NumPy 1.16.0 Release Notes — NumPy v2.3.dev0 Manual (original) (raw)

This NumPy release is the last one to support Python 2.7 and will be maintained as a long term release with bug fixes until 2020. Support for Python 3.4 been dropped, the supported Python versions are 2.7 and 3.5-3.7. The wheels on PyPI are linked with OpenBLAS v0.3.4+, which should fix the known threading issues found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29 and, if using OpenBLAS, OpenBLAS > v0.3.4.

This release has seen a lot of refactoring and features many bug fixes, improved code organization, and better cross platform compatibility. Not all of these improvements will be visible to users, but they should help make maintenance easier going forward.

Highlights#

New functions#

New deprecations#

Expired deprecations#

Future changes#

Compatibility notes#

f2py script on Windows#

On Windows, the installed script for running f2py is now an .exe file rather than a *.py file and should be run from the command line as f2pywhenever the Scripts directory is in the path. Running f2py as a modulepython -m numpy.f2py [...] will work without path modification in any version of NumPy.

NaT comparisons#

Consistent with the behavior of NaN, all comparisons other than inequality checks with datetime64 or timedelta64 NaT (“not-a-time”) values now always return False, and inequality checks with NaT now always return True. This includes comparisons between NaT values. For compatibility with the old behavior, use np.isnat to explicitly check for NaT or convert datetime64/timedelta64 arrays with .astype(np.int64) before making comparisons.

complex64/128 alignment has changed#

The memory alignment of complex types is now the same as a C-struct composed of two floating point values, while before it was equal to the size of the type. For many users (for instance on x64/unix/gcc) this means that complex64 is now 4-byte aligned instead of 8-byte aligned. An important consequence is that aligned structured dtypes may now have a different size. For instance,np.dtype('c8,u1', align=True) used to have an itemsize of 16 (on x64/gcc) but now it is 12.

More in detail, the complex64 type now has the same alignment as a C-structstruct {float r, i;}, according to the compiler used to compile numpy, and similarly for the complex128 and complex256 types.

nd_grid __len__ removal#

len(np.mgrid) and len(np.ogrid) are now considered nonsensical and raise a TypeError.

np.unravel_index now accepts shape keyword argument#

Previously, only the dims keyword argument was accepted for specification of the shape of the array to be used for unraveling. dims remains supported, but is now deprecated.

multi-field views return a view instead of a copy#

Indexing a structured array with multiple fields, e.g., arr[['f1', 'f3']], returns a view into the original array instead of a copy. The returned view will often have extra padding bytes corresponding to intervening fields in the original array, unlike before, which will affect code such asarr[['f1', 'f3']].view('float64'). This change has been planned since numpy 1.7. Operations hitting this path have emitted FutureWarnings since then. Additional FutureWarnings about this change were added in 1.12.

To help users update their code to account for these changes, a number of functions have been added to the numpy.lib.recfunctions module which safely allow such operations. For instance, the code above can be replaced with structured_to_unstructured(arr[['f1', 'f3']], dtype='float64'). See the “accessing multiple fields” section of theuser guide.

C API changes#

The NPY_FEATURE_VERSION was incremented to 0x0000D, due to the addition of:

New Features#

Integrated squared error (ISE) estimator added to histogram#

This method (bins='stone') for optimizing the bin number is a generalization of the Scott’s rule. The Scott’s rule assumes the distribution is approximately Normal, while the ISE is a non-parametric method based on cross-validation.

max_rows keyword added for np.loadtxt#

New keyword max_rows in numpy.loadtxt sets the maximum rows of the content to be read after skiprows, as in numpy.genfromtxt.

modulus operator support added for np.timedelta64 operands#

The modulus (remainder) operator is now supported for two operands of type np.timedelta64. The operands may have different units and the return value will match the type of the operands.

