Ruff linting and project workflow (devops) updates

- pylint, pycodestyle, pydocstyle, and flake8 to start - added pytest-cov package - added static analysis step to ct with ruff - added mypy typechecking step to ct - updated the state.py module tests - MIT license txt file added
mahdiolfat · Jan 31, 2024 · 9f63b7c · 9f63b7c
1 parent 6ab9aea
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diff --git a/.github/workflows/ContinuousTesting.yml b/.github/workflows/ContinuousTesting.yml
@@ -19,9 +19,9 @@ jobs:
                 python -m pip install --upgrade pip setuptools wheel
                 pip install -r requirements.txt
 
-            - name: Lint
+            - name: Static Lint
               run: |
-                python --version
+                ruff check --output-format=github pyssp --verbose
 
             - name: Test
               run: |

diff --git a/.gitignore b/.gitignore
@@ -1,6 +1,7 @@
 __pycache__
 *.egg-info
 env
-
+.env
+.coverage
 .vscode
 
diff --git a/LICENSE.txt b/LICENSE.txt
@@ -0,0 +1,20 @@
+Copyright (c) 2011-2023 GitHub Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/README.md b/README.md
@@ -2,14 +2,29 @@
 
 Direct impelementation of statistical digital signal processing algorithms from the book by the same name by Professor Monson H. Hayes.
 
-- Examples
-- Ready-to-Implement Algorithms
-- New Algorithms
+The book represents algorithms as:
 
-## Pour Moi
+1. set of instructions (pseudo-code)
+1. matlab code
+1. described in English in the textbook body
+
+## Chapters
+
+1. Introduction
+2. Background
+3. Discrete-Time Random Processes
+4. Signal Modeling
+5. The Levinson Recursion
+6. Lattice Filters
+7. Optimum Filters
+8. Spectrum Estimation
+9. Adaptive Filters
 
 ### TODO
 
+- add proper reference
+- lint markdown readme
+- License
 - Finalize test cases for all codes
 - pytest-cov?
 - python documentation generator
@@ -20,3 +35,5 @@ Direct impelementation of statistical digital signal processing algorithms from
 - stub files?
 - VS code add-ons
 - use matlab-equivalent psuedo-inverse ("X \ R") operation to match the book
+- switch linting to Ruff?
+- performance benchmarking would be cool
diff --git a/pyproject.toml b/pyproject.toml
@@ -6,9 +6,25 @@ build-backend = "hatchling.build"
 name = "pyssp"
 version = "0.0.1"
 readme = "README.md"
+license = "LICENSE.txt"
 
 [tool.pytest.ini_options]
 pythonpath = "."
 addopts = [
     "--import-mode=importlib",
+    "--cov=pyssp"
 ]
+
+[tool.ruff]
+line-length = 100
+indent-width = 4
+
+[tool.ruff.lint]
+select = ["E", "F", "B", "I", "SIM", "W", "D"]
+ignore = ["D213", "D401", "D211"]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
diff --git a/pyssp/__init__.py b/pyssp/__init__.py
@@ -0,0 +1,10 @@
+"""The import root for the book sections."""
+
+# import system as Chapter3
+# import modeling as Chapter4
+# import levinson as Chapter5
+# import lattice as Chapter6
+# import optimal as Chapter7
+# import spectrum as Chapter8
+# import adaptive as Chapter9
+# import state as Appendix
diff --git a/pyssp/adaptive.py b/pyssp/adaptive.py
@@ -1,14 +1,16 @@
+"""Chapter 9 algorithm implementations."""
+
 import numpy as np
 
 from .state import convm
 
 
 def lms(x, d, mu, nord, a0=None):
-    '''LMS Adaptive Filter
+    """LMS Adaptive Filter.
 
     Refernce Page 506, Figure 9.7
     Suffers from gradient noise amplification
-    '''
+    """
     X = convm(x, nord)
     M, N = X.shape
 
@@ -29,12 +31,12 @@ def lms(x, d, mu, nord, a0=None):
     return A, E
 
 
-def nlms(x, d, beta, nord, a0, delta=0.0001):
-    '''Normalized LMS Adaptive Filter
+def nlms(x, d, beta, nord, a0):
+    """Normalized LMS Adaptive Filter.
 
