def run_inversion(home,
project_name,
run_name,
fault_name,
model_name,
GF_list,
G_from_file,
G_name,
epicenter,
rupture_speed,
num_windows,
reg_spatial,
reg_temporal,
nfaults,
beta,
decimate,
bandpass,
solver,
bounds,
weight=False,
Ltype=2,
target_moment=None,
data_vector=None):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros, dot, array, squeeze, expand_dims, empty, tile, eye, ones, arange, load, size
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1 = datetime.now()
#Get data vector
if data_vector == None:
d = inv.getdata(home,
project_name,
GF_list,
decimate,
bandpass=bandpass)
else:
d = load(data_vector)
#Get GFs
G = inv.getG(home, project_name, fault_name, model_name, GF_list,
G_from_file, G_name, epicenter, rupture_speed, num_windows,
decimate, bandpass)
gc.collect()
#Get data weights
if weight == True:
print 'Applying data weights'
w = inv.get_data_weights(home, project_name, GF_list, d, decimate)
W = empty(G.shape)
W = tile(w, (G.shape[1], 1)).T
WG = empty(G.shape)
WG = W * G
wd = w * d.squeeze()
wd = expand_dims(wd, axis=1)
#Clear up extraneous variables
W = None
w = None
#Define inversion quantities
x = WG.transpose().dot(wd)
print 'Computing G\'G'
K = (WG.T).dot(WG)
else:
#Define inversion quantities if no weightd
x = G.transpose().dot(d)
print 'Computing G\'G'
K = (G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
static = False #Is it jsut a static inversion?
if size(reg_spatial) > 1:
if Ltype == 2: #Laplacian smoothing
Ls = inv.getLs(home, project_name, fault_name, nfaults,
num_windows, bounds)
elif Ltype == 0: #Tikhonov smoothing
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = eye(N)
elif Ltype == 3: #moment regularization
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = ones((1, N))
#Add rigidity and subfault area
mu, area = get_mu_and_area(home, project_name, fault_name,
model_name)
istrike = arange(0, N, 2)
Ls[0, istrike] = area * mu
idip = arange(1, N, 2)
Ls[0, idip] = area * mu
#Modify inversion quantities
x = x + Ls.T.dot(target_moment)
else:
print 'ERROR: Unrecognized regularization type requested'
return
Ninversion = len(reg_spatial)
else:
Ls = zeros(K.shape)
reg_spatial = array([0.])
Ninversion = 1
if size(reg_temporal) > 1:
Lt = inv.getLt(home, project_name, fault_name, num_windows)
Ninversion = len(reg_temporal) * Ninversion
else:
Lt = zeros(K.shape)
reg_temporal = array([0.])
static = True
#Make L's sparse
Ls = sparse(Ls)
Lt = sparse(Lt)
#Get regularization tranposes for ABIC
LsLs = Ls.transpose().dot(Ls)
LtLt = Lt.transpose().dot(Lt)
#inflate
Ls = Ls.todense()
Lt = Lt.todense()
LsLs = LsLs.todense()
LtLt = LtLt.todense()
#off we go
dt = datetime.now() - t1
print 'Preprocessing wall time was ' + str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal = datetime.now()
kout = 0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1 = datetime.now()
lambda_spatial = reg_spatial[ks]
lambda_temporal = reg_temporal[kt]
print 'Running inversion ' + str(kout + 1) + ' of ' + str(
Ninversion) + ' at regularization levels: ls =' + repr(
lambda_spatial) + ' , lt = ' + repr(lambda_temporal)
if static == True: #Only statics inversion no Lt matrix
Kinv = K + (lambda_spatial**2) * LsLs
Lt = eye(len(K))
LtLt = Lt.T.dot(Lt)
else: #Mixed inversion
Kinv = K + (lambda_spatial**2) * LsLs + (lambda_temporal**
2) * LtLt
if solver.lower() == 'lstsq':
sol, res, rank, s = lstsq(Kinv, x)
elif solver.lower() == 'nnls':
x = squeeze(x.T)
try:
sol, res = nnls(Kinv, x)
except:
print '+++ WARNING: No solution found, writting zeros.'
