| Commit message (Collapse) | Author | Age | Files | Lines |
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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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This should fix:
* http://projects.scipy.org/numpy/ticket/1848
* http://projects.scipy.org/scipy/ticket/1449
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