Human Activity Recognition Using Accelerometer Data.ipynb

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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aishwarya1111/Human-Activity-Recognition-Using-Accelerometer-Data/blob/main/Human%20Activity%20Recognition%20Using%20Accelerometer%20Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Group members:\n",
        "1. Aishwarya Shenoy 425828 fad64fap@rhrk.uni-kl.de\n",
        "2. Rohit Keswani 425350 myn34sir@rhrk.uni-kl.de\n",
        "3. Subhadeep Das 425752 deq34wim@rhrk.uni-kl.de"
      ],
      "metadata": {
        "id": "ugtrUSsO8dAI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Tensorflow 2.0 and other helper libraries "
      ],
      "metadata": {
        "id": "Ku8Bf6xGjQv4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQQ-ZkZ0m93G",
        "outputId": "73779718-4a8f-426f-d115-7d7d5956dcfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.2\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Pandas, numpy and scikit_learn"
      ],
      "metadata": {
        "id": "Tx4Tkh5RjfLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder"
      ],
      "metadata": {
        "id": "qKNsAK8jajos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Uploading the dataset to google colab\n"
      ],
      "metadata": {
        "id": "s41HJWtcjmw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
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          "height": 73
        },
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
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              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
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              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
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              "\n",
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              "\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving time_series_data_human_activities.csv to time_series_data_human_activities.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating a dataframe"
      ],
      "metadata": {
        "id": "qO5Bf0PVjwrB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['time_series_data_human_activities.csv']))\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeKzk3arcoAw",
        "outputId": "f82fceea-f7cc-4410-eddb-20c0f101a4d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         user  activity       timestamp  x-axis  y-axis    z-axis\n",
            "0           1   Walking   4991922345000    0.69   10.80 -2.030000\n",
            "1           1   Walking   4991972333000    6.85    7.44 -0.500000\n",
            "2           1   Walking   4992022351000    0.93    5.63 -0.500000\n",
            "3           1   Walking   4992072339000   -2.11    5.01 -0.690000\n",
            "4           1   Walking   4992122358000   -4.59    4.29 -1.950000\n",
            "...       ...       ...             ...     ...     ...       ...\n",
            "1073618    36  Standing  15049012250000   -0.91    9.43  2.533385\n",
            "1073619    36  Standing  15049062268000   -1.18    9.51  2.492524\n",
            "1073620    36  Standing  15049112287000   -1.50    9.53  2.533385\n",
            "1073621    36  Standing  15049162275000   -2.07    8.77  2.179256\n",
            "1073622    36  Standing  15049212262000   -2.14    9.89  3.255263\n",
            "\n",
            "[1073623 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Shortening the Dataframe from 36 participants to 5 participants"
      ],
      "metadata": {
        "id": "qoc3Q4auj5ML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(df[df.user > 5].index)\n",
        "print(df)\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "sMih0bPwi3zh",
        "outputId": "ea32cfcf-77a4-437e-9369-478a0ef3c674"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        user  activity      timestamp  x-axis  y-axis    z-axis\n",
            "0          1   Walking  4991922345000    0.69   10.80 -2.030000\n",
            "1          1   Walking  4991972333000    6.85    7.44 -0.500000\n",
            "2          1   Walking  4992022351000    0.93    5.63 -0.500000\n",
            "3          1   Walking  4992072339000   -2.11    5.01 -0.690000\n",
            "4          1   Walking  4992122358000   -4.59    4.29 -1.950000\n",
            "...      ...       ...            ...     ...     ...       ...\n",
            "128534     5  Standing  3222692272000   -1.38   10.34  0.313268\n",
            "128535     5  Standing  3222742199000   -1.18    9.81  0.081722\n",
            "128536     5  Standing  3222792278000   -0.89   10.61  0.040861\n",
            "128537     5  Standing  3222842205000   -0.95    9.96 -0.000000\n",
            "128538     5  Standing  3222892284000   -0.11    9.77  0.040861\n",
            "\n",
            "[128539 rows x 6 columns]\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user activity      timestamp  x-axis  y-axis  z-axis\n",
              "0     1  Walking  4991922345000    0.69   10.80   -2.03\n",
              "1     1  Walking  4991972333000    6.85    7.44   -0.50\n",
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              "          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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Shape of data"
      ],
      "metadata": {
        "id": "0oa0HDqAljON"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0XaLV7EliRW",
        "outputId": "c8c54e59-1195-4f7d-95c9-a443b1a2a437"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128539, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#More information about the dataset"
      ],
      "metadata": {
        "id": "WidgUp6FmKy_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PugBzi9hmHJM",
        "outputId": "0b0b369c-92b1-41f2-c048-18fc4bba18a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 128539 entries, 0 to 128538\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   user       128539 non-null  int64  \n",
            " 1   activity   128539 non-null  object \n",
            " 2   timestamp  128539 non-null  int64  \n",
            " 3   x-axis     128539 non-null  float64\n",
            " 4   y-axis     128539 non-null  float64\n",
            " 5   z-axis     128539 non-null  float64\n",
            "dtypes: float64(3), int64(2), object(1)\n",
            "memory usage: 6.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Checking for null values in the dataset"
      ],
      "metadata": {
        "id": "3-6lH0QrmZZk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYtsRssOmfZ5",
        "outputId": "7cd61db0-a772-4018-eea7-c9b00bafb1de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user         0\n",
              "activity     0\n",
              "timestamp    0\n",
              "x-axis       0\n",
              "y-axis       0\n",
              "z-axis       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Distribution of data in the dataset"
      ],
      "metadata": {
        "id": "eAYYyh4om5vw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['activity'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpFV18ZFm5Z7",
        "outputId": "26765d54-0e05-4161-b37a-6a14a38236d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Walking       55906\n",
              "Jogging       41160\n",
              "Downstairs    11309\n",
              "Upstairs      11295\n",
              "Sitting        4530\n",
              "Standing       4339\n",
              "Name: activity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Through this data distribution we can deduce that the data is unbalanced. Standing examples are very less in number compared to Walking examples. Thus, using this data directly will result in overfitting and the result will be skewed towards Walking."
      ],
      "metadata": {
        "id": "ppZoLJZpK5Yf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let us plot x, y, z to observe the variation in data. First we convert the data from type 'string' to type 'float'."
      ],
      "metadata": {
        "id": "aDi5OzmkMqH-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['x-axis'] = df['x-axis'].astype('float')\n",
        "df['y-axis'] = df['y-axis'].astype('float')\n",
        "df['z-axis'] = df['z-axis'].astype('float')"
      ],
      "metadata": {
        "id": "Ee7WI0n_XVbn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# We can observe how the data has changed"
      ],
      "metadata": {
        "id": "JIBnH8VdX1Jx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SzyEDR_X8Pu",
        "outputId": "9252518e-b038-4d43-f7ad-8d6b17d8b335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 128539 entries, 0 to 128538\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   user       128539 non-null  int64  \n",
            " 1   activity   128539 non-null  object \n",
            " 2   timestamp  128539 non-null  int64  \n",
            " 3   x-axis     128539 non-null  float64\n",
            " 4   y-axis     128539 non-null  float64\n",
            " 5   z-axis     128539 non-null  float64\n",
            "dtypes: float64(3), int64(2), object(1)\n",
            "memory usage: 6.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Basic exploration of the data"
      ],
      "metadata": {
        "id": "SVIw_t3HYEv3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Representation of data in a bar chart\n",
        "activity_stats = df.activity.value_counts()\n",
        "activities = activity_stats.index.tolist()\n",
        "activity_stats.plot(kind='bar')\n",
        "plt.grid()\n",
        "plt.show()\n",
        "print(activity_stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "-Q23nl4niZ-P",
        "outputId": "8bd135fe-1f9d-4f2d-9e4d-9e2f156e47ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Walking       55906\n",
            "Jogging       41160\n",
            "Downstairs    11309\n",
            "Upstairs      11295\n",
            "Sitting        4530\n",
            "Standing       4339\n",
            "Name: activity, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# activity vs user\n",
        "pd.crosstab(df.user, df.activity)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "D6rq5t1tit4E",
        "outputId": "ada9c30a-53a1-4b94-9050-17fcad8975d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "activity  Downstairs  Jogging  Sitting  Standing  Upstairs  Walking\n",
              "user                                                               \n",
              "1               2941    11056        0         0      3120    12861\n",
              "2                  0    11786        0         0         0    11739\n",
              "3               3326    11018     1609      2824      3411    12970\n",
              "4               1761      895     1257         0      1377     6079\n",
              "5               3281     6405     1664      1515      3387    12257"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca757b7e-bc42-45b6-b976-4d41519c2343\">\n",
