def main(band1, band2, band3, band4, band5, band6, band7, band8, band9, band10,
band11):
the_folder = sys.argv[
1] #'/glusterfs/surft/users/mp877190/data/datastore/EE/LANDSAT_8_C1/dungeness_checked/LC82010252015108LGN01/scenes/'
# Dictionary of the unique filenames for the bands needed.
dFilenames = {}
for i in [
band1, band2, band3, band4, band5, band6, band7, band8, band9,
band10, band11
]:
dFilenames[i] = sys.argv[2].format(
i) #"LC08_L1TP_201025_20150418_20170409_01_T1_B{}.TIF".format(i)
the_metafile = sys.argv[
3] #'LC08_L1TP_201025_20150418_20170409_01_T1_MTL.txt'
metadata = s.get_metadata(the_folder, the_metafile)
x_origin, y_origin, read_info_old_coord_sys, dataset = s.read_info(
the_folder, dFilenames[band10])
print(dataset)
print(x_origin)
print(y_origin)
lat_im, lon_im = s.latlon_image(the_folder, dFilenames[band10])
print(np.amin(lat_im))
print(np.amax(lat_im))
print(np.amin(lon_im))
print(np.amax(lon_im))
print("lat_im", lat_im.shape)
print("lon_im", lon_im.shape)
print(lat_im)
print(lon_im)
lines = np.int(metadata['THERMAL_LINES'])
samples = np.int(metadata['THERMAL_SAMPLES'])
# Dictionary of band data.
data = {}
#for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]:
for i in [
band1, band2, band3, band4, band5, band6, band7, band8, band9,
band10, band11
]:
# Open the files and store data in array.
data[i] = s.tiff_to_array(lines, samples, the_folder, dFilenames[i])
quit
A_rho = {}
M_rho = {}
refl = {}
refl_corr = {}
#for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]:
for i in [band1, band2, band3, band4, band5, band6, band7, band8, band9]:
# Function to read in metadata and extract radiance, reflectance, K1, K2
A_rho[i] = np.float(metadata['REFLECTANCE_ADD_BAND_%d' % i])
M_rho[i] = np.float(metadata['REFLECTANCE_MULT_BAND_%d' % i])
refl[i] = s.DN_to_refl(data[i], M_rho[i], A_rho[i])
refl_corr[i] = s.reflectance_corrected(
refl[i], np.float(metadata['SUN_ELEVATION']))
rad = {}
AL = {}
ML = {}
for i in [
band1, band2, band3, band4, band5, band6, band7, band8, band9,
band10, band11
]:
AL[i] = np.float(metadata['RADIANCE_ADD_BAND_%d' % i])
ML[i] = np.float(metadata['RADIANCE_MULT_BAND_%d' % i])
rad[i] = s.DN_to_radiance(data[i], ML[i], AL[i])
#A_rho_6 = np.float(metadata['REFLECTANCE_ADD_BAND_%d' % band6])
#M_rho_6 = np.float(metadata['REFLECTANCE_MULT_BAND_%d' % band6])
K1 = {}
K2 = {}
BT = {}
#for i in [10, 11]:
for i in [band10, band11]:
K1[i] = np.float(metadata['K1_CONSTANT_BAND_%d' % i])
K2[i] = np.float(metadata['K2_CONSTANT_BAND_%d' % i])
BT[i] = s.Radiance_to_satBT(data[i], rad[i], K1[i], K2[i])
print(np.shape(BT[10]))
print(np.shape(BT[11]))
#stack = np.concatenate([[lat_im], [lon_im], [refl_corr[1]], [BT[10]]])
#stack = np.concatenate([[lat_im], [lon_im], [refl_corr[1]], [refl_corr[2]], [refl_corr[3]], [refl_corr[4]], [refl_corr[5]], [refl_corr[6]], [refl_corr[7]], [data[9]], [BT[10]], [BT[11]]])
stack = np.concatenate([[refl_corr[1]], [refl_corr[2]], [refl_corr[3]],
[refl_corr[4]], [refl_corr[5]], [refl_corr[6]],
[refl_corr[7]], [data[9]], [BT[10]], [BT[11]]])
print(np.shape(stack))
"""
#coral reef
center_lat = -21.0 #-18.78
center_lon = 151.0 #147.63
dist_deg = 0.1
#dist_deg_smaller = 0.01
spacing = 0.0005
common_grid, min_lon, min_lat, spacing = p.creategrid(center_lat, center_lon , dist_deg, spacing)
#common_grid_smaller, min_lon, min_lat, spacing = p.creategrid(center_lat, center_lon , dist_deg_smaller, spacing)
print("common_grid shape", common_grid.shape)
print(common_grid)
#pdb.set_trace()
data_regridded, count, distance = r.regridding(stack, min_lon, min_lat, spacing, common_grid.shape)
#create a netcdf file
print('data regridded shape' , data_regridded.shape)
"""
name = sys.argv[4] #'dungeness_LC08_L1TP_201025_20150418_20170409_01_T1'
#conv_nc.create_netcdf(name, data_regridded, common_grid[0,:,:], common_grid[1,:,:])
#conv_nc.create_netcdf(name, stack, lat_im[:,0], lon_im[0,:])
conv_nc.create_netcdf(name, stack, lat_im[:, :], lon_im[:, :])
"""
def main(band1, band2, band3, band4, band5, band6, band7, band8, band9, band10,
band11):
#the_folder = 'E:/PHD/datastore/EE/LANDSAT_8_C1/hinckley/LC82030242013308LGN01/scenes/'
the_folder = sys.argv[
1] #'/glusterfs/surft/users/mp877190/data/datastore/EE/LANDSAT_8_C1/dungeness_checked/LC82010252015108LGN01/scenes/'
