[ENH] - Add triangle periodic fit mode

specparam/modes/definitions.py

@@ -6,7 +6,8 @@
 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, skewed_gaussian_function, cauchy_function)
+                                   gaussian_function, skewed_gaussian_function,
+                                   cauchy_function, triangle_function)
 from specparam.modes.jacobians import jacobian_gauss
 from specparam.utils.checks import check_selection
 
@@ -149,12 +150,33 @@
     powers_space='log10',
 )
 
+## PE - Triangle Mode
+
+params_triangle = ParamDefinition(OrderedDict({
+    'cf' : 'Center frequency of the peak.',
+    'pw' : 'Power of the peak, over and above the aperiodic component.',
+    'bw' : 'Bandwidth of the peak.',
+}))
+
+pe_triangle = Mode(
+    name='triangle',
+    component='periodic',
+    description='Triangle peak fit function.',
+    func=triangle_function,
+    jacobian=None,
+    params=params_triangle,
+    ndim=2,
+    freq_space='linear',
+    powers_space='log10',
+)
+
 
 # Collect available periodic modes
 PE_MODES = {
     'gaussian' : pe_gaussian,
     'skewed_gaussian' : pe_skewed_gaussian,
     'cauchy' : pe_cauchy,
+    'triangle' : pe_triangle,
 }
 
 ###################################################################################################

specparam/modes/funcs.py

@@ -53,9 +53,7 @@ def skewed_gaussian_function(xs, *params):
 
     ys = np.zeros_like(xs)
 
-    for ii in range(0, len(params), 4):
-
-        ctr, hgt, wid, skew = params[ii:ii+4]
+    for ctr, hgt, wid, skew in zip(*[iter(params)] * 4):
 
         ts = (xs - ctr) / wid
         temp = 2 / wid * (1 / np.sqrt(2 * np.pi) * np.exp(-ts**2 / 2)) * \
@@ -90,6 +88,35 @@ def cauchy_function(xs, *params):
     return ys
 
 
+def triangle_function(xs, *params):
+    """Triangle fitting function.
+
+    Parameters
+    ----------
+    xs : 1d array
+        Input x-axis values.
+    *params : float
+        Parameters that define a triangle function.
+
+    Returns
+    -------
+    ys : 1d array
+        Output values for triangle function.
+    """
+
+    ys = np.zeros_like(xs)
+    fres = xs[1] - xs[0]
+
+    for ctr, hgt, wid in zip(*[iter(params)] * 3):
+
+        n_samples = int(np.ceil(2 * wid / fres))
+        n_samples += 1 if n_samples % 2 == 0 else 0
+        temp = np.arccos(np.cos(np.linspace(0, 2 * np.pi, n_samples)))
+        ys[np.abs(xs - ctr) <= (n_samples / 2) * fres] += hgt * normalize(temp)
+
+    return ys
+
+
 ## APERIODIC FUNCTIONS
 
 def expo_function(xs, *params):

specparam/tests/modes/test_funcs.py

@@ -13,10 +13,8 @@
 def test_gaussian_function():
 
     ctr, hgt, wid = 50, 5, 10
-
-    xs = np.arange(1, 100)
+    xs = np.arange(1, 100, 1.)
     ys = gaussian_function(xs, ctr, hgt, wid)
-
     assert np.all(ys)
 
     # Check distribution matches generated gaussian from scipy
@@ -28,7 +26,7 @@ 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)
+    xs = np.arange(1, 100, 1.)
     ys_gaus = gaussian_function(xs, ctr, hgt, wid)
     ys_skew = skewed_gaussian_function(xs, ctr, hgt, wid, skew)
     np.allclose(ys_gaus, ys_skew, atol=0.001)
@@ -46,19 +44,26 @@ def test_skewed_gaussian_function():
 def test_cauchy_function():
 
     ctr, hgt, wid = 50, 5, 10
-
-    xs = np.arange(1, 100)
+    xs = np.arange(1, 100, 1.)
     ys = cauchy_function(xs, ctr, hgt, wid)
 
     assert np.all(ys)
 
+def test_triangle_function():
+
+    ctr, hgt, wid = 50, 5, 10
+    xs = np.arange(1, 100, 1.)
+    ys = triangle_function(xs, ctr, hgt, wid)
+
+    assert np.any(ys)
+
 ## Aperiodic functions
 
 def test_expo_function():
 
     off, knee, exp = 10, 5, 2
 
-    xs = np.arange(1, 100)
+    xs = np.arange(1, 100, 1.)
     ys = expo_function(xs, off, knee, exp)
 
     assert np.all(ys)
@@ -74,7 +79,7 @@ def test_expo_nk_function():
 
     off, exp = 10, 2
 
-    xs = np.arange(1, 100)
+    xs = np.arange(1, 100, 1.)
     ys = expo_nk_function(xs, off, exp)
 
     assert np.all(ys)
@@ -106,7 +111,7 @@ def test_linear_function():
 
     off, sl = 10, 2
 
-    xs = np.arange(1, 100)
+    xs = np.arange(1, 100, 1.)
     ys = linear_function(xs, off, sl)
 
     assert np.all(ys)
@@ -120,7 +125,7 @@ def test_quadratic_function():
 
     off, sl, curve = 10, 3, 2
 
-    xs = np.arange(1, 100)
+    xs = np.arange(1, 100, 1.)
     ys = quadratic_function(xs, off, sl, curve)
 
     assert np.all(ys)