def point_tabulation(self):
from reame.utils import tabulate_data_points
from reame.sensors import VAR
try:
return self._point_tabulation
except AttributeError:
variables = VAR[self.algorithm]
prod = self.cis_product()
data = prod.create_data_object(variables)
points = data.get_non_masked_points()
self._point_tabulation = tabulate_data_points(points, self.grid)
return self._point_tabulation
variables = reame.sensors.VAR[args.algorithm]
for i, fname in enumerate(args.files):
here_name = out_name.format(i)
if os.path.isfile(here_name) and not args.clobber:
print("Output already generated: " + here_name)
continue
# Fetch appropriate class
sensor = getattr(reame.sensors, args.algorithm)([fname], **kwargs)
try:
data = sensor.create_bounded_data_object(variables)
except NotImplementedError:
continue
points = data.get_non_masked_bounded_points()
hist, max_aod, mean_aod, mean_log = tabulate_data_points(points, grid)
# Save results
save = dict(files=[fname],
hist=hist,
max_aod=max_aod,
l=variables,
x=grid.lat_bins,
y=grid.lon_bins,
z=grid.aod_bins)
mean_aod.save(save)
mean_log.save(save)
np.savez_compressed(here_name, **save)
print(fname)
if args.mod03_path:
kwargs["mod03_path"] = args.mod03_path
if args.grid_path:
kwargs["grid_path"] = args.grid_path
variables = reame.sensors.VAR[args.algorithm]
for fname in args.files:
# Fetch appropriate class
sensor = getattr(reame.sensors, args.algorithm)([fname], **kwargs)
try:
data = sensor.create_bounded_data_object(variables)
except NotImplementedError:
continue
points = data.get_non_masked_bounded_points()
outputs = tabulate_data_points(points, grid)
hist += outputs[0]
max_aod = np.maximum(max_aod, outputs[1])
mean_aod += outputs[2]
mean_log += outputs[3]
# Save results
save = dict(files=args.files,
hist=hist,
max_aod=max_aod,
l=variables,
x=grid.lat_bins,
y=grid.lon_bins,
z=grid.aod_bins)
mean_aod.save(save)