goalie package

Submodules

goalie.adjoint module

Drivers for solving adjoint problems on sequences of meshes.

class AdjointMeshSeq(time_partition, initial_meshes, **kwargs)

Bases: MeshSeq

An extension of MeshSeq to account for solving adjoint problems on a sequence of meshes.

For time-dependent quantities of interest, the solver should access and modify J, which holds the QoI value.

Parameters:

• time_partition (TimePartition) – a partition of the temporal domain

• initial_meshes (list or MeshGeometry) – a list of meshes corresponding to the subinterval of the time partition, or a single mesh to use for all subintervals

• get_function_spaces – a function as described in get_function_spaces()

• get_initial_condition – a function as described in get_initial_condition()

• get_form – a function as described in get_form()

• get_solver – a function as described in get_solver()

• parameters (AdaptParameters) – parameters to apply to the mesh adaptation process

• get_qoi – a function as described in get_qoi()

check_qoi_convergence()

Check for convergence of the fixed point iteration due to the relative difference in QoI value being smaller than the specified tolerance.

Returns:

True if QoI convergence is detected, else False

Return type:

bool

get_checkpoints(solver_kwargs=None, run_final_subinterval=False)

Solve forward on the sequence of meshes, extracting checkpoints corresponding to the starting fields on each subinterval.

The QoI is also evaluated.

Parameters:

• solver_kwargs (dict with str keys and values which may take various types) – additional keyword arguments to be passed to the solver

• run_final_subinterval (bool) – if True, the solver is run on the final subinterval

Returns:

checkpoints for each subinterval

Return type:

list of firedrake.function.Functions

get_qoi(subinterval)

Get the function for evaluating the QoI, which has either zero or one arguments, corresponding to either an end time or time integrated quantity of interest, respectively. If the QoI has an argument then it is for the current time.

Signature for the function to be returned: ` :arg t: the current time (for time-integrated QoIs) :type t: :class:`float` :return: the QoI as a 0-form :rtype: :class:`ufl.form.Form` `

Parameters:

• solution_map (dict with str keys and values and firedrake.function.Function values) – a dictionary whose keys are the solution field names and whose values are the corresponding solutions

• subinterval (int) – the subinterval index

Returns:

the function for obtaining the QoI

Return type:

see docstring above

get_solve_blocks(field, subinterval, has_adj_sol=True)

Get all blocks of the tape corresponding to solve steps for prognostic solution field on a given subinterval.

Parameters:

• field (str) – name of the prognostic solution field

• subinterval (int) – subinterval index

• has_adj_sol (bool) – if True, only blocks with adj_sol attributes will be considered

Returns:

list of solve blocks

Return type:

list of pyadjoint.block.Blocks

property initial_condition

Get the initial conditions associated with the first subinterval.

Returns:

a dictionary whose keys are field names and whose values are the corresponding initial conditions applied on the first subinterval

Return type:

AttrDict with str keys and firedrake.function.Function values

solve_adjoint(solver_kwargs=None, adj_solver_kwargs=None, get_adj_values=False, test_checkpoint_qoi=False)

Solve an adjoint problem on a sequence of subintervals.

As well as the quantity of interest value, solution fields are computed - see AdjointSolutionData for more information.

Parameters:

• solver_kwargs (dict with str keys and values which may take various types) – parameters for the forward solver, as well as any parameters for the QoI, which should be included as a sub-dictionary with key ‘qoi_kwargs’

• adj_solver_kwargs (dict with str keys and values which may take various types) – parameters for the adjoint solver

• get_adj_values (bool) – if True, adjoint actions are also returned at exported timesteps

• test_checkpoint_qoi – solve over the final subinterval when checkpointing so that the QoI value can be checked across runs

Returns:

the solution data of the forward and adjoint solves

Return type:

AdjointSolutionData

static th(num)

Convert from cardinal to ordinal.

Parameters:

num (int) – the cardinal number to convert

Returns:

the corresponding ordinal number

Return type:

str

annotate_qoi(get_qoi)

Decorator that ensures QoIs are annotated properly.

