def test_reproject_gcps(self):
ds = gdal.Open(self.test_file)
d = Domain(ds=ds)
d.reproject_gcps(
'+proj=stere +datum=WGS84 +ellps=WGS84 +lat_0=75 +lon_0=10 +no_defs'
)
gcp = d.vrt.dataset.GetGCPs()[0]
self.assertTrue(gcp.GCPX > 636161)
self.assertTrue(gcp.GCPY < -288344)
def test_get_pixelsize_meters(self):
d = Domain(4326, "-te 25 70 35 72 -ts 500 500")
x, y = d.get_pixelsize_meters()
self.assertEqual(int(x), 444)
self.assertEqual(int(y), 723)
d = Domain(ds=gdal.Open(self.test_file_projected))
x, y = d.get_pixelsize_meters()
self.assertEqual(int(x), 500)
self.assertEqual(int(y), 500)
def test_export2thredds_arctic_long_lat(self):
n = Nansat(self.test_file_arctic, logLevel=40)
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_export2thredds_arctic.nc')
bands = {
'Bristol': {
'type': '>i2'
},
'Bootstrap': {
'type': '>i2'
},
'UMass_AES': {
'type': '>i2'
},
}
n.export2thredds(tmpfilename,
bands,
time=datetime.datetime(2016, 1, 20))
self.assertTrue(os.path.exists(tmpfilename))
g = gdal.Open(tmpfilename)
metadata = g.GetMetadata_Dict()
# Test that the long/lat values are set aproximately correct
ncg = 'NC_GLOBAL#'
easternmost_longitude = metadata.get(ncg + 'easternmost_longitude')
self.assertTrue(
float(easternmost_longitude) > 179,
'easternmost_longitude is wrong:' + easternmost_longitude)
westernmost_longitude = metadata.get(ncg + 'westernmost_longitude')
self.assertTrue(
float(westernmost_longitude) < -179,
'westernmost_longitude is wrong:' + westernmost_longitude)
northernmost_latitude = metadata.get(ncg + 'northernmost_latitude')
self.assertTrue(
float(northernmost_latitude) > 89.999,
'northernmost_latitude is wrong:' + northernmost_latitude)
southernmost_latitude = metadata.get(ncg + 'southernmost_latitude')
self.assertTrue(
float(southernmost_latitude) < 54,
'southernmost_latitude is wrong:' + southernmost_latitude)
self.assertTrue(
float(southernmost_latitude) > 53,
'southernmost_latitude is wrong:' + southernmost_latitude)
def __init__(self, filename, gdalDataset, gdalMetadata, **kwargs):
"""Create VRT"""
try:
ice_folder_name = kwargs['iceFolder']
except:
#iceFolderName = '/vol/istjenesten/data/metnoCharts/'
ice_folder_name = '/vol/data/metnoCharts/'
keyword_base = 'metno_local_hires_seaice'
if filename[0:len(keyword_base)] != keyword_base:
raise WrongMapperError
keyword_time = filename[len(keyword_base) + 1:]
requested_time = datetime.strptime(keyword_time, '%Y%m%d')
# Search for nearest available file, within the closest 3 days
found_dataset = False
for delta_day in [0, -1, 1, -2, 2, -3, 3]:
valid_time = (requested_time + timedelta(days=delta_day) +
timedelta(hours=15))
filename = (ice_folder_name + 'ice_conc_svalbard_' +
valid_time.strftime('%Y%m%d1500.nc'))
if os.path.exists(filename):
print('Found file:')
print(filename)
gdal_dataset = gdal.Open(filename)
gdal_metadata = gdalDataset.GetMetadata()
mg.Mapper.__init__(self, filename, gdal_dataset, gdal_metadata)
found_dataset = True
# Modify GeoTransform from netCDF file
# - otherwise a shift is seen!
self.dataset.SetGeoTransform(
(-1243508 - 1000, 1000, 0, -210526 - 7000, 0, -1000))
break # Data is found for this day
if found_dataset is False:
AttributeError("No local Svalbard-ice files available")
sys.exit()
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
GCP_COUNT=30,
**kwargs):
''' Create MODIS_L1 VRT '''
#list of available modis names:resolutions
modisResolutions = {
'MYD02QKM': 250,
'MOD02QKM': 250,
'MYD02HKM': 500,
'MOD02HKM': 500,
'MYD021KM': 1000,
'MOD021KM': 1000
}
#should raise error in case of not MODIS_L1
try:
mResolution = modisResolutions[gdalMetadata["SHORTNAME"]]
except:
raise WrongMapperError
# get 1st subdataset and parse to VRT.__init__()
# for retrieving geo-metadata
try:
gdalSubDataset = gdal.Open(gdalDataset.GetSubDatasets()[0][0])
except (AttributeError, IndexError):
raise WrongMapperError
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalSubDataset)
subDsString = 'HDF4_EOS:EOS_SWATH:"%s":MODIS_SWATH_Type_L1B:%s'
#provide all mappings
metaDict250SF = ['EV_250_RefSB']
metaDict250 = [{
'src': {
'SourceFilename': subDsString % (fileName, 'EV_250_RefSB'),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '645'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_250_RefSB'),
'SourceBand': 2
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '858'
}
}]
metaDict500SF = ['EV_250_Aggr500_RefSB', 'EV_500_RefSB']
metaDict500 = [{
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_250_Aggr500_RefSB'),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '645'
}
}, {
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_250_Aggr500_RefSB'),
'SourceBand': 2
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '858'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_500_RefSB'),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '469'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_500_RefSB'),
'SourceBand': 2
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '555'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_500_RefSB'),
'SourceBand': 3
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '1240'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_500_RefSB'),
'SourceBand': 4
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '1640'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_500_RefSB'),
'SourceBand': 5
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '2130'
}
}]
metaDict1000SF = [
'EV_250_Aggr1km_RefSB', 'EV_500_Aggr1km_RefSB', 'EV_1KM_RefSB',
'EV_1KM_Emissive'
]
metaDict1000 = [{
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_250_Aggr1km_RefSB'),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '645'
}
}, {
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_250_Aggr1km_RefSB'),
'SourceBand': 2
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '858'
}
}, {
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_500_Aggr1km_RefSB'),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '469'
}
}, {
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_500_Aggr1km_RefSB'),
'SourceBand': 2
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '555'
}
}, {
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_500_Aggr1km_RefSB'),
'SourceBand': 3
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '1240'
}
}, {
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_500_Aggr1km_RefSB'),
'SourceBand': 4
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '1640'
}
}, {
'src': {
'SourceFilename':
subDsString % (fileName, 'EV_500_Aggr1km_RefSB'),
'SourceBand': 5
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '2130'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '412'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 2
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '443'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 3
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '488'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 4
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '531'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 5
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '551'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 6
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '667'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 7
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '667'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 8
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '678'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 9
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '678'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 10
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '748'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 11
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '869'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 12
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '905'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 13
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '936'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 14
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '940'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_RefSB'),
'SourceBand': 15
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '1375'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '3750'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 2
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '3959'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 3
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '3959'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 4
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '4050'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 5
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '4465'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 6
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '4515'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 7
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '6715'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 8
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '7325'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 9
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '8550'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 10
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '9730'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 11
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '11030'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 12
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '12020'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 13
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '13335'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 14
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '13635'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 15
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '13935'
}
}, {
'src': {
'SourceFilename': subDsString % (fileName, 'EV_1KM_Emissive'),
'SourceBand': 16
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': '14235'
}
}]
# get proper mapping depending on resolution
metaDict = {
250: metaDict250,
500: metaDict500,
1000: metaDict1000,
}[mResolution]
# get proper mapping depending on resolution
metaDictSF = {
250: metaDict250SF,
500: metaDict500SF,
1000: metaDict1000SF,
}[mResolution]
# read all scales/offsets
rScales = {}
rOffsets = {}
for sf in metaDictSF:
dsName = subDsString % (fileName, sf)
ds = gdal.Open(dsName)
rScales[dsName] = map(
float,
ds.GetMetadataItem('radiance_scales').split(','))
rOffsets[dsName] = map(
float,
ds.GetMetadataItem('radiance_offsets').split(','))
self.logger.debug('radiance_scales: %s' % str(rScales))
# add 'band_name' to 'parameters'
for bandDict in metaDict:
SourceFilename = bandDict['src']['SourceFilename']
SourceBand = bandDict['src']['SourceBand']
bandDict['dst']['suffix'] = bandDict['dst']['wavelength']
scale = rScales[SourceFilename][SourceBand - 1]
offset = rOffsets[SourceFilename][SourceBand - 1]
self.logger.debug('band, scale, offset: %s_%d %s %s' %
(SourceFilename, SourceBand, scale, offset))
bandDict['src']['ScaleRatio'] = scale
bandDict['src']['ScaleOffset'] = offset
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
productDate = gdalMetadata["RANGEBEGINNINGDATE"]
productTime = gdalMetadata["RANGEBEGINNINGTIME"]
self.remove_geolocationArray()
# set required metadata
self.dataset.SetMetadataItem(
'time_coverage_start',
(parse(gdalMetadata["RANGEBEGINNINGDATE"] + ' ' +
gdalMetadata["RANGEBEGINNINGTIME"]).isoformat()))
self.dataset.SetMetadataItem(
'time_coverage_end',
(parse(gdalMetadata["RANGEENDINGDATE"] + ' ' +
gdalMetadata["RANGEENDINGTIME"]).isoformat()))
instrumentName = self.find_metadata(gdalMetadata,
'ASSOCIATEDINSTRUMENTSHORTNAME',
'MODIS')
platformName = self.find_metadata(gdalMetadata,
'ASSOCIATEDPLATFORMSHORTNAME',
'AQUA')
mm = pti.get_gcmd_instrument(instrumentName)
ee = pti.get_gcmd_platform(platformName)
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
lonSubdataset = [
subdatasetName[0]
for subdatasetName in gdalDataset.GetSubDatasets()
if 'Longitude' in subdatasetName[1]
][0]
latSubdataset = [
subdatasetName[0]
for subdatasetName in gdalDataset.GetSubDatasets()
if 'Latitude' in subdatasetName[1]
][0]
lons = gdal.Open(lonSubdataset).ReadAsArray()
lats = gdal.Open(latSubdataset).ReadAsArray()
gcps = []
rows = range(0, lons.shape[0], lons.shape[0] / GCP_COUNT)
cols = range(0, lons.shape[1], lons.shape[1] / GCP_COUNT)
factor = self.dataset.RasterYSize / lons.shape[0]
for r in rows:
for c in cols:
gcps.append(
gdal.GCP(float(lons[r, c]), float(lats[r, c]), 0,
factor * c + 0.5, factor * r + 0.5))
self.dataset.SetGCPs(gcps, self.dataset.GetGCPProjection())
self.tps = True
def __init__(self, fileName, gdalDataset, gdalMetadata,
manifestonly=False, **kwargs):
if zipfile.is_zipfile(fileName):
zz = zipfile.PyZipFile(fileName)
# Assuming the file names are consistent, the polarization
# dependent data should be sorted equally such that we can use the
# same indices consistently for all the following lists
# THIS IS NOT THE CASE...
mdsFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist() if 'measurement/s1a' in fn]
calFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/calibration/calibration-s1a' in fn]
noiseFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/calibration/noise-s1a' in fn]
annotationFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/s1a' in fn]
manifestFile = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'manifest.safe' in fn]
zz.close()
else:
mdsFiles = glob.glob('%s/measurement/s1a*' % fileName)
calFiles = glob.glob('%s/annotation/calibration/calibration-s1a*'
% fileName)
noiseFiles = glob.glob('%s/annotation/calibration/noise-s1a*'
% fileName)
annotationFiles = glob.glob('%s/annotation/s1a*'
% fileName)
manifestFile = glob.glob('%s/manifest.safe' % fileName)
if (not mdsFiles or not calFiles or not noiseFiles or
not annotationFiles or not manifestFile):
raise WrongMapperError
mdsDict = {}
for ff in mdsFiles:
mdsDict[
os.path.splitext(os.path.basename(ff))[0].split('-')[3]] = ff
self.calXMLDict = {}
for ff in calFiles:
self.calXMLDict[
os.path.splitext(
os.path.basename(ff))[0].split('-')[4]] = self.read_xml(ff)
self.noiseXMLDict = {}
for ff in noiseFiles:
self.noiseXMLDict[
os.path.splitext(
os.path.basename(ff))[0].split('-')[4]] = self.read_xml(ff)
self.annotationXMLDict = {}
for ff in annotationFiles:
self.annotationXMLDict[
os.path.splitext(
os.path.basename(ff))[0].split('-')[3]] = self.read_xml(ff)
self.manifestXML = self.read_xml(manifestFile[0])
# very fast constructor without any bands
if manifestonly:
self.init_from_manifest_only(self.manifestXML,
self.annotationXMLDict[
self.annotationXMLDict.keys()[0]])
return
gdalDatasets = {}
for key in mdsDict.keys():
# Open data files
gdalDatasets[key] = gdal.Open(mdsDict[key])
if not gdalDatasets:
raise WrongMapperError('No Sentinel-1 datasets found')
# Check metadata to confirm it is Sentinel-1 L1
for key in gdalDatasets:
metadata = gdalDatasets[key].GetMetadata()
break
if not 'TIFFTAG_IMAGEDESCRIPTION' in metadata.keys():
raise WrongMapperError
if (not 'Sentinel-1' in metadata['TIFFTAG_IMAGEDESCRIPTION']
and not 'L1' in metadata['TIFFTAG_IMAGEDESCRIPTION']):
raise WrongMapperError
warnings.warn('Sentinel-1 level-1 mapper is not yet adapted to '
'complex data. In addition, the band names should be '
'updated for multi-swath data - '
'and there might be other issues.')
