police_shootings.ipynb

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        "### Project 1: A Dive into Police Shootings and their City Dynamics\n",
        "*by Hannah Wendling and Krishna Tripathi*\n",
        "\n",
        "###Introduction\n",
        "\n",
        "Find the dataset [here](https://github.com/washingtonpost/data-police-shootings/blob/master/v1/fatal-police-shootings-data.csv) on GitHub.\n",
        "\n",
        "Steps we went through:\n",
        "*   Did an exploratory analysis, representing the data in various ways to see if anything appears striking right off the bat.\n",
        "  - Modularized each step so we could regenerate general stats about the dataset anytime.\n",
        "*   Adjusted some numbers based on general population info: adjusted age stats by population for each age,  divided shootings per city by the city population to get per capita shootings, etc.\n",
        "  - Note that we used city population in 2022 for this step. The shootings data is collected over many years, during which the population may have varied. However, we assumed the population of most cities *relative* to other citites remained fairly the same.\n",
        "- Ranked the cities by per-capita shootings. Picked an interesting city: Albuquerque, which had phenomenally high per-capita shootings, to focus our analysis on.\n",
        "- Created a sub-dataframe containing only Albequerque shootings. Generated the same exploratory numbers and charts that we did for our original dataframe, for Albuquerque.\n",
        "- Now used the functions we created earlier to generate visualizations of stats for police killings *in general* versus police killings *in Albuquerque*, to show that Albuquerque does stand out.\n",
        "- Compared racial group counts in Albuquerque and nationwide; saw that there is a stark difference between races regarding police killings, which is starker in Albuquerque.\n",
        "\n",
        "Possible uses for this:\n",
        "- Could be presented to authorities (in real life we're sure they already know about this, but this analysis can definitely form part of a larger report or call-to-action)\n",
        "  - Present to Albuquerque authorities, federal-level authorities or non-profits to help them decide where to focus their efforts.\n",
        "- Project could further be expanded with a larger/more detailed dataset (gender of police officers?)\n",
        "- Analysis could be deepened and made more granular by seeing relatioship between poverty rate, education rate, etc. and police killings - depending on which factor we specifically want to examine.\n",
        "- The same analysis we did could be replicated for multiple cities, or with machine-learning models could be expanded to create a heatmap for the US (which states are more dangerous than others, which cities in those states, etc.)\n",
        "  - The heatmap would *certainly* be very useful for federal-level analysis. Though out-of-scope for the current version of our project, a solid further research avenue.\n",
        "\n"
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      "source": [
        "###Setup\n",
        "Import the libraries and setup the data we'll use."
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# Path to the file on GitHub\n",
        "github_rawdata_filepath = \"https://raw.githubusercontent.com/washingtonpost/data-police-shootings/master/v1/fatal-police-shootings-data.csv\"\n",
        "\n",
        "# Read the CSV file into a DataFrame\n",
        "df = pd.read_csv(github_rawdata_filepath)"
      ],
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      "cell_type": "markdown",
      "source": [
        "Let's print the df's head (and tail) to see how it looks.\n",
        "\n"
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      "source": [
        "df.head()\n",
        "# print(df.tail)"
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              "   id                name        date   manner_of_death       armed   age  \\\n",
              "0   3          Tim Elliot  2015-01-02              shot         gun  53.0   \n",
              "1   4    Lewis Lee Lembke  2015-01-02              shot         gun  47.0   \n",
              "2   5  John Paul Quintero  2015-01-03  shot and Tasered     unarmed  23.0   \n",
              "3   8     Matthew Hoffman  2015-01-04              shot  toy weapon  32.0   \n",
              "4   9   Michael Rodriguez  2015-01-04              shot    nail gun  39.0   \n",
              "\n",
              "  gender race           city state  signs_of_mental_illness threat_level  \\\n",
              "0      M    A        Shelton    WA                     True       attack   \n",
              "1      M    W          Aloha    OR                    False       attack   \n",
              "2      M    H        Wichita    KS                    False        other   \n",
              "3      M    W  San Francisco    CA                     True       attack   \n",
              "4      M    H          Evans    CO                    False       attack   \n",
              "\n",
              "          flee  body_camera  longitude  latitude  is_geocoding_exact  \n",
              "0  Not fleeing        False   -123.122    47.247                True  \n",
              "1  Not fleeing        False   -122.892    45.487                True  \n",
              "2  Not fleeing        False    -97.281    37.695                True  \n",
              "3  Not fleeing        False   -122.422    37.763                True  \n",
              "4  Not fleeing        False   -104.692    40.384                True  "
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Tim Elliot</td>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>shot</td>\n",
              "      <td>gun</td>\n",
              "      <td>53.0</td>\n",
              "      <td>M</td>\n",
              "      <td>A</td>\n",
              "      <td>Shelton</td>\n",
              "      <td>WA</td>\n",
              "      <td>True</td>\n",
              "      <td>attack</td>\n",
              "      <td>Not fleeing</td>\n",
              "      <td>False</td>\n",
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              "      <td>47.247</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>Lewis Lee Lembke</td>\n",
              "      <td>2015-01-02</td>\n",
              "      <td>shot</td>\n",
              "      <td>gun</td>\n",
              "      <td>47.0</td>\n",
              "      <td>M</td>\n",
              "      <td>W</td>\n",
              "      <td>Aloha</td>\n",
              "      <td>OR</td>\n",
              "      <td>False</td>\n",
              "      <td>attack</td>\n",
              "      <td>Not fleeing</td>\n",
              "      <td>False</td>\n",
              "      <td>-122.892</td>\n",
              "      <td>45.487</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>John Paul Quintero</td>\n",
              "      <td>2015-01-03</td>\n",
              "      <td>shot and Tasered</td>\n",
              "      <td>unarmed</td>\n",
              "      <td>23.0</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>Wichita</td>\n",
              "      <td>KS</td>\n",
              "      <td>False</td>\n",
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              "      <td>Not fleeing</td>\n",
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              "      <td>37.695</td>\n",
              "      <td>True</td>\n",
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              "      <th>3</th>\n",
              "      <td>8</td>\n",
              "      <td>Matthew Hoffman</td>\n",
              "      <td>2015-01-04</td>\n",
              "      <td>shot</td>\n",
              "      <td>toy weapon</td>\n",
              "      <td>32.0</td>\n",
              "      <td>M</td>\n",
              "      <td>W</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>CA</td>\n",
              "      <td>True</td>\n",
              "      <td>attack</td>\n",
              "      <td>Not fleeing</td>\n",
              "      <td>False</td>\n",
              "      <td>-122.422</td>\n",
              "      <td>37.763</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9</td>\n",
              "      <td>Michael Rodriguez</td>\n",
              "      <td>2015-01-04</td>\n",
              "      <td>shot</td>\n",
              "      <td>nail gun</td>\n",
              "      <td>39.0</td>\n",
              "      <td>M</td>\n",
              "      <td>H</td>\n",
              "      <td>Evans</td>\n",
              "      <td>CO</td>\n",
              "      <td>False</td>\n",
              "      <td>attack</td>\n",
              "      <td>Not fleeing</td>\n",
              "      <td>False</td>\n",
              "      <td>-104.692</td>\n",
              "      <td>40.384</td>\n",
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          "metadata": {},
          "execution_count": 94
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    {
      "cell_type": "markdown",
      "source": [
        "### Formatting\n",
        "Let's drop some unneeded columns."
      ],
      "metadata": {
        "id": "jvEgciasaGS2"
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    {
      "cell_type": "code",
      "source": [
        "# Get rid of uneeded columns\n",
        "columns_to_drop = ['name' ,'longitude', 'latitude', 'is_geocoding_exact']\n",
        "df = df.drop(columns=columns_to_drop)"
      ],
      "metadata": {
        "id": "vDkH30I0YUHD"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
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        "colab": {
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        "id": "ti1ap2ZnW9xE",
        "outputId": "306e0b47-7dbc-419c-f11b-12064b60a912"
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        {
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          "data": {
            "text/plain": [
              "Index(['id', 'date', 'manner_of_death', 'armed', 'age', 'gender', 'race',\n",
              "       'city', 'state', 'signs_of_mental_illness', 'threat_level', 'flee',\n",
              "       'body_camera'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory data analysis\n",
        "Let's look at various features of the dataset in-general and visualize some of it to see what might stands out that we can investigate further.\n",
        "\n",
        "Begin with \"age\" variable."
      ],
      "metadata": {
        "id": "c8pdypmHaPeh"
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    },
    {
      "cell_type": "code",
      "source": [
        "df['age'].describe()  # Oh. This dataset is diff from the other one."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xm9P9A7GYAWs",
        "outputId": "8ce50f97-433b-44d7-c878-5b0be9cc4f5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    7499.000000\n",
              "mean       37.209228\n",
              "std        12.979490\n",
              "min         2.000000\n",
              "25%        27.000000\n",
              "50%        35.000000\n",
              "75%        45.000000\n",
              "max        92.000000\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sorted = df.sort_values(by='age')\n",
        "\n",
        "# Get the smallest 5 ages using the head method\n",
        "smallest_5_ages = df_sorted.head(8)\n",
        "\n",
        "# Print or display the result\n",
        "print(smallest_5_ages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vg75MK1e5Fz",
        "outputId": "a9bada49-de37-44e3-a6ca-9fda742855e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        id        date manner_of_death         armed   age gender race  \\\n",
            "7241  7981  2022-03-26            shot       unarmed   2.0      F  NaN   \n",
            "833    980  2015-11-03            shot       unarmed   6.0      M    W   \n",
            "2899  3229  2017-12-21            shot       unarmed   6.0      M    W   \n",
            "6642  7322  2021-08-27            shot       unarmed   8.0      F  NaN   \n",
            "1017  1165  2016-01-11            shot       unarmed  12.0      F    W   \n",
            "7163  7785  2022-03-01            shot           gun  12.0      M  NaN   \n",
            "6195  6729  2021-03-29            shot  undetermined  13.0      M    H   \n",
            "1680  1883  2016-09-14            shot    toy weapon  13.0      M    B   \n",
            "\n",
            "                city state  signs_of_mental_illness threat_level         flee  \\\n",
            "7241  Baxter Springs    KS                    False        other          NaN   \n",
            "833       Marksville    LA                    False        other          Car   \n",
            "2899         Schertz    TX                    False        other  Not fleeing   \n",
            "6642     Sharon Hill    PA                    False        other  Not fleeing   \n",
            "1017   Penn Township    PA                    False        other  Not fleeing   \n",
            "7163    Philadelphia    PA                    False        other         Foot   \n",
            "6195         Chicago    IL                    False        other         Foot   \n",
            "1680        Columbus    OH                    False        other         Foot   \n",
            "\n",
            "      body_camera  \n",
            "7241        False  \n",
            "833          True  \n",
            "2899        False  \n",
            "6642        False  \n",
            "1017        False  \n",
            "7163        False  \n",
            "6195         True  \n",
            "1680        False  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['age'].value_counts()\n",
        "print(value_counts.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2_4BbPVZeaO",
        "outputId": "7b7e47cd-3052-4020-87cf-736959879a05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31.0    261\n",
            "32.0    257\n",
            "27.0    257\n",
            "33.0    254\n",
            "34.0    251\n",
            "29.0    244\n",
            "35.0    243\n",
            "30.0    243\n",
            "25.0    240\n",
            "28.0    232\n",
            "Name: age, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It would be good to have a histogram showing the frequency of victims by age group.\n",
        "\n",
        "Let's prep for it by setting up bins:"
      ],
      "metadata": {
        "id": "gI7OAD_mVdIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bins = [0, 18, 28, 38, 48, 58, 100]\n",
        "labels = ['0-18', '19-28', '29-38', '39-48', '49-58', '58+']\n",
        "\n",
        "# Cut up the dataframe conveniently based on the bins\n",
        "df['age_range'] = pd.cut(df['age'], bins=bins, labels=labels, right=False)\n",
        "age_range_counts = df['age_range'].value_counts()\n",
        "\n",
        "print(age_range_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yen3ruMcZ08J",
        "outputId": "5e5c5cc4-4a2d-4784-e131-2ed978a7e0a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29-38    2429\n",
            "19-28    1771\n",
            "39-48    1563\n",
            "49-58     974\n",
            "58+       627\n",
            "0-18      135\n",
            "Name: age_range, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Examining the column we'll be using:"
      ],
      "metadata": {
        "id": "MfW3pcHyVuRd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "column_name = 'age_range'\n",
        "column_dtype = df[column_name].dtype\n",
        "\n",
        "print(f\"The data type of '{column_name}' is {column_dtype}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4h-ssjm6xl-J",
        "outputId": "e73dfe89-d557-4e1f-b0db-f669ddfa4af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The data type of 'age_range' is category.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See how the dataframe looks different now because of age RANGES vs \"ages\" as earlier\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xph_9OS3dCmj",
        "outputId": "a42fe7be-48b3-4e7f-8dec-a4940153e157"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id        date   manner_of_death       armed   age gender race  \\\n",
              "0   3  2015-01-02              shot         gun  53.0      M    A   \n",
              "1   4  2015-01-02              shot         gun  47.0      M    W   \n",
              "2   5  2015-01-03  shot and Tasered     unarmed  23.0      M    H   \n",
              "3   8  2015-01-04              shot  toy weapon  32.0      M    W   \n",
              "4   9  2015-01-04              shot    nail gun  39.0      M    H   \n",
              "\n",
              "            city state  signs_of_mental_illness threat_level         flee  \\\n",
              "0        Shelton    WA                     True       attack  Not fleeing   \n",
              "1          Aloha    OR                    False       attack  Not fleeing   \n",
              "2        Wichita    KS                    False        other  Not fleeing   \n",
              "3  San Francisco    CA                     True       attack  Not fleeing   \n",
              "4          Evans    CO                    False       attack  Not fleeing   \n",
              "\n",
              "   body_camera age_range  \n",
              "0        False     49-58  \n",
              "1        False     39-48  \n",
              "2        False     19-28  \n",
              "3        False     29-38  \n",
              "4        False     39-48  "
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f4131e08-e984-4f5b-a2b9-90ccf0f46fca button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f4131e08-e984-4f5b-a2b9-90ccf0f46fca');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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              "\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a plotting function and run it."
      ],
      "metadata": {
        "id": "NWIC3WRBWr2b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def show_frequency_of_victims_by_age_group(dataframe):\n",
        "  category_counts = dataframe['age_range'].value_counts()\n",
        "\n",
        "  # Sort the categories based on their natural order\n",
        "  sorted_category_counts = category_counts.sort_index()\n",
        "\n",
        "  print(\"Category Counts:\")\n",
        "  print(sorted_category_counts)\n",
        "\n",
        "  # Plotting the histogram with sorted bars\n",
        "  plt.bar(sorted_category_counts.index, sorted_category_counts)\n",
        "  plt.xlabel('Age Range')\n",
        "  plt.ylabel('Frequency')\n",
        "  plt.title('Frequency of Victims by Age Group')\n",
        "\n",
        "  # Uncomment to add text annotations\n",
        "  # for i, freq in enumerate(sorted_category_counts):\n",
        "  #     plt.text(i, freq + 0.1, str(freq), ha='center', va='bottom')\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "show_frequency_of_victims_by_age_group(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "1B-IeCNz3PKm",
        "outputId": "ce593df7-6dbe-481d-cee1-f0d9a65edc86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Category Counts:\n",
            "0-18      135\n",
            "19-28    1771\n",
            "29-38    2429\n",
            "39-48    1563\n",
            "49-58     974\n",
            "58+       627\n",
            "Name: age_range, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Examining the variable \"armed\". Was the victim armed or not?"
      ],
      "metadata": {
        "id": "i0n9PhwMaqVT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2xZgvMmaDCM",
        "outputId": "6fb17d4c-c544-4f3e-e542-514021bd7230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8002, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['armed'].unique()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNKQA6hTcV5f",
        "outputId": "c1c947ad-68e1-428d-e7e9-818e77dac0dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['gun', 'unarmed', 'toy weapon', 'nail gun', 'knife', nan, 'shovel',\n",
              "       'vehicle', 'hammer', 'hatchet', 'sword', 'machete', 'box cutter',\n",
              "       'undetermined', 'metal object', 'screwdriver', 'lawn mower blade',\n",
              "       'flagpole', 'guns and explosives', 'cordless drill', 'crossbow',\n",
              "       'BB gun', 'metal pole', 'Taser', 'metal pipe', 'metal hand tool',\n",
              "       'blunt object', 'metal stick', 'sharp object', 'meat cleaver',\n",
              "       'carjack', 'chain', \"contractor's level\", 'railroad spikes',\n",
              "       'stapler', 'beer bottle', 'unknown weapon', 'binoculars',\n",
              "       'pellet gun', 'bean-bag gun', 'baseball bat and fireplace poker',\n",
              "       'straight edge razor', 'gun and knife', 'ax', 'brick',\n",
              "       'baseball bat', 'hand torch', 'chain saw', 'garden tool',\n",
              "       'pair of scissors', 'pole', 'pick-axe', 'flashlight', 'baton',\n",
              "       'spear', 'chair', 'pitchfork', 'hatchet and gun', 'rock',\n",
              "       'piece of wood', 'pipe', 'glass shard', 'motorcycle',\n",
              "       'pepper spray', 'metal rake', 'crowbar', 'oar', 'machete and gun',\n",
              "       'tire iron', 'air conditioner', 'pole and knife',\n",
              "       'baseball bat and bottle', 'fireworks', 'pen', 'chainsaw',\n",
              "       'gun and sword', 'gun and car', 'claimed to be armed',\n",
              "       'incendiary device', 'samurai sword', 'bow and arrow',\n",
              "       'gun and vehicle', 'vehicle and gun', 'wrench', 'walking stick',\n",
              "       'barstool', 'BB gun and vehicle', 'wasp spray', 'air pistol',\n",
              "       'Airsoft pistol', 'baseball bat and knife', 'vehicle and machete',\n",
              "       'ice pick', 'car, knife and mace', 'bottle', 'gun and machete',\n",
              "       'microphone', 'knife and vehicle', 'machete and hammer', 'stake',\n",
              "       'incendiary weapon', 'ax and machete', 'hammer and garden tool',\n",
              "       'flare gun', 'knife, hammer and gasoline can', 'Gun', 'stone'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['armed'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1H49ExITd4ub",
        "outputId": "ce492235-43a0-46a3-ac14-03f6faf4601a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     7791\n",
              "unique     106\n",
              "top        gun\n",
              "freq      4573\n",
              "Name: armed, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "460/7791\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKAbYvyReTtB",
        "outputId": "6137aaf0-8871-4ce1-b8bf-c36ccd259b38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0590424849184957"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['armed'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhkckuVXa9RO",
        "outputId": "755da8a6-dae9-4c9b-d0c1-c051af7d4994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gun                    4573\n",
              "knife                  1142\n",
              "unarmed                 460\n",
              "undetermined            338\n",
              "vehicle                 284\n",
              "                       ... \n",
              "chainsaw                  1\n",
              "gun and sword             1\n",
              "claimed to be armed       1\n",
              "cordless drill            1\n",
              "stone                     1\n",
              "Name: armed, Length: 106, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's see how many times the victim had a gun or a knife, etc.\n",
        "value_counts = df['armed'].value_counts()\n",
        "print(value_counts.head(30))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0EGMb7BbL1R",
        "outputId": "f3b3f291-689a-46c4-e3c5-71b55535cfa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gun                 4573\n",
            "knife               1142\n",
            "unarmed              460\n",
            "undetermined         338\n",
            "vehicle              284\n",
            "toy weapon           248\n",
            "unknown weapon       121\n",
            "machete               65\n",
            "Taser                 39\n",
            "ax                    30\n",
            "sword                 27\n",
            "gun and knife         27\n",
            "gun and vehicle       27\n",
            "sharp object          25\n",
            "hammer                22\n",
            "baseball bat          20\n",
            "BB gun                19\n",
            "screwdriver           18\n",
            "metal pipe            18\n",
            "hatchet               16\n",
            "gun and car           15\n",
            "pair of scissors      14\n",
            "box cutter            14\n",
            "vehicle and gun       10\n",
            "crossbow              10\n",
            "piece of wood          9\n",
            "shovel                 8\n",
            "rock                   8\n",
            "pipe                   8\n",
            "metal object           7\n",
            "Name: armed, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's reorganize the dataframe so that weapons which were used in less than 100 cases are binned under \"other\".\n",
        "\n",
        "Note about the hard-coded list: we already had a list of the weapons in hand so we could do it in a hard-coded way."
