example_model.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "490a3ab6-52e3-46f9-85ae-70d637a3892a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "490a3ab6-52e3-46f9-85ae-70d637a3892a",
        "outputId": "808de180-630c-4d9b-9788-57f5a45b56b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.9/58.9 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.4/34.4 MB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q numerapi pandas lightgbm pyarrow scikit-learn scipy==1.10.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "552f701c-1594-4ed7-940d-98470159e961",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "552f701c-1594-4ed7-940d-98470159e961",
        "outputId": "3e2db4c9-c2c8-4eb7-ade3-c8b1b2e10794"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "signals/v2.0/train.parquet: 272MB [00:14, 19.0MB/s]                           \n",
            "signals/v2.0/validation.parquet: 422MB [00:13, 32.3MB/s]                           \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'signals/v2.0/validation.parquet'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from numerapi import NumerAPI\n",
        "import pandas as pd\n",
        "\n",
        "DATASET_VERSION = 'v2.0'\n",
        "\n",
        "# Download data\n",
        "napi = NumerAPI()\n",
        "napi.download_dataset(f'signals/{DATASET_VERSION}/train.parquet')\n",
        "napi.download_dataset(f'signals/{DATASET_VERSION}/validation.parquet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "4ec3babc-9d2d-4388-8fdc-5065805bb1a1",
      "metadata": {
        "id": "4ec3babc-9d2d-4388-8fdc-5065805bb1a1"
      },
      "outputs": [],
      "source": [
        "train = pd.read_parquet(f'signals/{DATASET_VERSION}/train.parquet')\n",
        "validation = pd.read_parquet(f'signals/{DATASET_VERSION}/validation.parquet')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4d97db1-cd89-4f0d-9533-c978950b629f",
      "metadata": {
        "id": "f4d97db1-cd89-4f0d-9533-c978950b629f"
      },
      "source": [
        "# Tickers\n",
        "\n",
        "The new dataset contains two tickers: `numerai_ticker` and `composite_figi`.\n",
        "\n",
        "`numerai_ticker` is given for the entire history, but `composite_figi` only goes back to September 2022.\n",
        "\n",
        "If you have Bloomberg tickers, you can map to `numerai_ticker` by replacing the exchange code with the ISO country code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "eb85e867-9635-44d0-a89c-b928cd405413",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "eb85e867-9635-44d0-a89c-b928cd405413",
        "outputId": "d3ef074c-47be-415d-b67b-fc2780942b69"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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leUy87jz//PNa52jYsKEgk8mERYsWaR6/c+eO4OrqqvV5Ft/PBg0aCLm5uZrHv//+ewGAsGzZMs17FhgYKISHhwv379/XbPfLL78IAIQ5c+ZozgFAeO+99wy+D+U/l0ePHhUACKtXr67wnoSGhlb4e8vPzxeCg4OFvn37GjxPeU2aNBFiY2O1HhOv82XP3aZNG6Fhw4bC3bt3NY/t27dPAFDheijlmtSuXTvBy8tLUhulvteCoP/vuzLfZbq+XwVB2rVcF/Hcrq6uwt9//615/PDhwxW+l/r06SO0adNG69qgVquFrl27CqGhoZrHxOtw9+7dJbVHbIOhz+XgwYMFAEJOTo4gCIJQUFAgqFSqCsdxdnYW3nrrLc1jVfXZJbJnnF5OVMs4Oztj7NixFR4vuwb27t27uHXrFnr06IH8/HycOXPG6HGfeuop+Pj4aP4t9mZfvHjR6L5RUVEICQnR/DsiIgKenp6afVUqFXbt2oVHH30U9evX12zXrFkz9O/f3+jxpRBHp+/evat5rLLviVwu14wuqtVqZGVloaSkBB07dkRSUpJmO29vb+Tl5WmNIJS3ceNG9OjRAz4+Prh165bmJyoqCiqVCr///rvJr7ms559/XmuKZI8ePaBSqXD58mWzjzlu3DitY3bu3BmCIGDcuHGax+RyOTp27Kj1Odm4cSO8vLzQt29frdfaoUMHeHh4VJieHxwcjOjo6ArnL/v7y8nJwa1bt9CrVy9cvHgROTk5Zr8uUxw7dgzp6emYNm1ahZE3Xcsi9u7di+joaPTp0webNm3SJCwUBAE//vgjBg0aBEEQtN6X6Oho5OTkaH2mAGD06NGS17aX3e7OnTvIyclBjx49KhwTkPb3umPHDjz66KNo3LixZrtWrVrp/D2VJ+U9y8jIQHJyMsaMGQNfX1+ttvTt2xfbt2+vcNznnntO8//i567859Hb2xstWrTQed0aNWoU6tatq/n3448/DqVSqTnXn3/+iZs3b+LFF1/UWsMcGxuLli1bYtu2bQBK32snJyfs27evwnIJcyQnJyMtLQ3Dhw/H7du3NZ+LvLw89OnTB7///nulqjPocv36dZw8eRKjRo3SKnXXq1cvtGnTxqxj5ubmar2/hkh9r81Rme8yKddyQx599FGtmQKdOnVC586dNZ+xrKws7NmzB08++aTmO+nWrVu4ffs2oqOjkZaWVmF6/fjx481KfqdL+e9JZ2dnTZ4IlUqF27dvw8PDAy1atNB57SjPFp9dIlvh9HKiWqZBgwY6p5meOnUKs2bNwp49eyqs/ZQSoJS9uQaguWmRclNZfl9xf3Hfmzdv4v79+2jWrFmF7XQ9Zg5xzWzZm77KvicA8PXXX+ODDz7AmTNnUFxcrHk8ODhY8/8vvvgivv/+e/Tv3x8NGjRAv3798OSTTyImJkazTVpaGk6cOKF3Ov3NmzcltUefyvz+pB7Ty8sLANCoUaMKj5c9T1paGnJychAYGKjzuOVfa9n3sqwDBw5g7ty5SExMRH5+vtZzOTk5mvZY04ULFwCUZjs3pqCgALGxsejQoQO+//57raSD//zzD7Kzs/H555/j888/17m/1PdFl19++QULFixAcnIyCgsLNY/r6hgw9vf6zz//4P79+wgNDa2wXYsWLXQGxGVJec/EziBxKUFZrVq1wo4dOyokjdL1eXRxcYG/v3+Fx2/fvl3huOVfj0wmQ7NmzTTrsA21qWXLlpps4M7Ozli8eDFeeeUV1KtXD126dMHAgQMxatQoKBQKva9Zn7S0NAClnSz65OTkaAWSlSW+Vn3XZCkBV3llO26knt/Ye22OylwLpVzLDdH1N9O8eXN8//33AIDz589DEATMnj0bs2fP1nmMmzdvagXuplwHjCn/PalWq7Fs2TJ8+umnSE9P18p3Ii41McQWn10iW2HQTVTL6Br5ys7ORq9eveDp6Ym33noLISEhcHFxQVJSEmbMmCGpp1lfT7ogoaRKZfa1FDFZjXgTaYn3ZO3atRgzZgweffRRvPbaawgMDIRcLsfChQs1gQUABAYGIjk5GTt27MCvv/6KX3/9FatXr8aoUaPw9ddfAyi9uenbty9ef/11necS16GbqzK/g/LJ9owdU9fjZc+jVqsRGBiIb7/9Vuf+5TsedH2mL1y4gD59+qBly5b48MMP0ahRIzg5OWH79u1YsmSJXY6eODs7Y8CAAdi8eTPi4+MxcOBAzXNie0eOHKn3BrX82n2po9x//PEHHnnkEfTs2ROffvoplEolHB0dsXr1aqxbt67C9vbw92oOXe221WuZNm0aBg0ahJ9//hk7duzA7NmzsXDhQuzZswft2rUz6VjiZ+O9997T5Ikor+xotL1q2bIljh07hqtXr1bomKsMmUym8/dp6nVLymdCyrW8MsTf9auvvqp31kj5jhBTKjkYk5KSgsDAQE1pzXfeeQezZ8/Gs88+i/nz58PX1xcODg6YNm2apGtsTfnsEknBoJuIsG/fPty+fRubNm3SSh6Unp5uw1b9JzAwEC4uLjh//nyF53Q9Zo5vvvkGMpkMffv2BWDae6Ive/oPP/yApk2bamqciubOnVthWycnJwwaNAiDBg2CWq3Giy++iM8++wyzZ89Gs2bNEBISgnv37iEqKsrg66hsJndDfHx8kJ2drfVYUVERMjIyLHqekJAQ7Nq1C926dTP7hnHr1q0oLCzEli1btEaudGWON4fU91mchp2SkiLpd/ftt99i8ODBeOKJJ/Drr79qsi4HBASgbt26UKlURo9jqh9//BEuLi7YsWOHZjo7UJr93RwBAQFwdXXVjGKVdfbsWaP7S3nPmjRpovd4Z86cgb+/v9HSSKYq/3oEQcD58+c1nR1l29S7d2+tbc+ePat5XhQSEoJXXnkFr7zyCtLS0tC2bVt88MEHWLt2rc7z6/vMie+Xp6enxT8b+oivxZLX5EGDBmH9+vVYu3YtZs6cKen8Ut5rHx8fnSPolVk6Y+jv39i13BBdfzPnzp3TJNdr2rQpgNLSnVX1uxYlJibiwoULWuXEfvjhBzz88MNYtWqV1rbZ2dlaM0js6bNLZCtc001Emp79sj35RUVF+PTTT23VJC1yuRxRUVH4+eefcf36dc3j58+fx6+//lrp4y9atAg7d+7EU089pZneZ8p74u7urnO6ua5jHD58GImJiVrblZ/K6uDgoLmRF6f6Pvnkk0hMTMSOHTsqnCc7OxslJSUAoKm5Xj44toSQkJAKa8c///xzvSNG5nryySehUqkwf/78Cs+VlJRIem263vucnByzA8ny3N3dJbWjffv2CA4OxtKlSytsr2vkzMnJCZs2bcKDDz6IQYMG4ciRIwBKX8/QoUPx448/6iwhpCu7tFRyuRwymUzr93jp0iX8/PPPZh8vOjoaP//8M65cuaJ5/PTp0zo/v+VJec+USiXatm2Lr7/+WmublJQU7Ny5EwMGDDCr7YaImaVFP/zwAzIyMjR5JTp27IjAwECsXLlSa4r+r7/+itOnT2uywOfn56OgoEDr2CEhIahbt67WfuWJnQjl35MOHTogJCQE77//vmb6b1mV+WzoU79+fYSHh+N///uf1jl/++03nDx50qxjPv7442jTpg3efvvtCtdIoHQdsVgtQOp7DZS+t2fOnNF6H44fP16pklT6fhdSruWG/Pzzz1prso8cOYLDhw9rPmOBgYF46KGH8Nlnn+ns7LTG7xoo7aAYM2YMnJyc8Nprr2kel8vlFa5jGzdurLCu3J4+u0S2wpFuIkLXrl3h4+OD0aNHY8qUKZDJZPjmm2/sarpoXFwcdu7ciW7dumHixIlQqVRYvnw5wsPDkZycLOkYJSUlmlGkgoICXL58GVu2bMGJEyfw8MMPa62VNeU96dChA7777ju8/PLLePDBB+Hh4YFBgwZh4MCB2LRpEx577DHExsYiPT0dK1euRFhYmNYNxnPPPYesrCz07t0bDRs2xOXLl/Hxxx+jbdu2aNWqFQDgtddew5YtWzBw4ECMGTMGHTp0QF5eHk6ePIkffvgBly5dgr+/P1xdXREWFobvvvsOzZs3h6+vL8LDwyWtKTbmueeewwsvvIChQ4eib9++OH78OHbs2FFhTWxl9erVCxMmTMDChQuRnJyMfv36wdHREWlpadi4cSOWLVuGxx9/3OAx+vXrpxlxmjBhAu7du4cvvvgCgYGBFhmZ79ChA1asWIEFCxagWbNmCAwMrDDiBpTedK9YsQKDBg1C27ZtMXbsWCiVSpw5cwanTp3SGYS6urril19+Qe/evdG/f3/89ttvCA8Px6JFi7B371507twZ48ePR1hYGLKyspCUlIRdu3YhKyvLrNcSGxuLDz/8EDExMRg+fDhu3ryJTz75BM2aNcOJEyfMOua8efMQHx+PHj164MUXX0RJSQk+/vhjtG7d2ugxpb5n7733Hvr374/IyEiMGzdOUzLMy8vLaE1oc/j6+qJ79+4YO3Ysbty4gaVLl6JZs2YYP348gNLRx8WLF2Ps2LHo1asXhg0bpiljFRQUhOnTpwMoHbns06cPnnzySYSFhaFOnTr46aefcOPGDTz99NN6zx8SEgJvb2+sXLkSdevWhbu7Ozp37ozg4GB8+eWX6N+/P1q3bo2xY8eiQYMGuHbtGvbu3QtPT09s3brV4u/HO++8g8GDB6Nbt24YO3Ys7ty5o7km6wqgjHF0dMSmTZsQFRWFnj174sknn0S3bt3g6OiIU6dOYd26dfDx8cHbb78t+b0GgGeffRYffvghoqOjMW7cONy8eRMrV65E69atDdauN6RDhw4AgClTpiA6OhpyuRxPP/20pGu5Ic2aNUP37t0xceJEFBYWYunSpfDz89NaVvTJJ5+ge/fuaNOmDcaPH4+mTZvixo0bSExMxN9//61VI9scSUlJWLt2LdRqNbKzs3H06FH8+OOPmu/AsstYBg4ciLfeegtjx45F165dcfLkSXz77beaEXmRvX12iWyiCjOlE1EV0lcyrHXr1jq3P3DggNClSxfB1dVVqF+/vvD6668LO3bs0Cp/JAj6y6zoKjOCcmVi9JUMmzRpUoV9y5ehEgRB2L17t9CuXTvByclJCAkJEb788kvhlVdeEVxcXPS8C/8ZPXq0AEDz4+bmJgQFBQlDhw4VfvjhhwplT0x5T+7duycMHz5c8Pb21iqXo1arhXfeeUdo0qSJ4OzsLLRr10745ZdfKryHP/zwg9CvXz8hMDBQcHJyEho3bixMmDBByMjI0GrP3bt3hZkzZwrNmjUTnJycBH9/f6Fr167C+++/LxQVFWm2O3jwoNChQwfBycnJaKkesazM0aNHtR4vX/pKEARBpVIJM2bMEPz9/QU3NzchOjpaOH/+vN6SYeWPKf7+//nnnwq/G13l3D7//HOhQ4cOgqurq1C3bl2hTZs2wuuvvy5cv35ds42u8kaiLVu2CBEREYKLi4sQFBQkLF68WPjqq68EAEJ6erpmO3NKhmVmZgqxsbFC3bp1BQCa/XW9b4JQWgasb9++Qt26dQV3d3chIiJCq8SWrvfg1q1bQlhYmKBQKIS0tDRBEAThxo0bwqRJk4RGjRoJjo6OgkKhEPr06SN8/vnnmv3ENmzcuFHy61m1apUQGhoqODs7Cy1bthRWr15d6b/X3377TfM5bNq0qbBy5Uqdx9TH2HsmCIKwa9cuoVu3boKrq6vg6ekpDBo0SEhNTdXaxtTPXfnrpPh+rl+/Xpg5c6YQGBgouLq6CrGxsVol0UTfffed0K5dO8HZ2Vnw9fUVRowYoVUG6tatW8KkSZOEli1bCu7u7oKXl5fQuXNn4fvvv6/QjvKfy82bNwthYWGaUollSzAdO3ZMGDJkiODn5yc4OzsLTZo0EZ588klh9+7dut9gPaSWDBMEQdiwYYPQsmVLwdnZWQgPDxe2bNkiDB06VGjZsqXWdsauQ2XduXNHmDNnjtCmTRvBzc1NcHFxEcLDw4WZM2dWuCYae69Fa9euFZo2bSo4OTkJbdu2FXbs2FGp77KSkhLhpZdeEgICAgSZTKb5TEu9lpdX9twffPCB0KhRI8HZ2Vno0aOHcPz48QrbX7hwQRg1apSgUCgER0dHoUGDBsLAgQOFH374QbONvuuwsTaIP3Xq1BF8fX2Fzp07CzNnztT5WS8oKBBeeeUVQalUCq6urkK3bt2ExMREm312ieyZTBDsaCiLiMhEjz76KE6dOqVzLRwRUWXt27cPDz/8MDZu3Gh0hgUBbdu2RUBAgNlls2qjS5cuITg4GO+99x5effVVWzeHiKyAa7qJqNq4f/++1r/T0tKwfft2TbIpIiKqGsXFxZpcEqJ9+/bh+PHjvCYTEZXDNd1EVG00bdoUY8aMQdOmTXH58mWsWLECTk5OestoEZnqn3/+MZgYzsnJCb6+vlXYIqLKU6lURpNSeXh4mFSe6dq1a4iKisLIkSNRv359nDlzBitXroRCocALL7xQ2SYTEdUoDLqJqNqIiYnB+vXrkZmZCWdnZ0RGRuKdd97RZBwnqqwHH3zQYCmhXr16Yd++fVXXICILuHr1KoKDgw1uM3fuXJMS0Pn4+KBDhw748ssv8c8//8Dd3R2xsbFYtGgR/Pz8KtliIqKahWu6iYiI/nXgwIEKyxjKEgMNouqkoKAA+/fvN7hN06ZNK2SdJiIiy2DQTURERERERGQlTKRGREREREREZCVc0y2BWq3G9evXUbduXchkMls3h4iIiIiIiGxMEATcvXsX9evXh4OD/vFsBt0SXL9+HY0aNbJ1M4iIiIiIiMjOXL16FQ0bNtT7PINuCerWrQug9M309PS0cWuIiIiIiIjI1nJzc9GoUSNNvKgPg24JxCnlnp6eDLqJiIiIiIhIw9gSZCZSIyIiIiIiIrISBt1EREREREREVsKgm4iIiIiIiMhKuKabiIiIiKiGUKlUKC4utnUziGoER0dHyOXySh+HQTcRERERUTUnCAIyMzORnZ1t66YQ1Sje3t5QKBRGk6UZwqCbiIiIiKiaEwPuwMBAuLm5VSpAIKLSjqz8/HzcvHkTAKBUKs0+FoNuIiIiIqJqTKVSaQJuPz8/WzeHqMZwdXUFANy8eROBgYFmTzVnIjUiIiIiompMXMPt5uZm45YQ1Tzi31VlciUw6CYiIiIiqgE4pZzI8izxd8Wgm4iIiIiIiMhKGHQTEREREVG1FxQUhKVLl9q6GTWeTCbDzz//bPb+Y8aMwaOPPipp20uXLkEmkyE5Odns89kDBt1ERERERAQAUKkFJF64jc3J15B44TZUasGq53vooYcwbdq0Co+vWbMG3t7eJh3r6NGjeP755y3TsEpYs2YNZDIZYmJitB7Pzs6GTCbDvn37bNMwC8nIyED//v11PieTyQz+xMXFYdmyZVizZk3VNtrGmL2ciIiIiIgQn5KBeVtTkZFToHlM6eWCuYPCEBNufrmkqhIQEGDrJmjUqVMHu3btwt69e/Hwww/bujlGqVQqyGQyODgYH5NVKBR6n8vIyND8/3fffYc5c+bg7Nmzmsc8PDzg4eFRucZWUnFxMRwdHav0nBzpJiIiIiKq5eJTMjBxbZJWwA0AmTkFmLg2CfEpGXr2rBrilOT3338fSqUSfn5+mDRpklZG6fLTy9PS0tCzZ0+4uLggLCwMCQkJWlOj9+3bB5lMhuzsbM0+ycnJkMlkuHTpkuax/fv3o0ePHnB1dUWjRo0wZcoU5OXlGWyvu7s7nn32Wbzxxht6t5FyfnHE/5dffkGLFi3g5uaGxx9/HPn5+fj6668RFBQEHx8fTJkyBSqVSnOcwsJCvPrqq2jQoAHc3d3RuXNnrRF28bhbtmxBWFgYnJ2dceXKFRw9ehR9+/aFv78/vLy80KtXLyQlJWm129D0coVCofnx8vKCTCbTeszDw6PC9HK1Wo13330XzZo1g7OzMxo3boy3335b5/FVKhWeffZZtGzZEleuXAEAbN68Ge3bt4eLiwuaNm2KefPmoaSkRKu9K1aswCOPPAJ3d3e8/fbbuHPnDkaMGIGAgAC4uroiNDQUq1ev1vu7qiyOdBMREVUjKrWAI+lZuHm3AIF1XdAp2BdyB2YsJiJtgiDgfrHK+IYova7M3XIKuiaSCwBkAOK2pKJbM39J1xtXR7lVMqnv3bsXSqUSe/fuxfnz5/HUU0+hbdu2GD9+fIVt1Wo1hgwZgnr16uHw4cPIycnROY3dmAsXLiAmJgYLFizAV199hX/++QeTJ0/G5MmTjQZpcXFxaNasGX744Qc8/vjjJp9blJ+fj48++ggbNmzA3bt3MWTIEDz22GPw9vbG9u3bcfHiRQwdOhTdunXDU089BQCYPHkyUlNTsWHDBtSvXx8//fQTYmJicPLkSYSGhmqOu3jxYnz55Zfw8/NDYGAgLl68iNGjR+Pjjz+GIAj44IMPMGDAAKSlpaFu3bpmvwZDZs6ciS+++AJLlixB9+7dkZGRgTNnzlTYrrCwEMOGDcOlS5fwxx9/ICAgAH/88QdGjRqFjz76CD169MCFCxc0Swzmzp2r2TcuLg6LFi3C0qVLUadOHcyePRupqan49ddf4e/vj/Pnz+P+/ftWeX0Ag24iIqJqo7pP/SSiqnO/WIWwOTssciwBQGZuAdrE7ZS0fepb0XBzsnyY4ePjg+XLl0Mul6Nly5aIjY3F7t27dQbdu3btwpkzZ7Bjxw7Ur18fAPDOO+/oXYusz8KFCzFixAhNwB4aGoqPPvoIvXr1wooVK+Di4qJ33/r162Pq1Kl48803JScO06W4uBgrVqxASEgIAODxxx/HN998gxs3bsDDwwNhYWF4+OGHsXfvXjz11FO4cuUKVq9ejStXrmhe+6uvvor4+HisXr0a77zzjua4n376KR544AHNuXr37q117s8//xze3t747bffMHDgQLNfgz53797FsmXLsHz5cowePRoAEBISgu7du2ttd+/ePcTGxqKwsBB79+6Fl5cXAGDevHl44403NPs2bdoU8+fPx+uvv64VdA8fPhxjx47V/PvKlSto164dOnbsCKB0loQ1cXo5ERFRNWDvUz+JiKytdevWkMvlmn8rlUrcvHlT57anT59Go0aNNEEnAERGRpp8zuPHj2PNmjWatcgeHh6Ijo6GWq1Genq60f1nzJiBf/75B1999ZXJ5xa5ublpAm4AqFevHoKCgrTWRterV0/zXpw8eRIqlQrNmzfXavdvv/2GCxcuaPZxcnJCRESE1rlu3LiB8ePHIzQ0FF5eXvD09MS9e/c0U7kt7fTp0ygsLESfPn0Mbjds2DDk5eVh586dmoAbKP39vPXWW1qvc/z48cjIyEB+fr5mOzG4Fk2cOBEbNmxA27Zt8frrr+PgwYOWfWHlcKSbiIjIzqnUAt7YdFLv1E8AiNtyCn3DFJxqTkQASqd4p74VLWnbI+lZGLP6qNHt1ox9EJ2CfSWdWypPT0/k5ORUeDw7O1sruAJQIfmVTCaDWq2WfK7yxKRhgvDf1bXsGnGgdIR1woQJmDJlSoX9GzdubPQc3t7emDlzJubNm1dhpFjK+QHdr9vQe3Hv3j3I5XL89ddfWp0UALQCdVdX1wrLAEaPHo3bt29j2bJlaNKkCZydnREZGYmioiKjr9Ucrq6ukrYbMGAA1q5di8TERK3R+Hv37mHevHkYMmRIhX3KzkJwd3fXeq5///64fPkytm/fjoSEBPTp0weTJk3C+++/b+YrMYxBNxERkZ1bvicN2fkVb8TKyswtxPI95zE1KrSKWkVE9kwmk0me4t0jNABKLxdk5hTo7NyTAVB4uaBHaIDFO/ZatGiBnTsrTltPSkpC8+bNzT5uq1atcPXqVWRkZECpLF1+c+jQIa1txGznGRkZ8PHxAYAK9aDbt2+P1NRUNGvWzOy2vPTSS/joo4+wbNkyk89vjnbt2kGlUuHmzZvo0aOHSfseOHAAn376KQYMGAAAuHr1Km7dulXpNukTGhoKV1dX7N69G88995ze7SZOnIjw8HA88sgj2LZtG3r16gWg9Pdz9uxZs34/AQEBGD16NEaPHo0ePXrgtddes1rQzenlREREdkylFvDVgUuStl2y6xynmRORyeQOMswdFAagNMAuS/z33EFhVplJM3HiRJw7dw5TpkzBiRMncPbsWXz44YdYv349XnnlFbOPGxUVhebNm2P06NE4fvw4/vjjD7z55pta2zRr1gyNGjVCXFwc0tLSsG3bNnzwwQda28yYMQMHDx7E5MmTkZycjLS0NGzevBmTJ0+W3BYXFxfMmzcPH330kcnnN0fz5s0xYsQIjBo1Cps2bUJ6ejqOHDmChQsXYtu2bQb3DQ0NxTfffIPTp0/j8OHDGDFihOTRaHO4uLhgxowZeP311/G///0PFy5cwKFDh7Bq1aoK27700ktYsGABBg4ciP379wMA5syZg//973+YN28eTp06hdOnT2PDhg2YNWuWwfPOmTMHmzdvxvnz53Hq1Cn88ssvaNWqlVVeI8Cgm4iIyK4t35OGnPuGR7nLmrc1FSq1rrEqIiL9YsKVWDGyPRRe2onBFF4uWDGyvdWSNTZt2hS///47zpw5g6ioKHTu3Bnff/89Nm7ciJiYGLOP6+DggJ9++gn3799Hp06d8Nxzz1UoQ+Xo6Ij169fjzJkziIiIwOLFi7FgwQKtbSIiIvDbb7/h3Llz6NGjB9q1a4c5c+ZorRWXYvTo0WjatKnJ5zfX6tWrMWrUKLzyyito0aIFHn30URw9etTolPhVq1bhzp07aN++PZ555hlMmTIFgYGBFmmTPrNnz8Yrr7yCOXPmoFWrVnjqqaf0rtWfNm0a5s2bhwEDBuDgwYOIjo7GL7/8gp07d+LBBx9Ely5dsGTJEjRp0sTgOZ2cnDBz5kxERESgZ8+ekMvl2LBhgzVeHgBAJpRdREA65ebmwsvLCzk5OfD09LR1c4iIqAYrWxLs0q18LNl1zuRjrB/fBZEhflZoHRHZo4KCAqSnpyM4ONhgNm0panpZQplMhp9++qlS2cSpdjH09yU1TuSabiIiIhsTb3ITUjPxc/J1ZOVVLmHNzbsFxjciItJB7iBjpx2RhTHoJiIisiFdtbcrK7Bu5Ua6iIiIyHIYdBMREdmIWHvbkuu8FJ7Okkr6EBHVRlxZS7bAoJuIiMgGVGoB87amWjTgBoCCEjUSUjMlJT0Sp7Vn5txHVl4RfD2cofCseWs4iYiIbIlBNxERkQ0cSc+y6JRyUU5+MSauTTKabXj7iQzM2pyic/240ssFcweFWS1bMRERUW3CoJuIiMgGrJXsTEBpXd15W1PRN0wBuYOsQjbiPWdu4Is/0vUeIyOnAC+sTcKnw9thQIRpZXGIiIhIG4NuIiIiG7BmsjMBpYHzkfQs5NwvMjtR26R1x/CxGhjYloE3ERGRuRxs3QAiIqLaqFOwL5Re1s0ynpCaiYlrk8yexi4AmLzhGBZuT7Vsw4iIiGoRBt1EREQ2IHeQYe6gMKue4+fk6xZJ1PbZ7+nYfiLDAkciIiKqfRh0ExER2UhMuBKfDm8HU/KEi9t6uznq3U8GwN1ZrjNJmrlm/XwSKrX+EF6lFpB44TY2J19D4oXbBrclIrKGoKAgLF261NbNqPH27dsHmUyG7OxsWzel2rB50H3t2jWMHDkSfn5+cHV1RZs2bfDnn39qnhcEAXPmzIFSqYSrqyuioqKQlpamdYysrCyMGDECnp6e8Pb2xrhx43Dv3j2tbU6cOIEePXrAxcUFjRo1wrvvvlslr4+IiEgXMUgtLFGjkY/0aeYKLxesHNkei4a0AYAKgbcMpdPC8wpVFmsrAGTlF2P5nvM6n4tPyUD3xXsw7ItDmLohGcO+OITui/cgPoWj40Rk2EMPPYRp06ZVeHzNmjXw9vY26VhHjx7F888/b5mGVVJRURHeffddPPDAA3Bzc4O/vz+6deuG1atXo7i42NbNq5SuXbsiIyMDXl5eAMz7XekTFBQEmUym9dOwYUOt58t3rBw7dgxPPfUUlEolnJ2d0aRJEwwcOBBbt27V1GW/dOkSZDIZkpOTLdJOU9k0kdqdO3fQrVs3PPzww/j1118REBCAtLQ0+Pj4aLZ599138dFHH+Hrr79GcHAwZs+ejejoaKSmpsLFpfQmZcSIEcjIyEBCQgKKi4sxduxYPP/881i3bh0AIDc3F/369UNUVBRWrlyJkydP4tlnn4W3t7fd/GESEVHtYahcV1lKLxfMjm0FH3dnTebxsjW0V4xsXyFJWj1PZxSUqJGdb/mbuiW7zqGFwkOrlFh8SgYmrk2qMI09M6dAUukyIrITexcCDnKg1+sVn/vtXUCtAh6eWfXtMkFAQICtmwCgNOCOjo7G8ePHMX/+fHTr1g2enp44dOgQ3n//fbRr1w5t27a1dTPN5uTkBIVCYbXjv/XWWxg/frzm33K5XO+2mzdvxpNPPomoqCh8/fXXaNasGQoLC3Hw4EHMmjULPXr0sFiHQGXYdKR78eLFaNSoEVavXo1OnTohODgY/fr1Q0hICIDSUe6lS5di1qxZGDx4MCIiIvC///0P169fx88//wwAOH36NOLj4/Hll1+ic+fO6N69Oz7++GNs2LAB169fBwB8++23KCoqwldffYXWrVvj6aefxpQpU/Dhhx/a6qUTEVEttXB7Kl5cl2Q04J4eFYr9M3pjQER9RIb4YXDbBogM8dME3EDp9PT9M3qjqb87AOCVvs3xwZNtrRJwi+ZtTdVMHVepBczbmqpz3bigY3sismMOcmDv26UBdlm/vVv6uIP+wKcqjBkzBo8++ijef/99KJVK+Pn5YdKkSVqjxuVHQdPS0tCzZ0+4uLggLCwMCQkJkMlkmjhC1zTp5ORkyGQyXLp0SfPY/v370aNHD7i6uqJRo0aYMmUK8vLy9LZ16dKl+P3337F7925MmjQJbdu2RdOmTTF8+HAcPnwYoaGhAIDCwkJMmTIFgYGBcHFxQffu3XH06FHNccT27dixA+3atYOrqyt69+6Nmzdv4tdff0WrVq3g6emJ4cOHIz8/X7PfQw89hMmTJ2Py5Mnw8vKCv78/Zs+erRn1BUoHP0eNGgUfHx+4ubmhf//+WrOJL1++jEGDBsHHxwfu7u5o3bo1tm/fXuF927dvH8aOHYucnBzNyHRcXJzm9b366qto0KAB3N3d0blzZ+zbt8/o77pu3bpQKBSaH32dKXl5eRg3bhxiY2Oxbds29OvXD02bNkWrVq0wbtw4HD9+XDMaX96dO3cwYsQIBAQEwNXVFaGhoVi9erXRtpnLpkH3li1b0LFjRzzxxBMIDAxEu3bt8MUXX2ieT09PR2ZmJqKiojSPeXl5oXPnzkhMTAQAJCYmwtvbGx07dtRsExUVBQcHBxw+fFizTc+ePeHk5KTZJjo6GmfPnsWdO3es/TKJiIgAAL8kX8dnv+uvjy2SAdhw9KqkY8odZGihqAsA8HCpg1v3CivTRKMycgpw6MJtAMCR9CyDmdHLli4joiomCEBRnvSfyElAz9dKA+w9C0of27Og9N89Xyt9XuqxBOt0tO3duxcXLlzA3r178fXXX2PNmjVYs2aNzm3VajWGDBkCJycnHD58GCtXrsSMGTNMPueFCxcQExODoUOH4sSJE/juu++wf/9+TJ48We8+3377LaKiotCuXbsKzzk6OsLdvbSj9PXXX8ePP/6Ir7/+GklJSWjWrBmio6ORlaV9zYyLi8Py5ctx8OBBXL16FU8++SSWLl2KdevWYdu2bdi5cyc+/vhjrX2+/vpr1KlTB0eOHMGyZcvw4Ycf4ssvv9Q8P2bMGPz555/YsmULEhMTIQgCBgwYoOnEmDRpEgoLC/H777/j5MmTWLx4MTw8PCq8nq5du2Lp0qXw9PRERkYGMjIy8OqrrwIAJk+ejMTERGzYsAEnTpzAE088gZiYmApLhc21c+dO3L59G6+/rmN2xr9kMt3ZT2bPno3U1FT8+uuvOH36NFasWAF/f3+LtEsXm04vv3jxIlasWIGXX34Z//d//4ejR49iypQpcHJywujRo5GZmQkAqFevntZ+9erV0zyXmZmJwMBArefr1KkDX19frW2Cg4MrHEN8rux0dqC0V6aw8L+bltzcXAu8WiIiqq1UagEf7U7Dst3SbjTKBquRIX5Gt2/g7QoAuHbnPloqPCvTVEkmrUvCoqFtUFiilrT9zbvmlSwjokoozgfeqW/evr+/V/qj79/G/N91wMndvHMb4OPjg+XLl0Mul6Nly5aIjY3F7t27taYii3bt2oUzZ85gx44dqF+/9H1455130L9/f5POuXDhQowYMUKz7jw0NBQfffQRevXqhRUrVmiWu5aVlpaGhx56yOBx8/LysGLFCqxZs0bTpi+++AIJCQlYtWoVXnvtNc22CxYsQLdu3QAA48aNw8yZM3HhwgU0bdoUAPD4449j7969Wp0KjRo1wpIlSyCTydCiRQucPHkSS5Yswfjx45GWloYtW7bgwIED6Nq1K4DSjoJGjRrh559/xhNPPIErV65g6NChaNOmNH+IeK7ynJyc4OXlBZlMpjXl/MqVK1i9ejWuXLmief9fffVVxMfHY/Xq1XjnnXf0vjczZszArFmzNP9+5513MGXKlArbnTt3DgDQokULzWNHjx7Fww8/rPn3hg0bMHDgwAr7XrlyBe3atdMM3AYFBeltjyXYNOhWq9Xo2LGj5k1v164dUlJSsHLlSowePdpm7Vq4cCHmzZtns/MTEZH9UKkFHEnPQmbOfWTlFcHXwxkKT+211YbEp2TgjU0nzZryLTVYVXqX3vQdvZSFh1sEQuHpghu5BSaVC1N6ueCJDg3xkZ5kaWVl3y/GxLVJmBYVKunYgXWtW4+ciGqH1q1ba63vVSqVOHnypM5tT58+jUaNGmkCPgCIjIw0+ZzHjx/HiRMn8O2332oeEwQBarUa6enpaNWqVYV9BAkj/RcuXEBxcbEmmAZKR8E7deqE06dPa20bERGh+f969erBzc1NKwiuV68ejhw5orVPly5dtEZ5IyMj8cEHH0ClUuH06dOoU6cOOnfurHnez88PLVq00Jx7ypQpmDhxInbu3ImoqCgMHTpUqx3GnDx5EiqVCs2bN9d6vLCwEH5+hjuTX3vtNYwZM0bzb1NGoCMiIjTJ0kJDQ1FSUqJzu4kTJ2Lo0KFISkpCv3798Oijj2o6IKzBpkG3UqlEWJh2jdJWrVrhxx9/BABNb8mNGzegVP6XhOXGjRua5AMKhQI3b97UOkZJSQmysrI0+ysUCty4cUNrG/HfupIAzJw5Ey+//LLm37m5uWjUqJE5L5GIiKqx+JSMConKREovF8wdFGYwSVh8SgZeWJtk9vmlBKvxKRmarOLH/87BiFWH4e3mCAH/ZTI3RgZg7qAw9A1T4LujV3HjrrQp6uuPXDEa4DvIgDsWLF1GRBI5upWOOJtq/5LSUW25E6AqKp1a3n266eeWyNPTEzk5ORUez87OrrAe19HRUevfMpkMarW0GTe6ODiUrrQtGySXzyx+7949TJgwQedIa+PGjXUet3nz5jhz5ozZ7Sqv7OuWyWQWfx90ee655xAdHa2Zvr5w4UJ88MEHeOmllyTtf+/ePcjlcvz1118VEqHpmqZelr+/P5o1a2b0HOLa+LNnz6JLly4AAGdnZ0n79u/fH5cvX8b27duRkJCAPn36YNKkSXj//feN7msOm67p7tatG86ePav12Llz59CkSRMAQHBwMBQKBXbv3q15Pjc3F4cPH9b0VEVGRiI7Oxt//fWXZps9e/ZArVZrem8iIyPx+++/a/0RJSQkoEWLFhWmlgOlvyxPT0+tHyIiql3ErNz61ixn/JudW19ZLJVawBubdI/AGCNDaVDfKdhXUhvvlBtFz/n3356u2jdm3q514OGs3d+u9HLRZBiXO8gwvLPum8jyBACZuYXoGORjMLBXC6XT0Vk+jKiKyWSlU7xN+Un8pDTgfvhNYPY/pf/9/b3Sx005jp51tLq0aNECSUkVOyeTkpIqjJKaolWrVrh69SoyMv679hw6dEhrGzFBV9ltypeUat++PVJTU9GsWbMKP2XzRZU1fPhw7Nq1C8eOHavwXHFxMfLy8hASEgInJyccOHBA67mjR49WGJQ0h5jbSnTo0CGEhoZCLpejVatWKCkp0drm9u3bOHv2rNa5GzVqhBdeeAGbNm3CK6+8opV7qywnJyeoVNplKtu1aweVSoWbN29WeN8slfm8X79+8PX1xeLFi83aPyAgAKNHj8batWuxdOlSfP755xZply42DbqnT5+OQ4cO4Z133sH58+exbt06fP7555g0aRKA0l6badOmYcGCBdiyZQtOnjyJUaNGoX79+nj00UcBlP5BxcTEYPz48Thy5AgOHDiAyZMn4+mnn9ZMJxk+fDicnJwwbtw4nDp1Ct999x2WLVumNZpNREQkUqkFxG3RnZW7LAH6s3N/vDutUlnE5w4KMzh93VjmcBkA+b/f8t6ujlg/vgv+mt0Px+f2w/rxXbDs6bZYP74L9s/orTVaH+Rv2jrMX05IC6aZxZzIzolZyh9+87+yYb1eL/23rqzmFjJx4kScO3cOU6ZMwYkTJ3D27Fl8+OGHWL9+PV555RWzjxsVFYXmzZtj9OjROH78OP744w+8+eabWts0a9YMjRo1QlxcHNLS0rBt2zZ88MEHWtvMmDEDBw8exOTJk5GcnIy0tDRs3rzZYCK1adOmoVu3bujTpw8++eQTHD9+HBcvXsT333+PLl26IC0tDe7u7pg4cSJee+01xMfHIzU1FePHj0d+fj7GjRtn9usWXblyBS+//DLOnj2L9evX4+OPP8bUqVMBlI4QDx48GOPHj8f+/ftx/PhxjBw5Eg0aNMDgwYM1r2HHjh1IT09HUlIS9u7dq3MqPVC6HvrevXvYvXs3bt26hfz8fDRv3hwjRozAqFGjsGnTJqSnp+PIkSNYuHAhtm3bVunXB5SOmH/55ZfYtm0bYmNjsWPHDly8eBEnTpzAu++Wfl71lRubM2cONm/ejPPnz+PUqVP45Zdf9L4+S7Dp9PIHH3wQP/30E2bOnIm33noLwcHBWLp0KUaMGKHZ5vXXX0deXh6ef/55ZGdno3v37oiPj9dKWvDtt99i8uTJ6NOnDxwcHDB06FB89NFHmue9vLywc+dOTJo0CR06dIC/vz/mzJnDGt1ERKTT8j1pyMyVtp5aV8KzX5KvY6nEpGnlSZm2DkjLHJ6VVxr0Nwv00GqfoeRs1lh/bWpiOCKyAbVKO+AWif9WqyruYwFNmzbF77//jjfffBNRUVEoKipCy5YtsXHjRsTExJh9XAcHB/z0008YN24cOnXqhKCgIHz00Udax3R0dMT69esxceJERERE4MEHH8SCBQvwxBNPaLaJiIjAb7/9hjfffBM9evSAIAgICQnBU089pffczs7OSEhIwJIlS/DZZ5/h1VdfhZubG1q1aoUpU6YgPDwcALBo0SKo1Wo888wzuHv3Ljp27IgdO3bonIlrqlGjRuH+/fvo1KkT5HI5pk6dqhX7rF69GlOnTsXAgQNRVFSEnj17Yvv27Zqp6yqVCpMmTcLff/8NT09PxMTEYMmSJTrP1bVrV7zwwgt46qmncPv2bcydOxdxcXFYvXo1FixYgFdeeQXXrl2Dv78/unTpojOxmbkee+wxHDx4EIsXL8aoUaOQlZUFLy8vdOzYUW8SNaB0dH7mzJm4dOkSXF1d0aNHD2zYsMFi7SpPJkhZ6V/L5ebmwsvLCzk5OZxqTkRUw5mzDvvZbkGYM