Filipe Saraiva's blog

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Archive for the ‘planetkde-english’ tag

Scilab backend for Cantor is reviving

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Back to 2011 I was a GSoC student at Scilab community. My project was to create a backend for Scilab in Cantor, the KDE mathematical software. My project ended very well and the objectives were accomplished. You can see several features of this project in reports from my blog.

The backend would be working with Scilab 5.4 because I implemented the standard streams support in Scilab. The backend was available in KDE 4.8, released in January 2012, but Scilab 5.4 was released in October 2012. Unfortunately, when this version of Scilab was released, the standard streams support were not working.

Since this date I tried some times fix it but I did not obtained success.

Therefore, Scilab release a 5.5-beta1 version in begin of this October and I did a test with Cantor and… voilá! The standard streams is working now and the Cantor backend is working too!

Now it is time to Scilab backend for Cantor revive! I am doing “nightly-builds” of Scilab to verify if the standard streams are working and I developed new features to the backend. Let me show them:

Predefined functions and variables

In the past Scilab backend had a giant-size XML file listing all predefined functions and variables. Now the backend run getscilabkeywords command to get this information, used in tab-complete and syntax highlighting.

Tab-Complete and Syntax Highlighting

These features were available in previous version of the backend to predefined functions and variables. Now user variables are too used in these features.


Tab-complete for predefined functions

Syntax highlighting and Tab-Complete for user variables



Syntax highlighting

Variable Management Panel

Scilab backend now have a preliminary variable management panel. All variable defined by user are showed in this panel.

scilab-backend-variable-management-panelVariable Management panel

With this feature the user can save and load variables, and clean the variable environment.

But it is a preliminary version based in Octave variable management: the user must define the variable presenting it (you can not use “;”). So the label and value will be send to panel.

Scilab backend reviving in KDE 4.12

I am working for this new version of Scilab backend in KDE 4.12.

But you can test it now: the code is hosted in scilab-backend branch from Cantor repository.

Written by Filipe Saraiva

October 21st, 2013 at 6:37 pm

Python Backend for Cantor: Append Plot Images in Cantor Workspace

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Other feature implemented in python backend for Cantor in last weeks was “append plot image to Cantor Workspace”.

In other backends you can, optionally, generate a plot image and this image will be append in Cantor workspace, not generating a separated window to the picture.

Below we have a command to generate a plot image in python using matplotlib and pyplot:


Now we have the result appended in Cantor workspace:

python_plot_resultIn python, to save a picture using pyplot, we type the command pyplot.savefig(). But, if a picture was saved, it can not be shown in separated window. Otherwise, if a picture is shown in a separated window, it can not be saved to a file.

To solve this problem, the python backend change the show() command to savefig(), with a random name to the picture. The image is saved in a temporary file and loaded in Cantor workspace.

The option to load figure in Cantor workspace or to use a separated window is configured in python backend configuration screen. The default is to use separated window because matplotlib/pyplot have several additional features in image screen.

I would like to see some feedback from you, in special if you are a python developer. The code is hosted in python-backend branch from Cantor repository.

Side panels in Python Backend for Cantor: Help and Variable Management

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In last weeks I developed some new features to python backend for Cantor. In this post I will write about two of their: the help and the variable management, implemented as panels.

python_backend_help_panel python_backend_variable_management

 Help Panel on left; Variable Management Panel on right

Help panel

Help panel shows the help output in a separated panel, facilitating the consult of this information. To use it, simply use help command as in python interactive mode, as in example below:


Below a bigger help output, from a python module:


In previous picture, did you see some change in variable management panel?

Variable Management panel

Variable management panel is a great feature provide by Cantor, but until now just Octave backend had it. This feature show the variables defined in the session, their values, and allow some interesting functions to manipulate these variables.

You can define a lot of variables in python session and these variables will be shown in the panel. See:


In previous picture, I defined a integer variables x and y, a string a, and two modules: numpy and scipy, this last as sc. All this values are shown in panel.

Now I will change some values defined previously and will add some others:


See, now I defined a variable aa by the concatenation of two a‘s; the value of a is now aa; div is the division of y by x. I have a 2-dimensional matrix mtr defined by matrix function from numpy module; a python class HelloWorldClass and a object hello.

I can change the value of some these variable manipulating their values in the panel. For example, I can rewrite the x variable from 35 to 350, clicking in value column and typing 350.

But the more interesting features are provide by the buttons bellow this panel. These buttons load python scripts to run some feature. There are: add variable, load variables from a file, save variables to a file and clear all variables in the session.

Add variable is quite simple: a dialog is open and you type the variable and their value:

python_backend_variable_management_usecase4Save/Load use shelve module to save and load the variables of the session. But, it is important to say, this feature don’t save all the variables because shelve module have some limitations. For example, I saved this session to a file named “python_session.txt”. The python code loaded is:


After save, I will clear the session. The python code loaded is:

python_backend_variable_management_usecase6And, finally, I will load the variables. The python code loaded ans the variable panel is show below:



It is working now but I need some more tests, in special the save/load python objects defined by user and modules.

I would like to see some feedback from you. The code is hosted in python-backend branch from Cantor repository, so you can test it.

Cantor – dynamic keywords in python backend

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In previous post, I mentioned about dynamic keywords in python backend. The idea is, after import a python module in Cantor workspace, functions, keywords, variables, and more from this module are load by Cantor and available to syntax highlighting and tab complete.

