def ResampleWind():
tt = np.load(coast_root + 'Left coast for each row.npy')
d = rd.SentinelData()
d.Get_List_NetCDF(ocn_root)
for indice in range(len(d.series)):
temp = rd.Data_Level2(d.series[indice], d.FList[indice])
temp.Get_WindData()
temp.CalWindVector()
mask = temp.windX.mask.reshape(-1)
Ind = np.where(mask == 0)[0]
lon = temp.lon.data.reshape(-1)
lat = temp.lat.data.reshape(-1)
U = temp.windX.data.reshape(-1)
V = temp.windY.data.reshape(-1)
lon, lat, U, V = lon[Ind], lat[Ind], U[Ind], V[Ind]
r, c, u, v = md2.GridLL_Wind(lon, lat, U, V)
ind = [xx for xx in range(len(r)) if tt[r[xx]] > c[xx]]
r_new = [r[xx] for xx in ind]
c_new = [c[xx] for xx in ind]
u_new = [u[xx] for xx in ind]
v_new = [v[xx] for xx in ind]
r, c, u, v = r_new, c_new, u_new, v_new
del r_new, c_new, u_new, v_new, ind
# md2.save_fig(d.series[indice], r, c, u, v)
if len(r) >= 4:
u10, v10 = Lc.InterpolationWinds(r, c, u, v)
np.save(level2_root + 'Interp/' + d.series[indice] + '-u10.npy',
u10)
np.save(level2_root + 'Interp/' + d.series[indice] + '-v10.npy',
v10)
u10 = np.zeros([15, 19], dtype=np.float16)
v10 = np.zeros_like(u10)
du = {}
dv = {}
for ii in range(len(r)):
r1, c1 = r[ii] // 150, c[ii] // 150
if (r1, c1) in du:
du[r1, c1].append(u[ii])
dv[r1, c1].append(v[ii])
else:
du[r1, c1] = [u[ii]]
dv[r1, c1] = [v[ii]]
for rr, cc in du.keys():
u10[rr, cc] = np.mean(du[rr, cc])
v10[rr, cc] = np.mean(dv[rr, cc])
np.save(level2_root + 'NotInterp/' + d.series[indice] + '-u10.npy',
u10)
np.save(level2_root + 'NotInterp/' + d.series[indice] + '-v10.npy',
v10)
return None
def Resample(n, mode='uniform'):
d = rd.SentinelData()
d.Get_List(file_root)
m = 1 # resize factor
for indice in range(n, len(d.series)):
stt = time.time()
temp = rd.Data_Level1(d.series[indice], d.FList[indice])
temp.OneStep()
GCP = temp.GCPs
SigmaNaught = temp.denoiseNRCS
name = 'Layer' + str(indice) + '-' + d.series[indice]
del temp
y1, x1 = SigmaNaught.shape
sub_img, up_left = cod.N_sub(tempN, SigmaNaught)
del SigmaNaught, up_left
jo.NMatrix_save(sub_img, 'SigmaNaught', temp_root)
del sub_img
md2.AllLonLat(name, x1, y1, GCP, m)
img, pixel_num = md2.GridLL_SigmaNaught(mode)
jo.Matrix_save(img, name, layer_root)
jo.Matrix_save(pixel_num, name + '-distribution', layer_root)
print('1 image:', (time.time() - stt) / 60, 'mins')
print('Done!\n')
return None