def get_params_global(data):
# First curve fit a single experiment to get a decent
# initial guess for the global fit
# parms = (2.0e-9*1e-4, 9.0e-3/1e-4, 1.0, 1.0)
#parms = 1e-7, 1e-3, -1000, 1000
parms = 1e-7, 1e-3, -3000, 3000
i = 1
pg, pcovg = curve_fit(lambda times, *p0: wvtr_vec_global(
data[i].ts, data[i].RH, data[i].temp, data[i].thickness, *p0),
data[i].ts,
data[i].js,
p0=parms)
# This is global fit using function above
eg = lambda par: esq_global(par, data)
pg2 = minimize(eg,
pg,
method='Nelder-Mead',
tol=1.0e-4,
options={
"maxfev": 100000,
"maxiter": 100000
})
print "Global fitting residual: ", eg(pg2.x)
return pg2.x
def esq_global(par, data):
L = 0.0
for i in range(len(data)):
fsim = wvtr_vec_global(data[i].ts, data[i].RH, data[i].temp,
data[i].thickness, *par)
L = L + np.sum((fsim - data[i].js)**2)
return L * 1e12 # Scale so that it is O(1)
def lnlike(par, data):
L = 0.0
for i in range(len(data)):
fsim = wvtr_vec_global(data[i].ts, data[i].RH, data[i].temp,
data[i].thickness, *par)
inv_sigma2 = 1 / (data[i].sigma * data[i].sigma)
L = L - 0.5 * np.sum((fsim - data[i].js)**2 * inv_sigma2) / len(fsim)
return L
# # plt.plot(err)
# # plt.show()
# plt.plot(fsim)
# plt.plot(data[i].js)
# plt.show()
# L = L - 0.5 * np.sum( (fsim - data[i].js)**2 * inv_sigma2)
#
# print "loglikelihood = ", L
#
# end explore likelihood computation
# Plot out global and fits of fits
for i in range(len(data)):
ax = plt.subplot(nrows, ncols, i)
thisfit_global_2step = wvtr_vec_global(data[i].ts, data[i].RH,
data[i].temp, data[i].thickness,
*pg_2step)
thisfit_global = wvtr_vec_global(data[i].ts, data[i].RH, data[i].temp,
data[i].thickness, *pg_1step)
thisfit = wvtr_vec(data[i].ts, data[i].RH, data[i].temp,
data[i].thickness, *pfits[i])
ax = plt.subplot(nrows, ncols, i)
l = ax.plot(data[i].ts, data[i].js, 'x')
ax.plot(data[i].ts, data[i].js + 2 * data[i].sigma, '--g')
ax.plot(data[i].ts, data[i].js - 2 * data[i].sigma, '--g')
ax.plot(data[i].ts, thisfit_global_2step, '--r', lw=2)
ax.plot(data[i].ts, thisfit_global, '--m', lw=2)
ax.plot(data[i].ts, thisfit, '--b', lw=2)