def _fill_algorithm_information(self, dataset, alg_src_info): alg_meta = ptype.AlgorithmMetadata( name=self.subset_name, version=str(alg_src_info['algorithm_version'])) if self.subset_name == 'brdf': alg_meta.doi = alg_src_info['nbar_doi'] else: alg_meta.doi = alg_src_info['nbar_terrain_corrected_doi'] dataset.lineage.algorithm = alg_meta
def _populate_lineage(md, mtl_): product_md = _get(mtl_, 'PRODUCT_METADATA') if not md.lineage: md.lineage = ptype.LineageMetadata() if not md.lineage.algorithm: md.lineage.algorithm = ptype.AlgorithmMetadata() # Example "LPGS_2.3.0" soft_v = _get(mtl_, 'METADATA_FILE_INFO', 'processing_software_version') md.lineage.algorithm.name, md.lineage.algorithm.version = soft_v.split('_') if not md.lineage.algorithm.parameters: md.lineage.algorithm.parameters = {} if not md.lineage.ancillary: md.lineage.ancillary = {} md.lineage.ancillary_quality = _get(product_md, 'ephemeris_type')
satellite_ref_point_start=ptype.Point(x=114, y=73), cloud_cover_percentage=0.0, sun_azimuth=102.37071009, sun_elevation=58.08261077, # sun_earth_distance=0.998137, # ground_control_points_version=2, # ground_control_points_model=47, # geometric_rmse_model=4.582, # geometric_rmse_model_x=3.370, # geometric_rmse_model_y=3.104, bands={} ), lineage=ptype.LineageMetadata( algorithm=ptype.AlgorithmMetadata( name='LPGS', version='12.5.0', parameters={} ), ancillary_quality='DEFINITIVE', ancillary={ 'cpf': ptype.AncillaryMetadata( name='L7CPF20050101_20050331.09' ), # We have the properties (quality) of the ancillary but not the file. 'ephemeris': ptype.AncillaryMetadata( properties={'type': 'DEFINITIVE'} ) } ) )
def fill_metadata(self, dataset, path, additional_files=()): """ :type additional_files: tuple[Path] :type dataset: ptype.DatasetMetadata :type path: Path :rtype: ptype.DatasetMetadata """ dataset.ga_level = 'P55' # Copy relevant fields from source nbar. if 'nbar' in dataset.lineage.source_datasets: source_ortho = dataset.lineage.source_datasets['nbar'] borrow_single_sourced_fields(dataset, source_ortho) # TODO, it'd be better to grab this from the images, but they're generated after # this code is run. Copying from Source will do for now dataset.grid_spatial = deepcopy( dataset.lineage.source_datasets['nbar'].grid_spatial) contiguous_data_bit = 0b100000000 dataset.grid_spatial.projection.valid_data = self.calculate_valid_data_region( path, contiguous_data_bit) dataset.format_ = ptype.FormatMetadata('GeoTIFF') with open(str(path.joinpath(self.METADATA_FILE))) as f: pq_metadata = yaml.load(f, Loader=Loader) if not dataset.lineage: dataset.lineage = ptype.LineageMetadata() dataset.lineage.algorithm = ptype.AlgorithmMetadata( name='pqa', version=str( pq_metadata['algorithm_information']['software_version']), doi=pq_metadata['algorithm_information']['pq_doi']) # Add ancillary files ancils = pq_metadata['ancillary'] ancil_files = {} for name, values in ancils.items(): ancil_files[name] = ptype.AncillaryMetadata( type_=name, name=values['data_source'], uri=values['data_file'], file_owner=values['user'], # PyYAML parses these as datetimes already. access_dt=values['accessed'], modification_dt=values['modified']) if ancil_files: dataset.lineage.ancillary = ancil_files product_flags = {} # Record which tests where run in 'product_flags' for test_name, val in pq_metadata['tests_run'].items(): product_flags['tested_%s' % test_name] = val dataset.product_flags = product_flags return dataset
