Upload cleanroom dataset

cleanroom/datasets/preprocessor_time.py

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+
+import pandas as pd
+import torch
+import h5py
+import json
+from typing import Dict, Set, List, Tuple
+from collections import defaultdict
+import re
+from io import StringIO
+
+# --- Helper Functions ---
+
+def load_boundary_nodes_from_csvs(boundary_csv_folder_path: str, target_column_name: str) -> Dict[str, Set[int]]:
+    # This function appears correct and has been kept as is.
+    # ... (function content is unchanged) ...
+    boundary_nodes_map: Dict[str, Set[int]] = {}
+    all_seen_nodes: Set[int] = set()
+    walls_nodes: Set[int] = set()
+    everything_csv_path = None
+
+    print(f"Scanning for boundary CSV files in: {boundary_csv_folder_path}")
+
+    if not os.path.isdir(boundary_csv_folder_path):
+        print(f"Error: Folder not found at {boundary_csv_folder_path}")
+        return boundary_nodes_map
+
+    # Step 1: Process 'walls.csv' first
+    walls_file_path = os.path.join(boundary_csv_folder_path, "walls.csv")
+    if os.path.exists(walls_file_path):
+        print(f"\nProcessing 'walls.csv'...")
+        try:
+            df_walls = pd.read_csv(walls_file_path, skiprows=5, header=0, engine='python')
+            if target_column_name in df_walls.columns:
+                walls_nodes = set(pd.to_numeric(df_walls[target_column_name], errors='coerce').dropna().astype(int).tolist())
+                boundary_nodes_map['walls'] = walls_nodes
+                all_seen_nodes.update(walls_nodes)
+                print(f"  - Loaded {len(walls_nodes)} nodes for boundary type: 'walls'.")
+            else:
+                print(f"  - Warning: 'walls.csv' does not contain the expected column '{target_column_name}'. Skipping walls assignment.")
+        except pd.errors.EmptyDataError:
+            print(f"  - Warning: 'walls.csv' is empty after skipping rows. Skipping walls assignment.")
+        except Exception as e:
+            print(f"  - Error reading 'walls.csv': {e}. Skipping walls assignment.")
+    else:
+        print("\n'walls.csv' not found in the specified directory.")
+
+    # Step 2: Process all other CSVs
+    print("\nProcessing other boundary CSVs (excluding walls.csv and everything.csv)...")
+    for filename in os.listdir(boundary_csv_folder_path):
+        if filename.endswith(".csv") and filename not in ["walls.csv", "everything.csv"]:
+            file_path = os.path.join(boundary_csv_folder_path, filename)
+            boundary_type = os.path.splitext(filename)[0]
+            try:
+                df = pd.read_csv(file_path, skiprows=5, header=0, engine='python')
+                if target_column_name in df.columns:
+                    nodes_from_file = set(pd.to_numeric(df[target_column_name], errors='coerce').dropna().astype(int).tolist())
+                    filtered_nodes = nodes_from_file - walls_nodes
+                    if filtered_nodes:
+                        boundary_nodes_map[boundary_type] = filtered_nodes
+                        all_seen_nodes.update(filtered_nodes)
+                        print(f"  - Loaded {len(filtered_nodes)} filtered nodes for boundary type: '{boundary_type}'. (Original: {len(nodes_from_file)})")
+                else:
+                    print(f"  - Warning: '{filename}' does not contain '{target_column_name}'. Skipping.")
+            except Exception as e:
+                print(f"  - Error reading '{filename}': {e}. Skipping.")
+        elif filename == "everything.csv":
+            everything_csv_path = os.path.join(boundary_csv_folder_path, filename)
+
+    # Step 3: Process 'everything.csv'
+    if everything_csv_path:
+        print(f"\nProcessing 'everything.csv' for interior nodes...")
+        try:
+            df_everything = pd.read_csv(everything_csv_path, skiprows=5, header=0, engine='python')
+            if target_column_name in df_everything.columns:
+                all_nodes_in_everything = set(pd.to_numeric(df_everything[target_column_name], errors='coerce').dropna().astype(int).tolist())