6g1gNI63FLKgpU3JrIJosOVkhO0bU6+hqkbkiUd+5EH6uOjYRVL1+iiUgvovniPwYDeXMuebovBbRtY/LhEtV1BQQHS09MRHBysM5s2/Ucmk+Gnn37SzJqtiR566CG0bdtWq2Y5mc/Q35fUONGm08uJiIjsiThl21Sbk69DpRaw/YS0OtzlfTq8HeIGhyMyxE9SwA2YNiJd/9+SYlLIHWSYOygM0ldkSscs5kREVBsx6CYiIvqXsSnb+tzOK8KhC7cxa3OKSft5ONfBypHtMSDC9Fq6nYJ9ofRyMRgcO/4bwBeVqExaTx0TrsSKke2h9NIdJPu6O2JaH+PZYcvydnM0mhiOiIioJrLpmm4iIiJ7IrUuti4HL9zSrKGWwt1ZjqTZfeFUx7z+b3FEeuLaJL2lwYr/DbS/OnAJv6ZkSlorLooJV6JvmAJH0rOw81QGvvvzb+QXla7pzMorxoajV+Ht5oic/GJJZcmy84uRkJop+fxERNZQG1bW7tu3z9ZNoHI40k1ERPSvykx//nK/adPKP3jiAbMDbpE4Iq3QMyJdVqaREme6yB1kyLlfhDUHL2sCbtGN3EJkSwy4gdJs6sxgTkREtRGDbiIion9JmbKtT2GJWtJ2DjLg0+HtLTbiGxOuxP4ZvfH1sw8a3E4MdU0JfKWUJfN2c5T0fpXNYE5ERFSbMOgmIiL6lzhl25o+eqodBkRYdoq13EGG+0Uqo8GvqYGvlLJkpox2A5Wbwk9EhqnV0jr/qpIgCLhXUILs/CLcKyipFdO7qWaxxN8V13QTERGVEROuxCfD2+O1H44jr8yUai/XOsgtKEFl7hcn9AzGwLamJ00zxtQyZ1IDX1MCZE8XR+QWGF/TzgzmRJbn5OQEBwcHXL9+HQEBAXBycoJMZo0aBKa5W1CEm7lFKCkTtNRxcECgpxPqujjZsGVExgmCgKKiIvzzzz9wcHCAk5P5n1kG3URERGXEp2Rg/rZUrYDb190Ro7o0wdLd580+7piuTTBzgOVH0VVqAXFbTpm0j9TA15QAOaCuI9yc5LiRW6Bz5FsGQOHlwgzmRFbg4OCA4OBgZGRk4Pr167ZuDgDgfpEKWXlFFa4HMgAZAHzdneDqJLdBy4hM4+bmhsaNG8PBwfxJ4gy6iYiI/hWfkoGJa5Mq3CTeySuuVMANANGtrZO1+0h6FjJzCyVvrzQh8BXXuGfm6A6ky7rwTz683Rw1a73Lby8AmB3bSnIdciIyjZOTExo3boySkhKoVCrjO1iBSi3g+JVsJF3NwubkjAoJGMsK8HDGt+O78JpAdk0ul6NOnTqVnjnCoJuIiAjiiLH+pGGVYUqgaypT10jPHRQm+SZXSlmysnLyS6eXe7k5Iju/4lTz+dtOw8FBpilFdvNuAQLrlr43vPEmqjyZTAZHR0c4OjpW+bnjUzLwxqaTOv/2dbl2Nx8nMvIRGeJn5ZYR2R6DbiIiIgAf705DZq51knyZEuiaypQp4NOjmpucNV0sSzZva6rBpGrAfxnN9S18z8wpwAtrk+DhXAf3Cks0jyu9XEyqIU5E9sXUvBIiJlak2oLZy4mIqFZTqQVMXpeEpbvTLH5sdyc5Vo60XHkwXToF+0Lh6Wx0O4WnMyb3bmbWOcSyZNOjmsNY34EAIPt+id7nAGgF3EBpRvUX1iZh3pYUJF64zVreRNWIOXklREysSLUFg24iIqq14lMy0H7+TvxyIsMqx//8mY5WH72VO8gQ90hro9vFPdK6UqPtCamZWLrrHKwZD68+eBnDvjiE7ov3ID7FOr8TIrIsU/NKAKUzYqy57IbI3jDoJiKiWkmcDpmjZ1RWF4WnMxSexkdmxBvKLlW0VjEmXImVI9vD263iOk4fN8dKj7ar1ALmbdW93t0aMnIKMHFtEgNvIjugUgs4kHYL7+84g/d3nMWB87e0ZqOYM0VcgHWX3RDZG67pJiKiWkcMIk0ljijrynBeXlXfUMaEK9E3TIFDF24j8eItADJEhvihS1O/SrfjSHqW0fXc1jBvayr6hil4Y05kI7qSoy3fex7ebo5YNKQNYsKV8PcwvrylPDeWCqNahkE3ERHVOuYEkWWTkBlKLGbLpGByBxm6hfqjW6i/RY9r6kiWm5PcYKkgKQSUjngfSc9idmMiK1KpBZ3VBAwlR8vOL8YLa5MwMEKJ3advmHzO/CIVJq5Nwgor57wgshcMuomIqNYxNYj0dXPUSkImjiofSc9CZs59ZOUVwdejdOp5TSx/ZWqyowk9Q7Bk1zmLnFvX70pfkEBEpolPyajQgaj0csHs2FZ46xfjs4Eqmw/DGrNZeH0ge8Sgm4iIah1Tg8gFj7apcNMmd5DVmhHYTsG+UHq5SJodoPRy+beDQsCSXZXPCH/pVj6A/26kd57KwA9J13C34L+1+N6ujhjbLQiTe4fy5ppIoviUDJ1LZTJzCvDiumNWP781ZrPo6kTg9YHsAROpERFRjaVSC0i8cBubk69plaK6kyc90+6EnsEYEFG7pz/KHWSYOygMxm5XZfhvLfvk3qHw0ZHYzVQbjl7B9hMZ6L54D4Z9cQirD17WCrgBIPt+MZbsSkOHBQlMvkZVQt+1pbowlByxql+JpWp1i50I5TsHeX0geyATBKF6XSVsIDc3F15eXsjJyYGnp6etm0NEREao1AKW7zmP1QfSkX3/vwRA4rTJ+dtOGx219XCW492hERgQUd/aza02dI0iiXStZd9+4nqVjJiVJQO4TpSsavuJDMzanIKsvCLNY9VtNDXxwm0M++KQrZsBoDRfxtSo0EodQ6UW0H3xHqPXdV4fyNKkxokMuiVg0E1EVH3oyrYrkkH6KM634zpbPCFZTSBO85a6ln3h9lR89nt6lbZR6eWC/TN6V4vgh6oXY5/nslm97dnm5GuYuiHZ1s3QmB4ViiB/d7PXYJvSicDrA1mS1DiRa7qJiKjGMJRtFzBt2uQtE6ag1yamrmWfOSAMDzT0qTAyqPRywVMdG2Lp7vMWb2NGTgEOXbjNThOyqO0nrhvtQMrOL64WWblNzWthLm83R50doOWVzf/g6+6IBYPDTZplZMoUdVZFIFtg0E1ERDWCubW39amqm9LaYECEEtHhigoZhQHguz//RmZOgcXXkY7/5k98+OQDdh34UPWhUguYtTlF0rYCgLgtp+y6xvydvEI4yABDS9GNPS/FoiFtcDbzrklJFbPyivHiumOY8Hc2Zg4Ik7SPqdfrhNRMBt1UpZhIjYiIaoRDF2+bXHtbFxlKR2HFoJAsQxwhH9y2ASJD/CB3kGkStAEwmqTNVGIdYCZOIks4kp6FrDzjI7aizNxCLN9j+VkclhCfkoFJ644ZDagrE3D7uDli5b+j/UH+7mYd47Pf07FdYkkyscKCVJuTr1e75HdUvTHoJiKiai8+JQPj//dnpY8jBn5iBm6yvphwJVaMbA9FuRtmmQXefgGldYB5c02VZU6G7SW7ztldp4+hrOWW4FxHhm+e7YQ/Z/XVzDKpzKyh2ZtTKvz96socX7YDT4rbeUU4kp5ldruITMWgm4iIqi2VWsCyXWl4YW0S8otUkvdTerng1X7NKzyu8HKx+7WYNVFMuBL7Z/TGsqfaah6zVJpXcX03UWWYGzjaW6fPkfQsi8wI0mfZ0+3Qo3mAVqdlp2BfeLuaVz6wfHC8/UQGHnx7F4Z9cQhTNyRj2BeH0H3xHsSnZCAmXImVI9tDan+ppUqVEUnBNd1ERFQtxadkIG7LKWTmmpbwTAZgdmwrnLyWCwAI8nPD1KjmBjNwk/XJHWSoI7fOez9pXRIWDbX/jNJkv6SsgdbFHpJ2iRUHbt4tQNqNexY5ppuTXKujU1fJQJHcQYax3YJMWtddlrj+Wl/m+IycAkxcm4RPhreDj7szPJzrILegxOhxL93KN6s9ROZgyTAJWDKMiMi+xKdkYOLaJJOnSLrUccAzkU3wy4kMrdEeQzeMVDVUagEdFiRIynRsDtbnJXOZe70RLXu6LQa3bWDRNkkVn5KBeVtTLT66/e24znBwkGklRjTUYVnZv+/xPYLwxR+XDG5jaqdIdbkmlO00MbekGlkPS4YREVGlqNQCDl24jcSLtwCUJsHq0tTP5l/2lVmTWFCixhd/VBwpyfx3pKQ63IDVVMv3pJl0Qy61FJGoOmSUJvtjiTXQtqiEoFIL+Hh3GpbuNm90WR8ZSpfhdAkx7btA7iDDoiFtDJZ0NORLIwE3YF7it3lbU+36mqCr04SdxNUT13QTEVEF8SkZ6LAgASNWHcbyvRewfO95jPjyMDosSLB5YiBrrEkU79Xsbf1lbaFSC1h94JJJ+4ztGoyVI9vD2036WlF7zihN9qky1xtbVUKIT8lA+/k7LR5wi8xNNCmuuTblb1ZkjauygP+m/9ubsvlKyn/+xE5iW38Xk2k40k1ERFriUzL0jkZk5xfjhbVJ+HR4OwyIqF/FLStlreQ3ZW/AWL+1ah1Jz0L2fdOmnQb5uyEmXIneLeuh/fwE3Cs0voYTKM0oDQiY3DvUbke3yH7sPJVp1n5VWQlBnH6cmXMf+8/fwo9J16x2rud7BldqhDUmXIm+YQos33Meqw+km/x3bw32klBN/D0mpGbip2PXcEfPTB4BpZ8vex+lJ20MuomISEOlFhC35ZTR7SavP4blkGFARNVPb7t0K8+qx7eXG7DaxJz3XJyy+9flO5IDbtGSXWlYf+Qq4h7hFE3Sb/uJ61hz8JKkbX3dnZCVV6T5t6KKpgBba822PluOZ+D1mFaVCvTkDjJMjQrF5N7NcCQ9CwfO/4Pley9YsJWmscX0//JM/T2yk7j64fRyIiLSOJKeJSkbuFoAXlxX9dPb4lMyzM6AK5U93IDVNqa85+Wn7JrbSZKZyymapF98SgZeXHdM0rRmP3cnHJrZB+H16wIA+rQMxPtPPIC+YQqrt3GijunH1mTJ6dhyh9JcIdP7toCvu3klxSrDVtP/y9t+IkPnNHIpElLNm4lBVY9BNxERaewy8Qu8KtdAiwmNrMVebsBqo07BvlB6ucDY2JmuKbuV7SThOn4qz9RrzeC29bHnzA1c/LcE1e4zNzHiy8Oa+tHWauMbm05aZa2zMZaeDSR3kOExK2d3L39tqcrp/4ZsP3Edk9ebl1wOAL46cIkdh9UEg24iIgJQOmqyysRkVlWZhMbUhEYOsoo3WvrYyw1YbSV3kGHuoDAAhn9nCi+XChnmpQbsuohTNA9duG3G3lRTmXqt8XJ1wsS1SVp1qwHrJrwyNdu/JVljNlCUFWcFjO8RDIWXdpt1XUuqmjibojJ9fuLabnYc2j8G3UREVKlR5KpaA23KeWQovdGSehvi7eZo8xuw2i4mXIkVI9tXuDn2dXfEuG5BWD++C/bP6F3hdyQ1YDdkkg2WSpD9MmXGj8LTGeuPXNF5rbFWVQRzsv1bgjVnA4mdZ9bQu2U97J/RGw819wcAPNmhoc5rSVWy1Mwte87Abi6VWkDihdvYnHwNiRdu15gOBSZSIyKqpsRMpzfvFiCwbumNkLmjtJUpi1NVa6ClJlDzc3fC24+FIyZcCTenOpLWgMsAq6+/JOPEzMamfq7FgN3chFLZ922flZ/sg6kzfoZ1amzwGmONhFfmZPuvLGvPBhI7z8yt423IzbsFkDvI0L6JL/aduwWZTGbzGU2WLn1ZUxKA1uS65Ay6iYiqIV1fTN6ujhjbLchoKSRdwXpmrvlf2HfKZOy1FpVawLrDV4xu5+vuiMSZfeBUp3QiV5C/u6TjZ+UXMwusnRCTK5mqbMCemXMfszefMjmruS2z8pPtmTL66CADlg9rj2K1WtL2lgyKrBFgebvWwdhuwWjs64YD528h4fRN5JQJ7KsiG3tMuBLjugWZvMzJGLFjuKGPKwDg7+zStfeW7Lg2lan5U4ypCQlAxcSA5ce1xWUa1X02GoNuIqJqRt8XU/b9YizZlYbVBy9h0ZA2Or+cdAbrbo4oVkm7cdRl1uaTKFSpofC03k3L8j1puHHXeFb10ZHBmoAbMO1GpKaMFNRmZQN2Vye5zr8TQ8Ss/CsdqvfNHZnHlNHH5cPaYUCEEokS8wFYMiiydIA1KEKBpU+311y7H2vf0GYBaVSYwmJBtwylnQXidPj6XqVB99nMu1i2Kw3rj1zR6nD2dXfEgsHhVp/tolIL+CnZMrXUy79GWzP3cyN2eBlaphG35VS1rkvOoJuIqBox9MUkys4vnSq7slyvsN5gvZLJeLLyijH9u2QA1pkGtv2E9DJhQf5uWv/uFOwLX3dHZOUZf401YaSA/iNOOX/jx5MmT8WdtzW1Wt/ckXmkdrw92y1IE5iJa5Ezcwr0XpcVns4WDYru5BXCQQZJCbh83ByxcEgbAKjQ4WooyDR3xklldQr2hcLT2WjpSgcZMK57MDb+9bfO77Dy0+HjUzIwe/MpAMCte0VYsutchX2y8orx4rpjmPB3NmYOCKv0a9HnSHqWpO8kKQTYTwLQykwNl9LhlZlbiOV7zmNqVKhF2lvVmEiNiKgaMWUk5pWNx1FUUjqCLSVYtwRLZ+s1tZxK+cBZ7iDDgsHhRvdjqbCaKSZciU9GtDd5v5qWmIikkdrxVjb/g5REfgUlaovVU45PycAkCRmv+4cr8O1znfHnrL6ICVciJlyJ/TN6Y/34Llj2dFusH98FR9/sa3c5DOQOMsQ90trodsuHtcObsWH4a1ZfTI9qDm9X7TrfZbOTix3O/0iYLQUAn/2ejl+Sr5vVfiksOavK263q65vroq/WuNR7AqnvyZJd56pt0kuOdBMRVSOmrAPLK1QhYt4OfPjEA/Bxd7Zo0hZ9BPxXwqSyI4ViORWp9AXOAyLqY8Lf2fjs93Sd+8lgPyMFZHldmvpB6eVi8udfvAkUp0tm5txHVl4RfD2crbqUgmznTp7xoEzXdUYzq2LTSZ2jrjn5xRZZk6pSC4jbYrjzVFxrrisvga1Gr00VE67ESj3vpzhyL76PcgcZpkaFYnLvZjqnNZvb4fzShtLvnoFtLd8pYcqsKoWnM4Z1aoyc+8X4Sse0e0t9tiqjtHNc93e11HsCU96T6joTiUE3EVE1YU4d7YJiNV5cdwx9WgZYp1E6WCJbrznlVAwFzjMHhOGBhj6YtTkFWWUSv9WUrKiknzgSaer67sC6LjqnS4r42alZVGoBb/1y2uh2s2N1X2f6hikQtyUVQMWg21Kdkcv3pBlNeqkWAB93J7OOb0/ExIiHLtxG4sVbAEo7DLo09dP5/unrUDA3S7gAYPKGYzh53fJTzaUsD/B2dcQnI9qjS9PS19R98R697bRUR7c5pHSOS7knEJdpSPldWboaQFVh0E1EVA1Utqbn7jP/WLA10lRmCt2hi7dNulGaHtXcaPAzIEKJ6HDTy1FR9WdqSTFvN0fcySvCpHX6A/WMGpJRl0pJCWgB/QHtkfQsg/tXtjMyPkV6bouakhRS7iBDt1B/dAv1N/sYlX0vPvs9HQ809LFYRQNxeYChDkAZgEVD26Bbs9LXnXjB8PehNcrSSWHqfYmh34WpJeOq42ecQTcRUTVg6ZqeVcHcxGTxKRl448eTkrdXeDpjcu9mkratLtMryfLKlhTbeSoDqw9e1rttdn4x/u/nk5JGxqvrVEf6jyUCWqlBgDnBgqnBDZNC/scS78Ubm05YpEKHlKnu4vKAsh15Uj8zCamZVfr9Zup9ibHfRUy4EtP6hGLpbuN/i9XxM85EakRE1YAte3VlKM1yawpzs/WKCW9MyTYd90hrBjwkidjpMmtga6MJiKRk9S87wkTVk6UCWqlBgDnBginBDZNCahOnLVdGbkEJpn+XjGFfHEL3xXvMTuQl5feoa3mA1M/MVwcuVWmSMVPuS6R8LuNTMrDh6BWD28gkHsseMegmIqoGbNWrK4ayCwaHQ+nlojc7b3nmZOs1NeGNgwz4dDin9pLpjqRnVbpUXlnVcaojlbJUQCsGd4aukd5ujmYFC6Z8vpgUUlvZ7PKWkJFTgBfWJmFpwlmopNRsK8Pc2RBSOw7Etd2mtstcptyXPPKA0uDnUuxwN1QqrnwZuOqGQTcRUTUg5YbOGsSyKwMi6pt04yJmVDWl193UqWov9Q612Do7ql0sHSRXx6mOtYFKLeBA2i28v+MM3t9xFgfO36oQkFgqoBWDO0PhTnZ+sVmlw6R+vqTktqiNYsKVmG7h2s5Ld59HhwUJJn3HmTsbQmrHQVXPvDHlvuTz39P1vldSO9zLloGrjhh0ExFVA5burTfE3VmOJU+V1nHdP6O35gsuJlyJT4a3g5QOZvHL05Red1MDoaYB7iZtTySyZJBcXac61nTxKRnosCABI1YdxvK9F7B873mM+PJwhUDJkgFt3zCFwWUL5o5ESgluTMltURtN7h0KhadlO8ey84vxggmdy8Z+j4amTseEKzGuW5Ck81TVzBtT70v0ffaldri///gD1TbgBhh0ExFVG6VBb3u4Osq1Hld6ueDT4e0w0EKjvh888QAea9cAkSEVS7P4uDsbLHNSlqm97qYGQhxdJHNZcuaIsWmTVPXiUzLwwtoknUsIygdKlgxojS1bMHcksmxwU76dsn9/mNvCMLmDDHGPhGneL0uS2pFiKEiVMnU6KkwhqT1V+d0oVoZwcTQcUhr67O+SOPvjVp7+qefVAYNuIqJqIj4lA/O3peJ+sUrzmK+7I2bHtsKAiPpYPrw9pvUxfwqdt5sjVhqZumVOD7rUfUxZt8bRRaoMS84cMTRtkqqeSi0gbsspo9uJgZLcQYZHHlDqndpqSkAr9VqXmXNf0nZlicGNr4d2kq3qPuW2KonvoaLc94zSywXjugeZfVxTOlLEzvM65T5PUn6PUjqIHGTAnbwiSW2xlL5hCjTycZW0bfm/kfiUDKw6cEnSvtW9o51BNxGRnVGpBSReuI3NydeQeOE2VGpBk2Sk/BSsO3nFmLTumOam/6U+5k+h+2SY8Rs3c770pO4jBkJSRiGqayIVsh+mLJcwpiqTF5FhpfWyjY+IiYFSfEoGPv89Xe92z/cMlhzQSr3Wzd922qyOmphwJeIGtgYABPm5VVgCRMbFhCuxf0ZvrB/fBcue/m8ZVVQraaPI+kjtcBE7z0vKXC/EznNjv0cpnYVqAXhxnWn5VCojPiUD3RfvQdrNPEnbX7qVr/l/lVrAG5uMlwetKR3trNNNRGRH4lMyMG9rqlZwrfB0RkGJWudIjID/1gmKtYLjHgnDxLVJkrOAy1Day95FQn1Psac9M6fA6PHF45r6Renl5qh3iqbSywVzB4XxJpMswpTlEvqUnTbJGvC2Z8psnMyc+3h3x1mD17ItxzPwekwrSZ18Uq+PWXlFeGFtEj4d3g4DIuprHlepBRy6cBuJF28BKC1v16Wp9jKf6/+Okj/QyJufNzOJpQPLquw6aCkdLmLnefnPhth5vsJBZvS7TewsnLz+mMFr19QNx/D5SDnq1HHArXuFCKxbuRrjuuh7PYYs2XUOgqDGS32aY/meNMmlGWtCRzuDbiIiO6HvC8zYqE35m35xCl354F0XU0twiD3tE9cmQQYYnJJpynEBYPuJDLy4Lknv89OjQjG5d2i1/+Il+2HJhEMsG2YfLt2SNuIGlAa/xq6RpnSolL0+SjF5/TEshwwDIpSIT8nAG5tOagUhy/eeh7ebIxYNaaMJxq7eKR0pbOTjJukcJE1lpi5LGYU1lKFbV+e5IVI6CwtLBIxec7RCOy3VaW1qic+ylu4+j9UHL6FYJW3vZ7sF1YiOdk4vJyKyA5X5AhOVvekvP4Xu23GdMa1PKLxdtTPrmrMeUN+6uLJ83B3xbLcgeLk6SZp2u/3EdUxer/9GVQZgw9GrkttIJIUl1whW9/WGtiYuq/kp6W+s+uMifjr23/IaqeJTMrBkV5qkbRWezvD1cJa0rSkdKuJIpJSuQXEq8NvbUo0mftt+4jpUagEn/84BABSpVFzSYEFSc4roIqVz2ViGblOS7JnbwZeZU2ByKU99TC3xWV7O/RLkF6mMb4jSNeM1AUe6iYjsQGW/wADdtT3Ljs50C/XHS31CcSQ9CzfvFlRqullMuBJ9wxSaY/m7OyP+VAa+OXQFcgcZsvKKserAJaw6cMlo73p8SgZeXHfM4Pk4hZesoVOwL3zdHZGVZ3yKoyG2SF5Uk+haViOSOjondlxKVVCixp7T0rImm9qh4uPubFIH6hd/6F9TLnpx3TF4OJ/EvcISAKUJ/LYez+ByGwspO0tB6u/OWe6A3q0CUdfFEUUlavx1+Y7e71apgbKU7czt4BNfV9yWU5JG1A2pqpk93m6O1X4tt4gj3UREdqAyX2CmJBkRA/HBbXWXBDNF2WPdLSzGT8euA0CF0ZeMnAK8sDYJy3adq/CcqTfKnMJLliR3kOGxtg0qfRy1AEyqwuRFNYm+JJEi8fph7L01teMyO78YW08YDrrNTeBkreuUGHCLLDlySf/N4io/4u3r7ojl/84Y6x9eT/N4oUqNX1MyMeLLw2gx61cM++IQpm5IxrAvDuHBtxOw/cR1zbZSA2Up23UK9q0wa80UmbmFWL7nvNn7A1U3s2ds1+Aas6SMQTcRkR0wZR1iWeasnbY0sSZu+RvC8pbsSkO3RXu0bhBNvVHmFF6yNKm1b6VgFnPTmLKs5o1NJ6FSC1CpBRxIu4X3d5zB+zvO4sD5W1CpBasFuuZcW6vqOiW+b/zcWY6u7OZH3+yLgf92Lv+ackPnfuXf/ay8Yry47hgWbi/tVL6TV2iwUoKpnedjuwVJe0F6LNl1rlKdNVLKl1WWt5sjJvduZsUzVC1OLycisjGVWsC6w1fM2ldh42zepo5UZ+aWjlpN69MML/VpbtKNck0oGUL2R7x5rOzyDi6BMJ0pnW7Z+cWYsv4YDly4pTPZ2NiuQRZv37So5mZdWy31mZKCnzvL05XdXGr99/I++z0dagH48o90o51LpnTwTO4ditUHL0nK/q2P1MRtupiaNNAci4a0qTGj3ABHuomIbG75njTcuGu8rqwuUmp7WpO5a9GX7j6PDgsSsPOUtDWVQM0oGUL2R0rtW1NwCYR0pr5X205m6E02tmRXGrzdHC068hbkb16GcPEzVZVXK37urEtq/XddVu03HHA7yIBPhpuW0FTuIMOiIW3Mao9IauI2ffqGKTCqa5MKjyu9XDC+R5DZn38fN0esNDHBa3Vg06A7Li4OMplM66dly5aa5wsKCjBp0iT4+fnBw8MDQ4cOxY0b2tM6rly5gtjYWLi5uSEwMBCvvfYaSkq0pzju27cP7du3h7OzM5o1a4Y1a9ZUxcsjIjLKlGy75ckAzN922qbTCitzo5edX4xtJ40H3Q4y4FMTb0iITBETrsTKke3h7aZ7naQpN49cAmGcmKn8XOZdix43J7+4UhUgyqvM71Lf+mBr4efOuirzXWfsK1otAD7uTiYf19h1S4rtJ6+bXCUAKL136b54D74+eFnzmLerI6ZHhWL/jN54M7Y1Phne3qw2OddxqDEZy8uy+Uh369atkZGRofnZv3+/5rnp06dj69at2LhxI3777Tdcv34dQ4YM0TyvUqkQGxuLoqIiHDx4EF9//TXWrFmDOXPmaLZJT09HbGwsHn74YSQnJ2PatGl47rnnsGPHjip9nURE5Zk6Nbs8U0qMWEtV3Oi91DsUAyIYcJN1xYQr8desvpge1Vxnab1Ph7czuIbR3KRbtY14sz7si0P4ZN8Fix5bDBsc5ZUfY7bE77Ls+uBezf0r3SZd+LmrGtb+rjM3qC973XKuY3pY982hKzoTvxmiL/lhzv1iLN2VhoTU0s70ARGlnQLuznKT2pSZW2jT+xprsfma7jp16kChqNibkZOTg1WrVmHdunXo3bs3AGD16tVo1aoVDh06hC5dumDnzp1ITU3Frl27UK9ePbRt2xbz58/HjBkzEBcXBycnJ6xcuRLBwcH44IMPAACtWrXC/v37sWTJEkRHR1fpayUiKssSZcIA204rFNcuZuYUWHSEqaymAe5WOjKRNrmDDFOjQjG5dzOdpfUcHGSYuDYJMmgnTrKHhIbVgXizbu25Oep/R+2e7xEMd2dHLNl1zuRjPP1gY4v8LsuuD/7t3K1KH688AfzcVYVOwb5QeDqbPcXcmMoE9eJ1a+JDIeiycDeyzChfKCZ+m/B3NmYO0L/cxlDyQwGl18Kya8VjwpXo3bKeye2qicslbD7SnZaWhvr166Np06YYMWIErlwpTSb0119/obi4GFFRUZptW7ZsicaNGyMxMREAkJiYiDZt2qBevf/S90dHRyM3NxenTp3SbFP2GOI24jF0KSwsRG5urtYPEZEU4rTJzcnXjE7ZstSXii2nFVp6PawunDZJVU1faT1xyrCi3JRhhZcLVtTANYiWpFILeGPTSasH3ACg+vckT3RshMm9m5k1/dbc9dz6iDXhLe3ZbkH83FUBuYMMcY+0NmtfB5n+JSqWnKngVMcB7zwWDhlMWxJT1me/p2P7Cf1ZzY0NFuiagVe2XVLVxO99mwbdnTt3xpo1axAfH48VK1YgPT0dPXr0wN27d5GZmQknJyd4e3tr7VOvXj1kZpZOW8jMzNQKuMXnxecMbZObm4v79+/rbNfChQvh5eWl+WnUqJElXi4R1XBlp02KtTo7zE/QWZ8asMyXip+7k82nFWoCEU9nix+b0ybJ3ohThsf8m0Coia8b3n/igRq5BtGSlu9Jq1SmZXM8s+qI2ee19E2/3EGGBYPDJW/vI7GjgJ+7qmPOGmoZgPE9gvU+B1h2poK+jkFTzPr5pN4BA6mDBeW3k3qfUJOXS9g06O7fvz+eeOIJREREIDo6Gtu3b0d2dja+//57WzYLM2fORE5Ojubn6tWrNm0PEdk/fWucsu+XZtWNmLdDa71U6Yh45acaDm5b3y6mFcaEK3HgjT6YHtXcosfltEmyRwmpmdhyvHQ06HJWPkZ8edhgB1ttp1ILWH3gUpWf90ZugVmJKq110z8goj4m9NQdgJUlA/D2o+HMIWCHxDXU347rjP7h9eBhYL2y8t8ZMDMHhGHFyPbwcNZe1WutGTJlcwksefIBk9dUZ+UXY/me8zqfk9oZpWs7Y/cJNX2Zjs3XdJfl7e2N5s2b4/z58+jbty+KioqQnZ2tNdp948YNzRpwhUKBI0eOaB1DzG5edpvyGc9v3LgBT09PuLq66myHs7MznJ0tP2JDRDWToTVOorxClWa9VLvGPpjx4wnk3C8xsIc09jTKIa4ra6HwwLytqZVar+4gA5YP43Rdsj/61iWLHWyrD17CoiFt+Nkt40h6FrLvV+0oNwCzp7Jb86Z/5oAwPNDQB7M2p+hc46r0csHcQWGICVcyh4CdkjvI0C3UH91C/aFSC5r8D/7uzjicfhsf7TmP0EB3xE/rpbU0Zf/5W1h76Ar6hdXD2G7BmlwR1mpjZIgfEi/cRl6hyuT9l+w6hxYKjwrXMWN5XGQo7UzQ1xlk6D5BUeazXxPZVdB97949XLhwAc888ww6dOgAR0dH7N69G0OHDgUAnD17FleuXEFkZCQAIDIyEm+//TZu3ryJwMBAAEBCQgI8PT0RFham2Wb79u1a50lISNAcg4ioskxJiPbZ7+kA0i1yXnsd5YgJV6JvmEJzI3LpVj4+++0C8oulf/EvH9aOGcvJ7kjpYMvOL8bEtUlc413GrlTjpQHtxfSo5lb/vQ2IUCI6vPQamZlzH1l5RfD1cIbC00UrEBOn5Na24KQ6KZsoDwA8XR3x0Z7zyL5fUiGgvpNX2vEUGeKntY81VSZ3TNmEaCK5gwyPPKD8915GNymdQeXvE8omrKypbBp0v/rqqxg0aBCaNGmC69evY+7cuZDL5Rg2bBi8vLwwbtw4vPzyy/D19YWnpydeeuklREZGokuXLgCAfv36ISwsDM888wzeffddZGZmYtasWZg0aZJmpPqFF17A8uXL8frrr+PZZ5/Fnj178P3332Pbtm22fOlEVINUdZbN6jDKUf5GpGOQD0Z8edjofuIINwNuskdSO9gE6L5hrY3iUzKwykpTyz2c6+BeYeVnDIkUns6Y3LuZxY5nSPlrpD61MTipzhr6lM6i/eduIQqKVXBx/G9q9617pZnP/TyqbjZtZXITiAnRyn5O41My8LmBgPv5nsGSO4Ok/g3UFDZd0/33339j2LBhaNGiBZ588kn4+fnh0KFDCAgIAAAsWbIEAwcOxNChQ9GzZ08oFAps2rRJs79cLscvv/wCuVyOyMhIjBw5EqNGjcJbb72l2SY4OBjbtm1DQkICHnjgAXzwwQf48ssvWS6MiCzGmlk2p0c1h7IGZEru0tSvwuvQ5aOnOMJN9suUDrbyGXxrI3FmgDUoPJ1x9M0oOEoMPqdHNdebAEvM9hz3SGu7DGb1ZdMn++Pt5gh3p9JA+3+Jl7WqmNz+dzmBv7tTlbVHnA5u7icmM+e/pNMqtYC4LYZn+mw5nsG8FnrYdKR7w4YNBp93cXHBJ598gk8++UTvNk2aNKkwfby8hx56CMeOHTOrjURExohfapaouV2W0ssFk3s301szuDoRS4sZqtE7oWcwBratX6XtIjKFqR1sNbHWrClMWXpTltLLBY88oNSMqOlazxz3SGskX81GsYQbfD93J821dPme81h9IF1rjTmna5Ol7DiVicISNQDgne2nAfy3Tv/2vyPd/nWrbqS77Hdv+dwAUszefArOdRzg5eqEZbvPITPX8N+zrtFxKmVXa7qJiKoj8UvthbVJFj1u2enjNeELTN/6RF93RywYHI4BEQy4yb6Z2sFWE2vNmkJqp8OYrk0Q3VpZoWOxXWMfg+uZNydfk3T8slUepkaF1oiOTLI/+pIsZuYUaD3uV4Uj3YD+714pQfi9whK8uM60gcva3tmoD4NuIiILEOt3Tll/DEWqyk2tqsmZu7k+kaozUzrYFJ7OdpnosCpJ7XSIbq3U2bFo7Hpx6VaepOOXr/JQ29aSkvUZSrJYfqaGt1vVBt2A7r+lwxdvY+lu00vqGVPbOxv1YdBNRGQhMeFKRLW6ju0plcvUW9Mzd/OGl6ozsYPtjU0nkZ2vvwxWQYkaCamZNbLzTKrKlhcC9F8v4lMyJNXgttcqD1SzSF1KUdeljs06mcv/LVljRJp/b/rZNJEaEVFNcy27NOlInxYBJu/r4+aIlSPbc5o1kZ2LCVfir1l9MT2qOdyc5Dq3yfm3dFh8SkYVt85+iDMDdKlMFQapCdpkZh6fyFRSA9g6DjK7STRmjRFp/r3px6CbiMhCVGoB6f9Od2wa6G7SvtP6NMOfs/rW6lExoupE7iDD5N7N4OmiOyO2eFs9b2uq3dxk24qXjqzh3m6OZldhkDqqOK0Kam4TAdID2Kz8YnRfvMcuOuPEmSiWUhU17qszBt1ERBYQn5KBrot2I7egtGbsF39cgpTOXnF0e1rfFuwdJqpmjqRnGczmK6B2lw4TE0vpmoZ/x8DUfGOkjioG+buZfQ4iU5gSwIqJ1WwdeBuaiWKqqqxxX10x6CYiqiTxxvJGbqHW48YGtwZFKDi6TVSNSQ3+amM2X0OJpYDSqd/mzgKQOqrIhE5UVeQOMsyObSVpW3uaBSPmqNBXw14Ke65xb08YdBMR6aFSC0i8cBubk68h8cJtnV+Oxm4sAVQY8fZ1d8Snw9vh4+Ed+CVFVI0x+NPP2BTwyswCEEcV9V09ZWBCJ6p6Pu7S62/b0ywYMUfF1D6hJu/rU4llIrUNs5cTEekQn5JRoaalskx9WJGUtYVqAZgd2wr+dZ1ZIouoBrFEdm5bUakFHLpwG4kXbwEozWrcpamfxa5N1pwFIE6Lnbg2qUKt4cokaCOqDHM+y/YyC0buIMP0vs3Rol5dvLjOeElEdyc5nu/ZFJN7h/LvTCIG3URE5YjTxcvfRIvrsMr26kr9wvSv64zBbRtYuKVEZEtlg7/y7Dn4i0/JqFDybPne83B1dEBsGyW6hQZA4Vm5DkJrzwKICVdixcj2FTpHFTo6R4mqgjmfZXubBTMgQomVDrpLIrrUccDDLQMxsksTi3bQ1RYMuomIylCpBbyx6aTOUSsB/61D7BumgNxBhkv/Zis3xt6+WInIMsTgL25rKjKrQfAXn5KBF3R0EgDA/WI1fki6hh+SrgEoXQqzYHC4SWUMVWoBR9KzsONUBmQyQNCz9sYSswBiwpXoG6bAkfQs3LxbwJlEZFPGZr6UZ69LIMS/K2vOhKmNGHQTEZWxfE+azky7orLrsHLuF2HJrjSDx7Pn6aVEZBniTepD7+3F1Tv3MSOmBZ7vGWJ3N6gqtYC4Lackb5+VV4wX1x3DhL+zMXOA4SzHKrWA5XvOY/WBdGTfl5aZ3BKzAOQOpQEBka2JM1/0dWqVNzvW/mbBiOQOMnQL9Ue3UH9bN6XGYNBNRLWKOAqja1REpRaw+sAlScfZcSoDO07dkLStPU4vJSLLkjvIEFbfE1fv3Iero9wu/+ZLS5wVGt+wnM9+T0eb+t4Y2Fb3iLeu6eqGOMiA5cOYfIlqnphwJaZHhRrtkAcAH3enKmgR2QsG3URk9wwFyqbQlRxN4emMYZ0aI8jfHbfuFkoeoVlz8LKk7aZFNeeNJVEt0di3tC70njM30ULhaXdTnXelZpq975TvjsHBQYYBEdrXM0PT1fVRCww4qOYK8neXtJ29JFGjqsGgm4jskhhoJ6Rm4ufk68jKK9I8pyuLuDF6k6PlFkrqkTZXkL+b1Y5NRPYjPiUD3//5NwDg97Rb+D3tllnXKmuJT8nAKokzeXRRC8CL65Kw0uG/EWoxB4Y5GHBQTcVSgqQLg24isgtly9ek3byHgxdu425Bic5tdWURN3ZsY7W0rYVfqkQ1nykVD2xBvAZawhubTmoSSRrLgWEIr41UU1XnUoJkPQ62bgARUXxKBjosSMCIVYexfO8F7Dh1Q2/ADfxXk3Xe1lSo1MZDaSm1tC1NBvvNTEpElmOoU8/Ua5W1WPIamJ1fjOV7zkOlFvCVmSPnvDZSTSYmVAP+Kx0osudSgmRdDLqJyKbE9YCmjpaUzSJujK2mMfJLlajmMxbQmnKtshZLXwNXH0zHx7vTkCMxB0Z5vDZSTSeWElR4ac/oUHi52HzmC9kGp5cTkc2YWr5GFyk3k1U9jZGZeYlqD6kBrS3XMFv6GpidX4ylu83LhTGdySWplmAdeSqLQTcR2Yy55WvKunQr3+g24vqqqppizsy8RLVHdUiadCfP+HXW190RsW2U+ObQFau1Q+HpjMm9m1nt+ET2hnXkScTp5URkEyq1gP8lXqr0cZbuOof4lAyD25RdX1VVmJmXqHYQO/X0jV3ZOr+DSi1g/rbTRrdbMLgN5j/aBtP6hFq8DbJ/f+Ieac1RPiKqlRh0E1GVExOn/Zpifs3YsqQkKYoJV2J6lOVvJvVhZl6i2sFQ0iSgdE337NhWNgs2pSZRE2fnvNQnFN5ujhZtA9exElFtx6CbiKqUuYnT9BGTFB26cNvotpN7hyKgrrPJ53B3kpu0PTPzEtUu+pImieZvO210Ro61mLrmXO4gw6IhbfSO3Ev1ePuGWPZ0W6wf3wX7Z/RmwE1EtRqDbiKqMpasFVve+G/+lDTNfHz3YMnH9HV3xKfD2+G9xyNMasvTDzbmFEqiWiYmXInZsbqXsYj1um0ReJuz5jwmXIlPhreDuZcxhaczFj8egcFtGyAyxI/XQyKq9ZhIjYiqjDXrZecXqfDC2iSsNDKFsbmiLgCgjoMMJWWmpPu6O2LwA/XR0McNvh7OUHj+l2U0UcIoellB/m7mvQgiqrZK107r7lQUUDr1fN7WVPQNU1RpECquOc/MKdBZS1yG0unf5Wfn+Lg7w9TS4uKr4tptIiJtDLqJqMrsSjVtDfekh0LQJdgPU79PRlZekaR9jN3U3rpXepwuTX0x6eFQSWU8TE2KxvXcRLWPKfW6qzKbsdxBhtmxYXhxXVKF58Qrnq662eYkg1R4uWDuoDBOJSciKodBNxFVCZVawE/J1yRvr/Rywcv9WkDuIMM7j4XjhbUVbxh1Edd3dwv11/n8rXulpXMC67pIvvE1JYjmem6i2sle63XHp2ToHYE3FCSb2nno6+6I2bGtGHATEenANd1EVCWOpGchK09a8jQZtEdeYsKVGNctSPK5DK3vvnW3NOj2NyGhmrGSQKLy7Sai2sMe63XHp2Rg4tokvSPwhoJk8bon1Z28Ykxad8xmCeOIiOwZg24iqhJSR3fcneQ6S8tEhSkkn0tc363r5k8c6fb3cJJ8PGMlgQDAx82RJXGIajGpQeodiUtlKktMXKlvWbYMpVnV9ZVbLJ2W3kry+cSjSCnhSERU2zDoJqIqcelWnqTtPn+mo87AtVOwLxSeppX7ittyqsLNn7im29/DtGPpKwnk7eqI6VGh+HNWXwbcRLWY1CB1/raqCUpNWWOuj4+7addJKcckIqqNuKabiCxGpRZwJD2rQnKy+JQMLNmVZnR/pZcLuuhZZy13kCHukdaS13YDQGZuIZbvOY+pUaGa9l2+XRr8/3O3ECq1YNJU8JhwJfqGKXS+RiIiKUFqVSVTs8Qac3PXn1f1unUiInvHoJuILCI+JQPztqZqjawovVwwO7YV5m87bXR/KeuhY8KVWDmyPV7+/jjyi1SS2rVk1zm0UHgAgFb7Fv56BmsOXjI5067cQValmYeJqPqQGmz++u/SF2t22kmdXWRojbm5689ZwYGISJtMEAQuvDEiNzcXXl5eyMnJgaenp62bQ2R3xGQ95S8mMkDvesLypkc114xIG3Pg/C2M+PKw5Pa5Ozkgr0hd4XHxVpdrsYnIEhIv3MawLw5J3l5ppRJb8SkZRmcFifW598/orTfwV6kFdF+8x+A09fKURo5JRFSTSI0TuaabiCqlqESNVzee0Blcm9KjF+TvJnnbLk39TFrfrSvgBpj4h4gsS2qlA1FmTgEm6kn6aC6VWsAbm05K2tbY7KKySSSlYgUHIqKKGHQTkdm2n8hAxLwduFdYUuljmTIdUVzfbQlM/ENEliKl0kFZ1uj4W74nDdn5xsszTotqLmmEPSZciU+Ht4OxONpBBnw6nLOGiIh0YdBNRGZZuD0VL65LQkGx7lFkqWQonY7YKdjXpP3E9d3uzvJKnV/ExD9EZAlipQNPV2lpcyzZ8adSC1h94JKkbU2ZXTQgoj6WD2tvcJvlw9phQAQDbiIiXRh0E5HJtp+4js9+T7fY8cydjhgTrsSx2f3g6y695rY+TPxDRJbSN0wBB5lp1zRLdPwdSc9C9n3jo9yA6de8ARGlHZ3la5ErvVywcmR7DIiob9LxiIhqE2YvJyKTqNQCZm1Osdjxnu8ZXKnpiE51HPDOY+EmlRIrz5yRdiIifY6kZ+GOhCneZVmi409q4O7t5mjWNY9lE4mIzMORbqJqRqUWkHjhNjYnX0PihdtVngDsSHoWsvJMu5k0ZMvxjEq/hphwJcZ1CzJ7fyb+ISJLMnXUWuHpbJGOP6mB+9iuwWZf88SyiYPbNkBkiB+vnUREEnCkm6ga0VcL2xolZ3RRqQX8L/GSRY8prmWsbO3rqDAFVklcyyhykAHLhzHxDxFZlqmj1gUlaiSkZlb6WnQnrxAOMsBQP6a3myMm925WqfMQEZFpONJNVE2ItbDL10u1RskZfefvsCABv6ZkWvzYlljL2CnYF77ujibtw8Q/RGQNYukwqXLyiyt9HY9PycCkdccMBtwAsGhIG45OExFVMQbdRNWASi1g3tZUg7WwrVlrOj4lAy+sTZJUhsYclljLKHeQYcHgcEnb+rg5MvEPEVmNWDpMamhb2eu4oe8IEUt6ERHZDoNuomrgSHpWhRHusqxZa1q8mbMGc8uF6TMgoj4m9Aw2uM20Ps3w56y+vPEkIqsSS4dJHfGuzHXc2HcEUDrl3McClR6IiMh0XNNNVA1InX5tjVrTUm7mKsPSScxmDgjDAw19MGtzCrLyijSPV+XadyIiQDvb968pGfhf4mWj+5hzHbfldwQRERnHoJuoGpA6/doataateZM2Laq5VYLgARFKRIezrA0R2Z6Y7RuApKDbnOu4Lb8jiIjIOAbdRFaiUgsWC/rEpDzGRpzvlBnZtRRr3qQF+btZ7dhlb3SJiGxNvI5n5hToXHstA6DwckGHJj5IvHDbpO8Oqce21FIeIiIyDYNuIivQVdrL190RCwaHm5W8S+4gw5v9W2HyhmMGt3vrl1OIDldYdET3Tl6hxY5VHkddiKi2EJOrTVybBBlQITgWADzygBK93ttrcllIuYMMjzygxGe/p+vdxtJLeYiISDomUiOyMH2lvbLyivHiumN4ad1fJmenjU/JwJytKUa3y8wtxPI95006tiEqtYD5204b3c7HzRGfDm8Hb1fpJbssmUCNiKg6EJOreblVvFa6Ocnx2e/pZpWFjE/JwOcGAu7newYznwURkQ0x6CayICllW7aeyETrufGYtyUFiRduGw3AxSA+K09aua4lu85ZrGa31CRqy4eVlt8a2y1I8rE56kJEtVWOjvKL+UUqndsaKycm5Xtny/EMq5WUJCIi4xh0E1mQ1CC1oFiN1QcvY9gXh9BhfgKW