvxNKscf5mCNZf5MiIiIiDfjhA9WtfA7ZBXzrfdgznt3enMUCvn/P8Z5VrVrZC6v+TZ3o4D7Sm89pPHZyAtKJ+JVTTkPpT34OLnoenHd+v0M5Lik/7X8AvLv6fAdwE3AEUMMEEREROW343t1h9WdoDBbmiQ6Nk2ozwWJ+DtLpsHWvV8zw+++BZ/xE3WHujs/PqoKgdIc7lMuQTtapp6QkWO8krEh9HPhvHvvUpIiIiMhg8GIBPzwOI6Ohae/oKP7wA/jFz8XOPa/1eRfmOhhlTOkMfuwIPj2JbTp71WHRjoegVMLG6p36EGnR/AKUyjDaeKtqLyUl3Xs8IcH6EyVXIiIicjqK/vO7EEUnCkhkhiC7QPmaq9ubeHYGKj0+45RKwWKWynVfqTvM77g1cedj5DQyOwflUr+jOEVSEqxjwJy7L/Q7EBEREZFO86hCdN+dJ1XeMzNYux4wvJUeVktzT03Q66PpZgYbzsJzi6uO8UMH8COHYhXCEGmFLeZ6/+FCDElJsK4HNprZ4/sdiIiIiEgnRQ9ugWwWKmVYv/HkN4eGIDsPR4+0PL9nF8KKUq+l0zA/h6+yguCHx8Gj3p4NkzNLPo+10P+t25KSYP1vYBb4qHWiVqmIiIhIAni5ROWmb+EH9sDwyCnJhpmBGZXvfgvP51q7yeIC9OHXJzODqII/8lDN931uVtUDpbtm5yFK3hbURCQz7r4TeA1wMbDVzH7TzJ5vZo+r9+hz2CIiIiL1zc5AIUflpm9BqVh7jBm+dyflKz+JV8pN38IX5vuzggVgRrTz4Zpv+d5dqh4oXWWP7Ox3CDUl6WOFh4GvA+8CLo8x3klW/CIiIiIn8WNHQiPUUgnWrK05xoZHQpPfqQl8fD/2uAvjz18uhTLt/TrnNLoG3/0IPjuNbdx0Ulw+PQnN9NYSadbMLD48FKuBbi8lYgXLzM4DbgN+Z+mlGI9ExC4iIiKymmj3IzA6hp21GavTCNjSaRgahqmJ5m4wOwOp1InKhD1mqRR4RLTjYXxu9sQ2x6OHQ2+uPsUlZ4BSCcsXwjnGhEnKCtBfAT8GzAN/Syh6cRQ1GhYREZFBNnmsqW1y3mSC5XMzzUbUeekhoh9+j+j2WyCdIfOLbyU6fBASWHxATiOz83g61esCmrEkJcF6NeGv59fd/Wv9DkZERESkXT43GxKmoeF4FwwNEz28ldQLXoLF3fK3mIWov2WqbWgoVDL0CDDKV34Se8KPgVavpJtmZhKZXEFyttltAvKEM1giIiIiA6/8hU9BIR+7AIVlMlDMh35ZMXl2oe8JFhDOgA0NQ6kAxSJMHFWBC+kq23sgGT/7NSQlwdoN4K5W3yIiInKaqJ4NaeocUmYIf+j+2MP94IFEJDKWyWAjo9imcyCdxiePqUS7dJU9vANGk9nEOikJ1ueAUTN7Vb8DEREREemIUhHWbWjumswQPnGUaNf2WMP96CHIJOyQfzWhtH6VjpfTXzYLM7MwnLCf/aqk/OR/EPge8Ckzu6TfwYiIiIi0ww/uh+xC0/2pzAxSaSrf+DK+Wt+spXuUipBb7F8PrFXY8Ag2tqbfYcjpbGomJPAJPeeXlLXbPwW+DzwLuNnMbgPuBw7Vu8jd/7IHsYmIiIg0JdqzA6KotTLlY2ugWMAPH8Qee8Hq4/K5vpZoF+mbXA5PaoULkpNgXUaoA7L0L8TPAD8d4zolWCIiItJXXizgRw9jmSF8YQ7buAk/NN5yk10zw6MI3/kQNEqwLFmrVyK9YLkCREqwGvkeiS20KCIiIlKbV8pEt98aekANj1ZLlQPu8cuz1zI8QrTtflKXXIqtcsbKc7nW5xcZZAsL4b+xhEpEguXuL+53DCIiIiLNiu66jeiO7wMG2XnIZI4nV9ZGdT9Lp/FiAWam4ZwfqT3oeO8pkTPM1Aykk7t6m4gES0RERGTQeLlEdN9dkBnCRkZx946fh/KZKWyVBMsXZqFS6ej9RAaBTU4lug1AclM/ERERkQSLtm+D3CI2EnrxdLzYRBQRbb1v1bd9/95E9MAS6Sl3ODaR2BLtkMAVLDP7eeDlwOOBMXe/dNl7awmVBt3db+tTiCIiIiL4PbdDqosJzvAIPr635sqYl4r4+L72znmJDKJSCSuW8IQ2GYYEJVhmdiHwZeCZSy9xauGLAnAl8Fgzu0RJloiIiPRD9MiD+JGDsHZ9925iBuUyLGZh7bqT3vLx/WCmZr5y5snl8XRye2BBQrYImtkG4HrC6tRh4F+A7Mpx7l4GPkFIvt7YyxhFREREAHzyGJVvfBlS6a72oDIzKBUp/+vH8ejks1bRD76X6DLVIl2zsICR3OQKGiRYZvY4M3tMD+J4N3AhcAdwkbu/A1hYZexXq19f0IO4REREJMGiA3sp33Btb++59V4AbM3a7t9sdE2oFjg7c/wlL5fxo4dgZKT79xdJGJucxqNkV89stEVwD3AI6HaS9QbCdsDfd/eZBmO3AWXgyV2OSURERBLK52chn6PylS9AuUy5VIKD+2HT2dhTLiL99Gd1576VMtGWu2C4N8mNDQ3h0QiVe24n/ZJXYpbCD49DKtXV1TORxJqYSnQPLIh3BqsX//U+ESgBtzca6O6Rmc0CG7selYiIiCRS5Sv/hs/NQLEIxQJ+/92hbPPcDL5vN2QXSD+v/c0unlvExtaceGFyAqCtHldNywzhd/4n5d2PkPnFtxL98HuQ8E/wRbrm2AQMJaaMRE2JOIMFpIGSe+NueRY+rllHjTNaIiIicmbwYgEW5mF0FDadDes2YOvWY2vWQSZDdMsNRHt3tXWPaPcOyp/5R3ziKJX778Y9wieP9vzTc0unYf0GmJ2m/Nl/DgUuVD1QzlA2MQmZ5JZoh+RUETwAPMnMHuXuRxqMfT4wQtgqKCIiImcYn5sJydVZm09sk1u2omRDw3gUUbnmKqKzNpP+5bdjTSYkvrhA5WtXweIi5S9+FvI5bHiY6PZb+7I9ydIZ3FKwMAvDozCSlM/IRXqoXMZm5/H16xqP7aOk/Nd5Y/XrO+oNMrMU8NeE81rXdTsoERERSR7ftzuUKK93Bml4BNzxqQmYONr0PSrXfTVsP9xwFsxOQ7lE5fprq8Ul+tN/x9auwzadE77q/JWciSan8JQlukQ7JCfB+hChcMX7zKxm+XUzexJwDfBSIAf8fe/CExERkaTwgwcajjEzbHQMDKLD483N7xF+6EDYdpjJYJvPCX2oFuZhzVolNyJ9YjOzp3bJTaA4WwQ3m9mNjYetyt390gYDdpjZu4DLgS+a2V5gE4CZfRt4HCeqBkbAb7r7oTZiEhERkQHlRw+dtCWwLkvhD9yDX/w8wkaYGI4ehijChk6Mt8wQvnxLooj03vQsrOgJl0RxEqwh4MVt3CNWnununzKzQ8DfEXpiLXnZsud7gXe5+zfbiEdEREQGlOcWw7a/uD2ghofxY0dhego2n9N4/uw8lW9/reY5KyVXIn12+GioFppwcSLMA1d3OxAAd/+GmV0HvAj4GeDRhAqDR4D/BG5093IvYhEREZHk8d2PAMRejTJL4ZkM0dZ7Sb/wZQ3HV77yBfzYkbAlUEQSxQagRDvES7Bm3f3tXY+kqlqq/abqQ0RERAQAd6dyx62QavII+dAQ0db7SF3y0rqJmRcLYbVLRSREkscdm5rBx/pTZKYZSSlyISIiIlLf/BzMzmDDMbcHLkmlQzPiOn2xfHaayn9cCcWCkiuRJCoW8Uq5+Q9Y+iD5EYqIiIhAqOzXQvJjZqFk+9HV62NFu7bjux6B9RvbCVFEumU+OxDJFSjBEhERkQHh4/sgilq7eGiY6M7b8HIpzLViHn/koVCWPW51QhHprQEp0Q7xzmCJiIiI9FVly11EW++FzFBL11smgxcLMHEMP2sT5U9+FDvvMaRf8ioYGg6rY6NjHY5aRDrFjk20/gFLj8VJsM7tehQiIiIiq3B3ou99JzT6jVFqfVVRRPkrX8Ae9Wgol/EdD1EpFo6XfdbZK5EE2zcOmcFYG4oT5bGuRyEiIiKymux8aPx7dpuf+Q4Nw8J8WMkaHYOxNWHlKpOB0TWdiVVEusKmpgeiRDsM4BZBM3tL9eld7r61r8GIiIhI10WPPNSReSydhg0nF7HwdRu0ciVnnkoFDh/BZufxpz+l39E0FkXYzCy+djA+CBm4BAv4DOGIm5vZvwN/qURLRETk9OTuRHf/4Pg2vk5TciVnIvvhXaRu+j64Uxkegic9od8h1Tc7hxuqIthlRoj9zcB9ZvalPscjIiIi3TAzBYtZbKi14hYiskK5jP3gTjyTwS1F+t/+A6am+x1VfbNzGIPzYcggrmBdWP16PvCS6uNV/QtHREREavFiASaPYY/+0dbnmJpoqfeViKxi3wGsUMTXrwPA5+awvQfwzZv6HNjq7MgxPKr0O4zYBi7Bcve91ad7gduAD5iZPtYSERFJCJ84gu/fQzS+H9+1nczvvBcbGm5trunJcF5ERDrC7t5ycjupzBC29SH82T/euZvML5D69Odh/Tqi17wczvuRtqaz+7fBcGv/hvRDnARrs5nd2MY93N0vbeP6ODcodXN+ERERaczdie69I5RUL5XAIzAjuvt20s+/pOn5Kj+8heiu28AG9USDSMJMTWPbd8C6dSdeGxmGg4dDj6lOnHFyx267Azt0GMadVKlM9M7faH3uUgk7OnF8xW0QxEmwhoAXt3GPAem5LCIiIu3whx8guuV6yGSwsVDty0sl/MEt+MXPJbrvTlIXPw8bHmk4V+X27xN9/0Yol+Cs5G5dEhkktnd/eLI82