Datasets:

ashiq24
/

FSI-pde-dataset

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

	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