xarray Extensions

Jua xarray Extensions

The Jua Python SDK extends the popular xarray library with weather-specific functionality to make working with meteorological data more intuitive and convenient.

Overview

Jua's xarray extensions provide:

1. Weather-specific data selection capabilities

2. Unit conversion utilities

3. Time handling enhancements

4. Point-based geographic selection

5. Enhanced type hints for better IDE support

These extensions are automatically applied when you use the SDK - no extra steps required.

Extended Functionality at a Glance

The SDK adds several powerful extensions to xarray:

• Enhanced sel() method with additional parameters:

  • prediction_timedelta - Select by forecast lead time

  • points - Select by geographic points

  • Support for proper North-to-South latitude slicing

• New accessor methods via .jua accessor or direct methods:

  • .to_celcius() - Convert temperature from Kelvin to Celsius

  • .to_absolute_time() - Convert from lead times to absolute dates

  • .select_point() - Select data at specific geographic locations

• Variable access with Jua's Variables enum for type safety

Key Features

Extended Data Selection

# Select data using prediction lead times
forecast_data.sel(prediction_timedelta=24)  # 24-hour forecast
forecast_data.sel(prediction_timedelta=slice(24, 72))  # 1-3 day forecast
forecast_data.sel(prediction_timedelta=slice(0, 120, 6))  # Every 6 hours up to 5 days

# Select geographic regions with natural ordering
# Note: latitude goes from North to South (higher to lower values)
europe_data = forecast_data.sel(
    latitude=slice(72, 36),    # North to South
    longitude=slice(-15, 35)   # West to East
)

Point-Based Selection

from jua.types.geo import LatLon

# Select data for specific locations
cities = [
    LatLon(lat=51.5074, lon=-0.1278, name="London"),
    LatLon(lat=40.7128, lon=-74.0060, name="New York")
]

# Using direct selection
city_data = forecast_data.sel(points=cities)

# Or using the accessor
city_data = forecast_data.jua.select_point(cities)

# Access data for a specific city
london_data = city_data.sel(points="London")

Unit Conversions

# Get temperature data
temperature = forecast_data["air_temperature_at_height_level_2m"]

# Convert from Kelvin to Celsius
celsius = temperature.to_celcius()
# or
celsius = temperature.jua.to_celcius()

Time Handling

# Convert from prediction lead times to absolute times
absolute_times = forecast_data.to_absolute_time()
# or
absolute_times = forecast_data.jua.to_absolute_time()

Type-Safe Variable Access

from jua.weather import Variables

# Access variables using enum members (with autocompletion and type checking)
temperature = forecast_data[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M]
wind_speed = forecast_data[Variables.WIND_SPEED_AT_HEIGHT_LEVEL_10M]

Best Practices

1. Use Variables enum for type-safe variable access

2. Use xarray's powerful data analysis capabilities - these extensions preserve all standard xarray functionality