localization.ipynb

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      "source": [
        "# Finding Dory\n",
        "## Politecnico di Milano, Internet of Things + Wireless Internet project\n",
        "### Processing and localization\n",
        "\n",
        "In the fist part of this project we have collected from the CoAP and MQTT server a total of 180 fingerprints and Dory's RSSI. In this part of the project we analyze the data and localize Dory using fingerprint-based methods.\n",
        "\n",
        "\n",
        "## Importing data\n",
        "\n",
        "The fist step is importing the retrieved data and set it up in a format suitable for analysis. In this section we import the data by parsing with simple custom parsers the hand-crafted text files and convert the entire dataset into a standard dataframe, suitable for further analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 448,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "57dce6d2-8e54-4520-ec94-8737f4ea67c4"
      },
      "outputs": [],
      "source": [
        "import ast\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_rows', 50)\n",
        "import numpy as np\n",
        "from numpy.ma.core import log10\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from matplotlib import cm\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
        "from sklearn.metrics import accuracy_score, mean_squared_error, ConfusionMatrixDisplay\n",
        "\n",
        "np.random.seed(31415)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7qPERDyE_aJ"
      },
      "source": [
        "### Import data from CoAP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 449,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RXwa_P3hE9dd",
        "outputId": "7107fe21-fd9a-475e-d927-b43d0b07e4fb"
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      "source": [
        "data_coap = []\n",
        "with open(\"coap/coap_coords.txt\") as f:\n",
        "  for l in f:\n",
        "    row = []\n",
        "    ls = l.split(\"||\")\n",
        "\n",
        "    # extract ground truth position\n",
        "    pos = list(map(int, map(float,ls[0].split(\",\"))))\n",
        "\n",
        "    # parse list and integer literals as python lists\n",
        "    anchors_readings = []\n",
        "    for anchor_readings in ls[1:]:\n",
        "      anchors_readings.append(list(map(ast.literal_eval , ast.literal_eval(anchor_readings.strip()))))\n",
        "    \n",
        "    # unroll experiments into one row per experiment\n",
        "    # appending ground truth and one station per column\n",
        "    for exp_idx in range(len(anchors_readings[0])):\n",
        "      row = pos[:] + [exp_idx]\n",
        "      for anchor_idx in range(len(anchors_readings)):\n",
        "\n",
        "        row.append(anchors_readings[anchor_idx][exp_idx])\n",
        "      data_coap.append(row)\n",
        "  \n",
        "  # build df\n",
        "  df_coap = pd.DataFrame(data=data_coap, columns=[\"X\", \"Y\", \"exp_id\", \"A1\", \"A2\", \"A3\", \"A4\", \"A5\", \"A6\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_OrSUZ5RJsI"
      },
      "source": [
        "### Import data from MQTT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 450,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dikizWyUFM1_",
        "outputId": "85b5f42f-f7c7-4b47-bff5-9c005d749729"
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      "outputs": [],
      "source": [
        "data_mqtt = []\n",
        "with open(\"mqtt/mqtt_coords.txt\") as f:\n",
        "  for l in f:\n",
        "    row = []\n",
        "    ls = l.split(\"|\")\n",
        "\n",
        "    # extract ground truth position\n",
        "    pos = list(map(int, map(float,ls[0].split(\",\"))))\n",
        "\n",
        "    # parse list and integet literals as python lists\n",
        "    anchors_readings = []\n",
        "    for anchor_readings in ls[1:]:\n",
        "      anchors_readings.append(list(map(int , anchor_readings.strip().split(\",\"))))\n",
        "    \n",
        "    # unroll experiments into one row per experiment\n",
        "    # appending ground truth and one station per column\n",
        "    for exp_idx in range(len(anchors_readings[0])):\n",
        "      row = pos[:] + [exp_idx]\n",
        "      for anchor_idx in range(len(anchors_readings)):\n",
        "        #print(anchors_readings[anchor_idx])\n",
        "        row.append(anchors_readings[anchor_idx][exp_idx])\n",
        "      data_mqtt.append(row)\n",
        "  \n",
        "  # build df\n",
        "  df_mqtt = pd.DataFrame(data=data_mqtt, columns=[\"X\", \"Y\", \"exp_id\", \"A1\", \"A2\", \"A3\", \"A4\", \"A5\", \"A6\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGhOCEwBs8aD"
      },
      "source": [
        "### Merging the two data sets plus Dory's RSSI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 451,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "w6TuZTjWRusx",
