Chronaxis user manual
Chronaxis analyzes time-series data from multiple sources. Bind input columns or message fields to signals, detect events and time intervals, calculate integrals, durations and statistics, then inspect or export the results. Typical inputs are BLF or ASC logs with a DBC; raw-message CSV, decoded-signal CSV and in-memory records are also supported.
Basic ASC/BLF examples include parallel settings. Start with the quick start, then choose continuous volumes from one recording or independent batch trials. See execution settings for the fields and resource choices.
This manual covers analysis configuration version 4 and its public interfaces. Use config_version: 4 for new analyses. See configuration syntax for writing an analysis. Install from a local wheel or source, then check the supported features. The HTML manual includes offline navigation and search. The AI bundle's manifest.json records its documentation revision and product baseline.
Reading path for configuration users
To ask AI to write configuration or Python code, open the AI guide for the source repository, offline manual, or installed package you have. Read its README and the matching task example directly.
- Follow installation, then run the supplied BLF/ASC quick start and check its 38 J integral.
- Read field paths and YAML indentation, configuration structure, and input sources. Replace the log directory, DBC, message ID and signal name with your own.
- Choose event rules, request/response matching, or a calculation interval, then select calculations and outputs.
- Compare the worked example results and consult result fields. Check units, status and reasons together with values.
Configuration validation checks declarations; it does not establish that file contents, a DBC, or engineering thresholds are correct. Run each complete example with its supplied inputs before replacing them.
Reading path for Python developers
Start with the Python quick start: load a plan, validate it, execute it, iterate over results, and close resources. Consult the public API reference and result structures as needed. Implementation details are separate from public usage.
Find a task
| Task | Read |
|---|---|
| Use your own BLF, ASC or CSV logs | Sources, CAN text inputs |
| Understand time, validity periods, unknown values and partial results | Time and result quality, glossary |
| Build expressions from signals | Expressions, configuration structure |
| Require several conditions for longer than a threshold, then integrate energy over the whole interval | Two-part ASC energy tutorial, including configuration, actual output and explanation |
| Detect activities, edges, or request/response pairs | Event rules, sequences |
| Use a domain preset | Presets |
| Adjust resources or run independent tasks in a batch | Execution settings, batch tasks |
| Inspect results or export signals/messages | Results, exports |
| Reuse definitions and inspect where fields come from | Imports, configuration tools |
| Maintain the implementation or build documentation | Architecture, documentation maintenance |
| Apply a calibration table | One-dimensional lookup and interpolation |
| Calculate rolling values or rates | Temporal transforms |
| Summarize event/window results | Result aggregation |
| Add a Python algorithm | Custom analysis operators |
| Accumulate same-frame per-message energy | High-performance integrator: multiply power by the duration represented by the message; accumulate existing energy contributions directly |
| Find complete runnable configurations | Example catalog |
Time-series transforms and host Python
Follow the ASC/DBC rolling and rate example or register your own Python calculation.