Datasets:

SachinSaud
/

Project_Fatigue

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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}")

	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}")