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# nipype.interfaces.niftyseg.maths module

Nipype interface for seg_maths.

The maths module provides higher-level interfaces to some of the operations that can be performed with the niftysegmaths (seg_maths) command-line program.

## BinaryMaths

Link to code

> Bases: MathsCommand

> Wrapped executable: `seg_maths`.

> Binary mathematical operations.

> See also

> Source code – Documentation

> Examples

>     >>> import copy
>     >>> from nipype.interfaces import niftyseg
>     >>> binary = niftyseg.BinaryMaths()
>     >>> binary.inputs.in_file = 'im1.nii'
>     >>> binary.inputs.output_datatype = 'float'

>     >>> # Test sub operation
>     >>> binary_sub = copy.deepcopy(binary)
>     >>> binary_sub.inputs.operation = 'sub'
>     >>> binary_sub.inputs.operand_file = 'im2.nii'
>     >>> binary_sub.cmdline
>     'seg_maths im1.nii -sub im2.nii -odt float im1_sub.nii'
>     >>> binary_sub.run()

>     >>> # Test mul operation
>     >>> binary_mul = copy.deepcopy(binary)
>     >>> binary_mul.inputs.operation = 'mul'
>     >>> binary_mul.inputs.operand_value = 2.0
>     >>> binary_mul.cmdline
>     'seg_maths im1.nii -mul 2.00000000 -odt float im1_mul.nii'
>     >>> binary_mul.run()

>     >>> # Test llsnorm operation
>     >>> binary_llsnorm = copy.deepcopy(binary)
>     >>> binary_llsnorm.inputs.operation = 'llsnorm'
>     >>> binary_llsnorm.inputs.operand_file = 'im2.nii'
>     >>> binary_llsnorm.cmdline
>     'seg_maths im1.nii -llsnorm im2.nii -odt float im1_llsnorm.nii'
>     >>> binary_llsnorm.run()

>     >>> # Test splitinter operation
>     >>> binary_splitinter = copy.deepcopy(binary)
>     >>> binary_splitinter.inputs.operation = 'splitinter'
>     >>> binary_splitinter.inputs.operand_str = 'z'
>     >>> binary_splitinter.cmdline
>     'seg_maths im1.nii -splitinter z -odt float im1_splitinter.nii'
>     >>> binary_splitinter.run()

> in_filea pathlike object or string representing an existing file

>   Image to operate on. Maps to a command-line argument: `%s` (position: 2).

> operand_filea pathlike object or string representing an existing file

>   Second image to perform operation with. Maps to a command-line argument: `%s` (position: 5). Mutually exclusive with inputs: `operand_value`, `operand_str`.

> operand_str‘x’ or ‘y’ or ‘z’

>   String value to perform operation splitinter. Maps to a command-line argument: `%s` (position: 5). Mutually exclusive with inputs: `operand_value`, `operand_file`.

> operand_valuea float

>   Float value to perform operation with. Maps to a command-line argument: `%.8f` (position: 5). Mutually exclusive with inputs: `operand_file`, `operand_str`.

> operation‘mul’ or ‘div’ or ‘add’ or ‘sub’ or ‘pow’ or ‘thr’ or ‘uthr’ or ‘smo’ or ‘edge’ or ‘sobel3’ or ‘sobel5’ or ‘min’ or ‘smol’ or ‘geo’ or ‘llsnorm’ or ‘masknan’ or ‘hdr_copy’ or ‘splitinter’

>   Operation to perform:

>   > - mul - <float/file> - Multiply image <float> value or by other image.

>   > - div - <float/file> - Divide image by <float> or by other image.

>   > - add - <float/file> - Add image by <float> or by other image.

>   > - sub - <float/file> - Subtract image by <float> or by other image.

>   > - pow - <float> - Image to the power of <float>.

>   > - thr - <float> - Threshold the image below <float>.

>   > - uthr - <float> - Threshold image above <float>.

