def plot(self):
"""
绘制真彩图
:return:
"""
if self.error:
return
try:
dv_rgb(self.data_r, self.data_g, self.data_b, self.out_picture)
except Exception as why:
print why
self.error = True
return
def _plot_rgb_fy3d_new(l1_file, out_file):
"""
对原数据和处理后的数据各出一张真彩图
"""
try:
with h5py.File(l1_file) as h5:
r = h5.get("CH_03/Ref")[:] # 第 8 通道 670
g = h5.get("CH_02/Ref")[:] # 第 6 通道 555
b = h5.get("CH_01/Ref")[:] # 第 4 通道 490
dv_rgb(r, g, b, out_file)
print ">>> {}".format(out_file)
except Exception as why:
print why
print "Error: plot RGB error".format(l1_file)
return
def _plot_rgb_fy3b_old(l1_file, out_file):
"""
对原数据和处理后的数据各出一张真彩图
"""
try:
with h5py.File(l1_file) as h5:
r = h5.get("EV_1KM_RefSB")[5] # 第 11 通道 650
g = h5.get("EV_1KM_RefSB")[4] # 第 10 通道 565
b = h5.get("EV_1KM_RefSB")[2] # 第 8 通道 490
dv_rgb(r, g, b, out_file)
print ">>> {}".format(out_file)
except Exception as why:
print why
print "Error: plot RGB error".format(l1_file)
return
def draw_dclc(self, ICFG, MCFG):
print u'回归图'
for Band in MCFG.chan1:
idx = np.where(self.MaskFine[Band] > 0)
if hasattr(self, 'S1_FovRefMean'):
if Band in self.S1_FovRefMean.keys() and Band in MCFG.axis_ref:
x = self.S1_FovRefMean[Band][idx]
y = self.S2_FovRefMean[Band][idx]
if len(x) >= 2:
value_min = value_max = None
flag = 'Ref'
print('ref', Band, len(x), np.min(x), np.max(x),
np.min(y), np.max(y))
if MCFG.AutoRange == 'ON':
value_min = np.min([np.min(x), np.min(y)])
value_max = np.max([np.max(x), np.max(y)])
elif len(MCFG.axis_ref) != 0:
value_min = MCFG.axis_ref[Band][0]
value_max = MCFG.axis_ref[Band][1]
print(Band, value_min, value_max)
if value_min is not None and value_max is not None:
regression(x, y, value_min, value_max, flag, ICFG,
MCFG, Band)
if hasattr(self, 'S1_FovRadMean'):
if Band in self.S1_FovRadMean.keys() and Band in MCFG.axis_rad:
x = self.S1_FovRadMean[Band][idx]
y = self.S2_FovRadMean[Band][idx]
if len(x) >= 2:
value_min = value_max = None
flag = 'Rad'
print('rad', Band, len(x), np.min(x), np.max(x),
np.min(y), np.max(y))
if MCFG.AutoRange == 'ON':
value_min = np.min([np.min(x), np.min(y)])
value_max = np.max([np.max(x), np.max(y)])
elif len(MCFG.axis_rad) != 0:
value_min = MCFG.axis_rad[Band][0]
value_max = MCFG.axis_rad[Band][1]
if value_min is not None and value_max is not None:
regression(x, y, value_min, value_max, flag, ICFG,
MCFG, Band)
if hasattr(self, 'S1_FovTbbMean'):
if Band in self.S1_FovTbbMean.keys() and Band in MCFG.axis_rad:
x = self.S1_FovTbbMean[Band][idx]
y = self.S2_FovTbbMean[Band][idx]
if len(x) >= 2:
value_min = value_max = None
flag = 'Tbb'
print('tbb', Band, len(x), np.min(x), np.max(x),
np.min(y), np.max(y))
if MCFG.AutoRange == 'ON':
value_min = np.min([np.min(x), np.min(y)])
value_max = np.max([np.max(x), np.max(y)])
elif len(MCFG.axis_tbb) != 0:
value_min = MCFG.axis_tbb[Band][0]
value_max = MCFG.axis_tbb[Band][1]
if value_min is not None and value_max is not None:
regression(x, y, value_min, value_max, flag, ICFG,
MCFG, Band)
if hasattr(self, 'S1_FovRefMean'):
a = set(self.S1_FovRefMean.keys())
b = set(['CH_01', 'CH_02', 'CH_03'])
c = a.intersection(b)
if len(c) == 3:
print '真彩图 显示匹配位置'
R = self.S1_FovRefMean['CH_03']
G = self.S1_FovRefMean['CH_02']
B = self.S1_FovRefMean['CH_01']
mask = self.MaskFine['CH_01']
# 把匹配点进行补点，图像好看
for i in xrange(3):
fill_points_2d(R, -999.)
fill_points_2d(G, -999.)
fill_points_2d(B, -999.)
