def allpsd(home, project_name, run_name, run_number, GF_list, d_or_s, v_or_d,
decimate, lowpass):
'''
Compute PSDs for either all the synthetics fo a aprticular run or all the data
'''
from numpy import genfromtxt, where, log10, savez
from obspy import read
from mudpy.forward import lowpass as lfilter
from mudpy.green import stdecimate
import nitime.algorithms as tsa
#Decide what I'm going to work on
sta = genfromtxt(home + project_name + '/data/station_info/' + GF_list,
usecols=0,
dtype='S')
gf = genfromtxt(home + project_name + '/data/station_info/' + GF_list,
usecols=[4, 5],
dtype='f')
datapath = home + project_name + '/data/waveforms/'
synthpath = home + project_name + '/output/inverse_models/waveforms/'
outpath = home + project_name + '/analysis/frequency/'
if v_or_d.lower() == 'd':
kgf = 0 #disp
datasuffix = 'kdisp'
synthsuffix = 'disp'
elif v_or_d.lower() == 'v':
kgf = 1 #disp
datasuffix = 'kvel'
synthsuffix = 'vel'
if d_or_s.lower() == 'd': #We're working on observed data
path = datapath
suffix = datasuffix
else: #We're looking at syntehtics from a certain run
path = synthpath
suffix = synthsuffix
i = where(gf[:, kgf] == 1)[0]
for k in range(len(i)):
print('Working on ' + sta[i[k]])
if d_or_s.lower() == 'd': #Read data
n = read(path + sta[i[k]] + '.' + suffix + '.n')
e = read(path + sta[i[k]] + '.' + suffix + '.e')
u = read(path + sta[i[k]] + '.' + suffix + '.u')
outname = sta[i[k]] + '.' + suffix + '.psd'
if lowpass != None:
fsample = 1. / e[0].stats.delta
e[0].data = lfilter(e[0].data, lowpass, fsample, 10)
n[0].data = lfilter(n[0].data, lowpass, fsample, 10)
u[0].data = lfilter(u[0].data, lowpass, fsample, 10)
if decimate != None:
n[0] = stdecimate(n[0], decimate)
e[0] = stdecimate(e[0], decimate)
u[0] = stdecimate(u[0], decimate)
else: #Read synthetics
n = read(path + run_name + '.' + run_number + '.' + sta[i[k]] +
'.' + suffix + '.n.sac')
e = read(path + run_name + '.' + run_number + '.' + sta[i[k]] +
'.' + suffix + '.e.sac')
u = read(path + run_name + '.' + run_number + '.' + sta[i[k]] +
'.' + suffix + '.u.sac')
outname = run_name + '.' + run_number + '.' + sta[
i[k]] + '.' + suffix + '.psd'
#Compute spectra
fn, npsd, nu = tsa.multi_taper_psd(n[0].data,
Fs=1. / n[0].stats.delta,
adaptive=True,
jackknife=False,
low_bias=True)
fe, epsd, nu = tsa.multi_taper_psd(e[0].data,
Fs=1. / e[0].stats.delta,
adaptive=True,
jackknife=False,
low_bias=True)
fu, upsd, nu = tsa.multi_taper_psd(u[0].data,
Fs=1. / u[0].stats.delta,
adaptive=True,
jackknife=False,
low_bias=True)
#Convert to dB
npsd = 10 * log10(npsd)
epsd = 10 * log10(epsd)
upsd = 10 * log10(upsd)
#Write to file
savez(outpath + outname,
fn=fn,
fe=fe,
fu=fu,
npsd=npsd,
epsd=epsd,
upsd=upsd)
def allcoherence(home, project_name, run_name, run_number, GF_list, v_or_d,
decimate, lowpass):
'''
Compute PSDs for either all the synthetics fo a aprticular run or all the data
'''
from numpy import genfromtxt, where, savez, c_, pi
from obspy import read
from mudpy.forward import lowpass as lfilter
from mudpy.green import stdecimate
import nitime.algorithms as tsa
#Regularized coherence
eps = 0.000001
al = 10.