sol = zeros(G.shape[1])
x = expand_dims(x, axis=1)
sol = expand_dims(sol, axis=1)
else:
print 'ERROR: Unrecognized solver \'' + solver + '\''
#Compute synthetics
ds = dot(G, sol)
#Get stats
L2, Lmodel = inv.get_stats(Kinv, sol, x)
VR = inv.get_VR(home, project_name, GF_list, sol, d, ds, decimate)
#VR=inv.get_VR(WG,sol,wd)
#ABIC=inv.get_ABIC(WG,K,sol,wd,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
ABIC = inv.get_ABIC(G, K, sol, d, lambda_spatial, lambda_temporal,
Ls, LsLs, Lt, LtLt)
#Get moment
Mo, Mw = inv.get_moment(home, project_name, fault_name, model_name,
sol)
#If a rotational offset was applied then reverse it for output to file
if beta != 0:
sol = inv.rot2ds(sol, beta)
#Write log
inv.write_log(home, project_name, run_name, kout, rupture_speed,
num_windows, lambda_spatial, lambda_temporal, beta,
L2, Lmodel, VR, ABIC, Mo, Mw, model_name, fault_name,
G_name, GF_list, solver)
#Write output to file
inv.write_synthetics(home, project_name, run_name, GF_list, G, sol,
ds, kout, decimate)
inv.write_model(home, project_name, run_name, fault_name,
model_name, rupture_speed, num_windows, epicenter,
sol, kout)
kout += 1
dt1 = datetime.now() - t1
dt2 = datetime.now() - ttotal
print '... inversion wall time was ' + str(
dt1) + ', total wall time elapsed is ' + str(dt2)
def run_inversion(home,
project_name,
run_name,
fault_name,
model_name,
GF_list,
G_from_file,
G_name,
epicenter,
rupture_speed,
num_windows,
reg_spatial,
reg_temporal,
nfaults,
beta,
decimate,
bandpass,
solver,
bounds,
weight=False,
Ltype=2,
target_moment=None,
data_vector=None,
weights_file=None,
onset_file=None,
GOD_inversion=False):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros, dot, array, squeeze, expand_dims, empty, tile, eye, ones, arange, load, size, genfromtxt
from numpy import where, sort, r_, diag
from numpy.linalg import lstsq
from scipy.linalg import norm
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
from matplotlib import path
t1 = datetime.now()
#Get data vector
if data_vector == None:
d = inv.getdata(home,
project_name,
GF_list,
decimate,
bandpass=bandpass)
else:
d = load(data_vector)
#Get GFs
G = inv.getG(home,
project_name,
fault_name,
model_name,
GF_list,
G_from_file,
G_name,
epicenter,
rupture_speed,
num_windows,
decimate,
bandpass,
onset_file=onset_file)
print(G.shape)
gc.collect()
#Get data weights
if weight == True:
print('Applying data weights')
if weights_file == None:
w = inv.get_data_weights(home, project_name, GF_list, d, decimate)
else: # Remember weights are "uncertainties" alrger value is less trustworthy
w = genfromtxt(weights_file)
w = 1 / w
#apply data weights
wd = w * d.squeeze()
#get norm after applying weights and normalize weghted data vector
data_norm = norm(wd)
wd = wd / data_norm
#reshape for inversion
wd = expand_dims(wd, axis=1)
#Apply weights to left hand side of the equation (the GFs)
W = empty(G.shape)
#W=tile(w,(G.shape[1],1)).T #why this and not a diagonal matrix??? ANSWER: they have the same effect, don;t rememebr why I chose to do it this way
W = diag(w)
#Normalization effect
W = W / data_norm
WG = W.dot(G)
#Clear up extraneous variables
W = None
w = None
#Define inversion quantities
x = WG.transpose().dot(wd)
print('Computing G\'G')
K = (WG.T).dot(WG)
else:
#Define inversion quantities if no weighted
x = G.transpose().dot(d)
print('Computing G\'G')
K = (G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
static = False #Is it jsut a static inversion?
