def est_SNR_1h(dstart,dend,ch1,ch2):
# here you load all the functions you need to use
from obspy.seg2.seg2 import readSEG2
from obspy.core import Stream
import numpy as np
from obspy.signal.cross_correlation import xcorr
from numpy import sign
dataDir = "/import/three-data/hadzii/STEINACH/STEINACH_longtime/"
SNR=[]
# loading the info for outfile-name
stream_start = readSEG2(dataDir + str(dstart) + ".dat")
t_start = stream_start[ch1].stats.seg2.ACQUISITION_TIME
stream_end = readSEG2(dataDir + str(dend) + ".dat")
t_end = stream_end[ch1].stats.seg2.ACQUISITION_TIME
for k in range(dstart, dend, 1):
st1 = merge_single(ch1,dstart,k+1)
st2 = merge_single(ch2,dstart,k+1)
st1.detrend('linear')
st2.detrend('linear')
st1.filter('lowpass',freq = 24, zerophase=True, corners=8)
st1.filter('highpass', freq= 0.05, zerophase=True, corners=2) #had to be reduced from 0.1Hz
st1.filter('bandstop', freqmin=8, freqmax=14, corners=4, zerophase=True)
st2.filter('lowpass',freq = 24, zerophase=True, corners=8)
st2.filter('highpass', freq= 0.05, zerophase=True, corners=2) #had to be reduced from 0.1Hz
st2.filter('bandstop', freqmin=8, freqmax=14, corners=4, zerophase=True)
# 1-bit normalization
tr1 = sign(st1[0].data)
tr2 = sign(st2[0].data)
# cross-correlation
index, value, acorr = xcorr(tr1, tr2, 25000, full_xcorr=True)
# check sanity
if np.max(acorr)>1:
acorr = zeros(50001)
corr += acorr
SNR_ges=[]
for isnrb3 in range(40000,49001,500): # steps of half a windowlength
endwb3=isnrb3 + 1000 # 1s windows
SNR_ges.append(np.max(np.abs(corr))/np.std(corr[isnrb3:endwb3]))
SNR.append(np.max(SNR_ges))
def merge_single(nch,dstart,dend):
'''Merges traces of one channel to larger traces. Used for cross-correlation'''
# here you load all the functions you need to use
from obspy.seg2.seg2 import readSEG2
from obspy.core import Stream
# directories:
dataDir2 = "/import/neptun-radler/STEINACH_feb/"
dataDir = "/import/three-data/hadzii/STEINACH/STEINACH_longtime/"
tr = []
for k in range(dstart, dend, 1):
fname = '%d' %(k)
fileName = fname + ".dat"
st = readSEG2(dataDir + fileName)
tr.append(st[nch-1])
new_stream = Stream(traces=tr)
new_stream.merge(method=1, fill_value='interpolate')
return new_stream
def merge_single(nch,dstart,dend):
'''Merges traces of one channel to larger traces. Used for cross-correlation'''
# here you load all the functions you need to use
from obspy.seg2.seg2 import readSEG2
from obspy.core import Stream
dataDir2 = "/import/neptun-radler/STEINACH_feb/"
dataDir = "/import/three-data/hadzii/STEINACH/STEINACH_longtime/"
outdir = "/home/jsalvermoser/Desktop/Processing/out_merged"
tr = []
for k in range(dstart, dend, 1):
fname = '%d' %(k)
fileName = fname + ".dat"
st = readSEG2(dataDir + fileName)
#st.detrend('linear')
tr.append(st[nch-1])
new_stream = Stream(traces=tr)
new_stream.merge(method=1, fill_value='interpolate')
start = new_stream[0].stats.starttime
end = new_stream[0].stats.endtime
timeframe = str(nch)+ "_" + str(start.year) +'.'+ str(start.julday) +'.'+ str(start.hour) +'.'+ str(start.minute) +'.'+ str(start.second) \
+'-'+ str(end.year) +'.'+ str(end.julday) +'.'+ str(end.hour) +'.'+ str(end.minute) +'.'+ str(end.second)
new_stream.write(outdir + timeframe + ".mseed", format="MSEED")
return new_stream[0]
def merge_single(nch,dstart,dend,dataDir):
'''Merges traces of one channel to larger traces. Used for cross-correlation'''
# here you load all the functions you need to use
from obspy.seg2.seg2 import readSEG2
from obspy.core import Stream
tr = []
for k in range(dstart, dend, 1):
fname = '%d' %(k)
fileName = fname + ".dat"
st = readSEG2(dataDir + fileName)
tr.append(st[nch-1])
new_stream = Stream(traces=tr)
new_stream.merge(method=1, fill_value='interpolate')
return new_stream
def summit_seg2_read(fname):
""" Read SEG2 data recorded with the DMT summit aquisition unit.
