.. only:: html

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

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

    .. _sphx_glr_auto_examples_voicelocalizer_rsa_pipeline.py:


Voice Localizer RSA analysis on a subject
=========================================

Load singletrial beta maps of a subject. First half of them correspond to
voice stimuli and second half to non-voice stimuli.
Then compute brain RDMs in the grey matter and finally compare (permutation
test) those RDMs with the Voice/Non-voice model RDM.




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

 Out:

 .. code-block:: none

    /hpc/banco/soft/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
      warnings.warn(msg, category=DeprecationWarning)






|


.. code-block:: default


    import os.path as op
    import nibabel as nb
    import numpy as np
    import matplotlib.pyplot as plt

    from nilearn.image import resample_to_img, concat_imgs
    from nilearn import plotting

    from brainrsa import SearchLightRSA
    from brainrsa.plotting import plot_rdm
    from brainrsa.utils import check_mask


    # ******************************************************************************
    # ***** Input parameters *******************************************************
    # ******************************************************************************
    # Number of betas (to load them and also construct the model RDM)
    n_betas = 40

    # Will compute brain RDMs only in grey matter
    process_mask = "/hpc/banco/cagna.b/projects/ageing/data/preprocessing/" + \
                   "sub-04/anat/c1sanlm_sub-04_T1w.nii"
    # Directory of the singletrial GLM
    beta_dir = "/hpc/banco/cagna.b/projects/ageing/data/analyses/sub-04/glm/" + \
               "uasub-04_task-voicelocalizer_model-singletrial"
    # Where the beta value sare actually available
    data_mask = op.join(beta_dir, "mask.nii")

    # ******************************************************************************
    # ***** Compute brain RDMs in the ROI ******************************************
    # ******************************************************************************
    # List beta files of the subject
    beta_imgs = []
    for b in range(n_betas):
        beta_imgs.append(op.join(beta_dir, "beta_{:04d}.nii".format(b+1)))

    # Then, load all the images and put them in a new one (4D)
    beta_imgs = concat_imgs(beta_imgs)

    # ******************************************************************************
    # ***** Compute brain RDMs in the ROI ******************************************
    # ******************************************************************************
    # Load the mask and check that it is binary
    data_mask = check_mask(data_mask, threshold=0.5)

    # Resample procmask to match datamask resolution
    process_mask = check_mask(
        resample_to_img(process_mask, data_mask), 
        threshold=0.5
    )

    # Initialise the searchlight and specify where to compute the values and the 
    # radius of the spege (in milimeters)
    rsa = SearchLightRSA(
        data_mask,
        process_mask_img=process_mask,
        distance='euclidean',
        radius=6,
        n_jobs=20,
        verbose=2
    )

    ## Compute beta distance matrix (RDMs) for each voxel of the process_mask using
    ## voxels included in data_mask
    #print("Start to fit brain RDMs")
    #rdms = rsa.fit(beta_imgs)

    ## Save the image that give RDM index at each voxel
    ## nb.save(rsa.index_image(), indx_map)

    ## Save RDMs
    ## np.save(dist_f, rdms)

    ## Compute the average RDM
    #avg_rdm = np.mean(rdms, axis=0)

    ## ******************************************************************************
    ## ***** Compare with Voice/Non-Voice model *************************************
    ## ******************************************************************************
    ## Create the model RDM
    #mid = int(n_betas/2)
    #model = np.zeros((n_betas, n_betas))
    #model[:mid, mid:] = 1
    #model[mid:, :mid] = 1

    ## Then, do the comparison with each brain RDMs (permuatation test)
    #print('start to compare brain RDMs to model RDM')
    #scores_img = rsa.compare_to(model, distance="spearmanr", n_perms=100)

    ## Compute just the distance with the model
    ##corr_img = rsa.distance_to(squareform(models[im]), distance, True)
    ##save_map(corr_img, brain, dist2model_tmpl.replace("[m]", model),
    ##         "Spearman ranked distance to " + model + " model")

    ## ******************************************************************************
    ## ***** Figure *****************************************************************
    ## ******************************************************************************
    #fig, axes = plt.subplots(2, 2, figsize=(12, 10))

    ## Show the mask
    #plotting.plot_roi(process_mask, data_mask, display_mode='x', cut_coords=1, 
    #                  axes=axes[0, 0], title="Process mask over data mask")

    ## The average RDM
    #plot_rdm(avg_rdm, "Average RDM", sigtri="lower", ax=axes[0, 1])

    ## The model
    #plot_rdm(model, "Voice/Non-voice model", sigtri="lower", ax=axes[0, 1])

    ## And the score map
    #plotting.plot_stat_map(
    #    scores_img, display_mode='x', cut_coords=1, colorbar=True, axes=axes[1, 1], 
    #    title="Comparison to V/NV model")

    plotting.show()



.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  3.534 seconds)


.. _sphx_glr_download_auto_examples_voicelocalizer_rsa_pipeline.py:


.. only :: html

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



  .. container:: sphx-glr-download sphx-glr-download-python

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



  .. container:: sphx-glr-download sphx-glr-download-jupyter

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


.. only:: html

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

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