Numpy 1.14.0 is the result of seven months of work and contains a large number
of bug fixes and new features, along with several changes with potential
compatibility issues. The major change that users will notice are the
stylistic changes in the way numpy arrays and scalars are printed, a change
that will affect doctests. See below for details on how to preserve the
old style printing when needed.
A major decision affecting future development concerns the schedule for
dropping Python 2.7 support in the runup to 2020. The decision has been made to
support 2.7 for all releases made in 2018, with the last release being
designated a long term release with support for bug fixes extending through
In 2019 support for 2.7 will be dropped in all new releases. More details
can be found in the relevant NEP_.
genfromtxt, loadtxt, fromregex and savetxt can now handle
files with arbitrary Python supported encoding.
Major improvements to printing of NumPy arrays and scalars.
New functions
parametrize: decorator added to numpy.testing
chebinterpolate: Interpolate function at Chebyshev points.
format_float_positional and format_float_scientific : format
floating-point scalars unambiguously with control of rounding and padding.
PyArray_ResolveWritebackIfCopy and PyArray_SetWritebackIfCopyBase,
new C-API functions useful in achieving PyPy compatibity.
Deprecations
Using np.bool_ objects in place of integers is deprecated. Previously
operator.index(np.bool_) was legal and allowed constructs such as
[1, 2, 3][np.True_]. That was misleading, as it behaved differently from
np.array([1, 2, 3])[np.True_].
Truth testing of an empty array is deprecated. To check if an array is not
empty, use array.size > 0.
Calling np.bincount with minlength=None is deprecated.
minlength=0 should be used instead.
Calling np.fromstring with the default value of the sep argument is
deprecated. When that argument is not provided, a broken version of
np.frombuffer is used that silently accepts unicode strings and -- after
encoding them as either utf-8 (python 3) or the default encoding
(python 2) -- treats them as binary data. If reading binary data is
desired, np.frombuffer should be used directly.
The style option of array2string is deprecated in non-legacy printing mode.
PyArray_SetUpdateIfCopyBase has been deprecated. For NumPy versions >= 1.14
use PyArray_SetWritebackIfCopyBase instead, see C API changes below for
more details.
The use of UPDATEIFCOPY arrays is deprecated, see C API changes below
for details. We will not be dropping support for those arrays, but they are
not compatible with PyPy.
Future Changes
np.issubdtype will stop downcasting dtype-like arguments.
It might be expected that issubdtype(np.float32, 'float64') and
issubdtype(np.float32, np.float64) mean the same thing - however, there
was an undocumented special case that translated the former into
issubdtype(np.float32, np.floating), giving the surprising result of True.
This translation now gives a warning that explains what translation is
occurring. In the future, the translation will be disabled, and the first
example will be made equivalent to the second.
np.linalg.lstsq default for rcond will be changed. The rcond
parameter to np.linalg.lstsq will change its default to machine precision
times the largest of the input array dimensions. A FutureWarning is issued
when rcond is not passed explicitly.
a.flat.__array__() will return a writeable copy of a when a is
non-contiguous. Previously it returned an UPDATEIFCOPY array when a was
writeable. Currently it returns a non-writeable copy. See gh-7054 for a
discussion of the issue.
Unstructured void array's .item method will return a bytes object. In the
future, calling .item() on arrays or scalars of np.void datatype will
return a bytes object instead of a buffer or int array, the same as
returned by bytes(void_scalar). This may affect code which assumed the
return value was mutable, which will no longer be the case. A
FutureWarning is now issued when this would occur.
Compatibility notes
The mask of a masked array view is also a view rather than a copy
There was a FutureWarning about this change in NumPy 1.11.x. In short, it is
now the case that, when changing a view of a masked array, changes to the mask
are propagated to the original. That was not previously the case. This change
affects slices in particular. Note that this does not yet work properly if the
mask of the original array is nomask and the mask of the view is changed.
See gh-5580 for an extended discussion. The original behavior of having a copy
of the mask can be obtained by calling the unshare_mask method of the view.
np.ma.masked is no longer writeable
Attempts to mutate the masked constant now error, as the underlying arrays
are marked readonly. In the past, it was possible to get away with::
emulating a function that sometimes returns np.ma.masked
val = random.choice([np.ma.masked, 10])
var_arr = np.asarray(val)
val_arr += 1 now errors, previously changed np.ma.masked.data
np.ma functions producing fill_values have changed
Previously, np.ma.default_fill_value would return a 0d array, but
np.ma.minimum_fill_value and np.ma.maximum_fill_value would return a
tuple of the fields. Instead, all three methods return a structured np.void
object, which is what you would already find in the .fill_value attribute.
