cc = ((rms-rms_o)>=0.0) & (t<5.0)
fc = np.zeros(rms.size)
fc[cc] = (rms-rms_o)[cc]
#b = B-5.0
#b[b<=0] = 0.0
b = B
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++
tau_o, bp = 3.0, -0.1 # LIBRES
q, off, bo = -5.53, 0.64, 10.0 # FIJOS
p = Parameters()
p.add('q', value=-5.53, vary=False)
p.add('tau', value=3.0, vary=False)
p.add('off', value=0.64, vary=False)
p.add('bp', value=-0.1, vary=False)
p.add('bo', value=10.0, vary=False)
TAUs = np.linspace(2.5, 12.0, 50)
BPs = np.linspace(-0.5, 0.0, 50)
for p['tau'].value in TAUs:
for p['bp'].value in BPs:
fit = ff.fit_forbush([t, fc, crs, b], [tau_o, q, off, bp, bo])
diff = fit.residuals(p)
#print diff
#diff = ff.nCR2([t, rms, B], tau, q, off, bp, bo)
cc = ((rms - rms_o) >= 0.0) & (t < 5.0) fc = np.zeros(rms.size) fc[cc] = (rms - rms_o)[cc] #tau = 3.0 #ncr = nCR2([t, fc], tau, q) #++++++++++++++++++++++++++++++++++++++++++++++++ figura fig = figure(1, figsize=(6, 4)) ax0 = fig.add_subplot(111) ax1 = ax0.twinx() #--- plot der ax1.plot(t[1:-1], fc[1:-1], c='gray') tau_o, q, off = 5., -6., 0.1 #2.0, -400.0 fit = ff.fit_forbush([t, fc, crs], [tau_o, q, off]) fit.make_fit() print fit.par #ncr = nCR2([t, fc], tau, q) """ #--- plot izq ax0.plot(t, ncr, label='$\\tau=%2.2g$'%tau) ax0.plot(t, crs, '-o', c='k', ms=3) ax0.axhline(y=0.0, c='g') ax0.axvline(x=0, ls='--', c='gray', lw=3) ax0.axvline(x=1, ls='--', c='gray', lw=3) ax0.axvline(x=4, ls='--', c='gray', lw=3) ax0.legend() ax0.grid() ax0.set_xlim(-2,+7)
tau = Lim(0.1, 5., n=nbin)
q = Lim(-20., -0.1, n=nbin)
off = Lim(0., 1., n=nbin)
bp = Lim(-1., 0., n=nbin)
bo = Lim(0., 20., n=nbin)
#--- slice object
rranges = (
slice(tau.min, tau.max, tau.delta()),
slice(q.min, q.max, q.delta()),
slice(off.min, off.max, off.delta()),
slice(bp.min, bp.max, bp.delta()),
slice(bo.min, bo.max, bo.delta()),
)
#--- start && run the fitter
data = np.array([t, fc, crs, b])
fit = ff.fit_forbush(data, [tau_, q_, off_, bp_, bo_])
fit.make_fit_brute(rranges)
print fit.par
#--- output en hdf5
fo = h5(fname_out, 'w')
for pname in fit.par.keys():
fo[pname] = fit.par[pname]
#--- guardamos la grilla de exploracion
fo['grids/tau'] = [tau.min, tau.max, tau.delta(), tau.n]
fo['grids/q'] = [q.min, q.max, q.delta(), q.n]
fo['grids/off'] = [off.min, off.max, off.delta(), off.n]
fo['grids/bp'] = [bp.min, bp.max, bp.delta(), bp.n]
fo['grids/bo'] = [bo.min, bo.max, bo.delta(), bo.n]
#------------------
b = B
_tau = np.linspace(*pa.seed_tau)
_q = np.linspace(*pa.seed_q)
_off = np.linspace(*pa.seed_off)
_bp = np.linspace(*pa.seed_bp)
_bo = np.linspace(*pa.seed_bo)
#tau_o, q, off = 3., -6., 0.1 #2.0, -400.0
#bp, bo = -0.1, 10.0
data = np.array([t, fc, crs, b])
_fit, it = [], 0
all_seeds = list(itertools.product(_tau, _q, _off, _bp, _bo))
for tau_o, q, off, bp, bo in all_seeds:
print " it, total: ", it + 1, len(all_seeds)
_fit += [ff.fit_forbush(data, [tau_o, q, off, bp, bo])]
_fit[it].make_fit(monit=True)
it += 1
MinRes = np.array([_fit[i].resid for i in range(len(_fit))])
ind = find(MinRes.min() == MinRes)
for i in ind:
print _fit[i].niter
print _fit[i].par
#sys.exit(0)
#print fit.par
"""
tau, bp = 2.36, 0.0
q, off, bo = -9.373, 0.89, 16.15
"""