# -*- coding: utf-8 -*-
"""Electrical resistivity tomography"""
import numpy as np
import pygimli as pg
from .ertModelling import ERTModelling
from .ertScheme import createData
createERTData = createData # backward compatibility
[docs]
def simulate(mesh, scheme, res, **kwargs):
"""Simulate an ERT measurement.
Perform the forward task for a given mesh, resistivity distribution &
measuring scheme and return data (apparent resistivity) or potentials.
For complex resistivity, the data contains an apparent phase or the
returned potentials are complex.
The forward operator itself only calculates potential values for the
electrodes in the given data scheme.
To calculate apparent resistivities, geometric factors (k) are needed.
If there are no values k in the DataContainerERT scheme, the function
tries to calculate them, either analytically or numerically by using a
p2-refined version of the given mesh.
TODO
----
* 2D + Complex + SR
Args
----
mesh : :gimliapi:`GIMLI::Mesh`
2D or 3D Mesh to calculate for.
res : float, array(mesh.cellCount()) | array(N, mesh.cellCount()) |
list
Resistivity distribution for the given mesh cells can be:
. float for homogeneous resistivity (e.g. 1.0)
. single array of length mesh.cellCount()
. matrix of N resistivity distributions of length mesh.cellCount()
. resistivity map as [[regionMarker0, res0],
[regionMarker0, res1], ...]
scheme : :gimliapi:`GIMLI::DataContainerERT`
Data measurement scheme.
Keyword Args
------------
verbose: bool[False]
Be verbose. Will override class settings.
calcOnly: bool [False]
Use fop.calculate instead of fop.response. Useful if you want
to force the calculation of impedances for homogeneous models.
No noise handling. Solution is put as token 'u' in the returned
DataContainerERT.
noiseLevel: float [0.0]
add normally distributed noise based on
scheme['err'] or on noiseLevel if error>0 is not contained
noiseAbs: float [0.0]
Absolute voltage error in V
returnArray: bool [False]
Returns an array of apparent resistivities instead of
a DataContainerERT
returnFields: bool [False]
Returns a matrix of all potential values (per mesh nodes)
for each injection electrodes.
sr : bool
use secondary field (singularity removal)
seed : int
numpy.random seed for repeatable noise in synthetic experiments
phiErr : float|iterable
absolute phase error, if not given, data['iperr'] or noiseLevel is used
contactImpedances float|iterables
contact impedances for being used with CEM model
current : float
current to be assumed
Returns
-------
DataContainerERT | array(data.size()) | array(N, data.size()) |
array(N, mesh.nodeCount()):
Data container with resulting apparent resistivity data['rhoa'] and
errors (if noiseLevel or noiseAbs is set).
Optionally return a Matrix of rhoa values
(for returnArray==True forces noiseLevel=0).
In case of complex-valued resistivity, phase values are contained in
data['phia'] or returned as additionally returned array.
Examples
--------
>>> from pygimli.physics import ert
>>> import pygimli as pg
>>> import pygimli.meshtools as mt
>>> world = mt.createWorld(start=[-50, 0], end=[50, -50],
... layers=[-1, -5], worldMarker=True)
>>> scheme = ert.createData(
... elecs=pg.utils.grange(start=-10, end=10, n=21),
... schemeName='dd')
>>> for pos in scheme.sensorPositions():
... _= world.createNode(pos)
... _= world.createNode(pos + [0.0, -0.1])
>>> mesh = mt.createMesh(world, quality=34)
>>> rhomap = [
... [1, 100. + 0j],
... [2, 50. + 0j],
... [3, 10.+ 1j],
... ]
>>> data = ert.simulate(mesh, res=rhomap, scheme=scheme, verbose=True)
"""
verbose = kwargs.pop('verbose', True)
calcOnly = kwargs.pop('calcOnly', False)
returnFields = kwargs.pop("returnFields", False)
returnArray = kwargs.pop('returnArray', False)
noiseLevel = kwargs.pop('noiseLevel', 0.0)
