ogstools.studies.convergence.convergence module#

ogstools.studies.convergence.convergence.resample(topology, meshes)[source]#
Return type:

list[UnstructuredGrid]

ogstools.studies.convergence.convergence.add_grid_spacing(mesh)[source]#
Return type:

UnstructuredGrid

ogstools.studies.convergence.convergence.grid_convergence(meshes, variable, topology, refinement_ratio)[source]#

Calculate the grid convergence field for the given meshes on the topology.

The calculation is based on the last three of the given meshes. For more information on this topic see <https://www.grc.nasa.gov/www/wind/valid/tutorial/spatconv.html> or <https://curiosityfluids.com/2016/09/09/establishing-grid-convergence/>.

Parameters:

  • meshes (list[UnstructuredGrid]) – At least three meshes with constant refinement.

  • variable (Variable) – The variable to be extrapolated.

  • topology (UnstructuredGrid) – The topology to evaluate.

  • refinement_ratio (float) – If not given, it is calculated automatically

Return type:

UnstructuredGrid

returns: Grid convergence field of the given variable.

ogstools.studies.convergence.convergence.richardson_extrapolation(meshes, variable, topology, refinement_ratio)[source]#

Estimate a better approximation of a variable on a mesh.

This function calculates the Richardson Extrapolation based on the change in results in the last three of the given meshes. For more information on this topic see <https://www.grc.nasa.gov/www/wind/valid/tutorial/spatconv.html> or <https://curiosityfluids.com/2016/09/09/establishing-grid-convergence/>.

Parameters:

  • meshes (list[UnstructuredGrid]) – At least three meshes with constant refinement.

  • variable (Variable) – The variable to be extrapolated.

  • topology (UnstructuredGrid) – The topology on which the extrapolation is done.

  • refinement_ratio (float) – Refinement ratio (spatial or temporal).

Return type:

UnstructuredGrid

Returns:

Richardson extrapolation of the given variable.

ogstools.studies.convergence.convergence.convergence_metrics(meshes, reference, variable, timestep_sizes)[source]#

Calculate convergence metrics for a given reference and variable.

Parameters:
Return type:

DataFrame

Returns:

A pandas Dataframe containing all metrics.

ogstools.studies.convergence.convergence.log_fit(x, y)[source]#
Return type:

tuple[float, ndarray]

ogstools.studies.convergence.convergence.convergence_order(metrics)[source]#

Calculates the convergence order for given convergence metrics.

Return type:

DataFrame

ogstools.studies.convergence.convergence.plot_convergence(metrics, variable)[source]#

Plot the absolute values of the convergence metrics.

Return type:

Figure

ogstools.studies.convergence.convergence.plot_convergence_errors(metrics)[source]#

Plot the relative errors of the convergence metrics in loglog scale.

Return type:

Figure

ogstools.studies.convergence.convergence.convergence_metrics_evolution(mesh_series, variable, refinement_ratio=2.0, units=('s', 's'))[source]#

Calculate convergence evolution metrics for given mesh series.

Contains convergence order and the relative error to the Richardson extrapolation for each timestep of the coarsest mesh series. and a variable

Parameters:

  • meshes_series – The List of mesh series to be analyzed.

  • variable (Variable) – The variable of interest.

  • refinement_ratio (float) – Refinement ratio between the discretizations.

Return type:

DataFrame

Returns:

A pandas Dataframe containing all metrics.

ogstools.studies.convergence.convergence.plot_convergence_error_evolution(evolution_metrics, error_type='relative')[source]#

Plot the evolution of relative errors.

Return type:

Figure

ogstools.studies.convergence.convergence.plot_convergence_order_evolution(evolution_metrics)[source]#

Plot the evolution of convergence orders.

Return type:

Figure