nipype.algorithms.misc module¶

Miscellaneous algorithms.

class nipype.algorithms.misc.AddCSVColumn(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_filea pathlike object or string representing an existing file: Input comma-separated value (CSV) files.

Optional Inputs¶

extra_column_headinga string: New heading to add for the added field.
extra_fielda string: New field to add to each row. This is useful for saving the group or subject ID in the file.
out_filea pathlike object or string representing a file: Output filename for merged CSV file. (Nipype default value: extra_heading.csv)

Outputs¶

csv_filea pathlike object or string representing a file: Output CSV file containing columns .

class nipype.algorithms.misc.AddCSVRow(infields=None, force_run=True, **kwargs)¶

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

Optional Inputs¶

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

Outputs¶

csv_filea pathlike object or string representing a file: Output CSV file containing rows .

class nipype.algorithms.misc.AddNoise(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_filea pathlike object or string representing an existing file: Input image that will be corrupted with noise.

Optional Inputs¶

in_maska pathlike object or string representing an existing file: Input mask, voxels outside this mask will be considered background.
out_filea pathlike object or string representing a file: Desired output filename.
snra float: Desired output SNR in dB. (Nipype default value: 10.0)

Outputs¶

out_filea pathlike object or string representing an existing file: Corrupted image.

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)

class nipype.algorithms.misc.CalculateMedian(*args, **kwargs)¶

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

Outputs¶

median_filesa list of items which are a pathlike object or string representing an existing file: One or more median images.

class nipype.algorithms.misc.CalculateNormalizedMoments(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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¶

momentan integer: Define which moment should be calculated, 3 for skewness, 4 for kurtosis.
timeseries_filea pathlike object or string representing an existing file: Text file with timeseries in columns and timepoints in rows, whitespace separated.

Outputs¶

momentsa list of items which are a float: Moments.

class nipype.algorithms.misc.CreateNifti(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

Bases: BaseInterface

Creates a nifti volume

Mandatory Inputs¶

data_filea pathlike object or string representing an existing file: ANALYZE img file.
header_filea pathlike object or string representing an existing file: Corresponding ANALYZE hdr file.

Optional Inputs¶

affinean array: Affine transformation array.

Outputs¶

nifti_file : a pathlike object or string representing an existing file

class nipype.algorithms.misc.Distance(**inputs)¶

Bases: Distance

Calculates distance between two volumes.

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

Mandatory Inputs¶

volume1a pathlike object or string representing an existing file: Has to have the same dimensions as volume2.
volume2a pathlike object or string representing an existing file: Has to have the same dimensions as volume1.

Optional Inputs¶

mask_volumea 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,)

class nipype.algorithms.misc.FuzzyOverlap(**inputs)¶

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_refa list of items which are a pathlike object or string representing an existing file: Reference image. Requires the same dimensions as in_tst.
in_tsta 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_maska pathlike object or string representing an existing file: Calculate overlap only within mask.
out_filea 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_fdia list of items which are a float: Array containing the fDIs of each computed class.
class_fjia list of items which are a float: Array containing the fJIs of each computed class.
dicea float: Fuzzy Dice Index (fDI), all the classes.
jaccarda float: Fuzzy Jaccard Index (fJI), all the classes.

class nipype.algorithms.misc.Gunzip(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_filea 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

class nipype.algorithms.misc.Gzip(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_filea 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

class nipype.algorithms.misc.Matlab2CSV(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_filea pathlike object or string representing an existing file: Input MATLAB .mat file.

Optional Inputs¶

reshape_matrixa 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

class nipype.algorithms.misc.MergeCSVFiles(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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

Optional Inputs¶

column_headingsa 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_headinga string: New heading to add for the added field.
extra_fielda string: New field to add to each row. This is useful for saving the group or subject ID in the file.
out_filea pathlike object or string representing a file: Output filename for merged CSV file. (Nipype default value: merged.csv)
row_heading_titlea string: Column heading for the row headings added. (Nipype default value: label)
row_headingsa 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_filea pathlike object or string representing a file: Output CSV file containing columns .

class nipype.algorithms.misc.MergeROIs(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_referencea pathlike object or string representing an existing file: Reference file.

