from copy import deepcopy
from warnings import warn
import types

from scipy.spatial.distance import cdist
import numpy as np
from nibabel.affines import apply_affine

from dipy.tracking.streamlinespeed import set_number_of_points
from dipy.tracking.streamlinespeed import length
from dipy.tracking.streamlinespeed import compress_streamlines
import dipy.tracking.utils as ut
from dipy.tracking.utils import streamline_near_roi
from dipy.core.geometry import dist_to_corner
import dipy.align.vector_fields as vfu


def unlist_streamlines(streamlines):
    """ Return the streamlines not as a list but as an array and an offset

    Parameters
    ----------
    streamlines: sequence

    Returns
    -------
    points : array
    offsets : array

    """

    points = np.concatenate(streamlines, axis=0)
    offsets = np.zeros(len(streamlines), dtype='i8')

    curr_pos = 0
    prev_pos = 0
    for (i, s) in enumerate(streamlines):

            prev_pos = curr_pos
            curr_pos += s.shape[0]
            points[prev_pos:curr_pos] = s
            offsets[i] = curr_pos

    return points, offsets


def relist_streamlines(points, offsets):
    """ Given a representation of a set of streamlines as a large array and
    an offsets array return the streamlines as a list of shorter arrays.

    Parameters
    -----------
    points : array
    offsets : array

    Returns
    -------
    streamlines: sequence
    """

    streamlines = []

    streamlines.append(points[0: offsets[0]])

    for i in range(len(offsets) - 1):
        streamlines.append(points[offsets[i]: offsets[i + 1]])

    return streamlines


def center_streamlines(streamlines):
    """ Move streamlines to the origin

    Parameters
    ----------
    streamlines : list
        List of 2D ndarrays of shape[-1]==3

    Returns
    -------
    new_streamlines : list
        List of 2D ndarrays of shape[-1]==3
    inv_shift : ndarray
        Translation in x,y,z to go back in the initial position

    """
    center = np.mean(np.concatenate(streamlines, axis=0), axis=0)
    return [s - center for s in streamlines], center


def transform_streamlines(streamlines, mat):
    """ Apply affine transformation to streamlines

    Parameters
    ----------
    streamlines : list
        List of 2D ndarrays of shape[-1]==3
    mat : array, (4, 4)
        transformation matrix

    Returns
    -------
    new_streamlines : list
        List of the transformed 2D ndarrays of shape[-1]==3
    """
    return [apply_affine(mat, s) for s in streamlines]


def select_random_set_of_streamlines(streamlines, select):
    """ Select a random set of streamlines

    Parameters
    ----------
    streamlines : list
        List of 2D ndarrays of shape[-1]==3

    select : int
        Number of streamlines to select. If there are less streamlines
        than ``select`` then ``select=len(streamlines)``.

    Returns
    -------
    selected_streamlines : list

    Notes
    -----
    The same streamline will not be selected twice.
    """
    len_s = len(streamlines)
    index = np.random.choice(len_s, min(select, len_s), replace=False)
    return [streamlines[i] for i in index]


def select_by_rois(streamlines, rois, include, mode=None, affine=None,
                   tol=None):
    """Select streamlines based on logical relations with several regions of
    interest (ROIs). For example, select streamlines that pass near ROI1,
    but only if they do not pass near ROI2.

    Parameters
    ----------
    streamlines : list
        A list of candidate streamlines for selection
    rois : list or ndarray
        A list of 3D arrays, each with shape (x, y, z) corresponding to the
        shape of the brain volume, or a 4D array with shape (n_rois, x, y,
        z). Non-zeros in each volume are considered to be within the region
    include : array or list
        A list or 1D array of boolean values marking inclusion or exclusion
        criteria. If a streamline is near any of the inclusion ROIs, it
        should evaluate to True, unless it is also near any of the exclusion
        ROIs.
    mode : string, optional
        One of {"any", "all", "either_end", "both_end"}, where a streamline is
        associated with an ROI if:

        "any" : any point is within tol from ROI. Default.

        "all" : all points are within tol from ROI.

        "either_end" : either of the end-points is within tol from ROI

        "both_end" : both end points are within tol from ROI.

    affine : ndarray
        Affine transformation from voxels to streamlines. Default: identity.
    tol : float
        Distance (in the units of the streamlines, usually mm). If any
        coordinate in the streamline is within this distance from the center
        of any voxel in the ROI, the filtering criterion is set to True for
        this streamline, otherwise False. Defaults to the distance between
        the center of each voxel and the corner of the voxel.

    Notes
    -----
    The only operation currently possible is "(A or B or ...) and not (X or Y
    or ...)", where A, B are inclusion regions and X, Y are exclusion regions.

    Returns
    -------
    generator
       Generates the streamlines to be included based on these criteria.

