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"""单个试井样本的数值求解器与正演代理模型对比。
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该脚本用一组指定地层/井筒参数和流量制度分别运行 C++ 数值求解器与 Python
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正演代理模型，随后在压力、压力导数和斜率三段曲线上计算误差指标，绘制逐点
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对比图，并导出 JSON 汇总，便于排查单个案例的代理误差来源。
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"""
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from __future__ import annotations
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import argparse
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import json
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.append(str(ROOT))
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import joblib
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from src.common.config import Config
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from src.common.experiment_paths import (
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config_for_stage,
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model_checkpoint_for_tag,
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normalize_tag,
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processed_path_for_tag,
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)
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from src.data.curve_processing import clean_curve_for_dataset, is_valid_curve, resample_curve_to_features
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from src.data.param_features import param_feature_transform_from_meta, transform_param_features
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from src.data.params import Params, Schedule
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from src.data.runner_client import CppRunner, read_result_bin
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from src.data.schedule_features import build_schedule_model_vector
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from src.evaluation.autofit_objective import dual_log_objective
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from src.models.forward_surrogate import ForwardSurrogate
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DEFAULT_SINGLE_CASE = {
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"config": "configs/data_gen_family_random.yaml",
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"tag": "family_random_50k",
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"no_schedule": False,
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"well_index": 0,
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"params": {
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"k": 0.025,
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"skin": 0.0,
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"wellboreC": 0.01,
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"phi": 0.0245,
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"h": 9.144,
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"Cf": 0.0004315,
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},
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}
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def parse_args() -> argparse.Namespace:
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"""解析单案例对比所需的路径、参数、井号和流量制度覆盖项。
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默认值对应一个可复现实验案例；命令行可覆盖模型 checkpoint、processed 数据、
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地层/井筒参数以及 `timeQ/q/sectionIndex`，方便快速定位某个具体样本的误差表现。
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"""
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parser = argparse.ArgumentParser(
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description="Compare solver output and surrogate prediction on a single parameter set"
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)
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parser.add_argument("--config", type=str, default=DEFAULT_SINGLE_CASE["config"], help="Config yaml path")
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parser.add_argument(
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"--stage",
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choices=["fixed_case", "case_neighborhood", "family_random", "family_random_v2_q"],
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default=None,
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help="Optional stage to infer config path",
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)
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parser.add_argument("--processed", type=str, default=None, help="Processed dataset path")
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parser.add_argument("--model", type=str, default=None, help="Surrogate checkpoint path")
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parser.add_argument("--output-dir", type=str, default=None, help="Output directory")
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parser.add_argument(
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"--tag",
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type=str,
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default=DEFAULT_SINGLE_CASE["tag"],
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help="Experiment tag for processed/model lookup",
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)
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parser.add_argument(
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"--no-schedule",
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action="store_true",
