def showsip1ddata(PHI, fr, ab2, mn2=None, cmax=None, ylab=True, cbar=True):
"""display SIP phase data as image plot."""
P.cla()
ax = P.gca()
pal = P.cm.get_cmap()
pal.set_under('w')
pal.set_bad('w')
if isinstance(PHI, pg.Vector):
PHI = N.asarray(PHI)
im = P.imshow(PHI.reshape((len(ab2), len(fr))),
interpolation='nearest',
cmap=pal)
if cmax is None:
cmax = N.max(PHI)
im.set_clim((0., cmax))
ax.xaxis.set_label_position('top')
P.xlabel('f in Hz')
a = []
df = 1
for f in fr[::df]:
a.append("%g" % rndig(f))
P.xticks(N.arange(0, len(fr), df), a)
xtl = ax.get_xticklabels()
for i, xtli in enumerate(xtl):
xtli.set_rotation('vertical')
if ylab:
a = []
yla = 'AB/2'
if mn2 is None:
for i in range(len(ab2)):
a.append(str(ab2[i]))
else:
yla = yla + '-MN/2'
for i in range(len(ab2)):
a.append('%g%g' % (rndig(ab2[i]), rndig(mn2[i])))
P.yticks(N.arange(len(ab2)), a)
P.ylabel(yla + ' in m')
if cbar:
P.colorbar(aspect=40, shrink=0.6)
P.ylim((len(ab2) - 0.5, -0.5))
P.show()
P.ylim((len(ab2) - 0.5, -0.5))
return
def showsip1ddata(PHI, fr, ab2, mn2=None, cmax=None, ylab=True, cbar=True):
"""display SIP phase data as image plot."""
P.cla()
ax = P.gca()
pal = P.cm.get_cmap()
pal.set_under('w')
pal.set_bad('w')
if isinstance(PHI, pg.RVector):
PHI = N.asarray(PHI)
im = P.imshow(PHI.reshape((len(ab2), len(fr))),
interpolation='nearest', cmap=pal)
if cmax is None:
cmax = N.max(PHI)
im.set_clim((0., cmax))
ax.xaxis.set_label_position('top')
P.xlabel('f in Hz')
a = []
df = 1
for f in fr[::df]:
a.append("%g" % rndig(f))
P.xticks(N.arange(0, len(fr), df), a)
xtl = ax.get_xticklabels()
for i, xtli in enumerate(xtl):
xtli.set_rotation('vertical')
if ylab:
a = []
yla = 'AB/2'
if mn2 is None:
for i in range(len(ab2)):
a.append(str(ab2[i]))
else:
yla = yla + '-MN/2'
for i in range(len(ab2)):
a.append('%g%g' % (rndig(ab2[i]), rndig(mn2[i])))
P.yticks(N.arange(len(ab2)), a)
P.ylabel(yla + ' in m')
if cbar:
P.colorbar(aspect=40, shrink=0.6)
P.ylim((len(ab2) - 0.5, -0.5))
P.show()
P.ylim((len(ab2) - 0.5, -0.5))
return
def showmymatrix(mat, x, y, dx=2, dy=1, xlab=None, ylab=None, cbar=None):
"""What is this good for?."""
plt.imshow(mat, interpolation='nearest')
plt.xticks(np.arange(0, len(x), dx), ["%g" % rndig(xi, 2) for xi in x])
plt.yticks(np.arange(0, len(y), dy), ["%g" % rndig(yi, 2) for yi in y])
plt.ylim((len(y) - 0.5, -0.5))
if xlab is not None:
plt.xlabel(xlab)
if ylab is not None:
plt.ylabel(ylab)
plt.axis('auto')
if cbar is not None:
plt.colorbar(orientation=cbar)
return
def showmymatrix(mat, x, y, dx=2, dy=1, xlab=None, ylab=None, cbar=None):
"""What is this good for?."""
plt.imshow(mat, interpolation='nearest')
plt.xticks(np.arange(0, len(x), dx), ["%g" % rndig(xi, 2) for xi in x])
plt.yticks(np.arange(0, len(y), dy), ["%g" % rndig(yi, 2) for yi in y])
plt.ylim((len(y) - 0.5, -0.5))
if xlab is not None:
plt.xlabel(xlab)
if ylab is not None:
plt.ylabel(ylab)
plt.axis('auto')
if cbar is not None:
plt.colorbar(orientation=cbar)
return
def draw1dmodel__Redundant(x, thk=None, xlab=None, zlab="z in m", islog=True,
fs=14, z0=0, **kwargs):
"""Draw 1d block model defined by value and thickness vectors."""