        "outputId": "8dc44481-0470-4265-da0d-3217f9725355"
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              "      <td>-63</td>\n",
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              "<p>180 rows × 9 columns</p>\n",
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            "text/plain": [
              "     X  Y  exp_id  A1  A2  A3  A4  A5  A6\n",
              "0   10  6       0 -64 -64 -61 -59 -59 -55\n",
              "1   10  6       1 -60 -60 -57 -55 -55 -51\n",
              "2   10  6       2 -62 -61 -58 -56 -57 -53\n",
              "3   10  6       3 -66 -66 -63 -61 -61 -57\n",
              "4   10  6       4 -64 -64 -61 -59 -59 -55\n",
              "..  .. ..     ...  ..  ..  ..  ..  ..  ..\n",
              "65   8  4       0 -59 -61 -54 -55 -57 -56\n",
              "66   8  4       1 -66 -67 -61 -60 -63 -63\n",
              "67   8  4       2 -64 -65 -59 -58 -61 -61\n",
              "68   8  4       3 -65 -66 -60 -59 -62 -62\n",
              "69   8  4       4 -63 -64 -58 -57 -60 -60\n",
              "\n",
              "[180 rows x 9 columns]"
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          "metadata": {},
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      ],
      "source": [
        "df = pd.concat([df_coap, df_mqtt])\n",
        "\n",
        "# manually define Dory's RSSI\n",
        "Dory_RSSI = [-57,-63,-58,-64,-63,-66]\n",
        "Dory_RSSI_np = np.array(Dory_RSSI)\n",
        "\n",
        "# dataset constraints to check if the data is valid\n",
        "assert(len(df) == 6*6*5)\n",
        "assert(df.isna().sum().sum() == 0)\n",
        "\n",
        "display(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data analysis\n",
        "\n",
        "We will show two analysis approaches.\n",
        "* pure mathematical: we aggregate all samples from the same position via averaging, fill  in missing values with linear and bilinear interpolation, compute the euclidean distances of all produced data points from the reference RSSI and selecting the minimum distance one\n",
        "* Machine Learning: we use all available fingerprints to train a KNN model, which we then use to classify the reference RSSI; the produced results are aggregated with a truncated weighted average."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pure mathematical approach\n",
        "Note: the Machine learning approach is documented [in this file](knn.ipynb) (`knn.ipynb`)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39DQ4mrK1JUH"
      },
      "source": [
        "#### Aggregating data and dealing with missing values\n",
        "Let us average all readings for an anchor at a position and build a dataframe of mean readings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 452,
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        "id": "1UrFAbo44CJ4",
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              "      <td>-63.0</td>\n",
              "      <td>-61.8</td>\n",
              "      <td>-64.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>-62.6</td>\n",
              "      <td>-61.6</td>\n",
              "      <td>-61.2</td>\n",
              "      <td>-64.0</td>\n",
              "      <td>-58.0</td>\n",
              "      <td>-62.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>-62.8</td>\n",
              "      <td>-56.8</td>\n",
              "      <td>-61.8</td>\n",
              "      <td>-63.0</td>\n",
              "      <td>-51.6</td>\n",
              "      <td>-59.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>-64.4</td>\n",
              "      <td>-56.6</td>\n",
              "      <td>-64.4</td>\n",
              "      <td>-65.6</td>\n",
              "      <td>-44.8</td>\n",
              "      <td>-59.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>-60.2</td>\n",
              "      <td>-66.0</td>\n",
              "      <td>-45.2</td>\n",
              "      <td>-57.4</td>\n",
              "      <td>-64.4</td>\n",
              "      <td>-66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>-61.2</td>\n",
              "      <td>-65.2</td>\n",
              "      <td>-52.2</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-63.2</td>\n",
              "      <td>-63.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>-63.2</td>\n",
              "      <td>-64.2</td>\n",
              "      <td>-58.0</td>\n",
              "      <td>-61.0</td>\n",
              "      <td>-61.4</td>\n",
              "      <td>-63.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>-63.8</td>\n",
              "      <td>-62.0</td>\n",
              "      <td>-60.8</td>\n",
              "      <td>-62.0</td>\n",
              "      <td>-57.4</td>\n",
              "      <td>-60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>-64.6</td>\n",
              "      <td>-60.6</td>\n",
              "      <td>-62.2</td>\n",
