COVID_Disease_Severity_Projection_github.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5Ianj720LwK"
      },
      "source": [
        "#Intitialization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hUg9gqAoobKi"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "from dateutil.parser import parse as dateparse\n",
        "import time\n",
        "from collections import Counter\n",
        "from tqdm.notebook import tqdm\n",
        "import pandas as pd\n",
        "from pandas import read_csv\n",
        "from pandas import DataFrame\n",
        "from datetime import datetime\n",
        "from dateutil.parser import parse as dateparse\n",
        "import pickle\n",
        "import csv\n",
        "import ast\n",
        "\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import backend as K\n",
        "\n",
        "from sklearn.preprocessing import MultiLabelBinarizer, QuantileTransformer, OneHotEncoder, LabelBinarizer, OrdinalEncoder\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, RandomizedSearchCV, StratifiedShuffleSplit\n",
        "from sklearn.metrics import accuracy_score,classification_report, make_scorer, balanced_accuracy_score, coverage_error, roc_auc_score, confusion_matrix, plot_confusion_matrix, multilabel_confusion_matrix, mean_squared_error\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from sklearn.utils import class_weight\n",
        "import sklearn as sk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CYy877h90eA1"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive, files\n",
        "# drive.mount('/content/drive')\n",
        "\n",
        "FILELOC = \"/content/drive/My Drive/COVID_Python/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLknMJwnPCfK",
        "outputId": "47156165-a4d2-4c7a-84f3-43af3fb1cfe5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on TPU  ['10.109.238.26:8470']\n",
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.109.238.26:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.109.238.26:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection\n",
        "    print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\n",
        "    tf.config.experimental_connect_to_cluster(tpu)\n",
        "    tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "    tpu_strategy = tf.distribute.TPUStrategy(tpu)\n",
        "    tpu_env=True\n",
        "except ValueError:\n",
        "    print('Not connected to a TPU runtime.')\n",
        "    tpu_env=False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_JNTtZptFcM"
      },
      "source": [
        "#Model definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nPWC9ukErZqE"
      },
      "outputs": [],
      "source": [
        "def reset_model(no_sequence_model=False, no_vars_model=False):\n",
        "\n",
        "    model_fn = AttModel\n",
        "    model = model_fn(L=ismlen,\n",
        "                     numvars=NVARS,\n",
        "                     vocab_size=len(aa_list)+1,\n",
        "                     embdim = ENCDIM,\n",
        "                     numheads = NHEADS,\n",
        "                     ffdim = FFDIM,\n",
        "                     num_dense = NDENSE,\n",
        "                     mask_zero=True,\n",
        "                     dropout_rate = DROPRATE,\n",
        "                     trans_drop = TRANSDROPRATE,\n",
        "                     Nt = NT,\n",
        "                     W = 1, Nc = NC, Nl = NL,\n",
        "                     )\n",
        "    if no_sequence_model:\n",
        "        model_fn = DenseModel\n",
        "        model = model_fn(numvars=NVARS,\n",
        "                         num_dense=NDENSE,\n",
        "                         dropout_rate=DROPRATE)\n",
        "    elif no_vars_model:\n",
        "        model_fn = AttModel_NoVars\n",
        "        model = model_fn(L=ismlen,\n",
        "                        vocab_size=len(aa_list)+1,\n",
        "                        embdim = ENCDIM,\n",
        "                        numheads = NHEADS,\n",
        "                        ffdim = FFDIM,\n",
        "                        num_dense = NDENSE,\n",
        "                        mask_zero=True,\n",
        "                        dropout_rate = DROPRATE,\n",
        "                        trans_drop = TRANSDROPRATE,\n",
        "                        Nt = NT,\n",
        "                        W = 1, Nc = NC, Nl = NL,\n",
        "                        )        \n",
        "\n",
        "    optimizer = keras.optimizers.Adam(learning_rate=LEARN_RATE)\n",
        "    loss = keras.losses.BinaryCrossentropy()\n",
        "    metrics = [keras.metrics.BinaryAccuracy(name='acc')]\n",
        "\n",
        "    model.compile(loss=loss, optimizer=optimizer, metrics=metrics,)\n",
        "    \n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mGlQoE_hrZbu"
      },
      "outputs": [],
      "source": [
        "class TransformerBlock(keras.layers.Layer):\n",
        "    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n",
        "        super(TransformerBlock, self).__init__()\n",
        "        self.att = keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n",
        "        self.ffn = keras.Sequential(\n",
        "            [keras.layers.Dense(ff_dim, activation=\"relu\"), keras.layers.Dense(embed_dim),]\n",
        "        )\n",
        "        self.layernorm1 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "        self.layernorm2 = keras.layers.LayerNormalization(epsilon=1e-6)\n",
        "        self.dropout1 = keras.layers.Dropout(rate)\n",
        "        self.dropout2 = keras.layers.Dropout(rate)\n",
        "\n",
        "    def call(self, inputs, training):\n",
        "        attn_output = self.att(inputs, inputs)\n",
        "        attn_output = self.dropout1(attn_output, training=training)\n",
        "        out1 = self.layernorm1(inputs + attn_output)\n",
        "        ffn_output = self.ffn(out1)\n",
        "        ffn_output = self.dropout2(ffn_output, training=training)\n",
        "        return self.layernorm2(out1 + ffn_output)\n",
        "\n",
        "class TokenAndPositionEmbedding(keras.layers.Layer):\n",
        "    def __init__(self, maxlen, vocab_size, embed_dim, mask_zero=False):\n",
        "        super(TokenAndPositionEmbedding, self).__init__()\n",
        "        self.token_emb = keras.layers.Embedding(input_dim=vocab_size,\n",
        "                                                output_dim=embed_dim,\n",
        "                                                mask_zero=mask_zero)\n",
        "        self.pos_emb = keras.layers.Embedding(input_dim=maxlen, output_dim=embed_dim,\n",
        "                                              mask_zero=mask_zero)\n",
        "\n",
        "    def call(self, x):\n",
        "        maxlen = tf.shape(x)[-1]\n",
        "        positions = tf.range(start=0, limit=maxlen, delta=1)\n",
        "        positions = self.pos_emb(positions)\n",
        "        x = self.token_emb(x)\n",
        "        return x + positions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0egOp97OrtaH"
      },
      "outputs": [],
      "source": [
        "def AttModel(L, numvars, vocab_size, embdim, numheads, ffdim, num_dense=False,\n",
        "             mask_zero=False, dropout_rate=False, trans_drop=0.1,\n",
        "             Nt=1, W=False, Nc=False, Nl=False):\n",
        "\n",
        "    inpSeq = keras.Input(shape=(L,))\n",
        "    inpVars = keras.Input(shape=(3,))\n",
        "    x = inpSeq\n",
        "    v = inpVars\n",
        "\n",
        "    if mask_zero:\n",
        "        x = keras.layers.Masking(mask_value=0)(x)   \n",
        "\n",
        "    x = TokenAndPositionEmbedding(L, vocab_size, embdim, mask_zero)(x)\n",
        "\n",
        "    if W and Nc and Nl:\n",
        "        for n in range(Nl):\n",
        "            x = keras.layers.Conv1D(filters = Nc,\n",
        "                                kernel_size = W,\n",
        "                                activation = 'relu',\n",
        "                                padding = 'same',\n",
        "                                )(x)\n",
        "            if n > 1 and n < Nl-1:\n",
        "                x = keras.layers.BatchNormalization()(x)\n",
        "\n",
        "    for n in range(Nt):\n",
        "        x = TransformerBlock(Nc, numheads, ffdim, rate=trans_drop)(x)\n",
        "\n",
        "    # Attention layer\n",
        "    h = keras.layers.TimeDistributed(keras.layers.Dense(Nc, activation='tanh'))(x)\n",
        "    attention = keras.layers.TimeDistributed(keras.layers.Dense(1, activation='tanh'))(h)\n",
        "    attention = keras.layers.Flatten()(attention)  \n",
        "    attention = keras.layers.Softmax(axis=1, name='attention')(attention) # normalize attention values\n",
        "    attention = keras.layers.RepeatVector(Nc)(attention)\n",
        "    attention = keras.layers.Permute([2, 1])(attention)\n",
        "    representation = keras.layers.multiply([h, attention])\n",
        "    representation = tf.math.reduce_sum(representation, axis = 1)\n",
        "    x = representation\n",
        "\n",
        "    h = keras.layers.concatenate([x, v])\n",
        "    x = keras.layers.Dense(num_dense, activation = 'relu')(h)\n",
        "    if dropout_rate:\n",
        "        x = keras.layers.Dropout(Params[dropout_rate])(x)\n",
        "\n",
        "    finalOut = keras.layers.Dense(1, activation='sigmoid')(x)       \n",
        "\n",
        "    # define the model's start and end points    \n",
        "    model = keras.Model([inpSeq,inpVars],finalOut)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qmx-trbm26QN"
      },
      "outputs": [],
      "source": [
        "def DenseModel(numvars, num_dense, dropout_rate=False):\n",
        "\n",
        "    inpVars = keras.Input(shape=(3,))\n",
        "    v = inpVars\n",
        "\n",
        "    x = keras.layers.Dense(num_dense, activation = 'relu')(v)\n",
        "    if dropout_rate:\n",
        "        x = keras.layers.Dropout(Params[dropout_rate])(x)\n",
        "\n",
        "    finalOut = keras.layers.Dense(1, activation='sigmoid')(x)       \n",
        "\n",
        "    # define the model's start and end points    \n",
        "    model = keras.Model(inpVars,finalOut)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4MrdS3013ODS"
      },
      "outputs": [],
      "source": [
        "def AttModel_NoVars(L, vocab_size, embdim, numheads, ffdim, num_dense=False,\n",
        "                    mask_zero=False, dropout_rate=False, trans_drop=0.1,\n",
        "                    Nt=1, W=False, Nc=False, Nl=False):\n",
        "\n",
        "    inpSeq = keras.Input(shape=(L,))\n",
        "    x = inpSeq\n",
        "\n",
        "    if mask_zero:\n",
        "        x = keras.layers.Masking(mask_value=0)(x)   \n",
        "\n",
        "    x = TokenAndPositionEmbedding(L, vocab_size, embdim, mask_zero)(x)\n",
        "\n",
        "    if W and Nc and Nl:\n",
        "        for n in range(Nl):\n",