extent=ptype.ExtentMetadata( coord=ptype.CoordPolygon( ul=ptype.Coord(lat=-24.98805, lon=133.97954), ur=ptype.Coord(lat=-24.9864, lon=136.23866), ll=ptype.Coord(lat=-26.99236, lon=133.96208), lr=ptype.Coord(lat=-26.99055, lon=136.25985) ), center_dt=datetime.datetime(2014, 10, 12, 0, 56, 6, 5785) ), image=ptype.ImageMetadata( satellite_ref_point_start=ptype.Point(x=101, y=78), bands={} ), lineage=ptype.LineageMetadata( source_datasets={'level1': test_ls8.EXPECTED_OUT}, algorithm=ptype.AlgorithmMetadata(name='terrain', version='1.0'), ), grid_spatial=ptype.GridSpatialMetadata( projection=ptype.ProjectionMetadata( geo_ref_points=ptype.PointPolygon( ul=ptype.Point( x=397012.5, y=7235987.5 ), ur=ptype.Point( x=625012.5, y=7235987.5 ), ll=ptype.Point( x=397012.5, y=7013987.5
def _build_ls8_ortho(): _reset_runtime_id() return ptype.DatasetMetadata( id_=uuid.UUID('17b92c16-51d3-11e4-909d-005056bb6972'), ga_label='LS8_OLITIRS_OTH_P51_GALPGS01-002_101_078_20141012', product_type='GAORTHO01', creation_dt=dateutil.parser.parse('2014-10-12 05:46:20'), size_bytes=2386550 * 1024, platform=ptype.PlatformMetadata(code='LANDSAT-8'), instrument=ptype.InstrumentMetadata(name='OLI_TIRS', type_="Multi-Spectral", operation_mode='PUSH-BROOM'), format_=ptype.FormatMetadata(name='GeoTiff', version=1), extent=ptype.ExtentMetadata( reference_system='WGS84', coord=ptype.CoordPolygon(ul=ptype.Coord(lat=-24.97, lon=133.97969), ur=ptype.Coord(lat=-24.96826, lon=136.24838), lr=ptype.Coord(lat=-26.96338, lon=136.26962), ll=ptype.Coord(lat=-26.96528, lon=133.96233)), from_dt=dateutil.parser.parse("2014-10-12T00:55:54"), center_dt=dateutil.parser.parse("2014-10-12T00:56:06"), to_dt=dateutil.parser.parse("2014-10-12T00:56:18"), ), grid_spatial=ptype.GridSpatialMetadata( dimensions=[ ptype.DimensionMetadata(name='sample', resolution=25.0, size=9161), ptype.DimensionMetadata(name='line', resolution=25.0, size=9161) ], projection=ptype.ProjectionMetadata( centre_point=ptype.Point(511512.500000, 7127487.500000), geo_ref_points=ptype.PointPolygon( ul=ptype.Point(397012.5, 7237987.5), ur=ptype.Point(626012.5, 7237987.5), ll=ptype.Point(397012.5, 7016987.5), lr=ptype.Point(626012.5, 7016987.5)), datum='GDA94', ellipsoid='GRS80', point_in_pixel='UL', map_projection='UTM', resampling_option='CUBIC_CONVOLUTION', zone=-53)), browse={ 'medium': ptype.BrowseMetadata(path=Path( 'product/LS8_OLITIRS_OTH_P51_GALPGS01-032_101_078_20141012.jpg' ), file_type='image/jpg', cell_size=219.75, red_band=7, green_band=5, blue_band=1), 'full': ptype.BrowseMetadata(path=Path( 'LS8_OLITIRS_OTH_P51_GALPGS01-032_101_078_20141012_FR.jpg'), file_type='image/jpg', cell_size=25.0, red_band=7, green_band=5, blue_band=1) }, image=ptype.ImageMetadata( satellite_ref_point_start=ptype.Point(101, 78), cloud_cover_percentage=0, cloud_cover_details=None, sun_elevation=58.00268508, sun_azimuth=59.41814014, ground_control_points_model=420, geometric_rmse_model=4.610, geometric_rmse_model_x=3.527, geometric_rmse_model_y=2.968, # TODO: What are these two? viewing_incidence_angle_long_track=0, viewing_incidence_angle_x_track=0, bands={ 'coastal_aerosol': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B1.TIF'), number=1, type_='reflective', cell_size=25.0, ), 'visible_blue': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B2.TIF'), number=2, type_='reflective', cell_size=25.0, ), 'visible_green': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B3.TIF'), number=3, type_='reflective', cell_size=25.0, ), 'visible_red': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B4.TIF'), number=4, type_='reflective', cell_size=25.0, ), 'near_infrared': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B5.TIF'), number=5, type_='reflective', cell_size=25.0, ), 