Handling Gaze Events#

What you will learn in this tutorial:#

how to detect different events using different algorithms like IDT, IVT and microsaccades
how to compute event properties like peak velocity and amplitude
how to save and load your event data

Preparations#

At first, we import pymovements as the alias pm for convenience.

import pymovements as pm

Then we download a dataset ToyDataset and load its data:

dataset = pm.Dataset('ToyDataset', path='data/ToyDataset')
dataset.download()
dataset.load()

INFO:pymovements.dataset.dataset:
        You are downloading the pymovements Toy Dataset. Please be aware that pymovements does not
        host or distribute any dataset resources and only provides a convenient interface to
        download the public dataset resources that were published by their respective authors.

        Please cite the referenced publication if you intend to use the dataset in your research.
        

Downloading https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip to data/ToyDataset/downloads/pymovements-toy-dataset.zip

Checking integrity of pymovements-toy-dataset.zip
Extracting pymovements-toy-dataset.zip to data/ToyDataset/raw

Extracting archive:   0%|          | 0/23 [00:00<?, ?file/s]

Extracting archive: 100%|██████████| 23/23 [00:00<00:00, 357.42file/s]

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  None
  
  None
- custom_read_kwargs:
  None
  
  None
- distance_column:
  None
  
  None
- experiment:
  Experiment
  Experiment
  - eyetracker:
    EyeTracker
    
    EyeTracker
    
    left:
    None
    
    None
    
    model:
    None
    
    None
    
    mount:
    None
    
    None
    
    right:
    None
    
    None
    
    sampling_rate:
    1000
    
    1000
    
    vendor:
    None
    
    None
    
    version:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - screen:
    Screen
    
    Screen
    
    distance_cm:
    68
    
    68
    
    height_cm:
    30.2
    
    30.2
    
    height_px:
    1024
    
    1024
    
    origin:
    'upper left'
    
    'upper left'
    
    width_cm:
    38
    
    38
    
    width_px:
    1280
    
    1280
    
    x_max_dva:
    15.599386487782953
    
    15.599386487782953
    
    x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
    
    y_max_dva:
    12.508044410882546
    
    12.508044410882546
    
    y_min_dva:
    -12.508044410882546
    
    -12.508044410882546
- extract:
  None
  
  None
- filename_format:
  dict (1 items)
  - gaze:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
- filename_format_schema_overrides:
  dict (1 items)
  - gaze:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
- has_resources:
  True
  
  True
- long_name:
  'pymovements Toy Dataset'
  
  'pymovements Toy Dataset'
- mirrors:
  dict (0 items)
- name:
  'ToyDataset'
  
  'ToyDataset'
- pixel_columns:
  None
  
  None
- position_columns:
  None
  
  None
- resources:
  list (1 items)
  - ResourceDefinition
    
    content:
    'gaze'
    
    'gaze'
    
    filename:
    'pymovements-toy-dataset.zip'
    
    'pymovements-toy-dataset.zip'
    
    filename_pattern:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
    
    filename_pattern_schema_overrides:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
    
    load_function:
    None
    
    None
    
    load_kwargs:
    dict (4 items)
    
    time_column:
    'timestamp'
    
    'timestamp'
    
    time_unit:
    'ms'
    
    'ms'
    
    (2 more)
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  None
  
  None
- time_unit:
  None
  
  None
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None
events:
tuple (20 items)
- Events
  - frame:
    DataFrame (4 columns, 0 rows)
    
    shape: (0, 4)
    name onset offset duration
    str i64 i64 i64
  - trial_columns:
    None
    
    None
- Events
  - frame:
    DataFrame (4 columns, 0 rows)
    
    shape: (0, 4)
    name onset offset duration
    str i64 i64 i64
  - trial_columns:
    None
    
    None
- (18 more)

fileinfo:

dict (1 items)

gaze:

DataFrame (3 columns, 20 rows)

shape: (20, 3)

text_id	page_id	filepath
i64	i64	str
0	1	"pymovements-toy-dataset-main/d…
0	2	"pymovements-toy-dataset-main/d…
0	3	"pymovements-toy-dataset-main/d…
0	4	"pymovements-toy-dataset-main/d…
0	5	"pymovements-toy-dataset-main/d…
…	…	…
3	1	"pymovements-toy-dataset-main/d…
3	2	"pymovements-toy-dataset-main/d…
3	3	"pymovements-toy-dataset-main/d…
3	4	"pymovements-toy-dataset-main/d…
3	5	"pymovements-toy-dataset-main/d…

gaze:

list (20 items)

Gaze

samples:

DataFrame (4 columns, 17223 rows)

shape: (17_223, 4)

time	stimuli_x	stimuli_y	pixel
i64	f64	f64	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]
1988146	-1.0	-1.0	[206.9, 152.1]
1988147	-1.0	-1.0	[207.0, 151.8]
1988148	-1.0	-1.0	[207.1, 151.7]
1988149	-1.0	-1.0	[207.0, 151.5]
…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]
2005364	-1.0	-1.0	[358.0, 414.5]
2005365	-1.0	-1.0	[355.8, 413.8]
2005366	-1.0	-1.0	[353.1, 413.2]
2005367	-1.0	-1.0	[351.2, 412.9]

events:
Events
Events
- frame:
  DataFrame (4 columns, 0 rows)
  
  shape: (0, 4)
  name onset offset duration
  str i64 i64 i64
- trial_columns:
  None
  
  None
trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Gaze

samples:

DataFrame (4 columns, 29799 rows)

shape: (29_799, 4)

time	stimuli_x	stimuli_y	pixel
i64	f64	f64	list[f64]
2008305	-1.0	-1.0	[141.4, 153.6]
2008306	-1.0	-1.0	[141.1, 153.2]
2008307	-1.0	-1.0	[140.7, 152.8]
2008308	-1.0	-1.0	[140.6, 152.7]
2008309	-1.0	-1.0	[140.5, 152.6]
…	…	…	…
2038099	-1.0	-1.0	[273.8, 773.8]
2038100	-1.0	-1.0	[273.8, 774.1]
2038101	-1.0	-1.0	[273.9, 774.5]
2038102	-1.0	-1.0	[274.0, 774.4]
2038103	-1.0	-1.0	[274.0, 773.9]

events:
Events
Events
- frame:
  DataFrame (4 columns, 0 rows)
  
  shape: (0, 4)
  name onset offset duration
  str i64 i64 i64
- trial_columns:
  None
  
  None
trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

(18 more)

path:
PosixPath('data/ToyDataset')

PosixPath('data/ToyDataset')
paths:
DatasetPaths
DatasetPaths
- dataset:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
- downloads:
  PosixPath('data/ToyDataset/downloads')
  
  PosixPath('data/ToyDataset/downloads')
- events:
  PosixPath('data/ToyDataset/events')
  
  PosixPath('data/ToyDataset/events')
- precomputed_events:
  PosixPath('data/ToyDataset/precomputed_events')
  
  PosixPath('data/ToyDataset/precomputed_events')
- precomputed_reading_measures:
  PosixPath
  
  PosixPath('data/ToyDataset/precomputed_reading_measures')
- preprocessed:
  PosixPath('data/ToyDataset/preprocessed')
  
  PosixPath('data/ToyDataset/preprocessed')
- raw:
  PosixPath('data/ToyDataset/raw')
  
  PosixPath('data/ToyDataset/raw')
- root:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
precomputed_events:
list (0 items)
precomputed_reading_measures:
list (0 items)

The dataset consists of gaze data in 20 files (check Dataset/gaze above). Every Gaze has some samples with six columns (check Gaze/samples): [time, stimuli_x, stimuli_y, text_id, page_id, pixel]. The Gaze/events DataFrame is empty so far. To be able to calculate events, we need to do some basic preprocessing, which will add new columns to the dataset samples DataFrame:

Dataset.pix2deg(): adds position column with degrees from the screen center needed by the idt algorithm
Dataset.pos2vel(): adds velocity column with gaze velocities needed by microsaccades and ivt algorithms

dataset.pix2deg()
dataset.pos2vel('smooth')
dataset.gaze[0]

Gaze

samples:

DataFrame (6 columns, 17223 rows)

shape: (17_223, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

events:
Events
Events
- frame:
  DataFrame (4 columns, 0 rows)
  
  shape: (0, 4)
  name onset offset duration
  str i64 i64 i64
- trial_columns:
  None
  
  None
trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Now every Gaze/samples DataFrame has two more columns: position and velocity which will be used by the event detection algorithms.

