startaim = max([tr.stats.starttime for tr in (AC + RLAS)])
endtaim = min([tr.stats.endtime for tr in (AC + RLAS)])
AC.trim(startaim, endtaim, nearest_sample=True)
RLAS.trim(startaim, endtaim, nearest_sample=True)
# **Resample, Filter and Rotate**
# In[4]:
RLAS.decimate(factor=4)
AC.decimate(factor=4)
f_cutoff = 1.0
RLAS.filter('lowpass', freq=f_cutoff, corners=2, zerophase=True)
AC.filter('lowpass', freq=f_cutoff, corners=2, zerophase=True)
# event location from event info
source_latitude = event.origins[0].latitude
source_longitude = event.origins[0].longitude
# station location (Wettzell)
station_latitude = 49.144001
station_longitude = 12.8782
# theoretical backazimuth and distance
baz = gps2dist_azimuth(source_latitude, source_longitude, station_latitude, station_longitude)
# get list of all days and of unique days
days_all = np.array([
datetime.strptime(a[-8:], '%Y.%j').strftime('%Y.%m.%d') for a in dayfiles
])
days = np.unique(days_all)
for dy in days:
file_list = dayfiles[np.where(
days_all == dy)] # files for this day, all stations
st = Stream()
for fl in file_list:
if fl not in bad_files and os.stat(fl).st_size != 0:
st += read(fl)
if len(st) > 0:
# decimate or resample to 1Hz
if np.all([tr.stats.sampling_rate == 100 for tr in st]):
st.decimate(100, no_filter=True)
else:
st.resample(1.0, no_filter=True)
st.merge(method=1, fill_value=0) # make sure 1 trace per sta/comp
for tr in st: # rename channels to L* to match [edited] dataless
tr.stats.channel = 'L' + tr.stats.channel[1:]
# write miniseed
ofile = os.path.join(mseed_dir_out,'EN-%s.%s.%s.mseed' % \
(dy.split('.')[0],dy.split('.')[1],dy.split('.')[2]))
st.write(ofile, format='MSEED')
for ax in [ax1,ax2,ax3,ax4]:
ax.legend(loc=2, prop={"size":12})
ax.yaxis.major.formatter.set_powerlimits((-1,2))
ax.set_xlim(0,max(AC[0].times()))
fig.tight_layout()
plt.show()
# -
# ### Resample, Filter and Rotate
# Resample seismograms using **decimate** in order to reduce the size of the arrays (speeds up processing).
# The seismograms are high-cut and low-cut filtered, depending on the frequency range of interest and the resolution of the instruments.
# +
RLAS.decimate(factor=4)
AC.decimate(factor=4)
high_cut = 1.0
low_cut = 0.005
RLAS.filter('bandpass', freqmax=high_cut, freqmin=low_cut, corners=2, zerophase=True)
AC.filter('bandpass', freqmax=high_cut, freqmin=low_cut, corners=2, zerophase=True)
# -
# In order to align the seismometer recordings with the event direction, we need to rotate the horizontal components<br>
# of the acceleration to transverse and radial.<br>
# <br>
# We can determine the theoretical rotation/direction angle (= backazimuth) from station and event location using **gps2dist_azimuth**.<br>
# This function also yields the epicentral distance and the azimuth angle.
# +
from obspy.geodetics.base import gps2dist_azimuth
