Merge model-development into main
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from __future__ import annotations
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from bisect import bisect_left
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import csv
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from dataclasses import dataclass
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from pathlib import Path
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from PythonModels.reporting.amesim_results import AmesimResults
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DEFAULT_TEST_MQL_ALIGNMENT_PATHS = (
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"temp3@pn_c1_8",
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"press3@pn_c1_8",
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"vvol1@pn_brp2_8",
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"vol1@pn_brp2_8",
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)
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@dataclass(frozen=True)
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class TestMqlComparisonMetric:
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data_path: str
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sample_count: int
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max_abs_error: float
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mean_abs_error: float
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max_rel_error: float
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final_abs_error: float
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@dataclass(frozen=True)
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class TestMqlComparisonResult:
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metrics: tuple[TestMqlComparisonMetric, ...]
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def metric(self, data_path: str) -> TestMqlComparisonMetric:
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for metric in self.metrics:
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if metric.data_path == data_path:
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return metric
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raise KeyError(data_path)
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@property
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def max_abs_error(self) -> float:
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return max((metric.max_abs_error for metric in self.metrics), default=0.0)
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@property
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def max_rel_error(self) -> float:
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return max((metric.max_rel_error for metric in self.metrics), default=0.0)
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class TestMqlComparisonError(ValueError):
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"""Raised when Python and AMESim series cannot be aligned."""
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def compare_test_mql_series(
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*,
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python_times: tuple[float, ...] | list[float],
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python_series_by_data_path: dict[str, tuple[float, ...] | list[float]],
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amesim_results: AmesimResults,
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data_paths: tuple[str, ...] | list[str] | None = None,
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relative_floor: float = 1.0e-12,
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) -> TestMqlComparisonResult:
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_validate_time_axis(python_times)
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selected_paths = _select_data_paths(python_series_by_data_path, amesim_results, data_paths)
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metrics = []
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for data_path in selected_paths:
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python_values = tuple(float(value) for value in python_series_by_data_path[data_path])
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if len(python_values) != len(python_times):
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raise TestMqlComparisonError(
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f"Python series length mismatch for {data_path!r}: "
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f"{len(python_values)} values for {len(python_times)} time samples."
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)
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amesim_values = amesim_results.series(data_path)
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abs_errors = []
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rel_errors = []
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for time_value, python_value in zip(python_times, python_values):
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amesim_value = interpolate_series_value(amesim_results.times, amesim_values, time_value)
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abs_error = abs(python_value - amesim_value)
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abs_errors.append(abs_error)
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rel_errors.append(abs_error / max(abs(amesim_value), relative_floor))
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final_amesim_value = interpolate_series_value(
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amesim_results.times,
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amesim_values,
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float(python_times[-1]),
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)
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metrics.append(
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TestMqlComparisonMetric(
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data_path=data_path,
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sample_count=len(python_times),
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max_abs_error=max(abs_errors, default=0.0),
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mean_abs_error=sum(abs_errors) / max(len(abs_errors), 1),
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max_rel_error=max(rel_errors, default=0.0),
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final_abs_error=abs(python_values[-1] - final_amesim_value),
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)
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)
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return TestMqlComparisonResult(metrics=tuple(metrics))
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def write_test_mql_amesim_baseline_csv(
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output_dir: Path,
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amesim_results: AmesimResults,
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data_paths: tuple[str, ...] | list[str] = DEFAULT_TEST_MQL_ALIGNMENT_PATHS,
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) -> Path:
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output_dir.mkdir(parents=True, exist_ok=True)
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csv_path = output_dir / "test_mql_amesim_baseline.csv"
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_validate_amesim_data_paths(amesim_results, data_paths)
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with csv_path.open("w", newline="", encoding="utf-8") as handle:
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writer = csv.writer(handle)
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writer.writerow(["time_s", *data_paths])
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for index, time_value in enumerate(amesim_results.times):
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writer.writerow(
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[time_value, *(amesim_results.series(data_path)[index] for data_path in data_paths)]
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)
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return csv_path
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def write_test_mql_comparison_csv(
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*,
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output_dir: Path,
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python_times: tuple[float, ...] | list[float],
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python_series_by_data_path: dict[str, tuple[float, ...] | list[float]],
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amesim_results: AmesimResults,
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data_paths: tuple[str, ...] | list[str] | None = None,
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) -> tuple[Path, Path, TestMqlComparisonResult]:
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output_dir.mkdir(parents=True, exist_ok=True)
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selected_paths = _select_data_paths(python_series_by_data_path, amesim_results, data_paths)