MainPath, _ = os.path.split(ICFG.ofile)
if not os.path.isdir(MainPath):
os.makedirs(MainPath)
file_name = '%s+%s_%s_Map321.png' % (ICFG.sat1, ICFG.sensor1,
ICFG.ymd)
path_name = os.path.join(MainPath, file_name)
dv_img.dv_rgb(R, G, B, path_name, 2, 1, mask)
def proj_modis(self):
# 进行数组的初始化
proj_data_ch38 = np.array([[[-999.] * self.col] * self.row] * 38)
proj_data_satz = np.array([[-999.] * self.col] * self.row)
proj_data_sata = np.array([[-999.] * self.col] * self.row)
proj_data_sunz = np.array([[-999.] * self.col] * self.row)
proj_data_suna = np.array([[-999.] * self.col] * self.row)
proj_LandSeaMask = np.array([[-999] * self.col] * self.row)
proj_src_row = np.array([[-999] * self.col] * self.row)
proj_src_col = np.array([[-999] * self.col] * self.row)
# 初始化投影参数
lookup_table = prj_core(self.cmd, self.res, self.row, self.col)
for L1File in self.ifile:
geoFile = find_modis_geo_file(L1File)
# 读取L1文件
try:
h4File = SD(L1File, SDC.READ)
# 读取1-2通道数据
in_data_r250 = h4File.select('EV_250_Aggr1km_RefSB').get()
in_data_r250_s = h4File.select(
'EV_250_Aggr1km_RefSB').attributes()['reflectance_scales']
in_data_r250_o = h4File.select(
'EV_250_Aggr1km_RefSB').attributes()['reflectance_offsets']
# 读取 3-7通道数据
in_data_r500 = h4File.select('EV_500_Aggr1km_RefSB').get()
in_data_r500_s = h4File.select(
'EV_500_Aggr1km_RefSB').attributes()['reflectance_scales']
in_data_r500_o = h4File.select(
'EV_500_Aggr1km_RefSB').attributes()['reflectance_offsets']
# 读取8-20通道， 包含26通道
in_data_r1km = h4File.select('EV_1KM_RefSB').get()
in_data_r1km_s = h4File.select(
'EV_1KM_RefSB').attributes()['reflectance_scales']
in_data_r1km_o = h4File.select(
'EV_1KM_RefSB').attributes()['reflectance_offsets']
# 读取20-36通道 不包含26通道
in_data_t1km = h4File.select('EV_1KM_Emissive').get()
in_data_t1km_s = h4File.select(
'EV_1KM_Emissive').attributes()['radiance_scales']
in_data_t1km_o = h4File.select(
'EV_1KM_Emissive').attributes()['radiance_offsets']
h4File.end()
except Exception as e:
print str(e)
# 读取GEO文件
try:
h4File = SD(geoFile, SDC.READ)
in_data_lon = h4File.select('Longitude').get()
in_data_lat = h4File.select('Latitude').get()
in_data_LandSeaMask = h4File.select(
'Land/SeaMask').get() # 多个/不知道是bug还是就这样
in_data_satz = h4File.select('SensorZenith').get()
in_data_satz_s = h4File.select(
'SensorZenith').attributes()['scale_factor']
in_data_sata = h4File.select('SensorAzimuth').get()
in_data_sata_s = h4File.select(
'SensorAzimuth').attributes()['scale_factor']
in_data_sunz = h4File.select('SolarZenith').get()
in_data_sunz_s = h4File.select(
'SolarZenith').attributes()['scale_factor']
in_data_suna = h4File.select('SolarAzimuth').get()
in_data_suna_s = h4File.select(
'SolarAzimuth').attributes()['scale_factor']
h4File.end()
except Exception as e:
print str(e)
# 获取经度数据集的行和列，制作一个一维的数组长度是 数据集行x列，分别存放行号和列号
rowh5, colh5 = in_data_lon.shape
ih5 = np.array([range(rowh5)] * colh5).T.reshape(
(-1)) # hdf5的行1d序列
jh5 = np.array([range(colh5)] * rowh5).reshape((-1)) # hdf5的列1d序列
# 返回投影查找表
# ii, jj, ih5, jh5 = lookup_table.lonslats2ij(in_data_lon, in_data_lat, ih5, jh5)
ii, jj = lookup_table.lonslats2ij(in_data_lon, in_data_lat)
# 投影方格以外的数据过滤掉
condition = np.logical_and(ii >= 0, ii < self.row)
condition = np.logical_and(condition, jj >= 0)
condition = np.logical_and(condition, jj < self.col)
index = np.where(condition)
ii = ii[index]
jj = jj[index]
ih5 = ih5[index]
jh5 = jh5[index]
# 角度和其他信息的投影和补点
proj_data_satz[ii, jj] = in_data_satz[ih5, jh5] * in_data_satz_s
proj_data_sata[ii, jj] = in_data_sata[ih5, jh5] * in_data_sata_s
proj_data_sunz[ii, jj] = in_data_sunz[ih5, jh5] * in_data_sunz_s
proj_data_suna[ii, jj] = in_data_suna[ih5, jh5] * in_data_suna_s
proj_LandSeaMask[ii, jj] = in_data_LandSeaMask[ih5, jh5]
# 1-2通道
for i in range(0, 2, 1):
# 过滤 无效值
condition = np.logical_and(in_data_r250[i, ih5, jh5] < 32767,
in_data_r250[i, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch38[i, tii,
tjj] = (in_data_r250[i, tih5, tjh5] -
in_data_r250_o[i]) * in_data_r250_s[i]
# 3-7通道
for i in range(2, 7, 1):
# 过滤 无效值
condition = np.logical_and(
in_data_r500[i - 2, ih5, jh5] < 32767,
in_data_r500[i - 2, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch38[
i, tii,
tjj] = (in_data_r500[i - 2, tih5, tjh5] -
in_data_r500_o[i - 2]) * in_data_r500_s[i - 2]
# 8-19 外加一个 26通道
for i in range(7, 22, 1):
# 过滤 无效值
condition = np.logical_and(
in_data_r1km[i - 7, ih5, jh5] < 32767,
in_data_r1km[i - 7, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch38[
i, tii,
tjj] = (in_data_r1km[i - 7, tih5, tjh5] -
in_data_r1km_o[i - 7]) * in_data_r1km_s[i - 7]
# 20-36通道 不包括26通道
for i in range(22, 38, 1):
# 过滤 无效值
condition = np.logical_and(
in_data_t1km[i - 22, ih5, jh5] < 32767,
in_data_t1km[i - 22, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch38[
i, tii,
tjj] = (in_data_t1km[i - 22, tih5, tjh5] -
in_data_t1km_o[i - 22]) * in_data_t1km_s[i - 22]
# 删除通道13hi 和 14 hi
proj_data_ch36 = np.delete(proj_data_ch38, (13, 15), 0)
# 把rad转亮温
radiance2tbb(proj_data_ch36)
# 进行补点
for i in range(36):
fill_points_2d(proj_data_ch36[i], -999.)
