# esys.downunder.forwardmodels.dcresistivity Package

Forward model for DC Resistivity

## Classes

- `Data`
- `DcRes`
- `FileWriter`
- `ForwardModel`
- `LinearPDE`
- `Locator`

class `esys.downunder.forwardmodels.dcresistivity.``Data`

  Bases: `Boost.Python.instance`

  Represents a collection of datapoints. It is used to store the values of a function. For more details please consult the c++ class documentation.

  `__init__`((object)arg1) → None

    __init__( (object)arg1, (object)value [, (object)p2 [, (object)p3 [, (object)p4]]]) -> None

  `conjugate`((Data)arg1) → Data
  `copy`((Data)arg1, (Data)other) → None :

    Make this object a copy of `other`

    > note: | The two objects will act independently from now on. That is, changing `other` after this call will not change this object and vice versa.

    copy( (Data)arg1) -> Data :

      note: | In the no argument form, a new object will be returned which is an independent copy of this object.

  `copyWithMask`((Data)arg1, (Data)other, (Data)mask) → None :

    Selectively copy values from `other` `Data`.Datapoints which correspond to positive values in `mask` will be copied from `other`

    Parameters:

    - other (`Data`) – source of values
    - mask (Scalar `Data`) –

  `delay`((Data)arg1) → Data :

    Convert this object into lazy representation

  `dump`((Data)arg1, (str)fileName) → None :

    Save the data as a netCDF file

    Parameters: | fileName (`string`) –

  `expand`((Data)arg1) → None :

    Convert the data to expanded representation if it is not expanded already.

  `getDomain`((Data)arg1) → Domain :

    Return type: | `Domain`

  `getFunctionSpace`((Data)arg1) → FunctionSpace :

    Return type: | `FunctionSpace`

  `getNumberOfDataPoints`((Data)arg1) → int :

    Return type: | `int`
    Returns: | Number of datapoints in the object

  `getRank`((Data)arg1) → int :

    Returns: | the number of indices required to address a component of a datapoint
    Return type: | positive `int`

  `getShape`((Data)arg1) → tuple :

    Returns the shape of the datapoints in this object as a python tuple. Scalar data has the shape `()`

    Return type: | `tuple`

  `getTagNumber`((Data)arg1, (int)dpno) → int :

    Return tag number for the specified datapoint

    Return type: | int
    Parameters: | dpno (int) – datapoint number

  `getTupleForDataPoint`((Data)arg1, (int)dataPointNo) → object :

    Returns: | Value of the specified datapoint
    Return type: | `tuple`
    Parameters: | dataPointNo (`int`) – datapoint to access

  `getTupleForGlobalDataPoint`((Data)arg1, (int)procNo, (int)dataPointNo) → object :

    Get a specific datapoint from a specific process

    Return type: | `tuple`

    Parameters:

    - procNo (positive `int`) – MPI rank of the process
    - dataPointNo (int) – datapoint to access

  `getX`((Data)arg1) → Data :

    Returns the spatial coordinates of the spatial nodes. :rtype: `Data`

  `hasInf`((Data)arg1) → bool :

    Returns return true if data contains +-Inf. [Note that for complex values, hasNaN and hasInf are not mutually exclusive.]

  `hasNaN`((Data)arg1) → bool :

    Returns return true if data contains NaN. [Note that for complex values, hasNaN and hasInf are not mutually exclusive.]

  `imag`((Data)arg1) → Data
  `internal_maxGlobalDataPoint`((Data)arg1) → tuple :

    Please consider using getSupLocator() from pdetools instead.

  `internal_minGlobalDataPoint`((Data)arg1) → tuple :

    Please consider using getInfLocator() from pdetools instead.

  `interpolate`((Data)arg1, (FunctionSpace)functionspace) → Data :

    Interpolate this object’s values into a new functionspace.

  `interpolateTable`((Data)arg1, (object)table, (float)Amin, (float)Astep, (Data)B, (float)Bmin, (float)Bstep[, (float)undef=1e+50[, (bool)check_boundaries=False]]) → Data :
    Creates a new Data object by interpolating using the source data (which are

      looked up in `table`) `A` must be the outer dimension on the table

      param table: | two dimensional collection of values
      param Amin: | The base of locations in table
      type Amin: | float
      param Astep: | size of gap between each item in the table
      type Astep: | float
      param undef: | upper bound on interpolated values
      type undef: | float
      param B: | Scalar representing the second coordinate to be mapped into the table
      type B: | `Data`
      param Bmin: | The base of locations in table for 2nd dimension
      type Bmin: | float
      param Bstep: | size of gap between each item in the table for 2nd dimension
      type Bstep: | float
      param check_boundaries:
      | if true, then values outside the boundaries will be rejected. If false, then boundary values will be used.
      raise RuntimeError(DataException):
      | if the coordinates do not map into the table or if the interpolated value is above `undef`
      rtype: | `Data`

    interpolateTable( (Data)arg1, (object)table, (float)Amin, (float)Astep [, (float)undef=1e+50 [, (bool)check_boundaries=False]]) -> Data

  `isComplex`((Data)arg1) → bool :

    Return type: | `bool`
    Returns: | True if this `Data` stores complex values.

  `isConstant`((Data)arg1) → bool :

    Return type: | `bool`
    Returns: | True if this `Data` is an instance of `DataConstant`
    Note: | This does not mean the data is immutable.

  `isEmpty`((Data)arg1) → bool :

    Is this object an instance of `DataEmpty`

    Return type: | `bool`
    Note: | This is not the same thing as asking if the object contains datapoints.

  `isExpanded`((Data)arg1) → bool :

    Return type: | `bool`
    Returns: | True if this `Data` is expanded.

  `isLazy`((Data)arg1) → bool :

    Return type: | `bool`
    Returns: | True if this `Data` is lazy.

  `isProtected`((Data)arg1) → bool :

    Can this instance be modified. :rtype: `bool`

  `isReady`((Data)arg1) → bool :

    Return type: | `bool`
    Returns: | True if this `Data` is not lazy.

  `isTagged`((Data)arg1) → bool :

    Return type: | `bool`
    Returns: | True if this `Data` is expanded.

  `nonuniformInterpolate`((Data)arg1, (object)in, (object)out, (bool)check_boundaries) → Data :

    1D interpolation with non equally spaced points

  `nonuniformSlope`((Data)arg1, (object)in, (object)out, (bool)check_boundaries) → Data :

    1D interpolation of slope with non equally spaced points

  `phase`((Data)arg1) → Data
  `promote`((Data)arg1) → None
  `real`((Data)arg1) → Data
  `replaceInf`((Data)arg1, (object)value) → None :

    Replaces +-Inf values with value. [Note, for complex Data, both real and imaginary components are replaced even if only one part is Inf].

  `replaceNaN`((Data)arg1, (object)value) → None :

    Replaces NaN values with value. [Note, for complex Data, both real and imaginary components are replaced even if only one part is NaN].

  `resolve`((Data)arg1) → None :

    Convert the data to non-lazy representation.

  `setProtection`((Data)arg1) → None :

    Disallow modifications to this data object

    Note: | This method does not allow you to undo protection.

  `setTaggedValue`((Data)arg1, (int)tagKey, (object)value) → None :

    Set the value of tagged Data.

    > param tagKey: | tag to update
    > type tagKey: | `int`

    setTaggedValue( (Data)arg1, (str)name, (object)value) -> None :

      param name: | tag to update
      type name: | `string`
      param value: | value to set tagged data to
      type value: | `object` which acts like an array, `tuple` or `list`

  `setToZero`((Data)arg1) → None :

    After this call the object will store values of the same shape as before but all components will be zero.

  `setValueOfDataPoint`((Data)arg1, (int)dataPointNo, (object)value) → None

    setValueOfDataPoint( (Data)arg1, (int)arg2, (object)arg3) -> None

    setValueOfDataPoint( (Data)arg1, (int)arg2, (float)arg3) -> None :

    > Modify the value of a single datapoint.

    > param dataPointNo:
    > type dataPointNo:
    > | int
    > param value: |
    > type value: | `float` or an object which acts like an array, `tuple` or `list`
    > warning: | Use of this operation is discouraged. It prevents some optimisations from operating.

  `tag`((Data)arg1) → None :

    Convert data to tagged representation if it is not already tagged or expanded

  `toListOfTuples`((Data)arg1[, (bool)scalarastuple=False]) → object :

    Return the datapoints of this object in a list. Each datapoint is stored as a tuple.

    Parameters: | scalarastuple – if True, scalar data will be wrapped as a tuple. True => [(0), (1), (2)]; False => [0, 1, 2]

class `esys.downunder.forwardmodels.dcresistivity.``DcRes`(domain, locator, delphiIn, sampleTags, phiPrimary, sigmaPrimary, w=1.0, coordinates=None, tol=1e-08, saveMemory=True, b=None)

  Bases: `esys.downunder.forwardmodels.base.ForwardModel`

  Forward Model for DC resistivity, with a given source pair. The cost function is defined as:

  Math: | defect = 1/2 (sum_s sum_pq w_pqs * ((phi_sp-phi_sq)-v_pqs)**2

  `__init__`(domain, locator, delphiIn, sampleTags, phiPrimary, sigmaPrimary, w=1.0, coordinates=None, tol=1e-08, saveMemory=True, b=None)

    setup new forward model

    Parameters:

    - domain – the domain of the model
    - locator – contains locator to the measurement pairs
    - sampleTags (list of tuples) – tags of measurement points from which potential differences will be calculated.
    - phiPrimary (`Scalar`) – primary potential.

    Type: | escript domain
    Type: | `list` of `Locator`
    Param: | delphiIn: this is v_pq, the potential difference for the current source and a set of measurement pairs. A list of measured potential differences is expected. Note this should be the secondary potential only.

  `getArguments`(sigma)

    Returns precomputed values shared by `getDefect()` and `getGradient()`.

    Parameters: | sigma (`Data` of shape (1,)) – conductivity
    Returns: | phi
    Return type: | `Data` of shape (1,)

  `getCoordinateTransformation`()

    returns the coordinate transformation being used

    Return type: | `CoordinateTransformation`

  `getDefect`(sigma, phi, loc_phi)

    Returns the defect value.