To be applied to the get_qoi() method.

Parameters:

get_qoi – a function mapping a dictionary of solution data and an integer index to a QoI function

goalie.error_estimation module

Tools to automate goal-oriented error estimation.

get_dwr_indicator(F, adjoint_error, test_space=None)

Given a 1-form and an approximation of the error in the adjoint solution, compute a dual weighted residual (DWR) error indicator.

Note that each term of a 1-form contains only one firedrake.ufl_expr.TestFunction. The 1-form most commonly corresponds to the variational form of a PDE. If the PDE is linear, it should be written as in the nonlinear case (i.e., with the solution field in place of any firedrake.ufl_expr.TrialFunctions.

Parameters:

• F (ufl.form.Form) – the form

• adjoint_error (firedrake.function.Function or dict with str keys and firedrake.function.Function values) – a dictionary whose keys are field names and whose values are the approximations to the corresponding components of the adjoint error, or a single such component

• test_space (firedrake.functionspaceimpl.WithGeometry) – a dictionary whose keys are field names and whose values are the test spaces for the corresponding fields, or a single such test space (or None to determine the test space(s) automatically)

Returns:

the DWR indicator

Return type:

firedrake.function.Function

goalie.function_data module

Nested dictionaries of solution data Functions.

class AdjointSolutionData(*args, **kwargs)

Bases: FunctionData

Class representing solution data for general adjoint problems.

For a given exported timestep, the field types are:

• 'forward': the forward solution after taking the timestep;

• 'forward_old': the forward solution before taking the timestep (provided the problem is not steady-state)

• 'adjoint': the adjoint solution after taking the timestep;

• 'adjoint_next': the adjoint solution before taking the timestep backwards (provided the problem is not steady-state).

Parameters:

• time_partition – the TimePartition used to discretise the problem in time

• function_spaces – the dictionary of FunctionSpaces used to discretise the problem in space

class ForwardSolutionData(*args, **kwargs)

Bases: FunctionData

Class representing solution data for general forward problems.

For a given exported timestep, the field types are:

• 'forward': the forward solution after taking the timestep;

• 'forward_old': the forward solution before taking the timestep (provided the problem is not steady-state).

Parameters:

• time_partition – the TimePartition used to discretise the problem in time

• function_spaces – the dictionary of FunctionSpaces used to discretise the problem in space

class IndicatorData(time_partition, meshes)

Bases: FunctionData

Class representing error indicator data.

Note that this class has a single dictionary with the field name as the key, rather than a doubly-nested dictionary.

Parameters:

• time_partition – the TimePartition used to discretise the problem in time

• meshes – the list of meshes used to discretise the problem in space

goalie.go_mesh_seq module

Drivers for goal-oriented error estimation on sequences of meshes.

class GoalOrientedMeshSeq(*args, **kwargs)

Bases: AdjointMeshSeq

An extension of AdjointMeshSeq to account for goal-oriented problems.

Parameters:

• time_partition (TimePartition) – a partition of the temporal domain

• initial_meshes (list or MeshGeometry) – a list of meshes corresponding to the subinterval of the time partition, or a single mesh to use for all subintervals

• get_function_spaces – a function as described in get_function_spaces()

• get_initial_condition – a function as described in get_initial_condition()

• get_form – a function as described in get_form()

• get_solver – a function as described in get_solver()

• parameters (AdaptParameters) – parameters to apply to the mesh adaptation process

• get_qoi – a function as described in get_qoi()

check_estimator_convergence()

Check for convergence of the fixed point iteration due to the relative difference in error estimator value being smaller than the specified tolerance.

Returns:

True if estimator convergence is detected, else False

Return type:

bool

error_estimate(absolute_value=False)

Deduce the error estimator value associated with error indicator fields defined over the mesh sequence.

