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Speeds up font compilation by around 200%

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Cython is used to compile some hot paths into native Python extensions.
These hot paths were identified through running ufocompile with the hotshot
profiler and then converting file by file to Cython, starting with the "hottest"
paths and continuing until returns were deminishing. This means that only a few
Python files were converted to Cython.

Closes #23
Closes #20 (really this time)
This commit is contained in:
Rasmus Andersson 2017-09-03 23:03:17 -04:00
parent 31ae014e0c
commit 8234b62ab7
108 changed files with 26933 additions and 110 deletions
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*.c
build

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A. HISTORY OF THE SOFTWARE
==========================
Python was created in the early 1990s by Guido van Rossum at Stichting
Mathematisch Centrum (CWI, see http://www.cwi.nl) in the Netherlands
as a successor of a language called ABC. Guido remains Python's
principal author, although it includes many contributions from others.
In 1995, Guido continued his work on Python at the Corporation for
National Research Initiatives (CNRI, see http://www.cnri.reston.va.us)
in Reston, Virginia where he released several versions of the
software.
In May 2000, Guido and the Python core development team moved to
BeOpen.com to form the BeOpen PythonLabs team. In October of the same
year, the PythonLabs team moved to Digital Creations, which became
Zope Corporation. In 2001, the Python Software Foundation (PSF, see
https://www.python.org/psf/) was formed, a non-profit organization
created specifically to own Python-related Intellectual Property.
Zope Corporation was a sponsoring member of the PSF.
All Python releases are Open Source (see http://www.opensource.org for
the Open Source Definition). Historically, most, but not all, Python
releases have also been GPL-compatible; the table below summarizes
the various releases.
Release Derived Year Owner GPL-
from compatible? (1)
0.9.0 thru 1.2 1991-1995 CWI yes
1.3 thru 1.5.2 1.2 1995-1999 CNRI yes
1.6 1.5.2 2000 CNRI no
2.0 1.6 2000 BeOpen.com no
1.6.1 1.6 2001 CNRI yes (2)
2.1 2.0+1.6.1 2001 PSF no
2.0.1 2.0+1.6.1 2001 PSF yes
2.1.1 2.1+2.0.1 2001 PSF yes
2.1.2 2.1.1 2002 PSF yes
2.1.3 2.1.2 2002 PSF yes
2.2 and above 2.1.1 2001-now PSF yes
Footnotes:
(1) GPL-compatible doesn't mean that we're distributing Python under
the GPL. All Python licenses, unlike the GPL, let you distribute
a modified version without making your changes open source. The
GPL-compatible licenses make it possible to combine Python with
other software that is released under the GPL; the others don't.
(2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
because its license has a choice of law clause. According to
CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
is "not incompatible" with the GPL.
Thanks to the many outside volunteers who have worked under Guido's
direction to make these releases possible.
B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
===============================================================
PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
--------------------------------------------
1. This LICENSE AGREEMENT is between the Python Software Foundation
("PSF"), and the Individual or Organization ("Licensee") accessing and
otherwise using this software ("Python") in source or binary form and
its associated documentation.
2. Subject to the terms and conditions of this License Agreement, PSF hereby
grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
analyze, test, perform and/or display publicly, prepare derivative works,
distribute, and otherwise use Python alone or in any derivative version,
provided, however, that PSF's License Agreement and PSF's notice of copyright,
i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
2011, 2012, 2013, 2014, 2015, 2016, 2017 Python Software Foundation; All Rights
Reserved" are retained in Python alone or in any derivative version prepared by
Licensee.
3. In the event Licensee prepares a derivative work that is based on
or incorporates Python or any part thereof, and wants to make
the derivative work available to others as provided herein, then
Licensee hereby agrees to include in any such work a brief summary of
the changes made to Python.
4. PSF is making Python available to Licensee on an "AS IS"
basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
INFRINGE ANY THIRD PARTY RIGHTS.
5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
6. This License Agreement will automatically terminate upon a material
breach of its terms and conditions.
7. Nothing in this License Agreement shall be deemed to create any
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Licensee. This License Agreement does not grant permission to use PSF
trademarks or trade name in a trademark sense to endorse or promote
products or services of Licensee, or any third party.
8. By copying, installing or otherwise using Python, Licensee
agrees to be bound by the terms and conditions of this License
Agreement.
BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
-------------------------------------------
BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
Individual or Organization ("Licensee") accessing and otherwise using
this software in source or binary form and its associated
documentation ("the Software").
2. Subject to the terms and conditions of this BeOpen Python License
Agreement, BeOpen hereby grants Licensee a non-exclusive,
royalty-free, world-wide license to reproduce, analyze, test, perform
and/or display publicly, prepare derivative works, distribute, and
otherwise use the Software alone or in any derivative version,
provided, however, that the BeOpen Python License is retained in the
Software, alone or in any derivative version prepared by Licensee.
