Archive for August, 2015

Matplotlib for Kivy Final Notes

August 26th, 2015 No comments

As outcome of one of the projects from the google summer of code 2015 two packages were created and are available as kivy garden packages.

These two packages can be used separately or can be combined as shown in


– If you are using dash_list attribute when creating a line in a figure, it will not work until the update is done in the next kivy version.
– The backend only works with the kivy >= 1.9.1. Some changes were done in the Widget class.
– Matplotlib backend has not been tested on android.

Both packages have been tested and debugged, however it is highly encouraged to submit any issue found while using them. Some of its advantages and weaknesses are exposed in the following paragraphs:

The first package is garden.matplotlib which is a matplotlib backend for kivy, it allows to create applications using pyplot instructions or to embed figures into a kivy application. There are two possible backends that can be invoked:

– backend_kivy which does the render using kivy graphics instructions.
– backend_kivyagg which does the render using an static image texture.

Both of the backends can be connected to the default matplotlib events Additionally another widget called NavigationToolbar can be instantiated and used with both backends, a better description can be found in A FigureCanvas can be used without a NavigationToolbar but a NavigationToolbar needs a FigureCanvas to be instantiated. The main advantage of this package is the ability to be added as another Kivy widget inside a Kivy application through the use of matplotlib instructions.

There are some features that were not implemented and the users should be aware of:

– backend_kivy and backend_kivyagg are non interactive backends (
– gouraud shading in backend_kivy
– configure_subplots button in the NavigationToolbar is not implemented.

Known bugs:

– When zooming on an area with not enough graphics instructions the render is not properly done.

In the same way there are some features implemented in backend_kivy that are not implemented in other graphics instructions based backends.

– clip path when rendering an image.
– draw_path_collection method for optimization.
– draw_markers for optimization.

We believe most of matplotlib current capabilities have been considered during this backend implementation. However, we encourage to submit any capability missed you may find.

Categories: GSOC2015, kivy, matplotlib Tags: , ,

N$ recognizer kivy implementation

August 18th, 2015 No comments
Categories: GSOC2015, kivy, python Tags: , , ,

Corner detection on Strokes and Strokes annotations on Matplotlib

August 6th, 2015 2 comments

Following my proposal for the Google Summer of Code 2015, an algorithm to detect corners on strokes was implemented and it is going to be polished and tested with a template matching classifier. This implementation follows Wolin approach in his paper: “ShortStraw: A Simple and Effective Corner Finder for Polylines” which can be downloaded from:

A brief explanation of the algorithm consists on sampling each one of the points in order to have equal distance between them. Then a window of +/-3 points is used and the average or mean of the distances is calculated. The distance between the initial point and the last point of the window form a straw. The straw is smaller in the cases that the window of points contain a corner.

corners strokesFrom the figure can be seen that pretty much corners are being detected. However a post processing still needs to be performed. In addition, a stroke canvas behavior was added to a figure canvas widget so annotations can be done on top of the graphs as can be seen in the figure below. The yellow background and the blue dots are just debugging information.

backend_kivy with annotations

backend_kivy and backend_kivyagg working with matplotlib 1.4.3, python2 and python3.

August 6th, 2015 No comments

Initially backend_kivy was implemented using matplotlib 1.5.1dev which is an unstable release. Small modifications were made based on the changes matplotlib has between 1.4.3 (stable) and 1.5.1dev. Some of the main points that were updated were the generation of MathText and the lines rendering in draw_path. Additionally in the case of backend_kivyagg a read-only memory view was sent from the agg renderer to the kivy backend, a writable copy had to be created due to an exception in cython for loading read-only buffers. Some other improvements involve a bug when the application was being closed which was fixed by adding an on_close event in the app and setting the global var app to None, another improvement is the generation of images from the plots. In the case of backend_kivy the figure canvas widget is being exported to png using the export_to_png(filename). A bug was reported when the FigureCanvasKivy is added to a BorderLayout and the window is resized. For instance if we have a button on top of the figure canvas, when resized the figure canvas is resized to the size of the window moving the button up but then is scaled down again and the dimensions are right. This arises in the backend_kivy due to a small delay that keeps the calculation of the positions in this case of the button out of the canvas. In backend_kivyagg this does not happen due to a fast transition so the button position is calculated correctly. (still an open issue and working on that for backend_kivy.)

Navigation Toolbar with matplotlib events connected

August 6th, 2015 No comments

NavigationToolbar is the last visual aspect from the backend that has been added with all the possible events connected to matplotlib. The main changes performed on the implementation involved to connect each one of the toolbar elements to the respective matplotlib calls. Additionally, the figure canvas is separated from the toolbar and both are children of a Float Layout inside an app generated via pyplot. The FigureCanvasKivy can be called independently of the NavigationToolbar but not in the opposite direction.

# updated version of the toolbar generation.
def _init_toolbar(self):
        basedir = os.path.join(rcParams['datapath'], 'images')
        actionview = ActionView()
        actionprevious = ActionPrevious(title="Navigation", with_previous=False)
        actionoverflow = ActionOverflow()
        actionview.use_separator = True
        for text, tooltip_text, image_file, callback in self.toolitems:
            if text is None:
            fname = os.path.join(basedir, image_file + '.png')
            if text in ['Pan', 'Zoom']:
                action_button = ActionToggleButton(text=text, icon=fname)
                action_button = ActionButton(text=text, icon=fname)
            action_button.bind(on_press=getattr(self, callback))

Navigation Toolbar Kivy

For matplotlib users the interface can look very familiar. However, I consider a brief explanation is necessary on the function of each button:

Home: Resets the plot axes to the initial state.
Left: Undo an operation performed.
Right: Redo an operation performed.
Pan: Allows to drag the plot.
Zoom: Allows to define a rectangular area to zoom in.
Configure: Not implemented yet but, loads a pop up for repositioning elements.
Save: Loads a Save Dialog to generate an image.
Configure and Save will be added at some point the next week.

Categories: GSOC2015, kivy, matplotlib Tags: , ,