# if xlab is None:
# xlab = "$\\rho$ in $\\Omega$m"
if thk is None: # gimli blockmodel (thk+x together) given
nl = int(np.floor((len(x) - 1) / 2.)) + 1
thk = np.asarray(x)[:nl - 1]
x = np.asarray(x)[nl - 1:nl * 2 - 1]
z1 = np.concatenate(([0], np.cumsum(thk))) + z0
z = np.concatenate((z1, [z1[-1] * 1.2]))
nl = len(x) # x.size()
px = np.zeros((nl * 2, 1))
pz = np.zeros((nl * 2, 1))
for i in range(nl):
px[2 * i] = x[i]
px[2 * i + 1] = x[i]
pz[2 * i + 1] = z[i + 1]
if i < nl - 1:
pz[2 * i + 2] = z[i + 1]
# plt.cla()
li = []
if islog:
li = plt.semilogx(px, pz, **kwargs)
else:
li = plt.plot(px, pz, **kwargs)
plt.gca().xaxis.set_label_position('top')
locs = plt.xticks()[0]
if len(locs) < 2:
locs = np.hstack((min(x), locs, max(x)))
elif len(locs) < 5:
locs[0] = max(locs[0], min(x))
locs[-1] = min(locs[-1], max(x))
a = []
for l in locs:
a.append('%g' % rndig(l))
plt.xticks(locs, a, fontsize=fs)
plt.yticks(fontsize=fs)
plt.xlim((np.min(x) * 0.9, np.max(x) * 1.1))
plt.ylim((max(z1) * 1.15, 0.))
if xlab is not None:
plt.xlabel(xlab, fontsize=fs)
if zlab is not None:
plt.ylabel(zlab, fontsize=fs)
plt.grid(which='both')
# plt.show()
return li
def makeSlmData(ab2, mn2, rhoa=None, filename=None):
"""generate a pygimli data container from ab/2 and mn/2 array."""
data = pg.DataContainer()
data.resize(len(ab2))
pos = N.unique(N.hstack((ab2, mn2)))
for elx in N.hstack((-pos[::-1], pos)):
data.createElectrode(elx, 0., 0.)
if filename is not None:
f = open(filename, 'w')
f.write(str(len(pos) * 2) + '\n#x y z\n')
for elx in N.hstack((-pos[::-1], pos)):
f.write(str(elx) + '\t0\t0\n')
f.write(str(len(ab2)) + '\n#a\tb\tm\tn\tk\trhoa\n')
lpos = len(pos)
iab = pos.searchsorted(ab2)
imn = pos.searchsorted(mn2)
if (filename is not None) & (rhoa is None):
rhoa = N.ones(len(ab2))
for i in range(len(iab)):
# print -pos[iab[i]], -pos[imn[i]], pos[imn[i]], pos[iab[i]]
k = (ab2[i]**2 - mn2[i]**2) * N.pi / mn2[i] / 2.0
if filename is not None:
f.write(str(lpos - iab[i]) + '\t' + str(lpos + iab[i] + 1) + '\t')
f.write(str(lpos - imn[i]) + '\t' + str(lpos + imn[i] + 1) + '\t')
f.write(str(rndig(k, 4)) + '\t' + str(rhoa[i]) + '\n')
data.createFourPointData(i, int(lpos - iab[i]), int(lpos + iab[i] + 1),
int(lpos - imn[i]), int(lpos + imn[i] + 1))
if filename is not None:
f.close()
data.set('rhoa', pg.asvector(rhoa))
return data
def makeSlmData(ab2, mn2, rhoa=None, filename=None):
"""generate a pygimli data container from ab/2 and mn/2 array."""
data = pg.DataContainer()
data.resize(len(ab2))
pos = N.unique(N.hstack((ab2, mn2)))
for elx in N.hstack((-pos[::-1], pos)):
data.createElectrode(elx, 0., 0.)
