def _display_function(rootname, argv, verbose, mapkwargs):
"""private"""
targetfile = "%s/_HerrMet.target" % rootname
paramfile = "%s/_HerrMet.param" % rootname
runfile = '%s/_HerrMet.run' % rootname
pngfile = '%s/_HerrMet.png' % rootname
#HerrLininitfile = '%s/_HerrLin.init' % rootname
# ------ Initiate the displayer using the target data if exists
if "-compact" in argv.keys(): # compact mode
which_displayer = DepthDispDisplayCompact
else:
which_displayer = DepthDispDisplay
if os.path.exists(targetfile):
rd = which_displayer(targetfile=targetfile)
d = makedatacoder(
targetfile, which=Datacoder_log) # datacoder based on observations
dobs, _ = d.target()
else:
print "no target file found in %s" % rootname
rd = which_displayer()
# ------ Display run results if exist
if os.path.exists(runfile) and ("-plot" in argv.keys()
or "-pdf" in argv.keys()):
with RunFile(runfile, verbose=verbose) as rundb:
s = rundb.select('select MODELID from MODELS limit 1')
if s is not None:
# --- display best models
if "-plot" in argv.keys():
assert argv["-plot"] == [] or len(
argv["-plot"]) == 4 # unexpected argument number
if argv["-plot"] == []:
plot_mode, plot_limit, plot_llkmin, plot_step = \
default_plot_mode, default_plot_limit, \
default_plot_llkmin, default_plot_step
elif len(argv['-plot']) == 4:
plot_mode, plot_limit, plot_llkmin, plot_step = argv[
'-plot']
else:
raise Exception()
print "plot : %s, limit %d, llkmin %f, step %d" % (
plot_mode, plot_limit, plot_llkmin, plot_step),
if plot_mode == "best":
chainids, weights, llks, ms, ds = \
rundb.getzip(limit=plot_limit,
llkmin=plot_llkmin,
step=plot_step,
algo="METROPOLIS")
elif plot_mode == "last":
chainids, weights, llks, ms, ds = \
rundb.getlastszip(limit=plot_limit,
llkmin=plot_llkmin,
step=plot_step,
algo="METROPOLIS")
else:
raise Exception('unexpected plot mode %s' % plot_mode)
vmin, vmax = llks.min(), llks.max()
# colors = values2colors(llks, vmin=vmin, vmax=vmax, cmap=argv['-cmap'])
if "-overdisp" in argv.keys():
"""note : recomputing dispersion with another frequency array might
result in a completely different dispersion curve in case
of root search failure """
waves, types, modes, freqs, _ = ds[0]
overwaves, overtypes, overmodes, _, _ = zip(*list(
groupbywtm(waves, types, modes, freqs,
np.arange(len(freqs)), None, True)))
overfreqs = [
freqspace(0.6 * min(freqs), 1.4 * max(freqs), 100,
"plog") for _ in xrange(len(overwaves))
]
overwaves, overtypes, overmodes, overfreqs = \
igroupbywtm(overwaves, overtypes, overmodes, overfreqs)
for llk, (mms, dds) in zip(
llks[::-1],
overdisp(ms[::-1],
overwaves,
overtypes,
overmodes,
overfreqs,
verbose=verbose,
**mapkwargs)):
# rd.plotmodel(color=clr, alpha=1.0, linewidth=3, *mms)
rd.addmodel(colorvalue=llk, *mms)
try:
# rd.plotdisp(color=clr, alpha=1.0, linewidth=3, *dds)
rd.adddisp(colorvalue=llk, *dds)
except KeyboardInterrupt:
raise
except Exception as e:
print "Error : could not plot dispersion curve (%s)" % str(
e)
# cb = makecolorbar(vmin=vmin, vmax=vmax, cmap=argv['-cmap'])
# pos = rd.axdisp[-1].get_position()
# cax = rd.fig.add_axes((pos.x0, 0.12, pos.width, 0.01))
# rd.fig.colorbar(cb, cax=cax, label="log likelyhood", orientation="horizontal")
else:
"display the dispersion curves as stored in the database"
for i in range(len(llks))[::-1]:
