def clip(self, path='', parameters=[], minval=-np.inf, maxval=np.inf):
""" Clips kernels by convolving them with a Gaussian. Wrapper over
xclip_sem utility.
"""
assert exists(path)
assert len(parameters) > 0
unix.cd(self.getpath)
for name in self.parameters:
self.mpirun(
PATH.SPECFEM_BIN +'/'+ 'xclip_sem '
+ str(minval) + ' '
+ str(maxval) + ' '
+ name + '_kernel' + ' '
+ path + '/ '
+ path + '/ ')
# move input files
src = path
dst = path + '_noclip'
unix.mkdir(dst)
for name in self.parameters:
unix.mv(glob(src+'/*'+name+'.bin'), dst)
# rename output files
unix.rename('_clip', '', glob(src+'/*'))
def smooth(self, path='', parameters='dummy', span=0. ):
""" Smooths SPECFEM2D kernels by convolving them with a Gaussian
"""
from seisflows.tools.array import meshsmooth, stack
#assert parameters == self.parameters
# implementing nproc > 1 would be straightforward, but a bit tedious
#assert self.mesh.nproc == 1
kernels = self.load(path, suffix='_kernel')
if not span:
return kernels
# set up grid
_,x = loadbypar(PATH.MODEL_INIT, ['x'], 0)
_,z = loadbypar(PATH.MODEL_INIT, ['z'], 0)
mesh = stack(x[0], z[0])
for key in self.parameters:
kernels[key] = [meshsmooth(kernels[key][0], mesh, span)]
unix.rm(path + '_nosmooth')
unix.mv(path, path + '_nosmooth')
self.save(path, kernels, suffix='_kernel')
def export_kernels(self, path):
unix.cd(self.kernel_databases)
# work around conflicting name conventions
files = []
files += glob('*proc??????_alpha_kernel.bin')
files += glob('*proc??????_alpha[hv]_kernel.bin')
files += glob('*proc??????_reg1_alpha_kernel.bin')
files += glob('*proc??????_reg1_alpha[hv]_kernel.bin')
unix.rename('alpha', 'vp', files)
files = []
files += glob('*proc??????_beta_kernel.bin')
files += glob('*proc??????_beta[hv]_kernel.bin')
files += glob('*proc??????_reg1_beta_kernel.bin')
files += glob('*proc??????_reg1_beta[hv]_kernel.bin')
unix.rename('beta', 'vs', files)
# hack to deal with problems on parallel filesystem
unix.mkdir(join(path, 'kernels'), noexit=True)
unix.mkdir(join(path, 'kernels', basename(self.getpath)))
src = join(glob('*_kernel.bin'))
dst = join(path, 'kernels', basename(self.getpath))
unix.mv(src, dst)
def export_residuals(self, path):
# hack deals with problems on parallel filesystem
unix.mkdir(join(path, 'residuals'), noexit=True)
src = join(self.getpath, 'residuals')
dst = join(path, 'residuals', basename(self.getpath))
unix.mv(src, dst)
def forward(self):
""" Calls SPECFEM3D_GLOBE forward solver
"""
solvertools.setpar('SIMULATION_TYPE', '1')
solvertools.setpar('SAVE_FORWARD', '.true.')
self.mpirun('bin/xspecfem3D')
unix.mv(self.data_wildcard, 'traces/syn')
def smooth(self, path='', parameters=[], span=0.):
""" Smooths kernels by convolving them with a Gaussian. Wrapper over
xsmooth_sem utility.
