{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation\n",
    "ParaDigMa requires the sensor data to be of a specific format. This tutorial provides examples of how to prepare your input data for subsequent analysis. In the end, the input for ParaDigMa is a dataframe consisting of:\n",
    "* A column `time`, representing the seconds relative to the first row of the dataframe;\n",
    "* One or multiple of the following sensor column categories:\n",
    "  * Accelerometer: `accelerometer_x`, `accelerometer_y` and `accelerometer_z` in _g_\n",
    "  * Gyroscope: `gyroscope_x`, `gyroscope_y` and `gyroscope_z` in _deg/s_\n",
    "  * PPG: `green` \n",
    "\n",
    "The final dataframe should be resampled to 100 Hz, have the correct units for the sensor columns, and the correct format for the `time` column. Also note that the _gait_ pipeline expects a specific orientation of sensor axes, as explained in [Coordinate system](../guides/coordinate_system.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "This example uses data of the [Personalized Parkinson Project](https://pubmed.ncbi.nlm.nih.gov/31315608/), which is stored in Time Series Data Format (TSDF). Inertial Measurements Units (IMU) and photoplethysmography (PPG) data are sampled at a different sampling frequency and therefore stored separately. Note that ParaDigMa works independent of data storage format; it only requires a `pandas` dataframe as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>acceleration_x</th>\n",
       "      <th>acceleration_y</th>\n",
       "      <th>acceleration_z</th>\n",
       "      <th>rotation_x</th>\n",
       "      <th>rotation_y</th>\n",
       "      <th>rotation_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>-2.684842</td>\n",
       "      <td>-115.670732</td>\n",
       "      <td>-32.012195</td>\n",
       "      <td>26.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-5.257034</td>\n",
       "      <td>6.115995</td>\n",
       "      <td>-2.497091</td>\n",
       "      <td>-110.609757</td>\n",
       "      <td>-34.634146</td>\n",
       "      <td>24.695122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-4.947244</td>\n",
       "      <td>6.392928</td>\n",
       "      <td>-2.468928</td>\n",
       "      <td>-103.231708</td>\n",
       "      <td>-36.768293</td>\n",
       "      <td>22.926829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-4.792349</td>\n",
       "      <td>6.735574</td>\n",
       "      <td>-2.605048</td>\n",
       "      <td>-96.280488</td>\n",
       "      <td>-38.719512</td>\n",
       "      <td>21.158537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.039795</td>\n",
       "      <td>-4.848675</td>\n",
       "      <td>7.115770</td>\n",
       "      <td>-2.731780</td>\n",
       "      <td>-92.560976</td>\n",
       "      <td>-41.280488</td>\n",
       "      <td>20.304878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  acceleration_x  acceleration_y  acceleration_z  rotation_x  \\\n",
       "0   0.000000       -5.402541        5.632536       -2.684842 -115.670732   \n",
       "1  10.040039       -5.257034        6.115995       -2.497091 -110.609757   \n",
       "2  10.040039       -4.947244        6.392928       -2.468928 -103.231708   \n",
       "3  10.040039       -4.792349        6.735574       -2.605048  -96.280488   \n",
       "4  10.039795       -4.848675        7.115770       -2.731780  -92.560976   \n",
       "\n",
       "   rotation_y  rotation_z  \n",
       "0  -32.012195   26.097561  \n",
       "1  -34.634146   24.695122  \n",
       "2  -36.768293   22.926829  \n",
       "3  -38.719512   21.158537  \n",
       "4  -41.280488   20.304878  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from paradigma.util import load_tsdf_dataframe\n",
    "\n",
    "path_to_raw_data = Path('../../tests/data/data_preparation_tutorial')\n",
    "path_to_imu_data = path_to_raw_data / 'imu'\n",
    "\n",
    "df_imu, imu_time, imu_values = load_tsdf_dataframe(\n",
    "    path_to_data=path_to_imu_data, \n",
    "    prefix='IMU'\n",
    ")\n",
    "\n",
    "df_imu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