if filename is not None:
f = open(filename, 'w')
f.write(str(len(pos) * 2) + '\n#x y z\n')
for elx in N.hstack((-pos[::-1], pos)):
f.write(str(elx) + '\t0\t0\n')
f.write(str(len(ab2)) + '\n#a\tb\tm\tn\tk\trhoa\n')
lpos = len(pos)
iab = pos.searchsorted(ab2)
imn = pos.searchsorted(mn2)
if (filename is not None) & (rhoa is None):
rhoa = N.ones(len(ab2))
for i in range(len(iab)):
# print -pos[iab[i]], -pos[imn[i]], pos[imn[i]], pos[iab[i]]
k = (ab2[i]**2 - mn2[i]**2) * N.pi / mn2[i] / 2.0
if filename is not None:
f.write(str(lpos - iab[i]) + '\t' + str(lpos + iab[i] + 1) + '\t')
f.write(str(lpos - imn[i]) + '\t' + str(lpos + imn[i] + 1) + '\t')
f.write(str(rndig(k, 4)) + '\t' + str(rhoa[i]) + '\n')
data.createFourPointData(i, int(lpos - iab[i]), int(lpos + iab[i] + 1),
int(lpos - imn[i]), int(lpos + imn[i] + 1))
if filename is not None:
f.close()
data.set('rhoa', pg.asvector(rhoa))
return data
def ReadAndRemoveEM(filename, readsecond=False, doplot=False,
dellast=True, ePhi=0.5, ePerc=1., lam=2000.):
"""
Read res1file and remove EM effects using a double-Cole-Cole model
fr,rhoa,phi,dphi = ReadAndRemoveEM(filename, readsecond/doplot bools)
"""
fr, rhoa, phi, drhoa, dphi = read1resfile(filename,
readsecond,
dellast=dellast)
# forward problem
mesh = pg.createMesh1D(1, 6) # 6 independent parameters
f = DoubleColeColeModelling(mesh, pg.asvector(fr), phi[2] / abs(phi[2]))
f.regionManager().loadMap("region.control")
model = f.createStartVector()
# inversion
inv = pg.RInversion(phi, f, True, False)
inv.setAbsoluteError(phi * ePerc * 0.01 + ePhi / 1000.)
inv.setRobustData(True)
# inv.setCWeight(pg.RVector(6, 1.0)) # wozu war das denn gut?
inv.setMarquardtScheme(0.8)
inv.setLambda(lam)
inv.setModel(model)
erg = inv.run()
inv.echoStatus()
chi2 = inv.chi2()
mod0 = pg.RVector(erg)
mod0[0] = 0.0 # set IP term to zero to obtain pure EM term
emphi = f.response(mod0)
resid = (phi - emphi) * 1000.
if doplot:
s = "IP: m= " + str(rndig(erg[0])) + " t=" + str(rndig(erg[1])) + \
" c =" + str(rndig(erg[2]))
s += " EM: m= " + str(rndig(erg[3])) + " t=" + str(rndig(erg[4])) + \
" c =" + str(rndig(erg[5]))
fig = P.figure(1)
fig.clf()
ax = P.subplot(111)
P.errorbar(
fr,
phi *
1000.,
yerr=dphi *
1000.,
fmt='x-',
label='measured')
ax.set_xscale('log')
P.semilogx(fr, emphi * 1000., label='EM term (CC)')
P.errorbar(fr, resid, yerr=dphi * 1000., label='IP term')
ax.set_yscale('log')
P.xlim((min(fr), max(fr)))
P.ylim((0.1, max(phi) * 1000.))
P.xlabel('f in Hz')
P.ylabel(r'-$\phi$ in mrad')
P.grid(True)
P.title(s)
P.legend(loc=2) # ('measured','2-cole-cole','residual'))
fig.show()
return N.array(fr), N.array(rhoa), N.array(resid), N.array(
phi) * 1e3, dphi, chi2, N.array(emphi) * 1e3
def showsip1dmodel(M, tau, thk, res=None, z=None,
cmin=None, cmax=None, islog=True):
"""
Display an SIP Debye block model as image.
