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Fatigue_Life_Combined_master/Visuize/life_D15_d5_r2/life_D15_d5_r2.txt

@@ -0,0 +1,515 @@
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Fatigue_Life_Combined_master/Visuize/read_node.py

@@ -0,0 +1,204 @@
+import csv
+import os
+
+def read_dat_file(dat_file_path):
+    nodes = {}       # node_id -> (x,y,z)
+    elements = []    # list of node_id lists for each element
+
+    in_nodes = False
+    in_elements = False
+
+    with open(dat_file_path, "r") as file:
+        lines = file.readlines()
+
+    for line in lines:
+        line = line.strip()
+
+        # Detect node block
+        if line.lower().startswith("nblock"):
+            in_nodes = True
+            in_elements = False
+            continue
+        if line == "-1":
+            if in_nodes:
+                in_nodes = False
+            elif in_elements:
+                in_elements = False
+            continue
+
+        # Detect element block
+        if line.lower().startswith("eblock"):
+            in_elements = True
+            in_nodes = False
+            continue
+
+        # Parse nodes
+        if in_nodes:
+            parts = line.split()
+            if len(parts) >= 4:
+                try:
+                    node_id = int(parts[0])
+                    x = float(parts[1])
+                    y = float(parts[2])
+                    z = float(parts[3])
+                    nodes[node_id] = (x, y, z)
+                except Exception:
+                    pass  # ignore malformed lines
+
+        # Parse elements (assuming EBLOCK contains element lines)
+        if in_elements:
+            parts = line.split()
+            if len(parts) >= 15:
+                # Extract the 4 node IDs from the standard positions
+                # Usually nodes start around index 10 or 11 — adjust if needed
+                try:
+                    # For tetrahedral elements, pick 4 node IDs.
+                    # You need to confirm the exact positions for your .dat file.
+                    node_ids = [int(parts[11]), int(parts[12]), int(parts[13]), int(parts[15])]
+                    elements.append(node_ids)
+                except Exception:
+                    pass  # ignore malformed lines
+
+    # Sort node IDs and create a mapping from node_id to zero-based index
+    sorted_node_ids = sorted(nodes.keys())
+    id_to_index = {nid: i for i, nid in enumerate(sorted_node_ids)}
+
+    # Build positions array ordered by zero-based index
+    positions = [nodes[nid] for nid in sorted_node_ids]
+
+    # Convert element connectivity node IDs to zero-based indices
+    connectivity = []
+    for elem_node_ids in elements:
+        try:
+            zero_based = [id_to_index[nid] for nid in elem_node_ids]
+            connectivity.append(zero_based)
+        except KeyError:
+            # Skip elements with invalid node IDs
+            continue
+
+    return positions, connectivity
+
+"""
+def read_dat_file(dat_file_path):
+    in_nodes = False
+    in_elements = False
+    nodes = []
+    cells_detail = []
+
+    with open(dat_file_path, "r") as file:
+        lines = file.readlines()
+
+    lines_iter = iter(lines)
+
+    for line in lines_iter:
+
+        line = line.strip()
+
+        # Detect node block
+        if line.lower().startswith("nblock"):
+            in_nodes = True
+            continue
+
+        if in_nodes and line.strip() == "-1":
+            in_nodes = False
+            continue
+
+        # Detect element block (EBLOCK)