fill_points_2d(proj_data_satz, -999.)
fill_points_2d(proj_data_sata, -999.)
fill_points_2d(proj_data_sunz, -999.)
fill_points_2d(proj_data_suna, -999)
# fill_points_2d(proj_LandSeaMask, -999)
# 整理通道顺序，把26通道放在对应的26通道
del26_proj_data_ch35 = np.delete(proj_data_ch36, 19, 0)
order_proj_data_ch36 = np.insert(del26_proj_data_ch35, 25,
proj_data_ch36[19], 0)
# 通过行列号计算经纬度
lon_x, lat_y = lookup_table.ij2lonlat()
proj_lon = lon_x.reshape([self.row, self.col])
proj_lat = lat_y.reshape([self.row, self.col])
# 原始数据行列号
proj_src_row[ii, jj] = ih5
proj_src_col[ii, jj] = jh5
fill_points_2d(proj_src_row, -999)
fill_points_2d(proj_src_col, -999)
# 写入HDF
opath = os.path.dirname(self.ofile)
if not os.path.isdir(opath):
os.makedirs(opath)
pic_name = self.ofile.split('.hdf')[0] + '.png'
picFile = urljoin(opath, pic_name)
dv_img.dv_rgb(order_proj_data_ch36[0], order_proj_data_ch36[3],
order_proj_data_ch36[2], picFile, 2, 1)
h5file_W = h5py.File(self.ofile, 'w')
h5file_W.create_dataset('36bands_L1B_Ref_BT_values',
dtype='f4',
data=order_proj_data_ch36,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SensorZenith',
dtype='f4',
data=proj_data_satz,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SensorAzimuth',
dtype='f4',
data=proj_data_sata,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SolarZenith',
dtype='f4',
data=proj_data_sunz,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SolarAzimuth',
dtype='f4',
data=proj_data_suna,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('LandSeaMask',
dtype='i2',
data=proj_LandSeaMask,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('Longitude',
dtype='f4',
data=proj_lon,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('Latitude',
dtype='f4',
data=proj_lat,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('IdxRow',
dtype='i4',
data=proj_src_row,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('IdxCol',
dtype='i4',
data=proj_src_col,
compression='gzip',
compression_opts=5,
shuffle=True)
def proj_fy3abc_mersi(self):
print 'start project %s %s' % (self.sat, self.sensor)
# 进行数组的初始化
proj_data_ch20 = np.array([[[-999] * self.col] * self.row] * 20)
proj_data_sv20 = np.array([[[-999.] * self.col] * self.row] * 20)
proj_data_bb20 = np.array([[[-999.] * self.col] * self.row] * 20)
proj_data_satz = np.array([[-999] * self.col] * self.row)
proj_data_sata = np.array([[-999] * self.col] * self.row)
proj_data_sunz = np.array([[-999] * self.col] * self.row)
proj_data_suna = np.array([[-999] * self.col] * self.row)
proj_LandCover = np.array([[-999] * self.col] * self.row)
proj_LandSeaMask = np.array([[-999] * self.col] * self.row)
proj_Cal_Coeff = np.array([[-999.] * 3] * 19)
# 初始化投影参数
lookup_table = prj_core(self.cmd, self.res, self.row, self.col)
for L1File in self.ifile:
if self.sat == 'FY3C':
# 根据输入的L1文件查找GEO文件
ipath = os.path.dirname(L1File)
iname = os.path.basename(L1File)
geoFile = urljoin(ipath, iname[0:-12] + 'GEO1K_MS.HDF')
# 读取L1文件
try:
h5File = h5py.File(L1File, 'r')
in_data_ch1 = h5File.get('/Data/EV_250_Aggr.1KM_RefSB')[:]
in_data_ch5 = h5File.get(
'/Data/EV_250_Aggr.1KM_Emissive')[:]
in_data_ch6 = h5File.get('/Data/EV_1KM_RefSB')[:]
in_data_svdn = h5File.get('/Calibration/SV_DN_average')[:]
in_data_bbdn = h5File.get('/Calibration/BB_DN_average')[:]
in_data_Cal_Coeff = h5File.get(
'/Calibration/VIS_Cal_Coeff')[:]
h5File.close()
except Exception as e:
print str(e)
try:
# 读取GEO文件
h5File = h5py.File(geoFile, 'r')
in_data_satz = h5File.get('/Geolocation/SensorZenith')[:]
in_data_sata = h5File.get('/Geolocation/SensorAzimuth')[:]
in_data_sunz = h5File.get('/Geolocation/SolarZenith')[:]
in_data_suna = h5File.get('/Geolocation/SolarAzimuth')[:]
in_data_LandCover = h5File.get('/Geolocation/LandCover')[:]
in_data_LandSeaMask = h5File.get(
'/Geolocation/LandSeaMask')[:]
in_data_lon = h5File.get('/Geolocation/Longitude')[:]
in_data_lat = h5File.get('/Geolocation/Latitude')[:]
h5File.close()
except Exception as e:
print str(e)
else:
# 读取L1文件
h5File = h5py.File(L1File, 'r')
try:
in_data_lon = h5File.get('/Longitude')[:]