"""
if z is None:
z = N.cumsum(N.hstack((0., thk)))
P.cla()
pal = P.cm.get_cmap()
pal.set_under('w')
pal.set_bad('w')
if isinstance(M, pg.RVector):
M = N.asarray(M)
if islog:
M = N.log10(M)
M = M.reshape((len(z), len(tau)))
im = P.imshow(M, interpolation='nearest', cmap=pal)
if cmax is None:
cmax = N.max(M)
if cmax is None:
cmax = N.max(M)
im.set_clim((cmin, cmax))
a = []
for t in tau[::2]:
a.append("%g" % rndig(t * 1000, 2))
P.xticks(N.arange(0, len(tau), 2), a)
a = []
for zi in z:
a.append(str(zi))
P.yticks(N.arange(len(z)) - 0.5, a)
P.xlabel(r'$\tau$ in ms')
P.ylabel('z in m')
P.ylim((len(z) - 0.5, -0.5))
P.colorbar(orientation='horizontal', aspect=40, shrink=0.6)
if res is not None:
xl = P.xlim()[1]
for i in range(len(res)):
P.text(xl, i, r' %g $\Omega$m' % rndig(res[i], 2))
lgm = N.zeros((len(z), 1))
tch = N.zeros((len(z), 1))
lgt = N.log(tau)
if islog:
M = 10**M
for n in range(len(M)):
m = N.abs(M[n])
tch[n] = N.sum(m)
lgm[n] = N.exp(N.sum(m * lgt) / N.sum(m))
tpos = N.interp(N.log(lgm), N.log(tau), N.arange(len(tau)))
P.plot(tpos, N.arange(len(z)), 'w*')
P.title('logarithmized spectral chargeability')
P.show()
return lgm, tch
def showsounding(ab2, rhoa, resp=None, mn2=None, islog=True, xlab=None):
"""
Display a sounding curve (rhoa over ab/2) and an additional response.
"""
if xlab is None:
xlab = r'$\rho_a$ in $\Omega$m'
ab2a = N.asarray(ab2)
rhoa = N.asarray(rhoa)
if mn2 is None:
if islog:
l1 = P.loglog(rhoa, ab2, 'rx-', label='observed')
else:
l1 = P.semilogy(rhoa, ab2, 'rx-', label='observed')
P.hold(True)
if resp is not None:
if islog:
l2 = P.loglog(resp, ab2, 'bo-', label='simulated')
else:
l2 = P.semilogy(resp, ab2, 'bo-', label='simulated')
P.legend((l1, l2), ('obs', 'sim'), loc=0)
else:
for unmi in N.unique(mn2):
if islog:
l1 = P.loglog(rhoa[mn2 == unmi], ab2a[mn2 == unmi],
'rx-', label='observed')
else:
l1 = P.semilogy(rhoa[mn2 == unmi], ab2a[mn2 == unmi],
'rx-', label='observed')
P.hold(True)
if resp is not None:
l2 = P.loglog(resp[mn2 == unmi], ab2a[mn2 == unmi],
'bo-', label='simulated')
P.legend((l1, l2), ('obs', 'sim'))
P.axis('tight')
P.ylim((max(ab2), min(ab2)))
locs = P.yticks()[0]
if len(locs) < 2:
locs = N.hstack((min(ab2), locs, max(ab2)))
else:
locs[0] = max(locs[0], min(ab2))
locs[-1] = min(locs[-1], max(ab2))
a = []
for l in locs:
a.append('%g' % rndig(l))
P.yticks(locs, a)
locs = P.xticks()[0]
a = []
for l in locs:
a.append('%g' % rndig(l))
P.xticks(locs, a)
P.grid(which='both')
P.xlabel(xlab)
P.ylabel('AB/2 in m')
# P.legend()
P.show()
return
def showqtresultfit(thk,
wc,
t2,
datvec,
resp,
t,
islog=True,
clim=None,
nu=3,
nv=2):
"""Show mrs qt result and data fit.
