def _pi_agm(prec):
from functions import _sqrt_fixed2
prec += 50
a = 1 << prec
if "verbose" in global_options:
print " computing initial square root..."
b = _sqrt_fixed2(a >> 1, prec)
t = a >> 2
p = 1
step = 1
while 1:
an = (a+b)>>1
adiff = a - an
if "verbose" in global_options:
base = global_options.get("verbose_base", 10)
try:
logdiff = _clog(adiff, base)
except ValueError:
logdiff = 0
digits = int(prec/_clog(base,2) - logdiff)
print " iteration", step, ("(accuracy ~= %i base-%i digits)" % (digits, base))
if p > 16 and abs(adiff) < 1000:
break
prod = (a*b)>>prec
b = _sqrt_fixed2(prod, prec)
t = t - p*((adiff**2) >> prec)
p = 2*p
a = an
step += 1
if "verbose" in global_options:
print " final division"
return ((((a+b)**2) >> 2) // t) >> 50
def gamma_fixed(prec):
# XXX: may need even more extra precision
prec += 30
# choose p such that exp(-4*(2**p)) < 2**-n
p = int(_clog((prec/4) * _clog(2), 2)) + 1
n = 1<<p; r=one=1<<prec
H, A, B, npow, k, d = 0, 0, 0, 1, 1, 1
while r:
A += (r * H) >> prec
B += r
r = r * (n*n) // (k*k)
H += one // k
k += 1
S = ((A<<prec) // B) - p*log2_fixed(prec)
return S >> 30
def _log_newton(x, prec):
# 50-bit approximation
r = int(_clog(Float((x, -prec), 64)) * 2.0 ** 50)
prevp = 50
for p in _quadratic_steps(50, prec + 8):
rb = _lshift(r, p - prevp)
e = _exp_series(-rb, p)
r = rb + ((_rshift(x, prec - p) * e) >> p) - (1 << p)
prevp = p
return r >> 8
def log(x, b=None):
"""
log(x) -> the natural (base e) logarithm of x
log(x, b) -> the base b logarithm of x
"""
# Basic input management
if b is not None:
Float._prec += 3
if b == 2:
blog = log2_float()
elif b == 10:
blog = log10_float()
else:
blog = log(b)
l = log(x) / blog
Float._prec -= 3
return l
if x == 0:
raise ValueError, "logarithm of 0"
if isinstance(x, (ComplexFloat, complex)):
mag = abs(x)
phase = atan2(x.imag, x.real)
return ComplexFloat(log(mag), phase)
if not isinstance(x, Float):
x = Float(x)
if x < 0:
return log(ComplexFloat(x))
if x == 1:
return Float((0, 0))
bc = bitcount(x.man)
# Estimated precision needed for log(t) + n*log(2)
prec = Float._prec + int(_clog(1 + abs(bc + x.exp), 2)) + 10
# Watch out for the case when x is very close to 1
if -1 < bc + x.exp < 2:
near_one = abs(x - 1)
if near_one == 0:
return Float((0, 0))
prec += -(near_one.exp) - bitcount(near_one.man)
# Separate mantissa and exponent, calculate fixed-point
# approximation and put it all together
t = _rshift(x.man, bc - prec)
l = _log_newton(t, prec)
a = (x.exp + bc) * log2_fixed(prec)
return Float((l + a, -prec))
def _exp_series(x, prec):
r = int(2.2 * prec ** 0.42)
# XXX: more careful calculation of guard bits
guards = r + 3
if prec > 60:
guards += int(_clog(prec))
prec2 = prec + guards
x = _rshift(x, r - guards)
s = (1 << prec2) + x
a = x
k = 2
# Sum exp(x/2**r)
while 1:
a = ((a * x) >> prec2) // k
if not a:
break
s += a
k += 1
# Calculate s**(2**r) by repeated squaring
for j in range(r):
s = (s * s) >> prec2
return s >> guards
