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

algorithms.misc
===============


.. _nipype.algorithms.misc.AddCSVColumn:


.. index:: AddCSVColumn

AddCSVColumn
------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L1104>`__

Short interface to add an extra column and field to a text file

Example
~~~~~~~

>>> import nipype.algorithms.misc as misc
>>> addcol = misc.AddCSVColumn()
>>> addcol.inputs.in_file = 'degree.csv'
>>> addcol.inputs.extra_column_heading = 'group'
>>> addcol.inputs.extra_field = 'male'
>>> addcol.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                Input comma-separated value (CSV) files

        [Optional]
        extra_column_heading: (a string)
                New heading to add for the added field.
        extra_field: (a string)
                New field to add to each row. This is useful for saving the group or
                subject ID in the file.
        in_file: (an existing file name)
                Input comma-separated value (CSV) files
        out_file: (a file name, nipype default value: extra_heading.csv)
                Output filename for merged CSV file

Outputs::

        csv_file: (a file name)
                Output CSV file containing columns

.. _nipype.algorithms.misc.CalculateNormalizedMoments:


.. index:: CalculateNormalizedMoments

CalculateNormalizedMoments
--------------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L1165>`__

Calculates moments of timeseries.

Example
~~~~~~~

>>> import nipype.algorithms.misc as misc
>>> skew = misc.CalculateNormalizedMoments()
>>> skew.inputs.moment = 3
>>> skew.inputs.timeseries_file = 'timeseries.txt'
>>> skew.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        moment: (an integer)
                Define which moment should be calculated, 3 for skewness, 4 for
                kurtosis.
        timeseries_file: (an existing file name)
                Text file with timeseries in columns and timepoints in rows,
                whitespace separated

        [Optional]
        moment: (an integer)
                Define which moment should be calculated, 3 for skewness, 4 for
                kurtosis.
        timeseries_file: (an existing file name)
                Text file with timeseries in columns and timepoints in rows,
                whitespace separated

Outputs::

        moments: (a list of items which are a float)
                Moments

.. _nipype.algorithms.misc.CreateNifti:


.. index:: CreateNifti

CreateNifti
-----------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L591>`__

Inputs::

        [Mandatory]
        data_file: (an existing file name)
                ANALYZE img file
        header_file: (an existing file name)
                corresponding ANALYZE hdr file

        [Optional]
        affine: (an array)
                affine transformation array
        data_file: (an existing file name)
                ANALYZE img file
        header_file: (an existing file name)
                corresponding ANALYZE hdr file
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run

Outputs::

        nifti_file: (an existing file name)

.. _nipype.algorithms.misc.Distance:


.. index:: Distance

Distance
--------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L245>`__

Calculates distance between two volumes.

Inputs::

        [Mandatory]
        volume1: (an existing file name)
                Has to have the same dimensions as volume2.
        volume2: (an existing file name)
                Has to have the same dimensions as volume1.

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        mask_volume: (an existing file name)
                calculate overlap only within this mask.
        method: ('eucl_min' or 'eucl_cog' or 'eucl_mean' or 'eucl_wmean' or
                 'eucl_max', nipype default value: eucl_min)
                ""eucl_min": Euclidean distance between two closest points
                "eucl_cog": mean Euclidian distance between the Center of Gravity of
                volume1 and CoGs of volume2 "eucl_mean": mean Euclidian minimum
                distance of all volume2 voxels to volume1 "eucl_wmean": mean
                Euclidian minimum distance of all volume2 voxels to volume1 weighted
                by their values "eucl_max": maximum over minimum Euclidian distances
                of all volume2 voxels to volume1 (also known as the Hausdorff
                distance)
        volume1: (an existing file name)
                Has to have the same dimensions as volume2.
        volume2: (an existing file name)
                Has to have the same dimensions as volume1.

Outputs::

        distance: (a float)
        histogram: (a file name)
        point1: (an array with shape (3,))
        point2: (an array with shape (3,))

.. _nipype.algorithms.misc.FuzzyOverlap:


.. index:: FuzzyOverlap

FuzzyOverlap
------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L489>`__

Calculates various overlap measures between two maps, using the fuzzy
definition proposed in: Crum et al., Generalized Overlap Measures for
Evaluation and Validation in Medical Image Analysis, IEEE Trans. Med.
Ima. 25(11),pp 1451-1461, Nov. 2006.

in_ref and in_tst are lists of 2/3D images, each element on the list
containing one volume fraction map of a class in a fuzzy partition
of the domain.

