NumPy 1.4.0 Release Notes#

This minor includes numerous bug fixes, as well as a few new features. It is backward compatible with 1.3.0 release.

Highlights#

  • New datetime dtype support to deal with dates in arrays

  • Faster import time

  • Extended array wrapping mechanism for ufuncs

  • New Neighborhood iterator (C-level only)

  • C99-like complex functions in npymath

New features#

Extended array wrapping mechanism for ufuncs#

An __array_prepare__ method has been added to ndarray to provide subclasses greater flexibility to interact with ufuncs and ufunc-like functions. ndarray already provided __array_wrap__, which allowed subclasses to set the array type for the result and populate metadata on the way out of the ufunc (as seen in the implementation of MaskedArray). For some applications it is necessary to provide checks and populate metadata on the way in. __array_prepare__ is therefore called just after the ufunc has initialized the output array but before computing the results and populating it. This way, checks can be made and errors raised before operations which may modify data in place.

Automatic detection of forward incompatibilities#

Previously, if an extension was built against a version N of NumPy, and used on a system with NumPy M < N, the import_array was successful, which could cause crashes because the version M does not have a function in N. Starting from NumPy 1.4.0, this will cause a failure in import_array, so the error will be caught early on.

New iterators#

A new neighborhood iterator has been added to the C API. It can be used to iterate over the items in a neighborhood of an array, and can handle boundaries conditions automatically. Zero and one padding are available, as well as arbitrary constant value, mirror and circular padding.

New polynomial support#

New modules chebyshev and polynomial have been added. The new polynomial module is not compatible with the current polynomial support in numpy, but is much like the new chebyshev module. The most noticeable difference to most will be that coefficients are specified from low to high power, that the low level functions do not work with the Chebyshev and Polynomial classes as arguments, and that the Chebyshev and Polynomial classes include a domain. Mapping between domains is a linear substitution and the two classes can be converted one to the other, allowing, for instance, a Chebyshev series in one domain to be expanded as a polynomial in another domain. The new classes should generally be used instead of the low level functions, the latter are provided for those who wish to build their own classes.

The new modules are not automatically imported into the numpy namespace, they must be explicitly brought in with an “import numpy.polynomial” statement.

New C API#

The following C functions have been added to the C API:

  1. PyArray_GetNDArrayCFeatureVersion: return the API version of the loaded numpy.

  2. PyArray_Correlate2 - like PyArray_Correlate, but implements the usual definition of correlation. Inputs are not swapped, and conjugate is taken for complex arrays.

  3. PyArray_NeighborhoodIterNew - a new iterator to iterate over a neighborhood of a point, with automatic boundaries handling. It is documented in the iterators section of the C-API reference, and you can find some examples in the multiarray_test.c.src file in numpy.core.

New ufuncs#

The following ufuncs have been added to the C API:

  1. copysign - return the value of the first argument with the sign copied from the second argument.

  2. nextafter - return the next representable floating point value of the first argument toward the second argument.

New defines#

The alpha processor is now defined and available in numpy/npy_cpu.h. The failed detection of the PARISC processor has been fixed. The defines are:

  1. NPY_CPU_HPPA: PARISC

  2. NPY_CPU_ALPHA: Alpha

Testing#

  1. deprecated decorator: this decorator may be used to avoid cluttering testing output while testing DeprecationWarning is effectively raised by the decorated test.

  2. assert_array_almost_equal_nulp: new method to compare two arrays of floating point values. With this function, two values are considered close if there are not many representable floating point values in between, thus being more robust than assert_array_almost_equal when the values fluctuate a lot.

  3. assert_array_max_ulp: raise an assertion if there are more than N representable numbers between two floating point values.

  4. assert_warns: raise an AssertionError if a callable does not generate a warning of the appropriate class, without altering the warning state.

Reusing npymath#

In 1.3.0, we started putting portable C math routines in npymath library, so that people can use those to write portable extensions. Unfortunately, it was not possible to easily link against this library: in 1.4.0, support has been added to numpy.distutils so that 3rd party can reuse this library. See coremath documentation for more information.

Improved set operations#

In previous versions of NumPy some set functions (intersect1d, setxor1d, setdiff1d and setmember1d) could return incorrect results if the input arrays contained duplicate items. These now work correctly for input arrays with duplicates. setmember1d has been renamed to in1d, as with the change to accept arrays with duplicates it is no longer a set operation, and is conceptually similar to an elementwise version of the Python operator ‘in’. All of these functions now accept the boolean keyword assume_unique. This is False by default, but can be set True if the input arrays are known not to contain duplicates, which can increase the functions’ execution speed.

Improvements#

  1. numpy import is noticeably faster (from 20 to 30 % depending on the platform and computer)

  2. The sort functions now sort nans to the end.

    • Real sort order is [R, nan]

    • Complex sort order is [R + Rj, R + nanj, nan + Rj, nan + nanj]

    Complex numbers with the same nan placements are sorted according to the non-nan part if it exists.

  3. The type comparison functions have been made consistent with the new sort order of nans. Searchsorted now works with sorted arrays containing nan values.

  4. Complex division has been made more resistant to overflow.

  5. Complex floor division has been made more resistant to overflow.

Deprecations#

The following functions are deprecated:

  1. correlate: it takes a new keyword argument old_behavior. When True (the default), it returns the same result as before. When False, compute the conventional correlation, and take the conjugate for complex arrays. The old behavior will be removed in NumPy 1.5, and raises a DeprecationWarning in 1.4.

  2. unique1d: use unique instead. unique1d raises a deprecation warning in 1.4, and will be removed in 1.5.

  3. intersect1d_nu: use intersect1d instead. intersect1d_nu raises a deprecation warning in 1.4, and will be removed in 1.5.

  4. setmember1d: use in1d instead. setmember1d raises a deprecation warning in 1.4, and will be removed in 1.5.

The following raise errors:

  1. When operating on 0-d arrays, numpy.max and other functions accept only axis=0, axis=-1 and axis=None. Using an out-of-bounds axes is an indication of a bug, so Numpy raises an error for these cases now.

  2. Specifying axis > MAX_DIMS is no longer allowed; Numpy raises now an error instead of behaving similarly as for axis=None.

Internal changes#

Use C99 complex functions when available#

The numpy complex types are now guaranteed to be ABI compatible with C99 complex type, if available on the platform. Moreover, the complex ufunc now use the platform C99 functions instead of our own.

split multiarray and umath source code#

The source code of multiarray and umath has been split into separate logic compilation units. This should make the source code more amenable for newcomers.

Separate compilation#

By default, every file of multiarray (and umath) is merged into one for compilation as was the case before, but if NPY_SEPARATE_COMPILATION env variable is set to a non-negative value, experimental individual compilation of each file is enabled. This makes the compile/debug cycle much faster when working on core numpy.

Separate core math library#

New functions which have been added:

  • npy_copysign

  • npy_nextafter

  • npy_cpack

  • npy_creal

  • npy_cimag

  • npy_cabs

  • npy_cexp

  • npy_clog

  • npy_cpow

  • npy_csqr

  • npy_ccos

  • npy_csin

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