data = read("data/SABOR/sabor_HyperPro_2014.txt", data_start=35)
header = read("data/SABOR/sabor_HyperPro_2014.txt", data_start=32, data_end=33)
header["col1"][0] = "date"
header = header[0].as_void()
for key, new_key in zip(data.keys(), header):
data.rename_column(key, new_key)
data.remove_columns(get_keys_with_label(data, "sd"))
data.remove_columns(get_keys_with_label(data, "Lu"))
data.rename_column("lat", "Latitude")
data.rename_column("lon", "Longitude")
rename_columns(data, "Ed", "Ed_")
rename_columns(data, "Rrs", "R_rs_")
convert_to_unit(data, "Ed", u.microwatt / (u.centimeter**2 * u.nanometer),
u.watt / (u.meter**2 * u.nanometer))
convert_to_unit(data, "R_rs", 1 / u.steradian)
data = add_Lw_from_Ed_Rrs(data)
map_data(data,
data_label="SABOR-H",
projection="gnom",
lat_0=37,
lon_0=-70,
llcrnrlon=-77,
urcrnrlon=-64,
files = list(folder.glob("Tara_HyperPro*.txt"))
data = table.vstack([read(file, data_start=35) for file in files])
header = read(files[0], data_start=32, data_end=33)
header["col1"][0] = "year"
header = header[0].as_void()
for key, new_key in zip(data.keys(), header):
data.rename_column(key, new_key)
data.remove_columns(get_keys_with_label(data, "LU"))
data.rename_column("lat", "Latitude")
data.rename_column("lon", "Longitude")
rename_columns(data, "ES", "Ed_", exclude="None")
rename_columns(data, "Rrs", "R_rs_", exclude="None")
convert_to_unit(data, "Ed", u.microwatt / (u.centimeter**2 * u.nanometer),
u.watt / (u.meter**2 * u.nanometer))
convert_to_unit(data, "R_rs", 1 / u.steradian)
data = add_Lw_from_Ed_Rrs(data)
map_data(data, data_label="TaraO", lon_0=0, resolution="i")
plot_spectra(data, data_label="TaraO", alpha=0.1)
write_data(data, label="TaraO")
from astropy import table
from astropy import units as u
from sba.plotting import plot_spectra, map_data
from sba.io import read, write_data
from sba.data_processing import split_spectrum, get_keys_with_label, remove_negative_R_rs, convert_to_unit, rename_columns
Ed = read("data/SOP4/SO-P4_irrad.tab", data_start=142, header_start=141)
Lu = read("data/SOP4/SO-P4_rad_up_40deg.tab", data_start=142, header_start=141)
Ls = read("data/SOP4/SO-P4_sky_rad_40deg.tab",
data_start=142,
header_start=141)
data = table.join(Ed, Lu, keys=["Date/Time"])
data = table.join(data, Ls, keys=["Date/Time"])
rename_columns(data, "Ed", "Ed", strip=True)
rename_columns(data, "Lu", "Lu", strip=True)
rename_columns(data, "Ls", "Ls", strip=True)
convert_to_unit(data, "Ed", u.watt / (u.meter**2 * u.nanometer))
convert_to_unit(data, "Lu",
u.microwatt / (u.centimeter**2 * u.nanometer * u.steradian),
u.watt / (u.meter**2 * u.nanometer * u.steradian))
convert_to_unit(data, "Ls", u.watt / (u.meter**2 * u.nanometer * u.steradian))
Ed_keys, Lu_keys, Ls_keys = get_keys_with_label(data, "Ed", "Lu", "Ls")
for Ed_k, Lu_k, Ls_k in zip(Ed_keys, Lu_keys, Ls_keys):
Lw_k = Lu_k.replace("Lu", "Lw")
Lw = data[Lu_k] - 0.028 * data[Ls_k]
Lw.name = Lw_k
data.add_column(Lw)
colnames_rrs = [f"R_rs_{wvl:.1f}" for wvl in wavelengths]
colnames_rrs2 = [f"R_rs_err_{wvl:.1f}" for wvl in wavelengths]
colnames = colnames + colnames_rrs + colnames_rrs2
for j, newname in enumerate(colnames, 1):
Rrs.rename_column(f"col{j}", newname)
Rrs.remove_columns(colnames_rrs2)
Rrs.remove_columns([
'Ratio (drho/rho, 750nm)', 'Ratio (dLsky/Lsky, 750nm)',
'Ratio (dLw/Lw, 750nm)', 'Ratio (dEd/Ed, 750nm)',
'Ratio (d(Lsk/Ed)/(Lsk/Ed), 750nm)'
])
data = table.join(Ed, Rrs, keys=["ID"])
rename_columns(data, "Ed [mW/m**2/nm] (", "Ed_", strip=True)
convert_to_unit(data, "Ed", u.milliwatt / (u.meter**2 * u.nanometer),
