def RTdeconv(data, om0, h, G, dt,corners=4, baselinelen=60., taperlen= 10.\
, fmin = 0.001, fmax = 0.005 ):
'''
Using the coef. of one station computes the displacement trace.
:param data: Raw data to be deconvoluted.
:param om0: Natural frequency.
:param h: damping constant.
:param s: sensitivity of the seismometer.
:param dt: sampling rate (sec).
:param corners : order of the Butterworth filter to be applied.
:param baselinelen: time (in Sec) to consider to define the baseline.
:param taperlen: Percentage of the trace to be tapered at the beginning.
:param (fmin, fmax): Corner frequencies of the Butterworth filter to be applied.
:return: Time serie with the deconvoluted displacement trace.
'''
baseline = np.mean(data[:int(baselinelen / dt)])
nwin = int(10. * len(data) / 100.)
if len(data) < 2 * nwin: nwin = len(data)
data -= baseline
taper = 0.5 * (1. - np.cos(np.pi * np.linspace(0., 1., nwin)))
data[:nwin] *= taper
#data[-nwin:]*= taper[::-1]
datap1 = data[1:]
datap2 = data[2:]
## Acceleration + double integration
c0 = 1. / G / dt
c1 = -2. * (1. + h * om0 * dt) / G / dt
c2 = (1. + 2. * h * om0 * dt + dt * dt * om0 * om0) / G / dt
accel = np.zeros(len(data))
aux = c2 * datap2 + c1 * datap1[:-1] + c0 * data[:-2]
accel[2:] = np.cumsum(aux)
accel = bp.bandpassfilter(accel, len(accel), dt, corners, 1, fmin, fmax)
vel = np.zeros(len(data))
vel[1:] = 0.5 * dt * np.cumsum(accel[:-1] + accel[1:])
dis = np.zeros(len(data))
dis[1:] = 0.5 * dt * np.cumsum(vel[:-1] + vel[1:])
return dis
def RTdeconv(data, om0, h, G, dt,corners=4, baselinelen=60., taperlen= 10.\
, fmin = 0.001, fmax = 0.005 ):
''' Using the coef. of one station computes
the displacement trace.
Input:
data: Raw data to be deconvoluted
om0, h, G: Natural frequency, damping constant, sensitivity of the seismometer
dt : sampling rate (sec)
corners : order of the Butterworth filter to be applied.
baselinelen : time (in Sec ) to consider to define the baseline.
taperlen: Percentage of the trace to be tapered at the beginning.
fmin, fmax: Corner frequencies of the Butterworth filter to be applied.
Output
Time serie with the deconvoluted displacement trace
'''
baseline = np.mean(data[:int(baselinelen/dt)])
nwin = int(10.*len(data)/100.)
if len(data) < 2*nwin: nwin = len(data)
data -= baseline
taper = 0.5*(1.-np.cos(np.pi*np.linspace(0.,1.,nwin)))
data[:nwin]*= taper
#data[-nwin:]*= taper[::-1]
datap1 = data[1:]
datap2 = data[2:]
## Acceleration + double integration
c0 = 1./G/dt
c1 = -2.*(1. + h*om0*dt)/G/dt
c2 = (1. + 2.*h*om0*dt + dt*dt*om0*om0)/G/dt
accel = np.zeros(len(data))
aux = c2*datap2 + c1*datap1[:-1] + c0*data[:-2]
accel[2:] = np.cumsum(aux)
accel = bp.bandpassfilter(accel,len(accel),dt ,corners , 1 , fmin,fmax)
vel = np.zeros(len(data))
vel[1:] = 0.5*dt*np.cumsum(accel[:-1]+accel[1:])
dis = np.zeros(len(data))
dis[1:] = 0.5*dt*np.cumsum(vel[:-1]+vel[1:])
return dis
def main(argv=sys.argv):
#Earth's parameters
#~ beta = 4.e3 #m/s
#~ rho = 3.e3 #kg/m^3
#~ mu = rho*beta*beta
PLotSt = ["IU.TRQA.00.LHZ",
"IU.LVC.00.LHZ",
"II.NNA.00.LHZ",
"IU.RAR.00.LHZ"]
#PlotSubf = [143, 133, 123, 113, 103, 93,
# 83, 73, 63, 53]
PlotSubf = [6,3]
#Set rup_vel = 0 to have a point source solution
RupVel = 2.1 #Chilean eq from Lay et al
t_h = 10. # Half duration for each sf
noiselevel = 0.0# L1 norm level of noise
mu =40e9
#W-Phase filter
corners = 4.
fmin = 0.001
fmax = 0.005
### Data from Chilean 2010 EQ (Same as W phase inv.)
strike = 18.
dip = 18.
rake = 104. # 109.
rakeA = rake + 45.
rakeB = rake - 45.
### Fault's grid parameters
nsx = 21 #Number of sf along strike
nsy = 11 #Number of sf along dip
flen = 600. #Fault's longitude [km] along strike
fwid = 300. #Fault's longitude [km] along dip
direc = 0 #Directivity 0 = bilateral
Min_h = 10. #Min depth of the fault
### Derivated parameters:
nsf = nsx*nsy
sflen = flen/float(nsx)
sfwid = fwid/float(nsy)
swp = [1, 0, 2] # useful to swap (lat,lon, depth)
mindist = flen*fwid # minimun dist to the hypcen (initializing)
###Chessboard
#weight = np.load("RealSol.npy")
weight = np.zeros(nsf)
weight[::2] = 1
#weight[::2] = 1
#~ weight[10]=15
#~ weight[5001]=10
#~ weight[3201]=2
## Setting dirs and reading files.
GFdir = "/home/roberto/data/GFS/"
workdir = os.path.abspath(".")+"/"
datadir = workdir + "DATA/"
tracesfilename = workdir + "goodtraces.dat"
tracesdir = workdir + "WPtraces/"
try:
reqfilename = glob.glob(workdir + '*.syn.req')[0]
except IndexError:
print "There is not *.syn.req file in the dir"
sys.exit()
basename = reqfilename.split("/")[-1][:-4]
if not os.path.exists(tracesfilename):
print tracesfilename, "does not exist."
exit()
if not os.path.exists(datadir):
os.makedirs(datadir)
if not os.path.exists(tracesdir):
os.makedirs(tracesdir)
tracesfile = open(tracesfilename)
reqfile = open(reqfilename)
trlist = readtraces(tracesfile)
eqdata = readreq(reqfile)
tracesfile.close()
reqfile.close()
####Hypocentre from
### http://earthquake.usgs.gov/earthquakes/eqinthenews/2010/us2010tfan/
cmteplat = -35.91#-35.85#-36.03#-35.83
cmteplon = -72.73#-72.72#-72.83# -72.67
cmtepdepth= 35.
eq_hyp = (cmteplat,cmteplon,cmtepdepth)
############
# Defining the sf system
grid, sblt = fault_grid('CL-2010',cmteplat,cmteplon,
cmtepdepth, direc,
Min_h, strike, dip, rake, flen,fwid ,nsx,nsy,
Verbose=False,ffi_io=True,gmt_io=True)
print ('CL-2010',cmteplat,cmteplon,
cmtepdepth, direc,
Min_h, strike, dip, rake, flen,fwid ,nsx,nsy)
print grid[0][1]
#sys.exit()
#This calculation is inside of the loop
#~ NP = [strike, dip, rake]
#~ M = np.array(NodalPlanetoMT(NP))
#~ Mp = np.sum(M**2)/np.sqrt(2)
#############################################################################
######Determining the sf closest to the hypocentre:
min_Dist_hyp_subf = flen *fwid
for subf in range(nsf):
sblat = grid[subf][1]
sblon = grid[subf][0]
sbdepth = grid[subf][2]
sf_hyp = (sblat,sblon, sbdepth)
Dist_hyp_subf = hypo2dist(eq_hyp,sf_hyp)
if Dist_hyp_subf < min_Dist_hyp_subf:
min_Dist_hyp_subf = Dist_hyp_subf
min_sb_hyp = sf_hyp
hyp_subf = subf
####Determining trimming times:
test_tr = read(GFdir + "H003.5/PP/GF.0001.SY.LHZ.SAC")[0]
t0 = test_tr.stats.starttime
TrimmingTimes = {} # Min. Distace from the fault to each station.
