mirror of
https://github.com/kuhyx/testsAndMisc.git
synced 2026-07-06 17:03:10 +02:00
Krzysztof kuhy Rudnicki
6b62f6bea9
Fixed violations: - PERF401: Use list comprehension (downloadCats, split) - SIM102: Combine nested if statements (keyboardCoop) - SIM113: Use enumerate (lichess_bot/tests) - B904: Add 'from None/e' to raises (engine, stockfish_analysis, generate_blunder_tests) - TRY300: Move return to else block (screen_locker) Per-file ignores added for TRY301: - lichess_bot/main.py: Null checks on subprocess streams - randomize_numbers/random_digits.py: Input validation Removed from global ignore (no violations): - RUF005, SIM103, SIM105, SIM108 Global ignore now only contains unavoidable conflicts: - D203/D213: Docstring style conflicts - COM812/ISC001: Formatter conflicts (per ruff docs)
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
"""Distribute values symmetrically across N parts."""
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def calculate_symmetric_weights(
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n: int, middle_weight: float, factors: list[float] | None = None
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) -> list[float]:
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"""Calculate symmetric weights for both even and odd N.
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Args:
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n: Number of parts to split into.
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middle_weight: The middle value for symmetry.
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factors: If provided, controls the difference in weights.
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Must have length n // 2 or n // 2 - 1 depending on n.
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Returns:
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List of symmetric weights.
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"""
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half_n = n // 2
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weights_left: list[float] = [middle_weight]
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if factors:
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for factor in factors:
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next_weight = weights_left[-1] + factor
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weights_left.append(next_weight)
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else:
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weights_left.extend(middle_weight - (i + 1) for i in range(half_n - 1))
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if n % 2 == 0:
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weights = weights_left[::-1] + weights_left
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else:
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weights = [*weights_left[::-1], middle_weight, *weights_left]
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return weights
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def scale_to_total(x: float, weights: list[float]) -> list[float]:
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"""Scale the weights so that their sum is proportional to X.
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Args:
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x: Total value to distribute.
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weights: The list of weights to be scaled.
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Returns:
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List of scaled values summing to x.
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"""
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total_weight = sum(weights)
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base_unit = x / total_weight
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return [base_unit * weight for weight in weights]
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def split_x_into_n_symmetrically(x: float, n: int, factors: list[float]) -> list[float]:
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"""Split X into N parts with symmetric weights controlled by factors."""
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weights = calculate_symmetric_weights(n, middle_weight=1, factors=factors)
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return scale_to_total(x, weights)
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def split_x_into_n_middle(x: float, n: int, middle_value: float) -> list[float]:
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"""Split X into N parts with symmetric weights using middle_value as peak."""
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weights = calculate_symmetric_weights(n, middle_weight=middle_value)
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return scale_to_total(x, weights)
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