"""
This module contains some not fully intuitive algorithm for splitting masked area.
They are designed for measurements, but should be also available as segmentation algorithm to
allow user to preview, for better intuition how it works and which parameters are good for their purpose.

Better option is to implement this utils as class based with base class :py:class:`AlgorithmDescribeBase`.
Then do not need to manage algorithm parameters in places where it is used.

Both class from this module are designed for spherical mask, but may be useful als for others.
"""
import typing

import numpy as np
import SimpleITK
from pydantic import Field
from scipy.ndimage import distance_transform_edt

from PartSegCore.algorithm_describe_base import AlgorithmDescribeBase
from PartSegCore.universal_const import UNIT_SCALE, Units
from PartSegCore.utils import BaseModel


class BorderRimParameters(BaseModel):
    distance: float = Field(500, ge=0, le=10**6)
    units: Units = Units.nm


class BorderRim(AlgorithmDescribeBase):
    """
    This class implement border rim (Annulus like) calculation.
    For given mask and image spacing it marks as 1 all pixels in given distance from mask border.

    https://en.wikipedia.org/wiki/Annulus_(mathematics)

    The algorithm is:

    1. For each image voxel calculate distance from background (0 labeled voxels in mask) with respect of voxel size
    2. Select these voxels which are closer than given distance.
    """

    __argument_class__ = BorderRimParameters

    @classmethod
    def get_name(cls) -> str:
        return "Border Rim"

    @staticmethod
    def border_mask(mask: np.ndarray, distance: float, units: Units, voxel_size, **_) -> typing.Optional[np.ndarray]:
        """
        This is function which implement calculation.

        :param mask: area for which rim should be calculated. 2d or 3d numpy array,
        :param distance: distance from border which will be marked.
        :param units: in which unit distance is given
        :param voxel_size: Image spacing in absolute units
        :param _: ignored arguments
        :return: border rim marked with 1
        """
        if mask is None:
            return None
        units_scalar = UNIT_SCALE[units.value]
        final_radius = [int((distance / units_scalar) / x) for x in reversed(voxel_size)]
        mask = np.array(mask > 0)
        mask = mask.astype(np.uint8)
        eroded = SimpleITK.GetArrayFromImage(
            SimpleITK.BinaryErode(SimpleITK.GetImageFromArray(mask.squeeze()), final_radius)
        )
        eroded = eroded.reshape(mask.shape)
        mask[eroded > 0] = 0
        return mask


class MaskDistanceSplitParameters(BaseModel):
    num_of_parts: int = Field(2, title="Number of Parts", ge=1, le=1024)
    equal_volume: bool = Field(
        False,
        title="Equal Volume",
        description="If split should be done in respect of parts volume of parts thickness.",
    )


class MaskDistanceSplit(AlgorithmDescribeBase):
    """
    This class contains implementation of splitting mask on parts based on distance from borders.
    Has two modes or working. It may split on parts with same thickness or same volume..

    Flow chart of algorithm:

    .. graphviz::

       digraph model {
          "Mask" -> "Voxel distance from background";
          "Voxel distance from background" -> "maximum distance"[label=<Equal thickness>]
          "maximum distance" -> "Bounds" -> "Split voxels in respect of bounds"
          "Bounds"[label=<Bounds<br />
          <FONT POINT-SIZE="10">[0, (maximum distance)/(Number of Parts),<br />
          2*(maximum distance)/(Number of Parts)<br />, ... ,maximum distance]</FONT>>]
          "Voxel distance from background" -> "Distance histogram"[label=<Equal volume>]
          "Distance histogram" -> "Create bounds base on histogram" -> "Split voxels in respect of bounds"
          "Create bounds base on histogram"[label=<Create bounds base on histogram<br />
          <FONT POINT-SIZE="10">using histogram bins approximate equal number of voxels in each part</FONT>>]
          "Distance histogram"[label=<Distance histogram<br /><font point-size="10">of voxels with positive distance
          <br /> from background </font>>]

       }

    """

    __argument_class__ = MaskDistanceSplitParameters

    @classmethod
    def get_name(cls) -> str:
        return "Mask Distance Split"

    @staticmethod
    def split(mask: np.ndarray, num_of_parts: int, equal_volume: bool, voxel_size, **_):
        """
        This is function which implement calculation.

        :param mask: area for which rim should be calculated. 2d or 3d numpy array
        :param num_of_parts: num of parts on which mask should be split
        :param equal_volume: if split should be on equal volume or equal thick
        :param voxel_size: image voxel size
        :return: mask region labelled starting from 1 near border
        """
        if len(voxel_size) == 2 and mask.ndim == 3:
            voxel_size = (1, *voxel_size)
        distance_arr = distance_transform_edt(mask, sampling=voxel_size)
        if equal_volume:
            # TODO add more bins, fix tests for more bins
            hist, bins = np.histogram(distance_arr[distance_arr > 0], bins=10 * num_of_parts)
            total = np.sum(hist)
            levels, step = np.linspace(0, total, num_of_parts + 1, True, retstep=True)
            bounds = [0]
            i = 1
            cum_sum = 0
            for val, begin, end in zip(hist, bins, bins[1:]):
                cum_sum += val
                if cum_sum > levels[i]:
                    exceed = (cum_sum - levels[i]) / step
                    bounds.append(begin + (end - begin) * exceed)
                    i += 1
        else:
            max_dist = np.max(distance_arr)
            bounds = np.linspace(0, max_dist, num_of_parts, False)
        mask = np.zeros(mask.shape, dtype=np.uint8 if num_of_parts < 255 else np.uint16)
        for bound in bounds:
            mask[distance_arr > bound] += 1
        return mask