Read the SEG2 formatted seismic data aquired with the summit recorder
and fill the ID fields in the stats.
:parameters:
------------
:type fname: string
:param fname: Path to the file containing the data to be read
:rtype: :class:`~obspy.core.Stream` object
:return: **st**: obspy.core.Stream object
Obspy stream object containing the data
"""
try:
from obspy.seg2.seg2 import readSEG2
except ImportError:
print "obspy.segy package not installed. Exit"
return
# read the stream
st = readSEG2(fname)
# enter ID in fields of the stream
for tr in st:
tr.stats['network'] = tr.stats.seg2.MEASURING_POINT
tr.stats['station'] = tr.stats.seg2.STATION_CODE
tr.stats['channel'] = tr.stats.seg2.REGISTRATION_DIRECTION
# This needs to be fixed
tr.stats['sac'] = {}
# longitude in degrees
tr.stats['sac']['stlo'] = \
float(tr.stats.seg2.RECEIVER_LOCATION.split(' ')[0]) * \
180 / 6371000 / 3.141592653589793
# longitude in degrees
tr.stats['sac']['stla'] = \
float(tr.stats.seg2.RECEIVER_LOCATION.split(' ')[1]) * \
180 / 6371000 / 3.141592653589793
# elevation in meters
tr.stats['sac']['stel'] = \
float(tr.stats.seg2.RECEIVER_LOCATION.split(' ')[2])
return st
# PERFORM STRETCHING
ch1=35
ch2=22
DEC_Dir= '/import/three-data/hadzii/STEINACH/Steinach/Steinach_ACTIVE/'
FEB_Dir= '/import/three-data/hadzii/STEINACH/Steinach/STEINACH_act_feb/'
DEC_fn='1002.dat'
FEB_fn='1000002.dat'
pos1D=6
pos2D=9
pos1F=6
pos2F=9
st_DEC = readSEG2(DEC_Dir + DEC_fn)
st_DEC.detrend('linear')
st_FEB = readSEG2(FEB_Dir + FEB_fn)
st_FEB.detrend('linear')
st_DEC.filter('lowpass',freq = 24, zerophase=True, corners=8)
st_DEC.filter('highpass', freq= 0.05, zerophase=True, corners=2) #had to be reduced from 0.1Hz
st_DEC.filter('bandstop', freqmin=8, freqmax=14, corners=4, zerophase=True)
st_FEB.filter('lowpass',freq = 24, zerophase=True, corners=8)
st_FEB.filter('highpass', freq= 0.05, zerophase=True, corners=2) #had to be reduced from 0.1Hz
st_FEB.filter('bandstop', freqmin=8, freqmax=14, corners=4, zerophase=True)
print st_DEC[0].stats.starttime
print st_FEB[0].stats.starttime
def crossc(dstart,dend,ch1,ch2,day):
# here you load all the functions you need to use
from obspy.seg2.seg2 import readSEG2
from obspy.core import Stream
import numpy as np
from obspy.signal.cross_correlation import xcorr
from numpy import sign
from obspy.signal.filter import lowpass
from obspy.signal.filter import highpass
from obspy.signal.filter import bandstop
from obspy.signal.filter import bandpass
dataDir = "/import/three-data/hadzii/STEINACH/STEINACH_longtime/"
outdir = "/home/jsalvermoser/Desktop/Processing/bands_SNR/" + "CH" + str(ch1) + "_CH" + str(ch2) + "/" + "JAN" + str(day) + "/"
# loading the info for outfile-name
stream_start = readSEG2(dataDir + str(dstart) + ".dat")
t_start = stream_start[ch1].stats.seg2.ACQUISITION_TIME
stream_end = readSEG2(dataDir + str(dend) + ".dat")
