Tutorial 1: Using ``neurocaps.extraction.TimeseriesExtractor``
==============================================================
The purpose of this module is to perform timeseries extraction, cleaning, and visualization. Additionally, it is used
to create the necessary dictionary structure needed for CAP. Technically, this dictionary structure can be manually
created if the BOLD images where not preprocessed using fMRIPrep. Output from the `Extracting Timeseries` section
is from a test from Github Actions using a truncated version of an open dataset provided by `Laumann & Poldrack <https://openfmri.org/dataset/ds000031/>`_
and used in `Laumann et al., 2015 <https://doi.org/10.1016/j.neuron.2015.06.037>`_ [1]_ was also utilized. This data was obtained from the OpenfMRI database, accession number ds000031.

Extracting Timeseries
---------------------

.. code-block:: python

    from neurocaps.extraction import TimeseriesExtractor

    dir = os.path.dirname(__file__)

    """If an asterisk '*' is after a name, all confounds starting with the
    term preceding the parameter will be used. in this case, all parameters
    starting with cosine will be used."""

    confounds=["Cosine*", "aComp*", "Rot*"]

    parcel_approach = {"Schaefer": {"n_rois": 100, "yeo_networks": 7, "resolution_mm": 2}}

    extractor = TimeseriesExtractor(space="MNI152NLin2009cAsym",
                                    parcel_approach=parcel_approach,
                                    standardize="zscore_sample",
                                    use_confounds=True,
                                    detrend=True,
                                    low_pass=0.15,
                                    high_pass=None,
                                    confound_names=confounds)

    bids_dir = os.path.join(dir, "ds000031_R1.0.4_ses001-022/ds000031_R1.0.4")

    extractor.get_bold(bids_dir=bids_dir,
                       session="002",
                       task="rest",
                       pipeline_name="fmriprep_1.0.0/fmriprep",
                       tr=1.2)

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

    .. code-block:: none

        2024-11-02 20:35:47,797 neurocaps._utils.extraction.check_confound_names [INFO] Confound regressors to be used if available: Cosine*, aComp*, Rot*.
        2024-11-02 20:35:48,898 neurocaps.extraction.timeseriesextractor [INFO] BIDS Layout: ...0.4_ses001-022\ds000031_R1.0.4 | Subjects: 1 | Sessions: 1 | Runs: 1
        2024-11-02 20:35:48,941 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 01 | SESSION: 002 | TASK: rest | RUN: 001] Preparing for Timeseries Extraction using [FILE: sub-01_ses-002_task-rest_run-001_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz].
        2024-11-02 20:35:48,946 neurocaps._utils.extraction.extract_timeseries [INFO] [SUBJECT: 01 | SESSION: 002 | TASK: rest | RUN: 001] The following confounds will be used for nuisance regression: Cosine00, Cosine01, Cosine02, Cosine03, Cosine04, Cosine05, Cosine06, aCompCor00, aCompCor01, aCompCor02, aCompCor03, aCompCor04, aCompCor05, RotX, RotY, RotZ.

.. code-block:: python

    print(extraction.subject_timeseries)

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

    .. code-block:: none

        {'01': {'run-001': array([[-0.12410211, -0.746016  , -0.9138416 , ...,  0.12293668,
        -0.3167036 , -0.4593077 ],
       [-1.0730965 ,  0.88747275,  0.83726895, ..., -0.9314818 ,
         0.5686499 ,  0.9783575 ],
       [-0.5288149 ,  0.62266237,  0.6349383 , ..., -0.5331197 ,
         0.5261529 ,  0.5858582 ],
       ...,
       [ 0.32443312, -0.42479128, -0.43596116, ...,  0.5425763 ,
        -0.2863486 , -0.31798226],
       [ 0.94420713, -0.7662241 , -0.6925075 , ...,  1.7636685 ,
        -0.4194046 , -0.5691561 ],
       [ 0.4901481 ,  0.33806482,  0.48850006, ..., -0.29197463,
        -0.08600576, -0.08736482]], dtype=float32)}}


Saving Timeseries
-----------------
.. code-block:: python

    extractor.timeseries_to_pickle(output_dir=dir,
                                   filename="rest_Schaefer.pkl")

Visualizing Timeseries
----------------------
.. code-block:: python

    # Visualizing a region
    extractor.visualize_bold(subj_id="01", region="Vis")

.. image:: embed/visualize_timeseries_regions.png
    :width: 1000

.. code-block:: python

    # Visualizing a several nodes
    extractor.visualize_bold(subj_id="01",
                             run="001",
                             roi_indx=[0, 1, 2])
    # or
    extractor.visualize_bold(subj_id="01",
                             run="001",
                             roi_indx=["LH_Vis_1", "LH_Vis_2", "LH_Vis_3"])

.. image:: embed/visualize_timeseries_nodes.png
    :width: 1000

==========

.. [1] Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M. Y., Gilmore, A. W., McDermott, K. B., Nelson, S. M., Dosenbach, N. U., Schlaggar, B. L., Mumford, J. A., Poldrack, R. A., & Petersen, S. E. (2015). Functional system and areal organization of a highly sampled individual human brain. Neuron, 87(3), 657–670. https://doi.org/10.1016/j.neuron.2015.06.037