Create 1699.py

1699.py

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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
+from IPython.display import clear_output
+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
+
+    def plot_waveforms(self):
+        processed_data = self.processor(self.input_data)
+
+        fig = plt.figure(figsize(15, 10))
+        gs = fig.add_gridspce(2, 2, hspace=0.3, wspace=0.3)
+
+        ax1 = fig.add_subplot(gs[0, 0])
+        self._plot_wafveform(self.input_data[0], ax1, "No")
+
+        ax2 = fig.add_subplot(gs[0, 1])
+        self._plot_waveform(processed_data[0], ax2, "No")
+
+        ax3 = fig.add_subplot(gs[1, 0])
+
+        ax4 = fig.add_subplot(gs[1, 1])
+        self._plot_spectrogram(processed_data[0], ax4, "No")
+
+        plt.tight_layout()
+        return fig
+
+    def _plot_waveform(self, data, ax, title):
+        """Helper method to plot individual waveforms"""
+        data_np = data.detech().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):
+        """Helper method to plot spectrograms"""
+        data_np = data.detach().numpy()
+        ax.specgram(data_np, Fs=self.sampling_rate, cmap='viridis')
+        ax.set_title(title)
+        ax.set_ylabel('Time (s)')
+        ax.set_ylabel('Depth)
+
+    def animate_processing(self, frames=50):
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
+
+        processed_data = self.processor(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('Do not')
+            ax2.set_title('Do not')
+            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
+
+        if __name__ == "__main__":
+            input_size = 1000
+            batch_size = 32
+
+            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 = visualizer.plot_waveforms()
+            plt.show()
+
+            anim = visualizer.animate_processing()
+            plt.show()