def _test_attributes(test_data, safe_path):
"""Compare dictionary attributes with given SAFE file."""
with s2reader.open(safe_path) as safe:
assert safe is not None
assert safe.product_start_time == test_data["product_start_time"]
assert safe.product_stop_time == test_data["product_stop_time"]
assert safe.generation_time == test_data["generation_time"]
assert len(safe.granules) == test_data["num_of_granules"]
assert safe.footprint.is_valid
assert safe.processing_level == test_data["processing_level"]
assert safe.product_type == test_data["product_type"]
assert safe.spacecraft_name == test_data["spacecraft_name"]
assert safe.sensing_orbit_number == test_data["sensing_orbit_number"]
assert safe.sensing_orbit_direction == test_data[
"sensing_orbit_direction"]
for granule_path in safe.granule_paths("02"):
assert isinstance(granule_path, str)
for granule in safe.granules:
assert granule.srid.startswith("EPSG")
assert isinstance(granule.metadata_path, str)
if granule.pvi_path:
assert isinstance(granule.pvi_path, str)
assert isinstance(granule.cloud_percent, float)
assert granule.footprint.is_valid
assert granule.cloudmask.is_valid
if not granule.cloudmask.is_empty:
assert granule.cloudmask.intersects(granule.footprint)
assert granule.nodata_mask.is_valid
if not granule.nodata_mask.is_empty:
assert granule.nodata_mask.intersects(granule.footprint)
assert isinstance(granule.band_path(2), str)
assert isinstance(granule.band_path("02", for_gdal=True), str)
def test_path(self, path):
if not HAVE_S2READER:
return False
try:
with s2reader.open(path):
pass
return True
except IOError:
return False
def update_original_package_location(folder):
ps = PostgresStorage()
safe_pkgs = os.listdir(folder)
print safe_pkgs
print "searching SAFE packages from the: '" + folder + "' directory"
for f in safe_pkgs:
print "------> " + f
if f.endswith(".SAFE"):
with s2reader.open(folder+f) as safe_pkg:
for g in safe_pkg.granules:
ps.update_original_package_location(f, g.granule_identifier);
def main(args):
scriptdir = os.path.dirname(os.path.realpath(__file__))
example = "data/S2A_OPER_PRD_MSIL1C_PDMC_20160905T104813_R002_V20160905T005712_20160905T010424.SAFE"
example_safe = os.path.join(scriptdir, example)
with s2reader.open(example_safe) as testfile:
print testfile
print testfile.product_start_time
print testfile.product_stop_time
print testfile.generation_time
print testfile.footprint
def _test_attributes(test_data, safe_path):
"""Compare dictionary attributes with given SAFE file."""
with s2reader.open(safe_path) as safe:
assert safe is not None
assert safe.product_start_time == test_data["product_start_time"]
assert safe.product_stop_time == test_data["product_stop_time"]
assert safe.generation_time == test_data["generation_time"]
assert len(safe.granules) == test_data["num_of_granules"]
assert safe.footprint.is_valid
assert safe.processing_level == test_data["processing_level"]
assert safe.product_type == test_data["product_type"]
assert safe.spacecraft_name == test_data["spacecraft_name"]
assert safe.sensing_orbit_number == test_data["sensing_orbit_number"]
assert safe.sensing_orbit_direction == test_data["sensing_orbit_direction"]
for granule_path in safe.granule_paths("02"):
assert isinstance(granule_path, str)
for granule in safe.granules:
assert granule.srid.startswith("EPSG")
assert isinstance(granule.metadata_path, str)
if granule.pvi_path:
assert isinstance(granule.pvi_path, str)
if granule.tci_path:
assert isinstance(granule.tci_path, str)
assert isinstance(granule.cloud_percent, float)
assert granule.footprint.is_valid
assert granule.cloudmask.is_valid
if not granule.cloudmask.is_empty:
assert granule.cloudmask.intersects(granule.footprint)
assert granule.nodata_mask.is_valid
if not granule.nodata_mask.is_empty:
assert granule.nodata_mask.intersects(granule.footprint)
assert isinstance(granule.band_path(2), str)
for bid in BAND_IDS:
abs_path = granule.band_path(bid, absolute=True)
assert os.path.isabs(abs_path)
rel_path = granule.band_path(bid, absolute=False)
abs_gdal_path = granule.band_path(
bid, absolute=True, for_gdal=True
)
rel_gdal_path = granule.band_path(
bid, absolute=False, for_gdal=True
)
if safe.is_zip:
assert abs_gdal_path.startswith("/vsizip/")
assert rel_gdal_path.startswith("/vsizip/")
assert rel_path in safe._zipfile.namelist()
else:
assert os.path.isfile(rel_path)
def main(args=None):
"""Print metadata as JSON strings."""