if size(reg_spatial) > 1:
if Ltype == 2: #Laplacian smoothing
Ls = inv.getLs(home, project_name, fault_name, nfaults,
num_windows, bounds)
elif Ltype == 0: #Tikhonov smoothing
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = eye(N)
elif Ltype == 3: #moment regularization
N = nfaults[0] * nfaults[
1] * num_windows * 2 #Get total no. of model parameters
Ls = ones((1, N))
#Add rigidity and subfault area
mu, area = get_mu_and_area(home, project_name, fault_name,
model_name)
istrike = arange(0, N, 2)
Ls[0, istrike] = area * mu
idip = arange(1, N, 2)
Ls[0, idip] = area * mu
#Modify inversion quantities
x = x + Ls.T.dot(target_moment)
else:
print('ERROR: Unrecognized regularization type requested')
return
Ninversion = len(reg_spatial)
else:
Ls = zeros(K.shape)
reg_spatial = array([0.])
Ninversion = 1
if size(reg_temporal) > 1:
Lt = inv.getLt(home, project_name, fault_name, num_windows)
Ninversion = len(reg_temporal) * Ninversion
else:
Lt = zeros(K.shape)
reg_temporal = array([0.])
static = True
#Make L's sparse
Ls = sparse(Ls)
Lt = sparse(Lt)
#Get regularization tranposes for ABIC
LsLs = Ls.transpose().dot(Ls)
LtLt = Lt.transpose().dot(Lt)
#inflate
Ls = Ls.todense()
Lt = Lt.todense()
LsLs = LsLs.todense()
LtLt = LtLt.todense()
#off we go
dt = datetime.now() - t1
print('Preprocessing wall time was ' + str(dt))
print('\n--- RUNNING INVERSIONS ---\n')
ttotal = datetime.now()
kout = 0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1 = datetime.now()
lambda_spatial = reg_spatial[ks]
lambda_temporal = reg_temporal[kt]
print('Running inversion ' + str(kout + 1) + ' of ' +
str(Ninversion) + ' at regularization levels: ls =' +
repr(lambda_spatial) + ' , lt = ' + repr(lambda_temporal))
if static == True: #Only statics inversion no Lt matrix
Kinv = K + (lambda_spatial**2) * LsLs
Lt = eye(len(K))
LtLt = Lt.T.dot(Lt)
else: #Mixed inversion
Kinv = K + (lambda_spatial**2) * LsLs + (lambda_temporal**
2) * LtLt
if solver.lower() == 'lstsq':
sol, res, rank, s = lstsq(Kinv, x)
elif solver.lower() == 'nnls':
x = squeeze(x.T)
try:
sol, res = nnls(Kinv, x)
except:
print('+++ WARNING: No solution found, writting zeros.')
