def e_ratio(a, b, e, x):
# Get S
bt2 = mp.beta(a, b - 1.0) # Beta function
bix = mp.betainc(a, b + 1.0, 0.0, e) # Incomplete Beta function
hf = mp.hyp2f1(1.0, a, a + b - 1.0,
-1.0) # 2F1, Gauss' hypergeometric function
hfre = mp.re(hf)
Sval = bix - x * bt2 * hfre
# Get U
c1 = mp.mpc(1.0 + a)
c2 = mp.mpc(-b)
c3 = mp.mpc(1.0)
c4 = mp.mpc(2.0 + a)
Uval = mp.appellf1(c1, c2, c3, c4, e, -e)
Ure = mp.re(Uval)
# Get P & Q
Pval = mp.hyp2f1(a + 1.0, 1.0 - b, a + 2.0,
e) # 2F1, Gauss' hypergeometric function
Pre = mp.re(Pval)
Qval = mp.hyp2f1(a + 1.0, 2.0 - b, a + 2.0,
e) # 2F1, Gauss' hypergeometric function
Qre = mp.re(Qval)
# Get T
Tval = ((e**(1.0 + a)) / (1.0 + a)) * (3.0 * Pre + 2.0 * Qre - Ure)
Tval = Tval + 4.0 * Sval
# Get Rval (ratio)
Rval = 0.25 * (1.0 - e * e) * (
(1.0 - e)**(1.0 - b)) * (e**(1.0 - a)) * Tval
return Rval
def e_ratio(a,b,e,x): # Get S bt2 = mp.beta(a,b-1.0) # Beta function bix = mp.betainc(a,b+1.0,0.0,e) # Incomplete Beta function hf = mp.hyp2f1(1.0,a,a+b-1.0,-1.0) # 2F1, Gauss' hypergeometric function hfre = mp.re(hf) Sval = bix - x*bt2*hfre # Get U c1 = mp.mpc(1.0 + a) c2 = mp.mpc(-b) c3 = mp.mpc(1.0) c4 = mp.mpc(2.0 + a) Uval = mp.appellf1(c1,c2,c3,c4,e,-e) Ure = mp.re(Uval) # Get P & Q Pval = mp.hyp2f1(a+1.0,1.0-b,a+2.0,e) # 2F1, Gauss' hypergeometric function Pre = mp.re(Pval) Qval = mp.hyp2f1(a+1.0,2.0-b,a+2.0,e) # 2F1, Gauss' hypergeometric function Qre = mp.re(Qval) # Get T Tval = ( (e**(1.0+a)) / (1.0+a) )*( 3.0*Pre + 2.0*Qre - Ure ) Tval = Tval + 4.0*Sval # Get Rval (ratio) Rval = 0.25*(1.0-e*e)*( (1.0-e)**(1.0-b) )*( e**(1.0-a) )*Tval return Rval
def next_upper_bracket(t, z_function): current_sign = np.sign( mp.re( z_function(t) ) ) next_sign = current_sign while current_sign == next_sign: t += 1 next_sign = np.sign( mp.re( z_function(t) ) ) return t
def one_side(x,y):
m = ((-1 + x)*(1 + y))/((1 + x)*(-1 + y))
k = sqrt(m)
u = asin(1/k)
EE = ellipe(m)
EF = re(ellipf(u,m))
n = (-1 + x)/(-1 + y)
EPI = ellippi(n/m,1/m)/k
#EPI = ellippi(n,u,m)
return re(-(EE*(1 + x)*(-1 + y) + (x - y)*(EF + EPI*(x-y) + EF*y))/sqrt(((1 + x)*(1 - y))))
def v(x,t,s,b):
"""
inverse map of w(v) = x+i\tau --> x-t.
"""
# c is atrificial parameter for findroot.
c = 10**(-9)
if x>b:
return j*mp.findroot(lambda y: re(w(j*y,s,b)-(x-t)), [-s/2+c,0-c],"bisect")
elif x<-b:
return j*mp.findroot(lambda y: re(w(j*y,s,b)-(x-t)), [0+c,s/2-c],"bisect")
else:
return 1/2+j*mp.findroot(lambda y: re(w(1/2+j*y,s,b)-(x-t)), 0)
def alpha(z, x, beta):
"""
Eq. (A4) from Ref[1]
Note that 'x' here corresponds to 'chi = x/rho',
and 'z' here corresponds to 'xi = z/2/rho' in the paper.
"""
arg1 = sqrt(2 * fabs(m(z, x, beta)))
arg2 = -2 * (m(z, x, beta) + nu(x, beta))
arg3 = 2 * eta(z, x, beta) / arg1
if z < 0:
return re(1 / 2 * (-arg1 + sqrt(fabs(arg2 + arg3))))
else:
return re(1 / 2 * (arg1 + sqrt(fabs(arg2 - arg3))))
def FDoubleDisc(x1,x2,t,s,b):
"""
inside of log() for disconnected HEE.
We take single interval subsystem A=[x1,x2].
This has very complicated minimizasion because the configuration of boundary surface Q
varys by Hawking-Page transition.
