fooof-tools · TomDonoghue · Apr 17, 2026 · Apr 17, 2026 · Apr 17, 2026 · Apr 17, 2026
diff --git a/specparam/algorithms/spectral_fit.py b/specparam/algorithms/spectral_fit.py
@@ -160,15 +160,15 @@ def _fit(self):
 
         # Take an initial fit of the aperiodic component
         temp_aperiodic_params = self._robust_ap_fit(self.data.freqs, self.data.power_spectrum)
-        temp_ap_fit = self.modes.aperiodic.func(self.data.freqs, *temp_aperiodic_params)
+        temp_ap_fit = self.modes.aperiodic.generate(self.data.freqs, *temp_aperiodic_params)
 
         # Find peaks from the flattened power spectrum, and fit them
         temp_spectrum_flat = self.data.power_spectrum - temp_ap_fit
         self.results.params.periodic.add_params('fit', self._fit_peaks(temp_spectrum_flat))
 
         # Calculate the peak fit
         #   Note: if no peaks are found, this creates a flat (all zero) peak fit
-        self.results.model._peak_fit = self.modes.periodic.func(\
+        self.results.model._peak_fit = self.modes.periodic.generate(\
             self.data.freqs, *np.ndarray.flatten(self.results.params.periodic.get_params('fit')))
 
         # Create peak-removed (but not flattened) power spectrum
@@ -178,7 +178,7 @@ def _fit(self):
         # Run final aperiodic fit on peak-removed power spectrum
         self.results.params.aperiodic.add_params('fit', \
             self._simple_ap_fit(self.data.freqs, self.results.model._spectrum_peak_rm))
-        self.results.model._ap_fit = self.modes.aperiodic.func(\
+        self.results.model._ap_fit = self.modes.aperiodic.generate(\
             self.data.freqs, *self.results.params.aperiodic.params)
 
         # Create remaining model components: flatspec & full power_spectrum model fit
@@ -305,7 +305,7 @@ def _robust_ap_fit(self, freqs, power_spectrum):
 
         # Do a quick, initial aperiodic fit
         popt = self._simple_ap_fit(freqs, power_spectrum)
-        initial_fit = self.modes.aperiodic.func(freqs, *popt)
+        initial_fit = self.modes.aperiodic.generate(freqs, *popt)
 
         # Flatten power_spectrum based on initial aperiodic fit
         flatspec = power_spectrum - initial_fit
@@ -406,7 +406,7 @@ def _fit_peaks(self, flatspec):
 
             # Fit and subtract guess peak from the spectrum
             guess = np.vstack((guess, cur_guess))
-            peak_fit = self.modes.periodic.func(self.data.freqs, *cur_guess)
+            peak_fit = self.modes.periodic.generate(self.data.freqs, *cur_guess)
             flat_iter = flat_iter - peak_fit
 
         # Check peaks based on edges, and on overlap, dropping any that violate requirements

diff --git a/specparam/modes/mode.py b/specparam/modes/mode.py
@@ -88,6 +88,25 @@ def n_params(self):
         return self.params.n_params
 
 
+    def generate(self, freqs, *params):
+        """Generate component values from component function.
+
+        Parameters
+        ----------
+        freqs : 1d array
+            Frequency values to generate component for.
+        *params
+            Parameters for the fit mode to generate with.
+
+        Returns
+        -------
+        1d array
+            Generated component values.
+        """
+
+        return self.func(freqs, *params)
+
+
     def check_params(self):
         """Check the description of the parameters for the current mode."""
 

diff --git a/specparam/sim/gen.py b/specparam/sim/gen.py
@@ -59,8 +59,7 @@ def gen_aperiodic(freqs, aperiodic_mode, aperiodic_params):
     """
 
     ap_mode = check_mode_definition(aperiodic_mode, 'aperiodic')
-
-    ap_vals = ap_mode.func(freqs, *aperiodic_params)
+    ap_vals = ap_mode.generate(freqs, *aperiodic_params)
 
     return ap_vals
 
@@ -84,8 +83,7 @@ def gen_periodic(freqs, periodic_mode, periodic_params):
     """
 
     pe_mode = check_mode_definition(periodic_mode, 'periodic')
-
-    pe_vals = pe_mode.func(freqs, *check_flat(periodic_params))
+    pe_vals = pe_mode.generate(freqs, *check_flat(periodic_params))
 