Parameters
----------
thk : array of length nlay-1
thickness vector
wc : array of length nlay
water content vector
t2 : array of length nlay
relaxation time vector
datvec : array of length len(t)*len(Q)
data vector
t : array
time vector
islog : bool [True]
use logarithms for colour scale
clim : tuple of 2 floats
color scale of data cube (in nV)
nu/nv : int
number of rows/columns for subplot
Returns
-------
ax : mpl.Axes object
Examples
--------
showqtresultfit(thk,wc,t2,datvec,resp,t,islog=True,clim=None,nu=3,nv=2)"""
if clim is None:
cma = max(datvec)
cmi = min(datvec)
if islog:
cma = np.log10(cma)
cmi = cma - 1.5
clim = (cmi, cma)
nt = len(t)
nq = len(datvec) / nt
si = (nq, nt)
fig = plt.figure(1)
ax1 = fig.add_subplot(nu, nv, 1)
draw1dmodel(wc, thk, islog=False, xlab=r'$\theta$')
ax3 = fig.add_subplot(nu, nv, 3)
draw1dmodel(t2, thk, xlab='T2* in ms')
ax3.set_xticks([0.02, 0.05, 0.1, 0.2, 0.5])
ax3.set_xticklabels(('0.02', '0.05', '0.1', '0.2', '0.5'))
ax2 = fig.add_subplot(nu, nv, 2)
if islog:
plt.imshow(np.log10(np.array(datvec).reshape(si)),
interpolation='nearest',
aspect='auto')
else:
plt.imshow(np.array(datvec).reshape(si),
interpolation='nearest',
aspect='auto')
plt.clim(clim)
ax4 = fig.add_subplot(nu, nv, 4)
if islog:
plt.imshow(np.log10(resp.reshape(si)),
interpolation='nearest',
aspect='auto')
else:
plt.imshow(resp.reshape(si), interpolation='nearest', aspect='auto')
misfit = np.array(datvec - resp)
plt.clim(clim)
ax5 = fig.add_subplot(nu, nv, 5)
plt.hist(misfit, bins=30)
plt.axis('tight')
plt.grid(which='both')
plt.text(plt.xlim()[0], np.mean(plt.ylim()),
' std=%g nV' % rndig(np.std(misfit), 3))
ax6 = fig.add_subplot(nu, nv, 6)
plt.imshow(misfit.reshape(si), interpolation='nearest', aspect='auto')
ax = [ax1, ax2, ax3, ax4, ax5, ax6]
return ax
def draw1dmodel__Redundant(x,
thk=None,
xlab=None,
zlab="z in m",
islog=True,
fs=14,
z0=0,
**kwargs):
"""Draw 1d block model defined by value and thickness vectors."""
# if xlab is None:
# xlab = "$\\rho$ in $\\Omega$m"
if thk is None: # gimli blockmodel (thk+x together) given
nl = int(np.floor((len(x) - 1) / 2.)) + 1
thk = np.asarray(x)[:nl - 1]
x = np.asarray(x)[nl - 1:nl * 2 - 1]
z1 = np.concatenate(([0], np.cumsum(thk))) + z0
z = np.concatenate((z1, [z1[-1] * 1.2]))
nl = len(x) # x.size()
px = np.zeros((nl * 2, 1))
pz = np.zeros((nl * 2, 1))
for i in range(nl):
px[2 * i] = x[i]
px[2 * i + 1] = x[i]
pz[2 * i + 1] = z[i + 1]
if i < nl - 1:
pz[2 * i + 2] = z[i + 1]
# plt.cla()
li = []
if islog:
li = plt.semilogx(px, pz, **kwargs)
else:
li = plt.plot(px, pz, **kwargs)
plt.gca().xaxis.set_label_position('top')
locs = plt.xticks()[0]
if len(locs) < 2:
locs = np.hstack((min(x), locs, max(x)))
elif len(locs) < 5:
locs[0] = max(locs[0], min(x))
locs[-1] = min(locs[-1], max(x))
a = []
for l in locs:
a.append('%g' % rndig(l))
plt.xticks(locs, a, fontsize=fs)
plt.yticks(fontsize=fs)
plt.xlim((np.min(x) * 0.9, np.max(x) * 1.1))
plt.ylim((max(z1) * 1.15, 0.))
if xlab is not None:
plt.xlabel(xlab, fontsize=fs)
if zlab is not None:
plt.ylabel(zlab, fontsize=fs)
plt.grid(which='both')
# plt.show()
return li
def ReadAndRemoveEM(filename, readsecond=False, doplot=False,
dellast=True, ePhi=0.5, ePerc=1., lam=2000.):
"""
Read res1file and remove EM effects using a double-Cole-Cole model
fr,rhoa,phi,dphi = ReadAndRemoveEM(filename, readsecond/doplot bools)
"""
fr, rhoa, phi, drhoa, dphi = read1resfile(filename,
readsecond,
dellast=dellast)
# forward problem
mesh = pg.createMesh1D(1, 6) # 6 independent parameters
f = DoubleColeColeModelling(mesh, pg.asvector(fr), phi[2] / abs(phi[2]))
f.regionManager().loadMap("region.control")
model = f.createStartVector()
# inversion
inv = pg.RInversion(phi, f, True, False)
inv.setAbsoluteError(phi * ePerc * 0.01 + ePhi / 1000.)
