def get_tbb_test2(self):
"""
return tbb
"""
data = dict()
if self.resolution == 4000: # 分辨率为 1000
satellite_type1 = ['FY4A']
# dn = self.get_dn()
if self.satellite in satellite_type1:
# rad转tbb的修正系数，所有时次都是固定值
tbb_k0 = self.get_tbb_k0()
tbb_k1 = self.get_tbb_k1()
rads = self.get_rad()
central_wave_numbers = self.get_central_wave_number()
# 逐个通道处理
for i in xrange(self.channels):
band = 'CH_{:02d}'.format(i + 1)
if band in rads.keys():
k0 = tbb_k0[band]
k1 = tbb_k1[band]
central_wave_number = central_wave_numbers[band]
rad = 36
tbb = planck_r2t(rad, central_wave_number)
data[band] = tbb * k1 + k0
else:
raise ValueError('Cant read this satellite`s data.: {}'.format(
self.satellite))
else:
raise ValueError(
'Cant read this data, please check its resolution: {}'.format(
self.in_file))
return data
def get_tbb(self):
data = dict()
if self.resolution == 1000: # 分辨率为 1000
satellite_type1 = ['FY3A', 'FY3B', 'FY3C']
# 红外转tbb的修正系数，所有时次都是固定值
tbb_k0 = self.get_tbb_k0()
tbb_k1 = self.get_tbb_k1()
if self.satellite in satellite_type1:
lut = self.get_lut_bt()
rads = self.get_rad()
central_wave_numbers = self.get_central_wave_number()
for band in rads:
rad = rads[band]
central_wave_number = central_wave_numbers[band]
if lut:
print 'f3 lut'
tbb = np.interp(rad, lut['rad'][band], lut['tbb'])
else:
print 'f3 plank'
tbb = planck_r2t(rad, central_wave_number)
k0 = tbb_k0[band]
k1 = tbb_k1[band]
tbb = tbb * k1 + k0
data[band] = tbb
else:
raise ValueError(
'Cant read this satellite`s data.: {}'.format(self.satellite))
else:
raise ValueError(
'Cant read this data, please check its resolution: {}'.format(self.in_file))
return data
def get_rad_tbb(self, D1, BandLst):
'''
D1是目标类的实例
'''
# iasi 的光谱波数范围
WaveNum2 = self.wavenumber
for Band in BandLst:
WaveNum1 = D1.waveNum[Band]
WaveRad1 = D1.waveRad[Band]
WaveRad2 = pb_sat.spec_interp(WaveNum1, WaveRad1, WaveNum2)
newRad = pb_sat.spec_convolution(WaveNum2, WaveRad2, self.radiance)
tbb = pb_sat.planck_r2t(newRad, D1.WN[Band])
tbb = tbb * D1.TeA[Band] + D1.TeB[Band]
self.Tbb[Band] = tbb.reshape(tbb.size, 1)
self.Rad[Band] = newRad.reshape(newRad.size, 1)
def get_tbb(self, wave_nums, wave_spec, band_list, central_wave_nums, a, b,
lut):
"""
use 卫星1的光谱和响应wave_nums,wave_spec（字典形式，key是通道信息，针对FY数据）
a,b 分别是红外tbb修正系数，字典 ，key是通道信息
return channel rad
"""
data = dict()
if self.resolution == 24000 or self.resolution == 24001:
satellite_type1 = ['METOP-A', 'METOP-B']
if self.satellite in satellite_type1:
# 目标数据通道信息
# 获取通道的rads
rads = self.get_rad(wave_nums, wave_spec, band_list,
central_wave_nums, a, b, lut)
for band in band_list:
rad = rads[band]
if lut:
print 'lut'
tbb = np.interp(rad, lut['rad'][band], lut['tbb'])
data[band] = tbb
else:
central_wave_number = central_wave_nums[band]
tbb = planck_r2t(rad, central_wave_number)
if a is not None:
k1 = a[band]
k0 = b[band]
data[band] = tbb * k1 + k0
else:
data[band] = tbb
else:
raise ValueError('Cant read this satellite`s data.: {}'.format(
self.satellite))
else:
raise ValueError(
'Cant read this data, please check its resolution: {}'.format(
self.in_file))
return data
def Load(self, L1File):
ipath = os.path.dirname(L1File)
iname = os.path.basename(L1File)
self.file_attr = self.read_file_attr(L1File)
if 'FY3C' in iname[:4]:
# 根据输入的L1文件自动拼接GEO文件
geoFile = os.path.join(ipath, iname[0:-12] + 'GEOXX_MS.HDF')
obcFile = os.path.join(ipath, iname[0:-12] + 'OBCXX_MS.HDF')
print u'%s' % L1File
print u'%s' % geoFile
print u'%s' % obcFile
#################### 读取L1文件 ######################
try:
with h5py.File(L1File, 'r') as h5File_R:
ary_ch3 = h5File_R.get('/Data/EV_Emissive')[:]
ary_ch7 = h5File_R.get('/Data/EV_RefSB')[:]
ary_offsets = h5File_R.get(
'/Data/Emissive_Radiance_Offsets')[:]
ary_scales = h5File_R.get(
'/Data/Emissive_Radiance_Scales')[:]
ary_ref_cal = h5File_R.attrs['RefSB_Cal_Coefficients']
ary_Nonlinear = h5File_R.attrs[
'Prelaunch_Nonlinear_Coefficients']
try:
ary_tb_coeff = h5File_R.attrs[
'Emmisive_BT_Coefficients']
except Exception:
ary_tb_coeff = h5File_R.attrs[
'Emissive_BT_Coefficients']