# create empty VRT dataset with geolocation only
for key in gdalDatasets:
VRT.__init__(self, gdalDatasets[key])
break
# Read annotation, noise and calibration xml-files
pol = {}
it = 0
for key in self.annotationXMLDict:
xml = Node.create(self.annotationXMLDict[key])
pol[key] = (xml.node('product').
node('adsHeader')['polarisation'].upper())
it += 1
if it == 1:
# Get incidence angle
pi = xml.node('generalAnnotation').node('productInformation')
self.dataset.SetMetadataItem('ORBIT_DIRECTION',
str(pi['pass']))
(X, Y, lon, lat, inc, ele, numberOfSamples,
numberOfLines) = self.read_geolocation_lut(
self.annotationXMLDict[key])
X = np.unique(X)
Y = np.unique(Y)
lon = np.array(lon).reshape(len(Y), len(X))
lat = np.array(lat).reshape(len(Y), len(X))
inc = np.array(inc).reshape(len(Y), len(X))
ele = np.array(ele).reshape(len(Y), len(X))
incVRT = VRT(array=inc, lat=lat, lon=lon)
eleVRT = VRT(array=ele, lat=lat, lon=lon)
incVRT = incVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=2)
eleVRT = eleVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=2)
self.bandVRTs['incVRT'] = incVRT
self.bandVRTs['eleVRT'] = eleVRT
for key in self.calXMLDict:
calibration_LUT_VRTs, longitude, latitude = (
self.get_LUT_VRTs(self.calXMLDict[key],
'calibrationVectorList',
['sigmaNought', 'betaNought',
'gamma', 'dn']
))
self.bandVRTs['LUT_sigmaNought_VRT_'+pol[key]] = (
calibration_LUT_VRTs['sigmaNought'].
get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
self.bandVRTs['LUT_betaNought_VRT_'+pol[key]] = (
calibration_LUT_VRTs['betaNought'].
get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
self.bandVRTs['LUT_gamma_VRT'] = calibration_LUT_VRTs['gamma']
self.bandVRTs['LUT_dn_VRT'] = calibration_LUT_VRTs['dn']
for key in self.noiseXMLDict:
noise_LUT_VRT = self.get_LUT_VRTs(self.noiseXMLDict[key],
'noiseVectorList',
['noiseLut'])[0]
self.bandVRTs['LUT_noise_VRT_'+pol[key]] = (
noise_LUT_VRT['noiseLut'].get_resized_vrt(
self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
metaDict = []
bandNumberDict = {}
bnmax = 0
for key in gdalDatasets.keys():
dsPath, dsName = os.path.split(mdsDict[key])
name = 'DN_%s' % pol[key]
# A dictionary of band numbers is needed for the pixel function
# bands further down. This is not the best solution. It would be
# better to have a function in VRT that returns the number given a
# band name. This function exists in Nansat but could perhaps be
# moved to VRT? The existing nansat function could just call the
# VRT one...
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
band = gdalDatasets[key].GetRasterBand(1)
dtype = band.DataType
metaDict.append({
'src': {
'SourceFilename': mdsDict[key],
'SourceBand': 1,
'DataType': dtype,
},
'dst': {
'name': name,
#'SourceTransferType': gdal.GetDataTypeName(dtype),
#'dataType': 6,
},
})
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
'''
Calibration should be performed as
s0 = DN^2/sigmaNought^2,
where sigmaNought is from e.g.
annotation/calibration/calibration-s1a-iw-grd-hh-20140811t151231-20140811t151301-001894-001cc7-001.xml,
and DN is the Digital Numbers in the tiff files.
Also the noise should be subtracted.
See
https://sentinel.esa.int/web/sentinel/sentinel-1-sar-wiki/-/wiki/Sentinel%20One/Application+of+Radiometric+Calibration+LUT
'''
# Get look direction
sat_heading = initial_bearing(longitude[:-1, :],
latitude[:-1, :],
longitude[1:, :],
latitude[1:, :])
look_direction = scipy.ndimage.interpolation.zoom(
np.mod(sat_heading + 90, 360),
(np.shape(longitude)[0] / (np.shape(longitude)[0]-1.), 1))
# Decompose, to avoid interpolation errors around 0 <-> 360
look_direction_u = np.sin(np.deg2rad(look_direction))
look_direction_v = np.cos(np.deg2rad(look_direction))
look_u_VRT = VRT(array=look_direction_u,
lat=latitude, lon=longitude)
look_v_VRT = VRT(array=look_direction_v,
lat=latitude, lon=longitude)
lookVRT = VRT(lat=latitude, lon=longitude)
lookVRT._create_band([{'SourceFilename': look_u_VRT.fileName,
'SourceBand': 1},
{'SourceFilename': look_v_VRT.fileName,
'SourceBand': 1}],
{'PixelFunctionType': 'UVToDirectionTo'}
)
# Blow up to full size
lookVRT = lookVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1)
# Store VRTs so that they are accessible later
self.bandVRTs['look_u_VRT'] = look_u_VRT
self.bandVRTs['look_v_VRT'] = look_v_VRT
self.bandVRTs['lookVRT'] = lookVRT
metaDict = []
# Add bands to full size VRT
for key in pol:
name = 'LUT_sigmaNought_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': {'SourceFilename':
(self.bandVRTs['LUT_sigmaNought_VRT_' +
pol[key]].fileName),
'SourceBand': 1
},
'dst': {'name': name
}
})
name = 'LUT_noise_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.bandVRTs['LUT_noise_VRT_' +
pol[key]].fileName,
'SourceBand': 1
},
'dst': {
'name': name
}
})
name = 'look_direction'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.bandVRTs['lookVRT'].fileName,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': name
}
})
for key in gdalDatasets.keys():
dsPath, dsName = os.path.split(mdsDict[key])
name = 'sigma0_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_%s' % pol[key]],
},
{'SourceFilename':
(self.bandVRTs['LUT_sigmaNought_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol[key],
'suffix': pol[key],
},
})
name = 'beta0_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_%s' % pol[key]]
},
{'SourceFilename':
(self.bandVRTs['LUT_betaNought_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_brightness_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol[key],
'suffix': pol[key],
},
})
self._create_bands(metaDict)
# Add incidence angle as band
name = 'incidence_angle'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = {'SourceFilename': self.bandVRTs['incVRT'].fileName,
'SourceBand': 1}
dst = {'wkv': 'angle_of_incidence',
'name': name}
self._create_band(src, dst)
self.dataset.FlushCache()
# Add elevation angle as band
name = 'elevation_angle'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = {'SourceFilename': self.bandVRTs['eleVRT'].fileName,
'SourceBand': 1}
dst = {'wkv': 'angle_of_elevation',
'name': name}
self._create_band(src, dst)
self.dataset.FlushCache()
# Add sigma0_VV
pp = [pol[key] for key in pol]
if 'VV' not in pp and 'HH' in pp:
name = 'sigma0_VV'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_HH'],
},
{'SourceFilename': (self.bandVRTs['LUT_noise_VRT_HH'].
fileName),
'SourceBand': 1
},
{'SourceFilename': (self.bandVRTs['LUT_sigmaNought_VRT_HH'].
fileName),
'SourceBand': 1,
},
{'SourceFilename': self.bandVRTs['incVRT'].fileName,
'SourceBand': 1}
]
dst = {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Sigma0HHToSigma0VV',
'polarization': 'VV',
'suffix': 'VV'}
self._create_band(src, dst)
self.dataset.FlushCache()
# set time as acquisition start time
n = Node.create(self.manifestXML)
meta = n.node('metadataSection')
for nn in meta.children:
if nn.getAttribute('ID') == u'acquisitionPeriod':
# set valid time
self.dataset.SetMetadataItem(
'time_coverage_start',
parse((nn.node('metadataWrap').
node('xmlData').
node('safe:acquisitionPeriod')['safe:startTime'])
).isoformat())
self.dataset.SetMetadataItem(
'time_coverage_end',
parse((nn.node('metadataWrap').
node('xmlData').
node('safe:acquisitionPeriod')['safe:stopTime'])
).isoformat())
# Get dictionary describing the instrument and platform according to
# the GCMD keywords
mm = pti.get_gcmd_instrument('sar')
ee = pti.get_gcmd_platform('sentinel-1a')
# TODO: Validate that the found instrument and platform are indeed what we
# want....
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
def __init__(self,
filename,
gdalDataset,
gdalMetadata,
emrange='VNIR',
**kwargs):
''' Create MODIS_L1 VRT '''
# check mapper
try:
INSTRUMENTSHORTNAME = gdalMetadata['INSTRUMENTSHORTNAME']
except:
raise WrongMapperError
if INSTRUMENTSHORTNAME != 'ASTER':
raise WrongMapperError
try:
SHORTNAME = gdalMetadata['SHORTNAME']
except:
raise WrongMapperError
if SHORTNAME != 'ASTL1B':
raise WrongMapperError
# set up metadict for data with various resolution
subDSString = 'HDF4_EOS:EOS_SWATH:"%s":%s:%s'
metaDictVNIR = [
{
'src': {
'SourceFilename':
subDSString % (filename, 'VNIR_Swath', 'ImageData1')
},
'dst': {
'wavelength': '560'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'VNIR_Swath', 'ImageData2')
},
'dst': {
'wavelength': '660'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'VNIR_Swath', 'ImageData3N')
},
'dst': {
'wavelength': '820'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'VNIR_Swath', 'ImageData3B')
},
'dst': {
'wavelength': '820'
}
},
]
metaDictSWIR = [
{
'src': {
'SourceFilename':
subDSString % (filename, 'SWIR_Swath', 'ImageData4')
},
'dst': {
'wavelength': '1650'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'SWIR_Swath', 'ImageData5')
},
'dst': {
'wavelength': '2165'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'SWIR_Swath', 'ImageData6')
},
'dst': {
'wavelength': '2205'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'SWIR_Swath', 'ImageData7')
},
'dst': {
'wavelength': '2260'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'SWIR_Swath', 'ImageData8')
},
'dst': {
'wavelength': '2330'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'SWIR_Swath', 'ImageData9')
},
'dst': {
'wavelength': '2395'
}
},
]
metaDictTIR = [
{
'src': {
'SourceFilename':
subDSString % (filename, 'TIR_Swath', 'ImageData10')
},
'dst': {
'wavelength': '8300'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'TIR_Swath', 'ImageData11')
},
'dst': {
'wavelength': '8650'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'TIR_Swath', 'ImageData12')
},
'dst': {
'wavelength': '9100'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'TIR_Swath', 'ImageData13')
},
'dst': {
'wavelength': '10600'
}
},
{
'src': {
'SourceFilename':
subDSString % (filename, 'TIR_Swath', 'ImageData14')
},
'dst': {
'wavelength': '11300'
}
},
]
# select appropriate metaDict based on <emrange> parameter
metaDict = {
'VNIR': metaDictVNIR,
'SWIR': metaDictSWIR,
'TIR': metaDictTIR,
}[emrange]
# get 1st EOS subdataset and parse to VRT.__init__()
# for retrieving geo-metadata
try:
gdalSubDataset0 = gdal.Open(metaDict[0]['src']['SourceFilename'])
except (AttributeError, IndexError):
raise WrongMapperError
# create empty VRT dataset with geolocation only
self._init_from_gdal_dataset(gdalSubDataset0, metadata=gdalMetadata)
# add source band, wkv and suffix
for metaEntry in metaDict:
metaEntry['src']['SourceBand'] = 1
metaEntry['dst']['wkv'] = 'toa_outgoing_spectral_radiance'
metaEntry['dst']['suffix'] = metaEntry['dst']['wavelength']
if 'ImageData3N' in metaEntry['src']['SourceFilename']:
metaEntry['dst']['suffix'] += 'N'
if 'ImageData3B' in metaEntry['src']['SourceFilename']:
metaEntry['dst']['suffix'] += 'B'
# add scale and offset
for metaEntry in metaDict:
bandNo = metaEntry['src']['SourceFilename'].strip().split(
':')[-1].replace('ImageData', '')
metaEntry['src']['ScaleRatio'] = float(gdalMetadata['INCL' +
bandNo])
metaEntry['src']['ScaleOffset'] = float(gdalMetadata['OFFSET' +
bandNo])
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
# set time
datetimeString = self.find_metadata(gdalMetadata,
"SETTINGTIMEOFPOINTING")
# Adding valid time to dataset
self.dataset.SetMetadataItem('time_coverage_start',
parse(datetimeString + '+00').isoformat())
self.dataset.SetMetadataItem('time_coverage_end',
parse(datetimeString + '+00').isoformat())
mm = pti.get_gcmd_instrument('ASTER')
ee = pti.get_gcmd_platform('TERRA')
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
self._remove_geolocation()
def __init__(self,
inputFileName,
gdalDataset,
gdalMetadata,
logLevel=30,
rmMetadatas=[
'NETCDF_VARNAME', '_Unsigned', 'ScaleRatio',
'ScaleOffset', 'dods_variable'
],
**kwargs):
# Remove 'NC_GLOBAL#' and 'GDAL_' and 'NANSAT_'
# from keys in gdalDataset
tmpGdalMetadata = {}
geoMetadata = {}
origin_is_nansat = False
if not gdalMetadata:
raise WrongMapperError
for key in gdalMetadata.keys():
newKey = key.replace('NC_GLOBAL#', '').replace('GDAL_', '')
if 'NANSAT_' in newKey:
geoMetadata[newKey.replace('NANSAT_', '')] = gdalMetadata[key]
origin_is_nansat = True
else:
tmpGdalMetadata[newKey] = gdalMetadata[key]
gdalMetadata = tmpGdalMetadata
fileExt = os.path.splitext(inputFileName)[1]
# Get file names from dataset or subdataset
subDatasets = gdalDataset.GetSubDatasets()
if len(subDatasets) == 0:
fileNames = [inputFileName]
else:
fileNames = [f[0] for f in subDatasets]
# add bands with metadata and corresponding values to the empty VRT
metaDict = []
xDatasetSource = ''
yDatasetSource = ''
firstXSize = 0
firstYSize = 0
for _, fileName in enumerate(fileNames):
subDataset = gdal.Open(fileName)
# choose the first dataset whith grid
if (firstXSize == 0 and firstYSize == 0
and subDataset.RasterXSize > 1
and subDataset.RasterYSize > 1):
firstXSize = subDataset.RasterXSize
firstYSize = subDataset.RasterYSize
firstSubDataset = subDataset
# get projection from the first subDataset
projection = firstSubDataset.GetProjection()
