"""
matplotlib includes a framework for arbitrary geometric
transformations that is used determine the final position of all
elements drawn on the canvas.

Transforms are composed into trees of :class:`TransformNode` objects
whose actual value depends on their children.  When the contents of
children change, their parents are automatically invalidated.  The
next time an invalidated transform is accessed, it is recomputed to
reflect those changes.  This invalidation/caching approach prevents
unnecessary recomputations of transforms, and contributes to better
interactive performance.

For example, here is a graph of the transform tree used to plot data
to the graph:

.. image:: ../_static/transforms.png

The framework can be used for both affine and non-affine
transformations.  However, for speed, we want use the backend
renderers to perform affine transformations whenever possible.
Therefore, it is possible to perform just the affine or non-affine
part of a transformation on a set of data.  The affine is always
assumed to occur after the non-affine.  For any transform::

  full transform == non-affine part + affine part

The backends are not expected to handle non-affine transformations
themselves.
"""

import numpy as np
from numpy import ma
from matplotlib._path import affine_transform
from numpy.linalg import inv

from weakref import WeakKeyDictionary
import warnings
import sets

import cbook
from path import Path
from _path import count_bboxes_overlapping_bbox, update_path_extents

DEBUG = False
if DEBUG:
    import warnings

MaskedArray = ma.MaskedArray

class TransformNode(object):
    """
    :class:`TransformNode` is the base class for anything that
    participates in the transform tree and needs to invalidate its
    parents or be invalidated.  This includes classes that are not
    really transforms, such as bounding boxes, since some transforms
    depend on bounding boxes to compute their values.
    """
    _gid = 0

    # Invalidation may affect only the affine part.  If the
    # invalidation was "affine-only", the _invalid member is set to
    # INVALID_AFFINE_ONLY
    INVALID_NON_AFFINE = 1
    INVALID_AFFINE     = 2
    INVALID            = INVALID_NON_AFFINE | INVALID_AFFINE

    # Some metadata about the transform, used to determine whether an
    # invalidation is affine-only
    is_affine = False
    is_bbox   = False

    # If pass_through is True, all ancestors will always be
    # invalidated, even if 'self' is already invalid.
    pass_through = False

    def __init__(self):
        """
        Creates a new TransformNode.
        """
        # Parents are stored in a WeakKeyDictionary, so that if the
        # parents are deleted, references from the children won't keep
        # them alive.
        self._parents = WeakKeyDictionary()

        # TransformNodes start out as invalid until their values are
        # computed for the first time.
        self._invalid = 1

    def __copy__(self, *args):
        raise NotImplementedError(
            "TransformNode instances can not be copied. " +
            "Consider using frozen() instead.")
    __deepcopy__ = __copy__

    def invalidate(self):
        """
        Invalidate this transform node and all of its ancestors.
        Should be called any time the transform changes.
        """
        # If we are an affine transform being changed, we can set the
        # flag to INVALID_AFFINE_ONLY
        value = (self.is_affine) and self.INVALID_AFFINE or self.INVALID

        # Shortcut: If self is already invalid, that means its parents
        # are as well, so we don't need to do anything.
        if self._invalid == value:
            return

        if not len(self._parents):
            self._invalid = value
            return

        # Invalidate all ancestors of self using pseudo-recursion.
        parent = None
        stack = [self]
        while len(stack):
            root = stack.pop()
            # Stop at subtrees that have already been invalidated
            if root._invalid != value or root.pass_through:
                root._invalid = value
                stack.extend(root._parents.keys())

    def set_children(self, *children):
        """
        Set the children of the transform, to let the invalidation
        system know which transforms can invalidate this transform.
        Should be called from the constructor of any transforms that
        depend on other transforms.
        """
        for child in children:
            child._parents[self] = None

    if DEBUG:
        _set_children = set_children
        def set_children(self, *children):
            self._set_children(*children)
            self._children = children
        set_children.__doc__ = _set_children.__doc__

    def frozen(self):
        """
        Returns a frozen copy of this transform node.  The frozen copy
        will not update when its children change.  Useful for storing
        a previously known state of a transform where copy.deepcopy()
        might normally be used.
        """
        return self

    if DEBUG:
        def write_graphviz(self, fobj, highlight=[]):
            """
            For debugging purposes.

            Writes the transform tree rooted at 'self' to a graphviz "dot"
            format file.  This file can be run through the "dot" utility
            to produce a graph of the transform tree.

            Affine transforms are marked in blue.  Bounding boxes are
            marked in yellow.

            fobj: A Python file-like object
            """
            seen = sets.Set()

            def recurse(root):
                if root in seen:
                    return
                seen.add(root)
                props = {}
                label = root.__class__.__name__
                if root._invalid:
                    label = '[%s]' % label
                if root in highlight:
                    props['style'] = 'bold'
                props['shape'] = 'box'
                props['label'] = '"%s"' % label
                props = ' '.join(['%s=%s' % (key, val) for key, val in props.items()])

                fobj.write('%s [%s];\n' %
                           (hash(root), props))

                if hasattr(root, '_children'):
                    for child in root._children:
                        name = '?'
                        for key, val in root.__dict__.items():
                            if val is child:
                                name = key
                                break
                        fobj.write('%s -> %s [label="%s", fontsize=10];\n' % (
                                hash(root),
                                hash(child),
                                name))
                        recurse(child)

            fobj.write("digraph G {\n")
            recurse(self)
            fobj.write("}\n")
    else:
        def write_graphviz(self, fobj, highlight=[]):
            return


class BboxBase(TransformNode):
    """
    This is the base class of all bounding boxes, and provides
    read-only access to its data.  A mutable bounding box is provided
    by the :class:`Bbox` class.

    The canonical representation is as two points, with no
    restrictions on their ordering.  Convenience properties are
    provided to get the left, bottom, right and top edges and width
    and height, but these are not stored explicity.
    """
    is_bbox = True
    is_affine = True

    #* Redundant: Removed for performance
    #
    # def __init__(self):
    #     TransformNode.__init__(self)

    if DEBUG:
        def _check(points):
            if ma.isMaskedArray(points):
                warnings.warn("Bbox bounds are a masked array.")
            points = np.asarray(points)
            if (points[1,0] - points[0,0] == 0 or
                points[1,1] - points[0,1] == 0):
                warnings.warn("Singular Bbox.")
        _check = staticmethod(_check)

    def frozen(self):
        return Bbox(self.get_points().copy())
    frozen.__doc__ = TransformNode.__doc__

    def __array__(self, *args, **kwargs):
        return self.get_points()

    def is_unit(self):
        """
        Returns True if the Bbox is the unit bounding box from (0, 0)
        to (1, 1).
        """
        return list(self.get_points().flatten()) == [0., 0., 1., 1.]

    def _get_x0(self):
        return self.get_points()[0, 0]
    x0 = property(_get_x0, None, None, """
         (property) :attr:`x0` is the first of the pair of *x* coordinates that
         define the bounding box.  :attr:`x0` is not guaranteed to be
         less than :attr:`x1`.  If you require that, use :attr:`xmin`.""")

    def _get_y0(self):
        return self.get_points()[0, 1]
    y0 = property(_get_y0, None, None, """
         (property) :attr:`y0` is the first of the pair of *y* coordinates that
         define the bounding box.  :attr:`y0` is not guaranteed to be
         less than :attr:`y1`.  If you require that, use :attr:`ymin`.""")

    def _get_x1(self):
        return self.get_points()[1, 0]
    x1 = property(_get_x1, None, None, """
         (property) :attr:`x1` is the second of the pair of *x* coordinates that
         define the bounding box.  :attr:`x1` is not guaranteed to be
         greater than :attr:`x0`.  If you require that, use :attr:`xmax`.""")

    def _get_y1(self):
        return self.get_points()[1, 1]
    y1 = property(_get_y1, None, None, """
         (property) :attr:`y1` is the second of the pair of *y* coordinates that
         define the bounding box.  :attr:`y1` is not guaranteed to be
         greater than :attr:`y0`.  If you require that, use :attr:`ymax`.""")

    def _get_p0(self):
        return self.get_points()[0]
    p0 = property(_get_p0, None, None, """
         (property) :attr:`p0` is the first pair of (*x*, *y*) coordinates that
         define the bounding box.  It is not guaranteed to be the bottom-left
         corner.  For that, use :attr:`min`.""")