Additionally, the dtype guessing now matches that of np.array - so when
passing a python scalar x, maximum_fill_value(x) is always the same as
maximum_fill_value(np.array(x)). Previously x = long(1) on Python 2
violated this assumption.
a.flat.__array__() returns non-writeable arrays when a is non-contiguous
The intent is that the UPDATEIFCOPY array previously returned when a was
non-contiguous will be replaced by a writeable copy in the future. This
temporary measure is aimed to notify folks who expect the underlying array be
modified in this situation that that will no longer be the case. The most
likely places for this to be noticed is when expressions of the form
np.asarray(a.flat) are used, or when a.flat is passed as the out
parameter to a ufunc.
np.tensordot now returns zero array when contracting over 0-length dimension
Previously np.tensordot raised a ValueError when contracting over 0-length
dimension. Now it returns a zero array, which is consistent with the behaviour
of np.dot and np.einsum.
numpy.testing reorganized
This is not expected to cause problems, but possibly something has been left
out. If you experience an unexpected import problem using numpy.testing
let us know.
np.asfarray no longer accepts non-dtypes through the dtype argument
This previously would accept dtype=some_array, with the implied semantics
of dtype=some_array.dtype. This was undocumented, unique across the numpy
functions, and if used would likely correspond to a typo.
1D np.linalg.norm preserves float input types, even for arbitrary orders
Previously, this would promote to float64 when arbitrary orders were
passed, despite not doing so under the simple cases::
This change affects only float32 and float16 arrays.
count_nonzero(arr, axis=()) now counts over no axes, not all axes
Elsewhere, axis==() is always understood as "no axes", but
count_nonzero had a special case to treat this as "all axes". This was
inconsistent and surprising. The correct way to count over all axes has always
been to pass axis == None.
__init__.py files added to test directories
This is for pytest compatibility in the case of duplicate test file names in
the different directories. As a result, run_module_suite no longer works,
i.e., python <path-to-test-file> results in an error.
.astype(bool) on unstructured void arrays now calls bool on each element
On Python 2, void_array.astype(bool) would always return an array of
True, unless the dtype is V0. On Python 3, this operation would usually
crash. Going forwards, astype matches the behavior of bool(np.void),
considering a buffer of all zeros as false, and anything else as true.
Checks for V0 can still be done with arr.dtype.itemsize == 0.
MaskedArray.squeeze never returns np.ma.masked
np.squeeze is documented as returning a view, but the masked variant would
sometimes return masked, which is not a view. This has been fixed, so that
the result is always a view on the original masked array.
This breaks any code that used masked_arr.squeeze() is np.ma.masked, but
fixes code that writes to the result of .squeeze().
Renamed first parameter of can_cast from from to from_
The previous parameter name from is a reserved keyword in Python, which made
it difficult to pass the argument by name. This has been fixed by renaming
the parameter to from_.
isnat raises TypeError when passed wrong type
The ufunc isnat used to raise a ValueError when it was not passed
variables of type datetime or timedelta. This has been changed to
raising a TypeError.
dtype.__getitem__ raises TypeError when passed wrong type
When indexed with a float, the dtype object used to raise ValueError.
User-defined types now need to implement __str__ and __repr__
Previously, user-defined types could fall back to a default implementation of
__str__ and __repr__ implemented in numpy, but this has now been
removed. Now user-defined types will fall back to the python default
object.__str__ and object.__repr__.
Many changes to array printing, disableable with the new "legacy" printing mode
The str and repr of ndarrays and numpy scalars have been changed in
a variety of ways. These changes are likely to break downstream user's
doctests.
These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by
enabling the new 1.13 "legacy" printing mode. This is enabled by calling
np.set_printoptions(legacy="1.13"), or using the new legacy argument to
np.array2string, as np.array2string(arr, legacy='1.13').
In summary, the major changes are:
For floating-point types:
The repr of float arrays often omits a space previously printed
in the sign position. See the new sign option to np.set_printoptions.
Floating-point arrays and scalars use a new algorithm for decimal
representations, giving the shortest unique representation. This will
usually shorten float16 fractional output, and sometimes float32 and
float128 output. float64 should be unaffected. See the new
floatmode option to np.set_printoptions.
Float arrays printed in scientific notation no longer use fixed-precision,
and now instead show the shortest unique representation.
The str of floating-point scalars is no longer truncated in python2.
For other data types:
Non-finite complex scalars print like nanj instead of nan*j.
NaT values in datetime arrays are now properly aligned.
Arrays and scalars of np.void datatype are now printed using hex
notation.
For line-wrapping:
The "dtype" part of ndarray reprs will now be printed on the next line
if there isn't space on the last line of array output.
The linewidth format option is now always respected.
The repr or str of an array will never exceed this, unless a single
element is too wide.
The last line of an array string will never have more elements than earlier
lines.
An extra space is no longer inserted on the first line if the elements are
too wide.
For summarization (the use of ... to shorten long arrays):
A trailing comma is no longer inserted for str.
Previously, str(np.arange(1001)) gave
'[ 0 1 2 ..., 998 999 1000]', which has an extra comma.
For arrays of 2-D and beyond, when ... is printed on its own line in
order to summarize any but the last axis, newlines are now appended to that
line to match its leading newlines and a trailing space character is
removed.
MaskedArray arrays now separate printed elements with commas, always
print the dtype, and correctly wrap the elements of long arrays to multiple
lines. If there is more than 1 dimension, the array attributes are now
printed in a new "left-justified" printing style.
recarray arrays no longer print a trailing space before their dtype, and
wrap to the right number of columns.
0d arrays no longer have their own idiosyncratic implementations of str
and repr. The style argument to np.array2string is deprecated.
Arrays of bool datatype will omit the datatype in the repr.
User-defined dtypes (subclasses of np.generic) now need to
implement __str__ and __repr__.
