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)
class TestRegularToIrregular:
NX = 2
nx = np.linspace(-NX + 0.5, NX - 0.5, num=2 * NX, endpoint=True)
vx = np.linspace(-NX, NX, num=2 * NX, endpoint=True)
meshx, meshy = np.meshgrid(nx, nx)
cartgrid = np.dstack((meshx, meshy))
values = np.repeat(vx[:, np.newaxis], 2 * NX, 1)
coord = georef.sweep_centroids(4, 1, NX, 0.0)
xx = coord[..., 0]
yy = coord[..., 1]
xxx = xx * np.cos(np.radians(90.0 - yy))
x = xx * np.sin(np.radians(90.0 - yy))
y = xxx
newgrid = np.dstack((x, y))
result = np.array([
[0.47140452, 1.41421356],
[0.47140452, 1.41421356],
[-0.47140452, -1.41421356],
[-0.47140452, -1.41421356],
])
def test_cart_to_irregular_interp(self):
newvalues = ipol.cart_to_irregular_interp(self.cartgrid,
self.values,
self.newgrid,
method="linear")
assert np.allclose(newvalues, self.result)
def test_cart_to_irregular_spline(self):
newvalues = ipol.cart_to_irregular_spline(self.cartgrid,
self.values,
self.newgrid,
order=1,
prefilter=False)
assert np.allclose(newvalues, self.result)
def test_cart_to_irregular_equality(self):
assert np.allclose(
ipol.cart_to_irregular_interp(self.cartgrid,
self.values,
self.newgrid,
method="linear"),
ipol.cart_to_irregular_spline(self.cartgrid,
self.values,
self.newgrid,
order=1,
prefilter=False),
)
def setUp(self):
# read the radar volume scan
filename = 'hdf5/20130429043000.rad.bewid.pvol.dbzh.scan1.hdf'
filename = util.get_wradlib_data_file(filename)
pvol = io.read_opera_hdf5(filename)
nrays = int(pvol["dataset1/where"]["nrays"])
nbins = int(pvol["dataset1/where"]["nbins"])
val = pvol["dataset%d/data1/data" % (1)]
gain = float(pvol["dataset1/data1/what"]["gain"])
offset = float(pvol["dataset1/data1/what"]["offset"])
self.val = val * gain + offset
self.rscale = int(pvol["dataset1/where"]["rscale"])
elangle = pvol["dataset%d/where" % (1)]["elangle"]
coord = georef.sweep_centroids(nrays, self.rscale, nbins, elangle)
sitecoords = (pvol["where"]["lon"], pvol["where"]["lat"],
pvol["where"]["height"])
coord, proj_radar = georef.spherical_to_xyz(coord[..., 0],
coord[..., 1],
coord[..., 2],
sitecoords,
re=6370040.,
ke=4. / 3.)
filename = 'hdf5/SAFNWC_MSG3_CT___201304290415_BEL_________.h5'
filename = util.get_wradlib_data_file(filename)
sat_gdal = io.read_safnwc(filename)
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))
self.val_sat = interp(val_sat.ravel()).reshape(val.shape)
timelag = 9 * 60
wind = 10
self.error = np.absolute(timelag) * wind
def setUp(self):
NX = 2
nx = np.linspace(-NX + 0.5, NX - 0.5, num=2 * NX, endpoint=True)
vx = np.linspace(-NX, NX, num=2 * NX, endpoint=True)
meshx, meshy = np.meshgrid(nx, nx)
self.cartgrid = np.dstack((meshx, meshy))
self.values = np.repeat(vx[:, np.newaxis], 2 * NX, 1)
coord = georef.sweep_centroids(4, 1, NX, 0.)
xx = coord[..., 0]
yy = np.degrees(coord[..., 1])
xxx = xx * np.cos(np.radians(90. - yy))
x = xx * np.sin(np.radians(90. - yy))
y = xxx
self.newgrid = np.dstack((x, y))
self.result = np.array([[0.47140452, 1.41421356],
[0.47140452, 1.41421356],
[-0.47140452, -1.41421356],
[-0.47140452, -1.41421356]])
def setUp(self):
NX = 2
nx = np.linspace(-NX + 0.5, NX - 0.5, num=2 * NX, endpoint=True)
vx = np.linspace(-NX, NX, num=2 * NX, endpoint=True)
meshx, meshy = np.meshgrid(nx, nx)
self.cartgrid = np.dstack((meshx, meshy))
self.values = np.repeat(vx[:, np.newaxis], 2 * NX, 1)
coord = georef.sweep_centroids(4, 1, NX, 0.)
xx = coord[..., 0]
yy = np.degrees(coord[..., 1])
xxx = xx * np.cos(np.radians(90. - yy))
x = xx * np.sin(np.radians(90. - yy))
y = xxx
self.newgrid = np.dstack((x, y))
self.result = np.array([[0.47140452, 1.41421356],
[0.47140452, 1.41421356],
[-0.47140452, -1.41421356],
[-0.47140452, -1.41421356]])
def test_sweep_centroids(self):
self.assertTrue(np.allclose(georef.sweep_centroids(1,100.,1,2.0),
np.array([[[50., 1.57079633, 2.]]])))
def test_sweep_centroids(self):
self.assertTrue(np.allclose(georef.sweep_centroids(1, 100., 1, 2.0),
np.array([[[50., 3.14159265, 2.]]])))
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_sweep_centroids(self):
np.testing.assert_array_equal(
georef.sweep_centroids(1, 100.0, 1, 2.0), np.array([[[50.0, 180.0, 2.0]]])
)
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()
class TestFilterCloudtype:
if has_data:
# read the radar volume scan
filename = "hdf5/20130429043000.rad.bewid.pvol.dbzh.scan1.hdf"
filename = util.get_wradlib_data_file(filename)
pvol = io.read_opera_hdf5(filename)
nrays = int(pvol["dataset1/where"]["nrays"])
nbins = int(pvol["dataset1/where"]["nbins"])
val = pvol["dataset%d/data1/data" % (1)]
gain = float(pvol["dataset1/data1/what"]["gain"])
offset = float(pvol["dataset1/data1/what"]["offset"])
val = val * gain + offset
rscale = int(pvol["dataset1/where"]["rscale"])
elangle = pvol["dataset%d/where" % (1)]["elangle"]
coord = georef.sweep_centroids(nrays, rscale, nbins, elangle)
sitecoords = (
pvol["where"]["lon"],
pvol["where"]["lat"],
pvol["where"]["height"],
)
coord, proj_radar = georef.spherical_to_xyz(
coord[..., 0],
coord[..., 1],
coord[..., 2],
sitecoords,
re=6370040.0,
ke=4.0 / 3.0,
)
filename = "hdf5/SAFNWC_MSG3_CT___201304290415_BEL_________.h5"
filename = util.get_wradlib_data_file(filename)
sat_gdal = io.read_safnwc(filename)
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)
timelag = 9 * 60
wind = 10
error = np.absolute(timelag) * wind
@requires_data
def test_filter_cloudtype(self):
nonmet = clutter.filter_cloudtype(self.val,
self.val_sat,
scale=self.rscale,
smoothing=self.error)
nclutter = np.sum(nonmet)
assert nclutter == 8141
nonmet = clutter.filter_cloudtype(self.val,
self.val_sat,
scale=self.rscale,
smoothing=self.error,
low=True)
nclutter = np.sum(nonmet)
assert nclutter == 17856