        "            x = keras.layers.Conv1D(filters = Nc,\n",
        "                                kernel_size = W,\n",
        "                                activation = 'relu',\n",
        "                                padding = 'same',\n",
        "                                )(x)\n",
        "            if n > 1 and n < Nl-1:\n",
        "                x = keras.layers.BatchNormalization()(x)\n",
        "\n",
        "    for n in range(Nt):\n",
        "        x = TransformerBlock(Nc, numheads, ffdim, rate=trans_drop)(x)\n",
        "\n",
        "    # Attention layer\n",
        "    h = keras.layers.TimeDistributed(keras.layers.Dense(Nc, activation='tanh'))(x)\n",
        "    attention = keras.layers.TimeDistributed(keras.layers.Dense(1, activation='tanh'))(h)\n",
        "    attention = keras.layers.Flatten()(attention)  \n",
        "    attention = keras.layers.Softmax(axis=1, name='attention')(attention) # normalize attention values\n",
        "    attention = keras.layers.RepeatVector(Nc)(attention)\n",
        "    attention = keras.layers.Permute([2, 1])(attention)\n",
        "    representation = keras.layers.multiply([h, attention])\n",
        "    representation = tf.math.reduce_sum(representation, axis = 1)\n",
        "    x = representation\n",
        "\n",
        "    x = keras.layers.Dense(num_dense, activation = 'relu')(x)\n",
        "    if dropout_rate:\n",
        "        x = keras.layers.Dropout(Params[dropout_rate])(x)\n",
        "\n",
        "    finalOut = keras.layers.Dense(1, activation='sigmoid')(x)       \n",
        "\n",
        "    # define the model's start and end points    \n",
        "    model = keras.Model(inpSeq,finalOut)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iwjr6D5QrtXF"
      },
      "outputs": [],
      "source": [
        "ismlen = 1273\n",
        "\n",
        "def tokenize_sequences(data_dataframe, SeqCol='ISM', seqlen=1273):\n",
        "    def f(x):\n",
        "        if len(x) < seqlen:\n",
        "            return x + '*'*(seqlen-len(x))\n",
        "        elif len(x) > seqlen:\n",
        "            return x[:seqlen]\n",
        "        else:\n",
        "            return x\n",
        "    data = np.vstack(data_dataframe[SeqCol].apply(f).apply(lambda x: np.array(list(x))))\n",
        "    aa_list = ['A', 'R', 'N', 'D', 'C', 'Q', 'E',\n",
        "            'G', 'H', 'I', 'L', 'K', 'M', 'F',\n",
        "            'P', 'S', 'T', 'W', 'Y', 'V', '-',\n",
        "            ]\n",
        "    aa_tokenizer = {aa_list[k]:k+1 for k in range(len(aa_list))}\n",
        "    aa_tokenizer['*'] = 0\n",
        "    aa_tokenizer['X'] = 0\n",
        "    # optionally handle B, J, Z ambiguities\n",
        "    # Asx\tB\tAspartic acid or Asparagine (D or N)\n",
        "    # Glx\tZ\tGlutamic acid or Glutamine (E or Q)\n",
        "    # Xaa\tX\tAny amino acid\n",
        "    # Xle\tJ\tLeucine or Isoleucine (L or I)\n",
        "    aa_tokenizer['B'] = 0\n",
        "    aa_tokenizer['Z'] = 0\n",
        "    aa_tokenizer['J'] = 0\n",
        "\n",
        "    return np.vectorize(aa_tokenizer.get)(data)\n",
        "\n",
        "aa_list = ['A', 'R', 'N', 'D', 'C', 'Q', 'E',\n",
        "        'G', 'H', 'I', 'L', 'K', 'M', 'F',\n",
        "        'P', 'S', 'T', 'W', 'Y', 'V', '-',\n",
        "        ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wOPQPrNrtArU"
      },
      "outputs": [],
      "source": [
        "# These parameters are currently hard-coded\n",
        "ENCDIM = 1500\n",
        "NC = 300\n",
        "NL = 1\n",
        "NT = 1\n",
        "NHEADS = 8\n",
        "FFDIM = 64\n",
        "NDENSE = 64\n",
        "\n",
        "TRANSDROPRATE = 0.4\n",
        "DROPRATE = 0\n",
        "\n",
        "LEARN_RATE = 0.0001\n",
        "\n",
        "BATCH_SIZE = 48*8\n",
        "\n",
        "STEPS_PER_EXECUTION = 50"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKYmX58xtHfQ"
      },
      "source": [
        "#Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z_Z6Dq-9tPfO"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(FILELOC + 'covid_aligned_crossval/covid_patient_0912_aligned_sequences.csv')\n",
        "valdata = pd.read_csv(FILELOC + 'covid_aligned_crossval/covid_patient_0912_valid1001_aligned.csv')\n",
        "mdf = pd.read_csv(FILELOC + 'covid_alldata_merged_align1001.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LBZfUKs_tPcd"
      },
      "outputs": [],
      "source": [
        "mdf = mdf[((mdf.Host == 'Human')|(mdf.Host.isna()))&(mdf.Label != -1)].copy()\n",
        "\n",
        "REFDATE = '2019-12-01'; refdt = dateparse(REFDATE)\n",
        "def f(x):\n",
        "    return (dateparse(x) - refdt).days\n",
        "mdf['date'] = mdf['Collection date'].apply(f)\n",
        "\n",
        "def f(x):\n",
        "    if x.lower() == 'male':\n",
        "        return 0\n",
        "    elif x.lower() == 'female':\n",
        "        return 1\n",
        "    else:\n",
        "        return -1\n",
        "mdf['gender'] = mdf['Gender_Raw'].apply(f)\n",
        "\n",
        "def f(x):\n",
        "    if x > 99:\n",
        "        return 99\n",
        "    else:\n",
        "        return x\n",
        "mdf['age'] = mdf['Age'].apply(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YITrxZxtbyj",
        "outputId": "ec264160-6620-4569-9d51-929c4c76afca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47587 44003\n",
            "23517 21913\n"
          ]
        }
      ],
      "source": [
        "trainmdf = mdf[mdf.ISM.isin(data.ISM)]\n",
        "valmdf = mdf[mdf.ISM.isin(valdata.ISM)]\n",
        "print(len(trainmdf), len(trainmdf[(trainmdf.age!=-1)&(trainmdf.gender!=-1)]))\n",
        "print(len(valmdf), len(valmdf[(valmdf.age!=-1)&(valmdf.gender!=-1)]))\n",
        "trainmdf = trainmdf[(trainmdf.age!=-1)&(trainmdf.gender!=-1)]\n",
        "valmdf = valmdf[(valmdf.age!=-1)&(valmdf.gender!=-1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "79k5d7AUd5jD"
      },
      "outputs": [],
      "source": [
        "# if EXPERIMENTAL:\n",
        "#     trainmdf.drop(trainmdf[trainmdf['Collection date'].apply(len) <= 4].index, inplace=True)\n",
        "#     valmdf.drop(valmdf[valmdf['Collection date'].apply(len) <= 4].index, inplace=True)\n",
        "#     print(len(trainmdf),len(valmdf))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nuTDJFXduBTp"
      },
      "outputs": [],
      "source": [
        "ytrain = trainmdf.Label.values; yval = valmdf.Label.values\n",
        "seqtrain = tokenize_sequences(trainmdf, 'ISM', ismlen)\n",
        "seqval = tokenize_sequences(valmdf, 'ISM', ismlen)\n",
        "vartrain = trainmdf[['age','date','gender']].values\n",
        "varval = valmdf[['age','date','gender']].values\n",
        "xtrain = [seqtrain,vartrain]\n",
        "xval = [seqval,varval]\n",
        "\n",
        "NVARS = 3   # age, gender, date = 3 variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjhypNjDuBQ5",
        "outputId": "9661136a-520d-4ed0-8370-cd0ffd702290"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.1281663419136498, 0.8979837557650708]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.utils import class_weight\n",
        "class_weights = list(class_weight.compute_class_weight(class_weight='balanced',\n",
        "                                                       classes=np.unique(ytrain), y=ytrain))\n",
        "sample_weights = np.array([class_weights[int(y)] for y in ytrain])\n",
        "print(class_weights)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-63OwaGqEhp8"
      },
      "source": [
        "#Evaluate model to predict disease severity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Y42jlINEpfk"
      },
      "outputs": [],
      "source": [
        "tf.keras.backend.clear_session()\n",
        "with tpu_strategy.scope():\n",
        "    model = reset_model()\n",
        "    model.load_weights(f'{FILELOC}covidout_trans_fulldata_weights/covid_regresstrans_20211212.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGbhHGrTEpcx",
        "outputId": "f38ced04-a8cf-4f07-f729-773efc21ce2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20\n",
            "20\n",
            "2\n"
          ]
        }
      ],
      "source": [
        "omicron = pd.read_csv(FILELOC + \"omicron_spike_20211209.csv\")\n",
        "# omicron = pd.read_csv(FILELOC + \"omicron_spike_20211209_highcov.csv\")\n",
        "# omicron = pd.read_csv(FILELOC + \"B1640_spike_20220107.csv\")\n",
        "# omicron = pd.read_csv(FILELOC + \"omicron_spike_BA2_20220127.csv\")\n",
        "omicron['seqlen'] = omicron.Align.apply(len)\n",
        "print(len(omicron))\n",
        "omicron.drop(omicron[omicron.seqlen<1273].index, inplace=True)\n",
        "omicron.reset_index(inplace=True, drop=True)\n",
        "print(len(omicron))\n",
        "omicron_raw = omicron.copy()\n",
        "omicron = omicron.groupby(['Align']).size().to_frame().reset_index(drop=False)\n",
        "print(len(omicron))\n",
        "\n",
        "\n",
        "otok = tokenize_sequences(omicron,'Align',ismlen)\n",
        "# otok = tokenize_sequences(omicron_raw,'Align',ismlen)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sdn3k12G3mOw",
        "outputId": "55e633a5-04a1-4ab3-8239-0a2e46af0761"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2975: StrategyBase.unwrap (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "use `experimental_local_results` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:2975: StrategyBase.unwrap (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "use `experimental_local_results` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 4s 4s/step\n"
          ]
        }
      ],
      "source": [
        "age = 50; date = 300; gender = 0\n",
        "vars = [age,date,gender]*np.ones((len(otok),3))\n",
        "omicron['pred'] = model.predict([otok, vars], verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PjWnYXVgVhcd",
        "outputId": "c4923b57-538c-4911-baf8-cebd1b76e522"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 29\n"
          ]
        }
      ],
      "source": [
        "maxpred = np.argwhere(omicron.pred.values==max(omicron.pred))[0][0]\n",
        "minpred = np.argwhere(omicron.pred.values==min(omicron.pred))[0][0]\n",
        "print(minpred,maxpred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kp90HyUeVj5Z",
        "outputId": "b6c9ce71-63f9-4c66-9d85-6258aed9133d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(array([ 68,  69, 140, 141, 143, 144]),)\n"
          ]
        }
      ],
      "source": [
        "a = omicron.iloc[maxpred].Align\n",
        "b = omicron.iloc[minpred].Align\n",
        "print(np.where(np.array(list(a) != np.array(list(b)))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4y-fdE6XSoZ",
        "outputId": "8c3b2bf0-66aa-4c27-892c-c9b74b2b244a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 6s 6s/step\n"
          ]
        }
      ],
      "source": [
        "with tpu_strategy.scope():\n",
        "    get_attention_model = keras.Model(inputs=model.input,outputs=model.get_layer('attention').output)\n",
