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Description:

candycan 0.0.1

candycan

Features:

Support both native SocketCAN and Python-CAN (SocketCAN, Kvaser,
Vector, etc.)
Production level data encapsulation with cached properties
Message parsing with DBC file
Automatic data validation via Pydantic
Virtual CAN testing inside Jupyter notebook with multiprocessing
support
Highly efficient a2l file handling with lazy loading and dynamic
properties
Fast file loading by streaming json processing

It’s based on Python-CAN, Scapy, cantools and dbc-editor
Install
Install all the dependencies listed in can-env.yaml and then install
candycan
pip install candycan

Data link layer (ISO 11898-1)

Frame sending and receiving
conversion from Python dict object to json and CAN frame
message parsing with given dbc
Multiprocessing support for testing inside the notebook
Secure CAN device add/remove with password proctected gpg encryption
Flexible arguments setting with argparse and subprocess pipes
on native
SocketCan
or Python-CAN virtual
CAN channel
Options with Python-CAN or
Scapy
backends
integrate can-utils, scapy
sniff for large
scale testing (tbd)
extend to Kvaser, Vector, etc. (tbd)

CCP

on physical CAN device (Kvaser) with Scapy application interface
on physical CAN device with Python-CAN and CCP logic
context manager for CCP functions
CCP/XCP data codecs and data processing in Numpy array
Bytes codecs (raw binary, hex, numeric) with endianess handling
Flexible arguments setting with argparse and interactive InquirerPy
Encapsulation of CAN specs in unified
Pydantic models (serialization,
schema, validation, annotation, etc.)
Self incremental command counter inside CAN specs object
Type systems for CAN (native SocketCAN, Python-CAN) and bus
(SocketCAN, virtual CAN, Kvaser, Vector, etc.)

A2L

a2l checking and fixing with
pya2l and
a2ltool
conversion a2l file to legit json files
al2 tree node, path and path segment definitions for positioning
semantics
searching of calibration terms
Calibration object with dynamic properties for easy access of class
attributes
Cached properties for automatic data conversion and derivative
properties like data size for corresponding data type
Exception handling with missing calibration properties
Unified processing of measurement, axis, conversion method and data
layouts
Lazy loading and streaming json file processing with events based
ijson
Encapsulation of calibration properties and values in Pydantic objects
(automatic validation, serialization, schemes, etc.)
Type systems for CCP/XCP data types
XCPConfig for CCP/XCP configuration
XCPData for encapsulating calibration data with automatic dimension,
data size validation, automatic codecs of hex, raw and numeric values
array view of table data as cached property with automatic type
conversion and validation
Create calibration data object from a2l file
Get calibration data from downloaded json file

DBC

load dbc files with cantools
append new messages and signals to dbc files
GUI for interactively modifying dbc on the command line with
dbc-editor

XCP (tbd)