1_Intro_and_data_preperation.ipynb

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  "cells": [
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      "metadata": {
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      },
      "source": [
        "# Intro to polysomnogram classification using movement, heart rate and step count data collected from Apple Watches"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KV-lfMqNKzbT"
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      "source": [
        "\n",
        "As part of our final year studying data science, we have been tasked with classifying polysomnogram (PSG) status by using data collected from an Apple Watch. The data used was from [this study](https://physionet.org/content/sleep-accel/1.0.0/), where there were 31 participants.  \n",
        "\n",
        "--- \n",
        "PSG status was recorded every 30 seconds in the study.  \n",
        "We began by preparing our data and creating heart rate, step count and x, y and z movement entries at 1 second intervals for each subject.  \n",
        "\n",
        "---\n",
        "We then passed this data to our feature extraction notebook, where we used the tsfresh feature extraction module. We gave each 30 second interval a unique session id whereby features would be extracted to represent each particular session for each subject.  \n",
        "We also used feature selection to reduce the feature space.  \n",
        "We then grouped the features formed from each session by PSG status. This gave us a set of features which represented each PSG status.  \n",
        "\n",
        "---  \n",
        "We then passed these features to our classification notebook. Here we read in data representing our approach of keeping sleep status, filtering the feature space.  \n",
        "We chose to try 3 classification algorithms, SVM Classifier, K neighbours and Decision Tree, to get a full perspective on how well each subject's data could be classified.  \n",
        "\n",
        "---\n",
        "To conclude, we tested the decisions that we made throughout the project.\n",
        "1. Was it a good decision to filter the features?  \n",
        "2. Was it a good decision to include the test data?\n",
        "3. Was it a good decision to use parameter optimisation?\n",
        "\n",
        "We also tried autoML and binary labels representing sleep / awake to see could we get even better results if this was the case."
      ]
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      "metadata": {
        "id": "Cric64Vloy1l"
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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59zzSDpHn7Uf"
      },
      "source": [
        "---\n",
        "---\n",
        "\n",
        "# 1. Setup\n",
        "\n",
        "---\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ptNp4gASm2H5"
      },
      "source": [
        "### Import Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_Y3AJ_7iMkB"
      },
      "source": [
        "##### Import general packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-9Kv0qNm7UZ"
      },
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm.auto import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUZu6uq-iPAm"
      },
      "source": [
        "##### Import & setup the Json package for exporting the data to a Json file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_Oy55AmiOj_"
      },
      "source": [
        "import json\n",
        "\n",
        "class JSONEncoder(json.JSONEncoder):\n",
        "    def default(self, obj):\n",
        "        if hasattr(obj, 'to_json'):\n",
        "            return obj.to_json(orient='records')\n",
        "        return json.JSONEncoder.default(self, obj)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrqfjdME18qj"
      },
      "source": [
        "### Mount the drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnU2Cgu818yz",
        "outputId": "070e6ce1-8256-436e-af08-89c29aaa206b"
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1Fqi2bHnKam"
      },
      "source": [
        "### Set the directory for reading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZjGedIWnFKj"
      },
      "source": [
        "os.chdir('/content/drive/My Drive/4th Year/CA4015/assignment2/Submission/data')    # Ivan's directory\n",
        "# os.chdir('/content/drive/My Drive/assignment2/Submission/data')                     # Nathan's directory"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caoopHQe9wpa"
      },
      "source": [
        "### Set the maximum number of rows and columns seen in the output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6QZAwlX920M"
      },
      "source": [
        "pd.set_option('max_rows', 25)\n",
        "pd.set_option('max_columns', 25)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0pQxo4jEocYc"
      },
      "source": [
        "---\n",
        "---\n",
        "\n",
        "# 2. Read in the data\n",
        "\n",
        "---\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThucCxw2SDpL"
      },
      "source": [
        "There are four different sensors for each subject that record a time and a sensor reading. The data from these sensors for each subject are stored in seperate files and need to be read in seperately so that we can then join them all into one dataframe for each subject."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLJOWP3rqXDW"
      },
      "source": [
        "*As provided by Eoin, The following types of data are provided:*\n",
        "\n",
        "**motion (acceleration)**: Saved as txt files with the naming convention '[subject-id-number]_acceleration.txt'\n",
        "\n",
        "Each line in this file has the format: date (in seconds since PSG start), x acceleration (in g), y acceleration, z acceleration\n",
        " \n",
        "**heart rate (bpm)**: Saved as txt files with the naming convention '[subject-id-number]_heartrate.txt'\n",
        "\n",
        "Each line in this file has the format: date (in seconds since PSG start), heart rate (bpm)\n",
        " \n",
        "**steps (count)**: Recorded from the Apple Watch and saved in the format '[subject-id-number]_steps.txt'\n",
        "\n",
        "Each line in this file has the format: date (in seconds since PSG start), steps (total in bin from this timestamp to next timestamp)\n",
        " \n",
        "**labeled sleep**: Recorded from polysomnography and saved in the format '[subject-id-number]_labeled_sleep.txt'\n",
        "\n",
        "Each line in this file has the format: date (in seconds since PSG start) stage (0-5, wake = 0, N1 = 1, N2 = 2, N3 = 3, REM = 5)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXJIR0kMi57t"
      },
      "source": [
        "### Create a function to read in, analyse and join the different data sources"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7KydPdVi6JC"
      },
      "source": [
        "def read_in_subject_data(subject_id):\n",
        "    # read in motion data\n",
        "    motion_df = pd.read_csv('../data/original_sensor_data_files/{}_cleaned_motion.out'.format(subject_id), sep=' ', names=[\"second\", \"x_move\", \"y_move\", \"z_move\"])\n",
        "\n",
        "    # read in heart rate data\n",
        "    hr_df = pd.read_csv('../data/original_sensor_data_files/{}_cleaned_hr.out'.format(subject_id), sep=' ', names=[\"second\", \"heart_rate\"])\n",
        "\n",
        "    # read in the step count data\n",
        "    steps_df = pd.read_csv('../data/original_sensor_data_files/{}_cleaned_counts.out'.format(subject_id), names=[\"second\", \"step_count\"])\n",
        "\n",
        "    # read in psg sleep status data\n",
        "    psg_df = pd.read_csv('../data/original_sensor_data_files/{}_cleaned_psg.out'.format(subject_id), sep=' ', names=[\"second\", \"psg_status\"])\n",
        "\n",
        "    # clean the psg data to remove '-1' values - turn values with '-1' to 'nan'\n",
        "    psg_df.replace(-1, float('nan'), inplace=True)\n",
        "\n",
        "    return (motion_df, hr_df, steps_df, psg_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zcw5R8f6thYv"
      },
      "source": [
        "def analyse_sensor_data(sensor_data_df):\n",
        "    print(\"   - Starts at\", round(min(sensor_data_df.second), 4), \"seconds\")\n",
        "    print(\"   - Ends at\", round(max(sensor_data_df.second), 4), \"seconds\")\n",
        "\n",
        "    print(\"\\n   - Collects data every\", round(max(sensor_data_df.second)/len(sensor_data_df), 4), \"seconds\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5Ox87iulG_P"
      },
      "source": [
        "def join_sensor_dataframes(motion_df, hr_df, steps_df, psg_df):\n",
        "  \n",
        "    # join the dataframes together\n",
        "    motion_nd_hr_df = pd.merge(motion_df, hr_df, on=\"second\", how=\"outer\")\n",
        "    motion_hr_nd_steps_df = pd.merge(motion_nd_hr_df, steps_df, on=\"second\", how=\"outer\")\n",
        "    all_sensor_df = pd.merge(motion_hr_nd_steps_df, psg_df, on=\"second\", how=\"outer\")\n",
        "\n",
        "    # sort df by second column\n",
        "    all_sensor_df[\"second\"] = all_sensor_df[\"second\"].astype(\"float\")\n",
        "    all_sensor_df = all_sensor_df.sort_values(\"second\")\n",
        "    all_sensor_df = all_sensor_df.reset_index(drop=True)\n",
        "\n",
        "    return all_sensor_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQfjU0lhnhFj"
      },
      "source": [
        "### Get a list of all the **subject ids** from the input files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WbS2bc_Aj5NS",
        "outputId": "c319240b-5e10-4634-b577-54538f00befd"
      },
      "source": [
        "subject_ids = []\n",
        "for n, filename in enumerate(os.listdir('../data/original_sensor_data_files/')):\n",
        "    terms = filename.split('_')\n",
        "    subject_id = int(terms[0])\n",
        "  \n",
        "    if subject_id not in subject_ids:\n",
        "        subject_ids.append(subject_id)\n",
        "    \n",
        "# sort the list\n",
        "sorted_subject_ids = sorted(subject_ids)\n",
        "\n",
        "print(\"There are\", len(sorted_subject_ids), \"subject id's\\nThey are:\\n\", sorted_subject_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 31 subject id's\n",
            "They are:\n",
            " [46343, 759667, 781756, 844359, 1066528, 1360686, 1449548, 1455390, 1818471, 2598705, 2638030, 3509524, 3997827, 4018081, 4314139, 4426783, 5132496, 5383425, 5498603, 5797046, 6220552, 7749105, 8000685, 8173033, 8258170, 8530312, 8686948, 8692923, 9106476, 9618981, 9961348]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdiJHEojl27o"
      },
      "source": [
        "### Read in the data, analyse it and create a map of subject_id to sensor data\n",
        "The print_analysis variable below can be changed to True to see an output of data for each subject."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4L6inkxw3BJi"
      },
      "source": [
        "print_analysis = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "c22f5053338740c195264b6c281620f9",
            "582ded2efa274055b0f776ee6b8fa683",
            "d958d4fb310749fc98dde9c0ea642f30",
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          ]
        },
        "id": "HYSbFFNklihc",
        "outputId": "3a9d35c9-159e-444d-9e4d-8dce4df01805"
      },
      "source": [
        "map_subject_id_to_sensor_dataframes = {}\n",
        "for subject_id in tqdm(sorted_subject_ids):\n",
        "\n",
        "    # read in the data\n",
        "    motion_df, hr_df, steps_df, psg_df = read_in_subject_data(subject_id)\n",
        "\n",
        "    if print_analysis:\n",
        "        # print which subject_id we are analysing\n",
        "        print(\"\\n--------------------------------------------------------------\")\n",
        "        print(\"==========================\", subject_id, \"==========================\")\n",
        "        print(\"--------------------------------------------------------------\")\n",
        "\n",
        "        # analyse the data using print statements\n",
        "        print(\"The motion sensor:\")\n",
        "        analyse_sensor_data(motion_df)\n",
