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ENH: Add kwarg support for vectorize (tickets #2100, #1156, and #1487) (clean)
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This is a substantial rewrite of vectorize to remove all introspection and
caching behaviour. This greatly simplifies the logic of the code, and allows
for much more generalized behaviour, simultaneously fixing tickets #1156,
#1487, and #2100. There will probably be a performance hit because caching is
no longer used (but should be able to be reinstated if needed).
As vectorize is a convenience function with poor performance in general,
perhaps this is okay. Rather than trying to inspect the function to determine
the number of arguments, defaults, and argument names, we just use the
arguments passed on the call to determine the behaviour on each call.
All tests pass and code is fully covered
Fixes:
Ticket #2100: kwarg support for vectorize
- API: Optional excluded argument to exclude some args from vectorization.
- Added documentation, examples, and coverage tests
- Added additional coverage test and base case for functions with no args
- Factored original behaviour into _vectorize_call
- Some minor documentation and error message corrections
Ticket #1156: Support vectorizing over instance methods
- No longer an issue since everything is determined by the call.
Ticket: #1487: result depends on execution order
- No longer caching, so the behaviour is as was expected.
ENH: Simple cache for vectorize
- Added simple cache to prevent vectorize from calling pyfunc twice on the first
argument when determining the output types and added regression test.
- Added documentation for excluded positional arguments.
- Documentation cleanups.
- Cleaned up variable names.
ENH: Performance improvements for backward compatibility of vectorize.
After some simple profiling, I found that the wrapping used to
support the caching of the previous commit wasted more time than
it saved, so I added a flag to allow the user to toggle. Moral:
caching makes sense only if the function is expensive and is off
by default.
I also compared performance with the original vectorize and opted
for keeping a cache of _ufunc if otypes is specified and there are
no kwargs/excluded vars. This case is easy to implement, and allows
users to reproduce (almost) the old performance characteristics if
needed. (The new version is about 5% slower in this case).
It would be much more complicated to add a similar cache in the case
where kwargs are used, and since a wrapper is used here, the
performance gain would be negligible (profiling showed that wrapping
was a more significant slowdown than the extra call to frompyfunc).
- API: Added cache kwarg which allows the user to toggle caching
of the first result.
- DOC: Added Notes section with a discussion of performance and a
warning that vectorize should not be used for performance.
- Added private _ufunc member to implement old-style of cache for
special case with no kwargs, excluded, and with otypes specified.
- Modified test case.
Partially address ticket #1982
- I tried to use hasattr(outputs, '__len__') rather than
isinstance(outputs, tuple) in order to allow for functions to return
lists. This, however, means that strings will get vectorized over
each character which breaks previous behaviour. Keeping old
behaviour for now.
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fix the wrapping problem of fill_diagonal with tall matrix.
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behavior.
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We are using the new tweak_* bento API wherever possible.
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The ipmt function was also fixed to handle broadcasting. The tests
were improved and extended to cover the broadcasting capability.
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PyArray_Diagonal is changed to return a copy of the diagonal (as in
numpy 1.6 and earlier), but with a new (hidden) WARN_ON_WRITE flag
set. Writes to this array (or views thereof) will continue to work as
normal, but the first write will trigger a DeprecationWarning.
We also issue this warning if someone extracts a non-numpy writeable
view of the array (e.g., by accessing the Python-level .data
attribute). There are likely still places where the data buffer is
exposed that I've missed -- review welcome!
New known-fail test: eye() for maskna arrays was only implemented by
exploiting ndarray.diagonal's view-ness, so it is now unimplemented
again, and the corresponding test is marked known-fail.
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Fix incorrect python version checks in test_print.py.
Fix missing build_err_msg import and wrong variable in test_io.py.
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Equal and nearly-equal size requirement is not true when passing a 1-D array of indices.
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versioned
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ENH: Give digitize left or right open interval option
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Rearrange some of the documentation and shorten lines. A few long
lines of code were also broken.
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The various padding functions are exposed as options to a public 'pad'
function. Example:
pad(a, 5, mode='mean')
Current modes are 'constant', 'edge', 'linear_ramp', 'maximum', 'mean',
'median', 'minimum', 'reflect', 'symmetric', 'wrap', and <function>
This commit includes unit tests and doctests and is based on feature
request ticket #655.
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Make imports from numpy.testing explicit.
Use np namespace.
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The interp function was computing slopes for all intervals, even when there
were only a few points to be interpolated. Now it only does so when the
number of interpolation points exceeds the number of sample points.
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Make unique use mergesort when return_index is true. This quarantees that
the returned indices are of the first occurrence of the unique elements and
makes the behavior better defined and consistent.
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This is caused by the inconsistent floating point handling of Python itself.
On Windows with 2.5:
>>> "%s" % 1e-6
'1e-006'
With 2.6:
>>> "%s" % 1e-6
'1e-06'
Reviewed as PR-225.
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This one wasn't actually converted to a test error, because it's not a
RuntimeWarning. Maybe need to add an option to raise on UserWarning too.
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This should fix the test errors seen on both MinGW and MSVC9 related to this.
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This allows these objects to be freed by refcount, rather than requiring
the gc, which can be useful in some situations.
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payment) functions. Added doctests and unit tests.
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* replace-noprefix: (30 commits)
BUG: Fix typo npydouble.
UPD: Remove now redundant typedef.
UPD: Fix a few more spots missing prefixes.
BUG: Fix scons build script so it works with umathmodule.c.
STY: Cleanup some prefixing that crept into comments.
UPD: Various fixes, Remove #define NPY_NO_PREFIX from files in core.
ENH: Add some needed macros to include files.
UPD: Use prefixed types in scalartypes.c.src.
UPD: Make multiarray *.c files use prefixed macros.
UPD: Use prefixed types in arraytypes.c.src.
ENH: Add a few needed macros to npy_common.h.
UPD: Include ndarrayobject.h instead of arrayobject.h in boolean_ops.c.src.
UPD: Use prefixed types in lowlevel_strided_loops.c.src template headers.
UPD: Use explicit prefixed types in einsum.c.src template headers.
UPD: Use prefixed versions of double and int in multiarray_tests.c.src.
UPD: Remove includes of noprefix.h in ufunc_object.c and _compiled_base.c.
BUG: Fix unprefixed reference to cdouble in ndarrayobject.h.
UPD: Use prefixed macros in numpy/core/src/scalarmathmodule.c.src.
UPD: Use prefixed macros in numpy/core/src/umath/funcs.inc.src.
MOV: Rename umathmodule.c.src umathmodule.c since it has no templates.
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Releases it only conditionally, as object arrays require refcounting to
be performed within the inner loop, making GIL release impractical.
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This makes subsequent thread-friendly modification easier.
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Sandwich certain potentially long running for loops that don't touch any
Python objects between NPY_BEGIN_ALLOW_THREADS and NPY_END_ALLOW_THREADS
so that the interpreter can potentially schedule another Python thread.
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