inv.setRobustData(True)
# inv.setCWeight(pg.RVector(6, 1.0)) # wozu war das denn gut?
inv.setMarquardtScheme(0.8)
inv.setLambda(lam)
inv.setModel(model)
erg = inv.run()
inv.echoStatus()
chi2 = inv.chi2()
mod0 = pg.RVector(erg)
mod0[0] = 0.0 # set IP term to zero to obtain pure EM term
emphi = f.response(mod0)
resid = (phi - emphi) * 1000.
if doplot:
s = "IP: m= " + str(rndig(erg[0])) + " t=" + str(rndig(erg[1])) + \
" c =" + str(rndig(erg[2]))
s += " EM: m= " + str(rndig(erg[3])) + " t=" + str(rndig(erg[4])) + \
" c =" + str(rndig(erg[5]))
fig = P.figure(1)
fig.clf()
ax = P.subplot(111)
P.errorbar(
fr,
phi *
1000.,
yerr=dphi *
1000.,
fmt='x-',
label='measured')
ax.set_xscale('log')
P.semilogx(fr, emphi * 1000., label='EM term (CC)')
P.errorbar(fr, resid, yerr=dphi * 1000., label='IP term')
ax.set_yscale('log')
P.xlim((min(fr), max(fr)))
P.ylim((0.1, max(phi) * 1000.))
P.xlabel('f in Hz')
P.ylabel(r'-$\phi$ in mrad')
P.grid(True)
P.title(s)
P.legend(loc=2) # ('measured','2-cole-cole','residual'))
fig.show()
return N.array(fr), N.array(rhoa), N.array(resid), N.array(
phi) * 1e3, dphi, chi2, N.array(emphi) * 1e3
def showsip1dmodel(M, tau, thk, res=None, z=None,
cmin=None, cmax=None, islog=True):
"""
Display an SIP Debye block model as image.
"""
if z is None:
z = N.cumsum(N.hstack((0., thk)))
P.cla()
pal = P.cm.get_cmap()
pal.set_under('w')
pal.set_bad('w')
if isinstance(M, pg.RVector):
M = N.asarray(M)
if islog:
M = N.log10(M)
M = M.reshape((len(z), len(tau)))
im = P.imshow(M, interpolation='nearest', cmap=pal)
if cmax is None:
cmax = N.max(M)
if cmax is None:
cmax = N.max(M)
im.set_clim((cmin, cmax))
a = []
for t in tau[::2]:
a.append("%g" % rndig(t * 1000, 2))
P.xticks(N.arange(0, len(tau), 2), a)
a = []
for zi in z:
a.append(str(zi))
P.yticks(N.arange(len(z)) - 0.5, a)
P.xlabel(r'$\tau$ in ms')
P.ylabel('z in m')
P.ylim((len(z) - 0.5, -0.5))
P.colorbar(orientation='horizontal', aspect=40, shrink=0.6)
if res is not None:
xl = P.xlim()[1]
for i in range(len(res)):
P.text(xl, i, r' %g $\Omega$m' % rndig(res[i], 2))
lgm = N.zeros((len(z), 1))
tch = N.zeros((len(z), 1))
lgt = N.log(tau)
if islog:
M = 10**M
for n in range(len(M)):
m = N.abs(M[n])
tch[n] = N.sum(m)
lgm[n] = N.exp(N.sum(m * lgt) / N.sum(m))
tpos = N.interp(N.log(lgm), N.log(tau), N.arange(len(tau)))
P.plot(tpos, N.arange(len(z)), 'w*')
P.title('logarithmized spectral chargeability')
P.show()
return lgm, tch
def showsounding(ab2, rhoa, resp=None, mn2=None, islog=True, xlab=None):
"""
Display a sounding curve (rhoa over ab/2) and an additional response.