Parameters:

absolute_value (bool) – if True, the modulus is taken on each element

Returns:

the error estimator value

Return type:

float

fixed_point_iteration(adaptor, update_params=None, enrichment_kwargs=None, adaptor_kwargs=None, solver_kwargs=None, indicator_fn=<cyfunction get_dwr_indicator>)

Apply goal-oriented mesh adaptation using a fixed point iteration loop approach.

Parameters:

• adaptor – function for adapting the mesh sequence. Its arguments are the mesh sequence and the solution and indicator data objects. It should return True if the convergence criteria checks are to be skipped for this iteration. Otherwise, it should return False.

• update_params – function for updating params at each iteration. Its arguments are the parameter class and the fixed point iteration

• enrichment_kwargs (dict with str keys and values which may take various types) – keyword arguments to pass to the global enrichment method

• solver_kwargs (dict with str keys and values which may take various types) – parameters to pass to the solver

• adaptor_kwargs (dict with str keys and values which may take various types) – parameters to pass to the adaptor

• indicator_fn – function which maps the form, adjoint error and enriched space(s) as arguments to the error indicator firedrake.function.Function

Returns:

solution and indicator data objects

Rtype1:

:class:`~.AdjointSolutionData

Rtype2:

:class:`~.IndicatorData

get_enriched_mesh_seq(enrichment_method='p', num_enrichments=1)

Construct a sequence of globally enriched spaces.

The following global enrichment methods are supported: * h-refinement (enrichment_method='h') - refine each mesh element

uniformly in each direction;

• p-refinement (enrichment_method='p') - increase the function space polynomial order by one globally.

Parameters:

• enrichment_method (str) – the method for enriching the mesh sequence

• num_enrichments (int) – the number of enrichments to apply

Returns:

the enriched mesh sequence

Type:

the type is inherited from the parent mesh sequence

indicate_errors(enrichment_kwargs=None, solver_kwargs=None, indicator_fn=<cyfunction get_dwr_indicator>)

Compute goal-oriented error indicators for each subinterval based on solving the adjoint problem in a globally enriched space.

Parameters:

• enrichment_kwargs (dict with str keys and values which may take various types) – keyword arguments to pass to the global enrichment method - see get_enriched_mesh_seq() for the supported enrichment methods and options

• solver_kwargs (dict with str keys and values which may take various types) – parameters for the forward solver, as well as any parameters for the QoI, which should be included as a sub-dictionary with key ‘qoi_kwargs’

• indicator_fn – function which maps the form, adjoint error and enriched space(s) as arguments to the error indicator firedrake.function.Function

Returns:

solution and indicator data objects

Rtype1:

:class:`~.AdjointSolutionData

Rtype2:

:class:`~.IndicatorData

property indicators

Returns:

the error indicator data object

Return type:

IndicatorData

goalie.log module

Loggers for Goalie.

Code mostly copied from the Thetis project.

critical(msg, *args, **kwargs)

Log ‘msg % args’ with severity ‘CRITICAL’.

To pass exception information, use the keyword argument exc_info with a true value, e.g.

logger.critical(“Houston, we have a %s”, “major disaster”, exc_info=1)

debug(msg, *args, **kwargs)

Log ‘msg % args’ with severity ‘DEBUG’.

To pass exception information, use the keyword argument exc_info with a true value, e.g.

logger.debug(“Houston, we have a %s”, “thorny problem”, exc_info=1)

error(msg, *args, **kwargs)

Log ‘msg % args’ with severity ‘ERROR’.

To pass exception information, use the keyword argument exc_info with a true value, e.g.

logger.error(“Houston, we have a %s”, “major problem”, exc_info=1)

info(msg, *args, **kwargs)

Log ‘msg % args’ with severity ‘INFO’.

To pass exception information, use the keyword argument exc_info with a true value, e.g.

logger.info(“Houston, we have a %s”, “interesting problem”, exc_info=1)

pyrint(msg, *args, **kwargs)

Log ‘msg % args’ with severity ‘INFO’.

