规范仿真模型库并完善前端交互

归档仿真模型并补充组件目录、建模规范与校验。

完善控制台、默认节点、视图适配及前端自动化测试。

app/simulation/reporting/testmodel_outputs.py

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+from __future__ import annotations
+
+from bisect import bisect_left
+import csv
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+
+PRIMARY_KEYS = (
+    "mytank.p",
+    "mytank.T",
+    "mycylinder.p",
+    "mycylinder.T",
+)
+
+MODELICA_COMPARISON_COLUMNS = {
+    "mytank.p": "mytank.p",
+    "mytank.T": "mytank.T",
+    "mycylinder.p": "mycylinder.p",
+    "mycylinder.T": "mycylinder.T",
+    "branch.upper_branch.p": "mypipe.p",
+    "branch.upper_branch.in": "myorifice.port_a.m_flow",
+    "branch.upper_branch.out": "mytee1.port_out2.m_flow",
+    "branch.lower_branch.p": "mypipe1.p",
+    "branch.lower_branch.in": "myorifice1.port_a.m_flow",
+    "branch.lower_branch.out": "mytee1.port_out1.m_flow",
+}
+
+COMPARISON_KEYS = tuple(MODELICA_COMPARISON_COLUMNS.keys())
+
+
+def _branch_series_values(
+    series: dict[str, list[float]],
+    branch_name: str,
+    legacy_key: str,
+) -> list[float]:
+    generic_key = f"branch.{branch_name}.{legacy_key.split('.')[-1]}"
+    if generic_key in series:
+        return series[generic_key]
+    return series[legacy_key]
+
+
+@dataclass(frozen=True)
+class TestModelArtifacts:
+    primary_csv_path: Path
+    temperature_csv_path: Path
+    temperature_svg_path: Path
+    run_report_path: Path
+    comparison_csv_path: Path | None = None
+    comparison_summary_path: Path | None = None
+
+
+def format_testmodel_run_report(
+    *,
+    network_summary: str,
+    initialization: Any,
+    raw_initial_state: tuple[float, ...],
+    consistent_initial_state: tuple[float, ...],
+    solution: Any,
+    series: dict[str, list[float]],
+    solve_diagnostics: Any,
+    artifacts: TestModelArtifacts,
+    comparison_summary: dict[str, tuple[float, float]] | None,
+) -> str:
+    lines = [
+        network_summary,
+        "",
+        f"Initialization converged: {initialization.converged}",
+        f"Initialization iterations: {initialization.iterations}",
+        f"Initialization max state delta: {initialization.max_state_delta:.6e}",
+        f"Initialization max flow delta: {initialization.max_flow_delta:.6e}",
+        f"Initialization max enthalpy delta: {initialization.max_enthalpy_delta:.6e}",
+        (
+            "Initialization downstream pressure spread: "
+            f"{initialization.downstream_pressure_spread:.6e}"
+        ),
+        "",
+        "Raw initial state vector:",
+        str(list(raw_initial_state)),
+        "",
+        "Constraint-consistent initial state vector:",
+        str(list(consistent_initial_state)),
+        "",
+        f"Solver success: {solution.success}",
+        f"Solver message: {solution.message}",
+        f"Final time: {solution.t[-1]:.2f} s",
+        f"Final tank pressure: {series['mytank.p'][-1]:.3f} Pa",
+        f"Final tank temperature: {series['mytank.T'][-1]:.3f} K",
+        f"Final cylinder pressure: {series['mycylinder.p'][-1]:.3f} Pa",
+        (
+            "Final branch inflow: "
+            f"{_branch_series_values(series, 'upper_branch', 'branch_upper.in')[-1] + _branch_series_values(series, 'lower_branch', 'branch_lower.in')[-1]:.6f} kg/s"
+        ),
+    ]
+
+    if solve_diagnostics is not None:
+        lines.extend(
+            [
+                "",
+                "Final closure solve diagnostics:",
+                (
+                    "Upper branch inlet solve: "
+                    f"converged={solve_diagnostics.upper_branch_inlet.converged}, "
+                    f"iterations={solve_diagnostics.upper_branch_inlet.iterations}, "
+                    f"residual={solve_diagnostics.upper_branch_inlet.residual:.6e}"
+                ),
+                (
