def setUp(self) -> None:
agri_l1_file_name = 'FY4A-_AGRI--_N_DISK_1047E_L1-_FDI-_MULT_' \
'NOM_20200101120000_20200101121459_4000M_V0001.HDF'
agri_l1_file_path = os.path.join(data_root_dir, '20200101', agri_l1_file_name)
agri_geo_file_name = 'FY4A-_AGRI--_N_DISK_1047E_L1-_GEO-_MULT_' \
'NOM_20200101120000_20200101121459_4000M_V0001.HDF'
agri_geo_file_path = os.path.join(data_root_dir, '20200101', agri_geo_file_name)
agri_clm_file_name = 'FY4A-_AGRI--_N_DISK_1047E_L2-_CLM-_MULT_' \
'NOM_20200101120000_20200101121459_4000M_V0001.NC'
agri_clm_file_path = os.path.join(data_root_dir, '20200101', agri_clm_file_name)
fy4_nav_file_name = 'fygatNAV.FengYun-4A.xxxxxxx.4km_M1.h5'
fy4_nav_file_path = os.path.join(data_root_dir, fy4_nav_file_name)
self.fy4_l1 = FY4AAGRIL1FDIDISK4KM(agri_l1_file_path)
self.fy4_geo = FY4AAGRIL1GEODISK4KM(agri_geo_file_path)
self.fy4_nav = FY4NavFile(fy4_nav_file_path)
self.fy4_clm = FY4AAGRICLM4KM(agri_clm_file_path)
def detect_cloud_mask(agri_l1_file_path, agri_geo_file_path,
agri_clm_tif_path):
fy4_l1 = FY4AAGRIL1FDIDISK4KM(agri_l1_file_path)
fy4_geo = FY4AAGRIL1GEODISK4KM(agri_geo_file_path)
month = fy4_l1.start_time_stamp.month
fy4_nav_file_path = get_nav_path(month)
fy4_nav = FY4NavFile(fy4_nav_file_path)
snow_mask = fy4_nav.get_snow_mask()
snow_mask = snow_mask == 3
pix_lat = fy4_nav.get_latitude()
pix_lon = fy4_nav.get_longitude()
dem = fy4_nav.get_dem()
sft = fy4_nav.prepare_surface_type_to_cspp()
coastal = fy4_nav.get_coastal()
coastal_mask = coastal > 0
space_mask = fy4_nav.get_space_mask(b=True)
sun_zen = fy4_geo.get_sun_zenith()
bt_372 = fy4_l1.get_band_by_channel('bt_372_low')
ems_372 = fy4_l1.get_band_by_channel('ems_372')
bt_850 = fy4_l1.get_band_by_channel('bt_850')
bt_1080 = fy4_l1.get_band_by_channel('bt_1080')
r = []
# 1 TStd
tstd = TStd(lut_file_path=TStd.get_lut_path(month))
x = tstd.prepare_feature(bt_1080)
valid_mask = tstd.prepare_valid_mask(bt_1080, dem, sft, coastal_mask,
space_mask)
ratio, prob = tstd.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 2 Bt1185
bt1185 = Bt1185(lut_file_path=Bt1185.get_lut_path(month))
x = bt1185.prepare_feature(bt_1080, bt_850)
valid_mask = bt1185.prepare_valid_mask(bt_1080, bt_850, sft, space_mask)
ratio, prob = bt1185.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 3 T11
t11 = T11(lut_file_path=T11.get_lut_path(month))
x = t11.prepare_feature(bt_1080)
valid_mask = t11.prepare_valid_mask(bt_1080, sft, space_mask)
ratio, prob = t11.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 4 Btd37511Night
btd37511 = Btd37511Night(lut_file_path=Btd37511Night.get_lut_path(month))
x = btd37511.prepare_feature(bt_372, bt_1080)
valid_mask = btd37511.prepare_valid_mask(bt_372, bt_1080, sft, sun_zen,
space_mask)
ratio, prob = btd37511.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 5 Tmax-T
tmax_t = TmaxT(lut_file_path=TmaxT.get_lut_path(month))
x = tmax_t.prepare_feature(bt_1080)
valid_mask = tmax_t.prepare_valid_mask(bt_1080, dem, sft, coastal_mask,
space_mask)
ratio, prob = tmax_t.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 6 GeoColor
geo_color_tif_path = fy4_l1.fname.replace('FDI', 'CLR').replace(
'V0001.HDF', 'GeoColor.tif')
geo_color = GeoColorRGB(lut_file_path=GeoColorRGB.get_lut_path(month))
x = geo_color.prepare_feature(geo_color_tif_path)
valid_mask = geo_color.prepare_valid_mask(x, sft, space_mask)
ratio, prob = geo_color.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 7 Emiss4Day
emiss4day = Emiss375Day(lut_file_path=Emiss375Day.get_lut_path(month))
x = emiss4day.prepare_feature(ems_372)
valid_mask = emiss4day.prepare_valid_mask(ems_372, sft, sun_zen,
space_mask)
ratio, prob = emiss4day.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 8 Emiss4Night
emiss4night = Emiss375Night(
lut_file_path=Emiss375Night.get_lut_path(month))
x = emiss4night.prepare_feature(ems_372)
valid_mask = emiss4night.prepare_valid_mask(ems_372, sft, sun_zen,
space_mask)
ratio, prob = emiss4night.infer(x, sft, valid_mask, space_mask, prob=True)
r.append(ratio)
# 融合
r_s = np.stack(r)
r_s_p = np.prod(r_s, 0)
prior_yes = t11.lut_ds['prior_yes'].data[sft[sft > 0] - 1]
p = np.ma.masked_array(np.zeros(x.shape), space_mask)
p[sft > 0] = 1.0 / (1.0 + r_s_p[sft > 0] / prior_yes - r_s_p[sft > 0])
space_mask_i = space_mask.astype(np.int)
dig_p = np.ones(space_mask.shape, np.uint8) * 4
dig_p[p >= 0.9] = 0
dig_p[np.logical_and(p >= 0.5, p < 0.9)] = 1
dig_p[np.logical_and(p > 0.1, p < 0.5)] = 2
dig_p[p <= 0.1] = 3
dig_p[space_mask_i == 1] = 126
tiff.imwrite(agri_clm_tif_path, dig_p)
return 0
def test_plot_ir(self) -> None:
l1 = FY4AAGRIL1FDIDISK4KM(self.agri_l1_file_path)
l1.plot(flag='ir')
plt.show()
def test_print_available_channels(self) -> None:
l1 = FY4AAGRIL1FDIDISK4KM(self.agri_l1_file_path)
l1.print_available_channels()
def test_get_fake_band_by_channel(self) -> None:
l1 = FY4AAGRIL1FDIDISK4KM(self.agri_l1_file_path)
array = l1.get_band_by_channel('ems_372')
# plt.imshow(array < 1) # night this value is less than 1
plt.imshow(array, vmin=0, vmax=10)
plt.show()
def test_get_band_by_channel(self) -> None:
l1 = FY4AAGRIL1FDIDISK4KM(self.agri_l1_file_path)
array = l1.get_band_by_channel('ref_065')
plt.imshow(array)
plt.show()