+        if line.lower().startswith("eblock"):
+            in_elements = True
+            continue
+
+        if in_elements and line.strip() == "-1":
+            in_elements = False
+            continue
+
+        # Parse nodes
+        if in_nodes:
+            parts = line.split()
+            if len(parts) == 4:
+                #node_id = int(parts[0])
+                x = float(parts[1])
+                y = float(parts[2])
+                z = float(parts[3])
+                nodes.append(( x, y, z))
+
+        # Parse elements (EBLOCK, 2-line format)
+        if in_elements:
+            parts = line.split()
+            num_nodes =8
+            if len(parts) > 14:
+                 # Ensure the line has 4 parts: ID, X, Y, Z
+                num_nodes = int(parts[8])
+                less = 0
+                if num_nodes < 8:
+                    less = 8-num_nodes
+                node_idsz = []
+                # print(parts)
+                element_id = [int(parts[10])]  # Element ID
+                body_id = [int(parts[0])]
+                for i in range(11, 19-less):
+                    # print(i)
+                    node_idsz.append(int(parts[i]))   # Node IDs
+                if num_nodes<=8:
+                    cells_detail.append(element_id+body_id+node_idsz)
+            if len(parts) == num_nodes-8 and num_nodes>8:  # Ensure the line has 4 parts: ID, X, Y, Z    
+                for i in range(0,num_nodes-8 ):
+                    node_idsz.append(int(parts[i])) 
+            #next_line = next(lines_iter).strip()   # this is only required if the next line contains more node ids in case of hexhedral elements
+            #node_ids += [int(x) for x in next_line.split()]
+            
+                # print(element_id, node_idsz)
+                cells_detail.append(element_id+body_id+node_idsz) 
+
+
+
+    # Sort nodes and cells
+    nodes.sort(key=lambda x: x[0])
+    cells_detail.sort(key=lambda x: x[0])
+
+    # Remove duplicates
+    cells_detail = [list(item) for item in dict.fromkeys(tuple(cell) for cell in cells_detail)]
+
+    # Extract 4-node face from each cell (can adjust as needed)
+    cells = [[cell[2], cell[3], cell[4], cell[6]] for cell in cells_detail]
+
+    return nodes, cells
+"""
+def read_result_file(result_file_path, encoding='utf-8'):
+    parameters = []
+    with open(result_file_path, mode='r', encoding=encoding) as file:
+        if result_file_path.endswith(".csv"):
+            reader = csv.reader(file)
+            next(reader)  # Skip header
+            for row in reader:
+                parameters.append(float(row[-1]))
+        elif result_file_path.endswith(".txt"):
+            for line in file:
+                if "Node Number" in line:
+                    continue
+                columns = line.split()
+                try:
+                    parameters.append(float(columns[-1]))
+                except ValueError:
+                    continue
+    return parameters
+
+def main(folder_path):
+    folder_base_name = os.path.basename(folder_path).replace("_data", "")
+    dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+    fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+    
+    dat_file_path = "path/to/your/dat_file.dat"
+    fatigue_life_path = "path/to/your/fatigue_life.txt"
+
+    nodes = read_dat_file(dat_file_path)
+    life = read_result_file(fatigue_life_path)
+
+    # Do something with the data
+    print("Nodes:")
+    for node in nodes:
+        print(node)
+
+    print("\nLife:")
+    for param in life:
+        print(param)