      ],
      "metadata": {
        "id": "gqju3xusXOP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "values_to_group = ['nail gun', 'shovel',\n",
        "       'vehicle', 'hammer', 'hatchet', 'sword', 'machete', 'box cutter',\n",
        "       'metal object', 'screwdriver', 'lawn mower blade',\n",
        "       'flagpole', 'guns and explosives', 'cordless drill', 'crossbow',\n",
        "       'BB gun', 'metal pole', 'Taser', 'metal pipe', 'metal hand tool',\n",
        "       'blunt object', 'metal stick', 'sharp object', 'meat cleaver',\n",
        "       'carjack', 'chain', \"contractor's level\", 'railroad spikes',\n",
        "       'stapler', 'beer bottle', 'binoculars',\n",
        "       'pellet gun', 'bean-bag gun', 'baseball bat and fireplace poker',\n",
        "       'straight edge razor', 'gun and knife', 'ax', 'brick',\n",
        "       'baseball bat', 'hand torch', 'chain saw', 'garden tool',\n",
        "       'pair of scissors', 'pole', 'pick-axe', 'flashlight', 'baton',\n",
        "       'spear', 'chair', 'pitchfork', 'hatchet and gun', 'rock',\n",
        "       'piece of wood', 'pipe', 'glass shard', 'motorcycle',\n",
        "       'pepper spray', 'metal rake', 'crowbar', 'oar', 'machete and gun',\n",
        "       'tire iron', 'air conditioner', 'pole and knife',\n",
        "       'baseball bat and bottle', 'fireworks', 'pen', 'chainsaw',\n",
        "       'gun and sword', 'gun and car', 'claimed to be armed',\n",
        "       'incendiary device', 'samurai sword', 'bow and arrow',\n",
        "       'gun and vehicle', 'vehicle and gun', 'wrench', 'walking stick',\n",
        "       'barstool', 'BB gun and vehicle', 'wasp spray', 'air pistol',\n",
        "       'Airsoft pistol', 'baseball bat and knife', 'vehicle and machete',\n",
        "       'ice pick', 'car, knife and mace', 'bottle', 'gun and machete',\n",
        "       'microphone', 'knife and vehicle', 'machete and hammer', 'stake',\n",
        "       'incendiary weapon', 'ax and machete', 'hammer and garden tool',\n",
        "       'flare gun', 'knife, hammer and gasoline can', 'Gun', 'stone']\n",
        "df['armed'] = df['armed'].replace(values_to_group, 'other')"
      ],
      "metadata": {
        "id": "RmNiWURRdn7C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look and verify if we've succeeded."
      ],
      "metadata": {
        "id": "bK9B69H0qBpX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['armed'].value_counts()\n",
        "print(value_counts.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYn4MVHagYZz",
        "outputId": "1a4c9414-fa32-49b5-bb1f-23275b4598d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gun               4573\n",
            "knife             1142\n",
            "other              909\n",
            "unarmed            460\n",
            "undetermined       338\n",
            "toy weapon         248\n",
            "unknown weapon     121\n",
            "Name: armed, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we're ready to have another function that generates a bar chart for \"use of weapon at time of shooting\" for which weapons were used."
      ],
      "metadata": {
        "id": "WJDHZ-aUXrHB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def show_use_of_weapon_at_time_of_shooting(dataframe):\n",
        "  category_counts = dataframe['armed'].value_counts()\n",
        "\n",
        "  # Plotting the bar chart with rotated x-axis labels\n",
        "  plt.bar(category_counts.index, category_counts)\n",
        "  plt.xlabel('Weapon')\n",
        "  plt.ylabel('Frequency')\n",
        "  plt.title('Use of Weapon by Victim at Time of Shooting')\n",
        "\n",
        "  # Rotate x-axis labels for better readability\n",
        "  plt.xticks(rotation=45, ha='right')\n",
        "\n",
        "  # Uncomment to add text annotations for each bar\n",
        "  # for i, freq in enumerate(category_counts):\n",
        "  #     plt.text(i, freq + 0.1, str(freq), ha='center', va='bottom')\n",
        "\n",
        "  # Adjust layout to prevent clipping of labels\n",
        "  plt.tight_layout()\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "show_use_of_weapon_at_time_of_shooting(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "OO23pM0NqhrC",
        "outputId": "3429df2e-4332-484e-ae54-82aa438751c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Just because this graph is so \"trendy,\" let's also see how it looks like vertically."
      ],
      "metadata": {
        "id": "K0nl1wuIYCCz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "category_counts = df['armed'].value_counts()\n",
        "\n",
        "# Plotting the horizontal bar chart\n",
        "plt.barh(category_counts.index, category_counts)\n",
        "plt.xlabel('Frequency')\n",
        "plt.ylabel('Weapon')\n",
        "plt.title('Use of Weapon by Victim at Time of Shooting')\n",
        "\n",
        "# Add text annotations for each bar\n",
        "# for i, freq in enumerate(category_counts):\n",
        "#     plt.text(freq + 0.1, i, str(freq), ha='left', va='center')\n",
        "\n",
        "# Adjust layout to prevent clipping of labels\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "CKni0KH6c73U",
        "outputId": "d2b7dd75-9629-4111-ff58-f2d308253471"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interesting thoughts we had at this point:\n",
        "\n",
        "If someone is driving away (fleeing by car), they'd be less of a threat so why kill them?\n",
        "\n",
        "Also because the data is police killings rather than \"shootings\" we can wonder how such a large proportion of people could be killed when they were in a car (compared to foot, in fact more!), because someone in a car is less vulnerable than a person on foot.\n",
        "\n",
        "Further, why do you want to kill a fleeing person in the first place (shoot) if they're not a drug-lord type criminal? Etc."
      ],
      "metadata": {
        "id": "nZ360ZjSqiMl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "amv88mf7kByl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Let's look at whether victims were fleeing at the time of shooting now."
      ],
      "metadata": {
        "id": "MaWkdbDgYVMT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['flee'].value_counts()\n",
        "print(value_counts.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOnBKPFJgb-c",
        "outputId": "2b7a6790-e075-4f6e-eb60-0fff605b285f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Not fleeing    4430\n",
            "Car            1289\n",
            "Foot           1022\n",
            "Other           295\n",
            "Name: flee, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1289+1022"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKwY0CE-9nwL",
        "outputId": "347bdb50-b97f-4455-dab4-b95e04f50929"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2311"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1289/2311"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4tY6-Ha-DzY",
        "outputId": "41e15512-bce9-493a-ab48-d60b5c8a51d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5577672003461704"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['flee'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yg7KDLvb9ZYR",
        "outputId": "a643a084-55d5-4591-d9b4-f6a595770ef4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count            7036\n",
              "unique              4\n",
              "top       Not fleeing\n",
              "freq             4430\n",
              "Name: flee, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "note in this by krishna: i wonder if the fact that there are body camera + shooting cases might imply that there's a flaw in the institution itself where police officers won't be prosecuted for unethical/unjust killings"
      ],
      "metadata": {
        "id": "daupRL76ryFG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Looking at Body Camera presence"
      ],
      "metadata": {
        "id": "j_m7k8bTamHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['body_camera'].value_counts()\n",
        "print(value_counts.head(30))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GGYndwjkUXE",
        "outputId": "e269aa3a-c378-40fd-fc18-136e4d5c6406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False    6865\n",
            "True     1137\n",
            "Name: body_camera, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['body_camera'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfBAIFzGaqVd",
        "outputId": "41d30341-f570-4a61-bd5e-829e8685cafb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      8002\n",
              "unique        2\n",
              "top       False\n",
              "freq       6865\n",
              "Name: body_camera, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['threat_level'].value_counts()\n",
        "print(value_counts.head(30))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmLOQdy8kiDT",
        "outputId": "bb1361f9-d5a8-4742-e7c9-7351b8894a54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "attack          5010\n",
            "other           2663\n",
            "undetermined     329\n",
            "Name: threat_level, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['threat_level'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nMiug4ah6Jvr",
        "outputId": "0c9715a3-8e66-46cc-f8ff-b53c8fd17302"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       8002\n",
              "unique         3\n",
              "top       attack\n",
              "freq        5010\n",
              "Name: threat_level, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note again: We didn't think that mental illness is a big factor to consider here, because a police officer in the heat of the moment won't know or have time to draw up the person's mental health history."
      ],
      "metadata": {
        "id": "eveVZQFEsL-R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's briefly look at manner of death and signs of mental illness."
      ],
      "metadata": {
        "id": "iveaH2lbYw9V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['signs_of_mental_illness'].value_counts()\n",
        "value_counts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NyLheKEkuzm",
        "outputId": "5cb73ba1-4369-4e92-ceae-57e14807cde0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    6331\n",
              "True     1671\n",
              "Name: signs_of_mental_illness, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['manner_of_death'].value_counts()\n",
        "value_counts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXYOc5Gtk2Tx",
        "outputId": "f2684619-edc6-42fb-8170-ab99f92b5bf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shot                7664\n",
              "shot and Tasered     338\n",
              "Name: manner_of_death, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['manner_of_death'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1CXyORkLEnXN",
        "outputId": "4f68952a-8156-48d7-8d52-adcbc8fbbc3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     8002\n",
              "unique       2\n",
              "top       shot\n",
              "freq      7664\n",
              "Name: manner_of_death, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below, note how the shooting counts for the \"head\" cities are SO MUCH HIGHER than the counts for tail cities."
      ],
      "metadata": {
        "id": "pvUK7uPxZM9r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['city'].value_counts()\n",
        "print(value_counts.head())\n",
        "value_counts.tail()  # Let's see if tail and head values are very off?"
      ],
      "metadata": {
        "id": "jdt2i1QmkQgI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2699e2c1-54d3-46a6-badc-a2393dc205f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Los Angeles    130\n",
            "Phoenix        105\n",
            "Houston         91\n",
            "Las Vegas       68\n",
            "San Antonio     68\n",
            "Name: city, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Crystal            1\n",
              "Gibson             1\n",
              "Neshoba County     1\n",
              "Providence         1\n",
              "Mariposa County    1\n",
              "Name: city, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['state'].value_counts()\n",
        "value_counts.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rciFbVo8lTDj",
        "outputId": "8389dc0d-5ce4-49f6-d0a1-b808292b7239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CA    1143\n",
              "TX     732\n",
              "FL     509\n",
              "AZ     363\n",
              "GA     306\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Race\n",
        "Let's look at one of our most interesting variables."
      ],
      "metadata": {
        "id": "MXrhLfBP6v9c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Primming up the dataframe to make it more convenient for plotting\n",
        "df['race'] = df['race'].replace('W', 'White')\n",
        "df['race'] = df['race'].replace('B', 'Black')\n",
        "df['race'] = df['race'].replace('H', 'Hispanic')\n",
        "df['race'] = df['race'].replace('A', 'Asian')\n",
        "df['race'] = df['race'].replace('Native Amer.', 'Native')\n",
        "df['race'] = df['race'].replace('O', 'Other')"
      ],
      "metadata": {
        "id": "Oyeg8oOR60yV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['race'].value_counts()\n",
        "print(value_counts.head(6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "racTmenblWDz",
        "outputId": "649bf02a-0d23-4c9a-8d0b-9e93298c75de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "White       3300\n",
            "Black       1766\n",
            "Hispanic    1166\n",
            "Asian        129\n",
            "N            105\n",
            "Other         19\n",
            "Name: race, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's have a function to plot this extremely important variable!"
      ],
      "metadata": {
        "id": "2Cq6exg7ZaCo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def show_race_of_victims(dataframe):\n",
        "  category_counts = dataframe['race'].value_counts()\n",
        "\n",
        "  # Plotting the bar chart\n",
        "  plt.bar(category_counts.index, category_counts)\n",
        "  plt.xlabel('Race')\n",
        "  plt.ylabel('Frequency')\n",
        "  plt.title('Race of Victims')\n",
        "\n",
        "  # for i, freq in enumerate(category_counts):\n",
        "  #     plt.text(i, freq + 0.1, str(freq), ha='center', va='bottom')\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "show_race_of_victims(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "oCCEtFPc7tWa",
        "outputId": "fd30dd92-e5c2-4443-c34f-609851db1d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gender\n",
        "Let's also look at whether the male-female disparity in shootings is different anywhere?\n",
        "\n",
        "We'll do some similar adjustments to our df again, and create a function."
      ],
      "metadata": {
        "id": "JAmZ6KmnAONx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['gender'] = df['gender'].replace('M', 'Male')\n",
        "df['gender'] = df['gender'].replace('F', 'Female')"
      ],
      "metadata": {
        "id": "fYDh2Qo3Ang5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['gender'].value_counts()\n",
        "print(value_counts.head(6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hi7_bKKmAQTg",
        "outputId": "f9ea3356-de8a-45d8-c449-aad66ebd20eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Male      7613\n",
            "Female     358\n",
            "Name: gender, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def show_gender_of_victims(dataframe):\n",
        "  category_counts = dataframe['gender'].value_counts()\n",
        "\n",
        "  # Plotting the bar chart\n",
        "  plt.bar(category_counts.index, category_counts)\n",
        "  plt.xlabel('Gender')\n",
        "  plt.ylabel('Frequency')\n",
        "  plt.title('Gender of Victims')\n",
        "\n",
        "  # for i, freq in enumerate(category_counts):\n",
        "  #     plt.text(i, freq + 0.1, str(freq), ha='center', va='bottom')\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "show_gender_of_victims(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "uA2uNOThA0bv",
        "outputId": "b92a2271-9b4f-4fc7-ab6f-de9faa61595c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "JE54oaaJ8Uwk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()  # Checking how the fundamentals look"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqG38LxKm5AT",
        "outputId": "90fac87d-b85d-48d4-d62e-a9bcfe752972"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                            0\n",
              "date                          0\n",
              "manner_of_death               0\n",
              "armed                       211\n",
              "age                         503\n",
              "gender                       31\n",
              "race                       1517\n",
              "city                          0\n",
              "state                         0\n",
              "signs_of_mental_illness       0\n",
              "threat_level                  0\n",
              "flee                        966\n",
              "body_camera                   0\n",
              "age_range                   503\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Date\n",
        "\n",
        "We haven't look at this (usually) the first-to-be-looked-at variable yet because we already knew that the # of shootings has more or less remained in the 950-1050 range for a decade.\n",
        "\n",
        "However, let's generate a plot for it so we have it visualized."
      ],
      "metadata": {
        "id": "c9NlORxlfyJM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'] = pd.to_datetime(df['date'])\n",
        "\n",
        "# Extract year from the datetime column\n",
        "df['year'] = df['date'].dt.year\n",
        "\n",
        "# Group by year and count the number of occurrences\n",
        "yearly_counts = df['year'].value_counts().sort_index()\n",
        "\n",
        "# Plotting the line graph\n",
        "plt.plot(yearly_counts.index, yearly_counts, marker='o', linestyle='-', color='teal')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Dates')\n",
        "plt.title('Line Graph of Dates Broken Up by Year')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "# Annotate each point with its value\n",
        "# for i, value in enumerate(yearly_counts):\n",
        "#     plt.annotate(f'{value}', (yearly_counts.index[i], value), textcoords=\"offset points\", xytext=(0,5), ha='center')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "9OSNN5-wfPBz",
        "outputId": "827cdd43-1b9f-4bad-ed89-3135ca8683bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some more interesting plotting."
      ],
      "metadata": {
        "id": "ugGAVRwlbHro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def show_histogram_date_broken_up_by_month(df):\n",
        "  # Convert 'date' column to datetime\n",
        "  df['date'] = pd.to_datetime(df['date'])\n",
        "\n",
        "  # Extract month from the datetime column\n",
        "  df['month'] = df['date'].dt.month\n",
        "\n",
        "  # Plotting the histogram\n",
        "  plt.hist(df['month'], bins=range(1, 14), edgecolor='white', align='left', color='teal')\n",
        "  plt.xlabel('Month')\n",
        "  plt.ylabel('Frequency')\n",
        "  plt.title('Histogram of Dates Broken Up by Month')\n",
        "  plt.xticks(range(1, 13), [month.strftime('%B') for month in pd.date_range(start='2022-01-01', periods=12, freq='MS')])\n",
        "\n",
        "  # Rotate x-axis labels and adjust layout\n",
        "  plt.xticks(range(1, 13), [month.strftime('%B') for month in pd.date_range(start='2022-01-01', periods=12, freq='MS')], rotation=45, ha='right')\n",
        "  plt.tight_layout()\n",
        "\n",
        "  # Add frequency count centered over each bar\n",
        "  for i, freq in enumerate(plt.hist(df['month'], bins=range(1, 14), edgecolor='black', align='left')[0]):\n",
        "      plt.text(i + 1, freq, str(int(freq)), ha='center', va='bottom')\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "show_histogram_date_broken_up_by_month(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "dL27HPKif4wW",
        "outputId": "b8f478e4-1a36-4ded-dced-193d85404782"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Location\n",
        "Anything interesting about the locations?\n",
        "**YES**. We see that Albuquerque has an interestingly high number of shootings - given that we know its population is low."
      ],
      "metadata": {
        "id": "SJRCL_a_nq-e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['city'].value_counts()\n",
        "print(value_counts.head(30))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3CccYP5kz8f",
        "outputId": "2856e92e-868f-4076-b306-631bdda7358b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Los Angeles      130\n",
            "Phoenix          105\n",
            "Houston           91\n",
            "Las Vegas         68\n",
            "San Antonio       68\n",
            "Albuquerque       59\n",
            "Chicago           50\n",
            "Columbus          48\n",
            "Denver            47\n",
            "Jacksonville      46\n",
            "Kansas City       45\n",
            "Tucson            44\n",
            "New York          43\n",
            "Oklahoma City     41\n",
            "Austin            41\n",
            "Miami             37\n",
            "St. Louis         37\n",
            "Tulsa             34\n",
            "Bakersfield       34\n",
            "Atlanta           33\n",
            "Louisville        32\n",
            "Mesa              30\n",
            "Philadelphia      30\n",
            "Springfield       28\n",
            "Baltimore         28\n",
            "Portland          27\n",
            "Washington        27\n",
            "Fresno            27\n",
            "San Diego         27\n",
            "Orlando           25\n",
            "Name: city, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tail cities appear to have very few shootings per year."
      ],
      "metadata": {
        "id": "GWtG17gJbbOs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['city'].value_counts()\n",
        "value_counts.tail(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6W5epJ6n0xC",
        "outputId": "6af2a7ca-b080-4483-b204-b9948aa956ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Canaan                1\n",
              "The Colony            1\n",
              "Valley Head           1\n",
              "Bremerton             1\n",
              "New Britain           1\n",
              "Rincon                1\n",
              "Rose Bud              1\n",
              "Old Forge             1\n",
              "Peshtigo              1\n",
              "Coral Springs         1\n",
              "Lauderdale Lakes      1\n",
              "Monticello            1\n",
              "North Hollywood       1\n",
              "Hillyard              1\n",
              "Odanah                1\n",
              "Penasco               1\n",
              "Holland Township      1\n",
              "Pell City             1\n",
              "Warren County         1\n",
              "West Monroe           1\n",
              "Cedar Lake            1\n",
              "Tremont               1\n",
              "St. Tammany Parish    1\n",
              "Dakota City           1\n",
              "Hinds County          1\n",
              "Crystal               1\n",
              "Gibson                1\n",
              "Neshoba County        1\n",
              "Providence            1\n",
              "Mariposa County       1\n",
              "Name: city, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we saw above, the tail cities have low shootings, so expectedly the average for the dataset overall should be dragged down by them. (We get an average of 2)"
      ],
      "metadata": {
        "id": "yy7I_woSbqkO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['city'].value_counts()\n",
        "\n",
        "# Calculate the average frequency\n",
        "average_frequency = value_counts.mean()\n",
        "\n",
        "print(f\"Average Frequency: {average_frequency}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKTO33oi3f-K",
        "outputId": "ad071b71-fba6-4788-b8d1-3e35150f7139"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Frequency: 2.4889580093312595\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Albuquerque\n",
        "###Bearing down on a city with disproportionately high shootings per-capita\n",
        "\n",
        "As we noticed above, Albuquerque's # shootings per year ranks among the highest, while its population ranks far, far below Chicago, Los Angeles etc.\n",
        "\n",
        "So, let us split our dataset up, and utilize the functions we created earlier to generate the same plots we did for the general data for Albuquerque now."