"""
assert exists(path)
assert len(parameters) > 0
# apply smoothing operator
unix.cd(self.getpath)
for name in parameters:
print ' smoothing', name
self.mpirun(
PATH.SPECFEM_BIN +'/'+ 'xsmooth_sem '
+ str(span) + ' '
+ str(span) + ' '
+ name + '_kernel' + ' '
+ path + '/ '
+ path + '/ ',
output=self.getpath+'/'+'OUTPUT_FILES/output_smooth_sem.txt')
print ''
# move input files
src = path
dst = path + '_nosmooth'
unix.mkdir(dst)
for name in self.parameters:
unix.mv(glob(src+'/*'+name+'.bin'), dst)
# rename output files
unix.rename('_smooth', '', glob(src+'/*'))
def save(self, path, v, backup=None):
if backup:
src = path +'/'+ 'gradient'
dst = path +'/'+ 'gradient_'+backup
unix.mv(src, dst)
solver.save(path +'/'+ 'gradient',
solver.split(v),
suffix='_kernel')
def export_kernels(self, path):
unix.cd(self.kernel_databases)
# work around conflicting name conventions
self.rename_kernels()
src = glob('*_kernel.bin')
dst = join(path, 'kernels', self.source_name)
unix.mkdir(dst)
unix.mv(src, dst)
def clean(self):
isready = self.solver_status()
if isready:
unix.rm(PATH.GRAD)
unix.mv(PATH.FUNC, PATH.GRAD)
unix.mkdir(PATH.FUNC)
unix.rm(PATH.SOLVER)
unix.mv(PATH.SOLVER+'_best', PATH.SOLVER)
else:
super(thrifty_inversion, self).clean()
def clean(self):
# can forward simulations from line search be carried over?
self.update_status()
if self.status==1:
unix.rm(PATH.GRAD)
unix.mv(PATH.FUNC, PATH.GRAD)
unix.mkdir(PATH.FUNC)
else:
super(thrifty_inversion, self).clean()
def iterate_search(self):
super(thrifty_inversion, self).iterate_search()
isdone = optimize.isdone
isready = self.solver_status()
# to avoid redundant forward simulation, save solver files associated
# with 'best' trial model
if isready and isdone:
unix.rm(PATH.SOLVER+'_best')
unix.mv(PATH.SOLVER, PATH.SOLVER+'_best')
def forward(self, path='traces/syn'):
""" Calls SPECFEM3D_GLOBE forward solver
"""
solvertools.setpar('SIMULATION_TYPE', '1')
solvertools.setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xspecfem3D')
if PAR.FORMAT in ['ASCII', 'ascii']:
src = glob('OUTPUT_FILES/*.sem.ascii')
dst = path
unix.mv(src, dst)
def update_aggregate_gradient(self):
""" Update stored aggregate gradient.
"""
names = self.check_source_names()
subset = [names[isrc] for isrc in self._source_subset]
print subset
for source_name in subset:
src = glob(join(PATH.GRAD, 'kernels', source_name, '*'))
dst = join(PATH.GRAD_AGG, source_name)
unix.mv(src, dst)
def forward(self, path='traces/syn'):
""" Calls SPECFEM2D forward solver
"""
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xmeshfem2D')
call_solver(system.mpiexec(), 'bin/xspecfem2D')
if PAR.FORMAT in ['SU', 'su']:
filenames = glob('OUTPUT_FILES/*.su')
unix.mv(filenames, path)
def clean(self):
"""
Determine if forward simulation from line search can be carried over
"""
self.update_status()
if self.status == 1:
print 'THRIFTY CLEAN'
unix.rm(PATH.GRAD)
unix.mv(PATH.FUNC, PATH.GRAD)
unix.mkdir(PATH.FUNC)
else:
super(thrifty_inversion_nz, self).clean()
def forward(self, path='traces/syn'):
""" Calls SPECFEM2D forward solver
"""
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xmeshfem2D')
call_solver(system.mpiexec(), 'bin/xspecfem2D')
if PAR.FORMAT in ['SU', 'su']:
filenames = glob('OUTPUT_FILES/*.su')
unix.mv(filenames, path)
def forward(self, path='traces/syn'):
""" Calls SPECFEM3D forward solver
"""
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xgenerate_databases')
call_solver(system.mpiexec(), 'bin/xspecfem3D')
if PAR.FORMAT in ['SU', 'su']:
src = glob('OUTPUT_FILES/*_d?_SU')
dst = path
unix.mv(src, dst)
def apply_hess(self, path='', hessian='Newton'):
""" Evaluates action of Hessian on a given model vector.
