{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resample Data and Concatenate Channels\n",
    "\n",
    "Informative as it is, raw Neon data is not always easy to work with. Different data streams (e.g., gaze, eye states, IMU) are sampled at different rates, don't necessarily share a common start timestamp, and within each stream data might not have been sampled at a constant rate. This tutorial demonstrates how to deal with these issues by resampling data streams and concatenating them into a single DataFrame.\n",
    "\n",
    "We will use the same ``OfficeWalk`` dataset as in the [previous tutorial](read_recording.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pyneon import get_sample_data, NeonRecording, time_to_ts\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "recording_dir = get_sample_data(\"OfficeWalk\") / \"Timeseries Data\" / \"walk1-e116e606\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now access raw data from gaze, eye states, and IMU streams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "recording = NeonRecording(recording_dir)\n",
    "gaze = recording.gaze\n",
    "eye_states = recording.eye_states\n",
    "imu = recording.imu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unequally sampled data\n",
    "\n",
    "Data from each stream has `timestamp [ns]` and `time [s]` columns, which denotes the UTC time of the sample in nanoseconds and the relative time since stream onset in seconds, respectively. But are they equally spaced in time? Let's explore that by computing the time differences between consecutive samples for each stream and check how many unique values we have. If the data was sampled at a constant rate, we should have only one unique value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique gaze time differences: 130, max: 115.123ms\n",
      "Unique eye states time differences: 130, max: 115.123ms\n",
      "Unique imu time differences: 1106, max: 230.444ms\n"
     ]
    }
   ],
   "source": [
    "# Get the data points\n",
    "gaze_ts = gaze.data[\"timestamp [ns]\"].values\n",
    "eye_states_ts = eye_states.data[\"timestamp [ns]\"].values\n",
    "imu_ts = imu.data[\"timestamp [ns]\"].values\n",
    "\n",
    "# Calculate the distances between subsequent data points\n",
    "gaze_diff = np.diff(gaze_ts)\n",
    "eye_states_diff = np.diff(eye_states_ts)\n",
    "imu_diff = np.diff(imu_ts)\n",
    "\n",
    "# Unique values\n",
    "gaze_diff_unique = np.unique(gaze_diff)\n",
    "eye_states_diff_unique = np.unique(eye_states_diff)\n",
    "imu_diff_unique = np.unique(imu_diff)\n",
    "\n",
    "print(\n",
    "    f\"Unique gaze time differences: {len(gaze_diff_unique)}, max: {np.max(gaze_diff_unique)/1e6}ms\"\n",
    ")\n",
    "print(\n",
    "    f\"Unique eye states time differences: {len(eye_states_diff_unique)}, max: {np.max(eye_states_diff_unique)/1e6}ms\"\n",
    ")\n",
    "print(\n",
    "    f\"Unique imu time differences: {len(imu_diff_unique)}, max: {np.max(imu_diff_unique)/1e6}ms\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oops, it seems the data are far from being continuously sampled. We can further explore the distribution of such differences by plotting a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaze_nominal_diff = 1e9 / gaze.sampling_freq_nominal\n",
    "eye_states_nominal_diff = 1e9 / eye_states.sampling_freq_nominal\n",
    "imu_nominal_diff = 1e9 / imu.sampling_freq_nominal\n",
    "\n",
    "fig, axs = plt.subplots(3, 1, tight_layout=True)\n",
    "\n",
    "axs[0].hist(gaze_diff, bins=50)\n",
    "axs[0].axvline(gaze_nominal_diff, color=\"red\", label=\"Nominal\")\n",
    "axs[0].set_title(\"Gaze\")\n",
    "\n",
    "axs[1].hist(eye_states_diff, bins=50)\n",
    "axs[1].axvline(eye_states_nominal_diff, color=\"red\", label=\"Nominal\")\n",
    "axs[1].set_title(\"Eye states\")\n",
    "\n",
    "axs[2].hist(imu_diff, bins=50)\n",
    "axs[2].axvline(imu_nominal_diff, color=\"red\", label=\"Nominal\")\n",
    "axs[2].set_title(\"IMU\")\n",
    "\n",
    "for i in range(3):\n",
    "    axs[i].set_yscale(\"log\")\n",
    "    axs[i].set_xlabel(\"Time difference [ns]\")\n",
    "    axs[i].set_ylabel(\"Counts\")\n",
    "    axs[i].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling data streams\n",
    "\n",
    "Given the unequal sampling, if we want to obtain continuous data streams, interpolation is necessary. PyNeon uses the `scipy.interpolate.interp1d` function to interpolate data streams. We can resample each stream by simply calling `resample()` which returns a new DataFrame with the resampled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        timestamp [ns]  time [s]  gaze x [px]  gaze y [px]  worn  fixation id  \\\n",