#Decide what I'm going to work on
sta = genfromtxt(home + project_name + '/data/station_info/' + GF_list,
usecols=0,
dtype='S')
gf = genfromtxt(home + project_name + '/data/station_info/' + GF_list,
usecols=[4, 5],
dtype='f')
datapath = home + project_name + '/data/waveforms/'
synthpath = home + project_name + '/output/inverse_models/waveforms/'
outpath = home + project_name + '/analysis/frequency/'
if v_or_d.lower() == 'd':
kgf = 0 #disp
datasuffix = 'kdisp'
synthsuffix = 'disp'
elif v_or_d.lower() == 'v':
kgf = 1 #disp
datasuffix = 'kvel'
synthsuffix = 'vel'
i = where(gf[:, kgf] == 1)[0]
for k in range(len(i)):
print('Working on ' + sta[i[k]])
#Read data
n = read(datapath + sta[i[k]] + '.' + datasuffix + '.n')
e = read(datapath + sta[i[k]] + '.' + datasuffix + '.e')
u = read(datapath + sta[i[k]] + '.' + datasuffix + '.u')
if lowpass != None:
fsample = 1. / e[0].stats.delta
e[0].data = lfilter(e[0].data, lowpass, fsample, 10)
n[0].data = lfilter(n[0].data, lowpass, fsample, 10)
u[0].data = lfilter(u[0].data, lowpass, fsample, 10)
if decimate != None:
n[0] = stdecimate(n[0], decimate)
e[0] = stdecimate(e[0], decimate)
u[0] = stdecimate(u[0], decimate)
#Read synthetics
nsyn = read(synthpath + run_name + '.' + run_number + '.' + sta[i[k]] +
'.' + synthsuffix + '.n.sac')
esyn = read(synthpath + run_name + '.' + run_number + '.' + sta[i[k]] +
'.' + synthsuffix + '.e.sac')
usyn = read(synthpath + run_name + '.' + run_number + '.' + sta[i[k]] +
'.' + synthsuffix + '.u.sac')
#What's the sampling rate correction?
Fs = 1. / n[0].stats.delta
Fsc = Fs / (2. * pi)
#Compute coherence
data = c_[n[0].data, nsyn[0].data].T
fn, cn = tsa.cohere.coherence_regularized(data,
epsilon=eps,
alpha=al,
csd_method={
'this_method':
'multi_taper_csd',
'adaptive': True,
'low_bias': True
})
fn = fn * Fsc
cn = cn[1, 0, :].real
data = c_[e[0].data, esyn[0].data].T
fe, ce = tsa.cohere.coherence_regularized(data,
epsilon=eps,
alpha=al,
csd_method={
'this_method':
'multi_taper_csd',
'adaptive': True,
'low_bias': True
})
fe = fe * Fsc
ce = ce[1, 0, :].real
data = c_[u[0].data, usyn[0].data].T
fu, cu = tsa.cohere.coherence_regularized(data,
epsilon=eps,
alpha=al,
csd_method={
'this_method':
'multi_taper_csd',
'adaptive': True,
'low_bias': True
})
fu = fu * Fsc
cu = cu[1, 0, :].real
#Write to file
outname = run_name + '.' + run_number + '.' + sta[
i[k]] + '.' + synthsuffix + '.coh'
savez(outpath + outname, fn=fn, fe=fe, fu=fu, cn=cn, ce=ce, cu=cu)
def allcoherence(home,project_name,run_name,run_number,GF_list,v_or_d,decimate,lowpass):
'''
Compute PSDs for either all the synthetics fo a aprticular run or all the data
'''
from numpy import genfromtxt,where,savez,c_,pi
from obspy import read
from mudpy.forward import lowpass as lfilter
from mudpy.green import stdecimate
import nitime.algorithms as tsa
#Regularized coherence
eps=0.000001
al=10.
