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()
Mandatory Inputs:

in_file (a pathlike object or string representing an existing file) – Input comma-separated value (CSV) files.

Optional Inputs:

  • 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 pathlike object or string representing a file) – Output filename for merged CSV file. (Nipype default value: extra_heading.csv)

Outputs:

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()
Mandatory Inputs:

in_file (a pathlike object or string representing a file) – Input comma-separated value (CSV) files.

Optional Inputs:

_outputs (a dictionary with keys which are any value and with values which are any value) – (Nipype default value: {})

Outputs:

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()
Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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)

Outputs:

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()
Optional Inputs:

  • in_files (a list of items which are a pathlike object or string representing an existing file)

  • median_file (a string) – Filename prefix to store median images.

  • median_per_file (a boolean) – Calculate a median file for each Nifti. (Nipype default value: False)

Outputs:

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()
Mandatory Inputs:

  • moment (an integer) – 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.

Outputs:

moments (a list of items which are a float) – Moments.

CreateNifti

Link to code

Bases: BaseInterface

Creates a nifti volume

Mandatory Inputs:

  • 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.

Optional Inputs:

affine (an array) – Affine transformation array.

Outputs:

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.

Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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 Euclidean distance between the Center of Gravity of volume1 and CoGs of volume2 “eucl_mean”: mean Euclidean minimum distance of all volume2 voxels to volume1 “eucl_wmean”: mean Euclidean minimum distance of all volume2 voxels to volume1 weighted by their values “eucl_max”: maximum over minimum Euclidean distances of all volume2 voxels to volume1 (also known as the Hausdorff distance). (Nipype default value: eucl_min)

Outputs:

  • 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.

Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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)

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.

  • jaccard (a float) – Fuzzy Jaccard Index (fJI), all the classes.

Gunzip

Link to code

Bases: Gzip

Gunzip wrapper

>>> from nipype.algorithms.misc import Gunzip
>>> gunzip = Gunzip(in_file='tpms_msk.nii.gz')
>>> res = gunzip.run()
>>> res.outputs.out_file  
'.../tpms_msk.nii'

>>> os.unlink('tpms_msk.nii')
Mandatory Inputs:

in_file (a pathlike object or string representing an existing file) – File to (de)compress.

Optional Inputs:

mode (‘decompress’) – Decompress or compress. (Nipype default value: decompress)

Outputs:

out_file (a pathlike object or string representing a file)

Gzip

Link to code

Bases: BaseInterface

Gzip wrapper

>>> from nipype.algorithms.misc import Gzip
>>> gzip = Gzip(in_file='tpms_msk.nii.gz', mode="decompress")
>>> res = gzip.run()
>>> res.outputs.out_file  
'.../tpms_msk.nii'

>>> gzip = Gzip(in_file='tpms_msk.nii')
>>> res = gzip.run()
>>> res.outputs.out_file  
'.../tpms_msk.nii.gz'

>>> os.unlink('tpms_msk.nii')
Mandatory Inputs:

in_file (a pathlike object or string representing an existing file) – File to (de)compress.

Optional Inputs:

mode (‘compress’ or ‘decompress’) – Compress or decompress. (Nipype default value: compress)

Outputs:

out_file (a pathlike object or string representing a 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()
Mandatory Inputs:

in_file (a pathlike object or string representing an existing file) – Input MATLAB .mat file.

Optional Inputs:

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)

Outputs:

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()
Mandatory Inputs:

in_files (a list of items which are a pathlike object or string representing an existing file) – Input comma-separated value (CSV) files.

Optional Inputs:

  • 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 pathlike object or string representing a file) – Output filename for merged CSV file. (Nipype default value: merged.csv)

  • row_heading_title (a string) – Column heading for the row headings added. (Nipype default value: label)

  • 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 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()
Optional Inputs:

  • 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.

Outputs:

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.

Mandatory Inputs:

volumes (a list of items which are a pathlike object or string representing an existing file) – Volumes which affine matrices will be modified.

Optional Inputs:

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 0x7fe882c24490>>, (array([[1., 0., 0., 0.],        [0., 1., 0., 0.],        [0., 0., 1., 0.],        [0., 0., 0., 1.]]),), None))

Outputs:

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()
Optional Inputs:

  • 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.

Outputs:

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.

Mandatory Inputs:

  • 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.

Optional Inputs:

  • 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)

Outputs:

  • 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) – 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).

Mandatory Inputs:

  • atlas (a pathlike object or string representing an existing file) – 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 Inputs:

  • dilation_size (an integer) – 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.

Outputs:

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

Mandatory Inputs:

  • 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.

Outputs:

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()
Mandatory Inputs:

in_file (a pathlike object or string representing an existing file) – File to be split.

Optional Inputs:

  • 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, an integer, an integer)) – Desired ROI size.

Outputs:

  • 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

Mandatory Inputs:

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.

Optional Inputs:

  • 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 (an 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)

Outputs:

  • 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