image.py

#!/usr/bin/env python
#
# image.py - Provides the :class:`Image` class, for representing 3D/4D NIFTI
#            images.
#
# Author: Paul McCarthy <pauldmccarthy@gmail.com>
#
"""This module provides the :class:`Nifti` and :class:`Image` classes, for
representing NIFTI1 and NIFTI2 images. The ``nibabel`` package is used
for file I/O.


It is very easy to load a NIFTI image::

    from fsl.data.image import Image
    myimg = Image('MNI152_T1_2mm.nii.gz')


A handful of other functions are also provided for working with image files
and file names:

.. autosummary::
   :nosignatures:

   canonicalShape
   looksLikeImage
   addExt
   splitExt
   getExt
   removeExt
   defaultExt
"""


import                      os
import os.path           as op
import itertools         as it
import                      json
import                      string
import                      logging
import                      tempfile

import                      six
import numpy             as np

import nibabel           as nib
import nibabel.fileslice as fileslice

import fsl.utils.meta        as meta
import fsl.transform.affine  as affine
import fsl.utils.notifier    as notifier
import fsl.utils.memoize     as memoize
import fsl.utils.path        as fslpath
import fsl.utils.bids        as fslbids
import fsl.data.constants    as constants
import fsl.data.imagewrapper as imagewrapper


log = logging.getLogger(__name__)


ALLOWED_EXTENSIONS = ['.nii.gz', '.nii', '.img', '.hdr', '.img.gz', '.hdr.gz']
"""The file extensions which we understand. This list is used as the default
if the ``allowedExts`` parameter is not passed to any of the ``*Ext``
functions, or the :func:`looksLikeImage` function.
"""


EXTENSION_DESCRIPTIONS = ['Compressed NIFTI images',
                          'NIFTI images',
                          'ANALYZE75 images',
                          'NIFTI/ANALYZE75 headers',
                          'Compressed NIFTI/ANALYZE75 images',
                          'Compressed NIFTI/ANALYZE75 headers']
"""Descriptions for each of the extensions in :data:`ALLOWED_EXTENSIONS`. """


FILE_GROUPS = [('.hdr',    '.img'),
               ('.hdr.gz', '.img.gz')]
"""File suffix groups used by :func:`addExt` to resolve file path
ambiguities - see :func:`fsl.utils.path.addExt`.
"""


PathError = fslpath.PathError
"""Error raised by :mod:`fsl.utils.path` functions when an error occurs.
Made available in this module for convenience.
"""


class Nifti(notifier.Notifier, meta.Meta):
    """The ``Nifti`` class is intended to be used as a base class for
    things which either are, or are associated with, a NIFTI image.
    The ``Nifti`` class is intended to represent information stored in
    the header of a NIFTI file - if you want to load the data from
    a file, use the :class:`Image` class instead.


    When a ``Nifti`` instance is created, it adds the following attributes
    to itself:


    ================= ====================================================
    ``header``        The :mod:`nibabel` NIFTI1/NIFTI2/Analyze header
                      object.

    ``shape``         A list/tuple containing the number of voxels along
                      each image dimension.

    ``pixdim``        A list/tuple containing the length of one voxel
                      along each image dimension.

    ``voxToWorldMat`` A 4*4 array specifying the affine transformation
                      for transforming voxel coordinates into real world
                      coordinates.

    ``worldToVoxMat`` A 4*4 array specifying the affine transformation
                      for transforming real world coordinates into voxel
                      coordinates.

    ``intent``        The NIFTI intent code specified in the header (or
                      :attr:`.constants.NIFTI_INTENT_NONE` for Analyze
                      images).
    ================= ====================================================


    The ``header`` field may either be a ``nifti1``, ``nifti2``, or
    ``analyze`` header object. Make sure to take this into account if you are
    writing code that should work with all three. Use the :meth:`niftiVersion`
    property if you need to know what type of image you are dealing with.


    The ``shape`` attribute may not precisely match the image shape as
    reported in the NIFTI header, because trailing dimensions of size 1 are
    squeezed out. See the :meth:`__determineShape` and :meth:`mapIndices`
    methods.