except Exception as e:
self.error = True
print str(e)
return
#################### 读取GEO文件 ######################
try:
with h5py.File(geoFile, 'r') as h5File_R:
ary_satz = h5File_R.get('/Geolocation/SensorZenith')[:]
ary_sata = h5File_R.get('/Geolocation/SensorAzimuth')[:]
ary_sunz = h5File_R.get('/Geolocation/SolarZenith')[:]
ary_suna = h5File_R.get('/Geolocation/SolarAzimuth')[:]
ary_lon = h5File_R.get('/Geolocation/Longitude')[:]
ary_lat = h5File_R.get('/Geolocation/Latitude')[:]
ary_height = h5File_R.get('/Geolocation/DEM')[:]
ary_LandCover = h5File_R.get('/Geolocation/LandCover')[:]
ary_LandSeaMask = h5File_R.get(
'/Geolocation/LandSeaMask')[:]
except Exception as e:
self.error = True
print str(e)
return
#################### 读取OBC文件 ####################
try:
with h5py.File(obcFile, 'r') as h5File_R:
ary_sv = h5File_R.get('/Calibration/Space_View')[:]
ary_bb = h5File_R.get('/Calibration/Blackbody_View')[:]
except Exception as e:
self.error = True
print str(e)
return
else:
# FY3A/FY3B VIRR
# 根据输入的L1文件自动拼接OBC文件
obcFile = os.path.join(ipath, iname[0:-12] + 'OBCXX_MS.HDF')
print u'%s' % L1File
print u'%s' % obcFile
#################### 读取L1文件 ####################
try:
with h5py.File(L1File, 'r') as h5File_R:
ary_ch3 = h5File_R.get('/EV_Emissive')[:, 0:1800, 0:2048]
ary_ch7 = h5File_R.get('/EV_RefSB')[:, 0:1800, 0:2048]
ary_offsets = h5File_R.get('/Emissive_Radiance_Offsets')[
0:1800, :]
ary_scales = h5File_R.get('/Emissive_Radiance_Scales')[
0:1800, :]
ary_ref_cal = h5File_R.attrs['RefSB_Cal_Coefficients']
ary_Nonlinear = h5File_R.attrs[
'Prelaunch_Nonlinear_Coefficients']
try:
ary_tb_coeff = h5File_R.attrs[
'Emmisive_BT_Coefficients']
except Exception:
ary_tb_coeff = h5File_R.attrs[
'Emissive_BT_Coefficients']
ary_satz = h5File_R.get('/SensorZenith')[0:1800, 0:2048]
ary_sata = h5File_R.get('/SensorAzimuth')[0:1800, 0:2048]
ary_sunz = h5File_R.get('/SolarZenith')[0:1800, 0:2048]
ary_suna = h5File_R.get('/SolarAzimuth')[0:1800, 0:2048]
ary_lon = h5File_R.get('/Longitude')[0:1800, 0:2048]
ary_lat = h5File_R.get('/Latitude')[0:1800, 0:2048]
ary_height = h5File_R.get('/Height')[0:1800, 0:2048]
ary_LandCover = h5File_R.get('/LandCover')[0:1800, 0:2048]
ary_LandSeaMask = h5File_R.get('/LandSeaMask')[0:1800,
0:2048]
except Exception as e:
self.error = True
print str(e)
return
#################### 读取OBC文件 ####################
try:
with h5py.File(obcFile, 'r') as h5File_R:
ary_sv = h5File_R.get('Space_View')[:]
ary_bb = h5File_R.get('Blackbody_View')[:]
except Exception as e:
self.error = True
print(str(e))
return
# 通道的中心波数和光谱响应
for i in xrange(self.Band):
BandName = 'CH_%02d' % (i + 1)
srfFile = os.path.join(
MainPath, 'SRF',
'%s_%s_SRF_CH%02d_Pub.txt' % (self.sat, self.sensor, (i + 1)))
dictWave = np.loadtxt(srfFile,
dtype={
'names': ('num', 'rad'),
'formats': ('f4', 'f4')
})
if BandName in ['CH_03', 'CH_04', 'CH_05']:
waveNum = 10**4 / dictWave['num'][::-1]
else:
waveNum = 10**7 / dictWave['num'][::-1]
waveRad = dictWave['rad'][::-1]
if BandName not in self.waveNum:
self.waveNum[BandName] = waveNum
self.waveRad[BandName] = waveRad
else:
self.waveNum[BandName] = np.concatenate(
(self.waveNum[BandName], waveNum))
self.waveRad[BandName] = np.concatenate(
(self.waveRad[BandName], waveRad))
############### 数据大小 使用经度维度 ###############
dshape = ary_lon.shape
##################可见光数据进行定标 ########
# LutAry = np.loadtxt(self.LutFile, dtype={'names': ('TBB', 'CH_03', 'CH_04', 'CH_05'),
# 'formats': ('i4', 'f4', 'f4', 'f4')})
# 定标系数b,a
proj_Cal_Coeff = np.full((7, 2), np.nan)
for i in range(7):
for j in range(2):
proj_Cal_Coeff[i, j] = ary_ref_cal[i * 2 + j - 1]
# 1 - 6 通道的ref
for i in xrange(self.Band):
BandName = 'CH_%02d' % (i + 1)
if i < 2 or i >= 5:
# 下标k (1 2 6 7 8 9 10存放在一个三维数组中)
if i < 2:
k = i
elif i >= 5:
k = i - 3
# DN值存放无效值用nan填充
DN = np.full(dshape, np.nan)
idx = np.logical_and(ary_ch7[k] < 32767, ary_ch7[k] > 0)
DN[idx] = ary_ch7[k][idx]
# 反射率值存放无效值用nan填充
k0 = proj_Cal_Coeff[k][1]
k1 = proj_Cal_Coeff[k][0]
Ref = (DN * k0 + k1) / 100.
