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# 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()

> in_filea pathlike object or string representing an existing file

>   Input comma-separated value (CSV) files.

> 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`)

> csv_filea 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()

> in_filea pathlike object or string representing a file

>   Input comma-separated value (CSV) files.

> _outputsa dictionary with keys which are any value and with values which are any value

>   (Nipype default value: `{}`)

> csv_filea 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()

> 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.

> 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`)

> out_filea 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()

> 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`)

> median_filesa 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()

> 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.

> momentsa list of items which are a float

>   Moments.

## CreateNifti

Link to code

> Bases: BaseInterface

> Creates a nifti volume

> 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.

> affinean 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.

> 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.

> 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`)

> 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_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.

> 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`)

> 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.

## 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')

> in_filea pathlike object or string representing an existing file

>   File to (de)compress.

> mode‘decompress’

>   Decompress or compress. (Nipype default value: `decompress`)

> 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')

> in_filea pathlike object or string representing an existing file

>   File to (de)compress.

> mode‘compress’ or ‘decompress’

>   Compress or decompress. (Nipype default value: `compress`)

> 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()

> in_filea pathlike object or string representing an existing file

>   Input MATLAB .mat file.

> 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`)

> 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()

> in_filesa list of items which are a pathlike object or string representing an existing file

>   Input comma-separated value (CSV) files.

> 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).

> csv_filea 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()

> 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.

> merged_filea 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.

> volumesa list of items which are a pathlike object or string representing an existing file

>   Volumes which affine matrices will be modified.

> transformation_matrixan array with shape (4, 4)

>   Transformation matrix that will be left multiplied by the affine matrix.

> 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()

> 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_filesa 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_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.

> 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`)

> 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.

## 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).

> 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.

> 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.

> mask_filea pathlike object or string representing an existing file

>   Output mask file.

## SimpleThreshold

Link to code

> Bases: BaseInterface

> Applies a threshold to input volumes

> 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.

> thresholded_volumesa 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()

> in_filea pathlike object or string representing an existing file

>   File to be split.

> 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.

> 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.

## TSNR

Link to code

> Bases: TSNR

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

> 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.

> 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`)

> 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