def _build_ls8_raw():
_reset_runtime_id()
raw = ptype.DatasetMetadata(
id_=uuid.UUID('1c76a8ca-51ae-11e4-8644-0050568d59ac'),
creation_dt=dateutil.parser.parse("2014-10-12 04:18:01"),
size_bytes=5680940 * 1024,
ga_label=
'MD_P00_LC81010700832014285LGN00_101_070-083_20141012T032336Z20141012T032910_1',
product_type='satellite_telemetry_data',
usgs=ptype.UsgsMetadata(interval_id='LC81010782014285LGN00'),
platform=ptype.PlatformMetadata(code='LANDSAT-8'),
instrument=ptype.InstrumentMetadata(name='OLI_TIRS'),
format_=ptype.FormatMetadata(name='MD'),
acquisition=ptype.AcquisitionMetadata(
aos=dateutil.parser.parse('2014-10-12T00:52:52'),
los=dateutil.parser.parse('2014-10-12T00:58:37'),
groundstation=ptype.GroundstationMetadata(
code='ASA',
antenna_coord=ptype.Coord(lat=-23.759,
lon=133.8824,
height=579.312)),
heading='D',
platform_orbit=8846),
extent=None,
grid_spatial=None,
browse=None,
image=ptype.ImageMetadata(satellite_ref_point_start=ptype.Point(
101, 70),
satellite_ref_point_end=ptype.Point(101,
83)),
lineage=ptype.LineageMetadata(machine=ptype.MachineMetadata()))
return raw
def test_aqua_pds_label(self):
ds = ptype.DatasetMetadata(
id_=UUID('d083fa45-1edd-11e5-8f9e-1040f381a756'),
product_type='satellite_telemetry_data',
creation_dt=datetime.datetime(2015, 6, 11, 5, 51, 50),
platform=ptype.PlatformMetadata(code='AQUA'),
instrument=ptype.InstrumentMetadata(name='MODIS'),
format_=ptype.FormatMetadata(name='PDS'),
rms_string='S1A1C1D1R1',
acquisition=ptype.AcquisitionMetadata(
aos=datetime.datetime(2014, 8, 7, 3, 16, 28, 750910),
los=datetime.datetime(2014, 8, 7, 3, 16, 30, 228023),
platform_orbit=65208
),
image=ptype.ImageMetadata(day_percentage_estimate=100.0),
lineage=ptype.LineageMetadata(
machine=ptype.MachineMetadata(),
source_datasets={}
)
)
self.assertEqual(
"AQUA_MODIS_STD-PDS_P00_65208.S1A1C1D1R1_0_0_20140807T031628Z20140807T031630",
drivers.RawDriver().get_ga_label(ds)
)
def test_pqa_fill(self):
input_folder = write_files({
'pqa.tif': '',
'pq_metadata.yaml': dedent(
"""
algorithm_information:
software_version: 1.0
pq_doi:
ancillary: {}
tests_run: {}
""")
})
dataset = ptype.DatasetMetadata(
id_=_EXPECTED_PQA.id_,
lineage=ptype.LineageMetadata(
source_datasets={
'nbar': _EXPECTED_NBAR,
'level1': test_ls8.EXPECTED_OUT
}
)
)
received_dataset = drivers.PqaDriver().fill_metadata(dataset, input_folder)
self.assert_same(_EXPECTED_PQA, received_dataset)
def init_existing_dataset(directory,
source_datasets,
uuid_=None,
source_hostname=None):
"""
Package an existing dataset folder (with mostly unknown provenance).
This is intended for old datasets where little information was recorded.
For brand new datasets, it's better to use init_locally_processed_dataset() to capture
local machine information.
:param uuid_: The existing dataset_id, if any.
:param source_hostname: Hostname where processed, if known.
