mypy passing finally ...

.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

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
 
@@ -24,10 +24,10 @@ The book represents algorithms as:
 
 - add proper reference to the book
 - lint markdown readme
-- Finalize test cases for all codes
+- finalize test cases for all codes
 - python documentation generator
 - 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

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

@@ -1,9 +1,10 @@
 """Chapter 4 modeling algorithm implementations."""
 
+from numpy.linalg import inv
 import numpy as np
 import scipy as sp
 
-from .state import convm
+from state import convm
 
 
 def pade(x, p, q):
@@ -20,7 +21,7 @@ def pade(x, p, q):
     # Linear difference matrix spanning the number of zeros
     Xq = X[q + 1:q + p + 1, 1:p + 1].copy()
     print(Xq.shape)
-    a = np.linalg.solve(-Xq, X[q + 1: q + p + 1, 0])
+    a = linalg.solve(-Xq, X[q + 1: q + p + 1, 0])
     # a(0) normalized to 1
     a = np.concatenate((np.ones(1), a)).reshape(-1, 1)
     b = X[:q + 1, :p + 1] @ a
@@ -59,7 +60,7 @@ def prony(x, p, q):
     Xq1 = X[q + 1:N + p, 0].copy()
     Xq_H = Xq.conjugate().transpose()
     rx = Xq_H @ Xq1
-    Xinv = np.linalg.inv(Xq_H @ Xq)
+    Xinv = linalg.inv(Xq_H @ Xq)
     a = -Xinv @ rx
     print(a.shape)
     # a(0) normalized to 1
@@ -93,7 +94,7 @@ def shanks(x, p, q):
     gx = G0_H @ x0
     # the factorization does not guarantee nonsingularity!
     # resulting matrix is positive *semi*-definite
-    Ginv = np.linalg.inv(G0_H @ G0)
+    Ginv = linalg.inv(G0_H @ G0)
     print(f"{x.shape=}")
     print(f"{Ginv.shape=}")
     b = Ginv @ gx
@@ -117,7 +118,7 @@ def spike(g, n0, n):
     G_H = np.transpose(G.conjugate())
 
     print(f"{G_H.shape=}, {G.shape=}")
-    Ginv = np.linalg.inv(G_H @ G)
+    Ginv = linalg.inv(G_H @ G)
     h = Ginv @ G_H @ d
 
     return h
@@ -141,7 +142,7 @@ def acm(x, p) -> tuple[np.ndarray, np.ndarray]:
     Xq = X[:N + p - 1, :p].copy()
     rx = X[1:N + p, 0].copy()
     Xq_H = Xq.copy().conjugate().transpose()
-    Xinv = np.linalg.inv(Xq_H @ Xq)
+    Xinv = inv(Xq_H @ Xq)
     a1 = -Xinv @ Xq_H @ rx
     a = np.concatenate((np.ones(1), a1)).reshape(-1, 1)
     err = np.abs(X[:N + p, 0].T @ X @ a)
@@ -161,7 +162,7 @@ def covm(x, p):
     cx = X[p:N, 0].copy()
     Xq_H = Xq.copy().conjugate().transpose()
     print(f"{Xq=}")
-    Xinv = np.linalg.inv(Xq_H @ Xq)
+    Xinv = linalg.inv(Xq_H @ Xq)
     a1 = -Xinv @ Xq_H @ cx
     a = np.concatenate((np.ones(1), a1)).reshape(-1, 1)
     err = np.abs(cx.transpose() @ X[p:N,] @ a)

requirements.txt

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