    Parameters:

    - sigma (`Data` of shape (1,)) – a suggestion for conductivity
    - phi (`Data` of shape (1,)) – potential field

    Return type: | `float`

  `getDomain`()

    Returns the domain of the forward model.

    Return type: | `Domain`

  `getGradient`(sigma, phi, loc_phi)

    Returns the gradient of the defect with respect to density.

    Parameters:

    - sigma (`Data` of shape (1,)) – a suggestison for conductivity
    - phi (`Data` of shape (1,)) – potential field

  `getPrimaryPotential`()

    returns the primary potential :rtype: `Data`

  `setUpPDE`()

    Return the underlying PDE.

    Return type: | `LinearPDE`

class `esys.downunder.forwardmodels.dcresistivity.``FileWriter`(fn, append=False, createLocalFiles=False)

  Bases: `object`

  Interface to write data to a file. In essence this class wrappes the standard `file` object to write data that are global in MPI to a file. In fact, data are writen on the processor with MPI rank 0 only. It is recommended to use `FileWriter` rather than `open` in order to write code that is running with as well as with MPI. It is safe to use `open` onder MPI to read data which are global under MPI.

  Variables:

  - name – name of file
  - mode – access mode (=’w’ or =’a’)
  - closed – True to indicate closed file
  - newlines – line seperator

  `__init__`(fn, append=False, createLocalFiles=False)

    Opens a file of name `fn` for writing. If running under MPI only the first processor with rank==0 will open the file and write to it. If `createLocalFiles` each individual processor will create a file where for any processor with rank>0 the file name is extended by its rank. This option is normally only used for debug purposes.

    Parameters:

    - fn (`str`) – filename.
    - append (`bool`) – switches on the creation of local files.
    - createLocalFiles (`bool`) – switches on the creation of local files.

  `close`()

    Closes the file

  `flush`()

    Flush the internal I/O buffer.

  `write`(txt)

    Write string `txt` to file.

    Parameters: | txt (`str`) – string `txt` to be written to file

  `writelines`(txts)

    Write the list `txt` of strings to the file.

    Parameters: | txts (any iterable object producing strings) – sequense of strings to be written to file
    Note: | Note that newlines are not added. This method is equivalent to call write() for each string.

class `esys.downunder.forwardmodels.dcresistivity.``ForwardModel`

  Bases: `object`

  An abstract forward model that can be plugged into a cost function. Subclasses need to implement `getDefect()`, `getGradient()`, and possibly `getArguments()` and ‘getCoordinateTransformation’.

  `__init__`()

    Initialize self. See help(type(self)) for accurate signature.

  `getArguments`(x)
  `getCoordinateTransformation`()
  `getDefect`(x, *args)
  `getGradient`(x, *args)

class `esys.downunder.forwardmodels.dcresistivity.``LinearPDE`(domain, numEquations=None, numSolutions=None, isComplex=False, debug=False)

  Bases: `esys.escriptcore.linearPDEs.LinearProblem`

  This class is used to define a general linear, steady, second order PDE for an unknown function u on a given domain defined through a `Domain` object.

  For a single PDE having a solution with a single component the linear PDE is defined in the following form:

  -(grad(A[j,l]+A_reduced[j,l])*grad(u)[l]+(B[j]+B_reduced[j])u)[j]+(C[l]+C_reduced[l])*grad(u)[l]+(D+D_reduced)=-grad(X+X_reduced)[j,j]+(Y+Y_reduced)

  where grad(F) denotes the spatial derivative of F. Einstein’s summation convention, ie. summation over indexes appearing twice in a term of a sum performed, is used. The coefficients A, B, C, D, X and Y have to be specified through `Data` objects in `Function` and the coefficients A_reduced, B_reduced, C_reduced, D_reduced, X_reduced and Y_reduced have to be specified through `Data` objects in `ReducedFunction`. It is also allowed to use objects that can be converted into such `Data` objects. A and A_reduced are rank two, B, C, X, B_reduced, C_reduced and X_reduced are rank one and D, D_reduced, Y and Y_reduced are scalar.

  The following natural boundary conditions are considered:

  n[j]*((A[i,j]+A_reduced[i,j])*grad(u)[l]+(B+B_reduced)[j]*u)+(d+d_reduced)*u=n[j]*(X[j]+X_reduced[j])+y

  where n is the outer normal field. Notice that the coefficients A, A_reduced, B, B_reduced, X and X_reduced are defined in the PDE. The coefficients d and y are each a scalar in `FunctionOnBoundary` and the coefficients d_reduced and y_reduced are each a scalar in `ReducedFunctionOnBoundary`.

  Constraints for the solution prescribe the value of the solution at certain locations in the domain. They have the form

  u=r where q>0

  r and q are each scalar where q is the characteristic function defining where the constraint is applied. The constraints override any other condition set by the PDE or the boundary condition.

  The PDE is symmetrical if

  A[i,j]=A[j,i] and B[j]=C[j] and A_reduced[i,j]=A_reduced[j,i] and B_reduced[j]=C_reduced[j]

  For a system of PDEs and a solution with several components the PDE has the form

  -grad((A[i,j,k,l]+A_reduced[i,j,k,l])*grad(u[k])[l]+(B[i,j,k]+B_reduced[i,j,k])*u[k])[j]+(C[i,k,l]+C_reduced[i,k,l])*grad(u[k])[l]+(D[i,k]+D_reduced[i,k]*u[k] =-grad(X[i,j]+X_reduced[i,j])[j]+Y[i]+Y_reduced[i]

  A and A_reduced are of rank four, B, B_reduced, C and C_reduced are each of rank three, D, D_reduced, X_reduced and X are each of rank two and Y and Y_reduced are of rank one. The natural boundary conditions take the form:

  n[j]*((A[i,j,k,l]+A_reduced[i,j,k,l])*grad(u[k])[l]+(B[i,j,k]+B_reduced[i,j,k])*u[k])+(d[i,k]+d_reduced[i,k])*u[k]=n[j]*(X[i,j]+X_reduced[i,j])+y[i]+y_reduced[i]

  The coefficient d is of rank two and y is of rank one both in `FunctionOnBoundary`. The coefficients d_reduced is of rank two and y_reduced is of rank one both in `ReducedFunctionOnBoundary`.

  Constraints take the form

  u[i]=r[i] where q[i]>0

  r and q are each rank one. Notice that at some locations not necessarily all components must have a constraint.

  The system of PDEs is symmetrical if

  > - A[i,j,k,l]=A[k,l,i,j]
  > - A_reduced[i,j,k,l]=A_reduced[k,l,i,j]
  > - B[i,j,k]=C[k,i,j]
  > - B_reduced[i,j,k]=C_reduced[k,i,j]
  > - D[i,k]=D[i,k]
  > - D_reduced[i,k]=D_reduced[i,k]
  > - d[i,k]=d[k,i]
  > - d_reduced[i,k]=d_reduced[k,i]

  `LinearPDE` also supports solution discontinuities over a contact region in the domain. To specify the conditions across the discontinuity we are using the generalised flux J which, in the case of a system of PDEs and several components of the solution, is defined as

  J[i,j]=(A[i,j,k,l]+A_reduced[[i,j,k,l])*grad(u[k])[l]+(B[i,j,k]+B_reduced[i,j,k])*u[k]-X[i,j]-X_reduced[i,j]

  For the case of single solution component and single PDE J is defined as

  J[j]=(A[i,j]+A_reduced[i,j])*grad(u)[j]+(B[i]+B_reduced[i])*u-X[i]-X_reduced[i]

  In the context of discontinuities n denotes the normal on the discontinuity pointing from side 0 towards side 1 calculated from `FunctionSpace.getNormal` of `FunctionOnContactZero`. For a system of PDEs the contact condition takes the form

  n[j]*J0[i,j]=n[j]*J1[i,j]=(y_contact[i]+y_contact_reduced[i])- (d_contact[i,k]+d_contact_reduced[i,k])*jump(u)[k]

  where J0 and J1 are the fluxes on side 0 and side 1 of the discontinuity, respectively. jump(u), which is the difference of the solution at side 1 and at side 0, denotes the jump of u across discontinuity along the normal calculated by `jump`. The coefficient d_contact is of rank two and y_contact is of rank one both in `FunctionOnContactZero` or `FunctionOnContactOne`. The coefficient d_contact_reduced is of rank two and y_contact_reduced is of rank one both in `ReducedFunctionOnContactZero` or `ReducedFunctionOnContactOne`. In case of a single PDE and a single component solution the contact condition takes the form

  n[j]*J0_{j}=n[j]*J1_{j}=(y_contact+y_contact_reduced)-(d_contact+y_contact_reduced)*jump(u)

  In this case the coefficient d_contact and y_contact are each scalar both in `FunctionOnContactZero` or `FunctionOnContactOne` and the coefficient d_contact_reduced and y_contact_reduced are each scalar both in `ReducedFunctionOnContactZero` or `ReducedFunctionOnContactOne`.

  Typical usage:

      p = LinearPDE(dom)
      p.setValue(A=kronecker(dom), D=1, Y=0.5)
      u = p.getSolution()

  `__init__`(domain, numEquations=None, numSolutions=None, isComplex=False, debug=False)

    Initializes a new linear PDE.