"""
unix.cd(self.getpath)
self.imprt(path, 'model')
self.forward()
unix.mv(self.wildcard, 'traces/lcg')
preprocess.prepare_apply_hess(self.getpath)
self.adjoint()
self.export_kernels(path)
def forward(self, path='traces/syn'):
""" Calls SPECFEM3D forward solver
"""
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xgenerate_databases')
call_solver(system.mpiexec(), 'bin/xspecfem3D')
if PAR.FORMAT in ['SU', 'su']:
src = glob('OUTPUT_FILES/*_d?_SU')
dst = path
unix.mv(src, dst)
def generate_data(self, **model_kwargs):
""" Generates data
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.getpath)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
self.call('bin/xspecfem3D')
unix.mv(self.data_wildcard, 'traces/obs')
self.export_traces(PATH.OUTPUT, 'traces/obs')
def generate_data(self, **model_kwargs):
""" Generates data
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.getpath)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
self.mpirun('bin/xspecfem3D')
unix.mv(self.data_wildcard, 'traces/obs')
self.export_traces(PATH.OUTPUT, 'traces/obs')
def export_kernels(self, path):
unix.cd(self.kernel_databases)
# work around conflicting name conventions
self.rename_kernels()
# two-step command used to work around parallel filesystem issue
unix.mkdir(join(path, 'kernels'), noexit=True)
unix.mkdir(join(path, 'kernels', basename(self.getpath)))
src = glob('*_kernel.bin')
dst = join(path, 'kernels', basename(self.getpath))
unix.mv(src, dst)
def finalize_search(self):
""" Cleans working directory and writes updated model
"""
unix.cd(PATH.OPTIMIZE)
m = self.load('m_new')
g = self.load('g_new')
p = self.load('p_new')
s = loadtxt('s_new')
x = self.step_lens()
f = self.func_vals()
# clean working directory
unix.rm('alpha')
unix.rm('m_try')
unix.rm('f_try')
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('s_new', 's_old')
# write updated model
alpha = x[f.argmin()]
savetxt('alpha', alpha)
self.save('m_new', m + alpha * p)
savetxt('f_new', f.min())
# append latest statistics
self.writer('factor',
-self.dot(g, g)**-0.5 * (f[1] - f[0]) / (x[1] - x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1] - f[0]) / (x[1] - x[0]))
self.writer('step_count', self.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180. * np.pi**-1 * angle(p, -g))
self.stepwriter.newline()
def export_kernels(self, path):
unix.mkdir_gpfs(join(path, 'kernels'))
unix.mkdir_gpfs(join(path, 'kernels', self.getname))
for name in self.kernel_map.values():
src = join(glob(self.databases + '/' + '*' + name + '.bin'))
dst = join(path, 'kernels', self.getname)
unix.mv(src, dst)
try:
name = 'rhop_kernel'
src = join(glob(self.databases + '/' + '*' + name + '.bin'))
dst = join(path, 'kernels', self.getname)
unix.mv(src, dst)
except:
pass
def clip(self, path='', thresh=1.):
""" Clips SPECFEM2D kernels
"""
parts = self.load(path)
if thresh >= 1.:
return parts
for key in self.parameters:
# scale to [-1,1]
minval = parts[key][0].min()
maxval = parts[key][0].max()
np.clip(parts[key][0], thresh*minval, thresh*maxval, out=parts[key][0])
unix.mv(path, path + '_noclip')
self.save(path, parts)
def apply_hess(self, path=''):
""" Computes action of Hessian on a given model vector.
"""
# a gradient evaluation must have already been carried out
unix.cd(self.getpath)
unix.mkdir('traces/lcg')
self.import_model(path)
self.forward()
unix.mv(self.data_wildcard, 'traces/lcg')
preprocess.prepare_apply_hess(self.getpath)
self.adjoint()
self.export_kernels(path)
def eval_func(self, path='', export_traces=False):
""" Evaluates misfit function by carrying out forward simulation and
comparing observations and synthetics.
"""
unix.cd(self.getpath)
self.import_model(path)
self.forward()
unix.mv(self.data_wildcard, 'traces/syn')
preprocess.prepare_eval_grad(self.getpath)
self.export_residuals(path)
if export_traces:
self.export_traces(path, prefix='traces/syn')
def generate_data(self, **model_kwargs):
""" Generates data
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.getpath)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xspecfem3D')
if PAR.FORMAT in ['SU', 'su']:
src = glob('OUTPUT_FILES/*_d?_SU')
dst = 'traces/obs'
unix.mv(src, dst)
def eval_func(self, path='', export_traces=False):
""" Evaluates misfit function by carrying out forward simulation and
comparing observations and synthetics.