Fatigue_Life_Combined_master/Visuize/test.py

@@ -0,0 +1,215 @@
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from read_node import read_dat_file, read_result_file
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial import distance_matrix
+
+"""
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_high\life_D15_d5_r1"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+
+positions, connectivity = read_dat_file(dat_file_path)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+
+# Calculate appropriate sphere size based on point density
+# Find minimum distance between points to avoid overlap
+from scipy.spatial.distance import pdist
+distances = pdist(positions)
+min_distance = np.min(distances)
+max_distance = np.max(distances)
+print(f"Min distance between points: {min_distance:.6f}")
+print(f"Max distance between points: {max_distance:.6f}")
+
+# Matplotlib version (for comparison)
+x = positions[:, 0]
+y = positions[:, 1]
+z = positions[:, 2]
+
+fig = plt.figure(figsize=(10, 8))
+ax = fig.add_subplot(111, projection='3d')
+ax.scatter(x, y, z, c='red', s=50, alpha=0.8)
+ax.set_xlabel('X')
+ax.set_ylabel('Y')
+ax.set_zlabel('Z')
+ax.set_title('3D Points Visualization - Matplotlib')
+ax.grid(True)
+plt.show()
+"""
+#.........................................................................................................................................................................................
+#.........................................................................................................................................................................................
+
+"""
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial.distance import pdist
+from read_node import read_dat_file
+
+# === Load dataset ===
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_high\life_D15_d5_r1"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+
+# Read point positions
+positions, connectivity = read_dat_file(dat_file_path)
+#print(connectivity)
+#print(positions)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+
+# === Compute spacing metrics ===
+distances = pdist(positions)
+min_distance = np.min(distances)
+max_distance = np.max(distances)
+print(f"Min distance between points: {min_distance:.6f}")
+print(f"Max distance between points: {max_distance:.6f}")
+
+# === Create PyVista point cloud ===
+point_cloud = pv.PolyData(positions)
+
+# === Option A: Use glyphs (spheres) for geometric visualization ===
+# Choose adaptive sphere size
+sphere_radius = min_distance * 0.35  # 10% of min spacing
+sphere = pv.Sphere(radius=sphere_radius)
+
+# Attach dummy scalar field (optional, for glyph binding)
+point_cloud["id"] = np.arange(len(positions))
+
+# Generate glyphs
+glyphs = point_cloud.glyph(geom=sphere, scale=False)
+
+# === PyVista Plot ===
+plotter = pv.Plotter()
+plotter.add_mesh(glyphs, color="red", opacity=1.0, show_scalar_bar=False)
+
+# Add axes and grid
+plotter.add_axes()
+plotter.show_grid()
+plotter.set_background("white")
+plotter.add_title("3D Point Cloud - PyVista (Spheres)")
+
+# Display
+plotter.show()
+"""
+#.........................................................................................................................................................................................
+#.........................................................................................................................................................................................
+
+"""
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial.distance import pdist
+from read_node import read_dat_file, read_result_file  # Make sure these are available
+
+# === Load dataset ===
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_high\life_D15_d5_r1"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+
+positions, connectivity = read_dat_file(dat_file_path)
+#print(connectivity)
+#print(positions)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+fatigue_life = np.array(read_result_file(fatigue_life_path))
+
+assert positions.shape[0] == fatigue_life.shape[0], "Mismatch between points and fatigue data!"
+
+# === Compute spacing metrics ===
+distances = pdist(positions)
+min_distance = np.min(distances)
+max_distance = np.max(distances)
+print(f"Min distance between points: {min_distance:.6f}")
+print(f"Max distance between points: {max_distance:.6f}")
+
+# === Create point cloud and attach fatigue life ===
+point_cloud = pv.PolyData(positions)
+point_cloud["fatigue_life"] = fatigue_life  # <-- this is used for coloring
+point_cloud["fatigue_life"] = np.log10(fatigue_life + 1e-12) # comment this if you want the absolute mapping of the life on nodes
+
+
+# === Create glyphs (spheres) ===
+sphere_radius = min_distance * 0.35
+sphere = pv.Sphere(radius=sphere_radius)
+glyphs = point_cloud.glyph(geom=sphere, scale=False)
+
+# === PyVista plot ===
+plotter = pv.Plotter()
+plotter.add_mesh(glyphs,
+                 scalars="fatigue_life",
+                 cmap="viridis",  # or try "plasma", "coolwarm", "inferno"
+                 opacity=1.0,
+                 show_scalar_bar=True)
+
+plotter.add_axes()
+plotter.show_grid()
+plotter.set_background("white")
+plotter.add_title("3D Point Cloud Colored by Fatigue Life")
+plotter.show()
+"""
+
+
+#.........................................................................................................................................................................................
+# visualization with connectivity
+#.........................................................................................................................................................................................
+
+import os
+import numpy as np
+import pyvista as pv
+from scipy.spatial.distance import pdist
+from read_node import read_dat_file, read_result_file  # Make sure these are available
+
+# === Load dataset ===
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\Visuize\life_D15_d5_r2"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+
+
+positions, connectivity = read_dat_file(dat_file_path)
+#print(connectivity)
+#print(positions)
+
+positions = np.array(positions)
+connectivity = np.array(connectivity)
+
+fatigue_life = np.array(read_result_file(fatigue_life_path))
+
+# Convert connectivity to VTK format for unstructured grid
+n_elements = len(connectivity)
+cell_types = np.full(n_elements, pv.CellType.TETRA, dtype=np.uint8)
+
+if np.min(connectivity) > 0:
+    connectivity -= 1
+
+# Format: [n_points, pt1, pt2, pt3, pt4, ...]
+cells = []
+for conn in connectivity:
+    cells.append(4)
+    cells.extend(conn)
+cells = np.array(cells)
+
+# Build unstructured grid
+grid = pv.UnstructuredGrid(cells, cell_types, positions)
+
+# Attach fatigue life as point data
+grid["fatigue_life"] = fatigue_life
+
+# Plot
+plotter = pv.Plotter()
+plotter.add_mesh(grid, scalars="fatigue_life", cmap="viridis_r", show_edges=True)
+plotter.add_axes()
+plotter.show_grid()
+plotter.set_background("white")
+plotter.add_title("Fatigue Mesh Visualization (Connected Elements)")
+plotter.show()