    Parameters:

    - domain (`Domain`) – domain of the PDE
    - numEquations – number of equations. If `None` the number of equations is extracted from the PDE coefficients.
    - numSolutions – number of solution components. If `None` the number of solution components is extracted from the PDE coefficients.
    - debug – if True debug information is printed

  `addPDEToLumpedSystem`(operator, a, b, c, hrz_lumping)

    adds a PDE to the lumped system, results depend on domain

    Parameters:

    - mat (`OperatorAdapter`) –
    - rhs (`Data`) –
    - a (`Data`) –
    - b (`Data`) –
    - c (`Data`) –
    - hrz_lumping (`bool`) –

  `addPDEToRHS`(righthandside, X, Y, y, y_contact, y_dirac)

    adds a PDE to the right hand side, results depend on domain

    Parameters:

    - mat (`OperatorAdapter`) –
    - righthandside (`Data`) –
    - X (`Data`) –
    - Y (`Data`) –
    - y (`Data`) –
    - y_contact (`Data`) –
    - y_dirac (`Data`) –

  `addPDEToSystem`(operator, righthandside, A, B, C, D, X, Y, d, y, d_contact, y_contact, d_dirac, y_dirac)

    adds a PDE to the system, results depend on domain

    Parameters:

    - mat (`OperatorAdapter`) –
    - rhs (`Data`) –
    - A (`Data`) –
    - B (`Data`) –
    - C (`Data`) –
    - D (`Data`) –
    - X (`Data`) –
    - Y (`Data`) –
    - d (`Data`) –
    - y (`Data`) –
    - d_contact (`Data`) –
    - y_contact (`Data`) –
    - d_dirac (`Data`) –
    - y_dirac (`Data`) –

  `addToRHS`(rhs, data)

    adds a PDE to the right hand side, results depend on domain

    Parameters:

    - mat (`OperatorAdapter`) –
    - righthandside (`Data`) –
    - data (`list`) –

  `addToSystem`(op, rhs, data)

    adds a PDE to the system, results depend on domain

    Parameters:

    - mat (`OperatorAdapter`) –
    - rhs (`Data`) –
    - data (`list`) –

  `alteredCoefficient`(name)

    Announces that coefficient `name` has been changed.

    Parameters: | name (`string`) – name of the coefficient affected
    Raises: | IllegalCoefficient – if `name` is not a coefficient of the PDE
    Note: | if `name` is q or r, the method will not trigger a rebuild of the system as constraints are applied to the solved system.

  `checkReciprocalSymmetry`(name0, name1, verbose=True)

    Tests two coefficients for reciprocal symmetry.

    Parameters:

    - name0 (`str`) – name of the first coefficient
    - name1 (`str`) – name of the second coefficient
    - verbose (`bool`) – if set to True or not present a report on coefficients which break the symmetry is printed

    Returns: | True if coefficients `name0` and `name1` are reciprocally symmetric.
    Return type: | `bool`

  `checkSymmetricTensor`(name, verbose=True)

    Tests a coefficient for symmetry.

    Parameters:

    - name (`str`) – name of the coefficient
    - verbose (`bool`) – if set to True or not present a report on coefficients which break the symmetry is printed.

    Returns: | True if coefficient `name` is symmetric
    Return type: | `bool`

  `checkSymmetry`(verbose=True)

    Tests the PDE for symmetry.

    Parameters: | verbose (`bool`) – if set to True or not present a report on coefficients which break the symmetry is printed.
    Returns: | True if the PDE is symmetric
    Return type: | `bool`
    Note: | This is a very expensive operation. It should be used for degugging only! The symmetry flag is not altered.

  `createCoefficient`(name)

    Creates a `Data` object corresponding to coefficient `name`.

    Returns: | the coefficient `name` initialized to 0
    Return type: | `Data`
    Raises: | IllegalCoefficient – if `name` is not a coefficient of the PDE

  `createOperator`()

    Returns an instance of a new operator.

  `createRightHandSide`()

    Returns an instance of a new right hand side.

  `createSolution`()

    Returns an instance of a new solution.

  `getCoefficient`(name)

    Returns the value of the coefficient `name`.

    Parameters: | name (`string`) – name of the coefficient requested
    Returns: | the value of the coefficient
    Return type: | `Data`
    Raises: | IllegalCoefficient – if `name` is not a coefficient of the PDE

  `getCurrentOperator`()

    Returns the operator in its current state.

  `getCurrentRightHandSide`()

    Returns the right hand side in its current state.

  `getCurrentSolution`()

    Returns the solution in its current state.

  `getDim`()

    Returns the spatial dimension of the PDE.

    Returns: | the spatial dimension of the PDE domain
    Return type: | `int`

  `getDomain`()

    Returns the domain of the PDE.

    Returns: | the domain of the PDE
    Return type: | `Domain`

  `getDomainStatus`()

    Return the status indicator of the domain

  `getFlux`(u=None)

    Returns the flux J for a given u.

    J[i,j]=(A[i,j,k,l]+A_reduced[A[i,j,k,l]]*grad(u[k])[l]+(B[i,j,k]+B_reduced[i,j,k])u[k]-X[i,j]-X_reduced[i,j]

    or

    J[j]=(A[i,j]+A_reduced[i,j])*grad(u)[l]+(B[j]+B_reduced[j])u-X[j]-X_reduced[j]

    Parameters: | u (`Data` or None) – argument in the flux. If u is not present or equals `None` the current solution is used.
    Returns: | flux
    Return type: | `Data`

  `getFunctionSpaceForCoefficient`(name)

    Returns the `FunctionSpace` to be used for coefficient `name`.

    Parameters: | name (`string`) – name of the coefficient enquired
    Returns: | the function space to be used for coefficient `name`
    Return type: | `FunctionSpace`
    Raises: | IllegalCoefficient – if `name` is not a coefficient of the PDE

  `getFunctionSpaceForEquation`()

    Returns the `FunctionSpace` used to discretize the equation.

    Returns: | representation space of equation
    Return type: | `FunctionSpace`

  `getFunctionSpaceForSolution`()

    Returns the `FunctionSpace` used to represent the solution.

    Returns: | representation space of solution
    Return type: | `FunctionSpace`

  `getNumEquations`()

    Returns the number of equations.

    Returns: | the number of equations
    Return type: | `int`
    Raises: | UndefinedPDEError – if the number of equations is not specified yet

  `getNumSolutions`()

    Returns the number of unknowns.

    Returns: | the number of unknowns
    Return type: | `int`
    Raises: | UndefinedPDEError – if the number of unknowns is not specified yet

  `getOperator`()

    Returns the operator of the linear problem.

    Returns: | the operator of the problem

  `getOperatorType`()

    Returns the current system type.

  `getRequiredOperatorType`()

    Returns the system type which needs to be used by the current set up.

  `getResidual`(u=None)

    Returns the residual of u or the current solution if u is not present.

    Parameters: | u (`Data` or None) – argument in the residual calculation. It must be representable in `self.getFunctionSpaceForSolution()`. If u is not present or equals `None` the current solution is used.
    Returns: | residual of u
    Return type: | `Data`

  `getRightHandSide`()

    Returns the right hand side of the linear problem.

    Returns: | the right hand side of the problem
    Return type: | `Data`

  `getShapeOfCoefficient`(name)

    Returns the shape of the coefficient `name`.

    Parameters: | name (`string`) – name of the coefficient enquired
    Returns: | the shape of the coefficient `name`
    Return type: | `tuple` of `int`
    Raises: | IllegalCoefficient – if `name` is not a coefficient of the PDE

  `getSolution`()

    Returns the solution of the PDE.

    Returns: | the solution
    Return type: | `Data`

  `getSolverOptions`()

    Returns the solver options

    Return type: | `SolverOptions`

  `getSystem`()

    Returns the operator and right hand side of the PDE.

    Returns: | the discrete version of the PDE
    Return type: | `tuple` of `Operator` and `Data`

  `getSystemStatus`()

    Return the domain status used to build the current system

  `hasCoefficient`(name)

    Returns True if `name` is the name of a coefficient.

    Parameters: | name (`string`) – name of the coefficient enquired
    Returns: | True if `name` is the name of a coefficient of the general PDE, False otherwise
    Return type: | `bool`

  `initializeSystem`()

    Resets the system clearing the operator, right hand side and solution.

  `insertConstraint`(rhs_only=False)

    Applies the constraints defined by q and r to the PDE.

    Parameters: | rhs_only (`bool`) – if True only the right hand side is altered by the constraint

  `introduceCoefficients`(**coeff)

    Introduces new coefficients into the problem.

    Use:

    p.introduceCoefficients(A=PDECoef(…), B=PDECoef(…))

    to introduce the coefficients A and B.

  `invalidateOperator`()

    Indicates the operator has to be rebuilt next time it is used.

  `invalidateRightHandSide`()

    Indicates the right hand side has to be rebuilt next time it is used.

  `invalidateSolution`()

    Indicates the PDE has to be resolved if the solution is requested.

  `invalidateSystem`()

    Announces that everything has to be rebuilt.

  `isComplex`()

    Returns true if this is a complex-valued LinearProblem, false if real-valued.

    Return type: | `bool`

  `isHermitian`()

    Checks if the pde is indicated to be Hermitian.

    Returns: | True if a Hermitian PDE is indicated, False otherwise
    Return type: | `bool`
    Note: | the method is equivalent to use getSolverOptions().isHermitian()

  `isOperatorValid`()

    Returns True if the operator is still valid.

  `isRightHandSideValid`()

    Returns True if the operator is still valid.

  `isSolutionValid`()

    Returns True if the solution is still valid.

  `isSymmetric`()

    Checks if symmetry is indicated.

    Returns: | True if a symmetric PDE is indicated, False otherwise
    Return type: | `bool`
    Note: | the method is equivalent to use getSolverOptions().isSymmetric()

  `isSystemValid`()

    Returns True if the system (including solution) is still vaild.

  `isUsingLumping`()

    Checks if matrix lumping is the current solver method.

    Returns: | True if the current solver method is lumping
    Return type: | `bool`

  `preservePreconditioner`(preserve=True)

    Notifies the PDE that the preconditioner should not be reset when making changes to the operator.

    Building the preconditioner data can be quite expensive (e.g. for multigrid methods) so if it is known that changes to the operator are going to be minor calling this method can speed up successive PDE solves.

    Note: | Not all operator types support this.
    Parameters: | preserve (`bool`) – if True, preconditioner will be preserved, otherwise it will be reset when making changes to the operator, which is the default behaviour.

  `reduceEquationOrder`()

    Returns the status of order reduction for the equation.

    Returns: | True if reduced interpolation order is used for the representation of the equation, False otherwise
    Return type: | `bool`

  `reduceSolutionOrder`()

    Returns the status of order reduction for the solution.

    Returns: | True if reduced interpolation order is used for the representation of the solution, False otherwise
    Return type: | `bool`

  `resetAllCoefficients`()

    Resets all coefficients to their default values.

  `resetOperator`()

    Makes sure that the operator is instantiated and returns it initialized with zeros.