      ],
      "metadata": {
        "id": "dorDeuDGbzit"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a sub-df containing only Albuquerque data\n",
        "albu_df = df[df[\"city\"] == \"Albuquerque\"]\n",
        "albu_df.head()"
      ],
      "metadata": {
        "id": "K_0nBwxO4Vr9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "c3054ed6-513a-426a-80a6-ab17335a5843"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id       date manner_of_death armed   age gender      race  \\\n",
              "24     46 2015-01-13            shot   gun  34.0   Male     White   \n",
              "333   445 2015-05-03            shot   gun  44.0   Male  Hispanic   \n",
              "567   692 2015-07-31            shot   gun  33.0   Male  Hispanic   \n",
              "854   997 2015-11-11            shot   NaN  31.0   Male  Hispanic   \n",
              "973  1117 2015-12-22            shot   gun  19.0   Male  Hispanic   \n",
              "\n",
              "            city state  signs_of_mental_illness  threat_level         flee  \\\n",
              "24   Albuquerque    NM                    False        attack         Foot   \n",
              "333  Albuquerque    NM                    False        attack  Not fleeing   \n",
              "567  Albuquerque    NM                    False        attack  Not fleeing   \n",
              "854  Albuquerque    NM                    False         other          Car   \n",
              "973  Albuquerque    NM                    False  undetermined         Foot   \n",
              "\n",
              "     body_camera age_range  year  month  \n",
              "24          True     29-38  2015      1  \n",
              "333        False     39-48  2015      5  \n",
              "567         True     29-38  2015      7  \n",
              "854        False     29-38  2015     11  \n",
              "973         True     19-28  2015     12  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-720611c6-ab00-431b-a0d0-8097a6c93581\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>manner_of_death</th>\n",
              "      <th>armed</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>race</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>signs_of_mental_illness</th>\n",
              "      <th>threat_level</th>\n",
              "      <th>flee</th>\n",
              "      <th>body_camera</th>\n",
              "      <th>age_range</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>46</td>\n",
              "      <td>2015-01-13</td>\n",
              "      <td>shot</td>\n",
              "      <td>gun</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>White</td>\n",
              "      <td>Albuquerque</td>\n",
              "      <td>NM</td>\n",
              "      <td>False</td>\n",
              "      <td>attack</td>\n",
              "      <td>Foot</td>\n",
              "      <td>True</td>\n",
              "      <td>29-38</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>333</th>\n",
              "      <td>445</td>\n",
              "      <td>2015-05-03</td>\n",
              "      <td>shot</td>\n",
              "      <td>gun</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>Albuquerque</td>\n",
              "      <td>NM</td>\n",
              "      <td>False</td>\n",
              "      <td>attack</td>\n",
              "      <td>Not fleeing</td>\n",
              "      <td>False</td>\n",
              "      <td>39-48</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>692</td>\n",
              "      <td>2015-07-31</td>\n",
              "      <td>shot</td>\n",
              "      <td>gun</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>Albuquerque</td>\n",
              "      <td>NM</td>\n",
              "      <td>False</td>\n",
              "      <td>attack</td>\n",
              "      <td>Not fleeing</td>\n",
              "      <td>True</td>\n",
              "      <td>29-38</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>854</th>\n",
              "      <td>997</td>\n",
              "      <td>2015-11-11</td>\n",
              "      <td>shot</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>Albuquerque</td>\n",
              "      <td>NM</td>\n",
              "      <td>False</td>\n",
              "      <td>other</td>\n",
              "      <td>Car</td>\n",
              "      <td>False</td>\n",
              "      <td>29-38</td>\n",
              "      <td>2015</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>973</th>\n",
              "      <td>1117</td>\n",
              "      <td>2015-12-22</td>\n",
              "      <td>shot</td>\n",
              "      <td>gun</td>\n",
              "      <td>19.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>Albuquerque</td>\n",
              "      <td>NM</td>\n",
              "      <td>False</td>\n",
              "      <td>undetermined</td>\n",
              "      <td>Foot</td>\n",
              "      <td>True</td>\n",
              "      <td>19-28</td>\n",
              "      <td>2015</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-720611c6-ab00-431b-a0d0-8097a6c93581')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-720611c6-ab00-431b-a0d0-8097a6c93581 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-720611c6-ab00-431b-a0d0-8097a6c93581');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e1e295f5-5add-425e-8e16-337698a61d72\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1e295f5-5add-425e-8e16-337698a61d72')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e1e295f5-5add-425e-8e16-337698a61d72 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generate plots for Albuquerque side-by-side with the general data for comparison."
      ],
      "metadata": {
        "id": "AOgzIWNfcW81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"For the charts below, for convenience, the first one for each category is the general data's \" \\\n",
        "      \"chart, and the second is for Albuquerque.\\n\")\n",
        "\n",
        "show_frequency_of_victims_by_age_group(df)  # general\n",
        "show_frequency_of_victims_by_age_group(albu_df)  # albu\n",
        "\n",
        "show_gender_of_victims(df)  # general\n",
        "show_gender_of_victims(albu_df)  # albu\n",
        "\n",
        "show_histogram_date_broken_up_by_month(df)\n",
        "show_histogram_date_broken_up_by_month(albu_df)\n",
        "\n",
        "show_race_of_victims(df)\n",
        "show_race_of_victims(albu_df)\n",
        "\n",
        "show_use_of_weapon_at_time_of_shooting(df)\n",
        "show_use_of_weapon_at_time_of_shooting(albu_df)"
      ],
      "metadata": {
        "id": "CYzFdfog4srr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1dd1eee2-86de-44e6-f465-9d36c12ff707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For the charts below, for convenience, the first one for each category is the general data's chart, and the second is for Albuquerque.\n",
            "\n",
            "Category Counts:\n",
            "0-18      135\n",
            "19-28    1771\n",
            "29-38    2429\n",
            "39-48    1563\n",
            "49-58     974\n",
            "58+       627\n",
            "Name: age_range, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Category Counts:\n",
            "0-18      0\n",
            "19-28    13\n",
            "29-38    16\n",
            "39-48     9\n",
            "49-58     6\n",
            "58+       3\n",
            "Name: age_range, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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x5bWLAADgGuDSQPTdd9/p6NGj5iMpKUmSdP/990uSRo4cqc8//1xLly7VunXrdOTIEfXs2dN8fUFBgaKjo5Wfn6+NGzdq4cKFio+P18SJE82a/fv3Kzo6Wh06dNDWrVs1YsQIDR48WImJieW7swAAoMKyGYZhuLqJIiNGjNDy5cu1Z88eORwO1a5dW4sWLVKvXr0kSbt27VKzZs2UnJysNm3aaMWKFeratauOHDmiwMBASdLcuXP1zDPP6NixY/L09NQzzzyjhIQE7dy509xO7969lZWVpZUrV15WXw6HQ35+fsrOzpbdbi/1/Q59NqHU1wQqiwOvRLu6BQDXqCv5/V1hziHKz8/XBx98oIEDB8pmsyklJUVnz55VZGSkWdO0aVPVq1dPycnJkqTk5GQ1b97cDEOSFBUVJYfDodTUVLPm/DWKaorWuJC8vDw5HA6nBwAAqLwqTCD65JNPlJWVpf79+0uS0tPT5enpKX9/f6e6wMBApaenmzXnh6Gi+aK5i9U4HA6dOXPmgr1MnTpVfn5+5iMkJORqdw8AAFRgFSYQLViwQPfee6+Cg4Nd3YrGjx+v7Oxs83Ho0CFXtwQAAMqQh6sbkKSDBw/qyy+/1LJly8yxoKAg5efnKysry+koUUZGhoKCgsyazZs3O61VdBXa+TV/vjItIyNDdrtd3t7eF+zHy8tLXl5eV71fAADg2lAhjhDFxcUpICBA0dH/d/Jky5YtVaVKFa1evdoc2717t9LS0hQRESFJioiI0I4dO5SZmWnWJCUlyW63KywszKw5f42imqI1AAAAXB6ICgsLFRcXp379+snD4/8OWPn5+WnQoEEaNWqUvvrqK6WkpGjAgAGKiIhQmzZtJEmdOnVSWFiYHn30UW3btk2JiYmaMGGCYmNjzSM8Q4cO1S+//KJx48Zp165deuutt7RkyRKNHDnSJfsLAAAqHpd/ZPbll18qLS1NAwcOLDY3a9Ysubm5KSYmRnl5eYqKitJbb71lzru7u2v58uV64oknFBERIV9fX/Xr108vvfSSWdOgQQMlJCRo5MiRmj17turWrav58+crKiqqXPYPAABUfBXqPkQVFfchAlyH+xABKKlr8j5EAAAArkIgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlkcgAgAAlufyQPTrr7/qkUceUa1ateTt7a3mzZtry5Yt5rxhGJo4caLq1Kkjb29vRUZGas+ePU5rHD9+XH369JHdbpe/v78GDRqknJwcp5rt27erXbt2qlq1qkJCQjRt2rRy2T8AAFDxuTQQnThxQm3btlWVKlW0YsUK/fjjj5oxY4Zq1Khh1kybNk2vv/665s6dq02bNsnX11dRUVHKzc01a/r06aPU1FQlJSVp+fLlWr9+vYYMGWLOOxwOderUSfXr11dKSoqmT5+uF198UW+//Xa57i8AAKiYbIZhGK7a+LPPPqsNGzbo66+/vuC8YRgKDg7W6NGjNWbMGElSdna2AgMDFR8fr969e+unn35SWFiYvvvuO7Vq1UqStHLlSnXp0kWHDx9WcHCw5syZo+eee07p6eny9PQ0t/3JJ59o165dl+zT4XDIz89P2dnZstvtpbT3/yf02YRSXxOoLA68Eu3qFgBco67k97dLjxB99tlnatWqle6//34FBATotttu0zvvvGPO79+/X+np6YqMjDTH/Pz81Lp1ayUnJ0uSkpOT5e/vb4YhSYqMjJSbm5s2bdpk1rRv394MQ5IUFRWl3bt368SJE8X6ysvLk8PhcHoAAIDKy6WB6JdfftGcOXPUuHFjJSYm6oknntDTTz+thQsXSpLS09MlSYGBgU6vCwwMNOfS09MVEBDgNO/h4aGaNWs61VxojfO3cb6pU6fKz8/PfISEhJTC3gIAgIrKpYGosLBQ4eHhmjJlim677TYNGTJEjz32mObOnevKtjR+/HhlZ2ebj0OHDrm0HwAAULZcGojq1KmjsLAwp7FmzZopLS1NkhQUFCRJysjIcKrJyMgw54KCgpSZmek0f+7cOR0/ftyp5kJrnL+N83l5eclutzs9AABA5eXSQNS2bVvt3r3baeznn39W/fr1JUkNGjRQUFCQVq9ebc47HA5t2rRJERERkqSIiAhlZWUpJSXFrFmzZo0KCwvVunVrs2b9+vU6e/asWZOUlKQbb7zR6Yo2AABgTS4NRCNHjtS3336rKVOmaO/evVq0aJHefvttxcbGSpJsNptGjBihl19+WZ999pl27Nihvn37Kjg4WD169JD0xxGlzp0767HHHtPmzZu1YcMGDRs2TL1791ZwcLAk6eGHH5anp6cGDRqk1NRUffjhh5o9e7ZGjRrlql0HAAAViIcrN3777bfr448/1vjx4/XSSy+pQYMGeu2119SnTx+zZty4cTp16pSGDBmirKws3XnnnVq5cqWqVq1q1vznP//RsGHD1LFjR7m5uSkmJkavv/66Oe/n56dVq1YpNjZWLVu21HXXXaeJEyc63asIAABYl0vvQ3St4D5EgOtwHyIAJXXN3IcIAACgIiAQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy3NpIHrxxRdls9mcHk2bNjXnc3NzFRsbq1q1aqlatWqKiYlRRkaG0xppaWmKjo6Wj4+PAgICNHbsWJ07d86pZu3atQoPD5eXl5caNWqk+Pj48tg9AABwjXD5EaKbbrpJR48eNR/ffPONOTdy5Eh9/vnnWrp0qdatW6cjR46oZ8+e5nxBQYGio6OVn5+vjRs3auHChYqPj9fEiRPNmv379ys6OlodOnTQ1q1bNWLECA0ePFiJiYnlup8AAKDi8nB5Ax4eCgoKKjaenZ2tBQsWaNGiRbr77rslSXFxcWrWrJm+/fZbtWnTRqtWrdKPP/6oL7/8UoGBgbr11ls1efJkPfPMM3rxxRfl6empuXPnqkGDBpoxY4YkqVmzZvrmm280a9YsRUVFleu+AgCAisnlR4j27Nmj4OBg3XDDDerTp4/S0tIkSSkpKTp79qwiIyPN2qZNm6pevXpKTk6WJCUnJ6t58+YKDAw0a6KiouRwOJSammrWnL9GUU3RGgAAAC49QtS6dWvFx8frxhtv1NGjRzVp0iS1a9dOO3fuVHp6ujw9PeXv7+/0msDAQKWnp0uS0tPTncJQ0XzR3MVqHA6Hzpw5I29v72J95eXlKS8vz3zucDiuel8BAEDF5dJAdO+995p/btGihVq3bq369etryZIlFwwq5WXq1KmaNGmSy7YPAADKl8s/Mjufv7+/mjRpor179yooKEj5+fnKyspyqsnIyDDPOQoKCip21VnR80vV2O32vwxd48ePV3Z2tvk4dOhQaeweAACooCpUIMrJydG+fftUp04dtWzZUlWqVNHq1avN+d27dystLU0RERGSpIiICO3YsUOZmZlmTVJSkux2u8LCwsya89coqila40K8vLxkt9udHgAAoPJyaSAaM2aM1q1bpwMHDmjjxo2677775O7uroceekh+fn4aNGiQRo0apa+++kopKSkaMGCAIiIi1KZNG0lSp06dFBYWpkcffVTbtm1TYmKiJkyYoNjYWHl5eUmShg4dql9++UXjxo3Trl279NZbb2nJkiUaOXKkK3cdAABUIC49h+jw4cN66KGH9Pvvv6t27dq688479e2336p27dqSpFmzZsnNzU0xMTHKy8tTVFSU3nrrLfP17u7uWr58uZ544glFRETI19dX/fr100svvWTWNGjQQAkJCRo5cqRmz56tunXrav78+VxyDwAATDbDMAxXN1HRORwO+fn5KTs7u0w+Pgt9NqHU1wQqiwOvRLu6BQDXqCv5/V2hziECAABwBQIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwvBIFol9++aW0+wAAAHCZEgWiRo0aqUOHDvrggw+Um5tb2j0BAACUqxIFou+//14tWrTQqFGjFBQUpMcff1ybN28u7d4AAADKRYkC0a233qrZs2fryJEjevfdd3X06FHdeeeduvnmmzVz5kwdO3astPsEAAAoM1d1UrWHh4d69uyppUuX6tVXX9XevXs1ZswYhYSEqG/fvjp69Ghp9QkAAFBmrioQbdmyRU8++aTq1KmjmTNnasyYMdq3b5+SkpJ05MgRde/evbT6BAAAKDMeJXnRzJkzFRcXp927d6tLly5677331KVLF7m5/ZGvGjRooPj4eIWGhpZmrwAAAGWiRIFozpw5GjhwoPr37686depcsCYgIEALFiy4quYAAADKQ4kC0Z49ey5Z4+npqX79+pVkeQAAgHJVonOI4uLitHTp0mLjS5cu1cKFC6+6KQAAgPJUokA0depUXXfddcXGAwICNGXKlKtuCgAAoDyVKBClpaWpQYMGxcbr16+vtLS0q24KAACgPJUoEAUEBGj79u3Fxrdt26ZatWpddVMAAADlqUSB6KGHHtLTTz+tr776SgUFBSooKNCaNWs0fPhw9e7du7R7BAAAKFMlusps8uTJOnDggDp27CgPjz+WKCwsVN++fTmHCAAAXHNKFIg8PT314YcfavLkydq2bZu8vb3VvHlz1a9fv7T7AwAAKHMlCkRFmjRpoiZNmpRWLwAAAC5RokBUUFCg+Ph4rV69WpmZmSosLHSaX7NmTak0BwAAUB5KFIiGDx+u+Ph4RUdH6+abb5bNZivtvgAAAMpNiQLR4sWLtWTJEnXp0qW0+wEAACh3Jbrs3tPTU40aNSrtXgAAAFyiRIFo9OjRmj17tgzDKO1+AAAAyl2JPjL75ptv9NVXX2nFihW66aabVKVKFaf5ZcuWlUpzAAAA5aFEgcjf31/33XdfafcCAADgEiUKRHFxcaXdBwAAgMuU6BwiSTp37py+/PJLzZs3TydPnpQkHTlyRDk5OaXWHAAAQHko0RGigwcPqnPnzkpLS1NeXp7uueceVa9eXa+++qry8vI0d+7c0u4TAACgzJToCNHw4cPVqlUrnThxQt7e3ub4fffdp9WrV5dacwAAAOWhRIHo66+/1oQJE+Tp6ek0Hhoaql9//bVEjbzyyiuy2WwaMWKEOZabm6vY2FjVqlVL1apVU0xMjDIyMpxel5aWpujoaPn4+CggIEBjx47VuXPnnGrWrl2r8PBweXl5qVGjRoqPjy9RjwAAoHIqUSAqLCxUQUFBsfHDhw+revXqV7zed999p3nz5qlFixZO4yNHjtTnn3+upUuXat26dTpy5Ih69uxpzhcUFCg6Olr5+fnauHGjFi5cqPj4eE2cONGs2b9/v6Kjo9WhQwdt3bpVI0aM0ODBg5WYmHjFfQIAgMqpRIGoU6dOeu2118znNptNOTk5euGFF6746zxycnLUp08fvfPOO6pRo4Y5np2drQULFmjmzJm6++671bJlS8XFxWnjxo369ttvJUmrVq3Sjz/+qA8++EC33nqr7r33Xk2ePFlvvvmm8vPzJUlz585VgwYNNGPGDDVr1kzDhg1Tr169NGvWrJLsOgAAqIRKFIhmzJihDRs2KCwsTLm5uXr44YfNj8teffXVK1orNjZW0dHRioyMdBpPSUnR2bNnncabNm2qevXqKTk5WZKUnJys5s2bKzAw0KyJioqSw+FQamqqWfPntaOiosw1AAAASnSVWd26dbVt2zYtXrxY27dvV05OjgYNGqQ+ffo4nWR9KYsXL9b333+v7777rthcenq6PD095e/v7zQeGBio9PR0s+b8MFQ0XzR3sRqHw6EzZ85csN+8vDzl5eWZzx0Ox2XvEwAAuPaUKBBJkoeHhx555JESb/jQoUMaPny4kpKSVLVq1RKvUxamTp2qSZMmuboNAABQTkoUiN57772Lzvft2/eSa6SkpCgzM1Ph4eHmWEFBgdavX6833nhDiYmJys/PV1ZWltNRooyMDAUFBUmSgoKCtHnzZqd1i65CO7/mz1emZWRkyG63/+XRrPHjx2vUqFHmc4fDoZCQkEvuEwAAuDaVKBANHz7c6fnZs2d1+vRpeXp6ysfH57ICUceOHbVjxw6nsQEDBqhp06Z65plnFBISoipVqmj16tWKiYmRJO3evVtpaWmKiIiQJEVEROif//ynMjMzFRAQIElKSkqS3W5XWFiYWfPFF184bScpKclc40K8vLzk5eV1yX0AAACVQ4kC0YkTJ4qN7dmzR0888YTGjh17WWtUr15dN998s9OYr6+vatWqZY4PGjRIo0aNUs2aNWW32/XUU08pIiJCbdq0kfTH1W5hYWF69NFHNW3aNKWnp2vChAmKjY01A83QoUP1xhtvaNy4cRo4cKDWrFmjJUuWKCEhoSS7DgAAKqESf5fZnzVu3FivvPJKsaNHV2PWrFnq2rWrYmJi1L59ewUFBWnZsmXmvLu7u5YvXy53d3dFRETokUceUd++ffXSSy+ZNQ0aNFBCQoKSkpJ0yy23aMaMGZo/f76ioqJKrU8AAHBtsxmGYZTWYlu3blX79u0r3VVZDodDfn5+ys7Olt1uL/X1Q5/laBXwVw68Eu3qFgBco67k93eJPjL77LPPnJ4bhqGjR4/qjTfeUNu2bUuyJAAAgMuUKBD16NHD6bnNZlPt2rV19913a8aMGaXRFwAAQLkpUSAqLCws7T4AAABcptROqgYAALhWlegI0fk3LbyUmTNnlmQTAAAA5aZEgeiHH37QDz/8oLNnz+rGG2+UJP38889yd3d3uvO0zWYrnS4BAADKUIkCUbdu3VS9enUtXLhQNWrUkPTHzRoHDBigdu3aafTo0aXaJAAAQFkq0TlEM2bM0NSpU80wJEk1atTQyy+/zFVmAADgmlOiQORwOHTs2LFi48eOHdPJkyevuikAAIDyVKJAdN9992nAgAFatmyZDh8+rMOHD+ujjz7SoEGD1LNnz9LuEQAAoEyV6ByiuXPnasyYMXr44Yd19uzZPxby8NCgQYM0ffr0Um0QAACgrJUoEPn4+Oitt97S9OnTtW/fPklSw4YN5evrW6rNAQAAlIerujHj0aNHdfToUTVu3Fi+vr4qxe+JBQAAKDclCkS///67OnbsqCZNmqhLly46evSoJGnQoEFccg8AAK45JQpEI0eOVJUqVZSWliYfHx9z/MEHH9TKlStLrTkAAIDyUKJziFatWqXExETVrVvXabxx48Y6ePBgqTQGAABQXkp0hOjUqVNOR4aKHD9+XF5eXlfdFAAAQHkqUSBq166d3nvvPfO5zWZTYWGhpk2bpg4dOpRacwAAAOWhRB+ZTZs2TR07dtSWLVuUn5+vcePGKTU1VcePH9eGDRtKu0cAAIAyVaIjRDfffLN+/vln3XnnnerevbtOnTqlnj176ocfflDDhg1Lu0cAAIAydcVHiM6ePavOnTtr7ty5eu6558qiJwAAgHJ1xUeIqlSpou3bt5dFLwAAAC5Roo/MHnnkES1YsKC0ewEAAHCJEp1Ufe7cOb377rv68ssv1bJly2LfYTZz5sxSaQ4AAKA8XFEg+uWXXxQaGqqdO3cqPDxckvTzzz871dhsttLrDgAAoBxcUSBq3Lixjh49qq+++krSH1/V8frrryswMLBMmgMAACgPV3QO0Z+/zX7FihU6depUqTYEAABQ3kp0UnWRPwckAACAa9EVBSKbzVbsHCHOGQIAANe6KzqHyDAM9e/f3/wC19zcXA0dOrTYVWbLli0rvQ4BAADK2BUFon79+jk9f+SRR0q1GQAAAFe4okAUFxdXVn0AAAC4zFWdVA0AAFAZEIgAAIDlEYgAAIDlEYgAAIDluTQQzZkzRy1atJDdbpfdbldERIRWrFhhzufm5io2Nla1atVStWrVFBMTo4yMDKc10tLSFB0dLR8fHwUEBGjs2LE6d+6cU83atWsVHh4uLy8vNWrUSPHx8eWxewAA4Brh0kBUt25dvfLKK0pJSdGWLVt09913q3v37kpNTZUkjRw5Up9//rmWLl2qdevW6ciRI+rZs6f5+oKCAkVHRys/P18bN27UwoULFR8fr4kTJ5o1+/fvV3R0tDp06KCtW7dqxIgRGjx4sBITE8t9fwEAQMVkMyrY92/UrFlT06dPV69evVS7dm0tWrRIvXr1kiTt2rVLzZo1U3Jystq0aaMVK1aoa9euOnLkiPkFs3PnztUzzzyjY8eOydPTU88884wSEhK0c+dOcxu9e/dWVlaWVq5ceVk9ORwO+fn5KTs7W3a7vdT3OfTZhFJfE6gsDrwS7eoWAFyjruT3d4U5h6igoECLFy/WqVOnFBERoZSUFJ09e1aRkZFmTdOmTVWvXj0lJydLkpKTk9W8eXMzDElSVFSUHA6HeZQpOTnZaY2imqI1LiQvL08Oh8PpAQAAKi+XB6IdO3aoWrVq8vLy0tChQ/Xxxx8rLCxM6enp8vT0lL+/v1N9YGCg0tPTJUnp6elOYahovmjuYjUOh0Nnzpy5YE9Tp06Vn5+f+QgJCSmNXQUAABWUywPRjTfeqK1bt2rTpk164okn1K9fP/34448u7Wn8+PHKzs42H4cOHXJpPwAAoGxd0Vd3lAVPT081atRIktSyZUt99913mj17th588EHl5+crKyvL6ShRRkaGgoKCJElBQUHavHmz03pFV6GdX/PnK9MyMjJkt9vl7e19wZ68vLzML7AFAACVn8uPEP1ZYWGh8vLy1LJlS1WpUkWrV68253bv3q20tDRFRERIkiIiIrRjxw5lZmaaNUlJSbLb7QoLCzNrzl+jqKZoDQAAAJceIRo/frzuvfde1atXTydPntSiRYu0du1aJSYmys/PT4MGDdKoUaNUs2ZN2e12PfXUU4qIiFCbNm0kSZ06dVJYWJgeffRRTZs2Tenp6ZowYYJiY2PNIzxDhw7VG2+8oXHjxmngwIFas2aNlixZooQEruwCAAB/cGkgyszMVN++fXX06FH5+fmpRYsWSkxM1D333CNJmjVrltzc3BQTE6O8vDxFRUXprbfeMl/v7u6u5cuX64knnlBERIR8fX3Vr18/vfTSS2ZNgwYNlJCQoJEjR2r27NmqW7eu5s+fr6ioqHLfXwAAUDFVuPsQVUTchwhwHe5DBKCkrsn7EAEAALgKgQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFieSwPR1KlTdfvtt6t69eoKCAhQjx49tHv3bqea3NxcxcbGqlatWqpWrZpiYmKUkZHhVJOWlqbo6Gj5+PgoICBAY8eO1blz55xq1q5dq/DwcHl5ealRo0aKj48v690DAADXCJcGonXr1ik2NlbffvutkpKSdPbsWXXq1EmnTp0ya0aOHKnPP/9cS5cu1bp163TkyBH17NnTnC8oKFB0dLTy8/O1ceNGLVy4UPHx8Zo4caJZs3//fkVHR6tDhw7aunWrRowYocGDBysxMbFc9xcAAFRMNsMwDFc3UeTYsWMKCAjQunXr1L59e2VnZ6t27dpatGiRevXqJUnatWuXmjVrpuTkZLVp00YrVqxQ165ddeTIEQUGBkqS5s6dq2eeeUbHjh2Tp6ennnnmGSUkJGjnzp3mtnr37q2srCytXLnykn05HA75+fkpOztbdru91Pc79NmEUl8TqCwOvBLt6hYAXKOu5Pd3hTqHKDs7W5JUs2ZNSVJKSorOnj2ryMhIs6Zp06aqV6+ekpOTJUnJyclq3ry5GYYkKSoqSg6HQ6mpqWbN+WsU1RSt8Wd5eXlyOBxODwAAUHlVmEBUWFioESNGqG3btrr55pslSenp6fL09JS/v79TbWBgoNLT082a88NQ0XzR3MVqHA6Hzpw5U6yXqVOnys/Pz3yEhISUyj4CAICKqcIEotjYWO3cuVOLFy92dSsaP368srOzzcehQ4dc3RIAAChDHq5uQJKGDRum5cuXa/369apbt645HhQUpPz8fGVlZTkdJcrIyFBQUJBZs3nzZqf1iq5CO7/mz1emZWRkyG63y9vbu1g/Xl5e8vLyKpV9AwAAFZ9LjxAZhqFhw4bp448/1po1a9SgQQOn+ZYtW6pKlSpavXq1ObZ7926lpaUpIiJCkhQREaEdO3YoMzPTrElKSpLdbldYWJhZc/4aRTVFawAAAGtz6RGi2NhYLVq0SJ9++qmqV69unvPj5+cnb29v+fn5adCgQRo1apRq1qwpu92up556ShEREWrTpo0kqVOnTgoLC9Ojjz6qadOmKT09XRMmTFBsbKx5lGfo0KF64403NG7cOA0cOFBr1qzRkiVLlJDA1V0AAMDFR4jmzJmj7Oxs3XXXXapTp475+PDDD82aWbNmqWvXroqJiVH79u0VFBSkZcuWmfPu7u5avny53N3dFRERoUceeUR9+/bVSy+9ZNY0aNBACQkJSkpK0i233KIZM2Zo/vz5ioqKKtf9BQAAFVOFug9RRcV9iADX4T5EAErqmr0PEQAAgCsQiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOW5NBCtX79e3bp1U3BwsGw2mz755BOnecMwNHHiRNWpU0fe3t6KjIzUnj17nGqOHz+uPn36yG63y9/fX4MGDVJOTo5Tzfbt29WuXTtVrVpVISEhmjZtWlnvGgAAuIa4NBCdOnVKt9xyi958880Lzk+bNk2vv/665s6dq02bNsnX11dRUVHKzc01a/r06aPU1FQlJSVp+fLlWr9+vYYMGWLOOxwOderUSfXr11dKSoqmT5+uF198UW+//XaZ7x8AALg22AzDMFzdhCTZbDZ9/PHH6tGjh6Q/jg4FBwdr9OjRGjNmjCQpOztbgYGBio+PV+/evfXTTz8pLCxM3333nVq1aiVJWrlypbp06aLDhw8rODhYc+bM0XPPPaf09HR5enpKkp599ll98skn2rVr12X15nA45Ofnp+zsbNnt9lLf99BnE0p9TaCyOPBKtKtbAHCNupLf3xX2HKL9+/crPT1dkZGR5pifn59at26t5ORkSVJycrL8/f3NMCRJkZGRcnNz06ZNm8ya9u3bm2FIkqKiorR7926dOHHigtvOy8uTw+FwegAAgMqrwgai9PR0SVJgYKDTeGBgoDmXnp6ugIAAp3kPDw/VrFnTqeZCa5y/jT+bOnWq/Pz8zEdISMjV7xAAAKiwKmwgcqXx48crOzvbfBw6dMjVLQEAgDJUYQNRUFCQJCkjI8NpPCMjw5wLCgpSZmam0/y5c+d0/Phxp5oLrXH+Nv7My8tLdrvd6QEAACqvChuIGjRooKCgIK1evdocczgc2rRpkyIiIiRJERERysrKUkpKilmzZs0aFRYWqnXr1mbN+vXrdfbsWbMmKSlJN954o2rUqFFOewMAACoylwainJwcbd26VVu3bpX0x4nUW7duVVpammw2m0aMGKGXX35Zn332mXbs2KG+ffsqODjYvBKtWbNm6ty5sx577DFt3rxZGzZs0LBhw9S7d28FBwdLkh5++GF5enpq0KBBSk1N1YcffqjZs2dr1KhRLtprAABQ0Xi4cuNbtmxRhw4dzOdFIaVfv36Kj4/XuHHjdOrUKQ0ZMkRZWVm68847tXLlSlWtWtV8zX/+8x8NGzZMHTt2lJubm2JiYvT666+b835+flq1apViY2PVsmVLXXfddZo4caLTvYoAAIC1VZj7EFVk3IcIcB3uQwSgpCrFfYgAAADKC4EIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYHoEIAABYnoerGwAAKwh9NsHVLQAV2oFXol26fY4QAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy7NUIHrzzTcVGhqqqlWrqnXr1tq8ebOrWwIAABWAZQLRhx9+qFGjRumFF17Q999/r1tuuUVRUVHKzMx0dWsAAMDFLBOIZs6cqccee0wDBgxQWFiY5s6dKx8fH7377ruubg0AALiYJQJRfn6+UlJSFBkZaY65ubkpMjJSycnJLuwMAABUBB6ubqA8/PbbbyooKFBgYKDTeGBgoHbt2lWsPi8vT3l5eebz7OxsSZLD4SiT/grzTpfJukBlUFY/d+WNn3Pg4sriZ71oTcMwLllriUB0paZOnapJkyYVGw8JCXFBN4C1+b3m6g4AlIey/Fk/efKk/Pz8LlpjiUB03XXXyd3dXRkZGU7jGRkZCgoKKlY/fvx4jRo1ynxeWFio48ePq1atWrLZbGXeL1zH4XAoJCREhw4dkt1ud3U7AMoIP+vWYBiGTp48qeDg4EvWWiIQeXp6qmXLllq9erV69Ogh6Y+Qs3r1ag0bNqxYvZeXl7y8vJzG/P39y6FTVBR2u51/JAEL4Ge98rvUkaEilghEkjRq1Cj169dPrVq10t/+9je99tprOnXqlAYMGODq1gAAgItZJhA9+OCDOnbsmCZOnKj09HTdeuutWrlyZbETrQEAgPVYJhBJ0rBhwy74ERlQxMvLSy+88EKxj0wBVC78rOPPbMblXIsGAABQiVnixowAAAAXQyACAACWRyACAACWRyACLuHAgQOy2WzaunWrq1sB4GKhoaF67bXXXN0GygCBCJVS//79ZbPZNHTo0GJzsbGxstls6t+/f/k3BuCyFf0c//mxd+9eV