# ----------------------------------------------------------------------------
# cocos2d
# Copyright (c) 2008-2012 Daniel Moisset, Ricardo Quesada, Rayentray Tappa,
# Lucio Torre
# Copyright (c) 2009-2023  Richard Jones, Claudio Canepa
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
#   * Redistributions of source code must retain the above copyright
#     notice, this list of conditions and the following disclaimer.
#   * Redistributions in binary form must reproduce the above copyright
#     notice, this list of conditions and the following disclaimer in
#     the documentation and/or other materials provided with the
#     distribution.
#   * Neither the name of cocos2d nor the names of its
#     contributors may be used to endorse or promote products
#     derived from this software without specific prior written
#     permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# ----------------------------------------------------------------------------

from __future__ import division, print_function, unicode_literals

__docformat__ = 'restructuredtext'

import operator as op
import math
import cocos.euclid as eu

# interfaces, abstract base clases ######################################

msg_abstract = "abstract method called, needs implementation"

# cshape reference interfase
class Cshape(object):
    """
    Represents an abstract geometric shape in the 2D space, and can
    answer questions about proximity or intersection with other shapes.

    Implementations are free to restrict the type of geometrical shapes
    that will accept, by example circles or axis aligned rectangles.
    """

    def overlaps(self, other):
        """
        Returns True if overlapping other, False otherwise

        :rtype: bool
        """
        raise NotImplementedError(msg_abstract)

    def distance(self, other):
        """
        Returns a float, distance from itself to other;

        Not necessarily  euclidean distance.
        It is distances between boundaries.

        :rtype: float
        """
        raise NotImplementedError(msg_abstract)

    def near_than(self, other, near_distance):
        """
        Returns a boolean, True if distance(self, other)<=near_distance

        :rtype: bool
        """
        raise NotImplementedError(msg_abstract)

    def touches_point(self, x, y):
        """
        Returns True if the point (x,y) overlaps the shape, False otherwise

        :rtype: bool
        """
        raise NotImplementedError(msg_abstract)

    def fits_in_box(self, packed_box):
        """
        Returns a boolean, True if the shape fully fits into the axis aligned
        rectangle defined by packed_box, False otherwise.

        :Parameters:
            `packed_box` : 4-tuple floats
                An axis aligned rectangle expressed as (minx, maxx, miny, maxy)
        :rtype: bool
        """
        raise NotImplementedError(msg_abstract)

    def minmax(self):
        """
        Returns the smallest axis aligned rectangle that contains all shape points.

        The rectangle is expressed as a 4-tuple of floats (minx, maxx, miny, maxy)
        Such a rectangle is also know as the Axis Aligned Bounding Box for shape;
        AABB for short.

        :rtype: 4-tuple of floats

        """
        raise NotImplementedError(msg_abstract)

    def copy(self):
        """
        Returns a copy of itself

        :rtype: Cshape
        """
        raise NotImplementedError(msg_abstract)


# collision manager interface
class CollisionManager(object):
    r"""
    Answers questions about proximity or collision with known objects.

    After instantiation or after calling its 'clear' method the instance
    don't knows any object.

    An object is made known to the CollisionManager instance by calling its
    'add' method with the object instance.

    Example questions are:

        - which known objects collides with <this object> ?
        - which known objects are near than 6.0 from <this object> ?

    Note that explicit objects in the question (call) don't need to be known to
    the collision manager answering the question.
    If the explicit object indeed is known, then it is omitted in the answer as a
    trivial case.

    There can be multiple CollisionManager instances in the same scope, and
    an object can be known to many collision managers at the same time.

    Objects that can be known or can be presented to a Collision Manager in
    a question must comply with:

        - obj has a member called cshape
        - obj.cshape supports the interface Cshape

    Such an object can be called 'a collidable' in the documentation, and when
    'obj' or 'other' is seen in the code you can assume it means collidable.

    While usually all collidables in a collision manager are of the same class
    (CircleShape or AARectShape) it is allowed to use both types into the same
    collision manager.

    The known objects collective for each CollisionManager instance is
    manipulated by calling the methods

        - clean() \: forgets all objects and empties internal data structures
        - add(obj) \: remember obj as a known object
        - remove_tricky(obj) \: forgets obj

    When objects are made known to a collision manager, internal data structures
    are updated based on the obj.cshape value at the 'add' moment.
    In particular, the obj.cshape indirectly tells where in the internal
    structures certain info will be stored.
    Later, the internal data structures are used to accelerate answers.

