diff options
| author | orivej <[email protected]> | 2022-02-10 16:45:01 +0300 |
|---|---|---|
| committer | Daniil Cherednik <[email protected]> | 2022-02-10 16:45:01 +0300 |
| commit | 2d37894b1b037cf24231090eda8589bbb44fb6fc (patch) | |
| tree | be835aa92c6248212e705f25388ebafcf84bc7a1 /contrib/tools/python3/src/Modules/mathmodule.c | |
| parent | 718c552901d703c502ccbefdfc3c9028d608b947 (diff) | |
Restoring authorship annotation for <[email protected]>. Commit 2 of 2.
Diffstat (limited to 'contrib/tools/python3/src/Modules/mathmodule.c')
| -rw-r--r-- | contrib/tools/python3/src/Modules/mathmodule.c | 4472 |
1 files changed, 2236 insertions, 2236 deletions
diff --git a/contrib/tools/python3/src/Modules/mathmodule.c b/contrib/tools/python3/src/Modules/mathmodule.c index 157a7e4858a..1f16849a3e6 100644 --- a/contrib/tools/python3/src/Modules/mathmodule.c +++ b/contrib/tools/python3/src/Modules/mathmodule.c @@ -1,82 +1,82 @@ -/* Math module -- standard C math library functions, pi and e */ - -/* Here are some comments from Tim Peters, extracted from the - discussion attached to http://bugs.python.org/issue1640. They - describe the general aims of the math module with respect to - special values, IEEE-754 floating-point exceptions, and Python - exceptions. - -These are the "spirit of 754" rules: - -1. If the mathematical result is a real number, but of magnitude too -large to approximate by a machine float, overflow is signaled and the -result is an infinity (with the appropriate sign). - -2. If the mathematical result is a real number, but of magnitude too -small to approximate by a machine float, underflow is signaled and the -result is a zero (with the appropriate sign). - -3. At a singularity (a value x such that the limit of f(y) as y -approaches x exists and is an infinity), "divide by zero" is signaled -and the result is an infinity (with the appropriate sign). This is -complicated a little by that the left-side and right-side limits may -not be the same; e.g., 1/x approaches +inf or -inf as x approaches 0 -from the positive or negative directions. In that specific case, the -sign of the zero determines the result of 1/0. - -4. At a point where a function has no defined result in the extended -reals (i.e., the reals plus an infinity or two), invalid operation is -signaled and a NaN is returned. - -And these are what Python has historically /tried/ to do (but not -always successfully, as platform libm behavior varies a lot): - -For #1, raise OverflowError. - -For #2, return a zero (with the appropriate sign if that happens by -accident ;-)). - -For #3 and #4, raise ValueError. It may have made sense to raise -Python's ZeroDivisionError in #3, but historically that's only been -raised for division by zero and mod by zero. - -*/ - -/* - In general, on an IEEE-754 platform the aim is to follow the C99 - standard, including Annex 'F', whenever possible. Where the - standard recommends raising the 'divide-by-zero' or 'invalid' - floating-point exceptions, Python should raise a ValueError. Where - the standard recommends raising 'overflow', Python should raise an - OverflowError. In all other circumstances a value should be - returned. - */ - -#include "Python.h" +/* Math module -- standard C math library functions, pi and e */ + +/* Here are some comments from Tim Peters, extracted from the + discussion attached to http://bugs.python.org/issue1640. They + describe the general aims of the math module with respect to + special values, IEEE-754 floating-point exceptions, and Python + exceptions. + +These are the "spirit of 754" rules: + +1. If the mathematical result is a real number, but of magnitude too +large to approximate by a machine float, overflow is signaled and the +result is an infinity (with the appropriate sign). + +2. If the mathematical result is a real number, but of magnitude too +small to approximate by a machine float, underflow is signaled and the +result is a zero (with the appropriate sign). + +3. At a singularity (a value x such that the limit of f(y) as y +approaches x exists and is an infinity), "divide by zero" is signaled +and the result is an infinity (with the appropriate sign). This is +complicated a little by that the left-side and right-side limits may +not be the same; e.g., 1/x approaches +inf or -inf as x approaches 0 +from the positive or negative directions. In that specific case, the +sign of the zero determines the result of 1/0. + +4. At a point where a function has no defined result in the extended +reals (i.e., the reals plus an infinity or two), invalid operation is +signaled and a NaN is returned. + +And these are what Python has historically /tried/ to do (but not +always successfully, as platform libm behavior varies a lot): + +For #1, raise OverflowError. + +For #2, return a zero (with the appropriate sign if that happens by +accident ;-)). + +For #3 and #4, raise ValueError. It may have made sense to raise +Python's ZeroDivisionError in #3, but historically that's only been +raised for division by zero and mod by zero. + +*/ + +/* + In general, on an IEEE-754 platform the aim is to follow the C99 + standard, including Annex 'F', whenever possible. Where the + standard recommends raising the 'divide-by-zero' or 'invalid' + floating-point exceptions, Python should raise a ValueError. Where + the standard recommends raising 'overflow', Python should raise an + OverflowError. In all other circumstances a value should be + returned. + */ + +#include "Python.h" #include "pycore_dtoa.h" -#include "_math.h" - -#include "clinic/mathmodule.c.h" - -/*[clinic input] -module math -[clinic start generated code]*/ -/*[clinic end generated code: output=da39a3ee5e6b4b0d input=76bc7002685dd942]*/ - - -/* - sin(pi*x), giving accurate results for all finite x (especially x - integral or close to an integer). This is here for use in the - reflection formula for the gamma function. It conforms to IEEE - 754-2008 for finite arguments, but not for infinities or nans. -*/ - -static const double pi = 3.141592653589793238462643383279502884197; -static const double logpi = 1.144729885849400174143427351353058711647; -#if !defined(HAVE_ERF) || !defined(HAVE_ERFC) -static const double sqrtpi = 1.772453850905516027298167483341145182798; -#endif /* !defined(HAVE_ERF) || !defined(HAVE_ERFC) */ - +#include "_math.h" + +#include "clinic/mathmodule.c.h" + +/*[clinic input] +module math +[clinic start generated code]*/ +/*[clinic end generated code: output=da39a3ee5e6b4b0d input=76bc7002685dd942]*/ + + +/* + sin(pi*x), giving accurate results for all finite x (especially x + integral or close to an integer). This is here for use in the + reflection formula for the gamma function. It conforms to IEEE + 754-2008 for finite arguments, but not for infinities or nans. +*/ + +static const double pi = 3.141592653589793238462643383279502884197; +static const double logpi = 1.144729885849400174143427351353058711647; +#if !defined(HAVE_ERF) || !defined(HAVE_ERFC) +static const double sqrtpi = 1.772453850905516027298167483341145182798; +#endif /* !defined(HAVE_ERF) || !defined(HAVE_ERFC) */ + /* Version of PyFloat_AsDouble() with in-line fast paths for exact floats and integers. Gives a substantial @@ -100,738 +100,738 @@ static const double sqrtpi = 1.772453850905516027298167483341145182798; } \ } -static double -m_sinpi(double x) -{ - double y, r; - int n; - /* this function should only ever be called for finite arguments */ - assert(Py_IS_FINITE(x)); - y = fmod(fabs(x), 2.0); - n = (int)round(2.0*y); - assert(0 <= n && n <= 4); - switch (n) { - case 0: - r = sin(pi*y); - break; - case 1: - r = cos(pi*(y-0.5)); - break; - case 2: - /* N.B. -sin(pi*(y-1.0)) is *not* equivalent: it would give - -0.0 instead of 0.0 when y == 1.0. */ - r = sin(pi*(1.0-y)); - break; - case 3: - r = -cos(pi*(y-1.5)); - break; - case 4: - r = sin(pi*(y-2.0)); - break; - default: - Py_UNREACHABLE(); - } - return copysign(1.0, x)*r; -} - -/* Implementation of the real gamma function. In extensive but non-exhaustive - random tests, this function proved accurate to within <= 10 ulps across the - entire float domain. Note that accuracy may depend on the quality of the - system math functions, the pow function in particular. Special cases - follow C99 annex F. The parameters and method are tailored to platforms - whose double format is the IEEE 754 binary64 format. - - Method: for x > 0.0 we use the Lanczos approximation with parameters N=13 - and g=6.024680040776729583740234375; these parameters are amongst those - used by the Boost library. Following Boost (again), we re-express the - Lanczos sum as a rational function, and compute it that way. The - coefficients below were computed independently using MPFR, and have been - double-checked against the coefficients in the Boost source code. - - For x < 0.0 we use the reflection formula. - - There's one minor tweak that deserves explanation: Lanczos' formula for - Gamma(x) involves computing pow(x+g-0.5, x-0.5) / exp(x+g-0.5). For many x - values, x+g-0.5 can be represented exactly. However, in cases where it - can't be represented exactly the small error in x+g-0.5 can be magnified - significantly by the pow and exp calls, especially for large x. A cheap - correction is to multiply by (1 + e*g/(x+g-0.5)), where e is the error - involved in the computation of x+g-0.5 (that is, e = computed value of - x+g-0.5 - exact value of x+g-0.5). Here's the proof: - - Correction factor - ----------------- - Write x+g-0.5 = y-e, where y is exactly representable as an IEEE 754 - double, and e is tiny. Then: - - pow(x+g-0.5,x-0.5)/exp(x+g-0.5) = pow(y-e, x-0.5)/exp(y-e) - = pow(y, x-0.5)/exp(y) * C, - - where the correction_factor C is given by - - C = pow(1-e/y, x-0.5) * exp(e) - - Since e is tiny, pow(1-e/y, x-0.5) ~ 1-(x-0.5)*e/y, and exp(x) ~ 1+e, so: - - C ~ (1-(x-0.5)*e/y) * (1+e) ~ 1 + e*(y-(x-0.5))/y - - But y-(x-0.5) = g+e, and g+e ~ g. So we get C ~ 1 + e*g/y, and - - pow(x+g-0.5,x-0.5)/exp(x+g-0.5) ~ pow(y, x-0.5)/exp(y) * (1 + e*g/y), - - Note that for accuracy, when computing r*C it's better to do - - r + e*g/y*r; - - than - - r * (1 + e*g/y); - - since the addition in the latter throws away most of the bits of - information in e*g/y. -*/ - -#define LANCZOS_N 13 -static const double lanczos_g = 6.024680040776729583740234375; -static const double lanczos_g_minus_half = 5.524680040776729583740234375; -static const double lanczos_num_coeffs[LANCZOS_N] = { - 23531376880.410759688572007674451636754734846804940, - 42919803642.649098768957899047001988850926355848959, - 35711959237.355668049440185451547166705960488635843, - 17921034426.037209699919755754458931112671403265390, - 6039542586.3520280050642916443072979210699388420708, - 1439720407.3117216736632230727949123939715485786772, - 248874557.86205415651146038641322942321632125127801, - 31426415.585400194380614231628318205362874684987640, - 2876370.6289353724412254090516208496135991145378768, - 186056.26539522349504029498971604569928220784236328, - 8071.6720023658162106380029022722506138218516325024, - 210.82427775157934587250973392071336271166969580291, - 2.5066282746310002701649081771338373386264310793408 -}; - -/* denominator is x*(x+1)*...*(x+LANCZOS_N-2) */ -static const double lanczos_den_coeffs[LANCZOS_N] = { - 0.0, 39916800.0, 120543840.0, 150917976.0, 105258076.0, 45995730.0, - 13339535.0, 2637558.0, 357423.0, 32670.0, 1925.0, 66.0, 1.0}; - -/* gamma values for small positive integers, 1 though NGAMMA_INTEGRAL */ -#define NGAMMA_INTEGRAL 23 -static const double gamma_integral[NGAMMA_INTEGRAL] = { - 1.0, 1.0, 2.0, 6.0, 24.0, 120.0, 720.0, 5040.0, 40320.0, 362880.0, - 3628800.0, 39916800.0, 479001600.0, 6227020800.0, 87178291200.0, - 1307674368000.0, 20922789888000.0, 355687428096000.0, - 6402373705728000.0, 121645100408832000.0, 2432902008176640000.0, - 51090942171709440000.0, 1124000727777607680000.0, -}; - -/* Lanczos' sum L_g(x), for positive x */ - -static double -lanczos_sum(double x) -{ - double num = 0.0, den = 0.0; - int i; - assert(x > 0.0); - /* evaluate the rational function lanczos_sum(x). For large - x, the obvious algorithm risks overflow, so we instead - rescale the denominator and numerator of the rational - function by x**(1-LANCZOS_N) and treat this as a - rational function in 1/x. This also reduces the error for - larger x values. The choice of cutoff point (5.0 below) is - somewhat arbitrary; in tests, smaller cutoff values than - this resulted in lower accuracy. */ - if (x < 5.0) { - for (i = LANCZOS_N; --i >= 0; ) { - num = num * x + lanczos_num_coeffs[i]; - den = den * x + lanczos_den_coeffs[i]; - } - } - else { - for (i = 0; i < LANCZOS_N; i++) { - num = num / x + lanczos_num_coeffs[i]; - den = den / x + lanczos_den_coeffs[i]; - } - } - return num/den; -} - -/* Constant for +infinity, generated in the same way as float('inf'). */ - -static double -m_inf(void) -{ -#ifndef PY_NO_SHORT_FLOAT_REPR - return _Py_dg_infinity(0); -#else - return Py_HUGE_VAL; -#endif -} - -/* Constant nan value, generated in the same way as float('nan'). */ -/* We don't currently assume that Py_NAN is defined everywhere. */ - -#if !defined(PY_NO_SHORT_FLOAT_REPR) || defined(Py_NAN) - -static double -m_nan(void) -{ -#ifndef PY_NO_SHORT_FLOAT_REPR - return _Py_dg_stdnan(0); -#else - return Py_NAN; -#endif -} - -#endif - -static double -m_tgamma(double x) -{ - double absx, r, y, z, sqrtpow; - - /* special cases */ - if (!Py_IS_FINITE(x)) { - if (Py_IS_NAN(x) || x > 0.0) - return x; /* tgamma(nan) = nan, tgamma(inf) = inf */ - else { - errno = EDOM; - return Py_NAN; /* tgamma(-inf) = nan, invalid */ - } - } - if (x == 0.0) { - errno = EDOM; - /* tgamma(+-0.0) = +-inf, divide-by-zero */ - return copysign(Py_HUGE_VAL, x); - } - - /* integer arguments */ - if (x == floor(x)) { - if (x < 0.0) { - errno = EDOM; /* tgamma(n) = nan, invalid for */ - return Py_NAN; /* negative integers n */ - } - if (x <= NGAMMA_INTEGRAL) - return gamma_integral[(int)x - 1]; - } - absx = fabs(x); - - /* tiny arguments: tgamma(x) ~ 1/x for x near 0 */ - if (absx < 1e-20) { - r = 1.0/x; - if (Py_IS_INFINITY(r)) - errno = ERANGE; - return r; - } - - /* large arguments: assuming IEEE 754 doubles, tgamma(x) overflows for - x > 200, and underflows to +-0.0 for x < -200, not a negative - integer. */ - if (absx > 200.0) { - if (x < 0.0) { - return 0.0/m_sinpi(x); - } - else { - errno = ERANGE; - return Py_HUGE_VAL; - } - } - - y = absx + lanczos_g_minus_half; - /* compute error in sum */ - if (absx > lanczos_g_minus_half) { - /* note: the correction can be foiled by an optimizing - compiler that (incorrectly) thinks that an expression like - a + b - a - b can be optimized to 0.0. This shouldn't - happen in a standards-conforming compiler. */ - double q = y - absx; - z = q - lanczos_g_minus_half; - } - else { - double q = y - lanczos_g_minus_half; - z = q - absx; - } - z = z * lanczos_g / y; - if (x < 0.0) { - r = -pi / m_sinpi(absx) / absx * exp(y) / lanczos_sum(absx); - r -= z * r; - if (absx < 140.0) { - r /= pow(y, absx - 0.5); - } - else { - sqrtpow = pow(y, absx / 2.0 - 0.25); - r /= sqrtpow; - r /= sqrtpow; - } - } - else { - r = lanczos_sum(absx) / exp(y); - r += z * r; - if (absx < 140.0) { - r *= pow(y, absx - 0.5); - } - else { - sqrtpow = pow(y, absx / 2.0 - 0.25); - r *= sqrtpow; - r *= sqrtpow; - } - } - if (Py_IS_INFINITY(r)) - errno = ERANGE; - return r; -} - -/* - lgamma: natural log of the absolute value of the Gamma function. - For large arguments, Lanczos' formula works extremely well here. -*/ - -static double -m_lgamma(double x) -{ - double r; - double absx; - - /* special cases */ - if (!Py_IS_FINITE(x)) { - if (Py_IS_NAN(x)) - return x; /* lgamma(nan) = nan */ - else - return Py_HUGE_VAL; /* lgamma(+-inf) = +inf */ - } - - /* integer arguments */ - if (x == floor(x) && x <= 2.0) { - if (x <= 0.0) { - errno = EDOM; /* lgamma(n) = inf, divide-by-zero for */ - return Py_HUGE_VAL; /* integers n <= 0 */ - } - else { - return 0.0; /* lgamma(1) = lgamma(2) = 0.0 */ - } - } - - absx = fabs(x); - /* tiny arguments: lgamma(x) ~ -log(fabs(x)) for small x */ - if (absx < 1e-20) - return -log(absx); - - /* Lanczos' formula. We could save a fraction of a ulp in accuracy by - having a second set of numerator coefficients for lanczos_sum that - absorbed the exp(-lanczos_g) term, and throwing out the lanczos_g - subtraction below; it's probably not worth it. */ - r = log(lanczos_sum(absx)) - lanczos_g; - r += (absx - 0.5) * (log(absx + lanczos_g - 0.5) - 1); - if (x < 0.0) - /* Use reflection formula to get value for negative x. */ - r = logpi - log(fabs(m_sinpi(absx))) - log(absx) - r; - if (Py_IS_INFINITY(r)) - errno = ERANGE; - return r; -} - -#if !defined(HAVE_ERF) || !defined(HAVE_ERFC) - -/* - Implementations of the error function erf(x) and the complementary error - function erfc(x). - - Method: we use a series approximation for erf for small x, and a continued - fraction approximation for erfc(x) for larger x; - combined with the relations erf(-x) = -erf(x) and erfc(x) = 1.0 - erf(x), - this gives us erf(x) and erfc(x) for all x. - - The series expansion used is: - - erf(x) = x*exp(-x*x)/sqrt(pi) * [ - 2/1 + 4/3 x**2 + 8/15 x**4 + 16/105 x**6 + ...] - - The coefficient of x**(2k-2) here is 4**k*factorial(k)/factorial(2*k). - This series converges well for smallish x, but slowly for larger x. - - The continued fraction expansion used is: - - erfc(x) = x*exp(-x*x)/sqrt(pi) * [1/(0.5 + x**2 -) 0.5/(2.5 + x**2 - ) - 3.0/(4.5 + x**2 - ) 7.5/(6.5 + x**2 - ) ...] - - after the first term, the general term has the form: - - k*(k-0.5)/(2*k+0.5 + x**2 - ...). - - This expansion converges fast for larger x, but convergence becomes - infinitely slow as x approaches 0.0. The (somewhat naive) continued - fraction evaluation algorithm used below also risks overflow for large x; - but for large x, erfc(x) == 0.0 to within machine precision. (For - example, erfc(30.0) is approximately 2.56e-393). - - Parameters: use series expansion for abs(x) < ERF_SERIES_CUTOFF and - continued fraction expansion for ERF_SERIES_CUTOFF <= abs(x) < - ERFC_CONTFRAC_CUTOFF. ERFC_SERIES_TERMS and ERFC_CONTFRAC_TERMS are the - numbers of terms to use for the relevant expansions. */ - -#define ERF_SERIES_CUTOFF 1.5 -#define ERF_SERIES_TERMS 25 -#define ERFC_CONTFRAC_CUTOFF 30.0 -#define ERFC_CONTFRAC_TERMS 50 - -/* - Error function, via power series. - - Given a finite float x, return an approximation to erf(x). - Converges reasonably fast for small x. -*/ - -static double -m_erf_series(double x) -{ - double x2, acc, fk, result; - int i, saved_errno; - - x2 = x * x; - acc = 0.0; - fk = (double)ERF_SERIES_TERMS + 0.5; - for (i = 0; i < ERF_SERIES_TERMS; i++) { - acc = 2.0 + x2 * acc / fk; - fk -= 1.0; - } - /* Make sure the exp call doesn't affect errno; - see m_erfc_contfrac for more. */ - saved_errno = errno; - result = acc * x * exp(-x2) / sqrtpi; - errno = saved_errno; - return result; -} - -/* - Complementary error function, via continued fraction expansion. - - Given a positive float x, return an approximation to erfc(x). Converges - reasonably fast for x large (say, x > 2.0), and should be safe from - overflow if x and nterms are not too large. On an IEEE 754 machine, with x - <= 30.0, we're safe up to nterms = 100. For x >= 30.0, erfc(x) is smaller - than the smallest representable nonzero float. */ - -static double -m_erfc_contfrac(double x) -{ - double x2, a, da, p, p_last, q, q_last, b, result; - int i, saved_errno; - - if (x >= ERFC_CONTFRAC_CUTOFF) - return 0.0; - - x2 = x*x; - a = 0.0; - da = 0.5; - p = 1.0; p_last = 0.0; - q = da + x2; q_last = 1.0; - for (i = 0; i < ERFC_CONTFRAC_TERMS; i++) { - double temp; - a += da; - da += 2.0; - b = da + x2; - temp = p; p = b*p - a*p_last; p_last = temp; - temp = q; q = b*q - a*q_last; q_last = temp; - } - /* Issue #8986: On some platforms, exp sets errno on underflow to zero; - save the current errno value so that we can restore it later. */ - saved_errno = errno; - result = p / q * x * exp(-x2) / sqrtpi; - errno = saved_errno; - return result; -} - -#endif /* !defined(HAVE_ERF) || !defined(HAVE_ERFC) */ - -/* Error function erf(x), for general x */ - -static double -m_erf(double x) -{ -#ifdef HAVE_ERF - return erf(x); -#else - double absx, cf; - - if (Py_IS_NAN(x)) - return x; - absx = fabs(x); - if (absx < ERF_SERIES_CUTOFF) - return m_erf_series(x); - else { - cf = m_erfc_contfrac(absx); - return x > 0.0 ? 1.0 - cf : cf - 1.0; - } -#endif -} - -/* Complementary error function erfc(x), for general x. */ - -static double -m_erfc(double x) -{ -#ifdef HAVE_ERFC - return erfc(x); -#else - double absx, cf; - - if (Py_IS_NAN(x)) - return x; - absx = fabs(x); - if (absx < ERF_SERIES_CUTOFF) - return 1.0 - m_erf_series(x); - else { - cf = m_erfc_contfrac(absx); - return x > 0.0 ? cf : 2.0 - cf; - } -#endif -} - -/* - wrapper for atan2 that deals directly with special cases before - delegating to the platform libm for the remaining cases. This - is necessary to get consistent behaviour across platforms. - Windows, FreeBSD and alpha Tru64 are amongst platforms that don't - always follow C99. -*/ - -static double -m_atan2(double y, double x) -{ - if (Py_IS_NAN(x) || Py_IS_NAN(y)) - return Py_NAN; - if (Py_IS_INFINITY(y)) { - if (Py_IS_INFINITY(x)) { - if (copysign(1., x) == 1.) - /* atan2(+-inf, +inf) == +-pi/4 */ - return copysign(0.25*Py_MATH_PI, y); - else - /* atan2(+-inf, -inf) == +-pi*3/4 */ - return copysign(0.75*Py_MATH_PI, y); - } - /* atan2(+-inf, x) == +-pi/2 for finite x */ - return copysign(0.5*Py_MATH_PI, y); - } - if (Py_IS_INFINITY(x) || y == 0.) { - if (copysign(1., x) == 1.) - /* atan2(+-y, +inf) = atan2(+-0, +x) = +-0. */ - return copysign(0., y); - else - /* atan2(+-y, -inf) = atan2(+-0., -x) = +-pi. */ - return copysign(Py_MATH_PI, y); - } - return atan2(y, x); -} - - -/* IEEE 754-style remainder operation: x - n*y where n*y is the nearest - multiple of y to x, taking n even in the case of a tie. Assuming an IEEE 754 - binary floating-point format, the result is always exact. */ - -static double -m_remainder(double x, double y) -{ - /* Deal with most common case first. */ - if (Py_IS_FINITE(x) && Py_IS_FINITE(y)) { - double absx, absy, c, m, r; - - if (y == 0.0) { - return Py_NAN; - } - - absx = fabs(x); - absy = fabs(y); - m = fmod(absx, absy); - - /* - Warning: some subtlety here. What we *want* to know at this point is - whether the remainder m is less than, equal to, or greater than half - of absy. However, we can't do that comparison directly because we +static double +m_sinpi(double x) +{ + double y, r; + int n; + /* this function should only ever be called for finite arguments */ + assert(Py_IS_FINITE(x)); + y = fmod(fabs(x), 2.0); + n = (int)round(2.0*y); + assert(0 <= n && n <= 4); + switch (n) { + case 0: + r = sin(pi*y); + break; + case 1: + r = cos(pi*(y-0.5)); + break; + case 2: + /* N.B. -sin(pi*(y-1.0)) is *not* equivalent: it would give + -0.0 instead of 0.0 when y == 1.0. */ + r = sin(pi*(1.0-y)); + break; + case 3: + r = -cos(pi*(y-1.5)); + break; + case 4: + r = sin(pi*(y-2.0)); + break; + default: + Py_UNREACHABLE(); + } + return copysign(1.0, x)*r; +} + +/* Implementation of the real gamma function. In extensive but non-exhaustive + random tests, this function proved accurate to within <= 10 ulps across the + entire float domain. Note that accuracy may depend on the quality of the + system math functions, the pow function in particular. Special cases + follow C99 annex F. The parameters and method are tailored to platforms + whose double format is the IEEE 754 binary64 format. + + Method: for x > 0.0 we use the Lanczos approximation with parameters N=13 + and g=6.024680040776729583740234375; these parameters are amongst those + used by the Boost library. Following Boost (again), we re-express the + Lanczos sum as a rational function, and compute it that way. The + coefficients below were computed independently using MPFR, and have been + double-checked against the coefficients in the Boost source code. + + For x < 0.0 we use the reflection formula. + + There's one minor tweak that deserves explanation: Lanczos' formula for + Gamma(x) involves computing pow(x+g-0.5, x-0.5) / exp(x+g-0.5). For many x + values, x+g-0.5 can be represented exactly. However, in cases where it + can't be represented exactly the small error in x+g-0.5 can be magnified + significantly by the pow and exp calls, especially for large x. A cheap + correction is to multiply by (1 + e*g/(x+g-0.5)), where e is the error + involved in the computation of x+g-0.5 (that is, e = computed value of + x+g-0.5 - exact value of x+g-0.5). Here's the proof: + + Correction factor + ----------------- + Write x+g-0.5 = y-e, where y is exactly representable as an IEEE 754 + double, and e is tiny. Then: + + pow(x+g-0.5,x-0.5)/exp(x+g-0.5) = pow(y-e, x-0.5)/exp(y-e) + = pow(y, x-0.5)/exp(y) * C, + + where the correction_factor C is given by + + C = pow(1-e/y, x-0.5) * exp(e) + + Since e is tiny, pow(1-e/y, x-0.5) ~ 1-(x-0.5)*e/y, and exp(x) ~ 1+e, so: + + C ~ (1-(x-0.5)*e/y) * (1+e) ~ 1 + e*(y-(x-0.5))/y + + But y-(x-0.5) = g+e, and g+e ~ g. So we get C ~ 1 + e*g/y, and + + pow(x+g-0.5,x-0.5)/exp(x+g-0.5) ~ pow(y, x-0.5)/exp(y) * (1 + e*g/y), + + Note that for accuracy, when computing r*C it's better to do + + r + e*g/y*r; + + than + + r * (1 + e*g/y); + + since the addition in the latter throws away most of the bits of + information in e*g/y. +*/ + +#define LANCZOS_N 13 +static const double lanczos_g = 6.024680040776729583740234375; +static const double lanczos_g_minus_half = 5.524680040776729583740234375; +static const double lanczos_num_coeffs[LANCZOS_N] = { + 23531376880.410759688572007674451636754734846804940, + 42919803642.649098768957899047001988850926355848959, + 35711959237.355668049440185451547166705960488635843, + 17921034426.037209699919755754458931112671403265390, + 6039542586.3520280050642916443072979210699388420708, + 1439720407.3117216736632230727949123939715485786772, + 248874557.86205415651146038641322942321632125127801, + 31426415.585400194380614231628318205362874684987640, + 2876370.6289353724412254090516208496135991145378768, + 186056.26539522349504029498971604569928220784236328, + 8071.6720023658162106380029022722506138218516325024, + 210.82427775157934587250973392071336271166969580291, + 2.5066282746310002701649081771338373386264310793408 +}; + +/* denominator is x*(x+1)*...*(x+LANCZOS_N-2) */ +static const double lanczos_den_coeffs[LANCZOS_N] = { + 0.0, 39916800.0, 120543840.0, 150917976.0, 105258076.0, 45995730.0, + 13339535.0, 2637558.0, 357423.0, 32670.0, 1925.0, 66.0, 1.0}; + +/* gamma values for small positive integers, 1 though NGAMMA_INTEGRAL */ +#define NGAMMA_INTEGRAL 23 +static const double gamma_integral[NGAMMA_INTEGRAL] = { + 1.0, 1.0, 2.0, 6.0, 24.0, 120.0, 720.0, 5040.0, 40320.0, 362880.0, + 3628800.0, 39916800.0, 479001600.0, 6227020800.0, 87178291200.0, + 1307674368000.0, 20922789888000.0, 355687428096000.0, + 6402373705728000.0, 121645100408832000.0, 2432902008176640000.0, + 51090942171709440000.0, 1124000727777607680000.0, +}; + +/* Lanczos' sum L_g(x), for positive x */ + +static double +lanczos_sum(double x) +{ + double num = 0.0, den = 0.0; + int i; + assert(x > 0.0); + /* evaluate the rational function lanczos_sum(x). For large + x, the obvious algorithm risks overflow, so we instead + rescale the denominator and numerator of the rational + function by x**(1-LANCZOS_N) and treat this as a + rational function in 1/x. This also reduces the error for + larger x values. The choice of cutoff point (5.0 below) is + somewhat arbitrary; in tests, smaller cutoff values than + this resulted in lower accuracy. */ + if (x < 5.0) { + for (i = LANCZOS_N; --i >= 0; ) { + num = num * x + lanczos_num_coeffs[i]; + den = den * x + lanczos_den_coeffs[i]; + } + } + else { + for (i = 0; i < LANCZOS_N; i++) { + num = num / x + lanczos_num_coeffs[i]; + den = den / x + lanczos_den_coeffs[i]; + } + } + return num/den; +} + +/* Constant for +infinity, generated in the same way as float('inf'). */ + +static double +m_inf(void) +{ +#ifndef PY_NO_SHORT_FLOAT_REPR + return _Py_dg_infinity(0); +#else + return Py_HUGE_VAL; +#endif +} + +/* Constant nan value, generated in the same way as float('nan'). */ +/* We don't currently assume that Py_NAN is defined everywhere. */ + +#if !defined(PY_NO_SHORT_FLOAT_REPR) || defined(Py_NAN) + +static double +m_nan(void) +{ +#ifndef PY_NO_SHORT_FLOAT_REPR + return _Py_dg_stdnan(0); +#else + return Py_NAN; +#endif +} + +#endif + +static double +m_tgamma(double x) +{ + double absx, r, y, z, sqrtpow; + + /* special cases */ + if (!Py_IS_FINITE(x)) { + if (Py_IS_NAN(x) || x > 0.0) + return x; /* tgamma(nan) = nan, tgamma(inf) = inf */ + else { + errno = EDOM; + return Py_NAN; /* tgamma(-inf) = nan, invalid */ + } + } + if (x == 0.0) { + errno = EDOM; + /* tgamma(+-0.0) = +-inf, divide-by-zero */ + return copysign(Py_HUGE_VAL, x); + } + + /* integer arguments */ + if (x == floor(x)) { + if (x < 0.0) { + errno = EDOM; /* tgamma(n) = nan, invalid for */ + return Py_NAN; /* negative integers n */ + } + if (x <= NGAMMA_INTEGRAL) + return gamma_integral[(int)x - 1]; + } + absx = fabs(x); + + /* tiny arguments: tgamma(x) ~ 1/x for x near 0 */ + if (absx < 1e-20) { + r = 1.0/x; + if (Py_IS_INFINITY(r)) + errno = ERANGE; + return r; + } + + /* large arguments: assuming IEEE 754 doubles, tgamma(x) overflows for + x > 200, and underflows to +-0.0 for x < -200, not a negative + integer. */ + if (absx > 200.0) { + if (x < 0.0) { + return 0.0/m_sinpi(x); + } + else { + errno = ERANGE; + return Py_HUGE_VAL; + } + } + + y = absx + lanczos_g_minus_half; + /* compute error in sum */ + if (absx > lanczos_g_minus_half) { + /* note: the correction can be foiled by an optimizing + compiler that (incorrectly) thinks that an expression like + a + b - a - b can be optimized to 0.0. This shouldn't + happen in a standards-conforming compiler. */ + double q = y - absx; + z = q - lanczos_g_minus_half; + } + else { + double q = y - lanczos_g_minus_half; + z = q - absx; + } + z = z * lanczos_g / y; + if (x < 0.0) { + r = -pi / m_sinpi(absx) / absx * exp(y) / lanczos_sum(absx); + r -= z * r; + if (absx < 140.0) { + r /= pow(y, absx - 0.5); + } + else { + sqrtpow = pow(y, absx / 2.0 - 0.25); + r /= sqrtpow; + r /= sqrtpow; + } + } + else { + r = lanczos_sum(absx) / exp(y); + r += z * r; + if (absx < 140.0) { + r *= pow(y, absx - 0.5); + } + else { + sqrtpow = pow(y, absx / 2.0 - 0.25); + r *= sqrtpow; + r *= sqrtpow; + } + } + if (Py_IS_INFINITY(r)) + errno = ERANGE; + return r; +} + +/* + lgamma: natural log of the absolute value of the Gamma function. + For large arguments, Lanczos' formula works extremely well here. +*/ + +static double +m_lgamma(double x) +{ + double r; + double absx; + + /* special cases */ + if (!Py_IS_FINITE(x)) { + if (Py_IS_NAN(x)) + return x; /* lgamma(nan) = nan */ + else + return Py_HUGE_VAL; /* lgamma(+-inf) = +inf */ + } + + /* integer arguments */ + if (x == floor(x) && x <= 2.0) { + if (x <= 0.0) { + errno = EDOM; /* lgamma(n) = inf, divide-by-zero for */ + return Py_HUGE_VAL; /* integers n <= 0 */ + } + else { + return 0.0; /* lgamma(1) = lgamma(2) = 0.0 */ + } + } + + absx = fabs(x); + /* tiny arguments: lgamma(x) ~ -log(fabs(x)) for small x */ + if (absx < 1e-20) + return -log(absx); + + /* Lanczos' formula. We could save a fraction of a ulp in accuracy by + having a second set of numerator coefficients for lanczos_sum that + absorbed the exp(-lanczos_g) term, and throwing out the lanczos_g + subtraction below; it's probably not worth it. */ + r = log(lanczos_sum(absx)) - lanczos_g; + r += (absx - 0.5) * (log(absx + lanczos_g - 0.5) - 1); + if (x < 0.0) + /* Use reflection formula to get value for negative x. */ + r = logpi - log(fabs(m_sinpi(absx))) - log(absx) - r; + if (Py_IS_INFINITY(r)) + errno = ERANGE; + return r; +} + +#if !defined(HAVE_ERF) || !defined(HAVE_ERFC) + +/* + Implementations of the error function erf(x) and the complementary error + function erfc(x). + + Method: we use a series approximation for erf for small x, and a continued + fraction approximation for erfc(x) for larger x; + combined with the relations erf(-x) = -erf(x) and erfc(x) = 