{
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        "OD2g9xsgxSYy"
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    {
      "cell_type": "markdown",
      "source": [
        "# Guide to Extracting Data w/ APIs from the Dreem Headband\n",
        "\n",
        "<!--<img src=\"https://i.imgur.com/RI0yuaz.png\" width=\"500\"/>-->\n",
        "<img src=\"https://imgur.com/hTJol4M.png\" width=\"500\"/>\n",
        "\n",
        "Why dream when you can dreem? Introducing the [Dreem 2](https://dreem.com/), a headband that is capable of providing not only raw, high frequency EEG measurements, but also sleep stage predictions throughout the night. In this notebook, we'll be *extracting* data from a Dreem headband, *visualizing* its raw EEG signals and its sleep stage predictions, and then *analyzing* this data.\n",
        "\n",
        "Note that this notebook assumes that you are conducting a clinical trial, and have registered this through Dreem.\n",
        "\n",
        "If you want to know more about the Dreem 2 headband, see the [README](https://github.com/alrojo/wearipedia/tree/main/wearables/dream-2-headband) for a detailed analysis of performances, sensors, data privacy, and extraction pipelines.\n",
        "\n",
        "TODO: FIX GITHUB README LINK\n",
        "\n",
        "While Dreem does not support an official API, as stated on the site ([\"do not have a public or private API for the moment\"](https://dreem.com/clinicaltrials)), we reverse-engineer the API implicitly used when visiting https://dreem-viewer.rythm.co/ and viewing clinical trial health records, allowing you to programmatically extract all of your clinical trial data at once (instead of having to click through a web interface).\n",
        "\n",
        "Overall, the Dreem API makes the following values available:\n",
        "\n",
        "Parameter Name  | Sampling Frequency \n",
        "-------------------|------------------\n",
        "Sleep stage classification (S0/1/2/3, MT, REM) | every 30 seconds\n",
        "7 separate raw EEG signals |  250Hz\n",
        "7 separate filtered EEG signals |  250Hz\n",
        "Raw accelerometer (x, y, z, norm) | 50Hz\n",
        "Filtered accelerometer (x, y, z) | 50Hz\n",
        "Positiongram | 10Hz\n",
        "\n",
        "Note that when we say \"filtered\" the EEG signal is filtered via a Butterworth bandpass filter of order 2 (0.4-35Hz) and the accelerometer signal is filtered via a Butterworth bandpass filter of order 2 (0.1-0.5Hz), see [this official article](https://support.dreem.com/hc/en-us/articles/360021664499-How-to-read-H5-file) and [this scientific article](https://academic.oup.com/sleep/article/43/11/zsaa097/5841249) for more.\n",
        "\n",
        "<br><br>\n",
        "In this guide, we sequentially cover the following **five** topics to extract from the unofficial Dreem API:\n",
        "1. **Set up**\n",
        "    - We include some details about what you'd be expected to do before running this notebook.\n",
        "2. **Authentication/Authorization**\n",
        "    - This is seamless, only requiring the clinical trial admin username and password.\n",
        "3. **Data extraction**\n",
        "    - Extraction is simple, requiring only clinical trial admin email and password. We provide some code to interface with the backend API.\n",
        "4. **Data visualization**\n",
        "    - 4.1: We visualize the hypnogram for the given sleep session.\n",
        "    - 4.2: We also visualize an EEG signal within a time interval of your choice.\n",
        "    - 4.3: We also visualize the variance of an EEG signal within a short time interval (1 second) over different sleep stages.\n",
        "5. **Data analysis**\n",
        "    - 5.1: We check whether the change in the variance of a representative EEG signal within a short time interval (1 second) is statistically significant between a wake stage vs. a sleep stage.\n",
        "    - 5.2: We check whether the change in the norm of the acceleration within a short time interval (1 second) is statistically significant between a wake stage vs. a sleep stage."
      ],
      "metadata": {
        "id": "OD2g9xsgxSYy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Set Up\n",
        "\n",
        "TODO: explain this. We aren't really sure because we didn't have to do this part. Maybe we can reach out to Dalia soon to get an overview?\n",
        "\n",
        "# 2. Authentication/Authorization\n",
        "\n",
        "Due to confidentiality requirements, we cannot share our own login credentials (though you can use your own, of course).\n",
        "\n",
        "Note that the website you would normally log into is https://dreem-viewer.rythm.co, so to get a more user-friendly table of records you can inspect in your browser, visit that link."
      ],
      "metadata": {
        "id": "jslKYojkmLfp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Enter login credentials\n",
        "email = \"\" #@param {type:\"string\"}\n",
        "password = \"\" #@param {type:\"string\"}\n",
        "\n",
        "import base64\n",
        "import json\n",
        "import requests\n",
        "\n",
        "authorization_str = 'Basic ' + base64.b64encode(bytes(email + ':' + password, 'utf-8')).decode()\n",
        "\n",
        "auth_dict = json.loads(requests.post('https://login.rythm.co/token/', headers={'Authorization': authorization_str}).text)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "gDUbC2uwmKyz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Data Extraction\n",
        "\n",
        "Now we extract data from the API.\n",
        "\n",
        "First, we get all available users. This gives us a mapping between usernames and user id's."
