@charris charris released this May 18, 2017 · 2378 commits to master since this release

NumPy 1.13.0 Release Notes

This release supports Python 2.7 and 3.4 - 3.6.


  • 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.


  • 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.
  •,, and
    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).
  • is deprecated, as it almost duplicates the
    functionality of np.MaskedArray.min. Exactly equivalent behaviour
    can be obtained with
  • The single-argument form of and is
    deprecated. np.maximum. should now be spelt, 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

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.
  • 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.
  • Deprecated boolean indexing behavior dropped. See Changes below for details.
  • Deprecated np.alterdot() and np.restoredot() removed.

FutureWarning to changed behavior

See Changes section for more detail.

  • numpy.average preserves subclasses
  • array == None and array != None do element-wise comparison.
  • np.equal, np.not_equal, object identity doesn't override comparison result.

dtypes are now always true

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

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

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.

.. _NEP:
.. _documentation:

New positive ufunc

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
.. 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::

>>> 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 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:

  1. A single scalar to specify a sample distance for all dimensions.
  2. N scalars to specify a constant sample distance for each dimension.
    i.e. dx, dy, dz, ...
  3. 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
  4. 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::

>>> 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]])]

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
Note that NumPy allocation will not show up in tracemalloc_ of earlier Python

.. _tracemalloc:

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.


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::

>>> 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.

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.

.. _MinGW:

.. _issues:

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.

.. _PPC long doubles:

.. _double double:

Better default repr for ndarray subclasses

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.

.. _LAPACK changelogs:

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.


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.

  • 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 matrix

It 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 now

Previously, 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 warnings

Some 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 objects

The offset attribute in a memmap object is now set to the
offset into the file. This is a behaviour change only for offsets

np.real and np.imag return scalars for scalar inputs

Previously, 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 polynomial convenience classes cannot be passed to ufuncs

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.

Output arguments to ufuncs can be tuples also for ufunc methods

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.



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