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Add z_obs output (#119)

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* Update SN model to output zobs too

* Add TF predicted zobs

* Add imoprt

* Update nb

* Calculate chi2

* Update nb

* Update script
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Richard Stiskalek 2024-03-22 18:10:40 +01:00 • committed by GitHub
parent f7285b2600
commit 4093186f9a
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6 changed files with 462 additions and 47 deletions
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145
notebooks/flow_calibration.ipynb
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9
notebooks/flow_calibration.py
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@ -30,7 +30,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
nsims = paths.get_ics(simname)
Vx, Vy, Vz, beta, sigma_v, alpha = [], [], [], [], [], []
BIC, AIC, logZ = [], [], []
BIC, AIC, logZ, chi2 = [], [], [], []
if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
alpha_cal, beta_cal, mag_cal, e_mu_intrinsic = [], [], [], []
@ -69,6 +69,10 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
BIC.append(f[f"sim_{nsim}/BIC"][...])
AIC.append(f[f"sim_{nsim}/AIC"][...])
logZ.append(f[f"sim_{nsim}/logZ"][...])
try:
chi2.append(f[f"sim_{nsim}/chi2"][...])
except KeyError:
chi2.append([0.])
if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
alpha_cal.append(f[f"sim_{nsim}/alpha_cal"][:])
@ -84,7 +88,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
Vx, Vy, Vz, alpha, beta, sigma_v = np.hstack(Vx), np.hstack(Vy), np.hstack(Vz), np.hstack(alpha), np.hstack(beta), np.hstack(sigma_v) # noqa
gof = np.hstack(BIC), np.hstack(AIC), np.hstack(logZ)
gof = np.hstack(BIC), np.hstack(AIC), np.hstack(logZ), np.hstack(chi2)
if catalogue in ["LOSS", "Foundation", "Pantheon+"]:
alpha_cal, beta_cal, mag_cal, e_mu_intrinsic = np.hstack(alpha_cal), np.hstack(beta_cal), np.hstack(mag_cal), np.hstack(e_mu_intrinsic) # noqa
@ -116,6 +120,7 @@ def read_samples(catalogue, simname, ksmooth, include_calibration=False,
print("BIC = {:4f} +- {:4f}".format(np.mean(gof[0]), np.std(gof[0])))
print("AIC = {:4f} +- {:4f}".format(np.mean(gof[1]), np.std(gof[1])))
print("logZ = {:4f} +- {:4f}".format(np.mean(gof[2]), np.std(gof[2])))
print("chi2 = {:4f} +- {:4f}".format(np.mean(gof[3]), np.std(gof[3])))
data = np.vstack(data).T