def get_all_results_by_param(self, mlmc_est, param_name):
"""
Get all result values by given param name, e.g. param_name = "temp" - return all temperatures...
:param mlmc_est: Estimate instance
:param param_name: Sample result param name
:return: moments means, moments vars -> two numpy arrays
"""
n_moments = 3
mlmc_est.mlmc.clean_select()
mlmc_est.mlmc.select_values({param_name: (0, ">=")})
domain = Estimate.estimate_domain(mlmc_est.mlmc)
moments_fn = Monomial(n_moments, domain, False, ref_domain=domain)
means, vars = mlmc_est.estimate_moments(moments_fn)
return means, vars
def plot_param(self, mlmc_est, ax, X, col, param_name, legend):
"""
Get all result values by given param name, e.g. param_name = "temp" - return all temperatures...
:param mlmc_est: Estimate instance
:param param_name: Sample result param name
:return: moments means, moments vars -> two numpy arrays
"""
n_moments = 3
mlmc_est.mlmc.clean_select()
mlmc_est.mlmc.select_values(None, selected_param=param_name)
print("plot param:", param_name)
samples = mlmc_est.mlmc.levels[0].sample_values[:,0,:]
print(" shape: ", samples.shape)
#print(np.any(np.isnan(samples), axis=1))
domain = Estimate.estimate_domain(mlmc_est.mlmc)
domain_diff = domain[1] - domain[0]
print(" domain: ", domain)
moments_fn = Monomial(n_moments, domain, False, ref_domain=domain)
N = mlmc_est.mlmc.n_samples[0]
mom_means, mom_vars = mlmc_est.estimate_moments(moments_fn)
mom_means, mom_vars = mom_means[1:, :], mom_vars[1:, :] # omit time=0
q_mean = mom_means[:, 1]
q_mean_err = np.sqrt(mom_vars[:, 1])
q_var = (mom_means[:, 2] - q_mean ** 2) * N / (N-1)
# print(" means: ", mom_means[-1, :])
# print(" vars: ", mom_vars[-1, :])
# print(" alt var: ", mom_vars[-1, 1]*N)
# print(" qvar : ", q_var[-1])
# print(" qvar_err : ", q_var[-1])
#q_var = q_mean_err * N
q_std = np.sqrt(q_var)
q_std_err = np.sqrt(q_var + np.sqrt(mom_vars[:, 2] * N / (N-1)))
ax.fill_between(X, q_mean - q_mean_err, q_mean + q_mean_err,
color=col, alpha=1, label=legend + " - mean")
ax.fill_between(X, q_mean - q_std, q_mean + q_std,
color=col, alpha=0.2, label=legend + " - std")
ax.fill_between(X, q_mean - q_std_err, q_mean - q_std,
color=col, alpha=0.4, label = None)
ax.fill_between(X, q_mean + q_std, q_mean + q_std_err,
color=col, alpha=0.4, label = None)