# Source code for pymunk.autogeometry

    """This module contain functions for automatic generation of geometry, for
    example from an image.

    Example::

        >>> import pymunk
        >>> from pymunk.autogeometry import march_soft
        >>> img = [
        ...     "  xx   ",
        ...     "  xx   ",
        ...     "  xx   ",
        ...     "  xx   ",
        ...     "  xx   ",
        ...     "  xxxxx",
        ...     "  xxxxx",
        ... ]
        >>> segments = []

        >>> def segment_func(v0, v1):
        ...     segments.append((tuple(v0), tuple(v1)))
        >>> def sample_func(point):
        ...     x = int(point.x)
        ...     y = int(point.y)
        ...     return 1 if img[y][x] == "x" else 0

        >>> march_soft(pymunk.BB(0,0,6,6), 7, 7, .5, segment_func, sample_func)
        >>> print(len(segments))
        13

    The information in segments can now be used to create geometry, for example as
    a Pymunk Poly or Segment::

        >>> s = pymunk.Space()
        >>> for (a,b) in segments:
        ...     segment = pymunk.Segment(s.static_body, a, b, 5)
        ...     s.add(segment)

    """
    __docformat__ = "reStructuredText"

    __all__ = ["is_closed", "simplify_curves", "simplify_vertexes",
        "to_convex_hull", "convex_decomposition", "PolylineSet", "march_soft",
        "march_hard"]

    import collections

    from ._chipmunk_cffi import lib, ffi
    from .vec2d import Vec2d
    from .bb import BB

    def _to_chipmunk(polyline):
        l = len(polyline)
        _line = ffi.new("cpPolyline *", {"verts": l})
        _line.count = l
        _line.capacity = l
        _line.verts = list(map(tuple, polyline))
        return _line

    def _from_polyline_set(_set):
        lines = []
        for i in range(_set.count):
            line = []
            for j in range(_set.lines[i].count):
                line.append(Vec2d._fromcffi(_set.lines[i].verts[j]))
            lines.append(line)
        return lines

    def is_closed(polyline):
        """Returns true if the first vertex is equal to the last.

        :param polyline: Polyline to simplify.
        :type polyline: [(float,float)]
        :rtype: `bool`
        """
        return bool(lib.cpPolylineIsClosed(_to_chipmunk(polyline)))

    def simplify_curves(polyline, tolerance):
        """Returns a copy of a polyline simplified by using the Douglas-Peucker
        algorithm.

        This works very well on smooth or gently curved shapes, but not well on
        straight edged or angular shapes.

        :param polyline: Polyline to simplify.
        :type polyline: [(float,float)]
        :param float tolerance: A higher value means more error is tolerated.
        :rtype: [(float,float)]
        """

        _line = lib.cpPolylineSimplifyCurves(_to_chipmunk(polyline), tolerance)
        simplified = []
        for i in range(_line.count):
            simplified.append(Vec2d._fromcffi(_line.verts[i]))
        return simplified

    def simplify_vertexes(polyline, tolerance):
        """Returns a copy of a polyline simplified by discarding "flat" vertexes.

        This works well on straigt edged or angular shapes, not as well on smooth
        shapes.

        :param polyline: Polyline to simplify.
        :type polyline: [(float,float)]
        :param float tolerance: A higher value means more error is tolerated.
        :rtype: [(float,float)]
        """
        _line = lib.cpPolylineSimplifyVertexes(_to_chipmunk(polyline), tolerance)
        simplified = []
        for i in range(_line.count):
            simplified.append(Vec2d._fromcffi(_line.verts[i]))
        return simplified

    def to_convex_hull(polyline, tolerance):
        """Get the convex hull of a polyline as a looped polyline.

        :param polyline: Polyline to simplify.
        :type polyline: [(float,float)]
        :param float tolerance: A higher value means more error is tolerated.
        :rtype: [(float,float)]
        """
        _line = lib.cpPolylineToConvexHull(_to_chipmunk(polyline), tolerance)
        hull = []
        for i in range(_line.count):
            hull.append(Vec2d._fromcffi(_line.verts[i]))
        return hull

    def convex_decomposition(polyline, tolerance):
        """Get an approximate convex decomposition from a polyline.

        Returns a list of convex hulls that match the original shape to within
        tolerance.

        .. note::
            If the input is a self intersecting polygon, the output might end up
            overly simplified.