+                    "Lower branch inlet solve: "
+                    f"converged={solve_diagnostics.lower_branch_inlet.converged}, "
+                    f"iterations={solve_diagnostics.lower_branch_inlet.iterations}, "
+                    f"residual={solve_diagnostics.lower_branch_inlet.residual:.6e}"
+                ),
+            ]
+        )
+        if solve_diagnostics.downstream_pressure_projection is not None:
+            lines.append(
+                "Downstream pressure projection: "
+                f"converged={solve_diagnostics.downstream_pressure_projection.converged}, "
+                f"iterations={solve_diagnostics.downstream_pressure_projection.iterations}, "
+                f"residual={solve_diagnostics.downstream_pressure_projection.residual:.6e}"
+            )
+
+    lines.extend(
+        [
+        f"Primary series CSV: {artifacts.primary_csv_path}",
+        f"Temperature CSV: {artifacts.temperature_csv_path}",
+        f"Temperature plot: {artifacts.temperature_svg_path}",
+        f"Run report TXT: {artifacts.run_report_path}",
+        ]
+    )
+
+    if (
+        artifacts.comparison_csv_path is not None
+        and artifacts.comparison_summary_path is not None
+    ):
+        lines.extend(
+            [
+                f"Modelica comparison CSV: {artifacts.comparison_csv_path}",
+                f"Modelica comparison summary: {artifacts.comparison_summary_path}",
+            ]
+        )
+        if comparison_summary is not None:
+            for key, (max_abs_error, max_rel_error) in comparison_summary.items():
+                lines.append(
+                    f"{key} max abs error: {max_abs_error:.6f}, "
+                    f"max rel error: {max_rel_error:.6%}"
+                )
+
+    return "\n".join(lines) + "\n"
+
+
+def write_testmodel_run_report(output_dir: Path, report_text: str) -> Path:
+    report_path = output_dir / "testmodel_run_report.txt"
+    report_path.write_text(report_text, encoding="utf-8")
+    return report_path
+
+
+def _write_primary_series_csv(output_dir: Path, series: dict[str, list[float]]) -> Path:
+    csv_path = output_dir / "testmodel_primary_series.csv"
+    with csv_path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.writer(handle)
+        writer.writerow(["time_s", *PRIMARY_KEYS])
+        for index, time_value in enumerate(series["time"]):
+            writer.writerow([time_value, *(series[key][index] for key in PRIMARY_KEYS)])
+    return csv_path
+
+
+def _write_temperature_csv(output_dir: Path, time_values: list[float], temperatures: list[float]) -> Path:
+    csv_path = output_dir / "testmodel_tank_temperature.csv"
+    with csv_path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.writer(handle)
+        writer.writerow(["time_s", "mytank_T_K"])
+        writer.writerows(zip(time_values, temperatures))
+    return csv_path
+
+
+def _write_temperature_svg(output_dir: Path, time_values: list[float], temperatures: list[float]) -> Path:
+    svg_path = output_dir / "testmodel_tank_temperature.svg"
+
+    width = 900
+    height = 520
+    left = 90
+    right = 40
+    top = 60
+    bottom = 70
+    plot_width = width - left - right
+    plot_height = height - top - bottom
+
+    min_time = min(time_values)
+    max_time = max(time_values)
+    min_temp = min(temperatures)
+    max_temp = max(temperatures)
+    temp_padding = max(1.0, (max_temp - min_temp) * 0.08)
+    min_temp -= temp_padding
+    max_temp += temp_padding
+
+    def scale_x(value: float) -> float:
+        return left + (value - min_time) / max(max_time - min_time, 1e-12) * plot_width
+
+    def scale_y(value: float) -> float:
+        return top + (max_temp - value) / max(max_temp - min_temp, 1e-12) * plot_height
+
+    points = " ".join(
+        f"{scale_x(time_value):.2f},{scale_y(temperature):.2f}"