    **Affine transformations**


    The ``Nifti`` class is aware of three coordinate systems:

      - The ``voxel`` coordinate system, used to access image data

      - The ``world`` coordinate system, where voxel coordinates are
        transformed into a millimetre coordinate system, defined by the
        ``sform`` and/or ``qform`` elements of the NIFTI header.

      - The ``fsl`` coordinate system, where voxel coordinates are scaled by
        the ``pixdim`` values in the NIFTI header, and the X axis is inverted
        if the voxel-to-world affine has a positive determinant.


    The :meth:`getAffine` method is a simple means of acquiring an affine
    which will transform between any of these coordinate systems.


    See `here <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT/FAQ#What_is_the_format_of_the_matrix_used_by_FLIRT.2C_and_how_does_it_relate_to_the_transformation_parameters.3F>`_
    for more details on the ``fsl`` coordinate system..


    The ``Nifti`` class follows the same process as ``nibabel`` in determining
    the ``voxel`` to ``world`` affine (see
    http://nipy.org/nibabel/nifti_images.html#the-nifti-affines):


     1. If ``sform_code != 0`` ("unknown") use the sform affine; else
     2. If ``qform_code != 0`` ("unknown") use the qform affine; else
     3. Use the fall-back affine.


    However, the *fall-back* affine used by the ``Nifti`` class differs to
    that used by ``nibabel``. In ``nibabel``, the origin (world coordinates
    (0, 0, 0)) is set to the centre of the image. Here in the ``Nifti``
    class, we set the world coordinate orign to be the corner of the image,
    i.e. the corner of voxel (0, 0, 0).


    You may change the ``voxToWorldMat`` of a ``Nifti`` instance (unless it
    is an Analyze image). When you do so:

     - Only the ``sform`` of the underlying ``Nifti1Header`` object is changed

     - The ``qform`` is not modified.

     - If the ``sform_code`` was previously set to ``NIFTI_XFORM_UNKNOWN``,
       it is changed to ``NIFTI_XFORM_ALIGNED_ANAT``. Otherwise, the
       ``sform_code`` is not modified.


    **ANALYZE support**


    A ``Nifti`` instance expects to be passed either a
    ``nibabel.nifti1.Nifti1Header`` or a ``nibabel.nifti2.Nifti2Header``, but
    can also encapsulate a ``nibabel.analyze.AnalyzeHeader``. In this case:

      - The image voxel orientation is assumed to be R->L, P->A, I->S.

      - The affine will be set to a diagonal matrix with the header pixdims as
        its elements (with the X pixdim negated), and an offset specified by
        the ANALYZE ``origin`` fields. Construction of the affine is handled
        by ``nibabel``.

      - The :meth:`niftiVersion` method will return ``0``.

      - The :meth:`getXFormCode` method will return
        :attr:`.constants.NIFTI_XFORM_ANALYZE`.


    **Metadata**


    The ``Image`` class inherits from the :class:`.Meta` class - its methods
    can be used to store and query any meta-data associated with the image.


    **Notification**


    The ``Nifti`` class implements the :class:`.Notifier` interface -
    listeners may register to be notified on the following topics:

    =============== ========================================================
    ``'transform'`` The affine transformation matrix has changed. This topic
                    will occur when the :meth:`voxToWorldMat` is changed.
    ``'header'``    A header field has changed. This will occur when the
                    :meth:`intent` is changed.
    =============== ========================================================
    """  # noqa


    def __init__(self, header):
        """Create a ``Nifti`` object.

        :arg header: A :class:`nibabel.nifti1.Nifti1Header`,
                       :class:`nibabel.nifti2.Nifti2Header`, or
                       ``nibabel.analyze.AnalyzeHeader`` to be used as the
                       image header.
        """

        # Nifti2Header is a sub-class of Nifti1Header,
        # and Nifti1Header a sub-class of AnalyzeHeader,
        # so we only need to test for the latter.
        if not isinstance(header, nib.analyze.AnalyzeHeader):
            raise ValueError('Unrecognised header: {}'.format(header))

        origShape, shape, pixdim = Nifti.determineShape(header)
        voxToWorldMat            = Nifti.determineAffine(header)
        affines, isneuro         = Nifti.generateAffines(voxToWorldMat,
                                                         shape,
                                                         pixdim)

        self.__header         = header
        self.__shape          = shape
        self.__origShape      = origShape
        self.__pixdim         = pixdim
        self.__affines        = affines
        self.__isNeurological = isneuro


    def __del__(self):
        """Clears the reference to the ``nibabel`` header object. """
        self.__header = None