u.watt / (u.meter**2 * u.nanometer))
convert_to_unit(data, "R_rs", 1 / u.steradian)
R_rs_keys = get_keys_with_label(data, "R_rs")
for R_rs_k in zip(R_rs_keys):
# Convert R_w to R_rs
data[R_rs_k] = data[R_rs_k] / np.pi
data = add_Lw_from_Ed_Rrs(data)
remove_negative_R_rs(data)
map_data(data,
import numpy as np
from astropy import table
from astropy import units as u
from sba.plotting import plot_spectra, map_data
from sba.io import read, write_data
from sba.data_processing import convert_to_unit, rename_columns, add_Lw_from_Ed_Rrs
Ed = read("data/HE302/HE302_irrad.tab", data_start=186, header_start=185)
Rrs = read("data/HE302/HE302_rrs.tab", data_start=186, header_start=185)
data = table.join(Ed, Rrs, keys=["Event"])
rename_columns(data, "Ed", "Ed", strip=True)
rename_columns(data, "Rrs", "R_rs", strip=True)
convert_to_unit(data, "Ed", u.watt / (u.meter**2 * u.nanometer))
convert_to_unit(data, "R_rs", 1 / u.steradian)
data = add_Lw_from_Ed_Rrs(data)
remove_indices = [i for i, row in enumerate(data) if row["R_rs_800"] >= 0.003]
data.remove_rows(remove_indices)
print(f"Removed {len(remove_indices)} rows with values of R_rs(800 nm) >= 0.003")
for key in ["Date/Time", "Latitude", "Longitude", "Altitude [m]"]:
data.rename_column(f"{key}_1", key)
data.remove_column(f"{key}_2")
map_data(data, data_label="HE302", projection='gnom', lat_0=55, lon_0=0, llcrnrlon=-10, urcrnrlon=11, llcrnrlat=50.5, urcrnrlat=59.5, resolution="h", parallels=np.arange(40, 70, 2), meridians=np.arange(-20, 20, 2))
plot_spectra(data, data_label="HE302", alpha=0.15)
mean_data = [*data_table[0]["year", "jd"], row["time_GMT"], *means]
data_table.add_row(mean_data)
data_table.remove_rows(np.arange(len(data_table) - 1))
# Finally, load lat/lon
*_, lon, lat = find_auxiliary_information_seabass(data_files[0])
data_table.add_column(table.Column(name="Longitude", data=[lon]))
data_table.add_column(table.Column(name="Latitude", data=[lat]))
data.append(data_table)
print(j, row["Station"])
data = table.vstack(data)
rename_columns("Es", "Ed_")
rename_columns("Lsky", "Ls_")
rename_columns("Lt", "Lt_")
convert_to_unit(data, "Ed", u.microwatt / (u.centimeter**2 * u.nanometer),
u.watt / (u.meter**2 * u.nanometer))
convert_to_unit(data, "Ls",
u.microwatt / (u.centimeter**2 * u.nanometer * u.steradian),
u.watt / (u.meter**2 * u.nanometer * u.steradian))
convert_to_unit(data, "Lt",
u.microwatt / (u.centimeter**2 * u.nanometer * u.steradian),
u.watt / (u.meter**2 * u.nanometer * u.steradian))
Ed_keys, Ls_keys, Lt_keys = get_keys_with_label(data, "Ed", "Ls", "Lt")
for Ed_k, Ls_k, Lt_k in zip(Ed_keys, Ls_keys, Lt_keys):
wavelength = int(Ed_k[3:])
wavelengths = np.arange(350, 1301, 1)
Ld = read("data/SeaSWIR/SeaSWIR_ASD_Ldspec.tab", data_start=974, header_start=973)
Ldkeys = get_keys_with_label(Ld, "Ld")
for Ldkey, wvl in zip(Ldkeys, wavelengths):
# multiply by pi to convert L to E (Mobley99)
# divide by 1e5 for normalisation to W/m^2/nm (empirical)
Ed = Ld[Ldkey] * np.pi / 1e5
Ed.name = f"Ed_{wvl}"
Ld.add_column(Ed)
Ld.remove_column(Ldkey)
Ed = Ld
# Units of Ld are not provided
Rrs = read("data/SeaSWIR/SeaSWIR_ASD_Rw.tab", data_start=974, header_start=973)
rename_columns(Rrs, "Refl (", "R_rs_", strip=True)
R_rs_keys = get_keys_with_label(Rrs, "R_rs")
for R_rs_k in R_rs_keys:
# Convert R_w to R_rs
Rrs[R_rs_k] = Rrs[R_rs_k] / np.pi
data = table.join(Ed, Rrs, keys=["Station"])
convert_to_unit(data, "Ed", u.watt / (u.meter**2 * u.nanometer))
convert_to_unit(data, "R_rs", 1 / u.steradian)
data = add_Lw_from_Ed_Rrs(data)
for key in data.keys():
if key[-2:] == "_1":
data.rename_column(key, key[:-2])
