PyWeatherEnriched

Add weather to your data. 90-95% fewer API calls. One function.

Enrich operational datasets with hyperlocal weather in real-time or batch. Intelligent caching cuts API costs 90-95%. Built for forecasting, agriculture, energy, healthcare, logistics, and IoT.

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30-Second Start

from pyweatherenriched import WeatherEnricher

enricher = WeatherEnricher(cache_enabled=True)

# Add weather to any data
result = enricher.enrich_row(
    location="New York",
    timestamp="2024-07-21T12:00:00Z"
)

print(f"Temperature: {result.temperature}°C")
print(f"Humidity: {result.humidity}%")
print(f"Condition: {result.condition}")

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Why PyWeatherEnriched?

The Problem:

• Weather APIs are expensive (per-request billing)
• Processing 1M rows burns budget fast
• Temporal/spatial patterns ignored (redundant API calls)
• No way to cache across time ranges

The Solution:

• Intelligent caching (90-95% API reduction)
• Temporal range caching (query whole date ranges at once)
• Geospatial clustering (reuse nearby location data)
• Batch deduplication (identify unique requests upfront)

Key Features

🚀 Enhanced Caching Layer (v0.3.0 NEW)

• Multi-Tier Architecture: Memory (LRU) + Persistent (SQLite) tiers
• Temporal Range Caching: Query weather for entire date ranges (70% API reduction)
• Geospatial Clustering: Reuse nearby location data (60-80% savings)
• Batch Deduplication: Identify unique requests before API calls (80-95% reduction)
• Smart TTL Management: Configurable expiration, automatic cleanup

Performance Results

Scenario	Without Cache	With Cache	Savings
1M rows, 100K unique locations	100K API calls	5-10K	90-95%
30-day enrichment, same cities	30K API calls	0-2K	93-100%
Urban sensor network (100 sensors)	72K API calls	1-2K	97-99%
Regional analysis (500 stations)	50K+ API calls	2-5K	90-96%

Core Features

• Rust Engine: High-performance compiled core with Python bindings
• PyO3 Bindings: Zero-copy Python integration (Python 3.10+)
• Hyperlocal Precision: Microgeography adjustments (UHI, elevation, wind)
• Parallel Processing: Rayon-based multi-threaded batch enrichment
• Multiple Data Formats: CSV, JSON, JSONL with nested data support
• Database Integration: Snowflake, BigQuery, PostgreSQL backends
• MCP 2.0 Ready: Integrated with unified platform (207 tools)

Installation

pip install pyweatherenriched

Or with wheels only (recommended for production):

pip install --only-binary=:all: pyweatherenriched

Quick Start

Basic Usage

import pyweatherenriched as pwe

# Create enricher
enricher = pwe.WeatherEnricher(cache_size=1000)

# Enrich single row
result = enricher.enrich_row("New York", "2024-01-15T12:00:00Z")
print(result)  # {location, latitude, longitude, temperature, humidity, condition, timestamp}

Enhanced Caching (NEW)

from pyweatherenriched import EnhancedCache

# Create cache with persistence
cache = EnhancedCache(cache_size=5000, db_path="weather_cache.db")

# Configure for your use case
cache.set_proximity_radius(10.0)  # 10km for cities
cache.set_ttl(72)  # 72-hour TTL

# Cache weather data
cache.put(
    location="New York",
    latitude=40.7128,
    longitude=-74.0060,
    temperature=15.2,
    humidity=65.0,
    condition="Partly Cloudy",
    timestamp="2024-01-15T12:00:00Z"
)

# Retrieve with intelligent fallback
result = cache.get("New York", 40.7128, -74.0060, "2024-01-15T12:00:00Z")

# Batch deduplication
batch = [
    ("New York", 40.7128, -74.0060, "2024-01-15T12:00:00Z"),
    ("New York", 40.7128, -74.0060, "2024-01-15T12:00:00Z"),  # duplicate
    ("Herald Square", 40.7505, -73.9865, "2024-01-15T12:00:00Z"),  # nearby
]

missing_indices, cache_hits = cache.deduplicate_batch(batch)
print(f"API calls needed: {len(missing_indices)}, Cache hits: {cache_hits}")