"""
v1 = v(x1,t,s,b)
v2 = v(x2,t,s,b)
vb1 = vb(x1,t,s,b)
vb2 = vb(x2,t,s,b)
h1 = v1-vb1
hp1 = h1+j*s/2
hm1 = h1-j*s/2
h2 = v2-vb2
hp2 = h2+j*s/2
hm2 = h2-j*s/2
F = (pi**(-4))*Dw(v1,s,b)*(-Dw(-vb1,s,b))*Dw(v2,s,b)*(-Dw(-vb2,s,b))
if s<1:
g1 = min([re(sinh(pi*hp1/s)**2),re(sinh(pi*hm1/s)**2)])
g2 = min([re(sinh(pi*hp2/s)**2),re(sinh(pi*hm2/s)**2)])
return F * (s**4)*g1*g2
else:
m1 = (sin(pi*hp1)*sin(pi*hp2))**2
m2 = ((sin(pi*hp1)*sin(pi*hm2))**2)*exp(2*pi*s)
m3 = ((sin(pi*hm1)*sin(pi*hp2))**2)*exp(2*pi*s)
m4 = (sin(pi*hm1)*sin(pi*hm2))**2
return F * min([re(m1),re(m2),re(m3),re(m4)])
def S_single_hol(x1, x2, t, a):
t1, t2, tb1, tb2 = theta(x1, t, a), theta(x2, t,
a), thetaB(x1, t,
a), thetaB(x2, t, a)
z1, z2, zb1, zb2 = j * exp(j * t1), j * exp(j * t2), j * exp(
-j * tb1), j * exp(-j * tb2)
deriv = a**2 / (exp(j * (t1 - tb1 + t2 - tb2) / 2) * cos(t1) * cos(t2) *
cos(tb1) * cos(tb2))
conn = (z1 - z2) * (zb1 - zb2)
disconn = (z1 - zb1) * (z2 - zb2)
return min([re(log(deriv * conn)),
re(log(deriv * disconn))]) / 6 - log((x2 - x1)**2) / 6
def v(x, t, s, b):
# c is atrificial parameter for findroot.
c = 10**(-10)
if x > b:
return j * mp.findroot(lambda y: re(w(j * y, s, b) - (x - t)),
[-s / 2 + c, 0 - c], "bisect")
elif x < -b:
return j * mp.findroot(lambda y: re(w(j * y, s, b) - (x - t)),
[0 + c, s / 2 - c], "bisect")
else:
tolerant = 10**(-20)
normparam = 10**11
return 1 / 2 + j * mp.findroot(
lambda y: tanh(re(w(1 / 2 + j * y, s, b) - (x - t)) / normparam),
0,
tol=tolerant)
def nPDF(self, x):
p = np.zeros(x.size)
for i, xx in enumerate(x):
gil_pelaez = lambda t: mp.re(
self.char_fn(t) * mp.exp(-1j * t * xx))
cutoff = self.find_cutoff(1e-30)
# Instead of finding roots, break up quadrature into degrees proportional to the
# expected number of oscillations of e^(i xx t) within t = [0, cutoff]
nosc = cutoff / (1 / max(10, np.abs(xx - self.mean())))
# roots = self.find_roots(gil_pelaez, cutoff)
# if np.abs(xx - self.mean()) < 3 * np.sqrt(self.variance()):
I = mp.quad(gil_pelaez, np.linspace(0, cutoff, nosc), maxdegree=10)
# I = mp.quadosc(gil_pelaez, (0, cutoff), zeros=roots)
# else:
# For now, do not trust any results out greater than 3sigma
# I = 0
# if np.abs(xx - self.mean()) >= 2 * np.sqrt(self.variance()):
# I = self.asymptotic_expansion(xx)
p[i] = 1 / np.pi * float(I)
print(i)
return p
def mp_get_Psca_from_expansion(k, R, dist, zeta):
csqrs = []
rhos = []
lbda = mp_get_lagrange_mul_from_expansion(k, R, dist, zeta, csqrs, rhos)
limit = len(csqrs)
print(limit)
print(lbda)
i = 0
lhs = mpmath.mpf(0.0)
rhs = lhs
oldlhs = lhs
oldrhs = rhs
tol = 1e-6
while (i < limit):
if (i == len(csqrs)): #here the l quantum number is i+1
cNl = mp_get_normalized_RgNl0_coeff_for_zdipole_field(
k, R, dist, i + 1)
csqrs.append(mpmath.re(mpmath.conj(cNl) * cNl))
rhos.append(mp_rho_N(i + 1, k * R))
denom = 1.0 + lbda * zeta * rhos[i]
lhs += zeta * csqrs[i] * (1.0 + lbda) * (
1.0 + 2 * zeta * rhos[i] * lbda) / 4 / denom**2
rhs += zeta * csqrs[i] * (1.0 + lbda) * lbda / 4 / denom**2
i += 1
if (i == limit) and ((lhs - oldlhs) > tol * lhs or
(rhs - oldrhs) > tol * rhs):
limit = int(np.ceil(limit * 1.2))
oldlhs = lhs
oldrhs = rhs
return 0.5 * k * (lhs - rhs)
def loop():
global a, listindex, pointer, loopdict
realpart = mpmath.re(a[listindex])
impart = mpmath.im(a[listindex])
if mpmath.workdps(mpmath.mp.dps * 3 / 4)(
mpmath.chop)(realpart - impart) > 0:
pointer = loopdict[pointer]
def getNthKFibonacciNumber( n, k ):
if real( n ) < 0:
raise ValueError( 'non-negative argument expected' )
if real( k ) < 2:
raise ValueError( 'argument <= 2 expected' )
if n < k - 1:
return 0
nth = int( n ) + 4
precision = int( fdiv( fmul( n, k ), 8 ) )
if ( mp.dps < precision ):
mp.dps = precision
poly = [ 1 ]
poly.extend( [ -1 ] * int( k ) )
roots = polyroots( poly )
nthPoly = getNthFibonacciPolynomial( k )
result = 0
exponent = fsum( [ nth, fneg( k ), -2 ] )
for i in range( 0, int( k ) ):
result += fdiv( power( roots[ i ], exponent ), polyval( nthPoly, roots[ i ] ) )
return floor( fadd( re( result ), fdiv( 1, 2 ) ) )
def write_g_im(filename, e, sigma):
nlines = len(e)
with open(filename, 'w') as f:
for iw in range(0, nlines):
s = '{0:18.8f}{1:18.12f}{2:18.12f}\n'. \
format(float(im(e[iw])), float(re(sigma[iw])), float(im(sigma[iw])))
f.write(s)
