pygimli.physics.ert#

Direct current electromagnetics

This package contains tools, modelling operators, and managers for Electrical Resistivity Tomography (ERT) & Induced polarization (IP)

Overview#

Functions

`coverageERT`	coverageDCtrans( (object)S, (object)dd, (object)mm) -> object :
`createData`(elecs[, schemeName])	Utility one-liner to create a BERT datafile
`createERTData`(args, *kwargs)
`createGeometricFactors`(args, *kwargs)
`createInversionMesh`(data, **kwargs)	Create default mesh for ERT inversion.
`drawERTData`(ax, data[, vals])	Plot ERT data as pseudosection matrix (position over separation).
`estimateError`(data[, absoluteError, ...])	Estimate error composed of an absolute and a relative part.
`generateDataPDF`(data[, filename])	Generate a multi-page pdf showing all data properties.
`geometricFactor`	geometricFactors( (object)data [, (object)dim=3 [, (object)forceFlatEarth=False]]) -> object :
`geometricFactors`(data [[, dim, forceFlatEarth])	Helper function to calculate configuration factors for a given DataContainerERT
`load`(fileName[, verbose])	Shortcut to load ERT data.
`show`(data[, vals])	Plot ERT data as pseudosection matrix (position over separation).
`showData`(data[, vals])	Plot ERT data as pseudosection matrix (position over separation).
`showERTData`(data[, vals])	Plot ERT data as pseudosection matrix (position over separation).
`simulate`(mesh, scheme, res, **kwargs)	Simulate an ERT measurement.

Classes

`DataContainer`	alias of `DataContainerERT`
`ERTManager`([data])	ERT Manager.
`ERTModelling`([sr, verbose])	Forward operator for Electrical Resistivity Tomography.
`ERTModellingReference`(**kwargs)	Reference implementation for 2.5D Electrical Resistivity Tomography.
`Manager`	alias of `ERTManager`
`VESManager`(**kwargs)	Vertical electrical sounding (VES) manager class.
`VESModelling`([ab2, mn2])	Vertical Electrical Sounding (VES) forward operator.

Functions#

pygimli.physics.ert.coverageERT()#

coverageDCtrans( (object)S, (object)dd, (object)mm) -> object :

C++ signature :
GIMLI::Vector<double> coverageDCtrans(GIMLI::MatrixBase,GIMLI::Vector<double>,GIMLI::Vector<double>)

pygimli.physics.ert.createData(elecs, schemeName='none', **kwargs)[source]#

Utility one-liner to create a BERT datafile

Parameters

elecs (int | list[pos] | array(x)) – Number of electrodes or electrode positions or x-positions
schemeName (str ['none']) – Name of the configuration. If you provide an unknown scheme name, all known schemes [‘wa’, ‘wb’, ‘pp’, ‘pd’, ‘dd’, ‘slm’, ‘hw’, ‘gr’] listed.
**kwargs –
Arguments that will be forwarded to the scheme generator.
- inversebool
  interchange AB MN with MN AB
- reciprocitybool
  interchange AB MN with BA NM
- addInversebool
  add additional inverse measurements
- spacingfloat [1]
  electrode spacing in meters
- closedbool
  Close the chain. Measure from the end of the array to the first electrode.

Returns

data

Return type

DataContainerERT

>>> import matplotlib.pyplot as plt
>>> from pygimli.physics import ert
>>>
>>> schemes = ['wa', 'wb', 'pp', 'pd', 'dd', 'slm', 'hw', 'gr']
>>> fig, ax = plt.subplots(3,3)
>>>
>>> for i, schemeName in enumerate(schemes):
...     s = ert.createData(elecs=41, schemeName=schemeName)
...     k = ert.geometricFactors(s)
...     _ = ert.show(s, vals=k, ax=ax.flat[i], label='k - ' + schemeName)
>>>
>>> plt.show()

(png, pdf)

Examples using pygimli.physics.ert.createData

2D ERT modeling and inversion

2D FEM modelling on two-layer example

Complex-valued electrical modeling

Naive complex-valued electrical inversion

pygimli.physics.ert.createERTData(*args, **kwargs)#

Examples using pygimli.physics.ert.createERTData

Hydrogeophysical modeling

Petrophysical joint inversion

pygimli.physics.ert.createGeometricFactors(*args, **kwargs)#

Examples using pygimli.physics.ert.createGeometricFactors

ERT field data with topography

Four-point sensitivities

Naive complex-valued electrical inversion

pygimli.physics.ert.createInversionMesh(data, **kwargs)[source]#

Create default mesh for ERT inversion.