    This  means that modifying obj.cshape after an 'add' can produce a memory
    leak in the next 'remove_tricky', and that in the same situation some
    answers can be partially wrong.
    What type of wrong ? It can sometimes miss a collision with a know
    object that changed it cshape.

    It is user code responsibility to drive the know objects update when
    obj.cshape values changes.

    Common use patterns that are safe and efficient:

    When most of the known objects update cshape each frame

    You do::

        # updating collision info
        collision_manager.clear() # fast, no leaks even if changed cshapes
        for actor in moving_actors:
            collision_manager.add(actor)

        # game logic
        # do what you need, but defer changes in cshape to next block
        # by example
        for actor in moving_actors:
            actor.new_pos = actor.cshape.center + dt * vel
            #other logic that potentially needs collision info;
            #it will be accurate because you have not changed cshapes
            ...

        # update cshapes for next frame
        for actor in moving actors:
            actor.cshape.center = actor.new_pos

    Example actors for this case are player, enemies, soldiers.

    All of the known objects don't change Cshapes

        - At level start you add all objects
        - When an actor reaches end of life use 'remove_tricky' to make it not known, no problem because his cshape has not changed

    Examples actors for this case are food, coins, trees, rocks.
    """

    def add(self, obj):
        """
        Makes obj a know entity
        """
        raise NotImplementedError(msg_abstract)

    def remove_tricky(self, obj):
        """
        *(obj should have the same .cshape value that when added)*
        Makes collision manager forget about obj, thus no further query will
        return obj.
        obj is required to be a known object.
        """

    def clear(self):
        """
        Empties the known set
        """
        raise NotImplementedError(msg_abstract)

    def they_collide(self, obj1, obj2):
        """
        Returns a boolean, True if obj1 overlaps objs2
        obj1, obj2 are not required to be known objects
        """
        raise NotImplementedError(msg_abstract)

    def objs_colliding(self, obj):
        """
        Returns a container with known objects that overlaps obj,
        excluding obj itself
        obj is not required to be a known object
        """
        raise NotImplementedError(msg_abstract)

    def iter_colliding(self, obj):
        """
        A lazy iterator over objects colliding with obj, allows to spare some
        CPU when the loop processing the collisions breaks before exhausting
        the collisions.
        obj is not required to be a known object

        Usage::

            for other in collision_manager.iter_colliding(obj):
                # process event 'obj touches other'

        """
        raise NotImplementedError(msg_abstract)

    def any_near(self, obj, near_distance):
        """
        Returns None if no know object (except itself) is near than
        near_distance, else an arbitrary known object with distance
        less than near_distance
        obj is not required to be a known object
        """
        raise NotImplementedError(msg_abstract)


    def objs_near(self, obj, near_distance):
        """
        Returns a container with the objects known by collision manager that
        are at distance to obj less or equal than near_distance, excluding
        itself.
        Notice that it includes the ones colliding with obj.
        obj is not required to be a known object
        """
        raise NotImplementedError(msg_abstract)

    def objs_near_wdistance(self, obj, near_distance):
        """
        Returns a list with the (other, distance) pairs that with all the
        known objects at distance less or equal than near_distance to obj,
        except obj itself.
        Notice that it includes the ones colliding with obj.
        obj is not required to be a known object
        If the game logic wants the list ordered by ascending distances, use
        ranked_objs_near instead.
        """
        raise NotImplementedError(msg_abstract)

    def ranked_objs_near(self, obj, near_distance):
        """
        Same as objs_near_wdistance but the list is ordered in increasing distance
        obj is not required to be a known object
        """
        raise NotImplementedError(msg_abstract)

    def iter_all_collisions(self):
        """
        Iterator that exposes all collisions between known objects.
        At each step it will yield a pair (obj, other).
        If (obj1, obj2) is seen when consuming the iterator, then (obj2, obj1)
        will not be seen.
        In other worlds, 'obj1 collides with obj2' means (obj1, obj2) or
        (obj2, obj1) will appear in the iterator output but not both.
        """

    def knows(self, obj):
        """Returns True if obj was added to the collision manager, false otherwise
        Used for debug and testing.
        """
        raise NotImplementedError(msg_abstract)

    def known_objs(self):
        """Returns a set with all the objects known by the CollisionManager
        Used for debug and testing.
        """
        raise NotImplementedError(msg_abstract)

    def objs_touching_point(self, x, y):
        """Returns a container with known objects touching point (x, y)

        Useful for mouse pick
        """
        raise NotImplementedError(msg_abstract)

    def objs_into_box(self, minx, maxx, miny, maxy):
        """Returns a container with know objects that fully fits into the axis
        aligned rectangle defined by params

        Useful for elastic box selection
        """
        raise NotImplementedError(msg_abstract)


# Cshape implementations #################################################


class CircleShape(Cshape):
    """
    Implements the Cshape interface that uses discs as geometric shape.

