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

    Click :ref:`here <sphx_glr_download_auto_examples_Time_Features_examples_plot_time_feature_example_1.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_Time_Features_examples_plot_time_feature_example_1.py:


============================================
      Feature Evolution and Distribution
============================================

This example shows how to plot feature evolution, or the distribution of a feature over trials
at a certain time point.




.. 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))







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 and keep only
2 electrodes of interest :



.. 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 discrete wavelet transform coefficients, The new features are automatically added



.. code-block:: python

    time_features.extract_feature('dwt')
    print(time_features)





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

 Out:

 .. code-block:: none

    Time Features subject_id_rec - 42 features, 180 time points, 356 trials
    2 labels : {1: 'Hits', 2: 'Correct rejects'}
    Feature types : Amp, DWT 4-8 Hz, DWT 8-16 Hz, DWT 16-32 Hz, DWT 32-64 Hz, DWT 64-128 Hz


Plot the evolution of feature 19 :



.. code-block:: python

    time_features.plot_feature_erp(feature_pos=2)




.. image:: /auto_examples/Time_Features_examples/images/sphx_glr_plot_time_feature_example_1_001.png
    :class: sphx-glr-single-img




Plot the evolution of feature DWT 16-32 Hz for channel TB'10-TB'11 :



.. code-block:: python

    time_features.plot_feature_erp(feature_type='DWT 16-32', feature_channame='EEG TP\'3-TP\'4')




.. image:: /auto_examples/Time_Features_examples/images/sphx_glr_plot_time_feature_example_1_002.png
    :class: sphx-glr-single-img




Plot the distribution of a feature at a certain time point. The 'time_points' parameter must be passed and contain
only 1 time point



.. code-block:: python

    time_point_sel = time_features.time2sample(0.55)
    time_features.plot_feature_distribution(time_points=time_point_sel, feature_pos=10)



.. image:: /auto_examples/Time_Features_examples/images/sphx_glr_plot_time_feature_example_1_003.png
    :class: sphx-glr-single-img




**Total running time of the script:** ( 0 minutes  0.736 seconds)


.. _sphx_glr_download_auto_examples_Time_Features_examples_plot_time_feature_example_1.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_time_feature_example_1.py <plot_time_feature_example_1.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_time_feature_example_1.ipynb <plot_time_feature_example_1.ipynb>`


.. only:: html

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

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