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# xradar furuno backend

In this example, we read scn/scnx (furuno) data files using the xradar `furuno` xarray backend.

```{code-cell} python
import glob
import gzip
import io
import wradlib as wrl
import wradlib_data
import warnings
from IPython.display import display

import matplotlib.pyplot as plt
import numpy as np
import xradar as xd
import xarray as xr

warnings.filterwarnings("ignore")

```

## Load furuno scn Data

Data provided by University of Graz, Austria.

```{code-cell} python
fpath = "furuno/0080_20210730_160000_01_02.scn.gz"
f = wradlib_data.DATASETS.fetch(fpath)
vol = xd.io.open_furuno_datatree(f, reindex_angle=False)
```

### Inspect scn RadarVolume

```{code-cell} python
display(vol)
```

### Inspect scn root group

The `sweep` dimension contains the number of scans in this radar volume. Further the dataset consists of variables (location coordinates, time_coverage) and attributes (Conventions, metadata).

```{code-cell} python
vol.root
```

### Inspect scn sweep group(s)

The sweep-groups can be accessed via their respective keys. The dimensions consist of `range` and `time` with added coordinates `azimuth`, `elevation`, `range` and `time`. There will be variables like radar moments (DBZH etc.) and sweep-dependent metadata (like `fixed_angle`, `sweep_mode` etc.).

```{code-cell} python
display(vol["sweep_0"])
```

### Georeferencing scn

```{code-cell} python
swp = vol["sweep_0"].to_dataset(inherit="all_coords")
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.wrl.georef.georeference()
```

### Inspect scn radar moments

The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of its parent dataset.

```{code-cell} python
display(swp.DBZH)
```

### Create scn simple plot

Using xarray features a simple plot can be created like this. Note the `sortby('time')` method, which sorts the radials by time.

For more details on plotting radar data see under [Visualization](../../visualisation/plotting).

```{code-cell} python
swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)
```

```{code-cell} python
fig = plt.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(crs={"latmin": 3e3}, fig=fig)
```

## Load furuno scnx Data

Data provided by GFZ German Research Centre for Geosciences.

```{code-cell} python
fpath = "furuno/2006_20220324_000000_000.scnx.gz"
f = wradlib_data.DATASETS.fetch(fpath)
vol = xd.io.open_furuno_datatree(f, reindex_angle=False)
```

### Inspect scnx RadarVolume

```{code-cell} python
display(vol)
```

### Inspect scnx root group

The `sweep` dimension contains the number of scans in this radar volume. Further the dataset consists of variables (location coordinates, time_coverage) and attributes (Conventions, metadata).

```{code-cell} python
vol.root
```

### Inspect scnx sweep group(s)

The sweep-groups can be accessed via their respective keys. The dimensions consist of `range` and `time` with added coordinates `azimuth`, `elevation`, `range` and `time`. There will be variables like radar moments (DBZH etc.) and sweep-dependent metadata (like `fixed_angle`, `sweep_mode` etc.).

```{code-cell} python
display(vol["sweep_0"])
```

### Georeferencing scnx

```{code-cell} python
swp = vol["sweep_0"].to_dataset(inherit="all_coords")
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.wrl.georef.georeference()
```

### Inspect scnx radar moments

The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of its parent dataset.

```{code-cell} python
display(swp.DBZH)
```

### Create scnx simple plot

Currently the data dynamic range is left as read from the file. That way the difference between shortpulse and longpulse can be clearly seen.

Using xarray features a simple plot can be created like this. Note the `sortby('time')` method, which sorts the radials by time.

For more details on plotting radar data see under [Visualization](../../visualisation/plotting).

```{code-cell} python
swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)
```

```{code-cell} python
fig = plt.figure(figsize=(5, 5))
pm = swp.DBZH.wrl.vis.plot(crs={"latmin": 3e3}, fig=fig)
```

## More Furuno loading mechanisms


### Use `xr.open_dataset` to retrieve explicit group

```{code-cell} python
swp_b = xr.open_dataset(f, engine="furuno", backend_kwargs=dict(reindex_angle=False))
display(swp_b)
```