20210504_Aula02_Victor.ipynb

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      "name": "20210504_Aula02_Victor.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/victorijsn/imersaodados3/blob/main/20210504_Aula02_Victor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCdJQJEcQonx"
      },
      "source": [
        "# Respostas aos desafios da aula 02 - ImersaoDados - Alura\n",
        "Victor Nunes Toscano\n",
        "\n",
        "Linkedin: https://www.linkedin.com/in/vntoscano/\n",
        "\n",
        "Data: 04/05/2021\n",
        "\n",
        "## Contexto\n",
        "\n",
        "- Visualizando os dados\n",
        "\n",
        "\n",
        "## Desafios:\n",
        "\n",
        "1. Desafio 01: Ordenar o gráfico countplot\n",
        "2. Desafio 02: Melhorar a visualização alterando tamanho da fonte...\n",
        "3. Desafio 03: Plotar os histogramas com seaborn\n",
        "4. Desafio 04: Estudar sobre as estatíticas retornadas no .describe()\n",
        "5. Desafio 05: Refletir sobre a manipulação do tamanho das visualizações.\n",
        "6. Desafio 06: Fazer outras análises com o boxplot e até com o histograma.\n",
        "7. Desafio 07: Resumo do que você aprendeu com os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vC0PwhyRh2b"
      },
      "source": [
        "Importar a biblioteca [pandas](https://pandas.pydata.org/docs/index.html) a base de dados:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oI6FJlFGQlF2"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "\n",
        "dados = pd.read_csv(url_dados, compression = 'zip')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkYSi2TBkWhb"
      },
      "source": [
        "# Importar as bibliotecas de gráficos\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFse2h0Vcrp4"
      },
      "source": [
        "mapa = {'droga': 'composto'}\n",
        "dados.rename(columns=mapa, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0UHuFzpKxXH"
      },
      "source": [
        "dados.columns = dados.columns.str.replace('-','_')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwZLZN9aR6bQ"
      },
      "source": [
        "### Resposta 01\n",
        "#### Ordenar o gráfico countplot\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "lGNjXR_gkYau",
        "outputId": "e1a2516b-2b91-4cd9-9074-05b8f1392a4c"
      },
      "source": [
        "cod_compostos = dados['composto'].value_counts().index[0:5]\n",
        "dados.query('composto in @cod_compostos')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>tempo</th>\n",
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              "      <th>composto</th>\n",
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              "      <th>7</th>\n",
              "      <td>id_0020d0484</td>\n",
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              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.2711</td>\n",
              "      <td>0.5133</td>\n",
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              "      <td>-0.1811</td>\n",
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              "      <td>1.0510</td>\n",
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              "      <td>-0.2991</td>\n",
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              "      <td>1.0140</td>\n",
              "      <td>...</td>\n",
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              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
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              "      <td>-2.9400</td>\n",
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              "      <td>1.1600</td>\n",
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              "      <td>-10.0000</td>\n",
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              "      <td>-10.0000</td>\n",
              "      <td>-10.0000</td>\n",
              "      <td>-10.0000</td>\n",
              "      <td>-10.0000</td>\n",
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              "      <td>-5.5520</td>\n",
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              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
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              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-9.9840</td>\n",
              "      <td>-6.7840</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>id_0054388ec</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-0.6696</td>\n",
              "      <td>-0.2718</td>\n",
              "      <td>-1.2230</td>\n",
              "      <td>-0.6226</td>\n",
              "      <td>-0.7220</td>\n",
              "      <td>0.1588</td>\n",
              "      <td>0.7785</td>\n",
              "      <td>0.7062</td>\n",
              "      <td>-0.7951</td>\n",
              "      <td>1.3770</td>\n",
              "      <td>0.2856</td>\n",
              "      <td>-0.8331</td>\n",
              "      <td>0.3879</td>\n",
              "      <td>1.3290</td>\n",
              "      <td>-0.4746</td>\n",
              "      <td>0.0857</td>\n",
              "      <td>-0.2745</td>\n",
              "      <td>-0.1093</td>\n",
              "      <td>0.2603</td>\n",
              "      <td>0.7386</td>\n",
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              "      <td>-2.2070</td>\n",
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              "      <td>0.2303</td>\n",
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              "      <td>-0.7447</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.4318</td>\n",
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              "      <td>0.2114</td>\n",
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              "      <td>-0.1056</td>\n",
              "      <td>0.5478</td>\n",
              "      <td>0.7477</td>\n",
              "      <td>1.2340</td>\n",
              "      <td>0.5035</td>\n",
              "      <td>-0.6591</td>\n",
              "      <td>0.3023</td>\n",
              "      <td>0.6171</td>\n",
              "      <td>0.6405</td>\n",
              "      <td>0.5429</td>\n",
              "      <td>0.3562</td>\n",
              "      <td>1.3290</td>\n",
              "      <td>0.5573</td>\n",
              "      <td>0.8837</td>\n",
              "      <td>0.5534</td>\n",
              "      <td>0.8976</td>\n",
              "      <td>1.0050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>id_0079af0fb</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-0.1636</td>\n",
              "      <td>-1.8230</td>\n",
              "      <td>-0.5211</td>\n",
              "      <td>0.3054</td>\n",
              "      <td>-1.1280</td>\n",
              "      <td>0.6041</td>\n",
              "      <td>0.3856</td>\n",
              "      <td>-0.1670</td>\n",
              "      <td>-0.5400</td>\n",
              "      <td>0.2721</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>0.0761</td>\n",
              "      <td>-0.4054</td>\n",
              "      <td>2.9760</td>\n",
              "      <td>-0.7679</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>0.0752</td>\n",
              "      <td>0.5943</td>\n",
              "      <td>0.4266</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0410</td>\n",
              "      <td>-0.1131</td>\n",
              "      <td>0.6593</td>\n",
              "      <td>-0.6890</td>\n",
              "      <td>0.5115</td>\n",
              "      <td>-0.2512</td>\n",
              "      <td>-0.8775</td>\n",
              "      <td>-1.3810</td>\n",
              "      <td>0.1419</td>\n",
              "      <td>-0.0363</td>\n",
              "      <td>0.2559</td>\n",
              "      <td>2.5290</td>\n",
              "      <td>0.2853</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.3626</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.3420</td>\n",
              "      <td>0.2815</td>\n",
              "      <td>-0.7493</td>\n",
              "      <td>-0.2701</td>\n",
              "      <td>0.1205</td>\n",
              "      <td>0.2396</td>\n",
              "      <td>0.3506</td>\n",
              "      <td>-0.9233</td>\n",
              "      <td>0.2490</td>\n",
              "      <td>-0.0824</td>\n",
              "      <td>0.4841</td>\n",
              "      <td>1.1940</td>\n",
              "      <td>-1.1650</td>\n",
              "      <td>-0.4853</td>\n",
              "      <td>1.1080</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>0.7438</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>-0.2761</td>\n",
              "      <td>-0.1748</td>\n",
              "      <td>0.3385</td>\n",
              "      <td>-0.3500</td>\n",
              "      <td>-0.0309</td>\n",
              "      <td>-0.0409</td>\n",
              "      <td>-0.0735</td>\n",
              "      <td>0.2732</td>\n",
              "      <td>-1.2100</td>\n",
              "      <td>0.0089</td>\n",
              "      <td>0.0317</td>\n",
              "      <td>0.4721</td>\n",
              "      <td>-0.3880</td>\n",
              "      <td>-0.6828</td>\n",
              "      <td>-0.6594</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.3571</td>\n",
              "      <td>-0.1319</td>\n",
              "      <td>0.2187</td>\n",
              "      <td>0.0737</td>\n",
              "      <td>0.6498</td>\n",
              "      <td>-1.4820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>id_007bfbb91</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>24</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>-1.3200</td>\n",
              "      <td>-1.7340</td>\n",
              "      <td>-0.0741</td>\n",
              "      <td>1.5440</td>\n",
              "      <td>-1.8290</td>\n",
              "      <td>-0.0951</td>\n",
              "      <td>0.3829</td>\n",
              "      <td>-1.8700</td>\n",
              "      <td>-0.0324</td>\n",
              "      <td>2.7670</td>\n",
              "      <td>-0.0438</td>\n",
              "      <td>-0.0147</td>\n",
              "      <td>0.1276</td>\n",
              "      <td>0.2968</td>\n",
              "      <td>-0.0704</td>\n",
              "      <td>-0.3767</td>\n",
              "      <td>2.5790</td>\n",
              "      <td>0.4069</td>\n",
              "      <td>-0.3868</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>1.8180</td>\n",
              "      <td>-2.9390</td>\n",
              "      <td>-0.1150</td>\n",
              "      <td>-0.8009</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>0.5412</td>\n",
              "      <td>-0.0804</td>\n",
              "      <td>-0.4444</td>\n",
              "      <td>2.9230</td>\n",
              "      <td>2.8270</td>\n",
              "      <td>-0.3437</td>\n",
              "      <td>1.6570</td>\n",
              "      <td>-1.9200</td>\n",
              "      <td>0.3442</td>\n",
              "      <td>1.7160</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4009</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2789</td>\n",
              "      <td>0.4465</td>\n",
              "      <td>0.9492</td>\n",
              "      <td>0.6385</td>\n",
              "      <td>1.5510</td>\n",
              "      <td>-0.1220</td>\n",
              "      <td>0.6688</td>\n",
              "      <td>0.4396</td>\n",
              "      <td>0.6947</td>\n",
              "      <td>0.9761</td>\n",
              "      <td>1.2430</td>\n",
              "      <td>1.4770</td>\n",
              "      <td>-0.0081</td>\n",
              "      <td>1.0040</td>\n",
              "      <td>1.4420</td>\n",
              "      <td>-0.3482</td>\n",
              "      <td>0.3297</td>\n",
              "      <td>1.2070</td>\n",
              "      <td>0.0524</td>\n",
              "      <td>0.5346</td>\n",
              "      <td>0.7643</td>\n",
              "      <td>0.4067</td>\n",
              "      <td>0.3817</td>\n",
              "      <td>0.4566</td>\n",
              "      <td>0.7736</td>\n",
              "      <td>0.5316</td>\n",
              "      <td>0.2790</td>\n",
              "      <td>0.9735</td>\n",
              "      <td>0.7412</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>0.7393</td>\n",
              "      <td>1.1020</td>\n",
              "      <td>0.3786</td>\n",
              "      <td>0.2636</td>\n",
              "      <td>-0.5751</td>\n",
              "      <td>0.3362</td>\n",
              "      <td>0.8543</td>\n",
              "      <td>0.9180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23793</th>\n",
              "      <td>id_ffd26f361</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.6008</td>\n",
              "      <td>0.2781</td>\n",
              "      <td>-0.3319</td>\n",
              "      <td>-0.8782</td>\n",
              "      <td>0.9281</td>\n",
              "      <td>0.7535</td>\n",
              "      <td>0.5971</td>\n",
              "      <td>1.6350</td>\n",
              "      <td>-0.3146</td>\n",
              "      <td>0.4984</td>\n",
              "      <td>-1.1890</td>\n",
              "      <td>0.3772</td>\n",
              "      <td>-0.8196</td>\n",
              "      <td>0.1083</td>\n",
              "      <td>-2.0350</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.7586</td>\n",
              "      <td>-0.4024</td>\n",
              "      <td>-0.0449</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.2358</td>\n",
              "      <td>0.7919</td>\n",
              "      <td>-0.8775</td>\n",
              "      <td>0.4232</td>\n",
              "      <td>1.8460</td>\n",
              "      <td>-2.0940</td>\n",
              "      <td>0.1438</td>\n",
              "      <td>-0.6604</td>\n",
              "      <td>0.7243</td>\n",
              "      <td>0.0338</td>\n",
