Usage

Command-line interface

The mdstools CLI provides commands for converting between YAML and tabular formats.

Flatten YAML to Excel/CSV

Convert a nested YAML metadata file to a tabular format:

mdstools flatten tests/example_metadata.yaml --output-dir generated

Or via pixi:

pixi run flatten tests/example_metadata.yaml --output-dir generated

Options:

• --output-dir — Output directory (default: generated/)

• --schema — Schema name for enrichment (adds descriptions and examples)

• --format — Output format: xlsx, csv, or md

Unflatten Excel/CSV to YAML

Convert a tabular file back to nested YAML:

mdstools unflatten generated/output.xlsx

Or via pixi:

pixi run unflatten generated/output.xlsx

Python API

Basic flattening

from mdstools.metadata.metadata import Metadata

data = {"curation": {"process": [{"role": "curator", "name": "Jane Doe"}]}}
metadata = Metadata(data)
flattened = metadata.flatten()
flattened.to_excel("output.xlsx")

With schema enrichment

from mdstools.metadata.metadata import Metadata
from mdstools.metadata.enriched_metadata import EnrichedFlattenedMetadata

metadata = Metadata(data)
flattened = metadata.flatten()
enriched = EnrichedFlattenedMetadata(flattened.rows, schema_dir="schemas")
enriched.to_excel("output.xlsx")

Multi-sheet Excel export

enriched.to_excel("output.xlsx", separate_sheets=True)

Schema validation

from mdstools.schema.validator import validate_with_json_schema, validate_with_pydantic

# JSON Schema validation
validate_with_json_schema(data, schema_name="minimum_echemdb")

# Pydantic validation
validate_with_pydantic(data, schema_name="minimum_echemdb")