args = sys.argv[1:]
parser = argparse.ArgumentParser()
parser.add_argument("safe_file", type=str, nargs='+')
parser.add_argument("--granules", action="store_true")
parsed = parser.parse_args(args)
pp = pprint.PrettyPrinter()
for safe_file in parsed.safe_file:
with s2reader.open(safe_file) as safe_dataset:
if parsed.granules:
pp.pprint(
dict(
safe_file=safe_file,
granules=[
dict(
granule_identifier=granule.granule_identifier,
footprint=str(granule.footprint),
srid=granule.srid,
# cloudmask_polys=str(granule.cloudmask),
# nodata_mask=str(granule.nodata_mask),
cloud_percent=granule.cloud_percent
)
for granule in safe_dataset.granules
]
)
)
else:
pp.pprint(
dict(
safe_file=safe_file,
product_start_time=safe_dataset.product_start_time,
product_stop_time=safe_dataset.product_stop_time,
generation_time=safe_dataset.generation_time,
footprint=str(safe_dataset.footprint),
granules=len(safe_dataset.granules),
granules_srids=list(set([
granule.srid
for granule in safe_dataset.granules
]))
)
)
print "\n"
def main(args=sys.argv[1:]):
"""Generate EO O&M XML metadata."""
parser = argparse.ArgumentParser()
parser.add_argument("filename", nargs=1)
parser.add_argument(
"--granule-id",
dest="granule_id",
help=("Optional. Specify a granule to export metadata from."))
parser.add_argument(
"--single-granule",
dest="single_granule",
action="store_true",
default=False,
help=
("When only one granule is contained in the package, include product "
"metadata from this one granule. Fails when more than one granule "
"is contained."))
parser.add_argument(
"--out-file",
"-f",
dest="out_file",
help=(
"Specify an output file to write the metadata to. By default, the "
"XML is printed on stdout."))
parser.add_argument(
"--resolution",
"-r",
dest="resolution",
default="10",
help=(
"Only produce metadata for bands of this resolution (in meters). "
"Default is 10."))
parsed = parser.parse_args(args)
try:
safe_pkg = s2reader.open(parsed.filename[0])
except IOError, e:
parser.error('Could not open SAFE package. Error was "%s"' % e)
def __init__(self, input_params, **kwargs):
"""Initialize."""
self.path = input_params["path"]
self.pyramid = input_params["pyramid"]
self.pixelbuffer = input_params["pixelbuffer"]
self.crs = self.pyramid.crs
self.srid = self.pyramid.srid
with s2reader.open(self.path) as s2dataset:
self.s2metadata = {
"path":
s2dataset.path,
"footprint":
s2dataset.footprint,
"granules": [{
"id":
granule.granule_identifier,
"datastrip_id":
granule.datastrip_identifier,
"srid":
granule.srid,
"footprint":
reproject_geometry(granule.footprint,
src_crs=CRS.from_epsg(4326),
dst_crs=self.crs),
"nodatamask":
granule.nodata_mask,
"cloudmask":
granule.cloudmask,
"band_path": {
index: granule.band_path(_id,
for_gdal=True,
absolute=True)
for index, _id in zip(range(1, 14), BAND_IDS)
}
} for granule in s2dataset.granules]
}
def main(args):
if len(args) > 1:
raise Error("too many parameters!")