              "      <td>-0.0182</td>\n",
              "      <td>-0.2022</td>\n",
              "      <td>0.8827</td>\n",
              "      <td>0.3147</td>\n",
              "      <td>-0.9263</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1934</td>\n",
              "      <td>1.2240</td>\n",
              "      <td>-0.1178</td>\n",
              "      <td>0.1459</td>\n",
              "      <td>-0.1242</td>\n",
              "      <td>0.3106</td>\n",
              "      <td>-0.7959</td>\n",
              "      <td>0.5681</td>\n",
              "      <td>0.3663</td>\n",
              "      <td>0.4874</td>\n",
              "      <td>-0.0548</td>\n",
              "      <td>0.1810</td>\n",
              "      <td>1.3470</td>\n",
              "      <td>1.6210</td>\n",
              "      <td>-0.8260</td>\n",
              "      <td>0.3377</td>\n",
              "      <td>0.3264</td>\n",
              "      <td>0.9241</td>\n",
              "      <td>0.3012</td>\n",
              "      <td>1.2980</td>\n",
              "      <td>-0.2789</td>\n",
              "      <td>0.7019</td>\n",
              "      <td>-0.3528</td>\n",
              "      <td>0.7383</td>\n",
              "      <td>1.0300</td>\n",
              "      <td>0.6527</td>\n",
              "      <td>1.6750</td>\n",
              "      <td>-0.5046</td>\n",
              "      <td>0.2942</td>\n",
              "      <td>0.2950</td>\n",
              "      <td>1.2470</td>\n",
              "      <td>0.9569</td>\n",
              "      <td>-0.2065</td>\n",
              "      <td>-0.4918</td>\n",
              "      <td>0.7863</td>\n",
              "      <td>0.0504</td>\n",
              "      <td>0.8813</td>\n",
              "      <td>0.7757</td>\n",
              "      <td>-0.5272</td>\n",
              "      <td>0.7082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23802</th>\n",
              "      <td>id_fff3976bd</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>87d714366</td>\n",
              "      <td>3.2890</td>\n",
              "      <td>2.1270</td>\n",
              "      <td>0.9770</td>\n",
              "      <td>2.0490</td>\n",
              "      <td>2.3170</td>\n",
              "      <td>-0.5355</td>\n",
              "      <td>-0.6357</td>\n",
              "      <td>-6.9060</td>\n",
              "      <td>-4.4830</td>\n",
              "      <td>-2.8400</td>\n",
              "      <td>-5.3310</td>\n",
              "      <td>0.6547</td>\n",
              "      <td>-2.2080</td>\n",
              "      <td>3.6030</td>\n",
              "      <td>0.6380</td>\n",
              "      <td>-2.1280</td>\n",
              "      <td>-3.1680</td>\n",
              "      <td>-4.2180</td>\n",
              "      <td>-0.3170</td>\n",
              "      <td>0.2140</td>\n",
              "      <td>-1.7660</td>\n",
              "      <td>-1.9700</td>\n",
              "      <td>-1.4820</td>\n",
              "      <td>-1.3710</td>\n",
              "      <td>-0.2628</td>\n",
              "      <td>-0.5276</td>\n",
              "      <td>-2.4160</td>\n",
              "      <td>-0.4638</td>\n",
              "      <td>-3.8630</td>\n",
              "      <td>-0.8880</td>\n",
              "      <td>-1.9850</td>\n",
              "      <td>2.0530</td>\n",
              "      <td>-1.4700</td>\n",
              "      <td>-3.3220</td>\n",
              "      <td>1.8800</td>\n",
              "      <td>...</td>\n",
              "      <td>-9.9170</td>\n",
              "      <td>-4.0200</td>\n",
              "      <td>-6.7560</td>\n",
              "      <td>-9.9170</td>\n",
              "      <td>-4.1240</td>\n",
              "      <td>-8.5110</td>\n",
              "      <td>-5.7500</td>\n",
              "      <td>-8.4760</td>\n",
              "      <td>-6.9470</td>\n",
              "      <td>-4.7650</td>\n",
              "      <td>-9.7690</td>\n",
              "      <td>-5.7900</td>\n",
              "      <td>-9.9170</td>\n",
              "      <td>-5.7450</td>\n",
              "      <td>-2.3570</td>\n",
              "      <td>-9.1190</td>\n",
              "      <td>-4.5980</td>\n",
              "      <td>-8.7760</td>\n",
              "      <td>-7.7980</td>\n",
              "      <td>-4.1620</td>\n",
              "      <td>-6.1510</td>\n",
              "      <td>-5.7400</td>\n",
              "      <td>-6.8700</td>\n",
              "      <td>-9.1000</td>\n",
              "      <td>-3.3360</td>\n",
              "      <td>-6.6420</td>\n",
              "      <td>-2.2750</td>\n",
              "      <td>-3.4720</td>\n",
              "      <td>-6.2670</td>\n",
              "      <td>-6.3980</td>\n",
              "      <td>-8.6300</td>\n",
              "      <td>-8.1490</td>\n",
              "      <td>-6.2900</td>\n",
              "      <td>-8.0970</td>\n",
              "      <td>-8.7330</td>\n",
              "      <td>-2.3780</td>\n",
              "      <td>-8.4480</td>\n",
              "      <td>-4.4550</td>\n",
              "      <td>-5.7930</td>\n",
              "      <td>-3.7000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23805</th>\n",
              "      <td>id_fff6df1c5</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>5628cb3ee</td>\n",
              "      <td>1.7380</td>\n",
              "      <td>-1.2900</td>\n",
              "      <td>-0.4533</td>\n",
              "      <td>-1.1640</td>\n",
              "      <td>-0.4841</td>\n",
              "      <td>-0.2392</td>\n",
              "      <td>-0.1774</td>\n",
              "      <td>-1.3980</td>\n",
              "      <td>-0.4059</td>\n",
              "      <td>0.1553</td>\n",
              "      <td>0.1083</td>\n",
              "      <td>-0.8020</td>\n",
              "      <td>-0.5039</td>\n",
              "      <td>-0.7167</td>\n",
              "      <td>-0.5016</td>\n",
              "      <td>-0.9369</td>\n",
              "      <td>-0.5864</td>\n",
              "      <td>-0.1009</td>\n",
              "      <td>0.8987</td>\n",
              "      <td>0.3389</td>\n",
              "      <td>-1.3460</td>\n",
              "      <td>-0.5087</td>\n",
              "      <td>-0.0892</td>\n",
              "      <td>-0.6559</td>\n",
              "      <td>0.0864</td>\n",
              "      <td>-0.6251</td>\n",
              "      <td>0.2471</td>\n",
              "      <td>-0.4523</td>\n",
              "      <td>-0.0389</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>0.0796</td>\n",
              "      <td>-0.2560</td>\n",
              "      <td>-0.7851</td>\n",
              "      <td>-0.1687</td>\n",
              "      <td>-1.2890</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2866</td>\n",
              "      <td>0.3854</td>\n",
              "      <td>-0.4986</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.5843</td>\n",
              "      <td>0.0907</td>\n",
              "      <td>-0.9303</td>\n",
              "      <td>0.1981</td>\n",
              "      <td>0.6515</td>\n",
              "      <td>0.2877</td>\n",
              "      <td>0.4389</td>\n",
              "      <td>-0.3859</td>\n",
              "      <td>0.5203</td>\n",
              "      <td>-1.1070</td>\n",
              "      <td>2.3870</td>\n",
              "      <td>0.1820</td>\n",
              "      <td>-0.6434</td>\n",
              "      <td>-0.9722</td>\n",
              "      <td>-0.4200</td>\n",
              "      <td>-0.5693</td>\n",
              "      <td>1.0700</td>\n",
              "      <td>1.1110</td>\n",
              "      <td>-0.5117</td>\n",
              "      <td>-1.2130</td>\n",
              "      <td>0.3769</td>\n",
              "      <td>0.5296</td>\n",
              "      <td>1.0400</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>1.0150</td>\n",
              "      <td>-0.7669</td>\n",
              "      <td>0.2865</td>\n",
              "      <td>0.5974</td>\n",
              "      <td>-0.2727</td>\n",
              "      <td>0.1188</td>\n",
              "      <td>1.0770</td>\n",
              "      <td>-1.0250</td>\n",
              "      <td>0.1516</td>\n",
              "      <td>0.4503</td>\n",
              "      <td>-0.6312</td>\n",
              "      <td>-0.8302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>com_controle</td>\n",
              "      <td>48</td>\n",
              "      <td>D2</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6845</td>\n",
              "      <td>0.7127</td>\n",
              "      <td>0.7294</td>\n",
              "      <td>0.4718</td>\n",
              "      <td>-0.2020</td>\n",
              "      <td>0.2783</td>\n",
              "      <td>0.4934</td>\n",
              "      <td>0.4144</td>\n",
              "      <td>0.5449</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3360</td>\n",
              "      <td>-0.6136</td>\n",
              "      <td>0.5011</td>\n",
              "      <td>0.9261</td>\n",
              "      <td>0.4419</td>\n",
              "      <td>0.0295</td>\n",
              "      <td>0.4220</td>\n",
              "      <td>0.4677</td>\n",
              "      <td>-0.1184</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3235 rows × 877 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id    tratamento  tempo dose  ...    c_96    c_97    c_98    c_99\n",
              "7      id_0020d0484     com_droga     48   D1  ...  0.6633  0.4562 -0.9622  0.0260\n",
              "16     id_002fb9c19     com_droga     48   D1  ... -9.9840 -9.9840 -9.9840 -6.7840\n",
              "25     id_0054388ec  com_controle     48   D1  ...  0.8837  0.5534  0.8976  1.0050\n",
              "38     id_0079af0fb  com_controle     24   D1  ...  0.2187  0.0737  0.6498 -1.4820\n",
              "40     id_007bfbb91  com_controle     24   D2  ... -0.5751  0.3362  0.8543  0.9180\n",
              "...             ...           ...    ...  ...  ...     ...     ...     ...     ...\n",
              "23793  id_ffd26f361  com_controle     48   D2  ...  0.8813  0.7757 -0.5272  0.7082\n",
              "23802  id_fff3976bd     com_droga     24   D1  ... -8.4480 -4.4550 -5.7930 -3.7000\n",
              "23805  id_fff6df1c5     com_droga     48   D2  ...  0.1516  0.4503 -0.6312 -0.8302\n",
              "23811  id_fffc1c3f4  com_controle     48   D2  ...  0.2256  0.7592  0.6656  0.3808\n",
              "23812  id_fffcb9e7c     com_droga     24   D1  ...  0.1732  0.7015 -0.6290  0.0740\n",
              "\n",
              "[3235 rows x 877 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ss7WJbkHmP6w"
      },
      "source": [
        "Fazendo o gráfico e ordenando as barras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7H7AD6OjpxWC"
      },
      "source": [
        "ordem=dados.query('composto in @cod_compostos')['composto'].value_counts().index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "QBopAeqdl80m",
        "outputId": "abb9f318-2f4f-47cb-e2c5-26ea03e8b3af"
      },
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "ax = sns.countplot(x = 'composto', data=dados.query('composto in @cod_compostos'),order=ordem)\n",
        "ax.set_title('Top 5 compostos', fontsize = 18)\n",
        "ax.set_xlabel('Compostos', fontsize = 14)\n",
        "ax.set_ylabel('Frequência', fontsize = 14)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgrcgIGQSzAo"
      },
      "source": [
        "### Resposta 02\n",
        "#### Melhorar a visualização alterando tamanho da fonte.\n",
        "\n",
        "Tá no gráfico acima.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tM1YnzZwTHVX"
      },
      "source": [
        "### Resposta 03\n",
        "#### Plotar os histogramas com seaborn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "clmw2QU4re50",
        "outputId": "041ceedc-82d1-493a-8e44-cfa89484f5a0"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "dados['g_0'].hist(bins = 100)\n",
        "plt.title('Histograma - g_0')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "tER4CbjXt5MN",
        "outputId": "4e680508-cb9f-4ba1-804b-bffa5634ecbe"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "dados['g_10'].hist(bins = 100)\n",
        "plt.title('Histograma - g_10')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-Bfu0myjCC4"
      },
      "source": [
        "Utilizando a forma apresentada pelo colega de imersão, [Pedro Henrique](https://github.com/pedrohnv/imersaodados3/blob/main/imersao_aula02.ipynb), vou gerar dois histogramas que comparam as médias das repostas do tratamento nas células."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4h0cZU82jr7_"
      },
      "source": [
        "tratamentos = dados['tratamento'].unique()\n",
        "df = pd.DataFrame({t : dados.query('tratamento == @t').loc[:,'c_0':'c_99'].describe().T['mean'] for t in tratamentos})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "nTU3kpknj3gz",
        "outputId": "d3183ab4-224e-4070-a45c-c031d3d13fdd"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "ax = sns.histplot(data=df, kde=True, bins=50)\n",
        "ax.set_xlabel('Médias das células (0 a 99)')\n",