+        for time_value, temperature in zip(time_values, temperatures)
+    )
+
+    x_ticks = 5
+    y_ticks = 5
+    x_tick_markup = []
+    y_tick_markup = []
+
+    for index in range(x_ticks + 1):
+        fraction = index / x_ticks
+        time_value = min_time + fraction * (max_time - min_time)
+        x = left + fraction * plot_width
+        x_tick_markup.append(
+            f'<line x1="{x:.2f}" y1="{top}" x2="{x:.2f}" y2="{top + plot_height}" '
+            'stroke="#d9e2ec" stroke-width="1" />'
+        )
+        x_tick_markup.append(
+            f'<text x="{x:.2f}" y="{height - 30}" text-anchor="middle" '
+            'font-size="14" fill="#102a43">'
+            f"{time_value:.1f}</text>"
+        )
+
+    for index in range(y_ticks + 1):
+        fraction = index / y_ticks
+        temp_value = min_temp + fraction * (max_temp - min_temp)
+        y = top + plot_height - fraction * plot_height
+        y_tick_markup.append(
+            f'<line x1="{left}" y1="{y:.2f}" x2="{left + plot_width}" y2="{y:.2f}" '
+            'stroke="#d9e2ec" stroke-width="1" />'
+        )
+        y_tick_markup.append(
+            f'<text x="{left - 12}" y="{y + 5:.2f}" text-anchor="end" '
+            'font-size="14" fill="#102a43">'
+            f"{temp_value:.1f}</text>"
+        )
+
+    svg_content = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">
+  <rect width="{width}" height="{height}" fill="#f7fafc" rx="18" ry="18" />
+  <text x="{width / 2:.0f}" y="32" text-anchor="middle" font-size="24" fill="#102a43">Python Testmodel Tank Temperature</text>
+  <text x="{width / 2:.0f}" y="{height - 8}" text-anchor="middle" font-size="16" fill="#486581">Time (s)</text>
+  <text x="26" y="{height / 2:.0f}" text-anchor="middle" font-size="16" fill="#486581" transform="rotate(-90 26 {height / 2:.0f})">Temperature (K)</text>
+  <rect x="{left}" y="{top}" width="{plot_width}" height="{plot_height}" fill="#ffffff" stroke="#bcccdc" stroke-width="1.5" />
+  {''.join(x_tick_markup)}
+  {''.join(y_tick_markup)}
+  <polyline fill="none" stroke="#d64545" stroke-width="3" stroke-linejoin="round" stroke-linecap="round" points="{points}" />
+</svg>
+"""
+    svg_path.write_text(svg_content, encoding="utf-8")
+    return svg_path
+
+
+def load_modelica_series(csv_path: Path, variable_names: tuple[str, ...]) -> dict[str, list[float]]:
+    series = {"time": []}
+    for variable_name in variable_names:
+        series[variable_name] = []
+
+    with csv_path.open("r", newline="", encoding="utf-8") as handle:
+        reader = csv.DictReader(handle)
+        available_variable_names = tuple(
+            variable_name
+            for variable_name in variable_names
+            if MODELICA_COMPARISON_COLUMNS.get(variable_name, variable_name) in (reader.fieldnames or ())
+        )
+        for row in reader:
+            series["time"].append(float(row["time"]))
+            for variable_name in available_variable_names:
+                modelica_column = MODELICA_COMPARISON_COLUMNS.get(variable_name, variable_name)
+                series[variable_name].append(float(row[modelica_column]))
+
+    return series
+
+
+def _interpolate_series_value(time_values: list[float], values: list[float], target_time: float) -> float:
+    if target_time <= time_values[0]:
+        return values[0]
+    if target_time >= time_values[-1]:
+        return values[-1]
+
+    right_index = bisect_left(time_values, target_time)
+    if right_index < len(time_values) and abs(time_values[right_index] - target_time) <= 1e-12:
+        return values[right_index]
+
+    left_index = right_index - 1
+    left_time = time_values[left_index]
+    right_time = time_values[right_index]
+    fraction = (target_time - left_time) / (right_time - left_time)
+    return values[left_index] + fraction * (values[right_index] - values[left_index])