    Distance is the euclidean distance.

    Look at Cshape for other class and methods documentation.
    """

    def __init__(self, center, r):
        """
        :Parameters:
            `center` : euclid.Vector2
                rectangle center
            `r` : float
                disc radius
        """
        self.center = center
        self.r = r

    def overlaps(self, other):
        if isinstance(other, CircleShape):
            return circle_overlaps_circle(self, other)
        elif isinstance(other, AARectShape):
            return aa_rect_overlaps_circle(other, self)
        raise NotImplementedError(
            "Collision between CircleShape and {0} is not implemented".format(other.__class__.__name__))

    def distance(self, other):
        if isinstance(other, CircleShape):
            return circle_distance_circle(self, other)
        elif isinstance(other, AARectShape):
            return aa_rect_distance_circle(other, self)
        raise NotImplementedError(
            "Distance between CircleShape and {0} is not implemented".format(other.__class__.__name__))

    def near_than(self, other, near_distance):
        return self.distance(other) <= near_distance

    def touches_point(self, x, y):
        return abs(self.center - (x, y)) <= self.r

    def fits_in_box(self, packed_box):
        r = self.r
        return (((packed_box[0] + r) <= self.center[0] <= (packed_box[1] - r)) and
                ((packed_box[2] + r) <= self.center[1] <= (packed_box[3] - r)))

    def minmax(self):
        r = self.r
        return (self.center[0] - r, self.center[0] + r,
                self.center[1] - r, self.center[1] + r)

    def copy(self):
        return CircleShape(eu.Vector2(*self.center), self.r)


class AARectShape(Cshape):
    """
    Implements the Cshape interface that uses rectangles with sides
    parallel to the coordinate axis as geometric shape.

    Distance is not the euclidean distance but the rectangular or max-min
    distance, max( min(x0 - x1), min(y0 - y1) : (xi, yi) in recti )

    Good if actors don't rotate.

    Look at Cshape for other class and methods documentation.
    """

    def __init__(self, center, half_width, half_height):
        """
        :Parameters:
            `center` : euclid.Vector2
                rectangle center
            `half_width` : float
                half width of rectangle
            `half_height` : float
                half height of rectangle
        """
        self.center = center
        self.rx = half_width
        self.ry = half_height

    def overlaps(self, other):
        if isinstance(other, AARectShape):
            return aa_rect_overlaps_aa_rect(self, other)
        elif isinstance(other, CircleShape):
            return aa_rect_overlaps_circle(self, other)
        raise NotImplementedError(
            "Collision between AARectShape and {0} is not implemented".format(other.__class__.__name__))

    def distance(self, other):
        if isinstance(other, AARectShape):
            return aa_rect_distance_aa_rect(self, other)
        elif isinstance(other, CircleShape):
            return aa_rect_distance_circle(self, other)
        raise NotImplementedError(
            "Distance between AARectShape and {0} is not implemented".format(other.__class__.__name__))

    def near_than(self, other, near_distance):
        return self.distance(other) <= near_distance

    def touches_point(self, x, y):
        return (abs(self.center[0] - x) < self.rx and
                abs(self.center[1] - y) < self.ry)

    def fits_in_box(self, packed_box):
        return ((packed_box[0] + self.rx <= self.center[0] <= packed_box[1] - self.rx) and
                (packed_box[2] + self.ry <= self.center[1] <= packed_box[3] - self.ry))

    def minmax(self):
        return (self.center[0] - self.rx, self.center[0] + self.rx,
                self.center[1] - self.ry, self.center[1] + self.ry)

    def copy(self):
        return AARectShape(eu.Vector2(*self.center), self.rx, self.ry)


def clamp(value, minimum, maximum):
        return max(min(value, maximum), minimum)


def aa_rect_overlaps_aa_rect(aa_rect, other):
    """
    Tells if two axis aligned rectangles overlap.

    The rects must have members 'center', 'rx', 'ry' where the latest two are
    the rect half_width and half_height.
    """
    return abs(aa_rect.center[0] - other.center[0]) < aa_rect.rx + other.rx and \
        abs(aa_rect.center[1] - other.center[1]) < aa_rect.ry + other.ry


def circle_overlaps_circle(circle, other):
    """
    Tells if two circles overlap.