        "ax.set_ylabel('Frequência')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JiIiIiALAgpqIiIiIKAAsqImIiIiIAsCCmoiIiIgoACyoiYiIiIgCwIKaiIiIiCgALKiJiIiIiALAgpqIiIiIKAAsqImIiIiIAsCCmoiIiIgoAGErqB944AEsW7YM06dPR2VlJQCgq6sL119/PVauXIlVq1bh5ptvRmdnZ7giEREREREFLGwF9fLly/Hiiy8iLy9v+DFJknDddddh48aNeOONNzBx4kQ8/PDD4YpERERERBSwsBXUCxYsQG5u7ojHUlNTsXjx4uGP586diyNHjoQrEhERERFRwCKmh1rTNLz88stYtmyZ6ChERERERD4ziA5wzL333guLxYJvfetbfu+bkZEUgkSxz263+bW9qmpQFN//BvN3ewCwWs1+bT+effx93iJFU9Z4xTGKfByj0LIE4TWYYxT5OEZji4iC+oEHHkBdXR1+97vfQZb9nzR3OPqhaXoIksUuu92G9vY+v/d5+rW9Pm9//epSv85ht9swMODyKxMAv/fx93mLMp4xovDiGEU+jlFo2e02DPrxGpyOE1+DOUaRj2MEyLI05gSu8IL6V7/6FcrKyvD73/8eJpNJdBwiIiIiIr+EraC+77778M4776CjowPXXHMNUlNT8dhjj+Gpp55CYWEhvvnNbwIA8vPz8eSTT4YrFhERERFRQMJWUK9fvx7r168/4fGDBw+GKwIRERERUdBFzCofRERERETRiAU1EREREVEAWFATEREREQWABTURERERUQBYUBMRERERBYAFNRERERFRAFhQExEREREFgAU1EREREVEAWFATEREREQWABTURERERUQBYUBMRERERBYAFNRERERFRAFhQExEREREFgAU1EREREVEAWFATEREREQWABTURERERUQAMogMQERGReOmpCVCMxpCeQ9dU2O22Ex4f7TEAUD0edHYPhTQTUTCwoCYiIiIoRiMa337er33yL1zj1/aSrJxwDovVjMEB1xjHZ0FNkY8tH0REREREAWBBTUREROOiDvZC7WyE1tMCXfWKjkMkDFs+iIiIyGe6rkPrqIPW2YC63W8Aunb0E5IEyZoOxV4EOW2C2JBEYcaCmiKOruto7x6CV9VgTTDAkmCE0cA3U4iIRNM1DWr9HmidDZASk5F61v9Df3sroHqh9bVB626Bt3Y75MGpUCbMhCRJoiMThQULaooojp4hHDjcia5+94jHs9MScfrUTJiMiqBkRETxTVc98NZsg97XASV3BuScaUg/55sY/PwiQznZDj13JtSGfdBaD0Ef6oOhcB4kJbQrhxBFAhbUFBG8qoYH/7wDH5W1IMGkoLQ4A6lWEwaGvOgZcKOqqQcf7mvGwhlZSLGaRMclIooruuqFt3ILdGcflEmnQ8koGHU7SZahFJRCSkyG2lgGb9WnMEw7C5LEdxkptrGgpojw8qZD+NeeJkybmILivBQYlKMvvilJZkzItCI7PRE7KtqxZV8z5k3LRG6GVXBiIqL4oTaWQXf2wjDlDMgp2WNuK0kSlKwiwGCCengn1KZyGPJnhSkpkRj8k5GEe39PE97b1YTV5xZjRkHacDF9vHRbAs4uzUWyxYhdlR3oG3SPciQiIgo2rfsINEcd5Oyppyymj6ek50POLITWVgWtuyWECYnEY0FNQlU2dOPFdyoxuygday4qGXPbBJMBC2dkQZEl7KzsgKrpYUpJRBSfdLcT3ro9kCwpUHJn+L2/kj8bUmIKvHW7oLsGQ5CQKDKwoCZhBoe8+M3rZchMScB3L5kFRT711eAJZgPmTs1E74Ab5XVdYUhJRBSfdF2Ht243oGswFM6HJPtfMkiyAkPRQkDX4T28E7rOiRCKTSyoSZh/bKtH74AbN359FiwJvl8FnpNuQWGODTVHerGroi2ECYmI4pfe3Qy9rx1K3ixICbZxH0cyW6Hkz4Y+0ImBA1uCmJAocrCgJiF6Btx4Z3s9Fs3MQmFOst/7zypMg81ixJN/28PWDyKiINN1Dd4j5ZASbJAzCwM+npxRACkxBY7NL0DXeEdFij0sqEmINz86DFXVcenZRePaX1FkzCpMR1uXE/WtfUFOR0QU3zRHA+DqD9rNWSRJgpI/G2pvB7TW6iAkJIosLKgp7Nq6nXh/TxO+UjoB2WmWcR/HnpqA2VMyUNnQDa+qBTEhEVH80jUVanMFJGsapJScoB1XtmXCOmMJ1NZD0N3OoB2XKBKEpaB+4IEHsGzZMkyfPh2VlZXDj9fW1uLyyy/HypUrcfnll+Pw4cPhiEOCvf6vGiiyhFVnFgZ0HEmScPWFM+HyaKht5iw1EVEwaO01gGcIyoSSoN86PH35GkDXoR45ENTjEokWloJ6+fLlePHFF5GXlzfi8TvvvBNXXnklNm7ciCuvvBI/+9nPwhGHBGrtGsTW/a1YviAfaTZzwMcrmZyBrLREVDX1wOPlLDURUSB01QO15RCk5CzItsygH9+YmgU5qwhaZyP0IU6EUOwIS0G9YMEC5ObmjnjM4XDgwIEDuPjiiwEAF198MQ4cOIDOzs5wRCJBPthzBJIk4fz5E4N2zJkFqfB4NVQ39QTtmERE8UjrqANUz7jWnPaVklUMSArUlkMhOwdRuAnroW5ubkZ2djYURQEAKIqCrKwsNDc3i4pEIebxatiyrxlzp2YGZXb6mJQkM3IzLKhp7mUvNRHROOmaCrW9BlJSBmRrWsjOIxnNkO2Tjs5SuwZCdh6icDKIDhAMGRlJoiNEJbvd/3VFrVbfC2FV1Uac48Pdjeh3evD1c4tPem5/jn/8PrOnZOLdbfVo6xnC1Ilj/yIYz/MWJZqyxiuOUeTjGPmmv/wTwO1E4pTTYfLjtdji5+u2xWqGVliC3vbDkBw1QHr6mMfg+EUGjsPYhBXUubm5aG1thaqqUBQFqqqira3thNYQXzgc/dC4FrFf7HYb2tv961+z220YGHD5vL2iyHj6tb3DH3/0WQssCQbsKDuCnftPfCfi+tWlfh3/mIEBFxIMElKsJlQc7kJuWuKYF9L4+7xFGc8YUXhxjCIfx8h37m1vACYLPAmZ8Pr4WpwOYNCP1+0vtlcgZxTA3XoYCQUlGPIqJ92e4ycef44AWZbGnMAV1vKRkZGBmTNn4s033wQAvPnmm5g5cybS09NFRaIQ6h/0wNE7hEnZSUG/ahw4uuJH0YRk9Ds96OgZCvrxiYhimdpWDVfjQShZRSF5jR6Nkj0V0HW4Gg+G5XxEoRSWgvq+++7D2WefjZaWFlxzzTW46KKLAAB33XUXXnjhBaxcuRIvvPAC7r777nDEIQEOt/ZBkoCJWaF7y2hCphUmo4za5t6QnYOIKBa5922EZLZAzigI2zklswVyej5cLTXQve6wnZcoFMLS8rF+/XqsX7/+hMenTJmC//7v/w5HBBJI1TQ0tPUjN92CBNPob+sFgyJLKMy2obKxBwNDHlgTjCE7FxFRrND6O+Gt3YGURRfB6QnvG9dy1hRonQ3QOuqg5EwN67mJgol3SqSQa+tywuPVUJAd+gsaJuXYIEnAYd7ohYjIJ57KfwG6huT5F4T93LIlBYaULKjtNdB1rtJE0YsFNYXckY5BmAwyMlMTQn6uRLMBuRkW1Lf1Q9X44kxENBZd1+Cp+BBKXgmMacG7zbg/zHnTAM8QtG4um0vRiwU1hZSqaWjtGkROhgVymC50Kci2wePV0NLpDMv5iIiildp0AHq/A8YZ5wjLYEjPBcxWaG3VwjIQBYoFNYVUW9cQvKqOCRnWsJ3TnpKARJOChrb+sJ2TiCgaeSo+BMxWGArnCcsgSRIU+2ToA13QBrqE5SAKBAtqCqkjjgEYDTIyU0Lf7nGMJEnIz0pCW5cTTpc3bOclIoom2lAfvId3wjj1LEiK2Iu45YwCQDZAa6sRmoNovFhQU8i4PSpaOweRm2GBLIen3eOYiVlHF19vbOdtbYmIRuOt/BjQVBhnnC06CiTFCDlzErSuJuhututR9GFBTSGz62Bb2Ns9jklKNCI92YyGtj7oOu+iSUR0PF3X4Tn4AeSsKVDS80XHAQAomYUAdGiOOtFRiPzGgppC5qO9R8Le7nG8iVlJ6Hd60dXv/+3MiYhimdZWDa3rSETMTh8jJSRBSs6C2lHHJfQo6rCgppBQNQ1b97cgNz387R7H5GVYocgSGlp5cSIR0fE8hz4GFCOMRYtERxlByZwMeIagd7eIjkLkFxbUFBId3UNwuryYkGkRlsFgkJGbYUFTxwDXpCYi+pyueuGp3gpD4TxIpkTRcUaQUrIBUyLU9lrRUYj84tetx8vLy7Fjxw50dXWN6Etdt25d0INRdGvtcsJsUpAhqN3jmHx7EhrbB9DW5USugF5uIqJI423YB7gGYJy6RHSUE0iSBCVzMtQjB6A5e0XHIfKZzzPUr7zyCq644gp8+umnePrpp1FZWYlnn30W9fX1ocxHUUjXdbR1OVFabIcii30TJDMlASaDjKYOrvZBRAQA3kMfQ0qwQcmfLTrKqOTMAkCSoXGWmqKIz9XOM888g2eeeQZPPvkkEhIS8OSTT+Lxxx+HweDXJDfFgX6nB4MuLxaUZIuOAlmWkJtpRWunE16VbR9EFN901wC89XtgKD4DkhyZv78lgxlyWh60zgZorkHRcYh84nNB7XA4sGDBgqM7yTI0TcM555yD9957L2ThKDq1dh1dQ3T+jCzBSY7Ky7RC1XS0dvKFmYjim6d2B6B6YSyOvHaP48n2yYCmou+zD0RHIfKJzwV1Tk4OGhsbAQCFhYXYtGkTduzYAaNR7N2VKPK0dTlhsxiRlSbugsTjZSSbkWBS2PZBRHHPe+hjyCk5RwvWCCZb0yBZUtG78x+8lwBFBZ8L6uuuuw7V1dUAgJtuugk//vGP8e1vfxtr164NWTiKPh6vBkfvELLTIufKcUmSMCHTirYuJ/qdHtFxiIiE0Po6oDYfhGHqmZAkMcuZ+kO2T4anoxFqc4XoKESn5HMD1erVq4f/f84552Dbtm3weDywWrlyAn2hvdsJXQeyI2R2+pi8TCtqjvTi08+OoHRyuug4RERh56neCgAR3+5xjJyWB73tEDz7N8EwYaboOERjGnOG+vi3WTRNG/HPYDAgMTERGtf3peO0dTlhUCSkJZtFRxkhNckEi9mAD3c3iY5CRCSEt3or5KwpkJPtoqP4RJIV2EqXwXt4F7SBLtFxiMY05gz1/PnzsWvXLgBASUnJCW8R6boOSZJQXl4euoQUNXRdR2uXE1mpiZAj7O1ESZKQZ7dib1UHegfcSLaaREciIgobtfsINEc9zEuuFB3FL8nzVqLn0zfgKX8f5gWXio5DdFJjFtR///vfh/+/adOmkIeh6NY74IbLoyI7PbLaPY7Jy7TiUGMPdh5sw3nz8kXHISIKG2/1NgASDEULRUfxizEtB8rEOfCUvw/T6asgKZG51B/RmC0fubm5w/+32+3IyspCXl7e8L+srCzY7dHx1hGFXlv3EADAnir27ognY7MYMTHbhq3lbaKjEBGFja7r8FZvhZI7HbI1TXQcv5lmLYfu7IH38E7RUYhOyudVPq655hrs379/xGP79+/HtddeG/RQFJ06eo4ul5dgiswZBEmScPbpeTjU0I3O3iHRcYiIwkJz1EPrboah+AzRUcZFmTgHks0Oz36+U06Ry+eCurKyEqWlpSMeO+2001BRweVsCFA1DY5eF+wpkbNc3mi+MjcPOoAdFZylJqL44K3eCkgKDJPni44yLpIkw1SyDGpLJdTOBtFxiEblc0Fts9nQ0dEx4rGOjg4kJkZ2AUXh0dnrgqbpEdvucUyePQmTsm3YWt4qOgoRUcjpug5P9VYo+bMgJ9hExxk34/SvAIqRs9QUsXwuqFesWIEf/ehHqKyshNPpxMGDB3HbbbfhwgsvDGU+ihIdPUOQJCAjObILagBYVJKF2uY+tHXxVuREFNu0tmro/Q4YpywWHSUgUkISDFPOgOfQJ9DdfO2myONzQf2DH/wAU6ZMwWWXXYZ58+bh8ssvx+TJk/HDH/4wlPkoSrR3O5GWZIbB4PO3lDCLZmQDALbx4kQiinGe6q2AYoChcJ7oKAEzzVoOeF3wVH4kOgrRCXy+esxsNuPOO+/Ez372M3R1dSEtLS0qbl1Koef2qujud1ZVRZsAACAASURBVGPaxBTRUXySkZKA4vwUbCtvxcVnFoqOQ0QUErqmwVuzHYaJpZBM0d+eqdgLIduL4Nm/CcZZ57MGoYji13IMfX19qK2txcDAwIjHlyyJjtuYUmh09BxbLi96XrAXz8zGi+9Woqm9H3n2JNFxiIiCTm05CH2wG4bi6G73OJ5p1nIMvf801CPlMOSViI5DNMzngvq1117DPffcA4vFgoSEL/pkJUniTV/iXEf3EBRZQlpSZN1ufCwLptvx0j8rsbW8DatZUBNRDPJWbQUMZhgKSk+9cZQwFC2E9Olf4Nm/iQU1RRSfC+pHH30Ujz/+OM4555xQ5qEo1N7tRGZKAmQ5et5+S0kyY0ZBGraVt+LSr0zmW4dEFFN0zQtP7XYYCk+HZIieyY5TkQwmGKd/Be59b0Prd0BOyhAdiQiAHxclqqqKpUuXhjILRaFBlxcDQ15kRvhyeaNZXJKNti4n6lr7REchIgoqtfEA4BqAcUp03sxlLMaS8wAd8JS/LzoK0TCfC+rrr78ev/3tb6FpWijzUJTp6HYCQMTf0GU086fbocgSth7gmtREFFs81VsBkwVK/mzRUYJOttmhFJTCU/EBdNUjOg4RAD9aPp577jl0dHTgmWeeQWpq6ojPvf/++8HORVHC0euCySDDZjGKjuI3a4IRc4oysK28DZedVwyZbR9EFAN0rxvewzthLFoESfFr7YGoYZq1HM6398BbuwPGYi6MQOL5/JP20EMPhSzEe++9h8cffxy6rkPXddx8881YsWJFyM5HwePoGUJ6ckLU9iAvmpmFPVUdqGrswbSJqafegYgownkb9gGeIRii/GYuY1HyZ0FKzoZ7/yYW1BQRfC6oFy1aFJIAuq7jJz/5CV588UVMmzYNFRUVuOKKK3D++edDliP/JiHxbNDlxaDLi6IJyaKjjNvcqZkwGWRsLW9lQU1EMcFb9SmkxGQoE2aIjhIykiTDVLIMrk9fhtpRByVzkuhIFOd8rljdbjceffRRLF++HPPnzwcAbNmyBS+88ELgIWQZfX1HLwzr6+tDVlYWi+ko4Ph8/emMlOi7IPGYBJMBpcWZ2FHRBpXXBxBRlNPdTnjr98JQtAiSrIiOE1LG6UsBxQTPgc2ioxCNXVCrqopHH30UAHD//fejsrISDz/88PDb+1OnTsXLL78cUABJkvDYY4/hpptuwnnnnYe1a9figQceCOiYFB6OniEYDTKSo7B/+niLZmajb9CDirpu0VGIiALiPbwLUD0wFsfe6h5fJpmtMBafAU/VJ9BdA6fegSiExmz5uOaaa3DllVcCADZt2oR33nkHFotlePY4Ozsbra2BrZDg9Xrx1FNP4Te/+Q3mz5+PnTt34pZbbsHf//53WK1Wn46RkcEbc4yH3W7zex+r9Yv1TDv7XchKsyAp6eQz1Mdv7+/xQ7XPl5/3slQLnn27HHtrOnHuosh623A8Y0ThxTGKfJE6Rrqm+jWL7Mv2zf/cAUNKFrJnzR3XtS2WcbwG+7vPaNuPdYyxxs+1dBWaDn4I85EdSFl0sf9fU9Xr14Wb/h4/lkTqz1GkGPO7SFEUuN1uAIDRaISqqiM+39nZecKKH/4qLy9HW1vbcBvJ/PnzkZiYiOrqapx22mk+HcPh6Iem6QHliDd2uw3t7f6tv2y32zAw4AIAOF1e9A96MCkrafix0Yz1uWBsP559Rnvepxdn4qN9R3DZOUUwGiKj3Wg8Y0ThxTGKfJE8Rna7DY1vP+/z9vkXrhnzuWjOXjhr98JU+jV0dPSPK8+gn6+n6YBf+4y2vcVqPukx0jH6a/YwxQ45uxid296Cq/AryMpK8ftrGswxiFWR/HMULrIsjTmBO2bl8Ic//AH19fUAgAsuuAC33XYbGhoaAABtbW245557cNFFFwUUMCcnBy0tLaipqQEAVFdXw+FwoKCgIKDjUmg5eqO/f/p4i0qy4XR5UVbrEB2FiGhcvDXbAV2DIQ7aPY5nKlkGvacVatMB0VEojo1ZUMuyjJtvvhkA8IMf/AD5+fm45JJL0Nvbi5UrVyIrKwtr164NKIDdbsddd92FdevW4ZJLLsEPfvAD/PznPw945ptCy9EzBIMiIcVqEh0lKGZOSkNSopE3eSGiqOWt3go5LR9Ker7oKGFlKFoIKcEGz/5NoqNQHPO5cchkMuH222/H7bffjs7OTqSlpQVt7eFLLrkEl1xySVCOReHh6B1CRhSvP/1lBkXGghlZ+LisGS63CrMpPnvkiCg6af0OqC2VMC38f6KjhJ2kGGGccQ7ce/8OT3eb6DgUp3xuFm1oaBj+NzAwgMbGxuGPKb4Mub3od3qRkRwb7R7HLJ6ZBbdHw97qDtFRiIj84qnaCgAwTomvdo9jjCXnAZDQu/MfoqNQnPJ5hvqrX/0qJEmCrn9x8d+x2cny8vLgJ6OI5eg5evFIrPRPHzM1PxWpSSZsPdCKRTOzRcchIvKZt/oTyFlTICfbRUcRQk7KgGHyfPTt+SeU6efG7C3XKXL5/B1XUVEx4uP29nY88cQTWLBgQdBDUWRz9A5BkSWkJMVG//Qxsixh0cxsbN7ViMEhDywJ0b2+NhHFB7WrCZqjAeYzrxIdRSjT7BUYrNkOqbMBin2y6DgUZ8a9Ppjdbscdd9yBX/3qV8HMQ1HA0XO0f1qOkf7p4y0uyYZX1bG9gn14RBQdvFWfApIEQ9Ei0VGEkrOLYc4thtpWM+LddKJwCGjB3ZqaGjidzmBloSjgcqvoc3qQkeL/4v/RoDDHhtwMCz4qaxEdhYjolHRdh6d6K5QJJZAtKaLjCCVJEpIXXQS4+qH3clKEwsvnlo8rr7xyxIoOTqcTVVVVAS+bR9FleP3pGLsg8RhJknDWnFz87f1qtHYOIjvdIjoSEdFJae210HvbYDx9legoESFp5hK0v/U7qO01kFN4LQyFj88F9WWXXTbi48TERMyYMQOFhYXBzkQR7Fj/dGpSbM5QA8CSWTl49YNqfFTWgtVnF4mOQ0R0Up6qTwHZAEPhPNFRIoKkGKFkTobaXAHd2QcpkbfLpvDwuaC+9NJLQ5mDokRHzxDSk82Q5djrnz4mzWbGrMJ0fFLWjG98ZXJM9ooTUfTTNQ3emm0wFJRCMltFx4kYcmYh1JZKqO01MBSUio5DccLngvrxxx/3abt169aNOwxFtp5+F/oGPcjLjP0X7jPn5OD3/3cAB+u7MXNSmug4REQnUJsroA92w1C8WHSUiCIZzZDT86E5GqBPmAnJEFsrUlFk8rmgrqurwzvvvIPZs2cjLy8PR44cwWeffYYVK1bAbI7dt//pCwdqHQBib/3p0cybakeiWcHHnzWzoCaiiOSt+gQwJsBQMFd0lIgj24ugOeqhddRByZkqOg7FAZ8Lal3X8cgjj2DlypXDj73zzjv4xz/+gV/84hchCUeRpazaEfP908eYjAoWzsjC1gNtuGqFFwkm3iSAiCKH7nXBU7MdhskLOQM7CtmSAikpE2p7LeTsKZCkgBY1Izoln7/DPvzwQ5x//vkjHlu2bBk++OCDoIeiyFRW7UCazQwlhvunj3fWnFy4PCq2l3P5JSKKLN7DuwHPEIzTzhQdJWIpWUWAxwm9u1l0FIoDPhfUkyZNwosvvjjisZdffhkFBQVBD0WRZ2DIg9rmnphdLm80xXkpmJBpxft7joiOQkQ0gufQR5CSMqDkThcdJWJJKTmAyQK1rUZ0FIoDPr+Pfd999+Hmm2/GM888g+zsbLS2tsJgMGDDhg2hzEcRorKhG7oeH/3Tx0iShHPmTsDL/zyE+tY+FGRz+SUiEk8b7IbaWAbT3IvZyjAGSZKgZBVBbSyDNtAF2crrYSh0fC6oS0pKsHHjRuzduxdtbW2w2+2YO3cujEZjKPNRhDhY3w2jQUaaLb569c6cnYO/vV+N9/ccwZqVnAkiIvG8VZ8Aug7jVLZ7nIqcUQD1SAW0thrIk+eLjkMxbNxXWi1cuBCDg4PweDywWHg3uVh3sKEb0wrSoMjxNRtiTTBi0YwsfLK/BZedOwWJZl6cSERi6JqKzMwkNNV8AnPeNGRPnTb29qoXkhLfr1mSYoScUXD0jpJ5JZBMiaIjUYzy+Sft4MGD+N73vgeTyYTW1lZ87Wtfw/bt2/E///M/eOyxx0KZkQQbHPKivrUP/37+NDgH3aLjhN05p+fho7IWbC1vxblz80THIaI4JckK6l/dAG9bPZSJp6Hx7efH3D7/wjWn3ObL28ciJasIWnsN1I7DMEyYKToOxSifpxvvuusufP/738c//vEPGAxH6/CFCxdi586dIQtHkaGqqQe6DswpyhQdRYgpE5KRb7fig928OJGIxNI66wFJgpzGP+59JZmtkFJyoHUchq6pouNQjPK5oK6qqsLXv/51AEcb/QHAYrHA5XKFJhlFjIMNXVBkCdML4/OCDkmScO7peahr7UNtc6/oOEQUp3TVA62zEVJKLtee9pOSVQR43dA6G0VHoRjlc0Gdl5eHsrKyEY/t27ePy+bFgcr6bkzOTY7rm5ssmZWDBJOCd3c0iI5CRHFqoHIH4HVDyeDvXX9JSZmQEpOhtdVA13XRcSgG+VxQr1u3DjfeeCN+/etfw+Px4KmnnsK6detwyy23hDIfCeZyqzjc0ofpBamiowiVaDbg7NIJ2F7ehs7eIdFxiCgO9e3ZBBgTISVniY4SdSRJgmwvgj7UC72/Q3QcikE+F9TnnXcennnmGXR2dmLhwoVoamrChg0bsHTp0lDmI8GqmnqgajqmTYzvghoAzp+fD03XsWkX3zIkovDS3U44a/ZAzpg43HZJ/pHT8wHFBLW9VnQUikE+vYevqipWrlyJt956C3fddVeII1EkOdjQBVmSUJyXIjqKcJmpiZg/PQsf7D6CVWcWxnULDBGFl+aoB6Cz3SMAkqwcXUKvrRq628kl9CiofJqhVhQFiqLwAsQ4dLC+G5Nykrj+8udWLJyIQZcXH33WIjoKEcUJXdehOuqRUDgHktkqOk5UU+yFAHSoHXWio1CM8bnlY82aNbjllluwbds21NfXo6GhYfgfxSa3R0Vtcy+mT4zP1T1GU5yXgikTkvHujgZoGi9sIaLQ0/s6APcgkkuXi44S9SSzFVJyFjTHYei6JjoOxZBTTju2t7fDbrfj3nvvBQB8/PHHI66QlSQJ5eXloUtIwtQc6YVX1TEtzi9I/LIViwrw29fLsPtQO+ZP58VBRBRamqMOUIywTF+E7gb2/wZKsU+Gt3or9G6+00jBc8oZ6pUrVwIAKioqUFFRgWXLlg3/v6KigsV0DDvY0A0JwLR89k8fb960TGSlJeKNjw9z+SUiCindMwSt+wjk9ImQjWbRcWKClJwNmBKhdvCPEwqeUxbUXy4Ytm/fHrIwFFkO1ndhYlYSLAlG0VEiiiLLWHVmIepb+7GnissvEVHoaI56QNehZBaKjhIzJEmCklkIva8D7g6u2kTBccqC+svL83BGLj54vBqqj/Sy3eMkzpiVjazURPzvllr+TBBRSOj60Yvnjt6UxCY6TkyRMyYBkoTe3e+KjkIx4pQ91Kqq4tNPPx0uGr78MQAsWbIkdAlJiNrmXni8Gi9IPAlFlnHxmYX441vl2FvlwNypmaIjEVGM0XvbAPcg5LwS0VFijmQ0Q07JRf9n70OZfi4kWREdiaLcKQvqjIwM3H777cMfp6amjvhYkiRs2rQpNOlImIMN3QCAaRPZP30yS2Zn482PD+N/t9SitDiDN1sgoqBS22sBw9HCj4JPzpwEb9UnkLpboKTniY5DUe6UBfXmzZvDkYMiTGV9F/LsVtgsJtFRIpYiy7jozEl49q0K7KnqwOlT7aIjEVGM0F2D0HtbIedMgyT7vMIt+UGy2WFIyYLqOMyCmgIWET+lLpcLd955J1asWIFVq1bhpz/9qehIcc2raqhq6sV03m78lJbMykF2ugV/e78aXpVrmhJRcKgdhwEASuYksUFimCRJsM1dDr2vA7prQHQcinIRUVA/9NBDMJvN2LhxI9544w2sW7dOdKS4VtfaB5dHxfQC9k+fikGRcfl5xWh2DOKDPUdExyGiGKBrKjRHHaSUHEgmi+g4Mc122nkAANVRLzgJRTvhBfXAwABef/11rFu3brgHNTOTF3iJVFn/ef8015/2SWlxBmZOSsP/bqnF4JBHdBwiinJaZyPgdUPJKhIdJeYZkjMgJWdDc9TzzokUEOEFdUNDA1JTU/HEE09g9erVuPrqq7Fjxw7RseLawYZu5KRbkJLEmwj4QpIkXL6sGANOD978uE50HCKKYrquQ2urgZSYDCmJk0vhoGROAjxD0HvaREehKHbKixJDTVVVNDQ0oKSkBLfddhv27t2L7373u3j33XeRlJTk0zEyMnzbjkay209c11TVdFQ19eArc/NG/bzV6l+RHertx7PPaM8rUHa7DecvKsA/dzZg9fJpyM20+rSfqmpQlJP/XRuKrBRcHKPIF8ljZPnS65enqxWeoV4kTlsIc1LCKbf39/jB3j5Y5xjrGP6On795rLkF6G3YB6mnEZa8U/esR/L3UyjF6/P2lfCCOjc3FwaDARdffDEAoLS0FGlpaaitrcWcOXN8OobD0Q9N4801/GG329De3nfC44dbejE45MUku/WEz9vtNgwMuPw6T6i3H88+oz3vYLhw0UT8a08T1v/uIyyemeXTMnrXry49aZ6TjRFFDo5R5IvkMbLbbRj80uuXp74CMJjhtWRD/dLn0oETth9LqLcP1jksVvNJj5EO/16zR/uaniqP0+mBlJYHT3sNBnp6IRlOXpD7mydWRPLPUbjIsjTmBK7wlo/09HQsXrwYH330EQCgtrYWDocDkybxymYRDn7eP80LEv2XmmTG1V+bibYuJ5o6eMU4EflHH+o/ulReZiFvNBJmckYBoOvQOptER6EoJbygBoC7774bTz31FFatWoUf/vCHePDBB5GcnCw6VlyqqOtCVmoi0mzsnx6Pi84qQlqSCWU1nXB5VNFxiCiKqO01gCRDsReKjhJ35MRkSIkp0Dq52geNj/CWDwCYOHEi/vznP4uOEfdUTcPBhm4sLskWHSVqKbKE0uJMfLD3CPbXdmLeNN7shYhOTfe4oHXUQ07Lg2Q8sXeaQk/OmAi1sQyasxdyIif1yD8RMUNNkeFwSx+G3CpmTmK7RyCSrSZMzUtBY/sA2rqcouMQURRQ22sAXYWSPVV0lLglp+UDkKA5GkRHoSjEgpqGVdR1AQBmsH86YFMnpiAp0YjdhzrY+kFEY9JVD7T2WkgpuZASuZKCKJLRDCklG1pXA9ekJr+xoKZhBw53Id9uRbLVJDpK1FNkGfOn2+Hxqth9qAO6zlVoiGh0WkcdoHqg5HB2WjQlfSLgcUHvbRcdhaIMC2oCAHi8KqqaejBzUrroKDEjxWrCrMnpaOtyoqa5V3QcIopAuqZCbauCZMuEbOW7g6JJKdmAYoTWybYP8g8LagIAVDf1wuPV2D8dZIU5NuSkJ+LA4S509/u/xjYRxTatswHwuNg7HSEkWYGclgetpwW66hUdh6IIC2oCAByo64IkAdMmpoqOElMkScLc4kyYjQq2V7Sxn5qIhumqF2rLIUiWVEg2rggUKeT0fEBTofW0iI5CUYQFNQE4ekHi5NxkWBIiYiXFmGIyKlg4Iwsuj4YdFW28qycRAQD69r0PuAeh5Ez36c6qFB6SNR0wJkLrbBQdhaIIC2qC0+VFbXMv2z1CKM1mxtziDDh6XfisxsGLFIninK560L3lv4/OTqdw7f9IIkkS5PQ86L1t0L1s1SPfsKAmHGrsgarpmMGCOqTy7UkozktBXWs/apv7RMchIoE8B/8Fb28HlAkzOTsdgY6uSa1D6zoiOgpFCb6/Tyiv64RBkTA1L0V0lJg3c1Iq+p1ulNV2wmzk37NE8Uj3uuHe/QbM+TOgsXc6IkmJyZASbNC6mqDYJ4uOQ1GAv9EJ+2s7MTU/FSajIjpKzJMkCfOn2ZGebMauQx3YdbBNdCQiCjNP+fvQB7qQfs43OTsdoSRJgpyWB73fAd01KDoORQEW1HGuu9+FxvYBzJ7M9afDRVFkLJ6ZDZvFiJ8/tw3VTT2iIxFRmOhuJ9x73oSSOwOJhXNEx6ExyOn5AACtixcn0qmxoI5z+2s7AQCzWFCHldEg44ySbKTbEvCrv+5F9REW1UTxwL33LejOXpgXXyY6Cp2CZLZCsqaxj5p8woI6zpXVdiLZakJ+VpLoKHEnwWTAfd87E7ZEIx75yx5UNbKoJoplWn8n3Ps2wjBlMZSsKaLjkA/k1Dzozh7oQ/2io1CEY0EdxzRdx/7aTswqTIPMPj4hstIs+MmVpyPFasIjf92DyoZu0ZGIKERcO14FdA3mRf8mOgr5SE6bAADQujlLTWNjQR3H6lv70O/0YPbkDNFR4lp6cgJ+cuU8pCWZ8atX9mBvVYfoSEQUZGpHHbyVH8M4+6uQubJH1JBMiWz7IJ9w2bw4VlZztH+6hP3TJ5WSagnp6ieqqsFut8Fut+Gh75+Nu5/5BBte+wzf//e5WP7548fzqhoMin9/B7s9Knq6eZU6xbf01AQoRqNf+6geDzq7hwI+t67rcH36F0hmK8ynXxzw8eKJrqknvA6Gm5yaB7WpjG0fNCYW1HFsf20nCrKSkGI1iY4SsUxGBU+/ttfn7a9fXerX8RVFHnH8aXkp6O5z4bG/7MZ7OxtQYLeOWFbr+tWlfuUZTyaiWKQYjWh8+3m/9sm/cA2AwAtqb+12qEfKYT7rW5DM1oCPF08kWfFr3I6OWXDJaROgNpWx7YPGxJaPODU45EFVUw9mFXF2OpIYDDIWl2QjL9OKvYc6sKfKAVXjbcqJopXudsL1ycuQMybBOHOZ6Dg0Dl+0fTSJjkIRjAV1nCqrPlqozS5kQR1pFFnCvGmZmF2UgYa2fnxc1gKXWxUdi4jGwbXrf6EPdCFh6dWQZP7KjVZyWh50Zy/cDs5S0+j40x2ndh1sg8koozg/VXQUGoUkSZhTnIn50+3oHXDjw31H0DPgFh2LiPygdjbC89k7MM44G0p2seg4FAA59ehqHwPlHwtOQpGKBXUc0nUd28tbUTIpHUYDvwUiWV6mFWfNyYGuA1v2NePTsmbRkYjIB7quw/XRnwFTIkyLeBOXaHe07SMdA+WfiI5CEYrVVBxq6hhAW+cgSou5XF40SE0y4+zTcodvVV7Z0A1dZ181USTzlL8PtfkgzIv/HXKC2FUqKDjktAlwtx2G1s2JDToRC+o4dGyd49OmZApOQr5KMBtw1uwcnD03HxX13dhxsB1eryY6FhGNQut3wLX1FSh5JTBOP1t0HAqSY20fnprtgpNQJGJBHYf2VHWgOD8FaTaz6CjkB0WR8aOr5qGkMA0tjkF8uK8Z/YMe0bGI6Di6rmPow2cBXUfC2deMWPaSoptkSoQ5fzq8LKhpFCyo40zvoBs1Tb1YVJIjOgqNgyRJKM5LwZJZ2XB7VXy47wiaHQOiYxHR57yVW6A2lsG8+DLeETEGJc08E1pnA9s+6AQsqOPMZ9UO6AAWzmJBHc0yUxNxTukEJCUasb2iHeV1XeyrJhJM63dg6JOXoORMg7GEa07HIuuMJQDY9kEnYkEdZ/ZUdSA1yYQpeSmio1CAEs0GnDUnBwVZSTjU2INPD7TC7eF61UQi6JqGofeeBjQNCedcC0nir9dYZEjOgJI9Fd6abaKjUIThT3wc8Xg1lNV2orQ4k319MUKRZcydmonTpmSgo2cIH+5tRk+/S3Qsorjj3vcPqM0VSDjzKsgp2aLjUAgZpiyC1tkIlbcip+OwoI4jlQ3dcLlVlBZzdY9YU5hjw9I5udB0HVs+a0FDW7/oSERxQ+04DPeOV2GYvACG6V8RHYdCzDB5AQDw4kQagQV1HNlT1QGTQUbJpDTRUSgE0mxmnF06AalJJuw+1IHPahzQNPZVE4WS7nFhaPNTkBJsSPjKf/DdvzggW9OOtn1Us6CmL7CgjhOarmNXZTtKCtNhMiqi41CIJJgULJmVg6IJyaht7sPH+1vQ1TskOhZRTNJ1HUP/eg5adwsSzrsBUkKS6EgUJoaihdC6GrnaBw1jQR0nao70oqvPhQUzuIxTrJNlCbMnp2PetEz09Ltxy6Pvo6qpR3QsopjjqfgA3qpPYJr/DRjySkTHoTA61vbB1T7omIgqqJ944glMnz4dlZWVoqPEnB0VbTAoEuYWs6COF/n2JCw9LRcmo4IHXtyF93Y1cmk9oiBR2w/D9dELUPJnwzRvleg4FGZyUjrk7GJ4a1lQ01ERU1Dv378fe/bsQV5enugoMUfXdew82IZZhemwJBhEx6EwSrGa8Ogt56CkMB1/fqcSf3yrHB4vl9YjCoTuGoDzn09CSkxGwrIbuURenDJOXgjN0QCtp0V0FIoAEfEq4Ha7cc899+Cuu+4SHSUm1TT3wtHrwoIZWaKjkABJFhPWXXYaLjmrEB991oKH/7IH/U7espxoPHRdg/O9p6H3dyLx/JsgJ9hERyJBDEXH2j52CE5CkSAipisff/xxXHLJJcjPzx/X/hkZvBBkLP/3SR0MioTzl0xGUqJx+HG73f9fBFarOaK293cfVdX8ft4in/NonxvP1yg7KxnXry7FjMmZ+NXLu/DAS7tw53VLkJtpPWFbVdWgKL7/re1VNRj82N7f40e68fwcUXgdGyOLnz87uqaeML7dn7yO/vo9yFjxHaTMPv2E7SXZv4u+/c0UadsH6xxjHSPSnvPw94TdhqYJU6HX74R9xRV+HSMa8bVubMIL6t27d6OsrAy33nrruI/hcPRzebCT0HUd/9rdhJLCdDj7h+DsP7rig91uQ3t7n1/HstttGBjw76Yhod7e330URcbTr+31efvrV5cKe85Wq3nUz43na3RsrGfkJ+PWb87Fhlf34YePfYAf/HspJucmj9jWbrf5/TXyVO0FkAAAIABJREFUd3t/v/ci1Xh+jii8jo2R3W7DoJ8/O+mygsa3nx/+WOvrgPfQx5BTJ6C3vQN9x30OAPIvXOPX94O/mdKBiNo+WOewWM0nPUakPed0YOQYF8yD+9NX0FpdDTk5dt8F5mvd0Qv+x5rAFT5NtH37dlRXV2P58uVYtmwZWlpacO2112LLli2io8WE2uY+OHqHsJDtHvS5aRNTcceaBUgwKXjw5d2oqOsSHYko4umeIXhrdwBmC5RJc7neNAHgah/0BeEF9Q033IAtW7Zg8+bN2Lx5M3JycvCHP/wBS5cuFR0tJuyoaIMiSzh9Ku+OSF/ISbfg//vWfGQkJ+BXf92LPYc6REciili6rsFbuxNQvTAULYKkGE+9E8UF2WaHbJ989I8timvCC2oKHU3Xsa2iFbMmp8OSwF8ANFKazYz/umoeJmZZ8cRrn2FbeavoSEQRST1SAb2/A0pBKeTE5FPvQHHFWLQQWnsttN520VFIoIgrqDdv3oxp06aJjhETKuq60Nnrwpmzc0RHoQiVlGjErd88HcV5yfj9/x3Ajoo20ZGIIorW0wKt9RDkzElQMiaKjvP/t3ff8VHV+f7HX+dMTe8JKQRIgNB7EwwoonRBLHAVdfXKet11/a27usvq1VXZXUW9utZrW3TX69oFBcUK0pQqSC+JoaRDeplMOef8/ohEIkETSHImmc/z8ZgHzJzvOec9800mn/nO95wj/JC1x0gAGaUOcH5XUIvWs2FXAUEOq0z3ED8pyGHlt1cNJi05nOc/2MPXu/LNjiSEX/CWF+M7/A1KUASWlIFmxxF+Sg2PQ43tLvOoA5wU1J2Uy+1j24HjjO6XgM3astM4icDjtFu5/crBdE8MY/G/tlJYWmt2JCFMZeg6xe/9D2BgTRvZ4tPhicBiTRuBfvw79Co5HiVQSUHdSW3dX4zHpzNuoEz3EM0T5LDyu6uG0CM5gm0HjlNaWWd2JCFMo+XvwV2QhTV1KIrj9PO1C3Eqm0z7CHhSUHdSG3YV0CU6mLREOYBGNF+Qw8p9N43BabewaV8xlbUesyMJ0e708gL04u8IHzkNNSrJ7DiiA1AjElBjUmXaRwCTgroTKiqr5WBuBeMGdpFzpYoWiwh1cF7/BFRFYeOeImrrfGZHEqLdGO4afEe2owRHEjPxOrPjiA7EmjYSvTgbvbrE7CjCBFJQd0Jf7SpEUWDsgESzo4gOKthpY0z/BHyazqZ9Rfh8utmRhGhzhq5//5W9gbXHCBSrnG5UNJ9M+whsUlB3Mpqus2F3Af26RxMV5jA7jujAIkLsjMiIp7rWy7aDxzEMw+xIQrQpLX8PRm25zJsWZ0WN7IIa3VWmfQQoKag7mR2HTlBa6Wbi0GSzo4hOID4qiAFp0RSVudgrlygXndjJedNqXJrMmxZnzZo2Er0oS6Z9BCApqDuZz7fmEhvhZHBPOfe0aB09EsPp3iWM7LxKjhZXmx1HiFZnuGsb5k1bkvuZHUd0YLb00QD4vttschLR3qSg7kSOFlVx4Fg5E4eloKpyMKJoPQPSoomNcLIzu4SKarfZcYRoNYZh4Du8DYzv503L+abFOVAjElDjeuDN2mR2FNHOpKDuRL7YlovdppI5WA5GFK1LVRSG947DblXZcuA4Hp9mdiQhWoVedAijphRL6iCZNy1ahS19FPqJw+gVhWZHEe1ICupOotrlZePeIsb270KIU45MF63PYbcwIiMOl9vH9oMn5CBF0eHpNWVo+ftRo5JRo1LMjiM6CWvaKAC82TLtI5BIQd1JrP02H69P56Lh8kdBtJ3ocCf9u9cfpJiVV2F2HCHOmqH56qd62BxYug6Sc/aLVqOGxmDp0htftkz7CCRSUHcCPk3ni2259O0WRXJcqNlxRCfXIzGMpJhg9h8pp7RKLk8uOiYtfy+4a7B2H4ZitZsdR3Qy1vTR6GV5aKW5ZkcR7UQK6k5g/c4CyqrcTB2TanYUEQAURWFwzxiCHFa+OXCCGpfX7EhCtIheXYJ+PKf+FHlhcWbHEZ2QNW0kKIqMUgcQq9kBOqqIyGDstuYfDe7xalSU17Z6Dp+ms+Lrw6QnhzN2SAoOe/O7NCIyuE0yCf+iaTpxcWGtuk2b1cKw3rFs2FXIs+98S0SwVb4yFx2CoWv4jmwHezCWpL5mxxEdjKFrzXw/DaOg+yC8OZuJmnQ1ZRVydqTOTgrqs2S3WXjxvW+b3X7BnMFtkmP9zgJKK938YmofHHZrszOFhDi4enKfNskk/IvForbJz2p0uJOM1EjW7shjSM8YUhNat2gXoi1o+fvrp3r0HItikT+BomUU1ULuyn81q62mq2jlRfiKc8AhFwvq7GTKRwd26uh0/+7RZscRAahXSgQD0mPYnVNKbZ3P7DhC/CS9pgy9OAs1thtquEz1EG1LjUwExUL1rjVmRxHtQArqDuzk6PSscT3k63ZhCkVR+H9zh2IYsCNLTqUn/Jfh86Id2Q42J5bk/mbHEQFAsdhQI7tQvW8DhiYDDp2dFNQdlMer1Y9OJ4XTv4eMTgvzdIkJoX+PaE5U1HG4sMrsOEI0qWzDOxh1VVhTh6BY5Fz9on2o0V3RXdX4jjV/2p3omKSg7qA+2niE0ko3l09Il9FpYbpuCaHERTrZe7hMzvoh/I5WcpTyr5aiRqegRiSYHUcEECU8DktIJL6DX5kdRbQxKag7oBPlLlZuOsrIPvH06RZldhwhUBSFIT1jURTYLlM/hB8xNB91X/4DS1AYlpSBZscRAUZRVEL6n4/v6LcYddVmxxFtSArqDuiNVVkoCsyd2NPsKEI0CHJYGZAWQ2mlm+/yK82OIwQAFZs+QC85QuyUBXIBF2GKsAETQPfh/U4uRd6ZSUHdwezJKeWbg8eZcV53osOdZscRopGucSEkRAWx72g51bUy9UOYS688Ttm6t7F2H05InzFmxxEByt6lB2pUEt5DMu2jM5OCugPx+jT+/flB4iODmDxKrooo/M/JqyhaVIXth46jy9QPYRLDMKjb8CqoKo6x15gdRwQwRVGw9hqHXpSFXl5odhzRRqSg7kDeXfMdBSW1XHNJb2xW6Trhn5x2K4PSoimr9pCdV2F2HBGgfIe3oR3bSdT4uaihciYkYS5b73GgqHgPrDU7imgjUpV1EHsPl/LplmNcOCyZgWkxZscR4iclxYaQGBPMgaPlVNV6zI4jAozhceH+6t+oMV2JGDnd7DhCoAZHYu02BO/B9XJO6k5KCuoOoNrl5R8f7qNLdDBXXSgHIgr/pygKg9JisFpUdhySs36I9uXetgyjphTn+dejqBaz4wgBgK3PeAxXJb6jO8yOItqAFNR+zjAM/u/TA1TWePjlpf1w2OSPg+gYHHYLA05O/ZCzfoh2opUcw7v7M2x9JmBJkAEI4T8sKYNQQqLw7pdLkXdGUlD7uc+2HGPzvmJmZ/age5dws+MI0SLJsSF0iQ5i/9FyquWCL6KNGYZO3fp/ojhCcIy60uw4QjSiqCq2jEy0Y7vRq0vMjiNamRTUfuzbrBO8uSqL4RlxTB3Tzew4QrSYoigMSo/BoijskAu+iDbm3b8WvSgLx5i5KM5Qs+MIcRpbRiZQ/7MqOhcpqP1U7vFqnvtgD6kJYdw0vR+qXF5cdFBOu5X+adGUVrrJKagyO47opHRXJe7Nb2NJzMDaa5zZcYRokhoWhyWlP94D6zB03ew4ohWZXlCXlZWxYMECJk+ezMyZM7n11lspLS01O5apyqrcPPnOTpx2C7+5fCAOu8ybFh1b17gQ4qOC2HekjMKSGrPjiE7Ivekt8NThOP86FBmAEH7M1mcCRk2pHJzYyZheUCuKwk033cQnn3zC8uXL6dq1K48++qjZsUxTVuXm4X9/Q7XLy22XD5KrIYpOQVEUBqfHoCjw1Fs7ZOqHaFW+ggP4Dq7HPngKlqhks+MI8ZOs3YehhETj3f2Z2VFEKzK9oI6MjGT06NEN94cMGUJ+fr6JicxTVuXm4de3U17j4XdXDaFHohyEKDqPIIeV/t2j2Zl1gjU7AvN3XLQ+Q/PhXv9PlNAY7MMuNTuOED9LUS3Y+l+Elr8PrfSY2XFEKzG9oD6Vruu8/vrrTJw40ewo7a6koo5HXt9OebWb3101mJ4pEWZHEqLVpSaEMrhXLG+tzqKkos7sOKIT8Oz6BL0sH+e4a1GsDrPjCNEs9j4TwGLHu/tzs6OIVmI1O8CpFi1aRHBwMPPnz2/RejEx5hzNHRLS/DdvTdOJiwtrcllWbjkPvraNOo/G/QvOo39aDJqmY7G0/PNOa2Vqje23R/v22IeZ7Zta1tFfo1sv6MVvHl3N66uyuG/BmA4/37Wlv0OidRi6hq+yhNxv3ie49yi6jMg8Y9uTfRR8Fr87LVnH0LUW/zy0NJO/tW+tffzUNvztOZ/Na3T6z0UYysDxVO9eS/TUG7AE+//7SFu+1xm61qKLMLW0fXvwm4J68eLFHDlyhOeeew5VbVkhWVJSja6375zMuLgwamrczW5vsai8+N63pz1eWFrLtgPHsVtVRvdL4KsduXy1I5cFcwZz/HjLzojQkkwhIY4zZjqTBXMGt+g5A23evj32YVb7kBBHk8s6+mvUJSaEyyek89pnB1m66iCZg5JatC9/EhcX1uLfU9E6YmNDyX31Pgxdw2uPIHflv5psFxzioLbGTcrU66ht4c91NLRonWjVcsYcTWlpphbnaeP2rbWPk33UVts3s/3JdZp6n9B6TsDY8TkFGz7EMWR6i7bZ3tr6vS4uLqzFvzvt/d6rqspPDuD6xZSPxx57jN27d/PMM89gt9vNjtMuDMPgUG4Fm/cVExpkI3NwIuEhgfHchbhwWDK9u0byxhdZlFW1/AOCEDX7N2JUFmFJ7INiDzY7jhAtZonuiiWpL949X2DomtlxxDkyvaA+dOgQzz//PMXFxcybN49Zs2bx61//2uxYbcrj09i8r5h9R8pIiglm3MAuOO1+82WBEG1OVRRumNYHTdN59ZMDctYP0SKGu4aST15CCYpAjU8zO44QZ80+4JL6U+hlbzI7ijhHpldxvXr14sCBA2bHaDdlVW62HTiOy+NjQI9oeiSGdfg5pEKcjYSoYOaMT+ONVVms21nA+MEdd+qHaF/uTW+j1VZizchEUUwfFxLirFm6DUaNSsGzfQXWnmPk57kDk55rJz5NZ//RMtbvLMAwDMYN6EJaUrgU0yKgTRrZlT6pkbz++SGKy2rNjiM6AF/BAbz7vyRi1AzU4Eiz4whxThRFxT50Bnp5Pr6cbWbHEedACup2UF3r5Q9PrePgsQqS40K4YGiSXLBFCOqnftw0ox+qqvDSin1ocile8RMMzYt73SsooTFEjZ9rdhwhWoU1bRRKeAKe7Stk+lsHJgV1GzIMg5yCStZ8m09hSQ0jMuIY1jsOm9W/TvUihJmiw51ce0lvsvIqWLnxqNlxhB/z7PgQvbwA5/nXo9plUEJ0Doqq4hgyHb3kCNqxXWbHEWdJCuo2Uuf2sXFvEbu+KyU63MFTd1xIUmyI2bGE8Euj+yUwqm8876/PITuvwuw4wg9pZfn180zTx2BNHWR2HCFalbXXWJSQaNzbP5BR6g5KCuo2kH+ihtU78imtdDMwLZox/RKIiQgyO5YQfktRFK6bnEFUmIPn3t9DTZ3X7EjCjxiGjnvdK2Bz4Bh7tdlxhGh1isWKfcg09KIstLw9ZscRZ0EK6lbk9el8c/A4Ww8cJ8RpZcLgJHokyoGHQjRHsNPGzbP6U17t5uWP9ssojWjg3b8WrfAgzjHzUIPCzY4jRJuw9ZmAEhaLe9ObGIYcT9LRSEHdSkoq6/hyRx55x2vo3TWC8wcmEhpsMzuWEB1KelIEV1yQzjcHj7Pqmzyz4wg/oFeX4N74JpbEPlh7n292HCHajGKx4Rh5OXrJMXyHvjY7jmghKajPkW4Y7D9SxoZdhSiKwrhBXeiTGoWqyqi0EGfjkpFdGZwew5urDsl86gBnGDp1a5aAoeOccKN82yc6PWv6aNTY7ri3vIvh85gdR7SAFNTnoKbOy4ZdhRzMrSAlLoQJg5OIDpMjz4U4F4qi8J8z+hEV5uDppbsor5ZLkwcq795VaHl7cIyZhxoeb3YcIdqcoqg4xszFqCnFs/tzs+OIFpCC+iwYhsGqrcdYsyOfqloPw3vHfn86PHk5hWgNoUE2fjNnEC63j2fe24XXJ/MJA41eUYh741tYug7E1vcCs+MI0W6sSX2xpA7Gs2M5el2V2XFEM0kFeBbW7Mjn8de/ITzEzgVDkkmOCzU7khCdTkp8KDdN70d2fiX/9+kBOUgxgBi6huvLl8BixTlepnqIwOMYdRV4Pbg3vmV2FNFMUlCfhbSkcH5z1RDGDuhCsNNqdhwhOq0RfeKZMbYb63YW8PEmuehLoPBsW4ZelIXz/OtQQ6LMjiNEu7NEJ2MfPAXfwXX48vaaHUc0gxTUZyE1IYxLRndDlVETIdrc7Mw0RvWN5+0vs/l6d6HZcUQb8+XuwbN9BbaM8dh6jjE7jhCmsQ+bhRIeT926f8oBih2AFNRCCL+mKgr/Ob0ffVIjWfLRPvbklJodSbQRvbaCutXPo0Ym4hh7jdlxhDCVYrXjzPwFRmURnu3LzY4jfoYU1EIIv2ezqtw6ZxCJMcE8vXQX2flyOr3OxtB16r58EcPjwjnpVyg2h9mRhDCdNbkf1t7j8Oz4CK1Epr35MymohRAdQrDTyu1XDSEi2M5jb+6QorqT8Wx9Fy13N46x12CJTjE7jhB+wzFmHkpQGHVf/C+Gt87sOOIM5Ig6P6VpOnFxYWbHEMKvRIU5+MPVQ3n439t57M0d/G7uENKTIhq1iYgMxm6ztGi7Hq9GRXlta0YVLeDN2ohnx4eEDb2EuPEzm7WOpsmpFEXnFB3pxGI79UrLYbgu+y0Fr90P294gbuZvGrXXvF5Ky6XQNpsU1H7KYlF58b1vW7TOgjmD2yiNEP4jOtzZqKi+7fJBZKT+cCYIu80ivzsdiHbiMHVrluDs2pf3ssMwnn6zWev9161z2ziZEOaw2GzkrvzXaY+rXXpTvfNLXBUVWGJSGx5PmXodIAW12WTKhxCiwzlZVEeGOvifN3ewaW+R2ZHEWdBrynB98iSKM5T4OXdgKC37ZkGIQGJJzEAJjUE7thPdVWl2HPEjUlALITqk6HAnf5o/nLTEcJ7/YA8rNx6Ri790IEZdNa6PHsXw1BI0+f9hDY00O5IQfk1RFKzdh4NqxZe9UeZT+xkpqIUQHVZokI3fzxvScJ7qF1fsxeX2mR1L/AzD56b2k7+jVxQRdMltWGK7mR1JiA5BsQdh7TkGfB58WRsxNK/ZkcT3pKAWQnRoNquFX17an8sye7BpbxG/f2INVbVyEQR/ZWg+XJ89g16UjXPizViT+5kdSYgORQ2OxNpjJIarEl/OVgxNBhH8gRTUQogOT1UUZo7rwR1zh1BV42XttwXkFFTKFBA/Y/jcuD59Au3YThznX4ctbaTZkYTokNSIBCypgzEqiyla+piMVPsBKaiFEJ1G3+7RPPH7C4gOd7Dru1K+2l1ItUv+0PgDw+PCtfIxtGO7cYy/AXu/C82OJESHZonthiVlALUHNuH6+O8YXrfZkQKaFNRCiE4lOtzJmH4JDOkZQ0WNhy935HPgaDk+OW+xaXRXJbUfPoJWeAjnxJux95lgdiQhOgVLfDpxM29Fy99L7UePYNRVmx2p1Rm6D8PjwnDXYHhq6//vh98+ynmohRCdjqIopCaEER8ZxO6cUg4cK+dIURV9UiPpGh+KoihmRwwY2onDuD55EqOuCufFt2LrPszsSEJ0KmGDLqSqDuq+eI6apfcTNOnXWOK6mx2rRQzDwKirQq8pw3BVYtRV1X848LrB0E5rX5GQCOn+9S2XFNRCiE7L6bAyok88JZV17M0pZUdWCVl5FfRMjiAlLhRVlcK6LXmzvqZuzRIUZxjBs+7GEtvd7EhCdEq2HiNQZy7E9fmz1L7/Fxxjr8bW90K/HjzQq47jO7YLLXc3R4qz0Gu/P7e2oqI4Q1FDosAWhGK1g9UOigKGARiEDsikzGVq/NNIQS2E6PRiwp2cPyiRgpJaDuZWsCOrhAPHyumRGE5qfKjZ8Todw1NL3Vev4zu4DktiBs5Jv0YNCjc7lhCdmiWhJ8GX30/d6hdwr/8XvqM7cY69BjU8zuxoQP0otH48B9/hb/Ad/ga9PB8AJTSGkJ7DcZWXoYZEg/Pnv0W0hkaBq6o9YjebFNRCiICgKApJsSEkxgRTXO4iK7eCvYfL2H+kDLdmMCojjl5dI1H9eESnI/Ad20Xd2pcxasuwD5mBfcRsFFX+1AjRHlRnGEFTbse76xPcW5dR8/Zd2IfOwD5oav1IbzszdA2t8CC+nG34Dn+DUVMKioolMQNHnwlYUgeiRiQSHx/e5OXWOxJ5lxNCBBRFUUiICiYhKpjKGg+HC6vYuLuQ1dtyiQpzMLpvAiP7xtOtS5gU1y2gVxTi3vwOvpytqJFJBM26B0t8mtmxhAg4iqJiHzQVa9po3BvfwLN1Kd49q7ANvBh73wtRHCFtun/DXYPv2C58x3aiHduFUVcFFhvWlAFYR87BmjoExdn5vhmUgloIEbDCQ+wMSo/h2un9+XzjYTbuKeSzrcf4ePNRIkLtDE6PZWBaDBmpkYQG2cyO65f06hI8Oz7Cu+9LsFixD5+NffC0cxoNMwwwAN0Azaj/F0BV6m8WBb88yl8If6KGRhM06Vf48ifi2bECz+Z38Gxfga3nGKxpo7AkZqColnPej2Ho6KV5+I59i3Z0J1rRITAMFEcolq4DsXYfhrXrQBSbsxWelf+SgloIEfCcDiuj+yUwul8C1S4vO7NPsCOrhM37ilj7bf08v5S4EDK6RpGRGknv1EjCg9v/61N/YRgGelEWnt2f4svZBoCt7wXYh81CDY5och2vT6Ok0s2JChclFXVU1HioqvFS5fJQWeOh1qNRWOxA+76Ihp/+dmDlHR9gVcNxWgzC7PW3UJtOqN0gzGYQ4TCIduok6wrBUnuLAGZN6oM1qQ9ayVE8Oz/Ge+hrvPu+RHGGYUkZgCU+DUt8Omp0ys9+EDY0H3plMXp5AXppLlpxFlrxd+CuAUCN7YZ9yAysqYNR49JQ1MA5O7NfFNQ5OTksXLiQ8vJyIiMjWbx4Md27dzc7lhAiAIUG2Rg7IJGxAxLxaTo5BZXsP1rOwaNlrNuVzxff5AKQEB1Mr66RJEQ66RofRmpCKBEhdr8+qv5cGIaBXpaLL3sz3u+2YFQUgj0Y28BLsPe/CC0omuJKNyeKSjhRUUdJRR0nKuo4UeHiREUdFdWnXw4+2GElLMROeLCN5LhQqCnDooBFBVUxsCg/jEpjgE79aLVuwJAR/Sk7uAuXD6o8KtVehbxqC9VehRrvj/+IO4jYspJINYQYZ32hHe3UiQn6/ubUcfrFX0Mh2o4lJpWgC3+Jkemun5Lx3Va0/H34sr7+oZEjBDUkun5aiKoCCugax3y1+GoqMVyVYJw8p7+CGpWMrcdwLAm9sKQMqD8zR4Dyi7eQP//5z1x99dXMmjWL999/n3vvvZd//atjT04XQnR8VotKr5RIeqVEwtju+DSdw4VVHDhaRk5BFYeOlbP+29qG9icLw/iooPpbZBBxkfX/d9r94u222QxdR68sRC/Kxpu3j4rc7yit9lKmh1AR1oeKqEsot8RQetBLydYDpxXMFlUhOtxBbEQQA9NiiI1wfn8LIibcSUSoHavlh8I3Li6M557OaXa++VP6kmtsaXKZpkOlR6G0TqVKt1NQoeOO68bRg1nkVavsOmHFqzf+4BNi04l2GsQ4fyiyM/YWYlSrRNgNQmwGnfSzkggwitWBrccIbD1G1J//uaYMrTgbvaIQo6as/uauwfD5wNBRVCu2qC4Q0x0lKAI1MhE1ogtqZCKKPcjsp+M3TH+HLykpYe/evbz88ssAzJgxg0WLFlFaWkp0dLTJ6YQQ4gdWi0rP5Ah6JtdPa4iLC+PIsVKOFVdztLiaY0XV5J2oYduB46dd8jws2EZEiIOIUDuRIXbCQ+1EhjgID7ET4rQS5Ki/BX//f7tVbZPRbsMwcHs1al0eXFVV1FZX4aquprqyiqrKKiqraqmqcVNZ66NGt1OhB1Gu98BHzx82Ug02q0p0uIuYcAcDe9QXzDERTuIig4iNcBIZ6jDtPN8WFaKcBlFOjeAQndpoNylTh5C7cuf3rwFUeRRK6lRK61RKXPX/P+FSKaxR2VPyfcF9cBMQBoBVNYiwG0Q4dCIc9f+G2uoL7ZO3umPl1LgUHBZwWAysKlKEC7+mKApKaDRq6E/XW3FxYRw/7l+nqfM3phfUBQUFJCQkYLHUT4y3WCzEx8dTUFDQ7IK6vd+09dpyKrdvpLe7qNnrVG4vore7uPntv2mq/U9PBKxfp+lMyo9WtesWKrcdIaPu5zL9sGLltoJmtP9BxdZzad+8SY8VW/Mb7ePnfhIqtuTRp+74GZaevs/69s14Dt+vWrE5lz6uM22/iTybj9H3DO2tmhWfx9fs9mfcx6am1jnz61u+6WiL9lG+sXntT/ZN+cYj+OpO//r/J7VgDmx5kJ1+rhMtWrl842F8PyqAacZBb+VBNuwuL+lAugNI/f4G1PkUSt0KJS4LpXUqZW6NKm8d1R4PxccVvstXv58r3DRVAZtqYFHBptQXZyfvK2d8TvUXPcAATTfQdPDqChoKPl1BMxQ8uuUMa1uBKBSiCLL6CI1ViA2y0iM0hMiocKLCHESGnrzZCXZaW73gDwsLblF7S9DPn61Addix6NbT2kcFQ9NfThsYhkaNV0HtN5HsjWupdCtUeU7eVKo8CtkuFfePr/J8eDOQ0HBXAeyW+n6zWwzsFrB//3+LCsFvb8d7Igbl+wMtVQVU1Wg48FKlfurLyWkvoWuyqSmO+mHjpzj17sluCVv/HVWerqSQAAARNUlEQVQnolA4vbBvqn34xsNUlbbsK/uwFq7TVHtrlQWft+m+b43tm9n+5Do1Nc1/zwsJKWlxppZs/2yEhJS06T5a+pwvqHC1e+33c/tTDJMPld69ezd//OMf+fDDDxsemzZtGo888gj9+/c3MZkQQgghhBA/z/TDLxMTEykqKkLT6q/VrmkaxcXFJCYmmpxMCCGEEEKIn2d6QR0TE0Pfvn1ZsWIFACtWrKBv374yf1oIIYQQQnQIpk/5AMjOzmbhwoVUVlYSHh7O4sWLSUuTK2wJIYQQQgj/5xcFtRBCCCGEEB2V6VM+hBBCCCGE6MikoBZCCCGEEOIcSEEthBBCCCHEOZCCWgghhBBCiHMgBbUQQgghhBDnQArqAOFyufjtb3/LxRdfzJQpU1i9evUZ2+7bt49rrrmGadOmMW3aNNasWdOOSQNXS/oIwO12M336dObMmdNOCUVz++jzzz9nzpw5zJgxg+nTp7NkyZJ2ThpYcnJymDt3LpMnT2bu3LkcPnz4tDaapnH//fczadIkLr74Yt5+++32DxrAmtNHzzzzDNOnT2fmzJnMmTOHdevWtX/QANacPjrpu+++Y/DgwSxevLj9Avo7QwSEp556yrj77rsNwzCMnJwcY+zYsUZ1dfVp7WpqaoyJEyca27dvNwzDMLxer1FaWtquWQNVc/vopAcffND405/+ZFx22WXtFTHgNbePduzYYRQWFhqGYRiVlZXGpEmTjC1btrRr1kBy7bXXGsuWLTMMwzCWLVtmXHvttae1Wbp0qXHjjTcamqYZJSUlRmZmpnHs2LH2jhqwmtNHa9euNWpraw3DMIx9+/YZw4cPN1wuV