if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("model_name", type=str, help="The model name.")
parsed_args = parser.parse_args()
model_name = parsed_args.model_name
grid = ParameterGrid(model_name)
image_dir = grid.config["directories"]["images"]
# Load metrics evaluated from faux observations
dfms = grid.load_faux_metrics()
# Get best hyper parameters for each faux sample
df = []
for dfm in dfms:
best_pars = grid.get_best_hyper_parameters(df=dfm, flatten=True, kstest_min=None)
df.append(best_pars)
df = pd.DataFrame(df)
# Plot hist for each parameter
for parname in df.columns:
filename = os.path.join(image_dir, f"faux_{parname}_{model_name}.png")
values = df[parname].values
plot_par(parname, values, filename=filename)
plt.show(block=False)
if filename is not None:
plt.savefig(filename, dpi=150, bbox_inches="tight")
plt.show(block=False)
if __name__ == "__main__":
n_samples = 10000
burnin = 1000
# Get best fitting hyper parameters
grid = ParameterGrid(MODEL_NAME)
# Get best fitting hyper parameters
hyper_params = grid.get_best_hyper_parameters()
# Sample the model with no recovery efficiency
model = create_model(UDG_MODEL_NAME, ignore_recov=True)
df = model.sample(burnin=burnin,
n_samples=n_samples,
hyper_params=hyper_params)
# Identify UDGs
cond = df["is_udg"].values == 1
df = df[cond].reset_index(drop=True)
# Get sizes
x = df["rec_phys"].values
# Make histogram