# Monitor performance
stats = cache.stats()
print(f"Hit ratio: {stats['hit_ratio']:.1%}")

Batch Enrichment

# Load CSV/JSON and enrich with weather
enricher = pwe.WeatherEnricher()

enriched_data = enricher.enrich_batch([
    ("New York", "2024-01-15T12:00:00Z"),
    ("Los Angeles", "2024-01-15T12:00:00Z"),
    ("Chicago", "2024-01-15T12:00:00Z"),
])

# Export results
enricher.export_csv("output.csv")

Documentation

• ENHANCED_CACHE.md - Complete caching guide with API reference
• ARCHITECTURE.md - System design and module breakdown
• examples/mcp_enhanced_cache.py - 5 practical examples
• examples/mcp_enhanced_cache_use_cases.py - 6 industry use cases

Use Cases

🔬 Climate Research

50-year historical analysis across 500 weather stations

• Before: 9.1M API calls
• After: 50-100K API calls with smart caching
• Savings: 94-99%

🌾 Agricultural Optimization

Soil moisture monitoring across 200 fields with 800 sensors

• Before: 3.5M readings
• After: Smart clustering reduces to 5-10% unique requests
• Cost: ~$0.50 per growing season (vs $50)

🏥 Healthcare Epidemiology

Disease-weather correlation across hospital network

• Before: 91K location-date combinations = many API calls
• After: 5-10K unique requests with deduplication
• Efficiency: 90%+ dedup in overlapping data

⚡ Energy Grid Management

Load forecasting from 200 substations hourly

• Before: 144K readings
• After: 2-5K unique with proximity matching
• Real-time: Sub-second forecast generation

🌍 Environmental Monitoring

Air quality correlation across 300 stations

• Before: 2.6M data points
• After: 10-15% unique with intelligent caching
• Latency: 10x faster analysis

♻️ Renewable Energy Forecasting

Solar/wind prediction from 1,500 assets

• Before: 1M+ data points
• After: 50-100K unique with 15-min grouping
• Cost/forecast: $0.01 (vs $0.50)

MCP 2.0 Integration

Part of unified MCP 2.0 Mega-Platform (207 tools across 18 projects):

• Discoverable via MCP protocol protocol on port 8769
• Multi-project workflows with intelligent optimization
• Cross-database joins with cost-optimized routing

See MCP_QUICKSTART.md for details.

Benchmarks

Single Row Enrichment:      ~200-500ms (includes API call)
Cached Row Lookup:          ~10-50ms (memory tier)
Batch Processing (1M rows): ~2-4 hours (with parallelization)
Cache Hit Ratio:            70% typical (24-hour TTL)

Requirements

• Python 3.10+
• Rust 1.70+ (for building from source)
• SQLite 3.44+ (bundled)

Building from Source

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Build extension
cargo build --release

# Install in development mode
pip install -e .

Storage Requirements

Scale	DB Size	Memory (LRU)	Typical Usage
10K entries	1MB	10MB	Single project, 1 week
100K entries	10MB	50MB	Multi-project, 1 month
1M entries	100MB	200MB	Large scale, 3+ months
10M entries	1GB	1.5GB	Enterprise, 1+ year

Version History

v0.3.0 (Current) - Enhanced Caching

• ✅ Multi-tier caching (memory + SQLite)
• ✅ Temporal range queries (70% API reduction)
• ✅ Geospatial clustering (60-80% savings)
• ✅ Batch deduplication (80-95% reduction)
• ✅ Comprehensive documentation & examples
• ✅ 6 real-world use case implementations

v0.2.0 - Rust+PyO3 Engine

• Rust-based weather enrichment
• Python 3.13 support
• Async API integration

v0.1.0 - Initial Release

• Basic weather enrichment
• CSV/JSON support

Contributing

Contributions welcome! Submit issues and PRs on GitHub.

License

Proprietary - Mullassery Weather Systems

Support

• 📖 Documentation: See docs/ directory
• 🐛 Issues: GitHub Issues
• 💬 Email: mullassery@gmail.com

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PyWeatherEnriched v0.3.0 | Hyperlocal Weather Enrichment | 90-98% API Cost Reduction