# take bands whose sizes are same as the first band.
if (subDataset.RasterXSize == firstXSize
and subDataset.RasterYSize == firstYSize):
if projection == '':
projection = subDataset.GetProjection()
if ('GEOLOCATION_X_DATASET' in fileName
or 'longitude' in fileName):
xDatasetSource = fileName
elif ('GEOLOCATION_Y_DATASET' in fileName
or 'latitude' in fileName):
yDatasetSource = fileName
else:
for iBand in range(subDataset.RasterCount):
subBand = subDataset.GetRasterBand(iBand + 1)
bandMetadata = subBand.GetMetadata_Dict()
if 'PixelFunctionType' in bandMetadata:
bandMetadata.pop('PixelFunctionType')
sourceBands = iBand + 1
# sourceBands = i*subDataset.RasterCount + iBand + 1
# generate src metadata
src = {
'SourceFilename': fileName,
'SourceBand': sourceBands
}
# set scale ratio and scale offset
scaleRatio = bandMetadata.get(
'ScaleRatio',
bandMetadata.get(
'scale', bandMetadata.get('scale_factor', '')))
if len(scaleRatio) > 0:
src['ScaleRatio'] = scaleRatio
scaleOffset = bandMetadata.get(
'ScaleOffset',
bandMetadata.get(
'offset', bandMetadata.get('add_offset', '')))
if len(scaleOffset) > 0:
src['ScaleOffset'] = scaleOffset
# sate DataType
src['DataType'] = subBand.DataType
# generate dst metadata
# get all metadata from input band
dst = bandMetadata
# set wkv and bandname
dst['wkv'] = bandMetadata.get('standard_name', '')
# first, try the name metadata
if 'name' in bandMetadata:
bandName = bandMetadata['name']
else:
# if it doesn't exist get name from NETCDF_VARNAME
bandName = bandMetadata.get('NETCDF_VARNAME', '')
if len(bandName) == 0:
bandName = bandMetadata.get(
'dods_variable', '')
# remove digits added by gdal in
# exporting to netcdf...
if (len(bandName) > 0 and origin_is_nansat
and fileExt == '.nc'):
if bandName[-1:].isdigit():
bandName = bandName[:-1]
if bandName[-1:].isdigit():
bandName = bandName[:-1]
# if still no bandname, create one
if len(bandName) == 0:
bandName = 'band_%03d' % iBand
dst['name'] = bandName
# remove non-necessary metadata from dst
for rmMetadata in rmMetadatas:
if rmMetadata in dst:
dst.pop(rmMetadata)
# append band with src and dst dictionaries
metaDict.append({'src': src, 'dst': dst})
# create empty VRT dataset with geolocation only
VRT.__init__(self, firstSubDataset, srcMetadata=gdalMetadata)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# Create complex data bands from 'xxx_real' and 'xxx_imag' bands
# using pixelfunctions
rmBands = []
for iBandNo in range(self.dataset.RasterCount):
iBand = self.dataset.GetRasterBand(iBandNo + 1)
iBandName = iBand.GetMetadataItem('name')
# find real data band
if iBandName.find("_real") != -1:
realBandNo = iBandNo
realBand = self.dataset.GetRasterBand(realBandNo + 1)
realDtype = realBand.GetMetadataItem('DataType')
bandName = iBandName.replace(iBandName.split('_')[-1],
'')[0:-1]
for jBandNo in range(self.dataset.RasterCount):
jBand = self.dataset.GetRasterBand(jBandNo + 1)
jBandName = jBand.GetMetadataItem('name')
# find an imaginary data band corresponding to the real
# data band and create complex data band from the bands
if jBandName.find(bandName + '_imag') != -1:
imagBandNo = jBandNo
imagBand = self.dataset.GetRasterBand(imagBandNo + 1)
imagDtype = imagBand.GetMetadataItem('DataType')
dst = imagBand.GetMetadata()
dst['name'] = bandName
dst['PixelFunctionType'] = 'ComplexData'
dst['dataType'] = 10
src = [{
'SourceFilename': fileNames[realBandNo],
'SourceBand': 1,
'DataType': realDtype
}, {
'SourceFilename': fileNames[imagBandNo],
'SourceBand': 1,
'DataType': imagDtype
}]
self._create_band(src, dst)
self.dataset.FlushCache()
rmBands.append(realBandNo + 1)
rmBands.append(imagBandNo + 1)
# Delete real and imaginary bands
if len(rmBands) != 0:
self.delete_bands(rmBands)
if len(projection) == 0:
# projection was not set automatically
# get projection from GCPProjection
projection = geoMetadata.get('GCPProjection', '')
if len(projection) == 0:
# no projection was found in dataset or metadata:
# generate WGS84 by default
projection = NSR().wkt
# fix problem with MET.NO files where a, b given in m and XC/YC in km
if ('UNIT["kilometre"' in projection
and ',SPHEROID["Spheroid",6378273,7.331926543631893e-12]'
in projection):
projection = projection.replace(
',SPHEROID["Spheroid",6378273,7.331926543631893e-12]', '')
# set projection
self.dataset.SetProjection(self.repare_projection(projection))
# check if GCPs were added from input dataset
gcps = firstSubDataset.GetGCPs()
gcpProjection = firstSubDataset.GetGCPProjection()
# if no GCPs in input dataset: try to add GCPs from metadata
if not gcps:
gcps = self.add_gcps_from_metadata(geoMetadata)
# if yet no GCPs: try to add GCPs from variables
if not gcps:
gcps = self.add_gcps_from_variables(inputFileName)
if gcps:
if len(gcpProjection) == 0:
# get GCP projection and repare
gcpProjection = self.repare_projection(
geoMetadata.get('GCPProjection', ''))
# add GCPs to dataset
self.dataset.SetGCPs(gcps, gcpProjection)
self.dataset.SetProjection('')
self._remove_geotransform()
# Find proper bands and insert GEOLOCATION ARRAY into dataset
if len(xDatasetSource) > 0 and len(yDatasetSource) > 0:
self.add_geolocationArray(
GeolocationArray(xDatasetSource, yDatasetSource))
elif not gcps:
# if no GCPs found and not GEOLOCATION ARRAY set:
# Set Nansat Geotransform if it is not set automatically
geoTransform = self.dataset.GetGeoTransform()
if len(geoTransform) == 0:
geoTransformStr = geoMetadata.get('GeoTransform',
'(0|1|0|0|0|0|1)')
geoTransform = eval(geoTransformStr.replace('|', ','))
self.dataset.SetGeoTransform(geoTransform)
subMetadata = firstSubDataset.GetMetadata()
### GET START TIME from METADATA
time_coverage_start = None
if 'start_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_time'])
elif 'start_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_date'])
elif 'time_coverage_start' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_start'])
### GET END TIME from METADATA
time_coverage_end = None
if 'stop_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['stop_time'])
elif 'stop_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['stop_date'])
elif 'time_coverage_stop' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_stop'])
elif 'end_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['end_time'])
elif 'end_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['end_date'])
elif 'time_coverage_end' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['time_coverage_end'])
### GET start time from time variable
if (time_coverage_start is None and cfunitsInstalled
and 'time#standard_name' in subMetadata
and subMetadata['time#standard_name'] == 'time'
and 'time#units' in subMetadata
and 'time#calendar' in subMetadata):
# get data from netcdf data
ncFile = netcdf_file(inputFileName, 'r')
timeLength = ncFile.variables['time'].shape[0]
timeValueStart = ncFile.variables['time'][0]
timeValueEnd = ncFile.variables['time'][-1]
ncFile.close()
try:
timeDeltaStart = Units.conform(
timeValueStart,
Units(subMetadata['time#units'],
calendar=subMetadata['time#calendar']),
Units('days since 1950-01-01'))
except ValueError:
self.logger.error('calendar units are wrong: %s' %
subMetadata['time#calendar'])
else:
time_coverage_start = (
datetime.datetime(1950, 1, 1) +
datetime.timedelta(float(timeDeltaStart)))
if timeLength > 1:
timeDeltaEnd = Units.conform(
timeValueStart,
Units(subMetadata['time#units'],
calendar=subMetadata['time#calendar']),
Units('days since 1950-01-01'))
else:
timeDeltaEnd = timeDeltaStart + 1
time_coverage_end = (datetime.datetime(1950, 1, 1) +
datetime.timedelta(float(timeDeltaEnd)))
## finally set values of time_coverage start and end if available
if time_coverage_start is not None:
self.dataset.SetMetadataItem('time_coverage_start',
time_coverage_start.isoformat())
if time_coverage_end is not None:
self.dataset.SetMetadataItem('time_coverage_end',
time_coverage_end.isoformat())
if 'sensor' not in gdalMetadata:
self.dataset.SetMetadataItem('sensor', 'unknown')
if 'satellite' not in gdalMetadata:
self.dataset.SetMetadataItem('satellite', 'unknown')
if 'source_type' not in gdalMetadata:
self.dataset.SetMetadataItem('source_type', 'unknown')
if 'platform' not in gdalMetadata:
self.dataset.SetMetadataItem('platform', 'unknown')
if 'instrument' not in gdalMetadata:
self.dataset.SetMetadataItem('instrument', 'unknown')
self.logger.info('Use generic mapper - OK!')
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
logLevel=30,
**kwargs):
if fileName[0:len(keywordBase)] != keywordBase:
raise WrongMapperError(
__file__, "Not Nora10 data converted from felt to netCDF")
requestedTime = datetime.strptime(fileName[len(keywordBase) + 1:],
'%Y%m%d%H%M')
# For correct rounding
fileTime = requestedTime + timedelta(minutes=30)
fileTime = fileTime - timedelta(minutes=fileTime.minute)
nc_file = (baseFolder + 'windspeed_10m' +
fileTime.strftime('/%Y/%m/') + 'windspeed_' +
fileTime.strftime('%Y%m%d%H.nc'))
nc_file_winddir = (baseFolder + 'winddir_10m' +
fileTime.strftime('/%Y/%m/') + 'winddir_' +
fileTime.strftime('%Y%m%d%H.nc'))
# Would prefer to use geotransform, but ob_tran
# (General Oblique Transformation) is not supported by GDAL
# Keeping lines below for potential future use:
#proj4 = gdalDataset.GetMetadataItem('projection_rotated_ll#proj4')
#proj4 = '+proj=ob_tran +o_proj=longlat +lon_0=-40 +o_lat_p=22 +a=6367470 +e=0'
#rlatmin = -13.25; rlatmax = 26.65; deltarlat = 0.1
#rlonmin = 5.75; rlonmax = 30.45; deltarlon = 0.1
# Needed due to precence of time dimension in netCDF file
gdal.SetConfigOption('GDAL_NETCDF_BOTTOMUP', 'No')
# Read relevant arrays into memory
g = gdal.Open('NETCDF:"' + nc_file + '":' + 'windspeed_10m')
ws_10m = np.flipud(g.GetRasterBand(1).ReadAsArray())
g = gdal.Open(nc_file_winddir)
wd_10m = np.flipud(g.GetRasterBand(1).ReadAsArray())
g = gdal.Open('NETCDF:"' + nc_file + '":' + 'latitude')
lat = np.flipud(g.GetRasterBand(1).ReadAsArray())
g = gdal.Open('NETCDF:"' + nc_file + '":' + 'longitude')
lon = np.flipud(g.GetRasterBand(1).ReadAsArray())
u10 = ws_10m * np.cos(np.deg2rad(wd_10m))
v10 = ws_10m * np.sin(np.deg2rad(wd_10m))
VRT_u10 = VRT(array=u10, lat=lat, lon=lon)
VRT_v10 = VRT(array=v10, lat=lat, lon=lon)
# Store bandVRTs so that they are available after reprojection etc
self.bandVRTs = {'u_VRT': VRT_u10, 'v_VRT': VRT_v10}
metaDict = []
metaDict.append({
'src': {
'SourceFilename': VRT_u10.fileName,
'SourceBand': 1
},
'dst': {
'wkv': 'eastward_wind',
'name': 'eastward_wind'
}
})
metaDict.append({
'src': {
'SourceFilename': VRT_v10.fileName,
'SourceBand': 1
},
'dst': {
'wkv': 'northward_wind',
'name': 'northward_wind'
}
})
# Add pixel function with wind speed
metaDict.append({
'src': [{
'SourceFilename': self.bandVRTs['u_VRT'].fileName,
'SourceBand': 1,
'DataType': 6
}, {
'SourceFilename': self.bandVRTs['v_VRT'].fileName,
'SourceBand': 1,
'DataType': 6
}],
'dst': {
'wkv': 'wind_speed',
'name': 'windspeed',
'height': '10 m',
'PixelFunctionType': 'UVToMagnitude'
}
})
# Add pixel function with wind direction
metaDict.append({
'src': [{
'SourceFilename': self.bandVRTs['u_VRT'].fileName,
'SourceBand': 1,
'DataType': 6
}, {
'SourceFilename': self.bandVRTs['v_VRT'].fileName,
'SourceBand': 1,
'DataType': 6
}],
'dst': {
'wkv': 'wind_to_direction',