A =0
for trid in trlist:
metafile = workdir + "DATA/" + "META." + trid + ".xml"
META = DU.getMetadataFromXML(metafile)[trid]
stlat = META['latitude']
stlon = META['longitude']
dist = locations2degrees(min_sb_hyp[0],min_sb_hyp[1],\
stlat,stlon)
parrivaltime = getTravelTimes(dist,min_sb_hyp[2])[0]['time']
ta = t0 + parrivaltime
tb = ta + round(15.*dist)
TrimmingTimes[trid] = (ta, tb)
###########################
DIST = []
# Ordering the stations in terms of distance
for trid in trlist:
metafile = workdir + "DATA/" + "META." + trid + ".xml"
META = DU.getMetadataFromXML(metafile)[trid]
lat = META['latitude']
lon = META['longitude']
trdist = locations2degrees(cmteplat,
cmteplon,lat,lon)
DIST.append(trdist)
DistIndex = lstargsort(DIST)
trlist = [trlist[i] for i in DistIndex]
stdistribution = StDistandAzi(trlist, eq_hyp , workdir + "DATA/")
StDistributionPlot(stdistribution)
#exit()
#Main loop
for subf in range(nsf):
print subf
sflat = grid[subf][1]
sflon = grid[subf][0]
sfdepth = grid[subf][2]
#~ strike = grid[subf][3] #+ 360.
#~ dip = grid[subf][4]
#~ rake = grid[subf][5] #
NP = [strike, dip, rake]
NPA = [strike, dip, rakeA]
NPB = [strike, dip, rakeB]
M = np.array(NodalPlanetoMT(NP))
MA = np.array(NodalPlanetoMT(NPA))
MB = np.array(NodalPlanetoMT(NPB))
#Time delay is calculated as the time in which
#the rupture reach the subfault
sf_hyp = (sflat, sflon, sfdepth)
Dist_ep_subf = hypo2dist(eq_hyp,sf_hyp)
if Dist_ep_subf < mindist:
mindist = Dist_ep_subf
minsubf = subf
if RupVel == 0:
t_d = eqdata['time_shift']
else:
t_d = round(Dist_ep_subf/RupVel) #-59.
print sflat, sflon, sfdepth
# Looking for the best depth dir:
depth = []
depthdir = []
for file in os.listdir(GFdir):
if file[-2:] == ".5":
depthdir.append(file)
depth.append(float(file[1:-2]))
BestDirIndex = np.argsort(abs(sfdepth\
- np.array(depth)))[0]
hdir = GFdir + depthdir[BestDirIndex] + "/"
###
SYN = np.array([])
SYNA = np.array([])
SYNB = np.array([])
for trid in trlist:
metafile = workdir + "DATA/" + "META." + trid + ".xml"
META = DU.getMetadataFromXML(metafile)[trid]
lat = META['latitude']
lon = META['longitude']
#Subfault loop
#GFs Selection:
##Change to folloing loop
dist = locations2degrees(sflat,sflon,lat,lon)
azi = -np.pi/180.*gps2DistAzimuth(lat,lon,
sflat,sflon)[2]
trPPsy, trRRsy, trRTsy, trTTsy = \
GFSelectZ(hdir,dist)
trROT = MTrotationZ(azi, trPPsy, trRRsy, trRTsy, trTTsy)
orig = trROT[0].stats.starttime
dt = trROT[0].stats.delta
trianglen = 2.*int(t_h/dt)-1.
FirstValid = int(trianglen/2.) + 1 # to delete
window = triang(trianglen)
window /= np.sum(window)
#window = np.array([1.])
parrivaltime = getTravelTimes(dist,sfdepth)[0]['time']
t1 = TrimmingTimes[trid][0] - t_d
t2 = TrimmingTimes[trid][1] - t_d
for trR in trROT:
trR.data *= 10.**-21 ## To get M in Nm
trR.data -= trR.data[0]
AUX1 = len(trR)
trR.data = convolve(trR.data,window,mode='valid')
AUX2 = len(trR)
mean = np.mean(np.hstack((trR.data[0]*np.ones(FirstValid),\
trR.data[:60./trR.stats.delta*1.-FirstValid+1])))
#mean = np.mean(trR.data[:60])
trR.data -= mean
trR.data = bp.bandpassfilter(trR.data,len(trR), trR.stats.delta,\
corners , 1 , fmin, fmax)
t_l = dt*0.5*(AUX1 - AUX2)
trR.trim(t1-t_l,t2-t_l, pad=True, fill_value=trR.data[0]) #We lost t_h due to the convolution
#~ for trR in trROT:
#~ trR.data *= 10.**-23 ## To get M in Nm
#~ trR.data -= trR.data[0]
#~ trR.data = convolve(trR.data,window,mode='same')
#~ #mean = np.mean(np.hstack((trR.data[0]*np.ones(FirstValid),\
#~ #trR.data[:60./trR.stats.delta*1.-FirstValid+1])))
#~ mean = np.mean(trR.data[:60])
#~ trR.data -= mean
#~ trR.data = bp.bandpassfilter(trR.data,len(trR), trR.stats.delta,\
#~ corners , 1 , fmin, fmax)
#~ trR.trim(t1,t2,pad=True, fill_value=trR.data[0])
trROT = np.array(trROT)
syn = np.dot(trROT.T,M)
synA = np.dot(trROT.T,MA)
synB = np.dot(trROT.T,MB)
SYN = np.append(SYN,syn)
SYNA = np.append(SYNA,synA)
SYNB = np.append(SYNB,synB)
print np.shape(A), np.shape(np.array([SYN]))
if subf == 0:
A = np.array([SYN])
AA = np.array([SYNA])
AB = np.array([SYNB])
else:
A = np.append(A,np.array([SYN]),0)
AA = np.append(AA,np.array([SYNA]),0)
AB = np.append(AB,np.array([SYNB]),0)
AC = np.vstack((AA,AB))
print np.shape(AC)
print np.shape(weight)
B = np.dot(A.T,weight)
stsyn = Stream()
n = 0
Ntraces= {}
for trid in trlist:
spid = trid.split(".")
