# NumPy 1.12.0 Release Notes#

This release supports Python 2.7 and 3.4 - 3.6.

## Highlights#

The NumPy 1.12.0 release contains a large number of fixes and improvements, but few that stand out above all others. That makes picking out the highlights somewhat arbitrary but the following may be of particular interest or indicate areas likely to have future consequences.

Order of operations in

`np.einsum`

can now be optimized for large speed improvements.New

`signature`

argument to`np.vectorize`

for vectorizing with core dimensions.The

`keepdims`

argument was added to many functions.New context manager for testing warnings

Support for BLIS in numpy.distutils

Much improved support for PyPy (not yet finished)

## Dropped Support#

Support for Python 2.6, 3.2, and 3.3 has been dropped.

## Added Support#

Support for PyPy 2.7 v5.6.0 has been added. While not complete (nditer

`updateifcopy`

is not supported yet), this is a milestone for PyPy’s C-API compatibility layer.

## Build System Changes#

Library order is preserved, instead of being reordered to match that of the directories.

## Deprecations#

### Assignment of ndarray object’s `data`

attribute#

Assigning the ‘data’ attribute is an inherently unsafe operation as pointed out in gh-7083. Such a capability will be removed in the future.

### Unsafe int casting of the num attribute in `linspace`

#

`np.linspace`

now raises DeprecationWarning when num cannot be safely
interpreted as an integer.

### Insufficient bit width parameter to `binary_repr`

#

If a ‘width’ parameter is passed into `binary_repr`

that is insufficient to
represent the number in base 2 (positive) or 2’s complement (negative) form,
the function used to silently ignore the parameter and return a representation
using the minimal number of bits needed for the form in question. Such behavior
is now considered unsafe from a user perspective and will raise an error in the
future.

## Future Changes#

In 1.13 NAT will always compare False except for

`NAT != NAT`

, which will be True. In short, NAT will behave like NaNIn 1.13

`np.average`

will preserve subclasses, to match the behavior of most other numpy functions such as np.mean. In particular, this means calls which returned a scalar may return a 0-d subclass object instead.

### Multiple-field manipulation of structured arrays#

In 1.13 the behavior of structured arrays involving multiple fields will change in two ways:

First, indexing a structured array with multiple fields (eg,
`arr[['f1', 'f3']]`

) will return a view into the original array in 1.13,
instead of a copy. Note the returned view will have extra padding bytes
corresponding to intervening fields in the original array, unlike the copy in
1.12, which will affect code such as `arr[['f1', 'f3']].view(newdtype)`

.

Second, for numpy versions 1.6 to 1.12 assignment between structured arrays occurs “by field name”: Fields in the destination array are set to the identically-named field in the source array or to 0 if the source does not have a field:

```
>>> a = np.array([(1,2),(3,4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> b = np.ones(2, dtype=[('z', 'i4'), ('y', 'i4'), ('x', 'i4')])
>>> b[:] = a
>>> b
array([(0, 2, 1), (0, 4, 3)],
dtype=[('z', '<i4'), ('y', '<i4'), ('x', '<i4')])
```

In 1.13 assignment will instead occur “by position”: The Nth field of the
destination will be set to the Nth field of the source regardless of field
name. The old behavior can be obtained by using indexing to reorder the fields
before
assignment, e.g., `b[['x', 'y']] = a[['y', 'x']]`

.

## Compatibility notes#

### DeprecationWarning to error#

Indexing with floats raises

`IndexError`

, e.g., a[0, 0.0].Indexing with non-integer array_like raises

`IndexError`

, e.g.,`a['1', '2']`

Indexing with multiple ellipsis raises

`IndexError`

, e.g.,`a[..., ...]`

.Non-integers used as index values raise

`TypeError`

, e.g., in`reshape`

,`take`

, and specifying reduce axis.

### FutureWarning to changed behavior#

`np.full`

now returns an array of the fill-value’s dtype if no dtype is given, instead of defaulting to float.`np.average`

will emit a warning if the argument is a subclass of ndarray, as the subclass will be preserved starting in 1.13. (see Future Changes)

`power`

and `**`

raise errors for integer to negative integer powers#

The previous behavior depended on whether numpy scalar integers or numpy integer arrays were involved.

For arrays

Zero to negative integer powers returned least integral value.

Both 1, -1 to negative integer powers returned correct values.

The remaining integers returned zero when raised to negative integer powers.

