Update all references to amp/amplitude to height or power

Author: TomDonoghue

doc/api.rst

@@ -52,7 +52,7 @@ Analysis Functions
 
     get_band_peak
     get_band_peak_group
-    get_highest_amp_peak
+    get_highest_peak
 
 Synth Code
 ----------

fooof/analysis.py

@@ -13,14 +13,14 @@ def get_band_peak_group(peak_params, band_def, n_fits):
     peak_params : 2d array
         Peak parameters, for a group fit, from FOOOF, with shape of [n_peaks, 4].
     band_def : [float, float]
-        Defines the band of interest, as [lower_frequency_bound, upper_frequency_bound].
+        Defines the band of interest, as [lower_frequency_bound, upper_frequency_bound], in Hz.
     n_fits : int
         The number of model fits in the FOOOFGroup data.
 
     Returns
     -------
     band_peaks : 2d array
-        Peak data. Each row is a peak, as [CF, Amp, BW].
+        Peak data. Each row is a peak, as [CF, PW, BW].
 
     Notes
     -----
@@ -59,12 +59,12 @@ def get_band_peak(peak_params, band_def, ret_one=True):
         Defines the band of interest, as [lower_frequency_bound, upper_frequency_bound].
     ret_one : bool, optional, default: True
         Whether to return single peak (if True) or all peaks within the range found (if False).
-        If True, returns the highest amplitude peak within the search range.
+        If True, returns the highest peak within the search range.
 
     Returns
     -------
     band_peaks : 1d or 2d array
-        Peak data. Each row is a peak, as [CF, Amp, BW]
+        Peak data. Each row is a peak, as [CF, PW, BW]
     """
 
     # Return nan array if empty input
@@ -82,17 +82,17 @@ def get_band_peak(peak_params, band_def, ret_one=True):
 
     band_peaks = peak_params[peak_inds, :]
 
-    # If results > 1 and ret_one, then we return the highest amplitude peak
+    # If results > 1 and ret_one, then we return the highest peak
     #    Call a sub-function to select highest power peak in band
     if n_peaks > 1 and ret_one:
-        band_peaks = get_highest_amp_peak(band_peaks)
+        band_peaks = get_highest_peak(band_peaks)
 
     # If results == 1, return peak - [cen, power, bw]
     return np.squeeze(band_peaks)
 
 
-def get_highest_amp_peak(band_peaks):
-    """Searches for the highest amplitude peak.
+def get_highest_peak(band_peaks):
+    """Searches for the highest peak.
 
     Parameters
     ----------
@@ -102,7 +102,7 @@ def get_highest_amp_peak(band_peaks):
     Returns
     -------
     band_peaks : array
-        Peak data. Each row is a peak, as [CF, Amp, BW].
+        Peak data. Each row is a peak, as [CF, PW, BW].
     """
 
     # Catch & return NaN if empty

fooof/core/funcs.py

@@ -32,9 +32,9 @@ def gaussian_function(xs, *params):
 
     for ii in range(0, len(params), 3):
 
-        ctr, amp, wid = params[ii:ii+3]
+        ctr, hgt, wid = params[ii:ii+3]
 
-        ys = ys + amp * np.exp(-(xs-ctr)**2 / (2*wid**2))
+        ys = ys + hgt * np.exp(-(xs-ctr)**2 / (2*wid**2))
 
     return ys
 

fooof/core/info.py

@@ -40,12 +40,12 @@ def get_data_indices(aperiodic_mode):
     """
 
     indices = {
-        'CF'  : 0,
-        'Amp' : 1,
-        'BW'  : 2,
+        'CF' : 0,
+        'PW' : 1,
+        'BW' : 2,
         'offset' : 0,
-        'knee'      : 1 if aperiodic_mode == 'knee' else None,
-        'exponent'  : 1 if aperiodic_mode == 'fixed' else 2
+        'knee' : 1 if aperiodic_mode == 'knee' else None,
+        'exponent' : 1 if aperiodic_mode == 'fixed' else 2
     }
 
     return indices

fooof/core/strings.py

@@ -59,7 +59,7 @@ def gen_settings_str(f_obj, description=False, concise=False):
     # Parameter descriptions to print out, if requested
     desc = {'peak_width_limits'     : 'Enforced limits for peak widths, in Hz.',
             'max_n_peaks'           : 'The maximum number of peaks that can be extracted.',
-            'min_peak_height'    : 'Minimum absolute amplitude of a peak, above aperiodic component.',
+            'min_peak_height'       : 'Minimum absolute height of a peak, above the aperiodic component.',
             'peak_threshold'        : 'Threshold at which to stop searching for peaks.',
             'aperiodic_mode'        : 'The aproach taken to fitting the aperiodic component.'}
 
@@ -81,9 +81,9 @@ def gen_settings_str(f_obj, description=False, concise=False):
                         '{}'.format(desc['peak_width_limits']),
                         'Max Number of Peaks : {}'.format(f_obj.max_n_peaks),
                         '{}'.format(desc['max_n_peaks']),
-                        'Minimum Amplitude : {}'.format(f_obj.min_peak_height),
+                        'Minimum Peak Height : {}'.format(f_obj.min_peak_height),
                         '{}'.format(desc['min_peak_height']),
-                        'Amplitude Threshold: {}'.format(f_obj.peak_threshold),
+                        'Peak Threshold: {}'.format(f_obj.peak_threshold),
                         '{}'.format(desc['peak_threshold']),
                         'Aperiodic Mode : {}'.format(f_obj.aperiodic_mode),
                         '{}'.format(desc['aperiodic_mode'])] if el != ''],
@@ -143,7 +143,7 @@ def gen_results_str_fm(fm, concise=False):
         # Peak parameters
         '{} peaks were found:'.format(
             len(fm.peak_params_)),
-        *['CF: {:6.2f}, Amp: {:6.3f}, BW: {:5.2f}'.format(op[0], op[1], op[2]) \
+        *['CF: {:6.2f}, PW: {:6.3f}, BW: {:5.2f}'.format(op[0], op[1], op[2]) \
           for op in fm.peak_params_],
         '',
 

fooof/data.py

@@ -18,9 +18,9 @@
 max_n_peaks : int, optional, default: inf
     Maximum number of gaussians to be fit in a single spectrum.
 min_peak_height : float, optional, default: 0
-    Minimum amplitude threshold for a peak to be modeled.
+    Absolute threshold for detecting peaks, in units of the input data.
 peak_threshold : float, optional, default: 2.0
-    Threshold for detecting peaks, units of standard deviation.
+    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.
 """
@@ -51,13 +51,14 @@
     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, Amp, BW].
+    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 input power spectrum and the full model fit.
 error : float
     Root mean squared error of the full model fit.
 gaussian_params : 2d array
-    Parameters that define the gaussian fit(s). Each row is a gaussian, as [mean, amp, std].
+    Parameters that define the gaussian fit(s).
+    Each row is a gaussian, as [mean, height, standard deviation].
 """
 
 
@@ -70,9 +71,10 @@
 ----------
 aperiodic_params : list, len 2 or 3
     Parameters that define the aperiodic fit. As [Offset, (Knee), Exponent].
-        The knee parameter is only included if aperiodic is fit with knee. Otherwise, length is 2.
+    The knee parameter is only included if aperiodic is fit with knee. Otherwise, length is 2.
 gaussian_params : list or list of lists
-    Fitted parameter values for the peaks. Each list is a peak, as [CF, Amp, BW].
+    Fitted parameter values for the peaks.
+    Each list is a peak, with a gaussian definition of [mean, height, standard deviation].
 nlv : float
     Noise level added to the generated power spectrum.
 """