      ],
      "metadata": {
        "id": "X4FUCtJxntgg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Get available users\n",
        "\n",
        "import pprint\n",
        "\n",
        "def get_aux_records(auth_dict):\n",
        "    url = 'https://api.rythm.co/v1/dreem/algorythm/restricted_list/9f38863b-8180-4dd4-81cf-9395800ae0cc/record/?limit=100&offset=0'\n",
        "\n",
        "    headers = {'Authorization': 'Bearer ' + auth_dict['token']}\n",
        "\n",
        "    out = requests.get(url, headers=headers)\n",
        "\n",
        "    out_dict = json.loads(out.text)\n",
        "\n",
        "    return out_dict\n",
        "\n",
        "def get_user2id_mapping(auth_dict, user_ids):\n",
        "    url = 'https://api.rythm.co/v1/dreem/dreemer/dreemer/details/'\n",
        "    headers = {'Authorization': 'Bearer ' + auth_dict['token'],\n",
        "            'Content-Type': 'application/json'}\n",
        "\n",
        "    payload = {\n",
        "        'id': [user_id for user_id in user_ids]\n",
        "    }\n",
        "\n",
        "    out = requests.post(url, headers=headers, data='{\"id\":[\"ce73192e-874c-4576-a2e3-27b0dc0ebcee\",\"1222a474-bc02-44ab-b4c5-d66dc34620b7\",\"e1ec95b0-b71b-4499-b73c-cf9b1e3576c2\"]}').text\n",
        "\n",
        "    return {x['pseudo']: x['dreemer'] for x in json.loads(out)}\n",
        "\n",
        "# we care about aux_records because:\n",
        "# (1) it contains the user_ids\n",
        "# (2) it contains a mapping between references and record id's\n",
        "aux_records = get_aux_records(auth_dict)\n",
        "\n",
        "# (1) here\n",
        "user_ids = set([result['user'] for result in aux_records['results']])\n",
        "\n",
        "map = get_user2id_mapping(auth_dict, user_ids)\n",
        "\n",
        "print('Available users:')\n",
        "pprint.pprint(map)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "WLbX7YVBriGt",
        "outputId": "b92a618e-cc41-41fa-d2d4-b47bc474a3d9"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Available users:\n",
            "{'61843_test01@dreem.com': 'e1ec95b0-b71b-4499-b73c-cf9b1e3576c2',\n",
            " '61843_test02@dreem.com': '1222a474-bc02-44ab-b4c5-d66dc34620b7',\n",
            " '61843_test03@dreem.com': 'ce73192e-874c-4576-a2e3-27b0dc0ebcee'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we filter to only download and display records from a particular user. At the end, we have a Pandas dataframe called `records_df` that contains the raw data behind the table you normally see through the admin portal.\n",
        "\n",
        "<img src=\"https://i.imgur.com/0P810M1.png\" width=1000></img>"
      ],
      "metadata": {
        "id": "3w9fjUP0-VI9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Filter to display only records from a specific user\n",
        "username = \"61843_test03@dreem.com\" #@param {type:\"string\"}\n",
        "\n",
        "uname_filter = username\n",
        "\n",
        "uid = [uid for uname, uid in map.items() if uname == uname_filter][0]\n",
        "\n",
        "# filter to only include this username\n",
        "filtered_aux_records = [record for record in aux_records['results'] if record['user'] == uid]\n",
        "\n",
        "record_ids = [record['id'] for record in filtered_aux_records]\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "def get_table_records():\n",
        "    # this is distinct from aux_records\n",
        "\n",
        "    # we get the *actual* info that is displayed on the table\n",
        "    # in the web portal\n",
        "    url = 'https://api.rythm.co/v1/dreem/record/report/details/'\n",
        "\n",
        "    headers = {\n",
        "        'Authorization': 'Bearer ' + auth_dict['token'],\n",
        "        'Content-Type': 'application/json'\n",
        "    }\n",
        "\n",
        "    payload = {\n",
        "        'id': record_ids\n",
        "    }\n",
        "\n",
        "    out_dict = json.loads(requests.post(url, headers=headers, data=json.dumps(payload)).text)\n",
        "\n",
        "    return out_dict\n",
        "\n",
        "def records_to_df(table_records, aux_records):\n",
        "    columns = {\n",
        "        'record_id': 'Record ID',\n",
        "        'record_start_iso': 'Start (ISO)',\n",
        "        'record_stop_iso': 'Stop (ISO)',\n",
        "        'timezone': 'Timezone',\n",
        "        'record_duration': 'Duration (seconds)',\n",
        "        'proportion_good_quality_eeg1': 'Channel quality 1',\n",
        "        'proportion_good_quality_eeg2': 'Channel quality 2',\n",
        "        'proportion_good_quality_eeg4': 'Channel quality 4',\n",
        "        'proportion_off_head': 'Proportion off head',\n",
        "        'proportion_scorable': 'Proportion scorable'\n",
        "    }\n",
        "\n",