Parameters: data (GIMLI::DataContainerERT) – Data Container needs at least sensors to define the geometry of the mesh.

:param Forwarded to pygimli.meshtools.createParaMesh:

Returns: mesh – Inversion mesh with default marker (1 for background, 2 parametric domain)
Return type: GIMLI::Mesh

pygimli.physics.ert.drawERTData(ax, data, vals=None, **kwargs)[source]#

Plot ERT data as pseudosection matrix (position over separation).

Parameters

data (DataContainerERT) – data container with sensorPositions and a/b/m/n fields
vals (iterable of data.size() [data['rhoa']]) – vector containing the vals to show
ax (mpl.axis) – axis to plot, if not given a new figure is created
cMin/cMax (float) – minimum/maximum color vals
logScale (bool) – logarithmic colour scale [min(A)>0]
label (string) – colorbar label
**kwargs –
- dxfloat
  x-width of individual rectangles
- indinteger iterable or IVector
  indices to limit display
- circularbool
  Plot in polar coordinates when plotting via patchValMap

Returns

ax – The used Axes
cbar – The used Colorbar or None

pygimli.physics.ert.estimateError(data, absoluteError=0.001, relativeError=0.03, absoluteUError=None, absoluteCurrent=0.1)[source]#

Estimate error composed of an absolute and a relative part.

Parameters

absoluteError (float [0.001]) – Absolute data error in Ohm m. Need ‘rhoa’ values in data.
relativeError (float [0.03]) – relative error level in %/100
absoluteUError (float [0.001]) – Absolute potential error in V. Need ‘u’ values in data. Or calculate them from ‘rhoa’, ‘k’ and absoluteCurrent if no ‘i’ is given
absoluteCurrent (float [0.1]) – Current level in A for reconstruction for absolute potential V

Returns

error

Return type

Array

Examples using pygimli.physics.ert.estimateError

ERT field data with topography

2D crosshole ERT inversion

3D surface ERT inversion

3D crosshole ERT inversion

Incorporating prior data into ERT inversion

Region-wise regularization

pygimli.physics.ert.generateDataPDF(data, filename='data.pdf')[source]#: Generate a multi-page pdf showing all data properties.

pygimli.physics.ert.geometricFactor()#

geometricFactors( (object)data [, (object)dim=3 [, (object)forceFlatEarth=False]]) -> object :

Helper function to calculate configuration factors for a given DataContainerERT

C++ signature :: GIMLI::Vector<double> geometricFactors(GIMLI::DataContainerERT [,int=3 [,bool=False]])

pygimli.physics.ert.geometricFactors((object)data[, (object)dim=3[, (object)forceFlatEarth=False]]) → object :#

Helper function to calculate configuration factors for a given DataContainerERT

C++ signature :: GIMLI::Vector<double> geometricFactors(GIMLI::DataContainerERT [,int=3 [,bool=False]])

Examples using pygimli.physics.ert.geometricFactors

2D crosshole ERT inversion

3D surface ERT inversion

3D crosshole ERT inversion

Incorporating prior data into ERT inversion

Region-wise regularization

pygimli.physics.ert.load(fileName, verbose=False, **kwargs)[source]#

Shortcut to load ERT data.

Import Data and try to assume the file format. Additionally to unified data format we support the wide-spread res2dinv format as well as ASCII column files generated by the processing software of various instruments (ABEM LS, Syscal Pro, Resecs, ?)

If this fails, install pybert and use its auto importer pybert.importData.

Parameters: fileName (str) –
Returns: data
Return type: pg.DataContainer

pygimli.physics.ert.show(data, vals=None, **kwargs)#

Plot ERT data as pseudosection matrix (position over separation).

Creates figure, axis and draw a pseudosection.

Parameters

data (BERT::DataContainerERT) –
**kwargs –
- axesmatplotlib.axes
  Axes to plot into. Default is None and a new figure and axes are created.
- valsArray[nData]
  Values to be plotted. Default is data[‘rhoa’].

Examples using pygimli.physics.ert.show

2D ERT modeling and inversion

ERT field data with topography

Incorporating prior data into ERT inversion

Region-wise regularization

pygimli.physics.ert.showData(data, vals=None, **kwargs)#

Plot ERT data as pseudosection matrix (position over separation).

