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 datatree as xt
import xarray as xr

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

/home/runner/micromamba/envs/wradlib-tests/lib/python3.11/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.3 when it was built against 1.14.2, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "

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)

Downloading file 'hdf5/knmi_polar_volume.h5' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/hdf5/knmi_polar_volume.h5' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

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 0x7f0b4ff5bc50>

../../../_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/wradlib/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/wradlib/wradlib-data'.

<xarray.Dataset>
Dimensions:            (volume_time: 2, azimuth: 360, range: 1200)
Coordinates:
    elevation          (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
    time               (volume_time, azimuth) datetime64[ns] 2018-12-20T06:06...
  * range              (range) float32 125.0 375.0 625.0 ... 2.996e+05 2.999e+05
    longitude          float64 151.2
    latitude           float64 -33.7
    altitude           float64 195.0
  * azimuth            (azimuth) float32 0.5 1.5 2.5 3.5 ... 357.5 358.5 359.5
Dimensions without coordinates: volume_time
Data variables: (12/17)
    DBZH               (volume_time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    DBZH_CLEAN         (volume_time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADDH             (volume_time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    VRADH              (volume_time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    WRADH              (volume_time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    TH                 (volume_time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    ...                 ...
    CLASS              (volume_time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
    sweep_mode         (volume_time) <U20 'azimuth_surveillance' 'azimuth_sur...
    sweep_number       (volume_time) int64 0 0
    prt_mode           (volume_time) <U7 'not_set' 'not_set'
    follow_mode        (volume_time) <U7 'not_set' 'not_set'
    sweep_fixed_angle  (volume_time) float64 0.5 0.5

[ ]:

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#