  `resetRightHandSide`()

    Sets the right hand side to zero.

  `resetRightHandSideCoefficients`()

    Resets all coefficients defining the right hand side

  `resetSolution`()

    Sets the solution to zero.

  `setDebug`(flag)

    Switches debug output on if `flag` is True otherwise it is switched off.

    Parameters: | flag (`bool`) – desired debug status

  `setDebugOff`()

    Switches debug output off.

  `setDebugOn`()

    Switches debug output on.

  `setHermitian`(flag=False)

    Sets the Hermitian flag to `flag`.

    Parameters: | flag (`bool`) – If True, the Hermitian flag is set otherwise reset.
    Note: | The method overwrites the Hermitian flag set by the solver options

  `setHermitianOff`()

    Clears the Hermitian flag. :note: The method overwrites the Hermitian flag set by the solver options

  `setHermitianOn`()

    Sets the Hermitian flag. :note: The method overwrites the Hermitian flag set by the solver options

  `setReducedOrderForEquationOff`()

    Switches reduced order off for equation representation.

    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setReducedOrderForEquationOn`()

    Switches reduced order on for equation representation.

    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setReducedOrderForEquationTo`(flag=False)

    Sets order reduction state for equation representation according to flag.

    Parameters: | flag (`bool`) – if flag is True, the order reduction is switched on for equation representation, otherwise or if flag is not present order reduction is switched off
    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setReducedOrderForSolutionOff`()

    Switches reduced order off for solution representation

    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set.

  `setReducedOrderForSolutionOn`()

    Switches reduced order on for solution representation.

    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setReducedOrderForSolutionTo`(flag=False)

    Sets order reduction state for solution representation according to flag.

    Parameters: | flag (`bool`) – if flag is True, the order reduction is switched on for solution representation, otherwise or if flag is not present order reduction is switched off
    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setReducedOrderOff`()

    Switches reduced order off for solution and equation representation

    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setReducedOrderOn`()

    Switches reduced order on for solution and equation representation.

    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setReducedOrderTo`(flag=False)

    Sets order reduction state for both solution and equation representation according to flag.

    Parameters: | flag (`bool`) – if True, the order reduction is switched on for both solution and equation representation, otherwise or if flag is not present order reduction is switched off
    Raises: | RuntimeError – if order reduction is altered after a coefficient has been set

  `setSolution`(u, validate=True)

    Sets the solution assuming that makes the system valid with the tolrance defined by the solver options

  `setSolverOptions`(options=None)

    Sets the solver options.

    Parameters: | options (`SolverOptions` or `None`) – the new solver options. If equal `None`, the solver options are set to the default.
    Note: | The symmetry flag of options is overwritten by the symmetry flag of the `LinearProblem`.

  `setSymmetry`(flag=False)

    Sets the symmetry flag to `flag`.

    Parameters: | flag (`bool`) – If True, the symmetry flag is set otherwise reset.
    Note: | The method overwrites the symmetry flag set by the solver options

  `setSymmetryOff`()

    Clears the symmetry flag. :note: The method overwrites the symmetry flag set by the solver options

  `setSymmetryOn`()

    Sets the symmetry flag. :note: The method overwrites the symmetry flag set by the solver options

  `setSystemStatus`(status=None)

    Sets the system status to `status` if `status` is not present the current status of the domain is used.

  `setValue`(**coefficients)

    Sets new values to coefficients.

    Parameters:

    - coefficients – new values assigned to coefficients
    - A (any type that can be cast to a `Data` object on `Function`) – value for coefficient `A`
    - A_reduced (any type that can be cast to a `Data` object on `ReducedFunction`) – value for coefficient `A_reduced`
    - B (any type that can be cast to a `Data` object on `Function`) – value for coefficient `B`
    - B_reduced (any type that can be cast to a `Data` object on `ReducedFunction`) – value for coefficient `B_reduced`
    - C (any type that can be cast to a `Data` object on `Function`) – value for coefficient `C`
    - C_reduced (any type that can be cast to a `Data` object on `ReducedFunction`) – value for coefficient `C_reduced`
    - D (any type that can be cast to a `Data` object on `Function`) – value for coefficient `D`
    - D_reduced (any type that can be cast to a `Data` object on `ReducedFunction`) – value for coefficient `D_reduced`
    - X (any type that can be cast to a `Data` object on `Function`) – value for coefficient `X`
    - X_reduced (any type that can be cast to a `Data` object on `ReducedFunction`) – value for coefficient `X_reduced`
    - Y (any type that can be cast to a `Data` object on `Function`) – value for coefficient `Y`
    - Y_reduced (any type that can be cast to a `Data` object on `ReducedFunction`) – value for coefficient `Y_reduced`
    - d (any type that can be cast to a `Data` object on `FunctionOnBoundary`) – value for coefficient `d`
    - d_reduced (any type that can be cast to a `Data` object on `ReducedFunctionOnBoundary`) – value for coefficient `d_reduced`
    - y (any type that can be cast to a `Data` object on `FunctionOnBoundary`) – value for coefficient `y`
    - d_contact (any type that can be cast to a `Data` object on `FunctionOnContactOne` or `FunctionOnContactZero`) – value for coefficient `d_contact`
    - d_contact_reduced (any type that can be cast to a `Data` object on `ReducedFunctionOnContactOne` or `ReducedFunctionOnContactZero`) – value for coefficient `d_contact_reduced`
    - y_contact (any type that can be cast to a `Data` object on `FunctionOnContactOne` or `FunctionOnContactZero`) – value for coefficient `y_contact`
    - y_contact_reduced (any type that can be cast to a `Data` object on `ReducedFunctionOnContactOne` or `ReducedFunctionOnContactZero`) – value for coefficient `y_contact_reduced`
    - d_dirac (any type that can be cast to a `Data` object on `DiracDeltaFunctions`) – value for coefficient `d_dirac`
    - y_dirac (any type that can be cast to a `Data` object on `DiracDeltaFunctions`) – value for coefficient `y_dirac`
    - r (any type that can be cast to a `Data` object on `Solution` or `ReducedSolution` depending on whether reduced order is used for the solution) – values prescribed to the solution at the locations of constraints
    - q (any type that can be cast to a `Data` object on `Solution` or `ReducedSolution` depending on whether reduced order is used for the representation of the equation) – mask for location of constraints

    Raises: | IllegalCoefficient – if an unknown coefficient keyword is used

  `shouldPreservePreconditioner`()

    Returns true if the preconditioner / factorisation should be kept even when resetting the operator.

    Return type: | `bool`

  `trace`(text)

    Prints the text message if debug mode is switched on.

    Parameters: | text (`string`) – message to be printed

  `validOperator`()

    Marks the operator as valid.

  `validRightHandSide`()

    Marks the right hand side as valid.

  `validSolution`()

    Marks the solution as valid.

class `esys.downunder.forwardmodels.dcresistivity.``Locator`(where, x=array([0., 0., 0.]))

  Bases: `object`

  Locator provides access to the values of data objects at a given spatial coordinate x.

  In fact, a Locator object finds the sample in the set of samples of a given function space or domain which is closest to the given point x.

  `__init__`(where, x=array([0., 0., 0.]))

    Initializes a Locator to access values in Data objects on the Doamin or FunctionSpace for the sample point which is closest to the given point x.

    Parameters:

    - where (`escript.FunctionSpace`) – function space
    - x (`numpy.ndarray` or `list` of `numpy.ndarray`) – location(s) of the Locator

  `getFunctionSpace`()

    Returns the function space of the Locator.

  `getId`(item=None)

    Returns the identifier of the location.

  `getValue`(data)

    Returns the value of `data` at the Locator if `data` is a `Data` object otherwise the object is returned.

  `getX`()

    Returns the exact coordinates of the Locator.

  `setValue`(data, v)

    Sets the value of the `data` at the Locator.

## Functions

`esys.downunder.forwardmodels.dcresistivity.``Abs`(arg)

  Returns the absolute value of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`.) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``C_GeneralTensorProduct`((Data)arg0, (Data)arg1[, (int)axis_offset=0[, (int)transpose=0]]) → Data :

  Compute a tensor product of two Data objects.

  Return type: | `Data`

  Parameters:

  - arg0 –
  - arg1 –
  - axis_offset (`int`) –
  - transpose (int) – 0: transpose neither, 1: transpose arg0, 2: transpose arg1

`esys.downunder.forwardmodels.dcresistivity.``DiracDeltaFunctions`((Domain)domain) → FunctionSpace :

  Return type: | `FunctionSpace`

`esys.downunder.forwardmodels.dcresistivity.``L2`(arg)

  Returns the L2 norm of `arg` at `where`.

  Parameters: | arg (`escript.Data` or `Symbol`) – function of which the L2 norm is to be calculated
  Returns: | L2 norm of `arg`
  Return type: | `float` or `Symbol`
  Note: | L2(arg) is equivalent to `sqrt(integrate(inner(arg,arg)))`

`esys.downunder.forwardmodels.dcresistivity.``Lsup`(arg)

  Returns the Lsup-norm of argument `arg`. This is the maximum absolute value over all data points. This function is equivalent to `sup(abs(arg))`.

  Parameters: | arg (`float`, `int`, `escript.Data`, `numpy.ndarray`) – argument
  Returns: | maximum value of the absolute value of `arg` over all components and all data points
  Return type: | `float`
  Raises: | TypeError – if type of `arg` cannot be processed

`esys.downunder.forwardmodels.dcresistivity.``Scalar`([(object)value=0.0[, (FunctionSpace)what=<esys.escriptcore.escriptcpp.FunctionSpace object at 0x7f6046e4d7b0>[, (bool)expanded=False]]]) → Data :

  Construct a Data object containing scalar data-points.

