def gen():
for rootname in rootnames:
yield Job(rootname,
argv,
verbose=verbose,
mapkwargs=mapkwargs,
return_fig=False)
def overdisp(ms,
overwaves,
overtypes,
overmodes,
overfreqs,
verbose=True,
**mapkwargs):
"""extrapolate dispersion curves"""
herrmanncaller = HerrmannCallerBasis(waves=overwaves,
types=overtypes,
modes=overmodes,
freqs=overfreqs,
h=0.005,
ddc=0.005)
fun = _OverdispCore(herrmanncaller)
gen = (Job(mms) for mms in ms)
with MapSync(fun, gen, **mapkwargs) as ma:
if verbose: wb = waitbar('overdisp')
Njobs = len(ms) - 1.
for jobid, (mms, overvalues), _, _ in ma:
if verbose: wb.refresh(jobid / Njobs)
dds = (overwaves, overtypes, overmodes, overfreqs, overvalues)
yield mms, dds
if verbose:
wb.close()
print()
print()
def overdisp(ms,
overwaves,
overtypes,
overmodes,
overfreqs,
verbose=True,
**mapkwargs):
"""extrapolate dispersion curves"""
def fun(mms):
ztop, vp, vs, rh = mms
try:
overvalues = dispersion(ztop,
vp,
vs,
rh,
overwaves,
overtypes,
overmodes,
overfreqs,
h=0.005,
dcl=0.005,
dcr=0.005)
except KeyboardInterrupt:
raise
except Exception as e:
h = ztop[1:] - ztop[:-1]
# assume failuer was caused by rounding issues
h[h <= 0.001] = 0.001001
ztop = np.concatenate(([0.], h.cumsum()))
try: #again
overvalues = dispersion(ztop,
vp,
vs,
rh,
overwaves,
overtypes,
overmodes,
overfreqs,
h=0.005,
dcl=0.005,
dcr=0.005)
except KeyboardInterrupt:
raise
except Exception as giveup:
overvalues = np.nan * np.ones(len(overwaves))
return mms, overvalues
with MapSync(fun, (Job(mms) for mms in ms), **mapkwargs) as ma:
if verbose: wb = waitbar('overdisp')
Njobs = len(ms) - 1.
for jobid, (mms, overvalues), _, _ in ma:
if verbose: wb.refresh(jobid / Njobs)
dds = (overwaves, overtypes, overmodes, overfreqs, overvalues)
yield mms, dds
if verbose:
wb.close()
print
print
def gen():
for rootname in rootnames:
yield Job(rootname,
extract_mode,
extract_limit,
extract_llkmin,
extract_step,
verbose,
percentiles=[.16, .5, .84],
mapkwargs=mapkwargs)
def gen(rootnames, runmode):
for rootname in rootnames:
targetfile = "%s/_HerrMet.target" % rootname
paramfile = "%s/_HerrMet.param" % rootname
runfile = "%s/_HerrMet.run" % rootname
if runmode == "append" and not os.path.exists(runfile):
runmode = "restart"
elif runmode == "restart" and os.path.exists(runfile):
os.remove(runfile)
elif runmode == "skip" and os.path.exists(runfile):
print "skip %s" % rootname
continue
# ------
p, logRHOM = load_paramfile(paramfile)
# ------
d = makedatacoder(targetfile, which=Datacoder_log) # datacoder based on observations
dobs, CDinv = d.target()
duncs = CDinv ** -.5
ND = len(dobs)
dinfs = d(0.1 * np.ones_like(d.values))
dsups = d(3.5 * np.ones_like(d.values))
logRHOD = LogGaussND(dobs, duncs, dinfs, dsups, k=1000., nanbehavior=1)
# ------
G = Theory(parameterizer=p, datacoder=d)
# ---------------------------------
if runmode == "restart" or runmode == "skip":
with RunFile(runfile, create=True, verbose=verbose) as rundb:
rundb.drop()
rundb.reset(p.NLAYER, d.waves, d.types, d.modes, d.freqs)
elif runmode == "append":
pass
else:
raise Exception('unexpected runmode %s' % runmode)
# ---------------------------------
for chainid in xrange(Nchain):
M0 = np.random.rand(len(p.MINF)) * (p.MSUP - p.MINF) + p.MINF
MSTD = p.MSTD
yield Job(runfile=runfile,
rootname=rootname,
chainid=chainid,
M0=M0,
MSTD=MSTD,
G=G,
ND=ND,
logRHOD=logRHOD,
logRHOM=logRHOM,
p=p, d=d,
nkeep=Nkeep,
verbose=verbose)
def gen(rootnames):
for rootname in rootnames:
targetfile = "%s/_HerrMet.target" % rootname
paramfile = "%s/_HerrMet.param" % rootname
runfile = "%s/_HerrMet.run" % rootname
# ------
p, logRHOM = load_paramfile(paramfile)
# ------
d = makedatacoder(targetfile, which=Datacoder_log) # datacoder based on observations
dobs, CDinv = d.target()
duncs = CDinv ** -.5
ND = len(dobs)
dinfs = d(0.1 * np.ones_like(d.values))
dsups = d(3.5 * np.ones_like(d.values))
logRHOD = LogGaussND(dobs, duncs, dinfs, dsups, k=1000., nanbehavior=1)
# ------
G = Theory(parameterizer=p, datacoder=d)
# ---------------------------------
with RunFile(runfile, verbose=verbose) as rundb:
best = list(rundb.get(llkmin=top_llkmin, limit=top_limit, step=top_step, algo=None))
# ---------------------------------
for modelid, chainid, weight, llk, nlayer, model, dat in best:
M0 = p(*model)
DM = 1.0 #p.MSTD
yield Job(runfile=runfile,
rootname=rootname,
chainid=chainid,
M0=M0,
DM=DM,
G=G,
ND=ND,
logRHOD=logRHOD,
logRHOM=logRHOM,
p=p, d=d,
verbose=verbose)
def gen():
for rootname in rootnames:
yield Job(rootname,
argv,
verbose=verbose,
mapkwargs=mapkwargs)
def job_generator():
for iy in range(ny):
for jx in range(nx): # order matters!!!!
vs = Mprior[:, iy, jx]
yield Job(iy, jx, ztop, vs)
def JobGen():
for weight, dm in zip(weights, dms):
yield Job(weight, dm)
def gen():
for i in xrange(1, 4 * len(ztop)):
modeli = model0.copy()
modeli[i] += dmodel[i]
yield Job(i, modeli)
def job_generator():
for iy in range(ny):
for jx in range(nx): # order matters!!!!
nnode = iy * nx + jx
yield Job(nnode, theorys[iy, jx], M[:, iy, jx])
def job_generator():
for i in range(self.shape[0]):
yield Job(i)
def job_generator():
ls = zip(parameterizer_strings, datacoder_strings)
for nnode, (ps, ds) in enumerate(ls):
yield Job(nnode, parameterizer_string=ps, datacoder_string=ds)
def job_generator():
for i in range(len(b)):
yield Job(i)
