mareforma 0.1.0

Automatic provenance for AI-driven research pipelines

mareforma

Automatic epistemic provenance for life sciences pipelines. Write transforms, run build, and mareforma figures out what kind of result you produced and how well-supported it is — no manual annotation required.

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Install

pip install mareforma

Requires Python ≥ 3.10.

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How it works

Write normal Python pipeline functions. mareforma auto-classifies each result.

from mareforma import transform, BuildContext
import pandas as pd

@transform("morphology.load")
def load(ctx: BuildContext) -> None:
    files = list(ctx.source_path("morphology").glob("*.swc"))
    ctx.save("skeletons", files, fmt="pickle")

@transform("morphology.features", depends_on=["morphology.load"])
def compute_features(ctx: BuildContext) -> None:
    skeletons = ctx.load("morphology.load.skeletons")
    df = pd.DataFrame([_extract_features(s) for s in skeletons])
    ctx.save("features", df, fmt="csv")

mareforma build
# ✓ morphology.load      done  (1.2s)
# ✓ morphology.features  done  (3.8s)

mareforma trace morphology.features
# morphology
# └── morphology.load ──────── RAW        ── SINGLE
#     └── morphology.features  ANALYSED   ── REPLICATED ◇

That's it. No annotations. mareforma reads your artifacts, classifies each transform, and tracks support level automatically.

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What gets classified automatically

Transform class — inferred from artifact content:

Class	Meaning
RAW	Root node — no upstream dependencies
PROCESSED	Output values ⊆ input values, row count ≤ input count
ANALYSED	New values computed, within input value range
INFERRED	Output values outside all input ranges

Support level — inferred from run history:

Level	Meaning
SINGLE	One run
REPLICATED ◇	Same output hash across ≥2 runs
CONVERGED ●	Same step name across ≥2 independent sources
CONSISTENT ◆	A run has a DOI-linked claim in supports
ESTABLISHED ●●	CONVERGED + CONSISTENT

SINGLE through CONVERGED require no annotation. CONSISTENT and ESTABLISHED require one DOI string in a claim.

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Quickstart

# 1. Init
cd my_project/
mareforma init

# 2. Register a data source
mareforma add-source morphology --path data/morphology/raw/ \
    --description "Neuron skeleton reconstructions"

# 3. Build — classification is automatic
mareforma build

# 4. Inspect the epistemic graph
mareforma trace morphology.features

# 5. Check overall health
mareforma status

# 6. Optional: link a result to literature (unlocks CONSISTENT)
mareforma claim add "Neuron size increases with cortical depth" \
    --source morphology --supports 10.64898/2026.03.05.709819

# 7. Export provenance graph
mareforma export

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BuildContext API

Method	Description
ctx.source_path("name")	Raw data path for a registered source
ctx.save("name", data, fmt=...)	Persist artifact (pickle, parquet, csv, numpy)
ctx.load("transform.artifact")	Load upstream artifact
ctx.claim("text", supports=[DOI])	Optional: link this run to literature
ctx.log("message")	Write to console

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CLI reference

Command	Description
mareforma init	Initialise project
mareforma add-source <name>	Register a data source
mareforma check	Validate paths and required fields
mareforma build [source]	Run the pipeline DAG (--dry-run, --force)
mareforma trace <transform>	Ancestry tree with class and support level (--json)
mareforma status	Epistemic health dashboard (--json)
mareforma diff <transform>	Compare the two most recent runs (--json)
mareforma log	Last build status (--json)
mareforma explain [source]	Dump project ontology (--json)
mareforma export	Write ontology.jsonld
mareforma claim add TEXT	Link a result to literature (--supports DOI)
mareforma claim list	List claims (--status, --source, --json)
mareforma claim show ID	Full claim detail
mareforma claim update ID	Update confidence, status, or supports

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Project structure

my_project/
├── .mareforma/
│   └── graph.db               ← provenance graph (commit this)
├── mareforma.project.toml     ← project ontology (commit this)
├── claims.toml                ← claims backup, auto-generated (commit this)
├── ontology.jsonld            ← JSON-LD export (commit this)
└── data/
    └── source_name/
        ├── raw/               ← your data
        └── preprocessing/
            └── build_transform.py