>   > - smo - <float> - Gaussian smoothing by std <float> (in voxels and up to 4-D).

>   > - edge - <float> - Calculate the edges of the image using a threshold <float>.

>   > - sobel3 - <float> - Calculate the edges of all timepoints using a Sobel filter with a 3x3x3 kernel and applying <float> gaussian smoothing.

>   > - sobel5 - <float> - Calculate the edges of all timepoints using a Sobel filter with a 5x5x5 kernel and applying <float> gaussian smoothing.

>   > - min - <file> - Get the min per voxel between <current> and <file>.

>   > - smol - <float> - Gaussian smoothing of a 3D label image.

>   > - geo - <float/file> - Geodesic distance according to the speed function <float/file>

>   > - llsnorm <file_norm> - Linear LS normalisation between current and <file_norm>

>   > - masknan <file_norm> - Assign everything outside the mask (mask==0) with NaNs

>   > - hdr_copy <file> - Copy header from working image to <file> and save in <output>.

>   > - splitinter <x/y/z> - Split interleaved slices in direction <x/y/z> into separate time points

>   Maps to a command-line argument: `-%s` (position: 4).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> out_filea pathlike object or string representing a file

>   Image to write. Maps to a command-line argument: `%s` (position: -2).

> output_datatype‘float’ or ‘char’ or ‘int’ or ‘short’ or ‘double’ or ‘input’

>   Datatype to use for output (default uses input type). Maps to a command-line argument: `-odt %s` (position: -3).

> out_filea pathlike object or string representing a file

>   Image written after calculations.

## BinaryMathsInteger

Link to code

> Bases: MathsCommand

> Wrapped executable: `seg_maths`.

> Integer mathematical operations.

> See also

> Source code – Documentation

> Examples

>     >>> import copy
>     >>> from nipype.interfaces.niftyseg import BinaryMathsInteger
>     >>> binaryi = BinaryMathsInteger()
>     >>> binaryi.inputs.in_file = 'im1.nii'
>     >>> binaryi.inputs.output_datatype = 'float'
>     >>> # Test dil operation
>     >>> binaryi_dil = copy.deepcopy(binaryi)
>     >>> binaryi_dil.inputs.operation = 'dil'
>     >>> binaryi_dil.inputs.operand_value = 2
>     >>> binaryi_dil.cmdline
>     'seg_maths im1.nii -dil 2 -odt float im1_dil.nii'
>     >>> binaryi_dil.run()
>     >>> # Test dil operation
>     >>> binaryi_ero = copy.deepcopy(binaryi)
>     >>> binaryi_ero.inputs.operation = 'ero'
>     >>> binaryi_ero.inputs.operand_value = 1
>     >>> binaryi_ero.cmdline
>     'seg_maths im1.nii -ero 1 -odt float im1_ero.nii'
>     >>> binaryi_ero.run()
>     >>> # Test pad operation
>     >>> binaryi_pad = copy.deepcopy(binaryi)
>     >>> binaryi_pad.inputs.operation = 'pad'
>     >>> binaryi_pad.inputs.operand_value = 4
>     >>> binaryi_pad.cmdline
>     'seg_maths im1.nii -pad 4 -odt float im1_pad.nii'
>     >>> binaryi_pad.run()

> in_filea pathlike object or string representing an existing file

>   Image to operate on. Maps to a command-line argument: `%s` (position: 2).

> operand_valuean integer

>   Int value to perform operation with. Maps to a command-line argument: `%d` (position: 5).

> operation‘dil’ or ‘ero’ or ‘tp’ or ‘equal’ or ‘pad’ or ‘crop’

>   Operation to perform:

>   > - equal - <int> - Get voxels equal to <int>

>   > - dil - <int> - Dilate the image <int> times (in voxels).

>   > - ero - <int> - Erode the image <int> times (in voxels).

>   > - tp - <int> - Extract time point <int>

>   > - crop - <int> - Crop <int> voxels around each 3D volume.