'name': 'winddir',
'height': '10 m',
'PixelFunctionType': 'UVToDirectionTo'
}
})
# create empty VRT dataset with geolocation only
VRT.__init__(self, lat=lat, lon=lon)
# add bands with metadata and corresponding values
# to the empty VRT
self._create_bands(metaDict)
# Add time
self._set_time(fileTime)
def __init__(self,
inputFileName,
gdalDataset,
gdalMetadata,
logLevel=30,
**kwargs):
# check if mapper fits
if not gdalMetadata:
raise WrongMapperError
if not os.path.splitext(inputFileName)[1] == '.mnt':
raise WrongMapperError
try:
mbNorthLatitude = float(gdalMetadata['NC_GLOBAL#mbNorthLatitude'])
mbSouthLatitude = float(gdalMetadata['NC_GLOBAL#mbSouthLatitude'])
mbEastLongitude = float(gdalMetadata['NC_GLOBAL#mbEastLongitude'])
mbWestLongitude = float(gdalMetadata['NC_GLOBAL#mbWestLongitude'])
mbProj4String = gdalMetadata['NC_GLOBAL#mbProj4String']
Number_lines = int(gdalMetadata['NC_GLOBAL#Number_lines'])
Number_columns = int(gdalMetadata['NC_GLOBAL#Number_columns'])
Element_x_size = float(gdalMetadata['NC_GLOBAL#Element_x_size'])
Element_y_size = float(gdalMetadata['NC_GLOBAL#Element_y_size'])
except:
raise WrongMapperError
# find subdataset with DEPTH
subDatasets = gdalDataset.GetSubDatasets()
dSourceFile = None
for subDataset in subDatasets:
if subDataset[0].endswith('.mnt":DEPTH'):
dSourceFile = subDataset[0]
if dSourceFile is None:
raise WrongMapperError
dSubDataset = gdal.Open(dSourceFile)
dMetadata = dSubDataset.GetMetadata()
try:
scale_factor = dMetadata['DEPTH#scale_factor']
add_offset = dMetadata['DEPTH#add_offset']
except:
raise WrongMapperError
geoTransform = [
mbWestLongitude, Element_x_size, 0, mbNorthLatitude, 0,
-Element_y_size
]
# create empty VRT dataset with geolocation only
VRT.__init__(self,
srcGeoTransform=geoTransform,
srcMetadata=gdalMetadata,
srcProjection=NSR(mbProj4String).wkt,
srcRasterXSize=Number_columns,
srcRasterYSize=Number_lines)
metaDict = [{
'src': {
'SourceFilename': dSourceFile,
'SourceBand': 1,
'ScaleRatio': scale_factor,
'ScaleOffset': add_offset
},
'dst': {
'wkv': 'depth'
}
}]
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
logLevel=30,
**kwargs):
if not gdalMetadata:
raise WrongMapperError
isHirlam = False
for key in gdalMetadata.keys():
if 'creation by fimex from file' in gdalMetadata[key]:
isHirlam = True
if not isHirlam:
raise WrongMapperError
#GeolocMetaDict = [{'src':
# {'SourceFilename': 'NETCDF:"' + fileName + '":longitude',
# 'SourceBand': 1,
# 'ScaleRatio': 1,
# 'ScaleOffset': 0},
# 'dst': {}},
# {'src':
# {'SourceFilename': 'NETCDF:"' + fileName + '":latitude',
# 'SourceBand': 1,
# 'ScaleRatio': 1,
# 'ScaleOffset': 0},
# 'dst': {}}]
subDataset = gdal.Open('NETCDF:"' + fileName + '":x_wind_10m')
#self.GeolocVRT = VRT(srcRasterXSize=subDataset.RasterXSize,
# srcRasterYSize=subDataset.RasterYSize)
#self.GeolocVRT._create_bands(GeolocMetaDict)
#GeolocObject = GeolocationArray(xVRT=self.GeolocVRT,
# yVRT=self.GeolocVRT,
# xBand=1, yBand=2,
# lineOffset=0, pixelOffset=0,
# lineStep=1, pixelStep=1)
## create empty VRT dataset with geolocation only
#VRT.__init__(self, srcRasterXSize = subDataset.RasterXSize,
# srcRasterYSize = subDataset.RasterYSize,
# geolocationArray = GeolocObject,
# srcProjection = GeolocObject.d['SRS'])
lon = gdal.Open('NETCDF:"' + fileName + '":longitude"').ReadAsArray()
lat = gdal.Open('NETCDF:"' + fileName + '":latitude"').ReadAsArray()
VRT.__init__(self, lat=lat, lon=lon)
# Add bands with wind components
metaDict = [{
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":x_wind_10m'),
'NODATA': -32767
},
'dst': {
'name': 'U',
'wkv': 'eastward_wind'
}
}, {
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":y_wind_10m'),
'NODATA': -32767
},
'dst': {
'name': 'V',
'wkv': 'northward_wind'
}
}]
# Add pixel function with wind speed
metaDict.append({
'src': [{
'SourceFilename': ('NETCDF:"' + fileName + '":x_wind_10m'),
'SourceBand': 1,
'DataType': 6
}, {
'SourceFilename': ('NETCDF:"' + fileName + '":y_wind_10m'),
'SourceBand': 1,
'DataType': 6
}],
'dst': {
'wkv': 'wind_speed',
'name': 'windspeed',
'height': '10 m',
'PixelFunctionType': 'UVToMagnitude',
'NODATA': 9999
}
})
# add bands with metadata and corresponding values
# to the empty VRT
self._create_bands(metaDict)
# Add time
validTime = datetime.datetime.utcfromtimestamp(
int(subDataset.GetRasterBand(1).GetMetadata()['NETCDF_DIM_time']))
self._set_time(validTime)
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
resolution='low',
**kwargs):
''' Create LANDSAT VRT from multiple tif files or single tar.gz file'''
mtlFileName = ''
bandFileNames = []
bandSizes = []
bandDatasets = []
fname = os.path.split(fileName)[1]
if (fileName.endswith('.tar') or fileName.endswith('.tar.gz')
or fileName.endswith('.tgz')):
# try to open .tar or .tar.gz or .tgz file with tar
try:
tarFile = tarfile.open(fileName)
except:
raise WrongMapperError
# collect names of bands and corresponding sizes
# into bandsInfo dict and bandSizes list
tarNames = sorted(tarFile.getnames())
for tarName in tarNames:
# check if TIF files inside TAR qualify
if (tarName[0] in ['L', 'M']
and os.path.splitext(tarName)[1] in ['.TIF', '.tif']):
# open TIF file from TAR using VSI
sourceFilename = '/vsitar/%s/%s' % (fileName, tarName)
gdalDatasetTmp = gdal.Open(sourceFilename)
# keep name, GDALDataset and size
bandFileNames.append(sourceFilename)
bandSizes.append(gdalDatasetTmp.RasterXSize)
bandDatasets.append(gdalDatasetTmp)
elif (tarName.endswith('MTL.txt')
or tarName.endswith('MTL.TXT')):
# get mtl file
mtlFileName = tarName
elif ((fname.startswith('L') or fname.startswith('M'))
and (fname.endswith('.tif') or fname.endswith('.TIF')
or fname.endswith('._MTL.txt'))):
# try to find TIF/tif files with the same name as input file
path, coreName = os.path.split(fileName)
coreName = os.path.splitext(coreName)[0].split('_')[0]
coreNameMask = coreName + '*[tT][iI][fF]'
tifNames = sorted(glob.glob(os.path.join(path, coreNameMask)))
for tifName in tifNames:
sourceFilename = tifName
gdalDatasetTmp = gdal.Open(sourceFilename)
# keep name, GDALDataset and size
bandFileNames.append(sourceFilename)
bandSizes.append(gdalDatasetTmp.RasterXSize)
bandDatasets.append(gdalDatasetTmp)
# get mtl file
mtlFiles = glob.glob(coreName + '*[mM][tT][lL].[tT][xX][tT]')
if len(mtlFiles) > 0:
mtlFileName = mtlFiles[0]
else:
raise WrongMapperError
# if not TIF files found - not appropriate mapper
if not bandFileNames:
raise WrongMapperError
# get appropriate band size based on number of unique size and
# required resoltuion
if resolution == 'low':
bandXSise = min(bandSizes)
elif resolution in ['high', 'hi']:
bandXSise = max(bandSizes)
else:
raise OptionError('Wrong resolution %s for file %s' %
(resolution, fileName))
# find bands with appropriate size and put to metaDict
metaDict = []
for bandFileName, bandSize, bandDataset in zip(bandFileNames,
bandSizes,
bandDatasets):
if bandSize == bandXSise:
# let last part of file name be suffix
bandSuffix = os.path.splitext(bandFileName)[0].split('_')[-1]
metaDict.append({
'src': {
'SourceFilename': bandFileName,
'SourceBand': 1,
'ScaleRatio': 0.1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'suffix': bandSuffix
}
})
gdalDataset4Use = bandDataset
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalDataset4Use)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
if len(mtlFileName) > 0:
mtlFileName = os.path.join(
os.path.split(bandFileNames[0])[0], mtlFileName)
mtlFileLines = [
line.strip() for line in self.read_xml(mtlFileName).split('\n')
]
dateString = [
line.split('=')[1].strip() for line in mtlFileLines
if ('DATE_ACQUIRED' in line or 'ACQUISITION_DATE' in line)
][0]
timeStr = [
line.split('=')[1].strip() for line in mtlFileLines
if ('SCENE_CENTER_TIME' in line
or 'SCENE_CENTER_SCAN_TIME' in line)
][0]
time_start = parse_time(dateString + 'T' + timeStr).isoformat()
time_end = (parse_time(dateString + 'T' + timeStr) +
datetime.timedelta(microseconds=60000000)).isoformat()
self.dataset.SetMetadataItem('time_coverage_start', time_start)
self.dataset.SetMetadataItem('time_coverage_end', time_end)
# set platform
platform = 'LANDSAT'
if fname[2].isdigit():
platform += '-' + fname[2]
ee = pti.get_gcmd_platform(platform)
self.dataset.SetMetadataItem('platform', json.dumps(ee))
# set instrument
instrument = {
'LANDSAT': 'MSS',
'LANDSAT-1': 'MSS',
'LANDSAT-2': 'MSS',
'LANDSAT-3': 'MSS',
'LANDSAT-4': 'TM',
'LANDSAT-5': 'TM',
'LANDSAT-7': 'ETM+',
'LANDSAT-8': 'OLI'
}[platform]
ee = pti.get_gcmd_instrument(instrument)
self.dataset.SetMetadataItem('instrument', json.dumps(ee))
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' OBPG L3 VRT '''
# test the product
try:
assert gdalMetadata['PlatformShortName'] == 'GCOM-W1'
assert gdalMetadata['SensorShortName'] == 'AMSR2'
assert gdalMetadata['ProductName'] == 'AMSR2-L3'
except:
raise WrongMapperError
# get list of similar (same date, A/D orbit) files in the directory
iDir, iFile = os.path.split(fileName)
iFileMask = iFile[:30] + '%02d' + iFile[32:]
simFiles = []
for freq in self.freqs:
simFile = os.path.join(iDir, iFileMask % freq)
#print simFile
if os.path.exists(simFile):
simFiles.append(simFile)
metaDict = []
for freq in self.freqs:
simFile = os.path.join(iDir, iFileMask % freq)
if simFile not in simFiles:
continue
#print 'simFile', simFile
# open file, get metadata and get parameter name
simSupDataset = gdal.Open(simFile)
if simSupDataset is None:
# skip this similar file
#print 'No dataset: %s not a supported SMI file' % simFile
continue
# get subdatasets from the similar file
simSubDatasets = simSupDataset.GetSubDatasets()
for simSubDataset in simSubDatasets:
#print 'simSubDataset', simSubDataset
if 'Brightness_Temperature' in simSubDataset[0]:
# get SourceFilename from subdataset
metaEntry = {
'src': {
'SourceFilename': simSubDataset[0],
'SourceBand': 1,
'ScaleRatio': 0.0099999998,
'ScaleOffset': 0
},
'dst': {
'wkv': 'brightness_temperature',
'frequency': '%02d' % freq,
'polarisation': simSubDataset[0][-2:-1],
'suffix':
('%02d%s' % (freq, simSubDataset[0][-2:-1]))
}
}
metaDict.append(metaEntry)
# initiate VRT for the NSIDC 10 km grid
VRT.__init__(self,
srcGeoTransform=(-3850000, 10000, 0.0, 5850000, 0.0,
-10000),
srcProjection=NSR(3411).wkt,
srcRasterXSize=760,
srcRasterYSize=1120)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# Adding valid time to dataset
self.dataset.SetMetadataItem(
'time_coverage_start',
parse(gdalMetadata['ObservationStartDateTime']).isoformat())
self.dataset.SetMetadataItem(
'time_coverage_end',
parse(gdalMetadata['ObservationStartDateTime']).isoformat())
mm = pti.get_gcmd_instrument('AMSR2')
ee = pti.get_gcmd_platform('GCOM-W1')
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
def __init__(self,
filename,
gdalDataset,
gdalMetadata,
GCP_COUNT=10,
bandNames=['VNIR_Band1', 'VNIR_Band2', 'VNIR_Band3N'],
bandWaves=[560, 660, 820],
**kwargs):
''' Create VRT
Parameters
-----------
GCP_COUNT : int
number of GCPs along each dimention
bandNames : list of string (band name)
bandWaves : list of integer (waves corresponding to band name)
Band name and waves
--------------------
'VNIR_Band3B' : 820, 'SWIR_Band4' : 1650, 'SWIR_Band5' : 2165,
'SWIR_Band6' : 2205, 'SWIR_Band7' : 2260, 'SWIR_Band8' : 2330,
'SWIR_Band9' : 2395, 'TIR_Band10' : 8300, 'TIR_Band11' : 8650,
'TIR_Band12' : 9100, 'TIR_Band13' : 10600, 'TIR_Band14' : 11300
'''
# check if it is ASTER L1A
try:
assert 'AST_L1A_' in filename
shortName = gdalMetadata['INSTRUMENTSHORTNAME']
assert shortName == 'ASTER'
except:
raise WrongMapperError
subDatasets = gdalDataset.GetSubDatasets()
# find datasets for each band and generate metaDict
metaDict = []
bandDatasetMask = 'HDF4_EOS:EOS_SWATH:"%s":%s:ImageData'
for bandName, bandWave in zip(bandNames, bandWaves):
metaEntry = {
'src': {
'SourceFilename': (bandDatasetMask % (filename, bandName)),
'SourceBand': 1,
'DataType': 6,
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'wavelength': str(bandWave),
'suffix': str(bandWave),
}
}
metaDict.append(metaEntry)
# create empty VRT dataset with geolocation only
gdalSubDataset = gdal.Open(metaDict[0]['src']['SourceFilename'])
self._init_from_gdal_dataset(gdalSubDataset,
metadata=gdalSubDataset.GetMetadata())
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
# find largest lon/lat subdatasets
latShape0 = 0
for subDataset in subDatasets:
if 'Latitude' in subDataset[1]:
ls = int(subDataset[1].strip().split('[')[1].split('x')[0])
if ls >= latShape0:
latShape0 = ls
latSubDS = subDataset[0]
if 'Longitude' in subDataset[1]:
ls = int(subDataset[1].strip().split('[')[1].split('x')[0])
if ls >= latShape0:
latShape0 = ls
lonSubDS = subDataset[0]
self.logger.debug(latSubDS)
self.logger.debug(lonSubDS)
# get lat/lon matrices
xDataset = gdal.Open(lonSubDS)
yDataset = gdal.Open(latSubDS)
longitude = xDataset.ReadAsArray()
latitude = yDataset.ReadAsArray()
step0 = longitude.shape[0] / GCP_COUNT
step1 = longitude.shape[1] / GCP_COUNT
# estimate pixel/line step
pixelStep = int(
ceil(
float(gdalSubDataset.RasterXSize) /
float(xDataset.RasterXSize)))
lineStep = int(
ceil(
float(gdalSubDataset.RasterYSize) /
float(xDataset.RasterYSize)))
self.logger.debug('steps: %d %d %d %d' %
(step0, step1, pixelStep, lineStep))
# generate list of GCPs
gcps = []
k = 0
for i0 in range(0, latitude.shape[0], step0):
for i1 in range(0, latitude.shape[1], step1):
# create GCP with X,Y,pixel,line from lat/lon matrices
lon = float(longitude[i0, i1])
lat = float(latitude[i0, i1])
if (lon >= -180 and lon <= 180 and lat >= -90 and lat <= 90):
gcp = gdal.GCP(lon, lat, 0, i1 * pixelStep, i0 * lineStep)
self.logger.debug(
'%d %d %d %f %f' %
(k, gcp.GCPPixel, gcp.GCPLine, gcp.GCPX, gcp.GCPY))
gcps.append(gcp)
k += 1
# append GCPs and lat/lon projection to the vsiDataset
self.dataset.SetGCPs(gcps, NSR().wkt)
# Adding valid time to dataset
self.dataset.SetMetadataItem(
'time_coverage_start',
parse(gdalMetadata['FIRSTPACKETTIME']).isoformat())
self.dataset.SetMetadataItem(
'time_coverage_end',
parse(gdalMetadata['LASTPACKETTIME']).isoformat())
mm = pti.get_gcmd_instrument('ASTER')
ee = pti.get_gcmd_platform('TERRA')
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
def test_init_from_GDALDataset(self):
ds = gdal.Open(self.test_file)
d = Domain(ds=ds)
self.assertEqual(type(d), Domain)
def __init__(self, inputFileName, gdalDataset, gdalMetadata, logLevel=30,
rmMetadatas=['NETCDF_VARNAME', '_Unsigned',
'ScaleRatio', 'ScaleOffset', 'dods_variable'],
**kwargs):
# Remove 'NC_GLOBAL#' and 'GDAL_' and 'NANSAT_'
# from keys in gdalDataset
tmpGdalMetadata = {}
geoMetadata = {}
origin_is_nansat = False
if not gdalMetadata:
raise WrongMapperError
for key in gdalMetadata.keys():
newKey = key.replace('NC_GLOBAL#', '').replace('GDAL_', '')
if 'NANSAT_' in newKey:
geoMetadata[newKey.replace('NANSAT_', '')] = gdalMetadata[key]
origin_is_nansat = True
else:
tmpGdalMetadata[newKey] = gdalMetadata[key]
gdalMetadata = tmpGdalMetadata
fileExt = os.path.splitext(inputFileName)[1]
# Get file names from dataset or subdataset
subDatasets = gdalDataset.GetSubDatasets()
if len(subDatasets) == 0:
filenames = [inputFileName]
else:
filenames = [f[0] for f in subDatasets]
# add bands with metadata and corresponding values to the empty VRT
metaDict = []
xDatasetSource = ''
yDatasetSource = ''
firstXSize = 0
firstYSize = 0
for _, filename in enumerate(filenames):
subDataset = gdal.Open(filename)
# choose the first dataset whith grid
if (firstXSize == 0 and firstYSize == 0 and
subDataset.RasterXSize > 1 and subDataset.RasterYSize > 1):
firstXSize = subDataset.RasterXSize
firstYSize = subDataset.RasterYSize
firstSubDataset = subDataset
# get projection from the first subDataset
projection = firstSubDataset.GetProjection()
# take bands whose sizes are same as the first band.
if (subDataset.RasterXSize == firstXSize and
subDataset.RasterYSize == firstYSize):
if projection == '':
projection = subDataset.GetProjection()
if ('GEOLOCATION_X_DATASET' in filename or
'longitude' in filename):
xDatasetSource = filename
elif ('GEOLOCATION_Y_DATASET' in filename or
'latitude' in filename):
yDatasetSource = filename
else:
for iBand in range(subDataset.RasterCount):
subBand = subDataset.GetRasterBand(iBand+1)
bandMetadata = subBand.GetMetadata_Dict()
if 'PixelFunctionType' in bandMetadata:
bandMetadata.pop('PixelFunctionType')
sourceBands = iBand + 1
# sourceBands = i*subDataset.RasterCount + iBand + 1
# generate src metadata
src = {'SourceFilename': filename,
'SourceBand': sourceBands}
# set scale ratio and scale offset
scaleRatio = bandMetadata.get(
'ScaleRatio',
bandMetadata.get(
'scale',
bandMetadata.get('scale_factor', '')))
if len(scaleRatio) > 0:
src['ScaleRatio'] = scaleRatio
scaleOffset = bandMetadata.get(
'ScaleOffset',
bandMetadata.get(
'offset',
bandMetadata.get(
'add_offset', '')))
if len(scaleOffset) > 0:
src['ScaleOffset'] = scaleOffset
# sate DataType
src['DataType'] = subBand.DataType
# generate dst metadata
# get all metadata from input band
dst = bandMetadata
# set wkv and bandname
dst['wkv'] = bandMetadata.get('standard_name', '')
# first, try the name metadata
if 'name' in bandMetadata:
bandName = bandMetadata['name']
else:
# if it doesn't exist get name from NETCDF_VARNAME
bandName = bandMetadata.get('NETCDF_VARNAME', '')
if len(bandName) == 0:
bandName = bandMetadata.get(
'dods_variable', ''
)
# remove digits added by gdal in
# exporting to netcdf...
if (len(bandName) > 0 and origin_is_nansat and
fileExt == '.nc'):
if bandName[-1:].isdigit():
bandName = bandName[:-1]
if bandName[-1:].isdigit():
bandName = bandName[:-1]
# if still no bandname, create one
if len(bandName) == 0:
bandName = 'band_%03d' % iBand
dst['name'] = bandName
# remove non-necessary metadata from dst
for rmMetadata in rmMetadatas:
if rmMetadata in dst:
dst.pop(rmMetadata)
# append band with src and dst dictionaries
metaDict.append({'src': src, 'dst': dst})
# create empty VRT dataset with geolocation only
self._init_from_gdal_dataset(firstSubDataset, metadata=gdalMetadata)
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
self._create_complex_bands(filenames)
if len(projection) == 0:
# projection was not set automatically
# get projection from GCPProjection
projection = geoMetadata.get('GCPProjection', '')
if len(projection) == 0:
# no projection was found in dataset or metadata:
# generate WGS84 by default
projection = NSR().wkt
# fix problem with MET.NO files where a, b given in m and XC/YC in km
if ('UNIT["kilometre"' in projection and
',SPHEROID["Spheroid",6378273,7.331926543631893e-12]' in
projection):
projection = projection.replace(
',SPHEROID["Spheroid",6378273,7.331926543631893e-12]',
'')
# set projection
self.dataset.SetProjection(self.repare_projection(projection))
# check if GCPs were added from input dataset
gcps = firstSubDataset.GetGCPs()
gcpProjection = firstSubDataset.GetGCPProjection()
# if no GCPs in input dataset: try to add GCPs from metadata
if not gcps:
gcps = self.add_gcps_from_metadata(geoMetadata)
# if yet no GCPs: try to add GCPs from variables
if not gcps:
gcps = self.add_gcps_from_variables(inputFileName)
if gcps:
if len(gcpProjection) == 0:
# get GCP projection and repare
gcpProjection = self.repare_projection(geoMetadata. get('GCPProjection', ''))
# add GCPs to dataset
self.dataset.SetGCPs(gcps, gcpProjection)
self.dataset.SetProjection('')
self._remove_geotransform()
# Find proper bands and insert GEOLOCATION ARRAY into dataset
if len(xDatasetSource) > 0 and len(yDatasetSource) > 0:
self._add_geolocation(Geolocation.from_filenames(xDatasetSource, yDatasetSource))
elif not gcps:
# if no GCPs found and not GEOLOCATION ARRAY set:
# Set Nansat Geotransform if it is not set automatically
geoTransform = self.dataset.GetGeoTransform()
if len(geoTransform) == 0:
geoTransformStr = geoMetadata.get('GeoTransform',
'(0|1|0|0|0|0|1)')
geoTransform = eval(geoTransformStr.replace('|', ','))
self.dataset.SetGeoTransform(geoTransform)
subMetadata = firstSubDataset.GetMetadata()
### GET START TIME from METADATA
time_coverage_start = None
if 'start_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_time'])
elif 'start_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_date'])
elif 'time_coverage_start' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_start'])
### GET END TIME from METADATA
time_coverage_end = None
if 'stop_time' in gdalMetadata:
time_coverage_end = parse_time(gdalMetadata['stop_time'])
elif 'stop_date' in gdalMetadata:
time_coverage_end = parse_time(gdalMetadata['stop_date'])
elif 'time_coverage_stop' in gdalMetadata:
time_coverage_end = parse_time(
gdalMetadata['time_coverage_stop'])
elif 'end_time' in gdalMetadata:
time_coverage_end = parse_time(gdalMetadata['end_time'])
elif 'end_date' in gdalMetadata:
time_coverage_end = parse_time(gdalMetadata['end_date'])
elif 'time_coverage_end' in gdalMetadata:
time_coverage_end = parse_time(
gdalMetadata['time_coverage_end'])
### GET start time from time variable
if (time_coverage_start is None and 'time#standard_name' in subMetadata and
subMetadata['time#standard_name'] == 'time' and 'time#units' in subMetadata):
# get data from netcdf data
ncFile = Dataset(inputFileName, 'r')
time_var = ncFile.variables['time']
t0 = time_var[0]
if len(time_var) == 1:
t1 = t0 + 1
else:
t1 = time_var[-1]
time_units_start = parse(time_var.units, fuzzy=True, ignoretz=True)
time_units_to_seconds = {'second' : 1.0,
'hour' : 60 * 60.0,
'day' : 24 * 60 * 60.0}
for key in time_units_to_seconds:
if key in time_var.units:
factor = time_units_to_seconds[key]
break
time_coverage_start = time_units_start + datetime.timedelta(seconds=t0 * factor)
time_coverage_end = time_units_start + datetime.timedelta(seconds=t1 * factor)
## finally set values of time_coverage start and end if available
if time_coverage_start is not None:
self.dataset.SetMetadataItem('time_coverage_start',
time_coverage_start.isoformat())
if time_coverage_end is not None:
self.dataset.SetMetadataItem('time_coverage_end',
time_coverage_end.isoformat())
if 'sensor' not in gdalMetadata:
self.dataset.SetMetadataItem('sensor', 'unknown')
if 'satellite' not in gdalMetadata:
self.dataset.SetMetadataItem('satellite', 'unknown')
if 'source_type' not in gdalMetadata:
self.dataset.SetMetadataItem('source_type', 'unknown')
if 'platform' not in gdalMetadata:
self.dataset.SetMetadataItem('platform', 'unknown')
if 'instrument' not in gdalMetadata:
self.dataset.SetMetadataItem('instrument', 'unknown')
self.logger.info('Use generic mapper - OK!')