    def _get_p1(self):
        return self.get_points()[1]
    p1 = property(_get_p1, None, None, """
         (property) :attr:`p1` is the second pair of (*x*, *y*) coordinates that
         define the bounding box.  It is not guaranteed to be the top-right
         corner.  For that, use :attr:`max`.""")

    def _get_xmin(self):
        return min(self.get_points()[:, 0])
    xmin = property(_get_xmin, None, None, """
        (property) :attr:`xmin` is the left edge of the bounding box.""")

    def _get_ymin(self):
        return min(self.get_points()[:, 1])
    ymin = property(_get_ymin, None, None, """
        (property) :attr:`ymin` is the bottom edge of the bounding box.""")

    def _get_xmax(self):
        return max(self.get_points()[:, 0])
    xmax = property(_get_xmax, None, None, """
        (property) :attr:`xmax` is the right edge of the bounding box.""")

    def _get_ymax(self):
        return max(self.get_points()[:, 1])
    ymax = property(_get_ymax, None, None, """
        (property) :attr:`ymax` is the top edge of the bounding box.""")

    def _get_min(self):
        return [min(self.get_points()[:, 0]),
                min(self.get_points()[:, 1])]
    min = property(_get_min, None, None, """
        (property) :attr:`min` is the bottom-left corner of the bounding box.""")

    def _get_max(self):
        return [max(self.get_points()[:, 0]),
                max(self.get_points()[:, 1])]
    max = property(_get_max, None, None, """
        (property) :attr:`max` is the top-right corner of the bounding box.""")

    def _get_intervalx(self):
        return self.get_points()[:, 0]
    intervalx = property(_get_intervalx, None, None, """
        (property) :attr:`intervalx` is the pair of *x* coordinates that define the
        bounding box. It is not guaranteed to be sorted from left to right.""")

    def _get_intervaly(self):
        return self.get_points()[:, 1]
    intervaly = property(_get_intervaly, None, None, """
        (property) :attr:`intervaly` is the pair of *y* coordinates that define the
        bounding box.  It is not guaranteed to be sorted from bottom to top.""")

    def _get_width(self):
        points = self.get_points()
        return points[1, 0] - points[0, 0]
    width = property(_get_width, None, None, """
        (property) The width of the bounding box.  It may be negative if :attr:`x1` <
        :attr:`x0`.""")

    def _get_height(self):
        points = self.get_points()
        return points[1, 1] - points[0, 1]
    height = property(_get_height, None, None, """
        (property) The height of the bounding box.  It may be negative if :attr:`y1` <
        :attr:`y0`.""")

    def _get_size(self):
        points = self.get_points()
        return points[1] - points[0]
    size = property(_get_size, None, None, """
        (property) The width and height of the bounding box.  May be negative, in the same
        way as :attr:`width` and :attr:`height`.""")

    def _get_bounds(self):
        x0, y0, x1, y1 = self.get_points().flatten()
        return (x0, y0, x1 - x0, y1 - y0)
    bounds = property(_get_bounds, None, None, """
        (property) Returns (:attr:`x0`, :attr:`y0`, :attr:`width`, :attr:`height`).""")

    def _get_extents(self):
        return self.get_points().flatten().copy()
    extents = property(_get_extents, None, None, """
        (property) Returns (:attr:`x0`, :attr:`y0`, :attr:`x1`, :attr:`y1`).""")

    def get_points(self):
        return NotImplementedError()

    def containsx(self, x):
        """
        Returns True if x is between or equal to :attr:`x0` and
        :attr:`x1`.
        """
        x0, x1 = self.intervalx
        return ((x0 < x1
                 and (x >= x0 and x <= x1))
                or (x >= x1 and x <= x0))

    def containsy(self, y):
        """
        Returns True if y is between or equal to :attr:`y0` and
        :attr:`y1`.
        """
        y0, y1 = self.intervaly
        return ((y0 < y1
                 and (y >= y0 and y <= y1))
                or (y >= y1 and y <= y0))

    def contains(self, x, y):
        """
        Returns True if (x, y) is a coordinate inside the bounding
        box or on its edge.
        """
        return self.containsx(x) and self.containsy(y)

    def overlaps(self, other):
        """
        Returns True if this bounding box overlaps with the given
        bounding box *other*.
        """
        ax1, ay1, ax2, ay2 = self._get_extents()
        bx1, by1, bx2, by2 = other._get_extents()

        if ax2 < ax1:
            ax2, ax1 = ax1, ax2
        if ay2 < ay1:
            ay2, ay1 = ay1, ay2
        if bx2 < bx1:
            bx2, bx1 = bx1, bx2
        if by2 < by1:
            by2, by1 = by1, by2

        return not ((bx2 < ax1) or
                    (by2 < ay1) or
                    (bx1 > ax2) or
                    (by1 > ay2))

    def fully_containsx(self, x):
        """
        Returns True if x is between but not equal to :attr:`x0` and
        :attr:`x1`.
        """
        x0, x1 = self.intervalx
        return ((x0 < x1
                 and (x > x0 and x < x1))
                or (x > x1 and x < x0))

    def fully_containsy(self, y):
        """
        Returns True if y is between but not equal to :attr:`y0` and
        :attr:`y1`.
        """
        y0, y1 = self.intervaly
        return ((y0 < y1
                 and (x > y0 and x < y1))
                or (x > y1 and x < y0))

    def fully_contains(self, x, y):
        """
        Returns True if (x, y) is a coordinate inside the bounding
        box, but not on its edge.
        """
        return self.fully_containsx(x) \
            and self.fully_containsy(y)

    def fully_overlaps(self, other):
        """
        Returns True if this bounding box overlaps with the given
        bounding box *other*, but not on its edge alone."""
        ax1, ay1, ax2, ay2 = self._get_extents()
        bx1, by1, bx2, by2 = other._get_extents()

        if ax2 < ax1:
            ax2, ax1 = ax1, ax2
        if ay2 < ay1:
            ay2, ay1 = ay1, ay2
        if bx2 < bx1:
            bx2, bx1 = bx1, bx2
        if by2 < by1:
            by2, by1 = by1, by2

        return not ((bx2 <= ax1) or
                    (by2 <= ay1) or
                    (bx1 >= ax2) or
                    (by1 >= ay2))

    def transformed(self, transform):
        """
        Return a new :class:`Bbox` object, statically transformed by
        the given transform.
        """
        return Bbox(transform.transform(self.get_points()))

    def inverse_transformed(self, transform):
        """
        Return a new :class:`Bbox` object, statically transformed by
        the inverse of the given transform.
        """
        return Bbox(transform.inverted().transform(self.get_points()))

    coefs = {'C':  (0.5, 0.5),
             'SW': (0,0),
             'S':  (0.5, 0),
             'SE': (1.0, 0),
             'E':  (1.0, 0.5),
             'NE': (1.0, 1.0),
             'N':  (0.5, 1.0),
             'NW': (0, 1.0),
             'W':  (0, 0.5)}
    def anchored(self, c, container = None):
        """
        Return a copy of the Bbox, shifted to position c within a
        container.

        c: may be either:

          * a sequence (cx, cy) where cx, cy range
            from 0 to 1, where 0 is left or bottom and 1 is right or top

          * a string:
            - C for centered
            - S for bottom-center
            - SE for bottom-left
            - E for left
            - etc.

