xarray ODIM backend#

In this example, we read ODIM_H5 (HDF5) data files using the xradar odim backend. Throughout the notebook xarray accessors are used to access wradlib functionality.

[1]:

import glob
import os
import wradlib as wrl
import warnings

warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import numpy as np
import xradar as xd
import xarray as xr

try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    plt.ion()

Load ODIM_H5 Volume Data#

[2]:

fpath = "hdf5/knmi_polar_volume.h5"
f = wrl.util.get_wradlib_data_file(fpath)
vol = xd.io.open_odim_datatree(f)

Georeferencing#

[6]:

swp = vol["sweep_0"].ds
swp = swp.assign_coords(sweep_mode=swp.sweep_mode)
swp = swp.wrl.georef.georeference()

Create 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.

[8]:

swp.DBZH.sortby("time").plot(x="range", y="time", add_labels=False)

[8]:

<matplotlib.collections.QuadMesh at 0x7fcdcd7e5370>

../../../_images/notebooks_fileio_backends_odim_backend_16_1.png

[9]:

fig = plt.figure(figsize=(10, 10))
pm = swp.DBZH.wrl.vis.plot(crs={"latmin": 33e3}, fig=fig)

../../../_images/notebooks_fileio_backends_odim_backend_17_0.png

Retrieve explicit group#

[10]:

swp_b = xr.open_dataset(
    f, engine="odim", group="sweep_13", backend_kwargs=dict(reindex_angle=False)
)
display(swp_b)

Use `xr.open_mfdataset` to retrieve timeseries of explicit group#

[11]:

flist = ["hdf5/71_20181220_060628.pvol.h5", "hdf5/71_20181220_061228.pvol.h5"]
flist = [wrl.util.get_wradlib_data_file(f) for f in flist]
ts = xr.open_mfdataset(
    flist, engine="odim", concat_dim="volume_time", combine="nested", group="sweep_0"
)
display(ts)

Downloading file 'hdf5/71_20181220_060628.pvol.h5' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/hdf5/71_20181220_060628.pvol.h5' to '/home/runner/work/wradlib-notebooks/wradlib-notebooks/wradlib-data'.
Downloading file 'hdf5/71_20181220_061228.pvol.h5' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/hdf5/71_20181220_061228.pvol.h5' to '/home/runner/work/wradlib-notebooks/wradlib-notebooks/wradlib-data'.

<xarray.Dataset> Size: 80MB
Dimensions:            (volume_time: 2, azimuth: 360, range: 1200)
Coordinates:
    elevation          (azimuth) float64 3kB dask.array<chunksize=(360,), meta=np.ndarray>
    time               (volume_time, azimuth) datetime64[ns] 6kB 2018-12-20T0...
  * range              (range) float32 5kB 125.0 375.0 ... 2.996e+05 2.999e+05
    longitude          float64 8B 151.2
    latitude           float64 8B -33.7
    altitude           float64 8B 195.0
  * azimuth            (azimuth) float32 1kB 0.5 1.5 2.5 ... 357.5 358.5 359.5
Dimensions without coordinates: volume_time
Data variables: (12/17)
    DBZH               (volume_time, azimuth, range) float64 7MB dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    DBZH_CLEAN         (volume_time, azimuth, range) float64 7MB dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADDH             (volume_time, azimuth, range) float64 7MB dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADH              (volume_time, azimuth, range) float64 7MB dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    WRADH              (volume_time, azimuth, range) float64 7MB dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    TH                 (volume_time, azimuth, range) float64 7MB dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    ...                 ...
    CLASS              (volume_time, azimuth, range) float32 3MB dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    sweep_mode         (volume_time) <U20 160B 'azimuth_surveillance' 'azimut...
    sweep_number       (volume_time) int64 16B 0 0
    prt_mode           (volume_time) <U7 56B 'not_set' 'not_set'
    follow_mode        (volume_time) <U7 56B 'not_set' 'not_set'
    sweep_fixed_angle  (volume_time) float64 16B 0.5 0.5
Attributes:
    Conventions:  ODIM_H5/V2_2

xarray ODIM backend

Contents

xarray ODIM backend#

Load ODIM_H5 Volume Data#

Inspect RadarVolume#

Inspect root group#

Inspect sweep group(s)#

Georeferencing#

Inspect radar moments#

Create simple plot#

Retrieve explicit group#

Use `xr.open_mfdataset` to retrieve timeseries of explicit group#

xarray ODIM backend

Contents

xarray ODIM backend#

Load ODIM_H5 Volume Data#

Inspect RadarVolume#

Inspect root group#

Inspect sweep group(s)#

Georeferencing#

Inspect radar moments#

Create simple plot#

Retrieve explicit group#

Use xr.open_mfdataset to retrieve timeseries of explicit group#

Use `xr.open_mfdataset` to retrieve timeseries of explicit group#