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classmatplotlib.backend_tools.ToolHome(toolmanager, name)[source]
Bases: matplotlib.backend_tools.ViewsPositionsBase Restore the original view limits. default_keymap=['h', 'r', 'home']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Reset original view'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
image='home'
Filename of the image. str: Filename of the image to use in a Toolbar. If None, the name is used as a label in the toolbar button. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolHome |
default_keymap=['h', 'r', 'home']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolHome.default_keymap |
description='Reset original view'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolHome.description |
image='home'
Filename of the image. str: Filename of the image to use in a Toolbar. If None, the name is used as a label in the toolbar button. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolHome.image |
classmatplotlib.backend_tools.ToolMinorGrid(toolmanager, name)[source]
Bases: matplotlib.backend_tools.ToolBase Tool to toggle the major and minor grids of the figure. default_keymap=['G']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Toggle major and minor grids'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolMinorGrid |
default_keymap=['G']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolMinorGrid.default_keymap |
description='Toggle major and minor grids'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolMinorGrid.description |
trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolMinorGrid.trigger |
classmatplotlib.backend_tools.ToolPan(*args)[source]
Bases: matplotlib.backend_tools.ZoomPanBase Pan axes with left mouse, zoom with right. cursor=4[source]
Cursor to use when the tool is active.
default_keymap=['p']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Pan axes with left mouse, zoom with right'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
image='move'
Filename of the image. str: Filename of the image to use in a Toolbar. If None, the name is used as a label in the toolbar button.
radio_group='default'
Attribute to group 'radio' like tools (mutually exclusive). str that identifies the group or None if not belonging to a group. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolPan |
cursor=4[source]
Cursor to use when the tool is active. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolPan.cursor |
default_keymap=['p']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolPan.default_keymap |
description='Pan axes with left mouse, zoom with right'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolPan.description |
image='move'
Filename of the image. str: Filename of the image to use in a Toolbar. If None, the name is used as a label in the toolbar button. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolPan.image |
radio_group='default'
Attribute to group 'radio' like tools (mutually exclusive). str that identifies the group or None if not belonging to a group. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolPan.radio_group |
classmatplotlib.backend_tools.ToolQuit(toolmanager, name)[source]
Bases: matplotlib.backend_tools.ToolBase Tool to call the figure manager destroy method. default_keymap=['ctrl+w', 'cmd+w', 'q']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Quit the figure'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuit |
default_keymap=['ctrl+w', 'cmd+w', 'q']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuit.default_keymap |
description='Quit the figure'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuit.description |
trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuit.trigger |
classmatplotlib.backend_tools.ToolQuitAll(toolmanager, name)[source]
Bases: matplotlib.backend_tools.ToolBase Tool to call the figure manager destroy method. default_keymap=[]
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Quit all figures'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuitAll |
default_keymap=[]
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuitAll.default_keymap |
description='Quit all figures'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuitAll.description |
trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolQuitAll.trigger |
matplotlib.backend_tools.ToolSetCursor[source]
alias of matplotlib.backend_tools.SetCursorBase | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolSetCursor |
classmatplotlib.backend_tools.ToolToggleBase(*args, **kwargs)[source]
Bases: matplotlib.backend_tools.ToolBase Toggleable tool. Every time it is triggered, it switches between enable and disable. Parameters
``*args``
Variable length argument to be used by the Tool. ``**kwargs``
toggled if present and True, sets the initial state of the Tool Arbitrary keyword arguments to be consumed by the Tool cursor=None
Cursor to use when the tool is active.
default_toggled=False
Default of toggled state.
disable(event=None)[source]
Disable the toggle tool. trigger call this method when toggled is True. This can happen in different circumstances. Click on the toolbar tool button. Call to matplotlib.backend_managers.ToolManager.trigger_tool. Another ToolToggleBase derived tool is triggered (from the same ToolManager).
enable(event=None)[source]
Enable the toggle tool. trigger calls this method when toggled is False.
radio_group=None
Attribute to group 'radio' like tools (mutually exclusive). str that identifies the group or None if not belonging to a group.
set_figure(figure)[source]
propertytoggled
State of the toggled tool.
