Bias in bootstrap ?
std seems to be biased in bootstrap estimation
n_bootstraps = np.logspace(np.log10(10), np.log10(1000), 10).astype(np.int)
mean_std = []
std_std = []
for n_bootstrap in n_bootstraps:
nm = bootstrap(filenames, n_bootstrap=n_bootstrap)
mean_std.append(np.mean(nm.uncertainty.array[~nm.mask]))
std_std.append(np.std(nm.uncertainty.array[~nm.mask]))
# We are actually limited by the number of input maps here...
plt.errorbar(n_bootstraps, mean_std, std_std)
plt.axhline(weighted_noise)