    See also
    --------
    :func:`dipy.tracking.utils.near_roi`
    :func:`dipy.tracking.utils.reduce_rois`

    Examples
    --------
    >>> streamlines = [np.array([[0, 0., 0.9],
    ...                          [1.9, 0., 0.]]),
    ...                np.array([[0., 0., 0],
    ...                          [0, 1., 1.],
    ...                          [0, 2., 2.]]),
    ...                np.array([[2, 2, 2],
    ...                          [3, 3, 3]])]
    >>> mask1 = np.zeros((4, 4, 4), dtype=bool)
    >>> mask2 = np.zeros_like(mask1)
    >>> mask1[0, 0, 0] = True
    >>> mask2[1, 0, 0] = True
    >>> selection = select_by_rois(streamlines, [mask1, mask2],
    ...                            [True, True],
    ...                            tol=1)
    >>> list(selection) # The result is a generator
    [array([[ 0. ,  0. ,  0.9],
           [ 1.9,  0. ,  0. ]]), array([[ 0.,  0.,  0.],
           [ 0.,  1.,  1.],
           [ 0.,  2.,  2.]])]
    >>> selection = select_by_rois(streamlines, [mask1, mask2],
    ...                            [True, False],
    ...                            tol=0.87)
    >>> list(selection)
    [array([[ 0.,  0.,  0.],
           [ 0.,  1.,  1.],
           [ 0.,  2.,  2.]])]
    >>> selection = select_by_rois(streamlines, [mask1, mask2],
    ...                            [True, True],
    ...                            mode="both_end",
    ...                            tol=1.0)
    >>> list(selection)
    [array([[ 0. ,  0. ,  0.9],
           [ 1.9,  0. ,  0. ]])]
    >>> mask2[0, 2, 2] = True
    >>> selection = select_by_rois(streamlines, [mask1, mask2],
    ...                            [True, True],
    ...                            mode="both_end",
    ...                            tol=1.0)
    >>> list(selection)
    [array([[ 0. ,  0. ,  0.9],
           [ 1.9,  0. ,  0. ]]), array([[ 0.,  0.,  0.],
           [ 0.,  1.,  1.],
           [ 0.,  2.,  2.]])]
    """
    if affine is None:
        affine = np.eye(4)
    # This calculates the maximal distance to a corner of the voxel:
    dtc = dist_to_corner(affine)
    if tol is None:
        tol = dtc
    elif tol < dtc:
        w_s = "Tolerance input provided would create gaps in your"
        w_s += " inclusion ROI. Setting to: %s" % dist_to_corner
        warn(w_s)
        tol = dtc
    include_roi, exclude_roi = ut.reduce_rois(rois, include)
    include_roi_coords = np.array(np.where(include_roi)).T
    x_include_roi_coords = apply_affine(affine, include_roi_coords)
    exclude_roi_coords = np.array(np.where(exclude_roi)).T
    x_exclude_roi_coords = apply_affine(affine, exclude_roi_coords)

    if mode is None:
        mode = "any"
    for sl in streamlines:
        include = streamline_near_roi(sl, x_include_roi_coords, tol=tol,
                                      mode=mode)
        exclude = streamline_near_roi(sl, x_exclude_roi_coords, tol=tol,
                                      mode=mode)
        if include & ~exclude:
            yield sl


def orient_by_rois(streamlines, roi1, roi2, affine=None, copy=True):
    """Orient a set of streamlines according to a pair of ROIs

    Parameters
    ----------
    streamlines : list
        List of 3d arrays. Each array contains the xyz coordinates of a single
        streamline.
    roi1, roi2 : ndarray
        Binary masks designating the location of the regions of interest, or
        coordinate arrays (n-by-3 array with ROI coordinate in each row).
    affine : ndarray
        Affine transformation from voxels to streamlines. Default: identity.
    copy : bool
        Whether to make a copy of the input, or mutate the input inplace.

    Returns
    -------
    streamlines : list
        The same 3D arrays, but reoriented with respect to the ROIs

    Examples
    --------
    >>> streamlines = [np.array([[0, 0., 0],
    ...                          [1, 0., 0.],
    ...                          [2, 0., 0.]]),
    ...                np.array([[2, 0., 0.],
    ...                          [1, 0., 0],
    ...                          [0, 0,  0.]])]
    >>> roi1 = np.zeros((4, 4, 4), dtype=bool)
    >>> roi2 = np.zeros_like(roi1)
    >>> roi1[0, 0, 0] = True
    >>> roi2[1, 0, 0] = True
    >>> orient_by_rois(streamlines, roi1, roi2)
    [array([[ 0.,  0.,  0.],
           [ 1.,  0.,  0.],
           [ 2.,  0.,  0.]]), array([[ 0.,  0.,  0.],
           [ 1.,  0.,  0.],
           [ 2.,  0.,  0.]])]

    """

    # If we don't already have coordinates on our hands:
    if len(roi1.shape) == 3:
        roi1 = np.asarray(np.where(roi1.astype(bool))).T
    if len(roi2.shape) == 3:
        roi2 = np.asarray(np.where(roi2.astype(bool))).T

    if affine is not None:
        roi1 = apply_affine(affine, roi1)
        roi2 = apply_affine(affine, roi2)

    # Make a copy, so you don't change the output in place:
    if copy:
        new_sl = deepcopy(streamlines)
    else:
        new_sl = streamlines

    for idx, sl in enumerate(streamlines):
        dist1 = cdist(sl, roi1, 'euclidean')
        dist2 = cdist(sl, roi2, 'euclidean')
        min1 = np.argmin(dist1, 0)
        min2 = np.argmin(dist2, 0)
        if min1[0] > min2[0]:
            new_sl[idx] = sl[::-1]

    return new_sl


def _extract_vals(data, streamlines, affine=None, threedvec=False):
    """
    Helper function for use with `values_from_volume`.

