├── Materiais
    ├── Aula 1
    │   └── Aula estatistica aula 1.pdf
    └── Aula 2
    │   ├── aula estatistica aula 2.pptx
    │   └── Medidas_de_tendencia_central.ipynb
└── README.md


/Materiais/Aula 1/Aula estatistica aula 1.pdf:
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https://raw.githubusercontent.com/acaicomdados/nivelamento-estatistica/main/Materiais/Aula 1/Aula estatistica aula 1.pdf


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/Materiais/Aula 2/aula estatistica aula 2.pptx:
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https://raw.githubusercontent.com/acaicomdados/nivelamento-estatistica/main/Materiais/Aula 2/aula estatistica aula 2.pptx


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/README.md:
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 1 | # Nivelamento em estatistica
 2 | 
 3 | Neste repositório vai conter os materiais das aulas e com todas anotações do que foi indicado durabte as aulas
 4 | A ideia é passar por tópicos principais e passar por uma questão prática (nas aulas será utilizado a linguagem de programação em python, mas se tiver alguma dificuldade, pode usar excel também), no qual vai girar em torno dos seguintes temas (a ementa pode mudar ao longo da aulas): 
 5 | 
 6 | - **Estatistica descritiva**
 7 |   - Conceitos (População, amostra, medidas quantitativas, medidas qualitativas)
 8 |   - Uso de gráficos, medidas de tendecia central, medidas de dispersão
 9 |   - Covariância e correlação
10 |   - Curtose e assimetria
11 | - **Probabilidade**
12 |   - (*em breve*)
13 | - **Inferência estatistica**
14 |   - (*em breve*)
15 | 
16 | Em breve será colocado uma pasta pra quem  deseja colocar os seu projetos e levar em discussão nas aulas 
17 | 
18 | ## Aula 1
19 | 
20 | Além do slide utilizado na primeira aula foi utilizados esse pra da conceitos iniciais em estatistica, que pode ser encontrado [aqui](https://github.com/acaicomdados/analise-reservatorios-federais/blob/main/Extra/Minicurso%20Jes%20/an%C3%A1lise%20de%20dados.pdf)
21 | 


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/Materiais/Aula 2/Medidas_de_tendencia_central.ipynb:
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   1 | {
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   4 |   "metadata": {
   5 |     "colab": {
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   7 |       "authorship_tag": "ABX9TyMP+gYSgNnzf7XLCjN2S3sD",
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  21 |       "metadata": {
  22 |         "id": "view-in-github",
  23 |         "colab_type": "text"
  24 |       },
  25 |       "source": [
  26 |         "<a href=\"https://colab.research.google.com/github/acaicomdados/nivelamento-estatistica/blob/main/Materiais/Aula%202/Medidas_de_tendencia_central.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  27 |       ]
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  29 |     {
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  31 |       "execution_count": null,
  32 |       "metadata": {
  33 |         "id": "VA8kZzaHTnis"
  34 |       },
  35 |       "outputs": [],
  36 |       "source": [
  37 |         "import numpy as np\n",
  38 |         "import pandas as pd\n",
  39 |         "from sklearn.datasets import load_iris\n",
  40 |         "import matplotlib.pyplot as plt\n",
  41 |         "import seaborn as sns"
  42 |       ]
  43 |     },
  44 |     {
  45 |       "cell_type": "code",
  46 |       "source": [
  47 |         "iris = load_iris()\n",
  48 |         "df_iris = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
  49 |         "\n",
  50 |         "df_iris['target'] = iris.target\n",
  51 |         "df_iris['target_names'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
  52 |         "\n",
  53 |         "df_iris.head()"
  54 |       ],
  55 |       "metadata": {
  56 |         "colab": {
  57 |           "base_uri": "https://localhost:8080/",
  58 |           "height": 206
  59 |         },
  60 |         "id": "p79RNhqg3Yzo",
  61 |         "outputId": "25e44eb8-71bf-448e-9ae9-2f4651315689"
  62 |       },
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  64 |       "outputs": [
  65 |         {
  66 |           "output_type": "execute_result",
  67 |           "data": {
  68 |             "text/plain": [
  69 |               "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
  70 |               "0                5.1               3.5                1.4               0.2   \n",
  71 |               "1                4.9               3.0                1.4               0.2   \n",
  72 |               "2                4.7               3.2                1.3               0.2   \n",
  73 |               "3                4.6               3.1                1.5               0.2   \n",
  74 |               "4                5.0               3.6                1.4               0.2   \n",
  75 |               "\n",
  76 |               "   target target_names  \n",
  77 |               "0       0       setosa  \n",
  78 |               "1       0       setosa  \n",
  79 |               "2       0       setosa  \n",
  80 |               "3       0       setosa  \n",
  81 |               "4       0       setosa  "
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 104 |               "      <th>sepal length (cm)</th>\n",
 105 |               "      <th>sepal width (cm)</th>\n",
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 368 |               "    </div>\n",
 369 |               "  </div>\n"
 370 |             ],
 371 |             "application/vnd.google.colaboratory.intrinsic+json": {
 372 |               "type": "dataframe",
 373 |               "variable_name": "df_iris",
 374 |               "summary": "{\n  \"name\": \"df_iris\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target_names\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
 375 |             }
 376 |           },
 377 |           "metadata": {},
 378 |           "execution_count": 2
 379 |         }
 380 |       ]
 381 |     },
 382 |     {
 383 |       "cell_type": "code",
 384 |       "source": [
 385 |         "sns.histplot(x='sepal length (cm)',data=df_iris,hue='target_names',kde=True)"
 386 |       ],
 387 |       "metadata": {
 388 |         "colab": {
 389 |           "base_uri": "https://localhost:8080/",
 390 |           "height": 467
 391 |         },
 392 |         "id": "KAQuTSeE4OGj",
 393 |         "outputId": "4130da1b-80c7-4422-83e4-c44912587cf7"
 394 |       },
 395 |       "execution_count": null,
 396 |       "outputs": [
 397 |         {
 398 |           "output_type": "execute_result",
 399 |           "data": {
 400 |             "text/plain": [
 401 |               "<Axes: xlabel='sepal length (cm)', ylabel='Count'>"
 402 |             ]
 403 |           },
 404 |           "metadata": {},
 405 |           "execution_count": 5
 406 |         },
 407 |         {
 408 |           "output_type": "display_data",
 409 |           "data": {
 410 |             "text/plain": [
 411 |               "<Figure size 640x480 with 1 Axes>"
 412 |             ],
 413 |             "image/png": 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\n"
 414 |           },
 415 |           "metadata": {}
 416 |         }
 417 |       ]
 418 |     },
 419 |     {
 420 |       "cell_type": "code",
 421 |       "source": [
 422 |         "sns.histplot(x='petal width (cm)',data=df_iris,hue='target_names',kde=True)"
 423 |       ],
 424 |       "metadata": {
 425 |         "colab": {
 426 |           "base_uri": "https://localhost:8080/",
 427 |           "height": 472
 428 |         },
 429 |         "id": "8kl8AaHg4taL",
 430 |         "outputId": "585b2f12-6e3e-46ad-d9bb-249519b3552e"
 431 |       },
 432 |       "execution_count": null,
 433 |       "outputs": [
 434 |         {
 435 |           "output_type": "execute_result",
 436 |           "data": {
 437 |             "text/plain": [
 438 |               "<Axes: xlabel='petal width (cm)', ylabel='Count'>"
 439 |             ]
 440 |           },
 441 |           "metadata": {},
 442 |           "execution_count": 7
 443 |         },
 444 |         {
 445 |           "output_type": "display_data",
 446 |           "data": {
 447 |             "text/plain": [
 448 |               "<Figure size 640x480 with 1 Axes>"
 449 |             ],
 450 |             "image/png": 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yMzNtMel0Ot5++23i4+M5cuQIf/3rX5k2bVq9JMqVSCKjgTOFvu7nPKcoCsHeBrKKKikor5ZERgghXFh2dja1tbXcdNNNxMXFAdCrVy8AnnvuOd544w1uuukmwNoCs3//fv75z38yceJEQkNDAQgODiYiIsJ2zNdff53p06dz++23A/Dqq6+yevVq5s6dy/z588nIyKBr165cdtllKIpiO2+dJ554wna/Y8eOvPTSSzz88MMum8hI15IGCsutiUzQH0Ys1alLcIoqa5wWkxBCCPvr06cPV111Fb169eKWW27hww8/pLCwkPLyctLS0njggQfqrXr90ksvkZaW1ujxSkpKOHHiBEOHDq23fejQoaSkpABw7733snPnThISEnjsscf4+eef6+27YsUKrrrqKjp06ICvry933303p06dosKJBdP2JImMBgorznQtNSTw9Pai0y03QgghXJNer+eXX37hp59+okePHrzzzjskJCSwd+9eAD788EN27txpu+3du5dNmza16Jz9+vUjPT2dF198kcrKSm699VZuvvlmwDoSasyYMfTu3Zuvv/6abdu2MX/+fMBaW+OKpGtJA3VdS0He53YtAfifbpGpq6URQgjhuhRFYejQoQwdOpRZs2YRFxfH+vXriYqK4siRI9x5550Nvq6uJsZsPjPww8/Pj6ioKNavX88VV1xh275+/XoGDRpUb7/bbruN2267jZtvvplrr72WgoICtm3bhsVi4Y033kCns7ZlfPnll454204jiYwG6rqWAhttkZGuJSGEaAs2b97MypUrueaaawgLC2Pz5s2cPHmSxMREXnjhBR577DH8/f259tprMZlMbN26lcLCQqZMmUJYWBienp4sW7aM6OhojEYj/v7+PP300zz33HN07tyZpKQkFixYwM6dO1m0aBFgHZUUGRlJ37590el0fPXVV0RERBAQEECXLl2oqanhnXfeYezYsaxfv573339f46vUMpLIaKDgdItMoHcjNTKe0rUkhBBNkZvReF2Jlufx8/Pj119/Ze7cuZSUlBAXF8cbb7zB6NGjAfDy8uLvf/87Tz/9NN7e3vTq1ctWjOvm5sbbb7/N7NmzmTVrFsOGDWPNmjU89thjFBcX89RTT5GXl0ePHj34/vvv6dq1KwC+vr689tprHDp0CL1ez8CBA/nxxx/R6XT06dOHN998k1dffZUZM2Zw+eWXM2fOHO655x67XidnUlRVVbUOwpFKSkrw9/enuLgYPz8/rcMB4P6FW1h1II9XJ/TitoHnTqj00bojvLQ0hbF9onjnT301iFAIIVqXqqoq0tPTiY+Px2g8s0ZdW57Ztz1o7OcKF//9LS0yGii4YNeStMgIIcTFiI2N5UBKiqy11I5JIqOBogt1LdXVyFRIjYwQQlxIbGysJBbtmAy/1sCZFpmGRy3VDcsulBYZIYQQ4rwkkXGyWrOFkirrgpEXGrVULC0yQgghxHlpnshkZWVx1113ERwcjKenJ7169WLr1q2251VVZdasWURGRuLp6cnIkSM5dOiQhhG3TPFZQ6r9Pc/fIlNqqqXGbHFKXEIIIYQr0jSRKSwsZOjQobi7u/PTTz+xf/9+3njjDQIDA237vPbaa7z99tu8//77bN68GW9vb0aNGkVVVZWGkTdfXXeRv6c7bvqGL//ZCY7UyQghhBCN07TY99VXXyUmJoYFCxbYttUtWw7W1pi5c+fy7LPPMm7cOAA++eQTwsPD+e6772wLZrmSuuUJGquPAdDrFPyMbpRU1VJcWU2or8FZ4QkhhBAuRdMWme+//54BAwZwyy23EBYWRt++ffnwww9tz6enp5OTk8PIkSNt2/z9/Rk8eDAbN25s8Jgmk4mSkpJ6t9bEVujbyIilOnXPF0qLjBBCCNEoTROZI0eO8N5779G1a1eWL1/OI488wmOPPca///1vAHJycgAIDw+v97rw8HDbc380Z84c/P39bbeYmBjHvokmsg29bqTQt45t5FK5jFwSQgghGqNp15LFYmHAgAH83//9HwB9+/Zl7969vP/++0ycOLFZx5wxYwZTpkyxPS4pKWlVyUxBeV3X0gUSGU9Zb0kIIS5GRkaGTIh3lqNHjxIfH8+OHTtISkpqdcezN00TmcjISHr06FFvW2JiIl9//TUAERERAOTm5hIZGWnbJzc3t9GLaTAYMBhab03JmRaZxmtkzn5eZvcVQojGZWRkkJjYnYqKSqed08vLk5SUA602mYmJiSE7O5uQkBCtQ3EKTROZoUOHkpqaWm/bwYMHiYuLA6yFvxEREaxcudKWuJSUlLB582YeeeQRZ4drFxdbIxNgW6ZAWmSEEKIx+fn5VFRU8un/u5XE2FCHny8l4yR3/d+X5Ofna5bI1NTU4O5+ngEjer2tIaC1qK6uxsPj/N97zaVpjcyTTz7Jpk2b+L//+z8OHz7MZ599xgcffMCkSZMAUBSFJ554gpdeeonvv/+ePXv2cM899xAVFcX48eO1DL3ZzoxaulAi415vfyGEEI1LjA2lX7cODr81NVn64IMPiIqKwmKpPyfYuHHjuP/++wH43//+R79+/TAajXTq1IkXXniB2tpa276KovDee+9xww034O3tzcsvv0xhYSF33nknoaGheHp60rVrV9sI4KNHj6IoCjt37rQdY9++fYwZMwY/Pz98fX0ZNmwYaWnWlbwtFguzZ88mOjoag8FAUlISy5YtO+/7Wrt2LYMGDcJgMBAZGcnf/va3ejEPHz6cyZMn88QTTxASEsKoUaOadN2aQtNEZuDAgXz77bd8/vnnXHLJJbz44ovMnTuXO++807bPtGnTePTRR3nooYcYOHAgZWVlLFu27JxVMl1F3TwyQd4X6lqShSOFEMLV3XLLLZw6dYrVq1fbthUUFLBs2TLuvPNO1q1bxz333MPjjz/O/v37+ec//8nChQt5+eWX6x3n+eef58Ybb2TPnj3cf//9zJw5k/379/PTTz+RkpLCe++912hXUlZWFpdffjkGg4FVq1axbds27r//flviMW/ePN544w1ef/11du/ezahRo7jhhhsanXw2KyuL6667joEDB7Jr1y7ee+89Pv74Y1566aV6+/373//Gw8OD9evX8/7777fkMp6X5otGjhkzhjFjxjT6vKIozJ49m9mzZzsxKsepS2QCLrJFRrqWhBDCdQUGBjJ69Gg+++wzrrrqKgD++9//EhISwpVXXsk111zD3/72N9sAl06dOvHiiy8ybdo0nnvuOdtx7rjjDu677z7b44yMDPr27cuAAQMA6NixY6MxzJ8/H39/fxYvXmzrkurWrZvt+ddff53p06fb5mZ79dVXWb16NXPnzmX+/PnnHO/dd98lJiaGf/zjHyiKQvfu3Tlx4gTTp09n1qxZ6HTWNpKuXbvy2muvNeeyNYnmSxS0N4XlTRx+LS0yQgjh0u68806+/vprTCYTAIsWLeL2229Hp9Oxa9cuZs+ejY+Pj+324IMPkp2dTUVFhe0YdQlLnUceeYTFixeTlJTEtGnT2LBhQ6Pn37lzJ8OGDWuwrqakpIQTJ04wdOjQetuHDh1KSkpKg8dLSUkhOTkZRVHq7V9WVsbx48dt2/r373+eq2I/ksg4kcWi2tZauvhRS9IiI4QQrmzs2LGoqsrSpUvJzMxk3bp1thKKsrIyXnjhBXbu3Gm77dmzh0OHDtUrofD29q53zNGjR3Ps2DGefPJJTpw4wVVXXcXUqVMbPL+np6fj3tx5/DFmR5FExokqasxYVOt9v0YWjKwT4Hm6RqZSWmSEEMKVGY1GbrrpJhYtWsTnn39OQkIC/fr1A6Bfv36kpqbSpUuXc251XTSNCQ0NZeLEiXz66afMnTuXDz74oMH9evfuzbp166ipOfcPYz8/P6Kioli/fn297evXrz9nepQ6iYmJbNy4EVVV6+3v6+tLdHT0eWN2BM1rZNqTsiprYZWbTsHgdv5f0IDTxcBVNRaqaswY3fUOj08IIYRj3HnnnYwZM4Z9+/Zx11132bbPmjWLMWPGEBsby80332zrbtq7d+85xbNnmzVrFv3796dnz56YTCaWLFlCYmJig/tOnjyZd955h9tvv50ZM2bg7+/Ppk2bGDRoEAkJCTz99NM899xzdO7cmaSkJBYsWMDOnTtZtGhRg8f761//yty5c3n00UeZPHkyqampPPfcc0yZMuWCyZcjSCLjRKVV1mzYx+hWr2+xIb4GN/Q6BbNFpbCimkh/bZoGhRDCFaRknGzV5xkxYgRBQUGkpqZyxx132LaPGjWKJUuWMHv2bF599VXc3d3p3r07f/7zn897PA8PD2bMmMHRo0fx9PRk2LBhLF68uMF9g4ODWbVqFU8//TRXXHEFer2epKQkW13MY489RnFxMU899RR5eXn06NGD77//nq5duzZ4vA4dOvDjjz/y9NNP06dPH4KCgnjggQd49tlnm3VtWkpRz24baoNKSkrw9/enuLgYPz8/TWPZnlHITe9uICbIk3XTRlxw//4v/sKp8mp+enwYiZHaxi6EEFqqqqoiPT2d+Pj4erUjMrOva2vs5woX//0tLTJOVNe15GM4f31MnQAvd06VV8vIJSGEaERsbCwpKQdkraV2TBIZJyozWRMZX8PFXXbrEOxyGbkkhBDnERsbK4lFOyajlpzI1iJjvLhEJtC2TIG0yAghhBANkUTGiUpNdV1LF5fI+BmtiUxpVe0F9hRCCCHaJ0lknOjsUUsXo26umZJK6VoSQgghGiKJjBPVdS35XmQiU7eftMgIIYQQDZNExomaWux7JpGRFhkhhBCiIZLIOFFTa2R8pUZGCCGEOC9JZJzozKili5tHpq7Yt0RaZIQQQogGyTwyTlTW5BYZqZERQogLycjIcNkJ8Z5//nm+++47du7c2aLjrFmzhiuvvJLCwkICAgIu6jX33nsvRUVFfPfddy06t9YkkXGiuloXKfYVQgj7yMjIoHtidyqduESBp5cnB+y0RMHUqVN59NFHW3ycIUOGkJ2djb+//0W/Zt68ebSFVYokkXGipo5asg2/lq4lIYRoUH5+PpUVlUx8eSIR8REOP19Oeg7/fubf5Ofn2yWR8fHxwcfHp9Hnq6ur8fDwuOBxPDw8iIho2vtvStLTmkmNjBM1vdjXul+ZqRaLxfWzZiGEcJSI+AhiE2MdfmtqsvTBBx8QFRWFxWKpt33cuHHcf//9PP/88yQlJdm233vvvYwfP56XX36ZqKgoEhISANiwYQNJSUkYjUYGDBjAd999h6Ioti6pNWvWoCgKRUVFACxcuJCAgACWL19OYmIiPj4+XHvttWRnZ59zrjoWi4XXXnuNLl26YDAYiI2N5eWXX7Y9P336dLp164aXlxedOnVi5syZ1NRo/4e2JDJOoqrqmRqZi22ROV3sq6pQVi3dS0II4WpuueUWTp06xerVq23bCgoKWLZsGXfeeWeDr1m5ciWpqan88ssvLFmyhJKSEsaOHUuvXr3Yvn07L774ItOnT7/guSsqKnj99df5z3/+w6+//kpGRgZTp05tdP8ZM2bwyiuvMHPmTPbv389nn31GeHi47XlfX18WLlzI/v37mTdvHh9++CFvvfVWE66GY0jXkpNUVJup64r0vcjVrw1uOtz1CjVmldKqWltiI4QQwjUEBgYyevRoPvvsM6666ioA/vvf/xISEsKVV17JunXrznmNt7c3H330ka1L6f3330dRFD788EOMRiM9evQgKyuLBx988Lznrqmp4f3336dz584ATJ48mdmzZze4b2lpKfPmzeMf//gHEydOBKBz585cdtlltn2effZZ2/2OHTsydepUFi9ezLRp05pwRexPWmScpK41Rq9TMLpf3GVXFOWs9Za0b74TQgjRdHfeeSdff/01JpMJgEWLFnH77bej0zX8XdCrV696dTGpqan07t0bo9Fo2zZo0KALntfLy8uWxABERkaSl5fX4L4pKSmYTCZbstWQL774gqFDhxIREYGPjw/PPvssGRkZF4zD0SSRcRLbOksGNxRFuejX1dXJlFRK15IQQriisWPHoqoqS5cuJTMzk3Xr1jXarQTWFhl7cHev34qvKEqjo5Q8PT3Pe6yNGzdy5513ct1117FkyRJ27NjBM888Q3V1tV1ibQlJZJyktIkjlur4SouMEEK4NKPRyE033cSiRYv4/PPPSUhIoF+/fhf9+oSEBPbs2WNr0QHYsmWLXWPs2rUrnp6erFy5ssHnN2zYQFxcHM888wwDBgyga9euHDt2zK4xNJckMk7S1Mnw6shcMkII4fruvPNOli5dyr/+9a/ztsY05I477sBisfDQQw+RkpLC8uXLef311wGa1MJ/PkajkenTpzNt2jQ++eQT0tLS2LRpEx9//DFgTXQyMjJYvHgxaWlpvP3223z77bd2OXdLSbGvkzR1Dpk6skyBEEJcWE56Tqs+z4gRIwgKCiI1NZU77rijSa/18/Pjhx9+4JFHHiEpKYlevXoxa9Ys7rjjjnp1My01c+ZM3NzcmDVrFidOnCAyMpKHH34YgBtuuIEnn3ySyZMnYzKZuP7665k5cybPP/+83c7fXIraFqb1O4+SkhL8/f0pLi7Gz89Pszi+3JrJtP/u5sqEUBbcd+EirTpPf7WLr7Yd5+lRCUy6sosDIxRCiNarqqqK9PR04uPj6315u/rMvs21aNEi7rvvPoqLiy9Y39KaNfZzhYv//pYWGSdp6oKRdXylRUYIIRoVGxvLgZQDLrvW0sX65JNP6NSpEx06dGDXrl1Mnz6dW2+91aWTGHuRRMZJ6mpcmloj4+cpNTJCCHE+sbGxmraOOENOTg6zZs0iJyeHyMhIbrnllnqz7rZnksg4SZnJ2qLi18xRSyWV0iIjhBDt1bRp0zSfeK61klFLTiKjloQQQgj7k0TGSWxdS00etVSXyEiLjBBCtPHxKe2OPX6eksg4SXNbZM4sUSAtMkKI9qtultqKigqNIxH2VPfz/OMsxE0hNTJO0tx5ZGTUkhBCgF6vJyAgwLZWkJeXl90mgxPOp6oqFRUV5OXlERAQgF6vb/axJJFxkjOjlpo6/FpqZIQQAiAiIgKg0YUPhesJCAiw/VybSxIZJ6nrWmp6i4x1/4pqM7VmC2566Q0UQrRPiqIQGRlJWFgYNTXSSu3q3N3dW9QSU0cSGSexrX7dzK4l6zFqCfT2OM/eQgjR9un1ert8AYq2Qf68dwJVVc+0yDSx2NfDTYfR3fpjku4lIYQQoj5JZJygssaM5fQIs6a2yIAU/AohhBCNkUTGCepGLOkU8HRvenOonxT8CiGEEA2SRMYJSs5aZ6k5wwWlRUYIIYRomKaJzPPPP4+iKPVu3bt3tz1fVVXFpEmTCA4OxsfHhwkTJpCbm6thxM1zZsRS8yb8kSHYQgghRMM0b5Hp2bMn2dnZtttvv/1me+7JJ5/khx9+4KuvvmLt2rWcOHGCm266ScNom6e5k+HVOTO7r7TICCGEEGfTfPi1m5tbg5PhFBcX8/HHH/PZZ58xYsQIABYsWEBiYiKbNm3i0ksvdXaozVa38nVTlyeo4+cpLTJCCCFEQzRvkTl06BBRUVF06tSJO++8k4yMDAC2bdtGTU0NI0eOtO3bvXt3YmNj2bhxo1bhNktzF4ysY6uRqZQWGSGEEOJsmrbIDB48mIULF5KQkEB2djYvvPACw4YNY+/eveTk5ODh4UFAQEC914SHh5OTk9PoMU0mEyaTyfa4pKTEUeFftOYuGFmnbu4ZaZERQggh6tM0kRk9erTtfu/evRk8eDBxcXF8+eWXeHp6NuuYc+bM4YUXXrBXiHZR2sIaGVuxr0laZIQQQoizad61dLaAgAC6devG4cOHiYiIoLq6mqKionr75ObmnneBqRkzZlBcXGy7ZWZmOjjqC2vpqCU/z7piX2mREUIIIc7WqhKZsrIy0tLSiIyMpH///ri7u7Ny5Urb86mpqWRkZJCcnNzoMQwGA35+fvVuWiutamHXktTICCGEEA3StGtp6tSpjB07lri4OE6cOMFzzz2HXq/nT3/6E/7+/jzwwANMmTKFoKAg/Pz8ePTRR0lOTnapEUtghxoZmUdGCCGEaJCmiczx48f505/+xKlTpwgNDeWyyy5j06ZNhIaGAvDWW2+h0+mYMGECJpOJUaNG8e6772oZcrOUNXPl6zp1iUyJJDJCCCFEPZomMosXLz7v80ajkfnz5zN//nwnReQYzV35uo6fLFEghBBCNKhV1ci0VWdGLTWz2Pf066prLZhqzXaLSwghhHB1ksg4QUsnxDv7dVInI4QQQpwhiYwTtLTYV69TbK+VREYIIYQ4QxIZB1NV9ax5ZJpfkmQr+JUh2EIIIYSNJDIOVlVjwWxRgea3yIAMwRZCCCEaIomMg9UtK6BTwMtD3+zj1BUKl8rIJSGEEMJGEhkHO3tWX0VRmn0cP2mREUIIIc4hiYyDlbVw6HUdX5lLRgghhDiHJDIO1tIRS3Vkdl8hhBDiXJLIOFhL55CpIzUyQgghxLkkkXEwe7XI+HlKjYwQQgjxR5LIOFhLF4ysY6uRkXlkhBBCCBtJZBysrgXFr4WJjIxaEkIIIc4liYyD2a1rqa5GxiQtMkIIIUQdSWQcrNSWyLR0+HXdEgXSIiOEEELUkUTGwcpk1JIQQgjhMJLIOJhtwUg7zSNTWlWLqqotjksIIYRoCySRcTB7tcj4eVpbZGotKlU1lhbHJYQQQrQFksg4WN2SAr4tTGS8PfTolPrHFEIIIdo7SWQczF6jlhRFsR1D6mSEEEIIK0lkHMxWI9PCFhnrMeoWjpSRS0IIIQRIIuNQqqqeqZFp4fBrOFMnI5PiCSGEEFaSyDiQqdZCrcU6wqilxb5w9lwy0rUkhBBCgCQyDlXXcqIo4OWub/HxZJkCIYQQoj5JZByorijXx+CGrm7IUQvIpHhCCCFEfZLIOJC9JsOrIy0yQgghRH2SyDiQvSbDq3Nm1JK0yAghhBAgiYxDldppDpk6vtIiI4QQQtQjiYwDnWmRafnQazh7+LW0yAghhBAgiYxD2btG5szwa2mREUIIIUASGYcqtdM6S3WkRkYIIYSoTxIZB5IaGSGEEMKxJJFxIHuPWvKTFhkhhBCiHklkHMheK1/X8fN0sx3XcnrpAyGEEKI9k0TGgepaZOxVI+N/etSSqkr3khBCCAGSyDjUmRoZ+wy/NrjpMbpbf2TFsnCkEEIIIYmMI9m7RQbOtMpIIiOEEEJIIuNQpabTi0ZKIiOEEEI4hCQyDmRrkbFTsS/IyCUhhBDibJLIOIiqqmdGLUmLjBBCCOEQ9vuGbYcyMjLIz89v8Llqs0qN2TpEOu3APrLd7ZMz1laWArD/8FG2uzV87uYICQkhNjbWbscTQgghnEESmWbKyMige2IilRUVDT6v8/In5tFFqKqFYZcOAuwz70vgVQ/hN+AG/vHPf/HSr/+2yzEBPL28OJCSIsmMEEIIl9JqEplXXnmFGTNm8PjjjzN37lwAqqqqeOqpp1i8eDEmk4lRo0bx7rvvEh4erm2wQH5+PpUVFdw5/e+Ex3Y+5/myGlieDe46hSnzv7bbefcX6UkpgaSrb6LfbePscszcjDQWvfo0+fn5ksgIIYRwKa0ikdmyZQv//Oc/6d27d73tTz75JEuXLuWrr77C39+fyZMnc9NNN7F+/XqNIj1XeGxnorv2PGd7bkkVZGdi9HAnums3u53vZEYhKSX5uHn5E9010m7HFUIIIVyR5sW+ZWVl3HnnnXz44YcEBgbathcXF/Pxxx/z5ptvMmLECPr378+CBQvYsGEDmzZt0jDii1NdawHAw82+l9jorgfAdPr4QgghRHumeSIzadIkrr/+ekaOHFlv+7Zt26ipqam3vXv37sTGxrJx48ZGj2cymSgpKal300K1+XQio7fvJTacToxMtWa7HlcIIYRwRZp2LS1evJjt27ezZcuWc57LycnBw8ODgICAetvDw8PJyclp9Jhz5szhhRdesHeoTeaoFhmDm7VFpqpGWmSEEEIIzVpkMjMzefzxx1m0aBFGo9Fux50xYwbFxcW2W2Zmpt2O3RQOS2TcpUVGCCGEqKNZIrNt2zby8vLo168fbm5uuLm5sXbtWt5++23c3NwIDw+nurqaoqKieq/Lzc0lIiKi0eMaDAb8/Pzq3bRQ17VksHeNjNuZGhlVtc+QbiGEEMJVada1dNVVV7Fnz5562+677z66d+/O9OnTiYmJwd3dnZUrVzJhwgQAUlNTycjIIDk5WYuQm6SuGNfuNTKnW2RU1Zos1XU1CSGEEO2RZomMr68vl1xySb1t3t7eBAcH27Y/8MADTJkyhaCgIPz8/Hj00UdJTk7m0ksv1SLkJnFU15KbTkGvKJhVFVONJDJCCCHat1Yxj0xj3nrrLXQ6HRMmTKg3IZ4rsI1asnMioygKBncdFdVmGYIthBCi3WtVicyaNWvqPTYajcyfP5/58+drE1ALVDuoawmsdTcV1WaqaqTgVwghRPum+TwybZWjupZAJsUTQggh6jTrW7ZTp06cOnXqnO1FRUV06tSpxUG1BY4atXT2MWUIthBCiPauWd+yR48exWw+90vUZDKRlZXV4qDaAke2yNQV+JpkUjwhhBDtXJNqZL7//nvb/eXLl+Pv7297bDabWblyJR07drRbcK7MoTUyp4dgV0mLjBBCiHauSYnM+PHjAevImYkTJ9Z7zt3dnY4dO/LGG2/YLThX5tAaGWmREUIIIYAmJjIWi/WLMz4+ni1bthASEuKQoFxdrcWC+fSsuw7pWpIWGSGEEAJo5vDr9PR0e8fRplSfNZrI3UHDr0FGLQkhhBDNnkdm5cqVrFy5kry8PFtLTZ1//etfLQ7MlZ1dH6NTFLsf3zb8WrqWhBBCtHPNSmReeOEFZs+ezYABA4iMjERxwJe1K3NkfQzI8GshhBCiTrMSmffff5+FCxdy99132zueNsG2PIEDupXgzPDrKmmREUII0c4165u2urqaIUOG2DuWNsPRLTJG9zMtMurpomIhhBCiPWrWN+2f//xnPvvsM3vH0mY4vmvJ2iJjUaHWIomMEEKI9qtZXUtVVVV88MEHrFixgt69e+Pu7l7v+TfffNMuwbkqk4O7ltz1CooCqgpVNWaHjIwSQgghXEGzEpndu3eTlJQEwN69e+s9J4W/jm+RURQFo5ueyhozploLvg45ixBCCNH6NSuRWb16tb3jaFMcnciAdVK8yhozVTUyckkIIUT7JX0SDuCMRMbz9FwyldWSyAghhGi/mtUic+WVV563C2nVqlXNDqgtqBt+bXBg7YotkZEWGSGEEO1YsxKZuvqYOjU1NezcuZO9e/ees5hke+SUFhkPSWSEEEKIZiUyb731VoPbn3/+ecrKyloUUFvgzK6lqmqZFE8IIUT7Zddv2rvuuqvdr7MEjh9+DWdaZCpqah12DiGEEKK1s+s37caNGzEajfY8pEsyne7uMbg7oUVGlikQQgjRjjWra+mmm26q91hVVbKzs9m6dSszZ860S2CuzHS6a6luBl5HkFFLQgghRDMTGX9//3qPdTodCQkJzJ49m2uuucYugbkqVVXPSmSk2FcIIYRwpGYlMgsWLLB3HG1G3dBrcHAic9bwa1VVZUZlIYQQ7VKzEpk627ZtIyUlBYCePXvSt29fuwTlykyna1b0OgU3JxT7mi0qNWYVDzdJZIQQQrQ/zUpk8vLyuP3221mzZg0BAQEAFBUVceWVV7J48WJCQ0PtGaNLqetWMjqwNQbAXa/DTadQa1GprDE7dKi3EEII0Vo169vv0UcfpbS0lH379lFQUEBBQQF79+6lpKSExx57zN4xupS6tY8cWehbxygFv0IIIdq5ZrXILFu2jBUrVpCYmGjb1qNHD+bPn9/ui31thb4OHHpdx8tDT5mpVgp+hRBCtFvN+ra1WCy4u7ufs93d3R2LpX3Pa2KqrWuRcXwiY5T1loQQQrRzzfq2HTFiBI8//jgnTpywbcvKyuLJJ5/kqquusltwrsgZc8jUsQ3Blq4lIYQQ7VSzEpl//OMflJSU0LFjRzp37kznzp2Jj4+npKSEd955x94xupS6UUvO6FqSFbCFEEK0d82qkYmJiWH79u2sWLGCAwcOAJCYmMjIkSPtGpwrcmbXkszuK4QQor1r0rftqlWr6NGjByUlJSiKwtVXX82jjz7Ko48+ysCBA+nZsyfr1q1zVKwu4czwa+d1LVVJi4wQQoh2qkmJzNy5c3nwwQfx8/M75zl/f3/+8pe/8Oabb9otOFdUl1R4SNeSEEII4XBN+rbdtWsX1157baPPX3PNNWzbtq3FQbkyZ6yzVEe6loQQQrR3Tfq2zc3NbXDYdR03NzdOnjzZ4qBcWbUGXUvSIiOEEKK9alIi06FDB/bu3dvo87t37yYyMrLFQbmyKg2KfU21FswW1eHnE0IIIVqbJn3bXnfddcycOZOqqqpznqusrOS5555jzJgxdgvOFZ0Zfu34FhmDu466pSKl4FcIIUR71KTh188++yzffPMN3bp1Y/LkySQkJABw4MAB5s+fj9ls5plnnnFIoK7AbFGpPd0y4owWGZ2iYHTXU1ljprLGjLehRYuZCyGEEC6nSd984eHhbNiwgUceeYQZM2agqtYvbUVRGDVqFPPnzyc8PNwhgbqCujlkAKetRu1Zl8hIwa