7vmDGTN6SPDMAyfz2fMnz/f+N3vfmc89NBD7RnRr8kIdYBYuXIlc+fOBaB79+4MGDCAtWvXntZuxYoVDB8+nCFDhgBgtVqJiopq16yBqrl9BLB161YOHz7MrFmz2jNiwGtuHw0ePJiEhAQAwsLCSE9PJy8vr12zBoqSkhL27t3LjBkzAJgxYwZ79+6ltLS0UbuPPvqIK6+8ElVViY6OZtKkSXz88cdmRA44ze2jzMxMgoKCAMjIyMAwDMrLy9s9byBqbh8BvPDCC1xwwQV07969nVP6NymoA0R+fj7JyckN9xMTEyksLDytXVZWFlarlQULFjBr1izuuusuKioq2jNqwGpuH9XW1vK3v/2N+++/vz3jCZrfR6fKzs5mx44djBkzpq3jBaSCggISEhKwWCwAWCwW4uPjKSgoOK1dUlJSw/3m9J1oHc3to1MtW7aM1NRUunTp0l4xA1pz+2j//v2sX7+eX/ziFyak9G9WswOI1nHZZZeRn5/f5LKvvvqq2dvRdZ2NGzfyxhtvEBsby4MPPshDDz3Egw8+2FpRA1Zr9dHDDz/M1VdfTUJCwk/OcRMt11p9dFJxcTG/+tWv+POf/9wwYi2E+GmbN2/miSeekGMP/IzX6+Wee+7hwQcfbCi8xQ+koO4kli5d+pPLk5KSyMvLIzo6Gqj/NDp69OjT2iUmJjJ69Gji4+MBmDlzJnfddVfrBw5ArdVH27ZtY+3atTz77LO43W4qKiqYOXMmy5cvb5PcgaS1+gjqv0K94YYbuOmmm5g6dWqrZxX1EhMTKSoqQtM0LBYLmqZRXFxMYmLiae3y8/MZNGgQcPqItWg7ze0jgO3bt3PnnXfy7LPPkpaWZkLawNScPjp+/DhHjx7ll7/8JQCVlZUYhkF1dTWLFi0yK7rfkCkfAWLKlCm8+eabABw+fJhdu3aRmZl5WrupU6eyc+dOqqurAVi7di0ZGRntmjVQNbePli9fzqpVq1i1ahWPPfYYvXv3lmK6nTS3j8rKyrjhhhu45ppruPLKK9s7ZkCJiYmhb9++rFixAqg/DqRv374NH3pOmjJlCm+//Ta6rlNaWsrnn3/O5MmTzYgccJrbRzt37uT222/nySefpH///mZEDVjN6aOkpCQ2bdrU8Pfn+uuv56qrrpJi+nuKYRiG2SFE26utrWXhwoXs27cPVVW58847mTRpEgBPPPEE8fHx/Md//AdQP3ftpZdeQlEUUlJSWLRoEbGxsWbGDwgt6aOTNm3axOLFi3nvvffMiBxwmttHixcv5rXXXqNHjx4N61533XVcfvnlZkXv1LKzs1m4cCGVlZWEh4ezePFi0tLSWLBgAbfddhsDBw5E0zQeeOABNmzYAMCCBQsaDjAVba85fXT55ZeTl5fXaHrUww8/LIM67aQ5fXSqp556itraWv74xz+alNi/SEEthBBCCCHEOZApH0IIIYQQQpwDKaiFEEIIIYQ4B1JQCyGEEEIIcQ6koBZCCCGEEOIcSEEthBBCCCHEOZCCWgjR6d1///08/vjjP9kmIyODI0eOAHDvvffyzDPPtGmmTZs2MX78+Dbdx5n8+PVoSZa2zD1v3jz27t3bJttuLa+++iqPPPKI2TGEEH5GCmohRIcyceJEBgwYQGlpaaPHZ8+eTUZGBrm5uY0ef/PNN7HZbNx+++3N3scDDzzAr3/961bJ62/O5vVoD6tWrSIkJIR+/fo1PPbKK68wbtw4hg0bxp/+9Cc8Hk+r7KuoqIhbbrmFUaNGMX78eF5//fXTssyYMYOhQ4cyb948srKyGpZdddVVLF++nJKSklbJIoToHKSgFkJ0OMnJyXz44YcN9w8cOIDL5Wqy7dy5c7nrrrvaK5rf89fX44033mDWrFkN99etW8cLL7zAK6+8wurVq8nNzeXJJ59slX3dcccdpKSksGHDBl544QUef/xxNm7cCNRfAfOOO+7gvvvuY8uWLVx44YXccsst+Hw+ABwOB+PHj2fZsmWtkkUI0TlIQS2E6HBmzZrVqKBZtmwZs2fPbtTG4/GwePFiLrjgAsaOHcu9995LXV1dw/KXXnqJ888/n/PPP5933nmn0boLFy5smBJRUVHBzTffzJgxYxg5ciQ333wzhYWFDW3fe+89LrroIoYOHcrEiRP54IMPmsxcV1fHwoULGTlyJNOmTWPXrl2Nlr/wwgtMmjSJoUOHMm3aND777LOGZUeOHGH+/PkMHz6c0aNH89vf/vaMr83WrVuZN28eI0aMYMKECQ1X0fy51+NUp05/+fHr8WOtkdvj8bBx40ZGjRrV8NiyZcu44oor6NWrFxEREfzqV79i6dKlZ3zet912G+PGjWP48OFcc801HDp0qMl2NTU1bN68mVtuuQWbzUafPn2YPHky7777LgDr169nxIgRjBgxAqvVyoIFCygqKmLLli0N2xg1ahRffvnlGbMIIQKPFNRCiA5nyJAhVFdXk52djaZpfPjhh1x66aWN2jz66KPk5OSwbNkyPv30U4qLixvmRa9du5YlS5awZMkSPv30U77++usz7kvXdebMmcPq1atZvXo1DoeDBx54AKi/FPlf/vIXXnzxRbZv384bb7xB3759m9zO008/zdGjR/nss8/4xz/+cdoIZ9euXXnttdfYtm0bt956K3feeSfFxcVA/WXNx40bx5YtW1i7di3z589vch95eXksWLCA+fPn8/XXX7Ns2bKGPD/1epyL1sh95MgRVFWlS5cuDY8dOnSIPn36NNzPyMjgxIkTlJWVNbmN8ePH88knn/D111/Tr18/7rjjjibbnbw48KkXCTYMo1EB/uNlhmFw8ODBhsfS09M5cODAGV8TIUTgkYJaCNEhnRyl3rBhA+np6SQkJDQsMwyDt956i7vuuovIyEhCQ0O5+eabG6aJrFy5kjlz5tC7d2+Cg4O59dZbz7ifqKgoJk+eTFBQEKGhodxyyy2NRitVVeXQoUPU1dURHx9Pr169mtzOypUr+a//+i8iIyNJTEzk2muvbbR86tSpJCQkoKoq06ZNo1u3buzcuRMAq9VKfn4+xcXFOBwORowY0eQ+VqxYwdixY5kxYwY2m42oqCj69u37s6/HuWiN3JWVlYSEhDR6rLa2ltDQ0Ib7YWFhQP0Ic1OuuOIKQkNDsdvt/OY3v2H//v1UVVWd1i40NJRhw4bx7LPP4na72bNnD59++mnDlKHzzjuPLVu2sGnTJjweD8899xxer7fRaH5ISEiT2xZCBC6r2QGEEOJszJo1i/nz55Obm9to7i1AaWkpLpeLOXPmNDxmGAa6rgNQXFzMgAEDGpYlJyefcT8ul4sHH3yQdevWUVFRAdQXdZqmERwczOOPP86SJUu4++67GTZsGH/84x9JT08/bTvFxcUkJiY23E9KSmq0fNmyZbz88svk5eUB9QXlydHYO++8kyeeeIIrrriCiIgIbrjhBq644orT9lFQUEBqauppj//c63EuWiN3RETEaYVycHAw1dXVDfdP/v/HhTeApmk8/vjjfPzxx5SWlqKq9WNFZWVlDYX4qR599FEeeOABJkyYQNeuXbn00ksbRqjT09N56KGHWLRoEcePH2fmzJn07Nmz0Qe2mpqaJrcrhAhcUlALITqk5ORkUlJSWLNmDX/9618bLYuKisLpdPLhhx82KoROio+Pp6CgoOF+fn7+GfezZMkScnJyeOutt4iLi2Pfvn3Mnj27YVpAZmYmmZmZ1NXV8fe//5177rmHf//736dtJy4ujoKCgoYR7FP3n5eXx3//93/zyiuvMHToUCwWS6MPCXFxcfzlL38B6udI33DDDYwcOZJu3bo12kdiYmLD6HBLXo8fCwoKanSQ5/Hjx5tcr7Vyp6amYhgGRUVFDfvp1asXBw4cYNq0aQDs37+f2NhYoqKiTsuxfPlyvvjiC15++WVSUlKoqqpi5MiRjaZunCo5OZnnn3++4f7vf/97Bg0a1HB/ypQpTJkyBagfPX/33XcZOHBgw/Ls7GwyMjLO8OoJIQKRTPkQQnRYf/3rX/nnP/9JcHBwo8dVVeXKK6/kb3/7W8PpzYqKili3bh1QXzAtXbqUrKwsXC4XTz/99Bn3UVNTg8PhIDw8nPLy8kZtT5w4weeff05tbS12u53g4OCG0dEfmzp1Ki+88AIVFRUUFhby6quvNixzuVwoikJ0dDQA7777bqM5vStXrmw4EDIiIgJFUZrcz8yZM/nqq6/46KOP8Pl8lJWVsW/fvp99PX6sT58+rFixAk3TWLt2baMpLqdqrdx2u53zzjuPzZs3Nzw2a9Ys3nnnHbKysqisrOR///d/ueyyy5rMUVNTg91uJyoqCpfLxWOPPdZku5Oys7Oprq7G4/Hw/vvvs379em644YaG5bt370bTNEpLS7nnnnuYOHFio28dtmzZYto5xIUQ/kkKaiFEh5Wamtpo5PBUd955J926deOqq65i2LBh/OIXvyAnJweACRMmcP3113P99ddz8cUXM2bMmDPu4/rrr8ftdjNmzBjmzp1LZmZmwzJd13nllVfIzMxk1KhRbNmyhfvuu6/J7dx6660kJSVx0UUXceONNzYaye3Zsyc33ngj8+bNY+zYsRw8eJBhw4Y1LN+1axdXXnklQ4cO5ZZbbuHuu++ma9eup+0jKSmJF198kZdffplRo0Yxe/Zs9u/f/7Ovx4/dfffdrF69mhEjRrB8+XImTZrUZLvWyg31F3V5//33G+6PHz+em266ieuuu44LLriA5ORkbrvttibXnT17NklJSWRmZjJ9+nSGDBnSZLuT1q1bx6RJkxg1ahRvvPEGL730UsOHAqj/oDZixAimTJlCREQEixYtaljmdrtZs2bNGYt7IURgUowzfScmhBBCtKN58+Zx7733Nrq4i7959dVXKSgo4A9/+IPZUYQQfkQKaiGEEEIIIc6BTPkQQgghhBDiHEhBLYQQQgghxDmQgloIIYQQQohzIAW1EEIIIYQQ50AKaiGEEEIIIc6BFNRCCCGEEEKcAymohRBCCCGEOAdSUAshhBBCCHEO/j/bs98Q/uA9cwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "4glWMNSLnCe2",
        "outputId": "3d814ca5-f616-4036-ce1a-1fba536164ce"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "ax = sns.boxplot(data=df, orient = \"h\")\n",
        "ax.set_xlabel('Médias das células (0 a 99)')\n",
        "ax.set_ylabel('Frequência')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9ujp16uT6lAAAAAA8wm0LhJeXl0aPHi1JGj9+vCpVqqSuXbvq2rVrCg0NVZkyZfTCCy/kS1AAAAAA7md8G9ciRYpo0qRJmjRpkhISEhQUFOS4mBoAAABA4WBcIHJOXcqRkpLieFy5cuW8SwQAAADAYxkXiGeeeUY2m02WZTmW5RyBiIuLy/tkAAAAADyOcYE4fPiw0/NLly7p7bffVpMmTfI8FAAAAADPZPxN1L8XHBysyZMn680338zLPAAAAAA82D0XCEn6+eeflZaWlldZAAAAAHg441OY+vfv73TXpbS0NB07dozbuAIAAACFiHGBCA8Pd3pevHhx1a5dWw899FBeZwIAAADgoYwLRI8ePVyZAwAAAEABYFwgFi5caLTd2LFj7zkMAAAAAM9mXCBOnjypTZs2qV69eqpYsaLOnTunQ4cO6U9/+pOKFi3qyowAAAAAPIRxgbAsS//1X/+l0NBQx7JNmzZp48aNmjNnjkvCAQAAAPAsxrdxjY2NVfv27Z2WtWvXTtu2bcvzUAAAAAA8k3GBqFq1qt5//32nZR9++KGqVKmS56EAAAAAeCbjU5hmzpyp0aNHa9myZSpbtqwuXrwoHx8fLVq0yJX5AAAAcvngg//V6dMn833cpKRESVJAQOA978PX11sZGfY7ble5clX17z/4nscBXMW4QDz66KP6xz/+oe+++07x8fEKDg5WgwYN5Ovr68p8AAAAuZw+fVK/HDuscv7Gf8rkiavJmZKkommX73kf1w22ufD/4wCe6J5/65o2barU1FRlZGTIz88vLzMBAADcUTl/Hw2t/0C+jrniYIIkuXzcnHEAT2RcII4cOaKRI0eqSJEiunjxosLCwrR3715FRUXpr3/9qyszAgAAAPAQxhdRT506VWPGjNHGjRvl45PdO5o2bapvv/3WZeEAAAAAeBbjAnHs2DF169ZNkmSz2SRJfn5+unHjhmuSAQAAAPA4xgWiYsWK+v77752WHTx4kNu4AgAAAIWI8TUQY8eO1YgRI9SvXz9lZGTonXfeUWRkpGbMmOHKfAAAAAA8iPERiKeeekrLli1TQkKCmjZtqrNnz2rRokVq2bKlK/MBAAAA8CBGRyDsdrtCQ0P1+eefa+rUqS6OBAAAAMBTGR2B8Pb2lre3NxdMAwAAAIWc8TUQgwcP1rhx4zRixAiVK1fOcScmSapcubJLwgEAAADwLHcsEJcuXVJwcLDjYuldu3bJsizHepvNpri4ONclBAAAAOAx7lggQkND9c9//lOHDx+WJL3wwgtavHixy4MBAAAA8Dx3vAbit0cbJGnv3r0uCwMAAADAs92xQPz2Wgcpd6EAAAAAUHjc8RQmu92uPXv2OIrD759LUkhIiOsSAgAAAPAYdywQpUuX1qRJkxzPAwMDnZ7bbDZt3rzZNekAAAAAeJQ7FogtW7bkRw4AAAAABYDRF8kBAAAAgESBAAAAAHAXKBAAAAAAjFEgAAAAABijQAAAAAAwRoEAAAAAYIwCAQAAAMAYBQIAAACAMQoEAAAAAGMUCAAAAADGKBAAAAAAjFEgAAAAABijQAAAAAAwRoEAAMCD7dwZq507Y90dA3AZ/o0XPD7uDgAAAG5tx45tkqQWLVq7OQngGvwbL3g4AgEAAADAGAUCAAAAgDEKBAAAAABjFAgAAAAAxigQAAAAAIxRIAAAAAAYo0AAAAAAMEaBAAAAAGCMAgEAAADAGAUCAAAAgDEKBAAAAABjFAgAAAAAxigQAAAAAIxRIAAAAAAYo0AAAAAAMEaBAAAAAGCMAgEAAADAGAUCAAAAgDEKBAAAAABjFAgAAAAAxigQAAAAAIxRIAAAAAAYo0AAAAAAMEaBAAAAAGCMAgEAAADAGAUCAAAAgDEKBAAAAABjFAgAAAAAxigQAAAAgIdITLyqiIjpSkpKdHrsSSgQhhYtWqT09PR7fu3cuXPzOBEAAADuNzExUfrppyNau/Yzp8eehAJh6O2331ZGRsZN12VmZuZzGgAAANxvEhOvaseObbIsS9u3b9P27dmPd+yI9aijED75NdD+/fs1b948paSkSJJeeukllSpVSrNmzVJqaqr8/Pw0efJk1a9fX2fOnFGvXr3Up08fbd++XdevX9f8+fMVGRmp7777TsWKFdOSJUsUHBx8y/GOHz+uWbNm6dKlS5KkYcOGqUePHjp58qSmTJmihIQE+fj4aPz48WrdurUkqVatWho/fry++OILJSYm6qWXXlJoaKimTZsmSerXr5+8vLy0atUqzZ49W97e3jpx4oRSUlK0Zs0aLV26VGvXrpUkPfbYY3r11VdVokSJXNmWLl2qTZs2yW63q2zZspoxY8ZtfxYAQOGVlJSopKQkzZ07w2h7X19vZWTYXZzK/U6dOik/Zbk7hsskp2cp/tRJ43kvyE6dOqmAgAB3x/AIMTFRysqyJGV/QG2zZS/PysrS2rWfadCgYW5M9y/5cgQiMTFRo0eP1oQJE7R27VpFRUWpdu3aGjNmjMaNG6eYmBiNHTtWY8aMcZwmlJiYqMaNGys6Olq9e/fWc889pwEDBigmJkZ169bVe++9d8vxMjMzNWrUKIWHhysmJkYxMTFq27atJOnFF19U586dFRMTozfeeEMTJkxQQkKC47X+/v5avXq15s2bp5kzZ0qSXn/9dUlSZGSk1qxZo1KlSkmS4uLitGzZMq1Zs0bbtm3T2rVrFRkZqZiYGNntdi1ZsiRXtjVr1uj06dP6+OOPFRUVpdatWysiIiJP3mcAAAAUXLt375TdnnNmiyXLyi4Tdnumdu/e6b5gv5MvRyAOHDig6tWrq1GjRpIkb29vXblyRb6+vgoJCZEkNW/eXL6+vjpx4oRKlCghPz8/xx/9devWVbly5VSnTh3H8127dt1yvBMnTigzM1MdO3Z0LAsKClJycrLi4uLUq1cvSdIjjzyiOnXq6MCBA2rXrp0kKSwsTJLUoEEDxcfH68aNGypatOhNx+nQoYP8/PwkSbt371ZYWJj8/f0lSX369NHs2bNzvWbLli36/vvv1aNHD0mS3W53vAYAgN8LCAhUQECgJk58zWj74OCSunTpVxencr+5c2fo+oVj7o7hMv5FvPRguarG816QFYajLKZCQlooNvar/y8RNtlskmVZ8vb2UUhIC3fHc8i3U5juVpEiRRyPvby8nJ57e3vLbnfN4dmcsuDt7S0p+2jGrQpETnm4G5ZlaeTIkerdu/e9hwQAAMB9p0uXHtqxY5vsdsnHJ/vP9MzMDHl5ealr155uTvcv+XIKU4MGDXT8+HHt379fUvan7qVLl1ZGRob27NkjKfsT/MzMTFWrVu0Pj1etWjX5+Phow4YNjmVXr16Vv7+/6tSpo6ioKEnZ10kcPnxYDRo0uOM+S5QooeTk5FuuDwkJ0YYNG5ScnCzLsvTpp5+qefPmubZr166dPvjgAyUlJUmS0tPTdfjw4bv9EQEAAHCfCQwMUsuWbWSz2dSqVRu1apX9uGXL1goICHR3PId8OQIRGBioRYsWKSIiQqmpqfLy8tLEiRP11ltvOV1EvXDhQqcjDffKx8dHS5Ys0fTp07VkyRLZbDYNGzZM3bt31/z58zVlyhStXLlSPj4+mjdvnh544IE77nPYsGEaPHiwihUrplWrVuVa36ZNGx05ckT9+vWTJNWrV08jR47MtV337t2VmJiogQMHSso+IvHss8+qdu3af/CnBgAAQEHXpUsPnT17Rl279pRlWY7HnsRm5VydgQLjypVkxxX6MFdYzgsuyJgjz8cc5b+c88O5BsJZzjUQQ+vf+UPAvLTiYPaNV1w97oqDCSpW7pFCdQ2EJ/2sheX36Ha8vGwqXfrm1+nyPRAAAAAAjHnsRdQmPvnkk5vezjUiIsJxxyYAAAAAeadAF4jw8HCFh4e7OwYAAABQaHAKEwAAAABjFAgAAAAAxigQAAAAAIxRIAAAAAAYo0AAAAAAMEaBAAAAAGCMAgEAAADAGAUCAAAAgDEKBAAAAABjFAgAAAAAxigQAAAAAIxRIAAAAAAYo0AAAAAAMEaBAAAAAGCMAgEAAADAGAUCAAAAgDEKBAAAAABjFAgAAAAAxigQAAAAAIxRIAAAAAAYo0AAAAAAMEaBAAAAAGCMAgEAAADAmI+7AwAAgFtr2bKNuyMALsW/8YKHAgEAgAdr0aK1uyMALsW/8YKHU5gAAAAAGKNAAAAAADBGgQAAAABgjAIBAAAAwBgFAgAAAIAxCgQAAAAAYxQIAAAAAMYoEAAAAACMUSAAAAAAGKNAAAAAADBGgQAAAABgjAIBAAAAwBgFAgAAAIAxCgQAAAAAYxQIAAAAAMYoEAAAAACMUSAAAAAAGKNAAAAAADBGgQAAAABgjAIBAAAAwBgFAgAAAIAxCgQAAAAAYxQIAAAAAMYoEAAAAACMUSAAAAAAGKNAAAAAADBGgQAAAABgjAIBAAAAwBgFAgAAAIAxCgQAAAAAYz7uDgAAAHAvLiRnasXBhHwfU5LLx72QnKmHXDoCcO8oEAAAoMCpXLmqW8YNSkqUJBULCLznffj6eisjw37bbR6S+35G4E4oEAAAoMDp33+wuyPcs+Dgkrp06Vd3xwDuGddAAAAAADBGgQAAAABgjAIBAAAAwBgFAgAAAIAxCgQAAAAAYxQIAAAAAMYoEAAAAACMUSAAAAAAGKNAAAAAADBGgQAAAABgjAIBAAAAwBgFAgAAAIAxCgQAAAAAYz7uDoC75+Vlc3eEAov3zvMxR56POfJ8zJHnY448X2Gfo9v9/DbLsqx8zAIAAACgAOMUJgAAAADGKBAAAAAAjFEgAAAAABijQAAAAAAwRoEAAAAAYIwCAQAAAMAYBQIAAACAMQoEAAAAAGMUCAAAAADGKBC4L6WlpWncuHF65pln1KFDB23duvWW28bFxWnAgAEKCwtTWFiYtm3blo9JC6+7mSNJunHjhjp16qSePXvmU0KYztGXX36pnj17qnPnzurUqZOWL1+ez0kLnxMnTqhv374KDQ1V37599csvv+Taxm63a9q0aWrfvr2eeeYZffLJJ/kftBAzmaPFixerU6dO6tKli3r27Knt27fnf9BCzGSOcvz88896/PHHNXfu3PwL6Mks4D60aNEia/LkyZZlWdaJEyes5s2bW8nJybm2S0lJsdq1a2ft37/fsizLysjIsBISEvI1a2FlOkc55syZY73yyitWjx498itioWc6RwcOHLAuXLhgWZZlXbt2zWrfvr21d+/efM1a2AwaNMiKjo62LMuyoqOjrUGDBuXaJioqyho2bJhlt9utK1euWK1atbJOnz6d31ELLZM5io2NtVJTUy3Lsqy4uDircePGVlpaWr7mLMxM5siyLCszM9MaOHCg9ec//9mKiIjIz4geiyMQuC9t2LBBffv2lSQ99NBDqlevnmJjY3Ntt27dOjVu3FgNGjSQJPn4+CgoKChfsxZWpnMkSfv27dMvv/yibt265WfEQs90jh5//HGVLVtWklSyZElVr15dZ8+ezdeshcmVK1f0448/qnPnzpKkzp0768cff1RCQoLTdp9//rnCw8Pl5eWlBx54QO3bt9fGjRvdEbnQMZ2jVq1aqXjx4pKkWrVqybIsJSYm5nvewsh0jiRp6dKlatu2rR566KF8Tum5KBC4L507d04VK1Z0PC9fvrwuXLiQa7tjx47Jx8dHw4cPV7du3TRp0iQlJSXlZ9RCy3SOUlNTNXv2bE2bNi0/40Hmc/Rbx48f14EDB/Tkk0+6Ol6hdf78eZUtW1be3t6SJG9vb5UpU0bnz5/PtV2FChUcz03mD3nDdI5+Kzo6WlWqVFG5cuXyK2ahZjpHhw8f1o4dO/Tcc8+5IaXn8nF3AOBe9OjRQ+fOnbvpul27dhnvJysrS3v27FFkZKQefPBBzZkzRxEREZozZ05eRS208mqO5s2bp/79+6ts2bK3PT8Vdy+v5ihHfHy8Ro0apddff91xRALAnX3zzTdauHAh1w95mIyMDL322muaM2eOo2ggGwUCBVJUVNRt11eoUEFnz57VAw88ICn7k4Ynnngi13bly5fXE088oTJlykiSunTpokmTJuV94EIor+bo22+/VWxsrJYsWaIbN24oKSlJXUE5NNMAAAyjSURBVLp0UUxMjEtyFyZ5NUdS9ukAQ4cO1fPPP6+OHTvmeVb8S/ny5XXx4kXZ7XZ5e3vLbrcrPj5e5cuXz7XduXPnVL9+fUm5j0jAdUznSJL279+vCRMmaMmSJXr44YfdkLZwMpmjS5cu6dSpU/r3f/93SdK1a9dkWZaSk5M1Y8YMd0X3CJzChPtShw4d9NFHH0mSfvnlFx06dEitWrXKtV3Hjh118OBBJScnS5JiY2NVq1atfM1aWJnOUUxMjLZs2aItW7bozTffVM2aNSkP+cR0jq5evaqhQ4dqwIABCg8Pz++YhU7p0qVVp04drVu3TlL2tVx16tRxFL0cHTp00CeffKKsrCwlJCToyy+/VGhoqDsiFzqmc3Tw4EGNHz9eb731lurWreuOqIWWyRxVqFBBX3/9teP/QUOGDFGfPn0KfXmQJJtlWZa7QwB5LTU1VS+//LLi4uLk5eWlCRMmqH379pKkhQsXqkyZMnr22WclZZ93umzZMtlsNlWqVEkzZszQgw8+6M74hcLdzFGOr7/+WnPnztVnn33mjsiFjukczZ07V++//76qVavmeO3gwYPVq1cvd0W/7x0/flwvv/yyrl27plKlSmnu3Ll6+OGHNXz4cI0ZM0aPPfaY7Ha7pk+frp07d0qShg8f7rgoHq5nMke9evXS2bNnnU75mzdvHh9k5ROTOfqtRYsWKTU1VRMnTnRTYs9BgQAAAABgjFOYAAAAABijQAAAAAAwRoEAAAAAYIwCAQAAAMAYBQIAAACAMQoEANxnpk2bpgULFtx2m1q1aunkyZOSpClTpmjx4sUuzfT111+rdevWLh3jVn7/ftxNFlfm7tevn3788UeX7DuvrFq1Sm+88Ya7YwDwMBQIAPBg7dq1U7169ZSQkOC0vHv37qpVq5bOnDnjtPyjjz6Sr6+vxo8fbzzG9OnT9cILL+RJXk9zL+9HftiyZYtKlCihRx991LFs5cqVatGihRo1aqRXXnlF6enpeTLWxYsXNXLkSDVr1kytW7fWhx9+mCtL586d1bBhQ/Xr10/Hjh1zrOvTp49iYmJ05cqVPMkC4P5AgQAAD1exYkWtX7/e8fzIkSNKS0u76bZ9+/bVpEmT8iuax/PU9yMyMlLdunVzPN++fbuWLl2qlStXauvWrTpz5ozeeuutPBnrxRdfVKVKlbRz504tXbpUCxYs0J49eyRlf8P4iy++qKlTp2rv3r166qmnNHLkSGVmZkqSihYtqtatWys6OjpPsgC4P1AgAMDDdevWzekPuOjoaHXv3t1pm/T0dM2dO1dt27ZV8+bNNWXKFF2/ft2xftmyZWrZsqVatmypTz/91Om1L7/8suMUn6SkJI0YMUJPPvmkmjZtqhEjRujChQuObT/77DM9/fTTatiwodq1a6e1a9feNPP169f18ssvq2nTpgoLC9OhQ4ec1i9dulTt27dXw4YNFRYWpi+++MKx7uTJkxo4cKAaN26sJ554QuPGjbvle7Nv3z7169dPTZo0UZs2bRzfUn6n9+O3fns61+/fj9/Li9zp6enas2ePmjVr5lgWHR2t3r17q0aNGgoICNCoUaMUFRV1y597zJgxatGihRo3bqwBAwbop59+uul2KSkp+uabbzRy5Ej5+vqqdu3aCg0N1erVqyVJO3bsUJMmTdSkSRP5+Pho+PDhunjxovbu3evYR7NmzfTVV1/dMguAwocCAQAerkGDBkpOTtbx48dlt9u1fv16de3a1Wmb+fPn68SJE4qOjtamTZsUHx/vuK4hNjZWy5cv1/Lly7Vp0ybt3r37lmNlZWWpZ8+e2rp1q7Zu3aqiRYtq+vTpkqTU1FTNnDlT7777rvbv36/IyEjVqVPnpvt5++23derUKX3xxRf6+9//nusT7MqVK+v999/Xt99+q9GjR2vChAmKj4+XJC1cuFAtWrTQ3r17FRsbq4EDB950jLNnz2r48OEaOHCgdu/erejoaEee270ff0Re5D558qS8vLxUrlw5x7KffvpJtWvXdjyvVauWLl++rKtXr950H61bt9Y//vEP7d69W48++qhefPHFm25nWZbTf3Me/7Zw/H6dZVk6evSoY1n16tV15MiRW74nAAofCgQAFAA5RyF27typ6tWrq2zZso51lmXp448/1qRJkxQYGCh/f3+NGDHCcdrThg0b1LNnT9WsWVN+fn4aPXr0LccJCgpSaGioihcvLn9/f40cOdLp02gvLy/99NNPun79usqUKaMaNWrcdD8bNmzQf/zHfygwMFDly5fXoEGDnNZ37NhRZcuWlZeXl8LCwlS1alUdPHhQkuTj46Nz584pPj5eRYsWVZMmTW46xrp169S8eXN17txZvr6+CgoKUp06de74fvwReZH72rVrKlGihNOy1NRU+fv7O56XLFlSUvYRhJvp3bu3/P39VaRIEf3nf/6nDh8+rF9//TXXdv7+/mrUqJGWLFmiGzdu6IcfftCmTZscp8CFhIRo7969+vrrr5Wenq7//u//VkZGhtPRmhIlStx03wAKLx93BwAA3Fm3bt00cOBAnTlzxunceUlKSEhQWlqaevbs6VhmWZaysrIkSfHx8apXr55jXcWKFW85TlpamubMmaPt27crKSlJUvYfsXa7XX5+flqwYIGWL1+uyZMnq1GjRpo4caKqV6+eaz/x8fEqX76843mFChWc1kdHR2vFihU6e/aspOw/oHM+bZ8wYYIWLlyo3r17KyAgQEOHDlXv3r1zjXH+/HlVqVIl1/I7vR9/RF7kDggIyFUM/Pz8lJyc7Hie8/j3RUOS7Ha7FixYoI0bNyohIUFeXtmfBV69etVRPH5r/vz5mj59utq0aaPKlSura9eujiMQ1atXV0REhGbMmKFLly6pS5cueuSRR5wKakpKyk33C6DwokAAQAFQsWJFVapUSdu2bdOsWbOc1gUFBalYsWJav3690x9+OcqUKaPz5887np87d+6W4yxfvlwnTpzQxx9/rODgYMXFxal79+6O01xatWqlVq1a6fr16/rrX/+q1157TR988EGu/QQHB+v8+fOOIxS/Hf/s2bN69dVXtXLlSjVs2FDe3t5OpSg4OFgzZ86UlH2Nw9ChQ9W0aVNVrVrVaYzy5cs7Pv2/m/fj94oXL+50UfqlS5du+rq8yl2lShVZlqWLFy86xqlRo4aOHDmisLAwSdLhw4f14IMPKigoKFeOmJgYbd68WStWrFClSpX066+/qmnTpk6nIv1WxYoV9c477zie/+Uvf1H9+vUdzzt06KAOHTpIyj46snr1aj322GOO9cePH1etWrVu8e4BKIw4hQkACohZs2bpf/7nf+Tn5+e03MvLS+Hh4Zo9e7bjdpsXL17U9u3bJWX/gRgVFaVjx44pLS1Nb7/99i3HSElJUdGiRVWqVCklJiY6bXv58mV9+eWXSk1NVZEiReTn5+f49Pv3OnbsqKVLlyopKUkXLlzQqlWrHOvS0tJks9n0wAMPSJJWr17tdE7+hg0bHBduBwQEyGaz3XScLl26aNeuXfr888+VmZmpq1evKi4u7o7vx+/Vrl1b69atk91uV2xsrNMpW7+VV7mLFCmikJAQffPNN45l3bp106effqpjx47p2rVr+tvf/qYePXrcNEdKSoqKFCmioKAgpaWl6c0337zpdjmOHz+u5ORkpaena82aNdqxY4eGDh3qWP/999/LbrcrISFBr732mtq1a+d0VGnv3r1u+w4PAJ6JAgEABUSVKlWcPhn+rQkTJqhq1arq06ePGjVqpOeee04nTpyQJLVp00ZDhgzRkCFD9Mwzz+jJJ5+85RhDhgzRjRs39OSTT6pv375q1aqVY11WVpZWrlypVq1aqVmzZtq7d6+mTp160/2MHj1aFSpU0NNPP61hw4Y5fVL/yCOPaNiwYerXr5+aN2+uo0ePqlGjRo71hw4dUnh4uBo2bKiRI0dq8uTJqly5cq4xKlSooHfffVcrVqxQs2bN1L17dx0+fPiO78fvTZ48WVu3blWTJk0UExOj9u3b33S7vMotZX+J3Jo1axzPW7dureeff16DBw9W27ZtVbFiRY0ZM+amr+3evbsqVKigVq1aqVOnTmrQoMFNt8uxfft2tW/fXs2aNVNkZKSWLVvmKEFSdjFt0qSJOnTooICAAM2YMcOx7saNG9q2bdstywyAwslm3eqYJwAAcJl+/fppypQpTl8m52lWrVql8+fP66WXXnJ3FAAehAIBAAAAwBinMAEAAAAwRoEAAAAAYIwCAQAAAMAYBQIAAACAMQoEAAAAAGMUCAAAAADGKBAAAAAAjFEgAAAAABj7P+ljskbQyWgHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_JRRHsPTio7"