7Tpn9s2ULpaq2S01idqtf6egT+4dCm8dIzhlsVYsEdVW5l7TDZUTk/K9Y62SkkREJA2DbiILMifTd/b9YizZlYYOCxIqjFCbW67LUjdYl27lSdpOXJstd5Bh0ZA2MDR+vXxYOwyIYMBNRLVPZUswJqRmVniM5cKIiOwfg24iE6nUAhIv3Mbm5GsVpodXJjFYdn4xXlibpDXqXZmbpMreYMWnZGDJrjSD28hQcW22uGZR6aX9Xii9XLByZHuzEskREdUElb0uf3XgUoXOWVM7R4mIqOoxezmRCbafyMCszSnIKlOaq2xm2dt3K5/pe8muNKw/chVxj4RV6ibJ3H1VagGHLtzGy98fl7S9rrXZMeFK9A1jnWwiorIsEfjGbTmFvmGlVSqkdo6yXBgRkW0x6CaSaOH2VJ3lWDJyCvDC2iR0aOKNvy5nW+Rcmbmlx/x0eDuDtVd1qcwNlq5SZ4ZMi2qud20262QTEWkT62lXZop5Zm4hPtqdhil9QjFva6qkfZi4kojItji9nEiC7SeuG6x/CsBiAXdZ//dzCmbHhkneXrylMucGa/uJDLygo9SZIUH+biadg4ioNhNrdVfWst1pGPlloqTrtaHOUSIiqhoMuomMUKkFzNpsvEa2NWTnFyPt5j2sGNke/h5OWs95uzlWyBTu5eqIaVGh6BumMOk8209cx+T1SSa3j2sEiYhMExOuxDgTyivqk3jxjqTt2DlKRGR7DLqJjDiSniW5RrY1fHXgIvqGKfDu0AgAQH1vV6wf3wV/zeqLv2b1xdhuTTTbipnQuy/eI7lW9/YTGXhx3TGYWmHM282RawSJiMwQZWLHaGWwc5SIyPYYdBMZYesyKzn3S7B8z3lk5ZcG/iEB7ogM8YPcQYaE1EysOXC5wj6ZOQWYuDbJaOBt7gg3AIztGsw1gkREZugU7Atfd0fjG1aCruoSRERkGwy6iYyw1CiBr5sjvn2uMz54PAKujqb96S3ZdQ77024BAALqOgMonfY+b2uqzgRr4mPztqZqlTQrKz7FvBFuoHSUe3LvZqbvSEREkDvI8FjbBlY9hwAmUCMishd2E3QvWrQIMpkM06ZN0zxWUFCASZMmwc/PDx4eHhg6dChu3Lihtd+VK1cQGxsLNzc3BAYG4rXXXkNJSYnWNvv27UP79u3h7OyMZs2aYc2aNVXwiqimELPNVtaCR9ugWzN/DO3YCEueagtTb4MSUjMBAAEepUH3kfQsg0l0BJRmVj+SnlXhOTFgN9eiIW14I0dEVAnWnmLu7eZocn4PIiKyDrsIuo8ePYrPPvsMERERWo9Pnz4dW7duxcaNG/Hbb7/h+vXrGDJkiOZ5lUqF2NhYFBUV4eDBg/j666+xZs0azJkzR7NNeno6YmNj8fDDDyM5ORnTpk3Dc889hx07dlTZ66PqTcw2W5kQc0LPYAyI+C97bEy4EitGtofC01nyMfKKVAAA/3+DbqnT3nVtZyxg18dBBnw6vD0z4RIRVZLYoWut7svs/GKdna5ERFT1bB5037t3DyNGjMAXX3wBHx8fzeM5OTlYtWoVPvzwQ/Tu3RsdOnTA6tWrcfDgQRw6dAgAsHPnTqSmpmLt2rVo27Yt+vfvj/nz5+OTTz5BUVERAGDlypUIDg7GBx98gFatWmHy5Ml4/PHHsWTJEpu8XqpeVGoBiRduo7BEjRd6hZi8v4ezHJ8Ob4eZAyqWiIkJV+LAG30wPaq5SccUp5dLnfZ+6VZ+hcd2/Ttqbqrlw9ppdR4QEZF5ypYPKx94y/796RsWWKlz2DonCRERlbJ50D1p0iTExsYiKipK6/G//voLxcXFWo+3bNkSjRs3RmJiIgAgMTERbdq0Qb169TTbREdHIzc3F6dOndJsU/7Y0dHRmmPoUlhYiNzcXK0fqn3iUzLQffEeDPviEKZuSMaK3y5I3tfb1RHTo0JxfG40BkTU17ud3EGGqVGhmB4VKvnY4kh3p2BfSSPlG45e0VrXrVIL+Cn5muTzAaXJeFaObG/wtRARkWk0s57KLWFSeLlgxcj2+GLUg/h0eHu4mJgHRMTM5URE9qGOLU++YcMGJCUl4ejRoxWey8zMhJOTE7y9vbUer1evHjIzMzXblA24xefF5wxtk5ubi/v378PV1bXCuRcuXIh58+aZ/bqo+otPycDEtUk6k5SJZIDO56dHhWJy71CT1jxP7h2K9UeuIjPX+KiEONItd5BhWKfGWLIrzeD24rruyBA/ANJLoMkAjOkahH6tFegU7Ms13EREVhATrkTfMAWOpGfh5t0CBNZ10brmDohQolClxvTvkk06LjOXExHZD5uNdF+9ehVTp07Ft99+CxcX++qJnTlzJnJycjQ/V69etXWTqAoZygou8natg3qeFT+3Lz4UgqlRzU0OUOUOMswZ2ErSthf/uacZuQ7yd5e0T0KZ6eRSp5aP7toEcx9prSlPRkRE1iF3kCEyxA+D2zbQec1V6Pi+MYaZy4mI7IfNRrr/+usv3Lx5E+3bt9c8plKp8Pvvv2P58uXYsWMHioqKkJ2drTXafePGDSgUpdk4FQoFjhw5onVcMbt52W3KZzy/ceMGPD09dY5yA4CzszOcnaUnuKKaRUqSsez7JZjaNRhpN+5ie8p/QeyANuavd/Zxl/aZm/htEpReLpg7KEzy1MGvDlzSjHisOnBJ0j7Rrbl2m4jIHohJ16QkwHSQAcuHMeElEZE9sdlId58+fXDy5EkkJydrfjp27IgRI0Zo/t/R0RG7d+/W7HP27FlcuXIFkZGRAIDIyEicPHkSN2/e1GyTkJAAT09PhIWFabYpewxxG/EYROVJTTyzbHcaPFy0+60qU1rMlIQ3GTkFmLg2CXfyCiWf8+XvkxG3RVqZME5LJCKyH6ZU0WDCSyIi+2OzoLtu3boIDw/X+nF3d4efnx/Cw8Ph5eWFcePG4eWXX8bevXvx119/YezYsYiMjESXLl0AAP369UNYWBieeeYZHD9+HDt27MCsWbMwadIkzUj1Cy+8gIsXL+L111/HmTNn8Omnn+L777/H9OnTbfXSyc6ZkngmIfW/WRROcgf4ujtVyXlF87edxuzYipnRdckvUktaMw5wWiIRkb0Rk67p62hlwksiIvtl00RqxixZsgQODg4YOnQoCgsLER0djU8//VTzvFwuxy+//IKJEyciMjIS7u7uGD16NN566y3NNsHBwdi2bRumT5+OZcuWoWHDhvjyyy8RHR1ti5dE1YAp0/ju5P+XkMzTtQ7UAiA3M1Y15bxAaRK3jJwC+Lg7YVy3IMnTxo15tlsQpyUSEdmhsknXMnPuIyuvCL4ezlB4ujDhJRGRHZMJgmAoXxQByM3NhZeXF3JycuDp6Wnr5lAViE/JwAtrk0zeT1xrbW7Qas55lz3dFoF1XTDsi0NmnbO89eO7aDKdExERERGRblLjRJvX6SayRzHhSpNqZ4sy/11rHZ+SUWXnFcvLeLs6mnXOsvzcnbiWm4iIiIjIghh0E+kxuXcoAjxMy2IvThuZtzVVU9bLnPNKKQ8jw38Jz+QOMoztFmTW+coa3LY+pycSEREREVkQg24iPeQOMowxI5AV11ofSc8y+7xxj0jLUls24dnk3qHwdqvcaHffMEWl9iciIiIiIm0MuokMCAnwAAA4mpEdzZQSYOVJyVK7YqR2HVa5gwyLhrQx+5wsE0ZEREREZHl2nb2cyNb+uVcIAHioeQCe7d4UN+8W4NbdQszfdtrovuaUACvLnCy1MeFKrBzZHq9sPI68QpVJ52OZMCIiIiIiy2PQTbWeSi3gSHoWbt4t0CQlE4PPf/6ta13Py0WT0VulFvDl/nRk5hRA16ptGQCFhUaN5Q4ykzOJx4Qr0btlPbSfn4B7hSVGt3eQAcuHtWeZMCIiIiIiK2DQTbVafEoG5m1N1aqNXbbslzjSHeDx36i13EGGuYPCMHFtEmSAVuAtjhPbetTYqY4D3n8iAhPXJunsGChr+bB2GBDBgJuIiIiIyBq4pptqLJVaQOKF29icfA2JF25XyCYen5KBiWuTtAJu4L+yX9tPXMfZzLsAgLsFxVr7i2uuFeXWXCt0rLW2FSnrwleObI8BEfWruGVERERERLWHTBAE8+oa1SJSi56T/dA1gu3uJEePUH90aOILX3cnvL39NLLyivQew0EGlI3Ty46AiwxNTbcXYhulrgsnIiIiIiLjpMaJDLolYNBdfajUApbvOY8lu85Z/NhieGovI9lERERERGQ7UuNErummGiM+JQNxW04hM7fQKscXUBp4z9uair5hCo4SExERERGRUVzTTTWCuD7bWgG3SACQkVOAI+lZVj0PERERERHVDAy6qdpTqQXM25pqNEu3Jd28W2B8IyIiIiIiqvUYdFO1dyQ9q0IGcmsLrKs7IzgREREREVFZXNNN1V5VjjrLUFoWrFOwb5Wdk4iIiIiIqi+OdFO1V5WjzgKAuYPCmESNiIiIiIgkYdBN1V6HJj7wdXeqknM92y2I5cKIiIiIiEgyTi+nakusyb36QDqy7xdXyTn7himq5DxERERERFQzMOimaik+JQNvbDqJ7HxpwbaPWx1ENvWDSgAOXriNuwUlJp/Tz92Ja7mJiIiIiMgkDLqp2olPycALa5OMbufi6IB3Hm0DpbcrOgX7atZhq9QCjqRnITPnPuZvO42svCJJ5x3ctj7XchMRERERkUkYdFO1ItbklqKgWI2rd+5jSIeGWo/LHWSIDPEDALg6ySUF8ACnlhMRERERkemYSI2qFVNrcq8+mA6VWtD7fEy4Ep8ObwdjA9hKlgkjIiIiIiIzMOimamVXaqZJ22fnF+NIepbBbQZE1MfyYe11Pif794dlwoiIiIiIyBwMuqnaUKkF/JR8zeT9bt41PjI+IEKJlSPbQ+mlXfNb4eWCFSPbs0wYERERERGZhWu6qdo4kp6FrDzTS4MF1nUxvhFKp5r3DVPgSHoWbt4tQGBdF60EbERERERERKZi0E3VhqlTy2UoHak2ZS122SRrRERERERElcXp5VQtxKdkYNWBS5K3F8emuRabiIiIiIhsiSPdZPdMKRMmUni5YO6gMK7FJiIiIiIim2LQTXbPlDJh47oFISpMwbXYRERERERkFxh0k92Tkn0cAJ7tFoTZg1pbuTVERERERETScU032T2p2cf7hims3BIiIiIiIiLTMOgmu9cp2BdKLxfomywuA6A0MUs5ERERERFRVWDQTXZP7iDD3EFhOp9jlnIiIiIiIrJnDLqpWogJV2LFyPZwc5JrPa7wcsGKke2ZpZyIiIiIiOwSE6lRtRETrsT6I1fw27lbGNapER55oAGzlBMRERERkV1j0E3Vilg6rH+4EpEhfjZuDRERERERkWGcXk7VSkZ2adBd39vVxi0hIiIiIiIyzqygu2nTprh9+3aFx7Ozs9G0adNKN4qoPJVawO7TN3C3sAQAUM/T2cYtIiIiIiIiMs6soPvSpUtQqVQVHi8sLMS1a9cq3SiisuJTMtB98R6M+/pPzWP9lvyO+JQMG7aKiIiIiIjIOJPWdG/ZskXz/zt27ICXl5fm3yqVCrt370ZQUJDFGkcUn5KBiWuTIJR7PDOnABPXJjFzORERERER2TWTgu5HH30UACCTyTB69Git5xwdHREUFIQPPvjAYo2j2k2lFjBva2qFgBsABJTW6J63NRV9wxTMYE5ERERERHbJpKBbrVYDAIKDg3H06FH4+/tbpVFEAHAkPUuTrVwXAaXZzI+kZzGTORERERER2SWzSoalp6dbuh1Uy6jUAo6kZ+Hm3QIE1nXRWW/75l39Abc52xEREREREVU1s+t07969G7t378bNmzc1I+Cir776qtINo5orPiUD87amao1iK71cMHdQmNb67MC6LpKOJ3U7IiIiIiKiqmZW9vJ58+ahX79+2L17N27duoU7d+5o/RDpIyZGKz9tXEyMVjYj+Z28QqPHU3qVjpITERERERHZI7NGuleuXIk1a9bgmWeesXR7qAYzlhgNAP7vp5Po3bIe5A4yzN922ugxZ8eGMYkaERERERHZLbOC7qKiInTt2tXSbaEazlhiNADIyitGl4W7MTqyidFtAcDH3clSzSMiIiIiIrI4s6aXP/fcc1i3bp2l20I1nNSEZ1l5RViyK82ixyQiIiIiIrIFs0a6CwoK8Pnnn2PXrl2IiIiAo6Oj1vMffvihRRpHNYs1Ep4xiRoREREREdkzs4LuEydOoG3btgCAlJQUredkMq6vJd06BftC4emMzFzjCdKMkQFQMIkaERERERHZObOC7r1791q6HVQLyB1kePrBxli6W9rUcX3Ebp25g5hEjYiIiIiI7JtZa7qJzBGfkoE1By9V+jjebo5YMbK9Vk1vIiIiIiIie2TWSPfDDz9scBr5nj17zG4Q1UxifW5d5cJMJQPQN0xhgSMRERERERFZl1lBt7ieW1RcXIzk5GSkpKRg9OjRlmgX1SAqtYA3Np20SMANAFn5xTiSnoXIED8LHZGIiIiIiMg6zAq6lyxZovPxuLg43Lt3r1INoppDpRZwJD0LXx9MR3Z+sUWPzVJhRERERERUHZgVdOszcuRIdOrUCe+//74lD0vVUHxKBuZtTUVGjnWCY5YKIyIiIiKi6sCiQXdiYiJcXBgM1XaWXL+ti5KlwoiIiIiIqJowK+geMmSI1r8FQUBGRgb+/PNPzJ492yINo+pJpRYwb2uq1QJuGVgqjIiIiIiIqg+zgm4vLy+tfzs4OKBFixZ466230K9fP4s0jKqnI+lZVptS7ufuhLcfC2epMCIiIiIiqjbMqtO9evVqrZ9Vq1Zh0aJFJgfcK1asQEREBDw9PeHp6YnIyEj8+uuvmucLCgowadIk+Pn5wcPDA0OHDsWNGze0jnHlyhXExsbCzc0NgYGBeO2111BSUqK1zb59+9C+fXs4OzujWbNmWLNmjTkvmyQwJ8GZl2sdKDxdYGjs2tfdEYkz+zDgJiIiIiKiasWsoFv0119/Ye3atVi7di2OHTtm8v4NGzbEokWL8Ndff+HPP/9E7969MXjwYJw6dQoAMH36dGzduhUbN27Eb7/9huvXr2tNbVepVIiNjUVRUREOHjyIr7/+GmvWrMGcOXM026SnpyM2NhYPP/wwkpOTMW3aNDz33HPYsWNHZV466WFOgrPFQyMQ90gYAFQIvGX//rzzWBs41anUx5WIiIiIiKjKyQRBMHn57c2bN/H0009j37598Pb2BgBkZ2fj4YcfxoYNGxAQEGB2g3x9ffHee+/h8ccfR0BAANatW4fHH38cAHDmzBm0atUKiYmJ6NKlC3799VcMHDgQ169fR7169QAAK1euxIwZM/DPP//AyckJM2bMwLZt25CSkqI5x9NPP43s7GzEx8dLalNubi68vLyQk5MDT09Ps19bbaBSC+i+eI+kKeYOMmD5sPYYEFE6eq0r47nSywVzB4VxhJuIiIiIiOyK1DjRrKHDl156CXfv3sWpU6eQlZWFrKwspKSkIDc3F1OmTDGrwSqVChs2bEBeXh4iIyPx119/obi4GFFRUZptWrZsicaNGyMxMRFAabb0Nm3aaAJuAIiOjkZubq5mtDwxMVHrGOI24jHIsuQOMswdFGZwqrho+bB2moAbAGLCldg/ozfWj++CZU+3xfrxXbB/Rm8G3EREREREVG2ZlUgtPj4eu3btQqtWrTSPhYWF4ZNPPjF5XffJkycRGRmJgoICeHh44KeffkJYWBiSk5Ph5OSkGUkX1atXD5mZmQCAzMxMrYBbfF58ztA2ubm5uH//PlxdXSu0qbCwEIWFhZp/5+bmmvSaaruYcCVWjGyPOZtP4ebdwgrPGxq9ljvIEBniVxXNJCIiIiIisjqzgm61Wg1HR8cKjzs6OkKtVpt0rBYtWiA5ORk5OTn44YcfMHr0aPz222/mNMtiFi5ciHnz5tm0DdVdTLgSSi9XDP7kADyc5Zge1Ry+Hs5QeJbW2GbJLyIiIiIiqg3Mml7eu3dvTJ06FdevX9c8du3aNUyfPh19+vQx6VhOTk5o1qwZOnTogIULF+KBBx7AsmXLoFAoUFRUhOzsbK3tb9y4AYVCAQBQKBQVspmL/za2jaenp85RbgCYOXMmcnJyND9Xr1416TVRKXGUu2mAB8b1aIrH2jVAZIgfA24iIiIiIqo1zAq6ly9fjtzcXAQFBSEkJAQhISEIDg5Gbm4uPv7440o1SK1Wo7CwEB06dICjoyN2796tee7s2bO4cuUKIiMjAQCRkZE4+f/t3XlcVXXi//H3BQQUBZcSREkpNXdTM0XL0lEh035mNTU1aWqLuJSaY9riMpoW0+qUY03fspnKtqkcc2lMM8sljVRA0jFHU0cWSwEl2e79/P7gwR1QVNBzvPdyX8/Hg8cD7zmc8zlvL8v7nnM/JzVV2dnZ7nVWr16t8PBwtWvXzr1O+W2UrVO2jcqEhIS4b2NW9oHqy8w9KUmKCq/+jOYAAAAAUBOc1+XlMTEx+v777/XFF19o165dkqS2bdueNmHZuUyfPl033nijLrvsMh0/flzvvvuu1q1bp88//1wREREaPXq0Jk+erIYNGyo8PFwTJkxQXFycevbsKUkaOHCg2rVrp3vuuUdJSUnKzMzUE088oXHjxikkJESSNGbMGL388suaOnWqRo0apbVr1+qDDz7Q8uXLz+fQUQ1ls5A3iaB0AwAAAPBP1Srda9eu1fjx47V582aFh4drwIABGjBggCQpNzdX7du316JFi3TddddVaXvZ2dkaPny4MjIyFBERoU6dOunzzz93b/OFF15QQECAbr31VhUWFio+Pl4LFy50f31gYKA+++wzJSYmKi4uTmFhYRoxYoT++Mc/uteJjY3V8uXLNWnSJL300ktq1qyZXn/9dcXHx1fn0FFNTpdR6qFcSVJRiUtOl+GycgAAAAB+p1r36b755pvVt29fTZo0qdLlCxYs0JdffqlPPvnEsgF6A+7TXT3cbxsAAABATWfLfbp37NihhISEMy4fOHCgkpOTq7NJ1DCr0jKU+Pb3FQq3JGXmFijx7e+1Ki3DQyMDAAAAgIuvWqU7Kyur0luFlQkKCtKRI0cueFDwTU6X0ax/pquySyfKHpu9LF1OV5UvrgAAAAAAn1at0t20aVOlpaWdcXlKSoqaNOHyYX/18to9yswrOONyo9LJ1bbsO3rxBgUAAAAAHlSt0j1o0CA9+eSTKig4vVidPHlSM2fO1ODBgy0bHHzHqrQMvfDFniqtm338zMUcAAAAAGqSas1e/sQTT+jjjz9W69atNX78eF155ZWSpF27dumVV16R0+nU448/bstA4b2cLqPZy9KrvH7jetxCDAAAAIB/qFbpjoyM1MaNG5WYmKjp06erbOJzh8Oh+Ph4vfLKK4qMjLRloPBeW/YdPW3itDNpEhGqa2Ib2jwiAAAAAPAO1SrdktS8eXOtWLFCx44d048//ihjjFq1aqUGDRrYMT74gLO9j/tUM4e0437dAAAAAPxGtUt3mQYNGqh79+5WjgU+aFVahp74NLVK607q35r7dAMAAADwK+dduoFVaRka8/b3VVo3KjxE4/u1tHlEAAAAAOBdqjV7OVCmupOn/e6a5lxWDgAAAMDvULpxXqozeZoktbikjo2jAQAAAADvROnGeanuvba5TRgAAAAAf0TpxnnZ/3N+ldflNmEAAAAA/BWlG9XmdBkt2XKgSus6xG3CAAAAAPgvSjeqbcu+o8rMKzznenVDgvSX33flNmEAAAAA/BalG9XidBn9bdP+Kq075/+1p3ADAAAA8GvcpxtVtiotQ9M+TlXOr8VVWj8qorbNIwIAAAAA70bpRpWsSsvQmLe/r9K6DklRTJ4GAAAAAFxejnNzuoymfZxa5fWNmDwNAAAAACRKN6rg5bV7qnxJuSSN6t2C93IDAAAAgCjdOAeny+jNDfur9TUD2kXZMxgAAAAA8DG8pxuVcrqMtuw7qm/2HFHOyaqf5W7Ce7kBAAAAwI3S7QfKCnT28QI1rldais/2fusVKRl6YmmajuYXVWs/DvFebgAAAAAoj9JdA5Uv2fuO5Otvm3+qUKDr166lkb1baHy/VhUKstNl9PB72/RZSka191k3JEjP3t6J93IDAAAAQDkOY4zx9CC8XV5eniIiIpSbm6vw8HBPD+esVqVlaPaydGXkFpxz3fp1aunpYR2V0KGJVqVl6NF/pCj3ZEm19xkWEqhtTw5UcBBTBAAAAADwD1XtiZzprkFWpGRo7LtVu5e2JOX8WqzEt7/XA31i9er6fee