def ex_coord():
pvol = io.read_OPERA_hdf5(os.path.dirname(__file__) + '/' + 'data/20130429043000.rad.bewid.pvol.dbzh.scan1.hdf')
# Count the number of dataset
ntilt = 1
for i in range(100):
try:
pvol["dataset%d/what" % ntilt]
ntilt += 1
except Exception:
ntilt -= 1
break
nrays = int(pvol["dataset1/where"]["nrays"])
nbins = int(pvol["dataset1/where"]["nbins"])
rscale = int(pvol["dataset1/where"]["rscale"])
coord = np.empty((ntilt, nrays, nbins, 3))
for t in range(ntilt):
elangle = pvol["dataset%d/where" % (t + 1)]["elangle"]
coord[t, ...] = georef.sweep_centroids(nrays, rscale, nbins, elangle)
ascale = math.pi / nrays
sitecoords = (pvol["where"]["lon"], pvol["where"]["lat"], pvol["where"]["height"])
proj_radar = georef.create_osr("aeqd", lat_0=pvol["where"]["lat"], lon_0=pvol["where"]["lon"])
radius = georef.get_earth_radius(pvol["where"]["lat"], proj_radar)
lon, lat, height = georef.polar2lonlatalt_n(coord[..., 0], np.degrees(coord[..., 1]), coord[..., 2], sitecoords,
re=radius, ke=4. / 3.)
x, y = georef.reproject(lon, lat, projection_target=proj_radar)
test = x[0, 90, 0:960:60]
print(test)
def test_create_osr(self):
self.maxDiff = None
radolan_wkt = ('PROJCS["Radolan projection",'
'GEOGCS["Radolan Coordinate System",'
'DATUM["Radolan Kugel",'
'SPHEROID["Erdkugel",6370040.0,0.0]],'
'PRIMEM["Greenwich",0.0,AUTHORITY["EPSG","8901"]],'
'UNIT["degree",0.017453292519943295],'
'AXIS["Longitude",EAST],'
'AXIS["Latitude",NORTH]],'
'PROJECTION["polar_stereographic"],'
'PARAMETER["central_meridian",10.0],'
'PARAMETER["latitude_of_origin",60.0],'
'PARAMETER["scale_factor",{0:8.10f}],'
'PARAMETER["false_easting",0.0],'
'PARAMETER["false_northing",0.0],'
'UNIT["m*1000.0",1000.0],'
'AXIS["X",EAST],'
'AXIS["Y",NORTH]]'.
format((1. + np.sin(np.radians(60.))) /
(1. + np.sin(np.radians(90.)))))
self.assertEqual(georef.create_osr('dwd-radolan').ExportToWkt(),
radolan_wkt)
aeqd_wkt = ('PROJCS["unnamed",'
'GEOGCS["WGS 84",'
'DATUM["unknown",'
'SPHEROID["WGS84",6378137,298.257223563]],'
'PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],'
'PROJECTION["Azimuthal_Equidistant"],'
'PARAMETER["latitude_of_center",{0:-f}],'
'PARAMETER["longitude_of_center",{1:-f}],'
'PARAMETER["false_easting",{2:-f}],'
'PARAMETER["false_northing",{3:-f}]]'.
format(49., 5., 0, 0))
self.assertEqual(georef.create_osr('aeqd',
lon_0=5.,
lat_0=49).ExportToWkt(),
aeqd_wkt)
self.assertEqual(georef.create_osr('aeqd',
lon_0=5.,
lat_0=49,
x_0=0,
y_0=0).ExportToWkt(),
aeqd_wkt)
self.assertRaises(ValueError, lambda: georef.create_osr('lambert'))
def setUp(self):
img = np.zeros((360, 10), dtype=np.float32)
img[2, 2] = 10 # isolated pixel
img[5, 6:8] = 10 # line
img[20, :] = 5 # spike
img[60:120, 2:7] = 11 # precip field
self.img = img
self.proj = georef.create_osr("dwd-radolan")
def setUp(self):
img = np.zeros((360, 10), dtype=np.float32)
img[2, 2] = 10 # isolated pixel
img[5, 6:8] = 10 # line
img[20, :] = 5 # spike
img[60:120, 2:7] = 11 # precip field
self.img = img
self.proj = georef.create_osr("dwd-radolan")
def test_create_osr(self):
self.maxDiff = None
radolan_wkt = ('PROJCS["Radolan projection",'
'GEOGCS["Radolan Coordinate System",'
'DATUM["Radolan Kugel",'
'SPHEROID["Erdkugel",6370040.0,0.0]],'
'PRIMEM["Greenwich",0.0,AUTHORITY["EPSG","8901"]],'
'UNIT["degree",0.017453292519943295],'
'AXIS["Longitude",EAST],'
'AXIS["Latitude",NORTH]],'
'PROJECTION["polar_stereographic"],'
'PARAMETER["central_meridian",10.0],'
'PARAMETER["latitude_of_origin",60.0],'
'PARAMETER["scale_factor",{0:8.10f}],'
'PARAMETER["false_easting",0.0],'
'PARAMETER["false_northing",0.0],'
'UNIT["m*1000.0",1000.0],'
'AXIS["X",EAST],'
'AXIS["Y",NORTH]]'.format(
(1. + np.sin(np.radians(60.))) /
(1. + np.sin(np.radians(90.)))))