Example
~~~~~~~

>>> overlap = FuzzyOverlap()
>>> overlap.inputs.in_ref = [ 'ref_class0.nii', 'ref_class1.nii' ]
>>> overlap.inputs.in_tst = [ 'tst_class0.nii', 'tst_class1.nii' ]
>>> overlap.inputs.weighting = 'volume'
>>> res = overlap.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_ref: (an existing file name)
                Reference image. Requires the same dimensions as in_tst.
        in_tst: (an existing file name)
                Test image. Requires the same dimensions as in_ref.

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        in_ref: (an existing file name)
                Reference image. Requires the same dimensions as in_tst.
        in_tst: (an existing file name)
                Test image. Requires the same dimensions as in_ref.
        out_file: (a file name, nipype default value: diff.nii)
                alternative name for resulting difference-map
        weighting: ('none' or 'volume' or 'squared_vol', nipype default
                 value: none)
                ""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

Outputs::

        class_fdi: (a list of items which are a float)
                Array containing the fDIs of each computed class
        class_fji: (a list of items which are a float)
                Array containing the fJIs of each computed class
        dice: (a float)
                Fuzzy Dice Index (fDI), all the classes
        diff_file: (an existing file name)
                resulting difference-map of all classes, using the chosen weighting
        jaccard: (a float)
                Fuzzy Jaccard Index (fJI), all the classes

.. _nipype.algorithms.misc.Gunzip:


.. index:: Gunzip

Gunzip
------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L710>`__

Inputs::

        [Mandatory]
        in_file: (an existing file name)

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        in_file: (an existing file name)

Outputs::

        out_file: (an existing file name)

.. _nipype.algorithms.misc.Matlab2CSV:


.. index:: Matlab2CSV

Matlab2CSV
----------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L775>`__

Simple interface to 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
~~~~~~~

>>> import nipype.algorithms.misc as misc
>>> mat2csv = misc.Matlab2CSV()
>>> mat2csv.inputs.in_file = 'cmatrix.mat'
>>> mat2csv.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                Input MATLAB .mat file

        [Optional]
        in_file: (an existing file name)
                Input MATLAB .mat file
        reshape_matrix: (a boolean, nipype default value: True)
                The output of this interface is meant for R, so matrices will be
                reshaped to vectors by default.

Outputs::

        csv_files: (a file name)

.. _nipype.algorithms.misc.MergeCSVFiles:


.. index:: MergeCSVFiles

MergeCSVFiles
-------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L968>`__

This interface is designed to facilitate data loading in the R environment.
It takes input CSV files and merges them into a single CSV file.
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
~~~~~~~

>>> import nipype.algorithms.misc as misc
>>> mat2csv = misc.MergeCSVFiles()
>>> mat2csv.inputs.in_files = ['degree.mat','clustering.mat']
>>> mat2csv.inputs.column_headings = ['degree','clustering']
>>> mat2csv.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_files: (an existing file name)
                Input comma-separated value (CSV) files

        [Optional]
        column_headings: (a 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_heading: (a string)
                New heading to add for the added field.
        extra_field: (a string)
                New field to add to each row. This is useful for saving the group or
                subject ID in the file.
        in_files: (an existing file name)
                Input comma-separated value (CSV) files
        out_file: (a file name, nipype default value: merged.csv)
                Output filename for merged CSV file
        row_heading_title: (a string, nipype default value: label)
                Column heading for the row headings added
        row_headings: (a 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_file: (a file name)
                Output CSV file containing columns

.. _nipype.algorithms.misc.ModifyAffine:


.. index:: ModifyAffine

ModifyAffine
------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L181>`__

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

Inputs::

        [Mandatory]
        volumes: (an existing file name)
                volumes which affine matrices will be modified

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        transformation_matrix: (an array with shape (4, 4), nipype default
                 value: (<bound method Array.copy_default_value of
                 <traits.trait_numeric.Array object at 0x6e7a050>>, (array([[ 1.,
                 0.,  0.,  0.],        [ 0.,  1.,  0.,  0.],        [ 0.,  0.,  1.,
                 0.],        [ 0.,  0.,  0.,  1.]]),), None))
                transformation matrix that will be left multiplied by the affine
                matrix
        volumes: (an existing file name)
                volumes which affine matrices will be modified

Outputs::

        transformed_volumes: (a file name)

.. _nipype.algorithms.misc.Overlap:


.. index:: Overlap

Overlap
-------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L408>`__

Calculates various overlap measures between two maps.

Example
~~~~~~~

