52 lines
2.1 KiB
Python
52 lines
2.1 KiB
Python
# move into dir single-core-regen before running
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from util.dataset import SlicedDataset
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from torch.utils.data import DataLoader
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from matplotlib import pyplot as plt
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import numpy as np
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def eye_dataset(dataset, no_symbols=None, offset=False, show=True):
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if no_symbols is None:
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no_symbols = len(dataset)
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_, axs = plt.subplots(2,2, sharex=True, sharey=True)
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xaxis = np.linspace(0,dataset.symbols_per_slice,dataset.samples_per_slice)
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roll = dataset.samples_per_symbol//2 if offset else 0
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for E_out, E_in in dataset[roll:dataset.samples_per_symbol*no_symbols+roll:dataset.samples_per_symbol]:
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E_in_x, E_in_y, E_out_x, E_out_y = E_in[0], E_in[1], E_out[0], E_out[1]
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axs[0,0].plot(xaxis, np.abs( E_in_x.numpy())**2, alpha=0.05, color='C0')
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axs[1,0].plot(xaxis, np.abs( E_in_y.numpy())**2, alpha=0.05, color='C0')
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axs[0,1].plot(xaxis, np.abs(E_out_x.numpy())**2, alpha=0.05, color='C0')
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axs[1,1].plot(xaxis, np.abs(E_out_y.numpy())**2, alpha=0.05, color='C0')
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if show:
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plt.show()
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# def plt_dataloader(dataloader, show=True):
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# _, axs = plt.subplots(2,2, sharex=True, sharey=True)
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# E_outs, E_ins = next(iter(dataloader))
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# for i, (E_out, E_in) in enumerate(zip(E_outs, E_ins)):
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# xaxis = np.linspace(dataset.symbols_per_slice*i,dataset.symbols_per_slice+dataset.symbols_per_slice*i,dataset.samples_per_slice)
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# E_in_x, E_in_y, E_out_x, E_out_y = E_in[0], E_in[1], E_out[0], E_out[1]
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# axs[0,0].plot(xaxis, np.abs(E_in_x.numpy())**2)
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# axs[1,0].plot(xaxis, np.abs(E_in_y.numpy())**2)
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# axs[0,1].plot(xaxis, np.abs(E_out_x.numpy())**2)
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# axs[1,1].plot(xaxis, np.abs(E_out_y.numpy())**2)
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# if show:
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# plt.show()
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if __name__ == "__main__":
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dataset = SlicedDataset("data/20241115-175517-128-16384-10000-0-0-17-0-PAM4-0.ini", symbols=1, drop_first=100)
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print(dataset[0][0].shape)
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eye_dataset(dataset, 1000, offset=True, show=False)
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train_loader = DataLoader(dataset, batch_size=10, shuffle=False)
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# plt_dataloader(train_loader, show=False)
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plt.show()
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