# Dictionary of the unique filenames for the bands needed.
dFilenames = {}
for i in [
band1, band2, band3, band4, band5, band6, band7, band8, band9,
band10, band11
]:
# dFilenames[i] = "LC82040212013155LGN00_B{}_crop.TIF".format(i)
dFilenames[i] = sys.argv[2].format(
i) #"LC08_L1TP_201025_20150418_20170409_01_T1_B{}.TIF".format(i)
the_metafile = sys.argv[
3] #'LC08_L1TP_201025_20150418_20170409_01_T1_MTL.txt'
metadata = s.get_metadata(the_folder, the_metafile)
#p = re.compile(r'(?<=_B)[^.]+')
#for fn in os.listdir(the_folder):
# try:
# file_name = fn
# file_band = p.search(fn).group(0)
# except AttributeError:
# if fn.endswith('MTL.txt'):
# the_metafile = fn
x_origin, y_origin, read_info_old_coord_sys, dataset = s.read_info(
the_folder, dFilenames[band10])
print(dataset)
print(x_origin)
print(y_origin)
lat_im, lon_im = s.latlon_image(the_folder, dFilenames[band10])
print(np.amin(lat_im))
print(np.amax(lat_im))
print(np.amin(lon_im))
print(np.amax(lon_im))
print("lat_im", lat_im.shape)
print("lon_im", lon_im.shape)
print(lat_im)
print(lon_im)
lines = np.int(metadata['THERMAL_LINES'])
samples = np.int(metadata['THERMAL_SAMPLES'])
# Dictionary of band data.
data = {}
#for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]:
for i in [
band1, band2, band3, band4, band5, band6, band7, band8, band9,
band10, band11
]:
# Open the files and store data in array.
data[i] = s.tiff_to_array(lines, samples, the_folder, dFilenames[i])
A_rho = {}
M_rho = {}
refl = {}
refl_corr = {}
#for i in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]:
for i in [band1, band2, band3, band4, band5, band6, band7, band8, band9]:
# Function to read in metadata and extract radiance, reflectance, K1, K2
A_rho[i] = np.float(metadata['REFLECTANCE_ADD_BAND_%d' % i])
M_rho[i] = np.float(metadata['REFLECTANCE_MULT_BAND_%d' % i])
refl[i] = s.DN_to_refl(data[i], M_rho[i], A_rho[i])
refl_corr[i] = s.reflectance_corrected(
refl[i], np.float(metadata['SUN_ELEVATION']))
rad = {}
AL = {}
ML = {}
for i in [
band1, band2, band3, band4, band5, band6, band7, band8, band9,
band10, band11
]:
AL[i] = np.float(metadata['RADIANCE_ADD_BAND_%d' % i])
ML[i] = np.float(metadata['RADIANCE_MULT_BAND_%d' % i])
rad[i] = s.DN_to_radiance(data[i], ML[i], AL[i])
#A_rho_6 = np.float(metadata['REFLECTANCE_ADD_BAND_%d' % band6])
#M_rho_6 = np.float(metadata['REFLECTANCE_MULT_BAND_%d' % band6])
K1 = {}
K2 = {}
BT = {}
#for i in [10, 11]:
for i in [band10, band11]:
K1[i] = np.float(metadata['K1_CONSTANT_BAND_%d' % i])
K2[i] = np.float(metadata['K2_CONSTANT_BAND_%d' % i])
BT[i] = s.Radiance_to_satBT(data[i], rad[i], K1[i], K2[i])
print(np.shape(BT[10]))
print(np.shape(BT[11]))
#stack = np.concatenate([[lat_im], [lon_im], [refl[1]], [refl[2]], [refl[3]], [refl[4]], [refl[5]], [refl[6]], [refl[7]], [refl[8]], [refl[9]], [BT[10]], [BT[11]]])
stack = np.concatenate([[lat_im], [lon_im], [refl_corr[1]], [refl_corr[2]],
[refl_corr[3]], [refl_corr[4]], [refl_corr[5]],
[refl_corr[6]], [refl_corr[7]], [data[9]],
[BT[10]], [BT[11]]])
print(np.shape(stack))
#current #Hartlepool #Dungeness #Hinckley #Hunterston-needs fix #Torness #Sizewell #Heysham
center_lat = 55.969752 #54.635 #50.913889 #51.209 #55.726366-needs fix #55.969752 #52.213461 #54.028889
center_lon = -2.397156 #-1.180833 #0.963889 #-3.127 #-4.898619-needs fix #-2.397156 #1.625419 #-2.916111
dist_deg = 0.1
dist_deg_smaller = 0.01
spacing = 0.0005
common_grid, min_lon, min_lat, spacing = p.creategrid(
center_lat, center_lon, dist_deg, spacing)
#common_grid_smaller, min_lon, min_lat, spacing = p.creategrid(center_lat, center_lon , dist_deg_smaller, spacing)
print("common_grid shape", common_grid.shape)
print(common_grid)
#pdb.set_trace()
data_regridded, count, distance = r.regridding(stack, min_lon, min_lat,
spacing, common_grid.shape)
#create a netcdf file
print('data regridded shape', data_regridded.shape)
name = sys.argv[4] #'dungeness_LC08_L1TP_201025_20150418_20170409_01_T1'
conv_nc.create_netcdf(name, data_regridded, common_grid[0, :, :],
common_grid[1, :, :])
"""