{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reading a Pupil Cloud-format dataset/recording\n", "In this tutorial, we will show how to load a single Neon recording downloaded from [Pupil Cloud](https://docs.pupil-labs.com/neon/pupil-cloud/) and give an overview of the data structure.\n", "\n", "## Reading sample data\n", "We will use a sample recording produced by our lab, called \"boardView\". This project (collection of recordings on Pupil Cloud) contains two recordings downloaded with the `Timeseries Data + Scene Video` option and a marker mapper enrichment. It can be downloaded with the `get_sample_data()` function. The function returns a `Pathlib.Path` [(reference)](https://docs.python.org/3/library/pathlib.html#pathlib.Path) instance pointing to the downloaded and unzipped directory. PyNeon accepts both `Path` and `string` objects but internally always uses `Path`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "D:\\GitHub\\PyNeon\\data\\boardView\n" ] } ], "source": [ "from pyneon import Dataset, Recording, get_sample_data\n", "\n", "# Download sample data (if not existing) and return the path\n", "sample_dir = get_sample_data(\"boardView\")\n", "print(sample_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `OfficeWalk` data has the following structure:\n", "\n", "```text\n", "boardView\n", "├── Timeseries Data + Scene Video\n", "│ ├── boardview1-d4fd9a27\n", "│ │ ├── info.json\n", "│ │ ├── gaze.csv\n", "│ │ └── ....\n", "│ ├── boardview2-713532d5\n", "│ │ ├── info.json\n", "│ │ ├── gaze.csv\n", "│ │ └── ....\n", "| ├── enrichment_info.txt\n", "| └── sections.csv\n", "└── boardView_MARKER-MAPPER_boardMapping_csv\n", "```\n", "\n", "The `Timeseries Data + Scene Video` folder contains what PyNeon refers to as a `Dataset`. It consists of two recordings, each with its own `info.json` file and data files. These recordings can be loaded either individually as a `Recording`, or as a collective `Dataset`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To load a `Dataset`, specify the path to the `Timeseries Data + Scene Video` folder:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset | 2 recordings\n" ] } ], "source": [ "dataset_dir = sample_dir / \"Timeseries Data + Scene Video\"\n", "dataset = Dataset(dataset_dir)\n", "print(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Dataset` provides an index-based access to its recordings. The recordings are stored in the `recordings` attribute, which contains a list of `Recording` instances. You can access individual recordings by index:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "D:\\GitHub\\PyNeon\\data\\boardView\\Timeseries Data + Scene Video\\boardview2-713532d5\n" ] } ], "source": [ "rec = dataset[0] # Internally accesses the recordings attribute\n", "print(type(rec))\n", "print(rec.recording_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can directly load a single `Recording` by specifying the recording's folder path:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "D:\\GitHub\\PyNeon\\data\\boardView\\Timeseries Data + Scene Video\\boardview1-d4fd9a27\n" ] } ], "source": [ "recording_dir = dataset_dir / \"boardview1-d4fd9a27\"\n", "rec = Recording(recording_dir)\n", "print(type(rec))\n", "print(rec.recording_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data and metadata of a Recording\n", "You can quickly get an overview of the metadata and contents of a `Recording` by printing the instance. The basic metadata (e.g., recording and wearer ID, recording start time and duration) and the path to available data will be displayed. At this point, the data is simply located from the recording's folder path, but it is not yet loaded into memory." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Data format: cloud\n", "Recording ID: d4fd9a27-3e28-45bf-937f-b9c14c3c1c5e\n", "Wearer ID: af6cd360-443a-4d3d-adda-7dc8510473c2\n", "Wearer name: Qian\n", "Recording start time: 2024-11-26 12:44:48.937000\n", "Recording duration: 32046000000 ns (32.046 s)\n", "\n" ] } ], "source": [ "print(rec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen in the output, this recording includes all data files. This tutorial will focus on non-video data. For processing video, refer to the [Neon video tutorial](video.ipynb).\n", "\n", "Individual data streams can be accessed as properties of the `Recording` instance. For example, the gaze data can be accessed as `recording.gaze`, and upon accessing, the tabular data is loaded into memory. On the other hand, if you try to access unavailable data, PyNeon will return `None` and a warning message." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "recording.gaze is \n", "recording.saccades is \n", "recording.scene_video is < cv2.VideoCapture 0000015E04120750>\n" ] } ], "source": [ "# Gaze and fixation data are available\n", "gaze = rec.gaze\n", "print(f\"recording.gaze is {gaze}\")\n", "\n", "saccades = rec.saccades\n", "print(f\"recording.saccades is {saccades}\")\n", "\n", "scene_video = rec.scene_video\n", "print(f\"recording.scene_video is {scene_video}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PyNeon reads tabular CSV file into specialized classes (e.g., gaze.csv to `NeonGaze`) which all have a `data` attribute that holds the tabular data as a `pandas.DataFrame` [(reference)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html). Depending on the nature of the data, such classes could be of `Stream` or `Events` super classes. `Stream` contains (semi)-continuous data streams, while `Events` (dubbed so to avoid confusion with the `Eventsent` subclass that holds data from `events.csv`) contains sparse event data.\n", "\n", "The class inheritance relationship is as follows:\n", "\n", "```text\n", "NeonTabular\n", "├── Stream\n", "│ ├── NeonGaze\n", "│ ├── NeonEyeStates\n", "│ └── NeonIMU\n", "└── Events\n", " ├── NeonBlinks\n", " ├── NeonSaccades\n", " ├── NeonFixations\n", " └── Eventsents\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data as DataFrames\n", "\n", "The essence of `NeonTabular` is the `data` attribute—a `pandas.DataFrame`. This is a common data structure in Python for handling tabular data. For example, you can print the first 5 rows of the gaze data by calling `gaze.data.head()`, and inspect the data type of each column by calling `gaze.data.dtypes`. \n", "\n", "Theoretically, you could re-assign `gaze.data` to `gaze_df`, however the conversion scripts written in the next section only work at the class level and not on the dataframe level." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " gaze x [px] gaze y [px] worn fixation id blink id \\\n", "timestamp [ns] \n", "1732621490425631343 697.829 554.242 1 1 \n", "1732621490430625343 698.096 556.335 1 1 \n", "1732621490435625343 697.810 556.360 1 1 \n", "1732621490440625343 695.752 557.903 1 1 \n", "1732621490445625343 696.108 558.438 1 1 \n", "\n", " azimuth [deg] elevation [deg] \n", "timestamp [ns] \n", "1732621490425631343 -7.581023 3.519804 \n", "1732621490430625343 -7.563214 3.385485 \n", "1732621490435625343 -7.581576 3.383787 \n", "1732621490440625343 -7.713686 3.284294 \n", "1732621490445625343 -7.690596 3.250055 \n", "gaze x [px] float64\n", "gaze y [px] float64\n", "worn Int8\n", "fixation id Int32\n", "blink id Int32\n", "azimuth [deg] float64\n", "elevation [deg] float64\n", "dtype: object\n" ] } ], "source": [ "print(gaze.data.head())\n", "print(gaze.data.dtypes)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " saccade id start timestamp [ns] end timestamp [ns] duration [ms] \\\n", "0 1 1732621490876132343 1732621490891115343 15 \n", "1 2 1732621491241357343 1732621491291481343 50 \n", "2 3 1732621491441602343 1732621491516601343 75 \n", "3 4 1732621491626723343 1732621491696847343 70 \n", "4 5 1732621491917092343 1732621491977090343 60 \n", "\n", " amplitude [px] amplitude [deg] mean velocity [px/s] peak velocity [px/s] \n", "0 14.938179 0.962102 1025.709879 1191.520740 \n", "1 130.743352 8.378644 2700.713283 3687.314947 \n", "2 241.003342 15.391730 3615.380044 5337.244676 \n", "3 212.619205 13.608618 3757.394092 6164.040944 \n", "4 220.842812 13.914266 4220.180601 6369.217052 \n", "saccade id Int32\n", "start timestamp [ns] int64\n", "end timestamp [ns] int64\n", "duration [ms] Int64\n", "amplitude [px] float64\n", "amplitude [deg] float64\n", "mean velocity [px/s] float64\n", "peak velocity [px/s] float64\n", "dtype: object\n" ] } ], "source": [ "print(saccades.data.head())\n", "print(saccades.data.dtypes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PyNeon performs the following preprocessing when reading the CSV files:\n", "1. Removes the redundant `section id` and `recording id` columns that are present in the raw CSVs.\n", "2. Sets the `timestamp [ns]` (or `start timestamp [ns]` for most event files) column as the DataFrame index.\n", "3. Automatically assigns appropriate data types to columns. For instance, `Int64` type is assigned to timestamps, `Int32` to event IDs (blink/fixation/saccade ID), and `float64` to float data (e.g. gaze location, pupil size)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like any other `pandas.DataFrame`, you can access individual rows, columns, or subsets of the data using the standard indexing and slicing methods. For example, `gaze.data.iloc[0]` returns the first row of the gaze data, and `gaze.data['gaze x [px]']` (or `gaze['gaze x [px]']`) returns the gaze x-coordinate column." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First row of gaze data:\n", "gaze x [px] 697.829\n", "gaze y [px] 554.242\n", "worn 1.0\n", "fixation id 1.0\n", "blink id \n", "azimuth [deg] -7.581023\n", "elevation [deg] 3.519804\n", "Name: 1732621490425631343, dtype: Float64\n", "\n", "All gaze x values:\n", "timestamp [ns]\n", "1732621490425631343 697.829\n", "1732621490430625343 698.096\n", "1732621490435625343 697.810\n", "1732621490440625343 695.752\n", "1732621490445625343 696.108\n", " ... \n", "1732621520958946343 837.027\n", "1732621520964071343 836.595\n", "1732621520969071343 836.974\n", "1732621520974075343 835.169\n", "1732621520979070343 833.797\n", "Name: gaze x [px], Length: 6091, dtype: float64\n" ] } ], "source": [ "print(f\"First row of gaze data:\\n{gaze.data.iloc[0]}\\n\")\n", "print(f\"All gaze x values:\\n{gaze['gaze x [px]']}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Useful attributes and methods for Stream and Events\n", "On top of analyzing `data` with `pandas.DataFrame` attributes and methods, you may also use attributes and methods of the `Stream` and `Events` instances containing the `data` to facilitate Neon-specific data analysis. For example, `Stream` class has a `ts` property that allows quick access of all timestamps in the data as a `numpy.ndarray` [(reference)](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html).