Revision: 3850
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=3850&view=rev
Author: mdboom
Date: 2007-09-14 05:24:20 -0700 (Fri, 14 Sep 2007)
Log Message:
-----------
Deleting this file to rename affine.py
Removed Paths:
-------------
branches/transforms/lib/matplotlib/transforms.py
Deleted: branches/transforms/lib/matplotlib/transforms.py
===================================================================
--- branches/transforms/lib/matplotlib/transforms.py 2007-09-14 12:23:06 UTC
(rev 3849)
+++ branches/transforms/lib/matplotlib/transforms.py 2007-09-14 12:24:20 UTC
(rev 3850)
@@ -1,652 +0,0 @@
-# """
-# The transforms module is broken into two parts, a collection of
-# classes written in the extension module _transforms to handle
-# efficient transformation of data, and some helper functions in
-# transforms to make it easy to instantiate and use those objects.
-# Hence the core of this module lives in _transforms.
-
-# The transforms class is built around the idea of a LazyValue. A
-# LazyValue is a base class that defines a method get that returns the
-# value. The concrete derived class Value wraps a float, and simply
-# returns the value of that float. The concrete derived class BinOp
-# allows binary operations on LazyValues, so you can add them, multiply
-# them, etc. When you do something like
-
-# inches = Value(8)
-# dpi = Value(72)
-# width = inches * dpi
-
-# width is a BinOp instance (that tells you the width of the figure in
-# pixels). Later, if the figure size in changed, ie we call
-
-# inches.set(10)
-
-# The width variable is automatically updated because it stores a
-# pointer to the inches variable, not the value. Since a BinOp is also
-# a lazy value, you can define binary operations on BinOps as well, such
-# as
-
-# middle = Value(0.5) * width
-
-# Pairs of LazyValue instances can occur as instances of two classes:
-
-# pt = Point( Value(x), Value(y)) # where x, y are numbers
-# pt.x(), pt.y() return Value(x), Value(y))
-
-# iv = Interval( Value(x), Value(y))
-# iv.contains(z) returns True if z is in the closed interval
-# iv.contains_open(z): same for open interval
-# iv.span() returns y-x as a float
-# iv.get_bounds() returns (x,y) as a tuple of floats
-# iv.set_bounds(x, y) allows input of new floats
-# iv.update(seq) updates the bounds to include all elements
-# in a sequence of floats
-# iv.shift(s) shifts the interval by s, a float
-
-# The bounding box class Bbox is also heavily used, and is defined by a
-# lower left point ll and an upper right point ur. The points ll and ur
-# are given by Point(x, y) instances, where x and y are LazyValues. So
-# you can represent a point such as
-
-# ll = Point( Value(0), Value(0) ) # the origin
-# ur = Point( width, height ) # the upper right of the figure
-
-# where width and height are defined as above, using the product of the
-# figure width in inches and the dpi. This is, in face, how the Figure
-# bbox is defined
-
-# bbox = Bbox(ll, ur)
-
-# A bbox basically defines an x,y coordinate system, with ll giving the
-# lower left of the coordinate system and ur giving the upper right.
-
-# The bbox methods are
-
-# ll() - return the lower left Point
-# ur() - return the upper right Point
-# contains(x,y) - return True if self contains point
-# overlaps(bbox) - return True if self overlaps bbox
-# overlapsx(bbox) - return True if self overlaps bbox in the x interval
-# overlapsy(bbox) - return True if self overlaps bbox in the y interval
-# intervalx() - return the x Interval instance
-# intervaly() - return the y interval instance
-# get_bounds() - get the left, bottom, width, height bounding tuple
-# update(xys, ignore) - update the bbox to bound all the xy tuples in
-# xys; if ignore is true ignore the current contents of bbox and
-# just bound the tuples. If ignore is false, bound self + tuples
-# width() - return the width of the bbox
-# height() - return the height of the bbox
-# xmax() - return the x coord of upper right
-# ymax() - return the y coord of upper right
-# xmin() - return the x coord of lower left
-# ymin() - return the y coord of lower left
-# scale(sx,sy) - scale the bbox by sx, sy
-# deepcopy() - return a deep copy of self (pointers are lost)
-
-
-# The basic transformation maps one bbox to another, with an optional
-# nonlinear transformation of one of coordinates (eg log scaling).
-
-# The base class for transformations is Transformation, and the concrete
-# derived classes are SeparableTransformation and Affine. Earlier
-# versions of matplotlib handled transformation of x and y separately
-# (ie we assumed all transformations were separable) but this makes it
-# difficult to do rotations or polar transformations, for example. All
-# artists contain their own transformation, defaulting to the identity
-# transform.