    @staticmethod
    def determineShape(header):
        """This method is called by :meth:`__init__`. It figures out the actual
        shape of the image data, and the zooms/pixdims for each data axis. Any
        empty trailing dimensions are squeezed, but the returned shape is
        guaranteed to be at least 3 dimensions. Returns:

         - A sequence/tuple containing the image shape, as reported in the
           header.
         - A sequence/tuple containing the effective image shape.
         - A sequence/tuple containing the zooms/pixdims.
        """

        # The canonicalShape function figures out
        # the data shape that we should use.
        origShape = list(header.get_data_shape())
        shape     = canonicalShape(origShape)
        pixdims   = list(header.get_zooms())

        # if get_zooms() doesn't return at
        # least len(shape) values, we'll
        # fall back to the pixdim field in
        # the header.
        if len(pixdims) < len(shape):
            pixdims = header['pixdim'][1:]

        pixdims = pixdims[:len(shape)]

        # should never happen, but if we only
        # have zoom values for the original
        # (< 3D) shape, pad them with 1s.
        if len(pixdims) < len(shape):
            pixdims = pixdims + [1] * (len(shape) - len(pixdims))

        return origShape, shape, pixdims


    @staticmethod
    def determineAffine(header):
        """Called by :meth:`__init__`. Figures out the voxel-to-world
        coordinate transformation matrix that is associated with this
        ``Nifti`` instance.
        """

        # We have to treat FSL/FNIRT images
        # specially, as FNIRT clobbers the
        # sform section of the NIFTI header
        # to store other data.
        intent = header.get('intent_code', -1)
        qform  = header.get('qform_code',  -1)
        sform  = header.get('sform_code',  -1)

        # FNIRT non-linear coefficient files
        # clobber the sform/qform/intent
        # and pixdims of the nifti header,
        # so we can't correctly place it in
        # the world coordinate system. See
        # $FSLDIR/src/fnirt/fnirt_file_writer.cpp
        # and fsl.transform.nonlinear for more
        # details.
        if intent in (constants.FSL_DCT_COEFFICIENTS,
                      constants.FSL_CUBIC_SPLINE_COEFFICIENTS,
                      constants.FSL_QUADRATIC_SPLINE_COEFFICIENTS,
                      constants.FSL_TOPUP_CUBIC_SPLINE_COEFFICIENTS,
                      constants.FSL_TOPUP_QUADRATIC_SPLINE_COEFFICIENTS):

            log.debug('FNIRT coefficient field detected - generating affine')

            # Knot spacing is stored in the pixdims
            # (specified in terms of reference image
            # voxels), and reference image pixdims
            # are stored as intent code parameters.
            # If we combine the two, we can at least
            # get the shape/size of the coefficient
            # field about right
            knotpix       =  header.get_zooms()[:3]
            refpix        = (header.get('intent_p1', 1) or 1,
                             header.get('intent_p2', 1) or 1,
                             header.get('intent_p3', 1) or 1)
            voxToWorldMat = affine.concat(
                affine.scaleOffsetXform(refpix,  0),
                affine.scaleOffsetXform(knotpix, 0))

        # If the qform or sform codes are unknown,
        # then we can't assume that the transform
        # matrices are valid. So we fall back to a
        # pixdim scaling matrix.
        #
        # n.b. For images like this, nibabel returns
        # a scaling matrix where the centre voxel
        # corresponds to world location (0, 0, 0).
        # This goes against the NIFTI spec - it
        # should just be a straight scaling matrix.
        elif qform == 0 and sform == 0:
            pixdims       = header.get_zooms()
            voxToWorldMat = affine.scaleOffsetXform(pixdims, 0)

        # Otherwise we let nibabel decide
        # which transform to use.
        else:
            voxToWorldMat = np.array(header.get_best_affine())

        return voxToWorldMat


    @staticmethod
    def generateAffines(voxToWorldMat, shape, pixdim):
        """Called by :meth:`__init__`, and the :meth:`voxToWorldMat` setter.
        Generates and returns a dictionary containing affine transformations
        between the ``voxel``, ``fsl``, and ``world`` coordinate
        systems. These affines are accessible via the :meth:`getAffine`
        method.

