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def animation_test():

import matplotlib.pyplot as plt

import matplotlib.animation as animation

import numpy as np

file = r'example.dat'

num_frames = 72

nx = 8

ny = 9

data = np.fromfile(file, np.float32).reshape(num_frames, ny, nx)

# Print data statistics to understand value range

print(f"Data min: {data.min()}, max: {data.max()}, mean: {data.mean()}")

print(f"First frame min: {data[0].min()}, max: {data[0].max()}, mean: {data[0].mean()}")

fig, ax = plt.subplots()

img = data[0,]

# Use data range for proper color scaling

vmax = np.percentile(data, 95) # Use 95th percentile to avoid outliers

vmin = np.percentile(data, 5) # Use 5th percentile to avoid outliers

h = ax.imshow(img, cmap=plt.get_cmap('CMRmap_r'), origin='lower',

interpolation='none', vmin=vmin, vmax=vmax, animated=True)

ax.set_xticks(range(nx))

ax.set_xticklabels(range(1, nx + 1))

ax.set_yticks(range(ny))

ax.set_yticklabels(range(1, ny + 1))

plt.colorbar(h)

fig.tight_layout()

def update(frame):

img = data[frame, ]

h.set_array(img)

return h,

# create animation

interval = 100

ani = animation.FuncAnimation(fig, update, frames=range(num_frames), interval=interval, blit=True)

# Save first to ensure consistent state

ani.save('example.gif', writer='pillow', fps=2, dpi=300)

plt.show()

if __name__ == '__main__':

animation_test()

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