K0 = np.full(dshape, k0, dtype=np.float32)
K1 = np.full(dshape, k1, dtype=np.float32)
if BandName not in self.Dn:
self.Dn[BandName] = DN
self.Ref[BandName] = Ref
self.K0[BandName] = K0
self.K1[BandName] = K1
else:
self.Dn[BandName] = np.concatenate((self.Dn[BandName], DN))
self.Ref[BandName] = np.concatenate(
(self.Ref[BandName], Ref))
self.K0[BandName] = np.concatenate((self.K0[BandName], K0))
self.K1[BandName] = np.concatenate((self.K1[BandName], K1))
if 2 <= i <= 4:
# DN Rad Tbb 值存放,默认 nan填充
k = i - 2
DN = np.full(dshape, np.nan)
Rad_pre = np.full(dshape, np.nan)
condition = np.logical_and(ary_ch3[k] < 32767, ary_ch3[k] > 0)
idx = np.where(condition)
# 下标i-2 (3,4,5通道DN存放在一个三维数组中)
DN[idx] = ary_ch3[k][idx]
Rad_pre[idx] = DN[idx] * ary_scales[idx[0], k] + \
ary_offsets[idx[0], k]
k0 = ary_Nonlinear[k]
k1 = ary_Nonlinear[3 + k]
k2 = ary_Nonlinear[6 + k]
Rad_nonlinearity = Rad_pre**2 * k2 + Rad_pre * k1 + k0
K0 = np.full(dshape, np.nan)
K0[:] = ary_scales[:, k].reshape(-1, 1)
K1 = np.full(dshape, np.nan)
K1[:] = ary_offsets[:, k].reshape(-1, 1)
self.Rad_pre[BandName] = Rad_pre
Rad = Rad_pre + Rad_nonlinearity
Tbb = pb_sat.planck_r2t(Rad, self.WN[BandName])
Tbb = Tbb * self.TeA[BandName] + self.TeB[BandName]
k0_coeff = ary_tb_coeff[k * 2 + 1]
k1_coeff = ary_tb_coeff[k * 2]
Tbb_coeff = Tbb * k0_coeff + k1_coeff
if BandName not in self.Dn:
self.Dn[BandName] = DN
self.Rad[BandName] = Rad
self.K0[BandName] = K0
self.K1[BandName] = K1
self.Tbb[BandName] = Tbb
self.Tbb_coeff[BandName] = Tbb_coeff
else:
self.Dn[BandName] = np.concatenate((self.Dn[BandName], DN))
self.Rad[BandName] = np.concatenate(
(self.Rad[BandName], Rad))
self.K0[BandName] = np.concatenate((self.K0[BandName], K0))
self.K1[BandName] = np.concatenate((self.K1[BandName], K1))
self.Tbb[BandName] = np.concatenate(
(self.Tbb[BandName], Tbb))
self.Tbb_coeff[BandName] = np.concatenate(
(self.Tbb_coeff[BandName], Tbb_coeff))
# SV, BB
for i in xrange(self.Band):
BandName = 'CH_%02d' % (i + 1)
SV = np.full(dshape, np.nan)
BB = np.full(dshape, np.nan)
SV[:] = ary_sv[i, :, 0].reshape(-1, 1)
BB[:] = ary_bb[i, :, 0].reshape(-1, 1)
if BandName not in self.SV:
self.SV[BandName] = SV
self.BB[BandName] = BB
else:
self.SV[BandName] = np.concatenate((self.SV[BandName], SV))
self.BB[BandName] = np.concatenate((self.BB[BandName], BB))
##################### 全局信息赋值 ############################
# 对时间进行赋值合并
Time = np.full(dshape, np.nan)
ymd = get_ymd(L1File)
hm = get_hm(L1File)
file_date = datetime.strptime(ymd + hm, '%Y%m%d%H%M')
secs = (file_date - datetime(1970, 1, 1, 0, 0, 0)).total_seconds()
Time[:] = secs
if self.Time == []:
self.Time = Time
else:
self.Time = np.concatenate((self.Time, Time))
# 土地覆盖
ary_LandCover_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_LandCover >= 0, ary_LandCover <= 254)
ary_LandCover_idx[condition] = ary_LandCover[condition]
if self.LandCover == []:
self.LandCover = ary_LandCover_idx
else:
self.LandCover = np.concatenate(
(self.LandCover, ary_LandCover_idx))
# 海陆掩码
ary_LandSeaMask_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_LandSeaMask >= 0, ary_LandSeaMask <= 7)
ary_LandSeaMask_idx[condition] = ary_LandSeaMask[condition]
if self.LandSeaMask == []:
self.LandSeaMask = ary_LandSeaMask_idx
else:
self.LandSeaMask = np.concatenate(
(self.LandSeaMask, ary_LandSeaMask_idx))
# 经纬度
ary_lon_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_lon > -180., ary_lon < 180.)
ary_lon_idx[condition] = ary_lon[condition]
if self.Lons == []:
self.Lons = ary_lon_idx
else:
self.Lons = np.concatenate((self.Lons, ary_lon_idx))
ary_lat_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_lat > -90., ary_lat < 90.)
