def to_matrix(self, smooth_fwhm=0, outdtype=None):
        """Return numpy.ndarray with the masked or flatten image data and
           the relevant information (mask indices and volume shape).

        Parameters
        ----------
        smooth__fwhm: int
            Integer indicating the size of the FWHM Gaussian smoothing kernel
            to smooth the subject volumes before creating the data matrix

        outdtype: dtype
            Type of the elements of the array, if None will obtain the dtype from
            the first nifti file.

        Returns
        -------
        outmat, mask_indices, vol_shape

        outmat: Numpy array with shape N x prod(vol.shape)
                containing the N files as flat vectors.

        mask_indices: matrix with indices of the voxels in the mask

        vol_shape: Tuple with shape of the volumes, for reshaping.
        """
        if not self.all_compatible:
            raise ValueError("`self.all_compatible` must be True in order to use this function.")

        if not outdtype:
            outdtype = self.items[0].dtype

        # extract some info from the mask
        n_voxels     = None
        mask_indices = None
        mask_shape   = self.items[0].shape[:3]
        if self.has_mask:
            mask_arr     = self.mask.get_data()
            mask_indices = np.nonzero(mask_arr)
            mask_shape   = self.mask.shape
            n_voxels     = np.count_nonzero(mask_arr)

        # if the mask is empty will use the whole image
        if n_voxels is None:
            log.debug('Non-zero voxels have not been found in mask {}'.format(self.mask))
            n_voxels     = np.prod(mask_shape)
            mask_indices = None

        # get the shape of the flattened subject data
        ndims = self.items[0].ndim
        if ndims == 3:
            subj_flat_shape = (n_voxels, )
        elif ndims == 4:
            subj_flat_shape = (n_voxels, self.items[0].shape[3])
        else:
            raise NotImplementedError('The subject images have {} dimensions. '
                                      'Still have not implemented t_matrix for this shape.'.format(ndims))

        # create and fill the big matrix
        outmat = np.zeros((self.n_subjs, ) + subj_flat_shape, dtype=outdtype)
        try:
            for i, image in enumerate(self.items):
                if smooth_fwhm > 0:
                    image.fwhm = smooth_fwhm

                if self.has_mask:
                    image.set_mask(self.mask)

                outmat[i, :], _, _ = image.mask_and_flatten()
                image.clear_data()

        except Exception as exc:
            raise Exception('Error flattening file {0}'.format(image)) from exc
        else:
            return outmat, mask_indices, mask_shape
def to_matrix(self, smooth_fwhm=0, outdtype=None):
        """Return numpy.ndarray with the masked or flatten image data and
           the relevant information (mask indices and volume shape).

        Parameters
        ----------
        smooth__fwhm: int
            Integer indicating the size of the FWHM Gaussian smoothing kernel
            to smooth the subject volumes before creating the data matrix

        outdtype: dtype
            Type of the elements of the array, if None will obtain the dtype from
            the first nifti file.

        Returns
        -------
        outmat, mask_indices, vol_shape

        outmat: Numpy array with shape N x prod(vol.shape)
                containing the N files as flat vectors.

        mask_indices: matrix with indices of the voxels in the mask

        vol_shape: Tuple with shape of the volumes, for reshaping.
        """
        if not self.all_compatible:
            raise ValueError("`self.all_compatible` must be True in order to use this function.")

        if not outdtype:
            outdtype = self.items[0].dtype

        # extract some info from the mask
        n_voxels     = None
        mask_indices = None
        mask_shape   = self.items[0].shape[:3]
        if self.has_mask:
            mask_arr     = self.mask.get_data()
            mask_indices = np.nonzero(mask_arr)
            mask_shape   = self.mask.shape
            n_voxels     = np.count_nonzero(mask_arr)

        # if the mask is empty will use the whole image
        if n_voxels is None:
            log.debug('Non-zero voxels have not been found in mask {}'.format(self.mask))
            n_voxels     = np.prod(mask_shape)
            mask_indices = None

        # get the shape of the flattened subject data
        ndims = self.items[0].ndim
        if ndims == 3:
            subj_flat_shape = (n_voxels, )
        elif ndims == 4:
            subj_flat_shape = (n_voxels, self.items[0].shape[3])
        else:
            raise NotImplementedError('The subject images have {} dimensions. '
                                      'Still have not implemented t_matrix for this shape.'.format(ndims))

        # create and fill the big matrix
        outmat = np.zeros((self.n_subjs, ) + subj_flat_shape, dtype=outdtype)
        try:
            for i, image in enumerate(self.items):
                if smooth_fwhm > 0:
                    image.fwhm = smooth_fwhm

                if self.has_mask:
                    image.set_mask(self.mask)

                outmat[i, :], _, _ = image.mask_and_flatten()
                image.clear_data()

        except Exception as exc:
            raise Exception('Error flattening file {0}'.format(image)) from exc
        else:
            return outmat, mask_indices, mask_shape
def to_matrix ( self , smooth_fwhm = 0 , outdtype = None ) : if not self . all_compatible : raise ValueError ( "`self.all_compatible` must be True in order to use this function." ) if not outdtype : outdtype = self . items [ 0 ] . dtype # extract some info from the mask n_voxels = None mask_indices = None mask_shape = self . items [ 0 ] . shape [ : 3 ] if self . has_mask : mask_arr = self . mask . get_data ( ) mask_indices = np . nonzero ( mask_arr ) mask_shape = self . mask . shape n_voxels = np . count_nonzero ( mask_arr ) # if the mask is empty will use the whole image if n_voxels is None : log . debug ( 'Non-zero voxels have not been found in mask {}' . format ( self . mask ) ) n_voxels = np . prod ( mask_shape ) mask_indices = None # get the shape of the flattened subject data ndims = self . items [ 0 ] . ndim if ndims == 3 : subj_flat_shape = ( n_voxels , ) elif ndims == 4 : subj_flat_shape = ( n_voxels , self . items [ 0 ] . shape [ 3 ] ) else : raise NotImplementedError ( 'The subject images have {} dimensions. ' 'Still have not implemented t_matrix for this shape.' . format ( ndims ) ) # create and fill the big matrix outmat = np . zeros ( ( self . n_subjs , ) + subj_flat_shape , dtype = outdtype ) try : for i , image in enumerate ( self . items ) : if smooth_fwhm > 0 : image . fwhm = smooth_fwhm if self . has_mask : image . set_mask ( self . mask ) outmat [ i , : ] , _ , _ = image . mask_and_flatten ( ) image . clear_data ( ) except Exception as exc : raise Exception ( 'Error flattening file {0}' . format ( image ) ) from exc else : return outmat , mask_indices , mask_shape
Return numpy.ndarray with the masked or flatten image data and
           the relevant information (mask indices and volume shape).

        Parameters
        ----------
        smooth__fwhm: int
            Integer indicating the size of the FWHM Gaussian smoothing kernel
            to smooth the subject volumes before creating the data matrix

        outdtype: dtype
            Type of the elements of the array, if None will obtain the dtype from
            the first nifti file.

        Returns
        -------
        outmat, mask_indices, vol_shape

        outmat: Numpy array with shape N x prod(vol.shape)
                containing the N files as flat vectors.

        mask_indices: matrix with indices of the voxels in the mask

        vol_shape: Tuple with shape of the volumes, for reshaping.
Return numpy . ndarray with the masked or flatten image data and the relevant information ( mask indices and volume shape ) .