def incidence_angle(self, rsfile):
n = Nansat(rsfile)
inc_min = float(n.get_metadata()['NEAR_RANGE_INCIDENCE_ANGLE'])-0.5
inc_max = float(n.get_metadata()['FAR_RANGE_INCIDENCE_ANGLE'])+0.5
inc = n['incidence_angle']
assert np.all(np.greater_equal(inc[np.isnan(inc)==False], inc_min))
assert np.all(np.less_equal(inc[np.isnan(inc)==False], inc_max))
def incidence_angle(self, rsfile):
sys.stderr.write("\nincidence_angle:" + rsfile + "\n")
n = Nansat(rsfile)
inc_min = float(n.get_metadata()["NEAR_RANGE_INCIDENCE_ANGLE"]) - 0.5
inc_max = float(n.get_metadata()["FAR_RANGE_INCIDENCE_ANGLE"]) + 0.5
inc = n["incidence_angle"]
assert np.all(np.greater_equal(inc[np.isnan(inc) == False], inc_min))
assert np.all(np.less_equal(inc[np.isnan(inc) == False], inc_max))
def test_issue_193(self):
fn = [
'/vagrant/shared/test_data/cmems/GLOBAL_ANALYSIS_FORECAST_PHY_001_024-TDS-x10-X30-y55-Y73-201705181200-201705271200.nc',
'/vagrant/shared/test_data/cmems/ARC-METNO-ARC-TOPAZ4_2_PHYS-FOR-TDS-x10-X30-y55-Y73-20170518-20170526.nc',
'/vagrant/shared/test_data/cmems/GLOBAL_ANALYSIS_FORECAST_BIO_001_014-TDS-x-180-X179.5-y-89-Y90-20170520-20170527.nc',
]
for f in fn:
n = Nansat(f)
self.assertTrue(n.get_metadata().has_key('time_coverage_start'))
self.assertTrue(n.get_metadata().has_key('time_coverage_end'))
self.assertTrue(n.get_metadata().has_key('instrument'))
self.assertTrue(n.get_metadata().has_key('platform'))
self.assertEqual(n.mapper, 'cmems')
def test_issue_193(self):
fn = [
'/vagrant/shared/test_data/cmems/GLOBAL_ANALYSIS_FORECAST_PHY_001_024-TDS-x10-X30-y55-Y73-201705181200-201705271200.nc',
'/vagrant/shared/test_data/cmems/ARC-METNO-ARC-TOPAZ4_2_PHYS-FOR-TDS-x10-X30-y55-Y73-20170518-20170526.nc',
'/vagrant/shared/test_data/cmems/GLOBAL_ANALYSIS_FORECAST_BIO_001_014-TDS-x-180-X179.5-y-89-Y90-20170520-20170527.nc',
]
for f in fn:
n = Nansat(f)
self.assertTrue(n.get_metadata().has_key('time_coverage_start'))
self.assertTrue(n.get_metadata().has_key('time_coverage_end'))
self.assertTrue(n.get_metadata().has_key('instrument'))
self.assertTrue(n.get_metadata().has_key('platform'))
self.assertEqual(n.mapper, 'cmems')
def _get_aux_wind_from_str(self, aux_wind_source, *args, **kwargs):
try:
wdir = int(aux_wind_source)
wdir, wdir_time, wspeed = self._get_aux_wind_from_int(wdir)
except ValueError:
import nansat.nansat
mnames = [
key.replace('mapper_', '')
for key in nansat.nansat.nansatMappers
]
# check if aux_wind_source is like 'ncep_wind_online', i.e. only
# mapper name is given. By adding the SAR image time stamp, we
# can then get the data online
if aux_wind_source in mnames:
aux_wind_source = aux_wind_source + \
datetime.strftime(self.SAR_image_time, ':%Y%m%d%H%M')
aux = Nansat(
aux_wind_source,
netcdf_dim={
'time': np.datetime64(self.SAR_image_time),
'height2': '10', # height dimension used in AROME arctic
# datasets
'height3': '10',
},
bands=[ # CF standard names of desired bands
'x_wind',
'y_wind', # or..:
'eastward_wind',
'northward_wind',
])
# Set filename of source wind in metadata
try:
wind_u_bandNo = aux._get_band_number({
'standard_name':
'eastward_wind',
})
except OptionError:
wind_u_bandNo = aux._get_band_number({
'standard_name': 'x_wind',
})
self.set_metadata(
'WIND_DIRECTION_SOURCE',
aux.get_metadata(bandID=wind_u_bandNo)['SourceFilename'])
wdir, wdir_time, wspeed = self._get_wind_direction_array(
aux, *args, **kwargs)
return wdir, wdir_time, wspeed
def resize(self, rsfile):
sys.stderr.write('\nresize:'+rsfile+'\n')
n = Nansat(rsfile)
inc_max = float(n.get_metadata()['FAR_RANGE_INCIDENCE_ANGLE'])+0.5
n.resize(0.5, eResampleAlg=0)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=1)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=2)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=3)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=4)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
def resize(self, rsfile):
sys.stderr.write("\nresize:" + rsfile + "\n")
n = Nansat(rsfile)
inc_max = float(n.get_metadata()["FAR_RANGE_INCIDENCE_ANGLE"]) + 0.5
n.resize(0.5, eResampleAlg=0)
