df = pd.read_csv('train.csv')
tstd = TStd()
tstd_lut_ds_x = copy.deepcopy(tstd.lut_ds)
for idx, csft in enumerate(tstd_lut_ds_x.cspp_sft.data.tolist()):
sft_cursor = idx + 1
sft_mask = sft == sft_cursor
start_idx = 40
for i in tqdm.trange(start_idx, 100, desc='%s' % csft): # ignore 01 01
agri_l1_file_path = df.loc[df.index[i], ('l1')]
agri_geo_file_path = df.loc[df.index[i], ('geo')]
agri_clm_file_path = df.loc[df.index[i], ('clm')]
l1 = FY4AAGRIL1FDIDISK4KM(agri_l1_file_path)
clm = FY4AAGRICLM4KM(agri_clm_file_path)
geo = FY4AAGRIL1GEODISK4KM(agri_geo_file_path)
bt_1080 = l1.get_band_by_channel('bt_1080')
tstd = TStd()
x = tstd.prepare_feature(bt_1080)
valid_mask = tstd.prepare_valid_mask(bt_1080, dem, sft, coastal_mask,
space_mask)
sft_valide_mask = np.logical_and(valid_mask, sft_mask)
clm_array = clm.get_clm()
label = clm_array[sft_valide_mask]
fea = x[sft_valide_mask]
def save_fy4_clm_to_tiff(tif_path, agri_clm_file_path):
if not os.path.exists(tif_path):
fy4_clm = FY4AAGRICLM4KM(agri_clm_file_path)
clm_data = fy4_clm.get_clm()
tiff.imwrite(tif_path, clm_data)