You may want to do something like::
FIXME: Set numpy array str/repr to legacy behaviour on numpy > 1.13
Some of these changes are described in more detail below.
C API changes
PyPy compatible alternative to UPDATEIFCOPY arrays
UPDATEIFCOPY arrays are contiguous copies of existing arrays, possibly with
different dimensions, whose contents are copied back to the original array when
their refcount goes to zero and they are deallocated. Because PyPy does not use
refcounts, they do not function correctly with PyPy. NumPy is in the process of
eliminating their use internally and two new C-API functions,
PyArray_SetWritebackIfCopyBase
PyArray_ResolveWritebackIfCopy,
have been added together with a complimentary flag,
NPY_ARRAY_WRITEBACKIFCOPY. Using the new functionality also requires that
some flags be changed when new arrays are created, to wit:
NPY_ARRAY_INOUT_ARRAY should be replaced by NPY_ARRAY_INOUT_ARRAY2 and
NPY_ARRAY_INOUT_FARRAY should be replaced by NPY_ARRAY_INOUT_FARRAY2.
Arrays created with these new flags will then have the WRITEBACKIFCOPY
semantics.
If PyPy compatibility is not a concern, these new functions can be ignored,
although there will be a DeprecationWarning. If you do wish to pursue PyPy
compatibility, more information on these functions and their use may be found
in the c-api documentation and the example in how-to-extend.
genfromtxt, loadtxt, fromregex and savetxt can now handle files
with arbitrary encoding supported by Python via the encoding argument.
For backward compatibility the argument defaults to the special bytes value
which continues to treat text as raw byte values and continues to pass latin1
encoded bytes to custom converters.
Using any other value (including None for system default) will switch the
functions to real text IO so one receives unicode strings instead of bytes in
the resulting arrays.
External nose plugins are usable by numpy.testing.Tester
numpy.testing.Tester is now aware of nose plugins that are outside the
nose built-in ones. This allows using, for example, nose-timer like
so: np.test(extra_argv=['--with-timer', '--timer-top-n', '20']) to
obtain the runtime of the 20 slowest tests. An extra keyword timer was
also added to Tester.test, so np.test(timer=20) will also report the 20
slowest tests.
parametrize decorator added to numpy.testing
A basic parametrize decorator is now available in numpy.testing. It is
intended to allow rewriting yield based tests that have been deprecated in
pytest so as to facilitate the transition to pytest in the future. The nose
testing framework has not been supported for several years and looks like
abandonware.
The new parametrize decorator does not have the full functionality of the
one in pytest. It doesn't work for classes, doesn't support nesting, and does
not substitute variable names. Even so, it should be adequate to rewrite the
NumPy tests.
chebinterpolate function added to numpy.polynomial.chebyshev
The new chebinterpolate function interpolates a given function at the
Chebyshev points of the first kind. A new Chebyshev.interpolate class
method adds support for interpolation over arbitrary intervals using the scaled
and shifted Chebyshev points of the first kind.
Support for reading lzma compressed text files in Python 3
With Python versions containing the lzma module the text IO functions can
now transparently read from files with xz or lzma extension.
sign option added to np.setprintoptions and np.array2string
This option controls printing of the sign of floating-point types, and may be
one of the characters '-', '+' or ' '. With '+' numpy always prints the sign of
positive values, with ' ' it always prints a space (whitespace character) in
the sign position of positive values, and with '-' it will omit the sign
character for positive values. The new default is '-'.
This new default changes the float output relative to numpy 1.13. The old
behavior can be obtained in 1.13 "legacy" printing mode, see compatibility
notes above.
hermitian option added tonp.linalg.matrix_rank
The new hermitian option allows choosing between standard SVD based matrix
rank calculation and the more efficient eigenvalue based method for
symmetric/hermitian matrices.
threshold and edgeitems options added to np.array2string
These options could previously be controlled using np.set_printoptions, but
now can be changed on a per-call basis as arguments to np.array2string.
concatenate and stack gained an out argument
A preallocated buffer of the desired dtype can now be used for the output of
these functions.
Support for PGI flang compiler on Windows
The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under
the Apache 2 license. It can be invoked by ::
There is little experience with this new compiler, so any feedback from people
using it will be appreciated.
Improvements
Numerator degrees of freedom in random.noncentral_f need only be positive.
Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be > 1, but
the distribution is valid for values > 0, which is the new requirement.
The GIL is released for all np.einsum variations
Some specific loop structures which have an accelerated loop version
did not release the GIL prior to NumPy 1.14.0. This oversight has been
fixed.
The np.einsum function will use BLAS when possible and optimize by default
The np.einsum function will now call np.tensordot when appropriate.
Because np.tensordot uses BLAS when possible, that will speed up execution.
By default, np.einsum will also attempt optimization as the overhead is
small relative to the potential improvement in speed.
f2py now handles arrays of dimension 0
f2py now allows for the allocation of arrays of dimension 0. This allows
for more consistent handling of corner cases downstream.
numpy.distutils supports using MSVC and mingw64-gfortran together
Numpy distutils now supports using Mingw64 gfortran and MSVC compilers
together. This enables the production of Python extension modules on Windows
containing Fortran code while retaining compatibility with the
binaries distributed by Python.org. Not all use cases are supported,
but most common ways to wrap Fortran for Python are functional.