assert np.nanmax(n["incidence_angle"]) <= inc_max
n.undo()
n.resize(0.5, eResampleAlg=1)
assert np.nanmax(n["incidence_angle"]) <= inc_max
n.undo()
n.resize(0.5, eResampleAlg=2)
assert np.nanmax(n["incidence_angle"]) <= inc_max
n.undo()
n.resize(0.5, eResampleAlg=3)
assert np.nanmax(n["incidence_angle"]) <= inc_max
n.undo()
n.resize(0.5, eResampleAlg=4)
assert np.nanmax(n["incidence_angle"]) <= inc_max
n.undo()
def resize(self, rsfile):
sys.stderr.write('\nresize:'+rsfile+'\n')
n = Nansat(rsfile)
inc_max = float(n.get_metadata()['FAR_RANGE_INCIDENCE_ANGLE'])+0.5
n.resize(0.5, eResampleAlg=0)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=1)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=2)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=3)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
n.resize(0.5, eResampleAlg=4)
assert (np.nanmax(n['incidence_angle']) <= inc_max)
n.undo()
def add_polarization(apps, schema_editor):
ds_model = apps.get_model('sar_doppler', 'dataset')
extra_model = apps.get_model('sar_doppler', 'sardopplerextrametadata')
for ds in ds_model.objects.filter(dataseturi__uri__endswith='.gsar'):
if ds.sardopplerextrametadata_set.all():
# This should only happen if the migration is interrupted
# No point in adding polarization if it was already added...
continue
fn = nansat_filename(ds.dataseturi_set.get(uri__endswith='.gsar').uri)
if not os.path.isfile(fn):
# a missing file will break the migration
# remove the dataset in case the file doesn't exist
ds.delete()
continue
n = Nansat(fn)
# Store the polarization and associate the dataset
extra = extra_model(dataset=ds,
polarization=n.get_metadata('polarization'))
extra.save()
ds.sardopplerextrametadata_set.add(extra)
def _get_aux_wind_from_str(self, aux_wind_source, *args, **kwargs):
try:
wdir = int(aux_wind_source)
wdir, wdir_time, wspeed = self._get_aux_wind_from_int(wdir)
except ValueError:
import nansat.nansat
mnames = [key.replace('mapper_','') for key in
nansat.nansat.nansatMappers]
# check if aux_wind_source is like 'ncep_wind_online', i.e. only
# mapper name is given. By adding the SAR image time stamp, we
# can then get the data online
if aux_wind_source in mnames:
aux_wind_source = aux_wind_source + \
datetime.strftime(self.SAR_image_time, ':%Y%m%d%H%M')
aux = Nansat(aux_wind_source, netcdf_dim={
'time': np.datetime64(self.SAR_image_time),
'height2': '10', # height dimension used in AROME arctic
# datasets
'height3': '10',
},
bands = [ # CF standard names of desired bands
'x_wind',
'y_wind', # or..:
'eastward_wind',
'northward_wind',
])
# Set filename of source wind in metadata
try:
wind_u_bandNo = aux._get_band_number({
'standard_name': 'eastward_wind',
})
except OptionError:
wind_u_bandNo = aux._get_band_number({
'standard_name': 'x_wind',
})
self.set_metadata('WIND_DIRECTION_SOURCE',
aux.get_metadata(bandID=wind_u_bandNo)['SourceFilename'])
wdir, wdir_time, wspeed = self._get_wind_direction_array(aux,
*args, **kwargs)
return wdir, wdir_time, wspeed
def get_or_create(self,
uri,
n_points=10,
uri_filter_args=None,
*args,
**kwargs):
''' Create dataset and corresponding metadata
Parameters:
----------
uri : str
URI to file or stream openable by Nansat
n_points : int
Number of border points (default is 10)
uri_filter_args : dict
Extra DatasetURI filter arguments if several datasets can refer to the same URI
Returns:
-------
dataset and flag
'''
if not uri_filter_args:
uri_filter_args = {}
# 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, **uri_filter_args)
if len(uris) > 0:
return uris[0].dataset, False
# Open file with Nansat
n = Nansat(nansat_filename(uri), **kwargs)
# 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)
default_char_fields = {
'entry_id': lambda: 'NERSC_' + str(uuid.uuid4()),
'entry_title': lambda: 'NONE',
'summary': lambda: 'NONE',
}
# set optional CharField metadata from Nansat or from default_char_fields
options = {}
for name in default_char_fields:
if name not in n_metadata:
warnings.warn('%s is not provided in Nansat metadata!' % name)
