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[MNT] - Update skewed_gaussian mode (for consistency) #369

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Merged
TomDonoghue merged 2 commits into from
Nov 1, 2025
Merged

[MNT] - Update skewed_gaussian mode (for consistency) #369

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09ac2ef
update skew gaussian naming
TomDonoghue Nov 1, 2025
f8f9e5a
update skewed gauss func for consistency
TomDonoghue Nov 1, 2025
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36 changes: 21 additions & 15 deletions specparam/modes/definitions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.',
Expand All @@ -30,7 +31,8 @@
)


# Knee
## AP - Knee Mode

params_knee = ParamDefinition(OrderedDict({
'offset' : 'Offset of the aperiodic component.',
'knee' : 'Knee of the aperiodic component.',
Expand All @@ -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.',
Expand All @@ -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',
Expand All @@ -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.',
Expand All @@ -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.',
Expand All @@ -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,
}

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4 changes: 2 additions & 2 deletions specparam/modes/funcs.py
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Expand Up @@ -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
----------
Expand Down
8 changes: 4 additions & 4 deletions specparam/tests/modes/test_funcs.py
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Expand Up @@ -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():
Expand Down