For scalars

Zero to negative integer powers returned least integral value.

Both 1, -1 to negative integer powers returned correct values.

The remaining integers sometimes returned zero, sometimes the correct float depending on the integer type combination.

All of these cases now raise a `ValueError`

except for those integer
combinations whose common type is float, for instance uint64 and int8. It was
felt that a simple rule was the best way to go rather than have special
exceptions for the integer units. If you need negative powers, use an inexact
type.

### Relaxed stride checking is the default#

This will have some impact on code that assumed that `F_CONTIGUOUS`

and
`C_CONTIGUOUS`

were mutually exclusive and could be set to determine the
default order for arrays that are now both.

### The `np.percentile`

‘midpoint’ interpolation method fixed for exact indices#

The ‘midpoint’ interpolator now gives the same result as ‘lower’ and ‘higher’ when the two coincide. Previous behavior of ‘lower’ + 0.5 is fixed.

`keepdims`

kwarg is passed through to user-class methods#

numpy functions that take a `keepdims`

kwarg now pass the value
through to the corresponding methods on ndarray sub-classes. Previously the
`keepdims`

keyword would be silently dropped. These functions now have
the following behavior:

If user does not provide

`keepdims`

, no keyword is passed to the underlying method.Any user-provided value of

`keepdims`

is passed through as a keyword argument to the method.

This will raise in the case where the method does not support a
`keepdims`

kwarg and the user explicitly passes in `keepdims`

.

The following functions are changed: `sum`

, `product`

,
`sometrue`

, `alltrue`

, `any`

, `all`

, `amax`

, `amin`

,
`prod`

, `mean`

, `std`

, `var`

, `nanmin`

, `nanmax`

,
`nansum`

, `nanprod`

, `nanmean`

, `nanmedian`

, `nanvar`

,
`nanstd`

`bitwise_and`

identity changed#

The previous identity was 1, it is now -1. See entry in Improvements for more explanation.

### ma.median warns and returns nan when unmasked invalid values are encountered#

Similar to unmasked median the masked median *ma.median* now emits a Runtime
warning and returns *NaN* in slices where an unmasked *NaN* is present.

### Greater consistency in `assert_almost_equal`

#

The precision check for scalars has been changed to match that for arrays. It is now:

```
abs(actual - desired) < 1.5 * 10**(-decimal)
```

Note that this is looser than previously documented, but agrees with the
previous implementation used in `assert_array_almost_equal`

. Due to the
change in implementation some very delicate tests may fail that did not
fail before.

`NoseTester`

behaviour of warnings during testing#

When `raise_warnings="develop"`

is given, all uncaught warnings will now
be considered a test failure. Previously only selected ones were raised.
Warnings which are not caught or raised (mostly when in release mode)
will be shown once during the test cycle similar to the default python
settings.

`assert_warns`

and `deprecated`

decorator more specific#

The `assert_warns`

function and context manager are now more specific
to the given warning category. This increased specificity leads to them
being handled according to the outer warning settings. This means that
no warning may be raised in cases where a wrong category warning is given
and ignored outside the context. Alternatively the increased specificity
may mean that warnings that were incorrectly ignored will now be shown
or raised. See also the new `suppress_warnings`

context manager.
The same is true for the `deprecated`

decorator.

### C API#

No changes.

## New Features#

### Writeable keyword argument for `as_strided`

#

`np.lib.stride_tricks.as_strided`

now has a `writeable`

keyword argument. It can be set to False when no write operation
to the returned array is expected to avoid accidental
unpredictable writes.

`axes`

keyword argument for `rot90`

#

The `axes`

keyword argument in `rot90`

determines the plane in which the
array is rotated. It defaults to `axes=(0,1)`

as in the original function.

### Generalized `flip`

#

`flipud`

and `fliplr`

reverse the elements of an array along axis=0 and
axis=1 respectively. The newly added `flip`

function reverses the elements of
an array along any given axis.

`np.count_nonzero`

now has an`axis`

parameter, allowing non-zero counts to be generated on more than just a flattened array object.

### BLIS support in `numpy.distutils`

#

Building against the BLAS implementation provided by the BLIS library is now
supported. See the `[blis]`

section in `site.cfg.example`

(in the root of
the numpy repo or source distribution).

