Battery Data Toolkit

The battery-data-toolkit, battdat, creates consistently-formatted collections of battery data. The library has three main purposes:

1. Storing battery data in standardized formats. battdat stores data in high-performance file formats and include extensive metadata alongside data.

2. Interfacing battery data with the PyData ecosystem. The core data model, BatteryDataset, is built atop Pandas DataFrames.

3. Providing standard implementations of common analysis techniques. battdat implements functions which ensure quality or perform common analyses.

Source code: ROVI-org/battery-data-toolkit

Contents:

• Getting Started
  • Installation
• User Guide
  • The BatteryDataset Object
  • Reading and Writing Datasets
  • File Formats
  • Describing Battery Data
  • Post-Processing
  • Consistency Checks
  • Streaming Battery Data
• battdat API
  • Dataset (battdat.data)
  • Schemas (battdat.schemas)
  • Extractors (battdat.io)
  • Postprocessing (battdat.postprocess)
  • Error Checking (battdat.consistency)
  • Streaming (battdat.streaming)