Merge pull request #1904 from anthrotype/transform-named-tuple
transform: make Transform class a NamedTuple
This commit is contained in:
Cosimo Lupo
GitHub
commit
70f0bb3570
@ -45,7 +45,8 @@ Examples:
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>>>
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"""
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from fontTools.misc.py23 import *
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from typing import NamedTuple
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__all__ = ["Transform", "Identity", "Offset", "Scale"]
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@ -65,7 +66,7 @@ def _normSinCos(v):
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return v
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class Transform(object):
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class Transform(NamedTuple):
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"""2x2 transformation matrix plus offset, a.k.a. Affine transform.
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Transform instances are immutable: all transforming methods, eg.
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@ -82,20 +83,68 @@ class Transform(object):
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>>>
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>>> t.scale(2, 3).transformPoint((100, 100))
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(200, 300)
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Transform's constructor takes six arguments, all of which are
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optional, and can be used as keyword arguments:
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>>> Transform(12)
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<Transform [12 0 0 1 0 0]>
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>>> Transform(dx=12)
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<Transform [1 0 0 1 12 0]>
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>>> Transform(yx=12)
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<Transform [1 0 12 1 0 0]>
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Transform instances also behave like sequences of length 6:
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>>> len(Identity)
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6
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>>> list(Identity)
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[1, 0, 0, 1, 0, 0]
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>>> tuple(Identity)
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(1, 0, 0, 1, 0, 0)
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Transform instances are comparable:
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>>> t1 = Identity.scale(2, 3).translate(4, 6)
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>>> t2 = Identity.translate(8, 18).scale(2, 3)
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>>> t1 == t2
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1
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But beware of floating point rounding errors:
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>>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
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>>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
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>>> t1
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> t2
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> t1 == t2
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0
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Transform instances are hashable, meaning you can use them as
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keys in dictionaries:
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>>> d = {Scale(12, 13): None}
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>>> d
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{<Transform [12 0 0 13 0 0]>: None}
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But again, beware of floating point rounding errors:
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>>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
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>>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
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>>> t1
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> t2
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> d = {t1: None}
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>>> d
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{<Transform [0.2 0 0 0.3 0.08 0.18]>: None}
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>>> d[t2]
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Traceback (most recent call last):
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File "<stdin>", line 1, in ?
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KeyError: <Transform [0.2 0 0 0.3 0.08 0.18]>
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"""
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def __init__(self, xx=1, xy=0, yx=0, yy=1, dx=0, dy=0):
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"""Transform's constructor takes six arguments, all of which are
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optional, and can be used as keyword arguments:
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>>> Transform(12)
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<Transform [12 0 0 1 0 0]>
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>>> Transform(dx=12)
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<Transform [1 0 0 1 12 0]>
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>>> Transform(yx=12)
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<Transform [1 0 12 1 0 0]>
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>>>
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"""
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self.__affine = xx, xy, yx, yy, dx, dy
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xx: float = 1
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xy: float = 0
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yx: float = 0
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yy: float = 1
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dx: float = 0
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dy: float = 0
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def transformPoint(self, p):
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"""Transform a point.