def __init__(self,
filename,
gdalDataset,
gdalMetadata,
GCP_COUNT=10,
**kwargs):
''' Create VRT
Parameters
----------
GCP_COUNT : int
number of GCPs along each dimention
'''
# extension must be .nc
if os.path.splitext(filename)[1] != '.nc':
raise WrongMapperError
# file must contain navigation_data/longitude
try:
ds = gdal.Open('HDF5:"%s"://navigation_data/longitude' % filename)
except RuntimeError:
raise WrongMapperError
else:
dsMetadata = ds.GetMetadata()
# title value must be known
if dsMetadata.get('title', '') not in self.titles:
raise WrongMapperError
# get geophysical data variables
subDatasets = gdal.Open(filename).GetSubDatasets()
metaDict = []
for subDataset in subDatasets:
groupName = subDataset[0].split('/')[-2]
if groupName not in ['geophysical_data', 'navigation_data']:
continue
varName = subDataset[0].split('/')[-1]
subds = gdal.Open(subDataset[0])
b = subds.GetRasterBand(1)
bMetadata = b.GetMetadata()
# set SRC/DST parameters
metaEntry = {
'src': {
'SourceFilename': subDataset[0],
'sourceBand': 1,
'DataType': b.DataType
},
'dst': {
'name': varName
}
}
# replace datatype for l2_flags
if varName == 'l2_flags':
metaEntry['src']['DataType'] = 4
metaEntry['src']['SourceType'] = 'SimpleSource'
# set scale if exist
metaKey = '%s_%s_scale_factor' % (groupName, varName)
if metaKey in bMetadata:
metaEntry['src']['ScaleRatio'] = bMetadata[metaKey]
# set offset if exist
metaKey = '%s_%s_add_offset' % (groupName, varName)
if metaKey in bMetadata:
metaEntry['src']['ScaleOffset'] = bMetadata[metaKey]
# set standard_name if exists
metaKey = '%s_%s_standard_name' % (groupName, varName)
if metaKey in bMetadata:
metaEntry['dst']['wkv'] = bMetadata[metaKey]
# set other metadata
for metaKey in bMetadata:
newMetaKey = metaKey.replace('%s_%s_' % (groupName, varName),
'')
if newMetaKey not in [
'scale_factor', 'add_offset', 'DIMENSION_LIST',
'_FillValue'
]:
metaEntry['dst'][newMetaKey] = bMetadata[metaKey]
metaDict.append(metaEntry)
# make GCPs
# get lat/lon grids
longitude = gdal.Open('HDF5:"%s"://navigation_data/longitude' %
filename).ReadAsArray()
latitude = gdal.Open('HDF5:"%s"://navigation_data/latitude' %
filename).ReadAsArray()
rasterYSize, rasterXSize = longitude.shape
step0 = max(1, int(float(latitude.shape[0]) / GCP_COUNT))
step1 = max(1, int(float(latitude.shape[1]) / GCP_COUNT))
gcps = []
k = 0
center_lon = 0
center_lat = 0
for i0 in range(0, latitude.shape[0], step0):
for i1 in range(0, latitude.shape[1], step1):
# create GCP with X,Y,pixel,line from lat/lon matrices
lon = float(longitude[i0, i1])
lat = float(latitude[i0, i1])
if (lon >= -180 and lon <= 180 and lat >= -90 and lat <= 90):
gcp = gdal.GCP(lon, lat, 0, i1 + 0.5, i0 + 0.5)
gcps.append(gcp)
center_lon += lon
center_lat += lat
k += 1
time_coverage_start = dsMetadata['time_coverage_start']
time_coverage_end = dsMetadata['time_coverage_end']
# create VRT
# x_size, y_size, geo_transform, projection, gcps=None, gcp_projection='', **kwargs
self._init_from_dataset_params(rasterXSize, rasterYSize,
(0, 1, 0, rasterYSize, 0, -1),
NSR().wkt, gcps,
NSR().wkt)
# add bands
self.create_bands(metaDict)
# reproject GCPs
center_lon /= k
center_lat /= k
srs = '+proj=stere +datum=WGS84 +ellps=WGS84 +lon_0=%f +lat_0=%f +no_defs' % (
center_lon, center_lat)
self.reproject_GCPs(srs)
### BAD, BAd, bad ...
self.dataset.SetProjection(self.dataset.GetGCPProjection())
# use TPS for reprojection
self.tps = True
# add NansenCloud metadata
self.dataset.SetMetadataItem('time_coverage_start',
str(time_coverage_start))
self.dataset.SetMetadataItem('time_coverage_end',
str(time_coverage_end))
self.dataset.SetMetadataItem('source_type', 'Satellite')
self.dataset.SetMetadataItem('mapper', 'obpg_l2_nc')
platform = {
'Orbview-2': 'SEASTAR'
}.get(dsMetadata.get('platform'), dsMetadata.get('platform'))
mm = pti.get_gcmd_instrument(dsMetadata.get('instrument'))
ee = pti.get_gcmd_platform(platform)
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
def __init__(self, filename, gdalDataset, gdalMetadata,
GCP_STEP=20, MAX_LAT=90, MIN_LAT=50, resolution='low',
**kwargs):
''' Create VRT
Parameters
----------
GCP_COUNT : int
number of GCPs along each dimention
'''
ifile = os.path.split(filename)[1]
if not ifile.startswith('GW1AM2_') or not ifile.endswith('.h5'):
raise WrongMapperError
try:
ProductName = gdalMetadata['ProductName']
PlatformShortName = gdalMetadata['PlatformShortName']
SensorShortName = gdalMetadata['SensorShortName']
except:
raise WrongMapperError
if (not ProductName == 'AMSR2-L1R' or
not PlatformShortName == 'GCOM-W1' or
not SensorShortName == 'AMSR2'):
raise WrongMapperError
if resolution == 'low':
subDatasetWidth = 243
else:
subDatasetWidth = 486
# get GCPs from lon/lat grids
latGrid = gdal.Open('HDF5:"%s"://Latitude_of_Observation_Point_for_89A' % filename).ReadAsArray()
lonGrid = gdal.Open('HDF5:"%s"://Longitude_of_Observation_Point_for_89A' % filename).ReadAsArray()
if subDatasetWidth == 243:
latGrid = latGrid[:, ::2]
lonGrid = lonGrid[:, ::2]
dx = .5
dy = .5
gcps = []
k = 0
maxY = 0
minY = latGrid.shape[0]
for i0 in range(0, latGrid.shape[0], GCP_STEP):
for i1 in range(0, latGrid.shape[1], GCP_STEP):
# create GCP with X,Y,pixel,line from lat/lon matrices
lon = float(lonGrid[i0, i1])
lat = float(latGrid[i0, i1])
if (lon >= -180 and
lon <= 180 and
lat >= MIN_LAT and
lat <= MAX_LAT):
gcp = gdal.GCP(lon, lat, 0, i1 + dx, i0 + dy)
gcps.append(gcp)
k += 1
maxY = max(maxY, i0)
minY = min(minY, i0)
yOff = minY
ySize = maxY - minY
# remove Y-offset from gcps
for gcp in gcps:
gcp.GCPLine -= yOff
metaDict = []
subDatasets = gdalDataset.GetSubDatasets()
metadata = gdalDataset.GetMetadata()
for subDataset in subDatasets:
# select subdatasets fro that resolution (width)
if (subDatasetWidth == int(subDataset[1].split(']')[0].split('x')[-1]) and
'Latitude' not in subDataset[0] and 'Longitude' not in subDataset[0]):
name = subDataset[0].split('/')[-1]
# find scale
scale = 1
for meta in metadata:
if name + '_SCALE' in meta:
scale = float(metadata[meta])
# create meta entry
metaEntry = {'src': {'SourceFilename': subDataset[0],
'sourceBand': 1,
'ScaleRatio': scale,
'ScaleOffset': 0,
'yOff': yOff,
'ySize': ySize,},
'dst': {'name': name}
}
metaDict.append(metaEntry)
# create VRT from one of the subdatasets
gdalSubDataset = gdal.Open(metaEntry['src']['SourceFilename'])
self._init_from_dataset_params(subDatasetWidth, ySize, (1,0,0,ySize,0,-1), NSR().wkt)
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
self.dataset.SetMetadataItem('time_coverage_start',
parse_time(gdalMetadata['ObservationStartDateTime']).isoformat())
self.dataset.SetMetadataItem('time_coverage_end',
parse_time(gdalMetadata['ObservationEndDateTime']).isoformat())
# append GCPs and lat/lon projection to the vsiDataset
self.dataset.SetGCPs(gcps, NSR().wkt)
self.reproject_gcps('+proj=stere +datum=WGS84 +ellps=WGS84 +lat_0=90 +lon_0=0 +no_defs')
self.tps = True
mm = pti.get_gcmd_instrument('AMSR2')
ee = pti.get_gcmd_platform('GCOM-W1')
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' OBPG L3 VRT '''
try:
assert 'Level-3 Standard Mapped Image' in gdalMetadata['Title']
except:
raise WrongMapperError
# get list of similar (same date) files in the directory
iDir, iFile = os.path.split(fileName)
iFileName, iFileExt = os.path.splitext(iFile)
simFilesMask = os.path.join(iDir, iFileName)
simFiles = glob.glob(simFilesMask + iFileExt[0:6] + '*')
#print 'simFilesMask, simFiles', simFilesMask, simFiles
metaDict = []
for simFile in simFiles:
#print 'simFile', simFile
# open file, get metadata and get parameter name
simSupDataset = gdal.Open(simFile)
if simSupDataset is None:
# skip this similar file
#print 'No dataset: %s not a supported SMI file' % simFile
continue
# get subdatasets from the similar file
simSubDatasets = simSupDataset.GetSubDatasets()
if len(simSubDatasets) > 0:
for simSubDataset in simSubDatasets:
#print 'simSubDataset', simSubDataset
if 'l3m_data' in simSubDataset[1]:
# get SourceFilename from subdataset
tmpSourceFilename = simSubDataset[0]
break
else:
# get SourceFilename from dataset
tmpSourceFilename = simFile
# open subdataset with GDAL
#print 'tmpSourceFilename', tmpSourceFilename
tmpGdalDataset = gdal.Open(tmpSourceFilename)
try:
# get metadata, get 'Parameter'
tmpGdalMetadata = tmpGdalDataset.GetMetadata()
simParameter = tmpGdalMetadata['Parameter']
except:
print 'No parameter: %s not a supported SMI file' % simFile
continue
else:
# set params of the similar file
simSourceFilename = tmpSourceFilename
simGdalDataset = tmpGdalDataset
simGdalMetadata = tmpGdalMetadata
# get WKV from the similar file
#print 'simParameter', simParameter
for param in self.param2wkv:
#print 'param', param
if param in simParameter:
simWKV = self.param2wkv[param]
break
# generate entry to metaDict
metaEntry = {
'src': {
'SourceFilename': simSourceFilename,
'SourceBand': 1,
'ScaleRatio': float(simGdalMetadata['Slope']),
'ScaleOffset': float(simGdalMetadata['Intercept'])
},
'dst': {
'wkv': simWKV
}
}
# add wavelength and BandName
if ' at ' in simParameter and ' nm' in simParameter:
simWavelength = simParameter.split(' at ')[1].split(' nm')[0]
metaEntry['dst']['suffix'] = simWavelength
metaEntry['dst']['wavelength'] = simWavelength
# add band with Rrsw
metaEntry2 = None
if simWKV == 'surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_air':
metaEntry2 = {'src': [metaEntry['src']]}
metaEntry2['dst'] = {
'wkv':
'surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_water',
'suffix': simWavelength,
'wavelength': simWavelength,
'PixelFunctionType': 'NormReflectanceToRemSensReflectance',
}
# append entry to metaDict
metaDict.append(metaEntry)
if metaEntry2 is not None:
metaDict.append(metaEntry2)
#get array with data and make 'mask'
a = simGdalDataset.ReadAsArray()
mask = np.zeros(a.shape, 'uint8') + 64
mask[a < -32000] = 1
self.bandVRTs = {'mask': VRT(array=mask)}
metaDict.append({
'src': {
'SourceFilename': self.bandVRTs['mask'].fileName,
'SourceBand': 1
},
'dst': {
'name': 'mask'
}
})
# create empty VRT dataset with geolocation only
# print 'simGdalMetadata', simGdalMetadata
latitudeStep = float(
simGdalMetadata.get('Latitude Step',
simGdalMetadata.get('Latitude_Step', 1)))
longitudeStep = float(
simGdalMetadata.get('Longitude Step',
simGdalMetadata.get('Longitude_Step', 1)))
numberOfColumns = int(
simGdalMetadata.get('Number of Columns',
simGdalMetadata.get('Number_of_Columns', 1)))
numberOfLines = int(
simGdalMetadata.get('Number of Lines',
simGdalMetadata.get('Number_of_Lines', 1)))
#longitudeStep = float(simGdalMetadata['Longitude Step'])
VRT.__init__(self,
srcGeoTransform=(-180.0, longitudeStep, 0.0, 90.0, 0.0,
-longitudeStep),
srcProjection=NSR().wkt,
srcRasterXSize=numberOfColumns,
srcRasterYSize=numberOfLines)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# Add valid time
startYear = int(
simGdalMetadata.get('Start Year',
simGdalMetadata.get('Start_Year', 1)))
startDay = int(
simGdalMetadata.get('Start Day',
simGdalMetadata.get('Start)Day', 1)))
self._set_time(
datetime.datetime(startYear, 1, 1) + datetime.timedelta(startDay))
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create LANDSAT VRT '''
# try to open .tar or .tar.gz or .tgz file with tar
try:
tarFile = tarfile.open(fileName)
except:
raise WrongMapperError
tarNames = tarFile.getnames()
#print tarNames
metaDict = []
for tarName in tarNames:
if ((tarName[0] == 'L' or tarName[0] == 'M')
and (tarName[-4:] == '.TIF' or tarName[-4:] == '.tif')):
#print tarName
bandNo = tarName[-6:-4]
metaDict.append({
'src': {
'SourceFilename':
'/vsitar/%s/%s' % (fileName, tarName),
'SourceBand': 1
},
'dst': {
'wkv': 'toa_outgoing_spectral_radiance',
'suffix': bandNo
}
})
if not metaDict:
raise WrongMapperError
#print metaDict
sizeDiffBands = []
for iFile in range(len(metaDict)):
tmpName = metaDict[iFile]['src']['SourceFilename']
gdalDatasetTmp = gdal.Open(tmpName)
if iFile == 0:
gdalDatasetTmp0 = gdalDatasetTmp
xSize = gdalDatasetTmp.RasterXSize
ySize = gdalDatasetTmp.RasterYSize
elif (xSize != gdalDatasetTmp.RasterXSize
or ySize != gdalDatasetTmp.RasterYSize):
sizeDiffBands.append(iFile)
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalDatasetTmp0)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# 8th band of LANDSAT8 is a double size band.