def F1_cubic(hstar,kappa):
'''
F1 function corresponding to a cubic viscosity profile (Nieuwstadt 1983)
Parameters
----------
hstar: float
Non-dimensional boundary-layer height
hstar = h*fc/utau
kappa: float
Von Karman constant
Returns
-------
F1: float
Value of F1 function
'''
C = hstar/kappa
alpha = 0.5+0.5*np.sqrt(1+4j*C)
F1 = np.zeros((1),dtype=np.float64)
F1[0] = 1./kappa*(-np.log(hstar)+
mpmath.re( mpmath.digamma(alpha+1)+
mpmath.digamma(alpha-1)-
2*mpmath.digamma(1.0) ) )
return np.asscalar(F1)
def end():
global a, listindex, pointer, loopdict
realpart = mpmath.re(a[listindex])
impart = mpmath.im(a[listindex])
if mpmath.workdps(mpmath.mp.dps * 3 / 4)(
mpmath.chop)(realpart - impart) <= 0:
pointer = next(key for key, value in loopdict.items()
if value == pointer)
def generate(c, num_iterations=200):
orbit_re = []
orbit_im = []
z = mpmath.mpc(real='0.0', imag='0.0')
for _ in range(num_iterations):
z = mpmath.fadd(mpmath.fmul(z, z), c)
orbit_re.append(mpmath.nstr(mpmath.re(z)))
orbit_im.append(mpmath.nstr(mpmath.im(z)))
return [orbit_re, orbit_im]
def get_moments(coef):
# returns moments of function calculated from coefficients of Pade decomposition
n = len(coef) / 2
# for i in range(0, n):
# p[i] = coef[i]
# q[i] = coef[i + n]
# m[0] = re(p[n - 1])
# m[1] = re(p[n - 2] - m[0] * q[n - 1])
# m[2] = re(p[n - 3] - p[n - 1] * q[n - 2] - (p[n - 2] - p[n - 1] * q[n - 1]) * q[n - 1])
m = [1025.615, 2025.615, 3025.615]
m[0] = float(re(coef[n - 1]))
m[1] = float(re(coef[n - 2] - coef[n - 1] * coef[2 * n - 1]))
m[2] = float(
re(coef[n - 3] - coef[n - 1] * coef[2 * n - 2] -
(coef[n - 2] - coef[n - 1] * coef[2 * n - 1]) * coef[2 * n - 1]))
# m[1] = float(re(coef[n-2] - m[0] * coef[2*n-1]))
return m
def formatOutput( output ):
# filter out text strings
for c in output:
if c in '+-.':
continue
# anything with embedded whitespace is a string
if c in string.whitespace or c in string.punctuation:
return output
#print( )
#print( 'formatOutput 1:', output )
# override settings with temporary settings if needed
# if g.tempCommaMode:
# comma = True
# else:
# comma = g.comma
# output settings, which may be overrided by temp settings
outputRadix = g.outputRadix
integerGrouping = g.integerGrouping
leadingZero = g.leadingZero
if g.tempHexMode:
integerGrouping = 4
leadingZero = True
outputRadix = 16
if g.tempLeadingZeroMode:
leadingZero = True
if g.tempOctalMode:
integerGrouping = 3
leadingZero = True
outputRadix = 8
mpOutput = mpmathify( output )
imaginary = im( mpOutput )
real = re( mpOutput )
result, negative = formatNumber( real, outputRadix, leadingZero, integerGrouping )
if negative:
result = '-' + result
if imaginary != 0:
strImaginary, negativeImaginary = formatNumber( imaginary, outputRadix, leadingZero )
result = '( ' + result + ( ' - ' if negativeImaginary else ' + ' ) + strImaginary + 'j )'
#print( 'formatOutput 2:', output )
#print( )
return result
def formatOutput( output ):
# filter out text strings
for c in output:
if c in '+-.':
continue
# anything with embedded whitespace is a string
if c in string.whitespace or c in string.punctuation:
return output
# output settings, which may be overrided by temp settings
outputRadix = g.outputRadix
integerGrouping = g.integerGrouping
leadingZero = g.leadingZero
integerDelimiter = g.integerDelimiter
# override settings with temporary settings if needed
if g.tempCommaMode:
integerGrouping = 3 # override whatever was set on the command-line
leadingZero = False # this one, too
integerDelimiter = ','
if g.tempHexMode:
integerGrouping = 4
leadingZero = True
outputRadix = 16
if g.tempLeadingZeroMode:
leadingZero = True
if g.tempOctalMode:
integerGrouping = 3
leadingZero = True
outputRadix = 8
mpOutput = mpmathify( output )
imaginary = im( mpOutput )
real = re( mpOutput )
result, negative = formatNumber( real, outputRadix, leadingZero, integerGrouping,
integerDelimiter, g.decimalDelimiter )
if negative:
result = '-' + result
if imaginary != 0:
strImaginary, negativeImaginary = \
formatNumber( imaginary, outputRadix, leadingZero, integerGrouping, integerDelimiter, g.DecimalDelimiter )
result = '( ' + result + ( ' - ' if negativeImaginary else ' + ' ) + strImaginary + 'j )'
#print( 'formatOutput 2:', output )
#print( )
return result
def hilbert(d):
'''
Compute Hilbert Class Polynomial.