        Optional argument *container* is the box within which the :class:`Bbox`
        is positioned; it defaults to the initial :class:`Bbox`.
        """
        if container is None:
            container = self
        l, b, w, h = container.bounds
        if isinstance(c, str):
            cx, cy = self.coefs[c]
        else:
            cx, cy = c
        L, B, W, H = self.bounds
        return Bbox(self._points +
                    [(l + cx * (w-W)) - L,
                     (b + cy * (h-H)) - B])

    def shrunk(self, mx, my):
        """
        Return a copy of the :class:`Bbox`, shurnk by the factor mx in
        the *x* direction and the factor my in the *y* direction.  The
        lower left corner of the box remains unchanged.  Normally mx
        and my will be less than 1, but this is not enforced.
        """
        w, h = self.size
        return Bbox([self._points[0],
                    self._points[0] + [mx * w, my * h]])

    def shrunk_to_aspect(self, box_aspect, container = None, fig_aspect = 1.0):
        """
        Return a copy of the :class:`Bbox`, shrunk so that it is as
        large as it can be while having the desired aspect ratio,
        *box_aspect*.  If the box coordinates are relative---that
        is, fractions of a larger box such as a figure---then the
        physical aspect ratio of that figure is specified with
        *fig_aspect*, so that *box_aspect* can also be given as a
        ratio of the absolute dimensions, not the relative dimensions.
        """
        assert box_aspect > 0 and fig_aspect > 0
        if container is None:
            container = self
        w, h = container.size
        H = w * box_aspect/fig_aspect
        if H <= h:
            W = w
        else:
            W = h * fig_aspect/box_aspect
            H = h
        return Bbox([self._points[0],
                     self._points[0] + (W, H)])

    def splitx(self, *args):
        """
        e.g., ``bbox.splitx(f1, f2, ...)``

        Returns a list of new :class:`Bbox` objects formed by
        splitting the original one with vertical lines at fractional
        positions *f1*, *f2*, ...
        """
        boxes = []
        xf = [0] + list(args) + [1]
        x0, y0, x1, y1 = self._get_extents()
        w = x1 - x0
        for xf0, xf1 in zip(xf[:-1], xf[1:]):
            boxes.append(Bbox([[x0 + xf0 * w, y0], [x0 + xf1 * w, y1]]))
        return boxes

    def splity(self, *args):
        """
        e.g., ``bbox.splitx(f1, f2, ...)``

        Returns a list of new :class:`Bbox` objects formed by
        splitting the original one with horizontal lines at fractional
        positions *f1*, *f2*, ...
        """
        boxes = []
        yf = [0] + list(args) + [1]
        x0, y0, x1, y1 = self._get_extents()
        h = y1 - y0
        for yf0, yf1 in zip(yf[:-1], yf[1:]):
            boxes.append(Bbox([[x0, y0 + yf0 * h], [x1, y0 + yf1 * h]]))
        return boxes

    def count_contains(self, vertices):
        """
        Count the number of vertices contained in the Bbox.

        vertices is a Nx2 numpy array.
        """
        if len(vertices) == 0:
            return 0
        vertices = np.asarray(vertices)
        x0, y0, x1, y1 = self._get_extents()
        dx0 = np.sign(vertices[:, 0] - x0)
        dy0 = np.sign(vertices[:, 1] - y0)
        dx1 = np.sign(vertices[:, 0] - x1)
        dy1 = np.sign(vertices[:, 1] - y1)
        inside = (abs(dx0 + dx1) + abs(dy0 + dy1)) <= 2
        return N.sum(inside)

    def count_overlaps(self, bboxes):
        """
        Count the number of bounding boxes that overlap this one.

        bboxes is a sequence of :class:`BboxBase` objects
        """
        return count_bboxes_overlapping_bbox(self, bboxes)

    def expanded(self, sw, sh):
        """
        Return a new :class:`Bbox` which is this :class:`Bbox`
        expanded around its center by the given factors *sw* and
        *sh*.
        """
        width = self.width
        height = self.height
        deltaw = (sw * width - width) / 2.0
        deltah = (sh * height - height) / 2.0
        a = np.array([[-deltaw, -deltah], [deltaw, deltah]])
        return Bbox(self._points + a)

    def padded(self, p):
        """
        Return a new :class:`Bbox` that is padded on all four sides by
        the given value.
        """
        points = self._points
        return Bbox(points + [[-p, -p], [p, p]])

    def translated(self, tx, ty):
        """
        Return a copy of the :class:`Bbox`, statically translated by
        tx and ty.
        """
        return Bbox(self._points + (tx, ty))

    def corners(self):
        """
        Return an array of points which are the four corners of this
        rectangle.  For example, if this :class:`Bbox` is defined by
        the points (*a*, *b*) and (*c*, *d*), :meth:`corners` returns
        (*a*, *b*), (*a*, *d*), (*c*, *b*) and (*c*, *d*).
        """
        l, b, r, t = self.get_points().flatten()
        return np.array([[l, b], [l, t], [r, b], [r, t]])

    def rotated(self, radians):
        """
        Return a new bounding box that bounds a rotated version of
        this bounding box by the given radians.  The new bounding box
        is still aligned with the axes, of course.
        """
        corners = self.corners()
        corners_rotated = Affine2D().rotate(radians).transform(corners)
        bbox = Bbox.unit()
        bbox.update_from_data_xy(corners_rotated, ignore=True)
        return bbox

    #@staticmethod
    def union(bboxes):
        """
        Return a :class:`Bbox` that contains all of the given bboxes.
        """
        assert(len(bboxes))

        if len(bboxes) == 1:
            return bboxes[0]

        x0 = np.inf
        y0 = np.inf
        x1 = -np.inf
        y1 = -np.inf

        for bbox in bboxes:
            points = bbox.get_points()
            xs = points[:, 0]
            ys = points[:, 1]
            x0 = min(x0, np.min(xs))
            y0 = min(y0, np.min(ys))
            x1 = max(x1, np.max(xs))
            y1 = max(y1, np.max(ys))

        return Bbox.from_extents(x0, y0, x1, y1)
    union = staticmethod(union)


class Bbox(BboxBase):
    """
    A mutable bounding box.
    """

    def __init__(self, points):
        """
        points: a 2x2 numpy array of the form [[x0, y0], [x1, y1]]

        If you need to create a :class:`Bbox` object from another form
        of data, consider the static methods unit, from_bounds and
        from_extents.
        """
        BboxBase.__init__(self)
        self._points = np.asarray(points, np.float_)
        self._minpos = np.array([0.0000001, 0.0000001])
        self._ignore = True

    if DEBUG:
        ___init__ = __init__
        def __init__(self, points):
            self._check(points)
            self.___init__(points)

        def invalidate(self):
            self._check(self._points)
            TransformNode.invalidate(self)

    _unit_values = np.array([[0.0, 0.0], [1.0, 1.0]], np.float_)
    #@staticmethod
    def unit():
        """
        (staticmethod) Create a new unit :class:`Bbox` from (0, 0) to
        (1, 1).
        """
        return Bbox(Bbox._unit_values.copy())
    unit = staticmethod(unit)

    #@staticmethod
    def from_bounds(x0, y0, width, height):
        """
        (staticmethod) Create a new :class:`Bbox` from x0, y0, width
        and height.

        width and height may be negative.
        """
        return Bbox.from_extents(x0, y0, x0 + width, y0 + height)
    from_bounds = staticmethod(from_bounds)

    #@staticmethod
    def from_extents(*args):
        """
        (staticmethod) Create a new Bbox from left, bottom, right and
        top.

        The y-axis increases upwards.
        """
        points = np.array(args, dtype=np.float_).reshape(2, 2)
        return Bbox(points)
    from_extents = staticmethod(from_extents)

    def __repr__(self):
        return 'Bbox(%s)' % repr(self._points)
    __str__ = __repr__

    def ignore(self, value):
        """
        Set whether the existing bounds of the box should be ignored
        by subsequent calls to :meth:`update_from_data` or
        :meth:`update_from_data_xy`.

        value:

           - When True, subsequent calls to :meth:`update_from_data`
             will ignore the existing bounds of the :class:`Bbox`.