     return pe_vals
 

diff --git a/specparam/sim/sim.py b/specparam/sim/sim.py
@@ -22,17 +22,22 @@ def sim_power_spectrum(freq_range, aperiodic_params, periodic_params,
         Frequency range to simulate power spectrum across, as [f_low, f_high], inclusive.
     aperiodic_params : dict of {str : array}
         Mode definition and parameters to create the aperiodic component of a power spectrum.
-        Should be organized as {mode : params}.
-    periodic_params : list of float or list of list of float
+        Should be organized as {mode : params}, where `mode` is a string label for a mode to
+        simulate with and `params` is a set of parameter values of length aperiodic_mode.n_params.
+    periodic_params : dict of {str : list of float or list of list of float}
         Mode definition and parameters to create the periodic component of a power spectrum.
-        Should be organized as {mode : params}.
+        Should be organized as {mode : params}, where `mode` is a string label for a mode to
+        simulate with and `params` corresponds to the number of desired peaks, with a total number
+        of values matching periodic_mode.n_params * n_peaks.
+        This can can be a flat list (ex: [10, 1, 1, 20, 0.5, 1]) or be grouped into a
+        list of lists (ex: [[10, 1, 1], [20, 0.5, 1]]).
     nlv : float, optional, default: 0.005
         Noise level to add to generated power spectrum.
     freq_res : float, optional, default: 0.5
         Frequency resolution for the simulated power spectrum.
     f_rotation : float, optional
-        Frequency value, in Hz, to rotate around.
-        Should only be set if spectrum is to be rotated.
+        Frequency value, in Hz, to rotate around. Should only be set if spectrum is to be rotated.
+        Can only be used with `powerlaw` aperiodic mode.
     return_params : bool, optional, default: False
         Whether to return the parameters for the simulated spectrum.
 
@@ -48,44 +53,21 @@ def sim_power_spectrum(freq_range, aperiodic_params, periodic_params,
 
     Notes
     -----
-    Aperiodic Parameters:
-
-    - The function for the aperiodic process to use is inferred from the provided parameters.
-    - If length of 2, the 'fixed' aperiodic mode is used, if length of 3, 'knee' is used.
-
-    Periodic Parameters:
-
-    - The periodic component is comprised of a set of 'peaks', each of which is described as:
-
-      * Mean (Center Frequency), height (Power), and standard deviation (Bandwidth).
-      * Make sure any center frequencies you request are within the simulated frequency range.
-
-    - The total number of parameters that need to be specified is number of peaks * 3
-
-      * These can be specified in as all together in a flat list (ex: [10, 1, 1, 20, 0.5, 1])
-      * They can also be grouped into a list of lists (ex: [[10, 1, 1], [20, 0.5, 1]])
+    - See `check_modes` for more information on the available modes to use to simulate,
+      as well as descriptions of the parameters for each mode.
 
     Rotating Power Spectra:
 
-    - You can optionally specify a rotation frequency, such that power spectra will be
-      simulated and rotated around that point to the specified aperiodic exponent.
+    - For the powerlaw aperiodic component only, you can optionally specify a rotation frequency,
+      such that power spectra will be simulated and rotated around that point to the specified
+      aperiodic exponent. Any power spectra simulated with the same rotation frequency will relate
+      to each other by having the specified rotation point.
 
-      * This can be used so that any power spectra simulated with the same 'f_rotation'
-        will relate to each other by having the specified rotation point.
-
-    - Note that rotating power spectra changes the offset.
-
-      * If you specify an offset value to simulate as well as 'f_rotation', the returned
-        spectrum will NOT have the requested offset. It instead will have the offset
-        value required to create the requested aperiodic exponent with the requested
-        rotation point.
-      * If you return SimParams, the recorded offset will be the calculated offset
-        of the data post rotation, and not the entered value.
-
-    - You cannot rotate power spectra simulated with a knee.
-
-      * The procedure we use to rotate does not support spectra with a knee, and so
-        setting 'f_rotation' with a knee will lead to an error.
+    - Note that rotating power spectra changes the offset. If a simulated spectrum is rotated,
+      the returned spectrum will NOT have the offset of the input parameters, and will instead
+      have offset value required to create the given aperiodic exponent with the requested
+      rotation point. If you return SimParams, the recorded offset will be the calculated offset
+      of the data post rotation, and not the entered value.
 