              "      <td>-64.0</td>\n",
              "      <td>-52.2</td>\n",
              "      <td>-57.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>-65.2</td>\n",
              "      <td>-59.6</td>\n",
              "      <td>-64.0</td>\n",
              "      <td>-64.6</td>\n",
              "      <td>-44.6</td>\n",
              "      <td>-56.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>-65.8</td>\n",
              "      <td>-70.0</td>\n",
              "      <td>-57.6</td>\n",
              "      <td>-54.0</td>\n",
              "      <td>-68.0</td>\n",
              "      <td>-67.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>-65.8</td>\n",
              "      <td>-68.0</td>\n",
              "      <td>-58.8</td>\n",
              "      <td>-56.2</td>\n",
              "      <td>-65.8</td>\n",
              "      <td>-66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>-63.4</td>\n",
              "      <td>-64.6</td>\n",
              "      <td>-58.4</td>\n",
              "      <td>-57.8</td>\n",
              "      <td>-60.6</td>\n",
              "      <td>-60.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>-64.0</td>\n",
              "      <td>-63.0</td>\n",
              "      <td>-60.6</td>\n",
              "      <td>-60.0</td>\n",
              "      <td>-56.0</td>\n",
              "      <td>-57.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>-66.4</td>\n",
              "      <td>-63.4</td>\n",
              "      <td>-63.6</td>\n",
              "      <td>-63.4</td>\n",
              "      <td>-56.4</td>\n",
              "      <td>-54.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>-69.0</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-68.0</td>\n",
              "      <td>-67.6</td>\n",
              "      <td>-56.8</td>\n",
              "      <td>-53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>-64.8</td>\n",
              "      <td>-67.8</td>\n",
              "      <td>-59.0</td>\n",
              "      <td>-24.8</td>\n",
              "      <td>-65.8</td>\n",
              "      <td>-65.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>-63.8</td>\n",
              "      <td>-65.8</td>\n",
              "      <td>-58.4</td>\n",
              "      <td>-50.2</td>\n",
              "      <td>-64.2</td>\n",
              "      <td>-61.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>-66.8</td>\n",
              "      <td>-67.6</td>\n",
              "      <td>-62.4</td>\n",
              "      <td>-58.4</td>\n",
              "      <td>-64.4</td>\n",
              "      <td>-61.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>-63.2</td>\n",
              "      <td>-63.0</td>\n",
              "      <td>-60.0</td>\n",
              "      <td>-58.0</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-54.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-63.0</td>\n",
              "      <td>-62.2</td>\n",
              "      <td>-61.0</td>\n",
              "      <td>-57.4</td>\n",
              "      <td>-49.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>-68.4</td>\n",
              "      <td>-65.4</td>\n",
              "      <td>-66.4</td>\n",
              "      <td>-65.4</td>\n",
              "      <td>-59.4</td>\n",
              "      <td>-25.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     X   Y    A1    A2    A3    A4    A5    A6\n",
              "0    0   0 -26.8 -66.6 -61.0 -66.8 -67.6 -69.8\n",
              "1    0   2 -51.4 -63.2 -59.8 -65.4 -64.6 -67.4\n",
              "2    0   4 -56.8 -60.4 -60.6 -65.4 -62.4 -66.0\n",
              "3    0   6 -61.4 -58.2 -64.4 -67.2 -62.0 -66.4\n",
              "4    0   8 -61.2 -49.6 -64.0 -65.6 -58.2 -64.0\n",
              "5    0  10 -66.0 -23.6 -65.2 -67.0 -58.0 -64.0\n",
              "6    2   0 -52.6 -66.6 -56.0 -65.0 -66.8 -68.6\n",
              "7    2   2 -54.4 -63.2 -56.4 -62.6 -63.8 -66.2\n",
              "8    2   4 -58.2 -61.2 -59.2 -64.2 -61.8 -65.2\n",
              "9    2   6 -62.0 -59.2 -61.6 -65.6 -60.0 -64.8\n",
              "10   2   8 -63.0 -54.6 -63.2 -65.6 -56.6 -63.0\n",
              "11   2  10 -64.8 -51.2 -65.2 -66.6 -54.2 -62.6\n",
              "12   4   0 -57.2 -66.0 -46.6 -61.2 -65.8 -66.8\n",
              "13   4   2 -60.0 -66.0 -54.4 -63.0 -65.0 -67.0\n",
              "14   4   4 -61.8 -63.0 -58.2 -63.0 -61.8 -64.4\n",
              "15   4   6 -62.6 -61.6 -61.2 -64.0 -58.0 -62.6\n",
              "16   4   8 -62.8 -56.8 -61.8 -63.0 -51.6 -59.8\n",
              "17   4  10 -64.4 -56.6 -64.4 -65.6 -44.8 -59.6\n",
              "18   6   0 -60.2 -66.0 -45.2 -57.4 -64.4 -66.0\n",
              "19   6   2 -61.2 -65.2 -52.2 -58.2 -63.2 -63.4\n",
              "20   6   4 -63.2 -64.2 -58.0 -61.0 -61.4 -63.2\n",
              "21   6   6 -63.8 -62.0 -60.8 -62.0 -57.4 -60.0\n",
              "22   6   8 -64.6 -60.6 -62.2 -64.0 -52.2 -57.6\n",