           - When False, subsequent calls to :meth:`update_from_data`
             will include the existing bounds of the :class:`Bbox`.
        """
        self._ignore = value

    def update_from_data(self, x, y, ignore=None):
        """
        Update the bounds of the :class:`Bbox` based on the passed in
        data.

        x: a numpy array of x-values

        y: a numpy array of y-values

        ignore:
           - when True, ignore the existing bounds of the Bbox.
           - when False, include the existing bounds of the Bbox.
           - when None, use the last value passed to :meth:`ignore`.
        """
        warnings.warn("update_from_data requires a memory copy -- please replace with update_from_data_xy")
        xy = np.hstack((x.reshape((len(x), 1)), y.reshape((len(y), 1))))
        return self.update_from_data_xy(xy, ignore)

    def update_from_data_xy(self, xy, ignore=None):
        """
        Update the bounds of the :class:`Bbox` based on the passed in
        data.

        xy: a numpy array of 2D points

        ignore:
           - when True, ignore the existing bounds of the Bbox.
           - when False, include the existing bounds of the Bbox.
           - when None, use the last value passed to :meth:`ignore`.
        """
        if ignore is None:
            ignore = self._ignore

        if len(xy) == 0:
            return
        xym = ma.masked_invalid(xy) # maybe add copy=False
        if (xym.count(axis=1)!=2).all():
            return

        points, minpos, changed = update_path_extents(
            Path(xy), None, self._points, self._minpos, ignore)

        if changed:
            self._points = points
            self._minpos = minpos
            self.invalidate()

    def _set_x0(self, val):
        self._points[0, 0] = val
        self.invalidate()
    x0 = property(BboxBase._get_x0, _set_x0)

    def _set_y0(self, val):
        self._points[0, 1] = val
        self.invalidate()
    y0 = property(BboxBase._get_y0, _set_y0)

    def _set_x1(self, val):
        self._points[1, 0] = val
        self.invalidate()
    x1 = property(BboxBase._get_x1, _set_x1)

    def _set_y1(self, val):
        self._points[1, 1] = val
        self.invalidate()
    y1 = property(BboxBase._get_y1, _set_y1)

    def _set_p0(self, val):
        self._points[0] = val
        self.invalidate()
    p0 = property(BboxBase._get_p0, _set_p0)

    def _set_p1(self, val):
        self._points[1] = val
        self.invalidate()
    p1 = property(BboxBase._get_p1, _set_p1)

    def _set_intervalx(self, interval):
        self._points[:, 0] = interval
        self.invalidate()
    intervalx = property(BboxBase._get_intervalx, _set_intervalx)

    def _set_intervaly(self, interval):
        self._points[:, 1] = interval
        self.invalidate()
    intervaly = property(BboxBase._get_intervaly, _set_intervaly)

    def _set_bounds(self, bounds):
        l, b, w, h = bounds
        points = np.array([[l, b], [l+w, b+h]], np.float_)
        if np.any(self._points != points):
            self._points = points
            self.invalidate()
    bounds = property(BboxBase._get_bounds, _set_bounds)

    def _get_minpos(self):
        return self._minpos
    minpos = property(_get_minpos)

    def _get_minposx(self):
        return self._minpos[0]
    minposx = property(_get_minposx)

    def _get_minposy(self):
        return self._minpos[1]
    minposy = property(_get_minposy)

    def get_points(self):
        """
        Get the points of the bounding box directly as a numpy array
        of the form: [[x0, y0], [x1, y1]].
        """
        self._invalid = 0
        return self._points

    def set_points(self, points):
        """
        Set the points of the bounding box directly from a numpy array
        of the form: [[x0, y0], [x1, y1]].  No error checking
        is performed, as this method is mainly for internal use.
        """
        if np.any(self._points != points):
            self._points = points
            self.invalidate()

    def set(self, other):
        """
        Set this bounding box from the "frozen" bounds of another Bbox.
        """
        if np.any(self._points != other.get_points()):
            self._points = other.get_points()
            self.invalidate()


class TransformedBbox(BboxBase):
    """
    A :class:`Bbox` that is automatically transformed by a given
    transform.  When either the child bounding box or transform
    changes, the bounds of this bbox will update accordingly.
    """
    def __init__(self, bbox, transform):
        """
        bbox: a child bbox

        transform: a 2D transform
        """
        assert bbox.is_bbox
        assert isinstance(transform, Transform)
        assert transform.input_dims == 2
        assert transform.output_dims == 2

        BboxBase.__init__(self)
        self._bbox = bbox
        self._transform = transform
        self.set_children(bbox, transform)
        self._points = None

    def __repr__(self):
        return "TransformedBbox(%s, %s)" % (self._bbox, self._transform)
    __str__ = __repr__

    def get_points(self):
        if self._invalid:
            points = self._transform.transform(self._bbox.get_points())
            if ma.isMaskedArray(points):
                points.putmask(0.0)
                points = np.asarray(points)
            self._points = points
            self._invalid = 0
        return self._points
    get_points.__doc__ = Bbox.get_points.__doc__

    if DEBUG:
        _get_points = get_points
        def get_points(self):
            points = self._get_points()
            self._check(points)
            return points

class Transform(TransformNode):
    """
    The base class of all TransformNodes that actually perform a
    transformation.

    All non-affine transformations should be subclasses of this class.
    New affine transformations should be subclasses of
    :class:`Affine2D`.

    Subclasses of this class should override the following members (at
    minimum):

      - :attr:`input_dims`
      - :attr:`output_dims`
      - :meth:`transform`
      - :attr:`is_separable`
      - :attr:`has_inverse`
      - :meth:`inverted` (if :meth:`has_inverse` can return True)

    If the transform needs to do something non-standard with
    :class:`mathplotlib.path.Path` objects, such as adding curves
    where there were once line segments, it should override:

      - :meth:`transform_path`
    """
    # The number of input and output dimensions for this transform.
    # These must be overridden (with integers) in the subclass.
    input_dims = None
    output_dims = None

    # True if this transform as a corresponding inverse transform.
    has_inverse = False

    # True if this transform is separable in the x- and y- dimensions.
    is_separable = False

    #* Redundant: Removed for performance
    #
    # def __init__(self):
    #     TransformNode.__init__(self)

    def __add__(self, other):
        """
        Composes two transforms together such that self is followed by other.
        """
        if isinstance(other, Transform):
            return composite_transform_factory(self, other)
        raise TypeError(
            "Can not add Transform to object of type '%s'" % type(other))

    def __radd__(self, other):
        """
        Composes two transforms together such that self is followed by other.
        """
        if isinstance(other, Transform):
            return composite_transform_factory(other, self)
        raise TypeError(
            "Can not add Transform to object of type '%s'" % type(other))

    def __array__(self, *args, **kwargs):
        """
        Used by C/C++ -based backends to get at the array matrix data.
        """
        return self.frozen().__array__()

    def transform(self, values):
        """
        Performs the transformation on the given array of values.

        Accepts a numpy array of shape (N x :attr:`input_dims`) and
        returns a numpy array of shape (N x :attr:`output_dims`).
        """
        raise NotImplementedError()

    def transform_affine(self, values):
        """
        Performs only the affine part of this transformation on the
        given array of values.

        ``transform(values)`` is always equivalent to
        ``transform_affine(transform_non_affine(values))``.

        In non-affine transformations, this is generally a no-op.  In
        affine transformations, this is equivalent to
        ``transform(values)``.

        Accepts a numpy array of shape (N x :attr:`input_dims`) and
        returns a numpy array of shape (N x :attr:`output_dims`).
        """
        return values

    def transform_non_affine(self, values):
        """
        Performs only the non-affine part of the transformation.

        ``transform(values)`` is always equivalent to
        ``transform_affine(transform_non_affine(values))``.

        In non-affine transformations, this is generally equivalent to
        ``transform(values)``.  In affine transformations, this is
        always a no-op.

        Accepts a numpy array of shape (N x :attr:`input_dims`) and
        returns a numpy array of shape (N x :attr:`output_dims`).
        """
        return self.transform(values)

    def get_affine(self):
        """
        Get the affine part of this transform.
        """
        return IdentityTransform()

    def transform_point(self, point):
        """
        A convenience function that returns the transformed copy of a
        single point.

        The point is given as a sequence of length :attr:`input_dims`.
        The transformed point is returned as a sequence of length
        :attr:`output_dims`.
        """
        assert len(point) == self.input_dims
        return self.transform(np.asarray([point]))[0]

    def transform_path(self, path):
        """
        Returns a transformed copy of path.

        path: a Path instance.

        In some cases, this transform may insert curves into the path
        that began as line segments.
        """
        return Path(self.transform(path.vertices), path.codes)

    def transform_path_affine(self, path):
        """
        Returns a copy of path, transformed only by the affine part of
        this transform.

        path: a Path instance

        ``transform_path(path)`` is equivalent to
        ``transform_path_affine(transform_path_non_affine(values))``.
        """
        return path

    def transform_path_non_affine(self, path):
        """
        Returns a copy of path, transformed only by the non-affine
        part of this transform.

        path: a Path instance

        ``transform_path(path)`` is equivalent to
        ``transform_path_affine(transform_path_non_affine(values))``.
        """
        return Path(self.transform_non_affine(path.vertices), path.codes)

    def inverted(self):
        """
        Return the corresponding inverse transformation.