        "        print(\"\\nThe heart rate sensor:\")\n",
        "        analyse_sensor_data(hr_df)\n",
        "        print(\"\\nThe PSG study data:\")\n",
        "        analyse_sensor_data(psg_df)\n",
        "        print(\"\\nThe step count sensor:\")\n",
        "        analyse_sensor_data(steps_df)\n",
        "\n",
        "    # join these sensor dataframes together\n",
        "    all_sensor_df = join_sensor_dataframes(motion_df, hr_df, steps_df, psg_df)\n",
        "\n",
        "    # add this subject to the map - map it to its sensor dataframe\n",
        "    map_subject_id_to_sensor_dataframes[subject_id] = all_sensor_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c22f5053338740c195264b6c281620f9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=31.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-iVsJLbM_2b"
      },
      "source": [
        "---\n",
        "---\n",
        "\n",
        "# 3. Fix the dataframe so that the seconds column is at a fixed interval\n",
        "\n",
        "---\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3NRTe0mKwBOV"
      },
      "source": [
        "### Define the functions to normalise the second values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5J8oRjy_Y27"
      },
      "source": [
        "def where_no_sensor_value_has_been_recorded_up_till_that_second_turn_this_to_a_val(df, val):\n",
        "\n",
        "    fixed_df = df.copy()\n",
        "    # iterate through these columns and fix the 'Nan' values\n",
        "    for col in fixed_df.drop(columns=[\"second\"], axis=1):\n",
        "\n",
        "        # iterate backwards over rows and turn \"Nan\" boxes to the specified val and find where the \"Nan\" values stop\n",
        "        i = len(fixed_df[col]) - 1\n",
        "        while np.isnan(fixed_df[col][i]):\n",
        "            fixed_df[col][i] = val\n",
        "            i = i - 1\n",
        "\n",
        "        # iterate forwards over rows and turn \"Nan\" boxes to the specified val and find where real values start\n",
        "        j = 0\n",
        "        while np.isnan(fixed_df[col][j]):\n",
        "            fixed_df[col][j] = val\n",
        "            j = j + 1\n",
        "\n",
        "    return fixed_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwqv42jLvPhY"
      },
      "source": [
        "def fill_in_the_dfs_nan_values(df):\n",
        "\n",
        "    # fill the \"Nan\" values with the value encountered before the \"Nan\" up until it hits the point where the rest of that column is only \"Nan\" values\n",
        "    filled_df = df.fillna(method=\"ffill\")\n",
        "\n",
        "    # fill the \"Nan\" values with an interpolated value using the value before this \"Nan\" value and after this \"Nan\" value\n",
        "    # filled_df = df.interpolate()\n",
        "\n",
        "    return filled_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwCrJlNKy-fv"
      },
      "source": [
        "def get_list_of_averages_for_each_col_of_all_rows(rows, val_to_fill_nans, cols_no_second_list):\n",
        "\n",
        "    # if there are no values between the specified seconds\n",
        "    if len(rows) == 0:\n",
        "        # add empty row\n",
        "        list_of_averages = list(np.repeat(float(\"nan\"), len(cols_no_second_list)))\n",
        "\n",
        "    # if there is at least one row with a value between these seconds\n",
        "    elif len(rows) > 0:\n",
        "        # average the values in these rows to get just 1 row with the values for each column\n",
        "        list_of_averages = []\n",
        "        for col in cols_no_second_list:\n",
        "\n",
        "            # make sure that the sleep labels column only contains whole numbers - they must maintain their classification label\n",
        "            if col == 'psg_status':\n",
        "                # count the number of occurances of each value and take the one that occurs most as this value\n",
        "                avg = float(rows.loc[:, col].mode()[0])\n",
        "                    \n",
        "            # first check if the values are all valid values - replace \"Nan\" with a value so these are invalid\n",
        "            elif set(rows.loc[:, col]) != {val_to_fill_nans} and val_to_fill_nans in list(rows.loc[:, col]):\n",
        "                count_nans = 0\n",
        "                non_nan_vals = []\n",
        "\n",
        "                # iterate through these row values to count how many invalid(-10) values there are\n",
        "                for v in rows.loc[:, col]:\n",
        "                    if v == val_to_fill_nans:\n",
        "                        count_nans += 1\n",
        "                      \n",
        "                    else:\n",
        "                        count_nans -= 1\n",
        "                        non_nan_vals.append(v)\n",
        "\n",
        "                # if there are more invalid rows than there are proper ones\n",
        "                if count_nans > 0:\n",
        "                    avg = sum(non_nan_vals)/len(non_nan_vals)\n",
        "\n",
        "                # if there are more proper values than invalid ones\n",
        "                elif count_nans < 0:\n",
        "                    avg = val_to_fill_nans\n",
        "\n",
        "                # if there is the exact same amout of invalid values and proper values, use the most common single value or the first of these most common values\n",
        "                else:\n",
        "                    print(rows)\n",
        "                    avg = float(rows.loc[:, col].mode()[0])\n",
        "\n",
        "            # if all the values are proper values, then just get the means of these values and populate the row with this\n",
        "            else:\n",
        "                avg = rows.loc[:, col].mean()\n",
        "                \n",
        "            list_of_averages.append(avg)\n",
        "\n",
        "    return list_of_averages"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzBr9Hm6_Y_v"
      },
      "source": [
        "def turn_seconds_to_a_set_interval(filled_sensor_df, second_column_step, val_to_fill_nans):\n",
        "\n",
        "    # define a variable for the second column\n",
        "    sec_col = filled_sensor_df.second\n",
        "\n",
        "    # get a ist of the cols exluding the second column\n",
        "    cols_no_second_list = filled_sensor_df.drop(columns=[\"second\"], axis=1).columns\n",
        "\n",
        "    # get the value of the maximum and minimum second in this dataframe\n",
        "    min_second_in_df = int(round(min(sec_col) - 0.5))\n",
        "    max_second_in_df = int(round(max(sec_col) + 0.5))\n",
        "\n",
        "    # create a new dataframe that we will populate\n",
        "    new_df = pd.DataFrame(columns=(filled_sensor_df.columns))\n",
        "\n",
        "    # iterate through each second interval in this dataframe\n",
        "    for i in tqdm(np.arange(min_second_in_df, max_second_in_df + second_column_step, second_column_step)):\n",
        "\n",
        "        # get the rows between second \"i - 1\" and second \"i\"\n",
        "        rows = filled_sensor_df.loc[(sec_col > i - (second_column_step - 0.00000001)) & (sec_col < i + 0.00000001)]\n",
        "        rows = rows.reset_index(drop=True)\n",
        "        \n",
        "        list_of_averages = get_list_of_averages_for_each_col_of_all_rows(rows, val_to_fill_nans, cols_no_second_list)\n",
        "\n",
        "        # create a dataframe row from this list\n",
        "        new_df_row = pd.DataFrame([i] + list_of_averages, columns=[i], index=filled_sensor_df.columns).T\n",
        "\n",
        "        # add this row to the new DataFrame\n",
        "        new_df = pd.concat([new_df, new_df_row], axis=0)\n",
        "\n",
        "    return new_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnzxIcVqS_hl"
      },
      "source": [
        "def is_not_nan(v):\n",
        "  return v == v"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQLn_fWkwAPS"
      },
      "source": [
        "### Iterate through the map we created to normalise the second data\n",
        "The second_column_step vairable below can be adjusted to a custom time interval\n",
        "\n",
        "This cell take a long time to run!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V57Tw-WWzBOS"
      },
      "source": [
        "val_to_fill_nans = -100\n",
        "second_column_step = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
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      },
      "source": [
        "map_subject_id_to_joined_and_uniform_second_sensor_df = {}\n",
        "for subject_id, all_sensors_df in tqdm(map_subject_id_to_sensor_dataframes.items()):\n",
        "\n",
        "    # print the subject_id\n",
        "    print(\"\\t\\t\", subject_id)\n",
        "\n",
        "    # set values where there was no sensor value taken to be a value instead of 'Nan'\n",
        "    half_filled_df = where_no_sensor_value_has_been_recorded_up_till_that_second_turn_this_to_a_val(all_sensors_df, val_to_fill_nans)\n",
        "\n",
        "    # fill the Nan values in the dataframe\n",
        "    filled_sensor_df = fill_in_the_dfs_nan_values(half_filled_df)\n",
        "\n",
        "    # replace the \"-10\" values with \"Nan\" values\n",
        "    df_val_to_fill_nan_replaced = filled_sensor_df.replace(val_to_fill_nans, float(\"nan\"))\n",
        "\n",
        "    # drop the rows that have an 'Nan' value for any of the sensors - this means that the sensor hadn't recorded it's first value yet\n",
        "    no_nan_values = df_val_to_fill_nan_replaced.dropna()\n",
        "\n",
        "    # turn the second column to a uniform interval by averaging the other columns\n",
        "    uniform_second_interval_df = turn_seconds_to_a_set_interval(no_nan_values, second_column_step, val_to_fill_nans)\n",
        "\n",
        "    # fill the nan values in the dataframe\n",
        "    df_with_one_sec_per_row = fill_in_the_dfs_nan_values(uniform_second_interval_df)\n",
        "\n",
        "    # reset the index\n",
        "    df_with_one_sec_per_row.reset_index(drop=True, inplace=True)\n",
        "\n",
        "    # clean out the rows where there is an invalid sleep state value\n",
        "    remove_invalid_sleep_state = df_with_one_sec_per_row[is_not_nan(df_with_one_sec_per_row[\"psg_status\"])]\n",
        "    remove_invalid_sleep_state.reset_index(drop=True, inplace=True)\n",
        "\n",
        "    # create a new map of the subject_id to this new dataframe\n",
        "    map_subject_id_to_joined_and_uniform_second_sensor_df[subject_id] = remove_invalid_sleep_state"
      ],
      "execution_count": null,
      "outputs": [
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            "\n",
            "\t\t 3997827\n"
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            "\t\t 7749105\n"
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            "\n",
            "\t\t 8000685\n"
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            "\t\t 8173033\n"
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            "\n",
            "\t\t 8530312\n"
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            "\n",
            "\t\t 8686948\n"
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            "\n",
            "\t\t 8692923\n"
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            "\t\t 9106476\n"
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            "\t\t 9618981\n"
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            "\n",
            "\t\t 9961348\n"
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            "\n",
            "\n"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zo1STHWKYH50"
      },
      "source": [
        "### Have a look at this created dictionary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrQuUDPkP7la",
        "outputId": "503003cc-cce2-4dbd-a299-61f5f0300896"
      },
      "source": [
        "i = 0\n",
        "for student_id, df in map_subject_id_to_joined_and_uniform_second_sensor_df.items():\n",