3. BeOpen is making the Software available to Licensee on an "AS IS"
basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
INFRINGE ANY THIRD PARTY RIGHTS.
4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
5. This License Agreement will automatically terminate upon a material
breach of its terms and conditions.
6. This License Agreement shall be governed by and interpreted in all
respects by the law of the State of California, excluding conflict of
law provisions. Nothing in this License Agreement shall be deemed to
create any relationship of agency, partnership, or joint venture
between BeOpen and Licensee. This License Agreement does not grant
permission to use BeOpen trademarks or trade names in a trademark
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7. By copying, installing or otherwise using the software, Licensee
agrees to be bound by the terms and conditions of this License
Agreement.
CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
---------------------------------------
1. This LICENSE AGREEMENT is between the Corporation for National
Research Initiatives, having an office at 1895 Preston White Drive,
Reston, VA 20191 ("CNRI"), and the Individual or Organization
("Licensee") accessing and otherwise using Python 1.6.1 software in
source or binary form and its associated documentation.
2. Subject to the terms and conditions of this License Agreement, CNRI
hereby grants Licensee a nonexclusive, royalty-free, world-wide
license to reproduce, analyze, test, perform and/or display publicly,
prepare derivative works, distribute, and otherwise use Python 1.6.1
alone or in any derivative version, provided, however, that CNRI's
License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
1995-2001 Corporation for National Research Initiatives; All Rights
Reserved" are retained in Python 1.6.1 alone or in any derivative
version prepared by Licensee. Alternately, in lieu of CNRI's License
Agreement, Licensee may substitute the following text (omitting the
quotes): "Python 1.6.1 is made available subject to the terms and
conditions in CNRI's License Agreement. This Agreement together with
Python 1.6.1 may be located on the Internet using the following
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Agreement may also be obtained from a proxy server on the Internet
using the following URL: http://hdl.handle.net/1895.22/1013".
3. In the event Licensee prepares a derivative work that is based on
or incorporates Python 1.6.1 or any part thereof, and wants to make
the derivative work available to others as provided herein, then
Licensee hereby agrees to include in any such work a brief summary of
the changes made to Python 1.6.1.
4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
INFRINGE ANY THIRD PARTY RIGHTS.
5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
6. This License Agreement will automatically terminate upon a material
breach of its terms and conditions.
7. This License Agreement shall be governed by the federal
intellectual property law of the United States, including without
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Notwithstanding the foregoing, with regard to derivative works based
on Python 1.6.1 that incorporate non-separable material that was
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License Agreement shall be deemed to create any relationship of
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License Agreement does not grant permission to use CNRI trademarks or
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services of Licensee, or any third party.
8. By clicking on the "ACCEPT" button where indicated, or by copying,
installing or otherwise using Python 1.6.1, Licensee agrees to be
bound by the terms and conditions of this License Agreement.
ACCEPT
CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
--------------------------------------------------
Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
The Netherlands. All rights reserved.
Permission to use, copy, modify, and distribute this software and its
documentation for any purpose and without fee is hereby granted,
provided that the above copyright notice appear in all copies and that
both that copyright notice and this permission notice appear in
supporting documentation, and that the name of Stichting Mathematisch
Centrum or CWI not be used in advertising or publicity pertaining to
distribution of the software without specific, written prior
permission.
STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

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from __future__ import absolute_import
from .copy import copy, deepcopy, Error

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"""Generic (shallow and deep) copying operations.
Interface summary:
import copy
x = copy.copy(y) # make a shallow copy of y
x = copy.deepcopy(y) # make a deep copy of y
For module specific errors, copy.Error is raised.
The difference between shallow and deep copying is only relevant for
compound objects (objects that contain other objects, like lists or
class instances).
- A shallow copy constructs a new compound object and then (to the
extent possible) inserts *the same objects* into it that the
original contains.
- A deep copy constructs a new compound object and then, recursively,
inserts *copies* into it of the objects found in the original.
Two problems often exist with deep copy operations that don't exist
with shallow copy operations:
a) recursive objects (compound objects that, directly or indirectly,
contain a reference to themselves) may cause a recursive loop
b) because deep copy copies *everything* it may copy too much, e.g.
administrative data structures that should be shared even between
copies
Python's deep copy operation avoids these problems by:
a) keeping a table of objects already copied during the current
copying pass
b) letting user-defined classes override the copying operation or the
set of components copied
This version does not copy types like module, class, function, method,
nor stack trace, stack frame, nor file, socket, window, nor array, nor
any similar types.
Classes can use the same interfaces to control copying that they use
to control pickling: they can define methods called __getinitargs__(),
__getstate__() and __setstate__(). See the documentation for module
"pickle" for information on these methods.
