salmonpy

Python mirror of the metasalmon R package for working with Salmon Data Packages (SDPs). Provides helpers to infer and validate dictionaries, search ontology terms, suggest semantics, suggest DwC-DP table/field mappings, and build/read Frictionless-style SDP bundles.

Quickstart

python3 -m venv .venv
. .venv/bin/activate
pip install -e .

import pandas as pd
from salmonpy import (
    create_salmon_datapackage,
    infer_dictionary,
    suggest_semantics,
    validate_dictionary,
)

df = pd.DataFrame({"species": ["Coho", "Chinook"], "count": [100, 200]})
dict_df = infer_dictionary(df, dataset_id="demo", table_id="observations")
dict_df.loc[dict_df["column_name"] == "count", "column_role"] = "measurement"

# Non-strict validation now warns (not aborts) if semantic IRIs are missing on measurement rows.
validate_dictionary(dict_df)

# If you want an explicit hard check for complete semantic coverage, use strict mode:
# validate_dictionary(dict_df, require_iris=True)

# Optional workflow:
# dict_df = suggest_semantics(df, dict_df)  # attach candidate term/property/entity/unit IRIs
# dict_df.loc[dict_df["column_name"] == "count", "term_iri"] = "https://w3id.org/gcdfo/salmon#..."
# validate_dictionary(dict_df, require_iris=True)
#
# See for guidance:
# https://dfo-pacific-science.github.io/metasalmon/articles/reusing-standards-salmon-data-terms.html

Access private CSVs from GitHub

from salmonpy import github_raw_url, read_github_csv

# Token discovery checks GITHUB_PAT/GH_TOKEN or your git credential store.
# Run metasalmon::ms_setup_github() once in R to create/store a PAT with repo scope.

dim_date = read_github_csv(
    "data/gold/dimension_tables/dim_date.csv",
    repo="dfo-pacific-science/qualark-data",
)
dim_date_pinned = read_github_csv(
    "data/gold/dimension_tables/dim_date.csv",
    ref="v0.3.0",
    repo="dfo-pacific-science/qualark-data",
)

print(
    github_raw_url(
        "data/gold/dimension_tables/dim_date.csv",
        repo="dfo-pacific-science/qualark-data",
    )
)

Running tests

. .venv/bin/activate
python -m unittest discover tests

Compatibility

• salmonpy 0.1.3 aligns with metasalmon 0.0.13 for the local package helpers, semantic suggestion workflow, NuSEDS method crosswalks, DwC-DP descriptor export, EDH XML bootstrap export, and term-request payload helpers.

Extras

• Validate an SDP: python -m salmonpy.scripts.validate_sdp --dataset dataset.csv --tables tables.csv --dictionary column_dictionary.csv [--codes codes.csv] [--require-semantics]
• Draft a new term request: python -m salmonpy.scripts.draft_new_term --label "<label>" --definition "<definition>" --term-type skos_concept --parent-iri <iri>
• Enable term search cache: set SALMONPY_CACHE=1

Publishing (PyPI)

1. Bump the version in pyproject.toml.
2. Install build tooling: pip install build twine.
3. Build artifacts: python -m build.
4. Upload: twine upload dist/* (requires PyPI credentials).
5. Tag the release in git to match the version.