        "    print(\"--------------------\", student_id, \"--------------------\")\n",
        "    print(df)\n",
        "\n",
        "    if i == 3:\n",
        "        break\n",
        "    i = i + 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-------------------- 46343 --------------------\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        390.0 -0.540527  0.680496 -0.271774        90.0   79.820000   \n",
            "1        391.0 -0.426881  0.920799 -0.093921        90.0  109.617059   \n",
            "2        392.0 -0.448256  0.816442 -0.228871        90.0  125.870000   \n",
            "3        393.0 -0.464479  0.765035 -0.432477        90.0  125.870000   \n",
            "4        394.0 -0.494360  0.798880 -0.076882        90.0  125.870000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "16556  16946.0 -0.441763 -0.525671  0.723509        73.0    0.000000   \n",
            "16557  16947.0 -0.441267 -0.525272  0.724310        73.0    0.000000   \n",
            "16558  16948.0 -0.441876 -0.525352  0.724083        73.0    0.000000   \n",
            "16559  16949.0 -0.442227 -0.525543  0.723603        73.0    0.000000   \n",
            "16560  16950.0 -0.441905 -0.525534  0.723882        73.0    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "16556         0.0  \n",
            "16557         0.0  \n",
            "16558         0.0  \n",
            "16559         0.0  \n",
            "16560         0.0  \n",
            "\n",
            "[16561 rows x 7 columns]\n",
            "-------------------- 759667 --------------------\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          7.0  0.176598  0.005585 -0.974216        75.0         0.0   \n",
            "1          8.0  0.179751  0.007542 -0.976257        75.0         0.0   \n",
            "2          9.0  0.171640  0.010070 -0.978475        75.0         0.0   \n",
            "3         10.0  0.177361  0.020753 -0.976957        75.0         0.0   \n",
            "4         11.0  0.157454  0.018493 -0.979429        75.0         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "14179  14186.0 -0.124983 -0.968178 -0.200968        66.0         0.0   \n",
            "14180  14187.0 -0.125358 -0.968028 -0.201479        66.0         0.0   \n",
            "14181  14188.0 -0.125288 -0.967798 -0.200318        66.0         0.0   \n",
            "14182  14189.0 -0.125436 -0.968168 -0.203684        66.0         0.0   \n",
            "14183  14190.0 -0.125521 -0.967810 -0.203962        66.0         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "14179         1.0  \n",
            "14180         1.0  \n",
            "14181         1.0  \n",
            "14182         1.0  \n",
            "14183         1.0  \n",
            "\n",
            "[14184 rows x 7 columns]\n",
            "-------------------- 781756 --------------------\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          2.0 -0.484159  0.827394 -0.144834        65.0      178.06   \n",
            "1          3.0 -0.504077  0.561891 -0.497203        65.0      178.06   \n",
            "2          4.0  0.166481  0.512248 -0.691838        65.0      178.06   \n",
            "3          5.0  0.951314  0.308661 -0.547155        65.0      178.06   \n",
            "4          6.0 -0.189213 -0.109087 -0.734049        65.0      178.06   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "29364  29366.0 -0.328940 -0.084280 -0.939244        46.0        0.00   \n",
            "29365  29367.0 -0.328674 -0.079961 -0.939474        46.0        0.00   \n",
            "29366  29368.0 -0.326509 -0.074846 -0.940762        46.0        0.00   \n",
            "29367  29369.0 -0.325748 -0.078187 -0.940861        46.0        0.00   \n",
            "29368  29370.0 -0.327856 -0.084254 -0.940070        46.0        0.00   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "29364         5.0  \n",
            "29365         5.0  \n",
            "29366         5.0  \n",
            "29367         5.0  \n",
            "29368         5.0  \n",
            "\n",
            "[29369 rows x 7 columns]\n",
            "-------------------- 844359 --------------------\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0       1320.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "1       1321.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "2       1322.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "3       1323.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "4       1324.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "26876  28196.0 -0.457123 -0.307096  0.829889   68.941176         0.0   \n",
            "26877  28197.0 -0.457172 -0.307249  0.829792   68.000000         0.0   \n",
            "26878  28198.0 -0.457106 -0.307052  0.829851   68.000000         0.0   \n",
            "26879  28199.0 -0.457199 -0.306877  0.830042   68.000000         0.0   \n",
            "26880  28200.0 -0.457377 -0.306842  0.830094   68.000000         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "26876         0.0  \n",
            "26877         0.0  \n",
            "26878         0.0  \n",
            "26879         0.0  \n",
            "26880         0.0  \n",
            "\n",
            "[26881 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkiDJR8iZYJ_"
      },
      "source": [
        "---\n",
        "---\n",
        "\n",
        "# 4. Store this dictionary in a json file\n",
        "\n",
        "---\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C72JF6CD9SMm"
      },
      "source": [
        "# put the dataframe in the json file\n",
        "with open('map_of_subject_id_to_its_cleaned_sensor_data.json', 'w') as fp:\n",
        "    json.dump(map_subject_id_to_joined_and_uniform_second_sensor_df, fp, cls=JSONEncoder)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oC73KvQ1cfM",
        "outputId": "904f89d0-8714-4032-d28e-08385c65537c"
      },
      "source": [
        "# make a copy with no steps - for further analysis later on\n",
        "# for student_id, df in map_subject_id_to_joined_and_uniform_second_sensor_df.items():\n",
        "#   print(df.drop(['step_count'], axis=1))\n",
        "# put the dataframe in the json file\n",
        "# with open('no_step_map_of_subject_id_to_its cleaned_sensor_data.json', 'w') as fp:\n",
        "#     json.dump(map_subject_id_to_joined_and_uniform_second_sensor_df, fp, cls=JSONEncoder)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        390.0 -0.540527  0.680496 -0.271774        90.0   79.820000   \n",
            "1        391.0 -0.426881  0.920799 -0.093921        90.0  109.617059   \n",
            "2        392.0 -0.448256  0.816442 -0.228871        90.0  125.870000   \n",
            "3        393.0 -0.464479  0.765035 -0.432477        90.0  125.870000   \n",
            "4        394.0 -0.494360  0.798880 -0.076882        90.0  125.870000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "16556  16946.0 -0.441763 -0.525671  0.723509        73.0    0.000000   \n",
            "16557  16947.0 -0.441267 -0.525272  0.724310        73.0    0.000000   \n",
            "16558  16948.0 -0.441876 -0.525352  0.724083        73.0    0.000000   \n",
            "16559  16949.0 -0.442227 -0.525543  0.723603        73.0    0.000000   \n",
            "16560  16950.0 -0.441905 -0.525534  0.723882        73.0    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "16556         0.0  \n",
            "16557         0.0  \n",
            "16558         0.0  \n",
            "16559         0.0  \n",
            "16560         0.0  \n",
            "\n",
            "[16561 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        390.0 -0.540527  0.680496 -0.271774        90.0         0.0\n",
            "1        391.0 -0.426881  0.920799 -0.093921        90.0         0.0\n",
            "2        392.0 -0.448256  0.816442 -0.228871        90.0         0.0\n",
            "3        393.0 -0.464479  0.765035 -0.432477        90.0         0.0\n",
            "4        394.0 -0.494360  0.798880 -0.076882        90.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "16556  16946.0 -0.441763 -0.525671  0.723509        73.0         0.0\n",
            "16557  16947.0 -0.441267 -0.525272  0.724310        73.0         0.0\n",
            "16558  16948.0 -0.441876 -0.525352  0.724083        73.0         0.0\n",
            "16559  16949.0 -0.442227 -0.525543  0.723603        73.0         0.0\n",
            "16560  16950.0 -0.441905 -0.525534  0.723882        73.0         0.0\n",
            "\n",
            "[16561 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          7.0  0.176598  0.005585 -0.974216        75.0         0.0   \n",
            "1          8.0  0.179751  0.007542 -0.976257        75.0         0.0   \n",
            "2          9.0  0.171640  0.010070 -0.978475        75.0         0.0   \n",
            "3         10.0  0.177361  0.020753 -0.976957        75.0         0.0   \n",
            "4         11.0  0.157454  0.018493 -0.979429        75.0         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "14179  14186.0 -0.124983 -0.968178 -0.200968        66.0         0.0   \n",
            "14180  14187.0 -0.125358 -0.968028 -0.201479        66.0         0.0   \n",
            "14181  14188.0 -0.125288 -0.967798 -0.200318        66.0         0.0   \n",
            "14182  14189.0 -0.125436 -0.968168 -0.203684        66.0         0.0   \n",
            "14183  14190.0 -0.125521 -0.967810 -0.203962        66.0         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "14179         1.0  \n",
            "14180         1.0  \n",
            "14181         1.0  \n",
            "14182         1.0  \n",
            "14183         1.0  \n",
            "\n",
            "[14184 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          7.0  0.176598  0.005585 -0.974216        75.0         0.0\n",
            "1          8.0  0.179751  0.007542 -0.976257        75.0         0.0\n",
            "2          9.0  0.171640  0.010070 -0.978475        75.0         0.0\n",
            "3         10.0  0.177361  0.020753 -0.976957        75.0         0.0\n",
            "4         11.0  0.157454  0.018493 -0.979429        75.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "14179  14186.0 -0.124983 -0.968178 -0.200968        66.0         1.0\n",
            "14180  14187.0 -0.125358 -0.968028 -0.201479        66.0         1.0\n",
            "14181  14188.0 -0.125288 -0.967798 -0.200318        66.0         1.0\n",
            "14182  14189.0 -0.125436 -0.968168 -0.203684        66.0         1.0\n",
            "14183  14190.0 -0.125521 -0.967810 -0.203962        66.0         1.0\n",
            "\n",
            "[14184 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          2.0 -0.484159  0.827394 -0.144834        65.0      178.06   \n",
            "1          3.0 -0.504077  0.561891 -0.497203        65.0      178.06   \n",
            "2          4.0  0.166481  0.512248 -0.691838        65.0      178.06   \n",
            "3          5.0  0.951314  0.308661 -0.547155        65.0      178.06   \n",
            "4          6.0 -0.189213 -0.109087 -0.734049        65.0      178.06   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "29364  29366.0 -0.328940 -0.084280 -0.939244        46.0        0.00   \n",
            "29365  29367.0 -0.328674 -0.079961 -0.939474        46.0        0.00   \n",
            "29366  29368.0 -0.326509 -0.074846 -0.940762        46.0        0.00   \n",
            "29367  29369.0 -0.325748 -0.078187 -0.940861        46.0        0.00   \n",
            "29368  29370.0 -0.327856 -0.084254 -0.940070        46.0        0.00   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "29364         5.0  \n",
            "29365         5.0  \n",
            "29366         5.0  \n",
            "29367         5.0  \n",
            "29368         5.0  \n",
            "\n",
            "[29369 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          2.0 -0.484159  0.827394 -0.144834        65.0         0.0\n",
            "1          3.0 -0.504077  0.561891 -0.497203        65.0         0.0\n",
            "2          4.0  0.166481  0.512248 -0.691838        65.0         0.0\n",
            "3          5.0  0.951314  0.308661 -0.547155        65.0         0.0\n",