"""
import types
import weakref
from copy_reg import dispatch_table
class Error(Exception):
pass
error = Error # backward compatibility
try:
from org.python.core import PyStringMap
except ImportError:
PyStringMap = None
__all__ = ["Error", "copy", "deepcopy"]
def copy(x):
"""Shallow copy operation on arbitrary Python objects.
See the module's __doc__ string for more info.
"""
cls = type(x)
copier = _copy_dispatch.get(cls)
if copier:
return copier(x)
copier = getattr(cls, "__copy__", None)
if copier:
return copier(x)
reductor = dispatch_table.get(cls)
if reductor:
rv = reductor(x)
else:
reductor = getattr(x, "__reduce_ex__", None)
if reductor:
rv = reductor(2)
else:
reductor = getattr(x, "__reduce__", None)
if reductor:
rv = reductor()
else:
raise Error("un(shallow)copyable object of type %s" % cls)
return _reconstruct(x, rv, 0)
_copy_dispatch = d = {}
def _copy_immutable(x):
return x
for t in (type(None), int, long, float, bool, str, tuple,
frozenset, type, xrange, types.ClassType,
types.BuiltinFunctionType, type(Ellipsis),
types.FunctionType, weakref.ref):
d[t] = _copy_immutable
for name in ("ComplexType", "UnicodeType", "CodeType"):
t = getattr(types, name, None)
if t is not None:
d[t] = _copy_immutable
def _copy_with_constructor(x):
return type(x)(x)
for t in (list, dict, set):
d[t] = _copy_with_constructor
def _copy_with_copy_method(x):
return x.copy()
if PyStringMap is not None:
d[PyStringMap] = _copy_with_copy_method
def _copy_inst(x):
if hasattr(x, '__copy__'):
return x.__copy__()
if hasattr(x, '__getinitargs__'):
args = x.__getinitargs__()
y = x.__class__(*args)
else:
y = _EmptyClass()
y.__class__ = x.__class__
if hasattr(x, '__getstate__'):
state = x.__getstate__()
else:
state = x.__dict__
if hasattr(y, '__setstate__'):
y.__setstate__(state)
else:
y.__dict__.update(state)
return y
d[types.InstanceType] = _copy_inst
del d
def deepcopy(x, memo=None, _nil=[]):
"""Deep copy operation on arbitrary Python objects.
See the module's __doc__ string for more info.
"""
if memo is None:
memo = {}
d = id(x)
y = memo.get(d, _nil)
if y is not _nil:
return y
cls = type(x)
copier = _deepcopy_dispatch.get(cls)
if copier:
y = copier(x, memo)
else:
try:
issc = issubclass(cls, type)
except TypeError: # cls is not a class (old Boost; see SF #502085)
issc = 0
if issc:
y = _deepcopy_atomic(x, memo)
else:
copier = getattr(x, "__deepcopy__", None)
if copier:
y = copier(memo)
else:
reductor = dispatch_table.get(cls)
if reductor:
rv = reductor(x)
else:
reductor = getattr(x, "__reduce_ex__", None)
if reductor:
rv = reductor(2)
else:
reductor = getattr(x, "__reduce__", None)
if reductor:
rv = reductor()
else:
raise Error(
"un(deep)copyable object of type %s" % cls)
y = _reconstruct(x, rv, 1, memo)
memo[d] = y
_keep_alive(x, memo) # Make sure x lives at least as long as d
return y
_deepcopy_dispatch = d = {}
def _deepcopy_atomic(x, memo):
return x
d[type(None)] = _deepcopy_atomic
d[type(Ellipsis)] = _deepcopy_atomic
d[int] = _deepcopy_atomic
d[long] = _deepcopy_atomic
d[float] = _deepcopy_atomic
d[bool] = _deepcopy_atomic
try:
d[complex] = _deepcopy_atomic
except NameError:
pass
d[str] = _deepcopy_atomic
try:
d[unicode] = _deepcopy_atomic
except NameError:
pass
try:
d[types.CodeType] = _deepcopy_atomic
except AttributeError:
pass
d[type] = _deepcopy_atomic
d[xrange] = _deepcopy_atomic
d[types.ClassType] = _deepcopy_atomic
d[types.BuiltinFunctionType] = _deepcopy_atomic
d[types.FunctionType] = _deepcopy_atomic
d[weakref.ref] = _deepcopy_atomic
def _deepcopy_list(x, memo):
y = []
memo[id(x)] = y
for a in x:
y.append(deepcopy(a, memo))
return y
d[list] = _deepcopy_list
def _deepcopy_tuple(x, memo):
y = []
for a in x:
y.append(deepcopy(a, memo))
d = id(x)
try:
return memo[d]
except KeyError:
pass
for i in range(len(x)):
if x[i] is not y[i]:
y = tuple(y)
break
else:
y = x
memo[d] = y
return y
d[tuple] = _deepcopy_tuple
def _deepcopy_dict(x, memo):
y = {}
memo[id(x)] = y
for key, value in x.iteritems():
y[deepcopy(key, memo)] = deepcopy(value, memo)
return y
d[dict] = _deepcopy_dict
if PyStringMap is not None:
d[PyStringMap] = _deepcopy_dict
def _deepcopy_method(x, memo): # Copy instance methods
return type(x)(x.im_func, deepcopy(x.im_self, memo), x.im_class)
_deepcopy_dispatch[types.MethodType] = _deepcopy_method
def _keep_alive(x, memo):
"""Keeps a reference to the object x in the memo.