8QQoh2qMl/wsfFxfHjjz9SWFjI4cOHUVWVrl27EhgY6Ij4XErdiCUPNx06RbnA3vZh9NBBhRT8CiGEaJ+a3RcRGBjIwIED7RmLy7PVxzipNQZkLhkhhBDtm/O+cRvw3nvv0bt3b/z8/PDz8yM5OZmffvrJ9nxVVRWTJk0iODgYHx8fJkyYQG5uroYRn58zlyeo4+VhzUUrpGtJCCFEO6RpIhMdHc0rr7zCtm3b2Lp1KyNGjGDcuHHs27cPgCeffJIffviBr776irVr13LixAluuukmLUM+r6oa580hU8fbYD1XuanWaecUQgghWgtNh7mMHTu23uOXX36Z9957j02bNhEdHc3HH3/MZ599xogRIwBYsGABiYmJbNq0iUsvvVSLkM/L1iLjhOUJ6vicHqlUJomMEEKIdkjTFpmzmc1mFi9eTHl5OcnJyWzbto2ampp6K2p3796d2NhYNm7c2OhxTCYTJSUl9W7OcmZ5Ame2yFgTGWmREUII0R5pnsjs2bMHHx8fDAYDDz/8MN9++y09evQgJycHDw8PAgIC6u0fHh5OTk5Oo8ebM2cO/v7+tltMTIyD38EZzlxnqY60yAghhGjPNE9kEhIS2LlzJ5s3b+aRRx5h4sSJ7N+/v9nHmzFjBsXFxbZbZmamHaM9P1ON84t96xKZqhoLtWaL084rhBBCtAaaTwXr4eFBly5dAOjfvz9btmxh3rx53HbbbVRXV1NUVFSvVSY3N5eIiIhGj2cwGDAYDI4Ou0G2FhknLE9Qx+CmQ69TMFtUyqvN+HtqnpsKIYQQTtPqvvUsFgsmk4n+/fvj7u7OypUrbc+lpqaSkZFBcnKyhhE2TouuJUVRznQvVUn3khBCiPZF0xaZGTNmMHr0aGJjYyktLeWzzz5jzZo1LF++HH9/fx544AGmTJlCUFAQfn5+PProoyQnJ7fKEUugzaglsHYvFVfWSJ2MEEKIdkfTRCYvL4977rmH7Oxs/P396d27N8uXL+fqq68G4K233kKn0zFhwgRMJhOjRo3i3Xff1TLk86qqcf6oJZC5ZIQQQrRfmiYyH3/88XmfNxqNzJ8/n/nz5zspopap1qBrCc4auVQtiYwQQoj2pdXVyLgqVVWpOt215MyZfeGsuWSkRkYIIUQ7I4mMndSYVVTVel+LGhmQuWSEEEK0P5LI2EnV6Tlk9DoFN53i1HNLIiOEEKK9kkTGTipPJzKe7noURZtEprzajFrXLCSEEEK0A5LI2Eldi4zRyd1KcKZGxmxRbSOnhBBCiPZAEhk7qaw+0yLjbHqdYjuvdC8JIYRoTySRsRNb15KH8xMZOKt7SRIZIYQQ7YgkMnZydo2MFuomxZMWGSGEEO2JJDJ2UmmrkdG2RUYSGSGEEO2JJDJ2UlVtLbLVrkVGupaEEEK0P5LI2ElrqZGRFhkhhBDtiSQydlKlcdeSn6c7ACWVksgIIYRoPySRsZMKDYdfAwScTmSKK2uwyKR4Qggh2glJZOzg7AUjNetaMrqhVxTMqkqZLB4phBCinZBExg5MtRbbgpFazOwLoFMU/DytdTJFlTWaxCCEEEI4myQydlBX6Ouh1+Gm0+6SBnh5AFBUUa1ZDEIIIYQzSSJjB1qus3S2ujoZaZERQgjRXkgiYwe2dZY0qo+pE+B1OpGpkERGCCFE++CmdQBtgdaz+tbxrxu5JImMaCUyMjLIz8936jkVczWeJWkollpQFExeUdQag1p0zJCQEGJjY+0UoRDCniSRsYO6RMZL40Qm8HSNTN0QbJ2iaBqPaN8yMjJITOxORUWlU843LsGNh/q7M7yjG17u9X/3U/PNLE+rZf6WGg6esjT52F5enqSkHJBkRohWSBIZO6hbnsCocdfSH4dg102SJ4QW8vPzqaio5NP/dyuJsaEOO4+xpojoki34VefYttXoDJgVDxRUPMxlJIToSQjR89hgA8WGDpzw7Uule+BFHT8l4yR3/d+X5OfnSyIjRCskiYwdaL3ydZ26IdiFFTUUVdZIIiNahcTYUPp16+CYg586DPt/AkstKHqIHghhPXD3DsW9rkWypgqKMyBnN5w6jL8pC3/TCYjqC/GXg5vRMbEJIZxCEhk7aC2JDFiHYBdW1FBUUU1skJfW4QjhODl7IPVHQIWAjtDtWvAMOHc/dyOEdLPeKgrg6K9w8gCc2G5NhBKuh8A4JwcvhLAXGbVkB1qvs3Q2GYIt2oW8FEhdCqgQfgn0uqXhJOaPvIKgx3jofTt4BoKpBHZ/DkdWg9r02hkhhPYkkbGDSo3XWTqbv5eMXBJtXFnu6SQGiOpnbVHRNfH/XmBH6H8fRPaxPs7cDLsXQ3W5XUMVQjieJDJ2YOta0rjYF85qkZFERrRF1RWw92trTUxgJ+gyEpo7Ok/vAd1GW1to9B5QlAHbF0JZnj0jFkI4mCQyLWRRrWstQetokQk4ewi2RVbBFm2IqsLBn6zdQZ6BkHgDKHb4CAvtDn3vAc8gMJXCzk+h8GjLjyuEcApJZFqo+qxudYPGSxQA+BndcNdbh2AXyJpLoi05eQBOHbImLz1utBbx2ot3iDWZ8Y8BczXs+dJaTCyEaPW0/+Z1cdVma7O20V3XKiagUxSFUF8DACdLTRpHI4Sd1FTC4V+s92OTwSfM/udwN0Lv2yCsh7XwN3UpHFuPbWl7IUSrJIlMC5lOt8i0hm6lOmG+1r9U80okkRFtRNpKqKkArxBrIuMoOjfoPhZiLrU+PrqOmJLf0f5PFCFEYySRaaG6RKY1DL2uE366RSa3tErjSISwg9IcyN1rvZ8w2ppsOJKiQKfh0PUaQCG04iAf3WAE1ezY8wohmkUSmRaqOt215O3ReuYWPLtrySLN4sKVqSqkrbLeD+sJfg6aIbghUf0gcSwqCvf39aDj9lfAIsmMEK2NJDItZEtkDK2nRSbQ2wN3vUKtRaWwXAp+hQsrSLMuL6DorcsJOFtYD9IDhlFjVgnKWgFLnpCaGSFaGUlkWqjq9B9o3obW0yKjUxRCfKTgV7g41WKdcResaygZ/TUJo8gzjj99XYmKDrZ/AsufkWRGiFZEEpkWqmyFXUsA4acLfnMlkRGuKi8FKk5ZF3WMvVTTUL5OqeVY0lTrg03zYcM7msYjhDhDEpkWOtMi03q6lgDC/KwtMnlS8CtckWqBjA3W+9GDWsUK1QWxo+Gal60PfpkJ+/+nbUBCCEASmRY7UyPTulpkws4q+FWlGVy4mpOpp1tjDNChv9bRnJE8CQY+aL3/zUOQtU3beIQQksi0iM6Nakvr7FoK9PbATadQY1YplHWXhCtR1TOtMR0GWpOZ1kJR4NpXrEOza6vgi3ug/JTWUQnRrkki0wJ670AAdIp1Zt/WRKcotu6lrMJKjaMRoglOHYLyk6A3QIcBWkdzLr0bTPgIgjpDyXH45s8yLFsIDbWub18Xo/cNAqzdSkorWJ7gj+KCvQE4eqpc40iEaILMzdZ/o/rZdz0lezL6w22fgruXdZ6bta9pHZEQ7ZYkMi2g9z6dyLSybqU68acTmczCCmotlgvsLUQrUHwcSrKs88a0ptqYhoT3gDFzrfd/fQ0yt2gajhDtlaaJzJw5cxg4cCC+vr6EhYUxfvx4UlNT6+1TVVXFpEmTCA4OxsfHhwkTJpCbm6tRxPXpfaxdS61txFKdEB8PvD301JhVThTJ6CXhAo7/bv03vCcYfLSN5WL0uc260KRqgW8fgmpp/RTC2TRNZNauXcukSZPYtGkTv/zyCzU1NVxzzTWUl5/5MHjyySf54Ycf+Oqrr1i7di0nTpzgpptu0jDqM9x8WneLjKIoZ7qX8uUDVrRyFQWQf9B6P3qQtrE0xejXwDcKCo7AzzO1jkaIdkfTb+Bly5bVe7xw4ULCwsLYtm0bl19+OcXFxXz88cd89tlnjBgxAoAFCxaQmJjIpk2buPRSbSfJ0vucqZFprTqGeLE/u4Sjp8q5nFCtwxGicVmnu2aCOoN3iLaxNIVnAIyfD/+5EbZ+DL1uhrghWkclRLvRqmpkiouLAQgKsiYI27Zto6amhpEjR9r26d69O7GxsWzcuLHBY5hMJkpKSurdHOVMItM6u5YAYoO80ClQWFFDUYWsuyRaqZpKyDm9wnWMC7XG1Ok8AvpNtN5fMgXMMuWBEM7SahIZi8XCE088wdChQ7nkkksAyMnJwcPDg4CAgHr7hoeHk5OT0+Bx5syZg7+/v+0WExPjsJhbe7EvgMFNT5S/JwDp0r0kWqvsXWCpAe8w8I/VOprmGfk8eAXDyRTYOF/raIRoN1pNIjNp0iT27t3L4sWLW3ScGTNmUFxcbLtlZmbaKcJznSn2bb2JDECnUGudzJ6sYpnlV7Q+qgVObLfejx5gnXTOFXkFwdUvWu+vfRWKHPfZI4Q4o1UkMpMnT2bJkiWsXr2a6Oho2/aIiAiqq6spKiqqt39ubi4RERENHstgMODn51fv5gi1FtU2IV5r7loC6BHlh4ebjsKKGo5Iq4xobfIPgqnEOidLWA+to2mZpDsgdgjUVMDK2VpHI0S7oGkio6oqkydP5ttvv2XVqlXEx8fXe75///64u7uzcuVK27bU1FQyMjJITk52drj1FFVZ52VRUPF0b92JjMFNT+8O/gBsO1aocTRC/MHxrdZ/I5NA17pbNy9IUeDa/7Pe3/MlnNihbTxCtAOaJjKTJk3i008/5bPPPsPX15ecnBxycnKorLROqe/v788DDzzAlClTWL16Ndu2beO+++4jOTlZ8xFLhaeXvTbqaZWz+v5RUkwAekUhu7iKE0WyZIFoJUqzrdP8KzrrTL5tQVRf6HWr9f7PM61rRwkhHEbTROa9996juLiY4cOHExkZabt98cUXtn3eeustxowZw4QJE7j88suJiIjgm2++0TBqq8JKa4uMUe8aH1LeBje6R/oCsCHtFGaLa8Qt2ri61pjQRNeYAO9iXTXTulbU0XVwcLnW0QjRpmnetdTQ7d5777XtYzQamT9/PgUFBZSXl/PNN980Wh/jTIWVZ1pkXEX/uEDcdApZRZWsSMmVwl+hLVOZdYQPWIt825KAWBj8F+v91S9Jq4wQDtQqin1dUUGVa7XIAAR6eXBdr0gUBQ7klLLqQB5lVbVahyXaq+wd1hFLfh3AN1LraOzvsifBwxdy9sCBJVpHI0SbJYlMM9W1yHi6UCIDEB/izdWJ4QDsPVHCx+vTWZvrRtDVD/PDwXJyimVNJuEEltozhbDRA7WNxVG8gs60yqx5FWThViEcQhKZZhrUwUjxhi8IMbhWIgOQGOnH9b0iifI3ApBv0uHbbwwLdpZwxd9X89KS/RSWyyzAwoFy91mHKBv8IKSb1tE4TvIka6tMrrTKCOEoksg004AoI0Xr/kOo0fUSGYAuYT7cMiCGe4d0pF9QLcUbvyQh2B1TrYWPfktnwvsbqKiWbifhAKp6ZpXrDgOsI5baKq8guPRh6/21r0mtjBAO0IY/QcTF8Pd0J97HQtGvn/B/I4JZeN9AwnwNHDlZzis/HdA6PNEWFaRBxSnrqJ7IPlpH43iX/hXcva2tMmkrL7y/EKJJJJERNoqiMDwhjDdutX65fLLxGL8ePKlxVKLNydxs/TcyCdwMmobiFF5B0P/0gpLr52kbixBtkCQy4hzDuoYyMTkOgKf/u4vKarPGEYk2oyQbijOt3UnR/bWOxnku/at11uL0XyFru9bRCNGmSCIjGvS30Yl0CPAkt8TEsn3ZWocj2oq62piwHtZC3/YiIAYuudl6X1plhLArSWREgzw99Nw2MAaA/247rnE0ok2oLIKTp+uuogdpGoomhj5m/Tfleyg8qmkoQrQlksiIRt3UrwNgXdLgeGGFxtEIl5e1BVAhMB58wrSOxvnCe0LnEdZJALd8rHU0QrQZLr7UrHCk6EAvhnQOZkPaKb7dnsWjV3XVOiThqmoqIXu39X57bI2pM+ghSFsF2z+B4TPAw0vriBxOVVXSS9LZnrudPfl7yCrN4kT5Ccpryqm11OKmc8Pf4E+gIZBYv1ji/OJIDErkkpBL8Df4ax2+cAGSyIjzurl/NBvSTvHf7ceZPKKLS6z0LVqhEzvAUgPeoRDYUetotNP1GgiIg6JjsPe/0O8erSNymKyyLL4//D1LjiwhozTjvPsWVBWQTjrb8+oXQncJ6MKw6GEM6zCMpLAk3HXujgxZuChJZMR5XXtJBDO/28uxUxVsPVbIwI5BWockXIzOUgPHt1gfxAyG9pwM6/Qw8M/wy0z4/QPoe3ebux7HSo7xwe4PWHpkKWbVOuLRoDfQK6QXfcP6Eu8fT5RPFD7uPrjr3Kmx1FBSXUJ+ZT7HSo5xpOgI+07tI6M0g8NFhzlcdJgFexfg7e5NcmQyV8ddzfCY4Xi5t/3WLHFxJJER5+Xl4caonhF8syOLlSl5ksiIJgutOAC1leAZaB2t1N71vQtWv2xdTDJzM8ReqnVEdlFRU8H7u97nk/2f2BKYwZGDGdd5HFfFXtXkxKOgqoBNJzbxW9ZvrD+xnoKqAlZkrGBFxgo83TwZHj2c0fGjGdphKB56D0e8JeEiJJERF3RZ1xC+2ZHFhrR8rUMRLsbXA8LL9lsfxA1t28sRXCyvIOh1C+z4D2z+p1MTmYyMDPLz7f//OLU8lQ8zP+RkjXUCzT6+fRgXNo7OXp2hGA7sad4s4RFEcLP3zdzU5SaOVR5jW8k2NhVv4mT1SX46+hM/Hf0JPw8/RsePZkynMfQJ7SPd3+2QJDLigoZ0DgFgb1YxxRU1+HtJP7W4OI8N9sBNrQbPIGmNOdugh6yJTMr31kkC/SIdfsqMjAy6J3ansqLSrscNGR1C+M3hKHqF6lPVZP8nm70797KIRXY9z9kCEgOY/M5k1p9az8nKk3yR+gVfpH5BrG8sYzuPZUynMUT7Rjvs/KJ1kURGXFCEv5FOod4cOVnOpvRTjOoZoXVIwgXoq4t5Kvn0EgTSGlNfZG+ITYaMjbBtIVw5w+GnzM/Pp7KikokvTyQivuX/h82qmV3mXWRbrBNmRumi6BXRC7dpjv1ayUnP4d/P/JsbA2/k+Suf5/ec3/kh7QdWZKwgozSD+TvnM3/nfPqF9eOGzjdwTcdr8PXwdWhMQluSyIiLMrRzCEdOlrPhcL4kMuKiRKb+m0BPhQq3QLzCErUOp/UZ9ODpRGYBDHsK3JxT5xERH0FsYmyLjmEym1iWvozs8mx0io7Loi6jR3APp3fr6HV6kqOSSY5K5tmaZ1mRsYLv077n9+zf2Z63ne1525nz+xyujLmSsZ3HkhyVLCOf2iBJZMRFGdolmP9sOsaGtFNahyJcwcmDhB79HwBZfv3pKq0x50q8AXwioCzH2sXU62atI7oolbWV/JD2A6eqTuGh8+Da+Gvp4NNB67Dwcvfihs43cEPnG8gpz2HpkaX8kPYDacVpLDu6jGVHlxFkDOK6+Ou4ofMNdA/qLvU0bYQkMuKiXNopGEWBQ3ll5JVUEeZn1Dok0Zr9/CyKauH71Bqihzu+/sMl6d1hwP2w5v9gy0cukciYzCaWHFnCqapTeLp5MqbTGEI8QzSJJSUl5bzP96UvSTFJHA09yobCDWws2khBVQGfpnzKpymf0sHQgaGBQ7nU/1KCPYKdFHV9ISEhxMa2rHVMSCIjLlKAlwc9o/zYm1XChrRTjO+r/V9gopVK/QkOLUdV9Ez9uYzFw7UOqBXrdw+sfdXaxZSXAq24C67aXM2SI0vIr8zH082TcZ3HEWgMdHocJfklANx1111Ne6EefC/xJWBIAL79fMkiiy9zvuTLnC+pPFpJybYSSraXYMoyOSDqhnl6eXIg5YAkMy0kiYy4aEM7h5xOZPIlkRENqyyCJU8CkNfpZg4VfKhtPK2dXyQkjIYDS6xFv6Nf1TqiBllUCz8f+5m8ijwMegNjO43VJIkBqCi1rvs27ulxdO/bvVnHqFFryLZkk2XJokAtwLOjJ54dPQmfEI4XXoTqQgnRhRCsBOOuOKampq5oOT8/XxKZFpJERly0gR2D+OevR9ieUaR1KKK1+mUmlGZDUGdOdL8PkETmgvrfZ01kdn0OI58Hd0+tI6pHVVXWHV9HZmkmbjo3xnQaQ7CnNl0xZwuJCWlR0XJnOgPWifyOlRwjvSSd46XHqVArOGY5xjHLMRQUwr3C6eDTgXDvcMK9wjG6Sbd6ayOJjLhoSbEBAKSdLKOkqgY/o1T/i7McXmFdDBHghndQCwzaxuMqOo+AgFgoyoB930LSHVpHVM+uk7vYX2Cd1HBk7EjCvNrWyuVe7l4kBieSGJxItbmarLIsjpceJ7M0k+LqYnIqcsipyLHtH2AIINwrnBDPEII9gwk2BktyozFJZMRFC/ExEBPkSWZBJbszi7msqzZFfqIVKsqErx+03h/4Z+g4FAq2n/81LuZCxaUtER5xNR2KPqZs7T84aGled8mFNCf+46XH2ZS9CYChUUOJ94+3d1itiofeg3j/eNv7LKku4XjpcXLKrclMsamYIlMRRaYiUgtTba/zdvMmwBiAv8Effw9//A3++Hn44Wfwu+Bwb0f+XjmL1kXLksiIJkmKCSSzoJKdmYWSyAirWhN8eQ9UFkBEb7jmJa0jsqvsglIUmlFc2gTh3gqZT/rgU7iPiaMHsjfP4pDzKJwplr2QkuoSfj72Myoq3QO70yukl0Nias38PPzoEdyDHsHWWakrayvJrcglryKPgsoCTlWdoqS6hPLacsrLyskqyzrnGN5u3vgZ/M5JcgryCwDH/l45i9ZFy5LIiCZJigngh10n2JlZpHUoojWwWOCHJ+DEdjAGwG3/aXU1Hi1VVFaFCvzjr9eQ3Lurw85TVriWwKoMVj7Vn+P+g+x+/I27DzH53Z9txbLnY7aYWX50OSaziVDPUIZFD5M5VwBPN086+nWko19H27ZqczUFVQUUmYooMZVQXF1MiamEouoiqs3V1iSntpzs8uz6B/OD7vO646v3JcovCn+dP/6KP554utS1bg1Fy5LIiCZJigkAYEdGEaqqutR/OGFnqgo/ToVdn1mXH5jwEQR21Doqh+kSFUi/bg4crVeQDHsyCDMdJazzGOs8M3Z0sgmLRW7M3kh+ZT5GvZFRHUfhppOvisZ46D2I8I4gwvvcGc+raqtsiU1xdTHFpjP3K2srcfN3o5JK0ixpcLoRztPNkw7eHejg04E4vzi83b2d/I5cj/x2iibpGeWHu17hVHk1xwsriQny0jokoQWLGZb/P9j6MaDA+Peh69VaR+XaAjtaW7WqiuBkirWbTgPpxensyd8DwIjYEbJOUQsY3YwY3YyEe4Wf89zGnzby9Ydfc9W0q/Ds4MnJypMUVBVQWVvJ4aLDHC46DEC4VzhdArrQNbArnm5tq7XTXiSREU1idNeTGOnH7uPF7MgskkSmPaosgm8ehEM/Wx/f8Db0uU3TkNoERYHIPpC+Fk7s1CSRKasuY1XmKgD6hPYhzi/O6TG0F3pVT2V6JeE14fSL6QdYu/RyK3LJKssiozSDvIo8cityya3IZeOJjcT7x9M7tDfhXuHSGn4WSWREkyXFBLD7eDE7M4q4oU+U1uEIZzr6G3z/GBSkgZsRbvgH9L5F66jajojecHQdlJ6AslzwOfcveUdRVZU1x9dQba4mzDOMwRGDnXZuYaXX6YnyiSLKJ4qBEQMprynnSNERUgtTOVl5krTiNNKK0wjzCmNA+ABifWMloQFkJTfRZH1PzyezM7NQ20CE8xQetQ6vXni9NYnxj4H7l0sSY28e3hDSzXo/e6dTT51SkEJmaSZ6Rc+I2BHodXqnnl+cy9vdm16hvbi5283c2u1Wugd1R6/oyavI48f0H/n28LfnFhG3Q5LIiCZLirFOTb73RAk1ZscMExWtgLkG0lZbh1a/3Rf2fAko1oUO//IrRCVpHWHbFJlk/Td3H5irnXLK0upSNpzYAMDgyMGaLT8gGhfsGcyVMVdyV+Jd9Antg17Rk1uRy3eHv2PFsRWU15RrHaJmpGtJNFlckBe+RjdKq2o5mFtKzyh/rUMS9lJTBUfWQMr3kPojVJ7V6tZ5BIx4Fjr01yy8diEgDjwDrdc+L8VaN+NAqqqyOnM1NZYaIrwi2uV8Ma7Ey92LIVFD6BPahy05W0gpSOFQ0SGOlRwjOSqZxKDEdtfdJImMaDKdTqF3tD/rD59iz/FiSWRcXU0lHFwGKT/AweVQXXbmOa9gSBwLgx6C8J7axdieKApE9IH0NdbuJQcnMvtO7SOrLAs3nRsjYkegU6Sh3hV4u3szPGY4PYN78uvxX8mrzGPt8bUcKTrClbFXtqth25LIiGbp1SGA9YdPsTurmNu1DkY0z8mDsOVD2P0FVBWf2e4bBYljIPEGiE0GvXxMOF1ELzj6q3UBztIc8D13jhJ7KDYVszF7IwCXRl6Kv0H+KHE1oV6h3Nj1Rnaf3M3vOb+TWZbJlwe/ZETMiHYz6kw+oUSz9I62fuDtOV58gT1Fq1OQDmtesda8qKdrnPxjoOeN1uSlQ3/QyV/lmvLwhpAE63wy2TvB91q7n0JVVVZlrqLWUkuUTxSXBF9i93MI59ApOpLCkoj1i2XFsRWcqjrFj+k/0j+8PwPDB7b5riZJZESz9OpgTWQO5JRgqjVjcJMRDq2exQKb34MVL4DZZN2WcJ11kcdOV0ry0tpEJlkTmbz91p+Pm31XE9+dv5uc8hzcde5cGXNlm/+yaw+CjEHc1PUmNp7YyN5Te9mWu42TFScZGTcSg77trkYvn1yiWaIDPQn0cqfGrJKaU6p1OOJCSnPhkxuss/GaTRB/BTy4Gv70OXS5SpKY1iggFjyDrCOX8uy7QnJhVSGbszcDMCRqCH4efnY9vtCOm86NYdHDGBEzAr2iJ6M0g+8Of0fZ2bVvbYx8eolmURSFXtEBAOyS7qXW7eRB+GikdaI1d28YOw/u+R906Kd1ZOJ86mb6BcjeYbfDqli7lMyqmRjfGBKDEu12bNF6JAQlcGOXG/Fy86KgqoBvDn9DQVWB1mE5hCQyotl6d6irkynSNhDRuMzf4eOroTgDgjrBw+ug/73WL0nR+kX0AkVvneW31D4Tn+X65JJXkYeHzoPh0cOlS6kNqysEDjAEUF5TzreHv+VE2Qmtw7I7SWREs/U6XfC7W1pkWqfcffDpzdZFCDsMgAd+geDOWkclmsLdC0ITrPdP7Gzx4QwdDJzws36RDe0wFB8PnxYfU7Rufh5+3NjlRiK8Iqg2V/PDkR9IK0rTOiy70jSR+fXXXxk7dixRUVEoisJ3331X73lVVZk1axaRkZF4enoycuRIDh06pE2w4hx9TnctHcoro7LarG0wor7CY/Cfm8BUDDGXwsTvwTtE66hEc0T2tf6bt886508zmVGJfjAaVVGJ84sjITDBTgGK1s7oZmRs57HE+8djUS38cuwXDhYe1Dosu9E0kSkvL6dPnz7Mnz+/wedfe+013n77bd5//302b96Mt7c3o0aNoqqqysmRioaE+xkI9TVgtqjsz5ZWmVbDVAqLboGyHAjrAXcstg7nFa7JPxq8w8BSCzm7m32Ylb4n8ezoid6i54roK6RLqZ1x07lxTdw1JAYloqKyMmMlqQWpWodlF5omMqNHj+all17ixhtvPOc5VVWZO3cuzz77LOPGjaN379588sknnDhx4pyWG6ENRVFsrTI7Moo0jUWcpqrww+OQn2qd2O6ur63T3QvXpShnloU4sf3M3D9NcKC6kBV+JwGILYptV7O+ijN0io4roq+gR3APAFZlruJAwQGNo2q5VjuPTHp6Ojk5OYwcOdK2zd/fn8GDB7Nx40Zuv73h+WRNJhMmk8n2uKSkxOGxtmd9YwNYkZLLzswirUNpUzIyMsjPz2/y60LSvyV279eoip6DfWZQfjgHyLF/gBchJcW+Q4bbtbAecGS1dQbmU4fPrJB9EWpUM8+c2ohFgeKtxQRGSmLbnimKwuUdLkdBYd+pfazOXA1A96DuGkfWfK02kcnJsX74hoeH19seHh5ue64hc+bM4YUXXnBobOKMpJgAAElk7CgjI4PExO5UVDStHqJ3uI4tD3qDXuHJn8qZ9/xEB0XYNKVlbXf+CqfRu1snyMvcBFnbmpTIvFe8l4M1RXib9aT8+wTK36RLqb1TFIVhHYYB2JIZBYWEINesm2q1iUxzzZgxgylTptgel5SUEBMTo2FEbVvvaH8UBY4XVpJfZiLEp+3OHuks+fn5VFRU8un/u5XE2NCLe5FqoXv+j3jUFlJkiOaee4dzz33afmH9+PtBZv7rF6lps5eovpC5GYqOWYdj+4Rf8CU7TSf5uGQ/ABOKothcusvRUQoXUZfMKCjsPbWX1Zmr8dB7EO8fr3VoTdZqE5mICOsiabm5uURGRtq25+bmkpSU1OjrDAYDBoN8mTqLr9GdLqE+HMorY2dGESN7XPjDVVycxNhQ+nXrcHE7H/0NagvBzZOAfjfSrxUU96ZknNQ6hLbF6A+h3a3LFhzfAt3HnHf3CksNM05txILKDd7x9D7u5aRAhatQFIXLOlxGjaWG1MJUfj72M9fHX0+0b7TWoTVJq51HJj4+noiICFauXGnbVlJSwubNm0lOTtYwMvFH0r2ksbI8yNhgvd/1ahmh1JZFD7L+m7cfTOev/3utcDvHa8uI1Hvxt8D+TghOuCJFURgeM9w2NPunoz+RW56rdVhNommLTFlZGYcPH7Y9Tk9PZ+fOnQQFBREbG8sTTzzBSy+9RNeuXYmPj2fmzJlERUUxfvx47YJuw5pbnBlEOQC/7s9gRGi5PUNqspCQEGJjYzWNwalUFQ4tt45kCe4KoTLdfJvmF2ldqbw401or0+nKBndbU3Gcr8vTUICXg5Px1Xk4N07hUnSKjqtjr+bH9B85XnacpelLGdd5HMGewVqHdlE0TWS2bt3KlVee+Y9YV9syceJEFi5cyLRp0ygvL+ehhx6iqKiIyy67jGXLlmE0GrUKuU0qKbB2Adx1113Ner17WDxR973DzoxC+vcfBah2jK5pPL28OJCS0n6Smdw9UJIFOndra4zMDdL2RQ+yJjIndkLskHNWxS4wV/Fcwe8A3OPbnYFG6e4VF6bX6bm247X8cOQHcityWXJkCTd2vdElFhTVNJEZPnw4qtr4l56iKMyePZvZs2c7Mar2p7LM2kR9/V+eIaF305ugLSp8f1wFgzd/nvs1fu72jvDi5GaksejVp8nPz28fiUxtFRxZY73f8TIwtP4PHGEHwV2sq2JXFsCJHRB7qe0pVVV5vuB3CixVdHH359GAPhoGKlyNu96d6+Kv439p/6OgqoClR5ZyY5cbMbq17saDVlvsK5wvOCqO6K49m/XaiNLjZBVVogZEEx3lb+fIRIOOroOaCvAKtq6lJNoHRYHYZEhdCsd/t06Wp7f+9bCoNJXVlcdxQ8ec4CEYFL3GwQpXY3Qzcn2n6/n20LcUmYr4Mf1HxnYei7tOo79QL0KrLfYVriXC35qxnyiSobZOUXEKsrZb73e5GnTyhdWuhPWwjmKqqYDsnQDsMuXzRtEOAKYG9qW7h0x8J5rHx92H6ztdj0FvILcil1+O/YKlGTNKO4skMsIuogM9ATheWKFxJO3EkdWAau1mCOyodTTC2XR662KgAJmbKaopZ2r+b9Sico1XLHf4XPyEeUI0JMgYxOiOo9Ereo6VHOPX47+etxRES5LICLuI8vdEUaCkqpaSyhqtw2nbCo9ap6lXdI2OWhHtQEQv8PCF6jJ+OvIDOeYK4tx8eSFosCwIKewi0ieSkXEjUVBIKUhha+5WrUNqkCQywi483HSE+1q7l7KKmja1vmgCVYUjq6z3o/pa62NE+6RzsxX6XnUyEz9V4Y2Qy/BpxbUMwvV08u/EZR0uA2Br7lb2n9qvcUTnkkRG2M2Z7iVJZBwmb791Ajy9AeKGah2N0NjmwAiy3PSEmc38s8aPBKmLEQ5wScgl9AvrB8Cvx3/laPFRbQP6A0lkhN1InYyDWcxw9Ffr/ZjB4C5TzrdnR2tKmFKwkfkB1lGCl+Qesg7JF8IBBkUMIiEwARWVX479Qk5544s3O5skMsJuIqVOxrGyd0BVsXUJgmgZbt2eFZlNTDq5hhJLNZlBcVi8QqxJTMZmrUMTbZSiKFwRcwWxvrHUqrX8lP4ThVWFWocFSCIj7EjqZByo1gTHTq+nFHcZ6GXK+fbKpJp5In8dGbVlROm9eSvscnTxV1ifPP47VBZpGp9ou/SKnmviriHUM5QqcxVLjyylStW+FVASGWFXdd1LmdK9ZF/Ht1jnDPEMhIjeWkcjNFKrWpiWv55tpjx8FHf+EXoFIXpP6zD8gDhQzWeKwYVwgLrZf/09/CmtKWVL7RZ0Rm1TCUlkhF3FBFnrNo7mV2BppXMOuJzqCutf2gAdL5fJ79opi6ryYsEWVlUexwMdb4deTlePAOuTigJdRgIK5B+0DtEXwkG83L24vtP1eLp5UqKWEPtoLLWWWs3ikURG2FWHAE8Mbjoqa8xkF2vf5NgmZGwAczX4hENod62jERqwqCovF27hm/I0dCi8FjL03MUgvUOhg3VkCYdXWIvDhXAQf4M/18Vfhx49Pj19+CLnC81ikURG2JVepxAf4g1A2skyjaNpA6qKrAsDAsQPl9Wt26G6JObLssMowItBg7nKK6bhneOGgbsnVORDxkanxinanzCvMPq79afyaCWjQ0drFockMsLuOof6AJCWV9Zqp7R2GenrrHUPAXEQFK91NMLJalQLz5zaaEtiXg5O5gafTo2/wN1oXXsLrC155SedEqdov0J1oaS9kEaQe5BmMUgiI+wuLtgLvU6hpKqW/LJqrcNxWZ41pyBvn/WBLEXQ7lRYanj05FqWVBxFj8L/BScz1vsiktnQRGvxr2qB1J+s/wrhSBr/vSqJjLA7d72OuNNFv9K91HwdSk6vbh3WA3wjtA1GONXx2jLuzv2F9VXZeCp63gm9gjEXk8SAtfux6yjr7M+lJ6SLSbR5ksgIh+gcdrp7SRKZZhndxQ2/6hxQ9BB/udbhCCfaXJXDn3KWc7CmiGCdkY/CrmKYZ1TTDmLwha6nu5iO/gZFmfYPVIhWQhIZ4RCdQrzRKZBfVk1uiYxeahKLmdeuNljvd+gPxgBNwxHOUata+EfRbh7MW0WRxUQPjyAWR4yityGkeQcMv8R6Q4UD3+OuSjevaJskkREOYXTX0zXMF4Bdx4u0DcbFBGcu45IwPbWKB8QO0Toc4QTpNSXcm7uCf5bsRQXGe3fi32EjiXDzbtmBu15jnUTRVEpv8070MuhNtEGSyAiH6RNjXczuYG4ZFdXaTZbkUqrLiTywAIAcn17WUSiizapRLXxUvI+bs39kV3U+Poo7rwYP4cXgSzHq3Fp+Ar0H9BgPOndC1FO8OcrQ8mMK0cpIIiMcJsLPSJivAbNFZe+JEq3DcQ0b5+NhOsWRQgsnvRO0jkY40PrKE0zI/pF5xbuoxsJQYyRfR17Hdd4d7Xsin3BIHAPAY4MNjDAfse/xhdCYJDLCYRRFISkmAIA9x4uxWGROmfMqyYb18wCYsbIKVZGlCNqi7VV5PJi3iodPriG9toQgnYGXgi7lvdDhRLW0K6kxIQkc1HUD4C7zLroc3+mY8wihAUlkhEN1DffB011PmamWfdIqc34/PwvVZZQF9uTLfdIV19Zsrcrjz7krmZi3gk1VObihcLdvAj9EjWWcTycUB8/anK7rxHtbq9EBI7d9Rcfs/Q49nxDOIomMcCg3nY6BHQMB2JCWT2W1rP/SoPRfYe9/QdGR2ftxraMRdlKrWlhRkcl9uSu4L28Fm025uKEwwbszS6LGMi2wP346D+cEoyhMWlrFel0MOtXCqN8/o1PWHuecWwgHskM1mRDn1yc6gP3ZJeSXVbM+LZ+RieEXflF7Yq6BH5+23h9wP5X+XbWNRzSoqKiQ7Ozsi9q3QDXxoyWbHy0nyMcEgBsK1+oiuU0XR3i1EU6Wkk2pI0Oup6ioEBX4WN+P8MgAumTtYdSWz1lbXcH++MFOi0MIe5NERjicTqdwZUIYX207zr4TJSRG+tEhwFPrsFqP9fPg5AHwCoYRz0JKutYRibNUVFQAsGrValK2rG10P4sOcqK9yejsy4kYH9TTY50NlbV0PFhCp9RiDOUH+c4ZQTcgu9S6VEF5pYlfBtyOyd2Tnkd/Z/iu7/AvL2BTz1GoijTSC9cjiYxwiqgAT3pE+rE/u4Slu7O5ZUA0gV5OalJvzU6mwtpXrfdH/Z91zg8kkWlNTNXWFpXYnrEMHhhX7zkVlQxdLVs9qtjhVkW57kxBe8daNy6r9qJPrQG3+CjQeM3PzVuOwfY0TNUmVEXH2j7jqTD4MDB1FX0P/0pwSbY1wfHw0jZQIZpIEhnhNFd0CyW/zEReqYnvdmRx64AYvA3t+FfQYob/TQJztXXist63aR2ROA+jlxH/EOvcSPlqDZsspWwwl5BDjW0ff/RcqvNliM6PGA8DtKKcwOj1hzmJFIUtiVdT6BvGlTu+JjbvELetfpsV/W7lROh5VtgWopVpx98iwtk83HTc0CeKr7Ydp7iyhi+3ZjKqZwRR7bWbaeN8OL4FDH4wZq51sT/RalXrYZ25mI2WUg6olbbtHij00/kwROdLouKF3sV+joej+1DoE8qoLZ8RUH6Kces/YmeXYWzpfhW1btJqKlo/SWSEU3kb3BifFMW3O7Ioqarlv9uO0zc2gD4xAfgZ3bUOz3mytsPK2db717wI/h20jUc0yKxayPQ3Ef2XaL7oD2Zznu257oonQ3R+9Nd54+nic/6cCojiyysf5bI9S+hxbCt9D/9Kp+y9rO1zI8fDumgdnhDnJYmMcLoALw/uGBzL2tSTpOSUsj2jiB0ZRcQEeRHlbyTU14CXwQ2jmw5Pdz0ebjqHz7HhVFUl8N/7wVIDiTdAv4laRyT+4GB1ET+Up7O04ignEyoJIAAzEIE7yXo/knW+hChtK/GudTOwpu8E0iN6cMXu/+FfXsANGz7maHh3Nva8lkI/GW0oWidJZIQmDG56rukZQZcwH3ZkFnG8sJKMggoyCirO2VcBvAx6Ar08CPEx0DHYi+hAL/Q6F0xuVBWWPAGF6eAfAze8LV1KrUS+uZIfy4/xQ3k6B2oKbdsNtQon1uRzT3gQN/WLa1tJdQOORSbyeUg8g1N+4ZL0TXTMPUBsbiopcQPYkjiSCqOf1iEKUY8kMkJTnUJ96BTqQ2FFNUfzyzlZaiK/vJrKajNVNWZqLSoqUG4yU26q5HhhJTszizC66bikgz/94wIxurtQs/6vr8Per0HnBhM+Pj1KSWjFpJpZXXGcH8rTWV+VjRnrqCM3dFzhGcVY73j2/nSIJz7dQ+hfgtp8ElOnxt3Ib73Hsjf+Ui7dv5xO2fvoeWwL3Y7vYk+nZHZ1uYxKg4/WYQoBSCIjWolALw8CY88tLKw1W6iqtVBWVUtBRTXZRZWknSynssbM1mOF7D5ezKD4IPrGBjg/6Kba9x2sfsl6/7q/Q6xMQqYFVVXZYTrJ9+Xp/FyRQal6ZtRRb49gxnrHc61XHAF660rRKephrULVXJFvKMsG30XEqaMM2fsjEYWZ9Du0lt5p69nfcSA7u1xOmVeA1mGKdk4SGdGquel1+Oh1+BjciPA30iPSjyu7q6Tnl7PxyClOlVXz2+F80k6W0bsVDXU9R/qv8O1frPcHPwID7tc2nnYoo6aUH8rT+aE8nSxzuW17pN6LMd7xjPWOJ95duk0akhPckW8uf4SOOSn0T11NeNFxeh/ZSM/03zkYk8TuzkM55R+pdZiinZJERrgcnaLQOdSHTiHe7MsuYd3BfLKLqzhZ4o5XwlCtwzvXsQ3w2W1QWwXdRsM1L2kdUbtRbDaxvCKDH8rT2Vmdb9vupbhxtVcs47zj6W8IQ9dOuoxaRFE4GtmDoxGJRJ9Mo9/B1UTnHyExYxuJGdvICo5nT6chpEcmoupcqLtXuDxJZITLUhSFS6L8iQ304uf9uWQVVRI6fgYLd5bQu48FN30rmG79yBpYfCfUVEDnq+CWhaCX/3aOVK2a+a0ymyXl6aypzKIG69T8OhSSjRGM9Y5nhGc0njr5OTSLonA8rAvHw7oQXnCMPmnr6XRiHx1OpdPhVDqlnv7sjb+UlLiBVBm8tY5WtAPyP1m4PD9Pd27q24Hl21I5WKLn+4Pl5H60mX/c0Y9QX4N2gW3/BJY8CZZaiL8Cbl8E7sYLv040WZWllvVV2fxSkcnayizKzqp76eYewA3e8Vzn3ZFQfTudfNFBcoPi+DkoDu/KYnqmb6bn0d/xrSwmef9yBh5YyeEOvUmJG0B2cEcZnSccRhIZ0SbodAq9Asz89u9XiLvlWTanFzDmnXW8e2d/+sc5eWRQTSX8Mgt+/8D6uNctcMM/JImxswpLLb9VneCXigzWVp6gUq21PRem92S0VxxjveNJ8JCRYY5W7unP7z2uYVvClXTJ2k2vtA2EFZ+ge+Z2umdup8g7mANxAzgQ048KT6lDEvYliYxoUyoPbuTVkSG8vb2Sw3ll3P7BRmaO6cHdlzpp/o+s7fDtw5Cfan18xXQYPqNd/zVaVFRIdnZ2i4+jqipZVLLFcootagG71SKqT3cbAYRiYJgulGFKGImKH7oqBaqqyKZl5y4rLW1p6K1KUW4RGSkZDjt+OqH8En4Dsf65DCxOoXfJYQLKT3Hp/uUM2v8zaV5R7Pbtwj6feCrcmt5CdirrlAOiFq5MEhnR5kT7ufG/SUOZ9t/dLN2Tzaz/7WPL0UKeH9uDYB8HdTWVZMOqF2HnZ4AKPuEw7l3oOtIx53MBFRXWyQ1XrVpNypa1zTpGubcb+RGe5Id7khflRYVv/dl0vUpriD5aSoejZQTmm1CA307f7GVPrhmAWnPtBfZs3QpLq1GAtZ+sZe0nzft5NIe3O9zS0537k9wZFudG14osulZkMS57DSuOmPnv/hqWHa4lq1S98MFOU4CygjLHBS1cikskMvPnz+fvf/87OTk59OnTh3feeYdBgwZpHZZoxbwNbvzjjr70/S2AOT8d4IddJ1h36CQzRndnQr9o+xUC5x2ATe/C7i+so5LA2pV07avgHWyfc7goU7UJgNiesQweGHfB/auwcFxfy3F9LZn6GtL1NRTqLPX20avQyexOYq2B7rUeRKh6lDgFLnz4Zju57CAcOI7FYnbcSZygvMo63d+kG2IYPNj56ycdBfJVE3GWQuIsBQTrKrm2ixvXdrF+DRUqRk4o/pzQ+XFS8cGsNPx/dPv2Y8z9+ghV5VXOC160aq0+kfniiy+YMmUK77//PoMHD2bu3LmMGjWK1NRUwsLCtA5PtGKKovDnYZ0Y0DGIGd/sISW7hOlf7+HdNWk8ckVnxiV1wNOjicNEVRVOHYaDy6wz9J7Ycea5mMFwzcsQM9C+b8TFGb2M+If42x5XqhZy1Gpy1Gqy1Wqy1RqOqyZyqTnntXogTjHSTfGkm85Id8ULYyNfcI7iYWxbK0B3CDHQPT5As/ObCecIkFVdQVBpLgFlp/CuKiZQrSJQraKnJRcVhQqDD2We/pQb/Sj39KPK3RMUHXkZeRc8h2hfWn0i8+abb/Lggw9y3333AfD++++zdOlS/vWvf/G3v/1N4+iEK0iKCeD7yUNZuP4o7645zLFTFfztmz3MXrKfkYnhXNk9lH6xgcQGedWvo6mthqIMKEiD3L2QswcyNkHpWTUXig4SroPkyRB7abuthVFVlRK1hkJzFUUWEwVmEymhFYRcH8LmjrCr5gSnqOWUWkM5lkaPE4wbsYqBOJ2BLoonnRUjBicnLsI5TB5eZAfHkx0cj95cg395AX7lp/CvKMCj1oS3qRRv05n6JAsKJg8v/AMsVFxpIFB/gpjcg5R7+lFm9Kfa3dhu//+1d606kamurmbbtm3MmDHDtk2n0zFy5Eg2btyoYWRQlPsrd43xI7b6G3xP/Hp6a1P6eBvZV63bfuFjNXqMBl7/x//eqqraXp/kfRSv8X4kuC8h6Pj2hs+lNnyuhmNoLK5zt5//Pfzx5X/cVz19DCtvUxGP3eJP4f657MwNQv3DsQcA7/ZVWJEVxZLsjuSavPl+1wm+33UCAB9dFTH6AqJ1JwmwlOBvKcFfKceXCjyoRYcFHd1Al0CFbxjFIfEUBXfEbPBCSc1FOfi/P4R7offW8PMn8/MJHX81H1a6E3rs3EU0m3aO87OgYlZV67+omFXL6X+tj2tVCxZUak8/V61aqFbNmFQzJsyYLGf+Nf/x/QRHEzwqmt0AxfWf8kJHoOJGoOJGAG4EKW6EKO54cSZpKQV2tujd2UeWt4pnt3COeESyuShW63CazSXeh6f15mauwVBTgdFUjrG6AkNtFTpVhUrAHQbWzXv5214CgADArOipdXOnRu9Ord6DWr07NW7Wf806NyyKDlWnQ0WHRaezPlas/1p0OqyfRsrpD5SzPpmU+nfqf+qAqpz9WPnDaxrnV21k+JWX4VNioHh3/oVf0FqdqmX4lZdRcLLlBf3Npagt/SR0oBMnTtChQwc2bNhAcnKybfu0adNYu3YtmzdvPuc1JpMJk8lke1xcXExsbCyZmZn4+dlv2N/Gfw4nuaD9rsHi6lQV9qjx/GLuzzZLN1LUOGpad14vhBCt1uTYYzz8wEN2PWZJSQkxMTEUFRXh7+/f6H5t7pN7zpw5vPDCC+dsj4mJ0SAa0brtPn0TQgjREtOB6VOedsixS0tLXTeRCQkJQa/Xk5ubW297bm4uERERDb5mxowZTJkyxfbYYrFQUFBAcHAwiqLYMjx7t9CI85Prrg257s4n11wbct214cjrrqoqpaWlREVFnXe/Vp3IeHh40L9/f1auXMn48eMBa2KycuVKJk+e3OBrDAYDBkP9uUICAgLO2c/Pz09+2TUg110bct2dT665NuS6a8NR1/18LTF1WnUiAzBlyhQmTpzIgAEDGDRoEHPnzqW8vNw2ikkIIYQQ7VerT2Ruu+02Tp48yaxZs8jJySEpKYlly5YRHh6udWhCCCGE0FirT2QAJk+e3GhXUlMZDAaee+65c7qfhGPJddeGXHfnk2uuDbnu2mgN171VD78WQgghhDgfmTJTCCGEEC5LEhkhhBBCuCxJZIQQQgjhsiSREUIIIYTLapOJzPz58+nYsSNGo