1.0 - erf(x), + this gives us erf(x) and erfc(x) for all x. + + The series expansion used is: + + erf(x) = x*exp(-x*x)/sqrt(pi) * [ + 2/1 + 4/3 x**2 + 8/15 x**4 + 16/105 x**6 + ...] + + The coefficient of x**(2k-2) here is 4**k*factorial(k)/factorial(2*k). + This series converges well for smallish x, but slowly for larger x. + + The continued fraction expansion used is: + + erfc(x) = x*exp(-x*x)/sqrt(pi) * [1/(0.5 + x**2 -) 0.5/(2.5 + x**2 - ) + 3.0/(4.5 + x**2 - ) 7.5/(6.5 + x**2 - ) ...] + + after the first term, the general term has the form: + + k*(k-0.5)/(2*k+0.5 + x**2 - ...). + + This expansion converges fast for larger x, but convergence becomes + infinitely slow as x approaches 0.0. The (somewhat naive) continued + fraction evaluation algorithm used below also risks overflow for large x; + but for large x, erfc(x) == 0.0 to within machine precision. (For + example, erfc(30.0) is approximately 2.56e-393). + + Parameters: use series expansion for abs(x) < ERF_SERIES_CUTOFF and + continued fraction expansion for ERF_SERIES_CUTOFF <= abs(x) < + ERFC_CONTFRAC_CUTOFF. ERFC_SERIES_TERMS and ERFC_CONTFRAC_TERMS are the + numbers of terms to use for the relevant expansions. */ + +#define ERF_SERIES_CUTOFF 1.5 +#define ERF_SERIES_TERMS 25 +#define ERFC_CONTFRAC_CUTOFF 30.0 +#define ERFC_CONTFRAC_TERMS 50 + +/* + Error function, via power series. + + Given a finite float x, return an approximation to erf(x). + Converges reasonably fast for small x. +*/ + +static double +m_erf_series(double x) +{ + double x2, acc, fk, result; + int i, saved_errno; + + x2 = x * x; + acc = 0.0; + fk = (double)ERF_SERIES_TERMS + 0.5; + for (i = 0; i < ERF_SERIES_TERMS; i++) { + acc = 2.0 + x2 * acc / fk; + fk -= 1.0; + } + /* Make sure the exp call doesn't affect errno; + see m_erfc_contfrac for more. */ + saved_errno = errno; + result = acc * x * exp(-x2) / sqrtpi; + errno = saved_errno; + return result; +} + +/* + Complementary error function, via continued fraction expansion. + + Given a positive float x, return an approximation to erfc(x). Converges + reasonably fast for x large (say, x > 2.0), and should be safe from + overflow if x and nterms are not too large. On an IEEE 754 machine, with x + <= 30.0, we're safe up to nterms = 100. For x >= 30.0, erfc(x) is smaller + than the smallest representable nonzero float. */ + +static double +m_erfc_contfrac(double x) +{ + double x2, a, da, p, p_last, q, q_last, b, result; + int i, saved_errno; + + if (x >= ERFC_CONTFRAC_CUTOFF) + return 0.0; + + x2 = x*x; + a = 0.0; + da = 0.5; + p = 1.0; p_last = 0.0; + q = da + x2; q_last = 1.0; + for (i = 0; i < ERFC_CONTFRAC_TERMS; i++) { + double temp; + a += da; + da += 2.0; + b = da + x2; + temp = p; p = b*p - a*p_last; p_last = temp; + temp = q; q = b*q - a*q_last; q_last = temp; + } + /* Issue #8986: On some platforms, exp sets errno on underflow to zero; + save the current errno value so that we can restore it later. */ + saved_errno = errno; + result = p / q * x * exp(-x2) / sqrtpi; + errno = saved_errno; + return result; +} + +#endif /* !defined(HAVE_ERF) || !defined(HAVE_ERFC) */ + +/* Error function erf(x), for general x */ + +static double +m_erf(double x) +{ +#ifdef HAVE_ERF + return erf(x); +#else + double absx, cf; + + if (Py_IS_NAN(x)) + return x; + absx = fabs(x); + if (absx < ERF_SERIES_CUTOFF) + return m_erf_series(x); + else { + cf = m_erfc_contfrac(absx); + return x > 0.0 ? 1.0 - cf : cf - 1.0; + } +#endif +} + +/* Complementary error function erfc(x), for general x. */ + +static double +m_erfc(double x) +{ +#ifdef HAVE_ERFC + return erfc(x); +#else + double absx, cf; + + if (Py_IS_NAN(x)) + return x; + absx = fabs(x); + if (absx < ERF_SERIES_CUTOFF) + return 1.0 - m_erf_series(x); + else { + cf = m_erfc_contfrac(absx); + return x > 0.0 ? cf : 2.0 - cf; + } +#endif +} + +/* + wrapper for atan2 that deals directly with special cases before + delegating to the platform libm for the remaining cases. This + is necessary to get consistent behaviour across platforms. + Windows, FreeBSD and alpha Tru64 are amongst platforms that don't + always follow C99. +*/ + +static double +m_atan2(double y, double x) +{ + if (Py_IS_NAN(x) || Py_IS_NAN(y)) + return Py_NAN; + if (Py_IS_INFINITY(y)) { + if (Py_IS_INFINITY(x)) { + if (copysign(1., x) == 1.) + /* atan2(+-inf, +inf) == +-pi/4 */ + return copysign(0.25*Py_MATH_PI, y); + else + /* atan2(+-inf, -inf) == +-pi*3/4 */ + return copysign(0.75*Py_MATH_PI, y); + } + /* atan2(+-inf, x) == +-pi/2 for finite x */ + return copysign(0.5*Py_MATH_PI, y); + } + if (Py_IS_INFINITY(x) || y == 0.) { + if (copysign(1., x) == 1.) + /* atan2(+-y, +inf) = atan2(+-0, +x) = +-0. */ + return copysign(0., y); + else + /* atan2(+-y, -inf) = atan2(+-0., -x) = +-pi. */ + return copysign(Py_MATH_PI, y); + } + return atan2(y, x); +} + + +/* IEEE 754-style remainder operation: x - n*y where n*y is the nearest + multiple of y to x, taking n even in the case of a tie. Assuming an IEEE 754 + binary floating-point format, the result is always exact. */ + +static double +m_remainder(double x, double y) +{ + /* Deal with most common case first. */ + if (Py_IS_FINITE(x) && Py_IS_FINITE(y)) { + double absx, absy, c, m, r; + + if (y == 0.0) { + return Py_NAN; + } + + absx = fabs(x); + absy = fabs(y); + m = fmod(absx, absy); + + /* + Warning: some subtlety here. What we *want* to know at this point is + whether the remainder m is less than, equal to, or greater than half + of absy. However, we can't do that comparison directly because we can't be sure that 0.5*absy is representable (the multiplication - might incur precision loss due to underflow). So instead we compare - m with the complement c = absy - m: m < 0.5*absy if and only if m < - c, and so on. The catch is that absy - m might also not be - representable, but it turns out that it doesn't matter: - - - if m > 0.5*absy then absy - m is exactly representable, by - Sterbenz's lemma, so m > c - - if m == 0.5*absy then again absy - m is exactly representable - and m == c - - if m < 0.5*absy then either (i) 0.5*absy is exactly representable, - in which case 0.5*absy < absy - m, so 0.5*absy <= c and hence m < - c, or (ii) absy is tiny, either subnormal or in the lowest normal - binade. Then absy - m is exactly representable and again m < c. - */ - - c = absy - m; - if (m < c) { - r = m; - } - else if (m > c) { - r = -c; - } - else { - /* - Here absx is exactly halfway between two multiples of absy, - and we need to choose the even multiple. x now has the form - - absx = n * absy + m - - for some integer n (recalling that m = 0.5*absy at this point). - If n is even we want to return m; if n is odd, we need to - return -m. - - So - - 0.5 * (absx - m) = (n/2) * absy - - and now reducing modulo absy gives us: - - | m, if n is odd - fmod(0.5 * (absx - m), absy) = | - | 0, if n is even - - Now m - 2.0 * fmod(...) gives the desired result: m - if n is even, -m if m is odd. - - Note that all steps in fmod(0.5 * (absx - m), absy) - will be computed exactly, with no rounding error - introduced. - */ - assert(m == c); - r = m - 2.0 * fmod(0.5 * (absx - m), absy); - } - return copysign(1.0, x) * r; - } - - /* Special values. */ - if (Py_IS_NAN(x)) { - return x; - } - if (Py_IS_NAN(y)) { - return y; - } - if (Py_IS_INFINITY(x)) { - return Py_NAN; - } - assert(Py_IS_INFINITY(y)); - return x; -} - - -/* - Various platforms (Solaris, OpenBSD) do nonstandard things for log(0), - log(-ve), log(NaN). Here are wrappers for log and log10 that deal with - special values directly, passing positive non-special values through to - the system log/log10. - */ - -static double -m_log(double x) -{ - if (Py_IS_FINITE(x)) { - if (x > 0.0) - return log(x); - errno = EDOM; - if (x == 0.0) - return -Py_HUGE_VAL; /* log(0) = -inf */ - else - return Py_NAN; /* log(-ve) = nan */ - } - else if (Py_IS_NAN(x)) - return x; /* log(nan) = nan */ - else if (x > 0.0) - return x; /* log(inf) = inf */ - else { - errno = EDOM; - return Py_NAN; /* log(-inf) = nan */ - } -} - -/* - log2: log to base 2. - - Uses an algorithm that should: - - (a) produce exact results for powers of 2, and - (b) give a monotonic log2 (for positive finite floats), - assuming that the system log is monotonic. -*/ - -static double -m_log2(double x) -{ - if (!Py_IS_FINITE(x)) { - if (Py_IS_NAN(x)) - return x; /* log2(nan) = nan */ - else if (x > 0.0) - return x; /* log2(+inf) = +inf */ - else { - errno = EDOM; - return Py_NAN; /* log2(-inf) = nan, invalid-operation */ - } - } - - if (x > 0.0) { -#ifdef HAVE_LOG2 - return log2(x); -#else - double m; - int e; - m = frexp(x, &e); - /* We want log2(m * 2**e) == log(m) / log(2) + e. Care is needed when - * x is just greater than 1.0: in that case e is 1, log(m) is negative, - * and we get significant cancellation error from the addition of - * log(m) / log(2) to e. The slight rewrite of the expression below - * avoids this problem. - */ - if (x >= 1.0) { - return log(2.0 * m) / log(2.0) + (e - 1); - } - else { - return log(m) / log(2.0) + e; - } -#endif - } - else if (x == 0.0) { - errno = EDOM; - return -Py_HUGE_VAL; /* log2(0) = -inf, divide-by-zero */ - } - else { - errno = EDOM; - return Py_NAN; /* log2(-inf) = nan, invalid-operation */ - } -} - -static double -m_log10(double x) -{ - if (Py_IS_FINITE(x)) { - if (x > 0.0) - return log10(x); - errno = EDOM; - if (x == 0.0) - return -Py_HUGE_VAL; /* log10(0) = -inf */ - else - return Py_NAN; /* log10(-ve) = nan */ - } - else if (Py_IS_NAN(x)) - return x; /* log10(nan) = nan */ - else if (x > 0.0) - return x; /* log10(inf) = inf */ - else { - errno = EDOM; - return Py_NAN; /* log10(-inf) = nan */ - } -} - - + might incur precision loss due to underflow). So instead we compare + m with the complement c = absy - m: m < 0.5*absy if and only if m < + c, and so on. The catch is that absy - m might also not be + representable, but it turns out that it doesn't matter: + + - if m > 0.5*absy then absy - m is exactly representable, by + Sterbenz's lemma, so m > c + - if m == 0.5*absy then again absy - m is exactly representable + and m == c + - if m < 0.5*absy then either (i) 0.5*absy is exactly representable, + in which case 0.5*absy < absy - m, so 0.5*absy <= c and hence m < + c, or (ii) absy is tiny, either subnormal or in the lowest normal + binade. Then absy - m is exactly representable and again m < c. + */ + + c = absy - m; + if (m < c) { + r = m; + } + else if (m > c) { + r = -c; + } + else { + /* + Here absx is exactly halfway between two multiples of absy, + and we need to choose the even multiple. x now has the form + + absx = n * absy + m + + for some integer n (recalling that m = 0.5*absy at this point). + If n is even we want to return m; if n is odd, we need to + return -m. + + So + + 0.5 * (absx - m) = (n/2) * absy + + and now reducing modulo absy gives us: + + | m, if n is odd + fmod(0.5 * (absx - m), absy) = | + | 0, if n is even + + Now m - 2.0 * fmod(...) gives the desired result: m + if n is even, -m if m is odd. + + Note that all steps in fmod(0.5 * (absx - m), absy) + will be computed exactly, with no rounding error + introduced. + */ + assert(m == c); + r = m - 2.0 * fmod(0.5 * (absx - m), absy); + } + return copysign(1.0, x) * r; + } + + /* Special values. */ + if (Py_IS_NAN(x)) { + return x; + } + if (Py_IS_NAN(y)) { + return y; + } + if (Py_IS_INFINITY(x)) { + return Py_NAN; + } + assert(Py_IS_INFINITY(y)); + return x; +} + + +/* + Various platforms (Solaris, OpenBSD) do nonstandard things for log(0), + log(-ve), log(NaN). Here are wrappers for log and log10 that deal with + special values directly, passing positive non-special values through to + the system log/log10. + */ + +static double +m_log(double x) +{ + if (Py_IS_FINITE(x)) { + if (x > 0.0) + return log(x); + errno = EDOM; + if (x == 0.0) + return -Py_HUGE_VAL; /* log(0) = -inf */ + else + return Py_NAN; /* log(-ve) = nan */ + } + else if (Py_IS_NAN(x)) + return x; /* log(nan) = nan */ + else if (x > 0.0) + return x; /* log(inf) = inf */ + else { + errno = EDOM; + return Py_NAN; /* log(-inf) = nan */ + } +} + +/* + log2: log to base 2. + + Uses an algorithm that should: + + (a) produce exact results for powers of 2, and + (b) give a monotonic log2 (for positive finite floats), + assuming that the system log is monotonic. +*/ + +static double +m_log2(double x) +{ + if (!Py_IS_FINITE(x)) { + if (Py_IS_NAN(x)) + return x; /* log2(nan) = nan */ + else if (x > 0.0) + return x; /* log2(+inf) = +inf */ + else { + errno = EDOM; + return Py_NAN; /* log2(-inf) = nan, invalid-operation */ + } + } + + if (x > 0.0) { +#ifdef HAVE_LOG2 + return log2(x); +#else + double m; + int e; + m = frexp(x, &e); + /* We want log2(m * 2**e) == log(m) / log(2) + e. Care is needed when + * x is just greater than 1.0: in that case e is 1, log(m) is negative, + * and we get significant cancellation error from the addition of + * log(m) / log(2) to e. The slight rewrite of the expression below + * avoids this problem. + */ + if (x >= 1.0) { + return log(2.0 * m) / log(2.0) + (e - 1); + } + else { + return log(m) / log(2.0) + e; + } +#endif + } + else if (x == 0.0) { + errno = EDOM; + return -Py_HUGE_VAL; /* log2(0) = -inf, divide-by-zero */ + } + else { + errno = EDOM; + return Py_NAN; /* log2(-inf) = nan, invalid-operation */ + } +} + +static double +m_log10(double x) +{ + if (Py_IS_FINITE(x)) { + if (x > 0.0) + return log10(x); + errno = EDOM; + if (x == 0.0) + return -Py_HUGE_VAL; /* log10(0) = -inf */ + else + return Py_NAN; /* log10(-ve) = nan */ + } + else if (Py_IS_NAN(x)) + return x; /* log10(nan) = nan */ + else if (x > 0.0) + return x; /* log10(inf) = inf */ + else { + errno = EDOM; + return Py_NAN; /* log10(-inf) = nan */ + } +} + + static PyObject * math_gcd(PyObject *module, PyObject * const *args, Py_ssize_t nargs) { PyObject *res, *x; Py_ssize_t i; - + if (nargs == 0) { return PyLong_FromLong(0); } @@ -863,31 +863,31 @@ math_gcd(PyObject *module, PyObject * const *args, Py_ssize_t nargs) } return res; } - + PyDoc_STRVAR(math_gcd_doc, "gcd($module, *integers)\n" "--\n" "\n" "Greatest Common Divisor."); - -static PyObject * + +static PyObject * long_lcm(PyObject *a, PyObject *b) -{ +{ PyObject *g, *m, *f, *ab; - + if (Py_SIZE(a) == 0 || Py_SIZE(b) == 0) { return PyLong_FromLong(0); } g = _PyLong_GCD(a, b); if (g == NULL) { - return NULL; + return NULL; } f = PyNumber_FloorDivide(a, g); Py_DECREF(g); if (f == NULL) { - return NULL; - } + return NULL; + } m = PyNumber_Multiply(f, b); Py_DECREF(f); if (m == NULL) { @@ -896,9 +896,9 @@ long_lcm(PyObject *a, PyObject *b) ab = PyNumber_Absolute(m); Py_DECREF(m); return ab; -} - - +} + + static PyObject * math_lcm(PyObject *module, PyObject * const *args, Py_ssize_t nargs) { @@ -945,259 +945,259 @@ PyDoc_STRVAR(math_lcm_doc, "Least Common Multiple."); -/* Call is_error when errno != 0, and where x is the result libm - * returned. is_error will usually set up an exception and return - * true (1), but may return false (0) without setting up an exception. - */ -static int -is_error(double x) -{ - int result = 1; /* presumption of guilt */ - assert(errno); /* non-zero errno is a precondition for calling */ - if (errno == EDOM) - PyErr_SetString(PyExc_ValueError, "math domain error"); - - else if (errno == ERANGE) { - /* ANSI C generally requires libm functions to set ERANGE - * on overflow, but also generally *allows* them to set - * ERANGE on underflow too. There's no consistency about - * the latter across platforms. - * Alas, C99 never requires that errno be set. - * Here we suppress the underflow errors (libm functions - * should return a zero on underflow, and +- HUGE_VAL on - * overflow, so testing the result for zero suffices to - * distinguish the cases). - * - * On some platforms (Ubuntu/ia64) it seems that errno can be - * set to ERANGE for subnormal results that do *not* underflow - * to zero. So to be safe, we'll ignore ERANGE whenever the +/* Call is_error when errno != 0, and where x is the result libm + * returned. is_error will usually set up an exception and return + * true (1), but may return false (0) without setting up an exception. + */ +static int +is_error(double x) +{ + int result = 1; /* presumption of guilt */ + assert(errno); /* non-zero errno is a precondition for calling */ + if (errno == EDOM) + PyErr_SetString(PyExc_ValueError, "math domain error"); + + else if (errno == ERANGE) { + /* ANSI C generally requires libm functions to set ERANGE + * on overflow, but also generally *allows* them to set + * ERANGE on underflow too. There's no consistency about + * the latter across platforms. + * Alas, C99 never requires that errno be set. + * Here we suppress the underflow errors (libm functions + * should return a zero on underflow, and +- HUGE_VAL on + * overflow, so testing the result for zero suffices to + * distinguish the cases). + * + * On some platforms (Ubuntu/ia64) it seems that errno can be + * set to ERANGE for subnormal results that do *not* underflow + * to zero. So to be safe, we'll ignore ERANGE whenever the * function result is less than 1.5 in absolute value. * * bpo-46018: Changed to 1.5 to ensure underflows in expm1() * are correctly detected, since the function may underflow * toward -1.0 rather than 0.0. - */ + */ if (fabs(x) < 1.5) - result = 0; - else - PyErr_SetString(PyExc_OverflowError, - "math range error"); - } - else - /* Unexpected math error */ - PyErr_SetFromErrno(PyExc_ValueError); - return result; -} - -/* - math_1 is used to wrap a libm function f that takes a double - argument and returns a double. - - The error reporting follows these rules, which are designed to do - the right thing on C89/C99 platforms and IEEE 754/non IEEE 754 - platforms. - - - a NaN result from non-NaN inputs causes ValueError to be raised - - an infinite result from finite inputs causes OverflowError to be - raised if can_overflow is 1, or raises ValueError if can_overflow - is 0. - - if the result is finite and errno == EDOM then ValueError is - raised - - if the result is finite and nonzero and errno == ERANGE then - OverflowError is raised - - The last rule is used to catch overflow on platforms which follow - C89 but for which HUGE_VAL is not an infinity. - - For the majority of one-argument functions these rules are enough - to ensure that Python's functions behave as specified in 'Annex F' - of the C99 standard, with the 'invalid' and 'divide-by-zero' - floating-point exceptions mapping to Python's ValueError and the - 'overflow' floating-point exception mapping to OverflowError. - math_1 only works for functions that don't have singularities *and* - the possibility of overflow; fortunately, that covers everything we - care about right now. -*/ - -static PyObject * -math_1_to_whatever(PyObject *arg, double (*func) (double), - PyObject *(*from_double_func) (double), - int can_overflow) -{ - double x, r; - x = PyFloat_AsDouble(arg); - if (x == -1.0 && PyErr_Occurred()) - return NULL; - errno = 0; - r = (*func)(x); - if (Py_IS_NAN(r) && !Py_IS_NAN(x)) { - PyErr_SetString(PyExc_ValueError, - "math domain error"); /* invalid arg */ - return NULL; - } - if (Py_IS_INFINITY(r) && Py_IS_FINITE(x)) { - if (can_overflow) - PyErr_SetString(PyExc_OverflowError, - "math range error"); /* overflow */ - else - PyErr_SetString(PyExc_ValueError, - "math domain error"); /* singularity */ - return NULL; - } - if (Py_IS_FINITE(r) && errno && is_error(r)) - /* this branch unnecessary on most platforms */ - return NULL; - - return (*from_double_func)(r); -} - -/* variant of math_1, to be used when the function being wrapped is known to - set errno properly (that is, errno = EDOM for invalid or divide-by-zero, - errno = ERANGE for overflow). */ - -static PyObject * -math_1a(PyObject *arg, double (*func) (double)) -{ - double x, r; - x = PyFloat_AsDouble(arg); - if (x == -1.0 && PyErr_Occurred()) - return NULL; - errno = 0; - r = (*func)(x); - if (errno && is_error(r)) - return NULL; - return PyFloat_FromDouble(r); -} - -/* - math_2 is used to wrap a libm function f that takes two double - arguments and returns a double. - - The error reporting follows these rules, which are designed to do - the right thing on C89/C99 platforms and IEEE 754/non IEEE 754 - platforms. - - - a NaN result from non-NaN inputs causes ValueError to be raised - - an infinite result from finite inputs causes OverflowError to be - raised. - - if the result is finite and errno == EDOM then ValueError is - raised - - if the result is finite and nonzero and errno == ERANGE then - OverflowError is raised - - The last rule is used to catch overflow on platforms which follow - C89 but for which HUGE_VAL is not an infinity. - - For most two-argument functions (copysign, fmod, hypot, atan2) - these rules are enough to ensure that Python's functions behave as - specified in 'Annex F' of the C99 standard, with the 'invalid' and - 'divide-by-zero' floating-point exceptions mapping to Python's - ValueError and the 'overflow' floating-point exception mapping to - OverflowError. -*/ - -static PyObject * -math_1(PyObject *arg, double (*func) (double), int can_overflow) -{ - return math_1_to_whatever(arg, func, PyFloat_FromDouble, can_overflow); -} - -static PyObject * + result = 0; + else + PyErr_SetString(PyExc_OverflowError, + "math range error"); + } + else + /* Unexpected math error */ + PyErr_SetFromErrno(PyExc_ValueError); + return result; +} + +/* + math_1 is used to wrap a libm function f that takes a double + argument and returns a double. + + The error reporting follows these rules, which are designed to do + the right thing on C89/C99 platforms and IEEE 754/non IEEE 754 + platforms. + + - a NaN result from non-NaN inputs causes ValueError to be raised + - an infinite result from finite inputs causes OverflowError to be + raised if can_overflow is 1, or raises ValueError if can_overflow + is 0. + - if the result is finite and errno == EDOM then ValueError is + raised + - if the result is finite and nonzero and errno == ERANGE then + OverflowError is raised + + The last rule is used to catch overflow on platforms which follow + C89 but for which HUGE_VAL is not an infinity. + + For the majority of one-argument functions these rules are enough + to ensure that Python's functions behave as specified in 'Annex F' + of the C99 standard, with the 'invalid' and 'divide-by-zero' + floating-point exceptions mapping to Python's ValueError and the + 'overflow' floating-point exception mapping to OverflowError. + math_1 only works for functions that don't have singularities *and* + the possibility of overflow; fortunately, that covers everything we + care about right now. +*/ + +static PyObject * +math_1_to_whatever(PyObject *arg, double (*func) (double), + PyObject *(*from_double_func) (double), + int can_overflow) +{ + double x, r; + x = PyFloat_AsDouble(arg); + if (x == -1.0 && PyErr_Occurred()) + return NULL; + errno = 0; + r = (*func)(x); + if (Py_IS_NAN(r) && !Py_IS_NAN(x)) { + PyErr_SetString(PyExc_ValueError, + "math domain error"); /* invalid arg */ + return NULL; + } + if (Py_IS_INFINITY(r) && Py_IS_FINITE(x)) { + if (can_overflow) + PyErr_SetString(PyExc_OverflowError, + "math range error"); /* overflow */ + else + PyErr_SetString(PyExc_ValueError, + "math domain error"); /* singularity */ + return NULL; + } + if (Py_IS_FINITE(r) && errno && is_error(r)) + /* this branch unnecessary on most platforms */ + return NULL; + + return (*from_double_func)(r); +} + +/* variant of math_1, to be used when the function being wrapped is known to + set errno properly (that is, errno = EDOM for invalid or divide-by-zero, + errno = ERANGE for overflow). */ + +static PyObject * +math_1a(PyObject *arg, double (*func) (double)) +{ + double x, r; + x = PyFloat_AsDouble(arg); + if (x == -1.0 && PyErr_Occurred()) + return NULL; + errno = 0; + r = (*func)(x); + if (errno && is_error(r)) + return NULL; + return PyFloat_FromDouble(r); +} + +/* + math_2 is used to wrap a libm function f that takes two double + arguments and returns a double. + + The error reporting follows these rules, which are designed to do + the right thing on C89/C99 platforms and IEEE 754/non IEEE 754 + platforms. + + - a NaN result from non-NaN inputs causes ValueError to be raised + - an infinite result from finite inputs causes OverflowError to be + raised. + - if the result is finite and errno == EDOM then ValueError is + raised + - if the result is finite and nonzero and errno == ERANGE then + OverflowError is raised + + The last rule is used to catch overflow on platforms which follow + C89 but for which HUGE_VAL is not an infinity. + + For most two-argument functions (copysign, fmod, hypot, atan2) + these rules are enough to ensure that Python's functions behave as + specified in 'Annex F' of the C99 standard, with the 'invalid' and + 'divide-by-zero' floating-point exceptions mapping to Python's + ValueError and the 'overflow' floating-point exception mapping to + OverflowError. +*/ + +static PyObject * +math_1(PyObject *arg, double (*func) (double), int can_overflow) +{ + return math_1_to_whatever(arg, func, PyFloat_FromDouble, can_overflow); +} + +static PyObject * math_2(PyObject *const *args, Py_ssize_t nargs, double (*func) (double, double), const char *funcname) -{ - double x, y, r; +{ + double x, y, r; if (!_PyArg_CheckPositional(funcname, nargs, 2, 2)) - return NULL; + return NULL; x = PyFloat_AsDouble(args[0]); if (x == -1.0 && PyErr_Occurred()) { - return NULL; + return NULL; } y = PyFloat_AsDouble(args[1]); if (y == -1.0 && PyErr_Occurred()) { return NULL; } - errno = 0; - r = (*func)(x, y); - if (Py_IS_NAN(r)) { - if (!Py_IS_NAN(x) && !Py_IS_NAN(y)) - errno = EDOM; - else - errno = 0; - } - else if (Py_IS_INFINITY(r)) { - if (Py_IS_FINITE(x) && Py_IS_FINITE(y)) - errno = ERANGE; - else - errno = 0; - } - if (errno && is_error(r)) - return NULL; - else - return PyFloat_FromDouble(r); -} - -#define FUNC1(funcname, func, can_overflow, docstring) \ - static PyObject * math_##funcname(PyObject *self, PyObject *args) { \ - return math_1(args, func, can_overflow); \ - }\ - PyDoc_STRVAR(math_##funcname##_doc, docstring); - -#define FUNC1A(funcname, func, docstring) \ - static PyObject * math_##funcname(PyObject *self, PyObject *args) { \ - return math_1a(args, func); \ - }\ - PyDoc_STRVAR(math_##funcname##_doc, docstring); - -#define FUNC2(funcname, func, docstring) \ + errno = 0; + r = (*func)(x, y); + if (Py_IS_NAN(r)) { + if (!Py_IS_NAN(x) && !Py_IS_NAN(y)) + errno = EDOM; + else + errno = 0; + } + else if (Py_IS_INFINITY(r)) { + if (Py_IS_FINITE(x) && Py_IS_FINITE(y)) + errno = ERANGE; + else + errno = 0; + } + if (errno && is_error(r)) + return NULL; + else + return PyFloat_FromDouble(r); +} + +#define FUNC1(funcname, func, can_overflow, docstring) \ + static PyObject * math_##funcname(PyObject *self, PyObject *args) { \ + return math_1(args, func, can_overflow); \ + }\ + PyDoc_STRVAR(math_##funcname##_doc, docstring); + +#define FUNC1A(funcname, func, docstring) \ + static PyObject * math_##funcname(PyObject *self, PyObject *args) { \ + return math_1a(args, func); \ + }\ + PyDoc_STRVAR(math_##funcname##_doc, docstring); + +#define FUNC2(funcname, func, docstring) \ static PyObject * math_##funcname(PyObject *self, PyObject *const *args, Py_ssize_t nargs) { \ return math_2(args, nargs, func, #funcname); \ - }\ - PyDoc_STRVAR(math_##funcname##_doc, docstring); - -FUNC1(acos, acos, 0, - "acos($module, x, /)\n--\n\n" + }\ + PyDoc_STRVAR(math_##funcname##_doc, docstring); + +FUNC1(acos, acos, 0, + "acos($module, x, /)\n--\n\n" "Return the arc cosine (measured in radians) of x.\n\n" "The result is between 0 and pi.") -FUNC1(acosh, m_acosh, 0, - "acosh($module, x, /)\n--\n\n" - "Return the inverse hyperbolic cosine of x.") -FUNC1(asin, asin, 0, - "asin($module, x, /)\n--\n\n" +FUNC1(acosh, m_acosh, 0, + "acosh($module, x, /)\n--\n\n" + "Return the inverse hyperbolic cosine of x.") +FUNC1(asin, asin, 0, + "asin($module, x, /)\n--\n\n" "Return the arc sine (measured in radians) of x.\n\n" "The result is between -pi/2 and pi/2.") -FUNC1(asinh, m_asinh, 0, - "asinh($module, x, /)\n--\n\n" - "Return the inverse hyperbolic sine of x.") -FUNC1(atan, atan, 0, - "atan($module, x, /)\n--\n\n" +FUNC1(asinh, m_asinh, 0, + "asinh($module, x, /)\n--\n\n" + "Return the inverse hyperbolic sine of x.") +FUNC1(atan, atan, 0, + "atan($module, x, /)\n--\n\n" "Return the arc tangent (measured in radians) of x.\n\n" "The result is between -pi/2 and pi/2.") -FUNC2(atan2, m_atan2, - "atan2($module, y, x, /)\n--\n\n" - "Return the arc tangent (measured in radians) of y/x.\n\n" - "Unlike atan(y/x), the signs of both x and y are considered.") -FUNC1(atanh, m_atanh, 0, - "atanh($module, x, /)\n--\n\n" - "Return the inverse hyperbolic tangent of x.") - -/*[clinic input] -math.ceil - - x as number: object - / - -Return the ceiling of x as an Integral. - -This is the smallest integer >= x. -[clinic start generated code]*/ - -static PyObject * -math_ceil(PyObject *module, PyObject *number) -/*[clinic end generated code: output=6c3b8a78bc201c67 input=2725352806399cab]*/ -{ - _Py_IDENTIFIER(__ceil__); - +FUNC2(atan2, m_atan2, + "atan2($module, y, x, /)\n--\n\n" + "Return the arc tangent (measured in radians) of y/x.\n\n" + "Unlike atan(y/x), the signs of both x and y are considered.") +FUNC1(atanh, m_atanh, 0, + "atanh($module, x, /)\n--\n\n" + "Return the inverse hyperbolic tangent of x.") + +/*[clinic input] +math.ceil + + x as number: object + / + +Return the ceiling of x as an Integral. + +This is the smallest integer >= x. +[clinic start generated code]*/ + +static PyObject * +math_ceil(PyObject *module, PyObject *number) +/*[clinic end generated code: output=6c3b8a78bc201c67 input=2725352806399cab]*/ +{ + _Py_IDENTIFIER(__ceil__); + if (!PyFloat_CheckExact(number)) { PyObject *method = _PyObject_LookupSpecial(number, &PyId___ceil__); if (method != NULL) { @@ -1205,64 +1205,64 @@ math_ceil(PyObject *module, PyObject *number) Py_DECREF(method); return result; } - if (PyErr_Occurred()) - return NULL; - } + if (PyErr_Occurred()) + return NULL; + } double x = PyFloat_AsDouble(number); if (x == -1.0 && PyErr_Occurred()) return NULL; return PyLong_FromDouble(ceil(x)); -} - -FUNC2(copysign, copysign, - "copysign($module, x, y, /)\n--\n\n" - "Return a float with the magnitude (absolute value) of x but the sign of y.\n\n" - "On platforms that support signed zeros, copysign(1.0, -0.0)\n" - "returns -1.0.\n") -FUNC1(cos, cos, 0, - "cos($module, x, /)\n--\n\n" - "Return the cosine of x (measured in radians).") -FUNC1(cosh, cosh, 1, - "cosh($module, x, /)\n--\n\n" - "Return the hyperbolic cosine of x.") -FUNC1A(erf, m_erf, - "erf($module, x, /)\n--\n\n" - "Error function at x.") -FUNC1A(erfc, m_erfc, - "erfc($module, x, /)\n--\n\n" - "Complementary error function at x.") -FUNC1(exp, exp, 1, - "exp($module, x, /)\n--\n\n" - "Return e raised to the power of x.") -FUNC1(expm1, m_expm1, 1, - "expm1($module, x, /)\n--\n\n" - "Return exp(x)-1.\n\n" - "This function avoids the loss of precision involved in the direct " - "evaluation of exp(x)-1 for small x.") -FUNC1(fabs, fabs, 0, - "fabs($module, x, /)\n--\n\n" - "Return the absolute value of the float x.") - -/*[clinic input] -math.floor - - x as number: object - / - -Return the floor of x as an Integral. - -This is the largest integer <= x. -[clinic start generated code]*/ - -static PyObject * -math_floor(PyObject *module, PyObject *number) -/*[clinic end generated code: output=c6a65c4884884b8a input=63af6b5d7ebcc3d6]*/ -{ +} + +FUNC2(copysign, copysign, + "copysign($module, x, y, /)\n--\n\n" + "Return a float with the magnitude (absolute value) of x but the sign of y.\n\n" + "On platforms that support signed zeros, copysign(1.0, -0.0)\n" + "returns -1.0.\n") +FUNC1(cos, cos, 0, + "cos($module, x, /)\n--\n\n" + "Return the cosine of x (measured in radians).") +FUNC1(cosh, cosh, 1, + "cosh($module, x, /)\n--\n\n" + "Return the hyperbolic cosine of x.") +FUNC1A(erf, m_erf, + "erf($module, x, /)\n--\n\n" + "Error function at x.") +FUNC1A(erfc, m_erfc, + "erfc($module, x, /)\n--\n\n" + "Complementary error function at x.") +FUNC1(exp, exp, 1, + "exp($module, x, /)\n--\n\n" + "Return e raised to the power of x.") +FUNC1(expm1, m_expm1, 1, + "expm1($module, x, /)\n--\n\n" + "Return exp(x)-1.