self.assertEqual(
georef.create_osr('dwd-radolan').ExportToWkt(), radolan_wkt)
aeqd_wkt = ('PROJCS["unnamed",'
'GEOGCS["WGS 84",'
'DATUM["unknown",'
'SPHEROID["WGS84",6378137,298.257223563]],'
'PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],'
'PROJECTION["Azimuthal_Equidistant"],'
'PARAMETER["latitude_of_center",{0:-f}],'
'PARAMETER["longitude_of_center",{1:-f}],'
'PARAMETER["false_easting",{2:-f}],'
'PARAMETER["false_northing",{3:-f}]]'.format(
49., 5., 0, 0))
self.assertEqual(
georef.create_osr('aeqd', lon_0=5., lat_0=49).ExportToWkt(),
aeqd_wkt)
self.assertEqual(
georef.create_osr('aeqd', lon_0=5., lat_0=49, x_0=0,
y_0=0).ExportToWkt(), aeqd_wkt)
with self.assertRaises(ValueError):
georef.create_osr('lambert')
def setUp(self):
img = np.zeros((360, 10), dtype=np.float32)
img[2, 2] = 10 # isolated pixel
img[5, 6:8] = 10 # line
img[20, :] = 5 # spike
img[60:120, 2:7] = 11 # precip field
self.r = np.arange(0, 100000, 10000)
self.az = np.arange(0, 360)
self.el = np.arange(0, 90)
self.th = np.zeros_like(self.az)
self.az1 = np.ones_like(self.el) * 225
self.img = img
self.proj = georef.create_osr("dwd-radolan")
self.da_ppi = io.create_xarray_dataarray(img, self.r, self.az, self.th)
self.da_rhi = io.create_xarray_dataarray(img[0:90], self.r, self.az1,
self.el)
def setUp(self):
img = np.zeros((360, 10), dtype=np.float32)
img[2, 2] = 10 # isolated pixel
img[5, 6:8] = 10 # line
img[20, :] = 5 # spike
img[60:120, 2:7] = 11 # precip field
self.r = np.arange(0, 100000, 10000)
self.az = np.arange(0, 360)
self.el = np.arange(0, 90)
self.th = np.zeros_like(self.az)
self.az1 = np.ones_like(self.el) * 225
self.img = img
self.proj = georef.create_osr("dwd-radolan")
self.da_ppi = io.create_xarray_dataarray(img, self.r, self.az, self.th)
self.da_rhi = io.create_xarray_dataarray(img[0:90], self.r, self.az1,
self.el)
def test_plot_ppi(self):
pl.figure()
proj = georef.create_osr("dwd-radolan")
ax, pm = vis.plot_ppi(self.img, re=6371000., ke=4./3.)
ax, pm = vis.plot_ppi(self.img, autoext=False)
vis.plot_ppi_crosshair(site=(0, 0),
ranges=[2, 4, 8],
angles=[0, 45, 90, 180, 270],
line=dict(color='white',
linestyle='solid'))
pl.figure()
ax, pm = vis.plot_ppi(self.img, site=(10., 45.), autoext=False, proj=proj)
vis.plot_ppi_crosshair(site=(0, 0),
ranges=[2, 4, 8],
angles=[0, 45, 90, 180, 270],
proj=proj,
line=dict(color='white',
linestyle='solid'))
def test_plot_ppi(self):
pl.figure()
proj = georef.create_osr("dwd-radolan")
ax, pm = vis.plot_ppi(self.img, re=6371000., ke=4. / 3.)
