def create_tasks(args):
x_list = range(args.x_min, args.x_max + 1)
y_list = range(args.y_min, args.y_max + 1)
dataset_types = [args.dataset_type]
if args.mask_pqa_apply:
dataset_types.append(DatasetType.PQ25)
from itertools import product
if args.file_per_statistic:
for (season, band, statistic) in product(args.get_seasons(), args.bands, args.statistics):
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(args.acq_min, args.acq_max, season,
seasons=SEASONS,
extend=True)
for cell in list_cells_as_list(x=x_list, y=y_list, satellites=args.satellites,
acq_min=acq_min_extended, acq_max=acq_max_extended,
dataset_types=dataset_types, include=criteria):
yield Arg25EpochStatisticsTask(x=cell.x, y=cell.y,
acq_min=acq_min_extended, acq_max=acq_max_extended,
season=season,
epochs = list(args.get_epochs()),
satellites=args.satellites,
dataset_type=args.dataset_type,
band=band,
bands=args.bands,
mask_pqa_apply=args.mask_pqa_apply, tidal_workflow=args.tidal_workflow,
tidal_ifile=args.tidal_ifile,
mask_pqa_mask=args.mask_pqa_mask,
x_chunk_size=args.x_chunk_size, y_chunk_size=args.y_chunk_size,
statistic = statistic,
statistics=args.statistics, interpolation=args.interpolation,
output_directory=args.output_directory)
return
for (acq_min, acq_max), season in product(args.get_epochs(), args.get_seasons()):
_log.debug("acq_min=[%s] acq_max=[%s] season=[%s]", acq_min, acq_max, season.name)
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(acq_min, acq_max, season,
seasons=SEASONS,
extend=True)
_log.debug("\tacq_min_extended=[%s], acq_max_extended=[%s], criteria=[%s]", acq_min_extended, acq_max_extended, criteria)
for cell in list_cells_as_list(x=x_list, y=y_list, satellites=args.satellites,
acq_min=acq_min_extended, acq_max=acq_max_extended,
dataset_types=dataset_types, include=criteria):
_log.debug("\t%3d %4d", cell.x, cell.y)
#yield args.create_task(x=cell.x, y=cell.y, acq_min=acq_min, acq_max=acq_max, season=season)
_log.debug("Creating task for %s %s %s %s %s", cell.x, cell.y, acq_min, acq_max, season)
yield Arg25BandStatisticsTask(x=cell.x, y=cell.y,
acq_min=acq_min_extended, acq_max=acq_max_extended, season=season,
satellites=args.satellites,
dataset_type=args.dataset_type, bands=args.bands,
mask_pqa_apply=args.mask_pqa_apply, mask_pqa_mask=args.mask_pqa_mask,
x_chunk_size=args.x_chunk_size, y_chunk_size=args.y_chunk_size,
statistics=args.statistics, interpolation=args.interpolation,
output_directory=args.output_directory)
def create_tasks(args):
x_list = range(args.x_min, args.x_max + 1)
y_list = range(args.y_min, args.y_max + 1)
dataset_types = [args.dataset_type]
if args.mask_pqa_apply:
dataset_types.append(DatasetType.PQ25)
from itertools import product
if args.file_per_statistic:
for (season, band, statistic) in product(args.get_seasons(), args.bands, args.statistics):
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(args.acq_min, args.acq_max, season,
seasons=SEASONS,
extend=True)
for cell in list_cells_as_list(x=x_list, y=y_list, satellites=args.satellites,
acq_min=acq_min_extended, acq_max=acq_max_extended,
dataset_types=dataset_types, include=criteria):
yield Arg25EpochStatisticsTask(x=cell.x, y=cell.y,
acq_min=acq_min_extended, acq_max=acq_max_extended,
season=season,
epochs = list(args.get_epochs()),
satellites=args.satellites,
dataset_type=args.dataset_type,
band=band,
bands=args.bands,
mask_pqa_apply=args.mask_pqa_apply, mask_pqa_mask=args.mask_pqa_mask,
x_chunk_size=args.x_chunk_size, y_chunk_size=args.y_chunk_size,
statistic = statistic,
statistics=args.statistics, interpolation=args.interpolation,
output_directory=args.output_directory)
return
for (acq_min, acq_max), season in product(args.get_epochs(), args.get_seasons()):
_log.debug("acq_min=[%s] acq_max=[%s] season=[%s]", acq_min, acq_max, season.name)
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(acq_min, acq_max, season,
seasons=SEASONS,
extend=True)
_log.debug("\tacq_min_extended=[%s], acq_max_extended=[%s], criteria=[%s]", acq_min_extended, acq_max_extended, criteria)
