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algorithms.rapidart
===================


.. _nipype.algorithms.rapidart.ArtifactDetect:


.. index:: ArtifactDetect

ArtifactDetect
--------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/algorithms/rapidart.py#L310>`__

Detects outliers in a functional imaging series

Uses intensity and motion parameters to infer outliers. If `use_norm` is
True, it computes the movement of the center of each face a cuboid centered
around the head and returns the maximal movement across the centers. If you
wish to use individual thresholds instead, import `Undefined` from
`nipype.interfaces.base` and set `....inputs.use_norm = Undefined`


Examples
~~~~~~~~

>>> ad = ArtifactDetect()
>>> ad.inputs.realigned_files = 'functional.nii'
>>> ad.inputs.realignment_parameters = 'functional.par'
>>> ad.inputs.parameter_source = 'FSL'
>>> ad.inputs.norm_threshold = 1
>>> ad.inputs.use_differences = [True, False]
>>> ad.inputs.zintensity_threshold = 3
>>> ad.run()  # doctest: +SKIP

Inputs::

        [Mandatory]
        mask_type: (u'spm_global' or u'file' or u'thresh')
                Type of mask that should be used to mask the functional data.
                *spm_global* uses an spm_global like calculation to determine the
                brain mask. *file* specifies a brain mask file (should be an image
                file consisting of 0s and 1s). *thresh* specifies a threshold to
                use. By default all voxels are used,unless one of these mask types
                are defined
        norm_threshold: (a float)
                Threshold to use to detect motion-related outliers when composite
                motion is being used
                mutually_exclusive: rotation_threshold, translation_threshold
        parameter_source: (u'SPM' or u'FSL' or u'AFNI' or u'NiPy' or
                 u'FSFAST')
                Source of movement parameters
        realigned_files: (a list of items which are an existing file name)
                Names of realigned functional data files
        realignment_parameters: (a list of items which are an existing file
                 name)
                Names of realignment parameters corresponding to the functional data
                files
        rotation_threshold: (a float)
                Threshold (in radians) to use to detect rotation-related outliers
                mutually_exclusive: norm_threshold
        translation_threshold: (a float)
                Threshold (in mm) to use to detect translation-related outliers
                mutually_exclusive: norm_threshold
        zintensity_threshold: (a float)
                Intensity Z-threshold use to detection images that deviate from the
                mean

        [Optional]
        bound_by_brainmask: (a boolean, nipype default value: False)
                use the brain mask to determine bounding boxfor composite norm
                (worksfor SPM and Nipy - currentlyinaccurate for FSL, AFNI
        global_threshold: (a float, nipype default value: 8.0)
                use this threshold when mask type equal's spm_global
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        intersect_mask: (a boolean)
                Intersect the masks when computed from spm_global.
        mask_file: (an existing file name)
                Mask file to be used if mask_type is 'file'.
        mask_threshold: (a float)
                Mask threshold to be used if mask_type is 'thresh'.
        plot_type: (u'png' or u'svg' or u'eps' or u'pdf', nipype default
                 value: png)
                file type of the outlier plot
        save_plot: (a boolean, nipype default value: True)
                save plots containing outliers
        use_differences: (a list of items which are a bool or None, nipype
                 default value: [True, False])
                Use differences between successive motion (first element) and
                intensity parameter (second element) estimates in order to determine
                outliers. (default is [True, False])
        use_norm: (a boolean, nipype default value: True)
                Uses a composite of the motion parameters in order to determine
                outliers.
                requires: norm_threshold

Outputs::

        displacement_files: (a list of items which are a file name)
                One image file for each functional run containing the voxel
                displacement timeseries
        intensity_files: (a list of items which are an existing file name)
                One file for each functional run containing the global intensity
                values determined from the brainmask
        mask_files: (a list of items which are a file name)
                One image file for each functional run containing the mask used for
                global signal calculation
        norm_files: (a list of items which are a file name)
                One file for each functional run containing the composite norm
        outlier_files: (a list of items which are an existing file name)
                One file for each functional run containing a list of 0-based
                indices corresponding to outlier volumes
        plot_files: (a list of items which are a file name)
                One image file for each functional run containing the detected
                outliers
        statistic_files: (a list of items which are an existing file name)
                One file for each functional run containing information about the
                different types of artifacts and if design info is provided then
                details of stimulus correlated motion and a listing or artifacts by
                event type.

.. _nipype.algorithms.rapidart.StimulusCorrelation:


.. index:: StimulusCorrelation

StimulusCorrelation
-------------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/algorithms/rapidart.py#L662>`__

Determines if stimuli are correlated with motion or intensity
parameters.

Currently this class supports an SPM generated design matrix and requires
intensity parameters. This implies that one must run
:ref:`ArtifactDetect <nipype.algorithms.rapidart.ArtifactDetect>`
and :ref:`Level1Design <nipype.interfaces.spm.model.Level1Design>` prior to
running this or provide an SPM.mat file and intensity parameters through
some other means.

Examples
~~~~~~~~

>>> sc = StimulusCorrelation()
>>> sc.inputs.realignment_parameters = 'functional.par'
>>> sc.inputs.intensity_values = 'functional.rms'
>>> sc.inputs.spm_mat_file = 'SPM.mat'
>>> sc.inputs.concatenated_design = False
>>> sc.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        concatenated_design: (a boolean)
                state if the design matrix contains concatenated sessions
        intensity_values: (a list of items which are an existing file name)
                Name of file containing intensity values
        realignment_parameters: (a list of items which are an existing file
                 name)
                Names of realignment parameters corresponding to the functional data
                files
        spm_mat_file: (an existing file name)
                SPM mat file (use pre-estimate SPM.mat file)

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run

Outputs::

        stimcorr_files: (a list of items which are an existing file name)
                List of files containing correlation values