-
-# The signature of a separable transformation instance is
-
-# trans = SeparableTransformation(bbox1, bbox2, funcx, funcy)
-
-# where funcx and funcy operate on x and y. The typical linear
-# coordinate transformation maps one bounding box to another, with funcx
-# and funcy both identity. Eg,
-
-# transData = Transformation(viewLim, displayLim,
-# Func(IDENTITY), Func(IDENTITY))
-
-# maps the axes view limits to display limits. If the xaxis scaling is
-# changed to log, one simply calls
-
-# transData.get_funcx().set_type(LOG10)
-
-# For more general transformations including rotation, the Affine class
-# is provided, which is constructed with 6 LazyValue instances:
-# a, b, c, d, tx, ty. These give the values of the matrix transformation
-
-# [xo = |a c| [xi + [tx
-# yo] |b d| yi] ty]
-
-# where if sx, sy are the scaling components, tx, y are the translation
-# components, and alpha is the rotation
-
-# a = sx*cos(alpha);
-# b = -sx*sin(alpha);
-# c = sy*sin(alpha);
-# d = sy*cos(alpha);
-
-# The affine transformation can be accomplished for row vectors with a
-# single matrix multiplication
-# X_new = X_old * M
-# where
-# M = [ a b 0
-# c d 0
-# tx ty 1]
-# and each X is the row vector [x, y, 1]. Hence M is
-# the transpose of the matrix representation given in
-# http://en.wikipedia.org/wiki/Affine_transformation,
-# which is for the more usual column-vector representation
-# of the position.)
-
-
-
-# From a user perspective, the most important Tranformation methods are
-
-# All transformations
-# -------------------
-# freeze() - eval and freeze the lazy objects
-# thaw() - release the lazy objects
-
-# xy_tup(xy) - transform the tuple (x,y)
-# seq_x_y(x, y) - transform the python sequences x and y
-# numerix_x_y(x, y) - x and y are numerix 1D arrays
-# numerix_xy(xy) - xy is a numerix array of shape (N,2)
-# inverse_numerix_xy(xy)- inverse of the above
-# seq_xy_tups(seq) - seq is a sequence of xy tuples or a (N,2) array
-# inverse_xy_tup(xy) - apply the inverse transformation to tuple xy
-
-# set_offset(xy, trans) - xy is an x,y tuple and trans is a
-# Transformation instance. This will apply a post transformational
-# offset of all future transformations by xt,yt = trans.xy_tup(xy[0],
xy[1])
-
-# deepcopy() - returns a deep copy; references are lost
-# shallowcopy() - returns a shallow copy excluding the offset
-
-# Separable transformations
-# -------------------------
-
-# get_bbox1() - return the input bbox
-# get_bbox2() - return the output bbox
-# set_bbox1() - set the input bbox
-# set_bbox2() - set the output bbox
-# get_funcx() - return the Func instance on x
-# get_funcy() - return the Func instance on y
-# set_funcx() - set the Func instance on x
-# set_funcy() - set the Func instance on y
-
-
-# Affine transformations
-# ----------------------
-
-# as_vec6() - return the affine as length 6 list of Values
-
-
-# In general, you shouldn't need to construct your own transformations,
-# but should use the helper functions defined in this module.
-
-
-# zero - return Value(0)
-# one - return Value(1)
-# origin - return Point(zero(), zero())
-# unit_bbox - return the 0,0 to 1,1 bounding box
-# identity_affine - An affine identity transformation
-# identity_transform - An identity separable transformation
-# translation_transform - a pure translational affine
-# scale_transform - a pure scale affine
-# scale_sep_transform - a pure scale separable transformation
-# scale_translation_transform - a scale and translate affine
-# bound_vertices - return the bbox that bounds all the xy tuples
-# bbox_all - return the bbox that bounds all the bboxes
-# lbwh_to_bbox - build a bbox from tuple
-# left, bottom, width, height tuple
-
-# multiply_affines - return the affine that is the matrix product
of
-# the two affines
-
-# get_bbox_transform - return a SeparableTransformation instance
that
-# transforms one bbox to another
-
-# blend_xy_sep_transform - mix the x and y components of two separable
-# transformations into a new transformation.
