def test_get_radolan_grid_equality(self):
# create radolan projection osr object
scale = (1. + np.sin(np.radians(60.))) / (1. + np.sin(np.radians(90.)))
dwd_string = ('+proj=stere +lat_0=90 +lat_ts=90 +lon_0=10 '
'+k={0:10.8f} +x_0=0 +y_0=0 +a=6370040 +b=6370040 '
'+to_meter=1000 +no_defs'.format(scale))
proj_stereo = georef.proj4_to_osr(dwd_string)
# create wgs84 projection osr object
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
# transform radolan polar stereographic projection to wgs84 and wgs84
# to polar stereographic
# using osr transformation routines
radolan_grid_ll = georef.reproject(self.radolan_grid_xy,
projection_source=proj_stereo,
projection_target=proj_wgs)
radolan_grid_xy = georef.reproject(self.radolan_grid_ll,
projection_source=proj_wgs,
projection_target=proj_stereo)
# check source and target arrays for equality
self.assertTrue(np.allclose(radolan_grid_ll, self.radolan_grid_ll))
self.assertTrue(np.allclose(radolan_grid_xy, self.radolan_grid_xy))
def test_get_radolan_grid_equality(self):
# create radolan projection osr object
scale = (1. + np.sin(np.radians(60.))) / (1. + np.sin(np.radians(90.)))
dwd_string = ('+proj=stere +lat_0=90 +lat_ts=90 +lon_0=10 '
'+k={0:10.8f} +x_0=0 +y_0=0 +a=6370040 +b=6370040 '
'+to_meter=1000 +no_defs'.format(scale))
proj_stereo = georef.proj4_to_osr(dwd_string)
# create wgs84 projection osr object
proj_wgs = osr.SpatialReference()
proj_wgs.ImportFromEPSG(4326)
# transform radolan polar stereographic projection to wgs84 and wgs84
# to polar stereographic
# using osr transformation routines
radolan_grid_ll = georef.reproject(self.radolan_grid_xy,
projection_source=proj_stereo,
projection_target=proj_wgs)
radolan_grid_xy = georef.reproject(self.radolan_grid_ll,
projection_source=proj_wgs,
projection_target=proj_stereo)
# check source and target arrays for equality
self.assertTrue(np.allclose(radolan_grid_ll, self.radolan_grid_ll))
self.assertTrue(np.allclose(radolan_grid_xy, self.radolan_grid_xy))
radolan_grid_xy = georef.get_radolan_grid(900, 900)
radolan_grid_ll = georef.get_radolan_grid(900, 900, wgs84=True)
# check source and target arrays for equality
self.assertTrue(np.allclose(radolan_grid_ll, self.radolan_grid_ll))
self.assertTrue(np.allclose(radolan_grid_xy, self.radolan_grid_xy))
def test_reproject(self):
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
proj_wgs84 = osr.SpatialReference()
proj_wgs84.ImportFromEPSG(4326)
x, y = georef.reproject(7., 53., projection_source=proj_wgs84, projection_target=proj_gk)
lon, lat = georef.reproject(x, y, projection_source=proj_gk, projection_target=proj_wgs84)
self.assertAlmostEqual(lon, 7.0)
self.assertAlmostEqual(lat, 53.0)
def test_reproject(self):
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
proj_wgs84 = osr.SpatialReference()
proj_wgs84.ImportFromEPSG(4326)
x, y = georef.reproject(7., 53., projection_source=proj_wgs84,
projection_target=proj_gk)
lon, lat = georef.reproject(x, y, projection_source=proj_gk,
projection_target=proj_wgs84)
self.assertAlmostEqual(lon, 7.0)
self.assertAlmostEqual(lat, 53.0)
def test_geoid_to_ellipsoid(self):
coords = np.array([[5.0, 50.0, 300.0], [2, 54, 300], [50, 5, 300]])
geoid = georef.get_earth_projection("geoid")
ellipsoid = georef.get_earth_projection("ellipsoid")
newcoords = georef.reproject(
coords, projection_source=geoid, projection_target=ellipsoid
)
assert np.any(np.not_equal(coords[..., 2], newcoords[..., 2]))
newcoords = georef.reproject(
newcoords, projection_source=ellipsoid, projection_target=geoid
)
np.testing.assert_allclose(coords, newcoords)
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 make_3d_grid(sitecoords,
proj,
maxrange,
maxalt,
horiz_res,
vert_res,
minalt=0.):
"""Generate Cartesian coordinates for a regular 3-D grid based on \
radar specs.
Parameters
----------
sitecoords : tuple
Radar location coordinates in lon, lat
proj : object
GDAL OSR Spatial Reference Object describing projection
maxrange : float
maximum radar range (same unit as SRS defined by ``proj``,
typically meters)
maxalt : float
maximum altitude to which the 3-d grid should extent (meters)
horiz_res : float
horizontal resolution of the 3-d grid (same unit as
SRS defined by ``proj``, typically meters)
vert_res : float
vertical resolution of the 3-d grid (meters)
Keyword Arguments
-----------------
minalt : float
minimum altitude to which the 3-d grid should extent (meters)
Returns
-------
output : :class:`numpy:numpy.ndarray`, tuple
float array of shape (num grid points, 3), a tuple of
3 representing the grid shape
"""
center = georef.reproject(sitecoords[0],
sitecoords[1],
projection_target=proj)
# minz = sitecoords[2]
llx = center[0] - maxrange
lly = center[1] - maxrange
x = np.arange(llx, llx + 2 * maxrange + horiz_res, horiz_res)
y = np.arange(lly, lly + 2 * maxrange + horiz_res, horiz_res)
z = np.arange(minalt, maxalt + vert_res, vert_res)
xyz = util.gridaspoints(z, y, x)
shape = (len(z), len(y), len(x))
return xyz, shape
def test_reproject(self):
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
proj_wgs84 = osr.SpatialReference()
proj_wgs84.ImportFromEPSG(4326)
x, y, z = georef.reproject(7.,
53.,
0.,
projection_source=proj_wgs84,
projection_target=proj_gk)
lon, lat = georef.reproject(x,
y,
projection_source=proj_gk,
projection_target=proj_wgs84)
self.assertAlmostEqual(lon, 7.0)
self.assertAlmostEqual(lat, 53.0)
lonlat = georef.reproject(np.stack((x, y), axis=-1),
projection_source=proj_gk,
projection_target=proj_wgs84)
self.assertAlmostEqual(lonlat[0], 7.0)
self.assertAlmostEqual(lonlat[1], 53.0)
self.assertRaises(
TypeError, lambda: georef.reproject(np.stack(
(x, y, x, y), axis=-1)))
lon, lat, alt = georef.reproject(x,
y,
z,
projection_source=proj_gk,
projection_target=proj_wgs84)
self.assertAlmostEqual(lon, 7., places=5)
self.assertAlmostEqual(lat, 53., places=3)
self.assertAlmostEqual(alt, 0., places=3)
self.assertRaises(TypeError, lambda: georef.reproject(x, y, x, y))
self.assertRaises(
TypeError,
lambda: georef.reproject([np.arange(10)], [np.arange(11)]))
self.assertRaises(
TypeError, lambda: georef.reproject([np.arange(
10)], [np.arange(10)], [np.arange(11)]))
def setUp(self):
f = 'gpm/2A-CS-151E24S154E30S.GPM.Ku.V7-20170308.20141206-S095002-E095137.004383.V05A.HDF5' # noqa
gpm_file = util.get_wradlib_data_file(f)
pr_data = read_generic_hdf5(gpm_file)
pr_lon = pr_data['NS/Longitude']['data']
pr_lat = pr_data['NS/Latitude']['data']
zenith = pr_data['NS/PRE/localZenithAngle']['data']
wgs84 = georef.get_default_projection()
a = wgs84.GetSemiMajor()
b = wgs84.GetSemiMinor()
rad = georef.proj4_to_osr(
('+proj=aeqd +lon_0={lon:f} ' +
'+lat_0={lat:f} +a={a:f} +b={b:f}' + '').format(lon=pr_lon[68, 0],
lat=pr_lat[68, 0],
a=a,
b=b))
pr_x, pr_y = georef.reproject(pr_lon,
pr_lat,
projection_source=wgs84,
projection_target=rad)
self.re = georef.get_earth_radius(pr_lat[68, 0], wgs84) * 4. / 3.