in_data_lat = h5File.get('/Latitude')[:]
in_data_ch1 = h5File.get('/EV_250_Aggr.1KM_RefSB')[:]
in_data_ch5 = h5File.get('/EV_250_Aggr.1KM_Emissive')[:]
in_data_ch6 = h5File.get('/EV_1KM_RefSB')[:]
in_data_Cal_Coeff = h5File.attrs['VIR_Cal_Coeff']
in_data_satz = h5File.get('/SensorZenith')[:]
in_data_sata = h5File.get('/SensorAzimuth')[:]
in_data_sunz = h5File.get('/SolarZenith')[:]
in_data_suna = h5File.get('/SolarAzimuth')[:]
in_data_LandCover = h5File.get('/LandCover')[:]
in_data_LandSeaMask = h5File.get('/LandSeaMask')[:]
except Exception as e:
print str(e)
return
try:
in_data_svdn = h5File.get('/SV_DN_average')[:]
in_data_bbdn = h5File.get('/BB_DN_average')[:]
except Exception as e:
print str(e)
in_data_svdn = np.full_like(in_data_lon, 0)
in_data_bbdn = np.full_like(in_data_lon, 0)
h5File.close()
# 获取经度数据集的行和列，制作一个一维的数组长度是 数据集行x列，分别存放行号和列号
rowh5, colh5 = in_data_lon.shape
ih5 = np.array([range(rowh5)] * colh5).T.reshape(
(-1)) # hdf5的行1d序列
jh5 = np.array([range(colh5)] * rowh5).reshape((-1)) # hdf5的列1d序列
# 返回投影查找表
ii, jj = lookup_table.lonslats2ij(in_data_lon, in_data_lat)
# 投影方格以外的数据过滤掉
condition = np.logical_and(ii >= 0, ii < self.row)
condition = np.logical_and(condition, jj >= 0)
condition = np.logical_and(condition, jj < self.col)
index = np.where(condition)
ii = ii[index]
jj = jj[index]
ih5 = ih5[index]
jh5 = jh5[index]
# 1-4通道的投影
for i in range(4):
# 过滤 无效值
condition = np.logical_and(in_data_ch1[i, ih5, jh5] < 10000,
in_data_ch1[i, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch20[i, tii, tjj] = in_data_ch1[i, tih5, tjh5]
# 5 通道投影
condition = np.logical_and(in_data_ch5[ih5, jh5] < 4095,
in_data_ch5[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch20[4, tii, tjj] = in_data_ch5[tih5, tjh5]
# 6-20通道投影
for i in range(15):
condition = np.logical_and(in_data_ch6[i, ih5, jh5] < 10000,
in_data_ch6[i, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch20[i + 5, tii, tjj] = in_data_ch6[i, tih5, tjh5]
for i in range(20):
# SV 进行过滤无效值投影
condition = np.logical_and(in_data_svdn[i, ih5 / 10] < 4095,
in_data_svdn[i, ih5 / 10] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_sv20[i, tii, tjj] = in_data_svdn[i, tih5 / 10]
# BB 进行过滤无效值投影
condition = np.logical_and(in_data_bbdn[i, ih5 / 10] < 4095,
in_data_bbdn[i, ih5 / 10] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_bb20[i, tii, tjj] = in_data_bbdn[i, tih5 / 10]
# 卫星天顶角
condition = np.logical_and(in_data_satz[ih5, jh5] < 18000,
in_data_satz[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_satz[tii, tjj] = in_data_satz[tih5, tjh5]
# 卫星方位角
condition = np.logical_and(in_data_sata[ih5, jh5] < 18000,
in_data_sata[ih5, jh5] > -18000)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_sata[tii, tjj] = in_data_sata[tih5, tjh5]
# 太阳天顶角
condition = np.logical_and(in_data_sunz[ih5, jh5] < 18000,
in_data_sunz[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_sunz[tii, tjj] = in_data_sunz[tih5, tjh5]
# 太阳方位角
condition = np.logical_and(in_data_suna[ih5, jh5] < 18000,
in_data_suna[ih5, jh5] > -18000)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_suna[tii, tjj] = in_data_suna[tih5, tjh5]
# land cover
condition = np.logical_and(in_data_LandCover[ih5, jh5] < 17,
in_data_LandCover[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_LandCover[tii, tjj] = in_data_LandCover[tih5, tjh5]
# land sea mask
condition = np.logical_and(in_data_LandSeaMask[ih5, jh5] < 7,
in_data_LandSeaMask[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_LandSeaMask[tii, tjj] = in_data_LandSeaMask[tih5, tjh5]
# RefSB_Cal_Coefficients这个属性直接写到hdf中，不进行操作，只是把14转成7*2
if self.sat == 'FY3C':
proj_Cal_Coeff = in_data_Cal_Coeff
else:
for i in range(19):
for j in range(3):
proj_Cal_Coeff[i, j] = in_data_Cal_Coeff[i * 3 + j]
# 通过行列号计算经纬度
lon_x, lat_y = lookup_table.ij2lonlat()
proj_lon = lon_x.reshape([self.row, self.col])
proj_lat = lat_y.reshape([self.row, self.col])
# 投影后进行补点操作
for i in range(20):
fill_points_2d(proj_data_sv20[i], -999)
fill_points_2d(proj_data_bb20[i], -999.)
