zwitschi
97b1c0360b
- Updated test functions in various test files to enhance code clarity by formatting long lines and improving indentation. - Adjusted assertions to use multi-line formatting for better readability. - Added new test cases for theme settings API to ensure proper functionality. - Ensured consistent use of line breaks and spacing across test files for uniformity.
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
from statistics import mean, median, pstdev
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from typing import Any, Dict, Iterable, List, Mapping, Union, cast
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def _extract_results(simulation_results: Iterable[object]) -> List[float]:
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values: List[float] = []
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for item in simulation_results:
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if not isinstance(item, Mapping):
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continue
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mapping_item = cast(Mapping[str, Any], item)
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value = mapping_item.get("result")
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if isinstance(value, (int, float)):
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values.append(float(value))
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return values
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def _percentile(values: List[float], percentile: float) -> float:
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if not values:
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return 0.0
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sorted_values = sorted(values)
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if len(sorted_values) == 1:
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return sorted_values[0]
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index = (percentile / 100) * (len(sorted_values) - 1)
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lower = int(index)
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upper = min(lower + 1, len(sorted_values) - 1)
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weight = index - lower
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return sorted_values[lower] * (1 - weight) + sorted_values[upper] * weight
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def generate_report(
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simulation_results: List[Dict[str, float]],
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) -> Dict[str, Union[float, int]]:
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"""Aggregate basic statistics for simulation outputs."""
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values = _extract_results(simulation_results)
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if not values:
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return {
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"count": 0,
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"mean": 0.0,
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"median": 0.0,
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"min": 0.0,
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"max": 0.0,
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"std_dev": 0.0,
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"variance": 0.0,
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"percentile_10": 0.0,
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"percentile_90": 0.0,
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"percentile_5": 0.0,
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"percentile_95": 0.0,
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"value_at_risk_95": 0.0,
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"expected_shortfall_95": 0.0,
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}
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summary: Dict[str, Union[float, int]] = {
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"count": len(values),
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"mean": mean(values),
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"median": median(values),
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"min": min(values),
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"max": max(values),
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"percentile_10": _percentile(values, 10),
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"percentile_90": _percentile(values, 90),
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"percentile_5": _percentile(values, 5),
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"percentile_95": _percentile(values, 95),
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}
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std_dev = pstdev(values) if len(values) > 1 else 0.0
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summary["std_dev"] = std_dev
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summary["variance"] = std_dev**2
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var_95 = summary["percentile_5"]
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summary["value_at_risk_95"] = var_95
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tail_values = [value for value in values if value <= var_95]
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if tail_values:
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summary["expected_shortfall_95"] = mean(tail_values)
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else:
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summary["expected_shortfall_95"] = var_95
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return summary
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