import numpy as np
oli = load_OLI()
def ylim(axs):
axs[0].set_ylim(-120, 120)
axs[0].set_yticks([-100, -50, 0, 50, 100])
axs[1].set_ylim(-1.5, 0.4)
axs[1].set_yticks([-1, -0.5, 0])
data_files = Path("data").glob("*processed.tab")
labels, wavelengths, Eds, Lws, R_rss = zip(
*[load_data_file(file) for file in data_files])
def get_differences(band):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
RrsR = [
band.convolve(wavelengths_data, R_rs)
for wavelengths_data, R_rs in zip(wavelengths, R_rss)
]
RrsL = [
band.convolve(wavelengths_data, Lw) /
band.convolve(wavelengths_data, Ed)
for wavelengths_data, Ed, Lw in zip(wavelengths, Eds, Lws)
]
"""
Generate boxcar and gaussian spectral response functions
"""
from sba.bandaveraging import calculate_differences
from sba.io import load_data_file
from sba.response_curves import load_all_sensors
from pathlib import Path
sensors = load_all_sensors()
data_files = Path("data").glob("*processed.tab")
for file in data_files:
label, wavelengths_data, Ed, Lw, R_rs = load_data_file(file)
for sensor in sensors:
reflectance_space = sensor.band_average(wavelengths_data, R_rs)
radiance_space = sensor.band_average(
wavelengths_data, Lw) / sensor.band_average(wavelengths_data, Ed)
difference_absolute, difference_relative = calculate_differences(
reflectance_space, radiance_space)
sensor.boxplot_absolute(difference_absolute, data_label=label)
sensor.boxplot_relative(difference_relative, data_label=label)