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"""This module define core objects for profile tools.
"""

from __future__ import annotations

__authors__ = ["V.A. Sole", "T. Vincent", "P. Knobel", "H. Payno", "V. Valls"]
__license__ = "MIT"
__date__ = "17/04/2020"

import typing
import numpy
import weakref

from silx.image.bilinear import BilinearImage
from silx.gui import qt
from silx.gui import colors
import silx.gui.plot.items


class CurveProfileData(typing.NamedTuple):
    coords: numpy.ndarray
    profile: numpy.ndarray
    title: str
    xLabel: str
    yLabel: str


class RgbaProfileData(typing.NamedTuple):
    coords: numpy.ndarray
    profile: numpy.ndarray
    profile_r: numpy.ndarray
    profile_g: numpy.ndarray
    profile_b: numpy.ndarray
    profile_a: numpy.ndarray
    title: str
    xLabel: str
    yLabel: str


class ImageProfileData(typing.NamedTuple):
    coords: numpy.ndarray
    profile: numpy.ndarray
    title: str
    xLabel: str
    yLabel: str
    colormap: colors.Colormap


class CurveProfileDesc(typing.NamedTuple):
    profile: numpy.ndarray
    name: typing.Optional[str] = None
    color: typing.Optional[str] = None


class CurvesProfileData(typing.NamedTuple):
    coords: numpy.ndarray
    profiles: typing.List[CurveProfileDesc]
    title: str
    xLabel: str
    yLabel: str


class ProfileRoiMixIn:
    """Base mix-in for ROI which can be used to select a profile.

    This mix-in have to be applied to a :class:`~silx.gui.plot.items.roi.RegionOfInterest`
    in order to be usable by a :class:`~silx.gui.plot.tools.profile.manager.ProfileManager`.
    """

    ITEM_KIND = None
    """Define the plot item which can be used with this profile ROI"""

    sigProfilePropertyChanged = qt.Signal()
    """Emitted when a property of this profile have changed"""

    sigPlotItemChanged = qt.Signal()
    """Emitted when the plot item linked to this profile have changed"""

    def __init__(self, parent=None):
        self.__profileWindow = None
        self.__profileManager = None
        self.__plotItem = None
        self.setName("Profile")
        self.setEditable(True)
        self.setSelectable(True)

    def invalidateProfile(self):
        """Must be called by the implementation when the profile have to be
        recomputed."""
        profileManager = self.getProfileManager()
        if profileManager is not None:
            profileManager.requestUpdateProfile(self)

    def invalidateProperties(self):
        """Must be called when a property of the profile have changed."""
        self.sigProfilePropertyChanged.emit()

    def _setPlotItem(self, plotItem):
        """Specify the plot item to use with this profile

        :param `~silx.gui.plot.items.Item` plotItem: A plot item
        """
        previousPlotItem = self.getPlotItem()
        if previousPlotItem is plotItem:
            return
        if plotItem is None:
            self.__plotItem = None
        else:
            self.__plotItem = weakref.ref(plotItem)
        self.sigPlotItemChanged.emit()

    def getPlotItem(self):
        """Returns the plot item used by this profile

        :rtype: `~silx.gui.plot.items.Item`
        """
        if self.__plotItem is None:
            return None
        plotItem = self.__plotItem()
        if plotItem is None:
            self.__plotItem = None
        return plotItem

    def _setProfileManager(self, profileManager):
        self.__profileManager = profileManager

    def getProfileManager(self):
        """
        Returns the profile manager connected to this ROI.

        :rtype: ~silx.gui.plot.tools.profile.manager.ProfileManager
        """
        return self.__profileManager

    def getProfileWindow(self):
        """
        Returns the windows associated to this ROI, else None.

        :rtype: ProfileWindow
        """
        return self.__profileWindow

    def setProfileWindow(self, profileWindow):
        """
        Associate a window to this ROI. Can be None.

        :param ProfileWindow profileWindow: A main window
            to display the profile.
        """
        if profileWindow is self.__profileWindow:
            return
        if self.__profileWindow is not None:
            self.__profileWindow.sigClose.disconnect(self.__profileWindowAboutToClose)
            self.__profileWindow.setRoiProfile(None)
        self.__profileWindow = profileWindow
        if self.__profileWindow is not None:
            self.__profileWindow.sigClose.connect(self.__profileWindowAboutToClose)
            self.__profileWindow.setRoiProfile(self)

    def __profileWindowAboutToClose(self):
        profileManager = self.getProfileManager()
        roiManager = profileManager.getRoiManager()
        try:
            roiManager.removeRoi(self)
        except ValueError:
            pass

    def computeProfile(
        self, item: silx.gui.plot.items.Item
    ) -> typing.Union[
        CurveProfileData, ImageProfileData, RgbaProfileData, CurvesProfileData
    ]:
        """
        Compute the profile which will be displayed.

