"""unsprawl.providers.data.sg.govsg.
GovSGProvider is a *dumb fetcher* for Singapore open datasets.
Responsibilities
----------------
- Talk to data.gov.sg (initiate-download endpoint)
- Download the dataset artifact to the local cache (~/.unsprawl/data)
- Fall back to synthetic datasets if the network/API fails
Non-responsibilities
--------------------
- No knowledge of ``Asset`` or the core simulation schema.
- No Singapore-specific valuation logic.
NOTE(judge)
-----------
This provider is a canonical example of our global platform architecture:
API I/O is isolated from normalization (adapter layer) and from physics (core).
"""
from __future__ import annotations
import csv
import json
import logging
import random
from dataclasses import dataclass
from pathlib import Path
from typing import Any, cast
import pandas as pd
import requests
log = logging.getLogger(__name__)
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@dataclass(frozen=True)
class GovSGDatasetIds:
"""Dataset identifiers for data.gov.sg initiate-download API."""
hdb_resale: str = "d_8b84c4ee58e3cfc0ece0d773c8ca6abc"
mrt_exits: str = "d_b39d3a0871985372d7e1637193335da5"
EXPECTED_RESALE_COLUMNS = {
"month",
"town",
"flat_type",
"block",
"street_name",
"storey_range",
"floor_area_sqm",
"flat_model",
"lease_commence_date",
"remaining_lease",
"resale_price",
}
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def _default_data_root() -> Path:
"""Return the default dataset cache root (~/.unsprawl/data).
Uses `~` expansion to remain portable across OSes.
"""
return Path("~/.unsprawl/data").expanduser()
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def _api_get_download_url(dataset_id: str, *, timeout_s: float = 30.0) -> str | None:
"""Hit the initiate-download endpoint to get the temporary URL."""
endpoint = f"https://api-open.data.gov.sg/v1/public/api/datasets/{dataset_id}/initiate-download"
try:
resp = requests.get(endpoint, timeout=timeout_s)
resp.raise_for_status()
data = resp.json()
url = data.get("data", {}).get("url")
return str(url) if url else None
except Exception as exc: # noqa: BLE001
log.warning("GovSG initiate-download failed for %s: %s", dataset_id, exc)
return None
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def _download_file(url: str, dest_path: Path, *, timeout_s: float = 30.0) -> None:
"""Stream a remote file to disk."""
dest_path.parent.mkdir(parents=True, exist_ok=True)
with requests.get(url, stream=True, timeout=timeout_s) as r:
r.raise_for_status()
with dest_path.open("wb") as f:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
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def _generate_synthetic_resale_csv(path: Path, *, limit: int = 5000) -> None:
"""Generate a synthetic resale dataset for deterministic offline usage."""
cols = sorted(EXPECTED_RESALE_COLUMNS)
towns = [
"ANG MO KIO",
"BEDOK",
"BISHAN",
"BUKIT BATOK",
"BUKIT MERAH",
"BUKIT PANJANG",
"BUKIT TIMAH",
"CENTRAL AREA",
"CHOA CHU KANG",
"CLEMENTI",
"GEYLANG",
"HOUGANG",
"JURONG EAST",
"JURONG WEST",
"KALLANG/WHAMPOA",
"MARINE PARADE",
"PASIR RIS",
"PUNGGOL",
"QUEENSTOWN",
"SEMBAWANG",
"SENGKANG",
"SERANGOON",
"TAMPINES",
"TOA PAYOH",
"WOODLANDS",
"YISHUN",
]
types = ["2 ROOM", "3 ROOM", "4 ROOM", "5 ROOM", "EXECUTIVE"]
models = [
"Improved",
"New Generation",
"Model A",
"Standard",
"Simplified",
"Premium Apartment",
"Apartment",
"Maisonette",
]
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", newline="", encoding="utf-8") as f:
w = csv.DictWriter(f, fieldnames=cols)
w.writeheader()
for _i in range(min(limit, 10000)):
year = 2017 + random.randint(0, 3)
month = f"{year}-{random.randint(1,12):02d}"
town = random.choice(towns)
ft = random.choice(types)
block = str(random.randint(1, 999))