for cell in list_cells_as_list(x=x_list, y=y_list, satellites=args.satellites,
acq_min=acq_min_extended, acq_max=acq_max_extended,
dataset_types=dataset_types, include=criteria):
_log.debug("\t%3d %4d", cell.x, cell.y)
#yield args.create_task(x=cell.x, y=cell.y, acq_min=acq_min, acq_max=acq_max, season=season)
_log.debug("Creating task for %s %s %s %s %s", cell.x, cell.y, acq_min, acq_max, season)
yield Arg25BandStatisticsTask(x=cell.x, y=cell.y,
acq_min=acq_min_extended, acq_max=acq_max_extended, season=season,
satellites=args.satellites,
dataset_type=args.dataset_type, bands=args.bands,
mask_pqa_apply=args.mask_pqa_apply, mask_pqa_mask=args.mask_pqa_mask,
x_chunk_size=args.x_chunk_size, y_chunk_size=args.y_chunk_size,
statistics=args.statistics, interpolation=args.interpolation,
output_directory=args.output_directory)
def requires(self):
dataset_types = [self.dataset_type]
if self.mask_pqa_apply:
dataset_types.append(DatasetType.PQ25)
for (acq_min, acq_max) in self.epochs:
_log.debug("acq_min=[%s] acq_max=[%s] season=[%s]", acq_min, acq_max, self.season.name)
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(acq_min, acq_max, self.season,
seasons=SEASONS,
extend=True)
_log.debug("\tacq_min_extended=[%s], acq_max_extended=[%s], criteria=[%s]", acq_min_extended, acq_max_extended, criteria)
for cell in list_cells_as_list(x=[self.x], y=[self.y], satellites=self.satellites,
acq_min=acq_min_extended, acq_max=acq_max_extended,
dataset_types=dataset_types, include=criteria):
_log.debug("\t%3d %4d", cell.x, cell.y)
# yield args.create_task(x=cell.x, y=cell.y, acq_min=acq_min, acq_max=acq_max, season=season)
_log.debug("Creating task for %s %s %s %s %s", cell.x, cell.y, acq_min, acq_max, self.season)
yield Arg25BandStatisticsTask(x=cell.x, y=cell.y,
acq_min=acq_min_extended, acq_max=acq_max_extended, season=self.season,
satellites=args.satellites,
dataset_type=args.dataset_type, bands=args.bands,
mask_pqa_apply=args.mask_pqa_apply, tidal_workflow=args.tidal_workflow,
mask_pqa_mask=args.mask_pqa_mask,
x_chunk_size=args.x_chunk_size, y_chunk_size=args.y_chunk_size,
statistics=args.statistics, interpolation=args.interpolation,
output_directory=args.output_directory)
def requires(self):
dataset_types = [self.dataset_type]
if self.mask_pqa_apply:
dataset_types.append(DatasetType.PQ25)
for (acq_min, acq_max) in self.epochs:
_log.debug("acq_min=[%s] acq_max=[%s] season=[%s]", acq_min, acq_max, self.season.name)
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(acq_min, acq_max, self.season,
seasons=SEASONS,
extend=True)
_log.debug("\tacq_min_extended=[%s], acq_max_extended=[%s], criteria=[%s]", acq_min_extended, acq_max_extended, criteria)
for cell in list_cells_as_list(x=[self.x], y=[self.y], satellites=self.satellites,
acq_min=acq_min_extended, acq_max=acq_max_extended,
dataset_types=dataset_types, include=criteria):
_log.debug("\t%3d %4d", cell.x, cell.y)
# yield args.create_task(x=cell.x, y=cell.y, acq_min=acq_min, acq_max=acq_max, season=season)
_log.debug("Creating task for %s %s %s %s %s", cell.x, cell.y, acq_min, acq_max, self.season)
yield Arg25BandStatisticsTask(x=cell.x, y=cell.y,
acq_min=acq_min_extended, acq_max=acq_max_extended, season=self.season,
satellites=args.satellites,
dataset_type=args.dataset_type, bands=args.bands,
mask_pqa_apply=args.mask_pqa_apply, mask_pqa_mask=args.mask_pqa_mask,
x_chunk_size=args.x_chunk_size, y_chunk_size=args.y_chunk_size,
statistics=args.statistics, interpolation=args.interpolation,
output_directory=args.output_directory)
def test_list_cells_120_020_2005_ls578_summer(config=None):
filename = "cells_120_020_2005_ls578_summer.csv"
acq_min, acq_max, include = build_season_date_criteria(ACQ_MIN_2005, ACQ_MAX_2005, Season.SUMMER)
list_cells_to_file(x=[CELL_X], y=[CELL_Y],
acq_min=acq_min, acq_max=acq_max,
satellites=SATELLITE_LS578,
dataset_types=DATASET_TYPE_ARG25,
filename=filename,
include=include,
config=config)
assert filecmp.cmp(filename, get_test_data_path(filename))