\n\n" + "This function avoids the loss of precision involved in the direct " + "evaluation of exp(x)-1 for small x.") +FUNC1(fabs, fabs, 0, + "fabs($module, x, /)\n--\n\n" + "Return the absolute value of the float x.") + +/*[clinic input] +math.floor + + x as number: object + / + +Return the floor of x as an Integral. + +This is the largest integer <= x. +[clinic start generated code]*/ + +static PyObject * +math_floor(PyObject *module, PyObject *number) +/*[clinic end generated code: output=c6a65c4884884b8a input=63af6b5d7ebcc3d6]*/ +{ double x; - _Py_IDENTIFIER(__floor__); - + _Py_IDENTIFIER(__floor__); + if (PyFloat_CheckExact(number)) { x = PyFloat_AS_DOUBLE(number); } @@ -1274,286 +1274,286 @@ math_floor(PyObject *module, PyObject *number) Py_DECREF(method); return result; } - if (PyErr_Occurred()) - return NULL; + if (PyErr_Occurred()) + return NULL; x = PyFloat_AsDouble(number); if (x == -1.0 && PyErr_Occurred()) return NULL; - } + } return PyLong_FromDouble(floor(x)); -} - -FUNC1A(gamma, m_tgamma, - "gamma($module, x, /)\n--\n\n" - "Gamma function at x.") -FUNC1A(lgamma, m_lgamma, - "lgamma($module, x, /)\n--\n\n" - "Natural logarithm of absolute value of Gamma function at x.") -FUNC1(log1p, m_log1p, 0, - "log1p($module, x, /)\n--\n\n" - "Return the natural logarithm of 1+x (base e).\n\n" - "The result is computed in a way which is accurate for x near zero.") -FUNC2(remainder, m_remainder, - "remainder($module, x, y, /)\n--\n\n" - "Difference between x and the closest integer multiple of y.\n\n" - "Return x - n*y where n*y is the closest integer multiple of y.\n" - "In the case where x is exactly halfway between two multiples of\n" - "y, the nearest even value of n is used. The result is always exact.") -FUNC1(sin, sin, 0, - "sin($module, x, /)\n--\n\n" - "Return the sine of x (measured in radians).") -FUNC1(sinh, sinh, 1, - "sinh($module, x, /)\n--\n\n" - "Return the hyperbolic sine of x.") -FUNC1(sqrt, sqrt, 0, - "sqrt($module, x, /)\n--\n\n" - "Return the square root of x.") -FUNC1(tan, tan, 0, - "tan($module, x, /)\n--\n\n" - "Return the tangent of x (measured in radians).") -FUNC1(tanh, tanh, 0, - "tanh($module, x, /)\n--\n\n" - "Return the hyperbolic tangent of x.") - -/* Precision summation function as msum() by Raymond Hettinger in - <http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/393090>, - enhanced with the exact partials sum and roundoff from Mark - Dickinson's post at <http://bugs.python.org/file10357/msum4.py>. - See those links for more details, proofs and other references. - - Note 1: IEEE 754R floating point semantics are assumed, - but the current implementation does not re-establish special - value semantics across iterations (i.e. handling -Inf + Inf). - - Note 2: No provision is made for intermediate overflow handling; - therefore, sum([1e+308, 1e-308, 1e+308]) returns 1e+308 while - sum([1e+308, 1e+308, 1e-308]) raises an OverflowError due to the - overflow of the first partial sum. - - Note 3: The intermediate values lo, yr, and hi are declared volatile so - aggressive compilers won't algebraically reduce lo to always be exactly 0.0. - Also, the volatile declaration forces the values to be stored in memory as - regular doubles instead of extended long precision (80-bit) values. This - prevents double rounding because any addition or subtraction of two doubles - can be resolved exactly into double-sized hi and lo values. As long as the - hi value gets forced into a double before yr and lo are computed, the extra - bits in downstream extended precision operations (x87 for example) will be - exactly zero and therefore can be losslessly stored back into a double, - thereby preventing double rounding. - - Note 4: A similar implementation is in Modules/cmathmodule.c. - Be sure to update both when making changes. - - Note 5: The signature of math.fsum() differs from builtins.sum() - because the start argument doesn't make sense in the context of - accurate summation. Since the partials table is collapsed before - returning a result, sum(seq2, start=sum(seq1)) may not equal the - accurate result returned by sum(itertools.chain(seq1, seq2)). -*/ - -#define NUM_PARTIALS 32 /* initial partials array size, on stack */ - -/* Extend the partials array p[] by doubling its size. */ -static int /* non-zero on error */ -_fsum_realloc(double **p_ptr, Py_ssize_t n, - double *ps, Py_ssize_t *m_ptr) -{ - void *v = NULL; - Py_ssize_t m = *m_ptr; - - m += m; /* double */ - if (n < m && (size_t)m < ((size_t)PY_SSIZE_T_MAX / sizeof(double))) { - double *p = *p_ptr; - if (p == ps) { - v = PyMem_Malloc(sizeof(double) * m); - if (v != NULL) - memcpy(v, ps, sizeof(double) * n); - } - else - v = PyMem_Realloc(p, sizeof(double) * m); - } - if (v == NULL) { /* size overflow or no memory */ - PyErr_SetString(PyExc_MemoryError, "math.fsum partials"); - return 1; - } - *p_ptr = (double*) v; - *m_ptr = m; - return 0; -} - -/* Full precision summation of a sequence of floats. - - def msum(iterable): - partials = [] # sorted, non-overlapping partial sums - for x in iterable: - i = 0 - for y in partials: - if abs(x) < abs(y): - x, y = y, x - hi = x + y - lo = y - (hi - x) - if lo: - partials[i] = lo - i += 1 - x = hi - partials[i:] = [x] - return sum_exact(partials) - - Rounded x+y stored in hi with the roundoff stored in lo. Together hi+lo - are exactly equal to x+y. The inner loop applies hi/lo summation to each - partial so that the list of partial sums remains exact. - - Sum_exact() adds the partial sums exactly and correctly rounds the final - result (using the round-half-to-even rule). The items in partials remain - non-zero, non-special, non-overlapping and strictly increasing in - magnitude, but possibly not all having the same sign. - - Depends on IEEE 754 arithmetic guarantees and half-even rounding. -*/ - -/*[clinic input] -math.fsum - - seq: object - / - -Return an accurate floating point sum of values in the iterable seq. - -Assumes IEEE-754 floating point arithmetic. -[clinic start generated code]*/ - -static PyObject * -math_fsum(PyObject *module, PyObject *seq) -/*[clinic end generated code: output=ba5c672b87fe34fc input=c51b7d8caf6f6e82]*/ -{ - PyObject *item, *iter, *sum = NULL; - Py_ssize_t i, j, n = 0, m = NUM_PARTIALS; - double x, y, t, ps[NUM_PARTIALS], *p = ps; - double xsave, special_sum = 0.0, inf_sum = 0.0; - volatile double hi, yr, lo; - - iter = PyObject_GetIter(seq); - if (iter == NULL) - return NULL; - - for(;;) { /* for x in iterable */ - assert(0 <= n && n <= m); - assert((m == NUM_PARTIALS && p == ps) || - (m > NUM_PARTIALS && p != NULL)); - - item = PyIter_Next(iter); - if (item == NULL) { - if (PyErr_Occurred()) - goto _fsum_error; - break; - } +} + +FUNC1A(gamma, m_tgamma, + "gamma($module, x, /)\n--\n\n" + "Gamma function at x.") +FUNC1A(lgamma, m_lgamma, + "lgamma($module, x, /)\n--\n\n" + "Natural logarithm of absolute value of Gamma function at x.") +FUNC1(log1p, m_log1p, 0, + "log1p($module, x, /)\n--\n\n" + "Return the natural logarithm of 1+x (base e).\n\n" + "The result is computed in a way which is accurate for x near zero.") +FUNC2(remainder, m_remainder, + "remainder($module, x, y, /)\n--\n\n" + "Difference between x and the closest integer multiple of y.\n\n" + "Return x - n*y where n*y is the closest integer multiple of y.\n" + "In the case where x is exactly halfway between two multiples of\n" + "y, the nearest even value of n is used. The result is always exact.") +FUNC1(sin, sin, 0, + "sin($module, x, /)\n--\n\n" + "Return the sine of x (measured in radians).") +FUNC1(sinh, sinh, 1, + "sinh($module, x, /)\n--\n\n" + "Return the hyperbolic sine of x.") +FUNC1(sqrt, sqrt, 0, + "sqrt($module, x, /)\n--\n\n" + "Return the square root of x.") +FUNC1(tan, tan, 0, + "tan($module, x, /)\n--\n\n" + "Return the tangent of x (measured in radians).") +FUNC1(tanh, tanh, 0, + "tanh($module, x, /)\n--\n\n" + "Return the hyperbolic tangent of x.") + +/* Precision summation function as msum() by Raymond Hettinger in + <http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/393090>, + enhanced with the exact partials sum and roundoff from Mark + Dickinson's post at <http://bugs.python.org/file10357/msum4.py>. + See those links for more details, proofs and other references. + + Note 1: IEEE 754R floating point semantics are assumed, + but the current implementation does not re-establish special + value semantics across iterations (i.e. handling -Inf + Inf). + + Note 2: No provision is made for intermediate overflow handling; + therefore, sum([1e+308, 1e-308, 1e+308]) returns 1e+308 while + sum([1e+308, 1e+308, 1e-308]) raises an OverflowError due to the + overflow of the first partial sum. + + Note 3: The intermediate values lo, yr, and hi are declared volatile so + aggressive compilers won't algebraically reduce lo to always be exactly 0.0. + Also, the volatile declaration forces the values to be stored in memory as + regular doubles instead of extended long precision (80-bit) values. This + prevents double rounding because any addition or subtraction of two doubles + can be resolved exactly into double-sized hi and lo values. As long as the + hi value gets forced into a double before yr and lo are computed, the extra + bits in downstream extended precision operations (x87 for example) will be + exactly zero and therefore can be losslessly stored back into a double, + thereby preventing double rounding. + + Note 4: A similar implementation is in Modules/cmathmodule.c. + Be sure to update both when making changes. + + Note 5: The signature of math.fsum() differs from builtins.sum() + because the start argument doesn't make sense in the context of + accurate summation. Since the partials table is collapsed before + returning a result, sum(seq2, start=sum(seq1)) may not equal the + accurate result returned by sum(itertools.chain(seq1, seq2)). +*/ + +#define NUM_PARTIALS 32 /* initial partials array size, on stack */ + +/* Extend the partials array p[] by doubling its size. */ +static int /* non-zero on error */ +_fsum_realloc(double **p_ptr, Py_ssize_t n, + double *ps, Py_ssize_t *m_ptr) +{ + void *v = NULL; + Py_ssize_t m = *m_ptr; + + m += m; /* double */ + if (n < m && (size_t)m < ((size_t)PY_SSIZE_T_MAX / sizeof(double))) { + double *p = *p_ptr; + if (p == ps) { + v = PyMem_Malloc(sizeof(double) * m); + if (v != NULL) + memcpy(v, ps, sizeof(double) * n); + } + else + v = PyMem_Realloc(p, sizeof(double) * m); + } + if (v == NULL) { /* size overflow or no memory */ + PyErr_SetString(PyExc_MemoryError, "math.fsum partials"); + return 1; + } + *p_ptr = (double*) v; + *m_ptr = m; + return 0; +} + +/* Full precision summation of a sequence of floats. + + def msum(iterable): + partials = [] # sorted, non-overlapping partial sums + for x in iterable: + i = 0 + for y in partials: + if abs(x) < abs(y): + x, y = y, x + hi = x + y + lo = y - (hi - x) + if lo: + partials[i] = lo + i += 1 + x = hi + partials[i:] = [x] + return sum_exact(partials) + + Rounded x+y stored in hi with the roundoff stored in lo. Together hi+lo + are exactly equal to x+y. The inner loop applies hi/lo summation to each + partial so that the list of partial sums remains exact. + + Sum_exact() adds the partial sums exactly and correctly rounds the final + result (using the round-half-to-even rule). The items in partials remain + non-zero, non-special, non-overlapping and strictly increasing in + magnitude, but possibly not all having the same sign. + + Depends on IEEE 754 arithmetic guarantees and half-even rounding. +*/ + +/*[clinic input] +math.fsum + + seq: object + / + +Return an accurate floating point sum of values in the iterable seq. + +Assumes IEEE-754 floating point arithmetic. +[clinic start generated code]*/ + +static PyObject * +math_fsum(PyObject *module, PyObject *seq) +/*[clinic end generated code: output=ba5c672b87fe34fc input=c51b7d8caf6f6e82]*/ +{ + PyObject *item, *iter, *sum = NULL; + Py_ssize_t i, j, n = 0, m = NUM_PARTIALS; + double x, y, t, ps[NUM_PARTIALS], *p = ps; + double xsave, special_sum = 0.0, inf_sum = 0.0; + volatile double hi, yr, lo; + + iter = PyObject_GetIter(seq); + if (iter == NULL) + return NULL; + + for(;;) { /* for x in iterable */ + assert(0 <= n && n <= m); + assert((m == NUM_PARTIALS && p == ps) || + (m > NUM_PARTIALS && p != NULL)); + + item = PyIter_Next(iter); + if (item == NULL) { + if (PyErr_Occurred()) + goto _fsum_error; + break; + } ASSIGN_DOUBLE(x, item, error_with_item); - Py_DECREF(item); - - xsave = x; - for (i = j = 0; j < n; j++) { /* for y in partials */ - y = p[j]; - if (fabs(x) < fabs(y)) { - t = x; x = y; y = t; - } - hi = x + y; - yr = hi - x; - lo = y - yr; - if (lo != 0.0) - p[i++] = lo; - x = hi; - } - - n = i; /* ps[i:] = [x] */ - if (x != 0.0) { - if (! Py_IS_FINITE(x)) { - /* a nonfinite x could arise either as - a result of intermediate overflow, or - as a result of a nan or inf in the - summands */ - if (Py_IS_FINITE(xsave)) { - PyErr_SetString(PyExc_OverflowError, - "intermediate overflow in fsum"); - goto _fsum_error; - } - if (Py_IS_INFINITY(xsave)) - inf_sum += xsave; - special_sum += xsave; - /* reset partials */ - n = 0; - } - else if (n >= m && _fsum_realloc(&p, n, ps, &m)) - goto _fsum_error; - else - p[n++] = x; - } - } - - if (special_sum != 0.0) { - if (Py_IS_NAN(inf_sum)) - PyErr_SetString(PyExc_ValueError, - "-inf + inf in fsum"); - else - sum = PyFloat_FromDouble(special_sum); - goto _fsum_error; - } - - hi = 0.0; - if (n > 0) { - hi = p[--n]; - /* sum_exact(ps, hi) from the top, stop when the sum becomes - inexact. */ - while (n > 0) { - x = hi; - y = p[--n]; - assert(fabs(y) < fabs(x)); - hi = x + y; - yr = hi - x; - lo = y - yr; - if (lo != 0.0) - break; - } - /* Make half-even rounding work across multiple partials. - Needed so that sum([1e-16, 1, 1e16]) will round-up the last - digit to two instead of down to zero (the 1e-16 makes the 1 - slightly closer to two). With a potential 1 ULP rounding - error fixed-up, math.fsum() can guarantee commutativity. */ - if (n > 0 && ((lo < 0.0 && p[n-1] < 0.0) || - (lo > 0.0 && p[n-1] > 0.0))) { - y = lo * 2.0; - x = hi + y; - yr = x - hi; - if (y == yr) - hi = x; - } - } - sum = PyFloat_FromDouble(hi); - + Py_DECREF(item); + + xsave = x; + for (i = j = 0; j < n; j++) { /* for y in partials */ + y = p[j]; + if (fabs(x) < fabs(y)) { + t = x; x = y; y = t; + } + hi = x + y; + yr = hi - x; + lo = y - yr; + if (lo != 0.0) + p[i++] = lo; + x = hi; + } + + n = i; /* ps[i:] = [x] */ + if (x != 0.0) { + if (! Py_IS_FINITE(x)) { + /* a nonfinite x could arise either as + a result of intermediate overflow, or + as a result of a nan or inf in the + summands */ + if (Py_IS_FINITE(xsave)) { + PyErr_SetString(PyExc_OverflowError, + "intermediate overflow in fsum"); + goto _fsum_error; + } + if (Py_IS_INFINITY(xsave)) + inf_sum += xsave; + special_sum += xsave; + /* reset partials */ + n = 0; + } + else if (n >= m && _fsum_realloc(&p, n, ps, &m)) + goto _fsum_error; + else + p[n++] = x; + } + } + + if (special_sum != 0.0) { + if (Py_IS_NAN(inf_sum)) + PyErr_SetString(PyExc_ValueError, + "-inf + inf in fsum"); + else + sum = PyFloat_FromDouble(special_sum); + goto _fsum_error; + } + + hi = 0.0; + if (n > 0) { + hi = p[--n]; + /* sum_exact(ps, hi) from the top, stop when the sum becomes + inexact. */ + while (n > 0) { + x = hi; + y = p[--n]; + assert(fabs(y) < fabs(x)); + hi = x + y; + yr = hi - x; + lo = y - yr; + if (lo != 0.0) + break; + } + /* Make half-even rounding work across multiple partials. + Needed so that sum([1e-16, 1, 1e16]) will round-up the last + digit to two instead of down to zero (the 1e-16 makes the 1 + slightly closer to two). With a potential 1 ULP rounding + error fixed-up, math.fsum() can guarantee commutativity. */ + if (n > 0 && ((lo < 0.0 && p[n-1] < 0.0) || + (lo > 0.0 && p[n-1] > 0.0))) { + y = lo * 2.0; + x = hi + y; + yr = x - hi; + if (y == yr) + hi = x; + } + } + sum = PyFloat_FromDouble(hi); + _fsum_error: - Py_DECREF(iter); - if (p != ps) - PyMem_Free(p); - return sum; + Py_DECREF(iter); + if (p != ps) + PyMem_Free(p); + return sum; error_with_item: Py_DECREF(item); goto _fsum_error; -} - -#undef NUM_PARTIALS - - -static unsigned long -count_set_bits(unsigned long n) -{ - unsigned long count = 0; - while (n != 0) { - ++count; - n &= n - 1; /* clear least significant bit */ - } - return count; -} - +} + +#undef NUM_PARTIALS + + +static unsigned long +count_set_bits(unsigned long n) +{ + unsigned long count = 0; + while (n != 0) { + ++count; + n &= n - 1; /* clear least significant bit */ + } + return count; +} + /* Integer square root Given a nonnegative integer `n`, we want to compute the largest integer @@ -1851,233 +1851,233 @@ math_isqrt(PyObject *module, PyObject *n) return NULL; } -/* Divide-and-conquer factorial algorithm - * - * Based on the formula and pseudo-code provided at: - * http://www.luschny.de/math/factorial/binarysplitfact.html - * - * Faster algorithms exist, but they're more complicated and depend on - * a fast prime factorization algorithm. - * - * Notes on the algorithm - * ---------------------- - * - * factorial(n) is written in the form 2**k * m, with m odd. k and m are - * computed separately, and then combined using a left shift. - * - * The function factorial_odd_part computes the odd part m (i.e., the greatest - * odd divisor) of factorial(n), using the formula: - * - * factorial_odd_part(n) = - * - * product_{i >= 0} product_{0 < j <= n / 2**i, j odd} j - * - * Example: factorial_odd_part(20) = - * - * (1) * - * (1) * - * (1 * 3 * 5) * - * (1 * 3 * 5 * 7 * 9) - * (1 * 3 * 5 * 7 * 9 * 11 * 13 * 15 * 17 * 19) - * - * Here i goes from large to small: the first term corresponds to i=4 (any - * larger i gives an empty product), and the last term corresponds to i=0. - * Each term can be computed from the last by multiplying by the extra odd - * numbers required: e.g., to get from the penultimate term to the last one, - * we multiply by (11 * 13 * 15 * 17 * 19). - * - * To see a hint of why this formula works, here are the same numbers as above - * but with the even parts (i.e., the appropriate powers of 2) included. For - * each subterm in the product for i, we multiply that subterm by 2**i: - * - * factorial(20) = - * - * (16) * - * (8) * - * (4 * 12 * 20) * - * (2 * 6 * 10 * 14 * 18) * - * (1 * 3 * 5 * 7 * 9 * 11 * 13 * 15 * 17 * 19) - * - * The factorial_partial_product function computes the product of all odd j in - * range(start, stop) for given start and stop. It's used to compute the - * partial products like (11 * 13 * 15 * 17 * 19) in the example above. It - * operates recursively, repeatedly splitting the range into two roughly equal - * pieces until the subranges are small enough to be computed using only C - * integer arithmetic. - * - * The two-valuation k (i.e., the exponent of the largest power of 2 dividing - * the factorial) is computed independently in the main math_factorial - * function. By standard results, its value is: - * - * two_valuation = n//2 + n//4 + n//8 + .... - * - * It can be shown (e.g., by complete induction on n) that two_valuation is - * equal to n - count_set_bits(n), where count_set_bits(n) gives the number of - * '1'-bits in the binary expansion of n. - */ - -/* factorial_partial_product: Compute product(range(start, stop, 2)) using - * divide and conquer. Assumes start and stop are odd and stop > start. - * max_bits must be >= bit_length(stop - 2). */ - -static PyObject * -factorial_partial_product(unsigned long start, unsigned long stop, - unsigned long max_bits) -{ - unsigned long midpoint, num_operands; - PyObject *left = NULL, *right = NULL, *result = NULL; - - /* If the return value will fit an unsigned long, then we can - * multiply in a tight, fast loop where each multiply is O(1). - * Compute an upper bound on the number of bits required to store - * the answer. - * - * Storing some integer z requires floor(lg(z))+1 bits, which is - * conveniently the value returned by bit_length(z). The - * product x*y will require at most - * bit_length(x) + bit_length(y) bits to store, based - * on the idea that lg product = lg x + lg y. - * - * We know that stop - 2 is the largest number to be multiplied. From - * there, we have: bit_length(answer) <= num_operands * - * bit_length(stop - 2) - */ - - num_operands = (stop - start) / 2; - /* The "num_operands <= 8 * SIZEOF_LONG" check guards against the - * unlikely case of an overflow in num_operands * max_bits. */ - if (num_operands <= 8 * SIZEOF_LONG && - num_operands * max_bits <= 8 * SIZEOF_LONG) { - unsigned long j, total; - for (total = start, j = start + 2; j < stop; j += 2) - total *= j; - return PyLong_FromUnsignedLong(total); - } - - /* find midpoint of range(start, stop), rounded up to next odd number. */ - midpoint = (start + num_operands) | 1; - left = factorial_partial_product(start, midpoint, +/* Divide-and-conquer factorial algorithm + * + * Based on the formula and pseudo-code provided at: + * http://www.luschny.de/math/factorial/binarysplitfact.html + * + * Faster algorithms exist, but they're more complicated and depend on + * a fast prime factorization algorithm. + * + * Notes on the algorithm + * ---------------------- + * + * factorial(n) is written in the form 2**k * m, with m odd. k and m are + * computed separately, and then combined using a left shift. + * + * The function factorial_odd_part computes the odd part m (i.e., the greatest + * odd divisor) of factorial(n), using the formula: + * + * factorial_odd_part(n) = + * + * product_{i >= 0} product_{0 < j <= n / 2**i, j odd} j + * + * Example: factorial_odd_part(20) = + * + * (1) * + * (1) * + * (1 * 3 * 5) * + * (1 * 3 * 5 * 7 * 9) + * (1 * 3 * 5 * 7 * 9 * 11 * 13 * 15 * 17 * 19) + * + * Here i goes from large to small: the first term corresponds to i=4 (any + * larger i gives an empty product), and the last term corresponds to i=0. + * Each term can be computed from the last by multiplying by the extra odd + * numbers required: e.g., to get from the penultimate term to the last one, + * we multiply by (11 * 13 * 15 * 17 * 19). + * + * To see a hint of why this formula works, here are the same numbers as above + * but with the even parts (i.e., the appropriate powers of 2) included. For + * each subterm in the product for i, we multiply that subterm by 2**i: + * + * factorial(20) = + * + * (16) * + * (8) * + * (4 * 12 * 20) * + * (2 * 6 * 10 * 14 * 18) * + * (1 * 3 * 5 * 7 * 9 * 11 * 13 * 15 * 17 * 19) + * + * The factorial_partial_product function computes the product of all odd j in + * range(start, stop) for given start and stop. It's used to compute the + * partial products like (11 * 13 * 15 * 17 * 19) in the example above. It + * operates recursively, repeatedly splitting the range into two roughly equal + * pieces until the subranges are small enough to be computed using only C + * integer arithmetic. + * + * The two-valuation k (i.e., the exponent of the largest power of 2 dividing + * the factorial) is computed independently in the main math_factorial + * function. By standard results, its value is: + * + * two_valuation = n//2 + n//4 + n//8 + .... + * + * It can be shown (e.g., by complete induction on n) that two_valuation is + * equal to n - count_set_bits(n), where count_set_bits(n) gives the number of + * '1'-bits in the binary expansion of n. + */ + +/* factorial_partial_product: Compute product(range(start, stop, 2)) using + * divide and conquer. Assumes start and stop are odd and stop > start. + * max_bits must be >= bit_length(stop - 2). */ + +static PyObject * +factorial_partial_product(unsigned long start, unsigned long stop, + unsigned long max_bits) +{ + unsigned long midpoint, num_operands; + PyObject *left = NULL, *right = NULL, *result = NULL; + + /* If the return value will fit an unsigned long, then we can + * multiply in a tight, fast loop where each multiply is O(1). + * Compute an upper bound on the number of bits required to store + * the answer. + * + * Storing some integer z requires floor(lg(z))+1 bits, which is + * conveniently the value returned by bit_length(z). The + * product x*y will require at most + * bit_length(x) + bit_length(y) bits to store, based + * on the idea that lg product = lg x + lg y. + * + * We know that stop - 2 is the largest number to be multiplied. From + * there, we have: bit_length(answer) <= num_operands * + * bit_length(stop - 2) + */ + + num_operands = (stop - start) / 2; + /* The "num_operands <= 8 * SIZEOF_LONG" check guards against the + * unlikely case of an overflow in num_operands * max_bits. */ + if (num_operands <= 8 * SIZEOF_LONG && + num_operands * max_bits <= 8 * SIZEOF_LONG) { + unsigned long j, total; + for (total = start, j = start + 2; j < stop; j += 2) + total *= j; + return PyLong_FromUnsignedLong(total); + } + + /* find midpoint of range(start, stop), rounded up to next odd number. */ + midpoint = (start + num_operands) | 1; + left = factorial_partial_product(start, midpoint, _Py_bit_length(midpoint - 2)); - if (left == NULL) - goto error; - right = factorial_partial_product(midpoint, stop, max_bits); - if (right == NULL) - goto error; - result = PyNumber_Multiply(left, right); - - error: - Py_XDECREF(left); - Py_XDECREF(right); - return result; -} - -/* factorial_odd_part: compute the odd part of factorial(n). */ - -static PyObject * -factorial_odd_part(unsigned long n) -{ - long i; - unsigned long v, lower, upper; - PyObject *partial, *tmp, *inner, *outer; - - inner = PyLong_FromLong(1); - if (inner == NULL) - return NULL; - outer = inner; - Py_INCREF(outer); - - upper = 3; + if (left == NULL) + goto error; + right = factorial_partial_product(midpoint, stop, max_bits); + if (right == NULL) + goto error; + result = PyNumber_Multiply(left, right); + + error: + Py_XDECREF(left); + Py_XDECREF(right); + return result; +} + +/* factorial_odd_part: compute the odd part of factorial(n). */ + +static PyObject * +factorial_odd_part(unsigned long n) +{ + long i; + unsigned long v, lower, upper; + PyObject *partial, *tmp, *inner, *outer; + + inner = PyLong_FromLong(1); + if (inner == NULL) + return NULL; + outer = inner; + Py_INCREF(outer); + + upper = 3; for (i = _Py_bit_length(n) - 2; i >= 0; i--) { - v = n >> i; - if (v <= 2) - continue; - lower = upper; - /* (v + 1) | 1 = least odd integer strictly larger than n / 2**i */ - upper = (v + 1) | 1; - /* Here inner is the product of all odd integers j in the range (0, - n/2**(i+1)]. The factorial_partial_product call below gives the - product of all odd integers j in the range (n/2**(i+1), n/2**i]. */ + v = n >> i; + if (v <= 2) + continue; + lower = upper; + /* (v + 1) | 1 = least odd integer strictly larger than n / 2**i */ + upper = (v + 1) | 1; + /* Here inner is the product of all odd integers j in the range (0, + n/2**(i+1)]. The factorial_partial_product call below gives the + product of all odd integers j in the range (n/2**(i+1), n/2**i]. */ partial = factorial_partial_product(lower, upper, _Py_bit_length(upper-2)); - /* inner *= partial */ - if (partial == NULL) - goto error; - tmp = PyNumber_Multiply(inner, partial); - Py_DECREF(partial); - if (tmp == NULL) - goto error; - Py_DECREF(inner); - inner = tmp; - /* Now inner is the product of all odd integers j in the range (0, - n/2**i], giving the inner product in the formula above. */ - - /* outer *= inner; */ - tmp = PyNumber_Multiply(outer, inner); - if (tmp == NULL) - goto error; - Py_DECREF(outer); - outer = tmp; - } - Py_DECREF(inner); - return outer; - - error: - Py_DECREF(outer); - Py_DECREF(inner); - return NULL; -} - - -/* Lookup table for small factorial values */ - -static const unsigned long SmallFactorials[] = { - 1, 1, 2, 6, 24, 120, 720, 5040, 40320, - 362880, 3628800, 39916800, 479001600, -#if SIZEOF_LONG >= 8 - 6227020800, 87178291200, 1307674368000, - 20922789888000, 355687428096000, 6402373705728000, - 121645100408832000, 2432902008176640000 -#endif -}; - -/*[clinic input] -math.factorial - - x as arg: object - / - -Find x!. - -Raise a ValueError if x is negative or non-integral. -[clinic start generated code]*/ - -static PyObject * -math_factorial(PyObject *module, PyObject *arg) -/*[clinic end generated code: output=6686f26fae00e9ca input=6d1c8105c0d91fb4]*/ -{ + /* inner *= partial */ + if (partial == NULL) + goto error; + tmp = PyNumber_Multiply(inner, partial); + Py_DECREF(partial); + if (tmp == NULL) + goto error; + Py_DECREF(inner); + inner = tmp; + /* Now inner is the product of all odd integers j in the range (0, + n/2**i], giving the inner product in the formula above. */ + + /* outer *= inner; */ + tmp = PyNumber_Multiply(outer, inner); + if (tmp == NULL) + goto error; + Py_DECREF(outer); + outer = tmp; + } + Py_DECREF(inner); + return outer; + + error: + Py_DECREF(outer); + Py_DECREF(inner); + return NULL; +} + + +/* Lookup table for small factorial values */ + +static const unsigned long SmallFactorials[] = { + 1, 1, 2, 6, 24, 120, 720, 5040, 40320, + 362880, 3628800, 39916800, 479001600, +#if SIZEOF_LONG >= 8 + 6227020800, 87178291200, 1307674368000, + 20922789888000, 355687428096000, 6402373705728000, + 121645100408832000, 2432902008176640000 +#endif +}; + +/*[clinic input] +math.factorial + + x as arg: object + / + +Find x!. + +Raise a ValueError if x is negative or non-integral. +[clinic start generated code]*/ + +static PyObject * +math_factorial(PyObject *module, PyObject *arg) +/*[clinic end generated code: output=6686f26fae00e9ca input=6d1c8105c0d91fb4]*/ +{ long x, two_valuation; - int overflow; + int overflow; PyObject *result, *odd_part, *pyint_form; - - if (PyFloat_Check(arg)) { + + if (PyFloat_Check(arg)) { if (PyErr_WarnEx(PyExc_DeprecationWarning, "Using factorial() with floats is deprecated", 1) < 0) { return NULL; } - PyObject *lx; - double dx = PyFloat_AS_DOUBLE((PyFloatObject *)arg); - if (!(Py_IS_FINITE(dx) && dx == floor(dx))) { - PyErr_SetString(PyExc_ValueError, - "factorial() only accepts integral values"); - return NULL; - } - lx = PyLong_FromDouble(dx); - if (lx == NULL) - return NULL; - x = PyLong_AsLongAndOverflow(lx, &overflow); - Py_DECREF(lx); - } + PyObject *lx; + double dx = PyFloat_AS_DOUBLE((PyFloatObject *)arg); + if (!(Py_IS_FINITE(dx) && dx == floor(dx))) { + PyErr_SetString(PyExc_ValueError, + "factorial() only accepts integral values"); + return NULL; + } + lx = PyLong_FromDouble(dx); + if (lx == NULL) + return NULL; + x = PyLong_AsLongAndOverflow(lx, &overflow); + Py_DECREF(lx); + } else { pyint_form = PyNumber_Index(arg); if (pyint_form == NULL) { @@ -2086,357 +2086,357 @@ math_factorial(PyObject *module, PyObject *arg) x = PyLong_AsLongAndOverflow(pyint_form, &overflow); Py_DECREF(pyint_form); } - - if (x == -1 && PyErr_Occurred()) { - return NULL; - } - else if (overflow == 1) { - PyErr_Format(PyExc_OverflowError, - "factorial() argument should not exceed %ld", - LONG_MAX); - return NULL; - } - else if (overflow == -1 || x < 0) { - PyErr_SetString(PyExc_ValueError, - "factorial() not defined for negative values"); - return NULL; - } - - /* use lookup table if x is small */ - if (x < (long)Py_ARRAY_LENGTH(SmallFactorials)) - return PyLong_FromUnsignedLong(SmallFactorials[x]); - - /* else express in the form odd_part * 2**two_valuation, and compute as - odd_part << two_valuation. */ - odd_part = factorial_odd_part(x); - if (odd_part == NULL) - return NULL; + + if (x == -1 && PyErr_Occurred()) { + return NULL; + } + else if (overflow == 1) { + PyErr_Format(PyExc_OverflowError, + "factorial() argument should not exceed %ld", + LONG_MAX); + return NULL; + } + else if (overflow == -1 || x < 0) { + PyErr_SetString(PyExc_ValueError, + "factorial() not defined for negative values"); + return NULL; + } + + /* use lookup table if x is small */ + if (x < (long)Py_ARRAY_LENGTH(SmallFactorials)) + return PyLong_FromUnsignedLong(SmallFactorials[x]); + + /* else express in the form odd_part * 2**two_valuation, and compute as + odd_part << two_valuation. */ + odd_part = factorial_odd_part(x); + if (odd_part == NULL) + return NULL; two_valuation = x - count_set_bits(x); result = _PyLong_Lshift(odd_part, two_valuation); - Py_DECREF(odd_part); - return result; -} - - -/*[clinic input] -math.trunc - - x: object - / - -Truncates the Real x to the nearest Integral toward 0. - -Uses the __trunc__ magic method. -[clinic start generated code]*/ - -static PyObject * -math_trunc(PyObject *module, PyObject *x) -/*[clinic end generated code: output=34b9697b707e1031 input=2168b34e0a09134d]*/ -{ - _Py_IDENTIFIER(__trunc__); - PyObject *trunc, *result; - + Py_DECREF(odd_part); + return result; +} + + +/*[clinic input] +math.trunc + + x: object + / + +Truncates the Real x to the nearest Integral toward 0. + +Uses the __trunc__ magic method. +[clinic start generated code]*/ + +static PyObject * +math_trunc(PyObject *module, PyObject *x) +/*[clinic end generated code: output=34b9697b707e1031 input=2168b34e0a09134d]*/ +{ + _Py_IDENTIFIER(__trunc__); + PyObject *trunc, *result; + if (PyFloat_CheckExact(x)) { return PyFloat_Type.tp_as_number->nb_int(x); } - if (Py_TYPE(x)->tp_dict == NULL) { - if (PyType_Ready(Py_TYPE(x)) < 0) - return NULL; - } - - trunc = _PyObject_LookupSpecial(x, &PyId___trunc__); - if (trunc == NULL) { - if (!PyErr_Occurred()) - PyErr_Format(PyExc_TypeError, - "type %.100s doesn't define __trunc__ method", - Py_TYPE(x)->tp_name); - return NULL; - } - result = _PyObject_CallNoArg(trunc); - Py_DECREF(trunc); - return result; -} - - -/*[clinic input] -math.frexp - - x: double - / - -Return the mantissa and exponent of x, as pair (m, e). - -m is a float and e is an int, such that x = m * 2.