+
+
+def write_modelica_comparison(
+    output_dir: Path,
+    python_series: dict[str, list[float]],
+    modelica_series: dict[str, list[float]],
+) -> tuple[Path, Path, dict[str, tuple[float, float]]]:
+    comparison_csv_path = output_dir / "testmodel_modelica_comparison.csv"
+    summary_path = output_dir / "testmodel_modelica_comparison_summary.txt"
+    summary: dict[str, tuple[float, float]] = {}
+
+    with comparison_csv_path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.writer(handle)
+        header = ["time_s"]
+        comparison_keys = tuple(
+            key
+            for key in COMPARISON_KEYS
+            if key in python_series and key in modelica_series and modelica_series[key]
+        )
+        for key in comparison_keys:
+            header.extend(
+                [
+                    f"python.{key}",
+                    f"modelica.{key}",
+                    f"abs_error.{key}",
+                    f"rel_error.{key}",
+                ]
+            )
+        writer.writerow(header)
+
+        max_abs_errors = {key: 0.0 for key in comparison_keys}
+        max_rel_errors = {key: 0.0 for key in comparison_keys}
+
+        for index, time_value in enumerate(python_series["time"]):
+            row = [time_value]
+            for key in comparison_keys:
+                python_value = python_series[key][index]
+                modelica_value = _interpolate_series_value(
+                    modelica_series["time"],
+                    modelica_series[key],
+                    time_value,
+                )
+                abs_error = abs(python_value - modelica_value)
+                rel_error = abs_error / max(abs(modelica_value), 1e-9)
+                max_abs_errors[key] = max(max_abs_errors[key], abs_error)
+                max_rel_errors[key] = max(max_rel_errors[key], rel_error)
+                row.extend([python_value, modelica_value, abs_error, rel_error])
+            writer.writerow(row)
+
+    summary_lines = []
+    for key in comparison_keys:
+        summary[key] = (max_abs_errors[key], max_rel_errors[key])
+        summary_lines.append(
+            f"{key}: max_abs_error={max_abs_errors[key]:.6f}, "
+            f"max_rel_error={max_rel_errors[key]:.6%}"
+        )
+    summary_path.write_text("\n".join(summary_lines) + "\n", encoding="utf-8")
+    return comparison_csv_path, summary_path, summary
+
+
+def export_testmodel_artifacts(
+    *,
+    output_dir: Path,
+    series: dict[str, list[float]],
+    modelica_series: dict[str, list[float]] | None = None,
+) -> tuple[TestModelArtifacts, dict[str, tuple[float, float]] | None]:
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    primary_csv_path = _write_primary_series_csv(output_dir, series)
+    temperature_csv_path = _write_temperature_csv(
+        output_dir,
+        series["time"],
+        series["mytank.T"],
+    )
+    temperature_svg_path = _write_temperature_svg(
+        output_dir,
+        series["time"],
+        series["mytank.T"],
+    )
+
+    comparison_csv_path = None
+    comparison_summary_path = None
+    comparison_summary = None
+    if modelica_series is not None:
+        (
+            comparison_csv_path,
+            comparison_summary_path,
+            comparison_summary,
+        ) = write_modelica_comparison(output_dir, series, modelica_series)
+
+    return (
+        TestModelArtifacts(
+            primary_csv_path=primary_csv_path,
+            temperature_csv_path=temperature_csv_path,
+            temperature_svg_path=temperature_svg_path,
+            run_report_path=output_dir / "testmodel_run_report.txt",
+            comparison_csv_path=comparison_csv_path,
+            comparison_summary_path=comparison_summary_path,
+        ),
+        comparison_summary,
+    )