def get_tiles(x, y, satellites, acq_min, acq_max, season, dataset_type, mask_pqa_apply):
acq_min, acq_max, criteria = build_season_date_criteria(acq_min, acq_max, season,
seasons=SEASONS, extend=True)
dataset_types = [dataset_type]
if mask_pqa_apply:
dataset_types.append(DatasetType.PQ25)
tiles = list_tiles_as_list(x=[x], y=[y], satellites=satellites,
acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, include=criteria)
return tiles
def test_list_tiles_120_020_2000_2010_ls578_summer_arg25_stats(config=None):
filename = "tiles_120_020_2000_2010_ls578_summer_arg25_stats.csv"
acq_min, acq_max, include = build_season_date_criteria(ACQ_MIN_2000, ACQ_MAX_2010, Season.SUMMER, seasons=SEASONS_ARG25_STATS)
list_tiles_to_file(x=[CELL_X], y=[CELL_Y],
acq_min=acq_min,
acq_max=acq_max,
satellites=SATELLITE_LS578,
dataset_types=DATASET_TYPE_ARG25_FC25_PQ25,
filename=filename,
include=include,
config=config)
assert filecmp.cmp(filename, get_test_data_path(filename))
def get_tiles(self):
acq_min, acq_max, criteria = build_season_date_criteria(self.acq_min, self.acq_max, self.season,
seasons=SEASONS, extend=True)
_log.info("\tcriteria is %s", criteria)
dataset_types = [self.dataset_type]
if self.mask_pqa_apply:
dataset_types.append(DatasetType.PQ25)
tiles = list_tiles_as_list(x=[self.x], y=[self.y], satellites=self.satellites,
acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, include=criteria)
return tiles
def get_tiles(self):
acq_min, acq_max, criteria = build_season_date_criteria(self.acq_min, self.acq_max, self.season,
seasons=SEASONS, extend=True)
_log.info("\tcriteria is %s", criteria)
dataset_types = [self.dataset_type]
if self.mask_pqa_apply:
dataset_types.append(DatasetType.PQ25)
tiles = list_tiles_as_list(x=[self.x], y=[self.y], satellites=self.satellites,
acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, include=criteria)
return tiles
def test_list_cells_120_020_2000_2010_ls578_summer_arg25_stats(config=None):
filename = "cells_120_020_2000_2010_ls578_wofs_summer_arg25_stats.csv"
acq_min = parse_date_min("2000")
acq_max = parse_date_max("2010")
acq_min, acq_max, include = build_season_date_criteria(acq_min, acq_max, Season.SUMMER, seasons=SEASONS_ARG25_STATS)
list_cells_to_file(x=[CELL_X], y=[CELL_Y],
acq_min=acq_min, acq_max=acq_max,
satellites=SATELLITE_LS578,
dataset_types=DATASET_TYPE_WOFS,
filename=filename,
include=include,
config=config)
assert filecmp.cmp(filename, get_test_data_path(filename))
def test_query():
workflow = Arg25BandStatisticsWorkflow()
workflow.x_min = workflow.x_max = TEST_X
workflow.y_min = workflow.y_max = TEST_Y
workflow.acq_min = parse_date_min("1985")
workflow.acq_max = parse_date_max("2014")
workflow.epoch = EpochParameter(5, 6)
workflow.seasons = Season
workflow.seasons = [Season.SUMMER]
workflow.satellites = [Satellite.LS5, Satellite.LS7]
workflow.mask_pqa_apply = True
workflow.mask_pqa_mask = [PqaMask.PQ_MASK_SATURATION, PqaMask.PQ_MASK_CONTIGUITY, PqaMask.PQ_MASK_CLOUD]
workflow.dataset_type = DatasetType.ARG25
workflow.bands = Ls57Arg25Bands
epochs = list(workflow.get_epochs())
print ""
print "epochs are", epochs
for season, epoch in product(workflow.seasons, epochs):
print season, epoch
from datacube.api.utils import build_season_date_criteria
acq_min, acq_max, criteria = build_season_date_criteria(epoch[0], epoch[1], season, seasons=SEASONS, extend=True)
print acq_min, acq_max, criteria
from datacube.api.query import list_tiles_as_list
tiles = list_tiles_as_list(x=[workflow.x_min], y=[workflow.y_min], satellites=workflow.satellites,
acq_min=acq_min, acq_max=acq_max,
dataset_types=[workflow.dataset_type], include=criteria)
print "Tiles found is ", len(tiles)
def run(self):
_log.info("*** Aggregating chunk NPY files into TIF")
ndv = get_dataset_type_ndv(self.dataset_type)
# TODO
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
# Create the output TIF
# TODO
gdal_type = gdal.GDT_Int16
if self.dataset_type == DatasetType.NDVI and self.statistic not in [
