def smooth(da_w): da, w = da_w mid = int(da.shape[0]/2) if (da.shape[-1]==0) | (w.shape[-1]==0): return da[mid], w[mid] data = np.array(smoothn(da, s=10., smoothOrder=1., axis=0, TolZ=0.001, verbose=False, isrobust=True, W = w))[[0, 3],] return data[0][mid], data[1][mid]
def do_aot_tcwv(aot_bands,
tcwv_bands,
cams_dir,
obs_time,
sun_ang_name,
view_ang_name,
dem,
tcwv_name=None,
aot_name=None):
toas = [
reproject_data(str(band),
tcwv_bands[0],
dstNodata=0,
resample=5,
xRes=120,
yRes=120).data for band in aot_bands
]
toas = np.array(toas) / 10000.
mask = np.all(toas >= 0.0001, axis=0)
time_ind = np.abs((obs_time.hour + obs_time.minute / 60. +
obs_time.second / 3600.) -
np.arange(0, 25, 3)).argmin()
prior_uncs = 0.1
prior_scale = 46.698
prior_f = cams_dir + '/'.join([
datetime.datetime.strftime(obs_time, '%Y_%m_%d'),
datetime.datetime.strftime(obs_time, '%Y_%m_%d') + '_gtco3.tif'
])
var_g = gdal.Open(prior_f)
prior_g = reproject_data(prior_f,
tcwv_bands[0],
dstNodata=0,
resample=1,
xRes=120,
yRes=120).g
g = var_g.GetRasterBand(int(time_ind + 1))
offset = g.GetOffset()
scale = g.GetScale()
tco3 = prior_g.GetRasterBand(
int(time_ind + 1)).ReadAsArray() * scale + offset
tco3[:] = np.nanmean(tco3) * prior_scale
saa, sza = reproject_data(str(sun_ang_name),
tcwv_bands[0],
dstNodata=0,
resample=1,
xRes=120,
yRes=120).data / 100.
vaa, vza = reproject_data(str(view_ang_name),
tcwv_bands[0],
dstNodata=0,
resample=1,
xRes=120,
yRes=120).data / 100.
raa = vaa - saa
sza = np.cos(np.deg2rad(sza))
vza = np.cos(np.deg2rad(vza))
raa = np.cos(np.deg2rad(raa))
ele = reproject_data(
dem, tcwv_bands[0], dstNodata=0, resample=1, xRes=120,
yRes=120).data / 10000.
X = np.vstack([toas[[9, 8]],
np.array([sza, vza, raa, tco3, ele])]).reshape(7, -1).T
iso_tcwv = Two_NN(np_model_file=file_path + '/emus/S2_TCWV.npz')
tcwv = iso_tcwv.predict(X)[0].reshape(toas[0].shape).astype(float)
bad = (tcwv < 0) | (tcwv > 8) | (~mask) | np.isnan(tcwv)
tcwv[bad] = np.nanmedian(tcwv[~bad])
bands_min = [
0.11572497, 0.08986528, 0.07280412, 0.05007033, 0.06228712, 0.06849915,
0.07015634, 0.06554198, 0.06809415, 0.02089167, 0.00080242, 0.04392302,
0.03012728
]
bands_max = [
0.32072931, 0.30801709, 0.32420026, 0.38214899, 0.3951169, 0.41631785,
0.44391895, 0.42444658, 0.46161492, 0.19927658, 0.00870671, 0.39533241,
0.32505772
]
for _, toa in enumerate(toas):
mask = mask & (toa >= bands_min[_]) & (toa <= bands_max[_])
X = np.vstack([toas, np.array([sza, vza, raa, tco3,
ele])]).reshape(18, -1).T
gbm = joblib.load(file_path + '/emus/lgb.pkl')
aot = gbm.predict(X).reshape(toas[0].shape).astype(float)
aot = np.exp(-1 * aot)
shape = toas[0].shape
if mask.sum() > 3:
aot_min, aot_median, aot_max = np.nanpercentile(aot[mask], [5, 50, 95])
good_aot = (aot >= aot_min) & (aot <= aot_max)
indx, indy = np.where(good_aot.reshape(shape))
myInterpolator = NearestNDInterpolator((indx, indy), aot[good_aot])
grid_x, grid_y = np.mgrid[0:shape[0]:1, 0:shape[1]:1, ]
aot = myInterpolator(grid_x, grid_y)
aot = smoothn(aot,
isrobust=True,
TolZ=1e-6,
W=100 * ((aot >= aot_min) & (aot <= aot_max)),
s=10,
MaxIter=1000)[0]
else:
aot = np.nanmedian(aot) * np.ones(shape)
if tcwv_name is not None:
g = gdal.Open(tcwv_bands[0])
ySize, xSize = tcwv.shape
dst = gdal.GetDriverByName('GTiff').Create(
tcwv_name,
xSize,
ySize,
1,
gdal.GDT_UInt16,
options=["TILED=YES", "COMPRESS=DEFLATE"])
dst.SetGeoTransform(g.GetGeoTransform())
dst.SetProjection(g.GetProjection())
dst.GetRasterBand(1).WriteArray((tcwv * 1000).astype(int))
dst.GetRasterBand(1).SetNoDataValue(65535)
dst = None
g = None
if aot_name is not None:
g = gdal.Open(aot_bands[0])
ySize, xSize = aot.shape
dst = gdal.GetDriverByName('GTiff').Create(
aot_name,
xSize,
ySize,
1,
gdal.GDT_UInt16,
options=["TILED=YES", "COMPRESS=DEFLATE"])
dst.SetGeoTransform(g.GetGeoTransform())
dst.SetProjection(g.GetProjection())
dst.GetRasterBand(1).WriteArray((aot * 1000).astype(int))
dst.GetRasterBand(1).SetNoDataValue(65535)
dst = None
g = None
return aot, tcwv