{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preprocessing: Cropping, Interpolation, and Concatenation\n", "\n", "Data streams from Neon devices (gaze, eye states, IMU) present three common challenges:\n", "1. **Irregular sampling**: Device limitations cause non-uniform time intervals between samples\n", "2. **Different sampling rates**: Each stream type has its own nominal frequency (gaze: 200 Hz, eye states: 200 Hz, IMU: 110 Hz)\n", "3. **Misaligned timestamps**: Streams may start and end at different times\n", "\n", "This tutorial shows how to address these challenges using PyNeon's preprocessing tools: `crop()` and `restrict()` for temporal selection, `interpolate()` for resampling, and `concat_streams()` for combining multiple streams. We use the same dataset as in the [previous tutorial](read_recording_cloud.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 Dataset, get_sample_data\n", "\n", "dataset = Dataset(get_sample_data(\"simple\", format=\"native\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the gaze, eye states, and IMU streams from the recording. Each `Stream` object contains continuous time-series data indexed by nanosecond-precision timestamps." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "rec = dataset.recordings[0]\n", "gaze = rec.gaze\n", "eye_states = rec.eye_states\n", "imu = rec.imu\n", "\n", "\n", "# Define a function to visualize the timestamp distribution across streams\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()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selecting Time Windows with `crop()` and `restrict()`\n", "\n", "Analyzing specific time windows requires selecting subsets of your data. PyNeon provides two complementary methods:\n", "- **`crop()`**: Extract data within a specified temporal range\n", "- **`restrict()`**: Align one stream to match another's temporal boundaries\n", "\n", "### Using `crop()` to Extract Time Ranges\n", "\n", "The `crop()` method accepts `tmin` and `tmax` bounds (both inclusive). The `by` parameter determines how these bounds are interpreted:\n", "- `by=\"timestamp\"`: Absolute Unix timestamps in nanoseconds\n", "- `by=\"time\"`: Relative time in seconds from the stream's first sample\n", "- `by=\"sample\"`: Zero-based sample indices\n", "\n", "Let's crop the gaze stream to the start of the recording:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gaze stream length before cropping: 1048\n", "Gaze stream length after cropping (0.3 seconds): 60\n", "Gaze stream length after cropping (200 samples): 201\n" ] } ], "source": [ "# Crop to the first 0.3 seconds of gaze data using relative time\n", "gaze_300ms = gaze.crop(tmin=0, tmax=0.3, by=\"time\")\n", "# Alternatively, crop to the first 200 data points\n", "gaze_200samples = gaze.crop(tmin=0, tmax=200, by=\"sample\")\n", "\n", "print(f\"Gaze stream length before cropping: {len(gaze)}\")\n", "print(f\"Gaze stream length after cropping (0.3 seconds): {len(gaze_300ms)}\")\n", "print(f\"Gaze stream length after cropping (200 samples): {len(gaze_200samples)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using `restrict()` to Align Streams\n", "\n", "The `restrict()` method aligns one stream to match another's temporal boundaries. It's a convenience wrapper equivalent to `crop(tmin=other.first_ts, tmax=other.last_ts, by=\"timestamp\")`.