        :param polyline: Polyline to simplify.
        :type polyline: [(float,float)]
        :param float tolerance: A higher value means more error is tolerated.
        :rtype: [(float,float)]
        """
        _line = _to_chipmunk(polyline)
        _set = lib.cpPolylineConvexDecomposition(_line, tolerance)
        return _from_polyline_set(_set)

    class PolylineSet(collections.Sequence):
        """A set of Polylines.

        Mainly intended to be used for its :py:meth:`collect_segment` function
        when generating geometry with the :py:func:`march_soft` and
        :py:func:`march_hard` functions.
        """
        def __init__(self):
            def free(_set):
                lib.cpPolylineSetFree(_set, True)
            self._set = ffi.gc(lib.cpPolylineSetNew(), free)

        def collect_segment(self, v0, v1):
            """Add a line segment to a polyline set.

            A segment will either start a new polyline, join two others, or add to
            or loop an existing polyline. This is mostly intended to be used as a
            callback directly from :py:func:`march_soft` or :py:func:`march_hard`.

            :param v0: Start of segment
            :type v0: (float,float)
            :param v1: End of segment
            :type v1: (float,float)
            """
            lib.cpPolylineSetCollectSegment(tuple(v0), tuple(v1), self._set)

        def __len__(self):
            return self._set.count

        def __getitem__(self, key):
            if key >= self._set.count:
                raise IndexError
            line = []
            for i in range(self._set.lines[key].count):
                line.append(Vec2d._fromcffi(self._set.lines[key].verts[i]))
            return line

    def march_soft(bb, x_samples, y_samples, threshold, segment_func, sample_func):
        """Trace an *anti-aliased* contour of an image along a particular threshold.

        The given number of samples will be taken and spread across the bounding
        box area using the sampling function and context.

        :param BB bb: Bounding box of the area to sample within
        :param int x_samples: Number of samples in x
        :param int y_samples: Number of samples in y
        :param float threshold: A higher value means more error is tolerated
        :param segment_func: The segment function will be called for each segment
            detected that lies along the density contour for threshold.
        :type segment_func: ``func(v0 : Vec2d, v1 : Vec2d)``
        :param sample_func: The sample function will be called for
            x_samples * y_samples spread across the bounding box area, and should
            return a float.
        :type sample_func: ``func(point: Vec2d) -> float``
        """

        @ffi.callback("typedef void (*cpMarchSegmentFunc)"
            "(cpVect v0, cpVect v1, void *data)")
        def _seg_f(v0, v1, _data):
            segment_func(Vec2d._fromcffi(v0), Vec2d._fromcffi(v1))

        @ffi.callback("typedef cpFloat (*cpMarchSampleFunc)"
            "(cpVect point, void *data)")
        def _sam_f(point, _data):
            return sample_func(Vec2d._fromcffi(point))

        lib.cpMarchSoft(bb._bb[0], x_samples, y_samples, threshold,
            _seg_f, ffi.NULL, _sam_f, ffi.NULL)

    def march_hard(bb, x_samples, y_samples, threshold, segment_func, sample_func):
        """Trace an *aliased* curve of an image along a particular threshold.

        The given number of samples will be taken and spread across the bounding
        box area using the sampling function and context.

        :param BB bb: Bounding box of the area to sample within
        :param int x_samples: Number of samples in x
        :param int y_samples: Number of samples in y
        :param float threshold: A higher value means more error is tolerated
        :param segment_func: The segment function will be called for each segment
            detected that lies along the density contour for threshold.
        :type segment_func: ``func(v0 : Vec2d, v1 : Vec2d)``
        :param sample_func: The sample function will be called for
            x_samples * y_samples spread across the bounding box area, and should
            return a float.
        :type sample_func: ``func(point: Vec2d) -> float``
        """

        @ffi.callback("typedef void (*cpMarchSegmentFunc)"
            "(cpVect v0, cpVect v1, void *data)")
        def _seg_f(v0, v1, _data):
            segment_func(Vec2d._fromcffi(v0), Vec2d._fromcffi(v1))

        @ffi.callback("typedef cpFloat (*cpMarchSampleFunc)"
            "(cpVect point, void *data)")
        def _sam_f(point, _data):
            return sample_func(Vec2d._fromcffi(point))

        lib.cpMarchHard(bb._bb[0], x_samples, y_samples, threshold,
            _seg_f, ffi.NULL, _sam_f, ffi.NULL)