mirror of
https://github.com/kuhyx/testsAndMisc.git
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Krzysztof kuhy Rudnicki
0e73b27d50
- R0914 (too many locals): Extract helper functions in generate_jpeg.py, engine.py, lichess_api.py, main.py - R0902 (too many instance attributes): Use dataclasses in keyboard_coop - W0621 (redefined outer name): Rename parameters/variables to avoid shadowing - W0201 (attribute outside init): Initialize all attrs in __init__ - R1705 (no-else-return): Remove unnecessary else after return - C1805 (implicit booleaness): Use implicit boolean checks - R1732 (consider-using-with): Use context manager for subprocess.Popen - E0401 (import-error): Add pylint disable for optional deps (selenium, mitmproxy) - Clean up pyproject.toml: update comments, remove redundant settings Pylint score: 10.00/10
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 - (idx + 1) for idx in range(half_n - 1))
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if not n % 2:
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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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