Improvements#

no-copy pickling of numpy arrays#

Up to protocol 4, numpy array pickling created 2 spurious copies of the data being serialized. With pickle protocol 5, and the PickleBuffer API, a large variety of numpy arrays can now be serialized without any copy using out-of-band buffers, and with one less copy using in-band buffers. This results, for large arrays, in an up to 66% drop in peak memory usage.

build shell independence#

NumPy builds should no longer interact with the host machine shell directly. exec_command has been replaced withsubprocess.check_output where appropriate.

np.polynomial.Polynomial classes render in LaTeX in Jupyter notebooks#

When used in a front-end that supports it, Polynomial instances are now rendered through LaTeX. The current format is experimental, and is subject to change.

randint and choice now work on empty distributions#

Even when no elements needed to be drawn, np.random.randint andnp.random.choice raised an error when the arguments described an empty distribution. This has been fixed so that e.g.np.random.choice([], 0) == np.array([], dtype=float64).

linalg.lstsq, linalg.qr, and linalg.svd now work with empty arrays#

Previously, a LinAlgError would be raised when an empty matrix/empty matrices (with zero rows and/or columns) is/are passed in. Now outputs of appropriate shapes are returned.

Chain exceptions to give better error messages for invalid PEP3118 format strings#

This should help track down problems.

Einsum optimization path updates and efficiency improvements#

Einsum was synchronized with the current upstream work.

numpy.angle and numpy.expand_dims now work on ndarray subclasses#

In particular, they now work for masked arrays.

NPY_NO_DEPRECATED_API compiler warning suppression#

Setting NPY_NO_DEPRECATED_API to a value of 0 will suppress the current compiler warnings when the deprecated numpy API is used.

np.diff Added kwargs prepend and append#

New kwargs prepend and append, allow for values to be inserted on either end of the differences. Similar to options for ediff1d. Now the inverse of cumsum can be obtained easily via prepend=0.

ARM support updated#

Support for ARM CPUs has been updated to accommodate 32 and 64 bit targets, and also big and little endian byte ordering. AARCH32 memory alignment issues have been addressed. CI testing has been expanded to include AARCH64 targets via the services of shippable.com.

Appending to build flags#

numpy.distutils has always overridden rather than appended to LDFLAGS and other similar such environment variables for compiling Fortran extensions. Now, if the NPY_DISTUTILS_APPEND_FLAGS environment variable is set to 1, the behavior will be appending. This applied to: LDFLAGS, F77FLAGS,F90FLAGS, FREEFLAGS, FOPT, FDEBUG, and FFLAGS. See gh-11525 for more details.

Generalized ufunc signatures now allow fixed-size dimensions#

By using a numerical value in the signature of a generalized ufunc, one can indicate that the given function requires input or output to have dimensions with the given size. E.g., the signature of a function that converts a polar angle to a two-dimensional cartesian unit vector would be ()->(2); that for one that converts two spherical angles to a three-dimensional unit vector would be (),()->(3); and that for the cross product of two three-dimensional vectors would be (3),(3)->(3).

Note that to the elementary function these dimensions are not treated any differently from variable ones indicated with a name starting with a letter; the loop still is passed the corresponding size, but it can now count on that size being equal to the fixed one given in the signature.

Generalized ufunc signatures now allow flexible dimensions#

Some functions, in particular numpy’s implementation of @ as matmul, are very similar to generalized ufuncs in that they operate over core dimensions, but one could not present them as such because they were able to deal with inputs in which a dimension is missing. To support this, it is now allowed to postfix a dimension name with a question mark to indicate that the dimension does not necessarily have to be present.