            "4          6.0 -0.189213 -0.109087 -0.734049        65.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "29364  29366.0 -0.328940 -0.084280 -0.939244        46.0         5.0\n",
            "29365  29367.0 -0.328674 -0.079961 -0.939474        46.0         5.0\n",
            "29366  29368.0 -0.326509 -0.074846 -0.940762        46.0         5.0\n",
            "29367  29369.0 -0.325748 -0.078187 -0.940861        46.0         5.0\n",
            "29368  29370.0 -0.327856 -0.084254 -0.940070        46.0         5.0\n",
            "\n",
            "[29369 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0       1320.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "1       1321.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "2       1322.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "3       1323.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "4       1324.0 -0.184235  0.037170  0.989243   68.000000         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "26876  28196.0 -0.457123 -0.307096  0.829889   68.941176         0.0   \n",
            "26877  28197.0 -0.457172 -0.307249  0.829792   68.000000         0.0   \n",
            "26878  28198.0 -0.457106 -0.307052  0.829851   68.000000         0.0   \n",
            "26879  28199.0 -0.457199 -0.306877  0.830042   68.000000         0.0   \n",
            "26880  28200.0 -0.457377 -0.306842  0.830094   68.000000         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "26876         0.0  \n",
            "26877         0.0  \n",
            "26878         0.0  \n",
            "26879         0.0  \n",
            "26880         0.0  \n",
            "\n",
            "[26881 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0       1320.0 -0.184235  0.037170  0.989243   68.000000         0.0\n",
            "1       1321.0 -0.184235  0.037170  0.989243   68.000000         0.0\n",
            "2       1322.0 -0.184235  0.037170  0.989243   68.000000         0.0\n",
            "3       1323.0 -0.184235  0.037170  0.989243   68.000000         0.0\n",
            "4       1324.0 -0.184235  0.037170  0.989243   68.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "26876  28196.0 -0.457123 -0.307096  0.829889   68.941176         0.0\n",
            "26877  28197.0 -0.457172 -0.307249  0.829792   68.000000         0.0\n",
            "26878  28198.0 -0.457106 -0.307052  0.829851   68.000000         0.0\n",
            "26879  28199.0 -0.457199 -0.306877  0.830042   68.000000         0.0\n",
            "26880  28200.0 -0.457377 -0.306842  0.830094   68.000000         0.0\n",
            "\n",
            "[26881 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          7.0  0.406087  0.447482 -0.797114        52.0    0.000000   \n",
            "1          8.0  0.405794  0.447517 -0.796961        52.0    0.000000   \n",
            "2          9.0  0.405773  0.447694 -0.796855        52.0    0.000000   \n",
            "3         10.0  0.405880  0.447645 -0.796586        52.0    0.000000   \n",
            "4         11.0  0.405632  0.447893 -0.796469        52.0    0.000000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28384  28391.0 -0.642338 -0.108089 -0.732913        67.0   15.350000   \n",
            "28385  28392.0 -0.612884 -0.075881 -0.761903        67.0   15.350000   \n",
            "28386  28393.0 -0.601741 -0.075485 -0.771609        67.0   15.350000   \n",
            "28387  28394.0 -0.599878 -0.075490 -0.773242        67.0   15.350000   \n",
            "28388  28395.0 -0.598183 -0.074388 -0.773787        67.0   14.070833   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28384         0.0  \n",
            "28385         0.0  \n",
            "28386         0.0  \n",
            "28387         0.0  \n",
            "28388         0.0  \n",
            "\n",
            "[28389 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          7.0  0.406087  0.447482 -0.797114        52.0         0.0\n",
            "1          8.0  0.405794  0.447517 -0.796961        52.0         0.0\n",
            "2          9.0  0.405773  0.447694 -0.796855        52.0         0.0\n",
            "3         10.0  0.405880  0.447645 -0.796586        52.0         0.0\n",
            "4         11.0  0.405632  0.447893 -0.796469        52.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28384  28391.0 -0.642338 -0.108089 -0.732913        67.0         0.0\n",
            "28385  28392.0 -0.612884 -0.075881 -0.761903        67.0         0.0\n",
            "28386  28393.0 -0.601741 -0.075485 -0.771609        67.0         0.0\n",
            "28387  28394.0 -0.599878 -0.075490 -0.773242        67.0         0.0\n",
            "28388  28395.0 -0.598183 -0.074388 -0.773787        67.0         0.0\n",
            "\n",
            "[28389 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0       1256.0  0.018397 -0.334409 -0.942973   72.673469         0.0   \n",
            "1       1257.0 -0.002067 -0.213810 -0.975119   69.000000         0.0   \n",
            "2       1258.0 -0.018828 -0.157115 -0.987791   69.000000         0.0   \n",
            "3       1259.0 -0.026951 -0.162920 -0.987225   69.000000         0.0   \n",
            "4       1260.0 -0.021368 -0.152223 -0.988947   69.000000         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "27690  28946.0 -0.036817 -0.270489  0.956521   75.000000         0.0   \n",
            "27691  28947.0 -0.037410 -0.272083  0.956103   75.000000         0.0   \n",
            "27692  28948.0 -0.037396 -0.271574  0.956266   75.000000         0.0   \n",
            "27693  28949.0 -0.036966 -0.269826  0.956565   75.000000         0.0   \n",
            "27694  28950.0 -0.036762 -0.270059  0.956664   76.132075         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "27690         2.0  \n",
            "27691         2.0  \n",
            "27692         2.0  \n",
            "27693         2.0  \n",
            "27694         2.0  \n",
            "\n",
            "[27695 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0       1256.0  0.018397 -0.334409 -0.942973   72.673469         0.0\n",
            "1       1257.0 -0.002067 -0.213810 -0.975119   69.000000         0.0\n",
            "2       1258.0 -0.018828 -0.157115 -0.987791   69.000000         0.0\n",
            "3       1259.0 -0.026951 -0.162920 -0.987225   69.000000         0.0\n",
            "4       1260.0 -0.021368 -0.152223 -0.988947   69.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "27690  28946.0 -0.036817 -0.270489  0.956521   75.000000         2.0\n",
            "27691  28947.0 -0.037410 -0.272083  0.956103   75.000000         2.0\n",
            "27692  28948.0 -0.037396 -0.271574  0.956266   75.000000         2.0\n",
            "27693  28949.0 -0.036966 -0.269826  0.956565   75.000000         2.0\n",
            "27694  28950.0 -0.036762 -0.270059  0.956664   76.132075         2.0\n",
            "\n",
            "[27695 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        330.0  0.452789 -0.240524 -0.858688   67.000000    0.000000   \n",
            "1        331.0  0.444282 -0.241134 -0.865195   67.000000    1.997937   \n",
            "2        332.0  0.433112 -0.243950 -0.869899   67.000000    3.070000   \n",
            "3        333.0  0.427771 -0.245177 -0.872137   67.000000    3.070000   \n",
            "4        334.0  0.444097 -0.284378 -0.850357   67.000000    3.070000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28556  28886.0 -0.077412 -0.100551 -0.994464   65.000000    0.000000   \n",
            "28557  28887.0 -0.077436 -0.100319 -0.994383   65.000000    0.000000   \n",
            "28558  28888.0 -0.077460 -0.100409 -0.994325   64.596774    0.000000   \n",
            "28559  28889.0 -0.077636 -0.101295 -0.994365   60.000000    0.000000   \n",
            "28560  28890.0 -0.077554 -0.101367 -0.994251   60.000000    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28556         0.0  \n",
            "28557         0.0  \n",
            "28558         0.0  \n",
            "28559         0.0  \n",
            "28560         0.0  \n",
            "\n",
            "[28561 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        330.0  0.452789 -0.240524 -0.858688   67.000000         0.0\n",
            "1        331.0  0.444282 -0.241134 -0.865195   67.000000         0.0\n",
            "2        332.0  0.433112 -0.243950 -0.869899   67.000000         0.0\n",
            "3        333.0  0.427771 -0.245177 -0.872137   67.000000         0.0\n",
            "4        334.0  0.444097 -0.284378 -0.850357   67.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28556  28886.0 -0.077412 -0.100551 -0.994464   65.000000         0.0\n",
            "28557  28887.0 -0.077436 -0.100319 -0.994383   65.000000         0.0\n",
            "28558  28888.0 -0.077460 -0.100409 -0.994325   64.596774         0.0\n",
            "28559  28889.0 -0.077636 -0.101295 -0.994365   60.000000         0.0\n",
            "28560  28890.0 -0.077554 -0.101367 -0.994251   60.000000         0.0\n",
            "\n",
            "[28561 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0       1020.0  0.940781  0.093765 -0.773560   53.000000   49.120000   \n",
            "1       1021.0  0.449129 -0.218798 -0.759470   53.000000  155.968039   \n",
            "2       1022.0  0.216543 -0.227609 -0.824963   53.000000  267.090000   \n",
            "3       1023.0  0.324577  0.050973 -1.026779   53.000000  267.090000   \n",
            "4       1024.0  0.118335 -0.289767 -0.847630   59.470588  267.090000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28616  29636.0 -0.252313 -0.517967 -0.820907   49.000000    0.000000   \n",
            "28617  29637.0 -0.251967 -0.518496 -0.820782   49.000000    0.000000   \n",
            "28618  29638.0 -0.253323 -0.518381 -0.820594   49.000000    0.000000   \n",
            "28619  29639.0 -0.253687 -0.518156 -0.820883   49.000000    0.000000   \n",
            "28620  29640.0 -0.252489 -0.497653 -0.832546   49.000000    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28616         0.0  \n",
            "28617         0.0  \n",
            "28618         0.0  \n",
            "28619         0.0  \n",
            "28620         0.0  \n",
            "\n",
            "[28621 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0       1020.0  0.940781  0.093765 -0.773560   53.000000         0.0\n",
            "1       1021.0  0.449129 -0.218798 -0.759470   53.000000         0.0\n",
            "2       1022.0  0.216543 -0.227609 -0.824963   53.000000         0.0\n",
            "3       1023.0  0.324577  0.050973 -1.026779   53.000000         0.0\n",
            "4       1024.0  0.118335 -0.289767 -0.847630   59.470588         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28616  29636.0 -0.252313 -0.517967 -0.820907   49.000000         0.0\n",
            "28617  29637.0 -0.251967 -0.518496 -0.820782   49.000000         0.0\n",
            "28618  29638.0 -0.253323 -0.518381 -0.820594   49.000000         0.0\n",
            "28619  29639.0 -0.253687 -0.518156 -0.820883   49.000000         0.0\n",
            "28620  29640.0 -0.252489 -0.497653 -0.832546   49.000000         0.0\n",
            "\n",
            "[28621 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        600.0 -0.237640  0.847092 -0.478195   86.000000        0.00   \n",
            "1        601.0 -0.237167  0.845061 -0.478347   85.326923       15.35   \n",
            "2        602.0 -0.247205  0.838956 -0.472265   85.000000       39.91   \n",
            "3        603.0 -0.246803  0.829943 -0.498903   85.000000       39.91   \n",
            "4        604.0 -0.238658  0.874044 -0.405521   85.000000       39.91   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28706  29306.0 -0.277758 -0.310836 -0.911762   73.000000        0.00   \n",
            "28707  29307.0 -0.276646 -0.309920 -0.913006   73.000000        0.00   \n",
            "28708  29308.0 -0.276631 -0.309579 -0.912616   69.372549        0.00   \n",
            "28709  29309.0 -0.276964 -0.309359 -0.912585   68.000000        0.00   \n",
            "28710  29310.0 -0.277566 -0.310752 -0.911819   68.000000        0.00   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28706         2.0  \n",
