import numpy
import copy

from UM.Math.Polygon import Polygon


##  Polygon representation as an array for use with Arrange
class ShapeArray:
    def __init__(self, arr, offset_x, offset_y, scale = 1):
        self.arr = arr
        self.offset_x = offset_x
        self.offset_y = offset_y
        self.scale = scale

    ##  Instantiate from a bunch of vertices
    #   \param vertices
    #   \param scale  scale the coordinates
    @classmethod
    def fromPolygon(cls, vertices, scale = 1):
        # scale
        vertices = vertices * scale
        # flip y, x -> x, y
        flip_vertices = numpy.zeros((vertices.shape))
        flip_vertices[:, 0] = vertices[:, 1]
        flip_vertices[:, 1] = vertices[:, 0]
        flip_vertices = flip_vertices[::-1]
        # offset, we want that all coordinates have positive values
        offset_y = int(numpy.amin(flip_vertices[:, 0]))
        offset_x = int(numpy.amin(flip_vertices[:, 1]))
        flip_vertices[:, 0] = numpy.add(flip_vertices[:, 0], -offset_y)
        flip_vertices[:, 1] = numpy.add(flip_vertices[:, 1], -offset_x)
        shape = [int(numpy.amax(flip_vertices[:, 0])), int(numpy.amax(flip_vertices[:, 1]))]
        arr = cls.arrayFromPolygon(shape, flip_vertices)
        return cls(arr, offset_x, offset_y)

    ##  Instantiate an offset and hull ShapeArray from a scene node.
    #   \param node source node where the convex hull must be present
    #   \param min_offset offset for the offset ShapeArray
    #   \param scale scale the coordinates
    @classmethod
    def fromNode(cls, node, min_offset, scale = 0.5):
        transform = node._transformation
        transform_x = transform._data[0][3]
        transform_y = transform._data[2][3]
        hull_verts = node.callDecoration("getConvexHull")
        # For one_at_a_time printing you need the convex hull head.
        hull_head_verts = node.callDecoration("getConvexHullHead") or hull_verts

        offset_verts = hull_head_verts.getMinkowskiHull(Polygon.approximatedCircle(min_offset))
        offset_points = copy.deepcopy(offset_verts._points)  # x, y
        offset_points[:, 0] = numpy.add(offset_points[:, 0], -transform_x)
        offset_points[:, 1] = numpy.add(offset_points[:, 1], -transform_y)
        offset_shape_arr = ShapeArray.fromPolygon(offset_points, scale = scale)

        hull_points = copy.deepcopy(hull_verts._points)
        hull_points[:, 0] = numpy.add(hull_points[:, 0], -transform_x)
        hull_points[:, 1] = numpy.add(hull_points[:, 1], -transform_y)
        hull_shape_arr = ShapeArray.fromPolygon(hull_points, scale = scale)  # x, y

        return offset_shape_arr, hull_shape_arr

    ##  Create np.array with dimensions defined by shape
    #   Fills polygon defined by vertices with ones, all other values zero
    #   Only works correctly for convex hull vertices
    #   Originally from: http://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array
    #   \param shape  numpy format shape, [x-size, y-size]
    #   \param vertices
    @classmethod
    def arrayFromPolygon(cls, shape, vertices):
        base_array = numpy.zeros(shape, dtype=float)  # Initialize your array of zeros

        fill = numpy.ones(base_array.shape) * True  # Initialize boolean array defining shape fill

        # Create check array for each edge segment, combine into fill array
        for k in range(vertices.shape[0]):
            fill = numpy.all([fill, cls._check(vertices[k - 1], vertices[k], base_array)], axis=0)

        # Set all values inside polygon to one
        base_array[fill] = 1

        return base_array

    ##  Return indices that mark one side of the line, used by arrayFromPolygon
    #   Uses the line defined by p1 and p2 to check array of
    #   input indices against interpolated value
    #   Returns boolean array, with True inside and False outside of shape
    #   Originally from: http://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array
    #   \param p1 2-tuple with x, y for point 1
    #   \param p2 2-tuple with x, y for point 2
    #   \param base_array boolean array to project the line on
    @classmethod
    def _check(cls, p1, p2, base_array):
        if p1[0] == p2[0] and p1[1] == p2[1]:
            return
        idxs = numpy.indices(base_array.shape)  # Create 3D array of indices

        p1 = p1.astype(float)
        p2 = p2.astype(float)

        if p2[0] == p1[0]:
            sign = numpy.sign(p2[1] - p1[1])
            return idxs[1] * sign

        if p2[1] == p1[1]:
            sign = numpy.sign(p2[0] - p1[0])
            return idxs[1] * sign

        # Calculate max column idx for each row idx based on interpolated line between two points

        max_col_idx = (idxs[0] - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1]
        sign = numpy.sign(p2[0] - p1[0])
        return idxs[1] * sign <= max_col_idx * sign

