This module customizes access to Harman CNLogger/TraceClient, targets (processing units in cars), GoogleMaps, and has a potential to be expanded on other tools for automotive software development. In addition to conventional Python environments, it supports notebook apps like IPython notebook and its up-to-date fork Jupyter.

Combined with the Jupyter/IPython and immence set of Python libraries, it creates convenient uniform environment for analysing traces and manipulating automotive software/hardware.

Though more work is still needed to make gAutoy a unification tool-of-choice in the automotive software community, we are on our way towards this goal.

Automotive software development is related to extensive use of different legacy tools, which are not customized under a single convenient environment. We designed gAutoy with an attempt to fill this gap, enabling you to manage different tasks in a single environment.

gAutoy was developed with the following goals in mind:

• automated issue reproduction, testing, and analysis
• "live" documentation (provide a unified, customizable, and convenient environment for creating documents that combine explanatory text with live codes driven by automotive software development utils)
• integration of automotive software development utils with IDEs

This tool is aimed to serve primarily to the following categories of people involved in automotive software industry:

• smart and "lazy" QA specialists who are interested in testing process automation
• allure and flabby developers who knows Python

Read details on installation process in INSTALL or on the documentation site.

For quick start We welcome users to start study gAutoy with the Cookbook -- a tiny introductory tutorial for those, who wish to get an at-a-glance impression of the gAutoy.

In addition, we collected some notable IPython notebook/Jupyter cells in this notebook. These can serve as introductory examples too.

Find more simple examples here. For sophisticated examples, we refer to the utils folder.

Complete documentation is available here. For gAutoy API details see API Reference.

Use question mark in notebooks (or .__doc__ in interactive Python console) to get dynamic info about class, method, function, and/or magic of interest.

The following features are not extremely time-consumung, but very "handy":

• Python 3 support
• Jupyter (so far works fine with Jupyter 4 and IPython 3.2.1+)
• set_option function to specify package options (similar to this) (mostly done)
• Enhanced RegExp support for log messages: make it consistent with Python re; enhanced C format parsing: use parse
• Unicode support
• Simple plugins for Sublime and Emacs
• multi-line patterns with multi-occurances (so far use only single occurance in multi-line) (mostly done by power taking syntax)
• improve KML viewer using OpenLayers 3 as map API and blogspot collection as examples (see more on KML here)
• Full support of pandas-like wraps for traces
• Documentation (fix API description, more examples)
• pip and conda installation

The following features are ordered by usefulness and usually take considerable time to implement:

• Link log massages and source codes: use Clang parser binds
• Play with C++ Cling kernel (and other)
• Extend loggers support with: dlt-viewer, etc
• Anvanced plugins for Sublime and Emacs editors Optional: plugins for IDEs (e.g., Eclipse, MS VS, etc)
• Add notebook widgets for GoogleMaps
• create custom IPython and Jupyter profiles/configs
• better support of MOST function catalogs (e.g., input and output parameters for MOST function calls)

Email: [email protected] (Sergiy Gogolenko)

So far only one contributor.

See the LICENSE file for license rights and limitations (GPLv3).

gAutoy Copyright © 2015 SGogolenko