noiseAbs = kwargs.pop('noiseAbs', 1e-4)
seed = kwargs.pop('seed', None)
sr = kwargs.pop('sr', True)
returnFOP = kwargs.pop("returnFOP", False)
fop = ERTModelling(sr=sr, verbose=verbose)
# fop = self.createForwardOperator(useBert=True, sr=sr, verbose=verbose)
fop.data = scheme
fop.setMesh(mesh, ignoreRegionManager=True)
cI = kwargs.pop("contactImpedances", None)
if cI is not None:
if isinstance(cI, float):
cI = pg.Vector(scheme.sensorCount(), cI)
fop._core.setContactImpedances([1e-3, 1e-4, 1e-5, 1e-6])
rhoa = None
phia = None
isArrayData = False
# parse the given res into mesh-cell-sized array
if isinstance(res, (int, float)):
res = np.ones(mesh.cellCount()) * float(res)
elif isinstance(res, complex):
res = np.ones(mesh.cellCount()) * res
elif hasattr(res[0], '__iter__'): # ndim == 2
if len(res[0]) == 2: # res seems to be a res map
# check if there are markers in the mesh that are not defined
# the rhomap. better signal here before it results in errors
meshMarkers = list(set(mesh.cellMarkers()))
mapMarkers = [m[0] for m in res]
if any([mark not in mapMarkers for mark in meshMarkers]):
left = [m for m in meshMarkers if m not in mapMarkers]
pg.critical("Mesh contains markers without assigned "
"resistivities {}. Please fix given "
"rhomap.".format(left))
res = pg.solver.parseArgToArray(res, mesh.cellCount(), mesh)
else: # probably nData x nCells array
# better check for array data here
isArrayData = True
if isinstance(res[0], complex) or isinstance(res, pg.CVector):
pg.info("Complex resistivity values found.")
fop.setComplex(True)
else:
fop.setComplex(False)
if not isinstance(scheme, pg.DataContainerERT):
raise TypeError("Scheme must be DataContainerERT!")
if not scheme.allNonZero('k') and not calcOnly:
if verbose:
pg.info('Calculate geometric factors.')
scheme.set('k', fop.calcGeometricFactor(scheme))
ret = pg.DataContainerERT(scheme)
# just to be sure that we don't work with artifacts
ret['u'] = 0.0
ret['i'] = 0.0
ret['r'] = 0.0
if isArrayData:
rhoa = np.zeros((len(res), scheme.size()))
for i, r in enumerate(res):
rhoa[i] = fop.response(r)
if verbose:
print(i, "/", len(res), " : ", pg.dur(), "s",
"min r:", min(r), "max r:", max(r),
"min r_a:", min(rhoa[i]), "max r_a:", max(rhoa[i]))
else: # res is single resistivity array
if len(res) == mesh.cellCount():
if calcOnly:
fop.mapERTModel(res, 0)
dMap = pg.core.DataMap()
fop.calculate(dMap)
if fop.complex():
pg.critical('Implement me')
else:
ret['r'] = dMap.data(scheme)
ret['i'] = kwargs.pop("current", 1.0)
ret['u'] = ret['r'] * ret['i']
if returnFields:
return pg.Matrix(fop.solution())
return ret
else:
if fop.complex():
res = pg.utils.squeezeComplex(res)
resp = fop.response(res)
if fop.complex():
rhoa, phia = pg.utils.toPolar(resp)
else:
rhoa = resp
else:
print(mesh)
print("res: ", res)
raise BaseException(
"Simulate called with wrong resistivity array.")
if not isArrayData:
ret['rhoa'] = rhoa
if phia is not None:
ret.set('phia', phia)
else:
ret.set('rhoa', rhoa[0])
if phia is not None:
ret.set('phia', phia[0])
if returnFields:
return pg.Matrix(fop.solution())
if noiseLevel > 0: # if errors in data noiseLevel=1 just triggers
if not ret.allNonZero('err'):
# 1A and #100µV
ret.set('err', estimateError(ret,
relativeError=noiseLevel,
absoluteUError=noiseAbs,
absoluteCurrent=1))
if verbose:
pg.info("Data error estimate (min:max) ",
min(ret['err']), ":", max(ret['err']))
rhoa *= 1. + pg.randn(ret.size(), seed=seed) * ret('err')
ret['rhoa'] = rhoa
if ret.allNonZero('k'): # also provide r if user changes k later
ret['r'] = ret['rhoa'] / ret['k']
ipError = None
if phia is not None:
if scheme.allNonZero('iperr'):
ipError = scheme['iperr']
else:
# np.abs(self.data("phia") +TOLERANCE) * 1e-4absoluteError
if noiseLevel > 0.5:
noiseLevel /= 100.
