nipype.algorithms.misc module

Miscellaneous algorithms.

AddCSVColumn

Link to code

Bases: BaseInterface

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

in_file : a pathlike object or string representing an existing file

Input comma-separated value (CSV) files.

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.

out_file : a pathlike object or string representing a file

Output filename for merged CSV file. (Nipype default value: extra_heading.csv)

csv_file : a pathlike object or string representing a file

Output CSV file containing columns .

AddCSVRow

Link to code

Bases: BaseInterface

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

in_file : a pathlike object or string representing a file

Input comma-separated value (CSV) files.

_outputs : a dictionary with keys which are any value and with values which are any value

(Nipype default value: {})

csv_file : a pathlike object or string representing a file

Output CSV file containing rows .

AddNoise

Link to code

Bases: BaseInterface

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

bg_dist : ‘normal’ or ‘rayleigh’

Desired noise distribution, currently only normal is implemented. (Nipype default value: normal)

dist : ‘normal’ or ‘rician’

Desired noise distribution. (Nipype default value: normal)

in_file : a pathlike object or string representing an existing file

Input image that will be corrupted with noise.

in_mask : a pathlike object or string representing an existing file

Input mask, voxels outside this mask will be considered background.

out_file : a pathlike object or string representing a file

Desired output filename.

snr : a float

Desired output SNR in dB. (Nipype default value: 10.0)

out_file : a pathlike object or string representing an existing file

Corrupted image.

AddNoise.gen_noise(image, mask=None, snr_db=10.0, dist='normal', bg_dist='normal')

Generates a copy of an image with a certain amount of added gaussian noise (rayleigh for background in mask)

CalculateMedian

Link to code

Bases: BaseInterface

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

in_files : a list of items which are a pathlike object or string representing an existing file median_file : a unicode string

Filename prefix to store median images.

median_per_file : a boolean

Calculate a median file for each Nifti. (Nipype default value: False)

median_files : a list of items which are a pathlike object or string representing an existing file

One or more median images.

CalculateNormalizedMoments

Link to code

Bases: BaseInterface

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

moment : an integer (int or long)

Define which moment should be calculated, 3 for skewness, 4 for kurtosis.

timeseries_file : a pathlike object or string representing an existing file

Text file with timeseries in columns and timepoints in rows, whitespace separated.

moments : a list of items which are a float

Moments.

CreateNifti

Link to code

Bases: BaseInterface

Creates a nifti volume

data_file : a pathlike object or string representing an existing file

ANALYZE img file.

header_file : a pathlike object or string representing an existing file

Corresponding ANALYZE hdr file.

affine : an array

Affine transformation array.

nifti_file : a pathlike object or string representing an existing file

Distance

Link to code

Bases: Distance

Calculates distance between two volumes.

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

volume1 : a pathlike object or string representing an existing file

Has to have the same dimensions as volume2.

volume2 : a pathlike object or string representing an existing file

Has to have the same dimensions as volume1.

mask_volume : a pathlike object or string representing an existing file

Calculate overlap only within this mask.

method : ‘eucl_min’ or ‘eucl_cog’ or ‘eucl_mean’ or ‘eucl_wmean’ or ‘eucl_max’

“”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). (Nipype default value: eucl_min)

distance : a float histogram : a pathlike object or string representing a file point1 : an array with shape (3,) point2 : an array with shape (3,)

FuzzyOverlap

Link to code

Bases: FuzzyOverlap

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

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

in_ref : a list of items which are a pathlike object or string representing an existing file

Reference image. Requires the same dimensions as in_tst.

in_tst : a list of items which are a pathlike object or string representing an existing file

Test image. Requires the same dimensions as in_ref.

in_mask : a pathlike object or string representing an existing file

Calculate overlap only within mask.

out_file : a pathlike object or string representing a file

Alternative name for resulting difference-map. (Nipype default value: diff.nii)

weighting : ‘none’ or ‘volume’ or ‘squared_vol’

‘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. (Nipype default value: none)

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.

jaccard : a float

Fuzzy Jaccard Index (fJI), all the classes.

Gunzip

Link to code

Bases: BaseInterface

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')

in_file : a pathlike object or string representing an existing file

out_file : a pathlike object or string representing an existing file

Matlab2CSV

Link to code

Bases: BaseInterface

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

in_file : a pathlike object or string representing an existing file

Input MATLAB .mat file.

reshape_matrix : a boolean

The output of this interface is meant for R, so matrices will be reshaped to vectors by default. (Nipype default value: True)

csv_files : a list of items which are a pathlike object or string representing a file

MergeCSVFiles

Link to code

Bases: BaseInterface

Merge several CSV files into a single CSV file.

This interface is designed to facilitate data loading in the R environment. 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

in_files : a list of items which are a pathlike object or string representing an existing file

Input comma-separated value (CSV) files.

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.

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.

out_file : a pathlike object or string representing a file

Output filename for merged CSV file. (Nipype default value: merged.csv)

row_heading_title : a unicode string

Column heading for the row headings added. (Nipype default value: label)

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).

csv_file : a pathlike object or string representing a file

Output CSV file containing columns .

MergeROIs

Link to code

Bases: BaseInterface

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

in_files : a list of items which are a pathlike object or string representing an existing file in_index : a list of items which are a pathlike object or string representing an existing file

Array keeping original locations.

in_reference : a pathlike object or string representing an existing file

Reference file.

merged_file : a pathlike object or string representing an existing file

The recomposed file.

