def get_or_create(self, uri, force):
# Validate uri - this should raise an exception if the uri doesn't
# point to a valid file or stream
validate_uri(uri)
# Several datasets can refer to the same uri (e.g., scatterometers and svp drifters), so we
# need to pass uri_filter_args
uris = DatasetURI.objects.filter(uri=uri)
# If the ingested uri is already in the database and not <force> ingestion then stop
if uris.exists() and not force:
return uris[0].dataset, False
elif uris.exists() and force:
uris[0].dataset.delete()
# Open file with Nansat
n = Nansat(nansat_filename(uri))
# get metadata from Nansat and get objects from vocabularies
n_metadata = n.get_metadata()
# set compulsory metadata (source)
platform, _ = Platform.objects.get_or_create(
json.loads(n_metadata['platform']))
instrument, _ = Instrument.objects.get_or_create(
json.loads(n_metadata['instrument']))
specs = n_metadata.get('specs', '')
source, _ = Source.objects.get_or_create(platform=platform,
instrument=instrument,
specs=specs)
footprint = Polygon(list(zip(*n.get_border())))
geolocation = GeographicLocation.objects.get_or_create(
geometry=footprint)[0]
data_center = DataCenter.objects.get_or_create(
json.loads(n_metadata['Data Center']))[0]
iso_category = ISOTopicCategory.objects.get_or_create(
pti.get_iso19115_topic_category('Oceans'))[0]
location = Location.objects.get_or_create(
json.loads(n_metadata['gcmd_location']))[0]
# create dataset
ds, created = Dataset.objects.get_or_create(
time_coverage_start=make_aware(n.time_coverage_start),
time_coverage_end=make_aware(
n.time_coverage_start +
timedelta(hours=23, minutes=59, seconds=59)),
source=source,
geographic_location=geolocation,
ISO_topic_category=iso_category,
data_center=data_center,
summary='',
gcmd_location=location,
access_constraints='',
entry_id=lambda: 'NERSC_' + str(uuid.uuid4()))
ds_uri, _ = DatasetURI.objects.get_or_create(
name=FILE_SERVICE_NAME,
service=LOCAL_FILE_SERVICE,
uri=uri,
dataset=ds)
return ds, created
#n = Nansat(iPath + fileName)
# list bands and georeference of the object
print n
# get dictionary with all bands metadata
print n.bands()
# get size of the object (Y and X dimensions, to follow Numpy style)
print n.shape()
# get list with coordinates of the object corners
print n.get_corners()
# get lists with coordinates of the object borders
print n.get_border()
raw_counts = n[1]
inc_angle = n[2]
#~ sigma0 = n[3]
sigma0 = raw_counts**2.0 * sin(deg2rad(inc_angle))
sigma0 = 10*log10(sigma0)
n.add_band(bandID=4, array=sigma0)
# 1. Remove speckle noise (using Lee-Wiener filter)
speckle_filter('wiener', 7)
# Reprojected image into Lat/Lon WGS84 (Simple Cylindrical) projection
# 1. Cancel previous reprojection
n = Nansat(iFileName, mapperName='generic', logLevel=10)
# list bands and georeference of the object
print 'Raw Nansat:', n
# get dictionary with metadata from all bands
print 'Bands:', n.bands()
# get size of the object (Y and X dimensions, to follow Numpy style)
print 'Shape:', n.shape()
# get list with coordinates of the object corners
print 'Corners:', n.get_corners()
# get lists with coordinates of the object borders
print 'Border:', n.get_border()
# get time of the image aquisition
print 'Time:', n.get_time()[0]
# Get band data and do some operations
# 1. Get data from 1st band as numpy array
# 2. Plot the array (pyplot image is save to a PNG file)
# 3. Save as Matlab file
a = n[1]
plt.imshow(a);plt.colorbar();plt.savefig(oFileName + '_imshow.png');plt.close()
savemat(oFileName + '.mat', {'band_1': a})
# make simple indexed image from 1st band with default colormap
n.write_figure(oFileName + '.png')