print trid
NMIN = 1. + (TrimmingTimes[trid][1] - TrimmingTimes[trid][0]) / dt
Ntraces[trid] = (n,NMIN + n)
trsyn = Trace(B[n:NMIN+n])
n += NMIN
trsyn.stats.network = spid[0]
trsyn.stats.station = spid[1]
trsyn.stats.location = spid[2]
trsyn.stats.channel = spid[3]
trsyn = AddNoise(trsyn,level = noiselevel)
#trsyn.stats.starttime =
stsyn.append(trsyn)
stsyn.write(workdir+"WPtraces/" + basename + ".decov.trim.mseed",
format="MSEED")
#####################################################
# Plotting:
#####################################################
#we are going to reflect the y axis later, so:
print minsubf
hypsbloc = [minsubf / nsy , -(minsubf % nsy) - 2]
#Creating the strike and dip axis:
StrikeAx= np.linspace(0,flen,nsx+1)
DipAx= np.linspace(0,fwid,nsy+1)
DepthAx = DipAx*np.sin(np.pi/180.*dip) + Min_h
hlstrike = StrikeAx[hypsbloc[0]] + sflen*0.5
hldip = DipAx[hypsbloc[1]] + sfwid*0.5
hldepth = DepthAx[hypsbloc[1]] + sfwid*0.5*np.sin(np.pi/180.*dip)
StrikeAx = StrikeAx - hlstrike
DipAx = DipAx - hldip
XX, YY = np.meshgrid(StrikeAx, DepthAx)
XX, ZZ = np.meshgrid(StrikeAx, DipAx )
sbarea = sflen*sfwid
SLIPS = weight.reshape(nsx,nsy).T#[::-1,:]
SLIPS /= mu*1.e6*sbarea
######Plot:#####################
plt.figure()
ax = host_subplot(111)
im = ax.pcolor(XX, YY, SLIPS, cmap="jet")
ax.set_ylabel('Depth [km]')
ax.set_ylim(DepthAx[-1],DepthAx[0])
# Creating a twin plot
ax2 = ax.twinx()
#im2 = ax2.pcolor(XX, ZZ, SLIPS[::-1,:], cmap="Greys")
im2 = ax2.pcolor(XX, ZZ, SLIPS[::-1,:], cmap="jet")
ax2.set_ylabel('Distance along the dip [km]')
ax2.set_xlabel('Distance along the strike [km]')
ax2.set_ylim(DipAx[0],DipAx[-1])
ax2.set_xlim(StrikeAx[0],StrikeAx[-1])
ax.axis["bottom"].major_ticklabels.set_visible(False)
ax2.axis["bottom"].major_ticklabels.set_visible(False)
ax2.axis["top"].set_visible(True)
ax2.axis["top"].label.set_visible(True)
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.1)
cb = plt.colorbar(im, cax=cax, orientation="horizontal")
cb.set_label("Slip [m]")
ax2.plot([0], [0], '*', ms=225./(nsy+4))
ax2.set_xticks(ax2.get_xticks()[1:-1])
#ax.set_yticks(ax.get_yticks()[1:])
#ax2.set_yticks(ax2.get_yticks()[:-1])
#########Plotting the selected traces:
nsp = len(PLotSt) * len(PlotSubf)
plt.figure(figsize=(13,11))
plt.title("Synthetics for rake = " + str(round(rake)))
mindis = []
maxdis = []
for i, trid in enumerate(PLotSt):
x = np.arange(0,Ntraces[trid][1]-Ntraces[trid][0],
dt)
for j, subf in enumerate(PlotSubf):
y = A[subf, Ntraces[trid][0]:Ntraces[trid][1]]
if j == 0:
yy = y
else:
yy = np.vstack((yy,y))
maxdis.append(np.max(yy))
mindis.append(np.min(yy))
for i, trid in enumerate(PLotSt):
x = np.arange(0,Ntraces[trid][1]-Ntraces[trid][0],
dt)
for j, subf in enumerate(PlotSubf):
y = A[subf, Ntraces[trid][0]:Ntraces[trid][1]]
plt.subplot2grid((len(PlotSubf), len(PLotSt)),
(j, i))
plt.plot(x,y, linewidth=2.5)
if j == 0:
plt.title(trid)
fig = plt.gca()
fig.axes.get_yaxis().set_ticks([])
fig.set_ylabel(str(subf),rotation=0)
fig.set_xlim((x[0],x[-1]))
fig.set_ylim((mindis[i],maxdis[i]))
if subf != PlotSubf[-1]:
fig.axes.get_xaxis().set_ticks([])
plt.show()
def main(argv=sys.argv):
#Earth's parameters
#~ beta = 4.e3 #m/s
#~ rho = 3.e3 #kg/m^3
#~ mu = rho*beta*beta
PLotSt = [
"IU.TRQA.00.LHZ", "IU.LVC.00.LHZ", "II.NNA.00.LHZ", "IU.RAR.00.LHZ"
]
#PlotSubf = [143, 133, 123, 113, 103, 93,
# 83, 73, 63, 53]
PlotSubf = [6, 3]
#Set rup_vel = 0 to have a point source solution
RupVel = 2.1 #Chilean eq from Lay et al
t_h = 10. # Half duration for each sf
noiselevel = 0.0 # L1 norm level of noise
mu = 40e9
#W-Phase filter
corners = 4.
fmin = 0.001
fmax = 0.005
### Data from Chilean 2010 EQ (Same as W phase inv.)
strike = 18.
dip = 18.
rake = 104. # 109.
rakeA = rake + 45.
rakeB = rake - 45.
### Fault's grid parameters
nsx = 21 #Number of sf along strike
nsy = 11 #Number of sf along dip
flen = 600. #Fault's longitude [km] along strike
fwid = 300. #Fault's longitude [km] along dip
direc = 0 #Directivity 0 = bilateral
Min_h = 10. #Min depth of the fault
### Derivated parameters:
nsf = nsx * nsy
sflen = flen / float(nsx)
sfwid = fwid / float(nsy)
swp = [1, 0, 2] # useful to swap (lat,lon, depth)
mindist = flen * fwid # minimun dist to the hypcen (initializing)
###Chessboard
#weight = np.load("RealSol.npy")
weight = np.zeros(nsf)
weight[::2] = 1
#weight[::2] = 1
#~ weight[10]=15
#~ weight[5001]=10
#~ weight[3201]=2
## Setting dirs and reading files.
GFdir = "/home/roberto/data/GFS/"
workdir = os.path.abspath(".") + "/"
datadir = workdir + "DATA/"
tracesfilename = workdir + "goodtraces.dat"
tracesdir = workdir + "WPtraces/"
try:
reqfilename = glob.glob(workdir + '*.syn.req')[0]
except IndexError:
print "There is not *.syn.req file in the dir"
sys.exit()
basename = reqfilename.split("/")[-1][:-4]
if not os.path.exists(tracesfilename):
print tracesfilename, "does not exist."
exit()
if not os.path.exists(datadir):
os.makedirs(datadir)
if not os.path.exists(tracesdir):
os.makedirs(tracesdir)
tracesfile = open(tracesfilename)
reqfile = open(reqfilename)
trlist = readtraces(tracesfile)
eqdata = readreq(reqfile)
tracesfile.close()
reqfile.close()
####Hypocentre from
### http://earthquake.usgs.gov/earthquakes/eqinthenews/2010/us2010tfan/
cmteplat = -35.91 #-35.85#-36.03#-35.83
cmteplon = -72.73 #-72.72#-72.83# -72.67
cmtepdepth = 35.
