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Now that Python < 2.6 is no longer supported we can use the errstate
context manager in places where constructs like
```
old = seterr(invalid='ignore')
try:
blah
finally:
seterr(**old)
```
were used.
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The numarray and oldnumeric modules are deprecated. This is a bit tricky
as raising a DeprecationWarning on import causes an error when tests are
run. To deal with that, a ModuleDeprecationWarning class is added to
numpy and NoseTester is modified to ignore that warning during testing.
Closes #2905
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specialize the sqrt ufunc for float and double and vectorize it using
SSE2.
improves performance by 4/2 for float/double if one is not memory bound
due to non-cached data.
performance is always better on all tested machines (amd phenom X2,
intel xeon 5xxx/7xxx, core2duo, corei7)
This version will not set errno on invalid input, but numpy only checks
the fpu flags so the behavior is the same.
In principle the compiler could autovectorize it when setting ffast-math
(for no errno) and specializing the loop for the vectorizable strides
and giving it some hints (restrict, __builtin_assume_aligned, etc.),
but its simpler and more reliable to simply vectorize it by hand.
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Python 3 removes the builtin types from the types module. The types
fixer replaces such references with the builtin types where possible
and also takes care of some special cases:
types.TypeNone <- type(None)
types.NotImplementedType <- type(NotImplemented)
types.EllipsisType <- type(Ellipsis)
The only two tricky substitutions are
types.StringType <- bytes
types.LongType <- int
These are fixed up to support both Python 3 and Python 2 code by
importing the long and bytes types from numpy.compat.
Closes #3240.
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In Python 3 `map` is an iterator while in Python 2 it returns a list.
The simple fix applied by the fixer is to inclose all instances of map
with `list(...)`. This is not needed in all cases, and even where
appropriate list comprehensions may be preferred for their clarity.
Consequently, this patch attempts to use list comprehensions where it
makes sense.
When the mapped function has two arguments there is another problem that
can arise. In Python 3 map stops execution when the shortest argument
list is exhausted, while in Python 2 it stops when the longest argument
list is exhausted. Consequently the two argument case might need special
care. However, we have been running Python3 converted versions of numpy
since 1.5 without problems, so it is probably not something that affects
us.
Closes #3068
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This replaces python backtics with repr(...). The backtics were mostly
used to generate strings for printing with a string format and it is
tempting to replace `'%s' % repr(x)` with `'%r' % x`. That would work
except where `x` happened to be a tuple or a dictionary but, because it
would be significant work to guarantee that and because there are not
many places where backtics are used, the safe path is to let the repr
replacements stand.
Closes #3083.
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Add `print_function` to all `from __future__ import ...` statements
and use the python3 print function syntax everywhere.
Closes #3078.
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2to3: Apply `imports` fixer.
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The `imports` fixer deals with the standard packages that have been
renamed, removed, or methods that have moved.
cPickle -- removed, use pickle
commands -- removed, getoutput, getstatusoutput moved to subprocess
urlparse -- removed, urlparse moved to urllib.parse
cStringIO -- removed, use StringIO or io.StringIO
copy_reg -- renamed copyreg
_winreg -- renamed winreg
ConfigParser -- renamed configparser
__builtin__ -- renamed builtins
In the case of `cPickle`, it is imported as `pickle` when python < 3 and
performance may be a consideration, but otherwise plain old `pickle` is
used.
Dealing with `StringIO` is a bit tricky. There is an `io.StringIO`
function in the `io` module, available since Python 2.6, but it expects
unicode whereas `StringIO.StringIO` expects ascii. The Python 3
equivalent is then `io.BytesIO`. What I have done here is used BytesIO
for anything that is emulating a file for testing purposes. That is more
explicit than using a redefined StringIO as was done before we dropped
support for Python 2.4 and 2.5.
Closes #3180.
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DOC: Formatting fixes using regex
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also other spacing or formatting mistakes
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The new import `absolute_import` is added the `from __future__ import`
statement and The 2to3 `import` fixer is run to make the imports
compatible. There are several things that need to be dealt with to make
this work.
1) Files meant to be run as scripts run in a different environment than
files imported as part of a package, and so changes to those files need
to be skipped. The affected script files are:
* all setup.py files
* numpy/core/code_generators/generate_umath.py
* numpy/core/code_generators/generate_numpy_api.py
* numpy/core/code_generators/generate_ufunc_api.py
2) Some imported modules are not available as they are created during
the build process and consequently 2to3 is unable to handle them
correctly. Files that import those modules need a bit of extra work.
