pymor.discretizers.builtin.gui.visualizers

Module Contents

class pymor.discretizers.builtin.gui.visualizers.OnedVisualizer(grid, codim=1, block=False, backend='jupyter_or_matplotlib')[source]

Bases: pymor.core.base.ImmutableObject

Visualize scalar data associated to a one-dimensional Grid as a plot.

The grid’s ReferenceElement must be the line. The data can either be attached to the subintervals or vertices of the grid.

Parameters:

  • grid – The underlying Grid.

  • codim – The codimension of the entities the data in U is attached to (either 0 or 1).

  • block – If True, block execution until the plot window is closed.

  • backend – Plot backend to use (‘jupyter_or_matplotlib’, ‘jupyter’, ‘matplotlib’).

Methods

visualize

Visualize the provided data.
visualize(U, title=None, legend=None, separate_plots=False, rescale_axes=False, block=None, columns=2, return_widget=False)[source]

Visualize the provided data.

Parameters:

  • U – VectorArray of the data to visualize. If len(U) > 1, the data is visualized as a time series of plots. Alternatively, a tuple of VectorArrays can be provided, in which case several plots are made into the same axes. The lengths of all arrays have to agree.

  • title – Title of the plot.

  • legend – Description of the data that is plotted. Most useful if U is a tuple in which case legend has to be a tuple of strings of the same length.

  • separate_plots – If True, use multiple figures to visualize multiple VectorArrays.

  • rescale_axes – If True, rescale axes to data in each frame.

  • block – If True, block execution until the plot window is closed. If None, use the default provided during instantiation.

  • columns – Number of columns the subplots are organized in.

  • return_widget – If True, create an interactive visualization that can be used as a jupyter widget.

class pymor.discretizers.builtin.gui.visualizers.PatchVisualizer(grid, codim=2, bounding_box=None, backend='jupyter_or_gl', block=False)[source]

Bases: pymor.core.base.ImmutableObject

Visualize scalar data associated to a two-dimensional Grid as a patch plot.

The grid’s ReferenceElement must be the triangle or square. The data can either be attached to the faces or vertices of the grid.

Parameters:

  • grid – The underlying Grid.

  • codim – The codimension of the entities the data in U is attached to (either 0 or 2).

  • bounding_box – A bounding box in which the grid is contained.

  • backend – Plot backend to use (‘jupyter_or_gl’, ‘jupyter’, ‘gl’, ‘matplotlib’).

  • block – If True, block execution until the plot window is closed.

Methods

visualize

Visualize the provided data.
visualize(U, title=None, legend=None, separate_colorbars=False, rescale_colorbars=False, block=None, filename=None, columns=2, return_widget=False, **kwargs)[source]

Visualize the provided data.

Parameters:

  • U – VectorArray of the data to visualize. If len(U) > 1, the data is visualized as a time series of plots. Alternatively, a tuple of VectorArrays can be provided, in which case a subplot is created for each entry of the tuple. The lengths of all arrays have to agree.

  • title – Title of the plot.

  • legend – Description of the data that is plotted. Most useful if U is a tuple in which case legend has to be a tuple of strings of the same length.

  • separate_colorbars – If True, use separate colorbars for each subplot.

  • rescale_colorbars – If True, rescale colorbars to data in each frame.

  • block – If True, block execution until the plot window is closed. If None, use the default provided during instantiation.

  • columns – The number of columns in the visualizer GUI in case multiple plots are displayed at the same time.

  • filename – If specified, write the data to a VTK-file using write_vtk instead of displaying it.

  • return_widget – If True, create an interactive visualization that can be used as a jupyter widget.

  • kwargs – Additional backend-specific arguments.