eq_hyp = (cmteplat, cmteplon, cmtepdepth)
############
# Defining the sf system
grid, sblt = fault_grid('CL-2010',
cmteplat,
cmteplon,
cmtepdepth,
direc,
Min_h,
strike,
dip,
rake,
flen,
fwid,
nsx,
nsy,
Verbose=False,
ffi_io=True,
gmt_io=True)
print('CL-2010', cmteplat, cmteplon, cmtepdepth, direc, Min_h, strike, dip,
rake, flen, fwid, nsx, nsy)
print grid[0][1]
#sys.exit()
#This calculation is inside of the loop
#~ NP = [strike, dip, rake]
#~ M = np.array(NodalPlanetoMT(NP))
#~ Mp = np.sum(M**2)/np.sqrt(2)
#############################################################################
######Determining the sf closest to the hypocentre:
min_Dist_hyp_subf = flen * fwid
for subf in range(nsf):
sblat = grid[subf][1]
sblon = grid[subf][0]
sbdepth = grid[subf][2]
sf_hyp = (sblat, sblon, sbdepth)
Dist_hyp_subf = hypo2dist(eq_hyp, sf_hyp)
if Dist_hyp_subf < min_Dist_hyp_subf:
min_Dist_hyp_subf = Dist_hyp_subf
min_sb_hyp = sf_hyp
hyp_subf = subf
####Determining trimming times:
test_tr = read(GFdir + "H003.5/PP/GF.0001.SY.LHZ.SAC")[0]
t0 = test_tr.stats.starttime
TrimmingTimes = {} # Min. Distace from the fault to each station.
A = 0
for trid in trlist:
metafile = workdir + "DATA/" + "META." + trid + ".xml"
META = DU.getMetadataFromXML(metafile)[trid]
stlat = META['latitude']
stlon = META['longitude']
dist = locations2degrees(min_sb_hyp[0],min_sb_hyp[1],\
stlat,stlon)
parrivaltime = getTravelTimes(dist, min_sb_hyp[2])[0]['time']
ta = t0 + parrivaltime
tb = ta + round(15. * dist)
TrimmingTimes[trid] = (ta, tb)
###########################
DIST = []
# Ordering the stations in terms of distance
for trid in trlist:
metafile = workdir + "DATA/" + "META." + trid + ".xml"
META = DU.getMetadataFromXML(metafile)[trid]
lat = META['latitude']
lon = META['longitude']
trdist = locations2degrees(cmteplat, cmteplon, lat, lon)
DIST.append(trdist)
DistIndex = lstargsort(DIST)
trlist = [trlist[i] for i in DistIndex]
stdistribution = StDistandAzi(trlist, eq_hyp, workdir + "DATA/")
StDistributionPlot(stdistribution)
#exit()
#Main loop
for subf in range(nsf):
print subf
sflat = grid[subf][1]
sflon = grid[subf][0]
sfdepth = grid[subf][2]
#~ strike = grid[subf][3] #+ 360.
#~ dip = grid[subf][4]
#~ rake = grid[subf][5] #
NP = [strike, dip, rake]
NPA = [strike, dip, rakeA]
NPB = [strike, dip, rakeB]
M = np.array(NodalPlanetoMT(NP))
MA = np.array(NodalPlanetoMT(NPA))
MB = np.array(NodalPlanetoMT(NPB))
#Time delay is calculated as the time in which
#the rupture reach the subfault
sf_hyp = (sflat, sflon, sfdepth)
Dist_ep_subf = hypo2dist(eq_hyp, sf_hyp)
if Dist_ep_subf < mindist:
mindist = Dist_ep_subf
minsubf = subf
if RupVel == 0:
t_d = eqdata['time_shift']
else:
t_d = round(Dist_ep_subf / RupVel) #-59.
print sflat, sflon, sfdepth
# Looking for the best depth dir:
depth = []
depthdir = []
for file in os.listdir(GFdir):
if file[-2:] == ".5":
depthdir.append(file)
depth.append(float(file[1:-2]))
BestDirIndex = np.argsort(abs(sfdepth\
- np.array(depth)))[0]
hdir = GFdir + depthdir[BestDirIndex] + "/"
###
SYN = np.array([])
SYNA = np.array([])
SYNB = np.array([])
for trid in trlist:
metafile = workdir + "DATA/" + "META." + trid + ".xml"
META = DU.getMetadataFromXML(metafile)[trid]
lat = META['latitude']
lon = META['longitude']
#Subfault loop
#GFs Selection:
##Change to folloing loop
dist = locations2degrees(sflat, sflon, lat, lon)
azi = -np.pi / 180. * gps2DistAzimuth(lat, lon, sflat, sflon)[2]
trPPsy, trRRsy, trRTsy, trTTsy = \
GFSelectZ(hdir,dist)
trROT = MTrotationZ(azi, trPPsy, trRRsy, trRTsy, trTTsy)
orig = trROT[0].stats.starttime
dt = trROT[0].stats.delta
trianglen = 2. * int(t_h / dt) - 1.
FirstValid = int(trianglen / 2.) + 1 # to delete
window = triang(trianglen)
window /= np.sum(window)
#window = np.array([1.])
parrivaltime = getTravelTimes(dist, sfdepth)[0]['time']
t1 = TrimmingTimes[trid][0] - t_d
t2 = TrimmingTimes[trid][1] - t_d
for trR in trROT:
trR.data *= 10.**-21 ## To get M in Nm
trR.data -= trR.data[0]
AUX1 = len(trR)
trR.data = convolve(trR.data, window, mode='valid')
AUX2 = len(trR)
mean = np.mean(np.hstack((trR.data[0]*np.ones(FirstValid),\
trR.data[:60./trR.stats.delta*1.-FirstValid+1])))
#mean = np.mean(trR.data[:60])
trR.data -= mean
trR.data = bp.bandpassfilter(trR.data,len(trR), trR.stats.delta,\
corners , 1 , fmin, fmax)
t_l = dt * 0.5 * (AUX1 - AUX2)
trR.trim(t1 - t_l, t2 - t_l, pad=True, fill_value=trR.data[0]
) #We lost t_h due to the convolution
#~ for trR in trROT:
#~ trR.data *= 10.**-23 ## To get M in Nm
#~ trR.data -= trR.data[0]
#~ trR.data = convolve(trR.data,window,mode='same')
#~ #mean = np.mean(np.hstack((trR.data[0]*np.ones(FirstValid),\
#~ #trR.data[:60./trR.stats.delta*1.-FirstValid+1])))
#~ mean = np.mean(trR.data[:60])
#~ trR.data -= mean
#~ trR.data = bp.bandpassfilter(trR.data,len(trR), trR.stats.delta,\
#~ corners , 1 , fmin, fmax)
#~ trR.trim(t1,t2,pad=True, fill_value=trR.data[0])
trROT = np.array(trROT)
syn = np.dot(trROT.T, M)
synA = np.dot(trROT.T, MA)
synB = np.dot(trROT.T, MB)
SYN = np.append(SYN, syn)
SYNA = np.append(SYNA, synA)
SYNB = np.append(SYNB, synB)
print np.shape(A), np.shape(np.array([SYN]))
if subf == 0:
A = np.array([SYN])
AA = np.array([SYNA])
AB = np.array([SYNB])
else:
A = np.append(A, np.array([SYN]), 0)
AA = np.append(AA, np.array([SYNA]), 0)
AB = np.append(AB, np.array([SYNB]), 0)
AC = np.vstack((AA, AB))
print np.shape(AC)
print np.shape(weight)
B = np.dot(A.T, weight)
stsyn = Stream()
n = 0
Ntraces = {}
for trid in trlist:
spid = trid.split(".")
print trid
NMIN = 1. + (TrimmingTimes[trid][1] - TrimmingTimes[trid][0]) / dt
Ntraces[trid] = (n, NMIN + n)
trsyn = Trace(B[n:NMIN + n])
n += NMIN
trsyn.stats.network = spid[0]
trsyn.stats.station = spid[1]
trsyn.stats.location = spid[2]
trsyn.stats.channel = spid[3]
trsyn = AddNoise(trsyn, level=noiselevel)
#trsyn.stats.starttime =
stsyn.append(trsyn)
stsyn.write(workdir + "WPtraces/" + basename + ".decov.trim.mseed",
format="MSEED")
#####################################################
# Plotting:
#####################################################
#we are going to reflect the y axis later, so:
print minsubf
hypsbloc = [minsubf / nsy, -(minsubf % nsy) - 2]
#Creating the strike and dip axis:
StrikeAx = np.linspace(0, flen, nsx + 1)
DipAx = np.linspace(0, fwid, nsy + 1)
DepthAx = DipAx * np.sin(np.pi / 180. * dip) + Min_h
hlstrike = StrikeAx[hypsbloc[0]] + sflen * 0.5
hldip = DipAx[hypsbloc[1]] + sfwid * 0.5
hldepth = DepthAx[hypsbloc[1]] + sfwid * 0.5 * np.sin(np.pi / 180. * dip)
StrikeAx = StrikeAx - hlstrike
DipAx = DipAx - hldip
XX, YY = np.meshgrid(StrikeAx, DepthAx)
XX, ZZ = np.meshgrid(StrikeAx, DipAx)
sbarea = sflen * sfwid
SLIPS = weight.reshape(nsx, nsy).T #[::-1,:]
SLIPS /= mu * 1.e6 * sbarea
######Plot:#####################
plt.figure()
ax = host_subplot(111)
im = ax.pcolor(XX, YY, SLIPS, cmap="jet")
ax.set_ylabel('Depth [km]')
ax.set_ylim(DepthAx[-1], DepthAx[0])
# Creating a twin plot
ax2 = ax.twinx()
#im2 = ax2.pcolor(XX, ZZ, SLIPS[::-1,:], cmap="Greys")
im2 = ax2.pcolor(XX, ZZ, SLIPS[::-1, :], cmap="jet")
ax2.set_ylabel('Distance along the dip [km]')
ax2.set_xlabel('Distance along the strike [km]')
ax2.set_ylim(DipAx[0], DipAx[-1])
ax2.set_xlim(StrikeAx[0], StrikeAx[-1])
ax.axis["bottom"].major_ticklabels.set_visible(False)
ax2.axis["bottom"].major_ticklabels.set_visible(False)
ax2.axis["top"].set_visible(True)
ax2.axis["top"].label.set_visible(True)
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.1)
cb = plt.colorbar(im, cax=cax, orientation="horizontal")
cb.set_label("Slip [m]")
ax2.plot([0], [0], '*', ms=225. / (nsy + 4))
ax2.set_xticks(ax2.get_xticks()[1:-1])
#ax.set_yticks(ax.get_yticks()[1:])
#ax2.set_yticks(ax2.get_yticks()[:-1])
#########Plotting the selected traces:
nsp = len(PLotSt) * len(PlotSubf)
plt.figure(figsize=(13, 11))
plt.title("Synthetics for rake = " + str(round(rake)))
mindis = []
maxdis = []
for i, trid in enumerate(PLotSt):
x = np.arange(0, Ntraces[trid][1] - Ntraces[trid][0], dt)
for j, subf in enumerate(PlotSubf):
y = A[subf, Ntraces[trid][0]:Ntraces[trid][1]]
if j == 0:
yy = y
else:
yy = np.vstack((yy, y))
maxdis.append(np.max(yy))
mindis.append(np.min(yy))
for i, trid in enumerate(PLotSt):
x = np.arange(0, Ntraces[trid][1] - Ntraces[trid][0], dt)
for j, subf in enumerate(PlotSubf):
y = A[subf, Ntraces[trid][0]:Ntraces[trid][1]]
plt.subplot2grid((len(PlotSubf), len(PLotSt)), (j, i))
plt.plot(x, y, linewidth=2.5)
if j == 0:
plt.title(trid)
fig = plt.gca()
fig.axes.get_yaxis().set_ticks([])
fig.set_ylabel(str(subf), rotation=0)
fig.set_xlim((x[0], x[-1]))
fig.set_ylim((mindis[i], maxdis[i]))
if subf != PlotSubf[-1]:
fig.axes.get_xaxis().set_ticks([])
plt.show()
def main(argv=sys.argv):
global eplat, eplon, epdepth, orig
GFdir = "/home/roberto/data/GFS/"
beta = 4.e3 #m/s
rho = 3.e3 #kg/m^3
mu = rho*beta*beta
mu =40e9
Lbdm0min = 1e-26*np.array([125.])
Lbdsmooth = 1e-26*np.array([100.])
#~ Lbdm0min = 1e-26*np.linspace(60.,500,40)
#~ Lbdsmooth = 1e-26*np.linspace(60.,500,40)#*0.5
corners = 4.
fmin = 0.001
fmax = 0.005
### Data from Chilean 2010 EQ (Same as W phase inv.)
strike = 18.
dip = 18.
rake = 104. # 109.
#rake = 45.
rakeA = rake + 45.
rakeB = rake - 45.
####################
nsx = 21
nsy = 11
Min_h = 10.
flen = 600. #Fault's longitude [km] along strike
fwid = 300. #Fault's longitude [km] along dip
sflen = flen/float(nsx)
sfwid = fwid/float(nsy)
swp = [1, 0, 2]
nsf = nsx*nsy
###################
t_h = 10.
MISFIT = np.array([])
#RUPVEL = np.arange(1.0, 5.0, 0.05)
RupVel = 2.1 # Best fit
#RupVel = 2.25 #From Lay et al.
#for RupVel in RUPVEL:
print "****************************"
print RupVel
print "****************************"
NP = [strike, dip, rake]
NPA = [strike, dip, rakeA]
NPB = [strike, dip, rakeB]
M = np.array(NodalPlanetoMT(NP))
MA = np.array(NodalPlanetoMT(NPA))
MB = np.array(NodalPlanetoMT(NPB))
Mp = np.sum(M**2)/np.sqrt(2)
#############
#Loading req file and EQparameters
parameters={}
with open(argv[1],'r') as file:
for line in file:
line = line.split()
key = line[0]
val = line[1:]
parameters[key] = val
#~ cmteplat = float(parameters['eplat'][0])
#~ cmteplon = float(parameters['eplon'][0])
#~ cmtepdepth=float(parameters['epdepth'][0])
orig = UTCDateTime(parameters['origin_time'][0])
####Hypocentre from
### http://earthquake.usgs.gov/earthquakes/eqinthenews/2010/us2010tfan/
cmteplat = -35.91#-35.85#-36.03#-35.83
cmteplon = -72.73#-72.72#-72.83# -72.67
cmtepdepth= 35.
eq_hyp = (cmteplat,cmteplon,cmtepdepth)
############
grid, sblt = fault_grid('CL-2010',cmteplat,cmteplon,cmtepdepth,0, Min_h,\
strike, dip, rake, flen, fwid, nsx, nsy, Verbose=False, ffi_io=True, gmt_io=True)
print ('CL-2010',cmteplat,cmteplon,cmtepdepth,0, Min_h,\
strike, dip, rake, flen, fwid, nsx, nsy,\
)
print grid[0][1]
#sys.exit()
#############
#Loading files and setting dirs:
inputfile = os.path.abspath(argv[1])
if not os.path.exists(inputfile): print inputfile, "does not exist."; exit()
workdir = "/".join(inputfile.split("/")[:-1])
basename = inputfile.split("/")[-1][:-4]
if workdir[-1] != "/": workdir += "/"
try :
os.mkdir(workdir+"WPinv")
except OSError:
pass#print "Directory WPtraces already exists. Skipping"
trfile = open(workdir+"goodtraces.dat")
trlist = []
#Loading Good traces files:
while 1:
line = trfile.readline().rstrip('\r\n')
if not line: break
trlist.append(line.split()[0])
trfile.close()
#############
# Reading traces:
st = read(workdir+"WPtraces/" + basename + ".decov.trim.mseed")
#############################################################################
######Determining the sf closest to the hypocentre:
min_Dist_hyp_subf = flen *fwid
for subf in range(nsf):
sblat = grid[subf][1]
sblon = grid[subf][0]
sbdepth = grid[subf][2]
sf_hyp = (sblat,sblon, sbdepth)
Dist_hyp_subf = hypo2dist(eq_hyp,sf_hyp)
if Dist_hyp_subf < min_Dist_hyp_subf:
min_Dist_hyp_subf = Dist_hyp_subf
min_sb_hyp = sf_hyp
hyp_subf = subf
print hyp_subf, min_sb_hyp, min_Dist_hyp_subf
####Determining trimming times:
test_tr = read(GFdir + "H003.5/PP/GF.0001.SY.LHZ.SAC")[0]
t0 = test_tr.stats.starttime
TrimmingTimes = {} # Min. Distace from the fault to each station.
A =0
for trid in trlist:
tr = st.select(id=trid)[0]
metafile = workdir + "DATA/" + "META." + tr.id + ".xml"
META = DU.getMetadataFromXML(metafile)[tr.id]
stlat = META['latitude']
stlon = META['longitude']
dist = locations2degrees(min_sb_hyp[0],min_sb_hyp[1],\
stlat,stlon)
parrivaltime = getTravelTimes(dist,min_sb_hyp[2])[0]['time']
ta = t0 + parrivaltime
tb = ta + round(15.*dist)
TrimmingTimes[trid] = (ta, tb)
##############################################################################
#####
DIST = []
# Ordering the stations in terms of distance
for trid in trlist:
metafile = workdir + "DATA/" + "META." + trid + ".xml"
META = DU.getMetadataFromXML(metafile)[trid]
lat = META['latitude']
lon = META['longitude']
trdist = locations2degrees(cmteplat,cmteplon,lat,lon)
DIST.append(trdist)
DistIndex = lstargsort(DIST)
if len(argv) == 3:
trlist = [argv[2]]
OneStation = True
else:
trlist = [trlist[i] for i in DistIndex]
OneStation = False
#####
client = Client()
ObservedDisp = np.array([])
gridlat = []
gridlon = []
griddepth = []
sbarea = []
mindist = flen*fwid # min distance hyp-subfault
##########Loop for each subfault
for subf in range(nsf):
print "**********"
print subf
eplat = grid[subf][1]
eplon = grid[subf][0]
epdepth = grid[subf][2]
## Storing the subfault's location centered in the hypcenter
gridlat.append(eplat-cmteplat)
gridlon.append(eplon-cmteplon)
griddepth.append(epdepth)
strike = grid[subf][3] #+ 360.
dip = grid[subf][4]
rake = grid[subf][5] #
NP = [strike, dip, rake]
M = np.array(NodalPlanetoMT(NP))
#Calculating the time dalay:
sf_hyp = (eplat,eplon, epdepth)
Dist_ep_subf = hypo2dist(eq_hyp,sf_hyp)
t_d = round(Dist_ep_subf/RupVel) #-59.
print eplat,eplon, epdepth
#t_d = 0.
# Determining depth dir:
depth = []
depthdir = []
for file in os.listdir(GFdir):
if file[-2:] == ".5":
depthdir.append(file)
depth.append(float(file[1:-2]))
BestDirIndex = np.argsort(abs(epdepth-np.array(depth)))[0]
hdir = GFdir + depthdir[BestDirIndex] + "/"
# hdir is the absolute path to the closest deepth.
SYN = np.array([])
SYNA = np.array([])
SYNB = np.array([])
#Main loop :
for trid in trlist:
tr = st.select(id=trid)[0]
metafile = workdir + "DATA/" + "META." + tr.id + ".xml"
META = DU.getMetadataFromXML(metafile)[tr.id]
lat = META['latitude']
lon = META['longitude']
trPPsy, trRRsy, trRTsy, trTTsy = \
GFSelectZ(lat,lon,hdir)
tr.stats.delta = trPPsy.stats.delta
azi = -np.pi/180.*gps2DistAzimuth(lat,lon,\
eplat,eplon)[2]
trROT = MTrotationZ(azi, trPPsy, trRRsy, trRTsy, trTTsy)
#Triangle
dt = trROT[0].stats.delta
trianglen = 2.*t_h/dt-1.
window = triang(trianglen)
window /= np.sum(window)
#window = np.array([1.])