        "    get_attention_model.compile()\n",
        "    att = get_attention_model.predict([otok, vars], verbose=True)\n",
        "omicron['att'] = [a for a in att]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-39uzTZbXe1",
        "outputId": "c8550f73-b597-4265-8427-271b2dbfd83a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "30.0"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "1500/50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNa3JqENbJL8",
        "outputId": "ea2b101b-c0e2-4166-ed83-90f77553ec01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([   0,   50,  100,  150,  200,  250,  300,  350,  400,  450,  500,\n",
              "        550,  600,  650,  700,  750,  800,  850,  900,  950, 1000, 1050,\n",
              "       1100, 1150, 1200, 1250])"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.arange(0,1300,50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "iXTc_kCBZ8pb",
        "outputId": "fe490678-a24c-4aea-b201-ae3f95564df7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in log10\n",
            "  after removing the cwd from sys.path.\n",
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig,ax = plt.subplots()\n",
        "fig.set_size_inches(8,4)\n",
        "\n",
        "ax.plot(range(1,1274), np.log10(att[maxpred]-att[minpred]), linewidth=1, color='black')\n",
        "\n",
        "ax.set_xlabel('Sequence Position', fontsize=18, fontweight='bold')\n",
        "ax.set_ylabel('Log Attention', fontsize=18, fontweight='bold')\n",
        "a = np.arange(0,1350,50)\n",
        "plt.xticks(a[:-1], rotation=45, fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "# plt.yscale(\"log\")\n",
        "# ax.set_xticks(a)\n",
        "# ax.set_xticklabels([str(aa) for aa in a], rotation=45, fontsize=10)\n",
        "ax.grid(axis='y', which='major', alpha=0.3)\n",
        "ax.grid(axis='x', which ='both', alpha=0.3)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ahgr6LBnLEP",
        "outputId": "b4a1585a-eb1a-4ded-ee02-adb472531bd7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 68,  69, 140, 141, 143, 144]),)"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.where(np.array(list(omicron.iloc[minpred].Align)) != np.array(list(omicron.iloc[maxpred].Align)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50DY5tHRuWlt",
        "outputId": "a83e8eea-ad97-436f-9714-c018f06651f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 0.42727992 (array([  4, 140, 141, 143, 144]),)\n",
            "1 0.3350014 (array([], dtype=int64),)\n",
            "2 0.3367828 (array([210, 211]),)\n",
            "3 0.40741944 (array([140, 141, 143, 144, 155, 156, 157, 439, 445, 451, 476, 483, 492,\n",
            "       495, 497, 500, 504]),)\n",
            "4 0.42769307 (array([140, 141, 143, 144, 171]),)\n",
            "5 0.42708147 (array([140, 141, 143, 144, 175]),)\n",
            "6 0.39644825 (array([140, 141, 143, 144, 345, 497, 500]),)\n",
            "7 0.42706034 (array([140, 141, 143, 144, 345]),)\n",
            "8 0.45399788 (array([140, 141, 143, 144, 416]),)\n",
            "9 0.45399636 (array([140, 141, 143, 144, 416, 700]),)\n",
            "10 0.45394132 (array([140, 141, 143, 144, 416, 439, 445, 451]),)\n",
            "11 0.4538765 (array([140, 141, 143, 144, 416, 439, 445, 451, 935]),)\n",
            "12 0.4274452 (array([140, 141, 143, 144, 495, 497]),)\n",
            "13 0.40166786 (array([140, 141, 143, 144, 795, 855, 949, 953, 968, 980]),)\n",
            "14 0.42725918 (array([ 140,  141,  143,  144, 1004]),)\n",
            "15 0.42780465 (array([ 140,  141,  143,  144, 1039]),)\n",
            "16 0.4272717 (array([140, 141, 143, 144]),)\n",
            "17 0.4272498 (array([ 140,  141,  143,  144, 1234]),)\n",
            "18 0.4271143 (array([ 140,  141,  143,  144, 1142]),)\n",
            "19 0.4267956 (array([140, 141, 143, 144, 935]),)\n",
            "20 0.4272546 (array([140, 141, 143, 144, 844]),)\n",
            "21 0.40184283 (array([140, 141, 143, 144, 763, 795, 855, 949, 953, 968, 980]),)\n",
            "22 0.4272717 (array([140, 141, 143, 144, 700]),)\n",
            "23 0.42744005 (array([140, 141, 143, 144, 483, 497]),)\n",
            "24 0.42768207 (array([140, 141, 143, 144, 483]),)\n",
            "25 0.42776555 (array([140, 141, 143, 144, 439]),)\n",
            "26 0.4274522 (array([140, 141, 143, 144, 210, 211, 370, 372, 374, 416, 439, 445, 476,\n",
            "       477, 483, 492, 495, 497, 500, 504]),)\n",
            "27 0.4274339 (array([140, 141, 143, 144, 210, 211, 338, 370, 372, 374, 416, 439, 445,\n",
            "       476, 477, 483, 492, 495, 497, 500, 504]),)\n",
            "28 0.40205193 (array([ 94, 140, 141, 143, 144, 374]),)\n",
            "29 0.5199139 (array([ 68,  69, 140, 141, 143, 144]),)\n"
          ]
        }
      ],
      "source": [
        "for ind in range(len(omicron)):\n",
        "    print(ind, omicron.iloc[ind].pred,\n",
        "          np.where(np.array(list(omicron.iloc[minpred].Align)) != np.array(list(omicron.iloc[ind].Align))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KKXETmdTv7P",
        "outputId": "6a6b31c4-0ded-43da-85df-36dfe0bd83d9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log10\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(array([  94,  142,  143,  144,  210,  416,  476,  731, 1257]),)"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.where(np.log10(att[maxpred]-att[minpred]) > -5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jDydU1m8Tv4Q"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(FILELOC + 'covid_aligned_crossval/covid_patient_0912_aligned_sequences.csv')\n",
        "mdf = pd.read_csv(FILELOC + 'covid_alldata_merged_align1001.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M1CK0yvgvuD7"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pnG1Hl7LvuBX"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wqjjEtazvt-n"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ifwnnFmWvt8W"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PHcR2xvtTv1R"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "DHllaxbysnrQ",
        "outputId": "78a5fc49-02f7-4be4-ab93-1821eb5e163e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'V'"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N-5hZT1Qdz_i"
      },
      "outputs": [],
      "source": [
        "with open(f'{FILELOC}covidout_trans_fulldata_weights/covid_regresspred_20211212.pkl','rb') as f:\n",
        "    trainmdf, valmdf, trainemb, valemb, trainatt, valatt, fsdf = pickle.load(f)\n",
        "linmean_train = trainmdf.groupby('Lineage').mean().join(trainmdf.groupby('Lineage').size().rename('Count'))\n",
        "linmean_val = valmdf.groupby('Lineage').mean().join(valmdf.groupby('Lineage').size().rename('Count'))\n",
        "linmean_train['Lineage'] = linmean_train.index\n",
        "linmean_val['Lineage'] = linmean_val.index\n",
        "ismtrain = trainmdf.groupby('ISM').mean().join(trainmdf.groupby('ISM').size().rename('Count'))\n",
        "ismval = valmdf.groupby('ISM').mean().join(valmdf.groupby('ISM').size().rename('Count'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "GkVoOKYo2NDI",
        "outputId": "e5e7a04d-8213-4b5f-cd3b-077b59117d5e"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Mean Severity</th>\n",
              "      <th>Relative Date</th>\n",
              "      <th>Transformer</th>\n",
              "      <th>XGBoost</th>\n",
              "      <th>Count</th>\n",
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              "      <th>Lineage</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AY.12</th>\n",
              "      <td>0.19</td>\n",
              "      <td>631.36</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.19</td>\n",
              "      <td>258</td>\n",
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              "      <th>AY.20</th>\n",
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              "      <td>0.43</td>\n",
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              "      <th>AY.4</th>\n",
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              "      <td>631.35</td>\n",
              "      <td>0.28</td>\n",
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              "      <th>B.1</th>\n",
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              "      <td>0.60</td>\n",
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              "      <td>0.63</td>\n",
              "      <td>0.54</td>\n",
              "      <td>229</td>\n",
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              "    <tr>\n",
              "      <th>B.1.1.519</th>\n",
              "      <td>0.68</td>\n",
              "      <td>485.36</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.69</td>\n",
              "      <td>1583</td>\n",
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              "    <tr>\n",
              "      <th>B.1.1.7</th>\n",
              "      <td>0.60</td>\n",
              "      <td>508.85</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.56</td>\n",
              "      <td>3298</td>\n",
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              "      <th>B.1.160</th>\n",
              "      <td>0.67</td>\n",
              "      <td>426.67</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.64</td>\n",
              "      <td>307</td>\n",
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              "      <th>B.1.177</th>\n",
              "      <td>0.60</td>\n",
              "      <td>413.79</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.54</td>\n",
              "      <td>269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B.1.351</th>\n",
              "      <td>0.52</td>\n",
              "      <td>480.60</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.44</td>\n",
              "      <td>849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B.1.617.2</th>\n",
              "      <td>0.42</td>\n",
              "      <td>604.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.34</td>\n",
              "      <td>3901</td>\n",
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              "      <th>B.1.621</th>\n",
              "      <td>0.79</td>\n",
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              "      <td>0.53</td>\n",
              "      <td>0.78</td>\n",
              "      <td>214</td>\n",
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              "      <td>0.85</td>\n",
              "      <td>509.03</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1080</td>\n",
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              "      <th>P.1</th>\n",
              "      <td>0.64</td>\n",
              "      <td>548.41</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.66</td>\n",
              "      <td>877</td>\n",
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            "text/plain": [
              "           Mean Severity  Relative Date  Transformer  XGBoost  Count\n",
