xarray ODIM backend¶
In this example, we read ODIM_H5 (HDF5) data files using the xarray odim backend.
[1]:
import glob
import os
import wradlib as wrl
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as pl
import numpy as np
import xarray as xr
try:
get_ipython().magic("matplotlib inline")
except:
pl.ion()
from wradlib.io import open_odim_dataset
Load ODIM_H5 Volume Data¶
[2]:
fpath = 'hdf5/knmi_polar_volume.h5'
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_odim_dataset(f)
Inspect RadarVolume¶
[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 14)
Elevation(s): (0.3, 0.4, 0.8, 1.1, 2.0, 3.0, 4.5, 6.0, 8.0, 10.0, 12.0, 15.0, 20.0, 25.0)
Inspect 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).
[4]:
vol.root
[4]:
<xarray.Dataset>
Dimensions: (sweep: 14)
Coordinates:
time datetime64[ns] 2011-06-10T11:40:02
sweep_mode <U20 'azimuth_surveillance'
longitude float64 4.79
altitude float64 50.0
latitude float64 52.95
Dimensions without coordinates: sweep
Data variables:
volume_number int64 0
platform_type <U5 'fixed'
instrument_type <U5 'radar'
primary_axis <U6 'axis_z'
time_coverage_start <U20 '2011-06-10T11:40:02Z'
time_coverage_end <U20 '2011-06-10T11:43:54Z'
sweep_group_name (sweep) <U8 'sweep_0' 'sweep_1' ... 'sweep_13'
sweep_fixed_angle (sweep) float64 0.3 0.4 0.8 1.1 ... 12.0 15.0 20.0 25.0
Attributes:
version: None
title: None
institution: None
references: None
source: None
history: None
comment: im/exported using wradlib
instrument_name: None
fixed_angle: 0.30000001192092896Inspect 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-dependend metadata (like fixed_angle, sweep_mode etc.).
[5]:
display(vol[0])
<xarray.Dataset>
Dimensions: (azimuth: 360, range: 320)
Coordinates:
* azimuth (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
elevation (azimuth) float32 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 0.3 0.3 0.3
rtime (azimuth) datetime64[ns] 2011-06-10T11:40:17.361118208 ... 20...
* range (range) float32 500.0 1.5e+03 2.5e+03 ... 3.185e+05 3.195e+05
time datetime64[ns] 2011-06-10T11:40:02
sweep_mode <U20 'azimuth_surveillance'
longitude float64 4.79
latitude float64 52.95
altitude float64 50.0
Data variables:
DBZH (azimuth, range) float32 ...
Attributes:
fixed_angle: 0.30000001192092896Goereferencing¶
[6]:
swp = vol[0].copy().pipe(wrl.georef.georeference_dataset)
Plotting¶
[7]:
swp.DBZH.plot.pcolormesh(x='x', y='y')
pl.gca().set_aspect('equal')
[8]:
fig = pl.figure(figsize=(10,10))
swp.DBZH.wradlib.plot_ppi(proj='cg', fig=fig)
[8]:
<matplotlib.collections.QuadMesh at 0x7fc755bca680>
[9]:
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
map_trans = ccrs.AzimuthalEquidistant(central_latitude=swp.latitude.values,
central_longitude=swp.longitude.values)
[10]:
map_proj = ccrs.AzimuthalEquidistant(central_latitude=swp.latitude.values,
central_longitude=swp.longitude.values)
pm = swp.DBZH.wradlib.plot_ppi(proj=map_proj)
ax = pl.gca()
ax.gridlines(crs=map_proj)
print(ax)
< GeoAxes: +proj=aeqd +ellps=WGS84 +lon_0=4.7899699211120605 +lat_0=52.953338623046875 +x_0=0.0 +y_0=0.0 +no_defs +type=crs >
[11]:
map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)
fig = pl.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection=map_proj)
pm = swp.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)
[11]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc7544a9480>
[12]:
import cartopy.feature as cfeature
def plot_borders(ax):
borders = cfeature.NaturalEarthFeature(category='physical',
name='coastline',
scale='10m',
facecolor='none')
ax.add_feature(borders, edgecolor='black', lw=2, zorder=4)
map_proj = ccrs.Mercator(central_longitude=swp.longitude.values)
fig = pl.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection=map_proj)
DBZH = swp.DBZH
pm = DBZH.where(DBZH > 0).wradlib.plot_ppi(ax=ax)
plot_borders(ax)
ax.gridlines(draw_labels=True)
[12]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc754517b80>
[13]:
import matplotlib.path as mpath
theta = np.linspace(0, 2*np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
map_proj = ccrs.AzimuthalEquidistant(central_latitude=swp.latitude.values,
central_longitude=swp.longitude.values,
)
fig = pl.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection=map_proj)
ax.set_boundary(circle, transform=ax.transAxes)
pm = swp.DBZH.wradlib.plot_ppi(proj=map_proj, ax=ax)
ax = pl.gca()
ax.gridlines(crs=map_proj)
[13]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc75de19690>
[14]:
fig = pl.figure(figsize=(10, 8))
proj=ccrs.AzimuthalEquidistant(central_latitude=swp.latitude.values,
central_longitude=swp.longitude.values)
ax = fig.add_subplot(111, projection=proj)
pm = swp.DBZH.wradlib.plot_ppi(ax=ax)
ax.gridlines()
[14]:
<cartopy.mpl.gridliner.Gridliner at 0x7fc75df48af0>
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7fc75418d9c0>
Inspect radar moments¶
The DataArrays can be accessed by key or by attribute. Each DataArray has dimensions and coordinates of it’s parent dataset. There are attributes connected which are defined by ODIM_H5 standard.