trigger(sender, event, data=None)[source]
Calls enable or disable based on toggled value. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase |
cursor=None
Cursor to use when the tool is active. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase.cursor |
default_toggled=False
Default of toggled state. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase.default_toggled |
disable(event=None)[source]
Disable the toggle tool. trigger call this method when toggled is True. This can happen in different circumstances. Click on the toolbar tool button. Call to matplotlib.backend_managers.ToolManager.trigger_tool. Another ToolToggleBase derived tool is triggered (from the same ToolManager). | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase.disable |
enable(event=None)[source]
Enable the toggle tool. trigger calls this method when toggled is False. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase.enable |
radio_group=None
Attribute to group 'radio' like tools (mutually exclusive). str that identifies the group or None if not belonging to a group. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase.radio_group |
set_figure(figure)[source] | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase.set_figure |
trigger(sender, event, data=None)[source]
Calls enable or disable based on toggled value. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolToggleBase.trigger |
classmatplotlib.backend_tools.ToolViewsPositions(*args, **kwargs)[source]
Bases: matplotlib.backend_tools.ToolBase Auxiliary Tool to handle changes in views and positions. Runs in the background and should get used by all the tools that need to access the figure's history of views and positions, e.g. ToolZoom ToolPan ToolHome ToolBack ToolForward add_figure(figure)[source]
Add the current figure to the stack of views and positions.
back()[source]
Back one step in the stack of views and positions.
clear(figure)[source]
Reset the axes stack.
forward()[source]
Forward one step in the stack of views and positions.
home()[source]
Recall the first view and position from the stack.
push_current(figure=None)[source]
Push the current view limits and position onto their respective stacks.
update_home_views(figure=None)[source]
Make sure that self.home_views has an entry for all axes present in the figure.
update_view()[source]
Update the view limits and position for each axes from the current stack position. If any axes are present in the figure that aren't in the current stack position, use the home view limits for those axes and don't update any positions. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions |
add_figure(figure)[source]
Add the current figure to the stack of views and positions. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.add_figure |
back()[source]
Back one step in the stack of views and positions. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.back |
clear(figure)[source]
Reset the axes stack. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.clear |
forward()[source]
Forward one step in the stack of views and positions. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.forward |
home()[source]
Recall the first view and position from the stack. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.home |
push_current(figure=None)[source]
Push the current view limits and position onto their respective stacks. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.push_current |
update_home_views(figure=None)[source]
Make sure that self.home_views has an entry for all axes present in the figure. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.update_home_views |
update_view()[source]
Update the view limits and position for each axes from the current stack position. If any axes are present in the figure that aren't in the current stack position, use the home view limits for those axes and don't update any positions. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolViewsPositions.update_view |
classmatplotlib.backend_tools.ToolXScale(*args, **kwargs)[source]
Bases: matplotlib.backend_tools.AxisScaleBase Tool to toggle between linear and logarithmic scales on the X axis. default_keymap=['k', 'L']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Toggle scale X axis'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
set_scale(ax, scale)[source] | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolXScale |
default_keymap=['k', 'L']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolXScale.default_keymap |
description='Toggle scale X axis'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolXScale.description |
set_scale(ax, scale)[source] | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolXScale.set_scale |
classmatplotlib.backend_tools.ToolYScale(*args, **kwargs)[source]
Bases: matplotlib.backend_tools.AxisScaleBase Tool to toggle between linear and logarithmic scales on the Y axis. default_keymap=['l']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Toggle scale Y axis'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
set_scale(ax, scale)[source] | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolYScale |
default_keymap=['l']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolYScale.default_keymap |
description='Toggle scale Y axis'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolYScale.description |
set_scale(ax, scale)[source] | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolYScale.set_scale |
classmatplotlib.backend_tools.ToolZoom(*args)[source]
Bases: matplotlib.backend_tools.ZoomPanBase A Tool for zooming using a rectangle selector. cursor=3[source]
Cursor to use when the tool is active.
default_keymap=['o']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas.
description='Zoom to rectangle'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar.
image='zoom_to_rect'
Filename of the image. str: Filename of the image to use in a Toolbar. If None, the name is used as a label in the toolbar button.
radio_group='default'
Attribute to group 'radio' like tools (mutually exclusive). str that identifies the group or None if not belonging to a group. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolZoom |
cursor=3[source]
Cursor to use when the tool is active. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolZoom.cursor |
default_keymap=['o']
Keymap to associate with this tool. list[str]: List of keys that will trigger this tool when a keypress event is emitted on self.figure.canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolZoom.default_keymap |
description='Zoom to rectangle'
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolZoom.description |
image='zoom_to_rect'
Filename of the image. str: Filename of the image to use in a Toolbar. If None, the name is used as a label in the toolbar button. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolZoom.image |
radio_group='default'
Attribute to group 'radio' like tools (mutually exclusive). str that identifies the group or None if not belonging to a group. | matplotlib.backend_tools_api#matplotlib.backend_tools.ToolZoom.radio_group |
classmatplotlib.backend_tools.ViewsPositionsBase(toolmanager, name)[source]
Bases: matplotlib.backend_tools.ToolBase Base class for ToolHome, ToolBack and ToolForward. trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ViewsPositionsBase |
trigger(sender, event, data=None)[source]
Called when this tool gets used. This method is called by ToolManager.trigger_tool. Parameters
eventEvent
The canvas event that caused this tool to be called.
senderobject
Object that requested the tool to be triggered.
dataobject
Extra data. | matplotlib.backend_tools_api#matplotlib.backend_tools.ViewsPositionsBase.trigger |
classmatplotlib.backend_tools.ZoomPanBase(*args)[source]
Bases: matplotlib.backend_tools.ToolToggleBase Base class for ToolZoom and ToolPan. disable(event)[source]
Release the canvas and disconnect press/release events.
enable(event)[source]
Connect press/release events and lock the canvas.
scroll_zoom(event)[source]
trigger(sender, event, data=None)[source]
Calls enable or disable based on toggled value. | matplotlib.backend_tools_api#matplotlib.backend_tools.ZoomPanBase |
disable(event)[source]
Release the canvas and disconnect press/release events. | matplotlib.backend_tools_api#matplotlib.backend_tools.ZoomPanBase.disable |
enable(event)[source]
Connect press/release events and lock the canvas. | matplotlib.backend_tools_api#matplotlib.backend_tools.ZoomPanBase.enable |
scroll_zoom(event)[source] | matplotlib.backend_tools_api#matplotlib.backend_tools.ZoomPanBase.scroll_zoom |
trigger(sender, event, data=None)[source]
Calls enable or disable based on toggled value. | matplotlib.backend_tools_api#matplotlib.backend_tools.ZoomPanBase.trigger |
backend_webagg Displays Agg images in the browser, with interactivity matplotlib.backends.backend_webagg.FigureCanvas[source]
alias of matplotlib.backends.backend_webagg.FigureCanvasWebAgg
classmatplotlib.backends.backend_webagg.FigureCanvasWebAgg(*args, **kwargs)[source]
Bases: matplotlib.backends.backend_webagg_core.FigureCanvasWebAggCore
classmatplotlib.backends.backend_webagg.ServerThread(group=None, target=None, name=None, args=(), kwargs=None, *, daemon=None)[source]
Bases: threading.Thread This constructor should always be called with keyword arguments. Arguments are: group should be None; reserved for future extension when a ThreadGroup class is implemented. target is the callable object to be invoked by the run() method. Defaults to None, meaning nothing is called. name is the thread name. By default, a unique name is constructed of the form "Thread-N" where N is a small decimal number. args is the argument tuple for the target invocation. Defaults to (). kwargs is a dictionary of keyword arguments for the target invocation. Defaults to {}. If a subclass overrides the constructor, it must make sure to invoke the base class constructor (Thread.__init__()) before doing anything else to the thread. run()[source]
Method representing the thread's activity. You may override this method in a subclass. The standard run() method invokes the callable object passed to the object's constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.
