linting is error free

pyproject.toml

@@ -17,7 +17,7 @@ addopts = [
 ]
 
 [tool.ruff]
-include = ["ssp/**"]
+include = ["ssp/**", "tests/**"]
 
 line-length = 100
 indent-width = 4

tests/test_adaptive.py

@@ -0,0 +1 @@
+"""Test the adaptive module."""

tests/test_lattice.py

@@ -0,0 +1 @@
+"""Test functions for the lattice module."""

tests/test_levinson.py

@@ -3,14 +3,14 @@
 import logging
 
 import numpy as np
-from ssp import levinson
 
+from ssp import levinson
 
 logger = logging.getLogger(__name__)
 
 
 def test_glev():
-    '''Example 5.3.1, Page 266'''
+    """Example 5.3.1, Page 266."""
     r = [4, 2, 1]
     b = [9, 6, 12]
 
@@ -23,7 +23,7 @@ def test_glev():
 
 
 def test_gtor() -> None:
-    '''Based on example 5.2.6'''
+    """Based on example 5.2.6."""
     expected_rx = np.array([2, -1, -1/4, 1/8])
 
     gamma = [1/2, 1/2, 1/2]
@@ -33,7 +33,7 @@ def test_gtor() -> None:
 
 
 def test_atog() -> None:
-
+    """The m-file for the step-down recursion."""
     a = [1, 0.5, -0.1, -0.5]
     expected_g = np.array([0.5, 0.2, -0.5])
 
@@ -45,6 +45,11 @@ def test_atog() -> None:
 
 
 def test_rtog() -> None:
+    """Companion m-file.
+
+    Performs mapping from a sequence of
+    autocorrelations r to the reflection coefficient g.
+    """
     rx = [2, -1, -1/4, 1/8]
     expected_g = np.array([0.5, 0.5, 0.5])
 
@@ -56,6 +61,7 @@ def test_rtog() -> None:
 
 
 def test_ator() -> None:
+    """Finds the autocorrelation sequence from a set of filter coefficients."""
     a = [1, 1, 7/8, 1/2]
     epsilon = 2 * (3 / 4)**3
     b = epsilon**2
@@ -69,6 +75,7 @@ def test_ator() -> None:
 
 
 def test_gtoa() -> None:
+    """The step-up recursion."""
     gamma = [0.5, 0.2, -0.5]
     expected_a = np.array([1, 0.5, -0.1, -0.5])
 
@@ -79,6 +86,7 @@ def test_gtoa() -> None:
     assert np.allclose(a, expected_a)
 
 def test_rtoa() -> None:
+    """m-file for the Levinson-Durbin recursion."""
     rx = np.array([2, -1, -1/4, 1/8])
     expected_a = [1, 1, 7/8, 1/2]
     expected_eps = 2 * (3 / 4)**3

tests/test_modeling.py

@@ -3,14 +3,15 @@
 import logging
 
 import numpy as np
-import scipy.signal as signal
-from ssp import modeling, system
+from scipy import signal
 
+from ssp import modeling, system
 
 logger = logging.getLogger(__name__)
 
 
 def test_pade() -> None:
+    """Pade Approximation."""
     x = [1, 1.5, 0.75, 0.1875, 0.0938]
     expected_a = [1, -1.5, 1.5]
     expected_b = [1]
@@ -39,6 +40,7 @@ def test_pade() -> None:
 
 
 def test_prony():
+    """Prony method."""
     N = 21
     T = 2 * (N - 1) + 1
     xn = np.ones(T)
@@ -49,6 +51,7 @@ def test_prony():
 
 
 def test_shanks():
+    """Shank's method."""
     N = 21
     T = 10 * (N - 1) + 1
     xn = np.ones(T)
@@ -66,6 +69,7 @@ def test_shanks():
 
 
 def test_spike():
+    """m-file to find the least squares inverse filter."""
     gn = np.array([-0.2, 0, 1])
     h, err = modeling.spike(gn, 4, 11)
     d = np.convolve(h, gn)
@@ -74,10 +78,12 @@ def test_spike():
     logger.info(f"{d=}, {np.argmax(d)=}")
 
 
-def test_ipf(): ...
+def test_ipf():
+    """Iterative prefiltering."""
 
 
 def test_acm():
+    """m-file for the autocorrelation method."""
     x = np.ones(20)
     x[1::2] = x[1::2] * -1
     logger.info(x)
@@ -88,6 +94,7 @@ def test_acm():
 
 
 def test_covm():
+    """Covariance method."""
     x = np.ones(20)
     x[1::2] = x[1::2] * -1
     logger.info(x)
@@ -98,7 +105,8 @@ def test_covm():
 
 
 def test_durbin():
-    N = 64
+    """Durbin's method."""
+    #N = 64
     ap = [1, 0.7348, 1.882, 0.7057, 0.8851]
 
     zeros, poles, _ = signal.tf2zpk([1], ap)

tests/test_optimal.py

@@ -1,10 +1,12 @@
-""""""
+"""Test optimal."""
 
 import numpy as np
+
 from ssp import optimal
 
 
 def test_kalman():
+    """Discrete Kalman filter."""
     av = 1
     aw = 0.36
     A = 0.8

tests/test_spectrum.py

@@ -0,0 +1 @@
+"""Spectrum."""

tests/test_state.py

@@ -7,6 +7,7 @@
 
 
 def test_convm():
+    """Set up a convoluston matrix."""
     x = np.array([1, 2, 3])
     p = 4
 
@@ -20,6 +21,7 @@ def test_convm():
 
 
 def test_covar():
+    """Form a covariance matrix."""
     x = np.array([1, 2, 3])
     p = 4
 

tests/test_system.py

@@ -0,0 +1 @@
+"""Discrete-time system."""