FirstValid = int(trianglen/2.) + 1
dist = locations2degrees(eplat,eplon,lat,lon)
parrivaltime = getTravelTimes(dist,epdepth)[0]['time']
t1 = TrimmingTimes[trid][0] - t_d
t2 = TrimmingTimes[trid][1] - t_d
#~ t1 = trROT[0].stats.starttime + parrivaltime- t_d
#~ t2 = t1+ round(MinDist[tr.id]*15. )
N = len(trROT[0])
for trR in trROT:
trR.data *= 10.**-21 ## To get M in Nm
trR.data -= trR.data[0]
AUX1 = len(trR)
trR.data = convolve(trR.data,window,mode='valid')
AUX2 = len(trR)
mean = np.mean(np.hstack((trR.data[0]*np.ones(FirstValid),\
trR.data[:60./trR.stats.delta*1.-FirstValid+1])))
#mean = np.mean(trR.data[:60])
trR.data -= mean
trR.data = bp.bandpassfilter(trR.data,len(trR), trR.stats. delta,
corners , 1 , fmin, fmax)
t_l = dt*0.5*(AUX1 - AUX2)
trR.trim(t1-t_l,t2-t_l, pad=True, fill_value=trR.data[0]) #We lost t_h due to the convolution
#~ for trR in trROT:
#~ trR.data *= 10.**-23 ## To get M in Nm
#~ trR.data -= trR.data[0]
#~ trR.data = convolve(trR.data,window,mode='same')
#~ # mean = np.mean(np.hstack((trR.data[0]*np.ones(FirstValid),\
#~ # trR.data[:60./trR.stats.delta*1.-FirstValid+1])))
#~ mean = np.mean(trR.data[:60])
#~ trR.data -= mean
#~ trR.data = bp.bandpassfilter(trR.data,len(trR), trR.stats.delta,\
#~ corners ,1 , fmin, fmax)
#~ trR.trim(t1,t2,pad=True, fill_value=trR.data[0])
nmin = min(len(tr.data),len(trROT[0].data))
tr.data = tr.data[:nmin]
for trR in trROT:
trR.data = trR.data[:nmin]
#############
trROT = np.array(trROT)
syn = np.dot(trROT.T,M)
synA = np.dot(trROT.T,MA)
synB = np.dot(trROT.T,MB)
SYN = np.append(SYN,syn)
SYNA = np.append(SYNA,synA)
SYNB = np.append(SYNB,synB)
if subf == 0 : ObservedDisp = np.append(ObservedDisp,tr.data,0)
sbarea.append(grid[subf][6])
print np.shape(A), np.shape(np.array([SYN]))
if subf == 0:
A = np.array([SYN])
AA = np.array([SYNA])
AB = np.array([SYNB])
else:
A = np.append(A,np.array([SYN]),0)
AA = np.append(AA,np.array([SYNA]),0)
AB = np.append(AB,np.array([SYNB]),0)
#Full matrix with the two rake's component
AC = np.vstack((AA,AB))
#MISFIT = np.array([])
########## Stabilizing the solution:
#### Moment minimization:
#~ constraintD = np.zeros(nsf)
#~ ObservedDispcons = np.append(ObservedDisp,constraintD)
#~ for lbd in Lbd:
#~ constraintF = lbd*np.eye(nsf,nsf)
#~ Acons = np.append(A,constraintF,1)
#~ print np.shape(Acons.T), np.shape(ObservedDispcons)
#~ R = nnls(Acons.T,ObservedDispcons)
#~ M = R[0]
#~ #M = np.zeros(nsf)
#~ #M[::2] = 1
#~ fit = np.dot(A.T,M)
#~ misfit = 100.*np.sum(np.abs(fit-ObservedDisp))\
#~ /np.sum(np.abs(ObservedDisp))
#~ MISFIT = np.append(MISFIT,misfit)
#~ plt.figure()
#~ plt.plot(Lbd,MISFIT)
#~ ###########################################
#~ ### Smoothing:
#~ constraintF_base = SmoothMatrix(nsx,nsy)
#~ constraintD = np.zeros(np.shape(constraintF_base)[0])
#~ ObservedDispcons = np.append(ObservedDisp,constraintD)
#~ for lbd in Lbd:
#~ constraintF = lbd*constraintF_base
#~ Acons = np.append(A,constraintF.T,1)
#~ #print np.shape(Acons.T), np.shape(ObservedDispcons)
#~ R = nnls(Acons.T,ObservedDispcons)
#~ M = R[0]
#~ fit = np.dot(A.T,M)
#~ misfit = 100.*np.sum(np.abs(fit-ObservedDisp))\
#~ /np.sum(np.abs(ObservedDisp))
#~ print lbd, misfit
#~ MISFIT = np.append(MISFIT,misfit)
#~ ###########################################
###########################################
#~ ##### Moment Minimization (including rake projections):
#~ constraintD = np.zeros(2*nsf)
#~ ObservedDispcons = np.append(ObservedDisp,constraintD)
#~ for lbd in Lbd:
#~ constraintF = lbd*np.eye(2*nsf,2*nsf)
#~ ACcons = np.append(AC,constraintF,1)
#~ print np.shape(ACcons.T), np.shape(ObservedDispcons)
#~ R = nnls(ACcons.T,ObservedDispcons)
#~ M = R[0]
#~ fit = np.dot(AC.T,M)
#~ misfit = 100.*np.sum(np.abs(fit-ObservedDisp))\
#~ /np.sum(np.abs(ObservedDisp))
#~ MISFIT = np.append(MISFIT,misfit)
#~ M = np.sqrt(M[:nsf]**2+M[nsf:]**2)
##############################################
### Smoothing (including rake projections):
#~ constraintF_base = SmoothMatrix(nsx,nsy)
#~ Nbase = np.shape(constraintF_base)[0]
#~ constraintD = np.zeros(2*Nbase)
#~ constraintF_base_big = np.zeros((2*Nbase, 2*nsf))
#~ constraintF_base_big[:Nbase,:nsf]= constraintF_base
#~ constraintF_base_big[Nbase:,nsf:]= constraintF_base
#~ ObservedDispcons = np.append(ObservedDisp,constraintD)
#~ for lbd in Lbd:
#~ constraintF = lbd*constraintF_base_big
#~ ACcons = np.append(AC,constraintF.T,1)
#~ #print np.shape(Acons.T), np.shape(ObservedDispcons)
#~ R = nnls(ACcons.T,ObservedDispcons)
#~ M = R[0]
#~ fit = np.dot(AC.T,M)
#~ misfit = 100.*np.sum(np.abs(fit-ObservedDisp))\
#~ /np.sum(np.abs(ObservedDisp))
#~ print lbd, misfit
#~ MISFIT = np.append(MISFIT,misfit)
#~ M = np.sqrt(M[:nsf]**2+M[nsf:]**2)
###########################################
#~ ##### Moment Minimization and Smoothing
#~ #### (including rake projections):
#~ mom0 = []
#~ constraintF_base = SmoothMatrix(nsx,nsy)
#~ Nbase = np.shape(constraintF_base)[0]
#~ constraintDsmoo = np.zeros(2*Nbase)
#~ constraintDmin = np.zeros(2*nsf)
#~ constraintF_base_big = np.zeros((2*Nbase, 2*nsf))
#~ constraintF_base_big[:Nbase,:nsf]= constraintF_base
#~ constraintF_base_big[Nbase:,nsf:]= constraintF_base
#~ ObservedDispcons = np.concatenate((ObservedDisp,
#~ constraintDmin,
