ML PINNS web.ipynb

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   "source": [
    "### <center> **Deep Learning** </center>\n",
    "<center> Ron Maor </center>\n",
    "<hr>"
   ]
  },
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     "text": [
      "Collecting flax\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/63/1f/63c720200f7a679d9fd408eb2641960d6fa99030c874ecba24091e694f91/flax-0.3.3-py3-none-any.whl (179kB)\n",
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      "\u001b[K     |████████████████████████████████| 184kB 4.3MB/s \n",
      "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from flax) (3.2.2)\n",
      "Requirement already satisfied: jax>=0.2.6 in /usr/local/lib/python3.7/dist-packages (from flax) (0.2.12)\n",
      "Requirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.7/dist-packages (from flax) (1.19.5)\n",
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      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax) (0.10.0)\n",
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      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->flax) (2.4.7)\n",
      "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.6->flax) (0.12.0)\n",
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      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->flax) (1.15.0)\n",
      "Installing collected packages: flax\n",
      "Successfully installed flax-0.3.3\n"
     ]
    }
   ],
   "source": [
    "pip install flax"
   ]
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     "text": [
      "Collecting pyDOE\n",
      "  Downloading https://files.pythonhosted.org/packages/bc/ac/91fe4c039e2744466621343d3b8af4a485193ed0aab53af5b1db03be0989/pyDOE-0.3.8.zip\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyDOE) (1.19.5)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pyDOE) (1.4.1)\n",
      "Building wheels for collected packages: pyDOE\n",
      "  Building wheel for pyDOE (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pyDOE: filename=pyDOE-0.3.8-cp37-none-any.whl size=18178 sha256=cc0cbd24b844aee705d90e5ee14722354f1a2dfe1133290dc98ea921bf182f14\n",
      "  Stored in directory: /root/.cache/pip/wheels/7c/c8/58/a6493bd415e8ba5735082b5e0c096d7c1f2933077a8ce34544\n",
      "Successfully built pyDOE\n",
      "Installing collected packages: pyDOE\n",
      "Successfully installed pyDOE-0.3.8\n"
     ]
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   ],
   "source": [
    "pip install pyDOE"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R61xXlEYVmZQ"
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   "source": [
    "from scipy import linspace\n",
    "from scipy.integrate import solve_ivp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "from jax import random, jit, vmap, grad, value_and_grad, jacfwd\n",
    "from jax.scipy.stats import multivariate_normal\n",
    "from jax.tree_util import tree_multimap\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "from jax.experimental import optimizers\n",
    "import jax.scipy.stats.norm as norm\n",
    "from jax.nn import sigmoid, log_sigmoid\n",
    "from jax.experimental import stax\n",
    "from jax.experimental.stax import BatchNorm, Conv, Dense, Flatten, Relu, Softmax\n",
    "from jax.experimental import optimizers\n",
    "\n",
    "import itertools\n",
    "import numpy.random as npr\n",
    "from tqdm import trange\n",
    "from functools import partial\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.notebook import trange, tqdm\n",
    "\n",
    "\n",
    "from flax import linen as nn        # The Linen API\n",
    "from flax import optim              # Optimizers\n",
    "\n",
    "import tensorflow_datasets as tfds  # TFDS for MNIST\n",
    "\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#from jupyterplot import ProgressPlot\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cAyoMNzbWJ3O"
   },
   "source": [
    "## Convolutional Neural Nets with MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tHWEreSWWMCj"
   },
   "source": [
    "implementing the following convolutional neural network to perform classification on MNIST data set. \n",
    "\n",
    "- Input layer: Accepts 28x28x1 images\n",
    "- Conv1 layer: 6 filters, filter size 5x5x1, stride 1\n",
    "- ReLU layer: Accepts and returns a 28x28x6 tensor\n",
    "- Max pooling layer: Pooling size 2x2, stride 2\n",
    "- Conv2 layer: 12 filters, filter size 5x5x12, stride 1\n",
    "- ReLU layer: Accepts and returns a 14x14x12 tensor\n",
    "- Max pooling layer: Pooling size 2x2, stride 2\n",
    "- Conv3 layer: 24 filters, filter size 5x5x24, stride 1\n",
    "- ReLU layer: Accepts and returns a 7x7x24 tensor\n",
    "- Fully connected network with 2 hidden layers: Accepts a flattened tensor of dimension 7x7x24 and outputs a 10-dimensional tensor containing the predicted class labels. The hidden layer dimensions should be [256,128,64,10]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OYv4RkKkWQPp"
   },
   "source": [
    "##### ***Answer***"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q24WqZsvWTTc"
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   "source": [
    "# Creating the required CNN\n",
    "class CNN(nn.Module):\n",
    "\n",
    "  @nn.compact\n",
    "  def __call__(self, x):\n",
    "    x = nn.Conv(features=6, kernel_size=(5, 5))(x)\n",
    "    x = nn.relu(x)\n",
    "    x = nn.max_pool(x, window_shape=(2, 2), strides=(2, 2))\n",
    "    x = nn.Conv(features=12, kernel_size=(5, 5))(x)\n",
    "    x = nn.relu(x)\n",
    "    x = nn.max_pool(x, window_shape=(2, 2), strides=(2, 2))\n",
    "    x = nn.Conv(features=24, kernel_size=(5, 5))(x)\n",
    "    x = nn.relu(x)\n",
    "    x = nn.max_pool(x, window_shape=(2, 2), strides=(2, 2))\n",
    "    x = x.reshape((x.shape[0], -1)) # Flatten\n",
    "    x = nn.Dense(features=256)(x)\n",
    "    x = nn.relu(x)\n",
    "    x = nn.Dense(features=10)(x)    # There are 10 classes in MNIST\n",
    "    x = nn.log_softmax(x)\n",
    "    return x\n",
    "\n",
    "# Loss and training calculations\n",
    "def cross_entropy_loss(logits, labels):\n",
    "  one_hot_labels = jax.nn.one_hot(labels, num_classes=10)\n",
    "  return -jnp.mean(jnp.sum(one_hot_labels * logits, axis=-1))\n",
    "\n",
    "def create_optimizer(params, learning_rate, beta):\n",
    "  optimizer_def = optim.Momentum(learning_rate=learning_rate, beta=beta)\n",
    "  optimizer = optimizer_def.create(params)\n",
    "  return optimizer\n",
    "\n",
    "def get_initial_params(key):\n",
    "  init_shape = jnp.ones((1, 28, 28, 1), jnp.float32)\n",
    "  initial_params = CNN().init(key, init_shape)['params']\n",
    "  return initial_params\n",
    "\n",
    "def compute_metrics(logits, labels):\n",
    "  loss = cross_entropy_loss(logits, labels)\n",
    "  accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)\n",
    "  metrics = {\n",
    "      'loss': loss,\n",
    "      'accuracy': accuracy\n",
    "  }\n",
    "  return metrics\n",
    "\n",
    "@jax.jit\n",
    "def train_step(optimizer, batch):\n",
    "  def loss_fn(params):\n",
    "    logits = CNN().apply({'params': params}, batch['image'])\n",
    "    loss = cross_entropy_loss(logits, batch['label'])\n",
    "    return loss, logits\n",
    "  grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n",
    "  (_, logits), grad = grad_fn(optimizer.target)\n",
    "  optimizer = optimizer.apply_gradient(grad)\n",
    "  metrics = compute_metrics(logits, batch['label'])\n",
    "  return optimizer, metrics\n",
    "\n",
    "\n",
    "\n",
    "# JIT compile\n",
    "@jax.jit\n",
    "def eval_step(params, batch):\n",
    "  logits = CNN().apply({'params': params}, batch['image'])\n",
    "  return compute_metrics(logits, batch['label'])\n",
    "\n",
    "def train_epoch(optimizer, train_ds, batch_size, epoch, rng):\n",
    "  train_ds_size = len(train_ds['image'])\n",
    "  steps_per_epoch = train_ds_size // batch_size\n",
    "\n",
    "  perms = jax.random.permutation(rng, len(train_ds['image']))\n",
    "  perms = perms[:steps_per_epoch * batch_size]  # Skip an incomplete batch\n",
    "  perms = perms.reshape((steps_per_epoch, batch_size))\n",
    "\n",
    "  batch_metrics = []\n",
    "\n",
    "  for perm in perms:\n",
    "    batch = {k: v[perm, ...] for k, v in train_ds.items()}\n",
    "    optimizer, metrics = train_step(optimizer, batch)\n",
    "    batch_metrics.append(metrics)\n",
    "\n",
    "  training_batch_metrics = jax.device_get(batch_metrics)\n",
    "  training_epoch_metrics = {\n",
    "      k: np.mean([metrics[k] for metrics in training_batch_metrics])\n",
    "      for k in training_batch_metrics[0]}\n",
    "\n",
    "  print('Training - epoch: %d, loss: %.4f, accuracy: %.2f' % (epoch, training_epoch_metrics['loss'], training_epoch_metrics['accuracy'] * 100))\n",
    "\n",
    "  return optimizer, training_epoch_metrics\n",
    "\n",
    "\n",
    "\n",
    "def eval_model(model, test_ds):\n",
    "  metrics = eval_step(model, test_ds)    # Evalue the model on the test set\n",
    "  metrics = jax.device_get(metrics)\n",
    "  eval_summary = jax.tree_map(lambda x: x.item(), metrics)\n",
    "  return eval_summary['loss'], eval_summary['accuracy']\n"
   ]
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     "text": [
      "\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
      "local data directory. If you'd instead prefer to read directly from our public\n",
      "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
      "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
      "\n"
     ]
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      "\n",
      "\n",
      "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
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   "source": [
    "def get_datasets():\n",
    "  ds_builder = tfds.builder('mnist')\n",
    "  ds_builder.download_and_prepare()\n",
    "  # Split into training/test sets\n",
    "  train_ds = tfds.as_numpy(ds_builder.as_dataset(split='train', batch_size=-1))\n",
    "  test_ds = tfds.as_numpy(ds_builder.as_dataset(split='test', batch_size=-1))\n",
    "  # Convert to floating-points\n",
    "  train_ds['image'] = jnp.float32(train_ds['image']) / 255.0\n",
    "  test_ds['image'] = jnp.float32(test_ds['image']) / 255.0\n",
    "  return train_ds, test_ds\n",
    "\n",
    "train_ds, test_ds = get_datasets()"
   ]
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      "\n"
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   "source": [
    "# Creating the dataset as required\n",
    "\n",
    "N = 1000 # Number of samples for each digit\n",
    "t_im = np.zeros((10*N,28,28,1))\n",
    "t_lbl = np.zeros(10*N)\n",
    "\n",
    "for k in tqdm((range(10))):\n",
    "  i = 0\n",
    "  for j in range(len(train_ds['label'])):\n",
    "    if train_ds['label'][j] == k:\n",
    "      t_im[k*N + i,:,:,:] = train_ds['image'][j,:,:,:]\n",
    "      t_lbl[k*N + i] = train_ds['label'][j]\n",
    "      i += 1\n",
    "      if i >= N:\n",
    "        break\n",
    "\n",
    "# Permute the data\n",
    "def shuffle(a, b):\n",
    "    assert len(a) == len(b)\n",
    "    p = np.random.permutation(len(a))\n",
    "    return a[p], b[p]\n",
    "\n",
    "t_im, t_lbl = shuffle(t_im, t_lbl)\n",
    "\n",
    "# Creating train and test dataset 3/4 & 1/4 from dataset\n",
    "train_ds2 = {}\n",
    "train_ds2['image'] = t_im[:7500,:,:,:]\n",
    "train_ds2['label'] = t_lbl[:7500].astype(int)\n",
    "\n",
    "test_ds2 = {}\n",
    "test_ds2['image'] = t_im[7500:,:,:,:]\n",
    "test_ds2['label'] = t_lbl[7500:].astype(int)"
   ]
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     "text": [
      "Training - epoch: 1, loss: 1.9010, accuracy: 41.15\n",
      "Testing - epoch: 1, loss: 0.95, accuracy: 74.60\n",
      "Training - epoch: 2, loss: 0.3952, accuracy: 88.09\n",
      "Testing - epoch: 2, loss: 0.21, accuracy: 93.60\n",
      "Training - epoch: 3, loss: 0.2050, accuracy: 93.37\n",
      "Testing - epoch: 3, loss: 0.17, accuracy: 95.72\n",
      "Training - epoch: 4, loss: 0.1217, accuracy: 96.35\n",
      "Testing - epoch: 4, loss: 0.15, accuracy: 95.68\n",
      "Training - epoch: 5, loss: 0.0871, accuracy: 97.29\n",
      "Testing - epoch: 5, loss: 0.12, accuracy: 97.28\n",
      "Training - epoch: 6, loss: 0.0828, accuracy: 97.33\n",
      "Testing - epoch: 6, loss: 0.13, accuracy: 96.24\n",
      "Training - epoch: 7, loss: 0.0598, accuracy: 97.95\n",
      "Testing - epoch: 7, loss: 0.11, accuracy: 97.32\n",
      "Training - epoch: 8, loss: 0.0369, accuracy: 98.83\n",
      "Testing - epoch: 8, loss: 0.12, accuracy: 97.40\n",
      "Training - epoch: 9, loss: 0.0433, accuracy: 98.46\n",
      "Testing - epoch: 9, loss: 0.12, accuracy: 97.36\n",
      "Training - epoch: 10, loss: 0.0395, accuracy: 98.65\n",
      "Testing - epoch: 10, loss: 0.14, accuracy: 96.64\n"
     ]
    }
   ],
   "source": [
    "rng = jax.random.PRNGKey(0)\n",
    "rng, init_rng = jax.random.split(rng)\n",
    "\n",
    "\n",
    "params = get_initial_params(init_rng)\n",
    "\n",
    "\n",
    "learning_rate = 0.1\n",
    "beta = 0.9\n",
    "num_epochs = 10\n",
    "batch_size = 256\n",
    "\n",
    "optimizer = create_optimizer(params, learning_rate=learning_rate, beta=beta)\n",
    "\n",
    "for epoch in range(1, num_epochs + 1):\n",
    "  # Use a separate PRNG key to permute image data during shuffling\n",
    "  rng, input_rng = jax.random.split(rng)\n",
    "  # Run an optimization step over a training batch\n",
    "  optimizer, train_metrics = train_epoch(optimizer, train_ds2, batch_size, epoch, input_rng)\n",
    "  # Evaluate on the test set after each training epoch\n",
