def load_ji():
ji13r = h5py.File('../../PaperRunaway/ji_radial_profile_SPHERE.hdf5', 'r')
ji13 = {"r": np.array(ji13r["r"]),
"omega": np.array(ji13r["omega"]),
"temp": np.array(ji13r["temp"]),
"dens": np.array(ji13r["dens"])}
mystar = sms.mhs_steve(0., 1e8, mintemp=3e7, stop_mindenserr=1e0, dontintegrate=True)
ji13["eint"] = np.zeros(len(ji13["r"]))
ji13["edeg"] = np.zeros(len(ji13["r"]))
ji13["press"] = np.zeros(len(ji13["r"]))
ji13["ent"] = np.zeros(len(ji13["r"]))
for i in range(len(ji13["r"])):
ji13["press"][i], ji13["ent"][i] = mystar.getpress_rhoT(ji13["dens"][i], ji13["temp"][i])
ji13["eint"][i], ji13["edeg"][i], dummy = mystar.gethelmeos_energies(ji13["dens"][i], ji13["temp"][i])
ji13["m"] = 4.*np.pi*scipyinteg.cumtrapz(ji13["r"]**2*ji13["dens"], x=ji13["r"], initial=0.)
ji13["m_privcom"] = 1.0675 # From Ji, private communication, of all mass below r = 1.5e9 cm
return ji13
def make_runaway_steve(starmass=1.2*1.9891e33, mymag=False, omega=0., omega_run_rat=0.8, S_arr=10**np.arange(7.5,8.2,0.25),
mlt_coeff="phil", uvs_k=3, uvs_s=None,
mintemp=1e5, S_old=False, mass_tol=1e-6, P_end_ratio=1e-8,
densest=False, stop_mindenserr=1e-10, L_tol=1e-6, keepstars=False,
omega_crit_tol=1e-3, omega_warn=10., de_err_tol=[1e6, 1e7], verbose=True):
"""Obtains runaway of a star of some given mass, magnetic field, and rotation. Outputs an object (usually several hundred megs large) that includes run inputs as "run_inputs", as well as all stellar output curves (hence its size) under "stars".
Arguments:
starmass : wanted mass
mymag : magnetic profile object. Defaults to false, meaning no magnetic field.
omega : rigid rotation angular velocity. Defaults to 0 (non-rotating). If < 0, attempts to estimate break-up omega with self.getomegamax(), if >= 0, uses user defined value.
S_arr : list of central entropy values in the runaway track
mintemp : temperature floor, effectively switches from adiabatic to isothermal profile if reached
S_old : if True, uses the S_old function of StarModSteve to prevent entropy from decreasing below previously calculated entropy curve (which should not occur in a runaway and leads to unphysical "right hooks" in runaway rho-T diagrams).
mlt_coeff : ["phil", "wwk", "kippw", "steve"]
Sets mixing length theory coefficients for calculating velocity
and superadiabatic temperature gradients. "phil" is the standard
faire coefficients suggested by Phil Chang; "wwk" is back-derived
from Woosley, Wunch and Kuhlen 2004; "kippw" is from
Kippenhahn & Wieigert (identical to Cox & Giuli); "steve"
is from Stevenson 1979. Since Stevenson's rotational and magnetic
corrections to convection are expressed as ratios of velocity and
temperature gradient, they can be used with any of these mlt_coeffs.
uvs_k : degree of the smoothing spline in scipy.interpolate.UnivariateSpline.
Must be <= 5; default is k=3.
uvs_s : UnivariateSpline positive smoothing factor used to choose the
number of knots. Default is None.
mass_tol : fractional tolerance between mass wanted and mass produced by self.getstarmodel()
P_end_ratio : ratio of P/P_c at which to terminate stellar integration
densest : central density initial estimate for self.getstarmodel()
stop_mindenserr : density floor, below which integration is halted.
Default is set to 1e-10 to prevent it from ever being reached.
Helmholtz sometimes has trouble below this 1e-8; try adjusting this
value to eliminate inverter errors.
