# NumPy 1.15.0 Release Notes#

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations of old functions, and improvements to many existing functions. Please read the detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer maintained nose framework. The old nose based interface remains for downstream projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg problems reported for NumPy 1.14.

## Highlights#

NumPy has switched to pytest for testing.

A new

`numpy.printoptions`

context manager.Many improvements to the histogram functions.

Support for unicode field names in python 2.7.

Improved support for PyPy.

Fixes and improvements to

`numpy.einsum`

.

## New functions#

`numpy.gcd`

and`numpy.lcm`

, to compute the greatest common divisor and least common multiple.`numpy.ma.stack`

, the`numpy.stack`

array-joining function generalized to masked arrays.`numpy.quantile`

function, an interface to`percentile`

without factors of 100`numpy.nanquantile`

function, an interface to`nanpercentile`

without factors of 100`numpy.printoptions`

, a context manager that sets print options temporarily for the scope of the`with`

block:>>> with np.printoptions(precision=2): ... print(np.array([2.0]) / 3) [0.67]

`numpy.histogram_bin_edges`

, a function to get the edges of the bins used by a histogram without needing to calculate the histogram.C functions

*npy_get_floatstatus_barrier*and*npy_clear_floatstatus_barrier*have been added to deal with compiler optimization changing the order of operations. See below for details.

## Deprecations#

Aliases of builtin

`pickle`

functions are deprecated, in favor of their unaliased`pickle.<func>`

names:*numpy.loads**numpy.core.numeric.load**numpy.core.numeric.loads**numpy.ma.loads*,*numpy.ma.dumps**numpy.ma.load*,*numpy.ma.dump*- these functions already failed on python 3 when called with a string.

Multidimensional indexing with anything but a tuple is deprecated. This means that the index list in

`ind = [slice(None), 0]; arr[ind]`

should be changed to a tuple, e.g.,`ind = [slice(None), 0]; arr[tuple(ind)]`

or`arr[(slice(None), 0)]`

. That change is necessary to avoid ambiguity in expressions such as`arr[[[0, 1], [0, 1]]]`

, currently interpreted as`arr[array([0, 1]), array([0, 1])]`

, that will be interpreted as`arr[array([[0, 1], [0, 1]])]`

in the future.Imports from the following sub-modules are deprecated, they will be removed at some future date.

`numpy.testing.utils`

*numpy.testing.decorators**numpy.testing.nosetester**numpy.testing.noseclasses*`numpy.core.umath_tests`

Giving a generator to

`numpy.sum`

is now deprecated. This was undocumented behavior, but worked. Previously, it would calculate the sum of the generator expression. In the future, it might return a different result. Use`np.sum(np.from_iter(generator))`

or the built-in Python`sum`

instead.Users of the C-API should call

`PyArrayResolveWriteBackIfCopy`

or`PyArray_DiscardWritbackIfCopy`

on any array with the`WRITEBACKIFCOPY`

flag set, before deallocating the array. A deprecation warning will be emitted if those calls are not used when needed.Users of

`nditer`

should use the nditer object as a context manager anytime one of the iterator operands is writeable, so that numpy can manage writeback semantics, or should call`it.close()`

. A*RuntimeWarning*may be emitted otherwise in these cases.The

`normed`

argument of`np.histogram`

, deprecated long ago in 1.6.0, now emits a`DeprecationWarning`

.

## Future Changes#

NumPy 1.16 will drop support for Python 3.4.

NumPy 1.17 will drop support for Python 2.7.

## Compatibility notes#

### Compiled testing modules renamed and made private#

The following compiled modules have been renamed and made private:

`umath_tests`

->`_umath_tests`

`test_rational`

->`_rational_tests`

`multiarray_tests`

->`_multiarray_tests`

`struct_ufunc_test`

->`_struct_ufunc_tests`

`operand_flag_tests`

->`_operand_flag_tests`

The `umath_tests`

module is still available for backwards compatibility, but
will be removed in the future.

### The `NpzFile`

returned by `np.savez`

is now a `collections.abc.Mapping`

#

This means it behaves like a readonly dictionary, and has a new `.values()`

method and `len()`

implementation.

For python 3, this means that `.iteritems()`

, `.iterkeys()`

have been
deprecated, and `.keys()`

and `.items()`

now return views and not lists.
This is consistent with how the builtin `dict`

type changed between python 2
and python 3.

### Under certain conditions, `nditer`

must be used in a context manager#

When using an `numpy.nditer`

with the `"writeonly"`

or `"readwrite"`

flags, there
are some circumstances where nditer doesn’t actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
`nditer`

should now be used as a context manager whenever it is used
with writeable arrays, e.g., `with np.nditer(...) as it: ...`

. You may also
explicitly call `it.close()`

for cases where a context manager is unusable,
for instance in generator expressions.

### Numpy has switched to using pytest instead of nose for testing#

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
`assert_almost_equal`

and such, are not be affected by this change except for
the nose specific functions `import_nose`

and `raises`

. Those functions are
not used in numpy, but are kept for downstream compatibility.

### Numpy no longer monkey-patches `ctypes`

with `__array_interface__`

#

Previously numpy added `__array_interface__`

attributes to all the integer
types from `ctypes`

.