ax, pm = vis.plot_ppi(self.img, autoext=False)
vis.plot_ppi_crosshair(site=(0, 0),
ranges=[2, 4, 8],
angles=[0, 45, 90, 180, 270],
line=dict(color='white', linestyle='solid'))
pl.figure()
ax, pm = vis.plot_ppi(self.img,
site=(10., 45.),
autoext=False,
proj=proj)
vis.plot_ppi_crosshair(site=(0, 0),
ranges=[2, 4, 8],
angles=[0, 45, 90, 180, 270],
proj=proj,
line=dict(color='white', linestyle='solid'))
def test_create_osr(self):
self.maxDiff = None
radolan_wkt = 'PROJCS["Radolan projection",' \
'GEOGCS["Radolan Coordinate System",' \
'DATUM["Radolan Kugel",' \
'SPHEROID["Erdkugel",6370040.0,0.0]],' \
'PRIMEM["Greenwich",0.0,AUTHORITY["EPSG","8901"]],' \
'UNIT["degree",0.017453292519943295],' \
'AXIS["Longitude",EAST],' \
'AXIS["Latitude",NORTH]],' \
'PROJECTION["polar_stereographic"],' \
'PARAMETER["central_meridian",10.0],' \
'PARAMETER["latitude_of_origin",60.0],' \
'PARAMETER["scale_factor",{0:8.10f}],' \
'PARAMETER["false_easting",0.0],' \
'PARAMETER["false_northing",0.0],' \
'UNIT["m*1000.0",1000.0],' \
'AXIS["X",EAST],' \
'AXIS["Y",NORTH]]'.format((1. + np.sin(np.radians(60.))) / (1. + np.sin(np.radians(90.))))
self.assertEqual(georef.create_osr('dwd-radolan').ExportToWkt(), radolan_wkt)
def test_create_osr(self):
self.maxDiff = None
radolan_wkt = 'PROJCS["Radolan projection",' \
'GEOGCS["Radolan Coordinate System",' \
'DATUM["Radolan Kugel",' \
'SPHEROID["Erdkugel",6370040.0,0.0]],' \
'PRIMEM["Greenwich",0.0,AUTHORITY["EPSG","8901"]],' \
'UNIT["degree",0.017453292519943295],' \
'AXIS["Longitude",EAST],' \
'AXIS["Latitude",NORTH]],' \
'PROJECTION["polar_stereographic"],' \
'PARAMETER["central_meridian",10.0],' \
'PARAMETER["latitude_of_origin",60.0],' \
'PARAMETER["scale_factor",{0:8.10f}],' \
'PARAMETER["false_easting",0.0],' \
'PARAMETER["false_northing",0.0],' \
'UNIT["m*1000.0",1000.0],' \
'AXIS["X",EAST],' \
'AXIS["Y",NORTH]]'.format((1. + np.sin(np.radians(60.))) / (1. + np.sin(np.radians(90.))))
self.assertEqual(
georef.create_osr('dwd-radolan').ExportToWkt(), radolan_wkt)
def ex_clutter_cloud():
# read the radar volume scan
path = os.path.dirname(__file__) + '/'
pvol = io.read_OPERA_hdf5(path + 'data/20130429043000.rad.bewid.pvol.dbzh.scan1.hdf')
# Count the number of dataset
ntilt = 1
for i in range(100):
try:
pvol["dataset%d/what" % ntilt]
ntilt += 1
except Exception:
ntilt -= 1
break
# Construct radar values
nrays = int(pvol["dataset1/where"]["nrays"])
nbins = int(pvol["dataset1/where"]["nbins"])
val = np.empty((ntilt, nrays, nbins))
for t in range(ntilt):
val[t, ...] = pvol["dataset%d/data1/data" % (t + 1)]
gain = float(pvol["dataset1/data1/what"]["gain"])
offset = float(pvol["dataset1/data1/what"]["offset"])
val = val * gain + offset
# Construct radar coordinates
rscale = int(pvol["dataset1/where"]["rscale"])
coord = np.empty((ntilt, nrays, nbins, 3))
for t in range(ntilt):
elangle = pvol["dataset%d/where" % (t + 1)]["elangle"]
coord[t, ...] = georef.sweep_centroids(nrays, rscale, nbins, elangle)
ascale = math.pi / nrays
sitecoords = (pvol["where"]["lon"], pvol["where"]["lat"], pvol["where"]["height"])
proj_radar = georef.create_osr("aeqd", lat_0=pvol["where"]["lat"], lon_0=pvol["where"]["lon"])
coord[..., 0], coord[..., 1], coord[..., 2] = georef.polar2lonlatalt_n(coord[..., 0], np.degrees(coord[..., 1]),
coord[..., 2], sitecoords, re=6370040.,
ke=4. / 3.)
