Preprocessing Raw Gaze Data#

What you will learn in this tutorial:#

how to transform pixel coordinates into degrees of visual angle
how to transform positional data into velocity data

Preparations#

We import pymovements as the alias pm for convenience.

[1]:

import pymovements as pm

Let’s start by downloading our ToyDataset and loading in its data:

[2]:

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.

Using already downloaded and verified file: data/ToyDataset/downloads/pymovements-toy-dataset.zip
Extracting pymovements-toy-dataset.zip to data/ToyDataset/raw

100%|██████████| 23/23 [00:00<00:00, 306.27it/s]

[2]:

Dataset

definition:
DatasetDefinition
DatasetDefinition
- acceleration_columns:
  None
  
  None
- column_map:
  dict (0 items)
- custom_read_kwargs:
  dict (1 items)
  - gaze:
    dict (4 items)
    
    columns:
    list (5 items)
    
    'timestamp'
    'x'
    (3 more)
    
    schema_overrides:
    dict (5 items)
    
    timestamp:
    Float64
    
    Float64
    
    x:
    Float64
    
    Float64
    
    (3 more)
    
    (2 more)
- 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:
  list (2 items)
  - 'x'
  - 'y'
- 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:
    None
    
    None
    
    md5:
    '256901852c1c07581d375eef705855d6'
    
    '256901852c1c07581d375eef705855d6'
    
    mirrors:
    None
    
    None
    
    url:
    str
    
    'https://github.com/pymovements/pymovements-toy-dataset/archive/refs/heads/main.zip'
- time_column:
  'timestamp'
  
  'timestamp'
- time_unit:
  'ms'
  
  'ms'
- trial_columns:
  None
  
  None
- velocity_columns:
  None
  
  None
events:
list (0 items)

fileinfo:

dict (1 items)

gaze:

DataFrame (5 columns, 20 rows)

shape: (20, 5)

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

gaze:

list (20 items)

Gaze

samples:

DataFrame (6 columns, 17223 rows)

shape: (17_223, 6)

time	stimuli_x	stimuli_y	text_id	page_id	pixel
i64	f64	f64	i64	i64	list[f64]
1988145	-1.0	-1.0	0	1	[206.8, 152.4]
1988146	-1.0	-1.0	0	1	[206.9, 152.1]
1988147	-1.0	-1.0	0	1	[207.0, 151.8]
1988148	-1.0	-1.0	0	1	[207.1, 151.7]
1988149	-1.0	-1.0	0	1	[207.0, 151.5]
…	…	…	…	…	…
2005363	-1.0	-1.0	0	1	[361.0, 415.4]
2005364	-1.0	-1.0	0	1	[358.0, 414.5]
2005365	-1.0	-1.0	0	1	[355.8, 413.8]
2005366	-1.0	-1.0	0	1	[353.1, 413.2]
2005367	-1.0	-1.0	0	1	[351.2, 412.9]

events:
Events
Events
- frame:
  DataFrame (6 columns, 0 rows)
  
  shape: (0, 6)
  text_id page_id name onset offset duration
  i64 i64 str i64 i64 i64
- trial_columns:
  list (2 items)
  - 'text_id'
  - 'page_id'
trial_columns:
list (2 items)
- 'text_id'
- 'page_id'
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	text_id	page_id	pixel
i64	f64	f64	i64	i64	list[f64]
2008305	-1.0	-1.0	0	2	[141.4, 153.6]
2008306	-1.0	-1.0	0	2	[141.1, 153.2]
2008307	-1.0	-1.0	0	2	[140.7, 152.8]
2008308	-1.0	-1.0	0	2	[140.6, 152.7]
2008309	-1.0	-1.0	0	2	[140.5, 152.6]
…	…	…	…	…	…
2038099	-1.0	-1.0	0	2	[273.8, 773.8]
2038100	-1.0	-1.0	0	2	[273.8, 774.1]
2038101	-1.0	-1.0	0	2	[273.9, 774.5]
2038102	-1.0	-1.0	0	2	[274.0, 774.4]
2038103	-1.0	-1.0	0	2	[274.0, 773.9]

events:
Events
Events
- frame:
  DataFrame (6 columns, 0 rows)
  
  shape: (0, 6)
  text_id page_id name onset offset duration
  i64 i64 str i64 i64 i64
- trial_columns:
  list (2 items)
  - 'text_id'
  - 'page_id'
trial_columns:
list (2 items)
- 'text_id'
- 'page_id'
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)

We can verify that all files have been loaded in by checking the fileinfo attribute:

[3]:

dataset.fileinfo

[3]:

{'gaze': shape: (20, 5)
 ┌─────────┬─────────┬─────────────────────────────────┬───────────────┬─────────────┐
 │ text_id ┆ page_id ┆ filepath                        ┆ load_function ┆ load_kwargs │
 │ ---     ┆ ---     ┆ ---                             ┆ ---           ┆ ---         │
 │ i64     ┆ i64     ┆ str                             ┆ null          ┆ null        │
 ╞═════════╪═════════╪═════════════════════════════════╪═══════════════╪═════════════╡
 │ 0       ┆ 1       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 0       ┆ 2       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 0       ┆ 3       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 0       ┆ 4       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 0       ┆ 5       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ …       ┆ …       ┆ …                               ┆ …             ┆ …           │
 │ 3       ┆ 1       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 3       ┆ 2       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 3       ┆ 3       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 3       ┆ 4       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 │ 3       ┆ 5       ┆ pymovements-toy-dataset-main/d… ┆ null          ┆ null        │
 └─────────┴─────────┴─────────────────────────────────┴───────────────┴─────────────┘}

Now let’s inpect our gaze dataframe:

[4]:

dataset.gaze[0]

[4]:

Gaze

samples:

DataFrame (6 columns, 17223 rows)

shape: (17_223, 6)

time	stimuli_x	stimuli_y	text_id	page_id	pixel
i64	f64	f64	i64	i64	list[f64]
1988145	-1.0	-1.0	0	1	[206.8, 152.4]
1988146	-1.0	-1.0	0	1	[206.9, 152.1]
1988147	-1.0	-1.0	0	1	[207.0, 151.8]
1988148	-1.0	-1.0	0	1	[207.1, 151.7]
1988149	-1.0	-1.0	0	1	[207.0, 151.5]
…	…	…	…	…	…
2005363	-1.0	-1.0	0	1	[361.0, 415.4]
2005364	-1.0	-1.0	0	1	[358.0, 414.5]
2005365	-1.0	-1.0	0	1	[355.8, 413.8]
2005366	-1.0	-1.0	0	1	[353.1, 413.2]
2005367	-1.0	-1.0	0	1	[351.2, 412.9]

events:
Events
Events
- frame:
  DataFrame (6 columns, 0 rows)
  
  shape: (0, 6)
  text_id page_id name onset offset duration
  i64 i64 str i64 i64 i64
- trial_columns:
  list (2 items)
  - 'text_id'
  - 'page_id'
trial_columns:
list (2 items)
- 'text_id'
- 'page_id'
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.

Preprocessing#

We now want to transform these pixel position coordinates into coordinates in degrees of visual angle. This is simply done by:

[5]:

dataset.pix2deg()

dataset.gaze[0]

[5]:

Gaze

samples:

DataFrame (7 columns, 17223 rows)

shape: (17_223, 7)

time	stimuli_x	stimuli_y	text_id	page_id	pixel	position
i64	f64	f64	i64	i64	list[f64]	list[f64]
1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]
1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]
1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]
1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]
1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]
…	…	…	…	…	…	…
2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]
2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]
2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]
2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]
2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]

events:
Events
Events
- frame:
  DataFrame (6 columns, 0 rows)
  
  shape: (0, 6)
  text_id page_id name onset offset duration
  i64 i64 str i64 i64 i64
- trial_columns:
  list (2 items)
  - 'text_id'
  - 'page_id'
trial_columns:
list (2 items)
- 'text_id'
- 'page_id'
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

The processed result has been added as a new column named position to our gaze dataframe.