            "28707         2.0  \n",
            "28708         2.0  \n",
            "28709         2.0  \n",
            "28710         2.0  \n",
            "\n",
            "[28711 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        600.0 -0.237640  0.847092 -0.478195   86.000000         0.0\n",
            "1        601.0 -0.237167  0.845061 -0.478347   85.326923         0.0\n",
            "2        602.0 -0.247205  0.838956 -0.472265   85.000000         0.0\n",
            "3        603.0 -0.246803  0.829943 -0.498903   85.000000         0.0\n",
            "4        604.0 -0.238658  0.874044 -0.405521   85.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28706  29306.0 -0.277758 -0.310836 -0.911762   73.000000         2.0\n",
            "28707  29307.0 -0.276646 -0.309920 -0.913006   73.000000         2.0\n",
            "28708  29308.0 -0.276631 -0.309579 -0.912616   69.372549         2.0\n",
            "28709  29309.0 -0.276964 -0.309359 -0.912585   68.000000         2.0\n",
            "28710  29310.0 -0.277566 -0.310752 -0.911819   68.000000         2.0\n",
            "\n",
            "[28711 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        780.0 -0.279175  0.049728 -0.962433   55.000000   70.610000   \n",
            "1        781.0 -0.272950  0.041645 -0.961628   55.000000   84.001552   \n",
            "2        782.0 -0.271310  0.043067 -0.962802   72.627119  141.220000   \n",
            "3        783.0 -0.277983  0.033025 -0.958815   81.000000  141.220000   \n",
            "4        784.0 -0.278549  0.052951 -0.955287   81.000000  141.220000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28586  29366.0 -0.336473 -0.169288 -0.925401   46.000000    0.000000   \n",
            "28587  29367.0 -0.336110 -0.169044 -0.925461   46.448276    0.000000   \n",
            "28588  29368.0 -0.335783 -0.169585 -0.925858   47.000000    0.000000   \n",
            "28589  29369.0 -0.335755 -0.172940 -0.925239   47.000000    0.000000   \n",
            "28590  29370.0 -0.336133 -0.169483 -0.924658   47.000000    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28586         2.0  \n",
            "28587         2.0  \n",
            "28588         2.0  \n",
            "28589         2.0  \n",
            "28590         2.0  \n",
            "\n",
            "[28591 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        780.0 -0.279175  0.049728 -0.962433   55.000000         0.0\n",
            "1        781.0 -0.272950  0.041645 -0.961628   55.000000         0.0\n",
            "2        782.0 -0.271310  0.043067 -0.962802   72.627119         0.0\n",
            "3        783.0 -0.277983  0.033025 -0.958815   81.000000         0.0\n",
            "4        784.0 -0.278549  0.052951 -0.955287   81.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28586  29366.0 -0.336473 -0.169288 -0.925401   46.000000         2.0\n",
            "28587  29367.0 -0.336110 -0.169044 -0.925461   46.448276         2.0\n",
            "28588  29368.0 -0.335783 -0.169585 -0.925858   47.000000         2.0\n",
            "28589  29369.0 -0.335755 -0.172940 -0.925239   47.000000         2.0\n",
            "28590  29370.0 -0.336133 -0.169483 -0.924658   47.000000         2.0\n",
            "\n",
            "[28591 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        390.0 -0.291748 -0.937607 -0.112351   68.000000   178.06000   \n",
            "1        391.0 -0.293551 -0.943818 -0.107300   68.000000    95.21873   \n",
            "2        392.0 -0.294197 -0.943658 -0.107564   68.793651    73.68000   \n",
            "3        393.0 -0.294690 -0.943979 -0.104516   69.000000    73.68000   \n",
            "4        394.0 -0.294653 -0.943670 -0.105675   69.000000    73.68000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28406  28796.0 -0.224860 -0.963208 -0.101005   55.000000     0.00000   \n",
            "28407  28797.0 -0.225408 -0.963210 -0.101067   55.000000     0.00000   \n",
            "28408  28798.0 -0.225090 -0.963249 -0.100623   55.000000     0.00000   \n",
            "28409  28799.0 -0.225219 -0.963232 -0.100749   55.000000     0.00000   \n",
            "28410  28800.0 -0.225568 -0.963102 -0.101022   55.000000     0.00000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28406         5.0  \n",
            "28407         5.0  \n",
            "28408         5.0  \n",
            "28409         5.0  \n",
            "28410         5.0  \n",
            "\n",
            "[28411 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        390.0 -0.291748 -0.937607 -0.112351   68.000000         0.0\n",
            "1        391.0 -0.293551 -0.943818 -0.107300   68.000000         0.0\n",
            "2        392.0 -0.294197 -0.943658 -0.107564   68.793651         0.0\n",
            "3        393.0 -0.294690 -0.943979 -0.104516   69.000000         0.0\n",
            "4        394.0 -0.294653 -0.943670 -0.105675   69.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28406  28796.0 -0.224860 -0.963208 -0.101005   55.000000         5.0\n",
            "28407  28797.0 -0.225408 -0.963210 -0.101067   55.000000         5.0\n",
            "28408  28798.0 -0.225090 -0.963249 -0.100623   55.000000         5.0\n",
            "28409  28799.0 -0.225219 -0.963232 -0.100749   55.000000         5.0\n",
            "28410  28800.0 -0.225568 -0.963102 -0.101022   55.000000         5.0\n",
            "\n",
            "[28411 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          3.0 -0.039845 -0.422124 -0.897008   82.000000         0.0   \n",
            "1          4.0  0.030084 -0.514437 -0.851242   82.000000         0.0   \n",
            "2          5.0  0.057260 -0.570795 -0.809798   82.000000         0.0   \n",
            "3          6.0  0.047477 -0.529191 -0.839760   82.000000         0.0   \n",
            "4          7.0 -0.059607 -0.392423 -0.902174   82.095238         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "12443  12446.0 -0.107203 -0.197328 -0.968576   86.000000         0.0   \n",
            "12444  12447.0 -0.107224 -0.197444 -0.968205   86.000000         0.0   \n",
            "12445  12448.0 -0.106984 -0.197312 -0.968329   86.000000         0.0   \n",
            "12446  12449.0 -0.107192 -0.197205 -0.968148   86.000000         0.0   \n",
            "12447  12450.0 -0.107212 -0.197598 -0.968562   86.000000         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "12443         2.0  \n",
            "12444         2.0  \n",
            "12445         2.0  \n",
            "12446         2.0  \n",
            "12447         2.0  \n",
            "\n",
            "[12448 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          3.0 -0.039845 -0.422124 -0.897008   82.000000         0.0\n",
            "1          4.0  0.030084 -0.514437 -0.851242   82.000000         0.0\n",
            "2          5.0  0.057260 -0.570795 -0.809798   82.000000         0.0\n",
            "3          6.0  0.047477 -0.529191 -0.839760   82.000000         0.0\n",
            "4          7.0 -0.059607 -0.392423 -0.902174   82.095238         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "12443  12446.0 -0.107203 -0.197328 -0.968576   86.000000         2.0\n",
            "12444  12447.0 -0.107224 -0.197444 -0.968205   86.000000         2.0\n",
            "12445  12448.0 -0.106984 -0.197312 -0.968329   86.000000         2.0\n",
            "12446  12449.0 -0.107192 -0.197205 -0.968148   86.000000         2.0\n",
            "12447  12450.0 -0.107212 -0.197598 -0.968562   86.000000         2.0\n",
            "\n",
            "[12448 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        510.0 -0.378189 -0.854553  0.352264   68.000000   92.100000   \n",
            "1        511.0 -0.378638 -0.854464  0.351274   68.000000   58.012414   \n",
            "2        512.0 -0.379009 -0.854253  0.351612   68.000000   21.490000   \n",
            "3        513.0 -0.378196 -0.855253  0.349989   68.000000   21.490000   \n",
            "4        514.0 -0.378478 -0.854559  0.350833   68.000000   21.490000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28706  29216.0 -0.502422  0.132781 -0.855973   57.000000    0.000000   \n",
            "28707  29217.0 -0.496858  0.110770 -0.864104   55.689655    0.000000   \n",
            "28708  29218.0 -0.496447  0.110380 -0.863645   55.000000    0.000000   \n",
            "28709  29219.0 -0.501906  0.132269 -0.856429   55.000000    0.000000   \n",
            "28710  29220.0 -0.496919  0.111184 -0.862977   55.000000    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28706         5.0  \n",
            "28707         5.0  \n",
            "28708         5.0  \n",
            "28709         5.0  \n",
            "28710         5.0  \n",
            "\n",
            "[28711 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        510.0 -0.378189 -0.854553  0.352264   68.000000         0.0\n",
            "1        511.0 -0.378638 -0.854464  0.351274   68.000000         0.0\n",
            "2        512.0 -0.379009 -0.854253  0.351612   68.000000         0.0\n",
            "3        513.0 -0.378196 -0.855253  0.349989   68.000000         0.0\n",
            "4        514.0 -0.378478 -0.854559  0.350833   68.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28706  29216.0 -0.502422  0.132781 -0.855973   57.000000         5.0\n",
            "28707  29217.0 -0.496858  0.110770 -0.864104   55.689655         5.0\n",
            "28708  29218.0 -0.496447  0.110380 -0.863645   55.000000         5.0\n",
            "28709  29219.0 -0.501906  0.132269 -0.856429   55.000000         5.0\n",
            "28710  29220.0 -0.496919  0.111184 -0.862977   55.000000         5.0\n",
            "\n",
            "[28711 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          2.0 -0.456478  0.554239 -0.694861   69.000000         0.0   \n",
            "1          3.0 -0.456989  0.554937 -0.694653   69.000000         0.0   \n",
            "2          4.0 -0.457043  0.561014 -0.690329   69.000000         0.0   \n",
            "3          5.0 -0.451954  0.572528 -0.684858   69.000000         0.0   \n",
            "4          6.0 -0.451066  0.570429 -0.685549   66.562500         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "14935  14937.0 -0.390103 -0.394325 -0.824656   59.000000         0.0   \n",
            "14936  14938.0 -0.389866 -0.394873 -0.824566   59.000000         0.0   \n",
            "14937  14939.0 -0.388760 -0.395637 -0.824656   59.787879         0.0   \n",
            "14938  14940.0 -0.389304 -0.395155 -0.824766   60.000000         0.0   \n",
            "14939  14941.0 -0.389999 -0.394158 -0.824448   60.000000         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "14935         0.0  \n",
            "14936         0.0  \n",
            "14937         0.0  \n",
            "14938         0.0  \n",
            "14939         0.0  \n",
            "\n",
            "[14940 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          2.0 -0.456478  0.554239 -0.694861   69.000000         0.0\n",
            "1          3.0 -0.456989  0.554937 -0.694653   69.000000         0.0\n",
            "2          4.0 -0.457043  0.561014 -0.690329   69.000000         0.0\n",
            "3          5.0 -0.451954  0.572528 -0.684858   69.000000         0.0\n",
            "4          6.0 -0.451066  0.570429 -0.685549   66.562500         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "14935  14937.0 -0.390103 -0.394325 -0.824656   59.000000         0.0\n",
            "14936  14938.0 -0.389866 -0.394873 -0.824566   59.000000         0.0\n",
            "14937  14939.0 -0.388760 -0.395637 -0.824656   59.787879         0.0\n",
            "14938  14940.0 -0.389304 -0.395155 -0.824766   60.000000         0.0\n",
            "14939  14941.0 -0.389999 -0.394158 -0.824448   60.000000         0.0\n",
            "\n",
            "[14940 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        240.0 -0.818787 -0.589890  0.032745   76.000000   92.100000   \n",
            "1        241.0 -0.786807 -0.612757  0.031372   76.000000  132.732353   \n",
            "2        242.0 -0.680668 -0.515934 -0.282820   76.000000  138.150000   \n",
            "3        243.0 -0.804740  0.253608 -0.500095   76.000000  138.150000   \n",
