.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_Time_Features_examples_plot_extract_and_save_features.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_Time_Features_examples_plot_extract_and_save_features.py:


============================================
         Extract and Save Features
============================================

This example shows how to create a TimeFeature instance from a SabDataset instance,
 how to extract features from the amplitude data and save them so that it can be used later on.




.. code-block:: python


    # import matplotlib
    # matplotlib.use('TkAgg')
    from os.path import isdir, join
    import sab_dataset
    import seaborn as sns
    sns.set()
    sns.set_context('paper')







Load the data : sab dataset



.. code-block:: python

    sab_dataset_dirpath = join('pySAB', 'sample_data') if isdir('pySAB') else join('..', '..', 'pySAB', 'sample_data')
    sab_dataset_filename = 'sab_dataset_rec_subject_id_040119_1153.p'
    rec_dataset = sab_dataset.load_sab_dataset(join(sab_dataset_dirpath, sab_dataset_filename))
    # sab_dataset_dirpath = 'sample_data_whole' if isdir('sample_data_whole') else join('..', '..', 'sample_data_whole')
    # subject_id = '042'
    # rec_dataset = sab_dataset.SabDataset(sab_dataset_dirpath, subject_id, 'rec')







Downsample the data



.. code-block:: python

    rec_dataset.downsample(2)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    New sampling rate is 256.0


Construct the features from the SabDataset object - Select only 'hits' and 'correct rejects' trials:



.. code-block:: python

    time_features = rec_dataset.create_features(trial_sel=(rec_dataset.hits | rec_dataset.correct_rejects))
    print(time_features)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Time Features subject_id_rec - 7 features, 180 time points, 356 trials
    2 labels : {1: 'Hits', 2: 'Correct rejects'}
    Feature types : Amp


Extract features, if called without any parameter, the function return the possible feature to extract



.. code-block:: python

    time_features.extract_feature()





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Possible features to compute : ['filt_bandpower', 'dwt', 'stft_bandpower', 'stft_phase', 'cwt_bandpower', 'cwt_phase', 'phase_hilbert']


Extract the phase
time_features.extract_feature('cwt_phase')


Save the time features instance so that it can be used later without having to re-compute the features
time_features.save(dir_path=sab_dataset_dirpath)


**Total running time of the script:** ( 0 minutes  0.064 seconds)


.. _sphx_glr_download_auto_examples_Time_Features_examples_plot_extract_and_save_features.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_extract_and_save_features.py <plot_extract_and_save_features.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_extract_and_save_features.ipynb <plot_extract_and_save_features.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_