        :arg voxToWorldMat: The voxel-to-world affine transformation
        :arg shape:         Image shape (number of voxels along each dimension
        :arg pixdim:        Image pixdims (size of one voxel along each
                            dimension)
        :returns:           A tuple containing:

                             - a dictionary of affine transformations between
                               each pair of coordinate systems
                             - ``True`` if the image is to be considered
                               "neurological", ``False`` otherwise - see the
                               :meth:`isNeurological` method.
        """

        import numpy.linalg as npla

        affines = {}
        shape             = list(shape[ :3])
        pixdim            = list(pixdim[:3])
        voxToScaledVoxMat = np.diag(pixdim + [1.0])
        isneuro           = npla.det(voxToWorldMat) > 0

        if isneuro:
            x                 = (shape[0] - 1) * pixdim[0]
            flip              = affine.scaleOffsetXform([-1, 1, 1],
                                                        [ x, 0, 0])
            voxToScaledVoxMat = affine.concat(flip, voxToScaledVoxMat)

        affines['fsl',   'fsl']   = np.eye(4)
        affines['voxel', 'voxel'] = np.eye(4)
        affines['world', 'world'] = np.eye(4)
        affines['voxel', 'world'] = voxToWorldMat
        affines['world', 'voxel'] = affine.invert(voxToWorldMat)
        affines['voxel', 'fsl']   = voxToScaledVoxMat
        affines['fsl',   'voxel'] = affine.invert(voxToScaledVoxMat)
        affines['fsl',   'world'] = affine.concat(affines['voxel', 'world'],
                                                  affines['fsl',   'voxel'])
        affines['world', 'fsl']   = affine.concat(affines['voxel',   'fsl'],
                                                  affines['world', 'voxel'])

        return affines, isneuro


    @staticmethod
    def identifyAffine(image, xform, from_=None, to=None):
        """Attempt to identify the source or destination space for the given
        affine.

        ``xform`` is assumed to be an affine transformation which can be used
        to transform coordinates between two coordinate systems associated with
        ``image``.

        If one of ``from_`` or ``to`` is provided, the other will be derived.
        If neither are provided, both will be derived. See the
        :meth:`.Nifti.getAffine` method for details on the valild values that
        ``from_`` and ``to`` may take.

        :arg image: :class:`.Nifti` instance associated with the affine.

        :arg xform: ``(4, 4)`` ``numpy`` array encoding an affine
                    transformation

        :arg from_: Label specifying the coordinate system which ``xform``
                    takes as input

        :arg to:    Label specifying the coordinate system which ``xform``
                    produces as output

        :returns:   A tuple containing:
                      - A label for the ``from_`` coordinate system
                      - A label for the ``to`` coordinate system
        """

        if (from_ is not None) and (to is not None):
            return from_, to

        if from_ is not None: froms = [from_]
        else:                 froms = ['voxel', 'fsl', 'world']
        if to    is not None: tos   = [to]
        else:                 tos   = ['voxel', 'fsl', 'world']

        for from_, to in it.product(froms, tos):

            candidate = image.getAffine(from_, to)

            if np.all(np.isclose(candidate, xform)):
                return from_, to

        raise ValueError('Could not identify affine')


    def strval(self, key):
        """Returns the specified NIFTI header field, converted to a python
        string, correctly null-terminated, and with non-printable characters
        removed.

        This method is used to sanitise some NIFTI header fields. The default
        Python behaviour for converting a sequence of bytes to a string is to
        strip all termination characters (bytes with value of ``0x00``) from
        the end of the sequence.