Compilation in this mode is usually enabled automatically, and can be
selected via the --fcompiler and --compiler options to
setup.py. Moreover, linking Fortran codes to static OpenBLAS is
supported; by default a gfortran compatible static archive
openblas.a is looked for.
np.linalg.pinv now works on stacked matrices
Previously it was limited to a single 2d array.
numpy.save aligns data to 64 bytes instead of 16
Saving NumPy arrays in the npy format with numpy.save inserts
padding before the array data to align it at 64 bytes. Previously
this was only 16 bytes (and sometimes less due to a bug in the code
for version 2). Now the alignment is 64 bytes, which matches the
widest SIMD instruction set commonly available, and is also the most
common cache line size. This makes npy files easier to use in
programs which open them with mmap, especially on Linux where an
mmap offset must be a multiple of the page size.
NPZ files now can be written without using temporary files
In Python 3.6+ numpy.savez and numpy.savez_compressed now write
directly to a ZIP file, without creating intermediate temporary files.
Better support for empty structured and string types
Structured types can contain zero fields, and string dtypes can contain zero
characters. Zero-length strings still cannot be created directly, and must be
constructed through structured dtypes::
It was always possible to work with these, but the following operations are
now supported for these arrays:
arr.sort()
arr.view(bytes)
arr.resize(...)
pickle.dumps(arr)
Support for decimal.Decimal in np.lib.financial
Unless otherwise stated all functions within the financial package now
support using the decimal.Decimal built-in type.
Float printing now uses "dragon4" algorithm for shortest decimal representation
The str and repr of floating-point values (16, 32, 64 and 128 bit) are
now printed to give the shortest decimal representation which uniquely
identifies the value from others of the same type. Previously this was only
true for float64 values. The remaining float types will now often be shorter
than in numpy 1.13. Arrays printed in scientific notation now also use the
shortest scientific representation, instead of fixed precision as before.
Additionally, the str of float scalars scalars will no longer be truncated
in python2, unlike python2 floats. np.double scalars now have a str
and repr identical to that of a python3 float.
New functions np.format_float_scientific and np.format_float_positional
are provided to generate these decimal representations.
A new option floatmode has been added to np.set_printoptions and
np.array2string, which gives control over uniqueness and rounding of
printed elements in an array. The new default is floatmode='maxprec' with
precision=8, which will print at most 8 fractional digits, or fewer if an
element can be uniquely represented with fewer. A useful new mode is
floatmode="unique", which will output enough digits to specify the array
elements uniquely.
Numpy complex-floating-scalars with values like inf*j or nan*j now
print as infj and nanj, like the pure-python complex type.
The FloatFormat and LongFloatFormat classes are deprecated and should
both be replaced by FloatingFormat. Similarly ComplexFormat and
LongComplexFormat should be replaced by ComplexFloatingFormat.
void datatype elements are now printed in hex notation
A hex representation compatible with the python bytes type is now printed
for unstructured np.void elements, e.g., V4 datatype. Previously, in
python2 the raw void data of the element was printed to stdout, or in python3
the integer byte values were shown.
printing style for void datatypes is now independently customizable
The printing style of np.void arrays is now independently customizable
using the formatter argument to np.set_printoptions, using the
'void' key, instead of the catch-all numpystr key as before.
Reduced memory usage of np.loadtxt
np.loadtxt now reads files in chunks instead of all at once which decreases
its memory usage significantly for large files.
Changes
Multiple-field indexing/assignment of structured arrays
The indexing and assignment of structured arrays with multiple fields has
changed in a number of ways, as warned about in previous releases.
First, indexing a structured array with multiple fields, e.g.,
arr[['f1', 'f3']], returns a view into the original array instead of a
copy. The returned view will have extra padding bytes corresponding to
intervening fields in the original array, unlike the copy in 1.13, which will
affect code such as arr[['f1', 'f3']].view(newdtype).
Second, assignment between structured arrays will now occur "by position"
instead of "by field name". The Nth field of the destination will be set to the
Nth field of the source regardless of field name, unlike in numpy versions 1.6
to 1.13 in which fields in the destination array were set to the
identically-named field in the source array or to 0 if the source did not have
a field.
Correspondingly, the order of fields in a structured dtypes now matters when
computing dtype equality. For example, with the dtypes ::
the expression x == y will now return False, unlike before.
This makes dictionary based dtype specifications like
dtype({'a': ('i4', 0), 'b': ('f4', 4)}) dangerous in python < 3.6
since dict key order is not preserved in those versions.
Assignment from a structured array to a boolean array now raises a ValueError,
unlike in 1.13, where it always set the destination elements to True.
Assignment from structured array with more than one field to a non-structured
array now raises a ValueError. In 1.13 this copied just the first field of the
source to the destination.
Using field "titles" in multiple-field indexing is now disallowed, as is
repeating a field name in a multiple-field index.
The documentation for structured arrays in the user guide has been
significantly updated to reflect these changes.
Integer and Void scalars are now unaffected by np.set_string_function
Previously, unlike most other numpy scalars, the str and repr of
integer and void scalars could be controlled by np.set_string_function.
This is no longer possible.
