def load_log(filename, enforce_float=False):
"""
loads system from pickled file
"""
import networks
import gmodes, pmodes
from mode_selection import compute_wo
network = networks.network()
f = open(filename, "r")
Porb = pickle.load(f)
eccentricity = pickle.load(f)
Mprim = pickle.load(f)
Mcomp = pickle.load(f)
Rprim = pickle.load(f)
wo = compute_wo(Mprim, Rprim)
for mode_tuple, mode_type in pickle.load(f):
if mode_type == "generic":
mode = networks.mode().from_tuple(mode_tuple)
elif mode_type == "gmode":
mode = gmodes.gmode().from_tuple(mode_tuple, wo=wo)
elif mode_type == "pmode":
mode = pmodes.pmode().from_tuple(mode_tuple)
else:
sys.exit("unknown mode_type : %s" % mode_type)
network.modes.append(mode)
network.K = pickle.load(f)
if enforce_float:
network.K = [[(i, j, float(k)) for i, j, k in coup] for coup in network.K]
network._update()
f.close()
system = networks.system(Mprim, Mcomp, Rprim, Porb, eccentricity, net=network)
return system
raise ValueError, "Mcomp mismatch" else: Mcomp = sdata["system"]["Mcomp/Mjup"] del clusters Edot_lin = [] resonances = set() O = np.linspace(minO, maxO, opts.n_pts) l = 2 ### dominant harmonic of the tide m = 2 ecc = 0.0 ### take zero eccentricity limit for o in O: n_res = int(round(opts.alpha*l/(m*o), 0)) edot_lin = 0.0 for n in range(max(1, n_res-opts.n_lin), n_res+opts.n_lin): mode = gm.gmode(n=n, l=l, m=m, alpha=opts.alpha, c=opts.c, wo=wo) mode.compute_forcing(2*np.pi/o, ecc, opts.Mprim, Mcomp, opts.Rprim) w, y, U = mode.get_wyU() resonances.add( w/m ) for u, k in U: edot_lin += 2*y*(w*u)**2 / ( (abs(w)-m*k*o)**2 + y**2 ) ### add linear result Edot_lin.append( edot_lin ) nmu.set_units(opts.unit_system) Eo = nmu.G*(opts.Mprim*nmu.Msun)**2 / (opts.Rprim*nmu.Rsun) Edot_lin = np.array(Edot_lin)*Eo ### plot linear result #ax.semilogy(O, Edot_lin, color='k', alpha=0.5) iax.plot(nmu.convert_time((2*np.pi/O), nmu.units["time"], time_unit), Edot_lin, color='k', alpha=0.5)