update skewed gauss func for consistency

Author: TomDonoghue

specparam/modes/definitions.py

@@ -5,7 +5,7 @@
 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
 
 ###################################################################################################
@@ -118,7 +118,7 @@
     name='skewed_gaussian',
     component='periodic',
     description='Skewed Gaussian peak fit function.',
-    func=skewnorm_function,
+    func=skewed_gaussian_function,
     jacobian=None,
     params=params_skewed_gaussian,
     ndim=2,

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():