def debug_plots(std):
print(std)
res = load_stan_from_folder(std, merge=True, cut=False)
chain, posterior, t, p, f, l, w, ow = res
print(w.mean(), np.std(w), np.mean(np.log(w)), np.std(np.log(w)))
# import matplotlib.pyplot as plt
# plt.hist(np.log(w), 100)
# plt.show()
# exit()
logw = np.log(w)
m = np.mean(logw)
s = np.std(logw)
print(m, s)
logw -= (m + 3 * s)
good = logw < 0
logw *= good
w = np.exp(logw)
sorti = np.argsort(w)
for key in chain.keys():
chain[key] = chain[key][sorti]
w = w[sorti]
ow = ow[sorti]
posterior = posterior[sorti]
c = ChainConsumer()
truth = [0.3, 0.14, 3.1, -19.365, 0, 0, 0.1, 1.0, 0.1, 0, 0, 0, 0, 0, 0]
c.add_chain(chain, name="uncorrected", posterior=posterior)
c.add_chain(chain, weights=w, name="corrected", posterior=posterior)
c.plot(filename="output.png", parameters=9, truth=truth, figsize=1.3)
# c = ChainConsumer()
# c.add_chain(chain, weights=w, name="corrected")
c.plot_walks(chains="corrected", filename="walks.png", truth=truth)
def debug_plots(std):
print(std)
res = load_stan_from_folder(std, merge=True, cut=False)
chain, posterior, t, p, f, l, w, ow = res
# print(w.mean())
# import matplotlib.pyplot as plt
# plt.hist(np.log(w), 100)
# plt.show()
# exit()
logw = np.log(w)
m = np.mean(logw)
s = np.std(logw)
print(m, s)
logw -= (m + 3 * s)
good = logw < 0
logw *= good
w = np.exp(logw)
c = ChainConsumer()
c.add_chain(chain, weights=w, name="corrected")
c.configure(summary=True)
c.plot(figsize=2.0, filename="output.png", parameters=9)
c = ChainConsumer()
c.add_chain(chain, name="uncorrected")
c.add_chain(chain, weights=w, name="corrected")
# c.add_chain(chain, name="calib")
c.plot(filename="output_comparison.png", parameters=9, figsize=1.3)
c.plot_walks(chains=1, filename="walks.png")
def debug_plots(std):
print(std)
do_weight = True
do_walk = True
load_second = True
res = load_stan_from_folder(std, merge=False, num=0, cut=False)
chain, posterior, t, p, f, l, w, ow = res[0]
if do_walk:
# print("owww ", ow.min(), ow.max())
# print("www ", w.min(), w.max())
chain2 = chain.copy()
logw = np.log10(w)
a = np.argsort(logw)
logw = logw[a]
for k in chain:
chain2[k] = chain2[k][a]
chain2["ow"] = np.log10(ow)
chain2["ww"] = logw
c = ChainConsumer()
c.add_chain(chain2, weights=w, name="calib")
c.plot_walks(truth=t, filename="walk_new.png")
c = ChainConsumer()
if do_weight:
c.add_chain(chain, weights=w, name="calib")
else:
c.add_chain(chain, name="calib")
if load_second:
res2 = load_stan_from_folder(std + "_calib_data_no_calib_model_and_bias", num=0, merge=False, cut=False)
chain, posterior, _, p, f, l, w, ow = res2[0]
if do_weight:
c.add_chain(chain, weights=w, name="nocalib")
else:
c.add_chain(chain, name="nocalib")
c.plot(filename="output.png", truth=t, figsize=0.75)
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)
if __name__ == "__main__":
dir_name = os.path.dirname(__file__)
std = dir_name + "/stan_output"
res = load_stan_from_folder(std, replace=False, merge=True, cut=False)
chain, posterior, t, p, f, l, w, ow = res
logw = np.log(w)
full_log_correction = logw + ow
if False:
mean = np.linspace(-2, 2, 17)
sigma = np.linspace(-2, 2, 17)
print(mean)
print(sigma)
ms = []
ss = []
ws = []
for m in mean:
for s in sigma:
