This is an experimental feature, for internal use.
Setting tkinter._debug = True before creating the root window enables
printing every executed Tcl command (or a Tcl command equivalent to the
used Tcl C API).
This will help to convert a Tkinter example into Tcl script to check
whether the issue is caused by Tkinter or exists in the underlying Tcl/Tk
library.
* Add PhotoImage.read() to read an image from a file.
* Add PhotoImage.data() to get the image data.
* Add background and grayscale parameters to PhotoImage.write().
* Add the PhotoImage method copy_replace() to copy a region
from one image to other image, possibly with pixel zooming and/or
subsampling.
* Add from_coords parameter to PhotoImage methods copy(), zoom() and subsample().
* Add zoom and subsample parameters to PhotoImage method copy().
The designated initializer syntax in static inline functions in pycore_backoff.h
causes problems for C++ or MSVC users who aren't yet using C++20.
While internal, pycore_backoff.h is included (indirectly, via pycore_code.h)
by some key 3rd party software that does so for speed.
This is *not* sufficient for the final 3.13 release, but it will do for beta 1:
- What's new entry
- Updated changelog entry (news blurb)
- Mention the proxy for f_globals in the datamodel and Python frame object docs
This doesn't have any C API details (what's new refers to the PEP).
Use the new public Raw functions:
* _PyTime_PerfCounterUnchecked() with PyTime_PerfCounterRaw()
* _PyTime_TimeUnchecked() with PyTime_TimeRaw()
* _PyTime_MonotonicUnchecked() with PyTime_MonotonicRaw()
Remove internal functions:
* _PyTime_PerfCounterUnchecked()
* _PyTime_TimeUnchecked()
* _PyTime_MonotonicUnchecked()
* Fix mangle_from_ default value in email.policy.Policy.__doc__
The docstring says it defaults to True, but it actually defaults
to False. Only the Compat32 subclass overrides that.
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Co-authored-by: Nikita Sobolev <mail@sobolevn.me>
For converting large ints to strings, CPython invokes a function in _pylong.py,
which uses the decimal module to implement an asymptotically waaaaay
sub-quadratic algorithm. But if the C decimal module isn't available, CPython
uses _pydecimal.py instead. Which in turn frequently does str(int). If the int
is very large, _pylong ends up doing the work, which in turn asks decimal to do
"big" arithmetic, which in turn calls str(big_int), which in turn ... it can
become infinite mutual recursion.
This change introduces a different int->str function that doesn't use decimal.
It's asymptotically worse, "Karatsuba time" instead of quadratic time, so
still a huge improvement. _pylong switches to that when the C decimal isn't
available. It is also used for not too large integers (less than 450_000 bits),
where it is faster (up to 2 times for 30_000 bits) than the asymptotically
better implementation that uses the C decimal.
Co-authored-by: Tim Peters <tim.peters@gmail.com>
* Initial stab.
* Test the tentative fix. Hangs "forever" without this change.
* Move the new test to a better spot.
* New comment to explain why _convert_to_str allows any poewr of 10.
* Fixed a comment, and fleshed out an existing test that appeared unfinished.
* Added temporary asserts. Or maybe permanent ;-)
* Update Lib/_pydecimal.py
Co-authored-by: Serhiy Storchaka <storchaka@gmail.com>
* Remove the new _convert_to_str().
Serhiy and I independently concluded that exact powers of 10
aren't possible in these contexts, so just checking the
string length is sufficient.
* At least for now, add the asserts to the other block too.
* 📜🤖 Added by blurb_it.
---------
Co-authored-by: Serhiy Storchaka <storchaka@gmail.com>
Co-authored-by: blurb-it[bot] <43283697+blurb-it[bot]@users.noreply.github.com>
We have only been tracking each module's PyModuleDef. However, there are some problems with that. For example, in some cases we load single-phase init extension modules from def->m_base.m_init or def->m_base.m_copy, but if multiple modules share a def then we can end up with unexpected behavior.
With this change, we track the following:
* PyModuleDef (same as before)
* for some modules, its init function or a copy of its __dict__, but specific to that module
* whether it is a builtin/core module or a "dynamic" extension
* the interpreter (ID) that owns the cached __dict__ (only if cached)
This also makes it easier to remember the module's kind (e.g. single-phase init) and if loading it previously failed, which I'm doing separately.