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
