def run(self):
print "****", self.output().path
dataset = self.tile.datasets[self.dataset_type]
metadata = get_dataset_metadata(dataset)
mask = None
# If doing PQA masking then get PQA mask
if self.mask_pqa_apply and DatasetType.PQ25 in self.tile.datasets:
mask = get_mask_pqa(self.tile.datasets[DatasetType.PQ25], self.mask_pqa_mask, mask=mask)
# If doing WOFS masking then get WOFS mask
if self.mask_wofs_apply and DatasetType.WATER in self.tile.datasets:
mask = get_mask_wofs(self.tile.datasets[DatasetType.WATER], self.mask_wofs_mask, mask=mask)
# TODO - no data value and data type
ndv = get_dataset_ndv(dataset)
data = get_dataset_data_masked(dataset, mask=mask, ndv=ndv)
raster_create(self.output().path, [data[b] for b in dataset.bands],
metadata.transform, metadata.projection, ndv, gdal.GDT_Int16,
dataset_metadata=self.generate_raster_metadata(dataset),
band_ids=[b.name for b in dataset.bands])
def retrieve_pixel_value(dataset, pqa, pqa_masks, wofs, wofs_masks, latitude, longitude, ndv=NDV):
_log.debug(
"Retrieving pixel value(s) at lat=[%f] lon=[%f] from [%s] with pqa [%s] and paq mask [%s] and wofs [%s] and wofs mask [%s]",
latitude, longitude, dataset.path, pqa and pqa.path or "", pqa and pqa_masks or "",
wofs and wofs.path or "", wofs and wofs_masks or "")
metadata = get_dataset_metadata(dataset)
x, y = latlon_to_xy(latitude, longitude, metadata.transform)
_log.info("Retrieving value at x=[%d] y=[%d] from %s", x, y, dataset.path)
x_size = y_size = 1
mask = None
if pqa:
mask = get_mask_pqa(pqa, pqa_masks, x=x, y=y, x_size=x_size, y_size=y_size)
if wofs:
mask = get_mask_wofs(wofs, wofs_masks, x=x, y=y, x_size=x_size, y_size=y_size, mask=mask)
data = get_dataset_data_masked(dataset, x=x, y=y, x_size=x_size, y_size=y_size, mask=mask, ndv=ndv)
_log.debug("data is [%s]", data)
return data
def retrieve_data(x, y, acq_dt, dataset, band_names, pqa, pqa_masks, wofs, wofs_masks, path, output_format,
overwrite=False, data_type=None, ndv=None, mask=None):
_log.info("Retrieving data from [%s] bands [%s] with pq [%s] and pq mask [%s] and wofs [%s] and wofs mask [%s] to [%s] file [%s]",
dataset.path,
band_names,
pqa and pqa.path or "",
pqa and pqa_masks or "",
wofs and wofs.path or "", wofs and wofs_masks or "",
output_format.name, path)
if os.path.exists(path) and not overwrite:
_log.error("Output file [%s] exists", path)
raise Exception("Output file [%s] already exists" % path)
metadata = get_dataset_metadata(dataset)
# mask = None
if pqa:
mask = get_mask_pqa(pqa, pqa_masks, mask=mask)
if wofs:
mask = get_mask_wofs(wofs, wofs_masks, mask=mask)
bands = []
for b in dataset.bands:
if b.name in band_names:
bands.append(b)
ndv = ndv or get_dataset_ndv(dataset)
data = get_dataset_data_masked(dataset, bands=bands, mask=mask, ndv=ndv)
_log.debug("data is [%s]", data)
data_type = data_type or get_dataset_datatype(dataset)
dataset_info = generate_raster_metadata(x, y, acq_dt, dataset, bands,
pqa is not None, pqa_masks,
wofs is not None, wofs_masks)
band_info = [b.name for b in bands]
if output_format == OutputFormat.GEOTIFF:
raster_create_geotiff(path, [data[b] for b in bands], metadata.transform, metadata.projection, ndv,
data_type, dataset_metadata=dataset_info, band_ids=band_info)
elif output_format == OutputFormat.ENVI:
raster_create_envi(path, [data[b] for b in bands], metadata.transform, metadata.projection, ndv,
data_type, dataset_metadata=dataset_info, band_ids=band_info)
def test_retrieve_data_ls5_arg_with_pqa_water_mask_dry(config=None):
filename = "LS5_TM_NBAR_WITH_PQA_WATER_DRY_{x:03d}_{y:04d}_{date}.{x_offset:04d}_{y_offset:04d}.{x_size:04d}x{y_size:04d}.tif".format(x=CELL_X, y=CELL_Y, date=DATE, x_offset=X_OFFSET, y_offset=Y_OFFSET, x_size=X_SIZE, y_size=Y_SIZE)
tiles = list_tiles_as_list(x=[CELL_X], y=[CELL_Y],
acq_min=ACQ_LS5, acq_max=ACQ_LS5,
satellites=[Satellite.LS5],
dataset_types=[ARG_DATASET_TYPE, PQ_DATASET_TYPE, WOFS_DATASET_TYPE],
config=config)
assert len(tiles) == 1
tile = tiles[0]
assert ARG_DATASET_TYPE in tile.datasets
dataset = tile.datasets[ARG_DATASET_TYPE]
assert PQ_DATASET_TYPE in tile.datasets
pqa = tile.datasets[PQ_DATASET_TYPE]
assert WOFS_DATASET_TYPE in tile.datasets
wofs = tile.datasets[WOFS_DATASET_TYPE]
mask = get_mask_pqa(pqa, x=X_OFFSET, y=Y_OFFSET, x_size=X_SIZE, y_size=Y_SIZE)
mask = get_mask_wofs(wofs, wofs_masks=[WofsMask.DRY, WofsMask.NO_DATA, WofsMask.SATURATION_CONTIGUITY,
WofsMask.SEA_WATER, WofsMask.TERRAIN_SHADOW, WofsMask.HIGH_SLOPE,
WofsMask.CLOUD_SHADOW, WofsMask.CLOUD],
x=X_OFFSET, y=Y_OFFSET, x_size=X_SIZE, y_size=Y_SIZE, mask=mask)
data = get_dataset_data_masked(dataset=dataset, x=X_OFFSET, y=Y_OFFSET, x_size=X_SIZE, y_size=Y_SIZE, mask=mask)
assert(data)
_log.info("data is [%s]\n%s", numpy.shape(data), data)
ndv = get_dataset_ndv(dataset)
assert(is_ndv(ndv, ARG_NDV))
data_type = get_dataset_datatype(dataset)
assert(data_type == ARG_DATA_TYPE)
metadata = generate_dataset_metadata(x=CELL_X, y=CELL_Y, acq_dt=ACQ_LS5,
dataset=dataset, bands=None,
mask_pqa_apply=False, mask_pqa_mask=None,
mask_wofs_apply=False, mask_wofs_mask=None)
raster_create_geotiff(filename, [data[b] for b in dataset.bands], CELL_GEO_TRANSFORM, CELL_PROJECTION, ndv, data_type,
dataset_metadata=metadata, band_ids=[b.name for b in dataset.bands])
assert filecmp.cmp(filename, get_test_data_path(filename))
def run(self):
print "****", self.output().path
dataset = self.tile.datasets[DatasetType.TCI]
print "***", dataset.path
transform = (self.x, 0.00025, 0.0, self.y+1, 0.0, -0.00025)
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
projection = srs.ExportToWkt()
# metadata = get_dataset_metadata(dataset)
mask = None
# If doing PQA masking then get PQA mask
if self.mask_pqa_apply and DatasetType.PQ25 in self.tile.datasets:
mask = get_mask_pqa(self.tile.datasets[DatasetType.PQ25], self.mask_pqa_mask, mask=mask)
# If doing WOFS masking then get WOFS mask
if self.mask_wofs_apply and DatasetType.WATER in self.tile.datasets:
mask = get_mask_wofs(self.tile.datasets[DatasetType.WATER], self.mask_wofs_mask, mask=mask)
# TODO - no data value and data type
ndv = get_dataset_ndv(dataset)
data = get_dataset_data_masked(dataset, mask=mask, ndv=ndv)
# Create ALL bands raster
# raster_create(self.output().path, [data[b] for b in dataset.bands],
# metadata.transform, metadata.projection, ndv, gdal.GDT_Float32,
# dataset_metadata=self.generate_raster_metadata(dataset),
# band_ids=[b.name for b in dataset.bands])
# Create just the WETNESS band raster
raster_create(self.output().path, [data[TciBands.WETNESS]],
transform, projection, ndv, gdal.GDT_Float32,
dataset_metadata=self.generate_raster_metadata(dataset),
band_ids=[TciBands.WETNESS.name])
def run(self):