>>> overlap = Overlap()
>>> overlap.inputs.volume1 = 'cont1.nii'
>>> overlap.inputs.volume1 = 'cont2.nii'
>>> res = overlap.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        volume1: (an existing file name)
                Has to have the same dimensions as volume2.
        volume2: (an existing file name)
                Has to have the same dimensions as volume1.

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        mask_volume: (an existing file name)
                calculate overlap only within this mask.
        out_file: (a file name, nipype default value: diff.nii)
        volume1: (an existing file name)
                Has to have the same dimensions as volume2.
        volume2: (an existing file name)
                Has to have the same dimensions as volume1.

Outputs::

        dice: (a float)
        diff_file: (an existing file name)
        jaccard: (a float)
        volume_difference: (an integer)

.. _nipype.algorithms.misc.PickAtlas:


.. index:: PickAtlas

PickAtlas
---------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L63>`__

Returns ROI masks given an atlas and a list of labels. Supports dilation
and left right masking (assuming the atlas is properly aligned).

Inputs::

        [Mandatory]
        atlas: (an existing file name)
                Location of the atlas that will be used.
        labels: (an 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]
        atlas: (an existing file name)
                Location of the atlas that will be used.
        dilation_size: (an integer, nipype default value: 0)
                Defines how much the mask will be dilated (expanded in 3D).
        hemi: ('both' or 'left' or 'right', nipype default value: both)
                Restrict the mask to only one hemisphere: left or right
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        labels: (an 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.
        output_file: (a file name)
                Where to store the output mask.

Outputs::

        mask_file: (an existing file name)
                output mask file

.. _nipype.algorithms.misc.SimpleThreshold:


.. index:: SimpleThreshold

SimpleThreshold
---------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L131>`__

Inputs::

        [Mandatory]
        threshold: (a float)
                volumes to be thresholdedeverything below this value will be set to
                zero
        volumes: (an existing file name)
                volumes to be thresholded

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        threshold: (a float)
                volumes to be thresholdedeverything below this value will be set to
                zero
        volumes: (an existing file name)
                volumes to be thresholded

Outputs::

        thresholded_volumes: (an existing file name)
                thresholded volumes

.. _nipype.algorithms.misc.TSNR:


.. index:: TSNR

TSNR
----

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L633>`__

Computes the time-course SNR for a time series

Typically you want to run this on a realigned time-series.

Example
~~~~~~~

>>> tsnr = TSNR()
>>> tsnr.inputs.in_file = 'functional.nii'
>>> res = tsnr.run() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                realigned 4D file or a list of 3D files

        [Optional]
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        in_file: (an existing file name)
                realigned 4D file or a list of 3D files
        regress_poly: (an integer >= 1)
                Remove polynomials

Outputs::

        detrended_file: (a file name)
                detrended input file
        mean_file: (an existing file name)
                mean image file
        stddev_file: (an existing file name)
                std dev image file
        tsnr_file: (an existing file name)
                tsnr image file

.. module:: nipype.algorithms.misc


.. _nipype.algorithms.misc.calc_moments:

:func:`calc_moments`
--------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L1193>`__



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:

:func:`makefmtlist`
-------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L921>`__






.. _nipype.algorithms.misc.maketypelist:

:func:`maketypelist`
--------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L905>`__






.. _nipype.algorithms.misc.matlab2csv:

:func:`matlab2csv`
------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L747>`__






.. _nipype.algorithms.misc.merge_csvs:

:func:`merge_csvs`
------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L855>`__






.. _nipype.algorithms.misc.remove_identical_paths:

:func:`remove_identical_paths`
------------------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L885>`__






.. _nipype.algorithms.misc.replaceext:

:func:`replaceext`
------------------

`Link to code <http://github.com/nipy/nipype/tree/49d76df8df526ae0790ff6079642565548bc4434/nipype/algorithms/misc.py#L738>`__





