.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.mrtrix.tensors
=========================


.. _nipype.interfaces.mrtrix.tensors.ConstrainedSphericalDeconvolution:


.. index:: ConstrainedSphericalDeconvolution

ConstrainedSphericalDeconvolution
---------------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/mrtrix/tensors.py#L117>`__

Wraps command **csdeconv**

Perform non-negativity constrained spherical deconvolution.

Note that this program makes use of implied symmetries in the diffusion profile.
First, the fact the signal attenuation profile is real implies that it has conjugate symmetry,
i.e. Y(l,-m) = Y(l,m)* (where * denotes the complex conjugate). Second, the diffusion profile should be
antipodally symmetric (i.e. S(x) = S(-x)), implying that all odd l components should be zero.
Therefore, this program only computes the even elements.    Note that the spherical harmonics equations used here
differ slightly from those conventionally used, in that the (-1)^m factor has been omitted. This should be taken
into account in all subsequent calculations. Each volume in the output image corresponds to a different spherical
harmonic component, according to the following convention:

* [0] Y(0,0)
* [1] Im {Y(2,2)}
* [2] Im {Y(2,1)}
* [3] Y(2,0)
* [4] Re {Y(2,1)}
* [5] Re {Y(2,2)}
* [6] Im {Y(4,4)}
* [7] Im {Y(4,3)}

Example
~~~~~~~

>>> import nipype.interfaces.mrtrix as mrt
>>> csdeconv = mrt.ConstrainedSphericalDeconvolution()
>>> csdeconv.inputs.in_file = 'dwi.mif'
>>> csdeconv.inputs.encoding_file = 'encoding.txt'
>>> csdeconv.run()                                          # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                diffusion-weighted image
                flag: %s, position: -3
        response_file: (an existing file name)
                the diffusion-weighted signal response function for a single fibre
                population (see EstimateResponse)
                flag: %s, position: -2
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        debug: (a boolean)
                Display debugging messages.
                flag: -debug
        directions_file: (an existing file name)
                a text file containing the [ el az ] pairs for the directions:
                Specify the directions over which to apply the non-negativity
                constraint (by default, the built-in 300 direction set is used)
                flag: -directions %s, position: -2
        encoding_file: (an existing file name)
                Gradient encoding, supplied as a 4xN text file with each line is in
                the format [ X Y Z b ], where [ X Y Z ] describe the direction of
                the applied gradient, and b gives the b-value in units (1000
                s/mm^2). See FSL2MRTrix
                flag: -grad %s, position: 1
        environ: (a dictionary with keys which are a value of type 'str' and
                 with values which are a value of type 'str', nipype default value:
                 {})
                Environment variables
        filter_file: (an existing file name)
                a text file containing the filtering coefficients for each even
                harmonic order.the linear frequency filtering parameters used for
                the initial linear spherical deconvolution step (default = [ 1 1 1 0
                0 ]).
                flag: -filter %s, position: -2
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        iterations: (an integer)
                the maximum number of iterations to perform for each voxel (default
                = 50)
                flag: -niter %s
        lambda_value: (a float)
                the regularisation parameter lambda that controls the strength of
                the constraint (default = 1.0).
                flag: -lambda %s
        mask_image: (an existing file name)
                only perform computation within the specified binary brain mask
                image
                flag: -mask %s, position: 2
        maximum_harmonic_order: (an integer)
                set the maximum harmonic order for the output series. By default,
                the program will use the highest possible lmax given the number of
                diffusion-weighted images.
                flag: -lmax %s
        normalise: (a boolean)
                normalise the DW signal to the b=0 image
                flag: -normalise, position: 3
        out_filename: (a file name)
                Output filename
                flag: %s, position: -1
        threshold_value: (a float)
                the threshold below which the amplitude of the FOD is assumed to be
                zero, expressed as a fraction of the mean value of the initial FOD
                (default = 0.1)
                flag: -threshold %s

Outputs::

        spherical_harmonics_image: (an existing file name)
                Spherical harmonics image

.. _nipype.interfaces.mrtrix.tensors.DWI2SphericalHarmonicsImage:


.. index:: DWI2SphericalHarmonicsImage

DWI2SphericalHarmonicsImage
---------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/mrtrix/tensors.py#L32>`__

Wraps command **dwi2SH**

Convert base diffusion-weighted images to their spherical harmonic representation.

This program outputs the spherical harmonic decomposition for the set measured signal attenuations.
The signal attenuations are calculated by identifying the b-zero images from the diffusion encoding supplied
(i.e. those with zero as the b-value), and dividing the remaining signals by the mean b-zero signal intensity.
The spherical harmonic decomposition is then calculated by least-squares linear fitting.
Note that this program makes use of implied symmetries in the diffusion profile.

First, the fact the signal attenuation profile is real implies that it has conjugate symmetry,
i.e. Y(l,-m) = Y(l,m)* (where * denotes the complex conjugate). Second, the diffusion profile should be
antipodally symmetric (i.e. S(x) = S(-x)), implying that all odd l components should be zero. Therefore,
this program only computes the even elements.

Note that the spherical harmonics equations used here differ slightly from those conventionally used,
in that the (-1)^m factor has been omitted. This should be taken into account in all subsequent calculations.

Each volume in the output image corresponds to a different spherical harmonic component, according to the following convention:

* [0] Y(0,0)
* [1] Im {Y(2,2)}
* [2] Im {Y(2,1)}
* [3] Y(2,0)
* [4] Re {Y(2,1)}
* [5] Re {Y(2,2)}
* [6] Im {Y(4,4)}
* [7] Im {Y(4,3)}

Example
~~~~~~~