sol = zeros(G.shape[1])
x = expand_dims(x, axis=1)
sol = expand_dims(sol, axis=1)
else:
print('ERROR: Unrecognized solver \'' + solver + '\'')
#Force faults outside a polygon to be zero
# print('WARNING: Using fault polygon to force solutions to zero')
# #load faulkt
# fault=genfromtxt(home+project_name+'/data/model_info/'+fault_name)
# polygon=genfromtxt('/Users/dmelgarm/Oaxaca2020/etc/zero_fault.txt')
# polygon=path.Path(polygon)
# i=where(polygon.contains_points(fault[:,1:3])==False)[0]
# i=sort(r_[i*2,i*2+1])
# N=nfaults[0]*2
# i=r_[i,i+N,i+2*N,i+3*N]
# sol[i]=0
#Compute synthetics
ds = dot(G, sol)
#Get stats
L2, Lmodel = inv.get_stats(Kinv, sol, x, Ls)
VR, L2data = inv.get_VR(home, project_name, GF_list, sol, d, ds,
decimate, WG, wd)
#VR=inv.get_VR(WG,sol,wd)
#ABIC=inv.get_ABIC(WG,K,sol,wd,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
ABIC = inv.get_ABIC(G, K, sol, d, lambda_spatial, lambda_temporal,
Ls, LsLs, Lt, LtLt)
#Get moment
Mo, Mw = inv.get_moment(home, project_name, fault_name, model_name,
sol)
#If a rotational offset was applied then reverse it for output to file
if beta != 0:
sol = inv.rot2ds(sol, beta)
#Write log
inv.write_log(home, project_name, run_name, kout, rupture_speed,
num_windows, lambda_spatial, lambda_temporal, beta,
L2, Lmodel, VR, ABIC, Mo, Mw, model_name, fault_name,
G_name, GF_list, solver, L2data)
#Write output to file
if GOD_inversion == True:
num = str(kout).rjust(4, '0')
np.save(
home + project_name + '/output/inverse_models/' +
run_name + '.' + num + '.syn.npy', ds)
inv.write_synthetics_GOD(home, project_name, run_name, GF_list,
ds, kout, decimate)
else:
inv.write_synthetics(home, project_name, run_name, GF_list, G,
sol, ds, kout, decimate)
inv.write_model(home,
project_name,
run_name,
fault_name,
model_name,
rupture_speed,
num_windows,
epicenter,
sol,
kout,
onset_file=onset_file)
kout += 1
dt1 = datetime.now() - t1
dt2 = datetime.now() - ttotal
print('... inversion wall time was ' + str(dt1) +
', total wall time elapsed is ' + str(dt2))
def run_inversion(home,project_name,run_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,coord_type,reg_spatial,reg_temporal,nfaults,beta,decimate,lowpass,
solver,bounds,segs):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy import degadds
from numpy import zeros,dot,array,squeeze,expand_dims,empty,tile,floor
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1=datetime.now()
#Get data vector
d=inv.getdata(home,project_name,GF_list,decimate,lowpass)
#Get GFs
G=inv.getG(home,project_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,coord_type,decimate,lowpass)
gc.collect()
#Get data weights
#w=inv.get_data_weights(home,project_name,GF_list,d,decimate)
#W=empty(G.shape)
#W=tile(w,(G.shape[1],1)).T
#WG=empty(G.shape)
#WG=W*G
#wd=w*d.squeeze()
#wd=expand_dims(wd,axis=1)
##Clear up extraneous variables
#W=None
#w=None
##Define inversion quantities
#x=WG.transpose().dot(wd)
#print 'Computing G\'G'
#K=(WG.T).dot(WG)
#Define inversion quantities
x=G.transpose().dot(d)
print 'Computing G\'G'
K=(G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
if type(reg_spatial)!=bool:
## DEG ADDED THIS IN TO DEAL WITH MULTIPLE FAULT SEGMENTS
#if segs==0:
# Ls=inv.getLs(home,project_name,fault_name,nfaults,num_windows,bounds,segs)
##LsLs=Ls.transpose().dot(Ls)
#elif segs==1: #This will make Ls a list of matrices, one index for each fault segment
# Ls=degadds.getLs_segs(home,project_name,fault_name,nfaults,num_windows,bounds,segs)
Ls=degadds.getLs_segs(home,project_name,fault_name,nfaults,num_windows,bounds,segs)
# LsLs=[]
# nstrike_segs=nfaults[0]
# for i in range(len(nstrike_segs)):
# LsLs0 = Ls[i].transpose().dot(Ls[i])
# LsLs.append(LsLs0)
Ninversion=len(reg_spatial)
else:
Ls=zeros(K.shape)
reg_spatial=array([0.])
Ninversion=1
if type(reg_temporal)!=bool:
Lt=inv.getLt(home,project_name,fault_name,num_windows)
Ninversion=len(reg_temporal)*Ninversion
else:
Lt=zeros(K.shape)
reg_temporal=array([0.])