0d array printing changed, style arg of array2string deprecated
Previously the str and repr of 0d arrays had idiosyncratic
implementations which returned str(a.item()) and 'array(' + repr(a.item()) + ')' respectively for 0d array a, unlike both numpy
scalars and higher dimension ndarrays.
Now, the str of a 0d array acts like a numpy scalar using str(a[()])
and the repr acts like higher dimension arrays using formatter(a[()]),
where formatter can be specified using np.set_printoptions. The
style argument of np.array2string is deprecated.
This new behavior is disabled in 1.13 legacy printing mode, see compatibility
notes above.
Seeding RandomState using an array requires a 1-d array
RandomState previously would accept empty arrays or arrays with 2 or more
dimensions, which resulted in either a failure to seed (empty arrays) or for
some of the passed values to be ignored when setting the seed.
MaskedArray objects show a more useful repr
The repr of a MaskedArray is now closer to the python code that would
produce it, with arrays now being shown with commas and dtypes. Like the other
formatting changes, this can be disabled with the 1.13 legacy printing mode in
order to help transition doctests.
The repr of np.polynomial classes is more explicit
It now shows the domain and window parameters as keyword arguments to make
them more clear::
This is a bugfix release for some problems found since 1.13.1. The most
important fixes are for CVE-2017-12852 and temporary elision. Users of earlier
versions of 1.13 should upgrade.
The Python versions supported are 2.7 and 3.4 - 3.6. The Python 3.6 wheels
available from PIP are built with Python 3.6.2 and should be compatible with
all previous versions of Python 3.6. It was cythonized with Cython 0.26.1,
which should be free of the bugs found in 0.27 while also being compatible with
Python 3.7-dev. The Windows wheels were built with OpenBlas instead ATLAS,
which should improve the performance of the linear algebra functions.
The NumPy 1.13.3 release is a re-release of 1.13.2, which suffered from a
bug in Cython 0.27.0.
Contributors
A total of 12 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.
Allan Haldane
Brandon Carter
Charles Harris
Eric Wieser
Iryna Shcherbina +
James Bourbeau +
Jonathan Helmus
Julian Taylor
Matti Picus
Michael Lamparski +
Michael Seifert
Ralf Gommers
Pull requests merged
A total of 22 pull requests were merged for this release.
9390 BUG: Return the poly1d coefficients array directly
9555 BUG: Fix regression in 1.13.x in distutils.mingw32ccompiler.
9556 BUG: Fix true_divide when dtype=np.float64 specified.
9557 DOC: Fix some rst markup in numpy/doc/basics.py.
9558 BLD: Remove -xhost flag from IntelFCompiler.
9559 DOC: Removes broken docstring example (source code, png, pdf)...
9580 BUG: Add hypot and cabs functions to WIN32 blacklist.
9732 BUG: Make scalar function elision check if temp is writeable.
9736 BUG: Various fixes to np.gradient
9742 BUG: Fix np.pad for CVE-2017-12852
9744 BUG: Check for exception in sort functions, add tests
9745 DOC: Add whitespace after "versionadded::" directive so it actually...
9746 BUG: Memory leak in np.dot of size 0
9747 BUG: Adjust gfortran version search regex
9757 BUG: Cython 0.27 breaks NumPy on Python 3.
9764 BUG: Ensure _npy_scaled_cexp{,f,l} is defined when needed.
9765 BUG: PyArray_CountNonzero does not check for exceptions
9766 BUG: Fixes histogram monotonicity check for unsigned bin values
9767 BUG: Ensure consistent result dtype of count_nonzero
9771 BUG: MAINT: Fix mtrand for Cython 0.27.
9772 DOC: Create the 1.13.2 release notes.
9794 DOC: Create 1.13.3 release notes.
=========================
1.13.1
==========================
This is a bugfix release for problems found in 1.13.0. The major changes are
fixes for the new memory overlap detection and temporary elision as well as
reversion of the removal of the boolean binary - operator. Users of 1.13.0
should upgrade.
Thr Python versions supported are 2.7 and 3.4 - 3.6. Note that the Python 3.6
wheels available from PIP are built against 3.6.1, hence will not work when
used with 3.6.0 due to Python bug 29943_. NumPy 1.13.2 will be released shortly
after Python 3.6.2 is out to fix that problem. If you are using 3.6.0 the
workaround is to upgrade to 3.6.1 or use an earlier Python version.
A total of 19 pull requests were merged for this release.
9240 DOC: BLD: fix lots of Sphinx warnings/errors.
9255 Revert "DEP: Raise TypeError for subtract(bool, bool)."
9261 BUG: don't elide into readonly and updateifcopy temporaries for...
9262 BUG: fix missing keyword rename for common block in numpy.f2py
9263 BUG: handle resize of 0d array
9267 DOC: update f2py front page and some doc build metadata.
9299 BUG: Fix Intel compilation on Unix.
9317 BUG: fix wrong ndim used in empty where check
9319 BUG: Make extensions compilable with MinGW on Py2.7
9339 BUG: Prevent crash if ufunc doc string is null
9340 BUG: umath: un-break ufunc where= when no out= is given
9371 DOC: Add isnat/positive ufunc to documentation
9372 BUG: Fix error in fromstring function from numpy.core.records...
9373 BUG: ')' is printed at the end pointer of the buffer in numpy.f2py.
9374 DOC: Create NumPy 1.13.1 release notes.
9376 BUG: Prevent hang traversing ufunc userloop linked list
9377 DOC: Use x1 and x2 in the heaviside docstring.
9378 DOC: Add $PARAMS to the isnat docstring
9379 DOC: Update the 1.13.1 release notes
Contributors
A total of 12 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.