\n", "\n", "This is particularly useful when streams have different start/end times. In our recording, the raw IMU stream starts earlier than gaze and eye states:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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VgD1HIFyOi5fR3+2XDQf/lhvL5Je+jctLZOZMfs8PxDqCYRuUIbzqOlS2QRmo61Bs1+Fy7ARqHYHmb7yW4Xui//rrL1myZInpgfYWQCsNoFWmTJnkvffek7Jly5pA+pFHHpGnn35axowZY+bPmTNHLl686H5d3759TXCuqSHq3nvvlRw5csiiRYtM5Y4fP16aNGkie/bsMUF5Yhpo6yPhm4LgowfniGV7RM8m1+z700zr37Si3/MDsY5g2AZlCK+6DpVtUAbqOhTbdbgcO4FaR3rJ8DnR+/btMz3NlStX9phesGBB08Osj2eeecZMGzBggDRu3Nj0Kt96663y8ssvy6xZs9yv0UC4aNGi5jF9+nT59ttv5csvvzSB8+rVq2X9+vUye/Zs0wNdsWJFeeutt0wKyWeffea1bMOHDzfBtutRqlSpVK4NpISe3bouxzj/e24zPxDrCIZtUIbwqutQ2QZloK5DsV2nxTaCoQyBWkd6yfBBtC8a8G7ZskWuvfZad2/wsmXLTM+x5kjnyZNHunTpYnqyEw881J7mgQMHysyZM6VSpUpm2tatW+XMmTNSoEABd3CuD0312L9/v9cyDBo0yFwKcD0OHz6cBnsOW3p56N9rFWL+6nOb+YFYRzBsgzKEV12HyjYoA3Udiu06LbYRDGUI1DrSS4ZP59C7cWi6xu7duz2mlytXzvzVXmR18OBBueOOO6RPnz7yyiuvmF5n7V3u2bOnSeHImTOnWW7nzp3SoUMHee2116R58+bu9WkAXaxYMVm+fHmSMmhvtDc6uFEfCG6aX6US5lvZzA/EOoJhG5QhvOo6VLZBGajrUGzXabGNYChDoNaRXkJiYGGLFi1M7rIG0onzonWw4A033CANGjSQjh07yvnz501utNJ0jueff15OnDhhAuE///xTateuLQ0bNpRJkyZ5rGfp0qVy2223mfQRTQdJCQYWAgAABDd/47WQSOfQgYGXL182ucqagrFr1y4TUE+dOtXcsSNz5symx/rSpUsyatQoM6jwk08+kXHjxnms5+677zY90sOGDTP3nXY94uLipGnTplK3bl1p166dGcioPdtr166V5557TjZu3Jhu+w4AAIC0FxI90SomJsbcoWPBggVy5MgRk0ZxzTXXmDtq6F04NDjW29G9+eab5lZ1t9xyi3Tq1Em6du3q7ol23cXD1y3uYmNjTdD8+eefyx9//GEGIOp6dAChP4MG6YkGAAAIbv7GayETRGcEBNEAAADBLazSOQAAAIC0RBANAAAAWCKIBgAAACwRRAMAAACWCKIBAAAASwTRAAAAgCWCaAAAAMASQTQAAABgiSAaAAAAsEQQDQAAAFgiiAYAAAAsEUQDAAAAlgiiAQAAAEsE0QAAAABBNAAAAJC66IkGAAAALBFEAwAAAJYIogEAAABLkbYvQMo5jmP+nj59mmoEAAAIQq44zRW3+UIQnYZiY2PN31KlSqXlZgEAAJCCuC06Otrn/AjnSmE2AiY+Pl6OHj0qefLkkYiIiLA4k9MThsOHD0tUVFR6FydDoy6py2BDm6QugxHtkroMBA2NNYAuXry4ZMrkO/OZnug0pG9EyZIlJdxoAE0QTV0GG9ol9RhsaJPUZTAK13YZnUwPtAsDCwEAAABLBNEAAACAJYJopJps2bLJ0KFDzV9Ql8GCdkk9BhvaJHUZjGiXV8bAQgAAAMASPdEAAACAJYJoAAAAwBJBNAAAAGCJIBoAAACwRBCNFBk+fLjceOON5tcXCxcuLO3atZPdu3cn+5opU6aYX2pM+MiePXvYvwNjx46V66+/3n1D+7p168qiRYuSrZfZs2dLlSpVTP1VrVpVFi5cGPb1mJK6pE3657XXXjPH64ABA2iXaVCXtEvvhg0bluQ7RD8Hk8NnZWDqkjbpHUE0UmTFihXSt29f+f7772Xp0qVy6dIlad68uZw9ezbZ12lgExMT434cOnQo7N8B/RVL/WLdtGmTbNy4UW699VZp27at7Nixw2vdrF27Vjp27Cg9e/aUH3/80ZzA6OOnn36iLi3rkjZ5ZRs2bJDx48ebk5Pk0C4DV5e0S9+uvfZaj++Q1atX0ybToC5pkz44QAAcP37c0ea0YsUKn8tMnjzZiY6Opr79kC9fPmfixIle57Vv395p1aqVx7TatWs7Dz/8MHVrWZe0yeTFxsY6FStWdJYuXeo0bNjQ6d+/v89laZeBq0vapXdDhw51qlWrdoWapk2mRl3SJr2jJxoBcerUKfM3f/78yS535swZKV26tJQqVeqKPYThKC4uTmbMmGF69DUVwZt169ZJ06ZNPaa1aNHCTIddXSrapG96talVq1ZJ2hvtMnXrknbp2969e6V48eJSrlw56dSpk/z666+0yTSoS9qkdwTR+M/i4+NNfl/9+vXluuuu87lc5cqVZdKkSTJv3jyZOnWqeV29evXkyJEjYf8ubN++XXLnzm1+Iap3797yxRdfyDXXXOO1Xo4dOyZFihTxmKbPdTrEqi5pk77pCcjmzZvN+Ad/0C4DV5e0S+9q165tcnMXL15sxj8cOHBAGjRoILGxsbTJVK5L2qQPPnqoAb/17t3bKV26tHP48GGrWrt48aJTvnx5Z/DgwWFf2xcuXHD27t3rbNy40Rk4cKBTsGBBZ8eOHV7rJUuWLM60adM8po0ePdopXLhw2NejbV3SJr379ddfTXvaunWre9qVUhBol4GrS9qlf06cOOFERUX5TNeiTQauLmmT3kX6Cq4Bf/Tr10/mz58vK1euNAPkbGTJkkWqV68u+/btC/vKzpo1q1SoUMHUQ82aNc0ApJEjR5pBSIkVLVpUfv/9d49p+lynQ6zqkjbpnQ7MPH78uNSoUcMjPUaP8/fff18uXLggmTNnpl2mUl3SLv2TN29eqVSpks/vED4rA1eXtEnvSOdAijiOYwJovVT+7bffStmyZa3XoV8keum9WLFivAuJaKqLfrl6o/m933zzjcc0vUNKcnm/4Sy5ukyMNvmvJk2amGNzy5Yt7ketWrVM3qT+7y3oo10Gri5pl/7R8Qz79+/3+R1CmwxcXdImffDRQw0kq0+fPuZOG8uXL3diYmLcj3PnzrmX6dKli7mc7vLCCy84X3/9tbN//35n06ZNTocOHZzs2bP7fak9VGkd6V1NDhw44Gzbts08j4iIcJYsWeK1HtesWeNERkY6b731lrNr1y4zylovW27fvt0Jd7Z1SZv0X+IUBNpl6tUl7dK7J554wnzn6PGtn4NNmzY16Vp6dyjaZOrWJW3SO9I5kCI6EEE1atTIY/rkyZOle/fu5n8d6Zsp0/+/2HHixAnp1auXGYCUL18+c6ld7y3ra9BXuNBLvV27djX36YyOjjb3kP3666+lWbNmXutRB2NOmzZNBg8eLM8++6xUrFhR5s6dm+ygznBhW5e0yZSjXQYO7dI/Oghd75H/119/SaFCheTmm282v1Wg/9MmU7cu+az0LkIjaR/zAAAAAHhBTjQAAABgiSAaAAAAsEQQDQAAAFgiiAYAAAAsEUQDAAAAlgiiAQAAAEsE0QAAAIAlgmgACCL6Y0Xt2rVL72JkCPpDEYULF5aDBw8GZH2LFy+WG264wfxUPILTypUrpXXr1lK8eHGJiIgwPzRlS3+AqU6dOpInTx7z4yJ33333f2pDc+bMkebNm0uBAgVMmfTn3K9Ef6hMl038aNWqlcdyu3btkjZt2pgfj8qVK5fceOON5odQElq3bp3ceuutZn5UVJTccsst8s8//7jn//333+Zn5nVe3rx5pWfPnuZnvl2GDRvmtSy6Pm9mzJhh5rdL5nOqd+/eZpkRI0Z4TNd9ueqqqyR79uzmJ8a7dOkiR48edc8/f/68+QysWrWqREZGpvizcMKECaaOdZ+1HCdPnkyyzJ49e6Rt27ZSsGBBs5z+4Mx3331ntR2CaABII96+qBI+9Mts5MiRMmXKlHR9TzJKIP/KK6+YL8EyZcoEZH0tW7aULFmyyKeffhqQ9SHwzp49K9WqVZPRo0en6PUHDhwwbUaDTg12NaD+888/5a677vL5Gj0uXb/E66tMGoC9/vrrVoG3/rKq6/HTTz9J5syZ5d5773Uvs3//frPeKlWqyPLly2Xbtm3y/PPPmwA0YQCt7VaD+PXr18uGDRukX79+Hr82qAH0jh07ZOnSpTJ//nxzIvLQQw+55z/55JMeZdGH/pJwwrK46MmGLt+gQQOf+/bFF1+YXz/UE53EGjduLLNmzZLdu3fL559/bvbxnnvucc+Pi4uTHDlyyGOPPSZNmzaVlDp37pypF/1VX1/uuOMOuXz5snz77beyadMm0650mv6qst98/Bw4ACDAYmJi3I8RI0Y4UVFRHtNiY2ODos67devmtG3b1glmZ8+eNfW3bt26gK73/fffd2rVqhXQdSJ1aAjzxRdfeEw7f/6888QTTzjFixd3cubM6dx0003Od999554/e/ZsJzIy0omLi3NP+/LLL52IiAjn4sWLXrczdOhQc0xcyYEDB0yZfvzxR+t9effdd508efI4Z86ccU+77777nM6dOyf7utq1azuDBw/2OX/nzp2mTBs2bHBPW7Rokdnf3377zetrtmzZYl6zcuVKj+mXL1926tWr50ycONHnZ8SRI0ecEiVKOD/99JNTunRps1/JmTdvns+6T+5zaO7cuU716tWdbNmyOWXLlnWGDRvmXLp0Kcly+t7rvpw4ccJj+h9//JFkH0+fPm2mLV261PEXPdEAkEaKFi3qfujlWe19Tjgtd+7cSXqB9ZLko48+KgMGDJB8+fJJkSJF5IMPPjC9Xw888IC5JF2hQgVZtGiRx7a0Z+u2224z69TX6GVT7XFz+eyzz8wlU+310cvQ2uuj69Ret48++kjmzZvn7iHXXjD1zDPPSKVKlSRnzpxSrlw50yt26dIl9zr1tZoOMWnSJHPJVrf9yCOPmN6lN954w+yjpl9oD3JCuo2xY8ea8mp5dN1avuQsXLhQsmXLZi7Lu2g5dV3ffPON1KpVy5SzXr16ptfLZevWraY3TOtNL+HWrFlTNm7c6J6vqQL6XHvIkPFoL6z2zmrKgfbcam+q9kju3bvXzNf3W3tpJ0+ebNrlqVOn5JNPPjHtX69CpJcPP/xQOnTo4E6h0JSiBQsWmOOtRYsW5ripXbu2R/rK8ePH5YcffjDztJ3rcd6wYUNZvXq1exmtC03h0OPBRfdV60Bf683EiRPNdhP3Nr/44otmW5oO4k18fLz5nHnqqafk2muvveI+a5qJXvXRstvU/apVq6Rr167Sv39/2blzp4wfP95cvUv8uZIc/cyrXLmyfPzxx+ZzT3ukdT26f9pG/EUQDQBBToNazdvTy7UaUPfp08cEB/rls3nzZnMpV7+89BKm0vw/vVxdvXp1ExBqru/vv/8u7du3N/P1cm3Hjh2lR48eJudSg0+9nK2de3qpVpfTwMN1aVe3ozTw1C8r/eLStBMN5t99912PsmrwqQG9bnP69OkmONA8zyNHjsiKFSvMJe/Bgwcn+QLXgFxzUzXI1cvPGlBo2ZL7IvX1Zffcc8/J22+/bfZd8yp1P1103SVLljSXvfUS7sCBAz2+wDX412BE14+MRXOFNTiePXu2CQDLly9v2rOmROh0VbZsWVmyZIm5zK8nYRpgatvUFIP0ose1nvQ++OCDHgGy5i2/9tpr5ljUMt95553mONXjSP3yyy/uk9devXqZY65GjRrSpEkT90mDpiZoYJiQHhP58+f3mragOcka2CYOlDUw12NZj3lfXn/9dbNuTcVIjp6M68mCBrL6nukJu40XXnjBHLfdunUzJ9zNmjWTl156yQTB/tKT7WXLlsmPP/5oPtc0Readd94xdaidFX7zu88aABAwkydPdqKjo694CbNhw4bOzTff7HFJNVeuXE6XLl3c0zQVRD/OXakNL730ktO8eXOP9R4+fNgss3v3bmfTpk3m/4MHD/6ndI4333zTqVmzpsdlb72ErpdFXVq0aOGUKVPG4/J55cqVneHDh7ufa1l69+6d5DJ1nz59fG5by9ejRw+vl26XLVvmnrZgwQIz7Z9//jHP9ZL5lClTkt0vvUysl4eRsdI55s+fb6bp8ZHwoekb7du3dx8rFStWdJ566iln8+bNzooVK8wx1qRJEyc+Pt4so5f4E74+S5YsZh0Jp02dOjVg6RwPPfSQU7VqVY9pmmqh6+rYsaPH9NatWzsdOnQw/69Zs8YsM2jQII9ldF0DBw40/7/yyitOpUqVkmyzUKFCzpgxY5JMnzZtmtnXY8eOuafp8azH8MKFC31+RmzcuNEpUqSIR4qIr3QOTaXQz6ElS5Y49evXd26//XZ33fvzOVSwYEEne/bsHu+HPte60DQvf9I5dHtt2rRxbrvtNmf16tXmM1E/bzQV5ejRo46/Iq3CfwBAmrv++uvd/+vgI+3B0VQMF+05dfVeKe3N1VHmmk6RmPYUa8+19lbpOvRSsT7XwT1X6oGZOXOmvPfee2Yd2kuml0A1JSIhHeSnPTsJy6ZlTjjQSae5yupSt27dJM+Tu8uB3n0g4QArX/WldwBQuj3tZf6///s/0+PnuoSvPfraY5mQppS4evWRcWib1LamVxj0b0KuY0EHJGoqlaYXuUydOlVKlSplro5oepCmPiRse9rmf/vtN4+Bg65j7r/SVAJNPdFUiYT0ypP26uoAv4Suvvpqd7qGq217W8Z1Bw9NoUp8rOlxq6kUOs9bKocOrku4f3q864BCTXVycd3BJjIy0qRL6ZUb1zHmoukyTzzxhLlDR8K7n+i+6UNTRrSsWvc6EDHxZ0By77P2RnsbDOrrMyExHUyogyxPnDjh/gwbM2aMGXypV/60p9sfBNEAEOQS5wvqpciE0/R5wi82/ZLRLzxvdwvQL14NMPTLYu3ateYy8ahRo0wKhAYRernbG82t1FQI/fLSwFsDEf3y17QJm7K6pv3X28jpl7B+AXqTXN3ope/777/f5Jtq2snQoUPNfuilchcNMPTWZ8hYNH1JAzcN5nzdPUJPjhKe0ClXwO1qI3oSpeMMXDT14fTp0x7TAkVTTy5cuCCdO3f2mJ41a1ZzO7uE+fyu27KVLl3afcKqd8DwtoyOL1AamGp6l55YuNKfNIDUfdUc68R3LtGT7y+//NJjut4dZPv27R7TNCUrNjbWpHVpEKzpZInvpqGfEzpdx2744qpzrQN/acqK7vN/eT9cJ8mJ24I+t/lsIogGgBCjXzJ6+yj9ktWeIm80uKxfv755DBkyxHwx662ptKdWv8A1GElIA25dRoNtl0OHDgWszNoTpYOFEj7XoMgXnac9iCmhPWD6ePzxx01uuObLuoJozQnVnrfkto30oyeI+/bt8wj8tNdYA119T/VET9uRntzpe/jHH3+YgaZ6dUJz8/Whefza86vvvQaCmh+tbTul77medGnPr+t+x66g1jVgWGmZSpQoIcOHD/d4reYZ60BivbqUmA7Qu++++8x9n3UwrObrfvXVV+6BvnoM6zJ6Iqi3Z9NBvdqL+vPPP7sH5mpPr+ZUa870uHHjzEBgHXypYw4S34JOBwTrSbYrAE/Yu3vdddd5TNNccuWaruVPvA96Mqv7rwP4lJ6k61gEzVHXq156nOlYCL0SlLAXWsdcXLx40dSrvj+uqwK6f0o/r7S3XHu99QqaBr569U3zyl9++WWzjOZ768PVVvQkQK+Q6Wu0rej2tAyaV63r0xMnzffW9pT4Xt3J8jvxAwCQLjnR/fv391jGW65hwvxQzUvUnMd77rnHWb9+vbNv3z5n8eLFTvfu3U1O9ffff29yJfW2V4cOHXJmzZrlZM2a1Z3zqPOuuuoq5+effzb5i3r7Kb0VleZKTp8+3axv5MiRTv78+T32QXOiq1Wrluz+eNsnLbvmOX744YcmV3LIkCFOpkyZnB07dvisv23btpny/P3338nmP2p+qk7TfNVz5845ffv2NctpPrjmQpYvX955+umnPdaRO3fuJLmVCA6u9zjxw3ULOm2r2n40h1dzmYsVK+bceeedpr24aBvWvHfNpdXjRHNjd+3a5XObV7rFnR7L3sqkr0vY5hOvQ48vXU5zg33RY6JChQom51ePLb21W2I6vqBkyZJmPELdunWdVatWecz/66+/TG61tmu9LeQDDzyQ5HaaOmZB1/Hss886/vBn3ETpRJ9T+h40btzYfG7oren0PdKxEHpbvMSv81afCennmd5uL0eOHGaf9FaGEyZMcM/Xuve2Dn2vXPTzT8eOaHl0rESdOnU88r79QRANACEWRKs9e/aY4CFv3rzmi6ZKlSrOgAEDzIAavXesDvjTAEK/zHTg0ahRo9yvPX78uNOsWTPzpavrdd1nVwdjFShQwEzXe9hqGQIVRI8ePdps0/XlOnPmzCvWoX5xjhs3zu8g+sKFC2ZQVqlSpcxJg95LuF+/fu5Bh65BXg8//PAVtw0AEVoF/vdbAwAQWHpZWlNJbH8lUfOa9XK2XsZNnNuYEnofbb30rLfG85UbDgAu5EQDADIkzV3U++HqnRN0cNN/pXcQ0BH6BNAA/EFPNAAgQ/ZEA0B6oicaAJCuyCoEkBHxs98AAACAJYJoAAAAwBJBNAAAAGCJIBoAAACwRBANAAAAWCKIBgAAACwRRAMAAACWCKIBAAAASwTRAAAAgNj5f1hjQJxBrxtcAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_timestamps(\n", " gaze_300ms, eye_states.crop(tmax=0.3, by=\"time\"), imu.crop(tmax=0.3, by=\"time\")\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `restrict()` to trim the eye states and IMU data to match gaze's temporal range:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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32ieffGIHDx60unXr2ksvvWS1atVKUz337t1rhQoVsj179ljBggXtZDpyNMGGfrPBFm36y86rWNR6NTvN4rNnS9d8sVBWtNc/FsqK9vrTFpHfrmyjyG9XtlHWaf8jYf5+CKe0xmtR3xP9559/2rRp01wPdHIBtCiAlmzZstkrr7xilSpVcoH07bffbvfff7+99tpr7vWJEyfaoUOHgsv16tXLBedKDZFrrrnG8uTJY1OnTnWNO3z4cGvevLmtW7fOBeVJKdDWI/FGySja4V6esc50hjRv/R9uWu8WVdM1XyyUFe31j4Wyor3+tEXktyvbKPLblW2Uddp/aJi/HzJD1OdEr1+/3vU0V69ePWR68eLFXQ+zHg888ICb1qdPH2vWrJnrVb7ooovsiSeesPHjxweXUSBcqlQp9xgzZox9/fXX9umnn7rAee7cubZw4UKbMGGC64GuWrWqPf/88y6F5MMPP0y2boMGDXLBduBRvnx5yyg6YwtcYvD+9zy988VCWdFe/1goK9rrT1tEfruyjSK/XdlGWaf9F4X5+yEzRH0QnRIFvMuWLbMzzzwz2Bs8Y8YM13OsHOkCBQpY586dXU920oGH6mnu27evjRs3zqpVq+amLV++3Pbv32/FihULBud6KNVjw4YNydbhwQcfdJcCAo8tW7ZYRtElj//638391fP0zhcLZUV7/WOhrGivP20R+e3KNor8dmUbZZ32Py/M3w+ZIerTOXQ3DqVrrF27NmR65cqV3V/1IsumTZvs0ksvtZ49e9qTTz7pep3Vu3zzzTe7FI68efO6+VavXm3XX3+9Pf3009aqVatgeQqgS5cubTNnzjymDuqNTo4GN+qRGZQzJIlziNI7XyyUFe31j4Wyor3+tEXktyvbKPLblW2Uddq/V5i/HzJDlhhY2Lp1a5e7rEA6aV60Bguec8451rhxY+vQoYMdOHDA5UaL0jkeffRR27VrlwuE//jjD6tXr541adLE3n777ZBypk+fbhdffLFLH1E6SHpk5MBCAAAAnLx4LUukc2hg4JEjR1yuslIw1qxZ4wLq0aNHuzt2ZM+e3fVYHz582IYMGeIGFb733ns2bNiwkHKuuuoq1yM9YMAAd9/pwOPo0aPWokULa9CggbVv394NZFTP9vz58+3hhx+2xYsXZ9q6AwAAIONliZ5o2b59u7tDx+TJk23r1q0ujeKMM85wd9TQXTgUHOt2dM8995y7Vd2FF15oHTt2tC5dugR7ogN38UjpFnf79u1zQfNHH31kv//+uxuAqHI0gDAtgwbpiQYAAIhsaY3XskwQHQ0IogEAACJbTKVzAAAAABmJIBoAAADwiSAaAAAA8IkgGgAAAPCJIBoAAADwiSAaAAAA8IkgGgAAAPCJIBoAAADwiSAaAAAA8IkgGgAAAPCJIBoAAADwiSAaAAAA8IkgGgAAAPCJIBoAAAAgiAYAAABOLnqiAQAAAJ8IogEAAACfCKIBAAAAn+L9LoD08zzP/d27dy/NCAAAEIECcVogbksJQXQG2rdvn/tbvnz5jHxbAAAApCNuK1SoUIqvx3nHC7MRNgkJCbZt2zYrUKCAxcXF0bKpnAHqRGPLli1WsGBB2ikD0faZi/an7WMV+z5tH0kUGiuALlOmjGXLlnLmMz3RGUgboly5chn5llFNATRBNG0fi9j3aftYxb5P20eK1HqgAxhYCAAAAPhEEA0AAAD4RBCNiJMrVy7r37+/+wvaPpaw79P2sYp9n7aPRgwsBAAAAHyiJxoAAADwiSAaAAAA8IkgGgAAAPCJIBoAAADwiSAaGer111+3s88+O3hD/QYNGtjUqVNTXWbChAlWo0YNy507t9WsWdOmTJmSYfWN9fYfNWqU+3XNxA9tB5y4p59+2rVnnz59Up2P/T9z2p59P3wGDBhwzHFEx/TUsN9nTtuz3/tDEI0MpV9s1BfYkiVLbPHixXbRRRfZ5ZdfbqtWrUp2/vnz51uHDh3s5ptvtu+//97at2/vHj/88ANbLgPaXxRsb9++PfjYvHkzbX+CFi1aZMOHD3cnNKlh/8+8thf2/fA588wzQ44jc+fOTXFe9vvMa3thv/fBAzJZkSJFvDfffDPZ16699lqvbdu2IdPq1avn3XbbbRlUu9hu/5EjR3qFChXK8DplZfv27fOqVq3qTZ8+3WvSpInXu3fvFOdl/8+8tmffD5/+/ft7tWrVSvP87PeZ1/bs9/7QE41Mc/ToURs7dqz9/fffLq0gOQsWLLAWLVqETGvdurWbjpPf/rJ//36rUKGClS9f/ri91ji+Xr16Wdu2bY/Zr5PD/p95bS/s++Hz008/WZkyZaxy5crWsWNH++WXX1Kcl/0+89pe2O/TLt7HvEBYrFy50gVtBw4csPz589vHH39sZ5xxRrLz7tixw0qWLBkyTc81HSe//atXr25vv/22u/S9Z88ee/75561hw4YukFZqCPzRScvSpUtdSkFasP9nXtuz74dPvXr1XK6t2lTpBAMHDrTGjRu7tLwCBQocMz/7fea1Pfu9PwTRyHD6kC5btswFZR9++KF17drVZs2alWIgh8xrfwXbiXupFUCffvrpLqf08ccfZ9P4sGXLFuvdu7dNnz6dwZlR0Pbs++Fz8cUXB//XCbkCO13dGj9+vBvvgshpe/Z7fwiikeFy5sxpVapUcf/XqVPH9QwNHjzYBWZJlSpVyn777beQaXqu6Tj57Z9Ujhw5rHbt2rZ+/Xqa3ycN5ty5c6ede+65ISk1s2fPtldffdUOHjxo2bNnD1mG/T/z2j4p9v3wKVy4sFWrVi3F4wj7fea1fVLs96kjJxqZLiEhwX2JJUdnxV999VXINPUmpZbDi/C1f1IKPJQOUrp0aZrZp+bNm7u201WAwKNu3bouR1H/JxfEsf9nXtsnxb4fPsq53bBhQ4rHEfb7zGv7pNjvj8PnQETghPTt29ebNWuWt3HjRm/FihXueVxcnDdt2jT3eufOnd20gHnz5nnx8fHe888/761Zs8aNNM6RI4e3cuVKtkQGtP/AgQO9L7/80tuwYYO3ZMkS7/rrr/dy587trVq1ivYPg6R3iGD/j5y2Z98Pn3vuucebOXOmO+7omN6iRQuvePHi3s6dO5Nte477mdf27Pf+kM6BDKVLql26dHEDHAoVKuRytL788ktr2bKle12jhrNlyxaSg/vBBx/YI488Yg899JBVrVrVJk2aZGeddRZbLgPaf9euXda9e3c30KdIkSIu/UP3cCV//eRg/8887Psnz9atW939/v/8808rUaKEXXDBBfbtt9+6/5Nre477mdf2HPP9iVMk7XMZAAAAIKaREw0AAAD4RBANAAAA+EQQDQAAAPhEEA0AAAD4RBANAAAA+EQQDQAAAPhEEA0AAAD4RBANABGkW7du1r59+8yuRlTQD0iccsoptmnTprCU98UXX9g555xjCQkJYSkP4Td79my77LLLrEyZMhYXF+d+fMsv/cBU/fr1rUCBAu5HR6666qoT2ocmTpxorVq1smLFirk66afkj6dp06Zu3qSPtm3bhsy3Zs0aa9eunftxrHz58tl5553nfiAlsQULFthFF13kXi9YsKBdeOGF9u+//wZf/+uvv9xP3Ou1woUL28033+x+/jtgwIABydZF5SVn7Nix7vX2qRynevTo4eZ5+eWXQ6ZrXU499VTLnTu3++nxzp0727Zt24KvHzhwwB0Da9asafHx8ek+Fo4YMcK1sdZZ9di9e/cx86xbt84uv/xyK168uJtPP0TzzTff+HofgmgAyCDJfVElfujLbPDgwTZq1KhM3SbREsg/+eST7kuwYsWKYSmvTZs2liNHDnv//ffDUh7C7++//7ZatWrZ0KFD07X8xo0b3T6joFPBrgLqP/74w6688soUl9HnUp+J1OqkAOyZZ57xFXjrl2MDjx9++MGyZ89u11xzTXCeDRs2uHJr1KhhM2fOtBUrVtijjz7qAtDEAbT2WwXxCxcutEWLFtkdd9wR8iuECqBXrVpl06dPt88//9ydiNx6663B1++9996QuuihX6VNXJcAnWxo/saNG6e4bh9//LH7VUSd6CTVrFkzGz9+vK1du9Y++ugjt45XX3118PWjR49anjx57K677rIWLVpYev3zzz+uXfRLxym59NJL7ciRI/b111/bkiVL3H6lafqF3jTz+TPhAIB02r59e/Dx8ssvewULFgyZtm/fvoho265du3qXX365F8n+/vtv134LFiwIa7mvvvqqV7du3bCWiZNDIczHH38cMu3AgQPePffc45UpU8bLmzevd/7553vffPNN8PUJEyZ48fHx3tGjR4PTPv30Uy8uLs47dOhQsu/Tv39/95k4no0bN7o6ff/9977X5aWXXvIKFCjg7d+/Pzjtuuuu8zp16pTqcvXq1fMeeeSRFF9fvXq1q9OiRYuC06ZOnerW99dff012mWXLlrllZs+eHTL9yJEjXsOGDb0333wzxWPE1q1bvbJly3o//PCDV6FCBbdeqfnkk09SbPvUjkOTJk3yateu7eXKlcurVKmSN2DAAO/w4cPHzKdtr3XZtWtXyPTff//9mHXcu3evmzZ9+nQvreiJBoAMUqpUqeBDl2fV+5x4Wv78+Y/pBdYlyTvvvNP69OljRYoUsZIlS9obb7zher9uvPFGd0m6SpUqNnXq1JD3Us/WxRdf7MrUMrpsqh63gA8//NBdMlWvjy5Dq9dHZarX7Z133rFPPvkk2EOuXjB54IEHrFq1apY3b16rXLmy6xU7fPhwsEwtq3SIt99+212y1Xvffvvtrnfp2Wefdeuo9Av1ICem93j99dddfVUfla36pWbKlCmWK1cud1k+QPVUWV999ZXVrVvX1bNhw4au1ytg+fLlrjdM7aZLuHXq1LHFixcHX1eqgJ6rhwzRR72w6p1VyoF6btWbqh7Jn376yb2u7a1e2pEjR7r9cs+ePfbee++5/V9XITLLW2+9Zddff30whUIpRZMnT3aft9atW7vPTb169ULSV3bu3Gnfffede037uT7nTZo0sblz5wbnUVsohUOfhwCtq9pAyybnzTffdO+btLf5sccec++ldJDkJCQkuOPMfffdZ2eeeeZx11lpJrrqo7r7afs5c+ZYly5drHfv3rZ69WobPny4u3qX9LiSGh3zqlevbu+++6477qlHWuVo/bSPpBVBNABEOAW1ytvT5VoF1D179nTBgb58li5d6i7l6stLlzBF+X+6XF27dm0XECrX97fffrNrr73Wva7LtR06dLCbbrrJ5Vwq+NTlbHXu6VKt5lPgEbi0q/cRBZ76stIXl9JOFMy/9NJLIXVV8KmAXu85ZswYFxwoz3Pr1q02a9Ysd8n7kUceOeYLXAG5clMV5OryswIK1S21L9KUvuwefvhhe+GFF9y6K69S6xmgssuVK+cue+sSbt++fUO+wBX8KxhR+YguyhVWcDxhwgQXAJ522mluf1ZKhKZLpUqVbNq0ae4yv07CFGBq31SKQWbR51onvbfccktIgKy85aefftp9FlXnK664wn1O9TmSn3/+OXjy2r17d/eZO/fcc6158+bBkwalJigwTEyfiaJFiyabtqCcZAW2SQNlBeb6LOszn5JnnnnGla1UjNToZFwnCwpktc10wu7HwIED3ee2a9eu7oS7ZcuW9vjjj7sgOK10sj1jxgz7/vvv3XFNKTIvvviia0N1VqRZmvusAQBhM3LkSK9QoULHvYTZpEkT74ILLgi5pJovXz6vc+fOwWlKBdHhPJDa8Pjjj3utWrUKKXfLli1unrVr13pLlixx/2/atOmE0jmee+45r06dOiGXvXUJXZdFA1q3bu1VrFgx5PJ59erVvUGDBgWfqy49evQ45jJ1z549U3xv1e+mm25K9tLtjBkzgtMmT57spv3777/uuS6Zjxo1KtX10mViXR5GdKVzfP75526aPh+JH0rfuPbaa4OflapVq3r33Xeft3TpUm/WrFnuM9a8eXMvISHBzaNL/ImXz5Ejhysj8bTRo0eHLZ3j1ltv9WrWrBkyTakWKqtDhw4h0y+77DLv+uuvd//PmzfPzfPggw+GzKOy+vbt6/5/8sknvWrVqh3zniVKlPBee+21Y6Z/8MEHbl137NgRnKbPsz7DU6ZMSfEYsXjxYq9kyZIhKSIppXMolULHoWnTpnmNGjXyLrnkkmDbp+U4VLx4cS937twh20PP1RZK80pLOofer127dt7FF1/szZ071x0TdbxRKsq2bdu8tIr3Ff4DADLc2WefHfxfg4/Ug6NUjAD1nAZ6r0S9uRplrnSKpNRTrJ5r9VapDF0q1nMN7jleD8y4cePslVdecWWol0yXQJUSkZgG+alnJ3HdVOfEA500LVDXgAYNGhzzPLW7HOjuA4kHWKXUXroDgOj91Mv8f//3f67HL3AJXz366rFMTCklgV59RA/tk9rXdIVBfxMLfBY0IFGpVEovChg9erSVL1/eXR1RepBSHxLve9rnf/3115CBg4HP3IlSKoFST5QqkZiuPKlXVwP8Ejv99NOD6RqBfTu5eQJ38FAKVdLPmj63SqXQa8mlcmhwXeL10+ddAwqV6hQQuINNfHy8S5fSlZvAZyxA6TL33HOPu0NH4rufaN30UMqI6qq210DEpMeA1LazeqOTGwya0jEhKQ0m1CDLXbt2BY9hr732