def get_new_cor(chain, mean_add=0, sigma_add=0):
mB_mean, mB_width = get_approximate_mb_correction()
data = {
"mB_mean": mB_mean,
"mB_width": mB_width,
"snana_dummy": True,
"sim": False
}
d = get_analysis_data(**data)
redshifts = d["redshifts"]
cosmologies = get_cosmology_dictionary()
weight = []
for i in range(chain["mean_MB"].size):
om = np.round(chain["Om"][i], decimals=3)
key = "%0.3f" % om
mus = cosmologies[key](redshifts)
mb = chain["mean_MB"][i] + mus - chain["alpha"][i] * chain["mean_x1"][i] + chain["beta"][i] * chain["mean_c"][i]
# cc = 1 - norm.cdf(mb, mB_mean, mB_width) + 0.001
cc = 1 - norm.cdf(mb, mB_mean + mean_add, mB_width + sigma_add)
w = np.sum(np.log(cc) - chain["mean_c"][i])
weight.append(w)
return np.array(weight)
def add_weight_to_chain(chain_dictionary, n_sne):
file = os.path.abspath(inspect.stack()[0][1])
dir_name = os.path.dirname(file)
data_dir = os.path.abspath(dir_name + "/../data")
pickle_file = data_dir + os.sep + "supernovae_passed.npy"
supernovae = np.load(pickle_file)
d = get_cosmology_dictionary()
weights = calculate_bias(chain_dictionary, supernovae, d)
existing = chain_dictionary["weight"]
logw = n_sne * weights - existing
logw -= logw.min()
weights = np.exp(-logw)
chain_dictionary["weight"] = weights
chain_dictionary["old_weight"] = existing
return chain_dictionary
def approx_bias():
file = os.path.abspath(inspect.stack()[0][1])
dir_name = os.path.dirname(file)
data_dir = os.path.abspath(dir_name + "/../data")
pickle_file = data_dir + os.sep + "supernovae_passed.npy"
supernovae = np.load(pickle_file)
d = get_cosmology_dictionary()
data = get_analysis_data()
n = 1000
init_vals = [init_fn(data=data) for i in range(n)]
keys = list(init_vals[0].keys())
chain_dictionary = {}
for key in keys:
chain_dictionary[key] = np.array([elem[key] for elem in init_vals])
weights, mean_abs = calculate_bias(chain_dictionary, supernovae, d, return_mbs=True)
import matplotlib.pyplot as plt
plt.scatter(mean_abs, weights)
plt.show()
def add_weight_to_chain(chain_dictionary, n_sne):
print(n_sne)
file = os.path.abspath(inspect.stack()[0][1])
dir_name = os.path.dirname(file)
data_dir = os.path.abspath(dir_name + "/../data")
dump_file = os.path.abspath(data_dir + "/SHINTON_SPEC_SALT2.npy")
supernovae = np.load(dump_file)
d = get_cosmology_dictionary()
# import matplotlib.pyplot as plt
# plt.hist(supernovae['S2mb'], 30)
# plt.show()
# exit()
weights = calculate_bias(chain_dictionary, supernovae, d)
existing = chain_dictionary["weight"]
logw = n_sne * weights - existing
logw -= logw.min()
weights = np.exp(-logw)
chain_dictionary["weight"] = weights
chain_dictionary["old_weight"] = existing
return chain_dictionary