Andras Deak +
Bob Eldering +
Charles Harris
Daniel Hrisca +
Eric Wieser
Joshua Leahy +
Julian Taylor
Michael Seifert
Pauli Virtanen
Ralf Gommers
Roland Kaufmann
Warren Weckesser
=========================
1.13.0
==========================
This release supports Python 2.7 and 3.4 - 3.6.
Highlights
Operations like a + b + c will reuse temporaries on some platforms,
resulting in less memory use and faster execution.
Inplace operations check if inputs overlap outputs and create temporaries
to avoid problems.
New __array_ufunc__ attribute provides improved ability for classes to
override default ufunc behavior.
New np.block function for creating blocked arrays.
New functions
New np.positive ufunc.
New np.divmod ufunc provides more efficient divmod.
New np.isnat ufunc tests for NaT special values.
New np.heaviside ufunc computes the Heaviside function.
New np.isin function, improves on in1d.
New np.block function for creating blocked arrays.
New PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API.
See below for details.
Deprecations
Calling np.fix, np.isposinf, and np.isneginf with f(x, y=out)
is deprecated - the argument should be passed as f(x, out=out), which
matches other ufunc-like interfaces.
Use of the C-API NPY_CHAR type number deprecated since version 1.7 will
now raise deprecation warnings at runtime. Extensions built with older f2py
versions need to be recompiled to remove the warning.
np.ma.argsort, np.ma.minimum.reduce, and np.ma.maximum.reduce
should be called with an explicit axis argument when applied to arrays with
more than 2 dimensions, as the default value of this argument (None) is
inconsistent with the rest of numpy (-1, 0, and 0, respectively).
np.ma.MaskedArray.mini is deprecated, as it almost duplicates the
functionality of np.MaskedArray.min. Exactly equivalent behaviour
can be obtained with np.ma.minimum.reduce.
The single-argument form of np.ma.minimum and np.ma.maximum is
deprecated. np.maximum. np.ma.minimum(x) should now be spelt
np.ma.minimum.reduce(x), which is consistent with how this would be done
with np.minimum.
Calling ndarray.conjugate on non-numeric dtypes is deprecated (it
should match the behavior of np.conjugate, which throws an error).
Calling expand_dims when the axis keyword does not satisfy
-a.ndim - 1 <= axis <= a.ndim, where a is the array being reshaped,
is deprecated.
Future Changes
Assignment between structured arrays with different field names will change
in NumPy 1.14. Previously, fields in the dst would be set to the value of the
identically-named field in the src. In numpy 1.14 fields will instead be
assigned 'by position': The n-th field of the dst will be set to the n-th
field of the src array. Note that the FutureWarning raised in NumPy 1.12
incorrectly reported this change as scheduled for NumPy 1.13 rather than
NumPy 1.14.
Build System Changes
numpy.distutils now automatically determines C-file dependencies with
GCC compatible compilers.
Compatibility notes
Error type changes
numpy.hstack() now throws ValueError instead of IndexError when
input is empty.
Functions taking an axis argument, when that argument is out of range, now
throw np.AxisError instead of a mixture of IndexError and
ValueError. For backwards compatibility, AxisError subclasses both of
these.
Tuple object dtypes
Support has been removed for certain obscure dtypes that were unintentionally
allowed, of the form (old_dtype, new_dtype), where either of the dtypes
is or contains the object dtype. As an exception, dtypes of the form
(object, [('name', object)]) are still supported due to evidence of
existing use.
DeprecationWarning to error
See Changes section for more detail.
partition, TypeError when non-integer partition index is used.
NpyIter_AdvancedNew, ValueError when oa_ndim == 0 and op_axes is NULL
negative(bool_), TypeError when negative applied to booleans.
subtract(bool_, bool_), TypeError when subtracting boolean from boolean.
Previously bool(dtype) would fall back to the default python
implementation, which checked if len(dtype) > 0. Since dtype objects
implement __len__ as the number of record fields, bool of scalar dtypes
would evaluate to False, which was unintuitive. Now bool(dtype) == True
for all dtypes.
__getslice__ and __setslice__ are no longer needed in ndarray subclasses
When subclassing np.ndarray in Python 2.7, it is no longer necessary to
implement __*slice__ on the derived class, as __*item__ will intercept
these calls correctly.
Any code that did implement these will work exactly as before. Code that
invokesndarray.__getslice__ (e.g. through super(...).__getslice__) will
now issue a DeprecationWarning - .__getitem__(slice(start, end)) should be
used instead.
Indexing MaskedArrays/Constants with ... (ellipsis) now returns MaskedArray
This behavior mirrors that of np.ndarray, and accounts for nested arrays in
MaskedArrays of object dtype, and ellipsis combined with other forms of
indexing.
C API changes
GUfuncs on empty arrays and NpyIter axis removal
It is now allowed to remove a zero-sized axis from NpyIter. Which may mean
that code removing axes from NpyIter has to add an additional check when
accessing the removed dimensions later on.