      },
      "source": [
        "### Resposta 04\n",
        "#### Estudar sobre as estatíticas retornadas no .describe()\n",
        "\n",
        "As medidas são as estatísticas descritivas já conhecidas.\n",
        "\n",
        "\n",
        "1. **Count:** É a contagem de observações;\n",
        "2. **Mean:** É a média das variáveis;\n",
        "3. **Std:** É o desvio padrão e fornece informações sobre a dispersão dos valores;\n",
        "4. **Min:** São os valores mínimos dos componentes genéticos;\n",
        "5. **25%:** Trata-se do primeiro quartil, ou seja, os valores que representam 1/4 da amostra;\n",
        "6. **50%:** Trata-se da mediana que é aponta a observação que divide a amostra pela metade;\n",
        "7. **75%:** Trata-se do 3º quartil e divide a amostra 3/4.\n",
        "8. **Max:** Valor máximo\n",
        "\n",
        "No caso abaixo, transpomos a base de dados para que verificarmos as estatísticas descritivas dos componentes genéticos como um todo.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "CLLxnZ_wvGqJ",
        "outputId": "bd91b310-0170-4d00-c6f2-d11ab3604863"
      },
      "source": [
        "estat_descr = dados.loc[:,'g_0':'g_771'].describe().T\n",
        "estat_descr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>g_0</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.248366</td>\n",
              "      <td>1.393399</td>\n",
              "      <td>-5.513</td>\n",
              "      <td>-0.473075</td>\n",
              "      <td>-0.00885</td>\n",
              "      <td>0.525700</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_1</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.095684</td>\n",
              "      <td>0.812363</td>\n",
              "      <td>-5.737</td>\n",
              "      <td>-0.562200</td>\n",
              "      <td>-0.04660</td>\n",
              "      <td>0.403075</td>\n",
              "      <td>5.039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_2</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.152253</td>\n",
              "      <td>1.035731</td>\n",
              "      <td>-9.104</td>\n",
              "      <td>-0.437750</td>\n",
              "      <td>0.07520</td>\n",
              "      <td>0.663925</td>\n",
              "      <td>8.257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_3</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.081971</td>\n",
              "      <td>0.950012</td>\n",
              "      <td>-5.998</td>\n",
              "      <td>-0.429575</td>\n",
              "      <td>0.00805</td>\n",
              "      <td>0.463400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_4</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.057347</td>\n",
              "      <td>1.032091</td>\n",
              "      <td>-6.369</td>\n",
              "      <td>-0.470925</td>\n",
              "      <td>-0.02690</td>\n",
              "      <td>0.465375</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_767</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.076251</td>\n",
              "      <td>1.115477</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.506200</td>\n",
              "      <td>0.00990</td>\n",
              "      <td>0.511175</td>\n",
              "      <td>6.317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_768</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.134162</td>\n",
              "      <td>0.951264</td>\n",
              "      <td>-4.269</td>\n",
              "      <td>-0.353100</td>\n",
              "      <td>0.00540</td>\n",
              "      <td>0.409075</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_769</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.128018</td>\n",
              "      <td>1.230636</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.544600</td>\n",
              "      <td>0.00060</td>\n",
              "      <td>0.498500</td>\n",
              "      <td>5.911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_770</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>-0.219210</td>\n",
              "      <td>1.326193</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.554400</td>\n",
              "      <td>0.02870</td>\n",
              "      <td>0.496400</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g_771</th>\n",
              "      <td>23814.0</td>\n",
              "      <td>0.101524</td>\n",
              "      <td>1.417674</td>\n",
              "      <td>-10.000</td>\n",
              "      <td>-0.523800</td>\n",
              "      <td>-0.00650</td>\n",
              "      <td>0.536950</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>772 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         count      mean       std     min       25%      50%       75%     max\n",
              "g_0    23814.0  0.248366  1.393399  -5.513 -0.473075 -0.00885  0.525700  10.000\n",
              "g_1    23814.0 -0.095684  0.812363  -5.737 -0.562200 -0.04660  0.403075   5.039\n",
              "g_2    23814.0  0.152253  1.035731  -9.104 -0.437750  0.07520  0.663925   8.257\n",
              "g_3    23814.0  0.081971  0.950012  -5.998 -0.429575  0.00805  0.463400  10.000\n",
              "g_4    23814.0  0.057347  1.032091  -6.369 -0.470925 -0.02690  0.465375  10.000\n",
              "...        ...       ...       ...     ...       ...      ...       ...     ...\n",
              "g_767  23814.0 -0.076251  1.115477 -10.000 -0.506200  0.00990  0.511175   6.317\n",
              "g_768  23814.0  0.134162  0.951264  -4.269 -0.353100  0.00540  0.409075  10.000\n",
              "g_769  23814.0 -0.128018  1.230636 -10.000 -0.544600  0.00060  0.498500   5.911\n",
              "g_770  23814.0 -0.219210  1.326193 -10.000 -0.554400  0.02870  0.496400  10.000\n",
              "g_771  23814.0  0.101524  1.417674 -10.000 -0.523800 -0.00650  0.536950  10.000\n",
              "\n",
              "[772 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "8uJFC2jcz4wr",
        "outputId": "05c2f670-944f-48db-990e-f691464d81b6"
      },
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(12,6))\n",
        "axs[0].hist(estat_descr['mean'], bins=20)\n",
        "axs[0].set_title('Media')\n",
        "axs[1].hist(estat_descr['50%'], bins=20)\n",
        "axs[1].set_title('Mediana')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2tlBkgZlHu9"
      },
      "source": [
        "Comparando os máximos e mínimos é possível notar valores truncados em um intervalo de 10 e -10. Talves, isso possa interferir nos resultados médios."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "1Xt09GB5MTow",
        "outputId": "30dce432-cd79-49eb-cd48-0d534d43ffea"
      },
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(12,6))\n",
        "axs[0].hist(estat_descr['min'], bins=20)\n",
        "axs[0].set_title('Minimo')\n",
        "axs[1].hist(estat_descr['max'], bins=20)\n",
        "axs[1].set_title('Maximo')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loj6WHd5T3Uu"
      },
      "source": [
        "### Resposta 05\n",
        "#### Refletir sobre a manipulação do tamanho das visualizações.\n",
        "\n",
        "Visualização de dados é uma área complexa. A mensagem do gráfico tem que ser direta. Existem muitas formas de representar os dados. Geralmente, utilizo um guia sobre como representar meus gráficos. O site [From data to viz](https://www.data-to-viz.com/) é execente e dá muitas dicas de como criar seus gráficos. Gráficos maiores, geralmente são mais fáceis de observar os padrões apontados pelo pesquisador."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1QaYFv1UOMo"
      },
      "source": [
        "### Resposta 06\n",
        "#### Fazer outras análises com o boxplot e até com o histograma.\n",
        "\n",
        "Com a observação de que haviam valores truncados em um intervalor, plotei o boxplot do gene 1 para verificar visualmente se há mudança qualitativa na interpretação dos gráficos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zIJsC82UIH8p"
      },
      "source": [
        "dados_filtrados=dados.query(\"g_1<10 & g_10>-10\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "aRwCZvvH6GTl",
        "outputId": "ec8c8de9-abe9-4fc7-fa90-ae85675efd9f"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.boxplot(y='tratamento', x= 'g_1', data=dados_filtrados)\n",
        "plt.title('Boxplot do gene 1 de acordo com o tratamento', fontsize=18)\n",
        "plt.xlabel('Expressões genéticas', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FITZvQUyl3gr"
      },
      "source": [
        "Observando em relação às médias gerais temos o seguinte:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeBIkVh4l9G0"
      },
      "source": [
        "tratamentos = dados['tratamento'].unique()\n",
        "df = pd.DataFrame({t : dados.query('tratamento == @t').loc[:,'g_0':'g_771'].describe().T['mean'] for t in tratamentos})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "QT6Sppb1mp_1",
        "outputId": "a90e5cca-92f7-4859-8fbb-6c44396868f7"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "ax = sns.boxplot(data=df, orient='h')\n",
        "ax.set_xlabel('Médias dos genes (0 a 771)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h2r1793VDANOB4HYRERH6y1/+Ikn6+eef9d1336lXr15lxvXv31/79u1Tdna2JCk1NVUhISFurdVbVLanycnJ2rJli7Zs2aI33nhDbdq0MS4AVEZl+3nx4kU9+eSTGjlypGJjY91dZrXWsGFDtWvXTuvXr5dUco9Pu3btnMHqqoiICK1Zs0bFxcW6cOGCvvjiC/Xr188TJVd7le3pvn37NHXqVC1ZskTt27f3RKleozI9DQ4O1u7du53fO8eOHathw4YRAMpR2X+jAwcO1Pbt22VZlgoLC7Vr1y61bdvWEyVXe5Xt6R133OG8d62goEA7d+5U69atr7ttH4tJWHCz3NxczZgxQ/v375fNZtO0adP06KOPSpIWL16sJk2a6De/+Y2kkvmsK1askI+Pj+644w7NnTtXjRo18mT51dLN9PSq3bt3KyEhQZ9++qknSq7WKtvPhIQE/fnPf1bLli2d644ZM0ZDhw71VOnVSnp6umbMmKFLly6pfv36SkhI0L333qsJEyZo8uTJeuCBB+RwODRnzhzt2LFDkjRhwgTnTdkoqzI9HTp0qI4fP15qatqiRYv4EKUClenptZYuXarc3FxNnz7dQxVXb5XpZ3FxsRISEpSamiqbzaawsDBNnz5dNhufTZenMj09cuSIXn31VZ07d04Oh0PdunVTXFyc7PaKJ/0QAgAAAADDELkAAAAAwxACAAAAAMMQAgAAAADDEAIAAAAAwxACAAAAAMMQAgCgGpk9e7befPPN644JCQnR4cOHJUmzZs3S22+/XaU17d69W717967SfVRXI0aM0Pfff+/pMm5JTEyMfvzxR0+XAaCaIQQAQBUJDw/X/fffrwsXLpR6Pzo6WiEhITp27Fip9//yl7+oVq1amjp1aqX3MWfOHP3Lv/yLS+pFaVu2bFHdunV13333Od9btWqVQkND1blzZ7300ksqKChwyb4GDBigTp06Of/cd999evbZZyVJ33zzTallnTp1UkhIiD7//HNJ0g8//KB//ud/Vrdu3cr9XQDjx4/XkiVLXFIngJqDEAAAVahFixbasGGD8/XBgweVl5dX7tjhw4dr5syZ7ioNN7B69WoNGjTI+Xrbtm1avny5Vlw9b6MAAApcSURBVK1apa1bt+rYsWMuO7nesGGD0tLSlJaWpv/5n/9Rs2bNFBERIUl66KGHnMvS0tL0+9//Xv7+/s7fYm232xUREaH58+eXu+1HHnlEu3fv1tmzZ11SK4CagRAAAFVo0KBBSkpKcr5OSkpSdHR0qTEFBQVKSEhQ37591bNnT82aNUv5+fnO5StWrFBYWJjCwsL08ccfl1p3xowZzulDWVlZeuaZZ9S9e3d17dpVzzzzjE6dOuUc++mnn+qRRx5Rp06dFB4ernXr1pVbc35+vmbMmKGuXbsqMjJS3333Xanl6enpGj16tB566CENGDBAX375pXNZSkqKIiMj1alTJ/Xq1Ut//OMfy92Hw+FQfHy8unXrpvDwcH3wwQcKCQlRUVGRJOny5cuaOXOmwsLC1KtXL7355ptyOBzO47j6G5u7du2q8PBwpaSkOLd9vXUPHz6sUaNGqUuXLurWrZumTJlSbn0FBQXatWuXHn74Yed7SUlJiomJUevWrRUYGKhJkyYpMTGx3PUlafLkyQoNDVWXLl00cuTISk/J2bNnjy5evKh/+qd/Knd5UlKSIiIi5O/vL0m69957FRsbq9atW5c7vnbt2mrfvr22b99eqf0DMAMhAACqUMeOHZWdna309HQ5HA5t2LBBjz/+eKkxr7/+ujIyMpSUlKTNmzfrzJkzznn+qampWrlypVauXKnNmzdr586dFe6ruLhYQ4YM0datW7V161bVrl1bc+bMkSTl5uZq3rx5+sMf/qC0tDStXr1a7dq1K3c7b731lo4cOaK//vWv+uMf/1gqxBQWFurZZ59VaGiovvrqK7388st64YUX9NNPP0mS4uLiNGfOHKWlpWn9+vXq3r17ufv46KOPlJqaqrVr1yoxMVFffPFFqeUzZsyQ3W7X5s2blZSUpB07dmjNmjXO5fv27VPLli21a9cuPfXUU4qLi5NlWTdcd/HixQoNDdWePXuUmpqqUaNGlVvf4cOHZbPZ1KxZM+d7P/74o9q2bet8HRISonPnzunixYvlbqN37976/PPPtXPnTt1333164YUXyh33jxITE9WvXz/nSf61cnNztWnTpjJB8kZatWqlAwcO3NQ6AGo2QgAAVLGrVwN27NihVq1aqWnTps5llmXpo48+0syZMxUUFKSAgAA988wzzilEGzdu1JAhQ9SmTRv5+/vrueeeq3A/DRo0UL9+/XTbbbcpICBAEydO1J49e5zLbTabfvzxR+Xn56tJkyYVfnK8ceNGPfvsswoKClLz5s01evRo57Jvv/1Wubm5evrpp+Xn56cePXro17/+tbNeu92uQ4cOKTs7W4GBgWrfvn2F+xgzZoyaNWumwMBAPf30085l586dU0pKimbOnCl/f381bNhQ48aNKzWtKjg4WMOGDZOvr68GDx6ss2fP6ty5czdc126368SJEzpz5oxq166thx56qNz6Ll26pLp165Z6Lzc3VwEBAc7X9erVkyTl5OSUu42YmBgFBATIz89Pv/3tb3XgwAFdvny53LFX5eXl6fPPP9fgwYPLXb5582Y1aNCg1BWKyqhbt64uXbp0U+sAqNnsni4AAGq6QYMGadSoUTp27FipOeaSdOHCBeXl5WnIkCHO9yzLUnFxsSTpzJkzuv/++53LWrRoUeF+8vLytHDhQm3btk1ZWVmSSk5QHQ6H/P399eabb2rlypWKi4tT586dNX36dLVq1arMds6cOaPmzZs7XwcHB5da1qxZM9lstlLLT58+LUlasmSJ3nnnHf37v/+7QkJC9G//9m/q1KnTDfdx7SfuJ06cUFFRkcLCwpzvFRcXlxrfqFEj59e33XabpJKT9KysrOuuO23aNC1evFgxMTEKDAzUk08+qZiYmDL1BQYGljm59/f3V3Z2tvP11a//MSxIJdOd3nzzTW3atEkXLlxw9uvixYvO8FCezZs3KygoqMKT/KvTyXx8fCrcRnlycnJUv379m1oHQM1GCACAKtaiRQvdcccdSklJKXPzZoMGDVSnTh1t2LCh1BWCq5o0aaKTJ086X584caLC/axcuVIZGRn66KOP1LhxY+3fv1/R0dHOaTK9evVSr169lJ+fr//4j//QK6+8og8//LDMdho3bqyTJ086rxRcu/8mTZro1KlTKi4udp7Ynjx5Uvfcc48kqUOHDnrnnXdUWFioP//5z5oyZUqp+frX7uPa+xWu/bpZs2by8/PTrl27ZLff3I+pG63buHFjzZs3T1LJU3eefPJJde3aVXfffXepcXfddZcsy9Lp06edfy+tW7fWwYMHFRkZKUk6cOCAGjVqpAYNGpTZT3Jysr788ku9++67uuOOO3T58mV17drV+XdRkaSkJA0aNKjck/yTJ0/q66+/dk7xuhnp6ellpqEBMBvTgQDADebPn68//elPZeZ522w2xcbGasGCBTp//rwk6fTp09q2bZskKSIiQomJiTp06JDy8vL01ltvVbiPnJwc1a5dW/Xr11dmZmapsefOndMXX3yh3Nxc+fn5yd/fv9Sn+dfq37+/li9frqysLJ06dUrvv/++c1mHDh1Up04drVixQoWFhdq9e7e2bNmiyMhIFRQUaN26dbp8+bJq1aqlunXrXncf7733nk6fPq1Lly7pD3/4g3NZkyZNFBoaqvj4eGVnZ6u4uFhHjhzR119/fYMu33jdjRs3OgNHYGCgfHx8yq3x6lSna/c5aNAgffzxxzp06JAuXbqkd955p8JpOzk5OfLz81ODBg2Ul5enN95444a1nzp1Srt3765wm2vXrlWnTp101113lXrfsixduXJFhYWFkqQrV66UenTplStX9Pe//109e/a8YQ0AzEEIAAA3uOuuu/TAAw+Uu2zatGm6++67NWzYMHXu3Fnjxo1TRkaGJKlPnz4aO3asxo4dq8cee6zCG20laezYsbpy5Yq6d++u4cOHOx8hKZVMiVm1apV69eqlhx9+WHv27NFrr71W7naee+45BQcH65FHHtH48eNLTWHy8/PT73//e6Wmpqp79+6aPXu2Fi1a5JxWtHbtWoWHh6tz585avXq1fve735W7j2HDhik0NFSPP/64oqOj1adPH9ntdvn6+kqSFi1apMLCQkVGRqpr166aPHlypR9xeb11v/vuO8XGxqpTp06aOHGi4uLidOedd5a7nREjRmjt2rXO171799ZTTz2lMWPGqG/fvmrRooUmT55c7rrR0dEKDg5Wr169NGDAAHXs2PGGda9du1YdO3Ysc5J/VXlPlpKk48ePq0OHDhowYICkkqB29fGiUsnvO3j44YfLvdIEwFw+1o2uTQIAUMVSUlL02muvaevWrZ4upZQRI0Zo1qxZpX5hmLeJjY3V/Pnz1aZNG0+XAqAaIQQAANwuPz9fu3fvVmhoqM6fP6/f/va3evDBBxUXF+fp0gDACIQAAIDb5eXladSoUfrpp59Up04d9e3bV3FxcaUewQkAqDqEAAAAAMAw3BgMAAAAGIYQAAAAABiGEAAAAAAYhhAAAAAAGIYQAAAAABiGEAAAAAAY5n8BeIXJY69GXfEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "jR91ooWi65CT",