Creates figure, axis and draw a pseudosection.

Parameters

data (BERT::DataContainerERT) –
**kwargs –
- axesmatplotlib.axes
  Axes to plot into. Default is None and a new figure and axes are created.
- valsArray[nData]
  Values to be plotted. Default is data[‘rhoa’].

Examples using pygimli.physics.ert.showData

ERT field data with topography

Petrophysical joint inversion

pygimli.physics.ert.showERTData(data, vals=None, **kwargs)[source]#

Plot ERT data as pseudosection matrix (position over separation).

Creates figure, axis and draw a pseudosection.

Parameters

data (BERT::DataContainerERT) –
**kwargs –
- axesmatplotlib.axes
  Axes to plot into. Default is None and a new figure and axes are created.
- valsArray[nData]
  Values to be plotted. Default is data[‘rhoa’].

Examples using pygimli.physics.ert.showERTData

Complex-valued electrical modeling

Naive complex-valued electrical inversion

pygimli.physics.ert.simulate(mesh, scheme, res, **kwargs)[source]#

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 apparent resistivities is complex as well.

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.

Parameters

mesh (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 (GIMLI::DataContainerERT) – Data measurement scheme.

Keyword Arguments

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.

Returns

DataContainerERT | array(data.size()) | array(N, data.size()) |
array(N, mesh.nodeCount()) – Data container with resulting apparent resistivity data and errors (if noiseLevel or noiseAbs is set). Optional returns a Matrix of rhoa values (for returnArray==True forces noiseLevel=0). In case of a complex valued resistivity model, phase values are returned in the DataContainerERT (see example below), or as an 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.+ 0j], # … ] # >>> ert = pg.ERTManager() # >>> data = ert.simulate(mesh, res=rhomap, scheme=scheme, verbose=True) # >>> rhoa = data.get(‘rhoa’).array() # >>> phia = data.get(‘phia’).array()

Examples using pygimli.physics.ert.simulate

2D ERT modeling and inversion

2D FEM modelling on two-layer example

3D modeling in a closed geometry

Complex-valued electrical modeling

Naive complex-valued electrical inversion

Petrophysical joint inversion

Classes#

pygimli.physics.ert.DataContainer#: alias of DataContainerERT

class pygimli.physics.ert.ERTManager(data=None, **kwargs)[source]#

Bases: MeshMethodManager

ERT Manager.

Method Manager for Electrical Resistivity Tomography (ERT)

__init__(data=None, **kwargs)[source]#

Create ERT Manager instance.

Parameters

data (GIMLI::DataContainerERT | str) – You can initialize the Manager with data or give them a dataset when calling the inversion.
useBert (*) – Use Bert forward operator instead of the reference implementation.
sr (*) – Calculate with singularity removal technique. Recommended but needs the primary potential. For flat earth cases the primary potential will be calculated analytical. For domains with topography the primary potential will be calculated numerical using a p2 refined mesh or you provide primary potentials with setPrimPot.

checkData(data=None)[source]#

Return data from container.

THINKABOUT: Data will be changed, or should the manager keep a copy?

checkErrors(err, dataVals)[source]#

Check (estimate) and return relative error.

By default we assume ‘err’ are relative values.

coverage()[source]#: Coverage vector considering the logarithmic transformation.

createForwardOperator(**kwargs)[source]#: Create and choose forward operator.

createMesh(data=None, **kwargs)[source]#

Create default inversion mesh.

Forwarded to pygimli.physics.ert.createInversionMesh

estimateError(data=None, **kwargs)[source]#

Estimate error composed of an absolute and a relative part.

Parameters

absoluteError (float [0.001]) – Absolute data error in Ohm m. Need ‘rhoa’ values in data.
relativeError (float [0.03]) – relative error level in %/100
absoluteUError (float [0.001]) – Absolute potential error in V. Need ‘u’ values in data. Or calculate them from ‘rhoa’, ‘k’ and absoluteCurrent if no ‘i’ is given
absoluteCurrent (float [0.1]) – Current level in A for reconstruction for absolute potential V

Returns

error

Return type

Array

load(fileName)[source]#

Load ERT data.

Forwarded to pygimli.physics.ert.load

Parameters: fileName (str) – Filename for the data.
Returns: data
Return type: GIMLI::DataContainerERT

saveResult(folder=None, size=(16, 10), **kwargs)[source]#

Save all results in the specified folder.

