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cut_template_intense.py
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cut_template_intense.py
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""" Cut template waveform for intense sequence
Inputs
data_dir: dir of continuous data
temp_pha: template phase file
out_root: root dir for template data
Outputs
temp_root/temp_name/net.sta.chn
Note: temp_name == ot (yyyymmddhhmmss.ss)
"""
import os, glob, shutil
import argparse
import numpy as np
import torch.multiprocessing as mp
from torch.utils.data import Dataset, DataLoader
from obspy import read, UTCDateTime
from dataset_gpu import read_ftemp, preprocess
import config
import warnings
warnings.filterwarnings("ignore")
# cut params
cfg = config.Config()
num_workers = cfg.num_workers
win_len = cfg.win_len
win_snr = cfg.win_snr
win_sta_lta = cfg.win_sta_lta
win_sta_lta_npts = [int(win*cfg.samp_rate) for win in win_sta_lta]
min_snr = cfg.min_snr
get_data_dict = cfg.get_data_dict
def get_sta_date(event_list):
sta_date_dict = {}
for i, [id_name, event_loc, pick_dict] in enumerate(event_list):
if i%1e3==0: print('%s/%s events done/total'%(i, len(event_list)))
# 1. get event info
event_name = id_name.split('_')[1]
event_dir = os.path.join(args.out_root, event_name)
ot = event_loc[0]
if not os.path.exists(event_dir): os.makedirs(event_dir)
for net_sta, [tp, ts] in pick_dict.items():
date = str(tp.date)
sta_date = '%s_%s'%(net_sta, date) # for one day's stream data
if sta_date not in sta_date_dict:
sta_date_dict[sta_date] = [[event_dir, tp, ts]]
else: sta_date_dict[sta_date].append([event_dir, tp, ts])
return sta_date_dict
# slice sac file with obspy
def obspy_slice(stream, t0, t1):
st = stream.slice(t0, t1)
for tr in st:
if not 'sac' in tr.stats: continue
tr.stats.sac.nzyear = t0.year
tr.stats.sac.nzjday = t0.julday
tr.stats.sac.nzhour = t0.hour
tr.stats.sac.nzmin = t0.minute
tr.stats.sac.nzsec = t0.second
tr.stats.sac.nzmsec = int(t0.microsecond / 1e3)
return st
def calc_sta_lta(data, win_lta_npts, win_sta_npts):
npts = len(data)
if npts < win_lta_npts + win_sta_npts:
print('input data too short!')
return np.zeros(1)
sta = np.zeros(npts)
lta = np.ones(npts)
data_cum = np.cumsum(data)
sta[:-win_sta_npts] = data_cum[win_sta_npts:] - data_cum[:-win_sta_npts]
sta /= win_sta_npts
lta[win_lta_npts:] = data_cum[win_lta_npts:] - data_cum[:-win_lta_npts]
lta /= win_lta_npts
sta_lta = sta/lta
sta_lta[0:win_lta_npts] = 0.
sta_lta[np.isinf(sta_lta)] = 0.
sta_lta[np.isnan(sta_lta)] = 0.
return sta_lta
class Cut_Templates(Dataset):
""" Dataset for cutting templates
"""
def __init__(self, sta_date_items):
self.sta_date_items = sta_date_items
self.data_dir = args.data_dir
def __getitem__(self, index):
data_paths_i = []
# get one sta-date
sta_date, samples = self.sta_date_items[index]
net_sta, date = sta_date.split('_')
net, sta = net_sta.split('.')
date = UTCDateTime(date)
# read & prep one day's data
print('reading %s %s'%(net_sta, date.date))
data_dict = get_data_dict(date, self.data_dir)
if net_sta not in data_dict: return data_paths_i
st_paths = data_dict[net_sta]
try:
stream = read(st_paths[0])
stream += read(st_paths[1])
stream += read(st_paths[2])
except: return data_paths_i
stream = preprocess(stream)
if len(stream)!=3: return data_paths_i
for [event_dir, tp, ts] in samples:
# time shift & prep
start_time = tp - win_len[0]
end_time = tp + win_len[1]
st = obspy_slice(stream, start_time, end_time)
if len(st)!=3: continue
st = st.detrend('demean') # note: no detrend here
# select with P SNR
if min_snr:
data_p = st.slice(tp-win_sta_lta[0]-win_snr[0], tp+win_sta_lta[1]+win_snr[1])[2].data
snr_p = calc_sta_lta(data_p**2, win_sta_lta_npts[0], win_sta_lta_npts[1])
if np.amax(snr_p)<min_snr: continue
# write & record out_paths
data_paths_i.append([])
for tr in st:
out_path = os.path.join(event_dir,'%s.%s'%(net_sta,tr.stats.channel))
if not 'sac' in tr.stats:
tr.write(out_path, format='sac')
tr = read(out_path)[0]
tr.stats.sac.t0, tr.stats.sac.t1 = tp-start_time, ts-start_time
tr.write(out_path, format='sac')
data_paths_i[-1].append(out_path)
return data_paths_i
def __len__(self):
return len(self.sta_date_items)
if __name__ == '__main__':
mp.set_start_method('spawn', force=True) # 'spawn' or 'forkserver'
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str,
default='/data/Example_data')
parser.add_argument('--temp_pha', type=str,
default='input/example.temp')
parser.add_argument('--out_root', type=str,
default='output/example_templates')
args = parser.parse_args()
# read fpha
temp_list = read_ftemp(args.temp_pha)
sta_date_dict = get_sta_date(temp_list)
sta_date_items = list(sta_date_dict.items())
# for sta-date pairs
data_paths = []
dataset = Cut_Templates(sta_date_items)
dataloader = DataLoader(dataset, num_workers=num_workers, batch_size=None)
for i, data_paths_i in enumerate(dataloader):
data_paths += data_paths_i
if i%10==0: print('%s/%s sta-date pairs done/total'%(i+1,len(dataset)))
fout_data_paths = os.path.join(args.out_root,'data_paths.npy')
np.save(fout_data_paths, data_paths)