add comments on knee convs

Author: TomDonoghue

fooof/utils/params.py

@@ -19,9 +19,27 @@ def compute_knee_frequency(knee, exponent):
     -------
     float
         Frequency value, in Hz, of the knee occurs.
+
+    Notes
+    -----
+    The knee frequency is an estimate of the frequency in spectrum at which the spectrum
+    moves from the plateau region to the exponential decay.
+
+    This approach for estimating the knee frequency comes from [1]_ (see [2]_ for code).
+
+    Note that this provides an estimate of the knee frequency, but is not, in the general case,
+    a precisely defined value. In particular, this conversion is based on the case of a Lorentzian
+    with exponent = 2, and for other exponent values provides a non-exact approximation.
+
+    References
+    ----------
+    .. [1] Gao, R., van den Brink, R. L., Pfeffer, T., & Voytek, B. (2020). Neuronal timescales
+           are functionally dynamic and shaped by cortical microarchitecture. Elife, 9, e61277.
+           https://doi.org/10.7554/eLife.61277
+    .. [2] https://github.com/rdgao/field-echos/blob/master/echo_utils.py#L64
     """
 
-    return knee ** (1./exponent)
+    return knee ** (1. / exponent)
 
 
 def compute_time_constant(knee_freq):
@@ -36,9 +54,20 @@ def compute_time_constant(knee_freq):
     -------
     float
         Calculated time constant value, tau, given the knee frequency.
+
+    Notes
+    -----
+    This approach for estimating the time constant comes from [1]_ (see [2]_ for code).
+
+    References
+    ----------
+    .. [1] Gao, R., van den Brink, R. L., Pfeffer, T., & Voytek, B. (2020). Neuronal timescales
+           are functionally dynamic and shaped by cortical microarchitecture. Elife, 9, e61277.
+           https://doi.org/10.7554/eLife.61277
+    .. [2] https://github.com/rdgao/field-echos/blob/master/echo_utils.py#L65
     """
 
-    return 1. / (2*np.pi*knee_freq)
+    return 1. / (2 * np.pi * knee_freq)
 
 
 def compute_fwhm(std):