fill_points_2d(proj_data_ch20[i], -999.)
fill_points_2d(proj_data_satz, -999)
fill_points_2d(proj_data_sata, -999)
fill_points_2d(proj_data_sunz, -999)
fill_points_2d(proj_data_suna, -999)
# 写入HDF
opath = os.path.dirname(self.ofile)
if not os.path.isdir(opath):
os.makedirs(opath)
pic_name = self.ofile.split('.hdf')[0] + '_321.png'
picFile = urljoin(opath, pic_name)
dv_img.dv_rgb(proj_data_ch20[2], proj_data_ch20[1], proj_data_ch20[0],
picFile, 2, 1)
pic_name2 = self.ofile.split('.hdf')[0] + '_643.png'
picFile = urljoin(opath, pic_name2)
dv_img.dv_rgb(proj_data_ch20[5], proj_data_ch20[3], proj_data_ch20[2],
picFile, 3, 1)
h5file_W = h5py.File(self.ofile, 'w')
h5file_W.create_dataset('20bands_L1B_DN_values',
dtype='i2',
data=proj_data_ch20,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('RefSBCoeffcients',
dtype='f4',
data=proj_Cal_Coeff,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SensorZenith',
dtype='i2',
data=proj_data_satz,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SensorAzimuth',
dtype='i2',
data=proj_data_sata,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SolarZenith',
dtype='i2',
data=proj_data_sunz,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SolarAzimuth',
dtype='i2',
data=proj_data_suna,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('LandSeaMask',
dtype='i2',
data=proj_LandSeaMask,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('LandCover',
dtype='i2',
data=proj_LandCover,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('Longitude',
dtype='f4',
data=proj_lon,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('Latitude',
dtype='f4',
data=proj_lat,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SV_DN_average',
dtype='f4',
data=proj_data_sv20,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('BB_DN_average',
dtype='f4',
data=proj_data_bb20,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.close()
def proj_fy3abc_virr(self):
# 进行数组的初始化
proj_data_ch10 = np.array([[[-999] * self.col] * self.row] * 10)
proj_data_sv10 = np.array([[[-999] * self.col] * self.row] * 10)
proj_data_rad = np.array([[[-999.] * self.col] * self.row] * 3)
proj_Cal_Coeff = np.array([[-999.] * 2] * 7)
proj_data_satz = np.array([[-999] * self.col] * self.row)
proj_data_sata = np.array([[-999] * self.col] * self.row)
proj_data_sunz = np.array([[-999] * self.col] * self.row)
proj_data_suna = np.array([[-999] * self.col] * self.row)
proj_LandCover = np.array([[-999] * self.col] * self.row)
proj_LandSeaMask = np.array([[-999] * self.col] * self.row)
# proj_src_row = np.array([[-999] * self.col] * self.row)
# proj_src_col = np.array([[-999] * self.col] * self.row)
# 初始化投影参数
lookup_table = prj_core(self.cmd, self.res, self.row, self.col)
for L1File in self.ifile:
if self.sat == 'FY3C':
# 根据输入的L1文件自动拼接GEO文件
ipath = os.path.dirname(L1File)
iname = os.path.basename(L1File)
geoFile = urljoin(ipath, iname[0:-12] + 'GEOXX_MS.HDF')
obcFile = urljoin(ipath, iname[0:-12] + 'OBCXX_MS.HDF')
# 读取L1文件
try:
h5File = h5py.File(L1File, 'r')
in_data_ch3 = h5File.get('/Data/EV_Emissive')[:]
in_data_ch7 = h5File.get('/Data/EV_RefSB')[:]
in_data_offsets = h5File.get(
'/Data/Emissive_Radiance_Offsets')[:]
in_data_scales = h5File.get(
'/Data/Emissive_Radiance_Scales')[:]
in_data_ref_cal = h5File.attrs['RefSB_Cal_Coefficients']
in_data_Nonlinear = h5File.attrs[
'Prelaunch_Nonlinear_Coefficients']
h5File.close()
except Exception as e:
print str(e)
try:
# 读取GEO文件
h5File = h5py.File(geoFile, 'r')
in_data_satz = h5File.get('/Geolocation/SensorZenith')[:]
in_data_sata = h5File.get('/Geolocation/SensorAzimuth')[:]
in_data_sunz = h5File.get('/Geolocation/SolarZenith')[:]
in_data_suna = h5File.get('/Geolocation/SolarAzimuth')[:]
in_data_lon = h5File.get('/Geolocation/Longitude')[:]
in_data_lat = h5File.get('/Geolocation/Latitude')[:]
in_data_LandCover = h5File.get('/Geolocation/LandCover')[:]
in_data_LandSeaMask = h5File.get(
'/Geolocation/LandSeaMask')[:]
h5File.close()
except Exception as e:
print str(e)
try:
# 读取OBC文件
h5File = h5py.File(obcFile, 'r')
in_data_sv = h5File.get('/Calibration/Space_View')[:]
h5File.close()
except Exception as e:
print str(e)
else: # FY3A FY3B
# 根据输入的L1文件自动拼接OBC文件
ipath = os.path.dirname(L1File)
iname = os.path.basename(L1File)
obcFile = urljoin(ipath, iname[0:-12] + 'OBCXX_MS.HDF')
# 读取L1文件
h5File = h5py.File(L1File, 'r')
in_data_ch3 = h5File.get('/EV_Emissive')[:]