self.pr_xy = np.dstack((pr_x, pr_y))
self.alpha = zenith
self.zt = 407000.
self.dr = 125.
self.bw_pr = 0.71
self.nbin = 176
self.nray = pr_lon.shape[1]
self.pr_out = np.array([[[[-58533.78453556, 124660.60390174],
[-58501.33048429, 124677.58873852]],
[[-53702.13393133, 127251.83656509],
[-53670.98686161, 127268.11882882]]],
[[[-56444.00788528, 120205.5374491],
[-56411.55421163, 120222.52300741]],
[[-51612.2360682, 122796.78620764],
[-51581.08938314, 122813.06920719]]]])
self.r_out = np.array(
[0., 125., 250., 375., 500., 625., 750., 875., 1000., 1125.])
self.z_out = np.array([
0., 119.51255112, 239.02510224, 358.53765337, 478.05020449,
597.56275561, 717.07530673, 836.58785786, 956.10040898,
1075.6129601
])
def ex_georef():
# --------------------------------------------------------------------------
# EXAMPLE 1: Full workflow for georeferencing radar data
# 1st step: generate the centroid coordinates of the radar bins
# define the polar coordinates and the site coordinates in lat/lon
r = np.arange(1, 129) * 1000
az = np.linspace(0, 360, 361)[0:-1]
# drs: 51.12527778 ; fbg: 47.87444444 ; tur: 48.58611111 ; muc: 48.3372222
# drs: 13.76972222 ; fbg: 8.005 ; tur: 9.783888889 ; muc: 11.61277778
sitecoords = (9.7839, 48.5861)
# these are the polgon vertices of the radar bins
polygons = georef.polar2polyvert(r, az, sitecoords)
# these are the corresponding centroids
cent_lon, cent_lat = georef.polar2centroids(r, az, sitecoords)
# plot the vertices and the centroids in one plot
fig = pl.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
polycoll = mpl.collections.PolyCollection(polygons, closed=True, facecolors='None')
ax.add_collection(polycoll, autolim=True)
ax.plot(cent_lon, cent_lat, 'r+')
ax.axis('tight')
pl.title('Zoom in to compare polygons and centroids.')
pl.show()
# 2nd step: reproject the centroid coordinates to Gauss-Krueger Zone 3
# by using the EPSG-Number 31467
# use it for projecting the centroids to Gauss-Krueger 3
proj_gk3 = georef.epsg_to_osr(31467)
x, y = georef.reproject(cent_lon, cent_lat, projection_targe=proj_gk3)
# export the projected centroid coordinates
# f = open('centroids.tab', 'w')
f = 'centroids.tab'
# f.write('x\ty\n')
np.savetxt(f, np.hstack((x.reshape((-1, 1)), y.reshape((-1, 1)))), fmt='%.2f', header='x\ty', delimiter='\t')
# f.close()
print('Exit.')
def setUp(self):
f = 'gpm/2A-CS-151E24S154E30S.GPM.Ku.V7-20170308.20141206-S095002-E095137.004383.V05A.HDF5' # noqa
gpm_file = util.get_wradlib_data_file(f)
pr_data = read_generic_hdf5(gpm_file)
pr_lon = pr_data['NS/Longitude']['data']
pr_lat = pr_data['NS/Latitude']['data']
zenith = pr_data['NS/PRE/localZenithAngle']['data']
wgs84 = georef.get_default_projection()
a = wgs84.GetSemiMajor()
b = wgs84.GetSemiMinor()
rad = georef.proj4_to_osr(('+proj=aeqd +lon_0={lon:f} ' +
'+lat_0={lat:f} +a={a:f} +b={b:f}' +
'').format(lon=pr_lon[68, 0],
lat=pr_lat[68, 0],
a=a, b=b))
pr_x, pr_y = georef.reproject(pr_lon, pr_lat,
projection_source=wgs84,
projection_target=rad)
self.re = georef.get_earth_radius(pr_lat[68, 0], wgs84) * 4. / 3.
self.pr_xy = np.dstack((pr_x, pr_y))
self.alpha = zenith
self.zt = 407000.
self.dr = 125.
self.bw_pr = 0.71
self.nbin = 176
self.nray = pr_lon.shape[1]
self.pr_out = np.array([[[[-58533.78453556, 124660.60390174],
[-58501.33048429, 124677.58873852]],
[[-53702.13393133, 127251.83656509],
[-53670.98686161, 127268.11882882]]],
[[[-56444.00788528, 120205.5374491],
[-56411.55421163, 120222.52300741]],
[[-51612.2360682, 122796.78620764],
[-51581.08938314, 122813.06920719]]]])
self.r_out = np.array([0., 125., 250., 375., 500., 625., 750., 875.,
1000., 1125.])
