# 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](https://docs.astral.sh/uv/) for managing dependencies and Python versions:
```bash
uv init && uv add jua
```
### Authentication
Generate an API key from the [Jua dashboard](https://app.jua.ai/api-keys) 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:
```python
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()
```
<details>
<summary>Show output</summary>
</details>
### Plot global forecast with 10-hour lead time
Generate a global wind speed visualization:
```python
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()
```
<details>
<summary>Show output</summary>
</details>
### Access historical weather data
Retrieve and visualize temperature data for Europe from a specific date:
```python
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()
```
<details>
<summary>Show output</summary>
</details>
## Documentation
For comprehensive documentation, visit [docs.jua.ai](https://docs.jua.ai).
## Contributing
See the [contribution guide](./CONTRIBUTING.md) to get started.
## Changes
See the [changelog](./CHANGELOG.md) for the latest changes.
## Support
If you encounter any issues or have questions, please:
- Check the [documentation](https://docs.jua.ai)
- Open an issue on GitHub
- Contact support@jua.ai
## License
This project is licensed under the MIT License - see the LICENSE file for details.
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"description": "# Jua Python SDK\n\n**Access industry-leading weather forecasts with ease**\n\nThe 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.\n\n## Getting Started \ud83d\ude80\n\n### Prerequisites\n\n- Python 3.11 or higher\n- Internet connection for API access\n\n### Installation\n\nInstall `jua` with pip:\n\n```\npip install jua\n```\n\nAlternatively, checkout [uv](https://docs.astral.sh/uv/) for managing dependencies and Python versions:\n\n```bash\nuv init && uv add jua\n```\n\n### Authentication\n\nGenerate an API key from the [Jua dashboard](https://app.jua.ai/api-keys) and save it to `~/.jua/default/api-key.json`.\n\n_Coming soon: Simply run `jua auth` to authenticate via your web browser._\n\n## Examples\n\n### Access the latest 20-day forecast for a point location\n\nRetrieve temperature forecasts for Zurich and visualize the data:\n\n```python\nimport matplotlib.pyplot as plt\nfrom jua import JuaClient\nfrom jua.types.geo import LatLon\nfrom jua.weather import Models, Variables\n\nclient = JuaClient()\nmodel = client.weather.get_model(Models.EPT1_5)\nzurich = LatLon(lat=47.3769, lon=8.5417)\n# Get latest forecast\nforecast = model.forecast.get_forecast(\n points=[zurich]\n)\ntemp_data = forecast[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M]\ntemp_data.to_celcius().to_absolute_time().plot()\nplt.show()\n```\n\n<details>\n<summary>Show output</summary>\n\n\n\n</details>\n\n### Plot global forecast with 10-hour lead time\n\nGenerate a global wind speed visualization:\n\n```python\nimport matplotlib.pyplot as plt\nfrom jua import JuaClient\nfrom jua.weather import Models, Variables\n\nclient = JuaClient()\nmodel = client.weather.get_model(Models.EPT1_5)\n\nlead_time = 10 # hours\ndataset = model.forecast.get_forecast(\n prediction_timedelta=lead_time,\n variables=[\n Variables.WIND_SPEED_AT_HEIGHT_LEVEL_10M,\n ],\n)\ndataset[Variables.WIND_SPEED_AT_HEIGHT_LEVEL_10M].plot()\nplt.show()\n```\n\n<details>\n<summary>Show output</summary>\n\n\n\n</details>\n\n### Access historical weather data\n\nRetrieve and visualize temperature data for Europe from a specific date:\n\n```python\nimport matplotlib.pyplot as plt\nfrom jua import JuaClient\nfrom jua.weather import Models, Variables\n\nclient = JuaClient()\nmodel = client.weather.get_model(Models.EPT1_5_EARLY)\n\ninit_time = \"2024-02-01 06:00:00\"\nhindcast = model.hindcast.get_hindcast(\n variables=[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M],\n init_time=init_time,\n prediction_timedelta=0,\n # Select Europe\n latitude=slice(71, 36),\n longitude=slice(-15, 50),\n method=\"nearest\",\n)\n\ndata = hindcast[Variables.AIR_TEMPERATURE_AT_HEIGHT_LEVEL_2M]\ndata.plot()\nplt.show()\n```\n\n<details>\n<summary>Show output</summary>\n\n\n\n</details>\n\n## Documentation\n\nFor comprehensive documentation, visit [docs.jua.ai](https://docs.jua.ai).\n\n## Contributing\n\nSee the [contribution guide](./CONTRIBUTING.md) to get started.\n\n## Changes\n\nSee the [changelog](./CHANGELOG.md) for the latest changes.\n\n## Support\n\nIf you encounter any issues or have questions, please:\n\n- Check the [documentation](https://docs.jua.ai)\n- Open an issue on GitHub\n- Contact support@jua.ai\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n",
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