              "    <div 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>activity</th>\n",
              "      <th>Downstairs</th>\n",
              "      <th>Jogging</th>\n",
              "      <th>Sitting</th>\n",
              "      <th>Standing</th>\n",
              "      <th>Upstairs</th>\n",
              "      <th>Walking</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2941</td>\n",
              "      <td>11056</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3120</td>\n",
              "      <td>12861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>11786</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3326</td>\n",
              "      <td>11018</td>\n",
              "      <td>1609</td>\n",
              "      <td>2824</td>\n",
              "      <td>3411</td>\n",
              "      <td>12970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1761</td>\n",
              "      <td>895</td>\n",
              "      <td>1257</td>\n",
              "      <td>0</td>\n",
              "      <td>1377</td>\n",
              "      <td>6079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3281</td>\n",
              "      <td>6405</td>\n",
              "      <td>1664</td>\n",
              "      <td>1515</td>\n",
              "      <td>3387</td>\n",
              "      <td>12257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca757b7e-bc42-45b6-b976-4d41519c2343')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-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-ca757b7e-bc42-45b6-b976-4d41519c2343 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-ca757b7e-bc42-45b6-b976-4d41519c2343');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Let us take a specific user and perform time-series plots. The chosen user is user 5."
      ],
      "metadata": {
        "id": "rFZobAqIjjt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_user = 5\n",
        "df_user = df[df.user==my_user]"
      ],
      "metadata": {
        "id": "Dlkc7oRLjgLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for act in activities:\n",
        "    df_temp = df_user.loc[df.activity==act].copy()\n",
        "    df_temp.reset_index(inplace=True)\n",
        "    \n",
        "    # convert time to seconds and start with 0\n",
        "    t_min = df_temp.timestamp.min()\n",
        "    df_temp['time_sec'] = (df_temp['timestamp'] - t_min)/1e9\n",
        "    \n",
        "    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14,6), sharex=True)\n",
        "    \n",
        "    ax1.plot(df_temp['time_sec'], df_temp['x-axis'], color='red', alpha=0.5)\n",
        "    ax1.set_ylim(-20,20)\n",
        "    ax1.set_title('user=' + str(my_user) + ' / ' + act + ' - x')\n",
        "    ax1.grid()\n",
        "    \n",
        "    ax2.plot(df_temp['time_sec'], df_temp['y-axis'], color='blue', alpha=0.5)\n",
        "    ax2.set_ylim(-20,20)\n",
        "    ax2.set_title('user=' + str(my_user) + ' / ' + act + ' - y')\n",
        "    ax2.grid()\n",
        "\n",
        "    ax3.plot(df_temp['time_sec'], df_temp['z-axis'], color='green', alpha=0.5)\n",
        "    ax3.set_ylim(-20,20)    \n",
        "    ax3.set_title('user=' + str(my_user) + ' / ' + act + ' - z')\n",
        "    ax3.grid()\n",
        "    \n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SWxImbAgj-Lj",
        "outputId": "8995ef78-641e-4c2d-d07c-e67f1cce2dd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 3 Axes>"
            ],
            "image/png": 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7P81Hpk8PnQ67joMHNbAB9Dvn5ABPPqnTd+8Gnn1Wz8XffutXeQVAz8mTJ2sgUFyMVnPn6rm4oEDnf/SRpuOpp7TSaO1avY4mUEGB5nnu7zxlSvD3P3hQy267dnl/p08/1d/H5jU7dmh+MHmy/qaFhcGf2bpVz/GbNjmfs+nPzdVgacaM4M/ZCjDLGGcfrVyp/51Fi/T99u3e6V250skXvvoK+M9/gH/9S/+b48YBtifU3r2a73r1jNq/X7e1erWm4VNfUX3FCu9t1hFRabkRkXcBjADQRkQ2AfgrgMcBvC8iNwBYD+Di0GtITB0mT9YdHNhP8ssv9QDeuBHo0SMuaasTPvlEr594+GGtxTzU2czrnHOcaeXl2iICOAXtmgq8lsSLMXqhpZeyMs2cSkqAM8/0r+1yn4Ctl17SWrxLLgm/TdvX2574Fy3SlonGjfV4sifyQPb3WbwYOPJIZ/qTT+pz4PGZl6fP7gx8zBjNIB5+2H/Zjz/WAM8dOG3frpnSuHHaynTGGdoK5G7N2b4daNECmD0b6NlTa/bC8XWNAKCFnVGjnAJBYaFO81r2+++1xvDbb/X9rbc684qLNWNt2tRp8cvKQreNG4EuXTRDPu648Omi2HMHvDbQDZwfz5abtWv1/2treEMpL9e0RuNcXlamBb6TTnJqxOfMAb74wr/2/5tv9PmkkyJbb0mJFpgDW4btPti2DfjvfyNb15Yt+hx4fisri2zgExtIff21PtyKipzXBQXO4CYffxz6uozcXG3BGOsbp2nUKE2L/W127gyu9PjmG2DgQODCC53foKICeOYZ/+0UFQUHXICed5o311Zie14NJy/PqZDZutU5x7kFTvvxRyf9kcjOBo45xnm/caMTNK1YAQwbpoX5WbP8P2eMfjYnR/9nV17pHHfl5bqP7H4tLdX/+bp1+n7OHH1etQq4+24gydcu4M5rX3lFe194BanuwHbDBsCru+Ps2c5rG+SvWaPPO3Zomho0cJbJywPefVfXde21TjBSVOQEgOvX6//LBkDuCkvLVuD/9JN+L/u/nzsXOPXU4OXriKiUOI0xl4WYNTIa66c6ytYuJfJIJdW1fbs2/1a3+xIAfPaZnkCs8nKtMWvbFrjmGj15NWsGNGrk/fmDB/Xk1aNH6CZz+9tv26aFF9tkHdjCYIzWTM2dqwGHLYR99ZX/ciUlmgkVFztdxbZudU6Qq1ZphjB3rnZ1O+YYXfeECU4AUFCgJ+sJE3RbV1yhAUZlPvlEg40WLUIvE5ixFBVp0Ghrvp5+2r9gAXgXVOx1ETk5WmBo00bTffnlQLdu/q1CNvAYNgw4/njvdLm7v/z4o3+m4Q5svNj1A9r6Y7lb57Kyfqm1FPt96uMF0XWV3Re24BA4b/JknXfOOdp1pLaUlGghvGtX4Prrwy/74ot6TsrK0jTPnKldLJs00cJbx45OoQ/QZVasAPr08Z++aZMGVN99p912jj9ea4+/+ELnv/MOcN99ofONAweADz7QY/Ovf9Vpubl6jM+cqQXbSy/VWupRo7RSxF2ocxeiJ07UypOMDOCGG/xbRBYsAM491zm/ZWXpuWrcOGeZbds0SBs6VD974IB/BUwknnrK//3Uqbo/mjfXwnfr1lqItQVda/Jkff7mG/0dvQJnwKlEWrvWf/qaNVqILi72P8cE+uSTyL/LkiXOa6/ABghuvbGqOhyyDQA++8yZtnmz7od33w1ePjfXOdeuXQs88ghw55363wu8wP6JJzRgsL+xtXevDjxz6aXA4Yc7FXVWqNY3dyvI669rwBkoL88Jpry4j4f8fKdXwYYNmiY3d0XhU08B/frpa3dPhG+/1cqvt992pgX+h9580+m23blzneo+y+r0UNzdqWz/fUALRok89GEiiXa3vfJypOzdq5lYVQsA//538LQFC7Tma8ECLaj/9reaIT/yiGbUAwdqxpuTowFGRoZm0mVlWri1BXNX+jB1KjB8uGY2hYV6Mli+XAvEgdyBjVVYqA9bq9auHXDbbfq6okILxrYf+AcfOE3bf/qTf60OoLWAixZpQGMzglNP1f924AWljzzivA5sLncL6O6W7O6TXljo3+Vs0iQ9MU6ZohmXO/N64QVnW1UZQaawUIM69zU4zzwD3H67FmDc3csAbaZ3n7ADA5tI2QJRqAwb0C4cOzwvLQxWWUBTHbaWmxKLu0DgVTgwxqm9/t//Qh8Pu3frcTNsmNPiUVysQVFxMTBvnl6nFXhNYVGRFpzsOWj/fm31vOIKp8Jhwwbd7r33aoVKeblua9Qo57ovd839xo16XG/cqOeoadO0dv+mm5xAZsUK4L339FqBYcP03Ni+vVZqWGVlmk/YVlib3sDCWnm5rnfiRD1fWzt2aKHVBkaW7X4TWDgNZFuFt2/X1pPA1onAdLgDG2vKFD3H29r5Tz4B+vYNv11Afx93MGCF6woWSqjAxvIaathdqK1LCgo0gHG3cLt5BTaAd8vTc8+F3k7AwB9+xo/XfN5r/0XCq5fCvHnhK6R27NDv3KKFdvULV8kXyKtL+bRp+rDWrAkOoLdtQ+aPP+p1Qb/5DTBgQOTbjDMGN6G4C8PuPo3upk7WjIbmriFYvVprDkJF/Xl5WlhNTtaMbtkyDSzcy2/dCrz0Ejrn5mrmdsklwRnI5s2a+V13nZMZ7typJwF3sJqToxmtu6l4yxa9ONTW3G3Zoo9OnZxuAI0bAxdc4BTgA8eKX7RIC6179mggsnKldo8oLPQPPF577ZeLCCu1Y4d+36Ii/U2nTw/u4gBo7Wtgemwtkrsfu62hmzcvsu1XossHHzjfxSujCKyVrKkPPgjuL56fr/vEq9Khskw/2kL1hQ8UcFF5jYUqEOfna03poEHR3R5Fzl3j6tX1JtKW7Wef1efvvtPHtddqQOs+j9nrPAA9f7m7Gtnp9rqfceO0UsDtvfe0W9i+fVqpN26cnkf/8hdnmeJip2C5YoVTK711qwYD11yj/+/evXV6dra2cHj14bfXwlTmb3/znm4rSaIhkm5XoQRev7N8eeWfSaCRYeucUAFMONE+57oDg2iorDxprwW9+mp9ru3rmT/6iMFNvWML2YF9OKtbCxxNNvCqSfcoLzk52jQeqrtTQYF+f3cLijH6cHdBALRAetZZ3v3/Cwv1AunOnbWpd/ZsPWmkpmqzb3GxFgheecX/c++9pwW21FTg5JO1wL9jh9bABV5M/6tf+b+3wUqg//wneJo9oQDa5cDdMmG7aCUn6zptodudsdkLEN2tWBs2VO0mWp9GMBZHItyUqzYCCa8LYQG2poby4ot6DAVWFlDs2S5oM2eiWW6uVgJs2xa8XGBL6Tff6PF8zDHa0tGkibamBApVs7xunXZVDeyG9MYbwCmn+LdyPP+8/zKbNmkh333er6jw77r5+ON6LV4o9vzq7n4U6uJke30GEUUmWgMPVUdhofdIpwmIwU0kPvgAuOOO4OkffRR6WNva8o9/BNes1dSmTZpBDR0KnH66vk9N1WtBrKef1kDm5pu1dvDmm7XmbtIk4J57grtI/fyzE9z8/LN2ldq3zwmENm3S603sxarjx2sf8HA3m7ItE+4L8bzEsoYj0otTieLBtqDm5dX+MOOHsqKiyEcX/OAD//d2xCd3hUVVum6GKvzk5obvVmk9/7x2L3MLbDkJvBaPiOq/ceOAW26JdyoiEsuhoOuPXbtCdzfZsUMzniVLtIYrGl3Vpk71bzEIZIx/V4ZoX7BvL2qzXSheeSW4hs9+T9vtYcUK55qKp58OvnB2yxbN7Ldt0+siXnsN+PBDHXbSzd2PNlp30a1OH2ai+iSa3Xeocna472iJZHSuaLKjdxERWV63RklQbLmJlFd3AsC5iPfzz7WwDmgrT5s21d9WZYXxt97SAKR586qtt6JCu5MtXKjdAUIN0WyvyzDGv9Xj4EHt/uZ1vUZg/1OvftSBgQwREVUu1DUnRES1Kd5D10eIwU2kQl2MZlso3Pco+eor/1G0IlFaqoFEJN2cbMuKV+1gSYnW8jVpou83b9aWpy++CL4hYXGxf5e64mL/YYdXrdKH9Y9/6Gg8sRjpiYhiKz+/9u+KTkRE9cc33+jlCgmOwU1tys3Vi8Nvu02vSZk1SwOhrCy92DPUzRdnztQLS3v0qLzb2z//qc/Dh+tF+qHuBg/okKATJ+pQn59/Htl3YGBDVDfVdtcmIiKqX2bMYHBzyLJjhefnaxDz9dd68XuTJs49B6ZMcUZ4WrAgdGDz44/O0L8PP+x9bxRAh0Yc6bpnaiTDFNobEbpbZ4iofpoxQ0fsatlSH02b1onuBURERFXB4CYWjNHrbwJv8GSHjnbfewAIPdRv4Ag5r78eemS2lSsjv8cGER165s93bloIaMVLeroT7NiHnRbt4eWJiIhqAYObWKnunWvDCdW6Q0QUiTvu0JvM7tmjLcv29fr1wfcpato0OOCxj+bNg+9nRURElAAY3BARHSratPEeydEYHRQlMOjZs0fvQbV0qf+Q80lJGvCEavlp3Jhd3oiI6ptTTol3CiLC4IaI6FAnotcENmkCdOoUPL+iQkdndAc9NhBasUIHJ3FLTQ3d3S093XsI+vokGvc7IyJKNH36xDsFEannOQwREQEALryw+p91t9R07x48/+DB4KBnzx69EfDq1f435xUBmjXz7u7WsqVeV8hWHyKixFNHrsVkcENEdCgYODB2627YEMjI0EcgY4DCwuDubnv26IiRixb5t3SkpIQf6CA1NXbfI1o47DYR1UetWsU7BRGJeXAjIqMA/AdAMoBXjDGPx3qbRESUIGxLTbNmQJcuwfPLyvQGxl4tPxs3Bt98uEkT76CnZUugRYvEGOhg9ux4p4CI6JAV0+BGRJIBPA/gdACbAMwRkYnGmGWx3C4REdURKSlA69b68GIHOggc7GDLFmDZsuCBDlq0CN3lrbYGOigsjP02iIhq07HHxjsFEYt1y80QAGuMMesAQETGAzgfAIMbIqJYO+444Kef4p2KmmncWB8dOwbPq6gACgqCu7vt2aM3Jw4MMho2DA54bCCUnq73/iEiomDnnBPvFEQs1sFNJwAbXe83ATguxtskIqqe3/0O+N//4p0Kf+efr0Mx798PbN1atc8OGlT3g5twbEtNixZAZmbw/IMHg6/1yc8Hdu8G1q4FSkv9l7cDHXi1/DRrFnmrT30fDY6IKIHF/QwsIjcDuBkAMjIykJ2dHd8E+WTm5uLgwYPIzc2Nd1LIpT7vk51Dh6LNjBlRWVfe8OFoO21atT5b1LkzmmzaBADYO2AAWixe7De/uF07FGVmosGePUg9eBALW7RA09xcNNi7t8bptvb274/my5ejvFEjpAQOM+xT0ro1dp1wAjp+9llE6zyYno6G+flhl8ldtgyZHv+v4vbt0Wjbtoi2E01FnTphx969QOfOOqF3b6T4WiOarlsHiKCiQQO09gUwRZ06IcV1jGyaMwepXbuiuF07lCfIuTWubCtQhw6AMUguLkZKQQFSCgv1uaAADfLykFJQgOSiIohroAOTnIyytDSUpaWhNC0NZc2aOY+0NFS4RhHKWLIEjbds+eV9fTpv5V57LZrm5ADl5Wj7ww+Rf+6qq4DkZHT68EM0SIBue1XdJztOPhntvv++StvYc8wxaDlvXsTLl7Rpg9SdO73ntWqF1N27q7T9usbuE5OcDPEYlCPvpJMi+s+tv+oqdHvrrWqlIX/AAKQH5Hn1wcEWLdAwII8ubdYMDQoKPJevSElBUlmZ33GSW4fykFgHN5sBuK8g7eyb9gtjzBgAYwBg8ODBZsSIETFOUgQOHgSys5Gbm4tMr9pAipta2Se9egGXXAI884zW1lZWW56aCowerbXI//ynTrvgAqBnT+02s3y5jjDy9ts674gjtNY44ELpzPPOAzZv9l/36NE6NO6zzwInnQQMGQJkZTnzL70UGD8+KEmZJ54IbNgQnNZwLRO33653pW/SBJg5U9OZk6Pdfqxhw4CRI/V1WRlmfvwxTrzoIq0NX75cv/Obb/p/xq1PH2DlSv9pF12k2wKc73bfffpcXg787W86/PDAgbredu30XizNmukyAwcCubma5kDdugHr1+vre+/VtOXl6T5evRpo25oJSwMAACAASURBVFbfd+wIbNmCzFNO0Ws8Fi/W3z0vz0nXhx8CS5b4/5b79unvPH26M/3cc7W1ZMeO0L9zy5bAU08B/foBJ54IPPecM3/4cMAGpg88gH6NG3uvx+3oo3XUsRtuQO711/9yjGQOG1ZnRrdJOOXlwQMdBA5z7S6INm7stPI0bOjXklSt89ZppwHffBOVrwJA07VnT+XLPfigLvfii56zM0eMAGw+ffrpekzMmaPv+/XTY/Wbb4BjjtH/9syZQH4+Mu15Y8QI4P/+D+jaVa+ZGjJEzwuhCqPDhvkfX+Fcd512RdywQY/Bvn2BRo2ABQt0/r336gAWEyfi+w4dcPIJJwD/+Y//UOV+XzZTz7s9eyIT0G6e77+v32v9ej1fDxniDCBx+unAlCnOx++80/98bQ0frueOhQv9p//lL/4DYhQU6Hl14ED9Hsbo7zlliv92Af29Tz8dePddTdvVV+v5DgBuvFE/++qr/tuzv+011wBjx4b/be+6S/Mhq1kzzecaNQLGjNHfygaLd97pf07z0rGj5jVr1vwyKTc3F5mnn675jsc+yRwyRG8qDAAnnKAt0y1aaH764YfOciNHhv7PPPywnuMnTNDtBLr3Xu99ZrVtq49lIa6uuPVWYN06Xffq1cDFFwM9egBFRf6/X6DLL9fW4nbttOyRn6+jT27fHvozAHDGGXqO79BBf4udO/UYaNtW83P7XbKygr+XfV9Wpo+UFODvf9dpqalASYnuk1tvBTIykOl1G4AEFevgZg6AXiLSHRrUXArg8hhvs+YSYbSd+uaWW4Bx47Rg/OmnzvQLL9STjNWmjX+BIUBhz57eGdHvf68nWFvLf8cdenKwAcWIEYCtdejbV997FfKvvdYplNx3n2aQ7uCmXz/9DqEufn7wQe3qkpbmTBsyRLvBWBddpM/uE81vf6snMnuNxB/+oNObNNHne+5xlm3Xzik4H364dzoCu8+MHKkZGaCF6927NRMEgNtu0wyqeXNn+RNP1OcePfTZZoLt2jnLpKTgYNu2+jo9XTMbm9biYj2O/vUvLSSOHq3T2rfXTHTvXs3cy8qcwCbwNwGA5GT977RqFXoI4N699ZGeDnzxhf7eAwbod7Lps669Fpg3T7+PiAafpaWa1qIiXebss/VRXg6UlDhdl377Wy2UbN6s67VDH/fqpeuZMUO7kB11lBY0XnlFM+KHH3YyjPvuc/4bDzwQ/F0GDgROPdUJbiIJbAD93xzn0eO3adPIPk/BkpP1fxcqOCwu9g56otHC99BDwK5dNQtu+vbVgrF1993O61CFtzZt9Dhr316PkaQkvSO5Xf73v/dfvls3fQT2xXdfeHzGGcHbsZUXXg47TP/3xx/vtFiOHKn5wn//q/OvvNIp5FvXXKNpAbSSpVEj4OST9fjt0kWPS3tevOYamOxsLaA/9BDw6KN6HhoyBJg8Wc/5N98cfJ1Xv37ObzFvnp4LTj7ZCTKGDnXSdNRR+nzZZc651mrcWI/zc87Rm+Aao9sPLHs0a6ZpskR0G0OHelfmNGqkAZ71l7/oOcj+jpdcAkydqkFQnz663aFD9XOXXqqF8tmz9Xy6apV+5qGHnHub3HMP8PTT+vree53tPPSQHi/Jyc60e+/ViuKmTYHHfYPk/vWvwXlTSYkGXTZPC5z/pz8Bjz0W/F0HDND/KQD076+fnzbNCbyvu06337lz8Hm0WTP9v9h9mZqqefrNN+v744/XvOnwwzU9qan6cOcn77yj3+388/X9t99qMNO+vT5sHmo1aqR57Qsv+E8/+mgNTHr3dqaNHu28tmm85RbgpZec6RddpJV86en+62vTRh+Vcf8mKSnBXWmHDwemTEH+oEH6e9QxMQ1ujDFlInIHgK+gQ0G/ZoxZGsttRgWDm+i47DIt9NkThM3QevcGnnxSXw8c6AQ3WVl6cn3zTT0RuFs27roLWLYMO8vKtFZ91y5nXlqa1lgMHAjMmqWZuj3A7TbT0pyTnnXnnVoz9sYb+v7++4MLg0OGaLDTrp3W/vXrp2kLxZ4EA9nuLe6C0kMP6X/NfVI56yx9hHPbbf6Fkwsu0EKQu7Uk3H/Y1jylp2uBzB2wBGre3NlW//7e9zEJJBKcmaSlOSfha67R58DrHULp0CGy5ezF4I0aeQ85DOj+HT7ceZ+U5OyvFi38l01OdoJLq10779/rtNO0MOj+b1x3nQZIdv8mJ/sHvW4PPqjLhvtvRWjrqFHIPO20yDI4qr5GjfS/6fX/nD8fmDix+utu2ND/v3LHHVqwd2vWzDnmL75Ya+ubNtXa8/37tSA7e7YG/IFOPlnPa0VFuuxhh2krpbuyxLa0AFpQTUqqnWD5qqu8p7dpowXJPn30HDNggAYSzZv7V/4Aekyfcorz+uijQ28vKcn/fGoL1+57L3k55hh9WPb807GjrsMGfLYAnpzs3P9owADnM/Z1VbnT165dcGEa0O/Wtavzvm9ffVgizjnn8MO1sJyTo61VNrhxb6d5c+D66515lteNHW3LOuDkdV7XrKWmAjfcoHlYbq6mYd06Z757kA8beG/bFhx4nnqqPiwb6EbiwQf9348aVflnLg+opw/cvpd27bSSzLYyHXUUcN554T9z550a2HfoAPz618Ann+h0d6VgOD16OL9nVpa26pSW+u8fL74gOr8OdUVzi/k1N8aYLwB4nF0TGO+OHR19+nhPb9rUqSEJZAsKF10EfPWV1sY0aaJBwUknaevLrbcC772nzdkXXhicObgLtqEKk4Az/GyjRloA9cq4RZwCfbgMsjpqcqffO+5wmqsHDdIT+TPPaEZw3HFaWLnrLq21mzDBuwB2000a3EQqksAm0LHHAj/+6B1sRXtkqkGDtLuBu6aztrgLCZa7JvPWW8MXDAMD4kGDnEJRFZW0b8/AJt5qkofYY6VZM62cadpU19ejh56DDh7U/du1q1Mo79dPH4B/8D5kiFYqBHattQV/t1Ct0TYtsXb44dqKEY5tDQGc3ymwAqKmevXSYcar8p1vucXJawLzthYtNPjs3l2P81hUnt52W3TW06yZtuyH07Wrf8AUicryutRU4JxzsLmkBJnDhwP2uhpbMTR6NPD111ox2qNH6G6EVXXhheHLCLHQv7+WURo2jKxl3j1M/pFHasXJwYORb+/yy/0rEmv7+8ZJ3AcUSEgMbhyBLShuaWnaOvPyy1Vfr7vWpXdvJxBq3NjJsK++Wvs3X3GF/2cbNHAKg+7aoOruN9sFLJZs7Ve0/luBTc92/U2baisC4HSp6d7dO6Nu2jT2NbFnnqldIGqjNTQpybv2MhFUNVC54ILYpIMSl73Wyt3C7C6IXH119dZru20muksuqby1xC0tTa9vi/Z3GzFCK2WqUgisrHXZBp7RFC4YjQbfNRe1qbRVK80j27fXG/j+8Y86Iz1dA0RA8/9oVYwNHBid9VRVYC+Bqrj++qot79XlLJy0tHpxny4GN7GUnq7df1avjt46b7gh+KLA6ho0CPj55+Dp554LTJqkrx94AHjkEf/599+vJ5eGDZ1uEU2aaMZ04IC+d1+/UZnA5l0rLS30gTx8uNauHXZY5NsJpTYK3i1a6AnG3dUjmmyw59UdqzZqXUMR8e+HTUT+OnfWbmI9elTerSXQrbdGpStjQhCpeuWPu1tYNNNRF2q3Dz9cr4WI1Y0VjzlGr+uJx/n78su11waHVK99t98eukK7DuE/J1bOOktrBRo0AObO1YsUK+Pui+m+CNmtSxe9wG7NGu2aFambbtJg4PPPnWkXXKA1JF995Uzr3Vu7PlRUaP9Qd2ZzxRVaSHfX9ttC9ZFHaobw9df6vmfPyNNWHRkZ/hfIAlWr9attDRroPo2VJk109K5Y1+YRUXT166ddoaqjml0XqZ6I5NqQ6jr9dA224xFgNG5ctWtmKHrscPl1HK+cD+Xaa6u2vB2NxDriCP2DpKRo7UpWVuW1TP37h57XoIEGNfZ1377B1xacfXbo9Xbq5F3Dc/zxzihL55yjNSYiuqw9udxzj9YQ9uoVfDF1w4Z6Md7ppzvdAy65JD53srVNzHWhC0YsZGSwpouorqlDw6vSIUSE+QnVWfznhuK+J8H99zuje3np00cvTNu5U2vS9u6terN24GgvVoMGel1Lhw7B/UzPPlsDpjff1Fr7tDS9l8G4cTr/zjv1wjN3f2A76o7t1y1S+QhdzZuH72ZmW2/atAk/PnysdegQ3+0TEQHeXXmOOUaHDyYiophicBPGzpNOQuZZZ4VvomvRQoMPQFtHgND3RRg0SDM3ewNBy10gv+UWYOlSzRwvvlgL7C1bht5+RoYGX1avXuEL+PEOQIiI6rv+/bWyy96vCAh9PQkHsCEiiip2SwujsGdPDViSkkKPqFWV6zzs0J0XX+w09154of8yHTroaFci2hc7XGBDRESJJylJr1fwXQNX2qKF3jeiQwfNS9yjNPE6OSKiqGLLTaRCjaV/5JFVX1eDBnqdypYtoW82SEREdduddwIlJdjyww/o1bKltswDeo3mokV6bWO07/dERHSIY8tNTXndDC0SyckMbIiI6rvUVJjAa3Cifd8rIiL6BVtuaoqZExERVcWgQcDatcCwYfFOCRFRvcPghoiIqDY1ahT65sVERFQj7JZWFenp+swLQImIiIiIEg5bbqrizjuB0lK9APRvf4t3aoiIiIiIyIXBTVUkJzs3Z0tOBsrL45seIiIiIiL6BYOb6rrlFmDduningoiIiIiIfGp0zY2IXCQiS0WkQkQGB8x7UETWiMhKETmzZslMQO3aAccfH+9UEBERERGRT01bbpYAuBDAS+6JItIPwKUAjgDQEcA3ItLbGMN+XEREREREFBM1arkxxiw3xqz0mHU+gPHGmBJjTA6ANQCG1GRbRERERERE4cRqKOhOADa63m/yTSMiIiIiIooJMcaEX0DkGwDtPWb9yRjzqW+ZbAD3GWPm+t7/F8CPxpi3fe9fBfClMeZDj/XfDOBmAMjIyDhm/Pjx1f82UVZYWIi0tLR4J4NcuE8SC/dH4uE+STzcJ4mH+yTxcJ8knkTeJ6eccso8Y8xgr3mVXnNjjDmtGtvcDKCL631n3zSv9Y8BMAYABg8ebEaMGFGNzcVGdnY2Eik9xH2SaLg/Eg/3SeLhPkk83CeJh/sk8dTVfRKrbmkTAVwqIqki0h1ALwCzY7QtIiIiIiKiGg8FfYGIbAJwAoDPReQrADDGLAXwPoBlACYDuJ0jpRERERERUSzVaChoY8zHAD4OMe8xAI/VZP1ERERERESRilW3NCIiIiIiolrF4IaIiIiIiOoFBjdERERERFQvMLghIiIiIqJ6gcENERERERHVCwxuiIiIiIioXmBwQ0RERERE9QKDGyIiIiIiqhcY3BARERERUb3A4IaIiIiIiOoFBjdERERERFQvMLghIiIiIqJ6gcENERERERHVCwxuiIiIiIioXmBwQ0RERERE9UKNghsReVJEVojIIhH5WETSXfMeFJE1IrJSRM6seVKJiIiIiIhCq2nLzRQA/Y0xAwGsAvAgAIhIPwCXAjgCwCgAL4hIcg23RUREREREFFKNghtjzNfGmDLf2x8BdPa9Ph/AeGNMiTEmB8AaAENqsi0iIiIiIqJwonnNzfUAvvS97gRgo2veJt80IiIiIiKimEipbAER+QZAe49ZfzLGfOpb5k8AygCMq2oCRORmADf73haKyMqqriOG2gDYGe9EkB/uk8TC/ZF4uE8SD/dJ4uE+STzcJ4knkfdJt1AzKg1ujDGnhZsvItcC+BWAkcYY45u8GUAX12KdfdO81j8GwJjK0hEPIjLXGDM43ukgB/dJYuH+SDzcJ4mH+yTxcJ8kHu6TxFNX90lNR0sbBeAPAM4zxhS5Zk0EcKmIpIpIdwC9AMyuybaIiIiIiIjCqek1N/8F0AzAFBFZKCIvAoAxZimA9wEsAzAZwO3GmPIabouIiChiIvKGiPzd93qEiGwKsdywBOsSTURE1VTT0dJ6GmO6GGOO9D1udc17zBhzmDGmjzHmy3DrSWAJ2V3uEMd9kli4PxJPre8TEblWRMpFpND1GFHJZy4TkXc8pq8UkUtc74eKiPGYViAilXatjoQxZroxpk801hUCj5PEw32SeLhPEk+d3CfRHC2t3vFdD0QJhPsksXB/JJ5o7ZNqBA6zjDFprkd2JcufA+ALj+nTAAx3vR8OYIXHtFmuWxEkNB4niYf7JPFwnySeurpPGNwQEdUxvpaMnq737u5XbUTkMxHJF5HdIjJdRJJ88zqKyEcikiciOSJyl2sdWSLyoYi8LSL7AFwbw/QnATgd2m05UGBwMwzAEx7TpvnW9YGIbBORvSIyTUSOiDANd4nIMhHpHNhlTURyReQ+EVnkW+97ItLINf8PIrJVRLaIyI2B+yNSItJeRIpEpLVr2tG+/dOgqusjIiIGN0RE9c290HuLtQWQAeAhAMYXUEwC8DP0vmMjAYwWkTNdnz0fwIcA0gGME5HLfUFSqEdX12ePEpGdIrJKRP5cScvPEADrjDFeQ4xOA3CEiLTypXkwgPcApLumDfUtB+j91XoBaAdgPiK4JYGI/AUavJ1sjPG8DgfAxQBGAegOYKBveTuQzj0ATgPQE8CIyrYXijFmG4Bs37asq6A3wS6t7nqJiA5lDG6IiOqXUgAdAHQzxpT6ricxAI4F0NYY86gx5qAxZh2AlwFc6vrsLGPMJ8aYCmPMAWPMO8aY9DCPDb7PTQPQHxpg/AbAZQDuD5PGUF3SYIxZD2ADtHVmEIDVxpgDAGa4pjUE8JNv+deMMQXGmBIAWQAGiUiLENsVEXkawBkATjHG5IVJ47PGmC3GmN3QoPBI3/SLAbxujFnqGyU0K8w6IjEWwJW+xCVDf7u3arhOIqJDFoMbIqL65UkAawB8LSLrROSPvundAHR0t7xAW3UyXJ/dWJ0NGmPWGWNyfEHRYgCPAvhtmI+cjRDBjY/tmjYcwHTftB9c02YbY0pEJFlEHheRtb6udLm+ZduEWG869KbR/zTG7K3ka21zvS4CkOZ73RH+v1PI38w3CpsdYGFpiMU+BdDPd9uE0wHsNcbw1glERNUUlZFmiIioVhUBaOJ63x7aFQ3GmAJo17R7RaQ/gG9FZA60EJ5jjOkVZr3G/UZErgDwUpjl+7labwLXI14fEJH20Jal+WHWOw3ALQDWA3jdN206gGt802yXtMuhXelOgwY2LQDsCbVt37wrAbwvIhcYY2aESUMoW6E3pra6hFrQGDMdTlAUapliEXnfl67DwVYbIqIaYcsNEVHdsxDA5b6Wi1EATrYzRORXItJTRATAXgDlACqgN1IuEJEHRKSx77P9ReTYUBsxxowLGAEt8LHBt82zRCTD9/pwAH+Gtkh4OQvAZF9XuVCmATgK2kpjA5DF0OtfToET3DQDUAJgFzTY+0eYddrvlA3gCgATRGRIZct7eB/AdSLSV0SaQL9rTb0JvabnPDC4ISKqEQY3RER1z90AzgWQDy2of+Ka1wvANwAKAcwC8IIx5jvfjZR/Bb12JAfATgCvQFs7amokgEUish/a3WwCQgcaIa+3sYwxqwDkAdhmjMn3TbMBWnMAM32LvgltydkMvWn0j5Ek1hgzBcD1ACaJyNGRfMb12S8BPAvgO2j3P7vNkqqsJ2CdM6AB6HzfNUdERFRNEr7yjIiIKDp8I6htA9DDGLMv3umJBhHpC2AJgNSa3HdHRL4F8I4x5pWoJY6I6BDElhsiIqotrQD8ua4HNiJygYikikhL6D14JtUwsDkWwNHQIa+JiKgGahzciEgXEfnOdzO0pSJyt296KxGZIiKrfc8ta55cIiKqq4wxO4wx/4t3OqLgFgA7AKyFXtP0u+quSETGQrsRjvYNBkFERDVQ425pItIBQAdjzHwRaQZgHoBfQy+O3G2Medw3FGlLY8wDNU0wERERERGRlxq33Bhjthpj5vteFwBYDr379fnQm5PB9/zrmm6LiIiIiIgolKgOKCAimXDuVL3BGJPumy4A9tj3RERERERE0Ra1m3iKSBqAj6D9hvdpPKOMMUZEPKMoEbkZesdoNG7c+JguXULeD61WlZUJCgqSkZ5ehqKiFDRoUAERoLAwBRUVgpSUCjRoUIEmTcrjndRDSkVFBZKSOA5GouD+SDzcJ4mhrEywf38KkpMNGjUqRUlJAyQlGZSWJkHEoLxckJ5eGu9kHrJ4nIRXUaFluKSk2htRl/sk8STyPlm1atVOY0xbr3lRCW5EpAE0sBlnjJngm7xdRDoYY7b6rsvZ4fVZY8wYAGMAYPDgwWbu3LnRSFKNvf468P33ucjKysQbb4Re7v77gaZNay1Zh7zs7GyMGDEiptuYMwfo0wdo3jymm6kXamN/1FV5ecDWrcDAgbW7Xe6T+Nu+Hfifa9iEioqlSEo6Imi5K68EevasxYTRL3ichJeVpc9//StQUFA7+SH3SeJJ5H0iIiHvCRaN0dIEwKsAlhtjnnbNmgjgGt/raxD6btUJyTY8/fBDfNNR15RVezDUyO3bB1S3N+W2bUB5mMa2ffuAzz8H3nwTKCqq3jZqQ04OkJtbs3UcPKgF8Ehs2eL/e2zYoJ8PZe9eYOrU6u+n+uD554EJEypfrqbWrgXW87aPCaUgYMwzWwse6O23a7adoiLgp58O7eOMYmv6dODpp4Hdu2tvm2VlWkFAVF3RaGsaCuAqAKeKyELf42wAjwM4XURWAzjN977O2LZNn9esCb9cdnbMk1Irpk8Hli6t2To2bQL+/vfKf7OayM9vgKefBmbNqvpn9+wBXnwR+PDD0MtUVOjzzp3Av/7lP8+Y4EJLvIwdC7zxRmS/tTvNBw44haF//1sL4MYA+flOZrJsGbBypf86xowBXntNX+/fr6/DFdw/+ED/U+EyqHXrNAiKtp9/1n1dl4QLuCvz1lva0kx105Yt1f/shAnAl186+VVdUFQUm+M+1oxx8odYrf/nn723UV5eewHsW28Bf/ub837dOn1+9lmt9AM0LVU5Z1VUAE88ASxcGNnyn32mLZ+Fhbqd/fvDL791K/Djj5GnJ55WrKg/5cZEFo3R0n4wxogxZqAx5kjf4wtjzC5jzEhjTC9jzGnGmFqM+2uuuDiy5bwO1rlztRAdbyUlmpZITopTp2qBtCY2bNBnezJ0M0bTUlJSs20UFGhPypycqn/WniCXLw+9zIEDoef9+CPw1FORt3bUhrffDl9QWL9e07xkib6fOFELQ5s2+X/XZ55xutG8/z7w7rvB69q5U59ti02oApUxzjxjdJ+vXh283JtvAv/9r/+06dM1cAvl3/8GPvkk9HwA+Phj4OWXveft3BnbAkp15ORoYcIeP1ZVWw5LS4ODUoqPqhREx4yp/nbsMVzd4HjlSg2M3en94AM9V7s9/zywYEH1tmF9+qkev089pc+lrsuNFiwI3mZt2b3bv7KsuFjzQ3f6jNHz46OPRmebO3cGF8bnz9dz1+zZ/tP37tXzw/z5Vd/OgQMaVGza5D2/ogL47jtd7sMPgeee05bgUP8nm7dPmqRpys8Pv/3HH9f8prRUt/HJJ5FVyNlzYV6efubJJ8MfUy+9BEyeXPl6E8H48dUPbvbs0crHqtq4sfJ9Vd8k5lVCdUhpaXD3oM8+q72atKwsfbgPfHsAfP65pmX+fODVV/XkUtWm5V279PutWKHbCVew9zr5bNyoJ+eNGzUtn30WvEx+ftUP2NWrtQsZoIXDf/xDa71efVUzqp9/rtr6rHfeCT3Pntij3Srw88/AvHnBXfqMCR9sWf/+twZsNtM4cAB47DHdb/Z/+OGHwFdfOQVm935wBxjuDN2rK8LChU5LUH4+UFoa3N1m2jTnuyxcCPzzn8C4cZoZTpzov2xpwPXUU6eGD1z37g1f+2f/g16Bwc6d+l0TrdbM/q9ef90J/tes0ZbDtWv1fWmpHn/Tpun/5dNPdf+6j7kvvtCgdPfuhrWZfKoFGzbUrHUvlPfe0woQd8C/dGnweTovT/9zNbFggR6/9ns89hjwyCNaWfLpp955QyTKy2vWZeqNN/TcaM878+drJcuzzzrnsXnzgB2eVw1Xzhjgm2+cc/Hu3RrQTp7s/7vbyjf7bAzw/fdOQd9WUFXF+vWaH0yfru+3bdPvAmjAM2aMbuO553T9u3YFryOw9X3lSifQeuaZ8NsvLtaeAu7/rrsrZnm5BnOBFU72soCxY4HFi/U1u14CL7yglY+RmDXLyT9efdXZV1Onhq9ArC8Y3ETBtGl6cNpAIJT339dajJqqqNAMyH2wu4OD//xHt2UDkUmTNLj43//0hF1YqJmMPWm61/PZZ86JaPt2Pem98YbWNgBag+dlyxZgyhR97RooD6++qtu0tf1ehc6XXgp9wGZlha6ptzVc33+v6//4Y/2eX32lrwN5tR4sXqyFxfJyDVoCu51t3x5cAK+q3budQCzQxIma1kmTgK+/9p83fbrWukXSFe699zTTePxx/UxpqX5+0SJnmVmznGsz3BmWO0N77DHn9b59uu/c++yTT5zuaQAwblw3LF2qta7z5um6vvvOmf/TT87r8vLIax937NCMePFi5zvYliNAA6vXXtPfLNRvG8j+jtOm6f/Knvgro4OLRLasl9de0/9kZYxxfjt7bNoaVxvkzpmj/5cFC/S4dHcltfvx4EHv6zuo9kgVd4Et0Obn63/TFmR37dJzw2uvOedXL6+9FvlxAOixkJPjnPtF9HjwOj8Hnn+efda7FaekRPOmkhLNY6wdO0Ifa8aEbrmyAf2CBbreBQt0Xe5KAECDBJuvWfv3V97Cnp/fACUlzrrGjtXXNqApKNDzj62Ycysr02MxVIG7uNiZd/CgXrv74ot6LnPnh+GuUd2wQc8HNv8L9Z9asyZ0/unev4CmYdIkff3KK07AFa6VOLCCzatV34v73B/YxTsrS4Okn37SBt8KdgAAIABJREFUSplHH9XzWnm54IknvIOsQFu3Rq+levZsTdOLL/r/jwA9TtzTDhwI3d364MHQlRChji9A/3fhgjd7bHmVRebP1/OFDVr379cKs6++0i6GgaZP969AdJcFN2yoP9eZR20o6EOZMVr4X7XKf/rixcCAAc57G4D07Am0aqUHZ//+Ou3DD3X+Qw/pdSudO+tIOo0aOZ/fsAHo0kUPxMmTdR2WPVnaWg5A1+9mM78DBzTQqajQA9r9mblztUCVnw+0bh36O2dlAT16AMceq+nxqm23Gbb7YA/sljZnTuWtEwsXAr/2uAXsDz8AJ54Y+nOPPQb86U/6eu9e/wKqHQnG8gqGAKer1g03ONO++05beNLSgHvuAZKSdH39+gEXX+wst2wZ0KyZBniAjjqzcaPuQxH937gL+zk5+ti+HRgyxNkvEyYAV1+t3UWOPRZo2TL0d3Z3p4y08F6ZwK5jgdzdGRs3rnx9q1f7/3e9vPCC//uBA/3T8dlnejxs2ADMnOnsT3cGkZUFXHABMGiQ9zbeegto2xa4/XZ9v369Fpwuu0z/+23aaEC1fr0+Tj658u/mxabzzDMrX9YWdmzLY2WF5Lp47cKhILCgkpeXig4dQi//5JP6/7Ktkh9+qHnDq686BSKvApVtTaio0IJi3746emfLlnouSE3V43PwYP2v7N6t2xkzxj9oKS8PLgjNm+cUhN3fa/dubWnp31/PMe3bayF5/HjgqKM0H9y/HxgxQh+Bx3Igd6UFoAXb554DDj9c33/6qf4G7uBu3jyne/HGjfpcXKzn5IMH9fcEgs/zVm4u8MknnYJagQNbR3btCu5GtWuX7qfp0zV/dufxgNMVbPhw4NRT/f8LgRV1X34JnH9+cPq2b3euo7P5Z+C5wBj9DSKpyRep3e64RUWVV+QuXQq0aOG8//lnoEOHtJDlgSeeAI47Tn9TQCtFAf99XFoKNGjgvJ8wQfdPr16h0zFnjgZYgP6P//Mf4KqrgK5dddrYsUB6OjB6tL4fM0YrQr3+W//4B9C7N3D55c66DztMv2fg8WWM7pe5czU/699f86vkZKeiKzdXj6VwwZ67N8TAgXrOcLdkugebcf9XcnI0/3vmGd3ePfc4FZfl5dXP7xIFg5soMCY4sAGAjz4KPvEB2kXHEtGToT2p/v3v+rxpk37+iiv0/cSJWhA+4wwnaHD/gadO1YPjo48qT+/Mmc6Jzpjg64tsbc7mzd6ft7UH69Z5X18zY4YW0tw1NzYT2bRJX3/1FXDJJdp1zq2iQmvjjzxSTyiWMfod9+1r4Lf8ggWhW8tKSzVDPPpo7/lVkZ3tBAs2aCws1Bone5Jbtkz3UVIScMQRwZnOF1/oye6ii3R+4GhjeXlOc3FpqVPzmJOjGfyyZdXrb1tTVbn2I5JudO7/P6Ctftu3a8EolMACUqh+24GFyoULNbiZNg349tvg5fPytPbvwAFn/9qayaws/4AqK8vJuEpLtc98UhIwdKjOz80FUlK0wFOVWvRAOTn+QYsx2kUQCB