    "  test_loss, test_accuracy = eval_model(optimizer.target, test_ds2)\n",
    "  print('Testing - epoch: %d, loss: %.2f, accuracy: %.2f' % (epoch, test_loss, test_accuracy * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 626
    },
    "id": "Zuz9e2v-WjE_",
    "outputId": "fb52d14d-f653-4045-c6fa-c9a9f97dbbec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating and plotting Confusion Matrix\n",
    "\n",
    "logits = CNN().apply({'params': optimizer.target}, test_ds2['image'])\n",
    "pred = np.argmax(logits, axis=-1)\n",
    "\n",
    "cm = confusion_matrix(test_ds2['label'], pred)\n",
    "# Normalise\n",
    "cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(cmn, annot=True, fmt='.2f')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.title('Normalized Confusion Matrix for Test Data')\n",
    "plt.show(block=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 867
    },
    "id": "LEaqU7cpWl2c",
    "outputId": "1abba619-76c0-4bc7-e1bc-3aca5eab6d3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting some of the error predictions\n",
    "\n",
    "error_idxs, = jnp.where(test_ds2['label'] != logits.argmax(axis=1))\n",
    "\n",
    "def show_img(img, ax=None, title=None):\n",
    "  \"\"\"Shows a single image.\"\"\"\n",
    "  if ax is None:\n",
    "    ax = plt.gca()\n",
    "  ax.imshow(img[..., 0], cmap='gray')\n",
    "  ax.set_xticks([])\n",
    "  ax.set_yticks([])\n",
    "  if title:\n",
    "    ax.set_title(title)\n",
    "\n",
    "def show_img_grid(imgs, titles):\n",
    "  \"\"\"Shows a grid of images.\"\"\"\n",
    "  n = int(np.ceil(len(imgs)**.5))\n",
    "  _, axs = plt.subplots(n, n, figsize=(3 * n, 3 * n))\n",
    "  for i, (img, title) in enumerate(zip(imgs, titles)):\n",
    "    show_img(img, axs[i // n][i % n], title)\n",
    "\n",
    "\n",
    "# Show some of them.\n",
    "show_img_grid(\n",
    "    [test_ds2['image'][idx] for idx in error_idxs[:25]],\n",
    "    [f'pred={logits[idx].argmax()}' for idx in error_idxs[:25]],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wfBPTDZ8XHqd"
   },
   "source": [
    "## Van de Pol Oscillator & LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AgCZuUnXXLRn"
   },
   "source": [
    "The Van der Pol oscillator is composed of two coupled first-order differential equations that are used to characterize many models in biological and physical sciences (e.g. as a model for action potential of neurons). The system of ODEs is given by:\n",
    "\n",
    "$$ \\dot{x} = \\mu (x - \\frac{1}{3} x^3 - y) $$\n",
    "\n",
    "$$ \\dot{y} = \\frac{1}{\\mu} x $$ <br>\n",
    "\n",
    "where $x$ is a state of the system, $y$ another state of the system, and $\\dot{x}$ and $\\dot{y}$ represent the evolution of the states with respect to time. The value $μ$ is a parameter (constant) is a positive constant. <br><br>\n",
    "\n",
    "1. Create a time-series data-set by using SciPy to integrate this system in the interval $t ∈ [ 0 , 50 ]$ using $2 , 000$ time-steps for $μ = 5$ using an initial condition of $x ( t = 0 ) = 0.1$ , $y ( t = 0 ) = 0.5$. Create two plots: one for the state trajectories (the $x$ -axis will represent time $t$ and the $y$ -axis the system states $( x , y )$ ) and one for the system trajectories, so the $x$ -axis will correspond to state x and the $y$ -axis to state $y$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2q-SlOVkXPjB"
   },
   "source": [
    "#### ***Answer (1)***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 427
    },
    "id": "JOm2JOGyXKeU",
    "outputId": "d5f862c0-cfa3-4585-b613-2595cb91b11d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'System Trajectories')"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U1FSOHj3KY4895jweK1ascDZgVKVv375kZ2dTUFBA165dq1ymT58+vPnmmxw4cKDK1uXypPrU4xUREUGLFi1Yvnx5hWO1bNkyEhMTKyW9Nantcbfb7QwdOpShQ4fy8MMP06VLF9577z2dLFtAl2H4MZvNxtVXX82bb77JN998w3XXXed8rn379iilmDlzJmlpaXz66ac88sgj9dpOx44d+eWXX1iwYAG7du3i2WefZd68eZWWCw0N5corr2TNmjWsWbOGqVOn0rNnT0aOHFnleu+//35SU1O5/PLLWbVqFWlpaSxdupTbbruN3bt31ytWd3rkkUdYsGABf/vb39iwYQO7du3iq6++4uqrryY/P7/a1/3zn//krrvuYunSpaSlpfHLL79w11138euvv3LhhRcCcPPNN1NaWsr48eP5/vvv2bNnDz/88AMPPPAAK1asqFOctTmON998M1988QW7du1i8+bNzJs3z1meo2matZKTk3nllVdYs2YNe/fuZfHixdx4441ERUUxfPhw53JDhgyhY8eO3HXXXVxyySUVPq9vvPEGs2fPZuPGjezdu5d3332XnJwcZ+NEYmIihw8f5qeffiIjI4O8vDySkpK46qqruPbaa3n77bfZuXMnGzdu5M033+TJJ5+0/Dj8UevWrXE4HDz//PPs2rWLxYsXc9ttt9XYCDBixAhSUlK46KKL+PTTT9m9ezdr167l+eefd5YpTpkyhdatWzNu3DgWLVpEWloaixcv5sMPP3Ru12az8cUXX5Cens7x48cBuO+++5zr2bFjB6+++iovv/wy999/f532qzbHfcGCBTzzzDOsXbuWffv28emnn7J///5qG5s0N/NqxbTmceUd/arq2PfCCy9IixYtJDg4WAYPHixffvmlALJ06VIRqb4j3x8fLyoqkuuuu06io6MlPDxcpkyZIs8//7yc+vYqHzru7bffltatW4vD4ZARI0bI7t27nctU1SFv06ZNMm7cOImKipLg4GBp166dXHvttTX2Eq9pfVU9XtVyb7/9tvzx4/H+++8LUOE4Ll++XEaOHClhYWESGhoqnTp1kttuu63aTpQiIkuWLJFJkyY5j0NsbKwMGjRI3nnnnQrL7dmzRy699FKJi4uToKAgadWqlVx22WXOY1bb/RM5/XG88cYbpX379hIcHCwxMTEyduxY+fXXX6vdB03TPOeJJ56QIUOGSOPGjcXhcEjLli3lsssuc46kcKpZs2Y5R2o41SeffCIDBw6UqKgoCQkJkbPOOqvCKA1FRUUyZcoUiY6OrjB0XElJiTz55JPSsWNHCQwMlNjYWBk6dKj897//db6WP3TGExGx2+0yZ86cCo85HA6ZPXt2rfb5j+tMTk6Wq6++utJyH330kSQlJYnD4ZCePXvKd999V2nbf1xX+egfbdq0kcDAQImPj5dzzz1XFi9e7Fzm0KFDcsUVVziHz+zYsWOFdT755JPSrFkzsdlsFYaOe+qpp6RNmzYSEBAgiYmJVQ4dd+qoF9U9frrjvmzZMhk+fLjExcWJw+GQpKQkeeKJJ2p1bDXXKZEarl9omqZpmuaz/v73v/Ptt9+yfv16b4fiEwoLCwkODmb+/PlMmDDB2+FofkLXLGuapmlaA3P8+HG2b9/Oa6+95tZpsBuy7Oxs5s+fj1Kq2vpkTasPnSxrmqZpWgMzfvx4Vq5cySWXXMLll1/u7XB8wh133MGXX37JjBkzat25WtNqQ5dhaJqmaZqmaVo19GgYmqZpmqZpmlYNnSxrmqZpmqZpWjV0sqxpmqZpmqZp1fD5Dn6//fZbnZaPi4vz6/nS/Xn/9L41XP68f/Xdt2bNmnkgGt9X1+/scr7yHvKVOMB3YtFx6DhOx1dicSWOmr6zdcuypmmapmmaplVDJ8uapmmapmmaVg2dLGuapmmapmlaNXy+ZlnTtDOPiFBQUIBhGCilvBrLkSNHKCwsrPI5EcFmsxEcHOz1ODVN0zTP0Mmypmk+p6CggMDAQAICvP8VFRAQgN1ur/b5kpISCgoKCAkJsTAqTdM0zSq6DEPTNJ9jGIZPJMq1ERAQgGEY3g5D0zRN8xCdLGua5nMaWklDQ4tX0zRNqz2dLGuapmmapmlaNXSyrGmapmmapmnV0MmypmlaNSZOnMiyZcsAePLJJ3nwwQe9HJGmaZpmtYbRg0bTtDOW8cFsZH+aW9epWiZiu+Ta0y531113MXPmTI4cOcKvv/7K3Llz3RqHpmma5vtcTpYzMjJ48cUXyc7ORilFSkoKY8eOrbCMiDBnzhzWr1+Pw+HgxhtvpG3btq5u2qdI5lEICUGFhnk7FK+QokLITEc1bentULxCRODgXmjWEmWrfpgxfybpv0GjcFSjcG+H4jZnn302IsJrr73Gxx9/XOMQcpqmVSZGKRSXQGkxlBRDcTEUFEDeScjNQfJyobAACvLNf0UFUFxUtmwJlJaY369AtiOI0oICsNlQ9kAIDICgYAgOBkcIBIdCeAQqLALCIiAsHAIdEBBg/rPbz9jvZ801LifLdrudK664grZt25Kfn8+9995L9+7dadGihXOZ9evXc/jwYZ577jl27NjB66+/zuOPP+7qpn2GZB3FeOhGcARje+hZVFSMt0OynPHsw7D9V2w33IvqPcjb4VhOvpmPfDwXNfRc1BU3eTscyxXv2Ynx4I0QE2d+BkIbuW3dtWkB9pTU1FSOHDlCVFQUYWFn5omw5j/EMMxktCAfsrOQg3vhwF7k8H44dAAy06t83RGL46zOqVMDSQ3L1fRcnbTvguqfjOreD6JiUDZduXqmcjlZjo6OJjo6GoCQkBCaN29OVlZWhWR5zZo1DB06FKUUHTp0IDc3l2PHjjlf19DJ1k1QVAhFhciHr6P++ndvh2QpKciH7b8CYLz/GrYuPVHBoV6OylqyYaX5d/nXSN8hqM49vByRtYrW/QRiQGY68uk7qEv/6u2QXHbkyBFuueUW3nrrLe6//36WLl3K8OHDvR2WdgaSkmLIz4Ojh5EdW5Adm2HHZsjL9XZo/m3HFvN4v/tyhYcrnDx07YPt4qnQrJVOpv2YW2uW09PTSUtLIykpqcLjWVlZxMXFOe/HxsaSlZVVZbK8aNEiFi1aBMCMGTMqvK42AgIC6vwaV508eZxcm51GF15G7if/IfLPUwnscJZHtuWN/Tud4l1byQJCL7yMvPnvErxoAeHTbqnzenxx32rraOZRgs4ZRfGOLfDRm8Q+8xbK/vvHqyHvW23kHD2MiogieNAI8r/5lMhxkwlsk3T6F1bjyJEjXp2UJC8vj2uvvZaHH36YDh06cOedd/Loo48yatSoKpd3OBx+/f+ruU5EzIT3WCayZT2yaTVs3VTjayxv0VU2SGgOTVugElpA4wRUVCzRie04hg1Cw7w6pnhcXBwZGRkeWbcYZSf7adthy3pk3c+QX4uTkV/XYvy6ttLDKmUcasLlKEewB6LVrOa2X6OCggJmzpzJtGnTCA2tf6tiSkoKKSkpzvt1/WB48sNUHeO3AxARSf6wsfDtQrJefwbb3U945EvFG/t3OrJ/LwAFHXugzjlK3mcfUtBrMKp5qzqtxxf3rTaktBTjWAaFUbGoC6/AeHkGRxd8iG3ouc5lGuq+1ZY9Ix2JjKZwzMWw/BuyZj+D/Y6H672+wsJCr9YHBwUFsXDhQuf9fv36sXDhQkpKSqpcvrCwsNL/b7NmzTwao+Z9UlRoJr/rf0J+Wgq/7bM2gPjmqLYdoW1HVIvWkNDC7Dfg5t+egLg4lB9/fwFmq3DjBFTjBOg/FKbdVu2ysVFRZCz/FmPh+7B3Z5XLyKKFyKLfv0OIisH2j2dQEf5xRf1M45ZkuaSkhJkzZ3LOOecwYMCASs/HxMRU+CHJzMwkJsZ/6nolP8884w4ORV1wCfLuK7BpNfTo7+3QLCG5J80bjcJRF/4FWfsjxoezsd3xyJkxs1lBnvk3tBH0GghJnZEF7yL9h6KCQ7wbm0XkRDaER6EahaMumIx8+Abyy1pUtz7eDk3T6kxEzM5n635CvvoEjh72zIbsdujWD9WtN6pdZ0hogTrlJNHfT7IbKhUQgOreD3v3fpWek6OHMd58BnamVnwiOwvjzqm/r+OKm1DnjD4zfiP9gMvJsojwyiuv0Lx5c84///wql+nbty9fffUVgwcPZseOHYSGhvpNvTJgXqop69CkhoxGFn2G8clb2Lr2qfDF57dyc8y/jcJR4RGocZchH7wGv6yBKr5M/E553WBII5RS2P58FcYTdyNfz0eNv9S7sVlECgugbBQMNWws8t1XGB+8hq3T86jAIC9Hp2mVSUE+smoZ8vFbFS63u1T60KwV6uxhZoewpi11DesZSDVOwH7Pk877IoKsWo68PrPCcvL2i8jbL5qvueZObAOSLY1TqxuXk+Vt27axfPlyWrVqxd133w3AlClTnGfDo0ePplevXqxbt45bb72VoKAgbrzxRlc361vyciEiCjDPOG0XXoHxygxkxWLUOaO9HJwF8spalstPGJLHIIsXYix4F1u3vv5/5pxvtiyrELP8SLXtiOp3DvLNPGTYn1CRfnRiWA0pLEAFOQBQAYHYLv0rxjMPIYs/Q4252MvRaWcqEYHDBzDefhF2bKn/ijp2QyWPQXXt4/yca1ptKKVQA5KhLBmWkhKMF/8Fv65zLiOvz6S0LJm2Pf/hGXNFsiFxOVnu1KkT//3vf2tcRinFNddc4+qmfFd+Hir+lPrE3gOhbUdk4XtI/2SUw+G92KxQVAT2AGcrugoIQJ03CZn73JnRulyWLBPy+3BpavxlyJofkS8+Qk25zkuBWUcKCyHo9/e56tITzuqFfPMpMuJ8ZyKtaZ4ihgE7UzGevq/Or1XD/oQacQGqaQtd+qB5lAoIwH7bPwHzZM54/lHzd7KMcctkAHMIzpaJ3ghRq4Kewc8dSoogMNB5VymF7eJpGE/fhyxagDpvkheDs0BJMQQEVnhIDRiG/O+/ZjnKWb39uxylqGz0z1OTxfhmqKGjkaVfIEPHgJ+PlCCFBRX2H8A2dpL5GVj+NSplnJci0/yViCBLPkc+mF27F/Q6G9uEy1HN6tbxWNM8RSmF/daHAJDsLIy7pzmfMx4xOxja7v8/VGIHb4SnnUIny+5QXEWy2OEs6NEf+eoTZOi5qPBILwVngeJicyalU6iAAGwTp2G8PAP5eSlqcEo1L/YDpcXm3z++ByZcjqxchnz2PvTo7YXArCNFBaigirXJqsNZ0LEb8sVHyJBR+tKi5jIpyMN46GY4VnPLr+36e6H3QP8vAdP8hoqKwT57odnafNdUOJENgPH4XQDY/v0OKjzCmyGe0XTvA3coLamUKAHYLvoLFBYi/6u5TKXBq6JlGTBHhmidhHz2gTmovr8qH07sj8lyWARq5AXI2h8p3lP18EL+QEpLzWNQRamF7aK/QM5xZPFnXohM8wcigjH/HUqvHYdxyyWVE+V2nbDNehf77IXOf6rPIJ0oaw2SUgr7zP9gn70QmjR1Pm787XJKr5vgnPpbs5ZOlt2hpNicd/4PVLNWqMEjkWVfIscyvRCYRapJlpVS2MZfZg70vmKJFwKzhjiT5SreA6MmQEgouR+8YXFUFiquXIZSTrXtaF5h+WY+Ut4RVNNqQUQofegmjOvGI19UbHCw3Tr99+T43qdQZSOxaJo/sT/2KrZX5//+gBjm5+GPw9JpHqeTZXcoKQF7FS2rgBr7ZzAM5Ot5FgdloeJiqG54sK69oU1781J8NRM6NHjlreZVJcuNwlAp4yhcuQzZt9viwCxSRc32qWzjpkBeLvLtwiqf91Vbt25l3Ljfa61/+eUXJk3y8/4HPsJYtBDjuvFwaL/zMTXxyt9bjvX43doZQtns2GcvxPbPF5yPGU/eQ+mdf/FiVGceXbPsIjFKwTAq1eyWU40TUAOHI8u/Rv400S+HEZNqWtahrHV53BSM5x5Bflrin0PplZ8E2Ks5BinjYPHnGJ9/gP3G+y0MzCKFZclyYNXJsmrVDnoPRBYtQFIuqHMr4OtrjpB2rMDVKCtIjA7mmr7xNS7ToUMH9u3bR2lpKQAPP/ww06dPd2scWmWl11bsDGq79ylUu05eikbTfINq3gr77IWU3pqz1rgAACAASURBVHEZnMyBE9kcuXAQtpc/QVVVBqm5lW5ZdpUzUar+zarG/hlKSvy3dbmKDo4VdO1j1i77a+tyafVlGAAqNIzQCybB+p/9s3W5qMj8W8PwcLZxl0JBPrKo4dQu22w2OnTowNatW/nf//5H8+bN6datm7fD8ltiGBUSZZUyzmxF1omypjnZn3kX292PO+8bN1yMeGqGSc1Jtyy7qoZL8OVUk2aoAclm7fKYi1FlE5j4jZLiCkPn/ZFZu3yp2bq8YjFq6LkWBmeBajr4nSr0gsnkfvYhxmfvY7/pAYsCs0jZZ0DV9Blo3tqsXf7uf+ZnoA5jj5+uBdiTevfuzerVq5kzZw7vvPOO1+I4E6RfPMR52zbzLVSE/12F0zR3UB26YnvpE4wbzQmfjPuvw3bLP8yZIzWP0C3LrqpFogSgzvszFJcg38yvcbkGqbrRME7VtQ8kdkD+91//a12uxQmTrVG42dlvw0r/a102DPPvaab2tY2+EE7mID8ttiAo9+jduzczZsxgzJgxNG3a9PQv0OqldNbv5S22Z9/XibKmnYYKDCR+/grnfeP5RzH0qEMeo5NlV9UiUQJQCS3MKZC/+xLJ9bNRAUpq6OBXRimF7bzJkHUUWf+TRYFZpLTmmuVyasR5EORAlvjZF5ph1vRiO83EM+27mCdM3y4wa/0bgKSkJIKCgrjpppu8HYrfEhHYvB4A2433o0IbneYVmqaVs8/+veO0fDAb4/MPvRiN/9JlGK6qZcsygBpzEbJqGfLdF/41q19x9R38KujWB5o0NZOlvkP8ZxzUkmJzuu/T7I8KDTM7e/64GJlwBSoqxqIAPUxq17KslMJ27oUYrzwJG1aZ08L7uDfeeIMHHniA0NBQb4dSLxs2bGDOnDkYhsHIkSOZMGFChefnzp3L5s2bASgqKuL48ePMnTsXgMmTJ9OqlTnbXVxcHPfcc49HYpTXnnbeVr3O9sg2NM2f2WcvpHT6zfDbPmTBuxhBQeaVPM1tdLLsqlq2LAPmPO9n9TKnaB01HlVDh6gGpaS4Vr1xlc2GShmHvPcq7Ew1Wxr9QUlJ7U4WADV6AvL9N8g381GTrvZwYBapZRkGAL3Ohrh4jG/mY/fhZHnPnj385S9/oV+/fkyePJmSBlg6ZBgGb7zxBg8++CCxsbHcd9999O3blxYtWjiXmTZtmvP2l19+SVpamvN+UFAQTz/9NJ4ma34AIOKWB8j1+NY0zT/ZH36B0kdug/1pyEdzMKJisfUf6u2w/IYuw3BV2Y9obYdusY25GE5k+9ckHacbDeMUatBICIvA+PJjDwdloZKqZ3CsimrSDNV/qDmUoL+U49QhWVY2O2rUeNi11acH1m/Tpg3Lly9n5syZ3g6l3nbu3ElCQgLx8fEEBAQwaNAgVq9eXe3yP/74I0OGDKn2eU8LGXGe17ataf7A/tCzEB4JgMz+P//rH+NFOll2VR1algHo2M0cRm3xZ0h5ktHQldahZdURbI47/MsaZH/a6V/QENRh/wHU6AuhsABZ/rUHg7JQ+ftY1e7rRA1OgdAwjG8/9WBQWlZWFrGxsc77sbGxZGVlVbns0aNHSU9Pp2vXrs7HiouLuffee3nggQdYtWqVx+PVNM119n+/7bxtPHo7kp/nxWj8hy7DcFUdapbBrNtUKRcgbzwDW9abo0Q0dKWlp+/cdQo1bKw55vKSz1FTb/FgYBYpKTlt575TqZaJ0LEb8t0XyOgJKHvtj51PqksZBmUnTMPGIl9+hKT/hmrSzIPBabXx448/cvbZZ2M75f/wpZdeIiYmhiNHjvDII4/QqlUrEhISKr120aJFLFq0CIAZM2YQFxdX6+1KQT7pZbcDAgLq9FpP8ZU4wHdi0XE0sDjmr+DIhYMAMG69hCbzfvR4HyGfPyaurtftazzTlLcs1yVZ6jsE+XguxuLPsftDsixGrRMlKJsC+uxhyE9LkYunosIiPBicBWqYwbA6thHnY7z8BGxsGB3dalTLDn6nUiPOQ76Zh3y7EHXZ9R4K7MwWExNDZmam835mZiYxMVV3Kl2xYgVXX12xhr582fj4eLp06cKePXuqTJZTUlJISUlx3s/IyKh1jHJwn/N2SUlJnV7rKXFxcT4RB/hOLDqOhheH7bUF5pTxQPpFgyuMmmF1LFZyJY5mzapvuNFlGK4qHwKrDq2DKiDQnJhj8zr/mHnHqFuyDKCGnwfFRcgP33ooKOtIHVuWAejRH2IaY3z3hWeCslIdW5YBVGQ0qt9Q5KclSF7lbl0i4q7oLOGL8bZr145Dhw6Rnp5OSUkJK1asoG/fvpWWO3jwILm5uXTo0MH52MmTJykuNhsCTpw4wbZt2yp0DHSbPD+p29c0H6OUwvbse877pTP+7sVoGj6dLLuqHokCgDrnXFAK+e5LDwRlsfokyy3aQIeuZke3hl67XVr3ZFnZ7eYJU+pG5PABDwVmkfp+BkacZ9Zu/7S00nM2m63BjEBRUlJSoXzBV9jtdq666ioee+wx7rjjDgYOHEjLli358MMPWbNmjXO5H3/8kUGDBlW4THvw4EHuvfde7r77bh5++GEmTJjgmWQ5qObx2TVNqz8VGoa67m7zzq6tyLZfvRtQA6bLMFxV30QhOhZ6DkBWLEImXI6qYbpon1ePZBlAJY9BZv8fbNkAXXt7IDCL1Hf/zxmFfPYBsuwr1ORrPBCYRer7GWjT3pykZOn/kOFjUae8Pjg4mIKCAgoLC70+HrfD4aCwsLDK50QEm81GcHCwxVHVTu/evendu+Jna/LkyRXuT5pUecz3jh07WjMSSEwTz29D085gtn7nUPruK5Cbg/F/92N7ZX7D7yfjBTpZdlUdRwI4lW3oGIx1PyHrf0I15PEQDQNU3T98qvdAJDwSY9mX2BtyslzHmu1yKiIa1etss3b7or+gTjMLoq9yXhmoz3tgxHlmZ9etG6FLr98fV4qQkBB3hegSX6nF80th4d6OQNP8nn3Wu5ReOw4A4/oLPV6/7I9879phQ1OPzk1OnXtAXHyDHkJMROqfLAYEooakwMbVSFYDTkYMqd//P6DOGQ25Oci6BjwFuHO663q8B/oMgfBIjKV+ULut1Zm3rxpo2pnC9sJHztvGvP94MZKGSSfLLpJ6XoKGshntzhkN235BDh90c2QWceVkgbLabaRhd/QTA+r7o9+pu3nC1ND3H+r3GQgMRA0aAZtWI8ePuTkwTdM0DUA5HKgJlwMgX36MFBZ4OaKGRSfLrnIhWYayCRrsduT7b9wYlIVc3f/GCdCpuzkqQkPt6FfPmmUoO2EaMgq2bkLSD7k5MIu4/BkYBYaB/Fy5o5+maZrmHrbzfu+fYNxcua+CVj2dLLtK6l+zDOYQWnTvh/y8FCktdWNgFnExUQJQA0dAxhGKUze6KSiLGXWblOWP1OCRoGzID2foCVPTFpDUGfnhW58cgk3TNM1f2F6Z77xtfD3Pi5E0LDpZdpUbkkXboBFwItscFaKhcUey3HsgOELI/+4rNwVlMRdalgFUVCx074v8uNgcs7mhcaGTazk1OAUOH4Td29wUlNbQSEG+t0PQNL+n7HbUBVMAkI/nejeYBkQny65yQ7JI1z4QFo78tMQ9MVnJHYmSIxjVZxCFPy5Gqhmiy6cZhkv7D2AbkmKeMKWeoSdMfQZDYBCycpmbgtIamgZ7sqxpDYxt3BTn7fJRMrSa6WTZVe5IFgMCzdnM1v+MNLQZrVzs4FdODRqB5OchG352Q1AWc7FlGTBPmELDGmay6Ib3gAoJhW59kTU/NMxyJM1leZ++6+0QNO2MYXtqjvN2gx6NyiJuSZZfeuklrrnmGu68884qn9+8eTNTp07l7rvv5u677+bjjz92x2Z9gztalimr2y0pRtaucENQFnLT/tP+LGyNE5AVDbB1XaT+o2GUUQGBqD6DkA0rG14v5dL6Dx13KtuAoZBzHLZtckNQWoPRvR8ApUd+83IgmnbmUNGxztvGPVd5MZKGwS3J8rBhw7j//vtrXKZz5848/fTTPP3000ycONEdm/UNbmpZpU0SNGmKrPnR9Zis5K6TBZuN4KGjYetG5OQJNwRmIXe0LANqwDBz+ucNK12PyUrO94CLs0J16wshocjK5a7HpDUYtrF/9nYImnZGsr22wHlbGmKfKQu5JVnu0qULYWFh7lhVw+OuZFEpVO9BsG0TktuASjHc1bIMBA8cZg4h1tCSxXpOylJJ+y4QGYOsa2BXF9xVihMYhOo1EFn/E1Jc5IbAtAahbUdvR6BpZySlFJTNnms885CXo/FtltUsb9++nbvvvpvHH3+c/fv3W7VZz3Nx6LhTqd4DobQU2diAkkU31GyXC2jbEWKbNLzZ7NzVsmyzoXoNgF/XIUUNqKOjG0+YVP+hkJ8Hv6x1eV1aw6Bn8dM077Hf9k/nbWPRguoXPMMFWLGRxMREXnrpJYKDg1m3bh1PP/00zz33XJXLLlq0iEWLFgEwY8YM4uLi6rStgICAOr/GFbkhIZwEYhs3xhbayKV1SWwsGXHxBP66lqhxk6tcxur9O53S0iIygPDISEJcjCsgIIDQwSPI++ITYkJDXD6eVslQioCQEKJq2P/a/r8VJp9L9ndfEr5/F8EDhrozTI85GRxMLhDXuAkqMNCldcmQ4Rx98xkCf1lN1OgL3BOgi3ztM6dpmuZOavylyIL3kA/fgJTx3g7HJ1mSLIeGhjpv9+7dmzfeeIMTJ04QERFRadmUlBRSUlKc9zMy6tZLMy4urs6vcYWRkwNA5rFjqDzXxwmVHv0pXPYVRw/sQwWHVnre6v07Hck0Y8nJzSPXxbji4uIo6NwTFn5AxtKvsA1IdkeIHldaXIRRVFLj/0tt/98koRWENuLEsq852a6LO8P0GOOkWTaUcSwL5WrdMkC3vhSu/Ymjhw+jAiz5iqpRfT9zzZo180A0/k0MA+WOkiZN02rNdv4llC54DwBjwXvYxl/q5Yh8jyXfStnZ2c6ZuXbu3IlhGISHh1uxac9z4yVowKxbLilGGsplaDfvP207ldXtNqBSDBH3/f8HBKC690c2rm44E5S4sRQHQPUcAPm5sP1Xt6xPa0DWN6DPvab5ETXlOgDk8w+8HIlvckuzzaxZs9iyZQs5OTlcf/31TJo0iZKyH/rRo0fz888/880332C32wkKCuL222/3nzo1NycKJHWC8EhYuwL6neOedXqSu08WbDZUr7ORFeYEJcrhcMt6PcowXB467lSq19nIz0thx2bo3MNt6/WYspptt32mO/eEoCBkw0pUl57uWafWIBifvIW9z2Bvh6FpZxzbiPMpff81AIyl/8M2/DwvR+Rb3JIs33777TU+P2bMGMaMGeOOTfketyeLdnNEgJXfIUWFqCAfTxbL9t+dl05V74HId1/A5nXQe6Db1usxburg53RWbzNZXP8TqiEky1Lq1v1XDgd07olsXIVMuc5/Tqy1aql+5yCrv4ejh70diqadsdT5k5HPP0TeexV0slyBLg5zlXggWew1AAoLYPtmt63TY9zdsg7Q/iwIDkE2r3ffOj3JXUPHlVEOB3Tpjaxf6Sxf8mnuPlmgrBQj6yjsT3PrejXfpCZc7u0QNO2MZxt/mfO2sfp7L0bie3Sy7CpD3JsoArTvCgGBDSNZdNekLKdQAQHQqTuyed2Zmyz26AfZmXBwr1vX6xGG4Z6OfadQ3fuBUg1vzG2tXlSTpt4OQdM0QA0ZBYC89rSXI/EtOll2lZsvQUNZy2L7LsiWBpAsu7uDXxl1Vi/ITIeGMAWuJ5LlslpdSd3o1vV6hCf2PyIKEjs0jM+Apmman7BNvcV5Ww4f8GIkvkUny67yQKIAoM7qDb/tQ7J8Z5i4KnkqWe7SC6BhJEtiuD7V8x+omMaQ0LxhTEHqqc9Apx6Qth3Jz3P7ujVN07RqOIIBMP5xo5cD8R06WXaVx5Ll8pZFH0+WPJUsN2kKjRMaRimKm0fDKKc694TtvyLFxW5ft1t54GQBQHXqZh7bhlC7r7mNHPKjGV41rQGyPf+h83aDGcLUw3Sy7CoPJcs0bwOR0eDryaInOviVUWf1gm2/ICU+nix66oSpS08oKoTdW92+brfy1EQSSZ0hMAjZusn969Z8ljHvbW+HoGlntFNHIDJuuMiLkfgOnSy7yjA8kygqherSE0ndgJQnpL7IKDX/eqJlsXNPc1SQtB1uX7dbeeg9QIeuYLMhW3y8btlTJwuBQZDUGdnq4/uvuUVAq7bmjQ0/ezcQTdOwzfyPt0PwKTpZdpUbZ2+rpGM3OJkDvlxk74HRMJzan2VuYoePX4Z389Bx5VRoI7OTW0MoxfHAyRKA6tQdDuxBTmR7ZP2a7wiberO3Q9A0rYyKiHLeNt5+yYuR+AadLLvKQ/WqACqpCwCyc4tH1u8WHqpZBlDhEdC0JbIz1e3rditPleIAqsNZsG8XUljokfW7hSf3v1N384ae+trvBfXs7+0QNE07hbrqDgBk+VdejsT7dLLsKg91bgKgSVOIiIIdZ2ayDKDad4GdqUh5uYePERGPXl1Q7bpAaSns8eFSFE/VLAO0amvWLe/a5pn1az7DY+8hTdPqxTZwuPO2ZB71YiTep7+dXGW4f5zlckopSOqCnMHJMu27QH4uHNznmfW7SjzXwRGApE7mZnz56oIHTxhVQCC0SUJ2+fjVBU3TND9m3Hu1t0PwKp0su8qDl6ChrGU1Mx3J8tGzOg+OhgGgfL1u2dMt643CzVKUXb47IoaUloLdQ1dXANWuM+zbjRT5cCmK5laSc8LbIWiaBthenuftEHyCTpZd5elkOakzgO9ehvZkBz9AxTaBmDjw1bplT7esU/Ye2JXqu6OieLIMA1DtOkFpCezd5bFtaL5FvtY/0JrmC1RAgPO28cYzXozEu3Sy7CpPDRtWrnkbsAfAPt9MFMSCZJE2HRBfrdm1Yv/bdYa8XDhy0HPbcIWHRgNxaldWiqJLMfxf2Q+zTpY1zXeoa+4EQH5e6uVIvEcny67y5NBxgAoMhOatEB9Nli1pWW3VFo4eRvJOemwb9ebhMhQA1bodAOKrLaseHDoOQIVHQpOmyG4fvbqiuY3681XeDkHTtD+wDUh23j5TS6R0suwi8eDQceVUq3ZmzaaIR7dTL1Yky62TzBv7dntsG/Xm4TIUABJaQEAg7PfB/QePdnItp1onwf40j25D8z41eJS3Q9A0rQbG3y73dgheoZNlV3m4Zhkwh886eQKOZXh2O/VhQcsqZcmyT7asGmUnMJ5sWQ4IgOatEV88WQCP1ywD0DIRMo745tUFzW2Uw+HtEDRNq4Jt1rveDsGrAk6/iFYjT9drYrYsC5h1yx06e3RbdebB6a7LqfAIiGkMe3d6bBv1JuX77+n3QFtk7QpExBxS0Jd4uAwDQLVMND8D+/dAx64e3ZY/2bBhA3PmzMEwDEaOHMmECRMqPP/dd9/x9ttvExMTA8CYMWMYOXKk87l588za4Ysuuohhw4ZZGrumab5DNQp33pZf1qC69fViNNbTybKrLEgUaNEGlM03WxatKEMAaNUW8cXL8FZ08APz6sL330DWUYht4tlt1ZUYYPPwV0nLtuam9u9G6WS5VgzD4I033uDBBx8kNjaW++67j759+9KiRYsKyw0aNIirr644hurJkyf5+OOPmTFjBgD33nsvffv2JSwszLL4pagQFaRbmjXNZ3TsBtt+wXjuEeyzF3o7GkvpZNlVVtRrOoLNDk6+ODGHRcmiatYK+WUNUlJsTlThK6za/5Zty1pW03wvWbagDENFRpuzWfriCZOP2rlzJwkJCcTHxwNmUrx69epKyXJVNmzYQPfu3Z3Jcffu3dmwYQNDhgzxaMynkhWLUcPGWrY9rX7EMKCwAArzoaAAiosq/isqQpz3iys/X1Ji/o6Wlprfp0YplBoghjmGe/lzwLHgYEqLi8FmQymb+b2rbGBT0Kk7tsEpXj4a/s12578wrhvv7TC8QifLrrKiZhmgaUv47cxNlmna0vzCPHIImrfy7Lbqwsr9B+TQAVTPAZ7dVl1Z9RlokYgc0MlybWVlZREbG+u8Hxsby44dlYdgXLlyJampqTRt2pSpU6cSFxdX6bUxMTFkZWVVuZ1FixaxaNEiAGbMmEFcXFy94g0ICCAuLo4jZfflg9nETfxLvdblivI4fIGnYhERKCrEOJmD5OaU/T2JkZuD5OciBflIfh6Sn4dRkM+Jgnzs+bnOx5zPF+QjBfn1C0IpCHKYjR92O8puN6/S2u3myXfZfWUPKEuKFZKbQ4BRaibRIuZ3jxhgGDiatyLcov83X3mPeCOO8s+nY8E7hF99u1djqYqn4tDJsqtEPNu5rYxq1hLZtAopLvb4turEypZlgEP7fDNZ9vB7QIU2gsgYOLTfo9upFytKkQDVtAXyQ6pv1m03UH369GHw4MEEBgby7bff8uKLLzJ9+vQ6rSMlJYWUlN9b9DIy6tcROS4ujoyMDNR5k5D//RdKS+u9LleUx+ELaopFRMwW3ZMnIDfHHIs97ySSexLyc+GUv/KH++Tnmi26NVEKgkPAEYy9URilAUHm/YhoVOOmzudUcAg4QsruO8zSmcCgyv+CgiAwEAId5l97QK0+x6eOARVbw/EoBAot+n/zlfeIN+JQf74K+ehN8j7/L4Xjfx8Zwx+OSbNmzap9TifLrjIM8xKQpzVrBYZB6aH9EBrh+e3VlkXJIgnNzZaF3/bhU2lS+XB+VrSsNmuJHD7g+e3UlVUty/HNzeQgOwuiY0+//BkuJiaGzMxM5/3MzExnR75y4eG/d9oZOXIk77zzjvO1W7ZscT6XlZVFly5dPByxSY0abybLZyApLIDjx+DEMTiRTV5pMcbhQ2ZCfPIEknO87HaO+be4qPqV2WwQGlb2rxGEhpkzop5yn0aNICQMVfaX0EYQEgKOUAgKciazvpIIad5nGz2B0o/eBDijGi50suwqwzAvF3mYatoSAUr2p0HHHh7fXq1Z1MFPBTkgLh5+87GWVefJgue/MFRCC+SnJb73BWXF0HGASmhutjIdPqCT5Vpo164dhw4dIj09nZiYGFasWMGtt95aYZljx44RHR0NwJo1a5z1zD179uT999/n5ElzqL6NGzdy6aWXWhL3qb3u/YUU5MGxTDiWgRzLMocBPX4MOZFtJsbHzeSYwoIKr8spvxHSCMIjICwCouPMiZrCIpz/VFh4pcQYR7BvfU9ofsd45UnsN9zr7TAsoZNlV4kBNgs6nCU0B2XzvWTZqppdgGatEF+r23buv+dPmGjaEgryfa9lVawpwyC+ubm5IwdRnX3oM+Cj7HY7V111FY899hiGYTB8+HBatmzJhx9+SLt27ejbty9ffvkla9aswW63ExYWxo033ghAWFgYF198Mffddx8AEydOtHQkjIZEjFI4lgUZh5GMI5CRXpYUm8kx2ZmQn1f5hY3CzU6rEVGoxA5lt6MhMgpVdjumTVuyinysU7N2xlNX3o7MmQXrVng7FMvoZNlVFl2CVkEOaBxPyf49Ht9WnViYLKv45sjm9YhFLZm1UjbOsiUtq01blNVt7/etZNmqMozoWHAEw+GDnt+Wn+jduze9e/eu8NjkyZOdty+99NJqW4xHjBjBiBEjPBrf6UhJiTkpj5dJcTGkH4LD+5H0w+YEORnmXzKPQukp9b9KmUlvdCwkNDdP7KJjzRbhsr9ExaACg067XXtMHEqXP2g+xjZoBKVzZgFnTimG97+FGjqLOjcB0LQlpQf2WLOt2rKyZblxApQUmy2rMd7vdQtYu/8J5iVyOXIQ1aWn57dXW4YBdgtOFpSC+GbIEZ0snylk9feogcOt215hAcXbt2Bs/QUOHUAO7YdDByDj8O+fdYCwcIhLQLVqB30GQVw8Ki4e4hIgJk63BGtnDHl9Jurau7wdhsfpZNlVFoyzXE41TqB02y8oXzqTs6qDH+b+C5g/XGdishwZbfYqzzhy+mWtZJSiLDphVPHNkbTtlmxL8z756E3wULIsxUWwPw3ZuxP27DT//rafrPJ+GPYAaNIUWrRB9RsCTVuiElpAfFNUcKhHYtK0hkJddgPy7svIquWgk+Xaeemll1i3bh2RkZHMnDmz0vMiwpw5c1i/fj0Oh4Mbb7yRtm3bumPT3idiSecuAOLikYJ8VM5xs77NFxgGKJs1yXtjc3IFOXoE1cFHZnGzcDQMpZT5Hjh62OPbqhOryjDAvLqwbgViYYKuWU8lj0GWfQU5x922TinIhx1bkNQNyLZf4OBe52QXhEdCm/aoXgOJ6NqDnEaRZsuxD5SAaJovsg37E6XvvgyUDWPo59zyTTBs2DDGjBnDiy++WOXz69ev5/Dhwzz33HPs2LGD119/nccff9wdm/Y+CxMFFVfWsnr0sO8ky2JdyzoxTcwW7KOHrNlebVg4GgZgjgjiay3LYmGyHNvYTHCysyCmsTXb1Cynxl1qJssuEBE4uAdZ9zOSuhHStpnvnYAAaNcZde5FqNZJ0CbJrCcu+wwHx8VxUtcJa1qtyecfwpU3ezsMj3JLstylSxfS09OrfX7NmjUMHToUpRQdOnQgNze3wpBFDZphmNNuWqG8ZTXjCKpdJ2u2eTpWniwEBJjlF0d9KFm0sgwDUHFNkJ1bfKtTRal1dfsqNr6sFCddJ8t+TLnQGCBHDyOrliMrl5mdYZWCVu1QoyaYne2SOpsdpjVNc4kacT6y5HNk4Xs6WXaHrKysCtMPxsbGkpWVVWWy7OrUqVZPuZihFAEhIURZsE0JDyMdCM3LIcwHppUEyHE4yLfb3XLMa/N/d6xZSyQ7gxgf2f+iw+EcAyKjogmqISZ3vS9zW7fl5NIviA12YAv3jclpjiLYLPrclSR1IBMIK8on5Ayb2vZMVZuSGxGBrZswvvwYUjeaDyZ1QV16ParPIJeSb03TqqYuuRZZ8rm3w7CEzxVkuTp1qtUzDZWWI/2MYQAAIABJREFUFGMUF1u2TVt0LHl7d1PgI5cJjdxcRCm37H9t/u+MiGjk17U+M5uUHMsC4HjOyRqHeHLX+1JCzLFuM7dvMS8h+wCjtATBPe+B0xFlfmXl7N1Nro9PbVvT1KlaHezeBknVzx4oW9ZjfPoupG2HyGjUhMtRZw9HxeorD5rmSade3Sxc/zO09I3fJE+w5NpxTExMhR+bqqZdbbCs7NwE2GIbI9mZp1/QKmUd/CwTFQMnjiPlHXO8zaIZDJ2iyxKA7CxrtlcbFg0dB2XjjYdHQmb1ZV+afzHeebnKxyU3B+PNWRjPTIec46jLbsD2xGxs503SibKmWaVsSNPsR/7m5UA8y5JfuL59+7J8+XJEhO3btxMaGuof9cpgbecmwB4d51uJksX7T1SMuc2cbOu2WRPDutEwAHP/AfGl94BhWDsyRWwTJPOoddvTvEINSDZvHNxb6TnZtxtj+i3IqmWo8yZhe+RFbMP+VKuJPjRNcx/b9Ge9HYIl3FKGMWvWLLZs2UJOTg7XX389kyZNoqTEnNFo9OjR9OrVi3Xr1nHrrbcSFBTknFLVL1jcsmqLafx7TZ4vsLhlXUXFmB28srMgygdmsROLR8OIiDK35WPJsuUnTL42IojmdiplnNlJ7w9k+68YL/wLQkKx3f9/5sQgmqZ5xakT8MjxY6hIP2kI/QO3JMu33357jc8rpbjmmmvcsSnfY3UZRkwcnMxBiotRgT4wS5TViVJkWYLsK8mi1aNh2O1mwnzcR/YfrD9hiohGdm21bHual1SRBEtmOsaLj0NkNLY7HkHpEVE0zWcYd03FPnuht8PwCAuzHD9lcaJgL5+5zleSJW+0KuJDZQgWJ8sARMb4zv5DWSmOhWUYkVFw8gRSdvVK80/qlM+UiCAiGK/PBDGw3fIPnShrmo+wPfqSt0PwOJ0su8rqMozosmTZV5Ilqzv4RUSZ2/OlkwWw/oTBxzp5Kiv3PyLanDnxpPtmd9N8XM5x2LgSdqai/nwVqokeaUTTfIUq6+QHIOW/iX5GJ8uusriDmy2mrAzh+DEy84pJO1Zg2barZPUl+PIyhOwsSgwh7VgBhhen2nR+MSjrWlZVZDQcPwbAgeOF5BV7b2QQETETV0vr1stq4o4fIzu/hIy8Ysu2rXmHrF2B8c2n0DgBNWikt8PRNK0a8trT3g7BI3Sy7Cqra5YjzERhe1YBN362m9u/2MPyPScs234lVl+CB4iMovD4ce7/dh+3f7GHl1Yetnb7pzLKElUrW1bDI+HkCf6zPp2bPk/jji/2UFDipbN5Z8u6he+Bss/ALwdPcO2CXfx1wS62Hs23bvua5eS9V2DHFtSgkeYJs6ZpPiX8WnPoOFn7o5cj8QydLLvK6pbl8AhKlI1nM2IID7ITHxbIp6levCRvdc0yQFgEHwS0Z1tGPu1iglm8+ziZ3mpdLG/VtnLq6bAINoe35pMtWXSIDebwyWJ+2OulE6ayZNnSMozIaAptgczaF0RsaACOABvztvhQWYrmNmr8pRXv9+jvpUg0TatJ6NiJ3g7Bo3Sy7CqLa3ZVkIPFLQZz0Ajm2n7x/Kl9FLuyCjma651kUcSwNlEEjoXF8UX4WQxPjOD2gU0xBNb9lmtpDE5eqFmWRhH8p915xDoU/0ppRZNGgaw8cNKy7VfgjZrtsEj+13wwmaUB3DKgKcMSI1l/KJfiUu+V42ieoZLH/n4nMAiatfJeMJqm1Yr40vC2bqKTZVdZ3LJqiPB580EkGdn0bx5G1/hQAFK9dRnaCy3LC0M7U4rikm5xtIwMItJhZ3N6nqUxOFk9gx+QaotiR0Qr/txMcATY6Bofytaj+Wb9sNWkvAzFukvjxfYAFrYcSm/bMc6KD6Vbk1CKSoWdWboUw9+o8Ijf70TH6RIMTfNlZWMsG//+h5cDcT+dLLvK4mRx1d5sDgbHcl7uVpRStI0OJjhAsTXjzEiWC0sMFtOUARm/Et/IjlKKzk1CvHuyAJYeg/9lhxBWnMfw0BwAOjcO4URhKQdziiyLwckL+//T/pOcCArjguI0ADo3CQG8eMKoWSO0kbcj0DStBrZHq56a3h/oZNkFv4+EYN1hnLfpN6JK8xmUtQUAu81MmHdneWlUDIvLUFbsy+EkAZz728+Qa5ZetIsx63Zzi7wwKoTFyWJmXjE/Z0HKoZUE5Zl1yh1igwHYnVVoSQwVeKFm+csd2TQtyqZbrjkNclRwAHGhAaR5Y/816/jKcJmaplVJhYQ6b/vbEHI6WXaFxYlSdkEJP+85xgjjIIEns52PJ0Y7SDtW6J0h1CxuWf56ZzbNAkvomr0LTprJYttoM1ncc8x7yaJVx2DZnhMYAqMOrXLuf4tIBwE25Z0TJotHw9iXXUjq0XxG5+/Alvt7nXZidDC7vT2MouZZvjS2+P+z9+Zxcp3lne/3nNqr9721L63dFt5kbMxijJVMLkwGDeFegxOSDCZcljiAweAYGBx8TZzBwITtE+B6ILmQhE8S8JDMTQDhawOWseVYwotkSa1WS5bU6qWW3mqvc+4fp051dXdV1zl1tlb5/f6jVndVv89bdU7X733e3/s8AoFgRdTvNVeWWYhlK7jsV/3l2RmKKtwcmi4LJYCt3WEyBYVLsx4c8nOxGsj4XI7jk2n29ypIAPMlsdxdyqx6IZZUd+ssP35mhp09YdbkkuVrwC9LbOoMelNz24PFgizBzYzD/Gz5+1u7Q1yYyXlXQk/gHG0dXkcgEAgMIt362wCoP/+xx5HYixDLVlBKmVyXhMJjZ2bY3tfCxlYfpFPldr9bSplVz8SSSzaMX5zVxNFr12rzZU77f3fET2fYx4inmWXnK4KMJjKMJrPcvKUDWtrK8wc9s5p1/5Cfi2JZVVV+eXaGVw1E6YoGoCKzvLUrjAqcTQorRrMh/8GdXocgEAgMIt32Hq9DcAQhlq2gVwJwQSxemMlxKpbhP+zqWzjoktEqQGzsCOKT8EYsuphZ/uXZGXb2hhno0U7Iq5WZxa6wt5lVF66Bx0dn8Enwuk1t2jWQWiiXt7UrzEy2SDxdcDyORai6Z9n5zPpwPMOluTyv39yuLRaWvP+Ad959gXO86nqvIxAIBAaRKkrJqnkPDp07hBDLVnAxq/bE2RkkYP+OPtBN9GlNLAd8Mhs6Qt6JRRfmf34my5lEltdtal9YLKQXysVt6QpxLpklX3R5G94lK46eVb16TQsdYT9EWlAzi+cPHhzyK5YWjC6U9PrF6Ax+GW7c0AbRVshly3+M+1r8tAZl4VtuQpr1w1cgaHaUL3zK6xBsQ4hlK7goln91fo4dvRH6WkNIEV0sLmQWt3SFvDvg5cL8f3l2Fgl47cY2CGmlwqgQi1u7wxRVODft8oepS9fAmUSWifkCN21s074RiS5aLGwuiWXXF0wuzV9RVX55dpZr17bSGvRpmWUoWzEkSdKsKKIiRnMTm/Q6AoFAUAfpXR/Uvjj9kreB2IgQy1ZwSShMzuc5Hc9w4/pW7RvhklhMLxaLiUyRhNvb8C6J5SfPzbK7L0JPNIDk90MwBOmFurqebcO7dA08dX4WWYLr15WugSViORrwsaYt4L4VR3WndNzpeIZYusBNGyoWC7B4wdQV4mwyS0ERnfyaFfWnj3gdgkAgqIP8hv/gdQi2I8SyFVzyqz5damV8gy4UatgQwKPMosPzn5jLM5rM8mp9sQCaWKoQSoNtASJ+D7bhXbJhPHV+jl29Ec2CAUjh6KKdBfDIt+1S6binz88hS3BdabEg6QvGTMWCqTtMXlE5Py2yy81Ks52wFwiaHXUm4XUItiDEshVcyir+6vws69uDrGsPat8oZdXURWJZL5/mQWbR4fkfvqAtFq6vFMvhxZlVWZJKVhSX5+/Cgml8LseZRFbz6upEWhbNH7QFk+vNWVy6Bw5fmGN3X4T2UEmUVxPLXt0DAseRDvye1yEIBIIGUD79Aa9DsAUhlq3gQlZxLlvkhfHUcqEEizKLrUEfA60Bb2wIDmeWD1+YY21bgPXtoYVvhiOLFgsAW7rDjCYzFN3chlcUkKRFh5Ds5qnSzsKyzHo2g1pcEMZbyyUEXRSLLojlyfk8ZxLZBQsKaIslWLS7sK49SNAniUN+TYj0m//Z6xAEAoEJ5A//mfZFan7lB14mCLFsBReEwjMX51BUuGGpUIJlmcWtXSH3hYLDnuVUvsjz46nFQgk0K0pm+fwzBZWxORcP+bng2X7q5Vk2dYRY0xZc+GZ0uVgc8qI5i+J86biny4uFigVjKbOsVmSWfbLEps4QZ0T5uKZDCgS8DkEgEJhAuuIar0OwFSGWreCCWP73C/N0hn1s6wmXvyf5AxAIVhHLYcZm86TyLm7Dq4qjh7t+PZaioKiLLRigiaUq8weXy6cpiqN+3flckWOT6eXz13cXKlbtnRE/XRG/u7sLSulac/AaePrCHGvbKmxIUNWGAdqC4YwXzVkEriHeW4Hg8kIdv+h1CJYRYtkKDh9uKioqRy7Nc82aFuSl2/zhyPLMardH2/AOC6WWoMzuvuii70vh6DKhtKEjhF92+ZCjUnR0/s9dSqGocO3alkXfLx9wyy5+Dba67dvWrUg+Z16DVL7IC+Pziy0oUFMsb+0KM59XGJ/zoPW7wB1OPO91BAKBwATKp97ndQiW8XsdwGVNKavmVGZ1JJFhNlvk2rWty38YDEFusSjSxfJIPMMV/dHlz3ECB8Wyoqr8+8U5rlvTin9pO+nI8moQAZ/Exg6X6007vFh4dmyOaEBmZ29k8Q+CJf92dsk10BXmyFiMbEEh5HdhLezwgvG5SykKCsttOMGQ5pVfKpa7S81ZEhkGK20rr1COHj3Kt7/9bRRF4dZbb+XAgQOLfv4v//Iv/OxnP8Pn89He3s773/9++vr6ALjtttvYuHEjAL29vXziE59wPf5qKF/4FL5v/cjrMAQCQR3ke7+A8rmPeh2GLQixbAWHbRjPXpxHAq4erCJ8gyHUJWK5O+KnM+xz37Pq0AG/0USW6UxxWVYVgFAIcsu9yVu7wzx9fg5VVR09dFfGwfmrqsqzF+e5ajC6fLEQLNlyllwDQ91hFBXOTWfZ3rNEYDuB4myd5aNj84T9yxcLkiSVdlcWi+VNnSFkSbPi3LTRkZAuGxRF4eGHH+ZTn/oUPT09/Omf/in79u1j/fr15cds3ryZBx98kFAoxE9+8hO++93v8pGPfASAYDDI5z//ea/CX86mbXB22OsoBAKBQaQt270OwTaEDcMKLojlbT1h2sNV1jTBGmLR7S5mqgpLhZxNHBnTMsdXrakilgMhKORRlcX+7K1dYWayRWJuNWdxsHTeyzM5plKF2jsLUGV3weW21w7fA0fG5tk7ECXgq3KNVRHLQZ/MhnYPDrquQoaHhxkcHGRgYAC/389NN93E4cOHFz3myiuvJBTSrpnt27cTj8e9CNUQ8gf+1OsQBAJBg6gXz3kdgiVEZtkK5Rq79m9Bz2WLnIylefsVPdUfEApBdrkg2Nod5tfHYuSKCkGHfKSLcPCA29FL82zqDNEdqbFYAG3BEF7IOm7t0sViht6oCyfoFQV8zsz/yEVtsXBNtcVCSeCouSyVMrK/JUBL0MXmLA7aMC7N5rg0l+c/7equ/oAqYhlgS3eIX19KVXnCK4t4PE5Pz8Lfj56eHk6dOlXz8Y8++ihXX311+f/5fJ577rkHn8/HW9/6Vl796ldXfd7Bgwc5ePAgAA8++CC9vb0Nxev3+1d+bm8v4+UvGxvDljhcZLXEIuIQcdSjViz6Pat85o8Z+OEhz+Kw/Htt/42vJPSspgOi9NeX5qse7CoTDMPczLJvb+0OUVThbNKtbfiiIzaEbEHh2ESat+zorP6AysxqhVje3BVGQsusLio15hRFZ+YP8OzFOda3B+lrqSL6a2SWJUliS1eY0275th1sd330krZYuLraYgEgEETNV99deezMDMl0gc5qCy3BMn7+858zMjLCfffdV/7e17/+dbq7uxkfH+ezn/0sGzduZHBwcNlz9+/fz/79+8v/n5qaaiiG3t5ew89tdAy743Ca1RKLiEPEUY9ascgfewDloU8Czt639eIwwtq1a2v+TNgwrODgFvSzY/O0BGV21BK8VQ74gQeNKRyyIbw4oZWMqymUaojFSEBmbXvQ3cyqA/PPFhRenEjXXiyFSp7lKrsLQ10hziaz7jRncfAeODo2T1/Uz9q2GjsEwSBUE8sVh/xeyXR3dxOLxcr/j8VidHcvz9I/99xz/PCHP+TjH/84gYp6xvpjBwYG2LNnD6Ojo47HbBR17LzXIQgEAgNIO/d6HYIt2PIJd/ToUT70oQ9x55138sgjjyz7+WOPPcYdd9zB3Xffzd13383PfvYzO4b1HodaHauqypGL81w92IKvhh9YCla3YQy0BogGZPcqQjgkFo+OzROQpdpVPWqIZdDLp7mYWXVg/i+Mp8granW/Mqw8/+4wuaLKhRkXmrM4JJaLispzl1Jcvaal9kHNQLDq/Ld4UW97FTI0NMTY2BgTExMUCgUOHTrEvn37Fj3mzJkzfOtb3+LjH/84HR0d5e/Pzc2Rz2vl92ZmZjhx4sSig4Feo3zxU16HIBAITKLOz3odQsNY3qM0cuIa4KabbuKOO+6wOtzqotyQwV6/5oXZHLF0gasGa2QVoWZmWZYkdzv5OSaWU+zuj9QsfyYFQ6hQM7v+i7OzzGSLtIecaxgCOObZfm48hV+W2NNXY2chUCqLlq29uzCSyLCxM7Ts57bikGf5VCzDfF6p7tfWCYagyh/fcuv3V3hm2efz8e53v5sHHngARVG45ZZb2LBhA9///vcZGhpi3759fPe73yWTyfDFL34RWCgRd+HCBb75zW8iyzKKonDgwIHVIZb7BmHyEiRX70FEgUBQHeUv/wzfvQ95HUZDWBbLlSeugfKJay/+sL4wnuJfnniRvb1B3rKzy/kBVWeyas+XDie9qlrJOJ0apdMAtnSH+fGpJEVFrZmZtg1VtX3+8XSBs9NZ/mBLX+0HBUtisUZmFbTmJCsuOOyg6ExTkucuzbOrb4XFgixrr0GV+a9rDxL0SZyOZ3jjlo4qz7YP1aHScUfHtLKJr1rh/ZMCQdQa94Anrd9XIddeey3XXnvtou/ddttt5a8//elPV33ezp07+cIXvuBobI0gf+SzKPe+1+swBAKBCaS3/yHqP34Hzpz0OpSGsSyWjZ64fuqppzh+/Dhr1qzhD/7gD2qeVrRysjo6n+BcYoInRxMMrenmdVtrVJKwiWxrG0mgo7uboI2nL08+PcVAa4grN69ZtgWtn/Sc6+xiPpehp6dn2WOu2ljkn19KkPK1sKXH2eYk46pCJNpCmw3z1+d2+Lh2fvaNu9fR21vdhpDr6ycBtIfDhJaMva+lAx59mfGsz/GTwslAgEIgUHccMyd0ZzJ5ziSyvPvGjSs+ZyIcJSxDe5XHbOu9wMtziuPzT7dGmQF8gaCtY70wdYHdA61sWTdQ8zHTbe3kioWq4165PsWTL58j0tZJS8jan7nVdOL8lY7Ut/yAoUAgWN1Iv3FAE8uXMa4cFb/uuut47WtfSyAQ4Kc//Slf+9rX+MxnPlP1sVZOVm9tgb/9/ev43//H03zv8Fl2tTt7wElNJACYnplFsumUp6KqPHMuwXVrWxYdztHRT3oqhSKoKlOXxpACizuV9fk1r+G/j4zRpjqbWaSokM5myNowf31uT5wapz3ko1NKMzVVPTuoprXvz0xNVH3te6J+nj8fZ2pT2HJcK1HMpLX3oc78zZzQffLlWVRgW9vK17/qD5CZniZX5TEb2/384uwMk5OTjjZnUZLTABRVlaRN90A6r3Ds0iwHdnevOH9FUVAz6aqPGQxpGe9nTl+03M2y0dPVK52sFggEglcKlTuPqlJEcqjcrJNY3js1cuK6ra2tfMr61ltvZWRkxOqwNfHLEq/f1MaL4ylS+WL9J1hB1T3L9m1Bn0tmmckWV9x+BlashrChI0TQJ7lzyE1RbK0zraoqL4ynuHIgirySyAsu1BmuhtacxaX523zjP39pnpBPql/6r4ZvHbT5z+cUJubztsa2DN2GYWOt6Zem0hRV2FvvHgiEqlbDANhSUW9b0JyoJ1/0OgSBQGAS9X/9g9chNIRllWfkxHWilIEFeOaZZxz3M1+9poWiqnmYHcWBSgDPl2LeO1AnG6ZXQ6hywMsnS2zqDDHicPk4VVVtrwYxMZ9nMlXgynrZwMqmJFXY2h3i4myObEGxLbaqOHDA8fnxFLv7a3StqyQUrr1YcKuTn2L/gvGF8RQ+CXb11lksBAI1FwsLrd9f2RUxmhnl86Kjn0Bw2bDjCgDUH/2tx4E0hmUbhpET1//6r//KM888g8/no7W1lQ984AN2xF6TXX0RfBKcmMo425jCAbH83HiKwdZA9UYUlej1UAvVM4dbu8L88twMqqo6tw3vwAFH04uFFTKrigqjySw764kuKyj2HvBLpgucm84ZO5jn99d8/zd1hpAlOB3P8JqNDt4DakU1DJtcT8+Pp9jWEyESqPO6BoOgKKiFApJ/8Z8ySZLY2hXmjDjk13zsuRqOHfU6CoFAYAL5g59C+dA7vQ6jYWzxLNc7cX377bdz++232zGUIYI+mc1dIU7GlrfCtRPV5rJZRUXlxfEUr91UX9xI/oCmTWqIpS1dIX48rG3DD7QGqz7GMnrTC5uziu0hHxs66sRczqxXF0Pl8mnxjMNi2d7MsuHFAoA/APnq73/QJ7Ohw4WKEJULxqJ1tZzOKwzH0vznPQYO5wZK10A+py0clrC1O8wPj8XIFxUCbrR+F7iC/J6Potz1Lq/DEAgEJpCiDlemcpim/QTZ3hNhOJZBUR085GfzFvRIQqstu3fAwEWli4NCoeqPh7pdaMygZxVtzFy/MJ7iiv5o/Wx4nfn3tfhpDcruiEUbPcvPj6eIBuTy+7cigUDNxRLo5dOctmHYWzqu7Fc2sljQywfma+0uaK3fz0270JxF4BpSm8OHlgUCgaOoo8srpq12mlgsh0nlFS462cXMZhuG6awi1N2Gd1Qs2jz/sZkMk6mCoflLkqQJ5mL1+evb8Kcd9+zanVme54r+iLH62P46Yrk7TCJdIJmuvqCwBZt3V8p+5VrNWCrRq8DU9K1rC47T4pBf06I6mQwRCASOoDzwUa9DME3TiuUdpUoCJ2OXj1h8/lKKDR1BuiIG3DF1xHLIL7O+PehsNQCb5//sea0M2ZVGFgsAvgDkawvBrd1hziaz5G2wB9TERs/yVCrPxdm8sZ0FNCtOrcw6LO7k5xj6NWBTNYznx1Ns74kQrtGMZRG6WK5REWOwNUBLQBZiuYlRn3nC6xAEAoFB5Lv/3OsQGqZpxfK69iBhv8ywk75lXShI1l/GoqJybDJdvwqEji6Wa3hWQROLjgoFm8XykfPTxvzKOoHamWWAbd1hCorKy9MOZpdtzCy/YGZnAVY84AcL5dOGHb0GNCuSHTYM3a9sdLEk1amIIkkSQz1hhp1cMAs8Rf3mf/M6BIFAYBCpVBHjcqRpxbJPlhjqDnHKjcyyDVm1M4ksmYLCHqNiuU41DNDEYiJTJJZyqNZuebFgT1bxyPlpruivU1+5Et/KmdVtPVpm1VmxaJ9n+dhEmmhAZlNnyNgT6tgwWoI+1rYFnRWLNt4DpvzKUDezDNo9MJrMkC86XEJQ4CrSG/6D1yEIBAILqBmHS/vaTNOKZdA+KM8kshQUh7bhbTzgd3xSu3Cu6DdYucFXsmoUPRSLNjZlGZ/LcWk2a1woQd3M6mBrgJag7LxYtCmzfHwyxa5eg35lqCuWQbsG3Mgs27FgMOVXhoVDnsXazYe03QU4mxSH/JoJ6f+4w+sQBAKBBZSH/7vXIZiiucVyT4S8onIu6dA2vI02hBcn0gy0BuiJ1qmvrFOyYagr2TC6wsgSzolFG0vHvTih2WUM+5WhJBZrLxYkSWJbd5hTTlpxbGrKMpMtcm46xx6jiyWoO3/QxGIsVSDh1CG/on33gCm/MlRURFlhd6W8YHS2jKTAXaSQs23sBQKBM0hvfLP2xdFfeRuISZpaLG93OrNqk2dZVVWOTabYbTSjBoZsGCG/VmvXObFsX+m4F8ZTdIT9xv3KAH4/ar3MaumQX86pbfhiEckGz/pLpZ2FPX0mFguBlTPrsHAPOOZdL2XWrTa+yRY0v7LhnRXQbDiw4oKhvyVAm9O7CwJPUVfYWRAIBKuLy3VXqKnF8mBrgNag7Fxm0aYOdmOzeaYzRa4w6leGutUwdLZ1a9vwjpRYsrGD3/HJFHvXthv3K4OxzGpPmKIKo07VG1YUW/y6xybS+GWJ7b0mMmYlz/ZK7+0Wx3cXirZYME7FMhRV2G12sQArHvLUDvlFnLWiCDxF/fEPvA5BIBAYRAoY3D1fZTS1WF7Yhnc4s2pRLB0rZRVNZZYNiuXtPWFmskUm5x3YhrfJhpLMFLg4m+dVa9vNPbGOZxlgW7f2mjrn27bHhnFsMs227jBBM53m6jRmAYgEtBKCji0YbSqdp3v2DfuVoZxZVg1YUc45ubsg8BT1h/+P1yEIBIIGUM9cPs1Jmlosg+ZbPpfMki048EGpb/9Z3IZ/cSJNW8jH+nZzFgTAUGYVHPJs2iSWX5rUYnvVGrNiObDiAUfQOvl1hHzOWlEsvv/ZgsLpeNqcXxkWrDgrZFZh4ZCfI7sLNmXWj0+m2dARpC1k4neZuAcc3V0QeIJ023u8DkEgEFhA+dzl05zkFSCWSx+UThzys0ksHp9MsacvYs73aTCzvLkzhF92aBveJs/28ck0AVliZ3+ruSf6/CvWmYbS7oKTFSGK1jOrJ2NpCgrmbDhQUWu7XmY1QjJTJObEIb9i0fL7r6gqL02lze2swEJFGANWJHC4hKDAdaRbf9vrEAQCQQPI77/H6xBM0/RiuXzIzwmxWPLsWmnIkEgXGJvNm85OeNw1AAAgAElEQVQqSj6fJlLqCKWAT6vb64hQKIllyWJm8fhkim09YYJGqyDoBOpnlgGGusO8PO3Q7oJqPbN6fCKNBOzqNSkWjfrWnbwHlKLl+b88nWM+p5jzK0NF6biVr4HeqJ+OsIO7CwJPsHqoVCAQeMQ1r/E6AtM0vVjuifjpDPuc8WzaUGP32ITuVzYpFMBQNQTQMouObMPbUGNXsyBkzGcVwVBmGTSxqKgOtX224Rp4cTLNxs4QrWYsCGCodBpouws+pw752dCU5Xgjnn0wbMPQzy6IzHLzcrk1OBAIXslULnQdsQc6QNOLZUmS2N7j0CE/G4TCsck0QZ/EUHcDdUN99ZtSgCYW53MKl+Zs7uSni2ULmcXheIaC0oBQAiQDnmWo2IZ36hqwYEMoKiovTabZ08hiwV+/dBpoJQQ3doY45cjugnUbyvGJNJ1hH4OtJk9JG5w/aPeAY7sLAs9Rvv2XXocgEAgaQD30M69DMETTi2XQMqsXZnKk8jbX47RBKBybSLGzN4LfaNe2Svx+Y0KhJBZtXzAUrWeWj5cO9+1qJLNuoBoGQE80QFfE75wVxcI1MJo02ea8AsmgDQO0a+B0LO3A7oL1zPrxkl/Z9La6v37pOJ1t3Q7uLgi859knvY5AIBCYoaUNAPU7X/Y4EGO8MsRyTxgVGInbfMjPolBI5YuMJrPmqyDoBIxlljd2hgjIkv2NKcqZ5cZfg5cmU6xvD9Ju1oIAhhcLUKo3vQo9u7oNp6FrwEBjGp1tPWFmcwoT8zbvLhStebZjqTzjc/mGbEiSXPLtG7gGhpzcXRB4hnz3n3sdgkAgaAD5E3/hdQimeMWIZXCgfJpFsXxyKoNithFDJX5jYtkvS2zpCjFst2+73Oq4MbGkqJoFwVRt3UoMzh+0a8CZ3QVrNozjk2n6on56jbY5r6RcDcOYbx0cEIsWd1f0soENedZhdewuCDxD2nGF1yEIBIIGkNas9zoEU7wixHJn2E9f1G+/DcGiWD4xpVVB2NHTgF8ZwB+o2+5ZRyuflkWxcxve4gG/8zM5ZnOKRaFkPLOsAmdW2e7Ciak0Oxudv8HSaQCbOoP4Zcl2saha9O0fL3n2tzbi2YfVsbsgEAgEgoZRDSR8vOYVIZZBa05if1bNmlA4MZVmfUeQlmCDv8PnW/AN12Fbd5hMQeHiTK6xsapRtHbAbyGraCGzbuCAHzhYa9eCWI6l8kylCuZLxunor7tS/xoI+GQ2d4YcWDBaa3d9fDLNjp5wY559MHcNOLW7IFgVqKOXTzcwgUCwgPrXq9+3/AoSy2EuzeWZzdr4QWlhC1pVVS2r2KhQAk2kGBXLPdo4tooliwf8jk+m6Aj5WNvWYK94nx+KRS27WYfOiJ/eqN/WEoKqqpbaXTc2/xNTWiw7Gr0GygfcjFV42N4T5nQ8Y+/ugoWmLOm8wkgi09jhTh3fKthdEKwKlAcun25gAoEApBtuBkB96nGPI6nPK0Ysl5uT2JlZtJBVvDibZy6nWBPLfr+hrCLA+vYgIZ/N2/AWD/gdL/mVG24uYOKAG2B/Jz+LHRxPTGUIyBJbuxq0IOgi3URmNZVXGJu1ccvLQlOWU7E0ikpjZfN0fD7j779eFcaJ1u8C79i7z+sIBAJBA0h/cKfXIRimqcSyOnmJ9M/+BTU1v+xnC6fhbfygtHC4S88qNrwFD5pIM5hZ9slaLWdbM8sWPMszGa1zoaX5m7AhAGzvjjA2m2fOrt0Fy2I5zdbuMAFfg4sFk/NfKCFo4z1gIbOs3wOWFow+/8L7UIfOiENnFwSeIt/xEa9DEAgEDSAFgl6HYJjmEsujp5j56ucgGVv2s9agtt1vr1hsPLN8YipNNCCzvsPCxVKyIRhlW0+YM4kMBcWebXi17Fn2m37uydL7YNmGAoZtCHpVFNuac1gQy/miyul4hp29DWaVoTx/1eA1sKEjRMgnraJ7IMO69qD5zoWV+HyoBjProFleTk6JzHIzIZXqtQoEgssXdexlr0NYkaYSy1Jg5VJath/ys+hX3d4TRm7UggAlz7IJodATIVdUGU3Y5Nm0YMM4MZVGlmCo0UogsJBZNfgabO8JI7GQ0bSM2nhmfTSZIVdU7cmsm9xdsFUsNnjAT1VVTsbSjVeC0fH5DC+WAHb0hpmYL5BMG79vBJcP6vys1yEIBIIGUL70Ga9DWJGmEsv4S1nafPWKD9u6w8TSBeI2fVCqxUJDfs10XuFsMmstqwra2Aa3oGEhi3vSrm14C3WWT8YybOoMEfZbuARN2hBagj7WdwTtE4vFxjPLlg/3genFAmjXwEgiS96EwFyRBjPLE/N5pjNFa/MH7doz+P4D7CwddD1hd81xwapA+bpoUiIQXE5I7/gj7YvElLeB1KG5xLLuf6lx4Kd8yM82sdhY97bhuHawyRaxbEIo9bX46Qz77MusNuhZVlSVU1NpdvTYIJTAVGZxZ2+Ek7GMPW2flcZL552YytAT8dPX0mAlEFiwv5hYMO3oDVNQVEbs2l1o8B44OWWDDQdKC0bjYnlrdxiftDC+oEloLVkxTr7gbRwCgcAU0pv+o9chGMK82bQKR48e5dvf/jaKonDrrbdy4MCBRT/P5/N89atfZWRkhLa2Nj784Q/T399vx9CLqWPD2NodRpa08mmvXm+Dz61YaMive2JS+6C2JatmwrMsSZImFu0SCg3WWb44k2M+r7DDil8XFsSyCbG0oyfCwdPTXJrLs6bN4uECfaHSkFhO2/P+V8ZhgPLugtWyhToN1ho/EdOakWzqDFkb38QhV4CQX2ZzV+gV41u28rf5hz/8IY8++iiyLPNf/st/4eqrr/ZiCoaQ7/5zlM/8sddhCAQCkzRcDctlLGeWFUXh4Ycf5t577+VLX/oSTzzxBOfPn1/0mEcffZSWlha+8pWv8Ja3vIXvfe97VoetTmBlG0bYL7OpM2RfZrXBrNqJWJq1bUHarRxsAiQTTUl0dvREuDibs6fedIOZZVssCNCgDSG8KAZLNHjAMZkuMD6Xt3a4Dxa84iYWCz3RAD0Rf/mApWUarDV+cirDULeFZiQ6Jg+5gnYPnIplKNp00NUsmYw7WW0rf5vPnz/PoUOH+OIXv8gnP/lJHn74YRQTOxhuI63d6HUIAoHAIurstNch1MSyWB4eHmZwcJCBgQH8fj833XQThw8fXvSYZ555hje+8Y0A3Hjjjbzwwgv2bIMvpZRZVmuIZaCcWbXlg7JYNC2UFpqRWBRKYHoLGihnc20pH9bgAb+TsYxWCaTdYma3ARvGho4QYb9kT2axwcyyLWUDYeHaMysW7awI0UBmOV9UGYln7MlsN3QPREgXFC7Y2c3SBB/60If48Y9/TNHk+2YWK3+bDx8+zE033UQgEKC/v5/BwUGGh4cdjdcuLofWuQKBYDnK33zN6xBqYlksx+Nxenp6yv/v6ekhHo/XfIzP5yMajTI768Cp5TqeZdDEsm0flA0c8Buf0w42eSUUttlZEaLBA34n7agEQimzDqZeA58ssa07bE9mtcHM8ompND5JswVZQn/dDXaw09nRq3WznM7YcNBVKSKZzCyPJjPkFdV6JQwwbcOAhQWjbTtMJvnkJz/JkSNH+PCHP8wTTzzh2DhW/jYvfW53d/ey565W1H/6jtchCAQCM+iNhY7+yts4VsAWz7KdHDx4kIMHDwLw4IMP0tvba/i5Rb/MFNAaChKt8bwbfS385ZNjXMj4uNbE765GTJKQwxG6TPyeZ6cmALhh+xp6e1tNj+n3+8uvyUy0hayqmnqNALb0XGB0RjH9vKXMh0PMAb39/UhBY97TTL7I2eRL/N6+DcvGr5ybEbJdXSSBzvY2Aiaed/WGOf7+yAXaOrsJWajGkZ9LEgfaO7sI1xm/cm4j0xfZ0d/KukFrvn1VUZgAouEwrSbm/+qhAH99ZJJLuSBD67stxTClqgSiUVPv3WMXLgJww/a19LZbE8yJcAQlnaLHxPy7e1TaQuc4N2/s3jF7XdZj48aN3HPPPRw7dozvfe97/OhHP+L222/nqquusm0MN7HyN7sSq6/zeOlf9Wf/TO8f/2nDv8fu99sKqyUWEYeIox5WYineeS9T730bgOX5OPWaWBbL3d3dxGILTUBisRjd3d1VH9PT00OxWCSVStHWVv2A3f79+9m/f3/5/1NTxsuJ6J375hIJUjWeF1FVWoMyz4xO8ppBa9MvZjPQ0mYqxmfOTBD2S3SoaaYaOGjX29tbHk/JF1DzeVPjAwx1Bnjy5WkmJyctmeuV2RkAphLJhSxvHV6cSFFUYUNUXRZ35dyMoM5p73cyFkPqMP689S0qBUXl8KkL7LLQalmNaWPOzs8zVydufW5FReXYpVn2b+s0/b5VRZJIzc6QMfG7en0KsgTPnBlnZ7s1H2oxl0MpFikUCobnc2R0iq6IH192lqmpOWvjF4uQy5p+Lbd1h3nufNLQ88xelzpr165d8ed79uzhgQce4KmnnuJb3/oW/f393H777Wzbts30WNWw8rd56XPj8fiy5+pY+ZtdSaOvs4589+dQPn+vpRjsiMNOVkssIg4RRz0sxSItaDGr87ESx0p/sy3bMIaGhhgbG2NiYoJCocChQ4fYt2/fosdcd911PPbYYwD86le/4oorrnDmBGSdA35QWRHCpgNepv2qGbb1RPBZPdgEpdJx5n2PO3ojzOUULs5a9PY1UGf4ZPlwnx2e7dK4pg94aWNbrjet2x9M2DDOJrNkiyo77bAgQENWHP2gq22+bZM2FL0ZiS1/Axq+B8Kcm86Szrt/aC2ZTHLkyBF+8IMf8NBDD/E3f/M3JBIJ0uk0X/ziF/nyl7/M3Jy1RQRY+9u8b98+Dh06RD6fZ2JigrGxMdtEvFNIO670OgSBQGAR1eTnmVtYziz7fD7e/e5388ADD6AoCrfccgsbNmzg+9//PkNDQ+zbt483velNfPWrX+XOO++ktbWVD3/4w3bEvhx/aTp1Dnjs7I3w7MV55nNFWoIWKlIoRSQTQiFXVBhNZvhPu6xtfZdpQCjBQvmwE1Np1lk5ZKdoiwUzoufEVIaB1gAdYRscQA2UjgOtIkRv1G/ds9pA6Ty91bTlSiA6JssH6uzsjfDz0RkUVbXmHTe5YJzJFrk4m+fWoc7Gx6xAkn2oDVRp2NETQVG1mud7B1psicUo73vf+1i3bh1DQ0Ps3buXAwcOsHnzZvx+P4VCgX/4h3/goYce4r777rM0jpW/zRs2bOA1r3kNd911F7Isc8cddyA32NbcC1SliNRgd1WBQOAhzx2Gq2/0Oopl2OJZvvbaa7n22msXfe+2224rfx0MBrnrrrvsGGpFJEmCYHDFzDJoQkFFEy5Xr7HwQWlSKJxJZCkoC81RLGOy3bXO+vYgYb/Myak0b9ra0fj4RfOtjk/G0lzRF218zEoarAYBekUIi4f8iuYzy6diaVqDMoOtFpqRVNJA6TTQsuv/dirJhZkcGzos1DouFsBnfC6n9J0FOzPrDdwD5d2FqYzrYvk73/kO4XD1+fv9ft75znfyh3/4h7aMZeVv89ve9jbe9ra32RKH26i/+CnSzb/ldRgCgcAkytc+h+9bP/I6jGVcPqkCg0j++mJ5u10VIUxWwxguZRW3W+1cp+PzmSqbVn6aLLGjJ2zdhqCYE8uxVJ5YqmCPBQMW7B8NZdfDTMznSVppfd5AZnk4nmFbt00WBNCsKA1mlgHrVoyCuXvgZCyNLGlVWWzBZLtrnfawnzVtAftav5ugllCu5DOf+YwLkTQv6ne/7nUIAoHABPL/+XGvQ1iRphPL+P11M00tQR8bOoL2bMObEIunYmk6wj56ozYVIZF9oCqNbUP3RhhNZMkWLHg2FcWkX9lmC0K5KUlj2/CgNYhpGJOl47IFhbPJLNvsWizpYzcgFte2B2kJyJywnF03V2v8xFSGDR0hogGbtsgbXDCCdg2cmLKp9bnNbNmyxesQLkuk3xdd/ASCy5LrXut1BCvSdGJZCgQM1Z3VGzNY+qA0ebjpVCzDdluzio15dkE74FRU4XTcglgq5Bd84gY4GUvjlyW2dllscazTQLtnnaHuMD4Ja1YMk01JRhIZFNVGGw40bMWRJYntvRFLmVVVUUBVDM9fVVVOlQ732UaDvn3Q7oFEusBUyoZ604JVgfTa/fUfJBAIVh2rve1184llf8DQtvTO3gizVitCmPAsp/JFLszk7LNgQEVmtYFt+FIclrahC+YWCydjGbZ0hQiY7PhXE33sBsRSyC+zuctiRQjFXGZ5wYZjs1hsOLMa5mzSQkUI/bozuGAam80zl1Ps21mAhhcLsPigq6A5qGyQozrcIVEgEDiDeuqY1yEso+nEsnbgqf6H5y47PihNbEGPxLOo2OjVhIrMqvkPhc6In/6WAC9NWpl/odxivB6KqnI6pvl1baMkuhv9UNxZyqw22vpcNelZPhXL0BXx0xO16XAfWBKLu/u0ihANtz4vlhaaRhcLcQcWC3Lji4UtXWFCPonjVu4BwapF/ckPvQ5BIBA0gPKX93kdwjKaTixLfj+qAfGwviNINCBbyyyayCzrgsTerGLjmVWAPX0Rjk9asKKYyCxfnM2RLijOLBYa8GwD7O6LkimojCazjY1v0oZxKpax9/0vjd1oXcqdvREkaFwsmlwsDMfSBH2SteobS7Fgw/DLmhVFiOXmRP3B33gdgkAgMIH0rg9oX2QtnqVxgKYTy/j9hjzLsqRVhHipQbGsqqopz/KpWIb+Fr899YV1GmzKobOrL0IyU+TSXGNWFNWEZ1m3INibWW7csw1aZhXg2ESqsfFNiMXZbIGLszm22zl/fewG3/+WoI+NnSELYtlc6bzTcc2G47ejIY+OBbEMsLs3wplExpPmJAJnkD/8Z16HIBAIGkB63W96HUJNmk4sa55lY9vSO3ojjXs29Wym0axaPGNvFQSoKJ3WaGZVi6dhsWQiszwcz9ifVbRwwA+gr0VrTuKGWDwxrnVkszWzDto10OD7D9ruwkuTDVpR9EWpgQWToqoMx7P2Lpag3JSl0d2RPf2aFcWLEnICh9h9ldcRCASCBpBWceOj1RtZo/iMZZZhwbPZ0AelCaE0kykwPpe3P6soWRPLGztDtARkjk82mlk17lk+HcuwpStsT5tvHQul43T29EUbt6KYyCwfH58FsH/BJFkTy7v6IqQLCuemG7CimJj/xZkcmYJi//x91u4By1YUwapj0SG/TIN/2wQCgaeo87Neh7CIphPLkt94R7NdfRFkqcFt+HIlhPovoX6wyf6sojUbgixJ7Oqz4NnM5w0tFoqKyul4xv75W7RhgHYNxNMFJuYbsKKYWDAdH59jsDVAe8jmFrwWM8uWdhdMzL98D9huQ7Hm27dsRRGsapSvPuB1CAKBoAHUv/um1yEsounEspGmJDrRgI9NnSGONSQUjJcNO1Xy6w7ZvgVdevtUa2Lp5ekcs9kGxEaxYGgL/sJsjmxRdWYLHhr27IK2DQ+NikXjmdWXxufsXyyAdg1YeP/7WwJ0Rxq0opTmLxmY/3AsQ8gnsa49aH6clZCt+fZBuwdONGpFEaxOevq1f088720cAoHAHHuuBkB96nGPA1lME4pl455lgD39UU5MpimY/aDM5xfGq8OpWIZ17UFagg5kFcFiZjEK0FgJuULe0PzLh/uc8OuCpczyxo4Q0YDsaGY1mS4wPpe1vxIGWM4sS5LE7r4IxxvZXTHhWR6OZ7RGMHbacMCyFQk0sdywFUWwKpE/+QWvQxAIBA0g/+77vQ6hKk0nliWfcRsGaAecskWVMwmTpUryOe3fOmJRVVWGY2n7/cpgi1je3hPGL9OYb7lgLLM8HM8Q9kusa3Moq2hh/j5ZYkej5cPyBZDluplVfWdhe7fNfl3QsusWFgugicXJVIGplEkrisHFQlFRGYlnGHJqsQCWd1cAjk0IK0azILV1eB2CQCBoAKl/jdchVKXpxLLR0nE6DX9Q6mPUOeAWSxdIZIqObMFLNojFkF9ma1e4MbFYyCP5jGWWt9p9uA9sySqCtmA6l8wylzMpOgs5YzsL8TSyBFudWjBZnL++u3Dc7D1g0IZyfsYhGw7YsmDSrSiWGvQIVi3q5CWvQxAIBA2gWvxss5OmE8tmSscB9EQDDLYGOGY2s1rIL4y3AuWsot1VAMA+sdgf5VQsQ95sVQkDnuWiomXtHfPrgg1iMYIKnDArlvLGbSibuqNEAg7cbharYQBs6QoR9kscN1tz3GBmebhUbWa1XgNlK0qjVWEEqxrl3vd6HYJAIGiEVdT2uunEstnMMmiHvI5PmCwfptsw6mSWh2MZfJImSGzHhi1o0CpC5BW1XLHAMIVCXbH48nSWnENZRcmm+e/o1aqimM6uF/IQWNlaoqoqp2IZdve3WohwBWzILPtkiR09DfiWC8Z8+8PxDBG/zFq7bThg64KpISuKYNUivfHNXocgEAgsoHz3a16HUKbpxLJkohqGzu6+KNPZIhdmc8afZFQoxNJs7AwR8jvwUtslFHobrAhhwLO8UDbPgcw62CIWw2UrikmxmK/fwXByvsBMtsjugTYLEa6AxWoYOrv6Iowms6TyJqwo+iHXOguG4ZjmV5Ylm204YNvuSsNWFMGqRXrnH3kdgkAgaADplrdoX1y64G0gFTSdWMZnXizv0WvNmvmg1MXyCpllVdXqC9teMk7HJrHcGfGzti1g3rNpoN31cCxDNCCzps1Y8xLT2CCWAXb3RzgZy5AvmthdMJBZHikdHN3R32IlvNrYNP89/VGtQc+U8d0Ftby7Uvs1KCgqZxIOdO7TsVhrXKdsRRFWjKZBkhe89I12eBQIBO4j/fY7vA5hGU0nljXPsrkPznXtQdpDPnO+5bxeNqu2CJxKFZjNKateLIOWWTs2mUYx86FSrN/uWi8Z5khWEewTy30RckW1LG6NoObrH/AbSWSQJdjWu7rF8s7eMLIEL5qxYpTLJ9a+Bs4ls+QV1fl7wGJ2Xa+K0lDNdcHq5+hTXkcgEAgMshqr2TSdWG7Es6wf8DFVEaNQv3TcSMmCsLXLIaFg0xY0wJUDUWazRV6eNmZFURVFW5SsMP980eGsImivgQ02hCtK2/CmxGIhX9ezPhLPsrYtSDhgc43tEpINB/xAa9Az1B3mhXEzYrl+Zlm34ThSYxpsXTDu7Y8ymsg21qBHsKpRvv45r0MQCAQNsFoqYjSdWJYasGEAXNEf5dJcnnja2HNVA37NkUQGCdjsxOE+sG0LGuCKUic7w2KpWL8hxbnpLAVFdaYKgo5NmdXOiJ/17UGTYtGIWHbQhgO2zR/gyv4oJ2MZsgWDv69gQCzHMrQEZAZbHbThgG0LRhU41kiDFsGqRH7fPV6HIBAIrHDsiNcRAE0olvWar2ZXIwv1lg1+UBo44Hc6nmVde5CwE4f7wLYtaICB1iD9LQGeNyoWDXRvK3fuczqzbJNY3DsQ5diEibbHdToYTmcKxNIFtnY7tFgC2w74gSYWC4rKCaMl5PL1ffvDpWYkkkM2nPLvtcGTur0nTNAn8bwQy02DdN1NXocgEAgsoHzzIa9DAJpQLJe7qZnMtm7tDhP2y8Yzi2WhUFssjiQyzjSi0LExqwaaWHpxImXMt1yo79kejqdpDcoMOJVVBNvFYrqgcNpoCb06B/xGElr7ZMdsOGBrZnlPv1ZCz/CCqU7L93xR4Wwy4+xiycZ7IOCT2dUXMbe7ILhsKO8GCgSCVY/0tt/XvkjPextIiaYTy2VrgskGG35Z4or+iInM6spCYSZTIJYqsNUpCwbYLpb3DkSZMepbLtY/3DUc0ywITmUVAdttCGDCilKndNxppz3rYOv8TfuWCznw+xfqXS9hNJmloDi8s2D3PSB8y02L8rX/y+sQBAKBQaQ3/UevQ1hE84nlBjPLoInF8zM5YkYaExRW9iyXs4qXkVAw5VvWszQ12l3niyrnprPO+nXBVrFY9i2bsOJIK2WW4xn6WwK0hpw53Adoi0MbD0CY8i3nV86snyndA45eA5J+v9u3uyB8y01G36D274urw/soEAjqI4Uc1g4maTqxXM5yNSSWtfJexsRiKftao3SaK1lFG6thgEnfsj7/YPXM+cvTWlZxi5PzB1vFMmgLpheN+pbrtLs+k8g461eGkmfbviyoKd9yndJ5I3HtcJ/jNhyw7TUQvuXmQ/70f/c6BIFAYIHVYKFqOrFcziybrLUMWmOClqDMc0bEYiEPvtpb0COJDP0tftoczSrad8BP58qBKC8Y8S3ntKyhFKyVWS8tFlwRi/bOP2PUt1zI1TzclsoXuTibd3axBLZ6tkE76CpLGMuu16kGMpLIsKUr5LwNB2x7DQI+mV29wrfcTEiRqNchCAQCC6hP/NTrEJpYLDeQafLJElf2Rw1mlgt1smpZd7Kq2FuHcG+p3vK5ZHblB5bEcq3M8kg8Q9gvs7Zt5Q53lrE5s3zlgAnf8grXgCsWBLB9/i1BH1u7DPqW8znwV39/i6XOfVvcmD/Yfg0I33Jzoj7zS69DEAgEJlG/91deh2BNLM/NzXH//ffzJ3/yJ9x///3Mzc1Vfdxtt93G3Xffzd13381f/MVfWBmyLpJcskU0+OG5d0CrtzwxVyftXyerODabc9avDI4IhbJvuV5mMbeyDWMkkWVLV8i5zn06ss/WzGpn2M+GDoO+5UJtG4LekGaLkwc8wXaxDJpYPDFV37esrpBZvjCbI1dU3cmsg+1iWfiWmxPlG//N6xAEAoFBpD/6mNchlLEklh955BH27t3Ll7/8Zfbu3csjjzxS9XHBYJDPf/7zfP7zn+cTn/iElSHrY8GGAZpYBnh+vE65khX8qqOJLCo4WwkDHBEKum+5bmZRzyxXOeClqFpW0fH5A0iS/WKxv75vudzBcIUDnh1hH92RlduBW8YBsbzXqG+5kKs9/7Jn3wUbDtj6GuwQvuWmQ/7UF70OQULbqWoAACAASURBVCAQmES6dvXUSbcklg8fPszNN98MwM0338zhw4dtCcoSPmun4zd2hmgP+eofcsvnoI5f1/EteAeEAui+5fSKvmV1hQN+Y7N5MgXF+cw6gCyj2phZBk0sZgoKp2Ir+JbrlA48k8gw1OVw2Tyw3bMNC77l5y7VuwdqZ5bPJLIEZIn1HZffglH3LT9fb/6CywZp0zavQxAIBCaRVijN6jaWIpmenqarqwuAzs5Opqenqz4un89zzz334PP5eOtb38qrX/3qmr/z4MGDHDx4EIAHH3yQ3t5eUzHlSh/eXe3t+E0+V2ffxhgvjs3S09NTU+wkVYVitJWeKmNcPBKnMxJgx4ZB28WS3+8vvyZFtcAU0NYSJdLgXKvxuu0Kj45ME1ci7BporfqYVDDALNA9uAbfkrGPxicBuHbLIL291Z9fjcq5GSUWDOLz++m0cf5vbOng87+8yMkZldftrv57lZkkk0BrTw/RJWPnCgrnpk/wuqG+8nwamZsRZltbSamKrb+7F7hi8BIvTmVX/L1xVKRoC129vcvm9/LsGEO9LQz299kWVzXy8W7iQHtbKyEbX4PXDKX5xqGzyJF2x947gTeok5eQ9HJyAoHgskDNZZFq2D7doK5Yvv/++0kmk8u+/453vGPR/yVJqikMv/71r9Pd3c34+Dif/exn2bhxI4OD1f9Y7d+/n/3795f/PzU1VS/ERejSLBGbQooYF2qV7Oj08eipLC+MjrGmxgG14tws+HxV4zs2Ns3mziCxWKyh8Veit7e3PKaa1BYnszPTzJt8nVZiqEXL0j12/AK9vp6qj1Hi2tzic3NISzYofn12Er8MbWqaqSmD3fBYPDejFBWFQiZj+nn12NYT5snTk/ynoeon6dWpcQDm8gVSS8YejmUoKiprwko5rkbmZgQlkwFFsf13X9kb4u+fn+LMhfGaFV2K6RSEIkxNTS2+LlWVExOz3LSxzZE5V6JOzwAwk5xGsnGs7e3av48ee5m3Xz/U0DzWrl1rWzwC+1DufS++b/3I6zAEAoEJ1Ef/Bem3fsez8euK5U9/+tM1f9bR0UEikaCrq4tEIkF7e3vVx3V3dwMwMDDAnj17GB0drSmWLSM3Xg1D51WDWr3lo2PzNcUy2QyEI8u+nS8qvDyd5Zo1LQ2PbxgHtqBBa86xpSvEkUvzvP3K6mJ5pQN+I4ksGzpCBHwOWxDAERsCwFWDLfzgWIxUvkg0UEUsZvVqIMutJgtl89yxoaCqqKpq6y7GVWui/N3z8NyleV67qfp9TTZbtXD85HyBuZzi/OE+qCgdZ2/liq1dYdqCMr++NM/bbf3NAq+Q3vFe1L//ptdhCAQCMwSDkMuh/tNfg4di2ZJned++fTz++OMAPP7441x//fXLHjM3N0e+VFB6ZmaGEydOsH79eivDrojka6zddSVr2wL0twQ4MrbCIb9spqpQOjedo6C44FcGx8QywDVrWnhpMkU6X+N31zjgp6oqZ+IZd+YPjhxwA23+ikpt32pOE8RSqMpiIZ4h6nQzDh2HroEdPRGiAbnOPZCGKmLZ9cUC2D5/nyzxqsEWjoylUOvVHF+FGKlUNDo6yic/+UnuuusuPvaxj3Ho0KHyz772ta/xwQ9+sFzFaHR01MXonUG+dXW1zxUIBPWRV0lFDEue5QMHDvClL32JRx99lL6+Pj7ykY8AcPr0aX7605/yvve9jwsXLvDNb34TWZZRFIUDBw44Kpat1FnWkSSJa9a08PjoDPmiWj1DmsvWFErgcOc+HQfF8tVrWvjBsTgvTqTYt66KnSWXhWBwWTYzli4wnS26M3+wvSmHzs7eCGG/xJGxeW7Y0Lb8AdmSvSS0fHdBb8bheNk8WHzI02dfAxxNLEb59aX52lnrbLamWJYl2Nzpgr/M4XvgiXOznImnqJFbX7XolYoOHDjAI488wiOPPMLv/d7vLXpMMBjkj//4j1mzZg3xeJx77rmHq666ipYWbVfsXe96FzfeeKMX4TuO1/5HgUBgkKtu8DoCwKJYbmtr47/+1/+67PtDQ0MMDQ0BsHPnTr7whS9YGcYcPmt1lnWuXdvCj4eTnJhKlxtVLCKbqSkUIn6ZwTYXsooOVcMArSJC0KeJxapiOZ+tbsFwq2SYjkOZ5YBPYu9AlKOXamRWdRvGkgVTUVEZTWT5zW2dtsdUFdla9ZeVuHqwhV+9PMfF2Tzr2pfvINTMLMezrG0LEvK70POoojGP3UuTq0t2rMPnkty64fISVocPH+a+++4DtEpF99133zKxXOmp7u7upqOjg5mZmbJYbmaU++7E9zlhyRAIVjuOV5QyyOqpy2ETkg2eZYBXDUbxSfDsxbkaYjlb3a8ad6kZBzjS7lon6JO5sj/K0Vrb8LkaYjmRRQI2u5ZZ9ll+r2tx9ZoWDl+Y59JsjsEl3nU1p2eWF8/z4myObFF1x4IAjnl2QZs/aN79pWKZQl4T6DUWjFf0u9Ri2MEFY39rgLVtgZJYHrD99zuJ0UpFOsPDwxQKBQYGFub5d3/3d/zjP/4jV155Jb/7u79LoEaZQKsVjHTcqDoy/cbfIvPYv8HkpZpjrabqJ6slFhGHiKMeTsYyXvq3p6trwWrrchxNJ5bL4qHBpiQ60YCPXX0RjozN8/vXLP6ZqqqaZ7VKVvFMIsNvuJZVdE4ogCaW/sezE0zO5+lrWfJBmctBoHpmeU1bkEjApU7qkgQOeUr1zOLRS/P81tKDnplSw44lCyZPMuvgyDWwpi3IYGuAo5fmecvOrsU/rGFDmckUiKUKTTF/0O6B87NF2w9Q2oEdlYoAEokEX/nKV/jgBz+IXHo9b7/9djo7OykUCnzjG9/gf/7P/8nb3179qKPVCkY6TlWMqUS9/f3w2L8BteN0Iw6jrJZYRBwijnq4EcvkD76LfMtbHItjpQpGzSeWbfAs61yzpoXv/nqKRLpAV2UntlxOE2hLsmpjelaxSYTCNRWZxaULADW7fLEAWjOOHb3LfbyO4ZANA2Bde5DeqJ+jY/P81vYlYjFX3YYx4lYzDh0XxOJjZ6p498tiefn8waXDfeDo7grAH+0boL+vb9V8IFViR6WiVCrFgw8+yDvf+U527NhR/r6elQ4EAtxyyy388z//s73Be0TlokHN55BqdKAUCASrD/VvvwF1xLJTuJT+cw87qmHoXLtW8+ouqwigb8EvzSp6JRQsZtFrsaEjSE/Ez79frGLFyKQhvHirfTZbZGK+4N7hPnBULEuSxLVrWzg6liJfXJK9zla3YYzEM2zuCuGXXcpCOiyWr13TQqagcGxySVWQTPXMsqsHXMHx+btip3IAI5WKCoUCDz30EG94wxuWHeRLJBKAtot2+PBhNmzY4HzQLqPcd6fXIQgEAgNId3zE6xCaTyzbmVne0hWiI+TjyFKxWCurFs/glyU2uJVVlJzNqkmSxL51rRwZmye/dPGRSUFksVg+41ab70ocqrOsc/26VtIFhRcnlojFbFYbu6LdtaqqjCQy7i4WHPTsAly1poWALHH4/JLSY3rpvPDSBWOGvqi/ZiMT23FYLF+uHDhwgOeee44/+ZM/4fnnn+fAgQOAVqnor/7qrwA4dOgQx48f57HHHltWIu7LX/4yH/3oR/nYxz7GzMwMv/M73tU3tRvphpu1LybGvA1EIBAYQrrhjV6H0Hw2DLsO+IGWVbp2bQuHL8xRUNSFbGG5EsJioXA6kWFTp3tZRUmSHBeLr17fyo+Hkzw/nipn2gFIp5DWLMkqJlz264KjmWXQmpMEfRJPX5grH3gDNLEYDi/a1p2YzzOXU9ji9vzBsdcg7Je5ajDK0xfmuOO6/oX56p7tpZn1RNa9nRUQYrkGRioVveENb+ANb3hD1ed/5jOfcTQ+L5HuuAv1qce9DkMgEBhkNZwXadrMsmqTNeGG9W3M5RSOVWYWM9rXUkUHP70Zh6tCEcDnrFh81WCUkE/i6aWZxUx6WWb5dDxLT9RPe9jFNZhDdZZ1Qn6ZqwZbOHx+bnFziszysmmu23DAcc8uwPXrWxmfy/PydG7hm1VsKOm8wsWZnMuZ9dLi2MH5C5qLRb5l/ToWCASXBV41iWpCsWxPnWWda9ZqmcWnKsViuiScK8TiVKrAbE5xVyiB45nVoE/m6jUtPH1hiVhMp5Z5lkfiLlsQwPH5g5Zdn5jPczaZLX9PTc9DZHE92pG4i804dFzIrF5fqrP99IWFe6AsMip8+6PJDCqwpdvN+ZeEj8gsCxpAufe9XocgEAjM8MKzngzbdGJZKosHezLL4VJm8enzs2WxqJbF8oItwfWDTTqSzxWxGEsVOFPKnKr5vFZntyKzni0oXJzNsdVNoUTJduPw/PWmLIcrxCKpeYguFstnEhnWt7vUjEPHBbHcEw0w1B1a7FuusmAciZcy603k2RY0J9Ktv619MbO89J5AIFi9KH/9FU/GbTqxXD7gZ2OFiBvWtzIxvyAWSZdEQ4VQOK23+HXbhuGwDQE0sSjBghVD96tWzH80mUVRvVgsyI41JdHpjvjZ3hNebEVJzUN0cWfD0/GsN/MHx8Xi9etaOTGVZjpT0L6RKr0WLRULxkSGtpCP3qjLNhwQNgyBKaTb3uN1CAKBwATS/rdqX0zHPRm/+cSyA+1/r1+/RCzqWbXo4qza2rYgYTezilCyITgrFjvDfnb0RvjV+VntGyXPdqUNw7PMuiw71pSkkleva+VULEMslde+kZ5HqrBhJDMF4umCBzYc59pdV3LD+jZUWLAjpebBH1hUp/ZMQvPsu3oYQxzwEzTAIt9y0psPX4FAYBzpt99R/0EO0nRiWbKxdJxOZ9jP7r4IT75cEouplJbRq6gxO5LIuC+UwBXPLsBrN7ZxJpHl/Ey2vFiQKjLLZxJZWoMyfS0uF1iRJFfmf9MmTSw+cU6/BuaXLJZKiwXXbSjOtbuuZEtXiLVtAX5xdkb7RmpuUVa5UFQ4m3T5cB8IsSywjHL3H3odgkAgqIO0xPboNk0nlp2wYQDctLGN0WSWc9NZSM9DJFrOTky73eK3EpfE8us2tSEBvxyd1eYPizzLI4kMW7rC7pd4cWn+69tDbO0K8fPRGc27vuSAn14JY0uTikVJknjdpnZeGE+RSBe0xULF/EfjaQqK6s0BVxBiWWAa6T0f9ToEgUBwmdC8Ytlma8LrN7UjS/D4mRkts1p5sKkklFxtxqHjcJ1lnZ5ogCv6I/zi7AzqbCm72qq10C0qKmeTWXfrC+u44NnWef2mdk7FMlyKz2mLsYqV7kg8w0BrgNagS804dFwUi6/f1I6iwqFzs6hLMssnJzV7husLRnHAT9Agst6cRCAQXFaoHhzMbTqxXG5KYkO760o6I35eNdjCz0dnUJZmFUtb8K5nFcG1zCrA6ze3c34mx5lkqWxYSxsAF2Zz5Iqq+1vw4Or8X7tJm+8vR0o3aqVYdrtzn46LYnljZ4hNHSF+eXZmWWb55OQ8IZ/EmrbgCr/BftxozCNoftSXnvM6BIFAYBD1ke+6PmbTiWVs7OC3lJs3tzMxn+elfHRRVu10PEN/i4stfitxMbN604Y2fBI8Pl1q8dyqiceFxULz2lAABlqD7OyN8Pj5FCqUSwfO54qMzeZd9ysDrmdWX7e5jWOTaS7lfYs8ZMOTc2zuCuFzqXvlImTZcc+2oLlRvvApr0MQCAT1GFwHgPqLn7g+dPOJZZ9z1QFu3NBKyCfxaHATUsmCADAcz7CtJ7LCMx3ERbHYHvazb10r/1+mk3wwghTUxOGZRJaALLG+wwux7Hyd5Ur2D3XwckrlpY7NZbF4urRY2O7FNeCyZ/fWrR3IEvy0bVe5dJ6qqpyanPcmsw6u3gOC5kJ+4BtehyAQCAwi/94HvBvbs5EdQpLlUoUE+zNN0YCPm7e084u2Hcy2dQMwky0yPpdnuxd+ZXCtdJrOm3d0MUOAQ+v2lb83Es+wsTOE34usostb8G/Y3E5UVvm3ta8p2zBOxTSxvM2raijg2u5CTzTAvrUt/KznKvKtHQCMz+WZyxW9qQYDrt8DguZB6l9T/tqrNroCgcAg2/Z4NnTTiWVAyzbaXA1D583bO8j5AhwMbgFgOKY16NjW45FQkGRUh+ssV/KqwShri7P868D1gHa472Qsw85eL4WSe/MP+2VuaZnjyb69JIOaDeVULMNga8A7Gw64umD4rXUBZoKtPOlfC8BLU9o9sN2ze8Cd8oGC5kb91kNehyAQCFagXBrYA5pTLPtkx8TyZn+OKxPD/HNhkExB4dhEGlnyUCy7vAUtSxJvnnmRk+FBjozNcyaRJVNQ2N0Xrf9kRwJyfwv+f5PHUCSZfzyvoqgqL02m2OGlDQdcfQ2uDqfYMH+Jf0j1UFRUjk2kaQ362OiFDQeEDUNgjZ17AVAP/8LjQAQCgVHUQsHV8ZpTLDvpY52d4fYzPyah+Pn+81M8dX6WXb0RogGPVjweVAL4jYl/p1+Z59vPTvCLszPIEuwd8EgsS5LrW/Dr5ie5ZeII/zYyy8HT0yQyRa5Z61HBdA9Kp8mzM7zjzE84n/Pz/55McPjCHNes7/DmcB8IsSywhHzX/V6HIBAIzPLrp10drknFsoMtoOdm2DVzlt/oKfKDY3HOTee4dajDmbGM4IFQCMwleQ/DnE1meeR4nOvWttAVcblzn44XQmkmyTuTz9AWlPnaU5doCcrcsL61/vOcwGXPMoA6O80NUy9ybbeP//vfJ4inC7x5z4Br4y9DlI4TWKDcBRNQJy95GIlAIDCK8k/fcXU8jxSOw8g+B8XyNADv3ROhb1prVHLLFo/FsptCqVCAuRmu7yhyz9XreH58nt+5ose18ZdROtylqqpr3QPV2STdET+fvXUj/+tkgtdvaqfF7WYkOl50sJudRkblrhv7+f5Ihu6wn9dv7SYWi7kXQyWyz9V7QNC8KPe+F354yOswBAJBDaSbbkU99DNweWHbnGLZ55wNQ53VxHKgvZPbNnY7MoYpJAkUF20IM0nN9tDRzWs2tvGajW3ujV2NSrHolvl/dhp6B9jYGeL9rx50Z8xaeNHBbnYafD5aO9p4z3XaQtH1NueVOLmTJHhFIN/7EMrnPuZ1GAKBoA7SLW/WxLLLNKkNw7lqGCTj2odzu4fZ5EpcziwzHQdA6lwFCwXwTCxKbavo/Qd3r4HZaWjtWLR97SnCsyywiLRlR/lrVVxLAsHqZf1mT4ZdJZ92NuNkpikZg/auhbbaXuO2UEhqYpnVIpZl55rQVENVippYbOt0Zby6lASrmx/w6kwS2trrP9AthFgW2Eji0x/0OgSBQFADyR/wZNzmFMs+HxQdsmEkYtDloUd3KU76s6ugljLLdKwWsaxnVl16DZIJTZh197ozXj288CzPJKF9lSwWQBzwE9iC9PrfBCB/7NceRyIQCIygZrOujdWcYtlJAbnqxLIHmWVpldlQwL3XIDEFgLRaxLLkgQ0jPoXUtUrmDyKzLLAF6V0ioywQXFacetG1oSyJ5SeffJK77rqL2267jdOnT9d83NGjR/nQhz7EnXfeySOPPGJlSGPIDna1S8ZWl1CQXG71m4xDe+fqsqGAezaMuCaWWS3XgNvzL+RhJrF6Muug3e+iGobAIpWHVJVf/MTDSAQCgRGUf/y2a2NZEssbNmzgYx/7GLt37675GEVRePjhh7n33nv50pe+xBNPPMH58+etDFsfhw74qZkUpFOrx68LILvb6ledTqyy+budWZ7U/n2FimWmE9ribLXMH0RmWWAfJcGs/s1XPQ5EIBDUQrr+9doXF866NqYlsbx+/XrWrl274mOGh4cZHBxkYGAAv9/PTTfdxOHDh60MWx+nSsclSn7d1SQU3PZrxiZWlw3F7WoY8SkIhSHqUce+pXgxf1hluyvud3EUNCfy1//J6xAEAkEdpLf9vutjOl5nOR6P09OzIK56eno4depUzccfPHiQgwcPAvDggw/S22vuQ9nv9xMIhZD8PrpMPrce2fMjJIHOzVsJ2vy7jeL3+xe9JslwmOKsTI8L8aiqykRsgui+m2hzYLylczNCqr2dWaC7sxOfC69Bcn6WQu8AvX19pp7XyNyMUJRhCmiNRom6MP/08QwzQNfQdvwV4zk1PyPEgiFkB+53HS/nJnAXyb/wkaieO420ccjDaAQCQTWkXvc7xtYVy/fffz/JZHLZ99/xjndw/fXX2x7Q/v372b9/f/n/U1NTpp7f29tLXlEgkzH93Hoop08CMB2IINn8u43S29u7aF7FQgHyOdvnWg11OgG5LOnWDrIOjLd0bkZQUikA4rEpJBfOqxYvXYCOroauSyfeI3VGuzfnZmdJuXANKKNnAEhIvkX3gFPzM0LRoftd5/9v787jo6rPxY9/vjMJCQkQMgmEXSGCFkQqhNUFlLhUq1K1SlFra6ulKFasVrAXbKUoLfXCz4pXtFZ78VZ7WxXE3qJEBDeQNSD7IptsgewbIcn5/v44SUggyyRzlpmT5/16+XIyOTnnOcOZmed8z3Oeb0v3ramrbiK8GTOn4H/lPbfDEEI0QldU1DnJtUuTW5g+fXpIGwgE6k6Dm52dTSBgc82rXZOSnDgKUdHhV7Pr1CX4k8cBd87qGuR0ze7JY6jBlzmzrWA4XYZx4ii0T0DFxjmzvWBIzbKwUPJflnDyvpvcDkMIEYwDeyD1Its3Y/tQXGpqKkePHiUrK4uKigq++OIL0tLS7N2o357WcfrEcUhOCZ+Zy8DsSuFQJwBdPRd7J5eneK7NwWRZFxdBUSF07mr7toLmcJ9pffxIeO0/SJ9lYSl/rXsydHaWi5EIIZpivPGiI9sJKetbs2YNEydOZNeuXcyePZtZs2YBZp3ys88+C4Df7+e+++5j1qxZTJkyhZEjR9KzZ8/QI2+M8tk3shxOiSKYNzc5PLJMUmdnthcMJ0dWTxw1NxlOyaLTI+tZR1Gdw6y8wOkp30WrYUz9qdshCCHqoS4baz74Zr8j2wup0GPYsGEMGzbsnOcDgQDTpk2r+Xnw4MEMHjw4lE01jw3dMLTWcOIYqu8AS9cbMicvQWcdhYQAqk2MM9sLhs+5STl0lpksh9XIqpMj62Vl5nTv4bT/YL4GFeVuRyE8xDf7VYypP3E7DCFEA9RtP0Z//pFj2wufegIr+XzWl2EUFcKp0jAcWXYuWdbHvoGuPRzZVrCUkyOr1clychgdA07uf9XIOilhOLIsZRjCQirpTLebmvIzIUTYUO07OLo9bybLdvRZzjoCgAq3ZNmhREFrDUcPocIsWXa2DOMYdExCxYTRyHrNdNcO9Bk+Zk4mFJZlGJIsC5sYTz7gdghCCJfZ32/DBcrnR1tcs6yPHjIfdLW53rq5nKrXzMsxR9bDcf/BmTKM44ehc7idLFVN0evECdPhg2ZyHo4nTJIs11FUVMTcuXM5ceIEnTp1YsqUKbRr1+6c5e6880569eoFmC3ynnjiCQCysrKYN28ehYWF9OnTh8mTJxPlQHumcOL7w2sYv/qx22EIIZqgiwtR8e1t3YY3R5Z9NnTDOHwQ2rSBcGqbBs4lClUnC6pLmCVKDpUhaK3hyCFUt/Ns3U6zOTiyrg/vh5Su4VWzDjKyXI9FixYxcOBAnn/+eQYOHMiiRYvqXa5NmzbMmTOHOXPm1CTKAG+88QY33ngjf/rTn4iPj2f58uVOhR42VO2uGMcOuxiJEKIxevm/bN+GN5Nlv/XdMPSRA9C1V1i1jQMcG1nWR81L8GE7smx3spRzEkqLoXt4JctKKedOmA4fCLv9B6QbRj3Wrl3L6NGjARg9ejRr164N+m+11mzdupURI0YAMGbMmGb9vRcZ03/udghCiAbo9/5m+za8eV3NjpGmwwdR/b9t7Tqt4NSo2rFD0DYeEhLt31ZzOJUsV7WnUT3Ot3c7LeHz2d5nWZedMrvBjLjK1u20iIwsnyM/P5/ERPO92rFjR/Lz8+tdrry8nKlTp+L3+7nlllsYNmwYhYWFxMXF4ff7AbO7UU5OToPbysjIICMjA4DZs2e3eGrwcJlWvHYcxuv/4sSPbgRwJbZwfE0kDomjPm7EUnz3RIreeAk48/60Kw6PJsvW3uCniwshPyd8R9UcuQR/ALr1NEcyw4lDZQj6G3Oa51Z7DBw5BFqjwnD/lfKhW+HI8syZM8nLyzvn+fHjx9f5WSnV4Pv2xRdfJBAIcPz4cZ5++ml69epFXFzzZmdMT08nPT295ueWTjvu5pTpwcRxYtn7qEtHhEUsTpM4JI6muBGLHj4GqpLl6m2HEke3bg3fvO7NZNnqGfwOmYlSOCYKTiRK2jDg0D7UyHAcVTRHv+yYsbGOwwcgqTOqbRhN81xNKdu7YejD+80HrfQ9EI6mT5/e4O8SEhLIzc0lMTGR3NxcOnSov81SIBAAICUlhf79+7N//36GDx9OSUkJlZWV+P1+cnJyapZrzYwXn8H/yntuhyGEqKJinfs+DrMCXIv4/JbWLOv9u80H511g2Toto3z2tw07eczshNGzj73baQmHumHob/ZDOJZggDPJ4jf7oU0MdAqzG1yh1SbLjUlLS2PlypUArFy5kqFDh56zTFFREeXl5mQuBQUF7Ny5kx49eqCUYsCAAaxevRqAFStWkJaW5lzwYca3oP6bI4UQrYc3R5at7oaxfw8kpzjeBDsoPvunu9YHvgZAnZdq63ZaxIGaZV1aAse+QaVdbts2QuLE1YX9u6FXKqp6JD+cSOu4c4wbN465c+eyfPnymtZxAHv37mXZsmVMnDiRw4cP8/LLL+Pz+TAMg3HjxtGjh9nt5q677mLevHm89dZb9O7dm6uvvtrN3XFV7Zu6K198Bv+kJ12MRghRH11ZifLb9/3kzWTZb+0Mfnr/blTvfpatz1LKhglYznZoL/ijoFsve7fTEk7c4Ld/t1mv2ydMjwGbk2VdWQmHvkZdeb1t2wiJjCyfo3379syYMeOc51NTU0lNNU96/4g7cAAAIABJREFUL7zwQp577rl6/z4lJYVnn33W1hgjibr2e+gP34WNq90ORQhRn01rYPBI21bv4TIMa748dWE+ZGfB+X0tWZ/lqtpmaRtLMfSBr6F7L1RUtG3baDEnRpb37TIfhO0Jk83J4tFDcPp0eJYhgbSOE7bzff/M5CS6rMzFSIQQ9THeft3W9Xs0WbZwZHn/HgBUOCfLYFuyoA0D9u8ibPffgW4Yet8uSOlu+wxBLebz25osVtfsh+0xICPLwkHGQ993OwQhRBU19ArzQdZRW7fj0WTZD1qbiV6I9P7dZreB88Lw5jawf2T16CEoKYbUb9mz/lDZfbKgNezbFb5lOGAen3Ymiwf2QNs46NzVvm2EQpJl4QDfrAVuhyCEOIu6eYIj2/Fmslxd5G1Fsrx3O3Tr5WiLkmapSRbtKcPQu7cBoPr2t2X9IavafytOjOqVcxLycyFc65XB/prlr3dV3dwXph8XFvdVF6I+qtbJorH0bRcjEUJUU126O7KdMP32C5FFvXd1ZSXs3RG+iSKYo4pgX7Kwd7s5a19yGLYMA/tH1vftBED1udCe9VvBxppdXVJs9tjuO8CW9VtCumEIp1w4EAD99l9dDkQI4SRvJsv+6gQqxLrlb/aZ/YXDOVGwOVnUu7fBBd8Kv5n7qimbyzB2bYGYWOh+vi3rt4SdI8t7t4M2UP3C/D1g83TfQgD4H5tV81ifKnExEiGEk7yZLFePLIfYEUPv3goQ3qNqNibLOjcbsrNQF4RpvTLYf7Kw4yvzZCEqjLss2pgs651bzLaBfS6yZf2WkJpl4QJj8vimFxJCOEYfPWTbur2ZLPstKsPYtdWcjCQxyYKgbKKsq88+m96x2dxEv4stX7dl7DxZKMiDo4dQF15i+botpXxoO0fWz78AFRNjy/otYefJwtaNnPr8I1tbM4rI4vujlGAIEY6Mxf9j27q9mSxXX5oPYcprbRiwe1t41ytDrRv8bLgMvS0T2nWAHr2tX7dVbGwdp3duMTdx0UDL120pm5JFfaoUDu4N75MlMOv2bUpmjWWLKHrr1fAtQxKOUwmJNY8rn/sPFyMRQgBQfZPf+i9s24Q3k2UrumF8sw+KCuCiQdbEZJeaG/ysTRa01ujtm1DfGhS+XRDA3jKMnZshti30CsNpvmuz6wa3XVugshJ1YQScLGht+eivLj8Nu7fSZtBQS9crIp/6/n3mg6qrb0II9/huu9f+bdi+BTdY0A1Db9kAgBpwqRUR2ceuZPHIIcjPgW+F+cmCjX2W9c6voO8AW+ebt4RdI8tb1kObGAjnm/vAvvfAnu1w+jQxg4ZZu14R8XzXjqt5rDevdTESIQSX2P8Z7c1k2YJuGHrrRujZu84lt7BkU7Kot28EQPVvnScL+sQxOHYY1T/MTxbAltZxWmvzhPGiS1DRbSxdt+VsKsXR2zPB7yf64m9bul7hEYNHAmD8aabLgQjRujlx9dubyXKI3TD0qRLYux01YLCFQdnErmRx2ybo3A2V1MnS9VrOrv2vGi1SDpyxhsyOkeXjh+HEMdTFQ6xdrx189tzkqrduhD4X4msbb+l6hTf4fz6t5rHOPuFiJEIIu3kyWVahdsPY8ZVZqxnuJRhgS7Koy0/Dri2o/hEwomZnstylR51Zu8KWDTe46S3rzVVfHEEnjBbe5KqzT8DBr1FSgiGCYEz9idshCCGoyl9s4MlkOdRuGPqrdRDTFsK5v3A1Oybl2LYJyk6hIuHGJhta5+nSEti5JTL2H2wZWdZfbYAu3VGduli6XlvYccK46UsASZZFo3wLFtU81sVFLkYihAAoXfquLev1ZrIcwsiyNirRmV+iBg5BRUVbHJgN7EgUNq6CtnFwUZj3F4Za+29h67xtmVBZgbokUpJlv6X7r8vKzCsLkVCCAfa8BzK/NE8WuvSwbJ3Ce2rXShqPTHAxEiEEQOHC/7JlvSFNS7Zq1Sr+8Y9/cPjwYZ555hlSU+tvsfXggw8SGxuLz+fD7/cze/bsUDbbtFBqGL/eBQV5cOkIa2OyifL50GBZoqArK9Gb1qAGpkXWyYKFI+t60xqIawepEXBlAczXoKLcuvVtz4SKctTA1pks65Ii82Qh/RZL1ie8zffSuxgTvweYo8sqvp3LEQnR+qj0m9EZ74FNZRghJcs9e/bkscce4+WXX25y2aeeeooOHTqEsrnghdANQ29cDf6oyBlVs7oTwJ5tUFSAqrrTO+xZnSgZleiv1qEuHhL+LeOqWdxnWa//wjxZ6Bfm/ZWrWfwe0F+tN+9Z+PZwS9YnvK3254TxyAT8r7znYjRCtE7qhu+bybJNQirD6NGjB926dbMqFuu0sBuG1tosQbhoICouQu6At3hkVW9cDVHREAmdQMCcWU0pC08WdpiT0URKvTLUTMphBV1ebl5ZuHQ4Kiqkc2nnWF2GsWkNtE+APv2sWZ/wPN+CM3WSurDAxUiEaJ1U+wRb1+9YzfKsWbN44oknyMjIsH9jLZ2U5MhBs13WpREyqgqWJgrmycJqGHApKrZtyOtzjIU3uOl1n0KbNpFTrwzWnixsz4TSYtSQy61ZnxMsPGHUZWXozetQ3x6O8kXIlQXhutrHivHo3S5GIoSwQ5NDRzNnziQvL++c58ePH8/QocElFDNnziQQCJCfn8/vfvc7unXrRv/+/etdNiMjoyahnj17NsnJyUFto1pUVBQJgQC5QId28cQ04++LPnyXYqVIuup6/IHmbdcpUVFRdV6Tso6J5AEJHTrQppmv1dnK9+4gJ+cEHSbcT9sQ19USZ+9bsI77/LSNjaF9iDHrykpOblxNmyGX0bFHz5DWdbaW7lsw8mJjqSz0kWTB+vO3rqcsrh3Jl1+Nig6+Zt3O/WtKaYcECoDEhASiQozh1OcfkV9WSsdrbqp5P7m5byJy+BYswviZObOfPrgX1av+e3iEEJGnyWR5+vTpIW8kEAgAkJCQwNChQ9mzZ0+DyXJ6ejrp6ek1P588ebJZ20pOTia/sBCAgtxcVJB/r41KjI+WQP9vk2sAzdyuU5KTk+u8JrpqX/Nzc4Le14YYy94Hn4+iPv0pdmH/z963oClFaXExZSHGrLdvwsjLofySoS2LoxEt3rcgVFZUQPnpkNevy8sxVq9EDRpGdn5+s/7Wzv1rilFcDEBuTjYqKiakdVVm/AsSAuSn9Kh5P7V038KyRE3YRvl80LkrZB3FmDlFapeFcInOOYmyeMDT9jKMU6dOUVpaWvN48+bN9OrVy96NtqQMY/tmyDmJuuwae2Kyi0VlGLqyEr16BQxMQ7V36EZMq1hUhqHXfQYxsTAwgkowwLoylK3roaQYNezK0NflJKveA8VFsGUdaugVUoIhWsQ/a0HNYyNjsYuRCNF6aRveeyEly2vWrGHixIns2rWL2bNnM2vWLABycnJ49tlnAcjPz2fGjBk8/vjjPPnkkwwePJhvf9vmmeH8zf/y1J9nQHz7yLsD3qqa5W0bIT8H36ixocfkNBX6DW76dBl67WdmrWpMaKOTTlM+vzUnC19+Yt7YdtEgC6JykFXJ8oYvoKICNTzCThZEWFE3jQdA//1VlyMRonXSy6xPlkO63X3YsGEMG3buDFeBQIBp06YBkJKSwpw5c0LZTPNVjQrpSgMVxOK6uBC9cRXqyuubVacZFixqm2V8ngHtOsAlaRYE5TCfL+RJOXTml+aNbZelN71wuFG+kG9u06dKzC4Yl6dHTheMKlb1GtdrPzUvo593gSVxidbJd/MEKpe8BUDlQ3fgf+F/XY5IiNZBjfkOesW/bVm3N2fwa2YZhv5ypTmiFImJkgWjarqoADatQQ0fHRkTkZzNgjIE/cVHEOgEF0ZIb+HafKF3w9AbVkH5adSw0RYF5SALThh1Xg7s2IwaNtpsRyhECHy/fcF8UHbK/HwVQthOXX2Tbev2ZrJc3SS+siKoxfXnGdCrD6pXHxuDskn1F3sIZQj6y08i92QBQk6Wdc5J2JaJGnV1nelrI4YFk5LoT5dB526QepFFQTnIgtZxetXHoDVqeASeLIiwo7qduS/HmCKt5IRwguraw7Z1R2BmEAR/1WXkyqZHlvXBvXDw6whOFEOY2ruK/qLqZKFnb4uCcliIyaJeXZUojbzawqAcFOrJwuGDsGcb6srrInNUNcSrK1pr84S5b39Ul+4WBiZaM9/LZ+omjcX/42IkQohQeTNZjm5j/j+IOcL1ZxkQFR25I0o1o2otq9nVh/aZJwujIvRkAUJKFs1E6SPoNwDVuavFgTnEF1rNsv70A4iKQo2K4JMFaPkJw97tcPxw5HXCEWFNKYW69V4A9Pt/RwfxfSSECE8eTZar6m7LyxtdTJefRn+5EnXpCFR8ewcCs0Goo2qfZ5iJUiR3AAglWdy1FbKOoCKxC0i1UE4WTpehV32MunSk7dOF2ibU98AnH0BMW9SQURYGJQT4vnNbzWNj0u0uRiKECIU3k+Xqm9SaOJPXmV9CSRHq8ggfVYUWJQq6oty8uXHQMFS7COutXFsIyaKR8R60a48aeoXFQTkohG4Yev0X5nvgyussDspBIdzgpwvzzZaBI6+KrCneRcTw/dc7NY+N/37BxUiEEC3lzWTZ7ze/QJsaWf4sw+yAcNElDgVmg6pkWQdRn32OTWuhqABfpNZrV2thsqxPHINNX5otA9tEVm/lOvz+oOrz66M/+cBslxaJXUCqtWQSoir6s2VQUY666gaLgxLCpKKiUD94AAD96YfovGyXIxLC+3RxkaXr82SyrJQySzEqGh5Z1tknYHsmatTYyJ6tqxk3M57N+Phf5slC/0stDsphyoduSbK8/F/g86HGRHiiFBUVdOeX2vQ3+80b+664NjJv7KvWwm4YurLS7Ml50SV1uhcIYTXf1d+teWw8/mN0iJMoCSGakHXE0tVF1uwDzRHdptEyDL3qI7MDQqTe1FStegKJZiZL+vBB2PkV6tZ7Uf4IPlmAFo0s69IS9GcfooZcjkpMsikwh/ijoKIFyfKyxdAmBnV5hN/Y1tJSpM1rIecEvjt/an1MLisqKmLu3LmcOHGCTp06MWXKFNq1a1dnmS1btvDXv/615ucjR47wi1/8gmHDhjF//ny2bdtGXFwcAA8++CDnn3++k7vgOb6XF2M8cAsAxgO34H/lPZcjEsLDTpVaujoPJ8vRDSYQ2jDMDggXXYLq1MXhwCzW0mT54/fNLiCRnihBi27w0198BKdKUen2NTF3jD8KDANtGEH3ida52ebNrVdeF9n16tDiZNlY/j4EkmHQubOQRrpFixYxcOBAxo0bx6JFi1i0aBF331233+/FF19cM7tqUVERkydPZtCgM1Od33PPPYwYMcLRuL1MKYXv93/BeOI+AIyX5+B74HGXoxLCo9pb+73myTIMoPGR5c1r4ORxbySK/qqbGZsxsqhLitCrV6CGX4my+IByha95NbvaqER/tARSL0L17mdjYA6Jan4pjl7+PhgG6ppbbArKQTWTEDVj/w/uNWfsG3Nj5F9ZqcfatWsZPdpshzl69GjWrl3b6PKrV6/m0ksvJSYmgmv3I4AKJKPu+AlgTq+uM790OSIhPCox2dLVeTdZjopusK+l8e+3IakzKu1yh4OyQTNnKwTQH/8flJ1Cjb3ZpqAcFh3dvJH1zevgxDHv7H9Nstz4Da3V9KkS9MqlqMEjI//KCtR0v9EVwe0/gP5wkdkubnQEdwFpRH5+PomJiQB07NiR/Pz8Rpf//PPPueyyy+o89+abb/LYY4/x+uuvU97EzdIieL5rboEE89/GmD8LnXXU5YiE8KC4dk0v0wzeLsOo5wNe790BX+9EjX/AGyNK1YlSkCPLuqwMnfEeXDwkcmfsO5s/qsnOJ7UZHy2BQDJq8Egbg3KQv5nHwKfLoLQYdd33bAzKQc19D2SfQK/9FDX2JpTFH6hOmjlzJnl5eec8P378+Do/K6UavYEzNzeXgwcP1inBmDBhAh07dqSiooIFCxawePFibr+9/j7BGRkZZGRkADB79mySk1s2ohMVFdXiv7WSI3G8/i+Of8/s6238+md0euNDfPHnHout6jWROCI2DgifWI5X/b9Tp06WrtfDyXL9ZRj6oyXQNh51WQRPQlGbv3k1y/qzZWa7uBu+b2NQDouOhrJTQS2qv9lnXn6/zQM3NlZrzvTuFRXmyVK/Ad4oQYEzfdWDHFnWH5k3VkX6lYXp06c3+LuEhARyc3NJTEwkNzeXDh0aLrdatWoVw4YNIyrqzNdB9ah0dHQ0V111FUuWLGnw79PT00lPP9N+8uTJk83ZjRrJyckt/lsrORWH/5X3qLzfPAZP3H0tvhf+FxUT60osTZE4JI6mhEMsulYe0JJYunXr1uDvPF2GcfZooz52GL3uc/OmJo9MQKCUCrobgq6oQH/4LlzQH9W3vwPROSSq4Zs5z6YzlpgdIK641uagHNSMkVW9/nOzA8S1HhlVhubtf0kR+pMPUWlXoJKsHXkIJ2lpaaxcuRKAlStXMnTo0AaXra8EIzc3FzCng1+7di09e/a0L9hWrHZHDOOhO9AW38EvRGtiPHSHbev2brLcJgZO1x1t1Ev/aU7tfK0HbmqqLcg+u3rNSjNRusFb064qf1RQo4q6IM/sADHq6sid3rw+/uBqlrXW5slSlx4wMM2BwBzSjJFl/emHUFaKunaczUG5a9y4cWzevJmHH36Yr776inHjzP3du3cvL730Us1yWVlZnDx5kv796548P//88/zyl7/kscceo6CggNtuuw1hD9/Li2seG5PvROfluBiNEJGpdu9y3+w/W75+z5ZhqPj26CMHa37W2VlmB4gxN6A6JLoYmQ38/iZH1bRhoP/9NvToDRcPcSgwh0RHB5corVxqztZ2tQfaxdUW7Mjqjs1w8GvUDx8KusVcRIgKriOMrig3ryxcdAnqvFQHAnNP+/btmTFjxjnPp6amkpp6Zt87d+7MggULzlnuqaeesjU+cYZSqm4P5sd/hO+J2agLPHT1TwibVb9/AFRSZ8vX76FvzLO0aw9FhTU/6qXvAArlpcvP1YIpw8hcDce+QX3ntsiera0+QZRh6LIys13awDRU1x4OBeYMFWTNsvHhIujQETVijP1BOanmZKGJkfW1n0FetrdKUIQnVCfM1YzfT8V4700XIxIiclROOdNDvtN/L7VlG95NluPbQ1mpOZqUl4P+bJl5+T3g/t2alotqvHWa1hrj//4Jnbui0i5rcLmIFdV0GUbNjY3X3+pQUA4KYmRZHz4AW9ajrv4uKrqNQ4E5Q/n85sQkje2/YaA/eAe69oSLBzsYnRDBUUrVqWHWS96s6ZghhDiX1tq8SbaoAAD141/gs2nuCO8my+2qalKLCs06zcpK1PUerbtrqmZ5+yY4sAd13a1mYuE1UY2XYejy0+h//xP6DYC+AxwMzCFB1CzrpW9DTCxqzHccCsphTZ0wZa6GwwdQ37nde1dWhKf4X3mvzoRZlfffjC4ubOQvhGh99Pov6pZeTPgZvlH2dTnzbrIcX3V28c1+9Ip/o0aMRnXu6m5MdmmiZtn49z+hYwA18moHg3JQE2UY+uP/g/wcfDf9wJuJUhMjyzrrKHrNJ2YXGC/d2FhbI1dXtGGYl7S7dEcNv9LhwIRoPt+9k/E9+VzNz8Yjd2G8+bKLEQkRHowvV1J5/80YL82uec4353V8V91o63Y9myyrZLPA23jlj+a0vjf9wOWIbOSPQjeUKO3dYfYVvuYWVHS0w4E5pGpkufbdsNV0STH63/+A/t9GXXSJC8E5oImaZb3kzaouMB6u1W1kYhq9/nNzVPmmH3jzyorwJNW7L53/+WnNz3r5++Yoc0Gui1EJ4TxddorKB8aZx/+fz5xEqrTLzSsxHQO2x+DZbhh0P8/8Ai0pQl07zhvT+jakkVE1472/QfsE1JXXOxyUg6JqTcwSVfeEQH/4LhQV4rv1hy4E5pBGRpb14YNmu7xrxznygeKa6Oh6y1B0ZSX6vb9B9/O8Mb29aFWU34//lfcwVvwf+n/Mln/GL+8FwLdgkbe62ghRi846ivHrn9X7O3VZOr4fPexoPJ5NllWbGNS9k+HwftTNE9wOx14N1CzrXVtgWybq+z/2zCQs9ardOqxWsqwLctHLFqPSLkedd4FLwTmgkZplY9EbENvWu/X61fxRUF7Pe2D1x3DsML6fT5PEQkQs35gb0KO/U6dG0/jZOGgbh+//venN8jLRqugDezB+92ijy/h+8ydU9/MciqguzybLAL6RV7kdgjPqmZRDV1SYNW4dk1Cjb3ApMIfUmZTizEmBfv9/zb7Kt9zlTlxOqdp/XV5O7a9MvXcHZK5G3XIXqp09dwiHjaho9DnvgXL0e29C735w6QiXAhPCGtXdMnRxIcYjVZ9ppSU1CbTvxbe9W2onPEXv2Y6x4PcQxAQ8vt+8gOrey4GoGufpZLnViImF/Lp1bHrtp/DNfnwTp6JiYlwKzCH19NnVJ46hP/kAdfm1qC7dXQrMITGx5v/LzsxYqbXG+MdfICERlX6zS4E5qJ5uGHrVx+aMlfdMkpE34Rkqvr2ZNB/9BmPGpJrnjUnm1SP1kyn4RrSSgSIRtnRlJfrLleg3F0AzpnH3Pf4Mqt/FNkbWMpIse4BqG4c+frjm55ppjbv2hMEjXYzMIW2qTgZOl9U8pRf/D/h9qJvudCkoB7WNM/9/quTMcxtXwd4dqHsmebsEp1pM27onCxXlZrvA8y6AAdJXWXiP6trj3JFmQL86l8pX5wLgmzYH1edCt0IUHqYNA3ZvxfjgXfhqHcdbsA417m6zpW1U+Kei4R+haFpsHJSelSh9sx/140daxYiaiotHA5QUA6AP7TNbpV1/K6pjkquxOaJ6ZLnq7F1XVGC8/d/QtSfqsmsa+UMPiYuHvOyaH/Xyf8GJY/gefqpVvAdE61U90gxg/GWueUWlivHs42eWu3kC6rt3yvtBNElnZ6HXfIL+9EM4cSzk9anrvof6zvdR8e0siM4dISXLCxcuZP369URFRZGSksKkSZOIj48/Z7nMzExee+01DMNg7NixjBs3LpTNirO1bXsmUTpdhvG/fzHv/h8+2uXAHBJX9QasSpaNdxdC2zjUdR6/qa2K8vnMkdXSqmPgk6WQdQTf5Okof+tolabaxqGPHgJAF+Sh338LLh6CGjjE5ciEcI7vvilw3xT0qVKMX90HpcU1v9Pv/c3sDFNFXTsONe4eqXNuBXRBHnr7Jsj8Er1xdeOTmLVE154k3PUzCvt8y7PHU0jJ8iWXXMKECRPw+/288cYbvPvuu9x99911ljEMg1dffZX/+I//ICkpiWnTppGWlkaPHj1CClzUEhsH5afNS88fLoLsLHyPzmw1iRJxVSdoJUVmB5Cv1qFuvTeiz2KbrW1bOFVi9pVe8hZcOBAGprkdlXPi4s9cWVj0Bpwuw3fHT1wOSgh3qNi2+J9/EzBrR42X/wAbVtVZRn+4yPy+qO1bg/D94AFU155OhSqCpMtPw5GD6N3b0Ds2w47NHK9Vemab1ItQV1yHunQ4Kq7h79TY5GSKTp60Px6XhJQsDxo0qOZxv379WL169TnL7Nmzhy5dupCSkgLAqFGjWLt2rSTLVkpINP+/b7dZpzl4FOpbgxr/Gy+pSop1YT562WJztsKrv+tyUA7rkIjOy4Glb0NRAb7v/7h1XW6N7wAlxeivd6I/W4YaezOqq3zGCKH8fvw/n1bzsy47Zc5+tmXDuQtv34Qx48Fznj4O0DYeNfp61OCRcF6qTPATBF1eDtlZcOwbszzw4NdwcC/knHA3sG8NQl06AnXJUEhMlraaQbCsZnn58uWMGjXqnOdzcnJISjpTN5qUlMTu3bsbXE9GRgYZGRkAzJ49m+Tk5GbFERUV1ey/iST17V9Z7wvIA/QLMwFI/tkv8Ufga9DSfzsdCJAVEwvvLIRTJXR46Enadg+vDhh2H5d5XbtTlrkGvesrYq+8loQhzrZKc/t9V9qnLwXaQM97CtU+geQfTcJn0dTebu+bEFZSMbH4f/GbOs/p4kL03xag13zS8B+WFqOXvo1e+nbzNhgTCz3OR/XqAz16o1K6QfsEs3wuNhbaxNqSrGmtwTDM/8pPQ0kRFBdBcQE6N9tMYrNPoLOzzOQ150SDs6CerSU3s9khqk8/Ki/ob85Oe34/iG8nia9NmkyWZ86cSV5e3jnPjx8/nqFDhwLwzjvv4Pf7ueKKK0IOKD09nfT09JqfTzZzWD85ObnZfxNJ6ts/3cGcmU2XFKNu/zG5vmiIwNcgpH+7br3Q+3ZBv4spumQ4xWG2/3Yfl0bn7mY3iJi2nL7hTsffA26/73RiJ/P/pSWouyeRU1oGpWVN/FVwWrpv3bp1s2T7QthNxbdH3f8Y3P9Ynee11nD8MG0+eo9TK5a2bOVlp2DvDrPvO5g3Y7dQuCSpIWsbB71SUX36oc7vZ3bt6ZAAUdHNuiKY5PF8J5w0mSxPnz690d+vWLGC9evXM2PGjHr/kQOBANnZZ+5Sz87OJhDw8LS7LlAdOqLu+jnk57SOnrr18N1xH/qzDNRt97au8oMqauxNkJ+LGnYFKqmT2+E4TvU435x8JiYWNTT0k3YhhDkRCl16kPCLGZTfNanRZXV5udmR5shB9MGv0Ye+hkP74GQEpbgJAUjujAp0gqTOEOhkfp4GOpnljrFtwR9Fp86dJUltZUIqw8jMzGTx4sX89re/JaaBiS9SU1M5evQoWVlZBAIBvvjiCx5+2Nk5vVsD35jvuB2Cq9QF/VEX9Hc7DNeo+Haoexr/MvM633dbQU9tIcKUio6GTl2gUxfUoGG2bMPtK1ii9QopWX711VepqKhg5kyzVrZv37488MAD5OTksGDBAqZNm4bf7+e+++5j1qxZGIbBVVddRc+ecqetEEIIIYQIfyEly3/605/qfT4QCDBt2pm7bwcPHszgwTKLlhCRPeABAAAMIUlEQVRCCCGEiCxy26QQQgghhBANkGRZCCGEEEKIBkiyLIQQQgghRAMkWRZCCCGEEKIBkiwLIYQQQgjRAEmWhRBCCCGEaIAky0IIIYQQQjRAkmUhhBBCCCEaIMmyEEIIIYQQDZBkWQghhBBCiAZIsiyEEEIIIUQDlNZaux2EEEIIIYQQ4chzI8tTp051OwRbeXn/ZN8il5f3z8v7Fk7C5XUOlzggfGKROOqSOM4VLrHYFYfnkmUhhBBCCCGsIsmyEEIIIYQQDfD/5je/+Y3bQVitT58+bodgKy/vn+xb5PLy/nl538JJuLzO4RIHhE8sEkddEse5wiUWO+KQG/yEEEIIIYRogJRhCCGEEEII0YAotwOwUmZmJq+99hqGYTB27FjGjRvndkghefHFF9mwYQMJCQk899xzABQVFTF37lxOnDhBp06dmDJlCu3atXM50uY7efIk8+fPJy8vD6UU6enp3HDDDZ7Yv9OnT/PUU09RUVFBZWUlI0aM4I477iArK4t58+ZRWFhInz59mDx5MlFRkfkWNAyDqVOnEggEmDp1qqf27cEHHyQ2Nhafz4ff72f27NmeOC7DzcKFC1m/fj1RUVGkpKQwadIk4uPjz1nO7s/1VatW8Y9//IPDhw/zzDPPkJqaWu9y9R0XVgs2Frtfk2CP9zvvvJNevXoBkJyczBNPPGHJ9pvav/Lycl544QW+/vpr2rdvzyOPPELnzp0t2XZz4lixYgULFy4kEAgAcP311zN27FjL46gvF6hNa81rr73Gxo0biYmJYdKkSbaUIjQVx9atW/nDH/5Q828xfPhwbr/9dsvjaCh/qM3y10R7RGVlpX7ooYf0sWPHdHl5uX7sscf0oUOH3A4rJFu3btV79+7Vjz76aM1zCxcu1O+++67WWut3331XL1y40K3wQpKTk6P37t2rtda6pKREP/zww/rQoUOe2D/DMHRpaanWWuvy8nI9bdo0vXPnTv3cc8/pzz77TGut9YIFC/QHH3zgZpghWbJkiZ43b55+9tlntdbaU/s2adIknZ+fX+c5LxyX4SYzM1NXVFRorc3Xt77X1InP9UOHDunDhw/rp556Su/Zs6fB5eo7LqwWTCxOvCbBHu933323pdvVOrj9W7p0qV6wYIHWWuvPPvtM/+d//qcrcXz88cf6z3/+s+XbPlt9uUBt69ev17NmzdKGYeidO3fqadOmuRLHli1bar4T7NRQ/lCb1a+JZ8ow9uzZQ5cuXUhJSSEqKopRo0axdu1at8MKSf/+/c85m1+7di2jR48GYPTo0RG7j4mJiTVneW3btqV79+7k5OR4Yv+UUsTGxgJQWVlJZWUlSim2bt3KiBEjABgzZkxE7htAdnY2GzZsqBlB0Vp7Zt8a4oXjMtwMGjQIv98PQL9+/cjJyTlnGSc+13v06EG3bt0sXWdLBROLE6+Jm8d7MPu3bt06xowZA8CIESPYsmUL2uLbr8Ipp6gvF6ht3bp1XHnllSil6NevH8XFxeTm5joeh1Mayh9qs/o1iczrpPXIyckhKSmp5uekpCR2797tYkT2yM/PJzExEYCOHTuSn5/vckShy8rKYt++fVxwwQWe2T/DMHjiiSc4duwY1113HSkpKcTFxdUkB4FAoN7kIBK8/vrr3H333ZSWlgJQWFjomX2rNmvWLACuueYa0tPTPXNchqvly5czatSoc54Pt8/1s48LNzjxmgR7vJeXlzN16lT8fj+33HILw4YNC3nbwexf7WX8fj9xcXEUFhbSoUOHkLffnDgAvvzyS7Zv307Xrl259957SU5OtiyGYOXk5NTZblJSEjk5OTX/hk7atWsXjz/+OImJidxzzz307NnT1u3Vzh9qs/o18Uyy3BoppVBKuR1GSE6dOsVzzz3Hj370I+Li4ur8LpL3z+fzMWfOHIqLi/njH//IkSNH3A7JEuvXrychIYE+ffqwdetWt8OxxcyZMwkEAuTn5/O73/3unJG+SD4unTZz5kzy8vLOeX78+PEMHToUgHfeeQe/388VV1zhahzBrOPs46J///6uxGKFxuKorbHj/cUXXyQQCHD8+HGefvppevXqRZcuXWyJNxwNGTKEyy67jOjoaJYtW8b8+fN56qmn3A7LNb179+bFF18kNjaWDRs2MGfOHJ5//nnbttdY/mA1zyTLgUCA7Ozsmp+zs7Nriu69JCEhgdzcXBITE8nNzbX0TNppFRUVPPfcc1xxxRUMHz4c8Nb+AcTHxzNgwAB27dpFSUkJlZWV+P1+cnJyIvL43LlzJ+vWrWPjxo2cPn2a0tJSXn/9dU/sW7Xq2BMSEhg6dCh79uzx3HHplOnTpzf6+xUrVrB+/XpmzJhRb0Jm1ed6U3EEo77joiXJcqixOPGaBHu8V283JSWF/v37s3///pCT5WD2r3qZpKQkKisrKSkpoX379iFttyVx1N7m2LFjeeONNyyNIViBQICTJ0/W/OxW/lM7YR08eDCvvvoqBQUFtnxe1pc/1Gb1a+KZmuXU1FSOHj1KVlYWFRUVfPHFF6SlpbkdluXS0tJYuXIlACtXrnR0JMJKWmteeuklunfvzne/+92a572wfwUFBRQXFwNmZ4zNmzfTvXt3BgwYwOrVqwEzSYjE43PChAm89NJLzJ8/n0ceeYSLL76Yhx9+2BP7BuZIRXV5yalTp9i8eTO9evXyxHEZbjIzM1m8eDFPPPEEMTEx9S4TLp/rDR0XbnDiNQnmeC8qKqK8vBwwP/N27txJjx49Qt52MPs3ZMgQVqxYAcDq1asZMGCA5Vd7gomjdg3sunXrLNn/lkhLS+OTTz5Ba82uXbuIi4tzpQQjLy+vpnZ8z549GIZh+UkMNJw/1Gb1a+KpSUk2bNjAX//6VwzD4KqrruLWW291O6SQzJs3j23btlFYWEhCQgJ33HEHQ4cOZe7cuZw8eTKiW1jt2LGDGTNm0KtXr5oPuR/84Af07ds34vfvwIEDzJ8/H8Mw0FozcuRIbr/9do4fP868efMoKiqid+/eTJ48mejoaLfDbbGtW7eyZMkSpk6d6pl9O378OH/84x8B8+bMyy+/nFtvvZXCwsKIPy7DzeTJk6moqKh5Hfv27csDDzxATk4OCxYsYNq0aYD9n+tr1qzhL3/5CwUFBcTHx3P++efz61//uk4cDR0XVgsmFrD/NWnoeN+7dy/Lli1j4sSJ7Ny5k5dffhmfz4dhGNx4441cffXVlmy/vv37+9//TmpqKmlpaZw+fZoXXniBffv20a5dOx555BFSUlIs2XZz4vjb3/7GunXr8Pv9tGvXjp/+9Kd0797d8jjqywUqKioAuPbaa9Fa8+qrr7Jp0ybatGnDpEmTGmw7aGccS5cu5cMPP8Tv99OmTRt++MMfcuGFF1oeR0P5Q/VIsh2viaeSZSGEEEIIIazkmTIMIYQQQgghrCbJshBCCCGEEA2QZFkIIYQQQogGSLIshBBCCCFEAyRZFkIIIYQQogGSLAshhBBCCNEASZZFqzRx4kT27dvndhhCCCGECHOSLItWp6CggPz8fNdmWxJCCCFE5JBJSUSrcuzYMX75y19SUVFBmzZtiI6O5pVXXsHv97sdmhBCiLO88cYbHDlyhF/96lcALFy4kH379vHkk08SFRXlcnSitZAjTbQqXbp04Z577mH79u1MmTLF7XCEEEI04pZbbuGhhx5i37597N69m8zMTGbOnCmJsnCUHG2i1Tlw4ADnn3++22EIIYRoQvv27bnxxhuZP38+JSUlPP3008TFxbkdlmhlpGZZtDr79+/nvPPOczsMIYQQQejduzcHDx5kwoQJJCcnux2OaIUkWRatimEYHDp0SEaWhRAiAhw8eJA///nPjB49mo8//tjtcEQrJcmyaFVOnz7N6dOnkftahRAivOXk5PD73/+e+++/n5/+9KccPHiQrVu3uh2WaIUkWRatSmxsLNdccw2PPvooEydOdDscIYQQ9SgpKeHZZ5/lxhtvJC0tjZiYGG666Sbeeustt0MTrZC0jhNCCCGEEKIBMrIshBBCCCFEAyRZFkIIIYQQogGSLAshhBBCCNEASZaFEEIIIYRogCTLQgghhBBCNECSZSGEEEIIIRogybIQQgghhBANkGRZCCGEEEKIBkiyLIQQQgghRAP+P7sPWiritmI/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def vdp(t, z):\n",
    "    x, y = z\n",
    "    return [mu*(x - (1/3)*x**3 - y), (1/mu)*x]\n",
    "\n",
    "t_0, t_f = 0, 50\n",
    "x0 = 0.1\n",
    "y0 = 0.5\n",
    "\n",
    "t = np.linspace(t_0, t_f, 2000)\n",
    "\n",
    "mu = 5\n",
    "sol = solve_ivp(vdp, [t_0, t_f], [x0, y0], t_eval=t)\n",