'short_wave_infrared1': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B6.TIF'), number=6, type_='reflective', cell_size=25.0, ), 'short_wave_infrared2': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B7.TIF'), number=7, type_='reflective', cell_size=25.0, ), 'panchromatic': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B8.TIF'), number=8, type_='panchromatic', cell_size=12.50, shape=ptype.Point(17761, 18241), ), 'cirrus': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B9.TIF'), number=9, type_='atmosphere', ), 'thermal_infrared1': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B10.TIF'), number=10, type_='thermal', cell_size=25.0, shape=ptype.Point(8881, 9121), ), 'thermal_infrared2': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_B11.TIF'), number=11, type_='thermal', cell_size=25.0, shape=ptype.Point(8881, 9121), ), 'quality': ptype.BandMetadata( path=Path('product/LC81010782014285LGN00_BQA.TIF'), number='QA', type_='quality', ) }), lineage=ptype.LineageMetadata( algorithm=ptype.AlgorithmMetadata( name='Pinkmatter Landsat Processor', version='3.3.3104', parameters={ 'resampling': 'CC', 'radiometric_correction': 'CPF', 'orientation': 'NUP', 'hemisphere': 'S', }), machine=ptype.MachineMetadata( hostname='rhe-jm-prod08.prod.lan', type_id='jobmanager', uname= 'Linux rhe-jm-dev08.dev.lan 2.6.32-279.22.1.el6.x86_64 #1 SMP Sun Oct ' '12 ' '09:21:40 EST 2014 x86_64 x86_64 x86_64 GNU/Linux'), ancillary={ 'cpf': ptype.AncillaryMetadata( name='L8CPF20141001_20141231.01', uri= '/eoancillarydata/sensor-specific/LANDSAT8/CalibrationParameterFile' '/L8CPF20141001_20141231.01'), 'bpf_tirs': ptype.AncillaryMetadata( name='LT8BPF20141012002432_20141012020301.01', uri= '/eoancillarydata/sensor-specific/LANDSAT8/BiasParameterFile/2014/10' '/LT8BPF20141012002432_20141012020301.01'), 'bpf_oli': ptype.AncillaryMetadata( name='LO8BPF20141012002825_20141012011100.01', uri= '/eoancillarydata/sensor-specific/LANDSAT8/BiasParameterFile/2014/10' '/LT8BPF20141012002432_20141012020301.01'), 'rlut': ptype.AncillaryMetadata(name='L8RLUT20130211_20431231v09.h5') }, source_datasets={'satellite_telemetry_data': _build_ls8_raw()}))
def _build_ls8_nbar(): _reset_runtime_id() nbar = ptype.DatasetMetadata( id_=uuid.UUID("249ae098-bd88-11e4-beaa-1040f381a756"), size_bytes=622208 * 1024, ga_label='LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012', product_type='GANBAR01', platform=ptype.PlatformMetadata(code='LANDSAT-8'), instrument=ptype.InstrumentMetadata(name='OLI_TIRS', type_="Multi-Spectral", operation_mode='PUSH-BROOM'), # acquisition=ptype.AcquisitionMetadata(), format_=ptype.FormatMetadata(name='GeoTiff', version=1), extent=ptype.ExtentMetadata( reference_system='WGS84', coord=ptype.CoordPolygon(ul=ptype.Coord(lat=-24.97, lon=133.97969), ur=ptype.Coord(lat=-24.96826, lon=136.24838), lr=ptype.Coord(lat=-26.96338, lon=136.26962), ll=ptype.Coord(lat=-26.96528, lon=133.96233)), from_dt=dateutil.parser.parse("2014-10-12T00:55:54"), to_dt=dateutil.parser.parse("2014-10-12T00:56:18"), ), grid_spatial=ptype.GridSpatialMetadata( dimensions=[ ptype.DimensionMetadata(name='sample', resolution=25.0, size=9161), ptype.DimensionMetadata(name='line', resolution=25.0, size=9161) ], projection=ptype.ProjectionMetadata( centre_point=ptype.Point(511512.500000, 7127487.500000), geo_ref_points=ptype.PointPolygon( ul=ptype.Point(397012.5, 7237987.5), ur=ptype.Point(626012.5, 7237987.5), ll=ptype.Point(397012.5, 7016987.5), lr=ptype.Point(626012.5, 7016987.5)), datum='GDA94', ellipsoid='GRS80', point_in_pixel='UL', map_projection='UTM', resampling_option='CUBIC_CONVOLUTION', zone=-53)), browse={ 