[16]:
display(swp.DBZH)
<xarray.DataArray 'DBZH' (azimuth: 360, range: 320)>
array([[ 22. , 17. , -8. , ..., -31.5, -31.5, -31.5],
[ 24. , 24.5, -9. , ..., -31.5, -31.5, -31.5],
[ 35.5, 42. , 12. , ..., -31.5, -31.5, -31.5],
...,
[ 23. , 14. , -13. , ..., -31.5, -31.5, -31.5],
[ 23. , 14. , -9. , ..., -31.5, -31.5, -31.5],
[ 22. , 18.5, -11.5, ..., -31.5, -31.5, -31.5]], dtype=float32)
Coordinates: (12/15)
* azimuth (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
elevation (azimuth) float32 0.3 0.3 0.3 0.3 0.3 ... 0.3 0.3 0.3 0.3 0.3
rtime (azimuth) datetime64[ns] 2011-06-10T11:40:17.361118208 ... 20...
* range (range) float32 500.0 1.5e+03 2.5e+03 ... 3.185e+05 3.195e+05
time datetime64[ns] 2011-06-10T11:40:02
sweep_mode <U20 'azimuth_surveillance'
... ...
x (azimuth, range) float32 4.363 13.09 ... -2.777e+03 -2.786e+03
y (azimuth, range) float32 500.0 1.5e+03 ... 3.183e+05 3.193e+05
z (azimuth, range) float32 53.0 58.0 64.0 ... 7.691e+03 7.734e+03
gr (azimuth, range) float32 500.0 1.5e+03 ... 3.183e+05 3.193e+05
rays (azimuth, range) float32 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
bins (azimuth, range) float32 500.0 1.5e+03 ... 3.185e+05 3.195e+05
Attributes:
_Undetect: 0.0
long_name: Equivalent reflectivity factor H
standard_name: radar_equivalent_reflectivity_factor_h
units: dBZCreate simple plot¶
Using xarray features a simple plot can be created like this. Note the sortby('rtime') method, which sorts the radials by time.
[17]:
swp.DBZH.sortby('rtime').plot(x="range", y="rtime", add_labels=False)
[17]:
<matplotlib.collections.QuadMesh at 0x7fc7541fc790>
[18]:
fig = pl.figure(figsize=(5,5))
pm = swp.DBZH.wradlib.plot_ppi(proj={'latmin': 33e3}, fig=fig)
Mask some values¶
[19]:
swp['DBZH'] = swp['DBZH'].where(swp['DBZH'] >= 0)
swp['DBZH'].plot()
[19]:
<matplotlib.collections.QuadMesh at 0x7fc75587bb20>
Export to ODIM and CfRadial2¶
[20]:
vol.to_odim('knmi_odim.h5')
vol.to_cfradial2('knmi_odim_as_cfradial.nc')
Import again¶
[21]:
vola = wrl.io.open_odim_dataset('knmi_odim.h5')
[22]:
volb = wrl.io.open_cfradial2_dataset('knmi_odim_as_cfradial.nc')
Check equality¶
[23]:
xr.testing.assert_allclose(vol.root, vola.root)
xr.testing.assert_equal(vol[0], vola[0])
xr.testing.assert_allclose(vol.root, volb.root)
xr.testing.assert_equal(vol[0], volb[0])
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_equal(vola[0], volb[0])
More ODIM loading mechanisms¶
Use xr.open_dataset to retrieve explicit group¶
[24]:
swp = xr.open_dataset(f, engine="odim", group="dataset14")
display(swp)
<xarray.Dataset>
Dimensions: (azimuth: 360, range: 240)
Coordinates:
* azimuth (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
elevation (azimuth) float32 25.0 25.0 25.0 25.0 ... 25.0 25.0 25.0 25.0
rtime (azimuth) datetime64[ns] 2011-06-10T11:43:48.763874560 ... 20...