        This default behaviour does not handle the case where a sequence of
        bytes which did contain a long string is subsequently overwritten with
        a shorter string - the short string will be terminated, but that
        termination character will be followed by the remainder of the
        original string.
        """

        val = self.header[key]

        try:              val = bytes(val).partition(b'\0')[0]
        except Exception: val = bytes(val)

        val = val.decode('ascii')

        return ''.join([c for c in val if c in string.printable]).strip()


    @property
    def header(self):
        """Return a reference to the ``nibabel`` header object. """
        return self.__header


    @header.setter
    def header(self, header):
        """Replace the ``nibabel`` header object managed by this ``Nifti``
        with a new header. The new header must have the same dimensions,
        voxel size, and orientation as the old one.
        """
        new = Nifti(header)
        if not (self.sameSpace(new) and self.ndim == new.ndim):
            raise ValueError('Incompatible header')
        self.__header = header


    @property
    def niftiVersion(self):
        """Returns the NIFTI file version:

           - ``0`` for ANALYZE
           - ``1`` for NIFTI1
           - ``2`` for NIFTI2
        """

        # nib.Nifti2 is a subclass of Nifti1,
        # and Nifti1 a subclass of Analyze,
        # so we have to check in order
        if   isinstance(self.header, nib.nifti2.Nifti2Header):   return 2
        elif isinstance(self.header, nib.nifti1.Nifti1Header):   return 1
        elif isinstance(self.header, nib.analyze.AnalyzeHeader): return 0

        else: raise RuntimeError('Unrecognised header: {}'.format(self.header))


    @property
    def shape(self):
        """Returns a tuple containing the image data shape. """
        return tuple(self.__shape)


    @property
    def ndim(self):
        """Returns the number of dimensions in this image. This number may not
        match the number of dimensions specified in the NIFTI header, as
        trailing dimensions of length 1 are ignored. But it is guaranteed to be
        at least 3.
        """
        return len(self.__shape)


    @property
    def pixdim(self):
        """Returns a tuple containing the image pixdims (voxel sizes)."""
        return tuple(self.__pixdim)


    @property
    def intent(self):
        """Returns the NIFTI intent code of this image. """
        return self.header.get('intent_code', constants.NIFTI_INTENT_NONE)

    @property
    def niftiDataType(self):
        """Returns the NIFTI data type code of this image. """
        return self.header.get('datatype', constants.NIFTI_DT_UNKNOWN)


    @intent.setter
    def intent(self, val):
        """Sets the NIFTI intent code of this image. """
        # analyze has no intent
        if (self.niftiVersion > 0) and (val != self.intent):
            self.header.set_intent(val, allow_unknown=True)
            self.notify(topic='header')


    @property
    def xyzUnits(self):
        """Returns the NIFTI XYZ dimension unit code. """

        # analyze images have no unit field
        if self.niftiVersion == 0:
            return constants.NIFTI_UNITS_MM

        # The nibabel get_xyzt_units returns labels,
        # but we want the NIFTI codes. So we use
        # the (undocumented) nifti1.unit_codes field
        # to convert back to the raw codes.
        units = self.header.get_xyzt_units()[0]
        units = nib.nifti1.unit_codes[units]
        return units


    @property
    def timeUnits(self):
        """Returns the NIFTI time dimension unit code. """

        # analyze images have no unit field
        if self.niftiVersion == 0:
            return constants.NIFTI_UNITS_SEC

        # See xyzUnits
        units = self.header.get_xyzt_units()[1]
        units = nib.nifti1.unit_codes[units]
        return units


    def getAffine(self, from_, to):
        """Return an affine transformation which can be used to transform
        coordinates from ``from_`` to ``to``.

        Valid values for the ``from_`` and ``to`` arguments are:

         - ``'voxel'``: The voxel coordinate system
         - ``'world'``: The world coordinate system, as defined by the image
           sform/qform
         - ``'fsl'``: The FSL coordinate system (scaled voxels, with a
           left-right flip if the sform/qform has a positive determinant)

        :arg from_: Source coordinate system
        :arg to:    Destination coordinate system
        :returns:   A ``numpy`` array of shape ``(4, 4)``
        """
        from_ = from_.lower()
        to    = to   .lower()

        if from_ not in ('voxel', 'fsl', 'world') or \
           to    not in ('voxel', 'fsl', 'world'):
            raise ValueError('Invalid source/reference spaces: "{}" -> "{}".'
                             'Recognised spaces are "voxel", "fsl", and '
                             '"world"'.format(from_, to))

        return np.copy(self.__affines[from_, to])


    @property
    def worldToVoxMat(self):
        """Returns a ``numpy`` array of shape ``(4, 4)`` containing an
        affine transformation from world coordinates to voxel coordinates.
        """
        return self.getAffine('world', 'voxel')


