pymor.discretizers.builtin.gui.jupyter package¶

This module provides plotting support inside the Jupyter notebook.

To use these routines you first have to execute

%matplotlib notebook

inside the given notebook.

Submodules¶

logging module¶

class pymor.discretizers.builtin.gui.jupyter.logging.LogViewer(out, accordion=None)[source]¶

Bases: logging.Handler

Methods

`LogViewer`	`close`, `emit`
`Handler`	`acquire`, `createLock`, `flush`, `format`, `get_name`, `handle`, `handleError`, `release`, `set_name`, `setFormatter`, `setLevel`
`Filterer`	`addFilter`, `filter`, `removeFilter`

Attributes

`LogViewer`	`empty`, `out`
`Handler`	`name`

__repr__()[source]¶: Return repr(self).

close()[source]¶

Tidy up any resources used by the handler.

This version removes the handler from an internal map of handlers, _handlers, which is used for handler lookup by name. Subclasses should ensure that this gets called from overridden close() methods.

emit(record)[source]¶

Do whatever it takes to actually log the specified logging record.

This version is intended to be implemented by subclasses and so raises a NotImplementedError.

class pymor.discretizers.builtin.gui.jupyter.logging.LoggingRedirector[source]¶

Bases: object

Methods

LoggingRedirector

start, stop

matplotlib module¶

class pymor.discretizers.builtin.gui.jupyter.matplotlib.MPLPlotBase(U, grid, codim, legend, bounding_box=None, separate_colorbars=False, columns=2, separate_plots=False, separate_axes=False)[source]¶: Bases: object

pymor.discretizers.builtin.gui.jupyter.matplotlib.visualize_matplotlib_1d(grid, U, codim=1, title=None, legend=None, separate_plots=True, separate_axes=False, columns=2)[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 in a single axes object or a series of axes, depending on the separate_axes switch. 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.
separate_axes: If True, use separate axes for each figure instead of an Animation.
column: Number of columns the subplots are organized in.

pymor.discretizers.builtin.gui.jupyter.matplotlib.visualize_patch(grid, U, bounding_box=[0, 0], [1, 1], codim=2, title=None, legend=None, separate_colorbars=False, rescale_colorbars=False, columns=2)[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.
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.

threejs module¶

class pymor.discretizers.builtin.gui.jupyter.threejs.ColorBarRenderer(*args, **kwargs)[source]¶

Bases: ipywidgets.widgets.widget_box.VBox

Methods

`ColorBarRenderer`	`freeze_camera`, `goto`
`DOMWidget`	`add_class`, `remove_class`
`Widget`	`add_traits`, `close`, `close_all`, `get_manager_state`, `get_state`, `get_view_spec`, `handle_comm_opened`, `hold_sync`, `notify_change`, `on_displayed`, `on_msg`, `on_widget_constructed`, `open`, `send`, `send_state`, `set_state`, `__del__`
`HasTraits`	`class_own_trait_events`, `class_own_traits`, `class_trait_names`, `class_traits`, `has_trait`, `hold_trait_notifications`, `observe`, `on_trait_change`, `set_trait`, `setup_instance`, `trait_events`, `trait_metadata`, `trait_names`, `traits`, `unobserve`, `unobserve_all`

Attributes

`Box`	`box_style`, `children`
`DOMWidget`	`layout`
`Widget`	`comm`, `keys`, `model_id`, `widget_types`, `widgets`
`LoggingHasTraits`	`log`
`HasTraits`	`cross_validation_lock`

class pymor.discretizers.builtin.gui.jupyter.threejs.Renderer(*args, **kwargs)[source]¶

Bases: ipywidgets.widgets.widget_box.VBox

Methods

`Renderer`	`freeze_camera`, `goto`
`DOMWidget`	`add_class`, `remove_class`
`Widget`	`add_traits`, `close`, `close_all`, `get_manager_state`, `get_state`, `get_view_spec`, `handle_comm_opened`, `hold_sync`, `notify_change`, `on_displayed`, `on_msg`, `on_widget_constructed`, `open`, `send`, `send_state`, `set_state`, `__del__`
`HasTraits`	`class_own_trait_events`, `class_own_traits`, `class_trait_names`, `class_traits`, `has_trait`, `hold_trait_notifications`, `observe`, `on_trait_change`, `set_trait`, `setup_instance`, `trait_events`, `trait_metadata`, `trait_names`, `traits`, `unobserve`, `unobserve_all`

Attributes

`Box`	`box_style`, `children`
`DOMWidget`	`layout`
`Widget`	`comm`, `keys`, `model_id`, `widget_types`, `widgets`
`LoggingHasTraits`	`log`
`HasTraits`	`cross_validation_lock`

class pymor.discretizers.builtin.gui.jupyter.threejs.ThreeJSPlot(*args, **kwargs)[source]¶

Bases: ipywidgets.widgets.widget_box.VBox

Methods

`ThreeJSPlot`	`finish_loading`
`DOMWidget`	`add_class`, `remove_class`
`Widget`	`add_traits`, `close`, `close_all`, `get_manager_state`, `get_state`, `get_view_spec`, `handle_comm_opened`, `hold_sync`, `notify_change`, `on_displayed`, `on_msg`, `on_widget_constructed`, `open`, `send`, `send_state`, `set_state`, `__del__`
`HasTraits`	`class_own_trait_events`, `class_own_traits`, `class_trait_names`, `class_traits`, `has_trait`, `hold_trait_notifications`, `observe`, `on_trait_change`, `set_trait`, `setup_instance`, `trait_events`, `trait_metadata`, `trait_names`, `traits`, `unobserve`, `unobserve_all`

Attributes

`Box`	`box_style`, `children`
`DOMWidget`	`layout`
`Widget`	`comm`, `keys`, `model_id`, `widget_types`, `widgets`
`LoggingHasTraits`	`log`
`HasTraits`	`cross_validation_lock`

pymor.discretizers.builtin.gui.jupyter.threejs.visualize_py3js(grid, U, bounding_box=([0, 0], [1, 1]), codim=2, title=None, legend=None, separate_colorbars=False, rescale_colorbars=False, columns=2, color_map=<matplotlib.colors.ListedColormap object>)[source]¶

Generate a pythreejs Plot and associated controls for scalar data associated to a two-dimensional Grid.

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.
columns: The number of columns in the visualizer GUI in case multiple plots are displayed at the same time.
color_map: a Matplotlib Colormap object