print "+++++ Sentinel2 User Product filename: '" + args[0] + "'"
storage = PostgresStorage()
tr = TemplatesResolver()
with s2reader.open(args[0]) as safe_pkg:
#mu.print_metadata(safe_pkg)
for granule in safe_pkg.granules:
print "--- Processing granule: '" + granule.granule_identifier + "'"
if (storage.check_granule_identifier(granule.granule_identifier)):
print "WARNING: Granule '" + granule.granule_identifier + "' already exist, skipping it..."
continue
(search_params, other_metadata,
product_abstract_metadata) = pgmap.collect_sentinel2_metadata(
safe_pkg, granule)
htmlAbstract = tr.generate_product_abstract(
product_abstract_metadata)
xml_doc = tr.generate_sentinel2_product_metadata(
du.join(search_params, other_metadata))
try:
search_params['htmlDescription'] = htmlAbstract
id = storage.persist_product_search_params(
du.wrap_keys_among_brackets(search_params), "SENTINEL2")
storage.persist_thumb(mu.create_thumb(granule.pvi_path), id)
except LookupError:
print "ERROR: No related collection found!"
break
storage.persist_product_metadata(xml_doc, id)
ogc_bbox = storage.get_product_OGC_BBOX(granule.granule_identifier)
storage.persist_ogc_links(
pgmap.create_ogc_links_dict(
tr.generate_ogc_links(
pgmap.ogc_links_href_dict(ogc_bbox, id))), id)
def execute(self, context):
products = list()
ids = []
if self.input_product is not None:
log.info("Processing single product: " + self.input_product)
products.append(self.input_product)
elif self.get_inputs_from is not None:
log.info("Getting inputs from: " + self.get_inputs_from)
inputs = context['task_instance'].xcom_pull(
task_ids=self.get_inputs_from, key=XCOM_RETURN_KEY)
for input in inputs:
products.append(input)
else:
self.downloaded_products = context['task_instance'].xcom_pull(
'dhus_download_task', key='downloaded_products')
if self.downloaded_products is not None and len(
self.downloaded_products) != 0:
products = self.downloaded_products.keys()
log.info(self.downloaded_products)
for p in self.downloaded_products:
ids.append(self.downloaded_products[p]["id"])
print "downloaded products keys :", self.downloaded_products.keys(
)[0]
if products is None or len(products) == 0:
log.info("Nothing to process.")
return
thumbnail_paths = list()
for product in products:
log.info("Processing {}".format(product))
with s2reader.open(product) as safe_product:
for granule in safe_product.granules:
try:
zipf = zipfile.ZipFile(product, 'r')
imgdata = zipf.read(granule.pvi_path, 'r')
img = Blob(imgdata)
img = Image(img)
img.scale(self.thumb_size_x + 'x' + self.thumb_size_y)
img.quality(80)
thumbnail_name = product.strip(
".zip") + "/thumbnail.jpg"
if os.path.isdir(product.strip(".zip")):
product_rmdir_cmd = "rm -r {} ".format(
product.strip(".zip"))
product_rmdir_BO = BashOperator(
task_id="product_rmdir_{}".format(
product.split("/")[-1].strip(".zip")),
bash_command=product_rmdir_cmd)
product_rmdir_BO.execute(context)
product_mkdir_cmd = "mkdir {} ".format(
product.strip(".zip"))
product_mkdir_BO = BashOperator(
task_id="product_mkdir_{}".format(
product.split("/")[-1].strip(".zip")),
bash_command=product_mkdir_cmd)
product_mkdir_BO.execute(context)
if self.output_dir is not None:
thumbnail_name = os.path.join(
self.output_dir, "thumbnail.jpeg")
log.info("Writing thumbnail to {}".format(
thumbnail_name))
img.write(thumbnail_name)
else:
img.write(str(thumbnail_name))
thumbnail_paths.append(thumbnail_name)
# XCOM expects a single file so we push it here:
context['task_instance'].xcom_push(
key='thumbnail_jpeg_abs_path',
value=str(thumbnail_name))
context['task_instance'].xcom_push(key='ids',
value=ids)
break
except BaseException as e:
log.error(
"Unable to extract thumbnail from {}: {}".format(
product, e))
return thumbnail_paths
def execute(self, context):
if self.get_inputs_from != None:
log.info("Getting inputs from: {}".format(self.get_inputs_from))
self.downloaded_products, self.archived_products = context[
'task_instance'].xcom_pull(task_ids=self.get_inputs_from,
key=XCOM_RETURN_KEY)
else:
log.info("Getting inputs from: dhus_download_task")
self.downloaded_products = context['task_instance'].xcom_pull(
'dhus_download_task', key='downloaded_products')
if self.downloaded_products is None:
log.info("Nothing to process.")