            "4        244.0 -0.622426  0.692686 -0.347310   76.000000  138.150000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28796  29036.0 -0.074907 -0.871517  0.479192   61.882353   18.420000   \n",
            "28797  29037.0 -0.075184 -0.872925  0.476714   61.000000   18.420000   \n",
            "28798  29038.0 -0.075231 -0.873311  0.475458   61.000000   18.420000   \n",
            "28799  29039.0 -0.075306 -0.872906  0.476966   61.000000   18.420000   \n",
            "28800  29040.0 -0.075332 -0.871545  0.479189   61.000000   17.357308   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28796         0.0  \n",
            "28797         0.0  \n",
            "28798         0.0  \n",
            "28799         0.0  \n",
            "28800         0.0  \n",
            "\n",
            "[28801 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        240.0 -0.818787 -0.589890  0.032745   76.000000         0.0\n",
            "1        241.0 -0.786807 -0.612757  0.031372   76.000000         0.0\n",
            "2        242.0 -0.680668 -0.515934 -0.282820   76.000000         0.0\n",
            "3        243.0 -0.804740  0.253608 -0.500095   76.000000         0.0\n",
            "4        244.0 -0.622426  0.692686 -0.347310   76.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28796  29036.0 -0.074907 -0.871517  0.479192   61.882353         0.0\n",
            "28797  29037.0 -0.075184 -0.872925  0.476714   61.000000         0.0\n",
            "28798  29038.0 -0.075231 -0.873311  0.475458   61.000000         0.0\n",
            "28799  29039.0 -0.075306 -0.872906  0.476966   61.000000         0.0\n",
            "28800  29040.0 -0.075332 -0.871545  0.479189   61.000000         0.0\n",
            "\n",
            "[28801 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          4.0 -0.853735  0.512627  0.062847   71.000000       55.26   \n",
            "1          5.0 -0.898391  0.403836 -0.086722   71.000000       55.26   \n",
            "2          6.0 -0.907429  0.393945 -0.096113   71.000000       55.26   \n",
            "3          7.0 -0.828271  0.157234 -0.440737   71.000000       55.26   \n",
            "4          8.0 -0.789488 -0.012383 -0.598302   71.000000       55.26   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "29332  29336.0 -0.111432 -0.955178  0.260838   61.862069        0.00   \n",
            "29333  29337.0 -0.111120 -0.955658  0.260191   62.000000        0.00   \n",
            "29334  29338.0 -0.111163 -0.954979  0.260074   62.000000        0.00   \n",
            "29335  29339.0 -0.111639 -0.954465  0.262678   62.000000        0.00   \n",
            "29336  29340.0 -0.111710 -0.954444  0.262536   62.000000        0.00   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "29332         5.0  \n",
            "29333         5.0  \n",
            "29334         5.0  \n",
            "29335         5.0  \n",
            "29336         5.0  \n",
            "\n",
            "[29337 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          4.0 -0.853735  0.512627  0.062847   71.000000         0.0\n",
            "1          5.0 -0.898391  0.403836 -0.086722   71.000000         0.0\n",
            "2          6.0 -0.907429  0.393945 -0.096113   71.000000         0.0\n",
            "3          7.0 -0.828271  0.157234 -0.440737   71.000000         0.0\n",
            "4          8.0 -0.789488 -0.012383 -0.598302   71.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "29332  29336.0 -0.111432 -0.955178  0.260838   61.862069         5.0\n",
            "29333  29337.0 -0.111120 -0.955658  0.260191   62.000000         5.0\n",
            "29334  29338.0 -0.111163 -0.954979  0.260074   62.000000         5.0\n",
            "29335  29339.0 -0.111639 -0.954465  0.262678   62.000000         5.0\n",
            "29336  29340.0 -0.111710 -0.954444  0.262536   62.000000         5.0\n",
            "\n",
            "[29337 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          7.0  0.649537 -0.561640 -0.480395  113.000000      285.51   \n",
            "1          8.0  0.585526 -0.727230 -0.355674  113.000000      285.51   \n",
            "2          9.0  0.106254 -0.650663 -0.711269  114.047619      285.51   \n",
            "3         10.0  0.067498 -0.826023 -0.555625  115.000000      285.51   \n",
            "4         11.0  0.034593 -0.805201 -0.563929  115.000000      285.51   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "13879  13886.0 -0.265626  0.121175 -0.946206   81.000000        0.00   \n",
            "13880  13887.0 -0.265698  0.119859 -0.943580   79.430769        0.00   \n",
            "13881  13888.0 -0.265685  0.122261 -0.944571   78.000000        0.00   \n",
            "13882  13889.0 -0.265099  0.122839 -0.946414   78.000000        0.00   \n",
            "13883  13890.0 -0.265502  0.120793 -0.947031   78.000000        0.00   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "13879         2.0  \n",
            "13880         2.0  \n",
            "13881         2.0  \n",
            "13882         2.0  \n",
            "13883         2.0  \n",
            "\n",
            "[13884 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          7.0  0.649537 -0.561640 -0.480395  113.000000         0.0\n",
            "1          8.0  0.585526 -0.727230 -0.355674  113.000000         0.0\n",
            "2          9.0  0.106254 -0.650663 -0.711269  114.047619         0.0\n",
            "3         10.0  0.067498 -0.826023 -0.555625  115.000000         0.0\n",
            "4         11.0  0.034593 -0.805201 -0.563929  115.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "13879  13886.0 -0.265626  0.121175 -0.946206   81.000000         2.0\n",
            "13880  13887.0 -0.265698  0.119859 -0.943580   79.430769         2.0\n",
            "13881  13888.0 -0.265685  0.122261 -0.944571   78.000000         2.0\n",
            "13882  13889.0 -0.265099  0.122839 -0.946414   78.000000         2.0\n",
            "13883  13890.0 -0.265502  0.120793 -0.947031   78.000000         2.0\n",
            "\n",
            "[13884 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          2.0 -0.305755  0.787998 -0.528217   67.000000         0.0   \n",
            "1          3.0 -0.305409  0.787309 -0.528058   67.000000         0.0   \n",
            "2          4.0 -0.306820  0.787134 -0.527904   67.000000         0.0   \n",
            "3          5.0 -0.307132  0.787018 -0.528143   67.000000         0.0   \n",
            "4          6.0 -0.305802  0.787311 -0.528218   67.000000         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "29274  29276.0 -0.310043  0.279532 -0.899242   71.000000         0.0   \n",
            "29275  29277.0 -0.307663  0.280708 -0.900917   71.000000         0.0   \n",
            "29276  29278.0 -0.311640  0.278817 -0.899907   71.583333         0.0   \n",
            "29277  29279.0 -0.310872  0.278600 -0.900331   72.000000         0.0   \n",
            "29278  29280.0 -0.308386  0.278777 -0.900293   72.000000         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "29274         2.0  \n",
            "29275         2.0  \n",
            "29276         2.0  \n",
            "29277         2.0  \n",
            "29278         2.0  \n",
            "\n",
            "[29279 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          2.0 -0.305755  0.787998 -0.528217   67.000000         0.0\n",
            "1          3.0 -0.305409  0.787309 -0.528058   67.000000         0.0\n",
            "2          4.0 -0.306820  0.787134 -0.527904   67.000000         0.0\n",
            "3          5.0 -0.307132  0.787018 -0.528143   67.000000         0.0\n",
            "4          6.0 -0.305802  0.787311 -0.528218   67.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "29274  29276.0 -0.310043  0.279532 -0.899242   71.000000         2.0\n",
            "29275  29277.0 -0.307663  0.280708 -0.900917   71.000000         2.0\n",
            "29276  29278.0 -0.311640  0.278817 -0.899907   71.583333         2.0\n",
            "29277  29279.0 -0.310872  0.278600 -0.900331   72.000000         2.0\n",
            "29278  29280.0 -0.308386  0.278777 -0.900293   72.000000         2.0\n",
            "\n",
            "[29279 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        330.0 -0.542007 -0.357147 -0.897308        68.0   49.120000   \n",
            "1        331.0 -0.654067 -0.404557 -0.559391        68.0  138.210196   \n",
            "2        332.0 -0.558549 -0.723995  0.320557        68.0  162.710000   \n",
            "3        333.0 -0.363137 -0.634709  0.541152        68.0  162.710000   \n",
            "4        334.0 -0.359056 -0.740199 -0.228460        68.0  162.710000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "22286  22616.0 -0.373494 -0.145858 -0.917257        76.0    0.000000   \n",
            "22287  22617.0 -0.373823 -0.145909 -0.917516        76.0    0.000000   \n",
            "22288  22618.0 -0.376649 -0.150658 -0.915261        76.0    0.000000   \n",
            "22289  22619.0 -0.375114 -0.145694 -0.917008        76.0    0.000000   \n",
            "22290  22620.0 -0.373701 -0.144820 -0.917782        76.0    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "22286         5.0  \n",
            "22287         5.0  \n",
            "22288         5.0  \n",
            "22289         5.0  \n",
            "22290         5.0  \n",
            "\n",
            "[22291 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        330.0 -0.542007 -0.357147 -0.897308        68.0         0.0\n",
            "1        331.0 -0.654067 -0.404557 -0.559391        68.0         0.0\n",
            "2        332.0 -0.558549 -0.723995  0.320557        68.0         0.0\n",
            "3        333.0 -0.363137 -0.634709  0.541152        68.0         0.0\n",
            "4        334.0 -0.359056 -0.740199 -0.228460        68.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "22286  22616.0 -0.373494 -0.145858 -0.917257        76.0         5.0\n",
            "22287  22617.0 -0.373823 -0.145909 -0.917516        76.0         5.0\n",
            "22288  22618.0 -0.376649 -0.150658 -0.915261        76.0         5.0\n",
            "22289  22619.0 -0.375114 -0.145694 -0.917008        76.0         5.0\n",
            "22290  22620.0 -0.373701 -0.144820 -0.917782        76.0         5.0\n",
            "\n",
            "[22291 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        660.0 -0.626404 -0.624619 -0.403564        60.0    0.000000   \n",
            "1        661.0 -0.605233 -0.633415 -0.462266        60.0   12.385862   \n",
            "2        662.0 -0.518739 -0.699511 -0.480976        60.0   18.420000   \n",
            "3        663.0 -0.778975 -0.485755 -0.351509        60.0   18.420000   \n",
            "4        664.0 -0.861930 -0.349778  0.141695        60.0   18.420000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28136  28796.0 -0.371396 -0.494701  0.788144        83.0    0.000000   \n",
            "28137  28797.0 -0.371439 -0.494165  0.788554        83.0    0.000000   \n",
            "28138  28798.0 -0.371343 -0.494495  0.788215        83.0    0.000000   \n",
            "28139  28799.0 -0.371276 -0.494685  0.787959        83.0    0.000000   \n",
            "28140  28800.0 -0.371038 -0.494759  0.788053        83.0    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28136         5.0  \n",
            "28137         5.0  \n",
            "28138         5.0  \n",
            "28139         5.0  \n",
            "28140         5.0  \n",
            "\n",
            "[28141 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        660.0 -0.626404 -0.624619 -0.403564        60.0         0.0\n",
            "1        661.0 -0.605233 -0.633415 -0.462266        60.0         0.0\n",
            "2        662.0 -0.518739 -0.699511 -0.480976        60.0         0.0\n",
            "3        663.0 -0.778975 -0.485755 -0.351509        60.0         0.0\n",
            "4        664.0 -0.861930 -0.349778  0.141695        60.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28136  28796.0 -0.371396 -0.494701  0.788144        83.0         5.0\n",
            "28137  28797.0 -0.371439 -0.494165  0.788554        83.0         5.0\n",