# rd.plotmodel(color=colors[i], alpha=1.0, linewidth=3, *ms[i])
# rd.plotdisp(color=colors[i], alpha=1.0, linewidth=3, *ds[i])
rd.addmodel(colorvalue=llks[i], *ms[i])
rd.adddisp(colorvalue=llks[i], *ds[i])
# cb = makecolorbar(vmin=vmin, vmax=vmax, cmap=argv['-cmap'])
# pos = rd.axdisp[-1].get_position()
# cax = rd.fig.add_axes((pos.x0, 0.12, pos.width, 0.01))
# rd.fig.colorbar(cb, cax=cax, label="log likelyhood", orientation="horizontal")
# cax.set_xticklabels(cax.get_xticklabels(), rotation=90., horizontalalignment="center")
rd.showdispcoll(vmin=vmin,
vmax=vmax,
cmap=argv['-cmap'],
alpha=1.0,
linewidth=3)
rd.showdepthcoll(vmin=vmin,
vmax=vmax,
cmap=argv['-cmap'],
alpha=1.0,
linewidth=3)
rd.colorbar(vmin=vmin,
vmax=vmax,
cmap=argv['-cmap'],
label="log likelyhood",
orientation="horizontal")
print rd.cax.get_position()
rd.cax.set_xticklabels(rd.cax.get_xticklabels(),
rotation=90.,
horizontalalignment="center")
# ---- display posterior pdf
if "-pdf" in argv.keys():
assert argv["-pdf"] == [] or len(
argv["-pdf"]) == 4 # unexpected argument number
if argv["-pdf"] == []:
pdf_mode, pdf_limit, pdf_llkmin, pdf_step = \
default_pdf_mode, default_pdf_limit, default_pdf_llkmin, default_pdf_step
elif len(argv['-pdf']) == 4:
pdf_mode, pdf_limit, pdf_llkmin, pdf_step = argv[
'-pdf']
else:
raise Exception()
print "pdf : %s, limit %d, llkmin %f, step %d" % (
pdf_mode, pdf_limit, pdf_llkmin, pdf_step),
if pdf_mode == "best":
chainids, weights, llks, ms, ds = \
rundb.getzip(limit=pdf_limit,
llkmin=pdf_llkmin,
step=pdf_step,
algo="METROPOLIS")
elif pdf_mode == "last":
chainids, weights, llks, ms, ds = \
rundb.getlastszip(limit=pdf_limit,
llkmin=pdf_llkmin,
step=pdf_step,
algo="METROPOLIS")
else:
raise Exception('unexpected pdf mode %s' % pdf_mode)
dms = [
depthmodel_from_arrays(ztop, vp, vs, rh)
for ztop, vp, vs, rh in ms
]
# display percentiles of model and data pdfs
clr = "b" if "-plot" not in argv.keys() else "k"
alp = 1.0 if "-plot" not in argv.keys() else 0.5
for p, (vppc, vspc, rhpc, prpc) in \
dmstats1(dms,
percentiles=[0.01, 0.16, 0.5, 0.84, 0.99],
Ndepth=100,
Nvalue=100,
weights=weights,
**mapkwargs):
try:
l = 3 if p == 0.5 else 1
for what, where in zip([vppc, vspc, rhpc, prpc], [
rd.axdepth['VP'], rd.axdepth['VS'],
rd.axdepth['RH'], rd.axdepth['PR']
]):
if where is not None:
what.show(where,
color=clr,
linewidth=l,
alpha=alp)
except KeyboardInterrupt:
raise
except Exception as e:
print "Error", str(e)
# display the disp pdf
for p, (wpc, tpc, mpc, fpc, vpc) in \
dispstats(ds,
percentiles=[0.01, 0.16, 0.5, 0.84, 0.99],
Ndisp=100,
weights=weights,
**mapkwargs):
try:
l = 3 if p == 0.5 else 1
rd.plotdisp(wpc,
tpc,
mpc,
fpc,
vpc,
dvalues=None,
color=clr,
alpha=alp,
linewidth=l)
except KeyboardInterrupt:
raise
except Exception as e:
print "Error", str(e)
# ------
if os.path.exists(paramfile):
p, _ = load_paramfile(paramfile)
showvp, showvs, showrh, showpr = True, True, True, True
if isinstance(p, Parameterizer_mZVSVPRH):
showpr = False
elif isinstance(p, Parameterizer_mZVSPRRH):
showvp = False
elif isinstance(p, Parameterizer_mZVSPRzRHvp):
showvp = showpr = showrh = False
elif isinstance(p, Parameterizer_mZVSPRzRHz):
showvp = showpr = showrh = False
else:
raise Exception('')
#
vplow, vphgh, vslow, vshgh, rhlow, rhhgh, prlow, prhgh = p.boundaries()
for what, where in zip(\