  Parameters:

  - value (float) – scalar value for all points
  - what (`FunctionSpace`) – FunctionSpace for Data
  - expanded (`bool`) – If True, a value is stored for each point. If False, more efficient representations may be used

  Return type: | `Data`

`esys.downunder.forwardmodels.dcresistivity.``acos`(arg)

  Returns the inverse cosine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``acosh`(arg)

  Returns the inverse hyperbolic cosine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``antihermitian`(arg)

  Returns the anti-hermitian part of the square matrix `arg`. That is, (arg-adjoint(arg))/2.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `Symbol`) – input matrix. Must have rank 2 or 4 and be square.
  Returns: | anti-hermitian part of `arg`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``antisymmetric`(arg)

  Returns the anti-symmetric part of the square matrix `arg`. That is, (arg-transpose(arg))/2.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `Symbol`) – input matrix. Must have rank 2 or 4 and be square.
  Returns: | anti-symmetric part of `arg`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``asin`(arg)

  Returns the inverse sine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``asinh`(arg)

  Returns the inverse hyperbolic sine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``atan`(arg)

  Returns inverse tangent of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``atan2`(arg0, arg1)

  Returns inverse tangent of argument `arg0` over `arg1`

`esys.downunder.forwardmodels.dcresistivity.``atanh`(arg)

  Returns the inverse hyperbolic tangent of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``boundingBox`(domain)

  Returns the bounding box of a domain

  Parameters: | domain (`escript.Domain`) – a domain
  Returns: | bounding box of the domain
  Return type: | `list` of pairs of `float`

`esys.downunder.forwardmodels.dcresistivity.``boundingBoxEdgeLengths`(domain)

  Returns the edge lengths of the bounding box of a domain

  Parameters: | domain (`escript.Domain`) – a domain
  Return type: | `list` of `float`

`esys.downunder.forwardmodels.dcresistivity.``clip`(arg, minval=None, maxval=None)

  Cuts the values of `arg` between `minval` and `maxval`.

  Parameters:

  - arg (`numpy.ndarray`, `escript.Data`, `Symbol`, `int` or `float`) – argument
  - minval (`float` or `None`) – lower range. If None no lower range is applied
  - maxval (`float` or `None`) – upper range. If None no upper range is applied

  Returns: | an object that contains all values from `arg` between `minval` and `maxval`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol`, `int` or `float` depending on the input
  Raises: | ValueError – if `minval>maxval`

`esys.downunder.forwardmodels.dcresistivity.``commonDim`(*args)

  Identifies, if possible, the spatial dimension across a set of objects which may or may not have a spatial dimension.

  Parameters: | args – given objects
  Returns: | the spatial dimension of the objects with identifiable dimension (see `pokeDim`). If none of the objects has a spatial dimension `None` is returned.
  Return type: | `int` or `None`
  Raises: | ValueError – if the objects with identifiable dimension don’t have the same spatial dimension.

`esys.downunder.forwardmodels.dcresistivity.``commonShape`(arg0, arg1)

  Returns a shape to which `arg0` can be extended from the right and `arg1` can be extended from the left.

  Parameters:

  - arg0 – an object with a shape (see `getShape`)
  - arg1 – an object with a shape (see `getShape`)

  Returns: | the shape of `arg0` or `arg1` such that the left part equals the shape of `arg0` and the right end equals the shape of `arg1`
  Return type: | `tuple` of `int`
  Raises: | ValueError – if no shape can be found

`esys.downunder.forwardmodels.dcresistivity.``condEval`(f, tval, fval)

  Wrapper to allow non-data objects to be used.

`esys.downunder.forwardmodels.dcresistivity.``convertToNumpy`(data)

  Writes `Data` objects to a numpy array.

  The keyword args are Data objects to save. If a scalar `Data` object is passed with the name `mask`, then only samples which correspond to positive values in `mask` will be output.

  Example usage:

  s=Scalar(..) v=Vector(..) t=Tensor(..) f=float() array = getNumpy(a=s, b=v, c=t, d=f)

`esys.downunder.forwardmodels.dcresistivity.``cos`(arg)

  Returns cosine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``cosh`(arg)

  Returns the hyperbolic cosine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``delay`(arg)

  Returns a lazy version of arg

`esys.downunder.forwardmodels.dcresistivity.``deviatoric`(arg)

  Returns the deviatoric version of `arg`.

`esys.downunder.forwardmodels.dcresistivity.``diameter`(domain)

  Returns the diameter of a domain.

  Parameters: | domain (`escript.Domain`) – a domain
  Return type: | `float`

`esys.downunder.forwardmodels.dcresistivity.``div`(arg, where=None)

  Returns the divergence of `arg` at `where`.

  Parameters:

  - arg (`escript.Data` or `Symbol`) – function of which the divergence is to be calculated. Its shape has to be (d,) where d is the spatial dimension.
  - where (`None` or `escript.FunctionSpace`) – `FunctionSpace` in which the divergence will be calculated. If not present or `None` an appropriate default is used.

  Returns: | divergence of `arg`
  Return type: | `escript.Data` or `Symbol`

`esys.downunder.forwardmodels.dcresistivity.``eigenvalues`(arg)

  Returns the eigenvalues of the square matrix `arg`.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `Symbol`) – square matrix. Must have rank 2 and the first and second dimension must be equal. It must also be symmetric, ie. `transpose(arg)==arg` (this is not checked).
  Returns: | the eigenvalues in increasing order
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input
  Note: | for `escript.Data` and `Symbol` objects the dimension is restricted to 3.

`esys.downunder.forwardmodels.dcresistivity.``eigenvalues_and_eigenvectors`(arg)

  Returns the eigenvalues and eigenvectors of the square matrix `arg`.

  Parameters: | arg (`escript.Data`) – square matrix. Must have rank 2 and the first and second dimension must be equal. It must also be symmetric, ie. `transpose(arg)==arg` (this is not checked).
  Returns: | the eigenvalues and eigenvectors. The eigenvalues are ordered by increasing value. The eigenvectors are orthogonal and normalized. If V are the eigenvectors then V[:,i] is the eigenvector corresponding to the i-th eigenvalue.
  Return type: | `tuple` of `escript.Data`
  Note: | The dimension is restricted to 3.

`esys.downunder.forwardmodels.dcresistivity.``erf`(arg)

  Returns the error function erf of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`.) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``escript_generalTensorProduct`(arg0, arg1, axis_offset, transpose=0)

  arg0 and arg1 are both Data objects but not necessarily on the same function space. They could be identical!!!

`esys.downunder.forwardmodels.dcresistivity.``escript_generalTensorTransposedProduct`(arg0, arg1, axis_offset)

  arg0 and arg1 are both Data objects but not necessarily on the same function space. They could be identical!!!

`esys.downunder.forwardmodels.dcresistivity.``escript_generalTransposedTensorProduct`(arg0, arg1, axis_offset)

  arg0 and arg1 are both Data objects but not necessarily on the same function space. They could be identical!!!

`esys.downunder.forwardmodels.dcresistivity.``escript_inverse`(arg)

  arg is a Data object!

`esys.downunder.forwardmodels.dcresistivity.``exp`(arg)

  Returns e to the power of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`.) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of arg
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``generalTensorProduct`(arg0, arg1, axis_offset=0)

  Generalized tensor product.

  `out[s,t]=Sigma_r arg0[s,r]*arg1[r,t]`

  where
    - s runs through `arg0.Shape[:arg0.ndim-axis_offset]`
    - r runs through `arg1.Shape[:axis_offset]`
    - t runs through `arg1.Shape[axis_offset:]`

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – first argument
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – second argument

  Returns: | the general tensor product of `arg0` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``generalTensorTransposedProduct`(arg0, arg1, axis_offset=0)

  Generalized tensor product of `arg0` and transpose of `arg1`.

  `out[s,t]=Sigma_r arg0[s,r]*arg1[t,r]`

  where
    - s runs through `arg0.Shape[:arg0.ndim-axis_offset]`
    - r runs through `arg0.Shape[arg1.ndim-axis_offset:]`
    - t runs through `arg1.Shape[arg1.ndim-axis_offset:]`

  The function call `generalTensorTransposedProduct(arg0,arg1,axis_offset)` is equivalent to `generalTensorProduct(arg0,transpose(arg1,arg1.ndim-axis_offset),axis_offset)`.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – first argument
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – second argument

  Returns: | the general tensor product of `arg0` and `transpose(arg1)` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``generalTransposedTensorProduct`(arg0, arg1, axis_offset=0)

  Generalized tensor product of transposed of `arg0` and `arg1`.

  `out[s,t]=Sigma_r arg0[r,s]*arg1[r,t]`

  where
    - s runs through `arg0.Shape[axis_offset:]`
    - r runs through `arg0.Shape[:axis_offset]`
    - t runs through `arg1.Shape[axis_offset:]`

  The function call `generalTransposedTensorProduct(arg0,arg1,axis_offset)` is equivalent to `generalTensorProduct(transpose(arg0,arg0.ndim-axis_offset),arg1,axis_offset)`.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – first argument
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – second argument

  Returns: | the general tensor product of `transpose(arg0)` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``getClosestValue`(arg, origin=0)

  Returns the value in `arg` which is closest to origin.

  Parameters:

  - arg (`escript.Data`) – function
  - origin (`float` or `escript.Data`) – reference value

  Returns: | value in `arg` closest to origin
  Return type: | `numpy.ndarray`

`esys.downunder.forwardmodels.dcresistivity.``getEpsilon`()

`esys.downunder.forwardmodels.dcresistivity.``getMPIRankWorld`() → int :

  Return the rank of this process in the MPI World.

`esys.downunder.forwardmodels.dcresistivity.``getMPIWorldMax`((int)arg1) → int :

  Each MPI process calls this function with a value for arg1. The maximum value is computed and returned.

  Return type: | int

`esys.downunder.forwardmodels.dcresistivity.``getMaxFloat`()

`esys.downunder.forwardmodels.dcresistivity.``getNumpy`(**data)

  Writes `Data` objects to a numpy array.

  The keyword args are Data objects to save. If a scalar `Data` object is passed with the name `mask`, then only samples which correspond to positive values in `mask` will be output.

