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import shutil
import numpy as np
import pandas as pd
from scipy.signal import iirnotch, lfilter
from data_utils import butter_bandpass_filter, correct_ecg_inversion
import matplotlib.pyplot as plt
from tqdm import tqdm
import shutil
import librosa
import pickle
import gc
BASE_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'datasets')
SAMPLING_RATE = 200
TARGET_RESAMPLE = 100
NOTCH_FREQ = 60
EEG_LOWCUT = 0.3
EEG_HIGHCUT = 40
EMG_LOWCUT = 10
EMG_HIGHCUT = 1000
ECG_LOWCUT = 0.3
ECG_HIGHCUT = 70
FLOW_LOWCUT = 0
FLOW_HIGHCUT = 15
do_resample = (SAMPLING_RATE != TARGET_RESAMPLE)
NUM_PROCESS = 4
import argparse
import multiprocessing
def process_ecg(args):
sig, chn_grp, chn, split = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
# data = data.reshape(-1, 1)
data = data.reshape(-1,)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, ECG_LOWCUT, ECG_HIGHCUT, SAMPLING_RATE)
b_notch, a_notch = iirnotch(NOTCH_FREQ, 30, SAMPLING_RATE)
filtered = lfilter(b_notch, a_notch, filtered)
filtered = filtered.reshape(-1, SAMPLING_RATE*30)
filtered_epochs = []
for epoch in filtered:
inv_epoch, _ = correct_ecg_inversion(epoch, SAMPLING_RATE)
filtered_epochs.append(inv_epoch)
filtered = np.stack(filtered_epochs, axis=0)
if do_resample:
filtered = np.reshape(filtered, (-1,1))
filtered = librosa.resample(filtered, orig_sr=SAMPLING_RATE, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def process_eeg(args):
sig, chn_grp, chn, split = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
# data = data.reshape(-1, 1)
data = data.reshape(-1,)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, EEG_LOWCUT, EEG_HIGHCUT, SAMPLING_RATE)
b_notch, a_notch = iirnotch(NOTCH_FREQ, 30, SAMPLING_RATE)
filtered = lfilter(b_notch, a_notch, filtered)
if do_resample:
filtered = librosa.resample(filtered, orig_sr=SAMPLING_RATE, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def process_emg(args):
sig, chn_grp, chn, split = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
# data = data.reshape(-1, 1)
data = data.reshape(-1,)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, EMG_LOWCUT, EMG_HIGHCUT, SAMPLING_RATE)
b_notch, a_notch = iirnotch(NOTCH_FREQ, 30, SAMPLING_RATE)
filtered = lfilter(b_notch, a_notch, filtered)
if do_resample:
filtered = librosa.resample(filtered, orig_sr=SAMPLING_RATE, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def process_flow(args):
sig, chn_grp, chn, split = args
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
# data = data.reshape(-1, 1)
data = data.reshape(-1,)
# Apply bandpass filter
filtered = butter_bandpass_filter(data, FLOW_LOWCUT, FLOW_HIGHCUT, SAMPLING_RATE)
# median filter?
if do_resample:
filtered = librosa.resample(filtered, orig_sr=SAMPLING_RATE, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
filtered = filtered.reshape(-1, TARGET_RESAMPLE*30)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, filtered.astype(np.float32))
return
else:
return filtered
def save_mmap(results, sig_list, chn_grp, chn, split='train'):
signame_list = [sig.split('.')[0] for sig in sig_list]
epoch_lengths = [sig.shape[0] for sig in results]
metadata = dict()
sig_info = dict()
start, end = 0, 0
for signame, sig in zip(signame_list, results):
length = sig.shape[0]
end = start + length
info = dict()
info['pos'] = (start, end)
info['stats'] = {'sum': float(np.sum(sig)), 'sumsq': float(np.sum(sig ** 2))}
sig_info[signame] = info
start = end
mmap_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.mmap')
signals_cat = np.concatenate(results, axis=0).astype(np.float32)
os.makedirs(os.path.dirname(mmap_path), exist_ok=True)
data = np.memmap(mmap_path, dtype=np.float32, mode='w+', shape=signals_cat.shape)
data[:] = signals_cat
data.flush()
metadata['sig_info'] = sig_info
metadata['data_shape'] = signals_cat.shape
info_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.pickle')
with open(info_path, 'wb') as f:
pickle.dump(metadata, f)
del data
def preprocess(sig_list, chn_grp=None, chn=None, split='train'):
def _process(sig_list, chn_grp, chn, split):
print(f"Processing {chn_grp}/{chn}")
if chn_grp in ['EEG','EOG']:
# sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)))
input_list = [(sig, chn_grp, chn, split) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_eeg, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
del results
elif chn_grp in ['EMG']:
# sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)))
input_list = [(sig, chn_grp, chn, split) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_emg, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
del results
elif chn_grp in ['ECG']:
# sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)))
input_list = [(sig, chn_grp, chn, split) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_ecg, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
del results
elif chn_grp in ['ABD', 'CHEST', 'AIRFLOW']:
# sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)))
input_list = [(sig, chn_grp, chn, split) for sig in sig_list]
with multiprocessing.Pool(NUM_PROCESS) as pool:
results = list(tqdm(pool.imap(process_flow, input_list), total=len(input_list)))
if cfg.save_type == 'mmap':
save_mmap(results, sig_list, chn_grp, chn, split)
del results
else:
results = []
for sig in tqdm(sig_list):
sig_path = os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn, sig)
data = np.load(sig_path)
if do_resample or chn == 'SaO2':
data = data.reshape(-1, 1)
if chn == 'SaO2':
data = librosa.resample(data, orig_sr=SAMPLING_RATE, target_sr=1, axis=0, res_type='soxr_qq')
data = data / 1e8
if round(np.max(data)) > 1 or round(np.min(data)) < 0:
print(f"Warning: SaO2 values out of range: {sig} with min {np.min(data)} and max {np.max(data)}")
data = data.reshape(-1, 1*30)
else:
data = librosa.resample(data, orig_sr=SAMPLING_RATE, target_sr=TARGET_RESAMPLE, axis=0, res_type='soxr_qq')
data = data.reshape(-1, TARGET_RESAMPLE*30)
# output_path = os.path.join(BASE_PATH, cfg.output_dir, chn_grp, chn, sig)
# shutil.copy(sig_path, output_path)
if cfg.save_type == 'npy':
# Save processed data
output_path = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, chn, sig)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
np.save(output_path, data)
elif cfg.save_type == 'mmap':
results.append(data)
# sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)))
save_mmap(results, sig_list, chn_grp, chn, split)
del results
gc.collect()
if chn_grp is not None and chn is not None:
_process(sig_list, chn_grp, chn, split)
return
else:
for chn_grp in os.listdir(os.path.join(BASE_PATH, cfg.data_dir)):
if not os.path.isdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp)):
continue
for chn in os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp)):
if not os.path.isdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)):
continue
_process(sig_list, chn_grp, chn, split)
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', default='isruc_all/signals_MAE', help='Physionet 2018 raw data location')
parser.add_argument('--output_dir', default='isruc_all/mmap_signals_MAE_processed', help='Physionet 2018 processed data location')
parser.add_argument('--save_type', default='mmap', help='Which data type to save. "npy" or "mmap"')
parser.add_argument('--chn_grp', default=None, help='If specified, only preprocess this channel group')
parser.add_argument('--chn', default=None, help='If specified, only preprocess this channel')
parser.add_argument('--ann_split', action='store_true', help='To split annotation files into train/val/test')
parser.add_argument('--num_train', type=int, default=50, help='Number of training samples')
cfg = parser.parse_args()
def get_isruc_filename(ids, ds_id):
if ds_id == 2:
return [f"sg{ds_id}_{id:03d}_1.npy" for id in ids] + [f"sg{ds_id}_{id:03d}_2.npy" for id in ids]
else:
return [f"sg{ds_id}_{id:03d}.npy" for id in ids]
if __name__ == "__main__":
skip_val, skip_test = False, False
if cfg.num_train < 90:
if os.path.exists(os.path.join(BASE_PATH, cfg.output_dir, 'val')):
skip_val = True
if os.path.exists(os.path.join(BASE_PATH, cfg.output_dir, 'test')):
skip_test = True
cfg.output_dir += f"_train{cfg.num_train}"
list_files_train = sorted(get_isruc_filename(list(range(1,93)), ds_id=1)) # subjet 8, 40 are missing
list_files_val = sorted(get_isruc_filename(list(range(93,101)), ds_id=1))
list_files_test = sorted(get_isruc_filename(list(range(1,9)), ds_id=2))
list_files_test += sorted(get_isruc_filename(list(range(1,11)), ds_id=3))
sig_list = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, "EEG", "C3-A2")))
for chn_grp in os.listdir(os.path.join(BASE_PATH, cfg.data_dir)):
# Skip any files that are not directories
if not os.path.isdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp)):
continue
for chn in os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp)):
# Skip any files that are not directories
if not os.path.isdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)):
continue
sig_list_chn = sorted(os.listdir(os.path.join(BASE_PATH, cfg.data_dir, chn_grp, chn)))
print(f"Number of records in {chn_grp}/{chn}:", len(sig_list_chn))
sig_list = list(set(sig_list).intersection(set(sig_list_chn)))
print(f"Number of records that exist in all chns:", len(sig_list)) # Total 7698 -> 7579 records
list_files_train = sorted(list(set(list_files_train).intersection(set(sig_list))))
list_files_val = sorted(list(set(list_files_val).intersection(set(sig_list))))
list_files_test = sorted(list(set(list_files_test).intersection(set(sig_list))))
# if cfg.num_train < 50:
list_files_train = list_files_train[:cfg.num_train]
print("Number of records in training set:", len(list_files_train))
print("Number of records in validation set:", len(list_files_val))
print("Number of records in test set:", len(list_files_test))
print("Processing training set...")
