def get_result(temp_dir, seed):
save_file = temp_dir + "/final%d.npy" % seed
if os.path.exists(save_file):
res = np.load(save_file)
if res.size == 0:
return None
else:
return res
else:
interp = generate_and_return()
z, t0, x0, x1, c, ston, lc = realise_light_curve(seed)
if ston < 5 or ston > 10:
np.save(save_file, np.array([]))
return None
try:
chainf, parametersf, means, cov = get_gaussian_fit(z, t0, x0, x1, c, lc, seed,
temp_dir, interp, type="iminuit")
mcmc_file = temp_dir + "/mcmc%d.npy" % seed
if os.path.exists(mcmc_file):
chainf2 = np.load(mcmc_file)
else:
chainf2, _, _, _ = get_gaussian_fit(z, t0, x0, x1, c, lc, seed, temp_dir, interp, type="mcmc")
np.save(mcmc_file, chainf2)
chain, parameters = get_posterior(z, t0, lc, seed, temp_dir, interp)
assert is_fit_good(t0, x0, x1, c, means, cov)
assert not is_unconstrained(chain, parameters)
except Exception as e:
print(e)
np.save(save_file, np.array([]))
return None
plot_results(chain, parameters, chainf, chainf2, parametersf, t0, x0, x1, c, temp_dir, seed, interp)
muf = chainf[:, -1]
muf2 = chainf2[:, -1]
mu = chain[:, -1]
mean2 = np.mean(chain[:, :-1], axis=0)
cov2 = np.cov(chain[:, :-1].T)
chain2 = np.random.multivariate_normal(mean2, cov2, size=int(1e6))
chain2, _ = add_mu_to_chain(interp, chain2, parameters[:-1])
mu2 = chain2[:, -1]
mus = [np.mean(mu), np.mean(mu2), np.mean(muf), np.mean(muf2)]
stds = [np.std(mu), np.std(mu2), np.std(muf), np.std(muf2)]
res = np.array([seed, z, t0, x0, x1, c, ston] + mus + stds)
np.save(save_file, res)
return res
def random_obs(temp_dir, seed):
np.random.seed(seed)
interp = generate_and_return()
x1 = np.random.normal()
# colour = np.random.normal(scale=0.1)
colour = 0
x0 = 1e-5
# t0 = np.random.uniform(low=1000, high=2000)
t0 = 1000
z = np.random.uniform(low=0.1, high=1.0)
# deltat = np.random.uniform(low=-20, high=0)
# num_obs = np.random.randint(low=10, high=40)
num_obs = 20
deltat = -35
filename = temp_dir + "/save_%d.npy" % seed
if not os.path.exists(filename):
ts = np.arange(t0 + deltat, (t0 + deltat) + 5 * num_obs, 5)
times = np.array([[t, t + 0.05, t + 0.1, t + 0.2] for t in ts]).flatten()
bands = [b for t in ts for b in ["desg", "desr", "desi", "desz"]]
gains = np.ones(times.shape)
skynoise = np.random.uniform(low=20, high=800) * np.ones(times.shape)
zp = 30 * np.ones(times.shape)
zpsys = ["ab"] * times.size
obs = Table({"time": times, "band": bands, "gain": gains, "skynoise": skynoise, "zp": zp, "zpsys": zpsys})
model = sncosmo.Model(source="salt2")
p = {"z": z, "t0": t0, "x0": x0, "x1": x1, "c": colour}
model.set(z=z)
print(seed, " Vals are ", p)
lc = sncosmo.realize_lcs(obs, model, [p])[0]
ston = (lc["flux"] / lc["fluxerr"]).max()
model.set(t0=t0, x1=x1, c=colour, x0=x0)
try:
res, fitted_model = sncosmo.fit_lc(
lc, model, ["t0", "x0", "x1", "c"], guess_amplitude=False, guess_t0=False
)
except ValueError:
return np.nan, np.nan, x1, colour, num_obs, ston, deltat, z, 0
fig = sncosmo.plot_lc(lc, model=fitted_model, errors=res.errors)
fig.savefig(temp_dir + os.sep + "lc_%d.png" % seed, bbox_inches="tight", dpi=300)
my_model = PerfectRedshift([lc], [z], t0, name="posterior%d" % seed)
sampler = EnsembleSampler(temp_dir=temp_dir, num_burn=400, num_steps=1500)
c = ChainConsumer()
my_model.fit(sampler, chain_consumer=c)
map = {"x0": "$x_0$", "x1": "$x_1$", "c": "$c$", "t0": "$t_0$"}
parameters = [map[a] for a in res.vparam_names]
mu1 = get_mu_from_chain(interped, c.chains[-1], c.parameters[-1])
c.parameteers[-1].append(r"$\mu$")
c.chains[-1] = np.hstack((c.chains[-1], mu1[:, None]))
chain2 = np.random.multivariate_normal(res.parameters[1:], res.covariance, size=int(1e5))
chain2 = np.hstack((chain2, get_mu_from_chain(interp, chain2, parameters)[:, None]))
c.add_chain(chain2, parameters=parameters, name="Gaussian")
figfilename = filename.replace(".npy", ".png")