Saved items are:: Inverted profile Resistivity vector Coverage vector Standardized coverage vector Mesh (bms and vtk with results)

setPrimPot(pot)[source]#: Set primary potential from external is not supported anymore.

setSingularityRemoval(sr=True)[source]#: Turn singularity removal on or off.

showMisfit(errorWeighted=False, **kwargs)[source]#: Show relative or error-weighted data misfit.

showModel(model=None, elecs=True, ax=None, **kwargs)[source]#

Show the last inversion result.

Parameters

ax (mpl axes) – Axes object to draw into. Create a new if its not given.
model (iterable [None]) – Model values to be draw. Default is self.model from the last run

Return type

ax, cbar

simulate(mesh, scheme, res, **kwargs)[source]#

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 apparent resistivities is complex as well.

Parameters

mesh (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 (GIMLI::DataContainerERT) – Data measurement scheme.

Keyword Arguments

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.

Returns

DataContainerERT | array(data.size()) | array(N, data.size()) |
array(N, mesh.nodeCount()) – Data container with resulting apparent resistivity data and errors (if noiseLevel or noiseAbs is set). Optional returns a Matrix of rhoa values (for returnArray==True forces noiseLevel=0). In case of a complex valued resistivity model, phase values are returned in the DataContainerERT (see example below), or as an 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.+ 0j], # … ] # >>> data = ert.simulate(mesh, res=rhomap, scheme=scheme, verbose=1) # >>> rhoa = data.get(‘rhoa’).array() # >>> phia = data.get(‘phia’).array()

standardizedCoverage(threshold=0.01)[source]#: Return standardized coverage vector (0|1) using thresholding.

class pygimli.physics.ert.ERTModelling(sr=True, verbose=False)[source]#

Bases: ERTModellingBase

Forward operator for Electrical Resistivity Tomography.

Note

Convention for complex resistiviy inversion: We want to use logarithm transformation for the imaginary part of model so we need the startmodel to have positive imaginary parts. The sign is flipped back to physical correct assumption before we call the response function. The Jacobian is calculated with negative imaginary parts and will be a conjugated complex block matrix for further calulations.

__init__(sr=True, verbose=False)[source]#

Initialize.

Variables

fop (pg.frameworks.Modelling) –
data (pg.DataContainer) –
modelTrans ([pg.trans.TransLog()]) –

Parameters

**kwargs – fop : Modelling

createJacobian(mod)[source]#: Compute Jacobian matrix and store but not return.

createStartModel(dataVals)[source]#: Create Starting model for ERT inversion.

flipImagPart(v)[source]#: Flip imaginary port (convention).

response(mod)[source]#: Forward response (apparent resistivity).

setDataPost(data)[source]#: Set data (at a later stage).

setDefaultBackground()[source]#: Set the default background behaviour.

setMeshPost(mesh)[source]#: Set mesh (at a later stage).

setVerbose(v)[source]#: Set verbosity.

class pygimli.physics.ert.ERTModellingReference(**kwargs)[source]#

Bases: ERTModellingBase

Reference implementation for 2.5D Electrical Resistivity Tomography.

__init__(**kwargs)[source]#

Initialize.

Variables

fop (pg.frameworks.Modelling) –
data (pg.DataContainer) –
modelTrans ([pg.trans.TransLog()]) –

Parameters

**kwargs – fop : Modelling

calcGeometricFactor(data)[source]#: Calculate geometry factors for a given dataset.

createJacobian(model)[source]#: Create Jacobian matrix for model and store it in self.jacobian().

createRHS(mesh, elecs)[source]#: Create right-hand-side vector.

getIntegrationWeights(rMin, rMax)[source]#: TODO WRITEME.

mixedBC(boundary, userData)[source]#: Apply mixed boundary conditions.

pointSource(cell, f, userData)[source]#

Define function for the current source term.

$\delta(x-pos), \int f(x) \delta(x-pos)=f(pos)=N(pos)$: Right hand side entries will be shape functions(pos)

response(model)[source]#: Solve forward task and return apparent resistivity for self.mesh.

uAnalytical(p, sourcePos, k)[source]#: Calculate analytical potential for homogeneous halfspace.

For sigma = 1 [S m]

pygimli.physics.ert.Manager#: alias of ERTManager

class pygimli.physics.ert.VESManager(**kwargs)[source]#

Bases: MethodManager1d

Vertical electrical sounding (VES) manager class.