93+du70zhBgAAAIBKULpriBUphzV+ybZqf52R9Nevz79wT+rfmkvKAQAAAOAMKN01wKq0DI19t/qFu4zrAt5g0OKSOuf/xQAAAABQw3FNsI9zuoxmL0v32P4b1wv12L4BAAAAwNtRun3cln1HqzRpmh24JzcAAAAAnB2l28dlH/dM4eae3AAAAABwbryn28d54vLuJhGhmjmkHROoAQAAAMA5ULp93DWxDdUkIlSZuQWy+obrYcGBeqDP5Uq8oaWSfzqm7OMFalyv9JJyznADAAAAwLlRun1cYIBDM4e0U+LbZ78/d1hwoPKLnNXadr3QII3v10qBAQ7FXdHoQoYJAAAAAH6J93TXAAkdmugvv++qeiEVX0NpGFZLo3u30JL7e2rbjIGqX7tWtbabmVeoLfuOWjlUAAAAAPArnOmuIRI6NNGXu4/o/a0HFd8uUvf2jj3tMvCRvVvohS/2VGu7npqoDQAAAABqAs501yCHjv0qSRrQPkpxVzQ67X3X4/u1Uv061TvbzX24AQAAAOD8UbprCKfL6N9ZxyVJJwqK5XSdPq1aYIBDTw/rWOVtNgoL5j7cAAAAAHABKN01wKq0DPV+eq2OHC+SJM1alq5rn1mrVWkZp62b0KGJFt7VRVWZfHzO/+vALOUAAAAAcAEo3T5uVVqGEt/+Xpl5Fd97nZlboMS3v6+0eA/qFK2Xf9f1rNt9sE+sBnXiPtwAAAAAcCEo3T7M6TKavSy90vtzlz02e1l6pZeaD+rURIt+31VNIiq+Z7thWC0tvKuLpg9qZ/2AAQAAAMDPMHu5D9uy76gycs88u7iRlJFboC37jlZ6n+2EDk00oF2Utuw7quzjBWpcL/S0Gc8BAAAAAOeP0u3Dqno7r7OtFxjgqLSQAwAAAAAuHJeX+7Cq3s6L234BAAAAgGdQun3YNbEN1SQiVGe6GNwhqUlEKLf9AgAAAAAPoXT7sMAAh2YOKZ3w7NTiXfbvmUPa8R5tAAAAAPAQSrePS+jQRH/5fVdFnTILeVREqP7y+65K6MBtvwAAAADAU5hIrQZgFnIAAAAA8E6U7hqCWcgBAAAAwPtweTkAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA28Wjpnj9/vrp376569eqpcePGGjp0qHbv3l1hnYKCAo0bN06NGjVS3bp1deuttyorK6vCOgcOHNBNN92kOnXqqHHjxvrDH/6gkpKSCuusW7dOXbt2VUhIiFq2bKnFixfbfXgAAAAAAD/n0dL91Vdfady4cdq8ebNWr16t4uJiDRw4UPn5+e51Jk2apGXLlunDDz/UV199pcOHD2vYsGHu5U6nUzfddJOKioq0ceNGvfXWW1q8eLFmzJjhXmffvn266aab1LdvX23fvl0TJ07Ufffdp88///yiHi8AAAAAwL84jDHG04Moc+TIETVu3FhfffWV+vTpo9zcXF166aV69913ddttt0mSdu3apbZt22rTpk3q2bOnVq5cqcGDB+vw4cOKjIyUJC1atEiPPvqojhw5ouDgYD366KNavny50tLS3Pu68847lZOTo1WrVp1zXHl5eYqIiFBubq7Cw8PtOXgAAAAAgM+oak/0qvd05+bmSpIaNmwoSUpOTlZxcbH69+/vXqdNmza67LLLtGnTJknSpk2b1LFjR3fhlqT4+Hjl5eVp586d7nXKb6NsnbJtnKqwsFB5eXkVPgAAAAAAqC6vKd0ul0sTJ05U79691aFDB0lSZmamgoODVb9+/QrrRkZGKjMz071O+cJdtrxs2dnWycvL08mTJ08by/z58xUREeH+iImJseQYAQAAAAD+xWtK97hx45SWlqb33nvP00PR9OnTlZub6/44ePCgp4cEAAAAAPBBQZ4egCSNHz9en332mdavX69mzZq5H4+KilJRUZFycnIqnO3OyspSVFSUe50tW7ZU2F7Z7Obl1zl1xvOsrCyFh4erdu3ap40nJCREISEhlhwbAAAAAMB/efRMtzFG48eP1yeffKK1a9cqNja2wvJu3bqpVq1aWrNmjfux3bt368CBA4qLi5MkxcXFKTU1VdnZ2e51Vq9erfDwcLVr1869TvltlK1Ttg0AAAAAAOzg0dnLx44dq3fffVdLly7VlVde6X48IiLCfQY6MTFRK1as0OLFixUeHq4JEyZIkjZu3Cip9JZhV111laKjo5WUlKTMzEzdc889uu+++zRv3jxJpbcM69Chg8aNG6dRo0Zp7dq1euihh7R8+XLFx8efc5zMXg4AAAAAKK+qPdGjpdvhcFT6+Jtvvql7771XklRQUKBHHnlES5YsUWFhoeLj47Vw4UL3peOS9NNPPykxMVHr1q1TWFiYRowYoaefflpBQf+7en7dunWaNGmS0tPT1axZMz355JPufZwLpRsAAAAAUJ5PlG5fQekGAAAAAJTnk/fpBgAAAACgJqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADbxaOlev369hgwZoujoaDkcDn366acVlhtjNGPGDDVp0kS1a9dW//79tWfPngrrHD16VHfffbfCw8NVv359jR49WidOnKiwTkpKiq677jqFhoYqJiZGSUlJdh8aAAAAAACeLd35+fnq3LmzXnnllUqXJyUlacGCBVq0aJG+/fZbhYWFKT4+XgUFBe517r77bu3cuVOrV6/WZ599pvXr1+uBBx5wL8/Ly9PAgQPVvHlzJScn609/+pNmzZql1157zfbjAwAAAAD4N4cxxnh6EJLkcDj0ySefaOjQoZJKz3JHR0frkUce0ZQpUyRJubm5ioyM1OLFi3XnnXfqhx9+ULt27bR161ZdffXVkqRVq1Zp0KBBOnTokKKjo/WXv/xFjz/+uDIzMxUcHCxJmjZtmj799FPt2rWrSmPLy8tTRESEcnNzFR4ebv3BAwAAAAB8SlV7ote+p3vfvn3KzMxU//793Y9FRESoR48e2rRpkyRp06ZNql+/vrtwS1L//v0VEBCgb7/91r1Onz593IVbkuLj47V7924dO3bsIh0NAAAAAMAfBXl6AGeSmZkpSYqMjKzweGRkpHtZZmamGjduXGF5UFCQGjZsWGGd2NjY07ZRtqxBgwan7buwsFCFhYXuf+fl5V3g0QAAAAAA/JHXnun2pPnz5ysiIsL9ERMT4+khAQAAAAB8kNeW7qioKElSVlZWhcezsrLcy6KiopSdnV1heUlJiY4ePVphncq2UX4fp5o+fbpyc3PdHwcPHrzwAwIAAAAA+B2vLd2xsbGKiorSmjVr3I/l5eXp22+/VVxcnCQpLi5OOTk5Sk5Odq+zdu1auVwu9ejRw73O+vXrVVxc7F5n9erVuvLKKyu9tFySQkJCFB4eXuEDAAAAAIDq8mjpPnHihLZv367t27dLKp08bfv27Tpw4IAcDocmTpyouXPn6p///KdSU1M1fPhwRUdHu2c4b9u2rRISEnT//fdry5Yt2rBhg8aPH68777xT0dHRkqS77rpLwcHBGj16tHbu3Kn3339fL730kiZPnuyhowYAAAAA+AuP3jJs3bp16tu372mPjxgxQosXL5YxRjNnztRrr72mnJwcXXvttVq4cKFat27tXvfo0aMaP368li1bpoCAAN16661asGCB6tat614nJSVF48aN09atW3XJJZdowoQJevTRR6s8Tm4ZBgAAAAAor6o90Wvu0+3NKN0AAAAAgPJ8/j7dAAAAAAD4Oko3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBNKNwAAAAAANqF0AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdAMAAAAAYBO/Kt2vvPKKWrRoodDQUPXo0UNbtmzx9JAAAAAAADVYkKcHcLG8//77mjx5shYtWqQePXroxRdfVHx8vHbv3q3GjRt7enjn78v50k8bpNg+kstZ8fMd70pySBHNpIBA6ej+0q9p2KLi5y7nuZfnHqz6tqqzrrduy9/2yzH49345Bv/eb004BrLzjm35235rwjGQnXdsy9/2W5XlxkjNe5d+vm/9/z53OaW+0+VrHMYY4+lBXAw9evRQ9+7d9fLLL0uSXC6XYmJiNGHCBE2bNu2sX5uXl6eIiAjl5uYqPDz8Ygy36r5Kkr58qvTz2D6lT0pJqt9cyvmp9PPQCKkg98zbqM5yu9b11m35236t3BbH4Hv7tXJbHIPv7dfKbfnbfq3cFsfge/u1clv+tl8rt8Ux+N5+z7W8fLcp+7zv49L1U8/89RdZVXuiX5TuoqIi1alTRx999JGGDh3qfnzEiBHKycnR0qVLz/r1Xl26pYrFu3nv0rPdAAAAAODrWvSR9ntf4Zaq3hP94vLyn3/+WU6nU5GRkRUej4yM1K5du05bv7CwUIWFhe5/5+Xl2T7GC3L9VMlZJK3/E4UbAAAAQM3hpYW7OvxqIrWqmj9/viIiItwfMTExnh7SuV07ydMjAAAAAABrBQb7dOGW/KR0X3LJJQoMDFRWVlaFx7OyshQVFXXa+tOnT1dubq774+DBgxdrqOdv48vVWNlxgcvtWtdbt+Vv++UY/Hu/HIN/77cmHIOn9ssx+Pd+a8IxeGq/HIN/7/ccyx2BpVf0fpVUjX16H78o3cHBwerWrZvWrFnjfszlcmnNmjWKi4s7bf2QkBCFh4dX+PBqXyVJ6+aVfh7bpwpfcK638Vfnbf5Wruut2/K3/XIM/r1fjsG/91sTjsFT++UY/Hu/NeEYPLVfjsG/93uO5cZZ2m++fMqni7dflG5Jmjx5sv7617/qrbfe0g8//KDExETl5+dr5MiRnh7ahTnb7OVlQiPOvo3qLLdrXW/dlr/t18ptcQy+t18rt8Ux+N5+rdyWv+3Xym1xDL63Xyu35W/7tXJbHIPv7fdcy8tOJu5b7/PF2y8mUpOkO+64Q0eOHNGMGTOUmZmpq666SqtWrTptcjWf43JKLa773725jeE+3b58DGTnHdvyt/1yDP6935pwDGTnHdvyt/3WhGMgO+/Ylr/tt6r36b6sV2nP2bf+f5+7nPJFfnHLsAvl9bcMAwAAAABcVFXtiX5zeTkAAAAAABcbpRsAAAAAAJtQugEAAAAAsAmlGwAAAAAAm1C6AQAAAACwCaUbAAAAAACbULoBAAAAALAJpRsAAAAAAJtQugEAAAAAsAmlGwAAAAAAm1C6AQAAAACwCaUbAAAAAACbULoBAAAAALAJpRsAAAAAAJtQugEAAAAAsEmQpwfgC4wxkqS8vDwPjwQAAAAA4A3K+mFZXzwTSncVHD9+XJIUExPj4ZEAAAAAALzJ8ePHFRERccblDnOuWg65XC4dPnxY9erVk8Ph8PRwKpWXl6eYmBgdPHhQ4eHhnh6OTyNLa5CjdcjSGuRoDXK0DllagxytQ5bWIEfreHuWxhgdP35c0dHRCgg48zu3OdNdBQEBAWrWrJmnh1El4eHhXvmE9EVkaQ1ytA5ZWoMcrUGO1iFLa5CjdcjSGuRoHW/O8mxnuMswkRoAAAAAADahdAMAAAAAYBNKdw0REhKimTNnKiQkxNND8XlkaQ1ytA5ZWoMcrUGO1iFLa5CjdcjSGuRonZqSJROpAQAAAABgE850AwAAAABgE0o3AAAAAAA2oXQDAAAAAGATSjcAAAAAADahdPsR5syzhsvl8vQQagyekxeODK1RWFjo6SEAsBE/K4GayVe+tyndfsDpdErynSelt/r5558lSQEBAe5McX727t2rY8eOyeFweHooPm3fvn368MMPlZub6+mh+LTdu3drwIAB+vHHHz09FJ+Xn5+voqIiHTt2TBIvUlqF39/n79Tf1zwnLwzPRXiLoqIiSf970dzbn5uU7hru3//+t6ZMmaJbb71Vc+fO1b59+zw9JJ/073//W5dffrkeeOABSVJgYCDF+zzt2LFDrVq10ieffOLpofi0lJQUXXPNNdq2bZuOHDkiiT8mz8f27dvVs2dPffPNN0pJSZFEjucrPT1dv/3tb3XDDTcoPj5emzdvVkAAf2ZU1+7duzVz5kzde++9evnll5WamiqHw+H1f1B6ox9++EETJkzQ0KFD9dhjjyk5OZnn5Hk4dOiQkpOTJYkXyy/Qjz/+qHnz5mnEiBF6/fXXtX//fk8PySft2rVLDz74oOLj4/Xggw8qLS3N65+b/OSpwVJTU9WrVy8dO3ZMLpdLK1eu1JIlS2SM4Zd3NaWnp6t27dpKTU3Vgw8+KKm0ePPHefXs2LFDvXv31tSpUzVq1ChPD8dnHTx4UEOGDNGIESM0f/58tWzZUpJUUlIiidJYVTt27FCvXr00ZcoU3X777Zo3b54k8Uf5eUhPT9e1116r1q1ba9iwYWrRooVmzpypgoICft9UQ1pamuLi4nTo0CHl5ORo+fLluv766/X55597/R+U3mbXrl3q2bOnfv31VwUFBSk5OVm9e/fW3//+d08Pzafs3r1bV199tRITE/XNN994ejg+LS0tTb169dKOHTu0Z88evfbaa3rmmWeUn5/v6aH5lLJ+ExISoiuuuEJZWVl6/vnnVVxc7N2/bwxqpL1795rmzZubxx9/3P3Y6NGjzUMPPWSMMaa4uNhTQ/NJK1asMK1btzZPP/206dixo3nwwQfdy44fP+7BkfmOH374wQQFBZk//vGPxhhjnE6nWbNmjXn11VfNhg0bzKFDhzw8Qt/x3nvvmRtuuMEYU5rj448/bu68804zbNgws2bNGg+Pzjds27bNBAcHm2nTphljjFm7dq1p3ry5ee+99zw8Mt9z8uRJc8stt5jExET3Y//3f/9n7r77blNUVGSOHDniwdH5jhMnTpj4+HgzZcoU92PJycmmQYMGJiQkxHzwwQfGmNLveZzb2LFjzdChQ93/zsrKMk888YQJDAw0CxcuNMYY43K5PDU8n5CRkWFuuOEG07t3b3PjjTeagQMHmvXr13t6WD7pwIEDpl27du7fOcYY88orr5jLL7/c/Pe///XgyHzLf/7zH3PFFVdU6DezZs0yo0aNMsaU/hw1xjt/TvJyfg3kdDq1evVq/eY3v9EjjzziftWndu3aSktL0w033KCRI0dq48aNHh6p7+jYsaO6deum++67TyNHjtSmTZv0yCOPaNSoUXrnnXdUXFzs6SF6NZfLpQ8++EBOp1O33XabJGnAgAGaPHmypk6dquHDh2vEiBHuy3txdgcPHlRERIQk6dprr9V3332n2rVry+FwqH///nrjjTckef/7mzzl6NGjuv/++zV58mTNnz9fUun3eMOGDbV8+XIPj873FBUVae/evWrfvr37sb179+rrr79W9+7d1b17dy1evFgSz8mzKSgo0MGDB9WzZ09JpVl17dpV/fr1U1xcnH7/+9/r22+/5UqMKsrMzFSjRo3c/27cuLHmzJmjOXPmaNy4cVqxYgWX7Z/DoUOHFBgYqKSkJD300EMKDAzU3Llz9fXXX3t6aD7FGKMvv/xSrVu31pgxY9xXo40ePVpS6ZVCqJqtW7eqd+/eeuihh9yP5eXl6bvvvlOPHj2UkJCgzz//XAEBAd73ve3Bwg8b7d2716Slpbn/PXv2bBMaGmrmzZtnZsyYYe644w5z+eWXm//85z8eHKXvyM/PN506dTLbtm0z+fn55rXXXjONGjUyDofDpKSkGGOMKSkp8fAovVtmZqZ54IEHTEhIiOnQoYMZNmyY2b59uykqKjIff/yxGThwoLn99tu5cqAK3nnnHRMZGWlef/11M2jQIPPLL7+4lz311FMmKCiowvc/Trdlyxb352Xfux9//LEJDQ0169at89SwfJLL5TK/+93vTMeOHc1HH31kpkyZYurUqWMWL15sli9fbubNm2cCAgI4Q3YO2dnZJi4uzsydO9ecPHnSGFN6Vic6Otr84x//MAkJCebuu+82JSUlnKGtglmzZpmYmBj3WcSyzIqKisyYMWNM27ZtTUZGhieH6BO2b9/u/nz58uXuM95fffWV+/Gys4reeHbRWyxfvtwsWrTI/W+Xy2WOHz9umjZtaj788EMPjsy3HDt2zOzatcv976SkJBMaGmpefPFFs2jRIpOYmGiCg4Pdf5t7E0p3DVb2C6agoMAMGjTIfPbZZ+5lX3/9tWncuLH517/+5anh+YyioiJTXFxsBg4caL7++mtjjDF33HGHCQ8PN61atXJfso9zy87ONomJiebqq6826enpFZa98MILJioqisvMq2D//v1myJAhplu3bhUuMzem9BLKVq1amffff9+TQ/QJpxaXffv2mW7dupknn3zSGMMfkNWxZs0a89vf/tYMHTrUtGzZ0rz66qvuZYWFhaZ9+/Zm5syZnhugj5g4caLp1KmTueuuu0xSUpKpW7euGTdunDHGmD/96U+mffv2vMB7FuW/Z7/99lvTu3dvM378eJOVlVVh+RdffGGio6PNtm3bPDFMr3e2n30rVqwwCQkJJj4+3v1C2sMPP2w2b958sYbnUyr7fi3/u6dbt25m6dKl7n+/9dZbZvfu3RdlbL6kshwLCwvN/fffX6HLlL1Q+be//e1iDq9Kgjx9ph3W2L9/v1avXq2AgAA1a9ZM8fHx7klXQkJCtGzZMgUEBMjlcikgIEANGzZUZGSkGjZs6OGRe5fyOcbExGjgwIGqVauWJKlLly768ccf9dprr2n9+vVatmyZUlNT9fTTTysoKEjPPfech0fvXSp7Tl566aWaPXu29uzZoyuuuEJS6dshAgMD1bJlSzVo0EDBwcEeHrl3qSzH5s2b6ze/+Y3mzp2rkpIS7du3T7GxsZKkunXrqn79+goJCfHwyL1LZd/bZT8jjTFyOBxq0aKFEhIStHDhQo0fP16NGzf28Ki9U/ksmzZtqoSEBPXr10/9+vXTL7/8omuvvVZNmzaVVJptSUmJwsPD1aRJEw+P3LuUzzE6Olo33nijXnjhBc2bN08bNmzQypUr9eSTT2rq1KmSpIiICNWuXdv7Lpn0Ajk5Oapfv777lp6BgYG65pprNGTIEH3wwQd69tln9fDDD7ufl23atFFYWBgTWJ2ifI5lfy+WKfs5eeONN8rhcGjBggWaN2+eQkNDtXTpUo0YMcKDI/c+ZVkGBgaqpKREQUH/q1zlJ0Usfxn0448/rj//+c/umeJRMcey722p9PkYHBysRYsWVXi+OhwONWnSRDExMR4eeSU82fhhjZSUFNOoUSPTs2dPc8UVV5i6deua++67zxw+fNi9zqmvEE2bNs10796dCW7KOVOOZWde58yZYxwOh4mNjTXJycnGmNLLXBYuXGj27t3ryaF7ncqyHDVqlMnMzDzj1zz88MNmwIAB7kkwUHmOI0eONMeOHTPGGPPss8+aqKgo06lTJ7N582aTmppqZsyYYVq0aGEOHDjg2cF7ker8jDx48KC56qqrzKxZszjTXYkzfW+Xz/KWW24xkydPNhkZGebkyZNmxowZ5rLLLuPtTOVUluOIESNMTk6Oe528vLwKXzNq1Chz6623msLCwos9XK+Wnp5uYmNj3VeoGFN6hVqZGTNmmB49epghQ4aY7du3mz179php06aZ5s2bc3l5OZXleOrPwPJnaJctW2YaNGhg6tevX+ESdFQtS2NKJ6G8/PLLzSeffGKefvppExoaar777ruLOVSvVt3npDHGPPbYY6Zz585e+b1N6fZxx48fN3FxcWbChAnGmNKZJleuXGkaNmxoEhISzI8//lhh/Z9++sn84Q9/MA0aNDA7duzwxJC90tlyHDhwoDl8+LApLi42iYmJ7veCln2j84d5RWfLMj4+/rQXKH766SczZcoU07BhQ698D46nnC3H/v37u0vO22+/bRISEozD4TDt27c3LVu2NN9//70nh+5Vqvsz0ul0moEDB5obbriBcnOKqmY5d+5c0717d9O4cWPTr18/Ex0dzXOynLPlOGDAgNOek9u3bzcPP/ywiYiIMKmpqZ4Ystc6cOCAueqqq0yrVq1Mhw4dzOzZs93Lyn//vvnmm+bGG280DofDdOjQwTRv3pznZDlny7GykuN0Os3EiRNNvXr1eE6eojpZOp1Oc+2115r27dubOnXqmK1bt17s4Xqt6uRojDG7du0ykyZNMg0aNPDaF4Eo3T7u5MmTpmvXrqfd5mb37t3mkksuMUOHDnWfwdm8ebMZO3as6dy5s9c+IT3lXDkOHjzYQyPzPdV5Tm7cuNGMGjXKtGnThvfWneJcOQ4ZMsT9