The affected files are:
* core/__init__.py,
* core/numeric.py,
* core/_internal.py,
* core/arrayprint.py,
* core/fromnumeric.py,
* numpy/__init__.py,
* lib/npyio.py,
* lib/function_base.py,
* fft/fftpack.py,
* random/__init__.py
Closes #3172
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This should be harmless, as we already are division clean. However,
placement of this import takes some care. In the future a script
can be used to append new features without worry, at least until
such time as it exceeds a single line. Having that ability will
make it easier to deal with absolute imports and printing updates.
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2to3: Apply `filter` fixes. Closes #3053.
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2to3 does a lot of list(filter(...)) sort of thing which can be
avoided by using list comprehensions instead of filters. This
also seems to clarify the code to a considerable degree.
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Generally, this involves using list comprehension, or explicit list
construction as `filter` is an iterator in Python 3.
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This changes the `exec` command to the `exec` function.
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Example: except ValueError,msg: -> except ValueError as msg:
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1) New function assert_no_warnings
2) Make assert_warns and assert_no_warnings pass through the
function's return value on success, so that it can be checked as
well.
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The original masked-NA-NEP branch contained a large number of changes
in addition to the core NA support. For example:
- ufunc.__call__ support for where= argument
- nditer support for arbitrary masks (in support of where=)
- ufunc.reduce support for simultaneous reduction over multiple axes
- a new "array assignment API"
- ndarray.diagonal() returning a view in all cases
- bug-fixes in __array_priority__ handling
- datetime test changes
etc. There's no consensus yet on what should be done with the
maskna-related part of this branch, but the rest is generally useful
and uncontroversial, so the goal of this branch is to identify exactly
which code changes are involved in maskna support.
The basic strategy used to create this patch was:
- Remove the new masking-related fields from ndarray, so no arrays
are masked
- Go through and remove all the code that this makes
dead/inaccessible/irrelevant, in a largely mechanical fashion. So
for example, if I saw 'if (PyArray_HASMASK(a)) { ... }' then that
whole block was obviously just dead code if no arrays have masks,
and I removed it. Likewise for function arguments like skipna that
are useless if there aren't any NAs to skip.
This changed the signature of a number of functions that were newly
exposed in the numpy public API. I've removed all such functions from
the public API, since releasing them with the NA-less signature in 1.7
would create pointless compatibility hassles later if and when we add
back the NA-related functionality. Most such functions are removed by
this commit; the exception is PyArray_ReduceWrapper, which requires
more extensive surgery, and will be handled in followup commits.
I also removed the new ndarray.setasflat method. Reason: a comment
noted that the only reason this was added was to allow easier testing
of one branch of PyArray_CopyAsFlat. That branch is now the main
branch, so that isn't an issue. Nonetheless this function is arguably
useful, so perhaps it should have remained, but I judged that since
numpy's API is already hairier than we would like, it's not a good
idea to add extra hair "just in case". (Also AFAICT the test for this
method in test_maskna was actually incorrect, as noted here:
https://github.com/njsmith/numpyNEP/blob/master/numpyNEP.py
so I'm not confident that it ever worked in master, though I haven't
had a chance to follow-up on this.)
I also removed numpy.count_reduce_items, since without skipna it
became trivial.
I believe that these are the only exceptions to the "remove dead code"
strategy.
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There are various docstrings show examples of how to run the tests, and
give example test output. Obviously the test output changes, and
running the doctests for the testing package:
import numpy.testing as npt
npt.test(doctests=True)
will cause several large sets of tests to be run in the rest of the
tree. So I skipped these.
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assert in non-testing files that should be checked for correctness.
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Closes #1543.
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With this patch, the latest scipy trunk (7087), built against NumPy
1.5.1, passes all tests when run against the numpy trunk. The single
failing test, test_imresize, fails because it tests all float types,
and the new 'half' type lacks the precision to pass that test.
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tolerances
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This new comparison raises an error if the number of representable
numbers between two arrays exceeds a tolerance.
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assert_array_almost_equal_nulp use spacing so that a single tolerance
number can be used independently on the amplitude of the floating point
number.
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