PyWeatherEnriched

Real geocoding + real historical weather, wired into a Rust core with pandas/numpy climate feature engineering on top.

Given a location name and a timestamp, PyWeatherEnriched geocodes the location (OpenStreetMap Nominatim) and looks up the real, genuinely observed historical weather for that place and hour (Open-Meteo's free Archive API) — no formula-generated or fabricated numbers. The lookup/geocoding core is Rust (via PyO3) for speed and a small memory footprint; a Python layer on top adds pandas/numpy feature engineering (rolling aggregates, heating/cooling degree-days, cyclical time encoding, anomaly z-scores) useful for feeding weather into an ML pipeline.

Install

pip install pyweatherenriched

Requires Python 3.10+. Ships as prebuilt wheels for Linux (x86_64/ARM64), macOS (Intel/Apple Silicon), and Windows (x86_64) — no Rust toolchain needed to install.

Quick start

import pyweatherenriched as pwe

enricher = pwe.WeatherEnricher()
result = enricher.enrich_row("New York", "2024-06-15T12:00:00")

print(result)
# {'location': 'New York', 'latitude': 40.7127281, 'longitude': -74.0060152,
#  'temperature': 19.4, 'humidity': 74.0, 'condition': 'Clear',
#  'timestamp': '2024-06-15T12:00:00'}

enrich_row makes two real network calls the first time it sees a location/timestamp pair (one to Nominatim to geocode location, one to Open-Meteo for the historical weather at those coordinates); the in-process cache means repeating the same lookup doesn't hit the network again:

fresh = pwe.WeatherEnricher()
fresh.enrich_row("New York", "2024-06-15T12:00:00")  # network
fresh.enrich_row("New York", "2024-06-15T12:00:00")  # cache hit
print(fresh.cache_stats())  # {'hits': 1, 'misses': 1, 'size': 1}

Feature engineering on a DataFrame

enrich_dataframe bridges the Rust core to pandas — fetch real weather for every row of a DataFrame in one call — and the features functions build standard climate ML features on top of the result:

import pandas as pd
import pyweatherenriched as pwe

orders = pd.DataFrame({
    "order_id": ["A1", "A2", "A3"],
    "location": ["Chicago", "Miami", "Denver"],
    "timestamp": ["2024-01-15T12:00:00", "2024-06-15T12:00:00", "2024-03-15T12:00:00"],
})

enricher = pwe.WeatherEnricher()
enriched = pwe.enrich_dataframe(enricher, orders)     # + latitude/longitude/temperature/humidity/condition
features = pwe.build_features(enriched)                # + hdd/cdd, cyclical time, rolling stats, anomaly z-score

print(features[["order_id", "temperature", "hdd", "cdd", "temperature_zscore"]])

Rows whose lookup fails (unresolvable location, upstream error) get NaN in the new columns instead of raising or fabricating a value, so one bad row doesn't lose the batch.

build_features is a convenience pipeline; each step is also a standalone function you can call individually with more control:

Caching

WeatherEnricher has a small built-in LRU cache. For larger workloads, EnhancedCache adds a second, SQLite-backed persistent tier with geospatial-proximity matching, TTL expiration, batch deduplication, and date-range queries:

from pyweatherenriched import EnhancedCache

cache = EnhancedCache(cache_size=5000, db_path="weather_cache.db")
cache.set_proximity_radius(10.0)  # treat lookups within 10km as cache hits
cache.set_ttl(72)                 # hours before an entry expires

cache.put("New York", 40.7128, -74.0060, 15.2, 65.0, "Partly Cloudy", "2024-01-15T12:00:00Z")
result = cache.get("New York", 40.7128, -74.0060, "2024-01-15T12:00:00Z")

# Deduplicate a batch before making any API calls.
batch = [
    ("New York", 40.7128, -74.0060, "2024-01-15T12:00:00Z"),
    ("New York", 40.7128, -74.0060, "2024-01-15T12:00:00Z"),  # duplicate
]
missing_indices, cache_hits = cache.deduplicate_batch(batch)

# Every observation in a date range near a point (requires db_path).
rows = cache.get_range(40.7128, -74.0060, "2024-01-01T00:00:00Z", "2024-01-31T23:59:59Z")

stats = cache.stats()
print(f"hit ratio: {stats['hit_ratio']:.1%}")

get() checks the memory tier, then the persistent tier (if db_path was given), then falls back to a proximity search; get_range only queries the persistent tier, so it always returns [] without db_path.

What's real vs. not (yet)

• Real: forward geocoding (Nominatim), historical weather (Open-Meteo Archive API), in-memory LRU cache, SQLite-backed EnhancedCache (proximity matching, TTL, dedup, date-range queries), pandas/numpy feature engineering.
• Not yet exposed to Python: the crate has additional Rust-side, independently unit-tested building blocks — elevation/lapse-rate adjustment (real SRTM GeoTIFF parsing), urban-heat-island modeling (real OSM building-density analysis), and OSM-based reverse geocoding — that aren't wired into the Python API yet. They live under src/geospatial/ if you want to build on them.
• Deliberately unimplemented: vegetation/NDVI, soil, and flood-risk layers, plus Google Maps/USPS reverse-geocoding sources, are framework stubs (src/geospatial/optional.rs) that return a clear "not yet implemented" error rather than fake data.

Development

git clone https://github.com/Mullassery/PyWeatherEnriched.git
cd PyWeatherEnriched

maturin develop --release   # build the Rust extension + install editable
cargo test                  # Rust unit tests (32 tests)
pip install -e ".[dev]"
pytest tests/ -v            # Python tests (33 tests, incl. live-network ones)

cargo run --bin pyweatherenriched-validate is a small smoke-test CLI that exercises real geocoding + weather fetch end to end — useful to confirm a build/environment can actually reach Nominatim and Open-Meteo.

See BUILD.md for wheel-building/release details.

Requirements

• Python 3.10+
• Rust 1.75+ (only if building from source — not needed to pip install)
• Network access to nominatim.openstreetmap.org and archive-api.open-meteo.com at call time (no API key needed for either)

License

Proprietary — see LICENSE. Source is public on GitHub for review; this isn't an open-source license, so redistribution/reuse isn't granted by default.

Support

• Issues: https://github.com/Mullassery/PyWeatherEnriched/issues
• Email: mullassery@gmail.com