{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interpolate data and concatenate streams\n", "\n", "Raw Neon data is not always easy to work with. A data stream (e.g., gaze, eye states, IMU) might not have been sampled at a constant rate. Additionally, streams may have differing sampling rates and lack synchronized start timestamps. All of these issues add complexity to continuous data analysis and sensor fusion.\n", "\n", "This tutorial demonstrates how to deal with these issues by interpolating data streams and concatenating them into a single DataFrame. We will use the same ``boardView`` dataset as in the [previous tutorial](read_recording.ipynb)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from pyneon import Recording, get_sample_data\n", "\n", "recording_dir = (\n", " get_sample_data(\"boardView\")\n", " / \"Timeseries Data + Scene Video\"\n", " / \"boardview1-d4fd9a27\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now access raw data from gaze, eye states, and IMU streams." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "rec = Recording(recording_dir)\n", "gaze = rec.gaze\n", "eye_states = rec.eye_states\n", "imu = rec.imu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Irregular sampling in data streams\n", "\n", "Data points from each stream are indexed by `timestamp [ns]`, which denotes the UTC time of the sample in nanoseconds. Are these samples uniformly distributed over time? We can examine the initial samples from each stream to assess their distribution, where, due to device boot-up, the sampling may be irregular." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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0H3/8IevWrTMZaHcBtNIAWqVKlUrGjx8vRYsWNYH0Sy+9JP369ZOPPvrIPL5kyRK5c+eOc7kePXqY4FxLQ1SrVq0kXbp0snr1arNyp06dKg0aNJAjR46YoDwmDbT1Fv1N8Rbd4D7ccET0CGnrsd/NtN4NSyZovkBoy8588O/30l/b8pS/9j8Q2kru/WddpJy2POWv/Z/kp/vXxJbs02zHjh0zmebSpUu7TM+RI4fJMOutf//+ZlqfPn2kfv36Jqv88MMPyzvvvCOLFi1yLqOBcJ48ecxt/vz58s0338jy5ctN4LxlyxbZuXOnLF682GSgS5YsKe+//74pIfn888/d9m3kyJEm2HbcChYsKN6iR2yOUwzW/+4ndL5AaMvOfPDv99Jf2/KUv/Y/ENpK7v1nXaSctjzlr/3f5af718SW7IPo2GjAu2/fPilXrpwzG7xhwwaTOdYa6UyZMkm7du1MJjvmwEPNNA8YMEAWLlwopUqVMtP2798v169fl+zZszuDc71pqcfx48fd9mHgwIHmVIDjdvbsWfEWPeXxT/5dzF+9n9D5AqEtO/PBv99Lf23LU/7a/0BoK7n3n3WRctrylL/2/z9+un9NbMm+nEOvxqHlGocPH3aZXqxYMfNXs8jq1KlT0qxZM+nevbsMHz7cZJ01u9y5c2dTwpE+fXoz308//SStW7eWUaNGSePGjZ3taQCdN29eCQ8Pv6cPmo12Rwc36s0XtGZIRa8hSuh8gdCWnfng3++lv7blKX/tfyC0ldz7z7pIOW15yl/738NP96+JLUUMLGzSpImpXdZAOmZdtA4WrFSpktSpU0eee+45uXXrlqmNVlrO8eabb8rly5dNIPz7779L9erVpW7dujJjxgyXdtavXy+PPPKIKR/RcpCE8ObAQgAAACRdvJYiyjl0YODdu3dNrbKWYBw6dMgE1HPmzDFX7AgODjYZ67///lsmTJhgBhV+9tlnMmXKFJd2nnrqKZORHjp0qLnutOMWGRkpDRs2lJo1a0rLli3NQEbNbG/btk0GDRoku3fv9tlrBwAAgPeliEy0On/+vLlCx8qVK+WXX34xZRRly5Y1V9TQq3BocKyXo3vvvffMpeoeeughadOmjbRv396ZiXZcxSO2S9xdu3bNBM1ffPGF/Pbbb2YAorajAwg9GTRIJhoAAMC/eRqvpZggOjkgiAYAAPBvAVXOAQAAAHgTQTQAAABgE0E0AAAAYBNBNAAAAGATQTQAAABgE0E0AAAAYBNBNAAAAGATQTQAAABgE0E0AAAAYBNBNAAAAGATQTQAAABgE0E0AAAAYBNBNAAAAGATQTQAAABgE0E0AAAAYBNBNAAAAGATQTQAAABgE0E0AAAAYFOI3QWQcJZlmb8RERG+7goAAADccMRpjrgtNgTRXnTt2jXzt2DBgr7uCgAAAOKJ2zJnzhzr40FWfGE2Ek1UVJScO3dOMmXKJEFBQb7ujt8fBerBxtmzZyUsLMzX3QkorHvfYv37Duved1j3vsX6d6WhsQbQ+fLlk1SpYq98JhPtRfpGFChQwNfdSFb0w8wH2jdY977F+vcd1r3vsO59i/X//8WVgXZgYCEAAABgE0E0AAAAYBNBNPxSaGioDBkyxPyFd7HufYv17zuse99h3fsW6z9hGFgIAAAA2EQmGgAAALCJIBoAAACwiSAaAAAAsIkgGgAAALCJIBo+NWrUKPPrjX369Il1nlmzZpl5ot/Spk3r1X6mFEOHDr1nXZYpUybOZRYvXmzm0XVevnx5WbVqldf6G+jrn20/cf3666/Stm1byZ49u6RLl85sz7t3745zmfDwcKlSpYq5akGJEiXMe4KkX/e63mNu+3q7cOGCV/udEhQpUsTtuuzRo0esy7Df9wy/WAif2bVrl0ydOlUqVKgQ77z6C0qHDx923udn0xOuXLlysmHDBuf9kJDYdwPbtm2T5557TkaOHCnNmjWTefPmScuWLWXv3r1y//33e6nHgbv+Fdt+4rh8+bLUrl1b6tevL6tXr5acOXPK0aNHJWvWrLEuc/LkSXnsscekW7duMnfuXPn666+lS5cukjdvXmnSpIlX+x9o695Bt/3ov6CXK1euJO5tyvyujYyMdN7/8ccfpVGjRtKqVSu387Pf9xxBNHzi+vXr0qZNG5k+fbq888478c6vgUOePHm80reUToM2T9fluHHjpGnTptK3b19z/+2335b169fLxIkTZcqUKUnc05TJzvpXbPuJY/To0VKwYEGZOXOmc1rRokXjXEa3cZ1nzJgx5v59990nW7ZskbFjxxJEJ/G6jx40Z8mSJQl7l/LpQUvMM8DFixeXunXrup2f/b7nKOeAT+hpJM3wNGzY0OOgu3DhwmZH3KJFCzl48GCS9zGl0gxQvnz5pFixYuZA5syZM7HOu3379nveIw0edDqSfv0rtv3EsXz5cqlatarJvmlgVrlyZXMQHxe2f9+te4dKlSqZzL9mTrdu3ZrkfU3p7ty5I3PmzJFOnTrFelaL7d5zBNHwugULFpjTQnqqyBOlS5eWGTNmyLJly8yHPyoqSmrVqiW//PJLkvc1palevbqp6VyzZo1MnjzZnK6uU6eOXLt2ze38Wn+YO3dul2l6n7pE76x/tv3Ec+LECbPOS5YsKWvXrpXu3btLr169ZPbs2bEuE9v2HxERITdv3vRCrwN33WvgrFnPL774wtz0ILJevXrmuwMJt3TpUrly5Yp07Ngx1nnY79ugv1gIeMuZM2esXLlyWfv373dOq1u3rtW7d2+P27hz545VvHhx64033kiiXgaOy5cvW2FhYdbHH3/s9vHUqVNb8+bNc5k2adIk8x4i6dd/TGz7Cafbcs2aNV2m9ezZ06pRo0asy5QsWdIaMWKEy7SVK1fqr/xaN27cSLK+pjQJWffuPPTQQ1bbtm0TuXeBpXHjxlazZs3inIf9vufIRMOr9uzZI5cuXTKj3bU2VG8bN26U8ePHm/9HH/wQm9SpU5vTgceOHfNKn1MyrTUsVapUrOtSa3EvXrzoMk3vU6PrnfUfE9t+wmlms2zZsi7TtMY5rnKa2LZ/HeimV5hA0q17d6pVq8a2/y+cPn3aDGrWwbFxYb/vOYJoeFWDBg3kwIEDsm/fPudNa+W0NlT/HxwcHG8bGmhrG7pjxr+j9bbHjx+PdV3WrFnTXJEgOh1gotOR9Os/Jrb9hNOrQ0S/yok6cuSIqTePDdu/79a9O/odwbafcDqwU2vSdTxSXNjubbCRtQaSRMxyjnbt2lkDBgxw3h82bJi1du1a6/jx49aePXus1q1bW2nTprUOHjzoox4nX6+++qoVHh5unTx50tq6davVsGFDK0eOHNalS5fcrnudJyQkxHr//fetQ4cOWUOGDDGn+g4cOODDVxE4659tP/Hs3LnTbMvDhw+3jh49as2dO9dKnz69NWfOHOc8uu71PXA4ceKEmadv375m+9dT2sHBwdaaNWt89CoCZ92PHTvWWrp0qZlf9zf6HZEqVSprw4YNPnoVyVtkZKRVqFAhq3///vc8xn4/4Qii4XdBtN7v0KGD836fPn3Mhz9NmjRW7ty5rUcffdTau3evj3qbvD377LNW3rx5zbrMnz+/uX/s2LFY171atGiRVapUKbNMuXLlTE0ovLP+2fYT14oVK6z777/fCg0NtcqUKWNNmzbN5XFd9/oeRPftt99alSpVMu9BsWLFrJkzZ3q514G57kePHm3q//WgMVu2bFa9evWsb775xgc9Txn0YFzzpocPH77nMfb7CRek/9jJXAMAAACBjppoAAAAwCaCaAAAAMAmgmgAAADAJoJoAAAAwCaCaAAAAMAmgmgAAADAJoJoAAAAwCaCaADwIx07dpSWLVv6uhvJwh9//GF+xvjUqVOJ0t6aNWukUqVKEhUVlSjtwTc2bdokjz/+uOTLl0+CgoJk6dKltttYu3at1KhRQzJlyiQ5c+aUp5566l9tZ0uWLJHGjRtL9uzZTZ/0J8zjU69ePTNvzFv0n+0eOnSolClTRjJkyCBZs2aVhg0byo4dO5yPa587d+4sRYsWlXTp0knx4sVlyJAhcufOHZfn0p8Mef/996VUqVISGhoq+fPnl+HDh7v0v1GjRmZdhIWFmZ8AX7t27b9a7926dTPzffjhhy7T9+7da54rS5YsZn29+OKLcv369XjXl92+aJsvv/yyFChQwKybsmXLypQpU2w9D0E0AHiJuy/E6Df9Qhw3bpzMmjXLp/1MLoG8fsm3aNFCihQpkijtNW3aVFKnTi1z585NlPbgG3/99ZdUrFhRJk2alKDlT548abarhx9