# TODO move the dicking around with bands stuff into utils?
import gdal
driver = raster = None
metadata = None
data_type = ndv = None
tiles = self.get_tiles()
_log.info("Total tiles found [%d]", len(tiles))
_log.info("Creating stack for band [%s]", self.band)
relevant_tiles = []
for tile in tiles:
dataset = self.dataset_type in tile.datasets and tile.datasets[self.dataset_type] or None
if not dataset:
_log.info("No applicable [%s] dataset for [%s]", self.dataset_type.name, tile.end_datetime)
continue
if self.band in [b.name for b in tile.datasets[self.dataset_type].bands]:
relevant_tiles.append(tile)
_log.info("Total tiles for band [%s] is [%d]", self.band, len(relevant_tiles))
for index, tile in enumerate(relevant_tiles, start=1):
dataset = tile.datasets[self.dataset_type]
assert dataset
band = dataset.bands[self.band]
assert band
pqa = (self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets) and tile.datasets[DatasetType.PQ25] or None
wofs = (self.mask_wofs_apply and DatasetType.WATER in tile.datasets) and tile.datasets[DatasetType.WATER] or None
if self.dataset_type not in tile.datasets:
_log.debug("No [%s] dataset present for [%s] - skipping", self.dataset_type.name, tile.end_datetime)
continue
filename = self.output().path
if not metadata:
metadata = get_dataset_metadata(dataset)
assert metadata
if not data_type:
data_type = get_dataset_datatype(dataset)
assert data_type
if not ndv:
ndv = get_dataset_ndv(dataset)
assert ndv
if not driver:
if self.output_format == OutputFormat.GEOTIFF:
driver = gdal.GetDriverByName("GTiff")
elif self.output_format == OutputFormat.ENVI:
driver = gdal.GetDriverByName("ENVI")
assert driver
if not raster:
if self.output_format == OutputFormat.GEOTIFF:
raster = driver.Create(filename, metadata.shape[0], metadata.shape[1], len(tiles), data_type, options=["BIGTIFF=YES", "INTERLEAVE=BAND"])
elif self.output_format == OutputFormat.ENVI:
raster = driver.Create(filename, metadata.shape[0], metadata.shape[1], len(tiles), data_type, options=["INTERLEAVE=BSQ"])
assert raster
# NOTE: could do this without the metadata!!
raster.SetGeoTransform(metadata.transform)
raster.SetProjection(metadata.projection)
raster.SetMetadata(self.generate_raster_metadata())
mask = None
if pqa:
mask = get_mask_pqa(pqa, self.mask_pqa_mask, mask=mask)
if wofs:
mask = get_mask_wofs(wofs, self.mask_wofs_mask, mask=mask)
_log.info("Stacking [%s] band data from [%s] with PQA [%s] and PQA mask [%s] and WOFS [%s] and WOFS mask [%s] to [%s]",
band.name, dataset.path,
pqa and pqa.path or "",
pqa and self.mask_pqa_mask or "",
wofs and wofs.path or "", wofs and self.mask_wofs_mask or "",
filename)
data = get_dataset_data_masked(dataset, mask=mask, ndv=ndv)
_log.debug("data is [%s]", data)
stack_band = raster.GetRasterBand(index)
stack_band.SetDescription(os.path.basename(dataset.path))
stack_band.SetNoDataValue(ndv)
stack_band.WriteArray(data[band])
stack_band.ComputeStatistics(True)
stack_band.SetMetadata({"ACQ_DATE": format_date(tile.end_datetime), "SATELLITE": dataset.satellite.name})
stack_band.FlushCache()
del stack_band
if raster:
raster.FlushCache()
raster = None
del raster
def run(self):
# TODO move the dicking around with bands stuff into utils?
import gdal
driver = raster = None
metadata = None
data_type = ndv = None
tiles = self.get_tiles()
_log.info("Total tiles found [%d]", len(tiles))
_log.info("Creating stack for band [%s]", self.band)
relevant_tiles = []
for tile in tiles:
dataset = self.dataset_type in tile.datasets and tile.datasets[
self.dataset_type] or None
if not dataset:
_log.info("No applicable [%s] dataset for [%s]",
self.dataset_type.name, tile.end_datetime)
continue
if self.band in [
b.name for b in tile.datasets[self.dataset_type].bands
]:
relevant_tiles.append(tile)
_log.info("Total tiles for band [%s] is [%d]", self.band,
len(relevant_tiles))
for index, tile in enumerate(relevant_tiles, start=1):
dataset = tile.datasets[self.dataset_type]
assert dataset
band = dataset.bands[self.band]
assert band
pqa = (self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets
) and tile.datasets[DatasetType.PQ25] or None
wofs = (self.mask_wofs_apply and DatasetType.WATER in tile.datasets
) and tile.datasets[DatasetType.WATER] or None
if self.dataset_type not in tile.datasets:
_log.debug("No [%s] dataset present for [%s] - skipping",
self.dataset_type.name, tile.end_datetime)
continue
filename = self.output().path
if not metadata:
metadata = get_dataset_metadata(dataset)
assert metadata
if not data_type:
data_type = get_dataset_datatype(dataset)
assert data_type
if not ndv:
ndv = get_dataset_ndv(dataset)
assert ndv
if not driver:
if self.output_format == OutputFormat.GEOTIFF:
driver = gdal.GetDriverByName("GTiff")
elif self.output_format == OutputFormat.ENVI:
driver = gdal.GetDriverByName("ENVI")
assert driver
if not raster:
if self.output_format == OutputFormat.GEOTIFF:
raster = driver.Create(
filename,
metadata.shape[0],
metadata.shape[1],
len(tiles),
data_type,
options=["BIGTIFF=YES", "INTERLEAVE=BAND"])
elif self.output_format == OutputFormat.ENVI:
raster = driver.Create(filename,
metadata.shape[0],
metadata.shape[1],
len(tiles),
data_type,
options=["INTERLEAVE=BSQ"])