  Example usage:

  s=Scalar(..) v=Vector(..) t=Tensor(..) f=float() array = getNumpy(a=s, b=v, c=t, d=f)

`esys.downunder.forwardmodels.dcresistivity.``getRank`(arg)

  Identifies the rank of the argument.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `float`, `int`, `Symbol`) – an object whose rank is to be returned
  Returns: | the rank of the argument
  Return type: | `int`
  Raises: | TypeError – if type of `arg` cannot be processed

`esys.downunder.forwardmodels.dcresistivity.``getShape`(arg)

  Identifies the shape of the argument.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `float`, `int`, `Symbol`) – an object whose shape is to be returned
  Returns: | the shape of the argument
  Return type: | `tuple` of `int`
  Raises: | TypeError – if type of `arg` cannot be processed

`esys.downunder.forwardmodels.dcresistivity.``getTagNames`(domain)

  Returns a list of tag names used by the domain.

  Parameters: | domain (`escript.Domain`) – a domain object
  Returns: | a list of tag names used by the domain
  Return type: | `list` of `str`

`esys.downunder.forwardmodels.dcresistivity.``getVersion`() → int :

  This method will only report accurate version numbers for clean checkouts.

`esys.downunder.forwardmodels.dcresistivity.``gmshGeo2Msh`(geoFile, mshFile, numDim, order=1, verbosity=0)

  Runs gmsh to mesh input `geoFile`. Returns 0 on success.

`esys.downunder.forwardmodels.dcresistivity.``grad`(arg, where=None)

  Returns the spatial gradient of `arg` at `where`.

  If `g` is the returned object, then

  > - if `arg` is rank 0 `g[s]` is the derivative of `arg` with respect to the `s`-th spatial dimension
  > - if `arg` is rank 1 `g[i,s]` is the derivative of `arg[i]` with respect to the `s`-th spatial dimension
  > - if `arg` is rank 2 `g[i,j,s]` is the derivative of `arg[i,j]` with respect to the `s`-th spatial dimension
  > - if `arg` is rank 3 `g[i,j,k,s]` is the derivative of `arg[i,j,k]` with respect to the `s`-th spatial dimension.

  Parameters:

  - arg (`escript.Data` or `Symbol`) – function of which the gradient is to be calculated. Its rank has to be less than 3.
  - where (`None` or `escript.FunctionSpace`) – FunctionSpace in which the gradient is calculated. If not present or `None` an appropriate default is used.

  Returns: | gradient of `arg`
  Return type: | `escript.Data` or `Symbol`

`esys.downunder.forwardmodels.dcresistivity.``grad_n`(arg, n, where=None)

`esys.downunder.forwardmodels.dcresistivity.``hermitian`(arg)

  Returns the hermitian part of the square matrix `arg`. That is, (arg+adjoint(arg))/2.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `Symbol`) – input matrix. Must have rank 2 or 4 and be square.
  Returns: | hermitian part of `arg`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``identity`(shape=())

  Returns the `shape` x `shape` identity tensor.

  Parameters: | shape (`tuple` of `int`) – input shape for the identity tensor
  Returns: | array whose shape is shape x shape where u[i,k]=1 for i=k and u[i,k]=0 otherwise for len(shape)=1. If len(shape)=2: u[i,j,k,l]=1 for i=k and j=l and u[i,j,k,l]=0 otherwise.
  Return type: | `numpy.ndarray` of rank 1, rank 2 or rank 4
  Raises: | ValueError – if len(shape)>2

`esys.downunder.forwardmodels.dcresistivity.``identityTensor`(d=3)

  Returns the `d` x `d` identity matrix.

  Parameters: | d (`int`, `escript.Domain` or `escript.FunctionSpace`) – dimension or an object that has the `getDim` method defining the dimension
  Returns: | the object u of rank 2 with u[i,j]=1 for i=j and u[i,j]=0 otherwise
  Return type: | `numpy.ndarray` or `escript.Data` of rank 2

`esys.downunder.forwardmodels.dcresistivity.``identityTensor4`(d=3)

  Returns the `d` x `d` x `d` x `d` identity tensor.

  Parameters: | d (`int` or any object with a `getDim` method) – dimension or an object that has the `getDim` method defining the dimension
  Returns: | the object u of rank 4 with u[i,j,k,l]=1 for i=k and j=l and u[i,j,k,l]=0 otherwise
  Return type: | `numpy.ndarray` or `escript.Data` of rank 4

`esys.downunder.forwardmodels.dcresistivity.``inf`(arg)

  Returns the minimum value over all data points.

  Parameters: | arg (`float`, `int`, `escript.Data`, `numpy.ndarray`) – argument
  Returns: | minimum value of `arg` over all components and all data points
  Return type: | `float`
  Raises: | TypeError – if type of `arg` cannot be processed

`esys.downunder.forwardmodels.dcresistivity.``inner`(arg0, arg1)

  Inner product of the two arguments. The inner product is defined as:

  `out=Sigma_s arg0[s]*arg1[s]`

  where s runs through `arg0.Shape`.

  `arg0` and `arg1` must have the same shape.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – first argument
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – second argument

  Returns: | the inner product of `arg0` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol`, `float` depending on the input
  Raises: | ValueError – if the shapes of the arguments are not identical

`esys.downunder.forwardmodels.dcresistivity.``insertTagNames`(domain, **kwargs)

  Inserts tag names into the domain.

  Parameters:

  - domain (`escript.Domain`) – a domain object
  - <tag_name> (`int`) – tag key assigned to <tag_name>

`esys.downunder.forwardmodels.dcresistivity.``insertTaggedValues`(target, **kwargs)

  Inserts tagged values into the target using tag names.

  Parameters:

  - target (`escript.Data`) – data to be filled by tagged values
  - <tag_name> (`float` or `numpy.ndarray`) – value to be used for <tag_name>

  Returns: | `target`
  Return type: | `escript.Data`

`esys.downunder.forwardmodels.dcresistivity.``integrate`(arg, where=None)

  Returns the integral of the function `arg` over its domain. If `where` is present `arg` is interpolated to `where` before integration.

  Parameters:

  - arg (`escript.Data` or `Symbol`) – the function which is integrated
  - where (`None` or `escript.FunctionSpace`) – FunctionSpace in which the integral is calculated. If not present or `None` an appropriate default is used.

  Returns: | integral of `arg`
  Return type: | `float`, `numpy.ndarray` or `Symbol`

`esys.downunder.forwardmodels.dcresistivity.``interpolate`(arg, where)

  Interpolates the function into the `FunctionSpace` `where`. If the argument `arg` has the requested function space `where` no interpolation is performed and `arg` is returned.

  Parameters:

  - arg (`escript.Data` or `Symbol`) – interpolant
  - where (`escript.FunctionSpace`) – `FunctionSpace` to be interpolated to

  Returns: | interpolated argument
  Return type: | `escript.Data` or `Symbol`

`esys.downunder.forwardmodels.dcresistivity.``interpolateTable`(tab, dat, start, step, undef=1e+50, check_boundaries=False)

`esys.downunder.forwardmodels.dcresistivity.``inverse`(arg)

  Returns the inverse of the square matrix `arg`.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `Symbol`) – square matrix. Must have rank 2 and the first and second dimension must be equal.
  Returns: | inverse of the argument. `matrix_mult(inverse(arg),arg)` will be almost equal to `kronecker(arg.getShape()[0])`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input
  Note: | for `escript.Data` objects the dimension is restricted to 3.

`esys.downunder.forwardmodels.dcresistivity.``jump`(arg, domain=None)

  Returns the jump of `arg` across the continuity of the domain.

  Parameters:

  - arg (`escript.Data` or `Symbol`) – argument
  - domain (`None` or `escript.Domain`) – the domain where the discontinuity is located. If domain is not present or equal to `None` the domain of `arg` is used.

  Returns: | jump of `arg`
  Return type: | `escript.Data` or `Symbol`

`esys.downunder.forwardmodels.dcresistivity.``kronecker`(d=3)

  Returns the kronecker delta-symbol.

  Parameters: | d (`int`, `escript.Domain` or `escript.FunctionSpace`) – dimension or an object that has the `getDim` method defining the dimension
  Returns: | the object u of rank 2 with u[i,j]=1 for i=j and u[i,j]=0 otherwise
  Return type: | `numpy.ndarray` or `escript.Data` of rank 2

`esys.downunder.forwardmodels.dcresistivity.``length`(arg)

  Returns the length (Euclidean norm) of argument `arg` at each data point.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol` depending on the type of `arg`

`esys.downunder.forwardmodels.dcresistivity.``listEscriptParams`() → list :

  Returns: | A list of tuples (p,v,d) where p is the name of a parameter for escript, v is its current value, and d is a description.

`esys.downunder.forwardmodels.dcresistivity.``log`(arg)

  Returns the natural logarithm of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`.) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``log10`(arg)

  Returns base-10 logarithm of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``longestEdge`(domain)

  Returns the length of the longest edge of the domain

  Parameters: | domain (`escript.Domain`) – a domain
  Returns: | longest edge of the domain parallel to the Cartesian axis
  Return type: | `float`

`esys.downunder.forwardmodels.dcresistivity.``makeTagMap`(fs)

  Produce an expanded Data over the function space where the value is the tag associated with the sample

`esys.downunder.forwardmodels.dcresistivity.``makeTransformation`(domain, coordinates=None)

  returns a `SpatialCoordinateTransformation` for the given domain

  Parameters:

  - domain (`esys.escript.AbstractDomain`) – domain in the domain of the coordinate transformation
  - coordinates (`ReferenceSystem` or `SpatialCoordinateTransformation`) – the reference system or spatial coordinate system.

  Returns: | the spatial coordinate system for the given domain of the specified reference system `coordinates`. If `coordinates` is already spatial coordinate system based on the riven domain `coordinates` is returned. Otherwise an appropriate spatial coordinate system is created.
  Return type: | `SpatialCoordinateTransformation`

`esys.downunder.forwardmodels.dcresistivity.``matchShape`(arg0, arg1)

  Returns a representation of `arg0` and `arg1` which have the same shape.

