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generate.py
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generate.py
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import numpy as np
def ICs_1comp(IC, xlist, nChains):
gamma = IC[0]
xl = IC[1]
xu = IC[2]
ICs = []
for i in range(nChains):
ICs.append({ "gamma": gamma, "xl": xl, "xu": xu, "cosi": [0. for i in range(len(xlist))] })
return ICs
def ICs_1comp_alltransit(IC, nChains):
gamma = IC[0]
xl = IC[1]
xu = IC[2]
ICs = []
for i in range(nChains):
ICs.append({ "gamma": gamma, "xl": xl, "xu": xu })
return ICs
def ICs_2comp_2xl_overlap(IC, xlist, nChains):
K = len(IC[0])
theta = [ IC[0] for i in range(nChains) ]
gamma = [ IC[1] + np.random.randn(2)*0.01 for i in range(nChains) ]
xl = [ IC[2] + np.random.randn(2)*0.01 for i in range(nChains) ]
xu = [ IC[3] for i in range(nChains) ]
ICs = []
for i in range(nChains):
ICs.append({ "theta": theta[i], "gamma": gamma[i], "xl": xl[i], "xu": xu[i], "cosi": [0. for i in range(len(xlist))] })
return ICs
def ICs_1comp_sigmas(IC, data, nChains):
gamma = IC[0]
xl = IC[1]
xu = IC[2]
per = data["per"]
log_rad = list(np.log10(data["rad"]))
log_Mpl = list(np.log10(data["Mpl"]))
# Mstar = data["Mstar"]
peri = data["peri"]
log_radi = list(np.log10(data["radi"]))
log_Mpli = list(np.log10(data["Mpli"]))
# Mstari = data["Mstari"]
N = len(per)
ICs = []
for i in range(nChains):
ICs.append({ "gamma": gamma, "xl": xl, "xu": xu, "cosi": [0. for i in range(N)],\
"per_true": per, "log_rad_true": log_rad, "log_Mpl_true": log_Mpl ,\
"peri_true": peri, "log_radi_true": log_radi, "log_Mpli_true": log_Mpli })
return ICs
def ICs_2comp_2xl_overlap_sigmas(IC, data, nChains):
K = len(IC[0])
theta = [ IC[0] for i in range(nChains) ]
gamma = [ IC[1] + np.random.randn(2)*0.01 for i in range(nChains) ]
xl = [ IC[2] + np.random.randn(2)*0.01 for i in range(nChains) ]
xu = [ IC[3]*1.05 for i in range(nChains) ]
per = data["per"]
log_rad = list(np.log10(data["rad"]))
log_Mpl = list(np.log10(data["Mpl"]))
peri = data["peri"]
log_radi = list(np.log10(data["radi"]))
log_Mpli = list(np.log10(data["Mpli"]))
N = len(per)
ICs = []
for i in range(nChains):
ICs.append({ "theta": theta[i], "gamma": gamma[i], "xl": xl[i], "xu": xu[i], "cosi": [0. for i in range(N)],\
"per_true": per, "log_rad_true": log_rad, "log_Mpl_true": log_Mpl ,\
"peri_true": peri, "log_radi_true": log_radi, "log_Mpli_true": log_Mpli })
return ICs
def ICs_2comp_2xl_overlap_alltransit(IC, nChains):
K = len(IC[0])
theta = [ IC[0] for i in range(nChains) ]
gamma = [ IC[1] for i in range(nChains) ]
xl = [ IC[2] for i in range(nChains) ]
xu = [ IC[3] for i in range(nChains) ]
ICs = []
for i in range(nChains):
ICs.append({ "theta": theta[i], "gamma": gamma[i], "xl": xl[i], "xu": xu[i] })
return ICs
def ICs_2comp_2xl_overlap_sigmas_alltransit(IC, data, nChains):
K = len(IC[0])
theta = [ IC[0] for i in range(nChains) ]
gamma = [ IC[1] + np.random.randn(2)*0.01 for i in range(nChains) ]
xl = [ IC[2] + np.random.randn(2)*0.01 for i in range(nChains) ]
xu = [ IC[3]*1.05 for i in range(nChains) ]
peri = data["peri"]
log_radi = list(np.log10(data["radi"]))
log_Mpli = list(np.log10(data["Mpli"]))
ICs = []
for i in range(nChains):
ICs.append({ "theta": theta[i], "gamma": gamma[i], "xl": xl[i], "xu": xu[i],\
"peri_true": peri, "log_radi_true": log_radi, "log_Mpli_true": log_Mpli })
return ICs