def mt_rekursi(res,tebal,freq):
k = np.sqrt(1j*2.*np.pi*4.*np.pi*1e-7*(freq/res))
t = tebal
c = k[-2]/k[-1]
for i in range(len(res)-2,0,-1):
c = k[i-1]/k[i]*mpm.coth(k[i]*t[i]+mpm.acoth(c))
c = 1./k[0]*mpm.coth(k[0]*t[0]+mpm.acoth(c))
# if c.real!=c.real and c.imag!=c.imag:
# print 'res',res[:5]
return c
def FJC(f, b=None, k_pN_nm=0.1, L_nm=20, S_pN=1e3):
if b == None:
b = 3 * kT / (k_pN_nm * L_nm)
x = f * b / kT
z = []
w = []
for xi in x:
z.append(L_nm * (mpmath.coth(xi) - 1 / xi))
w.append(np.log(np.sinh(xi) / xi))
z = np.asarray(z)
z += L_nm * f / S_pN
w = np.asarray(w)
w += f**2 / (2 * S_pN)
return z, w
def intReT(t):
return quad(lambda w: w**(-2) * Jw(w) *
(1 - cos(w * t)) * coth(w / (2 * T)),
0,
inf,
limit=subdiv)[0]
# -*- coding: utf-8 -*- """ Created by libsedmlscript v0.0.1 """ from sed_roadrunner import model, task, plot from mpmath import coth #---------------------------------------------- coth(0.5)
def func(t):
return mp.coth(g*mub*Hz/(kb*t))-1.0/(g*mub*Hz/(kb*t))
def eval(self, z):
return mpmath.coth(z)
def eval(self, z):
return mpmath.coth(z)
def sigma(iterative): #Boudreau 1997 pg 315. Sigma is the "amount of blending" between backward and central differences
sig = mpmath.coth((w*dz)/(2*find_Ds(iterative))) - ((2*find_Ds(iterative))/(w*dz))
return sig