UmnIarA0Ym2V5oAOHgYu+Me/NHnQSqFHZ3A7t+GP+sZrc23fzxsEx6grcJxEqw8cHUnb2pmbwd+CpgjbPX7AaHsesNkzMw2u/tUJ+MRERGR9vjCPJXvfB1KRWx0WSn0TAafnqDy+U/gkxPY5nOwJz218Xw7H4aREWz9hi5GLXJmsQe3127WGzl24CDegQTLdu4J5ejSaQB8dBi7676WEyw7OoFHUdtx9VKcBGvW3d/eqRua2YuAT6142YFZM7sW+LS731Rniteb2S8Cb3L3fKfiEhERkdZFP7g5bAtcu/6k180srGJNHoPMENEdt2KP/lFsbf3tPj473bXS7CJnJHc4eARqrSAPZcIq03Mvbu8e2UXsgQdPvsfoKLbvAHbLbfgLf7r5OY8cO56sDYp+bGouVL++DbgI+CXgI8Ae4FeB681sp5n9VzOrdZrtFuBJwD91P1QRERFpxLPzRNvuh7VrsVrnLDZuwjZugrE1+Pg+oi13husqZXx+9pTh0e4dsLg4MD1vRAbCsQkoFSFTI1kZHYVDRyDXxtpFoUDq8itgZjac61pihq9fh937QEjymmSHj8LQYH3Y0o9/ue4CthJWsS4DFoH3uftPABcA/wOoAP8I7DKz3zWz4z8J7r4LeDXwG2Z2cjt2ERER6bnowfCLk61SjGKpma+lUjAySnTP7ZSv/CTlf/oQ5Sv+iWjX9uNjvVKmcu2XwjmuATpzIZJ0dnQCVuslZRb++zw20fr8D+/A8gXYsP7U81LDw9j8POw70NyklQo2NQ0D1gev5wlWteLf64AbCY2CrwGmzew7wDuAh4CXA68FdgIfA7aY2cuXTfNKwk/IYP1ti4iInIZ8/25IxUyGhoYhn8OPHApVBisVKl+9ivI1V1F5+AF8946QrI2OdTdokTPN9h2r5lcAHlWwyRbLHFQq2He+i69WSt0MT2dIfeP65laxpmdwY+BWs/uy3lbtZfVKM3sq8CvA64GXApdyoupgDtgOHAGeBlxnZrcCjwUeB3zT3VtPs0VERKQjfOJo7YPzNVgqBetOLlzhi1n8oQfwPTtDqeiYc4lIfLba+avjA1Jw6DA8+5nNT75/HCuW6pdSXzMGMzMwvxBWuWKwQ0da2lbYb31NB939IXd/f3V74LnAG4EPEc5ZFYCLgfOqww24BHg88CDwy72PWERERJbz3CIsZiHVxiH0sTWw4Swol0NZ5xhl3EWkCdkszM3XP8s0Mozt3t/S9Hb3fY37MpkBho0fij/xzj0DVZ59Sd2PiNy9ZwlYtfT6V6oPAMzsbOBsYAyYB9YTtgy+EPg48Ou9ik9EREROFW25C1LW1nkpW+pxsy7ep9oiA6Vcxh7ZFarr7R8nesNr4cLH9zQE23+wcS+poSGYnQvJ2Nq18SfPLmKP7AorVI1kMtg3r8ef8HgYafBBShRhO3bBAG4XTvSGRnefdPft7n6fu++qfn0R8HvAhf2OT0RE5EwXPXCPyqmLrGZqmtRH/pnU567G7r0f5udJffnrUKn0No7944232plhKYNjk01NbTt2Q7kSr5T62CjkC9h3b2089tARrFSuXfUw4VpOsMzsIjM7r/HIznP3fwB+rh/3FhERSTIvFYn27ybas4Py175I5Qffww+P41Hnf6Hz3CIszCvBEqnlkZ2kPn0lZBfw8x6Fn/coOPtsLF8g9ZGPQ75w6jXu2D1bSH3yX7H7t3UsEbO9+2G1AhTLb1+JQln0Zjy8o7kkaGwMu/3uhomc3b2l8bbDhGopwTKzxwA/AMbN7JCZfbizYTWmAhciIiKnim67mcoXP0vl61/CH9xCdOtNlL/waaJbv9vxe/neneHM1ACekRDpqsUcqa98A6IKnH32SdvzfP06WMxhd98XxpbLMD0TtsTdeAupr34T23eA1Be/Suqq/wjb9tpRKmHHJmE4RvHtoQy2e2/8uSsVUrv2hD5acQ1lIJMhdcPNdYfZzt3NzZsgrX7k9AFgaXOmEc5FiYiISB95doHovjvDmYViEdasg3QKKhWie36AH9wHG84i8+o3dOR+0UP/j707D44su+47/z3v5Z4Aauvqlc1md1NskiLFXaYokaIoiiIlWzO2JGtsyZYiNOJ4iXDEeGz/4XCE5XHMHzMxGo3HlhdRMkWKbDbZ7CbZ+1LdXd3Vte97FaqAwlbYd2Qit/fumT9uAgVUASgABRS284lAA8h8uPmyuirxfnnvPefcioxjzGYjLdegFs1dLS8IIJdFDh71+5faOpGBQROSaWUAACAASURBVPSRh5HmFnT7Nh9CVJHWdsL/8N+If/e34PFl7o7p6vZX64spdZ5K+YbDqosrLjEyhqKLWx44U0MeWtthcBju2Xnr/RMFpDiJLrLa4Hqz5BksEbkH+B18OXUFvq6qbSt8XsYYY4xZIu1o9RdliSSSyyOpFBImfFU+FbS9Fb101i/tu9PHcjHa1e77WhljZrvYvHCgSaWgWiV49xAMD6PJBLR3ort23Kj0J4Ju34am0wRv7IPKHEsKF0G6e9HYLe7gMPRLF0dGFzd2Wzu4ZSzkm2o+3to2992Hj/stYxt0dnw5SwT/LjD1avqmqj53JycgIk0i8qqIfENE/pGILKP4vjHGGLO1ubMniN96hfk6iUo260uhhwnc4X13/oBDfqW+bLAGoMasOucIrrUvXFVPBBob0d33+Ip92ayf7Zrr31MuC319BE8v85K7q2fxveVE/FLF5pbFHd7cuvwiFOmU34t1M1VfcTG/8aoHTlnOq+LnZ3z9X+70BFR1HPgr4A+BPwP2iUjuTsc1xhhjtgJVhztznHjPCzA+tmDpYwkCSCRxZ0+ifUvoRXPzY04WiV/+kW8KbIyZrW8Adbq4JXmLIQLbt0N759L3Y6kinddvXxJ9pmQSOrpuf1wc++qESxl7plTK9+YqFGbdLKfOIsWiP48Najn/5z9R/1wBXl6Jk1DV7wGH8W+7NQC/tRLjGmOMMZve4ADxGy9BMonsvOe2BSckkQB1RE/+BfGZ48t6yHjfG+jIIKQ35gZ0Y1aTtHWs/JsPIiAgl64s7edGRqFWXdosUzKJ9C+illz/oH+eS91/NWVqmeCZCzducw556100u3Fnr2B5AWs3fu9Vh6qWVvBcZlYitBLsxhhjzDy0VvWfixO4cyd8Jb/U4sOOZLIQhui5k0t+7OiVH6PnTkA6a9UDjZmDNLcsrmLfUqXSyNGTSyvdPjwCssTL/WQCGR+fu4z8DNJ5HfQOg2Qu63tijY1Ddy/Bf/kmUpxcVEn59Ww5Aaup/rl7JU8EeAYYxM9ifXGFxzbGGGM2BZ0sEv3Ff0AH+4m++We486cXv79iplQa7b1OfOwAersGpFOPPTSAXjrnw5XtvTLmVqUSXO9ZndndTBqZmPDL8hZJevt9qfilEPHLG3v7Fj7swuU7L3JTf+0Kvvs04Tef9NUUGxvubMx1YDmvjlOzVivaVllVHbC3/u12EfmplRzfGGOM2Whc5zW0VkXjCABVJX7pWRgfI3r621Cc8I1+E0t/t1yCAFJp3Ltvoq3Ni/qZ+PghiCNkg7+7bMyq6eqpB5TVmd1VBbnauujjpbllWSFInUMW2oel6pcIrsRMXUMe6e1HRdB7di3vDaN1ZjnPYAi/T+rBFT4XgH3c2H/1QWCJC02NMcaYjU1V0WtXIJMlfvZJCENkxy544CGYLPrS6Nt2wMQYZHKQWf5SPUmlUVXc2RMEjz+x8HlFEXrlou+tZYyZk5w5P18hz5WRTiGtHSxqzrlW88FlOTNCySTScX3+xxkZhTiCcAX2SgUBeu/uOx9nHVlOwLoCPAI8KiI7VXV4Bc9n5pznQys4rjHGGLOuqSqMDqMjQ8Q/+f6N/i9xjA70wfUO/65xvhEJAjSb9xdBd7oPKplCO9vQni7kgffMf37tLaAOCTduZS9jVlUU+dml1SzQkEwhQ0N+f1TmNtX7Bob8TNpyXiNSKb9EcJ6Gw3LmAotLeVvTcgLWIeDL+Hz+W8Cfr+D5zCxZ0jTvUcYYY8waUXXohbPIAw/B9p1z7kVSdVCYQBq3EZ88guzaTfDeR+c4TsHFaHcn8fM/BBSiyFflSqVAghsBKpdHVae/X6llehIEKBA9/W0Sf/BPkKbtcz4fd2S/v9gyZrMYGyd44VUfVoIA96XPw+57Fu5ftZCubsQ5dLlV9RYjEP86cPEy+omFW8fK0LAvF78cYQjFSV9GfdtNl+SqyPFTtw94W9hyAtZLwL+pf/0vReQvVXWJu+dW9HyMMcaYu2d0hPiNFyGZIvylX0U++FGgHpaKE+j1DrRSwb3zOsFnfxH3zmt+Wd2Xvoa7dJbwi78KDU1QLhG/9hza2eargkU1P34qDenMnDNTq1W1T9IZdLKAa20m/PjP3npAz3V0oNfKspvNo1gk+MZf+x5MiQQSxwR/9T3IZXFf//1bQ8UiyIXLd+c9iFQaOXgM/fhHF56d6lp+rzvAz3719d/6Z3G9BypV3xjZzGnJgUZVD4nIBeDDwGPAvwf+9Qqdz/0zvh5ZoTGNMcaYFeO62mGyCIkK7vghZPsu4tNH/b6pILhRdGKy6MNVOgO1KvGLz0CtRtTS7JcQ1Wq+P00Q+mOyubUte57K4Pa9CbUawYd/BsnfuHjSwX5ArSy72RzaOgiffAZFYddOELmx2q1QIHjhNdzf/80lL62Tts67M6uTTsHoGExOQj4/9zGqyKVmyN7BmyJOkatt6AfeP+tmOXtx+WNuEcutsfrvZnz9r0TkD1fiZICfn/H1SpeBN8YYY5ZNiwV0dBi