Merge pull request #369 from fooof-tools/skewg

[MNT] - Update skewed_gaussian mode (for consistency)

Author: TomDonoghue

specparam/modes/definitions.py

@@ -5,13 +5,14 @@
 from specparam.modes.mode import Mode
 from specparam.modes.params import ParamDefinition
 from specparam.modes.funcs import (expo_function, expo_nk_function, double_expo_function,
-                                   gaussian_function, skewnorm_function, cauchy_function)
+                                   gaussian_function, skewed_gaussian_function, cauchy_function)
 from specparam.modes.jacobians import jacobian_gauss
 
 ###################################################################################################
 ## APERIODIC MODES
 
-# Fixed
+## AP - Fixed Mode
+
 params_fixed = ParamDefinition(OrderedDict({
     'offset' : 'Offset of the aperiodic component.',
     'exponent' : 'Exponent of the aperiodic component.',
@@ -30,7 +31,8 @@
 )
 
 
-# Knee
+## AP - Knee Mode
+
 params_knee = ParamDefinition(OrderedDict({
     'offset' : 'Offset of the aperiodic component.',
     'knee' : 'Knee of the aperiodic component.',
@@ -50,8 +52,9 @@
 )
 
 
-# Double exponent
-params_double_exp = ParamDefinition(OrderedDict({
+## AP - Double Exponent Mode
+
+params_doublexp = ParamDefinition(OrderedDict({
     'offset' : 'Offset of the aperiodic component.',
     'exponent0' : 'Exponent of the aperiodic component, before the knee.',
     'knee' : 'Knee of the aperiodic component.',
@@ -64,7 +67,7 @@
     description='Fit an function with 2 exponents and a knee.',
     func=double_expo_function,
     jacobian=None,
-    params=params_double_exp,
+    params=params_doublexp,
     ndim=1,
     freq_space='linear',
     powers_space='log10',
@@ -81,7 +84,8 @@
 ###################################################################################################
 ## PERIODIC MODES
 
-# Gaussian
+## PE - Gaussian Mode
+
 params_gauss = ParamDefinition(OrderedDict({
     'cf' : 'Center frequency of the peak.',
     'pw' : 'Power of the peak, over and above the aperiodic component.',
@@ -101,28 +105,30 @@
 )
 
 
-# Skewed Gaussian
-params_skew = ParamDefinition(OrderedDict({
+## PE - Skewed Gaussian Mode
+
+params_skewed_gaussian = ParamDefinition(OrderedDict({
     'cf' : 'Center frequency of the peak.',
     'pw' : 'Power of the peak, over and above the aperiodic component.',
     'bw' : 'Bandwidth of the peak.',
     'skew' : 'Skewness of the peak.',
 }))
 
-pe_skewnorm = Mode(
-    name='skewnorm',
+pe_skewed_gaussian = Mode(
+    name='skewed_gaussian',
     component='periodic',
     description='Skewed Gaussian peak fit function.',
-    func=skewnorm_function,
+    func=skewed_gaussian_function,
     jacobian=None,
-    params=params_skew,
+    params=params_skewed_gaussian,
     ndim=2,
     freq_space='linear',
     powers_space='log10',
 )
 
 
-# Cauchy
+## PE - Cauchy Mode
+
 params_cauchy = ParamDefinition(OrderedDict({
     'cf' : 'Center frequency of the peak.',
     'pw' : 'Power of the peak, over and above the aperiodic component.',
@@ -145,7 +151,7 @@
 # Collect available periodic modes
 PE_MODES = {
     'gaussian' : pe_gaussian,
-    'skewed_gaussian' : pe_skewnorm,
+    'skewed_gaussian' : pe_skewed_gaussian,
     'cauchy' : pe_cauchy,
 }
 

specparam/modes/funcs.py

@@ -35,8 +35,8 @@ def gaussian_function(xs, *params):
     return ys
 
 
-def skewnorm_function(xs, *params):
-    """Skewed normal distribution fitting function.
+def skewed_gaussian_function(xs, *params):
+    """Skewed gaussian fitting function.
 
     Parameters
     ----------

specparam/tests/modes/test_funcs.py

@@ -24,23 +24,23 @@ def test_gaussian_function():
     assert max(ys) == hgt
     assert np.allclose([ii/sum(ys) for ii in ys], norm.pdf(xs, ctr, wid))
 
-def test_skewnorm_function():
+def test_skewed_gaussian_function():
 
     # Check that with no skew, approximate gaussian
     ctr, hgt, wid, skew = 50, 5, 10, 1
     xs = np.arange(1, 100)
     ys_gaus = gaussian_function(xs, ctr, hgt, wid)
-    ys_skew = skewnorm_function(xs, ctr, hgt, wid, skew)
+    ys_skew = skewed_gaussian_function(xs, ctr, hgt, wid, skew)
     np.allclose(ys_gaus, ys_skew, atol=0.001)
 
     # Check with some skew - right skew (more density after center)
     skew1 = 2
-    ys_skew1 = skewnorm_function(xs, ctr, hgt, wid, skew1)
+    ys_skew1 = skewed_gaussian_function(xs, ctr, hgt, wid, skew1)
     assert sum(ys_skew1[xs<ctr]) < sum(ys_skew1[xs>ctr])
 
     # Check with some skew - left skew (more density before center)
     skew2 = -2
-    ys_skew2 = skewnorm_function(xs, ctr, hgt, wid, skew2)
+    ys_skew2 = skewed_gaussian_function(xs, ctr, hgt, wid, skew2)
     assert sum(ys_skew2[xs<ctr]) > sum(ys_skew2[xs>ctr])
 
 def test_cauchy_function():