+2AS7Giz+/vvv8uSTT8a6jH529XMTV58efPBBGT16tMf90MD1/PnzztuPP/4owcHB0qpVK+c8GvROnDhRDhw4IFu2bDGfBQ3Wf/vtN/P4zz//bA4Kp06dKgcPHpSxY8eaQPH11193ea7evXvLxx9/bAJpXWb58uVSrVo1l6BUA9tVq1bJnj17pH79+iZI/f777xO03r/88kv57rvvTJAb3blz58yBQIkSJczBgB7Yar/jWrfueNKX//u//zPtz5kzRw4dOiR9+vQxQbW+do/9i187BADYcP78eeftww8/tMLCwlymXbt2zfIH+hPALVq0sPzZX3/9Zdbf9u3bE7XdiRMnWlWrVk3UNuE7GuZ8+eWXLtNu3bplvfrqq1a+fPms9OnTW9WqVTM/7+6wePFiKyQkxIqMjHROW758uRUUFGTduXPH7fMMGTLknp/OdufkyZOmT99//73t1zJ27FgrU6ZM1vXr12Od5+rVq6b9DRs2xDrPu+++axUtWtR5/6effjKv9+eff7bVn7Jly1rDhg3zeL07/PLLL1b+/PmtH3/80SpcuLB5XQ5Tp061cuXK5bLuf/jhB9Pe0aNHndMOHDhgNW3a1MqQIYOZv23bttZvv/1mqy/6c+ZvvfWWy7QqVapYgwYNsjxFJhoAvCRPnjzOW+bMmU32Ofq0jBkz3pMF1lO6PXv2NFkSPV2bO3dumT59usm0vPDCC+Z0s2ZtVq9e7fJcmrV65JFHTJu6TLt27Uw2zeHzzz+X8uXLm9OYespUsz/apmbUZs+eLcuWLXNmyMPDw80y/fv3N5mv9OnTS7FixeTNN9+Uv//+29mmLqvlEDNmzJBChQqZ537ppZckMjJS3n33XfMatfwi+mlipc8xefJk01/tj7at/YuLZsT0tLOecnfQfmpbX3/9tVStWtX0s1atWnL48GHnPPv37zdZNF1velr6gQcekN27dzsf1+ya3j9+/LjNdxfJhWYbt2/fLgsWLJAffvjBZHb1LMTRo0fN47pNpEqVSmbOnGm23atXr8pnn31mPiN6psJXPvnkE2ndurUp3XBHSzSmTZtm9i2ahY2Nvp5s2bI5769YscJ85r766itT9qHZ7C5dusiff/4Zaxua3b527ZpLO57Q5XRf1LdvXylXrtw9j9++fVvSpElj1r+D7hOUZtrVlStXzFmCypUrm8+qZpMvXrwozzzzjK2+6L5Bs86//vqrKWf59ttv5ciRIyaT7zGPw20AQKKZOXOmlTlz5nizwHXr1jXZp7fffts6cuSI+RscHGw98sgj1rRp08y07t27W9mzZzfZWXX58mUrZ86c1sCBA61Dhw5Ze/futRo1amTVr1/fPH7u3DmTefrggw9MZkwzPZMmTTKZcL0988wzJsvjyJDfvn3bLKfPvXXrVrOMZuZy585tjR492iUblzFjRuvpp5+2Dh48aOZJkyaN1aRJE6tnz54m0zVjxgyTGfruu++cy+l97f/06dOtw4cPW2+88YZ5jZohi02vXr1MH6PTbKK2Vb16dSs8PNz0oU6dOlatWrVcsk+atdL1outu0aJF1r59+1za0del7w+Sv5hZyNOnT5tt69dff3WZr0GDBubz4qDbj2Y4dV5to2bNmuZzFZukzkTv2LHDLKd/Y1qxYoXJyGqmXLPrO3fujLUdzebqGRzddzj897//tUJDQ83nZtOmTeZzVKlSJef+wh393GfNmtW6ePGirezviBEjzL4oKirK3I+ZidbstO6bNFuu+50///zTeuqpp0x7uqxjP9S4cWOXds+ePWvm0f2Hp33RMxLt27c3j+tz6r5q9uzZsb5mt6/T1twAAK8H0Q8++KDz/t27d80XZrt27ZzTNNDVLwJHaUN8XzJ79uwx/z916tS/Kud47733rAceeMAlkNDT4xEREc5pGkAXKVLE5fRs6dKlrZEjRzrva1+6devm0rZ+oevBQWy0f506dXIbREc/lb1y5Uoz7ebNm+a+HpDMmjUrztdVuXJla+jQofG8eiQHMQOor776ykzTz1D0mwZRevDo+DyVLFnS6tu3rzkA3bhxo/kcaqDtCP402Iy+fOrUqU0b0afNmTMn0YLoF1980Spfvrzbx7S8Q4Nj/fzrZ0I/b+6CWy2jKF68uNW5c2eX6V27dr0nAHXsI9yVeMydO9d8ztevXx9rf90Frrt37zYHqNEPYGIG0Y72dT49gNHA9rXXXjP3R40aZR7Xg3Rd3zHfQ33OVatWedQXx/6rVKlS5mB///791oQJE0wSIK7XFVOIrdw3AMDrKlSo4Py/DizS8gstxXDQcg116dIlZ8mCnprUcoqYtExBT1c2aNDAtNGkSRNz/+mnnzblInFZuHChjB8/3rShI9vv3r1rSiKi01PBWioRvW/a5+inZ3Wao68OOto/5v24rmBw8+ZNSZs2bbzrK2/evOavPp+WmOhgIj1V7Tg9r6fy9YoF0enp4xs3bsS5LpA86Xar26MOjtO/0Tk+LzoYTUsitATJQQefFSxY0Ax20xIiLReKvn3q50LLAqIPHHR8Lv8tLbPS0pO33nrL7eNa3qElXXrTvpUsWdKUfgwcONBlwJ6WMWkJg5Z8RKefkZCQEFOq5XDfffeZv2fOnJHSpUs7p2s/9POzePFi8/mxY/Pmzc7PoYOWy7z66qvmCh2Oq588//zz5qYlGvratETrgw8+MCUnjvdQy67cDdJ0fN7jo/sPHVypAxwdVzvR/Ya+pzq40tPXRhANAH4uZh2mfqlEn6b3lePSbPF9yWjwsH79etm2bZusW7dOJkyYIIMGDTIBgtZEuqM1pG3atJFhw4aZwFuDDP1CHTNmjK2+Oqb928vI5ciRQy5fvuz2sbjWjdZt6xf0ypUrTR25Xu5LX8cTTzzhXEZrQfVSXkh5tI5WAzcN5urUqeN2Hj2Ain7QpxwBt2M70gMtDVodtDY4IiLCZVpi0YBVa4Xbtm3r0fzaR53fQYN7DaC11lvrvGO+ttq1a5sDYj04dhxQam2wKly4sHO++fPnS6dOncznJfpl9jyltdAxg1Pdl+h0Hd8Rk+MgRMdY6AGzXh1EValSRb744gtzwK7Bf0LoWA69uXuf7eybCKIBIIXx5EtGg0v98tTb4MGDzZelZmU0U6sDezTQiE4Dbp1Hg22H06dPJ1qf9XJX7du3d7mvAU9s9DHNDiaEZtz09sorr8hzzz1nAgtHEH3r1i0TTMT13PBvehB57Ngxl0vWaYZRA1193/VgULc1PQDU91kvB6eDUTUTqcGh3vRScJr51e1DB9Bp1lK3/4RuF3pgplldzQgrx2BXx6BipX3S6zOPHDnSZVnNKutgYz0DFTNDrYN0mzdvbg6OdeCwZtE1aHZcBk//r4OTte+aYXVc+s7x3EoDW91naICsGWENInv06GGCVkd2et68edKhQwdzCc7q1avLhQsXnAcTekAd33rX7LP2P+Zr0ANe7Uf0bLdesk8z5npmQA/2dRDiqFGjzHWjlfZNB1fre9OvXz/Tvj6vBvd6mT4NhOPri55Bq1u3rmlbX4Oun40bN8qnn35qst4e87jwAwDgk5ro3r17u8zjro4wet2f1hzqwEKtHdRBRseOHbPWrFljdezY0dRU66C+4cOHW7t27TIDrXRwndYeOuoJ9bFChQqZeki9bJRe1mvZsmWm5nP+/PmmvXHjxlnZsmVzeQ1aE12xYsU4X4+716R9z5Ejh/XJJ5+YuszBgwdbqVKlMgMDY6ODIbU/OvAoZk109AFgWnuq07QW9caNG1aPHj3MfFoPvmXLFlMj2q9fP5c2tC7SMUgTyY9jO4h5cwz80+1ZtzGtHdba2rx581pPPPGE2aYcdDvX2nittdXPUvPmzc1g1IQOLNTPu7s+6XLRPxcx29DPoM63bt26e9rUOn/ttw4m1M+vvg7tZ/SBhbE9b8zwT/cZTz75pNn2tf5Y9xV//PGHS9/iWqeerHd33O3LdLyH7lv0NVWoUMH69NNP71lOBwXra8+SJYuVLl06q0yZMlafPn2cNeue9EVr3/V16vpLmzatGasxZswYZxueIIgGgBQWRMf3JaNXvdABfxoc6Kh8HVyjg2ocLl26ZEbQ6xeqtuu4hq4OtNKraOj0Z5991vQhsYJovTqIPqf2R4ObhQsXxrsO9fq+U6ZM8TiI1tH+rVu3tgoWLGi+oPXL8+WXX3YOOnQM4NKrFQBAfIL0H8/z1gAAJC4tLdFSEru/kqh1zXo6Vq+JHbO2MSH0dLieVtZrz8ZWGw4ADtREAwCSJa1d1R/I0LpPvXLCv6VXB/joo48IoAF4hEw0ACBZZqIBwJfIRAMAfIpcDoDk6N8XkQEAAAABhiAaAAAAsIkgGgAAALCJIBoAAACwiSAaAAAAsIkgGgAAALCJIBoAAACwiSAaAAAAsIkgGgAAABB7/h97tulXmYFFrgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Take the first 0.3 seconds of gaze data\n", "gaze_begin = gaze.crop(0, 0.3, by=\"time\")\n", "# And the corresponding eye states and IMU data\n", "eye_states_begin = eye_states.restrict(gaze_begin)\n", "imu_begin = imu.restrict(gaze_begin)\n", "\n", "\n", "# Define a function to plot the timestamps of the gaze, eye states, and IMU data\n", "def plot_timestamps(gaze, eye_states, imu, concat_stream=None):\n", " _, ax = plt.subplots(figsize=(8, 2))\n", " ax.scatter(gaze.ts, np.ones_like(gaze.ts), s=5)\n", " ax.scatter(eye_states.ts, np.ones_like(eye_states.ts) * 2, s=5)\n", " ax.scatter(imu.ts, np.ones_like(imu.ts) * 3, s=5)\n", " # If a concatenated stream (explained later) is provided, plot its timestamps as well\n", " if concat_stream is not None:\n", " ax.scatter(concat_stream.ts, np.ones_like(concat_stream.ts) * 4, s=5)\n", " ax.set_yticks([1, 2, 3, 4])\n", " ax.set_yticklabels([\"Gaze\", \"Eye states\", \"IMU\", \"Concatenated\"])\n", " ax.set_ylim(0.5, 4.5)\n", " else:\n", " ax.set_yticks([1, 2, 3])\n", " ax.set_yticklabels([\"Gaze\", \"Eye states\", \"IMU\"])\n", " ax.set_ylim(0.5, 3.5)\n", " ax.set_xlabel(\"Timestamp (ns)\")\n", " plt.show()\n", "\n", "\n", "plot_timestamps(gaze_begin, eye_states_begin, imu_begin)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As shown in the figure above, in addition to the apparently later onset of IMU data, the first 0.3 second of gaze and eye states data indeed suffers from dropouts.