pymor.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.gui.jupyter.logging.LogViewer(out, accordion=None)

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__()

Return repr(self).

close()

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)

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.gui.jupyter.logging.LoggingRedirector

Bases: object

Methods

LoggingRedirector

start, stop

patch module

---

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

columns

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

threejs module

---

class pymor.gui.jupyter.threejs.ColorBarRenderer(render_size, color_map, vmin=None, vmax=None)

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.gui.jupyter.threejs.Renderer(U, grid, render_size, color_map, title, bounding_box=([0, 0], [1, 1]), codim=2, vmin=None, vmax=None)

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.gui.jupyter.threejs.ThreeJSPlot(grid, color_map, title, bounding_box, codim, U, vmins, vmaxs, separate_colorbars, size)

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.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>)

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