def plot_ml_powspec_with_band(sigmas, lmax=50, sample_fraction=0.68, label=None, color='red', mlcolor='blue',spec=0, burnin=0):
if isinstance(sigmas, str):
sigmas = pyfits.open(sigmas)[0].data
numsamps = np.sum(sigmas[0, :, 0, 0]) - len(sigmas[0, :, 0, 0]) * burnin
nsigmas = np.zeros(np.append(numsamps, sigmas.shape[2:]))
j = 0
for i in range(len(sigmas[0, :, 0, 0])):
jprev = j
j += sigmas[0, i, 0, 0] - burnin
nsigmas[jprev:j] = sigmas[burnin + 1:sigmas[0, i, 0, 0] + 1, i]
# sigmas = np.reshape(sigmas[1:], ((sigmas.shape[0] - 1) * sigmas.shape[1], 1, sigmas.shape[2], sigmas.shape[3]))
ls = np.arange(2, lmax + 1)
res = np.zeros((len(ls), 3))
i = 0
for l in ls:
samps = nsigmas[:, spec, l]
x = np.linspace(min(samps), max(samps), 100)
ml, upper, lower = gen_utils.calculate_ml_and_asymm_errorbars_from_samples(samps, x, sample_fraction, smooth=False)
res[i, 0] = ml
# res[i, 1] = ml - lower
# res[i, 2] = upper - ml
res[i, 1] = lower
res[i, 2] = upper
i += 1
plt.plot(ls, res[:, 1], color=color, label=label)
plt.plot(ls, res[:, 2], color=color)
plt.fill_between(ls, res[:, 2], res[:, 1], color=color, alpha=0.5)
plt.plot(ls, res[:, 0], color=mlcolor, label=label)
def plot_errbars_from_sigma_sample_marginals(ls, sigmas, burnin=0, spec=0, lmax=20, sample_fraction=0.68, label=None, color=None, shift=0):
if isinstance(sigmas, str):
sigmas = pyfits.open(sigmas)[0].data
numsamps = np.sum(sigmas[0, :, 0, 0]) - len(sigmas[0, :, 0, 0]) * burnin
nsigmas = np.zeros(np.append(numsamps, sigmas.shape[2:]))
j = 0
for i in range(len(sigmas[0, :, 0, 0])):
jprev = j
j += sigmas[0, i, 0, 0] - burnin
nsigmas[jprev:j] = sigmas[burnin + 1:sigmas[0, i, 0, 0] + 1, i]
# sigmas = np.reshape(sigmas[1:], ((sigmas.shape[0] - 1) * sigmas.shape[1], 1, sigmas.shape[2], sigmas.shape[3]))
res = np.zeros((len(ls), 3))
i = 0
for l in ls:
samps = nsigmas[:, spec, l]
x = np.linspace(min(samps), max(samps), 100)
ml, upper, lower = gen_utils.calculate_ml_and_asymm_errorbars_from_samples(samps, x, sample_fraction, smooth=False)
res[i, 0] = ml
res[i, 1] = ml - lower
res[i, 2] = upper - ml
i += 1
ls = ls + shift
plt.scatter(ls, res[:, 0], color=color, label=label)
plt.errorbar(ls, res[:, 0], res[:, 1:].T, ecolor=color, fmt=None)
def plot_ml_powspec_with_band(sigmas,
lmax=50,
sample_fraction=0.68,
label=None,
color='red',
mlcolor='blue',
spec=0,
burnin=0):
if isinstance(sigmas, str):
sigmas = pyfits.open(sigmas)[0].data
numsamps = np.sum(sigmas[0, :, 0, 0]) - len(sigmas[0, :, 0, 0]) * burnin
nsigmas = np.zeros(np.append(numsamps, sigmas.shape[2:]))
j = 0
for i in range(len(sigmas[0, :, 0, 0])):
jprev = j
j += sigmas[0, i, 0, 0] - burnin
nsigmas[jprev:j] = sigmas[burnin + 1:sigmas[0, i, 0, 0] + 1, i]
# sigmas = np.reshape(sigmas[1:], ((sigmas.shape[0] - 1) * sigmas.shape[1], 1, sigmas.shape[2], sigmas.shape[3]))
ls = np.arange(2, lmax + 1)
res = np.zeros((len(ls), 3))
i = 0
for l in ls:
samps = nsigmas[:, spec, l]
x = np.linspace(min(samps), max(samps), 100)
ml, upper, lower = gen_utils.calculate_ml_and_asymm_errorbars_from_samples(
samps, x, sample_fraction, smooth=False)
res[i, 0] = ml
# res[i, 1] = ml - lower
# res[i, 2] = upper - ml
res[i, 1] = lower
res[i, 2] = upper
i += 1
plt.plot(ls, res[:, 1], color=color, label=label)
plt.plot(ls, res[:, 2], color=color)
plt.fill_between(ls, res[:, 2], res[:, 1], color=color, alpha=0.5)
plt.plot(ls, res[:, 0], color=mlcolor, label=label)
def plot_errbars_from_sigma_sample_marginals(ls,
sigmas,
burnin=0,
spec=0,
lmax=20,
sample_fraction=0.68,
label=None,
color=None,
shift=0):
if isinstance(sigmas, str):
sigmas = pyfits.open(sigmas)[0].data
numsamps = np.sum(sigmas[0, :, 0, 0]) - len(sigmas[0, :, 0, 0]) * burnin
nsigmas = np.zeros(np.append(numsamps, sigmas.shape[2:]))
j = 0
for i in range(len(sigmas[0, :, 0, 0])):
jprev = j
j += sigmas[0, i, 0, 0] - burnin
nsigmas[jprev:j] = sigmas[burnin + 1:sigmas[0, i, 0, 0] + 1, i]
# sigmas = np.reshape(sigmas[1:], ((sigmas.shape[0] - 1) * sigmas.shape[1], 1, sigmas.shape[2], sigmas.shape[3]))
res = np.zeros((len(ls), 3))
i = 0
for l in ls:
samps = nsigmas[:, spec, l]
x = np.linspace(min(samps), max(samps), 100)
ml, upper, lower = gen_utils.calculate_ml_and_asymm_errorbars_from_samples(
samps, x, sample_fraction, smooth=False)
res[i, 0] = ml
res[i, 1] = ml - lower
res[i, 2] = upper - ml
i += 1
ls = ls + shift
plt.scatter(ls, res[:, 0], color=color, label=label)
plt.errorbar(ls, res[:, 0], res[:, 1:].T, ecolor=color, fmt=None)
