perfect-postcode/pipeline/download/test_naptan.py
2026-05-06 22:40:46 +01:00

91 lines
2.4 KiB
Python

import polars as pl
import pytest
from pipeline.download.naptan import (
canonical_station_name,
canonical_station_name_expr,
deduplicate_naptan,
)
def test_canonical_station_name_expr_normalizes_transport_suffixes():
names = [
"Bank",
"Bank Underground Station",
"Bank DLR Station",
"Pleasure Beach (Blackpool Tramway)",
"Earl's Court Tube Station",
]
df = pl.DataFrame(
{
"name": names,
}
)
result = df.select(canonical_station_name_expr().alias("key"))["key"].to_list()
assert result == [
"bank",
"bank",
"bank",
"pleasure beach",
"earls court",
]
assert [canonical_station_name(name) for name in names] == result
def test_deduplicate_naptan_merges_tube_station_variants_by_area():
df = pl.DataFrame(
{
"id": [
"bank",
"bank-lu",
"bank-dlr",
"other-bank",
"central-a",
"central-b",
],
"name": [
"Bank",
"Bank Underground Station",
"Bank DLR Station",
"Bank Underground Station",
"Central Tube Station",
"Central Tube Station",
],
"category": ["Tube station"] * 6,
"lat": [51.5129, 51.5134, 51.5132, 55.0140, 51.5, 53.0],
"lng": [-0.0889, -0.0890, -0.0885, -1.6781, -0.1, -2.0],
"locality": ["LOC1", "LOC1", "LOC2", "LOC1", None, None],
}
)
result = deduplicate_naptan(df).sort("lat")
assert len(result) == 4
assert result["name"].to_list() == [
"Central Tube Station",
"Bank",
"Central Tube Station",
"Bank Underground Station",
]
assert result.filter(pl.col("name") == "Bank")["lat"][0] == pytest.approx(
(51.5129 + 51.5134 + 51.5132) / 3
)
def test_deduplicate_naptan_does_not_merge_missing_locality_bus_stops():
df = pl.DataFrame(
{
"id": ["a", "b"],
"name": ["High Street", "High Street"],
"category": ["Bus stop", "Bus stop"],
"lat": [51.5, 52.5],
"lng": [-0.1, -1.1],
"locality": [None, None],
}
)
result = deduplicate_naptan(df)
assert len(result) == 2