[vplow, vphgh, vslow, vshgh, rhlow, rhhgh, prlow, prhgh],
[rd.axdepth['VP'], rd.axdepth['VP'], rd.axdepth['VS'], rd.axdepth['VS'], rd.axdepth['RH'], rd.axdepth['RH'], rd.axdepth['PR'], rd.axdepth['PR']]):
if where is not None:
what.show(where,
alpha=1.0,
color="k",
marker="o--",
linewidth=1,
markersize=3)
zmax = 1.1 * p.inv(p.MINF)[0][-1]
if isinstance(p, Parameterizer_mZVSPRzRHvp):
rd.axdepth['PR'].plot(p.PRz(np.linspace(0., zmax, 100)),
np.linspace(0., zmax, 100),
"r--",
linewidth=3)
legendtext(rd.axdepth['PR'], p.PRzName, loc=4)
legendtext(rd.axdepth['RH'], p.RHvpName, loc=4)
elif isinstance(p, Parameterizer_mZVSPRzRHz):
rd.axdepth['PR'].plot(p.PRz(np.linspace(0., zmax, 100)),
np.linspace(0., zmax, 100),
"r--",
linewidth=3)
rd.axdepth['RH'].plot(p.RHz(np.linspace(0., zmax, 100)),
np.linspace(0., zmax, 100),
"r--",
linewidth=3)
legendtext(rd.axdepth['PR'], p.PRzName, loc=4)
legendtext(rd.axdepth['RH'], p.RHzName, loc=4)
rd.set_zlim(np.array([0, zmax]))
else:
print "call option --param to see prior depth boundaries"
# --------------------
if "-m96" in argv.keys(): # plot user data on top
for m96 in argv['-m96']:
try:
dm = depthmodel_from_mod96(m96)
dm.vp.show(rd.axdepth['VP'], "m", linewidth=3, label=m96)
dm.vs.show(rd.axdepth['VS'], "m", linewidth=3)
dm.rh.show(rd.axdepth['RH'], "m", linewidth=3)
dm.pr().show(rd.axdepth['PR'], "m", linewidth=3)
except KeyboardInterrupt:
raise
except: #Exception as e:
print 'could not read or display %s (reason : %s)' % (m96,
str(e))
rd.axdepth['VP'].legend(loc=3)
if "-ritt" in argv.keys():
a = AsciiFile('/mnt/labex2/home/max/data/boreholes/GRT1/GRT1.logsonic')
for what, where in zip(
[a.data['VS'], a.data['VP'], a.data['VP'] / a.data['VS']],
[rd.axdepth['VS'], rd.axdepth['VP'], rd.axdepth['PR']]):
if where is not None:
where.plot(what, a.data['TVD'] / 1000., "m", alpha=0.5)
# --------------------
if os.path.exists(targetfile):
# plot data on top
rd.plotdisp(d.waves,
d.types,
d.modes,
d.freqs,
d.inv(dobs),
dvalues=d.dvalues,
alpha=.5,
color="r",
linewidth=2)
if "-overdisp" in argv.keys():
rd.set_vlim((0.5 * d.values.min(), 1.5 * d.values.max()))
rd.set_plim((0.8 / overfreqs.max(), 1.2 / overfreqs.min()))
else:
rd.set_vlim((0.8 * d.values.min(), 1.2 * d.values.max()))
rd.set_plim((0.8 / d.freqs.max(), 1.2 / d.freqs.min()))
rd.tick()
rd.grid()
rd.fig.suptitle(rootname.split('_HerrMet_')[-1])
if "-ftsz" in argv.keys():
chftsz(rd.fig, argv["-ftsz"][0])
else:
chftsz(rd.fig, default_fontsize)
if "-png" in argv.keys():
dpi = argv['-png'][0] if len(argv['-png']) else default_dpi
if verbose:
print "writing %s" % pngfile
rd.fig.savefig(pngfile, dpi=dpi)
elif "-inline" in argv.keys():
plt.show()
else:
showme()
plt.close(rd.fig)
def sker17(ztop, vp, vs, rh, \
waves, types, modes, freqs,
dz=0.001, dlogvs=0.01, dlogpr=0.01, dlogrh=0.01, norm=True,
h = 0.005, dcl = 0.005, dcr = 0.005):
"""sker17 : compute finite difference sensitivity kernels for surface waves dispersion curves
input:
-> depth model
ztop, vp, vs, rh : lists or arrays, see dispersion
-> required dispersion points
waves, types, modes, freqs : lists or arrays, see dispersion
-> sensitivity kernel computation
dz = depth increment in km
dlogvs = increment to apply to the logarithm of vs
dlogpr = increment to apply to the logarithm of vp/vs
dlogrh = increment to apply to the logarithm of rho