        The return value of this method should be treated as
        temporary.  An update to 'self' does not cause a corresponding
        update to its inverted copy.

        ``x === self.inverted().transform(self.transform(x))``
        """
        raise NotImplementedError()


class TransformWrapper(Transform):
    """
    A helper class that holds a single child transform and acts
    equivalently to it.

    This is useful if a node of the transform tree must be replaced at
    run time with a transform of a different type.  This class allows
    that replacement to correctly trigger invalidation.

    Note that :class:`TransformWrapper` instances must have the same
    input and output dimensions during their entire lifetime, so the
    child transform may only be replaced with another child transform
    of the same dimensions.
    """
    pass_through = True

    def __init__(self, child):
        """
        child: A Transform instance.  This child may later be replaced
        with :meth:`set`.
        """
        assert isinstance(child, Transform)

        Transform.__init__(self)
        self.input_dims = child.input_dims
        self.output_dims = child.output_dims
        self._set(child)
        self._invalid = 0

    def __repr__(self):
        return "TransformWrapper(%r)" % self._child
    __str__ = __repr__

    def frozen(self):
        return self._child.frozen()
    frozen.__doc__ = Transform.frozen.__doc__

    def _set(self, child):
        self._child = child
        self.set_children(child)

        self.transform                 = child.transform
        self.transform_affine          = child.transform_affine
        self.transform_non_affine      = child.transform_non_affine
        self.transform_path            = child.transform_path
        self.transform_path_affine     = child.transform_path_affine
        self.transform_path_non_affine = child.transform_path_non_affine
        self.get_affine                = child.get_affine
        self.inverted                  = child.inverted

    def set(self, child):
        """
        Replace the current child of this transform with another one.

        The new child must have the same number of input and output
        dimensions as the current child.
        """
        assert child.input_dims == self.input_dims
        assert child.output_dims == self.output_dims

        self._set(child)

        self._invalid = 0
        self.invalidate()
        self._invalid = 0

    def _get_is_affine(self):
        return self._child.is_affine
    is_affine = property(_get_is_affine)

    def _get_is_separable(self):
        return self._child.is_separable
    is_separable = property(_get_is_separable)

    def _get_has_inverse(self):
        return self._child.has_inverse
    has_inverse = property(_get_has_inverse)


class AffineBase(Transform):
    """
    The base class of all affine transformations of any number of
    dimensions.
    """
    is_affine = True

    def __init__(self):
        Transform.__init__(self)
        self._inverted = None

    def __array__(self, *args, **kwargs):
        return self.get_matrix()

    #@staticmethod
    def _concat(a, b):
        """
        Concatenates two transformation matrices (represented as numpy
        arrays) together.
        """
        return np.dot(b, a)
    _concat = staticmethod(_concat)

    def get_matrix(self):
        """
        Get the underlying transformation matrix as a numpy array.
        """
        raise NotImplementedError()

    def transform_non_affine(self, points):
        return points
    transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

    def transform_path_affine(self, path):
        return self.transform_path(path)
    transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__

    def transform_path_non_affine(self, path):
        return path
    transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__

    def get_affine(self):
        return self
    get_affine.__doc__ = Transform.get_affine.__doc__


class Affine2DBase(AffineBase):
    """
    The base class of all 2D affine transformations.

    2D affine transformations are performed using a 3x3 numpy array::

        a c e
        b d f
        0 0 1

    This class provides the read-only interface.  For a mutable 2D
    affine transformation, use :class:`Affine2D`.

    Subclasses of this class will generally only need to override a
    constructor and 'get_matrix' that generates a custom 3x3 matrix.
    """

    input_dims = 2
    output_dims = 2

    #* Redundant: Removed for performance
    #
    # def __init__(self):
    #     Affine2DBase.__init__(self)

    def frozen(self):
        return Affine2D(self.get_matrix().copy())
    frozen.__doc__ = AffineBase.frozen.__doc__

    def _get_is_separable(self):
        mtx = self.get_matrix()
        return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0
    is_separable = property(_get_is_separable)

    def __array__(self, *args, **kwargs):
        return self.get_matrix()

    def to_values(self):
        """
        Return the values of the matrix as a sequence (a,b,c,d,e,f)
        """
        mtx = self.get_matrix()
        return tuple(mtx[:2].swapaxes(0, 1).flatten())

    #@staticmethod
    def matrix_from_values(a, b, c, d, e, f):
        """
        (staticmethod) Create a new transformation matrix as a 3x3
        numpy array of the form::

          a c e
          b d f
          0 0 1
        """
        return np.array([[a, c, e], [b, d, f], [0.0, 0.0, 1.0]], np.float_)
    matrix_from_values = staticmethod(matrix_from_values)

    def transform(self, points):
        mtx = self.get_matrix()
        if isinstance(points, MaskedArray):
            tpoints = affine_transform(points.data, mtx)
            return ma.MaskedArray(tpoints, mask=ma.getmask(points))
        return affine_transform(points, mtx)

    def transform_point(self, point):
        mtx = self.get_matrix()
        return affine_transform(point, mtx)
    transform_point.__doc__ = AffineBase.transform_point.__doc__

    if DEBUG:
        _transform = transform
        def transform(self, points):
            # The major speed trap here is just converting to the
            # points to an array in the first place.  If we can use
            # more arrays upstream, that should help here.
            if (not ma.isMaskedArray(points) and
                not isinstance(points, np.ndarray)):
                warnings.warn(
                    ('A non-numpy array of type %s was passed in for ' +
                     'transformation.  Please correct this.')
                    % type(values))
            return self._transform(points)
    transform.__doc__ = AffineBase.transform.__doc__

    transform_affine = transform
    transform_affine.__doc__ = AffineBase.transform_affine.__doc__

    def inverted(self):
        if self._inverted is None or self._invalid:
            mtx = self.get_matrix()
            self._inverted = Affine2D(inv(mtx))
            self._invalid = 0
        return self._inverted
    inverted.__doc__ = AffineBase.inverted.__doc__


class Affine2D(Affine2DBase):
    """
    A mutable 2D affine transformation.
    """

    def __init__(self, matrix = None):
        """
        Initialize an Affine transform from a 3x3 numpy float array::

          a c e
          b d f
          0 0 1

        If matrix is None, initialize with the identity transform.
        """
        Affine2DBase.__init__(self)
        if matrix is None:
            matrix = np.identity(3)
        elif DEBUG:
            matrix = np.asarray(matrix, np.float_)
            assert matrix.shape == (3, 3)
        self._mtx = matrix
        self._invalid = 0

    def __repr__(self):
        return "Affine2D(%s)" % repr(self._mtx)
    __str__ = __repr__

    def __cmp__(self, other):
        if (isinstance(other, Affine2D) and
            (self.get_matrix() == other.get_matrix()).all()):
            return 0
        return -1

    #@staticmethod
    def from_values(a, b, c, d, e, f):
        """
        (staticmethod) Create a new Affine2D instance from the given
        values::

          a c e
          b d f
          0 0 1
        """
        return Affine2D(
            np.array([a, c, e, b, d, f, 0.0, 0.0, 1.0], np.float_)
            .reshape((3,3)))
    from_values = staticmethod(from_values)

    def get_matrix(self):
        """
        Get the underlying transformation matrix as a 3x3 numpy array::

          a c e
          b d f
          0 0 1
        """
        self._invalid = 0
        return self._mtx

    def set_matrix(self, mtx):
        """
        Set the underlying transformation matrix from a 3x3 numpy array::

          a c e
          b d f
          0 0 1
        """
        self._mtx = mtx
        self.invalidate()

    def set(self, other):
        """
        Set this transformation from the frozen copy of another
        :class:`Affine2DBase` object.
        """
        assert isinstance(other, Affine2DBase)
        self._mtx = other.get_matrix()
        self.invalidate()

    #@staticmethod
    def identity():
        """
        (staticmethod) Return a new :class:`Affine2D` object that is
        the identity transform.