`np.ma.notmasked_contiguous`

and `np.ma.flatnotmasked_contiguous`

always return lists#

This is the documented behavior, but previously the result could be any of slice, None, or list.

All downstream users seem to check for the `None`

result from
`flatnotmasked_contiguous`

and replace it with `[]`

. Those callers will
continue to work as before.

`np.squeeze`

restores old behavior of objects that cannot handle an `axis`

argument#

Prior to version `1.7.0`

, `numpy.squeeze`

did not have an `axis`

argument and
all empty axes were removed by default. The incorporation of an `axis`

argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

### unstructured void array’s `.item`

method now returns a bytes object#

`.item`

now returns a `bytes`

object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

`copy.copy`

and `copy.deepcopy`

no longer turn `masked`

into an array#

Since `np.ma.masked`

is a readonly scalar, copying should be a no-op. These
functions now behave consistently with `np.copy()`

.

### Multifield Indexing of Structured Arrays will still return a copy#

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
`numpy.lib.recfunctions.repack_fields`

has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the “accessing multiple fields” section of the
user guide.

## C API changes#

### New functions `npy_get_floatstatus_barrier`

and `npy_clear_floatstatus_barrier`

#

Functions `npy_get_floatstatus_barrier`

and `npy_clear_floatstatus_barrier`

have been added and should be used in place of the ```
npy_get_floatstatus``and
``npy_clear_status
```

functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339.

### Changes to `PyArray_GetDTypeTransferFunction`

#

`PyArray_GetDTypeTransferFunction`

now defaults to using user-defined
`copyswapn`

/ `copyswap`

for user-defined dtypes. If this causes a
significant performance hit, consider implementing `copyswapn`

to reflect the
implementation of `PyArray_GetStridedCopyFn`

. See #10898.

## New Features#

`np.gcd`

and `np.lcm`

ufuncs added for integer and objects types#

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision `Decimal`

and `long`

types.

### Support for cross-platform builds for iOS#

The build system has been modified to add support for the
`_PYTHON_HOST_PLATFORM`

environment variable, used by `distutils`

when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time. Creating a full iOS-compatible NumPy package requires building for the 5 architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and combining these 5 compiled builds products into a single “fat” binary.

`return_indices`

keyword added for `np.intersect1d`

#

New keyword `return_indices`

returns the indices of the two input arrays
that correspond to the common elements.

`np.quantile`

and `np.nanquantile`

#

Like `np.percentile`

and `np.nanpercentile`

, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. `np.percentile`

is now a thin wrapper
around `np.quantile`

with the extra step of dividing by 100.

### Build system#

Added experimental support for the 64-bit RISC-V architecture.

## Improvements#

`np.einsum`

updates#

Syncs einsum path optimization tech between `numpy`

and *opt_einsum*. In
particular, the *greedy* path has received many enhancements by @jcmgray. A
full list of issues fixed are:

Arbitrary memory can be passed into the

*greedy*path. Fixes gh-11210.The greedy path has been updated to contain more dynamic programming ideas preventing a large number of duplicate (and expensive) calls that figure out the actual pair contraction that takes place. Now takes a few seconds on several hundred input tensors. Useful for matrix product state theories.

Reworks the broadcasting dot error catching found in gh-11218 gh-10352 to be a bit earlier in the process.

Enhances the

*can_dot*functionality that previous missed an edge case (part of gh-11308).

`np.flip`

can operate over multiple axes#

`np.flip`

now accepts None, or tuples of int, in its `axis`

argument. If
axis is None, it will flip over all the axes.

`histogram`

and `histogramdd`

functions have moved to `np.lib.histograms`

#

These were originally found in `np.lib.function_base`

. They are still
available under their un-scoped `np.histogram(dd)`

names, and
to maintain compatibility, aliased at `np.lib.function_base.histogram(dd)`

.

Code that does `from np.lib.function_base import *`

will need to be updated
with the new location, and should consider not using `import *`

in future.

`histogram`

will accept NaN values when explicit bins are given#

Previously it would fail when trying to compute a finite range for the data. Since the range is ignored anyway when the bins are given explicitly, this error was needless.

Note that calling `histogram`

on NaN values continues to raise the
`RuntimeWarning`

s typical of working with nan values, which can be silenced
as usual with `errstate`

.

`histogram`

works on datetime types, when explicit bin edges are given#

Dates, times, and timedeltas can now be histogrammed. The bin edges must be passed explicitly, and are not yet computed automatically.

`histogram`

“auto” estimator handles limited variance better#

No longer does an IQR of 0 result in `n_bins=1`

, rather the number of bins
chosen is related to the data size in this situation.

### The edges returned by *histogram`* and `histogramdd`

now match the data float type#

When passed `np.float16`

, `np.float32`

, or `np.longdouble`

data, the
returned edges are now of the same dtype. Previously, `histogram`

would only
return the same type if explicit bins were given, and `histogram`

would
produce `float64`

bins no matter what the inputs.

`histogramdd`

allows explicit ranges to be given in a subset of axes#

The `range`

argument of `numpy.histogramdd`

can now contain `None`

values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

### The normed arguments of `histogramdd`

and `histogram2d`

have been renamed#

These arguments are now called `density`

, which is consistent with
`histogram`

. The old argument continues to work, but the new name should be
preferred.

`np.r_`

works with 0d arrays, and `np.ma.mr_`

works with `np.ma.masked`

#

0d arrays passed to the *r_* and *mr_* concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, `numpy.ma.mr_`

now works correctly on the `masked`

constant.

`np.ptp`

accepts a `keepdims`

argument, and extended axis tuples#

`np.ptp`

(peak-to-peak) can now work over multiple axes, just like `np.max`

and `np.min`

.

`MaskedArray.astype`

now is identical to `ndarray.astype`

#

This means it takes all the same arguments, making more code written for ndarray work for masked array too.

### Enable AVX2/AVX512 at compile time#

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously compilation for avx2 (or 512) with -march=native would still use the SSE code for the simd functions even when the rest of the code got AVX2.

`nan_to_num`

always returns scalars when receiving scalar or 0d inputs#

Previously an array was returned for integer scalar inputs, which is inconsistent with the behavior for float inputs, and that of ufuncs in general. For all types of scalar or 0d input, the result is now a scalar.

`np.flatnonzero`

works on numpy-convertible types#

`np.flatnonzero`

now uses `np.ravel(a)`

instead of `a.ravel()`

, so it
works for lists, tuples, etc.

`np.interp`

returns numpy scalars rather than builtin scalars#

Previously `np.interp(0.5, [0, 1], [10, 20])`

would return a `float`

, but
now it returns a `np.float64`

object, which more closely matches the behavior
of other functions.

Additionally, the special case of `np.interp(object_array_0d, ...)`

is no
longer supported, as `np.interp(object_array_nd)`

was never supported anyway.

As a result of this change, the `period`

argument can now be used on 0d
arrays.

### Allow dtype field names to be unicode in Python 2#

Previously `np.dtype([(u'name', float)])`

would raise a `TypeError`

in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the `ascii`

codec, raising a
`UnicodeEncodeError`

upon failure.

This change makes it easier to write Python 2/3 compatible code using
`from __future__ import unicode_literals`

, which previously would cause
string literal field names to raise a TypeError in Python 2.

### Comparison ufuncs accept `dtype=object`

, overriding the default `bool`

#

This allows object arrays of symbolic types, which override `==`

and other
operators to return expressions, to be compared elementwise with
`np.equal(a, b, dtype=object)`

.

`sort`

functions accept `kind='stable'`

#

Up until now, to perform a stable sort on the data, the user must do:

```
>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]
```

because merge sort is the only stable sorting algorithm available in NumPy. However, having kind=’mergesort’ does not make it explicit that the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind=’stable’ thus clarifying the intent.

### Do not make temporary copies for in-place accumulation#

When ufuncs perform accumulation they no longer make temporary copies because of the overlap between input an output, that is, the next element accumulated is added before the accumulated result is stored in its place, hence the overlap is safe. Avoiding the copy results in faster execution.

`linalg.matrix_power`

can now handle stacks of matrices#

Like other functions in `linalg`

, `matrix_power`

can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to `a`

(from `M`

), and the exceptions for non-square
matrices have been changed to `LinAlgError`

(from `ValueError`

).

### Increased performance in `random.permutation`

for multidimensional arrays#

`permutation`

uses the fast path in `random.shuffle`

for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

### Generalized ufuncs now accept `axes`

, `axis`

and `keepdims`

arguments#

One can control over which axes a generalized ufunc operates by passing in an
`axes`

argument, a list of tuples with indices of particular axes. For
instance, for a signature of `(i,j),(j,k)->(i,k)`

appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be `[(-2, -1), (-2, -1), (-2, -1)]`

. If one wanted to
use leading dimensions instead, one would pass in `[(0, 1), (0, 1), (0, 1)]`

.

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of `(i),(i)->()`

appropriate
for an inner product, one could pass in `axes=[0, 0]`

to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an `axis`

argument. This is equivalent to passing in
`axes`

with identical entries for all arguments with that core dimension
(e.g., for the example above, `axes=[(axis,), (axis,)]`

).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in `keepdims`

to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with `axes`

. For instance, for the
inner-product example, `keepdims=True, axes=[-2, -2, -2]`

would act on the
inner-product example, `keepdims=True, axis=-2`

would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

### float128 values now print correctly on ppc systems#

Previously printing float128 values was buggy on ppc, since the special double-double floating-point-format on these systems was not accounted for. float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23, especially if using float128. On ppc, glibc’s malloc in these version often misaligns allocated memory which can crash numpy when using float128 values.

### New `np.take_along_axis`

and `np.put_along_axis`

functions#

When used on multidimensional arrays, `argsort`

, `argmin`

, `argmax`

, and
`argpartition`

return arrays that are difficult to use as indices.
`take_along_axis`

provides an easy way to use these indices to lookup values
within an array, so that:

```
np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)
```

is the same as:

```
np.sort(a, axis=axis)
```

`np.put_along_axis`

acts as the dual operation for writing to these indices
within an array.