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help="When --model is omitted, infer the no-schedule checkpoint path",
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)
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parser.add_argument("--k", type=float, default=DEFAULT_SINGLE_CASE["params"]["k"])
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parser.add_argument("--skin", type=float, default=DEFAULT_SINGLE_CASE["params"]["skin"])
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parser.add_argument("--wellboreC", type=float, default=DEFAULT_SINGLE_CASE["params"]["wellboreC"])
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parser.add_argument("--phi", type=float, default=DEFAULT_SINGLE_CASE["params"]["phi"])
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parser.add_argument("--h", type=float, default=DEFAULT_SINGLE_CASE["params"]["h"])
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parser.add_argument("--Cf", type=float, default=DEFAULT_SINGLE_CASE["params"]["Cf"])
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parser.add_argument(
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"--well-index",
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type=int,
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default=DEFAULT_SINGLE_CASE["well_index"],
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help="Well index for solver output",
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)
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parser.add_argument(
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"--timeQ",
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type=str,
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default=None,
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help="Override schedule durations, e.g. '1000,1000,1000' or '0,1000,1000,1000'.",
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)
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parser.add_argument(
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"--q",
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type=str,
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default=None,
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help="Override schedule rates, e.g. '200,100,0' or '0,200,100,0'.",
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)
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parser.add_argument(
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"--section-index",
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type=int,
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default=None,
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help="Override tested section index. Defaults to the last section of the overridden schedule.",
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)
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return parser.parse_args()
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def calc_metrics(
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y_true: np.ndarray,
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y_pred: np.ndarray,
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eps_range: float = 1e-3,
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eps_var: float = 1e-6,
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) -> dict:
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"""计算一维曲线预测误差指标。
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`eps_range` 用于避免真实曲线几乎为常数时 NRMSE 分母过小，
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`eps_var` 用于避免 R2 在真实曲线方差接近 0 时失真；无效场景返回 NaN。
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"""
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err = y_pred - y_true
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mse = np.mean(err**2)
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rmse = float(np.sqrt(mse))
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mae = float(np.mean(np.abs(err)))
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bias = float(np.mean(err))
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value_range = float(np.max(y_true) - np.min(y_true))
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ss_tot = float(np.sum((y_true - np.mean(y_true)) ** 2))
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ss_res = float(np.sum(err**2))
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valid_nrmse = value_range > eps_range
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valid_r2 = ss_tot > eps_var
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nrmse = float(rmse / value_range) if valid_nrmse else np.nan
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r2 = float(1.0 - ss_res / ss_tot) if valid_r2 else np.nan
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return {
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"rmse": rmse,
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"mae": mae,
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"bias": bias,
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"abs_bias": float(abs(bias)),
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"nrmse": nrmse,
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"r2": r2,
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}
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def infer_curve_layout(meta: dict, curve_dim: int) -> dict:
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"""从预处理元数据推断曲线拼接布局。
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新版 processed 文件会保存 `curve_layout`；若旧数据缺失该字段，则按压力、
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压力导数、斜率三段等长拼接的历史约定回退，保证旧实验仍可比较。