Follows pseudo code from Algorithm 7.5.8
Args:
d: fundamental discriminant
Returns:
Hilbert class number, Hilbert class polynomial, and all reduced forms
'''
# initialize
t = [1]
b = d % 2
r = floor(sqrt((-d)/3))
h = 0
red = set()
reduced_forms = reduced_form(d) # print h1
a_inverse_sum = sum(1/mpf(form[0]) for form in reduced_forms)
precision = round(pi*sqrt(-d)*a_inverse_sum / log(10)) + 10
mpmath.mp.dps = precision
# outer loop
while b <= r:
m = (b*b - d) / 4
m_sqrt = int(floor(sqrt(m)))
for a in range(1, m_sqrt+1):
if m % a != 0:
continue
c = m/a
if b > a:
continue
# optional polynomial setup
tau = (-b + 1j * sqrt(-d)) / (2*a)
f = power(dedekind_eta(2 * tau, precision) / dedekind_eta(tau, precision), 24)
j = power((256 * f + 1), 3) / f
if b==a or c==a or b==0:
# T = T * (X-j)
t = polynomial_mul(t, [-j, 1])
h += 1
red.add((a, b, c))
else:
poly = [j.real * j.real + j.imag * j.imag, -2 * j.real, 1]
t = polynomial_mul(t, poly)
h += 2
red.add((a, b, c))
red.add((a, -b, c))
b += 2
if red != reduced_forms:
raise ValueError('Reduced form inconsistent.')
return h, [int(floor(mpmath.re(p) + 0.5)) for p in t], red
def root_find_mp(func, x_loc, y_loc, step_size, points,
solver='halley', wordy=True, tol=1e-15):
jump_limit = 0.1
maxsteps = 200
try:
grad = ((points[-1, 1] - points[-2, 1]) +
(points[-1, 1] - 2*points[-2, 1] + points[-3, 1])) * \
np.abs(step_size/(points[-1, 0] - points[-2, 0]))
root_mp = mp.findroot(func, points[-1, 1] + grad, solver, tol)
root = float(mp.re(root_mp)) + 1j * float(mp.im(root_mp))
if np.abs(root-points[-1, 1]) < 0.1:
points = np.vstack([points, [x_loc, root]])
else:
raise ValueError('Jump of {:.5f} at x = {:.5f}, y = {:.5f}'.format(
np.abs(root-points[-1, 1]), points[-1, 0], points[-1, 1]+grad))
x_error = None
except IndexError:
if points.all() == 0:
root_mp = mp.findroot(func, y_loc, solver, tol, maxsteps)
root = float(mp.re(root_mp)) + 1j*float(mp.im(root_mp))
points[0,] = x_loc, root
elif points.shape == (1, 2):
root_mp = mp.findroot(func, points[-1, 1], solver, tol, maxsteps)
root = float(mp.re(root_mp)) + 1j*float(mp.im(root_mp))
points = np.vstack([points, [x_loc, root]])
elif points.shape == (2, 2):
grad = (points[-1, 1] - points[-2, 1]) *\
np.abs(step_size/(points[-1, 0] - points[-2, 0]))
root_mp = mp.findroot(func, points[-1, 1] + grad, solver, tol, maxsteps)
root = float(mp.re(root_mp)) + 1j*float(mp.im(root_mp))
if np.abs(root - points[-1, 1]) < jump_limit:
points = np.vstack([points, [x_loc, root]])
else:
raise ValueError('Jump of {:.5f} at x = {:.5f}, y = {:.5f}'.format(
np.abs(root-points[-1, 1]), points[-1, 0], points[-1, 1]+grad))
x_error = None
return points, np.abs(step_size), x_error, x_loc
def S_double_hol_conn(x1, x2, t, s, b):
v1, v2, vb1, vb2 = v(x1, t, s, b), v(x2, t, s, b), vb(x1, t, s,
b), vb(x2, t, s, b)
deriv = (pi**(-4)) * Dw(v1, s, b) * (-Dw(-vb1, s, b)) * Dw(
v2, s, b) * (-Dw(-vb2, s, b))
if s < 1:
conn = (s**2) * sinh(pi * (v1 - v2) / s) * sinh(pi * (vb1 - vb2) / s)
else:
conn = sin(pi * (v1 - v2)) * sin(pi * (vb1 - vb2))
return re(log(deriv * conn**2)) / 12 - log((x2 - x1)**2) / 6
def find_root(t, method="riemann_siegel"):