L_tol : conservation of angular momentum error tolerance
omega_crit_tol : when using mystar.getomegamax(), absolute error tolerance for maximum omega
omega_warn : stop integration within mystar.getrotatingstarmodel() if self.omega approaches omega_warn*omega_crit estimate. Defaults to 10 to prevent premature stoppage.
de_err_tol : densities at which relaxed error tolerances kick in.
verbose : report happenings within code
"""
# Save a few details about the magnetic field (though we'll need better records for the actual paper)
try:
mymagsave = np.array([float(mymag.fBfld_r(0)), float(mymag.fBfld_r(2e8))])
except:
if verbose:
print "Magnetic field not found! Hopefully this is what you wanted."
mymagsave = False
r_in = {"mass": starmass,
"magprofile": mymagsave,
"omega": omega,
"omega_run_rat": omega_run_rat,
"S_arr": S_arr,
"mintemp": mintemp,
"S_old": S_old,
"mlt_coeff": mlt_coeff,
"composition": "CO",
"tog_coul": True,
"P_end_ratio": P_end_ratio,
"ps_eostol": 1e-8,
"fakeouterpoint": False,
"stop_invertererr": True,
"stop_mrat": 2.,
"stop_positivepgrad": True,
"stop_mindenserr": stop_mindenserr,
"densest": densest,
"mass_tol": mass_tol,
"L_tol": L_tol,
"omega_crit_tol": omega_crit_tol,
"omega_warn": omega_warn,
"lowerr_tol": de_err_tol[0],
"mederr_tol": de_err_tol[1],
"uvs_k": uvs_k,
"uvs_s": uvs_s}
if (omega != 0) or mymag:
print "*************You want to make an MHD/rotating star; let's first try making a stationary pure hydro star!************"
mymagzero = magprof.magprofile(None, None, None, None, blankfunc=True)
hstar = Star.mhs_steve(r_in["mass"], False, magprofile=mymagzero, omega=0., temp_c=5e6,
mintemp=r_in["mintemp"], composition=r_in["composition"], togglecoulomb=r_in["tog_coul"],
mlt_coeff=r_in["mlt_coeff"], P_end_ratio=r_in["P_end_ratio"],
ps_eostol=r_in["ps_eostol"], fakeouterpoint=r_in["fakeouterpoint"],
stop_invertererr=r_in["stop_invertererr"], stop_mrat=r_in["stop_mrat"],
stop_positivepgrad=r_in["stop_positivepgrad"], stop_mindenserr=r_in["stop_mindenserr"],
densest=r_in["densest"], mass_tol=r_in["mass_tol"], L_tol=r_in["L_tol"],
omega_crit_tol=r_in["omega_crit_tol"], nreps=100, verbose=verbose)
densest=0.9*hstar.data["rho"][0]
print "*************Okay, let's make a low-temperature (MHD/rotating) star************"
#Rest after this is identical to function call above
mystar = Star.mhs_steve(r_in["mass"], False, magprofile=mymag, omega=r_in["omega"], temp_c=5e6,
mintemp=r_in["mintemp"], composition=r_in["composition"], togglecoulomb=r_in["tog_coul"],
mlt_coeff=r_in["mlt_coeff"], P_end_ratio=r_in["P_end_ratio"],
ps_eostol=r_in["ps_eostol"], fakeouterpoint=r_in["fakeouterpoint"],
stop_invertererr=r_in["stop_invertererr"], stop_mrat=r_in["stop_mrat"],
stop_positivepgrad=r_in["stop_positivepgrad"], stop_mindenserr=r_in["stop_mindenserr"],
densest=r_in["densest"], mass_tol=r_in["mass_tol"], L_tol=r_in["L_tol"],
omega_crit_tol=r_in["omega_crit_tol"], nreps=100, verbose=verbose)
if r_in["omega"] < 0:
print "FOUND critical Omega = {0:.3e}! We'll use {1:.3e} of this value for the runaway.".format(mystar.omega, r_in["omega_run_rat"])
r_in["omega_crit_foundinfirststep"] = mystar.omega
mystar.omega *= r_in["omega_run_rat"]
mystar.getstarmodel(densest=0.9*mystar.data["rho"][0], P_end_ratio=r_in["P_end_ratio"], ps_eostol=r_in["ps_eostol"])
if r_in["omega"]:
if mystar.omega > mystar.getcritrot(max(mystar.data["M"]), mystar.data["R"][-1]):
print "WARNING: exceeding estimated critical rotation! Consider restarting this run."