ary_lat_idx[condition] = ary_lat[condition]
if self.Lats == []:
self.Lats = ary_lat_idx
else:
self.Lats = np.concatenate((self.Lats, ary_lat_idx))
# 高度
ary_height_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_height > -1000., ary_height < 10000.)
ary_height_idx[condition] = ary_height[condition]
if self.Height == []:
self.Height = ary_height_idx
else:
self.Height = np.concatenate((self.Height, ary_height_idx))
# 卫星方位角 天顶角
ary_sata_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_sata > -18000, ary_sata < 18000)
ary_sata_idx[condition] = ary_sata[condition]
if self.satAzimuth == []:
self.satAzimuth = ary_sata_idx / 100.
else:
self.satAzimuth = np.concatenate(
(self.satAzimuth, ary_sata_idx / 100.))
ary_satz_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_satz > 0, ary_satz < 18000)
ary_satz_idx[condition] = ary_satz[condition]
if self.satZenith == []:
self.satZenith = ary_satz_idx / 100.
else:
self.satZenith = np.concatenate(
(self.satZenith, ary_satz_idx / 100.))
# 太阳方位角 天顶角
ary_suna_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_suna > -18000, ary_suna < 18000)
ary_suna_idx[condition] = ary_suna[condition]
if self.sunAzimuth == []:
self.sunAzimuth = ary_suna_idx / 100.
else:
self.sunAzimuth = np.concatenate(
(self.sunAzimuth, ary_suna_idx / 100.))
ary_sunz_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_sunz > 0, ary_sunz < 18000)
ary_sunz_idx[condition] = ary_sunz[condition]
if self.sunZenith == []:
self.sunZenith = ary_sunz_idx / 100.
else:
self.sunZenith = np.concatenate(
(self.sunZenith, ary_sunz_idx / 100.))
self.get_extract_data()
def Load(self, L1File):
ipath = os.path.dirname(L1File)
iname = os.path.basename(L1File)
geoFile = os.path.join(ipath, iname[0:-12] + 'GEO1K_MS.HDF')
clm_name = iname.replace('GBAL_L1', 'ORBT_L2_CLM_MLT_NUL')
clm_file = os.path.join(ipath, clm_name)
print(u'读取 L1 %s' % L1File)
try:
h5File_R = h5py.File(L1File, 'r')
orbit_direction = h5File_R.attrs.get('Orbit Direction')
orbit_num = h5File_R.attrs.get('Orbit Number')
self.orbit_direction.append(orbit_direction)
self.orbit_num.append(orbit_num)
ary_ch1_4 = h5File_R.get('/Data/EV_250_Aggr.1KM_RefSB')[:]
ary_ch5_19 = h5File_R.get('/Data/EV_1KM_RefSB')[:]
ary_ch20_23 = h5File_R.get('/Data/EV_1KM_Emissive')[:]
ary_ch20_23_a = h5File_R.get(
'/Data/EV_1KM_Emissive').attrs['Slope']
ary_ch20_23_b = h5File_R.get(
'/Data/EV_1KM_Emissive').attrs['Intercept']
ary_ch24_25 = h5File_R.get('/Data/EV_250_Aggr.1KM_Emissive')[:]
ary_ch24_25_a = h5File_R.get(
'/Data/EV_250_Aggr.1KM_Emissive').attrs['Slope']
ary_ch24_25_b = h5File_R.get(
'/Data/EV_250_Aggr.1KM_Emissive').attrs['Intercept']
ary_IR_Coeff = h5File_R.get('/Calibration/IR_Cal_Coeff')[:]
ary_VIS_Coeff = h5File_R.get('/Calibration/VIS_Cal_Coeff')[:]
ary_sv = h5File_R.get('/Calibration/SV_DN_average')[:]
ary_bb = h5File_R.get('/Calibration/BB_DN_average')[:]
except Exception as e:
print str(e)
return
finally:
h5File_R.close()
print(u'读取geo %s' % geoFile)
try:
# 读取GEO文件
h5File_R = h5py.File(geoFile, 'r')
ary_satz = h5File_R.get('/Geolocation/SensorZenith')[:]
ary_sata = h5File_R.get('/Geolocation/SensorAzimuth')[:]
ary_sunz = h5File_R.get('/Geolocation/SolarZenith')[:]
ary_suna = h5File_R.get('/Geolocation/SolarAzimuth')[:]
ary_lon = h5File_R.get('/Geolocation/Longitude')[:]
ary_lat = h5File_R.get('/Geolocation/Latitude')[:]
ary_LandCover = h5File_R.get('/Geolocation/LandCover')[:]
ary_LandSeaMask = h5File_R.get('/Geolocation/LandSeaMask')[:]
ary_day = h5File_R.get('/Timedata/Day_Count')[:]
ary_time = h5File_R.get('/Timedata/Millisecond_Count')[:]
except Exception as e:
print str(e)
return
finally:
h5File_R.close()
print(u'读取 L1 %s' % L1File)
try:
h5File_R = h5py.File(clm_file, 'r')
