Validate output against the BDF reference#

battfeed emits conforming BDF by construction, but “check, don’t trust” is cheap: the bdf extra wraps the reference implementation’s checker so you can prove a file conforms.

pip install "battfeed[bdf]"

>>> from battfeed.sinks.bdf_csv import validate_file
>>> validate_file("LOCAL__DemoCell__20260811_001.bdf.csv")
{'ok': True, 'missing': [], 'extras': ['surface_temperature_celsius'], ...}

The report tells you whether the file is acceptable (ok), which required columns are missing if not, and which columns are extras beyond the required trio — extras are legitimate, the field exists so you can spot typos (surface_temp_celsius would show up here instead of matching an optional column).

When to run it#

  • In your source’s test suite, on a file collected from your source via a short Harvester run — this catches column-name mistakes check_source cannot see (it checks samples, not files).

  • Spot-checking a deployment, especially after changing a csvtail column map or unit scale.

  • There is no need to validate every production file in-line; conformance is a property of the writing code, not of individual runs.

What “conforming” covers#

Header naming and ordering (test_time_second, voltage_volt, current_ampere first, extras alphabetical), snake_case {quantity}_{unit} column names, and the sign convention are battfeed’s responsibility and specified in the output contract. Semantic plausibility of the values (a 40 V coin cell) is yours; battfeed records what the source reports.