/s2Nq3qrYaUOyVlCRXukxg62BZmf+xJ6LngQULgN/9Ts8f7prcGTP0AQA33aTXnZ13njNAiC0k2y5Kbl6BU2BgE5jG998Pbg13t+hkZ2twUxXFxc55c8UKZ3rgeS+wlRvQvGjJEv9upwsWaD504YWanxUXa95jA6dAkyY5hWfAuxZ782anRXXFCqBhQ20Vt/nQZZfp87Rp+nAXEgMDDNsz4L33nEL5/v3+w4hbNr+fPdspjLtlZem5qXdvJ212Hy5f7t/l2ivfrqnNm7UQP2EC0K5d5cvn5Gg+7zZrVmtkZnovX1Kiv+fevRoEeHnrLeD667VA36qVtpQtWqT/w8MP10DcbebM4P9SaalTyLcVY+7rVdzd0nfs0CGyH38cOOUUnWbLghUVTvnmhBOC0/rII1oRYctzS5ZoYHPBBXpsT5vm/R0B/U8fd1zwdHfPC8s92Iz7OBo7Vn8jm2faPAbQwGrQIK2cravEJFBHxkS6z01WFpCbm4vMUEdahI4+Wv/8ffo4NQ1V0bs3cO65ehEmoAdn9+7VGy3MywknaIG7NoZwrsx994W+4PCII7Smpzr7pHVr4NJLQ3epq6muXYMvBg9nwACtWe3eve73fY3GMWLdfbfWmlWHbYEMDO67dweuuSZ0Da7bgAH+rZj2ejavbb35plNAuPpqbbGq7Pju0kVraVu3dgKQrVt1mzNn6vvevbWmz9Z6nnqqBk929KLmzf0Dp9atg2v1cnNz8ZvfZOLccyv/zhQbq1f7B/HRPE7qkhEjEu8aNyvUPunUKXTFXiRatoz8usxu3bQQbFufKnP11c6IZeGMGlV3LrB3i/Q4CZfn3nWXdv1zBw7WX//qnHttRUGkHnhAz/M2T7D5Q1pacFe2O+7QQMKrQi2cxo313olbt4YeFMftmGOc7mixMHQo0KBBQt/nZp4xZrDnPAY33qIV3FDk0tMrH9GD+ySxRHN/NG4cWcuPl1tv9R6hsHlzDYyrUxlwySVaoxpo+PDwtWqVadJEg5JICjSDB2sGbbvgBAY3Xuw+iSSgo9hgcJP4EmGf2K6vpGpjn4werS1MjzwS081Um1ewFC8dOwK9e9fN4Ibd0ihhHGpDFZK/6gY2QOih1/ftq34rp1dgA9QssAG0q1GkXf0C63qq2t1t82btmnLDDf590Sm2EqjOkBIYA5vaV9kIb/GWKIENoF3GZ87MxK5dwDnn+F8Dnug4WhoRUT01ebJeQxc4uAgREVEkFi/2vs4tkTG4ISKqpzi4ABER1VTg9UuJjsENEVE99O23tTv8KxER1U81vd9fbWNwQ0RUD02b5gxZy2tAahd/byKqT9atq9qosPHG4IaIiCiKEmFofSKiaBo/Pt4piByDGyKies7rJo0UO+wOSET1TV1qkWZwQ0RUz3nd0Zxipy4VAoiIInHgAFBQEO9URIbBDRERURQxuCGi+qiuDAkdleBGRF4TkR0issQ1rZWITBGR1b7nltHYFhERUSJjcENE9VFycrxTEJlotdy8AWBUwLQ/AphqjOkFYKrvPRERUb3G4IaI6qOkOtLfKyrJNMZMA7A7YPL5AMb6Xo8F8OtobIuIiKpud+AZmmKGwQ0R1UfLlsU7BZGJZQyWYYzZ6nu9DUBGDLdFRERhPPtsvFNw6EhNjXcKiIiir67czDOlNjZijDEi4lmXJSI3A7gZADIyMpCdnV0bSapUbm4mDh48iNzc3HgnhVy4TxIL90fiCbdP7r+/BK1aHUSrVgfRsqU+GjRgM0O05eWlIje3wy/veZwkHu6TxMN9kngC94mIQXb2+vglKEKxDG62i0gHY8xWEekAYIfXQsaYMQDGAMDgwYPNiBEjYpikyGVnA7m5ucjMzIx3UsiF+ySxcH8knnD7pFs3vefN1q36EAFatgTat9dHRoY+N2+u86h6Nm4Eli513vM4STzcJ4mH+yTxeO2TESO6xycxVRDL4GYigGsAPO57/jSG2yIiokpcd51eD7J3rwY527Y5D3df6saNnUDHPrdtC6TUSls/ERFR9UUlqxKRdwGMANBGRDYB+Cs0qHlfRG4AsB7AxdHYFhERVV3z5vosAqSn66NPH2d+SQmwY4cT7GzfDsyb5/SxTkoC2rTxD3gyMoC0tNr/LomOAwoQEcVPVIIbY8xlIWaNjMb6iYioZu6+O/z81FSgSxd9WBUVwJ49/gFPbi6waJGzTFqaf8DTvj3QunXdGTKUiIgiM2BAvFMQGXYyICI6BFTn5mtJSRqotG4NHHGEM72oyOnWZp9zcoDycp2fkgK0a+cf8GRkAI0aRee7EBERhcLghoiIqqRJE6B7d31Y5eXAzp3+Ac/KlcCCBc4y6enB3dpatqx/gxewWxoR1Ud15dzG4IaIiGosOVmDlQzXHc2MAQoL/bu12aDHZpKpqc7nbMCTkQE0aBCf70FERN6OOy7eKYgMgxsiIooJEaBZM3306uVMLy11Bi+wAc+iRcCcOc7nWrcOHrGtWbO60cpTV2o3iYiqwn1NZiJjcENERLWqQQOgUyd9WMYA+fn+Ac+WLf73i2nSxHuI6upcT0RERJGrK4ENwOCGiIgSgL2haMuWQN++zvTiYg123PflmTMHKCvT+cnJzhDV7qCnSZP4fA+ALTdEVP+cdVa8UxA5BjcebKZJRFSX3XsvsHmz/4X/dU2jRkC3bvqwKiqAXbv8A55164Cff3aWadYsOOBp1YpDVBMRVUddqrRhcOOhLu1AIiIvN96oBfzDD493SqIvKUm7o7VtC/Tv70zfv98/4Nm+HVi7VoMhQLvDtWsXPGJbamp8vgcRUV1hbwRdFzC48cBReoiormvfPt4pqH1NmwI9eujDKitzhqi2Ac+yZcC8ec4yLVsG34i0RYvqD17ACjIiqm+aNYt3CiLH4IaIqB6qC6OK1YaUFCdgsYwBCgqCh6hescIJTBo18h+i2g5ewMovIjrUXH99vFNQNQxuiIjqIQY3oYloF4vmzYHevZ3pBw8GD1G9cKFOt59r08Y/4MnIANLS/H9vttwQUX3StWu8U1A1DG6ipHlzYN++6n9+4EBg40Zgz57opYmIiCLXsCHQubM+LGP0vOwOeDZuBJYscZZp2tS/W9v+/dFLU9u2QF5e9NZHRFQV7m6+dQWDmyg4+mit/Rs/vvrruPBCveh1/nzgs8/CL3v11cCbb1Z/W0R1Rfv2WpiMVNOm0S1YRlNgBcgZZ+jF/s8+G5vtseUmOkR0lLVWrYB+/ZzpBw44wY59nj07uqNtXnUVcNhhus68PG1Bev31yj938cXA++97z2vdWkeaA4D0dL23UDT17QssXx7dddbU0Udr3nqo6dYNWL8+3qmguiLUeePSS2s/LTXFQTFDuOaayJYbNAg47zwtpNx4o9ay/f73oUeVsDWCjRrpA3BujJSUBAweDPzqV6G3l5amUXSs+30fdxzw0EOx3UZV1GSUjjvuAEaPjl5aqPaEO6k2aQLcd59zHAHATTf5DxkcCa/m9jPOADp0CP0Z93DChx0W2XZuuQXIynLen3iiFphvuknPIZE4/njv6V73H2BwE1uNGwOZmbpPzj9f9++DDwK33aYDFHip6iAP9uakKSn6f+zWTQMeIPQN9UaO1CDszDP1v2VlZQF/+ANw663OtNGjgaFDI0tL797A/feHX+b3vwcuucR5/5vf6G8UbyNGAH36BE/v3197TVTFH/8IPPywc8ydcYYex24XXaTPKb7q46qek6rCdqvs2DF43vnne0+n6LrhBv/3Vf1PRYv7f3jSSVX7bP/+/pU31q9+tQUNG9YsXfHAlpsQIr0vRIsWzuvOnYHbb9fXl1wCvPyyM+/GG3W40dattYUmJUWf7ev/Z+++46Mo8z+Af57d9EpCeoEAAUIJCRISeoKKooh4WA476onY8E5PPQsavdPTU+/07n4W7BXksNMUxdCRjgFCCiQhlfSQhNTN8/vjyczO7M4mm2ST3cTvm1de7M7Mzjy7M/PM832eZ55RSkgQBbbiYmD3bpF5VVeLC+Pll4tlHn8c+PprIC9PbOvAAdvXTLm49L5mz1J3vb/8BXjhBevWcd114qT78EPxfU0tWwasWiVe33EH8O676hr/gICutxEcLGpfbem220SaFy8WhWFljUhyMrBtm223J5k2Ddi7t2/W3Rlru88EBoqaaKkL5sUXA2fOiP1leqxIQbyHB3D+vPhuM2aIIF8KMP70J+DHH0UBxtNTBOZSbaWPjzjH6uuBKVPUI2SFhgJz5gCjR4vRs7780jhvxgzxt2cP8P33YhpjxnspnnoK+Mc/gOZmUdjctg34+Wd12idPFrXYn30m3nt6GufFxRlfh4cbg3cPD3G8njmjXldSkjim4+KAwkLxJ7nqKiA+XhQiy8uBl1/W+tVJf9DrxVDToaHWdzH29RXHstbyXl7m00aOBH73O1GhtmqVsRUGEAHN9OnitfT/pEnGQF16sOl99xkDp3nzgF27xGsXF+P9RUrLl5sHZk89BTz7rPH91Vcbr4ezZ4s8deJEIDZWtKa+9JLl30ASESHy+4oKcQ4UFIihvAHg+uvF7/Tpp+JcnzgR+O9/RUVkYaH4rb75RlyD/fzE56OixLnv4wMsXAhkZop1RUeLa8nMmUB6ujoN7u6iVU76Tl98IV5fey2QnS2u44yJczIpScybMUPkOe+/LypcvLyACRNEPnfsmNjeG2+I/ZCTY9zWtGnigbQGg+XfJDlZ5E9S3jpsmDF/WLxY5DEnTojf+fXXxW8ncXYWlS/FxcCCBcCGDcZ5l10mvo8yPQPFQw+JfaCV111wgTgOTfNjrVasoCCRh9fWWr9tnU4EtEFBogeNl5eoaEhNFeeRm5u41vz6q/bnnZ3FNamzfQ4A11wj7v/z9QW++844fdgwcYxKx7LEwwNYsUIMlMKYSJdeb30545prxP933y2+w7/+Jd4HBGhkCANAnwc3jLH5AF4DoAfwDufcyiLtwBYeDjzxhHionJ+fug+3VCjT6Sw/UG7iRPF3ySXa8xkTFzhJQoIIbm69VZyoO3aoL3pBQeJEUXrySVGgXLdOZAiursCmTWKeVIi78kpgyxYRnOn1xprnW28VBffOREaKC9LWrSL4WrBA1CIdPiy2deedIq3r14uC6+TJ4mLj6wt88IGoeVA+qdy0tSopSdzsGxYmakwNBnGCS2lU1pIDYj9IBYj4eJHZffONeL94sciMpIt8d0REAK2t4jeWLpTSQwefekq851wUIhgDpk4V36WxUXRj0XL77cDmzeKiJAVeQ4eK1ydOdJ6e2bNFgcDafvr33CMuioAxODQNVObNE/vwlluAzz9XdxV74AHjDdXvvguUlIjpUiBfXS1+Xyl4uOsu8f/f/ib+V9Ywme4zT09RYJswQfyG3t7m54yrqzi2JOPHi2OvoEAcT4GB4iJ04YWiUHT8uDh2lC0bkyapgxvJ9OniHG5pERcOg8F4bjz0kHG55GRxsSkuNk5btEj8f+216pp80+8ofQdAnANTpwKvvqqeP3q0KCCZWrhQHMuAcWSv2Nha1NWZL0v6T3dbzQID1cHNkiWiYkmrYoYxY3B8770iD5s5U+Q/WhYvNp9mqcLn0UfFMdzSAnz8sZi2cqUxEAKABx80v3bde6/4DpKLLlKvVxnYA+LaU1srpmdmikL/+fMigHJzE8GI1M//7FmRXikNN94oCuw6nfFckiojJ0yAXMssfV6qOPDyEi1V+/aJ9Hl4FCM0NEp+XpL02TfeMAY3Y8aICpDt20W+MmGC9u8GiPze9Nx2cjKen1Kr1/nzosB5ySXiO82eLfKYsDBx3bv/flFADQszfpeUFHF92rNH5MHSfWDSPpeOh2XLxP4LCgJyc0V+OXeuKB94eYk8MSFBrH/CBJEPvvGG5Yq9JUvEdqQKHnuSKrgA45DEAQHiu9x/vziOfH2N+WR2tsgPDx4U+etttwHPPCNa8Ex7BHz+uThexo83BuHXXCPWUV4ufquxY0Ua9Hpjfv3ww+rKaWUr6JNPirJOTo46EHniCfH/P/+pXfErdVdWtsJwLq5bU6caW1eKioCmJnGeOjmJ67DytwHEvh81ShxHISHAm2+Ka3dQkLi2SeWfG24wfiY42PjatEVyIGG8D4d1YYzpAWQBmAegEMB+ANdzzjWLZwkJCfzAgQN9lp7uWro0D1EdbeqJidoF0dmzzTNyR/Cf/6iDm8cfFyeCj4+o+WJMfUADIvOUCnhSX29T0qAHkyapgwhlpj5qlLHrBCAy7q++EpmLVvO8VGA0bcEy9b//ARs2GPfJH/8oCgCW5Ocbh3OVvPeeyPzmzBHvKytFwVOnE0GDsnXl7rvFb3bsmMj4SktFJtfQINaZlSUuzqNHGz/z8cci2Jk1q+uug01NxtarhQvFhXTvXvG5lBTxu7S3i/dtbWKf6fUis5ZqcpKTjd0OVq8WaQ4OFvdxVFV1vn2JtP+GDBG/aXm5eH3okAh24+LUgXRDA/DDDyKzP3lyFxYvNubo7e3G2tzUVPW+NQ06tYLQoiJxjL7yivm87iguFrXaK1ZYn0EfOiR+/6uu6l1XDmkfTpnSvc+1tIjP1daqgxt/f/E9JNL+l2qITaWlpSElJaVHaSe2sW6desCByZN3YuLEWdiyxfwesqQkkR8dOCBq33/8UQQkXeWHtqR1Lh49KvLF2FjLn6uuFgU9qUWoM6dPiwq2MWMco9Bk6TyRCoCAKKD2536whx9/BHbu1J739NPiuiO1SlkyYYK47p88Ka6LSqGhosLL0v2Tf/iDuKb8/DOwd6/x+g6ISrUtW0RLxaOPimu0u7v1PWuA7h2jfUU6r4YNUw+p/MoromJZrxcV4iNGWK7wtkRZNujKpk3AL78YW/tra8XntK4jVVXiN9u713GvJ4yxg5zzBK15fX3aJgLI4Zyf7kjIGgCLAHRR9+x4Lr9cO7hx1H7to0aJgvvDDxtrzaRaoBtv7PyzkyZZvo8gMlK7r7dUMx8RYexvLImLE90DlF34lKw5KQFRO19YWIMpU0RrUFeZlVYgZTpW+9Chxtfjx4sg5vRpUeCQgiJpPcpWJMBYI6ekDOq64uYmMpX6enEceXuLzFyi16v73EumTBGFW2dnUTMjURZMrr5a1DZOmyYys/h4URvp7S2CF6kmTvo9HnnEuA2pBjYxUWzftHDj6WkMdsrKWlXzTDNm033bVcYdHi7+v/LK3j2EMiys+4HRBReIv96ydF9MV6Tz09dXrGPiROCdd9T7GBD7v7uBE+lfpnWGvr5tGDVKFNRMRUeLcyo5Wby/7rq+T5+pP/3J/NxUdp20xNK9RVpMH67q6G68cfAHNoBo0U5MFNeHjAwRaOzfL+ZJ5Rtlq9THH4uKmMsuE0F8XJzx2I2OFi0SSr6+orW+uVn0jJgwQbQWAeJ6J/VqGTkS8PEpxYQJUfjqKzFt5kzRWiGlQ+uekK505xjtK6mp4vubHk/K1v+e6s4xeuGFItCTrumWymSAY1RA9EZfn7rhAAoU7wsBJPXxNm3u6actz3PU4Ebqe23aHaAzUkuD8gZta02aZDxhtH6Tzk4ia3l4AJMn12D2bNFi1he/fWKiCG76q/B4zz3ATz91/wbEa6/t/Ib38HDRJRAwr+WKjhZ/bW3mffGVGBNdGLrr5pvV/b4ljz9u/T6zRZAxUDEGzJ8vXve05YqQ7rBF/jxYSHmUVm32YKTTGZ/5JFXoScGNFmUFnrJFGRC/2dixonJw0yZRkSZdQ1xdReEaEF2zysqMlVmAKH+EhTUhLk4EMe3tYvpAvJldi9SVzd5pkPbBYNfX3dKuATCfc/6Hjvc3A0jinN+nWGYZgGUAEBwcPGVNb8ZTtrFVq8Lg4uKCpUvzAAC1tc5obNRj82ZjlXJ8fDXi47txN5oD4xzIyPDB6NF1cHbu+rhobNSBMcDNrb0fUifU19fDq4+vOpw7btDqaHqzP06d8oSHhwGhoU02TtVvW3+cI6RzP/8ciPx8Y83SNdccg5eXF775JgzV1erS2sUXn0VERGN/J/E3z9J58u23oaiqcsUVVxQP2Jupe+uDD6IAQC779BfKuxyPI++TuXPn2q1bWhEAZSemiI5pMs75KgCrAHHPjSP17fvgA9H/MyUlSp7W1ib6lUrmzo2yqvl+oDDtAuNo6H4Cx9Kb/UG7sW/QOWJ/Z8+qK0i8vPKQkpKCjAzzm7eTkqJU9+2R/mHpPMnKEvfsTZ8epWpZ+C2JiRGjyU2bFtWv26W8y/EM1H3S18+52Q9gNGNsBGPMBcASAN/28TZtZuHCYlx9tXqak5PxOTT+/vYbz5wQQohj+q10/RiMpPtHrHmEwGAVEtLzewcJcQR9GtxwztsA3AfgewAZANZyzo/35TZtaejQFs2RYqSefKNGUfclQgghaoGB5g/2IwPD2LHiXjdHuEeCENIzfT4WCOd8I4CNfb2d/iTd6EaBDSGEEC2RkWIUsvPnjc+56OrBfYQQQnqvr7ulDUpSP1xLwyUTQgghvr7qUQ2l52sRQgjpOxTc9EBEhBjWduxYe6eEEELIQDFpkvG5HoQQQvoGBTc9NFjGXieEENJ/liwRD6kdNszeKSGEkMGJghtCCCGkn3h5iYfUUgUZIYT0jT4fUIAQQgghavPmicEGhg+3d0oIIWRwoeCGEEII6WfBwcCyZfZOBSGEDD7ULY0QQgghhBAyKFBwQwghhBBCCBkUKLghhBBCCCGEDAoU3BBCCCGEEEIGBQpuCCGEEEIIIYMCBTeEEEIIIYSQQYGCG0IIIYQQQsigQMENIYQQQgghZFCg4IYQQgghhBAyKPQquGGMXcsYO84Ya2eMJZjMe4wxlsMYy2SMXdq7ZBJCCCGEEEJI55x6+fljABYDeEs5kTE2HsASABMAhAH4kTE2hnNu6OX2CCGEEEIIIURTr1puOOcZnPNMjVmLAKzhnDdzznMB5ABI7M22CCGEEEIIIaQzfXXPTTiAAsX7wo5phBBCCCGEENInGOe88wUY+xFAiMasJzjn33Qskwbgz5zzAx3v/wtgL+f8k4737wLYxDlfp7H+ZQCWAUBwcPCUNWvW9Pzb2Fh9fT28vLzsnQyiQPvEsdD+cDy0TxwP7RPHQ/vE8dA+cTyOvE/mzp17kHOeoDWvy3tuOOcX92CbRQAiFe8jOqZprX8VgFUAkJCQwFNSUnqwub6RlpYGR0oPoX3iaGh/OB7aJ46H9onjoX3ieGifOJ6Buk/6qlvatwCWMMZcGWMjAIwGsK+PtkUIIYQQQgghvR4K+neMsUIA0wFsYIx9DwCc8+MA1gI4AWAzgHtppDRCCCGEEEJIX+rVUNCc868AfGVh3nMAnuvN+gkhhBBCCCHEWn3VLY0QQgghhBBC+hUFN4QQQgghhJBBgYIbQgghhBBCyKBAwQ0hhBBCCCFkUKDghhBCCCEUOqLiAAAgAElEQVSEEDIoUHBDCCGEEEIIGRQouCGEEEIIIYQMChTcEEIIIYQQQgYFCm4IIYQQQgghgwIFN4QQQgghhJBBgYIbQgghhBBCyKBAwQ0hhBBCCCFkUKDghhBCCCGEEDIoUHBDCCGEEEIIGRQouCGEEEIIIYQMCr0KbhhjLzHGTjLGfmWMfcUYG6KY9xhjLIcxlskYu7T3SSWEEEIIIYQQy3rbcrMFwETO+SQAWQAeAwDG2HgASwBMADAfwOuMMX0vt0UIIYQQQgghFvUquOGc/8A5b+t4uxdARMfrRQDWcM6bOee5AHIAJPZmW4QQQgghhBDSGVvec3M7gE0dr8MBFCjmFXZMI4QQQgghhJA+4dTVAoyxHwGEaMx6gnP+TccyTwBoA/BpdxPAGFsGYFnH23rGWGZ319GHAgBU2DsRRIX2iWOh/eF4aJ84Htonjof2ieOhfeJ4HHmfDLc0o8vghnN+cWfzGWNLAVwB4CLOOe+YXAQgUrFYRMc0rfWvArCqq3TYA2PsAOc8wd7pIEa0TxwL7Q/HQ/vE8dA+cTy0TxwP7RPHM1D3SW9HS5sP4BEAV3LOzytmfQtgCWPMlTE2AsBoAPt6sy1CCCGEEEII6Uxv77n5LwBvAFsYY0cYY28CAOf8OIC1AE4A2AzgXs65oZfbIoQQQqzGGPuAMfa3jtcpjLFCC8vNdrAu0YQQQnqot6OlRXPOIznn8R1/yxXznuOcj+Kcj+Wcb+psPQ7MIbvL/cbRPnEstD8cT7/vE8bYUsaYgTFWr/hL6eIz1zPGPtOYnskY+73i/UzGGNeYVscY67JrtTU45zs452NtsS4L6DxxPLRPHA/tE8czIPeJLUdLG3Q67gciDoT2iWOh/eF4bLVPehA47OGceyn+0rpYfgGAjRrTtwOYo3g/B8BJjWl7FI8icGh0njge2ieOh/aJ4xmo+4SCG0IIGWA6WjKiFe+V3a8CGGPrGWM1jLEqxtgOxpiuY14YY+wLxlg5YyyXMbZCsY5Uxtg6xtgnjLFzAJb2Yfp1AOZBdFs2ZRrczAbwosa07R3r+h9jrJQxVssY284Ym2BlGlYwxk4wxiJMu6wxxvIYY39mjP3asd7PGWNuivmPMMZKGGPFjLE/mO6P7ujYT1JrV0PHuqJ6si5CCCEU3BBCyGDzEMSzxQIBBAN4HADvCCi+A3AU4rljFwH4I2PsUsVnFwFYB2AIgE8ZYzd0FL4t/Q1TfHYyY6yCMZbFGFvZRctPIoDTnHOtIUa3A5jAGPPvSHMCgM8BDFFMm9mxHCCerzYaQBCAQ7DikQSMsacggrdkzrnmfTgArgMwH8AIAJM6lpcG0nkQwMUAogGkdLW9znDOh0itXQBeA7ADFkYXJYQQ0jWb9FcmhBDiMFoBhAIYzjnPgSgsgzGWCCCQc/5sx3KnGWNvA1gC4PuOaXs45193vG4E8FnHX1e2A5gIIB/ABIhgpA3A3y0sb6lLGjjn+YyxMxCtM2cAZHPOGxljuxTTXAD80rH8e9JnGWOpAKoZY76c81qN1TPG2D8hgqu5FpaR/JtzXtzxoe8AxHdMvw7A+x0D50jbvLGT9Vil456iGwBM5Zy39nZ9hBDyW0UtN4QQMri8BCAHwA+MsdOMsb90TB8OIEzZ8gLRqhOs+GxBTzbIOT/NOc/lnLdzztMBPAvgmk4+cjksBDcdpK5pc9ARnAHYqZi2j3PezBjTM8ZeYIyd6uhKl9exbICF9Q6BeGj037sIbACgVPH6PACvjtdhUP9OFn+zjlHYpC5nxztZbjLE6KO/45yXd5EuQgghnaDghhBCBp7zADwU70OkF5zzOs75Q5zzkQCuBPAgY+wiiEJ4bkc3KOnPm3N+uWI9XPEajLEbTUZAM/1TdkuDyXqY1gzGWAhEy9KhTr6fFNzMhjG42aGYJnVJuwGiK93FAHwBREmbsbDeaoiHTr/PGJvZyfY7UwLxYGpJpKUFO0ZhkwZY0LwXiDEWBOBriEcmHO5hmgghhHSg4IYQQgaeIwBu6Gi5mA8gWZrBGLuCMRbNGGMAagEYALRDPEi5jjH2KGPMveOzExljUy1thHP+qckIaKZ/Zzq2eRljLLjjdQyAlQC+sbDaywBs5pxzC/MBEbxMhghmdnVMS4e4/2UujMGNN4BmAJUQwd7znaxT+k5pEN3IvuzoqtddawHcxhgbxxjzgPiuPdJxX9I6AJ9wztf2dD2EEEKMKLghhJCB5wEACwHUQBTUv1bMGw3gRwD1APYAeJ1z/nPHg5SvgLh3JBdABYB3IFo8eusiAL8yxhogupt9CcuBhsX7bSSc8ywA5QBKOec1HdOkAM0HwO6ORT+CuM+nCOKh0XutSSznfAuA2wF8xxi7wJrPKD67CcC/AfwM0f1P2mZzd9bTIQKiJeqPVraIEUII6QLrvPKMEEIIsY2OlopSACM55+fsnR5bYIyNA3AMgOtAee4OIYQMZtRyQwghpL/4A1g50AMbxtjvGGOujDE/iGfwfEeBDSGEOIZeBzeMsUjG2M8dD0M7zhh7oGO6P2NsC2Msu+N/v94nlxBCyEDFOS/jnL9h73TYwF0AygCcgrin6W77JocQQoik193SGGOhAEI554cYY94ADgK4CuKBZ1Wc8xc6hiL145w/2tsEE0IIIYQQQoiWXrfccM5LOOeHOl7XAciAePr1IgAfdiz2IUTAQwghhBBCCCF9wqYDCjDGomB8UvUZzvmQjukMQLX0nhBCCCGEEEJszclWK2KMeQH4AsAfOefnRDwjcM45Y0wzimKMLYN4YjTc3d2nREZafB5av2tvb4dOR2MuOBLaJ46F9ofjoX3ieGifOB7aJ46H9onjceR9kpWVVcE5D9SaZ5PghjHmDBHYfMo5/7Jj8lnGWCjnvKTjvpwyrc9yzlcBWAUACQkJ/MCBA7ZIkk2kpaUhJSXF3skgCrRPHAvtD8dD+8Tx0D5xPLRPHA/tE8fjyPuEMZZvaZ4tRktjAN4FkME5/6di1rcAbu14fSssP62aEEIIIYQQQnrNFi03MwHcDCCdMXakY9rjAF4AsJYxdgfEE6Svs8G2CCGEEEIIIURTr4MbzvlOAMzC7It6u35CCCGEEEIIsYZj3iVECCGEEEIIId1EwQ0hhBBCCCFkUKDghhBCCCGEEDIoUHBDCCGEEEIIGRQouCGEEEIIIYQMChTcEEIIIYQQQgYFCm4IIcSOWg2taDW02jsZhBBCyKBAwQ0hhNjRczuewws7X7B3MgghhJBBgYIbQgixMwM32DsJhBBCyKBAwQ0hhBBCCCFkUKDghhBCCCGEEDIoUHBDCOl3dc11KK4rtncyCCGEEDLIUHBDCOl3r/3yGlYdXGXvZPS7qsYqVJyv6PV62nk7siuzcbb+rA1SRQgZKAztBvxw6gc0tTXZOymkm947/B5e2vWSxfk/nf4Jm3M292obzW3NqGqs6tU6BgMKbkiX2trbcLT0KDjn9k7KoMI5x/b87ahpqunX7Ta3NaOgtgAA0NjaiHPN5/p1+4A4pn5LSupKUFxXjH//8m/8d99/e72+7fnb8Wn6p3jjwBv48fSPNkghIbbRamhFc1tzr9axu2A3siqzNOcVnStCY2tjr9Y/kKWXpWN3wW78dPqnPt1OSV0J3jzwJloMLX26HWtxzvusDKJ1HTxZcRJ7Cvaopu0t3ItvM7+V369OX6050qWltJ6pPYOG1gZUN1bLlVythlb5N95xZgf2Fu7t1Xd5/8j7+Pcv/zabvj1/u+b0wYqCm0HuXPM5fJf5HQztPR+NaWvuVnx18itkV2XbMGXa2nk72nk7yhvKUddc1+P1NLQ0OHytdnVTNbbmbsXq9NVdLvvFiS/w/I7nbbLdtcfX4t3D76K5rRn//uXf+Oeef9pkvQPZsbJjSE1LRUNLg83W2dbeJl+03jr4llUtVXXNdRYLbnXNdUhNS0VGeYaqZm7nmZ0orS+1TaLJoJRZkYlfCn+xOL+hpcFmx9Brv7yGv+/8e48/vyFrA3449QM+S/9Mnnaw+CBK6koAAG8fehsfHPmgt8m0yvnW8/2yne6QruW2GmGRc47c6lyzwvgPp35AaX0pCs8Vdmt9x8qOIb8m32x6QW2B5vO82nk78mryulzvuhPr8My2Z7qVFkvqmuuQWZGJM7VnAAD/2vsvs+vgmmNr8P2p71XTNudsxqGSQwCAyvOVyKzMRFNbE9p5OzjnaOftAIBPfv0Ez2x7RjVN6bVfXpMruV7e/bLZtf109Wmcrj6NjPKMbn83S+fx1tyt3W7Rya3OxQd5HwzIXhY2CW4YY+8xxsoYY8cU0/wZY1sYY9kd//vZYluke9ZnrcfBkoP4777/dlnrYWg3IDUtFR8e+RCNrY1o5+34OfdnVJ6vBACLNWlKL+16CV+c+KLTZbROdskru1/By7tfxv/t/z+8sucVfJf5HQCgqa2pW7U2/7f///DGgTesXr67is4VIS0vTXNeWl4aduTvUE3LrMg0C7ak79Pa3vUDHNPL0uWC8s+5P5utvzu1WtK9LgZuQGNb92pAOefYX7R/wNScWnvc7CvaBwDd7jJW3ViNtLw0+fNKr+9/vdsB6St7xPGv1NTWhM05m3GsTGSvB0sOmn3OUWpXie1pHcO1TbXdevDr6mOrsSlnk8X5bx54E28eeLPHaVSqb6kHIApoJytOdvvz+4v3m037Lus7vHXwLfn92QbtiqvK85Xyb3W87LicFlP5NfkwcAPWZ6232HJ9tPQo/rHrH3JQpaWdt2NPwR60tbchvyZftZ8yyjN69XBezjmOlx3vVcWkKa1r7+HSw/jw6IdIL0u3+LnG1karrv+ACELeP/K+alpdcx3ePfwuvsn8xmz57fnb8cGRD+QAp9XQiqJzRQCAH0//iNS0VADA8fLjmts7WnoUX2V8JeeBR0uPqlpXlD4/9jn+tedfeGXPK1h9bDXeO/weAGP+mV1pfQXuf/b9R3797LZn8cmvn+DZbc8CAE5VnwIAvHPoHXmaJc0G81bOj45+hI+OfoTPj39uXK6t2azCt6yhzKpzbGP2xk7nb83divcPq/dZXk0ePjz6IQAMyPtjbdVy8wGA+SbT/gLgJ875aAA/dbwnCpxzi4WS5rZmixmztes+U3tGzmyrm6o7PUAzKzLl+bk1uVh7fC3Sz6ZjW/42ZFZmAgAOFB/AmdoznWb2Da0NnWaSJ8pP4Nltz+JwyWGRrsZqHC8zZloNrQ2q2rKDJQdxtv4sXtj5Ap7Z9gyqWqyreehujVuLoaVbwdPbh97uNLj5KdfYZeDjox9j9bHVeOPAG6pmbg6xPQam+nx+TT5e2PmCZp/q1emrsS1/m2r97bwdz2x7Ru6exDnHifITXV4UTbdrjaK6ImzI3oAXd70oB729UdNUI3eRs8aJ8hNW1T7VNNWgurEaL+x8AetOrLO6gNBiaEE7bzc7L4vOFWF7/naz5d888CbS8tKwMXujWYBjTTqV65Q+L9XI7i/aj8JzhXhh5wvYW7jXrBZRqSf7kjgWQ7sBvxT+oiqAVjVW4YWdL5gV+P+191/45NdPOl3fsbJjSD9rOS9WqmvpeSu5od2gWYj/6OhHWHNsTY/XC4h7FHae2WnVshXnK/Cfff/B9vztyK3Oxf9O/A8v737Z7DfIr8nH+0fex6bSTThQfADrs9bL86oaq/D8judR1VglF1DLGspQ31KPDVkbVPlIdWM1nt32LL4/9T3eP/w+3j/yPnYX7AYgCoOfH/+8y0KlqXbejr2Fe2FoNyCnKgf/O/E/bM3d2q11WFJcV4xntz2LnKoc1fTqxmoAMOsezZjIU1anr8aLu17EZ+mfdbtS6439b2B/0X65AK9VdihvKAcAudX8q5Nf4e1Db6OhpcGqff/Vya9w9OxRuSLpq5Nfya0rkuzKbKw5tgYZFRmoba5VzVNexz5N/xQAsOXUFmu/okw6XpSK6kSQdrr6tKqcI5Eqb6U0apG6H7518C28sucV1bzX97+ONcfWdBlEa1W+1TXXydeo7fnbkV+rbm3rr9bRvmKT4IZzvh2A6ZV8EYAPO15/COAqW2zLkR0uOdytk3/L6S14fsfzmgfmmwfexMu7X7YYkGzN3Wp2AisdKjmE9w6/Z3VXstXHVuPdw+/K76saqzTvi/jgyAd46+BbZoW/FkOLxfsoGlsb5cBBamb9JvMbuU/v/078r9O0KVtg9lSq+8CW1pdidfpqi4XXv277q2YhM7syW85UDe0GPL/jec2azfOt57Exe6PF7/bc9ufwj13/6DT9ykzv+1Pf4/X9ryM1LdVin+lt+dvQ1NYk114pSYGmkpQ2KQPLqszC2uNrsT1/OwztBuwr2qcqMElBlZaGloZO+8srfwcpwKppqsHW3K1m+6DifEWnXWEA4NW9r6qOu66sPb4Wr+9/XTXteNlx1DYZL1h1zXV4de+rWHt8rZhffhx/3f5X+fesaapBO29X/b5ScPBp+qf4MuNLPL/jeTS2NqLoXBHWZ63H24felgsZTW1NKGsoA6CudduYvbFbAXLhuUJVwcW0ILQhewPeOfSOVet69/C71HozwO0p3INNOZvw8dGP5fNVyru0as1NCyOm1p1Yhy8yvtAMcL7P+R4v7nzRbHpmRaZc4Go1tFpVwfbWwbfw3I7n5JZFLVtObcG6E+sAQO5u813md9iQtaHTdZ+pPaO6p0x5fu0t3KsqmB4sFi2aP+f9rKrN3l2wG6lpqXLhUQrkKppFK21WZRbqmuuwKXsTdhfsRouhBb+e/VXOEzg4NmZvxP7i/ar9oKzEk/IDqeVXqpjqzv2U9S31+Nv2v2FzzmbsLtgtt6qfaz4nt5hbKsRyzpFRnqHK51sNrdiYvRFNbU2oOF8hdy2yVIi2VHZR9izIrMzEM2nPWGzl++u2v8qvT5SfwNmGs9iQvUHOn6WASZJVmWXWIiOVeZQBvem1pdXQioPFB1WBqanUtFS8uvdV5FTl4NP0Ty22cChbYADR9WxXwS7VerIrs/FVxlfyNCkgtNZHRz/SLOcoW+GlwMrUjjOil4ayHLM+a72qG91zO54z+1xDi7i3x7QV61TVKRwoPoBX9rxidg9OaX0pTleftuIbOT6nPlx3MOdcCtNLAQT34bbsrqyhDN9kfoP1Wevx6KxH4aJ3ASAyuS8zvkTy8GSE+4Sj1dCK/Np8RPtH42jpUQAiMGhobUBJXQnGBY4DIFpaAGDVwVVYkbQCrYZWBHsFI6syC046J7nG94LQCzTTU9nY+1p1LVLm+fyO5/F08tNyZmWp+01dcx1e2fMK/Nz8cPfUu1XzMioyNJtku+Prk1+jtL4UZQ1lCPUOVaURELXg//7l30hNSVV9TspIUlNS5QL7kdIjuHz05ThbfxZvHHgDN026CSfKT+BQySGEeYchPiQegLqmp7W91apuZUrShTCjwtif9kDxAcSHxMNJ5yRfwKXf1tp7j6TlpVarc83nsLdwL7ac3gIGhnGB4+QWM1P1LfVw0jnhpd0vwdPZEw/PfNiqbRaeL0Ta3jQAgL+7v/wbAcC7h95FY1sjNuVswlPJT0HHOq9LqThfgcbWRgR6BsLNyc1svvS7KwOsfUX7sDF7I3xcffDg9AcBiNY/ACipV9cSvn3obdwQewM+S/8M/u7+qGqswtL4pWbBr1RIe3GXeeEvpypHrjE3PaYA4Jltz5hN16qE2JC1QbP7jTWK64o1g+0vTnyBzMpMzGyf2aP1EvuSChW5NbnYeWYn5gyfY1WwvCl7E0b5j8KYoWMAqG9QBoAvMozdhI+VHcPEoInYU7jHbD2AqOQCgPnR8/HmgTdR2Vhpdjx/fPRjnKo+JU+X8rN1J9ZhYtBEs3XWNtXKhcVWQ6tZBc2CMQvk193pxrU5ZzN2OO/A4nGL4eniqfpOvxQZK1WkfCCzMhOjh47WXJdpjfjB4oMY4TcCgLiemN770Nbehp9zf+4yjU1tTUhNS8XvJ/xevrYDomfGFxlfYLT/aMQExJhtX3ldPN96Xr7XpPx8OYI9RVHqUMkhXBZ9GZz1zsiqzMLnxz9HSlQKUqJSUHSuCEV1RdhXtA86poOPq48q7ZuyN+GikRfJ5RRABNdxIXGoaqySK4ZMfX3yawDG+zpyqnJQ31Iv5/vK+4CU6/j4148BiDz+QPEBJIQlAIDqd2WMoa29TQ4IlT0jpM8DohJ5f/F+q7pK1TTVdNnCaUrrhn7TwOO1X17r1jp7y7TL2IHiA2bLHC87Dr1OL79/abf2qGzK3xKAqiVPClpXJK3ocVodBbPV6BOMsSgA6znnEzve13DOhyjmV3POze67YYwtA7AMAIKDg6esWdO7Zmxbqq+vh5eXl1XLljeXY0OJqIUa4TkCyYHJAIBjtcdwoFociPOC5yG3IRc59TmYFzwPW86Kps8xXmOQVS9qhMb7jEeifyI+yPvAbBvXRVyHtYXqTGdp1FLN9Oyv2o/j59Q1IleEXoEA1wCzZU+eO4m9VeoT2kPvAU8nT5Q3l1v8zjcOuxHOOmcAMEvv0qil4Jzj+7Pfo7RJZITRXtEwcANyG3LN1iV9D63vreQDH1w57EocO3cMsb6x2FCyAVUtVVgYuhAuOhcUNBag4HwBSprUBdsLgy5Ek6EJYe5h0DM9Pi8QfVljfWPh7+KPbeXbjNtw9sG51nOI9IiEq84VOfU5SPBLgIfeAyO9RmJNwRo0GdRdxpT7QfoO1n4nySivUZgdMBvrS9ajorkC4e7h8Hbyxsk67RqnJZFL4KZ3Q0VzBdaXrIcTc8JNw29CVl0WdlfuRrRXNNz17kivTVcdY5LrI6/H6gLtwQwi3CMQ4R6BGJ8Y1fTSplJsLhVDVQ73GA7nNmfktIjM8YIhF4Axhok+E8EYw4d5H5q1El0UdBFcda7YWKpupVgatVT1Oy2NWoq61jpsPrsZDW0NmOY/TXWMLghdgKEuQ/FR/kfytFuH34ojNUdQ2FiIyhbrgntXnSua23sWYGudj1LaAev3u5aUwBSklaf16LOXDrkUoUNCe7xtYnuWriVNhiasL1mPsd5jcbDaWIs71nsspg+djoLzBfip7CcEuwUjxjsGoW6hWFNgvEYqz5tbht8CHdN1edwpP2PpWA10DZTz/mEew3Bh0IXyPGnZaf7TEOMTo/rs7yN/L+et1hrrPRa1rbXwdfZFZp15y7SSpfNVyrO72s70odOR25CLbeXb0NLSAhcXF4vLMzCz/Gtu4FwM9xyO9Np01f7SMz0M3IBor2jMCpiF4sZi/HD2BzgxJ7TxNgS6BmJBqDGIk/JoS98p1jcW9W31ZtfKIc5DUNNqbA2KHxKPSPdIVLRUYE/lHoz2Go0ozyhsObtF/k3GeY+Dp5OnXA5RfjbONw7flnyL6pbutUQA5sdRQ1sD/lfYeS8MAAhwDcAVoVcAAHZW7EROvbh+MDAE6YJwtr3nAwHNDpiNHRU7ul6wD8V4x1i8ZttCpEckCs5b35W7t6TzxFJZ057mzp17kHOeoDWvL1tuzjLGQjnnJYyxUABlWgtxzlcBWAUACQkJPCUlpQ+T1D1paWmwNj1F54pw/JAIJoK9gpGSID7nUuCCilOiqTob2YA7EBUQhWxkIyoqCgDQghZEBYjX53EecUlxiEKU2Tb2YZ/8GYml9LWcakFDgXrkp9Exo9Ha3qqqNTFwA0pPlCLKx3x7AOAJT4vfeRd2YXnCcrQaWs3Se9DlICJ8IuDG3OR5I/xHwNXJFbzMPKBOSUnBFye+0PzeSg0lDSgNKEWNrgaFQwrhw3zgAx+kIx3uTu5odG6Eq4+r2XpOQ9SKFkPU9ki/Y13HvyhP9fL+8AcAtKENUQFRqIDYh4unLcZ2tt3sxsz6sHqcrT+LOy64A2lpaWKbvqdx++Tb5fddMcCANKTBK9QLXhAFoSY0IWqo9m9ySH8Ij89+HKlpqfL3SZyZCJ8KHxRnFiM2JBYezh6oK6hTHWOSOTPnYM8u7RpcAChFKZanLJfftxha8OaBNxHlZlxPTUmNvO2qjp6plUGVuHrc1UiD+fc+BdFFz/Q4dhnlotpnO9gOGJwNCIwIRCACUQr1MXocx7Fyzkps3664D2Y4UJtfC28/b3jD2+L3shWt8xEwnpPW7nctecgzOyat5dnqaXW+RfqHpWvJkdIjCNAHoBKViPKNkqdHDY1CWmUaZsXMQpSHmJ6PfFS6VKqOuZSUFPk4y/XJxbxR87rMQ2MSYuRlZs2Zhaa2Js1zVZn3K9MubU/KH5TH+S/4RfOc6EwzmuEGNzSj2WJeZw0pz7YkxDcEabVpSB6fjPz8fOTl5XU7rWOjx2JaxDTwXI7KfGMFipPOCW3tbWhDG0ZNHoVh7cOQddRYmeTj6oOhI4fii4wvsGjsIvhX+yPK2/K2mSsDb+aICjRfZgjkOmO4eroivSEdcAWivKMQGxqLEK8QZGeLrmf+8EeYXxhOVZ9SHV8AUIMa8OEcvswXvvDt1u8AAAkzEuTjJmlmEl7c9aJVv2eETwSSJycjvSwdbRltqutST/aJUgEKEOXV88/bQmfXbFvp6hy3JWmfjJo8CpG+kf223d7qy+DmWwC3Anih43/zYTIcXE59DtLS0vDozEfh7uxucTnOuaoZXCI9bKu7etrk2dbeBj3Tm/VrlXx1UvQZjQ2KxeHSw71+WBQAi31v61rqVN2uANF9IcInwuK6OhuIQFLeXC43o+bWqGu1ujvyV0+8uvdVzelazcRnas9oTreVFkOLWXrWHl+L8YHjAYjfp7M+39YMunC45DDCvMPg5eKl2cxd0VwhB2KSY2XHMC1imjVfQWZ6nlgzzKlp9xZlP2l7OlZ2DJ7OlisF+lpZs7oeadeZXRjlPwohXiF2ShGxxFLPCWnkKNMbqk3vgVF2Pys4VyCP/tQZZZ79WfpnVvWx/6XwFySEJai6vQDAlxlfdvlZRyHdo2Rp1C1rbM7ZjM05mzEhcILFZQ4UH0Mrj4sAACAASURBVEBcSJxq2rnmc3L3QK0Rw0xZ++wx01HjDpUcgpNOXazTutFdojVIirWUozpqdeG1pPBcoc2GdCb9593D72p2xXZUNgluGGOrAaQACGCMFQJ4GiKoWcsYuwNAPoDrbLGt/pRxLgPeXt6oaqxCuHO4xeWK6orw69lf5fel9aUoqC3AkdIjfZ7GfUX7EOYdBn93f/xj1z8wb+Q8zBzWeZ/73jyDoDdqm2tRW16rOc9Wz3CxN2nYSklnNzzagmnwkleTJxeMurqZ1Zqx6625EGtRng99xbRfuKM8GFS6cdpedlbsxLVN18LXTdTGbjm9BVtObxlQF6bBILsyG3sL98LP3Q+eXDvYtRTEW3svYm/zTWtvHt6UswmbcjaZHUP9cZ7bWneHe9diGiCZ5j29Gem0txwlHyTEnmwS3HDOr7cw6yJbrN9epBYQS6NLcc6xNXerPJqFUndGgOoNaYSlZVOWARAjw6TlpXXaQuKIaKSn/tfbwRw6ozX0JOk/0vnUV0/0Jl2Tb0KuBmbwGWbz82vy+7zyg/S/o2eP4ujZo/ZOBiE2xzm32DPI0djqOTeDzunq06qhIvcU7EFzWzO+Pvm1PGRiUV2RZmBjD9+cFDXsDa0NaG1vNeuyRQj57ZAuQJ09MJf0ny8KvzDbF10N5eyIuhranRAyeD2z7RmbPlS2L/XlPTcDmnL4Vqlf6q9nf0VJfQm8XLxw8ciLHargYOmJzYSQ3y5HyqN+y5rbm9HY2ghPF2P3tK6GRndEWs8CI4T8dhi4AXrou17Qzii46QZpvPyBeFEihPx2NLU14WTFSfmJ6dIDCYn9fJb+Gdyc3HBz3M32TgohhAxqFNxY0FlhoLapFpzzPh0JixBCeuqz9M9Uo+FRhYz9FdUVARAPqnTWO1PASQgZcAbKfZwU3PTA0bNHUVxXjPLzlh9wSQgh9mI6zPdAuQn0t2DHmR24cMSFqG7q/oMTCSHEniwNsOVoKLjpIQpsCCEDBbUSOI7t+dsR4RNBLf+EkAFnoNzHSX0VCCFkkKNuaY7ls/TP7J0EQgjptoHSLY2ueIQQMshRtzRCCCG9NVC6pVFwQwghg1xTW5O9k0AIIWSAo5YbQgghhBBCyKBALTcD3JnaM/ZOAiGEkAEoozzD3kkghBCbowEFBrja5lp7J4EQQsgAJD3ThhBCBpMT5SfsnQSrUHBDCCGE2FCrodXeSSCEEJurbhwYz+ei4IYQQgixobb2NnsngRBCbG6g3HNDD/HU0GJosXcSCCHEptadWAc908NJ5wRnvTOcdE7yn7PO+F45r7PpOqajIaYtoOCGEELsp8+DG8bYfACvAdADeIdz/kJfb7O36IF3hJDB5kT5Cfi4+qCtvQ1t7W1oNbTCwA09Xh8D63ZApDW9u58ZCPnzQLnplhBCumOgDAXdp8ENY0wP4P8AzANQCGA/Y+xbzrlD35E0EC6ehBDSHf7u/rgv8T7VtHbeDkO7Aa3traqgR36tmN7ZPK3pzYZmNLQ2aM7rTeFfx3SqoKe/gqvutFINlK4bhBDSHSP8Rtg7CVbp65abRAA5nPPTAMAYWwNgEQAKbgghpB9p3eSuYzro9Do46537NS3tvN1mQZTpvPOt5y1+pjdBh6VgSCsgOlZ2zIa/FiGEOAY909s7CVbp6+AmHECB4n0hgKQ+3iYhhBATjjS8vY7p4OrkCle49ts2OecwcIPNgijTeY2tjfJrQggZjNadWIeVySvtnYwu2X1AAcbYMgDLACA4OBhpaWn2TVCHvLw8tLS0IC8vz95JIQq0TxwL7Q/H09k+cZT8dSBy6vjnBrcul82qy8Luyt3yezpPHA/tE8dD+8TxaO2TNJ5ml7R0R18HN0UAIhXvIzqmyTjnqwCsAoCEhASekpLSx