        "    list_of_dicts = []\n",
        "\n",
        "    for record in table_records:\n",
        "\n",
        "\n",
        "        d = {columns[key]: endpoint for key, endpoint in record['endpoints'].items() if key in columns.keys()}\n",
        "\n",
        "        d['Record ID'] = record['record']\n",
        "\n",
        "        list_of_dicts.append(d)\n",
        "\n",
        "    records_df = pd.DataFrame(list_of_dicts, columns=columns.values())\n",
        "\n",
        "    # (2) here\n",
        "    id2ref_map = {aux_record['id']: aux_record['reference'] for aux_record in aux_records}\n",
        "\n",
        "    records_df.insert(0, 'Ref', records_df['Record ID'].apply(lambda id: id2ref_map[id]))\n",
        "\n",
        "    return records_df\n",
        "\n",
        "table_records = get_table_records()\n",
        "\n",
        "#filtered_table_records = [record for record in table_records if record['user'] == uid]\n",
        "\n",
        "records_df = records_to_df(table_records, filtered_aux_records)\n",
        "\n",
        "records_df = records_df.sort_values('Ref', ascending=False,\n",
        "                                    ignore_index=True)\n",
        "\n",
        "records_df"
      ],
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        "colab": {
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          "height": 573
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        "id": "LzkYWRrDxYap",
        "outputId": "ca64133d-7417-4b84-ea2e-b9baa1a030e1"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "       Ref                             Record ID                Start (ISO)  \\\n",
              "0  2751857  6c2faef3-e9a6-46a0-b34d-9afdb8b24ec8  2022-06-14T19:20:27-05:00   \n",
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              "4  2751349  423eafa3-2a71-40b3-ac39-b6dcb6f3d43a  2022-06-14T03:38:12-05:00   \n",
              "5  2751348  5039f0c2-239e-41e8-9caa-abcfe071615c  2022-05-24T04:15:58-07:00   \n",
              "6  2751347  2ca402e4-c2fb-4c72-866d-c73e49d5ce65  2022-05-27T05:13:46-07:00   \n",
              "\n",
              "                  Stop (ISO)             Timezone  Duration (seconds)  \\\n",
              "0  2022-06-14T20:23:12-05:00      America/Chicago              3765.0   \n",
              "1  2022-06-14T19:18:14-05:00      America/Chicago               229.0   \n",
              "2  2022-06-15T02:44:37-05:00      America/Chicago              6561.0   \n",
              "3  2022-06-14T17:19:55-05:00      America/Chicago                23.0   \n",
              "4  2022-06-14T14:58:41-05:00      America/Chicago             40829.0   \n",
              "5  2022-05-24T10:49:51-07:00  America/Los_Angeles             23633.0   \n",
              "6  2022-05-27T06:16:06-07:00  America/Los_Angeles              3740.0   \n",
              "\n",
              "   Channel quality 1  Channel quality 2  Channel quality 4  \\\n",
              "0           0.226766           0.008497           0.353160   \n",
              "1           0.009009           0.000000           0.000000   \n",
              "2           0.050213           0.138466           0.426354   \n",
              "3           0.000000           0.090909           0.090909   \n",
              "4           0.710756           0.783470           0.734684   \n",
              "5           0.557961           0.656210           0.614526   \n",
              "6           0.305556           0.419872           0.565705   \n",
              "\n",
              "   Proportion off head  Proportion scorable  \n",
              "0             1.000000             0.296000  \n",
              "1             1.000000             0.000000  \n",
              "2             0.561644             0.853881  \n",
              "3                  NaN                  NaN  \n",
              "4             0.023495             0.969897  \n",
              "5             0.052030             0.779188  \n",
              "6             1.000000             0.604839  "
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we wish to extract a particular record from this table. Since we see that record 2751349 is the most complete (11 hours with [good enough data to be scored by a human 97% of the time](https://support.dreem.com/hc/en-us/articles/360021372739-How-to-read-the-Record-Quality-metric), we download all of its associated data (464MB of raw accelerometer + EEG data, and its hypnogram). The raw data is stored as a file `2751349.h5` on disk and the hypnogram is parsed and stored in a Pandas dataframe `hypnogram_df`.\n",
        "\n",
        "Since the raw data is stored in HDF5 format, we get all of the cool advantages of HDF5: an elegant hierarchical format with fast array-like indexing that loads into memory only when absolutely necessary."
