{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*PyPlr* and Pupil Core\n",
"==========================\n",
"\n",
"\n",
"\n",
"*PyPlr* was designed to work with [Pupil Core](https://pupil-labs.com/products/core/)—an affordable, open-source, versatile, research-grade eye tracking platform with high sampling rates, precise model-based 3D estimation of pupil size, and many other features which make it well-suited to our application (see [Kassner et al., 2014](https://arxiv.org/abs/1405.0006), for a detailed overview of the system). In particular, we leverage real-time data streaming with the forward facing scene camera to timestamp the onset of light stimuli with good temporal accuracy, opening the door to integration with virtually any light source given a suitable geometry.\n",
"\n",
"The best place to start learning more about Pupil Core is on the [Pupil Labs website](https://docs.pupil-labs.com/core/), but the features most relevant to *PyPlr* are:\n",
"\n",
"* [Pupil Capture](https://docs.pupil-labs.com/core/software/pupil-capture/#world-window): Software for interfacing with a Pupil Core headset\n",
"* [Pupil Player](https://docs.pupil-labs.com/core/software/pupil-player/#load-a-recording): Software for visualising and exporting data\n",
"* [Pupil Core Network API](https://docs.pupil-labs.com/developer/core/network-api/): Fast and reliable real-time communication and data streaming with [ZeroMQ](https://zeromq.org/), an open source universal messaging library, and [MessagePack](https://msgpack.org/index.html), a binary format for computer data interchange\n",
"\n",
"*PyPlr*'s `pupil.py` module\n",
"---------------------------\n",
"\n",
"*PyPlr* has a module called `pupil.py` which facilitates working with the Pupil Core Network API by wrapping all of the tricky ZeroMQ and MessagePack stuff into a single device class. The `PupilCore()` device class has a `.command(...)` method giving convenient access to all of the commands available via [Pupil Remote](https://docs.pupil-labs.com/developer/core/network-api/#pupil-remote). With Pupil Capture already running, we can make a short recording as follows:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'OK'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from time import sleep\n",
"from pyplr.pupil import PupilCore\n",
"\n",
"p = PupilCore()\n",
"\n",
"p.command('R our_recording')\n",
"\n",
"sleep(10)\n",
"\n",
"p.command('r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Annotations and notifications\n",
"-----------------------------\n",
"\n",
"To extract experimental events and calculate time-critical PLR parameters (e.g., constriction latency, time-to-peak constriction) we need a reliable indication in the pupil data of the time at which a light stimulus was administered. The Pupil Labs [Annotation Capture](https://docs.pupil-labs.com/core/software/pupil-capture/#annotations) plugin helps us with this. The plugin allows timestamps to be marked with a label manually via keypress or programmatically via the Network API in a process that is analogous to sending a 'trigger', 'message', or 'event marker'. With *PyPlr*'s `PupilCore()` interface, annotations can be generated programmatically with `.new_annotation(...)` and sent with `.send_annotation(...)`. It is important to make sure that the Annotation Capture plugin has been enabled. You can do this manually in the Pupil Capture GUI or programmatically by sending a [notification message](https://docs.pupil-labs.com/developer/core/network-api/#notification-message), which is a special kind of message that the Pupil software uses to coordinate all activities. The following example shows how to enable the Annotation Capture plugin programmatically and then send an annotation with the label `'my_event'` halfway through a 10 second recording."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'OK'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = PupilCore()\n",
"\n",
"p.annotation_capture_plugin(should='start') \n",
"\n",
"p.command('R our_recording')\n",
"\n",
"sleep(5.)\n",
"\n",
"annotation = p.new_annotation(\n",
" label='my_event', \n",
" custom_fields={'whatever':'info','you':'want'})\n",
"\n",
"p.send_annotation(annotation)\n",
"\n",
"sleep(5.)\n",
"\n",
"p.command('r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When the recording is finished, we can open it with Pupil Player and use the [Annotation Player](https://docs.pupil-labs.com/core/software/pupil-player/#annotation-export) plugin to view and export the annotations to CSV format. Any custom labels assigned to the annotation will be included as a column in the exported CSV file. By default, the timestamp of an annotation made with the `.new_annotation(...)` method is set with `.get_corrected_pupil_time(...)`, which gives the current pupil time (corrected for transmission delay); but this can be overridden at a later point if desired. \n",