mMvlMsnJyWbPnj3u9y6iVHWej2W3Ujxw4ADvqavEubK8+eab3Y8tX77cPPPMM2bRokWc4T7FuXK85ZZb3H9U5uTkmL///e+mS5cu/Iw8hcvlMs8884wZNGiQ+de//mVmzpxp2rRpc8bibUzp+7x37tzplWfBPKUqOZ56tWRKSopp2rQpZ2VPUd0si4uLTa9evTgRdorq5piWlua+gtebc6R0+7gTJ06Ypk2bVngill1WtWPHDhMWFmbmzJnjXrZhw4YKlwGiVFVyLLu/NM6uus/JL7/8ksnTKlGVHGfNmuWp4fmM6j4fmRX6zKqSJZOlnVt1n5O//vqr+y0lqCgjI8MsXrzYGFM6iWTZH+flfzaWv9QclatKjqeeXczNzb2oY/QV1c3yjTfeMHv27Lno4/R21c0xLS3N6086ULprgOeee840a9bMLFu2zP1Y2S+ZuXPnmh49evDe7SqoSo6//PILf5RXQVWyzM7O9tTwfAbPSWuQo3WqkuXPP//s/mOITCtX1RzJr3oOHz5c6R/nn376KbO+V8PZcix7nvLcrJozZfmPf/zDg6PyPTUhR2Yv9zEZGRk6ePCgjh07pv79+yswMFDDhg3T5s2blZSUpODg4Aozbl9yySXKy8tTnTp1PDxy73K+OYaGhlaYdRLnn2VYWJiHR+5deE5agxytc75Z1q5d2z3rMZleWI7kV1FlWUqSy+Vyz1r8wAMPSJLee+89GWOUm5url156SYcOHVJ0dLQnh+81rMiR52YpnpPW8IscPdv5UR07duwwzZs3N61btzYRERHmyiuvNEuWLDFFRUVm69atZvDgwaZ79+5myZIlxpjSV82nTp1qrr/++tNmQfVn5GgdsrQGOVqDHK1DltYgR+ucmmWbNm3Mu+++a3755RdjTOmlpmVnXw8fPmxmzJhhHA6HadCgAe89LoccrUOW1vCXHCndPiI7O9u0adPGPPbYY2bv3r3mv//9r7njjjtM69atzezZs01BQYHZvn27GTNmjAkKCjKdO3c2PXv2NA0aNGDylXLI0TpkaQ1ytAY5WocsrUGO1jlTlm3btjUzZ850v12p/CXP99xzjwkPDzc7d+701LC9Djlahyyt4U85Urp9xM6dO02LFi1Oe0Xn0UcfNe3btzfPPvuscblc5sSJE2bTpk1mzpw5ZtGiRUzOcApytA5ZWoMcrUGO1iFLa5Cjdc6WZceOHU1SUpLJz893P/7666+b+vXrc1uwU5CjdcjSGv6UI6XbR2zfvt00a9bMrF+/3hhTOptpmYceesg0b97cq6fJ9xbkaB2ytAY5WoMcrUOW1iBH65wry9jY2ApZZmZmcqu6SpCjdcjSGv6Uo8MYYzz9vnJUzTXXXKO6detq7dq1kqTCwkKFhIRIkrp3766WLVtqyZIlnhyiTyBH65ClNcjRGuRoHbK0Bjlap6pZOp1O9yRMOB05WocsreEvOQZ4egCoXH5+vo4fP668vDz3Y6+++qp27typu+66S5IUEhKikpISSVKfPn2Un5/vkbF6M3K0DllagxytQY7WIUtrkKN1LiRLX/6j3GrkaB2ytIY/50jp9kLp6ekaNmyYrr/+erVt21bvvPOOJKlt27Z66aWXtHr1at1+++0qLi5235YlOztbYWFhKikpERcvlCJH65ClNcjRGuRoHbK0BjlahyytQY7WIUtr+HuO3Kfby6Snp6tPnz4aPny4rr76aiUnJ2vkyJFq166dunTpoptvvllhYWEaO3asOnXqpDZt2ig4OFjLly/X5s2bFRTEf6lEjlYiS2uQozXI0TpkaQ1ytA5ZWoMcrUOW1iBHifd0e5GjR4/qd7/7ndq0aaOXXnrJ/Xjfvn3VsWNHLViwwP3Y8ePHNXfuXB09elShoaFKTExUu3btPDFsr0OO1iFLa5CjNcjROmRpDXK0DllagxytQ5bWIMdSvv+yQQ1SXFysnJwc3XbbbZIkl8ulgIAAxcbG6ujRo5IkUzrjvOrVq6dnnnmmwnooRY7WIUtrkKM1yNE6ZGkNcrQOWVqDHK1DltYgx1I150hqgMjISL399tu67rrrJElOp1OS1LRpU/eTzuFwKCAgoMIEBA6H4+IP1ouRo3XI0hrkaA1ytA5ZWoMcrUOW1iBH65ClNcixFKXby7Rq1UpS6as7tWrVklT66k92drZ7nfnz5+v11193z+xX056UViBH65ClNcjRGuRoHbK0BjlahyytQY7WIUtrkCOXl3utgIAAGWPcT7iyV4JmzJihuXPnatu2bTViUgG7kaN1yNIa5GgNcrQOWVqDHK1DltYgR+uQpTX8OUfOdHuxsjnugoKCFBMTo2effVZJSUn67rvv1LlzZw+PzneQo3XI0hrkaA1ytA5ZWoMcrUOW1iBH65ClNfw1x5r5UkINUfbqT61atfTXv/5V4eHh+uabb9S1a1cPj8y3kKN1yNIa5GgNcrQOWVqDHK1DltYgR+uQpTX8NUfOdPuA+Ph4SdLGjRt19dVXe3g0voscrUOW1iBHa5CjdcjSGuRoHbK0Bjlahyyt4W85cp9uH5Gfn6+wsDBPD8PnkaN1yNIa5GgNcrQOWVqDHK1DltYgR+uQpTX8KUdKNwAAAAAANuHycgAAAAAAbELpBgAAAADAJpRuAAAAAABsQukGAAAAAMAmlG4AAAAAAGxC6QYAAAAAwCaUbgAAAAAAbELpBgDAD917771yOBxyOByqVauWIiMjNWDAAL3xxhtyuVxV3s7ixYtVv359+wYKAICPo3QDAOCnEhISlJGRof3792vlypXq27evHn74YQ0ePFglJSWeHh4AADUCpRsAAD8VEhKiqKgoNW3aVF27dtVjjz2mpUuXauXKlVq8eLEk6fnnn1fHjh0VFhammJgYjR07VidOnJAkrVu3TiNHjlRubq77rPmsWbMkSYWFhZoyZYqaNm2qsLAw9ejRQ+vWrfPMgQIA4EGUbgAA4NavXz917txZH3/8sSQpICBACxYs0M6dO/XWW29p7dq1mjp1qiSpV69eevHFFxUeHq6MjAxlZGRoypQpkqTx48dr06ZNeu+995SSkqLbb79dCQkJ2rNnj8eODQAAT3AYY4ynBwEAAC6ue++9Vzk5Ofr0009PW3bnnXcqJSVF6enppy376KOPNGbMGP3888+SSt/TPXHiROXk5LjXOXDggC6//HIdOHBA0dHR7sf79++va665RvPmzbP8eAAA8FZBnh4AAADwLsYYORwOSdIXX3yh+fPna9euXcrLy1NJSYkKCgr066+/qk6dOpV+fWpqqpxOp1q3bl3h8cLCQjVq1Mj28QMA4E0o3QAAoIIffvhBsbGx2r9/vwYPHqzExEQ99dRTatiwob755huNHj1aRUVFZyzdJ06cUGBgoJKTkxUYGFhhWd26dS/GIQAA4DUo3QAAwG3t2rVKTU3VpEmTlJycLJfLpeeee04BAaXTwHzwwQcV1g8ODpbT6azwWJcuXeR0OpWdna3rrrvuoo0dAABvROkGAMBPFRYWKjMzU06nU1lZWVq1apXmz5+vwYMHa/jw4UpLS1NxcbH+/Oc/a8iQIdqwYYMWLVpUYRstWrTQiRMntGbNGnXu3Fl16tRR69atdffdd2v48OF67rnn1KVLFx05ckRr1qxRp06ddNNNN3noiAEAuPiYvRwAAD+1atUqNWnSRC1atFBCQoK+/PJLLViwQEuXLlVgYKA6d+6s559/Xs8884w6dOigd955R/Pnz6+wjV69emnMmDG64447dOmllyopKUmS9Oabb2r48OF65JFHdOWVV2ro0KHaunWrLrvsMk8cKgAAHsPs5QAAAAAA2IQz3QAAAAAA2ITSDQAAAACATSjdAAAAAADYhNINAAAAAIBNKN0AAAAAANiE0g0AAAAAgE0o3QAAAAAA2ITSDQAAAACATSjdAAAAAADYhNINAAAAAIBNKN0AAAAAANiE0g0AAAAAgE3+P+WIIKuRULFxAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_ticker_counts_per_date(df, title):\n",
        "    df['date'] = pd.to_datetime(df['date'])\n",
        "\n",
        "    # Count unique 'numerai_ticker' and 'composite_figi' per 'date'\n",
        "    nticker_count_per_date = df.groupby('date')['numerai_ticker'].nunique().reset_index(name='numerai_ticker_count')\n",
        "    figi_count_per_date = df.groupby('date')['composite_figi'].nunique().reset_index(name='figi_count')\n",
        "\n",
        "    # Merge the counts into a single DataFrame for plotting\n",
        "    merged_counts = pd.merge(nticker_count_per_date, figi_count_per_date, on='date')\n",
        "\n",
        "    # Plotting\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(merged_counts['date'], merged_counts['numerai_ticker_count'], label='Unique Numerai Tickers', marker='o')\n",
        "    plt.plot(merged_counts['date'], merged_counts['figi_count'], label='Unique Composite FIGIs', marker='x')\n",
        "\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Date')\n",
        "    plt.ylabel('Count')\n",
        "    plt.legend()\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "plot_ticker_counts_per_date(train, 'Training Dataset numerai_ticker and composite_figi Counts per Date')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "49621bcd-1c42-411e-8f1f-259fb4d9bda7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "49621bcd-1c42-411e-8f1f-259fb4d9bda7",
        "outputId": "d11716a9-74a2-4fe6-9e13-30fa1c486d8b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_ticker_counts_per_date(validation, 'Validation Dataset numerai_ticker and composite_figi Counts per Date')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c60a1d87-0cbd-4a4b-be6b-66a882fc7895",
      "metadata": {
        "id": "c60a1d87-0cbd-4a4b-be6b-66a882fc7895"
      },
      "source": [
        "## Mapping to `numerai_ticker`\n",
        "\n",
        "If you have Bloomberg tickers, you can map to `numerai_ticker` by replacing the exchange code with the ISO country code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "8b0f3b02-577c-4ead-a11c-01fbbef72144",
      "metadata": {
        "id": "8b0f3b02-577c-4ead-a11c-01fbbef72144"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "# Computed using https://stockmarketmba.com/globalstockexchanges.php\n",
        "# and https://www.isin.net/country-codes/\n",
        "# Converting Bloomberg exchange code -> Country -> ISO 3166\n",
        "TICKER_CTRY_MAP = {\n",
        "    \"AU\": \"AU\",\n",
        "    \"AV\": \"AT\",\n",
        "    \"BB\": \"BE\",\n",
        "    \"BZ\": \"BR\",\n",
        "    \"CA\": \"CA\",\n",
        "    \"CB\": \"CO\",\n",
        "    \"CH\": \"CN\",\n",
        "    \"CI\": \"CL\",\n",
        "    \"CN\": \"CA\",\n",
        "    \"CP\": \"CZ\",\n",
        "    \"DC\": \"DK\",\n",
        "    \"EY\": \"EG\",\n",
        "    \"FH\": \"FI\",\n",
        "    \"FP\": \"FR\",\n",
        "    \"GA\": \"GR\",\n",
        "    \"GR\": \"DE\",\n",
        "    \"GY\": \"DE\",\n",
        "    \"HB\": \"HU\",\n",
        "    \"HK\": \"HK\",\n",
        "    \"ID\": \"IE\",\n",
        "    \"IJ\": \"ID\",\n",
        "    \"IM\": \"IT\",\n",
        "    \"IN\": \"IN\",\n",
        "    \"IT\": \"IL\",\n",
        "    \"JP\": \"JP\",\n",
        "    \"KS\": \"KR\",\n",
        "    \"LN\": \"GB\",\n",
        "    \"MF\": \"MX\",\n",
        "    \"MK\": \"MY\",\n",
        "    \"NA\": \"NL\",\n",
        "    \"NO\": \"NO\",\n",
        "    \"NZ\": \"NZ\",\n",
        "    \"PE\": \"PE\",\n",
        "    \"PL\": \"PT\",\n",
        "    \"PM\": \"PH\",\n",
        "    \"PW\": \"PL\",\n",
        "    \"QD\": \"QA\",\n",
        "    \"RM\": \"RU\",\n",
        "    \"SJ\": \"ZA\",\n",
        "    \"SM\": \"ES\",\n",
        "    \"SP\": \"SG\",\n",
        "    \"SS\": \"SE\",\n",
        "    \"SW\": \"CH\",\n",
        "    \"TB\": \"TH\",\n",
        "    \"TI\": \"TR\",\n",
        "    \"TT\": \"TW\",\n",
        "    \"UH\": \"AE\",\n",
        "    \"US\": \"US\",\n",
        "    \"UQ\": \"US\",\n",
        "}\n",
        "\n",
        "def map_country_code(row):\n",
        "    if row[\"bloomberg_ticker\"] is None:\n",
        "        return None\n",
        "    split_ticker = row[\"bloomberg_ticker\"].split()\n",
        "    if len(split_ticker) < 2:\n",
        "        capture_message(f'No country code for {row[\"bloomberg_ticker\"]}')\n",
        "        return None\n",
        "\n",
        "    ticker = split_ticker[0]\n",
        "    country_code = split_ticker[-1]\n",
        "    iso_country_code = TICKER_CTRY_MAP.get(country_code)\n",
        "    return f\"{ticker} {iso_country_code}\"\n",
        "\n",
        "# create test dataframe with Bloomberg tickers\n",
        "df = pd.DataFrame([\n",
        "  {'bloomberg_ticker': '000640 KS', 'signal': random.random()},\n",
        "  {'bloomberg_ticker': '1103 TT', 'signal': random.random()},\n",
        "  {'bloomberg_ticker': 'A2A IM', 'signal': random.random()},\n",
        "  {'bloomberg_ticker': 'ABBN SW', 'signal': random.random()}\n",
        "])\n",
        "\n",
        "# convert to numerai_ticker\n",
        "df['numerai_ticker'] = df.apply(\n",
        "    map_country_code, axis=1\n",
        ")\n",
        "\n",
        "assert df.iloc[0]['numerai_ticker'] == '000640 KR'\n",
        "assert df.iloc[1]['numerai_ticker'] == '1103 TW'\n",
        "assert df.iloc[2]['numerai_ticker'] == 'A2A IT'\n",
        "assert df.iloc[3]['numerai_ticker'] == 'ABBN CH'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b09e5700-1208-4430-b623-c1b4265dc0f0",
      "metadata": {
        "id": "b09e5700-1208-4430-b623-c1b4265dc0f0"
      },
      "source": [
        "# Features\n",
        "\n",
        "The V1 features are:\n",
        "```\n",
        "feature_adv_20d_factor\n",
        "feature_beta_factor\n",
        "feature_book_to_price_factor\n",
        "feature_country\n",
        "feature_dividend_yield_factor\n",
        "feature_earnings_yield_factor\n",
        "feature_growth_factor\n",
        "feature_impact_cost_factor\n",
        "feature_market_cap_factor\n",
        "feature_momentum_12w_factor\n",
        "feature_momentum_26w_factor\n",