t_end = stream_end[ch1].stats.seg2.ACQUISITION_TIME
# initialization of the arrays and variables
TR = []
rms = []
sq = []
ncalm = 1
nbeat = 1
corr128_calm = 0
corr128_beat = 0
nerror = 0
mu1c=0
mu2c=0
mu3c=0
mu1b=0
mu2b=0
mu3b=0
var1c=0
var2c=0
var3c=0
var1b=0
var2b=0
var3b=0
SNR_calm_b1=[]
SNR_calm_b2=[]
SNR_calm_b3=[]
SNR_beat_b1=[]
SNR_beat_b2=[]
SNR_beat_b3=[]
#TAPER
taper_percentage=0.05
taper= np.blackman(int(len(time_vector) * taper_percentage))
taper_left, taper_right = np.array_split(taper,2)
taper = np.concatenate([taper_left,np.ones(len(time_vector)-len(taper)),taper_right])
for j in range(0, dend-dstart):
sq.append([])
for k in range(dstart, dend, 4):
start = k
end = k + 5 # only used to merge 5-1 = 4 files to one stream
try:
st1 = merge_single(ch1,start,end)
st2 = merge_single(ch2,start,end)
st1.detrend('linear')
st2.detrend('linear')
# calculate squares for rms
r = k-dstart
sq[r] = 0
for h in range(0,64000):
sq[r] += (st1[0].data[h])**2
# lowpass-filter the crossc_beat correlation function
st1.filter('lowpass',freq = 24, zerophase=True, corners=8)
st1.filter('highpass', freq= 0.05, zerophase=True, corners=2) #had to be reduced from 0.1Hz
st1.filter('bandstop', freqmin=8, freqmax=14, corners=4, zerophase=True)
st2.filter('lowpass',freq = 24, zerophase=True, corners=8)
st2.filter('highpass', freq= 0.05, zerophase=True, corners=2) #had to be reduced from 0.1Hz
st2.filter('bandstop', freqmin=8, freqmax=14, corners=4, zerophase=True)
# sometimes channels seem to fail, so I put this to prevent crashing of the program
# 1-bit normalization
tr1 = sign(st1[0].data)
tr2 = sign(st2[0].data)
# cross-correlation
index, value, acorr = xcorr(tr1, tr2, 25000, full_xcorr=True)
print sq[r]
# check sanity
if np.max(acorr)>1:
acorr = zeros(50001)
# sort the 128sec files into calm and beat:
# the value was chosen after observing calm files
if sq[r] < 1000000000000:
corr128_calm += acorr
ncalm += 1.
else:
corr128_beat += acorr
nbeat += 1.
print ncalm, nbeat # just to check if calm or noisy
except:
nerror += 1
print "%d : ERROR" %(r)
if ncalm<8:
corr128_calm = np.zeros(50001)
# normalization
else:
corr128_calm = (corr128_calm/ncalm) * taper
corr128_beat = (corr128_beat/nbeat) * taper
# filter again and divide into 3 bands which can be investigated separately
corr128_calm_band1 = highpass(corr128_calm, freq=0.1, corners=4, zerophase=True, df=500.)
corr128_calm_band1 = lowpass(corr128_calm_band1, freq=2, corners=4, zerophase=True, df=500.)
corr128_calm_band2 = bandpass(corr128_calm, freqmin=2, freqmax=8, df=500., corners=4, zerophase=True)
corr128_calm_band3 = bandpass(corr128_calm, freqmin=8, freqmax=24, df=500., corners=4, zerophase=True)
corr128_beat_band1 = highpass(corr128_beat, freq=0.1, df=500., corners=4, zerophase=True)
corr128_beat_band1 = lowpass(corr128_beat_band1, freq=2, corners=4, zerophase=True, df=500.)
