from sba.plotting import plot_spectra, map_data
from sba.io import read, write_data, find_auxiliary_information_seabass
from sba.data_processing import remove_negative_R_rs, remove_rows_based_on_threshold, convert_to_unit, add_Lw_from_Ed_Rrs
folder = Path("data/SMF/ASD/")
files = list(folder.glob("*ASD*"))
tabs = []
for file in files:
wavelengths, Es, Rrs = np.loadtxt(file,
delimiter="\t",
skiprows=50,
unpack=True,
usecols=[0, 1, 3])
date, time, lon, lat = find_auxiliary_information_seabass(file)
cols = ["Date", "Time", "Latitude", "Longitude"] + [
f"Ed_{wvl:.0f}" for wvl in wavelengths
] + [f"R_rs_{wvl:.0f}" for wvl in wavelengths]
dtype = [int, "S8", float, float] + 2 * [float for wvl in wavelengths]
tab = table.Table(rows=[[date, time, lat, lon, *Es, *Rrs]],
names=cols,
dtype=dtype)
tabs.append(tab)
data = table.vstack(tabs)
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)
from astropy import table
from astropy import units as u
from pathlib import Path
from sba.plotting import plot_spectra, map_data
from sba.io import read, write_data, find_auxiliary_information_seabass
from sba.data_processing import get_keys_with_label, split_spectrum, remove_rows_based_on_threshold
data = read("data/TAOM/ep1_hr3.avg.prod_1_1001.ftp", data_start=30)
header = read("data/TAOM/ep1_hr3.avg.prod_1_1001.ftp", data_start=27, data_end=28)
header["col1"][0] = "year"
header = header[0].as_void()
for key, new_key in zip(data.keys(), header):
data.rename_column(key, new_key)
date, time, lon, lat = find_auxiliary_information_seabass("data/TAOM/ep1_hr3.avg.prod_1_1001.ftp")
data.add_column(table.Column(name="Latitude", data=[lat]*len(data)))
data.add_column(table.Column(name="Longitude", data=[lon]*len(data)))
data.remove_columns(get_keys_with_label(data, "Lwn"))
Lw_keys, R_rs_keys = get_keys_with_label(data, "Lw", "Rrs")
for Lw_k, R_rs_k in zip(Lw_keys, R_rs_keys):
wavelength = float(Lw_k[2:])
data[Lw_k].unit = u.microwatt / (u.cm**2 * u.nm * u.steradian)
data[Lw_k] = data[Lw_k].to(u.watt / (u.m**2 * u.nm * u.steradian))
data.rename_column(Lw_k, f"Lw_{wavelength:.4f}")
data[R_rs_k].unit = 1 / u.steradian
remove_indices = np.where((time_table < time_start)
| (time_table > time_start + 3))[0]
data_table.remove_rows(remove_indices)
# If no data are available, go to the next file
if len(data_table) == 0:
continue
# Calculate median values and replace table with only these
means = [np.median(data_table[key]) for key in data_table.keys()[3:]]
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))