r_in["L_original"] = mystar.getmomentofinertia(mystar.data["R"], mystar.data["rho"])[-1]*mystar.omega
mystar.L_want = r_in["L_original"] # Store initial angular momentum for future use.
out_dict = {"temp_c": np.zeros(len(S_arr)+1),
"dens_c": np.zeros(len(S_arr)+1),
"omega": np.zeros(len(S_arr)+1),
"B_c": np.zeros(len(S_arr)+1),
"S_c": np.zeros(len(S_arr)+1),
"R": np.zeros(len(S_arr)+1),
"stars": []}
if "R_nuc" not in mystar.data.keys(): # Obtain timescale info if it's not already printed.
mystar.gettimescales()
out_dict["run_inputs"] = r_in
if r_in["omega"] < 0:
out_dict["omega_crit"] = r_in["omega_crit_foundinfirststep"]
out_dict["S_c"][0] = mystar.data["Sgas"][0]
out_dict["temp_c"][0] = mystar.data["T"][0]
out_dict["dens_c"][0] = mystar.data["rho"][0]
out_dict["omega"][0] = mystar.omega
out_dict["B_c"][0] = np.mean(mystar.data["B"][:10])
out_dict["R"][0] = mystar.data["R"][-1]
if keepstars:
out_dict["stars"].append(copy.deepcopy(mystar))
else:
out_dict["stars"].append(copy.deepcopy(mystar.data))
# Obtain tau_cc = tau_neutrino equality line
td = rtc.timescale_data(max_axes=[1e12,1e12])
if r_in["S_old"]:
ignition_line_f = td.get_tauneunuc_line()
for i in range(len(r_in["S_arr"])):
print "*************Star #{0:d}, entropy = {1:.3f}************".format(i, r_in["S_arr"][i])
# If we turn on S_old and, during the previous step, we passed the ignition line, start recording old entropy profiles
# and passing them on to the next star.
if r_in["S_old"] and mystar.data["T"][0] > ignition_line_f(mystar.data["rho"][0]):
# print "Using previous entropy profile"
# if r_in["rezero_Sold"]: # Bomb-proofing to keep extremely low entropy values from messing up integration
# mystar.data["Sgas"][mystar.data["Sgas"] < 0.] = 0.
myS_old = Star.sprof.entropy_profile(mystar.data["M"], mystar.data["Sgas"], mystar.data["v_conv_st"], spline_k=r_in["uvs_k"], spline_s=r_in["uvs_s"])
mystar.S_old = myS_old.S_old
mystar.dS_old = myS_old.dS_old
mystar.vconv_Sold = myS_old.vconv_Sold
outerr_code = obtain_model(mystar, i, r_in, omega_warn=r_in["omega_warn"], verbose=verbose)
if outerr_code:
print "===== RUNAWAY.PY REPORTS OUTERR: ", outerr_code, "for star", i, "so will stop model making! ====="
break
if "R_nuc" not in mystar.data.keys(): # Obtain timescale info if it's not already printed.
mystar.getconvection(td=td)
mystar.gettimescales()
out_dict["S_c"][i+1] = mystar.data["Sgas"][0]
out_dict["temp_c"][i+1] = mystar.data["T"][0]
out_dict["dens_c"][i+1] = mystar.data["rho"][0]
out_dict["omega"][i+1] = mystar.omega
out_dict["B_c"][i+1] = np.mean(mystar.data["B"][:10])
out_dict["R"][i+1] = mystar.data["R"][-1]
if keepstars:
out_dict["stars"].append(copy.deepcopy(mystar))
else:
out_dict["stars"].append(copy.deepcopy(mystar.data))
return out_dict
