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 NaN

  • In 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:

  1. If user does not provide keepdims, no keyword is passed to the underlying method.

  2. 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.

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