              "23   6  10 -65.2 -59.6 -64.0 -64.6 -44.6 -56.2\n",
              "24   8   0 -65.8 -70.0 -57.6 -54.0 -68.0 -67.8\n",
              "25   8   2 -65.8 -68.0 -58.8 -56.2 -65.8 -66.0\n",
              "26   8   4 -63.4 -64.6 -58.4 -57.8 -60.6 -60.4\n",
              "27   8   6 -64.0 -63.0 -60.6 -60.0 -56.0 -57.2\n",
              "28   8   8 -66.4 -63.4 -63.6 -63.4 -56.4 -54.4\n",
              "29   8  10 -69.0 -65.0 -68.0 -67.6 -56.8 -53.0\n",
              "30  10   0 -64.8 -67.8 -59.0 -24.8 -65.8 -65.2\n",
              "31  10   2 -63.8 -65.8 -58.4 -50.2 -64.2 -61.8\n",
              "32  10   4 -66.8 -67.6 -62.4 -58.4 -64.4 -61.8\n",
              "33  10   6 -63.2 -63.0 -60.0 -58.0 -58.2 -54.2\n",
              "34  10   8 -65.0 -63.0 -62.2 -61.0 -57.4 -49.0\n",
              "35  10  10 -68.4 -65.4 -66.4 -65.4 -59.4 -25.8"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "means_data = []\n",
        "for x in range(0,11,2):\n",
        "    for y in range(0,11,2):\n",
        "      row = [x,y]\n",
        "      row += df.loc[(df['X'] == x) & (df['Y'] == y)].mean()[[\"A1\", \"A2\",\"A3\",\"A4\",\"A5\",\"A6\"]].values.tolist()\n",
        "      means_data.append(row)\n",
        "df_means = pd.DataFrame(data=means_data, columns=['X', 'Y', \"A1\", \"A2\", \"A3\", \"A4\", \"A5\", \"A6\"])\n",
        "display(df_means)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fSEmpXR1jA3"
      },
      "source": [
        "#### Interpolation\n",
        "Let us now perform linear and bilinear interpolation to produce a new dataframe. The new dataframe has only one sample per `(x,y)` position.\n",
        "* if the position was in the original dataframe, the corresponding entry is the average of the entries in the original dataframe (mean)\n",
        "* if the position was not in the original dataframe and its adjacent positions were present, the corresponding entry is the average of the adjacent entries from the original dataframe (linear interpolation)\n",
        "* if the position was not in the original dataframe and its adjacent positions were also not present, the corresponding entry is the average of the adjacent entries computed in the new dataframe dataframe (bilinear interpolation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 453,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4LAH3F4o1Uay",
        "outputId": "9ef55722-8ca6-43d9-f28a-e7007ab26721"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>A1</th>\n",
              "      <th>A2</th>\n",
              "      <th>A3</th>\n",
              "      <th>A4</th>\n",
              "      <th>A5</th>\n",
              "      <th>A6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-26.8</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>-61.0</td>\n",
              "      <td>-66.8</td>\n",
              "      <td>-67.6</td>\n",
              "      <td>-69.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-51.4</td>\n",
              "      <td>-63.2</td>\n",
              "      <td>-59.8</td>\n",
              "      <td>-65.4</td>\n",
              "      <td>-64.6</td>\n",
              "      <td>-67.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-56.8</td>\n",
              "      <td>-60.4</td>\n",
              "      <td>-60.6</td>\n",
              "      <td>-65.4</td>\n",
              "      <td>-62.4</td>\n",
              "      <td>-66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>-61.4</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-64.4</td>\n",
              "      <td>-67.2</td>\n",
              "      <td>-62.0</td>\n",
              "      <td>-66.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-61.2</td>\n",
              "      <td>-49.6</td>\n",
              "      <td>-64.0</td>\n",
              "      <td>-65.6</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-64.3</td>\n",
              "      <td>-66.8</td>\n",
              "      <td>-58.7</td>\n",
              "      <td>-37.5</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-63.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-65.3</td>\n",
              "      <td>-66.7</td>\n",
              "      <td>-60.4</td>\n",
              "      <td>-54.3</td>\n",
              "      <td>-64.3</td>\n",
              "      <td>-61.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-65.3</td>\n",
              "      <td>-61.2</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-61.3</td>\n",
              "      <td>-58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>-64.1</td>\n",
              "      <td>-63.0</td>\n",
              "      <td>-61.1</td>\n",
              "      <td>-59.5</td>\n",
              "      <td>-57.8</td>\n",
              "      <td>-51.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-66.7</td>\n",
              "      <td>-64.2</td>\n",
              "      <td>-64.3</td>\n",
              "      <td>-63.2</td>\n",
              "      <td>-58.4</td>\n",