    "\n",
    "#Plotting\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n",
    "\n",
    "ax1.set_title('Variable Time-Series')\n",
    "ax1.plot(sol.t, sol.y[0], label=r'$x$')\n",
    "ax1.plot(sol.t, sol.y[1], label=r'$y$')\n",
    "ax1.set_xlabel(r'$t$')\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(sol.y[0], sol.y[1])\n",
    "ax2.set_xlabel(r'$x$')\n",
    "ax2.set_ylabel(r'$y$')\n",
    "ax2.set_title('System Trajectories')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n9KOT60CXZF3"
   },
   "source": [
    "2. Implement a Long Short-Term Memory (LSTM) recurrent neural network with 8 lags, one hidden layer with 20 neurons, and a hyperbolic tangent activation function. Train the network using the first $2/3$ of your data-set for $20 , 000$ stochastic gradient descent steps using the Adam optimizer with a learning rate of $10^{-3}$ and a mini-batch size of 128. Use the trained model to perform one-step-ahead predictions until the final time $T = 60$ , and compare them against the true data. Plot the predicted and exact signals, and report their discrepancy within the test interval in the relative $\\mathbb{L}_{2}$ norm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uheF9EVqXc0A"
   },
   "source": [
    "#### ***Answer (2)***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bTvqGaygXVFU"
   },
   "outputs": [],
   "source": [
    "import jax.numpy as np\n",
    "from jax import random, vmap, grad, jit\n",
    "from jax.experimental import optimizers\n",
    "from jax.ops import index_update, index\n",
    "\n",
    "import itertools\n",
    "from functools import partial\n",
    "from tqdm.notebook import trange, tqdm\n",
    "import numpy.random as npr\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from scipy.interpolate import griddata\n",
    "from jax.nn import sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HmXJCTQ2XkBj"
   },
   "outputs": [],
   "source": [
    "def RNN(in_dim, out_dim, hidden_dim, num_lags):\n",
    "    # Define init function\n",
    "    def init(rng_key):\n",
    "        # Define methods for initializing the weights\n",
    "        def glorot_normal(rng_key, size):\n",
    "          in_dim = size[0]\n",
    "          out_dim = size[1]\n",
    "          glorot_stddev = 1. / np.sqrt((in_dim + out_dim) / 2.)\n",
    "          return glorot_stddev*random.normal(rng_key, (in_dim, out_dim))\n",
    "        # Inputs\n",
    "        Ui = glorot_normal(rng_key, (in_dim, hidden_dim))\n",
    "        bi = np.zeros(hidden_dim)\n",
    "        Uf = glorot_normal(rng_key, (in_dim, hidden_dim))\n",
    "        bf = np.zeros(hidden_dim)\n",
    "        Uo = glorot_normal(rng_key, (in_dim, hidden_dim))\n",
    "        bo = np.zeros(hidden_dim)\n",
    "        Us = glorot_normal(rng_key, (in_dim, hidden_dim))\n",
    "        bs = np.zeros(hidden_dim)\n",
    "        # Transition dynamics\n",
    "        Wi = np.eye(hidden_dim)\n",
    "        Wf = np.eye(hidden_dim)\n",
    "        Wo = np.eye(hidden_dim)\n",
    "        Ws = np.eye(hidden_dim)\n",
    "        # Outputs\n",
    "        V = glorot_normal(rng_key, (hidden_dim, out_dim))\n",
    "        c = np.zeros(out_dim)\n",
    "        return (Ui, bi, Wi,\n",
    "                Uf, bf, Wf,\n",
    "                Uo, bo, Wo,\n",
    "                Us, bs, Ws,\n",
    "                V, c)\n",
    "    # Define apply function\n",
    "    def apply(params, input):\n",
    "        Ui,bi,Wi,Uf,bf,Wf,Uo,bo,Wo,Us,bs,Ws, V, c = params\n",
    "        H = np.zeros((input.shape[1], hidden_dim))\n",
    "        st = np.zeros((input.shape[1], hidden_dim))\n",
    "        for i in range(num_lags):\n",
    "            it = sigmoid(np.matmul(H, Wi) + np.matmul(input[i,:,:], Ui) + bi)\n",
    "            ft = sigmoid(np.matmul(H, Wf) + np.matmul(input[i,:,:], Uf) + bf)\n",
    "            st_t = np.tanh(np.matmul(H, Ws) + np.matmul(input[i,:,:], Us) + bs)\n",
    "            st = np.multiply(ft,st) + np.multiply(it, st_t)\n",
    "            ot = sigmoid(np.matmul(H, Wo) + np.matmul(input[i,:,:], Uo) + bo)\n",
    "            H = np.multiply( ot, np.tanh(st) )\n",
    "        H = np.matmul(H, V) + c\n",
    "        return H\n",
    "    return init, apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xFxJzZeYXmuO"
   },
   "outputs": [],
   "source": [
    "def create_lags(data, L):\n",
    "    N = data.shape[0] - L\n",
    "    D = data.shape[1]\n",
    "    X = np.zeros((L, N, D))\n",
    "    Y = np.zeros((N, D))\n",
    "    for i in range(0,N):\n",
    "        X = index_update(X, index[:,i,:], data[i:(i+L), :])\n",
    "        Y = index_update(Y, index[i,:], data[i+L, :])\n",
    "    return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SUZBBttjXowG"
   },
   "outputs": [],
   "source": [
    "class Seq2seq():\n",
    "  def __init__(self, dataset, num_lags, hidden_dim, rng_key = random.PRNGKey(0)):\n",
    "    # Normalize across data-points dimension\n",
    "    self.mean, self.std = dataset.mean(0), dataset.std(0)\n",
    "    dataset = (dataset - self.mean)/self.std\n",
    "\n",
    "    # Create the lagged normalized trainind data\n",
    "    # X: L x N x D\n",
    "    # Y: N x D\n",
    "    self.X, self.Y = create_lags(dataset, num_lags)\n",
    "    in_dim = self.X.shape[-1]\n",
    "    out_dim = self.Y.shape[-1]\n",
    "\n",
    "    # Initialization and evaluation functions\n",
    "    self.net_init, self.net_apply = RNN(in_dim, out_dim, hidden_dim, num_lags)\n",
    "\n",
    "    # Initialize parameters, not committing to a batch shape\n",
    "    self.net_params = self.net_init(rng_key)\n",
    "\n",
    "    # Use optimizers to set optimizer initialization and update functions\n",
    "    self.opt_init, \\\n",
    "    self.opt_update, \\\n",
    "    self.get_params = optimizers.adam(1e-3)\n",
    "    self.opt_state = self.opt_init(self.net_params)\n",
    "\n",
    "    # Logger to monitor the loss function\n",
    "    self.loss_log = []\n",
    "    self.itercount = itertools.count()\n",
    "\n",
    "  def loss(self, params, batch):\n",
    "    X, y = batch\n",
    "    y_pred = self.net_apply(params, X)\n",
    "    loss = np.mean((y - y_pred)**2)\n",
    "    return loss\n",
    "\n",
    "  # Define a compiled update step\n",
    "  @partial(jit, static_argnums=(0,))\n",
    "  def step(self, i, opt_state, batch):\n",
    "      params = self.get_params(opt_state)\n",
    "      g = grad(self.loss)(params, batch)\n",
    "      return self.opt_update(i, g, opt_state)\n",
    "\n",
    "  def data_stream(self, n, num_batches, batch_size):\n",
    "    rng = npr.RandomState(0)\n",
    "    while True:\n",
    "      perm = rng.permutation(n)\n",
    "      for i in range(num_batches):\n",
    "        batch_idx = perm[i*batch_size:(i+1)*batch_size]\n",
    "        yield self.X[:, batch_idx, :], self.Y[batch_idx, :]\n",
    "\n",
    "  def train(self, num_epochs = 100, batch_size = 64):\n",
    "    n = self.X.shape[1]\n",
    "    num_complete_batches, leftover = divmod(n, batch_size)\n",
    "    num_batches = num_complete_batches + bool(leftover)\n",
    "    batches = self.data_stream(n, num_batches, batch_size)\n",
    "    pbar = trange(num_epochs)\n",
    "    for epoch in pbar:\n",
    "      for _ in range(num_batches):\n",
    "        batch = next(batches)\n",
    "        self.opt_state = self.step(next(self.itercount), self.opt_state, batch)\n",
    "      self.net_params = self.get_params(self.opt_state)\n",
    "      loss_value = self.loss(self.net_params, batch)\n",
    "      self.loss_log.append(loss_value)\n",
    "      pbar.set_postfix({'Loss': loss_value})\n",
    "\n",
    "  @partial(jit, static_argnums=(0,))\n",
    "  def predict(self, params, inputs):\n",
    "    Y_pred = self.net_apply(params, inputs)\n",
    "    return Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K6fDSfWDXqcE"
   },
   "outputs": [],
   "source": [
    "rng_key = random.PRNGKey(0)\n",
    "\n",
    "dataset = np.stack( (sol.y[0], sol.y[1]) , axis=1)\n",
    "\n",
    "# Use 2/3 of all data as training Data\n",
    "train_size = int(len(dataset) * (2.0/3.0))\n",
    "train_data = dataset[0:train_size,:]\n",
    "\n",
    "# Model creation\n",
    "num_lags = 8\n",
    "hidden_dim = 20\n",
    "model = Seq2seq(train_data, num_lags, hidden_dim, rng_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "LrVUnVsBXsQ4",
    "outputId": "535efa38-b3fd-401d-e5b8-107199949aa3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f02e8c5b6d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = model.Y.shape[0]\n",
    "t1 = np.linspace(0, 50, len(dataset[num_lags:,0]))\n",
    "t2 = np.linspace(0, 50, len(pred))\n",
    "\n",
    "plt.figure(figsize=(25,7))\n",
    "plt.plot(np.arange(num_lags, N+num_lags), model.Y[:,0], label = '$y_t$')\n",
    "plt.plot(np.arange(num_lags, N+num_lags), model.Y[:,1], label = '$x_t$')\n",
    "for i in range(num_lags):\n",
    "  plt.plot(np.arange(num_lags-i+1, N+num_lags-i+1), model.X[num_lags-i+1,:,0], label = '$x_{t-%d}$' % (i+1))\n",
    "  plt.plot(np.arange(num_lags-i+1, N+num_lags-i+1), model.X[num_lags-i+1,:,1], label = '$y_{t-%d}$' % (i+1))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "referenced_widgets": [
      "c1e51ccc86184cd493324e0974927f5c",
      "9393358461af4b6a9a5e6f183fa23cfc",
      "7b2bc90619c740a79e73d5b15b7d12b5",
      "06c79e98faed4e9aab65aeccd1b008ff",
      "1a72231bf57b498fb3678789f8ce4202",
      "2470e28989aa454099c2a3f7674c407d",
      "01a438936b3d4a4cbfb2a411775d32dc",
      "514fbbb6fa8d4d16b5afecd4b30976ac"
     ]
    },
    "id": "lvNBJjkDYF1X",
    "outputId": "48d57643-3ed7-40c2-cfc4-dbd3a5ea746b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1e51ccc86184cd493324e0974927f5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=500.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model.train(num_epochs = 500, batch_size = 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87,
     "referenced_widgets": [
      "e55f921a1a574ee5ba972e60e585a681",
      "d485381d8b40452091784250d02e432f",
      "c11ed138881b4d819c1e239caf1cbf8e",
      "524f950d77084a5aa0e472ff546d3003",
      "bd1f58199e6b44c7bbaf04a76879079e",
      "73ef96d56e994a01854887f76f1445e6",
      "6a88cab2e84f4a2a89a205136bcff30e",
      "bc541b75e68b4fddbd1a2ae713825361"
     ]
    },
    "id": "dcpnytxCYlkt",
    "outputId": "f4af1e8b-fbb8-4c8d-dd8a-092c9a191b30"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e55f921a1a574ee5ba972e60e585a681",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1992.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Relative L2 prediction error: 1.326407e-01\n"
     ]
    }
   ],
   "source": [
    "opt_params = model.net_params\n",
    "# One-step ahead prediction (normalized)\n",
    "N, D = dataset.shape\n",
    "pred = np.zeros((N-num_lags, D))\n",
    "X_tmp =  model.X[:,0:1,:]\n",
    "for i in trange(N-num_lags):\n",
    "    pred = index_update(pred, index[i:i+1], model.net_apply(opt_params, X_tmp))\n",
    "    X_tmp = index_update(X_tmp, index[:-1,:,:], X_tmp[1:,:,:])\n",
    "    X_tmp = index_update(X_tmp, index[-1,:,:], pred[i])\n",
    "# De-normalize predictions\n",
    "pred = pred*model.std + model.mean\n",
    "error = np.linalg.norm(dataset[num_lags:] - pred, 2)/np.linalg.norm(dataset[num_lags:], 2)\n",
    "print('Relative L2 prediction error: %e' % (error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "id": "HLKGR4WkZXwH",
    "outputId": "88e22093-2f29-4663-950c-26dc69bfe114"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f02e63b0c90>"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,7))\n",
    "t1 = np.linspace(0, 50, len(dataset[num_lags:,0]))\n",
    "t2 = np.linspace(0, 50, len(pred[:,0]))\n",
    "plt.plot(t1, dataset[num_lags:,0], 'b-', linewidth = 2, label = \"Exact x\")\n",
    "plt.plot(t1, dataset[num_lags:,1], 'k-', linewidth = 2, label = \"Exact y\")\n",
    "plt.plot(t2, pred[:,0], 'r--', linewidth = 3, label = \"Prediction x\")\n",
    "plt.plot(t2, pred[:,1], 'y--', linewidth = 3, label = \"Prediction y\")\n",
    "# plt.plot(X.shape[1]*np.ones((2,1)), np.linspace(-1.75,1.75,2), 'k--', linewidth=2)\n",
    "plt.axvline(int(50 * (2.0/3.0)))\n",
    "plt.axis('tight')\n",
    "plt.xlabel('$t$')\n",
    "plt.ylabel('$y_t , x_t$')\n",
    "plt.legend(loc='lower left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4elD4lyFasRd"
   },
   "source": [
    "## Physics-Informed Neural Networks (PINNs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mQU95UWMaxM0"
   },
   "source": [
    "Implement a physics-informed neural network (Raissi, Perdikaris, & Karniadakis, 2017) to solve the Burgers’ equation:\n",
    "\n",
    "$$ u_t + u u_x - \\nu u_{xx} = 0, \\quad \\nu=0.01/\\pi, \\quad x\\in[-1, 1], t\\in[0,1] $$ <br>\n",
    "\n",
    "$$ u(0,x) = -\\sin(\\pi x), \\quad u(t,-1) = u(t,1) = 0 $$\n",
    "\n",
    "Plot your solution and report the relative $\\mathbb{L}_{2}$ prediction error against the reference solution provided"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SvGDaqsRa0sB"
   },
   "source": [
    "#### ***Answer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "o3hNsLZRawTs",
    "outputId": "975f4ada-8d18-46f4-cf75-33c43885cfcf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-c5ac9ef1-9303-485e-b857-660ac24e03af\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-c5ac9ef1-9303-485e-b857-660ac24e03af\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving burgers_shock.mat to burgers_shock.mat\n"
     ]
    }
   ],
   "source": [
    "#Upload burgers_shock.mat file\n",
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "02LEZwBsa5UB"
   },
   "outputs": [],
   "source": [