assert raster
# NOTE: could do this without the metadata!!
raster.SetGeoTransform(metadata.transform)
raster.SetProjection(metadata.projection)
raster.SetMetadata(self.generate_raster_metadata())
mask = None
if pqa:
mask = get_mask_pqa(pqa, self.mask_pqa_mask, mask=mask)
if wofs:
mask = get_mask_wofs(wofs, self.mask_wofs_mask, mask=mask)
_log.info(
"Stacking [%s] band data from [%s] with PQA [%s] and PQA mask [%s] and WOFS [%s] and WOFS mask [%s] to [%s]",
band.name, dataset.path, pqa and pqa.path or "",
pqa and self.mask_pqa_mask or "", wofs and wofs.path or "",
wofs and self.mask_wofs_mask or "", filename)
data = get_dataset_data_masked(dataset, mask=mask, ndv=ndv)
_log.debug("data is [%s]", data)
stack_band = raster.GetRasterBand(index)
stack_band.SetDescription(os.path.basename(dataset.path))
stack_band.SetNoDataValue(ndv)
stack_band.WriteArray(data[band])
stack_band.ComputeStatistics(True)
stack_band.SetMetadata({
"ACQ_DATE": format_date(tile.end_datetime),
"SATELLITE": dataset.satellite.name
})
stack_band.FlushCache()
del stack_band
if raster:
raster.FlushCache()
raster = None
del raster
def go(self):
# If we are applying a vector mask then calculate it not (once as it is the same for all tiles)
mask_vector = None
if self.mask_vector_apply:
mask_vector = get_mask_vector_for_cell(self.x, self.y, self.mask_vector_file, self.mask_vector_layer, self.mask_vector_feature)
# TODO move the dicking around with bands stuff into utils?
import gdal
if self.output_format == OutputFormat.GEOTIFF:
driver = gdal.GetDriverByName("GTiff")
elif self.output_format == OutputFormat.ENVI:
driver = gdal.GetDriverByName("ENVI")
assert driver
tiles = self.get_tiles()
_log.info("Total tiles found [%d]", len(tiles))
for band_name in self.bands:
_log.info("Creating stack for band [%s]", band_name)
relevant_tiles = []
for tile in tiles:
dataset = self.dataset_type in tile.datasets and tile.datasets[self.dataset_type] or None
if not dataset:
_log.info("No applicable [%s] dataset for [%s]", self.dataset_type.name, tile.end_datetime)
continue
if band_name in [b.name for b in tile.datasets[self.dataset_type].bands]:
relevant_tiles.append(tile)
_log.info("Total tiles for band [%s] is [%d]", band_name, len(relevant_tiles))
filename = None
raster = None
metadata = None
data_type = ndv = None
for index, tile in enumerate(relevant_tiles, start=1):
dataset = tile.datasets[self.dataset_type]
assert dataset
band = dataset.bands[band_name]
assert band
if self.list_only:
_log.info("Would stack band [%s] from dataset [%s]", band.name, dataset.path)
continue
pqa = (self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets) and tile.datasets[DatasetType.PQ25] or None
wofs = (self.mask_wofs_apply and DatasetType.WATER in tile.datasets) and tile.datasets[DatasetType.WATER] or None
if self.dataset_type not in tile.datasets:
_log.debug("No [%s] dataset present for [%s] - skipping", self.dataset_type.name, tile.end_datetime)
continue
if not filename:
filename = os.path.join(self.output_directory,
get_dataset_band_stack_filename(satellites=self.satellites,
dataset_type=self.dataset_type,
band=band,
x=self.x, y=self.y,
acq_min=self.acq_min, acq_max=self.acq_max,
season=self.season,
output_format=self.output_format,
mask_pqa_apply=self.mask_pqa_apply,
mask_wofs_apply=self.mask_wofs_apply,
mask_vector_apply=self.mask_vector_apply))
_log.info("Stacking [%s] band data from [%s] with PQA [%s] and PQA mask [%s] and WOFS [%s] and WOFS mask [%s] to band [%d] of [%s]",
band.name, dataset.path,
pqa and pqa.path or "",
pqa and self.mask_pqa_mask or "",
wofs and wofs.path or "", wofs and self.mask_wofs_mask or "",
index,
filename)
if not metadata:
metadata = get_dataset_metadata(dataset)
assert metadata
if not data_type:
data_type = get_dataset_datatype(dataset)
assert data_type
if not ndv:
ndv = get_dataset_ndv(dataset)
assert ndv
if not raster:
if self.output_format == OutputFormat.GEOTIFF:
raster = driver.Create(filename, metadata.shape[0], metadata.shape[1], len(relevant_tiles), data_type, options=["TILED=YES", "BIGTIFF=YES", "COMPRESS=LZW", "INTERLEAVE=BAND"])
elif self.output_format == OutputFormat.ENVI:
raster = driver.Create(filename, metadata.shape[0], metadata.shape[1], len(relevant_tiles), data_type, options=["INTERLEAVE=BSQ"])