"""
unix.cd(self.getpath)
self.import_model(path)
self.forward()
unix.mv(self.data_wildcard, 'traces/syn')
preprocess.prepare_eval_grad(self.getpath)
self.export_residuals(path)
if export_traces:
self.export_traces(path, prefix='traces/syn')
def apply_hess(self, path=''):
""" Computes action of Hessian on a given model vector.
"""
# a gradient evaluation must have already been carried out
unix.cd(self.getpath)
unix.mkdir('traces/lcg')
self.import_model(path)
self.forward()
unix.mv(self.data_wildcard, 'traces/lcg')
preprocess.prepare_apply_hess(self.getpath)
self.adjoint()
self.export_kernels(path)
def finalize_search(self):
""" Cleans working directory and writes updated model
"""
unix.cd(PATH.OPTIMIZE)
m = self.load("m_new")
g = self.load("g_new")
p = self.load("p_new")
s = loadtxt("s_new")
x = self.step_lens()
f = self.func_vals()
# clean working directory
unix.rm("alpha")
unix.rm("m_try")
unix.rm("f_try")
if self.iter > 1:
unix.rm("m_old")
unix.rm("f_old")
unix.rm("g_old")
unix.rm("p_old")
unix.rm("s_old")
unix.mv("m_new", "m_old")
unix.mv("f_new", "f_old")
unix.mv("g_new", "g_old")
unix.mv("p_new", "p_old")
unix.mv("s_new", "s_old")
# write updated model
alpha = x[f.argmin()]
savetxt("alpha", alpha)
self.save("m_new", m + alpha * p)
savetxt("f_new", f.min())
# append latest output
self.writer("factor", -self.dot(g, g) ** -0.5 * (f[1] - f[0]) / (x[1] - x[0]))
self.writer("gradient_norm_L1", np.linalg.norm(g, 1))
self.writer("gradient_norm_L2", np.linalg.norm(g, 2))
self.writer("misfit", f[0])
self.writer("restarted", self.restarted)
self.writer("slope", (f[1] - f[0]) / (x[1] - x[0]))
self.writer("step_count", self.step_count)
self.writer("step_length", x[f.argmin()])
self.writer("theta", 180.0 * np.pi ** -1 * angle(p, -g))
self.stepwriter.newline()
def finalize_search(self):
""" Cleans working directory and writes updated model
"""
unix.cd(PATH.OPTIMIZE)
m = self.load('m_new')
g = self.load('g_new')
p = self.load('p_new')
s = loadtxt('s_new')
x = self.step_lens()
f = self.func_vals()
# clean working directory
unix.rm('alpha')
unix.rm('m_try')
unix.rm('f_try')
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('s_new', 's_old')
# write updated model
alpha = x[f.argmin()]
savetxt('alpha', alpha)
self.save('m_new', m + alpha*p)
savetxt('f_new', f.min())
# append latest statistics
self.writer('factor', -self.dot(g,g)**-0.5 * (f[1]-f[0])/(x[1]-x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1]-f[0])/(x[1]-x[0]))
self.writer('step_count', self.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180.*np.pi**-1*angle(p,-g))
self.stepwriter.newline()
def forward(self, path='traces/syn'):
""" Calls SPECFEM2D forward solver
"""
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xmeshfem2D')
call_solver(system.mpiexec(), 'bin/xspecfem2D')
if PAR.FORMAT in ['SU', 'su']:
filenames = glob('OUTPUT_FILES/*.su')
# work around SPECFEM2D's different file names (depending on the
# version used :
unix.rename('single_p.su', 'single.su', filenames)
filenames = glob('OUTPUT_FILES/*.su')
unix.mv(filenames, path)
def clip(self, path='', tag='gradient', thresh=1.):
"""clips SPECFEM2D kernels"""
parts = self.load(path + '/' + tag)
if thresh >= 1.:
return parts
for key in self.inversion_parameters:
# scale to [-1,1]
minval = parts[key][0].min()
maxval = parts[key][0].max()
np.clip(parts[key][0],
thresh * minval,
thresh * maxval,
out=parts[key][0])
unix.mv(path + '/' + tag, path + '/' + '_noclip')
self.save(path + '/' + tag, parts)
def process_kernels(self, path, parameters):
""" Processes kernels in accordance with parameter settings
"""
fullpath = path + '/' + 'kernels'
assert exists(path)
if exists(fullpath + '/' + 'sum'):
unix.mv(fullpath + '/' + 'sum', fullpath + '/' + 'sum_nofix')
# mask sources and receivers
system.run('postprocess', 'fix_near_field', hosts='all', path=fullpath)
system.run('solver',
'combine',
hosts='head',
path=fullpath,
parameters=parameters)
def generate_data(self, **model_kwargs):
""" Generates data