coord = georef.reproject(coord, projection_target=proj_radar)
# Construct collocated satellite data
sat_gdal = io.read_safnwc(path + 'data/SAFNWC_MSG3_CT___201304290415_BEL_________.h5')
val_sat = georef.read_gdal_values(sat_gdal)
coord_sat = georef.read_gdal_coordinates(sat_gdal)
proj_sat = georef.read_gdal_projection(sat_gdal)
coord_sat = georef.reproject(coord_sat, projection_source=proj_sat, projection_target=proj_radar)
coord_radar = coord
interp = ipol.Nearest(coord_sat[..., 0:2].reshape(-1, 2), coord_radar[..., 0:2].reshape(-1, 2))
val_sat = interp(val_sat.ravel()).reshape(val.shape)
# Estimate localisation errors
timelag = 9 * 60
wind = 10
error = np.absolute(timelag) * wind
# Identify clutter based on collocated cloudtype
clutter = cl.filter_cloudtype(val[0, ...], val_sat[0, ...], scale=rscale, smoothing=error)
# visualize the result
plt.figure()
vis.plot_ppi(clutter)
plt.suptitle('clutter')
plt.savefig('clutter_cloud_example_1.png')
plt.figure()
vis.plot_ppi(val_sat[0, ...])
plt.suptitle('satellite')
plt.savefig('clutter_cloud_example_2.png')
plt.show()
def test_create_osr(self):
self.maxDiff = None
radolan_wkt = (
'PROJCS["Radolan projection",'
'GEOGCS["Radolan Coordinate System",'
'DATUM["Radolan Kugel",'
'SPHEROID["Erdkugel",6370040.0,0.0]],'
'PRIMEM["Greenwich",0.0,AUTHORITY["EPSG","8901"]],'
'UNIT["degree",0.017453292519943295],'
'AXIS["Longitude",EAST],'
'AXIS["Latitude",NORTH]],'
'PROJECTION["polar_stereographic"],'
'PARAMETER["central_meridian",10.0],'
'PARAMETER["latitude_of_origin",90.0],'
'PARAMETER["scale_factor",{0:8.10f}],'
'PARAMETER["false_easting",0.0],'
'PARAMETER["false_northing",0.0],'
'UNIT["m*1000.0",1000.0],'
'AXIS["X",EAST],'
'AXIS["Y",NORTH]]'
)
radolan_wkt3 = (
'PROJCS["Radolan Projection",'
'GEOGCS["Radolan Coordinate System",'
'DATUM["Radolan_Kugel",'
'SPHEROID["Erdkugel",6370040,0]],'
'PRIMEM["Greenwich",0,'
'AUTHORITY["EPSG","8901"]],'
'UNIT["degree",0.0174532925199433,'
'AUTHORITY["EPSG","9122"]]],'
'PROJECTION["Polar_Stereographic"],'
'PARAMETER["latitude_of_origin",90],'
'PARAMETER["central_meridian",10],'
'PARAMETER["scale_factor",{0:8.12f}],'
'PARAMETER["false_easting",0],'
'PARAMETER["false_northing",0],'
'UNIT["kilometre",1000,'
'AUTHORITY["EPSG","9036"]],'
'AXIS["Easting",SOUTH],'
'AXIS["Northing",SOUTH]]'
)
scale = (1.0 + np.sin(np.radians(60.0))) / (1.0 + np.sin(np.radians(90.0)))
aeqd_wkt = (
'PROJCS["unnamed",'
'GEOGCS["WGS 84",'
'DATUM["unknown",'
'SPHEROID["WGS84",6378137,298.257223563]],'
'PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],'
'PROJECTION["Azimuthal_Equidistant"],'
'PARAMETER["latitude_of_center",{0:-f}],'
'PARAMETER["longitude_of_center",{1:-f}],'
'PARAMETER["false_easting",{2:-f}],'
'PARAMETER["false_northing",{3:-f}],'
'UNIT["Meter",1]]'
)
aeqd_wkt3 = (
'PROJCS["unnamed",'
'GEOGCS["WGS 84",'
'DATUM["unknown",'
'SPHEROID["WGS84",6378137,298.257223563]],'
'PRIMEM["Greenwich",0],'
'UNIT["degree",0.0174532925199433]],'