# Finds root at the point nearest >= t.
n_max = 100000
eps = 1e-11
if t < 14:
lower = 14
else:
lower = t
if method == "euler":
z_function = lambda t: euler.calculate_z(t)
elif method == "dirichlet":
z_function = lambda t: dirichlet.calculate_z(t)
else:
z_function = lambda t: riemann_siegel.calculate_z(t)
# Need to find the lowest upper bracketing value.
upper = next_upper_bracket(t=t, z_function=z_function)
n = 0
while n <= n_max:
mid = (upper + lower) / 2
value = z_function(mid)
# print "Trying between {} and {}".format(lower, upper)
# print "Got Z({}) = {}".format(mid, value)
if abs(value) < eps: # Found a root.
print "The root closest to t={} in the upward direction is {}.".format(t, mid)
return mid
else:
n += 1
if np.sign( mp.re(z_function(lower)) ) == np.sign( mp.re(z_function(mid)) ):
lower = mid
else:
upper = mid
return None
def select():
global lastop, a, listindex, argindex, theFile
if lastop == 0:
print(
'Parse Error: SELECT. came before corresponding EXP. or LOG. at instruction '
+ str(pointer))
print(greporig(pointer))
left = val
middle = a[listindex]
right = val
if listindex > 0:
left = a[listindex - 1]
if listindex < len(a) - 1:
right = a[listindex + 1]
print('Nearby Tape Values: ' + mpmath.nstr(left) + ',' +
mpmath.nstr(middle) + ',' + mpmath.nstr(right))
sys.exit()
elif lastop == 1:
if a[argindex] != 0 or mpmath.im(
a[listindex]) != 0 or a[listindex] >= 0:
try:
a[argindex] = mpmath.power(a[argindex], a[listindex])
except OverflowError:
print(
'Number too large to represent. Try increasing precision or moving default tape value closer to 1.'
)
print(greporig(pointer))
a[argindex] = mpmath.chop(a[argindex], tol)
else:
a[argindex] = mpmath.mpc('inf')
else:
if a[listindex] == 1:
print('Tried to take a log base one at instruction ' +
str(pointer))
print(greporig(pointer))
left = val
middle = a[listindex]
right = val
if listindex > 0:
left = a[listindex - 1]
if listindex < len(a) - 1:
right = a[listindex + 1]
print('Nearby Tape Values: ' + mpmath.nstr(left) + ',' +
mpmath.nstr(middle) + ',' + mpmath.nstr(right))
sys.exit()
a[argindex] = mpmath.log(a[argindex], a[listindex])
#patch up nans when arg is infinite, since we usually want zero for the real part then
if mpmath.isinf(mpmath.im(a[argindex])) and mpmath.isnan(
mpmath.re(a[argindex])):
a[argindex] = mpmath.mpc(0, mpmath.im(a[argindex]))
a[argindex] = mpmath.chop(a[argindex], tol)
oparg = 0
def __call__(self, r, z=1e-10):
"""
Returns the intensity distribution near the focal spot.
"""
u = self.normalized_distance(z)
v = self.normalized_radius(r)
α = self.alpha()
β = self.beta(u, v)
I = 2 * α**2 * mp.exp(-α) / (mp.cosh(α) - 1) / (u**2 + 4 * α**2)
if mp.sqrt(u**2 + 4 * α**2) < v:
U1 = lommelU(u, v, 1)
U2 = lommelU(u, v, 2)
U1s = mp.re(U1)
U1c = mp.im(U1)
U2s = mp.re(U2)
U2c = mp.im(U2)
return I * ((U1c + U2s)**2 + (U1s - U2c)**2)
if mp.sqrt(u**2 + 4 * α**2) > v:
V0 = lommelV(u, v, 0)
V1 = lommelV(u, v, 1)
V0s = mp.re(V0)
V0c = mp.im(V0)
V1s = mp.re(V1)
V1c = mp.im(V1)
exp = mp.exp(α * (1 - β))
cos = mp.cos(u * (1 + β) / 2)
sin = mp.sin(u * (1 + β) / 2)
return I * ((V0c - V1s - exp * cos)**2 + (V0s + V1c + exp * sin)**2)
raise ValueError('invalid coordinates')
def linear_equilibrium(mu1, mu2, complex_guess_1, complex_guess_2):
"""The linear equilibrium function finds a two dimensional vector solution. The solutions we are looking for are real.