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       "      <th>1</th>\n",
       "      <td>9.959961</td>\n",
       "      <td>648214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.959961</td>\n",
       "      <td>646786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.959961</td>\n",
       "      <td>645334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.960205</td>\n",
       "      <td>644317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time   green\n",
       "0  0.000000  649511\n",
       "1  9.959961  648214\n",
       "2  9.959961  646786\n",
       "3  9.959961  645334\n",
       "4  9.960205  644317"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from paradigma.util import load_tsdf_dataframe\n",
    "\n",
    "path_to_ppg_data = os.path.join(path_to_raw_data, 'ppg')\n",
    "\n",
    "df_ppg, ppg_time, ppg_values = load_tsdf_dataframe(\n",
    "    path_to_data=path_to_ppg_data, \n",
    "    prefix='PPG'\n",
    ")\n",
    "\n",
    "df_ppg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The timestamps in this dataset correspond to delta milliseconds, and the data is not uniformly distributed as can be observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare dataframe\n",
    "\n",
    "#### Change column names\n",
    "To safeguard robustness of the pipeline, ParaDigMa fixes column names to a predefined standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>accelerometer_x</th>\n",
       "      <th>accelerometer_y</th>\n",
       "      <th>accelerometer_z</th>\n",
       "      <th>gyroscope_x</th>\n",
       "      <th>gyroscope_y</th>\n",
       "      <th>gyroscope_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>-2.684842</td>\n",
       "      <td>-115.670732</td>\n",
       "      <td>-32.012195</td>\n",
       "      <td>26.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-5.257034</td>\n",
       "      <td>6.115995</td>\n",
       "      <td>-2.497091</td>\n",
       "      <td>-110.609757</td>\n",
       "      <td>-34.634146</td>\n",
       "      <td>24.695122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-4.947244</td>\n",
       "      <td>6.392928</td>\n",
       "      <td>-2.468928</td>\n",
       "      <td>-103.231708</td>\n",
       "      <td>-36.768293</td>\n",
       "      <td>22.926829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-4.792349</td>\n",
       "      <td>6.735574</td>\n",
       "      <td>-2.605048</td>\n",
       "      <td>-96.280488</td>\n",
       "      <td>-38.719512</td>\n",
       "      <td>21.158537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.039795</td>\n",
       "      <td>-4.848675</td>\n",
       "      <td>7.115770</td>\n",
       "      <td>-2.731780</td>\n",
       "      <td>-92.560976</td>\n",
       "      <td>-41.280488</td>\n",
       "      <td>20.304878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  accelerometer_x  accelerometer_y  accelerometer_z  gyroscope_x  \\\n",
       "0   0.000000        -5.402541         5.632536        -2.684842  -115.670732   \n",
       "1  10.040039        -5.257034         6.115995        -2.497091  -110.609757   \n",
       "2  10.040039        -4.947244         6.392928        -2.468928  -103.231708   \n",
       "3  10.040039        -4.792349         6.735574        -2.605048   -96.280488   \n",
       "4  10.039795        -4.848675         7.115770        -2.731780   -92.560976   \n",
       "\n",
       "   gyroscope_y  gyroscope_z  \n",
       "0   -32.012195    26.097561  \n",
       "1   -34.634146    24.695122  \n",
       "2   -36.768293    22.926829  \n",
       "3   -38.719512    21.158537  \n",
       "4   -41.280488    20.304878  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from paradigma.constants import DataColumns\n",
    "\n",
    "accelerometer_columns = [DataColumns.ACCELEROMETER_X, DataColumns.ACCELEROMETER_Y, DataColumns.ACCELEROMETER_Z]\n",
    "gyroscope_columns = [DataColumns.GYROSCOPE_X, DataColumns.GYROSCOPE_Y, DataColumns.GYROSCOPE_Z]\n",
    "\n",
    "# Rename dataframe columns\n",
    "df_imu = df_imu.rename(columns={\n",
    "    'time': DataColumns.TIME,\n",