Statistic.COUNT, Statistic.COUNT_OBSERVED
]:
gdal_type = gdal.GDT_Float32
raster = driver.Create(
self.output().path,
4000,
4000,
len(self.epochs),
gdal_type,
options=["INTERLEAVE=BAND", "COMPRESS=LZW", "TILED=YES"])
assert raster
# TODO
raster.SetGeoTransform(transform)
raster.SetProjection(projection)
raster.SetMetadata(self.generate_raster_metadata())
from itertools import product
from datetime import date
for index, (acq_min, acq_max) in enumerate(self.epochs, start=1):
_log.info(
"Doing band [%s] statistic [%s] which is band number [%s]",
self.band.name, self.statistic.name, index)
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(
acq_min, acq_max, self.season, seasons=SEASONS, extend=True)
band = raster.GetRasterBand(index)
assert band
season = SEASONS[self.season]
acq_min_str = date(acq_min_extended.year, season[0][0].value,
season[0][1]).strftime("%Y%m%d")
acq_max_str = acq_max_extended.strftime("%Y%m%d")
# TODO
band.SetNoDataValue(ndv)
band.SetDescription("{band} {stat} {start}-{end}".format(
band=self.band.name,
stat=self.statistic.name,
start=acq_min_str,
end=acq_max_str))
for x_offset, y_offset in product(
range(0, 4000, self.x_chunk_size),
range(0, 4000, self.y_chunk_size)):
filename = self.get_statistic_filename(acq_min_extended,
acq_max_extended,
x_offset, y_offset)
_log.info("Processing chunk [%4d|%4d] for [%s] from [%s]",
x_offset, y_offset, self.statistic.name, filename)
# read the chunk
try:
data = numpy.load(filename)
except IOError:
_log.info("Failed to load chunk")
continue
_log.info("data is [%s]\n[%s]", numpy.shape(data), data)
_log.info("Writing it to (%d,%d)", x_offset, y_offset)
# write the chunk to the TIF at the offset
band.WriteArray(data, x_offset, y_offset)
band.FlushCache()
band.ComputeStatistics(True)
band.FlushCache()
del band
raster.FlushCache()
del raster
def run(self):
_log.info("*** Aggregating chunk NPY files into TIF")
ndv = get_dataset_type_ndv(self.dataset_type)
# TODO
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
# Create the output TIF
# TODO
gdal_type = gdal.GDT_Int16
if self.dataset_type == DatasetType.NDVI and self.statistic not in [Statistic.COUNT, Statistic.COUNT_OBSERVED]:
gdal_type = gdal.GDT_Float32
raster = driver.Create(self.output().path, 4000, 4000, len(self.epochs), gdal_type,
options=["INTERLEAVE=BAND", "COMPRESS=LZW", "TILED=YES"])
assert raster
# TODO
raster.SetGeoTransform(transform)
raster.SetProjection(projection)
raster.SetMetadata(self.generate_raster_metadata())
from itertools import product
from datetime import date
for index, (acq_min, acq_max) in enumerate(self.epochs, start=1):
_log.info("Doing band [%s] statistic [%s] which is band number [%s]", self.band.name, self.statistic.name, index)
acq_min_extended, acq_max_extended, criteria = build_season_date_criteria(acq_min, acq_max, self.season,
seasons=SEASONS,
extend=True)
band = raster.GetRasterBand(index)
assert band
season = SEASONS[self.season]
acq_min_str = date(acq_min_extended.year, season[0][0].value, season[0][1]).strftime("%Y%m%d")
acq_max_str = acq_max_extended.strftime("%Y%m%d")
# TODO
band.SetNoDataValue(ndv)
band.SetDescription("{band} {stat} {start}-{end}".format(band=self.band.name, stat=self.statistic.name, start=acq_min_str, end=acq_max_str))
for x_offset, y_offset in product(range(0, 4000, self.x_chunk_size),
range(0, 4000, self.y_chunk_size)):
filename = self.get_statistic_filename(acq_min_extended, acq_max_extended, x_offset, y_offset)
_log.info("Processing chunk [%4d|%4d] for [%s] from [%s]", x_offset, y_offset, self.statistic.name, filename)
# read the chunk
try:
data = numpy.load(filename)
except IOError:
_log.info("Failed to load chunk")
continue
_log.info("data is [%s]\n[%s]", numpy.shape(data), data)
_log.info("Writing it to (%d,%d)", x_offset, y_offset)
# write the chunk to the TIF at the offset
band.WriteArray(data, x_offset, y_offset)
band.FlushCache()
band.ComputeStatistics(True)
band.FlushCache()
del band
raster.FlushCache()
del raster