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"""
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curve_layout = meta.get("curve_layout")
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if curve_layout is not None:
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return curve_layout
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n_time_points = curve_dim // 3
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return {
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"n_time_points": int(n_time_points),
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"parts": [
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{"name": "log_pressure", "start": 0, "end": n_time_points},
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{"name": "log_derivative", "start": n_time_points, "end": 2 * n_time_points},
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{"name": "slope", "start": 2 * n_time_points, "end": 3 * n_time_points},
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],
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}
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def split_curve_by_layout(curve: np.ndarray, layout: dict) -> dict[str, np.ndarray]:
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"""按 `curve_layout` 把一维拼接曲线拆成命名片段。
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返回值通常包含 `log_pressure`、`log_derivative` 和 `slope`，
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后续绘图、分段指标和自动拟合目标函数都依赖这些片段边界一致。
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"""
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parts: dict[str, np.ndarray] = {}
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for part in layout["parts"]:
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start = int(part["start"])
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end = int(part["end"])
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parts[str(part["name"])] = curve[start:end]
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return parts
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def resolve_cf(args: argparse.Namespace, cfg: Config) -> float:
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"""解析综合压缩系数 Cf。
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Cf 可能由命令行显式给出，也可能在配置文件的 fixed_params 中固定；
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集中处理该兜底逻辑，避免构造 `Params` 时把缺失 Cf 静默写成错误默认值。
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"""
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if args.Cf is not None:
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return float(args.Cf)
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fixed_cfg = cfg.raw.get("params", {}).get("fixed_params", {}).get("Cf", {}) or {}
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if bool(fixed_cfg.get("enabled", False)):
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return float(fixed_cfg["value"])
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raise ValueError("Cf 未在命令行提供，且配置文件里也没有启用固定 Cf")
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def parse_float_list(raw: str, name: str) -> list[float]:
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"""把命令行传入的逗号/分号/空格分隔数值串转换为浮点列表。
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该函数用于解析 `--timeQ` 和 `--q`，允许用户用不同分隔符快速输入制度序列；
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若没有解析出任何有效数字，则抛出带参数名的错误。
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"""
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values: list[float] = []
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for token in raw.replace(";", ",").replace(" ", ",").split(","):
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token = token.strip()
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if not token:
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continue
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values.append(float(token))
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if not values:
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raise ValueError(f"{name} 不能为空")
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return values
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def resolve_case_schedule(cfg: Config, args: argparse.Namespace | None = None) -> Schedule:
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"""构造单案例对比使用的流量制度。
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默认从配置文件读取 `case_schedule`；当命令行提供 `--timeQ/--q` 时优先使用覆盖值，
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并按显式 `--section-index` 或最后一段规则确定测试段。
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"""
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if args is not None and (args.timeQ is not None or args.q is not None or args.section_index is not None):
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if args.timeQ is None or args.q is None:
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raise ValueError("覆盖流量制度时必须同时提供 --timeQ 和 --q")
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timeQ = parse_float_list(args.timeQ, "--timeQ")
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q = parse_float_list(args.q, "--q")
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if len(timeQ) != len(q):
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raise ValueError(f"--timeQ 和 --q 长度必须一致，当前分别为 {len(timeQ)} 和 {len(q)}")
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# Allow the reporting/table convention with an initial "0 0" row.
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if len(timeQ) >= 2 and timeQ[0] <= 0.0 and q[0] == 0.0:
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timeQ = timeQ[1:]
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q = q[1:]
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section_index = int(args.section_index) if args.section_index is not None else len(timeQ)
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schedule = Schedule(sectionIndex=section_index, timeQ=timeQ, q=q)
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if not schedule.validate():
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raise ValueError(
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"命令行覆盖的流量制度非法。注意 compare 脚本里的 timeQ 表示每段持续时间，"
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"如果有初始行请写成 0,0；有效段必须 timeQ>0 且 q>=0。"
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)
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return schedule
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schedule_cfg = cfg.raw["schedule"]["case_schedule"]
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timeQ = list(map(float, schedule_cfg["timeQ"]))
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q = list(map(float, schedule_cfg["q"]))
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policy = cfg.raw["schedule"].get("section_policy", {}) or {}
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mode = str(policy.get("mode", "fixed_last")).lower()
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n_sections = len(timeQ)
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if mode == "fixed_last":
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section_index = n_sections
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elif mode == "fixed_value":
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section_index = int(np.clip(int(policy.get("fixed_value", n_sections)), 1, n_sections))
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else:
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section_index = int(np.clip(int(schedule_cfg.get("default_section_index", n_sections)), 1, n_sections))
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schedule = Schedule(sectionIndex=section_index, timeQ=timeQ, q=q)
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if not schedule.validate():
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raise ValueError("配置文件中的 case_schedule 非法，无法用于单样本对比")
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return schedule
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def resolve_paths(args: argparse.Namespace) -> tuple[Config, Path, Path, Path]:
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"""解析配置、processed 数据、模型 checkpoint 和输出目录路径。
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路径优先级为命令行显式参数，其次为实验 tag 对应的标准目录；
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`--no-schedule` 只在自动推断模型路径时用于区分是否带制度分支的模型。
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"""
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tag = normalize_tag(args.tag)
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config_path = args.config
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if config_path is None:
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config_path = str(config_for_stage(args.stage) or Path("configs/data_gen_family_random.yaml"))
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processed_path = Path(args.processed) if args.processed is not None else processed_path_for_tag(tag)
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model_path = (
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Path(args.model)
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if args.model is not None
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else model_checkpoint_for_tag(tag, use_schedule=not args.no_schedule)
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)
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if args.output_dir is not None:
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output_dir = Path(args.output_dir)
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else:
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suffix = "" if not args.no_schedule else "_no_schedule"
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output_dir = Path("results") / (f"single_compare_{tag}{suffix}" if tag else f"single_compare{suffix}")
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return Config(config_path), processed_path, model_path, output_dir
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def build_params_from_args(args: argparse.Namespace, cfg: Config, schedule: Schedule) -> Params:
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"""把命令行中的地层/井筒参数组装为求解器和代理模型共用的 `Params`。
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|
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该对象同时持有物性参数与流量制度，是数值求解、参数特征变换和图标题展示的共同数据源。
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"""
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return Params(
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k=float(args.k),
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skin=float(args.skin),
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wellboreC=float(args.wellboreC),
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phi=float(args.phi),
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h=float(args.h),
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Cf=resolve_cf(args, cfg),
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schedule=schedule,
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)
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def run_solver_and_extract_curve(cfg: Config, params: Params, well_index: int) -> tuple[np.ndarray, dict]:
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|
|
"""运行 C++ 数值求解器并抽取可与代理输出对齐的曲线。
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|
|
|
|
|
原始求解结果会先做有效性检查和清洗，再按训练数据相同的重采样规则转换为
|
|
|
`log_pressure/log_derivative/slope` 拼接向量；同时返回原始 log-log 曲线供 JSON 留痕。
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|
"""
|
|
|
runner = CppRunner(cfg=cfg)
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|
|
try:
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|
ok = runner.run_simulation(params, override_schedule=params.schedule, include_schedule=True)
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|
result = read_result_bin(runner.result_bin) if runner.result_bin.exists() else None
|
|
|
if not ok and result is None:
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|
raise RuntimeError("求解器运行失败，未生成有效结果")
|
|
|
if not ok and result is not None:
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|
|
print("Warning: runner_client 返回失败，但 result.bin 可读取，继续使用求解器结果。")
|
|
|
|
|
|
if result is None:
|
|
|
raise RuntimeError("无法读取 result.bin")
|
|
|
if not result["loglog"]:
|
|
|
raise RuntimeError("求解器未返回 loglog 数据")
|
|
|
|
|
|
if well_index < 0 or well_index >= len(result["loglog"]):
|
|
|
raise IndexError(f"well-index={well_index} 超出范围，可用井数={len(result['loglog'])}")
|
|
|
|
|
|
loglog = result["loglog"][well_index]
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|
|
t = np.asarray(loglog["t"], dtype=np.float64)
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|
|
p = np.asarray(loglog["p"], dtype=np.float64)
|
|
|
d = np.asarray(loglog["deriv"], dtype=np.float64)
|
|
|
|
|
|
cleaned = clean_curve_for_dataset(cfg, t, p, d)
|
|
|
if cleaned is None:
|
|
|
raise RuntimeError("求解器返回曲线在清洗后无效")
|
|
|
|
|
|
t_clean, p_clean, d_clean = cleaned
|
|
|
valid, reason = is_valid_curve(cfg, t_clean, p_clean, d_clean)
|
|
|
if not valid:
|
|
|
raise RuntimeError(f"求解器曲线未通过有效性检查: {reason}")
|
|
|
|
|
|
curve_feat = resample_curve_to_features(cfg, t_clean, p_clean, d_clean)
|
|
|
raw = {
|
|
|
"t": t_clean.tolist(),
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|
"p": p_clean.tolist(),
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|
"d": d_clean.tolist(),
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|
"n_steps": int(result["nSteps"]),
|
|
|
"n_wells": int(result["nWells"]),
|
|
|
}
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|
return curve_feat, raw
|
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|
finally:
|
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|
runner.close()
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|
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|
|
|
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|
def build_schedule_vector(cfg: Config, schedule: Schedule) -> np.ndarray:
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|
"""把 `Schedule` 编码为正演代理模型的制度特征向量。
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|
|
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|
这里复用训练阶段同一套编码函数，确保单案例推理时的制度特征与 processed 数据一致。
|
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|
"""
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|
return build_schedule_model_vector(cfg, schedule)
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|
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|
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|
def load_model(checkpoint_path: Path) -> tuple[ForwardSurrogate, bool, torch.device]:
|
|
|
"""加载正演代理模型 checkpoint 并恢复网络结构。
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|
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|
checkpoint 中保存了输入维度、隐藏层宽度、dropout 和是否使用制度分支等信息；
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按这些元数据重建模型后再加载权重，才能保证推理结构与训练结构一致。
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"""
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checkpoint = torch.load(checkpoint_path, map_location="cpu")
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use_schedule = bool(checkpoint.get("use_schedule", True))
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|
model = ForwardSurrogate(
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param_dim=int(checkpoint["param_dim"]),
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|
schedule_dim=int(checkpoint["schedule_dim"]),
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|
curve_dim=int(checkpoint["curve_dim"]),
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|
hidden_dim=int(checkpoint["hidden_dim"]),
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|
dropout=float(checkpoint["dropout"]),
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|
use_schedule=use_schedule,
|
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|
)
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|
model.load_state_dict(checkpoint["model_state_dict"])
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|
model.eval()
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|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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|
model = model.to(device)
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|
return model, use_schedule, device
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|
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|
|
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|
def predict_surrogate_curve(
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processed: dict,
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|
model: ForwardSurrogate,
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|