classmatplotlib.backends.backend_webagg.WebAggApplication(url_prefix='')[source]
Bases: tornado.web.Application classAllFiguresPage(application, request, *, url_prefix='', **kwargs)[source]
Bases: tornado.web.RequestHandler get()[source]
classDownload(application:tornado.web.Application, request:tornado.httputil.HTTPServerRequest, **kwargs:Any)[source]
Bases: tornado.web.RequestHandler get(fignum, fmt)[source]
classFavIcon(application:tornado.web.Application, request:tornado.httputil.HTTPServerRequest, **kwargs:Any)[source]
Bases: tornado.web.RequestHandler get()[source]
classMplJs(application:tornado.web.Application, request:tornado.httputil.HTTPServerRequest, **kwargs:Any)[source]
Bases: tornado.web.RequestHandler get()[source]
classSingleFigurePage(application, request, *, url_prefix='', **kwargs)[source]
Bases: tornado.web.RequestHandler get(fignum)[source]
classWebSocket(application:tornado.web.Application, request:tornado.httputil.HTTPServerRequest, **kwargs:Any)[source]
Bases: tornado.websocket.WebSocketHandler on_close()[source]
Invoked when the WebSocket is closed. If the connection was closed cleanly and a status code or reason phrase was supplied, these values will be available as the attributes self.close_code and self.close_reason. Changed in version 4.0: Added close_code and close_reason attributes.
on_message(message)[source]
Handle incoming messages on the WebSocket This method must be overridden. Changed in version 4.5: on_message can be a coroutine.
open(fignum)[source]
Invoked when a new WebSocket is opened. The arguments to open are extracted from the tornado.web.URLSpec regular expression, just like the arguments to tornado.web.RequestHandler.get. open may be a coroutine. on_message will not be called until open has returned. Changed in version 5.1: open may be a coroutine.
send_binary(blob)[source]
send_json(content)[source]
supports_binary=True
classmethodinitialize(url_prefix='', port=None, address=None)[source]
initialized=False
classmethodstart()[source]
started=False
matplotlib.backends.backend_webagg.ipython_inline_display(figure)[source]
matplotlib.backends.backend_webagg.show()[source]
Show all figures. show blocks by calling mainloop if block is True, or if it is None and we are neither in IPython's %pylab mode, nor in interactive mode. | matplotlib.backend_webagg_api |
matplotlib.backends backend_mixed backend_template backend_agg backend_cairo backend_gtk3agg, backend_gtk3cairo backend_gtk4agg, backend_gtk4cairo backend_nbagg backend_pdf backend_pgf backend_ps backend_qtagg, backend_qtcairo backend_svg backend_tkagg, backend_tkcairo backend_webagg backend_wxagg, backend_wxcairo | matplotlib.index_backend_api |
matplotlib.backends.backend_agg.FigureCanvas[source]
alias of matplotlib.backends.backend_agg.FigureCanvasAgg | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvas |
classmatplotlib.backends.backend_agg.FigureCanvasAgg(figure=None)[source]
Bases: matplotlib.backend_bases.FigureCanvasBase buffer_rgba()[source]
Get the image as a memoryview to the renderer's buffer. draw must be called at least once before this function will work and to update the renderer for any subsequent changes to the Figure.
copy_from_bbox(bbox)[source]
draw()[source]
Render the Figure. It is important that this method actually walk the artist tree even if not output is produced because this will trigger deferred work (like computing limits auto-limits and tick values) that users may want access to before saving to disk.
get_renderer(cleared=False)[source]
print_jpeg(filename_or_obj, *args, pil_kwargs=None, **kwargs)[source]
Write the figure to a JPEG file. Parameters
filename_or_objstr or path-like or file-like
The file to write to. Other Parameters
pil_kwargsdict, optional
Additional keyword arguments that are passed to PIL.Image.Image.save when saving the figure.
print_jpg(filename_or_obj, *args, pil_kwargs=None, **kwargs)[source]
Write the figure to a JPEG file. Parameters
filename_or_objstr or path-like or file-like
The file to write to. Other Parameters
pil_kwargsdict, optional
Additional keyword arguments that are passed to PIL.Image.Image.save when saving the figure.