**e. -If x is 0, m and e are both 0. Else 0.5 <= abs(m) < 1.0. -[clinic start generated code]*/ - -static PyObject * -math_frexp_impl(PyObject *module, double x) -/*[clinic end generated code: output=03e30d252a15ad4a input=96251c9e208bc6e9]*/ -{ - int i; - /* deal with special cases directly, to sidestep platform - differences */ - if (Py_IS_NAN(x) || Py_IS_INFINITY(x) || !x) { - i = 0; - } - else { - x = frexp(x, &i); - } - return Py_BuildValue("(di)", x, i); -} - - -/*[clinic input] -math.ldexp - - x: double - i: object - / - -Return x * (2**i). - -This is essentially the inverse of frexp(). -[clinic start generated code]*/ - -static PyObject * -math_ldexp_impl(PyObject *module, double x, PyObject *i) -/*[clinic end generated code: output=b6892f3c2df9cc6a input=17d5970c1a40a8c1]*/ -{ - double r; - long exp; - int overflow; - - if (PyLong_Check(i)) { - /* on overflow, replace exponent with either LONG_MAX - or LONG_MIN, depending on the sign. */ - exp = PyLong_AsLongAndOverflow(i, &overflow); - if (exp == -1 && PyErr_Occurred()) - return NULL; - if (overflow) - exp = overflow < 0 ? LONG_MIN : LONG_MAX; - } - else { - PyErr_SetString(PyExc_TypeError, - "Expected an int as second argument to ldexp."); - return NULL; - } - - if (x == 0. || !Py_IS_FINITE(x)) { - /* NaNs, zeros and infinities are returned unchanged */ - r = x; - errno = 0; - } else if (exp > INT_MAX) { - /* overflow */ - r = copysign(Py_HUGE_VAL, x); - errno = ERANGE; - } else if (exp < INT_MIN) { - /* underflow to +-0 */ - r = copysign(0., x); - errno = 0; - } else { - errno = 0; - r = ldexp(x, (int)exp); - if (Py_IS_INFINITY(r)) - errno = ERANGE; - } - - if (errno && is_error(r)) - return NULL; - return PyFloat_FromDouble(r); -} - - -/*[clinic input] -math.modf - - x: double - / - -Return the fractional and integer parts of x. - -Both results carry the sign of x and are floats. -[clinic start generated code]*/ - -static PyObject * -math_modf_impl(PyObject *module, double x) -/*[clinic end generated code: output=90cee0260014c3c0 input=b4cfb6786afd9035]*/ -{ - double y; - /* some platforms don't do the right thing for NaNs and - infinities, so we take care of special cases directly. */ - if (!Py_IS_FINITE(x)) { - if (Py_IS_INFINITY(x)) - return Py_BuildValue("(dd)", copysign(0., x), x); - else if (Py_IS_NAN(x)) - return Py_BuildValue("(dd)", x, x); - } - - errno = 0; - x = modf(x, &y); - return Py_BuildValue("(dd)", x, y); -} - - -/* A decent logarithm is easy to compute even for huge ints, but libm can't - do that by itself -- loghelper can. func is log or log10, and name is - "log" or "log10". Note that overflow of the result isn't possible: an int - can contain no more than INT_MAX * SHIFT bits, so has value certainly less - than 2**(2**64 * 2**16) == 2**2**80, and log2 of that is 2**80, which is - small enough to fit in an IEEE single. log and log10 are even smaller. - However, intermediate overflow is possible for an int if the number of bits - in that int is larger than PY_SSIZE_T_MAX. */ - -static PyObject* -loghelper(PyObject* arg, double (*func)(double), const char *funcname) -{ - /* If it is int, do it ourselves. */ - if (PyLong_Check(arg)) { - double x, result; - Py_ssize_t e; - - /* Negative or zero inputs give a ValueError. */ - if (Py_SIZE(arg) <= 0) { - PyErr_SetString(PyExc_ValueError, - "math domain error"); - return NULL; - } - - x = PyLong_AsDouble(arg); - if (x == -1.0 && PyErr_Occurred()) { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - /* Here the conversion to double overflowed, but it's possible - to compute the log anyway. Clear the exception and continue. */ - PyErr_Clear(); - x = _PyLong_Frexp((PyLongObject *)arg, &e); - if (x == -1.0 && PyErr_Occurred()) - return NULL; - /* Value is ~= x * 2**e, so the log ~= log(x) + log(2) * e. */ - result = func(x) + func(2.0) * e; - } - else - /* Successfully converted x to a double. */ - result = func(x); - return PyFloat_FromDouble(result); - } - - /* Else let libm handle it by itself. */ - return math_1(arg, func, 0); -} - - -/*[clinic input] -math.log - - x: object - [ - base: object(c_default="NULL") = math.e - ] - / - -Return the logarithm of x to the given base. - -If the base not specified, returns the natural logarithm (base e) of x. -[clinic start generated code]*/ - -static PyObject * -math_log_impl(PyObject *module, PyObject *x, int group_right_1, - PyObject *base) -/*[clinic end generated code: output=7b5a39e526b73fc9 input=0f62d5726cbfebbd]*/ -{ - PyObject *num, *den; - PyObject *ans; - - num = loghelper(x, m_log, "log"); - if (num == NULL || base == NULL) - return num; - - den = loghelper(base, m_log, "log"); - if (den == NULL) { - Py_DECREF(num); - return NULL; - } - - ans = PyNumber_TrueDivide(num, den); - Py_DECREF(num); - Py_DECREF(den); - return ans; -} - - -/*[clinic input] -math.log2 - - x: object - / - -Return the base 2 logarithm of x. -[clinic start generated code]*/ - -static PyObject * -math_log2(PyObject *module, PyObject *x) -/*[clinic end generated code: output=5425899a4d5d6acb input=08321262bae4f39b]*/ -{ - return loghelper(x, m_log2, "log2"); -} - - -/*[clinic input] -math.log10 - - x: object - / - -Return the base 10 logarithm of x. -[clinic start generated code]*/ - -static PyObject * -math_log10(PyObject *module, PyObject *x) -/*[clinic end generated code: output=be72a64617df9c6f input=b2469d02c6469e53]*/ -{ - return loghelper(x, m_log10, "log10"); -} - - -/*[clinic input] -math.fmod - - x: double - y: double - / - -Return fmod(x, y), according to platform C. - -x % y may differ. -[clinic start generated code]*/ - -static PyObject * -math_fmod_impl(PyObject *module, double x, double y) -/*[clinic end generated code: output=7559d794343a27b5 input=4f84caa8cfc26a03]*/ -{ - double r; - /* fmod(x, +/-Inf) returns x for finite x. */ - if (Py_IS_INFINITY(y) && Py_IS_FINITE(x)) - return PyFloat_FromDouble(x); - errno = 0; - r = fmod(x, y); - if (Py_IS_NAN(r)) { - if (!Py_IS_NAN(x) && !Py_IS_NAN(y)) - errno = EDOM; - else - errno = 0; - } - if (errno && is_error(r)) - return NULL; - else - return PyFloat_FromDouble(r); -} - + if (Py_TYPE(x)->tp_dict == NULL) { + if (PyType_Ready(Py_TYPE(x)) < 0) + return NULL; + } + + trunc = _PyObject_LookupSpecial(x, &PyId___trunc__); + if (trunc == NULL) { + if (!PyErr_Occurred()) + PyErr_Format(PyExc_TypeError, + "type %.100s doesn't define __trunc__ method", + Py_TYPE(x)->tp_name); + return NULL; + } + result = _PyObject_CallNoArg(trunc); + Py_DECREF(trunc); + return result; +} + + +/*[clinic input] +math.frexp + + x: double + / + +Return the mantissa and exponent of x, as pair (m, e). + +m is a float and e is an int, such that x = m * 2.**e. +If x is 0, m and e are both 0. Else 0.5 <= abs(m) < 1.0. +[clinic start generated code]*/ + +static PyObject * +math_frexp_impl(PyObject *module, double x) +/*[clinic end generated code: output=03e30d252a15ad4a input=96251c9e208bc6e9]*/ +{ + int i; + /* deal with special cases directly, to sidestep platform + differences */ + if (Py_IS_NAN(x) || Py_IS_INFINITY(x) || !x) { + i = 0; + } + else { + x = frexp(x, &i); + } + return Py_BuildValue("(di)", x, i); +} + + +/*[clinic input] +math.ldexp + + x: double + i: object + / + +Return x * (2**i). + +This is essentially the inverse of frexp(). +[clinic start generated code]*/ + +static PyObject * +math_ldexp_impl(PyObject *module, double x, PyObject *i) +/*[clinic end generated code: output=b6892f3c2df9cc6a input=17d5970c1a40a8c1]*/ +{ + double r; + long exp; + int overflow; + + if (PyLong_Check(i)) { + /* on overflow, replace exponent with either LONG_MAX + or LONG_MIN, depending on the sign. */ + exp = PyLong_AsLongAndOverflow(i, &overflow); + if (exp == -1 && PyErr_Occurred()) + return NULL; + if (overflow) + exp = overflow < 0 ? LONG_MIN : LONG_MAX; + } + else { + PyErr_SetString(PyExc_TypeError, + "Expected an int as second argument to ldexp."); + return NULL; + } + + if (x == 0. || !Py_IS_FINITE(x)) { + /* NaNs, zeros and infinities are returned unchanged */ + r = x; + errno = 0; + } else if (exp > INT_MAX) { + /* overflow */ + r = copysign(Py_HUGE_VAL, x); + errno = ERANGE; + } else if (exp < INT_MIN) { + /* underflow to +-0 */ + r = copysign(0., x); + errno = 0; + } else { + errno = 0; + r = ldexp(x, (int)exp); + if (Py_IS_INFINITY(r)) + errno = ERANGE; + } + + if (errno && is_error(r)) + return NULL; + return PyFloat_FromDouble(r); +} + + +/*[clinic input] +math.modf + + x: double + / + +Return the fractional and integer parts of x. + +Both results carry the sign of x and are floats. +[clinic start generated code]*/ + +static PyObject * +math_modf_impl(PyObject *module, double x) +/*[clinic end generated code: output=90cee0260014c3c0 input=b4cfb6786afd9035]*/ +{ + double y; + /* some platforms don't do the right thing for NaNs and + infinities, so we take care of special cases directly. */ + if (!Py_IS_FINITE(x)) { + if (Py_IS_INFINITY(x)) + return Py_BuildValue("(dd)", copysign(0., x), x); + else if (Py_IS_NAN(x)) + return Py_BuildValue("(dd)", x, x); + } + + errno = 0; + x = modf(x, &y); + return Py_BuildValue("(dd)", x, y); +} + + +/* A decent logarithm is easy to compute even for huge ints, but libm can't + do that by itself -- loghelper can. func is log or log10, and name is + "log" or "log10". Note that overflow of the result isn't possible: an int + can contain no more than INT_MAX * SHIFT bits, so has value certainly less + than 2**(2**64 * 2**16) == 2**2**80, and log2 of that is 2**80, which is + small enough to fit in an IEEE single. log and log10 are even smaller. + However, intermediate overflow is possible for an int if the number of bits + in that int is larger than PY_SSIZE_T_MAX. */ + +static PyObject* +loghelper(PyObject* arg, double (*func)(double), const char *funcname) +{ + /* If it is int, do it ourselves. */ + if (PyLong_Check(arg)) { + double x, result; + Py_ssize_t e; + + /* Negative or zero inputs give a ValueError. */ + if (Py_SIZE(arg) <= 0) { + PyErr_SetString(PyExc_ValueError, + "math domain error"); + return NULL; + } + + x = PyLong_AsDouble(arg); + if (x == -1.0 && PyErr_Occurred()) { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + /* Here the conversion to double overflowed, but it's possible + to compute the log anyway. Clear the exception and continue. */ + PyErr_Clear(); + x = _PyLong_Frexp((PyLongObject *)arg, &e); + if (x == -1.0 && PyErr_Occurred()) + return NULL; + /* Value is ~= x * 2**e, so the log ~= log(x) + log(2) * e. */ + result = func(x) + func(2.0) * e; + } + else + /* Successfully converted x to a double. */ + result = func(x); + return PyFloat_FromDouble(result); + } + + /* Else let libm handle it by itself. */ + return math_1(arg, func, 0); +} + + +/*[clinic input] +math.log + + x: object + [ + base: object(c_default="NULL") = math.e + ] + / + +Return the logarithm of x to the given base. + +If the base not specified, returns the natural logarithm (base e) of x. +[clinic start generated code]*/ + +static PyObject * +math_log_impl(PyObject *module, PyObject *x, int group_right_1, + PyObject *base) +/*[clinic end generated code: output=7b5a39e526b73fc9 input=0f62d5726cbfebbd]*/ +{ + PyObject *num, *den; + PyObject *ans; + + num = loghelper(x, m_log, "log"); + if (num == NULL || base == NULL) + return num; + + den = loghelper(base, m_log, "log"); + if (den == NULL) { + Py_DECREF(num); + return NULL; + } + + ans = PyNumber_TrueDivide(num, den); + Py_DECREF(num); + Py_DECREF(den); + return ans; +} + + +/*[clinic input] +math.log2 + + x: object + / + +Return the base 2 logarithm of x. +[clinic start generated code]*/ + +static PyObject * +math_log2(PyObject *module, PyObject *x) +/*[clinic end generated code: output=5425899a4d5d6acb input=08321262bae4f39b]*/ +{ + return loghelper(x, m_log2, "log2"); +} + + +/*[clinic input] +math.log10 + + x: object + / + +Return the base 10 logarithm of x. +[clinic start generated code]*/ + +static PyObject * +math_log10(PyObject *module, PyObject *x) +/*[clinic end generated code: output=be72a64617df9c6f input=b2469d02c6469e53]*/ +{ + return loghelper(x, m_log10, "log10"); +} + + +/*[clinic input] +math.fmod + + x: double + y: double + / + +Return fmod(x, y), according to platform C. + +x % y may differ. +[clinic start generated code]*/ + +static PyObject * +math_fmod_impl(PyObject *module, double x, double y) +/*[clinic end generated code: output=7559d794343a27b5 input=4f84caa8cfc26a03]*/ +{ + double r; + /* fmod(x, +/-Inf) returns x for finite x. */ + if (Py_IS_INFINITY(y) && Py_IS_FINITE(x)) + return PyFloat_FromDouble(x); + errno = 0; + r = fmod(x, y); + if (Py_IS_NAN(r)) { + if (!Py_IS_NAN(x) && !Py_IS_NAN(y)) + errno = EDOM; + else + errno = 0; + } + if (errno && is_error(r)) + return NULL; + else + return PyFloat_FromDouble(r); +} + /* Given an *n* length *vec* of values and a value *max*, compute: - + max * sqrt(sum((x / max) ** 2 for x in vec)) The value of the *max* variable must be non-negative and @@ -2495,13 +2495,13 @@ vector_norm(Py_ssize_t n, double *vec, double max, int found_nan) #define NUM_STACK_ELEMS 16 -/*[clinic input] +/*[clinic input] math.dist - + p: object q: object - / - + / + Return the Euclidean distance between two points p and q. The points should be specified as sequences (or iterables) of @@ -2509,12 +2509,12 @@ coordinates. Both inputs must have the same dimension. Roughly equivalent to: sqrt(sum((px - qx) ** 2.0 for px, qx in zip(p, q))) -[clinic start generated code]*/ - -static PyObject * +[clinic start generated code]*/ + +static PyObject * math_dist_impl(PyObject *module, PyObject *p, PyObject *q) /*[clinic end generated code: output=56bd9538d06bbcfe input=74e85e1b6092e68e]*/ -{ +{ PyObject *item; double max = 0.0; double x, px, qx, result; @@ -2529,7 +2529,7 @@ math_dist_impl(PyObject *module, PyObject *p, PyObject *q) return NULL; } p_allocated = 1; - } + } if (!PyTuple_Check(q)) { q = PySequence_Tuple(q); if (q == NULL) { @@ -2539,14 +2539,14 @@ math_dist_impl(PyObject *module, PyObject *p, PyObject *q) return NULL; } q_allocated = 1; - } + } m = PyTuple_GET_SIZE(p); n = PyTuple_GET_SIZE(q); if (m != n) { PyErr_SetString(PyExc_ValueError, "both points must have the same number of dimensions"); - return NULL; + return NULL; } if (n > NUM_STACK_ELEMS) { @@ -2590,8 +2590,8 @@ math_dist_impl(PyObject *module, PyObject *p, PyObject *q) Py_DECREF(q); } return NULL; -} - +} + /* AC: cannot convert yet, waiting for *args support */ static PyObject * math_hypot(PyObject *self, PyObject *const *args, Py_ssize_t nargs) @@ -2603,7 +2603,7 @@ math_hypot(PyObject *self, PyObject *const *args, Py_ssize_t nargs) int found_nan = 0; double coord_on_stack[NUM_STACK_ELEMS]; double *coordinates = coord_on_stack; - + if (nargs > NUM_STACK_ELEMS) { coordinates = (double *) PyObject_Malloc(nargs * sizeof(double)); if (coordinates == NULL) { @@ -2651,251 +2651,251 @@ For example, the hypotenuse of a 3/4/5 right triangle is:\n\ 5.0\n\ "); -/* pow can't use math_2, but needs its own wrapper: the problem is - that an infinite result can arise either as a result of overflow - (in which case OverflowError should be raised) or as a result of - e.g. 0.