              "Lineage                                                             \n",
              "AY.12               0.19         631.36         0.28     0.19    258\n",
              "AY.20               0.69         618.55         0.43     0.64    364\n",
              "AY.4                0.26         631.35         0.28     0.21    817\n",
              "B.1                 0.73         314.45         0.65     0.79    994\n",
              "B.1.1               0.70         314.36         0.65     0.73    696\n",
              "B.1.1.222           0.60         421.69         0.63     0.54    229\n",
              "B.1.1.519           0.68         485.36         0.63     0.69   1583\n",
              "B.1.1.7             0.60         508.85         0.49     0.56   3298\n",
              "B.1.160             0.67         426.67         0.52     0.64    307\n",
              "B.1.177             0.60         413.79         0.64     0.54    269\n",
              "B.1.351             0.52         480.60         0.40     0.44    849\n",
              "B.1.617.2           0.42         604.00         0.33     0.34   3901\n",
              "B.1.621             0.79         571.63         0.53     0.78    214\n",
              "None                0.85         509.03         0.75     0.84   1080\n",
              "P.1                 0.64         548.41         0.58     0.66    877"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "linmean_val[linmean_val.Count>200].rename(columns={'Label':'Mean Severity',\n",
        "                                                   'date':'Relative Date',\n",
        "                                                   'age':'Age',\n",
        "                                                   'Pred_Trans':'Transformer',\n",
        "                                                   'Pred_XGB':'XGBoost'}).drop(columns=['Age','Lineage','gender']).round(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hzyVw49DtXJK"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d3wV8cKItX1h"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uSsxbJpbrzko"
      },
      "outputs": [],
      "source": [
        "# lineages = ['B.1/D614G','B.1.1.7/Alpha','P.1/Gamma','B.1.617.2/Delta']\n",
        "# gender = 0\n",
        "# ages = [25,50,75]\n",
        "# dates = np.linspace(100,700,21)\n",
        "# pred = {}\n",
        "# for lin in lineages:\n",
        "#     print(lin, lin.split(\"/\")[0])\n",
        "#     linism = valmdf[valmdf.Lineage==lin.split(\"/\")[0]].groupby('ISM').size()\n",
        "#     linism = linism.rename('Count').to_frame().reset_index()\n",
        "#     lintok = tokenize_sequences(linism.sort_values(by='Count').tail(30), 'ISM', ismlen)\n",
        "#     pred[lin] = {}\n",
        "#     for age in ages:\n",
        "#         pred[lin][age] = {}\n",
        "#         for date in dates:\n",
        "#             vars = [age,date,gender]*np.ones((len(lintok),3))\n",
        "#             pred[lin][age][date] = model.predict([lintok, vars], verbose=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9pNtDMKr8Ehe"
      },
      "outputs": [],
      "source": [
        "# with open(FILELOC + \"speculative_predictions_graph.pkl\", 'wb') as f:\n",
        "#     pickle.dump(pred,f)\n",
        "\n",
        "with open(FILELOC + \"speculative_predictions_graph.pkl\", 'rb') as f:\n",
        "    pred = pickle.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nEjtS7-fGF_o"
      },
      "outputs": [],
      "source": [
        "# gender = 0\n",
        "# ages = [25,50,75]\n",
        "# dates = np.linspace(100,700,21)\n",
        "# pred['B.1.529/Omicron'] = {}\n",
        "# for age in ages:\n",
        "#     pred['B.1.529/Omicron'][age] = {}\n",
        "#     for date in dates:\n",
        "#         vars = [age,date,gender]*np.ones((len(otok),3))\n",
        "#         pred['B.1.529/Omicron'][age][date] = model.predict([otok, vars], verbose=True, batch_size=32*8)\n",
        "\n",
        "# with open(FILELOC + \"speculative_predictions_graph_omicron.pkl\", 'wb') as f:\n",
        "#     pickle.dump(pred,f)\n",
        "\n",
        "with open(FILELOC + \"speculative_predictions_graph_omicron.pkl\", 'rb') as f:\n",
        "    pred = pickle.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zfVgtfnFEH5c"
      },
      "outputs": [],
      "source": [
        "# # lineages = ['B.1.617.2/Delta'] # ['B.1.1.7/Alpha','B.1.617.2/Delta']\n",
        "# lineages = ['B.1.351/Beta', 'B.1.617.2/Delta', 'B.1.1.7/Alpha']\n",
        "\n",
        "# gender = 0\n",
        "# ages = [50]\n",
        "# dates = np.linspace(100,700,21)\n",
        "# for lin in lineages:\n",
        "#     print(lin, lin.split(\"/\")[0])\n",
        "#     linism = trainmdf[trainmdf.Lineage==lin.split(\"/\")[0]].groupby('ISM').size()\n",
        "#     linism = linism.rename('Count').to_frame().reset_index()\n",
        "#     lintok = tokenize_sequences(linism.sort_values(by='Count').tail(30), 'ISM', ismlen)\n",
        "#     pred[lin] = {}\n",
        "#     for age in ages:\n",
        "#         pred[lin][age] = {}\n",
        "#         for date in dates:\n",
        "#             vars = [age,date,gender]*np.ones((len(lintok),3))\n",
        "#             pred[lin][age][date] = model.predict([lintok, vars], verbose=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gender = 0\n",
        "ages = [25,50,75]\n",
        "dates = np.linspace(100,700,21)\n",
        "# VARIANT_NAME = \"B.1.640\"\n",
        "VARIANT_NAME = \"BA.2\"\n",
        "pred[VARIANT_NAME] = {}\n",
        "for age in ages:\n",
        "    pred[VARIANT_NAME][age] = {}\n",
        "    for date in dates:\n",
        "        vars = [age,date,gender]*np.ones((len(otok),3))\n",
        "        pred[VARIANT_NAME][age][date] = model.predict([otok, vars], verbose=False, batch_size=32*8)"
      ],
      "metadata": {
        "id": "H4QLaUm9jb1y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_dqTpnMxPymU"
      },
      "outputs": [],
      "source": [
        "age = 50\n",
        "dates = np.linspace(100,700,21)\n",
        "# lineages = ['B.1.351/Beta', 'B.1.617.2/Delta', 'B.1.1.7/Alpha', 'B.1.529/Omicron']\n",
        "# lineages = ['B.1.617.2/Delta', 'B.1.1.7/Alpha', 'B.1.529/Omicron']\n",
        "lineages = ['B.1.617.2/Delta', 'B.1.1.7/Alpha', 'B.1.529/Omicron', VARIANT_NAME]\n",
        "\n",
        "severity = {}\n",
        "severity_mean = {}; severity_max = {}; severity_min= {}\n",
        "\n",
        "for lin in lineages:\n",
        "    severity_mean[lin] = np.array([np.mean(pred[lin][age][d]) for d in dates])\n",
        "    severity_max[lin] = np.array([np.max(pred[lin][age][d]) for d in dates])\n",
        "    severity_min[lin] = np.array([np.min(pred[lin][age][d]) for d in dates])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FC4yVD-nRJAh",
        "outputId": "40993cbf-7637-4c4e-9307-916db6b0dc8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4741503 0.16518871 0.7192841\n",
            "0.38688904 0.12225803 0.6304965\n"
          ]
        }
      ],
      "source": [
        "print(np.mean(np.subtract(severity_mean['B.1.617.2/Delta'], severity_mean['B.1.529/Omicron'])/np.array(severity_mean['B.1.617.2/Delta'])),\n",
        "      np.mean(np.subtract(severity_min['B.1.617.2/Delta'], severity_max['B.1.529/Omicron'])/np.array(severity_min['B.1.617.2/Delta'])),\n",
        "      np.mean(np.subtract(severity_max['B.1.617.2/Delta'], severity_min['B.1.529/Omicron'])/np.array(severity_max['B.1.617.2/Delta'])))\n",
        "\n",
        "print(np.mean(np.subtract(severity_mean['B.1.1.7/Alpha'], severity_mean['B.1.529/Omicron'])/np.array(severity_mean['B.1.1.7/Alpha'])),\n",
        "      np.mean(np.subtract(severity_min['B.1.1.7/Alpha'], severity_max['B.1.529/Omicron'])/np.array(severity_min['B.1.1.7/Alpha'])),\n",
        "      np.mean(np.subtract(severity_max['B.1.1.7/Alpha'], severity_min['B.1.529/Omicron'])/np.array(severity_max['B.1.1.7/Alpha'])))"
      ]
    },
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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "SMALL_SIZE = 8\n",
        "MEDIUM_SIZE = 10\n",
        "BIGGER_SIZE = 12\n",
        "\n",
        "plt.rcdefaults()\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=BIGGER_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
        "\n",
        "# fig,ax = plt.subplots(2,2)\n",
        "# fig.set_size_inches(8.5,8.5)\n",
        "\n",
        "# ind2d = np.unravel_index(range(len(lineages)), (2,2))\n",
        "# for l in range(len(lineages)):\n",
        "#     lineage = lineages[l]\n",
        "#     xi = ind2d[0][l]; yi = ind2d[1][l]\n",
        "#     for age in ages:\n",
        "#         linmean = [np.mean(pred[lineage][age][d]) for d in dates]\n",
        "#         ax[xi][yi].plot(dates, linmean, label=f'Age: {age}')\n",
        "\n",
        "#     ax[xi][yi].set_title(f'Projected Mean Severity ({lineage})')\n",
        "#     ax[xi][yi].set_xlabel('Days since Dec. 1, 2019')\n",
        "\n",
        "#     ax[xi][yi].legend(bbox_to_anchor=(1.0, 0.9))\n",
        "# plt.show()\n",
        "\n",
        "age = 50\n",
        "fig,ax = plt.subplots()\n",
        "fig.set_size_inches(8,5)\n",
        "\n",
        "lineage = 'B.1.1.7/Alpha'\n",
        "linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "ax.plot(dates, linmean, linestyle='solid', linewidth=3, color = 'red', label='Alpha (B.1.1.7)')\n",
        "# ax.fill_between(dates, linmin, linmax, alpha=0.1, color='red')\n",
        "lineage = 'B.1.617.2/Delta'\n",
        "linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "ax.plot(dates, linmean, linestyle='solid', color = 'blue', linewidth=3, label='Delta (B.1.617.2)')\n",
        "# ax.fill_between(dates, linmean-linstd, linmean+linstd, alpha=0.3, color='blue')\n",
        "# ax.fill_between(dates, linmin, linmax, alpha=0.1, color='blue')\n",
        "# lineage = 'B.1.351/Beta'\n",
        "# linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "# linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "# linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "# linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "# ax.plot(dates, linmean, linestyle='solid', color = 'red', linewidth=3, label='Beta (B.1.351)')\n",
        "# # ax.fill_between(dates, linmin, linmax, alpha=0.1, color='green')\n",
        "lineage = 'B.1.529/Omicron'\n",
        "linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "ax.plot(dates, linmean, linestyle='solid', color = 'green', linewidth=3, label='Omicron')\n",
        "ax.fill_between(dates, linmin, linmax, alpha=0.1, color='green')\n",
        "lineage = VARIANT_NAME\n",
        "linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "ax.plot(dates, linmean, linestyle='solid', color = 'purple', linewidth=3, label=VARIANT_NAME)\n",
        "ax.fill_between(dates, linmin, linmax, alpha=0.1, color='purple')\n",
        "\n",
        "ax.set_ylabel('Predicted Severity', fontsize=14, fontweight='bold')\n",
        "# ax.set_xlabel('Days since Dec. 1, 2019', fontsize=14, fontweight='bold')\n",