      ],
      "metadata": {
        "id": "a-9ekP_3AWe4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Record ref number\n",
        "record_ref = 2751349 #@param {type:\"integer\"}\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "def get_download_info(record_id, record_ref):\n",
        "    # get the record id\n",
        "    id = records_df[records_df['Ref'] == record_ref]['Record ID'].iloc[0]\n",
        "\n",
        "    url = f'https://api.rythm.co/v1/dreem/algorythm/record/{id}/h5/?filename={record_ref}'\n",
        "\n",
        "    headers = {\n",
        "        'Authorization': 'Bearer ' + auth_dict['token']\n",
        "    }\n",
        "\n",
        "    out_dict = json.loads(requests.get(url, headers=headers).text)\n",
        "\n",
        "    return out_dict\n",
        "\n",
        "def get_hypnogram(record_id):\n",
        "\n",
        "    url = f'https://api.rythm.co/v1/dreem/record/record/{record_id}/latest_hypnogram_as_txt/'\n",
        "\n",
        "    headers = {\n",
        "        'Authorization': 'Bearer ' + auth_dict['token']\n",
        "    }\n",
        "\n",
        "    hypnogram_text = requests.get(url, headers=headers).text\n",
        "\n",
        "    from io import StringIO\n",
        "\n",
        "    # get index of header\n",
        "    idx_start = [i for i,l in enumerate(hypnogram_text.split('\\n')) if 'Sleep Stage' in l][0]\n",
        "\n",
        "    hypnogram = pd.read_csv(StringIO('\\n'.join(hypnogram_text.split('\\n')[idx_start:])), sep='\\t')\n",
        "\n",
        "    return hypnogram\n",
        "\n",
        "record_id = records_df[records_df['Ref'] == record_ref]['Record ID'].iloc[0]\n",
        "\n",
        "print('Getting download URL...')\n",
        "download_info = get_download_info(record_id, record_ref)\n",
        "\n",
        "assert download_info['status_display'] == 'Available'\n",
        "\n",
        "download_url = download_info['data_url']\n",
        "\n",
        "def download_file(dowload_url, download_path):\n",
        "\n",
        "    # Streaming, so we can iterate over the response.\n",
        "    response = requests.get(download_url, stream=True)\n",
        "    total_size_in_bytes= int(response.headers.get('content-length', 0))\n",
        "    block_size = 1024 #1 Kibibyte\n",
        "    progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)\n",
        "    with open(download_path, 'wb') as file:\n",
        "        for data in response.iter_content(block_size):\n",
        "            progress_bar.update(len(data))\n",
        "            file.write(data)\n",
        "\n",
        "    progress_bar.close()\n",
        "\n",
        "    if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:\n",
        "        print(\"ERROR, something went wrong\")\n",
        "\n",
        "print('Now downloading text hypnogram...')\n",
        "\n",
        "hypnogram_df = get_hypnogram(record_id)\n",
        "\n",
        "print('Now downloading raw EEG/accelerometer data...\\n')\n",
        "\n",
        "download_file(download_url, str(record_ref) + '.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "lr1m6Sv6BNyZ",
        "outputId": "3e95e654-4e2c-4cc5-86eb-64303c7af411"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Getting download URL...\n",
            "Now downloading text hypnogram...\n",
            "Now downloading raw EEG/accelerometer data...\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 464M/464M [00:19<00:00, 23.8MiB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Data Visualization\n",
        "\n",
        "Now that we've downloaded all of the data, let's visualize some of it.\n",
        "\n",
        "## 4.1. Hypnogram Plot\n",
        "\n",
        "First, let's plot the hypnogram for this sleep session. This plot is very useful as it is a [standard plot](https://en.wikipedia.org/wiki/Hypnogram) to visualize the stages of sleep over time."
