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Viewing an ndarray as a np.recarray now automatically converts
the dtype to np.record.
This commit also fixes assignment to MaskedArray's dtype attribute,
fixes the repr of recarrays with non-structured dtype, and removes
recarray.view so that viewing a recarray as a non-structured dtype
no longer converts to ndarray type.
Fixes #3581
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In https://github.com/numpy/numpy/pull/5483, I solved the problem that a
"recarray" and a "record array" (nomenclature defined in
https://github.com/numpy/numpy/pull/5482) looked identical by making
sure that a type's subclass was listed in the repr. However, recarrays
are still represented using the function 'rec.array' even though this
function technically creates record arrays, not recarrays.
So I have updated recarray.__repr__.
Setup:
>>> a = np.array([(1,'ABC'), (2, "DEF")], dtype=[('foo', int), ('bar', 'S4')])
>>> recordarr = np.rec.array(a)
>>> recarr = a.view(np.recarray)
Behavior after https://github.com/numpy/numpy/pull/5483:
>>> recordarr
rec.array([(1, 'ABC'), (2, 'DEF')],
dtype=(numpy.record, [('foo', '<i8'), ('bar', 'S4')]))
>>> recarr
rec.array([(1, 'ABC'), (2, 'DEF')],
dtype=[('foo', '<i8'), ('bar', 'S4')])
New Behavior:
>>> recordarr
rec.array([(1, 'ABC'), (2, 'DEF')],
dtype=[('foo', '<i8'), ('bar', '|S4')])
>>> recarr
array([(1, 'ABC'), (2, 'DEF')],
dtype=[('foo', '<i8'), ('bar', 'S4')]).view(numpy.recarray)
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This commit makes changes to `__getitem__` and `__getattr__` of recarrays:
1. recarrays no longer convert string ndarrays to chararrays, and
instead simply return ndarrays of string type.
2. attribute access and index access of fields now behaves identically
3. dtype.type is now inherited when fields of structured type are accessed
Demonstration:
>>> rec = np.rec.array([('abc ', (1,1), 1), ('abc', (2,3), 1)],
... dtype=[('foo', 'S4'), ('bar', [('A', int), ('B', int)]), ('baz', int)])
Old Behavior:
>>> type(rec.foo), type(rec['foo'])
(numpy.core.defchararray.chararray, numpy.recarray)
>>> type(rec.bar), type(rec['bar']), rec.bar.dtype.type
(numpy.recarray, numpy.recarray, numpy.void)
>>> type(rec.baz), type(rec['baz'])
(numpy.ndarray, numpy.ndarray)
New behavior:
>>> type(rec.foo), type(rec['foo'])
(numpy.ndarray, numpy.ndarray)
>>> type(rec.bar), type(rec['bar']), rec.bar.dtype.type
(numpy.recarray, numpy.recarray, numpy.record)
>>> type(rec.baz), type(rec['baz'])
(numpy.ndarray, numpy.ndarray)
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This update adds a section better describing record arrays in the user
guide (numpy/doc/structured_arrays.py).
It also corrects nomenclature, such that "structured array" refers to
ndarrays with structured dtype, "record array" refers to modified
ndarrays as created by np.rec.array, and "recarray" refers to ndarrays
viewed as np.recarray. See the note at the end of the structured
array user guide.
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Add a link to f2py docs, which was missing.
[ci skip]
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[DOC] Fix small inaccuracy in broadcasting docs
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During broadcasting, dimensions with size 1 can be matched against 0-sized dimensions, and in this case it's the size 1 dimension that will be shrunk away to nothingness. So it's wrong to say that the *smaller* dimension is the one that changes.
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tostring returns bytes which are not equal to string, so provide a
tobytes function alias.
tostring does not emit a deprecation warning yet so rdepends do not need
to check two names to support older versions of numpy without warnings.
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Now is as good a time as any with open PR's at a low.
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Add missing part of usecols negative index explanation and other
minor redaction fixes.
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The behaviour documented did not match the actual behaviour of numpy. Explanation changed and the code example updated.
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Also mention np.intp, which at least personally I think is not an
unimportant type.
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The idioms fixer makes the following replacements.
1) int <- bool
2) comparison or identity of types <- isinstance
3) a.sort() <- sorted(a)
There were two problems that needed to be dealt with after the
application of the fixer. First, the replacement of comparison or
identity of types by isinstance was not always correct. The isinstance
function returns true for subtypes whereas many of the places where the
fixer made a substitution needed to check for exact type equality.
Second, the sorted function was applied to arrays, but because it treats
them as iterators and constructs a sorted list from the result, that is
the wrong thing to do.
Closes #3062.
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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 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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The int64 data type allows numbers from -9223372036854775808 to
9223372036854775807. The minus sign was missing.
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underflow, which remains ignored.
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