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#
#   See COPYING file distributed along with the NiBabel package for the
#   copyright and license terms.
#
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# module imports
""" Utilities to load and save image objects """

import os
import numpy as np

from .filename_parser import splitext_addext, _stringify_path
from .openers import ImageOpener
from .filebasedimages import ImageFileError
from .imageclasses import all_image_classes
from .arrayproxy import is_proxy
from .deprecated import deprecate_with_version


def load(filename, **kwargs):
    r''' Load file given filename, guessing at file type

    Parameters
    ----------
    filename : str or os.PathLike
       specification of file to load
    \*\*kwargs : keyword arguments
        Keyword arguments to format-specific load

    Returns
    -------
    img : ``SpatialImage``
       Image of guessed type
    '''
    filename = _stringify_path(filename)

    # Check file exists and is not empty
    try:
        stat_result = os.stat(filename)
    except OSError:
        raise FileNotFoundError("No such file or no access: '%s'" % filename)
    if stat_result.st_size <= 0:
        raise ImageFileError("Empty file: '%s'" % filename)

    sniff = None
    for image_klass in all_image_classes:
        is_valid, sniff = image_klass.path_maybe_image(filename, sniff)
        if is_valid:
            img = image_klass.from_filename(filename, **kwargs)
            return img

    raise ImageFileError('Cannot work out file type of "%s"' %
                         filename)


@deprecate_with_version('guessed_image_type deprecated.'
                        '2.1',
                        '4.0')
def guessed_image_type(filename):
    """ Guess image type from file `filename`

    Parameters
    ----------
    filename : str
        File name containing an image

    Returns
    -------
    image_class : class
        Class corresponding to guessed image type
    """
    sniff = None
    for image_klass in all_image_classes:
        is_valid, sniff = image_klass.path_maybe_image(filename, sniff)
        if is_valid:
            return image_klass

    raise ImageFileError('Cannot work out file type of "%s"' %
                         filename)


def save(img, filename):
    ''' Save an image to file adapting format to `filename`

    Parameters
    ----------
    img : ``SpatialImage``
       image to save
    filename : str or os.PathLike
       filename (often implying filenames) to which to save `img`.

    Returns
    -------
    None
    '''
    filename = _stringify_path(filename)

    # Save the type as expected
    try:
        img.to_filename(filename)
    except ImageFileError:
        pass
    else:
        return

    # Be nice to users by making common implicit conversions
    froot, ext, trailing = splitext_addext(filename, ('.gz', '.bz2'))
    lext = ext.lower()

    # Special-case Nifti singles and Pairs
    # Inline imports, as this module really shouldn't reference any image type
    from .nifti1 import Nifti1Image, Nifti1Pair
    from .nifti2 import Nifti2Image, Nifti2Pair

    klass = None
    converted = None

    if type(img) == Nifti1Image and lext in ('.img', '.hdr'):
        klass = Nifti1Pair
    elif type(img) == Nifti2Image and lext in ('.img', '.hdr'):
        klass = Nifti2Pair
    elif type(img) == Nifti1Pair and lext == '.nii':
        klass = Nifti1Image
    elif type(img) == Nifti2Pair and lext == '.nii':
        klass = Nifti2Image
    else:  # arbitrary conversion
        valid_klasses = [klass for klass in all_image_classes
                         if ext in klass.valid_exts]
        if not valid_klasses:  # if list is empty
            raise ImageFileError('Cannot work out file type of "%s"' %
                                 filename)

        # Got a list of valid extensions, but that's no guarantee
        #   the file conversion will work. So, try each image
        #   in order...
        for klass in valid_klasses:
            try:
                converted = klass.from_image(img)
                break
            except Exception as e:
                err = e
        # ... and if none of them work, raise an error.
        if converted is None:
            raise err

    # Here, we either have a klass or a converted image.
    if converted is None:
        converted = klass.from_image(img)
    converted.to_filename(filename)


@deprecate_with_version('read_img_data deprecated.'
                        'Please use ``img.dataobj.get_unscaled()`` instead.'
                        '2.0.1',
                        '4.0')
def read_img_data(img, prefer='scaled'):
    """ Read data from image associated with files

    If you want unscaled data, please use ``img.dataobj.get_unscaled()``
    instead.  If you want scaled data, use ``img.get_fdata()`` (which will cache
    the loaded array) or ``np.array(img.dataobj)`` (which won't cache the
    array). If you want to load the data as for a modified header, save the
    image with the modified header, and reload.