preprocess(list_files_train, cfg.chn_grp, cfg.chn, split='train')
if skip_val:
val_output_dir = cfg.output_dir
val_output_dir = val_output_dir.replace(f"_train{cfg.num_train}","")
os.symlink(os.path.join(BASE_PATH, val_output_dir, 'val'), os.path.join(BASE_PATH, cfg.output_dir, 'val'))
else:
print("Processing evaluation set...")
preprocess(list_files_val, cfg.chn_grp, cfg.chn, split='val')
if skip_test:
test_output_dir = cfg.output_dir
test_output_dir = test_output_dir.replace(f"_train{cfg.num_train}","")
os.symlink(os.path.join(BASE_PATH, test_output_dir, 'test'), os.path.join(BASE_PATH, cfg.output_dir, 'test'))
else:
print("Processing test set...")
preprocess(list_files_test, cfg.chn_grp, cfg.chn, split='test')
if cfg.ann_split:
# Reorganize annotations files
ANN_PATH = os.path.join(BASE_PATH, 'ISRUC_SG1/annotations')
NEW_ANN_PATH = os.path.join(BASE_PATH, 'ISRUC_SG1/annotations_split')
os.makedirs(NEW_ANN_PATH, exist_ok=True)
os.makedirs(os.path.join(NEW_ANN_PATH,'train'), exist_ok=True)
os.makedirs(os.path.join(NEW_ANN_PATH,'val'), exist_ok=True)
os.makedirs(os.path.join(NEW_ANN_PATH,'test'), exist_ok=True)
for fname in list_files_train:
ann_fname = fname.replace('.npy', '_ann.npy')
shutil.copy(os.path.join(ANN_PATH, ann_fname), os.path.join(NEW_ANN_PATH, 'train', ann_fname))
for fname in list_files_val:
ann_fname = fname.replace('.npy', '_ann.npy')
shutil.copy(os.path.join(ANN_PATH, ann_fname), os.path.join(NEW_ANN_PATH, 'val', ann_fname))
for fname in list_files_test:
ann_fname = fname.replace('.npy', '_ann.npy')
shutil.copy(os.path.join(ANN_PATH, ann_fname), os.path.join(NEW_ANN_PATH, 'test', ann_fname))
for split in ['train', 'val', 'test']:
# create link files to respiratory channels
os.makedirs(os.path.join(BASE_PATH, cfg.output_dir, split, 'RESP'), exist_ok=True)
for chn_grp in ['ABD', 'CHEST', 'AIRFLOW']:
chns = [f.split('.')[0] for f in os.listdir(os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp))]
for chn in chns:
src_mmap = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.mmap')
src_pickle = os.path.join(BASE_PATH, cfg.output_dir, split, chn_grp, f'{chn}.pickle')
dst_mmap = os.path.join(BASE_PATH, cfg.output_dir, split, 'RESP', f'{chn}.mmap')
dst_pickle = os.path.join(BASE_PATH, cfg.output_dir, split, 'RESP', f'{chn}.pickle')
if not os.path.exists(dst_mmap):
os.symlink(src_mmap, dst_mmap)
if not os.path.exists(dst_pickle):
os.symlink(src_pickle, dst_pickle) |