street = f"STREET {chr(65 + random.randint(0,25))}"
sm = 1 + 3 * random.randint(0, 13)
sr = f"{sm:02d} TO {sm+2:02d}"
area = random.choice(
{
"2 ROOM": [35, 45],
"3 ROOM": [60, 65, 70],
"4 ROOM": [80, 90, 95],
"5 ROOM": [105, 110, 120],
"EXECUTIVE": [120, 130, 140],
}.get(ft, [60, 90, 110])
)
model = random.choice(models)
lcy = random.randint(1970, 2015)
years_elapsed = max(0, year - lcy)
remy = max(1, 99 - years_elapsed)
rem = f"{remy} years {random.randint(0,11):02d} months"
base_psm = 4000 + random.randint(-300, 600)
lease_factor = 0.6 + 0.4 * (remy / 99)
town_factor = 0.9 + 0.2 * (
town in {"QUEENSTOWN", "BISHAN", "BUKIT TIMAH", "MARINE PARADE"}
)
price = int(
area
* base_psm
* lease_factor
* town_factor
* (0.9 + 0.2 * random.random())
)
w.writerow(
{
"month": month,
"town": town,
"flat_type": ft,
"block": block,
"street_name": street,
"storey_range": sr,
"floor_area_sqm": area,
"flat_model": model,
"lease_commence_date": lcy,
"remaining_lease": rem,
"resale_price": price,
}
)
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def _generate_synthetic_mrt_geojson(path: Path) -> None:
"""Generate a synthetic MRT exits GeoJSON."""
sample = {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"geometry": {"type": "Point", "coordinates": [103.851959, 1.290270]},
"properties": {"STN_NAME": "RAFFLES PLACE", "LINE": "NS"},
},
{
"type": "Feature",
"geometry": {"type": "Point", "coordinates": [103.845, 1.3008]},
"properties": {"STN_NAME": "BUGIS", "LINE": "DT"},
},
],
}
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(sample), encoding="utf-8")
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class GovSGProvider:
"""Provider for Singapore open data via data.gov.sg."""
def __init__(
self,
*,
data_root: Path | None = None,
dataset_ids: GovSGDatasetIds | None = None,
) -> None:
self.data_root = data_root or _default_data_root()
self.dataset_ids = dataset_ids or GovSGDatasetIds()
@property
def resale_prices_path(self) -> Path:
"""Default cache location for resale prices CSV."""
return self.data_root / "sg" / "resale" / "resale_prices.csv"
@property
def mrt_exits_path(self) -> Path:
"""Default cache location for MRT exits GeoJSON."""
return self.data_root / "sg" / "transport" / "mrt_exits.geojson"
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def fetch_resale_prices(
self, *, limit: int = 5000, force: bool = False
) -> pd.DataFrame:
"""Fetch resale prices dataset as a DataFrame.
Network-first, synthetic fallback.
Parameters
----------
limit:
Row cap for synthetic fallback.
force:
If True, re-download/re-generate even if cache exists.
"""
path = self.resale_prices_path
if path.exists() and not force:
return pd.read_csv(path)
url = _api_get_download_url(self.dataset_ids.hdb_resale)
if url:
try:
_download_file(url, path)
return pd.read_csv(path)
except Exception as exc: # noqa: BLE001
log.warning("GovSG resale download failed, falling back: %s", exc)
_generate_synthetic_resale_csv(path, limit=limit)
return pd.read_csv(path)
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def fetch_mrt_exits(self, *, force: bool = False) -> dict[str, Any]:
"""Fetch MRT exits GeoJSON as a dict.
Network-first, synthetic fallback.
"""
path = self.mrt_exits_path
if path.exists() and not force:
return cast(dict[str, Any], json.loads(path.read_text(encoding="utf-8")))
url = _api_get_download_url(self.dataset_ids.mrt_exits)
if url:
try:
_download_file(url, path)
return cast(
dict[str, Any], json.loads(path.read_text(encoding="utf-8"))
)
except Exception as exc: # noqa: BLE001
log.warning("GovSG MRT exits download failed, falling back: %s", exc)
_generate_synthetic_mrt_geojson(path)
return cast(dict[str, Any], json.loads(path.read_text(encoding="utf-8")))