    "    'acceleration_x': DataColumns.ACCELEROMETER_X,\n",
    "    'acceleration_y': DataColumns.ACCELEROMETER_Y,\n",
    "    'acceleration_z': DataColumns.ACCELEROMETER_Z,\n",
    "    'rotation_x': DataColumns.GYROSCOPE_X,\n",
    "    'rotation_y': DataColumns.GYROSCOPE_Y,\n",
    "    'rotation_z': DataColumns.GYROSCOPE_Z,\n",
    "})\n",
    "\n",
    "# Set columns to a fixed order\n",
    "df_imu = df_imu[[DataColumns.TIME] + accelerometer_columns + gyroscope_columns]\n",
    "\n",
    "df_imu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>649511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.959961</td>\n",
       "      <td>648214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.959961</td>\n",
       "      <td>646786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.959961</td>\n",
       "      <td>645334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.960205</td>\n",
       "      <td>644317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time   green\n",
       "0  0.000000  649511\n",
       "1  9.959961  648214\n",
       "2  9.959961  646786\n",
       "3  9.959961  645334\n",
       "4  9.960205  644317"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from paradigma.constants import DataColumns\n",
    "\n",
    "ppg_columns = [DataColumns.PPG]\n",
    "\n",
    "# Rename dataframe columns\n",
    "df_ppg = df_ppg.rename(columns={\n",
    "    'time': DataColumns.TIME,\n",
    "    'ppg': DataColumns.PPG,\n",
    "})\n",
    "\n",
    "# Set columns to a fixed order\n",
    "df_ppg = df_ppg[[DataColumns.TIME] + ppg_columns]\n",
    "\n",
    "df_ppg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change units\n",
    "ParaDigMa expects acceleration to be measured in g, and rotation in deg/s. Units can be converted conveniently using ParaDigMa functionalities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>accelerometer_x</th>\n",
       "      <th>accelerometer_y</th>\n",
       "      <th>accelerometer_z</th>\n",
       "      <th>gyroscope_x</th>\n",
       "      <th>gyroscope_y</th>\n",
       "      <th>gyroscope_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.550718</td>\n",
       "      <td>0.574163</td>\n",
       "      <td>-0.273684</td>\n",
       "      <td>-115.670732</td>\n",
       "      <td>-32.012195</td>\n",
       "      <td>26.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-0.535885</td>\n",
       "      <td>0.623445</td>\n",
       "      <td>-0.254545</td>\n",
       "      <td>-110.609757</td>\n",
       "      <td>-34.634146</td>\n",
       "      <td>24.695122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-0.504306</td>\n",
       "      <td>0.651675</td>\n",
       "      <td>-0.251675</td>\n",
       "      <td>-103.231708</td>\n",
       "      <td>-36.768293</td>\n",
       "      <td>22.926829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-0.488517</td>\n",
       "      <td>0.686603</td>\n",
       "      <td>-0.265550</td>\n",
       "      <td>-96.280488</td>\n",
       "      <td>-38.719512</td>\n",
       "      <td>21.158537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.039795</td>\n",
       "      <td>-0.494258</td>\n",
       "      <td>0.725359</td>\n",
       "      <td>-0.278469</td>\n",
       "      <td>-92.560976</td>\n",
       "      <td>-41.280488</td>\n",
       "      <td>20.304878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  accelerometer_x  accelerometer_y  accelerometer_z  gyroscope_x  \\\n",
       "0   0.000000        -0.550718         0.574163        -0.273684  -115.670732   \n",
       "1  10.040039        -0.535885         0.623445        -0.254545  -110.609757   \n",
       "2  10.040039        -0.504306         0.651675        -0.251675  -103.231708   \n",
       "3  10.040039        -0.488517         0.686603        -0.265550   -96.280488   \n",
       "4  10.039795        -0.494258         0.725359        -0.278469   -92.560976   \n",
       "\n",
       "   gyroscope_y  gyroscope_z  \n",
       "0   -32.012195    26.097561  \n",
       "1   -34.634146    24.695122  \n",
       "2   -36.768293    22.926829  \n",