              "      <td>-37.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>121 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       X    Y    A1    A2    A3    A4    A5    A6\n",
              "0    0.0  0.0 -26.8 -66.6 -61.0 -66.8 -67.6 -69.8\n",
              "1    0.0  2.0 -51.4 -63.2 -59.8 -65.4 -64.6 -67.4\n",
              "2    0.0  4.0 -56.8 -60.4 -60.6 -65.4 -62.4 -66.0\n",
              "3    0.0  6.0 -61.4 -58.2 -64.4 -67.2 -62.0 -66.4\n",
              "4    0.0  8.0 -61.2 -49.6 -64.0 -65.6 -58.2 -64.0\n",
              "..   ...  ...   ...   ...   ...   ...   ...   ...\n",
              "0   10.0  1.0 -64.3 -66.8 -58.7 -37.5 -65.0 -63.5\n",
              "0   10.0  3.0 -65.3 -66.7 -60.4 -54.3 -64.3 -61.8\n",
              "0   10.0  5.0 -65.0 -65.3 -61.2 -58.2 -61.3 -58.0\n",
              "0   10.0  7.0 -64.1 -63.0 -61.1 -59.5 -57.8 -51.6\n",
              "0   10.0  9.0 -66.7 -64.2 -64.3 -63.2 -58.4 -37.4\n",
              "\n",
              "[121 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# row-based\n",
        "df_interpolation = df_means.copy()\n",
        "for x in range(1,10,2):\n",
        "  for y in range(0,11,2):\n",
        "\n",
        "    row = pd.DataFrame(df_interpolation.loc[((df_interpolation['X'] == (x - 1)) |\n",
        "                                       (df_interpolation['X'] == (x + 1))) &\n",
        "                                       (df_interpolation['Y'] == y)].mean(axis=0).dropna()).transpose()\n",
        "    df_interpolation = pd.concat([df_interpolation, pd.DataFrame(row)])\n",
        "\n",
        "\n",
        "# column-based\n",
        "for x in range(0,11,1):\n",
        "  for y in range(1,10,1):\n",
        "\n",
        "    if not df_interpolation.loc[(df_interpolation['X'] == x) &\n",
        "                                (df_interpolation['Y'] == y)].empty:\n",
        "      continue\n",
        "    row = pd.DataFrame(df_interpolation.loc[((df_interpolation['Y'] == (y - 1)) |\n",
        "                                       (df_interpolation['Y'] == (y + 1))) &\n",
        "                                       (df_interpolation['X'] == x)].mean(axis=0).dropna()).transpose()\n",
        "    df_interpolation = pd.concat([df_interpolation, pd.DataFrame(row)])\n",
        "\n",
        "display(df_interpolation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the computed signal strength curved to visually inspect for any evident mistake."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 454,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x720 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(15,10))\n",
        "fig.patch.set_facecolor('xkcd:white')\n",
        "\n",
        "for idx, station in enumerate([\"A2\",\"A5\",\"A6\",\"A1\",\"A3\",\"A4\",]):\n",
        "  data = df_interpolation[[\"X\",\"Y\",station]].values\n",
        "  x, y, z = data[:,0], data[:,1], data[:,2]\n",
        "  ax = fig.add_subplot(2,3,(idx % 6)+ 1, projection='3d')\n",
        "  ax.set_title(station)\n",
        "  ax.plot_trisurf(x, y, z, linewidth=0.2, antialiased=True, cmap=cm.coolwarm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The plots above shows reasonable signals for wifi RSSI propagation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Euclidean Distance\n",
        "Now that we have managed average the values of repeated measurements and to approximate the missing ones, we can compute the Euclidean distance of each fingerprint in the interpolated dataframe from the Dory RSSI sample."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 455,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>A1</th>\n",
              "      <th>A2</th>\n",
              "      <th>A3</th>\n",
              "      <th>A4</th>\n",
              "      <th>A5</th>\n",
              "      <th>A6</th>\n",
              "      <th>Euclidean Distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-56.3</td>\n",
              "      <td>-62.20</td>\n",
              "      <td>-57.80</td>\n",
              "      <td>-63.4</td>\n",
              "      <td>-62.80</td>\n",
              "      <td>-65.7</td>\n",
              "      <td>1.288410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-58.6</td>\n",
              "      <td>-63.35</td>\n",
              "      <td>-57.05</td>\n",
              "      <td>-63.2</td>\n",
              "      <td>-63.10</td>\n",
              "      <td>-65.7</td>\n",
              "      <td>2.079663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-55.2</td>\n",
              "      <td>-62.00</td>\n",
              "      <td>-59.00</td>\n",
              "      <td>-64.4</td>\n",
              "      <td>-63.15</td>\n",
              "      <td>-66.2</td>\n",
              "      <td>2.337199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-61.20</td>\n",
              "      <td>-59.20</td>\n",
              "      <td>-64.2</td>\n",
              "      <td>-61.80</td>\n",
              "      <td>-65.2</td>\n",