assert raster
# NOTE: could do this without the metadata!!
raster.SetGeoTransform(metadata.transform)
raster.SetProjection(metadata.projection)
raster.SetMetadata(self.generate_raster_metadata())
mask = mask_vector
if pqa:
mask = get_mask_pqa(pqa, self.mask_pqa_mask, mask=mask)
if wofs:
mask = get_mask_wofs(wofs, self.mask_wofs_mask, mask=mask)
# _log.info("mask[3500,3500] is [%s]", mask[3500, 3500])
data = get_dataset_data_masked(dataset, mask=mask, ndv=ndv)
_log.debug("data is [%s]", data)
# _log.info("data[3500,3500] is [%s]", data[band][3500, 3500])
stack_band = raster.GetRasterBand(index)
stack_band.SetDescription(os.path.basename(dataset.path))
stack_band.SetNoDataValue(ndv)
stack_band.WriteArray(data[band])
stack_band.ComputeStatistics(True)
stack_band.SetMetadata({"ACQ_DATE": format_date(tile.end_datetime), "SATELLITE": dataset.satellite.name})
stack_band.FlushCache()
del stack_band
if raster:
raster.FlushCache()
raster = None
del raster
def run(self):
shape = (4000, 4000)
no_data_value = NDV
best_pixel_fc = dict()
for band in Fc25Bands:
# best_pixel_fc[band] = empty_array(shape=shape, dtype=numpy.int16, ndv=INT16_MIN)
best_pixel_fc[band] = empty_array(shape=shape, dtype=numpy.int16, ndv=NDV)
best_pixel_nbar = dict()
for band in Ls57Arg25Bands:
best_pixel_nbar[band] = empty_array(shape=shape, dtype=numpy.int16, ndv=NDV)
best_pixel_satellite = empty_array(shape=shape, dtype=numpy.int16, ndv=NDV)
best_pixel_date = empty_array(shape=shape, dtype=numpy.int32, ndv=NDV)
current_satellite = empty_array(shape=shape, dtype=numpy.int16, ndv=NDV)
current_date = empty_array(shape=shape, dtype=numpy.int32, ndv=NDV)
SATELLITE_DATA_VALUES = {Satellite.LS5: 5, Satellite.LS7: 7, Satellite.LS8: 8}
metadata_nbar = None
metadata_fc = None
for tile in self.get_tiles():
pqa = tile.datasets[DatasetType.PQ25]
nbar = tile.datasets[DatasetType.ARG25]
fc = tile.datasets[DatasetType.FC25]
wofs = DatasetType.WATER in tile.datasets and tile.datasets[DatasetType.WATER] or None
_log.info("Processing [%s]", fc.path)
data = dict()
# Create an initial "no mask" mask
mask = numpy.ma.make_mask_none((4000, 4000))
# _log.info("### mask is [%s]", mask[1000][1000])
# Add the PQA mask if we are doing PQA masking
if self.mask_pqa_apply:
mask = get_mask_pqa(pqa, pqa_masks=self.mask_pqa_mask, mask=mask)
# _log.info("### mask PQA is [%s]", mask[1000][1000])
# Add the WOFS mask if we are doing WOFS masking
if self.mask_wofs_apply and wofs:
mask = get_mask_wofs(wofs, wofs_masks=self.mask_wofs_mask, mask=mask)
# _log.info("### mask PQA is [%s]", mask[1000][1000])
# Get NBAR dataset
data[DatasetType.ARG25] = get_dataset_data_masked(nbar, mask=mask)
# _log.info("### NBAR/RED is [%s]", data[DatasetType.ARG25][Ls57Arg25Bands.RED][1000][1000])
# Get the NDVI dataset
data[DatasetType.NDVI] = calculate_ndvi(data[DatasetType.ARG25][Ls57Arg25Bands.RED],
data[DatasetType.ARG25][Ls57Arg25Bands.NEAR_INFRARED])
# _log.info("### NDVI is [%s]", data[DatasetType.NDVI][1000][1000])
# Add the NDVI value range mask (to the existing mask)
mask = self.get_mask_range(data[DatasetType.NDVI], min_val=0.0, max_val=0.3, mask=mask)
# _log.info("### mask NDVI is [%s]", mask[1000][1000])
# Get FC25 dataset
data[DatasetType.FC25] = get_dataset_data_masked(fc, mask=mask)
# _log.info("### FC/BS is [%s]", data[DatasetType.FC25][Fc25Bands.BARE_SOIL][1000][1000])
# Add the bare soil value range mask (to the existing mask)
mask = self.get_mask_range(data[DatasetType.FC25][Fc25Bands.BARE_SOIL], min_val=0, max_val=8000, mask=mask)
# _log.info("### mask BS is [%s]", mask[1000][1000])
# Apply the final mask to the FC25 bare soil data
data_bare_soil = numpy.ma.MaskedArray(data=data[DatasetType.FC25][Fc25Bands.BARE_SOIL], mask=mask).filled(NDV)
# _log.info("### bare soil is [%s]", data_bare_soil[1000][1000])
# Compare the bare soil value from this dataset to the current "best" value
best_pixel_fc[Fc25Bands.BARE_SOIL] = numpy.fmax(best_pixel_fc[Fc25Bands.BARE_SOIL], data_bare_soil)
# _log.info("### best pixel bare soil is [%s]", best_pixel_fc[Fc25Bands.BARE_SOIL][1000][1000])
# Now update the other best pixel datasets/bands to grab the pixels we just selected
for band in Ls57Arg25Bands:
best_pixel_nbar[band] = propagate_using_selected_pixel(best_pixel_fc[Fc25Bands.BARE_SOIL],
data_bare_soil,
data[DatasetType.ARG25][band],
best_pixel_nbar[band])
for band in [Fc25Bands.PHOTOSYNTHETIC_VEGETATION, Fc25Bands.NON_PHOTOSYNTHETIC_VEGETATION, Fc25Bands.UNMIXING_ERROR]:
best_pixel_fc[band] = propagate_using_selected_pixel(best_pixel_fc[Fc25Bands.BARE_SOIL],
data_bare_soil,
data[DatasetType.FC25][band],
best_pixel_fc[band])
# And now the other "provenance" data
# Satellite "provenance" data
current_satellite.fill(SATELLITE_DATA_VALUES[fc.satellite])
best_pixel_satellite = propagate_using_selected_pixel(best_pixel_fc[Fc25Bands.BARE_SOIL],
data_bare_soil,
current_satellite,
best_pixel_satellite)
# Date "provenance" data
current_date.fill(date_to_integer(tile.end_datetime))
best_pixel_date = propagate_using_selected_pixel(best_pixel_fc[Fc25Bands.BARE_SOIL],
data_bare_soil,
current_date,
best_pixel_date)
# Grab the metadata from the input datasets for use later when creating the output datasets
if not metadata_nbar:
metadata_nbar = get_dataset_metadata(nbar)
if not metadata_fc:
metadata_fc = get_dataset_metadata(fc)
# Create the output datasets
# FC composite
raster_create(self.get_dataset_filename("FC"),
[best_pixel_fc[b] for b in Fc25Bands],
metadata_fc.transform, metadata_fc.projection,
metadata_fc.bands[Fc25Bands.BARE_SOIL].no_data_value,
metadata_fc.bands[Fc25Bands.BARE_SOIL].data_type)
# NBAR composite
raster_create(self.get_dataset_filename("NBAR"),
[best_pixel_nbar[b] for b in Ls57Arg25Bands],
metadata_nbar.transform, metadata_nbar.projection,
metadata_nbar.bands[Ls57Arg25Bands.BLUE].no_data_value,
metadata_nbar.bands[Ls57Arg25Bands.BLUE].data_type)
# Satellite "provenance" composites
raster_create(self.get_dataset_filename("SAT"),
[best_pixel_satellite],
metadata_nbar.transform, metadata_nbar.projection, no_data_value,
gdal.GDT_Int16)
# Date "provenance" composites
raster_create(self.get_dataset_filename("DATE"),
[best_pixel_date],
metadata_nbar.transform, metadata_nbar.projection, no_data_value,
gdal.GDT_Int32)
def bs_workflow(tiles, percentile=90, xtile=None, ytile=None,
out_fnames=None):
"""
A baseline workflow for doing the baresoil percentile, NBAR, FC
corresponding mosaics.
