Chen, Y., A. Savvaidis, O. M. Saad, G.-C. D. Huang, D. Siervo, V. O’Sullivan, C. McCabe, B. Uku, P. Fleck, G. Burke, N. L. Alvarez, J. Domino, and I. Grigoratos, “TXED: the texas earthquake dataset for AI,” Seismological Research Letters, vol. 1, no. 1, p. doi: 10.1785/0220230327, 2024.

@Article{txed,
  author={Yangkang Chen and Alexandros Savvaidis and Omar M. Saad and Guo-Chin Dino Huang and Daniel Siervo and Vincent O’Sullivan and Cooper McCabe and Bede Uku and Preston Fleck and Grace Burke and Natalie L. Alvarez and Jessica Domino and Iason Grigoratos},
  title = {{TXED}: the Texas Earthquake Dataset for {AI}},
  journal={Seismological Research Letters},
  year=2024,
  volume=1,
  issue=1,
  number=1,
  pages={doi: 10.1785/0220230327},
  doi={10.1785/0220230327},
}

Developers of the TXED package, 2021-present

GNU General Public License, Version 3
(https://www.gnu.org/copyleft/gpl.html)   

git clone https://github.com/chenyk1990/txed

wget /address_TBD/TXED_20231111.h5
wget /address_TBD/ID_20231111.npy

import numpy as np
allid = np.load("ID_20231111.npy")
signalid=[ii for ii in allid if ii.split("_")[-1]=='EV']
print('Length of signalid is',len(signalid))

import numpy as np
allid = np.load("ID_20231111.npy")
noiseid=[ii for ii in allid if ii.split("_")[-1]=='NO']
print('Length of noiseid is',len(noiseid))

import h5py
import numpy as np
f = h5py.File("TXED_20231111.h5", 'r')
eventid=np.load("ID_20231111.npy")
idx=eventid[0]
dataset = f.get(idx)
print('TXED attributes are:',dataset.attrs.keys())

import h5py,os
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (7,8.2)

if os.path.isdir('./waveforms') == False:  
	os.makedirs('./waveforms',exist_ok=True)

h5fname="TXED_20231111.h5"
npyfname="ID_20231111.npy"

#open the h5file
f = h5py.File(h5fname, 'r')
eventid=np.load(npyfname)

#specificy a number of waveforms to plot
no=5
idxs=eventid[0:no]

#or specify an arbitrary TexNet eventID
# idxs=[ii for ii in eventid if ii.split('_')[0]=='texnet2023qnms']
# idxs=[ii for ii in eventid if ii.split('_')[0]=='texnet2022wmmd']
idxs=[ii for ii in eventid if ii.split('_')[0]=='texnet2023ncwh']

#loop over IDs and plot
id=0
for idx in idxs:
	id=id+1
	print('Plotting: %d/%d'%(id,len(idxs)))
	
	dataset = f.get(idx)
	data = np.array(dataset['data'])
	spt = int(dataset.attrs['p_arrival_sample']);
	sst = int(dataset.attrs['s_arrival_sample']);
	coda_end = int(dataset.attrs['coda_end_sample']);
	snr = dataset.attrs['snr_db'];
	t=dataset.attrs['origin_time']
	mag=dataset.attrs['magnitude']
	
	fig=plt.figure()
	ax1 = fig.add_subplot(311)
	plt.plot(data[:,0], 'k',label='Z')
	ymin,yma = ax1.get_ylim()
	plt.vlines(spt,ymin,yma,color='r',linewidth=2)
	plt.vlines(sst,ymin,yma,color='b',linewidth=2)
	legend_properties = {'weight':'bold'}
	ymin, ymax = ax1.get_ylim()
	plt.legend(loc = 'upper right', borderaxespad=0., prop=legend_properties)
	plt.ylabel('Amplitude', fontsize=12) 
	ax1.set_xticklabels([])
	ax = fig.add_subplot(312) 
	plt.plot(data[:,1], 'k',label='N')
	ymin,yma = ax1.get_ylim()
	plt.vlines(spt,ymin,yma,color='r',linewidth=2)
	plt.vlines(sst,ymin,yma,color='b',linewidth=2)
	legend_properties = {'weight':'bold'}
	ymin, ymax = ax.get_ylim()
	plt.legend(loc = 'upper right', borderaxespad=0., prop=legend_properties)
	plt.ylabel('Amplitude', fontsize=12) 
	ax.set_xticklabels([])
	ax = fig.add_subplot(313) 
	plt.plot(data[:,2], 'k',label='E')
	ymin,yma = ax1.get_ylim()
	plt.vlines(spt,ymin,yma,color='r',linewidth=2)
	plt.vlines(sst,ymin,yma,color='b',linewidth=2)
	legend_properties = {'weight':'bold'}
	ymin, ymax = ax.get_ylim()
	plt.legend(loc = 'upper right', borderaxespad=0., prop=legend_properties)
	plt.ylabel('Amplitude', fontsize=12) 
	plt.xlabel('Sample', fontsize=12) 

