def test_MissingErrors(self):
with pytest.raises(ipol.MissingSourcesError):
ipol.Nearest(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Nearest(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.Idw(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Idw(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.Linear(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.Linear(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.OrdinaryKriging(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.OrdinaryKriging(self.src, np.array([]))
with pytest.raises(ipol.MissingSourcesError):
ipol.ExternalDriftKriging(np.array([]), self.trg)
with pytest.raises(ipol.MissingTargetsError):
ipol.ExternalDriftKriging(self.src, np.array([]))
def test_Nearest_1(self):
"""testing the basic behaviour of the Idw class"""
ip = ipol.Nearest(self.src, self.trg)
# input more than one dataset
res = ip(self.vals)
self.assertTrue(
np.allclose(
res,
np.array([[1., 2., 3.], [1., 2., 3.], [1., 2., 3.],
[3., 2., 1.]])))
# input only one flat array
res = ip(self.vals[:, 2])
self.assertTrue(np.allclose(res, np.array([3., 3., 3., 1.])))
def test_Nearest_1(self):
"""testing the basic behaviour of the Idw class"""
ip = ipol.Nearest(self.src, self.trg)
# input more than one dataset
res = ip(self.vals)
assert np.allclose(
res,
np.array([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0], [1.0, 2.0, 3.0],
[3.0, 2.0, 1.0]]),
)
# input only one flat array
res = ip(self.vals[:, 2])
assert np.allclose(res, np.array([3.0, 3.0, 3.0, 1.0]))
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 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
def gridplot(interpolated, title=""):
plt.pcolormesh(xtrg, ytrg, interpolated.reshape((len(xtrg), len(ytrg))))
plt.axis("tight")
plt.scatter(src[:, 0], src[:, 1], facecolor="None", s=50, marker='s')
plt.title(title)
plt.xlabel("X")
plt.ylabel("Y")
# Interpolation IDW
idw = ipol.Idw(src, trg)
gridplot(idw(vals.ravel()), "IDW")
# Other approach for IDW
ip_near = ipol.Nearest(src, trg)
maxdist = trg[1, 0] - trg[0, 0]
result_near = ip_near(vals.ravel(), maxdist=maxdist)
# 5.2.3 Kriging ===============================
df_meg_nodes_np = np.array(df_meg_nodes)
gridx = np.arange(0.0, 5.5, 0.5)
gridy = np.arange(0.0, 5.5, 0.5)
# Ordninary Kriging
ok = ipol.OrdinaryKriging(src, trg)
gridplot(ok(vals.ravel()), "Ordinary Kriging")
# Universal Kriging
UK = UniversalKriging(df_meg_nodes_np[:, 0],
df_meg_nodes_np[:, 4],
def getRadarDataByExtent(radar_nc_path, save_path, extent):
x_min, x_max, y_min, y_max = extent
scale = 111.194925
nc_ds = nc.Dataset(radar_nc_path, "r")
lon = nc_ds.Longitude
lat = nc_ds.Latitude
zDr = np.array(nc_ds.variables["DifferentialReflectivity"])
Phi_dp = np.array(nc_ds.variables["DifferentialPhase"])
KDP = np.array(nc_ds.variables["KDP"])
reflectivity = np.array(nc_ds.variables["Reflectivity"])
'''
zDr_loc = np.where(zDr.flatten() != -8.125)[0]
reflect_loc = np.where(reflectivity.flatten() != -33.0)[0]
Kdp_loc = np.where(KDP.flatten() != -5.0)[0]
sample_loc = reduce(np.union1d, (zDr_loc, reflect_loc, Kdp_loc))
'''
# ------convert mm to m
GateWidth = np.array(nc_ds.variables["GateWidth"]) / 1000.0
w_r = GateWidth[0] / scale / 1000.0
dis_cum = np.cumsum(np.full(Phi_dp.shape[1], w_r))
# ---Determine the center coordinates of observed radar data
azimuth = np.array(nc_ds.variables["Azimuth"]) * np.pi / 180.0
x_obs_loc = lon + np.outer(np.cos(azimuth), dis_cum)
y_obs_loc = lat + np.outer(np.sin(azimuth), dis_cum)
xy_obs_loc = np.concatenate(
(np.reshape(x_obs_loc.flatten(),
(-1, 1)), np.reshape(y_obs_loc.flatten(), (-1, 1))),
axis=1)
# xy_obs_loc = xy_obs_loc[sample_loc]
# ---Determine the center coordinates of cells which are to be interpolated
num_row = int(np.ceil((y_max - y_min) / w_r))
num_col = int(np.ceil((x_max - x_min) / w_r))
x_center_loc = np.linspace(x_min, x_min +
(num_col - 1) * w_r, num_col) + 0.5 * w_r
y_center_loc = np.linspace(y_max -
(num_row - 1) * w_r, y_max, num_row) - 0.5 * w_r
x_center_loc, y_center_loc = np.meshgrid(x_center_loc, y_center_loc)
xy_center_loc = np.concatenate(
(np.reshape(x_center_loc.flatten(),
(-1, 1)), np.reshape(y_center_loc.flatten(), (-1, 1))),
axis=1)
# ---construct an Interpolation Object
interp_obj = ipol.Nearest(xy_obs_loc, xy_center_loc)
# zH_interploate = OrdinaryKriging_obj(reflectivity.flatten()[sample_loc])
zH_interploate = interp_obj(reflectivity.flatten())
zH_interploate = np.reshape(zH_interploate, (num_row, num_col))
# zDr_interploate = OrdinaryKriging_obj(zDr.flatten()[sample_loc])
zDr_interploate = interp_obj(zDr.flatten())
zDr_interploate = np.reshape(zDr_interploate, (num_row, num_col))
# Kdp_interploate = OrdinaryKriging_obj(KDP.flatten()[sample_loc])
Kdp_interploate = interp_obj(KDP.flatten())
Kdp_interploate = np.reshape(Kdp_interploate, (num_row, num_col))
# ---save the output .nc file
output_ds = nc.Dataset(save_path, mode="w")
output_ds.NetCDFRevision = "lyy_thu_data"
output_ds.GenDate = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
output_ds.num_col = num_col
output_ds.num_row = num_row
output_ds.Resolution = w_r
output_ds.X_min = x_min
output_ds.Y_max = y_max
RowDimId = output_ds.createDimension("row", num_row)
ColDimId = output_ds.createDimension("col", num_col)
zh_id = output_ds.createVariable("Reflectivity", np.float64,
("row", "col"))
zh_id.Units = "dBz"
zh_id[:, :] = zH_interploate
zdr_id = output_ds.createVariable("DifferentialReflectivity", np.float64,
("row", "col"))
zdr_id.Units = "dB"
zdr_id[:, :] = zDr_interploate
kdp_id = output_ds.createVariable("KDP", np.float64, ("row", "col"))
kdp_id.Units = "unitless"
kdp_id[:, :] = Kdp_interploate
output_ds.close()
nc_ds.close()
