def lnlike2(par, data_table, ebv_map=0, gaussian_ebv=False, return_fit=False):
g1, Teff1, g2, Teff2, EBV, sig_ext = par
if (g1 <= 5) or (g1 > 25): return -np.inf
if (g2 <= 5) or (g2 > 25): return -np.inf
if (Teff1 <= 3450) or (Teff1 > 8600): return -np.inf
if (Teff2 <= 3450) or (Teff2 > 8600): return -np.inf
if (sig_ext < 0): return -np.inf
if (EBV < 0): return -np.inf
if gaussian_ebv:
j = np.where(data_table['type'] == 'ebv')
v = data_table['value'][j]
s = data_table['error'][j]
lnprior = -0.5 * np.sum(((v - EBV) / s)**2)
elif (EBV > ebv_map):
lnprior = -0.5 * ((EBV - ebv_map) / 0.034)**2
else:
lnprior = 0.0
t = synth_binary(g1, Teff1, g2, Teff2, EBV)
j = joinTables(data_table, t, keys=("band", "type"))
wt = weights(j, sig_ext)
z2 = (j["value_1"] - j["value_2"])**2 * wt
j.add_column(Column(np.sqrt(z2), name="z", format="%.2f"))
if return_fit:
return j
else:
lnlike = -0.5 * (np.sum(z2 - np.log(wt)))
if np.isfinite(lnlike):
return lnlike + lnprior
else:
return -np.inf
def chisqd(param, data_table, sig_ext):
g1, Teff1, dg, Teff2, EBV = param
g2 = g1 + dg
t = synth_binary(g1, Teff1, g2, Teff2, EBV)
j = joinTables(data_table, t, keys=["band", "type"])
c = np.sum((j["value_1"] - j["value_2"])**2 * weights(j, sig_ext))
return c
def lnlike3(par,
data_table,
ebv_map=0,
ms_interp=None,
gaussian_ebv=False,
return_fit=False):
g1, Teff1, g2, Teff2, g3, Teff3, EBV, sig_ext = par
if (g1 <= 5) or (g1 > 25): return -np.inf
if (g2 <= 5) or (g2 > 25): return -np.inf
if (g3 <= 5) or (g3 > 25): return -np.inf
if (Teff1 <= 3450) or (Teff1 > 8600): return -np.inf
if (Teff2 <= 3450) or (Teff2 > 8600): return -np.inf
if (Teff3 <= 3450) or (Teff3 > 8600): return -np.inf
if (sig_ext < 0): return -np.inf
if (EBV < 0): return -np.inf
if gaussian_ebv:
j = np.where(data_table['type'] == 'ebv')
v = data_table['value'][j]
s = data_table['error'][j]
lnprior = -0.5 * np.sum(((v - EBV) / s)**2)
elif (EBV > ebv_map):
lnprior = -0.5 * ((EBV - ebv_map) / 0.033)**2
else:
lnprior = 0.0
if ms_interp is not None:
g_ZAMS_3, g_TAMS_3 = ms_interp(Teff3)
if g2 > g1:
g_ZAMS_B, g_TAMS_B = ms_interp(Teff2)
if ((g3 - g2) < (g_ZAMS_3 - g_ZAMS_B)):
return -np.inf
if ((g3 - g2) > (g_TAMS_3 - g_TAMS_B)):
return -np.inf
else:
g_ZAMS_B, g_TAMS_B = ms_interp(Teff1)
if ((g3 - g1) < (g_ZAMS_3 - g_ZAMS_B)):
return -np.inf
if ((g3 - g1) > (g_TAMS_3 - g_TAMS_B)):
return -np.inf
t = synth_triple(g1, Teff1, g2, Teff2, g3, Teff3, EBV)
j = joinTables(data_table, t, keys=("band", "type"))
wt = weights(j, sig_ext)
z2 = (j["value_1"] - j["value_2"])**2 * wt
j.add_column(Column(np.sqrt(z2), name="Z", format="%.3f"))
if return_fit:
return j
else:
lnlike = -0.5 * (np.sum(z2 - np.log(wt)))
if np.isfinite(lnlike):
return lnlike + lnprior
else:
return -np.inf
def chisq3(param, data_table, sig_ext):
g1, Teff1, g2, Teff2, g3, Teff3, EBV = param
t = synth_triple(g1, Teff1, g2, Teff2, g3, Teff3, EBV)
j = joinTables(data_table, t, keys=["band", "type"])
c = np.sum((j["value_1"] - j["value_2"])**2 * weights(j, sig_ext))
return c
def chisq1(param, data_table, sig_ext):
g1, Teff1, EBV = param
t = gTeff_to_magTable(g1, Teff1, EBV)
j = joinTables(data_table, t, keys=["band", "type"])
c = np.sum((j["value_1"] - j["value_2"])**2 * weights(j, sig_ext))
return c