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()
"""
#.........................................................................................................................................................................................
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"""
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()
"""
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"""
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()