def gen_background(xs, background_params, background_mode=None):
"""Generate background values, from parameter definition.
Parameters
----------
xs : 1d array
Frequency vector to create background from.
background_params : list of float
Paramters that define the background process.
background_mode : {'fixed', 'knee'}, optional
Which kind of background to generate power spectra with.
If not provided, is infered from the parameters.
Returns
-------
1d array
Generated background values
"""
if not background_mode:
background_mode = infer_bg_func(background_params)
bg_func = get_bg_func(background_mode)
return bg_func(xs, *background_params)
def _gen_power_vals(xs, bg_params, gauss_params, nlv):
"""Generate power values for a power spectrum.
Parameters
----------
xs : 1d array
Frequency vector to create power values from.
background_params : list of float
Parameters to create the background of power spectrum.
gauss_params : list of float
Parameters to create peaks. Length of n_peaks * 3.
nlv : float
Noise level to add to generated power spectrum.
Returns
-------
ys : 1d vector
Power values (linear).
"""
background = gen_background(xs, bg_params, infer_bg_func(bg_params))
peaks = gen_peaks(xs, gauss_params)
noise = np.random.normal(0, nlv, len(xs))
ys = np.power(10, background + peaks + noise)
return ys
def _check_loaded_settings(self, data):
"""Check if settings added, and update the object as needed.
Parameters
----------
data : dict
The dictionary of data that has been added to the object.
"""
# If settings not loaded from file, clear from object, so that default
# settings, which are potentially wrong for loaded data, aren't kept
if not set(get_obj_desc()['settings']).issubset(set(data.keys())):
# Reset all public settings to None
for setting in get_obj_desc()['settings']:
setattr(self, setting, None)
# Infer whether knee fitting was used, if background params have been loaded
if np.all(self.background_params_):
self.background_mode = infer_bg_func(self.background_params_)
# Reset internal settings so that they are consistent with what was loaded
# Note that this will set internal settings to None, if public settings unavailable
self._reset_internal_settings()