'PROJECTION["Azimuthal_Equidistant"],'
'PARAMETER["latitude_of_center",{0:d}],'
'PARAMETER["longitude_of_center",{1:d}],'
'PARAMETER["false_easting",{2:d}],'
'PARAMETER["false_northing",{3:d}],'
'UNIT["Meter",1],'
'AXIS["Easting",EAST],'
'AXIS["Northing",NORTH]]'
)
if gdal.VersionInfo()[0] >= "3":
radolan_wkt = radolan_wkt3.format(scale)
aeqd_wkt = aeqd_wkt3.format(49, 5, 0, 0)
else:
radolan_wkt = radolan_wkt.format(scale)
aeqd_wkt = aeqd_wkt.format(49, 5, 0, 0)
assert georef.create_osr("dwd-radolan").ExportToWkt() == radolan_wkt
assert georef.create_osr("aeqd", lon_0=5, lat_0=49).ExportToWkt() == aeqd_wkt
assert (
georef.create_osr("aeqd", lon_0=5, lat_0=49, x_0=0, y_0=0).ExportToWkt()
== aeqd_wkt
)
with pytest.raises(ValueError):
georef.create_osr("lambert")
class TestPolarPlot:
img = np.zeros((360, 10), dtype=np.float32)
img[2, 2] = 10 # isolated pixel
img[5, 6:8] = 10 # line
img[20, :] = 5 # spike
img[60:120, 2:7] = 11 # precip field
r = np.arange(0, 100000, 10000)
az = np.arange(0, 360)
el = np.arange(0, 90)
th = np.zeros_like(az)
az1 = np.ones_like(el) * 225
img = img
proj = georef.create_osr("dwd-radolan")
da_ppi = georef.create_xarray_dataarray(img, r, az, th)
da_ppi = georef.georeference_dataset(da_ppi, proj=None)
da_rhi = georef.create_xarray_dataarray(img[0:90], r, az1, el)
da_rhi = georef.georeference_dataset(da_rhi, proj=None)
def test_plot_ppi(self):
ax, pm = vis.plot_ppi(self.img, re=6371000.0, ke=(4.0 / 3.0))
ax, pm = vis.plot_ppi(self.img, self.r, self.az, re=6371000.0, ke=(4.0 / 3.0))
ax, pm = vis.plot_ppi(
self.img, self.r, self.az, re=6371000.0, ke=(4.0 / 3.0), ax=ax
)
ax, pm = vis.plot_ppi(
self.img, self.r, self.az, re=6371000.0, ke=(4.0 / 3.0), ax=212
)
ax, pm = vis.plot_ppi(self.img)
vis.plot_ppi_crosshair(site=(0, 0, 0), ranges=[2, 4, 8])
vis.plot_ppi_crosshair(
site=(0, 0, 0),
ranges=[2, 4, 8],
angles=[0, 45, 90, 180, 270],
line=dict(color="white", linestyle="solid"),
)
ax, pm = vis.plot_ppi(self.img, self.r, site=(10.0, 45.0, 0.0), proj=self.proj)
vis.plot_ppi_crosshair(
site=(10.0, 45.0, 0.0),
ranges=[2, 4, 8],
angles=[0, 45, 90, 180, 270],
proj=self.proj,
line=dict(color="white", linestyle="solid"),
)
ax, pm = vis.plot_ppi(self.img, func="contour")
ax, pm = vis.plot_ppi(self.img, func="contourf")
ax, pm = vis.plot_ppi(self.img, self.r, self.az, proj=self.proj, site=(0, 0, 0))
with pytest.warns(UserWarning):
ax, pm = vis.plot_ppi(self.img, site=(10.0, 45.0, 0.0), proj=self.proj)
with pytest.warns(UserWarning):
ax, pm = vis.plot_ppi(self.img, proj=None, site=(0, 0, 0))
with pytest.raises(TypeError):
ax, pm = vis.plot_ppi(self.img, proj=self.proj)
with pytest.raises(ValueError):
ax, pm = vis.plot_ppi(self.img, site=(0, 0), proj=self.proj)
with pytest.raises(ValueError):
vis.plot_ppi_crosshair(site=(0, 0), ranges=[2, 4, 8])
def test_plot_ppi_xarray(self):
self.da_ppi.wradlib.rays
self.da_ppi.wradlib.plot()
self.da_ppi.wradlib.plot_ppi()
self.da_ppi.wradlib.contour()
self.da_ppi.wradlib.contourf()