\n", "\n", "Useful as they are, UTC timestamps in nanoseconds are usually too large for human comprehension. Often we would want to simply know what is the relative time for each data point since the stream start (which is different from the recording start). In PyNeon, this is referred to as `times` and is in seconds. You can access it as a `numpy.ndarray` by calling the `times` property.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1732621490425631343 1732621490430625343 1732621490435625343 ...\n", " 1732621520969071343 1732621520974075343 1732621520979070343]\n", "[0.0000000e+00 4.9940000e-03 9.9940000e-03 ... 3.0543440e+01 3.0548444e+01\n", " 3.0553439e+01]\n" ] } ], "source": [ "print(gaze.ts)\n", "print(gaze.times)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Timestamps (UTC, in ns), relative time (relative to the stream start, in s), and index are the three units of time that are most commonly used in PyNeon. For example, you can crop the stream by either timestamp or relative time by calling the `crop()` method. The method takes `start` and `end` of the crop window in either UTC timestamps or relative time, and uses `by` to specify which time unit is used. The method returns a new `Stream` instance with the cropped data." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gaze data points before cropping: 6091\n", "Gaze data points after cropping: 999\n" ] } ], "source": [ "print(f\"Gaze data points before cropping: {len(gaze)}\")\n", "\n", "# Crop the gaze data to 5-10 seconds\n", "gaze_crop = gaze.crop(5, 10, by=\"time\") # Crop by time\n", "print(f\"Gaze data points after cropping: {len(gaze_crop)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may also want to restrict one stream to the temporal range of another stream. This can be done by calling the `restrict()` method. The method takes another `Stream` instance as an argument and crops the stream to the intersection of the two streams' temporal ranges." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IMU first timestamp: 1732621495435389343 > Gaze first timestamp: 1732621495430263343\n", "IMU last timestamp: 1732621500421101343 < Gaze last timestamp: 1732621500424901343\n" ] } ], "source": [ "imu_crop = rec.imu.restrict(gaze_crop)\n", "saccades_crop = saccades.restrict(gaze_crop)\n", "print(\n", " f\"IMU first timestamp: {imu_crop.first_ts} > Gaze first timestamp: {gaze_crop.first_ts}\"\n", ")\n", "print(\n", " f\"IMU last timestamp: {imu_crop.last_ts} < Gaze last timestamp: {gaze_crop.last_ts}\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many other attributes and methods available for `Stream` and `Events` classes. For a full list, refer to the [API reference](https://ncc-brain.github.io/PyNeon/reference/stream.html). We will also cover some of them in the following tutorials (e.g., [interpolation and concatenation of streams](interpolate_and_concat.ipynb))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## An example plot of cropped data\n", "\n", "Below we show how to easily plot the gaze and saccade data we cropped just now. Since PyNeon data are stored in `pandas.DataFrame`, you can use any plotting library that supports `pandas.DataFrame` as input. Here we use `matplotlib` to plot the gaze x, y coordinates and the saccade durations." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(8, 3))\n", "\n", "# Plot the gaze data\n", "(gaze_l,) = plt.plot(gaze_crop[\"gaze x [px]\"], label=\"Gaze x\")\n", "(gaze_r,) = plt.plot(gaze_crop[\"gaze y [px]\"], label=\"Gaze y\")\n", "\n", "# Visualize the saccades\n", "for sac_start, sac_end in zip(saccades_crop.start_ts, saccades_crop.end_ts):\n", " sac = plt.axvspan(sac_start, sac_end, color=\"lightgray\", label=\"Saccades\")\n", "\n", "plt.xlabel(\"timestamp [ns]\")\n", "plt.ylabel(\"gaze location [px]\")\n", "plt.legend(handles=[gaze_l, gaze_r, sac])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing gaze heatmap\n", "Finally, we will show how to plot a heatmap of the gaze/fixation data. Since it requires gaze, fixation, and video data, the input it takes is an instance of `Recording` that contains all necessary data. The method `plot_heatmap()`, by default, plots a gaze heatmap with fixations overlaid as circles." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = rec.plot_distribution()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see a clear centre-bias, as participants tend to look more centrally relative to head position." ] } ], "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 }