	eid=idx.split("_")[0]
	stname=idx.split("_")[1]
	sttime=t
	plt.text(-1200,(ymin-(ymax-ymin)*0.3),eid+' (Ml=%.2g)'%mag,fontsize=12,color='k')
	plt.text(1800,(ymin-(ymax-ymin)*0.3),stname,fontsize=12,color='k')
	plt.text(3700,(ymin-(ymax-ymin)*0.3),sttime,fontsize=12,color='k')
	ax1.set_title('Signal waveform: %s-%s'%(eid,stname), fontsize=14)
	plt.savefig(fname='./waveforms/signal-%s-%s'%(eid,stname)+'.png', format="png")
	plt.show()
	plt.close() 

f.close()

import h5py,os,random
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (7,8.2)

if os.path.isdir('./waveforms') == False:  
	os.makedirs('./waveforms',exist_ok=True)

h5fname="TXED_20231111.h5"
npyfname="ID_20231111.npy"

#open the h5file
f = h5py.File(h5fname, 'r')
allid=np.load(npyfname)

noiseid=[ii for ii in allid if ii.split("_")[-1]=='NO']
print('Length of noiseid is',len(noiseid))

#specificy a number of waveforms to plot
no=100

#random shuffling the noise waveforms
random.seed(2011)
random.shuffle(noiseid)
idxs=noiseid[0:no]

id=0
for idx in idxs:
	id=id+1
	print('Plotting: %d/%d'%(id,len(idxs)))
	dataset = f.get(idx)
	data = np.array(dataset['data'])
	t=dataset.attrs['origin_time']
	fig=plt.figure()
	ax1 = fig.add_subplot(311)
	plt.plot(data[:,0], 'k',label='Z')
	ymin,yma = ax1.get_ylim()
	legend_properties = {'weight':'bold'}
	ymin, ymax = ax1.get_ylim()
	plt.legend(loc = 'upper right', borderaxespad=0., prop=legend_properties)
	plt.ylabel('Amplitude', fontsize=12) 
	ax1.set_xticklabels([])
	ax = fig.add_subplot(312) 
	plt.plot(data[:,1], 'k',label='N')
	ymin,yma = ax1.get_ylim()
	legend_properties = {'weight':'bold'}
	ymin, ymax = ax.get_ylim()
	plt.legend(loc = 'upper right', borderaxespad=0., prop=legend_properties)
	plt.ylabel('Amplitude', fontsize=12) 
	ax.set_xticklabels([])
	ax = fig.add_subplot(313) 
	plt.plot(data[:,2], 'k',label='E')
	ymin,yma = ax1.get_ylim()
	legend_properties = {'weight':'bold'}
	ymin, ymax = ax.get_ylim()
	plt.legend(loc = 'upper right', borderaxespad=0., prop=legend_properties)
	plt.ylabel('Amplitude', fontsize=12) 
	plt.xlabel('Sample', fontsize=12) 

	no=idx.split("_")[0]
	eid='Noise #%s'%no
	stname=idx.split("_")[1]
	sttime=t
	plt.text(-1200,(ymin-(ymax-ymin)*0.3),eid,fontsize=12,color='k')
	plt.text(1200,(ymin-(ymax-ymin)*0.3),stname,fontsize=12,color='k')
	plt.text(3300,(ymin-(ymax-ymin)*0.3),sttime,fontsize=12,color='k')
	ax1.set_title('Noise waveform: #%s-%s'%(no,stname), fontsize=14)
	plt.savefig(fname='./waveforms/noise-%s-%s'%(no,stname)+'.png', format="png")
	plt.show()
	plt.close() 
f.close()

The development team welcomes voluntary contributions from any open-source enthusiast. 
If you want to make contribution to this project, feel free to contact the development team. 

Regarding any questions, bugs, developments, collaborations, please contact  
Yangkang Chen
[email protected]