Home · Quickstart · Documentation · Citation · NiPy

# algorithms.misc

## AddCSVColumn

Link to code

Short interface to add an extra column and field to a text file

### Example

    >>> from nipype.algorithms import misc
    >>> addcol = misc.AddCSVColumn()
    >>> addcol.inputs.in_file = 'degree.csv'
    >>> addcol.inputs.extra_column_heading = 'group'
    >>> addcol.inputs.extra_field = 'male'
    >>> addcol.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Input comma-separated value (CSV) files

    [Optional]
    extra_column_heading: (a string)
            New heading to add for the added field.
    extra_field: (a string)
            New field to add to each row. This is useful for saving the group or
            subject ID in the file.
    out_file: (a file name, nipype default value: extra_heading.csv)
            Output filename for merged CSV file

Outputs:

    csv_file: (a file name)
            Output CSV file containing columns

## AddCSVRow

Link to code

Simple interface to add an extra row to a csv file

Note

Requires pandas

Warning

Multi-platform thread-safe execution is possible with lockfile. Please recall that (1) this module is alpha software; and (2) it should be installed for thread-safe writing. If lockfile is not installed, then the interface is not thread-safe.

### Example

    >>> from nipype.algorithms import misc
    >>> addrow = misc.AddCSVRow()
    >>> addrow.inputs.in_file = 'scores.csv'
    >>> addrow.inputs.si = 0.74
    >>> addrow.inputs.di = 0.93
    >>> addrow.subject_id = 'S400'
    >>> addrow.inputs.list_of_values = [ 0.4, 0.7, 0.3 ]
    >>> addrow.run()

Inputs:

    [Mandatory]
    in_file: (a file name)
            Input comma-separated value (CSV) files

    [Optional]
    _outputs: (a dictionary with keys which are any value and with values
             which are any value, nipype default value: {})
    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:

    csv_file: (a file name)
            Output CSV file containing rows

## AddNoise

Link to code

Corrupts with noise the input image

### Example

    >>> from nipype.algorithms.misc import AddNoise
    >>> noise = AddNoise()
    >>> noise.inputs.in_file = 'T1.nii'
    >>> noise.inputs.in_mask = 'mask.nii'
    >>> noise.snr = 30.0
    >>> noise.run()

Inputs:

    [Mandatory]
    bg_dist: ('normal' or 'rayleigh', nipype default value: normal)
            desired noise distribution, currently only normal is implemented
    dist: ('normal' or 'rician', nipype default value: normal)
            desired noise distribution
    in_file: (an existing file name)
            input image that will be corrupted with noise

    [Optional]
    in_mask: (an existing file name)
            input mask, voxels outside this mask will be considered background
    out_file: (a file name)
            desired output filename
    snr: (a float, nipype default value: 10.0)
            desired output SNR in dB

Outputs:

    out_file: (an existing file name)
            corrupted image

## CalculateNormalizedMoments

Link to code

Calculates moments of timeseries.

### Example

    >>> from nipype.algorithms import misc
    >>> skew = misc.CalculateNormalizedMoments()
    >>> skew.inputs.moment = 3
    >>> skew.inputs.timeseries_file = 'timeseries.txt'
    >>> skew.run()

Inputs:

    [Mandatory]
    moment: (an integer)
            Define which moment should be calculated, 3 for skewness, 4 for
            kurtosis.
    timeseries_file: (an existing file name)
            Text file with timeseries in columns and timepoints in rows,
            whitespace separated

    [Optional]

Outputs:

    moments: (a list of items which are a float)
            Moments

## CreateNifti

Link to code

Creates a nifti volume

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, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run

Outputs:

    nifti_file: (an existing file name)

## Distance

Link to code

Calculates distance between two volumes.

Deprecated since version 0.10.0: Use `nipype.algorithms.metrics.Distance` instead.

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, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mask_volume: (an existing file name)
            calculate overlap only within this mask.
    method: ('eucl_min' or 'eucl_cog' or 'eucl_mean' or 'eucl_wmean' or
             'eucl_max', nipype default value: eucl_min)
            ""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 "eucl_max": maximum over minimum Euclidian distances
            of all volume2 voxels to volume1 (also known as the Hausdorff
            distance)

Outputs:

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

## FuzzyOverlap

Link to code

Calculates various overlap measures between two maps, using a fuzzy definition.

Deprecated since version 0.10.0: Use `nipype.algorithms.metrics.FuzzyOverlap` instead.

Inputs:

    [Mandatory]
    in_ref: (an existing file name)
            Reference image. Requires the same dimensions as in_tst.
    in_tst: (an existing file name)
            Test image. Requires the same dimensions as in_ref.