0myTlpaGvLy8hAVFWXvpBAF2ieOhfaH4zHdJ08nP403DryBGZEzEB8Sb7+E/Yb4lviiOLNYfk/nieOhfeJ4aJ84Hq194ijl9M709c0l+wGMZoyNYIy5AFgC4Ns+3iYhhJAOjDHcM/UeCmz60cSgifZOAiGE/Gb1aXDDOW8DcB+A7wFkAFjLOT/el9u0lRFDej8ixO8n/N4GKSGEkO67KuYqPDT9IXsn4zfJWe+Mu6bcZe9kEEKITd0z9R57J8EqfT4sGOd8I+d8DOd8FOf8ub7enq3cGn9rrz4/N2ouxgWOQ2pKqjztsujL5P//OO2PmD1stuozI/1GYpjvMPl9sGcwhrgN0Vz/QBmxgtiel4uXvZPQp34X8zt7J6FHLhpxEaaETjGb7u/uL7920ql7Ars7ufdJWq4dfy3iQ+Lh7erdJ+snXQv1Du3xZy8acZHZtEdmPoLlCctV0+6YfEePt0HIYOTr6mvvJJgZ7T/a3kkAAMwZPgcr56zEgtELcEPsDV0uH+YdJr+eETkDDyQ9gCDPoL5Mos3QmMc2Mj1iOmKDYjE9YjpSU1KRHJUsz/vzjD/j8dmPIykiCQ8kPYDE8EQMcRsiBzLSwTJr2CxMj5gufy4uJA63xd9mtq2UqBSHGa1ipN9IzemmF2F7ifSJtDhPq2Z1IAwDvnjcYjw842HcGte7ANwWpg+d3vVCVlDui7iQODyV/FS31zHKb5Tq/R+n/RF3TbkLj8x8BCuSVnS5bz2dPXHH5DvwVPJTuG7CdUgMT8TCMQtVyyybsgwXhF5g9lkd0yE+JB5DPYaazVOe0y56FwDGwmuYd5gcrNqipXfeyHm4LOQyjA8c3+t1EfvwdPbE7OGzMT96Pu5PvF+e7uHsgRCvEFXhLdLXcv62ImkFlk1ZhgtHXIhw73DNZVz1xtHqZkTOkCvg+sstcbf06/YkXdU+K89ZpZF+IxHoEdgXSTJzd8Ld/bKdwWb4kOEAgItHXowrxlyB2KBYs2Umh0zus+0nhSeZHSOJ4YmIDYrF3Ki58jQd05lds+JD4uXPRg2JwoPTH8Tloy/HIzMf6fSaeOcFd+KqmKu6TNtIv5HQ6/SYGj4VY4aO6bJy5LoJ18FdLyrgpkdMh5+7X5fbcBSOX5IbAO5LvA+XRl+Kq8dfjUujLzWb7+XiJRdq/Nz95IfBjR46Gg/PeBjLpizDdROuw0i/kRgXOA7XT7weABDuHQ5fN1+snLNSdRCmRKUAMK8FlkjbAoA/XPAHxIfEIyncfATu3kbgN026SRX9B3sGy69DvEKsXs+FIy7sdP69U++1el3KYCY1JRW3T75dfj85ZLKqsKqsWZ0UPAkrklbgsVmPdbmNuVFzNS+OI/1Gws1JeySlx2c/3uk6n05+usvtSvRMD08XT4zwG6H6frYwM3ImPJ09zaaPGTpGc/mx3mNV7511znJr44zIGZqfmRo2VfV+lN8os1puHdN1+ZtI54Hk5ribVe+HuA1BqHcoPJw94O/ujz/P+DPumXoPLhl1CZbGLzVrARvqMRSRvpHQMR3GB47H5aMvx5SwKXg6+Wk8OP1BPD77cYR5h2HhmIWq3/13Mb/DU8lPwdvVG4nhiWbpnBA0AU/MfgJ3XnCnvM1o/2gkhidiUcwi3BJ3C2YNm4WYgBhMCJwAAFg5ZyWuHX8tZg2bJa9nzvA5Zut20jnJ99MsHLMQMyJnINgtuFsPnCT2pwwypH03LWKaZrBs+iBWye8n/B6RPpEY6j4UTyc/DX93f4R5h2HO8Dm4bfJtuPOCO80+o3y+UUxADBLDEzVbjayxcMxCRPtHW5yvdfxaqhzrrWvHX4sVSSuwaOwi3D75dtw15S5cPvpyeX6QZxDih8RrFnwB4NLoS/HozEfN8tdb4m7pshB5/cTrLebLl0VfhlAv61r0gr2M19PuXAN70ppnKZiTBHkG4bb42+Cs69/nYVlj8bjFAAAGhpVzVsppdHNyQ0JYAq4efzVWzjFWBt8adysWxSzCn2f8WXN9N8beqHo/ZugYLByzUD5HpWP8L7P+IgcrMyNnyssHegbK5R/p+A70DMTV469GclQyJgVPAgA8lfwUbo67GakpqZgbNRcXj7wYV8VcJVdMXTv+Wvi4+iAxPBEezh7QMR1unnQzHp7xMFJTUvHYrMewZOISPJD0AMJ9whEfEo+nk5/GZdGX4e6Eu7Fg9AKzynHTnkCRvpFYOWclLh11KRi0rxnJgckYM3QMPF3MywWOzO5DQTuyy0Mux4mO543OjJyJaP9ofHj0Q7PlAjwCerwN6YBR1rSODRiLR2c+CndnETHrdXpE+kZiecJy5NXkycs9NP0hvLjrReiZHgZuUE3/+86/Y2bkTET4RCDCJwKcc8QExCBqSBRqmmqw7sQ63DTpJqw9vha5NbkARIHS0pPDw7zDUFxXjJSoFDS0NGDWsFnwdVM3/y6NX4q3D72NqsYqzXUsHrcYX2Z8aTbdzckN4d7hKKoTA+mNGDJCTtOyKcsQ6BmIh2c8DMYYdrbvxMXJF0PHdPj7jr+j2dAMQGQUx8uOY2LQRDyz7Rm5UMgYwzDfYThTewZXjLkCep0e32V9J297RdIK1LfUq7oDPpD0AF775TVVGmMCYnCy4qS8P4I8g3DTpJvwya+fYGbkTOwq2IWbJt0EHdPh46Mf41T1KdXnlQGnFmsKowwMKVEpqrQqX2uJ9o9Gq6EV+bX5mBs1Fz/n/YxRfqNU6XPVu8q/4/TI6Zg3ah5S01JV67kh9gZklGfg8+Ofd7q9J+Y8AQBobG2Eu7M7dhcYh8Md6TcSp6tPY+awmVgwZgHaeTtK6kosnj9av4ly36REpaDF0IKcqhw5E79j8h149/C7muvzcPaAh7OHHNT/cdofwTmHs94ZWZVZiPCJsJgOH1cf1fthvsOQmpIqf0+Jk84JQ9yGoKapRrUOZ70zwn3CERcchy2nt8DXzVcubPm4+uDikRcDAK4efzWuNFwJvU6PCUETMCFoAlKiUuR0bs/fDmedM1rbjQ/kDPIMsqrWjtiXm5MbmtqaNOc9MvMR/HX7XwGYtx7HBsVibICxEsH0gatL45eiurEa4wLHYVzgOM31O+mcEO4TjvnR87E5ZzNWJK3AuhPrMNx3OPYU7gEgWoysyYdCvUJRUl8CABg7dCwyKzMBiILTTZNuAgCz/OOBpAfg5+6HkroSZFdl4/7E+y1Wzs2InKHKNwDRsvvWwbdU06T8RMuEIFFJoOwSGuodio3ZG+X38UPikTI+Bell6ZrrcHd2xzDfYbh98u147/B78nTpmj09Yrr82ylJ+2reyHkI8QrBx79+LM9LikhCUkSS2e/TlUDPQDyV/BSe3fas5vzJIZNxuPQwwrzDEOkbidvib8P7R97vcr2Lxi6Sg2Ct76I0fMhwPDHnCVQ3VptdH7ty4YgLsTV3KwDjtTTEKwSl9aVWff7SUZfCRe+iunZLpGAh0icSep0eY4aOwcGSg6rWSr1Oj0CPQJSfL5ePCS8XL9w79V58k/kNbom7BXk1eYj0iVSdX4Eegbhm/DVw0btgSpjoctxiaEF5QzncnNyQHJWM2cNnQ8d04ODYXbAbzjpnjPQbifSydFwy6hIEeQapzumrYq7CgtELVN9B2dMnJSoF0yOna1aUjvI3tvS4OrkiJiBGNZ8xhqQIUZEtBcepKakobyiHl4uX6lql/G2mR07H9MjpON96Hv/Y9Q95npeLF0LcQpASm2L2OUdHwU0ngtyCUOJWguqmakwJm6LKKPua1kEY4hWiahFxd3bH47MfBwPDx79+jFZDK66dcC1cnVyxcs5K1QnFGMMIPzFIgp+7H+6cImrxbo67GXk1eWhoacCEoAk433oeAPDy7pcBiMwv0DMQET4RKGsoQ6BHoMULoLuzO+5PvN9igBQbFIvCc4VIDE9Eq6FVvlhxznHjpBuRVZmFQI9AhHmHoZ23Q68z3lckXVBcdC7y91oUswhrj69F1JAo6JgOscGiFk55nxMgCuYV5ytU65P4u/ub7Vet+5yWTFyCzTmbUXSuSG4Fi/aPxsMzHoaniyfmjZonL3vdhOvw951/l9+b1rrdGnerZpAMiCD3klGX4NW9r8ppWZG0ApXnKxHoqd0dYqj7ULg6uWJp/FI8v+N5AIC3izfiQ+Jx0UhRC9vW3gYnnRNG+I1AqFco0svS8W3mt0gKTwIHx76ifbgl7ha5ZeH2ybej1dCqujCPCxyHlXNW4kjpEXyX9Z24qJwVfZxjAmIwNdzYIiMdv8qAeWrYVNwQe4NcqNExHcJ9tLvLWGLaLH7JqEtwyahL5PeRvpF4eMbDVq1LWbiy1DLVFa3z1MPZQxXccM7l1zMiZ2BaxDTNYxEwPtzSUjofnP4gnHXOeHHXiz1KL7GPQNdAPJD0gOZ++8MFf1AdD6b3nF09/mqzzyyZuAS1TeKhrFFDohA1JMqqdEyLmIZpEdMAiIqjHfk7AIgCp9RKNDl0Mn7K/QmAaI0/23AWgOhukxyVDAaGl3e/DAM3YPbw2Rg+ZDh+OPWDKt8M8gxCWUOZWV68ZOISVDdVa7ZIDfMdhqghUbhwxIUoritGXk0ekocnI7MyE6HeoViRtAIMTK5U0zGdZpBgWuBTun7i9Wb3oV0z/hp4OHvg59yfUXCuAFeMucIsXUpD3IbgvsT74O/ujz2Fe5AYnojLR1+O9VnrVRU1M4fNRFcem/UY9Do9/rb9bwDUgSMg8jepm5KO6ZCakormtmbV9SUhLAFXjLkC86Pny8eR1DULEK1Nvq6++M++/wAAnpzzJDZmb0RSeJJcAG41GCtLuuLn7oc5w+dge/52edqVY6/ExuyNFh9gO2f4HGzN3SqnFQDqmuvwyp5X5PnS+vRMj5XJK7E6fbUcOCeGJ6Kdt+O7rO/g6+qL+5Pux+aczZg3Ulx3pQAHEMHlk3OeNAueb5x0I9LPpqsqqgI9A/GHC/4AQH0NSE1JReX5Svi6+Zqtx0XvorpuSeWRuVFz4ensidjgWDAwi60dWnm8EmPMYg+QnrJUdjDl4eyB0f6jkV2VrbpWD0QDN+X9zFKTnb1JLQKmzeCWCk+mdEyn6hogFW4XjF6AcJ9w1Q1llrqxLU9YjvyafADixJQGO7h45MUI9AjE6mOr5XnKrgFSbdyYoWPg4eyhGqrWmgETYgJiMGvYLFWTsBY3JzeLtfJaTIM3KfiZHz3fbFmtzEuZca2cs9JsX4zwG4HHZz8OzrnqIvXozEfhonfBueZz8jSpObqzzOn+JGO/fGedM0b4jTC7WVDKpKQLdVxwHCrPV2LO8DnQ6/QYFzBODn6Vy915wZ2q9Oh1ekwJm4K4kDjomR7bzm7Dn6b/yWLankp+Cs1tzdhfvB8xATHd7i4V4ROBwnOFXU5Tsnfz+fUTr0dGRQbS8tLkygKJ8vzoCenCvGjsInyT+U2/3yNBumfJxCVYc2wNItwjNANhAHLe5O/uj9igWNV5aElnBfjukIIiZbdlLxcvuTU6NjgWN4fcbNaF88k5T6KsoQzBXsEI9w7HpOBJqmWWxi/VbMHX6/RmLbVSS6fyGnbTpJvQamiFu7M75o4QXX+sqVx8cs6Tnd5bp2wBk0jDdnfWRW56xHRV7wrpOyiDN9OgSOLt4o26ljrVtNvib4OHs4d8rQj2DEa0f7RZq7lW115XJ1fcGncrfN18Vb+JaYH50ZmPwlnvbFZAddI54cqxV2qm1UnnhOsmXIfP0j+Dm5MbpkVMQ1pemtlysUGx2J6/HYvHLcauM7swMWgiNmVv0lynxDTQ9Xb1xhOzn4CTzgnnms/JwY3UQjI+cDwyKzMxN2ou9Do99NBj4ZiFGOU/Ck46J4u/t/Q9TA1xG4LZw2drLK1NKwDvjLPeWRXQ2vs69FtHwU0XOLjq/WOzHlMVSAcrZS18V0xblCRS17B7p96rmdmEeoeaZXjdoWM6uTtPd/i4+qgK7F3pzYhEysBmqPtQVDZWAjAGpfcl3icHzlLhR1mz1F1StzBr0qVsbbJUoAr3CUc4zFtXulOj4+rkqrp3pDtunnSz2fl2x+Q7zM5LRyLde7MtbxsA8zzEFiaHTsbk0L67KZbYRkxADJYnLEfG/gwAkLuyalmRtKI/kwbA2OfetAJmUvAk7CrYhZiAGM3RGRljcq0/Y8xsGakLqDWWJyxHY2ujapqTzsmqPEbq6pc8PBnjAsf1WU2z1r201npoxkNmLUzKlhUAuHtq9wYPsCYA1gqmxw41D+6Ajm6L3uGYPXy26r7VcQHjNIObQM9A+dottZrcGn8r3jn0DgDRJS+rMgv5tfmdplHqAubl4oUQrxAE+AXg0lGXyus1cAPiguPk5aXAh5CuUHDTTZ01JxJt1jaJ9pdlU5ahurG602VSU1KRWZGJIM+gHtXA3Jd4n9l9F8sTlqvulQC079fS6/RYNmUZVh1cNSBGb+tLyvNNupgyxhy2JVVpUcwi/Hj6R6sLeWRwCvEKwUkm7tfr7IZ7e9Fq5Q/2Cu5VxVN3uDm59bgbziMzHwHg+KNc/mXWX+yeZ5l2VVdijMld1ZWBprRfLI22pxThE4HlCcuRU5WDmcNmYnrkdJTWl6LF0NLlZ/U6PZYnLEdafZp8PDLGNEemJMQaFNyQ3xwvFy+rnhWj1YXBWgEeAWaBi7Pe2exmYEtCvUIxI3KG5uhbZGAYM3RMj+/lIYPXPVPvwaGSQ5gYNNGsxYJ0j6MHNZLuBG+J4Yk2eYi4KWu7qkvLBXoEwtfNF3dNucvqCkplLw4d06m6tRPSnyi4IcQBMcZUN8r/ljEwh+6GRkh3BHkGad6/RwgA1X2p9uCid8HNk26WA5PePIyWEHuh4MZKlm6EtndTMyGD3f1J96O8odzeySCEkN8E5ZDD5LdlUvAkZFdl9/o5iPZGwU0XlMO4KimfxUII6b0FoxdgV8Eus+law3UTQgghxLZig2Plx2oMZBTc9EBqSiraebvFB2oRQrpvavjUbo3SRwghhBBiamDcjeeApO5onY2NTwghhBBCCOk/1HLThbEBY7GvaB9c9eohoBljuC/xvl49k4QQQgghhBBiOxTcdGF+9HzMHjZb84FYWs8oIYQQQgghhNhHr7qlMcauZYwdZ4y1M8YSTOY9xhjLYYxlMsZ6/nhfO9MxHbxdve2dDEIIIYQQQkgXettycwz/3969xdpR1XEc//5SbgYMCJiiFAVjhRTDtSEQCSkXtSgBNcSUaEQkaUx4gERiQB6MJn0wJqIGJRJAUZGK1QrRgNyjL4Dc5F4tNykBqnKzkkDAvw97lW7KKRVOz5k5c76f5OTMWmuyZ3X/sqf9d9bMhs8APxrvTLIAWALsC7wXuC7Jh6rq1UkeT5IkSZImNKkrN1X1QFWtmmDoBGB5Vb1UVY8AqwG/al2SJEnSlJmqp6XtDjw+1l7T+iRJkiRpSmRTX1L52g7JdcBuEwydU1VXtH1uAs6sqtta+zzg5qr6eWtfBFxVVSsmeP2lwFKAuXPnHrx8+fK3/6fZwtatW8cOO+zQ9TQ0xkz6xTz6x0z6x0z6x0z6x0z6p8+ZHHnkkbdX1cKJxjZ7z01VHfM2jvkEsMdYe17rm+j1LwAuAFi4cGEtWrTobRxuatx00030aT4yk74xj/4xk/4xk/4xk/4xk/6ZqZlM1bK0K4ElSbZNshcwH7h1io4lSZIkSZN+FPSnk6wBDgN+n+QPAFV1H3A5cD9wNXCaT0qTJEmSNJUm9SjoqloJrNzE2DJg2WReX5IkSZL+X1O1LE2SJEmSppXFjSRJkqRBsLiRJEmSNAgWN5IkSZIGweJGkiRJ0iBY3EiSJEkaBIsbSZIkSYNgcSNJkiRpECxuJEmSJA2CxY0kSZKkQbC4kSRJkjQIFjeSJEmSBsHiRpIkSdIgWNxIkiRJGgSLG0mSJEmDMKniJsm3kzyY5O4kK5PsNDZ2dpLVSVYl+fjkpypJkiRJmzbZKzfXAh+uqv2AvwJnAyRZACwB9gUWAz9MMmeSx5IkSZKkTZpUcVNV11TVK615MzCvbZ8ALK+ql6rqEWA1cMhkjiVJkiRJb2ZL3nPzJeCqtr078PjY2JrWJ0mSJElTYqvN7ZDkOmC3CYbOqaor2j7nAK8Al77VCSRZCixtzXVJVr3V15hCuwL/7HoSeh0z6Rfz6B8z6R8z6R8z6R8z6Z8+Z/L+TQ1stripqmPebDzJF4HjgKOrqlr3E8AeY7vNa30Tvf4FwAWbm0cXktxWVQu7noc2MJN+MY/+MZP+MZP+MZP+MZP+mamZTPZpaYuBrwLHV9WLY0NXAkuSbJtkL2A+cOtkjiVJkiRJb2azV2424zxgW+DaJAA3V9WXq+q+JJcD9zNarnZaVb06yWNJkiRJ0iZNqripqg++ydgyYNlkXr8HerlcbpYzk34xj/4xk/4xk/4xk/4xk/6ZkZlkw20ykiRJkjRzbclHQUuSJElSZyxuJpBkcZJVSVYnOavr+cwWSS5OsjbJvWN9Oye5Nsnf2u93tf4k+X7L6O4kB3U38+FKskeSG5Pcn+S+JKe3fnPpSJLtktya5C8tk2+0/r2S3NLe+18m2ab1b9vaq9v4nl3Of6iSzElyZ5LftbZ5dCjJo0nuSXJXkttan+etDiXZKcmKJA8meSDJYWbSnSR7t8/H+p8XkpwxhEwsbjaSZA7wA+BYYAFwUpIF3c5q1vgJsHijvrOA66tqPnB9a8Mon/ntZylw/jTNcbZ5BfhKVS0ADgVOa58Hc+nOS8BRVbU/cACwOMmhwLeAc9u9kM8Cp7b9TwWebf3ntv205Z0OPDDWNo/uHVlVB4w9ytbzVre+B1xdVfsA+zP6vJhJR6pqVft8HAAcDLwIrGQAmVjcvNEhwOqqeriqXgaWAyd0PKdZoar+CDyzUfcJwCVt+xLgU2P9P62Rm4GdkrxnemY6e1TVk1V1R9v+N6O/jHbHXDrT3tt1rbl1+yngKGBF6984k/VZrQCOTkaPt9SWkWQe8EngwtYO5tFHnrc6kmRH4AjgIoCqermqnsNM+uJo4KGqeowBZGJx80a7A4+Ptde0PnVjblU9nbyTkgAAAtFJREFU2bafAua2bXOaZm35zIHALZhLp9oSqLuAtcC1wEPAc1X1Sttl/H1/LZM2/jywy/TOePC+y+g73/7b2rtgHl0r4JoktydZ2vo8b3VnL+AfwI/b8s0Lk2yPmfTFEuCytj3jM7G40YxRo0f7+Xi/DiTZAfg1cEZVvTA+Zi7Tr6pebUsJ5jG62rxPx1OatZIcB6ytqtu7note5/CqOojRUprTkhwxPuh5a9ptBRwEnF9VBwL/YcNyJ8BMutLuBzwe+NXGYzM1E4ubN3oC2GOsPa/1qRtPr7/s2X6vbf3mNE2SbM2osLm0qn7Tus2lB9qyjhuBwxgtEVj/3WXj7/trmbTxHYF/TfNUh+wjwPFJHmW0jPkoRvcWmEeHquqJ9nsto/sIDsHzVpfWAGuq6pbWXsGo2DGT7h0L3FFVT7f2jM/E4uaN/gzMb0+62YbRpborO57TbHYlcHLbPhm4Yqz/C+3pHYcCz49dRtUW0u4FuAh4oKq+MzZkLh1J8u4kO7XtdwAfZXQv1I3AiW23jTNZn9WJwA3lF5xtMVV1dlXNq6o9Gf19cUNVfQ7z6EyS7ZO8c/028DHgXjxvdaaqngIeT7J36zoauB8z6YOT2LAkDQaQiV/iOYEkn2C0hnoOcHFVLet4SrNCksuARcCuwNPA14HfApcD7wMeAz5bVc+0f3Sfx+jpai8Cp1TVbV3Me8iSHA78CbiHDfcTfI3RfTfm0oEk+zG6yXMOo/+guryqvpnkA4yuHOwM3Al8vqpeSrId8DNG90s9Ayypqoe7mf2wJVkEnFlVx5lHd9p7v7I1twJ+UVXLkuyC563OJDmA0UM3tgEeBk6hncMwk0604v/vwAeq6vnWN+M/JxY3kiRJkgbBZWmSJEmSBsHiRpIkSdIgWNxIkiRJGgSLG0mSJEmDYHEjSZIkaRAsbiRJkiQNgsWNJEmSpEGwuJEkSZI0CP8DrODAEMCF+mwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Let us now evaluate the standard deviation by user and activity and visualize each (user, activity) pair in a 3-D scatter plot"
      ],
      "metadata": {
        "id": "r45s4tG_k08W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats_all = df.groupby(['activity','user'], as_index=False)[['x-axis','y-axis','z-axis']].std()"
      ],
      "metadata": {
        "id": "fS-nKN-ylWAQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_rows', 200)\n",
        "stats_all"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "id": "ZJrXR6W-lnzB",
        "outputId": "2e418dc4-5f08-4d33-bc31-290988d41286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      activity  user     x-axis    y-axis    z-axis\n",
              "0   Downstairs     1   8.061251  4.761467  3.978546\n",
              "1   Downstairs     3   5.771033  5.208307  4.069624\n",
              "2   Downstairs     4   4.149014  3.826414  3.752217\n",
              "3   Downstairs     5   3.119087  4.166938  2.693416\n",
              "4      Jogging     1  10.706282  9.483499  5.653878\n",