# Reduce the size to same as the 1st band.
if len(sizeDiffBands) != 0:
vrtXML = self.read_xml()
node0 = Node.create(vrtXML)
for iBand in sizeDiffBands:
iBandNode = node0.nodeList('VRTRasterBand')[iBand]
iNodeDstRect = iBandNode.node('DstRect')
iNodeDstRect.replaceAttribute('xSize', str(xSize))
iNodeDstRect.replaceAttribute('ySize', str(ySize))
self.write_xml(node0.rawxml())
def __init__(self, filename, gdalDataset, gdalMetadata, **kwargs):
''' GLOBCOLOR L3M VRT '''
try:
print("=>%s<=" % gdalMetadata['NC_GLOBAL#title'])
except (TypeError, KeyError):
raise WrongMapperError
if 'GlobColour' not in gdalMetadata['NC_GLOBAL#title']:
raise WrongMapperError
# get list of similar (same date) files in the directory
iDir, iFile = os.path.split(filename)
iFileName, iFileExt = os.path.splitext(iFile)
print('idir:', iDir, iFile, iFileName[0:30], iFileExt[0:8])
simFilesMask = os.path.join(iDir, iFileName[0:30] + '*.nc')
simFiles = glob.glob(simFilesMask)
print('simFilesMask, simFiles', simFilesMask, simFiles)
metaDict = []
for simFile in simFiles:
print('simFile', simFile)
# open file, get metadata and get parameter name
simSupDataset = gdal.Open(simFile)
simSubDatasets = simSupDataset.GetSubDatasets()
simWKV = None
for simSubDataset in simSubDatasets:
if '_mean' in simSubDataset[0]:
simValidSupDataset = simSupDataset
simGdalDataset = gdal.Open(simSubDataset[0])
simBand = simGdalDataset.GetRasterBand(1)
simBandMetadata = simBand.GetMetadata()
simVarname = simBandMetadata['NETCDF_VARNAME']
# get WKV
print(' simVarname', simVarname)
if simVarname in self.varname2wkv:
simWKV = self.varname2wkv[simVarname]
break
# skipp adding this similar file if it is not valid
if simWKV is None:
continue
metaEntry = {
'src': {
'SourceFilename': simSubDataset[0],
'SourceBand': 1
},
'dst': {
'wkv': simWKV,
'original_name': simVarname
}
}
# add wavelength and name
longName = simBandMetadata['long_name']
if 'Fully normalised water leaving radiance' in longName:
simWavelength = simVarname.split('L')[1].split('_mean')[0]
metaEntry['dst']['suffix'] = simWavelength
metaEntry['dst']['wavelength'] = simWavelength
# add band with rrsw
metaEntry2 = None
if simWKV == 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water':
solarIrradiance = simBandMetadata['solar_irradiance']
metaEntry2 = {'src': metaEntry['src']}
metaEntry2['dst'] = {
'wkv':
'surface_ratio_of_upwelling_radiance_emerging_from_sea'
'_water_to_downwelling_radiative_flux_in_water',
'suffix':
simWavelength,
'wavelength':
simWavelength,
# 'expression': 'self["nLw_%s"] / %s / (0.52 + 1.7 * self["nLw
# _%s"] / %s)' % (simWavelength, solarIrradiance,
# simWavelength, solarIrradiance),
'expression':
'self["nLw_%s"] / %s' % (simWavelength, solarIrradiance)
}
print(' metaEntry', metaEntry)
metaDict.append(metaEntry)
if metaEntry2 is not None:
print(' metaEntry2', metaEntry2)
metaDict.append(metaEntry2)
print('simSubDatasets', simValidSupDataset.GetSubDatasets())
for simSubDataset in simValidSupDataset.GetSubDatasets():
print('simSubDataset', simSubDataset)
if '_flags ' in simSubDataset[1]:
print(' mask simSubDataset', simSubDataset[1])
flags = gdal.Open(simSubDataset[0]).ReadAsArray()
mask = np.ones(flags.shape) * 64
mask[np.bitwise_and(flags, np.power(2, 0)) > 0] = 1
mask[np.bitwise_and(flags, np.power(2, 3)) > 0] = 2
self.band_vrts = {'maskVRT': VRT(array=mask)}
metaDict.append({
'src': {
'SourceFilename': self.band_vrts['maskVRT'].filename,
'SourceBand': 1
},
'dst': {
'name': 'mask'
}
})
# create empty VRT dataset with geolocation only
simGdalDataset.SetProjection(
'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,'
'298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG",'
'"6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],'
'UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],'
'AUTHORITY["EPSG","4326"]]')
self._init_from_gdal_dataset(simGdalDataset)
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
# Add valid time
startYear = int(gdalMetadata['Start Year'])
startDay = int(gdalMetadata['Start Day'])
# Adding valid time to dataset
self.dataset.SetMetadataItem(
'time_coverage_start', (datetime.datetime(startYear, 1, 1) +
datetime.timedelta(startDay)).isoformat())
self.dataset.SetMetadataItem(
'time_coverage_end', (datetime.datetime(startYear, 1, 1) +
datetime.timedelta(startDay)).isoformat())
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Ocean Productivity website VRT '''
try:
assert 'IDL' in gdalMetadata['Projection Category']
assert '-9999' in gdalMetadata['Hole Value']
except:
raise WrongMapperError
print 'Ocean Productivity website data'
# get list of similar (same date) files in the directory
iDir, iFile = os.path.split(fileName)
iFileName, iFileExt = os.path.splitext(iFile)
simFilesMask = os.path.join(iDir, '*' + iFileName[4:11] + iFileExt)
#print 'simFilesMask', simFilesMask
simFiles = glob.glob(simFilesMask)
#print 'simFiles', simFiles
metaDict = []
for simFile in simFiles:
#print 'simFile',simFile
# open subdataset with GDAL
tmpSourceFilename = simFile
tmpGdalDataset = gdal.Open(tmpSourceFilename)
# get metadata, get 'Parameter'
tmpGdalMetadata = tmpGdalDataset.GetMetadata()
iDir, ifileName = os.path.split(tmpSourceFilename)
#print 'ifileName',ifileName
simParameter = ifileName[0:3]
# set params of the similar file
simSourceFilename = tmpSourceFilename
simGdalDataset = tmpGdalDataset
simGdalMetadata = tmpGdalMetadata
# get WKV from the similar file
for param in self.param2wkv:
#print 'param', param
if param in simParameter:
simWKV = self.param2wkv[param]
break
#print 'simWKV', simWKV
# generate entry to metaDict
metaEntry = {
'src': {
'SourceFilename': simSourceFilename,
'SourceBand': 1,
'ScaleRatio': float(simGdalMetadata['Slope']),
'ScaleOffset': float(simGdalMetadata['Intercept'])
},
'dst': {
'wkv': simWKV,
'name': self.bandNames[simWKV],
'Parameter': simParameter
}
}
#print 'metaEntry', metaEntry
# append entry to metaDict
metaDict.append(metaEntry)
#get array with data and make 'mask'
a = simGdalDataset.ReadAsArray()
mask = np.zeros(a.shape, 'uint8') + 128
mask[a < -9990] = 1
self.bandVRTs = {'maskVRT': VRT(array=mask)}
metaDict.append({
'src': {
'SourceFilename': (self.bandVRTs['maskVRT'].fileName),
'SourceBand': 1
},
'dst': {
'name': 'mask'
}
})
# create empty VRT dataset with geolocation only
# print 'simGdalMetadata', simGdalMetadata
latitudeStep = 0.08333334
longitudeStep = 0.08333334
numberOfColumns = 4320
numberOfLines = 2160
#longitudeStep = float(simGdalMetadata['Longitude Step'])
VRT.__init__(
self,
srcGeoTransform=(-180.0, longitudeStep, 0.0, 90.0, 0.0,
-longitudeStep),
srcProjection=
'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]',
srcRasterXSize=numberOfColumns,
srcRasterYSize=numberOfLines)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# Add valid time
startYear = int(iFile[4:8])
startDay = int(iFile[8:11])
self.dataset.SetMetadataItem(
'time_coverage_start', (datetime.datetime(startYear, 1, 1) +
datetime.timedelta(startDay)).isoformat())
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create Radarsat2 VRT '''
fPathName, fExt = os.path.splitext(fileName)
if zipfile.is_zipfile(fileName):
# Open zip file using VSI
fPath, fName = os.path.split(fPathName)
fileName = '/vsizip/%s/%s' % (fileName, fName)
if not 'RS' in fName[0:2]:
raise WrongMapperError('Provided data is not Radarsat-2')
gdalDataset = gdal.Open(fileName)
gdalMetadata = gdalDataset.GetMetadata()
#if it is not RADARSAT-2, return
if (not gdalMetadata
or not 'SATELLITE_IDENTIFIER' in gdalMetadata.keys()):
raise WrongMapperError
elif gdalMetadata['SATELLITE_IDENTIFIER'] != 'RADARSAT-2':
raise WrongMapperError
# read product.xml
productXmlName = os.path.join(fileName, 'product.xml')
productXml = self.read_xml(productXmlName)
# Get additional metadata from product.xml
rs2_0 = Node.create(productXml)
rs2_1 = rs2_0.node('sourceAttributes')
rs2_2 = rs2_1.node('radarParameters')
if rs2_2['antennaPointing'].lower() == 'right':
antennaPointing = 90
else:
antennaPointing = -90
rs2_3 = rs2_1.node('orbitAndAttitude').node('orbitInformation')
passDirection = rs2_3['passDirection']
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalDataset)
#define dictionary of metadata and band specific parameters
pol = []
metaDict = []
# Get the subdataset with calibrated sigma0 only
for dataset in gdalDataset.GetSubDatasets():
if dataset[1] == 'Sigma Nought calibrated':
s0dataset = gdal.Open(dataset[0])
s0datasetName = dataset[0][:]
band = s0dataset.GetRasterBand(1)
s0datasetPol = band.GetMetadata()['POLARIMETRIC_INTERP']
for i in range(1, s0dataset.RasterCount + 1):
iBand = s0dataset.GetRasterBand(i)
polString = iBand.GetMetadata()['POLARIMETRIC_INTERP']
suffix = polString
# The nansat data will be complex
# if the SAR data is of type 10
dtype = iBand.DataType
if dtype == 10:
# add intensity band
metaDict.append({
'src': {
'SourceFilename': ('RADARSAT_2_CALIB:SIGMA0:' +
fileName + '/product.xml'),
'SourceBand':
i,
'DataType':
dtype
},
'dst': {
'wkv':
'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'intensity',
'SourceTransferType':
gdal.GetDataTypeName(dtype),
'suffix': suffix,
'polarization': polString,
'dataType': 6
}
})
# modify suffix for adding the compled band below
suffix = polString + '_complex'
pol.append(polString)
metaDict.append({
'src': {
'SourceFilename': ('RADARSAT_2_CALIB:SIGMA0:' +
fileName + '/product.xml'),
'SourceBand':
i,
'DataType':
dtype
},
'dst': {
'wkv':
'surface_backwards_scattering_coefficient_of_radar_wave',
'suffix': suffix,
'polarization': polString
}
})
if dataset[1] == 'Beta Nought calibrated':
b0dataset = gdal.Open(dataset[0])
b0datasetName = dataset[0][:]
for j in range(1, b0dataset.RasterCount + 1):
jBand = b0dataset.GetRasterBand(j)
polString = jBand.GetMetadata()['POLARIMETRIC_INTERP']
if polString == s0datasetPol:
b0datasetBand = j
###############################
# Add SAR look direction
###############################
d = Domain(ds=gdalDataset)
lon, lat = d.get_geolocation_grids(100)
'''
(GDAL?) Radarsat-2 data is stored with maximum latitude at first
element of each column and minimum longitude at first element of each
row (e.g. np.shape(lat)=(59,55) -> latitude maxima are at lat[0,:],
and longitude minima are at lon[:,0])
In addition, there is an interpolation error for direct estimate along
azimuth. We therefore estimate the heading along range and add 90
degrees to get the "satellite" heading.
'''
if str(passDirection).upper() == 'DESCENDING':
sat_heading = initial_bearing(lon[:, :-1], lat[:, :-1], lon[:, 1:],
lat[:, 1:]) + 90
elif str(passDirection).upper() == 'ASCENDING':
sat_heading = initial_bearing(lon[:, 1:], lat[:, 1:], lon[:, :-1],
lat[:, :-1]) + 90
else:
print 'Can not decode pass direction: ' + str(passDirection)
# Calculate SAR look direction
SAR_look_direction = sat_heading + antennaPointing
# Interpolate to regain lost row
SAR_look_direction = np.mod(SAR_look_direction, 360)
SAR_look_direction = scipy.ndimage.interpolation.zoom(
SAR_look_direction, (1, 11. / 10.))