7eGSohAhEorJCREixcv1pkzZ8yx3NxcLVq0SPXq1XNhZwAuV+fOnXX06FGnR4MGDVzdFiohAhEqrfDwcIWEhGjZsmXm2LJly1SvXj3ddttt5tjKlSt15513yt/fX7Vq1VLXrl21b9++i669c+dO3XvvvapWrZoCAwP16KOP6rfffiuzfQGsysvLS0FBQU4Pd3d3ffrppwoPD1fVqlV1ww03aNKkSTp37pz5OpvNpnnz5qlr167y8fFRs2bNlJycrL179+quu+6Sr6+v7rjjDqef9X379ql79+4KDAxUtWrVdPvtt+vLL7+8aH9ZWVkaPHiwateuLbvdrrvvvlvbtm0rs/cDZYdAhEpt4MCBiouLM5+/++67xb6u5dSpUxo1apS2bNmi1atXy83NTffdd58KCwsvuGZWVpbuvvtu3XbbbdqyZYtWrlypjIwMPfDAA2W6LwD+8PXXX6tv374aPny4fvzxR82bN0/x8fH65z//6VQ3efJk9e3bV1u3blXTpk318MMP6/HHH9f48eO1ZcsWGYbhdLPenJwcdenSRatXr9YPP/ygzp07q1u3bkpLS/vLXu6//35lZmZqxYoVSklJUXh4uDp27Kjjx4+X2f6jjBhAJdSvXz+je/fuRmZmpuHl5WUcOHDAOHDggFG1alXj2LFjRvfu3Y1+/fpd8LXHjh0zJBk7duwwDMMw9u/fb0gyfvjhB8MwDGPy5MlGp06dnF5z6NAhQ5Kxe/fustwtwFL69etnuLu7G76+vuajV69eRseOHY0pU6Y41b7//vtGnTp1zOeSjAkTJpjPk5OTDUnGggULzLH//ve/RtWqVS/aw0033WT8+9//Np/Xr1/fmDVrlmEYhvH1118bdrvdyM3NdXpNw4YNjXnz5l3x/sK1LPXVHbCe2rVrKzo6WvHx8TIMQ9HR0bruuuucavbs2aOJEydq06ZN+u2338wjQ2lpabr55puLrblt2zZ99dVXqlatWrG5ffv2qUmTJmWzM4AFdejQQXPmzDGf+/r6qkWLFtqwYYPTEaGCggLl5ubq9OnT8vHxkSS1aNHCnC/63srmzZs7jeXm5srhcMhutysnJ0cvvviiEhISdPToUZ07d05nzpz5yyNE27ZtU05OjmrVquU0fubMmUt+7I6Kh0CESm/gwIHmYfE333yz2Hy3bt1Uv359vfPOOwoODlZhYaFuvvlm5efnX3C9nJwcdevWTa+++mqxuTp16pRu84DF+fr6qlGjRk5jOTk5mjRpknr27FmsvmrVquafq1SpYv7ZZrP95VjR/wSNGTNGSUlJ+te//qVGjRrJ29tbvXr1uui/BXXq1NHatWuLzfn7+1/eDqLCIBCh0uvcubPy8/Nls9kUFRXlNPf7779r9+7deuedd9SuXTtJ0jfffHPR9cLDw/XRRx8pNDRUHh78CAHlLTw8XLt37y4WlK7Whg0b1L9/f913332S/gg8Bw4cuGgf6enp8vDwUGhoaKn2gvLHSdWo9Nzd3fXTTz/pxx9/lLu7u9NcjRo1VKtWLb399tvau3ev1qxZo1GjRl10vdjYWB0/flwPPfSQvvvuO+3bt0+JiYkaMGCACgoKynJXAEiaOHGi3nvvPU2aNEmpqan66aeftHjxYk2YMOGq1m3cuLGWLVumrVu3atu2bXr44Yf/8uIKSYqMjFRERIR69OihVatW6cCBA9q4caOee+45bdmy5ap6QfkjEMES7Ha77HZ7sXE3NzctXrxYKSkpuvnmmzVy5EhNnz79omsFBwdrw4YNKigoUKdOndS8eXONGDFC/v7+cnPjRwooa1FRUVq+fLlWrVql22+/XW3atNGsWbNUv379q1p35syZqlGjhu644w5169ZNUVFRCg8P/8t6m82mL774Qu3bt9eAAQPUpEkT9e7dWwcPHjTPWcK1w2YYhuHqJgAAAFyJ/50FAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACAACWRyACgMtw1113acSIEa5uA0AZIRABuGakp6dr+PDhatSokapWrarAwEC1bdtWc+bM0enTp13dHoBrGN9MCeCa8Msvv6ht27by9/fXlClT1Lx5c3l5eWnHjh16++23df311+v//b//5+o2/1JBQYFsNhtf7wJUUPxkArgmPPnkk/Lw8NCWLVv0wAMPqFmzZrrhhhvUvXt3JSQkqFu3bpKkrKwsDR48WLVr15bdbtfdd9+tbdu2meu8+OKLuvXWW/X+++8rNDRUfn5+6t27t06ePGnWnDp1Sn379lW1atVUp04dzZgxo1g/eXl5GjNmjK6//nr5+vqqdevWWrt2rTkfHx8vf39/ffbZZwoLC5OXl5fS0tLK7g0CcFUIRAAqvN9//12rVq1SbGysfH19L1hjs9kkSffff78yMzO1YsUKpaSkKDw8XB07dtTx48fN2n379umTTz7R8uXLtXz5cq1bt06vvPKKOT927FitW7dOn376qVatWqW1a9fq+++/d9resGHDlJycrMWLF2v79u26//771blzZ+3Zs8esOX36tF599VXNnz9fqampCggIKM23BUBpMgCggvv2228NScayZcucxmvVqmX4+voavr6+xrhx44yvv/7asNvtRm5urlNdw4YNjXnz5hmGYRgvvPCC4ePjYzgcDnN+7NixRuvWrQ3DMIyTJ08anp6expIlS8z533//3fD29jaGDx9uGIZhHDx40HB3dzd+/fVXp+107NjRGD9+vGEYhhEXF2dIMrZu3Vo6bwKAMsU5RACuWZs3b1ZhYaH69OmjvLw8bdu2TTk5OapVq5ZT3ZkzZ7Rv3z7zeWhoqKpXr24+r1OnjjIzMyX9cfQoPz9frVu3Nudr1qypG2+80Xy+Y8cOFRQUqEmTJk7bycvLc9q2p6enWrRoUTo7C6BMEYgAVHiNGjWSzWbT7t27ncZvuOEGSZK3t7ckKScnR3Xq1HE6l6eIv7+/+ecqVao4zdlsNhUWFl52Pzk5OXJ3d1dKSorc3d2d5qpVq2b+2dvb2/woD0DFRiACUOHVqlVL99xzj9544w099dRTf3keUXh4uNLT0+Xh4aHQ0NASbathw4aqUqWKNm3apHr16kmSTpw4oZ9//ll///vfJUm33XabCgoKlJmZqXbt2pVoOwAqFk6qBnBNeOutt3Tu3Dm1atVKH374oX766Sft3r1bH3zwgXbt2iV3d3dFRkYqIiJCPXr00KpVq3TgwAFt3LhRzz33nLZs2XJZ26lWrZoGDRqksWPHas2aNdq5c6f69+/vdLl8kyZN1KdPH/Xt21fLli3T/v37tXnzZk2dOlUJCQll9RYAKEMcIQJwTWjYsKF++OEHTZkyRePHj9fhw4fl5eWlsLAwjRkzRk8++aRsNpu++OILPffccxowYICOHTumoKAgtW/fXoGBgZe9renTpysnJ0fdunVT9erVNXr0aGVnZzvVxMXF6eWXX9bo0aP166+/6rrrrlObNm3UtWvX0t51AOXAZhiG4eomAAAAXImPzAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOURiAAAgOX9f6h1PC/Wk/GTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-135-962869dc141d>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['date'] = pd.to_datetime(df['date'])\n",
            "<ipython-input-135-962869dc141d>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['month'] = df['date'].dt.month\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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CiGDnzp3f/X6SuLm5Kb+zwIfBMLlz51arc9euXfDw8FCb3snW1hZNmjRJ8evSj8VgR2nexx+a/fr1g4WFBYoXL46cOXMiICBA7fq0Fy9eIDw8HHPnzkXGjBnVflq2bAngw3UsX/LgwQPo6ekhR44cassdHBxgY2ODBw8epOg95M+fHzY2NmrhrXTp0sr78/DwUFvn5OSErFmzAvgwt9bVq1eTvZ9cuXIlez/Hjh1DhQoVYG5uDhsbG2TMmFGZMuXTYJcjR45kgSRpm0nXyTx48AC5c+dO9n7y5s2rrP9YUs1J0qVLBwB4/fr1V/cRAOTMmVPtcfbs2aGnp6d23U7z5s3x8OFDHDlyBACwb98+hIaGolmzZl/dvoGBARo0aIAjR47gyZMnAKCEvK99MN25cwe5c+f+4QMgPt1n1tbWAAAnJ6fPLv/avnz79i2GDBmiXH+VIUMGZMyYEeHh4cmOgdT0Pe8zMTFRqfXWrVsQEeTMmTPZ78D169f/9ff5xYsXePPmzReP4cTExG++Tu9TmTJlwuzZs/H06VPcvHkT06ZNQ8aMGTFkyBDMnz9fra2jo2Oy37V06dKp/Vs+ePAAOXPmTPbl4tPftQcPHiBz5sxqof9z7VLi03+jL9X56d9HAJ9dRprx6w3RIq2SdJHx27dvP7v+zZs3ahci582bFzdv3sS2bduwa9curF+/HrNmzcKQIUMwfPhwJCYmAgCaNm0Kf3//z26zYMGCX63rR08kq6enBw8PDxw/fhwigmPHjqnNUVeqVCksWLAAcXFxOH36tNr0BImJiShQoAAmTZr02W0nfTDeuXMH5cuXR548eTBp0iQ4OTnByMgIO3bswOTJk5V98zN9aaTdpz0Y3+pz/w6+vr6wt7fHsmXLULZsWSxbtgwODg6oUKHCN22zadOmmDFjBlauXInevXtj5cqVcHNz09gE01/aZyndl126dMHChQvRvXt3eHh4KJPtNmzYMFWOgS9J6ftMTEyESqXCzp07P9tW09OKqFQq5MqVC7ly5YKfnx9y5syJ5cuXo02bNkqbH/178bP8KnXSv2OwI41KGol28+bNZN/c37x5g0ePHqFSpUpqy83NzdGgQQM0aNAAcXFxqFOnDv766y8MGDAAGTNmhKWlJRISEr75g/7TehITE3Hr1i3lGzAAhIaGIjw8XKk3JTw9PbFz505s2bIFz58/V3rsgA/BbtCgQdixYwfevn2rduoze/bsuHjxIsqXL/+vgXPr1q2IjY3Fli1b1L55f+lU1e3btyEiatv8559/AEA5Nejs7IybN28me+6NGzeU9T/SrVu34OLiolZjYmKi2qlKfX19NG7cGIsWLcLYsWOxadMmtG3b9punbyhRogSyZ8+OFStWoGLFirh69Sr++uuvrz4ve/bsOHnyJN6/f692SvFjaeHOEuvWrYO/vz8mTpyoLHv37h3Cw8PV2qWFWr9F9uzZISJwcXFRepS/VcaMGWFmZvbFY1hPTy/Z353/wtXVFenSpcPTp0+/+7nOzs64dOkSEhMT1XrtPv1dc3Z2xr59+xAVFaXWa/e538mf8W/s7Oz82ZHu3zP6nX4unooljSpfvjyMjIwwe/bsZL0Jc+fORXx8PKpUqaIse/XqlVobIyMjuLm5QUTw/v176Ovro27duli/fj2uXLmS7PWS5qr6kqpVqwIApkyZorY8qbcsaY6zlEgKa2PHjoWZmZlaD1Hx4sVhYGCgTIHwcbCrX78+njx5gnnz5iXb5tu3bxETEwPgf9+2P/52HRERgYULF362npCQEGzcuFF5HBkZiSVLlsDd3R0ODg4APuyPU6dOITg4WGkXExODuXPnIlu2bHBzc/uuffA1M2fOVHs8ffp0AFA7BoAPE6y+fv0a7du3R3R09HfPp9akSROcP38eQ4cOhUqlQuPGjb/6nLp16+Lly5eYMWNGsnVJ+zxp4uNPQ1Rq0tfXT9bDMn36dCQkJKgtS7oeUZO1fos6depAX18fw4cPT/a+RCTZ34SP6evro1KlSti8ebPa6fzQ0FCsWLECnp6esLKy+u6aTp48qfzefezUqVN49erVZ0/9fk3VqlXx7NkzrF69WlkWHx+P6dOnw8LCAuXKlVPaJSQkJDsOJ0+eDJVKpfa7Ym5u/sP/fX19fREcHIwLFy4oy8LCwrB8+fIf+jqUcuyxI42ys7PDkCFDMHjwYJQtWxY1atSAmZkZjh8/jpUrV6JSpUpqk/pWqlQJDg4OKF26NOzt7XH9+nXMmDEDfn5+yrfXMWPGICgoCCVKlEDbtm3h5uaGsLAwnDt3Dvv27Ut2a6+PFSpUCP7+/pg7dy7Cw8NRrlw5nDp1CosXL0atWrXg7e2d4vdavHhxGBkZITg4GF5eXmrXapmZmaFQoUIIDg6GjY2N2r0lmzVrhjVr1qBDhw4ICgpC6dKlkZCQgBs3bmDNmjXYvXs3ihYtikqVKsHIyAjVq1dXAs+8efNgZ2f32R6EXLlyoXXr1jh9+jTs7e2xYMEChIaGqgXB/v37Y+XKlahSpQq6du0KW1tbLF68GPfu3cP69ev/dbBBSty7dw81atRA5cqVERwcjGXLlqFx48bJ5q777bffkD9/fmVgSeHChb/rdZo2bYo///wTmzdvRunSpf91zrIkzZs3x5IlS9CzZ0+cOnUKZcqUQUxMDPbt24dOnTqhZs2aMDU1hZubG1avXo1cuXLB1tYW+fPnT9V7hVarVg1Lly6FtbU13NzcEBwcjH379iF9+vRq7dzd3aGvr4+xY8ciIiICxsbGyhyIaUn27NkxcuRIDBgwAPfv30etWrVgaWmJe/fuYePGjWjXrh169+79xeePHDkSe/fuhaenJzp16gQDAwPMmTMHsbGxn51L7lssXboUy5cvR+3atVGkSBEYGRnh+vXrWLBgAUxMTFJ0K8B27dphzpw5aNGiBc6ePYts2bJh3bp1OHbsGKZMmaL8fatevTq8vb0xaNAg3L9/H4UKFcKePXuwefNmdO/eHdmzZ1e2WaRIEezbtw+TJk1C5syZ4eLiogzCSam+ffti2bJlqFixIrp06QJzc3P8/fffyJo1K8LCwn6ZnmCtlqpjcIm+YNmyZVKyZEkxNzcXY2NjyZMnjwwfPlyZ9y3JnDlzpGzZspI+fXoxNjaW7NmzS58+fSQiIkKtXWhoqAQEBIiTk5MYGhqKg4ODlC9fXubOnfvVWt6/fy/Dhw8XFxcXMTQ0FCcnJxkwYECyWr5nupMkHh4eytxhn0qaV+zT6R9EPkx7MHbsWMmXL58YGxtLunTppEiRIjJ8+HC1975lyxYpWLCgmJiYSLZs2WTs2LHKNB2fTm3g5+cnu3fvloIFCyr7fO3atcle+86dO1KvXj2xsbERExMTKV68uGzbtk2tTdKUFp8+P2kKiYULF/7rfkmaCuTatWtSr149sbS0lHTp0knnzp3l7du3n33OuHHjBICMGjXqX7f9JcWKFRMAMmvWrM+u/3TaDpEPU4kMGjRIOTYcHBykXr16alNpHD9+XIoUKSJGRkZqU598abqTgIAAtWVfmnbjS/v4U69fv5aWLVtKhgwZxMLCQnx9feXGjRvi7OycbOqLefPmiaurq+jr63/X1Ccpme7k07qTptw4ffq02vIvTYG0fv168fT0FHNzczE3N5c8efJIQECA3Lx586v1njt3Tnx9fcXCwkLMzMzE29tbmdMtyfdMd3Lp0iXp06ePFC5cWGxtbcXAwEAyZcokv//+u5w7dy7Z/vjcFE3+/v7J9mFoaKjyb2dkZCQFChT47O9OVFSU9OjRQzJnziyGhoaSM2dOGT9+vDLtTpIbN25I2bJlxdTUVAAo//5fmu7Ez88v2Wt97vfg/Pnzyjyejo6OMnr0aGUuyG+dC5F+HpUIr4ok0jXZsmVD/vz5sW3bNk2XkmJTp05Fjx49cP/+/c+O5iOi1NO9e3fMmTMH0dHRvF2ZhvEaOyL65YgI5s+fj3LlyjHUEaWyT2cxePXqFZYuXQpPT0+GujSA19gR0S8jJiYGW7ZsQVBQEC5fvozNmzdruiQinePh4QEvLy/kzZsXoaGhmD9/PiIjI/HHH39oujQCgx0R/UJevHiBxo0bw8bGBgMHDkSNGjU0XRKRzqlatSrWrVuHuXPnQqVSoXDhwpg/fz7Kli2r6dIIAK+xIyIiItISvMaOiIiISEvwVOw3SExMREhICCwtLTlHDxEREaUqEUFUVBQyZ8781flDGey+QUhIyA+97QwRERHR93r06BEcHR3/tQ2D3TdImvH70aNHKbr9DBEREVFKRUZGwsnJSe3+wF/CYPcNkk6/WllZMdgRERGRRnzL5WAcPEFERESkJRjsiIiIiLQEgx0RERGRlmCwIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEgaaLoD+J1v/7ZouIdXcH+On6RKIiIi0DnvsiIiIiLQEgx0RERGRlmCwIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEgx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIiIiLcFgR0RERKQlGOyIiIiItASDHREREZGWYLAjIiIi0hIMdkRERERagsGOiIiISEsw2BERERFpCQY7IiIiIi3BYEdERESkJRjsiIiIiLQEgx0RERGRlmCwIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItkWaC3ZgxY6BSqdC9e3dl2bt37xAQEID06dPDwsICdevWRWhoqNrzHj58CD8/P5iZmcHOzg59+vRBfHy8WpuDBw+icOHCMDY2Ro4cObBo0aJUeEdEREREqStNBLvTp09jzpw5KFiwoNryHj16YOvWrVi7di0OHTqEkJAQ1KlTR1mfkJAAPz8/xMXF4fjx41i8eDEWLVqEIUOGKG3u3bsHPz8/eHt748KFC+jevTvatGmD3bt3p9r7IyIiIkoNGg920dHRaNKkCebNm4d06dIpyyMiIjB//nxMmjQJPj4+KFKkCBYuXIjjx4/jxIkTAIA9e/bg2rVrWLZsGdzd3VGlShWMGDECM2fORFxcHAAgMDAQLi4umDhxIvLmzYvOnTujXr16mDx5skbeLxEREdHPovFgFxAQAD8/P1SoUEFt+dmzZ/H+/Xu15Xny5EHWrFkRHBwMAAgODkaBAgVgb2+vtPH19UVkZCSuXr2qtPl0276+vso2Pic2NhaRkZFqP0RERERpnYEmX3zVqlU4d+4cTp8+nWzds2fPYGRkBBsbG7Xl9vb2ePbsmdLm41CXtD5p3b+1iYyMxNu3b2FqaprstUePHo3hw4en+H0RERERaYLGeuwePXqEbt26Yfny5TAxMdFUGZ81YMAAREREKD+PHj3SdElEREREX6WxYHf27Fk8f/4chQsXhoGBAQwMDHDo0CFMmzYNBgYGsLe3R1xcHMLDw9WeFxoaCgcHBwCAg4NDslGySY+/1sbKyuqzvXUAYGxsDCsrK7UfIiIiorROY8GufPnyuHz5Mi5cuKD8FC1aFE2aNFH+39DQEPv371eec/PmTTx8+BAeHh4AAA8PD1y+fBnPnz9X2uzduxdWVlZwc3NT2ny8jaQ2SdsgIiIi0hYau8bO0tIS+fPnV1tmbm6O9OnTK8tbt26Nnj17wtbWFlZWVujSpQs8PDxQsmRJAEClSpXg5uaGZs2aYdy4cXj27BkGDx6MgIAAGBsbAwA6dOiAGTNmoG/fvmjVqhUOHDiANWvWYPv27an7homIiIh+Mo0OnviayZMnQ09PD3Xr1kVsbCx8fX0xa9YsZb2+vj62bduGjh07wsPDA+bm5vD398eff/6ptHFxccH27dvRo0cPTJ06FY6Ojvj777/h6+uribdERERE9NOoREQ0XURaFxkZCWtra0RERPzU6+2y9dedXsT7Y/w0XQIREdEv4XtyiMbnsSMiIiKiH4PBjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEgx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIiIiLcFgR0RERKQlGOyIiIiItASDHREREZGWYLAjIiIi0hIMdkRERERagsGOiIiISEsw2BERERFpCQY7IiIiIi3BYEdERESkJRjsiIiIiLQEgx0RERGRlmCwIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEgx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIiIiLcFgR0RERKQlGOyIiIiItASDHREREZGWYLAjIiIi0hIMdkRERERagsGOiIiISEsw2BERERFpCQY7IiIiIi3BYEdERESkJRjsiIiIiLQEgx0RERGRlmCwIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItodFgN3v2bBQsWBBWVlawsrKCh4cHdu7cqax/9+4dAgICkD59elhYWKBu3boIDQ1V28bDhw/h5+cHMzMz2NnZoU+fPoiPj1drc/DgQRQuXBjGxsbIkSMHFi1alBpvj4iIiChVaTTYOTo6YsyYMTh79izOnDkDHx8f1KxZE1evXgUA9OjRA1u3bsXatWtx6NAhhISEoE6dOsrzExIS4Ofnh7i4OBw/fhyLFy/GokWLMGTIEKXNvXv34OfnB29vb1y4cAHdu3dHmzZtsHv37lR/v0REREQ/k0pERNNFfMzW1hbjx49HvXr1kDFjRqxYsQL16tUDANy4cQN58+ZFcHAwSpYsiZ07d6JatWoICQmBvb09ACAwMBD9+vXDixcvYGRkhH79+mH79u24cuWK8hoNGzZEeHg4du3a9U01RUZGwtraGhEREbCysvrxb/r/Zeu//adtO625P8ZP0yUQERH9Er4nh6SZa+wSEhKwatUqxMTEwMPDA2fPnsX79+9RoUIFpU2ePHmQNWtWBAcHAwCCg4NRoEABJdQBgK+vLyIjI5Vev+DgYLVtJLVJ2sbnxMbGIjIyUu2HiIiIKK3TeLC7fPkyLCwsYGxsjA4dOmDjxo1wc3PDs2fPYGRkBBsbG7X29vb2ePbsGQDg2bNnaqEuaX3Sun9rExkZibdv3362ptGjR8Pa2lr5cXJy+hFvlYiIiOin0niwy507Ny5cuICTJ0+iY8eO8Pf3x7Vr1zRa04ABAxAREaH8PHr0SKP1EBEREX0LA00XYGRkhBw5cgAAihQpgtOnT2Pq1Klo0KAB4uLiEB4ertZrFxoaCgcHBwCAg4MDTp06pba9pFGzH7f5dCRtaGgorKysYGpq+tmajI2NYWxs/EPeHxEREVFq0XiP3acSExMRGxuLIkWKwNDQEPv371fW3bx5Ew8fPoSHhwcAwMPDA5cvX8bz58+VNnv37oWVlRXc3NyUNh9vI6lN0jaIiIiItIVGe+wGDBiAKlWqIGvWrIiKisKKFStw8OBB7N69G9bW1mjdujV69uwJW1tbWFlZoUuXLvDw8EDJkiUBAJUqVYKbmxuaNWuGcePG4dmzZxg8eDACAgKUHrcOHTpgxowZ6Nu3L1q1aoUDBw5gzZo12L5dd0agEhERkW5IUbC7e/cuXF1d//OLP3/+HM2bN8fTp09hbW2NggULYvfu3ahYsSIAYPLkydDT00PdunURGxsLX19fzJo1S3m+vr4+tm3bho4dO8LDwwPm5ubw9/fHn3/+qbRxcXHB9u3b0aNHD0ydOhWOjo74+++/4evr+5/rJyIiIkpLUjSPnZ6eHsqVK4fWrVujXr16MDEx+Rm1pRmcx+7H4zx2RERE3+anz2N37tw5FCxYED179oSDgwPat2+fbBADEREREaWuFAU7d3d3TJ06FSEhIViwYAGePn0KT09P5M+fH5MmTcKLFy9+dJ1ERERE9BX/aVSsgYEB6tSpg7Vr12Ls2LG4ffs2evfuDScnJ+XaOSIiIiJKHf8p2J05cwadOnVCpkyZMGnSJPTu3Rt37tzB3r17ERISgpo1a/6oOomIiIjoK1I0KnbSpElYuHAhbt68iapVq2LJkiWoWrUq9PQ+5EQXFxcsWrQI2bJl+5G1EhEREdG/SFGwmz17Nlq1aoUWLVogU6ZMn21jZ2eH+fPn/6fiiIiIiOjbpSjY3bp166ttjIyM4O/vn5LNExEREVEKpOgau4ULF2Lt2rXJlq9duxaLFy/+z0URERER0fdLUbAbPXo0MmTIkGy5nZ0dRo0a9Z+LIiIiIqLvl6Jg9/DhQ7i4uCRb7uzsjIcPH/7nooiIiIjo+6Uo2NnZ2eHSpUvJll+8eBHp06f/z0URERER0fdLUbBr1KgRunbtiqCgICQkJCAhIQEHDhxAt27d0LBhwx9dIxERERF9gxSNih0xYgTu37+P8uXLw8DgwyYSExPRvHlzXmNHREREpCEpCnZGRkZYvXo1RowYgYsXL8LU1BQFChSAs7Pzj66PiIiIiL5RioJdkly5ciFXrlw/qhYiIiIi+g9SFOwSEhKwaNEi7N+/H8+fP0diYqLa+gMHDvyQ4oiIiIjo26Uo2HXr1g2LFi2Cn58f8ufPD5VK9aPrIiIiIqLvlKJgt2rVKqxZswZVq1b90fUQERERUQqlaLoTIyMj5MiR40fXQkRERET/QYqCXa9evTB16lSIyI+uh4iIiIhSKEWnYo8ePYqgoCDs3LkT+fLlg6Ghodr6DRs2/JDiiIiIiOjbpSjY2djYoHbt2j+6FiIiIiL6D1IU7BYuXPij6yAiIiKi/yhF19gBQHx8PPbt24c5c+YgKioKABASEoLo6OgfVhwRERERfbsU9dg9ePAAlStXxsOHDxEbG4uKFSvC0tISY8eORWxsLAIDA390nURERET0FSnqsevWrRuKFi2K169fw9TUVFleu3Zt7N+//4cVR0RERETfLkU9dkeOHMHx48dhZGSktjxbtmx48uTJDymMiIiIiL5PinrsEhMTkZCQkGz548ePYWlp+Z+LIiIiIqLvl6JgV6lSJUyZMkV5rFKpEB0djaFDh/I2Y0REREQakqJTsRMnToSvry/c3Nzw7t07NG7cGLdu3UKGDBmwcuXKH10jEREREX2DFAU7R0dHXLx4EatWrcKlS5cQHR2N1q1bo0mTJmqDKYiIiIgo9aQo2AGAgYEBmjZt+iNrISIiIqL/IEXBbsmSJf+6vnnz5ikqhoiIiIhSLkXBrlu3bmqP379/jzdv3sDIyAhmZmYMdkREREQakKJRsa9fv1b7iY6Oxs2bN+Hp6cnBE0REREQakuJ7xX4qZ86cGDNmTLLePCIiIiJKHT8s2AEfBlSEhIT8yE0SERER0TdK0TV2W7ZsUXssInj69ClmzJiB0qVL/5DCiIiIiOj7pCjY1apVS+2xSqVCxowZ4ePjg4kTJ/6IuoiIiIjoO6Uo2CUmJv7oOoiIiIjoP/qh19gRERERkeakqMeuZ8+e39x20qRJKXkJIiIiIvpOKQp258+fx/nz5/H+/Xvkzp0bAPDPP/9AX18fhQsXVtqpVKofUyURERERfVWKgl316tVhaWmJxYsXI126dAA+TFrcsmVLlClTBr169fqhRRIRERHR16XoGruJEydi9OjRSqgDgHTp0mHkyJEcFUtERESkISkKdpGRkXjx4kWy5S9evEBUVNR/LoqIiIiIvl+Kgl3t2rXRsmVLbNiwAY8fP8bjx4+xfv16tG7dGnXq1PnRNRIRERHRN0jRNXaBgYHo3bs3GjdujPfv33/YkIEBWrdujfHjx//QAomIiIjo26Qo2JmZmWHWrFkYP3487ty5AwDInj07zM3Nf2hxRERERPTt/tMExU+fPsXTp0+RM2dOmJubQ0R+VF1ERERE9J1SFOxevXqF8uXLI1euXKhatSqePn0KAGjdujWnOiEiIiLSkBQFux49esDQ0BAPHz6EmZmZsrxBgwbYtWvXDyuOiIiIiL5dioLdnj17MHbsWDg6Oqotz5kzJx48ePDN2xk9ejSKFSsGS0tL2NnZoVatWrh586Zam3fv3iEgIADp06eHhYUF6tati9DQULU2Dx8+hJ+fH8zMzGBnZ4c+ffogPj5erc3BgwdRuHBhGBsbI0eOHFi0aNH3vWkiIiKiNC5FwS4mJkatpy5JWFgYjI2Nv3k7hw4dQkBAAE6cOIG9e/fi/fv3qFSpEmJiYpQ2PXr0wNatW7F27VocOnQIISEhalOqJCQkwM/PD3FxcTh+/DgWL16MRYsWYciQIUqbe/fuwc/PD97e3rhw4QK6d++ONm3aYPfu3Sl5+0RERERpkkpSMOKhatWqKFKkCEaMGAFLS0tcunQJzs7OaNiwIRITE7Fu3boUFfPixQvY2dnh0KFDKFu2LCIiIpAxY0asWLEC9erVAwDcuHEDefPmRXBwMEqWLImdO3eiWrVqCAkJgb29PYAP07H069cPL168gJGREfr164ft27fjypUryms1bNgQ4eHh33TqODIyEtbW1oiIiICVlVWK3tu3yNZ/+0/bdlpzf4yfpksgIiL6JXxPDklRj924ceMwd+5cVKlSBXFxcejbty/y58+Pw4cPY+zYsSkqGgAiIiIAALa2tgCAs2fP4v3796hQoYLSJk+ePMiaNSuCg4MBAMHBwShQoIAS6gDA19cXkZGRuHr1qtLm420ktUnaxqdiY2MRGRmp9kNERESU1qUo2OXPnx///PMPPD09UbNmTcTExKBOnTo4f/48smfPnqJCEhMT0b17d5QuXRr58+cHADx79gxGRkawsbFRa2tvb49nz54pbT4OdUnrk9b9W5vIyEi8ffs2WS2jR4+GtbW18uPk5JSi90RERESUmr57guL379+jcuXKCAwMxKBBg35YIQEBAbhy5QqOHj36w7aZUgMGDEDPnj2Vx5GRkQx3RERElOZ9d7AzNDTEpUuXfmgRnTt3xrZt23D48GG1kbYODg6Ii4tDeHi4Wq9daGgoHBwclDanTp1S217SqNmP23w6kjY0NBRWVlYwNTVNVo+xsfF3DQIhIiIiSgtSdCq2adOmmD9//n9+cRFB586dsXHjRhw4cAAuLi5q64sUKQJDQ0Ps379fWXbz5k08fPgQHh4eAAAPDw9cvnwZz58/V9rs3bsXVlZWcHNzU9p8vI2kNknbICIiItIGKbpXbHx8PBYsWIB9+/ahSJEiye4RO2nSpG/aTkBAAFasWIHNmzfD0tJSuSbO2toapqamsLa2RuvWrdGzZ0/Y2trCysoKXbp0gYeHB0qWLAkAqFSpEtzc3NCsWTOMGzcOz549w+DBgxEQEKD0unXo0AEzZsxA37590apVKxw4cABr1qzB9u26MwqViIiItN93Bbu7d+8iW7ZsuHLlCgoXLgwA+Oeff9TaqFSqb97e7NmzAQBeXl5qyxcuXIgWLVoAACZPngw9PT3UrVsXsbGx8PX1xaxZs5S2+vr62LZtGzp27AgPDw+Ym5vD398ff/75p9LGxcUF27dvR48ePTB16lQ4Ojri77//hq+v7/e8fSIiIqI07bvmsdPX18fTp09hZ2cH4MMtxKZNm5ZsxKm24Tx2Px7nsSMiIvo2P20eu08z4M6dO9XuEkFEREREmpOiwRNJUnDTCiIiIiL6Sb4r2KlUqmTX0H3PNXVERERE9PN81+AJEUGLFi2U0abv3r1Dhw4dko2K3bBhw4+rkIiIiIi+yXcFO39/f7XHTZs2/aHFEBEREVHKfVewW7hw4c+qg4iIiIj+o/80eIKIiIiI0g4GOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEgx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIiIiLcFgR0RERKQlGOyIiIiItASDHREREZGWYLAjIiIi0hIMdkRERERagsGOiIiISEsw2BERERFpCQY7IiIiIi3BYEdERESkJRjsiIiIiLQEgx0RERGRlmCwIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEgx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIiIiLcFgR0RERKQlGOyIiIiItASDHREREZGWYLAjIiIi0hIMdkRERERagsGOiIiISEsw2BERERFpCQNNF0D0PbL1367pElLF/TF+mi6BiIh+QeyxIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQmNBrvDhw+jevXqyJw5M1QqFTZt2qS2XkQwZMgQZMqUCaampqhQoQJu3bql1iYsLAxNmjSBlZUVbGxs0Lp1a0RHR6u1uXTpEsqUKQMTExM4OTlh3LhxP/utEREREaU6jQa7mJgYFCpUCDNnzvzs+nHjxmHatGkIDAzEyZMnYW5uDl9fX7x7905p06RJE1y9ehV79+7Ftm3bcPjwYbRr105ZHxkZiUqVKsHZ2Rlnz57F+PHjMWzYMMydO/envz8iIiKi1KTR6U6qVKmCKlWqfHadiGDKlCkYPHgwatasCQBYsmQJ7O3tsWnTJjRs2BDXr1/Hrl27cPr0aRQtWhQAMH36dFStWhUTJkxA5syZsXz5csTFxWHBggUwMjJCvnz5cOHCBUyaNEktABIRERH96tLsNXb37t3Ds2fPUKFCBWWZtbU1SpQogeDgYABAcHAwbGxslFAHABUqVICenh5OnjyptClbtiyMjIyUNr6+vrh58yZev3792deOjY1FZGSk2g8RERFRWpdmg92zZ88AAPb29mrL7e3tlXXPnj2DnZ2d2noDAwPY2tqqtfncNj5+jU+NHj0a1tbWyo+Tk9N/f0NEREREP1maDXaaNGDAAERERCg/jx490nRJRERERF+VZoOdg4MDACA0NFRteWhoqLLOwcEBz58/V1sfHx+PsLAwtTaf28bHr/EpY2NjWFlZqf0QERERpXVpNti5uLjAwcEB+/fvV5ZFRkbi5MmT8PDwAAB4eHggPDwcZ8+eVdocOHAAiYmJKFGihNLm8OHDeP/+vdJm7969yJ07N9KlS5dK74aIiIjo59NosIuOjsaFCxdw4cIFAB8GTFy4cAEPHz6ESqVC9+7dMXLkSGzZsgWXL19G8+bNkTlzZtSqVQsAkDdvXlSuXBlt27bFqVOncOzYMXTu3BkNGzZE5syZAQCNGzeGkZERWrdujatXr2L16tWYOnUqevbsqaF3TURERPRzaHS6kzNnzsDb21t5nBS2/P39sWjRIvTt2xcxMTFo164dwsPD4enpiV27dsHExER5zvLly9G5c2eUL18eenp6qFu3LqZNm6ast7a2xp49exAQEIAiRYogQ4YMGDJkCKc6Ia2Vrf92TZeQKu6P8dN0CUREaY5Gg52XlxdE5IvrVSoV/vzzT/z5559fbGNra4sVK1b86+sULFgQR44cSXGdRERERL+CNHuNHRERERF9HwY7IiIiIi3BYEdERESkJRjsiIiIiLQEgx0RERGRlmCwIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEgx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIiIiLcFgR0RERKQlGOyIiIiItASDHREREZGWYLAjIiIi0hIMdkRERERagsGOiIiISEsw2BERERFpCQY7IiIiIi3BYEdERESkJQw0XQARUWrL1n+7pktIFffH+Gm6BCJKZeyxIyIiItISDHZEREREWoLBjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCU43QkRESXDKWGIfk3ssSMiIiLSEgx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIiIiLcFRsURERCnAkcOUFrHHjoiIiEhLMNgRERERaQkGOyIiIiItwWBHREREpCUY7IiIiIi0BIMdERERkZZgsCMiIiLSEpzHjoiIiH4KzvWX+thjR0RERKQlGOyIiIiItASDHREREZGW0KlgN3PmTGTLlg0mJiYoUaIETp06pemSiIiIiH4YnQl2q1evRs+ePTF06FCcO3cOhQoVgq+vL54/f67p0oiIiIh+CJ0JdpMmTULbtm3RsmVLuLm5ITAwEGZmZliwYIGmSyMiIiL6IXRiupO4uDicPXsWAwYMUJbp6emhQoUKCA4OTtY+NjYWsbGxyuOIiAgAQGRk5E+tMzH2zU/dflqS0n2pK/vovxxr3Edfx330ddxHX8d99HXcRz92+yLy9caiA548eSIA5Pjx42rL+/TpI8WLF0/WfujQoQKAP/zhD3/4wx/+8CfN/Dx69OirmUcneuy+14ABA9CzZ0/lcWJiIsLCwpA+fXqoVCoNVvZjRUZGwsnJCY8ePYKVlZWmy0mTuI++jvvo67iPvo776Ou4j75OW/eRiCAqKgqZM2f+aludCHYZMmSAvr4+QkND1ZaHhobCwcEhWXtjY2MYGxurLbOxsfmZJWqUlZWVVv0C/AzcR1/HffR13Edfx330ddxHX6eN+8ja2vqb2unE4AkjIyMUKVIE+/fvV5YlJiZi//798PDw0GBlRERERD+OTvTYAUDPnj3h7++PokWLonjx4pgyZQpiYmLQsmVLTZdGRERE9EPoTLBr0KABXrx4gSFDhuDZs2dwd3fHrl27YG9vr+nSNMbY2BhDhw5NdtqZ/of76Ou4j76O++jruI++jvvo67iPAJXIt4ydJSIiIqK0TieusSMiIiLSBQx2RERERFqCwY6IiIhISzDYEREREWkJBjsiIvqpOEaPKPUw2BHRF/ED+cvu37+v6RLSvAsXLiAxMREqlYrHEqUYj53vw2BHRF+kUqmwePFizJo1S9OlpClbt26Fq6sr9u7dq+lS0qxNmzahadOmmDt3LkSE4e4LEhMTky3jfvqfpC8GAPDmzRsNV/NrYLAj+n/8Y/o/Sfvizp07CAgIQFRUlIYrSlv8/PzQqFEjNGrUCPv27dN0OWlSqVKlkCdPHqxYsQJz585lz91nJCYmQk/vw8fws2fP8Pz5cwDgfvpI0v4ZO3YsqlSpgvr16yMwMFDDVaVtnKBYC61cuRIvX76EtbU1mjdvruly0pSknoNr167h9evXiIiIgJeXF8zMzDRdWppz6tQpHDx4EC9evMD48eM1XU6akXQMAUDr1q2xYcMGbNiwAd7e3hquLO14//49DA0N8ebNG7Rp0wbPnz9H/fr10bZtWyW0JO1DAv744w+sXr0a+vr6KF68OBYvXqzpkjTu49A7ZcoUjBgxAp06dcKlS5fwzz//wM/PDxMmTNBwlWmUkFbp37+/WFhYSIkSJURPT08aNWokT58+1XRZGpOYmCgiIvHx8cr/r1+/XrJkySKlSpUSW1tbqVKliqxevVqTZaY5L1++lFq1aomZmZk0bNhQRD7sy6R9qMsSEhJEROTYsWOybt060dfXFycnJ9m3b5+GK0s7ko6Tmzdvyrhx4yRz5sySO3duWbBggbL/dPlYStoHIiKLFi0SBwcHmT9/vkyYMEEyZ84sPj4+EhkZqcEK044jR47I6NGjZdeuXSIi8uLFC5k8ebJky5ZNevbsqeHq0iaeitUijx49wokTJ3DkyBHs27cPp06dwu7du9GhQwc8ffpU0+VpRHBwMABAX18fKpUKwcHBaNu2LYYPH45jx45hy5Yt2LVrF8LCwjRcadqSPn16tG3bFl5eXti+fTvOnj3LHpb/p6enh82bN6NixYq4fPky2rZtC2dnZ9SrV4/X3P0/lUqFzZs3o0CBAggPD0fjxo2hUqkwdepU/P333zp/zV1ST9SOHTugr6+PCRMmoFWrVujVqxd27tyJe/fuoWbNmjp/CcTBgwfRoEEDTJ06Fba2tgCADBkyoFmzZujWrRs2btyIPn36aLjKNEjTyZJ+jDFjxkiFChWkbt26at/0Ll26JLa2tlKrVi0JCQnRYIWpr0ePHmJubi4vXryQ+Ph4ERGZOnWqVK9eXURE/vnnH8mePbu0bdtWec7Lly81UqumJfWevH37Vu34OXHihFSoUEEKFy4s586dU2urq6KioqRYsWLSr18/ZVlYWJg0btxYbGxsdL7nLjExUV6/fi0lSpSQoUOHKstfvXoltWrVkgIFCsiCBQuU40hXj6c7d+6Inp6eqFQqmT17ttq6S5cuiaurq1SoUEEiIiI0VKHm3bp1S/r06SNWVlYyZMgQtXWvXr2SqVOniqmpqUybNk