**-5. (for which ValueError needs to be raised.) -*/ - -/*[clinic input] -math.pow - - x: double - y: double - / - -Return x**y (x to the power of y). -[clinic start generated code]*/ - -static PyObject * -math_pow_impl(PyObject *module, double x, double y) -/*[clinic end generated code: output=fff93e65abccd6b0 input=c26f1f6075088bfd]*/ -{ - double r; - int odd_y; - - /* deal directly with IEEE specials, to cope with problems on various - platforms whose semantics don't exactly match C99 */ - r = 0.; /* silence compiler warning */ - if (!Py_IS_FINITE(x) || !Py_IS_FINITE(y)) { - errno = 0; - if (Py_IS_NAN(x)) - r = y == 0. ? 1. : x; /* NaN**0 = 1 */ - else if (Py_IS_NAN(y)) - r = x == 1. ? 1. : y; /* 1**NaN = 1 */ - else if (Py_IS_INFINITY(x)) { - odd_y = Py_IS_FINITE(y) && fmod(fabs(y), 2.0) == 1.0; - if (y > 0.) - r = odd_y ? x : fabs(x); - else if (y == 0.) - r = 1.; - else /* y < 0. */ - r = odd_y ? copysign(0., x) : 0.; - } - else if (Py_IS_INFINITY(y)) { - if (fabs(x) == 1.0) - r = 1.; - else if (y > 0. && fabs(x) > 1.0) - r = y; - else if (y < 0. && fabs(x) < 1.0) { - r = -y; /* result is +inf */ - if (x == 0.) /* 0**-inf: divide-by-zero */ - errno = EDOM; - } - else - r = 0.; - } - } - else { - /* let libm handle finite**finite */ - errno = 0; - r = pow(x, y); - /* a NaN result should arise only from (-ve)**(finite - non-integer); in this case we want to raise ValueError. */ - if (!Py_IS_FINITE(r)) { - if (Py_IS_NAN(r)) { - errno = EDOM; - } - /* - an infinite result here arises either from: - (A) (+/-0.)**negative (-> divide-by-zero) - (B) overflow of x**y with x and y finite - */ - else if (Py_IS_INFINITY(r)) { - if (x == 0.) - errno = EDOM; - else - errno = ERANGE; - } - } - } - - if (errno && is_error(r)) - return NULL; - else - return PyFloat_FromDouble(r); -} - - -static const double degToRad = Py_MATH_PI / 180.0; -static const double radToDeg = 180.0 / Py_MATH_PI; - -/*[clinic input] -math.degrees - - x: double - / - -Convert angle x from radians to degrees. -[clinic start generated code]*/ - -static PyObject * -math_degrees_impl(PyObject *module, double x) -/*[clinic end generated code: output=7fea78b294acd12f input=81e016555d6e3660]*/ -{ - return PyFloat_FromDouble(x * radToDeg); -} - - -/*[clinic input] -math.radians - - x: double - / - -Convert angle x from degrees to radians. -[clinic start generated code]*/ - -static PyObject * -math_radians_impl(PyObject *module, double x) -/*[clinic end generated code: output=34daa47caf9b1590 input=91626fc489fe3d63]*/ -{ - return PyFloat_FromDouble(x * degToRad); -} - - -/*[clinic input] -math.isfinite - - x: double - / - -Return True if x is neither an infinity nor a NaN, and False otherwise. -[clinic start generated code]*/ - -static PyObject * -math_isfinite_impl(PyObject *module, double x) -/*[clinic end generated code: output=8ba1f396440c9901 input=46967d254812e54a]*/ -{ - return PyBool_FromLong((long)Py_IS_FINITE(x)); -} - - -/*[clinic input] -math.isnan - - x: double - / - -Return True if x is a NaN (not a number), and False otherwise. -[clinic start generated code]*/ - -static PyObject * -math_isnan_impl(PyObject *module, double x) -/*[clinic end generated code: output=f537b4d6df878c3e input=935891e66083f46a]*/ -{ - return PyBool_FromLong((long)Py_IS_NAN(x)); -} - - -/*[clinic input] -math.isinf - - x: double - / - -Return True if x is a positive or negative infinity, and False otherwise. -[clinic start generated code]*/ - -static PyObject * -math_isinf_impl(PyObject *module, double x) -/*[clinic end generated code: output=9f00cbec4de7b06b input=32630e4212cf961f]*/ -{ - return PyBool_FromLong((long)Py_IS_INFINITY(x)); -} - - -/*[clinic input] -math.isclose -> bool - - a: double - b: double - * - rel_tol: double = 1e-09 - maximum difference for being considered "close", relative to the - magnitude of the input values - abs_tol: double = 0.0 - maximum difference for being considered "close", regardless of the - magnitude of the input values - -Determine whether two floating point numbers are close in value. - -Return True if a is close in value to b, and False otherwise. - -For the values to be considered close, the difference between them -must be smaller than at least one of the tolerances. - --inf, inf and NaN behave similarly to the IEEE 754 Standard. That -is, NaN is not close to anything, even itself. inf and -inf are -only close to themselves. -[clinic start generated code]*/ - -static int -math_isclose_impl(PyObject *module, double a, double b, double rel_tol, - double abs_tol) -/*[clinic end generated code: output=b73070207511952d input=f28671871ea5bfba]*/ -{ - double diff = 0.0; - - /* sanity check on the inputs */ - if (rel_tol < 0.0 || abs_tol < 0.0 ) { - PyErr_SetString(PyExc_ValueError, - "tolerances must be non-negative"); - return -1; - } - - if ( a == b ) { - /* short circuit exact equality -- needed to catch two infinities of - the same sign. And perhaps speeds things up a bit sometimes. - */ - return 1; - } - - /* This catches the case of two infinities of opposite sign, or - one infinity and one finite number. Two infinities of opposite - sign would otherwise have an infinite relative tolerance. - Two infinities of the same sign are caught by the equality check - above. - */ - - if (Py_IS_INFINITY(a) || Py_IS_INFINITY(b)) { - return 0; - } - - /* now do the regular computation - this is essentially the "weak" test from the Boost library - */ - - diff = fabs(b - a); - - return (((diff <= fabs(rel_tol * b)) || - (diff <= fabs(rel_tol * a))) || - (diff <= abs_tol)); -} - +/* pow can't use math_2, but needs its own wrapper: the problem is + that an infinite result can arise either as a result of overflow + (in which case OverflowError should be raised) or as a result of + e.g. 0.**-5. (for which ValueError needs to be raised.) +*/ + +/*[clinic input] +math.pow + + x: double + y: double + / + +Return x**y (x to the power of y). +[clinic start generated code]*/ + +static PyObject * +math_pow_impl(PyObject *module, double x, double y) +/*[clinic end generated code: output=fff93e65abccd6b0 input=c26f1f6075088bfd]*/ +{ + double r; + int odd_y; + + /* deal directly with IEEE specials, to cope with problems on various + platforms whose semantics don't exactly match C99 */ + r = 0.; /* silence compiler warning */ + if (!Py_IS_FINITE(x) || !Py_IS_FINITE(y)) { + errno = 0; + if (Py_IS_NAN(x)) + r = y == 0. ? 1. : x; /* NaN**0 = 1 */ + else if (Py_IS_NAN(y)) + r = x == 1. ? 1. : y; /* 1**NaN = 1 */ + else if (Py_IS_INFINITY(x)) { + odd_y = Py_IS_FINITE(y) && fmod(fabs(y), 2.0) == 1.0; + if (y > 0.) + r = odd_y ? x : fabs(x); + else if (y == 0.) + r = 1.; + else /* y < 0. */ + r = odd_y ? copysign(0., x) : 0.; + } + else if (Py_IS_INFINITY(y)) { + if (fabs(x) == 1.0) + r = 1.; + else if (y > 0. && fabs(x) > 1.0) + r = y; + else if (y < 0. && fabs(x) < 1.0) { + r = -y; /* result is +inf */ + if (x == 0.) /* 0**-inf: divide-by-zero */ + errno = EDOM; + } + else + r = 0.; + } + } + else { + /* let libm handle finite**finite */ + errno = 0; + r = pow(x, y); + /* a NaN result should arise only from (-ve)**(finite + non-integer); in this case we want to raise ValueError. */ + if (!Py_IS_FINITE(r)) { + if (Py_IS_NAN(r)) { + errno = EDOM; + } + /* + an infinite result here arises either from: + (A) (+/-0.)**negative (-> divide-by-zero) + (B) overflow of x**y with x and y finite + */ + else if (Py_IS_INFINITY(r)) { + if (x == 0.) + errno = EDOM; + else + errno = ERANGE; + } + } + } + + if (errno && is_error(r)) + return NULL; + else + return PyFloat_FromDouble(r); +} + + +static const double degToRad = Py_MATH_PI / 180.0; +static const double radToDeg = 180.0 / Py_MATH_PI; + +/*[clinic input] +math.degrees + + x: double + / + +Convert angle x from radians to degrees. +[clinic start generated code]*/ + +static PyObject * +math_degrees_impl(PyObject *module, double x) +/*[clinic end generated code: output=7fea78b294acd12f input=81e016555d6e3660]*/ +{ + return PyFloat_FromDouble(x * radToDeg); +} + + +/*[clinic input] +math.radians + + x: double + / + +Convert angle x from degrees to radians. +[clinic start generated code]*/ + +static PyObject * +math_radians_impl(PyObject *module, double x) +/*[clinic end generated code: output=34daa47caf9b1590 input=91626fc489fe3d63]*/ +{ + return PyFloat_FromDouble(x * degToRad); +} + + +/*[clinic input] +math.isfinite + + x: double + / + +Return True if x is neither an infinity nor a NaN, and False otherwise. +[clinic start generated code]*/ + +static PyObject * +math_isfinite_impl(PyObject *module, double x) +/*[clinic end generated code: output=8ba1f396440c9901 input=46967d254812e54a]*/ +{ + return PyBool_FromLong((long)Py_IS_FINITE(x)); +} + + +/*[clinic input] +math.isnan + + x: double + / + +Return True if x is a NaN (not a number), and False otherwise. +[clinic start generated code]*/ + +static PyObject * +math_isnan_impl(PyObject *module, double x) +/*[clinic end generated code: output=f537b4d6df878c3e input=935891e66083f46a]*/ +{ + return PyBool_FromLong((long)Py_IS_NAN(x)); +} + + +/*[clinic input] +math.isinf + + x: double + / + +Return True if x is a positive or negative infinity, and False otherwise. +[clinic start generated code]*/ + +static PyObject * +math_isinf_impl(PyObject *module, double x) +/*[clinic end generated code: output=9f00cbec4de7b06b input=32630e4212cf961f]*/ +{ + return PyBool_FromLong((long)Py_IS_INFINITY(x)); +} + + +/*[clinic input] +math.isclose -> bool + + a: double + b: double + * + rel_tol: double = 1e-09 + maximum difference for being considered "close", relative to the + magnitude of the input values + abs_tol: double = 0.0 + maximum difference for being considered "close", regardless of the + magnitude of the input values + +Determine whether two floating point numbers are close in value. + +Return True if a is close in value to b, and False otherwise. + +For the values to be considered close, the difference between them +must be smaller than at least one of the tolerances. + +-inf, inf and NaN behave similarly to the IEEE 754 Standard. That +is, NaN is not close to anything, even itself. inf and -inf are +only close to themselves. +[clinic start generated code]*/ + +static int +math_isclose_impl(PyObject *module, double a, double b, double rel_tol, + double abs_tol) +/*[clinic end generated code: output=b73070207511952d input=f28671871ea5bfba]*/ +{ + double diff = 0.0; + + /* sanity check on the inputs */ + if (rel_tol < 0.0 || abs_tol < 0.0 ) { + PyErr_SetString(PyExc_ValueError, + "tolerances must be non-negative"); + return -1; + } + + if ( a == b ) { + /* short circuit exact equality -- needed to catch two infinities of + the same sign. And perhaps speeds things up a bit sometimes. + */ + return 1; + } + + /* This catches the case of two infinities of opposite sign, or + one infinity and one finite number. Two infinities of opposite + sign would otherwise have an infinite relative tolerance. + Two infinities of the same sign are caught by the equality check + above. + */ + + if (Py_IS_INFINITY(a) || Py_IS_INFINITY(b)) { + return 0; + } + + /* now do the regular computation + this is essentially the "weak" test from the Boost library + */ + + diff = fabs(b - a); + + return (((diff <= fabs(rel_tol * b)) || + (diff <= fabs(rel_tol * a))) || + (diff <= abs_tol)); +} + static inline int _check_long_mult_overflow(long a, long b) { - + /* From Python2's int_mul code: Integer overflow checking for * is painful: Python tried a couple ways, but @@ -3449,83 +3449,83 @@ math_exec(PyObject *module) return 0; } -static PyMethodDef math_methods[] = { - {"acos", math_acos, METH_O, math_acos_doc}, - {"acosh", math_acosh, METH_O, math_acosh_doc}, - {"asin", math_asin, METH_O, math_asin_doc}, - {"asinh", math_asinh, METH_O, math_asinh_doc}, - {"atan", math_atan, METH_O, math_atan_doc}, +static PyMethodDef math_methods[] = { + {"acos", math_acos, METH_O, math_acos_doc}, + {"acosh", math_acosh, METH_O, math_acosh_doc}, + {"asin", math_asin, METH_O, math_asin_doc}, + {"asinh", math_asinh, METH_O, math_asinh_doc}, + {"atan", math_atan, METH_O, math_atan_doc}, {"atan2", (PyCFunction)(void(*)(void))math_atan2, METH_FASTCALL, math_atan2_doc}, - {"atanh", math_atanh, METH_O, math_atanh_doc}, - MATH_CEIL_METHODDEF + {"atanh", math_atanh, METH_O, math_atanh_doc}, + MATH_CEIL_METHODDEF {"copysign", (PyCFunction)(void(*)(void))math_copysign, METH_FASTCALL, math_copysign_doc}, - {"cos", math_cos, METH_O, math_cos_doc}, - {"cosh", math_cosh, METH_O, math_cosh_doc}, - MATH_DEGREES_METHODDEF + {"cos", math_cos, METH_O, math_cos_doc}, + {"cosh", math_cosh, METH_O, math_cosh_doc}, + MATH_DEGREES_METHODDEF MATH_DIST_METHODDEF - {"erf", math_erf, METH_O, math_erf_doc}, - {"erfc", math_erfc, METH_O, math_erfc_doc}, - {"exp", math_exp, METH_O, math_exp_doc}, - {"expm1", math_expm1, METH_O, math_expm1_doc}, - {"fabs", math_fabs, METH_O, math_fabs_doc}, - MATH_FACTORIAL_METHODDEF - MATH_FLOOR_METHODDEF - MATH_FMOD_METHODDEF - MATH_FREXP_METHODDEF - MATH_FSUM_METHODDEF - {"gamma", math_gamma, METH_O, math_gamma_doc}, + {"erf", math_erf, METH_O, math_erf_doc}, + {"erfc", math_erfc, METH_O, math_erfc_doc}, + {"exp", math_exp, METH_O, math_exp_doc}, + {"expm1", math_expm1, METH_O, math_expm1_doc}, + {"fabs", math_fabs, METH_O, math_fabs_doc}, + MATH_FACTORIAL_METHODDEF + MATH_FLOOR_METHODDEF + MATH_FMOD_METHODDEF + MATH_FREXP_METHODDEF + MATH_FSUM_METHODDEF + {"gamma", math_gamma, METH_O, math_gamma_doc}, {"gcd", (PyCFunction)(void(*)(void))math_gcd, METH_FASTCALL, math_gcd_doc}, {"hypot", (PyCFunction)(void(*)(void))math_hypot, METH_FASTCALL, math_hypot_doc}, - MATH_ISCLOSE_METHODDEF - MATH_ISFINITE_METHODDEF - MATH_ISINF_METHODDEF - MATH_ISNAN_METHODDEF + MATH_ISCLOSE_METHODDEF + MATH_ISFINITE_METHODDEF + MATH_ISINF_METHODDEF + MATH_ISNAN_METHODDEF MATH_ISQRT_METHODDEF {"lcm", (PyCFunction)(void(*)(void))math_lcm, METH_FASTCALL, math_lcm_doc}, - MATH_LDEXP_METHODDEF - {"lgamma", math_lgamma, METH_O, math_lgamma_doc}, - MATH_LOG_METHODDEF - {"log1p", math_log1p, METH_O, math_log1p_doc}, - MATH_LOG10_METHODDEF - MATH_LOG2_METHODDEF - MATH_MODF_METHODDEF - MATH_POW_METHODDEF - MATH_RADIANS_METHODDEF + MATH_LDEXP_METHODDEF + {"lgamma", math_lgamma, METH_O, math_lgamma_doc}, + MATH_LOG_METHODDEF + {"log1p", math_log1p, METH_O, math_log1p_doc}, + MATH_LOG10_METHODDEF + MATH_LOG2_METHODDEF + MATH_MODF_METHODDEF + MATH_POW_METHODDEF + MATH_RADIANS_METHODDEF {"remainder", (PyCFunction)(void(*)(void))math_remainder, METH_FASTCALL, math_remainder_doc}, - {"sin", math_sin, METH_O, math_sin_doc}, - {"sinh", math_sinh, METH_O, math_sinh_doc}, - {"sqrt", math_sqrt, METH_O, math_sqrt_doc}, - {"tan", math_tan, METH_O, math_tan_doc}, - {"tanh", math_tanh, METH_O, math_tanh_doc}, - MATH_TRUNC_METHODDEF + {"sin", math_sin, METH_O, math_sin_doc}, + {"sinh", math_sinh, METH_O, math_sinh_doc}, + {"sqrt", math_sqrt, METH_O, math_sqrt_doc}, + {"tan", math_tan, METH_O, math_tan_doc}, + {"tanh", math_tanh, METH_O, math_tanh_doc}, + MATH_TRUNC_METHODDEF MATH_PROD_METHODDEF MATH_PERM_METHODDEF MATH_COMB_METHODDEF MATH_NEXTAFTER_METHODDEF MATH_ULP_METHODDEF - {NULL, NULL} /* sentinel */ -}; - + {NULL, NULL} /* sentinel */ +}; + static PyModuleDef_Slot math_slots[] = { {Py_mod_exec, math_exec}, {0, NULL} }; - -PyDoc_STRVAR(module_doc, + +PyDoc_STRVAR(module_doc, "This module provides access to the mathematical functions\n" "defined by the C standard."); - -static struct PyModuleDef mathmodule = { - PyModuleDef_HEAD_INIT, + +static struct PyModuleDef mathmodule = { + PyModuleDef_HEAD_INIT, .m_name = "math", .m_doc = module_doc, .m_size = 0, .m_methods = math_methods, .m_slots = math_slots, -}; - -PyMODINIT_FUNC -PyInit_math(void) -{ +}; + +PyMODINIT_FUNC +PyInit_math(void) +{ return PyModuleDef_Init(&mathmodule); -} +} |