    The circles must have members 'center', 'r', where the latest is the radius.
    """
    return (circle.center - other.center).magnitude_squared() < (circle.r + other.r) ** 2


def aa_rect_overlaps_circle(aa_rect, circle):
    """
    Tells if an axis aligned rectangle and a circle overlap.

    The rect must have members 'center', 'rx', 'ry' where the latest two are
    the rect half_width and half_height.
    The circle must have members 'center', 'r', where the latest is the radius.
    """
    d = circle.center - aa_rect.center
    # Point in the rect nearest to circle center.
    d_clamped = eu.Vector2(clamp(d.x, -aa_rect.rx, aa_rect.rx),
                           clamp(d.y, -aa_rect.ry, aa_rect.ry))

    return (d - d_clamped).magnitude_squared() < circle.r ** 2


def circle_distance_circle(circle, other):
    """
    Give the distance between two circles.

    The circles must have members 'center', 'r', where the latest is the radius.
    """
    d = abs(circle.center - other.center) - circle.r - other.r
    if d < 0.0:
        d = 0.0
    return d


def aa_rect_distance_circle(aa_rect, circle):
    """
    Give the distance between an axis-aligned rectangle and a circle.

    The rect must have members 'center', 'rx', 'ry' where the latest two are
    the rect half_width and half_height.
    The circle must have members 'center', 'r', where the latest is the radius.
    """
    d = circle.center - aa_rect.center
    # Point in the rect nearest to circle center.
    d_clamped = eu.Vector2(clamp(d.x, -aa_rect.rx, aa_rect.rx),
                           clamp(d.y, -aa_rect.ry, aa_rect.ry))

    d = abs(d - d_clamped) - circle.r
    if d < 0.0:
        d = 0.0
    return d


def aa_rect_distance_aa_rect(aa_rect, other):
    """
    Give the distance between two axis-aligned rectangles.

    The rect must have members 'center', 'rx', 'ry' where the latest two are
    the rect half_width and half_height.
    """
    d = max((abs(aa_rect.center[0] - other.center[0]) - aa_rect.rx - other.rx,
             abs(aa_rect.center[1] - other.center[1]) - aa_rect.ry - other.ry))
    if d < 0.0:
        d = 0.0
    return d

# CollisionManager implementations #######################################


class CollisionManagerBruteForce(CollisionManager):
    """
    Implements the CollisionManager interface with with the simpler code possible.

    Intended for reference and debugging, it has very bad performance.

    Look at CollisionManager for other class and methods documentation.
    """

    def __init__(self):
        self.objs = set()

    def add(self, obj):
        # ? use weakref ? python 2.7 has weakset
        self.objs.add(obj)

    def remove_tricky(self, obj):
        self.objs.remove(obj)

    def clear(self):
        self.objs.clear()

    def they_collide(self, obj1, obj2):
        return obj1.cshape.overlaps(obj2.cshape)

    def objs_colliding(self, obj):
        f_overlaps = obj.cshape.overlaps
        return [other for other in self.objs if
                (other is not obj) and f_overlaps(other.cshape)]

    def iter_colliding(self, obj):
        f_overlaps = obj.cshape.overlaps
        for other in self.objs:
            if other is not obj and f_overlaps(other.cshape):
                yield other

    def any_near(self, obj, near_distance):
        f_near_than = obj.cshape.near_than
        for other in self.objs:
            if other is not obj and f_near_than(other.cshape, near_distance):
                return other
        return None

    def objs_near(self, obj, near_distance):
        f_near_than = obj.cshape.near_than
        return [other for other in self.objs if
                (other is not obj) and f_near_than(other.cshape, near_distance)]

    def objs_near_wdistance(self, obj, near_distance):
        f_distance = obj.cshape.distance
        res = []
        for other in self.objs:
            if other is obj:
                continue
            d = f_distance(other.cshape)
            if d <= near_distance:
                res.append((other, d))
        return res

