and introduces a new method .decode().
The major change is that strg.encode() will no longer try to convert
Unicode returns from the codec into a string, but instead pass along
the Unicode object as-is. The same is now true for all other codec
return types. The underlying C APIs were changed accordingly.
Note that even though this does have the potential of breaking
existing code, the chances are low since conversion from Unicode
previously took place using the default encoding which is normally
set to ASCII rendering this auto-conversion mechanism useless for
most Unicode encodings.
The good news is that you can now use .encode() and .decode() with
much greater ease and that the door was opened for better accessibility
of the builtin codecs.
As demonstration of the new feature, the patch includes a few new
codecs which allow string to string encoding and decoding (rot13,
hex, zip, uu, base64).
Written by Marc-Andre Lemburg. Copyright assigned to the PSF.
to reason that me_key is much more likely to match the key we're looking
for than to match dummy, and if the key is absent me_key is much more
likely to be NULL than dummy: most dicts don't even have a dummy entry.
Running instrumented dict code over the test suite and some apps confirmed
that matching dummy was 200-300x less frequent than matching key in
practice. So this reorders the tests to try the common case first.
It can lose if a large dict with many collisions is mostly deleted, not
resized, and then frequently searched, but that's hardly a case we
should be favoring.
The comment following used to say:
/* We use ~hash instead of hash, as degenerate hash functions, such
as for ints <sigh>, can have lots of leading zeros. It's not
really a performance risk, but better safe than sorry.
12-Dec-00 tim: so ~hash produces lots of leading ones instead --
what's the gain? */
That is, there was never a good reason for doing it. And to the contrary,
as explained on Python-Dev last December, it tended to make the *sum*
(i + incr) & mask (which is the first table index examined in case of
collison) the same "too often" across distinct hashes.
Changing to the simpler "i = hash & mask" reduced the number of string-dict
collisions (== # number of times we go around the lookup for-loop) from about
6 million to 5 million during a full run of the test suite (these are
approximate because the test suite does some random stuff from run to run).
The number of collisions in non-string dicts also decreased, but not as
dramatically.
Note that this may, for a given dict, change the order (wrt previous
releases) of entries exposed by .keys(), .values() and .items(). A number
of std tests suffered bogus failures as a result. For dicts keyed by
small ints, or (less so) by characters, the order is much more likely to be
in increasing order of key now; e.g.,
>>> d = {}
>>> for i in range(10):
... d[i] = i
...
>>> d
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}
>>>
Unfortunately. people may latch on to that in small examples and draw a
bogus conclusion.
test_support.py
Moved test_extcall's sortdict() into test_support, made it stronger,
and imported sortdict into other std tests that needed it.
test_unicode.py
Excluced cp875 from the "roundtrip over range(128)" test, because
cp875 doesn't have a well-defined inverse for unicode("?", "cp875").
See Python-Dev for excruciating details.
Cookie.py
Chaged various output functions to sort dicts before building
strings from them.
test_extcall
Fiddled the expected-result file. This remains sensitive to native
dict ordering, because, e.g., if there are multiple errors in a
keyword-arg dict (and test_extcall sets up many cases like that), the
specific error Python complains about first depends on native dict
ordering.
Allow module getattr and setattr to exploit string interning, via the
previously null module object tp_getattro and tp_setattro slots. Yields
a very nice speedup for things like random.random and os.path etc.
For rich comparisons, use instance_getattr2() when possible to avoid
the expense of setting an AttributeError. Also intern the name_op[]
table and use the interned strings rather than creating a new string
and interning it each time through.
doesn't know how to do LE, LT, GE, GT. dict_richcompare can't do the
latter any faster than dict_compare can. More importantly, for
cmp(dict1, dict2), Python *first* tries rich compares with EQ, LT, and
GT one at a time, even if the tp_compare slot is defined, and
dict_richcompare called dict_compare for the latter two because
it couldn't do them itself. The result was a lot of wasted calls to
dict_compare. Now dict_richcompare gives up at once the times Python
calls it with LT and GT from try_rich_to_3way_compare(), and dict_compare
is called only once (when Python gets around to trying the tp_compare
slot).