self.z_out = np.array([0., 119.51255112, 239.02510224, 358.53765337,
478.05020449, 597.56275561, 717.07530673,
836.58785786, 956.10040898, 1075.6129601])
def test_reproject(self):
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
proj_wgs84 = osr.SpatialReference()
proj_wgs84.ImportFromEPSG(4326)
x, y, z = georef.reproject(7., 53., 0., projection_source=proj_wgs84,
projection_target=proj_gk)
lon, lat = georef.reproject(x, y, projection_source=proj_gk,
projection_target=proj_wgs84)
self.assertAlmostEqual(lon, 7.0)
self.assertAlmostEqual(lat, 53.0)
lonlat = georef.reproject(np.stack((x, y), axis=-1),
projection_source=proj_gk,
projection_target=proj_wgs84)
self.assertAlmostEqual(lonlat[0], 7.0)
self.assertAlmostEqual(lonlat[1], 53.0)
self.assertRaises(TypeError,
lambda: georef.reproject(np.stack((x, y, x, y),
axis=-1)))
lon, lat, alt = georef.reproject(x, y, z, projection_source=proj_gk,
projection_target=proj_wgs84)
self.assertAlmostEqual(lon, 7., places=5)
self.assertAlmostEqual(lat, 53., places=3)
self.assertAlmostEqual(alt, 0., places=3)
self.assertRaises(TypeError,
lambda: georef.reproject(x, y, x, y))
self.assertRaises(TypeError,
lambda: georef.reproject([np.arange(10)],
[np.arange(11)]))
self.assertRaises(TypeError,
lambda: georef.reproject([np.arange(10)],
[np.arange(10)],
[np.arange(11)]))
def get_raster_extent(dataset, geo=False, window=True):
"""Get the coordinates of the 4 corners of the raster dataset
Parameters
----------
dataset : gdal.Dataset
raster image with georeferencing (GeoTransform at least)
geo : bool
True to get geographical coordinates
window : bool
True to get the window containing the corners
Returns
-------
extent : :class:`numpy:numpy.ndarray`
corner coordinates [ul,ll,lr,ur] or
window extent [xmin, xmax, ymin, ymax]
"""
x_size = dataset.RasterXSize
y_size = dataset.RasterYSize
geotrans = dataset.GetGeoTransform()
xmin = geotrans[0]
ymax = geotrans[3]
xmax = geotrans[0] + geotrans[1] * x_size
ymin = geotrans[3] + geotrans[5] * y_size
extent = np.array([[xmin, ymax], [xmin, ymin], [xmax, ymin], [xmax, ymax]])
if geo:
projection = read_gdal_projection(dataset)
extent = georef.reproject(extent, projection_source=projection)
if window:
x = extent[:, 0]
y = extent[:, 1]
extent = np.array([x.min(), x.max(), y.min(), y.max()])
return extent
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):
global skip
# setup test grid and catchment
lon = 7.071664
lat = 50.730521
r = np.array(range(50, 100 * 1000 + 50, 100))
a = np.array(range(0, 360, 1))
rays = a.shape[0]
bins = r.shape[0]
# create polar grid polygon vertices in lat,lon
radar_ll = georef.polar2polyvert(r, a, (lon, lat))
# create polar grid centroids in lat,lon
rlon, rlat = georef.polar2centroids(r, a, (lon, lat))
radar_llc = np.dstack((rlon, rlat))
# setup OSR objects
self.proj_gk = osr.SpatialReference()
self.proj_gk.ImportFromEPSG(31466)
self.proj_ll = osr.SpatialReference()
self.proj_ll.ImportFromEPSG(4326)
# project ll grids to GK2
self.radar_gk = georef.reproject(radar_ll,
projection_source=self.proj_ll,
projection_target=self.proj_gk)
self.radar_gkc = georef.reproject(radar_llc,
projection_source=self.proj_ll,
projection_target=self.proj_gk)
# reshape
self.radar_gk.shape = (rays, bins, 5, 2)
self.radar_gkc.shape = (rays, bins, 2)
self.box0 = np.array([[2600000., 5630000.], [2600000., 5630100.],
[2600100., 5630100.], [2600100., 5630000.],
[2600000., 5630000.]])
self.box1 = np.array([[2600100., 5630000.], [2600100., 5630100.],
[2600200., 5630100.], [2600200., 5630000.],
[2600100., 5630000.]])
self.data = np.array([self.box0, self.box1])
# create catchment bounding box
buffer = 5000.
bbox = zonalstats.get_bbox(self.data[..., 0], self.data[..., 1])
bbox = dict(left=bbox['left'] - buffer,
right=bbox['right'] + buffer,
bottom=bbox['bottom'] - buffer,
top=bbox['top'] + buffer)
mask, shape = zonalstats.mask_from_bbox(self.radar_gkc[..., 0],
self.radar_gkc[..., 1],
bbox,
polar=True)
self.radar_gkc = self.radar_gkc[mask, :]
self.radar_gk = self.radar_gk[mask]
self.zdpoly = zonalstats.ZonalDataPoly(self.radar_gk,
self.data,
srs=self.proj_gk)
# self.zdpoly.dump_vector('test_zdpoly')
self.zdpoint = zonalstats.ZonalDataPoint(self.radar_gkc,
self.data,
srs=self.proj_gk)
# self.zdpoint.dump_vector('test_zdpoint')
isec_poly0 = np.array([
np.array([[2600000., 5630000.], [2600000., 5630057.83273596],
[2600018.65014816, 5630000.], [2600000., 5630000.]]),
np.array([[2600000., 5630057.83273596], [2600000., 5630100.],
[2600091.80406488,
5630100.], [2600100., 5630074.58501104],
[2600100., 5630000.], [2600018.65014816, 5630000.],
[2600000., 5630057.83273596]]),
np.array([[2600091.80406488, 5630100.], [2600100., 5630100.],
[2600100., 5630074.58501104],
[2600091.80406488, 5630100.]])
])
isec_poly1 = np.array([
np.array([[2600100., 5630000.], [2600100., 5630074.58501104],
[2600124.05249566, 5630000.], [2600100., 5630000.]]),
np.array([[2600100., 5630074.58501104], [2600100., 5630100.],
[2600197.20644071,
5630100.], [2600200., 5630091.33737992],
[2600200., 5630000.], [2600124.05249566, 5630000.],
[2600100., 5630074.58501104]]),
np.array([[2600197.20644071, 5630100.], [2600200., 5630100.],
[2600200., 5630091.33737992],
[2600197.20644071, 5630100.]])
])
isec_point0 = np.array([[2600062.31245173, 5630031.20266055]])
isec_point1 = np.array([[2600157.8352244, 5630061.85098382]])
self.isec_poly = np.array([isec_poly0, isec_poly1])
self.isec_point = np.array([isec_point0, isec_point1])
def setUp(self):
global skip
# setup test grid and catchment
lon = 7.071664
lat = 50.730521
r = np.array(range(50, 100 * 1000 + 50, 100))
a = np.array(range(0, 360, 1))
rays = a.shape[0]
bins = r.shape[0]
# setup OSR objects
self.proj_gk = osr.SpatialReference()
self.proj_gk.ImportFromEPSG(31466)
self.proj_ll = osr.SpatialReference()
self.proj_ll.ImportFromEPSG(4326)
# create polar grid polygon vertices in lat,lon
radar_ll = georef.spherical_to_polyvert(r, a, 0, (lon, lat),
proj=self.proj_ll)[..., 0:2]
# create polar grid centroids in lat,lon
coords = georef.spherical_to_centroids(r, a, 0, (lon, lat),
proj=self.proj_ll)
radar_llc = coords[..., 0:2]
# project ll grids to GK2
self.radar_gk = georef.reproject(radar_ll,
projection_source=self.proj_ll,
projection_target=self.proj_gk)
self.radar_gkc = georef.reproject(radar_llc,
projection_source=self.proj_ll,
projection_target=self.proj_gk)
# reshape
self.radar_gk.shape = (rays, bins, 5, 2)
self.radar_gkc.shape = (rays, bins, 2)
self.box0 = np.array([[2600000., 5630000.], [2600000., 5630100.],
[2600100., 5630100.], [2600100., 5630000.],
[2600000., 5630000.]])
self.box1 = np.array([[2600100., 5630000.], [2600100., 5630100.],
[2600200., 5630100.], [2600200., 5630000.],
[2600100., 5630000.]])
self.data = np.array([self.box0, self.box1])
# create catchment bounding box
buffer = 5000.