To pass exception information, use the keyword argument exc_info with a true value, e.g.

logger.info(“Houston, we have a %s”, “interesting problem”, exc_info=1)

set_log_level(level)

warning(msg, *args, **kwargs)

Log ‘msg % args’ with severity ‘WARNING’.

To pass exception information, use the keyword argument exc_info with a true value, e.g.

logger.warning(“Houston, we have a %s”, “bit of a problem”, exc_info=1)

goalie.math module

bessi0(x)

Modified Bessel function of the first kind.

Code taken from [].

bessk0(x)

Modified Bessel function of the second kind.

Code taken from [].

goalie.mesh_seq module

Sequences of meshes corresponding to a TimePartition.

class MeshSeq(time_partition, initial_meshes, **kwargs)

Bases: object

A sequence of meshes for solving a PDE associated with a particular TimePartition of the temporal domain.

Parameters:

• time_partition (TimePartition) – a partition of the temporal domain

• initial_meshes (list or MeshGeometry) – a list of meshes corresponding to the subinterval of the time partition, or a single mesh to use for all subintervals

• get_function_spaces – a function as described in get_function_spaces()

• get_initial_condition – a function as described in get_initial_condition()

• get_form – a function as described in get_form()

• get_solver – a function as described in get_solver()

• transfer_method (str) – the method to use for transferring fields between meshes. Options are “project” (default) and “interpolate”. See animate.interpolation.transfer() for details

• transfer_kwargs (dict with str keys and values which may take various types) – kwargs to pass to the chosen transfer method

• parameters (AdaptParameters) – parameters to apply to the mesh adaptation process

check_element_count_convergence()

Check for convergence of the fixed point iteration due to the relative difference in element count being smaller than the specified tolerance.

Returns:

an array, whose entries are True if convergence is detected on the corresponding subinterval

Return type:

list of bools

count_elements()

Count the number of elements in each mesh in the sequence.

Returns:

list of element counts

Return type:

list of ints

count_vertices()

Count the number of vertices in each mesh in the sequence.

Returns:

list of vertex counts

Return type:

list of ints

debug(msg)

Print a debug message.

Parameters:

msg (str) – the message to print

fixed_point_iteration(adaptor, update_params=None, solver_kwargs=None, adaptor_kwargs=None)

Apply mesh adaptation using a fixed point iteration loop approach.

Parameters:

• adaptor – function for adapting the mesh sequence. Its arguments are the mesh sequence and the solution data object. It should return True if the convergence criteria checks are to be skipped for this iteration. Otherwise, it should return False.

• update_params – function for updating params at each iteration. Its arguments are the parameter class and the fixed point iteration

• solver_kwargs (dict with str keys and values which may take various types) – parameters to pass to the solver

• adaptor_kwargs (dict with str keys and values which may take various types) – parameters to pass to the adaptor

Returns:

solution data object

Return type:

ForwardSolutionData

property form

See get_form().

property function_spaces

Get the function spaces associated with the mesh sequence.

Returns:

a dictionary whose keys are field names and whose values are the corresponding function spaces

Return type:

AttrDict with str keys and firedrake.functionspaceimpl.FunctionSpace values

get_checkpoints(run_final_subinterval=False, solver_kwargs=None)

Get checkpoints corresponding to the starting fields on each subinterval.

Parameters:

• run_final_subinterval (bool) – if True, the solver is run on the final subinterval

• solver_kwargs (dict with str keys and values which may take various types) – parameters for the forward solver

Returns:

checkpoints for each subinterval

Return type:

list of firedrake.function.Functions

get_form()

Get the function mapping a subinterval index and a solution dictionary to a dictionary containing parts of the PDE weak form corresponding to each solution component.

Signature for the function to be returned: ` :arg index: the subinterval index :type index: :class:`int` :arg solutions: map from fields to tuples of current and previous solutions :type solutions: :class:`dict` with :class:`str` keys and :class:`tuple` values :return: map from fields to the corresponding forms :rtype: :class:`dict` with :class:`str` keys and :class:`ufl.form.Form` values `

Returns:

the function for obtaining the form

Return type:

see docstring above

get_function_spaces(mesh)

Construct the function spaces corresponding to each field, for a given mesh.

