def mpf_pow(s, t, prec, rnd=round_fast):
"""
Compute s**t. Raises ComplexResult if s is negative and t is
fractional.
"""
ssign, sman, sexp, sbc = s
tsign, tman, texp, tbc = t
if ssign and texp < 0:
raise ComplexResult("negative number raised to a fractional power")
if texp >= 0:
return mpf_pow_int(s, (-1)**tsign * (tman<<texp), prec, rnd)
# s**(n/2) = sqrt(s)**n
if texp == -1:
if tman == 1:
if tsign:
return mpf_div(fone, mpf_sqrt(s, prec+10,
reciprocal_rnd[rnd]), prec, rnd)
return mpf_sqrt(s, prec, rnd)
else:
if tsign:
return mpf_pow_int(mpf_sqrt(s, prec+10,
reciprocal_rnd[rnd]), -tman, prec, rnd)
return mpf_pow_int(mpf_sqrt(s, prec+10, rnd), tman, prec, rnd)
# General formula: s**t = exp(t*log(s))
# TODO: handle rnd direction of the logarithm carefully
c = mpf_log(s, prec+10, rnd)
return mpf_exp(mpf_mul(t, c), prec, rnd)
def mpf_acosh(x, prec, rnd=round_fast):
# acosh(x) = log(x+sqrt(x**2-1))
wp = prec + 15
if mpf_cmp(x, fone) == -1:
raise ComplexResult("acosh(x) is real only for x >= 1")
q = mpf_sqrt(mpf_add(mpf_mul(x,x), fnone, wp), wp)
return mpf_log(mpf_add(x, q, wp), prec, rnd)
def mpf_asin(x, prec, rnd=round_fast):
sign, man, exp, bc = x
if bc+exp > 0 and x not in (fone, fnone):
raise ComplexResult("asin(x) is real only for -1 <= x <= 1")
flag_nr = True
if prec < 1000 or exp+bc < -13:
flag_nr = False
else:
ebc = exp + bc
if ebc < -13:
flag_nr = False
elif ebc < -3:
if prec < 3000:
flag_nr = False
if not flag_nr:
# asin(x) = 2*atan(x/(1+sqrt(1-x**2)))
wp = prec + 15
a = mpf_mul(x, x)
b = mpf_add(fone, mpf_sqrt(mpf_sub(fone, a, wp), wp), wp)
c = mpf_div(x, b, wp)
return mpf_shift(mpf_atan(c, prec, rnd), 1)
# use Newton's method
extra = 10
extra_p = 10
prec2 = prec + extra
r = math.asin(to_float(x))
r = from_float(r, 50, rnd)
for p in giant_steps(50, prec2):
wp = p + extra_p
c, s = cos_sin(r, wp, rnd)
tmp = mpf_sub(x, s, wp, rnd)
tmp = mpf_div(tmp, c, wp, rnd)
r = mpf_add(r, tmp, wp, rnd)
sign, man, exp, bc = r
return normalize(sign, man, exp, bc, prec, rnd)
def mpf_agm(a, b, prec, rnd=round_fast):
"""
Computes the arithmetic-geometric mean agm(a,b) for
nonnegative mpf values a, b.