#Decide what I'm going to work on
sta=genfromtxt(home+project_name+'/data/station_info/'+GF_list,usecols=0,dtype='S')
gf=genfromtxt(home+project_name+'/data/station_info/'+GF_list,usecols=[4,5],dtype='f')
datapath=home+project_name+'/data/waveforms/'
synthpath=home+project_name+'/output/inverse_models/waveforms/'
outpath=home+project_name+'/analysis/frequency/'
if v_or_d.lower()=='d':
kgf=0 #disp
datasuffix='kdisp'
synthsuffix='disp'
elif v_or_d.lower()=='v':
kgf=1 #disp
datasuffix='kvel'
synthsuffix='vel'
i=where(gf[:,kgf]==1)[0]
for k in range(len(i)):
print 'Working on '+sta[i[k]]
#Read data
n=read(datapath+sta[i[k]]+'.'+datasuffix+'.n')
e=read(datapath+sta[i[k]]+'.'+datasuffix+'.e')
u=read(datapath+sta[i[k]]+'.'+datasuffix+'.u')
if lowpass!=None:
fsample=1./e[0].stats.delta
e[0].data=lfilter(e[0].data,lowpass,fsample,10)
n[0].data=lfilter(n[0].data,lowpass,fsample,10)
u[0].data=lfilter(u[0].data,lowpass,fsample,10)
if decimate!=None:
n[0]=stdecimate(n[0],decimate)
e[0]=stdecimate(e[0],decimate)
u[0]=stdecimate(u[0],decimate)
#Read synthetics
nsyn=read(synthpath+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+synthsuffix+'.n.sac')
esyn=read(synthpath+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+synthsuffix+'.e.sac')
usyn=read(synthpath+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+synthsuffix+'.u.sac')
#What's the sampling rate correction?
Fs=1./n[0].stats.delta
Fsc=Fs/(2.*pi)
#Compute coherence
data=c_[n[0].data,nsyn[0].data].T
fn,cn=tsa.cohere.coherence_regularized(data,epsilon=eps,alpha=al,
csd_method={'this_method':'multi_taper_csd','adaptive':True,'low_bias':True})
fn=fn*Fsc
cn=cn[1,0,:].real
data=c_[e[0].data,esyn[0].data].T
fe,ce=tsa.cohere.coherence_regularized(data,epsilon=eps,alpha=al,
csd_method={'this_method':'multi_taper_csd','adaptive':True,'low_bias':True})
fe=fe*Fsc
ce=ce[1,0,:].real
data=c_[u[0].data,usyn[0].data].T
fu,cu=tsa.cohere.coherence_regularized(data,epsilon=eps,alpha=al,
csd_method={'this_method':'multi_taper_csd','adaptive':True,'low_bias':True})
fu=fu*Fsc
cu=cu[1,0,:].real
#Write to file
outname=run_name+'.'+run_number+'.'+sta[i[k]]+'.'+synthsuffix+'.coh'
savez(outpath+outname,fn=fn,fe=fe,fu=fu,cn=cn,ce=ce,cu=cu)
def synthetics(home,project_name,run_name,run_number,gflist,vord,decimate,lowpass,t_lim,sort,scale,k_or_g):
'''
Plot synthetics vs real data
gflist: The GF control file that decides what to plot/not plot
datapath
'''
from obspy import read
from numpy import genfromtxt,where,argsort
import matplotlib.pyplot as plt
import matplotlib
from mudpy.green import stdecimate
from mudpy.forward import lowpass as lfilter
matplotlib.rcParams.update({'font.size': 14})
#Decide what to plot
sta=genfromtxt(home+project_name+'/data/station_info/'+gflist,usecols=0,dtype='S')
lon=genfromtxt(home+project_name+'/data/station_info/'+gflist,usecols=[1],dtype='f')
lat=genfromtxt(home+project_name+'/data/station_info/'+gflist,usecols=[2],dtype='f')