5HBgwfz+++/n3f/r776iu7du2M0GunVqxc//vijkyJtW5py3RcuXIiiKPVuRqPRidG6vl9//ZWxY8cSFRWFoih89913F3zNmjVr6NevHwaDgS5durBw4UKHx9nWNPW6r1mz5pzfdUVRyMnJcU7AbcCcOXMYOHAgvr6+hIWFMX78eFJTUy/4Ovlsb5nmXHctPtvbXCLzxRdfMGXKFJ577jm2b99Onz59GDVqFHl5eQ3uv2HDBv70pz/xwAMPsGPHDsaPH8/48ePZu3evkyN3bU297mCdCTI7O9t2O3bsmBMjdn3l5eX06dOH+fPnX9T+6enpXH/99Vx55ZXs3LmTJ554gj//+c8sX77cwZG2LU297nVSU1Pr/b6HhYU5KMK2Z+3atUyaNIlNmzbxyy+/UFNTwzXXXEN5eXmjr5HP9pZrznUHDT7b1TZm0KBB6qRJk2yPzWazGhUVpc6ZM6fB/W+99Vb1+uuvr7dt8ODB6l/+8heHxtnWNPW6L1iwQPX393dSdG0foH777bfn3WfatGlqz54962277bbb1FGjRjkwsrbtYq776tWrVUAtLCx0SkztQV5engqoa9eubXQf+Wy3v4u57lp8trepFpnq6mq2bdvGyJEjbdt0Oh0jR45k48aNDb5m48aN9fYHGDVqVKP7i3M157oDlJWVERcXR0xMDOPGjWPfvn3OCLfdkt91bSUlJREZGcnVV1/N+vXrtQ7HpRUXFwMQFBTU6D7y+25/F3Pdwfmf7W0qkcnPz8dsNp+zfEF4eHij/dE5OTlN2l+cqznXPSEhgX/961/873//49NPP8VisTBkyBCOHz/ujJDbpcZ+10tKSqisrNQoqrYvMjKS999/n6+//pqvv/6amJgYhg8fzvbt27UOzSVZLBaeeOIJhg4dyiWXXNLofvLZbl8Xe921+Gx3iSUKRNuTnJxMcnKy7fGQIUNITEzkn//8Jy+++KKGkQlhXwkJCSQkJNgeDxkyhLS0NN566y3+85//aBiZa5o0aRJ79+7lt99+0zqUduVir7sWn+1tqkUmJCQEvV5Pbm5uve25ublEREQ0+JqIiIgm7S/O1Zzr/kfu7u707duXw4cPOyJEQeO/635+fnh6emoUVfs0aNAg+V1vhsmTJ7NkyRJWr15NdHT0efeVz3b7acp1/yNnfLa3qUTGw8OD/v37s3LlSts2i8XCypUr62WIZ0tOTq63P8Avv/zS6P7iXM257n9kNpvZs2cPkZGRjgqz3ZPf9dZj586d8rveBKqqMnnyZL799ltWrVpFfHz8BV8jv+8t15zr/kdO+Wx3ammxEyxevFg1GAzqwoUL1f3796sPPfSQGhAQoObk5Kiqqqp33323+re//c22//r161U3Nzf19ddfV1NSUtTnnntOdXd3V/fs2aPVW3BJTb3uL7zwgrp8+XI1LS1N3bZtm3r77berRqNR3bdvn1ZvweWUlpaqO3bsUHfs2KEC6ptvvqnu2LFDPXbsmKqqqvq3v/1Nvfvuu237HzlyRPXy8lKffvppNSUlRZ0/f76q1+vVZcuWafUWXFJTr/tbb72lfvfdd+qhQ4fUPXv2qI8//riq0+nUFStWaPUWXM4jjzyi+vv7q2vWrFGzs7Ntt4qKCts+8tluf8257lp8tre5REZVVfWdd95RY2NjVQ8PD3XQoEHqpk2bbM9dccUV6sSJE+vt/+WXX6rdunVTPTw81J49e6pLly51csRtQ1Ou+xNPPGHbNzw8XL3uuuvU7du3axC166ob1vvHW911njhxonrFFVec85qkpCTVw8ND7dSpk7pgwQKnx+3qmnrdX331VbVz586q0WhUg4KC1OHDh6urVq3SJngX1dD1Bur9/spnu/0157pr8dmunA5WCCGEEMLltKkaGSGEEEK0L5LICCGEEMJlSSIjhBBCCJcliYwQQgghXJYkMkIIIYRwWZLICCGEEMJlSSIjhBBCCJcliYwQwmHWrFmDoigUFRXZ5Xj33nsv48ePP+8+w4cP54knnjjvPgsXLiQgIKBZMcycOZOHHnqoWa+9WH/729949NFHHXoOIdoKSWSEEBfUki9+e5o3bx4LFy5s0ms6duzI3Llz7XL+nJwc5s2bxzPPPGOX4zVm6tSp/Pvf/+bIkSMOPY8QbYEkMkIIl+Hv769pQvXRRx8xZMgQ4uLiHHqekJAQRo0axXvvvefQ8wjRFkgiI0QbN3z4cCZPnszkyZPx9/cnJCSEmTNncvbqJCaTialTp9KhQwe8vb0ZPHgwa9asAazdQ/fddx/FxcUoioKiKDz//PMA/Oc//2HAgAH4+voSERHBHXfcQV5e3kXHNnXqVMaMGWN7PHfuXBRFYdmyZbZtXbp04aOPPgLO7VoqLy/nnnvuwcfHh8jISN54441z3vuxY8d48sknbbGfbfny5SQmJuLj48O1115Ldnb2eeNdvHgxY8eOrbfNYrHw2muv0aVLFwwGA7Gxsbz88ssAHD16FEVR+PLLLxk2bBienp4MHDiQgwcPsmXLFgYMGICPjw+jR4/m5MmT9Y47duxYFi9efIErKISQREaIduDf//43bm5u/P7778ybN48333zTlhwATJ48mY0bN7J48WJ2797NLbfcwrXXXsuhQ4cYMmQIc+fOxc/Pj+zsbLKzs5k6dSoANTU1vPjii+zatYvvvvuOo0ePcu+99150XFdccQW//fYbZrMZgLVr1xISEmJLorKyskhLS2P48OENvv7pp59m7dq1/O9//+Pnn39mzZo1bN++3fb8N998Q3R0NLNnz7bFXqeiooLXX3+d//znP/z6669kZGTY3ldDCgoK2L9/PwMGDKi3fcaMGbzyyivMnDmT/fv389lnnxEeHl5vn+eee45nn32W7du34+bmxh133MG0adOYN28e69at4/Dhw8yaNaveawYNGsTx48c5evTohS6jEO2bQ5ekFEJo7oorrlATExNVi8Vi2zZ9+nQ1MTFRVVVVPXbsmKrX69WsrKx6r7vqqqvUGTNmqKqqqgsWLFD9/f0veK4tW7aogFpaWqqq6pmVogsLCxvcv7CwUNXpdOqWLVtUi8WiBgUFqXPmzFEHDx6sqqqqfvrpp2qHDh1s+0+cOFEdN26cqqqqWlpaqnp4eKhffvml7flTp06pnp6e6uOPP27bFhcXp7711lv1zrtgwQIVUA8fPmzbNn/+fDU8PLzR97Zjxw4VUDMyMmzbSkpKVIPBoH744YcNviY9PV0F1I8++si27fPPP1cBdeXKlbZtc+bMURMSEuq9tri4WAXUNWvWNBqTEEJVpUVGiHbg0ksvrdetkpyczKFDhzCbzezZswez2Uy3bt3w8fGx3dauXUtaWtp5j7tt2zbGjh1LbGwsvr6+XHHFFQBkZGRcVFwBAQH06dOHNWvWsGfPHjw8PHjooYfYsWMHZWVlrF271nbMP0pLS6O6uprBgwfbtgUFBZGQkHBR5/by8qJz5862x5GRkeftFqusrATAaDTatqWkpGAymbjqqqvOe67evXvb7te11vTq1avetj+e29PTE7C2HAkhGuemdQBCCG2VlZWh1+vZtm0ber2+3nM+Pj6Nvq68vJxRo0YxatQoFi1aRGhoKBkZGYwaNYrq6uqLPv/w4cNZs2YNBoOBK664gqCgIBITE/ntt99Yu3YtTz31VLPf2/m4u7vXe6woSr26oT8KCQkBoLCwkNDQUOBMstGUc9UllH/cZrFY6r2moKAAwHYuIUTDpEVGiHZg8+bN9R5v2rSJrl27otfr6du3L2azmby8PLp06VLvFhERAYCHh4etjqXOgQMHOHXqFK+88grDhg2je/fuTSr0rVNXJ7Ny5UpbLczw4cP5/PPPOXjwYKP1MZ07d8bd3b3eeyssLOTgwYP19mso9ubo3Lkzfn5+7N+/37ata9eueHp6snLlyhYf/4/27t2Lu7s7PXv2tPuxhWhLJJERoh3IyMhgypQppKam8vnnn/POO+/w+OOPA9CtWzfuvPNO7rnnHr755hvS09P5/fffmTNnDkuXLgWsc7GUlZWxcuVK8vPzqaioIDY2Fg8PD9555x2OHDnC999/z4svvtjk2C6//HJKS0tZsmRJvURm0aJFREZG0q1btwZf5+PjwwMPPMDTTz/NqlWr2Lt3L/feey86Xf2PtY4dO/Lrr7+SlZVFfn5+k+Oro9PpGDlyJL/99pttm9FoZPr06UybNo1PPvmEtLQ0Nm3axMcff9zs89RZt26dbaSTEKJxksgI0Q7cc889VFZWMmjQICZNmsTjjz9eb3baBQsWcM899/DUU0+RkJDA+PHj2bJlC7GxsQAMGTKEhx9+mNtuu43Q0FBee+01QkNDWbhwIV999RU9evTglVde4fXXX29ybIGBgfTq1YvQ0FC6d+8OWJMbi8XSaH1Mnb///e8MGzaMsWPHMnLkSC677DL69+9fb5/Zs2dz9OhROnfu3OJumj//+c8sXry4XjfQzJkzeeqpp5g1axaJiYncdtttzWqZ+qPFixfz4IMPtvg4QrR1inq+TmEhhMsbPnw4SUlJdpvdtj1TVZXBgwfz5JNP8qc//clh5/npp5946qmn2L17N25uUsooxPlIi4wQQlwkRVH44IMPqK2tdeh5ysvLWbBggSQxQlwE+V8ihBBNkJSURFJSkkPPcfPNNzv0+EK0JdK1JIQQQgiXJV1LQgghhHBZksgIIYQQwmVJIiOEEEIIlyWJjBBCCCFcliQyQgghhHBZksgIIYQQwmVJIiOEEEIIlyWJjBBCCCFcliQyQgghhHBZ/x+guL8CDboqggAAAABJRU5ErkJggg==\n"
 451 |           },
 452 |           "metadata": {}
 453 |         }
 454 |       ]
 455 |     },
 456 |     {
 457 |       "cell_type": "code",
 458 |       "source": [
 459 |         "df_iris.groupby('target_names').mean()"
 460 |       ],
 461 |       "metadata": {
 462 |         "colab": {
 463 |           "base_uri": "https://localhost:8080/",
 464 |           "height": 232
 465 |         },
 466 |         "id": "kK_ZBvmt4786",
 467 |         "outputId": "ac836aa2-0d6f-4b9f-d0a9-78239f9ddc3d"
 468 |       },
 469 |       "execution_count": null,
 470 |       "outputs": [
 471 |         {
 472 |           "output_type": "stream",
 473 |           "name": "stderr",
 474 |           "text": [
 475 |             "<ipython-input-8-cc6fcf70a8d1>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
 476 |             "  df_iris.groupby('target_names').mean()\n"
 477 |           ]
 478 |         },
 479 |         {
 480 |           "output_type": "execute_result",
 481 |           "data": {
 482 |             "text/plain": [
 483 |               "              sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
 484 |               "target_names                                                           \n",
 485 |               "setosa                    5.006             3.428              1.462   \n",
 486 |               "versicolor                5.936             2.770              4.260   \n",
 487 |               "virginica                 6.588             2.974              5.552   \n",
 488 |               "\n",
 489 |               "              petal width (cm)  target  \n",
 490 |               "target_names                            \n",
 491 |               "setosa                   0.246     0.0  \n",
 492 |               "versicolor               1.326     1.0  \n",
 493 |               "virginica                2.026     2.0  "
 494 |             ],
 495 |             "text/html": [
 496 |               "\n",
 497 |               "  <div id=\"df-c93566ec-1631-4615-9b53-90cb727c9135\" class=\"colab-df-container\">\n",
 498 |               "    <div>\n",
 499 |               "<style scoped>\n",
 500 |               "    .dataframe tbody tr th:only-of-type {\n",
 501 |               "        vertical-align: middle;\n",
 502 |               "    }\n",
 503 |               "\n",
 504 |               "    .dataframe tbody tr th {\n",
 505 |               "        vertical-align: top;\n",
 506 |               "    }\n",
 507 |               "\n",
 508 |               "    .dataframe thead th {\n",
 509 |               "        text-align: right;\n",
 510 |               "    }\n",
 511 |               "</style>\n",
 512 |               "<table border=\"1\" class=\"dataframe\">\n",
 513 |               "  <thead>\n",
 514 |               "    <tr style=\"text-align: right;\">\n",
 515 |               "      <th></th>\n",
 516 |               "      <th>sepal length (cm)</th>\n",
 517 |               "      <th>sepal width (cm)</th>\n",
 518 |               "      <th>petal length (cm)</th>\n",
 519 |               "      <th>petal width (cm)</th>\n",
 520 |               "      <th>target</th>\n",
 521 |               "    </tr>\n",
 522 |               "    <tr>\n",
 523 |               "      <th>target_names</th>\n",
 524 |               "      <th></th>\n",
 525 |               "      <th></th>\n",
 526 |               "      <th></th>\n",
 527 |               "      <th></th>\n",
 528 |               "      <th></th>\n",
 529 |               "    </tr>\n",
 530 |               "  </thead>\n",
 531 |               "  <tbody>\n",
 532 |               "    <tr>\n",
 533 |               "      <th>setosa</th>\n",
 534 |               "      <td>5.006</td>\n",
 535 |               "      <td>3.428</td>\n",
 536 |               "      <td>1.462</td>\n",
 537 |               "      <td>0.246</td>\n",
 538 |               "      <td>0.0</td>\n",
 539 |               "    </tr>\n",
 540 |               "    <tr>\n",
 541 |               "      <th>versicolor</th>\n",
 542 |               "      <td>5.936</td>\n",
 543 |               "      <td>2.770</td>\n",
 544 |               "      <td>4.260</td>\n",
 545 |               "      <td>1.326</td>\n",
 546 |               "      <td>1.0</td>\n",
 547 |               "    </tr>\n",
 548 |               "    <tr>\n",
 549 |               "      <th>virginica</th>\n",
 550 |               "      <td>6.588</td>\n",
 551 |               "      <td>2.974</td>\n",
 552 |               "      <td>5.552</td>\n",
 553 |               "      <td>2.026</td>\n",
 554 |               "      <td>2.0</td>\n",
 555 |               "    </tr>\n",
 556 |               "  </tbody>\n",
 557 |               "</table>\n",
 558 |               "</div>\n",
 559 |               "    <div class=\"colab-df-buttons\">\n",
 560 |               "\n",
 561 |               "  <div class=\"colab-df-container\">\n",
 562 |               "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c93566ec-1631-4615-9b53-90cb727c9135')\"\n",
 563 |               "            title=\"Convert this dataframe to an interactive table.\"\n",
 564 |               "            style=\"display:none;\">\n",
 565 |               "\n",
 566 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
 567 |               "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
 568 |               "  </svg>\n",
 569 |               "    </button>\n",
 570 |               "\n",
 571 |               "  <style>\n",
 572 |               "    .colab-df-container {\n",
 573 |               "      display:flex;\n",
 574 |               "      gap: 12px;\n",
 575 |               "    }\n",
 576 |               "\n",
 577 |               "    .colab-df-convert {\n",