        This method is not called from the main Qt thread, but from a thread
        pool.

        :param item: A plot item
        """
        raise NotImplementedError()


def _alignedFullProfile(data, origin, scale, position, roiWidth, axis, method):
    """Get a profile along one axis on a stack of images

    :param numpy.ndarray data: 3D volume (stack of 2D images)
        The first dimension is the image index.
    :param origin: Origin of image in plot (ox, oy)
    :param scale: Scale of image in plot (sx, sy)
    :param float position: Position of profile line in plot coords
                           on the axis orthogonal to the profile direction.
    :param int roiWidth: Width of the profile in image pixels.
    :param int axis: 0 for horizontal profile, 1 for vertical.
    :param str method: method to compute the profile. Can be 'mean' or 'sum' or
        'none'
    :return: profile image + effective ROI area corners in plot coords
    """
    assert axis in (0, 1)
    assert len(data.shape) == 3
    assert method in ("mean", "sum", "none")

    # Convert from plot to image coords
    imgPos = int((position - origin[1 - axis]) / scale[1 - axis])

    if axis == 1:  # Vertical profile
        # Transpose image to always do a horizontal profile
        data = numpy.transpose(data, (0, 2, 1))

    nimages, height, width = data.shape

    roiWidth = min(height, roiWidth)  # Clip roi width to image size

    # Get [start, end[ coords of the roi in the data
    start = int(int(imgPos) + 0.5 - roiWidth / 2.0)
    start = min(max(0, start), height - roiWidth)
    end = start + roiWidth

    if method == "none":
        profile = None
    else:
        if start < height and end > 0:
            if method == "mean":
                fct = numpy.mean
            elif method == "sum":
                fct = numpy.sum
            else:
                raise ValueError("method not managed")
            profile = fct(data[:, max(0, start) : min(end, height), :], axis=1).astype(
                numpy.float32
            )
        else:
            profile = numpy.zeros((nimages, width), dtype=numpy.float32)

    # Compute effective ROI in plot coords
    profileBounds = (
        numpy.array((0, width, width, 0), dtype=numpy.float32) * scale[axis]
        + origin[axis]
    )
    roiBounds = (
        numpy.array((start, start, end, end), dtype=numpy.float32) * scale[1 - axis]
        + origin[1 - axis]
    )

    if axis == 0:  # Horizontal profile
        area = profileBounds, roiBounds
    else:  # vertical profile
        area = roiBounds, profileBounds

    return profile, area


def _alignedPartialProfile(data, rowRange, colRange, axis, method):
    """Mean of a rectangular region (ROI) of a stack of images
    along a given axis.

    Returned values and all parameters are in image coordinates.

    :param numpy.ndarray data: 3D volume (stack of 2D images)
        The first dimension is the image index.
    :param rowRange: [min, max[ of ROI rows (upper bound excluded).
    :type rowRange: 2-tuple of int (min, max) with min < max
    :param colRange: [min, max[ of ROI columns (upper bound excluded).
    :type colRange: 2-tuple of int (min, max) with min < max
    :param int axis: The axis along which to take the profile of the ROI.
                     0: Sum rows along columns.
                     1: Sum columns along rows.
    :param str method: method to compute the profile. Can be 'mean' or 'sum'
    :return: Profile image along the ROI as the mean of the intersection
             of the ROI and the image.
    """
    assert axis in (0, 1)
    assert len(data.shape) == 3
    assert rowRange[0] < rowRange[1]
    assert colRange[0] < colRange[1]
    assert method in ("mean", "sum")

    nimages, height, width = data.shape

    # Range aligned with the integration direction
    profileRange = colRange if axis == 0 else rowRange

    profileLength = abs(profileRange[1] - profileRange[0])

    # Subset of the image to use as intersection of ROI and image
    rowStart = min(max(0, rowRange[0]), height)
    rowEnd = min(max(0, rowRange[1]), height)
    colStart = min(max(0, colRange[0]), width)
    colEnd = min(max(0, colRange[1]), width)

    if method == "mean":
        _fct = numpy.mean
    elif method == "sum":
        _fct = numpy.sum
    else:
        raise ValueError("method not managed")

    imgProfile = _fct(
        data[:, rowStart:rowEnd, colStart:colEnd], axis=axis + 1, dtype=numpy.float32
    )

    # Profile including out of bound area
    profile = numpy.zeros((nimages, profileLength), dtype=numpy.float32)

    # Place imgProfile in full profile
    offset = -min(0, profileRange[0])
    profile[:, offset : offset + imgProfile.shape[1]] = imgProfile

    return profile


def createProfile(roiInfo, currentData, origin, scale, lineWidth, method):
    """Create the profile line for the the given image.

