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        Click :ref:`here <sphx_glr_download_auto_examples_utils_subject_to_mni.py>`
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.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_utils_subject_to_mni.py:


===================================
Transform from Subject to MNI Space
===================================

Pycortex has built-in functionality for linearly transforming data to and from
standard atlas spaces (e.g. MNI-152). This functionality is built on top of FSL.

This example shows how to create a transform from some subject functional space
to MNI space, and how to apply that transform to a dataset.

.. GENERATED FROM PYTHON SOURCE LINES 13-40

.. code-block:: default


    import cortex

    # First let's do this "manually", using cortex.mni
    from cortex import mni

    import numpy as np
    np.random.seed(1234)

    # This transform is gonna be from one specific functional space for a subject
    # which is defined by the transform (xfm)
    s1_to_mni = mni.compute_mni_transform(subject='S1', xfm='fullhead')
    # s1_to_mni is a 4x4 array describing the transformation in homogeneous corods

    # Transform data from subject to MNI space
    # first we will create a dataset to transform
    data = cortex.Volume.random('S1', 'fullhead')

    # then transform it!
    mni_data = mni.transform_to_mni(data, s1_to_mni)
    # mni_data is a nibabel Nifti1Image

    mni_data_vol = mni_data.get_data() # the actual array, shape=(182,218,182)

    # That was the manual method. pycortex can also cache these transforms for you
    # if you get them using the pycortex database
    s1_to_mni_db = cortex.db.get_mnixfm('S1', 'fullhead')
    # this is the same as s1_to_mni, but will return instantly on subsequent calls

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.. _sphx_glr_download_auto_examples_utils_subject_to_mni.py:


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

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