ModifyAffine

Link to code

Bases: BaseInterface

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

volumes : a list of items which are a pathlike object or string representing an existing file

Volumes which affine matrices will be modified.

transformation_matrix : an array with shape (4, 4)

Transformation matrix that will be left multiplied by the affine matrix. (Nipype default value: (<bound method AbstractArray.copy_default_value of <traits.trait_numeric.Array object at 0x7f622d9f18d0>>, (array([[1., 0., 0., 0.],        [0., 1., 0., 0.],        [0., 0., 1., 0.],        [0., 0., 0., 1.]]),), None))

transformed_volumes : a list of items which are a pathlike object or string representing a file

NormalizeProbabilityMapSet

Link to code

Bases: BaseInterface

Returns the input tissue probability maps (tpms, aka volume fractions).

The tissue probability maps are 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

in_files : a list of items which are a pathlike object or string representing an existing file in_mask : a pathlike object or string representing an existing file

Masked voxels must sum up 1.0, 0.0 otherwise.

out_files : a list of items which are a pathlike object or string representing an existing file

Normalized maps.

Overlap

Link to code

Bases: Overlap

Calculates various overlap measures between two maps.

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

bg_overlap : a boolean

Consider zeros as a label. (Nipype default value: False)

vol_units : ‘voxel’ or ‘mm’

Units for volumes. (Nipype default value: voxel)

volume1 : a pathlike object or string representing an existing file

Has to have the same dimensions as volume2.

volume2 : a pathlike object or string representing an existing file

Has to have the same dimensions as volume1.

mask_volume : a pathlike object or string representing an existing file

Calculate overlap only within this mask.

out_file : a pathlike object or string representing a file

(Nipype default value: diff.nii)

weighting : ‘none’ or ‘volume’ or ‘squared_vol’

‘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. (Nipype default value: none)

dice : a float

Averaged dice index.

diff_file : a pathlike object or string representing an existing file

Error map of differences.

jaccard : a float

Averaged jaccard index.

labels : a list of items which are an integer (int or long)

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

Bases: BaseInterface

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

atlas : a pathlike object or string representing an existing file

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.

dilation_size : an integer (int or long)

Defines how much the mask will be dilated (expanded in 3D). (Nipype default value: 0)

hemi : ‘both’ or ‘left’ or ‘right’

Restrict the mask to only one hemisphere: left or right. (Nipype default value: both)

output_file : a pathlike object or string representing a file

Where to store the output mask.

mask_file : a pathlike object or string representing an existing file

Output mask file.

SimpleThreshold

Link to code

Bases: BaseInterface

Applies a threshold to input volumes

threshold : a float

Volumes to be thresholdedeverything below this value will be set to zero.

volumes : a list of items which are a pathlike object or string representing an existing file

Volumes to be thresholded.

thresholded_volumes : a list of items which are a pathlike object or string representing an existing file

Thresholded volumes.

SplitROIs

Link to code

Bases: BaseInterface

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

in_file : a pathlike object or string representing an existing file

File to be splitted.

in_mask : a pathlike object or string representing an existing file

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.

out_files : a list of items which are a pathlike object or string representing an existing file

The resulting ROIs.

out_index : a list of items which are a pathlike object or string representing an existing file

Arrays keeping original locations.

out_masks : a list of items which are a pathlike object or string representing an existing file

A mask indicating valid values.

TSNR

Link to code

Bases: TSNR

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

in_file : a list of items which are a pathlike object or string representing an existing file

Realigned 4D file or a list of 3D files.

detrended_file : a pathlike object or string representing a file

Input file after detrending. (Nipype default value: detrend.nii.gz)

mean_file : a pathlike object or string representing a file

Output mean file. (Nipype default value: mean.nii.gz)

regress_poly : a long integer >= 1

Remove polynomials.

stddev_file : a pathlike object or string representing a file

Output tSNR file. (Nipype default value: stdev.nii.gz)

tsnr_file : a pathlike object or string representing a file

Output tSNR file. (Nipype default value: tsnr.nii.gz)

detrended_file : a pathlike object or string representing a file

Detrended input file.

mean_file : a pathlike object or string representing an existing file

Mean image file.

stddev_file : a pathlike object or string representing an existing file

Std dev image file.

tsnr_file : a pathlike object or string representing an existing file

Tsnr image file.

nipype.algorithms.misc.calc_moments(timeseries_file, moment)

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

nipype.algorithms.misc.makefmtlist(output_array, typelist, rowheadingsBool, shape, extraheadingBool)

nipype.algorithms.misc.maketypelist(rowheadings, shape, extraheadingBool, extraheading)

nipype.algorithms.misc.merge_csvs(in_list)

nipype.algorithms.misc.merge_rois(in_files, in_idxs, in_ref, dtype=None, out_file=None)

Re-builds an image resulting from a parallelized processing

nipype.algorithms.misc.normalize_tpms(in_files, in_mask=None, out_files=None)

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

nipype.algorithms.misc.remove_identical_paths(in_files)

nipype.algorithms.misc.replaceext(in_list, ext)

nipype.algorithms.misc.split_rois(in_file, mask=None, roishape=None)

Splits an image in ROIs for parallel processing