 578 |               "      background-color: #E8F0FE;\n",
 579 |               "      border: none;\n",
 580 |               "      border-radius: 50%;\n",
 581 |               "      cursor: pointer;\n",
 582 |               "      display: none;\n",
 583 |               "      fill: #1967D2;\n",
 584 |               "      height: 32px;\n",
 585 |               "      padding: 0 0 0 0;\n",
 586 |               "      width: 32px;\n",
 587 |               "    }\n",
 588 |               "\n",
 589 |               "    .colab-df-convert:hover {\n",
 590 |               "      background-color: #E2EBFA;\n",
 591 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 592 |               "      fill: #174EA6;\n",
 593 |               "    }\n",
 594 |               "\n",
 595 |               "    .colab-df-buttons div {\n",
 596 |               "      margin-bottom: 4px;\n",
 597 |               "    }\n",
 598 |               "\n",
 599 |               "    [theme=dark] .colab-df-convert {\n",
 600 |               "      background-color: #3B4455;\n",
 601 |               "      fill: #D2E3FC;\n",
 602 |               "    }\n",
 603 |               "\n",
 604 |               "    [theme=dark] .colab-df-convert:hover {\n",
 605 |               "      background-color: #434B5C;\n",
 606 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 607 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 608 |               "      fill: #FFFFFF;\n",
 609 |               "    }\n",
 610 |               "  </style>\n",
 611 |               "\n",
 612 |               "    <script>\n",
 613 |               "      const buttonEl =\n",
 614 |               "        document.querySelector('#df-c93566ec-1631-4615-9b53-90cb727c9135 button.colab-df-convert');\n",
 615 |               "      buttonEl.style.display =\n",
 616 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 617 |               "\n",
 618 |               "      async function convertToInteractive(key) {\n",
 619 |               "        const element = document.querySelector('#df-c93566ec-1631-4615-9b53-90cb727c9135');\n",
 620 |               "        const dataTable =\n",
 621 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 622 |               "                                                    [key], {});\n",
 623 |               "        if (!dataTable) return;\n",
 624 |               "\n",
 625 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
 626 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 627 |               "          + ' to learn more about interactive tables.';\n",
 628 |               "        element.innerHTML = '';\n",
 629 |               "        dataTable['output_type'] = 'display_data';\n",
 630 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
 631 |               "        const docLink = document.createElement('div');\n",
 632 |               "        docLink.innerHTML = docLinkHtml;\n",
 633 |               "        element.appendChild(docLink);\n",
 634 |               "      }\n",
 635 |               "    </script>\n",
 636 |               "  </div>\n",
 637 |               "\n",
 638 |               "\n",
 639 |               "<div id=\"df-ed0209f0-247e-4772-9c52-4bd5e4c4bbb1\">\n",
 640 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed0209f0-247e-4772-9c52-4bd5e4c4bbb1')\"\n",
 641 |               "            title=\"Suggest charts\"\n",
 642 |               "            style=\"display:none;\">\n",
 643 |               "\n",
 644 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 645 |               "     width=\"24px\">\n",
 646 |               "    <g>\n",
 647 |               "        <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",
 648 |               "    </g>\n",
 649 |               "</svg>\n",
 650 |               "  </button>\n",
 651 |               "\n",
 652 |               "<style>\n",
 653 |               "  .colab-df-quickchart {\n",
 654 |               "      --bg-color: #E8F0FE;\n",
 655 |               "      --fill-color: #1967D2;\n",
 656 |               "      --hover-bg-color: #E2EBFA;\n",
 657 |               "      --hover-fill-color: #174EA6;\n",
 658 |               "      --disabled-fill-color: #AAA;\n",
 659 |               "      --disabled-bg-color: #DDD;\n",
 660 |               "  }\n",
 661 |               "\n",
 662 |               "  [theme=dark] .colab-df-quickchart {\n",
 663 |               "      --bg-color: #3B4455;\n",
 664 |               "      --fill-color: #D2E3FC;\n",
 665 |               "      --hover-bg-color: #434B5C;\n",
 666 |               "      --hover-fill-color: #FFFFFF;\n",
 667 |               "      --disabled-bg-color: #3B4455;\n",
 668 |               "      --disabled-fill-color: #666;\n",
 669 |               "  }\n",
 670 |               "\n",
 671 |               "  .colab-df-quickchart {\n",
 672 |               "    background-color: var(--bg-color);\n",
 673 |               "    border: none;\n",
 674 |               "    border-radius: 50%;\n",
 675 |               "    cursor: pointer;\n",
 676 |               "    display: none;\n",
 677 |               "    fill: var(--fill-color);\n",
 678 |               "    height: 32px;\n",
 679 |               "    padding: 0;\n",
 680 |               "    width: 32px;\n",
 681 |               "  }\n",
 682 |               "\n",
 683 |               "  .colab-df-quickchart:hover {\n",
 684 |               "    background-color: var(--hover-bg-color);\n",
 685 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 686 |               "    fill: var(--button-hover-fill-color);\n",
 687 |               "  }\n",
 688 |               "\n",
 689 |               "  .colab-df-quickchart-complete:disabled,\n",
 690 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
 691 |               "    background-color: var(--disabled-bg-color);\n",
 692 |               "    fill: var(--disabled-fill-color);\n",
 693 |               "    box-shadow: none;\n",
 694 |               "  }\n",
 695 |               "\n",
 696 |               "  .colab-df-spinner {\n",
 697 |               "    border: 2px solid var(--fill-color);\n",
 698 |               "    border-color: transparent;\n",
 699 |               "    border-bottom-color: var(--fill-color);\n",
 700 |               "    animation:\n",
 701 |               "      spin 1s steps(1) infinite;\n",
 702 |               "  }\n",
 703 |               "\n",
 704 |               "  @keyframes spin {\n",
 705 |               "    0% {\n",
 706 |               "      border-color: transparent;\n",
 707 |               "      border-bottom-color: var(--fill-color);\n",
 708 |               "      border-left-color: var(--fill-color);\n",
 709 |               "    }\n",
 710 |               "    20% {\n",
 711 |               "      border-color: transparent;\n",
 712 |               "      border-left-color: var(--fill-color);\n",
 713 |               "      border-top-color: var(--fill-color);\n",
 714 |               "    }\n",
 715 |               "    30% {\n",
 716 |               "      border-color: transparent;\n",
 717 |               "      border-left-color: var(--fill-color);\n",
 718 |               "      border-top-color: var(--fill-color);\n",
 719 |               "      border-right-color: var(--fill-color);\n",
 720 |               "    }\n",
 721 |               "    40% {\n",
 722 |               "      border-color: transparent;\n",
 723 |               "      border-right-color: var(--fill-color);\n",
 724 |               "      border-top-color: var(--fill-color);\n",
 725 |               "    }\n",
 726 |               "    60% {\n",
 727 |               "      border-color: transparent;\n",
 728 |               "      border-right-color: var(--fill-color);\n",
 729 |               "    }\n",
 730 |               "    80% {\n",
 731 |               "      border-color: transparent;\n",
 732 |               "      border-right-color: var(--fill-color);\n",
 733 |               "      border-bottom-color: var(--fill-color);\n",
 734 |               "    }\n",
 735 |               "    90% {\n",
 736 |               "      border-color: transparent;\n",
 737 |               "      border-bottom-color: var(--fill-color);\n",
 738 |               "    }\n",
 739 |               "  }\n",
 740 |               "</style>\n",
 741 |               "\n",
 742 |               "  <script>\n",
 743 |               "    async function quickchart(key) {\n",
 744 |               "      const quickchartButtonEl =\n",
 745 |               "        document.querySelector('#' + key + ' button');\n",
 746 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
 747 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
 748 |               "      try {\n",
 749 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
 750 |               "            'suggestCharts', [key], {});\n",
 751 |               "      } catch (error) {\n",
 752 |               "        console.error('Error during call to suggestCharts:', error);\n",
 753 |               "      }\n",
 754 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
 755 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
 756 |               "    }\n",
 757 |               "    (() => {\n",
 758 |               "      let quickchartButtonEl =\n",
 759 |               "        document.querySelector('#df-ed0209f0-247e-4772-9c52-4bd5e4c4bbb1 button');\n",
 760 |               "      quickchartButtonEl.style.display =\n",
 761 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 762 |               "    })();\n",
 763 |               "  </script>\n",
 764 |               "</div>\n",
 765 |               "\n",
 766 |               "    </div>\n",
 767 |               "  </div>\n"
 768 |             ],
 769 |             "application/vnd.google.colaboratory.intrinsic+json": {
 770 |               "type": "dataframe",
 771 |               "summary": "{\n  \"name\": \"df_iris\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"target_names\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7950605846936022,\n        \"min\": 5.006,\n        \"max\": 6.587999999999999,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.006,\n          5.936,\n          6.587999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3368224062222306,\n        \"min\": 2.77,\n        \"max\": 3.428,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.428,\n          2.77,\n          2.9739999999999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0907003611230377,\n        \"min\": 1.462,\n        \"max\": 5.5520000000000005,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.462,\n          4.26,\n          5.5520000000000005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8967348177322731,\n        \"min\": 0.24600000000000002,\n        \"max\": 2.026,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.24600000000000002,\n          1.3259999999999998,\n          2.026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