return
for product in self.downloaded_products.keys():
log.info("Processing: {}".format(product))
with s2reader.open(product) as s2_product:
coords = []
links = []
metadata = s2_product._product_metadata
granule = s2_product.granules[0]
granule_metadata = granule._metadata
product_footprint = [
[[m.replace(" ", ",")]
for m in str(s2_product.footprint).replace(", ", ",").
partition('((')[-1].rpartition('))')[0].split(",")]
]
for item in product_footprint[0]:
[x_coordinate, y_coordinate] = item[0].split(",")
coords.append([float(x_coordinate), float(y_coordinate)])
final_metadata_dict = {
"type": "Feature",
"geometry": {
"type": "Polygon",
"coordinates": [coords]
},
"properties": {
"eop:identifier":
s2_product.manifest_safe_path.rsplit('.SAFE', 1)[0],
"timeStart":
s2_product.product_start_time,
"timeEnd":
s2_product.product_stop_time,
"originalPackageLocation":
os.path.join(
self.original_package_download_base_url,
os.path.basename(self.archived_products.pop(0))),
"thumbnailURL":
None,
"quicklookURL":
None,
"eop:parentIdentifier":
"SENTINEL2",
"eop:productionStatus":
None,
"eop:acquisitionType":
None,
"eop:orbitNumber":
s2_product.sensing_orbit_number,
"eop:orbitDirection":
s2_product.sensing_orbit_direction,
"eop:track":
None,
"eop:frame":
None,
"eop:swathIdentifier":
metadata.find('.//Product_Info/Datatake').
attrib['datatakeIdentifier'],
"opt:cloudCover":
int(
float(
metadata.findtext(
".//Cloud_Coverage_Assessment"))),
"opt:snowCover":
None,
"eop:productQualityStatus":
None,
"eop:productQualityDegradationStatus":
None,
"eop:processorName":
None,
"eop:processingCenter":
None,
"eop:creationDate":
None,
"eop:modificationDate":
None,
"eop:processingDate":
None,
"eop:sensorMode":
None,
"eop:archivingCenter":
granule_metadata.findtext('.//ARCHIVING_CENTRE'),
"eop:processingMode":
None,
"eop:availabilityTime":
s2_product.generation_time,
"eop:acquisitionStation":
None,
"eop:acquisitionSubtype":
None,
"eop:startTimeFromAscendingNode":
None,
"eop:completionTimeFromAscendingNode":
None,
"eop:illuminationAzimuthAngle":
metadata.findtext('.//Mean_Sun_Angle/AZIMUTH_ANGLE'),
"eop:illuminationZenithAngle":
metadata.findtext('.//Mean_Sun_Angle/ZENITH_ANGLE'),
"eop:illuminationElevationAngle":
None,
"eop:resolution":
None
}
}
for i in self.bands_res.values():
features_list = []
granule_counter = 1
for granule in s2_product.granules:
granule_coords = []
granule_coordinates = [[
[m.replace(" ", ",")]
for m in str(granule.footprint).replace(", ", ",").