corr128_beat_band2 = bandpass(corr128_beat, freqmin=2, freqmax=8, df=500., corners=4, zerophase=True)
corr128_beat_band3 = bandpass(corr128_beat, freqmin=8, freqmax=24, df=500., corners=4, zerophase=True)
# SNR (Signal-to-Noise Ratio):print 222222
# for the signal-to-noise ratio one divides the maximum of the signal by the
# variance of a late window (noise). As we don't know which window has the
# lowest signal fraction, we loop over some windows. We need windows of
# different lengths for the different bands as different frequencies are
# contained. For every band the minimum-frequency fmin is chosen (e.g. 4Hz), then
# the time for one cyle is 1/fc (e.g. 0.25s) and as we take windows of 3-4
# cycles we choose a window length of 4*0.25s = 1s
## CALM + BEAT
for isnrb1 in range(45000,50000,2500): # steps of half a windowlength
endwb1=isnrb1 + 2500 # 5s window
SNR_calm_b1.append(np.max(np.abs(corr128_calm_band1))/np.std(corr128_calm_band1[isnrb1:endwb1]))
SNR_beat_b1.append(np.max(np.abs(corr128_beat_band1))/np.std(corr128_beat_band1[isnrb1:endwb1]))
SNR_calm_b1 = max(SNR_calm_b1)
SNR_beat_b1 = max(SNR_beat_b1)
for isnrb2 in range(45000,49001,500): # steps of half a windowlength
endwb2=isnrb2 + 1000 # 2s windows
SNR_calm_b2.append(np.max(np.abs(corr128_calm_band2))/np.std(corr128_calm_band2[isnrb2:endwb2]))
SNR_beat_b2.append(np.max(np.abs(corr128_beat_band2))/np.std(corr128_beat_band2[isnrb2:endwb2]))
SNR_beat_b2 = max(SNR_beat_b2)
SNR_calm_b2 = max(SNR_calm_b2)
for isnrb3 in range(45000,49751,125): # steps of half a windowlength
endwb3=isnrb3 + 250 # 0.5s windows
SNR_calm_b3.append(np.max(np.abs(corr128_calm_band3))/np.std(corr128_calm_band3[isnrb3:endwb3]))
SNR_beat_b3.append(np.max(np.abs(corr128_beat_band3))/np.std(corr128_beat_band3[isnrb3:endwb3]))
SNR_beat_b3 = max(SNR_beat_b3)
SNR_calm_b3 = max(SNR_calm_b3)
if ncalm<8:
SNR_calm_b1 = 0
SNR_calm_b2 = 0
SNR_calm_b3 = 0
print SNR_calm_b1, SNR_calm_b2, SNR_calm_b3
print SNR_beat_b1, SNR_beat_b2, SNR_beat_b3
# RMS for histogram and sifting:
#for s in range(0,dend-dstart):
# rms.append((sq[s]/16000)**(0.5))
# save into files:
np.save(outdir + t_start + "-" + t_end + "CH" + str(ch1) + "_" +"xcorr128s_beat_0-2Hz" + "_" + "CH" + str(ch2), corr128_beat_band1)
np.save(outdir + t_start + "-" + t_end + "CH" + str(ch1) + "_" +"xcorr128s_beat_2-8Hz" + "_" + "CH" + str(ch2), corr128_beat_band2)
np.save(outdir + t_start + "-" + t_end + "CH" + str(ch1) + "_" +"xcorr128s_beat_8-24Hz" + "_" + "CH" + str(ch2), corr128_beat_band3)
np.save(outdir + t_start + "-" + t_end + "CH" + str(ch1) + "_" +"xcorr128s_calm_0-2Hz" + "_" + "CH" + str(ch2), corr128_calm_band1)
np.save(outdir + t_start + "-" + t_end + "CH" + str(ch1) + "_" +"xcorr128s_calm_2-8Hz" + "_" + "CH" + str(ch2), corr128_calm_band2)
np.save(outdir + t_start + "-" + t_end + "CH" + str(ch1) + "_" +"xcorr128s_calm_8-24Hz" + "_" + "CH" + str(ch2), corr128_calm_band3)
# np.save(outdir + "JAN_"+"CH" + str(ch1) + "_" +"RMS" + "_" + "CH" + str(ch2) + str(dstart) + "-" + str(dend), rms)
return corr128_beat_band1,corr128_beat_band2,corr128_beat_band3, corr128_calm_band1,corr128_calm_band2,corr128_calm_band3, ncalm, nbeat, SNR_beat_b1, SNR_beat_b2, SNR_beat_b3, SNR_calm_b1, SNR_calm_b2, SNR_calm_b3
def merge(nch,dstart,dend):
''' read the file 'fileName' that is in directory 'dataDir' into
a stream called 'st'
The i-loop is over the channels/geophones, so the merged data of i geophones
is plotted in the end.