#~ constraintDsmoo ))
#~ for lbdm0 in Lbdm0min:
#~ constraintFmin = lbdm0*np.eye(2*nsf,2*nsf)
#~ for lbdsm in Lbdsmooth:
#~ constraintFsmoo = lbdsm*constraintF_base_big
#~ ACcons = np.hstack((AC, constraintFmin, constraintFsmoo.T))
#~ print lbdm0, lbdsm
#~ R = nnls(ACcons.T,ObservedDispcons)
#~ M = R[0]
#~ fit = np.dot(AC.T,M)
#~ misfit = 100.*np.sum(np.abs(fit-ObservedDisp))\
#~ /np.sum(np.abs(ObservedDisp))
#~ MISFIT = np.append(MISFIT,misfit)
#~ MA = M[:nsf]
#~ MB = M[nsf:]
#~ M = np.sqrt(MA**2+MB**2)
#~ mom0.append(np.sum(M))
##############################################
# Rotation to the rake's conventional angle:
#MB, MA = Rot2D(MB,MA,-rakeB)
print np.shape(M), np.shape(A.T)
R = nnls(A.T,ObservedDisp)
M = R[0]
#~ M = np.zeros(nsf)
#~ M[::2] = 1
fit = np.dot(A.T,M)
MA = M
MB = M
np.save("RealSol", M)
nm0 = np.size(Lbdm0min)
nsmth = np.size(Lbdsmooth)
#~ plt.figure()
#~ plt.pcolor(1./Lbdsmooth, 1./Lbdm0min,MISFIT.reshape(nm0,nsmth))
#~ plt.xlabel(r'$1/ \lambda_{2}$', fontsize = 24)
#~ plt.ylabel(r'$1/ \lambda_{1}$',fontsize = 24 )
#~ plt.ylim((1./Lbdm0min).min(),(1./Lbdm0min).max() )
#~ plt.ylim((1./Lbdsmooth).min(),(1./Lbdsmooth).max() )
#~ cbar = plt.colorbar()
#~ cbar.set_label("Misfit %")
#~ print np.shape(Lbdm0min), np.shape(mom0)
#~ plt.figure()
#~ CS = plt.contour(1./Lbdsmooth, 1./Lbdm0min,MISFIT.reshape(nm0,nsmth) )
#~ plt.xlabel(r'$1/ \lambda_{2}$', fontsize = 24)
#~ plt.ylabel(r'$1/ \lambda_{1}$',fontsize = 24 )
#~ plt.clabel(CS, inline=1, fontsize=10)
#~ plt.title('Misfit')
#~ plt.figure()
#~ plt.plot(1./Lbdm0min,MISFIT)
#~ plt.xlabel(r'$1/ \lambda_{2}$', fontsize = 24)
#~ plt.ylabel("Misfit %")
#~ plt.figure()
#~ plt.plot(Lbdm0min,mom0)
#~ plt.ylabel(r'$M_0\, [Nm]$', fontsize = 24)
#~ plt.xlabel(r'$\lambda_{M0}$', fontsize = 24)
misfit = 100.*np.sum(np.abs(fit-ObservedDisp))/np.sum(np.abs(ObservedDisp))
print "Residual: ", 1000.*R[1]
print misfit
#SLIP = M*Mp/mu/(1.e6*np.array(sbarea))
sbarea = sflen*sfwid
SLIP = M/(mu*1.e6*sbarea)
SLIP = SLIP.reshape(nsx,nsy).T[::-1]
moment = M.reshape(nsx,nsy).T[::-1]
plt.figure(figsize = (13,5))
plt.plot(fit,'b' ,label="Fit")
plt.plot(ObservedDisp,'r',label="Observed")
plt.xlabel("Time [s]")
plt.ylabel("Displacement [m]")
plt.legend()
np.set_printoptions(linewidth=1000,precision=3)
print "***********"
print sbarea
print SLIP
print np.mean(SLIP)
print "Moment:"
print np.sum(M)
### SLIPS Distribution (as the synthetics) :
SLIPS = M.reshape(nsx,nsy).T
SLIPS /= mu*1.e6*sbarea
#~ #########Ploting slip distribution:
#~ #we are going to reflect the y axis later, so:
hypsbloc = [hyp_subf / nsy , -(hyp_subf % nsy) - 2]
#Creating the strike and dip axis:
StrikeAx= np.linspace(0,flen,nsx+1)
DipAx= np.linspace(0,fwid,nsy+1)
DepthAx = DipAx*np.sin(np.pi/180.*dip) + Min_h
print DepthAx
hlstrike = StrikeAx[hypsbloc[0]] + sflen*0.5
#we are going to reflect the axis later, so:
hldip = DipAx[hypsbloc[1]] + sfwid*0.5
hldepth = DepthAx[hypsbloc[1]] + sfwid*0.5*np.sin(np.pi/180.*dip)
StrikeAx = StrikeAx - hlstrike
DipAx = DipAx - hldip
XX, YY = np.meshgrid(StrikeAx, DepthAx)
XX, ZZ = np.meshgrid(StrikeAx, DipAx )
######Plot: (Old colormap: "gist_rainbow_r")
plt.figure(figsize = (13,6))
ax = host_subplot(111)
im = ax.pcolor(XX, YY, SLIPS, cmap="jet")
ax.set_ylabel('Depth [km]')
ax.set_ylim(DepthAx[-1],DepthAx[0])
# Creating a twin plot
ax2 = ax.twinx()
im2 = ax2.pcolor(XX, ZZ, SLIPS[::-1,:], cmap="jet")
ax2.set_ylabel('Distance along the dip [km]')
ax2.set_xlabel('Distance along the strike [km]')
ax2.set_ylim(DipAx[0],DipAx[-1])
ax2.set_xlim(StrikeAx[0],StrikeAx[-1])
ax.axis["bottom"].major_ticklabels.set_visible(False)
ax2.axis["bottom"].major_ticklabels.set_visible(False)
ax2.axis["top"].set_visible(True)
ax2.axis["top"].label.set_visible(True)
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.1)
cb = plt.colorbar(im, cax=cax, orientation="horizontal")
cb.set_label("Slip [m]")
ax2.plot([0], [0], '*', ms=225./(nsy+4))
ax2.set_xticks(ax2.get_xticks()[1:-1])
#~ ### Rake plot:
plt.figure(figsize = (13,6))
fig = host_subplot(111)
XXq, ZZq = np.meshgrid(StrikeAx[:-1]+sflen, DipAx[:-1]+sfwid )
Q = plt.quiver(XXq,ZZq, MB.reshape(nsx,nsy).T[::-1,:]/(mu*1.e6*sbarea),
MA.reshape(nsx,nsy).T[::-1,:]/(mu*1.e6*sbarea),
SLIPS[::-1,:],
units='xy',scale = 0.5 , linewidths=(2,),
edgecolors=('k'), headaxislength=5 )
fig.set_ylim([ZZq.min()-80,ZZq.max()+80])
fig.set_xlim([XXq.min()-20, XXq.max()+20 ])
fig.set_ylabel('Distance along dip [km]')
fig.set_xlabel('Distance along the strike [km]')
fig2 = fig.twinx()
fig2.set_xlabel('Distance along the strike [km]')
fig.axis["bottom"].major_ticklabels.set_visible(False)
fig.axis["bottom"].label.set_visible(False)
fig2.axis["top"].set_visible(True)
fig2.axis["top"].label.set_visible(True)
fig2.axis["right"].major_ticklabels.set_visible(False)
divider = make_axes_locatable(fig)
cax = divider.append_axes("bottom", size="5%", pad=0.1)
cb = plt.colorbar(im, cax=cax, orientation="horizontal")
cb.set_label("Slip [m]")
plt.show()
#############
#~ print np.shape(MISFIT), np.shape(RUPVEL)
#~ plt.figure()
#~ plt.plot(RUPVEL,MISFIT)
#~ plt.xlabel("Rupture Velocity [km/s]")
#~ plt.ylabel("Misfit %")
#~ plt.show()
print np.shape(MB.reshape(nsx,nsy).T)
print np.shape(ZZ)