        Unless this transform will be mutated later on, consider using
        the faster :class:`IdentityTransform` class instead.
        """
        return Affine2D(np.identity(3))
    identity = staticmethod(identity)

    def clear(self):
        """
        Reset the underlying matrix to the identity transform.
        """
        self._mtx = np.identity(3)
        self.invalidate()
        return self

    def rotate(self, theta):
        """
        Add a rotation (in radians) to this transform in place.

        Returns self, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg, :meth:`translate`
        and :meth:`scale`.
        """
        a = np.cos(theta)
        b = np.sin(theta)
        rotate_mtx = np.array(
            [[a, -b, 0.0], [b, a, 0.0], [0.0, 0.0, 1.0]],
            np.float_)
        self._mtx = np.dot(rotate_mtx, self._mtx)
        self.invalidate()
        return self

    def rotate_deg(self, degrees):
        """
        Add a rotation (in degrees) to this transform in place.

        Returns self, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg, :meth:`translate`
        and :meth:`scale`.
        """
        return self.rotate(degrees*np.pi/180.)

    def rotate_around(self, x, y, theta):
        """
        Add a rotation (in radians) around the point (x, y) in place.

        Returns self, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg, :meth:`translate`
        and :meth:`scale`.
        """
        return self.translate(-x, -y).rotate(theta).translate(x, y)

    def rotate_deg_around(self, x, y, degrees):
        """
        Add a rotation (in degrees) around the point (x, y) in place.

        Returns self, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg, :meth:`translate`
        and :meth:`scale`.
        """
        return self.translate(-x, -y).rotate_deg(degrees).translate(x, y)

    def translate(self, tx, ty):
        """
        Adds a translation in place.

        Returns self, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg, :meth:`translate`
        and :meth:`scale`.
        """
        translate_mtx = np.array(
            [[1.0, 0.0, tx], [0.0, 1.0, ty], [0.0, 0.0, 1.0]],
            np.float_)
        self._mtx = np.dot(translate_mtx, self._mtx)
        self.invalidate()
        return self

    def scale(self, sx, sy=None):
        """
        Adds a scale in place.

        If sy is None, the same scale is applied in both the x- and
        y-directions.

        Returns self, so this method can easily be chained with more
        calls to :meth:`rotate`, :meth:`rotate_deg, :meth:`translate`
        and :meth:`scale`.
        """
        if sy is None:
            sy = sx
        scale_mtx = np.array(
            [[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]],
            np.float_)
        self._mtx = np.dot(scale_mtx, self._mtx)
        self.invalidate()
        return self

    def _get_is_separable(self):
        mtx = self.get_matrix()
        return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0
    is_separable = property(_get_is_separable)


class IdentityTransform(Affine2DBase):
    """
    A special class that does on thing, the identity transform, in a
    fast way.
    """
    _mtx = np.identity(3)

    def frozen(self):
        return self
    frozen.__doc__ = Affine2DBase.frozen.__doc__

    def __repr__(self):
        return "IdentityTransform()"
    __str__ = __repr__

    def get_matrix(self):
        return self._mtx
    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__

    def transform(self, points):
        return points
    transform.__doc__ = Affine2DBase.transform.__doc__

    transform_affine = transform
    transform_affine.__doc__ = Affine2DBase.transform_affine.__doc__

    transform_non_affine = transform
    transform_non_affine.__doc__ = Affine2DBase.transform_non_affine.__doc__

    def transform_path(self, path):
        return path
    transform_path.__doc__ = Affine2DBase.transform_path.__doc__

    transform_path_affine = transform_path
    transform_path_affine.__doc__ = Affine2DBase.transform_path_affine.__doc__

    transform_path_non_affine = transform_path
    transform_path_non_affine.__doc__ = Affine2DBase.transform_path_non_affine.__doc__

    def get_affine(self):
        return self
    get_affine.__doc__ = Affine2DBase.get_affine.__doc__

    inverted = get_affine
    inverted.__doc__ = Affine2DBase.inverted.__doc__


class BlendedGenericTransform(Transform):
    """
    A "blended" transform uses one transform for the x-direction, and
    another transform for the y-direction.

    This "generic" version can handle any given child transform in the
    x- and y-directions.
    """
    input_dims = 2
    output_dims = 2
    is_separable = True
    pass_through = True

    def __init__(self, x_transform, y_transform):
        """
        Create a new "blended" transform using x_transform to
        transform the x-axis and y_transform to transform the y_axis.

        You will generally not call this constructor directly but use
        the :func:`blended_transform_factory` function instead, which
        can determine automatically which kind of blended transform to
        create.
        """
        # Here we ask: "Does it blend?"

        Transform.__init__(self)
        self._x = x_transform
        self._y = y_transform
        self.set_children(x_transform, y_transform)
        self._affine = None

    def _get_is_affine(self):
        return self._x.is_affine and self._y.is_affine
    is_affine = property(_get_is_affine)

    def frozen(self):
        return blended_transform_factory(self._x.frozen(), self._y.frozen())
    frozen.__doc__ = Transform.frozen.__doc__

    def __repr__(self):
        return "BlendedGenericTransform(%s,%s)" % (self._x, self._y)
    __str__ = __repr__

    def transform(self, points):
        x = self._x
        y = self._y

        if x is y and x.input_dims == 2:
            return x.transform(points)

        if x.input_dims == 2:
            x_points = x.transform(points)[:, 0:1]
        else:
            x_points = x.transform(points[:, 0])
            x_points = x_points.reshape((len(x_points), 1))

        if y.input_dims == 2:
            y_points = y.transform(points)[:, 1:]
        else:
            y_points = y.transform(points[:, 1])
            y_points = y_points.reshape((len(y_points), 1))

        if isinstance(x_points, MaskedArray) or isinstance(y_points, MaskedArray):
            return ma.concatenate((x_points, y_points), 1)
        else:
            return np.concatenate((x_points, y_points), 1)
    transform.__doc__ = Transform.transform.__doc__

    def transform_affine(self, points):
        return self.get_affine().transform(points)
    transform_affine.__doc__ = Transform.transform_affine.__doc__

    def transform_non_affine(self, points):
        if self._x.is_affine and self._y.is_affine:
            return points
        return self.transform(points)
    transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

    def inverted(self):
        return BlendedGenericTransform(self._x.inverted(), self._y.inverted())
    inverted.__doc__ = Transform.inverted.__doc__

    def get_affine(self):
        if self._invalid or self._affine is None:
            if self._x.is_affine and self._y.is_affine:
                if self._x == self._y:
                    self._affine = self._x.get_affine()
                else:
                    x_mtx = self._x.get_affine().get_matrix()
                    y_mtx = self._y.get_affine().get_matrix()
                    # This works because we already know the transforms are
                    # separable, though normally one would want to set b and
                    # c to zero.
                    mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0]))
                    self._affine = Affine2D(mtx)
            else:
                self._affine = IdentityTransform()
            self._invalid = 0
        return self._affine
    get_affine.__doc__ = Transform.get_affine.__doc__


class BlendedAffine2D(Affine2DBase):
    """
    A "blended" transform uses one transform for the x-direction, and
    another transform for the y-direction.

    This version is an optimization for the case where both child
    transforms are of type Affine2DBase.
    """
    is_separable = True

    def __init__(self, x_transform, y_transform):
        """
        Create a new "blended" transform using x_transform to
        transform the x-axis and y_transform to transform the y_axis.

        Both x_transform and y_transform must be 2D affine transforms.

        You will generally not call this constructor directly but use
        the :func:`blended_transform_factory` function instead, which
        can determine automatically which kind of blended transform to
        create.
        """
        assert x_transform.is_affine
        assert y_transform.is_affine
        assert x_transform.is_separable
        assert y_transform.is_separable

        Transform.__init__(self)
        self._x = x_transform
        self._y = y_transform
        self.set_children(x_transform, y_transform)

        Affine2DBase.__init__(self)
        self._mtx = None

    def __repr__(self):
        return "BlendedAffine2D(%s,%s)" % (self._x, self._y)
    __str__ = __repr__

    def get_matrix(self):
        if self._invalid:
            if self._x == self._y:
                self._mtx = self._x.get_matrix()
            else:
                x_mtx = self._x.get_matrix()
                y_mtx = self._y.get_matrix()
                # This works because we already know the transforms are
                # separable, though normally one would want to set b and
                # c to zero.
                self._mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0]))
            self._inverted = None
            self._invalid = 0
        return self._mtx
    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__


def blended_transform_factory(x_transform, y_transform):
    """
    Create a new "blended" transform using x_transform to
    transform the x-axis and y_transform to transform the y_axis.