+                interior_nodes = all_nodes_in_everything - all_seen_nodes
+                boundary_nodes_map['interior'] = interior_nodes
+                print(f"  - Loaded {len(interior_nodes)} unique interior nodes from 'everything.csv'.")
+            else:
+                print(f"  - Warning: 'everything.csv' does not contain '{target_column_name}'.")
+        except Exception as e:
+            print(f"  - Error reading 'everything.csv': {e}.")
+    else:
+        print("\n'everything.csv' not found.")
+
+    print("\nFinished loading boundary nodes from CSVs.")
+    return boundary_nodes_map
+
+def load_velocity_lookup(csv_path: str) -> Dict[str, float]:
+    velocity_lookup = {}
+    if not os.path.exists(csv_path):
+        print(f"Warning: Velocity lookup file not found at {csv_path}")
+        return velocity_lookup
+    try:
+        df = pd.read_csv(csv_path)
+        velocity_lookup = {row['Design Points'].strip(): float(row['Inlet velocity']) for _, row in df.iterrows()}
+        print(f"Loaded velocity lookup table with {len(velocity_lookup)} design points.")
+    except Exception as e:
+        print(f"Error loading velocity lookup from {csv_path}: {e}")
+    return velocity_lookup
+
+def get_design_point_info(folder_name: str, velocity_lookup: Dict[str, float]) -> Tuple[str, float]:
+    # <<< FIXED: Corrected regex to get the full DP name, e.g., 'DP1' >>>
+    dp_match = re.search(r'(DP\d+)', folder_name)
+    design_point = dp_match.group(1) if dp_match else 'DP_Unknown'
+    velocity = velocity_lookup.get(design_point, 0.0)
+    if velocity == 0.0 and design_point != 'DP_Unknown':
+        print(f"Warning: No velocity found for {design_point} in lookup table.")
+    return design_point, velocity
+
+
+# <<< FIXED: Rewritten to parse the new CSV format >>>
+def read_velocity_csv_file(csv_file_path: str) -> Tuple[List[int], List[List[float]], List[List[float]]]:
+    """
+    Reads a time-stepped velocity CSV file with the new column format.
+    """
+    print(f"  Reading file: {os.path.basename(csv_file_path)}")
+    try:
+        # The data starts after the '[Data]' line, so we skip until we find it.
+        with open(csv_file_path, 'r') as f:
+            lines = f.readlines()
+        
+        data_start_index = -1
+        for i, line in enumerate(lines):
+            if '[Data]' in line:
+                data_start_index = i + 1
+                break
+
+        if data_start_index == -1:
+            print("    Warning: '[Data]' section not found. Cannot read file.")
+            return [], [], []
+
+        # The line after '[Data]' is the header.
+        header_line = lines[data_start_index]
+        data_lines = lines[data_start_index + 1:]
+        
+        # Use pandas to parse the data from the identified lines
+        df = pd.read_csv(StringIO("".join(data_lines)), header=None, sep=',\s*', engine='python')
+        
+        # Assign column names based on the header line for clarity (optional but good practice)
+        df.columns = [h.strip() for h in header_line.split(',')]
+
+        # Select columns based on their position (0-indexed) as per your requirement
+        # X, Y, Z are columns 0, 1, 2
+        # Domain Node Number is column 3
+        # Velocity u, v, w are columns 5, 6, 7
+        coordinates = df.iloc[:, [0, 1, 2]].values.tolist()
+        nodes = df.iloc[:, 3].astype(int).tolist()
+        velocities = df.iloc[:, [5, 6, 7]].values.tolist()
+
+    except Exception as e:
+        print(f"  An error occurred while reading {csv_file_path}: {e}")
+        return [], [], []
+        
+    print(f"  Successfully read {len(nodes)} nodes.")
+    return nodes, coordinates, velocities
+
+def create_node_features(
+    velocity_node_ids: List[int],
+    boundary_nodes_map: Dict[str, Set[int]]
+) -> Tuple[torch.Tensor, Dict[str, int], Dict[str, str]]:
+    # This function appears correct and has been kept as is.
+    # ... (function content is unchanged) ...
+    all_node_types = sorted(list(boundary_nodes_map.keys()))
+    class_to_index = {name: i for i, name in enumerate(all_node_types)}
+    num_classes = len(all_node_types)