in_data_ch7 = h5File.get('/EV_RefSB')[:]
in_data_offsets = h5File.get('/Emissive_Radiance_Offsets')[:]
in_data_scales = h5File.get('/Emissive_Radiance_Scales')[:]
in_data_ref_cal = h5File.attrs['RefSB_Cal_Coefficients']
in_data_Nonlinear = h5File.attrs[
'Prelaunch_Nonlinear_Coefficients']
in_data_satz = h5File.get('/SensorZenith')[:]
in_data_sata = h5File.get('/SensorAzimuth')[:]
in_data_sunz = h5File.get('/SolarZenith')[:]
in_data_suna = h5File.get('/SolarAzimuth')[:]
in_data_lon = h5File.get('/Longitude')[:]
in_data_lat = h5File.get('/Latitude')[:]
in_data_LandCover = h5File.get('/LandCover')[:]
in_data_LandSeaMask = h5File.get('/LandSeaMask')[:]
h5File.close()
# 读取OBC文件
h5File = h5py.File(obcFile, 'r')
in_data_sv = h5File.get('Space_View')[:]
h5File.close()
# 获取经度数据集的行和列，制作一个一维的数组长度是 数据集行x列，分别存放行号和列号
rowh5, colh5 = in_data_lon.shape
ih5 = np.array([range(rowh5)] * colh5).T.reshape(
(-1)) # hdf5的行1d序列
jh5 = np.array([range(colh5)] * rowh5).reshape((-1)) # hdf5的列1d序列
# 返回投影查找表
# ii, jj, ih5, jh5 = lookup_table.lonslats2ij(in_data_lon, in_data_lat, ih5, jh5)
ii, jj = lookup_table.lonslats2ij(in_data_lon, in_data_lat)
# 投影方格以外的数据过滤掉
condition = np.logical_and(ii >= 0, ii < self.row)
condition = np.logical_and(condition, jj >= 0)
condition = np.logical_and(condition, jj < self.col)
index = np.where(condition)
ii = ii[index]
jj = jj[index]
ih5 = ih5[index]
jh5 = jh5[index]
# 通道信息进行投影 1-2通道
for i in range(0, 2, 1):
# 过滤EV_RefSB的无效值
condition = np.logical_and(in_data_ch7[i, ih5, jh5] < 32767,
in_data_ch7[i, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch10[i, tii, tjj] = in_data_ch7[i, tih5, tjh5]
# 通道信息进行投影 3-5通道
for i in range(2, 5, 1):
# 过滤 EV_Emissive的无效值
condition = np.logical_and(
in_data_ch3[i - 2, ih5, jh5] < 32767,
in_data_ch3[i - 2, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
# 把3-5通道用EV_Emissive来进行赋值
proj_data_ch10[i, tii, tjj] = in_data_ch3[i - 2, tih5, tjh5]
# 用定标系数修正EV_Emissive的值
proj_data_rad[
i - 2, tii,
tjj] = in_data_ch3[i - 2, tih5, tjh5] * in_data_scales[
tih5, i - 2] + in_data_offsets[tih5, i - 2]
k0 = in_data_Nonlinear[3 * (i - 2)]
k1 = in_data_Nonlinear[3 * (i - 2) + 1] + 1
k2 = in_data_Nonlinear[3 * (i - 2) + 2]
proj_data_rad[i - 2, tii, tjj] = proj_data_rad[
i - 2, tii, tjj]**2 * k2 + proj_data_rad[i - 2, tii,
tjj] * k1 + k0
# 通道信息进行投影 6-10通道
for i in range(5, 10, 1):
condition = np.logical_and(
in_data_ch7[i - 3, ih5, jh5] < 32767,
in_data_ch7[i - 3, ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_ch10[i, tii, tjj] = in_data_ch7[i - 3, tih5, tjh5]
# SV 投影
for i in range(0, 10, 1):
condition = np.logical_and(in_data_sv[i, ih5, 0] < 1023,
in_data_sv[i, ih5, 0] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_sv10[i, tii, tjj] = in_data_sv[i, tih5, 0]
# 卫星天顶角
condition = np.logical_and(in_data_satz[ih5, jh5] < 18000,
in_data_satz[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_satz[tii, tjj] = in_data_satz[tih5, tjh5]
# 卫星方位角
condition = np.logical_and(in_data_sata[ih5, jh5] < 18000,
in_data_sata[ih5, jh5] > -18000)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_sata[tii, tjj] = in_data_sata[tih5, tjh5]
# 太阳天顶角
condition = np.logical_and(in_data_sunz[ih5, jh5] < 18000,
in_data_sunz[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_sunz[tii, tjj] = in_data_sunz[tih5, tjh5]
# 太阳方位角
condition = np.logical_and(in_data_suna[ih5, jh5] < 18000,
in_data_suna[ih5, jh5] > -18000)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_data_suna[tii, tjj] = in_data_suna[tih5, tjh5]
# land cover
condition = np.logical_and(in_data_LandCover[ih5, jh5] < 17,
in_data_LandCover[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_LandCover[tii, tjj] = in_data_LandCover[tih5, tjh5]
# land sea mask
condition = np.logical_and(in_data_LandSeaMask[ih5, jh5] < 7,
in_data_LandSeaMask[ih5, jh5] > 0)
index = np.where(condition)
tii = ii[index]
tjj = jj[index]
tih5 = ih5[index]
tjh5 = jh5[index]
proj_LandSeaMask[tii, tjj] = in_data_LandSeaMask[tih5, tjh5]
# RefSB_Cal_Coefficients这个属性直接写到hdf中，不进行操作，只是把14转成7*2
for i in range(7):
for j in range(2):
if j == 0:
proj_Cal_Coeff[i, j] = in_data_ref_cal[i * 2 + j + 1]
if j == 1:
proj_Cal_Coeff[i, j] = in_data_ref_cal[i * 2 + j - 1]
# 通过行列号计算经纬度
lon_x, lat_y = lookup_table.ij2lonlat()
proj_lon = lon_x.reshape([self.row, self.col])
proj_lat = lat_y.reshape([self.row, self.col])