bbox = zonalstats.get_bbox(self.data[..., 0], self.data[..., 1])
bbox = dict(left=bbox['left'] - buffer, right=bbox['right'] + buffer,
bottom=bbox['bottom'] - buffer, top=bbox['top'] + buffer)
mask, shape = zonalstats.mask_from_bbox(self.radar_gkc[..., 0],
self.radar_gkc[..., 1],
bbox,
polar=True)
self.radar_gkc = self.radar_gkc[mask, :]
self.radar_gk = self.radar_gk[mask]
self.zdpoly = zonalstats.ZonalDataPoly(self.radar_gk, self.data,
srs=self.proj_gk)
# self.zdpoly.dump_vector('test_zdpoly')
self.zdpoint = zonalstats.ZonalDataPoint(self.radar_gkc, self.data,
srs=self.proj_gk)
# self.zdpoint.dump_vector('test_zdpoint')
isec_poly0 = np.array([np.array([[2600000., 5630000.],
[2600000., 5630100.],
[2600009.61157242, 5630100.],
[2600041.77844048, 5630000.],
[2600000., 5630000.]]),
np.array([[2600009.61157242, 5630100.],
[2600100., 5630100.],
[2600100., 5630000.],
[2600041.77844048, 5630000.],
[2600009.61157242, 5630100.]]),
np.array([[2600091.80406488, 5630100.],
[2600100., 5630100.],
[2600100., 5630074.58501104],
[2600091.80406488, 5630100.]])])
isec_poly1 = np.array([np.array([[2600100., 5630000.],
[2600100., 5630100.],
[2600114.66582085, 5630100.],
[2600146.83254704, 5630000.],
[2600100., 5630000.]]),
np.array([[2600114.66582085, 5630100.],
[2600200., 5630100.],
[2600200., 5630000.],
[2600146.83254704, 5630000.],
[2600114.66582085, 5630100.]]),
np.array([[2600197.20644071, 5630100.],
[2600200., 5630100.],
[2600200., 5630091.33737992],
[2600197.20644071, 5630100.]])])
isec_point0 = np.array([[2600077.2899581, 5630056.0874306]])
isec_point1 = np.array([[2600172.498418, 5630086.7127034]])
self.isec_poly = np.array([isec_poly0, isec_poly1])
self.isec_point = np.array([isec_point0, isec_point1])
def setUp(self):
global skip
# setup test grid and catchment
lon = 7.071664
lat = 50.730521
r = np.array(range(50, 100 * 1000 + 50, 100))
a = np.array(range(0, 360, 1))
rays = a.shape[0]
bins = r.shape[0]
# setup OSR objects
self.proj_gk = osr.SpatialReference()
self.proj_gk.ImportFromEPSG(31466)
self.proj_ll = osr.SpatialReference()
self.proj_ll.ImportFromEPSG(4326)
# create polar grid polygon vertices in lat,lon
radar_ll = georef.spherical_to_polyvert(r,
a,
0, (lon, lat),
proj=self.proj_ll)[..., 0:2]
# create polar grid centroids in lat,lon
coords = georef.spherical_to_centroids(r,
a,
0, (lon, lat),
proj=self.proj_ll)
radar_llc = coords[..., 0:2]
# project ll grids to GK2
self.radar_gk = georef.reproject(radar_ll,
projection_source=self.proj_ll,
projection_target=self.proj_gk)
self.radar_gkc = georef.reproject(radar_llc,
projection_source=self.proj_ll,
projection_target=self.proj_gk)
# reshape
self.radar_gk.shape = (rays, bins, 5, 2)
self.radar_gkc.shape = (rays, bins, 2)
self.box0 = np.array([[2600000., 5630000.], [2600000., 5630100.],
[2600100., 5630100.], [2600100., 5630000.],
[2600000., 5630000.]])
self.box1 = np.array([[2600100., 5630000.], [2600100., 5630100.],
[2600200., 5630100.], [2600200., 5630000.],
[2600100., 5630000.]])
self.data = np.array([self.box0, self.box1])
# create catchment bounding box
buffer = 5000.
bbox = zonalstats.get_bbox(self.data[..., 0], self.data[..., 1])
bbox = dict(left=bbox['left'] - buffer,
right=bbox['right'] + buffer,
bottom=bbox['bottom'] - buffer,
top=bbox['top'] + buffer)
mask, shape = zonalstats.mask_from_bbox(self.radar_gkc[..., 0],
self.radar_gkc[..., 1],
bbox,
polar=True)
self.radar_gkc = self.radar_gkc[mask, :]
self.radar_gk = self.radar_gk[mask]
self.zdpoly = zonalstats.ZonalDataPoly(self.radar_gk,
self.data,
srs=self.proj_gk)
# self.zdpoly.dump_vector('test_zdpoly')
self.zdpoint = zonalstats.ZonalDataPoint(self.radar_gkc,
self.data,
srs=self.proj_gk)
# self.zdpoint.dump_vector('test_zdpoint')
isec_poly0 = np.array([
np.array([[2600000., 5630000.], [2600000., 5630100.],
[2600009.61157242, 5630100.],
[2600041.77844048, 5630000.], [2600000., 5630000.]]),
np.array([[2600009.61157242, 5630100.], [2600100., 5630100.],
[2600100., 5630000.], [2600041.77844048, 5630000.],
[2600009.61157242, 5630100.]]),
np.array([[2600091.80406488, 5630100.], [2600100., 5630100.],
[2600100., 5630074.58501104],
[2600091.80406488, 5630100.]])
])
isec_poly1 = np.array([
np.array([[2600100., 5630000.], [2600100., 5630100.],
[2600114.66582085, 5630100.],
[2600146.83254704, 5630000.], [2600100., 5630000.]]),
np.array([[2600114.66582085, 5630100.], [2600200., 5630100.],
[2600200., 5630000.], [2600146.83254704, 5630000.],
[2600114.66582085, 5630100.]]),
np.array([[2600197.20644071, 5630100.], [2600200., 5630100.],
[2600200., 5630091.33737992],
[2600197.20644071, 5630100.]])
