File size: 3,701 Bytes
3452229 |
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 |
#!/usr/bin/env python3
"""
Plot a single sample from the shallow water dataset.
"""
import numpy as np
import matplotlib.pyplot as plt
from shallow_water_dataset import ShallowWaterDataset, build_s2_coord_vertices
def plot_shallow_water_sample(sample, save_path="sample_plot.png"):
"""Plot a 4-panel view of vorticity evolution"""
# Extract data from comprehensive dataset output
vorticity_traj = sample["vorticity_trajectory"]
alpha = sample["alpha"]
beta = sample["beta"]
time_traj = sample["time_coordinates"]
# Get shape directly from vorticity trajectory
vorticity_shape = vorticity_traj.shape[1:]
Nphi, Ntheta = vorticity_shape
# Use 1D phi/theta for plotting, as in plot_sphere.py
phi_1d = np.linspace(0, 2 * np.pi, Nphi, endpoint=False)
theta_1d = np.linspace(0, np.pi, Ntheta)
phi_vert, theta_vert = build_s2_coord_vertices(phi_1d, theta_1d)
x = np.sin(theta_vert) * np.cos(phi_vert)
y = np.sin(theta_vert) * np.sin(phi_vert)
z = -np.cos(theta_vert) # Flip poles
# Select 4 time points: initial, two middle, and final
n_times = len(time_traj)
time_indices = [0, n_times // 3, 2 * n_times // 3, n_times - 1]
time_labels = ["Initial", "Early", "Late", "Final"]
# Get global vorticity range for consistent color scaling
vmax_global = np.max(np.abs(vorticity_traj))
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(20, 5))
axes = [fig.add_subplot(1, 4, i + 1, projection="3d") for i in range(4)]
for i, (time_idx, label) in enumerate(zip(time_indices, time_labels)):
ax = axes[i]
vort_data = vorticity_traj[time_idx]
# Use normalization as in plot_sphere.py
import matplotlib
norm = matplotlib.colors.Normalize(-vmax_global, vmax_global)
fc = plt.cm.RdBu_r(norm(vort_data))
ax.plot_surface(
x,
y,
z,
facecolors=fc,
cstride=1,
rstride=1,
linewidth=0,
antialiased=False,
shade=False,
)
ax.set_title(f"{label} Vorticity (t={time_traj[time_idx]:.1f}h)", fontsize=12)
ax.set_axis_off()
ax.set_box_aspect([1, 1, 1])
mappable = plt.cm.ScalarMappable(cmap="RdBu_r", norm=norm)
mappable.set_array(vort_data)
# Position colorbar on the right side
plt.subplots_adjust(right=0.88)
cbar_ax = fig.add_axes([0.90, 0.15, 0.02, 0.7])
fig.colorbar(mappable, cax=cbar_ax, label="Vorticity (1/s)")
fig.suptitle(
f"Shallow Water Vorticity Evolution\\nα={alpha:.3f}, β={beta:.3f}",
fontsize=16,
fontweight="bold",
)
plt.tight_layout(rect=[0, 0, 0.88, 1])
plt.savefig(save_path, dpi=150, bbox_inches="tight")
plt.close()
print(f"Sample visualization saved to {save_path}")
print(f"Parameters: α={alpha:.3f}, β={beta:.3f}")
print(f"Time steps saved: {len(time_traj)}")
print(f"Vorticity grid size: {Nphi}×{Ntheta}")
if __name__ == "__main__":
# Set random seed for reproducibility
np.random.seed(1)
# Create dataset with default parameters
dataset = ShallowWaterDataset(
Nphi=128,
Ntheta=64,
stop_sim_time=600,
save_interval=1,
)
# Generate a single sample
print("Generating shallow water sample...")
sample = next(iter(dataset))
print("Sample keys:", list(sample.keys()))
for key, value in sample.items():
if isinstance(value, np.ndarray):
print(f"{key}: shape {value.shape}")
else:
print(f"{key}: {type(value)}")
# Create 4-panel plot
plot_shallow_water_sample(sample)
|