c = d["sim_c"]
x1 = d["sim_x1"]
alpha = 0.15
beta = 4.0
hist_all, bins = np.histogram(mB, bins=200)
hist_passed, _ = np.histogram(mB[mask], bins=bins)
binc = 0.5 * (bins[:-1] + bins[1:])
ratio = 1.0 * hist_passed / hist_all
inter = interp1d(ratio, binc)
mean = inter(0.5)
width = 0.5 * (inter(0.16) - inter(0.84))
width += alpha * np.std(x1) + beta * np.std(c)
return mean, width + 0.02
if __name__ == "__main__":
file = os.path.abspath(__file__)
stan_model = os.path.dirname(file) + "/model.stan"
mB_mean, mB_width = get_approximate_mb_correction()
print(mB_mean, mB_width)
data = {
"mB_mean": mB_mean,
"mB_width": mB_width
}
print("Running %s" % file)
run(data, stan_model, file, weight_function=add_weight_to_chain)
import os
from dessn.models.d_simple_stan.run import run
if __name__ == "__main__":
file = os.path.abspath(__file__)
stan_model = os.path.dirname(file) + "/model.stan"
data = {"add_sim": 3000}
print("Running %s" % file)
run(data, stan_model, file)