+    feature_vectors = []
+    node_type_counts = defaultdict(int)
+
+    for node_id in velocity_node_ids:
+        vector = [0] * num_classes
+        assigned_type = "interior" # Default
+        for boundary_type_name in all_node_types:
+            if node_id in boundary_nodes_map.get(boundary_type_name, set()):
+                assigned_type = boundary_type_name
+                break
+        
+        index = class_to_index[assigned_type]
+        vector[index] = 1
+        node_type_counts[assigned_type] += 1
+        feature_vectors.append(vector)
+    
+    final_zone_map = {name: name for name in all_node_types}
+    return torch.tensor(feature_vectors, dtype=torch.float32), dict(node_type_counts), final_zone_map
+
+def create_time_stepped_velocity_pairs(folder_path: str, boundary_nodes_map: Dict[str, Set[int]]) -> List[Dict]:
+    # This function appears correct and has been kept as is.
+    # ... (function content is unchanged) ...
+    print(f"  Creating time-stepped velocity pairs from: {folder_path}")
+    csv_files = sorted([f for f in os.listdir(folder_path) if f.endswith('.csv')])
+    
+    if len(csv_files) < 2:
+        print(f"    Need at least 2 time steps, found {len(csv_files)}")
+        return []
+    
+    trajectory_data = []
+    
+    for i in range(len(csv_files) - 1):
+        current_file = csv_files[i]
+        target_file = csv_files[i + 1]
+        
+        current_path = os.path.join(folder_path, current_file)
+        target_path = os.path.join(folder_path, target_file)
+        
+        print(f"    Processing time step {i+1}: {os.path.basename(current_file)} -> {os.path.basename(target_file)}")
+        
+        current_nodes, current_coordinates, current_velocities = read_velocity_csv_file(current_path)
+        target_nodes, _, target_velocities = read_velocity_csv_file(target_path)
+        
+        if not current_nodes or not target_nodes or current_nodes != target_nodes:
+            print(f"    Warning: Node mismatch or empty data. Skipping step.")
+            continue
+            
+        node_type_tensor, node_type_counts, zone_map = create_node_features(current_nodes, boundary_nodes_map)
+        
+        step_data = {
+            'step_number': i + 1, 'current_file': current_file, 'target_file': target_file,
+            'node_ids': current_nodes, 'coordinates': current_coordinates,
+            'current_velocities': current_velocities, 'target_velocities': target_velocities,
+            'node_type_tensor': node_type_tensor, 'node_type_counts': node_type_counts,
+            'num_nodes': len(current_nodes), 'zone_map': zone_map
+        }
+        trajectory_data.append(step_data)
+    
+    print(f"    Created {len(trajectory_data)} time-stepped velocity pairs")
+    return trajectory_data
+
+def save_data_to_h5(
+    base_folder_path: str,
+    design_point_range: range,
+    boundary_csv_folder_path: str,
+    output_folder_path: str,
+    velocity_lookup_csv_path: str
+):
+    device = torch.device('cpu')
+    os.makedirs(output_folder_path, exist_ok=True)
+
+    print("="*60 + "\nSTEP 1: LOADING VELOCITY LOOKUP TABLE\n" + "="*60)
+    velocity_lookup = load_velocity_lookup(velocity_lookup_csv_path)
+    
+    print("\n" + "="*60 + "\nSTEP 2: READING MESH BOUNDARY INFORMATION\n" + "="*60)
+    target_column_name = ' Domain Node Number [  ]' 
+    boundary_nodes_map = load_boundary_nodes_from_csvs(boundary_csv_folder_path, target_column_name)
+    if not boundary_nodes_map:
+        print("CRITICAL ERROR: Failed to read boundary nodes. Cannot proceed.")
+        return
+
+    basename = "final_data_timestep"
+    h5_path = os.path.join(output_folder_path, f"{basename}_data.h5")
+    meta_json_path = os.path.join(output_folder_path, f"{basename}_data.json")
+
+    print("\n" + "="*60 + "\nSTEP 3: PROCESSING TIME-STEPPED DATA\n" + "="*60)
+
+    group_to_folder_map = {}
+    final_class_map = {}
+    group_counter = 0
+    
+    with h5py.File(h5_path, 'w') as f:
+        for dp_num in design_point_range:
+            dp_folder = f"dataset_DP{dp_num}"