      ],
      "metadata": {
        "id": "550PqGvgFBLe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Plot hypnogram\n",
        "\n",
        "from datetime import datetime, timezone\n",
        "import numpy as np\n",
        "import h5py\n",
        "import matplotlib.pyplot as plt\n",
        "import pytz\n",
        "\n",
        "# get user's timezone\n",
        "timezone_user = records_df[records_df['Ref'] == record_ref]['Timezone'].iloc[0]\n",
        "\n",
        "def utc_to_local(utc_dt):\n",
        "    return utc_dt.replace(tzinfo=timezone.utc).astimezone(tz=pytz.timezone(timezone_user))\n",
        "\n",
        "# we do this stage_to_int thing to ensure that the\n",
        "# stages appear in order on the y-axis\n",
        "stage_to_int = [\n",
        "    'SLEEP-MT',\n",
        "    'SLEEP-S3',\n",
        "    'SLEEP-S2',\n",
        "    'SLEEP-S1',\n",
        "    'SLEEP-REM',\n",
        "    'SLEEP-S0'\n",
        "]\n",
        "\n",
        "stages_to_real_names = {\n",
        "    'SLEEP-MT': None,\n",
        "    'SLEEP-S3': 'DEEP',\n",
        "    'SLEEP-S2': 'N2',\n",
        "    'SLEEP-S1': 'N1',\n",
        "    'SLEEP-REM': 'REM',\n",
        "    'SLEEP-S0': 'WAKE'\n",
        "}\n",
        "\n",
        "stage_ints = np.array(hypnogram_df['Sleep Stage'].apply(lambda x: stage_to_int.index(x)))\n",
        "\n",
        "num_stages = len(stage_to_int)\n",
        "\n",
        "# load in hdf5 file\n",
        "f = h5py.File('2751349.h5', 'r')\n",
        "\n",
        "# turn into seconds so we can plot\n",
        "hypnogram_df['seconds'] = hypnogram_df['Time [hh:mm:ss]'].apply(lambda x: 3600 * int(x.split(':')[0]) + 60 * int(x.split(':')[1]) + int(x.split(':')[-1]))\n",
        "\n",
        "# get the date this was taken\n",
        "date = datetime.fromtimestamp(f.attrs['start_time'])\n",
        "\n",
        "timezone_str = utc_to_local(date).strftime('%Z')\n",
        "date = utc_to_local(date).strftime('%B %d, %Y')\n",
        "\n",
        "# get the contiguous regions in stage_ints to plot\n",
        "# these separately\n",
        "empty_idxes = np.where(stage_ints != 0)[0]\n",
        "\n",
        "regions = []\n",
        "\n",
        "cur_region_start = -1\n",
        "\n",
        "cur_region_end = -1\n",
        "\n",
        "for i, (idx1, idx2) in enumerate(zip(empty_idxes[:-1], empty_idxes[1:])):\n",
        "    if cur_region_start == -1:\n",
        "        cur_region_start = idx1\n",
        "        cur_region_end = idx2\n",
        "\n",
        "    # either diff > 1 or we're at the end\n",
        "    if idx2 - idx1 > 1 or i == empty_idxes[:-1].shape[0] - 1:\n",
        "        cur_region_end = idx1\n",
        "        # add 1 to account for extra dummy entry in heart_rate_day\n",
        "        # at the beginning\n",
        "        regions.append((cur_region_start+1, cur_region_end+1))\n",
        "\n",
        "        cur_region_start = -1\n",
        "        cur_region_end = -1\n",
        "\n",
        "with plt.style.context('ggplot'):\n",
        "    plt.figure(figsize=(14, 2))\n",
        "\n",
        "    plt.title(f'Hypnogram for record {record_ref} taken on {date} ({timezone_str})',\n",
        "              fontsize=20)\n",
        "    \n",
        "    for region_start, region_end in regions:\n",
        "        plt.plot(hypnogram_df['seconds'].iloc[region_start:region_end] / 3600,\n",
        "                stage_ints[region_start:region_end], color='red')\n",
        "\n",
        "    plt.yticks(ticks=list(range(1, num_stages)),\n",
        "            labels=[stages_to_real_names[stage] for stage in stage_to_int[1:]])\n",
        "    \n",
        "    plt.xticks(ticks=[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],\n",
        "               labels=['4AM', '5AM', '6AM', '7AM', '8AM', '9AM', '10AM', '11AM', '12PM', '1PM', '2PM'])\n",
        "\n",
        "    # just set the xlim so that it extends all the way left\n",
        "    # and right\n",
        "    plt.xlim(*(hypnogram_df['seconds'] / 3600).iloc[[0, -1]])\n",
        "\n",
        "    plt.tight_layout()\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "cellView": "form",
        "id": "tgWLJUaeMHDB",
        "outputId": "2e41791a-a4d0-4c3a-80d5-34c96ff0700c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x144 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.2. Hypnogram + EEG plot\n",
        "\n",
        "Now let's plot some EEG signals (filtered). This will allow us to obtain a very granular view at the EEG signal throughout the entire sleep session. In particular, we will reproduce the below plot that you can see yourself on the admin portal. Feel free to tweak the inputs to the plot and see how it reacts!\n",
        "\n",
        "<img src=\"https://imgur.com/YCMQoPP.png\" width=1000></img>\n",
        "\n",
        "*Above is a plot taken directly from the online admin web interface!*"
      ],
      "metadata": {
        "id": "bF_PLMxgMHTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Plot EEG signals\n",
        "date = \"2022-06-14\" #@param {type:\"date\"}\n",
        "start_hour =  6 #@param {type:\"integer\"}\n",
        "start_minute =   40#@param {type:\"integer\"}\n",