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.cwd)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xspecfem3D')
if PAR.FORMAT in ['ASCII', 'ascii']:
src = glob('OUTPUT_FILES/*.sem.ascii')
dst = 'traces/obs'
unix.mv(src, dst)
if PAR.SAVETRACES:
self.export_traces(PATH.OUTPUT+'/'+'traces/obs')
def search_status(self):
""" Checks line search status
"""
if PAR.VERBOSE:
print " trial step", optimize.step
self.evaluate_function()
isdone, isbest = optimize.search_status()
if not PATH.LOCAL:
if isbest and isdone:
unix.rm(PATH.SOLVER + '_best')
unix.mv(PATH.SOLVER, PATH.SOLVER + '_best')
elif isbest:
unix.rm(PATH.SOLVER + '_best')
unix.cp(PATH.SOLVER, PATH.SOLVER + '_best')
return isdone
def generate_data(self, **model_kwargs):
""" Generates data
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.cwd)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
call_solver(system.mpiexec(), 'bin/xspecfem3D')
if PAR.FORMAT in ['ASCII', 'ascii']:
src = glob('OUTPUT_FILES/*.sem.ascii')
dst = 'traces/obs'
unix.mv(src, dst)
if PAR.SAVETRACES:
self.export_traces(PATH.OUTPUT + '/' + 'traces/obs')
def compute_precond(self, model_path=None, model_name=None, model_type='gll'):
assert(model_name)
assert(model_type)
assert (exists(model_path))
unix.cd(solver.cwd)
src = model_path
dst = solver.model_databases
solver.save(dst, solver.load(src))
solver.forward()
unix.mv(solver.data_wildcard, 'traces/syn')
solver.initialize_adjoint_traces('traces/syn')
self.process_traces(solver.cwd)
solver.adjoint()
solver.export_kernels(PATH.GLOBAL)
def save(self, g, path='', parameters=[], backup=None):
""" Utility for saving dictionary representation of gradient
"""
if not exists(path):
raise Exception
if not parameters:
parameters = solver.parameters
if backup:
src = path +'/'+ 'gradient'
dst = path +'/'+ 'gradient_'+backup
unix.mv(src, dst)
solver.save(solver.split(g),
path +'/'+ 'gradient',
parameters=parameters,
suffix='_kernel')
def smooth(self, path='', span=0., parameters=[]):
""" Smooths SPECFEM2D kernels by convolving them with a Gaussian
"""
from seisflows.tools.array import meshsmooth, stack
kernels = self.load(path, suffix='_kernel')
if not span:
return kernels
x = kernels['x'][0]
z = kernels['z'][0]
mesh = stack(x, z)
for key in parameters:
kernels[key] = [meshsmooth(kernels[key][0], mesh, span)]
unix.mv(path, path + '_nosmooth')
self.save(path, kernels)
def process_kernels(self, path, parameters):
""" Processes kernels in accordance with parameter settings
"""
fullpath = path +'/'+ 'kernels'
assert exists(path)
if exists(fullpath +'/'+ 'sum'):
unix.mv(fullpath +'/'+ 'sum', fullpath +'/'+ 'sum_nofix')
# mask sources and receivers
system.run('postprocess', 'fix_near_field',
hosts='all',
path=fullpath)
system.run('solver', 'combine',
hosts='head',
path=fullpath,
parameters=parameters)
def save(self, gradient, path='', parameters=[], backup=None):
""" Convience function for saving dictionary representation of
gradient
"""
if not exists(path):
raise Exception
if not parameters:
parameters = solver.parameters
if backup:
src = path +'/'+ 'gradient'
dst = path +'/'+ 'gradient_'+backup
unix.mv(src, dst)
solver.save(solver.split(gradient),
path +'/'+ 'gradient',
parameters=parameters,
suffix='_kernel')
def write_gradient(self, path):
super(mumford_shah, self).write_gradient(path)
g = solver.load(path +'/'+ 'gradient', suffix='_kernel')
m = solver.load(path +'/'+ 'model')
mesh = self.getmesh()
for parameter in solver.parameters:
for iproc in range(PAR.NPROC):
g[parameter][iproc] += PAR.GAMMA *\
sem.read(PATH.MUMFORD_SHAH_OUTPUT, parameter+'_dm', iproc)
# save gradient
self.save(path, solver.merge(g), backup='noregularize')
# save edges
src = PATH.MUMFORD_SHAH_OUTPUT
dst = PATH.OUTPUT+'/'+'mumford_shah'+('_%04d' % optimize.iter)
unix.mv(src, dst)
def generate_data(self, **model_kwargs):
""" Generates data
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.cwd)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.false.')