#   def objs_near_wdistance(self, obj, near_distance):
#       # alternative version, needs python 2.5+
#       f_distance = obj.cshape.distance
#       def f(other):
#       return other, f_distance(other.cshape)
#       import itertools as it
#       return [(other, d) for other,d in it.imap(f, self.objs) if
#                                                      (other is not obj) and
#                                                      (d <= near_distance)]
    def ranked_objs_near(self, obj, near_distance):
        tmp = self.objs_near_wdistance(obj, near_distance)
        tmp.sort(key=op.itemgetter(1))
        return tmp

    def iter_all_collisions(self):
        # O(n**2)
        for i, obj in enumerate(self.objs):
            f_overlaps = obj.cshape.overlaps
            for j, other in enumerate(self.objs):
                if j >= i:
                    break
                if f_overlaps(other.cshape):
                    yield (obj, other)

    def knows(self, obj):
        return obj in self.objs

    def known_objs(self):
        return self.objs

    def objs_touching_point(self, x, y):
        touching = set()
        for obj in self.objs:
            if obj.cshape.touches_point(x, y):
                touching.add(obj)
        return touching

    def objs_into_box(self, minx, maxx, miny, maxy):
        into = set()
        packed_box = minx, maxx, miny, maxy
        for obj in self.objs:
            if obj.cshape.fits_in_box(packed_box):
                into.add(obj)
        return into


class CollisionManagerGrid(CollisionManager):
    """
    Implements the CollisionManager interface based on the scheme
    known as spatial hashing.

    The idea behind is to divide the space in rectangles with a given width and
    height, and have a table telling which objects overlaps each rectangle.

    Later, when the question 'which know objects has such and such spatial
    relation with <some object>' arrives, only the objects in rectangles
    overlapping <some object> (or nearby ones) needs to be examined for the
    condition.

    Look at CollisionManager for other class and methods documentation.
    """

    def __init__(self, xmin, xmax, ymin, ymax, cell_width, cell_height):
        """
        Cell width and height have impact on performance.
        For objects with same with, and with width==height, a good value
        is 1.25 * (object width).
        For mixed widths, a good guess can be
        ~ 1.25 * { width(object): all objects not exceptionlly big}

        :Parameters:
            `xmin` : float
                minimum x coordinate for a point in world
            `xmax` : float
                maximum x coordinate for a point in world
            `ymin` : float
                minimum y coordinate for a point in world
            `ymax` : float
                maximum y coordinate for a point in world
            `cell_width` : float
                width for the rectangles the space will be broken
            `cell_height` : float
                height for the rectangles the space will be broken
        """
        self.xmin = xmin
        self.xmax = xmax
        self.ymin = ymin
        self.ymax = ymax
        self.cell_width = cell_width
        self.cell_height = cell_height

        cols = int(math.ceil((xmax - xmin) / cell_width))
        rows = int(math.ceil((ymax - ymin) / cell_height))
        self.cols = cols
        self.rows = rows
        numbuckets = cols * rows
        # buckets maps cell identifier -> objs that potentially overlaps the cell
        self.buckets = [set() for k in range(numbuckets)]

    def add(self, obj):
        # add to any bucket it overlaps
        # for the collision logic algorithm is fine if a number of buckets
        # that don't overlap are included; this allows to use a faster
        # 'buckets_for_objects' at the cost of potentially some extra buckets
        for cell_idx in self._iter_cells_for_aabb(obj.cshape.minmax()):
            self.buckets[cell_idx].add(obj)

    def remove_tricky(self, obj):
        for cell_idx in self._iter_cells_for_aabb(obj.cshape.minmax()):
            self.buckets[cell_idx].remove(obj)

    def clear(self):
        for bucket in self.buckets:
            bucket.clear()

    def they_collide(self, obj1, obj2):
        return obj1.cshape.overlaps(obj2.cshape)

    def objs_colliding(self, obj):
        aabb = obj.cshape.minmax()
        f_overlaps = obj.cshape.overlaps
        collides = set()
        collides.add(obj)
        # do brute force with others in all the buckets obj overlaps
        for cell_id in self._iter_cells_for_aabb(aabb):
            for other in self.buckets[cell_id]:
                if other not in collides and f_overlaps(other.cshape):
                    collides.add(other)
        collides.remove(obj)
        return collides

    def iter_colliding(self, obj):
        aabb = obj.cshape.minmax()
        f_overlaps = obj.cshape.overlaps
        collides = set()
        collides.add(obj)
        # do brute force with others in all the buckets obj overlaps
        for cell_id in self._iter_cells_for_aabb(aabb):
            for other in self.buckets[cell_id]:
                if (other not in collides) and f_overlaps(other.cshape):
                    collides.add(other)
                    yield other