Parameters:

mesh (firedrake.mesh.MeshGeometry) – the mesh to base the function spaces on

Returns:

a dictionary whose keys are field names and whose values are the corresponding function spaces

Return type:

dict with str keys and firedrake.functionspaceimpl.FunctionSpace values

get_initial_condition()

Get the initial conditions applied on the first mesh in the sequence.

Returns:

the dictionary, whose keys are field names and whose values are the corresponding initial conditions applied

Return type:

dict with str keys and firedrake.function.Function values

get_solver()

Get the function mapping a subinterval index and an initial condition dictionary to a dictionary of solutions for the corresponding solver step.

Signature for the function to be returned: ``` :arg index: the subinterval index :type index: int :arg ic: map from fields to the corresponding initial condition components :type ic: dict with str keys and

firedrake.function.Function values

Returns:

map from fields to the corresponding solutions

Return type:

dict with str keys and firedrake.function.Function values

```

Returns:

the function for obtaining the solver

Return type:

see docstring above

info(msg)

Print an info level message.

Parameters:

msg (str) – the message to print

property initial_condition

Get the initial conditions associated with the first subinterval.

Returns:

a dictionary whose keys are field names and whose values are the corresponding initial conditions applied on the first subinterval

Return type:

AttrDict with str keys and firedrake.function.Function values

plot(fig=None, axes=None, **kwargs)

Plot the meshes comprising a 2D MeshSeq.

Parameters:

• fig (matplotlib.figure.Figure) – matplotlib figure to use

• axes (matplotlib.axes._axes.Axes) – matplotlib axes to use

Returns:

matplotlib figure and axes for the plots

Rtype1:

matplotlib.figure.Figure

Rtype2:

matplotlib.axes._axes.Axes

All keyword arguments are passed to firedrake.pyplot.triplot().

set_meshes(meshes)

Set all meshes in the sequence and deduce various properties.

Parameters:

meshes (list of firedrake.MeshGeometrys or firedrake.MeshGeometry) – list of meshes to use in the sequence, or a single mesh to use for all subintervals

property solutions

Returns:

the solution data object

Return type:

FunctionData

solve_forward(solver_kwargs=None)

Solve a forward problem on a sequence of subintervals.

A dictionary of solution fields is computed - see ForwardSolutionData for more details.

Parameters:

solver_kwargs (dict whose keys are strs and whose values may take various types) – parameters for the forward solver

Returns:

the solution data of the forward solves

Return type:

ForwardSolutionData

property solver

See get_solver().

warning(msg)

Print a warning message.

Parameters:

msg (str) – the message to print

goalie.metric module

Driver functions for metric-based mesh adaptation.

enforce_variable_constraints(metrics, h_min=1e-30, h_max=1e+30, a_max=100000.0, boundary_tag=None)

Post-process a list of metrics to enforce minimum and maximum element sizes, as well as maximum anisotropy.

Parameters:

• metrics (list of RiemannianMetrics) – the metrics

• h_min (firedrake.function.Function, float, or int) – minimum tolerated element size

• h_max (firedrake.function.Function, float, or int) – maximum tolerated element size

• a_max (firedrake.function.Function, float, or int) – maximum tolerated element anisotropy

• boundary_tag (str or int) – optional tag to enforce sizes on.

ramp_complexity(base, target, iteration, num_iterations=3)

Ramp up the target complexity over the first few iterations.

Parameters:

• base (float) – the base complexity to start from

• target (float) – the desired complexity

• iteration (int) – the current iteration

• num_iterations (int) – how many iterations to ramp over?

Returns:

the ramped target complexity

Return type:

float

space_time_normalise(metrics, time_partition, metric_parameters, global_factor=None, boundary=False, restrict_sizes=True, restrict_anisotropy=True)

Apply \(L^p\) normalisation in both space and time.

Based on Equation (1) in [].

Parameters:

• metrics (list of RiemannianMetrics) – the metrics associated with each subinterval

• time_partition (TimePartition) – temporal discretisation for the problem at hand