print_png(filename_or_obj, *args, metadata=None, pil_kwargs=None)[source]
Write the figure to a PNG file. Parameters
filename_or_objstr or path-like or file-like
The file to write to.
metadatadict, optional
Metadata in the PNG file as key-value pairs of bytes or latin-1 encodable strings. According to the PNG specification, keys must be shorter than 79 chars. The PNG specification defines some common keywords that may be used as appropriate: Title: Short (one line) title or caption for image. Author: Name of image's creator. Description: Description of image (possibly long). Copyright: Copyright notice. Creation Time: Time of original image creation (usually RFC 1123 format). Software: Software used to create the image. Disclaimer: Legal disclaimer. Warning: Warning of nature of content. Source: Device used to create the image. Comment: Miscellaneous comment; conversion from other image format. Other keywords may be invented for other purposes. If 'Software' is not given, an autogenerated value for Matplotlib will be used. This can be removed by setting it to None. For more details see the PNG specification.
pil_kwargsdict, optional
Keyword arguments passed to PIL.Image.Image.save. If the 'pnginfo' key is present, it completely overrides metadata, including the default 'Software' key.
print_raw(filename_or_obj, *args)[source]
print_rgba(filename_or_obj, *args)[source]
print_tif(filename_or_obj, *, pil_kwargs=None)[source]
print_tiff(filename_or_obj, *, pil_kwargs=None)[source]
print_to_buffer()[source]
restore_region(region, bbox=None, xy=None)[source]
tostring_argb()[source]
Get the image as ARGB bytes. draw must be called at least once before this function will work and to update the renderer for any subsequent changes to the Figure.
tostring_rgb()[source]
Get the image as RGB bytes. draw must be called at least once before this function will work and to update the renderer for any subsequent changes to the Figure. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg |
buffer_rgba()[source]
Get the image as a memoryview to the renderer's buffer. draw must be called at least once before this function will work and to update the renderer for any subsequent changes to the Figure. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.buffer_rgba |
copy_from_bbox(bbox)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.copy_from_bbox |
draw()[source]
Render the Figure. It is important that this method actually walk the artist tree even if not output is produced because this will trigger deferred work (like computing limits auto-limits and tick values) that users may want access to before saving to disk. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.draw |
get_renderer(cleared=False)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.get_renderer |
print_jpeg(filename_or_obj, *args, pil_kwargs=None, **kwargs)[source]
Write the figure to a JPEG file. Parameters
filename_or_objstr or path-like or file-like
The file to write to. Other Parameters
pil_kwargsdict, optional
Additional keyword arguments that are passed to PIL.Image.Image.save when saving the figure. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_jpeg |
print_jpg(filename_or_obj, *args, pil_kwargs=None, **kwargs)[source]
Write the figure to a JPEG file. Parameters
filename_or_objstr or path-like or file-like
The file to write to. Other Parameters
pil_kwargsdict, optional
Additional keyword arguments that are passed to PIL.Image.Image.save when saving the figure. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_jpg |
print_png(filename_or_obj, *args, metadata=None, pil_kwargs=None)[source]
Write the figure to a PNG file. Parameters
filename_or_objstr or path-like or file-like
The file to write to.
metadatadict, optional
Metadata in the PNG file as key-value pairs of bytes or latin-1 encodable strings. According to the PNG specification, keys must be shorter than 79 chars. The PNG specification defines some common keywords that may be used as appropriate: Title: Short (one line) title or caption for image. Author: Name of image's creator. Description: Description of image (possibly long). Copyright: Copyright notice. Creation Time: Time of original image creation (usually RFC 1123 format). Software: Software used to create the image. Disclaimer: Legal disclaimer. Warning: Warning of nature of content. Source: Device used to create the image. Comment: Miscellaneous comment; conversion from other image format. Other keywords may be invented for other purposes. If 'Software' is not given, an autogenerated value for Matplotlib will be used. This can be removed by setting it to None. For more details see the PNG specification.