mht8qSt/6ulOC4JoAIhwSfMFdSky8TQ9T/zFpi8ZfeEld7cAffEqwNCXxfz5891l4iFDhrgUCAURutydHOVWKhVCX14KvBWI6MtfaRN+6hqYdqK3kdOXsL4Ak5Na2+jS9w033ODyTZV20r9/f7ceulQeoABDtz5DdFH6kgI3BXMp3T1CJ0eJT+gkEHAH9hGdRGmcQYBSH/bu3RsyLVyUenLw4EHr1KlTyPScOXO629klzucP3JatQoUKwRNW3QEjuXk0vkAUmCq9SycWgfQnBZBaV+VYJ71ziU6+P/3005DpujvIypUrQ6YpJWvfvn0urUtBsNLJkt5NQ8cJTdfYjZQE2lxtkFZKWdE6n8j2CJwkJ90X9NzPsYkgGgCyGH3J6PZR+pJVT1FyFFw2atTIPfr16+e+mHVrKvXU6gtcwUhiCrg1j4LtgM2bN4etzuqJ0mChxM8VFKVEr6kHMT3UA6bH3Xff7XLDlS8bCKKVE6qet9TeG5lHJ4jr168PCfzUa6xAV9tUJ3raj3Ryp234+++/u4Gmujqh3Hw9lMevnl9tewWCyo/Wvp3eba6TLvX8Bu53HAhqAwOGRXUqW7asDRo0KGRZ5RlrILGuLiWlAXrXXXedu++zBsMqX/ezzz4LDvTVZ1jz6ERQt2fToF71ov7444/Bgbnq6VVOtXKmhw0b5gYCa/ClxhwkvQWdBgTrJDsQgCfu3T3rrLNCpimXXALTVf+k66CTWa2/BvCJTtI1FkE56rrqpc+ZxkLoSlDiXmiNuTh06JBrV22fwFUBrZ/oeKXecvV66wqaAl9dfVNe+RNPPOHmUb63HoF9RScBukKmZbSv6P1UB+VVqzydOCnfW/tT0nt1pyrNiR8AgEzJie7du3fIPMnlGibOD1VeonIer776am/hwoXe+vXrvS+++MLr1q2by6n+9ttvXa6kbnu1efNmb/z48V7OnDmDOY967dRTT/V+/PFHl7+o20/pVlTKlRwzZowrb/DgwV7RokVD1kE50bVq1Up1fZJbJ9VdeY5vvfWWy5Xs16+fly1bNm/VqlUptt+KFStcff76669U8x+Vn6ppylf9559/vF69ern5lA+uXMjTTjvNu//++0PKyJ8//zG5lYgMgW2c9BG4BZ32Ve0/yuFVLnPp0qW9K664wu0vAdqHlfeuXFp9TpQbu2bNmhTf83i3uNNnObk6abnE+3zSMvT50nzKDU6JPhNVqlRxOb/6bOnWbklpfEG5cuXceIQGDRp4c+bMCXn9zz//dLnV2q91W8gbb7zxmNtpasyCynjooYe8tEjLuIkKSY5T2gbNmjVzxw3dmk7bSGMhdFu8pMsl156J6Xim2+3lyZPHrZNuZThixIjg62r75MrQtgrQ8U9jR1QfjZWoX79+SN53WhBEA0AWC6Jl3bp1LngoXLiw+6KpUaOG16dPHzegRveO1YA/BRD6MtPAoyFDhgSX3blzp9eyZUv3patyA/fZ1WCsYsWKuem6h63qEK4geujQoe49A1+u48aNO24b6otz2LBhaQ6iDx486AZllS9f3p006F7Cd9xxR3DQYWCQ12233Xbc9waAODVB2vutAQAIL12WViqJ319JVF6zLmfrMm7S3Mb00H20delZt8ZLKTccAALIiQYARCXlLup+uLpzggY3nSjdQUAj9AmgAaQFPdEAgKjsiQaAzERPNAAgU5FVCCAa8bPfAAAAgE8E0QAAAIBPBNEAAACATwTRAAAAgE8E0QAAAIBPBNEAAACATwTRAAAAgE8E0QAAAIBPBNEAAACA+fP/igU+r88BpggAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Restrict eye_states and imu data to match gaze_300ms's time range\n", "eye_states_300ms = eye_states.restrict(gaze_300ms)\n", "imu_300ms = imu.restrict(gaze_300ms)\n", "\n", "plot_timestamps(gaze_300ms, eye_states_300ms, imu_300ms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding Sampling Irregularities\n", "\n", "To quantify sampling irregularities, let's examine the distribution of time intervals between consecutive samples and compare them to the nominal sampling rates specified by Pupil Labs:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nominal sampling frequency of gaze: 200 Hz. Actual: 199.1 Hz\n", "Nominal sampling frequency of eye states: 200 Hz. Actual: 199.1 Hz\n", "Nominal sampling frequency of IMU: 110 Hz. Actual: 114.7 Hz\n" ] }, { "data": { "image/png": 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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": [ "The histograms reveal that while most intervals cluster around the nominal rate (red line), all three streams show irregularities:\n", "- **Gaze and eye states**: Integer multiples of the nominal interval indicate occasional dropped frames from the eye camera\n", "- **IMU**: Broader distribution suggests more variable sampling intervals\n", "\n", "These irregularities motivate the need for interpolation when analyses require uniform sampling." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resampling to Uniform Timestamps with `interpolate()`\n", "\n", "Many analyses require uniformly-sampled data. The `interpolate()` method resamples streams to regular intervals using `scipy.interpolate.interp1d` [(API reference)](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html).