SC7s Python Telemetry System

This folder contains programs (written primarily in python) responsible for gathering, parsing, storing, sending, and receiving CAN data from various components in the solar car.

Directory Structure

telemetry-python
├── example-data
├── scripts
└── src
    ├── can
    ├── resources
    └── xbee-tests

The scripts directory contains executable python scripts.

• virtual_system.py simulates the entire telemetry pipeline without need for a real CAN bus connection or XBees. This is useful for testing without connecting to a live CAN bus.
• onboard.py parses CAN data from the bus, sends the data as JSON from one XBee radio device to another. This program is intended to run onboard the car and send parsed data via XBees to the basestation.
• virtual_onboard.py functions similar to onboard.py, except that, like virtual_system.py, the CAN traffic is simulated.
• remote.py, running on the basestation, receives parsed data from onboard.py and stores it in a database.
• dbc_builder.py provides a way to conveniently create dbc files for devices on the CAN bus based off 'abstract' dbc files for these devices. It can, for example, create a dbc file for three MPPT devices (say, one at base adress 0x600, another at 0x610, and the third at 0x620) by using abstract_mppt.dbc (in src/resources).

The example-data folder contains CAN data, both raw and parsed, collected off MPPTs, motor controllers, etc.

The src contains classes and functions leveraged by the programs in scripts.

• sql.py contains helper functions for database interactions.
• util.py contains various minor utilites.
• Modules in the can directory contains logic for managing, simulating, and serializing aggregrate CAN data.
• resources houses all non-source-code files which are relevant to the system, including dbc files, SQLite databases, etc.

Setup

First, download the repository and go this directory

$ git clone https://github.com/nusolar/SC7s
$ cd telemetry-python

It is highly recommended to use a virtual environment like venv or direnv. Refer to this article to familiarize yourself with virtual environments.

Once in a virtual environment, you can run

$ pip install -e .

to install all the required packages.