Resolve #2: mypy linting integration

- some minor gitignore and readme todo updates
- mypy passing with github workflow

.github/workflows/ContinuousTesting.yml

@@ -23,6 +23,10 @@ jobs:
               run: |
                 ruff check --output-format=github pyssp --verbose
 
+            - name: Static Type Check
+              run: |
+                mypy pyssp --explicit-package-bases
+
             - name: Test
               run: |
                 pytest

.gitignore

@@ -1,4 +1,5 @@
 __pycache__
+*cache
 *.egg-info
 env
 .env

README.md

@@ -4,9 +4,9 @@ Direct impelementation of statistical digital signal processing algorithms from
 
 The book represents algorithms as:
 
-1. set of instructions (pseudo-code)
-1. matlab code
-1. described in English in the textbook body
+1. set of instructions (pseudo-code) annotated in Tables.
+1. matlab code annotated in Figures.
+1. described in words in the body of the text.
 
 ## Chapters
 
@@ -22,18 +22,13 @@ The book represents algorithms as:
 
 ### TODO
 
-- add proper reference
+- add proper reference to the book
 - lint markdown readme
-- License
-- Finalize test cases for all codes
-- pytest-cov?
+- finalize test cases for all codes
 - python documentation generator
-- linting workflow
-- pass linting
 - mypy static type checking
-- Release package scripts
+- release package scripts
 - 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

pyproject.toml

@@ -28,3 +28,8 @@ quote-style = "double"
 indent-style = "space"
 skip-magic-trailing-comma = false
 line-ending = "auto"
+
+[tool.mypy]
+mypy_path = "pyssp"
+ignore_missing_imports = true
+#plugins = "numpy.typing.mypy_plugin"

pyssp/levinson.py

@@ -17,7 +17,7 @@ def rtoa(r) -> tuple[np.ndarray, np.ndarray]:
         gamma = -np.transpose(r[1:1 + j,]) @ np.flipud(a) / epsilon
         an = np.concatenate((a, np.zeros((1, 1)))).reshape(-1, 1)
         anT = np.conjugate(np.flipud(a))
-        a = an + gamma * np.concatenate(([0], anT.ravel())).reshape(-1, 1)
+        a = an + gamma * np.concatenate((np.zeros(1), anT.ravel())).reshape(-1, 1)
         epsilon = epsilon * (1 - np.abs(gamma)**2)
         print(f"{gamma=},\n{a=},\n{epsilon=}\n")
     return a, epsilon

pyssp/modeling.py

@@ -1,5 +1,6 @@
 """Chapter 4 modeling algorithm implementations."""
 
+
 import numpy as np
 import scipy as sp
 

requirements.txt

@@ -5,4 +5,4 @@ matplotlib
 ruff
 pytest
 pytest-cov
-mypy
+mypy