            "28138  28798.0 -0.371343 -0.494495  0.788215        83.0         5.0\n",
            "28139  28799.0 -0.371276 -0.494685  0.787959        83.0         5.0\n",
            "28140  28800.0 -0.371038 -0.494759  0.788053        83.0         5.0\n",
            "\n",
            "[28141 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        810.0 -0.597305  0.067917 -0.793396        69.0   92.100000   \n",
            "1        811.0 -0.600928  0.074565 -0.793367        69.0   82.733898   \n",
            "2        812.0 -0.717070 -0.082613 -0.638098        69.0   36.840000   \n",
            "3        813.0 -0.797868  0.000172 -0.596950        69.0   36.840000   \n",
            "4        814.0 -0.777789  0.007262 -0.609437        69.0   36.840000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28586  29396.0 -0.172488 -0.132721  0.979756        60.0    0.000000   \n",
            "28587  29397.0 -0.172483 -0.132819  0.979620        60.0    0.000000   \n",
            "28588  29398.0 -0.172267 -0.133250  0.979564        60.0    0.000000   \n",
            "28589  29399.0 -0.172036 -0.132870  0.979674        60.0    0.000000   \n",
            "28590  29400.0 -0.172474 -0.132970  0.979528        60.0    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28586         2.0  \n",
            "28587         2.0  \n",
            "28588         2.0  \n",
            "28589         2.0  \n",
            "28590         2.0  \n",
            "\n",
            "[28591 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        810.0 -0.597305  0.067917 -0.793396        69.0         0.0\n",
            "1        811.0 -0.600928  0.074565 -0.793367        69.0         0.0\n",
            "2        812.0 -0.717070 -0.082613 -0.638098        69.0         0.0\n",
            "3        813.0 -0.797868  0.000172 -0.596950        69.0         0.0\n",
            "4        814.0 -0.777789  0.007262 -0.609437        69.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28586  29396.0 -0.172488 -0.132721  0.979756        60.0         2.0\n",
            "28587  29397.0 -0.172483 -0.132819  0.979620        60.0         2.0\n",
            "28588  29398.0 -0.172267 -0.133250  0.979564        60.0         2.0\n",
            "28589  29399.0 -0.172036 -0.132870  0.979674        60.0         2.0\n",
            "28590  29400.0 -0.172474 -0.132970  0.979528        60.0         2.0\n",
            "\n",
            "[28591 rows x 6 columns]\n",
            "      second    x_move    y_move    z_move  heart_rate  step_count  psg_status\n",
            "0      360.0  0.131165  0.362488 -0.920334        80.0  150.430000         0.0\n",
            "1      361.0  0.131647  0.366169 -0.918581        80.0  150.430000         0.0\n",
            "2      362.0  0.140300  0.373990 -0.913917        80.0  139.314483         0.0\n",
            "3      363.0  0.138696  0.375260 -0.913630        80.0  107.450000         0.0\n",
            "4      364.0  0.130869  0.374363 -0.915467        80.0  107.450000         0.0\n",
            "...      ...       ...       ...       ...         ...         ...         ...\n",
            "3661  4021.0 -0.321640  0.211945 -0.914276        71.0    0.000000         3.0\n",
            "3662  4022.0 -0.321640  0.211945 -0.914276        71.0    0.000000         3.0\n",
            "3663  4023.0 -0.321640  0.211945 -0.914276        71.0    0.000000         3.0\n",
            "3664  4024.0 -0.321640  0.211945 -0.914276        71.0    0.000000         3.0\n",
            "3665  4025.0 -0.322794  0.203171 -0.917783        71.0    0.000000         3.0\n",
            "\n",
            "[3666 rows x 7 columns]\n",
            "      second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0      360.0  0.131165  0.362488 -0.920334        80.0         0.0\n",
            "1      361.0  0.131647  0.366169 -0.918581        80.0         0.0\n",
            "2      362.0  0.140300  0.373990 -0.913917        80.0         0.0\n",
            "3      363.0  0.138696  0.375260 -0.913630        80.0         0.0\n",
            "4      364.0  0.130869  0.374363 -0.915467        80.0         0.0\n",
            "...      ...       ...       ...       ...         ...         ...\n",
            "3661  4021.0 -0.321640  0.211945 -0.914276        71.0         3.0\n",
            "3662  4022.0 -0.321640  0.211945 -0.914276        71.0         3.0\n",
            "3663  4023.0 -0.321640  0.211945 -0.914276        71.0         3.0\n",
            "3664  4024.0 -0.321640  0.211945 -0.914276        71.0         3.0\n",
            "3665  4025.0 -0.322794  0.203171 -0.917783        71.0         3.0\n",
            "\n",
            "[3666 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        150.0 -0.644287  0.148071 -0.779541   68.000000    3.070000   \n",
            "1        151.0 -0.526846  0.149023 -0.750051   68.000000  367.674364   \n",
            "2        152.0  0.097396 -0.202447 -1.004455   68.000000  439.010000   \n",
            "3        153.0  0.397575  0.051838 -0.894276   68.843137  439.010000   \n",
            "4        154.0 -0.112778  0.644497 -0.722835   69.000000  439.010000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28706  28856.0 -0.840077  0.216720 -0.494479   67.000000    0.000000   \n",
            "28707  28857.0 -0.840099  0.216644 -0.494361   67.000000    0.000000   \n",
            "28708  28858.0 -0.839801  0.216710 -0.494655   67.000000    0.000000   \n",
            "28709  28859.0 -0.839762  0.216356 -0.494515   67.000000    0.000000   \n",
            "28710  28860.0 -0.840041  0.216697 -0.494732   65.301887    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28706         1.0  \n",
            "28707         1.0  \n",
            "28708         1.0  \n",
            "28709         1.0  \n",
            "28710         1.0  \n",
            "\n",
            "[28711 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        150.0 -0.644287  0.148071 -0.779541   68.000000         0.0\n",
            "1        151.0 -0.526846  0.149023 -0.750051   68.000000         0.0\n",
            "2        152.0  0.097396 -0.202447 -1.004455   68.000000         0.0\n",
            "3        153.0  0.397575  0.051838 -0.894276   68.843137         0.0\n",
            "4        154.0 -0.112778  0.644497 -0.722835   69.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28706  28856.0 -0.840077  0.216720 -0.494479   67.000000         1.0\n",
            "28707  28857.0 -0.840099  0.216644 -0.494361   67.000000         1.0\n",
            "28708  28858.0 -0.839801  0.216710 -0.494655   67.000000         1.0\n",
            "28709  28859.0 -0.839762  0.216356 -0.494515   67.000000         1.0\n",
            "28710  28860.0 -0.840041  0.216697 -0.494732   65.301887         1.0\n",
            "\n",
            "[28711 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        840.0  0.150436 -0.887863  0.413330   70.000000    21.49000   \n",
            "1        841.0  0.152270 -0.889634  0.412469   70.000000    18.11902   \n",
            "2        842.0  0.152289 -0.889179  0.413210   70.000000     0.00000   \n",
            "3        843.0  0.152224 -0.888816  0.414621   69.764706     0.00000   \n",
            "4        844.0  0.152345 -0.889031  0.413802   68.000000     0.00000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28616  29456.0 -0.694993 -0.104232 -0.713901   62.000000     0.00000   \n",
            "28617  29457.0 -0.694135 -0.104212 -0.715265   62.000000     0.00000   \n",
            "28618  29458.0 -0.693075 -0.104085 -0.715963   62.000000     0.00000   \n",
            "28619  29459.0 -0.692616 -0.103431 -0.716024   62.000000     0.00000   \n",
            "28620  29460.0 -0.692406 -0.103410 -0.715939   62.811321     0.00000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28616         1.0  \n",
            "28617         1.0  \n",
            "28618         1.0  \n",
            "28619         1.0  \n",
            "28620         1.0  \n",
            "\n",
            "[28621 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        840.0  0.150436 -0.887863  0.413330   70.000000         0.0\n",
            "1        841.0  0.152270 -0.889634  0.412469   70.000000         0.0\n",
            "2        842.0  0.152289 -0.889179  0.413210   70.000000         0.0\n",
            "3        843.0  0.152224 -0.888816  0.414621   69.764706         0.0\n",
            "4        844.0  0.152345 -0.889031  0.413802   68.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28616  29456.0 -0.694993 -0.104232 -0.713901   62.000000         1.0\n",
            "28617  29457.0 -0.694135 -0.104212 -0.715265   62.000000         1.0\n",
            "28618  29458.0 -0.693075 -0.104085 -0.715963   62.000000         1.0\n",
            "28619  29459.0 -0.692616 -0.103431 -0.716024   62.000000         1.0\n",
            "28620  29460.0 -0.692406 -0.103410 -0.715939   62.811321         1.0\n",
            "\n",
            "[28621 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          1.0 -0.028203 -0.165014 -0.985585        59.0         0.0   \n",
            "1          2.0 -0.032825 -0.167729 -0.984657        59.0         0.0   \n",
            "2          3.0 -0.034003 -0.172530 -0.983026        59.0         0.0   \n",
            "3          4.0 -0.038129 -0.175084 -0.982936        59.0         0.0   \n",
            "4          5.0 -0.045175 -0.160327 -0.986380        59.0         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "29095  29096.0 -0.030788 -0.697993  0.714224        59.0         0.0   \n",
            "29096  29097.0 -0.030956 -0.696544  0.717718        59.0         0.0   \n",
            "29097  29098.0 -0.031194 -0.698024  0.716801        59.0         0.0   \n",
            "29098  29099.0 -0.030355 -0.698666  0.716319        59.0         0.0   \n",
            "29099  29100.0 -0.031020 -0.696816  0.717255        59.0         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "29095         5.0  \n",
            "29096         5.0  \n",
            "29097         5.0  \n",
            "29098         5.0  \n",
            "29099         5.0  \n",
            "\n",
            "[29100 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          1.0 -0.028203 -0.165014 -0.985585        59.0         0.0\n",
            "1          2.0 -0.032825 -0.167729 -0.984657        59.0         0.0\n",
            "2          3.0 -0.034003 -0.172530 -0.983026        59.0         0.0\n",
            "3          4.0 -0.038129 -0.175084 -0.982936        59.0         0.0\n",
            "4          5.0 -0.045175 -0.160327 -0.986380        59.0         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "29095  29096.0 -0.030788 -0.697993  0.714224        59.0         5.0\n",
            "29096  29097.0 -0.030956 -0.696544  0.717718        59.0         5.0\n",
            "29097  29098.0 -0.031194 -0.698024  0.716801        59.0         5.0\n",
            "29098  29099.0 -0.030355 -0.698666  0.716319        59.0         5.0\n",
            "29099  29100.0 -0.031020 -0.696816  0.717255        59.0         5.0\n",
            "\n",
            "[29100 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        480.0 -0.352509  0.061035 -0.930130   57.000000    0.000000   \n",
            "1        481.0 -0.353399  0.057744 -0.927474   57.000000   59.176897   \n",
            "2        482.0 -0.352646  0.056110 -0.926366   57.000000   79.820000   \n",
            "3        483.0 -0.354494  0.056523 -0.925543   57.000000   79.820000   \n",
            "4        484.0 -0.345746 -0.017889 -0.927804   57.000000   79.820000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28436  28916.0  0.013232 -0.188708  0.987871   74.000000    0.000000   \n",
            "28437  28917.0  0.013040 -0.188297  0.987942   74.000000    0.000000   \n",
            "28438  28918.0  0.012685 -0.188376  0.987582   73.931034    0.000000   \n",
            "28439  28919.0  0.012851 -0.188040  0.987618   73.000000    0.000000   \n",
            "28440  28920.0  0.012508 -0.188495  0.987472   73.000000    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28436         1.0  \n",
            "28437         1.0  \n",
            "28438         1.0  \n",
            "28439         1.0  \n",
            "28440         1.0  \n",
            "\n",
