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[ENH] Aperiodic Knee Fit #235

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[ENH] Aperiodic Knee Fit #235

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48b7cb7
directly fit knee frequency
ryanhammonds Nov 23, 2021
851c1e8
add knee_constant aperiodic mode
ryanhammonds Apr 18, 2022
fbaf4d8
add knee_constant aperiodic mode tests
ryanhammonds Apr 18, 2022
0198465
add double exponential function
ryanhammonds May 28, 2022
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8 changes: 4 additions & 4 deletions fooof/core/funcs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,8 +51,8 @@ def expo_function(xs, *params):
xs : 1d array
Input x-axis values.
*params : float
Parameters (offset, knee, exp) that define Lorentzian function:
y = 10^offset * (1/(knee + x^exp))
Parameters (offset, knee_freq, exp) that define Lorentzian function:
y = 10^offset * (1/(knee_freq^exp + x^exp))

Returns
-------
Expand All @@ -62,9 +62,9 @@ def expo_function(xs, *params):

ys = np.zeros_like(xs)

offset, knee, exp = params
offset, knee_freq, exp = params

ys = ys + offset - np.log10(knee + xs**exp)
ys = ys + offset - np.log10(knee_freq**exp + xs**exp)

return ys

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2 changes: 1 addition & 1 deletion fooof/objs/fit.py
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Expand Up @@ -172,7 +172,7 @@ def __init__(self, peak_width_limits=(0.5, 12.0), max_n_peaks=np.inf, min_peak_h
# Guess parameters for aperiodic fitting, [offset, knee, exponent]
# If offset guess is None, the first value of the power spectrum is used as offset guess
# If exponent guess is None, the abs(log-log slope) of first & last points is used
self._ap_guess = (None, 0, None)
self._ap_guess = (None, 1, None)
# Bounds for aperiodic fitting, as: ((offset_low_bound, knee_low_bound, exp_low_bound),
# (offset_high_bound, knee_high_bound, exp_high_bound))
# By default, aperiodic fitting is unbound, but can be restricted here, if desired
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I wonder if

self._ap_bounds = ((-np.inf, -np.inf, -np.inf), (np.inf, np.inf, np.inf))

should also be updated, as the knee parameters shouldn't be below 0 in any case?

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