xarray GAMIC backend¶
In this example, we read GAMIC (HDF5) data files using the xarray gamic
backend.
[1]:
import glob
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()
Load ODIM_H5 Volume Data¶
[2]:
fpath = 'hdf5/DWD-Vol-2_99999_20180601054047_00.h5'
f = wrl.util.get_wradlib_data_file(fpath)
vol = wrl.io.open_gamic_dataset(f)
Inspect RadarVolume¶
[3]:
display(vol)
<wradlib.RadarVolume>
Dimension(s): (sweep: 10)
Elevation(s): (28.0, 18.0, 14.0, 11.0, 8.2, 6.0, 4.5, 3.1, 1.7, 0.6)
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: 10) Coordinates: time datetime64[ns] 2018-06-01T05:40:47.040999936 sweep_mode <U20 'azimuth_surveillance' longitude float64 6.457 altitude float64 310.0 latitude float64 50.93 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 '2018-06-01T05:40:47Z' time_coverage_end <U20 '2018-06-01T05:44:16Z' sweep_group_name (sweep) <U7 'sweep_0' 'sweep_1' ... 'sweep_8' 'sweep_9' sweep_fixed_angle (sweep) float64 28.0 18.0 14.0 11.0 ... 4.5 3.1 1.7 0.6 Attributes: version: None title: None institution: None references: None source: None history: None comment: im/exported using wradlib instrument_name: None fixed_angle: 28.0
Inspect 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: 360) Coordinates: * azimuth (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5 elevation (azimuth) float64 28.0 28.0 28.0 28.0 ... 28.0 28.0 28.0 28.0 rtime (azimuth) datetime64[ns] 2018-06-01T05:40:57.362999808 ... 20... * range (range) float32 50.0 150.0 250.0 ... 3.585e+04 3.595e+04 time datetime64[ns] 2018-06-01T05:40:47.040999936 sweep_mode <U20 'azimuth_surveillance' longitude float64 6.457 latitude float64 50.93 altitude float64 310.0 Data variables: DBZH (azimuth, range) float32 ... DBZV (azimuth, range) float32 ... KDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... DBTH (azimuth, range) float32 ... DBTV (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... VRADV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... WRADV (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... Attributes: fixed_angle: 28.0
Goereferencing¶
[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 0x7f82ae0eb430>
[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=6.4569489 +lat_0=50.9287272 +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 0x7f82adabe290>
[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 0x7f82ac949030>
[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 0x7f82f224b370>
[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 0x7f82b690b550>
[15]:
swp.DBZH.wradlib.plot_ppi()
[15]:
<matplotlib.collections.QuadMesh at 0x7f82ae1795a0>
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: 360)> array([[13.177166, 11.671261, 19.200787, ..., nan, nan, nan], [11.169292, 11.671261, 17.192913, ..., nan, nan, nan], [12.173229, 11.671261, 19.702755, ..., nan, nan, nan], ..., [10.165356, 11.169292, 19.702755, ..., nan, nan, nan], [11.169292, 11.671261, 16.188976, ..., nan, nan, nan], [12.173229, 12.675198, 19.200787, ..., nan, nan, nan]], dtype=float32) Coordinates: (12/15) * azimuth (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5 elevation (azimuth) float64 28.0 28.0 28.0 28.0 ... 28.0 28.0 28.0 28.0 rtime (azimuth) datetime64[ns] 2018-06-01T05:40:57.362999808 ... 20... * range (range) float32 50.0 150.0 250.0 ... 3.585e+04 3.595e+04 time datetime64[ns] 2018-06-01T05:40:47.040999936 sweep_mode <U20 'azimuth_surveillance' ... ... x (azimuth, range) float64 0.3852 1.156 1.926 ... -275.7 -276.4 y (azimuth, range) float64 44.14 132.4 ... 3.159e+04 3.168e+04 z (azimuth, range) float64 333.5 380.4 ... 1.72e+04 1.725e+04 gr (azimuth, range) float64 44.15 132.4 ... 3.159e+04 3.168e+04 rays (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5 bins (azimuth, range) float32 50.0 150.0 ... 3.585e+04 3.595e+04 Attributes: format: UV8 is_dft: 0 unit: dBZ long_name: Equivalent reflectivity factor H standard_name: radar_equivalent_reflectivity_factor_h units: dBZ _Undetect: 0.0
Create 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 0x7f82ac75fbb0>
[18]:
fig = pl.figure(figsize=(5,5))
pm = swp.DBZH.wradlib.plot_ppi(proj={'latmin': 3e3}, fig=fig)
Mask some values¶
[19]:
swp['DBZH'] = swp['DBZH'].where(swp['DBZH'] >= 0)
swp['DBZH'].plot()
[19]:
<matplotlib.collections.QuadMesh at 0x7f82adfaddb0>
Export to ODIM and CfRadial2¶
[20]:
vol.to_odim('gamic_as_odim.h5')
vol.to_cfradial2('gamic_as_cfradial2.nc')
Import again¶
[21]:
vola = wrl.io.open_odim_dataset('gamic_as_odim.h5')
[22]:
volb = wrl.io.open_cfradial2_dataset('gamic_as_cfradial2.nc')
Check equality¶
We have to drop the time variable when checking equality since GAMIC has millisecond resolution, ODIM has seconds.
[23]:
xr.testing.assert_allclose(vol.root.drop("time"), vola.root.drop("time"))
xr.testing.assert_allclose(vol[0].drop(["rtime", "time"]), vola[0].drop(["rtime", "time"]))
xr.testing.assert_allclose(vol.root.drop("time"), volb.root.drop("time"))
xr.testing.assert_equal(vol[0].drop("time"), volb[0].drop("time"))
xr.testing.assert_allclose(vola.root, volb.root)
xr.testing.assert_allclose(vola[0].drop("rtime"), volb[0].drop("rtime"))
More GAMIC loading mechanisms¶
Use xr.open_dataset
to retrieve explicit group¶
[24]:
swp = xr.open_dataset(f, engine="gamic", group="scan9")
display(swp)
<xarray.Dataset> Dimensions: (azimuth: 360, range: 1000) Coordinates: * azimuth (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5 elevation (azimuth) float64 0.5988 0.5988 0.5988 ... 0.5988 0.5988 0.5988 rtime (azimuth) datetime64[ns] 2018-06-01T05:43:49.404000 ... 2018-... * range (range) float32 75.0 225.0 375.0 ... 1.498e+05 1.499e+05 time datetime64[ns] 2018-06-01T05:43:40.504000 sweep_mode <U20 'azimuth_surveillance' longitude float64 6.457 latitude float64 50.93 altitude float64 310.0 Data variables: DBZH (azimuth, range) float32 ... DBZV (azimuth, range) float32 ... KDP (azimuth, range) float32 ... RHOHV (azimuth, range) float32 ... DBTH (azimuth, range) float32 ... DBTV (azimuth, range) float32 ... ZDR (azimuth, range) float32 ... VRADH (azimuth, range) float32 ... VRADV (azimuth, range) float32 ... WRADH (azimuth, range) float32 ... WRADV (azimuth, range) float32 ... PHIDP (azimuth, range) float32 ... Attributes: fixed_angle: 0.6