              "      <td>2.870540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-57.5</td>\n",
              "      <td>-60.80</td>\n",
              "      <td>-59.90</td>\n",
              "      <td>-64.8</td>\n",
              "      <td>-62.10</td>\n",
              "      <td>-65.6</td>\n",
              "      <td>3.210919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-54.4</td>\n",
              "      <td>-63.20</td>\n",
              "      <td>-56.40</td>\n",
              "      <td>-62.6</td>\n",
              "      <td>-63.80</td>\n",
              "      <td>-66.2</td>\n",
              "      <td>3.464102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-57.2</td>\n",
              "      <td>-64.60</td>\n",
              "      <td>-55.40</td>\n",
              "      <td>-62.8</td>\n",
              "      <td>-64.40</td>\n",
              "      <td>-66.6</td>\n",
              "      <td>3.622154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-60.0</td>\n",
              "      <td>-62.10</td>\n",
              "      <td>-58.70</td>\n",
              "      <td>-63.6</td>\n",
              "      <td>-61.80</td>\n",
              "      <td>-64.8</td>\n",
              "      <td>3.652396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-56.8</td>\n",
              "      <td>-60.40</td>\n",
              "      <td>-60.60</td>\n",
              "      <td>-65.4</td>\n",
              "      <td>-62.40</td>\n",
              "      <td>-66.0</td>\n",
              "      <td>3.984972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-54.1</td>\n",
              "      <td>-61.80</td>\n",
              "      <td>-60.20</td>\n",
              "      <td>-65.4</td>\n",
              "      <td>-63.50</td>\n",
              "      <td>-66.7</td>\n",
              "      <td>4.170132</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     X    Y    A1     A2     A3    A4     A5    A6  Euclidean Distance\n",
              "0  2.0  3.0 -56.3 -62.20 -57.80 -63.4 -62.80 -65.7            1.288410\n",
              "0  3.0  3.0 -58.6 -63.35 -57.05 -63.2 -63.10 -65.7            2.079663\n",
              "0  1.0  3.0 -55.2 -62.00 -59.00 -64.4 -63.15 -66.2            2.337199\n",
              "8  2.0  4.0 -58.2 -61.20 -59.20 -64.2 -61.80 -65.2            2.870540\n",
              "0  1.0  4.0 -57.5 -60.80 -59.90 -64.8 -62.10 -65.6            3.210919\n",
              "7  2.0  2.0 -54.4 -63.20 -56.40 -62.6 -63.80 -66.2            3.464102\n",
              "0  3.0  2.0 -57.2 -64.60 -55.40 -62.8 -64.40 -66.6            3.622154\n",
              "0  3.0  4.0 -60.0 -62.10 -58.70 -63.6 -61.80 -64.8            3.652396\n",
              "2  0.0  4.0 -56.8 -60.40 -60.60 -65.4 -62.40 -66.0            3.984972\n",
              "0  0.0  3.0 -54.1 -61.80 -60.20 -65.4 -63.50 -66.7            4.170132"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def compute_euclidean_distance(row):\n",
        "  return np.linalg.norm(Dory_RSSI_np - row[[\"A1\", \"A2\", \"A3\", \"A4\", \"A5\", \"A6\"]])\n",
        "\n",
        "df_interpolation['Euclidean Distance'] = df_interpolation.apply(compute_euclidean_distance, axis=1)\n",
        "\n",
        "display(df_interpolation.nsmallest(10, 'Euclidean Distance', 'all'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visual inspection of the smallest computed euclidean distances shows reasonable values and clustering around similar coordinates.\n",
        "\n",
        "This method selects the position **(2,3)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Repeating the same operation for the original dataframe, with no interpolation and all individual samples, shows coherent results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 456,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>exp_id</th>\n",
              "      <th>A1</th>\n",
              "      <th>A2</th>\n",
              "      <th>A3</th>\n",
              "      <th>A4</th>\n",
              "      <th>A5</th>\n",
              "      <th>A6</th>\n",
              "      <th>Euclidean Distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>-58</td>\n",
              "      <td>-61</td>\n",
              "      <td>-59</td>\n",
              "      <td>-64</td>\n",
              "      <td>-62</td>\n",
              "      <td>-65</td>\n",
              "      <td>2.828427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-58</td>\n",
              "      <td>-61</td>\n",
              "      <td>-59</td>\n",
              "      <td>-64</td>\n",
              "      <td>-61</td>\n",
              "      <td>-65</td>\n",
              "      <td>3.316625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>-54</td>\n",
              "      <td>-63</td>\n",
              "      <td>-56</td>\n",
              "      <td>-63</td>\n",
              "      <td>-64</td>\n",
              "      <td>-66</td>\n",