-# This allows you to specify x and y in
-# different coordinate systems
-
-# transform_bbox - apply a transformation to a bbox and return
the
-# transformed bbox
-
-# inverse_transform_bbox - apply the inverse transformation of a bbox
-# and return the inverse transformed bbox
-
-# offset_copy - make a copy with an offset
-
-
-# The units/transform_unit.py code has many examples.
-
-# A related and partly overlapping class, PBox, has been added to the
-# original transforms module to facilitate Axes repositioning and resizing.
-# At present, the differences between Bbox and PBox include:
-
-# Bbox works with the bounding box, the coordinates of the lower-left
-# and upper-right corners; PBox works with the lower-left coordinates
-# and the width, height pair (left, bottom, width, height, or 'lbwh').
-# Obviously, these are equivalent, but lbwh is what is used by
-# Axes._position, and it is the natural specification for the types of
-# manipulations for which the PBox class was made.
-
-# Bbox uses LazyValues grouped in pairs as 'll' and 'ur' Point objects;
-# PBox uses a 4-element list, subclassed from the python list.
-
-# Bbox and PBox methods are mostly quite different, reflecting their
-# different original purposes. Similarly, the CXX implementation of
-# Bbox is good for methods such as update and for lazy evaluation, but
-# for PBox intended uses, involving very little calculation, pure
-# python probably is adequate.
-
-# In the future we may reimplement the PBox using Bbox
-# and transforms, or eliminate it entirely by adding its methods
-# and attributes to Bbox and/or putting them elsewhere in this module.
-# """
-# from __future__ import division
-# import math
-# import numpy as npy
-
-# from matplotlib._transforms import Value, Point, Interval, Bbox, Affine
-# from matplotlib._transforms import IDENTITY, LOG10, POLAR, Func, FuncXY
-# from matplotlib._transforms import SeparableTransformation
-# from matplotlib._transforms import NonseparableTransformation
-
-# 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 zero(): return Value(0)
-
-# def one() : return Value(1)
-
-# def origin():
-# return Point( zero(), zero() )
-
-# def unit_bbox():
-# """
-# Get a 0,0 -> 1,1 Bbox instance
-# """
-# return Bbox( origin(), Point( one(), one() ) )
-
-# def identity_affine():
-# """
-# Get an affine transformation that maps x,y -> x,y
-# """
-
-# return Affine(one(), zero(), zero(), one(), zero(), zero())
-
-# def identity_transform():
-# """
-# Get an affine transformation that maps x,y -> x,y
-# """
-# return SeparableTransformation(unit_bbox(), unit_bbox(),
-# Func(IDENTITY),
-# Func(IDENTITY))
-
-# def translation_transform(tx, ty):
-# """
-# return a pure tranlational transformation tx and ty are LazyValue
-# instances (Values or binary operations on values)
-# """
-# return Affine(one(), zero(), zero(), one(), tx, ty)
-
-# def scale_transform(sx, sy):
-# """
-# Return a pure scale transformation as an Affine instance; sx and
-# sy are LazyValue instances (Values or binary opertations on
-# values)
-# """
-# return Affine(sx, zero(), zero(), sy, zero(), zero())
-
-# def scale_sep_transform(sx, sy):
-# """
-# Return a pure scale transformation as a SeparableTransformation;
-# sx and sy are LazyValue instances (Values or binary opertations on
-# values)
-# """
-
-# bboxin = unit_bbox()
-# bboxout = Bbox( Point( zero(), zero() ),
-# Point( sx, sy ) )
-# return SeparableTransformation(
-# bboxin, bboxout,
-# Func(IDENTITY), Func(IDENTITY))
-
-
-
-# def bound_vertices(verts):
-# """
-# Return the Bbox of the sequence of x,y tuples in verts
-# """
-# # this is a good candidate to move to _transforms
-# bbox = unit_bbox()
-# bbox.update(verts, 1)
-# return bbox
-
-# def bbox_all(bboxes):
-# """
-# Return the Bbox that bounds all bboxes
-# """
-# # this is a good candidate to move to _transforms
-# assert(len(bboxes))
-
-# if len(bboxes)==1: return bboxes[0]
-
-# bbox = bboxes[0]
-# minx = bbox.xmin()
-# miny = bbox.ymin()
-# maxx = bbox.xmax()
-# maxy = bbox.ymax()
-
-# for bbox in bboxes[1:]:
-# thisminx = bbox.xmin()
-# thisminy = bbox.ymin()
-# thismaxx = bbox.xmax()
-# thismaxy = bbox.ymax()
-
-# if thisminx < minx : minx = thisminx
-# if thismaxx > maxx : maxx = thismaxx
-# if thisminy < miny : miny = thisminy
-# if thismaxy > maxy : maxy = thismaxy
-
-# return Bbox( Point( Value(minx), Value(miny) ),
-# Point( Value(maxx), Value(maxy) )
-# )
-
-# def lbwh_to_bbox(l,b,w,h):
-# return Bbox( Point( Value(l), Value(b)),
-# Point( Value(l+w), Value(b + h) ) )
-
-
-# def invert_vec6(v):
-# """
-# v is a,b,c,d,tx,ty vec6 repr of an affine transformation.
