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...).
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.
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.
This macro is defined to a positive inf. The corresponding single and extension precision macro are available with the suffix F and L.
This macro is defined to positive zero. The corresponding single and extension precision macro are available with the suffix F and L.
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.
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.
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 nor an infinity.
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).
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).
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.
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.
Base of natural logarithm ()
Logarithm to base 2 of the Euler constant ()
Logarithm to base 10 of the Euler constant ()
Natural logarithm of 2 ()
Natural logarithm of 10 ()
Pi ()
Pi divided by 2 ()
Pi divided by 4 ()
Reciprocal of pi ()
Two times the reciprocal of pi ()
Those can be useful for precise floating point comparison.
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.
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.
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);
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.
New in version 2.0.0.
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 is 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:
This macro is defined to positive zero.
This macro is defined to positive zero.
This macro is defined to negative zero.
This macro is defined to 1.0.
This macro is defined to -1.0.
This macro is defined to +inf.
This macro is defined to -inf.
This macro is defined to a NaN value, guaranteed to have its sign bit unset.
Converts a half-precision float to a single-precision float.
Converts a half-precision float to a double-precision float.
Converts a single-precision float to a half-precision float. The value is rounded to the nearest representable half, with ties going to the nearest even. If the value is too small or too big, the system’s floating point underflow or overflow bit will be set.
Converts a double-precision float to a half-precision float. The value is rounded to the nearest representable half, with ties going to the nearest even. If the value is too small or too big, the system’s floating point underflow or overflow bit will be set.
Compares two half-precision floats (h1 == h2).
Compares two half-precision floats (h1 != h2).
Compares two half-precision floats (h1 <= h2).
Compares two half-precision floats (h1 < h2).
Compares two half-precision floats (h1 >= h2).
Compares two half-precision floats (h1 > h2).
Compares two half-precision floats that are known to not be NaN (h1 == h2). If a value is NaN, the result is undefined.
Compares two half-precision floats that are known to not be NaN (h1 < h2). If a value is NaN, the result is undefined.
Compares two half-precision floats that are known to not be NaN (h1 <= h2). If a value is NaN, the result is undefined.
Tests whether the half-precision float has a value equal to zero. This may be slightly faster than calling npy_half_eq(h, NPY_ZERO).
Tests whether the half-precision float is a NaN.
Tests whether the half-precision float is plus or minus Inf.
Tests whether the half-precision float is finite (not NaN or Inf).
Returns 1 is h is negative, 0 otherwise.
Returns the value of x with the sign bit copied from y. Works for any value, including Inf and NaN.
This is the same for half-precision float as npy_spacing and npy_spacingf described in the low-level floating point section.
This is the same for half-precision float as npy_nextafter and npy_nextafterf described in the low-level floating point section.
Low-level function which converts a 32-bit single-precision float, stored as a uint32, into a 16-bit half-precision float.
Low-level function which converts a 64-bit double-precision float, stored as a uint64, into a 16-bit half-precision float.
Low-level function which converts a 16-bit half-precision float into a 32-bit single-precision float, stored as a uint32.
Low-level function which converts a 16-bit half-precision float into a 64-bit double-precision float, stored as a uint64.