pil_kwargsdict, optional
Keyword arguments passed to PIL.Image.Image.save. If the 'pnginfo' key is present, it completely overrides metadata, including the default 'Software' key. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_png |
print_raw(filename_or_obj, *args)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_raw |
print_rgba(filename_or_obj, *args)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_rgba |
print_tif(filename_or_obj, *, pil_kwargs=None)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_tif |
print_tiff(filename_or_obj, *, pil_kwargs=None)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_tiff |
print_to_buffer()[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.print_to_buffer |
restore_region(region, bbox=None, xy=None)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.restore_region |
tostring_argb()[source]
Get the image as ARGB bytes. draw must be called at least once before this function will work and to update the renderer for any subsequent changes to the Figure. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.tostring_argb |
tostring_rgb()[source]
Get the image as RGB bytes. draw must be called at least once before this function will work and to update the renderer for any subsequent changes to the Figure. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.FigureCanvasAgg.tostring_rgb |
matplotlib.backends.backend_agg.get_hinting_flag()[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.get_hinting_flag |
classmatplotlib.backends.backend_agg.RendererAgg(width, height, dpi)[source]
Bases: matplotlib.backend_bases.RendererBase The renderer handles all the drawing primitives using a graphics context instance that controls the colors/styles buffer_rgba()[source]
clear()[source]
draw_mathtext(gc, x, y, s, prop, angle)[source]
Draw mathtext using matplotlib.mathtext.
draw_path(gc, path, transform, rgbFace=None)[source]
Draw a Path instance using the given affine transform.
draw_tex(gc, x, y, s, prop, angle, *, mtext=None)[source]
draw_text(gc, x, y, s, prop, angle, ismath=False, mtext=None)[source]
Draw the text instance. Parameters
gcGraphicsContextBase
The graphics context.
xfloat
The x location of the text in display coords.
yfloat
The y location of the text baseline in display coords.
sstr
The text string.
propmatplotlib.font_manager.FontProperties
The font properties.
anglefloat
The rotation angle in degrees anti-clockwise.
mtextmatplotlib.text.Text
The original text object to be rendered. Notes Note for backend implementers: When you are trying to determine if you have gotten your bounding box right (which is what enables the text layout/alignment to work properly), it helps to change the line in text.py: if 0: bbox_artist(self, renderer)
to if 1, and then the actual bounding box will be plotted along with your text.
get_canvas_width_height()[source]
Return the canvas width and height in display coords.
get_content_extents()[source]
[Deprecated] Notes Deprecated since version 3.4:
get_text_width_height_descent(s, prop, ismath)[source]
Get the width, height, and descent (offset from the bottom to the baseline), in display coords, of the string s with FontProperties prop.
lock=<unlocked _thread.RLock object owner=0 count=0>
option_image_nocomposite()[source]
Return whether image composition by Matplotlib should be skipped. Raster backends should usually return False (letting the C-level rasterizer take care of image composition); vector backends should usually return not rcParams["image.composite_image"].
option_scale_image()[source]
Return whether arbitrary affine transformations in draw_image() are supported (True for most vector backends).
points_to_pixels(points)[source]
Convert points to display units. You need to override this function (unless your backend doesn't have a dpi, e.g., postscript or svg). Some imaging systems assume some value for pixels per inch: points to pixels = points * pixels_per_inch/72 * dpi/72
Parameters
pointsfloat or array-like
a float or a numpy array of float Returns
Points converted to pixels
restore_region(region, bbox=None, xy=None)[source]
Restore the saved region. If bbox (instance of BboxBase, or its extents) is given, only the region specified by the bbox will be restored. xy (a pair of floats) optionally specifies the new position (the LLC of the original region, not the LLC of the bbox) where the region will be restored. >>> region = renderer.copy_from_bbox()
>>> x1, y1, x2, y2 = region.get_extents()
>>> renderer.restore_region(region, bbox=(x1+dx, y1, x2, y2),
... xy=(x1-dx, y1))
start_filter()[source]
Start filtering. It simply create a new canvas (the old one is saved).
stop_filter(post_processing)[source]
Save the plot in the current canvas as a image and apply the post_processing function. def post_processing(image, dpi):
# ny, nx, depth = image.shape # image (numpy array) has RGBA channels and has a depth of 4. ... # create a new_image (numpy array of 4 channels, size can be # different). The resulting image may have offsets from # lower-left corner of the original image return new_image, offset_x, offset_y The saved renderer is restored and the returned image from post_processing is plotted (using draw_image) on it.
