esys.modellib.temperature Package¶

Classes¶

Data
IterationDivergenceError
Model
TemperatureAdvection

class esys.modellib.temperature.Data¶

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, (object)dpno) → int :¶

Return tag number for the specified datapoint

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

getTupleForDataPoint((Data)arg1, (object)dataPointNo) → object :¶

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

getTupleForGlobalDataPoint((Data)arg1, (object)procNo, (object)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, (object)Amin, (object)Astep, (Data)B, (object)Bmin, (object)Bstep[, (object)undef=1e+50[, (object)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, (object)Amin, (object)Astep [, (object)undef=1e+50 [, (object)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, (object)check_boundaries) → Data :¶: 1D interpolation with non equally spaced points

nonuniformSlope((Data)arg1, (object)in, (object)out, (object)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, (object)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, (object)dataPointNo, (object)value) → None¶

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

setValueOfDataPoint( (Data)arg1, (object)arg2, (object)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[, (object)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.modellib.temperature.IterationDivergenceError¶

Exception which is thrown if there is no convergence of the iteration process at a time step.

But there is a chance that a smaller step could help to reach convergence.

__init__(*args, **kwargs)¶

class esys.modellib.temperature.Model(parameters=[], **kwargs)¶

A Model object represents a process marching over time until a finalizing condition is fulfilled. At each time step an iterative process can be performed and the time step size can be controlled. A Model has the following work flow:

doInitialization()
while not terminateInitialIteration(): doInitialStep()
doInitialPostprocessing()
while not finalize():
    dt=getSafeTimeStepSize(dt)
    doStepPreprocessing(dt)
    while not terminateIteration(): doStep(dt)
    doStepPostprocessing(dt)
doFinalization()

where doInitialization, finalize, getSafeTimeStepSize, doStepPreprocessing, terminateIteration, doStepPostprocessing, doFinalization are methods of the particular instance of a Model. The default implementations of these methods have to be overwritten by the subclass implementing a Model.

__init__(parameters=[], **kwargs)¶

Creates a model.

Just calls the parent constructor.

UNDEF_DT = 1e+300¶

doFinalization()¶

Finalizes the time stepping.

This function may be overwritten.

doInitialPostprocessing()¶

Finalises the initialization iteration process. This method is not called in case of a restart.

This function may be overwritten.

doInitialStep()¶

Performs an iteration step in the initialization phase. This method is not called in case of a restart.

This function may be overwritten.

doInitialization()¶

Initializes the time stepping scheme. This method is not called in case of a restart.

This function may be overwritten.

doStep(dt)¶

Executes an iteration step at a time step.

dt is the currently used time step size.

This function may be overwritten.

doStepPostprocessing(dt)¶

Finalises the time step.

dt is the currently used time step size.

This function may be overwritten.

doStepPreprocessing(dt)¶

Sets up a time step of step size dt.

This function may be overwritten.

finalize()¶

Returns False if the time stepping is finalized.

This function may be overwritten.

getSafeTimeStepSize(dt)¶

Returns a time step size which can be safely used.

dt gives the previously used step size.

This function may be overwritten.

setUp()¶

Sets up the model.

This function may be overwritten.

terminateInitialIteration()¶: Returns True if iteration at the inital phase is terminated.

terminateIteration()¶: Returns True if iteration on a time step is terminated.

toDom(esysxml, node)¶: toDom method of Model class.

class esys.modellib.temperature.TemperatureAdvection(**kwargs)¶

The conservation of internal heat energy is given by

rho c_p ( dT/dt+v[j] * grad(T)[j])-grad(kappa grad(T)_{,i}=Q

n_i kappa T_{,i}=0

it is assummed that *

ho c_p* is constant in time.

solved by Taylor Galerkin method

__init__(**kwargs)¶

Creates a model.

Just calls the parent constructor.

G(T, alpha)¶: tangential operator for taylor galerikin

doInitialization()¶

Initializes the time stepping scheme. This method is not called in case of a restart.

This function may be overwritten.

doStepPostprocessing(dt)¶: perform taylor galerkin step

getSafeTimeStepSize(dt)¶: returns new step size

Functions¶

esys.modellib.temperature.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.modellib.temperature.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.modellib.temperature.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.modellib.temperature.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.modellib.temperature.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.modellib.temperature Package¶

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