Many solutions that can be found are complex and because of the numerical methods in which python finds these solutions
we have decided to use the mpmath modules. This method of finding the solution for our non linear heat flux uses
the muller method of numerical calculation. For more information on mpmath root finding:
http://mpmath.org/doc/1.1.0/calculus/optimization.html"""
import numpy as np
import sympy
import mpmath
#List of Constants
I0 = 1367.0 #Solar constant W/m^2
a = 2.8 #Unitless parameter needed for linear albedo feedback relationships more info in Budyko 1969
b = .009 #Another parameter needed for linear feedback relationship more info in Budyko 1969
sig = 5.67 * 10**-8 #Stephan boltzmann constant m^2 kg s^-2 K^-1
e = .64 #Emmisivity of earth
A = 2.7 #Heat flux parameter for linear heat forcing
solution_array = []
for x in mu1:
for y in mu2:
#Below we find the two-dimensional vector solutions in form of [polar_solution, tropical_solution]
f = [
lambda T1, T2:
(.156 * x * I0 * (1 - a) + .156 * x * I0 * b * T1 - e * sig *
(T1**4) + (A * (T2 - T1))), lambda T1, T2:
(.288 * y * I0 * (1 - a) + .288 * y * I0 * b * T2 - e * sig *
(T2**4) + (A * (T1 - T2)))
]
solution = np.array(
mpmath.findroot(f, ([complex_guess_1, complex_guess_2]),
solver='muller',
verify=False))
if abs(mpmath.im(solution[0])) > 1e-10:
solution[0] = np.nan
if abs(mpmath.im(solution[1])) > 1e-10:
solution[1] = np.nan
solution_array.append(
[mpmath.re(solution[0]),
mpmath.re(solution[1]), x, y])
return (solution_array)
def propagate(self, T):
"""
Returns new Gaussian beam with updated parameters from transfer matrix.
"""
z = self.origin
k = self.wavenumber
q = self.parameter
q = (T[0, 0] * q + T[0, 1]) / (T[1, 0] * q + T[1, 1])
z = mp.re(q) + z
w = mp.sqrt(2 * mp.im(q) / k)
return Gaussian(waist=w, origin=z, wavelength=self.wavelength)
def S_double_dirac(x1, x2, t, s, b):
v1, v2, vb1, vb2 = v(x1, t, s, b), v(x2, t, s, b), vb(x1, t, s,
b), vb(x2, t, s, b)
deriv = ((2 * pi)**(-4)) * Dw(v1, s, b) * (-Dw(-vb1, s, b)) * Dw(
v2, s, b) * (-Dw(-vb2, s, b))
conn = jt1(v1 - v2, j * s) * jt1(vb2 - vb1, j * s) / eta(j * s)**6
disconn = jt1(v1 - vb1 + j * s / 2, j * s) * jt1(v2 - vb2 + j * s / 2,
j * s)
cross = jt1(v1 - vb2 + j * s / 2, j * s) * jt1(v2 - vb1 + j * s / 2, j * s)
return re(log(deriv * (conn * disconn / cross)**2)) / 12 - log(
(x2 - x1)**2) / 6
def S_single_dirac(x1, x2, t, a):
t1, t2, tb1, tb2 = theta(x1, t, a), theta(x2, t,
a), thetaB(x1, t,
a), thetaB(x2, t, a)
z1, z2, zb1, zb2 = j * exp(j * t1), j * exp(j * t2), j * exp(
-j * tb1), j * exp(-j * tb2)
deriv = a**2 / (exp(j * (t1 - tb1 + t2 - tb2) / 2) * cos(t1) * cos(t2) *
cos(tb1) * cos(tb2))
conn = (z1 - z2) * (zb1 - zb2)
disconn = (z1 - zb1) * (z2 - zb2)
cross = (z1 - zb2) * (z2 - zb1)
return re(log(deriv * conn * disconn / cross)) / 6 - log((x2 - x1)**2) / 6
def non_linear_albedo_solver(mu1, mu2, guess_1, guess_2):
"""This solution is a modeling of a situation where solar intensity is decreased sufficiently until the earth
produces enough ice that the earth will reflect sufficient radiation to keep the earth in an ice age.
Ice ages are still subject to a heat flux, the below function finds the equilibria for the ice ages for
made with a non-linear heat flux."""
