def lognormal_adsolver(pts): ec = util.ecdf(np.array(pts), issorted=False) x = ec[:,0] xrev = util.reverse(x) n = float((len(x))) i = np.array(range(len(x)), dtype=float) l1 = logn.Lognormal.fromFit(pts) imu = l1.mu() isig = l1.sigma() ivs = [imu, isig] ovs = (i,x,xrev,n) print ovs (fvals, infodict, ier, mesg) = opt.fsolve(solve_admin, ivs, ovs, None, 1, 0) f_mu = fvals[0] f_sigma = fvals[1] if ier != 1: raise logn.LognormalConvergenceError(mesg, (f_mu, f_sigma)) return logn.Lognormal(f_mu, f_sigma)
def lognormal_adsolver(pts):
ec = util.ecdf(np.array(pts), issorted=False)
x = ec[:, 0]
xrev = util.reverse(x)
n = float((len(x)))
i = np.array(range(len(x)), dtype=float)
l1 = logn.Lognormal.fromFit(pts)
imu = l1.mu()
isig = l1.sigma()
ivs = [imu, isig]
ovs = (i, x, xrev, n)
print ovs
(fvals, infodict, ier, mesg) = opt.fsolve(solve_admin, ivs, ovs, None, 1,
0)
f_mu = fvals[0]
f_sigma = fvals[1]
if ier != 1:
raise logn.LognormalConvergenceError(mesg, (f_mu, f_sigma))
return logn.Lognormal(f_mu, f_sigma)
def lognormal_nrsolver(pts): ec = util.ecdf(np.array(pts), issorted=False) x = ec[:,0] xrev = util.reverse(x) n = float(len(x)) i = np.array(range(len(x)), dtype=float) l1 = logn.Lognormal.fromFit(pts) imu = l1.mu() isig = l1.sigma() ivs = [imu, isig] ovs = (i, x, xrev, n) [mu, sigma] = opt.root(solve_admin, ivs, ovs) return [mu, sigma]
def lognormal_nrsolver(pts):
ec = util.ecdf(np.array(pts), issorted=False)
x = ec[:, 0]
xrev = util.reverse(x)
n = float(len(x))
i = np.array(range(len(x)), dtype=float)
l1 = logn.Lognormal.fromFit(pts)
imu = l1.mu()
isig = l1.sigma()
ivs = [imu, isig]
ovs = (i, x, xrev, n)
[mu, sigma] = opt.root(solve_admin, ivs, ovs)
return [mu, sigma]
def tlladmin_solver(pts):
ec = util.ecdf(pts)
x = ec[:, 1]
xrev = util.reverse(x)
i = np.array(range(len(x)), dtype=float)
n = float(len(x))
ib = 1.0
ic = float(np.median(x))
id = ic / float(x.max())
ivs = [ib, ic, id]
ovs = (i, x, xrev, n)
(fvals, infodict, ier, mesg) = opt.fsolve(tll_admin, ivs, ovs, None, 1, 0)
f_b = fvals[0]
f_c = fvals[1]
f_d = fvals[2]
if ier != 1:
raise ml.ModLavConvergenceError(mesg, (f_b, f_c, f_d))
return ml.ModLav(f_b, f_c, f_d)
def tlladmin_solver(pts): ec = util.ecdf(pts) x = ec[:,1] xrev = util.reverse(x) i = np.array(range(len(x)), dtype=float) n = float(len(x)) ib = 1.0 ic = float(np.median(x)) id = ic/float(x.max()) ivs = [ib,ic,id] ovs = (i,x,xrev,n) (fvals, infodict, ier, mesg) = opt.fsolve(tll_admin, ivs, ovs, None, 1, 0) f_b = fvals[0] f_c = fvals[1] f_d = fvals[2] if ier != 1: raise ml.ModLavConvergenceError(mesg, (f_b,f_c,f_d)) return ml.ModLav(f_b, f_c, f_d)