        "import datetime\n",
        "datebinnames = datebinnames = [(datetime.datetime(2019,12,1) + datetime.timedelta(dates[d]+15)).strftime('%b %Y')\\\n",
        "                for d in range(len(dates)-1)]\n",
        "ax.set_xticks(dates)\n",
        "ax.set_xticklabels(datebinnames, fontsize=12, ha='center', rotation=45)\n",
        "plt.yticks(fontsize=12)\n",
        "ax.legend(bbox_to_anchor=(1,1), framealpha=1.0, fontsize=12)\n",
        "plt.show()\n",
        "plt.rcdefaults()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "BaLbIbGpkDQA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ua6KNur7kDKw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lnvW7wHVXhU",
        "outputId": "c15a0f3a-75dc-4c1f-a864-6a3c7dd424a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "B.1.351/Beta B.1.351\n",
            "B.1.617.2/Delta B.1.617.2\n",
            "B.1.1.7/Alpha B.1.1.7\n"
          ]
        }
      ],
      "source": [
        "# seq = {}\n",
        "# for lin in ['B.1.351/Beta', 'B.1.617.2/Delta', 'B.1.1.7/Alpha']:\n",
        "#     print(lin, lin.split(\"/\")[0])\n",
        "#     linism = trainmdf[trainmdf.Lineage==lin.split(\"/\")[0]].groupby('ISM').size()\n",
        "#     linism = linism.rename('Count').to_frame().reset_index()\n",
        "#     seq[lin] = linism.sort_values(by='Count').tail(1)['ISM'].values[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOzJDjurWpYk",
        "outputId": "7413bd1a-5b31-46b7-e059-f1bdec69d9d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([  66,   94,  141,  142,  144,  210,  211,  338,  370,  372,  374,\n",
              "         416,  439,  445,  476,  477,  483,  492,  495,  497,  504,  546,\n",
              "         569,  654,  678,  715,  763,  795,  855,  953,  968,  980,  981,\n",
              "        1117]),)"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# np.where(np.array(list(omicron.iloc[16]['Align'])) != np.array(list(seq['B.1.1.7/Alpha'])))\n",
        "# np.where(np.array(list(omicron.iloc[16]['Align'])) != np.array(list(seq['B.1.351/Beta'])))\n",
        "# np.where(np.array(list(omicron.iloc[16]['Align'])) != np.array(list(seq['B.1.617.2/Delta'])))\n",
        "# seq['B.1.617.2/Delta'][476]\n",
        "# omicron.iloc[16]['Align'][476]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dohs8zrvVt77"
      },
      "outputs": [],
      "source": [
        "# with open(f'{FILELOC}covidout_trans_fulldata_weights/covid_regresspred_20211212.pkl','rb') as f:\n",
        "#     trainmdf, valmdf, trainemb, valemb, trainatt, valatt, fsdf = pickle.load(f)\n",
        "# tdf = trainmdf.reset_index(drop=False)\n",
        "# tdf[tdf.ISM==seq['B.1.617.2/Delta']].index\n",
        "# delta = np.mean(trainatt[tdf[tdf.ISM==seq['B.1.617.2/Delta']].index],axis=0)\n",
        "# beta = np.mean(trainatt[tdf[tdf.ISM==seq['B.1.351/Beta']].index], axis=0)\n",
        "# alpha = np.mean(trainatt[tdf[tdf.ISM==seq['B.1.1.7/Alpha']].index], axis=0)\n",
        "# for ind in omicron.index:\n",
        "#     print(omicron.loc[ind]['att'][498])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "UQlpNluZiYiJ",
        "outputId": "1171b059-5aee-4a87-e194-f750f2a2469b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Sp08fOXr0qOTk5EjFihWlXLlywZQ0zZYtW6R///7SqlUrueOOO0pcb+jQoTJkyBDPz3l5eZKZmSnp6emSnJxseZ8ul0sMw5D09HTTD0xfdVNSUoqtGxMTIxkZGT7ruEfqi6pYsWJQ/frarykpKUHXLUmo9zF1RRITE4utFxcXV+JxFky/SUlJxZZVqlTpjNtywj6mrv3rWlmbupFZ18rakVY3WAkJCX6tV+qgm5+fL7Vq1RIRkWuuuUamT58uiYmJPl/YwmXv3r1y7bXXSkpKisyYMUNiY2NLXDc+Pl7i4+OLLY+JiQnZA2oYhiXb81W3pG2UZtvB9usrPJtR92zbDNU+pm7Jj3Fptxtov+77WVHb321Tl7pW16ZuZNa1snak1Q2Gv72UOuhWqFBBTpw4IUeOHDnj3Ndwyc3Nlc6dO8uhQ4dk2bJlIb3EWSSw6wd37NADzMPjCQCwg4CieePGjUXk1Dw4Ozl27Jh07dpVtm7dKnPmzJEGDRqEuyXbsWvQBQLFMQ0AKElAQfcf//iHqKpMmzZNdu3aZXZPASksLJSbbrpJvv32W/n444+lVatW4W4JJSCYOB/X0QUA2EFAH0Y7dOiQXHHFFbJs2TJp1KiR9O3bV+rVqyfly5cvtm7fvn2DbtIfjzzyiMyaNUu6du0qBw8eLPYFEbfddltI+rA7u4YCO/QA8xB0AQB2EFDQ7devn+fDJrm5ufLaa6+VuG6ogu66detERGT27NmeryAuiqB7SmlDQUkfKjIbwQQAAJgtqMuLnSkEqWrIQpKIyNKlS0O2rWhiRQBlBM75GNEFANhBwEGXF5LIZNdQYIceYB6CLgDADgIKunb7Gjj4j1AAp+GYBgCUxD5X/kXUIJg4H48nAMAOgpqjKyKyfv16+fbbbyUnJ0cqVaokrVu39lxnF/Zj15Bphx5gHqYuAADsIOCgu2/fPrn11ltlyZIlxW5r3769TJkyRapUqRJUcwgNO4QCO/SAyETQBQCUJKCpC0ePHpX27dvLkiVLir2gqKp89dVXcs0118jRo0dNaRLmsUMAIJg4H48nAMAOAgq6b7zxhmzevNnzs6p6/nP/vGnTJnnzzTfN6RKmsWvItEMPMA9TFwAAdhBQ0P3kk088/3788cdl48aNcuDAAdm4caM89thjnts+/vjj4DuE5QgFiGQEXQBASQKao7t582YxDEN69+4to0eP9ixPTU2VF198UbZv3y4fffSRbNmyxbRGYQ47BACCifMxogsAsIOARnT/+usvERGpX7++z9vr1avntR7sw66hwA49wDw8ngAAOwgo6KampoqIyMKFC4u9oLlcLlm4cKHXesDZEIwQKLu+eQMAhF9AUxcuueQSmTt3rqxcuVKuuOIKue2226Ry5cqyb98+ef/992XlypViGIZceumlZveLIJU2FBiGEfYeEHmYugAAsIOAgu6dd94pc+fOFRGRb7/9Vr799tsS14O9lDYUhCowEEychaALALCDgKYu9OzZU3r37u31YnL6C0vv3r3lhhtuCK472BIhAgAARIKAgq6IyIcffijDhw+XSpUqeV0/t1KlSvLcc8/J1KlTTWsS5rHD6JcdeoC1GNEFANhBwF8BHBMTI08//bQ89dRT8vPPP8vBgwclNTVVLrzwQkvmdcIcZoQCK0IEwQSBIugCAEoScNB1MwzDczkxRAeCLs6GEV0AgB0EFHS//PJL+eKLL0REZPDgwVKzZk3PbTt27JCxY8eKiEiXLl2kQ4cOJrQJs9ghFNihB1iLxxMAYAcBBd3XXntNZs2aJfXq1ZNXXnnF67bMzExZsmSJrF+/Xn799VeCrs0wdQFOwxsnAEBJAvow2g8//CAiIu3bt/d5+1VXXSWqKmvXrg28M9gWIQJnw9QFAIAdBBR09+/fLyIi6enpPm+vVKmSiIhkZ2cH2BasYodQYIceYK1QPsYcTwCAkgQUdOPi4kREZN26dT5vdy+Pj48PsC1YhakLAAAgWgQUdOvWrSuqKrNmzZLp06d73TZ9+nT5/PPPxTAMqVOnjilNwl4IujgbX4+nVZcdZEQXAFCSgILuNddcIyIiLpdLbrnlFmnUqJF07dpVGjVqJLfccovnReZvf/ubeZ3CFHYIBXboAdZiji4AwA4CCroDBw6UChUqiGEYoqqyadMmmTt3rmzatMnzAlOhQgUZMGCAqc0ieKUNBb5G4RjRBQAAkSCgoFu9enX58MMPJTEx0bOsaFBJTEyUadOmSfXq1YPvEGHFaBkCwYguAMAOAv5mtC5dusimTZtk3Lhxsnz5cs9XALdp00YGDhzo9SUSsA8+jIZQIOgCAOwgqK8ArlmzpvzrX/8yqxeEiR3m6LpcrpD2AOcg6AIAShLQ1AVELrsGAIKus9j1OAMARBdTgu5///tf6d69uzRs2FAuv/xyef755+Wvv/4yozRMZtepC4WFhabXRPgwdQEAYAd+B93x48dLamqqpKWlyeLFiz3LP/nkE7n66qtl9uzZsnnzZlm5cqU888wz0q5dOzlx4oQlTSOy+QohBF0EiqALACiJ30F39erVcujQIVFVad++vWf5448/7vmzs/tyY6oq33//vbz11lvmd4ygMKKLUGBEFwBgB34H3fXr14thGNK2bVuJiTl1tzVr1si2bds811qtV6+eNG3a1HOfTz75xOR2ESyCLkKBoAkAsAO/g+7evXtFRKRRo0aeZcuWLfP8u1atWvLjjz/KmjVrpGXLlqKqsnHjRhNbhVMwdQFmYkQXAFASv4PuwYMHReTUN565/fDDD55/9+jRQ8qUKSMxMTGerwjOzc01q0+YhBFdhAJTFwAAduB30HUHkf3793uWrVy50vPv1q1be/6dkJAgIiLlypULukGYy65B9+TJk6bXRPgQdAEAduB30K1SpYqoqsyYMUN27twps2fPlt9//91ze6tWrTz/3r17t4iIZGRkmNgqnIKpCwAAIBT8/ma01q1by/bt22Xnzp1Sq1Ytz3LDMKRhw4ZSrVo1z7JVq1aJYRhSo0YNc7tF0Eo7+uX+oKG/6weKoOssoRxlZUQXAFASv0d0BwwY4Ak97kuIuX8eNGiQZ72dO3fK999/LyIil19+uZm9wgSlDQWhChEEXWdh6gIAwA78DrotW7aUt956yzP/1m3QoEFy9913e35+6623PC8yRa+3C7gxdSE6ET4BAKHm99QFEZF77rlHevbsKd98842cPHlSmjRpIrVr1/Zap0ePHtKhQwcRYUTXjkobNpi6gEAwdQEAYAelCroiIpUqVZKuXbuWeHuzZs2CagjWKikAFJ2Kcrb1Cbo4G6YuAADswO+pC4BbsCHC1/25vJjzMaILAAg1gm6UOdOIbjgxousshE8AgB0QdKMMQRehwNQFAIAdEHRRalZMXSDoOh9TFwAAoUbQjTKM6CIUuOoCAMAOCLpRprRBl8uLAQCASEXQxRlZMVrG1AXnY0QXAGAHBN0ow9QFhAJBFwBgBwEH3SVLlkiXLl0kPT1dypQpI7GxscX+K1Om1N9HAZsJ1dQFrqOLQBF0AQAlCSiJzpo1S2644QZRVV5QIkxpR3SZuoBAED4BAHYQ0Iju8OHDxeVymd0LQoCpCwgFpi4AAOwgoBHdTZs2iWEYEhsbKw899JBceOGFEh8f7/PP3IhsXHUBdkfQBQCUJKCgm5SUJAcPHpR7771XXnnlFbN7goXsOnWBvxA4CyO6AAA7CGjqQocOHUREJDEx0dRmYD0zpi4woouzIXwCAOwgoKD73HPPSVJSkkycOFHWr19vdk+IQgRdBIpQDQAoSUBTF0aOHCm1a9eW9evXS7NmzaRVq1Zy7rnnStmyZb3WMwxD3n33XVMahTnsMKLr6/6hvLzYvn37RFWlSpUqIdtmtGHqAgDADgIKuhMnThTDMMQwDHG5XLJixQpZsWKF1zqqGvKgW1BQIP/85z9lypQpkpOTIxdddJE8//zzcs0114SsB7uL9qsuvPrqq/Loo4+Kqsro0aPlscceC8l2ow3hEwBgB0F9M5r7hct9Pd2i/4VDv3795JVXXpFbb71Vxo0bJ7GxsdKlSxf55ptvwtIP/BeKoHv06FF5+umnpbCwUFwulzzzzDNy+PBhy7cLaxGqAQAlCWhEt23btra7lNjq1atl2rRpMmbMGHn00UdFRKRv377SqFEjefzxx4uNOEer0o7oWnF5sXB9YcSvv/7qFWyPHj0qmzZtkssuu8zybUcbpi4AAOwgoKC7dOlSk9sI3owZMyQ2Nlbuu+8+z7KEhAS5++675cknn5QdO3ZIZmZmGDv0duzYMVm9erW4XC7JycmRSpUqSUxMyQPspX1jUVLd3377zef6y5cvl/Llyxdbfvz48WLL1q5dK0ePHj1jv2eybNmyYst+/PFHWbly5Vn3QyDc+yIrK6vYbV9//bUUFBQEVdfsnp1Qd8OGDcXWO3z4sM/HPth+9+/fX2zZ1q1bz7itwsJCOXToUETvYyfWPX78uMTExJT49fF26zecta2qy7lhbV0ra4eibnx8vKjqGT9Xk5aWJg0aNDBt+8Ey1CFDH9dcc43s2rVLNm3a5LX8yy+/lA4dOsisWbOka9euZ62Tl5cnKSkpkpubK8nJyVa1K1lZWVK7dm3L6gMAAIRajx495NNPP7V8O/7mtYBGdN1yc3Nl0qRJ8u2333rSfuvWreWOO+6wNCT6smfPHqlatWqx5e5lu3fv9nm/goICrxG9vLw8ETn1DsbKLzHgCxIAAIDTqGpIMo6/2wg46P73v/+VXr16ycGDB72Wf/TRR/Lcc8/JJ598IldccUWg5Uvt6NGjEh8fX2x5QkKC53ZfRo0aJcOHDy+2PDs7W44dO2Zuk0X8+eefltUGAAAIh4KCAp9TysyWn5/v13oBBd1du3ZJ9+7dJTc31zN31H05MRGRAwcOSLdu3WTDhg1SvXr1QDZRaomJiT7nWrrDaknf4jZ06FAZMmSI5+e8vDzJzMyU9PR0S0eljx07JqmpqSLive/cDh8+XGx+bGJiYqm+jc5XXTf3G5TY2FhJSUk5Y52ib2ZSU1PPWPdsTn9jZFbds3HXPnLkiOeYiIuLk6SkJFPqms0pdU8/dsyqe6btVKxY0ef8NFWVnJycYstLWj9QTnnsQln3TM8LwdQtzX1C8fxj17olnRuBnLNn2oZI6T9v4m/tSKprZW2r6pbmHE1LS5OMjAzTezideyDzrDQAjzzyiBqGoTExMWoYhmZkZGjjxo01IyNDDcPw3Pboo48GUj4gHTp00Pr16xdbvnjxYhURnTVrll91cnNzVUQ0NzfX7BZ9Kiws1D179mhhYaHX8gceeEBFxOu/MWPGBF3Xqn79tW/fvmK/V8OGDS3rV9W++4K6oa1bWFhY7NgTET169Kgp9d3biIR9Ybe6P/zwg9djUqlSJZ/3tUu/dqhtZt28vDyf54aZImE/hKKulbWtrLtz505t1aqV59i44YYbTN1GIPzNawENYyxYsEBERMqVKydz586Vffv2yfr162Xfvn3yxRdfSLly5UREZN68eYGUD0iTJk1k69atnjm2bqtWrfLcjvCz22XpAIRfkyZNpHPnzp6fhw4davon3QEELjY2VmbMmCEjR46UV199NaK+9TagZ5KsrCwxDEPuvPNO6dSpk9dtnTt3lrvuuktU1eflnKzSq1cvKSwslPHjx3uWFRQUyIQJE6RFixa2urSYP3wFQkIiELiSzh/Oq/AzDENmz54tixYtku+++45vLAwxzgH4o0qVKjJ06FAZNGiQVKxYMdzt+C2gObru66eVNL/RvTxUX+sqItKiRQvp3bu3DB06VPbv3y/nnXeeTJo0SbKysiLqnYfT8YQKwJfY2Fjp0KFDuNuISjwvw8kCGtGtWrWqqKp8+OGHxa4ecODAAZk6dapnvVCaPHmyDB48WKZMmSIDBw6UEydOyJw5c6Rt27Yh7cMMjOgC5mJEFwCiT0AjuldccYVkZWXJH3/8IXXr1pVOnTpJ5cqVZd++fTJ//nzJy8sTwzBCHjATEhJkzJgxMmbMmJBu1wpODbpWfKUwEAwnnFdAMDgH4GQBBd1BgwbJBx98IKoqeXl58vHHH3tuc4eWmJgYGThwoDldwjF4QgUAALnwPFQAACAASURBVKES0NSFZs2aycsvv1zi7YZhyMsvvyzNmjULuLFo59QRXcBuOK8Q7TgH4GQBX79l8ODBsmTJEunWrZukp6dLbGyspKenS/fu3WXp0qUyaNAgM/uMOk4NukxdgN044bwCgsE5ACcL+CuARUTatm0bkR/0QvjwhAoAAEKFK3LblFNHdAG74bxCtOMcgJP5NaI7YsQIERHp2LGjtGzZ0vOzP/75z38G1lmUc2rQZeoC7MYJ5xUQDM4BOJlfQffZZ58VwzAkKSlJWrZs6fnZHwRdFMUTKgAACJWg5uiebSSOUBM4p47oAnbDeYVoxzkAJ/Mr6NasWVMMw5CUlBSvn2EdpwZdpi4AgL044bUFKIlfQTcrK+uMPwP+4gkVAACEClddsCmnjugCAOyF1xY4WUBBNyYmRsqUKSOvvPKKz9s/++wzuf7666Vbt25BNRfNnBp0nfA7AICT8LwMJwv4w2hnmlf522+/yZw5czh5UAxzdAEAQKhYMnUhPz/firJRxakjugAAe+G1BU7m94iury+JWLhwoRw+fNhr2ZEjR2TixImnipcJ6uplcCCeUAHAXnhehpP5nURP/5IIVZVFixbJokWLfK5vGIbUrFkz+A6jlFNHdJm6AAAAQqXUQ65FQ8nZAkrfvn1L3xFExLlBFwAAIFT8DrpFvyRi+/btYhiGVKxYUZKTk73WK1u2rFSvXl1uuOEG6d+/v7ndIuIR1gEAQKj4HXSLfklETMypz7ANGzZMhgwZYnpTiK4RXaYuAAAAKwT0abG+ffuKYRjSqFEjs/vB/+fUoOuE3wEAAESGgIKu+6oKQGkRdAEAQKgEfP2v48ePy2effSZr1qyRnJwccblcxdYxDEPefffdoBqMVk4d0fWFqQsAAMAKAQXdnTt3SocOHeSXX34pcR1VJegGwalB1wm/AwAAiAwBBd0hQ4bI1q1bze4FUYCgCwAAQiWgoLt48WIxDENUVTIyMqR27doSHx9PiDGRU0d0fWHqAgAAsEJAQbegoEBERNq0aSNfffUVX/VrAacGXSf8DgAAIDLEBHIn92XF2rVrR8hFqRB0AQBAqAQUdB9++GFRVVm4cKEUFhaa3RPEuSO6vjB1AQAAWCGg4dhq1apJmzZtZPny5XLFFVfI3XffLeeee66ULVu22Lpt27YNuslo5NSg64TfAQAARIaAgu5VV13l+TDaqlWrZNWqVT7XMwxDTp48GVSDAAAAQCCCmmBbdHSu6J+f3SEYgXPqiK4vHCsAAMAKAQfdM4UTggsAAADCLaCgu23bNrP7wGmiaUQXAADACgEF3Vq1apndB04TTUGXvwAAAAArmHIR3K1bt0p2drace+65Ur16dTNKAgAAAEEJ6Dq6IqdG4UaPHi0ZGRlSv359adu2rUyfPl0+++wzad++vVx99dWyb98+M3uNKtE0ogsAAGCFgIPuzTffLMOGDZM///zT60/Pl19+uSxbtkyWLl0qH330kSlNRqNoCrpMXQAAAFYIKOhOnTrVE2JPDynp6enSokULERH58ssvg2wPAAAACExAQffdd98VEZGyZcvKSy+9VOz2Sy+9VFRV1q9fH1x3USyaRnQBAACsEFDQXbt2rRiGIbfffrs8+uijxW6vUqWKiIjs3bs3uO6iGEEXAAAgOAEF3b/++ktERGrXru3z9vz8fBFh7iX8w3ECAACsEFDQTUtLExEpcWrCokWLREQkIyMjwLbAiC4AAEBwAgq6l112maiqzJgxQ4YPH+5Z/tNPP8nNN98s3333nRiG4flQGkqPoAsAABCcgL4w4u6775ZZs2aJqsqIESNE5NSfnydNmuS13l133RV8h3A8pi4AAAArBDSi27VrV7nttts8AcUwDM9oo3vZ7bffLp06dTKpzejDiC4AAEBwAv7CiEmTJsnIkSMlLS1NVNXzX1pamrzwwgsyYcIEM/uMOgRdAACA4AQ0dUHkVOj6xz/+IU888YT8/PPPcvDgQUlNTZULL7yQQIZSYeoCAACwQkBBd/LkySIi0rx5c6lfv77Uq1fP6/aCggLPJchSU1ODbDE6MaILAAAQnICmLvTr10/uvPNOmTdvns/bX3vtNUlPT+fyYgAAAAibgKcunIl7vi4CF00juhwrAADACgF/GO1MNm3aZEXZqBJNQRcAAMAKfo/o1qlTp9iykSNHymuvvea17MiRI5KdnS0iIuXLlw+yPQAAACAwfgfdrKwsMQzD82dmVZWDBw/KwYMHi63rvq5ukyZNzOs0ykTTiC5TFwAAgBVKNXXB30CiqpKYmOj51jSUXjQFXQAAACv4PaL7zDPPeP49fPhwMQxDrrnmGmnVqpXXemXLlpXq1atLp06dpHLlyuZ1CgAAAJRCwEFXVaVjx44yZMgQSxqLdtE0osvUBQAAYIWALi+2ZMkSMQxD6tata3Y/+P+iKegCAABYIaDLi7Vr107at28v06dP93n7Z599Jtdff71069YtqOYAAACAQAX8hRFn+nPzb7/9JnPmzGEEMgjRNKLL1AUAAGAFS74wIj8/34qyJfryyy/lrrvukgsuuEDKlSsnderUkXvuuUf27NkT0j7MFE1BFwAAwAp+j+j6ulTYwoUL5fDhw17Ljhw5IhMnTjxVvIwl3zBczBNPPCEHDx6U3r17y/nnny+///67vPbaazJnzhxZt26dVKlSJSR9AAAAwD78TqLPPvus14iiqsqiRYtk0aJFPtc3DENq1qwZfId+eOWVV6RNmzYSE/O/AepOnTrJlVdeKa+99po8//zzIenDTNE0osvUBQAAYIVSD7kWDSVnCyh9+/YtfUcBaNu2rc9lqampsnnz5pD0AAAAAHvxO+jWrFnTM6K4fft2MQxDKlasKMnJyV7rub8w4oYbbpD+/fub220pHD58WA4fPiznnHPOGdcrKCiQgoICz895eXkiIuJyucTlclnao3s7qurXtvxdr7R1SyPS6lpZm7qRWdfXdsysFUn7grrW1rWydijOD84Njgkr6wbL3378DrpZWVmef7unCAwbNsy2Xxjx6quvyvHjx+Wmm24643qjRo2S4cOHF1uenZ0tx44ds6o9D5fLJbm5uaKqXlMvTp/7LCKSm5sr+/fvD6quVf0Go7CwUPbv329JvyKRtS+oa33d0/l7Tvkj0vYFda2ta2XtUJwfnBscE1bWDZa/Fz4I6NNiffv2FcMwpFGjRoHc/YxcLpccP37cr3Xj4+N9zlv9+uuvZfjw4XLjjTdK+/btz1hj6NChXmE9Ly9PMjMzJT09vdhotRVcLpcYhiHp6eleB1BSUlKxdStWrCgZGRlB1bWq32DExMRIRkaGJf2KRNa+oK71dU/n7znlj0jbF9S1tq6VtUNxfnBucExYWTdYCQkJfq0XUNB1X1WhJAcPHpQPPvhAJk6cKN9//32pan/99dfSrl07v9bdvHmz1KtXz2vZli1bpEePHtKoUSN55513zlojPj5e4uPjiy2PiYkJ2QNqGEax7cXGxhZbLzY2tlQ9+aprBivqxsTEWNavSGTtC+paX7eoSOmZupFZ18raVp8fkdJvpNW1snak1Q2Gv72Ydv0vl8slc+fOlYkTJ8qcOXPkxIkTAdWpV6+eTJgwwa91q1at6vXzjh07pGPHjpKSkiJz586VChUqBNSDHUTTVRcAAACsEHTQ3bhxo0yYMEE++OADz3we99UYAglmVapUkX79+pX6fn/++ad07NhRCgoK5MsvvywWgmFfXF4MAABYIaCgm5OTI1OnTpWJEyfKDz/8ICK+w8q5554bVHP++uuvv6RLly6ya9cuWbJkiZx//vkh2a6VGNEFAAAIjt9B1+Vyyfz582XixIkye/ZszwfGVNUTwNz/b9Cggfz73//2e65tsG699VZZvXq13HXXXbJ582ava+cmJSVJ9+7dQ9KHmQi6AAAAwfE76GZmZsrevXtFpPjobXx8vFx77bXyySefiGEY0rhx45CFXBGRdevWiYjIe++9J++9957XbbVq1YrIoBtNmLoAAACs4HfQ3bNnjxiG4QklZcuWlQ4dOsjNN98s3bt3l6SkpLB9Gq/oNX6dghFdAACA4JR6jq5hGHLBBRfIpEmT5LLLLrOiJwhBFwAAIFgBDcFu3bpVWrVqJZdddpmMHTtWdu3aZXZfiCJMXQAAAFbwO+g2aNBAVNUTSlRVvv/+e3n00UelVq1a0rZtW8uajEaM6AIAAATH76D7008/yerVq+WBBx6QihUrisj/RuJcLpcsX77cs+7q1atl2rRpUlBQYHK70YOgCwAAEJxSTV249NJL5fXXX5c9e/bItGnTpHPnzp4PoBW9zNi2bdvk1ltvlWrVqpnfMRyHqQsAAMAKAc3RjYuLkxtvvFG++OIL2bFjh4wePVrq169fbGrDoUOHTG02mjCiCwAAEJygrwdWpUoVefzxx2Xjxo2ycuVK+fvf/y4pKSlm9AYAAAAEzNQL31522WXyxhtvyJ49e2Tq1KnSsWNHM8tHlWga0WXqAgAAsIIl3/AQHx8vffr0kXnz5llRPipEU9AFAACwQni+ygwAAACwGEHXpqJpRJepCwAAwAoEXZuKpqALAABgBYIuAAAAHImga1PRNKLL1AUAAGAFgq5NRVPQBQAAsAJBFwAAAI5E0LWpaBrRZeoCAACwAkHXpqIp6AIAAFiBoAsAAABHIujaVDSN6DJ1AQAAWIGga1PRFHQBAACsQNAFAACAIxF0bYoRXQAAgOAQdAEAAOBIBF2biqYRXT6MBgAArEDQtaloCroAAABWIOgCAADAkQi6NhVNI7pMXQAAAFYg6NpUNAVdAAAAKxB0AQAA4EgEXZuKphFdpi4AAAArEHRtKpqCLgAAgBUIugg7RnQBAIAVCLo2xYguAABAcAi6NkXQBQAACA5BF2HH1AUAAGAFgm4EYUQXAADAfwTdCELQBQAA8B9BF2HH1AUAAGAFgm4EYUQXAADAfwRdAAAAOBJBN4I4dUSXqQsAAMAKBN0I4tSgCwAAYAWCLgAAAByJoBtBnDqiy9QFAABgBYJuBHFq0AUAALACQRcAAACORNCNIE4d0WXqAgAAsAJBN4I4NegCAABYgaALAAAARyLoRhBGdAEAAPxH0I0gBF0AAAD/EXQRdnwYDQAAWIGgG0EY0QUAAPAfQRcAAACORNCNIE4d0WXqAgAAsAJBN4I4NegCAABYgaALAAAAR3Jk0L333nvFMAy57rrrwt2KqZw6osvUBQAAYAXHBd3vvvtOJk6cKAkJCeFuxXRODboAAABWcFTQVVUZOHCg9O3bVypXrhzudgAAABBGjgq6U6ZMkZ9++kleeOGFcLdiCUZ0AQAA/Fcm3A2YJT8/X5544gl58sknpUqVKn7fr6CgQAoKCjw/5+XliYiIy+USl8tlep+nc7lcoqrFtuVr277WK23dYEVaXStrUzcy6/rajpm1ImlfUNfaulbWDsX5wbnBMWFl3WD5249jgu6IESMkMTFRHn744VLdb9SoUTJ8+PBiy7Ozs+XYsWNmtVcil8slubm5oqoSE/O/Afbc3Nxi6x44cEBiY2ODqmtVv8Hav3+/JXVFIm9fUNfauqfbv3+/abUibV9Q19q6VtYOxfnBucExYWXdYOXn5/u1nu2CrsvlkuPHj/u1bnx8vBiGIVu3bpVx48bJhx9+KPHx8aXa3tChQ2XIkCGen/Py8iQzM1PS09MlOTm5VLUC4XK5xDAMSU9P9zqAUlJSiq2bnp4uGRkZQdW1qt9gZWRkWFJXJPL2BXWtrXs6f88pf0TavqCutXWtrB2K84Nzg2PCyrrB8veiA7YLul9//bW0a9fOr3U3b94s9erVk0GDBknr1q2lZ8+epd5efHy8z3AcExMTsgfUMIxi2/O17djY2FL15KuuGayoGxMTY1m/IpG1L6hrfd2iIqVn6kZmXStrW31+REq/kVbXytqRVjcY/vZiu6Bbr149mTBhgl/rVq1aVb766iuZP3++fPrpp5KVleW57eTJk3L06FHJysqS1NTUkIzOAgAAwD5sF3SrVKki/fr183v9P/74Q0REbrjhhmK37dq1S2rXri1jx46VwYMHm9Vi2HDVBQAAAP/ZLuiWVvv27WXmzJnFlt93331Sq1YtGTZsmDRu3DgMnZmPoAsAAOC/iA+6NWvWlJo1axZbPnjwYKlcubJ07949DF0BAAAg3OwzqxhnxYguAACA/yJ+RLckRT+YBgAAgOjDiG4EYUQXAADAfwTdCELQBQAA8B9BFwAAAI5E0I0gjOgCAAD4j6AbQQi6AAAA/iPoAgAAwJEIuhGEEV0AAAD/EXQjCEEXAADAfwRdAAAAOBJBN4IwogsAAOA/gm4EIegCAAD4j6ALAAAARyLoRhBGdAEAAPxH0I0gBF0AAAD/EXQBAADgSATdCMKILgAAgP8IugAAAHAkgi4AAAAciaALAAAARyLoAgAAwJEIugAARLkxY8Z4/fzmm2+GqRPAXARdAACi3J133ilXX321lClTRjp27Cg33XRTuFsCTEHQtalmzZoVWxYXFxeGTqzXuHHjcLeAKDFixAivn5988skwdQLYS1pamixevFgKCgpk0qRJkpycHO6WAFMQdG2qevXq8uCDD3p+fumllyQ2NjaMHZnngw8+8PqZP5EhVO6//35p3ry5iJx6g1X0HAMAOE+ZcDeAkr3++uty3333SXx8vNSrVy/c7Zjm5ptvluPHj8uKFSuka9eu0rp1a3G5XOFuC1EgPT1dVqxYITk5OVJQUCBVq1YNd0sAAAsRdG3u4osvDncLpjMMQ/r16yf9+vULdyuIQmXKlJG0tDTZv39/uFsBAFiMqQsAAABwJIIuAAAAHImgCwAAAEci6AIAAMCRCLoAAABwJIIuAAAAHImgCwAAAEci6AIAAMCRCLoAAABwJIIuAAAAHImgCwAAAEci6AIAAMCRCLoAAABwpDLhbsBuVFVERPLy8kKyPZfLJfn5+ZKQkCAxMea976Cu9bWpG5l1raxNXeqGqjZ1I7OulbUjrW6w3DnNndtKQtA9TX5+voiIZGZmhrkTAAAAnEl+fr6kpKSUeLuhZ4vCUcblcsnu3bulQoUKYhiG5dvLy8uTzMxM2bFjhyQnJ1PX5LpW1qZuZNa1sjZ1qRuq2tSNzLpW1o60usFSVcnPz5dq1aqdcaSZEd3TxMTESI0aNUK+3eTkZEsOIOpaX5u6kVnXytrUpW6oalM3MutaWTvS6gbjTCO5bvaZbAEAAACYiKALAAAAR4p99tlnnw13E9EuNjZWrrrqKilTxtyZJNS1vjZ1I7OulbWpS91Q1aZuZNa1snak1Q0FPowGAAAAR2LqAgAAAByJoAsAAABHIugCAADAkQi6AAAAcCSCLgAAAByJoAsAAABHIuiGmRVXd1NVT12uHgcAiARWvV7xOhjdCLohlp+fL/v375djx46JiIhhGKafhIZhiGEYnn+LhO5Ed7lcEVM3knq1urZZdU+cOCFHjx41pVYoa7tF2gutled1pPUczXWtOjdCcc4V5X69cjNrH1lV9/TnTeqaW9csfGFECH388cfy+uuvy+bNm6VatWpy8cUXy6hRo6Rq1aqmbWPBggUya9YsycrKkvr160vPnj2ladOmkpCQIC6XS2JizH1vk5WVJdnZ2RIXFyc1atSQtLQ029aNpF6trm1F3VmzZsn06dNl48aNUr9+fWnTpo3079/fhG6tq713717Jy8uTpKQkSU5OlqSkJBO6jby6VtamrrV1rTo3rDyfT7d48WL56quv5JdffpE2bdpIixYtpGXLliJyKjSdHlTDXXfevHkyZ84c2bx5s1x22WXSokUL6dGjR0C1qBsCipCYMWOGli1bVq+//np96qmntEePHpqWlqaZmZk6depUzc3NDXob77//viYkJGjTpk21RYsWmpqaqqmpqXr33XfrwYMHVVW1sLAw6O0U3V6dOnU0MTFRExMTtXr16vruu+/q7t27bVc3knqNxJ6nTp2qcXFxevnll2uPHj20QYMGahiGXnfddbpq1aqgjjurak+dOlUbNGig5cqV00qVKmnz5s11wYIFevz48YB7jcS6kdgzdf9X16pzw6rz+XRTpkzRcuXK6QUXXKAXXXSRxsXFac2aNfXxxx/3rONyuWxT9/3339f4+Hi9+OKLtV27dlqxYkVNTk7W2267TU+cOFHqetS1HkHXYi6XS/Py8rRDhw7ao0cP3bVrl6qqHjt2TL/55hu98sortUKFCjp27FjNyckJeDvbtm3TmjVr6n333ad//PGHqqoeOHBA+/Tpo2lpadqqVSvNzs5WVXPC7vz58zU+Pl7vuecenTlzpo4fP167du2qhmHo7bffrt9//71t6kZSr5HY8+7du7Vp06bar18/zcrKUlXVP/74Q9977z3NyMjQiy66SOfMmRPQcWdV7dmzZ2vZsmX19ttv14kTJ+o///lPveSSSzQ2NlaHDRvm2VZpRVrdSOyZuqdYdW5YeT6fbsuWLVq1alV96KGH9JdfflFV1VWrVun111+vsbGx2rt3b8+6pQmlVtXdtm2b1qlTR++//37dtm2bqqpu2rRJBw8erPHx8dq+fXvdvn273/WoGxoE3RA4duyY1q1bV++55x5V9T6xsrOztWvXrlquXDkdP368Hj16NKBtrF27VhMSEvSzzz5TVdWTJ096tv3cc89pWlqaNmvWTP/8889iPZSG+34DBgzQSy65xOtgPnLkiI4ZM0YNw9COHTvqqlWrwlo3knqN5J53796tFStW1FGjRhXb5ooVK7RGjRraoEED/eqrr0pV14raLpdLT548qbfccou2bdvW88ZTVfW3337TwYMHq2EYeu+99+qvv/7qd5+RVjcSe6auN6vOOyvP59OtWrVKExMTde7cuV7Ld+zYocOGDdPY2Fjt0aOHVw/hrLtlyxZNTEzUKVOmeC3/888/dfz48ZqWlqZt27bVAwcOUDeAulYh6IZAfn6+NmrUSHv27Kmqp0ZUi74bPnTokP7tb3/TjIwM/e677zzrlMZPP/2khmHopEmTPMvcYffEiRP64osvalpaml5//fWan58f7K+kPXv21CZNmvi87b333lPDMLR79+76888/h71uJPUaiT3//PPPWqlSJR05cqSqqhYUFHjd/t1332nVqlX1iiuu8PxVwd8nPqtqt2nTRjt27Kiq3udaYWGhPvfcc2oYhg4aNMjzRO2vSKsbiT1T9xSrzg0rz+fTLVmyRA3D0P/+97+qeuq1yl0rOztbn332WTUMwzNIFO6669atU8MwdPr06cXqHj58WCdOnKjJycl6/fXXUzeAulYh6IbIyy+/rIZh6MyZMz3Lij45bNmyRS+88EJt2bKl3zWPHTvm+ffu3bv1/PPP1y5duuiOHTs8y91h9/jx4zpo0CCtUKGCvvnmm8W2X1r9+/fX9PR03blzp6p6H+iqqq+//roahqFPP/20qvof3K2oG0m9RkrPpx87N910k6alpXnquo87t1mzZml8fLw+/PDDfvVZ9P5m11Y9Ffjr1avnc3uqqk8++aTGxsbqe++9p6r+799IqxuJPUdzXSvPO6vPOV/++OMPrV27tvbq1csrNLt/z7179+rf//53rVChgtcgTrjqHj58WFu1aqXNmzf3TBEsWjc3N1dHjRqlsbGxnjcKZ1I0HJpZN1L7tQpBN0T27NmjTZs21fr16+uyZcs8y90HRkFBgb744ouakJCg33zzzVnrjR8/XgcNGqSHDx/2LPvPf/6jhmHov/71L68PORQd2W3QoIFeddVVpeq96AHsnmS+efNmTU5O1n79+nltp+gTsXu+zoYNG866DXePwdaNpF4jueeivauqzpw5UytXrqzXXnut7t+/X1W9X7BzcnK0X79+WrlyZf39999LrLdixQrNy8vzWvbZZ58FVbvo/nWfFwsWLNCYmBh94oknvPaD28GDB7Vnz55aqVKlM/Z7+n3NqBuKfiOx52ju11f/quacd1acc6UxYMAAjYuL07ffftszda/o47N161atUaOG9urVq8QaO3bs8Br4UVUdOHBg0HWLct9v9OjRWrZsWR02bJjnQ95F6+7YsUNbtGihrVq1KtaT2+bNm4t9AP2ll14Kum4k92slgm4IrVu3TjMyMrRNmza6YsUKz3L3E+2OHTvUMAx9//33z1hn4sSJahiG9u/fX/Py8jwH1smTJ/WOO+7QhIQEnTx5stcTk/tEf+ONNzQxMVE3bNhQqhHd09fNycnRRx55RMuUKeMZ/XP3oHrqSXHVqlWanJyszz//vM+aU6ZM0VdffdVrWX5+ftB1I6nXSOv5u+++0+nTp+vYsWN1w4YNXh+gHDx4sKakpOidd97pGUUpGqK/+OILNQyjxDnA7777riYnJ+uMGTOK3TZkyJCgap9u165d2rNnT83IyPD8haPoflA9FSLi4+P1gw8+8Fnj008/LXbb3r17g65rVb+R2DP9nmLVeRfKc27btm26evVqXbduXbFpGm3bttXKlSvrzJkzPaHI5XJ5XhtHjhyp5cqV84wwF/XWW2/pJZdcops3b1ZV70B+1VVXBVx306ZNumjRIp03b16xD1b16NFDExIS9NVXX/U8Fi6XyzNQMXnyZDUMQ7ds2VKs7vjx4/X888/X5cuXe+7n1rNnz4DrRlq/oUTQDbGVK1d6Li8zf/58r9vmzp2rqampOm/evBLvP2HCBDUMQx955BHPnwyK2rp1q3bu3FkTExN1/Pjxnndabi+88IJmZGT4fSmp2bNn680336zNmzfXW2+9VadOnep5979582a99tprtWLFijpixAjPfYqOJlerVk0ffPDBEn+PoUOH6pEjR7xu+/XXXwOqG0m9RmLPkydP1vT0dE1JSdHy5ctrfHy83njjjV7H8c0336wVKlTQPn36FDvGpkyZoqmpqbp27doS+x08eLDu2bPHZ0+3TXlEpQAAIABJREFU3XZbqWvPnz9fH3zwQe3SpYs+8sgjumLFCs/+WL16tTZs2FBr166t77zzjuc+RUcfypcv7/WGwM09n3n06NHFPkD6ww8/BFzXqn4jsWf6PcWq886qc86Xki5p6J5m99tvv2nTpk21WrVq+uGHHxYbPRw6dKhWr1692Miz+3d44IEHPGFc9X9vIrZu3aqXXHJJqetOmTJFq1evrklJSZqQkKBJSUn6zDPP6A8//OCpf/XVV2v58uV11KhRunfvXq/7v/LKK3rOOecU22fufvv376/79u3zLHfv84KCAu3YsWOp60Zav6FG0A2D7777Ts877zxNSkrS559/Xr/99ludNWuWXnvttVqzZk2f7y5VT43kxsTE6JAhQ7zW2b9/v27ZssVzEv/xxx/avXt3LVu2rA4YMMAzerxq1Sr929/+pq1atfLrUmZFr6XYvXt3rVOnjiYnJ2u7du08T4xr167VDh06aPny5XXAgAFe91+9erXWqlVLX3zxRa/l7pPn9N+j6AjHjz/+WKq6kdRrJPa8fPlyTUpK0iFDhujy5cv1999/12effVYzMjK0Ro0aOmHCBM+6d999t6alpXk+mb19+3ZdsWKFdurUSevXr+/1glS038GDB/t881bUvffe63ftkq4rfdddd3lGlJYuXarnnXeeZmZmFvuU+bJly7Ry5cr67rvvlthvSfv3m2++KXVdq/qNxJ7p9xSrzjurzjlfznRJw9tuu80TxjZt2qQtWrTQChUq6NChQz1Xoli3bp126NBBr7zySq9A6v7L5pAhQ0r8HQoLC3XDhg3aqlUrv+t+9dVXmpiYqA888IAuXrxYv/jiC73//vvVMAy96qqr9IsvvlDVU1er6dy5s8bExOhNN92k33//vbpcLl2zZo126tRJL7nkEq/XWXe/Dz/88Bn3eX5+vnbp0sXvupHWbzgQdMPk119/1dtvv13j4uK0bNmympqaqnXr1tUff/zR5/q///67xsTEaNWqVb3m8P7973/XOnXqqGEYmpmZqXfccYceP35cjxw5oo8//riWLVtWy5cvrzVq1NBq1appenq6rl+//qz97d69Wy+88ELt16+f54n7yJEj+o9//EMrV66smZmZnj9HrF+/Xu+44w4tU6aMtmjRQl988UV98cUXtUOHDnrOOed4XTrH/aeMRx55xHP9PdVTLwhF//Shqrphwwa/6kZSr5G4f1VV33nnHc3IyCh2fH7++efasmVLjY+P9xqpGjt2rDZt2lQNw9CUlBStWrWqVq1atdj9P/30UzUMQwcMGOA12vLhhx/qmDFj9LHHHtOVK1d6vZj6U3vbWa4r3aJFC88IxfLly/XKK6/UsmXL6nXXXafTpk3TN998Uzt37qxVqlTx2o8l7V/341fUt99+63ddq/qNxJ7p93+sOO+sOudO5+8lDa+55hpds2aNqp66AlGvXr00MTFRK1asqE2aNNHatWtrWlqa12cG3CFs4MCBXiONq1ev1jlz5uiSJUu8RhFzcnLOWtfd77PPPqvnn39+sefsKVOmaHJysl588cX6+eefe5b3799fq1evrmXKlNFzzz1Xq1evrhkZGV6vs+43Fg899JDXtI1FixbplClTdPLkycW299BDD52xbqT1G04E3TA6efKkbtiwQadOnaoLFy484/B+QUGBjhs3TsuXL6/9+vXTXbt2aefOnTUpKUn79OmjL730knbs2FFjYmK0efPmnifYxYsX67hx4/Tee+/Vl19+2e/rNW7btk3Lly/v+SSwe87N8ePHdfLkyZ4D2f2CsWPHDp0+fbpecsklmpqaqpUrV9bWrVt7HeTLly9XwzD00ksv9frT3pNPPqlt27bVqlWras+ePfX999/3fMhu586dZ60bSb1Gas/PPfecxsXFeUaoil5yaOnSpdqmTRutWLGi11y/7OxsnTp1qr7wwgv6zjvvFLsw/smTJ/W2227TmJgYr2taduvWTcuWLauJiYlapkwZjYuL0/vvv9/rhe7AgQNnrO3PdaWbNGni+aDNli1bdPTo0Vq3bl2Ni4vT1NRUbdKkidcL+cKFC9UwDG3Xrp3XJfqef/557datmzZt2lQfeOAB/eabbzz7/+effz5rXav6jcSe6deb2eedledcSfy9pGHReZwzZszQYcOGae/evXXYsGG6detWz21//fWXXnzxxWoYhv7nP//xLO/Tp4+WL19eDcNQwzC0SpUqXgHvbHXdHnroIa1Ro4bXZ1/cZs6cqeecc45edtll+vXXX3uWf/PNN/rvf/9b77//fh0zZoz+9ttvXjVbtWrlmUrm1qtXL42Li/P0m5iYqOPGjfMcf6qnntPPVDcS+w0Hgm4EOX78uP773//WsmXLanp6utatW1fnzp3rCS25ubk6duxYjY+P12uvvTaobe3atUvj4uK8/nxd9ANLH374oVatWlUbN27s+RIK9zqbNm3S7du3F/tzxapVq7Rjx44aFxen48aNU1XVa6+9VuPj4/Xyyy/Xrl27apUqVTQhIUEfeeQRrxeOM9WNpF4jrWd3AP/88881Pj5e33rrLa8+3RYsWKANGjTQFi1a6KZNm3z+zr7k5ubqvffeqykpKdq3b1/t1q2bZmRk6Lhx43Tjxo26evVqHThwoBqGoXfeeafXRffPxN/rSnft2tUzquVyufTw4cO6bNky3bRpU7E/yc6fP18vvPBCrVatmk6bNk1VVa+77jotU6aMNmrUSJs3b66JiYlarVo1/c9//uP1Se8z1bWqX1XVefPmRVTP8+bN0wsuuCBi+l2wYIFl+1fVmvPOqnOuJKW5pKG/H5B2z+nNzMzUTz75RLt06aIVK1bUp556Sr/++mt9+eWXtUWLFpqQkODzg3ZnMmrUKI2Pj/d8+KqwsNCrr48++sjzTZKnfwamJC6XS9u2basJCQn6r3/9S7t06aKpqak6cuRI/fHHH3Xq1Knas2dPLVOmjI4ZM8bR/YYDQTfCnDhxQv/v//5PMzIy9Omnn/Z8mMH9JJifn6+33HKLJiUleT6FWlou16mvLW7Xrl2xkQb3dk6ePKljx47V5ORkffzxx/XEiRPFrrXoy9q1a/W6665TwzD0wgsv1OrVq+vMmTP1r7/+8vR/9dVXa0JCgr7xxhtaWFhY7Huzi57EhYWFeujQIVN6Pf1J9vvvvw+615Lk5ORYsn/XrFljSs+nfxXwoUOH9IILLtBLL73U67gq+qL79ttvq2EYnnmDJfV7eu28vDy96667tFy5cpqZmamff/55scvRPPHEExoTE+P5oKava40Gel3pN954w+uTwqcrOjr+5ZdfapMmTbRq1arapEkTrVKlin700Ud6+PBhLSws1C1btmjDhg01PT3dMzeupLpF/yS4c+dO0/pVVa/gtHjxYtN6XrhwoSew/PHHH6b1vGDBAk/dBQsWmNZvUWb2e3rvVvSrau55V3S7Zp1zp7PqUolF67p/v23btmmjRo3UMAw977zzdP78+V6/w5o1a/Sqq67S/9femYfXdG//f+3M89iIKSOCJMZEBFExZKghWjVUhSBKUFO1QX17TVf0XvfS9qJ1XYq4aogW9ZhCiSSGFEkIqoZoQwVBM5CIJO/fH3nO/p19ppycnCORu17Pkyfn7L0/77PO5+xh7bXXZ33s7e1rFVl8/Pgx3NzcJMEiRXtlaRcnT54U7VOH/G/ds2dPCIKANm3a4ODBg5Jzy927d8V0xqysLK37QR/2yuvK92Fd7W0osKP7GlJWVoakpCSlNATZAXX48GEIgoDk5ORaa8sfADt27BDzoORHU8pHFgYMGABfX98apy6WP/levHgRUVFRcHd3xzfffCM+jpP9Lysrg6enJ/r06aP2BPL8+XPJum3btunF1ufPn0tszcjIqLOtQLVjm5eXJ5lxLDExsc42y3TlH/vJ5nTX1WZZDl9MTIxkeUpKCuzs7PDuu+9KHlfJ91fv3r3FOs210S4qKkJMTAxiY2MlUQfZxTU3Nxd2dnYYM2aMSm1D1ZWW6coPVjl27Bg6deqEFi1aYOvWraK2zNa7d+/C2dkZI0aMUKu7b98+vPfee/jqq6/0aq+8tnxpOX3YvHPnTgiCgKFDh4rLvvrqqzrbLNMdMmSIuCw5ObnO9v766684deoUtm3bJqZyrV69us72ynS3bt0q6h4+fLjO9paXl6uctfLkyZN1Ou7U6db1mFOHIUolqvv8O3fuoFevXhg9erR47Ms75LJc3s2bN6vVlXdEq6qqUFlZiTVr1ijNnCZzHisrK5GXlwdPT09MmjRJyalUpSv/ul+/fhg4cKB4TpG1raqqwpEjRyAIApYsWaJ1f798+VIv9qpD3/bWB+zovqbIDmb5nU7G4sWLYW1trXU+7okTJ3Dw4EHxYJQ/gS5YsABGRkZYtmyZJEokc7wOHToEQRBUTnKhqCt/Ajp79izmzp0riazI686ZMwfm5ubIycmRfLcjR45g7Nix8PLyQt++fSXlseLj43W2VZNuWlqaTrbK2Lt3L/r37w8nJyc0b94cw4cPF7eTRU10sVmTbnp6uk42l5WVYfz48RAEAe7u7hKHtKSkBOvWrYO5uTlGjRqlciRueHg4evfurbS8Jm2gOtqsWP9Rfv9u2rSpJCokw1B1pdXpAtX7S0xMjNJjXZmDM2zYMDRr1gy//fabkm5iYiIsLS0xatQo/Pjjj5K2Y8eOrVMdbHXaQLUzpqvNGzduhCAIsLW1haurq7g/lpeX16mP1enWtY937NgBX19fMWfTz89PvFDXpY816R46dEhne/fu3YuoqCh4eXmhf//+iI+PF53oly9fYu3atTAzM6v1cadJF6h2dnU55lRhqFKJqnTlHffff/9dadIK2TXszz//FEu+KXLmzBnxtWKE/f79+5g+fToEQUBcXJy4XH5/6dChA0aOHKm1rvxrxQizTLeyshImJiaYP3++km5aWhoWLVqE8PBwzJ49G+fPnxc1//jjD53tVdS9cOGC0lMBXextSLCj2wiQ35kvXryIXr164c0339SqpMeePXsgCAK6dOmCY8eOKeWBFRcXY/LkyTA2Nsann36qtMOvW7cOjo6OuHHjRq10AUiib4DUWR87diy8vb0lkTRZ3cZu3bph+PDhaNeuHWxsbMTyWKWlpTrZWpOuLrbKa9vY2GDQoEFYunQp3n//fZiZmYkn9KKiIp1t1qQLQExXUHUzpMnmdevWwcrKCkOGDIGDgwNiY2PFdQ8ePMDq1athZWWFAQMGSJ4aXLlyBQEBARg/frxSHp422orI2/3jjz/C2dkZq1atkqyTjQ7Wd13pmnQBKB1f8t938ODB6NSpk9Lj4DNnzsDFxQWzZ8+WRPdl5OXlYeDAgbCwsMD69etrVQe7Jm2g+sIvb6s2Nsv64i9/+QsOHDgAc3NzfPzxx+L6a9euYfDgwTr3sTpdAEr7pzb2JiUlwcLCAmPHjsXWrVvxz3/+E3Z2dpg5c6Zo76BBg2BpaVmrPlan++GHH4rbyOf2amvvf//7X5iZmaFHjx6YOHGiWN2gQ4cOSE5ORnl5OUpKSrBq1SpYWloiLCxMq+NOnW7Hjh2RnJws6VtFezUdc6owVKlEdbr9+vUTqy2osl3G5s2bYWFhgYMHD0p0d+/eDUEQMHDgQHGZorObm5uLSZMmQRAEjBo1SvLULTMzE+3bt0d8fLwkJ7YmXXkHUpW9O3fuhJWVFbZv3y6xJTExES4uLvDx8YGPjw+srKzQrl07HDhwQGyvi73qdGUpNvI3IrWxt6HBjm4jYteuXRgwYAAcHBxw5cqVGre/dOkSOnbsCA8PD7i6usLPzw/JyclKd3OPHz/GrFmzxMeWslHLqampGDRoEDp16iSJRtakqyrXSzHa6+vrixEjRogOZkpKClxdXTFz5kzR6cvPz0dYWBiaNm2K8+fPA6jOTayNrZp0mzVrhoyMDCX7arJVxqFDh/DGG2/gww8/FB3YZ8+ewdfXFxEREeJ2xcXF4t14VFRUjTZr0o2MjJTYoKrPNdkMVNd57tWrFzIyMhAdHQ0bGxtMnDhRPME9ffoUu3btQtOmTeHg4ICIiAjExsYiMDAQjo6OGnPDVWnHxsaK2qr2DdkgO09PT0n0yVB1pTXpXr9+XZJbq+ppRVpaGry9vTFx4kQlp2b9+vVo06aNJE958+bNWL16NVatWoX8/HxkZ2cjOjoaJiYmtaqDrU77iy++wN///nfk5+eL+0NVVZVSjVdVNssirh999BHu3buHsrIyjBw5Era2tuLgF6C6VN3w4cO17mNNuvKzRsqjTR/fu3cPXbp0wYQJE8SqAC9fvkRISAimTJkibnfr1i0MGzZM6z7WpDt16lSVdmpj7+3bt8XlsqjkkydPEB8fD0EQ0K5dOyQmJuLFixcoLS3Fzp074eLiAicnJ43HXU267du3R2JioiQyKm+vumNOFYYqlViTrru7u1g1QdV57uLFixgwYAD8/f0lE2JkZGTA09MTzs7OEAQBgwcPFtcpOrt5eXlYtGgR7O3t0bp1a0ybNg3z5s1DSEgInJ2dJVUbaqOrqs8vXLiA8PBw+Pj4SM47hw8fhpOTE2bMmIGcnByUlpYiPT0dnp6eGDBggM721ka3NvY2RNjRbQSUl5cjNjYWHh4eaN++vdqEfsU2CxcuFAcxZGZmwt3dHf7+/iqdXaA6h9DV1RXGxsZwcHCAi4sLmjZtKhlMpYuuPCdOnEBYWBicnJzEk2NJSQmmTJmCjh07KiW9Z2VlwczMTFJmRltbtdFdu3ZtrWyV8eTJE4wYMQJ9+/aVTE1ZVlaGgQMHYsCAAUhLS0NaWpp417xmzRo0adIERkZGam3WRjc9PR2pqamSu3FtbJZRWlqKZs2aYf369SgsLMSoUaNgZ2eHiRMnittcunQJjx49wrRp0xAUFAR/f38MGzasxhssddrykV35m4v4+Hi8+eabcHFxkfSDoepKa6Pr4eGB6OhosX/lL2JpaWkIDw+Hi4uLyqhqbGwsfH19xfeDBw+GmZkZLCwsRO0NGzbg6tWrWLZsGUxMTLSug12TdsuWLbFhwwalCKY6mw8dOgQjIyPMmjVLEt2UjeJetGiRROfBgwdYuHBhjX2sra66wU+a+viXX36BlZWVOPGC7Hwzbtw4TJkyBfHx8Vi+fDn++OMP3L9/H4sWLdKqj2vSnT9/PpYuXYp79+4pRT412fvzzz/DzMwMP/zwg+Q7l5SUIDg4WNwn9u/fL7a5e/cu4uLiNB532uh6enqKuvLpPuqOOXUYolSitrotW7YUnSz5m7h//etf6NOnDxwdHSW6xcXFmDFjBgRBwNdff42//OUvSvnhik5pUVERzpw5g8jISHh7e8PT0xNhYWGS66wuujKqqqqQkJCAN998E05OThJ7CwoKMHToUISHhysFEL744guVA8y0sbc2urWxt6HCjm4j4dKlS/j8889rvPuW59ChQ5JH3ampqXB3d1eKwMqfuLOyspCUlIT58+fjP//5j1JuVG105SktLcWQIUPg6ekJLy8vycFTUlKC0NBQLFy4UFwmGyWan5+PZs2aiY/CamNrbXRV2erl5aVkq4zy8nKsXbsW27Ztkyz/+uuvxYtMREQE7Ozs4OfnJ+Z1XbhwQaPN2ura29vD399f1C0pKanRZuD/XywmTJggOrb3798XHdKYmBhERUVh0KBBKC4uFiODz549k9T41EV7/PjxiIqKwtChQ3H79m389NNP8PLyQmhoqFLpJEPVldZW18TEBIGBgaJuUVERRo8eDR8fH7i7u6t1EOLj49GuXTtUVlZixowZcHJywvbt23H58mXs378foaGhsLGxwZo1awBUz3qkbR3smrT79esHa2trrFmzBi9evEBJSYlGm2/cuIF169ZJomEy3n77bbVTe548eVKjzbXRlT+enz9/XmMfZ2dnQxAEcZBfRUWFWJWgXbt28Pb2hpWVFTw8PMQnJ9r0sTa61tbWcHNzw969e1FZWVlj/wLAqVOnIAiC6HBWVlaKN1AzZ85EcHAw/Pz84OPjI7lBkQ0yUnfcaavbtm1bUbesrEzjMacOQ5RKrI1ux44dxfaymsheXl7o1q2b0g1AYWEhZs+ejblz5wKofjo1b948rZ3S/Px8PHjwQGlwn6665eXl4pOYnj17Ktn766+/QhAELF++XMmWc+fOwcjISCxrVxt7ddWtyd6GCju6jQhtyr8ooujMpqen1+iUGkp3xYoVmDhxosoLze3bt0UnXvF7BgQEYNiwYZLP1RZddZcvX67WVhmKj61//PFHCIIgJvyXlpbiu+++g6+vL1q3bq00D7s+dNu2bSvm4mnqX0U2btwIFxcX8TGtLJJsbm4OIyMj7Ny5E0Dt+7smbWNjY1H7+fPnSqkC8hiqrrS2uhYWFoiKigJQnTIzd+5cREdHK+VTyyMbpbxp0ybMnDkT8fHxkj4sKChAREQEXFxclAYS1oQ22pGRkWjatCny8vJQVFSEOXPmaLRZ3e8rmw1swYIF4sW7NqOua6MrIz8/v8Y+LisrwzvvvANBEDBgwAC89dZbEAQB8+bNw507d1BRUYGTJ0+iR48eaNmypdZ9XBtdd3d35OXlobi4uMb+LSgoQLNmzdCvXz+lJ3FhYWEYO3Ys1q9fD0EQVN7Mq0MXXaB639d0zCliqFKUtdWdN2+euL/cvHkT+/btU3kjBUBMlZHx6NEjzJ8/X8kprenGXV+6d+/eRWpqqqSihjzff/+9OMBR/hqVl5cHZ2dncTppbUvW1VX33r17Gu1tiLCjyygh75QePXpUXH7u3DmNs7fpQ1fRidOE7ITfs2dPyYkEgNaz9tRFt6aSaop6O3fuFB/Zy3j27Bm++eYbCIIgKQelT13Z5BGA9v17/vx5uLm5ifPQA0BkZCTMzMxgbW2tcRDZq9Q2VF1pbXWtra3FvLfS0lKlKV8VKSoqEiOrRkZGKkcrp6amwsTERHyEr60Dqa22sbGxOPL92bNnNdqsioqKCgQEBMDPz09pAFZdUKWrWBmhJnvv3r2LGTNmoEOHDujYsSP8/PwkN3dlZWXYv3+/eHOore210ZVF9rTpX1n0tX///ti/fz9SU1Mxbdo0mJiYiE9egoOD0b1791r1cW10dQmSGKoUpaF0VX1H2Wepc0orKytx9epVcWCvPnWvXLmisuyb4ndUR1FREdzc3DBv3jzJ8oKCAo3neV11Hz16pPU1r6HBji6jEplT6u/vj+PHj+Po0aNo1aoVQkNDVeZ+1kXX29tbJ13ZARsWFiZJnr969SqCg4OVBmbpQ7d79+6iri4XdlXlZh48eABBEJRGHNe3rr+/P+bPn4/y8nJERUXB0dERGzZswOjRo5UiQfWpbai60trqHjlypFa6OTk5cHV1hSAIeO+998RKCPKVTpydnZUqEOhTW+aI6YLs+EhKSoIgCFi2bJnOWobSraysRGFhIVJSUiT7kiyaVlRUBGdnZ8kANX3qTp48uVa6KSkp4iAmMzMz2NjYYOvWraLDsnr1ajg4OOD333+vlVOqb91XVYpSV13Fc7KirqpztiqnVPakJj09Hb169cLUqVMlbfWlGxcXp1FXE4WFhfDw8JDsh1euXMH48ePx17/+9ZXovi6wo8uo5dy5c3B3d0ebNm3g7e0NBwcHpVmtGoLu22+/jeDgYJSXl+PKlSsYPHgw7O3txUFNDU0XkEYBvv32W9jY2CgNDNG3bm01Jk2ahIEDB4qlwGQ5W48fP0ZsbKzawWz1oa3PutKvQjcjI0OcN37FihWSSMmpU6fQvHlzrF69Wukz61tbnjt37sDf3x8dOnTQ65z2+tQ9e/YsjIyMcPjwYXGZrNC9s7MzEhISxGX1rXv37l18//33+Pbbb5XGWkyePBlt2rTRKcigL936KkWpb11VyH4n2YBKQRDQu3dvBAUFwdraWpL+0RB0geobbj8/P/EJ2KVLlzBo0CCYmZlJcmcNpfs6wY4uo4T8yVk285Gjo6NW1RzqQ3fMmDHo1KkTsrKyMHjwYFhbW+tlSkJD6co7o5mZmejfvz86d+4seTTXEHQPHjwIQRDg4OCApKQkyeMwXXO3X4V2XepKv0rdzMxMtGzZEoIg4P3338fmzZuxfv16hIaGwtXVFbm5uTrbakhtebZs2QJBELBlyxa96Olb9+nTp+jQoQN69OghRvSTk5Px1ltvoXnz5jr3g6F0VXHx4kUEBwcjOjoaZWVleouo1Ua3vkpRGlpXHvkpdaOjoyEIApycnCT5wQ1JFwCCgoLw7rvv4saNG3jrrbdga2uLzMxMg+u+brCjy6glOTkZ3bp1g52dnV7v5PSlKx8dbNGiBUJCQmBtbV3nA9JQuookJiaif//+cHBwqLOzbwjdiooKfPfdd/jhhx805qg1NG0Zta0rXR+6t27dwgcffABLS0vR8ff399dLyR5Dast4+PAhWrZsiY4dO6K8vFxvTpg+dc+dOwd7e3sIggAXFxc4OzujVatWWpXOqg9dxQkPBg0aBGdn51rlmOtTtyGVonwVJS5TUlLQuXNnODg4SCpPNDTdyspKBAUFoUePHhg+fLhSIMZQuq8j7OgyKpGV8jE3N6/zidvQuosXL4YgCLCzs9OrrYbSLS8vx9SpU+Hl5YUuXbogJyenwepWVlbWOcL6qrV1qStdn7pVVVX45ZdfsHfvXqSmpoozPjV0bRmrVq0yyCNNfepeu3YNH330EUaMGIG//e1veou4GkoXqC575uvrCz8/P73eCOuiW9+lKF9FiUug+gYrIiICgiCovCFsaLqRkZFihFhVIMZQuq8b7Ogyarl586ZkJpWGqpuTk4OuXbtqXfexvnUB4Pr169i0aZPeZ5QxlO7rhi51petT93XEUINSXsfBLoagrKwM586dE0tA1bduQypFaSjdFy9eYN++fRrP+Q1Jd/ny5TA3N9d4Q2go3dcJdnSZRkFtypLK4eJCAAAPIUlEQVQ1BF2AHQVDo0vJpPrUZZjXjfosRfm/rHv27FnxRkU+N7m+dRsqAgAQwzAMwzBMLTl9+jSNHj2a7Ozs6Msvv6TKykqaOnUqubm50dGjR8nU1JR19awbFxdH7u7uDVK3IcKOLsMwDMMwOpORkUEjRowgc3NzqqyspCdPntDx48epa9eurPs/qNvQMF68ePHi+jaCYRiGYZjXCwAkCAK1aNGCTExMaPv27URElJqaSp06dWLd/zHdhopJfRvAMAzDMMzrhyAIRER07NgxSkxMJFtbW0pLSyNfX1/W/R/Ubaiwo8swDMMwjE6UlpbSpk2b6NKlS5SRkaE3Z4l1X0/dhgjn6DIMwzAMozO3bt2iqqoqatOmDeuyboODHV2GYRiGYRimUWJU3wYwDMMwDMMwjCFgR5dhGIZhGIZplLCjyzAMwzAMwzRK2NFlGIZhGIZhGiXs6DIMwzAMwzCNEnZ0GYZhGIZhmEYJO7oMwzAMwzBMo4QdXYZhGIZhGKZRwo4uwzAM89px584dEgRB/Dt58uQrbc8wzOsBO7oMwxiMHTt2UEREBLm6upKpqSnZ29uTl5cXhYaG0qxZs+jIkSP1bSKjAUVnUP7PysqKWrduTRMnTqTs7Oz6NlUCO7EMw8gwqW8DGIZpnIwbN44SExMly4qKiqioqIju3LlDKSkp9Ntvv1FEREQ9WcjUhdLSUrp16xbdunWLEhMTaePGjTRu3LhX9vlOTk60cuVK8X2rVq1eaXuGYV4P2NFlGEbvHD58WOLkBgQEUEREBNnY2NCjR4/o4sWLdObMmXq0kNGFsLAwCg8Pp8rKSsrOzqadO3dSVVUVVVRUUFxcHIWFhVGzZs1eiS12dnb08ccf11t7hmFeE8AwDKNn5syZAyICEaF169aoqKhQ2qawsBBpaWkq2586dQqjRo2Cm5sbzMzMYGtri+DgYKxZswbl5eUq2yQlJSEwMBAWFhZo0qQJJk2ahIcPH6JPnz6iLTExMeL2J06cEJcTEXJzcyV6Hh4e4rpFixYpfV5WVhYmTJgAb29vWFhYwNraGp07d8by5ctRUlKitL2i3vnz5zFo0CDY29vD0tISISEhSE1NVfndCgoKsHTpUnTv3h0ODg4wMzND8+bNER4ejh07duil/1SRm5sr6SPFfli4cKFk/caNGyXrr1+/jri4OPj4+MDS0hKWlpZo06YNJk+ejGvXril9XklJCZYsWYIuXbrAxsYGJiYmcHFxQadOnTBp0iQcOnRIrW0nTpxQ6mdVf3369NHYXp6kpCQMHDgQrq6uMDU1hYODA3r06IF//OMfePbsmdL28nrffvstjh49itDQUFhbW8PGxgaRkZHIycnRuv8Zhqk77OgyDKN3ZsyYIV7w33jjDdy8eVPrtp9++qlGR6V3795KjuTatWtVbuvt7Q0/Pz+9O7rr1q2DiYmJWht9fX1x//59tXpBQUEwNTVVamdubo6rV69K2mVkZKBp06ZqP2vo0KF17j911OToHjhwQLJ++fLl4rpdu3bBwsJCrR3m5ub47rvvJHqhoaEabR81apRa2/Tp6FZUVGDkyJEaddq3b48//vhDYr/8+l69ekEQBKV2zs7OePjwoVb9zzBM3eHUBYZh9E7Xrl3F1wUFBeTj40OdO3embt26UUBAAPXt25dat26t1G7Hjh2UkJAgvo+IiKBevXrRgwcPaMuWLVRSUkKpqak0Z84c+ve//01ERHl5efTRRx+JbWxtbSk2NpaMjIxo06ZN9Oeff+r1u50+fZo+/PBDqqqqIiKi4OBgioyMpOLiYtqyZQsVFBTQ1atXady4cXT06FGVGhkZGdSyZUsaM2YM5eXl0fbt24mI6MWLF/Tll1/SN998Q0RExcXFFBUVRfn5+WLbfv36Ua9evaioqIjS0tIkurr0X11QTD9p2rQpERHdvHmTxo4dSy9evCAiImdnZ4qJiSFBEMQ+evHiBcXExFBAQAC1adOGrl27Jg4aMzIyonHjxpGPjw8VFBRQbm6u1gPKFi5cSHfu3JH0Q1xcnJiD6+bmVqNGQkIC7dq1S3wfHBxM4eHhdO3aNdq9ezcREV27do3GjBlDP/30k0qN9PR0ateuHQ0bNoyysrLo4MGDRET0+PFj2rhxI82fP1+r78MwTB2pb0+bYZjGx8uXLxEYGKgxIhYSEoKsrCxJuy5duojrx40bJ1m3a9cucZ2JiQkeP34MAEhISJDoHjt2TGyTnp4uWaePiO4777wjLg8NDUVlZaW4LiMjQ6KZnZ2tUs/a2hr37t0T17399tviuq5du4rLv/rqK7URUxm3bt2qU/9pQjHqGRYWhpUrV+Lzzz/H+++/