def inverse_local(local_prob, hyper): # R(0,i) for fix mass prob_R0 = local_prob[0:n_fixm] R0 = piece_power_frac(hyper, M0, prob_R0) # M(1,i) for variable mass prob_M1 = local_prob[n_fixm:n_fixm+n_varm] M1 = 10.**( uniform.ppf(prob_M1, -4., 10.) ) # R(1,i) for varibable mass prob_R1 = local_prob[n_fixm+n_varm:] R1 = piece_power_frac(hyper, M1, prob_R1) local = np.hstack((R0, M1, R1)) return local
def plt_power():
### data
hyper = np.loadtxt(dir + "hyper.out")
loglike = np.loadtxt(dir + "loglike.out")
repeat = np.loadtxt(dir + "repeat.out")
### split data
c0 = hyper[:, 0]
power = hyper[:, 1:5]
sigma = hyper[:, 5:9]
trans = hyper[:, 9:12]
### plot
plt.clf()
row = 2
col = 4
f, ((a00, a01, a02, a03), (a10, a11, a12, a13)) = plt.subplots(row, col, figsize=(col * 5, row * 5))
ax = ((a00, a01, a02, a03), (a10, a11, a12, a13))
# repeat
ax[0][0].plot(repeat)
ax[0][0].set_yscale("log")
ax[0][0].set_xlabel("repeat")
# loglike
ax[0][1].plot(loglike)
ax[0][1].set_xlabel("L")
# over plot
dat = data()
ax[0][2].errorbar(dat[:, 0], dat[:, 2], xerr=dat[:, 1], yerr=dat[:, 3], fmt=".")
hyper_last = hyper[-1, :]
trans_last = hyper_last[-3:]
m_sample = np.logspace(np.log10(np.min(dat[:, 0])), np.log10(np.max(dat[:, 0])), 1000)
r_sample = piece_power_frac(hyper_last, m_sample, prob_R=0.5 * np.ones_like(m_sample))
r_upper = piece_power_frac(hyper_last, m_sample, prob_R=0.84 * np.ones_like(m_sample))
r_lower = piece_power_frac(hyper_last, m_sample, prob_R=0.16 * np.ones_like(m_sample))
ax[0][2].plot(m_sample, r_sample, "r-")
ax[0][2].fill_between(m_sample, r_lower, r_upper, color="grey", alpha=0.2)
r_trans = piece_power_frac(hyper_last, trans_last, prob_R=0.5 * np.ones_like(trans_last))
ax[0][2].plot(trans_last, r_trans, "rx")
ax[0][2].set_xscale("log")
ax[0][2].set_yscale("log")
ax[0][2].set_xlabel(r"M [M$_\oplus$]")
ax[0][2].set_ylabel(r"R [R$_\oplus$]")
# C
ax[1][0].plot(c0)
ax[1][0].set_yscale("log")
ax[1][0].set_xlabel("c0")
ax[1][0].set_ylim([1e-3, 1e3])
# power
for i in range(4):
ax[1][1].plot(power[:, i])
ax[1][1].set_xlabel("power")
# sigma
for i in range(4):
ax[1][2].plot(sigma[:, i])
ax[1][2].set_yscale("log")
ax[1][2].set_xlabel("sigma")
ax[1][2].set_ylim([1e-3, 1e2])
# transition
for i in range(3):
ax[1][3].plot(trans[:, i])
ax[1][3].set_yscale("log")
ax[1][3].set_xlabel("transition")
ax[1][3].set_ylim([1e-4, 1e6])
plt.savefig("plt_power.png")
return None