    Parameters
    ----------
    img : ``SpatialImage``
       Image with valid image file in ``img.file_map``.  Unlike the
       ``img.get_fdata()`` method, this function returns the data read
       from the image file, as specified by the *current* image header
       and *current* image files.
    prefer : str, optional
       Can be 'scaled' - in which case we return the data with the
       scaling suggested by the format, or 'unscaled', in which case we
       return, if we can, the raw data from the image file, without the
       scaling applied.

    Returns
    -------
    arr : ndarray
       array as read from file, given parameters in header

    Notes
    -----
    Summary: please use the ``get_data`` method of `img` instead of this
    function unless you are sure what you are doing.

    In general, you will probably prefer ``prefer='scaled'``, because
    this gives the data as the image format expects to return it.

    Use `prefer` == 'unscaled' with care; the modified Analyze-type
    formats such as SPM formats, and nifti1, specify that the image data
    array is given by the raw data on disk, multiplied by a scalefactor
    and maybe with the addition of a constant.  This function, with
    ``unscaled`` returns the data on the disk, without these
    format-specific scalings applied.  Please use this funciton only if
    you absolutely need the unscaled data, and the magnitude of the
    data, as given by the scalefactor, is not relevant to your
    application.  The Analyze-type formats have a single scalefactor +/-
    offset per image on disk. If you do not care about the absolute
    values, and will be removing the mean from the data, then the
    unscaled values will have preserved intensity ratios compared to the
    mean-centered scaled data.  However, this is not necessarily true of
    other formats with more complicated scaling - such as MINC.
    """
    if prefer not in ('scaled', 'unscaled'):
        raise ValueError('Invalid string "%s" for "prefer"' % prefer)
    hdr = img.header
    if not hasattr(hdr, 'raw_data_from_fileobj'):
        # We can only do scaled
        if prefer == 'unscaled':
            raise ValueError("Can only do unscaled for Analyze types")
        return np.array(img.dataobj)
    # Analyze types
    img_fh = img.file_map['image']
    img_file_like = (img_fh.filename if img_fh.fileobj is None
                     else img_fh.fileobj)
    if img_file_like is None:
        raise ImageFileError('No image file specified for this image')
    # Check the consumable values in the header
    hdr = img.header
    dao = img.dataobj
    default_offset = hdr.get_data_offset() == 0
    default_scaling = hdr.get_slope_inter() == (None, None)
    # If we have a proxy object and the header has any consumed fields, we load
    # the consumed values back from the proxy
    if is_proxy(dao) and (default_offset or default_scaling):
        hdr = hdr.copy()
        if default_offset and dao.offset != 0:
            hdr.set_data_offset(dao.offset)
        if default_scaling and (dao.slope, dao.inter) != (1, 0):
            hdr.set_slope_inter(dao.slope, dao.inter)
    with ImageOpener(img_file_like) as fileobj:
        if prefer == 'scaled':
            return hdr.data_from_fileobj(fileobj)
        return hdr.raw_data_from_fileobj(fileobj)


@deprecate_with_version('which_analyze_type deprecated.'
                        '2.1',
                        '4.0')
def which_analyze_type(binaryblock):
    """ Is `binaryblock` from NIfTI1, NIfTI2 or Analyze header?

    Parameters
    ----------
    binaryblock : bytes
        The `binaryblock` is 348 bytes that might be NIfTI1, NIfTI2, Analyze,
        or None of the the above.

    Returns
    -------
    hdr_type : str
        * a nifti1 header (pair or single) -> return 'nifti1'
        * a nifti2 header (pair or single) -> return 'nifti2'
        * an Analyze header -> return 'analyze'
        * None of the above -> return None

    Notes
    -----
    Algorithm:

    * read in the first 4 bytes from the file as 32-bit int ``sizeof_hdr``
    * if ``sizeof_hdr`` is 540 or byteswapped 540 -> assume nifti2
    * Check for 'ni1', 'n+1' magic -> assume nifti1
    * if ``sizeof_hdr`` is 348 or byteswapped 348 assume Analyze
    * Return None
    """
    from .nifti1 import header_dtype
    hdr_struct = np.ndarray(shape=(), dtype=header_dtype, buffer=binaryblock)
    bs_hdr_struct = hdr_struct.byteswap()
    sizeof_hdr = hdr_struct['sizeof_hdr']
    bs_sizeof_hdr = bs_hdr_struct['sizeof_hdr']
    if 540 in (sizeof_hdr, bs_sizeof_hdr):
        return 'nifti2'
    if hdr_struct['magic'] in (b'ni1', b'n+1'):
        return 'nifti1'
    if 348 in (sizeof_hdr, bs_sizeof_hdr):
        return 'analyze'
    return None