>>> import nipype.interfaces.mrtrix as mrt
>>> dwi2SH = mrt.DWI2SphericalHarmonicsImage()
>>> dwi2SH.inputs.in_file = 'diffusion.nii'
>>> dwi2SH.inputs.encoding_file = 'encoding.txt'
>>> dwi2SH.run()                                    # doctest: +SKIP

Inputs::

        [Mandatory]
        encoding_file: (an existing file name)
                Gradient encoding, supplied as a 4xN text file with each line is in
                the format [ X Y Z b ], where [ X Y Z ] describe the direction of
                the applied gradient, and b gives the b-value in units (1000
                s/mm^2). See FSL2MRTrix
                flag: -grad %s, position: 1
        in_file: (an existing file name)
                Diffusion-weighted images
                flag: %s, position: -2
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        environ: (a dictionary with keys which are a value of type 'str' and
                 with values which are a value of type 'str', nipype default value:
                 {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        maximum_harmonic_order: (a float)
                set the maximum harmonic order for the output series. By default,
                the program will use the highest possible lmax given the number of
                diffusion-weighted images.
                flag: -lmax %s
        normalise: (a boolean)
                normalise the DW signal to the b=0 image
                flag: -normalise, position: 3
        out_filename: (a file name)
                Output filename
                flag: %s, position: -1

Outputs::

        spherical_harmonics_image: (an existing file name)
                Spherical harmonics image

.. _nipype.interfaces.mrtrix.tensors.Directions2Amplitude:


.. index:: Directions2Amplitude

Directions2Amplitude
--------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/mrtrix/tensors.py#L386>`__

Wraps command **dir2amp**

convert directions image to amplitudes

Example
~~~~~~~

>>> import nipype.interfaces.mrtrix as mrt
>>> amplitudes = mrt.Directions2Amplitude()
>>> amplitudes.inputs.in_file = 'peak_directions.mif'
>>> amplitudes.run()                                          # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                the input directions image. Each volume corresponds to the x, y & z
                component of each direction vector in turn.
                flag: %s, position: -2
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        display_debug: (a boolean)
                Display debugging messages.
                flag: -debug
        display_info: (a boolean)
                Display information messages.
                flag: -info
        environ: (a dictionary with keys which are a value of type 'str' and
                 with values which are a value of type 'str', nipype default value:
                 {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        num_peaks: (an integer)
                the number of peaks to extract (default is 3)
                flag: -num %s
        out_file: (a file name)
                the output amplitudes image
                flag: %s, position: -1
        peak_directions: (a list of from 2 to 2 items which are a float)
                phi theta. the direction of a peak to estimate. The algorithm will
                attempt to find the same number of peaks as have been specified
                using this option phi: the azimuthal angle of the direction (in
                degrees). theta: the elevation angle of the direction (in degrees,
                from the vertical z-axis)
                flag: -direction %s
        peaks_image: (an existing file name)
                the program will try to find the peaks that most closely match those
                in the image provided
                flag: -peaks %s
        quiet_display: (a boolean)
                do not display information messages or progress status.
                flag: -quiet

Outputs::

        out_file: (an existing file name)
                amplitudes image

.. _nipype.interfaces.mrtrix.tensors.EstimateResponseForSH:


.. index:: EstimateResponseForSH

EstimateResponseForSH
---------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/mrtrix/tensors.py#L185>`__

Wraps command **estimate_response**

Estimates the fibre response function for use in spherical deconvolution.

Example
~~~~~~~

>>> import nipype.interfaces.mrtrix as mrt
>>> estresp = mrt.EstimateResponseForSH()
>>> estresp.inputs.in_file = 'dwi.mif'
>>> estresp.inputs.mask_image = 'dwi_WMProb.mif'
>>> estresp.inputs.encoding_file = 'encoding.txt'
>>> estresp.run()                                   # doctest: +SKIP

Inputs::