call_solver(system.mpiexec(), 'bin/xmeshfem2D', output='mesher.log')
call_solver(system.mpiexec(), 'bin/xspecfem2D')
if PAR.FORMAT in ['SU', 'su']:
src = glob('OUTPUT_FILES/*.su')
dst = 'traces/obs'
unix.mv(src, dst)
if PAR.SAVETRACES:
self.export_traces(PATH.OUTPUT + '/' + 'traces/obs')
def forward(self, path='traces/syn'):
""" Calls SPECFEM3D forward solver and then moves files into path
"""
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.true.')
setpar('ATTENUATION ', '.true.')
call_solver(system.mpiexec(), 'bin/xgenerate_databases')
call_solver(system.mpiexec(), 'bin/xspecfem3D')
# seismic unix output format
if PAR.FORMAT in ['SU', 'su']:
src = glob('OUTPUT_FILES/*_d?_SU')
dst = path
unix.mv(src, dst)
# ascii sem output format
elif PAR.FORMAT == "ascii":
src = glob('OUTPUT_FILES/*sem?')
dst = path
unix.mv(src, dst)
def finalize_search(self):
""" Prepares algorithm machinery and scratch directory for next
model upate
"""
m = self.load('m_new')
print("finalize_search")
print(m)
g = self.load('g_new')
p = self.load('p_new')
x = self.line_search.search_history()[0]
f = self.line_search.search_history()[1]
# clean scratch directory
unix.cd(PATH.OPTIMIZE)
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('m_try', 'm_new')
self.savetxt('f_new', f.min())
# output latest statistics
self.writer('factor',
-self.dot(g, g)**-0.5 * (f[1] - f[0]) / (x[1] - x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1] - f[0]) / (x[1] - x[0]))
self.writer('step_count', self.line_search.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180. * np.pi**-1 * angle(p, -g))
self.line_search.writer.newline()
def smooth(self, path='', tag='gradient', span=0.):
"""smooths SPECFEM2D kernels by convolving them with a Gaussian"""
from seisflows.tools.array import meshsmooth
parts = self.load(path + '/' + tag)
if not span:
return parts
# set up grid
x = parts['x'][0]
z = parts['z'][0]
lx = x.max() - x.min()
lz = z.max() - z.min()
nn = x.size
nx = np.around(np.sqrt(nn * lx / lz))
nz = np.around(np.sqrt(nn * lx / lz))
# perform smoothing
for key in self.inversion_parameters:
parts[key] = [meshsmooth(x, z, parts[key][0], span, nx, nz)]
unix.mv(path + '/' + tag, path + '/' + '_nosmooth')
self.save(path + '/' + tag, parts)
def clip(self, path='', parameters=[], minval=-np.inf, maxval=np.inf):
""" Clips kernels by convolving them with a Gaussian. Wrapper over
xclip_sem utility.