    @property
    def voxToWorldMat(self):
        """Returns a ``numpy`` array of shape ``(4, 4)`` containing an
        affine transformation from voxel coordinates to world coordinates.
        """
        return self.getAffine('voxel', 'world')


    @voxToWorldMat.setter
    def voxToWorldMat(self, xform):
        """Update the ``voxToWorldMat``. The ``worldToVoxMat`` value is also
        updated. This will result in notification on the ``'transform'``
        topic.
        """

        # Can't do much with
        # an analyze image
        if self.niftiVersion == 0:
            raise Exception('voxToWorldMat cannot be '
                            'changed for an ANALYZE image')

        header    = self.header
        sformCode = int(header['sform_code'])

        if sformCode == constants.NIFTI_XFORM_UNKNOWN:
            sformCode = constants.NIFTI_XFORM_ALIGNED_ANAT

        header.set_sform(xform, code=sformCode)

        affines, isneuro = Nifti.generateAffines(xform,
                                                 self.shape,
                                                 self.pixdim)

        self.__affines        = affines
        self.__isNeurological = isneuro

        log.debug('Affine changed:\npixdims: '
                  '%s\nsform: %s\nqform: %s',
                  header.get_zooms(),
                  header.get_sform(),
                  header.get_qform())

        self.notify(topic='transform')


    @property
    def voxToScaledVoxMat(self):
        """Returns a transformation matrix which transforms from voxel
        coordinates into scaled voxel coordinates, with a left-right flip
        if the image appears to be stored in neurological order.

        See http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT/FAQ#What_is_the\
        _format_of_the_matrix_used_by_FLIRT.2C_and_how_does_it_relate_to\
        _the_transformation_parameters.3F
        """
        return self.getAffine('voxel', 'fsl')


    @property
    def scaledVoxToVoxMat(self):
        """Returns a transformation matrix which transforms from scaled voxels
        into voxels, the inverse of the :meth:`voxToScaledVoxMat` transform.
        """
        return self.getAffine('fsl', 'voxel')


    def mapIndices(self, sliceobj):
        """Adjusts the given slice object so that it may be used to index the
        underlying ``nibabel`` NIFTI image object.

        See the :meth:`__determineShape` method.

        :arg sliceobj: Something that can be used to slice a
                       multi-dimensional array, e.g. ``arr[sliceobj]``.
        """

        # How convenient - nibabel has a function
        # that does the dirty work for us.
        return fileslice.canonical_slicers(sliceobj, self.__origShape)


    def getXFormCode(self, code=None):
        """This method returns the code contained in the NIFTI header,
        indicating the space to which the (transformed) image is oriented.

        The ``code`` parameter may be either ``sform`` (the default) or
        ``qform`` in which case the corresponding matrix is used.

        :returns: one of the following codes:
                    - :data:`~.constants.NIFTI_XFORM_UNKNOWN`
                    - :data:`~.constants.NIFTI_XFORM_SCANNER_ANAT`
                    - :data:`~.constants.NIFTI_XFORM_ALIGNED_ANAT`
                    - :data:`~.constants.NIFTI_XFORM_TALAIRACH`
                    - :data:`~.constants.NIFTI_XFORM_MNI_152`
                    - :data:`~.constants.NIFTI_XFORM_ANALYZE`
        """

        if self.niftiVersion == 0:
            return constants.NIFTI_XFORM_ANALYZE

        if   code == 'sform' : code = 'sform_code'
        elif code == 'qform' : code = 'qform_code'
        elif code is not None:
            raise ValueError('code must be None, sform, or qform')

        if code is not None:
            code = self.header[code]

        # If the caller did not specify
        # a code, we check both. If the
        # sform is present, we return it.
        # Otherwise, if the qform is
        # present, we return that.
        else:

            sform_code = self.header['sform_code']
            qform_code = self.header['qform_code']

            if   sform_code != constants.NIFTI_XFORM_UNKNOWN: code = sform_code
            elif qform_code != constants.NIFTI_XFORM_UNKNOWN: code = qform_code

        # Invalid values
        if code not in range(5):
            code = constants.NIFTI_XFORM_UNKNOWN

        return int(code)


