doc/source/release/1.13.0-notes.rst
This release supports Python 2.7 and 3.4 - 3.6.
a + b + c will reuse temporaries on some platforms,
resulting in less memory use and faster execution.__array_ufunc__ attribute provides improved ability for classes to
override default ufunc behavior.np.block function for creating blocked arrays.np.positive ufunc.np.divmod ufunc provides more efficient divmod.np.isnat ufunc tests for NaT special values.np.heaviside ufunc computes the Heaviside function.np.isin function, improves on in1d.np.block function for creating blocked arrays.PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API.See below for details.
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.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.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.ndarray.conjugate on non-numeric dtypes is deprecated (it
should match the behavior of np.conjugate, which throws an error).expand_dims when the axis keyword does not satisfy
-a.ndim - 1 <= axis <= a.ndim, where a is the array being reshaped,
is deprecated.FutureWarning raised in NumPy 1.12
incorrectly reported this change as scheduled for NumPy 1.13 rather than
NumPy 1.14.numpy.distutils now automatically determines C-file dependencies with
GCC compatible compilers.numpy.hstack() now throws ValueError instead of IndexError when
input is empty.np.AxisError instead of a mixture of IndexError and
ValueError. For backwards compatibility, AxisError subclasses both of
these.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.
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 NULLnegative(bool_), TypeError when negative applied to booleans.subtract(bool_, bool_), TypeError when subtracting boolean from boolean.np.equal, np.not_equal, object identity doesn't override failed comparison.np.equal, np.not_equal, object identity doesn't override non-boolean comparison.np.alterdot() and np.restoredot() removed.See Changes section for more detail.
numpy.average preserves subclassesarray == None and array != None do element-wise comparison.np.equal, np.not_equal, object identity doesn't override comparison result.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 subclassesWhen 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
invokes ndarray.__getslice__ (e.g. through super(...).__getslice__) will
now issue a DeprecationWarning - .__getitem__(slice(start, end)) should be
used instead.
... (ellipsis) now returns MaskedArrayThis behavior mirrors that of np.ndarray, and accounts for nested arrays in MaskedArrays of object dtype, and ellipsis combined with other forms of indexing.
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-APISimilar 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.
__array_ufunc__ addedThis 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 NEP 13_ and
documentation_ for more details.
.. _NEP 13: http://www.numpy.org/neps/nep-0013-ufunc-overrides.html
.. _documentation: https://github.com/numpy/numpy/blob/master/doc/source/reference/arrays.classes.rst
positive ufuncThis ufunc corresponds to unary +, but unlike + on an ndarray it will raise
an error if array values do not support numeric operations.
divmod ufuncThis 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 valuesThe 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 functionThe 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 arraysAdd 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::
>>> A = np.eye(2) * 2
>>> B = np.eye(3) * 3
>>> np.block([
... [A, np.zeros((2, 3))],
... [np.ones((3, 2)), B ]
... ])
array([[ 2., 0., 0., 0., 0.],
[ 0., 2., 0., 0., 0.],
[ 1., 1., 3., 0., 0.],
[ 1., 1., 0., 3., 0.],
[ 1., 1., 0., 0., 3.]])
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 in1dThe new function isin tests whether each element of an N-dimensional
array is present anywhere within a second array. It is an enhancement
of in1d that preserves the shape of the first array.
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 uniqueIn 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 dataUsers can now specify a not-constant spacing for data.
In particular np.gradient can now take:
dx, dy, dz, ...This means that, e.g., it is now possible to do the following::
>>> f = np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float_)
>>> dx = 2.
>>> y = [1., 1.5, 3.5]
>>> np.gradient(f, dx, y)
[array([[ 1. , 1. , -0.5], [ 1. , 1. , -0.5]]),
array([[ 2. , 2. , 2. ], [ 2. , 1.7, 0.5]])]
apply_along_axisPreviously, 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 .shapeFor consistency with ndarray and broadcast, d.ndim is a shorthand
for len(d.shape).
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.
.. _tracemalloc: https://docs.python.org/3/library/tracemalloc.html
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.
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::
>>> np.add(x[1:].copy(), x[:-1].copy(), out=x[1:])
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.
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.
.. _issues: https://mingwpy.github.io/issues.html
packbits and unpackbitsThe functions numpy.packbits with boolean input and numpy.unpackbits have
been optimized to be a significantly faster for contiguous data.
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.
.. _PPC long doubles: https://www.ibm.com/support/knowledgecenter/en/ssw_aix_71/com.ibm.aix.genprogc/128bit_long_double_floating-point_datatype.htm
.. _double double: https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic
ndarray subclassesSubclasses of ndarray with no repr specialization now correctly indent
their data and type lines.
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 failed whenever one attempts to use it with booleans, e.g.,
np.matrix('True'). Now, this works as expected.
linalg operations now accept empty vectors and matricesAll 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.
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.
.. _LAPACK changelogs: http://www.netlib.org/lapack/release_notes.html#_4_history_of_lapack_releases
reduce of np.hypot.reduce and np.logical_xor allowed in more casesThis now works on empty arrays, returning 0, and can reduce over multiple axes.
Previously, a ValueError was thrown in these cases.
repr of object arraysObject 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.
argsort on masked arrays takes the same default arguments as sortBy 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 subclassesFor 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 comparisonPreviously these operations returned scalars False and True respectively.
np.equal, np.not_equal for object arrays ignores object identityPreviously, 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 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.
Boolean indexes used on the lhs of an assignment must match the dimensions of the rhs.
Boolean indexing into scalar arrays return a new 1-d array. This means that
array(1)[array(True)] gives array([1]) and not the original array.
np.random.multivariate_normal behavior with bad covariance matrixIt is now possible to adjust the behavior the function will have when dealing with the covariance matrix by using two new keyword arguments:
tol can be used to specify a tolerance to use when checking that
the covariance matrix is positive semidefinite.
check_valid can be used to configure what the function will do in the
presence of a matrix that is not positive semidefinite. Valid options are
ignore, warn and raise. The default value, warn keeps the
the behavior used on previous releases.
assert_array_less compares np.inf and -np.inf nowPreviously, np.testing.assert_array_less ignored all infinite values. This
is not the expected behavior both according to documentation and intuitively.
Now, -inf < x < inf is considered True for any real number x and all
other cases fail.
assert_array_ and masked arrays assert_equal hide less warningsSome warnings that were previously hidden by the assert_array_
functions are not hidden anymore. In most cases the warnings should be
correct and, should they occur, will require changes to the tests using
these functions.
For the masked array assert_equal version, warnings may occur when
comparing NaT. The function presently does not handle NaT or NaN
specifically and it may be best to avoid it at this time should a warning
show up due to this change.
offset attribute value in memmap objectsThe offset attribute in a memmap object is now set to the
offset into the file. This is a behaviour change only for offsets
greater than mmap.ALLOCATIONGRANULARITY.
np.real and np.imag return scalars for scalar inputsPreviously, np.real and np.imag used to return array objects when
provided a scalar input, which was inconsistent with other functions like
np.angle and np.conj.
The ABCPolyBase class, from which the convenience classes are derived, sets
__array_ufun__ = None in order of opt out of ufuncs. If a polynomial
convenience class instance is passed as an argument to a ufunc, a TypeError
will now be raised.
For calls to ufuncs, it was already possible, and recommended, to use an
out argument with a tuple for ufuncs with multiple outputs. This has now
been extended to output arguments in the reduce, accumulate, and
reduceat methods. This is mostly for compatibility with __array_ufunc;
there are no ufuncs yet that have more than one output.