1DFaZNDHZaYtOmTWJpaSlZs2aVJ0+eiMj/uvuvXLkidnZ24unpKS9evNBkmanm6NGjYm9vr4SRpFA7YMAA6dy5s4iIZMmSRdq1a6d8sKxdu1bmzJkjcXFxmilaQ5Le/7Zt26Rq1aqSP39++f3332Xr1q2SmJgohw4dkurVq0vRokXl/Pnzas/RRREREZI7d26ZN2+eiPzv9+zly5dSrFgxyZ49u3LaSFfFx8dLsWLFZMSIEcpjkQ8BOEeOHFKwYEGZMmWK2ilJXRMXFye7du0SFxcX5cvmxy5fviympqbSpUsXDVSX+r50LDx8+FD69u0rrq6uMn78eLV1L168kDVr1ijHF33AYPeLu379usTExIiIyN69e8XMzEzatm0rb9++FZH/fQCfP39eqlSpojN/SK9cuSK5c+eWzZs3y+bNm6Vy5cry7t072b59u5ibm4uVlZV0795dLaC0a9dOmjVrpuxPXbJ161YxMzOTP/74Qw4ePCilS5cWFxcXOX36tIiI7Nu3T2rVqiXZs2eXixcvarhazatataqUL18+2fViTZs2FT09PXF2dtbJ40jkw76Ijo6W8uXLq12fmfTh26ZNG0mfPr3UqlVLwsLCNFlqqvn07+7Hf3d27twpNjY20qhRo2Tr79y5oxOh5eP9s2LFChkzZoz069dPrl27JiIiT58+lX79+kmuXLlkwoQJn92GLuynb8Vg9wtbv369WFhYyOrVq+XNmzciIrJ9+3YxMTGRDh06KOHu0z8q2hzukt7bo0ePpGXLllK4cGFRqVSycuVKEfnQY9C1a1exs7OTrVu3isiHnpaBAwdKxowZlT8kuiIxMVEiIiKkQoUKMmrUKBERiYmJEUdHx2Q9Bbt375aGDRvK3bt3NVGqRiR9wL569UpCQ0OVHu9t27aJu7u7dO/eXa19ly5dZNeuXfLs2bNUr1VTkvbR8+fPJTw8XEJDQ0Xkw+ASQ0ND+eOPP9Tad+nSRWbPnq2cWdB2H/+9nT59urRr1058fHxk6dKlcvv2bRH5EO6sra2lcePGn92GroSWnj17ip2dnZQrV04KFCggFhYWMmPGDImNjZUnT55I//79xc3NTe0UPyXHYPeLq1mzpri6usratWvVwp2pqal06tRJWaYLJkyYIJs3b1Y+aMaNGyd6enri5uYmmzdvVtqdOHFCWrRoIQYGBpI/f34pVqyYODs7K6dtdU1cXJyULl1arl+/Lo8ePZLMmTNLu3btlPXbt29XPoR1qRcq6TjatGmTeHt7i7Ozs1SvXl3+/PNPEREZP368FClSREqXLi3jxo2Tpk2birW1tdy6dUuTZaeqpH20ZcsWKVGihLi7u4uzs7PMnj1boqKiZOnSpWJgYCC1a9eWnj17Sps2bcTc3FwePHig4cpTX9++fSV9+vTSuXNnqV69uri4uEjDhg2Vvzs7d+6UDBkySOXKlTVcqWZs2bJF7Ozs5MKFC8rlMP369ZOMGTPKkiVLRORDD2bHjh2lUaNGOn05yNcw2GmBunXrirOzs1q427Fjh6hUqmTXJGir9+/fS40aNeTGjRvKshEjRsjw4cPl999/F29vb1m+fLmy7tWrV7Jv3z4ZP368rF+/Xic/aEQ+9Ca8e/dOChYsKH379pUcOXJIu3btlD+sT58+lZo1a8qqVas0XKlm7NixQ0xMTGTKlCly4sQJGTRokKhUKjl8+LC8efNGtm3bJnXr1pUSJUpIhQoV5MKFC5ouOdXt3LlTTE1NZcqUKXLz5k0ZMGCAso9ERE6ePCk1a9YUHx8fqVSpkk7uo+DgYHFxcZHg4GBl2erVq6VChQrSokULefXqlcTHx8umTZukcuXKWn1W5UsWLlwov/32m0RGRsr79++V5V26dBF7e3tlEMmzZ890fuDN1zDY/WJmzpwpQUFByX7xa9euLfb29rJ27VqlV+X48eNqvyDa6tNf8iNHjsi2bduUx+fOnVM+WD4Od7omPj5eOW6ioqJE5H/7bOHChWJtbS0eHh5qzxk0aJDkzZtX7t+/n7rFpgHv3r2T5s2bKwMAnj9//tlT1CIib968kXfv3qV2iRqXkJAg/v7+0r9/fxERuX//vuTMmVPatGkjIv87vpK+cOrSGYSPHTt2TOzt7ZNdn7pkyRJJnz69XLlyRUTUg4quhbuZM2dKunTplC+VScfK3bt3JWPGjHLo0CG19gx1X8Zg94txd3cXR0dHOXbsWLJffHd3dylUqJAsXrxY7UNGF8KdyP8m0C1fvrzkypVL1q9fr+yH8+fPS61atcTHx0fnJiMOCgpSu55p69atUrVqVSlTpozMmzdPnjx5IjExMdKjRw+xtbWVrl27ysiRI6V169ZiZWWljITVNQkJCVK6dGlZunSphISESJYsWdSmxlm9erXs3r1bgxVqXmxsrPz222+yadMmiY6OVk7jJ33oTp8+Xa5fv66014UP44+/aCb9/+HDh8XOzk4JJx+PvHd0dJSZM2emfqEa8qXAGhERIXnz5pVatWqptbl69apkz55dGchFX8cJin8RK1aswMqVK3H+/HnkypULTZo0QXBwMBISEgB8uM1R7ty58eDBA2zduhXGxsbKcw0MDDRVdqpSqVRQqVTYsmULXF1dMWrUKGzbtg2xsbFwd3fHsGHDkC5dOowdOxYbNmzQdLmp4siRI2jZsiWmTZuGqKgoXLx4Eb///jvy588Pa2trTJ8+HcOGDUN4eDiGDx+OsWPH4tChQ9i3bx/i4+MRHBwMd3d3Tb+NVCciiI+PR+7cuXHmzBmULl0aVatWxdy5cwEAr1+/xu7du3Hnzh3ld1AXGRkZoWzZsli8eDFy586NWrVqYcaMGVCpVHj37h2CgoKwYcMG5Ub32j7J9cc3rP/4/8uUKYOiRYvC398fDx8+hKGhIQAgNDQUVlZWyJQpk8ZqTk0iokzOvHTpUgwYMADTpk3DgQMHYGVlhVGjRuHOnTuoVKkSzp07h0OHDqFfv36ws7ND4cKFNVz9L0TDwZK+wZUrV+S3336TggULyvbt20VExMvLS7JlyyaHDx9WTr22bNlSrl27plNd+EnfiENDQyUmJkaZXDcmJkbKly8vRYsWlXXr1ik9d2fOnJEmTZro1KnFwYMHS5EiRWTIkCEybNgwtesuZ82aJSVLlpRWrVrJvXv3ROR/vQm6NJ9f0nEUFhYm8fHxyijE1atXi0qlkqJFi6pN3Dxw4EBxdXWVO3fuaKReTUjaR+Hh4WrzYa5evVpy5swpRYsWVaYvSUxMlAEDBujUPvq4N3LSpElSo0YNadKkiXIq/9WrV1K2bFmxt7eXiRMnSmBgoFSuXFnc3d11YtTrx/tn4MCBYmFhIZUrV5ZcuXJJrly5lEFJe/fulRIlSoiVlZXkyZNHvLy8lL9FurCffgQGuzSud+/eUrduXeW+pq6urrJp0yYREalYsaK4uLhIpUqVxMPDQ/Lmzasc+LoU7jZu3ChFihSR3LlzS5cuXeTo0aMi8r9wV6RIEdmwYYMS7nTlWqiPg9mwYcOkWLFikjNnzmSztCeFu7Zt28rVq1eV5bpw2uxjmzZtEnd3dylXrpy0b99euQ5xxowZolKppHHjxtK0aVNp2rSp2NjY6OQo6g0bNkjx4sUlZ86c0qFDB+UU/4gRI6RgwYJSqlQp6dChg9SuXVtsbW11Zh99/LsycuRIsbS0lM6dOyt3I6latapER0dLYmKitGnTRooVKyaFCxeWevXq6VxoOXv2rPj4+MiRI0dEROTJkycyYcIEcXR0lLFjxyrtLl68KPfu3VM+y3TlkqIfgcEuDVu4cKHY2NjI2bNnJSwsTJ4+fSqVKlWSokWLypYtW0Tkw63E2rdvL506dVIOfF0KdZcvXxYbGxsZN26c9OvXTypVqiRlypSRvXv3isiHcOfr6yvZs2dX9pk2B5bP/dsnjfgdO3asMjFs0lxjSebMmSN58+aVzp0761RPXZKLFy9K+vTp5c8//5TOnTtLyZIl1Xrp1q5dK23bthU/Pz8ZOHCg2nVj2uzj35UTJ05IhgwZpH///jJx4kRJnz69VKhQQdkXGzZskE6dOkm1atWkf//+aiPUdUVwcLC0adNG9uzZoyw7e/asODk5Sc2aNZVlYWFhEhkZqexfXQktM2bMkOrVq4uPj4/ardKeP38uf/zxh3h4eChnU3R5IMl/xWCXhg0aNEg8PT0lISFBObAfP34sJUqUkGzZssm6deuUtrr4reby5cvy119/qd1DcP/+/VK7dm0pVaqUEu6io6OlZs2aOjOx7u3bt6V+/foi8mESa2dnZ2Ui1JEjR0qBAgVk4MCBySbRXbBggXI6Vhd8/MFx+vRpGTx4sIh8+B06cOCAFC1aVH777TflA+jTu7los0/D/c2bN2XBggXKaUURkXv37omLi4uUL19eLl++nNolpjmbNm2SQoUKibOzc7JRrocOHZL06dPLhg0b1JZ/+v/absmSJWJjYyOWlpZKj12SpAmtjx07pqHqtAcHT6RBIgIAMDY2xrt37xAXFwc9PT28f/8eWbJkwejRo/H8+XPMnDkTq1evBgDlglRtHyiRtG/u3buH/v37Y/LkyYiMjFTW+/j4oHPnzrC3t8eIESOwY8cOmJubY9OmTXBxcdFU2akqNDQU27dvR8mSJVGvXj2MHDkS2bNnBwAMGjQItWvXxs6dOzF16lSEhoYqz2vZsiWyZcumoapTl4hApVLh8OHDmDVrFmbNmoWQkBAAH36HypYti/Hjx0NfXx8VKlRAZGQkTExMAGj/AIDRo0dj7dq1EBEkJCQgIiIC+fPnR+vWrfHy5UulXbZs2bBv3z7cuXMHffv2RXBwsAar1jwHBwfkyJEDISEh2LdvH4D/HSs5c+aElZUVwsPD1ZZ/+v/aJOlv9ceaNWuGpUuXwszMDPPmzcPly5eVdVmyZIGLiwvi4uJSs0ztpNFYSf/q0qVLoq+vL8OGDVNbvmvXLqlbt674+PhIhQoVJDY2VkMVasa2bdtk9uzZsnTpUilevLjkypVLLl26pNYmKChIfHx8xNfXV2JiYnTqW7GIyJ9//ikqlUp+++03ZdnHx8mQIUOkePHi0rVrV3n+/LkmStS4zZs3i7Gxsbi5uYmLi4s4OzvL06dPlfUJCQly6NAhyZEjh3h5eenMMZQ0CEvkf9d9nTlzRjJkyCClSpVSen+T9sfdu3fFyspK6tatqzPXr37p1OClS5ekXr164u7uLosXL1aWv337VnLnzi2zZs1KrRI16uP98/Tp02R3Y1m9erVkyZJF/Pz8ZOHChbJ3717x8/OT/Pnz68y1hj8Tg10at3DhQjE0NJQ+ffrImTNn5M6dO+Ln5yd//fWXXLt2TVQqlXLKURtt375dmdQz6YOkTp06ygCAdevWSbly5aR27drJJv88fPiwPHr0KHUL1qCPg8fq1atl4MCB4uTkJJUqVVKWf/zBO2DAACldurROBruoqChp3769LFq0SKKjo+XkyZNStGhRyZs3r7x8+VJpl5CQIEePHtWJ0/ifBtdDhw7JggUL5PXr1yLyIdyZmppKgwYNlOs2k55z7949nbmV2seh5ezZs3LgwAG5evWqchnM6dOnpUGDBuLk5CRdu3aVcePGSc2aNSVnzpw6canMx/tn6NChUqRIEbGwsJDatWvLmjVrlPVr1qwROzs7UalU0qBBA+nUqZMS6hju/hsGu1/AunXrxM7OThwdHSVLlizy22+/ydu3b5VZ3j8NNNri2bNn4uLiIi1btlQbrVmmTBm1KTtWrVolPj4+UrNmzWQ9d7oi6QP24sWLsnPnTtmyZYtERETIoUOHJHv27FKxYkW19kmjFV+9epXqtWrauXPnxNHRUcqUKaN2Pc/169elaNGikidPHp3cL59q0KCBODo6yuLFiyU8PFxERE6dOvXFcKcLPn6vAwYMkLx584qtra14eXlJQECA0it+9uxZqVu3rpiZmYmPj4/MnTtXea6uhJahQ4eKvb29rF69Wm7evClFihSRokWLSmBgoLIPtmzZIpkyZZI+ffoovcQcKPHfMdj9Ih4/fizBwcFy+PBh5cDv37+/5MmTR+30kbY5e/asFCtWTNq0aaNcoF2pUiVZtmyZWrvly5eLj4+PeHt76+yF3GvXrhVbW1txd3cXlUolnp6eMmXKFOV0YqVKleTevXsyaNAgyZkzZ7KRsdrs44vY79+/L5UrVxaVSqWMlE5y/fp1KVmypNjb2ytzsumyxo0bS968eWXhwoVq4c7KykqqVKmiUz3iHxs1apTY29tLUFCQvH37Vtq3by/m5ubSuHFjpVf83Llz0rBhQ6lSpYqsWLFCea4uBOHjx49LoUKF5MCBAyLy4eyJqamp/Pbbb5I/f375+++/lc+xFStWiKOjowQEBOjsF/MfjcHuF3TlyhVp1qyZpE+fXidu93Tu3DkpXLiwtGrVSi5fviwNGjSQ/fv3J2s3c+ZMadKkiU5+2Jw7d04yZMggf//9tzI1TvPmzcXb21umT58uwcHB4uzsLC4uLpI5c2advD3PgQMHRKVSycaNGyUsLEy8vLzE1dVV6SlIcuXKFfH29lauJdMFSWEjJCREwsLClDn8RETq16+fLNwdO3ZMMmXKJI8fP9ZIvZp0/fp18fDwkB07doiIyJ49e8Tc3FyaNGkiuXLlkubNmys9dydPnpQGDRpIuXLlZOHChRqsOnU9ePBA5s2bJ/Hx8bJv3z5Jnz69LFiwQGJjY8XV1VUKFy4s48ePV8LdunXrxNTUVHr16qWT0y39aAx2v5j379/LuXPnpFevXsqQel1w7tw5KVq0qPj7+4uFhYW4urpKhQoVpGLFilKuXDmpUKGCdOzYUat7L//N8uXLxc3NTSIiIpQP6adPn0rjxo2lXLlykpCQINHR0bJ37161+8bqijt37siwYcNk8uTJyrLXr19L6dKlJWfOnMnCnS5+uKxfv17y5csnWbNmlc6dO6t9eUoKd4sXL1auuUua/kUXLV68WJ49eyZHjhyRTJkyydy5c0Xkw37S19cXX19fJdxduHBBqlatKlWqVFGbu01bfO7UaVxcnLx+/VpiY2OlTp06MnDgQOX0q5+fnzg5OUm3bt3UTktv2rRJ/vnnn1SrW5upRD4zJpnSvPfv3yv3G9QV586dQ4sWLaCnp4d8+fLB19cX4eHhCAsLg6GhIWrXrg03NzdNl6kRq1atwuDBg3H06FE4ODggPj4eBgYGuH//PlxdXbFz5074+vpqukyNuHr1Kjp37oz79+9j/PjxqFevnrJ/wsPDUa1aNbx+/RqrV69G/vz5NV2uRty6dQsVKlRA7969ERUVhf3790NPTw9dunRBjRo1AABNmjTB/v37MWnSJDRq1AiA9k7VkSQxMVGZSupzunfvjrdv32L69OkwMjLC0KFDcezYMeTPnx8TJkxQpp+6dOkS0qdPjyxZsqRW6ani4/1z/PhxREZGwtraGoUKFYKZmRkSExPh6ekJHx8fjBw5EiICf39/NGnSBBUrVoSenh4SEhKgr6+v4XeiXbR70jMtpmuhDgAKFy6MJUuWoF27djAzM0PZsmV1Zt61rylWrBgeP36MmTNnYsSIEcoHikqlQr58+WBjY6PZAjXIzMwMTk5OOHfuHA4fPox69erBwMAACQkJsLGxwfbt21G6dGm0aNECx48fh5GRkaZLThXy/3P5AR8+oCtVqoQuXboAADw9PTFp0iRMmTIFAFCjRg0sX74crVq1QokSJbQ+0AHqoWXz5s24e/cuzM3NUbBgQZQsWRIA8OjRI4SHh8PIyAgigmvXrqFevXro0KEDAChfIAoWLKix9/EzJe2fAQMGYOXKlciQIQMeP34MLy8vdO7cGcWKFYODgwOOHz+Onj174uLFi3j58iUWLVoEPT09JCYmMtT9DBrtLyRKgXPnzkmxYsWkQYMGyU6h6bJly5aJkZGR9O/fX27duiWhoaEyaNAgcXJy0snTrx97+PChtG/fXtzc3NROxyadCgoPD9fJu27s27dPevfuLT169JAmTZqotTl06JDUrFlTKlasKGvXrtVEmWlCnz59JEuWLFKrVi3x8vKS3LlzS2BgoIh8uBXfb7/9JqVLlxYPDw9xc3NTjildGCQh8uHaZgcHBzl+/LiIfBgNa2pqqpzKv3fvntSvX18qVaokderUUS5z4OjXn4fBjn5Jp06dknLlyklISIimS0kzEhMTZeXKlWJpaSlZs2aVXLlyiaOjo5w9e1bTpaWajyfNDQ4Olhs3bij3e71z5460a9dOSpYsKVOmTFGeoyvTT3xq+/btYmBgIGXLlpUsWbKIoaGh2m0KRUSOHDki3t7eUqNGDYmKitKZsJIkaSLd4OBgEREJDAwUExMTWbVqlYh8+ELw999/S5s2baRLly7KPHW6dEy1atVK/vjjDxH5MAjC2tpaZs+eLSIfbuco8uGau/j4eJ27N66m8Bo7+mW9e/dOuc0T/c+DBw9w48YNJCQkoGDBgnB0dNR0SalC/v/U4saNGzFgwADExMTAwcEBbm5uGD16NDJnzoy7d+9i7NixuHbtGvz8/NC/f39Nl60RISEh2L59OxITE9G+fXucOXMG06dPx9mzZzFixAjUrl1baRscHAwnJyedOY4+9ueff+LGjRtYsWIF1q9fj5YtW2L8+PFo3749IiMj8fLlS7i6uqo9J+n0qzb69JrD2NhY1KxZEx06dECWLFng4+OD8ePHo0OHDoiPj8eMGTPg6uqqXKcJqF8CQD8H7xVLvyyGus9zdnaGr68vqlatqlMfxiqVCnv27EHLli0REBCAf/75B40aNcLGjRvRsmVLPHr0CK6urujXrx+yZs2KAwcO4PXr15ouO9XduXMHefPmVcIuABQtWhTdu3dH8eLFMWjQIGzatElp7+HhoRPH0cd9HO/fvwcA6Ovrw9XVFXv37kWLFi0wbtw4tG/fHiKC7du3Y926dYiIiFDbjraGOuB/19TNnTsXb9++hbGxMYoVK4YWLVrA09MTc+bMUa4vjI6OxtatW3H9+nW1bTDUpQJNdhcSEf0oYWFhUr16dfnrr79EROT58+fi5OQkFStWlCJFikjFihWVU/f37t3Tqalxkk6BBQUFyYIFC6R3795iZmaW7N6l58+flzZt2oiDg4Ns3bpVE6Vq3NSpU5XbNK5YsUJUKpXo6+vLokWLlDZRUVFSsWJF6dmzp6bK1JjIyEjJlSuXNG3aVEREHj16JDVr1pRs2bLJw4cPJT4+XkJCQqRy5cpSvHhxnTotnVawx46ItEK6dOnQsmVLlC9fHq9evYK3tzf8/PywZ88e+Pn5Yd++fahZsyYePXqEbNmywcHBQdMlpxqVSoWDBw+iVq1asLW1RYcOHdCyZUv06NED69atU9q5u7ujffv2qFevHvLmzavBijVn9+7d6NixIwCgUaNGGDx4MFQqFczNzXHt2jVcuXIFdevWxcuXLzF27FgNV/vzJSYmqj02MzPDgAED8PTpU+zfvx+Ojo5o27YtcubMidy5c8Pd3R3VqlVDWFgYjh49Cn19fSQkJGioet3Ea+yI6Jck/3Ktzvz587F69WosW7YMdnZ2WL16NSZPngxXV1eMHj0azs7OqVytZj19+hTTp0+HhYUFBg4cCAB4+PAhJkyYgMWLF2P+/PmoV6+e0j42NhbGxsaaKjfVfG6eupCQENSuXRs+Pj4YPXo0oqKiMGTIEMyfPx+mpqZwcnKClZUVdu/eDUNDQ52Zhy0wMBCFChWCh4cHIiIiUK9ePVhbWytfDF69eoW9e/ciMjIS9vb2qFatGvT19bX6msO0inubiH45SaHuzJkzuH37NqysrFC1alVlfUhICG7evKnMSXfu3DmULVsWgwcPhpWVlabK1ojr16+jWrVqSEhIQL9+/ZTlWbNmRa9evQAAHTp0QGxsLJo0aQIAOhHqgP9dMzZmzBiYmprCz88POXLkQP369bFjxw5cunQJBQsWxOTJk+Hv74+YmBhlLjs9PT2dCS3BwcHo1KkTbGxsMHToUFSsWBELFy5Evnz5MHXqVHTr1g3p06dHw4YN1Z6XkJCgE/snrWGPHRH9kjZt2oQGDRogb968uHTpEpo1a4Z+/frBzc0Ne/fuxR9//AEAcHJywo4dO3DmzBmdPb3Ys2dPTJs2DY0bN8akSZOQIUMGZd3Dhw8xbNgw