def get_fft(evnm,p_before, p_after,stns='001',ext='[E,N,Z]'):
#Reading in the Events
st = readandrotate(evnm,stns=stns,ext='[E,N,Z]')
# Removing this we are going to use multiple traces and stack them
##Get the P components
#st = get_p(st)
#assert len(st) ==1
# Calculate P picks
st_filt = st.copy()
st_filt = st.filter('bandpass',freqmin=20.0,freqmax=200.0)
p_pick, s_picks = picker(st_filt)
#Set P picks to sac header this is probably bad style
if not(np.isnan(p_pick)):
for tr in st:
tr.stats.sac.t0 = p_pick
else:
for tr in st:
tr.stats.sac.t0 = -12345.0
# Slice of the P-wave windows
st_p = window_trace(st,p_before,p_after)
assert len(st_p) != 0
window_len_time = p_before + p_after
# Calculates the noise window
# Note uses the beginning of the trace if the P wave is near the beginning it will overlap
st_n = noise_window_trace(st,window_len_time)
#Calulate power spectral density of P,Noise for event and reference
fft_p = np.zeros(len(st_n[0])+1)
fft_n = np.zeros(len(st_n[0])+1)
for i in range(3):
fft_p_cmp,freq = calc_mtspec(st_p[i])
fft_n_cmp,freq = calc_mtspec(st_n[i])
fft_p += fft_p_cmp
fft_n += fft_n_cmp
return(fft_p,fft_n,freq,st,st_p,st_n)
def dts_p(evnm1,evnm_ref,fmin,fmax,snr_limit,station='001',p_before=0.1,p_after=0.2,diagnostic_plot=False):
output_linefit=True
#Reading in the Events
st = readandrotate(evnm1,stns=station,ext='[E,N,Z]')
st_ref = readandrotate(evnm_ref,stns=station,ext='[E,N,Z]')
#Get the P components
st = get_p(st)
st_ref = get_p(st_ref)
assert len(st) ==1
assert len(st_ref) ==1
# Slice of the P-wave windows
st_p = window_trace(st,p_before,p_after)
st_ref_p = window_trace(st_ref,p_before,p_after)
assert len(st_p) != 0
assert len(st_ref_p) != 0
# Finds the length of P window in samples and checks all the windows are the same length
window_len_samples = get_and_check_window_len(st_p,st_ref_p)
window_len_time = p_before + p_after
# Calculates the noise window
# Note uses the beginning of the trace if the P wave is near the beginning it will overlap
st_n = noise_window_trace(st,window_len_time)
st_ref_n = noise_window_trace(st,window_len_time)
#Calulate power spectral density of P,Noise for event and reference
fft_p,freq = calc_mtspec(st_p[0])
fft_n,freq = calc_mtspec(st_n[0])
fft_ref_p,freq = calc_mtspec(st_ref_p[0])
fft_ref_n,freq = calc_mtspec(st_ref_n[0])
meas_tstar,meas_tstar_error,linefit = bs_lsr(fft_p,
fft_ref_p,freq,fmin,fmax,fft_n,fft_ref_n,snr_limit=snr_limit)
if diagnostic_plot:
plt.figure(figsize=(8,12))
# --------------- #
plt.subplot(5,2,1)
plt.title('Event Whole Trace')
plt.plot(st[0].times(),st[0].data)
ppick = st[0].stats.sac.t0
plt.axvspan(ppick-p_before,ppick+p_after,color='r',alpha=0.5)
plt.xlabel('Time (sec)')
plt.ylabel('Amplitude')
# --------------- #
plt.subplot(5,2,2)
plt.title('Reference Whole Trace')
plt.plot(st_ref[0].times(),st_ref[0].data,'g')
ppick = st_ref[0].stats.sac.t0
plt.axvspan(ppick-p_before,ppick+p_after,color='r',alpha=0.5)
plt.xlabel('Time (sec)')
plt.ylabel('Amplitude')
# --------------- #
plt.subplot(6,2,3)
plt.title('Trace Window')
plt.plot(st_p[0].times(),st_p[0].data,label='P-wave')
plt.plot(st_n[0].times(),st_n[0].data,'r',label='Noise')
plt.xlabel('Time (sec)')
plt.ylabel('Amplitude')
# --------------- #
plt.subplot(6,2,4)
plt.title('Reference Window')
plt.plot(st_ref_p[0].times(),st_ref_p[0].data,'g',label='P-wave')
plt.plot(st_ref_n[0].times(),st_ref_n[0].data,'r',label='Noise')
plt.xlabel('Time (sec)')
plt.ylabel('Amplitude')
# --------------- #
plt.subplot(6,2,5)
plt.title('Trace FFT')
plt.semilogy(freq,fft_p)
plt.semilogy(freq,fft_n,'r')
plt.xlabel('Freq (Hz)')
plt.ylabel('Amplitude')
# --------------- #
plt.subplot(6,2,6)
plt.title('Reference FFT')
plt.semilogy(freq,fft_ref_p,'g')
plt.semilogy(freq,fft_ref_n,'r')
plt.xlabel('Freq (Hz)')
plt.ylabel('Amplitude')
# --------------- #
plt.subplot(6,2,7)
plt.title('Log-spectral-ratio')
plt.plot(freq,np.log(fft_p/fft_ref_p))
plt.axvspan(fmin,fmax,color='r',alpha=0.5)
plt.xlabel('Freq (Hz)')
plt.ylabel('LSR')
ind = (freq>fmin) & (freq<fmax)
linefit_freq = freq[ind]
line = linefit_freq*linefit.params[0]+linefit.params[1]
plt.plot(linefit_freq,line,'-w')
# --------------- #
plt.subplot(6,2,8)
plt.title('Signal/Noise')
snr=fft_p/fft_n
snr_ref=fft_ref_p/fft_ref_n
plt.semilogy(freq,snr)
plt.semilogy(freq,snr_ref,'g')
plt.axhline(snr_limit,color='r',ls='--')
plt.xlabel('Freq (Hz)')
plt.ylabel('SNR')
# --------------- #
# --------------- #
plt.subplot(3,1,3)
plt.title('LSR in frequency band')
plt.plot(freq,np.log(fft_p/fft_ref_p))
plt.axvspan(fmin,fmax,color='r',alpha=0.5)
plt.xlabel('Freq (Hz)')
plt.ylabel('LSR')
plt.plot(linefit_freq,line,'-w')
extra = 0.1*(fmax-fmin)
plt.xlim(fmin-extra,fmax+extra)
#TODO autoscale y axis
# --------------- #
plt.tight_layout()
return meas_tstar,meas_tstar_error,linefit
def mean_corr(evnm1,evnm2):
"""Calculate the mean correlation between two events"""
st = readandrotate(evnm1,stns='*',ext='[E,N,Z]')
st2 = readandrotate(evnm2,stns='*',ext='[E,N,Z]')
