Saving and Loading Event Data#
What you will learn in this tutorial:#
how to save your event data
how to load your event data
Preparations#
We import pymovements as the alias pm for convenience.
[1]:
import pymovements as pm
/home/docs/checkouts/readthedocs.org/user_builds/pymovements/envs/v0.15.0/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Let’s start by downloading our ToyDataset and loading in its data:
[2]:
dataset = pm.Dataset('ToyDataset', path='data/ToyDataset')
dataset.download()
dataset.load()
Using already downloaded and verified file: data/ToyDataset/downloads/pymovements-toy-dataset.zip
Extracting pymovements-toy-dataset.zip to data/ToyDataset/raw
100%|██████████| 20/20 [00:00<00:00, 195.41it/s]
[2]:
<pymovements.dataset.dataset.Dataset at 0x7fb94b2be8e0>
Now let’s do some preprocessing:
[3]:
dataset.pix2deg()
dataset.pos2vel()
dataset.gaze[0].frame.head()
100%|██████████| 20/20 [00:00<00:00, 722.53it/s]
100%|██████████| 20/20 [00:00<00:00, 658.52it/s]
[3]:
shape: (5, 9)
| text_id | page_id | time | x_right_pix | y_right_pix | y_right_pos | x_right_pos | x_right_vel | y_right_vel |
|---|---|---|---|---|---|---|---|---|
| i64 | i64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 0 | 1 | 1.988145e6 | 206.8 | 152.4 | -12.005591 | -7.528075 | 1.221164 | -3.589697 |
| 0 | 1 | 1.988146e6 | 206.9 | 152.1 | -12.01277 | -7.525633 | 2.442343 | -7.179203 |
| 0 | 1 | 1.988147e6 | 207.0 | 151.8 | -12.019949 | -7.52319 | 1.628238 | -5.184827 |
| 0 | 1 | 1.988148e6 | 207.1 | 151.7 | -12.022342 | -7.520748 | 0.407059 | -4.386968 |
| 0 | 1 | 1.988149e6 | 207.0 | 151.5 | -12.027128 | -7.52319 | 0.407069 | -3.190445 |
Detect some events:
[4]:
dataset.detect_events('ivt')
dataset.events[0].frame.head()
20it [00:00, 355.75it/s]
[4]:
shape: (5, 6)
| name | onset | offset | duration | text_id | page_id |
|---|---|---|---|---|---|
| str | i64 | i64 | i64 | i64 | i64 |
| "fixation" | 1988145 | 1988322 | 177 | 0 | 1 |
| "fixation" | 1988351 | 1988546 | 195 | 0 | 1 |
| "fixation" | 1988592 | 1988736 | 144 | 0 | 1 |
| "fixation" | 1988788 | 1989013 | 225 | 0 | 1 |
| "fixation" | 1989060 | 1989170 | 110 | 0 | 1 |
[5]:
dataset.detect_events('microsaccades')
dataset.events[0].frame.head()
20it [00:00, 73.26it/s]
[5]:
shape: (5, 6)
| name | onset | offset | duration | text_id | page_id |
|---|---|---|---|---|---|
| str | i64 | i64 | i64 | i64 | i64 |
| "fixation" | 1988145 | 1988322 | 177 | 0 | 1 |
| "fixation" | 1988351 | 1988546 | 195 | 0 | 1 |
| "fixation" | 1988592 | 1988736 | 144 | 0 | 1 |
| "fixation" | 1988788 | 1989013 | 225 | 0 | 1 |
| "fixation" | 1989060 | 1989170 | 110 | 0 | 1 |
We now have event dataframes available with our detected event data.
Saving#
Saving your event data is as simple as:
[6]:
dataset.save_events()
100%|██████████| 20/20 [00:00<00:00, 3109.31it/s]
[6]:
<pymovements.dataset.dataset.Dataset at 0x7fb94b2be8e0>
All of the event data is saved into this directory:
[7]:
dataset.paths.events
[7]:
PosixPath('data/ToyDataset/events')
Let’s confirm it by printing all the new files in this directory:
[8]:
print(list(dataset.paths.events.glob('*/*/*')))
[PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_1.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_3.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_2.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_3.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_3.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_5.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_4.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_4.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_2.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_1.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_2.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_4.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_5.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_1.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_3.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_5.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_5.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_2.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_1.feather'), PosixPath('data/ToyDataset/events/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_4.feather')]
All of the files have been saved into the Dataset.paths.events as feather files.
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:
[9]:
dataset.save_events(events_dirname='events_csv', extension='csv')
100%|██████████| 20/20 [00:00<00:00, 2194.25it/s]
[9]:
<pymovements.dataset.dataset.Dataset at 0x7fb94b2be8e0>
Let’s confirm again by printing all the new files in this alternative directory:
[10]:
alternative_dirpath = dataset.path / 'events_csv'
print(list(alternative_dirpath.glob('*/*/*')))
[PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_2.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_4.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_5.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_1.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_2.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_5.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_2.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_5.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_4.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_5.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_2.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_3.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_3.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_3.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_3.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_1.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_4.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_1.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_4.csv'), PosixPath('data/ToyDataset/events_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_1.csv')]
Loading#
Now let’s imagine that this preprocessing and saving was done in another file and we only want to load the preprocessed data.
We simulate this by initializing a new dataset object. We don’t need to download any additional data.
[11]:
preprocessed_dataset = pm.Dataset('ToyDataset', path='data/ToyDataset')
The preprocessed data can now simply be loaded by setting preprocessed to True:
[12]:
preprocessed_dataset.load(events=True)
dataset.events[0].frame.head()
100%|██████████| 20/20 [00:00<00:00, 197.91it/s]
100%|██████████| 20/20 [00:00<00:00, 1631.90it/s]
[12]:
shape: (5, 6)
| name | onset | offset | duration | text_id | page_id |
|---|---|---|---|---|---|
| str | i64 | i64 | i64 | i64 | i64 |
| "fixation" | 1988145 | 1988322 | 177 | 0 | 1 |
| "fixation" | 1988351 | 1988546 | 195 | 0 | 1 |
| "fixation" | 1988592 | 1988736 | 144 | 0 | 1 |
| "fixation" | 1988788 | 1989013 | 225 | 0 | 1 |
| "fixation" | 1989060 | 1989170 | 110 | 0 | 1 |
By default, the events directory and the feather extension will be chosen.
In case of alternative directory names or other file formats you can use the following:
[13]:
preprocessed_dataset.load(
events=True,
events_dirname='events_csv',
extension='csv',
)
dataset.events[0].frame.head()
100%|██████████| 20/20 [00:00<00:00, 196.50it/s]
100%|██████████| 20/20 [00:00<00:00, 1254.97it/s]
[13]:
shape: (5, 6)
| name | onset | offset | duration | text_id | page_id |
|---|---|---|---|---|---|
| str | i64 | i64 | i64 | i64 | i64 |
| "fixation" | 1988145 | 1988322 | 177 | 0 | 1 |
| "fixation" | 1988351 | 1988546 | 195 | 0 | 1 |
| "fixation" | 1988592 | 1988736 | 144 | 0 | 1 |
| "fixation" | 1988788 | 1989013 | 225 | 0 | 1 |
| "fixation" | 1989060 | 1989170 | 110 | 0 | 1 |
What you have learned in this tutorial:#
saving your preprocesed data using
Dataset.save_preprocessed()load your preprocesed data using
Dataset.load(preprocessed=True)using custom directory names by specifying
preprocessed_dirnameusing other file formats than the default
featherformat by specifyingextension