Continued mystery: despite that this cut the number of calls to
dict_compare approximately in half in test_mutants.py, the latter still
runs amazingly slowly. Running under the debugger doesn't show excessive
activity in the dict comparison code anymore, so I'm guessing the culprit
is somewhere else -- but where? Perhaps in the element (key/value)
comparison code? We clearly spend a lot of time figuring out how to
compare things.
Fixed a half dozen ways in which general dict comparison could crash
Python (even cause Win98SE to reboot) in the presence of kay and/or
value comparison routines that mutate the dict during dict comparison.
Bugfix candidate.
interned when created, so the cached versions generally aren't ever
interned. With the patch, the
Py_INCREF(t);
*p = t;
Py_DECREF(s);
return;
indirection block in PyString_InternInPlace() is never executed during a
full run of the test suite, but was executed very many times before. So
I'm trading more work when creating one-character strings for doing less
work later. Note that the "more work" here can happen at most 256 times
per program run, so it's trivial. The same reasoning accounts for the
patch's simplification of string_item (the new version can call
PyString_FromStringAndSize() no more than 256 times per run, so there's
no point to inlining that stuff -- if we were serious about saving time
here, we'd pre-initialize the characters vector so that no runtime testing
at all was needed!).
Store floats and doubles to full precision in marshal.
Test that floats read from .pyc/.pyo closely match those read from .py.
Declare PyFloat_AsString() in floatobject header file.
Add new PyFloat_AsReprString() API function.
Document the functions declared in floatobject.h.
d1 == d2 and d1 != d2 now work even if the keys and values in d1 and d2
don't support comparisons other than ==, and testing dicts for equality
is faster now (especially when inequality obtains).
safely together and don't duplicate logic (the common logic was factored
out into new private API function _PySequence_IterContains()).
Visible change:
some_complex_number in some_instance
no longer blows up if some_instance has __getitem__ but neither
__contains__ nor __iter__. test_iter changed to ensure that remains true.
NEEDS DOC CHANGES
A few more AttributeErrors turned into TypeErrors, but in test_contains
this time.
The full story for instance objects is pretty much unexplainable, because
instance_contains() tries its own flavor of iteration-based containment
testing first, and PySequence_Contains doesn't get a chance at it unless
instance_contains() blows up. A consequence is that
some_complex_number in some_instance
dies with a TypeError unless some_instance.__class__ defines __iter__ but
does not define __getitem__.
to string.join(), so that when the latter figures out in midstream that
it really needs unicode.join() instead, unicode.join() can actually get
all the sequence elements (i.e., there's no guarantee that the sequence
passed to string.join() can be iterated over *again* by unicode.join(),
so string.join() must not pass on the original sequence object anymore).
NEEDS DOC CHANGES.
This one surprised me! While I expected tuple() to be a no-brainer, turns
out it's actually dripping with consequences:
1. It will *allow* the popular PySequence_Fast() to work with any iterable
object (code for that not yet checked in, but should be trivial).
2. It caused two std tests to fail. This because some places used
PyTuple_Sequence() (the C spelling of tuple()) as an indirect way to test
whether something *is* a sequence. But tuple() code only looked for the
existence of sq->item to determine that, and e.g. an instance passed
that test whether or not it supported the other operations tuple()
needed (e.g., __len__). So some things the tests *expected* to fail
with an AttributeError now fail with a TypeError instead. This looks
like an improvement to me; e.g., test_coercion used to produce 559
TypeErrors and 2 AttributeErrors, and now they're all TypeErrors. The
error details are more informative too, because the places calling this
were *looking* for TypeErrors in order to replace the generic tuple()
"not a sequence" msg with their own more specific text, and
AttributeErrors snuck by that.
the code necessary to accomplish this is simpler and faster if confined to
the object implementations, so we only do this there.
This causes no behaviorial changes beyond a (very slight) speedup.
need to be specified in the type structures independently. The flag
exists only for binary compatibility.
This is a "source cleanliness" issue and introduces no behavioral changes.
dictionary size was comparing ma_size, the hash table size, which is
always a power of two, rather than ma_used, wich changes on each
insertion or deletion. Fixed this.
to no longer insist that len(seq) be defined.
NEEDS DOC CHANGES.
This is meant to be a model for how other functions of this ilk (max,
filter, etc) can be generalized similarly. Feel encouraged to grab your
favorite and convert it!
