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 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