options[name] = default_char_fields[name]()
else:
options[name] = n_metadata[name]
default_foreign_keys = {
'gcmd_location': {
'model': Location,
'value': pti.get_gcmd_location('SEA SURFACE')
},
'data_center': {
'model': DataCenter,
'value': pti.get_gcmd_provider('NERSC')
},
'ISO_topic_category': {
'model': ISOTopicCategory,
'value': pti.get_iso19115_topic_category('Oceans')
},
}
# set optional ForeignKey metadata from Nansat or from default_foreign_keys
for name in default_foreign_keys:
value = default_foreign_keys[name]['value']
model = default_foreign_keys[name]['model']
if name not in n_metadata:
warnings.warn('%s is not provided in Nansat metadata!' % name)
else:
try:
value = json.loads(n_metadata[name])
except:
warnings.warn(
'%s value of %s metadata provided in Nansat is wrong!'
% (n_metadata[name], name))
options[name], _ = model.objects.get_or_create(value)
# Find coverage to set number of points in the geolocation
if len(n.vrt.dataset.GetGCPs()) > 0:
n.reproject_gcps()
geolocation = GeographicLocation.objects.get_or_create(
geometry=WKTReader().read(n.get_border_wkt(nPoints=n_points)))[0]
# create dataset
ds, created = Dataset.objects.get_or_create(
time_coverage_start=n.get_metadata('time_coverage_start'),
time_coverage_end=n.get_metadata('time_coverage_end'),
source=source,
geographic_location=geolocation,
**options)
# create dataset URI
ds_uri, _ = DatasetURI.objects.get_or_create(uri=uri, dataset=ds)
return ds, created
def get_or_create(self,
uri,
n_points=10,
uri_filter_args=None,
uri_service_name=FILE_SERVICE_NAME,
uri_service_type=LOCAL_FILE_SERVICE,
*args,
**kwargs):
""" Create dataset and corresponding metadata
Parameters:
----------
uri : str
URI to file or stream openable by Nansat
n_points : int
Number of border points (default is 10)
uri_filter_args : dict
Extra DatasetURI filter arguments if several datasets can refer to the same URI
uri_service_name : str
name of the service which is used ('dapService', 'fileService', 'http' or 'wms')
uri_service_type : str
type of the service which is used ('OPENDAP', 'local', 'HTTPServer' or 'WMS')
Returns:
-------
dataset and flag
"""
if not uri_filter_args:
uri_filter_args = {}
# 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, **uri_filter_args)
if len(uris) > 0:
return uris[0].dataset, False
# Open file with Nansat
n = Nansat(nansat_filename(uri), **kwargs)
# get metadata from Nansat and get objects from vocabularies
n_metadata = n.get_metadata()
entry_id = n_metadata.get('entry_id', None)
# 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)
default_char_fields = {
# Adding NERSC_ in front of the id violates the string representation of the uuid
#'entry_id': lambda: 'NERSC_' + str(uuid.uuid4()),
'entry_id': lambda: str(uuid.uuid4()),
'entry_title': lambda: 'NONE',
'summary': lambda: 'NONE',
}
# set optional CharField metadata from Nansat or from default_char_fields
options = {}
try:
existing_ds = Dataset.objects.get(entry_id=entry_id)
except Dataset.DoesNotExist:
existing_ds = None
for name in default_char_fields:
if name not in n_metadata:
warnings.warn('%s is not provided in Nansat metadata!' % name)
# prevent overwriting of existing values by defaults
if existing_ds:
options[name] = existing_ds.__getattribute__(name)
else:
options[name] = default_char_fields[name]()
else:
options[name] = n_metadata[name]
default_foreign_keys = {
'gcmd_location': {
'model': Location,
'value': pti.get_gcmd_location('SEA SURFACE')
},
'data_center': {
'model': DataCenter,
'value': pti.get_gcmd_provider('NERSC')
},
'ISO_topic_category': {
'model': ISOTopicCategory,
'value': pti.get_iso19115_topic_category('Oceans')
},
}
# set optional ForeignKey metadata from Nansat or from default_foreign_keys
for name in default_foreign_keys:
value = default_foreign_keys[name]['value']
model = default_foreign_keys[name]['model']
if name not in n_metadata:
warnings.warn('%s is not provided in Nansat metadata!' % name)
else:
try:
value = json.loads(n_metadata[name])
except:
warnings.warn(
'%s value of %s metadata provided in Nansat is wrong!'