Detecting Events#

pymovements provides a range of event detection methods for several types of gaze events.

See the reference for Events to get an overview of all the supported methods.

For this tutorial we will use the I-DT and I-VT (idt and ivt) algorithms for detecting fixations and the microsaccades algorithm for detecting saccades.

Let’s start with fixations detection using the idt algorithm with the dispersion_threshold equal to 2.7:

dataset.detect_events('idt', dispersion_threshold=2.7)

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  None
  
  None
- custom_read_kwargs:
  None
  
  None
- distance_column:
  None
  
  None
- experiment:
  Experiment
  Experiment
  - eyetracker:
    EyeTracker
    
    EyeTracker
    
    left:
    None
    
    None
    
    model:
    None
    
    None
    
    mount:
    None
    
    None
    
    right:
    None
    
    None
    
    sampling_rate:
    1000
    
    1000
    
    vendor:
    None
    
    None
    
    version:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - screen:
    Screen
    
    Screen
    
    distance_cm:
    68
    
    68
    
    height_cm:
    30.2
    
    30.2
    
    height_px:
    1024
    
    1024
    
    origin:
    'upper left'
    
    'upper left'
    
    width_cm:
    38
    
    38
    
    width_px:
    1280
    
    1280
    
    x_max_dva:
    15.599386487782953
    
    15.599386487782953
    
    x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
    
    y_max_dva:
    12.508044410882546
    
    12.508044410882546
    
    y_min_dva:
    -12.508044410882546
    
    -12.508044410882546
- extract:
  None
  
  None
- filename_format:
  dict (1 items)
  - gaze:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
- filename_format_schema_overrides:
  dict (1 items)
  - gaze:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
- has_resources:
  True
  
  True
- long_name:
  'pymovements Toy Dataset'
  
  'pymovements Toy Dataset'
- mirrors:
  dict (0 items)
- name:
  'ToyDataset'
  
  'ToyDataset'
- pixel_columns:
  None
  
  None
- position_columns:
  None
  
  None
- resources:
  list (1 items)
  - ResourceDefinition
    
    content:
    'gaze'
    
    'gaze'
    
    filename:
    'pymovements-toy-dataset.zip'
    
    'pymovements-toy-dataset.zip'
    
    filename_pattern:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
    
    filename_pattern_schema_overrides:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
    
    load_function:
    None
    
    None
    
    load_kwargs:
    dict (4 items)
    
    time_column:
    'timestamp'
    
    'timestamp'
    
    time_unit:
    'ms'
    
    'ms'
    
    (2 more)
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  None
  
  None
- time_unit:
  None
  
  None
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None

events:

tuple (20 items)

Events

frame:

DataFrame (4 columns, 56 rows)

shape: (56, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988563	418
"fixation"	1988564	1988750	186
"fixation"	1988751	1989178	427
"fixation"	1989179	1989436	257
"fixation"	1989437	1989600	163
…	…	…	…
"fixation"	2003929	2004090	161
"fixation"	2004091	2004363	272
"fixation"	2004364	2004883	519
"fixation"	2004885	2005116	231
"fixation"	2005117	2005298	181

trial_columns:
None

None

Events

frame:

DataFrame (4 columns, 94 rows)

shape: (94, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	2008305	2008621	316
"fixation"	2008622	2008821	199
"fixation"	2008822	2009214	392
"fixation"	2009215	2009433	218
"fixation"	2009434	2009704	270
…	…	…	…
"fixation"	2036840	2037175	335
"fixation"	2037176	2037424	248
"fixation"	2037462	2037644	182
"fixation"	2037645	2037824	179
"fixation"	2037825	2038103	278

trial_columns:
None

None

(18 more)

fileinfo:

dict (1 items)

gaze:

DataFrame (3 columns, 20 rows)

shape: (20, 3)

text_id	page_id	filepath
i64	i64	str
0	1	"pymovements-toy-dataset-main/d…
0	2	"pymovements-toy-dataset-main/d…
0	3	"pymovements-toy-dataset-main/d…
0	4	"pymovements-toy-dataset-main/d…
0	5	"pymovements-toy-dataset-main/d…
…	…	…
3	1	"pymovements-toy-dataset-main/d…
3	2	"pymovements-toy-dataset-main/d…
3	3	"pymovements-toy-dataset-main/d…
3	4	"pymovements-toy-dataset-main/d…
3	5	"pymovements-toy-dataset-main/d…

gaze:

list (20 items)

Gaze

samples:

DataFrame (6 columns, 17223 rows)

shape: (17_223, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

events:

Events

frame:

DataFrame (4 columns, 56 rows)

shape: (56, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988563	418
"fixation"	1988564	1988750	186
"fixation"	1988751	1989178	427
"fixation"	1989179	1989436	257
"fixation"	1989437	1989600	163
…	…	…	…
"fixation"	2003929	2004090	161
"fixation"	2004091	2004363	272
"fixation"	2004364	2004883	519
"fixation"	2004885	2005116	231
"fixation"	2005117	2005298	181

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Gaze

samples:

DataFrame (6 columns, 29799 rows)

shape: (29_799, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
2008305	-1.0	-1.0	[141.4, 153.6]	[-12.268583, -8.823284]	[null, null]
2008306	-1.0	-1.0	[141.1, 153.2]	[-12.275749, -8.832989]	[null, null]
2008307	-1.0	-1.0	[140.7, 152.8]	[-12.285302, -8.842695]	[-5.572617, -6.065816]
2008308	-1.0	-1.0	[140.6, 152.7]	[-12.28769, -8.845121]	[-3.582268, -4.043733]
2008309	-1.0	-1.0	[140.5, 152.6]	[-12.290078, -8.847547]	[-2.388085, -2.021821]
…	…	…	…	…	…
2038099	-1.0	-1.0	[273.8, 773.8]	[-9.071149, 6.490168]	[1.21962, 1.635403]
2038100	-1.0	-1.0	[273.8, 774.1]	[-9.071149, 6.497527]	[1.626175, 4.497406]
2038101	-1.0	-1.0	[273.9, 774.5]	[-9.06871, 6.50734]	[1.626186, 1.635423]
2038102	-1.0	-1.0	[274.0, 774.4]	[-9.066271, 6.504886]	[null, null]
2038103	-1.0	-1.0	[274.0, 773.9]	[-9.066271, 6.492621]	[null, null]

events:

Events

frame:

DataFrame (4 columns, 94 rows)

shape: (94, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	2008305	2008621	316
"fixation"	2008622	2008821	199
"fixation"	2008822	2009214	392
"fixation"	2009215	2009433	218
"fixation"	2009434	2009704	270
…	…	…	…
"fixation"	2036840	2037175	335
"fixation"	2037176	2037424	248
"fixation"	2037462	2037644	182
"fixation"	2037645	2037824	179
"fixation"	2037825	2038103	278