    [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
    out_file: (a file name, nipype default value: diff.nii)
            alternative name for resulting difference-map
    weighting: ('none' or 'volume' or 'squared_vol', nipype default
             value: none)
            'none': no class-overlap weighting is performed. 'volume': computed
            class-overlaps are weighted by class volume 'squared_vol': computed
            class-overlaps are weighted by the squared volume of the class

Outputs:

    class_fdi: (a list of items which are a float)
            Array containing the fDIs of each computed class
    class_fji: (a list of items which are a float)
            Array containing the fJIs of each computed class
    dice: (a float)
            Fuzzy Dice Index (fDI), all the classes
    diff_file: (an existing file name)
            resulting difference-map of all classes, using the chosen weighting
    jaccard: (a float)
            Fuzzy Jaccard Index (fJI), all the classes

## Gunzip

Link to code

Gunzip wrapper

Inputs:

    [Mandatory]
    in_file: (an existing file name)

    [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:

    out_file: (an existing file name)

## Matlab2CSV

Link to code

Simple interface to save the components of a MATLAB .mat file as a text file with comma-separated values (CSVs).

CSV files are easily loaded in R, for use in statistical processing. For further information, see cran.r-project.org/doc/manuals/R-data.pdf

### Example

    >>> from nipype.algorithms import misc
    >>> mat2csv = misc.Matlab2CSV()
    >>> mat2csv.inputs.in_file = 'cmatrix.mat'
    >>> mat2csv.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            Input MATLAB .mat file

    [Optional]
    reshape_matrix: (a boolean, nipype default value: True)
            The output of this interface is meant for R, so matrices will be
            reshaped to vectors by default.

Outputs:

    csv_files: (a file name)

## MergeCSVFiles

Link to code

This interface is designed to facilitate data loading in the R environment. It takes input CSV files and merges them into a single CSV file. If provided, it will also incorporate column heading names into the resulting CSV file.

CSV files are easily loaded in R, for use in statistical processing. For further information, see cran.r-project.org/doc/manuals/R-data.pdf

### Example

    >>> from nipype.algorithms import misc
    >>> mat2csv = misc.MergeCSVFiles()
    >>> mat2csv.inputs.in_files = ['degree.mat','clustering.mat']
    >>> mat2csv.inputs.column_headings = ['degree','clustering']
    >>> mat2csv.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            Input comma-separated value (CSV) files

    [Optional]
    column_headings: (a list of items which are a string)
            List of column headings to save in merged CSV file (must be equal to
            number of input files). If left undefined, these will be pulled from
            the input filenames.
    extra_column_heading: (a string)
            New heading to add for the added field.
    extra_field: (a string)
            New field to add to each row. This is useful for saving the group or
            subject ID in the file.
    out_file: (a file name, nipype default value: merged.csv)
            Output filename for merged CSV file
    row_heading_title: (a string, nipype default value: label)
            Column heading for the row headings added
    row_headings: (a list of items which are a string)
            List of row headings to save in merged CSV file (must be equal to
            number of rows in the input files).

Outputs:

    csv_file: (a file name)
            Output CSV file containing columns

## MergeROIs

Link to code

Splits a 3D image in small chunks to enable parallel processing. ROIs keep time series structure in 4D images.

### Example

    >>> from nipype.algorithms import misc
    >>> rois = misc.MergeROIs()
    >>> rois.inputs.in_files = ['roi%02d.nii' % i for i in xrange(1, 6)]
    >>> rois.inputs.in_reference = 'mask.nii'
    >>> rois.inputs.in_index = ['roi%02d_idx.npz' % i for i in xrange(1, 6)]
    >>> rois.run()

Inputs:

    [Mandatory]

    [Optional]
    in_files: (an existing file name)
    in_index: (an existing file name)
            array keeping original locations
    in_reference: (an existing file name)
            reference file

Outputs:

    merged_file: (an existing file name)
            the recomposed file

## ModifyAffine

Link to code

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, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    transformation_matrix: (an array with shape (4, 4), nipype default
             value: (<bound method Array.copy_default_value of
             <traits.trait_numeric.Array object at 0x10e678e50>>, (array([[ 1.,
             0.,  0.,  0.],        [ 0.,  1.,  0.,  0.],        [ 0.,  0.,  1.,
             0.],        [ 0.,  0.,  0.,  1.]]),), None))
            transformation matrix that will be left multiplied by the affine
            matrix

Outputs:

    transformed_volumes: (a file name)

## NormalizeProbabilityMapSet

Link to code

Returns the input tissue probability maps (tpms, aka volume fractions) normalized to sum up 1.0 at each voxel within the mask.