### Hook in `numpy/__init__.py`

to run distribution-specific checks#

Binary distributions of numpy may need to run specific hardware checks or load specific libraries during numpy initialization. For example, if we are distributing numpy with a BLAS library that requires SSE2 instructions, we would like to check the machine on which numpy is running does have SSE2 in order to give an informative error.

Add a hook in `numpy/__init__.py`

to import a `numpy/_distributor_init.py`

file that will remain empty (bar a docstring) in the standard numpy source,
but that can be overwritten by people making binary distributions of numpy.

### New nanfunctions `nancumsum`

and `nancumprod`

added#

Nan-functions `nancumsum`

and `nancumprod`

have been added to
compute `cumsum`

and `cumprod`

by ignoring nans.

`np.interp`

can now interpolate complex values#

`np.lib.interp(x, xp, fp)`

now allows the interpolated array `fp`

to be complex and will interpolate at `complex128`

precision.

### New polynomial evaluation function `polyvalfromroots`

added#

The new function `polyvalfromroots`

evaluates a polynomial at given points
from the roots of the polynomial. This is useful for higher order polynomials,
where expansion into polynomial coefficients is inaccurate at machine
precision.

### New array creation function `geomspace`

added#

The new function `geomspace`

generates a geometric sequence. It is similar
to `logspace`

, but with start and stop specified directly:
`geomspace(start, stop)`

behaves the same as
`logspace(log10(start), log10(stop))`

.

### New context manager for testing warnings#

A new context manager `suppress_warnings`

has been added to the testing
utils. This context manager is designed to help reliably test warnings.
Specifically to reliably filter/ignore warnings. Ignoring warnings
by using an “ignore” filter in Python versions before 3.4.x can quickly
result in these (or similar) warnings not being tested reliably.

The context manager allows to filter (as well as record) warnings similar
to the `catch_warnings`

context, but allows for easier specificity.
Also printing warnings that have not been filtered or nesting the
context manager will work as expected. Additionally, it is possible
to use the context manager as a decorator which can be useful when
multiple tests give need to hide the same warning.

### New masked array functions `ma.convolve`

and `ma.correlate`

added#

These functions wrapped the non-masked versions, but propagate through masked values. There are two different propagation modes. The default causes masked values to contaminate the result with masks, but the other mode only outputs masks if there is no alternative.

### New `float_power`

ufunc#

The new `float_power`

ufunc is like the `power`

function except all
computation is done in a minimum precision of float64. There was a long
discussion on the numpy mailing list of how to treat integers to negative
integer powers and a popular proposal was that the `__pow__`

operator should
always return results of at least float64 precision. The `float_power`

function implements that option. Note that it does not support object arrays.

`np.loadtxt`

now supports a single integer as `usecol`

argument#

Instead of using `usecol=(n,)`

to read the nth column of a file
it is now allowed to use `usecol=n`

. Also the error message is
more user friendly when a non-integer is passed as a column index.

### Improved automated bin estimators for `histogram`

#

Added ‘doane’ and ‘sqrt’ estimators to `histogram`

via the `bins`

argument. Added support for range-restricted histograms with automated
bin estimation.

`np.roll`

can now roll multiple axes at the same time#

The `shift`

and `axis`

arguments to `roll`

are now broadcast against each
other, and each specified axis is shifted accordingly.

### The `__complex__`

method has been implemented for the ndarrays#

Calling `complex()`

on a size 1 array will now cast to a python
complex.

`pathlib.Path`

objects now supported#

The standard `np.load`

, `np.save`

, `np.loadtxt`

, `np.savez`

, and similar
functions can now take `pathlib.Path`

objects as an argument instead of a
filename or open file object.

### New `bits`

attribute for `np.finfo`

#

This makes `np.finfo`

consistent with `np.iinfo`

which already has that
attribute.

### New `signature`

argument to `np.vectorize`

#

This argument allows for vectorizing user defined functions with core
dimensions, in the style of NumPy’s
generalized universal functions. This allows
for vectorizing a much broader class of functions. For example, an arbitrary
distance metric that combines two vectors to produce a scalar could be
vectorized with `signature='(n),(n)->()'`

. See `np.vectorize`

for full
details.