       "3   -38.719512    21.158537  \n",
       "4   -41.280488    20.304878  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from paradigma.util import convert_units_accelerometer, convert_units_gyroscope\n",
    "\n",
    "accelerometer_units = 'm/s^2'\n",
    "gyroscope_units = 'deg/s'\n",
    "\n",
    "accelerometer_data = df_imu[accelerometer_columns].values\n",
    "gyroscope_data = df_imu[gyroscope_columns].values\n",
    "\n",
    "# Convert units to expected format\n",
    "df_imu[accelerometer_columns] = convert_units_accelerometer(accelerometer_data, accelerometer_units)\n",
    "df_imu[gyroscope_columns] = convert_units_gyroscope(gyroscope_data, gyroscope_units)\n",
    "\n",
    "df_imu.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Account for watch side\n",
    "For the Gait & Arm Swing pipeline, it is essential to ensure correct sensor axes orientation. For more information please read [Coordinate System](../guides/coordinate_system.md) and set the axes of the data accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>accelerometer_x</th>\n",
       "      <th>accelerometer_y</th>\n",
       "      <th>accelerometer_z</th>\n",
       "      <th>gyroscope_x</th>\n",
       "      <th>gyroscope_y</th>\n",
       "      <th>gyroscope_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.550718</td>\n",
       "      <td>-0.574163</td>\n",
       "      <td>0.273684</td>\n",
       "      <td>-115.670732</td>\n",
       "      <td>32.012195</td>\n",
       "      <td>-26.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-0.535885</td>\n",
       "      <td>-0.623445</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>-110.609757</td>\n",
       "      <td>34.634146</td>\n",
       "      <td>-24.695122</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-0.651675</td>\n",
       "      <td>0.251675</td>\n",
       "      <td>-103.231708</td>\n",
       "      <td>36.768293</td>\n",
       "      <td>-22.926829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.040039</td>\n",
       "      <td>-0.488517</td>\n",
       "      <td>-0.686603</td>\n",
       "      <td>0.265550</td>\n",
       "      <td>-96.280488</td>\n",
       "      <td>38.719512</td>\n",
       "      <td>-21.158537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.039795</td>\n",
       "      <td>-0.494258</td>\n",
       "      <td>-0.725359</td>\n",
       "      <td>0.278469</td>\n",
       "      <td>-92.560976</td>\n",
       "      <td>41.280488</td>\n",
       "      <td>-20.304878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  accelerometer_x  accelerometer_y  accelerometer_z  gyroscope_x  \\\n",
       "0   0.000000        -0.550718        -0.574163         0.273684  -115.670732   \n",
       "1  10.040039        -0.535885        -0.623445         0.254545  -110.609757   \n",
       "2  10.040039        -0.504306        -0.651675         0.251675  -103.231708   \n",
       "3  10.040039        -0.488517        -0.686603         0.265550   -96.280488   \n",
       "4  10.039795        -0.494258        -0.725359         0.278469   -92.560976   \n",
       "\n",
       "   gyroscope_y  gyroscope_z  \n",
       "0    32.012195   -26.097561  \n",
       "1    34.634146   -24.695122  \n",
       "2    36.768293   -22.926829  \n",
       "3    38.719512   -21.158537  \n",
       "4    41.280488   -20.304878  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the orientation of the sensor according to the documented coordinate system\n",
    "df_imu[DataColumns.ACCELEROMETER_Y] *= -1\n",
    "df_imu[DataColumns.ACCELEROMETER_Z] *= -1\n",
    "df_imu[DataColumns.GYROSCOPE_Y] *= -1\n",
    "df_imu[DataColumns.GYROSCOPE_Z] *= -1\n",
    "\n",
    "df_imu.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change time column\n",
    "ParaDigMa expects the data to be in seconds relative to the first row, which should be equal to 0. The toolbox has the built-in function `transform_time_array` to help users transform their time column to the correct format if the timestamps have been sampled in delta time between timestamps. In the near future, the functionalities for transforming other types (e.g., datetime format) shall be provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>accelerometer_x</th>\n",