"""
# Get some basic image info
ds_type = DatasetType.FC25
ds = tiles[0]
dataset = ds.datasets[ds_type]
md = get_dataset_metadata(dataset)
if md is None:
_log.info("Tile path not exists %s",dataset.path)
return
samples, lines = md.shape
#_log.info("dataset shape %s for %s", md.shape, out_fnames)
time_slices = len(tiles)
_log.info("length of time slices [%d] for %s", time_slices, out_fnames)
geobox = GriddedGeoBox.from_gdal_dataset(gdal.Open(dataset.path))
lat_lon = ""
for line in out_fnames:
lat_lon = line.split("/")[-2]
break;
# Initialise the tiling scheme for processing
if xtile is None:
xtile = samples
if ytile is None:
ytile = lines
chunks = generate_tiles(samples, lines, xtile=samples, ytile=ytile,
generator=False)
# Define no-data
no_data_value = NDV
nan = numpy.float32(numpy.nan) # for the FC dtype no need for float64
# Define the output files
if out_fnames is None:
nbar_outfname = 'nbar_best_pixel'
all_outfname = 'all_best_pixel'
#fc_outfname = 'fc_best_pixel'
#sat_outfname = 'sat_best_pixel'
#date_outfnme = 'date_best_pixel'
#count_outfnme = 'count_best_pixel'
else:
nbar_outfname = out_fnames[0]
all_outfname = out_fnames[1]
#fc_outfname = out_fnames[1]
#sat_outfname = out_fnames[2]
#date_outfnme = out_fnames[3]
#count_outfnme = out_fnames[4]
nbar_outnb = len(Ls57Arg25Bands)
all_outnb = len(BareSoil)
#fc_outnb = len(Fc25Bands)
out_dtype = gdal.GDT_Int16
#_log.info("input xtile [%d] ytile [%d] for %s", xtile, ytile, out_fnames)
nbar_outds = TiledOutput(nbar_outfname, samples=samples, lines=lines,
bands=nbar_outnb, dtype=out_dtype,
nodata=no_data_value, geobox=geobox, fmt="GTiff")
all_outds = TiledOutput(all_outfname, samples=samples, lines=lines,
bands=all_outnb, dtype=out_dtype,
nodata=no_data_value, geobox=geobox, fmt="GTiff")
satellite_code = {Satellite.LS5: 5, Satellite.LS7: 7, Satellite.LS8: 8}
fc_bands_subset = [Fc25Bands.PHOTOSYNTHETIC_VEGETATION,
Fc25Bands.NON_PHOTOSYNTHETIC_VEGETATION,
Fc25Bands.UNMIXING_ERROR]
count=0
# Loop over each spatial tile/chunk and build up the time series
for chunk in chunks:
count=0
ys, ye = chunk[0]
xs, xe = chunk[1]
ysize = ye - ys
xsize = xe - xs
dims = (time_slices, ysize, xsize)
#_log.info("got chunk [%s] for %s", chunk, out_fnames)
# Initialise the intermediate and best_pixel output arrays
data = {}
best_pixel_nbar = {}
best_pixel_fc = {}
stack_bare_soil = numpy.zeros(dims, dtype='float32')
stack_sat = numpy.zeros(dims, dtype='int16')
#stack_date = numpy.zeros(dims, dtype='int32')
stack_year = numpy.zeros(dims, dtype='int16')
stack_md = numpy.zeros(dims, dtype='int16')
stack_count = numpy.zeros(dims, dtype='int16')
best_pixel_satellite = numpy.zeros((ysize, xsize), dtype='int16')
#best_pixel_date = numpy.zeros((ysize, xsize), dtype='int32')
best_pixel_year = numpy.zeros((ysize, xsize), dtype='int16')
best_pixel_md = numpy.zeros((ysize, xsize), dtype='int16')
best_pixel_count = numpy.zeros((ysize, xsize), dtype='int16')
best_pixel_satellite.fill(no_data_value)
#best_pixel_date.fill(no_data_value)
best_pixel_count.fill(no_data_value)
stack_nbar = {}
#_log.info("all initialised successfully")
for band in Ls57Arg25Bands:
stack_nbar[band] = numpy.zeros(dims, dtype='int16')
best_pixel_nbar[band] = numpy.zeros((ysize, xsize),
dtype='int16')
best_pixel_nbar[band].fill(no_data_value)
stack_fc = {}
for band in fc_bands_subset:
stack_fc[band] = numpy.zeros(dims, dtype='int16')
best_pixel_fc[band] = numpy.zeros((ysize, xsize),
dtype='int16')
best_pixel_fc[band].fill(no_data_value)
for idx, ds in enumerate(tiles):
pqa = ds.datasets[DatasetType.PQ25]
nbar = ds.datasets[DatasetType.ARG25]
fc = ds.datasets[DatasetType.FC25]
#_log.info("Processing nbar for index %d ", idx)
try:
wofs = ds.datasets[DatasetType.WATER]
except KeyError:
print "Missing water for:\n {}".format(ds.end_datetime)
wofs = None
# mask = numpy.zeros((ysize, xsize), dtype='bool')
# TODO update to use the api's version of extract_pq
#pq_data = get_dataset_data(pqa, x=xs, y=ys, x_size=xsize,
# y_size=ysize)[Pq25Bands.PQ]
#mask = extract_pq_flags(pq_data, combine=True)
#mask = ~mask
mask = get_mask_pqa(pqa, x=xs, y=ys, x_size=xsize, y_size=ysize)
# WOfS
if wofs is not None:
mask = get_mask_wofs(wofs, x=xs, y=ys, x_size=xsize,
y_size=ysize, mask=mask)
# NBAR
data[DatasetType.ARG25] = get_dataset_data(nbar, x=xs, y=ys,
x_size=xsize,
y_size=ysize)
# NDVI
'''
red = None
nir = None
if satellite_code[fc.satellite] == 8:
red = data[DatasetType.ARG25][Ls8Arg25Bands.RED]
nir = data[DatasetType.ARG25][Ls8Arg25Bands.NEAR_INFRARED]
else:
red = data[DatasetType.ARG25][Ls57Arg25Bands.RED]
nir = data[DatasetType.ARG25][Ls57Arg25Bands.NEAR_INFRARED]
ndvi = calculate_ndvi(red, nir)
ndvi[mask] = no_data_value
#mask |= numexpr.evaluate("(ndvi < 0.0) | (ndvi > 0.3)")
'''
# FC
data[DatasetType.FC25] = get_dataset_data(fc, x=xs, y=ys,
x_size=xsize,
y_size=ysize)