Fatigue_Life_Combined_master/Visuize/visualize.py

@@ -0,0 +1,65 @@
+from read_node import read_dat_file, read_result_file
+import os
+
+import numpy as np
+import pyvista as pv
+folder_path = r"D:\AnK\FatigueNet\Fatigue_Life\datasets\Raw data\shaft_high\life_D15_d5_r1"
+folder_base_name = os.path.basename(folder_path).replace("_data", "")
+dat_file_path = os.path.join(folder_path, f"{folder_base_name}.dat")
+fatigue_life_path = os.path.join(folder_path, f"{folder_base_name}.txt")
+
+#dat_file_path = "path/to/your/dat_file.dat"
+#fatigue_life_path = "path/to/your/fatigue_life.txt"
+
+positions,cells = read_dat_file(dat_file_path)
+fatigue_life = read_result_file(fatigue_life_path)
+#print(positions)
+#print(fatigue_life)
+
+# Step 3: Log scale for better contrast
+log_fatigue_life = np.log10(fatigue_life)
+
+# Step 4: Create point cloud
+point_cloud = pv.PolyData(positions)
+point_cloud["Fatigue Life (log10)"] = log_fatigue_life
+
+# Step 5: Alternative approach - create glyphs from points
+# This is often more reliable for point-based visualizations
+sphere_source = pv.Sphere(radius=0.15)
+glyphs = point_cloud.glyph(geom=sphere_source, scale=False)
+
+# Step 6: Plotting
+plotter = pv.Plotter()
+
+# Add spheres with color mapping
+plotter.add_mesh(
+    glyphs,
+    scalars="Fatigue Life (log10)",
+    cmap="viridis",
+    opacity=1.0,
+    show_edges=False
+)
+
+# Add scalar bar with better formatting
+plotter.add_scalar_bar(
+    title="log₁₀(Fatigue Life)",
+    n_labels=6,
+    fmt="%.1f"
+)
+
+# Set background color for better contrast
+plotter.set_background('white')
+
+# Set camera position for better view
+plotter.camera_position = 'iso'
+
+# Add axes for reference
+plotter.add_axes()
+
+# Show the plot
+plotter.show()
+
+# Print some debug info
+print(f"Created {len(positions)} spheres")
+print(f"Fatigue life range: {fatigue_life.min():.0e} to {fatigue_life.max():.0e}")
+print(f"Log fatigue life range: {log_fatigue_life.min():.2f} to {log_fatigue_life.max():.2f}")