 772 |             }
 773 |           },
 774 |           "metadata": {},
 775 |           "execution_count": 8
 776 |         }
 777 |       ]
 778 |     },
 779 |     {
 780 |       "cell_type": "code",
 781 |       "source": [
 782 |         "df_iris.groupby('target_names').median()"
 783 |       ],
 784 |       "metadata": {
 785 |         "colab": {
 786 |           "base_uri": "https://localhost:8080/",
 787 |           "height": 232
 788 |         },
 789 |         "id": "3LRIPujb5bxW",
 790 |         "outputId": "f43cbe24-c80b-4015-df61-fe42b89cbbcc"
 791 |       },
 792 |       "execution_count": null,
 793 |       "outputs": [
 794 |         {
 795 |           "output_type": "stream",
 796 |           "name": "stderr",
 797 |           "text": [
 798 |             "<ipython-input-9-8bd1e834e166>:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
 799 |             "  df_iris.groupby('target_names').median()\n"
 800 |           ]
 801 |         },
 802 |         {
 803 |           "output_type": "execute_result",
 804 |           "data": {
 805 |             "text/plain": [
 806 |               "              sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
 807 |               "target_names                                                           \n",
 808 |               "setosa                      5.0               3.4               1.50   \n",
 809 |               "versicolor                  5.9               2.8               4.35   \n",
 810 |               "virginica                   6.5               3.0               5.55   \n",
 811 |               "\n",
 812 |               "              petal width (cm)  target  \n",
 813 |               "target_names                            \n",
 814 |               "setosa                     0.2     0.0  \n",
 815 |               "versicolor                 1.3     1.0  \n",
 816 |               "virginica                  2.0     2.0  "
 817 |             ],
 818 |             "text/html": [
 819 |               "\n",
 820 |               "  <div id=\"df-197726f0-00e9-40f6-a7e0-06a42e244c7b\" class=\"colab-df-container\">\n",
 821 |               "    <div>\n",
 822 |               "<style scoped>\n",
 823 |               "    .dataframe tbody tr th:only-of-type {\n",
 824 |               "        vertical-align: middle;\n",
 825 |               "    }\n",
 826 |               "\n",
 827 |               "    .dataframe tbody tr th {\n",
 828 |               "        vertical-align: top;\n",
 829 |               "    }\n",
 830 |               "\n",
 831 |               "    .dataframe thead th {\n",
 832 |               "        text-align: right;\n",
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 834 |               "</style>\n",
 835 |               "<table border=\"1\" class=\"dataframe\">\n",
 836 |               "  <thead>\n",
 837 |               "    <tr style=\"text-align: right;\">\n",
 838 |               "      <th></th>\n",
 839 |               "      <th>sepal length (cm)</th>\n",
 840 |               "      <th>sepal width (cm)</th>\n",
 841 |               "      <th>petal length (cm)</th>\n",
 842 |               "      <th>petal width (cm)</th>\n",
 843 |               "      <th>target</th>\n",
 844 |               "    </tr>\n",
 845 |               "    <tr>\n",
 846 |               "      <th>target_names</th>\n",
 847 |               "      <th></th>\n",
 848 |               "      <th></th>\n",
 849 |               "      <th></th>\n",
 850 |               "      <th></th>\n",
 851 |               "      <th></th>\n",
 852 |               "    </tr>\n",
 853 |               "  </thead>\n",
 854 |               "  <tbody>\n",
 855 |               "    <tr>\n",
 856 |               "      <th>setosa</th>\n",
 857 |               "      <td>5.0</td>\n",
 858 |               "      <td>3.4</td>\n",
 859 |               "      <td>1.50</td>\n",
 860 |               "      <td>0.2</td>\n",
 861 |               "      <td>0.0</td>\n",
 862 |               "    </tr>\n",
 863 |               "    <tr>\n",
 864 |               "      <th>versicolor</th>\n",
 865 |               "      <td>5.9</td>\n",
 866 |               "      <td>2.8</td>\n",
 867 |               "      <td>4.35</td>\n",
 868 |               "      <td>1.3</td>\n",
 869 |               "      <td>1.0</td>\n",
 870 |               "    </tr>\n",
 871 |               "    <tr>\n",
 872 |               "      <th>virginica</th>\n",
 873 |               "      <td>6.5</td>\n",
 874 |               "      <td>3.0</td>\n",
 875 |               "      <td>5.55</td>\n",
 876 |               "      <td>2.0</td>\n",
 877 |               "      <td>2.0</td>\n",
 878 |               "    </tr>\n",
 879 |               "  </tbody>\n",
 880 |               "</table>\n",
 881 |               "</div>\n",
 882 |               "    <div class=\"colab-df-buttons\">\n",
 883 |               "\n",
 884 |               "  <div class=\"colab-df-container\">\n",
 885 |               "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-197726f0-00e9-40f6-a7e0-06a42e244c7b')\"\n",
 886 |               "            title=\"Convert this dataframe to an interactive table.\"\n",
 887 |               "            style=\"display:none;\">\n",
 888 |               "\n",
 889 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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 891 |               "  </svg>\n",
 892 |               "    </button>\n",
 893 |               "\n",
 894 |               "  <style>\n",
 895 |               "    .colab-df-container {\n",
 896 |               "      display:flex;\n",
 897 |               "      gap: 12px;\n",
 898 |               "    }\n",
 899 |               "\n",
 900 |               "    .colab-df-convert {\n",
 901 |               "      background-color: #E8F0FE;\n",
 902 |               "      border: none;\n",
 903 |               "      border-radius: 50%;\n",
 904 |               "      cursor: pointer;\n",
 905 |               "      display: none;\n",
 906 |               "      fill: #1967D2;\n",
 907 |               "      height: 32px;\n",
 908 |               "      padding: 0 0 0 0;\n",
 909 |               "      width: 32px;\n",
 910 |               "    }\n",
 911 |               "\n",
 912 |               "    .colab-df-convert:hover {\n",
 913 |               "      background-color: #E2EBFA;\n",
 914 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 915 |               "      fill: #174EA6;\n",
 916 |               "    }\n",
 917 |               "\n",
 918 |               "    .colab-df-buttons div {\n",
 919 |               "      margin-bottom: 4px;\n",
 920 |               "    }\n",
 921 |               "\n",
 922 |               "    [theme=dark] .colab-df-convert {\n",
 923 |               "      background-color: #3B4455;\n",
 924 |               "      fill: #D2E3FC;\n",
 925 |               "    }\n",
 926 |               "\n",
 927 |               "    [theme=dark] .colab-df-convert:hover {\n",
 928 |               "      background-color: #434B5C;\n",
 929 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 930 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 931 |               "      fill: #FFFFFF;\n",
 932 |               "    }\n",
 933 |               "  </style>\n",
 934 |               "\n",
 935 |               "    <script>\n",
 936 |               "      const buttonEl =\n",
 937 |               "        document.querySelector('#df-197726f0-00e9-40f6-a7e0-06a42e244c7b button.colab-df-convert');\n",
 938 |               "      buttonEl.style.display =\n",
 939 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 940 |               "\n",
 941 |               "      async function convertToInteractive(key) {\n",
 942 |               "        const element = document.querySelector('#df-197726f0-00e9-40f6-a7e0-06a42e244c7b');\n",
 943 |               "        const dataTable =\n",
 944 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 945 |               "                                                    [key], {});\n",
 946 |               "        if (!dataTable) return;\n",
 947 |               "\n",
 948 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
 949 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 950 |               "          + ' to learn more about interactive tables.';\n",
 951 |               "        element.innerHTML = '';\n",
 952 |               "        dataTable['output_type'] = 'display_data';\n",
 953 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
 954 |               "        const docLink = document.createElement('div');\n",
 955 |               "        docLink.innerHTML = docLinkHtml;\n",
 956 |               "        element.appendChild(docLink);\n",
 957 |               "      }\n",
 958 |               "    </script>\n",
 959 |               "  </div>\n",
 960 |               "\n",
 961 |               "\n",
 962 |               "<div id=\"df-eecb0209-1dec-463a-81fa-195d83f816a4\">\n",
 963 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eecb0209-1dec-463a-81fa-195d83f816a4')\"\n",
 964 |               "            title=\"Suggest charts\"\n",
 965 |               "            style=\"display:none;\">\n",
 966 |               "\n",
 967 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 968 |               "     width=\"24px\">\n",
 969 |               "    <g>\n",
 970 |               "        <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",
 971 |               "    </g>\n",
 972 |               "</svg>\n",
 973 |               "  </button>\n",
 974 |               "\n",
 975 |               "<style>\n",
 976 |               "  .colab-df-quickchart {\n",
 977 |               "      --bg-color: #E8F0FE;\n",
 978 |               "      --fill-color: #1967D2;\n",
 979 |               "      --hover-bg-color: #E2EBFA;\n",
 980 |               "      --hover-fill-color: #174EA6;\n",
 981 |               "      --disabled-fill-color: #AAA;\n",
 982 |               "      --disabled-bg-color: #DDD;\n",