tostring_argb()[source]
tostring_rgb()[source]
tostring_rgba_minimized()[source]
[Deprecated] Notes Deprecated since version 3.4: | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg |
buffer_rgba()[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.buffer_rgba |
clear()[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.clear |
draw_mathtext(gc, x, y, s, prop, angle)[source]
Draw mathtext using matplotlib.mathtext. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.draw_mathtext |
draw_path(gc, path, transform, rgbFace=None)[source]
Draw a Path instance using the given affine transform. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.draw_path |
draw_tex(gc, x, y, s, prop, angle, *, mtext=None)[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.draw_tex |
draw_text(gc, x, y, s, prop, angle, ismath=False, mtext=None)[source]
Draw the text instance. Parameters
gcGraphicsContextBase
The graphics context.
xfloat
The x location of the text in display coords.
yfloat
The y location of the text baseline in display coords.
sstr
The text string.
propmatplotlib.font_manager.FontProperties
The font properties.
anglefloat
The rotation angle in degrees anti-clockwise.
mtextmatplotlib.text.Text
The original text object to be rendered. Notes Note for backend implementers: When you are trying to determine if you have gotten your bounding box right (which is what enables the text layout/alignment to work properly), it helps to change the line in text.py: if 0: bbox_artist(self, renderer)
to if 1, and then the actual bounding box will be plotted along with your text. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.draw_text |
get_canvas_width_height()[source]
Return the canvas width and height in display coords. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.get_canvas_width_height |
get_content_extents()[source]
[Deprecated] Notes Deprecated since version 3.4: | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.get_content_extents |
get_text_width_height_descent(s, prop, ismath)[source]
Get the width, height, and descent (offset from the bottom to the baseline), in display coords, of the string s with FontProperties prop. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.get_text_width_height_descent |
lock=<unlocked _thread.RLock object owner=0 count=0> | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.lock |
option_image_nocomposite()[source]
Return whether image composition by Matplotlib should be skipped. Raster backends should usually return False (letting the C-level rasterizer take care of image composition); vector backends should usually return not rcParams["image.composite_image"]. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.option_image_nocomposite |
option_scale_image()[source]
Return whether arbitrary affine transformations in draw_image() are supported (True for most vector backends). | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.option_scale_image |
points_to_pixels(points)[source]
Convert points to display units. You need to override this function (unless your backend doesn't have a dpi, e.g., postscript or svg). Some imaging systems assume some value for pixels per inch: points to pixels = points * pixels_per_inch/72 * dpi/72
Parameters
pointsfloat or array-like
a float or a numpy array of float Returns
Points converted to pixels | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.points_to_pixels |
restore_region(region, bbox=None, xy=None)[source]
Restore the saved region. If bbox (instance of BboxBase, or its extents) is given, only the region specified by the bbox will be restored. xy (a pair of floats) optionally specifies the new position (the LLC of the original region, not the LLC of the bbox) where the region will be restored. >>> region = renderer.copy_from_bbox()
>>> x1, y1, x2, y2 = region.get_extents()
>>> renderer.restore_region(region, bbox=(x1+dx, y1, x2, y2),
... xy=(x1-dx, y1)) | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.restore_region |
start_filter()[source]
Start filtering. It simply create a new canvas (the old one is saved). | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.start_filter |
stop_filter(post_processing)[source]
Save the plot in the current canvas as a image and apply the post_processing function. def post_processing(image, dpi):
# ny, nx, depth = image.shape # image (numpy array) has RGBA channels and has a depth of 4. ... # create a new_image (numpy array of 4 channels, size can be # different). The resulting image may have offsets from # lower-left corner of the original image return new_image, offset_x, offset_y The saved renderer is restored and the returned image from post_processing is plotted (using draw_image) on it. | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.stop_filter |
tostring_argb()[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.tostring_argb |
tostring_rgb()[source] | matplotlib.backend_agg_api#matplotlib.backends.backend_agg.RendererAgg.tostring_rgb |
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