    # TODO Check what has worse performance - hashing
    #      a 4x4 array (via memoizeMD5), or the call
    #      to aff2axcodes. I'm guessing the latter,
    #      but am not 100% sure.
    @memoize.Instanceify(memoize.memoizeMD5)
    def axisMapping(self, xform):
        """Returns the (approximate) correspondence of each axis in the source
        coordinate system to the axes in the destination coordinate system,
        where the source and destinations are defined by the given affine
        transformation matrix.
        """

        inaxes = [[-1, 1], [-2, 2], [-3, 3]]

        return nib.orientations.aff2axcodes(xform, inaxes)


    def isNeurological(self):
        """Returns ``True`` if it looks like this ``Nifti`` object has a
        neurological voxel orientation, ``False`` otherwise. This test is
        purely based on the determinant of the voxel-to-mm transformation
        matrix - if it has a positive determinant, the image is assumed to
        be in neurological orientation, otherwise it is assumed to be in
        radiological orientation.

        ..warning:: This method will return ``True`` for images with an
                    unknown orientation (e.g. the ``sform_code`` and
                    ``qform_code`` are both set to ``0``). Therefore, you
                    must check the orientation via the :meth:`getXFormCode`
                    before trusting the result of this method.

        See http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT/FAQ#What_is_the\
        _format_of_the_matrix_used_by_FLIRT.2C_and_how_does_it_relate_to\
        _the_transformation_parameters.3F
        """
        return self.__isNeurological


    def sameSpace(self, other):
        """Returns ``True`` if the ``other`` image (assumed to be a
        :class:`Nifti` instance) has the same dimensions and is in the
        same space as this image.
        """
        return np.all(np.isclose(self .shape[:3],
                                 other.shape[:3]))  and \
               np.all(np.isclose(self .pixdim[:3],
                                 other.pixdim[:3])) and \
               np.all(np.isclose(self .voxToWorldMat,
                                 other.voxToWorldMat))


    def getOrientation(self, axis, xform):
        """Returns a code representing the orientation of the specified
        axis in the input coordinate system of the given transformation
        matrix.

        :arg xform: A transformation matrix which is assumed to transform
                    coordinates from some coordinate system (the one
                    which you want an orientation for) into the image
                    world coordinate system.

                    For example, if you pass in the voxel-to-world
                    transformation matrix, you will get an orientation
                    for axes in the voxel coordinate system.

        This method returns one of the following values, indicating the
        direction in which coordinates along the specified axis increase:

          - :attr:`~.constants.ORIENT_L2R`:     Left to right
          - :attr:`~.constants.ORIENT_R2L`:     Right to left
          - :attr:`~.constants.ORIENT_A2P`:     Anterior to posterior
          - :attr:`~.constants.ORIENT_P2A`:     Posterior to anterior
          - :attr:`~.constants.ORIENT_I2S`:     Inferior to superior
          - :attr:`~.constants.ORIENT_S2I`:     Superior to inferior
          - :attr:`~.constants.ORIENT_UNKNOWN`: Orientation is unknown

        The returned value is dictated by the XForm code contained in the
        image file header (see the :meth:`getXFormCode` method). Basically, if
        the XForm code is *unknown*, this method will return
        ``ORIENT_UNKNOWN`` for all axes. Otherwise, it is assumed that the
        image is in RAS orientation (i.e. the X axis increases from left to
        right, the Y axis increases from posterior to anterior, and the Z axis
        increases from inferior to superior).
        """

        if self.getXFormCode() == constants.NIFTI_XFORM_UNKNOWN:
            return constants.ORIENT_UNKNOWN

        code = nib.orientations.aff2axcodes(
            xform,
            ((constants.ORIENT_R2L, constants.ORIENT_L2R),
             (constants.ORIENT_A2P, constants.ORIENT_P2A),
             (constants.ORIENT_S2I, constants.ORIENT_I2S)))[axis]

        return code


    def adjust(self, pixdim=None, shape=None, origin=None):
        """Return a new ``Nifti`` object with the specified ``pixdim`` or
        ``shape``. The affine of the new ``Nifti`` is adjusted accordingly.

        Only one of ``pixdim`` or ``shape`` can be specified.

        See :func:`.affine.rescale` for the meaning of the ``origin`` argument.

