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def animation_test():

import matplotlib.pyplot as plt

import matplotlib.animation as animation

import numpy as np

from matplotlib.colors import Normalize # Add explicit import for Normalize

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()

# Calculate normalization limits once for all frames

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

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

# Create a single normalizer to be used consistently throughout the animation

norm = Normalize(vmin=vmin, vmax=vmax)

# Use the normalizer in imshow instead of vmin/vmax

h = ax.imshow(data[0], cmap=plt.get_cmap('CMRmap_r'), origin='lower',

interpolation='none', norm=norm, 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):

# Simply update the array data - normalization is handled by our Normalize object

h.set_array(data[frame])

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

# The Normalize object will ensure consistent color scaling in the saved file

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

plt.show()

if __name__ == '__main__':

animation_test()

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