#Make L's sparse <-- does this actually change anything??--DEG
#for i in range(len(nstrike_segs)):
# Ls=sparse(Ls[:,:,i])
Ls=sparse(Ls)
Lt=sparse(Lt)
#Get regularization tranposes for ABIC
LsLs=Ls.transpose().dot(Ls)
LtLt=Lt.transpose().dot(Lt)
#inflate
Ls=Ls.todense()
Lt=Lt.todense()
LsLs=LsLs.todense()
LtLt=LtLt.todense()
#off we go
dt=datetime.now()-t1
print 'Preprocessing wall time was '+str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal=datetime.now()
kout=0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1=datetime.now()
lambda_spatial=reg_spatial[ks]
lambda_temporal=reg_temporal[kt]
print 'Running inversion '+str(kout+1)+' of '+str(Ninversion)+' at regularization levels: ls ='+repr(lambda_spatial)+' , lt = '+repr(lambda_temporal)
# DEG!! HERE THERE NEEDS TO BE SOME LOOP THROUGH THE LAMBDA_SPATIALS FOR EACH FAULT SEGMENT
Kinv=K+(lambda_spatial**2)*+(lambda_temporal**2)*LtLt
if solver.lower()=='lstsq':
sol,res,rank,s=lstsq(Kinv,x)
elif solver.lower()=='nnls':
x=squeeze(x.T)
sol,res=nnls(Kinv,x)
x=expand_dims(x,axis=1)
sol=expand_dims(sol,axis=1)
else:
print 'ERROR: Unrecognized solver \''+solver+'\''
#Compute synthetics
ds=dot(G,sol)
#Get stats
L2,Lmodel=inv.get_stats(Kinv,sol,x)
VR=inv.get_VR(G,sol,d)
ABIC=inv.get_ABIC(G,K,sol,d,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
## CHANGE THIS FOR SEGS!!!! <-- DEG
#ABIC=inv.get_ABIC(G,K,sol,d,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt,nfaults,segs)
#Get moment
Mo,Mw=inv.get_moment(home,project_name,fault_name,model_name,sol)
#If a rotational offset was applied then reverse it for output to file
if beta !=0:
sol=inv.rot2ds(sol,beta)
#Write log
inv.write_log(home,project_name,run_name,kout,rupture_speed,num_windows,lambda_spatial,lambda_temporal,beta,
L2,Lmodel,VR,ABIC,Mo,Mw,model_name,fault_name,G_name,GF_list,solver)
#Write output to file
inv.write_synthetics(home,project_name,run_name,GF_list,G,sol,ds,kout,decimate)
inv.write_model(home,project_name,run_name,fault_name,model_name,rupture_speed,num_windows,epicenter,sol,kout)
kout+=1
dt1=datetime.now()-t1
dt2=datetime.now()-ttotal
print '... inversion wall time was '+str(dt1)+', total wall time elapsed is '+str(dt2)
def run_inversion(home,project_name,run_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,reg_spatial,reg_temporal,nfaults,beta,decimate,bandpass,
solver,bounds,weight=False,Ltype=2,target_moment=None,data_vector=None):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros,dot,array,squeeze,expand_dims,empty,tile,eye,ones,arange,load
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1=datetime.now()
#Get data vector
if data_vector==None:
d=inv.getdata(home,project_name,GF_list,decimate,bandpass=None)
else:
d=load(data_vector)
#Get GFs
G=inv.getG(home,project_name,fault_name,model_name,GF_list,G_from_file,G_name,epicenter,
rupture_speed,num_windows,decimate,bandpass)
gc.collect()
#Get data weights
if weight==True:
print 'Applying data weights'
w=inv.get_data_weights(home,project_name,GF_list,d,decimate)
W=empty(G.shape)
W=tile(w,(G.shape[1],1)).T
WG=empty(G.shape)
WG=W*G
wd=w*d.squeeze()
wd=expand_dims(wd,axis=1)
#Clear up extraneous variables
W=None
w=None
#Define inversion quantities
x=WG.transpose().dot(wd)
print 'Computing G\'G'
K=(WG.T).dot(WG)
else:
#Define inversion quantities if no weightd
x=G.transpose().dot(d)
print 'Computing G\'G'
K=(G.T).dot(G)
#Get regularization matrices (set to 0 matrix if not needed)
static=False #Is it jsut a static inversion?
