Skip to content

Latest commit

 

History

History
453 lines (279 loc) · 13.9 KB

coremath.rst

File metadata and controls

453 lines (279 loc) · 13.9 KB
Raw

NumPy core libraries

David Cournapeau

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 <numpy/npy_math.h> - please refer to this header when in doubt.

Floating point classification

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.

Low-level floating point manipulation

Those can be useful for precise floating point comparison.

Complex functions

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

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_util 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.

Half-precision functions

1.6.0

The header file <numpy/halffloat.h> provides functions to work with IEEE 754-2008 16-bit floating point values. While this format is not typically used for numerical computations, it is useful for storing values which require floating point but do not need much precision. It can also be used as an educational tool to understand the nature of floating point round-off error.

Like for other types, NumPy includes a typedef npy_half for the 16 bit float. Unlike for most of the other types, you cannot use this as a normal type in C, since it is a typedef for npy_uint16. For example, 1.0 looks like 0x3c00 to C, and if you do an equality comparison between the different signed zeros, you will get -0.0 != 0.0 (0x8000 != 0x0000), which is incorrect.

For these reasons, NumPy provides an API to work with npy_half values accessible by including <numpy/halffloat.h> and linking to 'npymath'. For functions that are not provided directly, such as the arithmetic operations, the preferred method is to convert to float or double and back again, as in the following example.

npy_half sum(int n, npy_half *array) {
    float ret = 0;
    while(n--) {
        ret += npy_half_to_float(*array++);
    }
    return npy_float_to_half(ret);
}

External Links: