Numpy core libraries

New in version 1.3.0.

Starting from numpy 1.3.0, we are working on separating the pure C, “computational” code from the python dependent code. The goal is twofolds: making the code cleaner, and enabling code reuse by other extensions outside numpy (scipy, etc...).

Numpy core math library

The numpy core math library (‘npymath’) is a first step in this direction. This library contains most math-related C99 functionality, which can be used on platforms where C99 is not well supported. The core math functions have the same API as the C99 ones, except for the npy_* prefix.

The available functions are defined in npy_math.h - please refer to this header in doubt.

Floating point classification

NPY_NAN

This macro is defined to a NaN (Not a Number), and is guaranteed to have the signbit unset (‘positive’ NaN). The corresponding single and extension precision macro are available with the suffix F and L.

NPY_INFINITY

This macro is defined to a positive inf. The corresponding single and extension precision macro are available with the suffix F and L.

NPY_PZERO

This macro is defined to positive zero. The corresponding single and extension precision macro are available with the suffix F and L.

NPY_NZERO

This macro is defined to negative zero (that is with the sign bit set). The corresponding single and extension precision macro are available with the suffix F and L.

int npy_isnan(x)

This is a macro, and is equivalent to C99 isnan: works for single, double and extended precision, and return a non 0 value is x is a NaN.

int npy_isfinite(x)

This is a macro, and is equivalent to C99 isfinite: works for single, double and extended precision, and return a non 0 value is x is neither a NaN or a infinity.

int npy_isinf(x)

This is a macro, and is equivalent to C99 isinf: works for single, double and extended precision, and return a non 0 value is x is infinite (positive and negative).

int npy_signbit(x)

This is a macro, and is equivalent to C99 signbit: works for single, double and extended precision, and return a non 0 value is x has the signbit set (that is the number is negative).

double npy_copysign(double x, double y)

This is a function equivalent to C99 copysign: return x with the same sign as y. Works for any value, including inf and nan. Single and extended precisions are available with suffix f and l.

New in version 1.4.0.

Useful math constants

The following math constants are available in npy_math.h. Single and extended precision are also available by adding the F and L suffixes respectively.

NPY_E

Base of natural logarithm ()

NPY_LOG2E

Logarithm to base 2 of the Euler constant ()

NPY_LOG10E

Logarithm to base 10 of the Euler constant ()

NPY_LOGE2

Natural logarithm of 2 ()

NPY_LOGE10

Natural logarithm of 10 ()

NPY_PI

Pi ()

NPY_PI_2

Pi divided by 2 ()

NPY_PI_4

Pi divided by 4 ()

NPY_1_PI

Reciprocal of pi ()

NPY_2_PI

Two times the reciprocal of pi ()

NPY_EULER

The Euler constant ()

Low-level floating point manipulation

Those can be useful for precise floating point comparison.

double npy_nextafter(double x, double y)

This is a function equivalent to C99 nextafter: return next representable floating point value from x in the direction of y. Single and extended precisions are available with suffix f and l.

New in version 1.4.0.

double npy_spacing(double x)

This is a function equivalent to Fortran intrinsic. Return distance between x and next representable floating point value from x, e.g. spacing(1) == eps. spacing of nan and +/- inf return nan. Single and extended precisions are available with suffix f and l.

New in version 1.4.0.

Complex functions

New in version 1.4.0.

C99-like complex functions have been added. Those can be used if you wish to implement portable C extensions. Since we still support platforms without C99 complex type, you need to restrict to C90-compatible syntax, e.g.:

/* a = 1 + 2i \*/
npy_complex a = npy_cpack(1, 2);
npy_complex b;

b = npy_log(a);

Linking against the core math library in an extension

New in version 1.4.0.

To use the core math library in your own extension, you need to add the npymath compile and link options to your extension in your setup.py:

>>> from numpy.distutils.misc_utils import get_info
>>> info = get_info('npymath')
>>> config.add_extension('foo', sources=['foo.c'], extra_info=**info)

In other words, the usage of info is exactly the same as when using blas_info and co.