# 原始数据行列号
# proj_src_row[ii, jj] = ih5
# proj_src_col[ii, jj] = jh5
# fill_points_2d(proj_src_row, -999)
# fill_points_2d(proj_src_col, -999)
# 进行补点
for i in range(10):
fill_points_2d(proj_data_ch10[i], -999)
fill_points_2d(proj_data_sv10[i], -999)
for i in range(3):
fill_points_2d(proj_data_rad[i], -999.)
fill_points_2d(proj_data_satz, -999)
fill_points_2d(proj_data_sata, -999)
fill_points_2d(proj_data_sunz, -999)
fill_points_2d(proj_data_suna, -999)
# fill_points_2d(proj_LandCover, -999)
# fill_points_2d(proj_LandSeaMask, -999)
# 写入HDF
opath = os.path.dirname(self.ofile)
if not os.path.isdir(opath):
os.makedirs(opath)
pic_name = self.ofile.split('.hdf')[0] + '.png'
picFile = urljoin(opath, pic_name)
dv_img.dv_rgb(proj_data_ch10[0], proj_data_ch10[8], proj_data_ch10[6],
picFile, 2, 1)
h5file_W = h5py.File(self.ofile, 'w')
h5file_W.create_dataset('10bands_L1B_DN_values',
dtype='i2',
data=proj_data_ch10,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('RAD_Emissive',
dtype='f4',
data=proj_data_rad,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('RefSBCoeffcients',
dtype='f4',
data=proj_Cal_Coeff,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SensorZenith',
dtype='i2',
data=proj_data_satz,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SensorAzimuth',
dtype='i2',
data=proj_data_sata,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SolarZenith',
dtype='i2',
data=proj_data_sunz,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SolarAzimuth',
dtype='i2',
data=proj_data_suna,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('LandSeaMask',
dtype='i2',
data=proj_LandSeaMask,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('LandCover',
dtype='i2',
data=proj_LandCover,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('Longitude',
dtype='f4',
data=proj_lon,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('Latitude',
dtype='f4',
data=proj_lat,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.create_dataset('SV_DN_average',
dtype='i2',
data=proj_data_sv10,
compression='gzip',
compression_opts=5,
shuffle=True)
def proj_fy3d_mersi(self):
print 'start project %s %s' % (self.sat, self.sensor)
# 进行数组的初始化
proj_data_ch = np.array([[[-999] * self.col] * self.row] * 25)
proj_data_sv = np.array([[[-999.] * self.col] * self.row] * 25)
proj_data_bb = np.array([[[-999.] * self.col] * self.row] * 25)
proj_data_satz = np.array([[-999] * self.col] * self.row)
proj_data_sata = np.array([[-999] * self.col] * self.row)
proj_data_sunz = np.array([[-999] * self.col] * self.row)
proj_data_suna = np.array([[-999] * self.col] * self.row)
proj_LandCover = np.array([[-999] * self.col] * self.row)
proj_LandSeaMask = np.array([[-999] * self.col] * self.row)
proj_Cal_Coeff = np.array([[-999.] * 3] * 19)
print proj_Cal_Coeff.shape
# 初始化投影参数
# lookup_table = prj_core(self.cmd, self.res, self.row, self.col)
for L1File in self.ifile:
mersi = CLASS_MERSI2_L1_1000M()
mersi.Load(L1File)
# 创建投影查找表
lookup_table = prj_core(self.cmd, self.res, self.row, self.col)
# 获取投影后网格经纬度信息
lon_x, lat_y = lookup_table.ij2lonlat()
# 经纬度转成投影后的数据维度
proj_lon = lon_x.reshape([self.row, self.col])
proj_lat = lat_y.reshape([self.row, self.col])
# 获取源数据经纬度位置与投影后位置的对应关系，并转成与数据大小一致维度
proj1_i, proj1_j = lookup_table.lonslats2ij(mersi.Lons, mersi.Lats)
if len(proj1_i.shape) == 1:
proj1_i = proj1_i.reshape(mersi.Lons.shape)
proj1_j = proj1_j.reshape(mersi.Lons.shape)
# 根据投影前数据别分获取源数据维度，制作一个和数据维度一致的数组，分别存放行号和列号
data1_row, data1_col = mersi.Lons.shape
data1_i, data1_j = np.mgrid[0:data1_row:1, 0:data1_col:1]
# 投影方格以外的数据过滤掉，第一块数据原始数据的行列, data1_i, data1_j 第一块数据投影后的行列 proj1_i,proj1_j
condition = np.logical_and(proj1_i >= 0, proj1_i < self.row)
condition = np.logical_and(condition, proj1_j >= 0)
condition = np.logical_and(condition, proj1_j < self.col)
index = np.where(condition)
p1_i = proj1_i[index]
p1_j = proj1_j[index]
d1_i = data1_i[index]
d1_j = data1_j[index]
# 1-4通道的投影
i = 0
for key in sorted(mersi.DN.keys()):
index = np.where(~np.isnan(mersi.DN[key][d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_data_ch[i, pi, pj] = mersi.DN[key][di, dj]
index = np.where(~np.isnan(mersi.SV[key][d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_data_sv[i, pi, pj] = mersi.SV[key][di, dj]
index = np.where(~np.isnan(mersi.BB[key][d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_data_bb[i, pi, pj] = mersi.BB[key][di, dj]
fill_points_2d(proj_data_ch[i], -999)
fill_points_2d(proj_data_sv[i], -999.)
