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 run(self): ndv = NDV nbar = self.tile.datasets[DatasetType.ARG25] _log.info("Processing tile [%s]", nbar.path) # Apply PQA if specified pqa = None if self.mask_pqa_apply and DatasetType.PQ25 in self.tile.datasets: pqa = self.tile.datasets[DatasetType.PQ25] mask = None log_mem("Before get PQA mask") if pqa: mask = get_mask_pqa(pqa, self.mask_pqa_mask) data = get_dataset_data_masked(nbar, mask=mask, ndv=ndv) log_mem("After get data (masked)") metadata = get_dataset_metadata(nbar) data = calculate_tassel_cap_index(data, coefficients=TCI_COEFFICIENTS[nbar.satellite][TasselCapIndex.WETNESS]) raster_create(self.output().path, [data], metadata.transform, metadata.projection, numpy.nan, gdal.GDT_Float32)
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 run(self): _log.info("Creating stack for band [%s]", self.band.name) data_type = get_dataset_type_datatype(self.dataset_type) ndv = get_dataset_type_ndv(self.dataset_type) metadata = None driver = None raster = None acq_min, acq_max, criteria = build_season_date_criteria(self.acq_min, self.acq_max, self.season, seasons=SEASONS, extend=True) _log.info("\tacq %s to %s criteria is %s", acq_min, acq_max, 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) for index, tile in enumerate(tiles, start=1): dataset = tile.datasets[self.dataset_type] assert dataset # band = dataset.bands[self.band] # assert band band = self.band pqa = (self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets) and tile.datasets[DatasetType.PQ25] 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 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) _log.info("Stacking [%s] band data from [%s] with PQA [%s] and PQA mask [%s] to [%s]", band.name, dataset.path, pqa and pqa.path or "", pqa and self.mask_pqa_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() del raster raster = None
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 run(self): _log.info("Creating stack for band [%s]", self.band.name) data_type = get_dataset_type_datatype(self.dataset_type) ndv = get_dataset_type_ndv(self.dataset_type) metadata = None driver = None raster = None acq_min, acq_max, criteria = build_season_date_criteria( self.acq_min, self.acq_max, self.season, seasons=SEASONS, extend=True) _log.info("\tacq %s to %s criteria is %s", acq_min, acq_max, 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) for index, tile in enumerate(tiles, start=1): dataset = tile.datasets[self.dataset_type] assert dataset # band = dataset.bands[self.band] # assert band band = self.band pqa = (self.mask_pqa_apply and DatasetType.PQ25 in tile.datasets ) and tile.datasets[DatasetType.PQ25] 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 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) _log.info( "Stacking [%s] band data from [%s] with PQA [%s] and PQA mask [%s] to [%s]", band.name, dataset.path, pqa and pqa.path or "", pqa and self.mask_pqa_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() del raster raster = None
def preview_cloudfree_mosaic(x,y,start,end, bands, satellite,iterations=0,xsize=2000,ysize=2000,file_format="GTiff",data_type=gdal.GDT_CInt16): def resize_array(arr,size): r = numpy.array(arr).astype(numpy.int16) i = Image.fromarray(r) i2 = i.resize(size,Image.NEAREST) r2 = numpy.array(i2) del i2 del i del r return r2 StartDate = start EndDate = end best_data = {} band_str = "+".join([band.name for band in bands]) sat_str = "+".join([sat.name for sat in satellite]) cache_id = ["preview",str(x),str(y),str(start),str(end),band_str,sat_str,str(xsize),str(ysize),file_format,str(iterations)] f_name = "_".join(cache_id) f_name = f_name.replace(" ","_") c_name = f_name cached_res = cache.get(c_name) if cached_res: return str(cached_res) f_name = os.path.join("/tilestore/tile_cache",f_name) tiles = list_tiles(x=[x], y=[y],acq_min=StartDate,acq_max=EndDate,satellites=satellite,dataset_types=[DatasetType.ARG25,DatasetType.PQ25], sort=SortType.ASC) tile_metadata = None tile_count = 0 tile_filled = False for tile in tiles: if tile_filled: break print "merging on tile "+str(tile.x)+", "+str(tile.y) tile_count+=1 dataset = DatasetType.ARG25 in tile.datasets and tile.datasets[DatasetType.ARG25] or None if dataset is None: print "No dataset availible" tile_count-=1 continue tile_metadata = get_dataset_metadata(dataset) if tile_metadata is None: print "NO METADATA" tile_count-=1 continue pqa = DatasetType.PQ25 in tile.datasets and tile.datasets[DatasetType.PQ25] or None mask = None mask = get_mask_pqa(pqa,[PqaMask.PQ_MASK_CLEAR],mask=mask) band_data = get_dataset_data_masked(dataset, mask=mask,bands=bands) swap_arr = None for band in band_data: if not band in best_data: print "Adding "+band.name bd = resize_array(band_data[band],(2000,2000)) print bd best_data[band]=bd del bd else: best = resize_array(best_data[band],(2000,2000)) swap_arr=numpy.in1d(best.ravel(),-999).reshape(best.shape) b_data = numpy.array(band_data[band]) best[swap_arr]=b_data[swap_arr] best_data[band]=numpy.copy(best) del b_data del best del swap_arr