"\n",
"Notifications can be used for many things, but there is no single exhaustive document. One way to find out what you can manipulate with a notification is to open [the codebase](https://github.com/pupil-labs/pupil) and search for `.notify_all(` and `def on_notify(`. Alternatively, if you just want to access the pupil detector properties, there is a handy method:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'subject': 'pupil_detector.properties.1.Detector2DPlugin',\n",
" 'topic': 'notify.pupil_detector.properties.1.Detector2DPlugin',\n",
" 'values': {'blur_size': 5,\n",
" 'canny_aperture': 5,\n",
" 'canny_ration': 2,\n",
" 'canny_treshold': 160,\n",
" 'coarse_detection': True,\n",
" 'coarse_filter_max': 280,\n",
" 'coarse_filter_min': 128,\n",
" 'contour_size_min': 5,\n",
" 'ellipse_roundness_ratio': 0.1,\n",
" 'ellipse_true_support_min_dist': 2.5,\n",
" 'final_perimeter_ratio_range_max': 1.2,\n",
" 'final_perimeter_ratio_range_min': 0.6,\n",
" 'initial_ellipse_fit_treshhold': 1.8,\n",
" 'intensity_range': 23,\n",
" 'pupil_size_max': 100,\n",
" 'pupil_size_min': 10,\n",
" 'strong_area_ratio_range_max': 1.1,\n",
" 'strong_area_ratio_range_min': 0.6,\n",
" 'strong_perimeter_ratio_range_max': 1.1,\n",
" 'strong_perimeter_ratio_range_min': 0.8,\n",
" 'support_pixel_ratio_exponent': 2.0}}\n"
]
}
],
"source": [
"from pprint import pprint\n",
"\n",
"p = PupilCore()\n",
"\n",
"properties = p.get_pupil_detector_properties(\n",
" detector_name='Detector2DPlugin', \n",
" eye_id=1)\n",
"pprint(properties)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This shows some of the pupil detector properties that can be modified with notifications. Bare in mind that for most use cases it will be best to verify manually in Pupil Capture that all of your most important settings are as they should be."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting pupil data in real-time\n",
"-------------------------------\n",
"\n",
"The Pupil Capture continuously generates data from the camera frames it receives from a Pupil Core headset and makes them available via the [IPC backbone](https://docs.pupil-labs.com/developer/core/network-api/#reading-from-the-ipc-backbone). `PupilCore()` has a `.pupil_grabber(...)` method which simplifies access to these data, empowering users to design lightweight applications that bypass the record-load-export routine of the Pupil Player software. Just specify the topic of interest and how long you want to spend grabbing data:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Grabbing 10 seconds of pupil.1.3d\n",
"PupilGrabber done grabbing 10 seconds of pupil.1.3d\n"
]
}
],
"source": [
"p = PupilCore()\n",
"pgr_future = p.pupil_grabber(topic='pupil.1.3d', seconds=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of note, `.pupil_grabber(...)` does its work in a thread using Python's `concurrent.futures` framework which means the grabbed data can be accessed via a call to the `.result()` method of a returned `Future` object once the work is done:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 1,\n",
" 'topic': 'pupil.1.3d',\n",
" 'method': 'pye3d 0.0.6 real-time',\n",
" 'norm_pos': [0.5360164625892022, 0.6350228570342871],\n",
" 'diameter': 32.04137148235498,\n",
" 'confidence': 1.0,\n",
" 'timestamp': 55693.943421,\n",
" 'sphere': {'center': [6.045138284134944,\n",
" -0.6276101187813217,\n",
" 49.29280081519474],\n",
" 'radius': 10.392304845413264},\n",
" 'projected_sphere': {'center': [130.62958410928272, 92.31126323451176],\n",
" 'axes': [142.37459004874788, 142.37459004874788],\n",
" 'angle': 0.0},\n",
" 'circle_3d': {'center': [0.7818853071011702,\n",
" -3.159317419301394,\n",
" 40.696951453773934],\n",
" 'normal': [-0.5064567538505914, -0.24361364857743406, -0.8271359904549626],\n",
" 'radius': 2.0321335156335523},\n",
" 'diameter_3d': 3.6276776361286016,\n",
" 'ellipse': {'center': [102.91516081712683, 70.07561144941687],\n",
" 'axes': [27.30486055019297, 32.04137148235498],\n",
" 'angle': 32.95347595832888},\n",
" 'location': [102.91516081712683, 70.07561144941687],\n",
" 'model_confidence': 1.0,\n",
" 'theta': 1.8168863457712132,\n",
" 'phi': -2.120212108874702}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pgr_future.result()\n",
"data[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data are returned as a list of dictionaries, with each dictionary representing a single [data point](https://docs.pupil-labs.com/developer/core/overview/#timing-data-conventions). With the `unpack_data_pandas(...)` helper function from `pyplr.utils`, we can organise the whole lot and inspect the pupil timecourse:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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