bare_soil = data[DatasetType.FC25][Fc25Bands.BARE_SOIL]
#mask |= numexpr.evaluate("(bare_soil < 0) | (bare_soil > 8000)")
errcnt=0
# apply the mask to each dataset and insert into the 3D array
if satellite_code[fc.satellite] == 8:
for band in Ls57Arg25Bands:
for oband in Ls8Arg25Bands:
try:
if oband.name == band.name:
data[DatasetType.ARG25][oband][mask] = no_data_value
stack_nbar[band][idx] = data[DatasetType.ARG25][oband]
break
except ValueError:
errcnt=1
_log.info("Data converting error LS8")
except IOError:
errcnt=1
_log.info("reading error LS8")
except KeyError:
errcnt=1
_log.info("Key error LS8")
except:
errcnt=1
_log.info("Unexpected error for LS8: %s",sys.exc_info()[0])
else:
for band in Ls57Arg25Bands:
try:
data[DatasetType.ARG25][band][mask] = no_data_value
stack_nbar[band][idx] = data[DatasetType.ARG25][band]
except ValueError:
errcnt=1
_log.info("Data converting error LS57")
except IOError:
errcnt=1
_log.info("NBAR reading error LS57")
except KeyError:
errcnt=1
_log.info("Key error LS57")
except:
errcnt=1
_log.info("Unexpected error LS57: %s",sys.exc_info()[0])
for band in fc_bands_subset:
try:
data[DatasetType.FC25][band][mask] = no_data_value
stack_fc[band][idx] = data[DatasetType.FC25][band]
except ValueError:
errcnt=2
_log.info("FC Data converting error")
except IOError:
errcnt=2
_log.info("FC reading error LS57")
except KeyError:
errcnt=2
_log.info("FC Key error")
except:
errcnt=2
_log.info("FC Unexpected error: %s",sys.exc_info()[0])
if errcnt != 0:
if errcnt == 1:
_log.info("nbar tile has problem %s",nbar.path)
else:
_log.info("fc tile has problem %s",fc.path)
errcnt=0
continue
# Add bare soil, satellite and date to the 3D arrays
try:
#_log.info("bare soil for %s %s",bare_soil, out_fnames)
stack_bare_soil[idx] = bare_soil
stack_bare_soil[idx][mask] = nan
stack_sat[idx][:] = satellite_code[fc.satellite]
#dtime = int(ds.end_datetime.strftime('%Y%m%d'))
dtime = int(ds.end_datetime.strftime('%Y'))
#_log.info("year of acquisition %d",dtime)
stack_year[idx][:] = dtime
#stack_date[idx][:] = dtime
mtime = int(ds.end_datetime.strftime('%m%d'))
stack_md[idx][:] = mtime
count = count+1
#count = int(numpy.ma.count(numpy.ma.masked_less(bare_soil, 1),axis=0)[0])
#_log.info("count observed [%d] on %d", count, dtime)
count1 = int(numpy.ma.count(numpy.ma.masked_less(bare_soil, 1)))
if count1 < 1 :
_log.info("no data present on %d and year %d for tile %s reducing count by one", mtime, dtime, lat_lon )
count=count-1
stack_count[idx][:] = count
except:
_log.info("stacking - Unexpected error: %s",sys.exc_info()[0])
# Calcualte the percentile
pct_fc = numpy.nanpercentile(stack_bare_soil, percentile,
axis=0, interpolation='nearest')
# Loop over each time slice and generate a mosaic for each dataset_type
try:
for idx in range(time_slices):
pct_idx = pct_fc == stack_bare_soil[idx]
for band in Ls57Arg25Bands:
band_data = stack_nbar[band]
best_pixel_nbar[band][pct_idx] = band_data[idx][pct_idx]
for band in fc_bands_subset:
band_data = stack_fc[band]
best_pixel_fc[band][pct_idx] = band_data[idx][pct_idx]
best_pixel_satellite[pct_idx] = stack_sat[idx][pct_idx]
#best_pixel_date[pct_idx] = stack_date[idx][pct_idx]
best_pixel_year[pct_idx] = stack_year[idx][pct_idx]
best_pixel_md[pct_idx] = stack_md[idx][pct_idx]
best_pixel_count[pct_idx] = stack_count[idx][pct_idx]
#best_pixel_count[pct_idx] = time_slices
# Output the current spatial chunk for each dataset
for band in Ls57Arg25Bands:
bn = band.value
band_data = best_pixel_nbar[band]
nbar_outds.write_tile(band_data, chunk, raster_band=bn)
'''
for band in fc_bands_subset:
bn = band.value
band_data = best_pixel_fc[band]
fc_outds.write_tile(band_data, chunk, raster_band=bn)
'''
for band in BareSoil:
bn = band.value
if bn < 5:
if bn == 1:
all_outds.write_tile(pct_fc, chunk,raster_band=BareSoil.BARE_SOIL.value)
for oband in fc_bands_subset:
if oband.name == band.name:
band_data = best_pixel_fc[oband]
all_outds.write_tile(band_data, chunk, raster_band=bn)
break
elif bn < 11:
for oband in Ls57Arg25Bands:
if oband.name == band.name:
band_data = best_pixel_nbar[oband]
all_outds.write_tile(band_data, chunk, raster_band=bn)
break
elif bn == 11:
all_outds.write_tile(best_pixel_satellite, chunk, raster_band=bn)
elif bn == 12:
all_outds.write_tile(best_pixel_year, chunk, raster_band=bn)
elif bn == 13:
all_outds.write_tile(best_pixel_md, chunk, raster_band=bn)
elif bn == 14:
all_outds.write_tile(best_pixel_count, chunk, raster_band=bn)
except ValueError:
_log.info("Data converting final error")
except IOError:
_log.info("writing error LS57")
except KeyError:
_log.info("Key error final")
except:
_log.info("Final Unexpected error: %s",sys.exc_info()[0])
_log.info("total dataset counts for each chunk is %d for tile %s", count, lat_lon)
# Close the output files
nbar_outds.close()
all_outds.close()
def go(self):
import numpy
from datacube.api.query import list_cells_as_list, list_tiles_as_list