>   > - pad - <int> - Pad <int> voxels with NaN value around each 3D volume.

>   Maps to a command-line argument: `-%s` (position: 4).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> out_filea pathlike object or string representing a file

>   Image to write. Maps to a command-line argument: `%s` (position: -2).

> output_datatype‘float’ or ‘char’ or ‘int’ or ‘short’ or ‘double’ or ‘input’

>   Datatype to use for output (default uses input type). Maps to a command-line argument: `-odt %s` (position: -3).

> out_filea pathlike object or string representing a file

>   Image written after calculations.

## MathsCommand

Link to code

> Bases: NiftySegCommand

> Wrapped executable: `seg_maths`.

> Base Command Interface for seg_maths interfaces.

> The executable seg_maths enables the sequential execution of arithmetic operations, like multiplication (-mul), division (-div) or addition (-add), binarisation (-bin) or thresholding (-thr) operations and convolution by a Gaussian kernel (-smo). It also allows mathematical morphology based operations like dilation (-dil), erosion (-ero), connected components (-lconcomp) and hole filling (-fill), Euclidean (- euc) and geodesic (-geo) distance transforms, local image similarity metric calculation (-lncc and -lssd). Finally, it allows multiple operations over the dimensionality of the image, from merging 3D images together as a 4D image (-merge) or splitting (-split or -tp) 4D images into several 3D images, to estimating the maximum, minimum and average over all time-points, etc.

> in_filea pathlike object or string representing an existing file

>   Image to operate on. Maps to a command-line argument: `%s` (position: 2).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> out_filea pathlike object or string representing a file

>   Image to write. Maps to a command-line argument: `%s` (position: -2).

> output_datatype‘float’ or ‘char’ or ‘int’ or ‘short’ or ‘double’ or ‘input’

>   Datatype to use for output (default uses input type). Maps to a command-line argument: `-odt %s` (position: -3).

> out_filea pathlike object or string representing a file

>   Image written after calculations.

## Merge

Link to code

> Bases: MathsCommand

> Wrapped executable: `seg_maths`.

> Merge image files.

> See also

> Source code – Documentation

> Examples

>     >>> from nipype.interfaces import niftyseg
>     >>> node = niftyseg.Merge()
>     >>> node.inputs.in_file = 'im1.nii'
>     >>> files = ['im2.nii', 'im3.nii']
>     >>> node.inputs.merge_files = files
>     >>> node.inputs.dimension = 2
>     >>> node.inputs.output_datatype = 'float'
>     >>> node.cmdline
>     'seg_maths im1.nii -merge 2 2 im2.nii im3.nii -odt float im1_merged.nii'

> dimensionan integer

>   Dimension to merge the images.

> in_filea pathlike object or string representing an existing file

>   Image to operate on. Maps to a command-line argument: `%s` (position: 2).

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

>   List of images to merge to the working image <input>. Maps to a command-line argument: `%s` (position: 4).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> out_filea pathlike object or string representing a file

>   Image to write. Maps to a command-line argument: `%s` (position: -2).

> output_datatype‘float’ or ‘char’ or ‘int’ or ‘short’ or ‘double’ or ‘input’

>   Datatype to use for output (default uses input type). Maps to a command-line argument: `-odt %s` (position: -3).

> out_filea pathlike object or string representing a file

>   Image written after calculations.

## TupleMaths

Link to code

> Bases: MathsCommand

> Wrapped executable: `seg_maths`.

> Mathematical operations on tuples.

