pymor.discretizers.builtin.gui.qt

Visualization of grid data using Qt.

This module provides a few methods and classes for visualizing data associated to grids. We use the Qt widget toolkit for the GUI.

Submodules

• gl
• matplotlib

Package Contents

class pymor.discretizers.builtin.gui.qt.PlotMainWindow(U, vmins, vmaxs, plot, length=1, title=None, save_action=None)

Bases: qtpy.QtWidgets.QWidget

Base class for plot main windows.

Methods

closeEvent	This is directly called from CI.
rewind
set
slider_changed
speed_changed
step_backward
step_forward
to_end
toggle_play
update_solution

closeEvent(event=None)

This is directly called from CI.

Xvfb (sometimes) raises errors on interpreter shutdown when there are still ‘live’ MPL plot objects. This happens even if the referencing MainWindow was already destroyed

rewind()

set(ind)

slider_changed(ind)

speed_changed(val)

step_backward()

step_forward()

to_end()

toggle_play(checked)

update_solution()

pymor.discretizers.builtin.gui.qt.background_visualization_method(method='ipython_if_possible')

pymor.discretizers.builtin.gui.qt.visualize_matplotlib_1d(grid, U, codim=1, title=None, legend=None, separate_plots=False, rescale_axes=False, columns=2, block=False)

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 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.

• 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 subplots to visualize multiple VectorArrays.

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

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

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

pymor.discretizers.builtin.gui.qt.visualize_patch(grid, U, bounding_box=([0, 0], [1, 1]), codim=2, title=None, legend=None, separate_colorbars=False, rescale_colorbars=False, backend='gl', block=False, columns=2)

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.

• 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.

• backend – Plot backend to use (‘gl’ or ‘matplotlib’).

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

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