7N+/H1euXIGFhYVOXeB+//59eHp6IiIiAg0bNkThwoXRrl07FClSBCVLlkRgYOBnn/e1u1L8yj733ho3bowdO3agQYMGePHiBdzd3WFra4vVq1dj+vTpcHd310yxlIx2HpVEpJWSvoe+ePECs2bNQmBgII4ePYpjx45h586dGDp0KG7fvo0KFSqgS5cucHNzg6GhIU6ePKkzoe5z39UnTZqEHj16YP/+/Vi2bBnCwsKUdVmzZsXw4cNx6tQpWFpaan2o+3T/ZMuWDR07doSenh7Kli2Lv//+GwEBAejUqRP+/vtvbNy48bPb0dZQB/zvvZ08eRL3798HAAwfPhzVq1dHzpw50bFjR6xZswYTJkzArVu3sHnzZsTHx2uwYvoYe+yI6Jeye/durF27FlFRUZg6daoyCOLMmTPw8/ND6dKlMXHiRLi4uADQrfsqJ52iPnz4MDZv3oy3b9/C0dFRua6uZ8+e2LhxI3r06IGmTZvC1tZWwxVrzpYtWxAfH486deoAAHx9fZEjRw6MHTsWzZo1Q2JiInbv3g13d3esXbsWTk5OGq449YgILl68iBIlSqBOnTqoVq0amjRpgj/++ANv3rzBxIkTERcXh4EDB2LBggUoXrw4du3apemyKYlGxuISEX2HpOk6EhIS5NChQ6JSqcTExETOnTuntv706dPi6OgoFStWlKtXr6qt0xUbNmwQa2tr8ff3l/79+4uxsbHUrFlTmXaie/fukjNnThk9erSEhYVpuFrNePDggVSoUEHy5s0rXbt2lfj4eNm7d680a9ZMzpw5IwkJCbJ+/XqpWrWqlCpVSmdvf7V3717p0aOHZM6cWXr06CF79uyRzJkzy7x580Tkwx02zp49q3O3UUvr2GNHRL+EI0eOYOnSpcrpnxIlSqBZs2YYPXo0HBwclN6qEydOwN/fHwcOHECWLFk0XXaqevToESpWrIhOnTqha9euePr0KQoVKoT69etj2rRpyim21q1b4+zZswgKCkK6dOk0XLVmPHjwAOfOnUP79u3h7u6OihUr4syZMyhYsCAGDRoE4MPoYCMjI6hUKq2+pu5T8tGI8/DwcFy4cAEtWrSAh4cHHj9+jKdPn2LNmjUoXLiw8hxdGR38K9CNo5SIfnm3b9/Gtm3bMGDAAOTJkwcHDx7E0qVLMXToUDx79gwqlQoigpIlS+LSpUs6E+pERJlrLCIiAiYmJujatSsePXqEYsWKoU6dOpgxYwb09PQQFBQE4MN0MLt379bZUAd8uENL7dq1cevWLTg4OCA4OBiHDx/GH3/8gfXr1wP4MDpY10IdoH53CBsbG3h5eeHKlSvImjUrDA0NcffuXRw9elTtOQx1aYgmuwuJiL7H4sWLJWfOnNK+fXuJiYmRw4cPi76+vnTs2FGePHmitNPmU0JJpwXj4uKUZUl30bh3754ULFhQ1q1bJ9myZZP27dsrN1y/du2a1KhRQ06ePJn6RWvI146DpFOIb9++lZ07d0qHDh1EpVKJv79/KlSned96ijlpP8XFxcnVq1dlxIgRynFFaQ9PxRJRmnX37l2YmJgo9zQFgEWLFmH06NEoV64cZsyYgVOnTqFs2bLo1q0bJkyYoBM9B3fu3MGyZcvQr18/bN26FQ0aNMCDBw9gZmaGVq1a4cCBA6hSpQrWrFmjPKdfv344duwYNmzYADs7Ow1Wn/oWLVoEMzMz1K9fP9k6+WSi61WrVqFevXpaP//ax72QV69eRbp06WBsbIz06dN/dvLvzy3TlXn8fjX8FyGiNOn169fw9PREy5Yt0blzZ2TKlAkA0KJFCyQkJKB9+/YwMDDAuHHjcPz4cVhbW+tEqAOAQ4cOYfz48bh48SJ27tyJBQsWKKM2AwICcPHiRejp6WHdunWws7PD+vXrsXjxYhw+fFjnQt3r168xa9YseHp6on79+skCStL/Jy1PmmRX20NLUqgbOHAgli9fDpVKhUKFCuGPP/5A0aJFk51+/tw0ONq8f35pGuwtJCL6V0FBQZItWzYZPny42qlWEZEiRYqIpaWl9OrVS6tPvX5JQECAqFQqqVq1qrx69Upt3YYNG8TPz09sbGykYMGC4unpKRcuXNBQpZq3fPlysbS0lIsXL2q6FI37+Hdl7969kiVLFtmzZ49MmzZN6tWrJzlz5pTjx4+LyLefqqW0hadiiShNO3r0KBo1aoR27dqhTZs2yJQpE968eYNevXoha9asaNCgAVxdXTVdZqpJuo9rr169EB4ejj179qBx48bo2LEjsmXLprSLiIhAeHg4jI2NYW5uDktLS80VnUrkk964pJGaoaGhaNasGXx8fNC/f3+dGwzxOcuWLcP169eRIUMG9OjRAwBw+vRpTJw4EWfOnMHSpUvh4eHBffULYrAjojTv6NGjaNasGapVq4ZSpUrh6tWr2LhxI4KDg3Xm3q9JoSUuLk65By4ATJ8+HWPGjEGTJk0QEBAAZ2dnAMCtW7eQM2dOTZWrUTNnzoSrqytKliypjPzt378/Vq9erdxD+NMQqO0+fr8PHjxAkyZNcOHCBQwcOFCZwBr4MNH3hAkTcP78ecyZMwdeXl4aqphSisGOiH4JZ86cQc+ePXH//n1YWlpi6dKlavNoabOkD+WkW4JFRUXBxsYGs2fPhqGhIQIDA/HXX3+hUaNGaNSoEbZu3YoRI0YgLCxM5+79+uTJE3Tv3h1bt25F1apVUaRIEQwaNAjh4eGoWbMmKlSooNxHWBddvHgRhQoVwu7duzFmzBjcvXsXu3btUrvl3tmzZzFgwADY2NioDcChXwODHRH9MqKiohAeHg4TExNkzJhR0+Wkqk2bNqFp06Zo164dnJ2dMXnyZFhaWuLgwYNInz495s2bh3HjxsHY2Bjh4eHYsGEDihcvrumyf7ovnSo8fPgwDh8+jOnTpyN37twoXLgwnj59ClNTUyxatCj1C00Ddu3ahUGDBmHNmjXInj07du7ciSlTpiAqKgoLFixAnjx5lLY3btxArly5eBr2F8RgR0SUBn0cWF6+fInKlSujcePG6NmzJ0JCQlCiRAn4+fkhMDBQec7Jkyfx5s0bZM+eHVmzZtVU6anm4320f/9+vHr1CqampqhYsSJMTEwAfLhzwpQpU3Djxg2l92nVqlWfnfpE23wael++fInixYujQoUKmDt3LgBg+/btmDFjBiIjI7FgwQLkzp37X7dBaR+DHRFRGjJx4kSULl0aJUuWVD5UHz58iAoVKuDcuXOIiopCsWLFUK1aNSXUbdy4EbVr19Zw5ZrTp08fJbQlhZDt27fDzc0NwP9OZW/YsAHz5s1D+vTpMX/+fOV2YdruypUryJgxI+zt7XHo0CHUqVMHEydORIsWLQAAO3fuxIwZM/DPP/9g3759ynWa9GtiDCciSiPevHmDffv2wcfHB2fPnlVCSsaMGZExY0ZlpGK1atUwffp0AMDDhw+xcOFC7N27V5Ola8zChQuxYMECrFu3DidOnFACXcWKFfHw4UMAUG65VqdOHbRs2RLbtm1DSEiIToS6RYsWoWDBgujXrx9OnTqFcuXKoWXLlti9ezdu3rwJAKhSpQratGmDGjVqwNHRUcMV03/FYEdElEaYmZlh4cKFqFGjBry9vXHmzBkAH6btyJ49O/r27Qt3d3cEBgbC0NAQADBr1iw8efIE+fLl02TpqSYppCW5desWKleujGLFiiFTpkxwc3PDypUrkSNHDjRr1kyZ8iTp5FT9+vXh6OiI69eva6L8ny5p/4gIRAQODg7Inj07QkJCUKlSJcybNw958uTB1atXleMLAGrXro2JEydCX18fCQkJmiqffgAGOyKiNCDpA9nBwQF//fUXKlSogGrVquHChQuwsLBAv379kD17dkRFRWHixInYsGEDOnbsiMDAQCxYsEDttmvaSkSUXsz3798D+HBniQsXLihtEhISYGVlhdatWyM0NBQvX74E8L87J0ycOBG3bt1C/vz5U7f4VJK0f169egWVSoWSJUuiRIkSKFy4MHbv3o2lS5fizp07ePXqFfr164dHjx4l24au3MFFWzHYERGlAUnBY8uWLWjbti3evXuH58+fw8fHBydPnkS+fPmwcOFCODo6IjAwEH/++ScePnyII0eOoFChQhqu/ucLCgrCihUrAAAdOnRAt27dAAB169aFSqXClClTEB8fr4QSOzs76OnpIS4uTm077u7uOHXqlFYPLtmwYQMKFiyIzZs3w8bGBn/99RcCAwMRGhqK9evXw9XVFbly5UJISAj+/vtvTZdLPxgHTxARpRHHjx+Hl5cXZsyYAS8vL9y/fx9Tp07FkSNHsHfvXpQoUQJv375FQkICRAQGBgYwNTXVdNk/XVRUFOrWrYu4uDhYWVnh0KFDOHr0KAoUKIDw8HD07dsXt27dgpeXF7p3746wsDAEBAQA+DCIQtuvpUsaHJL039OnT2PVqlWYNWsW/P390bJlS7x8+RILFizAmDFjkDNnToSHh2PChAkYNmwY7/mqZRjsiIjSiHnz5mHx4sU4dOiQ0vP08OFDtGvXDqdPn8aBAwd0onfuc8LCwlCqVCn8888/GD16NPr166ese/HiBUaNGoXdu3fjzp07yJUrF0xMTHD8+HEYGhpq9ZQdH7+3d+/eKdO8AB96f+fPn4+nT58iQ4YMSJ8+PcqVK4c2bdqobSM+Pp7hTosw2BERpREzZ85E//798eLFC5iYmCg9MJs3b0bt2rWV3hhduePGx8LDw9GkSRNER0fD2NgY/v7+aNKkibL+3bt3iImJwaFDh5AxY0aUKlUK+vr6Wh1aPg51U6dOxbFjx/DmzRvkz58fAwcOhJWVFW7duoXTp09j+PDhuHXrFvT19RESEqJzE3zrEgY7IiIN+Ny9Sm/duoX69eujSpUq6NevH6ytrQEAFy5cwPDhw5EhQwb06tVL7Q4BuubZs2do3bo13r59i9atW6uFu0975pJGxGq7AQMGYN68eejUqRNu376NGzduIDo6GidOnICtrS0AIDo6Gp07d8bjx4+xe/dundgvuorBjogolSWFujNnzuD27duwsrJC1apVkZiYiKFDh+LAgQPw9PTEwIEDoa+vj9GjR+PcuXNYv349zMzMNF2+xt27dw9dunRBXFwcGjdujGbNmqFixYooUaIERo8erenyUtW1a9dQs2ZNzJw5E5UqVQIAXLp0CZ06dUJ0dDSCg4OV6zATEhKgp6cHlUqlM6FXFzHYERFpwKZNm9CgQQPkzZsXly5dQuPGjTF27FjY29tjxIgR2LZtGy5fvoz8+fPjzp07OHz4sM5eX/c59+7dQ+/evXH9+nXExsbCzMwMZ8+ehZGRkaZLS1XHjh1D5cqVcfbsWeTKlQvAh57L4OBgtG/fHqNGjUKNGjXUgtzneotJe2jnhQdERGlQ0gfqixcvMGvWLAQGBuL333/H5cuXUbNmTbx58wZTpkzBsGHD0KlTJ+zcuRPm5uYoUqQIXF1dNV1+muLi4oIZM2bg7NmzCA0Nhb+/PwwMDLT6mrrPBbKcOXPCyckJu3btUoKdnp4eChQogMjISNy/fx+A+tx0DHXaTTuPfiKiNEilUmH37t1Yu3Yt0qVLhypVqsDCwgIeHh7YsWMH/Pz80KNHD/z111/IkyePci9P+rxMmTKhWrVqyuOEhAStDXUfXz/49u1biAjMzMxgYWGBwoULY+PGjciWLRtq1KgB4EO4c3BwgJWVlSbLJg3gqVgiop8sqaclMTERR48ehZeXF4yNjXH8+HH89ttvatfc1a5dG/ny5cPkyZORN29eTZdOaczIkSNx6NAhhIWFoXfv3mjUqBFCQ0OVEcP58uVD8eLFsXr1arx8+RLnzp3T2rBLn6edE/sQEaUhKpUKR44cQYcOHeDu7o7Tp0/j/fv3mDZtGp49e6ZMLlu0aFGsXbsW9+7dY08LAVC/N+7EiRMxY8YMeHh4IH/+/GjSpAmGDRsGe3t7rFy5ElWrVsWNGzewbNky2Nvb4+zZszAwMOC9X3UMYzwRUSq4ffs2tm3bBkNDQ4wbNw4HDx6El5cXjIyMMHz4cDg4OEBEULJkSVy6dAnGxsaaLpnSgKTTrzdv3kRMTAyWLFmijH4tU6YM2rVrBxHB8OHDMWTIEAwZMgRRUVGwtLQEwMmHdRH/tYmIUkHLli2hr6+PkSNHolevXpg0aRKCgoLg7e0NfX19DB48GJkzZwYAnRvZScl9PFDi4MGD8PHxgbW1NZYuXaq0SbqDRPv27WFgYIDu3bvD0tJSCXVJt50j3cJTsUREP8ndu3cREhKiPG7evDkGDhyIoKAgdO/eHSVKlMDBgwcRGBiI8ePHK6fMOGpRt30c6mJiYuDl5YWxY8ciIiICV69exceXxrdp0wbz5s3D0KFDsX79erXt8DjSTYzyREQ/wevXr+Hp6YmWLVuic+fOyJQpEwCgRYsWSEhIUHpZxo0bh+PHj8Pa2poTxpLa6NexY8ciNDQUkyZNQp8+ffD27VsMGjQI9vb2aiOmW7VqBTs7O1SuXFlDVVNawh47IqKfIF26dFixYgVWrFiBefPmqfXctW7dGu7u7li2bBmGDRuGEiVKcASsjuvVqxciIyOhp6eH9+/fAwCCgoKUuekAYMiQIRg8eDDatGmDRYsWqT2/WrVqyjx+pNvYY0dE9JN4eXlh6dKlaNSoEVQqFdq0aYNMmTLhzZs3KFasGOrWrYsGDRrwlJmOe/ToEZYtW4agoCAcPnwYFhYWAICIiAiYmJiotR02bBgAoEOHDoiJiUFAQIDael5TRzwCiIh+Ik9PT6xcuRLNmjXD8+fPUapUKVy9ehWHDx9GcHAwpzUhODk54cCBA2jatCk8PT1x+PBhWFlZITExUQn98fHx0NPTg56eHoYNG4aoqCisXr0anTp14hcDUsMJiomIUsGZM2fQs2dP3L9/H5aWlli6dCkKFy6s6bIoDbl69SoaN24MEcHp06fx+++/o06dOmjRogWio6Ohp6cHMzMzPHr0CE5OTsogC977lT7GYEdElEqioqIQHh4OExMTZMyYUdPlkIZ9GshEBFevXkX9+vURGxuLuLg4JCYmIl26dAgLC4O+vj6MjIxQrFgxrFq16rPbIGKwIyIiSmUfj359/fo1VCoVbGxsAHzouevWrRsOHTqENWvWIHPmzEqwi4+PR6VKlXgtHX0Rgx0REZGGDB06FPv378eTJ0/Qq1cvNG7cGLa2trhy5QqaNWsGU1NT7N+/H6ampmrPS0hI4PQ49Fmc7oSIiCiVfHzv11mzZmHu3LmoW7cuatWqhZ49e+LPP//E48ePkT9/fixfvhwxMTFwcXHBmzdv1LbDUEdfwr5cIiKiVJJ0+vXixYt4+vQpAgMDUbNmTQBAyZIllXu/9u3bF25ubli6dCnGjx/PewfTN2OwIyIiSiWJiYk4deoUSpUqBWNjYyxYsEBZ16BBAwAf7v2qp6eH7t27o2DBgsr9YXn6lb4FT8USERH9RB9fyq6np4eSJUsiMDAQsbGxOHHiBF6/fq2sb9CgAebNm4epU6di48aNatthqKNvwR47IiKinyhpOpLly5cjMTERzZo1Q7t27fDu3Tt0794dmTJlQseOHWFtbQ0A+P3335E+fXqULVtWk2XTL4rBjoiI6CeLjo7GpEmTYGZmBlNTU9SrVw9du3ZFQkICevXqBQDo1KmTcicSHx8fAB/uOMGpTeh78GghIiL6wT6dONjCwgJbtmxBy5YtMXPmTCQmJqJ+/fro0aMHVCoV+vTpg6ioKAwcOBDm5ubK8xjq6HtxHjsiIqKf5PHjx3B0dFQeP3nyBM2bN0diYiI6d+6MunXrAgBGjBiBXbt24ejRo7yTBP0nDHZEREQ/wdy5c7F06VKMGjUKZcqUUZY/fvwYNWrUgIhg8ODBSrjjvV/pR+CoWCIiop/Aw8MDz58/x4QJE3D06FFluaOjIyZOnIjbt29j1KhR2L9/v7KOoY7+KwY7IiKin6BAgQLYtGkTbt++jTFjxuDIkSPKujdv3qBGjRrw9PSEt7c3gA+jZxnq6L/iqVgiIqKf6Nq1a6hfvz5cXV1Rt25deHt7o3PnzihVqhT69+8P4MPExUl3pSD6LxjsiIiIfrIbN26gR48euHLlCgDAwcEBx48fh6GhIU+/0g/FYEdERJQKXr58iYcPH+LVq1fw8fGBvr4+56mjH47BjoiISAN471f6GRjsiIiIiLQEr9QkIiIi0hIMdkRERERagsGOiIiISEsw2BERERFpCQY7IiIiIi3BYEdERESkJRjsiIiIiLQEgx0RERGRlmCwIyKdFRgYCEtLS8THxyvLoqOjYWhoCC8vL7W2Bw8ehEqlwp07d1K5SiKib8dgR0Q6y9vbG9HR0Thz5oyy7MiRI3BwcMDJkyfx7t07ZXlQUBCyZs2K7Nmza6JUIqJvwmBHRDord+7cyJQpEw4ePKgsO3jwIGrWrAkXFxecOHFCbbm3tzcSExMxevRouLi4wNTUFIUKFcK6deuUdgkJCWjdurWyPnfu3Jg6dara67Zo0QK1atXC8OHDkTFjRlhZWaFDhw6Ii4tT2sTGxqJr166ws7ODiYkJPD09cfr0abV6VCoV9u/fj6JFi8LMzAylSpXCzZs3f8KeIqJfBYMdEek0b29vBAUFKY+DgoLg5eWFcuXKKcvfvn2LkydPwtvbG6NHj8aSJUsQGBiIq1evokePHmjatCkOHToEAEhMTISjoyPWrl2La9euYciQIRg4cCDWrFmj9rr79+/H9evXcfDgQaxcuRIbNmzA8OHDlfV9+/bF+vXrsXjxYpw7dw45cuSAr68vwsLC1LYzaNAgTJw4EWfOnIGBgQFatWr1s3YVEf0KhIhIh82bN0/Mzc3l/fv3EhkZKQYGBvL8+XNZsWKFlC1bVkRE9u/fLwDk/v37YmZmJsePH1fbRuvWraVRo0ZffI2AgACpW7eu8tjf319sbW0lJiZGWTZ79myxsLCQhIQEiY6OFkNDQ1m+fLmyPi4uTjJnzizjxo0TEZGgoCABIPv27VPabN++XQDI27dv/9tOIaJfloGGcyURkUZ5eXkhJiYGp0+fxuvXr5ErVy5kzJgR5cqVQ8uWLfHu3TscPHgQrq6uiI6Oxps3b1CxYkW1bcTFxeG3335THs+cORMLFizAw4cP8fbtW8TFxcHd3V3tOYUKFYKZmZny2MPDA9HR0Xj06BEiIiLw/v17lC5dWllvaGiI4sWL4/r162rbKViwoPL/mTJlAgA8f/4cWbNm/c/7hoh+PQx2RKTTcuTIAUdHRwQFBeH169coV64cACBz5sxwcnLC8ePHERQUBB8fH0RHRwMAtm/fjixZsqhtx9jYGACwatUq9O7dGxMnToSHhwcsLS0xfvx4nDx58qfUb2hoqPy/SqUC8OF0MBHpJgY7ItJ53t7eOHjwIF6/fo0+ffooy8uWLYudO3fi1KlT6NixI9zc3GBsbIyHDx8qAfBTx44dQ6lSpdCpUydl2eemSLl48SLevn0LU1NTAMCJEydgYWEBJycnZMiQAUZGRjh27BicnZ0BAO/fv8fp06fRvXv3H/jOiUjbMNgRkc7z9vZGQEAA3r9/rxbYypUrh86dOyMuLg7e3t6wtLRE79690aNHDyQmJsLT0xMRERE4duwYrKys4O/vj5w5c2LJkiXYvXs3XFxcsHTpUpw+fRouLi5qrxkXF4fWrVtj8ODBuH//PoYOHYrOnTtDT08P5ubm6NixI/r06QNbW1tkzZoV48aNw5s3b9C6devU3j1E9AthsCMineft7Y23b98iT548sLe3V5aXK1cOUVFRyrQoADBixAhkzJgRo0ePxt27d2FjY4PChQtj4MCBAID27dvj/PnzaNCgAVQqFRo1aoROnTph586daq9Zvnx55MyZE2XLlkVsbCwaNWqEYcOGKevHjBmDxMRENGvWDFFRUShatCh2796NdOnS/fwdQkS/LJWIiKaLICLSJS1atEB4eDg2bdqk6VKISMtwHjsiIiIiLcFgR0RERKQleCqWiIiISEuwx46IiIhISzDYEREREWkJBjsiIiIiLcFgR0RERKQlGOyIiIiItASDHREREZGWYLAjIiIi0hIMdkRERERa4v8A17wnTJutMTYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}