      "0  1725032224852161732     0.000  1067.486000   620.856000  True            1   \n",
      "1  1725032224857161732     0.005  1066.920463   617.120061  True            1   \n",
      "2  1725032224862161732     0.010  1072.699000   615.780000  True            1   \n",
      "3  1725032224867161732     0.015  1067.447000   617.062000  True            1   \n",
      "4  1725032224872161732     0.020  1071.564000   613.158000  True            1   \n",
      "\n",
      "   blink id  azimuth [deg]  elevation [deg]  \n",
      "0      <NA>      16.213030        -0.748998  \n",
      "1      <NA>      16.176315        -0.511927  \n",
      "2      <NA>      16.546413        -0.426618  \n",
      "3      <NA>      16.210049        -0.508251  \n",
      "4      <NA>      16.473521        -0.260388  \n",
      "Only one unique time difference: [5000000]\n"
     ]
    }
   ],
   "source": [
    "# Resample to the nominal sampling frequency\n",
    "gaze_resampled_data = gaze.interpolate()\n",
    "print(gaze_resampled_data.head())\n",
    "\n",
    "ts = gaze_resampled_data[\"timestamp [ns]\"].values\n",
    "ts_diffs = np.diff(ts)\n",
    "ts_diff_unique = np.unique(ts_diffs)\n",
    "print(f\"Only one unique time difference: {np.unique(ts_diffs)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, we resampled the gaze data with default parameters, which means that the resampled data will have the same start and timestamps as the original data, and the sampling rate is set to the nominal sampling frequency (200 Hz, as specified by Pupil Labs). Notice that resampling would not change the data type of the columns. For example, the `bool`-type `worn` column and the integer-type `fixation_id` column are preserved. \n",
    "\n",
    "Alternatively, one can also resample the gaze data to any desired timestamps by specifying the `new_ts` parameter. This is especially helpful when synchronizing different data streams. For example, we can resample the gaze data (~200Hz) to the timestamps of the IMU data (~110Hz)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original gaze data length: 18769\n",
      "Original IMU data length: 10919\n",
      "Data length after resampling to IMU: 10919\n"
     ]
    }
   ],
   "source": [
    "print(f\"Original gaze data length: {gaze.data.shape[0]}\")\n",
    "print(f\"Original IMU data length: {imu.data.shape[0]}\")\n",
    "gaze_resampled_to_imu_data = gaze.interpolate(new_ts=imu.ts)\n",
    "print(f\"Data length after resampling to IMU: {gaze_resampled_to_imu_data.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenating different streams\n",
    "\n",
    "Based on the resampling method, it is then possible to concatenate different streams into a single DataFrame by resampling them to common timestamps. The method `concat_streams()` provides such functionality. It takes a list of stream names and resamples them to common timestamps, defined by the latest start and earliest end timestamps of the streams. The news ampling frequency can either be directly specified or taken from the lowest/highest sampling frequency of the streams.\n",
    "\n",
    "In the following example, we will concatenate the gaze, eye states, and IMU streams into a single DataFrame using the default parameters (e.g., using the lowest sampling frequency of the streams)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concatenating streams:\n",
      "\tGaze\n",
      "\t3D eye states\n",
      "\tIMU\n",
      "Using lowest sampling rate: 110 Hz (['imu'])\n",
      "Using latest start timestamp: 1725032224878547732 (['imu'])\n",
      "Using earliest last timestamp: 1725032319533909732 (['imu'])\n",
      "        timestamp [ns]  time [s]  gaze x [px]  gaze y [px]  worn  fixation id  \\\n",
      "0  1725032224878547732  0.000000  1073.410354   611.095861  True            1   \n",
      "1  1725032224887638641  0.009091  1069.801082   613.382535  True            1   \n",
      "2  1725032224896729550  0.018182  1070.090109   613.439696  True            1   \n",
      "3  1725032224905820459  0.027273  1069.891351   612.921757  True            1   \n",
      "4  1725032224914911368  0.036364  1069.692588   612.403803  True            1   \n",
      "\n",
      "   blink id  azimuth [deg]  elevation [deg]  pupil diameter left [mm]  ...  \\\n",
      "0      <NA>      16.591703        -0.129540                  5.036588  ...   \n",
      "1      <NA>      16.360605        -0.274666                  5.093205  ...   \n",
      "2      <NA>      16.379116        -0.278283                  5.078107  ...   \n",
      "3      <NA>      16.366379        -0.245426                  5.077680  ...   \n",