if reg_spatial!=None:
if Ltype==2: #Laplacian smoothing
Ls=inv.getLs(home,project_name,fault_name,nfaults,num_windows,bounds)
elif Ltype==0: #Tikhonov smoothing
N=nfaults[0]*nfaults[1]*num_windows*2 #Get total no. of model parameters
Ls=eye(N)
elif Ltype==3: #moment regularization
N=nfaults[0]*nfaults[1]*num_windows*2 #Get total no. of model parameters
Ls=ones((1,N))
#Add rigidity and subfault area
mu,area=get_mu_and_area(home,project_name,fault_name,model_name)
istrike=arange(0,N,2)
Ls[0,istrike]=area*mu
idip=arange(1,N,2)
Ls[0,idip]=area*mu
#Modify inversion quantities
x=x+Ls.T.dot(target_moment)
else:
print 'ERROR: Unrecognized regularization type requested'
return
Ninversion=len(reg_spatial)
else:
Ls=zeros(K.shape)
reg_spatial=array([0.])
Ninversion=1
if reg_temporal!=None:
Lt=inv.getLt(home,project_name,fault_name,num_windows)
Ninversion=len(reg_temporal)*Ninversion
else:
Lt=zeros(K.shape)
reg_temporal=array([0.])
static=True
#Make L's sparse
Ls=sparse(Ls)
Lt=sparse(Lt)
#Get regularization tranposes for ABIC
LsLs=Ls.transpose().dot(Ls)
LtLt=Lt.transpose().dot(Lt)
#inflate
Ls=Ls.todense()
Lt=Lt.todense()
LsLs=LsLs.todense()
LtLt=LtLt.todense()
#off we go
dt=datetime.now()-t1
print 'Preprocessing wall time was '+str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal=datetime.now()
kout=0
for kt in range(len(reg_temporal)):
for ks in range(len(reg_spatial)):
t1=datetime.now()
lambda_spatial=reg_spatial[ks]
lambda_temporal=reg_temporal[kt]
print 'Running inversion '+str(kout+1)+' of '+str(Ninversion)+' at regularization levels: ls ='+repr(lambda_spatial)+' , lt = '+repr(lambda_temporal)
if static==True: #Only statics inversion no Lt matrix
Kinv=K+(lambda_spatial**2)*LsLs
Lt=eye(len(K))
LtLt=Lt.T.dot(Lt)
else: #Mixed inversion
Kinv=K+(lambda_spatial**2)*LsLs+(lambda_temporal**2)*LtLt
if solver.lower()=='lstsq':
sol,res,rank,s=lstsq(Kinv,x)
elif solver.lower()=='nnls':
x=squeeze(x.T)
try:
sol,res=nnls(Kinv,x)
except:
print '+++ WARNING: No solution found, writting zeros.'