    "import jax.numpy as np\n",
    "from jax import random, vmap, grad, jit\n",
    "from jax.experimental import optimizers\n",
    "\n",
    "import itertools\n",
    "from functools import partial\n",
    "from tqdm.notebook import trange, tqdm\n",
    "import numpy.random as npr\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from scipy.interpolate import griddata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LQ3jAw6YbADa"
   },
   "outputs": [],
   "source": [
    "#Building the MLP\n",
    "\n",
    "def MLP(layers):\n",
    "    def init(rng_key):\n",
    "        def init_layer(key, d_in, d_out):\n",
    "            k1, k2 = random.split(key)\n",
    "            glorot_stddev = 1. / np.sqrt((d_in + d_out) / 2.)\n",
    "            W = glorot_stddev*random.normal(k1, (d_in, d_out))\n",
    "            b = np.zeros(d_out)\n",
    "            return W, b\n",
    "        key, *keys = random.split(rng_key, len(layers))\n",
    "        params = list(map(init_layer, keys, layers[:-1], layers[1:]))\n",
    "        return params\n",
    "    def apply(params, inputs):\n",
    "        for W, b in params[:-1]:\n",
    "            outputs = np.dot(inputs, W) + b\n",
    "            inputs = np.tanh(outputs)\n",
    "        W, b = params[-1]\n",
    "        outputs = np.dot(inputs, W) + b\n",
    "        return outputs\n",
    "    return init, apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UF5G3tmObCjj"
   },
   "outputs": [],
   "source": [
    "class NeuralNetRegression():\n",
    "  def __init__(self, X, y, X_f, layers, rng_key = random.PRNGKey(0)):\n",
    "    # Normalize data\n",
    "    self.Xmean, self.Xstd = X.mean(0), X.std(0)\n",
    "    self.Ymean, self.Ystd = y.mean(0), y.std(0)\n",
    "    #X = (X - self.Xmean)/self.Xstd\n",
    "    #y = (y - self.Ymean)/self.Ystd\n",
    "\n",
    "    # Store the normalized trainind data\n",
    "    self.X = X\n",
    "    self.y = y\n",
    "    self.layers = layers\n",
    "    self.X_f = X_f\n",
    "\n",
    "    # Use stax to set up network initialization and evaluation functions\n",
    "    self.net_init, self.net_apply = MLP(layers)\n",
    "\n",
    "    # Initialize parameters, not committing to a batch shape\n",
    "    params = self.net_init(rng_key)\n",
    "\n",
    "    # Use optimizers to set optimizer initialization and update functions\n",
    "    self.opt_init, \\\n",
    "    self.opt_update, \\\n",
    "    self.get_params = optimizers.adam(1e-4)\n",
    "    self.opt_state = self.opt_init(params)\n",
    "\n",
    "    # Logger to monitor the loss function\n",
    "    self.loss_log = []\n",
    "    self.itercount = itertools.count()\n",
    "\n",
    "  def u_loss1(self, params, batch):\n",
    "    X, y = batch\n",
    "    y_pred = self.net_apply(params, X)\n",
    "    loss_u = (y - y_pred)**2\n",
    "    return loss_u\n",
    "\n",
    "  def net_u(self, params, x, t):\n",
    "    inputs = np.stack([x, t])\n",
    "    u = self.net_apply(params, inputs)\n",
    "    return u[0]\n",
    "\n",
    "  def net_f1(self, params, X_f):\n",
    "    x, t = X_f\n",
    "    #Derivatives\n",
    "    u = self.net_u(params, x, t)\n",
    "    u_t = grad(self.net_u, 2)(params, x, t)\n",
    "    u_x = grad(self.net_u, 1)(params, x, t)\n",
    "    u_xx = grad(grad(self.net_u, 1), 1)(params, x, t)\n",
    "    #Residual\n",
    "    res = (u_t + u*u_x - (0.01/np.pi)*u_xx)**2\n",
    "    return res\n",
    "\n",
    "  def loss(self, params, batch):\n",
    "    u_loss = lambda x: self.u_loss1(params, x)\n",
    "    f_loss = lambda x: self.net_f1(params, x)\n",
    "    loss_u = (1/300)*np.sum(vmap(u_loss)(batch))\n",
    "    loss_res = (1/10000)*np.sum(vmap(f_loss)(self.X_f))\n",
    "    loss = loss_u\n",
    "    return loss\n",
    "\n",
    "  # Define a compiled update step\n",
    "  @partial(jit, static_argnums=(0,))\n",
    "  def step(self, i, opt_state, batch):\n",
    "      params = self.get_params(opt_state)\n",
    "      g = grad(self.loss)(params, batch)\n",
    "      return self.opt_update(i, g, opt_state)\n",
    "\n",
    "  def data_stream(self, n, num_batches, batch_size):\n",
    "    rng = npr.RandomState(0)\n",
    "    while True:\n",
    "      perm = rng.permutation(n)\n",
    "      for i in range(num_batches):\n",
    "        batch_idx = perm[i*batch_size:(i+1)*batch_size]\n",
    "        yield self.X[batch_idx, :], self.y[batch_idx]\n",
    "\n",
    "  def train(self, num_epochs = 100, batch_size = 64):\n",
    "    n = self.X.shape[0]\n",
    "    num_complete_batches, leftover = divmod(n, batch_size)\n",
    "    num_batches = num_complete_batches + bool(leftover)\n",
    "    batches = self.data_stream(n, num_batches, batch_size)\n",
    "    pbar = trange(num_epochs)\n",
    "    for epoch in pbar:\n",
    "      for _ in range(num_batches):\n",
    "        batch = next(batches)\n",
    "        self.opt_state = self.step(next(self.itercount), self.opt_state, batch)\n",
    "      params = self.get_params(self.opt_state)\n",
    "      loss_value = self.loss(params, batch)\n",
    "      self.loss_log.append(loss_value)\n",
    "      pbar.set_postfix({'Loss': loss_value})\n",
    "\n",
    "  def predict(self, params, X_star):\n",
    "    #X_star = (X_star - self.Xmean)/self.Xstd\n",
    "    pred_fn = lambda x: self.net_apply(params, x)\n",
    "    y_pred = vmap(pred_fn)(X_star)\n",
    "    #y_pred = y_pred*self.Ystd + self.Ymean\n",
    "    return y_pred\n",
    "\n",
    "  def compute_activations(self, params, X_star):\n",
    "    X_star = (X_star - self.Xmean)/self.Xstd\n",
    "    def MLP_pass(params, inputs):\n",
    "        H = []\n",
    "        for W, b in params[:-1]:\n",
    "              outputs = np.dot(inputs, W) + b\n",
    "              inputs = np.tanh(outputs)\n",
    "              H.append(inputs)\n",
    "        return H\n",
    "    # Get predictions\n",
    "    pred_fn = lambda x: MLP_pass(params, x)\n",
    "    activations = vmap(pred_fn)(X_star)\n",
    "    return activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "C_DvRqyVbE88"
   },
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "import numpy as np\n",
    "from pyDOE import lhs\n",
    "\n",
    "\n",
    "np.random.seed(1234)\n",
    "\n",
    "data = scipy.io.loadmat('burgers_shock.mat')\n",
    "\n",
    "t = data['t'].flatten()[:,None]\n",
    "x = data['x'].flatten()[:,None]\n",
    "\n",
    "# Keeping the 2D data for the solution data (real() is maybe to make it float by default, in case of zeroes)\n",
    "Exact_u = np.real(data['usol']).T # T x N\n",
    "\n",
    "X, T = np.meshgrid(x,t)\n",
    "\n",
    "X_star = np.hstack((X.flatten()[:,None], T.flatten()[:,None]))\n",
    "u_star = Exact_u.flatten()[:,None]\n",
    "\n",
    "N_u = 300 #Boundary data\n",
    "N_f = 1000 #Collocation points\n",
    "\n",
    "# Domain bounds (lowerbounds upperbounds) [x, t]\n",
    "lb = X_star.min(axis=0)\n",
    "ub = X_star.max(axis=0)\n",
    "# Getting the initial conditions (t=0)\n",
    "xx1 = np.hstack((X[0:1,:].T, T[0:1,:].T))\n",
    "uu1 = Exact_u[0:1,:].T\n",
    "# Getting the lowest boundary conditions (x=-1)\n",
    "xx2 = np.hstack((X[:,0:1], T[:,0:1]))\n",
    "uu2 = Exact_u[:,0:1]\n",
    "# Getting the highest boundary conditions (x=1)\n",
    "xx3 = np.hstack((X[:,-1:], T[:,-1:]))\n",
    "uu3 = Exact_u[:,-1:]\n",
    "# Stacking them in multidimensional tensors for training (X_u_train is for now the continuous boundaries)\n",
    "X_u_train = np.vstack([xx1, xx2, xx3])\n",
    "u_train = np.vstack([uu1, uu2, uu3])\n",
    "\n",
    "\n",
    "# Generating the x and t collocation points for f, with each having a N_f size\n",
    "# We pointwise add and multiply to spread the LHS over the 2D domain\n",
    "X_f_train = lb + (ub-lb)*lhs(2, N_f)\n",
    "\n",
    "# Generating a uniform random sample from ints between 0, and the size of x_u_train, of size N_u (initial data size) and without replacement (unique)\n",
    "idx = np.random.choice(X_u_train.shape[0], N_u, replace=False)\n",
    "# Getting the corresponding X_u_train (which is now scarce boundary/initial coordinates)\n",
    "X_u_train = X_u_train[idx,:]\n",
    "# Getting the corresponding u_train\n",
    "u_train = u_train [idx,:]\n",
    "\n",
    "idx = np.random.choice(X_star.shape[0], 3000, replace=False)\n",
    "Xff_train = X_star[idx,:]\n",
    "Xff_u_train = u_star[idx,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z08alHhNbVs1"
   },
   "outputs": [],
   "source": [
    "import jax.numpy as np\n",
    "\n",
    "#8 hidden layers with tanh activation\n",
    "layers = [2, 20, 20, 20, 20, 20, 20, 20, 20, 1]\n",
    "model = NeuralNetRegression(Xff_train, Xff_u_train, X_f_train, layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68,
     "referenced_widgets": [
      "77c85bd87261499ba9bc701462e7cb33",
      "1bcbde38517c46dc95e6d1466bc2ec61",
      "4bdf48147feb47269493c4417cecf245",
      "0aa29420f3df4766bb299e35e950e335",
      "b939bfd58196481090aa4bf12c37b4f2",
      "8c610bf1b8aa4450a92dd24bc04ea1cf",
      "ed1a06beec9e4269aa510981a7d4fe94",
      "96472b165d7e4775ade4a80abbf061cc"
     ]
    },
    "id": "8xZp7Wr8bYIN",
    "outputId": "4cb36bdc-7672-474a-efa9-70d98c721378"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77c85bd87261499ba9bc701462e7cb33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=300.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model.train(num_epochs = 300, batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ADXJLMbjbazm",
    "outputId": "da43754d-8b8d-4208-c3ef-ed413bd15cc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The L2 error is: 0.08621\n"
     ]
    }
   ],
   "source": [
    "#Calculating predictions and L2 error\n",
    "\n",
    "opt_params = model.get_params(model.opt_state)\n",
    "u_pred = model.predict(opt_params, X_star)\n",
    "\n",
    "l2_err = np.linalg.norm(u_star - u_pred, 2) / np.linalg.norm(u_star, 2)\n",
    "print('The L2 error is: {:0.5f}'.format(l2_err))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "id": "d1wC3rAgcduV",
    "outputId": "8ebf6162-957c-48ad-ed19-fc649c7647be"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting\n",
    "\n",
    "from mpl_toolkits import mplot3d\n",
    "from scipy.interpolate import griddata\n",
    "import matplotlib.gridspec as gridspec\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "def plot_results(X_star, u_pred, X_u_train, u_train, Exact_u, X, T, x, t,):\n",
    "    fig, ax = plt.subplots(figsize=(15,15))\n",
    "    #ax.axis('off')\n",
    "\n",
    "    U_pred = griddata(X_star, u_pred.flatten(), (X, T), method='cubic')\n",
    "\n",
    "    ####### Row 0: u(t,x) ##################\n",
    "    gs0 = gridspec.GridSpec(1, 2)\n",
    "    gs0.update(top=1-0.06, bottom=1-1.0/3.0+0.06, left=0.15, right=0.85, wspace=0)\n",
    "    ax = plt.subplot(gs0[:, :])\n",
    "\n",
    "    h = ax.imshow(U_pred.T, interpolation='nearest', cmap='jet',\n",
    "                  extent=[t.min(), t.max(), x.min(), x.max()],\n",
    "                  origin='lower', aspect='auto')\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    fig.colorbar(h, cax=cax)\n",
    "\n",
    "    ax.plot(X_u_train[:,1], X_u_train[:,0], 'kx', label = 'Data (%d points)' % (u_train.shape[0]), markersize = 7, clip_on = False)\n",
    "\n",
    "    line = np.linspace(x.min(), x.max(), 2)[:,None]\n",
    "    ax.plot(t[25]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
    "    ax.plot(t[50]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
    "    ax.plot(t[75]*np.ones((2,1)), line, 'w-', linewidth = 1)\n",
    "\n",
    "    ax.set_xlabel('$t$')\n",
    "    ax.set_ylabel('$x$')\n",
    "    ax.legend(loc='upper center', bbox_to_anchor=(1.0, -0.125), ncol=5, frameon=False)\n",
    "    ax.set_title('$u(t,x)$', fontsize = 10)\n",
    "\n",
    "    ####### Row 1: u(t,x) slices ##################\n",
    "    gs1 = gridspec.GridSpec(1, 3)\n",
    "    gs1.update(top=1-1.0/3.0-0.1, bottom=1.0-2.0/3.0, left=0.1, right=0.9, wspace=0.5)\n",
    "\n",
    "    ax = plt.subplot(gs1[0, 0])\n",
    "    ax.plot(x,Exact_u[25,:], 'b-', linewidth = 2, label = 'Exact')\n",
    "    ax.plot(x,U_pred[25,:], 'r--', linewidth = 2, label = 'Prediction')\n",
    "    ax.set_xlabel('$x$')\n",
    "    ax.set_ylabel('$u(t,x)$')\n",
    "    ax.set_title('$t = 0.25$', fontsize = 10)\n",
    "    ax.axis('square')\n",
    "    ax.set_xlim([-1.1,1.1])\n",
    "    ax.set_ylim([-1.1,1.1])\n",
    "\n",
    "    ax = plt.subplot(gs1[0, 1])\n",
    "    ax.plot(x,Exact_u[50,:], 'b-', linewidth = 2, label = 'Exact')\n",
    "    ax.plot(x,U_pred[50,:], 'r--', linewidth = 2, label = 'Prediction')\n",
    "    ax.set_xlabel('$x$')\n",
    "    ax.set_ylabel('$u(t,x)$')\n",
    "    ax.axis('square')\n",
    "    ax.set_xlim([-1.1,1.1])\n",
    "    ax.set_ylim([-1.1,1.1])\n",
    "    ax.set_title('$t = 0.50$', fontsize = 10)\n",
    "    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.35), ncol=5, frameon=False)\n",
    "\n",
    "    ax = plt.subplot(gs1[0, 2])\n",
    "    ax.plot(x,Exact_u[75,:], 'b-', linewidth = 2, label = 'Exact')\n",
    "    ax.plot(x,U_pred[75,:], 'r--', linewidth = 2, label = 'Prediction')\n",
    "    ax.set_xlabel('$x$')\n",
    "    ax.set_ylabel('$u(t,x)$')\n",
    "    ax.axis('square')\n",
    "    ax.set_xlim([-1.1,1.1])\n",
    "    ax.set_ylim([-1.1,1.1])\n",
    "    ax.set_title('$t = 0.75$', fontsize = 10)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_results(X_star, u_pred.flatten(), X_u_train, u_train,\n",
    "  Exact_u, X, T, x, t)"
   ]
  },
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    "id": "T3jADI8Bcg6I"
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