>>> import numpy as np
>>> import pygimli as pg
>>> from pygimli.physics import VESManager
>>> ab2 = np.logspace(np.log10(1.5), np.log10(100), 32)
>>> mn2 = 1.0
>>> # 3 layer with 100, 500 and 20 Ohmm
>>> # and layer thickness of 4, 6, 10 m
>>> # over a Halfspace of 800 Ohmm
>>> synthModel = pg.cat([4., 6., 10.], [100., 5., 20., 800.])
>>> ves = VESManager()
>>> ra, err = ves.simulate(synthModel, ab2=ab2, mn2=mn2, noiseLevel=0.01)
>>> ax = ves.showData(ra, error=err)
>>> model = ves.invert(ra, err, nLayers=4, showProgress=0, verbose=0)
>>> ax, _ = ves.showModel(synthModel)
>>> _ = ves.showResult(ax=ax)

../../_images/pygimli-physics-ert-2_00.png

(png, pdf)#

../../_images/pygimli-physics-ert-2_01.png

(png, pdf)#

__init__(**kwargs)[source]#

Initialize instance.

Parameters: complex (bool) – Accept complex resistivities.
Variables: complex (bool) – Accept complex resistivities.

property complex#: Return whether the computations are complex.

createForwardOperator(**kwargs)[source]#

Create Forward Operator.

Create Forward Operator based on complex attribute.

exportData(fileName, data=None, error=None)[source]#

Export data into simple ascii matrix.

Usefull?

invert(data=None, err=None, ab2=None, mn2=None, **kwargs)[source]#

Invert measured data.

Parameters

data (iterable) – data vector
err (iterable) – error vector
ab2 (iterable) – AB/2 vector (otherwise taken from data)
mn2 (iterable) – MN/2 vector (otherwise taken from data)

Keyword Arguments

**kwargs – Additional kwargs inherited from %(MethodManager1d.invert) and %(Inversion.run)

Returns

model – inversion result

Return type

pg.Vector

loadData(fileName, **kwargs)[source]#: Load simple data matrix.

preErrorCheck(err, dataVals=None)[source]#: Fct to be called before the validity check of the error values.

simulate(model, ab2=None, mn2=None, **kwargs)[source]#: Simulate measurement data.

class pygimli.physics.ert.VESModelling(ab2=None, mn2=None, **kwargs)[source]#

Bases: Block1DModelling

Vertical Electrical Sounding (VES) forward operator.

Variables

ab2 – Half distance between the current electrodes A and B.
mn2 – Half distance between the potential electrodes M and N. Only used for input (feeding am etc.) or plotting.
am – Part of data basis. Distances between A and M electrodes. A is first current, M is first potential electrode.
bm – Part of data basis. Distances between B and M electrodes. B is second current, M is first potential electrode.
an – Part of data basis. Distances between A and N electrodes. A is first current, N is second potential electrode.
bn – Part of data basis. Distances between B and N electrodes. B is second current, N is second potential electrode.

__init__(ab2=None, mn2=None, **kwargs)[source]#: Initialize with distances.

createStartModel(rhoa)[source]#: Create starting model.

drawData(ax, data, error=None, label=None, **kwargs)[source]#

Draw modeled apparent resistivity data.

Parameters

ax (axes) – Matplotlib axes object to draw into.
data (iterable) – Apparent resistivity values to draw.
error (iterable [None]) – Adds an error bar if you have error values.
label (str ['$rho_a$']) – Set legend label for the amplitude.
ab2 (iterable) – Override ab2 that fits data size.
mn2 (iterable) – Override mn2 that fits data size.
plot (function name) – Matplotlib plot function, e.g., plot, loglog, semilogx or semilogy

drawModel(ax, model, **kwargs)[source]#: Draw model as 1D block model.

response(par)[source]#: Model response.

response_mt(par, i=0)[source]#: Multi-threading model response.

setDataSpace(ab2=None, mn2=None, am=None, bm=None, an=None, bn=None, **kwargs)[source]#

Set data basis, i.e., arrays for all am, an, bm, bn distances.

You can set either * AB/2 and (optionally) MN/2 spacings for a classical sounding, or * all distances AM, AN, BM, BN for arbitrary arrays :param ab2: AB/2 distances :type ab2: iterable :param mn2: MN/2 distance(s) :type mn2: iterable | float :param am: :type am: distances between current and potential electrodes :param an: :type an: distances between current and potential electrodes :param bm: :type bm: distances between current and potential electrodes :param bn: :type bn: distances between current and potential electrodes