* range (range) float32 250.0 750.0 1.25e+03 ... 1.192e+05 1.198e+05
time datetime64[ns] 2011-06-10T11:43:45
sweep_mode <U20 'azimuth_surveillance'
longitude float64 4.79
latitude float64 52.95
altitude float64 50.0
Data variables:
DBZH (azimuth, range) float32 -31.5 -0.5 0.0 ... -31.5 -31.5 -31.5
Attributes:
fixed_angle: 25.0Use xr.open_mfdataset to retrieve timeseries of explicit group¶
[25]:
fpath = os.path.join(wrl.util.get_wradlib_data_path(), "hdf5/71*.h5")
f = glob.glob(fpath)
ts = xr.open_mfdataset(f, engine="odim", concat_dim="time", combine="nested", group="dataset1")
display(ts)
<xarray.Dataset>
Dimensions: (time: 2, azimuth: 360, range: 1200)
Coordinates:
* azimuth (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
elevation (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
rtime (time, azimuth) datetime64[ns] 2018-12-20T06:12:41.009703424 ...
* range (range) float32 125.0 375.0 625.0 ... 2.996e+05 2.999e+05
* time (time) datetime64[ns] 2018-12-20T06:12:28 2018-12-20T06:06:28
sweep_mode <U20 'azimuth_surveillance'
longitude float64 151.2
latitude float64 -33.7
altitude float64 195.0
Data variables:
DBZH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
DBZH_CLEAN (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
VRADDH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
VRADH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
WRADH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
TH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
ZDR (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
RHOHV (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
PHIDP (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
KDP (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
SNRH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
Attributes:
fixed_angle: 0.5Use wrl.io.open_odim_mfdataset to retrieve volume timeseries¶
[26]:
fpath = os.path.join(wrl.util.get_wradlib_data_path(), "hdf5/71*.h5")
f = glob.glob(fpath)
ts = wrl.io.open_odim_mfdataset(f)
display(ts)
100%|██████████| 14/14 [00:02<00:00, 5.66it/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/micromamba/envs/wradlib-notebooks/lib/python3.10/site-packages/IPython/core/formatters.py:707, in PlainTextFormatter.__call__(self, obj)
700 stream = StringIO()
701 printer = pretty.RepresentationPrinter(stream, self.verbose,
702 self.max_width, self.newline,
703 max_seq_length=self.max_seq_length,
704 singleton_pprinters=self.singleton_printers,
705 type_pprinters=self.type_printers,
706 deferred_pprinters=self.deferred_printers)
--> 707 printer.pretty(obj)
708 printer.flush()
709 return stream.getvalue()
File ~/micromamba/envs/wradlib-notebooks/lib/python3.10/site-packages/IPython/lib/pretty.py:410, in RepresentationPrinter.pretty(self, obj)
407 return meth(obj, self, cycle)
408 if cls is not object \
409 and callable(cls.__dict__.get('__repr__')):
--> 410 return _repr_pprint(obj, self, cycle)
412 return _default_pprint(obj, self, cycle)
413 finally:
File ~/micromamba/envs/wradlib-notebooks/lib/python3.10/site-packages/IPython/lib/pretty.py:778, in _repr_pprint(obj, p, cycle)
776 """A pprint that just redirects to the normal repr function."""
777 # Find newlines and replace them with p.break_()
--> 778 output = repr(obj)
779 lines = output.splitlines()
780 with p.group():
File ~/micromamba/envs/wradlib-notebooks/lib/python3.10/site-packages/wradlib/io/xarray.py:1922, in RadarVolume.__repr__(self)
1920 summary.append(f"{dims} ({dims_summary})")
1921 dim0 = list(set(self[0].dims) & {"azimuth", "elevation", "time"})[0]
-> 1922 angle = f"{self._dims[dim0].capitalize()}(s):"
1923 angle_summary = [f"{v.attrs['fixed_angle']:.1f}" for v in self]
1924 angle_summary = ", ".join(angle_summary)
KeyError: 'time'
[27]:
display(ts[0])
<xarray.Dataset>
Dimensions: (time: 2, azimuth: 360, range: 1200)
Coordinates:
* azimuth (azimuth) float32 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
elevation (azimuth) float32 dask.array<chunksize=(360,), meta=np.ndarray>
rtime (time, azimuth) datetime64[ns] 2018-12-20T06:12:41.009703424 ...
* range (range) float32 125.0 375.0 625.0 ... 2.996e+05 2.999e+05
* time (time) datetime64[ns] 2018-12-20T06:12:28 2018-12-20T06:06:28
sweep_mode <U20 'azimuth_surveillance'
longitude float64 151.2
latitude float64 -33.7
altitude float64 195.0
Data variables:
DBZH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
DBZH_CLEAN (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
VRADDH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
VRADH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
WRADH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
TH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
ZDR (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
RHOHV (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
PHIDP (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
KDP (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
SNRH (time, azimuth, range) float32 dask.array<chunksize=(1, 360, 1200), meta=np.ndarray>
Attributes:
fixed_angle: 0.5