With this addition, the signature for matmul can be expressed as(m?,n),(n,p?)->(m?,p?). This indicates that if, e.g., the second operand has only one dimension, for the purposes of the elementary function it will be treated as if that input has core shape (n, 1), and the output has the corresponding core shape of (m, 1). The actual output array, however, has the flexible dimension removed, i.e., it will have shape (..., m). Similarly, if both arguments have only a single dimension, the inputs will be presented as having shapes (1, n) and (n, 1) to the elementary function, and the output as (1, 1), while the actual output array returned will have shape (). In this way, the signature allows one to use a single elementary function for four related but different signatures,(m,n),(n,p)->(m,p), (n),(n,p)->(p), (m,n),(n)->(m) and(n),(n)->().

np.clip and the clip method check for memory overlap#

The out argument to these functions is now always tested for memory overlap to avoid corrupted results when memory overlap occurs.

New value unscaled for option cov in np.polyfit#

A further possible value has been added to the cov parameter of thenp.polyfit function. With cov='unscaled' the scaling of the covariance matrix is disabled completely (similar to setting absolute_sigma=True inscipy.optimize.curve_fit). This would be useful in occasions, where the weights are given by 1/sigma with sigma being the (known) standard errors of (Gaussian distributed) data points, in which case the unscaled matrix is already a correct estimate for the covariance matrix.

Detailed docstrings for scalar numeric types#

The help function, when applied to numeric types such as numpy.intc,numpy.int_, and numpy.longlong, now lists all of the aliased names for that type, distinguishing between platform -dependent and -independent aliases.

__module__ attribute now points to public modules#

The __module__ attribute on most NumPy functions has been updated to refer to the preferred public module from which to access a function, rather than the module in which the function happens to be defined. This produces more informative displays for functions in tools such as IPython, e.g., instead of<function 'numpy.core.fromnumeric.sum'> you now see<function 'numpy.sum'>.

Large allocations marked as suitable for transparent hugepages#

On systems that support transparent hugepages over the madvise system call numpy now marks that large memory allocations can be backed by hugepages which reduces page fault overhead and can in some fault heavy cases improve performance significantly. On Linux the setting for huge pages to be used,/sys/kernel/mm/transparent_hugepage/enabled, must be at least madvise. Systems which already have it set to always will not see much difference as the kernel will automatically use huge pages where appropriate.

Users of very old Linux kernels (~3.x and older) should make sure that_/sys/kernel/mm/transparent_hugepage/defrag_ is not set to always to avoid performance problems due concurrency issues in the memory defragmentation.

Alpine Linux (and other musl c library distros) support#

We now default to use fenv.h for floating point status error reporting. Previously we had a broken default that sometimes would not report underflow, overflow, and invalid floating point operations. Now we can support non-glibc distributions like Alpine Linux as long as they ship fenv.h.

Speedup np.block for large arrays#

Large arrays (greater than 512 * 512) now use a blocking algorithm based on copying the data directly into the appropriate slice of the resulting array. This results in significant speedups for these large arrays, particularly for arrays being blocked along more than 2 dimensions.

arr.ctypes.data_as(...) holds a reference to arr#

Previously the caller was responsible for keeping the array alive for the lifetime of the pointer.

Speedup np.take for read-only arrays#

The implementation of np.take no longer makes an unnecessary copy of the source array when its writeable flag is set to False.

Support path-like objects for more functions#

The np.core.records.fromfile function now supports pathlib.Pathand other path-like objects in addition to a file object. Furthermore, thenp.load function now also supports path-like objects when using memory mapping (mmap_mode keyword argument).

Better behaviour of ufunc identities during reductions#

Universal functions have an .identity which is used when .reduce is called on an empty axis.

As of this release, the logical binary ufuncs, logical_and, logical_or, and logical_xor, now have identity s of type bool, where previously they were of type int. This restores the 1.14 behavior of getting bool s when reducing empty object arrays with these ufuncs, while also keeping the 1.15 behavior of getting int s when reducing empty object arrays with arithmetic ufuncs like add and multiply.

Additionally, logaddexp now has an identity of -inf, allowing it to be called on empty sequences, where previously it could not be.

This is possible thanks to the newPyUFunc_FromFuncAndDataAndSignatureAndIdentity, which allows arbitrary values to be used as identities now.