if 'phiErr' in kwargs:
ipError = np.ones(ret.size()) * kwargs.pop('phiErr') / 1000
else:
ipError = abs(ret['phia']) * noiseLevel
if verbose:
print("Data IP abs error estimate (min:max) ",
min(ipError), ":", max(ipError))
phia += pg.randn(ret.size(), seed=seed) * ipError
ret['iperr'] = ipError
ret['phia'] = phia
# check what needs to be setup and returned
if returnArray:
if phia is not None:
return rhoa, phia
else:
return rhoa
ret['valid'] = 1
if returnFOP:
return ret, fop
else:
return ret
def simulateOld(mesh, scheme, res, sr=True, useBert=True,
verbose=False, **kwargs):
"""ERT forward calculation.
Convenience function to use the ERT modelling operator
if you like static functions.
See :py:mod:`pygimli.ert.ERTManager.simulate` for description
of the arguments.
Parameters
----------
mesh: :gimliapi:`GIMLI::Mesh` | str
Modelling domain. Mesh can be a file name here.
scheme: :gimliapi:`GIMLI::DataContainerERT` | str
Data configuration. Scheme can be a file name here.
res: see :py:mod:`pygimli.ert.ERTManager.simulate`
Resistivity distribution.
sr: bool [True]
Use singularity removal technique.
useBert: bool [True]
Use Bert forward operator instead of the reference implementation.
**kwargs:
Forwarded to :py:mod:`pygimli.ert.ERTManager.simulate`
"""
from .ertManager import ERTManager
ert = ERTManager(useBert=useBert, sr=sr, verbose=verbose)
if isinstance(mesh, str):
mesh = pg.load(mesh)
if isinstance(scheme, str):
scheme = pg.physics.ert.load(scheme)
return ert.simulate(mesh=mesh, res=res, scheme=scheme,
verbose=verbose, **kwargs)
@pg.cache
def createGeometricFactors(scheme, numerical=None, mesh=None, dim=3,
h2=True, p2=True, verbose=False):
"""Create geometric factors for a given data scheme.
Create geometric factors for a data scheme with and without topography.
Calculation will be done analytical (only for half space geometry)
or numerical.
This function caches the result depending on scheme, mesh and pg.version()
Parameters
----------
scheme: :gimliapi:`GIMLI::DataContainerERT`
Datacontainer of the scheme.
numerical: bool | None [False]
If numerical is None, False is assumed, we try to guess topography
and warn if we think we found them.
If set to True or False, numerical calculation will used or not.
mesh: :gimliapi:`GIMLI::Mesh` | str
Mesh for numerical calculation. If not given, analytical geometric
factors for halfspace earth are guessed or a default mesh will be
created (and h/p refined according to h2/p2). If given topo is set to
True. If the numerical effort is to high or the accuracy to low
you should consider calculating the factors manually.
h2: bool [True]
Default spatial refinement to achieve high accuracy
p2: bool [True]
Default polynomial refinement to achieve high accuracy
verbose: bool
Give some output.
"""
if numerical is None:
numerical = False
if min(pg.z(scheme)) != max(pg.z(scheme)):
verbose = True
pg.warn('Sensor z-coordinates not equal. Is there topography?')
if numerical is False and mesh is None:
if verbose:
pg.info('Calculate analytical flat earth geometric factors.')
return pg.core.geometricFactors(scheme, forceFlatEarth=True, dim=dim)
if mesh is None:
pg.info('Create default mesh for geometric factor calculation.')
m = createInversionMesh(scheme)
else:
m = mesh
if verbose:
pg.info('mesh', m)
if h2 is True:
m = m.createH2()
if verbose:
pg.info('h2 refine', m)
if p2 is True:
m = m.createP2()
if verbose:
pg.info('p2 refine', m)
if verbose:
pg.info('Calculate numerical geometric factors.')
d = simulate(m, res=1.0, scheme=scheme, sr=False, useBert=True,
calcOnly=True, verbose=True)
return 1./d['u']
[docs]
def createInversionMesh(data, **kwargs):
"""Create default mesh for ERT inversion.
Parameters
----------
data: :gimliapi:`GIMLI::DataContainerERT`
Data Container needs at least sensors to define the geometry of the
mesh.