% (n_metadata[name], name))
if existing_ds:
options[name] = existing_ds.__getattribute__(name)
else:
options[name], _ = model.objects.get_or_create(value)
# Find coverage to set number of points in the geolocation
if len(n.vrt.dataset.GetGCPs()) > 0:
n.reproject_gcps()
geolocation = GeographicLocation.objects.get_or_create(
geometry=WKTReader().read(n.get_border_wkt(nPoints=n_points)))[0]
# create dataset
# - the get_or_create method should use get_or_create here as well,
# or its name should be changed - see issue #127
ds, created = Dataset.objects.update_or_create(
entry_id=options['entry_id'],
defaults={
'time_coverage_start': n.get_metadata('time_coverage_start'),
'time_coverage_end': n.get_metadata('time_coverage_end'),
'source': source,
'geographic_location': geolocation,
'gcmd_location': options["gcmd_location"],
'ISO_topic_category': options["ISO_topic_category"],
"data_center": options["data_center"],
'entry_title': options["entry_title"],
'summary': options["summary"]
})
# create parameter
all_band_meta = n.bands()
for band_id in range(1, len(all_band_meta) + 1):
band_meta = all_band_meta[band_id]
standard_name = band_meta.get('standard_name', None)
short_name = band_meta.get('short_name', None)
units = band_meta.get('units', None)
if standard_name in ['latitude', 'longitude', None]:
continue
params = Parameter.objects.filter(standard_name=standard_name)
if params.count() > 1 and short_name is not None:
params = params.filter(short_name=short_name)
if params.count() > 1 and units is not None:
params = params.filter(units=units)
if params.count() >= 1:
ds.parameters.add(params[0])
# create dataset URI
DatasetURI.objects.get_or_create(name=uri_service_name,
service=uri_service_type,
uri=uri,
dataset=ds)
return ds, created
def get_or_create(self, uri, reprocess=False, *args, **kwargs):
# ingest file to db
ds, created = super(DatasetManager,
self).get_or_create(uri, *args, **kwargs)
fn = nansat_filename(uri)
n = Nansat(fn)
# Reproject to leaflet projection
xlon, xlat = n.get_corners()
d = Domain(
NSR(3857), '-lle %f %f %f %f -tr 1000 1000' %
(xlon.min(), xlat.min(), xlon.max(), xlat.max()))
n.reproject(d)
# Get band numbers of required bands according to standard names
speedBandNum = n._get_band_number({'standard_name': 'wind_speed'})
dirBandNum = n._get_band_number(
{'standard_name': 'wind_from_direction'})
# Get numpy arrays of the bands
speed = n[speedBandNum]
dir = n[dirBandNum]
## It probably wont work with nansatmap...
#nmap = Nansatmap(n, resolution='l')
#nmap.pcolormesh(speed, vmax=18)
#nmap.quiver(-speed*np.sin(dir), speed*np.cos(dir), step=10, scale=300,
# width=0.002)
# Set paths - this code should be inherited but I think there is an
# issue in generalising the first line that defines the current module
mm = self.__module__.split('.')
