jua 0.4.0

Easy access to Jua's weather & power services

Jua Python SDK

Access industry-leading weather forecasts with ease

The Jua Python SDK provides a simple and powerful interface to Jua's state-of-the-art weather forecasting capabilities. Easily integrate accurate weather data into your applications, research, or analysis workflows.

Getting Started 🚀

Prerequisites

• Python 3.11 or higher
• Internet connection for API access

Installation

We strongly recommend using uv to manage dependencies:

uv init && uv add jua

Alternatively, you can install with pip:

pip install jua

Authentication

Generate an API key from the Jua dashboard and save it to ~/.jua/default/api-key.json.

Coming soon: Simply run jua auth to authenticate via your web browser.

Examples

Access the latest 20-day forecast for a point location

Retrieve temperature forecasts for Zurich and visualize the data:

import matplotlib.pyplot as plt
from jua import JuaClient
from jua.types.geo import LatLon
from jua.weather import Models, Variables

client = JuaClient()
model = client.weather.get_model(Models.EPT1_5)
zurich = LatLon(lat=47.3769, lon=8.5417)
# Get latest forecast
forecast = model.forecast.get_forecast(
    points=[zurich]
)
temp_data = forecast[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M]
temp_data.to_celcius().to_absolute_time().plot()
plt.show()

Show output

Plot global forecast with 10-hour lead time

Generate a global wind speed visualization:

import matplotlib.pyplot as plt
from jua import JuaClient
from jua.weather import Models, Variables

client = JuaClient()
model = client.weather.get_model(Models.EPT1_5)

lead_time = 10 # hours
dataset = model.forecast.get_forecast(
    prediction_timedelta=lead_time,
    variables=[
        Variables.WIND_SPEED_AT_HEIGHT_LEVEL_10M,
    ],
)
dataset[Variables.WIND_SPEED_AT_HEIGHT_LEVEL_10M].plot()
plt.show()

Show output

Access historical weather data

Retrieve and visualize temperature data for Europe from a specific date:

import matplotlib.pyplot as plt
from jua import JuaClient
from jua.weather import Models, Variables

client = JuaClient()
model = client.weather.get_model(Models.EPT1_5_EARLY)

init_time = "2024-02-01 06:00:00"
hindcast = model.hindcast.get_hindcast(
    variables=[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M],
    init_time=init_time,
    prediction_timedelta=0,
    # Select Europe
    latitude=slice(71, 36),
    longitude=slice(-15, 50),
    method="nearest",
)

data = hindcast[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M]
data.plot()
plt.show()

Show output

Development Setup

Installing development dependencies

Install all required dependencies including extras:

uv sync --all-extras

Running examples

Execute example scripts:

uv run examples/weather/forecast.py

For example notebooks, ensure you select the correct Python interpreter in your notebook environment.

Documentation

For comprehensive documentation, visit docs.jua.ai.

Support

If you encounter any issues or have questions, please:

• Check the documentation
• Open an issue on GitHub
• Contact [e-mail]

License

This project is licensed under the MIT License - see the LICENSE file for details.