       "      <th>accelerometer_y</th>\n",
       "      <th>accelerometer_z</th>\n",
       "      <th>gyroscope_x</th>\n",
       "      <th>gyroscope_y</th>\n",
       "      <th>gyroscope_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.550718</td>\n",
       "      <td>-0.574163</td>\n",
       "      <td>0.273684</td>\n",
       "      <td>-115.670732</td>\n",
       "      <td>32.012195</td>\n",
       "      <td>-26.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01004</td>\n",
       "      <td>-0.535885</td>\n",
       "      <td>-0.623445</td>\n",
       "      <td>0.254545</td>\n",
       "      <td>-110.609757</td>\n",
       "      <td>34.634146</td>\n",
       "      <td>-24.695122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02008</td>\n",
       "      <td>-0.504306</td>\n",
       "      <td>-0.651675</td>\n",
       "      <td>0.251675</td>\n",
       "      <td>-103.231708</td>\n",
       "      <td>36.768293</td>\n",
       "      <td>-22.926829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03012</td>\n",
       "      <td>-0.488517</td>\n",
       "      <td>-0.686603</td>\n",
       "      <td>0.265550</td>\n",
       "      <td>-96.280488</td>\n",
       "      <td>38.719512</td>\n",
       "      <td>-21.158537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.04016</td>\n",
       "      <td>-0.494258</td>\n",
       "      <td>-0.725359</td>\n",
       "      <td>0.278469</td>\n",
       "      <td>-92.560976</td>\n",
       "      <td>41.280488</td>\n",
       "      <td>-20.304878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      time  accelerometer_x  accelerometer_y  accelerometer_z  gyroscope_x  \\\n",
       "0  0.00000        -0.550718        -0.574163         0.273684  -115.670732   \n",
       "1  0.01004        -0.535885        -0.623445         0.254545  -110.609757   \n",
       "2  0.02008        -0.504306        -0.651675         0.251675  -103.231708   \n",
       "3  0.03012        -0.488517        -0.686603         0.265550   -96.280488   \n",
       "4  0.04016        -0.494258        -0.725359         0.278469   -92.560976   \n",
       "\n",
       "   gyroscope_y  gyroscope_z  \n",
       "0    32.012195   -26.097561  \n",
       "1    34.634146   -24.695122  \n",
       "2    36.768293   -22.926829  \n",
       "3    38.719512   -21.158537  \n",
       "4    41.280488   -20.304878  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from paradigma.constants import TimeUnit\n",
    "from paradigma.util import transform_time_array\n",
    "\n",
    "df_imu[DataColumns.TIME] = transform_time_array(\n",
    "    time_array=df_imu[DataColumns.TIME], \n",
    "    input_unit_type=TimeUnit.DIFFERENCE_MS, \n",
    "    output_unit_type=TimeUnit.RELATIVE_S,\n",
    ")\n",
    "\n",
    "df_imu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>649511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00996</td>\n",
       "      <td>648214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01992</td>\n",
       "      <td>646786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.02988</td>\n",
       "      <td>645334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03984</td>\n",
       "      <td>644317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      time   green\n",
       "0  0.00000  649511\n",
       "1  0.00996  648214\n",
       "2  0.01992  646786\n",
       "3  0.02988  645334\n",
       "4  0.03984  644317"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from paradigma.constants import TimeUnit\n",
    "from paradigma.util import transform_time_array\n",
    "\n",
    "df_ppg[DataColumns.TIME] = transform_time_array(\n",
    "    time_array=df_ppg[DataColumns.TIME], \n",
    "    input_unit_type=TimeUnit.DIFFERENCE_MS, \n",
    "    output_unit_type=TimeUnit.RELATIVE_S,\n",
    ")\n",
    "\n",
    "df_ppg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These dataframes are ready to be processed by ParaDigMa."
   ]
  }
 ],
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