在Numpy源代码中的哪里定义了常量? [英] Where in the Numpy source code are the constants defined?

问题描述

我试图找到将常量存储在numpy中的位置.某个方向会很好.

I am trying to find the location of where the constants are stored in numpy. Some direction would be nice.

推荐答案

在当前定义的是(从第48行开始):

The ones currently defined are (starting at line 48):

#define NPY_INFINITYF __npy_inff()
#define NPY_NANF __npy_nanf()
#define NPY_PZEROF __npy_pzerof()
#define NPY_NZEROF __npy_nzerof()

#define NPY_INFINITY ((npy_double)NPY_INFINITYF)
#define NPY_NAN ((npy_double)NPY_NANF)
#define NPY_PZERO ((npy_double)NPY_PZEROF)
#define NPY_NZERO ((npy_double)NPY_NZEROF)

#define NPY_INFINITYL ((npy_longdouble)NPY_INFINITYF)
#define NPY_NANL ((npy_longdouble)NPY_NANF)
#define NPY_PZEROL ((npy_longdouble)NPY_PZEROF)
#define NPY_NZEROL ((npy_longdouble)NPY_NZEROF)

/*
 * Useful constants
 */
#define NPY_E         2.718281828459045235360287471352662498  /* e */
#define NPY_LOG2E     1.442695040888963407359924681001892137  /* log_2 e */
#define NPY_LOG10E    0.434294481903251827651128918916605082  /* log_10 e */
#define NPY_LOGE2     0.693147180559945309417232121458176568  /* log_e 2 */
#define NPY_LOGE10    2.302585092994045684017991454684364208  /* log_e 10 */
#define NPY_PI        3.141592653589793238462643383279502884  /* pi */
#define NPY_PI_2      1.570796326794896619231321691639751442  /* pi/2 */
#define NPY_PI_4      0.785398163397448309615660845819875721  /* pi/4 */
#define NPY_1_PI      0.318309886183790671537767526745028724  /* 1/pi */
#define NPY_2_PI      0.636619772367581343075535053490057448  /* 2/pi */
#define NPY_EULER     0.577215664901532860606512090082402431  /* Euler constant */
#define NPY_SQRT2     1.414213562373095048801688724209698079  /* sqrt(2) */
#define NPY_SQRT1_2   0.707106781186547524400844362104849039  /* 1/sqrt(2) */

#define NPY_Ef        2.718281828459045235360287471352662498F /* e */
#define NPY_LOG2Ef    1.442695040888963407359924681001892137F /* log_2 e */
#define NPY_LOG10Ef   0.434294481903251827651128918916605082F /* log_10 e */
#define NPY_LOGE2f    0.693147180559945309417232121458176568F /* log_e 2 */
#define NPY_LOGE10f   2.302585092994045684017991454684364208F /* log_e 10 */
#define NPY_PIf       3.141592653589793238462643383279502884F /* pi */
#define NPY_PI_2f     1.570796326794896619231321691639751442F /* pi/2 */
#define NPY_PI_4f     0.785398163397448309615660845819875721F /* pi/4 */
#define NPY_1_PIf     0.318309886183790671537767526745028724F /* 1/pi */
#define NPY_2_PIf     0.636619772367581343075535053490057448F /* 2/pi */
#define NPY_EULERf    0.577215664901532860606512090082402431F /* Euler constan*/
#define NPY_SQRT2f    1.414213562373095048801688724209698079F /* sqrt(2) */
#define NPY_SQRT1_2f  0.707106781186547524400844362104849039F /* 1/sqrt(2) */

#define NPY_El        2.718281828459045235360287471352662498L /* e */
#define NPY_LOG2El    1.442695040888963407359924681001892137L /* log_2 e */
#define NPY_LOG10El   0.434294481903251827651128918916605082L /* log_10 e */
#define NPY_LOGE2l    0.693147180559945309417232121458176568L /* log_e 2 */
#define NPY_LOGE10l   2.302585092994045684017991454684364208L /* log_e 10 */
#define NPY_PIl       3.141592653589793238462643383279502884L /* pi */
#define NPY_PI_2l     1.570796326794896619231321691639751442L /* pi/2 */
#define NPY_PI_4l     0.785398163397448309615660845819875721L /* pi/4 */
#define NPY_1_PIl     0.318309886183790671537767526745028724L /* 1/pi */
#define NPY_2_PIl     0.636619772367581343075535053490057448L /* 2/pi */
#define NPY_EULERl    0.577215664901532860606512090082402431L /* Euler constan*/
#define NPY_SQRT2l    1.414213562373095048801688724209698079L /* sqrt(2) */
#define NPY_SQRT1_2l  0.707106781186547524400844362104849039L /* 1/sqrt(2) */

此处

从numpy 1.3.0开始，我们正在努力将纯C的计算"代码与依赖于python的代码分开.目标是双重的:使代码更整洁，并使numpy之外的其他扩展(scipy等)重新使用代码.

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核心数学库(npymath)是朝着这个方向迈出的第一步.该库包含大多数与数学相关的C99功能，可以在不完全支持C99的平台上使用.核心数学函数具有与C99相同的API，除了npy_ *前缀.

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.

可用的功能在-中定义-如有疑问，请参阅此标头.

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

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