])
isec_point0 = np.array([[2600077.2899581, 5630056.0874306]])
isec_point1 = np.array([[2600172.498418, 5630086.7127034]])
self.isec_poly = np.array([isec_poly0, isec_poly1])
self.isec_point = np.array([isec_point0, isec_point1])
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
class TestZonalData:
global skip
# setup test grid and catchment
lon = 7.071664
lat = 50.730521
r = np.array(range(50, 100 * 1000 + 50, 100))
a = np.array(range(0, 360, 1))
rays = a.shape[0]
bins = r.shape[0]
# setup OSR objects
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
proj_ll = osr.SpatialReference()
proj_ll.ImportFromEPSG(4326)
# create polar grid polygon vertices in lat,lon
radar_ll = georef.spherical_to_polyvert(r, a, 0, (lon, lat),
proj=proj_ll)[..., 0:2]
# create polar grid centroids in lat,lon
coords = georef.spherical_to_centroids(r, a, 0, (lon, lat), proj=proj_ll)
radar_llc = coords[..., 0:2]
# project ll grids to GK2
radar_gk = georef.reproject(radar_ll,
projection_source=proj_ll,
projection_target=proj_gk)
radar_gkc = georef.reproject(radar_llc,
projection_source=proj_ll,
projection_target=proj_gk)
# reshape
radar_gk.shape = (rays, bins, 5, 2)
radar_gkc.shape = (rays, bins, 2)
box0 = np.array([
[2600000.0, 5630000.0],
[2600000.0, 5630100.0],
[2600100.0, 5630100.0],
[2600100.0, 5630000.0],
[2600000.0, 5630000.0],
])
box1 = np.array([
[2600100.0, 5630000.0],
[2600100.0, 5630100.0],
[2600200.0, 5630100.0],
[2600200.0, 5630000.0],
[2600100.0, 5630000.0],
])
data = np.array([box0, box1])
# create catchment bounding box
buffer = 5000.0
bbox = zonalstats.get_bbox(data[..., 0], data[..., 1])
bbox = dict(
left=bbox["left"] - buffer,
right=bbox["right"] + buffer,
bottom=bbox["bottom"] - buffer,
top=bbox["top"] + buffer,
)
mask, shape = zonalstats.mask_from_bbox(radar_gkc[..., 0],
radar_gkc[..., 1],
bbox,
polar=True)
radar_gkc = radar_gkc[mask, :]
radar_gk = radar_gk[mask]
zdpoly = zonalstats.ZonalDataPoly(radar_gk, data, srs=proj_gk)
# zdpoly.dump_vector('test_zdpoly')
zdpoint = zonalstats.ZonalDataPoint(radar_gkc, data, srs=proj_gk)
# zdpoint.dump_vector('test_zdpoint')
isec_poly0 = np.array(
[
np.array([
[2600000.0, 5630000.0],
[2600000.0, 5630100.0],
[2600009.61157242, 5630100.0],
[2600041.77844048, 5630000.0],
[2600000.0, 5630000.0],
]),
np.array([
[2600009.61157242, 5630100.0],
[2600100.0, 5630100.0],
[2600100.0, 5630000.0],
[2600041.77844048, 5630000.0],
[2600009.61157242, 5630100.0],
]),
np.array([
[2600091.80406488, 5630100.0],
[2600100.0, 5630100.0],
[2600100.0, 5630074.58501104],
[2600091.80406488, 5630100.0],
]),
],
dtype=object,
)
isec_poly1 = np.array(
[
np.array([
[2600100.0, 5630000.0],
[2600100.0, 5630100.0],
[2600114.66582085, 5630100.0],
[2600146.83254704, 5630000.0],
[2600100.0, 5630000.0],
]),
np.array([
[2600114.66582085, 5630100.0],
[2600200.0, 5630100.0],
[2600200.0, 5630000.0],
[2600146.83254704, 5630000.0],
[2600114.66582085, 5630100.0],
]),
np.array([
[2600197.20644071, 5630100.0],
[2600200.0, 5630100.0],
[2600200.0, 5630091.33737992],
[2600197.20644071, 5630100.0],
]),
],
dtype=object,
)
isec_point0 = np.array([[2600077.2899581, 5630056.0874306]])
isec_point1 = np.array([[2600172.498418, 5630086.7127034]])
isec_poly = np.array([isec_poly0, isec_poly1])
isec_point = np.array([isec_point0, isec_point1])
def test_srs(self):
assert self.zdpoly.srs == self.proj_gk
assert self.zdpoint.srs == self.proj_gk
def test_isecs(self):
# need to iterate over nested array for correct testing
for i, ival in enumerate(self.zdpoly.isecs):
for k, kval in enumerate(ival):
np.testing.assert_allclose(kval.astype(float),
self.isec_poly[i, k],
rtol=1e-6)
np.testing.assert_allclose(self.zdpoint.isecs.astype(float),
self.isec_point,
rtol=1e-6)
def test_get_isec(self):
for i in [0, 1]:
for k, arr in enumerate(self.zdpoly.get_isec(i)):
np.testing.assert_allclose(arr.astype(float),
self.isec_poly[i, k],
rtol=1e-6)
np.testing.assert_allclose(self.zdpoint.get_isec(i).astype(float),
self.isec_point[i],
rtol=1e-6)
def test_get_source_index(self):
np.testing.assert_array_equal(self.zdpoly.get_source_index(0),
np.array([2254, 2255]))
np.testing.assert_array_equal(self.zdpoly.get_source_index(1),
np.array([2255, 2256]))
np.testing.assert_array_equal(self.zdpoint.get_source_index(0),
np.array([2255]))
np.testing.assert_array_equal(self.zdpoint.get_source_index(1),
np.array([2256]))
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 setUp(self):
global skip
# setup test grid and catchment
lon = 7.071664
lat = 50.730521
r = np.array(range(50, 100 * 1000 + 50, 100))
a = np.array(range(0, 360, 1))
rays = a.shape[0]
bins = r.shape[0]
# create polar grid polygon vertices in lat,lon
radar_ll = georef.polar2polyvert(r, a, (lon, lat))
# create polar grid centroids in lat,lon
rlon, rlat = georef.polar2centroids(r, a, (lon, lat))
radar_llc = np.dstack((rlon, rlat))
# setup OSR objects
self.proj_gk = osr.SpatialReference()
self.proj_gk.ImportFromEPSG(31466)
self.proj_ll = osr.SpatialReference()
self.proj_ll.ImportFromEPSG(4326)
# project ll grids to GK2
self.radar_gk = georef.reproject(radar_ll, projection_source=self.proj_ll,
projection_target=self.proj_gk)
self.radar_gkc = georef.reproject(radar_llc, projection_source=self.proj_ll,
projection_target=self.proj_gk)
# reshape
self.radar_gk.shape = (rays, bins, 5, 2)
self.radar_gkc.shape = (rays, bins, 2)
self.box0 = np.array([[2600000., 5630000.], [2600000., 5630100.],
[2600100., 5630100.], [2600100., 5630000.],
[2600000., 5630000.]])
self.box1 = np.array([[2600100., 5630000.], [2600100., 5630100.],
[2600200., 5630100.], [2600200., 5630000.],
[2600100., 5630000.]])
self.data = np.array([self.box0, self.box1])
# create catchment bounding box
buffer = 5000.
bbox = zonalstats.get_bbox(self.data[..., 0], self.data[..., 1])
bbox = dict(left=bbox['left'] - buffer, right=bbox['right'] + buffer,
bottom=bbox['bottom'] - buffer, top=bbox['top'] + buffer)
mask, shape = zonalstats.mask_from_bbox(self.radar_gkc[..., 0],
self.radar_gkc[..., 1],
bbox,
polar=True)
self.radar_gkc = self.radar_gkc[mask, :]
self.radar_gk = self.radar_gk[mask]
self.zdpoly = zonalstats.ZonalDataPoly(self.radar_gk, self.data, srs=self.proj_gk)
# self.zdpoly.dump_vector('test_zdpoly')
self.zdpoint = zonalstats.ZonalDataPoint(self.radar_gkc, self.data, srs=self.proj_gk)
# self.zdpoint.dump_vector('test_zdpoint')
isec_poly0 = np.array([np.array([[2600000., 5630000.],
[2600000., 5630057.83273596],
[2600018.65014816, 5630000.],
[2600000., 5630000.]]),
np.array([[2600000., 5630057.83273596],
[2600000., 5630100.],
[2600091.80406488, 5630100.],
[2600100., 5630074.58501104],
[2600100., 5630000.],
[2600018.65014816, 5630000.],
[2600000., 5630057.83273596]]),
np.array([[2600091.80406488, 5630100.],
[2600100., 5630100.],
[2600100., 5630074.58501104],
[2600091.80406488, 5630100.]])])
isec_poly1 = np.array([np.array([[2600100., 5630000.],
[2600100., 5630074.58501104],
[2600124.05249566, 5630000.],
[2600100., 5630000.]]),
np.array([[2600100., 5630074.58501104],
[2600100., 5630100.],
[2600197.20644071, 5630100.],
[2600200., 5630091.33737992],
[2600200., 5630000.],
[2600124.05249566, 5630000.],
[2600100., 5630074.58501104]]),
np.array([[2600197.20644071, 5630100.],
[2600200., 5630100.],
[2600200., 5630091.33737992],
[2600197.20644071, 5630100.]])])
