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Taxonomic types, projections and async taxonomy services for Polli-Labs.

Project description

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Typus

CI

Shared taxonomy & geo‑temporal types for the Polli‑Labs ecological stack

Documentation: https://docs.polli.ai/typus

Typus centralises every domain object that the rest of our platform — linnaeus, pollinalysis-server, dashboards … — needs: taxon records, clades, hierarchical classification results, projection helpers and async database services. Anything that speaks taxonomy imports Typus and stays DRY.


Features

  • Wide ancestry viewexpanded_taxa ORM exposes each rank (L10 → L70) on a single row for constant‑time lineage queries.
  • Async servicesPostgresTaxonomyService (ltree) & SQLiteTaxonomyService (fixture) share one interface.
  • Pydantic v2 modelsTaxon, Clade, HierarchicalClassificationResult, all JSON‑Schema‑exportable.
  • Taxonomy summaries & pollinator groupsTaxonSummary trails plus coarse PollinatorGroup helpers for UI labels.
  • Projection utils – lat/lon ↔ unit‑sphere, cyclical‑time features, multi‑scale elevation sinusoids.
  • Optional drivers only when you need them – install polli-typus[postgres], [sqlite], or [loader]; core install stays lightweight.
  • Offline SQLite loadertypus-load-sqlite CLI builds and caches the offline dataset

Requirements

  • Python ≥ 3.10

Installation

Core (no DB drivers)

uv pip install polli-typus        # import typus

With Postgres backend

uv pip install "polli-typus[postgres]"    # adds asyncpg

With SQLite only (CI, offline, sandboxes)

uv pip install "polli-typus[sqlite]"

With SQLite loader tooling (TSV/HTTP ingest)

uv pip install "polli-typus[loader]"

Development / tests / lint

uv pip install -e ".[dev,sqlite,loader]"   # pytest, ruff, ty, pre-commit, loader deps …

Quick start

SQLite (recommended for getting started)

# Download or build the expanded taxonomy dataset locally (~475MB sqlite / ~440MB tsv.gz)
# The loader creates recommended indexes by default for fast name search
typus-load-sqlite --sqlite expanded_taxa.sqlite
from pathlib import Path
from typus.services import SQLiteTaxonomyService

svc = SQLiteTaxonomyService(Path("expanded_taxa.sqlite"))
bee = await svc.get_taxon(630955)           # Anthophila
print(bee.scientific_name, bee.rank_level)  # Anthophila RankLevel.L32

You can also load on-demand in code (will download if missing):

from pathlib import Path
from typus.services import load_expanded_taxa, SQLiteTaxonomyService

db_path = load_expanded_taxa(Path("expanded_taxa.sqlite"))  # create_indexes=True by default
svc = SQLiteTaxonomyService(db_path)

Name search (v0.4.0+)

# Scientific prefix match
taxa = await svc.search_taxa("Apis", scopes={"scientific"}, match="prefix")

# Vernacular (common name) exact
taxa = await svc.search_taxa("honey bee", scopes={"vernacular"}, match="exact")

Taxonomy summaries & pollinator groups (v0.4.2)

from pathlib import Path
from typus import PollinatorGroup
from typus.services import SQLiteTaxonomyService

svc = SQLiteTaxonomyService(Path("expanded_taxa.sqlite"))

# Compact trail for UIs
bee_summary = await svc.taxon_summary(630955)  # Anthophila
print(bee_summary.format_trail())  # Animalia → Arthropoda → Insecta → Hymenoptera → Apidae → Anthophila

# Coarse pollinator grouping
groups = await svc.pollinator_groups_for_taxon(47219)  # Apis mellifera
assert PollinatorGroup.BEE in groups

Elevation (Postgres only)

import os
from typus import PostgresRasterElevation

dsn = os.getenv("ELEVATION_DSN") or os.getenv("TYPUS_TEST_DSN")
elev = PostgresRasterElevation(dsn, raster_table=os.getenv("ELEVATION_TABLE", "elevation_raster"))

la = await elev.elevation(34.0522, -118.2437)
vals = await elev.elevations([
    (34.0522, -118.2437),  # LA
    (0.0, -30.0),          # ocean (likely None)
])

Geo helpers

from typus import latlon_to_unit_sphere
print(latlon_to_unit_sphere(31.5, -110.4))  # → x, y, z on S²

Postgres (optional for production deployments)

from typus import PostgresTaxonomyService
svc = PostgresTaxonomyService("postgresql+asyncpg://user:pw@host/db")
bee = await svc.get_taxon(630955)

Developer guide

  • Lint & tests (one‑liner)

    make format && make lint && make typecheck && make test
    
  • Format whole repo

    make format
    
  • JSON Schemasmake schemastypus/schemas/

  • Type checkingmake typecheck

  • SQLite fixtureuv run python scripts/gen_fixture_sqlite.py

  • Pre‑commit hooksmake dev-install

Environment Variables

  • TYPUS_TEST_DSN: Postgres DSN for tests and perf harness (e.g., postgresql+asyncpg://user:pw@host/db).
  • POSTGRES_DSN: Alternate Postgres DSN; used if TYPUS_TEST_DSN is unset.
  • Optional test/ops normalization maps legacy /ibrida-v0-r1 DSNs to /ibrida-v0.
  • ELEVATION_DSN: Optional DSN override for elevation tests; falls back to TYPUS_TEST_DSN.
  • ELEVATION_TABLE: Elevation raster table name (default: elevation_raster).
  • TYPUS_ELEVATION_TEST: Set 1 to enable guarded elevation tests.
  • Perf harness:
    • TYPUS_PERF_WRITE=1: write report to dev/agents/perf_report.md.
    • TYPUS_PERF_VERIFY=1: enable result sanity checks.
    • TYPUS_PERF_EXPLAIN=1: append PG EXPLAIN snippets.

Publishing (maintainers)

See build/typus_publish.md for tag → TestPyPI → PyPI workflow.


License

MIT © 2025 Polli Labs

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