> See also

> Source code – Documentation

> Examples

>     >>> import copy
>     >>> from nipype.interfaces import niftyseg
>     >>> tuple = niftyseg.TupleMaths()
>     >>> tuple.inputs.in_file = 'im1.nii'
>     >>> tuple.inputs.output_datatype = 'float'

>     >>> # Test lncc operation
>     >>> tuple_lncc = copy.deepcopy(tuple)
>     >>> tuple_lncc.inputs.operation = 'lncc'
>     >>> tuple_lncc.inputs.operand_file1 = 'im2.nii'
>     >>> tuple_lncc.inputs.operand_value2 = 2.0
>     >>> tuple_lncc.cmdline
>     'seg_maths im1.nii -lncc im2.nii 2.00000000 -odt float im1_lncc.nii'
>     >>> tuple_lncc.run()

>     >>> # Test lssd operation
>     >>> tuple_lssd = copy.deepcopy(tuple)
>     >>> tuple_lssd.inputs.operation = 'lssd'
>     >>> tuple_lssd.inputs.operand_file1 = 'im2.nii'
>     >>> tuple_lssd.inputs.operand_value2 = 1.0
>     >>> tuple_lssd.cmdline
>     'seg_maths im1.nii -lssd im2.nii 1.00000000 -odt float im1_lssd.nii'
>     >>> tuple_lssd.run()

>     >>> # Test lltsnorm operation
>     >>> tuple_lltsnorm = copy.deepcopy(tuple)
>     >>> tuple_lltsnorm.inputs.operation = 'lltsnorm'
>     >>> tuple_lltsnorm.inputs.operand_file1 = 'im2.nii'
>     >>> tuple_lltsnorm.inputs.operand_value2 = 0.01
>     >>> tuple_lltsnorm.cmdline
>     'seg_maths im1.nii -lltsnorm im2.nii 0.01000000 -odt float im1_lltsnorm.nii'
>     >>> tuple_lltsnorm.run()

> in_filea pathlike object or string representing an existing file

>   Image to operate on. Maps to a command-line argument: `%s` (position: 2).

> operand_file1a pathlike object or string representing an existing file

>   Image to perform operation 1 with. Maps to a command-line argument: `%s` (position: 5). Mutually exclusive with inputs: `operand_value1`.

> operand_file2a pathlike object or string representing an existing file

>   Image to perform operation 2 with. Maps to a command-line argument: `%s` (position: 6). Mutually exclusive with inputs: `operand_value2`.

> operand_value1a float

>   Float value to perform operation 1 with. Maps to a command-line argument: `%.8f` (position: 5). Mutually exclusive with inputs: `operand_file1`.

> operand_value2a float

>   Float value to perform operation 2 with. Maps to a command-line argument: `%.8f` (position: 6). Mutually exclusive with inputs: `operand_file2`.

> operation‘lncc’ or ‘lssd’ or ‘lltsnorm’

>   Operation to perform:

>   > - lncc <file> <std> Local CC between current img and <file> on a kernel with <std>

>   > - lssd <file> <std> Local SSD between current img and <file> on a kernel with <std>

>   > - lltsnorm <file_norm> <float> Linear LTS normalisation assuming <float> percent outliers

>   Maps to a command-line argument: `-%s` (position: 4).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> out_filea pathlike object or string representing a file

>   Image to write. Maps to a command-line argument: `%s` (position: -2).

> output_datatype‘float’ or ‘char’ or ‘int’ or ‘short’ or ‘double’ or ‘input’

>   Datatype to use for output (default uses input type). Maps to a command-line argument: `-odt %s` (position: -3).

> out_filea pathlike object or string representing a file

>   Image written after calculations.

## UnaryMaths

Link to code

> Bases: MathsCommand

> Wrapped executable: `seg_maths`.

> Unary mathematical operations.