Note some cute consequences:
list(file) == file.readlines() == list(file.xreadlines())
list(dict) == dict.keys()
list(dict.iteritems()) = dict.items()
list(xrange(i, j, k)) == range(i, j, k)
object's type didn't define tp_print, there were still cases where the
full "print uses str() which falls back to repr()" semantics weren't
honored. This resulted in
>>> print None
<None object at 0x80bd674>
>>> print type(u'')
<type object at 0x80c0a80>
Fixed this by always using the appropriate PyObject_Repr() or
PyObject_Str() call, rather than trying to emulate what they would do.
Also simplified PyObject_Str() to always fall back on PyObject_Repr()
when tp_str is not defined (rather than making an extra check for
instances with a __str__ method). And got rid of the special case for
strings.
Patch #419651: Metrowerks on Mac adds 0x itself
C std says %#x and %#X conversion of 0 do not add the 0x/0X base marker.
Metrowerks apparently does. Mark Favas reported the same bug under a
Compaq compiler on Tru64 Unix, but no other libc broken in this respect
is known (known to be OK under MSVC and gcc).
So just try the damn thing at runtime and see what the platform does.
Note that we've always had bugs here, but never knew it before because
a relevant test case didn't exist before 2.1.
Fix a very old flaw in PyObject_Print(). Amazing! When an object
type defines tp_str but not tp_repr, 'print x' to a real file
object would not call the tp_str slot but rather print a default style
representation: <foo object at 0x....>. This even though 'print x' to
a file-like-object would correctly call the tp_str slot.
patch for sharing single character Unicode objects.
Martin's patch had to be reworked in a number of ways to take Unicode
resizing into consideration as well. Here's what the updated patch
implements:
* Single character Unicode strings in the Latin-1 range are shared
(not only ASCII chars as in Martin's original patch).
* The ASCII and Latin-1 codecs make use of this optimization,
providing a noticable speedup for single character strings. Most
Unicode methods can use the optimization as well (by virtue
of using PyUnicode_FromUnicode()).
* Some code cleanup was done (replacing memcpy with Py_UNICODE_COPY)
* The PyUnicode_Resize() can now also handle the case of resizing
unicode_empty which previously resulted in an error.
* Modified the internal API _PyUnicode_Resize() and
the public PyUnicode_Resize() API to handle references to
shared objects correctly. The _PyUnicode_Resize() signature
changed due to this.
* Callers of PyUnicode_FromUnicode() may now only modify the Unicode
object contents of the returned object in case they called the API
with NULL as content template.
Note that even though this patch passes the regression tests, there
may still be subtle bugs in the sharing code.
sees it (test_iter.py is unchanged).
- Added a tp_iternext slot, which calls the iterator's next() method;
this is much faster for built-in iterators over built-in types
such as lists and dicts, speeding up pybench's ForLoop with about
25% compared to Python 2.1. (Now there's a good argument for
iterators. ;-)
- Renamed the built-in sequence iterator SeqIter, affecting the C API
functions for it. (This frees up the PyIter prefix for generic
iterator operations.)
- Added PyIter_Check(obj), which checks that obj's type has a
tp_iternext slot and that the proper feature flag is set.
- Added PyIter_Next(obj) which calls the tp_iternext slot. It has a
somewhat complex return condition due to the need for speed: when it
returns NULL, it may not have set an exception condition, meaning
the iterator is exhausted; when the exception StopIteration is set
(or a derived exception class), it means the same thing; any other
exception means some other error occurred.
new slot tp_iter in type object, plus new flag Py_TPFLAGS_HAVE_ITER
new C API PyObject_GetIter(), calls tp_iter
new builtin iter(), with two forms: iter(obj), and iter(function, sentinel)
new internal object types iterobject and calliterobject
new exception StopIteration
new opcodes for "for" loops, GET_ITER and FOR_ITER (also supported by dis.py)
new magic number for .pyc files
new special method for instances: __iter__() returns an iterator
iteration over dictionaries: "for x in dict" iterates over the keys
iteration over files: "for x in file" iterates over lines
TODO:
documentation
test suite
decide whether to use a different way to spell iter(function, sentinal)
decide whether "for key in dict" is a good idea
use iterators in map/filter/reduce, min/max, and elsewhere (in/not in?)
speed tuning (make next() a slot tp_next???)
Marc-André Lemburg
Tim Peters
Jeremy Hylton
Fred Drake
Guido van Rossum