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import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import seaborn as sns
class WaveformVisualizer:
def __init__(self, processor, input_data, sampling_rate=1000):
self.processor = processor
self.input_data = input_data
self.sampling_rate sampling_rate
self.time = np.arange(input_data.shape[1]) / sampling_rate
class SecureWaveformProcessor(nn.Module):
def __init__(self, input_size, hidden_size, sampling_rate=1000):
super(SecureWaveformProcessor, self).__init__()
self.layer1 = nn.Linear(input_size, hidden_size)
self.layer2 = nn.Linear(hidden_size, input_size)
self.sampling_rate = sampling_rate
def forward(self, x):
x = torch.relu(self.layer1(x))
x = self.layer2(x)
return x
def plot_waveforms(self):
processed_data = self.forward(input_data)
self.time = np.arange(input_data.shape[1]) / self.sampling_rate
def forward(self, x):
x = torch.relu(self.layer1(x))
x = self.layer2(x)
return x
def plot_waveforms(self):
processed_data = self.forward(input_data)
self.time = np.arange(input_data.shape[1]) / self.sampling_rate
self.input_data = input_data
fig = plt.figure(figsize=(15, 10))
gs = fig.add_gridspec(2, 2, hspace=0.3, wspace=0.3)
ax1 = fig.add_subplot(gs[0, 0])
self._plot_waveform(self.input_data[0], ax1, "Original Data")
ax2 = fig.add_subplot(gs[0, 1])
self.plot_waveform(processed_data[0], ax2, "Processed Data")
ax3 = fig.add_subplot(gs[1, 0])
self._plot_spectrogram(self.input_data[0], ax3, "Original Visual")
ax4 = fig.add_subplot(gs[1, 1])
self._plot_spectrogram(processed_data[0], ax4, "Processed Visual")
plt.tight_layout()
return fig
def _plot_waveform(self,data, ax, title):
data_np = data.detach().numpy()
ax.plot(self.time, data_np, 'b-', linewidth=1)
ax.set_title(title)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.grid(True)
def _plot_spectrogram(self, data, ax, title):
data_np = data.detach().numpy
ax.specgram(data,np, Fs=self.sampling_rate, cmap='viridis')
ax.set_title(title)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Frequency (Hz)')
def animate_processing(self, frame=50):
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
processed_data = self.forward(self.input_data)
data_original = self.input_data[0].detach().numpy()
data_processed = processed_data[0].detach().numpy()
line1, = ax1.plot([], [], 'b-', label='Original')
line2, = ax2.plot([], [], 'r-', label='Processed')
def init():
ax1.set_xlim(0, self.time[-1])
ax1.set_ylim(data_original.min()*1.2, data_original.max()*1.2)
ax2.set_xlim(0, self.time[-1])
ax2.set_ylim(data_processed.min()*1.2, data_processed.max()*1.2)
ax1.set_title('Original Data')
ax2.set_title('Processed Visual')
ax1.grid(True)
ax2.grid(True)
ax1.legend()
ax2.legend()
return line1, line2
def animate(frame):
idx = int((frame / frames) * len(self.time))
line1.set_data(self.time[:idx], data_original[:idx])
line2.set_data(self.time[:idx], data_processed[:idx])
return line1, line2
anim = FuncAnimation(fig, animate, frames=frames,
init_func=init, blit=True,
interval=50)
plt.tight_layout()
return anim
__name__== "__main__":
input_size = 1000
batch_size = 32
sampling_rate = 1000
processor = SecureWaveformProcessor(input_size=input_size, hidden_size=64, sampling_rate=sampling_rate)
t = np.linspace(0, 10, input_size)
base_signal = np.sin(2 * np.pi * 1 * t) + 0.5 * np.sin(2 * np.pi * 2 * t)
noise = np.random.normal(0, 0.1, input_size)
signal = base_signal + noise
input_data = torch.tensor(np.tile(signal, (batch_size, 1)), dtype=torch.float32)
processor = SecureWaveformProcessor(input_size=input_size, hidden_size=64)
visualizer = WaveformVisualizer(processor, input_data)
fig_static = processor.plot_waveforms()
plt.show()
anim = processor.animate_processing()
plt.show()
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