sol=zeros(G.shape[1])
x=expand_dims(x,axis=1)
sol=expand_dims(sol,axis=1)
else:
print 'ERROR: Unrecognized solver \''+solver+'\''
#Compute synthetics
ds=dot(G,sol)
#Get stats
L2,Lmodel=inv.get_stats(Kinv,sol,x)
VR=inv.get_VR(home,project_name,GF_list,sol,d,ds,decimate)
#VR=inv.get_VR(WG,sol,wd)
#ABIC=inv.get_ABIC(WG,K,sol,wd,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
ABIC=inv.get_ABIC(G,K,sol,d,lambda_spatial,lambda_temporal,Ls,LsLs,Lt,LtLt)
#Get moment
Mo,Mw=inv.get_moment(home,project_name,fault_name,model_name,sol)
#If a rotational offset was applied then reverse it for output to file
if beta !=0:
sol=inv.rot2ds(sol,beta)
#Write log
inv.write_log(home,project_name,run_name,kout,rupture_speed,num_windows,lambda_spatial,lambda_temporal,beta,
L2,Lmodel,VR,ABIC,Mo,Mw,model_name,fault_name,G_name,GF_list,solver)
#Write output to file
inv.write_synthetics(home,project_name,run_name,GF_list,G,sol,ds,kout,decimate)
inv.write_model(home,project_name,run_name,fault_name,model_name,rupture_speed,num_windows,epicenter,sol,kout)
kout+=1
dt1=datetime.now()-t1
dt2=datetime.now()-ttotal
print '... inversion wall time was '+str(dt1)+', total wall time elapsed is '+str(dt2)
def run_inversion(home,
project_name,
run_name,
source_name,
model_name,
GF_list,
G_from_file,
G_name,
reg_spatial,
nsources,
solver,
forceMT,
mt,
weight=False,
Ltype=0):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros, dot, array, squeeze, expand_dims, empty, tile, eye, ones, arange, load
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1 = datetime.now()
#Get data vector
d = inv.getdata(home, project_name, GF_list, decimate=None, bandpass=None)
#Get GFs
G = getG(home, project_name, source_name, model_name, GF_list, G_from_file,
G_name, forceMT)
#Get data weights
if weight == True:
print 'Applying data weights'
w = inv.get_data_weights(home, project_name, GF_list, d, None)
W = empty(G.shape)
W = tile(w, (G.shape[1], 1)).T
WG = empty(G.shape)
WG = W * G
wd = w * d.squeeze()
wd = expand_dims(wd, axis=1)
#Clear up extraneous variables
W = None
w = None
#Define inversion quantities
x = WG.transpose().dot(wd)
print 'Computing G\'G'
K = (WG.T).dot(WG)
#And cleanup
W = None
WG = None
wd = None
else:
#Define inversion quantities if no weightd
x = G.transpose().dot(d)
print 'Computing G\'G'
K = (G.T).dot(G)
#Cleanup
#G=None
#Get regularization matrices (set to 0 matrix if not needed)
if forceMT == False:
Ls = eye(nsources * 6)
else:
Ls = eye(nsources)
print 'Nsources: ' + str(nsources)
Ninversion = len(reg_spatial)
#Make L's sparse
Ls = sparse(Ls)
#Get regularization tranposes for ABIC
LsLs = Ls.transpose().dot(Ls)
#inflate
Ls = Ls.todense()
LsLs = LsLs.todense()
#off we go
dt = datetime.now() - t1
print 'Preprocessing wall time was ' + str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal = datetime.now()
kout = 0
for ks in range(len(reg_spatial)):
t1 = datetime.now()
lambda_spatial = reg_spatial[ks]
print 'Running inversion ' + str(kout + 1) + ' of ' + str(
Ninversion) + ' at regularization levels: ls =' + repr(
lambda_spatial)
Kinv = K + (lambda_spatial**2) * LsLs
if solver == 'lstsq':
sol, res, rank, s = lstsq(Kinv, x)
elif solver == 'nnls':
sol, res = nnls(Kinv, squeeze(x.T))
#Compute synthetics
ds = dot(G, sol)
#Get stats
L2, Lmodel = inv.get_stats(Kinv, sol, x)
VR = inv.get_VR(home, project_name, GF_list, sol, d, ds, None)
