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# Getting Started
The `xcube-resampling` package can be installed into an existing Python environment
using
```bash
pip install xcube-resampling
```
or
```bash
conda install -c conda-forge xcube-resampling
```
After installation, you are ready to go and use `resample_in_space` to resample you
datasets:
### Generate a sample dataset
```python
import numpy as np
import xarray as xr
res = 0.1
source_ds = xr.Dataset(
data_vars=dict(
refl=xr.DataArray(
np.array(
[
[0, 1, 0, 2, 0, 3, 0, 4],
[2, 0, 3, 0, 4, 0, 1, 0],
[0, 4, 0, 1, 0, 2, 0, 3],
[1, 0, 2, 0, 3, 0, 4, 0],
[0, 3, 0, 4, 0, 1, 0, 2],
[4, 0, 1, 0, 2, 0, 3, 0],
],
dtype=np.float64,
),
dims=("lat", "lon"),
)
),
coords=dict(
lon=xr.DataArray(50.0 + res * np.arange(0, 8) + 0.5 * res, dims="lon"),
lat=xr.DataArray(10.6 - res * np.arange(0, 6) - 0.5 * res, dims="lat"),
),
)
```
### Apply resampling
```python
from xcube_resampling.spatial import resample_in_space
from xcube_resampling.gridmapping import GridMapping
target_gm = GridMapping.regular((3, 3), (50.05, 10.05), 0.2, "EPSG:4326")
target_ds = resample_in_space(source_ds, target_gm=target_gm)
```
The resulting dataset `target_ds` has a size of 3×3, as defined by the target
grid mapping:
```text
<xarray.Dataset> Size: 128B
Dimensions: (lat: 3, lon: 3)
Coordinates:
spatial_ref int64 8B 0
* lon (lon) float64 24B 50.15 50.35 50.55
* lat (lat) float64 24B 10.55 10.35 10.15
Data variables:
refl (lat, lon) float64 72B 0.875 1.375 1.375 1.5 ... 1.25 1.5 1.0
```
For more examples, including visualization, please refer to the
[Example Notebooks](examples/affine.ipynb).
|
