def setup(self):
""" Sets up nonlinear optimization machinery
"""
unix.mkdir(PATH.OPTIMIZE)
# prepare output writers
self.writer = Writer(path=PATH.OUTPUT)
self.stepwriter = StepWriter(path=PATH.SUBMIT)
# prepare algorithm machinery
if PAR.SCHEME in ['NLCG']:
self.NLCG = NLCG(path=PATH.OPTIMIZE,
maxiter=PAR.NLCGMAX,
thresh=PAR.NLCGTHRESH,
precond=self.precond())
elif PAR.SCHEME in ['LBFGS']:
self.LBFGS = LBFGS(path=PATH.OPTIMIZE,
memory=PAR.LBFGSMEM,
maxiter=PAR.LBFGSMAX,
thresh=PAR.LBFGSTHRESH,
precond=self.precond())
# write initial model
if exists(PATH.MODEL_INIT):
import solver
src = PATH.MODEL_INIT
dst = join(PATH.OPTIMIZE, 'm_new')
savenpy(dst, solver.merge(solver.load(src)))
def write_gradient(self, path):
super(regularize, self).write_gradient(path)
g = solver.load(path + '/' + 'gradient', suffix='_kernel')
if not PAR.LAMBDA:
return solver.merge(g)
m = solver.load(path + '/' + 'model')
mesh = self.getmesh()
for key in solver.parameters:
for iproc in range(PAR.NPROC):
g[key][iproc] += PAR.LAMBDA *\
self.nabla(mesh, m[key][iproc], g[key][iproc])
self.save(path, solver.merge(g), backup='noregularize')
def setup(self):
""" Sets up nonlinear optimization machinery
"""
unix.mkdir(PATH.OPTIMIZE)
# prepare output writers
self.writer = Writer(path=PATH.OUTPUT)
self.stepwriter = StepWriter(path=PATH.SUBMIT)
# prepare algorithm machinery
if PAR.SCHEME in ["NLCG"]:
self.NLCG = NLCG(path=PATH.OPTIMIZE, maxiter=PAR.NLCGMAX, thresh=PAR.NLCGTHRESH, precond=self.precond)
elif PAR.SCHEME in ["LBFGS"]:
self.LBFGS = LBFGS(
path=PATH.OPTIMIZE,
memory=PAR.LBFGSMEM,
maxiter=PAR.LBFGSMAX,
thresh=PAR.LBFGSTHRESH,
precond=self.precond,
)
# write initial model
if exists(PATH.MODEL_INIT):
src = PATH.MODEL_INIT
dst = join(PATH.OPTIMIZE, "m_new")
savenpy(dst, solver.merge(solver.load(src)))
def regularize3d(self, path):
assert (exists(path))
g = solver.load(path + '/' + 'gradient', suffix='_kernel')
if not PAR.LAMBDA:
return solver.merge(g)
m = solver.load(path + '/' + 'model')
mesh = self.getmesh()
for key in solver.parameters:
for iproc in range(PAR.NPROC):
g[key][iproc] += PAR.LAMBDA *\
self.nabla(mesh, m[key][iproc], g[key][iproc])
return solver.merge(g)
def regularize(self, path):
assert (exists(path))
g = solver.load(path +'/'+ 'gradient', suffix='_kernel')
if not PAR.LAMBDA:
return solver.merge(g)
m = solver.load(path +'/'+ 'model')
mesh = self.getmesh()
for key in solver.parameters:
for iproc in range(PAR.NPROC):
g[key][iproc] += PAR.LAMBDA *\
self.nabla(mesh, m[key][iproc], g[key][iproc])
#self.nabla(m[key][iproc], g[key][iproc] , mesh, h)
return solver.merge(g)
def evaluate_gradient(self):
""" Performs adjoint simulation to evaluate gradient
"""
system.run('solver',
'eval_grad',
hosts='all',
path=PATH.GRAD,
export_traces=divides(optimize.iter, PAR.SAVETRACES))
postprocess.write_gradient(path=PATH.GRAD)
src = join(PATH.GRAD, 'gradient')
dst = join(PATH.OPTIMIZE, 'g_new')
savenpy(dst, solver.merge(solver.load(src, suffix='_kernel')))
def evaluate_gradient(self):
""" Performs adjoint simulation to evaluate gradient
"""
system.run('solver', 'eval_grad',
hosts='all',
path=PATH.GRAD,
export_traces=divides(optimize.iter, PAR.SAVETRACES))
postprocess.write_gradient(
path=PATH.GRAD)
src = join(PATH.GRAD, 'gradient')
dst = join(PATH.OPTIMIZE, 'g_new')
savenpy(dst, solver.merge(solver.load(src, suffix='_kernel')))
def write_gradient(self, path):
""" Reads kernels and writes gradient of objective function
"""
if not exists(path):
raise Exception()
self.combine_kernels(path, solver.parameters)
self.process_kernels(path, solver.parameters)
g = solver.merge(solver.load(
path +'/'+ 'kernels/sum',
suffix='_kernel',
verbose=True))
if PAR.LOGARITHMIC:
# convert from logarithmic to absolute perturbations
g *= solver.merge(solver.load(path +'/'+ 'model'))
self.save(path, g)
if PATH.MASK:
# apply mask
g *= solver.merge(solver.load(PATH.MASK))
self.save(path, g, backup='nomask')
def search(self):
self.bestWords = []
self.prog.value = 0
sekwencja = self.literki.text.replace(' ', '')
if len(sekwencja) < 3:
self.opis.text = 'Wprowadź 3 lub więcej liter i wyszukaj ' + \
'jeszcze raz.'