module = '%s.%s' % (mm[0], mm[1])
mp = media_path(module, n.fileName)
ppath = product_path(module, n.fileName)
filename = os.path.basename(n.fileName).split('.')[0] + '.' + \
os.path.basename(n.fileName).split('.')[1] + '.png'
# check uniqueness of parameter
param1 = Parameter.objects.get(standard_name=n.get_metadata(
bandID=speedBandNum, key='standard_name'))
param2 = Parameter.objects.get(standard_name=n.get_metadata(
bandID=dirBandNum, key='standard_name'))
n.write_figure(os.path.join(mp, filename),
bands=speedBandNum,
mask_array=n['swathmask'],
mask_lut={0: [128, 128, 128]},
transparency=[128, 128, 128])
# Get DatasetParameter
dsp1, created = DatasetParameter.objects.get_or_create(
dataset=ds, parameter=param1)
# Create Visualization
geom, created = GeographicLocation.objects.get_or_create(
geometry=WKTReader().read(n.get_border_wkt()))
vv, created = Visualization.objects.get_or_create(
uri='file://localhost%s/%s' % (mp, filename),
title='%s' % (param1.standard_name),
geographic_location=geom)
# Create VisualizationParameter
vp, created = VisualizationParameter.objects.get_or_create(
visualization=vv, ds_parameter=dsp1)
return ds, True
def get_or_create(self, uri, *args, **kwargs):
""" Ingest gsar file to geo-spaas db
"""
ds, created = super(DatasetManager,
self).get_or_create(uri, *args, **kwargs)
connection.close()
# TODO: Check if the following is necessary
if not type(ds) == Dataset:
return ds, False
fn = nansat_filename(uri)
n = Nansat(fn, subswath=0)
# set Dataset entry_title
ds.entry_title = n.get_metadata('title')
ds.save()
if created:
from sar_doppler.models import SARDopplerExtraMetadata
# Store the polarization and associate the dataset
extra, _ = SARDopplerExtraMetadata.objects.get_or_create(
dataset=ds, polarization=n.get_metadata('polarization'))
if not _:
raise ValueError(
'Created new dataset but could not create instance of ExtraMetadata'
)
ds.sardopplerextrametadata_set.add(extra)
connection.close()
gg = WKTReader().read(n.get_border_wkt())
#lon, lat = n.get_border()
#ind_near_range = 0
#ind_far_range = int(lon.size/4)
#import pyproj
#geod = pyproj.Geod(ellps='WGS84')
#angle1,angle2,img_width = geod.inv(lon[ind_near_range], lat[ind_near_range],
# lon[ind_far_range], lat[ind_far_range])
# If the area of the dataset geometry is larger than the area of the subswath border, it means that the dataset
# has already been created (the area should be the total area of all subswaths)
if np.floor(ds.geographic_location.geometry.area) > np.round(gg.area):
return ds, False
swath_data = {}
lon = {}
lat = {}
astep = {}
rstep = {}
az_left_lon = {}
ra_upper_lon = {}
az_right_lon = {}
ra_lower_lon = {}
az_left_lat = {}
ra_upper_lat = {}
az_right_lat = {}
ra_lower_lat = {}
num_border_points = 10
border = 'POLYGON(('
for i in range(self.N_SUBSWATHS):
# Read subswaths
swath_data[i] = Nansat(fn, subswath=i)
lon[i], lat[i] = swath_data[i].get_geolocation_grids()
astep[i] = int(
max(1, (lon[i].shape[0] / 2 * 2 - 1) / num_border_points))
rstep[i] = int(
max(1, (lon[i].shape[1] / 2 * 2 - 1) / num_border_points))
az_left_lon[i] = lon[i][0:-1:astep[i], 0]
az_left_lat[i] = lat[i][0:-1:astep[i], 0]
az_right_lon[i] = lon[i][0:-1:astep[i], -1]
az_right_lat[i] = lat[i][0:-1:astep[i], -1]
ra_upper_lon[i] = lon[i][-1, 0:-1:rstep[i]]
ra_upper_lat[i] = lat[i][-1, 0:-1:rstep[i]]
ra_lower_lon[i] = lon[i][0, 0:-1:rstep[i]]
ra_lower_lat[i] = lat[i][0, 0:-1:rstep[i]]
lons = np.concatenate(
(az_left_lon[0], ra_upper_lon[0], ra_upper_lon[1], ra_upper_lon[2],
ra_upper_lon[3], ra_upper_lon[4], np.flipud(az_right_lon[4]),
np.flipud(ra_lower_lon[4]), np.flipud(ra_lower_lon[3]),
np.flipud(ra_lower_lon[2]), np.flipud(ra_lower_lon[1]),
np.flipud(ra_lower_lon[0]))).round(decimals=3)
# apply 180 degree correction to longitude - code copied from
# get_border_wkt...
# TODO: simplify using np.mod?
for ilon, llo in enumerate(lons):
lons[ilon] = copysign(
acos(cos(llo * pi / 180.)) / pi * 180, sin(llo * pi / 180.))