device: torch.device,
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|
use_schedule: bool,
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|
params: Params,
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|
schedule: Schedule,
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|
|
cfg: Config,
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|
) -> np.ndarray:
|
|
|
"""使用代理模型预测单个参数点的反标准化曲线。
|
|
|
|
|
|
参数和制度先按 processed 文件中的 transform/scaler 进入训练时的标准化空间；
|
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|
模型输出再通过 `scaler_curve.inverse_transform` 还原成可与数值求解器直接比较的曲线值。
|
|
|
"""
|
|
|
scaler_params = processed["scaler_params"]
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|
|
scaler_schedule = processed["scaler_schedule"]
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|
scaler_curve = processed["scaler_curve"]
|
|
|
param_transform = param_feature_transform_from_meta(processed.get("meta", {}))
|
|
|
|
|
|
params_vec = np.asarray(
|
|
|
[params.k, params.skin, params.wellboreC, params.phi, params.h, params.Cf],
|
|
|
dtype=np.float32,
|
|
|
).reshape(1, -1)
|
|
|
schedule_vec = build_schedule_vector(cfg, schedule).reshape(1, -1)
|
|
|
|
|
|
params_x = scaler_params.transform(transform_param_features(params_vec, param_transform)).astype(np.float32)
|
|
|
schedule_x = scaler_schedule.transform(schedule_vec).astype(np.float32)
|
|
|
|
|
|
with torch.no_grad():
|
|
|
params_t = torch.tensor(params_x, dtype=torch.float32, device=device)
|
|
|
if use_schedule:
|
|
|
schedule_t = torch.tensor(schedule_x, dtype=torch.float32, device=device)
|
|
|
pred_scaled = model(params_t, schedule_t).cpu().numpy()
|
|
|
else:
|
|
|
pred_scaled = model(params_t, None).cpu().numpy()
|
|
|
|
|
|
return scaler_curve.inverse_transform(pred_scaled)[0].astype(np.float32)
|
|
|
|
|
|
|
|
|
def plot_comparison(
|
|
|
curve_true: np.ndarray,
|
|
|
curve_pred: np.ndarray,
|
|
|
curve_layout: dict,
|
|
|
output_path: Path,
|
|
|
params: Params,
|
|
|
schedule: Schedule,
|
|
|
model_path: Path,
|
|
|
use_schedule: bool,
|
|
|
) -> dict:
|
|
|
"""绘制数值求解器曲线与代理预测曲线的分段对比图。
|
|
|
|
|
|
左列展示每个曲线片段的真实值与预测值，右列展示逐点误差；
|
|
|
图标题汇总参数、制度、模型名称和整体指标，返回的 summary 会同步写入 JSON。
|
|
|
"""
|
|
|
true_parts = split_curve_by_layout(curve_true, curve_layout)
|
|
|
pred_parts = split_curve_by_layout(curve_pred, curve_layout)
|
|
|
overall = calc_metrics(curve_true, curve_pred)
|
|
|
autofit = dual_log_objective(curve_true, curve_pred, curve_layout)
|
|
|
overall_r2_text = "nan" if np.isnan(overall["r2"]) else f"{overall['r2']:.4f}"
|
|
|
|
|
|
part_names = ["log_pressure", "log_derivative", "slope"]
|
|
|
title_map = {
|
|
|
"log_pressure": "Log Pressure",
|
|
|
"log_derivative": "Log |Derivative|",
|
|
|
"slope": "Slope of Log Pressure vs Log Time",
|
|
|
}
|
|
|
|
|
|
fig, axes = plt.subplots(3, 2, figsize=(14, 12))
|
|
|
fig.suptitle(
|
|
|
"Solver vs Surrogate\n"
|
|
|
f"k={params.k:.6g}, skin={params.skin:.6g}, wellboreC={params.wellboreC:.6g}, "
|
|
|
f"phi={params.phi:.6g}, h={params.h:.6g}, Cf={params.Cf:.6g}\n"
|
|
|
f"sectionIndex={schedule.sectionIndex}, timeQ={schedule.timeQ}, q={schedule.q}\n"
|
|
|
f"use_schedule={use_schedule}, model={model_path.name}, "
|
|
|
f"Overall RMSE={overall['rmse']:.4f}, MAE={overall['mae']:.4f}, "
|
|
|
f"AutoFitObj={autofit['dual_log_objective']:.4f}, "
|
|
|
f"R2={overall_r2_text}"
|
|
|
)
|
|
|
|
|
|
summary = {"overall": overall, "autofit": autofit, "parts": {}}
|
|
|
|
|
|
for row, name in enumerate(part_names):
|
|
|
y_true = true_parts[name]
|
|
|
y_pred = pred_parts[name]
|
|
|
err = y_pred - y_true
|
|
|
x = np.arange(len(y_true))
|
|
|
metrics = calc_metrics(y_true, y_pred)
|
|
|
summary["parts"][name] = metrics
|
|
|
|
|
|
nrmse_text = "nan" if np.isnan(metrics["nrmse"]) else f"{metrics['nrmse']:.4f}"
|
|
|
r2_text = "nan" if np.isnan(metrics["r2"]) else f"{metrics['r2']:.4f}"
|
|
|
|
|
|
ax_l = axes[row, 0]
|
|
|
ax_l.plot(x, y_true, label="Solver", linewidth=2, alpha=0.85)
|
|
|
ax_l.plot(x, y_pred, label="Surrogate", linewidth=2, alpha=0.85)
|
|
|
ax_l.set_title(
|
|
|
f"{title_map[name]} | RMSE={metrics['rmse']:.4f}, MAE={metrics['mae']:.4f}, "
|
|
|
f"Bias={metrics['bias']:.4f}, NRMSE={nrmse_text}, R2={r2_text}"
|
|
|
)
|
|
|
ax_l.set_xlabel("Resampled Time Index")
|
|
|
ax_l.set_ylabel("Value")
|
|
|
ax_l.grid(True, alpha=0.3)
|
|
|
ax_l.legend()
|
|
|
|
|
|
ax_r = axes[row, 1]
|
|
|
ax_r.plot(x, err, linewidth=1.5, alpha=0.85)
|
|
|
ax_r.axhline(0.0, linestyle="--", linewidth=1)
|
|
|
ax_r.set_title(f"{title_map[name]} Error")
|
|
|
ax_r.set_xlabel("Resampled Time Index")
|
|
|
ax_r.set_ylabel("Surrogate - Solver")
|
|
|
ax_r.grid(True, alpha=0.3)
|
|
|
|
|
|
plt.tight_layout(rect=[0, 0, 1, 0.94])
|
|
|
plt.savefig(output_path, dpi=150, bbox_inches="tight")
|
|
|
plt.close()
|
|
|
return summary
|
|
|
|
|
|
|
|
|
def main() -> None:
|
|
|
"""执行单案例完整对比流程。
|
|
|
|
|
|
流程包括解析路径和制度、加载 processed 与 checkpoint、运行数值求解器、
|
|
|
调用代理模型预测、绘制对比图、写出 JSON 汇总并打印核心指标。
|
|
|
"""
|
|
|
args = parse_args()
|
|
|
cfg, processed_path, model_path, output_dir = resolve_paths(args)
|
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
|
if not processed_path.exists():
|
|
|
raise FileNotFoundError(f"Processed 数据不存在: {processed_path}")
|
|
|
if not model_path.exists():
|
|
|
raise FileNotFoundError(f"模型 checkpoint 不存在: {model_path}")
|
|
|
|
|
|
processed = joblib.load(processed_path)
|
|
|
curve_layout = infer_curve_layout(processed["meta"], int(processed["meta"]["curve_dim"]))
|
|
|
|
|
|
schedule = resolve_case_schedule(cfg, args)
|
|
|
params = build_params_from_args(args, cfg, schedule)
|
|
|
|
|
|
print("Running solver...")
|
|
|
curve_solver, raw_solver = run_solver_and_extract_curve(cfg, params, args.well_index)
|
|
|
|
|
|
print("Running surrogate...")
|
|
|
model, use_schedule, device = load_model(model_path)
|
|
|
curve_pred = predict_surrogate_curve(
|
|
|
processed=processed,
|
|
|
model=model,
|
|
|
device=device,
|
|
|
use_schedule=use_schedule,
|
|
|
params=params,
|
|
|
schedule=schedule,
|
|
|
cfg=cfg,
|
|
|
)
|
|
|
|
|
|
plot_path = output_dir / "single_case_comparison.png"
|
|
|
summary = plot_comparison(
|
|
|
curve_true=curve_solver,
|
|
|
curve_pred=curve_pred,
|
|
|
curve_layout=curve_layout,
|
|
|
output_path=plot_path,
|
|
|
params=params,
|
|
|
schedule=schedule,
|
|
|
model_path=model_path,
|
|
|
use_schedule=use_schedule,
|
|
|
)
|
|
|
|
|
|
summary_payload = {
|
|
|
"config_path": str(cfg.path),
|
|
|
"processed_path": str(processed_path),
|
|
|
"model_path": str(model_path),
|
|
|
"use_schedule": use_schedule,
|
|
|
"device": str(device),
|
|
|
"params": {
|
|
|
"k": params.k,
|
|
|
"skin": params.skin,
|
|
|
"wellboreC": params.wellboreC,
|
|
|
"phi": params.phi,
|
|
|
"h": params.h,
|
|
|
"Cf": params.Cf,
|
|
|
},
|
|
|
"schedule": {
|
|
|
"sectionIndex": schedule.sectionIndex,
|
|
|
"timeQ": schedule.timeQ,
|
|
|
"q": schedule.q,
|
|
|
},
|
|
|
"metrics": summary,
|
|
|
"solver_raw_loglog": raw_solver,
|
|
|
}
|
|
|
|
|
|
with open(output_dir / "single_case_summary.json", "w", encoding="utf-8") as f:
|
|
|
json.dump(summary_payload, f, ensure_ascii=False, indent=2)
|
|
|
|
|
|
overall = summary["overall"]
|
|
|
autofit = summary["autofit"]
|
|
|
overall_r2_text = "nan" if np.isnan(overall["r2"]) else f"{overall['r2']:.6f}"
|
|
|
print("\nSingle-case comparison complete.")
|
|
|
print(f"Output dir: {output_dir}")
|
|
|
print(
|
|
|
f"Overall RMSE={overall['rmse']:.6f}, MAE={overall['mae']:.6f}, "
|
|
|
f"Bias={overall['bias']:.6f}, "
|
|
|
f"AutoFitObj={autofit['dual_log_objective']:.6f}, "
|
|
|
f"R2={overall_r2_text}"
|
|
|
)
|
|
|
print(f"Plot saved: {plot_path}")
|
|
|
print(f"Summary saved: {output_dir / 'single_case_summary.json'}")
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
main()
|