isec_point0 = np.array([[2600062.31245173, 5630031.20266055]])
isec_point1 = np.array([[2600157.8352244, 5630061.85098382]])
self.isec_poly = np.array([isec_poly0, isec_poly1])
self.isec_point = np.array([isec_point0, isec_point1])
def test_reproject(self):
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
proj_wgs84 = osr.SpatialReference()
proj_wgs84.ImportFromEPSG(4326)
x, y, z = georef.reproject(
7.0, 53.0, 0.0, projection_source=proj_wgs84, projection_target=proj_gk
)
lon, lat = georef.reproject(
x, y, projection_source=proj_gk, projection_target=proj_wgs84
)
assert pytest.approx(lon) == 7.0
assert pytest.approx(lat) == 53.0
lonlat = georef.reproject(
np.stack((x, y), axis=-1),
projection_source=proj_gk,
projection_target=proj_wgs84,
)
assert pytest.approx(lonlat[0]) == 7.0
assert pytest.approx(lonlat[1]) == 53.0
with pytest.raises(TypeError):
georef.reproject(np.stack((x, y, x, y), axis=-1))
lon, lat, alt = georef.reproject(
x, y, z, projection_source=proj_gk, projection_target=proj_wgs84
)
assert pytest.approx(lon, abs=1e-5) == 7.0
assert pytest.approx(lat, abs=1e-3) == 53.0
assert pytest.approx(alt, abs=1e-3) == 0.0
with pytest.raises(TypeError):
georef.reproject(x, y, x, y)
with pytest.raises(TypeError):
georef.reproject([np.arange(10)], [np.arange(11)])
with pytest.raises(TypeError):
georef.reproject([np.arange(10)], [np.arange(10)], [np.arange(11)])
class TestSatellite:
f = "gpm/2A-CS-151E24S154E30S.GPM.Ku.V7-20170308.20141206-S095002-E095137.004383.V05A.HDF5" # noqa
gpm_file = util.get_wradlib_data_file(f)
pr_data = wradlib.io.read_generic_hdf5(gpm_file)
pr_lon = pr_data["NS/Longitude"]["data"]
pr_lat = pr_data["NS/Latitude"]["data"]
zenith = pr_data["NS/PRE/localZenithAngle"]["data"]
wgs84 = georef.get_default_projection()
a = wgs84.GetSemiMajor()
b = wgs84.GetSemiMinor()
rad = georef.proj4_to_osr(
("+proj=aeqd +lon_0={lon:f} " + "+lat_0={lat:f} +a={a:f} +b={b:f}" + "").format(
lon=pr_lon[68, 0], lat=pr_lat[68, 0], a=a, b=b
)
)
pr_x, pr_y = georef.reproject(
pr_lon, pr_lat, projection_source=wgs84, projection_target=rad
)
re = georef.get_earth_radius(pr_lat[68, 0], wgs84) * 4.0 / 3.0
pr_xy = np.dstack((pr_x, pr_y))
alpha = zenith
zt = 407000.0
dr = 125.0
bw_pr = 0.71
nbin = 176
nray = pr_lon.shape[1]
pr_out = np.array(
[
[
[
[-58533.78453556, 124660.60390174],
[-58501.33048429, 124677.58873852],
],
[
[-53702.13393133, 127251.83656509],
[-53670.98686161, 127268.11882882],
],
],
[
[
[-56444.00788528, 120205.5374491],
[-56411.55421163, 120222.52300741],
],
[
[-51612.2360682, 122796.78620764],
[-51581.08938314, 122813.06920719],
],
],
]
)
r_out = np.array(
[0.0, 125.0, 250.0, 375.0, 500.0, 625.0, 750.0, 875.0, 1000.0, 1125.0]
)
z_out = np.array(
[
0.0,
119.51255112,
239.02510224,
358.53765337,
478.05020449,
597.56275561,
717.07530673,
836.58785786,
956.10040898,
1075.6129601,
]
)
def test_correct_parallax(self):
xy, r, z = georef.correct_parallax(self.pr_xy, self.nbin, self.dr, self.alpha)
pr_out = np.array(
[
[
[
[-16582.50734831, 35678.47219358],
[-16547.94607589, 35696.40777009],
],
[
[-11742.02016667, 38252.32622057],
[-11708.84553319, 38269.52268457],
],
],
[
[
[-14508.62005182, 31215.98689653],
[-14474.05905935, 31233.92329553],
],
[
[-9667.99183645, 33789.86576047],
[-9634.81750708, 33807.06305397],
],
],
]
)
r_out = np.array(
[0.0, 125.0, 250.0, 375.0, 500.0, 625.0, 750.0, 875.0, 1000.0, 1125.0]
)
z_out = np.array(
[
0.0,
118.78164113,
237.56328225,
356.34492338,
475.1265645,
593.90820563,
712.68984675,
831.47148788,
950.25312901,
1069.03477013,
]
)
np.testing.assert_allclose(xy[60:62, 0:2, 0:2, :], pr_out, rtol=1e-12)
np.testing.assert_allclose(r[0:10], r_out, rtol=1e-12)
np.testing.assert_allclose(z[0, 0, 0:10], z_out, rtol=1e-10)
def test_dist_from_orbit(self):
beta = abs(-17.04 + np.arange(self.nray) * self.bw_pr)
xy, r, z = georef.correct_parallax(self.pr_xy, self.nbin, self.dr, self.alpha)
dists = georef.dist_from_orbit(self.zt, self.alpha, beta, r, re=self.re)
bd = np.array(
[
426553.58667772,
426553.50342119,
426553.49658156,
426553.51025979,
426553.43461609,
426553.42515894,
426553.46559985,
426553.37020786,
426553.44407286,
426553.42173696,
]
)
sd = np.array(
[
426553.58667772,
424895.63462839,
423322.25176564,
421825.47714885,
420405.9414294,
419062.44208923,
417796.86827302,
416606.91482435,
415490.82582636,
414444.11587979,
]
)
np.testing.assert_allclose(dists[0:10, 0, 0], bd, rtol=1e-12)
np.testing.assert_allclose(dists[0, 0:10, 0], sd, rtol=1e-12)
def plot_scan_strategy(
ranges,
elevs,
sitecoords,
beamwidth=1.0,
vert_res=500.0,
maxalt=10000.0,
range_res=None,
maxrange=None,
units="m",
terrain=None,
az=0.0,
cg=False,
ax=111,
cmap="tab10",
):
"""Plot the vertical scanning strategy.
Parameters
----------
ranges : sequence of floats
array of ranges
elevs : sequence of floats
elevation angles
sitecoords : sequence of floats
radar site coordinates (longitude, latitude, altitude)
beamwidth : float
3dB width of the radar beam, defaults to 1.0 deg.
vert_res : float
Vertical resolution in [m].
maxalt : float
Maximum altitude in [m].
range_res : float
Horizontal resolution in [m].
maxrange : float
Maximum range in [m].
units : str
Units to plot in, can be 'm' or 'km'. Defaults to 'm'.
terrain : bool | :class:`numpy:numpy.ndarray`
If True, downloads srtm data and add orography for given `az`.
az : float
Used to specify azimuth for terrain plots.
cg : bool
If True, plot in curvelinear grid, defaults to False (cartesian grid).
ax : :class:`matplotlib:matplotlib.axes.Axes` | matplotlib grid definition
If matplotlib Axes object is given, the scan strategy will be plotted into this
axes object.
If matplotlib grid definition is given (nrows/ncols/plotnumber),
axis are created in the specified place.
Defaults to '111', only one subplot/axis.
cmap : str
matplotlib colormap string.
Returns
-------
ax : :class:`matplotlib:matplotlib.axes.Axes` | matplotlib toolkit axisartist Axes object
matplotlib Axes or curvelinear Axes (r-theta-grid) depending on keyword argument `cg`.
"""
if units == "m":
scale = 1.0
elif units == "km":
scale = 1000.0
else:
raise ValueError(
f"wradlib: unknown value for keyword argument units={units}")
az = np.array([az])
if maxrange is None:
maxrange = ranges.max()
xyz, rad = georef.spherical_to_xyz(ranges,
az,
elevs,
sitecoords,
squeeze=True)
add_title = ""
if terrain is True:
add_title += f" - Azimuth {az[0]}°"
ll = georef.reproject(xyz, projection_source=rad)
# (down-)load srtm data
ds = io.get_srtm(
[
ll[..., 0].min(), ll[..., 0].max(), ll[..., 1].min(),
ll[..., 1].max()
],
download={},
)
rastervalues, rastercoords, proj = georef.extract_raster_dataset(
ds, nodata=-32768.0)
# map rastervalues to polar grid points
terrain = ipol.cart_to_irregular_spline(rastercoords,
rastervalues,
ll[-1, ..., :2],
order=3,
prefilter=False)
if ax == 111:
fig = pl.figure(figsize=(16, 8))
else:
fig = pl.gcf()
legend2 = {}
if cg is True:
ax, caax, paax = create_cg(fig=fig, subplot=ax, rot=0, scale=1)
# for nice plotting we assume earth_radius = 6370000 m
er = 6370000
# calculate beam_height and arc_distance for ke=1
# means line of sight
ade = georef.bin_distance(ranges, 0, sitecoords[2], re=er, ke=1.0)
nn0 = np.zeros_like(ranges)
ecp = nn0 + er
# theta (arc_distance sector angle)
thetap = -np.degrees(ade / er) + 90.0
# zero degree elevation with standard refraction
(bes, ) = paax.plot(thetap,
ecp,
"-k",
linewidth=3,
label="_MSL",
zorder=3)
legend2["MSL"] = bes
if terrain is not None:
paax.fill_between(thetap,
ecp.min() - 2500,
ecp + terrain,
color="0.75",
zorder=2)
# axes layout
ax.set_xlim(0, np.max(ade))
ax.set_ylim([ecp.min() - maxalt / 5, ecp.max() + maxalt])
caax.grid(True, axis="x")
ax.grid(True, axis="y")
ax.axis["top"].toggle(all=False)
gh = ax.get_grid_helper()
yrange = maxalt + maxalt / 5
nbins = ((yrange // vert_res) * 2 + 1) // np.sqrt(2)
gh.grid_finder.grid_locator2._nbins = nbins
else:
ax = fig.add_subplot(ax)
paax = ax
caax = ax
if terrain is not None:
paax.fill_between(ranges, 0, terrain, color="0.75", zorder=2)
ax.set_xlim(0.0, maxrange)
ax.set_ylim(0.0, maxalt)
ax.grid()
# axes ticks and formatting
if range_res is not None:
xloc = range_res
caax.xaxis.set_major_locator(MultipleLocator(xloc))
else:
caax.xaxis.set_major_locator(MaxNLocator())
yloc = vert_res
caax.yaxis.set_major_locator(MultipleLocator(yloc))
import functools
hform = functools.partial(_height_formatter, cg=cg, scale=scale)
rform = functools.partial(_range_formatter, scale=scale)
caax.yaxis.set_major_formatter(FuncFormatter(hform))
caax.xaxis.set_major_formatter(FuncFormatter(rform))
# color management
from cycler import cycler
NUM_COLORS = len(elevs)
cmap = pl.get_cmap(cmap)
if cmap.N >= 256:
colors = [cmap(1.0 * i / NUM_COLORS) for i in range(NUM_COLORS)]
else:
colors = cmap.colors
cycle = cycler(color=colors)
paax.set_prop_cycle(cycle)
# plot beams
for i, el in enumerate(elevs):
alt = xyz[i, ..., 2]
groundrange = np.sqrt(xyz[i, ..., 0]**2 + xyz[i, ..., 1]**2)
if cg:
plrange = thetap
plalt = ecp + alt
beamradius = util.half_power_radius(ranges, beamwidth)
else:
plrange = np.insert(groundrange, 0, 0)
plalt = np.insert(alt, 0, sitecoords[2])
beamradius = util.half_power_radius(plrange, beamwidth)
_, center, edge = _plot_beam(plrange,
plalt,
beamradius,
label=f"{el:4.1f}°",
ax=paax)
# legend 1
handles, labels = ax.get_legend_handles_labels()
leg1 = ax.legend(
handles,
labels,
prop={"family": "monospace"},
loc="upper left",
bbox_to_anchor=(1.04, 1),
borderaxespad=0,
)
# legend 2
legend2["Center"] = center[0]
legend2["3 dB"] = edge[0]
ax.legend(
legend2.values(),
legend2.keys(),
prop={"family": "monospace"},
loc="lower left",
bbox_to_anchor=(1.04, 0),
borderaxespad=0,
)
# add legend 1
ax.add_artist(leg1)
# set axes labels
ax.set_title(f"Radar Scan Strategy - {sitecoords}" + add_title)
caax.set_xlabel(f"Range ({units})")
caax.set_ylabel(f"Altitude ({units})")
return ax
def reproject_raster_dataset(src_ds, **kwargs):
"""Reproject/Resample given dataset according to keyword arguments
Parameters
----------
src_ds : gdal.Dataset
raster image with georeferencing (GeoTransform at least)
Keyword Arguments
-----------------
spacing : float
float or tuple of two floats
pixel spacing of destination dataset, same unit as pixel coordinates
size : int
tuple of two ints
X/YRasterSize of destination dataset
resample : GDALResampleAlg
defaults to GRA_Bilinear
GRA_NearestNeighbour = 0, GRA_Bilinear = 1, GRA_Cubic = 2,
GRA_CubicSpline = 3, GRA_Lanczos = 4, GRA_Average = 5, GRA_Mode = 6,
GRA_Max = 8, GRA_Min = 9, GRA_Med = 10, GRA_Q1 = 11, GRA_Q3 = 12
projection_target : osr object
destination dataset projection, defaults to None
align : bool or Point
If False, there is no destination grid aligment.
If True, aligns the destination grid to the next integer multiple of
destination grid.
If Point (tuple, list of upper-left x,y-coordinate), the destination
grid is aligned to this point.