37w3I5SGbR7vaib7/TfTMCRgf8+EKoFKBHbuQhiYkmULSGchkYdt2SCZhdNiXTk6lkVweCcM1Dy+SSEBUw739OtG3/9us+7SvG9TCldkE4pjglTfRRAhNTbeGqHwe2jthoQp6cykUYXTU//tebSJIIL6K33yKRSiV76wiXz6HnL84u++WKnK1BTI2m72QZf2pq+rTIvIHwNfwe7H+m4i8V1X/7XJPRETywO9MPQRwcLljGWOMMSspvnoR98pPIJ2G8dHpvVe6fSc4h2RzaBT5i495LrBkal9GKg0712nFrFzeL1WsVXCnj6FxjDuyz/f2seWBZhOQ/Yd9r6Xt2+Y5wBeFCL7/I9w/+/qig4R0Xp/+2btBnSJXWtBHH5n7gO7eOz+fMERqEXL+EvozP+1va+9CxguoLQ9c0J10Cfx94Bo+DAXAvxGRQyLyqWWO938CO+vjnVjh6oTGGGPMsujQAO7V56BagbFR2L5jurCFiEwHJ0kk5g1XG4WIIElfXCN+/Xncobf9cw4TfobLmI2sbwA5cMSXLV8oeDQ2+IqAl68ufuzmq3e3ql42g1xonrfwjLR1rkhRGk0mkeOn698owTsH/OyfLRde0LIDlqoOAl8BOmfc/LPAERF5UUS+JItY7yAiSRH5U+Afz7j5/13ueRljjDErJXrlx0Tf/QaUy0hDEzQ0IcEqVghbJySd8bNZ1Sps24HYciCz0UURwct7wOnils0lk8iJ04sbu1TyYadhnv1QqyGR8DPLg0O33jc2TnD05MrsB8ukoacP2bsfeetdP1OXy935uJvcHb0dpaotIvJZ4Gn8/inFLxn8av1jUEReAo4B5/Bl2CeAPPBe4AvAH+CLW0j9548BT93JeRljjDF3yrU2o+dO+WWBDb5R51wl2TcrSSR9sQ5j1itVvxTuoQdue6hcuoK0d6I7dixu7EwGevp8gLln18JjN7cgqujdfH0QAQW50Iz+4j2z7zp30Vc1XYlWDiKQCAne2oemUmgua7NXi3DH8/2q2isiXwD+Ob6i4NTbXALsBv5h/WM+U8EKYAD4LVV1d3pexhhjzHJpTxfxcz+AbNYvmTPGrC/9AwQ/eRnp7cd99Uu+ZPl8S3RVkaMn0WzWlx5fjKl+c2cuoF/6/PzHqSJvvIOuRU+oXNYvefzwE7D7RgiU85furHrgLY+TQ23WaklWJGqr9yfAR4G/BKpTd804TOb4mDpG8DNcn1XVmUsOjTHGmLsqPn+K6Jnv+A3eFq6MWV8KBeTgUcJvPgkDg2g+R/Di6wR/9t+RC5dv3XdUKCCnziLXe5YeOrJZv/+oWJz/mMEhXzV0hRp/L0kiAc4RvLXvxm3DI/6c1uJ8zLQVnctU1RZV/SPgEeDfAmeYHaZuJvhCGf8U+LSqtq3k+ZjZRKRRRP5YRM6KSEFExkTkqIj8byJyR/8SReQ+EfkTEbksIiURGRaRfSLyPy9mL94c470sIlr/2Hsn52aMMYulpUnc6y9Apeyr/Rlj1o/xCYK/+h7Ba2+hIr7MeiqF7toJ4+MET/2I4Bvfhiut08eH//EbBM+9gmYzS1/alkxCtYocPjHvIXK99+4Wt7hZUyNcbfVLJQE5fa4+dWHL+NbSqpQEUtV+/HLBfy8iO4FPAe/HLxlMAKNAH3BIVVtW4xzMbCLyCLAXeF/9pkkgDXy6/vG7IvLLqrrkBs/1ypGvAlPz0wWgEfiF+sdvichvqGp1niFuHu8P8Hv4jDHmrtLONl8xL3cXN6sbYxY2Okbw0utIaxsaBOjOHbMDhAjs3OH3HfUPEj75Q7SpEalUfRDb1rT8flC5LHLiDPpzn/YNwmdSRY6dhOQaVtgUgSBA9h+G9z9GsO+QlVBfB1b9b0S93Prr9Q+zBkQkATyPD1c9wD9U1T0iEgC/DXwD+ATwHeDXlzj2NuAFfLi6BPwDVT1WnxH7I+BPgV/FV4b8J4sY737g/8GH8B7gQ0s5H2OMuRM60OsbABtj1gfnCL79fRgbR7c1wUKFJERg+za0UoVSGU2n/FK5O5nNSSahNI6cOof+3Gdm33e1FekbWPtAk8sRnDkPF5vRdPrOmgubFbF1yiFtbb+P3x8H8JuqugdAVZ2qfh/4X+r3/ZqI/PISx/4X+CqQJeDXVPVYfeyqqv4ZfqkowNdF5AOLGO8/AzuAfwn0L/FcjDFmWXR8zPe7OnkEbN+VMetHVzdSLMKO7QuHq5nSKb90Lp1emaVy2Szy7iGo1W7cVigQ7N2PJpNrvxwvCNDd96D5HOStGMV6YBF3a/j9+ue3VPXgHPc/BfwfwKP4io9vLGHsqQqRT6nqtTnu/4/AvwYagN/lRuC6hYj8XeBvA2/ji6X83hLOwxhjFk1V0XMn0WoFogh3YO90TVvJ294rY9YF5whOnEbdWm5yoj6LVUb2vI1+4XMEr72FXL6KopBfJ8uJg8AHSrMuWMDa5EQkh+9RBvDyXMeoqorIK/hmz19ZwthP4PuZLTR2QUT2AV+rjz1nwBKRXfgwVgG+Xj+nxZ6KMcYsipYmoVYlfvrb6NgIxDGEIWRzgCz+HXJjtqqePti1486q1BWLfs+QU/RXvuj/Dc6lvRM5cRbduX35j7VSMmmCQ8fh+GkoldDt21e2FLrZVJYUsERkoX5Wd9spVT2z1iexAXyIG0tBzy1w3NR994vIzvreudv5yBw/P9/YXwM+vMAx/x9wL/BvVLV5EY9tjDFLoj1dvvx6HEO5BE3b/dKeOEZCe7/RbBKlMvT2+VmX7U3TTbJXRP8A4TefxH38I+jXvry8pXGqBE/9CGltgyDA7dyB/uwnoRb58374IV9q/UoLwWt7fWPb9bCnKJVCd2yb3udlzEKW+jf2r1jbYpQz/Tt8GXizsAdnfH19geNm3vcgsJiAtdSxm0SkQVULM+8Ukb8F/H18EPu/FvG4xhizJBpFRE9/G1C/xyqdQaZmq2zWymwWHV0EP3oRGR6BQNBHHsb97m/P34B3sVSRi80Ez7+CAsGJM7gd228t+rAIcvYC0tOH3n8fRBGy523k3EWoVJGBQfQjH0Tvv4/g1bfQdBIa11FFvPUQ9MyGsJy/KbZua2OZ+co0ucBxM+9b7KvZcseeDlj1KoT/FXDAH6lqjWUQka8DXwd473vfe5ujjTFbiU6ME/3gW/5irmEdXawZs5KuthJ+71k0kfBlzJ2Dzm7kxdfR/+Fryy/EUIsInn0eudKKZtKQz6PlCsFrbxHvvgfe/+jix3IOOXkGTdULQ0wViOgbAEBzOeT4aSSR8BUDrVmu2aCWGrD+3aqcxfLsXesTMCviT/AzYf9JVQ8tdxBV/XPgzwE+/elPr5dZVmPMGtPSJPErP4aBXrBwZTarUongxy+h2Sxk6oUOwhC2b0MuXEY/+2m4/96lj1upEPzgJ9DZ5QPPVEjLpFHnCJ57GffPvr7omR3ZdxDp7PZjTUkkoPHGMka9/776wfZ+vtm4lhSwVHU9BSyzOBMzvl6odufM+ybmPWrhsceXMraIfBn4Q6ALX2nQGGNWhKpD268Rv/C0fye/aTtiy3vMJiXHTkO15kuTz7pDIBCCN97B/e5vLX3cA0ehvdM36r058OSyyEQBOq/Do4/cfrCRUWT/YbShYeHwZMHKbAL222bz657x9UPMv2/toXl+ZiljzxewpsYev2n/1Tfqn/8VoCJy807cqbJC4Yz7SqpqXUCNMXPSqEb86nNo21Wol2DHOdi+c61PzZhVIxcu+d5Pc8nnkSstPgg9/NDcx8ylb4DgwBG0cf5ApCIEh4/jFhGw5Nwlv4s/MU/FQGM2ka26s3crLSG7iN/fBLOr/t1s6r7eRVYQhNmVAxcz9oWbbn9f/fOT+Jmtmz9+oX7/L8y47W8t8tyMMVuQXrmIXrngG4Kms75SYEMT1vbBbFo9fTA4PP9+JRE0kyF46kcwMLjoYYM396Ei85dQB8hl4VoHVCoLD1aLkMPHIJtd9OMbs5Ft1YC1ZX7TquoksL/+7VfnOkb8lcev1r99bQnDNwMdtxk7D3x+GWMbY8ySaLWCO3oAwiSSyyOJBCKC3GkFNWPWMdl/GMJg4aV1+RwUJgi+9RSML2IXwPgEtHVAw22a6E5V4GzvWvi4rutIFEHK/i2arWHLBSxVDeofW2mO+lv1z78kIn9jjvt/G3is/vW3FzuoquqM4/8nEXnfHIf9U6ABiIHv3vTzstAH8Hb90Ldn3P7jxZ6fMWZrid/Zg44MWuUxs3UMjyDNLZBbaIs1Pnzt3g3VKnLkxG2HlWMnfS+qxbQwCAKC/YcXPuTAEXTrvLdtzNYLWFvUt4Cz+Jm7Z0TklwFEJBCR3+bGXqiXVfWNmT8oIn8sIlr/eN8cY//fQC++kMWLIvKp+s+lROQfA/++ftyfWwNhY8xqce0t6KmjkEzZckCzZchUsFlsL7dcnuDYqYVnscoV5Phpv/xvUWNmobvH7/GaS0sbtHX6WTRjtggLWFuAqkbAbwBt+IITe0SkCBSBHwBNwEngd5cx9hjwN4Eh4MPAMREZx/e6+s9ACr808H+94ydijDFz0DjGHXoH0mkkYUuQzBYxPuELRywluCRC1MXInrfnPUSutPjiMIutuikCiQTBq2/6Wa+ZhkcInnkO0mmrDmi2FAtYW4SqtgE/A/zv+OIUCtSA48C/AD6rqiPLHPs48NPAnwJXgCQ+vL0L/BHwNVW9zQ5YY4xZOi2XiP7rn6A9XZBKr/XpGHPXyDsHfKBZqAjFXBoakEtXYHiOX/mVCvL6Xh+IliKXQwYGoWt2EWLZ8zbE7kZvLmO2CCvTvoWo6gTwb+sfi/2ZPwb+eBHH9QH/vP6xIlT1iys1ljFmc1EX4w7tw507CbUKZPO2NNBsHdVqffbqNkUo5hIEgCLnLqJf+NyN24eGCb71fV8RsHGJTblF0CAk+NGLuF/7Mjz6CHLuInKldVYTYWO2CgtYxhhjNhx3aB/uyD5/sWjhymwxcvWa7+8WLnMhUi6H7DsI9+5GP/hTAAQvvIoMj6D37V7emPkcTBQIn3oWfex9SMs1NJdf/P4wYzYRC1jGGGM2DFWHXjqHO/Q2pLNWgt1sPcMjyNv74U7+7icSkEwR/ODHuK992e+56uz24epO3qxobEAnS3CtA21qWvryRWM2CQtYxhhjNgTXdpX4hR/6JUyZjIUrM7cogt5+P6OyrWljzaDEsS+73tOL9PShH/4g+jMfvhFUhkcIvvnk8pbx3SyXRZMJgtfe8g2FG/IrU4gilwWsobDZ2ixgGWOMWfd0Yoz4+af9hWW+AbF3xs0c5Mx5gpdehyiGOMZ97mfRL/78nc32rLZKBYZHkdPnkLMXkUIBTaYgERC0tqNnzuN+46tIb78vGlGt+XC1EmEomUS3reM/G2M2KAtYxhhj1j135oSfmWhssv1WZk5y+Sry/KtoIoTt2yCKfIPbkVHcb/6t5S1XGxn1VfASoZ8NW8zfvbYOgn2H0F074MH70YYGeOyRGzNptRpy6Jg/x/ECcvIMMjwCUYTmcug9u6YfR1Whu5fgz/4SUYem01Y0wpgNwAKWMcaYdUsnxol/8j10cMD3ubJwZW6mCldakGeeh0wG0il/eyKB7tyBNLf4vUb/469DNrO4MctlZO9+35Q3jv3D7NqJfuyn0V/47I2g5Zz/WgR5ax/S2oH09KJhSNDcgqYSBE7Re3ahH/4AIMiFS0hvv6+8l0xAmECnwtvNyxlFpgPVTR2mjDHrmAUsY4wxd406BxNjyLYdc98/MQaZLJJMoaq4o/vR/j5IWRNhMzd59xCy75BfBjgVrqbvFB9emluRY6fQz3/29gOqIi/tQc5eQLdvmw5QlCvI2/uR7l4futJp5PJV9N7d6AP3Ehw/jYYhmstBKolOBSPnYHCIYP8RPyOVSqL33evHsKWuxmxKFrCMMcbcNXrtCvGLzxD8+m8SPv7ErPtcVzvxM9/x7+InEv4CtFqFXB7ZSIUKzN3T1oG8c9AXaJgvrIhAUwOy/zBENd/7ab5jnUNe20tw4TK6c8fsJYGZtC+LfvUaCEi5iu7YBsPDftYqk4Zc7tYxgwB27bx1BsrClTGblv3GMsYYc1doHOH2vwW1Ku6FHxKfPoa6+vKrkSHi1567cXBhwu+5yjdYuDJzK5cJXngNUsnbh5VkElJJgr37Cf7yOzA8cusxlQryyhsEx06i8xWRSCb9XqymJvTee/z3uRzs2D53uDLGbEk2g2WMMWZVqTrc+dPoicPoYB80NEGthnv9eZgsEHz6c0Tf+XMol6Bpu99nlV7kXhmz9agiB44gR05AubL4og+plC8gMThM8L1ncL/xNXj4Ib+Ha2iE4PW34EprvX+ThXpjzPJZwDLGGLNq3PUO4peehfExiGo3AlQqhQaCO7ofd/oYqLtxnzHzGRkleGkPcqkZzWahaYm9oKaKRkwUCf/6+8R/+A+Qs+cJDhwF1VuXBRpjzDJYwDLGGLMqXMc14me/6y9Y51jqJ4mk3/RfKkE2Z+Fqs+jtR7q6kXMXwcXoxz+KfvJjdzamqu8T9da7MF7wM1HL3cNUb6qrk5OE//27EMe+IEUYWrgyxqwIC1jGGGNWnE4WiF/5EQQBksnOe5wkU5BMzXu/2UBqEXLwCPLuYT8jGSYARV54Ddo60M9/DnbvWtbQsu8gwd79aC4Lu+auQLlkuRxaKvm9U7Yk0BizgixgGWOMWVHqHNE3/wyiCMnaxv8tIY6Rl14jOHsRzed88YcpUeybALe04X7nb8N737OkoeXEaeTtg77c+kpX3svOH/6NMWa57C0bY4wxK0Yni8Sv/NiXWF9g5spsLvLOAYIzF9GmxtnhCiARQqPfKxV++/vIyTOLH/f8JeTlPdCQs7LmxpgNwwKWMcaYFRO/+RJ67gQkEranaqvo6EIOHEUb877n03yyWTSVInjlTRgZve2wcuoc8pOX/RK+m0ObMcasYxawjDHGrAjX1Y42X4DG7UjCLoi3BOcIXn3TB6DEInYd5LKoCME3vo2cOQ+12uz7VWFsHPnxSwTPvwLZjIUrY8yGY3uwjDHG3DGNIty+PZBMIbaUa8uQ5hakf9AvDVyshjwUigQ/ehE9dxH3935zunqfvPwGwfFTaCC37uUyxpgNwgKWMcaYO6I9XUTPfhdKk9C4ba1Px9wt4xPIK2+g6dTSy5s35NF8DmltR1563Re+GJ9ATpz2xSwWWmpojDHrnAUsY4wxS6Iuxp08ArUa2t+DtrX4pV75Btt3tVWoErzwKkyWlt7sd4oI2tCAnDwLp876UJXPWbgyxmx4FrCMMcYsmqoSP/VNtLvTXwgHASTTC/a6MptQyzWkrQNtXGa4mpIIYbvNehpjNhcLWMYYY26hlTJUK2jzRWhsQn7qQ352amwEHeyHfKPttdqq4pjgzX1oMrn0pYHGGLMFWMAyxpgtTgd60WoVSSTQni7ctatoxzV/8VwpgwjBz/8SwWMfIH7hGQALV1uYnD7nC1tsa1rrUzHGmHXJApYxxmxRqorb/ybuyLsQhL45sDrf0DWTRcIEZLK+QuDhd3EH9volgbmGtT51s1aGhpHX96K5rM1eGWPMPCxgGWPMFqOquCPvokMD6OVzkM0jYYiqAtxSqEISCUgkUFUrYrFR9PYjx08hHdfRxx+BsQl48H700UfgwfuXN2alQvCjF0GBVGpFT9cYYzYTC1jGGLPFuOOHcO+8Dqk0pDLTy/1uF54sXG0A3b0E+w4il6+Cc2gigYyMQhjApatIeAD9yi+hH/vppfWYUkVeeA16+8GWBhpjzIIsYBljzBahLibe8yJ67hQ0NNk+qs2krQM5dwk5fRYU3/g3cdOv+DxQrRG8+ib69n70q7+M/vQHFzW8HDpOcKEZ3d5kSwONMeY2LGAZY8w6pIUJqJRxJw+jHdeQj3yC4DM/Pz2LpHEMhXFoaPR7paZ+Th0iQf1r9VX/eroAiA+/CyODkMsj1mtoc1CFlmuE33sGDUJoaPSzVfNJJdFUEsoV5EcvIkdPoI8/ij7+6NxLB51D3j2E7DuINuYtXBljzCJYwDLGmHUkvnwebW1GL5/3BScAEPSd13FHDxB+9gtoqYg7ewIqFX/vI48RPPI4ruUy2tNF+ItfQT70UeKfPIV2dfiLYuf8jEbOmgFvCtUqdPch5y8iJ8+g2Rxk0ov/+Uwakgm43ot09SBv74dHHoYwxH3+53zD31IZOX+J4MgJtKHB96wyxhhzWxawjDFmDen4GOTyUKuil8/h9r7m70hnIAhuzFg5B+US8btv+PvjyO+hSibRznbizjaIIkgkiF9/HvbtARdDNosEdmG8YUUR9A/CPTuRk2dhogAPPeBLpTe3oIkENG2DYBmhOQxv7KdyDjq7AQha2vx9ddrYMOt7Y4wxC7OAZYwxayQ+fQz35suQy4FTKBV90Yk5KrRJEPggNpdMaP9AQQAAIABJREFUZta3ms74C/N0ymarNhpVKBSRax2+at/e/VAs+oIUcYyGYT1MCbpj+8ot2QsCaGpcmbGMMWaLs4BljDF3gboY9EaD3vjkYV/JL5P1y72CAPKNKxKIRGRpFeLM2lKF1nakswvpuI60dYAqGoZoJg337PIzTCK2B8oYYzYAC1jGGLOKtFImfv159HoHkkoT/sbvoL3Xce/sgWTKBy5bfrVx1HuFrUjQ6e4leOtduN6DlEqoCKRTc89MWVESY4zZMCxgGWPMKtGBPqKXnoHREQgTaKVC9J0/h1oNsjkkYbNM697YOPLmO8jVNnj4Qejpg2wW9+Vf9N+nl1BYAuBaO3LmAqjzvapiB8kEunOHzU4ZY8wmYQHLGGNWgRYmiJ75a7/8L5OdtfRPM2p7o9azKEKutCJnziPtnWgtgmwW2rt8JcaxMcKnnkW3NeH+zt+Ehx5YeDxVZO+7yLUOpL0LTaf8PqpkEvJLDGjGGGPWPQtYxphNScsliCK0qx15/xN3ZbZIuzth+060u5N4z4tQqyKZ7C3HWbhap0ZGkbMXkHMXkZExNBA0k/FFSABS9b9D6RQKUCgSvPAq7vd+G/IzCpCowti4rwLZ1uHHu3oNzWT88j8rd26MMZuaBSxjzKah1Qp69RKu9QractmXMo9jgi/8CsFnPrfscuWutRl3dD+ECeT9H4RsDnqvE3zq5yCbRc8cJz5+CMZHQQKIY0il5gxXZh2oVKA46RvoXm1DqhX0oQeht88v30ym0Kny5QvJ52B4hPBP/yvuox+C+++DRAI5cAQZHfPHKGgygW7fZvuojDFmFfw/bR38XnMz9//859f6VKZZwDLGbDiqN5bYqSru5BF0qB+9cOZGEYJUylfoi2Pc/jdxxw4Q/NrfIXz0pxb/OJUy8ZsvoZfO+/0xQYD2dIIC1QruzHF/YLXie1Llb5S5tlmqdaBSQY6d8mG3ucXPHJUrSOd1cA4NBHI5NJuF7h4IE9C0iGA1RQQaG9E4Rk5fgLMX/W3JpA9UqhaqjDFmFXUXJwGolcprfCazWcAyxmwocWsz7tXnkMc/QPDoT+EunEZbm30lvlR6ugz6tEQCzTVAsYD78VPoznsIvvgVv6+mqw15+H0Ejz8BgJYm0a52H8quXkKvXvRhKp2+dYlhJotWq4AijdvuxlM3SyAXLiOvvukb8yYSEAaA+GDV2OCDz8wQnJ+nx9hihCHs3D7HSVjINsaY1XRkYBCAYJ1V47WAZYzZEFQdevq47x0F6IXTxJfO+TtzDQvOGEkQQGMTGsfoQB/xs0+Ci31voWMH0E/8DeT+B4mPHoCBXj+TESYgnfX7aOYZe66GwGYNFYvI+cvIgcPIRNHvn1rJZrzGGGPWjckoor1QJLEOX+MtYBlj1jWNIhCIn38abWvxe1xSy6u8JmGINjb5YFUPTuoc7uwJOHHIB6omPxNhS/w2FrnYjLy+189YpdN+iZ79PzTGmE3rwsgoAqzHhdgWsIwx645O7W9SxZ04DOUSCL531B1eNIvIrMa+EgSQy6OaRWQ9vkybeUWRr/p36hxyvRtNpmAxxSmMMcZsaE6VY4NDRKqk1uGbaRawjDHrhuu8hjt7Am2/BsX63plEAtIZEFnVWSULVxvI8AjS2Y289Q5SLKGANjVZQQljjNkiro5PUHO61qcxLwtYxpg1p7Uq7uh+3LGDvvJaMgWNTbZMz0Atgp5eZLLk91idPIv0DfgKgInk4sqpG2OM2TQi53iru5eac2t9KvOygGWMWXPxW6+g505BLrfsXlVmk3EOOXoSOXQMKU6i4isAIoLmc35m0xhjzJZzbniEqovX+jQWZL+hjDFrynW0opfOQS7v90OZzS2KIAyRS80wPAqDQ74x84MPQKns91V1dCH9gxDHaDaLNjXeflxjjDGbXu9kiXd6+4l0/S4PBAtYxpg1oupgaJD41ef8/ioLV5tToYBcuoJcuQaDQ8jYOJpKIdUqCr75r1O4eGV6hopUCs1lZxUjMcYYs7WVopjnOzrXfbgCC1jGmDXgLp0j3vMCRDUIE0g6s9anZFaSKnR1I8dPIxcu+WbNYQippJ+NUkWzGSujbowxZlFKUcwPr7VRrEVrfSqLYgHLGHPX6GAf7upl3MG369UBs4jNUmwe9WAVHDwKV6/52xoarLqfMcaYZVFVWiYKvNTRhaKs37IWs1nAMsasOlWHO3YAt+9NiCNoaERCe/nZVMYnCF59Ey41+/DckLdgZYwxW0g1dojAWLXKtlSK5B38DhgslzkxOExHochkFG2IZYEz2RWOMWbVqCqMj+JOHMadOgrZLAShlV/fTOLYl05/c58vYLF9uy39M8aYLcKpcnF0jKMDgwxXqggQiuCATBDwQD7LB7dt49HGBlK3WbFSqNXY39dP63iBqnM4VTZWrLrBApYxZtW4o/tx+98ECSBjywE3nXKZ4IfPIW2daC4Huexan5Exxpi7pBRFPNfeRX+pRK0+w6QwPds0Gce0jBfoKBSpOaUxmeDxxkZ2ZdIkg4BYlcFyhUoc01mcpBjVUGXDhqqZLGAZY1acqsM1X8AdehsyOQtWm1GxSPDkszA45Jv92qyVMcZsGYPlMk+3tlONY27XkarmfGSaqEWcGh4hKQICKNPBbLOxgGWMWTGqirtyEbfnBShNQtbC1YZQKCCd3TA6BuUK1Gp+OeeuHegD90Fjg+9VVSj6/lSt7ci5i76sekPewpUxxsxDVWkrFLhenGR7Ks3OdIoHctklL5UfrVYZLldIBAGRczzckF/WHqfeyRIt4xOEItScI5dI8J6GHE3JJNlFNnDvLBT5SXsnVbe8khO1zTJNtQALWMaYFaET48T730SbL9SLHDTZXqv1bGQUudiMnL0AQyP+3cTYzQ5LIgRx7O9D0ED8ck9Vvxxwkb+MjTFmpQyXKxSjiKZUksZkkuAOfs+oKt2TJS6OjtJdLBGrkg4DPnvvbt7X2HBHY1djx6H+Ac4Mj6Cq1FTrMzd+zMcaG/jZ3fewOzt/m5JSFHNhZITzo2OMVqrT56NAQoRfevB+nti2uN+1NefY293LxdEx4hnL+UIRwn4hVuWhfI7P3LOL+3JZMnO8OVqNY/b19nN+ZHTDFZ242+y3ozHriKqDSmW62arIxqjCpqPDRD/4FhQnbEngehbHyOWryKlz0Nbub0ul/QzVPL+gdeYvUQvMxpg1MFyucHJomNaJCUpRTCCCq782fWTndj53371zBoKFdBcnebWrm0JUm17CNuXFzuuEInz1PQ8uK2gNVyr8pK2T8VptOszA1MyN//7K2Dgt4xN8fNdOPrN716zZo2rseKe3jwsjoyjcGGPmWMBrXd2cHBzmK+95kF2Z9JznoqpcHZ9gb08vk1E863yojz11W0ehSO9kicg58skE72ts4JGGBtJhwEQ1Yl9vHzXnLFwtggUsY9YJdTHxa8+jl84BEHz51wg/8sk1PquFaRzjDr2NO3kEVJF841qf0tYyNg59A0i1imazkElDU/3/QXESogiZKECpBO1dBJevgovRMITGxsUFJgtVxpg1Uo5j3u7p49Lo2OyKcjMu8M8Nj3JueJQPbGviA9uaeKShgTCY/3Wrr1Ti/Ij/mfmCQs05asALHV1kEwm+cP+9PLF9223PV1U51D/I0YFB4ttUwHP4CnynhoY5MThEUyrJB7dvoxzF07NMtwsykSq9pRLfudrKfdkMn9i1k0caG8iEIarKcKXKa13dDFbKt4TI+Uwt+5uoRZwdHuXy6Lh/buiixzAWsIxZN+I9L6KXz0MqBXGMnjkBH/kkqroul9ppsUD8k6fQgT5IJJDU3O+emTukCiOjfv9T3wB0diHFEoxP+IBV3ygcBAJO0fr3BOLvcvV3TIMAzWZsWZ8xZt3Teunvt7p7qaljoev6qRByYXSMq+MTOFUeb2rkY7t2kEskyIYhY9Uag+UyRwYGKdSi24afmWNP1Gq82tXNyaFhfv2976ExmZz3+Hd7+zk5NLykGZ6pY0erNQ73Dy55a9LUDFf3ZInBcg+Rc4RBMD3Dd6elzpe7z2qrs9+0xqwBdQ76utEowl04DYUJtKsd0hkkCNAwgQ72E7/5Mu7SWWTbTsK/+w+RZArt6YJUGtm1e+3OvzR5I1xllr5Z1ywgjqFUhrFxpLkFaeuAnl4kCNE4hjCAMPQfM5b2zfUL1N5rNMZsNAOlMq9d72aoXFnyUrSpMNA8Ns61iQKuvvwtEQRwBzMwkSq9kyW+efkqX3nPgzze1DirwETkHIf7B5ccrm52p6/ZU8/fWShacxawjLmLVB3uwhncgb0wPkq9eoC/aE5nkfoLtoigYYg7fhCyOXSwD3fuFMHDjxA99U3YsYvE7/+jNdmjpQO9RD/5PkwWLVytBFXoH0QGh6DlGsH5yzf2PamDRBIaG1H7czbGbGKxKicHh9jfN3DLPqGlUmbPvNRWIHBMLel7/XoPr3Z181A+x3sb8gyXKzSP+WV0tjfJTLGAZcxd5E4dw73xImRzt62yJ8kUbEsBoLUq7tgB9OwJCAIYG8GdOEzw4MO41ivwwEOEj31g1c8/Pn4Q965vHCyZNW4qW6shp88j5y6iuaxfPheG6MMPwv33oR943M/wLKOM7aqpVpHWdmhpRQaGYHQcmZz04UkBAc3n19c5G2MMPgC1jk/QXihQiR2BCA/n87x/W+OSC0w4VSpxTG+pTE9xkmuFAgPlCqFwx+FqtU2FtY5Cka5CEZsrMnOxgGXMXaDqcOdP4/btgXwDEi7tn54kU2i5hE4WIJMDF+P2vYETgVrVz3J86auEP/Pp6cdjYhwam1Zslst1teP2v+WXJ97tKoFTv3DHJ5DO63ClFWm5BpUqJJOIOkgmfYA5dwnOXEBeewtJhOiunejuXXDvbhgc8kUgJPCzQ6PjvpdTLuvHmlqC5xSeeD/a1AjptD9W1T/GVGgrlf3yvEzaf6/qb6vV/LkGgb+/XEEGBuFqK8H5S6Dq90WlUpBMoA3rLAQaY8wMqsrp4REO9g0QqZu1zO7q2DivX+8mn0jwWFMjP7v7HppSt+5RilW5Nj7B1fEJekslxqo1tL50r+bc9NK4jVZDwcKVmY8FLGNWmdZqxD/8NtpzHTKZJYerKZKe0SsjTKCJGIIAyeb8Xq6DbxN86GNIMol7Zw/u+CHkQx8h/MpvLPsxp7i2FuLnfwBBePfCVXESunsJTp5BrrSCCNOdO4IAMhn/cbP0jWIbGse+39PAEJy75H8ujm9UxpvqSeJcPRDhf8MLcLGZQOr75YL6c3bOL+p0zveEQnzRiGwGEiEMj+J/6KZzmuojlcv6vVPGGHMHYqcLVsoDH4xGqzU6i0V6JicJJSB2jkwiQWMyQVMyySONDfM2q42c40K90IQw9/K3Wv22QhRxfniE8yOjZMKQx5saeG9DAzXnaB2foK1QAOSWpXpWQMFsVhawjFlFWikTP/+0LwaRb1jR/Uozq/ZJIoGWS7i3XyX45GdxZ477vVtXLhEP9BH+vT/0Sw6XQft7iV98xoe5ZY6xaKUS0tKGnLsIrW2IBKgq2tjg71/qTE8YQnb5SxnnLBwx9Rbr1MVNHEMU+RmwpkWWPjfGrIrxao2Bcpn2iQI9pRJNySS7sxkea2xgVzpz21Cy1mrO0TNZYqhcYbBSZqhcoVCLiOqV9AQ/G1R1jmQgNCSS3JfN+CV6AmPVGjXnSIchPZOTVGI3ZzgKRQhFiJzj3myGHek0uTBB1cUkgoDRSpWOYpFgCUv2YgBVipEv731pdBxl5v6nDTY9ZcwdsIBlzArTagUKE5BMEj/zXXRs5O4Ug0ilcRfPoB3XAPxMU5hFR0dwxw4S/twvLmoYVQeFAtSqxMcPTvflmjWDttJU/X6qPW9DterDS0MDuh6Xzt18gTZV0W/+yr3GmFVUiiKODQ7RPDpOIYoIRaaXnfWVyrSMT3Ck3/cleiCX5ZP37OLxpkbCm16Tx6s1RqtVAAq1GiOVCskgJB0G7EineTCXrVejW1mRc3QWi7SOF7g4OgbMbv46n5pTRqpVRurnvBQzx+8tlektlec5bslDA7cWmTBmq7GAZcwK0jgmfvIv0NFhZPtOP3PVtP2uVNqTIEAlQIsFmNmTKpnCHT+I3Hs/weNP3HrO6nAXz6Itzb4yYLmEjg77mRjV1d1zFcfI1WvInr3I8Ciay91olGuM2TBKUcSV8Qk6C0WKke8z1JBIkAlDnti+jYfzuRV/HRyv1jg1NMypoeHpXkBw64zLVPU3gO7JEgNd3ThVGpNJUkFAQzLBcKXKRK02HbqmZl4EP9sT1Gd77slk+OQ9O3m4Ib9gP6TbiZyjdaLA5dExrk0UCGaEQmPMxmcBy5gVoupwJ4+g42OQTKPjo9C07a6WMZ+r2a+EIRpHxC8+g3v8CYKPfwZ58D2+2EJHG+7sCfRas7+iCEMfqlZzxs056O5FTp5BLjYjtQhNpdDt22x5nTEbTCWOOTIwyMnBYYQbe3JmujQ2Rj6R4KM7dvDE9m1zFkGYSVUpxTF9pXK9F5IjFCGXSDBQKjMZRfSXK4zVZ26WWnVuasna1GxV/4zJm5vHUurL6+q395fLvNHdg1NIiLA9nSIVBDQmkygwVq3iFJqSCVJhyH3ZTL0ohBJKQCoMODM8Qt9kibBe4KH+pJf0HIwx65sFLGNWiLZewb39KmTzSCLhK86tE5JKoy5Gr1wkbrnsQw7U9zSp78G12uFmsoScvYAcPo4UJ32vp3zOz1oZYzaUUhRxuH+QM8MjwML9f2rOF1s40D/Awf4BHmnI80hDnseaGmlKpSjHMb2TJfpLJVonCgyUy0ROSQYBkXM4IADCQJbdKHYlTZ1DrErfPEvrekv+86VR8ftI8f0NA7nx89YM1pjNywKWMSsljv1yusT6/GclQQi5/I0mtrD6oUoVunuQ85eR0+f9/qps5kbRCmPMhjJSqXJ8cIgLI6OzluUtxtSxLRMF2gtF3unt92Wu6+W6pwo5TJm5h8cBbh2Eq6WaFTxVl72nyRizsazPK0FjzKq5a0sWO68TvLYX6ev3+wqyGcg23Z3HNsasCFVfSKFtosDl0XH6y2VU9Y77/9w842UFEYwxm4kFLGPMyiqXkb0HkOOnIJHws1W2t8qYdUdV/fK8UonhSpVyFDNR82W+u4qTs0qDw8LLAI0xxtxgAcuYu61cQdo70Pc8CPn8jdv7B5C3DyDdvXDvPbhf/8qtFfU6ugieewX91MfQn/vM3T3vhVQqMDqONF9Fjp6EchkaG5bet8oYcwtVpVCLmKjViNRRc77EdjoMyIQhTckkmTCcnp2OVRmrVinUIq5NTNA2UaQUR8ROiVSnOw1EThEgEQSLKgtujDFmcSxgGXM3TZYInvwh0tOH7tyO+/2/Bw15GBkl+Osf+Ga12Qy0thP89Q9wv/JF5MpVCBLw8IPIy3vAKfLmPvSRh+HB+/0+p8nJ2WHtblCFzuvIiTPIxcuAgDrIZKHJlgKazUdVV32JbeQcvaUS7YUi3cVJhioVqrFfPhfM89iRKm4qONUzUiiC1EuL37z4buY+IOtXZIwxK88CljF3kbz4GvQP+JLko+MEz76A+5UvEry+F6q1GzNW25JQKBA++zxav7iSU2fQRALyOSgWCV55A/d7v03w9HNIyzXcr/8K+qmP350nMjBE8OJryPUe3ww4n7fZKrPhOVX6SiXaJ4oMlMv0l8vUnCNySlTvUZQOQ3akU3ygqZH3b2tiWyq1qLFVlYlaRCWOKcUxQ+UKlTimGPmZqViVYi1ipFqd1Sj3pkFuc/6zn4uV/jbGmLVhAcuY1VYqI/sP+Ya6g8Potia/J6mpEa73EHzrKT/z03BTZb18ftYF1qxLpVwO6ekj+E9/AeUK2tSIvPYWet+98J4HYXwCevtgxw7YvWvlnkst8k2BT56FQNCmRttfZdYVV19ON1atMl6rAb5fUSoMSAUhuUSCQKAUxQCM12oMlstcL07SWy+5Hc8x6zOlHMf0TJboL5V5t2+AXCLkvmyWB3NZdmcy9eV5NfrLJYbLVWJVqi6mUIuAG7NQCy3Js6V6xhizsVnAMmY19fYTPPlDvycpnZ4dSET8PqXlEEEb8hBFfgwRSCQJvvcs+qmPIcdOQux8M+Gvfgn95Mfu7HmUy0hzK7LvAIyN+zBoM1bmLpkqxlCJHcUoIlY/q1SMfJAarlQZrVQpRhHlOCYUIRC50ZJAQOqlGhQFnV1Nc87ZotuYCkETtYiJ2gTXJgqEiwhPNqtkjDGbnwUsY1aLKsErb9SX/q3CnqQw9B9TshmoVJGDR/3XuRREEcFLe3D9g+gXfx4ymVvHqVaR85ehvRMZGIRyxV8ERhGoIpUKKKgIpJK2v8osmapSdY7JKGasWkWBoB56SlFEKY6pOkehVmOwXKFYi6g5Nx1UYlVUdXpf0RSnOmdlu+jm5XE6/Z+ZJ7Wiz9GKRBhjjJliAcuYlTY6hhw5gfT2+T1KN1cCXE3plP+Ykkj45YMnziDnLqKf+QT6/sdg106YnETau5C978JkCcIAEkn/GaZnqLTRlgGaxavEMaPVKj2TJToLRXomSxSiiEDqhReY/XdJUVR9QLldPLF9RcYYYzYCC1jGrBRV6Oom+NHLUK1CGK6PcBIEfr9XrYbsP0Jw4Ciqzp+XAuk0bLNZKbN0NefoK5XoKBTpnSzRXy5TjmISQXDL7JLTekBa8mI8Y4wxZmOxgGXMSjlzlsR3fojmsrf2r1oPkklIJu3y1tzC1fc4laKYmnPUnGMyiihGEZXYUY5jqnFMIYoo1CImo4iqczhVkkFwyx4mK/ttjDFmK7OAZcxKmZxEkwnIZtf6TMwm5VQpRTHlOPazQQIhQiIQSnE8XVLc1WeOnCphfTap6hwTtRqFWo1SFNNbKjEZ+cp2kVOCemEIAabaKS1mX5GFKWOMMWY2C1jGmHVrqjhC5JSxWpVy5MtdV12M4gOEIIhAPpGgKZVkRypNNhHO25R1PfHV8RzjtSqT9dmiUhRTdb7ow2C5QqFWoxzHTEYxcb3Qw1QQAh+EVHX6tvnikLJw+XEr0mCMMcasDAtYxpg1V4oiBsplOguTDJTLDFcqFKOIyClSrzY3VQLbqZ+hmRkFBAiDAOFGUEgGAZkwIBkEqDI9TiCQDAJSQcDOdJpkICTDkGwYkk8kyNQrM0b1/ULpMCQVBNOfQ5HpvUUJEWJVKrHzW9qm9hnVv56MfOPYYq1GKY4ZrVQZrVaZjGIqsQ9Mgdx4bopO71WaL+rMG4QsHBljjDHrggUsY8xdpaoUooihcoVrEwVaxicoRBEJkTn7EWm9xPZCsysKuJuWqk3tJVpIW6EI1ANafWZo3vFnBLuZs0cCs35u5giBCIoSO7WZI2OMMWaLsIBlzAYyFU6yYUhigzT6najWaC8U6Z6cpHeyxEi1Oh1KZgaq6hqGDGWO3km3OX7m19ZU1hhjjDFTLGAZc5d1Fyc5NzLKww15Pritabpxas052iYKJIOAh/N5wmD2bMpAucwb13voK5UJRfiVhx7gA9uapi/wk+skcKkqXcVJLoyO0To+QdU5AoGaW91Gr8YYY4wx64EFLGPuohMDQ7zb10+kyuXRMU4ODvEzO3cyGdU4Ojg0nTki59iZSXN/Nkslnqr4Fk8vUYtVebWrm329/UxGEU6V9+Tz/OID97E7m1n156GqjFSrXC9OMlypUIpiCrUa47UaE7WIQIRoxuxUbFnKGGOMMVuEBSxjVpGqcm2iQGexyGC5wvXi5HSBhJoqvaUyQ909xPXiBjMNlisMlivzjh2pMl6rTX/fUSzyvZZrNCQTJCSgGEUkgoCHcln+//buPUyOqszj+PfNFXIhhCDEoJCAgCj6CMFVFFkwCoo+rshFvCyoIIi46yoI3hYiXhcv6y6CiguKosICwnJRVBQFhFWJq3INCEFECCEmJJMLmUny7h/nVPqkUt3TM91TPd31+zxPPVNV55zqU++cme63q/r0PtvPYObWW226WpbasDEcZ2DjBrYeN46tx45lrBkDGz1+39GGTYnS6oH1PLJqNQtXrGTdhg1YPI8tjqmrUyIiIlJRSrAqxMymAqcCRwBzgA3A/cClwLnu3t/CsXcETgdeD+wMrAXuBi4GLnRv/IrbzHaL7Q8Bngn0Ab8DLnD3K4fbr05Y0d/Pyv4Bxphx+xNLeHzt2i1vj0sUJSjDtd6dp/prSRcbNrBwxQAPruxj/Ngx7Dp1KjMnbc1YM1YPrOfBvr54y2GYYW9jnHDBgTGEiR/ySVnRRBQiIiIiEijBqggz2wX4BTA77loDTAT2i8vbzGyeuy8fxrHnAj8GZsRdq4CpwAFxOdLM3lAvgTOzw4DLgUlx10pgO0KydYiZfRM4frAkrdP6li/n+seXsGRgYNOscusbfO9QWZyQxA2s38Bdy5/ivqdWYMZmM9tt9KxmzUbilOOjO+wiIiIio8ro+FS8jCgzGwdcS0iuHgde7e6TCQnNMYSrRfsAlwzj2NOA6wjJ1X3Ai919KjAZeB8wABwKfLlO+znAf8e+/ArY092nAdOAs2O1dwIfGmrfyvbI3ffwRH8/6+OX4/aPguSqyHp3BhpMGy4iIiIiw6cEqxqOA14Q149w9xsB3H2ju18GnBTLDjOzeUM89mnATMItgYe5+x3x2P3ufh5wVqx3opntUdD+bEIythh4vbvfH9uvcvezgAtivY+Z2fQh9q109b5HSURERESqQQlWNRwXf97k7rcXlF8KLIrrxw7x2Fn9S919UUH5uYRbBscCb0sLzGwy4fNgAF9196cK2n82/twGeOMQ+yYiIiIiUiolWD3OzCYBL4+bPyqqEz/bdEPcPGQIx96TMKFFo2OvAm6pc+wDgK0Haf8wcO9Q+yYiIiIi0glKsHrfXtR+z3c1qJeVzTSz7Zo89t4F7Rsd+3kttn9+k/0SEREREekIJVi9b1YySA7pAAAXiklEQVSy/tcG9dKyWXVrtXbsbcxsSkH75e6+ton2zfarY/T9TyIiIiLlWT8KX3tpmvbeNzVZX9OgXlo2tW6t9hx7Va59o7Zped1+mdmJwIlxc5WZLRzkmEO1PbC0YYWxY2ZOgp3a/LhdZRUwZdBa1aYYNUdxao7i1BzFqTmKU3MUp+aUGaez3nvKA6vec/LKkh4utUvRTiVY0hPc/QJqMw62nZnd4e77jdTxe4WZ3fE3xakhxag5ilNzFKfmKE7NUZyaozg1p8px0i2Cva8vWZ9Ut9bmZX11a7X32H0F5Y3aN9svEREREZGOUILV+x5L1hvdvpaWPVa3VmvHXhlnFcy3n25mW1Nf1r7ZfomIiIiIdIQSrN53L7Axru/doF5WttjdlzV57HTmv2aOfU+L7e9usl8jYcRuP+wxitPgFKPmKE7NUZyaozg1R3FqjuLUnMrGyXwUzrwh7WVmNwOvAH7u7vMKyg34E7Ar8G13Py5fp85xDXiY8F1YF7n78QV1JgOLCZ9zPNvdz8qVPUn4Lqwz3f2TBe13iY8B8C53/2YzfRMRERER6QRdwaqGi+PPg83sJQXlRxGSK4BvN3vQ+AXFWf1jzGx2QbVTCMnVBuC7ufargSvj5slmNq2g/RnxZx9wdbN9ExERERHpBCVY1XAxcCdgwJVmNg/AzMaY2VHAN2K9H7n7z9KGZjbfzDwuswuO/QXCFapJwPVmNje2m2BmJwPZVakL3P3+gvZnAquBZwLXmtnusf1kMzsTeE+s9yl3Xz6McxcRERERKY1uEayImBzdBMyOu9YQEuyt4vb/AfPySYyZzQey2/rmuPvDBceeC/wYmBF39cXjjo/bPwHe4O7r6vTtMOByarMFriBc9Robt78JHO8arCIiIiIyyukKVkXExOiFwNmEySUcGAAWAKcBLx3uFSJ3XwA8H/h34AFCYrUauBV4N/DaeslVbP/D2LdvED5vtRWwHPgpcKS7v6vs5MrMpsard3ea2SozW2FmvzWzU81sQpl9GSlmtq+ZnWVm15jZfWb2NzMbiD9/ZWYfM7PtBjnGjmb2RTNbaGZrzWyZmd1iZifEz+gN1ofdzOzrZrbIzJ42syfN7MdmdkT7zrQ9zGwbMzvDzG6L/VxnZo+a2U1xrGxbp11LY6nVGJfJzF5hZpfFuKwzsyVm9lMze0sTbbt+LJnZJDN7rZl93Mx+YGZ/Tu4AmD9I253M7L1mdrmZ/SnGYG08n++b2Sub7MOoj2OLcUrvqmi0PGeQ4+xrZpckY/VxM7tqCHE+ONZ/PPlfcImZ7TuEUAz2GMOOU3KMI83sWjN7zMz6zWx1HBvfMLMXNdG+G8bTDDN7Z4z/PfEcs9/J1WZ2eIO2Lcc4Hqen49TgmB9O/+6aqF+Z50PcXYsWLclC+FbuRYQk1AnJ4tPJ9u+A6Z3uZxvO8yvJOTmwFliZ2/cksH+d9nOBpUndPkLSnm3fAExo8PiHxdhm9VcQPquXbV9EvMre6QU4mHArbNa3dYQ3AdJYvajdY6nVGJcco8/l4rEc6E+2fwCM6+WxBByUi0G6zG/Q7tmE2V7T+qsJdxqk+y4Exo7UeCkrjsONU2w7P9brj3+T9ZbZDY5xQi4uT+Xi32wfPLZ7KtkeAE7o5HiKbScC1+Ta9BH+d2XbG4AP9MB4SvuUPZetyu37ITCpnTGuUpzqHG/PeIxN7QepX5nnQ3en4x3QomU0LcA44I/xj/Ux4FVx/xjgzdQSkOs73dc2nOuxxKuXwLbJ/imxbEk81yeAabm204DHY/m9wH5x/wTCxCbZC+vz6zz2nOQf+63AHsljfyL5h3n6KIjTy6m90L0S2C97siPc1vpi4FOEW2jbNpZajXHJMTop+Z19H3hW3D8ROC75XX+p3ec5msYS4cXaMuBG4BzgmOTc5jdoNzvWuTH+7c1KxsrzCBP8ZOfxyZEYL2XGcbhxim3nx3q/GOZj7w+sj8e4KhmrM4CvJed5dJ32Ryd1vgbMiPufFY/n8fiFb0yVGKf0d3YesFMypuYCt1BLEOd2+Xhy4NfAycCuub+r/0oe6zvtjHGV4lRwrDHAr2L927K2DepX5vlwU5873QEtWkbTAhyf/JPZ4gkSeEtSPq/T/R3hWBySnOvbcmWfjPvXkEssYvlHqL3Q2KOg/Dux/HGS5C4p/zq1d/I6drWQkEA9GPvyn2WOpVZjXGKMxlG7urcAGFNQ5z2xfCB9Yu+1sUTB1SXCbc+DJVjTgH0blBvwI2rv2m7V7vFSZhyHG6dYbz6tJVhZYvFHYHxB+Q2xfFG+n4TPBWf9/FFB2wmECaUcuKVT4ynWW9QoTnHM9cU6n+3y8XTwIOVp4vzsdsW4SnEqqPv+WO+S5G/SG9SvxPPhZn3qdAe0aBlNC3Bz/CP9eZ1yAx6KdS7udH9HOBbbJP/wzsiV/Tnuv6hO2ynJk/cncmWTqV0ROrNO+9nJY7+zgzHIrsw8TsGL2pEcS63EuOQYvST5Xb21Tp2x1G6pPDNX1tNjiSG8WBvkOEcl57FPO8fLaIhjs3GihQSL8HUk2TkcW6fO3yd1Ds6VvTIpO7BO++OSOlu8ECwxTtmtV19oUGdBrHNur42n3GO9OHmsw9sV46rGidpVt6XAM2guwarE82G6aJILkcjMJhFuB4PwbvEWPPwl3xA3DymjXx30imT9wWzFzPYkfLk01I/TKsI7xbBlnA4gfLl0o/YPE24DKGpfpmPjz8vd/elmG7U6ltoQ4zLtkqzfU1TB3TcA2dc0bOprxcZSq9LxNzYtUByb9upk/YY6dW4lvFCDLc8za99HuD2qSBq/TsbpofhzblGhhe+d3CNu3pEr67XxVPdvpxUVjtM3CMnhB939ycEOWrHnw02UYInU7EXtb+KuBvWyspk2yCx73cbMJprZbDN7H+HWBYA/Adcm1fZO1puJ0/Ny+4fa/vkN6owYM5tI+LwVwAIz29nMLjCzv8TZuJ6Is3O9rqB5q2Op1Rh3SqMn5awsPbdKjKU2OSj+7KeWrGaqGMfnm9ldZrYmzkaWzYy3T4M22XkucfclRRXimwH3ZY9Rp/29sV5R+yWEyYGK2pfpq/HnQWZ2npntBGDBvsB1hHf9byfc5pXqtfF0ULJ+ZxuPW7k4mdm7gXnAje7+7SaPW8nnQyVYIjWzkvW/NqiXls2qW6uLxGlhnfAO1iLgXGA64V3aeb75NPtDjdM2ZjaloP1yd1/bRPtOxXg24TMVEG4tuovwtQM7EGY/2gF4PXBdfGGXThHb6lhqNcZlejhZ37uoQpx+d/e4Oc3MJsf1qoyllpjZHGpfun6Zu6/MValiHLcnvHBbS5hMZQ/C7IALzOxTddpk/W4Uo7Q8f56tti/TeYRJGzYC7wUeNbM+wv/4BcBzCDN/zitIFntmPFn4+oyPxM1b3H1hGw9fqTjFJP3zhL+5k4Zw+Co9H26iBEukZmqyvqZBvbRsat1a3WUxYbbA1cm+m4B/cfdHcnVbjdPUgvJG7TsV4+nJ+scJEzQcBUxx9+mEW+Muj+UnAB9I6rcrRsNtX6bfEcYOwBlmNq6gzj8RPtOXydarMpaGzcy2pvZF7EuBDxdUq1IcHwBOJ0wRvZW7zyDcrnQoIXEw4GNmdmpB21bPs2vi5O4bCS+Y30X4vAyEK1bZm0ZbESa6mLxl694YT2Y2hnAnxjMJieX72vwQVYvT1wljZr67P1SnTpEqPR9uogRLRHD32e4+092nADsSpm9/EfAbMzu7s73rmDG59ePd/Qp3HwCIiecxwB9inY/WSS56mruvJ3yBOYQrCtdZ+BLXCWY208w+BHyWkKBmNpbdz24Ux9P3CJ+jGSDM5vlYZ3vVWe7+XXf/vLvfn/wt9rv7TwifafltrDo/fs6oksxse+BnwLcItwEeAGxLeBH9JsJtjCcDv85uH+xB/0G4ywDgFHf/Yyc7M4oNGiczezvwOuD3wJdK7FvXUoIlUtOXrE9qUC8t66tbq0u5+xJ3/yLwGsKMPP9qZq9PqrQap76C8kbtOxXj9HEfcPer8xXiu8RfiJszqH2gvF0xGm77Urn7+dTikF1JWEeYffEcwm2E5yRNlsefVRlLQ2ZmY4HvAm8kTD381phEFFEcgTgRzUfj5hTCZ0VSrZ5nN8XpYsJnan4JHOruv3L3Fe6+2N2vIiRcSwm3P38u17brx5OZfYHalZgPuPtF7X4MKhInM9sR+DLhy4/fHd9UG4pKPR9mlGCJ1KTvDDd6Ry8t69l3k939N4QZtQBOTIqGGqeVcYaffPvp8fanwdp3Ksbpvd731a21+cx52Yx6rY6lVmNcOnf/EOFF27eAu4G/AL8h3F65D+HJGeDP7t4f16syloYkJleXEL7UdgPwdne/okETxbHm9mR911xZ1u/BrtjUO89W25fCzPYCDoubX4wztG0mTsaRTVLwptxnSLt6PJnZOUB2i+hp7v7ldh4/UZU4fY7wBuIFwH1mNiVdqN12SrJ/QtK+cs+HoARLJHUvtVuXCj+snytb7O7LRrZLHZclGc9J9qWz+DQTp/zU3UNtf3eDOiMm/m4H+zA7hM98