DyMjI3GdpaWlGOGcNWuWuNzIyAiXL18WNS9fvixpN2vWLADAxYsXJdHSqqoqiS0VFRW4c+eOWtvkI7LapCWo26ayshJOTk7i8h49ekhSb+Lj4yXtMjMzxXXyy93c3FBUVKTytxk2bFiNfc8wjH7g8mIMw+gdExMT+umnn2jBggXk6uqqcpu0tDQKCwujR48eERHR8+fPKSsrS1y/detWSYmokSNHiusqKiooIyODiIguXLggLnd1daX+/fuL73v27EleXl56/W7p6eni65MnT5KxsbFoY1BQkGTb06dPq9QYOnQoNW/eXHzftm1b8fXTp0/F1/IRW1tbW5o3b56Slre3NxHp3n+1ITk5mT755BOaP38+bd++XYxqGxsb09q1a8WBaPKR3oCAAPL39xff+/v7U0BAgPhetm379u3J2dmZiKqjpa1bt6bhw4fTp59+Sjt27KCnT5+Sh4dHrW2uLdevX6cnT56I76Ojo8nY2Fh8HxMTI9le3aDKsWPHkq2trfjex8dHfC3/GzMMY1jY0WUYxiDY2tpSQkIC3b9/n3Jycmjjxo0UExMjufg/evRIrM7w9OlTAqC1vsxBlk9NaNKkidJ26hxtRRQ/W/bYXRF5J6gmZDYq4unpKXlvbm4uvpY5j4qf5ebmJnG4FNG1/3TF3NycvL29KSYmhn7++WeaMGGCuE7eblX9L79M5vRZWFjQrl27yN3dnYiIbt++TXv27KEVK1bQ6NGjqUWLFrRq1ao62awNir+vov2K79U5rdr+xgzDGBbO0WUYxqAIgkB+fn7k5+dHEydOpMWLF1OrVq3Ei/2NGzeIiMjBwUHSLioqinr37q1WV5YHLN/u4cOHSts9ePBAZXsjI+l9fmlpqfi6qKhIbTsnJyfxc0JCQmjo0KFqbezZs6fK5aamppL3giCo/SwZeXl5VFlZqdbZ1bX/asOiRYto8eLFNW4nb7eqfpRf5ujoKL7u168f5ebm0sWLFykrK4tu3rxJp0+fptTUVCovL6dPPvmEoqKiVOZ36wt521XZr/he3n55tP2NGYYxLOzoMgyjd7Zs2UJlZWU0evRosrOzk6yztrYmIyMj0dGVOWjW1tbUuXNn8fH748ePadasWUoOQ2FhIR06dIj8/PyIiCgwMJD27NlDRNVOyPHjx8X0hdOnT1Nubq5KGxUdw7Nnz5Kvry8REa1YsUJtdLRnz560d+9eIiLKz8+nyZMnK33H0tJS2r17t1pHV1tCQkLEQVHFxcW0cuVKpUFMv/32G3l4eOjcf4agZ8+ektSSK1euiJ+Xk5MjSTeR9VFZWRnl5uZS+/btKTAwkAIDA4moOtLu6OhIhYWFVFVVRdnZ2TU6uorf+fnz51rb3rZtW3JychIju9u2baMpU6aINxhbtmxR+q4MwzRc2NFlGEbv5Obm0pIlS2j27NkUEhJCnTt3JicnJ3r8+DElJSVRRUWFuG1kZKT4+pNPPqExY8YQUXUubMeOHWnIkCHk6OhIjx8/pszMTEpLS6NmzZrRe++9R0REY8aMocWLF4upBu+88w5NmjSJBEGgTZs2qbWxXbt2ZGtrS8XFxURENG3aNDpw4ADl5+drnMxi7ty5tG/fPgJAN2/eJH9/fxo2bBi5urpSYWEhXb58mVJSUujZs2d1nils/PjxtHz5cjGKuGDBAjp+/Dj16NGDnj9/TmfPnqU33nhDdLx16T9DMH36dPr666/pxYsXVFVVRX369JFUXZDd5JiZmdH06dOJqDoFxdfXl/z8/CgoKIiaN29OlpaWlJaWRoWFhaK24g2KKlxcXMjU1JRevnxJRNWVGLKzs8nU1JRCQ0NFJ1oVRkZGNGfOHPrss8+IqDoHNyQkhMLDw+mXX36RVGPo27cvderUqfYdxDDMq6M+R8IxDNM4WbRokcaKC7K/Dz74QKntggULamzn4eEhabNmzRqV23l6esLX11dl1QUA+L//+z+V7QIDA9GkSROVVReA6rq9muroyv7k0VSXV76/FL9bRkYGXF1d1X6GYh1dXfpPHTXV0dVEbevo3r9/v0a7g4KC8PLlS5W2KVZWkK+OIf+3cuXKGttXVFRgxIgRGm1p3769pHIGoDxhhDwxMTHiOlktX4ZhDA8PRmMYRu/Mnj2bkpKSaNq0aRQUFETu7u5kaWlJZmZm1KJFC4qKiqI9e/aorOWakJBA6enpFB0dTV5eXmRubk6mpqbUokULCg8Pp4SEBDp+/LikzfTp0ykpKYkCAgLI3NycXFxcaPz48XT27FlycXFRa+fSpUspISGBvLy8yNTUlDw8PGjBggWUkpJClpaWattNmzaNMjMzafLkyeTj40NWVlZkYmJCrq6u1KdPH/rss88oOztb9w6Uo1u3bnTlyhVasmQJdevWjezs7MjExISaNGlC/fr1U4rM6tJ/hmDEiBGUlZVFcXFx1Lp1a7KwsCALCwtq1aoVffDBB5SZmSmx3dHRkdasWUOjR48mX19fcnJyImNjY7Kzs6PAwEBatmwZHT9+nExMtHsQuWHDBoqJiSFXV1elfOyaMDY2pl27dtHu3btp4MCB1KRJEzIxMSF7e3vq3r07rVy5kn7++WdJ5QyGYRomAlCLYboMwzCvGaGhoZSSkkJE1aWhNm/eXL8GMQzDMK8MjugyDMMwDMMwjRJ2dBmGYRiGYZhGCTu6DMMwDMMwTKOEc3QZhmEYhmGYRglHdBmGYRiGYZhGCTu6DMMwDMMwTKOEHV2GYRiGYRimUcKOLsMwDMMwDNMoYUeXYRiGYRiGaZSwo8swDMMwDMM0StjRZRiGYRiGYRol7OgyDMMwDMMwjZL/B6ZnVGfpAt51AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# fig,ax = plt.subplots()\n",
        "# fig.set_size_inches(8,5)\n",
        "\n",
        "# # ax.plot(alpha, label='alpha')\n",
        "# # ax.plot(beta,label='beta')\n",
        "# # ax.plot(delta,label='delta')\n",
        "# # ax.plot(omicron.iloc[16]['att'],label='omicron')\n",
        "\n",
        "# # ax.plot(omicron.iloc[16]['att'] - alpha, label='alpha')\n",
        "# # ax.plot(omicron.iloc[16]['att'] - beta,label='beta')\n",
        "# # ax.plot(omicron.iloc[16]['att'] - delta,label='delta')\n",
        "\n",
        "# ax.plot(1000*(omicron.iloc[16]['att'] - delta),color='black',linewidth=3)\n",
        "\n",
        "# ax.set_xlabel('Sequence Position', fontsize=14, fontweight='bold')\n",
        "# ax.set_ylabel('Attention Score', fontsize=14, fontweight='bold')\n",
        "# a = np.arange(0,1350,50)\n",
        "# plt.xticks(a[:-1], rotation=45, fontsize=12)\n",
        "# plt.yticks(fontsize=12)\n",
        "# # plt.yscale('symlog')\n",
        "# # ax.set_xticks(a)\n",
        "# # ax.set_xticklabels([str(aa) for aa in a], rotation=45, fontsize=10)\n",
        "# ax.grid(axis='y', which='major', alpha=0.3)\n",
        "# ax.grid(axis='x', which ='both', alpha=0.3)\n",
        "# # ax.legend(bbox_to_anchor=(1.0, 1.0), framealpha=1.0, fontsize=12)\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nh9thBTHVtxu",
        "outputId": "77fd868a-7822-419e-8ba4-37525f7a8031"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 18, 141, 500, 949]),)"
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# np.where((omicron.iloc[16]['att'] - delta) < -0.0001)\n",
        "# np.where((omicron.iloc[16]['att'] - delta) > 0.0001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VhK4bFaRVsfY"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "65jImqgNd9Yd",
        "outputId": "77f9b44b-4bcf-4a42-dc68-960cea8b4039"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "SMALL_SIZE = 8\n",
        "MEDIUM_SIZE = 10\n",
        "BIGGER_SIZE = 12\n",
        "\n",
        "plt.rcdefaults()\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=BIGGER_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
        "\n",
        "# fig,ax = plt.subplots(2,2)\n",
        "# fig.set_size_inches(8.5,8.5)\n",
        "\n",
        "# ind2d = np.unravel_index(range(len(lineages)), (2,2))\n",
        "# for l in range(len(lineages)):\n",
        "#     lineage = lineages[l]\n",
        "#     xi = ind2d[0][l]; yi = ind2d[1][l]\n",
        "#     for age in ages:\n",
        "#         linmean = [np.mean(pred[lineage][age][d]) for d in dates]\n",
        "#         ax[xi][yi].plot(dates, linmean, label=f'Age: {age}')\n",
        "\n",
        "#     ax[xi][yi].set_title(f'Projected Mean Severity ({lineage})')\n",
        "#     ax[xi][yi].set_xlabel('Days since Dec. 1, 2019')\n",
        "\n",
        "#     ax[xi][yi].legend(bbox_to_anchor=(1.0, 0.9))\n",
        "# plt.show()\n",
        "\n",
        "fig,ax = plt.subplots()\n",
        "fig.set_size_inches(5,5)\n",
        "lineage = 'B.1.1.7/Alpha'\n",
        "colors = {25:'blue', 50:'orange', 75:'brown'}\n",
        "for age in ages:\n",
        "    linmean = [np.mean(pred[lineage][age][d]) for d in dates]\n",
        "    ax.plot(dates, linmean, linestyle='solid', color = colors[age], label=f'{lineage} Age: {age}')\n",
        "lineage = 'B.1.617.2/Delta'\n",
        "for age in ages:\n",
        "    linmean = [np.mean(pred[lineage][age][d]) for d in dates]\n",
        "    ax.plot(dates, linmean, linestyle='dashed', color = colors[age], label=f'{lineage} Age: {age}')\n",
        "lineage = 'B.1.529/Omicron'\n",
        "for age in ages:\n",
        "    linmean = [np.mean(pred[lineage][age][d]) for d in dates]\n",
        "    ax.plot(dates, linmean, linestyle='dotted', color = colors[age], label=f'{lineage} Age: {age}')\n",
        "\n",
        "ax.set_title(f'Projected Mean Severity')\n",
        "ax.set_xlabel('Days since Dec. 1, 2019')\n",
        "ax.legend(bbox_to_anchor=(0.9, 0.9), framealpha=1.0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "4hLlQsE-d9S5",
        "outputId": "695c61e6-c1ad-475a-bbc9-5d2ece2286ff"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "SMALL_SIZE = 8\n",
        "MEDIUM_SIZE = 10\n",
        "BIGGER_SIZE = 12\n",
        "\n",
        "plt.rcdefaults()\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=BIGGER_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
        "\n",
        "# fig,ax = plt.subplots(2,2)\n",
        "# fig.set_size_inches(8.5,8.5)\n",
        "\n",
        "# ind2d = np.unravel_index(range(len(lineages)), (2,2))\n",
        "# for l in range(len(lineages)):\n",
        "#     lineage = lineages[l]\n",
        "#     xi = ind2d[0][l]; yi = ind2d[1][l]\n",
        "#     for age in ages:\n",
        "#         linmean = [np.mean(pred[lineage][age][d]) for d in dates]\n",
        "#         ax[xi][yi].plot(dates, linmean, label=f'Age: {age}')\n",
        "\n",
        "#     ax[xi][yi].set_title(f'Projected Mean Severity ({lineage})')\n",
        "#     ax[xi][yi].set_xlabel('Days since Dec. 1, 2019')\n",
        "\n",
        "#     ax[xi][yi].legend(bbox_to_anchor=(1.0, 0.9))\n",
        "# plt.show()\n",
        "\n",
        "age = 50\n",
        "fig,ax = plt.subplots()\n",
        "fig.set_size_inches(8,5)\n",
        "\n",
        "lineage = 'B.1.1.7/Alpha'\n",
        "linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "ax.plot(dates, linmean, linestyle='solid', linewidth=3, color = 'red', label='Alpha (B.1.1.7)')\n",
        "# ax.fill_between(dates, linmin, linmax, alpha=0.1, color='red')\n",
        "lineage = 'B.1.617.2/Delta'\n",
        "linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "ax.plot(dates, linmean, linestyle='solid', color = 'blue', linewidth=3, label='Delta (B.1.617.2)')\n",
        "# ax.fill_between(dates, linmean-linstd, linmean+linstd, alpha=0.3, color='blue')\n",
        "# ax.fill_between(dates, linmin, linmax, alpha=0.1, color='blue')\n",
        "# lineage = 'B.1.351/Beta'\n",
        "# linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "# linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "# linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "# linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "# ax.plot(dates, linmean, linestyle='solid', color = 'red', linewidth=3, label='Beta (B.1.351)')\n",
        "# # ax.fill_between(dates, linmin, linmax, alpha=0.1, color='green')\n",
        "lineage = 'B.1.529/Omicron'\n",
        "linmean = np.array([np.mean(pred[lineage][age][d]) for d in dates])\n",
        "linmax = np.array([np.max(pred[lineage][age][d]) for d in dates])\n",
        "linmin = np.array([np.min(pred[lineage][age][d]) for d in dates])\n",
        "linstd = np.array([np.std(pred[lineage][age][d]) for d in dates])\n",
        "ax.plot(dates, linmean, linestyle='solid', color = 'black', linewidth=3, label='Omicron')\n",
        "ax.fill_between(dates, linmin, linmax, alpha=0.1, color='black')\n",
        "\n",
        "ax.set_ylabel('Predicted Severity', fontsize=14, fontweight='bold')\n",
        "# ax.set_xlabel('Days since Dec. 1, 2019', fontsize=14, fontweight='bold')\n",
        "import datetime\n",
        "datebinnames = datebinnames = [(datetime.datetime(2019,12,1) + datetime.timedelta(dates[d]+15)).strftime('%b %Y')\\\n",
        "                for d in range(len(dates)-1)]\n",
        "ax.set_xticks(dates)\n",
        "ax.set_xticklabels(datebinnames, fontsize=12, ha='center', rotation=45)\n",
        "plt.yticks(fontsize=12)\n",
        "ax.legend(bbox_to_anchor=(1,1), framealpha=1.0, fontsize=12)\n",
        "plt.show()\n",
        "plt.rcdefaults()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ENECyppOdz7X"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jIvO6Kd7dz4R"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QG0wSP_8I-s8"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kQvEBm5VJA9t"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [],
      "name": "COVID_Disease_Severity_Projection_github.ipynb",
      "toc_visible": true,
      "provenance": [],
      "mount_file_id": "1rN7n0OBv6mThnyglCEDK1TSHSKgi_5wC",
      "authorship_tag": "ABX9TyMUILIdLMhoMSkiGb9xor0Q"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}