        "feature_momentum_52w_factor\n",
        "feature_momentum_52w_less_4w_factor\n",
        "feature_ppo_60d_130d_country_ranknorm\n",
        "feature_ppo_60d_90d_country_ranknorm\n",
        "feature_price_factor\n",
        "feature_rsi_130d_country_ranknorm\n",
        "feature_rsi_60d_country_ranknorm\n",
        "feature_rsi_90d_country_ranknorm\n",
        "feature_trix_130d_country_ranknorm\n",
        "feature_trix_60d_country_ranknorm\n",
        "feature_value_factor\n",
        "feature_volatility_factor\n",
        "```\n",
        "\n",
        "Features with `{n}(d|w)` in the name (for example, `feature_adv_20d_factor`) are time-series features that are computed over `n` days or `n` weeks.\n",
        "\n",
        "Features with `country_ranknorm` in the name are grouped by country, then ranked, then gaussianized.\n",
        "\n",
        "Features with `factor` in the name refer to risk factors that most of the targets are neutral to.\n",
        "\n",
        "PPO, RSI and TRIX are examples of technical indicators.\n",
        "\n",
        "PPO is a percentage price oscillator that compares shorter and longer moving averages in a ratio\n",
        "RSI is the relative strength index usually used as an overbought/oversold indicator\n",
        "TRIX is a triple exponential moving average indicator usually used as momentum or reversal feature\n",
        "\n",
        "`momentum_52w_less_4w` refers to one year return of a stock excluding the last 4 weeks.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21782d68-7d7f-4df7-864d-0cdd92a00adb",
      "metadata": {
        "id": "21782d68-7d7f-4df7-864d-0cdd92a00adb"
      },
      "source": [
        "# Modeling\n",
        "\n",
        "The V1 dataset includes a small set of features that can be used on its own or in addition to your existing dataset.\n",
        "\n",
        "In this example, we will show how to use the V1 features to train and submit predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "7da5fa8a-d8a1-4bc7-8fe9-e2664aaaf55f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7da5fa8a-d8a1-4bc7-8fe9-e2664aaaf55f",
        "outputId": "04636413-3211-4533-d2fd-05c7eb9c4b94"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
            "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
            "\n",
            "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
            "This will raise in a future version.\n",
            "\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.458827 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 5610\n",
            "[LightGBM] [Info] Number of data points in the train set: 2536318, number of used features: 22\n",
            "[LightGBM] [Info] Start training from score 0.500004\n",
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            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
            "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
              "              n_estimators=2000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
              "              n_estimators=2000)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
              "              n_estimators=2000)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import lightgbm as lgb\n",
        "\n",
        "feature_cols = [col for col in train.columns if col.startswith('feature_')]\n",
        "\n",
        "# there are two non-numerical feature cols\n",
        "feature_cols = [s for s in feature_cols if s not in (\"feature_country\", \"feature_exchange_code\")]\n",
        "\n",
        "# https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html\n",
        "model = lgb.LGBMRegressor(\n",
        "  n_estimators=2000,\n",
        "  learning_rate=0.01,\n",
        "  max_depth=5,\n",
        "  num_leaves=2**5-1,\n",
        "  colsample_bytree=0.1\n",
        ")\n",
        "\n",
        "# This will take a few minutes 🍵\n",
        "model.fit(\n",
        "  train[feature_cols],\n",
        "  train[\"target\"]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84168430-581c-4d71-bc26-55ad94fe9c3c",
      "metadata": {
        "id": "84168430-581c-4d71-bc26-55ad94fe9c3c"
      },
      "source": [
        "# Live Submission\n",
        "\n",
        "To make a live submission, you only need to submit a ticker column with its signal.\n",
        "\n",
        "We accept the following tickers for live submissions:\n",
        "\n",
        "* cusip\n",
        "* sedol\n",
        "* bloomberg_ticker\n",
        "* composite_figi\n",
        "* numerai_ticker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "680c8c24-0e66-48ed-915c-055d81f50fe2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "680c8c24-0e66-48ed-915c-055d81f50fe2",
        "outputId": "8031aca5-0dd4-4be4-fe3b-e8657a81e582"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "signals/v2.0/live.parquet: 968kB [00:00, 3.94MB/s]                           \n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"submission\",\n  \"rows\": 6657,\n  \"fields\": [\n    {\n      \"column\": \"numerai_ticker\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6657,\n        \"samples\": [\n          \"DIB AE\",\n          \"095700 KR\",\n          \"MDA CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"signal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005963242110890815,\n        \"min\": 0.445869181037109,\n        \"max\": 0.5567841073493948,\n        \"num_unique_values\": 6657,\n        \"samples\": [\n          0.5009788393873139,\n          0.5034310964627485,\n          0.5149118188590079\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "submission"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-4ad5e5ac-4a41-41fc-823d-408e8a722aa2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>numerai_ticker</th>\n",
              "      <th>signal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>000080 KR</td>\n",
              "      <td>0.496785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000100 KR</td>\n",
              "      <td>0.505226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>000120 KR</td>\n",
              "      <td>0.499629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>000150 KR</td>\n",
              "      <td>0.502670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>000210 KR</td>\n",
              "      <td>0.489368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6652</th>\n",
              "      <td>ZUO US</td>\n",
              "      <td>0.501888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6653</th>\n",
              "      <td>ZURN CH</td>\n",
              "      <td>0.498486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6654</th>\n",
              "      <td>ZVRA US</td>\n",
              "      <td>0.499622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6655</th>\n",
              "      <td>ZWS US</td>\n",
              "      <td>0.500102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6656</th>\n",
              "      <td>ZYME US</td>\n",
              "      <td>0.502035</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6657 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ad5e5ac-4a41-41fc-823d-408e8a722aa2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4ad5e5ac-4a41-41fc-823d-408e8a722aa2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4ad5e5ac-4a41-41fc-823d-408e8a722aa2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-14fd9dac-55d3-4738-8c33-2110a438cbac\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14fd9dac-55d3-4738-8c33-2110a438cbac')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-14fd9dac-55d3-4738-8c33-2110a438cbac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_90efecb3-1ad6-4969-8bf1-729f639d353e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('submission')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_90efecb3-1ad6-4969-8bf1-729f639d353e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('submission');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     numerai_ticker    signal\n",
              "0         000080 KR  0.496785\n",
              "1         000100 KR  0.505226\n",
              "2         000120 KR  0.499629\n",
              "3         000150 KR  0.502670\n",
              "4         000210 KR  0.489368\n",
              "...             ...       ...\n",
              "6652         ZUO US  0.501888\n",
              "6653        ZURN CH  0.498486\n",
              "6654        ZVRA US  0.499622\n",
              "6655         ZWS US  0.500102\n",
              "6656        ZYME US  0.502035\n",
              "\n",
              "[6657 rows x 2 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "napi.download_dataset(f'signals/{DATASET_VERSION}/live.parquet')\n",
        "live = pd.read_parquet(f'signals/{DATASET_VERSION}/live.parquet')\n",
        "\n",
        "live['signal'] = model.predict(live[feature_cols])\n",
        "\n",
        "submission = live[['numerai_ticker', 'signal']]\n",
        "submission"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "3f38bb35-5c5b-4a88-a7ed-c0174c2d3a94",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "3f38bb35-5c5b-4a88-a7ed-c0174c2d3a94",
        "outputId": "b184a7a0-2b05-462c-93ef-d82ae91d6f69"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": "download(\"download_7743b82b-6112-4b50-bdfb-508a6ca9bd14\", \"signals_886_live_preds.csv\", 179357)",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Save and download your predictions\n",
        "filename = f'signals_example_preds.csv'\n",
        "submission.to_csv(filename, index=False)\n",
        "\n",
        "# Download file if running in Google Colab\n",
        "try:\n",
        "    from google.colab import files\n",
        "    files.download(filename)\n",
        "except:\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "SjZUhwDoeOhZ",
      "metadata": {
        "id": "SjZUhwDoeOhZ"
      },
      "source": [
        "\n",
        "\n",
        "Now, visit the [Submissions page](https://signals.numer.ai/submissions) to upload your submissions to your model.\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}