fill_points_2d(proj_data_bb[i], -999.)
fill_points_2d(proj_data_ch[i], -999)
fill_points_2d(proj_data_sv[i], -999.)
fill_points_2d(proj_data_bb[i], -999.)
fill_points_2d(proj_data_ch[i], -999)
fill_points_2d(proj_data_sv[i], -999.)
fill_points_2d(proj_data_bb[i], -999.)
i = i + 1
# 卫星方位角 天顶角
index = np.where(~np.isnan(mersi.satZenith[d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_data_satz[ pi, pj] = mersi.satZenith[di, dj] * 100
index = np.where(~np.isnan(mersi.satAzimuth[d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_data_sata[pi, pj] = mersi.satAzimuth[di, dj] * 100
# 卫星方位角 天顶角
index = np.where(~np.isnan(mersi.sunAzimuth[d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_data_suna[pi, pj] = mersi.sunAzimuth[di, dj] * 100
index = np.where(~np.isnan(mersi.sunZenith[d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_data_sunz[ pi, pj] = mersi.sunZenith[di, dj] * 100
# 海陆掩码等
index = np.where(~np.isnan(mersi.LandCover[d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_LandCover[ pi, pj] = mersi.LandCover[di, dj]
index = np.where(~np.isnan(mersi.LandSeaMask[d1_i, d1_j]))
pi = p1_i[index]
pj = p1_j[index]
di = d1_i[index]
dj = d1_j[index]
proj_LandSeaMask[ pi, pj] = mersi.LandSeaMask[di, dj]
proj_Cal_Coeff = mersi.VIS_Coeff
fill_points_2d(proj_data_satz, -999)
fill_points_2d(proj_data_sata, -999)
fill_points_2d(proj_data_sunz, -999)
fill_points_2d(proj_data_suna, -999)
fill_points_2d(proj_data_satz, -999)
fill_points_2d(proj_data_sata, -999)
fill_points_2d(proj_data_sunz, -999)
fill_points_2d(proj_data_suna, -999)
fill_points_2d(proj_data_satz, -999)
fill_points_2d(proj_data_sata, -999)
fill_points_2d(proj_data_sunz, -999)
fill_points_2d(proj_data_suna, -999)
# 写入HDF
opath = os.path.dirname(self.ofile)
if not os.path.isdir(opath):
os.makedirs(opath)
pic_name = self.ofile.split('.hdf')[0] + '_321.png'
picFile = urljoin(opath, pic_name)
dv_img.dv_rgb(proj_data_ch[2], proj_data_ch[1], proj_data_ch[0], picFile, 2, 1)
pic_name2 = self.ofile.split('.hdf')[0] + '_643.png'
picFile = urljoin(opath, pic_name2)
dv_img.dv_rgb(proj_data_ch[5], proj_data_ch[3], proj_data_ch[2], picFile, 2, 1)
h5file_W = h5py.File(self.ofile, 'w')
h5file_W.create_dataset('L1B_DN_values', dtype='i2', data=proj_data_ch, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('RefSBCoeffcients', dtype='f4', data=proj_Cal_Coeff, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('SensorZenith', dtype='i4', data=proj_data_satz, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('SensorAzimuth', dtype='i4', data=proj_data_sata, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('SolarZenith', dtype='i4', data=proj_data_sunz, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('SolarAzimuth', dtype='i4', data=proj_data_suna, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('LandSeaMask', dtype='i2', data=proj_LandSeaMask, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('LandCover', dtype='i2', data=proj_LandCover, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('Longitude', dtype='f4', data=proj_lon, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('Latitude', dtype='f4', data=proj_lat, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('SV_DN_average', dtype='f4', data=proj_data_sv, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.create_dataset('BB_DN_average', dtype='f4', data=proj_data_bb, compression='gzip', compression_opts=5, shuffle=True)
h5file_W.close()