data_pre = h5File_R.get('Cloud_Mask')[0, :, :]
clm_flag = np.full(data_pre.shape, -999)
z = data_pre
# 0 表示无效值
z0 = z & 0b1
z12 = (z >> 1) & 0b11
z4 = (z >> 4) & 0b1
z67 = (z >> 6) & 0b11
# Invalid 0
mask = (z == 0)
idx = np.where(mask)
clm_flag[idx] = 0
# Coastlines
mask = (z67 == 0b01)
idx = np.where(mask)
clm_flag[idx] = 1
# Uncertain
mask = (z12 == 0b01) & (z0 == 0b1)
idx = np.where(mask)
clm_flag[idx] = 2
# Cloud
mask = (z12 == 0b00) & (z0 == 0b1)
idx = np.where(mask)
clm_flag[idx] = 3
# Poss Land Clear
mask = ((z67 == 0b11) |
(z67 == 0b10)) & (z12 == 0b10) & (z0 == 0b1)
idx = np.where(mask)
clm_flag[idx] = 4
# Land Clear
mask = ((z67 == 0b11) |
(z67 == 0b10)) & (z12 == 0b11) & (z0 == 0b1)
idx = np.where(mask)
clm_flag[idx] = 5
# Poss Sea Clear
mask = (z67 == 0b00) & (z12 == 0b10) & (z0 == 0b1)
idx = np.where(mask)
clm_flag[idx] = 6
# Sea Clear
mask = (z67 == 0b00) & (z12 == 0b11) & (z4 == 0b1) & (z0 == 0b1)
idx = np.where(mask)
clm_flag[idx] = 7
# Sun Glint
mask = (z67 == 0b00) & (z12 == 0b11) & (z4 == 0b0) & (z0 == 0b1)
idx = np.where(mask)
clm_flag[idx] = 8
# print clm_flag
data = clm_flag
if self.CloudMask == []:
self.CloudMask = data
else:
self.CloudMask = np.concatenate((self.CloudMask, data))
except Exception as e:
print str(e)
if hasattr(self, 'CloudMask'):
delattr(self, 'CloudMask')
finally:
h5File_R.close()
# 通道的中心波数和光谱响应
for i in xrange(self.Band):
BandName = 'CH_%02d' % (i + 1)
srfFile = os.path.join(
MainPath, 'SRF',
'%s_%s_SRF_CH%02d_Pub.txt' % (self.sat, self.sensor, (i + 1)))
dictWave = np.loadtxt(srfFile,
dtype={
'names': ('num', 'rad'),
'formats': ('f4', 'f4')
})
waveNum = 10**7 / dictWave['num'][::-1]
waveRad = dictWave['rad'][::-1]
self.waveNum[BandName] = waveNum
self.waveRad[BandName] = waveRad
# 数据大小 使用经度维度
dshape = ary_lon.shape
# 1-19通道的可见光数据进行定标
K = ary_VIS_Coeff
for i in range(19):
if i < 4:
# 统一下标
j = i
indata = ary_ch1_4[j]
else:
j = i - 4
indata = ary_ch5_19[j]
# 初始存放dn数据的结构，初始值 Nan
DN = np.full(dshape, np.nan)
idx = np.logical_and(indata < 65500, indata >= 0)
DN[idx] = indata[idx]
Ref = ((DN**2 * K[i, 2]) + DN * K[i, 1] + K[i, 0]) / 100.
BandName = 'CH_%02d' % (i + 1)
if BandName not in self.Dn.keys():
self.Dn[BandName] = DN
self.Ref[BandName] = Ref
else:
self.Dn[BandName] = np.concatenate((self.Dn[BandName], DN))
self.Ref[BandName] = np.concatenate((self.Ref[BandName], Ref))
if BandName not in self.cal_coeff1.keys():
self.cal_coeff1[BandName] = [K[i, 0], K[i, 1], K[i, 2]]
# 20-25通道的红外光数据进行定标 ########
K = ary_IR_Coeff
a = np.concatenate((ary_ch20_23_a, ary_ch24_25_a))
b = np.concatenate((ary_ch20_23_b, ary_ch24_25_b))
for i in range(6):
if i < 4:
# 统一下标
j = i
indata = ary_ch20_23[j]
else:
j = i - 4
indata = ary_ch24_25[j]
BandName = 'CH_%02d' % (i + 20)
# 初始存放dn数据的结构，初始值 Nan
DN = np.full(dshape, np.nan)
Rad = np.full(dshape, np.nan)
# 存放定标系数
if BandName not in self.cal_coeff1.keys():
self.cal_coeff1[BandName] = [
K[i, 0, 0], K[i, 2, 0], K[i, 3, 0]
]
# k1 = np.full(dshape, np.nan)
# 把红外定标系数拆成4个数据 2000*2048
k1 = (np.repeat(K[i, 1, :], 10 * 2048)).reshape(dshape)
if BandName not in self.cal_coeff2.keys():
self.cal_coeff2[BandName] = k1
else:
self.cal_coeff2[BandName] = np.concatenate(
(self.cal_coeff2[BandName], k1))
# 对无效DN值进行过滤
idx = np.logical_and(indata < 65500, indata > 0)
DN[idx] = indata[idx]
Rad[idx] = DN[idx] * a[i] + b[i]
Tbb = pb_sat.planck_r2t(Rad, self.WN[BandName])
Tbb = Tbb * self.TeA[BandName] + self.TeB[BandName]
# 对类成员进行赋值
if BandName not in self.Dn.keys():
self.Dn[BandName] = DN
self.Rad[BandName] = Rad
self.Tbb[BandName] = Tbb
else:
self.Dn[BandName] = np.concatenate((self.Dn[BandName], DN))
self.Rad[BandName] = np.concatenate((self.Rad[BandName], Rad))
self.Tbb[BandName] = np.concatenate((self.Tbb[BandName], Tbb))