:rtype: ptype.DatasetMetadata
"""
md = ptype.DatasetMetadata(
id_=uuid_,
# Default creation time is creation of an image.
creation_dt=datetime.datetime.utcfromtimestamp(
directory.stat().st_ctime),
lineage=ptype.LineageMetadata(
machine=ptype.MachineMetadata(hostname=source_hostname),
source_datasets=source_datasets))
_note_package_vers(md)
return md
def test_gqa():
md = ptype.DatasetMetadata(lineage=ptype.LineageMetadata(
machine=ptype.MachineMetadata()))
gqa.populate_from_gqa(md, _GQA_PATH)
print(repr(md.gqa))
assert md.gqa == {
"cep90": 0.41,
"colors": {
"blue": 600,
"green": 30164,
"red": 336,
"teal": 1340,
"yellow": 399
},
"final_gcp_count": 32582,
"ref_date": datetime.date(2000, 9, 4),
"ref_source": "GLS_v2",
"ref_source_path":
"/g/data/v10/eoancillarydata/GCP/GQA_v2/wrs2/091/081/LE70910812000248ASA00_B5.TIF",
"residual": {
"abs": {
"x": 0.2,
"y": 0.23
},
"abs_iterative_mean": {
"x": 0.15,
"y": 0.17
},
"iterative_mean": {
"x": 0.02,
"y": 0
},
"iterative_stddev": {
"x": 0.32,
"y": 0.52
},
"mean": {
"x": 0.01,
"y": -0.03
},
"stddev": {
"x": 1.27,
"y": 3.94
}
}
}
assert 'gqa' in md.lineage.machine.software_versions
assert md.lineage.machine.software_versions['gqa'] == {
'repo_url': "https://github.com/GeoscienceAustralia/gqa.git",
'version': "0.4+20.gb0d00dc"
}
def test_override_gqa_version():
md = ptype.DatasetMetadata(lineage=ptype.LineageMetadata(
machine=ptype.MachineMetadata()))
md.lineage.machine.note_software_version('gqa', '0.0.0')
gqa.populate_from_gqa(md, _GQA_PATH)
# It should have overridden it (more specific).
assert 'gqa' in md.lineage.machine.software_versions
assert md.lineage.machine.software_versions['gqa'] == {
'repo_url': "https://github.com/GeoscienceAustralia/gqa.git",
'version': "0.4+20.gb0d00dc"
}
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')
def test_nbar_fill_metadata(self):
input_folder = write_files({
'reflectance_brdf_1.tif': '',
'reflectance_brdf_2.tif': '',
'reflectance_brdf_3.tif': '',
'reflectance_brdf_4.tif': '',
'reflectance_brdf_5.tif': '',
'reflectance_brdf_6.tif': '',
'reflectance_brdf_7.tif': '',
'reflectance_terrain_1.tif': '',
'reflectance_terrain_2.tif': '',
'reflectance_terrain_3.tif': '',
'reflectance_terrain_4.tif': '',
'reflectance_terrain_5.tif': '',
'reflectance_terrain_6.tif': '',
'reflectance_terrain_7.tif': '',
'reflectance_lambertian_1.tif': '',
'reflectance_lambertian_2.tif': '',
'reflectance_lambertian_3.tif': '',
'reflectance_lambertian_4.tif': '',
'reflectance_lambertian_5.tif': '',
'reflectance_lambertian_6.tif': '',
'reflectance_lambertian_7.tif': '',
'nbar_metadata.yaml': dedent(
"""
algorithm_information:
software_version: 1.0
algorithm_version: 1.0
arg25_doi:
nbar_doi:
nbar_terrain_corrected_doi:
ancillary_data: {}
"""),
})
dataset = ptype.DatasetMetadata(
id_=_EXPECTED_NBAR.id_,
lineage=ptype.LineageMetadata(
source_datasets={
'level1': test_ls8.EXPECTED_OUT
}
)
)
received_dataset = drivers.NbarDriver('terrain').fill_metadata(dataset, input_folder)
self.assert_same(_EXPECTED_NBAR, received_dataset)
def init_locally_processed_dataset(directory, source_datasets, uuid_=None):
"""
Create a blank dataset for a newly created dataset on this machine.
:param uuid_: The existing dataset_id, if any.
:rtype: ptype.DatasetMetadata
"""
md = ptype.DatasetMetadata(
id_=uuid_,
# Default creation time is creation of an image.
creation_dt=datetime.datetime.utcfromtimestamp(
directory.stat().st_ctime),
lineage=ptype.LineageMetadata(machine=ptype.MachineMetadata(
hostname=socket.getfqdn(),
runtime_id=_RUNTIME_ID,
uname=' '.join(os.uname())),
source_datasets=source_datasets))
_note_package_vers(md)
return md
# 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'}
)
}
)
)
class TestMtlRead(unittest.TestCase):
def test_ls7_equivalence(self):
assert_expected_mtl(
Path(os.path.join(os.path.dirname(__file__), FILENAME)),
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
def fill_metadata(self, dataset, path, additional_files=()):
"""
:type additional_files: tuple[Path]
:type dataset: ptype.DatasetMetadata
:type path: Path
:rtype: ptype.DatasetMetadata
"""
with open(str(path.joinpath(self.METADATA_FILE))) as f:
nbar_metadata = yaml.load(f, Loader=Loader)