Note

Please recall this is not a spatial normalization algorithm

### Example

    >>> from nipype.algorithms import misc
    >>> normalize = misc.NormalizeProbabilityMapSet()
    >>> normalize.inputs.in_files = [ 'tpm_00.nii.gz', 'tpm_01.nii.gz', 'tpm_02.nii.gz' ]
    >>> normalize.inputs.in_mask = 'tpms_msk.nii.gz'
    >>> normalize.run()

Inputs:

    [Mandatory]

    [Optional]
    in_files: (an existing file name)
    in_mask: (an existing file name)
            Masked voxels must sum up 1.0, 0.0 otherwise.

Outputs:

    out_files: (an existing file name)
            normalized maps

## Overlap

Link to code

Calculates various overlap measures between two maps.

Deprecated since version 0.10.0: Use `nipype.algorithms.metrics.Overlap` instead.

Inputs:

    [Mandatory]
    bg_overlap: (a boolean, nipype default value: False)
            consider zeros as a label
    vol_units: ('voxel' or 'mm', nipype default value: voxel)
            units for volumes
    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, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mask_volume: (an existing file name)
            calculate overlap only within this mask.
    out_file: (a file name, nipype default value: diff.nii)
    weighting: ('none' or 'volume' or 'squared_vol', nipype default
             value: none)
            'none': no class-overlap weighting is performed. 'volume': computed
            class-overlaps are weighted by class volume 'squared_vol': computed
            class-overlaps are weighted by the squared volume of the class

Outputs:

    dice: (a float)
            averaged dice index
    diff_file: (an existing file name)
            error map of differences
    jaccard: (a float)
            averaged jaccard index
    labels: (a list of items which are an integer)
            detected labels
    roi_di: (a list of items which are a float)
            the Dice index (DI) per ROI
    roi_ji: (a list of items which are a float)
            the Jaccard index (JI) per ROI
    roi_voldiff: (a list of items which are a float)
            volume differences of ROIs
    volume_difference: (a float)
            averaged volume difference

## PickAtlas

Link to code

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.
    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.

    [Optional]
    dilation_size: (an integer, nipype default value: 0)
            Defines how much the mask will be dilated (expanded in 3D).
    hemi: ('both' or 'left' or 'right', nipype default value: both)
            Restrict the mask to only one hemisphere: left or right
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    output_file: (a file name)
            Where to store the output mask.

Outputs:

    mask_file: (an existing file name)
            output mask file

## SimpleThreshold

Link to code

Applies a threshold to input volumes

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, nipype default value: False)
            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

## SplitROIs

Link to code

Splits a 3D image in small chunks to enable parallel processing. ROIs keep time series structure in 4D images. >>> from nipype.algorithms import misc >>> rois = misc.SplitROIs() >>> rois.inputs.in_file = ‘diffusion.nii’ >>> rois.inputs.in_mask = ‘mask.nii’ >>> rois.run() # doctest: +SKIP

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            file to be splitted

    [Optional]
    in_mask: (an existing file name)
            only process files inside mask
    roi_size: (a tuple of the form: (an integer, an integer, an integer))
            desired ROI size

Outputs:

    out_files: (an existing file name)
            the resulting ROIs
    out_index: (an existing file name)
            arrays keeping original locations
    out_masks: (an existing file name)
            a mask indicating valid values

## TSNR

Link to code

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 or a list of 3D files

    [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
    regress_poly: (an integer >= 1)
            Remove polynomials

Outputs:

    detrended_file: (a file name)
            detrended input file
    mean_file: (an existing file name)
            mean image file
    stddev_file: (an existing file name)
            std dev image file
    tsnr_file: (an existing file name)
            tsnr image file

## `calc_moments()`

Link to code

Returns nth moment (3 for skewness, 4 for kurtosis) of timeseries (list of values; one per timeseries).

Keyword arguments: timeseries_file – text file with white space separated timepoints in rows

## `makefmtlist()`

Link to code

## `maketypelist()`

Link to code

## `matlab2csv()`

Link to code

## `merge_csvs()`

Link to code

## `merge_rois()`

Link to code

Re-builds an image resulting from a parallelized processing

## `normalize_tpms()`

Link to code

Returns the input tissue probability maps (tpms, aka volume fractions) normalized to sum up 1.0 at each voxel within the mask.

## `remove_identical_paths()`

Link to code

## `replaceext()`

Link to code

## `split_rois()`

Link to code

Splits an image in ROIs for parallel processing