Spaces:
Sleeping
Sleeping
File size: 6,211 Bytes
8e5ba9e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | """Matplotlib visualization functions for the Gradio demo.
Four figure types:
1. Prediction comparison bar chart (neural vs analytical)
2. Deformed shape diagram with exaggerated deflection
3. Safety factor gauge
4. Error bar plot with uncertainty bands
"""
import io
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
def create_comparison_chart(
neural_stress: float,
analytical_stress: float,
neural_deflection: float,
analytical_deflection: float,
stress_ci: tuple[float, float] | None = None,
deflection_ci: tuple[float, float] | None = None,
) -> plt.Figure:
"""Bar chart comparing neural prediction vs analytical solution."""
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
# Stress comparison
bars1 = ax1.bar(
["Neural", "Analytical"],
[neural_stress / 1e6, analytical_stress / 1e6],
color=["#4A90D9", "#7CB342"],
width=0.5,
)
if stress_ci:
ax1.errorbar(
0, neural_stress / 1e6,
yerr=[[neural_stress / 1e6 - stress_ci[0] / 1e6],
[stress_ci[1] / 1e6 - neural_stress / 1e6]],
fmt="none", color="black", capsize=5,
)
error_pct = abs(neural_stress - analytical_stress) / analytical_stress * 100
ax1.set_title(f"Max Stress (Error: {error_pct:.2f}%)")
ax1.set_ylabel("Stress [MPa]")
ax1.bar_label(bars1, fmt="%.1f")
# Deflection comparison
bars2 = ax2.bar(
["Neural", "Analytical"],
[neural_deflection * 1e3, analytical_deflection * 1e3],
color=["#4A90D9", "#7CB342"],
width=0.5,
)
if deflection_ci:
ax2.errorbar(
0, neural_deflection * 1e3,
yerr=[[neural_deflection * 1e3 - deflection_ci[0] * 1e3],
[deflection_ci[1] * 1e3 - neural_deflection * 1e3]],
fmt="none", color="black", capsize=5,
)
error_pct_d = abs(neural_deflection - analytical_deflection) / analytical_deflection * 100
ax2.set_title(f"Max Deflection (Error: {error_pct_d:.2f}%)")
ax2.set_ylabel("Deflection [mm]")
ax2.bar_label(bars2, fmt="%.3f")
fig.tight_layout()
return fig
def create_beam_deformation(
length: float,
height: float,
max_deflection: float,
config_id: str,
n_points: int = 100,
) -> plt.Figure:
"""Beam deformation diagram with exaggerated deflection."""
fig, ax = plt.subplots(figsize=(10, 4))
x = np.linspace(0, length, n_points)
# Deflection shape depends on config
if "ss" in config_id and "point" in config_id:
# Simply supported, central point: cubic segments
mid = length / 2
y = np.where(
x <= mid,
max_deflection * (3 * length * x**2 - 4 * x**3) / length**3,
max_deflection * (3 * length * (length - x)**2 - 4 * (length - x)**3) / length**3,
)
elif "ss" in config_id and "udl" in config_id:
# Simply supported, UDL: quartic
y = max_deflection * 16 * x * (length - x) * (length**2 + x * (length - x) - x * (length - x)) / (5 * length**4)
# Simplified shape
y = max_deflection * np.sin(np.pi * x / length)
elif "cantilever" in config_id and "point" in config_id:
# Cantilever, tip load: cubic
y = max_deflection * (3 * length * x**2 - x**3) / (2 * length**3)
elif "cantilever" in config_id and "udl" in config_id:
# Cantilever, UDL: quartic
y = max_deflection * (6 * length**2 * x**2 - 4 * length * x**3 + x**4) / (3 * length**4)
elif "fixed" in config_id and "point" in config_id:
# Fixed-fixed, central point
mid = length / 2
y_half = max_deflection * 16 * (x[:n_points//2])**2 * (3 * mid - 2 * x[:n_points//2]) / (3 * length**3) * (3 * length)
y = np.concatenate([y_half, y_half[::-1]])
y = y[:n_points]
else:
# Default: sinusoidal
y = max_deflection * np.sin(np.pi * x / length)
# Scale factor for visibility
scale = max(length / (20 * max_deflection), 1.0) if max_deflection > 0 else 1.0
scale = min(scale, 100.0)
# Undeformed beam
ax.fill_between(x, -height/2, height/2, alpha=0.15, color="gray", label="Undeformed")
ax.plot(x, np.full_like(x, height/2), "k--", linewidth=0.5, alpha=0.3)
ax.plot(x, np.full_like(x, -height/2), "k--", linewidth=0.5, alpha=0.3)
# Deformed beam (exaggerated)
y_scaled = -y * scale
ax.fill_between(x, y_scaled - height/2, y_scaled + height/2, alpha=0.6, color="#4A90D9", label="Deformed")
ax.plot(x, y_scaled + height/2, "b-", linewidth=1.5)
ax.plot(x, y_scaled - height/2, "b-", linewidth=1.5)
ax.set_xlabel("Position along beam [m]")
ax.set_ylabel("Displacement [m]")
ax.set_title(f"Deformed Shape (scale: {scale:.0f}x)")
ax.legend(loc="upper right")
ax.set_aspect("auto")
ax.grid(True, alpha=0.3)
fig.tight_layout()
return fig
def create_safety_gauge(safety_factor: float) -> plt.Figure:
"""Semicircular gauge showing safety factor value."""
fig, ax = plt.subplots(figsize=(5, 3), subplot_kw={"projection": "polar"})
# Gauge range: 0 to 4 (mapped to 0 to pi)
max_sf = 4.0
sf_clamped = min(safety_factor, max_sf)
angle = np.pi * (1 - sf_clamped / max_sf)
# Color zones
theta_fail = np.linspace(np.pi, np.pi * (1 - 1.0/max_sf), 50)
theta_marg = np.linspace(np.pi * (1 - 1.0/max_sf), np.pi * (1 - 2.0/max_sf), 50)
theta_safe = np.linspace(np.pi * (1 - 2.0/max_sf), 0, 50)
for theta, color in [(theta_fail, "#EF5350"), (theta_marg, "#FFA726"), (theta_safe, "#66BB6A")]:
ax.barh(1, np.diff(theta).mean(), left=theta[:-1], height=0.3, color=color, alpha=0.5)
# Needle
ax.plot([angle, angle], [0, 0.9], color="black", linewidth=2)
ax.plot(angle, 0.9, "ko", markersize=8)
ax.set_ylim(0, 1.3)
ax.set_thetamin(0)
ax.set_thetamax(180)
ax.set_rticks([])
ax.set_thetagrids([0, 45, 90, 135, 180], ["4.0", "3.0", "2.0", "1.0", "0"])
ax.set_title(f"Safety Factor: {safety_factor:.2f}", pad=20, fontsize=14, fontweight="bold")
fig.tight_layout()
return fig
|