Python Dataset.fa[fattr]方法代码示例

# 需要导入模块: from mvpa2.datasets.base import Dataset [as 别名]
# 或者: from mvpa2.datasets.base.Dataset import fa[fattr] [as 别名]

#.........这里部分代码省略.........
    The default attribute name is listed in parenthesis, but may be altered by
    the corresponding prefix arguments. The validity of the attribute values
    relies on correct settings in the NIfTI image header.

    Parameters
    ----------
    samples : str or NiftiImage or list
      fMRI timeseries, specified either as a filename (single file 4D image),
      an image instance (4D image), or a list of filenames or image instances
      (each list item corresponding to a 3D volume).
    targets : scalar or sequence
      Label attribute for each volume in the timeseries, or a scalar value that
      is assigned to all samples.
    chunks : scalar or sequence
      Chunk attribute for each volume in the timeseries, or a scalar value that
      is assigned to all samples.
    mask : str or NiftiImage
      Filename or image instance of a 3D volume mask. Voxels corresponding to
      non-zero elements in the mask will be selected. The mask has to be in the
      same space (orientation and dimensions) as the timeseries image
    sprefix : str or None
      Prefix for attribute names describing spatial properties of the
      timeseries. If None, no such attributes are stored in the dataset.
    tprefix : str or None
      Prefix for attribute names describing temporal properties of the
      timeseries. If None, no such attributes are stored in the dataset.
    add_fa : dict or None
      Optional dictionary with additional volumetric data that shall be stored
      as feature attributes in the dataset. The dictionary key serves as the
      feature attribute name. Each value might be of any type supported by the
      'mask' argument of this function.

    Returns
    -------
    Dataset
    """
    # load the samples
    imgdata, imghdr, img = _load_anyimg(samples, ensure=True, enforce_dim=4)

    # figure out what the mask is, but only handle known cases, the rest
    # goes directly into the mapper which maybe knows more
    maskimg = _load_anyimg(mask)
    if maskimg is None:
        pass
    else:
        # take just data and ignore the header
        mask = maskimg[0]

    # compile the samples attributes
    sa = {}
    if not targets is None:
        sa['targets'] = _expand_attribute(targets, imgdata.shape[0], 'targets')
    if not chunks is None:
        sa['chunks'] = _expand_attribute(chunks, imgdata.shape[0], 'chunks')

    # create a dataset
    ds = Dataset(imgdata, sa=sa)
    if sprefix is None:
        space = None
    else:
        space = sprefix + '_indices'
    ds = ds.get_mapped(FlattenMapper(shape=imgdata.shape[1:], space=space))

    # now apply the mask if any
    if not mask is None:
        flatmask = ds.a.mapper.forward1(mask)
        # direct slicing is possible, and it is potentially more efficient,
        # so let's use it
        #mapper = StaticFeatureSelection(flatmask)
        #ds = ds.get_mapped(StaticFeatureSelection(flatmask))
        ds = ds[:, flatmask != 0]

    # load and store additional feature attributes
    if not add_fa is None:
        for fattr in add_fa:
            value = _load_anyimg(add_fa[fattr], ensure=True)[0]
            ds.fa[fattr] = ds.a.mapper.forward1(value)

    # store interesting NIfTI props in the dataset in a more portable way
    ds.a['imgaffine'] = img.get_affine()
    ds.a['imgtype'] = img.__class__.__name__
    # stick the header instance in as is, and ...
    ds.a['imghdr'] = imghdr
    # ... let strip_nibabel() be the central place to take care of any header
    # conversion into non-NiBabel dtypes
    strip_nibabel(ds)

    # If there is a space assigned , store the extent of that space
    if sprefix is not None:
        ds.a[sprefix + '_dim'] = imgdata.shape[1:]
        # 'voxdim' is (x,y,z) while 'samples' are (t,z,y,x)
        ds.a[sprefix + '_eldim'] = _get_voxdim(imghdr)
        # TODO extend with the unit
    if tprefix is not None:
        ds.sa[tprefix + '_indices'] = np.arange(len(ds), dtype='int')
        ds.sa[tprefix + '_coords'] = \
            np.arange(len(ds), dtype='float') * _get_dt(imghdr)
        # TODO extend with the unit

    return ds