gf=genfromtxt(home+project_name+'/data/station_info/'+gflist,usecols=[4,5],dtype='f')
datapath=home+project_name+'/data/waveforms/'
synthpath=home+project_name+'/output/inverse_models/waveforms/'
if vord.lower()=='d':
kgf=0 #disp
if k_or_g.lower()=='kal':
#datasuffix='kdisp'
datasuffix='HX'
else:
#datasuffix='disp'
datasuffix='HX'
synthsuffix='disp'
elif vord.lower()=='v':
kgf=1 #disp
datasuffix='kvel'
synthsuffix='vel'
#Decide on sorting
i=where(gf[:,kgf]==1)[0]
if sort.lower()=='lon':
j=argsort(lon[i])[::-1]
i=i[j]
elif sort.lower()=='lat':
j=argsort(lat[i])[::-1]
i=i[j]
nsta=len(i)
fig, axarr = plt.subplots(nsta, 3)
for k in range(len(i)):
n=read(datapath+sta[i[k]]+'.'+datasuffix+'.n')
e=read(datapath+sta[i[k]]+'.'+datasuffix+'.e')
u=read(datapath+sta[i[k]]+'.'+datasuffix+'.u')
if lowpass!=None:
fsample=1./e[0].stats.delta
e[0].data=lfilter(e[0].data,lowpass,fsample,10)
n[0].data=lfilter(n[0].data,lowpass,fsample,10)
u[0].data=lfilter(u[0].data,lowpass,fsample,10)
if decimate!=None:
n[0]=stdecimate(n[0],decimate)
e[0]=stdecimate(e[0],decimate)
u[0]=stdecimate(u[0],decimate)
ns=read(synthpath+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+synthsuffix+'.n.sac')
es=read(synthpath+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+synthsuffix+'.e.sac')
us=read(synthpath+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+synthsuffix+'.u.sac')
if scale!=None:
n[0].data=n[0].data/scale
ns[0].data=ns[0].data/scale
e[0].data=e[0].data/scale
es[0].data=es[0].data/scale
u[0].data=u[0].data/scale
us[0].data=us[0].data/scale
#Make plot
axn=axarr[k,0]
axe=axarr[k,1]
axu=axarr[k,2]
axn.plot(n[0].times(),n[0].data,'k',ns[0].times(),ns[0].data,'r')
axn.grid(which='both')
axe.plot(e[0].times(),e[0].data,'k',es[0].times(),es[0].data,'r')
axe.grid(which='both')
axu.plot(u[0].times(),u[0].data,'k',us[0].times(),us[0].data,'r')
axu.grid(which='both')
axe.yaxis.set_ticklabels([])
axu.yaxis.set_ticklabels([])
axe.set_xlim(t_lim)
axn.set_xlim(t_lim)
axu.set_xlim(t_lim)
axn.yaxis.set_ticklabels([])
axe.yaxis.set_ticklabels([])
axu.yaxis.set_ticklabels([])
axn.yaxis.grid(False)
axe.yaxis.grid(False)
axu.yaxis.grid(False)
axn.yaxis.set_ticks([])
axe.yaxis.set_ticks([])
axu.yaxis.set_ticks([])
#Annotations
trange=t_lim[1]-t_lim[0]
sign=1.
if abs(min(n[0].data))>max(n[0].data):
sign=-1.
nmax='%.3f' % (sign*max(abs(n[0].data)))
sign=1.
if abs(min(ns[0].data))>max(ns[0].data):
sign=-1.
nsmax='%.3f' % (sign*max(abs(ns[0].data)))
sign=1.
nlims=axn.get_ylim()
nrange=nlims[1]-nlims[0]
if abs(min(e[0].data))>max(e[0].data):
sign=-1.
emax='%.3f' % (sign*max(abs(e[0].data)))
sign=1.
if abs(min(es[0].data))>max(es[0].data):
sign=-1.
esmax='%.3f' % (sign*max(abs(es[0].data)))
sign=1.
elims=axe.get_ylim()
erange=elims[1]-elims[0]
if abs(min(u[0].data))>max(u[0].data):
sign=-1.
umax='%.3f' % (sign*max(abs(u[0].data)))
sign=1.
if abs(min(us[0].data))>max(us[0].data):
sign=-1
usmax='%.3f' % (sign*max(abs(us[0].data)))
sign=1.