+            folder_path = os.path.join(base_folder_path, dp_folder)
+            
+            design_point, velocity = get_design_point_info(dp_folder, velocity_lookup)
+            
+            print(f"\n{'='*40}\nProcessing Design Point: {design_point} (Inlet Vel: {velocity} m/s)\n{'='*40}")
+            
+            if not os.path.exists(folder_path):
+                print(f"WARNING: Folder {folder_path} does not exist. Skipping...")
+                continue
+            
+            trajectory_data = create_time_stepped_velocity_pairs(folder_path, boundary_nodes_map)
+            
+            if not trajectory_data:
+                print(f"No valid trajectory data found in {dp_folder}")
+                continue
+            
+            if not final_class_map:
+                zone_map = trajectory_data[0].get('zone_map', {})
+                final_class_map = {name: i for i, name in enumerate(sorted(list(set(zone_map.values()))))}
+            
+            # Each DP folder's trajectory becomes one group in the H5 file
+            group_name = f"group_{group_counter}"
+            dataset_group = f.create_group(group_name)
+            
+            num_steps = len(trajectory_data)
+            num_nodes = trajectory_data[0]['num_nodes']
+            
+            # Stack all time steps into single arrays for this group
+            all_current_vel = torch.tensor([s['current_velocities'] for s in trajectory_data], dtype=torch.float32)
+            all_target_vel = torch.tensor([s['target_velocities'] for s in trajectory_data], dtype=torch.float32)
+            
+            # Static data (same for all steps in a trajectory)
+            coordinates = torch.tensor(trajectory_data[0]['coordinates'], dtype=torch.float32)
+            node_type = trajectory_data[0]['node_type_tensor']
+            
+            dataset_group.create_dataset("current_velocities", data=all_current_vel.cpu().numpy())
+            dataset_group.create_dataset("target_velocities", data=all_target_vel.cpu().numpy())
+            dataset_group.create_dataset("coordinates", data=coordinates.cpu().numpy())
+            dataset_group.create_dataset("node_type", data=node_type.cpu().numpy())
+            
+            group_to_folder_map[group_name] = {
+                'design_point': design_point, 'velocity': velocity,
+                'source_folder': dp_folder, 'num_steps': num_steps, 'num_nodes': num_nodes,
+                'node_type_counts': trajectory_data[0]['node_type_counts']
+            }
+            
+            print(f"  Saved {num_steps} time steps to H5 group: {group_name}")
+            group_counter += 1
+
+    metadata = {
+        "file_info": group_to_folder_map,
+        "class_map": final_class_map, # <<< FIXED: Use the populated class map
+        "total_groups": group_counter
+    }
+    
+    with open(meta_json_path, 'w') as json_file:
+        json.dump(metadata, json_file, indent=4)
+    
+    print("\n" + "="*60 + "\nPROCESSING COMPLETE\n" + "="*60)
+    print(f"Total groups created: {group_counter}")
+    print(f"H5 file saved to: {h5_path}")
+    print(f"Metadata saved to: {meta_json_path}")
+
+# --- Main Execution Block ---
+def main():
+    # --- CONFIGURATION ---
+    # Base folder path where all 'dataset_DPx' folders are located
+    BASE_FOLDER_PATH = r"C:\Users\accel\OneDrive\Desktop"
+    # Range of design points to process (e.g., range(1, 10) for DP1 to DP9)
+    DESIGN_POINT_RANGE = range(1, 10)
+    # Path to the DesignPoints.csv file for velocity lookup
+    VELOCITY_LOOKUP_PATH = r"d:\Python_and_machine_learning_tutorials\DesignPoints.csv"
+    # Path to the folder containing boundary definition CSVs
+    BOUNDARY_CSV_PATH = r"D:\data_validation\node_type_timestepped"
+    # Path where the final H5 and JSON files will be saved
+    OUTPUT_FOLDER_PATH = r"D:\ANK_official\a_Dataset_generated\preprocessed_for_model\timestep_data"
+    
+    save_data_to_h5(
+        BASE_FOLDER_PATH,
+        DESIGN_POINT_RANGE,
+        BOUNDARY_CSV_PATH,
+        OUTPUT_FOLDER_PATH,
+        VELOCITY_LOOKUP_PATH
+    )
+
+if __name__ == '__main__':
+    main()

cleanroom/datasets/time_pt.py

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+import os
+import h5py
+import torch
+import json
+import numpy as np
+import random
+from typing import List, Dict, Any
+
+def create_pt_files_from_h5(h5_path: str, output_dir: str):
+    """
+    Reads the consolidated time-stepped H5 file.
+    - Flattens the training data into individual time-step samples.
+    - Groups the testing data by full trajectory.
+    """
+    print("="*60)
+    print("STARTING H5 TO .PT CONVERSION FOR TIME-STEPPED DATA")
+    print("="*60)
+
+    # --- 1. SETUP PATHS AND LOAD METADATA ---
+    os.makedirs(output_dir, exist_ok=True)
+    meta_json_path = h5_path.replace('.h5', '.json')
+
+    if not os.path.exists(h5_path):
+        raise FileNotFoundError(f"H5 file not found at: {h5_path}")
+    if not os.path.exists(meta_json_path):
+        raise FileNotFoundError(f"Metadata JSON file not found at: {meta_json_path}")
+
+    with open(meta_json_path, 'r') as f:
+        metadata = json.load(f)
+    
+    file_info = metadata.get("file_info", {})
+    group_names = sorted(list(file_info.keys()))
+    
+    if not group_names:
+        print("No groups found in the H5 file. Exiting.")
+        return
+        
+    print(f"Found {len(group_names)} total simulation trajectories in the metadata.")
+
+    # --- 2. SPLIT DATASET ---
+    random.seed(42) # Use a fixed seed for reproducibility
+    random.shuffle(group_names)
+
+    train_end_idx = round(len(group_names) * 0.8)
+    splits = {
+        'train': group_names[:train_end_idx],
+        'test': group_names[train_end_idx:]
+    }
+
+    print("\nSplitting data into sets based on trajectories:")
+    for split_name, groups in splits.items():
+        print(f"  - {split_name.upper()} set: {len(groups)} trajectories")
+
+    # --- 3. PROCESS EACH SPLIT AND SAVE TO A .PT FILE ---
+    with h5py.File(h5_path, 'r') as h5_file:
+        for split_name, groups_in_split in splits.items():
+            if not groups_in_split:
+                print(f"\n--- No trajectories for '{split_name}' split. Skipping. ---")
+                continue
+                
+            print(f"\n--- Processing '{split_name}' split ---")
+            
+            final_data_list: List[Dict[str, Any]] = []
+
+            # <<< MODIFIED: Conditional logic based on the split type >>>
+            if split_name == 'train':
+                # --- FLATTENING LOGIC FOR THE TRAINING SET ---
+                print("  -> Applying FLATTENING for the training set.")
+                for group_name in groups_in_split:
+                    if group_name not in h5_file: continue
+                    group = h5_file[group_name]
+                    meta_info = file_info.get(group_name, {})
+                    num_steps = meta_info.get('num_steps', 0)
+
+                    # Load static data once per trajectory
+                    coordinates = torch.tensor(group['coordinates'][:], dtype=torch.float32)
+                    node_type = torch.tensor(group['node_type'][:], dtype=torch.float32)
+                    
+                    # Load all time-varying data for the trajectory
+                    current_velocities_all_steps = torch.tensor(group['current_velocities'][:], dtype=torch.float32)
+                    target_velocities_all_steps = torch.tensor(group['target_velocities'][:], dtype=torch.float32)
