open_patient_data.ipynb

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   "source": [
    "# Notebook to get all raw data from one single patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these lines only need to be run one time, if the packages are not already installed in your python environment\n",
    "# %pip install fastparquet\n",
    "# %pip install mne\n",
    "# %pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import useful packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils_patient import Patient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Patient \n",
    "\n",
    "### 1.1. Get directory\n",
    "\n",
    "All the data of a single patient needs to be contained in one folder with the patient id name (following the path structure in the OneDrive)\n",
    "\n",
    "- example: \"BD-SEER -> MSEL_01110 -> MSEL_01110_SeerAnnotations.csv \"\n",
    "- id = 'MSEL_01110'\n",
    "- dir = 'BD-SEER'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# id should be a string, the patient data should be inside a folder with the same name\n",
    "id = 'MSEL_01110' \n",
    "# dir can be \"\" if the id folder is inside the notebook directory, otherwise it should be the path to the folder \n",
    "dir = 'data'\n",
    "# patient class is created\n",
    "patient_class = Patient(id=id, dir=dir)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# patient directory\n",
    "patient_class.pat_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Get Annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get patient annotations (they should return nothing since it starts empty)\n",
    "patient_class.annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get patient annotations\n",
    "annotations = patient_class.get_seizures_annotations()\n",
    "annotations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Get sensor data\n",
    "\n",
    "- get data from one sensor opening all files inside patient directory\n",
    "- data will be saved in a designated savedir, make sure to create the folder before sending the data there\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "           Acc x      Acc y      Acc z    Acc Mag  \\\n",
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       "                                timestamp  \n",
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   "source": [
    "sensor = 'acc'\n",
    "savedir = f'savedir/raw_data_{patient_class.id}_{sensor}.parquet'\n",
    "\n",
    "data = patient_class.get_sensor_data(sensor=sensor, savedir=savedir)\n",
    "\n",
    "data.head(5)"
   ]
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   "cell_type": "code",
   "execution_count": 15,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HR</th>\n",
       "      <th>timestamp</th>\n",
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       "              HR                           timestamp\n",
       "index                                               \n",
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    "sensor = 'hr'\n",
    "\n",
    "savedir = f'savedir/raw_data_{patient_class.id}_{sensor}.parquet'\n",
    "\n",
    "data = patient_class.get_sensor_data(sensor=sensor, savedir=savedir)\n",
    "\n",
    "data.head(5)"
   ]
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