     Examples
     --------
@@ -139,22 +121,22 @@ def sim_group_power_spectra(n_spectra, freq_range, aperiodic_params, periodic_pa
         The number of power spectra to generate.
     freq_range : list of [float, float]
         Frequency range to simulate power spectra across, as [f_low, f_high], inclusive.
-    aperiodic_params : list of float or generator
-        Parameters for the aperiodic component of the power spectra.
-    aperiodic_mode : Mode or str
-        Which kind of aperiodic component to simulate.
-    periodic_params : list of float or generator
-        Parameters for the periodic component of the power spectra.
-        Length of n_peaks * 3.
-    periodic_mode : Mode or str
-        Which kind of periodic component to simulate.
+    aperiodic_params : dict of {str : array}
+        Mode definition and parameters to create the aperiodic components of the power spectra.
+        Should be organized as {mode : params}, where `mode` is a string label for a mode to
+        simulate with and `params` (array_like or generator) defines the aperiodic parameters.
+    periodic_params : dict of {str : list of float or list of list of float}
+        Mode definition and parameters to create the periodic components of the power spectras.
+        Should be organized as {mode : params}, where `mode` is a string label for a mode to
+        simulate with and `params` (array_like or generator) defines the periodic parameters.
     nlvs : float or list of float or generator, optional, default: 0.005
         Noise level to add to generated power spectrum.
     freq_res : float, optional, default: 0.5
         Frequency resolution for the simulated power spectra.
     f_rotation : float, optional
-        Frequency value, in Hz, to rotate around.
-        Should only be set if spectra are to be rotated.
+        Frequency value, in Hz, to rotate around. Should only be set if spectrum is to be rotated.
+        Can only be used with `powerlaw` aperiodic mode.
+        See additional notes in `sim_power_spectra` on rotating simulated power spectra.
     return_params : bool, optional, default: False
         Whether to return the parameters for the simulated spectra.
 
@@ -179,40 +161,6 @@ def sim_group_power_spectra(n_spectra, freq_range, aperiodic_params, periodic_pa
     - A generator object that returns parameters for a power spectrum.
       If so, each spectrum has parameters sampled from the generator.
 
-    Aperiodic Parameters:
-
-    - The function for the aperiodic process to use is inferred from the provided parameters.
-    - If length of 2, the 'fixed' aperiodic mode is used, if length of 3, 'knee' is used.
-
-    Periodic Parameters:
-
-    - The periodic component is comprised of a set of 'peaks', each of which is described as:
-
-      * Mean (Center Frequency), height (Power), and standard deviation (Bandwidth).
-      * Make sure any center frequencies you request are within the simulated frequency range.
-
-    Rotating Power Spectra:
-
-    - You can optionally specify a rotation frequency, such that power spectra will be
-      simulated and rotated around that point to the specified aperiodic exponent.
-
-      * This can be used so that any power spectra simulated with the same 'f_rotation'
-        will relate to each other by having the specified rotation point.
-
-    - Note that rotating power spectra changes the offset.
-
-      * If you specify an offset value to simulate as well as 'f_rotation', the returned
-        spectrum will NOT have the requested offset. It instead will have the offset
-        value required to create the requested aperiodic exponent with the requested
-        rotation point.
-      * If you return SimParams, the recorded offset will be the calculated offset
-        of the data post rotation, and not the entered value.
-
-    - You cannot rotate power spectra simulated with a knee.
-
-      * The procedure we use to rotate does not support spectra with a knee, and so
-        setting 'f_rotation' with a knee will lead to an error.
-
     Examples
     --------
     Generate 2 power spectra using the same parameters:

diff --git a/specparam/tests/modes/test_mode.py b/specparam/tests/modes/test_mode.py
@@ -2,7 +2,10 @@
 
 from collections import OrderedDict
 
+import numpy as np
+
 from specparam.modes.params import ParamDefinition
+from specparam.modes.definitions import ap_powerlaw
 
 from specparam.modes.mode import *
 
@@ -41,3 +44,11 @@ def tfit2(xs, *params):
                  ndim=2, freq_space='linear', powers_space='linear')
     assert tmode
     assert isinstance(tmode.params, ParamDefinition)
+
+def test_mode_generate():
+
+    freqs = np.linspace(1, 40, 40)
+    params = [1, 1]
+
+    output = ap_powerlaw.generate(freqs, *params)
+    assert isinstance(output, np.ndarray)
diff --git a/specparam/tests/sim/test_utils.py → specparam/tests/utils/test_random.py b/specparam/tests/sim/test_utils.py → specparam/tests/utils/test_random.py
@@ -1,6 +1,6 @@
-"""Test functions for specparam.sim.utils."""
+"""Test functions for specparam.utils.random"""
 
-from specparam.sim.utils import *
+from specparam.utils.random import *
 
 ###################################################################################################
 ###################################################################################################

diff --git a/specparam/sim/utils.py → specparam/utils/random.py b/specparam/sim/utils.py → specparam/utils/random.py
@@ -1,4 +1,4 @@
-"""Utilities related to simulations."""
+"""Utilities related to managing randomness."""
 
 import numpy as np