      "4      <NA>      16.353641        -0.212567                  5.077253  ...   \n",
      "\n",
      "   acceleration x [g]  acceleration y [g]  acceleration z [g]  roll [deg]  \\\n",
      "0            0.063477           -0.058594            0.940430   -2.550682   \n",
      "1            0.057486           -0.048916            0.942273   -2.602498   \n",
      "2            0.051494           -0.039238            0.944117   -2.654314   \n",
      "3            0.046721           -0.045800            0.944336   -2.699777   \n",
      "4            0.042113           -0.054556            0.944336   -2.744383   \n",
      "\n",
      "   pitch [deg]   yaw [deg]  quaternion w  quaternion x  quaternion y  \\\n",
      "0    -4.461626 -172.335180      0.067636     -0.024792      0.037342   \n",
      "1    -4.493445 -172.562393      0.065682     -0.025185      0.037639   \n",
      "2    -4.525265 -172.789613      0.063728     -0.025579      0.037936   \n",
      "3    -4.556964 -173.013398      0.061802     -0.025917      0.038238   \n",
      "4    -4.588647 -173.236726      0.059879     -0.026247      0.038541   \n",
      "\n",
      "   quaternion z  \n",
      "0     -0.996703  \n",
      "1     -0.996810  \n",
      "2     -0.996917  \n",
      "3     -0.997017  \n",
      "4     -0.997115  \n",
      "\n",
      "[5 rows x 36 columns]\n"
     ]
    }
   ],
   "source": [
    "concat_data = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"])\n",
    "print(concat_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We show an exemplary sampling of eye, imu and concatenated data below. It can be seen that imu data has subsequent missing values which can in turn be interpolated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fb49a7bb60>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = 5\n",
    "end_time = 5.3\n",
    "start_ts = time_to_ts(start_time, concat_data)\n",
    "end_ts = time_to_ts(end_time, concat_data)\n",
    "\n",
    "raw_gaze_data_slice = gaze.data[\n",
    "    (gaze.data[\"timestamp [ns]\"] >= start_ts) & (gaze.data[\"timestamp [ns]\"] <= end_ts)\n",
    "]\n",
    "raw_eye_states_data_slice = eye_states.data[\n",
    "    (eye_states.data[\"timestamp [ns]\"] > start_ts)\n",
    "    & (eye_states.data[\"timestamp [ns]\"] <= end_ts)\n",
    "]\n",
    "raw_imu_data_slice = imu.data[\n",
    "    (imu.data[\"timestamp [ns]\"] >= start_ts) & (imu.data[\"timestamp [ns]\"] <= end_ts)\n",
    "]\n",
    "concat_data_slice = concat_data[\n",
    "    (concat_data[\"timestamp [ns]\"] >= start_ts)\n",
    "    & (concat_data[\"timestamp [ns]\"] <= end_ts)\n",
    "]\n",
    "\n",
    "# plot all data in the same scatter plot\n",
    "plt.figure(figsize=(15, 4))\n",
    "plt.scatter(\n",
    "    raw_gaze_data_slice[\"timestamp [ns]\"],\n",
    "    np.zeros_like(raw_gaze_data_slice[\"timestamp [ns]\"]) + 2,\n",
    "    label=\"Raw gaze data\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.scatter(\n",
    "    raw_eye_states_data_slice[\"timestamp [ns]\"],\n",
    "    np.zeros_like(raw_eye_states_data_slice[\"timestamp [ns]\"]) + 1,\n",
    "    label=\"Raw eye states data\",\n",
    "    color=\"orange\",\n",
    ")\n",
    "plt.scatter(\n",
    "    raw_imu_data_slice[\"timestamp [ns]\"],\n",
    "    np.zeros_like(raw_imu_data_slice[\"timestamp [ns]\"]),\n",
    "    label=\"Raw IMU data\",\n",
    "    color=\"blue\",\n",
    ")\n",
    "plt.scatter(\n",
    "    concat_data_slice[\"timestamp [ns]\"],\n",
    "    np.zeros_like(concat_data_slice[\"timestamp [ns]\"]) - 1,\n",
    "    label=\"Concatenated data\",\n",
    "    color=\"green\",\n",
    ")\n",
    "# set x-ticks with higher frequency and add gridlines\n",
    "plt.xticks(concat_data_slice[\"timestamp [ns]\"], labels=None)\n",
    "plt.yticks([])\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A linear interpolation allows us to estimate missing values. In the end, the concatenated dataframe combines all continuous data into one central location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fb49cd3d10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot imu data and interpolated data in same plot\n",
    "plt.figure(figsize=(16, 3))\n",
    "plt.scatter(\n",
    "    raw_imu_data_slice[\"time [s]\"],\n",
    "    raw_imu_data_slice[\"acceleration z [g]\"],\n",
    "    label=\"Raw imu data\",\n",
    "    color=\"blue\",\n",
    ")\n",
    "plt.plot(\n",
    "    concat_data_slice[\"time [s]\"],\n",
    "    concat_data_slice[\"acceleration z [g]\"],\n",
    "    label=\"Interpolated imu data\",\n",
    "    color=\"green\",\n",
    ")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pyneon",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}