    Parameters
    ----------
    data : 3D or 4D array
        Scalar (for 3D) and vector (for 4D) values to be extracted. For 4D
        data, interpolation will be done on the 3 spatial dimensions in each
        volume.

    streamlines : ndarray or list
        If array, of shape (n_streamlines, n_nodes, 3)
        If list, len(n_streamlines) with (n_nodes, 3) array in
        each element of the list.

    affine : ndarray, shape (4, 4)
        Affine transformation from voxels (image coordinates) to streamlines.
        Default: identity.

    threedvec : bool
        Whether the last dimension has length 3. This is a special case in
        which we can use :func:`vfu.interpolate_vector_3d` for the
        interploation of 4D volumes without looping over the elements of the
        last dimension.

    Return
    ------
    array or list (depending on the input) : values interpolate to each
        coordinate along the length of each streamline
    """
    data = data.astype(np.float)
    if (isinstance(streamlines, list) or
            isinstance(streamlines, types.GeneratorType)):
        if affine is not None:
            streamlines = ut.move_streamlines(streamlines,
                                              np.linalg.inv(affine))

        vals = []
        for sl in streamlines:
            if threedvec:
                vals.append(list(vfu.interpolate_vector_3d(data,
                                 sl.astype(np.float))[0]))
            else:
                vals.append(list(vfu.interpolate_scalar_3d(data,
                                 sl.astype(np.float))[0]))

    elif isinstance(streamlines, np.ndarray):
        sl_shape = streamlines.shape
        sl_cat = streamlines.reshape(sl_shape[0] *
                                     sl_shape[1], 3).astype(np.float)

        if affine is not None:
            inv_affine = np.linalg.inv(affine)
            sl_cat = (np.dot(sl_cat, inv_affine[:3, :3]) +
                      inv_affine[:3, 3])

        # So that we can index in one operation:
        if threedvec:
            vals = np.array(vfu.interpolate_vector_3d(data, sl_cat)[0])
        else:
            vals = np.array(vfu.interpolate_scalar_3d(data, sl_cat)[0])
        vals = np.reshape(vals, (sl_shape[0], sl_shape[1], -1))
        if vals.shape[-1] == 1:
            vals = np.reshape(vals, vals.shape[:-1])
    else:
        raise RuntimeError("Extracting values from a volume ",
                           "requires streamlines input as an array, ",
                           "a list of arrays, or a streamline generator.")

    return vals


def values_from_volume(data, streamlines, affine=None):
    """Extract values of a scalar/vector along each streamline from a volume.

    Parameters
    ----------
    data : 3D or 4D array
        Scalar (for 3D) and vector (for 4D) values to be extracted. For 4D
        data, interpolation will be done on the 3 spatial dimensions in each
        volume.

    streamlines : ndarray or list
        If array, of shape (n_streamlines, n_nodes, 3)
        If list, len(n_streamlines) with (n_nodes, 3) array in
        each element of the list.

    affine : ndarray, shape (4, 4)
        Affine transformation from voxels (image coordinates) to streamlines.
        Default: identity. For example, if no affine is provided and the first
        coordinate of the first streamline is ``[1, 0, 0]``, data[1, 0, 0]
        would be returned as the value for that streamline coordinate

    Return
    ------
    array or list (depending on the input) : values interpolate to each
        coordinate along the length of each streamline.

    Notes
    -----
    Values are extracted from the image based on the 3D coordinates of the
    nodes that comprise the points in the streamline, without any interpolation
    into segments between the nodes. Using this function with streamlines that
    have been resampled into a very small number of nodes will result in very
    few values.
    """
    data = np.asarray(data)
    if len(data.shape) == 4:
        if data.shape[-1] == 3:
            return _extract_vals(data, streamlines, affine=affine,
                                 threedvec=True)
        if isinstance(streamlines, types.GeneratorType):
            streamlines = list(streamlines)
        vals = []
        for ii in range(data.shape[-1]):
            vals.append(_extract_vals(data[..., ii], streamlines,
                        affine=affine))

        if isinstance(vals[-1], np.ndarray):
            return np.swapaxes(np.array(vals), 2, 1).T
        else:
            new_vals = []
            for sl_idx in range(len(streamlines)):
                sl_vals = []
                for ii in range(data.shape[-1]):
                    sl_vals.append(vals[ii][sl_idx])
                new_vals.append(np.array(sl_vals).T)
            return new_vals

    elif len(data.shape) == 3:
        return _extract_vals(data, streamlines, affine=affine)
    else:
        raise ValueError("Data needs to have 3 or 4 dimensions")