  Parameters:

  - arg0 (`numpy.ndarray`,`escript.Data`,``float``, `int`, `Symbol`) – first argument
  - arg1 (`numpy.ndarray`,`escript.Data`,``float``, `int`, `Symbol`) – second argument

  Returns: | `arg0` and `arg1` where copies are returned when the shape has to be changed
  Return type: | `tuple`

`esys.downunder.forwardmodels.dcresistivity.``matchType`(arg0=0.0, arg1=0.0)

  Converts `arg0` and `arg1` both to the same type `numpy.ndarray` or `escript.Data`

  Parameters:

  - arg0 (`numpy.ndarray`,`escript.Data`,``float``, `int`, `Symbol`) – first argument
  - arg1 (`numpy.ndarray`,`escript.Data`,``float``, `int`, `Symbol`) – second argument

  Returns: | a tuple representing `arg0` and `arg1` with the same type or with at least one of them being a `Symbol`
  Return type: | `tuple` of two `numpy.ndarray` or two `escript.Data`
  Raises: | TypeError – if type of `arg0` or `arg1` cannot be processed

`esys.downunder.forwardmodels.dcresistivity.``matrix_mult`(arg0, arg1)

  matrix-matrix or matrix-vector product of the two arguments.

  `out[s0]=Sigma_{r0} arg0[s0,r0]*arg1[r0]`

  or

  `out[s0,s1]=Sigma_{r0} arg0[s0,r0]*arg1[r0,s1]`

  The second dimension of `arg0` and the first dimension of `arg1` must match.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`) – first argument of rank 2
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`) – second argument of at least rank 1

  Returns: | the matrix-matrix or matrix-vector product of `arg0` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input
  Raises: | ValueError – if the shapes of the arguments are not appropriate

`esys.downunder.forwardmodels.dcresistivity.``matrix_transposed_mult`(arg0, arg1)

  matrix-transposed(matrix) product of the two arguments.

  `out[s0,s1]=Sigma_{r0} arg0[s0,r0]*arg1[s1,r0]`

  The function call `matrix_transposed_mult(arg0,arg1)` is equivalent to `matrix_mult(arg0,transpose(arg1))`.

  The last dimensions of `arg0` and `arg1` must match.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`) – first argument of rank 2
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`) – second argument of rank 1 or 2

  Returns: | the product of `arg0` and the transposed of `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input
  Raises: | ValueError – if the shapes of the arguments are not appropriate

`esys.downunder.forwardmodels.dcresistivity.``matrixmult`(arg0, arg1)

  See `matrix_mult`.

`esys.downunder.forwardmodels.dcresistivity.``maximum`(*args)

  The maximum over arguments `args`.

  Parameters: | args (`numpy.ndarray`, `escript.Data`, `Symbol`, `int` or `float`) – arguments
  Returns: | an object which in each entry gives the maximum of the corresponding values in `args`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol`, `int` or `float` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``maxval`(arg)

  Returns the maximum value over all components of `arg` at each data point.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``meanValue`(arg)

  return the mean value of the argument over its domain

  Parameters: | arg (`escript.Data`) – function
  Returns: | mean value
  Return type: | `float` or `numpy.ndarray`

`esys.downunder.forwardmodels.dcresistivity.``minimum`(*args)

  The minimum over arguments `args`.

  Parameters: | args (`numpy.ndarray`, `escript.Data`, `Symbol`, `int` or `float`) – arguments
  Returns: | an object which gives in each entry the minimum of the corresponding values in `args`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol`, `int` or `float` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``minval`(arg)

  Returns the minimum value over all components of `arg` at each data point.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``mkDir`(*pathname)

  creates a directory of name `pathname` if the directory does not exist.

  Parameters: | pathname (`str` or `sequence of strings`) – valid path name
  Note: | The method is MPI safe.

`esys.downunder.forwardmodels.dcresistivity.``mult`(arg0, arg1)

  Product of `arg0` and `arg1`.

  Parameters:

  - arg0 (`Symbol`, `float`, `int`, `escript.Data` or `numpy.ndarray`) – first term
  - arg1 (`Symbol`, `float`, `int`, `escript.Data` or `numpy.ndarray`) – second term

  Returns: | the product of `arg0` and `arg1`
  Return type: | `Symbol`, `float`, `int`, `escript.Data` or `numpy.ndarray`
  Note: | The shape of both arguments is matched according to the rules used in `matchShape`.

`esys.downunder.forwardmodels.dcresistivity.``negative`(arg)

  returns the negative part of arg

`esys.downunder.forwardmodels.dcresistivity.``nonsymmetric`(arg)

  Deprecated alias for antisymmetric

`esys.downunder.forwardmodels.dcresistivity.``normalize`(arg, zerolength=0)

  Returns the normalized version of `arg` (=``arg/length(arg)``).

  Parameters:

  - arg (`escript.Data` or `Symbol`) – function
  - zerolength (`float`) – relative tolerance for arg == 0

  Returns: | normalized `arg` where `arg` is non-zero, and zero elsewhere
  Return type: | `escript.Data` or `Symbol`

`esys.downunder.forwardmodels.dcresistivity.``outer`(arg0, arg1)

  The outer product of the two arguments. The outer product is defined as:

  `out[t,s]=arg0[t]*arg1[s]`

  where
    - s runs through `arg0.Shape`
    - t runs through `arg1.Shape`

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – first argument
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`, `float`, `int`) – second argument

  Returns: | the outer product of `arg0` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``phase`(arg)

  return the “phase”/”arg”/”angle” of a number

`esys.downunder.forwardmodels.dcresistivity.``pokeDim`(arg)

  Identifies the spatial dimension of the argument.

  Parameters: | arg (any) – an object whose spatial dimension is to be returned
  Returns: | the spatial dimension of the argument, if available, or `None`
  Return type: | `int` or `None`

`esys.downunder.forwardmodels.dcresistivity.``polarToCart`(r, phase)

  conversion from cartesian to polar coordinates

  Parameters:

  - r (any float type object) – length
  - phase (any float type object) – the phase angle in rad

  Returns: | cartesian representation as complex number
  Return type: | appropriate complex

`esys.downunder.forwardmodels.dcresistivity.``positive`(arg)

  returns the positive part of arg

`esys.downunder.forwardmodels.dcresistivity.``printParallelThreadCounts`() → None

`esys.downunder.forwardmodels.dcresistivity.``reorderComponents`(arg, index)

  Resorts the components of `arg` according to index.

`esys.downunder.forwardmodels.dcresistivity.``resolve`(arg)

  Returns the value of arg resolved.

`esys.downunder.forwardmodels.dcresistivity.``safeDiv`(arg0, arg1, rtol=None)

  returns arg0/arg1 but return 0 where arg1 is (almost) zero

`esys.downunder.forwardmodels.dcresistivity.``saveDataCSV`(filename, append=False, refid=False, sep=', ', csep='_', **data)

  Writes `Data` objects to a CSV file. These objects must have compatible FunctionSpaces, i.e. it must be possible to interpolate all data to one `FunctionSpace`. Note, that with more than one MPI rank this function will fail for some function spaces on some domains.

  Parameters:

  - filename (`string`) – file to save data to.
  - append (`bool`) – If `True`, then open file at end rather than beginning
  - refid (`bool`) – If `True`, then a list of reference ids will be printed in the first column
  - sep (`string`) – separator between fields
  - csep – separator for components of rank 2 and above (e.g. ‘_’ -> c0_1)

  The keyword args are Data objects to save. If a scalar `Data` object is passed with the name `mask`, then only samples which correspond to positive values in `mask` will be output. Example:

      s=Scalar(..)
      v=Vector(..)
      t=Tensor(..)
      f=float()
      saveDataCSV("f.csv", a=s, b=v, c=t, d=f)

  Will result in a file

  a, b0, b1, c0_0, c0_1, .., c1_1, d 1.0, 1.5, 2.7, 3.1, 3.4, .., 0.89, 0.0 0.9, 8.7, 1.9, 3.4, 7.8, .., 1.21, 0.0

  The first line is a header, the remaining lines give the values.

`esys.downunder.forwardmodels.dcresistivity.``saveESD`(datasetName, dataDir='.', domain=None, timeStep=0, deltaT=1, dynamicMesh=0, timeStepFormat='%04d', **data)

  Saves `Data` objects to files and creates an `escript dataset` (ESD) file for convenient processing/visualisation.

  Single timestep example:

      tmp = Scalar(..)
      v = Vector(..)
      saveESD("solution", "data", temperature=tmp, velocity=v)

  Time series example:

      while t < t_end:
          tmp = Scalar(..)
          v = Vector(..)
          # save every 10 timesteps
          if t % 10 == 0:
              saveESD("solution", "data", timeStep=t, deltaT=10, temperature=tmp, velocity=v)
          t = t + 1

  tmp, v and the domain are saved in native format in the “data” directory and the file “solution.esd” is created that refers to tmp by the name “temperature” and to v by the name “velocity”.

  Parameters:

  - datasetName (`str`) – name of the dataset, used to name the ESD file
  - dataDir (`str`) – optional directory where the data files should be saved
  - domain (`escript.Domain`) – domain of the `Data` object(s). If not specified, the domain of the given `Data` objects is used.
  - timeStep (`int`) – current timestep or sequence number - first one must be 0
  - deltaT (`int`) – timestep or sequence increment, see example above
  - dynamicMesh (`int`) – by default the mesh is assumed to be static and thus only saved once at timestep 0 to save disk space. Setting this to 1 changes the behaviour and the mesh is saved at each timestep.
  - timeStepFormat (`str`) – timestep format string (defaults to “%04d”)
  - <name> (`Data` object) – writes the assigned value to the file using <name> as identifier

  Note: | The ESD concept is experimental and the file format likely to change so use this function with caution.
  Note: | The data objects have to be defined on the same domain (but not necessarily on the same `FunctionSpace`).
  Note: | When saving a time series the first timestep must be 0 and it is assumed that data from all timesteps share the domain. The dataset file is updated in each iteration.

`esys.downunder.forwardmodels.dcresistivity.``showEscriptParams`()

  Displays the parameters escript recognises with an explanation and their current value.

`esys.downunder.forwardmodels.dcresistivity.``sign`(arg)

  Returns the sign of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``sin`(arg)

  Returns sine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`.) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``sinh`(arg)

  Returns the hyperbolic sine of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``sqrt`(arg)

  Returns the square root of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``sup`(arg)

  Returns the maximum value over all data points.

