def __init__(self, dsm_path=None, terrain_buffer=0):
self.dsm_path = dsm_path
self.terrain_buffer = terrain_buffer
self.output_measurements = {m['name']: Measurement(**m) for m in WOFS_OUTPUT}
if dsm_path is None:
# TODO: This should be recorded in the dataset metadata, but we haven't allowed for this
# in Transformation classes.
_LOG.warning('WARNING: Path or URL to a DSM is not set. Terrain shadow mask will not be calculated.')
def measurements(self, input_measurements):
return {
'classification':
Measurement(name='classification',
dtype='uint8',
nodata=0,
units='1')
}
def measurements(self, input_measurements):
return {
'vegetat_veg_cat':
Measurement(name='vegetat_veg_cat',
dtype='float32',
nodata=float('nan'),
units='1')
}
def __init__(self, dsm_path=None, terrain_buffer=0, c2=False):
self.dsm_path = dsm_path
self.terrain_buffer = terrain_buffer
self.c2 = c2
self.output_measurements = {
m['name']: Measurement(**m)
for m in WOFS_OUTPUT
}
if dsm_path is None:
_LOG.warning(
'WARNING: Path or URL to a DSM is not set. Terrain shadow mask will not be calculated.'
)
def __init__(self, median_path=None, indstr=None, n_workers=2):
# directory or location where the long term mean of indices locate
self.median_path = median_path
# Patterns of indices file,.e.g '56HLH_{}_2015-01-01_2020-12-31-cog.tif'
self.indstr = indstr
# number of workers of Dask client
self.n_workers = n_workers
self.output_measurements = {
m['name']: Measurement(**m)
for m in NMASK_OUTPUT
}
def measurements(self, input_measurements):
return {'cultman_agr_cat': Measurement(name='cultman_agr_cat', dtype='float32', nodata=float('nan'), units='1')}