def runInv(self, uncertainty=False):
"""run actual inversion (assumes one was created before)"""
self.model = np.array(mrsves.INV.run())
if uncertainty:
self.modelL, self.modelU = iterateBounds(self.INV,
dchi2=self.INV.chi2() / 2,
change=1.2)
def run(self, verbose=True, uncertainty=False, **kwargs):
"""Easiest variant doing all (create fop and inv) in one call."""
self.invert(verbose=verbose, **kwargs)
if uncertainty:
if verbose:
print("Computing uncertainty...")
self.modelL, self.modelU = iterateBounds(
self.INV, dchi2=self.INV.chi2() / 2, change=1.2)
if verbose:
print("ready")
def run(self,nlay=3,lam=10.,startvec=None,verbose=True,uncertainty=False,**kwargs):
''' even easier variant returning all in one call '''
if self.INV is None:
self.INV = self.createInv(nlay,lam,verbose,**kwargs)
if startvec is not None:
self.INV.setModel( pg.asvector( startvec ) )
if verbose: print("Doing inversion...")
self.model = np.array( self.INV.run() )
if uncertainty:
if verbose: print("Computing uncertainty...")
self.modelL, self.modelU = iterateBounds( self.INV, dchi2=self.INV.chi2()/2, change=1.2 )
def block1dInversion(self,
nlay=2,
lam=100.,
show=False,
verbose=True,
uncertainty=False):
"""Invert all data together by a 1D model (more general solution)."""
data, error = pg.Vector(), pg.Vector()
for mrs in self.mrs:
data = pg.cat(data, mrs.data)
error = pg.cat(error, np.real(mrs.error))
# f = JointMRSModelling(self.mrs, nlay)
f = MultiFOP(self.mrs, nlay)
mrsobj = self.mrs[0]
for i in range(3):
f.region(i).setParameters(mrsobj.startval[i], mrsobj.lowerBound[i],
mrsobj.upperBound[i])
INV = pg.Inversion(data, f, verbose)
INV.setLambda(lam)
INV.setMarquardtScheme(0.8)
# INV.stopAtChi1(False) # should be already in MarquardtScheme
INV.setDeltaPhiAbortPercent(0.5)
INV.setAbsoluteError(error)
model = INV.run()
m0 = self.mrs[0]
m0.model = np.asarray(model)
if uncertainty:
from pygimli.utils import iterateBounds
m0.modelL, m0.modelU = iterateBounds(INV,
dchi2=INV.chi2() / 2,
change=1.2)
if show:
self.show1dModel()
# %% fill up 2D model (for display only)
self.WMOD, self.TMOD = [], []
thk = model[0:nlay - 1]
wc = model[nlay - 1:2 * nlay - 1]
t2 = model[2 * nlay - 1:3 * nlay - 1]
for i in range(len(self.mrs)):
self.WMOD.append(np.hstack((thk, wc)))
self.TMOD.append(np.hstack((thk, t2)))
return model
def run(self, nlay=3, lam=100., startvec=None,
verbose=True, uncertainty=False, **kwargs):
""" easiest variant doing all (create fop and inv) in one call """
if self.INV is None or self.nlay != nlay:
self.INV = self.createInv(nlay, lam, verbose, **kwargs)
self.INV.setVerbose(verbose)
if startvec is not None:
self.INV.setModel(startvec)
if verbose:
print("Doing inversion...")
self.model = np.array(self.INV.run())
if uncertainty:
if verbose:
print("Computing uncertainty...")
self.modelL, self.modelU = iterateBounds(
self.INV, dchi2=self.INV.chi2() / 2, change=1.2)
if verbose:
print("ready")
def run(self, nlay=3, lam=100., startvec=None,
verbose=True, uncertainty=False, **kwargs):
"""Easiest variant doing all (create fop and inv) in one call."""
if self.INV is None or self.nlay != nlay:
self.INV = self.createInv(nlay, lam, verbose, **kwargs)
self.INV.setVerbose(verbose)
if startvec is not None:
self.INV.setModel(startvec)
if verbose:
print("Doing inversion...")
self.model = np.array(self.INV.run())
if uncertainty:
if verbose:
print("Computing uncertainty...")
self.modelL, self.modelU = iterateBounds(
self.INV, dchi2=self.INV.chi2() / 2, change=1.2)
if verbose:
print("ready")
def block1dInversion(self, nlay=2, lam=100., show=False, verbose=True,
uncertainty=False):
"""Invert all data together by a 1D model (more general solution)."""
data, error = pg.RVector(), pg.RVector()
for mrs in self.mrs:
data = pg.cat(data, mrs.data)
error = pg.cat(error, np.real(mrs.error))
# f = JointMRSModelling(self.mrs, nlay)
f = MultiFOP(self.mrs, nlay)
mrsobj = self.mrs[0]
for i in range(3):
f.region(i).setParameters(mrsobj.startval[i], mrsobj.lowerBound[i],
mrsobj.upperBound[i])
INV = pg.RInversion(data, f, verbose)
INV.setLambda(lam)
INV.setMarquardtScheme(0.8)
# INV.stopAtChi1(False) # should be already in MarquardtScheme
INV.setDeltaPhiAbortPercent(0.5)
INV.setAbsoluteError(error)
model = INV.run()
m0 = self.mrs[0]
m0.model = np.asarray(model)
if uncertainty:
from pygimli.utils import iterateBounds
m0.modelL, m0.modelU = iterateBounds(
INV, dchi2=INV.chi2() / 2, change=1.2)
if show:
self.show1dModel()
# %% fill up 2D model (for display only)
self.WMOD, self.TMOD = [], []
thk = model[0:nlay-1]
wc = model[nlay-1:2*nlay-1]
t2 = model[2*nlay-1:3*nlay-1]
for i in range(len(self.mrs)):
self.WMOD.append(np.hstack((thk, wc)))
self.TMOD.append(np.hstack((thk, t2)))
return model
def runInv(self,uncertainty=False):
""" run actual inversion (assumes one was created before) """
self.model = np.array( mrsves.INV.run() )
if uncertainty:
self.modelL, self.modelU = iterateBounds(self.INV,
dchi2=self.INV.chi2()/2, change=1.2)