  Parameters: | arg (`float`, `int`, `escript.Data`, `numpy.ndarray`) – argument
  Returns: | maximum value of `arg` over all components and all data points
  Return type: | `float`
  Raises: | TypeError – if type of `arg` cannot be processed

`esys.downunder.forwardmodels.dcresistivity.``swap_axes`(arg, axis0=0, axis1=1)

  Returns the swap of `arg` by swapping the components `axis0` and `axis1`.

  Parameters:

  - arg (`escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  - axis0 (`int`) – first axis. `axis0` must be non-negative and less than the rank of `arg`.
  - axis1 (`int`) – second axis. `axis1` must be non-negative and less than the rank of `arg`.

  Returns: | `arg` with swapped components
  Return type: | `escript.Data`, `Symbol` or `numpy.ndarray` depending on the type of `arg`

`esys.downunder.forwardmodels.dcresistivity.``symmetric`(arg)

  Returns the symmetric part of the square matrix `arg`. That is, (arg+transpose(arg))/2.

  Parameters: | arg (`numpy.ndarray`, `escript.Data`, `Symbol`) – input matrix. Must have rank 2 or 4 and be square.
  Returns: | symmetric part of `arg`
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``tan`(arg)

  Returns tangent of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``tanh`(arg)

  Returns the hyperbolic tangent of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``tensor_mult`(arg0, arg1)

  The tensor product of the two arguments.

  For `arg0` of rank 2 this is

  `out[s0]=Sigma_{r0} arg0[s0,r0]*arg1[r0]`

  or

  `out[s0,s1]=Sigma_{r0} arg0[s0,r0]*arg1[r0,s1]`

  and for `arg0` of rank 4 this is

  `out[s0,s1,s2,s3]=Sigma_{r0,r1} arg0[s0,s1,r0,r1]*arg1[r0,r1,s2,s3]`

  or

  `out[s0,s1,s2]=Sigma_{r0,r1} arg0[s0,s1,r0,r1]*arg1[r0,r1,s2]`

  or

  `out[s0,s1]=Sigma_{r0,r1} arg0[s0,s1,r0,r1]*arg1[r0,r1]`

  In the first case the second dimension of `arg0` and the last dimension of `arg1` must match and in the second case the two last dimensions of `arg0` must match the two first dimensions of `arg1`.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`) – first argument of rank 2 or 4
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`) – second argument of shape greater than 1 or 2 depending on the rank of `arg0`

  Returns: | the tensor product of `arg0` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``tensor_transposed_mult`(arg0, arg1)

  The tensor product of the first and the transpose of the second argument.

  For `arg0` of rank 2 this is

  `out[s0,s1]=Sigma_{r0} arg0[s0,r0]*arg1[s1,r0]`

  and for `arg0` of rank 4 this is

  `out[s0,s1,s2,s3]=Sigma_{r0,r1} arg0[s0,s1,r0,r1]*arg1[s2,s3,r0,r1]`

  or

  `out[s0,s1,s2]=Sigma_{r0,r1} arg0[s0,s1,r0,r1]*arg1[s2,r0,r1]`

  In the first case the second dimension of `arg0` and `arg1` must match and in the second case the two last dimensions of `arg0` must match the two last dimensions of `arg1`.

  The function call `tensor_transpose_mult(arg0,arg1)` is equivalent to `tensor_mult(arg0,transpose(arg1))`.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`) – first argument of rank 2 or 4
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`) – second argument of shape greater of 1 or 2 depending on rank of `arg0`

  Returns: | the tensor product of the transposed of `arg0` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``tensormult`(arg0, arg1)

  See `tensor_mult`.

`esys.downunder.forwardmodels.dcresistivity.``testForZero`(arg)

  Tests if the argument is identical to zero.

  Parameters: | arg (typically `numpy.ndarray`, `escript.Data`, `float`, `int`) – the object to test for zero
  Returns: | True if the argument is identical to zero, False otherwise
  Return type: | `bool`

`esys.downunder.forwardmodels.dcresistivity.``trace`(arg, axis_offset=0)

  Returns the trace of `arg` which is the sum of `arg[k,k]` over k.

  Parameters:

  - arg (`escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  - axis_offset (`int`) – `axis_offset` to components to sum over. `axis_offset` must be non-negative and less than the rank of `arg` +1. The dimensions of component `axis_offset` and axis_offset+1 must be equal.

  Returns: | trace of arg. The rank of the returned object is rank of `arg` minus 2.
  Return type: | `escript.Data`, `Symbol` or `numpy.ndarray` depending on the type of `arg`

`esys.downunder.forwardmodels.dcresistivity.``transpose`(arg, axis_offset=None)

  Returns the transpose of `arg` by swapping the first `axis_offset` and the last `rank-axis_offset` components.

  Parameters:

  - arg (`escript.Data`, `Symbol`, `numpy.ndarray`, `float`, `int`) – argument
  - axis_offset (`int`) – the first `axis_offset` components are swapped with the rest. `axis_offset` must be non-negative and less or equal to the rank of `arg`. If `axis_offset` is not present `int(r/2)` where r is the rank of `arg` is used.

  Returns: | transpose of `arg`
  Return type: | `escript.Data`, `Symbol`, `numpy.ndarray`, `float`, `int` depending on the type of `arg`

`esys.downunder.forwardmodels.dcresistivity.``transposed_matrix_mult`(arg0, arg1)

  transposed(matrix)-matrix or transposed(matrix)-vector product of the two arguments.

  `out[s0]=Sigma_{r0} arg0[r0,s0]*arg1[r0]`

  or

  `out[s0,s1]=Sigma_{r0} arg0[r0,s0]*arg1[r0,s1]`

  The function call `transposed_matrix_mult(arg0,arg1)` is equivalent to `matrix_mult(transpose(arg0),arg1)`.

  The first dimension of `arg0` and `arg1` must match.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`) – first argument of rank 2
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`) – second argument of at least rank 1

  Returns: | the product of the transpose of `arg0` and `arg1` at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input
  Raises: | ValueError – if the shapes of the arguments are not appropriate

`esys.downunder.forwardmodels.dcresistivity.``transposed_tensor_mult`(arg0, arg1)

  The tensor product of the transpose of the first and the second argument.

  For `arg0` of rank 2 this is

  `out[s0]=Sigma_{r0} arg0[r0,s0]*arg1[r0]`

  or

  `out[s0,s1]=Sigma_{r0} arg0[r0,s0]*arg1[r0,s1]`

  and for `arg0` of rank 4 this is

  `out[s0,s1,s2,s3]=Sigma_{r0,r1} arg0[r0,r1,s0,s1]*arg1[r0,r1,s2,s3]`

  or

  `out[s0,s1,s2]=Sigma_{r0,r1} arg0[r0,r1,s0,s1]*arg1[r0,r1,s2]`

  or

  `out[s0,s1]=Sigma_{r0,r1} arg0[r0,r1,s0,s1]*arg1[r0,r1]`

  In the first case the first dimension of `arg0` and the first dimension of `arg1` must match and in the second case the two first dimensions of `arg0` must match the two first dimensions of `arg1`.

  The function call `transposed_tensor_mult(arg0,arg1)` is equivalent to `tensor_mult(transpose(arg0),arg1)`.

  Parameters:

  - arg0 (`numpy.ndarray`, `escript.Data`, `Symbol`) – first argument of rank 2 or 4
  - arg1 (`numpy.ndarray`, `escript.Data`, `Symbol`) – second argument of shape greater of 1 or 2 depending on the rank of `arg0`

  Returns: | the tensor product of transpose of arg0 and arg1 at each data point
  Return type: | `numpy.ndarray`, `escript.Data`, `Symbol` depending on the input

`esys.downunder.forwardmodels.dcresistivity.``unitVector`(i=0, d=3)

  Returns a unit vector u of dimension d whose non-zero element is at index i.

  Parameters:

  - i (`int`) – index for non-zero element
  - d (`int`, `escript.Domain` or `escript.FunctionSpace`) – dimension or an object that has the `getDim` method defining the dimension

  Returns: | the object u of rank 1 with u[j]=1 for j=index and u[j]=0 otherwise
  Return type: | `numpy.ndarray` or `escript.Data` of rank 1

`esys.downunder.forwardmodels.dcresistivity.``vol`(arg)

  Returns the volume or area of the oject `arg`

  Parameters: | arg (`escript.FunctionSpace` or `escript.Domain`) – a geometrical object
  Return type: | `float`

`esys.downunder.forwardmodels.dcresistivity.``whereNegative`(arg)

  Returns mask of negative values of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``whereNonNegative`(arg)

  Returns mask of non-negative values of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``whereNonPositive`(arg)

  Returns mask of non-positive values of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``whereNonZero`(arg, tol=0.0)

  Returns mask of values different from zero of argument `arg`.

  Parameters:

  - arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  - tol (`float`) – absolute tolerance. Values with absolute value less than tol are accepted as zero. If `tol` is not present `rtol``*```Lsup` (arg)` is used.

  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`

  Raises:

  - ValueError – if `rtol` is non-negative.
  - TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``wherePositive`(arg)

  Returns mask of positive values of argument `arg`.

  Parameters: | arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`.) – argument
  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`
  Raises: | TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``whereZero`(arg, tol=None, rtol=1.4901161193847656e-08)

  Returns mask of zero entries of argument `arg`.

  Parameters:

  - arg (`float`, `escript.Data`, `Symbol`, `numpy.ndarray`) – argument
  - tol (`float`) – absolute tolerance. Values with absolute value less than tol are accepted as zero. If `tol` is not present `rtol``*```Lsup` (arg)` is used.
  - rtol (non-negative `float`) – relative tolerance used to define the absolute tolerance if `tol` is not present.

  Return type: | `float`, `escript.Data`, `Symbol`, `numpy.ndarray` depending on the type of `arg`

  Raises:

  - ValueError – if `rtol` is non-negative.
  - TypeError – if the type of the argument is not expected

`esys.downunder.forwardmodels.dcresistivity.``zeros`(shape=())

  Returns the `shape` zero tensor.

  Parameters: | shape (`tuple` of `int`) – input shape for the identity tensor
  Returns: | array of shape filled with zeros
  Return type: | `numpy.ndarray`

## Others

- DBLE_MAX
- EPSILON

## Packages