"""
if xlab is None:
xlab = r'$\rho_a$ in $\Omega$m'
ab2a = N.asarray(ab2)
rhoa = N.asarray(rhoa)
if mn2 is None:
if islog:
l1 = P.loglog(rhoa, ab2, 'rx-', label='observed')
else:
l1 = P.semilogy(rhoa, ab2, 'rx-', label='observed')
P.hold(True)
if resp is not None:
if islog:
l2 = P.loglog(resp, ab2, 'bo-', label='simulated')
else:
l2 = P.semilogy(resp, ab2, 'bo-', label='simulated')
P.legend((l1, l2), ('obs', 'sim'), loc=0)
else:
for unmi in N.unique(mn2):
if islog:
l1 = P.loglog(rhoa[mn2 == unmi], ab2a[mn2 == unmi],
'rx-', label='observed')
else:
l1 = P.semilogy(rhoa[mn2 == unmi], ab2a[mn2 == unmi],
'rx-', label='observed')
P.hold(True)
if resp is not None:
l2 = P.loglog(resp[mn2 == unmi], ab2a[mn2 == unmi],
'bo-', label='simulated')
P.legend((l1, l2), ('obs', 'sim'))
P.axis('tight')
P.ylim((max(ab2), min(ab2)))
locs = P.yticks()[0]
if len(locs) < 2:
locs = N.hstack((min(ab2), locs, max(ab2)))
else:
locs[0] = max(locs[0], min(ab2))
locs[-1] = min(locs[-1], max(ab2))
a = []
for l in locs:
a.append('%g' % rndig(l))
P.yticks(locs, a)
locs = P.xticks()[0]
a = []
for l in locs:
a.append('%g' % rndig(l))
P.xticks(locs, a)
P.grid(which='both')
P.xlabel(xlab)
P.ylabel('AB/2 in m')
# P.legend()
P.show()
return
def showqtresultfit(thk, wc, t2, datvec, resp, t,
islog=True, clim=None, nu=3, nv=2):
"""Show mrs qt result and data fit.
Parameters
----------
thk : array of length nlay-1
thickness vector
wc : array of length nlay
water content vector
t2 : array of length nlay
relaxation time vector
datvec : array of length len(t)*len(Q)
data vector
t : array
time vector
islog : bool [True]
use logarithms for colour scale
clim : tuple of 2 floats
color scale of data cube (in nV)
nu/nv : int
number of rows/columns for subplot
Returns
-------
ax : mpl.Axes object
Examples
--------
showqtresultfit(thk,wc,t2,datvec,resp,t,islog=True,clim=None,nu=3,nv=2)"""
if clim is None:
cma = max(datvec)
cmi = min(datvec)
if islog:
cma = np.log10(cma)
cmi = cma - 1.5
clim = (cmi, cma)
nt = len(t)
nq = len(datvec) / nt
si = (nq, nt)
fig = plt.figure(1)
ax1 = fig.add_subplot(nu, nv, 1)
draw1dmodel(wc, thk, islog=False, xlab=r'$\theta$')
ax3 = fig.add_subplot(nu, nv, 3)
draw1dmodel(t2, thk, xlab='T2* in ms')
ax3.set_xticks([0.02, 0.05, 0.1, 0.2, 0.5])
ax3.set_xticklabels(('0.02', '0.05', '0.1', '0.2', '0.5'))
ax2 = fig.add_subplot(nu, nv, 2)
if islog:
plt.imshow(np.log10(np.array(datvec).reshape(si)),
interpolation='nearest', aspect='auto')
else:
plt.imshow(np.array(datvec).reshape(si),
interpolation='nearest', aspect='auto')
plt.clim(clim)
ax4 = fig.add_subplot(nu, nv, 4)
if islog:
plt.imshow(np.log10(resp.reshape(si)),
interpolation='nearest', aspect='auto')
else:
plt.imshow(resp.reshape(si),
interpolation='nearest', aspect='auto')
misfit = np.array(datvec - resp)
plt.clim(clim)
ax5 = fig.add_subplot(nu, nv, 5)
plt.hist(misfit, bins=30)
plt.axis('tight')
plt.grid(which='both')
plt.text(plt.xlim()[0], np.mean(plt.ylim()),
' std=%g nV' % rndig(np.std(misfit), 3))
ax6 = fig.add_subplot(nu, nv, 6)
plt.imshow(misfit.reshape(si), interpolation='nearest', aspect='auto')
ax = [ax1, ax2, ax3, ax4, ax5, ax6]
return ax
