58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
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import torch
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import numpy as np
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from svd import calculate_svd, to_remove, temporal_filter, svd_denoise
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if __name__ == "__main__":
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filename: str = "example_data_crop"
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window_size: int = 2
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kernel_size_pooling: int = 2
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orig_freq: int = 30
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new_freq: int = 3
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filtfilt_chuck_size: int = 10
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bp_low_frequency: float = 0.1
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bp_high_frequency: float = 1.0
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torch_device: torch.device = torch.device(
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"cuda:0" if torch.cuda.is_available() else "cpu"
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)
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print("Load data")
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input = np.load(filename + str(".npy"))
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data = torch.tensor(input, device=torch_device)
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print("Movement compensation [MISSING!!!!]")
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print("(include ImageAlignment.py into processing chain)")
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print("SVD")
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whiten_mean, whiten_k, eigenvalues = calculate_svd(data)
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print("Calculate to_remove")
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data = torch.tensor(input, device=torch_device)
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to_remove_data = to_remove(data, whiten_k, whiten_mean)
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data -= to_remove_data
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del to_remove_data
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print("apply temporal filter")
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data = temporal_filter(
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data,
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device=torch_device,
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orig_freq=orig_freq,
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new_freq=new_freq,
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filtfilt_chuck_size=filtfilt_chuck_size,
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bp_low_frequency=bp_low_frequency,
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bp_high_frequency=bp_high_frequency,
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)
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print("SVD Denosing")
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data_out = svd_denoise(data, window_size=window_size)
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print("Pooling")
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avage_pooling = torch.nn.AvgPool2d(
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kernel_size=(kernel_size_pooling, kernel_size_pooling),
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stride=(kernel_size_pooling, kernel_size_pooling),
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)
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data_out = avage_pooling(data_out)
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np.save(filename + str("_decorrelated.npy"), data_out.cpu())
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