def genMod(self, individual):
"""Generate (GA) model from random vector (0-1) using model bounds."""
model = np.asarray(individual) * (self.lUB - self.lLB) + self.lLB
if self.logpar:
return pg.exp(model)
else:
return model
def genMod(self, individual):
""" generate (GA) model from random vector (0-1) using model bounds """
model = np.asarray(individual) * (self.lUB - self.lLB) + self.lLB
if self.logpar:
return pg.exp(model)
else:
return model
def genMods( individual ):
""" generate MRS and VES models from unit vector """
model = pg.asvector( individual ) * ( self.lUB - self.lLB ) + self.lLB
if self.logpar:
model = pg.exp( model )
modMRS = model(0,nlay*3-1)
modVES = pg.cat(model(0,nlay-1),model(nlay*3-1,nlay*4-1))
return modMRS, modVES
def genMods(individual):
"""generate MRS and VES models from unit vector"""
model = individual * (self.lUB - self.lLB) + self.lLB
# model = pg.asvector(individual) * (self.lUB - self.lLB) + self.lLB
if self.logpar:
model = pg.exp(model)
modMRS = model(0, nlay * 3 - 1)
modVES = pg.cat(model(0, nlay - 1),
model(nlay * 3 - 1, nlay * 4 - 1))
return modMRS, modVES
def invert(self, data, values=None, verbose=0, **kwargs):
"""
Invert the given data.
A parametric mesh for the inversion will be created if non is given
before.
Parameters
----------
"""
self.fop.setVerbose(verbose)
self.inv.setVerbose(verbose)
self.inv.setMaxIter(kwargs.pop('maxiter', 10))
self.inv.setLambda(kwargs.pop('lambd', 10))
if self.paraMesh is None:
self.paraMesh = createParaMesh2dGrid(data.sensorPositions(),
**kwargs)
self.setParaMesh(self.paraMesh)
if verbose:
print(self.paraMesh)
# pg.show(self.paraMesh)
err = data('err')
rhoa = data('rhoa')
startModel = pg.RVector(self.fop.regionManager().parameterCount(),
pg.median(rhoa))
self.fop.setData(data)
self.inv.setForwardOperator(self.fop)
# check err here
self.inv.setData(rhoa)
self.inv.setError(err)
self.inv.setModel(startModel)
model = self.inv.run()
if values is not None:
if isinstance(values, pg.RVector):
values = [values]
elif isinstance(values, np.ndarray):
if values.ndim == 1:
values = [values]
allModel = pg.RMatrix(len(values), len(model))
self.inv.setVerbose(False)
for i in range(len(values)):
print(i)
tic = time.time()
self.inv.setModel(model)
self.inv.setReferenceModel(model)
dData = pg.abs(values[i] / rhoa)
relModel = self.inv.invSubStep(pg.log(dData))
allModel[i] = model * pg.exp(relModel)
print(i, "/", len(values), " : ", time.time()-tic,
"s min/max: ", min(allModel[i]), max(allModel[i]))
return allModel
return model
def invert(self, data, values=None, verbose=0, **kwargs):
"""
Invert the given data.
A parametric mesh for the inversion will be created if non is given
before.
Parameters
----------
"""
self.fop.setVerbose(verbose)
self.inv.setVerbose(verbose)
self.inv.setMaxIter(kwargs.pop('maxiter', 10))
self.inv.setLambda(kwargs.pop('lambd', 10))
if self.paraMesh is None:
self.paraMesh = createParaMesh2dGrid(data.sensorPositions(),
**kwargs)
self.setParaMesh(self.paraMesh)
if verbose:
print(self.paraMesh)
# pg.show(self.paraMesh)
err = data('err')
rhoa = data('rhoa')
startModel = pg.RVector(self.fop.regionManager().parameterCount(),
pg.median(rhoa))
self.fop.setData(data)
self.inv.setForwardOperator(self.fop)
# check err here
self.inv.setData(rhoa)
self.inv.setError(err)
self.inv.setModel(startModel)
model = self.inv.run()
if values is not None:
if isinstance(values, pg.RVector):
values = [values]
elif isinstance(values, np.ndarray):
if values.ndim == 1:
values = [values]
allModel = pg.RMatrix(len(values), len(model))
self.inv.setVerbose(False)
for i in range(len(values)):
print(i)
tic = time.time()
self.inv.setModel(model)
self.inv.setReferenceModel(model)
dData = pg.abs(values[i] / rhoa)
relModel = self.inv.invSubStep(pg.log(dData))
allModel[i] = model * pg.exp(relModel)
print(i, "/", len(values), " : ",
time.time() - tic, "s min/max: ", min(allModel[i]),
max(allModel[i]))
return allModel
return model
def response(self, model):
return model[0] * pg.exp(-self.x / model[1])
def genMod( self, individual ):
model = pg.asvector( individual ) * ( self.lUB - self.lLB ) + self.lLB
if self.logpar:
return pg.exp( model )
else:
return model
def response(self, model):
return model[0] * pg.exp(-self.x / model[1])
