| Commit message (Collapse) | Author | Age | Files | Lines |
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This removes files and code supporting scons builds. After this change
numpy will only support builds using distutils or bento. The removal of
scons has been discussed on the list several times and a decision has been
made that scons support is no longer needed. This was originally discussed
for numpy 1.7 and because the distutils and bento methods are still
available we are skipping the usual deprecation period.
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The different sort kinds are now located in
quicksort.c.src
mergesort.c.src
heapsort.c.src
Both direct and indirect sorts are included in each file. This cleanup
was done so that additional sorts or quickselect could be added without
undue clutter.
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There are two reasons to want to keep PyArray_ReduceWrapper out of the
public multiarray API:
- Its signature is likely to change if/when masked arrays are added
- It is essentially a wrapper for array->scalar transformations
(*not* just reductions as its name implies -- the whole reason it
is in multiarray.so in the first place is to support count_nonzero,
which is not actually a reduction!). It provides some nice
conveniences (like making it easy to apply such functions to
multiple axes simultaneously), but, we already have a general
mechanism for writing array->scalar transformations -- generalized
ufuncs. We do not want to have two independent, redundant
implementations of this functionality, one in multiarray and one in
umath! So in the long run we should add these nice features to the
generalized ufunc machinery. And in the short run, we shouldn't add
it to the public API and commit ourselves to supporting it.
However, simply removing it from numpy_api.py is not easy, because
this code was used in both multiarray and umath. This commit:
- Moves ReduceWrapper and supporting code to umath/, and makes
appropriate changes (e.g. renaming it to PyUFunc_ReduceWrapper and
cleaning up the header files).
- Reverts numpy.count_nonzero to its previous implementation, so that
it loses the new axis= and keepdims= arguments. This is
unfortunate, but this change isn't so urgent that it's worth tying
our APIs in knots forever. (Perhaps in the future it can become a
generalized ufunc.)
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This removes the include dependency on PY_UNICODE_WIDE in preparation
for defining more general unicode functions in the ucsnarray.c file.
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Doing this so that it can be used both in multiarray and umath, to make
writing new reduction operations generally easier.
Also made PyArray_Squeeze work with NA-masked arrays.
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Some bugs in the NA reductions got tracked back to a need for an
assignment operation which doesn't overwrite NA values. Making a decent
array assignment API seems like a good aproach to deal with that.
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Highlights:
* Tighten up date unit vs time unit casting rules,
and integrate the NPY_CASTING enum deeper into the datetime conversions
* Determine a unit when converting from a string array,
similar to when converting from lists of strings
* Switch local/utc handling to a timezone= parameter,
which also accepts a datetime.tzinfo object. This, for
example, enables the use of the pytz library with numpy.datetime64
* Remove the events metadata, make the old API functions raise
exceptions, and rename the "frequency" metadata name to "timeunit"
* Abstract the flexible dtype mechanism into a function,
so that it can be more easily changed without having to
add code to many places
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Also move datetime string functions to their own source file.
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install it.
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Fix the build to deal with the new name and location.
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Also rename the busdaydef parameters to busdaycal parameters. This
change was motivated by Chuck's code review feedback.
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Moving lowlevel_strided_loops.h to private needed a setup.py update.
Added the rest of the needed files in the scons configuration.
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The problem was that if endian.h was not detected, @DEFINE_NPY_HAVE_ENDIAN_H@
was still present in _numpyconfig.h, causing compilation to fail.
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We should use this header in npy_endian.h if available.
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object
To use PEP 3118 buffers as ndarray bases, we need to have a way to
release the buffers on garbage collection. The easiest way to do this is
to use a Memoryview object -- but they are not available on Python 2.6.
Hence, we implement a minimal memory view object that only handles the
garbage collection, and use it on Python 2.6. It also implements the
new buffer protocol, to allow testing the functionality.
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headers.
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Universal builds break the configure stage: we have to harcode the
arch-specific values in the case of mac os x, as we have only one configuration
stage per compile, but several arch per compile with the braindead -arch
machinery.
We rename the old numpyconfig.h to a private header, and numpyconfig.h itself
post-fix the values in the case of mac os x.
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breaks it).
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