Improved conversion from ctypes objects#

Numpy has always supported taking a value or type from ctypes and converting it into an array or dtype, but only behaved correctly for simpler types. As of this release, this caveat is lifted - now:

A new ndpointer.contents member#

This matches the .contents member of normal ctypes arrays, and can be used to construct an np.array around the pointers contents. This replacesnp.array(some_nd_pointer), which stopped working in 1.15. As a side effect of this change, ndpointer now supports dtypes with overlapping fields and padding.

matmul is now a ufunc#

numpy.matmul is now a ufunc which means that both the function and the__matmul__ operator can now be overridden by __array_ufunc__. Its implementation has also changed. It uses the same BLAS routines asnumpy.dot, ensuring its performance is similar for large matrices.

Start and stop arrays for linspace, logspace and geomspace#

These functions used to be limited to scalar stop and start values, but can now take arrays, which will be properly broadcast and result in an output which has one axis prepended. This can be used, e.g., to obtain linearly interpolated points between sets of points.

CI extended with additional services#

We now use additional free CI services, thanks to the companies that provide:

These are in addition to our continued use of travis, appveyor (for wheels) and LGTM

Changes#

Comparison ufuncs will now error rather than return NotImplemented#

Previously, comparison ufuncs such as np.equal would return_NotImplemented_ if their arguments had structured dtypes, to help comparison operators such as __eq__ deal with those. This is no longer needed, as the relevant logic has moved to the comparison operators proper (which thus do continue to return NotImplemented as needed). Hence, like all other ufuncs, the comparison ufuncs will now error on structured dtypes.

Positive will now raise a deprecation warning for non-numerical arrays#

Previously, +array unconditionally returned a copy. Now, it will raise a DeprecationWarning if the array is not numerical (i.e., if np.positive(array) raises a TypeError. For ndarraysubclasses that override the default __array_ufunc__ implementation, the TypeError is passed on.

NDArrayOperatorsMixin now implements matrix multiplication#

Previously, np.lib.mixins.NDArrayOperatorsMixin did not implement the special methods for Python’s matrix multiplication operator (@). This has changed now that matmul is a ufunc and can be overridden using__array_ufunc__.

The scaling of the covariance matrix in np.polyfit is different#

So far, np.polyfit used a non-standard factor in the scaling of the the covariance matrix. Namely, rather than using the standard chisq/(M-N), it scaled it with chisq/(M-N-2) where M is the number of data points and N is the number of parameters. This scaling is inconsistent with other fitting programs such as e.g. scipy.optimize.curve_fit and was changed to chisq/(M-N).

maximum and minimum no longer emit warnings#

As part of code introduced in 1.10, float32 and float64 set invalid float status when a Nan is encountered in numpy.maximum and numpy.minimum, when using SSE2 semantics. This caused a RuntimeWarning to sometimes be emitted. In 1.15 we fixed the inconsistencies which caused the warnings to become more conspicuous. Now no warnings will be emitted.

Umath and multiarray c-extension modules merged into a single module#

The two modules were merged, according to NEP 15. Previously _np.core.umath_and np.core.multiarray were separate c-extension modules. They are now python wrappers to the single np.core/_multiarray_math c-extension module.

getfield validity checks extended#

numpy.ndarray.getfield now checks the dtype and offset arguments to prevent accessing invalid memory locations.

NumPy functions now support overrides with __array_function__#

NumPy has a new experimental mechanism for overriding the implementation of almost all NumPy functions on non-NumPy arrays by defining an__array_function__ method, as described in NEP 18.

This feature is not yet been enabled by default, but has been released to facilitate experimentation by potential users. See the NEP for details on setting the appropriate environment variable. We expect the NumPy 1.17 release will enable overrides by default, which will also be more performant due to a new implementation written in C.

Arrays based off readonly buffers cannot be set writeable#

We now disallow setting the writeable flag True on arrays created from fromstring(readonly-buffer).