              "5      Jogging     2  10.047730  4.599569  6.223608\n",
              "6      Jogging     3   8.114999  7.966063  6.021514\n",
              "7      Jogging     4   8.557923  9.179301  5.857416\n",
              "8      Jogging     5   5.405740  7.600087  5.162815\n",
              "9      Sitting     3   0.163183  0.149645  0.040073\n",
              "10     Sitting     4   0.595684  1.116342  0.815846\n",
              "11     Sitting     5   0.241884  0.187076  0.193030\n",
              "12    Standing     3   0.125622  0.073809  0.175041\n",
              "13    Standing     5   0.560478  0.287220  0.510066\n",
              "14    Upstairs     1   7.779192  6.492359  4.751230\n",
              "15    Upstairs     3   7.321590  6.639742  3.980631\n",
              "16    Upstairs     4   2.657667  3.587390  3.181474\n",
              "17    Upstairs     5   2.135585  3.348893  2.321837\n",
              "18     Walking     1   6.967989  5.396160  3.220753\n",
              "19     Walking     2   3.151260  3.480465  3.433426\n",
              "20     Walking     3   6.186944  4.394928  3.907049\n",
              "21     Walking     4   4.714031  4.093288  4.339591\n",
              "22     Walking     5   2.596243  4.231367  3.115958"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45139e72-a847-4490-8cae-cf9b8252caea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>activity</th>\n",
              "      <th>user</th>\n",
              "      <th>x-axis</th>\n",
              "      <th>y-axis</th>\n",
              "      <th>z-axis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Downstairs</td>\n",
              "      <td>1</td>\n",
              "      <td>8.061251</td>\n",
              "      <td>4.761467</td>\n",
              "      <td>3.978546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Downstairs</td>\n",
              "      <td>3</td>\n",
              "      <td>5.771033</td>\n",
              "      <td>5.208307</td>\n",
              "      <td>4.069624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Downstairs</td>\n",
              "      <td>4</td>\n",
              "      <td>4.149014</td>\n",
              "      <td>3.826414</td>\n",
              "      <td>3.752217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Downstairs</td>\n",
              "      <td>5</td>\n",
              "      <td>3.119087</td>\n",
              "      <td>4.166938</td>\n",
              "      <td>2.693416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jogging</td>\n",
              "      <td>1</td>\n",
              "      <td>10.706282</td>\n",
              "      <td>9.483499</td>\n",
              "      <td>5.653878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Jogging</td>\n",
              "      <td>2</td>\n",
              "      <td>10.047730</td>\n",
              "      <td>4.599569</td>\n",
              "      <td>6.223608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Jogging</td>\n",
              "      <td>3</td>\n",
              "      <td>8.114999</td>\n",
              "      <td>7.966063</td>\n",
              "      <td>6.021514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Jogging</td>\n",
              "      <td>4</td>\n",
              "      <td>8.557923</td>\n",
              "      <td>9.179301</td>\n",
              "      <td>5.857416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Jogging</td>\n",
              "      <td>5</td>\n",
              "      <td>5.405740</td>\n",
              "      <td>7.600087</td>\n",
              "      <td>5.162815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Sitting</td>\n",
              "      <td>3</td>\n",
              "      <td>0.163183</td>\n",
              "      <td>0.149645</td>\n",
              "      <td>0.040073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Sitting</td>\n",
              "      <td>4</td>\n",
              "      <td>0.595684</td>\n",
              "      <td>1.116342</td>\n",
              "      <td>0.815846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Sitting</td>\n",
              "      <td>5</td>\n",
              "      <td>0.241884</td>\n",
              "      <td>0.187076</td>\n",
              "      <td>0.193030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Standing</td>\n",
              "      <td>3</td>\n",
              "      <td>0.125622</td>\n",
              "      <td>0.073809</td>\n",
              "      <td>0.175041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Standing</td>\n",
              "      <td>5</td>\n",
              "      <td>0.560478</td>\n",
              "      <td>0.287220</td>\n",
              "      <td>0.510066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Upstairs</td>\n",
              "      <td>1</td>\n",
              "      <td>7.779192</td>\n",
              "      <td>6.492359</td>\n",
              "      <td>4.751230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Upstairs</td>\n",
              "      <td>3</td>\n",
              "      <td>7.321590</td>\n",
              "      <td>6.639742</td>\n",
              "      <td>3.980631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Upstairs</td>\n",
              "      <td>4</td>\n",
              "      <td>2.657667</td>\n",
              "      <td>3.587390</td>\n",
              "      <td>3.181474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Upstairs</td>\n",
              "      <td>5</td>\n",
              "      <td>2.135585</td>\n",
              "      <td>3.348893</td>\n",
              "      <td>2.321837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Walking</td>\n",
              "      <td>1</td>\n",
              "      <td>6.967989</td>\n",
              "      <td>5.396160</td>\n",
              "      <td>3.220753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Walking</td>\n",
              "      <td>2</td>\n",
              "      <td>3.151260</td>\n",
              "      <td>3.480465</td>\n",
              "      <td>3.433426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Walking</td>\n",
              "      <td>3</td>\n",
              "      <td>6.186944</td>\n",
              "      <td>4.394928</td>\n",
              "      <td>3.907049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Walking</td>\n",
              "      <td>4</td>\n",
              "      <td>4.714031</td>\n",
              "      <td>4.093288</td>\n",
              "      <td>4.339591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Walking</td>\n",
              "      <td>5</td>\n",
              "      <td>2.596243</td>\n",
              "      <td>4.231367</td>\n",
              "      <td>3.115958</td>\n",
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              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as p\n",
        "figure = p.scatter_3d(data_frame=stats_all,\n",
        "                    x='x-axis', y='y-axis', z='z-axis',\n",
        "                    color='activity',\n",
        "                    opacity=0.25)\n",
        "figure.update_layout(title='')\n",
        "figure.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "-aIHxCKIlqZr",
        "outputId": "42e2e172-5ce9-48c6-bcff-44d0e4debb42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finally, let's evaluate the mean (across users) of the standard deviations by activity and use a heatmap to represent it:"
      ],
      "metadata": {
        "id": "p5Bp9ApemUGu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats_all_mean_std = stats_all.groupby('activity')[['x-axis','y-axis','z-axis']].mean()\n",
        "stats_all_mean_std"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "YSbkTXeRmW5u",
        "outputId": "38a9eef1-f2e8-4d44-87a5-cc33cae98d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              x-axis    y-axis    z-axis\n",
              "activity                                \n",
              "Downstairs  5.275096  4.490781  3.623451\n",
              "Jogging     8.566535  7.765704  5.783846\n",
              "Sitting     0.333584  0.484354  0.349650\n",
              "Standing    0.343050  0.180514  0.342553\n",
              "Upstairs    4.973509  5.017096  3.558793\n",
              "Walking     4.723293  4.319242  3.603355"
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              "\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sb\n",
        "sb.heatmap(stats_all_mean_std,\n",
        "            annot=True, linewidths=1, linecolor='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "2Sn27CHJmo0R",
        "outputId": "cddb8085-ab5c-4bbe-d965-07df3c68f241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Now, let us drop the columns 'user' and 'timestamp' as they are not necessary"
      ],
      "metadata": {
        "id": "zUPeRi0iMgOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['user', 'timestamp'], axis = 1).copy()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wI6VjSaQME5l",
        "outputId": "a19079f4-a1e8-476c-b215-239c9cfc26df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  activity  x-axis  y-axis  z-axis\n",
              "0  Walking    0.69   10.80   -2.03\n",
              "1  Walking    6.85    7.44   -0.50\n",
              "2  Walking    0.93    5.63   -0.50\n",
              "3  Walking   -2.11    5.01   -0.69\n",
              "4  Walking   -4.59    4.29   -1.95"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa1598ac-1a40-44bb-9ffb-9dd029735f86\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>activity</th>\n",
              "      <th>x-axis</th>\n",
              "      <th>y-axis</th>\n",
              "      <th>z-axis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walking</td>\n",
              "      <td>0.69</td>\n",
              "      <td>10.80</td>\n",
              "      <td>-2.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Walking</td>\n",
              "      <td>6.85</td>\n",
              "      <td>7.44</td>\n",
              "      <td>-0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Walking</td>\n",
              "      <td>0.93</td>\n",
              "      <td>5.63</td>\n",
              "      <td>-0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Walking</td>\n",
              "      <td>-2.11</td>\n",
              "      <td>5.01</td>\n",
              "      <td>-0.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Walking</td>\n",
              "      <td>-4.59</td>\n",
              "      <td>4.29</td>\n",
              "      <td>-1.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa1598ac-1a40-44bb-9ffb-9dd029735f86')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aa1598ac-1a40-44bb-9ffb-9dd029735f86 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-aa1598ac-1a40-44bb-9ffb-9dd029735f86');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Now let us balance the dataset by taking only the first 4339 lines for each activity and create a balanced dataframe df_bal."