 983 |               "  }\n",
 984 |               "\n",
 985 |               "  [theme=dark] .colab-df-quickchart {\n",
 986 |               "      --bg-color: #3B4455;\n",
 987 |               "      --fill-color: #D2E3FC;\n",
 988 |               "      --hover-bg-color: #434B5C;\n",
 989 |               "      --hover-fill-color: #FFFFFF;\n",
 990 |               "      --disabled-bg-color: #3B4455;\n",
 991 |               "      --disabled-fill-color: #666;\n",
 992 |               "  }\n",
 993 |               "\n",
 994 |               "  .colab-df-quickchart {\n",
 995 |               "    background-color: var(--bg-color);\n",
 996 |               "    border: none;\n",
 997 |               "    border-radius: 50%;\n",
 998 |               "    cursor: pointer;\n",
 999 |               "    display: none;\n",
1000 |               "    fill: var(--fill-color);\n",
1001 |               "    height: 32px;\n",
1002 |               "    padding: 0;\n",
1003 |               "    width: 32px;\n",
1004 |               "  }\n",
1005 |               "\n",
1006 |               "  .colab-df-quickchart:hover {\n",
1007 |               "    background-color: var(--hover-bg-color);\n",
1008 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1009 |               "    fill: var(--button-hover-fill-color);\n",
1010 |               "  }\n",
1011 |               "\n",
1012 |               "  .colab-df-quickchart-complete:disabled,\n",
1013 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
1014 |               "    background-color: var(--disabled-bg-color);\n",
1015 |               "    fill: var(--disabled-fill-color);\n",
1016 |               "    box-shadow: none;\n",
1017 |               "  }\n",
1018 |               "\n",
1019 |               "  .colab-df-spinner {\n",
1020 |               "    border: 2px solid var(--fill-color);\n",
1021 |               "    border-color: transparent;\n",
1022 |               "    border-bottom-color: var(--fill-color);\n",
1023 |               "    animation:\n",
1024 |               "      spin 1s steps(1) infinite;\n",
1025 |               "  }\n",
1026 |               "\n",
1027 |               "  @keyframes spin {\n",
1028 |               "    0% {\n",
1029 |               "      border-color: transparent;\n",
1030 |               "      border-bottom-color: var(--fill-color);\n",
1031 |               "      border-left-color: var(--fill-color);\n",
1032 |               "    }\n",
1033 |               "    20% {\n",
1034 |               "      border-color: transparent;\n",
1035 |               "      border-left-color: var(--fill-color);\n",
1036 |               "      border-top-color: var(--fill-color);\n",
1037 |               "    }\n",
1038 |               "    30% {\n",
1039 |               "      border-color: transparent;\n",
1040 |               "      border-left-color: var(--fill-color);\n",
1041 |               "      border-top-color: var(--fill-color);\n",
1042 |               "      border-right-color: var(--fill-color);\n",
1043 |               "    }\n",
1044 |               "    40% {\n",
1045 |               "      border-color: transparent;\n",
1046 |               "      border-right-color: var(--fill-color);\n",
1047 |               "      border-top-color: var(--fill-color);\n",
1048 |               "    }\n",
1049 |               "    60% {\n",
1050 |               "      border-color: transparent;\n",
1051 |               "      border-right-color: var(--fill-color);\n",
1052 |               "    }\n",
1053 |               "    80% {\n",
1054 |               "      border-color: transparent;\n",
1055 |               "      border-right-color: var(--fill-color);\n",
1056 |               "      border-bottom-color: var(--fill-color);\n",
1057 |               "    }\n",
1058 |               "    90% {\n",
1059 |               "      border-color: transparent;\n",
1060 |               "      border-bottom-color: var(--fill-color);\n",
1061 |               "    }\n",
1062 |               "  }\n",
1063 |               "</style>\n",
1064 |               "\n",
1065 |               "  <script>\n",
1066 |               "    async function quickchart(key) {\n",
1067 |               "      const quickchartButtonEl =\n",
1068 |               "        document.querySelector('#' + key + ' button');\n",
1069 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
1070 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
1071 |               "      try {\n",
1072 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
1073 |               "            'suggestCharts', [key], {});\n",
1074 |               "      } catch (error) {\n",
1075 |               "        console.error('Error during call to suggestCharts:', error);\n",
1076 |               "      }\n",
1077 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1078 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1079 |               "    }\n",
1080 |               "    (() => {\n",
1081 |               "      let quickchartButtonEl =\n",
1082 |               "        document.querySelector('#df-eecb0209-1dec-463a-81fa-195d83f816a4 button');\n",
1083 |               "      quickchartButtonEl.style.display =\n",
1084 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1085 |               "    })();\n",
1086 |               "  </script>\n",
1087 |               "</div>\n",
1088 |               "\n",
1089 |               "    </div>\n",
1090 |               "  </div>\n"
1091 |             ],
1092 |             "application/vnd.google.colaboratory.intrinsic+json": {
1093 |               "type": "dataframe",
1094 |               "summary": "{\n  \"name\": \"df_iris\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"target_names\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.754983443527075,\n        \"min\": 5.0,\n        \"max\": 6.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0,\n          5.9,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3055050463303894,\n        \"min\": 2.8,\n        \"max\": 3.4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.4,\n          2.8,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.080264406271472,\n        \"min\": 1.5,\n        \"max\": 5.55,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.5,\n          4.35,\n          5.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9073771725877466,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.2,\n          1.3,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
1095 |             }
1096 |           },
1097 |           "metadata": {},
1098 |           "execution_count": 9
1099 |         }
1100 |       ]
1101 |     },
1102 |     {
1103 |       "cell_type": "code",
1104 |       "source": [
1105 |         "sns.boxplot(x='sepal length (cm)',data=df_iris,y='target_names')"
1106 |       ],
1107 |       "metadata": {
1108 |         "colab": {
1109 |           "base_uri": "https://localhost:8080/",
1110 |           "height": 467
1111 |         },
1112 |         "id": "oD43rPb35nAT",
1113 |         "outputId": "17a2f932-f6fd-4a82-943d-b066b7035948"
1114 |       },
1115 |       "execution_count": null,
1116 |       "outputs": [
1117 |         {
1118 |           "output_type": "execute_result",
1119 |           "data": {
1120 |             "text/plain": [
1121 |               "<Axes: xlabel='sepal length (cm)', ylabel='target_names'>"
1122 |             ]
1123 |           },
1124 |           "metadata": {},
1125 |           "execution_count": 10
1126 |         },
1127 |         {
1128 |           "output_type": "display_data",
1129 |           "data": {
1130 |             "text/plain": [
1131 |               "<Figure size 640x480 with 1 Axes>"
1132 |             ],
1133 |             "image/png": 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\n"
1134 |           },
1135 |           "metadata": {}
1136 |         }
1137 |       ]
1138 |     },
1139 |     {
1140 |       "cell_type": "code",
1141 |       "source": [
1142 |         "sns.violinplot(x='sepal length (cm)',data=df_iris,y='target_names')"
1143 |       ],
1144 |       "metadata": {
1145 |         "colab": {
1146 |           "base_uri": "https://localhost:8080/",
1147 |           "height": 467
1148 |         },
1149 |         "id": "g9ZE42fK54sL",
1150 |         "outputId": "8b8c3d97-5600-4821-87d9-4ac489e83e0d"
1151 |       },
1152 |       "execution_count": null,
1153 |       "outputs": [
1154 |         {
1155 |           "output_type": "execute_result",
1156 |           "data": {
1157 |             "text/plain": [
1158 |               "<Axes: xlabel='sepal length (cm)', ylabel='target_names'>"
1159 |             ]
1160 |           },
1161 |           "metadata": {},
1162 |           "execution_count": 11
1163 |         },
1164 |         {
1165 |           "output_type": "display_data",
1166 |           "data": {
1167 |             "text/plain": [
1168 |               "<Figure size 640x480 with 1 Axes>"
1169 |             ],
1170 |             "image/png": 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\n"
1171 |           },
1172 |           "metadata": {}
1173 |         }
1174 |       ]
1175 |     },
1176 |     {
1177 |       "cell_type": "code",
1178 |       "source": [
1179 |         "\n",
1180 |         "sns.boxplot(x='petal width (cm)',data=df_iris,y='target_names')"
1181 |       ],
1182 |       "metadata": {
1183 |         "colab": {
1184 |           "base_uri": "https://localhost:8080/",
1185 |           "height": 467
1186 |         },
1187 |         "id": "5aFEOXqI6S-y",
1188 |         "outputId": "14987142-d692-427b-fc9e-6369c50e893d"
1189 |       },
1190 |       "execution_count": null,
1191 |       "outputs": [
1192 |         {
1193 |           "output_type": "execute_result",
1194 |           "data": {
1195 |             "text/plain": [
1196 |               "<Axes: xlabel='petal width (cm)', ylabel='target_names'>"
1197 |             ]
1198 |           },
1199 |           "metadata": {},
1200 |           "execution_count": 12
1201 |         },
1202 |         {
1203 |           "output_type": "display_data",
1204 |           "data": {
1205 |             "text/plain": [
1206 |               "<Figure size 640x480 with 1 Axes>"
1207 |             ],
1208 |             "image/png": 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\n"
1209 |           },
1210 |           "metadata": {}
1211 |         }
1212 |       ]
1213 |     }
1214 |   ]
1215 | }


--------------------------------------------------------------------------------