else:
if not self.BAZY:
self.importFiles()
self.hand = getFrequencyDict(sekwencja)
lettersNr = len(sekwencja)
self.opis.text = 'Obliczanie... \n\nCzekaj'
wordlists = self.wordlist[:lettersNr - 2]
crpWordlist = list(merge(*wordlists))
# TODO implement thread search
work = chooseWords(
self, crpWordlist, lettersNr + 1,
lambda x: Clock.schedule_once(partial(self.update_bar, x)))
work.start()
self.prog.value = 1.0
def fix_near_field(self, path=''):
"""
"""
import preprocess
preprocess.setup()
name = solver.check_source_names()[solver.getnode]
fullpath = path + '/' + name
#print 'DB: name=', name
#print 'DB: fullpath=', fullpath
g = solver.load(fullpath, suffix='_kernel')
g_vec = solver.merge(g)
nproc = solver.mesh.nproc
#print 'DB: len(g_vec)=', len(g_vec)
if not PAR.FIXRADIUS:
return
x, y, z = self.getcoords()
#print 'DB: len(g)=', len(g)
#print 'DB: len(g[vp][0])=', len(g['vp'][0])
#print 'DB: x.shape=', x.shape
#print 'DB: len(x)=', len(x)
##sys.exit("DB: stop from postporcess-regularize")
lx = x.max() - x.min()
ly = y.max() - y.min()
lz = z.max() - z.min()
nn = x.size
nx = np.around(np.sqrt(nn * lx / (lz * ly)))
ny = np.around(np.sqrt(nn * ly / (lx * lz)))
nz = np.around(np.sqrt(nn * lz / (lx * ly)))
dx = lx / nx * 1.25
dy = ly / ny * 1.25
dz = lz / nz * 1.25
#print 'DB: lx=', lx
#print 'DB: ly=', ly
#print 'DB: lz=', lz
#print 'DB: nn=', nn
#print 'DB: nx=', nx
#print 'DB: ny=', ny
#print 'DB: nz=', nz
#print 'DB: dx=', dx
#print 'DB: dy=', dy
#print 'DB: dz=', dz
sigma = PAR.FIXRADIUS * (dx + dz + dy) / 3.0
_, h = preprocess.load(solver.getpath + '/' + 'traces/obs')
# mask sources
mask = np.exp(-0.5 * ((x - h.sx[0])**2. + (y - h.sy[0])**2. +
(z - h.sz[0])**2.) / sigma**2.)
# mask top
# for matlab
# z_sqrt=(abs(z).^(0.25)); depth_scale=1-z_sqrt/max(z_sqrt); figure; plot(depth_scale,z);
z_factor = np.power(abs(z), 0.5)
#max_z_factor = np.amax(z_factor)
#scale_depth = 1.0 - z_factor/max_z_factor
#print 'DB: max(z_factor)=',max_z_factor
#print 'DB: max(scale_depth)=',np.amax(scale_depth)
#print 'DB: min(scale_depth)=',np.amin(scale_depth)
#mask *= scale_depth
#mask_depth = solver.split(z)
mask_depth = solver.split(z_factor)
mask_d = solver.split(mask)
##print 'DB: sigma=',sigma
##print 'DB: mask=',mask
#print 'DB: len(mask)=', len(mask)
#print 'DB: len(mask_d)=', len(mask_d)
##print 'DB: len(g)=', len(g)
##print 'DB: len(g)[vp][0]=', len(g['vp'][0])
for key in solver.parameters:
for iproc in range(nproc):
#print 'DB: key, iproc=', key, iproc
#print 'DB: len(g[key][iproc])=', len(g[key][iproc])
#print 'DB: len(mask_d[key][iproc])=', len(mask_d[key][iproc])
weight = np.sum(mask_d['vp'][iproc] * g[key][iproc]) / np.sum(
mask_d['vp'][iproc])
#print 'DB: key, iproc, weigth= ', key, iproc, weight
g[key][iproc] *= 1. - mask_d['vp'][iproc]
g[key][iproc] *= mask_depth['vp'][iproc]
#g[key][iproc] += mask_d['vp'][iproc]*weight
#weight = np.sum(mask_d['vp'][iproc]*g[key][iproc])/np.sum(mask_d['vp'][iproc])
##print 'DB: key, iproc, weigth= ', key, iproc, weight
#g[key][iproc] *= 1.-mask_d['vp'][iproc]
#g[key][iproc] += mask_d['vp'][iproc]*weight
# mask receivers
#for ir in range(h.nr):
