pymovements in 10 minutes#

What you will learn in this tutorial:#

how to download one of the publicly available datasets
how to load a subset of the data into your memory
how to transform pixel coordinates into degrees of visual angle
how to transform positional data into velocity data
how to detect fixations by using the I-VT algorithm
how to detect saccades by using the microsaccades algorithm
how to compute additional event properties for your analysis
how to save your preprocessed data
how to plot the main saccadic sequence from your data

Downloading one of the public datasets#

We import pymovements as the alias pm for convenience.

import polars as pl

import pymovements as pm

pymovements provides a library of publicly available datasets.

You can browse through the available dataset definitions here: Dataset

For this tutorial we will limit ourselves to the ToyDataset due to its minimal space requirements.

Other datasets can be downloaded by simply replacing ToyDataset with one of the other available datasets.

We can initialize and download by passing the desired dataset name as a string argument.

Additionally, we need the root directory path of your data.

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

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, 312.43file/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 (0 items)
fileinfo:
DataFrame (0 columns, 0 rows)

shape: (0, 0)
gaze:
list (0 items)
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)

Our downloaded dataset will be placed in a new directory with the name of the dataset:

dataset.path

PosixPath('data/ToyDataset')

Archive files are automatically extracted into the path specified by Dataset.paths.raw:

dataset.paths.raw

PosixPath('data/ToyDataset/raw')

Loading in your data into memory#

Next, we load our dataset into memory to be able to work with it:

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)

This way we fill two attributes with data. First we have the fileinfo attribute which holds all the basic information for files:

dataset.fileinfo['gaze'].head()

shape: (5, 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…

We notice that for each filepath a text_id and page_id is specified.

We have also loaded our gaze data into the dataframes in the gaze attribute:

dataset.gaze[0]

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

Apart from some trial identifier columns we see the columns time and pixel.

The last two columns refer to the pixel coordinates at the timestep specified by time.

We are also able to just take a subset of the data by specifying values of the fileinfo columns. The key refers to the column in the fileinfo dataframe. The values in the dictionary can be of type bool, int, float or str, but also lists and ranges

subset = {
    'text_id': 0,
    'page_id': [0, 1],
}
dataset.load(subset=subset)

dataset.fileinfo

{'gaze': shape: (1, 3)
 ┌─────────┬─────────┬─────────────────────────────────┐
 │ text_id ┆ page_id ┆ filepath                        │
 │ ---     ┆ ---     ┆ ---                             │
 │ i64     ┆ i64     ┆ str                             │
 ╞═════════╪═════════╪═════════════════════════════════╡
 │ 0       ┆ 1       ┆ pymovements-toy-dataset-main/d… │
 └─────────┴─────────┴─────────────────────────────────┘}

Now we selected only a small subset of our data.

Preprocessing raw gaze data#

We now want to preprocess our gaze data by transforming pixel coordinates into degrees of visual angle and then computing velocity data from our positional data.

dataset.pix2deg()

dataset.gaze[0]

Gaze

samples:

DataFrame (5 columns, 17223 rows)

shape: (17_223, 5)

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

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

We notice that a new column has appeared: position. This column specifies the position coordinates in degrees of visual angle (dva).

For transforming our positional data into velocity data, we will use the Savitzky-Golay differentiation filter.

We can also specify some additional parameters for this method:

dataset.pos2vel(method='savitzky_golay', degree=2, window_length=7)

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]	[1.207641, -3.119165]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]
…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]

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

There is also the more general apply() method, which can be used to apply both transformation and event detection methods.

dataset.apply('pos2acc', degree=2, window_length=7)

dataset.gaze[0]

Gaze

samples:

DataFrame (7 columns, 17223 rows)

shape: (17_223, 7)

time	stimuli_x	stimuli_y	pixel	position	velocity	acceleration
i64	f64	f64	list[f64]	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]	[690.085837, -1501.799767]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]	[0.001831, -866.371365]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]	[-575.06741, -57.655244]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]	[-230.013049, 1328.57081]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]	[690.12611, 2021.586565]
…	…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]	[-1099.087619, 1477.17518]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]	[1834.348384, 2599.156806]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]	[9396.15507, 4547.960553]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]	[16194.183852, 5079.286997]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]	[16598.914618, 4193.246498]

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

Detecting events#

Now let’s detect some events.

