from fsi_reader import FsiDataReader import matplotlib.pyplot as plt import numpy as np from matplotlib.tri import Triangulation from matplotlib.animation import FuncAnimation from scipy.interpolate import griddata def single_plot(data, mesh_points): data = np.squeeze(data) # Shape becomes (1317,) print(data.shape) print(mesh_points.shape) x, y = mesh_points[:, 0], mesh_points[:, 1] # Create figure with subplots fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6), gridspec_kw={'width_ratios': [1, 1.2]}) # Approach 1: Triangulation-based contour plot tri = Triangulation(x, y) contour = ax1.tricontourf(tri, data, levels=40, cmap='viridis') fig.colorbar(contour, ax=ax1, label='Value', shrink=0.3) ax1.set_title('Contour Plot of Field Data') ax1.set_aspect('equal') # Approach 2: Scatter plot with interpolated background grid_x, grid_y = np.mgrid[x.min():x.max():100j, y.min():y.max():100j] grid_z = griddata((x, y), data, (grid_x, grid_y), method='cubic') im = ax2.imshow(grid_z.T, origin='lower', extent=[x.min(), x.max(), y.min(), y.max()], cmap='plasma') ax2.scatter(x, y, c=data, edgecolor='k', lw=0.3, cmap='plasma', s=15) fig.colorbar(im, ax=ax2, label='Interpolated Value', shrink=0.3) ax2.set_title('Interpolated Surface with Sample Points') # Common formatting for ax in (ax1, ax2): ax.set_xlabel('X Coordinate') ax.set_ylabel('Y Coordinate') ax.grid(True, alpha=0.3) plt.tight_layout() plt.show() def create_field_animation(data_frames, mesh_frames, interval=100, save_path=None): """ Create an animation of time-varying 2D field data on a mesh. Parameters: ----------- data_frames : list of arrays List of data arrays for each time frame (each with shape [1, 1317, 1] or similar) mesh_frames : list of arrays or single array Either a list of mesh coordinates for each frame or a single fixed mesh interval : int Delay between animation frames in milliseconds save_path : str, optional Path to save the GIF animation """ plt.rcParams.update({ 'font.size': 20, # Base font size 'axes.titlesize': 20, # Title font size 'axes.labelsize': 20, # Axis label size 'xtick.labelsize': 20, # X-tick label size 'ytick.labelsize': 18, # Y-tick label size 'figure.titlesize': 22 # Super title size (if used) }) # Determine if mesh is fixed or time-varying mesh_varying = isinstance(mesh_frames, list) # Get initial mesh and data mesh_initial = mesh_frames[0] if mesh_varying else mesh_frames data_initial = np.squeeze(data_frames[0]) # Extract coordinates x, y = mesh_initial[:, 0], mesh_initial[:, 1] # Create figure fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(50, 10), gridspec_kw={'width_ratios': [1.5, 1.5]}) # Calculate global min/max for consistent colorbars all_data = np.concatenate([np.squeeze(frame) for frame in data_frames]) vmin, vmax = all_data.min(), all_data.max() # Create initial triangulation tri_initial = Triangulation(x, y) # Set up first subplot - contour contour = ax1.tricontourf(tri_initial, data_initial, levels=40, cmap='viridis', vmin=vmin, vmax=vmax) # Add contour lines for better visibility contour_lines = ax1.tricontour(tri_initial, data_initial, levels=15, colors='black', linewidths=0.5, alpha=0.7) fig.colorbar(contour, ax=ax1, label='Value', shrink=0.3) ax1.set_title('Contour Plot of Field Data') ax1.set_aspect('equal') # Set up second subplot - interpolated surface with scatter points grid_x, grid_y = np.mgrid[x.min():x.max():100j, y.min():y.max():100j] grid_z = griddata((x, y), data_initial, (grid_x, grid_y), method='cubic') im = ax2.imshow(grid_z.T, origin='lower', extent=[x.min(), x.max(), y.min(), y.max()], cmap='plasma', vmin=vmin, vmax=vmax) scat = ax2.scatter(x, y, c=data_initial, edgecolor='k', lw=0.3, cmap='plasma', s=15, vmin=vmin, vmax=vmax) fig.colorbar(im, ax=ax2, label='Interpolated Value', shrink=0.3) ax2.set_title('Interpolated Surface with Sample Points') # Common formatting for ax in (ax1, ax2): ax.set_xlabel('X Coordinate') ax.set_ylabel('Y Coordinate') ax.grid(True, alpha=0.3) # Add frame counter time_text = ax1.text(0.02, 0.98, '', transform=ax1.transAxes, fontsize=10, va='top', ha='left') plt.tight_layout() # Update function for animation def update(frame): # Get current data data = np.squeeze(data_frames[frame]) # Get current mesh if varying if mesh_varying: mesh = mesh_frames[frame] x, y = mesh[:, 0], mesh[:, 1] tri = Triangulation(x, y) else: mesh = mesh_frames x, y = mesh[:, 0], mesh[:, 1] tri = tri_initial # Update contour plot for c in ax1.collections: c.remove() new_contour = ax1.tricontourf(tri, data, levels=40, cmap='viridis', vmin=vmin, vmax=vmax) new_lines = ax1.tricontour(tri, data, levels=15, colors='black', linewidths=0.5, alpha=0.7) # Update interpolated surface grid_z = griddata((x, y), data, (grid_x, grid_y), method='cubic') im.set_array(grid_z.T) # Update scatter points scat.set_offsets(mesh) scat.set_array(data) # Update frame counter time_text.set_text(f'Frame: {frame+1}/{len(data_frames)}') return [new_contour, new_lines, im, scat, time_text] # Create animation anim = FuncAnimation(fig, update, frames=len(data_frames), interval=interval, blit=False) # Save if path provided if save_path: print(f"Saving animation to {save_path}...") if save_path.endswith('.gif'): anim.save(save_path, writer='pillow', dpi=150) else: anim.save(save_path, writer='ffmpeg', dpi=150) return anim