def gen_aperiodic(freqs, aperiodic_params, aperiodic_mode=None):
"""Generate aperiodic values.
Parameters
----------
freqs : 1d array
Frequency vector to create aperiodic component for.
aperiodic_params : list of float
Parameters that define the aperiodic component.
aperiodic_mode : {'fixed', 'knee'}, optional
Which kind of aperiodic component to generate.
If not provided, is inferred from the parameters.
Returns
-------
ap_vals : 1d array
Aperiodic values, in log10 spacing.
"""
if not aperiodic_mode:
aperiodic_mode = infer_ap_func(aperiodic_params)
ap_func = get_ap_func(aperiodic_mode)
ap_vals = ap_func(freqs, *aperiodic_params)
return ap_vals
def update_sim_ap_params(sim_params, delta, field=None):
"""Update the aperiodic parameter definition in a SimParams object.
Parameters
----------
sim_params : SimParams
Object storing the current parameter definition.
delta : float or list of float
Value(s) by which to update the parameters.
field : {'offset', 'knee', 'exponent'} or list of string
Field of the aperiodic parameter(s) to update.
Returns
-------
new_sim_params : SimParams
Updated object storing the new parameter definition.
Raises
------
InconsistentDataError
If the input parameters and update values are inconsistent.
"""
# Grab the aperiodic parameters that need updating
ap_params = sim_params.aperiodic_params.copy()
# If field isn't specified, check shapes line up and update across parameters
if not field:
if not len(ap_params) == len(delta):
raise InconsistentDataError(
"The number of items to update and "
"number of new values is inconsistent.")
ap_params = [param + update for param, update in zip(ap_params, delta)]
# If labels are given, update deltas according to their labels
else:
# This loop checks & casts to list, to work for single or multiple passed in values
for cur_field, cur_delta in zip(
list([field]) if not isinstance(field, list) else field,
list([delta]) if not isinstance(delta, list) else delta):
data_ind = get_indices(infer_ap_func(ap_params))[cur_field]
ap_params[data_ind] = ap_params[data_ind] + cur_delta
# Replace parameters. Note that this copies a new object, as data objects are immutable
new_sim_params = sim_params._replace(aperiodic_params=ap_params)
return new_sim_params
def update_syn_ap_params(syn_params, delta, field=None):
"""Update the aperiodic parameter definition in a SynParams object.
Parameters
----------
syn_params : SynParams object
Object storing the current parameter definitions.
delta : float or list
Value(s) by which to update the parameters.
field : {'offset', 'knee', 'exponent'} or list of string
Field of the aperiodic parameters to update.
Returns
-------
new_syn_params : SynParams object
Updated object storing the new parameter definitions.
Notes
-----
SynParams is a `namedtuple`, which is immutable.
Therefore, this function constructs and returns a new `SynParams` object.
"""
ap_params = syn_params.aperiodic_params.copy()
if not field:
if not len(ap_params) == len(delta):
raise ValueError('')
ap_params = [ii + jj for ii, jj in zip(ap_params, delta)]
else:
field = list([field]) if not isinstance(field, list) else field
delta = list([delta]) if not isinstance(delta, list) else delta
for cur_field, cur_delta in zip(field, delta):
dat_ind = get_data_indices(
infer_ap_func(syn_params.aperiodic_params))[cur_field]
ap_params[dat_ind] = ap_params[dat_ind] + cur_delta
new_syn_params = SynParams(ap_params, *syn_params[1:])
return new_syn_params
def _check_loaded_settings(self, data):
"""Check if settings added, and update the object as needed.
Parameters
----------
data : dict
A 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 aperiodic params have been loaded
if np.all(self.aperiodic_params_):
self.aperiodic_mode = infer_ap_func(self.aperiodic_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()