Other Parameters
----------------
Forwarded to :py:mod:`pygimli.meshtools.createParaMesh`
Returns
-------
mesh: :gimliapi:`GIMLI::Mesh`
Inversion mesh with default marker (1 for background,
2 parametric domain)
"""
mesh = pg.meshtools.createParaMesh(data.sensors(), **kwargs)
return mesh
def createERTDataNotUsedAnymore(elecs, schemeName='none', **kwargs):
"""Create data scheme for compatibility (advanced version in BERT).
Parameters
----------
sounding : bool [False]
Create a 1D VES Schlumberger configuration.
elecs need to be an array with elecs[0] = mn/2 and elecs[1:] = ab/2.
"""
if kwargs.pop('sounding', False):
data = pg.DataContainerERT()
data.setSensors(pg.cat(-elecs[::-1], elecs))
nElecs = len(elecs)
for i in range(nElecs-1):
data.createFourPointData(i, i, 2*nElecs-i-1, nElecs-1, nElecs)
return data
if schemeName != "dd":
return createData(elecs, schemeName, **kwargs)
isClosed = kwargs.pop('closed', False)
data = pg.DataContainerERT()
data.setSensors(elecs)
nElecs = len(elecs)
a = []
b = []
m = []
n = []
eb = 0
for i in range(nElecs):
for j in range(eb + 2, nElecs):
ea = i
eb = ea + 1
em = j
en = em + 1
if isClosed:
en = en % nElecs
if en < nElecs and en != ea:
a.append(ea)
b.append(eb)
m.append(em)
n.append(en)
data.resize(len(a))
data.add('a', a)
data.add('b', b)
data.add('m', m)
data.add('n', n)
data.set('valid', np.ones(len(a)))
return data
[docs]
def estimateError(data, relativeError=0.03, absoluteUError=None,
absoluteError=0, absoluteCurrent=0.1):
"""Estimate relative error based on relative and absolute parts.
Parameters
----------
relativeError : float [0.03]
relative error level in %/100 for u, R or rhoa
absoluteUError : float [None]
Absolute potential error in V. Needs 'u' values in data. Otherwise
calculate them from 'rhoa', 'k' and absoluteCurrent if no 'i' given.
absoluteError : float [0.0]
Absolute data error in Ohm m. Needs 'R' or 'rhoa' values.
absoluteCurrent : float [0.1]
Current level in A for reconstruction for absolute potential V
Returns
-------
error : Array
"""
if relativeError >= 0.5:
print("relativeError set to a value > 0.5 .. assuming this "
"is a percentage Error level dividing them by 100")
relativeError /= 100.0
if absoluteUError is None:
if not data.allNonZero('rhoa'):
pg.critical("We need apparent resistivity values "
"(rhoa) in the data to estimate a "
"data error.")
error = relativeError + pg.abs(absoluteError / data['rhoa'])
else:
u = None
i = absoluteCurrent
if data.haveData('i'):
i = data['i']
if data.haveData('u'):
u = data['u']
else:
if data.haveData('r'):
u = data['r'] * i
elif data.haveData('rhoa'):
if data.haveData('k'):
u = data['rhoa'] / data['k'] * i
else:
pg.critical("We need (rhoa) and (k) in the"
"data to estimate data error.")
else:
pg.critical("We need apparent resistivity values "
"(rhoa) or impedances (r) "
"in the data to estimate data error.")
error = pg.abs(absoluteUError / u) + relativeError
return error
def __DataContainerERT_createGeometricFactors(self, *args,**kwargs):
self['k'] = createGeometricFactors(self, *args, **kwargs)
pg.DataContainerERT.createGeometricFactors = __DataContainerERT_createGeometricFactors
pg.DataContainerERT.createGeometricFactors.__doc__ = createGeometricFactors.__doc__
def __DataContainerERT_estimateError(self, *args,**kwargs):
if not self.haveData('k'):
self.createGeometricFactors()
self['err'] = estimateError(self, *args, **kwargs)
pg.DataContainerERT.estimateError = __DataContainerERT_estimateError
pg.DataContainerERT.estimateError.__doc__ = estimateError.__doc__.replace(
"Estimate ", "Set ")[:-4]
if __name__ == "__main__":
pass