The largest followup change is that gufuncs are now allowed to have zero-sized
inner dimensions. This means that a gufunc now has to anticipate an empty inner
dimension, while this was never possible and an error raised instead.
For most gufuncs no change should be necessary. However, it is now possible
for gufuncs with a signature such as (..., N, M) -> (..., M) to return
a valid result if N=0 without further wrapping code.
PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API
Similar to PyArray_MapIterArray but with an additional copy_if_overlap
argument. If copy_if_overlap != 0, checks if input has memory overlap with
any of the other arrays and make copies as appropriate to avoid problems if the
input is modified during the iteration. See the documentation for more complete
documentation.
New Features
__array_ufunc__ added
This is the renamed and redesigned __numpy_ufunc__. Any class, ndarray
subclass or not, can define this method or set it to None in order to
override the behavior of NumPy's ufuncs. This works quite similarly to Python's
__mul__ and other binary operation routines. See the documentation for a
more detailed description of the implementation and behavior of this new
option. The API is provisional, we do not yet guarantee backward compatibility
as modifications may be made pending feedback. See the NEP and
documentation for more details.
This ufunc corresponds to unary +, but unlike + on an ndarray it will raise
an error if array values do not support numeric operations.
New divmod ufunc
This ufunc corresponds to the Python builtin divmod, and is used to implement
divmod when called on numpy arrays. np.divmod(x, y) calculates a result
equivalent to (np.floor_divide(x, y), np.remainder(x, y)) but is
approximately twice as fast as calling the functions separately.
np.isnat ufunc tests for NaT special datetime and timedelta values
The new ufunc np.isnat finds the positions of special NaT values
within datetime and timedelta arrays. This is analogous to np.isnan.
np.heaviside ufunc computes the Heaviside function
The new function np.heaviside(x, h0) (a ufunc) computes the Heaviside
function:
.. code::
{ 0 if x < 0,
heaviside(x, h0) = { h0 if x == 0,
{ 1 if x > 0.
np.block function for creating blocked arrays
Add a new block function to the current stacking functions vstack,
hstack, and stack. This allows concatenation across multiple axes
simultaneously, with a similar syntax to array creation, but where elements
can themselves be arrays. For instance::
While primarily useful for block matrices, this works for arbitrary dimensions
of arrays.
It is similar to Matlab's square bracket notation for creating block matrices.
isin function, improving on in1d
The new function isin tests whether each element of an N-dimensonal
array is present anywhere within a second array. It is an enhancement
of in1d that preserves the shape of the first array.
Temporary elision
On platforms providing the backtrace function NumPy will try to avoid
creating temporaries in expression involving basic numeric types.
For example d = a + b + c is transformed to d = a + b; d += c which can
improve performance for large arrays as less memory bandwidth is required to
perform the operation.
axes argument for unique
In an N-dimensional array, the user can now choose the axis along which to look
for duplicate N-1-dimensional elements using numpy.unique. The original
behaviour is recovered if axis=None (default).
np.gradient now supports unevenly spaced data
Users can now specify a not-constant spacing for data.
In particular np.gradient can now take:
A single scalar to specify a sample distance for all dimensions.
N scalars to specify a constant sample distance for each dimension.
i.e. dx, dy, dz, ...
N arrays to specify the coordinates of the values along each dimension of F.
The length of the array must match the size of the corresponding dimension
Any combination of N scalars/arrays with the meaning of 2. and 3.
This means that, e.g., it is now possible to do the following::
Support for returning arrays of arbitrary dimensions in apply_along_axis
Previously, only scalars or 1D arrays could be returned by the function passed
to apply_along_axis. Now, it can return an array of any dimensionality
(including 0D), and the shape of this array replaces the axis of the array
being iterated over.
.ndim property added to dtype to complement .shape
For consistency with ndarray and broadcast, d.ndim is a shorthand
for len(d.shape).
Support for tracemalloc in Python 3.6
NumPy now supports memory tracing with tracemalloc_ module of Python 3.6 or
newer. Memory allocations from NumPy are placed into the domain defined by
numpy.lib.tracemalloc_domain.
Note that NumPy allocation will not show up in tracemalloc_ of earlier Python
versions.
NumPy may be built with relaxed stride checking debugging
Setting NPY_RELAXED_STRIDES_DEBUG=1 in the environment when relaxed stride
checking is enabled will cause NumPy to be compiled with the affected strides
set to the maximum value of npy_intp in order to help detect invalid usage of
the strides in downstream projects. When enabled, invalid usage often results
in an error being raised, but the exact type of error depends on the details of
the code. TypeError and OverflowError have been observed in the wild.
It was previously the case that this option was disabled for releases and
enabled in master and changing between the two required editing the code. It is
now disabled by default but can be enabled for test builds.
Improvements
Ufunc behavior for overlapping inputs
Operations where ufunc input and output operands have memory overlap
produced undefined results in previous NumPy versions, due to data
dependency issues. In NumPy 1.13.0, results from such operations are
now defined to be the same as for equivalent operations where there is
no memory overlap.