      ],
      "metadata": {
        "id": "j6CuhhmdQtUh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Walking = df[df['activity'] == 'Walking'].head(4339).copy()\n",
        "Jogging = df[df['activity'] == 'Jogging'].head(4339).copy()\n",
        "Downstairs = df[df['activity'] == 'Downstairs'].head(4339).copy()\n",
        "Upstairs = df[df['activity'] == 'Upstairs'].head(4339).copy()\n",
        "Sitting = df[df['activity'] == 'Sitting'].head(4339).copy()\n",
        "Standing = df[df['activity'] == 'Standing'].head(4339).copy()\n",
        "\n",
        "df_bal = pd.DataFrame()\n",
        "df_bal = df_bal.append([Walking, Jogging, Downstairs, Upstairs, Sitting, Standing])\n",
        "df_bal.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppDhSuvDRGhC",
        "outputId": "9f21ab3d-06b8-4537-f49f-1ed84e982920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26034, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bal['activity'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hz-YrE1LTvU6",
        "outputId": "a4bbd923-7357-4eb9-b314-21b20bb64ee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Walking       4339\n",
              "Jogging       4339\n",
              "Downstairs    4339\n",
              "Upstairs      4339\n",
              "Sitting       4339\n",
              "Standing      4339\n",
              "Name: activity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_bal.head()"
      ],
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        "colab": {
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          "height": 206
        },
        "id": "ZcFKSXHvT5Tb",
        "outputId": "83fb0b3f-332b-4aec-df40-ea4ea69df52d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  activity  x-axis  y-axis  z-axis\n",
              "0  Walking    0.69   10.80   -2.03\n",
              "1  Walking    6.85    7.44   -0.50\n",
              "2  Walking    0.93    5.63   -0.50\n",
              "3  Walking   -2.11    5.01   -0.69\n",
              "4  Walking   -4.59    4.29   -1.95"
            ],
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Walking</td>\n",
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              "      <td>5.01</td>\n",
              "      <td>-0.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Walking</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Here we observe that the values in x, y and z columns are of type string and we need to convert them to numeric values using LabelEncoder from sklearn."
      ],
      "metadata": {
        "id": "17KyecaaUm8N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label = LabelEncoder()\n",
        "df_bal['label'] = label.fit_transform(df_bal['activity'])\n",
        "df_bal['label'].value_counts()"
      ],
      "metadata": {
        "id": "0RswZ_HJUmWN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8f041a9-d5ec-4dab-a7bc-f06decb91939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    4339\n",
              "1    4339\n",
              "0    4339\n",
              "4    4339\n",
              "2    4339\n",
              "3    4339\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pi3O1Y5t-C4Z",
        "outputId": "f31e7543-96d7-4bf0-c7e0-c7a8f0ad4cec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Downstairs', 'Jogging', 'Sitting', 'Standing', 'Upstairs',\n",
              "       'Walking'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Standardization of data\n"
      ],
      "metadata": {
        "id": "Gqq-hqsD-4Xp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_bal[['x-axis','y-axis','z-axis']]\n",
        "y = df_bal['label']"
      ],
      "metadata": {
        "id": "_M6EpkFU-8Zb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "scaled_X = pd.DataFrame(data = X, columns = ['x', 'y', 'z'])\n",
        "scaled_X['label'] = y.values\n",
        "\n",
        "scaled_X.head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Kc83tY0o_zY9",
        "outputId": "e6c5aead-1155-4246-d31b-2e94643d67b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          x         y         z  label\n",
              "0  0.125562  0.747120 -0.647604      5\n",
              "1  0.971597  0.285962 -0.338025      5\n",
              "2  0.158525  0.037540 -0.338025      5\n",
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              "\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Frame Preparation"
      ],
      "metadata": {
        "id": "Q7me2oNxCLbI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dividing the data into frames of 4 seconds. We will multiply the frequency by 4 seconds, thus taking 80 observations at a time and our hop size will be 40 but there will be some overlapping due to this."
      ],
      "metadata": {
        "id": "Z49JtWsFDa-D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats"
      ],
      "metadata": {
        "id": "-IYZzmaoCPgy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Fs = 20\n",
        "frame_size = Fs*4\n",
        "hop_size = Fs*2"
      ],
      "metadata": {
        "id": "uuU4fwnfCpAU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_frames(data, frame_size, hop_size):\n",
        "  \n",
        "  features = 3\n",
        "  frames = []\n",
        "  labels = []\n",
        "  for i in range(0, len(data)- frame_size, hop_size):\n",
        "    x = data['x'].values[i:i+frame_size]\n",
        "    y = data['y'].values[i:i+frame_size]\n",
        "    z = data['z'].values[i:i+frame_size]\n",
        "\n",
        "    label = stats.mode(data['label'][i : i+frame_size])[0][0]\n",
        "    frames.append([x,y,z])\n",
        "    labels.append(label)\n",
        "\n",
        "  frames = np.asarray(frames).reshape(-1, frame_size, features)\n",
        "  labels = np.asarray(labels)\n",
        "\n",
        "  return frames, labels\n",
        "\n",
        "X, y = get_frames(scaled_X, frame_size, hop_size)\n",
        "\n",
        "X.shape, y.shape\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KC_kDuFD611",
        "outputId": "3aee4c33-65d5-44be-95e1-9a1565f0dcbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((649, 80, 3), (649,))"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# We have 6 six activities and each activity has 4339 examples thus we have a total of (4339*6) observations. When this is divided by our chosen hop size of 40, we get a value of approximately 649."
      ],
      "metadata": {
        "id": "d81kgUUkH9zs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(4339*6)/40"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izrKod5NH0m7",
        "outputId": "9ac68160-673c-4a59-8128-a8a17a9aa50c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "650.85"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Now we split the data into training data and testing data."
      ],
      "metadata": {
        "id": "cO89vgfOIcFr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "ss = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
        "for train_index, test_index in ss.split(X, y):\n",
        "    train_features, test_features = X[train_index], X[test_index]\n",
        "    train_labels, test_labels = y[train_index], y[test_index]"
      ],
      "metadata": {
        "id": "nBY39aVLIa2z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_features.shape, test_features.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mv4a2F1rRSZt",
        "outputId": "46d6258a-044d-43eb-c61c-d4778f082447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((585, 80, 3), (64, 80, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Now the dataset is 3-dimensional, however each sample in the dataset is 2- dimesional"
      ],
      "metadata": {
        "id": "fTdkifQvUEss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_features[0].shape, test_features[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2xU_nk7Tf8-",
        "outputId": "5c4d059a-b2d6-4558-c4c9-91e5daf7df85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((80, 3), (80, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Thus, we need to reshape the data samples as CNN accepts 3-dimensional data"
      ],
      "metadata": {
        "id": "NqWfbD2mUfgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_features = train_features.reshape(585, 80, 3, 1)\n",
        "test_features = test_features.reshape(64, 80, 3, 1)"
      ],
      "metadata": {
        "id": "ZIKCYmblUkm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Now we have each sample as 3-dimensional"
      ],
      "metadata": {
        "id": "_FEYT0U3VKgY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_features[0].shape, test_features[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8i9bMsKVUdF",
        "outputId": "d2573b39-620e-4d81-a0a4-54dc52f25111"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((80, 3, 1), (80, 3, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2D CNN model\n",
        "\n",
        "A Sequential() model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.\n",
        "\n",
        "Conv2D() is a 2D Convolution Layer, this layer creates a convolution kernel that is wind with layers input which helps produce a tensor of outputs. In image processing kernel is a convolution matrix or masks which can be used for blurring, sharpening, embossing, edge detection, and more by doing a convolution between a kernel and an image. In the first Conv2D() layer we are learning a total of 16 filters each having size (2,2). We will be using ReLu activation function. The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero.\n",
        "\n",
        "Dropout layer is used to by randomly set the outgoing edges of hidden units to 0 at each update of the training phase. The value passed in dropout specifies the probability at which outputs of the layer are dropped out.\n",
        "\n",
        "Flatten() is used to convert the data into a 1-dimensional array for inputting it to the next layer.\n",
        "\n",
        "Dense layer is the regular deeply connected neural network layer with 64 neurons. The output layer is also a dense layer with 6 neurons for the 6 classes. The activation function used is softmax. Softmax converts a real vector to a vector of categorical probabilities. The elements of the output vector are in range (0, 1) and sum to 1. Softmax is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution."
      ],
      "metadata": {
        "id": "w3-lhNwrTR-G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(16, (2, 2), activation = 'relu', input_shape = train_features[0].shape))\n",
        "model.add(Dropout(0.1))\n",
        "\n",
        "model.add(Conv2D(32, (2, 2), activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64, activation = 'relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(6, activation='softmax'))"
      ],
      "metadata": {
        "id": "PX5fUIDBU_3s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we are compiling the model and fitting it to the training data. We will use 10 epochs to train the model. An epoch is an iteration over the entire data provided. validation_data is the data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. As metrics = ['accuracy'] the model will be evaluated based on the accuracy."
      ],
      "metadata": {
        "id": "KWLxxYEEVLIW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=Adam(learning_rate = 0.001), loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n",
        "history = model.fit(train_features, train_labels, epochs = 10, validation_data= (test_features, test_labels), verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VPGZMPsVRC8",
        "outputId": "ae38d1f1-f067-4bc5-99ec-51cdd8f90699"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "19/19 [==============================] - 1s 14ms/step - loss: 1.2421 - accuracy: 0.5402 - val_loss: 0.6639 - val_accuracy: 0.7812\n",
            "Epoch 2/10\n",
            "19/19 [==============================] - 0s 7ms/step - loss: 0.5497 - accuracy: 0.8085 - val_loss: 0.4378 - val_accuracy: 0.7969\n",
            "Epoch 3/10\n",
            "19/19 [==============================] - 0s 8ms/step - loss: 0.4199 - accuracy: 0.8581 - val_loss: 0.3672 - val_accuracy: 0.8281\n",
            "Epoch 4/10\n",
            "19/19 [==============================] - 0s 8ms/step - loss: 0.3495 - accuracy: 0.8684 - val_loss: 0.2995 - val_accuracy: 0.8906\n",
            "Epoch 5/10\n",
            "19/19 [==============================] - 0s 8ms/step - loss: 0.2590 - accuracy: 0.9179 - val_loss: 0.2519 - val_accuracy: 0.8906\n",
            "Epoch 6/10\n",
            "19/19 [==============================] - 0s 7ms/step - loss: 0.2182 - accuracy: 0.9265 - val_loss: 0.2429 - val_accuracy: 0.8906\n",
            "Epoch 7/10\n",
            "19/19 [==============================] - 0s 8ms/step - loss: 0.2036 - accuracy: 0.9316 - val_loss: 0.2331 - val_accuracy: 0.9062\n",
            "Epoch 8/10\n",
            "19/19 [==============================] - 0s 7ms/step - loss: 0.2035 - accuracy: 0.9214 - val_loss: 0.2778 - val_accuracy: 0.8594\n",
            "Epoch 9/10\n",
            "19/19 [==============================] - 0s 7ms/step - loss: 0.1693 - accuracy: 0.9385 - val_loss: 0.2255 - val_accuracy: 0.8906\n",
            "Epoch 10/10\n",
            "19/19 [==============================] - 0s 7ms/step - loss: 0.1239 - accuracy: 0.9675 - val_loss: 0.2535 - val_accuracy: 0.8750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will plot the model accuracy and overall model loss. Training accuracy and validation accuracy will be plotted in case of model accuracy and training loss and validation loss will be plotted in model loss."
      ],
      "metadata": {
        "id": "voaGY5O3Vn0v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_Curve(history, epochs):\n",
        "  # Plot training & validation accuracy values\n",
        "  epoch_range = range(1, epochs+1)\n",
        "  plt.plot(epoch_range, history.history['accuracy'])\n",
        "  plt.plot(epoch_range, history.history['val_accuracy'])\n",
        "  plt.title('Model accuracy')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Val'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "  # Plot training & validation loss values\n",
        "  plt.plot(epoch_range, history.history['loss'])\n",
        "  plt.plot(epoch_range, history.history['val_loss'])\n",
        "  plt.title('Model loss')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Val'], loc='upper left')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "IJg256bgWGkK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_Curve(history, 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "k1YJJE37We9S",
        "outputId": "18989010-efa0-43cc-b455-8960823104d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Confusion Matrix\n",
        "\n",
        "A Confusion Matrix is a table that is used to denote the performance of a classification model on a set of test data for which the ground truth values are known.\n",
        "\n",
        "Each row of the matrix represents the instances of data in the predicted class. Each column of the matrix represents the instances of data in the actual class. The name arises from the fact that it makes very easy to spot when the system is confusing two classes (i.e. commonly mislabeling one class as another)."
      ],
      "metadata": {
        "id": "toP2Ao2SLN-2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "4B7S5CZ4Mfzl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_labels = np.argmax(model.predict(test_features), axis=-1)\n",
        "classes=['Downstairs', 'Jogging', 'Sitting', 'Standing', 'Upstairs','Walking']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V6sYZ0RAMyUd",
        "outputId": "3b79d730-6ed6-4542-f42d-5a66c1ea783c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c_matrix = confusion_matrix(test_labels, predicted_labels)\n",
        "for i in range(6):\n",
        "  print(i, \":\", classes[i])\n",
        "plot_confusion_matrix(conf_mat=c_matrix, show_normed=True, figsize=(10,10))\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=c_matrix, display_labels=label.classes_)\n",
        "disp.plot()\n",
        "plt.show()\n",
        "label.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DGGXJtpKNHPO",
        "outputId": "4c4e072b-82bf-461e-b241-c940f1380f9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 : Downstairs\n",
            "1 : Jogging\n",
            "2 : Sitting\n",
            "3 : Standing\n",
            "4 : Upstairs\n",
            "5 : Walking\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Downstairs', 'Jogging', 'Sitting', 'Standing', 'Upstairs',\n",
              "       'Walking'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    }
  ]
}