c.plot(filename=figfilename, truth={"$t_0$": t0, "$x_0$": x0, "$x_1$": x1, "$c$": colour})
means = []
stds = []
isgood = (
(np.abs(x1 - res.parameters[3]) < 4) & (np.abs(colour - res.parameters[4]) < 2) & (res.parameters[2] > 0.0)
)
isgood *= 1.0
for i in range(len(c.chains)):
a = c.chains[i][:, -1]
means.append(a.mean())
stds.append(np.std(a))
diffmu = np.diff(means)[0]
diffstd = np.diff(stds)[0]
np.save(filename, np.array([diffmu, diffstd, ston, 1.0 * isgood]))
else:
vals = np.load(filename)
diffmu = vals[0]
diffstd = vals[1]
ston = vals[2]
isgood = vals[3]
return diffmu, diffstd, x1, colour, num_obs, ston, deltat, z, isgood
def get_result(temp_dir, zps, seed, scatter, special=False, full=True):
save_file = temp_dir + "/final%d.npy" % seed
if os.path.exists(save_file):
res = np.load(save_file)
if res.size == 0:
return None
else:
return res
else:
interp = generate_and_return()
if full:
cosmology = None
else:
cosmology = WMAP9
z, t0, x0, x1, c, ston, lc = realise_light_curve(temp_dir, zps, seed, scatter=scatter, cosmology=cosmology)
if ston < 4:
print("Failed ", z)
np.save(save_file, np.array([]))
return None
try:
chainf, parametersf, means, cov = get_gaussian_fit(z, t0, x0, x1, c, lc, seed,
temp_dir, interp, type="iminuit")
mcmc_file = temp_dir + "/mcmc%d.npy" % seed
if os.path.exists(mcmc_file):
chainf2 = np.load(mcmc_file)
else:
chainf2, _, _, _ = get_gaussian_fit(z, t0, x0, x1, c, lc, seed, temp_dir, interp, type="mcmc")
np.save(mcmc_file, chainf2)
if full:
nestle_file = temp_dir + "/nestle%d.npy" % seed
if os.path.exists(nestle_file):
chainf3 = np.load(nestle_file)
else:
chainf3, _, _, _ = get_gaussian_fit(z, t0, x0, x1, c, lc, seed, temp_dir, interp, type="nestle")
np.save(nestle_file, chainf3)
chain, parameters = get_posterior(z, t0, lc, seed, temp_dir, interp, special)
assert not is_unconstrained(chain, parameters)
assert is_fit_good(t0, x0, x1, c, means, cov)
except Exception as e:
print(e)
np.save(save_file, np.array([]))
return None
if special:
try:
plot_results(chain, parameters, chainf, chainf2, chainf3, parametersf, t0, x0, x1, c, temp_dir, seed, interp)
except Exception as e:
print(e)
pass
muf = chainf[:, -1]
muf2 = chainf2[:, -1]
if full:
muf3 = chainf3[:, -1]
mu = chain[:, -1]
mean2 = np.mean(chain[:, :-1], axis=0)
cov2 = np.cov(chain[:, :-1].T)
chain2 = np.random.multivariate_normal(mean2, cov2, size=int(1e6))
chain2, _ = add_mu_to_chain(interp, chain2, parameters[:-1])
mu2 = chain2[:, -1]
mus = [np.mean(mu), np.mean(mu2), np.mean(muf), np.mean(muf2), np.mean(muf3)]
stds = [np.std(mu), np.std(mu2), np.std(muf), np.std(muf2), np.std(muf3)]
res = np.array([seed, z, t0, x0, x1, c, ston] + mus + stds + [skew(mu)])
else:
mus = [np.mean(muf), np.mean(muf2)]
stds = [np.std(muf), np.std(muf2)]
res = np.array([seed, z, t0, x0, x1, c, ston] + mus + stds)
# print("Faster", res)
np.save(save_file, res)
return res
c.configure_bar(shade=True)
c.plot(filename=surface, figsize=(7, 7))
if False:
fig = sncosmo.plot_lc(lcs[0], model=fitted_model, errors=res.errors)
fig.savefig(temp_dir + os.sep + "lc_simple.png", bbox_inches="tight", dpi=300)
alpha = 0.14
beta = 3.15
c2 = ChainConsumer()
means = []
stds = []
print("Add chains")
for i in range(len(c.chains)):
chain = c.chains[i]
apparent_interp = generate_and_return()
x0s = chain[:, c.parameters[i].index("$x_0$")]
x1s = chain[:, c.parameters[i].index("$x_1$")]
cs = chain[:, c.parameters[i].index("$c$")]
a = apparent_interp(x1s, cs, grid=False) - (0.4 * np.log10(x0s / 1e-5))
a += alpha * x1s - beta * cs
means.append(a.mean())
stds.append(np.std(a))
c2.add_chain(a, parameters=[r"$\mu+M$"], name=c.names[i])
print(means, stds, np.diff(means), np.diff(stds))
actual = apparent_interp(x1, colour, grid=False) - (0.4 * np.log10(x0 / 1e-5))
actual += alpha * x1 - beta * colour
c2.plot(filename=mu_simple, figsize=(7, 4), truth=[actual], legend=True)