Additionally we would like to have velocity data available too. We have four different methods available:

preceding: this will just take the single preceding sample in account for velocity calculation. Most noisy variant.
neighbors: this will take the neighboring samples in account for velocity calculation. A bit less noisy.
smooth: this will increase the neighboring samples to two on each side. You can get a smooth conversion this way.
savitzky_golay: this is using the Savitzky-Golay differentiation filter for conversion. You can specify additional parameters like window_length and degree. Depending on your parameters this will lead to the best results.

Let’s use the fivepoint method first:

[6]:

dataset.pos2vel(method='fivepoint')

dataset.gaze[0]

[6]:

Gaze

samples:

DataFrame (8 columns, 17223 rows)

shape: (17_223, 8)

time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]	[null, null]
1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[null, null]
1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.610194, -5.256267]
1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[0.402548, -4.447465]
1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[0.402561, -3.234462]
…	…	…	…	…	…	…	…
2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-63.266374, -21.085616]
2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-63.249652, -19.431326]
2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-60.359624, -15.710061]
2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[null, null]
2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[null, null]

events:
Events
Events
- frame:
  DataFrame (6 columns, 0 rows)
  
  shape: (0, 6)
  text_id page_id name onset offset duration
  i64 i64 str i64 i64 i64
- trial_columns:
  list (2 items)
  - 'text_id'
  - 'page_id'
trial_columns:
list (2 items)
- 'text_id'
- 'page_id'
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

The processed result has been added as a new column named velocity to our gaze dataframe.

We can also use the Savitzky-Golay differentiation filter with some additional parameters like this:

[7]:

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

dataset.gaze[0]

[7]:

Gaze

samples:

DataFrame (8 columns, 17223 rows)

shape: (17_223, 8)

time	stimuli_x	stimuli_y	text_id	page_id	pixel	position	velocity
i64	f64	f64	i64	i64	list[f64]	list[f64]	list[f64]
1988145	-1.0	-1.0	0	1	[206.8, 152.4]	[-10.697598, -8.852399]	[1.207641, -3.119165]
1988146	-1.0	-1.0	0	1	[206.9, 152.1]	[-10.695183, -8.859678]	[1.20764, -4.072198]
1988147	-1.0	-1.0	0	1	[207.0, 151.8]	[-10.692768, -8.866956]	[1.035119, -4.765267]
1988148	-1.0	-1.0	0	1	[207.1, 151.7]	[-10.690352, -8.869381]	[1.207654, -4.245382]
1988149	-1.0	-1.0	0	1	[207.0, 151.5]	[-10.692768, -8.874233]	[1.552735, -2.339263]
…	…	…	…	…	…	…	…
2005363	-1.0	-1.0	0	1	[361.0, 415.4]	[-6.932438, -2.386672]	[-62.062479, -20.465552]
2005364	-1.0	-1.0	0	1	[358.0, 414.5]	[-7.006376, -2.408998]	[-61.343786, -18.073031]
2005365	-1.0	-1.0	0	1	[355.8, 413.8]	[-7.060582, -2.426362]	[-53.501231, -14.617634]
2005366	-1.0	-1.0	0	1	[353.1, 413.2]	[-7.12709, -2.441245]	[-41.879965, -10.276475]
2005367	-1.0	-1.0	0	1	[351.2, 412.9]	[-7.173881, -2.448686]	[-27.710881, -6.112645]

events:
Events
Events
- frame:
  DataFrame (6 columns, 0 rows)
  
  shape: (0, 6)
  text_id page_id name onset offset duration
  i64 i64 str i64 i64 i64
- trial_columns:
  list (2 items)
  - 'text_id'
  - 'page_id'
trial_columns:
list (2 items)
- 'text_id'
- 'page_id'
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

This has overwritten our velocity columns. As we see, the values in the velocity columns are slightly different.

What you have learned in this tutorial:#

transforming pixel coordinates into degrees of visual angle by using Dataset.pix2deg()
transforming positional data into velocity data by using Dataset.pos2vel()
passing additional keyword arguments when using the Savitzky-Golay differentiation filter

Preprocessing Raw Gaze Data#

What you will learn in this tutorial:#

Preparations#

Preprocessing#

What you have learned in this tutorial:#

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