"""
assert exists(path)
assert len(parameters) > 0
unix.cd(self.getpath)
for name in self.parameters:
self.call(PATH.SPECFEM_BIN + '/' + 'xclip_sem ' + str(minval) +
' ' + str(maxval) + ' ' + name + '_kernel' + ' ' + path +
'/ ' + path + '/ ')
# move input files
src = path
dst = path + '_noclip'
unix.mkdir(dst)
for name in self.parameters:
unix.mv(glob(src + '/*' + name + '.bin'), dst)
# rename output files
unix.rename('_clip', '', glob(src + '/*'))
def smooth_legacy(path='', parameters=[], span=0.):
solver = sys.modules['seisflows_solver']
PATH = sys.modules['seisflows_paths']
# intialize arrays
kernels = {}
for key in parameters or solver.parameters:
kernels[key] = []
coords = {}
for key in ['x', 'z']:
coords[key] = []
# read kernels
for key in parameters or solver.parameters:
kernels[key] += solver.io.read_slice(path, key + '_kernel', 0)
if not span:
return kernels
# read coordinates
for key in ['x', 'z']:
coords[key] += solver.io.read_slice(PATH.MODEL_INIT, key, 0)
mesh = array.stack(coords['x'][0], coords['z'][0])
#mesh = array.stack(solver.mesh_properties.coords['x'][0],
# solver.mesh_properties.coords['z'][0])
for key in parameters or solver.parameters:
kernels[key] = [array.meshsmooth(kernels[key][0], mesh, span)]
unix.rm(path + '_nosmooth')
unix.mv(path, path + '_nosmooth')
unix.mkdir(path)
for key in parameters or solver.parameters:
solver.io.write_slice(kernels[key][0], path, key + '_kernel', 0)
def finalize_search(self):
""" Prepares algorithm machinery and scratch directory for next
model upate
"""
m = self.load('m_new')
g = self.load('g_new')
p = self.load('p_new')
x = self.line_search.search_history()[0]
f = self.line_search.search_history()[1]
# clean scratch directory
unix.cd(PATH.OPTIMIZE)
if self.iter > 1:
unix.rm('m_old')
unix.rm('f_old')
unix.rm('g_old')
unix.rm('p_old')
unix.rm('s_old')
unix.mv('m_new', 'm_old')
unix.mv('f_new', 'f_old')
unix.mv('g_new', 'g_old')
unix.mv('p_new', 'p_old')
unix.mv('m_try', 'm_new')
self.savetxt('f_new', f.min())
# output latest statistics
self.writer('factor', -self.dot(g,g)**-0.5 * (f[1]-f[0])/(x[1]-x[0]))
self.writer('gradient_norm_L1', np.linalg.norm(g, 1))
self.writer('gradient_norm_L2', np.linalg.norm(g, 2))
self.writer('misfit', f[0])
self.writer('restarted', self.restarted)
self.writer('slope', (f[1]-f[0])/(x[1]-x[0]))
self.writer('step_count', self.line_search.step_count)
self.writer('step_length', x[f.argmin()])
self.writer('theta', 180.*np.pi**-1*angle(p,-g))
self.line_search.writer.newline()
def generate_data(self, **model_kwargs):
""" Generates data (perform meshing and database generation first)
"""
self.generate_mesh(**model_kwargs)
unix.cd(self.cwd)
setpar('SIMULATION_TYPE', '1')
setpar('SAVE_FORWARD', '.false.')
call_solver(system.mpiexec(), 'bin/xmeshfem2D', output='mesher.log')
call_solver(system.mpiexec(), 'bin/xspecfem2D', output='solver.log')
if PAR.FORMAT in ['SU', 'su']:
src = glob('OUTPUT_FILES/*.su')
# work around SPECFEM2D's different file names (depending on the
# version used :
unix.rename('single_p.su', 'single.su', src)
src = glob('OUTPUT_FILES/*.su')
dst = 'traces/obs'
unix.mv(src, dst)
if PAR.SAVETRACES:
self.export_traces(PATH.OUTPUT + '/' + 'traces/obs')
def combine(self, path=''):
""" combines SPECFEM3D_GLOBE kernels
"""
dirs = unix.ls(path)
# initialize kernels
unix.cd(path)
for key in self.model_parameters:
if key not in self.inversion_parameters:
for i in range(PAR.NPROC):
proc = '%06d' % i
name = self.kernel_map[key]
src = PATH.GLOBAL + '/' + 'mesh' + '/' + key + '/' + proc
dst = path + '/' + 'sum' + '/' + 'proc' + proc + '_' + name + '.bin'
savebin(np.load(src), dst)