ulims=axu.get_ylim()
urange=ulims[1]-ulims[0]
axn.annotate(nmax,xy=(t_lim[1]-0.3*trange,nlims[0]+0.02*nrange),fontsize=12)
axe.annotate(emax,xy=(t_lim[1]-0.3*trange,elims[0]+0.02*erange),fontsize=12)
axu.annotate(umax,xy=(t_lim[1]-0.3*trange,ulims[0]+0.02*urange),fontsize=12)
axn.annotate(nsmax,xy=(t_lim[1]-0.3*trange,nlims[0]+0.7*nrange),fontsize=12,color='red')
axe.annotate(esmax,xy=(t_lim[1]-0.3*trange,elims[0]+0.7*erange),fontsize=12,color='red')
axu.annotate(usmax,xy=(t_lim[1]-0.3*trange,ulims[0]+0.7*urange),fontsize=12,color='red')
#Station name
axn.set_ylabel(sta[i[k]],rotation=0)
if k==0:
if vord.lower()=='d':
axn.set_title('North (m)')
axe.set_title('East (m)')
axu.set_title('Up (m)')
else:
axn.set_title('North (m/s)')
axe.set_title('East (m/s)')
axu.set_title('Up (m/s)')
if k!=len(i)-1:
axn.xaxis.set_ticklabels([])
axe.xaxis.set_ticklabels([])
axu.xaxis.set_ticklabels([])
xtick=axn.xaxis.get_majorticklocs()
ix=[1,3,5]
xtick=xtick[ix]
xticklabel=['','50','','150','','250','']
if k==len(i)-1: #Last plot
axe.set_xlabel('Time (s)')
axn.xaxis.set_ticklabels(xticklabel)
axe.xaxis.set_ticklabels(xticklabel)
axu.xaxis.set_ticklabels(xticklabel)
#axn.xaxis.set_ticks(xtick)
#axe.xaxis.set_ticks(xtick)
#axu.xaxis.set_ticks(xtick)
plt.subplots_adjust(left=0.2, bottom=0.05, right=0.8, top=0.95, wspace=0, hspace=0)
def allpsd(home,project_name,run_name,run_number,GF_list,d_or_s,v_or_d,decimate,lowpass):
'''
Compute PSDs for either all the synthetics fo a aprticular run or all the data
'''
from numpy import genfromtxt,where,log10,savez
from obspy import read
from mudpy.forward import lowpass as lfilter
from mudpy.green import stdecimate
import nitime.algorithms as tsa
#Decide what I'm going to work on
sta=genfromtxt(home+project_name+'/data/station_info/'+GF_list,usecols=0,dtype='S')
gf=genfromtxt(home+project_name+'/data/station_info/'+GF_list,usecols=[4,5],dtype='f')
datapath=home+project_name+'/data/waveforms/'
synthpath=home+project_name+'/output/inverse_models/waveforms/'
outpath=home+project_name+'/analysis/frequency/'
if v_or_d.lower()=='d':
kgf=0 #disp
datasuffix='kdisp'
synthsuffix='disp'
elif v_or_d.lower()=='v':
kgf=1 #disp
datasuffix='kvel'
synthsuffix='vel'
if d_or_s.lower()=='d': #We're working on observed data
path=datapath
suffix=datasuffix
else: #We're looking at syntehtics from a certain run
path=synthpath
suffix=synthsuffix
i=where(gf[:,kgf]==1)[0]
for k in range(len(i)):
print 'Working on '+sta[i[k]]
if d_or_s.lower()=='d': #Read data
n=read(path+sta[i[k]]+'.'+suffix+'.n')
e=read(path+sta[i[k]]+'.'+suffix+'.e')
u=read(path+sta[i[k]]+'.'+suffix+'.u')
outname=sta[i[k]]+'.'+suffix+'.psd'
if lowpass!=None:
fsample=1./e[0].stats.delta
e[0].data=lfilter(e[0].data,lowpass,fsample,10)
n[0].data=lfilter(n[0].data,lowpass,fsample,10)
u[0].data=lfilter(u[0].data,lowpass,fsample,10)
if decimate!=None:
n[0]=stdecimate(n[0],decimate)
e[0]=stdecimate(e[0],decimate)
u[0]=stdecimate(u[0],decimate)
else: #Read synthetics
n=read(path+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+suffix+'.n.sac')
e=read(path+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+suffix+'.e.sac')
u=read(path+run_name+'.'+run_number+'.'+sta[i[k]]+'.'+suffix+'.u.sac')
outname=run_name+'.'+run_number+'.'+sta[i[k]]+'.'+suffix+'.psd'
#Compute spectra
fn, npsd, nu = tsa.multi_taper_psd(n[0].data,Fs=1./n[0].stats.delta,adaptive=True,jackknife=False,low_bias=True)
fe, epsd, nu = tsa.multi_taper_psd(e[0].data,Fs=1./e[0].stats.delta,adaptive=True,jackknife=False,low_bias=True)
fu, upsd, nu = tsa.multi_taper_psd(u[0].data,Fs=1./u[0].stats.delta,adaptive=True,jackknife=False,low_bias=True)
#Convert to dB
npsd=10*log10(npsd)
epsd=10*log10(epsd)
upsd=10*log10(upsd)
#Write to file
savez(outpath+outname,fn=fn,fe=fe,fu=fu,npsd=npsd,epsd=epsd,upsd=upsd)