The k to m loops are over the data files, so traces from files 1 to 113 are
merged together which equals one hour of measurements.
For each channel i the corresponding trace is picked from all the 113 files
and then written into a new stream 'new_stream'. In this stream all
traces (consecutive in time) are merged then into one long trace using the
'merge-method 1'. As the new_stream only consists of one trace the index '[0]'
can always be used.
't' is an array, built to merge a combined time axis for all the merged traces.
The new_stream data is finally plotted against the time axis. This procedure
is iterated for each channel, whereas subplots are built. '''
# here you load all the functions you need to use
from obspy.seg2.seg2 import readSEG2
from obspy.core import Stream
import matplotlib.pyplot as plt
import numpy as np
from numpy import matrix
import time
time_start = time.clock()
dataDir = "/home/johannes/~home/Steinach/DEC_JAN/DEC_night/"
outdir = "/home/johannes/~home/Steinach/outmseed/"
ax = plt.subplot(111)
TR = []
for a in range(0,nch):
TR.append([])
for k in range(dstart, dend, 1):
fname = '%d' %(k)
fileName = fname + ".dat"
st = readSEG2(dataDir + fileName)
for i in range(0,nch):
TR[i].append(st[i])
for m in range(0,nch):
new_stream = Stream(traces=TR[m])
new_stream.merge(method=0, fill_value='interpolate')
start = new_stream[0].stats.starttime
end = new_stream[0].stats.endtime
timeframe = str(m+1)+ "_" + str(start.year) +'.'+ str(start.julday) +'.'+ str(start.hour) +'.'+ str(start.minute) +'.'+ str(start.second) \
+'-'+ str(end.year) +'.'+ str(end.julday) +'.'+ str(end.hour) +'.'+ str(end.minute) +'.'+ str(end.second)
new_stream.write(outdir + "CH" + str(m+1) + "/" + timeframe + ".mseed", format="MSEED")
new_stream[0].normalize()
dt = new_stream[0].stats.starttime.timestamp
t = np.linspace(new_stream[0].stats.starttime.timestamp - dt,
new_stream[0].stats.endtime.timestamp -dt, new_stream[0].stats.npts)
ax.plot(t, new_stream[0].data + 1.5* m)
time_elapsed = (time.clock() - time_start)
print time_elapsed
plt.show()
#! /usr/bin/env python # -*- coding: utf-8 -*- from obspy.seg2.seg2 import readSEG2 dataDir = "/home/jsalvermoser/Desktop/STEINACH_act_feb/" path = "/home/jsalvermoser/Desktop/STEINACH_act_feb/*.dat" import matplotlib.pyplot as plt import numpy as np import glob spec = 0 k=0 for fname in glob.glob(path): st=readSEG2(fname) spec += np.fft.rfft(st[5].data) k+=1 print fname print k spec_sum = spec/k #fileName = '1000006.dat' #st = readSEG2(dataDir + fileName) #data = st[5].data #spec1 = np.fft.rfft(data) fmax = 1000 # sampling frequency divided by 2 --> Nyquist frequency freq = np.linspace(0,fmax,len(spec)) # lowpass-filter the crossc_beat correlation function
