s1 = s0n
#%% training dataset for MLP, CNN, and CWT-CNN
traces_step = 4
traces_train = np.arange(0, s1.shape[1], traces_step)
X = np.zeros((int(len(traces_train)*nt/tw), int(tw/tinc) ))
Xcwt = np.zeros((int(len(traces_train)*nt/tw), len(freq_index), int(tw/tinc)))
Y = np.zeros((int(len(traces_train)*nt/tw), ))
for i in traces_train:
itrace = s1[:,i]
itrace_samps = f.trace_samp(itrace, tw, tinc)
X[int(i/traces_step*nt/tw):int((i/traces_step+1)*nt/tw),:] = itrace_samps
isamps_cwt = f.trace_cwt_samp(itrace, dt, freq_index, tw, tinc)
Xcwt[int(i/traces_step*nt/tw):int((i/traces_step+1)*nt/tw),:,:]=isamps_cwt
ilabel = s1_labels[:,i]
Y[int(i/traces_step*nt/tw):int((i/traces_step+1)*nt/tw),] = ilabel
'''plot the selected traces'''
fig,ax = f.seisplot_wig(s1, scale=0.8, lw=0.5,
highlight=True, lightstep=traces_step)
#ax.set_yticks(np.arange(0,nt,50))
#ax.set_yticklabels(np.arange(0,nt,50)*dt )
ax.set_ylabel('Time (ms)', fontsize=13)
#fig.savefig('fig/s0n_partition_snr=%.2f.pdf'%ssnr, dpi=200)
ax.set_ylabel('Time (s)', fontsize=13)
#fig.savefig('fig/micro_labels.pdf', dpi=200)
#%% training dataset for MLP, CNN, and CWT-CNN
traces_step = 4
traces_train = np.arange(0, s1.shape[1], traces_step)
X = np.zeros((int(len(traces_train) * nt / tw), int(tw / tinc)))
Xcwt = np.zeros(
(int(len(traces_train) * nt / tw), len(freq_index), int(tw / tinc)))
Y = np.zeros((int(len(traces_train) * nt / tw), ))
for i in traces_train:
itrace = s1[:, i]
itrace_samps = f.trace_samp(itrace, tw, tinc)
X[int(i / traces_step * nt / tw):int((i / traces_step + 1) * nt /
tw), :] = itrace_samps
isamps_cwt = f.trace_cwt_samp(itrace, dt, freq_index, tw, tinc)
Xcwt[int(i / traces_step * nt / tw):int((i / traces_step + 1) * nt /
tw), :, :] = isamps_cwt
ilabel = s1_labels[:, i]
Y[int(i / traces_step * nt / tw):int((i / traces_step + 1) * nt /
tw), ] = ilabel
'''plot the selected traces'''
fig, ax = f.seisplot_wig(s1,
scale=0.8,
lw=0.5,
ax.set_yticks(np.arange(0, nt / tw + 1, 400 / tw))
ax.set_yticklabels(np.arange(0, nt + 1, 400) * dt)
ax.set_xticks(np.arange(0, 967, 100))
ax.set_xticklabels((np.arange(0, 967, 100) / 10).astype(int))
#fig.savefig('fig/obs_label', dpi=200)
#%% training dataset for MLP, CNN, and CWT-CNN
traces_step = 80
traces_train = np.arange(0, 967, traces_step)
X = np.zeros((int(len(traces_train) * nt / tw), int(tw / tinc)))
Xcwt = np.zeros((int(len(traces_train) * nt / tw), 15, int(tw / tinc)))
Y = np.zeros((int(len(traces_train) * nt / tw), ))
for i in traces_train:
itrace = Obs[:, i]
itrace_samps = f.trace_samp(itrace, tw, tinc)
X[int(i / traces_step * nt / tw):int((i / traces_step + 1) * nt /
tw), :] = itrace_samps
isamps_cwt = f.trace_cwt_samp(itrace, dt, freq_index, tw, tinc)
Xcwt[int(i / traces_step * nt / tw):int((i / traces_step + 1) * nt /
tw), :, :] = isamps_cwt
ilabel = Obs_labels[:, i]
Y[int(i / traces_step * nt / tw):int((i / traces_step + 1) * nt /
tw), ] = ilabel
'''plot the selected traces'''
fig, ax = f.seisplot_wig(Obs,
inc=10,
scale=30,
lw=0.2,