First, we will detect fixations using the I-VT algorithm using its default parameters:

dataset.detect_events('ivt')

dataset.events[0]

Events

frame:

DataFrame (4 columns, 72 rows)

shape: (72, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation"	1988145	1988322	177
"fixation"	1988351	1988546	195
"fixation"	1988592	1988736	144
"fixation"	1988788	1989012	224
"fixation"	1989044	1989170	126
…	…	…	…
"fixation"	2004114	2004349	235
"fixation"	2004399	2004687	288
"fixation"	2004714	2004878	164
"fixation"	2004930	2005109	179
"fixation"	2005138	2005286	148

trial_columns:
None

None

Next we detect some saccades. This time we don’t use the default parameters but specify our own:

dataset.detect_events('microsaccades', minimum_duration=8)

dataset.events[0].frame.filter(pl.col('name') == 'saccade').head()

shape: (5, 4)

name	onset	offset	duration
str	i64	i64	i64
"saccade"	1988323	1988337	14
"saccade"	1988341	1988351	10
"saccade"	1988546	1988567	21
"saccade"	1988570	1988583	13
"saccade"	1988736	1988760	24

We can also use the more general interface of the apply() method:

dataset.apply('idt', dispersion_threshold=2.7, name='fixation.ivt')

dataset.events[0].frame.filter(pl.col('name') == 'fixation.ivt').head()

shape: (5, 4)

name	onset	offset	duration
str	i64	i64	i64
"fixation.ivt"	1988145	1988563	418
"fixation.ivt"	1988564	1988750	186
"fixation.ivt"	1988751	1989178	427
"fixation.ivt"	1989179	1989436	257
"fixation.ivt"	1989437	1989600	163

Computing event properties#

The event dataframe currently only holds the name, onset, offset and duration of an event (additionally we have some more identifier columns at the beginning).

We now want to compute some additional properties for each event. Event properties are things like peak velocity, amplitude and dispersion during an event.

We start out with computing the dispersion:

dataset.compute_event_properties("dispersion")

dataset.events[0]

Events

frame:

DataFrame (5 columns, 264 rows)

shape: (264, 5)

name	onset	offset	duration	dispersion
str	i64	i64	i64	f64
"fixation"	1988145	1988322	177	0.154958
"fixation"	1988351	1988546	195	0.291833
"fixation"	1988592	1988736	144	0.296297
"fixation"	1988788	1989012	224	0.271854
"fixation"	1989044	1989170	126	0.349
…	…	…	…	…
"fixation.ivt"	2003929	2004090	161	2.814851
"fixation.ivt"	2004091	2004363	272	2.819008
"fixation.ivt"	2004364	2004883	519	2.768099
"fixation.ivt"	2004885	2005116	231	2.805674
"fixation.ivt"	2005117	2005298	181	2.744494

trial_columns:
None

None

We notice that a new column with the name dispersion has appeared in the event dataframe.

We can also pass a list of properties to compute all of our desired properties in a single run. Let’s add the amplitude and peak velocity:

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

dataset.events[0]

Events

frame:

DataFrame (7 columns, 264 rows)

shape: (264, 7)

name	onset	offset	duration	dispersion	amplitude	peak_velocity
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
…	…	…	…	…	…	…
"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

trial_columns:
None

None

Plotting our data#

pymovements provides a range of plotting functions.

You can browse through the available plotting functions here: Plotting

In this tutorial we will plot the saccadic main sequence of our data.

pm.plotting.main_sequence_plot(dataset.events[0])

../_images/b0f0b4dffaa76ec7ed35ab67c21f060ce11ba2c344f8757e8cc64efb7d81b93d.png

(<Figure size 1500x500 with 1 Axes>,
 <Axes: xlabel='Amplitude [dva]', ylabel='Peak Velocity [dva/s]'>)

Saving and loading your dataframes#

If we want to save interim results, we can simply use the save() method like this:

dataset.save()

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 (1 items)

Events

frame:

DataFrame (7 columns, 264 rows)

shape: (264, 7)

name	onset	offset	duration	dispersion	amplitude	peak_velocity
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
…	…	…	…	…	…	…
"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

trial_columns:
None

None

fileinfo:
dict (1 items)
- gaze:
  DataFrame (3 columns, 1 rows)
  
  shape: (1, 3)
  text_id page_id filepath
  i64 i64 str
  0 1 "pymovements-toy-dataset-main/d…

gaze:

list (1 items)

Gaze

samples:

DataFrame (7 columns, 17223 rows)

shape: (17_223, 7)

time	stimuli_x	stimuli_y	pixel	position	velocity	acceleration
i64	f64	f64	list[f64]	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]	[690.085837, -1501.799767]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]	[0.001831, -866.371365]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]	[-575.06741, -57.655244]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]	[-230.013049, 1328.57081]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]	[690.12611, 2021.586565]
…	…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]	[-1099.087619, 1477.17518]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]	[1834.348384, 2599.156806]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]	[9396.15507, 4547.960553]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]	[16194.183852, 5079.286997]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]	[16598.914618, 4193.246498]

events:

Events

frame:

DataFrame (7 columns, 264 rows)

shape: (264, 7)

name	onset	offset	duration	dispersion	amplitude	peak_velocity
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
…	…	…	…	…	…	…
"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

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

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 test this out by initializing a new PublicDataset object in the same directory and loading in the preprocessed gaze and event data.

This time we don’t need to download anything.

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

dataset.load(events=True, preprocessed=True, subset=subset)

display(dataset.gaze[0])
display(dataset.events[0])

Gaze

samples:

DataFrame (7 columns, 17223 rows)

shape: (17_223, 7)

time	stimuli_x	stimuli_y	pixel	position	velocity	acceleration
i64	f64	f64	list[f64]	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]	[690.085837, -1501.799767]
1988146	-1.0	-1.0	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]	[0.001831, -866.371365]
1988147	-1.0	-1.0	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]	[-575.06741, -57.655244]
1988148	-1.0	-1.0	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]	[-230.013049, 1328.57081]
1988149	-1.0	-1.0	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]	[690.12611, 2021.586565]
…	…	…	…	…	…	…
2005363	-1.0	-1.0	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]	[-1099.087619, 1477.17518]
2005364	-1.0	-1.0	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]	[1834.348384, 2599.156806]
2005365	-1.0	-1.0	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]	[9396.15507, 4547.960553]
2005366	-1.0	-1.0	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]	[16194.183852, 5079.286997]
2005367	-1.0	-1.0	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]	[16598.914618, 4193.246498]

events:

Events

frame:

DataFrame (7 columns, 264 rows)

shape: (264, 7)

name	onset	offset	duration	dispersion	amplitude	peak_velocity
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
…	…	…	…	…	…	…
"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

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

Events

frame:

DataFrame (7 columns, 264 rows)

shape: (264, 7)

name	onset	offset	duration	dispersion	amplitude	peak_velocity
str	i64	i64	i64	f64	f64	f64
"fixation"	1988145	1988322	177	0.154958	0.110074	16.24151
"fixation"	1988351	1988546	195	0.291833	0.206397	18.88542
"fixation"	1988592	1988736	144	0.296297	0.209546	17.690373
"fixation"	1988788	1989012	224	0.271854	0.192719	19.130211
"fixation"	1989044	1989170	126	0.349	0.304362	18.616167
…	…	…	…	…	…	…
"fixation.ivt"	2003929	2004090	161	2.814851	2.527788	212.117446
"fixation.ivt"	2004091	2004363	272	2.819008	2.518967	244.333244
"fixation.ivt"	2004364	2004883	519	2.768099	2.46208	194.527643
"fixation.ivt"	2004885	2005116	231	2.805674	2.507902	203.067333
"fixation.ivt"	2005117	2005298	181	2.744494	2.578767	329.741947

trial_columns:
None

None

pymovements in 10 minutes#

What you will learn in this tutorial:#

Downloading one of the public datasets#

Loading in your data into memory#

Preprocessing raw gaze data#

Detecting events#

Computing event properties#

Plotting our data#

Saving and loading your dataframes#

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