# 定标方法仿真数据已有描述
# for row_i in range(DN.shape[0]):
# # dn数据每10行使用一个定标系数
# jj = int(row_i / 10)
# k3 = K[i, 3, jj]
# k2 = K[i, 2, jj]
# k1 = K[i, 1, jj]
# k0 = K[i, 0, jj]
# Rad[row_i, :] = (DN[row_i, :] * k3 ** 3 + DN[row_i, :] * k2 ** 2 +
# DN[row_i, :] * k1 + k0) # / 100.
# 全局信息赋值 ############################
# 对时间进行赋值合并
v_ymd2seconds = np.vectorize(fy3_ymd2seconds)
T1 = v_ymd2seconds(ary_day, ary_time)
Time = np.full(dshape, -999)
for i in xrange(ary_lon.shape[0]):
Time[i, :] = T1[i / 10, 0]
if self.Time == []:
self.Time = Time
else:
self.Time = np.concatenate((self.Time, Time))
# SV, BB
for i in xrange(self.Band):
BandName = 'CH_%02d' % (i + 1)
SV = np.full(dshape, np.nan)
BB = np.full(dshape, np.nan)
for j in xrange(ary_lon.shape[0]):
SV[j, :] = ary_sv[i][j / 10]
BB[j, :] = ary_bb[i][j / 10]
# SV = np.ma.masked_where(SV < 0, SV)
# BB = np.ma.masked_where(BB < 0, BB)
# np.ma.filled(SV, np.nan)
# np.ma.filled(BB, np.nan)
if BandName not in self.SV.keys():
self.SV[BandName] = SV
self.BB[BandName] = BB
else:
self.SV[BandName] = np.concatenate((self.SV[BandName], SV))
self.BB[BandName] = np.concatenate((self.BB[BandName], BB))
# 土地覆盖
ary_LandCover_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_LandCover >= 0, ary_LandCover <= 254)
ary_LandCover_idx[condition] = ary_LandCover[condition]
if self.LandCover == []:
self.LandCover = ary_LandCover_idx
else:
self.LandCover = np.concatenate(
(self.LandCover, ary_LandCover_idx))
# 海陆掩码
ary_LandSeaMask_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_LandSeaMask >= 0, ary_LandSeaMask <= 7)
ary_LandSeaMask_idx[condition] = ary_LandSeaMask[condition]
if self.LandSeaMask == []:
self.LandSeaMask = ary_LandSeaMask_idx
else:
self.LandSeaMask = np.concatenate(
(self.LandSeaMask, ary_LandSeaMask_idx))
# 经纬度
ary_lon_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_lon > -180., ary_lon < 180.)
ary_lon_idx[condition] = ary_lon[condition]
if self.Lons == []:
self.Lons = ary_lon_idx
else:
self.Lons = np.concatenate((self.Lons, ary_lon_idx))
ary_lat_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_lat > -90., ary_lat < 90.)
ary_lat_idx[condition] = ary_lat[condition]
if self.Lats == []:
self.Lats = ary_lat_idx
else:
self.Lats = np.concatenate((self.Lats, ary_lat_idx))
# 卫星方位角 天顶角
ary_sata_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_sata > 0, ary_sata < 36000)
ary_sata_idx[condition] = ary_sata[condition]
if self.satAzimuth == []:
self.satAzimuth = ary_sata_idx / 100.
else:
self.satAzimuth = np.concatenate(
(self.satAzimuth, ary_sata_idx / 100.))
ary_satz_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_satz > 0, ary_satz < 18000)
ary_satz_idx[condition] = ary_satz[condition]
if self.satZenith == []:
self.satZenith = ary_satz_idx / 100.
else:
self.satZenith = np.concatenate(
(self.satZenith, ary_satz_idx / 100.))
# 太阳方位角 天顶角
ary_suna_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_suna > 0, ary_suna < 36000)
ary_suna_idx[condition] = ary_suna[condition]
if self.sunAzimuth == []:
self.sunAzimuth = ary_suna_idx / 100.
else:
self.sunAzimuth = np.concatenate(
(self.sunAzimuth, ary_suna_idx / 100.))
ary_sunz_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_sunz > 0, ary_sunz < 18000)
ary_sunz_idx[condition] = ary_sunz[condition]
if self.sunZenith == []:
self.sunZenith = ary_sunz_idx / 100.
else:
self.sunZenith = np.concatenate(
(self.sunZenith, ary_sunz_idx / 100.))
# 系数先不合并，暂时未用，数据格式无法统一了
# self.IR_Coeff = ary_IR_Coeff
self.VIS_Coeff = ary_VIS_Coeff
def Load(self, L1File):
ipath = os.path.dirname(L1File)
iname = os.path.basename(L1File)
geoFile = os.path.join(ipath, iname[0:-12] + 'GEO1K_MS.HDF')
print L1File
print geoFile
if 'FY3C' in iname[:4]:
try:
h5File_R = h5py.File(L1File, 'r')
ary_ch1 = h5File_R.get('/Data/EV_250_Aggr.1KM_RefSB')[:]
ary_ch5 = h5File_R.get('/Data/EV_250_Aggr.1KM_Emissive')[:]
ary_ch6 = h5File_R.get('/Data/EV_1KM_RefSB')[:]
ary_Cal_Coeff = h5File_R.get('/Calibration/VIS_Cal_Coeff')[:]
ary_svdn = h5File_R.get('/Calibration/SV_DN_average')[:]
ary_bbdn = h5File_R.get('/Calibration/BB_DN_average')[:]
except Exception as e:
print str(e)
return
finally:
h5File_R.close()
print sys.getsizeof(ary_ch1) / 1024. / 1024.