# create temporary files and directories
unix.cd(self.getpath)
with open('kernels_list.txt', 'w') as file:
file.write('\n'.join(dirs) + '\n')
unix.mkdir('INPUT_KERNELS')
unix.mkdir('OUTPUT_SUM')
for dir in dirs:
src = path + '/' + dir
dst = 'INPUT_KERNELS' + '/' + dir
unix.ln(src, dst)
# sum kernels
self.mpirun(PATH.SOLVER_BINARIES + '/' + 'xsum_kernels')
unix.mv('OUTPUT_SUM', path + '/' + 'sum')
# remove temporary files and directories
unix.rm('INPUT_KERNELS')
unix.rm('kernels_list.txt')
unix.cd(path)
def generate_precond(self, process_traces=None, model_path=None, model_name=None, model_type='gll'):
assert(model_name)
assert(model_type)
assert (exists(model_path))
self.initialize_solver_directories()
unix.cd(self.getpath)
assert(exists(model_path))
self.check_mesh_properties(model_path)
src = model_path
dst = join(self.getpath, 'DATA/proc000000_rho_vp_vs.dat')
unix.cp(src, dst)
self.export_model(PATH.OUTPUT +'/'+ model_name)
self.forward()
unix.mv(self.data_wildcard, 'traces/syn')
self.initialize_adjoint_traces('traces/syn')
process_traces(self.getpath)
self.adjoint()
self.export_kernels(PATH.GLOBAL)
def smooth(self, path='', tag='gradient', span=0.):
""" smooths SPECFEM3D_GLOBE kernels
"""
unix.cd(self.getpath)
# list kernels
kernels = []
for name in self.model_parameters:
if name in self.inversion_parameters:
flag = True
else:
flag = False
region, name = name.split('_')
kernels = kernels + [[name, flag]]
# smooth kernels
for name, flag in kernels:
if flag:
print ' smoothing', name
self.mpirun(PATH.SOLVER_BINARIES + '/' + 'xsmooth_sem ' +
str(span) + ' ' + str(span) + ' ' + name + ' ' +
path + '/' + tag + '/ ' + self.databases + '/ ')
# move kernels
src = path + '/' + tag
dst = path + '/' + '_nosmooth'
unix.mkdir(dst)
for name, flag in kernels:
if flag:
unix.mv(glob(src + '/*' + name + '.bin'), dst)
else:
unix.cp(glob(src + '/*' + name + '.bin'), dst)
unix.rename('_smooth', '', glob(src + '/*'))
print ''
unix.cd(path)
def process_kernels(self, path, parameters):
""" Processes kernels in accordance with parameter settings
"""
fullpath = path +'/'+ 'kernels'
assert exists(path)
if exists(fullpath +'/'+ 'sum'):
unix.mv(fullpath +'/'+ 'sum', fullpath +'/'+ 'sum_nofix')
print('==========in process_kernels, path==========')
print(path)
# mask sources and receivers
system.run('postprocess', 'fix_near_field',
#hosts='all',
#path=fullpath)
path=path)
print('==========fix_near_field end1==========')
system.run('solver', 'combine',
#hosts='head',
input_path=path,
output_path=path+'/'+'sum',
parameters=parameters)
print('==========combine end==========')
def smooth(self, path='', parameters=None, span=0.):
""" For a long time SPECFEM2D lacked its own smoothing utility; this
method was intended only as a crude workaround
"""
from seisflows.tools import array
assert self.mesh_properties.nproc == 1,\
msg.SmoothingError_SPECFEM2D
kernels = self.load(path, suffix='_kernel')
if not span:
return kernels
# set up grid
x = sem.read(PATH.MODEL_INIT, 'x', 0)
z = sem.read(PATH.MODEL_INIT, 'z', 0)
mesh = array.stack(x, z)
for key in parameters or self.parameters:
kernels[key] = [array.meshsmooth(kernels[key][0], mesh, span)]
unix.rm(path + '_nosmooth')
unix.mv(path, path + '_nosmooth')
self.save(path, kernels, suffix='_kernel')
def save_residuals(self):
src = join(PATH.GRAD, 'residuals')
dst = join(PATH.OUTPUT, 'residuals_%04d' % optimize.iter)
unix.mv(src, dst)
def save_traces(self):
src = join(PATH.GRAD, 'traces')
dst = join(PATH.OUTPUT, 'traces_%04d' % optimize.iter)
unix.mv(src, dst)
def save_gradient(self):
src = join(PATH.GRAD, 'gradient')
dst = join(PATH.OUTPUT, 'gradient_%04d' % optimize.iter)
unix.mv(src, dst)