#Get moment
M0total, M0, Mw = get_moment(home, project_name, source_name, sol,
forceMT)
#Write log
write_log(home, project_name, run_name, kout, lambda_spatial, L2,
Lmodel, VR, M0total, Mw, model_name, source_name, G_name,
GF_list)
#Write output to file
inv.write_synthetics(home, project_name, run_name, GF_list, G, sol, ds,
kout, None)
write_model(home, project_name, run_name, source_name, model_name, sol,
kout, forceMT, mt)
kout += 1
dt1 = datetime.now() - t1
dt2 = datetime.now() - ttotal
print '... inversion wall time was ' + str(
dt1) + ', total wall time elapsed is ' + str(dt2)
def run_inversion(home,project_name,run_name,source_name,model_name,GF_list,G_from_file,G_name,
reg_spatial,nsources,solver,forceMT,mt,weight=False,Ltype=0):
'''
Assemble G and d, determine smoothing and run the inversion
'''
from mudpy import inverse as inv
from mudpy.forward import get_mu_and_area
from numpy import zeros,dot,array,squeeze,expand_dims,empty,tile,eye,ones,arange,load
from numpy.linalg import lstsq
from scipy.sparse import csr_matrix as sparse
from scipy.optimize import nnls
from datetime import datetime
import gc
t1=datetime.now()
#Get data vector
d=inv.getdata(home,project_name,GF_list,decimate=None,bandpass=None)
#Get GFs
G=getG(home,project_name,source_name,model_name,GF_list,G_from_file,G_name,forceMT)
#Get data weights
if weight==True:
print 'Applying data weights'
w=inv.get_data_weights(home,project_name,GF_list,d,None)
W=empty(G.shape)
W=tile(w,(G.shape[1],1)).T
WG=empty(G.shape)
WG=W*G
wd=w*d.squeeze()
wd=expand_dims(wd,axis=1)
#Clear up extraneous variables
W=None
w=None
#Define inversion quantities
x=WG.transpose().dot(wd)
print 'Computing G\'G'
K=(WG.T).dot(WG)
#And cleanup
W=None
WG=None
wd=None
else:
#Define inversion quantities if no weightd
x=G.transpose().dot(d)
print 'Computing G\'G'
K=(G.T).dot(G)
#Cleanup
#G=None
#Get regularization matrices (set to 0 matrix if not needed)
if forceMT==False:
Ls=eye(nsources*6)
else:
Ls=eye(nsources)
print 'Nsources: '+str(nsources)
Ninversion=len(reg_spatial)
#Make L's sparse
Ls=sparse(Ls)
#Get regularization tranposes for ABIC
LsLs=Ls.transpose().dot(Ls)
#inflate
Ls=Ls.todense()
LsLs=LsLs.todense()
#off we go
dt=datetime.now()-t1
print 'Preprocessing wall time was '+str(dt)
print '\n--- RUNNING INVERSIONS ---\n'
ttotal=datetime.now()
kout=0
for ks in range(len(reg_spatial)):
t1=datetime.now()
lambda_spatial=reg_spatial[ks]
print 'Running inversion '+str(kout+1)+' of '+str(Ninversion)+' at regularization levels: ls ='+repr(lambda_spatial)
Kinv=K+(lambda_spatial**2)*LsLs
if solver=='lstsq':
sol,res,rank,s=lstsq(Kinv,x)
elif solver=='nnls':
sol,res=nnls(Kinv,squeeze(x.T))
#Compute synthetics
ds=dot(G,sol)
#Get stats
L2,Lmodel=inv.get_stats(Kinv,sol,x)
VR=inv.get_VR(home,project_name,GF_list,sol,d,ds,None)
#Get moment
M0total,M0,Mw=get_moment(home,project_name,source_name,sol,forceMT)
#Write log
write_log(home,project_name,run_name,kout,lambda_spatial,
L2,Lmodel,VR,M0total,Mw,model_name,source_name,G_name,GF_list)
#Write output to file
inv.write_synthetics(home,project_name,run_name,GF_list,G,sol,ds,kout,None)
write_model(home,project_name,run_name,source_name,model_name,sol,kout,forceMT,mt)
kout+=1
dt1=datetime.now()-t1
dt2=datetime.now()-ttotal
print '... inversion wall time was '+str(dt1)+', total wall time elapsed is '+str(dt2)