        "end_hour =  6 #@param {type:\"integer\"}\n",
        "end_minute =   50#@param {type:\"integer\"}\n",
        "channel_1 = 1 #@param {type:\"integer\"}\n",
        "channel_2 = 2 #@param {type:\"integer\"}\n",
        "channel_3 = 4 #@param {type:\"integer\"}\n",
        "channel_4 = 5 #@param {type:\"integer\"}\n",
        "channel_5 = 6 #@param {type:\"integer\"}\n",
        "\n",
        "eeg_channel_nums = [channel_1, channel_2, channel_3,\n",
        "                    channel_4, channel_5]\n",
        "\n",
        "#eeg_channel_nums = [1, 2, 4, 5, 6]\n",
        "\n",
        "date_provided = date\n",
        "\n",
        "fig, ax = plt.subplots(6, 1, figsize=(16, 8))\n",
        "\n",
        "# get user's timezone\n",
        "timezone_user = records_df[records_df['Ref'] == record_ref]['Timezone'].iloc[0]\n",
        "\n",
        "def utc_to_local(utc_dt):\n",
        "    return utc_dt.replace(tzinfo=timezone.utc).astimezone(tz=pytz.timezone(timezone_user))\n",
        "\n",
        "# we do this stage_to_int thing to ensure that the\n",
        "# stages appear in order on the y-axis\n",
        "stage_to_int = [\n",
        "    'SLEEP-MT',\n",
        "    'SLEEP-S3',\n",
        "    'SLEEP-S2',\n",
        "    'SLEEP-S1',\n",
        "    'SLEEP-REM',\n",
        "    'SLEEP-S0'\n",
        "]\n",
        "\n",
        "stages_to_real_names = {\n",
        "    'SLEEP-MT': None,\n",
        "    'SLEEP-S3': 'DEEP',\n",
        "    'SLEEP-S2': 'N2',\n",
        "    'SLEEP-S1': 'N1',\n",
        "    'SLEEP-REM': 'REM',\n",
        "    'SLEEP-S0': 'WAKE'\n",
        "}\n",
        "\n",
        "stage_ints = np.array(hypnogram_df['Sleep Stage'].apply(lambda x: stage_to_int.index(x)))\n",
        "\n",
        "num_stages = len(stage_to_int)\n",
        "\n",
        "# load in hdf5 file\n",
        "f = h5py.File('2751349.h5', 'r')\n",
        "\n",
        "# turn into seconds so we can plot\n",
        "hypnogram_df['seconds'] = hypnogram_df['Time [hh:mm:ss]'].apply(lambda x: 3600 * int(x.split(':')[0]) + 60 * int(x.split(':')[1]) + int(x.split(':')[-1]))\n",
        "\n",
        "# get the date this was taken\n",
        "date = datetime.fromtimestamp(f.attrs['start_time'])\n",
        "\n",
        "timezone_str = utc_to_local(date).strftime('%Z')\n",
        "date = utc_to_local(date).strftime('%B %d, %Y')\n",
        "\n",
        "# get the contiguous regions in stage_ints to plot\n",
        "# these separately\n",
        "empty_idxes = np.where(stage_ints != 0)[0]\n",
        "\n",
        "regions = []\n",
        "\n",
        "cur_region_start = -1\n",
        "\n",
        "cur_region_end = -1\n",
        "\n",
        "for i, (idx1, idx2) in enumerate(zip(empty_idxes[:-1], empty_idxes[1:])):\n",
        "    if cur_region_start == -1:\n",
        "        cur_region_start = idx1\n",
        "        cur_region_end = idx2\n",
        "\n",
        "    # either diff > 1 or we're at the end\n",
        "    if idx2 - idx1 > 1 or i == empty_idxes[:-1].shape[0] - 1:\n",
        "        cur_region_end = idx1\n",
        "        # add 1 to account for extra dummy entry in heart_rate_day\n",
        "        # at the beginning\n",
        "        regions.append((cur_region_start+1, cur_region_end+1))\n",
        "\n",
        "        cur_region_start = -1\n",
        "        cur_region_end = -1\n",
        "\n",
        "\n",
        "date_tz = datetime.strptime(date_provided, '%Y-%m-%d').replace(tzinfo=pytz.timezone(timezone_user))\n",
        "\n",
        "start_timestamp = date_tz.timestamp() + start_hour * 3600 + start_minute * 60\n",
        "end_timestamp = date_tz.timestamp() + end_hour * 3600 + end_minute * 60\n",
        "\n",
        "start_time_offset = start_timestamp - f.attrs['start_time']\n",
        "end_time_offset = end_timestamp - f.attrs['start_time']\n",
        "\n",
        "start_time_idx = int(start_time_offset * 250)\n",
        "end_time_idx = int(end_time_offset * 250)\n",
        "\n",
        "eeg_signals = [f[f'eeg{eeg_channel_num}/filtered'][start_time_idx:end_time_idx] for eeg_channel_num in eeg_channel_nums]\n",
        "\n",
        "\n",
        "with plt.style.context('ggplot'):\n",
        "    \n",
        "    \n",
        "    for region_start, region_end in regions:\n",
        "        ax[0].plot(hypnogram_df['seconds'].iloc[region_start:region_end] / 3600,\n",
        "                stage_ints[region_start:region_end], color='black')\n",
        "\n",
        "    # now fill in an interval\n",
        "    ax[0].axvspan(start_hour + start_minute / 60, end_hour + end_minute / 60,\n",
        "                  color='#ff95d4')\n",
        "\n",
        "    ax[0].set_yticks(ticks=list(range(1, num_stages)))\n",
        "    ax[0].set_yticklabels(labels=[stages_to_real_names[stage] for stage in stage_to_int[1:]])\n",
        "    \n",
        "    ticks = [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
        "    ax[0].set_xticks(ticks=ticks)\n",
        "    ax[0].set_xticklabels(labels=['0' + str(tick) + ':00' for tick in ticks])#['04:00', '06:00', '', '9AM', '10AM', '11AM', '12PM', '1PM', '2PM'])\n",
        "    ax[0].tick_params(colors='silver', which='ticks')\n",