"""
asign, aman, aexp, abc = a
bsign, bman, bexp, bbc = b
if asign or bsign:
raise ComplexResult("agm of a negative number")
# Handle inf, nan or zero in either operand
if not (aman and bman):
if a == fnan or b == fnan:
return fnan
if a == finf:
if b == fzero:
return fnan
return finf
if b == finf:
if a == fzero:
return fnan
return finf
# agm(0,x) = agm(x,0) = 0
return fzero
wp = prec + 20
amag = aexp + abc
bmag = bexp + bbc
mag_delta = amag - bmag
# Reduce to roughly the same magnitude using floating-point AGM
abs_mag_delta = abs(mag_delta)
if abs_mag_delta > 10:
while abs_mag_delta > 10:
a, b = mpf_shift(mpf_add(a,b,wp),-1), \
mpf_sqrt(mpf_mul(a,b,wp),wp)
abs_mag_delta //= 2
asign, aman, aexp, abc = a
bsign, bman, bexp, bbc = b
amag = aexp + abc
bmag = bexp + bbc
mag_delta = amag - bmag
#print to_float(a), to_float(b)
# Use agm(a,b) = agm(x*a,x*b)/x to obtain a, b ~= 1
min_mag = min(amag, bmag)
max_mag = max(amag, bmag)
n = 0
# If too small, we lose precision when going to fixed-point
if min_mag < -8:
n = -min_mag
# If too large, we waste time using fixed-point with large numbers
elif max_mag > 20:
n = -max_mag
if n:
a = mpf_shift(a, n)
b = mpf_shift(b, n)
#print to_float(a), to_float(b)
af = to_fixed(a, wp)
bf = to_fixed(b, wp)
g = agm_fixed(af, bf, wp)
return from_man_exp(g, -wp - n, prec, rnd)
def mpf_asin(x, prec, rnd=round_fast):
sign, man, exp, bc = x
if bc + exp > 0 and x not in (fone, fnone):
raise ComplexResult("asin(x) is real only for -1 <= x <= 1")
# asin(x) = 2*atan(x/(1+sqrt(1-x**2)))
wp = prec + 15
a = mpf_mul(x, x)
b = mpf_add(fone, mpf_sqrt(mpf_sub(fone, a, wp), wp), wp)
c = mpf_div(x, b, wp)
return mpf_shift(mpf_atan(c, prec, rnd), 1)
def mpf_atanh(x, prec, rnd=round_fast):
# atanh(x) = log((1+x)/(1-x))/2
sign, man, exp, bc = x
mag = bc + exp
if mag > 0:
raise ComplexResult("atanh(x) is real only for -1 < x < 1")
wp = prec + 15
a = mpf_add(x, fone, wp)
b = mpf_sub(fone, x, wp)
return mpf_shift(mpf_log(mpf_div(a, b, wp), prec, rnd), -1)
def mpf_atanh(x, prec, rnd=round_fast):
# atanh(x) = log((1+x)/(1-x))/2
sign, man, exp, bc = x
if (not man) and exp:
if x in (fzero, fnan):
return x
raise ComplexResult("atanh(x) is real only for -1 <= x <= 1")
mag = bc + exp
if mag > 0:
if mag == 1 and man == 1:
return [finf, fninf][sign]
raise ComplexResult("atanh(x) is real only for -1 <= x <= 1")
wp = prec + 15
if mag < -8:
if mag < -wp:
return mpf_perturb(x, sign, prec, rnd)
wp += (-mag)
a = mpf_add(x, fone, wp)
b = mpf_sub(fone, x, wp)
return mpf_shift(mpf_log(mpf_div(a, b, wp), prec, rnd), -1)
def mpf_acos(x, prec, rnd=round_fast):
# acos(x) = 2*atan(sqrt(1-x**2)/(1+x))
sign, man, exp, bc = x
if bc + exp > 0:
if x not in (fone, fnone):
raise ComplexResult("acos(x) is real only for -1 <= x <= 1")
if x == fnone:
return mpf_pi(prec, rnd)
wp = prec + 15
a = mpf_mul(x, x)
b = mpf_sqrt(mpf_sub(fone, a, wp), wp)
c = mpf_div(b, mpf_add(fone, x, wp), wp)
return mpf_shift(mpf_atan(c, prec, rnd), 1)
def mpf_ei(x, prec, rnd=round_fast, e1=False):
if e1:
x = mpf_neg(x)
sign, man, exp, bc = x
if e1 and not sign:
if x == fzero:
return finf
raise ComplexResult("E1(x) for x < 0")
if man:
xabs = 0, man, exp, bc
xmag = exp + bc
wp = prec + 20
can_use_asymp = xmag > wp
if not can_use_asymp:
if exp >= 0:
xabsint = man << exp
else:
xabsint = man >> (-exp)
can_use_asymp = xabsint > int(wp * 0.693) + 10
if can_use_asymp:
if xmag > wp:
v = fone
else:
v = from_man_exp(ei_asymptotic(to_fixed(x, wp), wp), -wp)
v = mpf_mul(v, mpf_exp(x, wp), wp)
v = mpf_div(v, x, prec, rnd)
else:
wp += 2 * int(to_int(xabs))
u = to_fixed(x, wp)
v = ei_taylor(u, wp) + euler_fixed(wp)
t1 = from_man_exp(v, -wp)
t2 = mpf_log(xabs, wp)
v = mpf_add(t1, t2, prec, rnd)
else:
if x == fzero: v = fninf
elif x == finf: v = finf
elif x == fninf: v = fzero
else: v = fnan
if e1:
v = mpf_neg(v)
return v
def mpf_log(x, prec, rnd=round_fast):
"""
Compute the natural logarithm of the mpf value x. If x is negative,
ComplexResult is raised.
