def get_tile_csv_filename(self):
acq_min = workflow.format_date(self.acq_min)
acq_max = workflow.format_date(self.acq_max)
# TODO other distinguishing characteristics (e.g. dataset types)
return os.path.join(
self.output_directory,
"tiles_{satellites}_{x_min:03d}_{x_max:03d}_{y_min:04d}_{y_max:04d}_{acq_min}_{acq_max}.csv".format(
satellites=get_satellite_string(self.satellites),
x_min=self.x, x_max=self.x, y_min=self.y, y_max=self.y,
acq_min=acq_min, acq_max=acq_max
))
def get_tile_csv_filename(self):
acq_min = workflow.format_date(self.acq_min)
acq_max = workflow.format_date(self.acq_max)
# TODO other distinguishing characteristics (e.g. dataset types)
return os.path.join(
self.output_directory,
"tiles_{satellites}_{x_min:03d}_{x_max:03d}_{y_min:04d}_{y_max:04d}_{acq_min}_{acq_max}.csv".format(
satellites=get_satellite_string(self.satellites),
x_min=self.x, x_max=self.x, y_min=self.y, y_max=self.y,
acq_min=acq_min, acq_max=acq_max
))
def output(self):
from datacube.api.workflow import format_date
from datacube.api.utils import get_satellite_string
acq_min = format_date(self.acq_min)
acq_max = format_date(self.acq_max)
filename = "{satellites}_OUTPUT_{x:03d}_{y:04d}_{acq_min}_{acq_max}.tif".format(
satellites=get_satellite_string(self.satellites), x=self.x, y=self.y, acq_min=acq_min, acq_max=acq_max
)
filename = os.path.join(self.output_directory, filename)
return luigi.LocalTarget(filename)
def get_statistic_filename(self, statistic, ulx, uly, lrx, lry):
from datacube.api.utils import get_satellite_string
from datacube.api.workflow import format_date
# if statistic in [Statistic.PERCENTILE_25, Statistic.PERCENTILE_50, Statistic.PERCENTILE_75,
# Statistic.PERCENTILE_90, Statistic.PERCENTILE_95]:
# statistic_name = "PERCENTILE"
# else:
# statistic_name = statistic.name
statistic_name = statistic.name
filename = "{satellites}_WETNESS_{statistic}_{x:03d}_{y:04d}_{acq_min}_{acq_max}_{ulx:04d}_{uly:04d}_{lrx:04d}_{lry:04d}.npy".format(
satellites=get_satellite_string(self.satellites), statistic=statistic_name,
x=self.x, y=self.y, acq_min=format_date(self.acq_min), acq_max=format_date(self.acq_max),
ulx=ulx, uly=uly, lrx=lrx, lry=lry)
return os.path.join(self.output_directory, filename)
def get_dataset_filename(self, dataset):
from datacube.api.workflow import format_date
from datacube.api.utils import get_satellite_string
satellites = get_satellite_string(self.satellites)
acq_min = format_date(self.acq_min)
acq_max = format_date(self.acq_max)
return os.path.join(self.output_directory,
"{satellites}_{dataset}_{x:03d}_{y:04d}_{acq_min}_{acq_max}.tif".format(
satellites=satellites,
dataset=dataset,
x=self.x, y=self.y,
acq_min=acq_min,
acq_max=acq_max))
def output(self):
from datacube.api.workflow import format_date
from datacube.api.utils import get_satellite_string
acq_min = format_date(self.acq_min)
acq_max = format_date(self.acq_max)
filename = "{satellites}_OUTPUT_{x:03d}_{y:04d}_{acq_min}_{acq_max}_{ulx:04d}_{uly:04d}_{lrx:04d}_{lry:04d}.tif".format(
satellites=get_satellite_string(self.satellites), x=self.x, y=self.y, acq_min=acq_min, acq_max=acq_max,
ulx=self.x_offset, uly=self.y_offset,
lrx=(self.x_offset + self.chunk_size_x),
lry=(self.y_offset + self.chunk_size_y)
)
filename = os.path.join(self.output_directory, filename)
return luigi.LocalTarget(filename)
def run(self):
tiles = self.get_tiles()
transform = (self.x, 0.00025, 0.0, self.y+1, 0.0, -0.00025)
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
projection = srs.ExportToWkt()
driver = gdal.GetDriverByName("GTiff")
assert driver
# TODO
raster = driver.Create(self.output().path, 4000, 4000, len(tiles), gdal.GDT_Float32, options=["BIGTIFF=YES", "INTERLEAVE=BAND"])
assert raster
# TODO
raster.SetGeoTransform(transform)
raster.SetProjection(projection)
raster.SetMetadata(self.generate_raster_metadata())
for index, tile in enumerate(tiles, start=1):
# The Tassel Cap dataset is a virtual dataset derived from the NBAR so it's path is actually the NBAR path
filename = tile.datasets[DatasetType.TCI].path
filename = map_filename_nbar_to_wetness(filename)
filename = os.path.join(self.output_directory, filename)
print "+++", filename
log_mem("Before get data")
data = read_dataset_data(filename, bands=[TciBands.WETNESS])
log_mem("After get data")
band = raster.GetRasterBand(index)
band.SetDescription(os.path.basename(filename))
band.SetNoDataValue(numpy.nan)
band.WriteArray(data)
band.ComputeStatistics(True)
band.SetMetadata({"ACQ_DATE": format_date(tile.end_datetime), "SATELLITE": tile.datasets[DatasetType.TCI].satellite.name})
band.FlushCache()
del band
raster.FlushCache()
del raster
