def test_plot():
pathlist = glob.glob(
os.path.join('tests', 'data', 'datastore', 'BTCN', '*'))
sm = SiteMeasurements.from_pathlist(
pathlist)[dt.datetime(2018, 5, 28):dt.datetime(2018, 5, 29)]
srfs = [Sentinel2Blue(), Sentinel2Green()]
# make sure all below modes don't fail:
for with_errors in [False, True]:
for types in ['toa', 'sr', ['toa', 'sr']]:
plot(types, sm, srfs, with_errors=with_errors, show=False)
# some spectral plots
if (0):
import numpy as np
import matplotlib.pyplot as plt
from calval.satellites.srf import Sentinel2Green, Sentinel2Red, Landsat8Blue, NewsatBlue, NewsatRed
from pyspectral.solar import (SolarIrradianceSpectrum,
TOTAL_IRRADIANCE_SPECTRUM_2000ASTM)
srr = SolarIrradianceSpectrum(TOTAL_IRRADIANCE_SPECTRUM_2000ASTM,
dlambda=0.0005)
srr.interpolate(ival_wavelength=(0.200, 2.000))
print(srr.units)
print(srr.ipol_wavelength, srr.ipol_irradiance)
plt.figure()
plt.plot(srr.ipol_wavelength, srr.ipol_irradiance, 'k-.')
srf = Sentinel2Green()
x = srr.ipol_wavelength * 1000
vals = srf(x) * srr.ipol_irradiance
avg = np.dot(srf(x), srr.ipol_irradiance) / np.sum(srf(x))
plt.plot(srr.ipol_wavelength, vals, 'g-')
plt.plot([srf.start / 1000, srf.end / 1000], [avg, avg], 'g--')
srf = Sentinel2Red()
x = srr.ipol_wavelength * 1000
vals = srf(x) * srr.ipol_irradiance
avg = np.dot(srf(x), srr.ipol_irradiance) / np.sum(srf(x))
plt.plot(srr.ipol_wavelength, vals, 'r-')
plt.plot([srf.start / 1000, srf.end / 1000], [avg, avg], 'r--')
srf = Landsat8Blue()
x = srr.ipol_wavelength * 1000
def __init__(self):
super().__init__([
Sentinel2Blue(), Sentinel2Green(), Sentinel2Red()
])
def test_plot():
# checks plot_srf doesn't fail
srfs = [Sentinel2Red(), Sentinel2Green(), Sentinel2Blue()]
plot_srfs(srfs, show=False, normalize_max=True)
def test_sentinel():
# comparing central wavelength to https://en.wikipedia.org/wiki/Sentinel-2
assert Sentinel2Blue().center == pytest.approx(490, abs=10)
assert Sentinel2Green().center == pytest.approx(560, abs=.10)
assert Sentinel2Red().center == pytest.approx(665, abs=10)
assert Sentinel2Nir().center == pytest.approx(833, abs=10)
def test_as_dataframe():
srf = Sentinel2Green()
df = srf.as_dataframe()
assert df['response'][srf.start] == srf(srf.start)
class SentinelSceneData(SceneData):
band_ex_irradiance = {
_band_aliases['blue']: srf_exatmospheric_irradiance(Sentinel2Blue()),
_band_aliases['green']: srf_exatmospheric_irradiance(Sentinel2Green()),
_band_aliases['red']: srf_exatmospheric_irradiance(Sentinel2Red()),
_band_aliases['nir']: srf_exatmospheric_irradiance(Sentinel2Nir())
}
def __init__(self, sceneinfo, path=None):
super().__init__(sceneinfo, path)
self.granule = self.sceneinfo._get_granule()
self._read_product_metadata()
self._read_l1_metadata()
def _granule_mtd_path(self):
return os.path.join(self.path, 'GRANULE', self.granule, 'MTD_TL.xml')
def _product_mtd_path(self):
return os.path.join(self.path,
'MTD_{}.xml'.format(self.sceneinfo._data.product))
def _read_product_metadata(self):
self.product_meta = metadata = parse_xml_metadata(
self._product_mtd_path())
data = metadata['General_Info']['Product_Image_Characteristics'][
'Reflectance_Conversion']
self.esuns = [
data['Solar_Irradiance_List']['SOLAR_IRRADIANCE_{}'.format(
_band_id(_band_aliases[band]))] for band in band_names
]
self.sun_distance = np.sqrt(
1.0 / data['U']) # assuming U is `d(t)` of the TG
data = metadata['Geometric_Info']['Product_Footprint'][
'Product_Footprint']
coords = data['Global_Footprint']['EXT_POS_LIST']
self.corners = [(coords[i + 1], coords[i]) for i in range(0, 8, 2)]
self.center = tuple(np.average(self.corners, axis=0))
self.cloud_coverage = metadata['Quality_Indicators_Info'][
'Cloud_Coverage_Assessment']
def _read_l1_metadata(self):
self.metadata = metadata = parse_tile_metadata(
self._granule_mtd_path())
self.timestamp = metadata['General_Info']['SENSING_TIME']
def _view_angle(bandid):
mean_view_list = metadata['Geometric_Info']['Tile_Angles'][
'Mean_Viewing_Incidence_Angle_List']
return mean_view_list['Mean_Viewing_Incidence_Angle_{}'.format(
bandid)]
assert _view_angle(_band_id(
_band_aliases['green']))['AZIMUTH_ANGLE_unit'] == 'deg'
# NOTE: Seem to be large differences between viewing angles of the channels
# TODO: make per-channel calcs!
angles = [
_view_angle(_band_id(_band_aliases[band]))['AZIMUTH_ANGLE']
for band in band_names
]
view_average_azimuth = np.average(angles)
angles = [
_view_angle(_band_id(_band_aliases[band]))['ZENITH_ANGLE']
for band in band_names
]
view_average_zenith = np.average(angles)
self.sat_average_angle = IncidenceAngle(view_average_azimuth,
90 - view_average_zenith)
sun_azimuth = metadata['Geometric_Info']['Tile_Angles'][
'Mean_Sun_Angle']['AZIMUTH_ANGLE']
sun_zenith = metadata['Geometric_Info']['Tile_Angles'][
'Mean_Sun_Angle']['ZENITH_ANGLE']
self.sun_average_angle = IncidenceAngle(sun_azimuth, 90 - sun_zenith)
def get_metadata(self):
meta = {
'center': self.center,
'esuns': self.esuns,
'sun_distance': self.sun_distance, # based on `U` from metadata
'sat_average_angle': self.sat_average_angle.to_dict(),
'sun_average_angle': self.sun_average_angle.to_dict(),
'cloud_cover': self.cloud_coverage
}
return meta
def get_scale(self, band, product):
""" TODO: compute from metadata """
return _scale_values[product]