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

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


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


.. index:: ConstrainedSphericalDeconvolution

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

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/mrtrix/tensors.py#L194>`__

Wraps the executable 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
                argument: ``%s``, position: -3
        response_file: (an existing file name)
                the diffusion-weighted signal response function for a single fibre
                population (see EstimateResponse)
                argument: ``%s``, position: -2

        [Optional]
        out_filename: (a file name)
                Output filename
                argument: ``%s``, position: -1
        mask_image: (an existing file name)
                only perform computation within the specified binary brain mask
                image
                argument: ``-mask %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
                argument: ``-grad %s``, position: 1
        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 ]).
                argument: ``-filter %s``, position: -2
        lambda_value: (a float)
                the regularisation parameter lambda that controls the strength of
                the constraint (default = 1.0).
                argument: ``-lambda %s``
        maximum_harmonic_order: (an integer (int or long))
                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.
                argument: ``-lmax %s``
        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)
                argument: ``-threshold %s``
        iterations: (an integer (int or long))
                the maximum number of iterations to perform for each voxel (default
                = 50)
                argument: ``-niter %s``
        debug: (a boolean)
                Display debugging messages.
                argument: ``-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)
                argument: ``-directions %s``, position: -2
        normalise: (a boolean)
                normalise the DW signal to the b=0 image
                argument: ``-normalise``, position: 3
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a 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', nipype default value: {})
                Environment variables

Outputs::

        spherical_harmonics_image: (an existing file name)
                Spherical harmonics image

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


.. index:: DWI2SphericalHarmonicsImage

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

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/mrtrix/tensors.py#L50>`__

Wraps the executable 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]
        in_file: (an existing file name)
                Diffusion-weighted images
                argument: ``%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
                argument: ``-grad %s``, position: 1

        [Optional]
        out_filename: (a file name)
                Output filename
                argument: ``%s``, position: -1
        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.
                argument: ``-lmax %s``
        normalise: (a boolean)
                normalise the DW signal to the b=0 image
                argument: ``-normalise``, position: 3
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a 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', nipype default value: {})
                Environment variables

Outputs::

        spherical_harmonics_image: (an existing file name)
                Spherical harmonics image

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


.. index:: Directions2Amplitude

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

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/mrtrix/tensors.py#L597>`__

Wraps the executable 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.
                argument: ``%s``, position: -2

        [Optional]
        peaks_image: (an existing file name)
                the program will try to find the peaks that most closely match those
                in the image provided
                argument: ``-peaks %s``
        num_peaks: (an integer (int or long))
                the number of peaks to extract (default is 3)
                argument: ``-num %s``
        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)
                argument: ``-direction %s``
        display_info: (a boolean)
                Display information messages.
                argument: ``-info``
        quiet_display: (a boolean)
                do not display information messages or progress status.
                argument: ``-quiet``
        display_debug: (a boolean)
                Display debugging messages.
                argument: ``-debug``
        out_file: (a file name)
                the output amplitudes image
                argument: ``%s``, position: -1
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a 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', nipype default value: {})
                Environment variables

Outputs::

        out_file: (an existing file name)
                amplitudes image

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


.. index:: EstimateResponseForSH

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

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/mrtrix/tensors.py#L294>`__

Wraps the executable 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]
        in_file: (an existing file name)
                Diffusion-weighted images
                argument: ``%s``, position: -3
        mask_image: (an existing file name)
                only perform computation within the specified binary brain mask
                image
                argument: ``%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
                argument: ``-grad %s``, position: 1

        [Optional]
        out_filename: (a file name)
                Output filename
                argument: ``%s``, position: -1
        maximum_harmonic_order: (an integer (int or long))
                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.
                argument: ``-lmax %s``
        normalise: (a boolean)
                normalise the DW signal to the b=0 image
                argument: ``-normalise``
        quiet: (a boolean)
                Do not display information messages or progress status.
                argument: ``-quiet``
        debug: (a boolean)
                Display debugging messages.
                argument: ``-debug``
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a 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', nipype default value: {})
                Environment variables

Outputs::

        response: (an existing file name)
                Spherical harmonics image

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


.. index:: FindShPeaks

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

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/mrtrix/tensors.py#L529>`__

Wraps the executable 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]
        in_file: (an existing file name)
                the input image of SH coefficients.
                argument: ``%s``, position: -3
        directions_file: (an existing file name)
                the set of directions to use as seeds for the peak finding
                argument: ``%s``, position: -2

        [Optional]
        peaks_image: (an existing file name)
                the program will try to find the peaks that most closely match those
                in the image provided
                argument: ``-peaks %s``
        num_peaks: (an integer (int or long))
                the number of peaks to extract (default is 3)
                argument: ``-num %s``
        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)
                argument: ``-direction %s``
        peak_threshold: (a float)
                only peak amplitudes greater than the threshold will be considered
                argument: ``-threshold %s``
        display_info: (a boolean)
                Display information messages.
                argument: ``-info``
        quiet_display: (a boolean)
                do not display information messages or progress status.
                argument: ``-quiet``
        display_debug: (a boolean)
                Display debugging messages.
                argument: ``-debug``
        out_file: (a file name)
                the output image. Each volume corresponds to the x, y & z component
                of each peak direction vector in turn
                argument: ``%s``, position: -1
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a 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', nipype default value: {})
                Environment variables

Outputs::

        out_file: (an existing file name)
                Peak directions image

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


.. index:: GenerateDirections

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

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/mrtrix/tensors.py#L454>`__

Wraps the executable 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 (int or long))
                the number of directions to generate.
                argument: ``%s``, position: -2

        [Optional]
        power: (a float)
                specify exponent to use for repulsion power law.
                argument: ``-power %s``
        niter: (an integer (int or long))
                specify the maximum number of iterations to perform.
                argument: ``-niter %s``
        display_info: (a boolean)
                Display information messages.
                argument: ``-info``
        quiet_display: (a boolean)
                do not display information messages or progress status.
                argument: ``-quiet``
        display_debug: (a boolean)
                Display debugging messages.
                argument: ``-debug``
        out_file: (a file name)
                the text file to write the directions to, as [ az el ] pairs.
                argument: ``%s``, position: -1
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a 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', nipype default value: {})
                Environment variables

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 <file:///build/nipype-1.1.9/nipype/interfaces/mrtrix/tensors.py#L332>`__





