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Doublecone/extract_artist_logs.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jan 14 11:04:01 2026
+
+@author: T0NGXU
+"""
+
+import os
+import re
+import pandas as pd
+from pathlib import Path
+
+def extract_artist_log_data(root_folder):
+    """
+    从rawdata文件夹下的所有snapshot_*文件夹中提取artist.log文件的数据
+    提取第11到19行的关键参数信息
+    """
+    # 存储所有提取的数据
+    all_data = []
+    
+    # 查找所有的snapshot_*文件夹
+    snapshot_folders = sorted([f for f in Path(root_folder).iterdir() 
+                             if f.is_dir() and f.name.startswith('snapshot_')])
+    
+    print(f"找到 {len(snapshot_folders)} 个snapshot文件夹")
+    
+    for folder in snapshot_folders:
+        log_file = folder / "artist.log"
+        
+        if not log_file.exists():
+            print(f"警告: {log_file} 不存在")
+            continue
+        
+        try:
+            # 提取文件夹名中的数字（snapshot编号）
+            snapshot_num = int(folder.name.split('_')[1])
+            
+            with open(log_file, 'r', encoding='utf-8') as f:
+                lines = f.readlines()
+            
+            # 提取第11到19行（索引10到18）
+            if len(lines) >= 19:
+                data_lines = lines[10:19]
+                
+                # 解析关键参数
+                params = {'snapshot': snapshot_num}
+                
+                for line in data_lines:
+                    line = line.strip()
+                    if '=' in line:
+                        # 分割键值对
+                        parts = line.split('=')
+                        if len(parts) == 2:
+                            key = parts[0].strip()
+                            value = parts[1].strip()
+                            
+                            # 清理键名，移除特殊字符
+                            key = re.sub(r'[^a-zA-Z0-9]', '', key)
+                            
+                            # 尝试转换为数值
+                            try:
+                                # 处理科学计数法
+                                if 'E' in value or 'e' in value:
+                                    value = float(value)
+                                else:
+                                    # 尝试整数，失败则尝试浮点数
+                                    try:
+                                        value = int(value)
+                                    except ValueError:
+                                        value = float(value)
+                            except ValueError:
+                                # 如果转换失败，保持字符串
+                                pass
+                            
+                            params[key] = value
+                
+                all_data.append(params)
+                print(f"成功提取 snapshot_{snapshot_num} 的数据")
+            else:
+                print(f"警告: {log_file} 行数不足")
+                
+        except Exception as e:
+            print(f"处理 {log_file} 时出错: {e}")
+    
+    # 创建DataFrame
+    if all_data:
+        df = pd.DataFrame(all_data)
+        # 按snapshot编号排序
+        df = df.sort_values('snapshot').reset_index(drop=True)
+        
+        # 保存到CSV文件
+        output_file = Path(root_folder) / "extracted_artist_log_data.csv"
+        df.to_csv(output_file, index=False, encoding='utf-8')
+        print(f"\n数据已保存到: {output_file}")
+        
+        # 显示数据概览
+        print(f"\n提取的数据概览:")
+        print(f"总共提取了 {len(df)} 个snapshot的数据")
+        print(f"包含的字段: {list(df.columns)}")
+        print(f"\n前5行数据:")
+        print(df.head())
+        
+        return df
+    else:
+        print("未提取到任何数据")
+        return None
+
+# 使用示例
+if __name__ == "__main__":
+    # 设置rawdata文件夹的路径
+    rawdata_folder = "raw_data"  # 请根据实际情况修改路径
+    
+    if Path(rawdata_folder).exists():
+        df = extract_artist_log_data(rawdata_folder)
+    else:
+        print(f"错误: 文件夹 {rawdata_folder} 不存在")

Doublecone/preprocess.py

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+import os
+import re
+import glob
+import numpy as np
+import torch
+from scipy.interpolate import griddata
+from tqdm import tqdm
+
+# ================= 配置区域 =================
+DATA_ROOT = r'D:\生成肖新算例\raw_data'
+OUTPUT_FILE = r'D:\生成肖新算例\raw_data\double_cone_dataset_final.pt'
+
+N_NODES_EXPECTED = 1120638
+N_VARS_TOTAL = 68
+TARGET_H = 17          # 我们想要的法向层数
+TARGET_W = 384         # 我们想要的流向点数
+
+# 变量索引 (x=0, y=1, rho=66, u=60, v=61, p=63)
+VAR_INDICES = [0, 1, 66, 60, 61, 63] 
+# ============================================
+
+def read_raw_points(filepath):
+    """
+    只读取点云，不关心形状
+    """
+    try:
+        with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
+            content = f.read()
+        
+        # 提取数字
+        pattern = re.compile(r'[-+]?\d*\.?\d+(?:[eEdD][-+]?\d+)?')