if iterations > 0: if tile_count>iterations: print "Exiting after "+str(iterations)+" iterations" break numberOfBands=len(bands) if numberOfBands == 0: return "None" if bands[0] not in best_data: print "No data was merged for "+str(x)+", "+str(y) return "None" numberOfPixelsInXDirection=len(best_data[bands[0]]) numberOfPixelsInYDirection=len(best_data[bands[0]][0]) if tile_count <1: print "No tiles found for "+str(x)+", "+str(y) return "None" driver = gdal.GetDriverByName(file_format) if driver is None: print "No driver found for "+file_format return "None" print f_name+'.tif' raster = driver.Create(f_name+'.tif', numberOfPixelsInXDirection, numberOfPixelsInYDirection, numberOfBands, data_type, options=["BIGTIFF=YES", "INTERLEAVE=BAND"]) gt = tile_metadata.transform gt2 = (gt[0],gt[1]*2.0,gt[2],gt[3],gt[4],gt[5]*2.0) tile_metadata.transform = gt2 raster.SetGeoTransform(tile_metadata.transform) print tile_metadata.transform raster.SetProjection(tile_metadata.projection) index = 1 for band in bands: stack_band = raster.GetRasterBand(index) stack_band.SetNoDataValue(-999) stack_band.WriteArray(best_data[band]) stack_band.ComputeStatistics(True) index+=1 stack_band.FlushCache() del stack_band raster.FlushCache() del raster cache.set(c_name,f_name+".tif") return f_name+".tif"
def obtain_cloudfree_mosaic(x,y,start,end, bands, satellite,iterations=0,xsize=4000,ysize=4000,file_format="GTiff",data_type=gdal.GDT_CInt16,months=None): StartDate = start EndDate = end best_data = {} band_str = "+".join([band.name for band in bands]) sat_str = "+".join([sat.name for sat in satellite]) cache_id = [str(x),str(y),str(start),str(end),band_str,sat_str,str(xsize),str(ysize),file_format,str(iterations)] f_name = "_".join(cache_id) f_name = f_name.replace(" ","_") c_name = f_name cached_res = cache.get(c_name) if cached_res: return str(cached_res) f_name = os.path.join("/tilestore/tile_cache",f_name) tiles = list_tiles(x=[x], y=[y],acq_min=StartDate,acq_max=EndDate,satellites=satellite,dataset_types=[DatasetType.ARG25,DatasetType.PQ25], sort=SortType.ASC) tile_metadata = None tile_count = 0 tile_filled = False stats_file = open(f_name+'.csv','w+') total_ins = 0 for tile in tiles: if tile_filled: break if months: print tile.start_datetime.month if not tile.start_datetime.month in months: continue #print "merging on tile "+str(tile.x)+", "+str(tile.y) tile_count+=1 dataset = DatasetType.ARG25 in tile.datasets and tile.datasets[DatasetType.ARG25] or None if dataset is None: print "No dataset availible" tile_count-=1 continue tile_metadata = get_dataset_metadata(dataset) if tile_metadata is None: print "NO METADATA" tile_count-=1 continue pqa = DatasetType.PQ25 in tile.datasets and tile.datasets[DatasetType.PQ25] or None mask = None mask = get_mask_pqa(pqa,[PqaMask.PQ_MASK_CLEAR],mask=mask) band_data = get_dataset_data_masked(dataset, mask=mask,bands=bands) swap_arr = None best = None good_ins = None for band in band_data: if not band in best_data: #print "Adding "+band.name #print band_data[band] best_data[band]=band_data[band] best = numpy.array(best_data[band]) swap_arr=numpy.in1d(best.ravel(),-999).reshape(best.shape) good_ins = len(numpy.where(best[swap_arr]!=-999)[0]) else: best = numpy.array(best_data[band]) swap_arr=numpy.in1d(best.ravel(),-999).reshape(best.shape) b_data = numpy.array(band_data[band]) best[swap_arr]=b_data[swap_arr] best_data[band]=numpy.copy(best) good_ins = len(numpy.where(b_data[swap_arr]!=-999)[0]) del b_data total_ins+=good_ins stats_file.write(str(tile.x)+','+str(tile.y)+','+str(tile.start_datetime.year)+','+str(tile.start_datetime.month)+','+str(len(best[swap_arr]))+','+str(good_ins)+','+str(total_ins)+','+str(tile.dataset)+"\n") del swap_arr del best del good_ins if iterations > 0: if tile_count>iterations: print "Exiting after "+str(iterations)+" iterations" break numberOfBands=len(bands) if numberOfBands == 0: return "None" if bands[0] not in best_data: print "No data was merged for "+str(x)+", "+str(y) return "None" numberOfPixelsInXDirection=len(best_data[bands[0]]) print numberOfPixelsInXDirection numberOfPixelsInYDirection=len(best_data[bands[0]][0]) print numberOfPixelsInYDirection pixels = numberOfPixelsInXDirection if numberOfPixelsInYDirection > numberOfPixelsInXDirection: pixels = numberOfPixelsInYDirection if tile_count <1: print "No tiles found for "+str(x)+", "+str(y) return "None" driver = gdal.GetDriverByName(file_format) if driver is None: print "No driver found for "+file_format return "None" #print f_name+'.tif' raster = driver.Create(f_name+'.tif', pixels, pixels, numberOfBands, data_type, options=["BIGTIFF=YES", "INTERLEAVE=BAND"]) raster.SetGeoTransform(tile_metadata.transform) raster.SetProjection(tile_metadata.projection) index = 1 stats_file.close() for band in bands: stack_band = raster.GetRasterBand(index) stack_band.SetNoDataValue(-999) stack_band.WriteArray(best_data[band]) stack_band.ComputeStatistics(True) index+=1 stack_band.FlushCache() del stack_band raster.FlushCache() del raster cache.set(c_name,f_name+".tif") return f_name+".tif"