Returns
-------
dst_ds : gdal.Dataset
reprojected/resampled raster dataset
"""
# checking kwargs
spacing = kwargs.pop('spacing', None)
size = kwargs.pop('size', None)
resample = kwargs.pop('resample', gdal.GRA_Bilinear)
src_srs = kwargs.pop('projection_source', None)
dst_srs = kwargs.pop('projection_target', None)
align = kwargs.pop('align', False)
# Get the GeoTransform vector
src_geo = src_ds.GetGeoTransform()
x_size = src_ds.RasterXSize
y_size = src_ds.RasterYSize
# get extent
ulx = src_geo[0]
uly = src_geo[3]
lrx = src_geo[0] + src_geo[1] * x_size
lry = src_geo[3] + src_geo[5] * y_size
extent = np.array([[[ulx, uly], [lrx, uly]], [[ulx, lry], [lrx, lry]]])
if dst_srs:
src_srs = osr.SpatialReference()
src_srs.ImportFromWkt(src_ds.GetProjection())
# Transformation
extent = georef.reproject(extent,
projection_source=src_srs,
projection_target=dst_srs)
# wkt needed
src_srs = src_srs.ExportToWkt()
dst_srs = dst_srs.ExportToWkt()
(ulx, uly, urx, ury, llx, lly, lrx,
lry) = tuple(list(extent.flatten().tolist()))
# align grid to destination raster or UL-corner point
if align:
try:
ulx, uly = align
except TypeError:
pass
ulx = int(max(np.floor(ulx), np.floor(llx)))
uly = int(min(np.ceil(uly), np.ceil(ury)))
lrx = int(min(np.ceil(lrx), np.ceil(urx)))
lry = int(max(np.floor(lry), np.floor(lly)))
# calculate cols/rows or xspacing/yspacing
if spacing:
try:
x_ps, y_ps = spacing
except TypeError:
x_ps = spacing
y_ps = spacing
cols = int(abs(lrx - ulx) / x_ps)
rows = int(abs(uly - lry) / y_ps)
elif size:
cols, rows = size
x_ps = x_size * src_geo[1] / cols
y_ps = y_size * abs(src_geo[5]) / rows
else:
raise NameError("Whether keyword 'spacing' or 'size' must be given")
# create destination in-memory raster
mem_drv = gdal.GetDriverByName('MEM')
# and set RasterSize according ro cols/rows
dst_ds = mem_drv.Create('', cols, rows, 1, gdal.GDT_Float32)
# Create the destination GeoTransform with changed x/y spacing
dst_geo = (ulx, x_ps, src_geo[2], uly, src_geo[4], -y_ps)
# apply GeoTransform to destination dataset
dst_ds.SetGeoTransform(dst_geo)
# apply Projection to destination dataset
if dst_srs is not None:
dst_ds.SetProjection(dst_srs)
# nodata handling, need to initialize dst_ds with nodata
src_band = src_ds.GetRasterBand(1)
nodata = src_band.GetNoDataValue()
dst_band = dst_ds.GetRasterBand(1)
if nodata is not None:
dst_band.SetNoDataValue(nodata)
dst_band.WriteArray(np.ones((rows, cols)) * nodata)
dst_band.FlushCache()
# resample and reproject dataset
gdal.ReprojectImage(src_ds, dst_ds, src_srs, dst_srs, resample)
return dst_ds
def plot_ppi_crosshair(site,
ranges,
angles=None,
proj=None,
elev=0.,
ax=None,
**kwargs):
"""Plots a Crosshair for a Plan Position Indicator (PPI).
Parameters
----------
site : tuple
Tuple of coordinates of the radar site.
If `proj` is not used, this simply becomes the offset for the origin
of the coordinate system.
If `proj` is used, values must be given as (longitude, latitude,
altitude) tuple of geographical coordinates.
ranges : list
List of ranges, for which range circles should be drawn.
If ``proj`` is None arbitrary units may be used (such that they fit
with the underlying PPI plot.
Otherwise the ranges must be given in meters.
angles : list
List of angles (in degrees) for which straight lines should be drawn.
These lines will be drawn starting from the center and until the
largest range.
proj : osr spatial reference object
GDAL OSR Spatial Reference Object describing projection
The function will calculate lines and circles according to
georeferenced coordinates taking beam propagation, earth's curvature
and scale effects due to projection into account.
Depending on the projection, crosshair lines might not be straight and
range circles might appear elliptical (also check if the aspect of the
axes might not also be responsible for this).
elev : float or array of same shape as az
Elevation angle of the scan or individual azimuths.
May improve georeferencing coordinates for larger elevation angles.
ax : :class:`matplotlib:matplotlib.axes.Axes`
If given, the crosshair will be plotted into this axes object. If None
matplotlib's current axes (function gca()) concept will be used to
determine the axes.
Keyword Arguments
-----------------
line : dict
dictionary, which will be passed to the crosshair line objects using
the standard keyword inheritance mechanism. If not given defaults will
be used.
circle : dict
dictionary, which will be passed to the range circle line objects using
the standard keyword inheritance mechanism. If not given defaults will
be used.
See also
--------
:func:`~wradlib.vis.plot_ppi` - plotting a PPI in cartesian coordinates
Returns
-------
ax : :class:`matplotlib:matplotlib.axes.Axes`
The axes object into which the PPI was plotted
Examples
--------
See :ref:`/notebooks/visualisation/wradlib_plot_ppi_example.ipynb`.
"""
# check coordinate tuple
if site and len(site) < 3:
raise ValueError("WRADLIB: `site` need to be a tuple of coordinates "
"(longitude, latitude, altitude).")
# if we didn't get an axes object, find the current one
if ax is None:
ax = pl.gca()
if angles is None:
angles = [0, 90, 180, 270]
# set default line keywords
linekw = dict(color='gray', linestyle='dashed')
# update with user settings
linekw.update(kwargs.get('line', {}))
# set default circle keywords
circkw = dict(edgecolor='gray', linestyle='dashed', facecolor='none')
# update with user settings
circkw.update(kwargs.get('circle', {}))
# determine coordinates for 'straight' lines
if proj:
# projected
# reproject the site coordinates
psite = georef.reproject(*site, projection_target=proj)
# these lines might not be straigt so we approximate them with 10
# segments. Produce polar coordinates
rr, az = np.meshgrid(np.linspace(0, ranges[-1], 10), angles)
# convert from spherical to projection
coords = georef.spherical_to_proj(rr, az, elev, site, proj=proj)
nsewx = coords[..., 0]
nsewy = coords[..., 1]
else:
# no projection
psite = site
rr, az = np.meshgrid(np.linspace(0, ranges[-1], 2), angles)
# use simple trigonometry to calculate coordinates
nsewx, nsewy = (psite[0] + rr * np.cos(np.radians(90 - az)),
psite[1] + rr * np.sin(np.radians(90 - az)))
# mark the site, just in case nothing else would be drawn
ax.plot(*psite[:2], marker='+', **linekw)
# draw the lines
for i in range(len(angles)):
ax.add_line(lines.Line2D(nsewx[i, :], nsewy[i, :], **linekw))
# draw the range circles
if proj:
# produce an approximation of the circle
x, y = np.meshgrid(ranges, np.arange(360))
poly = georef.spherical_to_proj(ranges,
np.arange(360),
elev,
site,
proj=proj)[..., :2]
poly = np.swapaxes(poly, 0, 1)
for p in poly:
ax.add_patch(patches.Polygon(p, **circkw))
else:
# in the unprojected case, we may use 'true' circles.
for r in ranges:
ax.add_patch(patches.Circle(psite, r, **circkw))
# there should be not much wrong, setting the axes aspect to equal
# by default
ax.set_aspect('equal')
# return the axes object for later use
return ax
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()