def set_noise(data, **kwargs): """Set the noise level This method calculates the noise standard deviation using the median absolute deviation (MAD) of the input data and adds it to the keyword arguments. Parameters ---------- data : np.ndarray Input noisy data (3D array) Returns ------- dict Updated keyword arguments """ # It the noise is not already provided calculate it using the MAD if isinstance(kwargs['noise_est'], type(None)): kwargs['noise_est'] = sigma_mad(data) print ' - Noise Estimate:', kwargs['noise_est'] kwargs['log'].info(' - Noise Estimate: ' + str(kwargs['noise_est'])) return kwargs
def get_sigma_map(data, opt=None): # Get wavelet residuals. wavelet_residuals = call_mr_transform(data, opt=opt, remove_files=True) # Return sigma map. return np.array([sigma_mad(a) * np.ones(data.shape) for a in wavelet_residuals])
def get_sigma(data): return stats.sigma_mad(data) / fake_data_norm