from datacube.config import Config
x_min, x_max, y_max, y_min = self.extract_bounds_from_vector()
_log.debug("The bounds are [%s]", (x_min, x_max, y_min, y_max))
cells_vector = self.extract_cells_from_vector()
_log.debug("Intersecting cells_vector are [%d] [%s]", len(cells_vector), cells_vector)
config = Config()
_log.debug(config.to_str())
x_list = range(x_min, x_max + 1)
y_list = range(y_min, y_max + 1)
_log.debug("x = [%s] y=[%s]", x_list, y_list)
cells_db = list()
for cell in list_cells_as_list(x=x_list, y=y_list, acq_min=self.acq_min, acq_max=self.acq_max,
satellites=[satellite for satellite in self.satellites],
dataset_types=[self.dataset_type]):
cells_db.append((cell.x, cell.y))
_log.debug("Cells from DB are [%d] [%s]", len(cells_db), cells_db)
cells = intersection(cells_vector, cells_db)
_log.debug("Combined cells are [%d] [%s]", len(cells), cells)
for (x, y) in cells:
_log.info("Processing cell [%3d/%4d]", x, y)
tiles = list_tiles_as_list(x=x_list, y=y_list, acq_min=self.acq_min, acq_max=self.acq_max,
satellites=[satellite for satellite in self.satellites],
dataset_types=[self.dataset_type])
_log.info("There are [%d] tiles", len(tiles))
if self.list_only:
for tile in tiles:
_log.info("Would process [%s]", tile.datasets[self.dataset_type].path)
continue
# Calculate the mask for the cell
mask_aoi = self.get_mask_aoi_cell(x, y)
pixel_count = 4000 * 4000
pixel_count_aoi = (mask_aoi == False).sum()
_log.debug("mask_aoi is [%s]\n[%s]", numpy.shape(mask_aoi), mask_aoi)
metadata = None
with self.get_output_file() as csv_file:
csv_writer = csv.writer(csv_file)
import operator
header = reduce(operator.add, [["DATE", "INSTRUMENT", "# PIXELS", "# PIXELS IN AOI"]] + [
["%s - # DATA PIXELS" % band_name,
"%s - # DATA PIXELS AFTER PQA" % band_name,
"%s - # DATA PIXELS AFTER PQA WOFS" % band_name,
"%s - # DATA PIXELS AFTER PQA WOFS AOI" % band_name,
"%s - MIN" % band_name, "%s - MAX" % band_name, "%s - MEAN" % band_name] for band_name in self.bands])
csv_writer.writerow(header)
for tile in tiles:
_log.info("Processing tile [%s]", tile.datasets[self.dataset_type].path)
if self.list_only:
continue
if not metadata:
metadata = get_dataset_metadata(tile.datasets[self.dataset_type])
# Apply PQA if specified
pqa = None
mask_pqa = None
if self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets:
pqa = tile.datasets[DatasetType.PQ25]
mask_pqa = get_mask_pqa(pqa, self.mask_pqa_mask)
_log.debug("mask_pqa is [%s]\n[%s]", numpy.shape(mask_pqa), mask_pqa)
# Apply WOFS if specified
wofs = None
mask_wofs = None
if self.mask_wofs_apply and DatasetType.WATER in tile.datasets:
wofs = tile.datasets[DatasetType.WATER]
mask_wofs = get_mask_wofs(wofs, self.mask_wofs_mask)
_log.debug("mask_wofs is [%s]\n[%s]", numpy.shape(mask_wofs), mask_wofs)
dataset = tile.datasets[self.dataset_type]
bands = []
dataset_band_names = [b.name for b in dataset.bands]
for b in self.bands:
if b in dataset_band_names:
bands.append(dataset.bands[b])
data = get_dataset_data(tile.datasets[self.dataset_type], bands=bands)
_log.debug("data is [%s]\n[%s]", numpy.shape(data), data)
pixel_count_data = dict()
pixel_count_data_pqa = dict()
pixel_count_data_pqa_wofs = dict()
pixel_count_data_pqa_wofs_aoi = dict()
mmin = dict()
mmax = dict()
mmean = dict()
for band_name in self.bands:
# Add "zeroed" entries for non-present bands - should only be if outputs for those bands have been explicitly requested
if band_name not in dataset_band_names:
pixel_count_data[band_name] = 0
pixel_count_data_pqa[band_name] = 0
pixel_count_data_pqa_wofs[band_name] = 0
pixel_count_data_pqa_wofs_aoi[band_name] = 0
mmin[band_name] = numpy.ma.masked
mmax[band_name] = numpy.ma.masked
mmean[band_name] = numpy.ma.masked
continue
band = dataset.bands[band_name]
data[band] = numpy.ma.masked_equal(data[band], NDV)
_log.debug("masked data is [%s] [%d]\n[%s]", numpy.shape(data), numpy.ma.count(data), data)
pixel_count_data[band_name] = numpy.ma.count(data[band])
if pqa:
data[band].mask = numpy.ma.mask_or(data[band].mask, mask_pqa)
_log.debug("PQA masked data is [%s] [%d]\n[%s]", numpy.shape(data[band]), numpy.ma.count(data[band]), data[band])
pixel_count_data_pqa[band_name] = numpy.ma.count(data[band])
if wofs:
data[band].mask = numpy.ma.mask_or(data[band].mask, mask_wofs)
_log.debug("WOFS masked data is [%s] [%d]\n[%s]", numpy.shape(data[band]), numpy.ma.count(data[band]), data[band])
pixel_count_data_pqa_wofs[band_name] = numpy.ma.count(data[band])
data[band].mask = numpy.ma.mask_or(data[band].mask, mask_aoi)
_log.debug("AOI masked data is [%s] [%d]\n[%s]", numpy.shape(data[band]), numpy.ma.count(data[band]), data[band])
pixel_count_data_pqa_wofs_aoi[band_name] = numpy.ma.count(data[band])
mmin[band_name] = numpy.ma.min(data[band])
mmax[band_name] = numpy.ma.max(data[band])
mmean[band_name] = numpy.ma.mean(data[band])