# mask = np.exp(-0.5*((x-h.rx[ir])**2.+(z-h.ry[ir])**2.)/sigma**2.)
# for key in solver.parameters:
# weight = np.sum(mask*g[key][0])/np.sum(mask)
# g[key][0] *= 1.-mask
# g[key][0] += mask*weight
solver.save(fullpath, g, suffix='_kernel')
def importFiles(self):
self.ltrVals = letterValues(PUNKTACJA)
self.wordlist = loadWords(DICTIONARY)
self.BAZY = True
printDbg('imported {:d} words.'.format(len(list(
merge(*self.wordlist)))))
def fix_near_field(self, path=''):
"""
"""
import preprocess
preprocess.setup()
name = solver.check_source_names()[solver.getnode]
fullpath = path + '/' + name
g = solver.load(fullpath, suffix='_kernel')
g_vec = solver.merge(g)
nproc = solver.mesh.nproc
if not PAR.FIXRADIUS:
return
x, y, z = self.getcoords()
lx = x.max() - x.min()
ly = y.max() - y.min()
lz = z.max() - z.min()
nn = x.size
nx = np.around(np.sqrt(nn * lx / (lz * ly)))
ny = np.around(np.sqrt(nn * ly / (lx * lz)))
nz = np.around(np.sqrt(nn * lz / (lx * ly)))
dx = lx / nx * 1.25
dy = ly / ny * 1.25
dz = lz / nz * 1.25
sigma = PAR.FIXRADIUS * (dx + dz + dy) / 3.0
_, h = preprocess.load(solver.getpath + '/' + 'traces/obs')
mask = np.exp(-0.5 * ((x - h.sx[0])**2. + (y - h.sy[0])**2. +
(z - h.sz[0])**2.) / sigma**2.)
scale_z = np.power(abs(z), 0.5)
power_win = 10
win_x = np.power(x, power_win)
win_y = np.power(y, power_win)
win_z = np.power(z, power_win)
win_x = win_x / win_x.max()
win_y = win_y / win_y.max()
win_z = win_z / win_z.max()
win_x = 1.0 - win_x[::-1]
win_y = 1.0 - win_y[::-1]
win_z = 1.0 - win_z[::-1]
win_x_rev = win_x[::-1]
win_y_rev = win_y[::-1]
win_z_rev = win_z[::-1]
taper_x = x * 0.0 + 1.0
taper_y = y * 0.0 + 1.0
taper_z = z * 0.0 + 1.0
taper_x *= win_x
taper_y *= win_y
taper_z *= win_z
taper_x *= win_x_rev
taper_y *= win_y_rev
taper_z *= win_z_rev
scale_z = scale_z * taper_z + 0.1
mask_x = solver.split(taper_x)
mask_y = solver.split(taper_y)
mask_z = solver.split(scale_z)
mask_d = solver.split(mask)
for key in solver.parameters:
for iproc in range(nproc):
weight = np.sum(mask_d['vp'][iproc] * g[key][iproc]) / np.sum(
mask_d['vp'][iproc])
g[key][iproc] *= 1. - mask_d['vp'][iproc]
g[key][iproc] *= mask_z['vp'][iproc]
g[key][iproc] *= mask_x['vp'][iproc]
g[key][iproc] *= mask_y['vp'][iproc]
#sigma = 1.0
## mask receivers
#for ir in range(h.nr):
# mask = np.exp(-0.5*((x-h.rx[ir])**2.+(y-h.ry[ir])**2.+(z-h.rz[ir])**2.)/sigma**2.)
# mask_d = solver.split(mask)
# #mask = np.exp(-0.5*((x-h.rx[ir])**2.+(z-h.ry[ir])**2.)/sigma**2.)
# for key in solver.parameters:
# for iproc in range(nproc):
# #weight = np.sum(mask*g[key][0])/np.sum(mask)
# g[key][iproc] *= 1.-mask_d['vp'][iproc]
# #g[key][0] += mask*weight
solver.save(fullpath, g, suffix='_kernel')