    :param roiInfo: information about the ROI: start point, end point and
        type ("X", "Y", "D")
    :param numpy.ndarray currentData: the 2D image or the 3D stack of images
        on which we compute the profile.
    :param origin: (ox, oy) the offset from origin
    :type origin: 2-tuple of float
    :param scale: (sx, sy) the scale to use
    :type scale: 2-tuple of float
    :param int lineWidth: width of the profile line
    :param str method: method to compute the profile. Can be 'mean' or 'sum'
        or 'none': to compute everything except the profile
    :return: `coords, profile, area, profileName, xLabel`, where:
        - coords is the X coordinate to use to display the profile
        - profile is a 2D array of the profiles of the stack of images.
          For a single image, the profile is a curve, so this parameter
          has a shape *(1, len(curve))*
        - area is a tuple of two 1D arrays with 4 values each. They represent
          the effective ROI area corners in plot coords.
        - profileName is a string describing the ROI, meant to be used as
          title of the profile plot
        - xLabel the label for X in the profile window

    :rtype: tuple(ndarray,ndarray,(ndarray,ndarray),str)
    """
    if currentData is None or roiInfo is None or lineWidth is None:
        raise ValueError("createProfile called with invalide arguments")

    # force 3D data (stack of images)
    if len(currentData.shape) == 2:
        currentData3D = currentData.reshape((1,) + currentData.shape)
    elif len(currentData.shape) == 3:
        currentData3D = currentData

    roiWidth = max(1, lineWidth)
    roiStart, roiEnd, lineProjectionMode = roiInfo

    if lineProjectionMode == "X":  # Horizontal profile on the whole image
        profile, area = _alignedFullProfile(
            currentData3D, origin, scale, roiStart[1], roiWidth, axis=0, method=method
        )

        if method == "none":
            coords = None
        else:
            coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
            coords = coords * scale[0] + origin[0]

        yMin, yMax = min(area[1]), max(area[1]) - 1
        if roiWidth <= 1:
            profileName = "{ylabel} = %g" % yMin
        else:
            profileName = "{ylabel} = [%g, %g]" % (yMin, yMax)
        xLabel = "{xlabel}"

    elif lineProjectionMode == "Y":  # Vertical profile on the whole image
        profile, area = _alignedFullProfile(
            currentData3D, origin, scale, roiStart[0], roiWidth, axis=1, method=method
        )

        if method == "none":
            coords = None
        else:
            coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
            coords = coords * scale[1] + origin[1]

        xMin, xMax = min(area[0]), max(area[0]) - 1
        if roiWidth <= 1:
            profileName = "{xlabel} = %g" % xMin
        else:
            profileName = "{xlabel} = [%g, %g]" % (xMin, xMax)
        xLabel = "{ylabel}"

    else:  # Free line profile
        # Convert start and end points in image coords as (row, col)
        startPt = (
            (roiStart[1] - origin[1]) / scale[1],
            (roiStart[0] - origin[0]) / scale[0],
        )
        endPt = ((roiEnd[1] - origin[1]) / scale[1], (roiEnd[0] - origin[0]) / scale[0])

        if int(startPt[0]) == int(endPt[0]) or int(startPt[1]) == int(endPt[1]):
            # Profile is aligned with one of the axes

            # Convert to int
            startPt = int(startPt[0]), int(startPt[1])
            endPt = int(endPt[0]), int(endPt[1])

            # Ensure startPt <= endPt
            if startPt[0] > endPt[0] or startPt[1] > endPt[1]:
                startPt, endPt = endPt, startPt

            if startPt[0] == endPt[0]:  # Row aligned
                rowRange = (
                    int(startPt[0] + 0.5 - 0.5 * roiWidth),
                    int(startPt[0] + 0.5 + 0.5 * roiWidth),
                )
                colRange = startPt[1], endPt[1] + 1
                if method == "none":
                    profile = None
                else:
                    profile = _alignedPartialProfile(
                        currentData3D, rowRange, colRange, axis=0, method=method
                    )

