pymor.discretizers.builtin.gui.jupyter.matplotlib

Module Contents

class pymor.discretizers.builtin.gui.jupyter.matplotlib.PatchVisualizer(grid, U, bounding_box=None, codim=2, title=None, legend=None, separate_colorbars=False, rescale_colorbars=False, columns=2)[source]

Bases: pymor.core.base.BasicObject

Patch visualizer.

Methods

set

set(U=None, idx=0)[source]
pymor.discretizers.builtin.gui.jupyter.matplotlib.visualize_matplotlib_1d(grid, U, codim=1, title=None, legend=None, separate_plots=False, rescale_axes=False, columns=2, return_widget=True)[source]

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.

  • U – VectorArray of the data to visualize. If len(U) > 1, the data is visualized as an animation. It is also possible to provide a tuple of VectorArrays, in which case several plots are made into one or multiple figures, depending on the separate_plots switch. The lengths of all arrays have to agree.

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

  • 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.

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

pymor.discretizers.builtin.gui.jupyter.matplotlib.visualize_patch(grid, U, bounding_box=None, codim=2, title=None, legend=None, separate_colorbars=False, rescale_colorbars=False, columns=2, return_widget=True)[source]

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.

  • 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.

  • bounding_box – A bounding box in which the grid is contained (defaults to grid.bounding_box()).

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

  • 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.

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