norm = if True, I divide the sensitivity values by the thickness of each layer
=> this corrects for the difference of sensitivity due to the variable thicknesss
-> Herrmann's parameters, see CPS documentation
h, dcl, dcr = passed to dispersion
output:
-> yields a tuple (w, t, m, F, DLOGVADZ, DLOGVADLOGVS, DLOGVADLOGPR, DLOGVADLOGRH) for each wave, type and mode
w = string, wave letter (L = Love or R = Rayleigh)
t = string, type letter (C = phase or U = group)
m = int, mode number (0= fundamental)
F = array, 1D, frequency array in Hz
DLOGVADZ = array, 2D, [normed] sensitivity kernel relative to top depth of each layer (lines) and frequency (columns)
DLOGVADLOGVS = array, 2D, [normed] sensitivity kernel relative to Pwave velocity of each layer (lines) and frequency (columns)
DLOGVADLOGPR = array, 2D, [normed] sensitivity kernel relative to Swave velocity of each layer (lines) and frequency (columns)
DLOGVADLOGRH = array, 2D, [normed] sensitivity kernel relative to density of each layer (lines) and frequency (columns)
note that these arrays might contain nans
see also :
sker17_1
dispersion
"""
waves, types, modes, freqs = [
np.asarray(_) for _ in waves, types, modes, freqs
]
nlayer = len(ztop)
H = np.array(ztop) # NOT ASARRAY
H[:-1], H[-1] = H[1:] - H[:-1], np.inf #layer thickness in km
model0 = np.concatenate((ztop, np.log(vs), np.log(vp / vs), np.log(rh)))
dmodel = np.concatenate(
(dz * np.ones_like(ztop), dlogvs * np.ones_like(vs),
dlogpr * np.ones_like(vs), dlogrh * np.ones_like(rh)))
logvalues0 = lognofail(
dispersion(ztop,
vp,
vs,
rh,
waves,
types,
modes,
freqs,
h=h,
dcl=dcl,
dcr=dcr))
IZ = np.arange(nlayer)
IVS = np.arange(nlayer, 2 * nlayer)
IPR = np.arange(2 * nlayer, 3 * nlayer)
IRH = np.arange(3 * nlayer, 4 * nlayer)
DVADP = np.zeros((4 * nlayer, len(waves)), float) * np.nan
# ----
# parallel
# ----
def fun(i, modeli):
ztopi, logvsi, logpri, logrhi = \
modeli[IZ], modeli[IVS], modeli[IPR], modeli[IRH]
n = len(ztopi)
ilayer = i % n
if ilayer == n - 1:
Hi = 1.e50 # thickness of the half-space
else:
Hi = ztopi[ilayer + 1] - ztopi[ilayer]
try:
logvaluesi = lognofail(
dispersion(ztopi,
np.exp(logvsi + logpri),
np.exp(logvsi),
np.exp(logrhi),
waves,
types,
modes,
freqs,
h=h,
dcl=dcl,
dcr=dcr))
except CPiSDomainError as err:
print("error during gradient computation %s" % str(err))
return i, None
except:
raise
if norm:
# sensitivity corrected from the layer thicknesses
DVAVPi = (logvaluesi - logvalues0) / (modeli[i] - model0[i]) / Hi
else:
# absolute sensitivity regardless the thickness differences
DVAVPi = (logvaluesi - logvalues0) / (modeli[i] - model0[i])
return i, DVAVPi
# ----
def gen():
for i in xrange(1, 4 * len(ztop)):
modeli = model0.copy()
modeli[i] += dmodel[i]
yield Job(i, modeli)
# ----
with MapSync(fun, gen()) as ma:
for _, (i, DVAVPi), _, _ in ma:
if DVAVPi is None: continue
DVADP[i, :] = DVAVPi
for w, t, m, F, Iwtm in groupbywtm(waves, types, modes, freqs,
np.arange(len(waves))):
DLOGVADZ = DVADP[IZ, :][:, Iwtm]
DLOGVADLOGPR = DVADP[IPR, :][:, Iwtm]
DLOGVADLOGVS = DVADP[IVS, :][:, Iwtm]
DLOGVADLOGRH = DVADP[IRH, :][:, Iwtm]
DLOGVADZ, DLOGVADLOGVS, DLOGVADLOGPR, DLOGVADLOGRH = \
[np.ma.masked_where(np.isnan(_), _) for _ in
[DLOGVADZ, DLOGVADLOGVS, DLOGVADLOGPR, DLOGVADLOGRH]]
yield w, t, m, F, DLOGVADZ, DLOGVADLOGVS, DLOGVADLOGPR, DLOGVADLOGRH