"""
sign, man, exp, bc = x
#------------------------------------------------------------------
# Handle special values
if not man:
if x == fzero: return fninf
if x == finf: return finf
if x == fnan: return fnan
if sign:
raise ComplexResult("logarithm of a negative number")
wp = prec + 20
#------------------------------------------------------------------
# Handle log(2^n) = log(n)*2.
# Here we catch the only possible exact value, log(1) = 0
if man == 1:
if not exp:
return fzero
return from_man_exp(exp*ln2_fixed(wp), -wp, prec, rnd)
mag = exp+bc
abs_mag = abs(mag)
#------------------------------------------------------------------
# Handle x = 1+eps, where log(x) ~ x. We need to check for
# cancellation when moving to fixed-point math and compensate
# by increasing the precision. Note that abs_mag in (0, 1) <=>
# 0.5 < x < 2 and x != 1
if abs_mag <= 1:
# Calculate t = x-1 to measure distance from 1 in bits
tsign = 1-abs_mag
if tsign:
tman = (MP_ONE<<bc) - man
else:
tman = man - (MP_ONE<<(bc-1))
tbc = bitcount(tman)
cancellation = bc - tbc
if cancellation > wp:
t = normalize(tsign, tman, abs_mag-bc, tbc, tbc, 'n')
return mpf_perturb(t, tsign, prec, rnd)
else:
wp += cancellation
# TODO: if close enough to 1, we could use Taylor series
# even in the AGM precision range, since the Taylor series
# converges rapidly
#------------------------------------------------------------------
# Another special case:
# n*log(2) is a good enough approximation
if abs_mag > 10000:
if bitcount(abs_mag) > wp:
return from_man_exp(exp*ln2_fixed(wp), -wp, prec, rnd)
#------------------------------------------------------------------
# General case.
# Perform argument reduction using log(x) = log(x*2^n) - n*log(2):
# If we are in the Taylor precision range, choose magnitude 0 or 1.
# If we are in the AGM precision range, choose magnitude -m for
# some large m; benchmarking on one machine showed m = prec/20 to be
# optimal between 1000 and 100,000 digits.
if wp <= LOG_TAYLOR_PREC:
m = log_taylor_cached(lshift(man, wp-bc), wp)
if mag:
m += mag*ln2_fixed(wp)
else:
optimal_mag = -wp//LOG_AGM_MAG_PREC_RATIO
n = optimal_mag - mag
x = mpf_shift(x, n)
wp += (-optimal_mag)
m = -log_agm(to_fixed(x, wp), wp)
m -= n*ln2_fixed(wp)
return from_man_exp(m, -wp, prec, rnd)
def mpf_nthroot(s, n, prec, rnd=round_fast):
"""nth-root of a positive number
Use the Newton method when faster, otherwise use x**(1/n)
"""
sign, man, exp, bc = s
if sign:
raise ComplexResult("nth root of a negative number")
if not man:
if s == fnan:
return fnan
if s == fzero:
if n > 0:
return fzero
if n == 0:
return fone
return finf
# Infinity
if not n:
return fnan
if n < 0:
return fzero
return finf
flag_inverse = False
if n < 2:
if n == 0:
return fone
if n == 1:
return mpf_pos(s, prec, rnd)
if n == -1:
return mpf_div(fone, s, prec, rnd)
# n < 0
rnd = reciprocal_rnd[rnd]
flag_inverse = True
extra_inverse = 5
prec += extra_inverse
n = -n
if n > 20 and (n >= 20000 or prec < int(233 + 28.3 * n**0.62)):
prec2 = prec + 10
fn = from_int(n)
nth = mpf_rdiv_int(1, fn, prec2)
r = mpf_pow(s, nth, prec2, rnd)
s = normalize(r[0], r[1], r[2], r[3], prec, rnd)
if flag_inverse:
return mpf_div(fone, s, prec-extra_inverse, rnd)
else:
return s
# Convert to a fixed-point number with prec2 bits.
prec2 = prec + 2*n - (prec%n)
# a few tests indicate that
# for 10 < n < 10**4 a bit more precision is needed
if n > 10:
prec2 += prec2//10
prec2 = prec2 - prec2%n
# Mantissa may have more bits than we need. Trim it down.
shift = bc - prec2
# Adjust exponents to make prec2 and exp+shift multiples of n.
sign1 = 0
es = exp+shift
if es < 0:
sign1 = 1
es = -es
if sign1:
shift += es%n
else:
shift -= es%n
man = rshift(man, shift)
extra = 10
exp1 = ((exp+shift-(n-1)*prec2)//n) - extra
rnd_shift = 0
if flag_inverse:
if rnd == 'u' or rnd == 'c':
rnd_shift = 1
else:
if rnd == 'd' or rnd == 'f':
rnd_shift = 1
man = nthroot_fixed(man+rnd_shift, n, prec2, exp1)
s = from_man_exp(man, exp1, prec, rnd)
if flag_inverse:
return mpf_div(fone, s, prec-extra_inverse, rnd)
else:
return s
def mpf_log(x, prec, rnd=round_fast):
"""
Compute the natural logarithm of the mpf value x. If x is negative,
ComplexResult is raised.
"""
sign, man, exp, bc = x
if not man:
if x == fzero: return fninf
if x == finf: return finf
if x == fnan: return fnan
if sign:
raise ComplexResult("logarithm of a negative number")
# log(2^n)
if man == 1:
if not exp:
return fzero
return from_man_exp(exp * ln2_fixed(prec + 20), -prec - 20, prec, rnd)
# Assume 20 bits to be sufficient for cancelling rounding errors
wp = prec + 20
mag = bc + exp
# Already on the standard interval, (0.5, 1)
if not mag:
t = rshift(man, bc - wp)
log2n = 0
# Watch out for "x = 0.9999"
# Proceed only if 1-x lost at most 15 bits of accuracy
res = (MP_ONE << wp) - t
if not (res >> (wp - 15)):
# Find out extra precision needed
delta = mpf_sub(x, fone, 10)
delta_bits = -(delta[2] + delta[3])
# O(x^2) term vanishes relatively
if delta_bits > wp + 10:
xm1 = mpf_sub(x, fone, prec, rnd)
return mpf_perturb(xm1, 1, prec, rnd)
else:
wp += delta_bits
t = rshift(man, bc - wp)
# Already on the standard interval, (1, 2)
elif mag == 1:
t = rshift(man, bc - wp - 1)
log2n = 0
# Watch out for "x = 1.0001"
# Similar to above; note that we flip signs
# to obtain a positive residual, ensuring that
# the following shift rounds down
res = t - (MP_ONE << wp)
if not (res >> (wp - 15)):
# Find out extra precision needed
delta = mpf_sub(x, fone, 10)
delta_bits = -(delta[2] + delta[3])
# O(x^2) term vanishes relatively
if delta_bits > wp + 10:
xm1 = mpf_sub(x, fone, prec, rnd)
return mpf_perturb(xm1, 1, prec, rnd)
else:
wp += delta_bits
t = rshift(man, bc - wp - 1)
# Rescale
else:
# Estimated precision needed for n*log(2) to
# be accurate relatively
wp += int(math.log(1 + abs(mag), 2))
log2n = mag * ln2_fixed(wp)
# Rescaled argument as a fixed-point number
t = rshift(man, bc - wp)
# Use the faster method
if wp < LOG_TAYLOR_PREC:
a = log_taylor(t, wp)
else:
a = log_newton(t, wp)
return from_man_exp(a + log2n, -wp, prec, rnd)