+        # 寻找数据区
+        if "ZONETYPE" in content:
+            content = content.split("ZONETYPE")[-1]
+        elif "POINT" in content:
+            content = content.split("POINT")[-1]
+            
+        all_tokens = pattern.findall(content)
+        
+        limit = N_NODES_EXPECTED * N_VARS_TOTAL
+        if len(all_tokens) < limit:
+             # 容错
+             actual_nodes = len(all_tokens) // N_VARS_TOTAL
+             limit = actual_nodes * N_VARS_TOTAL
+        
+        # 转为 numpy 矩阵 [N, 68]
+        data_flat = np.array(all_tokens[:limit], dtype=np.float32)
+        actual_nodes = len(data_flat) // N_VARS_TOTAL
+        data_matrix = data_flat.reshape(actual_nodes, N_VARS_TOTAL)
+        
+        # 提取 [N, 6] (x, y, rho, u, v, p)
+        return data_matrix[:, VAR_INDICES]
+
+    except Exception as e:
+        print(f"Read error: {e}")
+        return None
+
+def generate_target_grid(points_data):
+    """
+    根据点云的几何边界，生成一个完美的 17x384 结构化网格
+    """
+    x = points_data[:, 0]
+    y = points_data[:, 1]
+    
+    # 1. 找到流向范围 (X轴)
+    x_min, x_max = x.min(), x.max()
+    
+    # 我们需要在 X 方向生成 384 个切片
+    # 注意：双锥激波通常在 X > 0 的区域，我们需要覆盖整个区域
+    target_x_line = np.linspace(x_min, x_max, TARGET_W)
+    
+    # 2. 构建上下边界
+    # 我们将空间划分为 TARGET_W 个竖条，在每个竖条里找 Y_min (壁面) 和 Y_max (远场)
+    # 为了加速，我们先对原始数据按 X 排序
+    # 但 griddata 不需要排序，我们直接定义目标网格的坐标
+    
+    # 更好的策略：
+    # 原始网格是贴体的，意味着 Y 坐标随 X 变化。
+    # 我们利用几何特征：壁面通常是 Y=0 (或接近0)，远场是 Y=Max
+    
+    # 为了保持贴体特性（这一点对激波很重要），我们不能用矩形网格。
+    # 我们需要“扇形”网格。
+    
+    # 简单策略：利用极坐标思想或比例思想
+    # 在每个 X 位置，Y 从 0 到 Y_max_at_that_x 分布
+    
+    # 这里我们使用 griddata 的 'nearest' 或 'linear' 直接映射到一个规则的 Logical Grid
+    # 既然源数据很密，我们可以直接定义一个物理空间的网格吗？不行，因为远场边界是斜的。
+    
+    # === 核心算法：边界重建 ===
+    # 1. 把所有点按 X 坐标分桶
+    num_bins = TARGET_W
+    x_bins = np.linspace(x_min, x_max, num_bins)
+    
+    grid_x = np.zeros((TARGET_H, TARGET_W))
+    grid_y = np.zeros((TARGET_H, TARGET_W))
+    
+    # 对每一列（流向位置）
+    for j in range(TARGET_W):
+        current_x = x_bins[j]
+        # 找到原始数据中 X 坐标接近 current_x 的点 (取一个薄切片)
+        # 宽度容差
+        dx = (x_max - x_min) / TARGET_W
+        mask = (x >= current_x - dx/1.5) & (x <= current_x + dx/1.5)
+        
+        slice_y = y[mask]
+        
+        if len(slice_y) == 0:
+            # 如果切片没数据（可能是坏点），沿用上一列
+            if j > 0:
+                grid_x[:, j] = grid_x[:, j-1]
+                grid_y[:, j] = grid_y[:, j-1]
+            continue
+            
+        # 在这个切片里，找到 Y_min (壁面) 和 Y_max (远场)
+        y_bot = slice_y.min()
+        y_top = slice_y.max()
+        
+        # 在 Y_bot 和 Y_top 之间均匀生成 17 个点
+        new_y_col = np.linspace(y_bot, y_top, TARGET_H)
