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_polar2lonlatalt_n(self):
self.assertTrue(np.allclose(georef.polar2lonlatalt_n(self.r, self.az, self.th, self.csite),
self.result_n, rtol=1e-04))
def test_polar2lonlatalt_n(self):
self.assertTrue(np.allclose(
georef.polar2lonlatalt_n(self.r, self.az, self.th, self.csite),
self.result_n, rtol=1e-04))
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 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_polar2lonlatalt_n(self):
lon, lat, alt = georef.polar2lonlatalt_n(self.r, self.az,
self.th, self.csite)
self.assertTrue(np.allclose(lon, self.result_n[0]))
self.assertTrue(np.allclose(lat, self.result_n[1]))
self.assertTrue(np.allclose(alt, self.result_n[2]))