Outputs¶

merged_filea pathlike object or string representing an existing file: The recomposed file.

class nipype.algorithms.misc.ModifyAffine(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

Bases: BaseInterface

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

Mandatory Inputs¶

volumesa list of items which are a pathlike object or string representing an existing file: Volumes which affine matrices will be modified.

Optional Inputs¶

transformation_matrixan array with shape (4, 4): Transformation matrix that will be left multiplied by the affine matrix.

Outputs¶

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

class nipype.algorithms.misc.NormalizeProbabilityMapSet(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_filesa list of items which are a pathlike object or string representing an existing file: Normalized maps.

class nipype.algorithms.misc.Overlap(**inputs)¶

Bases: Overlap

Calculates various overlap measures between two maps.

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

Mandatory Inputs¶

bg_overlapa boolean: Consider zeros as a label. (Nipype default value: False)
vol_units‘voxel’ or ‘mm’: Units for volumes. (Nipype default value: voxel)
volume1a pathlike object or string representing an existing file: Has to have the same dimensions as volume2.
volume2a pathlike object or string representing an existing file: Has to have the same dimensions as volume1.

Optional Inputs¶

mask_volumea pathlike object or string representing an existing file: Calculate overlap only within this mask.
out_filea 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¶

dicea float: Averaged dice index.
diff_filea pathlike object or string representing an existing file: Error map of differences.
jaccarda float: Averaged jaccard index.
labelsa list of items which are an integer: Detected labels.
roi_dia list of items which are a float: The Dice index (DI) per ROI.
roi_jia list of items which are a float: The Jaccard index (JI) per ROI.
roi_voldiffa list of items which are a float: Volume differences of ROIs.
volume_differencea float: Averaged volume difference.

class nipype.algorithms.misc.PickAtlas(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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¶

atlasa pathlike object or string representing an existing file: Location of the atlas that will be used.
labelsan 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_sizean 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_filea pathlike object or string representing a file: Where to store the output mask.

Outputs¶

mask_filea pathlike object or string representing an existing file: Output mask file.

class nipype.algorithms.misc.SimpleThreshold(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

Bases: BaseInterface

Applies a threshold to input volumes

Mandatory Inputs¶

thresholda float: Volumes to be thresholdedeverything below this value will be set to zero.
volumesa list of items which are a pathlike object or string representing an existing file: Volumes to be thresholded.

Outputs¶

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

class nipype.algorithms.misc.SplitROIs(from_file=None, resource_monitor=None, ignore_exception=False, **inputs)¶

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_filea pathlike object or string representing an existing file: File to be split.

Optional Inputs¶

in_maska pathlike object or string representing an existing file: Only process files inside mask.
roi_sizea tuple of the form: (an integer, an integer, an integer): Desired ROI size.

Outputs¶

out_filesa list of items which are a pathlike object or string representing an existing file: The resulting ROIs.
out_indexa list of items which are a pathlike object or string representing an existing file: Arrays keeping original locations.
out_masksa list of items which are a pathlike object or string representing an existing file: A mask indicating valid values.

class nipype.algorithms.misc.TSNR(**inputs)¶

Bases: TSNR

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

Mandatory Inputs¶

in_filea 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_filea pathlike object or string representing a file: Input file after detrending. (Nipype default value: detrend.nii.gz)
mean_filea pathlike object or string representing a file: Output mean file. (Nipype default value: mean.nii.gz)
regress_polyan integer >= 1: Remove polynomials.
stddev_filea pathlike object or string representing a file: Output tSNR file. (Nipype default value: stdev.nii.gz)
tsnr_filea pathlike object or string representing a file: Output tSNR file. (Nipype default value: tsnr.nii.gz)

Outputs¶

detrended_filea pathlike object or string representing a file: Detrended input file.
mean_filea pathlike object or string representing an existing file: Mean image file.
stddev_filea pathlike object or string representing an existing file: Std dev image file.
tsnr_filea 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

Release	Devel
1.12.0	1.12.0
Download	Github