              "      <td>3.872983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>-57</td>\n",
              "      <td>-61</td>\n",
              "      <td>-61</td>\n",
              "      <td>-66</td>\n",
              "      <td>-63</td>\n",
              "      <td>-66</td>\n",
              "      <td>4.123106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-54</td>\n",
              "      <td>-61</td>\n",
              "      <td>-56</td>\n",
              "      <td>-63</td>\n",
              "      <td>-63</td>\n",
              "      <td>-65</td>\n",
              "      <td>4.358899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-61</td>\n",
              "      <td>-62</td>\n",
              "      <td>-57</td>\n",
              "      <td>-62</td>\n",
              "      <td>-61</td>\n",
              "      <td>-64</td>\n",
              "      <td>5.477226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>-58</td>\n",
              "      <td>-62</td>\n",
              "      <td>-62</td>\n",
              "      <td>-67</td>\n",
              "      <td>-64</td>\n",
              "      <td>-68</td>\n",
              "      <td>5.656854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>-53</td>\n",
              "      <td>-62</td>\n",
              "      <td>-55</td>\n",
              "      <td>-62</td>\n",
              "      <td>-62</td>\n",
              "      <td>-65</td>\n",
              "      <td>5.656854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>-58</td>\n",
              "      <td>-64</td>\n",
              "      <td>-53</td>\n",
              "      <td>-61</td>\n",
              "      <td>-63</td>\n",
              "      <td>-65</td>\n",
              "      <td>6.082763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>-59</td>\n",
              "      <td>-65</td>\n",
              "      <td>-53</td>\n",
              "      <td>-62</td>\n",
              "      <td>-64</td>\n",
              "      <td>-66</td>\n",
              "      <td>6.164414</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    X  Y  exp_id  A1  A2  A3  A4  A5  A6  Euclidean Distance\n",
              "48  2  4       3 -58 -61 -59 -64 -62 -65            2.828427\n",
              "47  2  4       2 -58 -61 -59 -64 -61 -65            3.316625\n",
              "85  2  2       0 -54 -63 -56 -63 -64 -66            3.872983\n",
              "53  0  4       3 -57 -61 -61 -66 -63 -66            4.123106\n",
              "87  2  2       2 -54 -61 -56 -63 -63 -65            4.358899\n",
              "21  4  4       1 -61 -62 -57 -62 -61 -64            5.477226\n",
              "54  0  4       4 -58 -62 -62 -67 -64 -68            5.656854\n",
              "89  2  2       4 -53 -62 -55 -62 -62 -65            5.656854\n",
              "33  4  2       3 -58 -64 -53 -61 -63 -65            6.082763\n",
              "34  4  2       4 -59 -65 -53 -62 -64 -66            6.164414"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Euclidean Distance'] = df.apply(compute_euclidean_distance, axis=1)\n",
        "display(df.nsmallest(10, 'Euclidean Distance', 'all'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 457,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "      <th>A1</th>\n",
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              "    <tr>\n",
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              "      <td>-60</td>\n",
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              "      <td>-51</td>\n",
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              "      <td>6</td>\n",
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              "      <td>-62</td>\n",
              "      <td>-61</td>\n",
              "      <td>-58</td>\n",
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              "      <td>-57</td>\n",
              "      <td>-53</td>\n",
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              "      <td>-64</td>\n",
              "      <td>-64</td>\n",
              "      <td>-61</td>\n",
              "      <td>-59</td>\n",
              "      <td>-59</td>\n",
              "      <td>-55</td>\n",
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              "    <tr>\n",
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              "      <th>65</th>\n",
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              "      <td>-59</td>\n",
              "      <td>-61</td>\n",
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              "      <td>-57</td>\n",
              "      <td>-56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-66</td>\n",
              "      <td>-67</td>\n",
              "      <td>-61</td>\n",
              "      <td>-60</td>\n",
              "      <td>-63</td>\n",
              "      <td>-63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-64</td>\n",
              "      <td>-65</td>\n",
              "      <td>-59</td>\n",
              "      <td>-58</td>\n",
