def sta_lta_div (trace, nsta, nlta):
from obspy import Trace
import numpy as np
station = trace.stats.station
npts = trace.stats.npts
srt = trace.stats.sampling_rate
S1 = nlta
L1 = L2 = S2 = 0
stlt = np.zeros(npts)
max = trace.data.argmax()
while S2 < max:
S2 = S1 + nsta
L2 = (S1 + S2) // 2
L1 = L2 - nlta
norm1 = np.mean (trace.data[S1:S2] ** 2) ** 2
norm2 = np.mean (trace.data[L1:L2] ** 2) ** 2
StaLta = np.round(norm1 / norm2, 3)
stlt[S1:S2] = StaLta
S1 += nsta
data = Trace (data=stlt, header=trace.stats)
data.taper (max_percentage = 0.1)
max = data.data.argmax()
return max
def _taper_stream(tr: obspy.Trace, taper_buffer: float) -> obspy.Trace:
"""Taper the stream, return new stream"""
start = tr.stats.starttime
end = tr.stats.endtime
dur = end - start
buffer = taper_buffer
tr.taper(type="hann", max_percentage=0.05, max_length=dur * buffer)
return tr
def test_taper(self):
"""
Test taper method of trace
"""
data = np.ones(10)
tr = Trace(data=data)
tr.taper()
for i in range(len(data)):
self.assertTrue(tr.data[i] <= 1.)
self.assertTrue(tr.data[i] >= 0.)
def test_taper(self):
"""
Test taper method of trace
"""
data = np.ones(10)
tr = Trace(data=data)
tr.taper()
for i in range(len(data)):
self.assertTrue(tr.data[i] <= 1.)
self.assertTrue(tr.data[i] >= 0.)
f_in.seek( ns * nbytes_station + nc * nbytes_trace + nbytes_stationname +\
8 + ntimesteps * size_of_float + 12 )
#for nt in range(ntimesteps):
# values[nt] = np.fromfile(f_in,dtype=dtype_output,count=1)
values = np.fromfile(f_in, dtype=dtype_output, count=ntimesteps)
tr = Trace(data=values)
# Filter and downsample
# Since the same filter will be applied to all synthetics consistently, non-zero-phase should be okay
# ToDo: Think about whether zerophase would be better
# taper first
#ToDo: Discuss with Andreas whether this tapering makes sense!
tr.taper(type='cosine', max_percentage=0.001)
tr.data = sosfilt(sos, tr.data)
tr.stats.sampling_rate = fs_old
tr.interpolate(fs_new)
# Differentiate
if output_quantity == 'VEL' or output_quantity == 'ACC':
tr.differentiate()
if output_quantity == 'ACC':
tr.differentiate()
# Remove the extra time that specfem added
tr.trim(starttime=tr.stats.starttime + offset_seconds)
# Set data type
tr.data = tr.data.astype(dtype_output)
def __tapper(data, max_percentage=0.05):
tr = Trace(data)
tr.taper(max_percentage=max_percentage, type='blackman')
return tr.data
8 + ntimesteps * size_of_float + 12 )
#for nt in range(ntimesteps):
# values[nt] = np.fromfile(f_in,dtype=dtype_output,count=1)
values = np.fromfile(f_in,dtype=dtype_output,count=ntimesteps)
tr = Trace(data=values)
# Filter and downsample
# Since the same filter will be applied to all synthetics consistently, non-zero-phase should be okay
# ToDo: Think about whether zerophase would be better
# taper first
#ToDo: Discuss with Andreas whether this tapering makes sense!
tr.taper(type='cosine',max_percentage=0.001)
tr.data = sosfilt(sos,tr.data)
tr.stats.sampling_rate = fs_old
tr.interpolate(fs_new)
# Differentiate
if output_quantity == 'VEL' or output_quantity == 'ACC':
tr.differentiate()
if output_quantity == 'ACC':
tr.differentiate()
# Remove the extra time that specfem added
tr.trim(starttime = tr.stats.starttime+offset_seconds)
# Set data type