-# Return the inverse of v as a vec6
-# """
-# a,b,c,d,tx,ty = v
-# M = npy.array([ [a,b,0], [c,d,0], [tx,ty,1]], dtype=npy.float64)
-# Mi = npy.linalg.inv(M)
-# return Mi[:,:2].ravel()
-
-# def multiply_affines( v1, v2):
-# """
-# v1 and v2 are Affine instances
-# """
-
-# a1, b1, c1, d1, tx1, ty1 = v1.as_vec6()
-# a2, b2, c2, d2, tx2, ty2 = v2.as_vec6()
-
-# a = a1 * a2 + c1 * b2
-# b = b1 * a2 + d1 * b2
-# c = a1 * c2 + c1 * d2
-# d = b1 * c2 + d1 * d2
-# tx = a1 * tx2 + c1 * ty2 + tx1
-# ty = b1 * tx2 + d1 * ty2 + ty1
-# return Affine(a,b,c,d,tx,ty)
-
-# def get_bbox_transform(boxin, boxout):
-# """
-# return the transform that maps transform one bounding box to
-# another
-# """
-# return SeparableTransformation(
-# boxin, boxout, Func(IDENTITY), Func( IDENTITY))
-
-
-# def blend_xy_sep_transform(trans1, trans2):
-# """
-# If trans1 and trans2 are SeparableTransformation instances, you can
-# build a new SeparableTransformation from them by extracting the x and y
-# bounding points and functions and recomposing a new
SeparableTransformation
-
-# This function extracts all the relevant bits from trans1 and
-# trans2 and returns the new Transformation instance. This is
-# useful, for example, if you want to specify x in data coordinates
-# and y in axes coordinates.
-# """
-
-# inboxx = trans1.get_bbox1()
-# inboxy = trans2.get_bbox1()
-
-# outboxx = trans1.get_bbox2()
-# outboxy = trans2.get_bbox2()
-
-# xminIn = inboxx.ll().x()
-# xmaxIn = inboxx.ur().x()
-# xminOut = outboxx.ll().x()
-# xmaxOut = outboxx.ur().x()
-
-# yminIn = inboxy.ll().y()
-# ymaxIn = inboxy.ur().y()
-# yminOut = outboxy.ll().y()
-# ymaxOut = outboxy.ur().y()
-
-# funcx = trans1.get_funcx()
-# funcy = trans2.get_funcy()
-
-# boxin = Bbox( Point(xminIn, yminIn), Point(xmaxIn, ymaxIn) )
-# boxout = Bbox( Point(xminOut, yminOut), Point(xmaxOut, ymaxOut) )
-
-# return SeparableTransformation(boxin, boxout, funcx, funcy)
-
-
-# def transform_bbox(trans, bbox):
-# 'transform the bbox to a new bbox'
-# xmin, xmax = bbox.intervalx().get_bounds()
-# ymin, ymax = bbox.intervaly().get_bounds()
-
-# xmin, ymin = trans.xy_tup((xmin, ymin))
-# xmax, ymax = trans.xy_tup((xmax, ymax))
-
-# return Bbox(Point(Value(xmin), Value(ymin)),
-# Point(Value(xmax), Value(ymax)))
-
-# def inverse_transform_bbox(trans, bbox):
-# 'inverse transform the bbox'
-# xmin, xmax = bbox.intervalx().get_bounds()
-# ymin, ymax = bbox.intervaly().get_bounds()
-
-# xmin, ymin = trans.inverse_xy_tup((xmin, ymin))
-# xmax, ymax = trans.inverse_xy_tup((xmax, ymax))
-# return Bbox(Point(Value(xmin), Value(ymin)),
-# Point(Value(xmax), Value(ymax)))
-
-
-# def offset_copy(trans, fig=None, x=0, y=0, units='inches'):
-# '''
-# Return a shallow copy of a 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'
-# '''
-# newtrans = trans.shallowcopy()
-# if units == 'dots':
-# newtrans.set_offset((x,y), identity_transform())
-# return newtrans
-# 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')
-# tx = Value(x) * fig.dpi
-# ty = Value(y) * fig.dpi
-# newtrans.set_offset((0,0), translation_transform(tx, ty))
-# return newtrans
-
-
-# def get_vec6_scales(v):
-# 'v is an affine vec6 a,b,c,d,tx,ty; return sx, sy'
-# a,b,c,d = v[:4]
-# sx = math.sqrt(a**2 + b**2)
-# sy = math.sqrt(c**2 + d**2)
-# return sx, sy
-
-# def get_vec6_rotation(v):
-# 'v is an affine vec6 a,b,c,d,tx,ty; return rotation in degrees'
-# sx, sy = get_vec6_scales(v)
-# c,d = v[2:4]
-# angle = math.atan2(c,d)/math.pi*180
-# return angle
-
-
-
-# class PBox(list):
-# '''
-# A left-bottom-width-height (lbwh) specification of a bounding box,
-# such as is used to specify the position of an Axes object within
-# a Figure.
