52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
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import pdb, os, random, copy
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import pandas as pd
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import numpy as np
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import pickle as pkl
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import torch
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from torch.utils.data import DataLoader,Dataset
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def str2bool(v):
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if v.lower() in ('yes', 'true', 't', 'y', '1'):
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return True
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elif v.lower() in ('no', 'false', 'f', 'n', '0'):
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return False
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else:
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raise argparse.ArgumentTypeError('Unsupported value encountered.')
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def set_seed(seed):
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed) # Numpy module.
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random.seed(seed) # Python random module.
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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def DFT(data, clicks=None):
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for idx, channel in enumerate(range(data.shape[-1])):
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freq = np.fft.fft(data[:,:,channel])
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freq = freq[:, :data.shape[1]//2] # symmetric
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mag = np.abs(freq)
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phase = np.angle(freq)
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mag = (mag - np.min(mag)) / (np.max(mag) - np.min(mag))
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phase = (phase - np.min(phase)) / (np.max(phase) - np.min(phase))
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if idx==0:
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mags, phases = mag, phase
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else:
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mags, phases = np.stack([mags, mag], axis=-1), np.stack([phases, phase], axis=-1)
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data = np.concatenate([data, mags, phases], axis=1)
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return data, clicks
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def MakeDir(dirName):
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if not os.path.exists(dirName):
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os.makedirs(dirName)
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class simpleSet(Dataset):
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def __init__(self, features, labels):
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self.features = features
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labels = labels.astype(int)
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self.labels = labels - np.min(labels)
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def __getitem__(self, index):
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return self.features[index], self.labels[index]
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def __len__(self):
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return len(self.features)
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