\n", "\n", "By default, `interpolate()` generates uniformly-spaced timestamps at the stream's nominal sampling frequency (200 Hz for gaze/eye states, 110 Hz for IMU), starting from the first sample and ending at the last." ] }, { "cell_type": "code", "execution_count": 7, "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", "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": [ "Column data types are preserved during interpolation. You can also resample to custom timestamps using the `new_ts` parameter—useful for synchronizing streams with different sampling rates. For example, resample gaze data (~200 Hz) to IMU timestamps (~110 Hz):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original gaze data length: 1048\n", "Original IMU data length: 622\n", "Gaze data length after interpolating to IMU timestamps: 622\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\User\\Documents\\GitHub\\PyNeon\\pyneon\\preprocess\\preprocess.py:67: UserWarning: 18 out of 622 requested timestamps are outside the data time range and will have empty data.\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": [ "The warning above is expected: remember that IMU recording starts earlier than gaze, so interpolating gaze to IMU timestamps will include extrapolation for the initial IMU samples. The resulting `interpolated_gaze` stream contains values at all IMU timestamps, with NaNs for timestamps outside the original gaze range." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How Interpolation Works\n", "\n", "By default, float columns use linear interpolation (`float_kind='linear'`), while non-float columns use nearest-neighbor (`other_kind='nearest'`). Linear interpolation estimates values at new timestamps by drawing straight lines between existing data points:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imu_500ms = imu.crop(tmax=0.5, by=\"time\")\n", "\n", "plt.figure(figsize=(8, 2))\n", "plt.plot(imu_500ms[\"acceleration x [g]\"], marker=\"o\", label=\"Raw\")\n", "plt.plot(\n", " imu_500ms.interpolate()[\"acceleration x [g]\"],\n", " marker=\"^\",\n", " label=\"Interpolated\",\n", ")\n", "plt.xlabel(\"Timestamp [ns]\")\n", "plt.ylabel(\"Acceleration x [g]\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interpolation creates new uniformly-spaced samples (orange triangles) while preserving most original data points (blue circles) that happen to fall near the new timestamps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combining Streams with `concat_streams()`\n", "\n", "Joint analysis of multiple sensors requires combining streams into a single DataFrame. The `concat_streams()` method:\n", "\n", "1. Determines the common temporal overlap (from the latest start to the earliest end across all streams)\n", "2. Interpolates all streams to uniform timestamps within this range\n", "3. Combines columns into a single `Stream` object\n", "\n", "The sampling rate defaults to the lowest nominal frequency among the selected streams (110 Hz when including IMU)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Concatenating streams:\n", "\tgaze\n", "\teye_states\n", "\timu\n", "Using lowest sampling rate: 110 Hz (\n", "['imu']\n", "Length: 1, dtype: str)\n", "Using latest start timestamp: 1766074431275967547 (\n", "['gaze', 'eye_states']\n", "Length: 2, dtype: str)\n", "Using earliest last timestamp: 1766074436535834547 (\n", "['gaze', 'eye_states']\n", "Length: 2, dtype: str)\n", " gaze x [px] gaze y [px] worn azimuth [deg] \\\n", "timestamp [ns] \n", "1766074431275967547 731.885864 503.253845 -1 -4.384848 \n", "1766074431285058456 735.780922 499.996515 -1 -4.134103 \n", "1766074431294149365 735.862011 502.149531 -1 -4.128883 \n", "1766074431303240274 737.018387 503.200361 -1 -4.054441 \n", "1766074431312331183 739.273033 502.726029 -1 -3.909299 \n", "\n", " elevation [deg] pupil diameter left [mm] \\\n", "timestamp [ns] \n", "1766074431275967547 6.207878 4.582340 \n", "1766074431285058456 6.416890 4.536907 \n", "1766074431294149365 6.278738 4.528555 \n", "1766074431303240274 6.211310 4.611737 \n", "1766074431312331183 6.241746 4.558960 \n", "\n", " eyeball center left x [mm] eyeball center left y [mm] \\\n", "timestamp [ns] \n", "1766074431275967547 -30.562500 14.062500 \n", "1766074431285058456 -30.448863 14.085226 \n", "1766074431294149365 -30.534090 14.082387 \n", "1766074431303240274 -30.613637 14.096591 \n", "1766074431312331183 -30.596600 14.073850 \n", "\n", " eyeball center left z [mm] optical axis left x ... \\\n", "timestamp [ns] ... \n", "1766074431275967547 -32.062500 -0.098742 ... \n", "1766074431285058456 -32.056819 -0.100264 ... \n", "1766074431294149365 -32.058237 -0.091252 ... \n", "1766074431303240274 -32.144887 -0.097646 ... \n", "1766074431312331183 -32.011375 -0.082156 ... \n", "\n", " gyro x [deg/s] gyro y [deg/s] gyro z [deg/s] \\\n", "timestamp [ns] \n", "1766074431275967547 0.270761 -3.295624 1.393119 \n", "1766074431285058456 -0.352645 -3.417501 1.581498 \n", "1766074431294149365 -0.661864 -3.321655 1.665359 \n", "1766074431303240274 -1.197607 -3.654104 1.829147 \n", "1766074431312331183 -1.479553 -3.961838 1.829147 \n", "\n", " acceleration x [g] acceleration y [g] \\\n", "timestamp [ns] \n", "1766074431275967547 -0.033417 0.028384 \n", "1766074431285058456 -0.046604 0.024029 \n", "1766074431294149365 -0.029812 0.024873 \n", "1766074431303240274 -0.033384 0.013343 \n", "1766074431312331183 -0.034372 0.017812 \n", "\n", " acceleration z [g] quaternion w quaternion x \\\n", "timestamp [ns] \n", "1766074431275967547 1.012492 0.130887 -0.010311 \n", "1766074431285058456 1.014113 0.131004 -0.010321 \n", "1766074431294149365 1.006813 0.131136 -0.010400 \n", "1766074431303240274 1.016316 0.131278 -0.010472 \n", "1766074431312331183 1.007866 0.131435 -0.010574 \n", "\n", " quaternion y quaternion z \n", "timestamp [ns] \n", "1766074431275967547 -0.010332 -0.991290 \n", "1766074431285058456 -0.010347 -0.991274 \n", "1766074431294149365 -0.010343 -0.991256 \n", "1766074431303240274 -0.010282 -0.991237 \n", "1766074431312331183 -0.010180 -0.991216 \n", "\n", "[5 rows x 35 columns]\n", "Index(['gaze x [px]', 'gaze y [px]', 'worn', 'azimuth [deg]',\n", " 'elevation [deg]', 'pupil diameter left [mm]',\n", " 'eyeball center left x [mm]', 'eyeball center left y [mm]',\n", " 'eyeball center left z [mm]', 'optical axis left x',\n", " 'optical axis left y', 'optical axis left z',\n", " 'pupil diameter right [mm]', 'eyeball center right x [mm]',\n", " 'eyeball center right y [mm]', 'eyeball center right z [mm]',\n", " 'optical axis right x', 'optical axis right y', 'optical axis right z',\n", " 'eyelid angle top left [rad]', 'eyelid angle bottom left [rad]',\n", " 'eyelid aperture left [mm]', 'eyelid angle top right [rad]',\n", " 'eyelid angle bottom right [rad]', 'eyelid aperture right [mm]',\n", " 'gyro x [deg/s]', 'gyro y [deg/s]', 'gyro z [deg/s]',\n", " 'acceleration x [g]', 'acceleration y [g]', 'acceleration z [g]',\n", " 'quaternion w', 'quaternion x', 'quaternion y', 'quaternion z'],\n", " dtype='str')\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": [ "Compare a 0.3-second window from the middle of the recording. The concatenated stream (bottom row) shows perfectly uniform timestamps that roughly matches the sampling rate of IMU (110 Hz)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Extract a time window from the middle of the recording to highlight sampling differences\n", "gaze_middle = gaze.crop(tmin=3.0, tmax=3.3, by=\"time\")\n", "eye_states_middle = eye_states.restrict(gaze_middle)\n", "imu_middle = imu.restrict(gaze_middle)\n", "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 resampling to uniform timestamps, interpolation preserves the data's essential characteristics. Comparing the same columns before (blue circles) and after (orange triangles) concatenation shows how values remain faithful to the originals:" ] }, { "cell_type": "code", "execution_count": 12, "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": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 2 }