        "\n",
        "    # just set the xlim so that it extends all the way left\n",
        "    # and right\n",
        "    ax[0].set_xlim(*(hypnogram_df['seconds'] / 3600).iloc[[0, -1]])\n",
        "\n",
        "    ax[0].spines['top'].set_visible(False)\n",
        "    ax[0].spines['right'].set_visible(False)\n",
        "    ax[0].spines['bottom'].set_visible(True)\n",
        "    ax[0].spines['left'].set_visible(True)\n",
        "\n",
        "    plt.setp(ax[0].get_xticklines(), color='silver')\n",
        "    plt.setp(ax[0].get_yticklines(), color='silver')\n",
        "\n",
        "    ax[0].spines['bottom'].set_edgecolor('silver')\n",
        "    ax[0].spines['left'].set_edgecolor('silver')\n",
        "\n",
        "\n",
        "for i in range(1, 6):\n",
        "    ax[i].plot(eeg_signals[i-1], color='black', linewidth=.7)\n",
        "\n",
        "    ax[i].set_ylim(-150, 150)\n",
        "\n",
        "    ax[i].set_xlim(0, eeg_signals[i-1].shape[0])\n",
        "\n",
        "    # leave only the left edge of the frame in\n",
        "    ax[i].spines['top'].set_visible(False)\n",
        "    ax[i].spines['right'].set_visible(False)\n",
        "    ax[i].spines['bottom'].set_visible(False)\n",
        "    ax[i].spines['left'].set_visible(True)\n",
        "    ax[i].get_xaxis().set_ticks([])\n",
        "\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "cellView": "form",
        "id": "fQzlijN7EOWo",
        "outputId": "082f7d36-eab9-4413-bea2-ad9d1dc92789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Above is a plot we created ourselves!*\n",
        "\n",
        "Now let's combine both of the above plots together. What happens to the EEG signal across different sleep stages? Does it become more chaotic during some vs. others? Intuitively, here we'd expect that the EEG signal becomes very active and chaotic when the user is awake, and the EEG signal becomes quiet in deep sleep. We measure this notion \"chaos\" with the variance of the signal, taken during a 1 second time interval. Below we aggregate all 1 second time intervals, pair them to their apppropriate sleep stages, and plot the variance vs. sleep stage."
      ],
      "metadata": {
        "id": "ZBMWLbv_shVu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Visualize EEG signal vs. sleep stage\n",
        "import seaborn as sns\n",
        "\n",
        "def strided_window_reshape(x, window_size):\n",
        "    # reshape 1D array according to strided window size (windows do not overlap)\n",
        "    num_windows = x.shape[0] // window_size\n",
        "\n",
        "    return x[:num_windows * window_size].reshape((-1, window_size))\n",
        "\n",
        "# get variance for each 1-second interval in EEG signal\n",
        "windows = strided_window_reshape(f['eeg1/filtered'][:], 250)\n",
        "\n",
        "variances = np.var(windows, axis=1)\n",
        "\n",
        "# get median for each 30-second interval in variance signal\n",
        "variances_windowed = strided_window_reshape(variances, 30)\n",
        "\n",
        "median_variances = np.median(variances_windowed, axis=1)\n",
        "\n",
        "sleep_stages = np.unique(hypnogram_df['Sleep Stage'])\n",
        "\n",
        "sleep_stage_variances = {}\n",
        "\n",
        "df_dict = []\n",
        "\n",
        "for sleep_stage in sleep_stages:\n",
        "    median_variance = median_variances[np.where(hypnogram_df['Sleep Stage'] == sleep_stage)[0]]\n",
        "\n",
        "    for v in median_variance:\n",
        "        df_dict.append({'Sleep Stage': stages_to_real_names[sleep_stage],\n",
        "                        'variance': v})\n",
        "\n",
        "\n",
        "eeg_var_df = pd.DataFrame(df_dict).dropna()\n",
        "\n",
        "with plt.style.context('ggplot'):\n",
        "    plt.figure(figsize=(16, 8))\n",
        "\n",
        "    plt.title('EEG signal variance vs. sleep stage (1 second time intervals)',\n",
        "              fontsize=20)\n",
        "\n",
        "    sns.barplot(x='Sleep Stage', y='variance', data=eeg_var_df,\n",
        "                capsize=.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "cellView": "form",
        "id": "55iIewh0x1Oc",
        "outputId": "6cb31918-f9e0-498d-e58a-39b5734653ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Indeed, we see that there is a large amount of variability in this variance across different sleep stages. In particular, we see that the WAKE stage is particularly chaotic. However, at least visually, we see very little difference between deep sleep and REM sleep in terms of the variance of the EEG signal.\n",
        "\n",
        "# 5. Data analysis\n",
        "\n",
        "DISCLAIMER: the analyses below may not be 100% biologically or scientifically grounded; the code is here to assist in your process, if you are interested in asking these kinds of questions.\n",
        "\n",
        "## 5.1. Variance of EEG signal vs. stage\n",
        "\n",
        "Here, we'll just use what we already computed earlier in section 4.3 (`eeg_var_df`, a DataFrame hidden behind the code fold) and compute a statistical test on that, for due diligence."
