Query: python code for elastic collision of objects

Relevant code:
def query_collision(collision_object):
        """
        Check to see if the specified object is colliding with any of the objects currently in the Collision Manager
        Returns the first object we are colliding with if there was a collision and None if no collisions was found
        """
        global collidable_objects
        # Note that we use a Brute Force approach for the time being.
        # It performs horribly under heavy loads, but it meets
        # our needs for the time being.
        for obj in collidable_objects:
            # Make sure we don't check ourself against ourself.
            if obj.obj_id is not collision_object.obj_id:
                if collision_object.is_colliding(obj):
                    # A collision has been detected. Return the object that we are colliding with.
                    return obj

        # No collision was noticed. Return None.
        return None

Similar but not relevant code:
def check_player_collision(self):
        """Check to see if we are colliding with the player."""
        player_tiles = r.TileMapManager.active_map.grab_collisions(self.char.coords)
        enemy_tiles = r.TileMapManager.active_map.grab_collisions(self.coords)

        #Check to see if any of the tiles are the same. If so, there is a collision.
        for ptile in player_tiles:
            for etile in enemy_tiles:
                if r.TileMapManager.active_map.pixels_to_tiles(ptile.coords) == r.TileMapManager.active_map.pixels_to_tiles(etile.coords):
                    return True

        return False

Similar but not relevant code:
def connect(self, A, B, distance=1):
        """Add a link from A and B of given distance, and also add the inverse
        link if the graph is undirected."""
        self.connect1(A, B, distance)
        if not self.directed: self.connect1(B, A, distance)

Similar but not relevant code:
def compute_jaccard_index(x_set, y_set):
    """Return the Jaccard similarity coefficient of 2 given sets.

    Args:
        x_set (set): first set.
        y_set (set): second set.

    Returns:
        float: Jaccard similarity coefficient.

    """
    if not x_set or not y_set:
        return 0.0

    intersection_cardinal = len(x_set & y_set)
    union_cardinal = len(x_set | y_set)

    return intersection_cardinal / float(union_cardinal)

Similar but not relevant code:
def benchmark(store, n=10000):
    """
    Iterates over all of the referreds, and then iterates over all of the
    referrers that refer to each one.

    Fairly item instantiation heavy.
    """
    R = Referrer

    for referred in store.query(Referred):
        for _reference in store.query(R, R.reference == referred):
            pass

Similar but not relevant code:
def line_line_collide(line1, line2):
    """Determine if two line segments meet.

    This is a helper for :func:`convex_hull_collide` in the
    special case that the two convex hulls are actually
    just line segments. (Even in this case, this is only
    problematic if both segments are on a single line.)

    Args:
        line1 (numpy.ndarray): ``2 x 2`` array of start and end nodes.
        line2 (numpy.ndarray): ``2 x 2`` array of start and end nodes.

    Returns:
        bool: Indicating if the line segments collide.
    """
    s, t, success = segment_intersection(
        line1[:, 0], line1[:, 1], line2[:, 0], line2[:, 1]
    )
    if success:
        return _helpers.in_interval(s, 0.0, 1.0) and _helpers.in_interval(
            t, 0.0, 1.0
        )

    else:
        disjoint, _ = parallel_lines_parameters(
            line1[:, 0], line1[:, 1], line2[:, 0], line2[:, 1]
        )
        return not disjoint