self.da_ppi.wradlib.pcolormesh()
self.da_ppi.wradlib.plot(proj="cg")
self.da_ppi.wradlib.plot_ppi(proj="cg")
self.da_ppi.wradlib.contour(proj="cg")
self.da_ppi.wradlib.contourf(proj="cg")
self.da_ppi.wradlib.pcolormesh(proj="cg")
with pytest.raises(TypeError):
self.da_ppi.wradlib.pcolormesh(proj=self.proj)
fig = pl.figure()
ax = fig.add_subplot(111)
with pytest.raises(TypeError):
self.da_ppi.wradlib.pcolormesh(proj={"rot": 0, "scale": 1}, ax=ax)
@pytest.mark.skipif("cartopy" not in sys.modules, reason="without Cartopy")
def test_plot_ppi_cartopy(self):
if cartopy:
if (LooseVersion(cartopy.__version__) < LooseVersion("0.18.0")) and (
LooseVersion(mpl.__version__) >= LooseVersion("3.3.0")
):
pytest.skip("fails for cartopy < 0.18.0 and matplotlib >= 3.3.0")
site = (7, 45, 0.0)
map_proj = cartopy.crs.Mercator(central_longitude=site[1])
ax, pm = vis.plot_ppi(self.img, self.r, self.az, proj=map_proj)
assert isinstance(ax, cartopy.mpl.geoaxes.GeoAxes)
fig = pl.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection=map_proj)
self.da_ppi.wradlib.plot_ppi(ax=ax)
ax.gridlines(draw_labels=True)
def test_plot_rhi(self):
ax, pm = vis.plot_rhi(self.img[0:90, :])
ax, pm = vis.plot_rhi(self.img[0:90, :], th_res=0.5)
ax, pm = vis.plot_rhi(self.img[0:90, :], th_res=0.5, ax=212)
ax, pm = vis.plot_rhi(self.img[0:90, :], r=np.arange(10), th=np.arange(90))
ax, pm = vis.plot_rhi(self.img[0:90, :], func="contour")
ax, pm = vis.plot_rhi(self.img[0:90, :], func="contourf")
ax, pm = vis.plot_rhi(
self.img[0:90, :],
r=np.arange(10),
th=np.arange(90),
proj=self.proj,
site=(0, 0, 0),
)
def test_plot_rhi_xarray(self):
assert (
repr(self.da_rhi.wradlib).split("\n", 1)[1]
== repr(self.da_rhi).split("\n", 1)[1]
)
self.da_rhi.wradlib.rays
self.da_rhi.wradlib.plot()
self.da_rhi.wradlib.plot_rhi()
self.da_rhi.wradlib.contour()
self.da_rhi.wradlib.contourf()
self.da_rhi.wradlib.pcolormesh()
self.da_rhi.wradlib.plot(proj="cg")
self.da_rhi.wradlib.plot_rhi(proj="cg")
self.da_rhi.wradlib.contour(proj="cg")
self.da_rhi.wradlib.contourf(proj="cg")
self.da_rhi.wradlib.pcolormesh(proj="cg")
def test_plot_cg_ppi(self):
cgax, pm = vis.plot_ppi(self.img, elev=2.0, proj="cg")
cgax, pm = vis.plot_ppi(self.img, elev=2.0, proj="cg", site=(0, 0, 0))
cgax, pm = vis.plot_ppi(self.img, elev=2.0, proj="cg", ax=cgax)
fig, ax = pl.subplots(2, 2)
with pytest.raises(TypeError):
vis.plot_ppi(self.img, elev=2.0, proj="cg", ax=ax[0, 0])
cgax, pm = vis.plot_ppi(self.img, elev=2.0, proj="cg", ax=111)
cgax, pm = vis.plot_ppi(self.img, elev=2.0, proj="cg", ax=121)
cgax, pm = vis.plot_ppi(self.img, proj="cg")
cgax, pm = vis.plot_ppi(self.img, func="contour", proj="cg")
cgax, pm = vis.plot_ppi(self.img, func="contourf", proj="cg")
cgax, pm = vis.plot_ppi(self.img, func="contourf", proj="cg")
def test_plot_cg_rhi(self):
cgax, pm = vis.plot_rhi(self.img[0:90, :], proj="cg")
cgax, pm = vis.plot_rhi(self.img[0:90, :], proj="cg", ax=cgax)
fig, ax = pl.subplots(2, 2)