    A faster version of the blended transform is returned for the case
    where both child transforms are affine.
    """
    if (isinstance(x_transform, Affine2DBase)
        and isinstance(y_transform, Affine2DBase)):
        return BlendedAffine2D(x_transform, y_transform)
    return BlendedGenericTransform(x_transform, y_transform)


class CompositeGenericTransform(Transform):
    """
    A composite transform formed by applying transform a then transform b.

    This "generic" version can handle any two arbitrary transformations.
    """
    pass_through = True

    def __init__(self, a, b):
        """
        Create a new composite transform that is the result of
        applying transform a then transform b.

        You will generally not call this constructor directly but use
        the :func:`composite_transform_factory` function instead,
        which can automatically choose the best kind of composite
        transform instance to create.
        """
        assert a.output_dims == b.input_dims
        self.input_dims = a.input_dims
        self.output_dims = b.output_dims

        Transform.__init__(self)
        self._a = a
        self._b = b
        self.set_children(a, b)

    def frozen(self):
        self._invalid = 0
        frozen = composite_transform_factory(self._a.frozen(), self._b.frozen())
        if not isinstance(frozen, CompositeGenericTransform):
            return frozen.frozen()
        return frozen
    frozen.__doc__ = Transform.frozen.__doc__

    def _get_is_affine(self):
        return self._a.is_affine and self._b.is_affine
    is_affine = property(_get_is_affine)

    def _get_is_separable(self):
        return self._a.is_separable and self._b.is_separable
    is_separable = property(_get_is_separable)

    def __repr__(self):
        return "CompositeGenericTransform(%s, %s)" % (self._a, self._b)
    __str__ = __repr__

    def transform(self, points):
        return self._b.transform(
            self._a.transform(points))
    transform.__doc__ = Transform.transform.__doc__

    def transform_affine(self, points):
        return self.get_affine().transform(points)
    transform_affine.__doc__ = Transform.transform_affine.__doc__

    def transform_non_affine(self, points):
        if self._a.is_affine and self._b.is_affine:
            return points
        return self._b.transform_non_affine(
            self._a.transform(points))
    transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__

    def transform_path(self, path):
        return self._b.transform_path(
            self._a.transform_path(path))
    transform_path.__doc__ = Transform.transform_path.__doc__

    def transform_path_affine(self, path):
        return self._b.transform_path_affine(
            self._a.transform_path(path))
    transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__

    def transform_path_non_affine(self, path):
        if self._a.is_affine and self._b.is_affine:
            return path
        return self._b.transform_path_non_affine(
            self._a.transform_path(path))
    transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__

    def get_affine(self):
        if self._a.is_affine and self._b.is_affine:
            return Affine2D(np.dot(self._b.get_affine().get_matrix(),
                                    self._a.get_affine().get_matrix()))
        else:
            return self._b.get_affine()
    get_affine.__doc__ = Transform.get_affine.__doc__

    def inverted(self):
        return CompositeGenericTransform(self._b.inverted(), self._a.inverted())
    inverted.__doc__ = Transform.inverted.__doc__


class CompositeAffine2D(Affine2DBase):
    """
    A composite transform formed by applying transform a then transform b.

    This version is an optimization that handles the case where both a
    and b are 2D affines.
    """
    def __init__(self, a, b):
        """
        Create a new composite transform that is the result of
        applying transform a then transform b.

        Both a and b must be instances of :class:`Affine2DBase`.

        You will generally not call this constructor directly but use
        the :func:`composite_transform_factory` function instead,
        which can automatically choose the best kind of composite
        transform instance to create.
        """
        assert a.output_dims == b.input_dims
        self.input_dims = a.input_dims
        self.output_dims = b.output_dims
        assert a.is_affine
        assert b.is_affine

        Affine2DBase.__init__(self)
        self._a = a
        self._b = b
        self.set_children(a, b)
        self._mtx = None

    def __repr__(self):
        return "CompositeAffine2D(%s, %s)" % (self._a, self._b)
    __str__ = __repr__

    def get_matrix(self):
        if self._invalid:
            self._mtx = np.dot(
                self._b.get_matrix(),
                self._a.get_matrix())
            self._inverted = None
            self._invalid = 0
        return self._mtx
    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__


def composite_transform_factory(a, b):
    """
    Create a new composite transform that is the result of applying
    transform a then transform b.

    Shortcut versions of the blended transform are provided for the
    case where both child transforms are affine, or one or the other
    is the identity transform.

    Composite transforms may also be created using the '+' operator,
    e.g.:

      c = a + b
    """
    if isinstance(a, IdentityTransform):
        return b
    elif isinstance(b, IdentityTransform):
        return a
    elif isinstance(a, AffineBase) and isinstance(b, AffineBase):
        return CompositeAffine2D(a, b)
    return CompositeGenericTransform(a, b)


class BboxTransform(Affine2DBase):
    """
    BboxTransform linearly transforms points from one Bbox to another Bbox.
    """
    is_separable = True

    def __init__(self, boxin, boxout):
        """
        Create a new BboxTransform that linearly transforms points
        from boxin to boxout.
        """
        assert boxin.is_bbox
        assert boxout.is_bbox

        Affine2DBase.__init__(self)
        self._boxin = boxin
        self._boxout = boxout
        self.set_children(boxin, boxout)
        self._mtx = None
        self._inverted = None

    def __repr__(self):
        return "BboxTransform(%s, %s)" % (self._boxin, self._boxout)
    __str__ = __repr__

    def get_matrix(self):
        if self._invalid:
            inl, inb, inw, inh = self._boxin.bounds
            outl, outb, outw, outh = self._boxout.bounds
            x_scale = outw / inw
            y_scale = outh / inh
            if DEBUG and (x_scale == 0 or y_scale == 0):
                raise ValueError("Transforming from or to a singular bounding box.")
            self._mtx = np.array([[x_scale, 0.0    , (-inl*x_scale+outl)],
                                   [0.0    , y_scale, (-inb*y_scale+outb)],
                                   [0.0    , 0.0    , 1.0        ]],
                                  np.float_)
            self._inverted = None
            self._invalid = 0
        return self._mtx
    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__


class BboxTransformTo(Affine2DBase):
    """
    BboxTransformTo is a transformation that linearly transforms
    points from the unit bounding box to a given :class:`Bbox`.
    """
    is_separable = True

    def __init__(self, boxout):
        """
        Create a new :class:`BboxTransformTo` that linearly transforms
        points from the unit bounding box to boxout.
        """
        assert boxout.is_bbox