partition('((')[-1].rpartition('))')[0].split(",")
]]
for item in granule_coordinates[0]:
[granule_x_coordinate,
granule_y_coordinate] = item[0].split(",")
granule_coords.append([
float(granule_x_coordinate),
float(granule_y_coordinate)
])
zipped_product = zipfile.ZipFile(product)
for file_name in zipped_product.namelist():
if file_name.endswith(
'.jp2'
) and not file_name.endswith('PVI.jp2'):
features_list.append({"type": "Feature", "geometry": { "type": "Polygon", "coordinates": [granule_coords]},\
"properties": {\
"location":os.path.join(self.remote_dir, granule.granule_path.rsplit("/")[-1], "IMG_DATA", file_name.rsplit("/")[-1]), "band": self.bands_dict[file_name.rsplit("/")[-1].rsplit(".")[0][-3:]]},\
"id": "GRANULE.{}".format(granule_counter)})
granule_counter += 1
final_granules_dict = {
"type": "FeatureCollection",
"features": features_list
}
timeStart, timeEnd = final_metadata_dict["properties"][
"timeStart"], final_metadata_dict["properties"]["timeEnd"]
# create description.html and dump it to file
log.info("Creating description.html")
tr = TemplatesResolver()
htmlAbstract = tr.generate_product_abstract({
"timeStart":
timeStart,
"timeEnd":
timeEnd,
"originalPackageLocation":
final_metadata_dict["properties"]["originalPackageLocation"]
})
log.debug(pprint.pformat(htmlAbstract))
final_metadata_dict['htmlDescription'] = htmlAbstract
with open(product.strip(".zip") + '/description.html',
'w') as product_outfile:
product_outfile.write(htmlAbstract)
# Note here that the SRID is a property of the granule not the product
final_metadata_dict["properties"]["crs"] = granule.srid
with open(product.strip(".zip") + '/product.json',
'w') as product_outfile:
json.dump(final_metadata_dict, product_outfile, indent=4)
with open(product.strip(".zip") + '/granules.json',
'w') as granules_outfile:
json.dump(final_granules_dict, granules_outfile, indent=4)
product_identifier = s2_product.manifest_safe_path.rsplit(
'.SAFE', 1)[0]
bbox = get_bbox_from_granules_coordinates(granule_coordinates)
ows_links_dict = create_owslinks_dict(
product_identifier=product_identifier,
timestart=timeStart,
timeend=timeEnd,
granule_bbox=bbox,
gs_workspace=self.gs_workspace,
gs_wms_layer=self.gs_wms_layer,
gs_wms_width=self.gs_wms_width,
gs_wms_height=self.gs_wms_height,
gs_wms_format=self.gs_wms_format,
gs_wms_version=self.gs_wms_version,
gs_wfs_featuretype=self.gs_wfs_featuretype,
gs_wfs_format=self.gs_wfs_format,
gs_wfs_version=self.gs_wfs_version,
gs_wcs_coverage_id=self.gs_wcs_coverage_id,
gs_wcs_scale_i=self.gs_wcs_scale_i,
gs_wcs_scale_j=self.gs_wcs_scale_j,
gs_wcs_format=self.gs_wcs_format,
gs_wcs_version=self.gs_wcs_version,
)
log.info("ows links: {}".format(pprint.pformat(ows_links_dict)))
with open(product.strip(".zip") + '/owsLinks.json',
'w') as owslinks_outfile:
json.dump(ows_links_dict, owslinks_outfile, indent=4)