Because we remember objects by their id, we have
to assure that possibly temporary objects are kept
alive by referencing them.
We store a reference at the id of the memo, which should
normally not be used unless someone tries to deepcopy
the memo itself...
"""
try:
memo[id(memo)].append(x)
except KeyError:
# aha, this is the first one :-)
memo[id(memo)]=[x]
def _deepcopy_inst(x, memo):
if hasattr(x, '__deepcopy__'):
return x.__deepcopy__(memo)
if hasattr(x, '__getinitargs__'):
args = x.__getinitargs__()
args = deepcopy(args, memo)
y = x.__class__(*args)
else:
y = _EmptyClass()
y.__class__ = x.__class__
memo[id(x)] = y
if hasattr(x, '__getstate__'):
state = x.__getstate__()
else:
state = x.__dict__
state = deepcopy(state, memo)
if hasattr(y, '__setstate__'):
y.__setstate__(state)
else:
y.__dict__.update(state)
return y
d[types.InstanceType] = _deepcopy_inst
def _reconstruct(x, info, deep, memo=None):
if isinstance(info, str):
return x
assert isinstance(info, tuple)
if memo is None:
memo = {}
n = len(info)
assert n in (2, 3, 4, 5)
callable, args = info[:2]
if n > 2:
state = info[2]
else:
state = None
if n > 3:
listiter = info[3]
else:
listiter = None
if n > 4:
dictiter = info[4]
else:
dictiter = None
if deep:
args = deepcopy(args, memo)
y = callable(*args)
memo[id(x)] = y
if state is not None:
if deep:
state = deepcopy(state, memo)
if hasattr(y, '__setstate__'):
y.__setstate__(state)
else:
if isinstance(state, tuple) and len(state) == 2:
state, slotstate = state
else:
slotstate = None
if state is not None:
y.__dict__.update(state)
if slotstate is not None:
for key, value in slotstate.iteritems():
setattr(y, key, value)
if listiter is not None:
for item in listiter:
if deep:
item = deepcopy(item, memo)
y.append(item)
if dictiter is not None:
for key, value in dictiter:
if deep:
key = deepcopy(key, memo)
value = deepcopy(value, memo)
y[key] = value
return y
del d
del types
# Helper for instance creation without calling __init__
class _EmptyClass:
pass
def _test():
l = [None, 1, 2L, 3.14, 'xyzzy', (1, 2L), [3.14, 'abc'],
{'abc': 'ABC'}, (), [], {}]
l1 = copy(l)
print l1==l
l1 = map(copy, l)
print l1==l
l1 = deepcopy(l)
print l1==l
class C:
def __init__(self, arg=None):
self.a = 1
self.arg = arg
if __name__ == '__main__':
import sys
file = sys.argv[0]
else:
file = __file__
self.fp = open(file)
self.fp.close()
def __getstate__(self):
return {'a': self.a, 'arg': self.arg}
def __setstate__(self, state):
for key, value in state.iteritems():
setattr(self, key, value)
def __deepcopy__(self, memo=None):
new = self.__class__(deepcopy(self.arg, memo))
new.a = self.a
return new
c = C('argument sketch')
l.append(c)
l2 = copy(l)
print l == l2
print l
print l2
l2 = deepcopy(l)
print l == l2
print l
print l2
l.append({l[1]: l, 'xyz': l[2]})
l3 = copy(l)
import repr
print map(repr.repr, l)
print map(repr.repr, l1)
print map(repr.repr, l2)
print map(repr.repr, l3)
l3 = deepcopy(l)
import repr
print map(repr.repr, l)
print map(repr.repr, l1)
print map(repr.repr, l2)
print map(repr.repr, l3)
class odict(dict):
def __init__(self, d = {}):
self.a = 99
dict.__init__(self, d)
def __setitem__(self, k, i):
dict.__setitem__(self, k, i)
self.a
o = odict({"A" : "B"})
x = deepcopy(o)
print(o, x)
if __name__ == '__main__':
_test()

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from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
ext_modules = [
Extension("copy", ["copy.pyx"]),
]
setup(
name = 'copy',
cmdclass = {'build_ext': build_ext},
ext_modules = ext_modules
)