        [Mandatory]
        encoding_file: (an existing file name)
                Gradient encoding, supplied as a 4xN text file with each line is in
                the format [ X Y Z b ], where [ X Y Z ] describe the direction of
                the applied gradient, and b gives the b-value in units (1000
                s/mm^2). See FSL2MRTrix
                flag: -grad %s, position: 1
        in_file: (an existing file name)
                Diffusion-weighted images
                flag: %s, position: -3
        mask_image: (an existing file name)
                only perform computation within the specified binary brain mask
                image
                flag: %s, position: -2
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        debug: (a boolean)
                Display debugging messages.
                flag: -debug
        environ: (a dictionary with keys which are a value of type 'str' and
                 with values which are a value of type 'str', nipype default value:
                 {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        maximum_harmonic_order: (an integer)
                set the maximum harmonic order for the output series. By default,
                the program will use the highest possible lmax given the number of
                diffusion-weighted images.
                flag: -lmax %s
        normalise: (a boolean)
                normalise the DW signal to the b=0 image
                flag: -normalise
        out_filename: (a file name)
                Output filename
                flag: %s, position: -1
        quiet: (a boolean)
                Do not display information messages or progress status.
                flag: -quiet

Outputs::

        response: (an existing file name)
                Spherical harmonics image

.. _nipype.interfaces.mrtrix.tensors.FindShPeaks:


.. index:: FindShPeaks

FindShPeaks
-----------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/mrtrix/tensors.py#L349>`__

Wraps command **find_SH_peaks**

identify the orientations of the N largest peaks of a SH profile

Example
~~~~~~~

>>> import nipype.interfaces.mrtrix as mrt
>>> shpeaks = mrt.FindShPeaks()
>>> shpeaks.inputs.in_file = 'csd.mif'
>>> shpeaks.inputs.directions_file = 'dirs.txt'
>>> shpeaks.inputs.num_peaks = 2
>>> shpeaks.run()                                          # doctest: +SKIP

Inputs::

        [Mandatory]
        directions_file: (an existing file name)
                the set of directions to use as seeds for the peak finding
                flag: %s, position: -2
        in_file: (an existing file name)
                the input image of SH coefficients.
                flag: %s, position: -3
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        display_debug: (a boolean)
                Display debugging messages.
                flag: -debug
        display_info: (a boolean)
                Display information messages.
                flag: -info
        environ: (a dictionary with keys which are a value of type 'str' and
                 with values which are a value of type 'str', nipype default value:
                 {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        num_peaks: (an integer)
                the number of peaks to extract (default is 3)
                flag: -num %s
        out_file: (a file name)
                the output image. Each volume corresponds to the x, y & z component
                of each peak direction vector in turn
                flag: %s, position: -1
        peak_directions: (a list of from 2 to 2 items which are a float)
                phi theta. the direction of a peak to estimate. The algorithm will
                attempt to find the same number of peaks as have been specified
                using this option phi: the azimuthal angle of the direction (in
                degrees). theta: the elevation angle of the direction (in degrees,
                from the vertical z-axis)
                flag: -direction %s
        peak_threshold: (a float)
                only peak amplitudes greater than the threshold will be considered
                flag: -threshold %s
        peaks_image: (an existing file name)
                the program will try to find the peaks that most closely match those
                in the image provided
                flag: -peaks %s
        quiet_display: (a boolean)
                do not display information messages or progress status.
                flag: -quiet

Outputs::

        out_file: (an existing file name)
                Peak directions image

.. _nipype.interfaces.mrtrix.tensors.GenerateDirections:


.. index:: GenerateDirections

GenerateDirections
------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/mrtrix/tensors.py#L313>`__

Wraps command **gendir**

generate a set of directions evenly distributed over a hemisphere.

Example
~~~~~~~

>>> import nipype.interfaces.mrtrix as mrt
>>> gendir = mrt.GenerateDirections()
>>> gendir.inputs.num_dirs = 300
>>> gendir.run()                                          # doctest: +SKIP

Inputs::

        [Mandatory]
        num_dirs: (an integer)
                the number of directions to generate.
                flag: %s, position: -2
        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
                Control terminal output: `stream` - displays to terminal
                immediately, `allatonce` - waits till command is finished to display
                output, `file` - writes output to file, `none` - output is ignored

        [Optional]
        args: (a string)
                Additional parameters to the command
                flag: %s
        display_debug: (a boolean)
                Display debugging messages.
                flag: -debug
        display_info: (a boolean)
                Display information messages.
                flag: -info
        environ: (a dictionary with keys which are a value of type 'str' and
                 with values which are a value of type 'str', nipype default value:
                 {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        niter: (an integer)
                specify the maximum number of iterations to perform.
                flag: -niter %s
        out_file: (a file name)
                the text file to write the directions to, as [ az el ] pairs.
                flag: %s, position: -1
        power: (a float)
                specify exponent to use for repulsion power law.
                flag: -power %s
        quiet_display: (a boolean)
                do not display information messages or progress status.
                flag: -quiet

Outputs::

        out_file: (an existing file name)
                directions file

.. module:: nipype.interfaces.mrtrix.tensors


.. _nipype.interfaces.mrtrix.tensors.concat_files:

:func:`concat_files`
--------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/interfaces/mrtrix/tensors.py#L221>`__





