cellpy-core¶

The core engine of cellpy: fast, thread-safe processing of battery-cycling raw data. Given a raw data frame (polars, native schema — or pandas via the legacy bridge), it finds all steps and cycles and builds per-step and per-cycle summary tables. It is designed to be small, schema-injected, and easy for cellpy developers to build on and extend; instrument loaders, file IO, and unit handling stay out of the core.

import polars as pl
from cellpycore import Data, make_step_table, make_summary

data = Data.from_raw_frame(pl.read_parquet("my_native_raw.parquet"))
make_step_table(data, nom_cap=1.0)
make_summary(data)
print(data.steps, data.summary)

Where to go next¶

• Getting started — install the package and run the pipeline for the first time.
• Examples — runnable notebooks demonstrating the full pipeline on mock and real cycling data.
• Standalone use — the slim-consumer guide: step tables and per-cycle summaries without full cellpy.
• Harmonized raw format — the authoritative input-format specification.
• Development guide — practices and workflows for contributors.

Design in one paragraph¶

The engine is polars-native, schema-agnostic (column names are injected via a Schema object), and thread-safe (no module-level mutable state). Order matters: the step table is built before the summary, because make_summary reads data.steps. Units are passed by value (plain conversion factors), and populated cell metadata is never required — see the contract the caller must honor.