# Copy relevant fields from source ortho.
if 'level1' in dataset.lineage.source_datasets:
source_ortho = dataset.lineage.source_datasets['level1']
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['level1'].grid_spatial)
dataset.grid_spatial.projection.valid_data = self.calculate_valid_data_region(
path)
if not dataset.lineage:
dataset.lineage = ptype.LineageMetadata()
self._fill_algorithm_information(
dataset, nbar_metadata['algorithm_information'])
dataset.product_doi = nbar_metadata['algorithm_information'][
'arg25_doi']
# Extract ancillary file data and values
parameters = {}
ancils = nbar_metadata['ancillary_data']
brdfs = ancils.pop('brdf', {})
brdf_ancils = {
'_'.join((band_name, 'brdf', ancil_type)): values
for band_name, ancil_types in brdfs.items()
for ancil_type, values in ancil_types.items()
}
ancils.update(brdf_ancils)
# Add algorithm parameters
for name, values in ancils.items():
parameters[name] = values['value']
if parameters:
dataset.lineage.algorithm.parameters = parameters
# Add ancillary files
ancil_files = {}
for name, values in ancils.items():
if 'data_file' not in values:
continue
ancil_files[name] = ptype.AncillaryMetadata(
type_=name,
name=values['data_file'].rpartition('/')[2],
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
# All NBARs are P54. (source: Lan Wei)
dataset.ga_level = 'P54'
dataset.format_ = ptype.FormatMetadata('GeoTIFF')
return dataset
sun_azimuth=59.57807899,
sun_elevation=57.89670734,
sun_earth_distance=0.998137,
ground_control_points_model=420,
geometric_rmse_model=4.61,
geometric_rmse_model_x=2.968,
geometric_rmse_model_y=3.527,
bands={}),
lineage=ptype.LineageMetadata(
algorithm=ptype.AlgorithmMetadata(name='LPGS',
version='2.3.0',
parameters={}),
ancillary={
'rlut':
ptype.AncillaryMetadata(name='L8RLUT20130211_20431231v09.h5'),
'bpf_tirs':
ptype.AncillaryMetadata(
name='LT8BPF20141012002432_20141012011154.02'),
'bpf_oli':
ptype.AncillaryMetadata(
name='LO8BPF20141012002825_20141012011100.01'),
'cpf':
ptype.AncillaryMetadata(name='L8CPF20141001_20141231.01')
}))
class TestMtlRead(unittest.TestCase):
def test_ls8_equivalence(self):
assert_expected_mtl(
Path(os.path.join(os.path.dirname(__file__), FILENAME)),
EXPECTED_OUT)
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?
}))
format_=ptype.FormatMetadata(name='RCC'),
acquisition=ptype.AcquisitionMetadata(
aos=datetime.datetime(2005, 6, 1, 1, 51, 10),
los=datetime.datetime(2005, 6, 1, 2, 0, 25),
groundstation=ptype.GroundstationMetadata(code='ASA'),
platform_orbit=113025
),
usgs=ptype.UsgsMetadata(
interval_id='L5TB2005152015110ASA111'
),
lineage=ptype.LineageMetadata(
machine=ptype.MachineMetadata(
hostname='niggle.local',
runtime_id=UUID('b2af5545-e894-11e4-b3b0-1040f381a756'),
type_id='jobmanager',
version='2.4.0',
uname='Darwin niggle.local 14.3.0 Darwin Kernel Version 14.3.0: Mon Mar 23 11:59:05 PDT 2015; '
'root:xnu-2782.20.48~5/RELEASE_X86_64 x86_64'
),
source_datasets={}
)
)
_LS7_RAW = ptype.DatasetMetadata(
id_=UUID('c50c6bd4-e895-11e4-9814-1040f381a756'),
ga_level='P00',
product_type='satellite_telemetry_data',
creation_dt=datetime.datetime(2015, 4, 15, 1, 42, 47),
size_bytes=7698644992,
checksum_path=Path('package.sha1'),
usgs=ptype.UsgsMetadata(
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