-# It is a 4-element list with methods for changing the size, shape,
-# and position relative to its container.
-# '''
-# 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 __init__(self, box, container=None, llur=False):
-# if len(box) != 4:
-# raise ValueError("Argument must be iterable of length 4")
-# if llur:
-# box = [box[0], box[1], box[2]-box[0], box[3]-box[1]]
-# list.__init__(self, box)
-# self.set_container(container)
-
-# def as_llur(self):
-# return [self[0], self[1], self[0]+self[2], self[1]+self[3]]
-
-# def set_container(self, box=None):
-# if box is None:
-# box = self
-# if len(box) != 4:
-# raise ValueError("Argument must be iterable of length 4")
-# self._container = list(box)
-
-# def get_container(self, box):
-# return self._container
-
-# def anchor(self, c, container=None):
-# '''
-# Shift to position c within its container.
-
-# c can be a sequence (cx, cy) where cx, cy range from 0 to 1,
-# where 0 is left or bottom and 1 is right or top.
-
-# Alternatively, c can be a string: C for centered,
-# S for bottom-center, SE for bottom-left, E for left, etc.
-
-# Optional arg container is the lbwh box within which the
-# PBox is positioned; it defaults to the initial
-# PBox.
-# '''
-# if container is None:
-# container = self._container
-# l,b,w,h = container
-# if isinstance(c, str):
-# cx, cy = self.coefs[c]
-# else:
-# cx, cy = c
-# W,H = self[2:]
-# self[:2] = l + cx * (w-W), b + cy * (h-H)
-# return self
-
-# def shrink(self, mx, my):
-# '''
-# Shrink the box by mx in the x direction and my in the y direction.
-# The lower left corner of the box remains unchanged.
-# Normally mx and my will be <= 1, but this is not enforced.
-# '''
-# assert mx >= 0 and my >= 0
-# self[2:] = mx * self[2], my * self[3]
-# return self
-
-# def shrink_to_aspect(self, box_aspect, fig_aspect = 1):
-# '''
-# Shrink the box 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
-# l,b,w,h = self._container
-# H = w * box_aspect/fig_aspect
-# if H <= h:
-# W = w
-# else:
-# W = h * fig_aspect/box_aspect
-# H = h
-# self[2:] = W,H
-# return self
-
-# def splitx(self, *args):
-# '''
-# e.g., PB.splitx(f1, f2, ...)
-
-# Returns a list of new PBoxes formed by
-# splitting the original one (PB) with vertical lines
-# at fractional positions f1, f2, ...
-# '''
-# boxes = []
-# xf = [0] + list(args) + [1]
-# l,b,w,h = self[:]
-# for xf0, xf1 in zip(xf[:-1], xf[1:]):
-# boxes.append(PBox([l+xf0*w, b, (xf1-xf0)*w, h]))
-# return boxes
-
-# def splity(self, *args):
-# '''
-# e.g., PB.splity(f1, f2, ...)
-
-# Returns a list of new PBoxes formed by
-# splitting the original one (PB) with horizontal lines
-# at fractional positions f1, f2, ..., with y measured
-# positive up.
-# '''
-# boxes = []
-# yf = [0] + list(args) + [1]
-# l,b,w,h = self[:]
-# for yf0, yf1 in zip(yf[:-1], yf[1:]):
-# boxes.append(PBox([l, b+yf0*h, w, (yf1-yf0)*h]))
-# return boxes
-
-
-
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