Saving and Loading Preprocessed Data#
What you will learn in this tutorial:#
how to save your preprocessed data
how to load your preprocessed 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/v0.16.1/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, 45.10it/s]
[2]:
<pymovements.dataset.dataset.Dataset at 0x7fe0f592daf0>
Now let’s load in the data and do some preprocessing:
[3]:
dataset.pix2deg()
dataset.pos2vel()
dataset.gaze[0].frame.head()
100%|██████████| 20/20 [00:00<00:00, 22.56it/s]
100%|██████████| 20/20 [00:00<00:00, 47.18it/s]
[3]:
shape: (5, 8)
| text_id | page_id | time | stimuli_x | stimuli_y | pixel | position | velocity |
|---|---|---|---|---|---|---|---|
| i64 | i64 | f64 | f64 | f64 | list[f64] | list[f64] | list[f64] |
| 0 | 1 | 1.988145e6 | -1.0 | -1.0 | [206.8, 152.4] | [-10.697598, -8.852399] | [null, null] |
| 0 | 1 | 1.988146e6 | -1.0 | -1.0 | [206.9, 152.1] | [-10.695183, -8.859678] | [null, null] |
| 0 | 1 | 1.988147e6 | -1.0 | -1.0 | [207.0, 151.8] | [-10.692768, -8.866956] | [1.610194, -5.256267] |
| 0 | 1 | 1.988148e6 | -1.0 | -1.0 | [207.1, 151.7] | [-10.690352, -8.869381] | [0.402548, -4.447465] |
| 0 | 1 | 1.988149e6 | -1.0 | -1.0 | [207.0, 151.5] | [-10.692768, -8.874233] | [0.402561, -3.234462] |
We have now added some additional columns for degrees in visual angle and velocity.
Saving#
Saving your preprocessed data is as simple as:
[4]:
dataset.save_preprocessed()
100%|██████████| 20/20 [00:00<00:00, 314.54it/s]
[4]:
<pymovements.dataset.dataset.Dataset at 0x7fe0f592daf0>
All of the preprocessed data is saved into this directory:
[5]:
dataset.paths.preprocessed
[5]:
PosixPath('data/ToyDataset/preprocessed')
Let’s confirm it by printing all the new files in this directory:
[6]:
print(list(dataset.paths.preprocessed.glob('*/*/*')))
[PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_4.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_3.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_1.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_2.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_1.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_5.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_5.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_4.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_4.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_4.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_2.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_2.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_3.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_3.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_5.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_2.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_1.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_5.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_3.feather'), PosixPath('data/ToyDataset/preprocessed/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_1.feather')]
All of the files have been saved into the Dataset.paths.preprocessed 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:
[7]:
dataset.save_preprocessed(preprocessed_dirname='preprocessed_csv', extension='csv')
100%|██████████| 20/20 [00:00<00:00, 40.57it/s]
[7]:
<pymovements.dataset.dataset.Dataset at 0x7fe0f592daf0>
Let’s confirm again by printing all the new files in this alternative directory:
[8]:
alternative_dirpath = dataset.path / 'preprocessed_csv'
print(list(alternative_dirpath.glob('*/*/*')))
[PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_5.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_4.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_5.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_1.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_3.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_3.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_1.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_4.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_2.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_4.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_1.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_2.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_2_5.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_2.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_2.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_0_3.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_3.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_4.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_1_1.csv'), PosixPath('data/ToyDataset/preprocessed_csv/aeye-lab-pymovements-toy-dataset-6cb5d66/data/trial_3_5.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.
[9]:
events_dataset = pm.Dataset('ToyDataset', path='data/ToyDataset')
The preprocessed data can now simply be loaded by setting preprocessed to True:
[10]:
events_dataset.load(preprocessed=True)
events_dataset.gaze[0].frame.head()
100%|██████████| 20/20 [00:00<00:00, 1316.69it/s]
[10]:
shape: (5, 8)
| text_id | page_id | time | stimuli_x | stimuli_y | pixel | position | velocity |
|---|---|---|---|---|---|---|---|
| i64 | i64 | f64 | f64 | f64 | list[f64] | list[f64] | list[f64] |
| 0 | 1 | 1.988145e6 | -1.0 | -1.0 | [206.8, 152.4] | [-10.697598, -8.852399] | [null, null] |
| 0 | 1 | 1.988146e6 | -1.0 | -1.0 | [206.9, 152.1] | [-10.695183, -8.859678] | [null, null] |
| 0 | 1 | 1.988147e6 | -1.0 | -1.0 | [207.0, 151.8] | [-10.692768, -8.866956] | [1.610194, -5.256267] |
| 0 | 1 | 1.988148e6 | -1.0 | -1.0 | [207.1, 151.7] | [-10.690352, -8.869381] | [0.402548, -4.447465] |
| 0 | 1 | 1.988149e6 | -1.0 | -1.0 | [207.0, 151.5] | [-10.692768, -8.874233] | [0.402561, -3.234462] |
By default, the preprocessed directory and the feather extension will be chosen.
In case of alternative directory names or other file formats you can use the following:
[11]:
events_dataset.load(
preprocessed=True,
preprocessed_dirname='preprocessed_csv',
extension='csv',
)
events_dataset.gaze[0].frame.head()
100%|██████████| 20/20 [00:00<00:00, 79.99it/s]
[11]:
shape: (5, 11)
| text_id | page_id | time | stimuli_x | stimuli_y | pixel_x | pixel_y | position_x | position_y | velocity_x | velocity_y |
|---|---|---|---|---|---|---|---|---|---|---|
| i64 | i64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 0 | 1 | 1.988145e6 | -1.0 | -1.0 | 206.8 | 152.4 | -10.697598 | -8.852399 | null | null |
| 0 | 1 | 1.988146e6 | -1.0 | -1.0 | 206.9 | 152.1 | -10.695183 | -8.859678 | null | null |
| 0 | 1 | 1.988147e6 | -1.0 | -1.0 | 207.0 | 151.8 | -10.692768 | -8.866956 | 1.610194 | -5.256267 |
| 0 | 1 | 1.988148e6 | -1.0 | -1.0 | 207.1 | 151.7 | -10.690352 | -8.869381 | 0.402548 | -4.447465 |
| 0 | 1 | 1.988149e6 | -1.0 | -1.0 | 207.0 | 151.5 | -10.692768 | -8.874233 | 0.402561 | -3.234462 |
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