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@ -107,7 +156,7 @@ class Transform(object):
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(250.0, 550.0)
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"""
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(x, y) = p
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xx, xy, yx, yy, dx, dy = self.__affine
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xx, xy, yx, yy, dx, dy = self
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return (xx*x + yx*y + dx, xy*x + yy*y + dy)
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def transformPoints(self, points):
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@ -119,7 +168,7 @@ class Transform(object):
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[(0, 0), (0, 300), (200, 300), (200, 0)]
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>>>
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"""
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xx, xy, yx, yy, dx, dy = self.__affine
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xx, xy, yx, yy, dx, dy = self
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return [(xx*x + yx*y + dx, xy*x + yy*y + dy) for x, y in points]
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def translate(self, x=0, y=0):
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@ -188,7 +237,7 @@ class Transform(object):
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>>>
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"""
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xx1, xy1, yx1, yy1, dx1, dy1 = other
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xx2, xy2, yx2, yy2, dx2, dy2 = self.__affine
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xx2, xy2, yx2, yy2, dx2, dy2 = self
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return self.__class__(
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xx1*xx2 + xy1*yx2,
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xx1*xy2 + xy1*yy2,
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@ -210,7 +259,7 @@ class Transform(object):
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<Transform [8 6 6 3 21 15]>
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>>>
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"""
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xx1, xy1, yx1, yy1, dx1, dy1 = self.__affine
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xx1, xy1, yx1, yy1, dx1, dy1 = self
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xx2, xy2, yx2, yy2, dx2, dy2 = other
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return self.__class__(
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xx1*xx2 + xy1*yx2,
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@ -232,9 +281,9 @@ class Transform(object):
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(10.0, 20.0)
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>>>
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"""
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if self.__affine == (1, 0, 0, 1, 0, 0):
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if self == Identity:
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return self
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xx, xy, yx, yy, dx, dy = self.__affine
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xx, xy, yx, yy, dx, dy = self
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det = xx*yy - yx*xy
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xx, xy, yx, yy = yy/det, -xy/det, -yx/det, xx/det
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dx, dy = -xx*dx - yx*dy, -xy*dx - yy*dy
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@ -247,77 +296,7 @@ class Transform(object):
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'[2 0 0 3 8 15]'
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>>>
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"""
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return "[%s %s %s %s %s %s]" % self.__affine
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def __len__(self):
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"""Transform instances also behave like sequences of length 6:
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>>> len(Identity)
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6
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>>>
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"""
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return 6
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def __getitem__(self, index):
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"""Transform instances also behave like sequences of length 6:
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>>> list(Identity)
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[1, 0, 0, 1, 0, 0]
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>>> tuple(Identity)
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(1, 0, 0, 1, 0, 0)
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>>>
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"""
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return self.__affine[index]
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def __ne__(self, other):
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return not self.__eq__(other)
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def __eq__(self, other):
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"""Transform instances are comparable:
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>>> t1 = Identity.scale(2, 3).translate(4, 6)
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>>> t2 = Identity.translate(8, 18).scale(2, 3)
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>>> t1 == t2
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1
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>>>
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But beware of floating point rounding errors:
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>>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
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>>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
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>>> t1
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> t2
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> t1 == t2
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0
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>>>
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"""
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xx1, xy1, yx1, yy1, dx1, dy1 = self.__affine
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xx2, xy2, yx2, yy2, dx2, dy2 = other
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return (xx1, xy1, yx1, yy1, dx1, dy1) == \
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(xx2, xy2, yx2, yy2, dx2, dy2)
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def __hash__(self):
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"""Transform instances are hashable, meaning you can use them as
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keys in dictionaries:
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>>> d = {Scale(12, 13): None}
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>>> d
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{<Transform [12 0 0 13 0 0]>: None}
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>>>
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But again, beware of floating point rounding errors:
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>>> t1 = Identity.scale(0.2, 0.3).translate(0.4, 0.6)
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>>> t2 = Identity.translate(0.08, 0.18).scale(0.2, 0.3)
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>>> t1
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> t2
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<Transform [0.2 0 0 0.3 0.08 0.18]>
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>>> d = {t1: None}
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>>> d
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{<Transform [0.2 0 0 0.3 0.08 0.18]>: None}
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>>> d[t2]
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Traceback (most recent call last):
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File "<stdin>", line 1, in ?
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KeyError: <Transform [0.2 0 0 0.3 0.08 0.18]>
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>>>
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"""
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return hash(self.__affine)
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return "[%s %s %s %s %s %s]" % self
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def __bool__(self):
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"""Returns True if transform is not identity, False otherwise.
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@ -336,13 +315,10 @@ class Transform(object):
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>>> bool(Offset(2))
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True
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"""
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return self.__affine != Identity.__affine
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__nonzero__ = __bool__
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return self != Identity
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def __repr__(self):
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return "<%s [%g %g %g %g %g %g]>" % ((self.__class__.__name__,) \
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+ self.__affine)
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return "<%s [%g %g %g %g %g %g]>" % ((self.__class__.__name__,) + self)
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Identity = Transform()
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