\n", "\n", "In addition to dropouts, irregular sampling may also occur, especially for IMU data from our experience. For example, it can be observed in the middle of this recording (5 - 5.3 seconds):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gaze_middle = gaze.crop(5, 5.3, by=\"time\")\n", "eye_states_middle = eye_states.restrict(gaze_middle)\n", "imu_middle = imu.restrict(gaze_middle)\n", "\n", "plot_timestamps(gaze_middle, eye_states_middle, imu_middle)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How frequent are these irregularities? We now examine the distribution of the time differences between consecutive samples, and compare them to the expected time difference for a regular, nominal (as specified by Pupil Labs) sampling rate." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nominal sampling frequency of gaze: 200 Hz. Actual: 199.4 Hz\n", "Nominal sampling frequency of eye states: 200 Hz. Actual: 199.4 Hz\n", "Nominal sampling frequency of IMU: 110 Hz. Actual: 113.9 Hz\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\n", " f\"Nominal sampling frequency of gaze: {gaze.sampling_freq_nominal} Hz. \"\n", " f\"Actual: {gaze.sampling_freq_effective:.1f} Hz\"\n", ")\n", "print(\n", " f\"Nominal sampling frequency of eye states: {eye_states.sampling_freq_nominal} Hz. \"\n", " f\"Actual: {eye_states.sampling_freq_effective:.1f} Hz\"\n", ")\n", "print(\n", " f\"Nominal sampling frequency of IMU: {imu.sampling_freq_nominal} Hz. \"\n", " f\"Actual: {imu.sampling_freq_effective:.1f} Hz\"\n", ")\n", "\n", "fig, axs = plt.subplots(3, 1, figsize=(8, 5), tight_layout=True)\n", "\n", "axs[0].hist(gaze.ts_diff, bins=50)\n", "axs[0].axvline(1e9 / gaze.sampling_freq_nominal, c=\"red\", label=\"Nominal\")\n", "axs[0].set_title(\"Gaze\")\n", "\n", "axs[1].hist(eye_states.ts_diff, bins=50)\n", "axs[1].axvline(1e9 / eye_states.sampling_freq_nominal, c=\"red\", label=\"Nominal\")\n", "axs[1].set_title(\"Eye states\")\n", "\n", "axs[2].hist(imu.ts_diff, bins=50)\n", "axs[2].axvline(1e9 / imu.sampling_freq_nominal, c=\"red\", label=\"Nominal\")\n", "axs[2].set_title(\"IMU\")\n", "axs[2].set_xlabel(\"Time difference [ns]\")\n", "\n", "for i in range(3):\n", " axs[i].set_yscale(\"log\")\n", " axs[i].set_ylabel(\"Counts\")\n", " axs[i].legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For gaze and eye states data, the empirical distribution of time differences is close to the expected value. However, some integer multiples of the nominal sampling rate suggest possible eye video frame drops. For IMU data, the distribution is much wider." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolating data streams\n", "\n", "Given the presence of irregular sampling, if you want to perform analyses that assume continuous data streams, interpolation is necessary. PyNeon uses the `scipy.interpolate.interp1d` [(API reference)](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) function to interpolate data streams. The `interpolate()` method of `Stream` creates a new object with interpolated data.