     Refernce Page 515, Figure 9.12
-    '''
-
+    """
+    delta = 0.0001
     X = convm(x, nord)
     M, N = X.shape
 
@@ -59,14 +61,13 @@ def nlms(x, d, beta, nord, a0, delta=0.0001):
 
 
 def rls(x, d, nord, lamda=1, delta=0.001):
-    '''Recursive Least Squares
+    """Recursive Least Squares.
 
     Reference 545, Table 9.6
 
     For special case of lambda == 1, this is known as growing window RLS algorithm,
     For all other values of lambda <1 and >0, this is the exponentially weighted RLS algorithm.
-    '''
-
+    """
     X = convm(x, nord)
     M, N = X.shape
     P = np.eye(N) / delta

diff --git a/pyssp/lattice.py b/pyssp/lattice.py
@@ -1,14 +1,16 @@
+"""Implementation of algorithm from Chapter 6."""
+
+
 import numpy as np
 import scipy as sp
 
+
 def fcov(x, p):
-    '''
-    Figure 6.15, Page 310.
+    """Figure 6.15, Page 310.
 
     Using the forward covariance method the reflection co-efficients of the lattice filter
-    are found by sequentially minimizing the sum of the squares of the forward prediction error. 
-    '''
-
+    are found by sequentially minimizing the sum of the squares of the forward prediction error.
+    """
     if p >= len(x):
         raise ValueError("Model order must be less than length of signal")
 
@@ -23,8 +25,8 @@ def fcov(x, p):
     for j in range(p):
         print(j)
         N = N - 1
-        #print(f"{eplus=}, {eplus.shape=}")
-        #print(f"{eminus=}, {eminus.shape=}")
+        # print(f"{eplus=}, {eplus.shape=}")
+        # print(f"{eminus=}, {eminus.shape=}")
         gamma[j] = (np.transpose(-eminus) @ eplus) / (np.transpose(eminus) @ eminus)
         temp1 = eplus + gamma[j] * eminus
         temp2 = eminus + np.conjugate(gamma[j]) * eplus
@@ -39,12 +41,10 @@ def fcov(x, p):
 
 
 def burg(x, p):
-    '''
-    Sequentially minimizes the sum of the forward and backward covariance errors.
+    """Sequentially minimizes the sum of the forward and backward covariance errors.
 
     Guaranteed to be stable. All reflection coefficients will be <|1|
-    '''
-
+    """
     if p > len(x):
         raise ValueError("Model order must be less than length of signal")
 
@@ -59,10 +59,10 @@ def burg(x, p):
     for j in range(p):
         print(j)
         N = N - 1
-        #print(f"{eplus=}, {eplus.shape=}")
-        #print(f"{eminus=}, {eminus.shape=}")
-        eplusmag = (np.transpose(eplus) @ eplus)
-        eminusmag = (np.transpose(eplus) @ eplus)
+        # print(f"{eplus=}, {eplus.shape=}")
+        # print(f"{eminus=}, {eminus.shape=}")
+        eplusmag = np.transpose(eplus) @ eplus
+        eminusmag = np.transpose(eplus) @ eplus
         gamma[j] = (np.transpose(-2 * eminus) @ eplus) / (eplusmag + eminusmag)
         temp1 = eplus + gamma[j] * eminus
         temp2 = eminus + np.conjugate(gamma[j]) * eplus
@@ -74,17 +74,18 @@ def burg(x, p):
     return gamma, err
 
 
-def bcov(x, p):
-    '''
-    Sequentially minimizes the backward covariance error.
-    '''
-    pass
+def bcov():
+    """Sequentially minimizes the backward covariance error.
+
+    Arguements: (x, p)
+    """
+
 
 def mcov(x, p):
-    '''
-    Modified covariance method. Unlike the forward/backward algorithms,
-    it *does not* minimize an error term sequentially.
-    '''
+    """Modified covariance method. Unlike the forward/backward algorithms.
+
+    It *does not* minimize an error term sequentially.
+    """
     _x = np.array(x).reshape(-1, 1)
     N = len(x)