'medium': ptype.BrowseMetadata(path=Path( 'LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012.tif'), file_type='image/jpg', cell_size=219.75, red_band=7, green_band=5, blue_band=2), 'full': ptype.BrowseMetadata(path=Path( 'LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_FR.tif'), file_type='image/jpg', cell_size=25.0, red_band=7, green_band=5, blue_band=2) }, image=ptype.ImageMetadata( satellite_ref_point_start=ptype.Point(101, 78), cloud_cover_percentage=0.01, cloud_cover_details=None, # TODO: What are these two? viewing_incidence_angle_long_track=0, viewing_incidence_angle_x_track=0, bands={ '1': ptype.BandMetadata(path=Path( 'product/LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_B1.tif' ), ), '2': ptype.BandMetadata(path=Path( 'product/LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_B2.tif' ), ), '3': ptype.BandMetadata(path=Path( 'product/LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_B3.tif' ), ), '4': ptype.BandMetadata(path=Path( 'product/LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_B4.tif' ), ), '5': ptype.BandMetadata(path=Path( 'product/LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_B5.tif' ), ), '6': ptype.BandMetadata(path=Path( 'product/LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_B6.tif' ), ), '7': ptype.BandMetadata(path=Path( 'product/LS8_OLI_TIRS_NBAR_P54_GANBAR01-015_101_078_20141012_B7.tif' ), ) }), lineage=ptype.LineageMetadata( algorithm=ptype.AlgorithmMetadata(name='GANBAR', version='3.2.1', parameters={}), machine=ptype.MachineMetadata(), source_datasets={'level1': _build_ls8_ortho()}, ancillary={})) return nbar
def _build_ls7_wofs(): return ptype.DatasetMetadata( ga_label='LS7_ETM_WATER_140_-027_2013-07-24T00-32-27.952897', product_type='GAWATER', size_bytes=616 * 1024, platform=ptype.PlatformMetadata(code='LS7'), instrument=ptype.InstrumentMetadata(name='ETM', type_='Multi-Spectral'), format_=ptype.FormatMetadata('GeoTIFF', version=1), extent=ptype.ExtentMetadata( reference_system='WGS84', coord=ptype.CoordPolygon(ul=ptype.Coord(140.0000000, -26.0000000), ll=ptype.Coord(140.0000000, -27.0000000), ur=ptype.Coord(141.0000000, -26.0000000), lr=ptype.Coord(141.0000000, -27.0000000)), # TODO: Should we store the center coordinate? from_dt=dateutil.parser.parse('2013-07-24 00:32:27.952897'), to_dt=dateutil.parser.parse('2013-07-24 00:33:15.899670')), grid_spatial=ptype.GridSpatialMetadata( dimensions=[ ptype.DimensionMetadata(name='x', resolution=27.1030749476, size=4000), ptype.DimensionMetadata(name='y', resolution=27.1030749476, size=4000) ], # TODO: Should WOfS have tile coordinates here? # georectified=ptype.GeoRectifiedSpacialMetadata( # geo_ref_points=PointPolygon( # ul=ptype.Point(255012.500, 7229987.500), # ur=ptype.Point(497012.500, 7229987.500), # ll=ptype.Point(255012.500, 7019987.500), # lr=ptype.Point(497012.500, 7229987.500) # ), # checkpoint_availability=0, # datum='GDA94', # ellipsoid='GRS80', # point_in_pixel='UL', # projection='UTM', # zone=-54 # ) ), image=ptype.ImageMetadata( satellite_ref_point_start=ptype.Point(98, 78), satellite_ref_point_end=ptype.Point(98, 79), cloud_cover_percentage=0.76494375, cloud_cover_details='122391 count', sun_elevation=33.0061002772, sun_azimuth=38.2433049177, bands={ 'W': ptype.BandMetadata(path=Path( 'LS7_ETM_WATER_140_-027_2013-07-24T00-32-27.952897.tif'), # TODO: Nodata value? ) }), lineage=ptype.LineageMetadata( algorithm=ptype.AlgorithmMetadata(name='WOfS', version='1.3', parameters={}), machine=ptype.MachineMetadata(), source_datasets={ # TODO: LS7 dataset? }))