def CSMViErr(p, t, L, L_err, z, DM, Mej, Eej):
from Cosmology import wave_0, bands
from LCFitting import earlyFit
#CSM component p0=epoch (in rest frame), p1=Mext, p2=Rext
V_pred = np.array([CSMFit(ti, wave_0[bands['V']], z, DM, Mej, Eej,
p[1], p[2], p[0]) for ti in t[0]])
I_pred = np.array([CSMFit(ti, wave_0[bands['i']], z, DM, Mej, Eej,
p[1], p[2], p[0]) for ti in t[1]])
#Power law component
V_pred = np.array(V_pred) + earlyFit(t[0], p[3]*(1.+z), p[4], p[6])
I_pred = np.array(I_pred) + earlyFit(t[1], p[3]*(1.+z), p[5], p[7])
#Error
V_err = (V_pred - L[0])/L_err[0]
I_err = (I_pred - L[1])/L_err[1]
return np.concatenate([V_err, I_err],axis=0)
def ShockCoolingViErr(p, t, L, L_err, z, DM, Mej):
from Cosmology import wave_0, bands
from LCFitting import earlyFit
#Shock cooling component p0=epoch (in rest frame), p1=R8
#Fix t5=0.1 temperature, because assume late time
V_pred = np.array([ShockCoolingFit(ti, wave_0[bands['V']], z, DM, Mej,
p[1], 0.1, p[0]) for ti in t[0]])
I_pred = np.array([ShockCoolingFit(ti, wave_0[bands['i']], z, DM, Mej,
p[1], 0.1, p[0]) for ti in t[1]])
#Power law component, p2=epoch (in rest frame)
V_pred = np.array(V_pred) + earlyFit(t[0], p[2]*(1.+z), p[3], p[5])
I_pred = np.array(I_pred) + earlyFit(t[1], p[2]*(1.+z), p[4], p[6])
#Error
V_err = (V_pred - L[0])/L_err[0]
I_err = (I_pred - L[1])/L_err[1]
return np.concatenate([V_err, I_err],axis=0)
def kasenViErr(p, t, L, L_err, z, DM, m_c, e_51):
from Cosmology import wave_0, bands
from LCFitting import earlyFit
#Kasen component p0=epoch (in rest frame), p1=a13, p2=theta
V_pred = np.array([KasenFit(ti, p[1], 1.0, wave_0[bands['V']], z,
m_c, e_51, DM, p[0])
for ti in t[0]])*Kasen_isocorr(p[2])
I_pred = np.array([KasenFit(ti, p[1], 1.0, wave_0[bands['i']], z,
m_c, e_51, DM, p[0])
for ti in t[1]])*Kasen_isocorr(p[2])
#Power law component
V_pred = np.array(V_pred) + earlyFit(t[0], p[3]*(1.+z), p[4], p[6])
I_pred = np.array(I_pred) + earlyFit(t[1], p[3]*(1.+z), p[5], p[7])
#Error
V_err = (V_pred - L[0])/L_err[0]
I_err = (I_pred - L[1])/L_err[1]
return np.concatenate([V_err, I_err],axis=0)
def NiSTMultiErr(p, t, L, L_err, z, DM, taus, Mej, Ek):
from Cosmology import wave_0, bands
from LCFitting import earlyFit
#Ni shell component p0=epoch (in rest frame), p1=t_s, p2=a_s, p3=tau_s
B_pred = predNiSmod(t[0], wave_0[bands['B']], z, DM, p[3] * taus[0], Mej,
Ek, p[1], p[2], p[0])
V_pred = predNiSmod(t[1], wave_0[bands['V']], z, DM, p[3] * taus[1], Mej,
Ek, p[1], p[2], p[0])
I_pred = predNiSmod(t[2], wave_0[bands['i']], z, DM, p[3] * taus[2], Mej,
Ek, p[1], p[2], p[0])
#Power law component
B_pred = B_pred + earlyFit(t[0], p[4] * (1. + z), p[5], p[8])
V_pred = V_pred + earlyFit(t[1], p[4] * (1. + z), p[6], p[9])
I_pred = I_pred + earlyFit(t[2], p[4] * (1. + z), p[7], p[10])
#Error
B_err = (B_pred - L[0]) / L_err[0]
V_err = (V_pred - L[1]) / L_err[1]
I_err = (I_pred - L[2]) / L_err[2]
err = np.concatenate([B_err, V_err, I_err], axis=0)
return np.array(err, dtype=float)
def kasenPowMultiErr(p, t, L, L_err, z, DM, m_c, e_51, sep, angle):
from Cosmology import wave_0, bands
from LCFitting import earlyFit
#Kasen component p0=epoch (in rest frame), p1=a13, p2=theta
B_pred = np.array([KasenFit(ti, sep, 1.0, wave_0[bands['B']], z,
m_c, e_51, DM, p[0])
for ti in t[0]])*Kasen_isocorr(angle)
V_pred = np.array([KasenFit(ti, sep, 1.0, wave_0[bands['V']], z,
m_c, e_51, DM, p[0])
for ti in t[1]])*Kasen_isocorr(angle)
I_pred = np.array([KasenFit(ti, sep, 1.0, wave_0[bands['i']], z,
m_c, e_51, DM, p[0])
for ti in t[2]])*Kasen_isocorr(angle)
#Power law component, p3=epoch
B_pred = np.array(B_pred) + earlyFit(t[0], p[1]*(1.+z), p[2], p[5])
V_pred = np.array(V_pred) + earlyFit(t[1], p[1]*(1.+z), p[3], p[6])
I_pred = np.array(I_pred) + earlyFit(t[2], p[1]*(1.+z), p[4], p[7])
#Error
B_err = (B_pred - L[0])/L_err[0]
V_err = (V_pred - L[1])/L_err[1]
I_err = (I_pred - L[2])/L_err[2]
return np.concatenate([B_err, V_err, I_err],axis=0)