\n", "\n", "With the default parameters, the interpolated data will have the same start timestamp as the original data, and the sampling rate is set to the nominal sampling frequency specified by Pupil Labs (200 Hz for gaze and eye states, 110 Hz for IMU)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nominal sampling frequency of gaze: 200 Hz. Actual (after interpolation): 200.0 Hz\n", "Only one unique time difference: [5000000]\n", "The new gaze stream is uniformly sampled: True\n", "gaze x [px] float64\n", "gaze y [px] float64\n", "worn float64\n", "fixation id float64\n", "blink id float64\n", "azimuth [deg] float64\n", "elevation [deg] float64\n", "dtype: object\n" ] } ], "source": [ "# Interpolate to the nominal sampling frequency\n", "gaze_interp = gaze.interpolate()\n", "\n", "# Three ways you can check if the interpolation was successful:\n", "# 1. Compare the effective sampling frequency to the nominal sampling frequency\n", "print(\n", " f\"Nominal sampling frequency of gaze: {gaze_interp.sampling_freq_nominal} Hz. \"\n", " f\"Actual (after interpolation): {gaze_interp.sampling_freq_effective:.1f} Hz\"\n", ")\n", "# 2. Check the number of unique time differences\n", "print(f\"Only one unique time difference: {np.unique(gaze_interp.ts_diff)}\")\n", "# 3. Call the `is_uniformly_sampled` property (boolean)\n", "print(f\"The new gaze stream is uniformly sampled: {gaze_interp.is_uniformly_sampled}\")\n", "print(gaze_interp.data.dtypes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that after interpolation, the data types of the columns are preserved.\n", "\n", "Alternatively, one can also interpolate 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 interpolate the gaze data (~200Hz) to the timestamps of the IMU data (~110Hz)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original gaze data length: 6091\n", "Original IMU data length: 3459\n", "Gaze data length after interpolating to IMU timestamps: 3459\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\GitHub\\PyNeon\\pyneon\\preprocess\\preprocess.py:66: UserWarning: new_ts contains timestamps after the data end time; These samples will be NaN.\n", " warn(\n" ] } ], "source": [ "print(f\"Original gaze data length: {len(gaze)}\")\n", "print(f\"Original IMU data length: {len(imu)}\")\n", "gaze_interp_to_imu = gaze.interpolate(new_ts=imu.ts)\n", "print(\n", " f\"Gaze data length after interpolating to IMU timestamps: {len(gaze_interp_to_imu)}\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, float-type data is interpolated using the `'linear'` method. We are examine its behavior by comparing interpolated data with the raw data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gaze_interp_begin = gaze_interp.restrict(gaze_begin)\n", "\n", "plt.figure(figsize=(8, 2))\n", "plt.plot(gaze_begin[\"gaze x [px]\"], marker=\"o\", label=\"Raw\")\n", "plt.plot(\n", " gaze_interp_begin[\"gaze x [px]\"],\n", " marker=\"^\",\n", " label=\"Interpolated\",\n", ")\n", "plt.xlabel(\"Timestamp [ns]\")\n", "plt.ylabel(\"Gaze x [px]\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Concatenating different streams\n", "\n", "There might be cases where you want to concatenate different streams into a single one to facilitate further analysis (e.g., [epoching](pupil_size_and_epoching.ipynb)). This is possible by interpolating the streams' data to common timestamps and concatenating them into a single DataFrame.