# Convert the mean to an int...taking into account masking....
if not numpy.ma.is_masked(mmean[band_name]):
mmean[band_name] = mmean[band_name].astype(numpy.int16)
pixel_count_data_pqa_wofs_aoi_all_bands = reduce(operator.add, pixel_count_data_pqa_wofs_aoi.itervalues())
if pixel_count_data_pqa_wofs_aoi_all_bands == 0 and not self.output_no_data:
_log.info("Skipping dataset with no non-masked data values in ANY band")
continue
row = reduce(
operator.add,
[[tile.end_datetime,
self.decode_satellite_as_instrument(tile.datasets[self.dataset_type].satellite),
pixel_count, pixel_count_aoi]] +
[[pixel_count_data[band_name], pixel_count_data_pqa[band_name],
pixel_count_data_pqa_wofs[band_name], pixel_count_data_pqa_wofs_aoi[band_name],
mmin[band_name], mmax[band_name], mmean[band_name]] for band_name in self.bands])
csv_writer.writerow(row)
def go(self):
import numpy
from datacube.api.query import list_cells_as_list, list_tiles_as_list
from datacube.config import Config
# Verify that all the requested satellites have the same band combinations
dataset_bands = get_bands(self.dataset_type, self.satellites[0])
_log.info("dataset bands is [%s]", " ".join([b.name for b in dataset_bands]))
for satellite in self.satellites:
if dataset_bands != get_bands(self.dataset_type, satellite):
_log.error("Satellites [%s] have differing bands", " ".join([satellite.name for satellite in self.satellites]))
raise Exception("Satellites with different band combinations selected")
bands = []
dataset_bands_list = list(dataset_bands)
if not self.bands:
bands = dataset_bands_list
else:
for b in self.bands:
bands.append(dataset_bands_list[b - 1])
_log.info("Using bands [%s]", " ".join(band.name for band in bands))
x_min, x_max, y_max, y_min = self.extract_bounds_from_vector()
_log.debug("The bounds are [%s]", (x_min, x_max, y_min, y_max))
cells_vector = self.extract_cells_from_vector()
_log.debug("Intersecting cells_vector are [%d] [%s]", len(cells_vector), cells_vector)
config = Config(os.path.expanduser("~/.datacube/config"))
_log.debug(config.to_str())
x_list = range(x_min, x_max + 1)
y_list = range(y_min, y_max + 1)
_log.debug("x = [%s] y=[%s]", x_list, y_list)
cells_db = list()
for cell in list_cells_as_list(x=x_list, y=y_list, acq_min=self.acq_min, acq_max=self.acq_max,
satellites=[satellite for satellite in self.satellites],
dataset_types=[self.dataset_type]):
cells_db.append((cell.x, cell.y))
_log.debug("Cells from DB are [%d] [%s]", len(cells_db), cells_db)
cells = intersection(cells_vector, cells_db)
_log.debug("Combined cells are [%d] [%s]", len(cells), cells)
for (x, y) in cells:
_log.info("Processing cell [%3d/%4d]", x, y)
tiles = list_tiles_as_list(x=x_list, y=y_list, acq_min=self.acq_min, acq_max=self.acq_max,
satellites=[satellite for satellite in self.satellites],
dataset_types=[self.dataset_type])
_log.info("There are [%d] tiles", len(tiles))
if self.list_only:
for tile in tiles:
_log.info("Would process [%s]", tile.datasets[self.dataset_type].path)
continue
# Calculate the mask for the cell
mask_aoi = self.get_mask_aoi_cell(x, y)
pixel_count = 4000 * 4000
pixel_count_aoi = (mask_aoi == False).sum()
_log.debug("mask_aoi is [%s]\n[%s]", numpy.shape(mask_aoi), mask_aoi)
metadata = None
with self.get_output_file() as csv_file:
csv_writer = csv.writer(csv_file)
import operator
header = reduce(operator.add, [["DATE", "INSTRUMENT", "# PIXELS", "# PIXELS IN AOI"]] + [
["%s - # DATA PIXELS" % b.name,
"%s - # DATA PIXELS AFTER PQA" % b.name,
"%s - # DATA PIXELS AFTER PQA WOFS" % b.name,
"%s - # DATA PIXELS AFTER PQA WOFS AOI" % b.name,
"%s - MIN" % b.name, "%s - MAX" % b.name, "%s - MEAN" % b.name] for b in bands])
csv_writer.writerow(header)
for tile in tiles:
_log.info("Processing tile [%s]", tile.datasets[self.dataset_type].path)
if self.list_only:
continue
if not metadata:
metadata = get_dataset_metadata(tile.datasets[self.dataset_type])
# Apply PQA if specified
pqa = None
mask_pqa = None
if self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets:
pqa = tile.datasets[DatasetType.PQ25]
mask_pqa = get_mask_pqa(pqa, self.mask_pqa_mask)
_log.debug("mask_pqa is [%s]\n[%s]", numpy.shape(mask_pqa), mask_pqa)
# Apply WOFS if specified
wofs = None
mask_wofs = None
if self.mask_wofs_apply and DatasetType.WATER in tile.datasets:
wofs = tile.datasets[DatasetType.WATER]
mask_wofs = get_mask_wofs(wofs, self.mask_wofs_mask)
_log.debug("mask_wofs is [%s]\n[%s]", numpy.shape(mask_wofs), mask_wofs)
data = get_dataset_data(tile.datasets[self.dataset_type], bands=bands)
_log.debug("data is [%s]\n[%s]", numpy.shape(data), data)
pixel_count_data = dict()
pixel_count_data_pqa = dict()
pixel_count_data_pqa_wofs = dict()
pixel_count_data_pqa_wofs_aoi = dict()
mmin = dict()
mmax = dict()
mmean = dict()
for band in bands:
data[band] = numpy.ma.masked_equal(data[band], NDV)
_log.debug("masked data is [%s] [%d]\n[%s]", numpy.shape(data), numpy.ma.count(data), data)
pixel_count_data[band] = numpy.ma.count(data[band])
if pqa:
data[band].mask = numpy.ma.mask_or(data[band].mask, mask_pqa)
_log.debug("PQA masked data is [%s] [%d]\n[%s]", numpy.shape(data[band]), numpy.ma.count(data[band]), data[band])
pixel_count_data_pqa[band] = numpy.ma.count(data[band])
if wofs:
data[band].mask = numpy.ma.mask_or(data[band].mask, mask_wofs)
_log.debug("WOFS masked data is [%s] [%d]\n[%s]", numpy.shape(data[band]), numpy.ma.count(data[band]), data[band])
pixel_count_data_pqa_wofs[band] = numpy.ma.count(data[band])
data[band].mask = numpy.ma.mask_or(data[band].mask, mask_aoi)
_log.debug("AOI masked data is [%s] [%d]\n[%s]", numpy.shape(data[band]), numpy.ma.count(data[band]), data[band])
pixel_count_data_pqa_wofs_aoi[band] = numpy.ma.count(data[band])
mmin[band] = numpy.ma.min(data[band])
mmax[band] = numpy.ma.max(data[band])
mmean[band] = numpy.ma.mean(data[band])
# Convert the mean to an int...which is actually trickier than you would expect due to masking....
if numpy.ma.count(mmean[band]) != 0:
mmean[band] = mmean[band].astype(numpy.int16)
# Should we output if no data values found?
pixel_count_data_pqa_wofs_aoi_all_bands = reduce(operator.add, pixel_count_data_pqa_wofs_aoi.itervalues())
if pixel_count_data_pqa_wofs_aoi_all_bands == 0 and not self.output_no_data:
_log.info("Skipping dataset with no non-masked data values in ANY band")
continue
row = reduce(
operator.add,
[[tile.end_datetime,
self.decode_satellite_as_instrument(tile.datasets[self.dataset_type].satellite),
pixel_count, pixel_count_aoi]] +
[[pixel_count_data[band], pixel_count_data_pqa[band],
pixel_count_data_pqa_wofs[band], pixel_count_data_pqa_wofs_aoi[band],
mmin[band], mmax[band], mmean[band]] for band in bands])
csv_writer.writerow(row)
