jua 0.14.0

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

Install jua with pip:

pip install jua

Alternatively, checkout uv for managing dependencies and Python versions:

uv init && uv add 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

Documentation

For comprehensive documentation, visit docs.jua.ai.

Contributing

See the contribution guide to get started.

Changes

See the changelog for the latest changes.

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.