try:
# 读取GEO文件
h5File_R = h5py.File(geoFile, 'r')
ary_satz = h5File_R.get('/Geolocation/SensorZenith')[:]
ary_sata = h5File_R.get('/Geolocation/SensorAzimuth')[:]
ary_sunz = h5File_R.get('/Geolocation/SolarZenith')[:]
ary_suna = h5File_R.get('/Geolocation/SolarAzimuth')[:]
ary_lon = h5File_R.get('/Geolocation/Longitude')[:]
ary_lat = h5File_R.get('/Geolocation/Latitude')[:]
ary_LandCover = h5File_R.get('/Geolocation/LandCover')[:]
ary_LandSeaMask = h5File_R.get('/Geolocation/LandSeaMask')[:]
ary_day = h5File_R.get('/Timedata/Day_Count')[:]
ary_time = h5File_R.get('/Timedata/Millisecond_Count')[:]
except Exception as e:
print str(e)
return
finally:
h5File_R.close()
else:
# FY3A/FY3B MERSI
# 读取L1文件
try:
h5File_R = h5py.File(L1File, 'r')
ary_lon = h5File_R.get('/Longitude')[:]
ary_lat = h5File_R.get('/Latitude')[:]
ary_svdn = np.full_like(ary_lon, -999.)
ary_bbdn = np.full_like(ary_lon, -999.)
ary_ch1 = h5File_R.get('/EV_250_Aggr.1KM_RefSB')[:]
ary_ch5 = h5File_R.get('/EV_250_Aggr.1KM_Emissive')[:]
ary_ch6 = h5File_R.get('/EV_1KM_RefSB')[:]
ary_Cal_Coeff = h5File_R.attrs['VIR_Cal_Coeff']
ary_satz = h5File_R.get('/SensorZenith')[:]
ary_sata = h5File_R.get('/SensorAzimuth')[:]
ary_sunz = h5File_R.get('/SolarZenith')[:]
ary_suna = h5File_R.get('/SolarAzimuth')[:]
ary_LandCover = h5File_R.get('/LandCover')[:]
ary_LandSeaMask = h5File_R.get('/LandSeaMask')[:]
ary_svdn = h5File_R.get('/SV_DN_average')[:]
ary_bbdn = h5File_R.get('/BB_DN_average')[:]
except Exception as e:
print str(e)
return
finally:
h5File_R.close()
# 通道的中心波数和光谱响应
# for i in xrange(self.Band):
# BandName = 'CH_%02d' % (i + 1)
# srfFile = os.path.join(
# MainPath, 'SRF', '%s_%s_SRF_CH%02d_Pub.txt' % (self.sat, self.sensor, (i + 1)))
# dictWave = np.loadtxt(
# srfFile, dtype={'names': ('num', 'rad'), 'formats': ('f4', 'f4')})
# waveNum = 10 ** 7 / dictWave['num'][::-1]
# waveRad = dictWave['rad'][::-1]
# self.waveNum[BandName] = waveNum
# self.waveRad[BandName] = waveRad
# 数据大小 使用经度维度 ###############
dshape = ary_lon.shape
# 通道信息赋值 #######################
# 读取FY3C查找表
# LutAry = np.loadtxt(self.LutFile, dtype={'names': ('TBB', '05'),
# 'formats': ('i4', 'f4')})
# RefSB_Cal_Coefficients这个属性直接写到hdf中，FY3A/B 需要转成19*3
proj_Cal_Coeff = np.full((19, 3), -999.)
if 'FY3C' in iname[:4]:
proj_Cal_Coeff = ary_Cal_Coeff
else:
for i in range(19):
for j in range(3):
proj_Cal_Coeff[i, j] = ary_Cal_Coeff[i * 3 + j]
# 定标系数 19*3 转 20*3
values = np.array([0, 0, 0])
K = np.insert(proj_Cal_Coeff, 4, values, 0)
pat = u'\w{4}_\w{5}_\w{4}_L1_(\d{8})_(\d{4})_\w{5}_MS.HDF$'
g = re.match(pat, iname)
if g:
ymd = g.group(1)
hms = g.group(2)
else:
raise ValueError('Cant get the ymdhms from file name.')
# 可见
for i in xrange(self.Band):
BandName = 'CH_%02d' % (i + 1)
if i < 4:
DN = np.full(dshape, np.nan)
idx = np.logical_and(ary_ch1[i] < 10000, ary_ch1[i] > 0)
DN[idx] = ary_ch1[i][idx]
if int(ymd + hms) <= 201303060015:
Ref = (DN ** 2 * K[i, 2] + DN * K[i, 1] + K[i, 0]) / 100.
print 'ref', Ref, BandName
else:
Ref = (DN ** 2 * K[i, 2] + DN * K[i, 1] + K[i, 0]) / 10000.
self.Dn[BandName] = DN
self.Ref[BandName] = Ref
elif i > 4:
k = i - 5
DN = np.full(dshape, np.nan)
idx = np.logical_and(ary_ch6[k] < 10000, ary_ch6[k] > 0)
DN[idx] = ary_ch6[k][idx]
if int(ymd + hms) <= 201303060015:
Ref = (DN ** 2 * K[i, 2] + DN * K[i, 1] + K[i, 0]) / 100.
print 'ref', Ref, BandName
else:
Ref = (DN ** 2 * K[i, 2] + DN * K[i, 1] + K[i, 0]) / 10000.
self.Dn[BandName] = DN
self.Ref[BandName] = Ref
# 红外
elif i == 4:
# 数据空间
DN = np.full(dshape, np.nan)
Rad = np.full(dshape, np.nan)
Tbb = np.full(dshape, np.nan)
# 过滤无效值
idx = np.logical_and(ary_ch5 < 10000, ary_ch5 >= 0)
# dn
DN[idx] = ary_ch5[idx]
self.Dn[BandName] = DN
# rad
Rad = DN / 100.
idx1 = np.where(Rad <= 0.)