• metric_parameters (list of dicts or a single dict to use for all subintervals) – dictionary containing the target space-time metric complexity under dm_plex_metric_target_complexity and the normalisation order under dm_plex_metric_p, or a list thereof

• global_factor (float) – pre-computed global normalisation factor

• boundary (bool) – if True, the normalisation to be performed over the boundary

• restrict_sizes (bool) – if True, minimum and maximum metric magnitudes are enforced

• restrict_anisotropy (bool) – if True, maximum anisotropy is enforced

Returns:

the space-time normalised metrics

Return type:

list of RiemannianMetrics

goalie.options module

class AdaptParameters(parameters=None)

Bases: AttrDict

A class for holding parameters associated with adaptive mesh fixed point iteration loops.

Parameters:

parameters (dict with str keys and values which may take various types) – parameters to set

class GoalOrientedMetricParameters(parameters=None)

Bases: GoalOrientedParameters, MetricParameters

A class for holding parameters associated with metric-based, goal-oriented adaptive mesh fixed point iteration loops.

Parameters:

parameters (dict with str keys and values which may take various types) – parameters to set

class GoalOrientedParameters(parameters=None)

Bases: AdaptParameters

A class for holding parameters associated with goal-oriented adaptive mesh fixed point iteration loops.

Parameters:

parameters (dict with str keys and values which may take various types) – parameters to set

class MetricParameters(parameters=None)

Bases: AdaptParameters

A class for holding parameters associated with metric-based adaptive mesh fixed point iteration loops.

Parameters:

parameters (dict with str keys and values which may take various types) – parameters to set

export(metric)

Set parameters appropriate to a given RiemannianMetric.

Parameters:

metric (animate.metric.RiemannianMetric) – the metric to apply parameters to

goalie.plot module

Driver functions for plotting solution data.

plot_indicator_snapshots(indicators, time_partition, field, **kwargs)

Plot a sequence of snapshots associated with indicators and a given TimePartition

Any keyword arguments are passed to firedrake.plot.tricontourf().

Parameters:

• indicators – list of list of indicators, indexed by mesh sequence index, then timestep

• time_partition – the TimePartition object used to solve the problem

plot_snapshots(solutions, time_partition, field, label, **kwargs)

Plot a sequence of snapshots associated with solutions.field.label and a given TimePartition.

Any keyword arguments are passed to firedrake.plot.tricontourf().

Parameters:

• solutions – AttrDict of solutions computed by solving a forward or adjoint problem

• time_partition – the TimePartition object used to solve the problem

• field – solution field of choice

• label – choose from 'forward', 'forward_old' 'adjoint' and 'adjoint_next'

goalie.point_seq module

class PointSeq(time_partition, **kwargs)

Bases: MeshSeq

A simplified subset of MeshSeq for ODE problems.

In this version, a single mesh comprised of a single vertex is shared across all subintervals.

Parameters:

• time_partition – the TimePartition which partitions the temporal domain

• get_function_spaces – a function, whose only argument is a MeshSeq, which constructs prognostic firedrake.functionspaceimpl.FunctionSpaces for each subinterval

• get_initial_condition – a function, whose only argument is a MeshSeq, which specifies initial conditions on the first mesh

• get_form – a function, whose only argument is a MeshSeq, which returns a function that generates the ODE weak form

• get_solver – a function, whose only argument is a MeshSeq, which returns a function that integrates initial data over a subinterval

• get_bcs – a function, whose only argument is a MeshSeq, which returns a function that determines any Dirichlet boundary conditions

• parameters – AdaptParameters instance

set_meshes(mesh)

Update the mesh associated with the PointSeq, as well as the associated attributes.

Parameters:

mesh – the vertex-only mesh

goalie.time_partition module

Partitioning for the temporal domain.

class TimeInstant(field_names, **kwargs)

Bases: TimeInterval

A TimePartition for steady-state problems.

Under the hood this means dividing \([0,1)\) into a single timestep.

Parameters:

• end_time (float or int) – end time of the interval of interest

• num_subintervals (int) – number of subintervals in the partition

