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memmap needs to call it in __array_finalize__ to determine if it can
drop the references on copies.
The python version if may_share_memory caused significant slowdowns when
slicing these maps.
closes gh-3364
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New padding method which scales much better with dimensionality.
This new implementation is fully vectorized, builds each abstracted
n-dimensional padding block in a single step, and takes advantage
of separability. The API is completely preserved, and the old
algorithm is used if a vector function is input for `mode`.
The new algorithm is faster for all tested combinations of inputs,
and scales much better with dimensionality. Execution time reductions
from ~25% for small rank 1 arrays to >99% for rank 4+ arrays observed.
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BUG: np.insert must copy index array
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Otherwise it would do in-place changes to it. Fixes gh-3279.
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The unicode fixer strips the u from u'hi' and converts the unicode type
to str. The first won't work for Python 2 and instead we replace the u
prefix with the sixu function borrowed from the six compatibility
package. That function calls the unicode constructor with the
'unicode_escape' encoder so that the many tests using escaped unicode
characters like u'\u0900' will be handled correctly. That makes the
sixu function a bit different from the asunicode function currently in
numpy.compat and also provides a target that can be converted back to
the u prefix when support for Python 3.2 is dropped. Python 3.3
reintroduced the u prefix for compatibility.
The unicode fixer also replaces 'unicode' with 'str' as 'unicode' is no
longer a builtin in Python 3. For code compatibility, 'unicode' is
defined either as 'str' or 'unicode' in numpy.compat so that checks like
if isinstance(x, unicode):
...
will work properly for all python versions.
Closes #3089.
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Various functions have been moved around in the stdlib for Python 3,
this fixes that up so that the code is valid in both Python 2 and
Python 3.
Note: monkey patching the stlib urlopen for testing looks a bit hokey
to me, but I don't see an easier, more reliable way to do the test.
Closes #3090.
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In Python 3 zip returns an iterator instead of a list. Consequently, in
places where an iterator won't do it must be enclosed in list(...).
Lists instead of iterators are also used in array constructors as using
iterators there usually results in an object array containing an
iterator object.
Closes #3094
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The numliterals fixer replaces the old style octal number like '01' by
'0o1' removes the 'L' suffix.
Octal values were previously mistakenly specified in some dates, those
uses have been corrected by removing the leading zeros.
Simply Removing the 'L' suffix should not be a problem, but in some
testing code it looks neccesary, so in those places the Python long
constructor is used instead.
The 'long' type is no longer defined in Python 3. Because we need to
have it defined for Python 2 it is added to numpy/compat/np3k.py where
it is defined as 'int' for Python 3 and 'long' for Python 2. The `long`
fixer then needs to be skipped so that it doesn't undo the good work.
Closes #3074, #3067.
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There were several smaller to larger problems for these two functions,
that this addresses:
* delete did not handle out of bound values graciously (ignoring negative
ones)
* both were unnecessarily slow due to use of sets
* insert did not handle unsorted indices correctly
Further changes:
* Add FutureWarning for boolean obj, so it can be handled similar to a
boolean mask with indexing.
* Add FutureWarning to remove inconsistent special cases for 0-d arrays
(neither insertion nor deletion along an axis make sense for a scalar)
* Allow insertion of an array with more then one element along axis when
obj is a sequence with a single item. (i.e. array([1])).
* Reintroduce speed optimization for scalar in insert that existed in 1.6.
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2to3: apply `dict` fixer.
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In Python3 `dict.items()`, `dict.keys()`, and `dict.values()` are
iterators. This causes problems when a list is needed so the 2to3 fixer
explicitly constructs a list when is finds on of those functions.
However, that is usually not necessary, so a lot of the work here has
been cleaning up those places where the fix is not needed. The big
exception to that is the `numpy/f2py/crackfortran.py` file. The code
there makes extensive use of loops that modify the contents of the
dictionary being looped through, which raises an error. That together
with the obscurity of the code in that file made it safest to let the
`dict` fixer do its worst.
Closes #3050.
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MAINT: Cleanup some imports involving reduce.
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Because reduce has been available in functools since Python 2.6 we
can get rid of the version checks we currently have before we import
it.
Also removes some reduce related skips in tools/py3tool.py. We were
already skipping the reduce fixer so this has no effect other than
cleaning up the code.
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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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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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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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In python3 range is an iterator and `xrange` has been removed. This has
two consequence for code:
1) Where a list is needed `list(range(...))` must be used.
2) `xrange` must be replaced by `range`
Both of these changes also work in python2 and this patch makes both.
There are three places fixed that do not need it, but I left them in
so that the result would be `xrange` clean.
Closes #3092
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I'm not sure this is the right fix, but test_closing_fid need to check
that garbage collection will close a file that goes through a bunch of
openings followed by dropping the reference. So the fix is to ignore
warnings during the test. I'd just ignore ResourceWarning, but it does
not look to be a built in warning in Python 2.7.
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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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Example: except ValueError,msg: -> except ValueError as msg:
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This broke when function_base._nannop tried to fill a boolean array with
integer zeros, raising a 'safe_casting' error. It looks like nanargmax and
nanargmin would also break, and were probably incorrect for booleans in any
case. The fix is not to use fill values for boolean and integer dtypes.
Previously that was only done for the integer dtypes.
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Fix ndindex for 0-d arrays.
Add tests for tuple arguments to ndindex
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The behavior of in1d is not really defined here, but it should
be at least consistent over different execution branches. This is
what it has been for most usages.
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There was a regression introduced by the speed improvement in
commit 6441c2a. This fixes it, and generally ravels the arrays for
np.in1d. However it can be argued that at least the first array should
not be ravelled in the future.
Fixes "Issue gh-2755"
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This switches us back to the behaviour seen in numpy 1.6 and earlier,
which it turns out that scikit-learn (and probably others) relied on.
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BF bug #808
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the argument passed to be used as the item to be insterted, and a list was
passed as the positions. This was fixed by simply duplicating the item to
be inserted so that it was a list of equal length and then control was
passed to the already exsisting code to handel this case
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Meshgrid enhancements (>2-D, sparse grids, matrix indexing)
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(test_io.TestSavezLoad)
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BUG: make genfromtxt work with comments=None. Closes Github issue 329.
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Small arrays are sorted with insertion sort, which is a stable
sort. Consequently larger arrays are needed to check that the
sort used is properly stable.
The test was also refactored to make it more compact.
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otherwise zipfile of python3 gets confused to receive bytes for the header
whenever handle is opened for a text (unicode) file
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