+
+                    # Loop through each time-step and append it as an individual sample
+                    for step_idx in range(num_steps):
+                        sample_data = {
+                            'coordinates': coordinates, 'node_type': node_type,
+                            'current_velocities': current_velocities_all_steps[step_idx],
+                            'target_velocities': target_velocities_all_steps[step_idx],
+                            'meta_info': meta_info
+                        }
+                        final_data_list.append(sample_data)
+            
+            else: # For 'test' split
+                # --- GROUPING LOGIC FOR THE TESTING SET ---
+                print("  -> Applying GROUPING for the testing set.")
+                for group_name in groups_in_split:
+                    if group_name not in h5_file: continue
+                    group = h5_file[group_name]
+                    meta_info = file_info.get(group_name, {})
+
+                    # Load all data for the entire trajectory at once
+                    trajectory_sample = {
+                        'coordinates': torch.tensor(group['coordinates'][:], dtype=torch.float32),
+                        'node_type': torch.tensor(group['node_type'][:], dtype=torch.float32),
+                        'current_velocities': torch.tensor(group['current_velocities'][:], dtype=torch.float32),
+                        'target_velocities': torch.tensor(group['target_velocities'][:], dtype=torch.float32),
+                        'meta_info': meta_info
+                    }
+                    final_data_list.append(trajectory_sample)
+
+            # --- Saving Logic (same for both splits) ---
+            base_filename = os.path.splitext(os.path.basename(h5_path))[0]
+            output_pt_path = os.path.join(output_dir, f"{base_filename}_{split_name}.pt")
+            
+            torch.save(final_data_list, output_pt_path)
+
+            if split_name == 'train':
+                print(f"  -> Successfully saved {len(final_data_list)} FLATTENED time-step samples to: {output_pt_path}")
+            else:
+                print(f"  -> Successfully saved {len(final_data_list)} GROUPED trajectories to: {output_pt_path}")
+
+    print("\n" + "="*60)
+    print("CONVERSION COMPLETE")
+    print(f"Output .pt files are located in: {output_dir}")
+    print("="*60)
+
+def main():
+    H5_FILE_PATH = r"D:\ANK_official\a_Dataset_generated\preprocessed_for_model\timestep_data\final_data_timestep_data.h5"
+    PT_OUTPUT_DIR = r"D:\ANK_official\a_Dataset_generated\preprocessed_for_model\timestep_data\pt_files"
+    create_pt_files_from_h5(H5_FILE_PATH, PT_OUTPUT_DIR)
+
+if __name__ == '__main__':
+    main()

cleanroom/output/best_model.pth

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cleanroom/output/checkpoint_epoch_40.pth

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cleanroom/output/checkpoint_epoch_400.pth

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cleanroom/output/checkpoint_epoch_50.pth

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cleanroom/output/checkpoint_epoch_60.pth

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cleanroom/output/checkpoint_epoch_70.pth

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