lats = np.concatenate(
(az_left_lat[0], ra_upper_lat[0], ra_upper_lat[1], ra_upper_lat[2],
ra_upper_lat[3], ra_upper_lat[4], np.flipud(az_right_lat[4]),
np.flipud(ra_lower_lat[4]), np.flipud(ra_lower_lat[3]),
np.flipud(ra_lower_lat[2]), np.flipud(ra_lower_lat[1]),
np.flipud(ra_lower_lat[0]))).round(decimals=3)
poly_border = ','.join(
str(llo) + ' ' + str(lla) for llo, lla in zip(lons, lats))
wkt = 'POLYGON((%s))' % poly_border
new_geometry = WKTReader().read(wkt)
# Get or create new geolocation of dataset
# Returns False if it is the same as an already created one (this may happen when a lot of data is processed)
ds.geographic_location, cr = GeographicLocation.objects.get_or_create(
geometry=new_geometry)
connection.close()
return ds, True
def export2netcdf(self, n, ds, history_message=''):
if not history_message:
history_message = 'Export to netCDF [geospaas sar_doppler version %s]' % os.getenv(
'GEOSPAAS_SAR_DOPPLER_VERSION', 'dev')
ii = int(n.get_metadata('subswath'))
date_created = datetime.now(timezone.utc)
fn = self.nc_name(ds, ii)
original = Nansat(n.get_metadata('Originating file'), subswath=ii)
metadata = original.get_metadata()
def pretty_print_gcmd_keywords(kw):
retval = ''
value_prev = ''
for key, value in kw.items():
if value:
if value_prev:
retval += ' > '
retval += value
value_prev = value
return retval
# Set global metadata
metadata['Conventions'] = metadata['Conventions'] + ', ACDD-1.3'
# id - the ID from the database should be registered in the file if it is not already there
try:
entry_id = n.get_metadata('entry_id')
except ValueError:
n.set_metadata(key='entry_id', value=ds.entry_id)
try:
id = n.get_metadata('id')
except ValueError:
n.set_metadata(key='id', value=ds.entry_id)
metadata['date_created'] = date_created.strftime('%Y-%m-%d')
metadata['date_created_type'] = 'Created'
metadata['date_metadata_modified'] = date_created.strftime('%Y-%m-%d')
metadata['processing_level'] = 'Scientific'
metadata['creator_role'] = 'Investigator'
metadata['creator_name'] = 'Morten Wergeland Hansen'
metadata['creator_email'] = '*****@*****.**'
metadata['creator_institution'] = pretty_print_gcmd_keywords(
pti.get_gcmd_provider('NO/MET'))
metadata[
'project'] = 'Norwegian Space Agency project JOP.06.20.2: Reprocessing and analysis of historical data for future operationalization of Doppler shifts from SAR'
metadata['publisher_name'] = 'Morten Wergeland Hansen'
metadata['publisher_url'] = 'https://www.met.no/'
metadata['publisher_email'] = '*****@*****.**'
metadata['references'] = 'https://github.com/mortenwh/openwind'
metadata['dataset_production_status'] = 'Complete'
# Get image boundary
lon, lat = n.get_border()
boundary = 'POLYGON (('
for la, lo in list(zip(lat, lon)):
boundary += '%.2f %.2f, ' % (la, lo)
boundary = boundary[:-2] + '))'
# Set bounds as (lat,lon) following ACDD convention and EPSG:4326
metadata['geospatial_bounds'] = boundary
metadata['geospatial_bounds_crs'] = 'EPSG:4326'
# history
try:
history = n.get_metadata('history')
except ValueError:
metadata['history'] = date_created.isoformat(
) + ': ' + history_message
else:
metadata['history'] = history + '\n' + date_created.isoformat(
) + ': ' + history_message
# Set metadata from dict (export2thredds could take it as input..)
for key, val in metadata.items():
n.set_metadata(key=key, value=val)
# Export data to netcdf
logging.info('Exporting %s to %s (subswath %d)' %
(n.filename, fn, ii + 1))
n.export(filename=fn)
#ww.export2thredds(thredds_fn, mask_name='swathmask', metadata=metadata, no_mask_value=1)
# Clean netcdf attributes
history = n.get_metadata('history')
self.clean_nc_attrs(fn, history)
# Add netcdf uri to DatasetURIs
ncuri = 'file://localhost' + fn
new_uri, created = DatasetURI.objects.get_or_create(uri=ncuri,
dataset=ds)
connection.close()
return new_uri, created
