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Krzysztof kuhy Rudnicki
faef20dc45
598 findings to none, and the suite ran through `./run.sh run` after each step rather than at the end -- 18 passed, 27s, every time. The shape of this one was different from the other courses. PORR is four implementations of the same linear algebra (sequential, threads, processes, dask), so nearly every finding came in groups of four: - 229 missing annotations. Rather than `object`, which mypy strict rejects on every attribute access, each module declares what these actually are: `Matrix = np.ndarray | list[list[float]]` and the vector equivalent. The backends really are called with both -- comparing them is the point of the course. - 76 names: `A` -> matrix, `Ab` -> augmented, `Ax` -> product, `I` -> identity, `dA` -> dask_matrix, and the two CamelCase functions. Renamed through `tokenize`, not regex, so no string or comment was touched. - 121 missing docstrings, one per method name since the names repeat across all four backends. - Seven functions over the argument limit, all taking the same seven or nine loose values. They are two frozen dataclasses now, in a new richardson_problem.py: `Problem(matrix, rhs, lambda_min, lambda_max)` and `Settings(max_iterations, x0, tol)`. `RichardsonMethod.__init__` also took a `size` argument it never read. - threads_indep_old's iteration did "split into chunks, submit, wait" four times by hand (C901 11, PLR0915 54). One `_run_chunked` helper. - tests.py's two bare asserts became explicit raises, per the no-ignores rule. `assert False` was also B011: `python -O` deletes it, so the non-convergence branch would have silently passed under optimisation. Real fixes rather than style: np.random -> Generator, `os.path.exists` -> Path, an `except Exception` around dask narrowed to the errors it actually raises, and two runnable modules that logged at INFO without ever calling basicConfig -- caught by scripts/check_refactor_safety.py, which is now doing the job it was written for. Repo-wide: 1,781 findings -> 1,183, all of them in the five courses left. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
35 lines
868 B
Python
Executable File
35 lines
868 B
Python
Executable File
#!/usr/bin/env python3
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"""The system to solve, and the settings to solve it with.
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Every Richardson backend in this project took the same seven or nine loose
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arguments -- matrix, right-hand side, both eigenvalue bounds, iteration cap,
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starting guess and tolerance -- which is what put all of them over ruff's
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argument limit. They travel together, so they are two dataclasses.
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"""
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from dataclasses import dataclass
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import numpy as np
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Matrix = np.ndarray | list[list[float]]
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Vector = np.ndarray | list[float]
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@dataclass(frozen=True)
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class Problem:
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"""One linear system, with the eigenvalue bounds Richardson needs."""
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matrix: Matrix
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rhs: Vector
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lambda_min: float
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lambda_max: float
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@dataclass(frozen=True)
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class Settings:
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"""How hard to try before giving up."""
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max_iterations: int
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x0: Vector | None = None
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tol: float = 1e-05
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