with pytest.raises(TypeError):
vis.plot_rhi(self.img[0:90, :], proj="cg", ax=ax[0, 0])
cgax, pm = vis.plot_rhi(self.img[0:90, :], th_res=0.5, proj="cg")
cgax, pm = vis.plot_rhi(self.img[0:90, :], proj="cg")
cgax, pm = vis.plot_rhi(
self.img[0:90, :], r=np.arange(10), th=np.arange(90), proj="cg"
)
cgax, pm = vis.plot_rhi(self.img[0:90, :], func="contour", proj="cg")
cgax, pm = vis.plot_rhi(self.img[0:90, :], func="contourf", proj="cg")
def test_create_cg(self):
cgax, caax, paax = vis.create_cg()
cgax, caax, paax = vis.create_cg(subplot=121)
def ex_clutter_cloud():
# read the radar volume scan
path = os.path.dirname(__file__) + '/'
pvol = io.read_OPERA_hdf5(
path + 'data/20130429043000.rad.bewid.pvol.dbzh.scan1.hdf')
# Count the number of dataset
ntilt = 1
for i in range(100):
try:
pvol["dataset%d/what" % ntilt]
ntilt += 1
except Exception:
ntilt -= 1
break
# Construct radar values
nrays = int(pvol["dataset1/where"]["nrays"])
nbins = int(pvol["dataset1/where"]["nbins"])
val = np.empty((ntilt, nrays, nbins))
for t in range(ntilt):
val[t, ...] = pvol["dataset%d/data1/data" % (t + 1)]
gain = float(pvol["dataset1/data1/what"]["gain"])
offset = float(pvol["dataset1/data1/what"]["offset"])
val = val * gain + offset
# Construct radar coordinates
rscale = int(pvol["dataset1/where"]["rscale"])
coord = np.empty((ntilt, nrays, nbins, 3))
for t in range(ntilt):
elangle = pvol["dataset%d/where" % (t + 1)]["elangle"]
coord[t, ...] = georef.sweep_centroids(nrays, rscale, nbins, elangle)
ascale = math.pi / nrays
sitecoords = (pvol["where"]["lon"], pvol["where"]["lat"],
pvol["where"]["height"])
proj_radar = georef.create_osr("aeqd",
lat_0=pvol["where"]["lat"],
lon_0=pvol["where"]["lon"])
coord[..., 0], coord[..., 1], coord[..., 2] = georef.polar2lonlatalt_n(
coord[..., 0],
np.degrees(coord[..., 1]),
coord[..., 2],
sitecoords,
re=6370040.,
ke=4. / 3.)
coord = georef.reproject(coord, projection_target=proj_radar)
# Construct collocated satellite data
sat_gdal = io.read_safnwc(
path + 'data/SAFNWC_MSG3_CT___201304290415_BEL_________.h5')
val_sat = georef.read_gdal_values(sat_gdal)
coord_sat = georef.read_gdal_coordinates(sat_gdal)
proj_sat = georef.read_gdal_projection(sat_gdal)
coord_sat = georef.reproject(coord_sat,
projection_source=proj_sat,
projection_target=proj_radar)
coord_radar = coord
interp = ipol.Nearest(coord_sat[..., 0:2].reshape(-1, 2),
coord_radar[..., 0:2].reshape(-1, 2))
val_sat = interp(val_sat.ravel()).reshape(val.shape)
# Estimate localisation errors
timelag = 9 * 60
wind = 10
error = np.absolute(timelag) * wind
# Identify clutter based on collocated cloudtype
clutter = cl.filter_cloudtype(val[0, ...],
val_sat[0, ...],
scale=rscale,
smoothing=error)
# visualize the result
plt.figure()
vis.plot_ppi(clutter)
plt.suptitle('clutter')
plt.savefig('clutter_cloud_example_1.png')
plt.figure()
vis.plot_ppi(val_sat[0, ...])
plt.suptitle('satellite')
plt.savefig('clutter_cloud_example_2.png')
plt.show()