import numpy as np
import sympy
import mpmath
#List of Constants
I0 = 1367.0 #Solar constant W/m^2
a = 2.8 #Unitless parameter needed for linear albedo feedback relationships more info in Budyko 1969
b = .009 #Another parameter needed for linear feedback relationship more info in Budyko 1969
sig = 5.67 * 10**-8 #Stephan boltzmann constant m^2 kg s^-2 K^-1
e = .64 #Emmisivity of earth
A = 600 #Heat flux parameter for non-linear heat forcing
solution_array = []
for x in mu1:
for y in mu2:
#Below we find the two-dimensional vector solutions in form of [polar_solution, tropical_solution]
f = [
lambda T1, T2: (.156 * x * I0 * (1 - .75) - e * sig * (T1**4) +
(A / (T2 - T1))), lambda T1, T2:
(.288 * y * I0 * (1 - .75) - e * sig * (T2**4) + (A /
(T1 - T2)))
]
solution = np.array(
mpmath.findroot(f, ([guess_1, guess_2]),
solver='muller',
verify=False))
if abs(mpmath.im(solution[0])) > 1e-10:
solution[0] = np.nan
if abs(mpmath.im(solution[1])) > 1e-10:
solution[1] = np.nan
solution_array.append(
[mpmath.re(solution[0]),
mpmath.re(solution[1]), x, y])
return (solution_array)
def put_price_calc(given, op):
#Option data
K = given[0] #Strike
R = given[1] #Risk free rate of return (in %)
T = given[2] #Time to expiry
y = given[3] #Annual dividend yield
S0 = given[4] #Stock price
#Parameters over which data is being optimized
sigma = op[0] #Log return diffusion
lemu = op[1] #Arrival rate of up jumps
lemd = op[2] #Arrival rate of down jumps
etau = op[3] #Strength of up jump
etad = op[4] #Strength of down jump
k = math.log(K) #Log of strike rate
r = R / 100 #Risk free rate of return
mu = r - y - lemu / (etau - 1) + lemd / (
etad + 1) #Mu for the given data and parameters
#Risk neutral probability of finishing in the money
pi2 = 0.5 + mp.fdiv(1, mp.pi) * mp.quad(
lambda u: mp.re(
mp.fdiv(
mp.exp(-mp.mpc(0, 1) * u * k) * char_fun(
u, mu, sigma, lemu, lemd, etau, etad, T, S0),
mp.mpc(0, 1) * u)), [0, mp.inf])
#Delta of the option
pi1 = 0.5 + mp.fdiv(1, mp.pi) * mp.quad(
lambda u: mp.re(
mp.fdiv(
mp.exp(-mp.mpc(0, 1) * u * k) * char_fun(
u - mp.mpc(0, 1), mu, sigma, lemu, lemd, etau, etad, T, S0),
mp.mpc(0, 1) * u * char_fun(-mp.mpc(0, 1), mu, sigma, lemu,
lemd, etau, etad, T, S0))),
[0, mp.inf])
#Price of the put option
P_alternate = K * mp.exp(-r * T) * (1 - pi2) - S0 * mp.exp(
-y * T) * (1 - pi1)
return P_alternate
def S_double_hol_disconn(x1, x2, t, s, b):
v1, v2, vb1, vb2 = v(x1, t, s, b), v(x2, t, s, b), vb(x1, t, s,
b), vb(x2, t, s, b)
#difference from mirror twist op
p1, n1, p2, n2 = v1 - vb1 + j * s / 2, v1 - vb1 - j * s / 2, v2 - vb2 + j * s / 2, v2 - vb2 - j * s / 2
deriv = (pi**(-4)) * Dw(v1, s, b) * (-Dw(-vb1, s, b)) * Dw(
v2, s, b) * (-Dw(-vb2, s, b))
if s < 1:
g1 = min([re(sinh(pi * p1 / s)**2), re(sinh(pi * n1 / s)**2)])
g2 = min([re(sinh(pi * p2 / s)**2), re(sinh(pi * n2 / s)**2)])
disconnSquare = (s**4) * g1 * g2
else:
m1 = sin(pi * p1) * sin(pi * p2)
m2 = sin(pi * p1) * sin(pi * n2) * exp(pi * s)
m3 = sin(pi * n1) * sin(pi * p2) * exp(pi * s)
m4 = sin(pi * n1) * sin(pi * n2)
disconnSquare = min([re(m1**2), re(m2**2), re(m3**2), re(m4**2)])
return re(log(deriv * disconnSquare)) / 12 - log((x2 - x1)**2) / 6
def elliptic_core_g(x,y): x = x/2 y = y/2 factor = (cos(x)/sin(y) + sin(y)/cos(x) - (cos(y)/tan(y)/cos(x) + sin(x)*tan(x)/sin(y)))/pi k = tan(x)/tan(y) m = k*k n = (sin(x)/sin(y))*(sin(x)/sin(y)) u = asin(tan(y)/tan(x)) complete = ellipk(m) - ellippi(n, m) incomplete = ellipf(u,m) - ellippi(n/k/k,1/m)/k #incomplete = ellipf(u,m) - ellippi(n,u,m) return re(1.0 - factor*(incomplete + complete))
def getNthPadovanNumber( arg ):
n = fadd( real( arg ), 4 )
a = root( fsub( fdiv( 27, 2 ), fdiv( fmul( 3, sqrt( 69 ) ), 2 ) ), 3 )
b = root( fdiv( fadd( 9, sqrt( 69 ) ), 2 ), 3 )
c = fadd( 1, fmul( mpc( 0, 1 ), sqrt( 3 ) ) )
d = fsub( 1, fmul( mpc( 0, 1 ), sqrt( 3 ) ) )
e = power( 3, fdiv( 2, 3 ) )
r = fadd( fdiv( a, 3 ), fdiv( b, e ) )
s = fsub( fmul( fdiv( d, -6 ), a ), fdiv( fmul( c, b ), fmul( 2, e ) ) )
t = fsub( fmul( fdiv( c, -6 ), a ), fdiv( fmul( d, b ), fmul( 2, e ) ) )
return nint( re( fsum( [ fdiv( power( r, n ), fadd( fmul( 2, r ), 3 ) ),
fdiv( power( s, n ), fadd( fmul( 2, s ), 3 ) ),
fdiv( power( t, n ), fadd( fmul( 2, t ), 3 ) ) ] ) ) )
def isKthPower( n, k ):
if not isint( k, gaussian=True ) and isint( k ):
raise ValueError( 'integer arguments expected' )
if k == 1:
return 1
elif k < 1:
raise ValueError( 'a positive power k is expected' )
if im( n ):
# I'm not sure why this is necessary...
if re( n ) == 0:
return isKthPower( im( n ), k )
# We're looking for a Gaussian integer among any of the roots.
for i in [ autoprec( root )( n, k, i ) for i in arange( k ) ]:
if isint( i, gaussian=True ):
return 1
return 0
else:
rootN = autoprec( root )( n, k )
return 1 if isint( rootN, gaussian=True ) else 0
def g(theta,phi):
R = abs(re(spherharm(l,m,theta,phi)))
x = R*cos(phi)*sin(theta)
y = R*sin(phi)*sin(theta)
z = R*cos(theta)
return [x,y,z]
def handleOutput( valueList, indent=0, file=sys.stdout ):
'''
Once the evaluation of terms is complete, the results need to be
translated into output.