\n", "\n", "The method `concat_streams()` of `Recording` provides such functionality. It takes a list of stream names and interpolate them to common timestamps, defined by the latest start and earliest end timestamps of the streams. The new sampling frequency can either be directly specified or taken from the lowest/highest sampling frequency of the streams.\n", "\n", "In the following example, we concatenate the gaze, eye states, and IMU streams into a single DataFrame using the default parameters." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Concatenating streams:\n", "\tgaze\n", "\teye_states\n", "\timu\n", "Using lowest sampling rate: 110 Hz (['imu'])\n", "Using latest start timestamp: 1732621490607650343 (['imu'])\n", "Using earliest last timestamp: 1732621520979070343 (['gaze' 'eye_states'])\n", " gaze x [px] gaze y [px] worn fixation id blink id \\\n", "timestamp [ns] \n", "1732621490607650343 705.518840 554.991000 1 1 \n", "1732621490616741252 704.882473 553.793142 1 1 \n", "1732621490625832161 707.703762 556.712119 1 1 \n", "1732621490634923070 711.389933 553.846792 1 1 \n", "1732621490644013979 709.281792 555.543786 1 1 \n", "\n", " azimuth [deg] elevation [deg] pupil diameter left [mm] \\\n", "timestamp [ns] \n", "1732621490607650343 -7.085339 3.473196 3.346414 \n", "1732621490616741252 -7.126717 3.550039 3.363306 \n", "1732621490625832161 -6.944035 3.363050 3.368352 \n", "1732621490634923070 -6.707335 3.547818 3.365432 \n", "1732621490644013979 -6.842681 3.438366 3.374732 \n", "\n", " pupil diameter right [mm] eyeball center left x [mm] \\\n", "timestamp [ns] \n", "1732621490607650343 3.360563 -32.282935 \n", "1732621490616741252 3.359459 -32.249418 \n", "1732621490625832161 3.350918 -32.216782 \n", "1732621490634923070 3.361014 -32.249156 \n", "1732621490644013979 3.365781 -32.234158 \n", "\n", " ... acceleration x [g] acceleration y [g] \\\n", "timestamp [ns] ... \n", "1732621490607650343 ... -0.067383 -0.340820 \n", "1732621490616741252 ... -0.062619 -0.315917 \n", "1732621490625832161 ... -0.052682 -0.329993 \n", "1732621490634923070 ... -0.058795 -0.334090 \n", "1732621490644013979 ... -0.060815 -0.322199 \n", "\n", " acceleration z [g] roll [deg] pitch [deg] yaw [deg] \\\n", "timestamp [ns] \n", "1732621490607650343 0.932129 1.923968 -20.230545 132.920122 \n", "1732621490616741252 0.925714 1.923949 -20.227639 132.924402 \n", "1732621490625832161 0.924432 1.920479 -20.228228 132.927574 \n", "1732621490634923070 0.931812 1.916587 -20.228839 132.931756 \n", "1732621490644013979 0.925476 1.916104 -20.227092 