self.custom_archived = []
for archive_line in self.downloaded_products.keys():
jp2_files_paths = []
archive_path = archive_line
archived_product = zipfile.ZipFile(archive_line, 'r')
for file_name in archived_product.namelist():
if file_name.endswith(
'.jp2') and not file_name.endswith('PVI.jp2'):
archived_product.extract(file_name,
archive_path.strip(".zip"))
jp2_files_paths.append(
os.path.join(archive_path.strip(".zip"), file_name))
parent_dir = os.path.dirname(jp2_files_paths[0])
if file_name.endswith('MTD_TL.xml'):
archived_product.extract(file_name,
archive_path.strip(".zip"))
mtd_tl_xml = os.path.join(archive_path.strip(".zip"),
file_name)
tree = ET.parse(mtd_tl_xml)
root = tree.getroot()
geometric_info = root.find(
root.tag.split('}', 1)[0] + "}Geometric_Info")
tile_geocoding = geometric_info.find("Tile_Geocoding")
wld_files = []
prj_files = []
for jp2_file in jp2_files_paths:
wld_name = os.path.splitext(jp2_file)[0]
gdalinfo_cmd = "gdalinfo {} > {}".format(
jp2_file, wld_name + ".prj")
gdalinfo_BO = BashOperator(
task_id="bash_operator_gdalinfo_{}".format(wld_name[-3:]),
bash_command=gdalinfo_cmd)
gdalinfo_BO.execute(context)
sed_cmd = "sed -i -e '1,4d;29,$d' {}".format(wld_name + ".prj")
sed_BO = BashOperator(task_id="bash_operator_sed_{}".format(
wld_name[-3:]),
bash_command=sed_cmd)
sed_BO.execute(context)
prj_files.append(wld_name + ".prj")
wld_file = open(wld_name + ".wld", "w")
wld_files.append(wld_name + ".wld")
for key, value in self.bands_res.items():
if wld_name[-3:] in value:
element = key
geo_position = tile_geocoding.find(
'.//Geoposition[@resolution="{}"]'.format(element))
wld_file.write(
geo_position.find("XDIM").text + "\n" + "0" + "\n" + "0" +
"\n")
wld_file.write(geo_position.find("YDIM").text + "\n")
wld_file.write(geo_position.find("ULX").text + "\n")
wld_file.write(geo_position.find("ULY").text + "\n")
parent_dir = os.path.dirname(jp2_files_paths[0])
self.custom_archived.append(os.path.dirname(parent_dir))
log.info(os.path.dirname(parent_dir))
log.info(self.custom_archived)
context['task_instance'].xcom_push(key='downloaded_products',
value=self.downloaded_products)
context['task_instance'].xcom_push(
key='downloaded_products_with_wldprj',
value=' '.join(self.custom_archived))
return self.custom_archived
def test_product_abstract_generation(pkg_path):
tr = TemplatesResolver()
with s2reader.open(pkg_path) as safe_pkg:
for granule in safe_pkg.granules:
(search_params, other_metadata, product_abstract_metadata) = s2.collect_sentinel2_metadata(safe_pkg, granule)
print tr.generate_product_abstract(product_abstract_metadata)
def test_metadata_read(pkg_path):
with s2reader.open(pkg_path) as safe_pkg:
mu.print_metadata(safe_pkg)
def file_bbox(
input_file,
tile_pyramid
):
"""Return the bounding box of a raster or vector file in a given CRS."""
