![openeo-logo](../_static/services/openeo_logo.png)

# How to use OpenEO

## Install

```
pip install openeo
```

## Connect, authenticate, and run a job

```python
import openeo

# Connect and authenticate via EGI Check-In
con = openeo.connect("https://openeo.eodc.eu/v1.0/")
con = con.authenticate_oidc(provider_id="egi")

# Load a collection with spatial and temporal filters
sig0 = con.load_collection(
    "SENTINEL1_GRD",
    spatial_extent={"west": 22, "south": 39.483774, "east": 22.225465, "north": 39.6},
    temporal_extent=["2018-02-28T04:00:00Z", "2018-02-28T05:00:00Z"],
    bands=["VV"]
)

# Apply a process
sig0_reduced = sig0.reduce_dimension("t", "mean")

# Save and submit as a batch job
sig0_save = sig0_reduced.save_result(format="netcdf")
sig0_job = sig0_save.create_job()
sig0_job.start_job()
```

**Step by step:**

1. `openeo.connect(...)` — connects to the eodc OpenEO backend.
2. `authenticate_oidc(provider_id="egi")` — authenticates using EGI Check-In (OIDC). A browser window will open on first use to complete the login flow.
3. `con.load_collection(...)` — defines a lazy data cube filtered by collection, spatial extent, time range, and bands. Nothing is downloaded yet.
4. `reduce_dimension("t", "mean")` — applies a temporal mean reduction. OpenEO supports many built-in processes; see the [OpenEO Processes reference](../reference/openeo-processes).
5. `save_result(format="netcdf")` — specifies the output format.
6. `create_job()` / `start_job()` — submits the processing request as a batch job on the eodc backend.

## Tips

- Use `con.list_collections()` to browse available datasets.
- Results can be saved directly to a [User Workspace](workspaces) using the `save_result` process with a workspace target.
- Monitor running jobs at `https://openeo.eodc.eu`.