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comparison = compare_test_mql_series(
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python_times=python_times,
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python_series_by_data_path=python_series_by_data_path,
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amesim_results=amesim_results,
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data_paths=selected_paths,
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)
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csv_path = output_dir / "test_mql_amesim_comparison.csv"
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summary_path = output_dir / "test_mql_amesim_comparison_summary.txt"
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with csv_path.open("w", newline="", encoding="utf-8") as handle:
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writer = csv.writer(handle)
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header = ["time_s"]
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for data_path in selected_paths:
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header.extend(
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[
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f"python.{data_path}",
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f"amesim.{data_path}",
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f"abs_error.{data_path}",
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f"rel_error.{data_path}",
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]
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)
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writer.writerow(header)
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for index, time_value in enumerate(python_times):
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row = [time_value]
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for data_path in selected_paths:
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python_value = float(python_series_by_data_path[data_path][index])
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amesim_value = interpolate_series_value(
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amesim_results.times,
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amesim_results.series(data_path),
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float(time_value),
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)
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abs_error = abs(python_value - amesim_value)
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rel_error = abs_error / max(abs(amesim_value), 1.0e-12)
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row.extend([python_value, amesim_value, abs_error, rel_error])
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writer.writerow(row)
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summary_lines = [
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(
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f"{metric.data_path}: samples={metric.sample_count}, "
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f"max_abs_error={metric.max_abs_error:.12g}, "
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f"mean_abs_error={metric.mean_abs_error:.12g}, "
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f"max_rel_error={metric.max_rel_error:.12%}, "
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f"final_abs_error={metric.final_abs_error:.12g}"
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)
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for metric in comparison.metrics
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]
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summary_path.write_text("\n".join(summary_lines) + "\n", encoding="utf-8")
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return csv_path, summary_path, comparison
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def interpolate_series_value(
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time_values: tuple[float, ...] | list[float],
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values: tuple[float, ...] | list[float],
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target_time: float,
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) -> float:
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if len(time_values) != len(values):
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raise TestMqlComparisonError("time and value series lengths differ.")
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if not time_values:
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raise TestMqlComparisonError("cannot interpolate an empty series.")
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if target_time <= time_values[0]:
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return float(values[0])
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if target_time >= time_values[-1]:
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return float(values[-1])
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right_index = bisect_left(time_values, target_time)
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if right_index < len(time_values) and abs(time_values[right_index] - target_time) <= 1.0e-12:
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return float(values[right_index])
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left_index = right_index - 1
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left_time = float(time_values[left_index])
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right_time = float(time_values[right_index])
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fraction = (target_time - left_time) / (right_time - left_time)
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return float(values[left_index]) + fraction * (float(values[right_index]) - float(values[left_index]))
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def _select_data_paths(
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python_series_by_data_path: dict[str, tuple[float, ...] | list[float]],
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amesim_results: AmesimResults,
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data_paths: tuple[str, ...] | list[str] | None,
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) -> tuple[str, ...]:
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if data_paths is None:
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data_paths = tuple(
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data_path
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for data_path in python_series_by_data_path
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if data_path in amesim_results.series_by_data_path
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)
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selected_paths = tuple(data_paths)
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if not selected_paths:
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raise TestMqlComparisonError("no common Data_Path values are available for comparison.")
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missing_python = [data_path for data_path in selected_paths if data_path not in python_series_by_data_path]
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if missing_python:
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raise TestMqlComparisonError(f"Python series missing Data_Path values: {missing_python}")
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_validate_amesim_data_paths(amesim_results, selected_paths)
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return selected_paths
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def _validate_amesim_data_paths(
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amesim_results: AmesimResults,
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data_paths: tuple[str, ...] | list[str],
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) -> None:
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missing_amesim = [data_path for data_path in data_paths if data_path not in amesim_results.series_by_data_path]
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if missing_amesim:
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raise TestMqlComparisonError(f"AMESim results missing Data_Path values: {missing_amesim}")
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def _validate_time_axis(time_values: tuple[float, ...] | list[float]) -> None:
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if not time_values:
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raise TestMqlComparisonError("Python time axis is empty.")
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previous = float(time_values[0])
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for value in time_values[1:]:
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value = float(value)
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if value < previous:
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raise TestMqlComparisonError("Python time axis must be monotonically increasing.")
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previous = value
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