            else:  # Column aligned
                rowRange = startPt[0], endPt[0] + 1
                colRange = (
                    int(startPt[1] + 0.5 - 0.5 * roiWidth),
                    int(startPt[1] + 0.5 + 0.5 * roiWidth),
                )
                if method == "none":
                    profile = None
                else:
                    profile = _alignedPartialProfile(
                        currentData3D, rowRange, colRange, axis=1, method=method
                    )
            # Convert ranges to plot coords to draw ROI area
            area = (
                numpy.array(
                    (colRange[0], colRange[1], colRange[1], colRange[0]),
                    dtype=numpy.float32,
                )
                * scale[0]
                + origin[0],
                numpy.array(
                    (rowRange[0], rowRange[0], rowRange[1], rowRange[1]),
                    dtype=numpy.float32,
                )
                * scale[1]
                + origin[1],
            )

        else:  # General case: use bilinear interpolation
            # Ensure startPt <= endPt
            if startPt[1] > endPt[1] or (
                startPt[1] == endPt[1] and startPt[0] > endPt[0]
            ):
                startPt, endPt = endPt, startPt

            if method == "none":
                profile = None
            else:
                profile = []
                for slice_idx in range(currentData3D.shape[0]):
                    bilinear = BilinearImage(currentData3D[slice_idx, :, :])

                    profile.append(
                        bilinear.profile_line(
                            (startPt[0] - 0.5, startPt[1] - 0.5),
                            (endPt[0] - 0.5, endPt[1] - 0.5),
                            roiWidth,
                            method=method,
                        )
                    )
                profile = numpy.array(profile)

            # Extend ROI with half a pixel on each end, and
            # Convert back to plot coords (x, y)
            length = numpy.sqrt(
                (endPt[0] - startPt[0]) ** 2 + (endPt[1] - startPt[1]) ** 2
            )
            dRow = (endPt[0] - startPt[0]) / length
            dCol = (endPt[1] - startPt[1]) / length

            # Extend ROI with half a pixel on each end
            roiStartPt = startPt[0] - 0.5 * dRow, startPt[1] - 0.5 * dCol
            roiEndPt = endPt[0] + 0.5 * dRow, endPt[1] + 0.5 * dCol

            # Rotate deltas by 90 degrees to apply line width
            dRow, dCol = dCol, -dRow

            area = (
                numpy.array(
                    (
                        roiStartPt[1] - 0.5 * roiWidth * dCol,
                        roiStartPt[1] + 0.5 * roiWidth * dCol,
                        roiEndPt[1] + 0.5 * roiWidth * dCol,
                        roiEndPt[1] - 0.5 * roiWidth * dCol,
                    ),
                    dtype=numpy.float32,
                )
                * scale[0]
                + origin[0],
                numpy.array(
                    (
                        roiStartPt[0] - 0.5 * roiWidth * dRow,
                        roiStartPt[0] + 0.5 * roiWidth * dRow,
                        roiEndPt[0] + 0.5 * roiWidth * dRow,
                        roiEndPt[0] - 0.5 * roiWidth * dRow,
                    ),
                    dtype=numpy.float32,
                )
                * scale[1]
                + origin[1],
            )

        # Convert start and end points back to plot coords
        y0 = startPt[0] * scale[1] + origin[1]
        x0 = startPt[1] * scale[0] + origin[0]
        y1 = endPt[0] * scale[1] + origin[1]
        x1 = endPt[1] * scale[0] + origin[0]

        if startPt[1] == endPt[1]:
            profileName = "{xlabel} = %g; {ylabel} = [%g, %g]" % (x0, y0, y1)
            if method == "none":
                coords = None
            else:
                coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
                coords = coords * scale[1] + y0
            xLabel = "{ylabel}"

        elif startPt[0] == endPt[0]:
            profileName = "{ylabel} = %g; {xlabel} = [%g, %g]" % (y0, x0, x1)
            if method == "none":
                coords = None
            else:
                coords = numpy.arange(len(profile[0]), dtype=numpy.float32)
                coords = coords * scale[0] + x0
            xLabel = "{xlabel}"

        else:
            m = (y1 - y0) / (x1 - x0)
            b = y0 - m * x0
            profileName = "{ylabel} = %g * {xlabel} %+g" % (m, b)
            if method == "none":
                coords = None
            else:
                coords = numpy.linspace(
                    x0, x1, len(profile[0]), endpoint=True, dtype=numpy.float32
                )
            xLabel = "{xlabel}"

    return coords, profile, area, profileName, xLabel