              "      <td>-61</td>\n",
              "      <td>-61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>-65</td>\n",
              "      <td>-66</td>\n",
              "      <td>-60</td>\n",
              "      <td>-59</td>\n",
              "      <td>-62</td>\n",
              "      <td>-62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>-63</td>\n",
              "      <td>-64</td>\n",
              "      <td>-58</td>\n",
              "      <td>-57</td>\n",
              "      <td>-60</td>\n",
              "      <td>-60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>180 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     X  Y  exp_id  A1  A2  A3  A4  A5  A6\n",
              "0   10  6       0 -64 -64 -61 -59 -59 -55\n",
              "1   10  6       1 -60 -60 -57 -55 -55 -51\n",
              "2   10  6       2 -62 -61 -58 -56 -57 -53\n",
              "3   10  6       3 -66 -66 -63 -61 -61 -57\n",
              "4   10  6       4 -64 -64 -61 -59 -59 -55\n",
              "..  .. ..     ...  ..  ..  ..  ..  ..  ..\n",
              "65   8  4       0 -59 -61 -54 -55 -57 -56\n",
              "66   8  4       1 -66 -67 -61 -60 -63 -63\n",
              "67   8  4       2 -64 -65 -59 -58 -61 -61\n",
              "68   8  4       3 -65 -66 -60 -59 -62 -62\n",
              "69   8  4       4 -63 -64 -58 -57 -60 -60\n",
              "\n",
              "[180 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
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          "data": {
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              "      <td>-60.4</td>\n",
              "      <td>-60.6</td>\n",
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              "      <td>-62.4</td>\n",
              "      <td>-66.0</td>\n",
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              "    <tr>\n",
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              "      <td>-61.4</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-64.4</td>\n",
              "      <td>-67.2</td>\n",
              "      <td>-62.0</td>\n",
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              "      <td>-49.6</td>\n",
              "      <td>-64.0</td>\n",
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              "      <td>-58.7</td>\n",
              "      <td>-37.5</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-63.5</td>\n",
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              "    <tr>\n",
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              "      <td>-65.3</td>\n",
              "      <td>-66.7</td>\n",
              "      <td>-60.4</td>\n",
              "      <td>-54.3</td>\n",
              "      <td>-64.3</td>\n",
              "      <td>-61.8</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>5.0</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>-65.3</td>\n",
              "      <td>-61.2</td>\n",
              "      <td>-58.2</td>\n",
              "      <td>-61.3</td>\n",
              "      <td>-58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>-64.1</td>\n",
              "      <td>-63.0</td>\n",
              "      <td>-61.1</td>\n",
              "      <td>-59.5</td>\n",
              "      <td>-57.8</td>\n",
              "      <td>-51.6</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-66.7</td>\n",
              "      <td>-64.2</td>\n",
              "      <td>-64.3</td>\n",
              "      <td>-63.2</td>\n",
              "      <td>-58.4</td>\n",
              "      <td>-37.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>121 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       X    Y    A1    A2    A3    A4    A5    A6\n",
              "0    0.0  0.0 -26.8 -66.6 -61.0 -66.8 -67.6 -69.8\n",
              "1    0.0  2.0 -51.4 -63.2 -59.8 -65.4 -64.6 -67.4\n",
              "2    0.0  4.0 -56.8 -60.4 -60.6 -65.4 -62.4 -66.0\n",
              "3    0.0  6.0 -61.4 -58.2 -64.4 -67.2 -62.0 -66.4\n",
              "4    0.0  8.0 -61.2 -49.6 -64.0 -65.6 -58.2 -64.0\n",
              "..   ...  ...   ...   ...   ...   ...   ...   ...\n",
              "0   10.0  1.0 -64.3 -66.8 -58.7 -37.5 -65.0 -63.5\n",
              "0   10.0  3.0 -65.3 -66.7 -60.4 -54.3 -64.3 -61.8\n",
              "0   10.0  5.0 -65.0 -65.3 -61.2 -58.2 -61.3 -58.0\n",
              "0   10.0  7.0 -64.1 -63.0 -61.1 -59.5 -57.8 -51.6\n",
              "0   10.0  9.0 -66.7 -64.2 -64.3 -63.2 -58.4 -37.4\n",
              "\n",
              "[121 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = df.drop(columns='Euclidean Distance')\n",
        "df_interpolation = df_interpolation.drop(columns='Euclidean Distance')\n",
        "\n",
        "df.to_csv(\"data/raw_data.csv\", index=False)\n",
        "df_interpolation.to_csv(\"data/processed_data.csv\", index=False)"
      ]
    }
  ],
  "metadata": {
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