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
Simply run jua auth to authenticate via your web browser. Make sure you are already logged in the Jua platform.
Alternatively, generate an API key from the Jua platform and save it to ~/.jua/default/api-key.json.
Datetime resolution
Forecast datetime values are normalized to millisecond resolution. Applications requiring another resolution must explicitly convert datetime values before further processing or persistence. Timestamp instants and timezone semantics remain unchanged.
Examples
Obtaining the metadata for a model
from jua import JuaClient
from jua.weather import Models
client = JuaClient()
model = client.weather.get_model(Models.EPT1_5)
metadata = model.get_metadata()
# Print the metadata
print(metadata)
Getting the forecast runs available for a model
from jua import JuaClient
from jua.weather import Models
client = JuaClient()
# Getting metadata the latest forecast run
latest = model.get_latest_init_time()
print(latest)
# Fetching model runs
available_forecasts = model.get_available_forecasts()
# Fetching all model runs for January 2025
# Results are paginated so we might need to iterate through
result = model.get_available_forecasts(
since=datetime(2025, 1, 1),
before=datetime(2025, 1, 31, 23, 59),
limit=100,
)
all_forecasts = list(result.forecasts)
while result.has_more:
print("Fetching next page")
result = result.next()
all_forecasts.extend(result.forecasts)
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)
# Check if 10-day forecast is ready for the latest available init_time
is_ten_day_ready = model.is_ready(forecasted_hours=240)
# Get latest forecast
if is_ten_day_ready:
forecast = model.get_forecasts(points=[zurich], max_lead_time=240)
temp_data = forecast[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M]
temp_data.to_celcius().to_absolute_time().plot()
plt.show()
Show output
Access historical weather data
Historical data can be accessed in the same way. In this case, we get all EPT2 forecasts from January 2024, and plot the first 5 together.
from datetime import datetime
import matplotlib.pyplot as plt
from jua import JuaClient
from jua.weather import Models, Variables
client = JuaClient()
zurich = LatLon(lat=47.3769, lon=8.5417)
model = client.weather.get_model(Models.EPT2)
hindcast = model.get_forecasts(
init_time=slice(
datetime(2024, 1, 1, 0),
datetime(2024, 1, 31, 0),
),
points=[zurich],
min_lead_time=0,
max_lead_time=(5 * 24),
variables=[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M],
method="nearest",
)
data = hindcast[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M]
# Compare the first 5 runs of January
fig, ax = plt.subplots(figsize=(15, 8))
for i in range(5):
forecast_data = data.isel(init_time=i, points=0).to_celcius().to_absolute_time()
forecast_data.plot(ax=ax, label=forecast_data.init_time.values)
plt.legend()
plt.show()
Show output
Accessing Market Aggregates
The AggregateVariables enum provides the following variables:
WIND_SPEED_AT_HEIGHT_LEVEL_10M- Wind speed at 10m height (Weighting.WIND_CAPACITY)WIND_SPEED_AT_HEIGHT_LEVEL_100M- Wind speed at 100m height (Weighting.WIND_CAPACITY)SURFACE_DOWNWELLING_SHORTWAVE_FLUX_SUM_1H- Surface downwelling shortwave flux (Weighting.SOLAR_CAPACITY)AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M- Air temperature at 2m height (Weighting.POPULATION)
Comparing the latest EPT2 and ECMWF IFS run for the Ireland and Northern Ireland market zones:
from jua import JuaClient
from jua.market_aggregates import AggregateVariables, ModelRuns
from jua.types import Countries, MarketZones
from jua.weather import Models, Variables
client = JuaClient()
# Create energy market using MarketZones enum
ir_nir = client.market_aggregates.get_market([MarketZones.IE, MarketZones.GB_NIR])
# Get the market aggregates for the latest EPT2 and ECMWF IFS runs
model_runs = [ModelRuns(Models.EPT2, 0), ModelRuns(Models.ECMWF_IFS_SINGLE, 0)]
ds = ir_nir.compare_runs(
agg_variable=AggregateVariables.WIND_SPEED_AT_HEIGHT_LEVEL_10M,
model_runs=model_runs,
max_lead_time=24,
)
print("Retrieved dataset:")
print(ds)
print()
Obtaining all market zones for a country:
from jua.types import Countries, MarketZones
norway_zones = MarketZones.filter_by_country(Countries.NORWAY)
print(f"Norwegian zones: {[z.zone_name for z in norway_zones]}")
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 support@jua.ai
License
This project is licensed under the MIT License - see the LICENSE file for details.
1 maintainer
Metadata
1 maintainer
Release files for jua 0.42.0
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