+        
+        grid_x[:, j] = current_x
+        grid_y[:, j] = new_y_col
+        
+    return grid_x, grid_y
+
+def process_regrid(filepath):
+    # 1. 读取原始散乱点
+    raw_data = read_raw_points(filepath)
+    if raw_data is None: return None
+    
+    # 2. 生成目标网格坐标 (物理空间)
+    # [17, 384]
+    grid_x, grid_y = generate_target_grid(raw_data)
+    
+    # 3. 插值 (核心步骤)
+    # 将原始数据 (x, y, rho...) 插值到新的 (grid_x, grid_y) 上
+    # raw_data[:, 0:2] 是源坐标 (N, 2)
+    # raw_data[:, 2:] 是源值 (N, 4)
+    # (grid_x, grid_y) 是目标坐标
+    
+    points = raw_data[:, 0:2]
+    values = raw_data[:, 2:] # rho, u, v, p
+    
+    # method='linear' 保证平滑，但比 nearest 慢。
+    # 鉴于数据量大，我们可以先尝试 'linear'。如果太慢，用 'nearest'。
+    # 这里为了质量，一定要用 linear
+    try:
+        grid_values = griddata(points, values, (grid_x, grid_y), method='linear', fill_value=0)
+    except Exception as e:
+        print(f"Interpolation failed: {e}")
+        return None
+    
+    # 4. 组合结果
+    # grid_x: [17, 384]
+    # grid_values: [17, 384, 4]
+    
+    # 转换为 [6, 17, 384] -> (x, y, rho, u, v, p)
+    # 先把 x, y 扩充维度
+    grid_x_exp = grid_x[:, :, np.newaxis]
+    grid_y_exp = grid_y[:, :, np.newaxis]
+    
+    full_grid = np.concatenate([grid_x_exp, grid_y_exp, grid_values], axis=2)
+    
+    # Transpose to [C, H, W]
+    final_tensor = np.transpose(full_grid, (2, 0, 1))
+    
+    return final_tensor
+
+def main():
+    print(f"Regridding Mode: {DATA_ROOT}")
+    file_list = sorted(glob.glob(os.path.join(DATA_ROOT, 'snapshot_*', 'flowPoint.plt')))
+    
+    processed_list = []
+    for fname in tqdm(file_list, desc="Regridding"):
+        tensor = process_regrid(fname)
+        if tensor is not None:
+            processed_list.append(tensor)
+            
+    if not processed_list:
+        print("Failed.")
+        return
+        
+    full_tensor = np.stack(processed_list, axis=0)
+    full_tensor = torch.from_numpy(full_tensor).float()
+    
+    # 处理 NaN (插值可能产生空洞)
+    if torch.isnan(full_tensor).any():
+        print("Warning: 插值包含 NaN，正在填充...")
+        full_tensor = torch.nan_to_num(full_tensor, 0.0)
+    
+    print(f"\n✅ Done! Shape: {full_tensor.shape}")
+    
+    # Save
+    input_data = full_tensor[:, 0:2]
+    target_data = full_tensor[:, 2:]
+    
+    stats = {
+        'x_mean': input_data[:,0].mean(), 'x_std': input_data[:,0].std(),
+        'rho_mean': target_data[:,0].mean(), 'rho_std': target_data[:,0].std(),
+    }
+    
+    torch.save({'x': input_data, 'y': target_data, 'stats': stats}, OUTPUT_FILE)
+    print(f"Saved to {OUTPUT_FILE}")
+    print("现在去运行 check_data.py，你会看到完美光滑的图像。")
+
+if __name__ == "__main__":
+    main()