"""Data objects for FOOOF.
Notes on FOOOF data objects:
- these data objects are NamedTuples, immutable data types with attribute labels
- the namedtuples are wrapped as classes (they are still immutable when doing this)
- wrapping in objects helps to be able to render well formed documentation for them.
- setting `__slots__` as empty voids the dynamic dictionary that usually stores attributes
- this means no additional attributes can be defined (which is more memory efficient)
"""
from collections import namedtuple
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[docs]class FOOOFSettings(namedtuple('FOOOFSettings', ['peak_width_limits', 'max_n_peaks',
'min_peak_height', 'peak_threshold',
'aperiodic_mode'])):
"""User defined settings for the fitting algorithm.
Parameters
----------
peak_width_limits : tuple of (float, float)
Limits on possible peak width, in Hz, as (lower_bound, upper_bound).
max_n_peaks : int
Maximum number of peaks to fit.
min_peak_height : float
Absolute threshold for detecting peaks, in units of the input data.
peak_threshold : float
Relative threshold for detecting peaks, in units of standard deviation of the input data.
aperiodic_mode : {'fixed', 'knee'}
Which approach to take for fitting the aperiodic component.
Notes
-----
This object is a data object, based on a NamedTuple, with immutable data attributes.
"""
__slots__ = ()
[docs]class FOOOFRunModes(namedtuple('FOOOFRunModes', ['debug', 'check_freqs', 'check_data'])):
"""Checks performed and errors raised by the model.
Parameters
----------
debug : bool
Whether to run in debug mode.
check_freqs : bool
Whether to run in check freqs mode.
check_data : bool
Whether to run in check data mode.
Notes
-----
This object is a data object, based on a NamedTuple, with immutable data attributes.
"""
__slots__ = ()
[docs]class FOOOFResults(namedtuple('FOOOFResults', ['aperiodic_params', 'peak_params',
'r_squared', 'error', 'gaussian_params'])):
"""Model results from parameterizing a power spectrum.
Parameters
----------
aperiodic_params : 1d array
Parameters that define the aperiodic fit. As [Offset, (Knee), Exponent].
The knee parameter is only included if aperiodic is fit with knee.
peak_params : 2d array
Fitted parameter values for the peaks. Each row is a peak, as [CF, PW, BW].
r_squared : float
R-squared of the fit between the full model fit and the input data.
error : float
Error of the full model fit.
gaussian_params : 2d array
Parameters that define the gaussian fit(s).
Each row is a gaussian, as [mean, height, standard deviation].
Notes
-----
This object is a data object, based on a NamedTuple, with immutable data attributes.
"""
__slots__ = ()
[docs]class SimParams(namedtuple('SimParams', ['aperiodic_params', 'periodic_params', 'nlv'])):
"""Parameters that define a simulated power spectrum.
Parameters
----------
aperiodic_params : list
Parameters that define the aperiodic component.
periodic_params : list or list of lists
Parameters that define the periodic component.
nlv : float
Noise level added to simulated spectrum.
Notes
-----
This object is a data object, based on a NamedTuple, with immutable data attributes.
"""
__slots__ = ()