Fatigue_Life_Combined_master/Visuize/visualize_combined_result.py

@@ -0,0 +1,103 @@
+import pyvista as pv
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import sys
+
+# Add the parent directory to the Python path to find other modules
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+
+
+def visualize_sample(eval_dir, sample_idx):
+    """
+    Finds the geometry and prediction files for a specific sample,
+    and creates a 3D visualization comparing ground truth and prediction.
+
+    Args:
+        eval_dir (str): The path to the directory containing the evaluation output.
+        sample_idx (int): The index of the sample to visualize (e.g., 0, 1, 2...).
+    """
+    eval_path = Path(eval_dir)
+    
+    # --- 1. Find and Load the Correct Files ---
+    geometry_file = eval_path / f"geometry_sample_{sample_idx}.npz"
+    prediction_file = eval_path / f"prediction_sample_{sample_idx}.csv"
+
+    if not geometry_file.is_file():
+        raise FileNotFoundError(f"Geometry file not found: {geometry_file}")
+    if not prediction_file.is_file():
+        raise FileNotFoundError(f"Prediction CSV file not found: {prediction_file}")
+
+    print(f"Loading geometry from: {geometry_file}")
+    print(f"Loading predictions from: {prediction_file}")
+
+    geom_data = np.load(geometry_file)
+    df = pd.read_csv(prediction_file)
+
+    # --- 2. Extract and Prepare Data ---
+    positions = geom_data['mesh_pos']
+    connectivity = geom_data['cells']
+    ground_truth_life = df['ground_truth_life'].values
+    predicted_life = df['final_predicted_life'].values
+
+    # --- 3. Build the PyVista Unstructured Grid ---
+    num_cells = connectivity.shape[0]
+    padding = np.full((num_cells, 1), 4)
+    cells_for_pyvista = np.hstack((padding, connectivity)).flatten()
+
+    # --- THIS IS THE FIX ---
+    # Create an array of cell types with the same length as the number of cells.
+    # Each element specifies that the corresponding cell is a tetrahedron.
+    cell_types = np.full(num_cells, pv.CellType.TETRA, dtype=np.uint8)
+    
+    # Create the grid object using the new cell_types array
+    grid = pv.UnstructuredGrid(cells_for_pyvista, cell_types, positions)
+    # --- END OF FIX ---
+
+    grid["Ground Truth Life"] = ground_truth_life
+    grid["Predicted Life"] = predicted_life
+
+    # --- 4. Create and Configure the Plot ---
+    plotter = pv.Plotter(shape=(1, 2), window_size=[1600, 800])
+    
+    min_val = min(ground_truth_life.min(), predicted_life.min())
+    max_val = max(ground_truth_life.max(), predicted_life.max())
+    clim = [min_val, max_val]
+
+    # Subplot 1: Ground Truth
+    plotter.subplot(0, 0)
+    plotter.add_text("Ground Truth Life", font_size=15)
+    plotter.add_mesh(grid, scalars="Ground Truth Life", cmap="viridis", show_edges=False, clim=clim, log_scale=True)
+    plotter.add_axes()
+    plotter.set_background("white")
+
+    # Subplot 2: Prediction
+    plotter.subplot(0, 1)
+    plotter.add_text("Predicted Life (Combined Model)", font_size=15)
+    plotter.add_mesh(grid.copy(), scalars="Predicted Life", cmap="viridis", show_edges=False, clim=clim, log_scale=True)
+    plotter.add_axes()
+    plotter.set_background("white")
+
+    plotter.link_views()
+    plotter.camera_position = 'xy'
+
+    #output_filename = eval_path / f"visualization_sample_{sample_idx}.png"
+    #plotter.screenshot(output_filename)
+    #print(f"\nSaved visualization to {output_filename}")
+
+    print("Showing interactive plot. Close the window to exit.")
+    plotter.show()
+
+def main():
+    """
+    Main function to define the file paths and run the visualizer.
+    """
+    # --- USER ACTION REQUIRED ---
+    evaluation_directory = "/home/gd_user1/AnK/project_PINN/Project_Fatigue/Fatigue_Life_Combined/combined_test_results_pre_masking"
+    sample_index = 0
+    # --- END OF USER ACTION SECTION ---
+    
+    visualize_sample(evaluation_directory, sample_index)
+
+if __name__ == "__main__":
+    main()

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