• timesteps (list of floats or float) – a list timesteps to be used on each subinterval, or a single timestep to use for all subintervals

• field_names (list of strs or str) – the list of field names to consider

• num_timesteps_per_export (list of :class`int`s or int) – a list of numbers of timesteps per export for each subinterval, or a single number to use for all subintervals

• start_time (float or int) – start time of the interval of interest

• subinterals – sequence of subintervals (which need not be of uniform length), or None to use uniform subintervals (the default)

• field_types (list of strs or str) – a list of strings indicating whether each field is ‘unsteady’ or ‘steady’, i.e., does the corresponding equation involve time derivatives or not?

class TimeInterval(*args, **kwargs)

Bases: TimePartition

A trivial TimePartition with a single subinterval.

Parameters:

• end_time (float or int) – end time of the interval of interest

• num_subintervals (int) – number of subintervals in the partition

• timesteps (list of floats or float) – a list timesteps to be used on each subinterval, or a single timestep to use for all subintervals

• field_names (list of strs or str) – the list of field names to consider

• num_timesteps_per_export (list of :class`int`s or int) – a list of numbers of timesteps per export for each subinterval, or a single number to use for all subintervals

• start_time (float or int) – start time of the interval of interest

• subinterals – sequence of subintervals (which need not be of uniform length), or None to use uniform subintervals (the default)

• field_types (list of strs or str) – a list of strings indicating whether each field is ‘unsteady’ or ‘steady’, i.e., does the corresponding equation involve time derivatives or not?

property timestep

Returns:

the timestep used on the single interval

Return type:

float

class TimePartition(end_time, num_subintervals, timesteps, field_names, num_timesteps_per_export=1, start_time=0.0, subintervals=None, field_types=None)

Bases: object

A partition of the time interval of interest into subintervals.

The subintervals are assumed to be uniform in length. However, different timestep values may be used on each subinterval.

Parameters:

• end_time (float or int) – end time of the interval of interest

• num_subintervals (int) – number of subintervals in the partition

• timesteps (list of floats or float) – a list timesteps to be used on each subinterval, or a single timestep to use for all subintervals

• field_names (list of strs or str) – the list of field names to consider

• num_timesteps_per_export (list of :class`int`s or int) – a list of numbers of timesteps per export for each subinterval, or a single number to use for all subintervals

• start_time (float or int) – start time of the interval of interest

• subinterals – sequence of subintervals (which need not be of uniform length), or None to use uniform subintervals (the default)

• field_types (list of strs or str) – a list of strings indicating whether each field is ‘unsteady’ or ‘steady’, i.e., does the corresponding equation involve time derivatives or not?

debug(attr)

Print attribute ‘msg’ for debugging purposes.

Parameters:

attr – the attribute to display debugging information for

property num_timesteps

:returns the total number of timesteps :rtype: int

goalie.utility module

Utility functions and classes for mesh adaptation.

class AttrDict(*args, **kwargs)

Bases: dict

Dictionary that provides both self[key] and self.key access to members.

Disclaimer: Copied from stackoverflow.

create_directory(path, comm=<mpi4py.MPI.Intracomm object>)

Create a directory on disk.

Disclaimer: Code copied from Thetis.

Parameters:

• path (str) – path to the directory

• comm (mpi4py.MPI.Intracomm) – MPI communicator

Returns:

the path in absolute form

Rtype path:

str

effectivity_index(error_indicator, Je)

Compute the overestimation factor of some error estimator for the QoI error.

Note that this is only typically used for simple steady-state problems with analytical solutions.

Parameters:

• error_indicator (firedrake.function.Function) – a \(\mathbb P0\) error indicator which localises contributions to an error estimator to individual elements

• Je (float) – the error in the quantity of interest

Returns:

the effectivity index

Return type:

float

Module contents