bGoWf7Y6llqNcUfEd8nf6e57u/vO7v4Sd/+0u6+mNiPjbUmTSoyloUiuXB1DLbm6bJBmimNzsvPcwcyeUVQhxv+5cTN/nln7vWK9ovY7EL4bqKh9WdJZ1B6sWyt8ng3CO/87JPu7djyZ2eeBD8XN0+PdGCOlKnGaE3+eTLgylF8+ktTN9qV3LFTy+VAJlkjk7muofbfJa4rqxHf5Do2b9W7X6SXZO8Dp5fb7gWzii3pxmkzte7TycbqVMAtRo/a7ED7PU9S+TNlj79WgTvpiZhG0ZSy1GuNRJd5i8qq4mU7tW6WxNKj4ov17wJupJVeXNtFUcax5abK+KFf202S98DwJ39eTfUA+f55Z+6nAy+q0T4/bqTiln3HcpW6t8HnbTPqOf1eOp3i722lx83R3/3w7jtuA4tSEyj4fjsS3F2vR0q0LcDzhKsRG4CUF5UdT+6bzeZ3ubwvnORawQerMi3Fw4N9yZZ+M+1cDswvanh7L1wN7FJR/J5Y/BkwrKD8/lq8EpncwTq9Ift9vLCgfQ5jkwoFHgTHtGkutxni0LHGsXRX7+uv8uOv1sUT4/JkTZt4aLE6XxroDwJuH+DhdHcdm4tTE/6yJwP/G46wCti2oc0ss/z0wvqD8h7H8YWBswe8o6+f1BW3HJ/8PbunUeCIkVdn/lmvq1JlMuLrlwB+6fTwRPgOanfOpIx1jxWmzY87PjtmgTuWeDzveAS1aRtMCjAP+SO0F87y4fwxhiu4VseyHne5ri+c5m/AC4yTCVSpLyp5NmAp6VTzXvwEzc+2nESYwcMJtC3Pj/gmE2wjWxbLz6zz+nOT4NwO7x/2TgTOpJXanj4JYXR77shQ4AhgX9+8MXJY8KRzXzrHUaoxLjtGuwKeBfQlTZ2fn+XLCTGZOmNhir3af52gbS4Tp/bdPlkfi45+T2z8laTMW+D615OqoYTxuV8VxmHH6e+BG4B+BZyX7xxPeEPpN8vdY2E/Claf1sc6VwE5x/3bUXsw6cHSd9ukLwfOB7eL+neLxshd5+3cqTrHdNUk/vwPsRrideXyMwW+T8mO7eTzFWGTn8oGyYly1ODU47vzsuA3qVOb5cFOfO90BLVpG20JIPhYl/4hWEy7jZ9u/o4NXVdp4jp4s6wjT9q7K7X8I2KfOMeYSko6s7kqgP9n+MTCxQR8Oi7HN6j9F7YWPAxcxyDvWJcVqMmEmrqxfTwPLcnGaPxJjqdUYlxijF+XisSzXzz8D+47UeY6msUTt3e/Blm8lbQ5M9vcTvpeu0VJ4daub4jjMOB2UK1tD+L+VnuMG4NODPPYJhEQ2a7Oc2gvZun/PSfv5Sd2NsX22PQCc0MnxFNttD9yRq7M6d94OnNPNf5eEN7rS3/1gfzuntSvGVYtTM38Pg9SbTQWeDzf1t9Md0KJlNC6Ee+w/AdxJSDpWEp6sTgUmdLp/bTi/CcCRwFcI72T+lZBkrSG8GL6GcEl/60GOsyPhOzHuj/8olxNuwTmB5Ha5Bu13I8xMtIiQuDxJuH/6iE7HKNfPMfGcfkm4otdPeBfu+8DLRnIstRrjkuKzbTzHXyZjaSnhMwYfBCaN9HmOlrFEexKHwZZ3dHschxmnGfHv5gpgYfxbHCC8+/174FzgBU0+/r6EyUQejeN1MeFW1lc22f6Vsf7i2P7ReLy5nY5T0nYc4f/4DbGf/XFMPEi4qnVAt/9dsuWbhYMt89sZ4yrFqcGx52ftmqjb88+H2WKxwyIiIiIiItIizSIoIiIiIiLSJkqwRERERERE2kQJloiIiIiISJsowRIREREREWkTJVgiIiIiIiJtogRLRERERESkTZRgiYiIiIiItIkSLBERERERkTZRgiUiIiIiItImSrBEREQEADN7tZldYWaPmdk6M/uTmR3R6X6JiHQTc/dO90FEREQ6yMzGAOcDJxUUrwee6+4PltsrEZHupCtYIiIicjabJ1drkvVxwFHldkdEpHspwRIREakwM9sNOCPZtRDYHehP9u1eaqdERLqYEiwREZFqO4NwlSpzors/BixP9o0vt0siIt1LCZaIiEhFmdk04G3Jrpvc/WYzGwtMT/Y/VW7PRES6lxIsERGR6joSmJRsnxd/zgEmJPufKK1HIiJdTgmWiIhIdb05WV8GXBvXX5irt7Cc7oiIdD8lWCIiIhVkZtsAByW7rnP3bGKLubnq95TSKRGRHqAES0REpJpew+aTV/xPsv7SZH0FcG8pPRIR6QFKsERERKrp0GR9A/BzADObCLwsKbvd3b3MjomIdDMlWCIiItU0L1lf4O7ZTIEvA7ZKym4sr0siIt1PCZaIiEjFmNmuwC7JrpuT9Xm56tePfI9ERHqHEiwREZHqOTC3fVuyfnSy/oC731dCf0REeoYSLBERkeo5ILd9O4CZzQV2T/Z/r7QeiYj0CCVYIiIi1fPyZP1Rd18c19+Sq6cES0RkiJRgiYiIVIiZbQvsmexaEPePY/ME61Z3v7/MvomI9AIlWCIiItXyd4Al2wvizyOBWcn+r5bWIxGRHqIES0REpFpenNu+M/7852TfEuCKcrojItJblGCJiIhUywty23eb2X7A/sm+r7h7f4l9EhHpGeM63QEREREpVfr5q7XAg8B3k32rgfNK7ZGISA/RFSwREZGKMDNj82nYHwCey+bffXWBuy8rtWMiIj3E3L3TfRAREZESmNks4K/Jrh8A66klWGuAXd39ibL7JiLSK3SLoIiISHVsl9ueCByebJ+n5EpEpDW6giUiIlIRZrY/cFuyawAYH9dXAru5+9LSOyYi0kP0GSwREZHqmJLbHp+sf1bJlYhI65RgiYiIVMeEOvsfAb5cZkdERHqVEiwREZHq6Kuz/yPu/nSpPRER6VFKsERERKpjRcG+W9z9e6X3RESkRynBEhERqY58grUBOKUTHRER6VVKsERERKpjZm77Qne/syM9ERHpUUqwREREKsDMxgDn5naPL6orIiLDpy8aFhERqYZTgP1y+w4xM/Pcl2Ka2WRgTtx0d7+7jA6KiPQCXcESERHpcWY2C/hUQdFOwLyC/RcDd8bl9BHsmohIz1GCJSIi0vsuBLaJ6yuBNUnZZ8xs0x0tZnYWcETcfAh4fyk9FBHpEZa7K0BERER6iJm9Fzgv2fUOYBbwmWTfL4BLgdcC/xD3LQUOdPd7R76XIiK9QwmWiIhIjzKz3YHfA5Pirqvd/fB4xepC4Ng6Te8FDnf3hSV0U0SkpyjBEhER6VFm9jxg12TXbe6+LCk/EjgJ2BuYDiwEvgWc7+7rSuyqiEjPUIIlIiIiIiLSJprkQkREREREpE2UYImIiIiIiLSJEiwREREREZE2UYIlIiIiIiLSJkqwRERERERE2kQJloiIiIiISJsowRIREREREWkTJVgiIiIiIiJtogRLRERERESkTZRgiYiIiIiItIkSLBERERERkTZRgiUiIiIiItImSrBERERERETaRAmWiIiIiIhIm/w/jp5Qag1ZQ9kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.fill_between(new_ells, 0, percentiles[2,:], color = 'salmon', label = '99%', alpha=0.8)\n",
    "plt.fill_between(new_ells, 0, percentiles[1,:], color = 'red', label = '95%', alpha = 0.7)\n",
    "plt.fill_between(new_ells, 0, percentiles[0,:], color = 'darkred', label = '68%', alpha = 1)\n",
    "\n",
    "plt.ylim(0, 0.2)\n",
    "\n",
    "plt.legend(frameon=False, fontsize=30, loc='upper left')\n",
    "plt.ylabel(r'$\\frac{| C_{\\ell, \\rm{emulated}}^{\\rm{TT}} - C_{\\ell, \\rm{true}}^{\\rm{TT}}|} {\\sigma_{\\ell, \\rm{CMB}}^{\\rm{TT}}}$', fontsize=50)\n",
    "plt.xlabel(r'$\\ell$',  fontsize=50)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "ax.yaxis.set_major_locator(plt.MaxNLocator(5))\n",
    "\n",
    "plt.setp(ax.get_xticklabels(), fontsize=25)\n",
    "plt.setp(ax.get_yticklabels(), fontsize=25)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./accuracy_emu_TT_wide.pdf')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "anaconda-cloud": {},
  "colab": {
   "collapsed_sections": [
    "iFVu43WWssWj",
    "8NQyktNjPrHg",
    "ayqZj7aj1XFL",
    "7GIndU8g2qTk",
    "83cCKejC81q5",
    "ZH-y08FC54bH"
   ],
   "name": "cosmopower_CMB_NN_training.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}