Operations affected now make temporary copies, as needed to eliminate
data dependency. As detecting these cases is computationally
expensive, a heuristic is used, which may in rare cases result to
needless temporary copies. For operations where the data dependency
is simple enough for the heuristic to analyze, temporary copies will
not be made even if the arrays overlap, if it can be deduced copies
are not necessary. As an example,np.add(a, b, out=a) will not
involve copies.
To illustrate a previously undefined operation::
>>> x = np.arange(16).astype(float)
>>> np.add(x[1:], x[:-1], out=x[1:])
In NumPy 1.13.0 the last line is guaranteed to be equivalent to::
A similar operation with simple non-problematic data dependence is::
>>> x = np.arange(16).astype(float)
>>> np.add(x[1:], x[:-1], out=x[:-1])
It will continue to produce the same results as in previous NumPy
versions, and will not involve unnecessary temporary copies.
The change applies also to in-place binary operations, for example::
>>> x = np.random.rand(500, 500)
>>> x += x.T
This statement is now guaranteed to be equivalent to x[...] = x + x.T,
whereas in previous NumPy versions the results were undefined.
Partial support for 64-bit f2py extensions with MinGW
Extensions that incorporate Fortran libraries can now be built using the free
MinGW toolset, also under Python 3.5. This works best for extensions that only
do calculations and uses the runtime modestly (reading and writing from files,
for instance). Note that this does not remove the need for Mingwpy; if you make
extensive use of the runtime, you will most likely run into issues. Instead,
it should be regarded as a band-aid until Mingwpy is fully functional.
Extensions can also be compiled using the MinGW toolset using the runtime
library from the (moveable) WinPython 3.4 distribution, which can be useful for
programs with a PySide1/Qt4 front-end.
Performance improvements for packbits and unpackbits
The functions numpy.packbits with boolean input and numpy.unpackbits have
been optimized to be a significantly faster for contiguous data.
Fix for PPC long double floating point information
In previous versions of NumPy, the finfo function returned invalid
information about the double double_ format of the longdouble float type
on Power PC (PPC). The invalid values resulted from the failure of the NumPy
algorithm to deal with the variable number of digits in the significand
that are a feature of PPC long doubles. This release by-passes the failing
algorithm by using heuristics to detect the presence of the PPC double double
format. A side-effect of using these heuristics is that the finfo
function is faster than previous releases.
Subclasses of ndarray with no repr specialization now correctly indent
their data and type lines.
More reliable comparisons of masked arrays
Comparisons of masked arrays were buggy for masked scalars and failed for
structured arrays with dimension higher than one. Both problems are now
solved. In the process, it was ensured that in getting the result for a
structured array, masked fields are properly ignored, i.e., the result is equal
if all fields that are non-masked in both are equal, thus making the behaviour
identical to what one gets by comparing an unstructured masked array and then
doing .all() over some axis.
np.matrix with booleans elements can now be created using the string syntax
np.matrix failed whenever one attempts to use it with booleans, e.g.,
np.matrix('True'). Now, this works as expected.
More linalg operations now accept empty vectors and matrices
All of the following functions in np.linalg now work when given input
arrays with a 0 in the last two dimensions: det, slogdet, pinv,
eigvals, eigvalsh, eig, eigh.
Bundled version of LAPACK is now 3.2.2
NumPy comes bundled with a minimal implementation of lapack for systems without
a lapack library installed, under the name of lapack_lite. This has been
upgraded from LAPACK 3.0.0 (June 30, 1999) to LAPACK 3.2.2 (June 30, 2010). See
the LAPACK changelogs_ for details on the all the changes this entails.
While no new features are exposed through numpy, this fixes some bugs
regarding "workspace" sizes, and in some places may use faster algorithms.
reduce of np.hypot.reduce and np.logical_xor allowed in more cases
This now works on empty arrays, returning 0, and can reduce over multiple axes.
Previously, a ValueError was thrown in these cases.
Better repr of object arrays
Object arrays that contain themselves no longer cause a recursion error.
Object arrays that contain list objects are now printed in a way that makes
clear the difference between a 2d object array, and a 1d object array of lists.
Changes
argsort on masked arrays takes the same default arguments as sort
By default, argsort now places the masked values at the end of the sorted
array, in the same way that sort already did. Additionally, the
end_with argument is added to argsort, for consistency with sort.
Note that this argument is not added at the end, so breaks any code that
passed fill_value as a positional argument.
average now preserves subclasses
For ndarray subclasses, numpy.average will now return an instance of the
subclass, matching the behavior of most other NumPy functions such as mean.
As a consequence, also calls that returned a scalar may now return a subclass
array scalar.
array == None and array != None do element-wise comparison
Previously these operations returned scalars False and True respectively.
np.equal, np.not_equal for object arrays ignores object identity
Previously, these functions always treated identical objects as equal. This had
the effect of overriding comparison failures, comparison of objects that did
not return booleans, such as np.arrays, and comparison of objects where the
results differed from object identity, such as NaNs.
Boolean indexing changes
Boolean array-likes (such as lists of python bools) are always treated as
boolean indexes.
Boolean scalars (including python True) are legal boolean indexes and
never treated as integers.
Boolean indexes must match the dimension of the axis that they index.
pyup-bot
commented
6 years ago
Updates
Here's a list of all the updates bundled in this pull request. I've added some links to make it easier for you to find all the information you need.
Changelogs
numpy 1.12.1 -> 1.14.0