            "[28441 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        480.0 -0.352509  0.061035 -0.930130   57.000000         0.0\n",
            "1        481.0 -0.353399  0.057744 -0.927474   57.000000         0.0\n",
            "2        482.0 -0.352646  0.056110 -0.926366   57.000000         0.0\n",
            "3        483.0 -0.354494  0.056523 -0.925543   57.000000         0.0\n",
            "4        484.0 -0.345746 -0.017889 -0.927804   57.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28436  28916.0  0.013232 -0.188708  0.987871   74.000000         1.0\n",
            "28437  28917.0  0.013040 -0.188297  0.987942   74.000000         1.0\n",
            "28438  28918.0  0.012685 -0.188376  0.987582   73.931034         1.0\n",
            "28439  28919.0  0.012851 -0.188040  0.987618   73.000000         1.0\n",
            "28440  28920.0  0.012508 -0.188495  0.987472   73.000000         1.0\n",
            "\n",
            "[28441 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        450.0  0.329025  0.667206 -0.665939   81.000000    9.210000   \n",
            "1        451.0  0.328910  0.668118 -0.667113   81.000000    4.250769   \n",
            "2        452.0  0.329240  0.667693 -0.667091   81.000000    0.000000   \n",
            "3        453.0  0.331037  0.670360 -0.663862   81.000000    0.000000   \n",
            "4        454.0  0.332303  0.673308 -0.659938   81.000000    0.000000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28616  29066.0 -0.215714  0.735003 -0.636632   72.000000    0.000000   \n",
            "28617  29067.0 -0.216228  0.734970 -0.636730   70.470588    0.000000   \n",
            "28618  29068.0 -0.216266  0.734944 -0.636504   69.000000    0.000000   \n",
            "28619  29069.0 -0.216328  0.734508 -0.636422   69.000000    0.000000   \n",
            "28620  29070.0 -0.215638  0.734940 -0.636478   69.000000    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28616         2.0  \n",
            "28617         2.0  \n",
            "28618         2.0  \n",
            "28619         2.0  \n",
            "28620         2.0  \n",
            "\n",
            "[28621 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        450.0  0.329025  0.667206 -0.665939   81.000000         0.0\n",
            "1        451.0  0.328910  0.668118 -0.667113   81.000000         0.0\n",
            "2        452.0  0.329240  0.667693 -0.667091   81.000000         0.0\n",
            "3        453.0  0.331037  0.670360 -0.663862   81.000000         0.0\n",
            "4        454.0  0.332303  0.673308 -0.659938   81.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28616  29066.0 -0.215714  0.735003 -0.636632   72.000000         2.0\n",
            "28617  29067.0 -0.216228  0.734970 -0.636730   70.470588         2.0\n",
            "28618  29068.0 -0.216266  0.734944 -0.636504   69.000000         2.0\n",
            "28619  29069.0 -0.216328  0.734508 -0.636422   69.000000         2.0\n",
            "28620  29070.0 -0.215638  0.734940 -0.636478   69.000000         2.0\n",
            "\n",
            "[28621 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        870.0 -0.611481 -0.086685 -0.774353   71.000000   27.630000   \n",
            "1        871.0 -0.610363 -0.084030 -0.777015   71.000000   25.248103   \n",
            "2        872.0 -0.604861 -0.087808 -0.781001   71.000000    0.000000   \n",
            "3        873.0 -0.603511 -0.088307 -0.781833   71.000000    0.000000   \n",
            "4        874.0 -0.603166 -0.088407 -0.782295   71.000000    0.000000   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28016  28886.0  0.158362 -0.229361 -0.955963   64.000000    0.000000   \n",
            "28017  28887.0  0.157091 -0.227011 -0.957808   64.000000    0.000000   \n",
            "28018  28888.0  0.156143 -0.224949 -0.958017   64.000000    0.000000   \n",
            "28019  28889.0  0.156214 -0.225033 -0.958315   63.344828    0.000000   \n",
            "28020  28890.0  0.157941 -0.229536 -0.956746   63.000000    0.000000   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28016         5.0  \n",
            "28017         5.0  \n",
            "28018         5.0  \n",
            "28019         5.0  \n",
            "28020         5.0  \n",
            "\n",
            "[28021 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        870.0 -0.611481 -0.086685 -0.774353   71.000000         0.0\n",
            "1        871.0 -0.610363 -0.084030 -0.777015   71.000000         0.0\n",
            "2        872.0 -0.604861 -0.087808 -0.781001   71.000000         0.0\n",
            "3        873.0 -0.603511 -0.088307 -0.781833   71.000000         0.0\n",
            "4        874.0 -0.603166 -0.088407 -0.782295   71.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28016  28886.0  0.158362 -0.229361 -0.955963   64.000000         5.0\n",
            "28017  28887.0  0.157091 -0.227011 -0.957808   64.000000         5.0\n",
            "28018  28888.0  0.156143 -0.224949 -0.958017   64.000000         5.0\n",
            "28019  28889.0  0.156214 -0.225033 -0.958315   63.344828         5.0\n",
            "28020  28890.0  0.157941 -0.229536 -0.956746   63.000000         5.0\n",
            "\n",
            "[28021 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0        420.0 -0.549332  0.273941 -0.787247   79.000000         0.0   \n",
            "1        421.0 -0.547925  0.277964 -0.793456   79.000000         0.0   \n",
            "2        422.0 -0.547017  0.278005 -0.794117   79.000000         0.0   \n",
            "3        423.0 -0.549509  0.278710 -0.791815   79.000000         0.0   \n",
            "4        424.0 -0.550670  0.279755 -0.791027   78.607843         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28766  29186.0 -0.458928  0.060116 -0.888502   71.000000         0.0   \n",
            "28767  29187.0 -0.460114  0.060457 -0.887954   71.000000         0.0   \n",
            "28768  29188.0 -0.459230  0.058250 -0.888350   71.000000         0.0   \n",
            "28769  29189.0 -0.457897  0.057137 -0.889381   71.000000         0.0   \n",
            "28770  29190.0 -0.457490  0.057088 -0.889593   70.773585         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28766         0.0  \n",
            "28767         0.0  \n",
            "28768         0.0  \n",
            "28769         0.0  \n",
            "28770         0.0  \n",
            "\n",
            "[28771 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0        420.0 -0.549332  0.273941 -0.787247   79.000000         0.0\n",
            "1        421.0 -0.547925  0.277964 -0.793456   79.000000         0.0\n",
            "2        422.0 -0.547017  0.278005 -0.794117   79.000000         0.0\n",
            "3        423.0 -0.549509  0.278710 -0.791815   79.000000         0.0\n",
            "4        424.0 -0.550670  0.279755 -0.791027   78.607843         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28766  29186.0 -0.458928  0.060116 -0.888502   71.000000         0.0\n",
            "28767  29187.0 -0.460114  0.060457 -0.887954   71.000000         0.0\n",
            "28768  29188.0 -0.459230  0.058250 -0.888350   71.000000         0.0\n",
            "28769  29189.0 -0.457897  0.057137 -0.889381   71.000000         0.0\n",
            "28770  29190.0 -0.457490  0.057088 -0.889593   70.773585         0.0\n",
            "\n",
            "[28771 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          1.0 -0.472725  0.447848 -0.739899   57.000000         0.0   \n",
            "1          2.0 -0.435472  0.395195 -0.797007   57.000000         0.0   \n",
            "2          3.0 -0.435210  0.393356 -0.797561   57.000000         0.0   \n",
            "3          4.0 -0.436220  0.394887 -0.795830   57.000000         0.0   \n",
            "4          5.0 -0.436898  0.394214 -0.795357   57.000000         0.0   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "28285  28286.0  0.573508  0.723908 -0.384454   62.000000         0.0   \n",
            "28286  28287.0  0.571822  0.724339 -0.387415   62.093023         0.0   \n",
            "28287  28288.0  0.571820  0.724301 -0.386943   63.000000         0.0   \n",
            "28288  28289.0  0.576170  0.724127 -0.380714   63.000000         0.0   \n",
            "28289  28290.0  0.578143  0.723886 -0.377255   63.000000         0.0   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "28285         2.0  \n",
            "28286         2.0  \n",
            "28287         2.0  \n",
            "28288         2.0  \n",
            "28289         2.0  \n",
            "\n",
            "[28290 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          1.0 -0.472725  0.447848 -0.739899   57.000000         0.0\n",
            "1          2.0 -0.435472  0.395195 -0.797007   57.000000         0.0\n",
            "2          3.0 -0.435210  0.393356 -0.797561   57.000000         0.0\n",
            "3          4.0 -0.436220  0.394887 -0.795830   57.000000         0.0\n",
            "4          5.0 -0.436898  0.394214 -0.795357   57.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "28285  28286.0  0.573508  0.723908 -0.384454   62.000000         2.0\n",
            "28286  28287.0  0.571822  0.724339 -0.387415   62.093023         2.0\n",
            "28287  28288.0  0.571820  0.724301 -0.386943   63.000000         2.0\n",
            "28288  28289.0  0.576170  0.724127 -0.380714   63.000000         2.0\n",
            "28289  28290.0  0.578143  0.723886 -0.377255   63.000000         2.0\n",
            "\n",
            "[28290 rows x 6 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  step_count  \\\n",
            "0          1.0 -0.278607  0.254209 -0.920258   52.000000       18.42   \n",
            "1          2.0 -0.280708  0.252022 -0.920253   52.000000       18.42   \n",
            "2          3.0 -0.283092  0.246838 -0.919934   52.000000       18.42   \n",
            "3          4.0 -0.280484  0.248970 -0.920957   52.000000       18.42   \n",
            "4          5.0 -0.281661  0.248239 -0.920686   52.000000       18.42   \n",
            "...        ...       ...       ...       ...         ...         ...   \n",
            "21535  21536.0 -0.260058  0.254656 -0.924000   66.086207        0.00   \n",
            "21536  21537.0 -0.259622  0.253925 -0.924361   68.000000        0.00   \n",
            "21537  21538.0 -0.263577  0.250459 -0.924090   68.000000        0.00   \n",
            "21538  21539.0 -0.268807  0.246552 -0.923361   68.000000        0.00   \n",
            "21539  21540.0 -0.265418  0.250343 -0.923984   68.000000        0.00   \n",
            "\n",
            "       psg_status  \n",
            "0             0.0  \n",
            "1             0.0  \n",
            "2             0.0  \n",
            "3             0.0  \n",
            "4             0.0  \n",
            "...           ...  \n",
            "21535         0.0  \n",
            "21536         0.0  \n",
            "21537         0.0  \n",
            "21538         0.0  \n",
            "21539         0.0  \n",
            "\n",
            "[21540 rows x 7 columns]\n",
            "        second    x_move    y_move    z_move  heart_rate  psg_status\n",
            "0          1.0 -0.278607  0.254209 -0.920258   52.000000         0.0\n",
            "1          2.0 -0.280708  0.252022 -0.920253   52.000000         0.0\n",
            "2          3.0 -0.283092  0.246838 -0.919934   52.000000         0.0\n",
            "3          4.0 -0.280484  0.248970 -0.920957   52.000000         0.0\n",
            "4          5.0 -0.281661  0.248239 -0.920686   52.000000         0.0\n",
            "...        ...       ...       ...       ...         ...         ...\n",
            "21535  21536.0 -0.260058  0.254656 -0.924000   66.086207         0.0\n",
            "21536  21537.0 -0.259622  0.253925 -0.924361   68.000000         0.0\n",
            "21537  21538.0 -0.263577  0.250459 -0.924090   68.000000         0.0\n",
            "21538  21539.0 -0.268807  0.246552 -0.923361   68.000000         0.0\n",
            "21539  21540.0 -0.265418  0.250343 -0.923984   68.000000         0.0\n",
            "\n",
            "[21540 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}