        Affine2DBase.__init__(self)
        self._boxout = boxout
        self.set_children(boxout)
        self._mtx = None
        self._inverted = None

    def __repr__(self):
        return "BboxTransformTo(%s)" % (self._boxout)
    __str__ = __repr__

    def get_matrix(self):
        if self._invalid:
            outl, outb, outw, outh = self._boxout.bounds
            if DEBUG and (outw == 0 or outh == 0):
                raise ValueError("Transforming to a singular bounding box.")
            self._mtx = np.array([[outw,  0.0, outl],
                                   [ 0.0, outh, outb],
                                   [ 0.0,  0.0,  1.0]],
                                  np.float_)
            self._inverted = None
            self._invalid = 0
        return self._mtx
    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__


class BboxTransformFrom(Affine2DBase):
    """
    BboxTransform linearly transforms points from a given
    :class:`Bbox` to the unit bounding box.
    """
    is_separable = True

    def __init__(self, boxin):
        assert boxin.is_bbox

        Affine2DBase.__init__(self)
        self._boxin = boxin
        self.set_children(boxin)
        self._mtx = None
        self._inverted = None

    def __repr__(self):
        return "BboxTransformFrom(%s)" % (self._boxin)
    __str__ = __repr__

    def get_matrix(self):
        if self._invalid:
            inl, inb, inw, inh = self._boxin.bounds
            if DEBUG and (inw == 0 or inh == 0):
                raise ValueError("Transforming from a singular bounding box.")
            x_scale = 1.0 / inw
            y_scale = 1.0 / inh
            self._mtx = np.array([[x_scale, 0.0    , (-inl*x_scale)],
                                   [0.0    , y_scale, (-inb*y_scale)],
                                   [0.0    , 0.0    , 1.0        ]],
                                  np.float_)
            self._inverted = None
            self._invalid = 0
        return self._mtx
    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__


class ScaledTranslation(Affine2DBase):
    """
    A transformation that translates by xt and yt, after xt and yt
    have been transformaed by the given transform scale_trans.
    """
    def __init__(self, xt, yt, scale_trans):
        Affine2DBase.__init__(self)
        self._t = (xt, yt)
        self._scale_trans = scale_trans
        self.set_children(scale_trans)
        self._mtx = None
        self._inverted = None

    def __repr__(self):
        return "ScaledTranslation(%s)" % (self._t,)
    __str__ = __repr__

    def get_matrix(self):
        if self._invalid:
            xt, yt = self._scale_trans.transform_point(self._t)
            self._mtx = np.array([[1.0, 0.0, xt],
                                   [0.0, 1.0, yt],
                                   [0.0, 0.0, 1.0]],
                                  np.float_)
            self._invalid = 0
            self._inverted = None
        return self._mtx
    get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__


class TransformedPath(TransformNode):
    """
    A TransformedPath caches a non-affine transformed copy of the
    path.  This cached copy is automatically updated when the
    non-affine part of the transform changes.
    """
    def __init__(self, path, transform):
        """
        Create a new TransformedPath from the given path and transform.
        """
        assert isinstance(transform, Transform)
        TransformNode.__init__(self)

        self._path = path
        self._transform = transform
        self.set_children(transform)
        self._transformed_path = None
        self._transformed_points = None

    def _revalidate(self):
        if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE)
            or self._transformed_path is None):
            self._transformed_path = \
                self._transform.transform_path_non_affine(self._path)
            self._transformed_points = \
                Path(self._transform.transform_non_affine(self._path.vertices))
        self._invalid = 0

    def get_transformed_points_and_affine(self):
        """
        Return a copy of the child path, with the non-affine part of
        the transform already applied, along with the affine part of
        the path necessary to complete the transformation.  Unlike
        get_transformed_path_and_affine, no interpolation will be
        performed.
        """
        self._revalidate()
        return self._transformed_points, self.get_affine()

    def get_transformed_path_and_affine(self):
        """
        Return a copy of the child path, with the non-affine part of
        the transform already applied, along with the affine part of
        the path necessary to complete the transformation.
        """
        self._revalidate()
        return self._transformed_path, self.get_affine()

    def get_fully_transformed_path(self):
        """
        Return a fully-transformed copy of the child path.
        """
        if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE)
            or self._transformed_path is None):
            self._transformed_path = \
                self._transform.transform_path_non_affine(self._path)
        self._invalid = 0
        return self._transform.transform_path_affine(self._transformed_path)

    def get_affine(self):
        return self._transform.get_affine()


def nonsingular(vmin, vmax, expander=0.001, tiny=1e-15, increasing=True):
    '''
    Ensure the endpoints of a range are not too close together.

    "too close" means the interval is smaller than 'tiny' times
    the maximum absolute value.

    If they are too close, each will be moved by the 'expander'.
    If 'increasing' is True and vmin > vmax, they will be swapped,
    regardless of whether they are too close.
    '''
    swapped = False
    if vmax < vmin:
        vmin, vmax = vmax, vmin
        swapped = True
    if vmax - vmin <= max(abs(vmin), abs(vmax)) * tiny:
        if vmin == 0.0:
            vmin = -expander
            vmax = expander
        else:
            vmin -= expander*abs(vmin)
            vmax += expander*abs(vmax)
    if swapped and not increasing:
        vmin, vmax = vmax, vmin
    return vmin, vmax


def interval_contains(interval, val):
    a, b = interval
    return (
        ((a < b) and (a <= val and b >= val))
        or (b <= val and a >= val))

def interval_contains_open(interval, val):
    a, b = interval
    return (
        ((a < b) and (a < val and b > val))
        or (b < val and a > val))

def offset_copy(trans, fig, x=0.0, y=0.0, units='inches'):
    '''
    Return a new transform with an added offset.
      args:
        trans is any transform
      kwargs:
        fig is the current figure; it can be None if units are 'dots'
        x, y give the offset
        units is 'inches', 'points' or 'dots'
    '''
    if units == 'dots':
        return trans + Affine2D().translate(x, y)
    if fig is None:
        raise ValueError('For units of inches or points a fig kwarg is needed')
    if units == 'points':
        x /= 72.0
        y /= 72.0
    elif not units == 'inches':
        raise ValueError('units must be dots, points, or inches')
    return trans + ScaledTranslation(x, y, fig.dpi_scale_trans)

if __name__ == '__main__':
    import copy
    from random import random
    import timeit

    bbox = Bbox.from_extents(10., 15., 20., 25.)
    assert bbox.x0 == 10
    assert bbox.y0 == 15
    assert bbox.x1 == 20
    assert bbox.y1 == 25

    assert np.all(bbox.min == [10, 15])
    assert np.all(bbox.max == [20, 25])
    assert np.all(bbox.intervalx == (10, 20))
    assert np.all(bbox.intervaly == (15, 25))

    assert bbox.width == 10
    assert bbox.height == 10

    assert bbox.bounds == (10, 15, 10, 10)

    assert tuple(np.asarray(bbox).flatten()) == (10, 15, 20, 25)

    bbox.intervalx = (11, 21)
    bbox.intervaly = (16, 26)

    assert bbox.bounds == (11, 16, 10, 10)

    bbox.x0 = 12
    bbox.y0 = 17
    bbox.x1 = 22
    bbox.y1 = 27

    assert bbox.bounds == (12, 17, 10, 10)

    bbox = Bbox.from_bounds(10, 11, 12, 13)
    assert bbox.bounds == (10, 11, 12, 13)

    bbox_copy = copy.deepcopy(bbox)
    assert (bbox.extents == bbox_copy.extents).all()
    bbox_copy.p1 = (14, 15)
    assert bbox.bounds == (10, 11, 12, 13)
    assert bbox_copy.bounds == (10, 11, 4, 4)

    bbox1 = Bbox([[10., 15.], [20., 25.]])
    bbox2 = Bbox([[30., 35.], [40., 45.]])
    trans = BboxTransform(bbox1, bbox2)
    bbox3 = bbox1.transformed(trans)
    assert (bbox3.extents == bbox2.extents).all()

    translation = Affine2D().translate(10, 20)
    assert translation.to_values() == (1, 0, 0, 1, 10, 20)
    scale = Affine2D().scale(10, 20)
    assert scale.to_values() == (10, 0, 0, 20, 0, 0)
    rotation = Affine2D().rotate_deg(30)
    assert rotation.to_values() == (0.86602540378443871, 0.49999999999999994,
                                   -0.49999999999999994, 0.86602540378443871,
                                   0.0, 0.0)

    points = np.array([[1, 2], [3, 4], [5, 6], [7, 8]], np.float_)
    translated_points = translation.transform(points)
    assert (translated_points == [[11., 22.], [13., 24.], [15., 26.], [17., 28.]]).all()
    scaled_points = scale.transform(points)
    print scaled_points
    rotated_points = rotation.transform(points)
    print rotated_points

    tpoints1 = rotation.transform(translation.transform(scale.transform(points)))
    trans_sum = scale + translation + rotation
    tpoints2 = trans_sum.transform(points)
    # Need to do some sort of fuzzy comparison here?
    assert (tpoints1.round() == tpoints2.round()).all()

    print points

    # Here are some timing tests
    points = np.asarray([(random(), random()) for i in xrange(10000)])
    t = timeit.Timer("trans_sum.transform(points)", "from __main__ import trans_sum, points")
    print "Time to transform 10000 x 10 points:", t.timeit(10)
