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Interfaces and Algorithms

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nipype.algorithms.modelgen

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nipype.algorithms.misc

CreateNifti

Inputs:

[Mandatory]
data_file : (an existing file name)
        ANALYZE img file
header_file : (an existing file name)
        corresponding ANALYZE hdr file

[Optional]
affine : (an array)
        affine transformation array
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run

Outputs:

nifti_file : (an existing file name)
        Unknown

Distance

Calculates distance between two volumes.

Inputs:

[Mandatory]
volume1 : (an existing file name)
        Has to have the same dimensions as volume2.
volume2 : (an existing file name)
        Has to have the same dimensions as volume1.

[Optional]
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run
method : ('eucl_min' or 'eucl_cog' or 'eucl_mean' or 'eucl_wmean')
        ""eucl_min": Euclidean distance between two closest points    "eucl_cog": mean Euclidian distance between the Center of Gravity of volume1 and CoGs of volume2    "eucl_mean": mean Euclidian minimum distance of all volume2 voxels to volume1    "eucl_wmean": mean Euclidian minimum distance of all volume2 voxels to volume1 weighted by their values

Outputs:

distance : (a float)
        Unknown
histogram : (a file name)
        Unknown
point1 : (an array with shape (3,))
        Unknown
point2 : (an array with shape (3,))
        Unknown

ModifyAffine

Left multiplies the affine matrix with a specified values. Saves the volume as a nifti file.

Inputs:

[Mandatory]
volumes : (an existing file name)
        volumes which affine matrices will be modified

[Optional]
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run
transformation_matrix : (an array with shape (4, 4))
        transformation matrix that will be left multiplied by the affine matrix

Outputs:

transformed_volumes : (a file name)
        Unknown

Overlap

Calculates various overlap measures between two maps.

Example

>>> overlap = Overlap()
>>> overlap.inputs.volume1 = 'cont1.nii'
>>> overlap.inputs.volume1 = 'cont2.nii'
>>> res = overlap.run() 

Inputs:

[Mandatory]
volume1 : (an existing file name)
        Has to have the same dimensions as volume2.
volume2 : (an existing file name)
        Has to have the same dimensions as volume1.

[Optional]
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run
out_file : (a file name)
        Unknown

Outputs:

dice : (a float)
        Unknown
diff_file : (an existing file name)
        Unknown
jaccard : (a float)
        Unknown
volume_difference : (an integer)
        Unknown

PickAtlas

Returns ROI masks given an atlas and a list of labels. Supports dilation and left right masking (assuming the atlas is properly aligned).

Inputs:

[Mandatory]
atlas : (an existing file name)
        Location of the atlas that will be used.

[Optional]
dilation_size : (an integer)
        Defines how much the mask will be dilated (expanded in 3D).
hemi : ('both' or 'left' or 'right')
        Restrict the mask to only one hemisphere: left or right
ignore_exception : (a boolean)
        Print an error message instead of throwing an exception in case the interface fails to run
labels : (an integer or a list of items which are an integer)
        Labels of regions that will be included in the mask. Must be compatible with the atlas used.
output_file : (a file name)
        Where to store the output mask.

Outputs:

mask_file : (an existing file name)
        output mask file

SimpleThreshold

Inputs:

[Mandatory]
threshold : (a float)
        volumes to be thresholdedeverything below this value will be set to zero
volumes : (an existing file name)
        volumes to be thresholded

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

Outputs:

thresholded_volumes : (an existing file name)
        thresholded volumes

TSNR

Computes the time-course SNR for a time series

Typically you want to run this on a realigned time-series.

Example

>>> tsnr = TSNR()
>>> tsnr.inputs.in_file = 'functional.nii'
>>> res = tsnr.run() 

Inputs:

[Mandatory]
in_file : (an existing file name)
        realigned 4D file

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

Outputs:

tsnr_file : (an existing file name)
        tsnr image file