    def any_near(self, obj, near_distance):
        minx, maxx, miny, maxy = obj.cshape.minmax()
        minx -= near_distance
        maxx += near_distance
        miny -= near_distance
        maxy += near_distance
        f_distance = obj.cshape.distance
        # do brute force with others in all the buckets inflated shape overlaps
        for cell_id in self._iter_cells_for_aabb((minx, maxx, miny, maxy)):
            for other in self.buckets[cell_id]:
                if other is not obj and f_distance(other.cshape) < near_distance:
                    return other
        return None

    def objs_near(self, obj, near_distance):
        minx, maxx, miny, maxy = obj.cshape.minmax()
        minx -= near_distance
        maxx += near_distance
        miny -= near_distance
        maxy += near_distance
        f_distance = obj.cshape.distance
        collides = set()
        # do brute force with others in all the buckets inflated shape overlaps
        for cell_id in self._iter_cells_for_aabb((minx, maxx, miny, maxy)):
            for other in self.buckets[cell_id]:
                if other not in collides and (f_distance(other.cshape) < near_distance):
                    collides.add(other)
        collides.discard(obj)
        return collides

    def objs_near_wdistance(self, obj, near_distance):
        minx, maxx, miny, maxy = obj.cshape.minmax()
        minx -= near_distance
        maxx += near_distance
        miny -= near_distance
        maxy += near_distance
        f_distance = obj.cshape.distance
        collides = {}
        collides[obj] = 0.0
        # do brute force with others in all the buckets inflated shape overlaps
        for cell_id in self._iter_cells_for_aabb((minx, maxx, miny, maxy)):
            for other in self.buckets[cell_id]:
                if other not in collides:
                    d = f_distance(other.cshape)
                    if d <= near_distance:
                        collides[other] = d
                        # yield (other, d)
        del collides[obj]
        return [(other, collides[other]) for other in collides]

    def ranked_objs_near(self, obj, near_distance):
        tmp = self.objs_near_wdistance(obj, near_distance)
        tmp.sort(key=op.itemgetter(1))
        return tmp

    def iter_all_collisions(self):
        # implemented using the fact: 'a collides b' iff (there is a bucket B
        # with a in B, b in B and 'a collides b')
        known_collisions = set()
        for bucket in self.buckets:
            for i, obj in enumerate(bucket):
                f_overlaps = obj.cshape.overlaps
                for j, other in enumerate(bucket):
                    if j >= i:
                        break
                    if f_overlaps(other.cshape):
                        if id(obj) < id(other):
                            coll_id = (id(obj), id(other))
                        else:
                            coll_id = (id(other), id(obj))
                        if coll_id not in known_collisions:
                            known_collisions.add(coll_id)
                            yield (obj, other)

    def knows(self, obj):
        for bucket in self.buckets:
            if obj in bucket:
                return True
        return False

    def known_objs(self):
        objs = set()
        for bucket in self.buckets:
            objs |= bucket
        return objs

    def objs_touching_point(self, x, y):
        touching = set()
        for cell_id in self._iter_cells_for_aabb((x, x, y, y)):
            for obj in self.buckets[cell_id]:
                if obj.cshape.touches_point(x, y):
                    touching.add(obj)
        return touching

    def objs_into_box(self, minx, maxx, miny, maxy):
        into = set()
        buckets = self.buckets
        packed_box = (minx, maxx, miny, maxy)
        for cell_idx in self._iter_cells_for_aabb(packed_box):
            for obj in buckets[cell_idx]:
                if (obj not in into) and (obj.cshape.fits_in_box(packed_box)):
                    into.add(obj)
        return into

    def _iter_cells_for_aabb(self, aabb):
        # iterate all buckets overlapping the rectangle minmax
        minx, maxx, miny, maxy = aabb
        ix_lo = int(math.floor((minx - self.xmin) / self.cell_width))
        ix_sup = int(math.ceil((maxx - self.xmin) / self.cell_width))
        iy_lo = int(math.floor((miny - self.ymin) / self.cell_height))
        iy_sup = int(math.ceil((maxy - self.ymin) / self.cell_height))

        # but disregard cells outside world, can come from near questions
        if ix_lo < 0:
            ix_lo = 0
        if ix_sup > self.cols:
            ix_sup = self.cols
        if iy_lo < 0:
            iy_lo = 0
        if iy_sup > self.rows:
            iy_sup = self.rows

        for iy in range(iy_lo, iy_sup):
            contrib_y = iy * self.cols
            for ix in range(ix_lo, ix_sup):
                cell_id = ix + contrib_y
                yield cell_id