        "outputId": "caef75d7-c3bf-4e2b-dc42-3ff58ad7e6c9"
      },
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "sns.boxplot(y='g_1', x='tempo', hue='tratamento', data=dados)\n",
        "plt.title('Boxplot do g-1 de acordo com o tempo e tratamento', fontsize=18)\n",
        "plt.xlabel('Tempo', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G9+7dW2fbAIAbN27g4cOHGDp0KOrUqaMdbmFhAX9/f2g0Gpw6darYZfHzzz8jNzcX06dPR/Xq1Qt8LpfLtb8P0H+/BAAvLy/Ur19fW65RowaaNm0KuVyOsWPH6ozbsWNH5ObmltiU6fz588jIyICfn5/OXRkbGxv4+fkhIyNDpwlYab3I9tWuXTu4ublpyxYWFnBxcYEgCPDz8yvwOwEgKiqqwLwnTJgACwsLbdnR0RE+Pj74559/tL0nnTp1CoIgwN/fX2fcOnXqYMiQIYiNjcXNmzeL/Y1HjhxB1apV0bt3b53fl5aWBk9PT8TGxuL+/fv6LbBijB49utA21dbW1tr/09PTkZycDLlcDjc3N519ryTPTiczMxPJycnIzMxE165dERkZWWjTovHjx+uU89fHoEGDdLanVq1awcbGRmc9ibFtAEDXrl2hUqn0arb37LmhVatW2uEymQxTp04FUPK5i6THhxfJIBo3bozu3btryx4eHujcuTNGjBiB1atXY+3atQDy2mfK5XI0b968wDRatGiBkydP4sGDB9p2vRYWFggMDMTAgQNx584drF69Wtt843mFTbN27dqoXr06YmJiio3/wYMHcHZ2LpCwm5mZoUmTJiWezIqTP+/Cbr+2aNGi0Fhq166tc4vy2fEjIiKQnJxcIGnLl5SUhIyMjAKJGoBChyUlJem05VMoFKK0qwbyThwZGRl6//bIyEgIgoD+/fsXOc0nT56UON+ffvoJ27dvR0REBHJzc3U+S01N1f4fFRUFmUymc1IrzIusk8Ju2cfExEAulxf6WfPmzXHv3j2deeZP/3ktW7bUTq84+QlU69atix2vtPslAJ3mC/lsbW3h4OCgkxQC0Cb1KSkpJcYB/Pf7nqXvby7Oi2xfRf1OAAWORcX9zsKWbf6wmJgYNG/eXO/fX1wPPZGRkUhPT9c5Hj8vMTGx0GNBaRT1/ejoaKxduxZhYWFIS0vT+ezZi7aSJCYmYt26dTh16lShz2OkpaUVaBL3/LrKXx+FnTNsbW11nrkQa9uws7MDUPK2DhS/jzdr1gxyubxM2zuVDybWVG7c3NxQrVo1vR6YKc7Zs2e1id/Nmzfh4+MjRnj0/4YNG6ZTu1K/fn2EhoZKEosgCJDJZNiyZYtOO+hnFXYSetYvv/yCd999F66urpg/fz7q1q0LS0tLqNVq+Pv7QxAEnfGfrZkWk6n3kFDU+ilqOIACy768vcj2VdzvKeozKX+nIAioUaMGAgMDixynsMS9tPLvCj0rPT0dY8eORWZmJsaPHw+lUomqVatCLpdj06ZNep8LBEHAxIkTERkZiddffx3Ozs6oVq0aFAoFvv/+exw9erTQu3Yvsk0+O08xtw2pt3UqP0ysqVzld8OUr2HDhtBoNIiMjCxQS5h/O/TZ2oXw8HAEBgbC3d0d9vb22L59O9zd3dGjR48C8yrsZTTx8fFIS0srtGbhWQ0bNsS9e/egUql0aq1VKhXu37+v8/3SJmH5v+eff/4p8Nndu3cLjeXXX39FWlpagdv3kZGRsLGxgb29fZHzq1GjBqytrXVqP/MVNmzVqlXIzs7Wlp9vElEW+bHo+9ubNGmCX3/9FfXq1Su0hk8fP/74IywtLbFr1y6d5Law7aNJkyY4d+4cbt26VexDkWVdJ89OR6PR4P79+wWSm+fjy99uCltO+cNK2q7za8YjIiKKraEs7X5pKPm/586dOwUezNT3Nxe3f4qxfb2o4pZt/m969vc3atRIZ1x9f3/jxo1x//59uLm5vXD3ji96oXnx4kXEx8dj+fLlGDp0qM5n69at03s6t2/f1vaUMWPGDJ3PStsjjr4MuW0UtTyL28f/+ecfaDSaEtc3SY9trKnc5LeXbNu2rXZYfhvozZs361zR//333wgNDUWHDh20t5vT09Px3nvvwdbWFitXrsTixYvRoEEDzJ07t9Bbg/fu3SvQFnrLli068y1KfnvE5w/ahw4dQlJSks7389v+PdukoDi1atVCu3btEBoaqpPY5uTkFPrWsd69e0Oj0Wi79Mp39uxZ3Lx5E56entq2sYVRKBR4+eWXcf36dfz+++86n+V3+fasDh06oHv37tq/59tll4VCoYCHhwfCw8N1aqsEQcDWrVsLjP/aa68BANasWVNoV1P6NANRKBSQyWQ6NVqCIODrr78uMG7+3Y81a9boXAA++z2g7OskX36vGNu2bdMZfvLkyQIXPW3btkW9evVw+PBhbZdhQF6vMdu2bYNMJiuxl41+/frB3NwcX375ZaFtUp/9fYB++6Uhubu7a3uEeDbep0+fYs+ePbC2toa7u3ux0yhu/xRj+3pRO3bs0NnG4uLiEBwcjKZNm2oTOU9PT8hkMmzbtk2nCVN8fDwOHz6M+vXro02bNsXOZ/DgwdBoNFizZk2hn+vzG0t7jMuXX4P7fG1tWFgYrl27VmD8qlWrIjU1tcD4+fvS88P//vtvg7U5NuS2kb88n28eUrNmTbRv3x6nT5/G33//rR0uCIL2WOPt7f3C86XywRprMoibN2/ixx9/BJCXMN69exeHDh2Cubk5Zs2apR3P3d0d/fv3x7Fjx5CamgoPDw8kJCRg3759sLS0xEcffaQdd9GiRYiOjsbWrVtRq1YtAHkHvTFjxmDu3LnYsmWLTk2AUqnE+++/j+HDh6Nx48a4fPkyTpw4gc6dO+PVV18tNn5/f3/8/PPPWLJkCW7evInWrVsjIiIC3333HZo2barzoJ2Liwvkcjk2btyI1NRUWFtbo0GDBgUeYnnWvHnz4Ofnh9GjR2Ps2LGoVq0afvrpp0IP4L6+vggKCsKWLVsQGxuLjh07Ijo6Gvv27UOtWrXw3nvvlbA2gFmzZiEsLAz+/v4YN24cHB0dcebMGe0DaKWpkfrqq68AQNuf+O3bt7XDOnXqhE6dOpUYy7lz5zBlyhRtLKdPny7wMBwAuLq64p133sH69esxePBg9O3bF3Xq1EF8fDxu3LiBc+fOITw8vNj59e3bFydOnMD48eMxePBgqFQqnDx5EpmZmYXO780338SWLVswZMgQ9O/fHw4ODnjw4AFOnDiBb7/9FtWrVxdlnQDAyy+/DA8PDwQFBSElJQUvv/wyYmJicPDgQSiVSp2Tq0KhwMKFCzF9+nQMGzYMI0aMQNWqVXH8+HH89ddfmDJlSoldrzk6OmL+/PlYsmQJfHx8MGjQINSvXx+PHz/GqVOnsHz5crRu3bpU+6UhVa9eHXPmzMGSJUswYsQI+Pr6Ash7Q19UVBSWLFmCatWqFTsNe3t7NG7cGMeOHUPDhg1Rq1YtWFlZwdPTU5Tt60Wp1WqMHTsWAwYMQHp6Og4cOIDs7GydZdusWTNMmjQJW7duxbhx49C/f3+kp6fj0KFDyMjIwOrVq0ts1pDfXeiePXtw48YNeHh4wN7eHnFxcfjrr78QFRVV4kOvL3KMA/Iu0h0cHBAQEIDY2Fg4OjoiIiICP/74Y4HtG8hrLnj69GksWbIE7du3h0KhQNeuXdG8eXO0bNkSW7duRVZWFpo2bYp79+5p95MbN26UsLRLz5DbhpubG/bs2aN9T4K5uTlcXV3RsGFDLFiwAH5+fhg7dizGjBkDBwcHnD59GmFhYRg4cCBfylUBMLEmgzh69CiOHj0KIK+2wc7ODu7u7pg8eXKBW+yrV69GmzZtEBQUhBUrVsDa2hqdOnXCzJkztX2T/vDDDzhy5Aj8/f11mn24urpi1qxZWLVqFbZv367Ty0Xbtm3x4YcfYu3atThw4ABsbGwwbtw4vPvuuyXWJlarVg379+/HF198gdDQUBw+fBg1a9bEqFGj8M477+g8JFOvXj0sX74cW7ZsweLFi5GbmwtfX99iTzrt27fH9u3bERgYiM2bN6NatWro27cvRo8eXaDNuLm5ObZt24avv/4aP/30E0JCQlCtWjX069cPs2bNKra/7HzNmjXD3r17ERAQgF27dsHS0hK9evXCxx9/jN69e5equcfnn3+uU75586b2Yc7p06eXmFg3atRIG8uePXtgYWGBl19+GStXriz0Aavp06fD2dkZu3fvxq5du5CRkYGaNWuiZcuWWLBgQYnx5icuO3bsQEBAAGxtbeHh4YHZs2dr+wp/1pw5c9CqVSvs2bMHW7duhSAIcHR0xCuvvKJtRyrGOsm3bt06rFu3DsHBwbhw4QKUSiXWr1+Po0ePFkg8PD09sWPHDnz99dfaWszmzZtj2bJlGD58uF7zGzNmDBo1aoRt27Zh9+7dyMnJQe3atdGtWzc4Ojpqx9NnvywPY8eORe3atbFt2zZ8+eWXAPJ6cch/GYk+Vq9ejeXLl2Pt2rXIzMxE/fr1tf0Kl3X7elEBAQE4cOAAtmzZgrS0NDg5OWHFihUFauDff/99NG7cGPv27UNgYCDMzc3h5uaGwMBAbS8XJfnss8/QpUsXHDp0CJs2bUJubi4cHBzQpk0bzJ49u8Tvv8gxDsi7MNq6dStWrVqFPXv2QKVSwdnZGVu2bMF3331XYPueMGECYmJicOLECRw4cAAajQa7du1Cly5dsGnTJgQEBCAoKAiZmZlo2bIlAgICcOvWLYMk1oDhto2BAwciIiICx44dw88//wyNRoPPPvsMDRs2hIuLCw4cOIAvvvgC+/fvR0ZGBho2bIg5c+aUey9O9GJkAlvUkwlycnKCr68vVqxYIXUoRi08PBxDhw7F7NmztV0REpHhrF+/Hhs2bMCpU6fKpZ06EZUvtrEmqiSefxX8s+2ai+uKi4iIiPTDpiBElcSgQYPQtWtXKJVKZGZm4vTp07h69SpeffXVYl8LTURERPphYk1USXh5eeH06dM4cuQIVCoVGjRogJkzZ+LNN9+UOjQiIiKTwDbWREREREQiYBtrIiIiIiIRMLEmIiIiIhKBSbWxTk5Oh0bDli1EREREZBhyuQz29lUL/cykEmuNRmBiTURERESSYFMQIiIiIiIRMLEmIiIiIhIBE2siIiIiIhGYVBtrIiIiImOgVquQnJwAlSpH6lDoBZmZWcDe3gEKhf7pMhNrIiIiIpElJyegShVrVK3qCJlMJnU4VEqCICA9PQ3JyQmoVauu3t9jUxAiIiIikalUOahatTqT6gpKJpOhatXqpb7jwMSaiIiIyACYVFdsL7L+mFgTEREREYmAiTVRKaWkJGPFiiVITU2ROhQyItwuiKg8DRvmg99+u/zC3//000XYvPkrESMSjzHHVhIm1kSlFBwchDt3buPIkcNSh0JGhNsFERkLlUoldQiVFhNrolJISUlGWNhZCIKAsLBzrJ0kANwuiKh8LV26EI8fx2Hu3Pfg7f0y9u7diR49OuLo0R8wZMgAzJw5FQDw0Udz8dprfdG3b09Mm/Ym/vknEgDw44+H8csvx7Fv3y54e7+MDz54FwCwe/cOjBgxCN7er2DcuOE4e/a0dp4//RSMqVMn4osvAtGvXy8MHz4I//vfNfz0UzCGDBmAgQO9cfz4Ue34OTk52LBhHYYMGQAfnz5YtWo5srOzAAB//HEVvr6vYv/+PRg40BuDBvXFsWNHio3t/v17mD59Mvr164Vx40YgLOys4Rf0C2BiTVQKwcFB0GgEAIBGo2HtJAHgdkFE5WvhwqWoU8cRAQFrEBLyKzw9vQEAf/75B/bu/Q5r1qwHAHTt2h0HDhxGcHAIlMpWWLJkIQBg0KAh6NOnP8aMeR0hIb9i5cq1AID69Rvgq6+24sSJM3jjjTexdOlCPHnyRDvfmzdvoHnzljh27BS8vfvik0/mIyLiJg4cCMLHHy/FmjUrkZGRAQDYuHE9YmKisGPHPhw8GISEhARs375VO62kpESkpz/FDz8cx7x5C7FmTQDS0tIKjU2lUmHu3HfRuXNXBAeH4N1338fixQsRHX2/PBZ3qTCxJiqFixfPQ63Ou8WmVqtw8eJ5iSMiY8DtgoiMwcSJk2FlZQVLyyoAgIEDB8HauiosLCwwceJk3L37N54+fVrk9z09e6NWLQfI5XJ4efVBgwaNEBERrv28bt16GDDgNSgUCnh59UF8/GO88YY/LCws0LlzV5ibmyM2NgaCIODIkSDMmDEb1avbwtq6Kl5//Q2cOvWLdloKhRkmTPCHmZkZunXrARBn4ZQAACAASURBVCsra0RHRxUa140b/0NmZibGjZsAc3NzdOjQCd2790BIyAmRlpx4+IIYolLo1s0d586dgVqtgkJhhm7d3KUOiYwAtwsiMgZ16jhq/1er1di8+SucPn0SKSkpkMvzuo5LTU2BjY1Nod8/fvwoDh7ch7i4hwCAzMxMnaZt9vY1tP9bWloCAGrUqKkzLCMjEykpycjKysKkSeO0nwmCAI1Goy3b2trCzOy/NLRKlSrIzMwoNK4nTxJQu3YdyOX/1Qc7OtbFkycJxSwNaTCxJioFHx9fhIWdhVoNyOVyvPbaEKlDIiPA7YKIyltJfSyHhPyMsLCzWLfuK9StWw9Pnz5F//4eEASh0O/HxT3CypWfYt26r+Hs7AKFQoEJE8bg/0cvFVtbO1haWmL37kNwcKhd6u8/H1utWg6Ij38MjUajTa4fP45Dw4aNSh+cgbEpCFEp2NnZo0ePnpDJZOjR4xXY2tpJHRIZAW4XRFTe7O1r4OHD2CI/z8jIgLm5BWxtbZGVlYVNm74s9vuZmZmQyWSwt887fh07dgT37kW+UGxyuRw+Pr744os1SE5OAgAkJMTj8uWLen3/+djatHGGpWUV7N27CyqVCn/8cRXnz/+K3r37vlB8hsTEmqiUfHx80bKlE2slSQe3CyIqT35+E7Bz5zb069cLp0+fKvB5v34D4OjoiMGDX8W4ccPRtq2zzucDBw7C/fv30K9fL3z44Ww0bdoMI0eOxVtvTcRrr/XBP//chYuL2wvHN3XqO2jQoCEmT34Dffr0xKxZbxfZhvp5z8dmbm6OgIA1uHz5AgYM8MKaNQH46KPFaNy4yQvHZygyQXiRSn7jlJj4VPtkPhEREZFU4uKi4OjYWOowqIwKW49yuQw1axbeTp011kRERFRh8a2nZEyYWBMRiYAndyJp8K2nZEyYWBMRiYAnd6Lyx7eekrFhYk1EVEY8uRNJg289JWPDxJqIqIx4cieSBt96SsaGiTURURnx5E4kjW7d3KFQ5L3rjm89JWPAxJqIqIzyTu4KAIBCoeDJnaic+Pj4al/VzbeekjFgYk1EVEY+Pr7a1wQLgsCTO1E54VtPydiYSR0AEZEp+C+xljgQokrGx8cXsbEPjP6CdsXKFUhOSRV9uvZ2tpj3wTzRp0svhok1EVEZBQcHQSaTQxDUkMlkOHLkMPz8JkodFlGlYGdnj3nzPpY6jBIlp6Qiy76b+NNNvij6NA1l2DAfrFy5Fs2atZA6FINhUxAiojK6ePE8NBo1AECjUfPhRSKiUlCpVFKHIBrWWBMRlVG3bu44c+YUBEGATCbjw4tEZJTCw6/jyy8/R0ZGBgBg2rSZqFatGtatW42srExUqWKFWbPmoHXrtnj06CH8/f3g4+OLy5cvIDs7Gx9/vAw//vg9bt4Mh4WFJVasCETNmrWKnN+1a38iMHAFAKBdu5e0TeaAvNprL68++OOP39CsWQvMnDkH69atQkTEDQBAv34DMHbseADAvXv/YPnyxcjKykTLlk548CAG48dPgrv7y9i/fw9OnfoFarUKFhaWmDNnHlq2dDLUIiwRa6yJiMqoZ09PnYcXe/XykjgiIiJdaWmpmD//fbz99gzs3Lkf33yzBy1atMSCBR/gzTenYufOA/D3n4IFCz5Abm4uACA1NRWuru2wffs+DBw4CLNmTcWQIcOxc+cBODm1xvffHypyfjk5Ofjkk/mYNet97Np1EG5uL+Hx4zidcdLT07Flyy58+OHH2LFjKzQaDXbtOoiNG7/B8ePHtHf/li79GMOGjcTu3YcwYsRo3Lp1UzuNfv0GYOvWXdi+fR/8/adg1arPDLD09MfEmoiojM6eDdUpnzlzSqJIiIgKFx7+PzRp0hQuLm4A8roGTU5Ohrm5OTp27AwA6NSpC8zNzREdHQUAsLKyRvfuPQAASmUrODjU1tYGt2rVCrGxMUXOLzo6ClWqVMFLL3UEAHh5ecPGxkZnnH79Bmj/v3r1Cnx8fCGTyVC1qg169+6Dq1evID39Ke7di4S3d7//n28bNG/+Xxvt27cjMG3am/DzG4ENG9bi7t2/y7ScyoqJNRFRGV24EFZsmYgMJzr6PqZNm4SYmCipQzE5Fhbm2v/lcjksLCyfKSugVqtLOUWZTsna2kr/b8pkBYbl5uZi4cK5mDFjNnbvPoTAwPXIyckpZUziYmJNRFRGNWvWLLZMRIazefOXyMzMxKZNG6QOxag5O7vg/v17CA+/DgBQq9Wwt7dHbm4u/vjjKgDg999/g0qlQqNGjcs8v0aNGiM7OxvXrv0JADh9+iSePv23yPE7duyMY8d+hCAIyMhIx6lTv6BTpy6oWtUGTZs2Q0jICQDA7du38M8/kQCAnJxsqNVq1K5dBwBw+PC3ZY67rPjwIhFRGT158qTYMhEZRnT0fTx8GAsAePgwFjExUWjYsOxJoSHY29kapGs8eztbvcarXt0Wn366EuvXr0VWViZkMjmmTZuJTz9dqfPw4rJlATA3Ny95giWwsLDAokWfIjBwBWQyGdzc2qNOHccix58wwR9r167E66+PBAD07fsqunbtDgD46KPF+OyzJdizZzuaNWuBZs2aw8bGBlWr2mDSpLfw5puvo3p1W3h4SP98i0wQTOd1BomJT6HRmMzPIaIK4qOP3tee3AGgXr36WLZslYQREVUOxrzvxcVFwdHROJP8iiYjIwNWVlaQyWS4d+8fvPPOW9i373tUr17d4PMubD3K5TLUrGlT6PissSYiKqPExMRiy0RkGM8m1YWVyTTkdxMI5FWezp27oFyS6hdhlIn1hg0bsH79egQHB0OpVEodDhFRsbp376HTj3X+U/REZFj16tUvUGNN5Ss4+IdCu91bsOAT0fqT7ty5Kzp37irKtAzN6BLrGzdu4K+//kL9+tw5XkRKSjI2blyPqVNnwNbWTupwiCoFHx9fnD79Xxd7r702RMJoiCqPyZOnYdGi+dryW29NlzCaysnHZzB8fAZLHYbRMKpeQXJycrBkyRIsWrRI6lAqrODgINy5cxtHjhyWOhSiSua/F8QQUflo1KgJ6tSpCwBwdKxrtA8uUuVhVIn1559/jtdeew0NGjSQOpQKKSUlGWFhZyEIAsLCziE1NUXqkIgqhe++O6BT/vbb/RJFQlT55Pc0UVyPE0TlxWiagvz5558IDw/HnDlzXngaRT2hWVl8++3uZ16rrEFIyFFMnTpV4qiITN/lyxcKlD/88AOJoiGqPJKSkhAefg0A8L//XYOZmQr29vYSR5UnPl4OMzOjqr+kFyCXy+HgUE3v8Y0msf7tt98QGRkJL6+8Pgjj4uIwadIkfPbZZ+jRQ78HgSp7d3unT5+BSqUCAKhUKoSGnsawYeMkjoqockpIKPpFCEQkjq1bt0Kj0QAANBoNNm7cAn9/46hQ0mg0UKk02vLnq5fiaWqS6POxsa2BmXMWij5dyqPRaAoczytEd3uTJ0/G5MmTtWVPT09s3LiRvYKUQrdu7jh37gzUahUUCjN06+YudUhElUKtWrXx+PEjbdnBobaE0RBVHoXdLTKWxPp5T1OT4NdM/Onu/kf8ZN1Ybdu2Ca+/PvGFXmCzbdsmZGZmYvr0WQaI7D+8R2FCfHx8IZPl/S+TydgzAVE5SUlJ1iknJycXMSaVRUpKMlasWMLnR0jr+YeF+fCwadu+fQtyc3ML/Sz/jr3UjKbG+nmhoaFSh1Dh2NnZo3btOnj4MBa1a9dmd3tE5aR79x44ffqkTpnE92yvR35+E6UOh4yAg0MdnbtFtWvXkTAa45f/opWMjAwAwLRpM1GtWjWdV5rPmjUHrVu3xaNHD+Hv7wcfH19cvnwB2dnZ+PjjZfjxx+9x82Y4LCwssWJFIGrWrFXk/O7fv4fPP1+NpKRECIKA0aP90L//QDx4EINVq5YjJSUZCoUCkydP076+vEePjpg8+W2cO3cGqampmDZtBnr18kJgYAAAYOrUiZDJ5Fi/fhO++CIQCoUC0dFRyMjIwI4d+7Bnzw6cOPETAKB167aYNet9WFtbF4htz54dOHs2FGq1GrVq1cbcuQuK/S36Yo21CUlJSUZ8fDwAICEhnrU6ROUk725R3u0i3i0yDPZ6RIXh3SL9paWlYv789/H22zOwc+d+fPPNHrRo0RILFnyAN9+cip07D8DffwoWLPhAWyucmpoKV9d22L59HwYOHIRZs6ZiyJDh2LnzAJycWhf6Yph8KpUK8+bNho+PL3buPIBduw5qKx0WL/4I3t59sXPnASxcuBRLly7UWXdVq1bF1q27sHDhYqxbtxoAMHv2XADA119/gx079qFatbwHCu/c+RuBgeuxY8c+XLx4HidO/ISNG7/Brl0HoVarsWPH1gKxnTjxE2JjY7Fp0w58881edOvmjg0b1omynJlYm5Dg4CAIwn8PcbAvayIpyKQOwCQFBwdpH07n8Y3yde/eQ+eilneLihYe/j80adIULi5uAACFQoHk5GSYm5ujY8fOAIBOnbrA3Nwc0dFRAAArK2vtMlUqW8HBobb2bYqtWrVCbGxMkfOLjo6CWq2Gp2dv7TBbWztkZKTj7t2/8eqrrwEAmjZthhYtnHDjxv+043l59QUAtG3rgidPEpCdnV3kfHr18oKVlRUA4OrVK/Dy6oOqVW20lRxXr14p8J2wsHO4evUKJk4chwkTxuDw4UOIi3tYwhLUDxNrE3Lx4nmo1WoAgFqtxsWL5yWOiKhyCA4OeubkDiZ9BpB3fMtrQ6lWq3h8IwB5d4vkcgWAvESRd4vEZWHx30OCcrkcFhaWz5QV2pxD/PlaAMhbpwCKnY+1tVWppy8IAsaPn4gdO/Zhx4592L37EL7++psXC/Y5TKxNyEsvddQpd+jQSaJIiCqXixfP63T5xaRPfN26uUOhyHssiL0eUT47O3vUquUAAKhVy4HPFhXD2dkF9+/fQ3j4dQB5yaq9vT1yc3Pxxx9XAQC///4bVCoVGjUq+xssGzVqDIVCgdDQ/54/SU1NgbV1VbRoocTx40cB5LXDjoz8G23bupQ4TWvrqkhPf1rk5x07dkZoaAgyMtIhCAKOHv0BnTp1KTBejx6vICjoO6SlpQHIe/P3nTt/l/YnFspoH16ksuPT0UTl46WXOuLChV+1ZV7Uis/HxxdhYWehVufVnLFmkoD8Z4seAwDi4x8jNTXFaJNrG9saBukaz8a2hl7jVa9ui08/XYn169ciKysTMpkc06bNxKefrtR5eHHZsoAX6s7ueWZmZlixIhBr167Ejh1bIJPJMXr0OPTrNwCffLIMq1Ytx6FD+6BQKPDRR0v0erHPqFFjMWPGFFhaVsH69ZsKfN6tmzsiI+/grbfeAAC0atUG48dPKjBev34DkJqagnfeyevmWaPRwNd3OFq2LHsXzzLBhLKvyv6CmLffnoSsrExtuUoVK3z11TYJIyKqHFatWo6IiHBtuW1bZ8yePV/CiEzT7t3f4MyZU+jVy4u9ghAAYOvWr3Uuat3dX8GkSVMkjOg/cXFRcHQse80vSauw9VjcC2LYFMSEdOvmrm1rJpcreKuUqJw8m1QDwI0b4UWMSWXh4+OLli2dWFtNWs+/IObSJTbDImmxKYgJyb9VqtGo+RAHEZkcOzt7zJv3sdRhENEzgoN/KLTbvQULPtH2IFKZMLE2IXZ29ujRoyfOnDmFHj1eMdp2ZkRERGLo0qW7TlOQLl26SxhN5eTjMxg+PoOlDsNosCmIieGtUqLyZ25uoVPO7yqKiAyrT5/+OuW+fV+VKJLCmdBjbJXSi6w/JtYmJv9WKWuricqPSpWrU85/axkRGdbZs6E65TNnTkkUSUFmZhZIT09jcl1BCYKA9PQ0mJmVrqKETUGIiMro+RMnT6RE5ePZZiD5ZWPpMcbe3gHJyQl4+jRF6lBEodFo8O+//6JatWqQyytHvayZmQXs7R1K9x0DxUJERERkUHkvDcp+rmwcFAoz1KpVV+owRMPuLvVTOS45KpGUlGSsWLEEqammcYVMVBFYWVkVWyYiw8jISC+2TOJISUlGWNhZCIKAsLBzzDGKwcTaxAQHB+HOnds4cuSw1KEQVRoqlbrYMomDFQf0PEvLKsWWSRzBwUHaF/BpNBrmGMVgYm1CeEVJJI0aNWoWWyZxsOKAnpednVVsmcRx8eJ5qNUqAIBarcLFi3wRT1GYWJsQXlESSSMh4XGxZSo7VhwQSadbN3dt+3WFwoxvdi4GE2sTwitKImloNJpiy1R2rDigwlhZWRdbJnH4+PhCLpcBAORyOd+VUQwm1iaEV5REZKpYcUCFUavVxZZJHPlvdpbJZHyzcwmYWJsQXlESSaNOHcdiy1R2rDigwnTs2LnYMomHb3bWDxNrE8IrSiJpjBs3QafMPl7Fx4oD0odMJpM6BKrkmFibGF5REpW/55slPP82OCo7VhxQYa5evaJT/u23yxJFYvrYK49+mFibGDs7e8yb9zFPOkTl6PnEmu1/DYMVB/Q8MzOzYsskDvbKoz8m1kREZSQImmLLJA5WHNDz+ObF8sFeefTHxNrE8M1kRERUWeQ/0FpUmcTBXnn0x8TaxLANFBERVRb5yV5RZRIHe+XRHxNrE8I2UERkynhHjp5Xr179YsskDvbKoz8m1iaEbaCIyJTxjhw9b/RoP53ymDHjJYrEtLFXHv0xsTYhbANFRKaKd+SoMCEhJ3TKJ0/+LFEkpo+98uiHibUJ6dbNHXK5AgAglyvYBoqITAbvyFFhrl//U6f8119/SBQJUR4m1ibEx8cXGo0aQN6Jh1eVRGQqeEeOSFpsiqUfJtYmS5A6ACIi0bBXAiLpsCmW/phYm5B9+3Y9V94pUSREROJirwRUGDs7e52yvX0NiSIxbWyKpT8m1ibk99+v6JSvXr1SxJhERBULeyWgwqSlpemUU1NTJYrEtLEplv6YWJsQQRCKLRMRVWTslYAKev48x/OeIbAplv6YWBMRUYVgZ2ePefM+Zm01UTljUyz9MbE2IQXbmtkXMSYREVHFp9Foii2TOOzs7NGpU1cAQKdOXXhxWwwm1iYkJSVZp5ycnFzEmEQkJl7UElFlIZPJpA7BqDGxJiIqI7lc/lxZIVEkRETiS0lJxm+/XQIAXLlyid3tFYOJtQmxtLQstkxEhpGUlKhTTkx8IlEkpi0lJRkrVizhSZ2onLG7Pf0xsTYh2dnZxZaJiCoyvvmNSBrsbk9/TKyJiMjo8c1vRNLp1s1d27ZaJpOxu71iMLEmIiKjx1vRRNLp2dNT+24MQRDQq5eXxBEZLybWRERk9Hgrmkg6Z8+GAsjvDUSGM2dOSRmOUWNiTURERo9vfiOSTt6FbP5bLQVe2BbDTOoAiIiISuLj44uwsLNQq/nmN6LinD9/DmFhZ0WdpqWlJbKyMnXKAQFLRZl2jx494e7+iijTMgZGU2OdnJyMN998E3379oWPjw+mT5+OpKQkqcMiIiIjYGdnjx49ekImk6FHj1f45jeiclSrVi3t/zKZDLVqOUgYjXEzmhprmUwGf39/dOnSBQAQEBCA1atXY/ny5RJHRkRExsDHxxexsQ9YW01UDHf3VwxSA/zuu28jNTUFvXp5wc9voujTNxVGU2NtZ2enTaoBoF27dnj48KGEERERkTGxs7PHvHkfs7aaSAK1atWClZUVL2xLYDSJ9bM0Gg32798PT09PqUMhIiIiqvTMzMzRqFETXtiWwGiagjxr6dKlsLa2xrhx40r1vZo1bQwUUcVgYWGBnJwcnbKDQzUJI5JWaGgoQkJCRJ9uSkreiyns7MQ/uHh7e/OC0kRU5n2PSErc9wzD3FwBgMu3JEaXWAcEBCAqKgobN26EXF66CvXExKfaFwhURs8m1fnlhIR/JYpGemlpmcjNVYs+3cTEvIdqq1YV/+CSlpZZqdeZKeF6JJIG9z3DyD+fcvkCcrmsyMpco0qs16xZg/DwcGzevBkWFhZSh0MVnKEe4MjvYmju3IWiT5vIVBiiy6/815gb4la0qXX5ZYwMsU0URqxu4ABuF1R6RpNY37lzB5s2bUKTJk0watQoAECDBg3w5ZdfShwZEREZg9TUVACGSayJiMRgNIl1y5Ytcfv2banDKDe8ciciU2aIO0a8W1SxGWKbmDbNH5mZGdqytbU1tw+SlNEk1kRE5YEXtUSmY+7cj7Bo0fxnykyqSVpMrCViiCv3GzeuIzBwhbY8Z858tGnjLOo8iIiIjEWjRk0gk8kgCAKsra3RsGFjqUOiSo6JtQlp29ZV+3+VKlWYVBMVwhAXtZcvX8CmTRu05alTZ6BTp66izoOICteoUWNER0extpqMglG+IIZeXP36DQAA06e/J3EkRJVHly7dtf/LZDIm1UTlqEoVKyiVrVhbTUaBibWJsbGpBien1qytJipnjo51AQBTprwjcSRERCQVNgUhIhKBra0dbG3tWFtNRFSJscaaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEwMSaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEwMSaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEwMSaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEwMSaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEYCZ1AERERGRc9u3bhZiYKKnD0Et0dF6cAQFLJY5Efw0bNsaYMa9LHQYZABNrIiIi0hETE4Xbd+5CUcVO6lBKpFErAAB3Y55IHIl+1FkpUodABsTEugQV6aod4JU7mQ7ue4bHfY+Ko6hiB+vGXlKHYXIyok5JHQIZEBPrElSkq3aAV+5kOrjvGRb3PSIi8TGx1gOv2g0nI+oU4uPjKlQtH2smyw/3PcPJrzWrSHcGuO+RKalI+x5Q8fY/qfY9JtYkuaysbNy/ewuONhVjc7SGBgCQFXdX4kj0E/dUJXUIZMRiYqIqzP7HfY9MSUXa94CKtf9Jue9VjLVJJs/RxgxvuNaQOgyTtP16ktQhkJHj/mcY3PeoJNz3DEPKfY/9WBMRERERiYCJNRERERGRCJhYExERERGJwKgS63v37mHkyJHo27cvRo4cifv370sdEhERERGRXowqsf7kk08wZswYnDhxAmPGjMHHH38sdUhERERERHoxmsQ6MTERN2/exMCBAwEAAwcOxM2bN5GUxKeqiYiIiMj4GU13e48ePUKdOnWgUOS9vUyhUKB27dp49OgRatRgVzRERETlJTU1BeqsFL5+2wDUWSlITTWDrW3FeKsslY7RJNZiqFnTRvRpPn2axoOLAamzUqA2k0kdhskzN1fAwaGa1GGUCvc9w1JnpeDpUwvY29sjS+pgTFhF3PcAQKEwmhvaJkmhkMPcXMF9z4Ck2vfKnFg/fPgQ9erVK3MgdevWxePHj6FWq6FQKKBWqxEfH4+6devqPY3ExKfQaIQyx/IstVoj6vSoIEHcVUaFyM1VIyHhX6nDKBXue4anVmuQm6uWOgyTVhH3PQCwsakORZUcWDf2kjoUk5MRdQo2NtW57xmYIfc9uVxWZGVumRLrnJwceHl5ISIioiyTAQDUrFkTrVu3xtGjRzFo0CAcPXoUrVu3lrwZiK2tHRLSVDy4GEhG1CmYCekAcqUOhYwM9z3Dyog6xVvRREQiKzGx/u2334r8LCcnR9RgFi1ahHnz5uGrr75C9erVERAQIOr0iYiIiIgMpcTE2s/PDw4ODpDLDd/eqnnz5vj2228NPh8iIiIiKaWmpiD5qQrbr7P3M7HFPVXBPjVFknmXmFjXq1cPq1evxksvvVTgs+zsbLRr184ggRERkeHx5G44Up7ciUgaJSbWzs7OCA8PLzSxlslkpXq4kIiIiIjyniOxzHyCN1zZpbDYtl9PQhWJniEpMbEODAws8jMLCwuEhoaKGhBVPiqVCnE5rDEzFNaaUXF4cjccKU/uRCSNEhtOm5ubw9zcXK+JTZ48ucwBERERERFVRKK+IObq1atiTo4qCTMzM9S2EFhjZiCsNSMiIioffLUSEREREZEImFgTEREREYmAiTURERERkQhEbWMtCIKYkyOiSk6dlYKMqFNSh6EXjSoLACA3qyJxJPpRZ6UAqCV1GGTEKsr+x32PjIneifXnn39e6HALCws4Ojri5ZdfxpQpU0QLzJhUlIMLUEEPMJb69TpDlUvDho2lDqFUoqOjAACNGlaUE2YtNGzYGDExUVIHQkaoIu1/3PfImOidWN+/fx8hISFwdXVF3bp18ejRI1y/fh2enp44ffo0Fi9eXGTyXZFVpIMLUDEPMI8fxwFCrtSBkJEZM+Z1qUMolYCApQCAuXMXShxJ6eTHTfSsirT/cd8jY6J3Yq3RaLB27Vp4e3trh508eRJHjx7FoUOHEBQUhDVr1qBnz54GCVQqFengAlTMA0xAwFJkxd2VOgwiIiKiMtH74cWwsDB4enrqDPPw8MC5c+cAAK+99hpiYmLEjY6IiIiIqILQO7Fu1KgR9u/frzPswIEDaNSoEQAgOTkZVlZW4kZHRERERFRB6N0UZNmyZXjnnXewZcsW1KlTB48fP4ZCocD69esBAPfu3cPMmTMNFigRERlG3FMVtl9PkjqMEj3N0QAAbCwqRk+xcU9VaCJ1EERUrvROrNu2bYsTJ07g2rVriI+Ph4ODA9q1awdz87weHTp16oROnToZLFAiIhJfRXpAO/7/H86u5VgxYm6CirV8iajsStWPtbm5OTp27GioWIiIqJxVpAe0K+LD2URUuVSM+2lEREREREaOiTURERERkQhEfaU5EREREemnojw4DFSsh4elfHCYiTUZBR5ckQnD8AAAFslJREFUDIc9ExARGZ+K9mBrRXp4uAmkW75MrElyPLgYVhNUvGVMRGTqKtKDwwAfHtYXE2uSHA8uREREZAoqxr1sIiIiIiIjx8SaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEwMSaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEwMSaiIiIiEgETKyJiIiIiETAxJqIiIiISARMrImIiIiIRMDEmoiIiIhIBEysiYiIiIhEwMSaiIiIiEgETKyJiETw9Om/uH07AjdvhksdChERSYSJNRGRCGJjHwAA1q1bKXEkRJWLSpWL6Oj7SE1NkToUIibWRERldePGde3/KpWKtdZE5SgmJhqZmZlYuzZA6lCIYCZ1AACwePFiXLx4ERYWFrC2tsaCBQvg4uIidVgVUlJSIhIS4nH27Cn07OkldThERuf8+XMICzsr6jRv347QKa9evRxOTq1Fm36PHj3h7v6KaNMjMhUpKcnIyckBAERHRyE1NQW2tnYSR0WVmVEk1q+88grmz58Pc3NznD59Gu+++y5OnjwpdVgVUkJCPABg585tTKyJiMhoGOKi9t69SJ3y3Lmz0LRpc9Gmz4taKi2jSKw9PDy0/7dr1w5xcXHQaDSQy9lSpTRCQ0N0yqy1JirI3f0V0U+UEyeOKTBs7tyFos6DiArKr60uqkxU3owisX7W3r170atXL5NPqsvjdvTOndtw6dIF0abPK3ciInpRvKilyqBcEmtfX188fPiw0M8uXLgAhUIBADh27BiCg4Oxd+/eF5pPzZo2Lxxjeate3Qrm5gqDz0fMeVSvbgUHh2qiTa+iyl+mXBZUHG4f4uO+R/rg9mEY3P/0Uy6JdVBQUInjhISEYO3atdixYwdq1ar1QvNJTHwKjUZ4oe+WNxeXTnBx6STqNAu7cn/vvfmiziMh4V9Rp1cR5eaqAXBZUPG4fYiP+x7pg9uHYXD/+49cLiuyMtcomoKcPn0an332GbZv344GDRpIHQ4RERERUakZRWL94YcfwtzcHDNmzNAO27FjB+zt7SWMioiIiIhIf0aRWF+6dEnqEIiIiIiIysS0u94gIiKTwVdXE5GxY2JNREQVQnx8PDIzM/Htt/ulDoWIqFBG0RSEiIhMi9h99atUufj33zQAwIULv+Lx4ziYmYl3CmM//UQkBtZYExGR0YuPj3+u/FiiSIiIisYaayIiEp3Yb9nz9x+nU05Pf8o37BGR0WGNNRERGT2NRlNsmYjIGDCxJiIiIiISARNrIiIyenXqOBZbJiIyBmxjTURERm/q1BlYtGi+tvz22zMljIbIeIndI0++6OgoAEBAwFJRp2tqPfKwxpqIiIxeo0ZNtLXUdeo4omHDxhJHRFS52NrawtbWVuowjB5rrImIqEIYN24C1qwJgJ/fRKlDITJaYvfIQ6XDGmsiIqoQ/vjjKgDg99+vSBwJEVHhmFgTEZHRS0lJxq+/noUgCAgLO4vU1BSpQyIiKoCJNRERGb3g4CCo1SoAgEqlwpEjhyWOiIioILaxJpNV0Z6MBkzv6WgisVy4EAZBEAAAgiDgwoUwtrUmIqPDGmuiUuKT0UTlr2bNmsWWiciwoqPvY9q0SYiJiZI6FKPGGmsTYmVlhczMTJ1yZcYno4lMx5MnT4otE5Fhbd78JTIzM7Fp0wYsW7ZK6nCMFmusTcizSXVhZSKiisre3v7/2rv/2Kbq/Y/jr56O7QsO6FbuNscvMREkcr0i49cc4h2QELJCJpAMgnIjBLzZxPgjBjT+moZgggiKucAfIg7FILBgowhykR+DIT+FIGqQ5AsXNjfYWsNEwbX9/tEv9W6yCq47pz19PhKSvduT7l3+2Hn1c949n6g1kpNhOFvUTqezjSPRHmfP/q9qas5LkmpqzrNqHQXBGgAQ9y5cuBC1RnIKBgMt6kAg0MaRaI9Vq95uUa9cudyiTuIfwRoAEPccjug1gI5zbbW6rRq/IVgDAOLe8OH5LeoRI+6zqBMg+eTm9oxa4zcEa+Am+f0+LVpUzgYVgImmTCmRYYRPWYZhaMqUEos7ApLHnDmlLeq5c8ss6iT+EayBm+T1VurUqe/YoAIwkcuVEVmlHjmyQN27uyzuCEgeffrcFlmlzs3tqd69+1rcUfwiWAM3we/3qarq2rbKu1m1hiTpb38b3Kq+16JO7G3KlBL1738nq9WABebMKVXnzp1Zrf4DBGsbaX3f6mS/j3VH8HorFQyGd38LBoOsWkOSNHPm7Bb1P/4xu40j0R4uV4bmz3+B1WpEOJ0pUWvETrdu3dW7d19168YGadEQrG2kubk5ao32q67eq0Ag/P8aCDSrunqvxR0hHrhcGUpLS5MkpaWlEfwAk1z7e9xWjdhhDPLGEKxtpFOn1Kg12m/kyPsiKyJOZ4pGjuTOBAhvnnDlyhVJ0pUrV9g8ATAJd6swB2OQN45gbSOXL/8UtUb7eTzFMozwDXQNw9DEiQ9a3BHiAZsnANaYNu2hFvX06TMt6sTeGIO8cQRrG+GTe8dzuTI0dOgISdKwYSO45A9JbJ4AWOXIkUMt6sOHD1jUib0xBnnjCNY2wid3c4VCIatbQJzgi8OANVoHPAJfx2AM8sYRrG1k164vWtX/tqgT+/L7fTp4cL8k6eDBL5kzgyRF5qvbqgF0jHvvzWtRDxky1KJO7I0xyBtHsLaR1pfADh3iklisMWeG6wkGg1FrAObgSmLHcLkyVFAwWg6HQwUF9zMGGQXB2kZa/0HhD0zsMWeG63E4HFFrAB2j9Yx16xqx4/EU6447BrBa/QcI1sBNYM4M15ORkdmizszMbONIALHEKIh52KDpxhCsbYRVs47HnBmux+drbFE3Nja2cSSAjsSVWliNYG0jQ4YMa1Hn5Q1r40j8WcyZ4XoYwwKswSgI4g3B2kamT3+4Vc3t9joCc2YAEB9GjrxPhuGUJBmGk/E8WI5gbSMuV4by8oZLkoYOHc5qagdhzgytXTuxt1UD6BgeT7GcznCUcTqdLHjAcgRrm5k+/WH1738nq9WAiUaMyG9Rs2oGmIPxPMQbgrXNsJoKmG/KlJLIl4UdDoemTCmxuCMgeTCeh3hCsAaAdnK5MpSVlSNJys7O4YMtYCIWlBBPCNYA0E5+v08NDRckSRcvXmSrewBIUgRrAGgnr7cycou9UIit7gEgWRGsAaCdwlvdByRJgUCAre4BIEkRrAGgndhWGQAgEaxtx+/3adGicmY8AQux8yIAJKe4CtZffvmlBg4cqLVr11rdSsLyeit16tR3zHgCJmJbZQCAFEfBuqmpSYsXL9b9999vdSsJy+/3qapql0KhkKqqdrNqDZiEbZUBAFIcBetFixZp1qxZysjIsLqVhOX1VioYDF+CDga5MwFgFo+nWIYR3iDGMAw2qgCAJBUXwXrXrl26dOmSxo8fb3UrCS18Z4JmSVIg0MydCQCThDeIyZYkZWVlsVEFACSpFDN+SXFxsWpqaq773GeffabXX39dq1evbvfvcbvT2/0aiezvf39An3/+uZqbm5WSkqLCwr/rL3/panVbgO01Njaqvr5OklRfX6eUlGauvgFAEjIlWFdWVrb53KFDh3ThwgVNnTpVkuTz+fTFF1/I7/errKzspn5PQ0NTZBQiGY0bV6Tt27dLkhwOQ+PGFenChUsWdwXYX0VFhZqbw/exbm4OaPXq9/TQQ49Y3BUAoCMYhqPNxVxTgnU0eXl5qq6ujtTz58/XoEGDNGPGDAu7SkwuV4YKCkZr585/q6Dgfi5HAybZt69K0rUP9SHt21dFsAaAJBQXM9aIHY+nWHfcMYAvTwEmcrvdUWsAQHJwhGy0k0Gyj4IAsMY///mIrlz5JVKnpf2P/vWvdyzsCADQUaKNgrBiDQDtlJ9fELUGACQHgrXNsKU5YD6Pp1gOR/g+1g6Hg1EsAEhSBGubYUtzwBr/HawBAMmJYG0jbGkOWMPrrWwRrPlgC5iHK7WIJwRrG2FLc8Aa4V1Pw/exDgQC7HoKmIgrtYgnBGsbYUtzwBojR94npzO8LYDTmaKRI++zuCMgOXClFvGGYG0jnNwBa3g8xTKM8CiIYRh8eREwCVdqEW8I1jYSvjNB+GfuTACY59qupw6Hg11PARNxpRbxhmBtIy5XhrKysiVJWVlZnNwBE7HrKWA+rtQi3hCsbcTv96m+vl6SdOFCPbNmgIlcrgzNn/8CH2gBEzGGhXhDsLYRr7dS13aoDwZDzJoBAGyNMSzEG4K1jTBrBgBINoxhIZ4QrG2EWTMAQLJhDAvxhGBtI8yaAQAAWIdgbSPMmgEAAFgnxeoGEFseT7HOnz/HajUAAIDJHKFrt5GwgYaGpsgOTAAAAECsGYZDbnf69Z8zuRcAAADAlgjWAAAAQAwQrG3G7/dp0aJydl0EAAAwGcHaZrzeSp069R27LgIAAJiMYG0jfr9PVVW7FAqFVFW1m1VrAAAAExGsbcTrrVQgEJQkBQIBVq0BAABMRLC2kerqvQoGA5KkYDCg6uq9FncEAACQPAjWNnLvvXlRawAAAHQcgrWNORwOq1sAAABIGgRrGzl8+GCL+tChAxZ1AgAAkHwI1jbidruj1gAAAOg4BGsbaWhoiFoDAACg4xCsbSQ/v0DStblqx//XAAAAMAPB2kY8nmKlpDglSSkpKZo48UGLOwIAAEgeBGsbcbkyNGrUA3I4HBo1arS6d3dZ3RIAAEDSSLG6AcSWx1Os8+fPsVoNAABgMkcoFApZ3USsNDQ0KRi0zdsBAABAnDEMh9zu9Os/Z3IvAAAAgC0RrAEAQMLy+31atKhcP/7ot7oVgGANAAASl9dbqVOnvtPHH2+yuhWAYA0AABKT3+9TVdUuhUIhVVXtZtUaliNYAwCAhOT1VioYDEqSgsEAq9awHMEaAAAkpOrqvQoEApKkQCCg6uq9FneEZEewBgAACWnQoLtb1H/9691tHAmYg2ANAAAS0n/+c6ZFffbsmTaOBMxBsAYAAAmpru6HqDVgNoI1AABISLm5PaPWgNkI1gAAICHNmVPaop47t8yiToAwgjUAAEhIffrcFlmlzs3tqd69+1rcEZJd3ATriooKjR8/Xh6PR5MmTbK6HQAAkADmzClV586dWa1GXHCEQqGQ1U1s27ZNa9as0cqVK5Wenq6LFy+qR48eN/06DQ1NCgYtfzsAAACwKcNwyO1Ov/5zJvdyXe+8847KysqUnh5u8s+EagAAAMBKcRGsT58+rWPHjqmkpEQPPvig1q9fb3VLAAAAwE1JMeOXFBcXq6am5rrP7du3T4FAQLW1tfrggw/k8/k0bdo09evXT0OHDr2p39PWsjwAAADQ0UwJ1pWVlVGfz83NVVFRkQzDkNvtVn5+vo4fP37TwZoZawAAAHSkuJ+xLioq0p49eyRJly9f1uHDh3XnnXda3BUAAABw4+LiriC//PKLnn/+eZ08eVKSNGnSJM2ZM+emX4cVawAAAHSkaCvWcRGsY4VgDQAAgI4U96MgAAAAQKIz5cuLZjEMh9UtAAAAwMai5U1bjYIAAAAAVmEUBAAAAIgBgjUAAAAQAwRrAAAAIAYI1gAAAEAMEKwBAACAGCBYAwAAADFAsAYAAABigGANAAAAxADBGgAAAIgBW21pjvjm8/n0zDPP6OzZs0pNTVXfvn1VXl6uzMzMyDELFizQpk2bdOTIEd1yyy0WdgvY0/Lly/XWW2/J6/Wqf//+2rBhg9asWSPDMOR0OvXss88qLy/P6jYB2zh37pxKS0sj9aVLl9TU1KStW7f+4TkRiYcVa5jG4XBo9uzZ2rp1q7xer3r37q3FixdHnt+xY4ccDoeFHQL29vXXX+urr75Sz549JYU/7C5cuFCrV6/W5s2bVVpaqhdeeMHiLgF76dWrlzZv3hz5N2bMGBUVFf3hORGJiWAN07hcLg0fPjxS33PPPaqpqZEUPsEvX75cCxYssKo9wNauXr2q8vJyvfTSS5HHQqGQQqGQfvrpJ0nhlbScnByLOgTs7+rVq/J6vZo8eXLUcyISF6MgsEQwGNS6detUWFgoSSovL9e8efPUtWtXizsD7GnZsmWaOHGievXqFXksMzNT5eXlKi4uVrdu3RQMBlVRUWFhl4C97dixQ9nZ2brrrrtaPN76nIjExYo1LPHKK6+oS5cumjFjhj799FN16tRJDzzwgNVtAbZ09OhRnThxQtOnT2/xeFNTk95//31t2LBBO3fu1Pz581VWVqZQKGRRp4C9bdy4UZMnT/7d4/99TkRiI1jDdK+99prOnDmjpUuXyjAMHThwQPv371dhYWHk03pRUZG+//57izsF7OHgwYM6ffq0xowZo8LCQv3www+aNWuWdu/era5du+r222+XJE2YMEFnz56Vz+ezuGPAfurq6nTw4EF5PJ4Wj7c+JyKxOUIsTcBES5Ys0dGjR7Vq1Sp17tz5uscMGDCAu4IAHaiwsFArVqzQ1atXNXfuXH388cdyu93av3+/nnrqKVVVVfFFYiDGVqxYoW+++UbLli2LPHYj50QkFmasYZpTp05p5cqVuu2221RSUiIp/G3pt99+2+LOgOQ0aNAgzZ49WzNmzFCnTp2UmpqqZcuWEaqBDlBZWannnnsuUnNOtCdWrAEAAIAYYJgHAAAAiAGCNQAAABADBGsAAAAgBgjWAAAAQAwQrAEAAIAYIFgDAAAAMcB9rAEggQwePDjy888//6zU1FQ5nU5J0ssvv6yJEyda1RoAJD3uYw0ACaqwsFCvvvqq8vPzrW4FACBGQQDAFoLBoFatWqWxY8dq+PDhevzxx+X3+yVJ586d04ABA7Rx40aNHj1aQ4cO1bp163T8+HF5PB7l5eWpvLw88lqbNm1SSUmJysvLNWTIEI0fP17V1dWR5+vq6vToo49q2LBhGjdunNavX2/6+wWAeESwBgAbqKio0Pbt27V27Vrt2bNH3bt3bxGWJenYsWPatm2b3njjDS1cuFArVqzQu+++q08++URbtmzRgQMHIsceP35cffr00f79+zVv3jyVlZVFgvqTTz6pnJwc7dmzR2+++aaWLFnSIngDQLIiWAOADXz44Yd64oknlJOTo9TUVJWVlWnr1q1qbm6OHFNaWqq0tDQVFBSoS5cuKioqktvtVnZ2tvLy8nTy5MnIsZmZmZo5c6Y6deqkCRMmqF+/ftq5c6dqa2t15MgRPf3000pLS9PAgQM1depUbd682Yq3DQBxhS8vAoAN1NTUqLS0VIbx23qJYRhqaGiI1G63O/JzWlra7+rLly9H6uzsbDkcjkidm5ur+vp61dfXq3v37kpPT2/x3IkTJ2L+ngAg0RCsAcAGcnJytHDhQg0ZMuR3z507d+6mX6+urk6hUCgSrmtra1VYWKisrCz9+OOPampqioTr2tpaZWdnt+8NAIANMAoCADYwbdo0LV26VOfPn5ckNTY2avv27X/69RobG/Xee+/p119/1ZYtW3T69GmNHj1at956qwYPHqwlS5boypUr+vbbb7VhwwZu8wcAYsUaAGzh4YcfVigU0iOPPKL6+nq53W5NmDBBY8eO/VOvd/fdd+vMmTMaMWKEevTooTfffFMZGRmSpCVLlujFF1/UqFGj1K1bNz322GPc8g8AxH2sAQCtbNq0SR999JHWrVtndSsAkFAYBQEAAABigGANAAAAxACjIAAAAEAMsGINAAAAxADBGgAAAIgBgjUAAAAQAwRrAAAAIAYI1gAAAEAMEKwBAACAGPg/FBMzabwwDswAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thgMC3Gfcore"
      },
      "source": [
        "### Resposta 07\n",
        "#### Resumo do que você aprendeu com os dados\n",
        "\n",
        "1. Estamos estudando as cadeias genéticas de células e suas variações molecurares (não sei se to colocando certo) normalizadas dentro do intervalo de -10 a 10;\n",
        "2. Os dados mostraram uma variabilidade que se aproxima de uma distribuição normal o que pode ser observado nos histogramas dos genes 1  e 10.\n",
        "3. Também foi possível observar uma diferença no formato da distribuição das médias e das medianas das variações moleculares dos genes de forma geral, com a distribuição das medianas, mais concentradas em valores próximos à zero.\n",
        "4. Nos boxplots é possível nota a presença de muitos outliers. Tentei tirar os valores truncados, mas sem muito sucesso na observação dos genes.\n",
        "5. Também pude observar diferenças entre os grupos de tratamento e controle em relação às médias do gene 1. Em geral, tem uma mediana maior em comparação com o grupo de controle, também se comparado com o tempo de administração do composto.\n",
        "\n"
      ]
    }
  ]
}