### Emit py3kwarnings for division of integer arrays#

To help people migrate their code bases from Python 2 to Python 3, the python interpreter has a handy option -3, which issues warnings at runtime. One of its warnings is for integer division:

```
$ python -3 -c "2/3"
-c:1: DeprecationWarning: classic int division
```

In Python 3, the new integer division semantics also apply to numpy arrays. With this version, numpy will emit a similar warning:

```
$ python -3 -c "import numpy as np; np.array(2)/np.array(3)"
-c:1: DeprecationWarning: numpy: classic int division
```

### numpy.sctypes now includes bytes on Python3 too#

Previously, it included str (bytes) and unicode on Python2, but only str (unicode) on Python3.

## Improvements#

`bitwise_and`

identity changed#

The previous identity was 1 with the result that all bits except the LSB were masked out when the reduce method was used. The new identity is -1, which should work properly on twos complement machines as all bits will be set to one.

### Generalized Ufuncs will now unlock the GIL#

Generalized Ufuncs, including most of the linalg module, will now unlock the Python global interpreter lock.

### Caches in *np.fft* are now bounded in total size and item count#

The caches in *np.fft* that speed up successive FFTs of the same length can no
longer grow without bounds. They have been replaced with LRU (least recently
used) caches that automatically evict no longer needed items if either the
memory size or item count limit has been reached.

### Improved handling of zero-width string/unicode dtypes#

Fixed several interfaces that explicitly disallowed arrays with zero-width
string dtypes (i.e. `dtype('S0')`

or `dtype('U0')`

, and fixed several
bugs where such dtypes were not handled properly. In particular, changed
`ndarray.__new__`

to not implicitly convert `dtype('S0')`

to
`dtype('S1')`

(and likewise for unicode) when creating new arrays.

### Integer ufuncs vectorized with AVX2#

If the cpu supports it at runtime the basic integer ufuncs now use AVX2 instructions. This feature is currently only available when compiled with GCC.

### Order of operations optimization in `np.einsum`

#

`np.einsum`

now supports the `optimize`

argument which will optimize the
order of contraction. For example, `np.einsum`

would complete the chain dot
example `np.einsum(‘ij,jk,kl->il’, a, b, c)`

in a single pass which would
scale like `N^4`

; however, when `optimize=True`

`np.einsum`

will create
an intermediate array to reduce this scaling to `N^3`

or effectively
`np.dot(a, b).dot(c)`

. Usage of intermediate tensors to reduce scaling has
been applied to the general einsum summation notation. See `np.einsum_path`

for more details.

### quicksort has been changed to an introsort#

The quicksort kind of `np.sort`

and `np.argsort`

is now an introsort which
is regular quicksort but changing to a heapsort when not enough progress is
made. This retains the good quicksort performance while changing the worst case
runtime from `O(N^2)`

to `O(N*log(N))`

.

`ediff1d`

improved performance and subclass handling#

The ediff1d function uses an array instead on a flat iterator for the subtraction. When to_begin or to_end is not None, the subtraction is performed in place to eliminate a copy operation. A side effect is that certain subclasses are handled better, namely astropy.Quantity, since the complete array is created, wrapped, and then begin and end values are set, instead of using concatenate.

### Improved precision of `ndarray.mean`

for float16 arrays#

The computation of the mean of float16 arrays is now carried out in float32 for improved precision. This should be useful in packages such as Theano where the precision of float16 is adequate and its smaller footprint is desirable.

## Changes#

### All array-like methods are now called with keyword arguments in fromnumeric.py#

Internally, many array-like methods in fromnumeric.py were being called with positional arguments instead of keyword arguments as their external signatures were doing. This caused a complication in the downstream ‘pandas’ library that encountered an issue with ‘numpy’ compatibility. Now, all array-like methods in this module are called with keyword arguments instead.

### Operations on np.memmap objects return numpy arrays in most cases#

Previously operations on a memmap object would misleadingly return a memmap
instance even if the result was actually not memmapped. For example,
`arr + 1`

or `arr + arr`

would return memmap instances, although no memory
from the output array is memmapped. Version 1.12 returns ordinary numpy arrays
from these operations.

Also, reduction of a memmap (e.g. `.sum(axis=None`

) now returns a numpy
scalar instead of a 0d memmap.

### stacklevel of warnings increased#

The stacklevel for python based warnings was increased so that most warnings
will report the offending line of the user code instead of the line the
warning itself is given. Passing of stacklevel is now tested to ensure that
new warnings will receive the `stacklevel`

argument.

This causes warnings with the “default” or “module” filter to be shown once for every offending user code line or user module instead of only once. On python versions before 3.4, this can cause warnings to appear that were falsely ignored before, which may be surprising especially in test suits.