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

(18 more)

path:
PosixPath('data/ToyDataset')

PosixPath('data/ToyDataset')
paths:
DatasetPaths
DatasetPaths
- dataset:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
- downloads:
  PosixPath('data/ToyDataset/downloads')
  
  PosixPath('data/ToyDataset/downloads')
- events:
  PosixPath('data/ToyDataset/events')
  
  PosixPath('data/ToyDataset/events')
- precomputed_events:
  PosixPath('data/ToyDataset/precomputed_events')
  
  PosixPath('data/ToyDataset/precomputed_events')
- precomputed_reading_measures:
  PosixPath
  
  PosixPath('data/ToyDataset/precomputed_reading_measures')
- preprocessed:
  PosixPath('data/ToyDataset/preprocessed')
  
  PosixPath('data/ToyDataset/preprocessed')
- raw:
  PosixPath('data/ToyDataset/raw')
  
  PosixPath('data/ToyDataset/raw')
- root:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
precomputed_events:
list (0 items)
precomputed_reading_measures:
list (0 items)

The detected events are added as rows with the name fixation to the event dataframe:

dataset.events[0]

Events

frame:

DataFrame (4 columns, 56 rows)

shape: (56, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988563	418
"fixation"	1988564	1988750	186
"fixation"	1988751	1989178	427
"fixation"	1989179	1989436	257
"fixation"	1989437	1989600	163
…	…	…	…
"fixation"	2003929	2004090	161
"fixation"	2004091	2004363	272
"fixation"	2004364	2004883	519
"fixation"	2004885	2005116	231
"fixation"	2005117	2005298	181

trial_columns:
None

None

As you can see, 56 fixations were found for the first file.

Now let’s try another algorithm ivt with velocity_threshold=20. Because we don’t want to mix fixations found by different algorithms we add name parameter with ‘fixation.ivt’

dataset.detect_events('ivt', velocity_threshold=20, name='fixation.ivt')
dataset.events[0]

Events

frame:

DataFrame (4 columns, 129 rows)

shape: (129, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988563	418
"fixation"	1988564	1988750	186
"fixation"	1988751	1989178	427
"fixation"	1989179	1989436	257
"fixation"	1989437	1989600	163
…	…	…	…
"fixation.ivt"	2004132	2004331	199
"fixation.ivt"	2004399	2004687	288
"fixation.ivt"	2004714	2004878	164
"fixation.ivt"	2004931	2005109	178
"fixation.ivt"	2005138	2005287	149

trial_columns:
None

None

Now we have additional rows with name=’fixations.ivt’.

Let’s try to use the microsaccades algorithm to detect fixations.

dataset.detect_events('microsaccades', minimum_duration=12)

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  None
  
  None
- custom_read_kwargs:
  None
  
  None
- distance_column:
  None
  
  None
- experiment:
  Experiment
  Experiment
  - eyetracker:
    EyeTracker
    
    EyeTracker
    
    left:
    None
    
    None
    
    model:
    None
    
    None
    
    mount:
    None
    
    None
    
    right:
    None
    
    None
    
    sampling_rate:
    1000
    
    1000
    
    vendor:
    None
    
    None
    
    version:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - screen:
    Screen
    
    Screen
    
    distance_cm:
    68
    
    68
    
    height_cm:
    30.2
    
    30.2
    
    height_px:
    1024
    
    1024
    
    origin:
    'upper left'
    
    'upper left'
    
    width_cm:
    38
    
    38
    
    width_px:
    1280
    
    1280
    
    x_max_dva:
    15.599386487782953
    
    15.599386487782953
    
    x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
    
    y_max_dva:
    12.508044410882546
    
    12.508044410882546
    
    y_min_dva:
    -12.508044410882546
    
    -12.508044410882546
- extract:
  None
  
  None
- filename_format:
  dict (1 items)
  - gaze:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
- filename_format_schema_overrides:
  dict (1 items)
  - gaze:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
- has_resources:
  True
  
  True
- long_name:
  'pymovements Toy Dataset'
  
  'pymovements Toy Dataset'
- mirrors:
  dict (0 items)
- name:
  'ToyDataset'
  
  'ToyDataset'
- pixel_columns:
  None
  
  None
- position_columns:
  None
  
  None
- resources:
  list (1 items)
  - ResourceDefinition
    
    content:
    'gaze'
    
    'gaze'
    
    filename:
    'pymovements-toy-dataset.zip'
    
    'pymovements-toy-dataset.zip'
    
    filename_pattern:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
    
    filename_pattern_schema_overrides:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
    
    load_function:
    None
    
    None
    
    load_kwargs:
    dict (4 items)
    
    time_column:
    'timestamp'
    
    'timestamp'
    
    time_unit:
    'ms'
    
    'ms'
    
    (2 more)
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  None
  
  None
- time_unit:
  None
  
  None
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None

events:

tuple (20 items)

Events

frame:

DataFrame (4 columns, 222 rows)

shape: (222, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988563	418
"fixation"	1988564	1988750	186
"fixation"	1988751	1989178	427
"fixation"	1989179	1989436	257
"fixation"	1989437	1989600	163
…	…	…	…
"saccade"	2004373	2004385	12
"saccade"	2004688	2004704	16
"saccade"	2004879	2004901	22
"saccade"	2005110	2005126	16
"saccade"	2005288	2005345	57

trial_columns:
None

None

Events

frame:

DataFrame (4 columns, 366 rows)

shape: (366, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	2008305	2008621	316
"fixation"	2008622	2008821	199
"fixation"	2008822	2009214	392
"fixation"	2009215	2009433	218
"fixation"	2009434	2009704	270
…	…	…	…
"saccade"	2036849	2036861	12
"saccade"	2037161	2037188	27
"saccade"	2037412	2037503	91
"saccade"	2037638	2037654	16
"saccade"	2037812	2037830	18

trial_columns:
None

None

(18 more)

fileinfo:

dict (1 items)

gaze:

DataFrame (3 columns, 20 rows)

shape: (20, 3)

text_id	page_id	filepath
i64	i64	str
0	1	"pymovements-toy-dataset-main/d…
0	2	"pymovements-toy-dataset-main/d…
0	3	"pymovements-toy-dataset-main/d…
0	4	"pymovements-toy-dataset-main/d…
0	5	"pymovements-toy-dataset-main/d…
…	…	…
3	1	"pymovements-toy-dataset-main/d…
3	2	"pymovements-toy-dataset-main/d…
3	3	"pymovements-toy-dataset-main/d…
3	4	"pymovements-toy-dataset-main/d…
3	5	"pymovements-toy-dataset-main/d…

gaze:

list (20 items)

Gaze

samples:

DataFrame (6 columns, 17223 rows)

shape: (17_223, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

events:

Events

frame:

DataFrame (4 columns, 222 rows)

shape: (222, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988563	418
"fixation"	1988564	1988750	186
"fixation"	1988751	1989178	427
"fixation"	1989179	1989436	257
"fixation"	1989437	1989600	163
…	…	…	…
"saccade"	2004373	2004385	12
"saccade"	2004688	2004704	16
"saccade"	2004879	2004901	22
"saccade"	2005110	2005126	16
"saccade"	2005288	2005345	57

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Gaze

samples:

DataFrame (6 columns, 29799 rows)

shape: (29_799, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
2008305	-1.0	-1.0	[141.4, 153.6]	[-12.268583, -8.823284]	[null, null]
2008306	-1.0	-1.0	[141.1, 153.2]	[-12.275749, -8.832989]	[null, null]
2008307	-1.0	-1.0	[140.7, 152.8]	[-12.285302, -8.842695]	[-5.572617, -6.065816]
2008308	-1.0	-1.0	[140.6, 152.7]	[-12.28769, -8.845121]	[-3.582268, -4.043733]
2008309	-1.0	-1.0	[140.5, 152.6]	[-12.290078, -8.847547]	[-2.388085, -2.021821]
…	…	…	…	…	…
2038099	-1.0	-1.0	[273.8, 773.8]	[-9.071149, 6.490168]	[1.21962, 1.635403]
2038100	-1.0	-1.0	[273.8, 774.1]	[-9.071149, 6.497527]	[1.626175, 4.497406]
2038101	-1.0	-1.0	[273.9, 774.5]	[-9.06871, 6.50734]	[1.626186, 1.635423]
2038102	-1.0	-1.0	[274.0, 774.4]	[-9.066271, 6.504886]	[null, null]
2038103	-1.0	-1.0	[274.0, 773.9]	[-9.066271, 6.492621]	[null, null]

events:

Events

frame:

DataFrame (4 columns, 366 rows)

shape: (366, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	2008305	2008621	316
"fixation"	2008622	2008821	199
"fixation"	2008822	2009214	392
"fixation"	2009215	2009433	218
"fixation"	2009434	2009704	270
…	…	…	…
"saccade"	2036849	2036861	12
"saccade"	2037161	2037188	27
"saccade"	2037412	2037503	91
"saccade"	2037638	2037654	16
"saccade"	2037812	2037830	18

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

(18 more)

path:
PosixPath('data/ToyDataset')

PosixPath('data/ToyDataset')
paths:
DatasetPaths
DatasetPaths
- dataset:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
- downloads:
  PosixPath('data/ToyDataset/downloads')
  
  PosixPath('data/ToyDataset/downloads')
- events:
  PosixPath('data/ToyDataset/events')
  
  PosixPath('data/ToyDataset/events')
- precomputed_events:
  PosixPath('data/ToyDataset/precomputed_events')
  
  PosixPath('data/ToyDataset/precomputed_events')
- precomputed_reading_measures:
  PosixPath
  
  PosixPath('data/ToyDataset/precomputed_reading_measures')
- preprocessed:
  PosixPath('data/ToyDataset/preprocessed')
  
  PosixPath('data/ToyDataset/preprocessed')
- raw:
  PosixPath('data/ToyDataset/raw')
  
  PosixPath('data/ToyDataset/raw')
- root:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
precomputed_events:
list (0 items)
precomputed_reading_measures:
list (0 items)

The detected events are added as rows with the name saccade to the event dataframe:

dataset.events[0]

Events

frame:

DataFrame (4 columns, 222 rows)

shape: (222, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988563	418
"fixation"	1988564	1988750	186
"fixation"	1988751	1989178	427
"fixation"	1989179	1989436	257
"fixation"	1989437	1989600	163
…	…	…	…
"saccade"	2004373	2004385	12
"saccade"	2004688	2004704	16
"saccade"	2004879	2004901	22
"saccade"	2005110	2005126	16
"saccade"	2005288	2005345	57

trial_columns:
None

None

Now there are three sets of events in the dataset.events DataFrame with different values in the ‘name’ column:

set(dataset.events[0].frame['name'])

{'fixation', 'fixation.ivt', 'saccade'}

Computing Event Properties#

pymovements provides a range of event properties.

See the reference for Events to get an overview of all the supported properties.

For this tutorial we will compute several properties of saccades.

We start out with the peak velocity:

dataset.compute_event_properties("peak_velocity")

dataset.events[0]

Events

frame:

DataFrame (5 columns, 222 rows)

shape: (222, 5)

name	onset	offset	duration	peak_velocity
str	i64	i64	i64	f64
"fixation"	1988145	1988563	418	200.144558
"fixation"	1988564	1988750	186	249.67823
"fixation"	1988751	1989178	427	211.598748
"fixation"	1989179	1989436	257	189.183243
"fixation"	1989437	1989600	163	255.077509
…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183
"saccade"	2004688	2004704	16	175.646379
"saccade"	2004879	2004901	22	209.46361
"saccade"	2005110	2005126	16	137.917594
"saccade"	2005288	2005345	57	352.550667

trial_columns:
None

None

Check above that a new column with the name peak_velocity has appeared in the event DataFrame.

We can also pass a list of properties. Let’s add the amplitude and dispersion:

dataset.compute_event_properties(["amplitude", "dispersion"])

dataset.events[0]

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

This way we can compute all of our desired properties in a single run.

Saving Event Data#

Saving your event data is as simple as:

dataset.save_events()

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  None
  
  None
- custom_read_kwargs:
  None
  
  None
- distance_column:
  None
  
  None
- experiment:
  Experiment
  Experiment
  - eyetracker:
    EyeTracker
    
    EyeTracker
    
    left:
    None
    
    None
    
    model:
    None
    
    None
    
    mount:
    None
    
    None
    
    right:
    None
    
    None
    
    sampling_rate:
    1000
    
    1000
    
    vendor:
    None
    
    None
    
    version:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - screen:
    Screen
    
    Screen
    
    distance_cm:
    68
    
    68
    
    height_cm:
    30.2
    
    30.2
    
    height_px:
    1024
    
    1024
    
    origin:
    'upper left'
    
    'upper left'
    
    width_cm:
    38
    
    38
    
    width_px:
    1280
    
    1280
    
    x_max_dva:
    15.599386487782953
    
    15.599386487782953
    
    x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
    
    y_max_dva:
    12.508044410882546
    
    12.508044410882546
    
    y_min_dva:
    -12.508044410882546
    
    -12.508044410882546
- extract:
  None
  
  None
- filename_format:
  dict (1 items)
  - gaze:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
- filename_format_schema_overrides:
  dict (1 items)
  - gaze:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
- has_resources:
  True
  
  True
- long_name:
  'pymovements Toy Dataset'
  
  'pymovements Toy Dataset'
- mirrors:
  dict (0 items)
- name:
  'ToyDataset'
  
  'ToyDataset'
- pixel_columns:
  None
  
  None
- position_columns:
  None
  
  None
- resources:
  list (1 items)
  - ResourceDefinition
    
    content:
    'gaze'
    
    'gaze'
    
    filename:
    'pymovements-toy-dataset.zip'
    
    'pymovements-toy-dataset.zip'
    
    filename_pattern:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
    
    filename_pattern_schema_overrides:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
    
    load_function:
    None
    
    None
    
    load_kwargs:
    dict (4 items)
    
    time_column:
    'timestamp'
    
    'timestamp'
    
    time_unit:
    'ms'
    
    'ms'
    
    (2 more)
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  None
  
  None
- time_unit:
  None
  
  None
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None

events:

tuple (20 items)

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

Events

frame:

DataFrame (7 columns, 366 rows)

shape: (366, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	2008305	2008621	316	167.343877	2.283024	2.706135
"fixation"	2008622	2008821	199	314.396904	2.578854	2.79657
"fixation"	2008822	2009214	392	305.525917	2.612895	2.908934
"fixation"	2009215	2009433	218	216.195201	2.612208	2.765062
"fixation"	2009434	2009704	270	208.05132	2.566305	2.70311
…	…	…	…	…	…	…
"saccade"	2036849	2036861	12	54.743137	0.472141	0.529715
"saccade"	2037161	2037188	27	223.056103	2.358604	2.587752
"saccade"	2037412	2037503	91	406.701444	16.94863	18.346458
"saccade"	2037638	2037654	16	138.382767	1.411621	1.827761
"saccade"	2037812	2037830	18	240.193236	2.739312	3.024326

trial_columns:
None

None

(18 more)

fileinfo:

dict (1 items)

gaze:

DataFrame (3 columns, 20 rows)

shape: (20, 3)

text_id	page_id	filepath
i64	i64	str
0	1	"pymovements-toy-dataset-main/d…
0	2	"pymovements-toy-dataset-main/d…
0	3	"pymovements-toy-dataset-main/d…
0	4	"pymovements-toy-dataset-main/d…
0	5	"pymovements-toy-dataset-main/d…
…	…	…
3	1	"pymovements-toy-dataset-main/d…
3	2	"pymovements-toy-dataset-main/d…
3	3	"pymovements-toy-dataset-main/d…
3	4	"pymovements-toy-dataset-main/d…
3	5	"pymovements-toy-dataset-main/d…

gaze:

list (20 items)

Gaze

samples:

DataFrame (6 columns, 17223 rows)

shape: (17_223, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

events:

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Gaze

samples:

DataFrame (6 columns, 29799 rows)

shape: (29_799, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
2008305	-1.0	-1.0	[141.4, 153.6]	[-12.268583, -8.823284]	[null, null]
2008306	-1.0	-1.0	[141.1, 153.2]	[-12.275749, -8.832989]	[null, null]
2008307	-1.0	-1.0	[140.7, 152.8]	[-12.285302, -8.842695]	[-5.572617, -6.065816]
2008308	-1.0	-1.0	[140.6, 152.7]	[-12.28769, -8.845121]	[-3.582268, -4.043733]
2008309	-1.0	-1.0	[140.5, 152.6]	[-12.290078, -8.847547]	[-2.388085, -2.021821]
…	…	…	…	…	…
2038099	-1.0	-1.0	[273.8, 773.8]	[-9.071149, 6.490168]	[1.21962, 1.635403]
2038100	-1.0	-1.0	[273.8, 774.1]	[-9.071149, 6.497527]	[1.626175, 4.497406]
2038101	-1.0	-1.0	[273.9, 774.5]	[-9.06871, 6.50734]	[1.626186, 1.635423]
2038102	-1.0	-1.0	[274.0, 774.4]	[-9.066271, 6.504886]	[null, null]
2038103	-1.0	-1.0	[274.0, 773.9]	[-9.066271, 6.492621]	[null, null]

events:

Events

frame:

DataFrame (7 columns, 366 rows)

shape: (366, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	2008305	2008621	316	167.343877	2.283024	2.706135
"fixation"	2008622	2008821	199	314.396904	2.578854	2.79657
"fixation"	2008822	2009214	392	305.525917	2.612895	2.908934
"fixation"	2009215	2009433	218	216.195201	2.612208	2.765062
"fixation"	2009434	2009704	270	208.05132	2.566305	2.70311
…	…	…	…	…	…	…
"saccade"	2036849	2036861	12	54.743137	0.472141	0.529715
"saccade"	2037161	2037188	27	223.056103	2.358604	2.587752
"saccade"	2037412	2037503	91	406.701444	16.94863	18.346458
"saccade"	2037638	2037654	16	138.382767	1.411621	1.827761
"saccade"	2037812	2037830	18	240.193236	2.739312	3.024326

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

(18 more)

path:
PosixPath('data/ToyDataset')

PosixPath('data/ToyDataset')
paths:
DatasetPaths
DatasetPaths
- dataset:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
- downloads:
  PosixPath('data/ToyDataset/downloads')
  
  PosixPath('data/ToyDataset/downloads')
- events:
  PosixPath('data/ToyDataset/events')
  
  PosixPath('data/ToyDataset/events')
- precomputed_events:
  PosixPath('data/ToyDataset/precomputed_events')
  
  PosixPath('data/ToyDataset/precomputed_events')
- precomputed_reading_measures:
  PosixPath
  
  PosixPath('data/ToyDataset/precomputed_reading_measures')
- preprocessed:
  PosixPath('data/ToyDataset/preprocessed')
  
  PosixPath('data/ToyDataset/preprocessed')
- raw:
  PosixPath('data/ToyDataset/raw')
  
  PosixPath('data/ToyDataset/raw')
- root:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
precomputed_events:
list (0 items)
precomputed_reading_measures:
list (0 items)

All the event data is saved into this directory:

dataset.paths.events

PosixPath('data/ToyDataset/events')

Let’s confirm it by printing all files in this directory:

print(list(dataset.paths.events.glob('*/*/*')))

[PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_0_1.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_1_2.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_0_2.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_3_5.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_1_4.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_0_4.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_3_4.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_2_3.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_2_2.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_3_1.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_1_5.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_3_3.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_2_4.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_3_2.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_2_5.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_1_3.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_0_3.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_1_1.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_0_5.feather'), PosixPath('data/ToyDataset/events/pymovements-toy-dataset-main/data/trial_2_1.feather')]

All files have been saved into the Dataset.paths.events as files in Feather format.

If we want to save the data into an alternative directory and also use a different file format like csv we can use the following:

dataset.save_events(events_dirname='events_csv', extension='csv')

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  None
  
  None
- custom_read_kwargs:
  None
  
  None
- distance_column:
  None
  
  None
- experiment:
  Experiment
  Experiment
  - eyetracker:
    EyeTracker
    
    EyeTracker
    
    left:
    None
    
    None
    
    model:
    None
    
    None
    
    mount:
    None
    
    None
    
    right:
    None
    
    None
    
    sampling_rate:
    1000
    
    1000
    
    vendor:
    None
    
    None
    
    version:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - screen:
    Screen
    
    Screen
    
    distance_cm:
    68
    
    68
    
    height_cm:
    30.2
    
    30.2
    
    height_px:
    1024
    
    1024
    
    origin:
    'upper left'
    
    'upper left'
    
    width_cm:
    38
    
    38
    
    width_px:
    1280
    
    1280
    
    x_max_dva:
    15.599386487782953
    
    15.599386487782953
    
    x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
    
    y_max_dva:
    12.508044410882546
    
    12.508044410882546
    
    y_min_dva:
    -12.508044410882546
    
    -12.508044410882546
- extract:
  None
  
  None
- filename_format:
  dict (1 items)
  - gaze:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
- filename_format_schema_overrides:
  dict (1 items)
  - gaze:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
- has_resources:
  True
  
  True
- long_name:
  'pymovements Toy Dataset'
  
  'pymovements Toy Dataset'
- mirrors:
  dict (0 items)
- name:
  'ToyDataset'
  
  'ToyDataset'
- pixel_columns:
  None
  
  None
- position_columns:
  None
  
  None
- resources:
  list (1 items)
  - ResourceDefinition
    
    content:
    'gaze'
    
    'gaze'
    
    filename:
    'pymovements-toy-dataset.zip'
    
    'pymovements-toy-dataset.zip'
    
    filename_pattern:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
    
    filename_pattern_schema_overrides:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
    
    load_function:
    None
    
    None
    
    load_kwargs:
    dict (4 items)
    
    time_column:
    'timestamp'
    
    'timestamp'
    
    time_unit:
    'ms'
    
    'ms'
    
    (2 more)
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  None
  
  None
- time_unit:
  None
  
  None
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None

events:

tuple (20 items)

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

Events

frame:

DataFrame (7 columns, 366 rows)

shape: (366, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	2008305	2008621	316	167.343877	2.283024	2.706135
"fixation"	2008622	2008821	199	314.396904	2.578854	2.79657
"fixation"	2008822	2009214	392	305.525917	2.612895	2.908934
"fixation"	2009215	2009433	218	216.195201	2.612208	2.765062
"fixation"	2009434	2009704	270	208.05132	2.566305	2.70311
…	…	…	…	…	…	…
"saccade"	2036849	2036861	12	54.743137	0.472141	0.529715
"saccade"	2037161	2037188	27	223.056103	2.358604	2.587752
"saccade"	2037412	2037503	91	406.701444	16.94863	18.346458
"saccade"	2037638	2037654	16	138.382767	1.411621	1.827761
"saccade"	2037812	2037830	18	240.193236	2.739312	3.024326

trial_columns:
None

None

(18 more)

fileinfo:

dict (1 items)

gaze:

DataFrame (3 columns, 20 rows)

shape: (20, 3)

text_id	page_id	filepath
i64	i64	str
0	1	"pymovements-toy-dataset-main/d…
0	2	"pymovements-toy-dataset-main/d…
0	3	"pymovements-toy-dataset-main/d…
0	4	"pymovements-toy-dataset-main/d…
0	5	"pymovements-toy-dataset-main/d…
…	…	…
3	1	"pymovements-toy-dataset-main/d…
3	2	"pymovements-toy-dataset-main/d…
3	3	"pymovements-toy-dataset-main/d…
3	4	"pymovements-toy-dataset-main/d…
3	5	"pymovements-toy-dataset-main/d…

gaze:

list (20 items)

Gaze

samples:

DataFrame (6 columns, 17223 rows)

shape: (17_223, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

events:

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Gaze

samples:

DataFrame (6 columns, 29799 rows)

shape: (29_799, 6)

time	stimuli_x	stimuli_y	pixel	position	velocity
i64	f64	f64	list[f64]	list[f64]	list[f64]
2008305	-1.0	-1.0	[141.4, 153.6]	[-12.268583, -8.823284]	[null, null]
2008306	-1.0	-1.0	[141.1, 153.2]	[-12.275749, -8.832989]	[null, null]
2008307	-1.0	-1.0	[140.7, 152.8]	[-12.285302, -8.842695]	[-5.572617, -6.065816]
2008308	-1.0	-1.0	[140.6, 152.7]	[-12.28769, -8.845121]	[-3.582268, -4.043733]
2008309	-1.0	-1.0	[140.5, 152.6]	[-12.290078, -8.847547]	[-2.388085, -2.021821]
…	…	…	…	…	…
2038099	-1.0	-1.0	[273.8, 773.8]	[-9.071149, 6.490168]	[1.21962, 1.635403]
2038100	-1.0	-1.0	[273.8, 774.1]	[-9.071149, 6.497527]	[1.626175, 4.497406]
2038101	-1.0	-1.0	[273.9, 774.5]	[-9.06871, 6.50734]	[1.626186, 1.635423]
2038102	-1.0	-1.0	[274.0, 774.4]	[-9.066271, 6.504886]	[null, null]
2038103	-1.0	-1.0	[274.0, 773.9]	[-9.066271, 6.492621]	[null, null]

events:

Events

frame:

DataFrame (7 columns, 366 rows)

shape: (366, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	2008305	2008621	316	167.343877	2.283024	2.706135
"fixation"	2008622	2008821	199	314.396904	2.578854	2.79657
"fixation"	2008822	2009214	392	305.525917	2.612895	2.908934
"fixation"	2009215	2009433	218	216.195201	2.612208	2.765062
"fixation"	2009434	2009704	270	208.05132	2.566305	2.70311
…	…	…	…	…	…	…
"saccade"	2036849	2036861	12	54.743137	0.472141	0.529715
"saccade"	2037161	2037188	27	223.056103	2.358604	2.587752
"saccade"	2037412	2037503	91	406.701444	16.94863	18.346458
"saccade"	2037638	2037654	16	138.382767	1.411621	1.827761
"saccade"	2037812	2037830	18	240.193236	2.739312	3.024326

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

(18 more)

path:
PosixPath('data/ToyDataset')

PosixPath('data/ToyDataset')
paths:
DatasetPaths
DatasetPaths
- dataset:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
- downloads:
  PosixPath('data/ToyDataset/downloads')
  
  PosixPath('data/ToyDataset/downloads')
- events:
  PosixPath('data/ToyDataset/events')
  
  PosixPath('data/ToyDataset/events')
- precomputed_events:
  PosixPath('data/ToyDataset/precomputed_events')
  
  PosixPath('data/ToyDataset/precomputed_events')
- precomputed_reading_measures:
  PosixPath
  
  PosixPath('data/ToyDataset/precomputed_reading_measures')
- preprocessed:
  PosixPath('data/ToyDataset/preprocessed')
  
  PosixPath('data/ToyDataset/preprocessed')
- raw:
  PosixPath('data/ToyDataset/raw')
  
  PosixPath('data/ToyDataset/raw')
- root:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
precomputed_events:
list (0 items)
precomputed_reading_measures:
list (0 items)

Let’s confirm again by printing all the new files in this alternative directory:

alternative_dirpath = dataset.path / 'events_csv'
print(list(alternative_dirpath.glob('*/*/*')))

[PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_2_2.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_2_5.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_3_1.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_2_1.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_1_1.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_3_3.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_3_2.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_3_4.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_1_3.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_2_3.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_0_5.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_0_2.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_1_4.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_2_4.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_0_3.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_1_5.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_1_2.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_0_4.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_0_1.csv'), PosixPath('data/ToyDataset/events_csv/pymovements-toy-dataset-main/data/trial_3_5.csv')]

Loading Previously Computed Events Data#

Let’s initialize a new dataset object from the same ToyDataset.

preprocessed_dataset = pm.Dataset('ToyDataset', path='data/ToyDataset')

When we load the dataset using load() without any parameters there will be no events loaded:

preprocessed_dataset.load()

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  None
  
  None
- custom_read_kwargs:
  None
  
  None
- distance_column:
  None
  
  None
- experiment:
  Experiment
  Experiment
  - eyetracker:
    EyeTracker
    
    EyeTracker
    
    left:
    None
    
    None
    
    model:
    None
    
    None
    
    mount:
    None
    
    None
    
    right:
    None
    
    None
    
    sampling_rate:
    1000
    
    1000
    
    vendor:
    None
    
    None
    
    version:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - screen:
    Screen
    
    Screen
    
    distance_cm:
    68
    
    68
    
    height_cm:
    30.2
    
    30.2
    
    height_px:
    1024
    
    1024
    
    origin:
    'upper left'
    
    'upper left'
    
    width_cm:
    38
    
    38
    
    width_px:
    1280
    
    1280
    
    x_max_dva:
    15.599386487782953
    
    15.599386487782953
    
    x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
    
    y_max_dva:
    12.508044410882546
    
    12.508044410882546
    
    y_min_dva:
    -12.508044410882546
    
    -12.508044410882546
- extract:
  None
  
  None
- filename_format:
  dict (1 items)
  - gaze:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
- filename_format_schema_overrides:
  dict (1 items)
  - gaze:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
- has_resources:
  True
  
  True
- long_name:
  'pymovements Toy Dataset'
  
  'pymovements Toy Dataset'
- mirrors:
  dict (0 items)
- name:
  'ToyDataset'
  
  'ToyDataset'
- pixel_columns:
  None
  
  None
- position_columns:
  None
  
  None
- resources:
  list (1 items)
  - ResourceDefinition
    
    content:
    'gaze'
    
    'gaze'
    
    filename:
    'pymovements-toy-dataset.zip'
    