out_crs = tile_pyramid.crs
# Read raster data with rasterio, vector data with fiona.
file_ext = os.path.splitext(input_file)[1][1:]
if file_ext in ["shp", "geojson"]:
is_vector_file = True
else:
is_vector_file = False
if is_vector_file:
with fiona.open(input_file) as inp:
inp_crs = CRS(inp.crs)
bounds = inp.bounds
else:
if file_ext in ["SAFE", "zip", "ZIP"]:
with s2reader.open(input_file) as s2dataset:
inp_crs = CRS().from_epsg(4326)
if inp_crs != out_crs:
bounds = reproject_geometry(
s2dataset.footprint,
src_crs=inp_crs,
dst_crs=out_crs
).bounds
inp_crs = out_crs
else:
bounds = s2dataset.footprint.bounds
else:
with rasterio.open(input_file) as inp:
inp_crs = inp.crs
try:
assert inp_crs.is_valid
except AssertionError:
raise IOError("CRS could not be read from %s" % input_file)
bounds = (
inp.bounds.left, inp.bounds.bottom, inp.bounds.right,
inp.bounds.top)
out_bbox = bbox = box(*bounds)
# If soucre and target CRSes differ, segmentize and reproject
if inp_crs != out_crs:
if not is_vector_file:
segmentize = _get_segmentize_value(input_file, tile_pyramid)
try:
ogr_bbox = ogr.CreateGeometryFromWkb(bbox.wkb)
ogr_bbox.Segmentize(segmentize)
segmentized_bbox = loads(ogr_bbox.ExportToWkt())
bbox = segmentized_bbox
except:
raise
try:
out_bbox = reproject_geometry(
bbox,
src_crs=inp_crs,
dst_crs=out_crs
)
except:
raise
else:
out_bbox = bbox
# Validate and, if necessary, try to fix output geometry.
try:
assert out_bbox.is_valid
except AssertionError:
try:
cleaned = out_bbox.buffer(0)
assert cleaned.is_valid
except Exception as e:
raise TypeError("invalid file bbox geometry: %s" % e)
out_bbox = cleaned
return out_bbox
def read_path(self, path):
values = {}
with s2reader.open(path) as ds:
metadata = ds._product_metadata
granule = ds.granules[0]
granule_metadata = granule._metadata
values['identifier'] = metadata.findtext('.//PRODUCT_URI')
values['begin_time'] = ds.product_start_time
values['end_time'] = ds.product_stop_time
values['footprint'] = ds.footprint.wkt
values['masks'] = [
('clouds', self._read_mask(granule, 'MSK_CLOUDS')),
('nodata', self._read_mask(granule, 'MSK_NODATA')),
]
def tci_path(granule):
tci_paths = [
path for path in granule.dataset._product_metadata.xpath(
".//Granule[@granuleIdentifier='%s']/IMAGE_FILE/text()"
% granule.granule_identifier) if path.endswith('TCI')
]
try:
return os.path.join(ds._zip_root if ds.is_zip else ds.path,
tci_paths[0]) + '.jp2'
except IndexError:
raise IOError("TCI path does not exist")
values['browses'] = [(None, tci_path(granule))]
# TODO: extended metadata
# values['parent_identifier']
# values['production_status']
# values['acquisition_type']
values['orbit_number'] = ds.sensing_orbit_number
values['orbit_direction'] = ds.sensing_orbit_direction
# values['track']
# values['frame']
values['swath_identifier'] = metadata.find(
'.//Product_Info/Datatake').attrib['datatakeIdentifier']
values['product_version'] = metadata.findtext(
'.//Product_Info/PROCESSING_BASELINE')
# values['product_quality_status']
# values['product_quality_degradation_tag']
# values['processor_name']
# values['processing_center']
# values['creation_date']
# values['modification_date']
values['processing_date'] = ds.generation_time
# values['sensor_mode']
values['archiving_center'] = granule_metadata.findtext(
'.//ARCHIVING_CENTRE')
# values['processing_mode']
values['availability_time'] = ds.generation_time
# values['acquisition_station']
# values['acquisition_sub_type']
# values['start_time_from_ascending_node']
# values['completion_time_from_ascending_node']
values['illumination_azimuth_angle'] = metadata.findtext(
'.//Mean_Sun_Angle/AZIMUTH_ANGLE')
values['illumination_zenith_angle'] = metadata.findtext(
'.//Mean_Sun_Angle/ZENITH_ANGLE')
# values['illumination_elevation_angle']
# values['polarisation_mode']
# values['polarization_channels']
# values['antenna_look_direction']
# values['minimum_incidence_angle']
# values['maximum_incidence_angle']
# values['doppler_frequency']
# values['incidence_angle_variation']
values['cloud_cover'] = metadata.findtext(
".//Cloud_Coverage_Assessment")
# values['snow_cover']
# values['lowest_location']
# values['highest_location']
return values
