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 all the dependencies listed in can-env.yaml and then install candycan

pip install candycan

• 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)

• 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 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

• 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