If the result is a list or a generator, special formatting turns those
into text output. Date-time values and measurements also require special
formatting.
Setting file to an io.StringIO objects allows for 'printing' to a string,
which is used by makeHelp.py to generate actual rpn output for the examples.
'''
if valueList is None:
return file
indentString = ' ' * indent
if len( valueList ) != 1:
if g.checkForSingleResults:
print( 'valueList', valueList )
raise ValueError( 'unexpected multiple results!' )
valueList = [ valueList ]
if isinstance( valueList[ 0 ], RPNFunction ):
print( indentString + 'rpn: unexpected end of input in function definition', file=file )
else:
mp.pretty = True
result = valueList.pop( )
if result is nan:
return file
if g.comma:
g.integerGrouping = 3 # override whatever was set on the command-line
g.leadingZero = False # this one, too
g.integerDelimiter = ','
else:
g.integerDelimiter = ' '
if isinstance( result, RPNGenerator ):
formatListOutput( result.getGenerator( ), indent=indent, file=file )
elif isinstance( result, list ):
formatListOutput( result, indent=indent, file=file )
else:
# single result
if isinstance( result, RPNDateTime ):
outputString = formatDateTime( result )
elif isinstance( result, str ):
result = checkForVariable( result )
outputString = result
else:
# output the answer with all the extras according to command-line arguments
# handle the units if we are displaying a measurement
if isinstance( result, RPNMeasurement ):
outputString = formatOutput( nstr( result.value, g.outputAccuracy, min_fixed=-g.maximumFixed - 1 ) )
outputString += ' ' + formatUnits( result )
# handle a complex output (mpmath type: mpc)
elif isinstance( result, mpc ):
#print( 'result', result, type( result ) )
#print( 'im', im( result ), type( im( result ) ) )
#print( 're', re( result ), type( re( result ) ) )
imaginary = im( result )
if im( result ) > 0:
outputString = '(' + formatOutput( nstr( mpmathify( re( result ) ), g.outputAccuracy, min_fixed=-g.maximumFixed - 1 ) ) + \
' + ' + formatOutput( nstr( mpmathify( im( result ) ), g.outputAccuracy, min_fixed=-g.maximumFixed - 1 ) ) + 'j)'
elif im( result ) < 0:
outputString = '(' + formatOutput( nstr( mpmathify( re( result ) ), g.outputAccuracy, min_fixed=-g.maximumFixed - 1 ) ) + \
' - ' + formatOutput( nstr( fneg( mpmathify( im( result ) ) ), g.outputAccuracy, min_fixed=-g.maximumFixed - 1 ) ) + 'i)'
else:
outputString = formatOutput( nstr( re( result ), g.outputAccuracy, min_fixed=-g.maximumFixed - 1 ) )
# otherwise, it's a plain old mpf
else:
outputString = formatOutput( nstr( result, g.outputAccuracy, min_fixed=-g.maximumFixed - 1 ) )
print( indentString + outputString, file=file )
# handle --identify
if g.identify:
handleIdentify( result, file )
saveResult( result )
if g.timer or g.tempTimerMode:
print( '\n' + indentString + '{:.3f} seconds'.format( time.process_time( ) - g.startTime ), file=file )
def g(theta,phi):
R = abs(re(a*spherharm(l,m1,theta,phi)+b*spherharm(l,m2,theta,phi)))**2
x = R*cos(phi)*sin(theta)
y = R*sin(phi)*sin(theta)
z = R*cos(theta)
return [x,y,z]
def z_function(t, N=100000): zeta = zeta_function(1/2 + (1.j)*t, N) return mp.re( np.exp( 1.j * riemann_siegel_theta(t) ) * zeta )
def isPower( n, k ):
#print( 'log( n )', log( n ) )
#print( 'log( k )', log( k ) )
#print( 'divide', autoprec( lambda n, k: fdiv( re( log( n ) ), re( log( k ) ) ) )( n, k ) )
return isInteger( autoprec( lambda n, k: fdiv( re( log( n ) ), re( log( k ) ) ) )( n, k ) )
def arFpDensity(Da, r0a, La, ta):
D, r0, L, t = (m.mpmathify(str(x)) for x in (Da, r0a, La, ta))
pref = m.exp(-r0 ** 2 / (4 * D * t)) * D / (4 * m.sqrt(m.pi) * (D * t) ** 1.5)
z = (1j * L * r0) / (2 * D * t)
q = m.exp(-L ** 2 / (D * t))
return float(m.re(2 * pref * (r0 * m.jtheta(4, z, q) - 0 * 1j * L * m.djtheta(4, z, q))))
def getReal( n ):
return re( n )