      ],
      "metadata": {
        "id": "epMUQPa72N87"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "wake_var = eeg_var_df[eeg_var_df['Sleep Stage'] == 'WAKE']['variance']\n",
        "\n",
        "sleep_var = eeg_var_df[eeg_var_df['Sleep Stage'] != 'WAKE']['variance']\n",
        "\n",
        "res = ttest_ind(wake_var, sleep_var)\n",
        "\n",
        "print(f'P-value: {res.pvalue:.3g}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwreCuIM4av_",
        "outputId": "4ab3ee62-50d4-41c9-af53-ea1bda406db8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-value: 2.42e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looks significant!\n",
        "\n",
        "## 5.2. Norm of acceleration vs. stage\n",
        "\n",
        "For our next analysis, we'll do a similar analysis (with 1 second time intervals as well), but with the norm of the acceleration instead. First, we'll do the same plot, just to get an idea of what's going on."
      ],
      "metadata": {
        "id": "3ImiHLZ254lu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Plot accelerometer norm vs. sleep stage\n",
        "acc_norm = np.sum([f[k]['filtered'][:] ** 2 for k in ['accelerometer_x', 'accelerometer_y', 'accelerometer_z']], axis=0)\n",
        "\n",
        "# get variance for each 1-second interval in acc norm\n",
        "windows = strided_window_reshape(acc_norm, 50)\n",
        "\n",
        "variances = np.median(windows, axis=1)\n",
        "\n",
        "# get median for each 30-second interval in variance signal\n",
        "variances_windowed = strided_window_reshape(variances, 30)\n",
        "\n",
        "median_variances = np.median(variances_windowed, axis=1)\n",
        "\n",
        "sleep_stages = np.unique(hypnogram_df['Sleep Stage'])\n",
        "\n",
        "sleep_stage_variances = {}\n",
        "\n",
        "df_dict = []\n",
        "\n",
        "for sleep_stage in sleep_stages:\n",
        "    median_variance = median_variances[np.where(hypnogram_df['Sleep Stage'] == sleep_stage)[0]]\n",
        "\n",
        "    for v in median_variance:\n",
        "        df_dict.append({'Sleep Stage': stages_to_real_names[sleep_stage],\n",
        "                        'norm': v})\n",
        "\n",
        "\n",
        "acc_norm_df = pd.DataFrame(df_dict).dropna()\n",
        "\n",
        "with plt.style.context('ggplot'):\n",
        "    plt.figure(figsize=(16, 8))\n",
        "\n",
        "    plt.title('Accelerometer norm vs. sleep stage (1 second time intervals)',\n",
        "              fontsize=20)\n",
        "\n",
        "    sns.barplot(x='Sleep Stage', y='norm', data=acc_norm_df,\n",
        "                capsize=.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "cellView": "form",
        "id": "SW0WwKGq6PHx",
        "outputId": "d5cc16b7-3d9a-49cd-ed0f-014574b981e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's again compute a two-tailed T-test, again for due diligence."
      ],
      "metadata": {
        "id": "w_JJwTgwucYl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "wake_var = acc_norm_df[acc_norm_df['Sleep Stage'] == 'WAKE']['norm']\n",
        "\n",
        "sleep_var = acc_norm_df[acc_norm_df['Sleep Stage'] != 'WAKE']['norm']\n",
        "\n",
        "res = ttest_ind(wake_var, sleep_var)\n",
        "\n",
        "print(f'P-value: {res.pvalue:.3g}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VTARsmE7h-8",
        "outputId": "d6d1c1d9-d791-4da7-a68b-b296d62818be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-value: 1.46e-69\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Significant, as expected."
      ],
      "metadata": {
        "id": "6_CtlTeV7xO1"
      }
    }
  ]
}