132.937429 \n", "\n", " quaternion w quaternion x quaternion y quaternion z \n", "timestamp [ns] \n", "1732621490607650343 0.395828 -0.085287 -0.154390 0.901227 \n", "1732621490616741252 0.395796 -0.085271 -0.154370 0.901246 \n", "1732621490625832161 0.395766 -0.085242 -0.154389 0.901258 \n", "1732621490634923070 0.395728 -0.085207 -0.154411 0.901275 \n", "1732621490644013979 0.395683 -0.085190 -0.154403 0.901297 \n", "\n", "[5 rows x 34 columns]\n", "Index(['gaze x [px]', 'gaze y [px]', 'worn', 'fixation id', 'blink id',\n", " 'azimuth [deg]', 'elevation [deg]', 'pupil diameter left [mm]',\n", " 'pupil diameter right [mm]', 'eyeball center left x [mm]',\n", " 'eyeball center left y [mm]', 'eyeball center left z [mm]',\n", " 'eyeball center right x [mm]', 'eyeball center right y [mm]',\n", " 'eyeball center right z [mm]', 'optical axis left x',\n", " 'optical axis left y', 'optical axis left z', 'optical axis right x',\n", " 'optical axis right y', 'optical axis right z', 'gyro x [deg/s]',\n", " 'gyro y [deg/s]', 'gyro z [deg/s]', 'acceleration x [g]',\n", " 'acceleration y [g]', 'acceleration z [g]', 'roll [deg]', 'pitch [deg]',\n", " 'yaw [deg]', 'quaternion w', 'quaternion x', 'quaternion y',\n", " 'quaternion z'],\n", " dtype='object')\n" ] } ], "source": [ "concat_stream = rec.concat_streams([\"gaze\", \"eye_states\", \"imu\"])\n", "print(concat_stream.data.head())\n", "print(concat_stream.data.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We show an exemplary sampling of eye, imu and concatenated data below. It can be seen that the concatenated data is regularly sampled at the nominal sampling rate of IMU." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "concat_stream_middle = concat_stream.restrict(gaze_middle)\n", "plot_timestamps(gaze_middle, eye_states_middle, imu_middle, concat_stream_middle)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Despite different sampling from the original streams, the concatenated data still respects the original values of the data. For example, the `gaze x [px]` and `acceleration x [g]` data from the raw and concatenated stream are quite comparable." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(2, 1, figsize=(8, 4), tight_layout=True)\n", "axs[0].plot(\n", " gaze_middle[\"gaze x [px]\"],\n", " marker=\"o\",\n", " label=\"Raw gaze\",\n", ")\n", "axs[0].plot(\n", " concat_stream_middle[\"gaze x [px]\"],\n", " marker=\"^\",\n", " label=\"Concatenated\",\n", ")\n", "axs[0].set_ylabel(\"gaze x [px]\")\n", "axs[0].legend()\n", "axs[1].plot(\n", " imu_middle[\"acceleration x [g]\"],\n", " marker=\"o\",\n", " label=\"Raw IMU\",\n", ")\n", "axs[1].plot(\n", " concat_stream_middle[\"acceleration x [g]\"],\n", " marker=\"^\",\n", " label=\"Concatenated\",\n", ")\n", "axs[1].set_ylabel(\"acceleration x [g]\")\n", "axs[1].set_xlabel(\"timestamp [ns]\")\n", "axs[1].legend()\n", "plt.show()" ] } ], "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": "3.13.1" } }, "nbformat": 4, "nbformat_minor": 2 }