Rad[idx1] = np.nan
self.Rad[BandName] = Rad
# tbb
Tbb = pb_sat.planck_r2t(
Rad, self.WN[BandName], self.TeA[BandName], self.TeB[BandName])
self.Tbb[BandName] = Tbb
# CA = interpolate.InterpolatedUnivariateSpline(LutAry['%02d' % (i + 1)], LutAry['TBB'])(DN[idx])
# self.CA[BandName] = np.full(dshape, np.nan)
# self.CA[BandName][idx] = CA / 100.
# 全局信息赋值 ############################
# 对时间进行赋值合并
if 'FY3C' in iname[:4]:
v_ymd2seconds = np.vectorize(fy3_ymd2seconds)
T1 = v_ymd2seconds(ary_day, ary_time)
Time = np.full(dshape, -999)
for i in xrange(ary_lon.shape[0]):
Time[i, :] = T1[i / 10, 0]
if self.Time == []:
self.Time = Time
else:
self.Time = np.concatenate((self.Time, Time))
else:
time = np.full(dshape, -999.)
name_class = pb_name.nameClassManager()
info = name_class.getInstance(iname)
secs = int(
(info.dt_s - datetime(1970, 1, 1, 0, 0, 0)).total_seconds())
time[:] = secs
if not self.Time:
self.Time = time
else:
self.Time = np.concatenate((self.Time, time))
# SV, BB
for i in xrange(self.Band):
SV = np.full(dshape, np.nan)
BB = np.full(dshape, np.nan)
for j in xrange(ary_lon.shape[0]):
SV[j, :] = ary_svdn[i][j / 10]
BB[j, :] = ary_bbdn[i][j / 10]
SV = np.ma.masked_where(SV < 0, SV)
BB = np.ma.masked_where(BB < 0, BB)
np.ma.filled(SV, np.nan)
np.ma.filled(BB, np.nan)
# 数据合并
BandName = 'CH_%02d' % (i + 1)
if BandName not in self.SV.keys():
self.SV[BandName] = SV
self.BB[BandName] = BB
else:
self.SV[BandName] = np.concatenate((self.SV[BandName], SV))
self.BB[BandName] = np.concatenate((self.BB[BandName], BB))
# 土地覆盖
ary_LandCover_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_LandCover >= 0, ary_LandCover <= 254)
ary_LandCover_idx[condition] = ary_LandCover[condition]
if self.LandCover == []:
self.LandCover = ary_LandCover_idx
else:
self.LandCover = np.concatenate(
(self.LandCover, ary_LandCover_idx))
# 海陆掩码
ary_LandSeaMask_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_LandSeaMask >= 0, ary_LandSeaMask <= 7)
ary_LandSeaMask_idx[condition] = ary_LandSeaMask[condition]
if self.LandSeaMask == []:
self.LandSeaMask = ary_LandSeaMask_idx
else:
self.LandSeaMask = np.concatenate(
(self.LandSeaMask, ary_LandSeaMask_idx))
# 经纬度
ary_lon_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_lon > -180., ary_lon < 180.)
ary_lon_idx[condition] = ary_lon[condition]
if self.Lons == []:
self.Lons = ary_lon_idx
else:
self.Lons = np.concatenate((self.Lons, ary_lon_idx))
ary_lat_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_lat > -90., ary_lat < 90.)
ary_lat_idx[condition] = ary_lat[condition]
if self.Lats == []:
self.Lats = ary_lat_idx
else:
self.Lats = np.concatenate((self.Lats, ary_lat_idx))
# 卫星方位角 天顶角
ary_sata_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_sata > -18000, ary_sata < 18000)
ary_sata_idx[condition] = ary_sata[condition]
if self.satAzimuth == []:
self.satAzimuth = ary_sata_idx / 100.
else:
self.satAzimuth = np.concatenate(
(self.satAzimuth, ary_sata_idx / 100.))
ary_satz_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_satz > 0, ary_satz < 18000)
ary_satz_idx[condition] = ary_satz[condition]
if self.satZenith == []:
self.satZenith = ary_satz_idx / 100.
else:
self.satZenith = np.concatenate(
(self.satZenith, ary_satz_idx / 100.))
# 太阳方位角 天顶角
ary_suna_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_suna > -18000, ary_suna < 18000)
ary_suna_idx[condition] = ary_suna[condition]
if self.sunAzimuth == []:
self.sunAzimuth = ary_suna_idx / 100.
else:
self.sunAzimuth = np.concatenate(
(self.sunAzimuth, ary_suna_idx / 100.))
ary_sunz_idx = np.full(dshape, np.nan)
condition = np.logical_and(ary_sunz > 0, ary_sunz < 18000)
ary_sunz_idx[condition] = ary_sunz[condition]
if self.sunZenith == []:
self.sunZenith = ary_sunz_idx / 100.
else:
self.sunZenith = np.concatenate(
(self.sunZenith, ary_sunz_idx / 100.))