This feature is implemented for now. You can test it compiling Cantor from python-backend branch.

But, let me show more information about this feature.

There are several ways to import a python module in python console. You have “import modulename”, “import modulename as modulevariable”, “from modulename import *”, “from modulename import function_1, function_2, …”, and more. Each import way causes different consequences to user experience.

The four import ways mentioned in previous paragraph are supported by python backend. I will show these different import ways and how python backend behave for each one.

import modulename

The more basic import way. After this command, a variable named “modulename” is defined and the functions and more keywords of this module are available to access using “modulename.keyword”.

python_import_modulenameTab Complete


Syntax Highlighting

import modulename as modulevariable

This way the user define a name “modulevariable” to reference “modulename”, and “modulename” is not defined. So, you can access the functions and more from “modulename” using “modulevariable.keyword”.


Tab Complete


Syntax Highlighting

from modulename import *

This way the user import all functions and keywords from “modulename” but anything variable is defined to access “modulename”. The functions of the module are accessed directly.


Tab Complete


Syntax Highlighting

from modulename import function_1, function_2, …

The user import only specific functions from a “modulename”, no all functions.


Tab Complete


Syntax Highlighting

Cantor plugin

I developed a Cantor plugin to import modules. This plugin open a dialog to user enter a modulename and, after press Ok, Cantor run “import modulename” and keywords are available. The diaglog is accessible by “Packaging” menu, in toolbar.


Cantor Plugin

Handling Errors

The backend can identify several errors during import.


Handling Errors


Well, the feature is working and it is mature for use, however it don’t support all import ways in python. But, I think these five ways cover the most commons import ways used by most python scientific users.

The important thing is, this feature enable python backend to support the several python modules, and no only scipy, numpy, and matplotlib, as I proposed in begin of this project.

Let me know how you import a module in python. I will develop support to more import ways in future versions of the backend.

For now, wait for more news of this project soon!

Python backend for Cantor – Syntax Highlighting, Tab Complete, and Interactive Mode

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My GSoC’2013 project for development a python backend for Cantor reach some objectives in before weeks.

Firstly I developed preliminary versions of syntax highlighting and tab complete features. This version use XML file processing to provide a basic set of keywords and built-in functions to these features. To activate tab complete, press Tab button after some letter.  You can see some screenshots of these features below (click mouse right-button and enlarge it):


A simple syntax highlighting


More syntax highlighting and tab complete

Next, the preliminary version  of the backend developed during LaKademy was build to support python script mode. But it would be more interesting to support python interactive mode in Cantor console. So, to do it, I implemented preprocessing in python command before send it to python interpreter, adding some commands to simulate python interactive mode.

This solution use PyRun_SimpleString from python/C API to process python commands in python script mode. There is other functions from python/C API to process python commands in interactive mode, for example, PyRun_InteractiveOne, but this function requires the python commands in a file to process and the output of commands is a bit of hard to get. So, for now, PyRun_SimpleString + preprocessing commands is working very well.

You can see in screenshots below and above that variable values and operation values are show in Cantor console without use of print command. It is a one of characteristic of python interactive mode:

python_backend_aninhado1More examples of syntax highlighting and python interactive mode


More examples of syntax highlighting and python interactive mode

Now I am developing a feature to list all functions and keywords from a specific python module imported during the program execution. I am using dir command to do it. This feature will provide these keywords to use in syntax highlighting and tab complete in a dynamic way. Then, python backend will support any python module, and not scipy, numpy, and matplotlib only.

Python backend is available in python-backend branch in Cantor repository. You can test it, but remember this code is not stable and finalized for now.

Google Summer of Code 2013: Cantor + Python

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This year I have a project accepted to Google Summer of Code. This is my second time in the program and I am very happy and grateful for it

This year I come back to Cantor, the KDE mathematical environment for several mathematical softwares like Maxima, Sage, Scilab, etc. In 2011 I developed the Scilab backend during Google Summer of Code. My mentor was Sylvestre Ledru, from Scilab team.

This time my project is conclude the scientific python backend for Cantor. I began the development of this piece of software during LaKademy. The communication between Cantor and python is working properly. The software miss some features of IDE, like tab-complete and syntax highlight. I intent provide support to scipy, numpy and matplotlib too. Alexander Rieder, from KDE, is my supervisor.

There are some ideas about how to implement tab-complete and syntax highlight. It is possible using XML files (like I implemented in Scilab backend) or use the Kdevelop technology. I am researching which I will use.

More updates will come soon! Follow the tag gsoc2013-python-backend this blog!

I am a Mage!

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This is just a manner of speaking. = D

May 27, 2013. I officially became a packer on Mageia, Mandriva-based Linux distro. The date marks the creation of an account with access permission to repository and build system for me.

I started contributing to Mageia since the early days of the project, translating the manifesto to Portuguese. Since then I helped on forums, IRC, mailing lists, got mirrors with high performance in Brazil, and did more work in promotion. However, I wanted to provide software for Mageia, because this is a job who not everyone can contribute.

To become a packers I had to participate in a mentoring program where I was guided by my Portuguese friend José Jorge or zezinho. I learned a lot about the art of creating RPM packages. Thanks zezinho!

I’m packing SWI-Prolog and abnTeX2. I intend to maintain some scientific-related software and KDE stuff like themes and plasma applets.

Well, this is my way to contribute to free software. Thank you, community!

Written by Filipe Saraiva

June 2nd, 2013 at 5:56 pm