> See also

> Source code – Documentation

> Examples

>     >>> import copy
>     >>> from nipype.interfaces import niftyseg
>     >>> unary = niftyseg.UnaryMaths()
>     >>> unary.inputs.output_datatype = 'float'
>     >>> unary.inputs.in_file = 'im1.nii'

>     >>> # Test sqrt operation
>     >>> unary_sqrt = copy.deepcopy(unary)
>     >>> unary_sqrt.inputs.operation = 'sqrt'
>     >>> unary_sqrt.cmdline
>     'seg_maths im1.nii -sqrt -odt float im1_sqrt.nii'
>     >>> unary_sqrt.run()

>     >>> # Test sqrt operation
>     >>> unary_abs = copy.deepcopy(unary)
>     >>> unary_abs.inputs.operation = 'abs'
>     >>> unary_abs.cmdline
>     'seg_maths im1.nii -abs -odt float im1_abs.nii'
>     >>> unary_abs.run()

>     >>> # Test bin operation
>     >>> unary_bin = copy.deepcopy(unary)
>     >>> unary_bin.inputs.operation = 'bin'
>     >>> unary_bin.cmdline
>     'seg_maths im1.nii -bin -odt float im1_bin.nii'
>     >>> unary_bin.run()

>     >>> # Test otsu operation
>     >>> unary_otsu = copy.deepcopy(unary)
>     >>> unary_otsu.inputs.operation = 'otsu'
>     >>> unary_otsu.cmdline
>     'seg_maths im1.nii -otsu -odt float im1_otsu.nii'
>     >>> unary_otsu.run()

>     >>> # Test isnan operation
>     >>> unary_isnan = copy.deepcopy(unary)
>     >>> unary_isnan.inputs.operation = 'isnan'
>     >>> unary_isnan.cmdline
>     'seg_maths im1.nii -isnan -odt float im1_isnan.nii'
>     >>> unary_isnan.run()

> in_filea pathlike object or string representing an existing file

>   Image to operate on. Maps to a command-line argument: `%s` (position: 2).

> operation‘sqrt’ or ‘exp’ or ‘log’ or ‘recip’ or ‘abs’ or ‘bin’ or ‘otsu’ or ‘lconcomp’ or ‘concomp6’ or ‘concomp26’ or ‘fill’ or ‘euc’ or ‘tpmax’ or ‘tmean’ or ‘tmax’ or ‘tmin’ or ‘splitlab’ or ‘removenan’ or ‘isnan’ or ‘subsamp2’ or ‘scl’ or ‘4to5’ or ‘range’

>   Operation to perform:

>   > - sqrt - Square root of the image).

>   > - exp - Exponential root of the image.

>   > - log - Log of the image.

>   > - recip - Reciprocal (1/I) of the image.

>   > - abs - Absolute value of the image.

>   > - bin - Binarise the image.

>   > - otsu - Otsu thresholding of the current image.

>   > - lconcomp - Take the largest connected component

>   > - concomp6 - Label the different connected components with a 6NN kernel

>   > - concomp26 - Label the different connected components with a 26NN kernel

>   > - fill - Fill holes in binary object (e.g. fill ventricle in brain mask).

>   > - euc - Euclidean distance transform

>   > - tpmax - Get the time point with the highest value (binarise 4D probabilities)

>   > - tmean - Mean value of all time points.

>   > - tmax - Max value of all time points.

>   > - tmin - Mean value of all time points.

>   > - splitlab - Split the integer labels into multiple timepoints

>   > - removenan - Remove all NaNs and replace then with 0

>   > - isnan - Binary image equal to 1 if the value is NaN and 0 otherwise

>   > - subsamp2 - Subsample the image by 2 using NN sampling (qform and sform scaled)

>   > - scl - Reset scale and slope info.

>   > - 4to5 - Flip the 4th and 5th dimension.

>   > - range - Reset the image range to the min max.

>   Maps to a command-line argument: `-%s` (position: 4).

> argsa string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> out_filea pathlike object or string representing a file

>   Image to write. Maps to a command-line argument: `%s` (position: -2).

> output_datatype‘float’ or ‘char’ or ‘int’ or ‘short’ or ‘double’ or ‘input’

>   Datatype to use for output (default uses input type). Maps to a command-line argument: `-odt %s` (position: -3).

> out_filea pathlike object or string representing a file

>   Image written after calculations.