Source code for dipy.io.image

import nibabel as nib
import numpy as np
from packaging.version import Version

from dipy.testing.decorators import warning_for_keywords




@warning_for_keywords()
def load_nifti_data(fname, *, as_ndarray=True):
    """Load only the data array from a nifti file.

    Parameters
    ----------
    fname : str
        Full path to the file.
    as_ndarray: bool, optional
        convert nibabel ArrayProxy to a numpy.ndarray.
        If you want to save memory and delay this casting, just turn this
        option to False.

    Returns
    -------
    data: np.ndarray or nib.ArrayProxy

    See Also
    --------
    load_nifti

    """
    img = nib.load(fname)
    return np.asanyarray(img.dataobj) if as_ndarray else img.dataobj





@warning_for_keywords()
def load_nifti(
    fname,
    *,
    return_img=False,
    return_voxsize=False,
    return_coords=False,
    as_ndarray=True,
):
    """Load data and other information from a nifti file.

    Parameters
    ----------
    fname : str
        Full path to a nifti file.

    return_img : bool, optional
        Whether to return the nibabel nifti img object.

    return_voxsize: bool, optional
        Whether to return the nifti header zooms.

    return_coords : bool, optional
        Whether to return the nifti header aff2axcodes.

    as_ndarray: bool, optional
        convert nibabel ArrayProxy to a numpy.ndarray.
        If you want to save memory and delay this casting, just turn this
        option to False.

    Returns
    -------
    A tuple, with (at the most, if all keyword args are set to True):
    (data, img.affine, img, vox_size, nib.aff2axcodes(img.affine))

    See Also
    --------
    load_nifti_data

    """
    img = nib.load(fname)
    data = np.asanyarray(img.dataobj) if as_ndarray else img.dataobj
    vox_size = img.header.get_zooms()[:3]

    ret_val = [data, img.affine]

    if return_img:
        ret_val.append(img)
    if return_voxsize:
        ret_val.append(vox_size)
    if return_coords:
        ret_val.append(nib.aff2axcodes(img.affine))

    return tuple(ret_val)





@warning_for_keywords()
def save_nifti(fname, data, affine, *, hdr=None, dtype=None):
    """Save a data array into a nifti file.

    Parameters
    ----------
    fname : str
        The full path to the file to be saved.

    data : ndarray
        The array with the data to save.

    affine : 4x4 array
        The affine transform associated with the file.

    hdr : nifti header, optional
        May contain additional information to store in the file header.

    Returns
    -------
    None

    """
    NIBABEL_4_0_0_PLUS = Version(nib.__version__) >= Version("4.0.0")
    # See GitHub issues
    #  * https://github.com/nipy/nibabel/issues/1046
    #  * https://github.com/nipy/nibabel/issues/1089
    # This only applies to NIfTI because the parent Analyze formats did
    # not support 64-bit integer data, so `set_data_dtype(int64)` would
    # already fail.
    danger_dts = (np.dtype("int64"), np.dtype("uint64"))
    if (
        hdr is None
        and dtype is None
        and data.dtype in danger_dts
        and NIBABEL_4_0_0_PLUS
    ):
        msg = f"Image data has type {data.dtype}, which may cause "
        msg += "incompatibilities with other tools. Indeed, Analyze formats "
        msg += "did not support 64-bit integer data.\n\n"
        msg += "To silent this, please specify the `header` or `dtype` "
        msg += "You could also use `np.asarray(data, dtype=np.int32)`. "
        msg += "This cast will make sure that you data is compatible with "
        msg += "other software."

        raise ValueError(msg)

    kwargs = {"dtype": dtype} if NIBABEL_4_0_0_PLUS else {}
    result_img = nib.Nifti1Image(data, affine, header=hdr, **kwargs)
    result_img.to_filename(fname)





def save_qa_metric(fname, xopt, fopt):
    """Save Quality Assurance metrics.

    Parameters
    ----------
    fname: string
        File name to save the metric values.
    xopt: numpy array
        The metric containing the
        optimal parameters for
        image registration.
    fopt: int
        The distance between the registered images.

    """
    np.savetxt(fname, xopt, header="Optimal Parameter metric")
    with open(fname, "a") as f:
        f.write("# Distance after registration\n")
        f.write(str(fopt))