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() # doctest: +SKIP

Inputs:

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

[Optional]
out_file: (a file name, nipype default value: extra_heading.csv)
        Output filename for merged CSV file
extra_column_heading: (a unicode string)
        New heading to add for the added field.
extra_field: (a unicode string)
        New field to add to each row. This is useful for saving the group or
        subject ID in the 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.inputs.subject_id = 'S400'
>>> addrow.inputs.list_of_values = [ 0.4, 0.7, 0.3 ]
>>> addrow.run() # doctest: +SKIP

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: {})

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() # doctest: +SKIP

Inputs:

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

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

Outputs:

out_file: (an existing file name)
        corrupted image

CalculateMedian

Link to code

Computes an average of the median across one or more 4D Nifti timeseries

Example

>>> from nipype.algorithms.misc import CalculateMedian
>>> mean = CalculateMedian()
>>> mean.inputs.in_files = 'functional.nii'
>>> mean.run() # doctest: +SKIP

Inputs:

[Optional]
in_files: (a list of items which are an existing file name)
median_file: (a unicode string)
        Filename prefix to store median images
median_per_file: (a boolean, nipype default value: False)
        Calculate a median file for each Nifti

Outputs:

median_files: (a list of items which are an existing file name)
        One or more median images

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() # doctest: +SKIP

Inputs:

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

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

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]
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)
mask_volume: (an existing file name)
        calculate overlap only within this mask.

Outputs:

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

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: (a list of items which are an existing file name)
        Reference image. Requires the same dimensions as in_tst.
in_tst: (a list of items which are an existing file name)
        Test image. Requires the same dimensions as in_ref.

[Optional]
in_mask: (an existing file name)
        calculate overlap only within mask
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
out_file: (a file name, nipype default value: diff.nii)
        alternative name for resulting difference-map

Outputs:

jaccard: (a float)
        Fuzzy Jaccard Index (fJI), all the classes
dice: (a float)
        Fuzzy Dice Index (fDI), all the classes
class_fji: (a list of items which are a float)
        Array containing the fJIs of each computed class
class_fdi: (a list of items which are a float)
        Array containing the fDIs of each computed class

Gunzip

Link to code

Gunzip wrapper

>>> from nipype.algorithms.misc import Gunzip
>>> gunzip = Gunzip(in_file='tpms_msk.nii.gz')
>>> res = gunzip.run()
>>> res.outputs.out_file  # doctest: +ELLIPSIS
'.../tpms_msk.nii'
>>> os.unlink('tpms_msk.nii')

Inputs:

[Mandatory]
in_file: (an existing file name)

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() # doctest: +SKIP

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 list of items which are 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() # doctest: +SKIP

Inputs:

[Mandatory]
in_files: (a list of items which are an existing file name)
        Input comma-separated value (CSV) files

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

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 range(1, 6)]
>>> rois.inputs.in_reference = 'mask.nii'
>>> rois.inputs.in_index = ['roi%02d_idx.npz' % i for i in range(1, 6)]
>>> rois.run() # doctest: +SKIP

Inputs:

[Optional]
in_files: (a list of items which are an existing file name)
in_index: (a list of items which are 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: (a list of items which are an existing file name)
        volumes which affine matrices will be modified

[Optional]
transformation_matrix: (an array with shape (4, 4), nipype default
          value: (<bound method AbstractArray.copy_default_value of
          <traits.trait_numeric.Array object at 0x7f702e0fe2b0>>,
          (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 list of items which are 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() # doctest: +SKIP

Inputs:

[Optional]
in_files: (a list of items which are an existing file name)
in_mask: (an existing file name)
        Masked voxels must sum up 1.0, 0.0 otherwise.

Outputs:

out_files: (a list of items which are 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]
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.
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

[Optional]
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:

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

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 (int or long) or a list of items which are an
          integer (int or long))
        Labels of regions that will be included in the mask. Must be
        compatible with the atlas used.

[Optional]
hemi: ('both' or 'left' or 'right', nipype default value: both)
        Restrict the mask to only one hemisphere: left or right
dilation_size: (an integer (int or long), nipype default value: 0)
        Defines how much the mask will be dilated (expanded in 3D).
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]
volumes: (a list of items which are an existing file name)
        volumes to be thresholded
threshold: (a float)
        volumes to be thresholdedeverything below this value will be set to
        zero

Outputs:

thresholded_volumes: (a list of items which are 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.

Example

>>> 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 (int or long), an integer
          (int or long), an integer (int or long)))
        desired ROI size

Outputs:

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

TSNR

Link to code

Deprecated since version 0.12.1: Use nipype.algorithms.confounds.TSNR instead

Inputs:

[Mandatory]
in_file: (a list of items which are an existing file name)
        realigned 4D file or a list of 3D files

[Optional]
regress_poly: (a long integer >= 1)
        Remove polynomials
tsnr_file: (a file name, nipype default value: tsnr.nii.gz)
        output tSNR file
mean_file: (a file name, nipype default value: mean.nii.gz)
        output mean file
stddev_file: (a file name, nipype default value: stdev.nii.gz)
        output tSNR file
detrended_file: (a file name, nipype default value: detrend.nii.gz)
        input file after detrending

Outputs:

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