    'pymovements-toy-dataset.zip'
    
    filename_pattern:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
    
    filename_pattern_schema_overrides:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
    
    load_function:
    None
    
    None
    
    load_kwargs:
    dict (4 items)
    
    time_column:
    'timestamp'
    
    'timestamp'
    
    time_unit:
    'ms'
    
    'ms'
    
    (2 more)
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  None
  
  None
- time_unit:
  None
  
  None
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None
events:
tuple (20 items)
- Events
  - frame:
    DataFrame (4 columns, 0 rows)
    
    shape: (0, 4)
    name onset offset duration
    str i64 i64 i64
  - trial_columns:
    None
    
    None
- Events
  - frame:
    DataFrame (4 columns, 0 rows)
    
    shape: (0, 4)
    name onset offset duration
    str i64 i64 i64
  - trial_columns:
    None
    
    None
- (18 more)

fileinfo:

dict (1 items)

gaze:

DataFrame (3 columns, 20 rows)

shape: (20, 3)

text_id	page_id	filepath
i64	i64	str
0	1	"pymovements-toy-dataset-main/d…
0	2	"pymovements-toy-dataset-main/d…
0	3	"pymovements-toy-dataset-main/d…
0	4	"pymovements-toy-dataset-main/d…
0	5	"pymovements-toy-dataset-main/d…
…	…	…
3	1	"pymovements-toy-dataset-main/d…
3	2	"pymovements-toy-dataset-main/d…
3	3	"pymovements-toy-dataset-main/d…
3	4	"pymovements-toy-dataset-main/d…
3	5	"pymovements-toy-dataset-main/d…

gaze:

list (20 items)

Gaze

samples:

DataFrame (4 columns, 17223 rows)

shape: (17_223, 4)

time	stimuli_x	stimuli_y	pixel
i64	f64	f64	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]
1988146	-1.0	-1.0	[206.9, 152.1]
1988147	-1.0	-1.0	[207.0, 151.8]
1988148	-1.0	-1.0	[207.1, 151.7]
1988149	-1.0	-1.0	[207.0, 151.5]
…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]
2005364	-1.0	-1.0	[358.0, 414.5]
2005365	-1.0	-1.0	[355.8, 413.8]
2005366	-1.0	-1.0	[353.1, 413.2]
2005367	-1.0	-1.0	[351.2, 412.9]

events:
Events
Events
- frame:
  DataFrame (4 columns, 0 rows)
  
  shape: (0, 4)
  name onset offset duration
  str i64 i64 i64
- trial_columns:
  None
  
  None
trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Gaze

samples:

DataFrame (4 columns, 29799 rows)

shape: (29_799, 4)

time	stimuli_x	stimuli_y	pixel
i64	f64	f64	list[f64]
2008305	-1.0	-1.0	[141.4, 153.6]
2008306	-1.0	-1.0	[141.1, 153.2]
2008307	-1.0	-1.0	[140.7, 152.8]
2008308	-1.0	-1.0	[140.6, 152.7]
2008309	-1.0	-1.0	[140.5, 152.6]
…	…	…	…
2038099	-1.0	-1.0	[273.8, 773.8]
2038100	-1.0	-1.0	[273.8, 774.1]
2038101	-1.0	-1.0	[273.9, 774.5]
2038102	-1.0	-1.0	[274.0, 774.4]
2038103	-1.0	-1.0	[274.0, 773.9]

events:
Events
Events
- frame:
  DataFrame (4 columns, 0 rows)
  
  shape: (0, 4)
  name onset offset duration
  str i64 i64 i64
- trial_columns:
  None
  
  None
trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

(18 more)

path:
PosixPath('data/ToyDataset')

PosixPath('data/ToyDataset')
paths:
DatasetPaths
DatasetPaths
- dataset:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
- downloads:
  PosixPath('data/ToyDataset/downloads')
  
  PosixPath('data/ToyDataset/downloads')
- events:
  PosixPath('data/ToyDataset/events')
  
  PosixPath('data/ToyDataset/events')
- precomputed_events:
  PosixPath('data/ToyDataset/precomputed_events')
  
  PosixPath('data/ToyDataset/precomputed_events')
- precomputed_reading_measures:
  PosixPath
  
  PosixPath('data/ToyDataset/precomputed_reading_measures')
- preprocessed:
  PosixPath('data/ToyDataset/preprocessed')
  
  PosixPath('data/ToyDataset/preprocessed')
- raw:
  PosixPath('data/ToyDataset/raw')
  
  PosixPath('data/ToyDataset/raw')
- root:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
precomputed_events:
list (0 items)
precomputed_reading_measures:
list (0 items)

But when we load it with the events=True parameter the events will be loaded:

preprocessed_dataset.load(events=True)

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  None
  
  None
- custom_read_kwargs:
  None
  
  None
- distance_column:
  None
  
  None
- experiment:
  Experiment
  Experiment
  - eyetracker:
    EyeTracker
    
    EyeTracker
    
    left:
    None
    
    None
    
    model:
    None
    
    None
    
    mount:
    None
    
    None
    
    right:
    None
    
    None
    
    sampling_rate:
    1000
    
    1000
    
    vendor:
    None
    
    None
    
    version:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - screen:
    Screen
    
    Screen
    
    distance_cm:
    68
    
    68
    
    height_cm:
    30.2
    
    30.2
    
    height_px:
    1024
    
    1024
    
    origin:
    'upper left'
    
    'upper left'
    
    width_cm:
    38
    
    38
    
    width_px:
    1280
    
    1280
    
    x_max_dva:
    15.599386487782953
    
    15.599386487782953
    
    x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
    
    y_max_dva:
    12.508044410882546
    
    12.508044410882546
    
    y_min_dva:
    -12.508044410882546
    
    -12.508044410882546
- extract:
  None
  
  None
- filename_format:
  dict (1 items)
  - gaze:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
- filename_format_schema_overrides:
  dict (1 items)
  - gaze:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
- has_resources:
  True
  
  True
- long_name:
  'pymovements Toy Dataset'
  
  'pymovements Toy Dataset'
- mirrors:
  dict (0 items)
- name:
  'ToyDataset'
  
  'ToyDataset'
- pixel_columns:
  None
  
  None
- position_columns:
  None
  
  None
- resources:
  list (1 items)
  - ResourceDefinition
    
    content:
    'gaze'
    
    'gaze'
    
    filename:
    'pymovements-toy-dataset.zip'
    
    'pymovements-toy-dataset.zip'
    
    filename_pattern:
    'trial_{text_id:d}_{page_id:d}.csv'
    
    'trial_{text_id:d}_{page_id:d}.csv'
    
    filename_pattern_schema_overrides:
    dict (2 items)
    
    text_id:
    <class 'int'>
    
    <class 'int'>
    
    page_id:
    <class 'int'>
    
    <class 'int'>
    
    load_function:
    None
    
    None
    
    load_kwargs:
    dict (4 items)
    
    time_column:
    'timestamp'
    
    'timestamp'
    
    time_unit:
    'ms'
    
    'ms'
    
    (2 more)
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  None
  
  None
- time_unit:
  None
  
  None
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None

events:

tuple (20 items)

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

Events

frame:

DataFrame (7 columns, 366 rows)

shape: (366, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	2008305	2008621	316	167.343877	2.283024	2.706135
"fixation"	2008622	2008821	199	314.396904	2.578854	2.79657
"fixation"	2008822	2009214	392	305.525917	2.612895	2.908934
"fixation"	2009215	2009433	218	216.195201	2.612208	2.765062
"fixation"	2009434	2009704	270	208.05132	2.566305	2.70311
…	…	…	…	…	…	…
"saccade"	2036849	2036861	12	54.743137	0.472141	0.529715
"saccade"	2037161	2037188	27	223.056103	2.358604	2.587752
"saccade"	2037412	2037503	91	406.701444	16.94863	18.346458
"saccade"	2037638	2037654	16	138.382767	1.411621	1.827761
"saccade"	2037812	2037830	18	240.193236	2.739312	3.024326

trial_columns:
None

None

(18 more)

fileinfo:

dict (1 items)

gaze:

DataFrame (3 columns, 20 rows)

shape: (20, 3)

text_id	page_id	filepath
i64	i64	str
0	1	"pymovements-toy-dataset-main/d…
0	2	"pymovements-toy-dataset-main/d…
0	3	"pymovements-toy-dataset-main/d…
0	4	"pymovements-toy-dataset-main/d…
0	5	"pymovements-toy-dataset-main/d…
…	…	…
3	1	"pymovements-toy-dataset-main/d…
3	2	"pymovements-toy-dataset-main/d…
3	3	"pymovements-toy-dataset-main/d…
3	4	"pymovements-toy-dataset-main/d…
3	5	"pymovements-toy-dataset-main/d…

gaze:

list (20 items)

Gaze

samples:

DataFrame (4 columns, 17223 rows)

shape: (17_223, 4)

time	stimuli_x	stimuli_y	pixel
i64	f64	f64	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]
1988146	-1.0	-1.0	[206.9, 152.1]
1988147	-1.0	-1.0	[207.0, 151.8]
1988148	-1.0	-1.0	[207.1, 151.7]
1988149	-1.0	-1.0	[207.0, 151.5]
…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]
2005364	-1.0	-1.0	[358.0, 414.5]
2005365	-1.0	-1.0	[355.8, 413.8]
2005366	-1.0	-1.0	[353.1, 413.2]
2005367	-1.0	-1.0	[351.2, 412.9]

events:

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

Gaze

samples:

DataFrame (4 columns, 29799 rows)

shape: (29_799, 4)

time	stimuli_x	stimuli_y	pixel
i64	f64	f64	list[f64]
2008305	-1.0	-1.0	[141.4, 153.6]
2008306	-1.0	-1.0	[141.1, 153.2]
2008307	-1.0	-1.0	[140.7, 152.8]
2008308	-1.0	-1.0	[140.6, 152.7]
2008309	-1.0	-1.0	[140.5, 152.6]
…	…	…	…
2038099	-1.0	-1.0	[273.8, 773.8]
2038100	-1.0	-1.0	[273.8, 774.1]
2038101	-1.0	-1.0	[273.9, 774.5]
2038102	-1.0	-1.0	[274.0, 774.4]
2038103	-1.0	-1.0	[274.0, 773.9]

events:

Events

frame:

DataFrame (7 columns, 366 rows)

shape: (366, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	2008305	2008621	316	167.343877	2.283024	2.706135
"fixation"	2008622	2008821	199	314.396904	2.578854	2.79657
"fixation"	2008822	2009214	392	305.525917	2.612895	2.908934
"fixation"	2009215	2009433	218	216.195201	2.612208	2.765062
"fixation"	2009434	2009704	270	208.05132	2.566305	2.70311
…	…	…	…	…	…	…
"saccade"	2036849	2036861	12	54.743137	0.472141	0.529715
"saccade"	2037161	2037188	27	223.056103	2.358604	2.587752
"saccade"	2037412	2037503	91	406.701444	16.94863	18.346458
"saccade"	2037638	2037654	16	138.382767	1.411621	1.827761
"saccade"	2037812	2037830	18	240.193236	2.739312	3.024326

trial_columns:
None

None

trial_columns:
None

None
experiment:
Experiment
Experiment
- eyetracker:
  EyeTracker
  EyeTracker
  - left:
    None
    
    None
  - model:
    None
    
    None
  - mount:
    None
    
    None
  - right:
    None
    
    None
  - sampling_rate:
    1000
    
    1000
  - vendor:
    None
    
    None
  - version:
    None
    
    None
- sampling_rate:
  1000
  
  1000
- screen:
  Screen
  Screen
  - distance_cm:
    68
    
    68
  - height_cm:
    30.2
    
    30.2
  - height_px:
    1024
    
    1024
  - origin:
    'upper left'
    
    'upper left'
  - width_cm:
    38
    
    38
  - width_px:
    1280
    
    1280
  - x_max_dva:
    15.599386487782953
    
    15.599386487782953
  - x_min_dva:
    -15.599386487782953
    
    -15.599386487782953
  - y_max_dva:
    12.508044410882546
    
    12.508044410882546
  - y_min_dva:
    -12.508044410882546
    
    -12.508044410882546

(18 more)

path:
PosixPath('data/ToyDataset')

PosixPath('data/ToyDataset')
paths:
DatasetPaths
DatasetPaths
- dataset:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
- downloads:
  PosixPath('data/ToyDataset/downloads')
  
  PosixPath('data/ToyDataset/downloads')
- events:
  PosixPath('data/ToyDataset/events')
  
  PosixPath('data/ToyDataset/events')
- precomputed_events:
  PosixPath('data/ToyDataset/precomputed_events')
  
  PosixPath('data/ToyDataset/precomputed_events')
- precomputed_reading_measures:
  PosixPath
  
  PosixPath('data/ToyDataset/precomputed_reading_measures')
- preprocessed:
  PosixPath('data/ToyDataset/preprocessed')
  
  PosixPath('data/ToyDataset/preprocessed')
- raw:
  PosixPath('data/ToyDataset/raw')
  
  PosixPath('data/ToyDataset/raw')
- root:
  PosixPath('data/ToyDataset')
  
  PosixPath('data/ToyDataset')
precomputed_events:
list (0 items)
precomputed_reading_measures:
list (0 items)

By default, the events directory and the feather extension will be chosen.

In the case of alternative directory names or other file formats, you can use the following:

preprocessed_dataset.load(
    events=True,
    events_dirname='events_csv',
    extension='csv',
)
dataset.events[0]

Events

frame:

DataFrame (7 columns, 222 rows)

shape: (222, 7)

name	onset	offset	duration	peak_velocity	amplitude	dispersion
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988563	418	200.144558	2.492864	2.712569
"fixation"	1988564	1988750	186	249.67823	2.651198	2.865026
"fixation"	1988751	1989178	427	211.598748	2.585906	2.779518
"fixation"	1989179	1989436	257	189.183243	2.614347	2.77424
"fixation"	1989437	1989600	163	255.077509	2.594651	2.729391
…	…	…	…	…	…	…
"saccade"	2004373	2004385	12	70.374183	0.7073	0.766684
"saccade"	2004688	2004704	16	175.646379	1.807485	1.875716
"saccade"	2004879	2004901	22	209.46361	2.933818	3.086169
"saccade"	2005110	2005126	16	137.917594	1.405354	1.501217
"saccade"	2005288	2005345	57	352.550667	14.682541	16.101153

trial_columns:
None

None

What you have learned in this tutorial:#

detecting different events with different algorithms by using Dataset.detect_events()
computing event properties by using Dataset.compute_event_properties()
saving your preprocesed data using Dataset.save_preprocessed()
load your preprocesed data using Dataset.load(events=True)
using custom directory names by specifying preprocessed_dirname
using other file formats than the default feather format by specifying extension

Handling Gaze Events#

What you will learn in this tutorial:#

Preparations#

Detecting Events#

Computing Event Properties#

Saving Event Data#

Loading Previously Computed Events Data#

What you have learned in this tutorial:#

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