        Only the spatial dimensions may be adjusted - use the functions in
        the :mod:`.image.resample` module if you need to adjust non-spatial
        dimensions.

        :arg pixdim: New voxel dimensions
        :arg shape:  New image shape
        :arg origin: Voxel grid alignment - either ``'centre'`` (the default)
                     or ``'corner'``
        :returns:    A new ``Nifti`` object based on this one, with adjusted
                     pixdims, shape and affine.
        """

        if ((pixdim is not None) and (shape is not None)) or \
           ((pixdim is     None) and (shape is     None)):
            raise ValueError('Exactly one of pixdim or '
                             'shape must be specified')

        if shape is not None: ndim = len(shape)
        else:                 ndim = len(pixdim)

        # We only allow adjustment of
        # the spatial dimensions
        if ndim != 3:
            raise ValueError('Three dimensions must be specified')

        oldShape  = np.array(self.shape[ :ndim])
        oldPixdim = np.array(self.pixdim[:ndim])
        newShape  = shape
        newPixdim = pixdim

        # if pixdims were specified,
        # convert them into a shape,
        # and vice versa
        if newPixdim is not None:
            newShape = oldShape * (oldPixdim / newPixdim)
        else:
            newPixdim = oldPixdim * (oldShape / newShape)

        # Rescale the voxel-to-world affine
        xform = affine.rescale(oldShape, newShape, origin)
        xform = affine.concat(self.getAffine('voxel', 'world'), xform)

        # Now that we've got our spatial
        # scaling/offset matrix, pad the
        # new shape/pixdims with those
        # from any non-spatial dimensions
        newShape  = list(newShape)  + list(self.shape[ 3:])
        newPixdim = list(newPixdim) + list(self.pixdim[3:])

        # And create the new header
        # and we're away
        header = self.header.copy()
        header.set_data_shape(newShape)
        header.set_zooms(     newPixdim)
        header.set_sform(     xform)
        header.set_qform(     xform)
        return Nifti(header)


class Image(Nifti):
    """Class which represents a NIFTI image. Internally, the image is
    loaded/stored using a :mod:`nibabel.nifti1.Nifti1Image` or
    :mod:`nibabel.nifti2.Nifti2Image`, and data access managed by a
    :class:`.ImageWrapper`.


    In addition to the attributes added by the :meth:`Nifti.__init__` method,
    the following attributes/properties are present on an ``Image`` instance
    as properties (https://docs.python.org/2/library/functions.html#property):


    ============== ===========================================================
    ``name``       The name of this ``Image`` - defaults to the image
                   file name, sans-suffix.

    ``dataSource`` The data source of this ``Image`` - the name of the
                   file from where it was loaded, or some other string
                   describing its origin.

    ``nibImage``   A reference to the ``nibabel`` NIFTI image object.

    ``saveState``  A boolean value which is ``True`` if this image is
                   saved to disk, ``False`` if it is in-memory, or has
                   been edited.

    ``dataRange``  The minimum/maximum values in the image. Depending upon
                   the value of the ``calcRange`` parameter to
                   :meth:`__init__`, this may be calculated when the ``Image``
                   is created, or may be incrementally updated as more image
                   data is loaded from disk.
    ============== ===========================================================


    The ``Image`` class adds some :class:`.Notifier` topics to those which are
    already provided by the :class:`Nifti` class - listeners may register to
    be notified of changes to the above properties, by registering on the
    following _topic_ names (see the :class:`.Notifier` class documentation):


    =============== ======================================================
    ``'data'``      This topic is notified whenever the image data changes
                    (via the :meth:`__setitem__` method). The indices/
                    slices of the portion of data that was modified is
                    passed to registered listeners as the notification
                    value (see :meth:`.Notifier.notify`).

    ``'saveState'`` This topic is notified whenever the saved state of the
                    image changes (i.e. data or ``voxToWorldMat`` is
                    edited, or the image saved to disk).

    ``'dataRange'`` This topic is notified whenever the image data range
                    is changed/adjusted.
    =============== ======================================================
    """


    def __init__(self,
                 image,
                 name=None,
                 header=None,
                 xform=None,
                 loadData=True,
                 calcRange=True,
                 threaded=False,