# Decompose, to avoid interpolation errors around 0 <-> 360
SAR_look_direction_u = np.sin(np.deg2rad(SAR_look_direction))
SAR_look_direction_v = np.cos(np.deg2rad(SAR_look_direction))
look_u_VRT = VRT(array=SAR_look_direction_u, lat=lat, lon=lon)
look_v_VRT = VRT(array=SAR_look_direction_v, lat=lat, lon=lon)
# Note: If incidence angle and look direction are stored in
# same VRT, access time is about twice as large
lookVRT = VRT(lat=lat, lon=lon)
lookVRT._create_band([{
'SourceFilename': look_u_VRT.fileName,
'SourceBand': 1
}, {
'SourceFilename': look_v_VRT.fileName,
'SourceBand': 1
}], {'PixelFunctionType': 'UVToDirectionTo'})
# Blow up to full size
lookVRT = lookVRT.get_resized_vrt(gdalDataset.RasterXSize,
gdalDataset.RasterYSize)
# Store VRTs so that they are accessible later
self.bandVRTs['look_u_VRT'] = look_u_VRT
self.bandVRTs['look_v_VRT'] = look_v_VRT
self.bandVRTs['lookVRT'] = lookVRT
# Add band to full sized VRT
lookFileName = self.bandVRTs['lookVRT'].fileName
metaDict.append({
'src': {
'SourceFilename': lookFileName,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': 'SAR_look_direction'
}
})
###############################
# Create bands
###############################
self._create_bands(metaDict)
###################################################
# Add derived band (incidence angle) calculated
# using pixel function "BetaSigmaToIncidence":
###################################################
src = [{
'SourceFilename': b0datasetName,
'SourceBand': b0datasetBand,
'DataType': dtype
}, {
'SourceFilename': s0datasetName,
'SourceBand': 1,
'DataType': dtype
}]
dst = {
'wkv': 'angle_of_incidence',
'PixelFunctionType': 'BetaSigmaToIncidence',
'SourceTransferType': gdal.GetDataTypeName(dtype),
'_FillValue': -10000, # NB: this is also hard-coded in
# pixelfunctions.c
'dataType': 6,
'name': 'incidence_angle'
}
self._create_band(src, dst)
self.dataset.FlushCache()
###################################################################
# Add sigma0_VV - pixel function of sigma0_HH and beta0_HH
# incidence angle is calculated within pixel function
# It is assummed that HH is the first band in sigma0 and
# beta0 sub datasets
###################################################################
if 'VV' not in pol and 'HH' in pol:
s0datasetNameHH = pol.index('HH') + 1
src = [{
'SourceFilename': s0datasetName,
'SourceBand': s0datasetNameHH,
'DataType': 6
}, {
'SourceFilename': b0datasetName,
'SourceBand': b0datasetBand,
'DataType': 6
}]
dst = {
'wkv':
'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sigma0HHBetaToSigma0VV',
'polarization': 'VV',
'suffix': 'VV'
}
self._create_band(src, dst)
self.dataset.FlushCache()
############################################
# Add SAR metadata
############################################
if antennaPointing == 90:
self.dataset.SetMetadataItem('ANTENNA_POINTING', 'RIGHT')
if antennaPointing == -90:
self.dataset.SetMetadataItem('ANTENNA_POINTING', 'LEFT')
self.dataset.SetMetadataItem('ORBIT_DIRECTION',
str(passDirection).upper())
# Set time
validTime = gdalDataset.GetMetadata()['ACQUISITION_START_TIME']
self.logger.info('Valid time: %s', str(validTime))
self._set_time(parse(validTime))
# set SADCAT specific metadata
self.dataset.SetMetadataItem(
'start_date', (parse(gdalMetadata['FIRST_LINE_TIME']).isoformat()))
self.dataset.SetMetadataItem(
'stop_date', (parse(gdalMetadata['LAST_LINE_TIME']).isoformat()))
self.dataset.SetMetadataItem('sensor', 'SAR')
self.dataset.SetMetadataItem('satellite', 'Radarsat2')
self.dataset.SetMetadataItem('mapper', 'radarsat2')
def test_dont_init_if_gdal_AutoCreateWarpedVRT_fails(self, mock_gdal):
mock_gdal.AutoCreateWarpedVRT.return_value = None
with self.assertRaises(NansatProjectionError):
Domain(ds=gdal.Open(self.test_file),
srs="+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs")
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
product_type='RVL',
GCP_COUNT=10,
**kwargs):
'''
Parameters
----------
product_type: string
Sentinel-1 level-2 ocean product type/component, i.e. ocean swell
spectra (OSW), ocean wind field (OWI), or radial surface velocity
(RVL) (RVL is the default)
GCP_COUNT : int
number of GCPs along each dimention
'''
fPathName, fExt = os.path.splitext(fileName)
# List of Sentinel-1 level-2 components
unwanted_product_components = ['osw', 'owi', 'rvl']
# Remove requested 'product_type' from list of unwanted
unwanted_product_components.pop(
unwanted_product_components.index(product_type.lower()))
# Check if it is Sentinel-1 (or ASAR) level-2 (in S1 data format)
if not gdalMetadata or not 'NC_GLOBAL' in gdalMetadata.keys():
raise WrongMapperError
else:
title = gdalMetadata['NC_GLOBAL#TITLE']
# Raise error if it is not Sentinel-1 format
if not 'Sentinel-1' or 'ASA' in title:
raise WrongMapperError
metadata = {}
for key, val in gdalMetadata.iteritems():
new_key = key.split('#')[-1]
metadata[new_key] = val
subDatasets = gdalDataset.GetSubDatasets()
fileNames = [f[0] for f in subDatasets]
rm_bands = []
# Find all data that is not relevant for the selected product type
# and get bands of longitude, latitude and zero doppler time
for i, f in enumerate(fileNames):
if f.split(':')[-1][:3] in unwanted_product_components:
rm_bands.append(i)
if 'Lon' in f.split(':')[-1]:
lon_ds = gdal.Open(f)
rm_bands.append(i)
if 'Lat' in f.split(':')[-1]:
lat_ds = gdal.Open(f)
rm_bands.append(i)
if 'ZeroDopplerTime' in f.split(':')[-1]:
zdt_ds = gdal.Open(f)
rm_bands.append(i)
# Remove bands in rm_bands from the list of bands to add to the Nansat
# object
fileNames = [f for i, f in enumerate(fileNames) if not i in rm_bands]
# (
# 'Lon' in f.split(':')[-1] or
# 'Lat' in f.split(':')[-1] or
# 'ZeroDopplerTime' in f.split(':')[-1] )]
# create empty VRT dataset
VRT.__init__(self, gdal.Open(subDatasets[0][0]), srcMetadata=metadata)
# The zero Doppler time grid is 3-dimensional - the last dimension is a
# char array with the time as year, month, day, etc.
# Will not bother with it yet...
#for iBand in range(zdt_ds.RasterCount):
# subBand = zdt_ds.GetRasterBand(iBand+1)
XSize = lon_ds.RasterXSize
YSize = lon_ds.RasterYSize
# get projection from the lon and lat datasets
longitude = lon_ds.ReadAsArray()
latitude = lat_ds.ReadAsArray()
# estimate step of GCPs
step0 = max(1, int(float(latitude.shape[0]) / GCP_COUNT))
step1 = max(1, int(float(latitude.shape[1]) / GCP_COUNT))
self.logger.debug('gcpCount: >%s<, %d %d %f %d %d', title,
latitude.shape[0], latitude.shape[1], GCP_COUNT,
step0, step1)
# estimate pixel/line step of the geolocation arrays
pixelStep = 1
lineStep = 1
self.logger.debug('pixel/lineStep %f %f' % (pixelStep, lineStep))
# generate list of GCPs
dx = .5
dy = .5
gcps = []
k = 0
for i0 in range(0, latitude.shape[0], step0):
for i1 in range(0, latitude.shape[1], step1):
# create GCP with X,Y,pixel,line from lat/lon matrices
lon = float(longitude[i0, i1])
lat = float(latitude[i0, i1])
if (lon >= -180 and lon <= 180 and lat >= -90 and lat <= 90):
gcp = gdal.GCP(lon, lat, 0, i1 * pixelStep + dx,
i0 * lineStep + dy)
self.logger.debug('%d %d %d %f %f', k, gcp.GCPPixel,
gcp.GCPLine, gcp.GCPX, gcp.GCPY)
gcps.append(gcp)
k += 1
# append GCPs and lat/lon projection to the vsiDataset
self.dataset.SetGCPs(gcps, NSR().wkt)
# define band specific parameters
metaDict = []
geoFileDict = {}
xDatasetSource = ''
yDatasetSource = ''
for i, fileName in enumerate(fileNames):
band = gdal.Open(fileName)
# check that the band size is the same size as the latitude and
# longitude grids
if (band.RasterXSize != XSize or band.RasterYSize != YSize):
raise IndexError(('Size of sub-dataset is different from size '
'of longitude and latitude grids'))
bandMetadata = band.GetMetadata()
# generate src metadata
src = {'SourceFilename': fileName, 'SourceBand': 1}
# Generate dst metadata
short_name = fileName.split(':')[-1]
dst = {
'name': short_name,
'short_name': short_name,
'long_name': bandMetadata[short_name + '#long_name'],
'units': bandMetadata[short_name + '#units'],
#'wkv': ,
}
# append band with src and dst dictionaries
metaDict.append({'src': src, 'dst': dst})
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
metaDict = []
for i in range(self.dataset.RasterCount):
if 'Nrcs' in self.dataset.GetRasterBand(i +
1).GetMetadata()['name']:
metaDict.append({
'src': {
'SourceFilename': (self.dataset.GetRasterBand(
i + 1).GetMetadata()['SourceFilename']),
'SourceBand':
1
},
'dst': {
'short_name':
'sigma0',
'wkv':
'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType':
'dB2pow',
'polarization':
(self.dataset.GetMetadata()['POLARISATION']),
'suffix':
self.dataset.GetMetadata()['POLARISATION'],
'dataType':
6,
}
})
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# set time
self.dataset.SetMetadataItem(
'time_coverage_start',
parse(self.dataset.GetMetadata()
['SOURCE_ACQUISITION_UTC_TIME']).isoformat())
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create CSKS VRT '''
if fileName.split('/')[-1][0:4] != "CSKS":
raise WrongMapperError
# Get coordinates
metadata = gdalMetadata['Estimated_Bottom_Left_Geodetic_Coordinates']
bottom_left_lon = float(metadata.split(' ')[1])
bottom_left_lat = float(metadata.split(' ')[0])
metadata = gdalMetadata['Estimated_Bottom_Right_Geodetic_Coordinates']
bottom_right_lon = float(metadata.split(' ')[1])
bottom_right_lat = float(metadata.split(' ')[0])
metadata = gdalMetadata['Estimated_Top_Left_Geodetic_Coordinates']
top_left_lon = float(metadata.split(' ')[1])
top_left_lat = float(metadata.split(' ')[0])
metadata = gdalMetadata['Estimated_Top_Right_Geodetic_Coordinates']
top_right_lon = float(metadata.split(' ')[1])
top_right_lat = float(metadata.split(' ')[0])
metadata = gdalMetadata['Scene_Centre_Geodetic_Coordinates']
center_lon = float(metadata.split(' ')[1])
center_lat = float(metadata.split(' ')[0])
# Get sub-datasets
subDatasets = gdalDataset.GetSubDatasets()
# Get file names from dataset or subdataset
if subDatasets.__len__() == 1:
fileNames = [fileName]
else:
fileNames = [f[0] for f in subDatasets]
for i, elem in enumerate(fileNames):
if fileNames[i][-3:] == 'QLK':
fileNames.pop(i)
#print fileNames
subDataset = gdal.Open(fileNames[0])
# generate list of GCPs
gcps = []
# create GCP with X,Y,Z(?),pixel,line from lat/lon matrices
gcp = gdal.GCP(float(bottom_left_lon), float(bottom_left_lat), 0, 0, 0)
gcps.append(gcp)
#self.logger.debug('%d %d %d %f %f', 0, gcp.GCPPixel, gcp.GCPLine,
# gcp.GCPX, gcp.GCPY)
gcp = gdal.GCP(float(bottom_right_lon), float(bottom_right_lat), 0,
subDataset.RasterXSize, 0)
gcps.append(gcp)
#self.logger.debug('%d %d %d %f %f', 1, gcp.GCPPixel, gcp.GCPLine,
# gcp.GCPX, gcp.GCPY)
gcp = gdal.GCP(float(top_left_lon), float(top_left_lat), 0, 0,
subDataset.RasterYSize)
gcps.append(gcp)
#self.logger.debug('%d %d %d %f %f', 2, gcp.GCPPixel, gcp.GCPLine,
# gcp.GCPX, gcp.GCPY)
gcp = gdal.GCP(float(top_right_lon), float(top_right_lat), 0,
subDataset.RasterXSize, subDataset.RasterYSize)
gcps.append(gcp)
#self.logger.debug('%d %d %d %f %f', 3, gcp.GCPPixel, gcp.GCPLine,
# gcp.GCPX, gcp.GCPY)
gcp = gdal.GCP(float(center_lon), float(center_lat), 0,
int(np.round(subDataset.RasterXSize / 2.)),
int(round(subDataset.RasterYSize / 2.)))
gcps.append(gcp)
#self.logger.debug('%d %d %d %f %f', 4, gcp.GCPPixel, gcp.GCPLine,
# gcp.GCPX, gcp.GCPY)
# append GCPs and lat/lon projection to the vsiDataset
latlongSRS = osr.SpatialReference()
latlongSRS.ImportFromProj4(
"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
latlongSRSWKT = latlongSRS.ExportToWkt()
# create empty VRT dataset with geolocation only
VRT.__init__(self,
srcRasterXSize=subDataset.RasterXSize,
srcRasterYSize=subDataset.RasterYSize,
srcGCPs=gcps,
srcGCPProjection=latlongSRSWKT)
#print self.fileName
# Read all bands later
#band='S01'
#res='SBI'
# Use only full size "original" datasets
for i, elem in enumerate(fileNames):
if fileNames[i][-3:] == 'SBI':
# Add real and imaginary raw counts as bands
src = {
'SourceFilename': fileNames[i],
'SourceBand': 1,
'DataType': gdal.GDT_Int16
}
dst = {
'dataType':
gdal.GDT_Float32,
'name':
'RawCounts_%s_real' %
gdalMetadata[fileNames[i][-7:-4] + '_Polarisation']
}
self._create_band(src, dst)
src = {
'SourceFilename': fileNames[i],
'SourceBand': 2,
'DataType': gdal.GDT_Int16
}
dst = {
'dataType':
gdal.GDT_Float32,
'name':
'RawCounts_%s_imaginary' %
gdalMetadata[fileNames[i][-7:-4] + '_Polarisation']
}
self._create_band(src, dst)
self.dataset.FlushCache()
for i, elem in enumerate(fileNames):
if fileNames[i][-3:] == 'SBI':
# Calculate sigma0 scaling factor
Rref = float(gdalMetadata['Reference_Slant_Range'])
Rexp = float(gdalMetadata['Reference_Slant_Range_Exponent'])
alphaRef = float(gdalMetadata['Reference_Incidence_Angle'])
F = float(gdalMetadata['Rescaling_Factor'])
K = float(gdalMetadata[fileNames[i][-7:-4] +
'_Calibration_Constant'])
Ftot = Rref**(2. * Rexp)
Ftot *= np.sin(alphaRef * np.pi / 180.0)
Ftot /= F**2.
Ftot /= K
#print Ftot
src = [{
'SourceFilename': self.fileName,
'DataType': gdal.GDT_Float32,
'SourceBand': 2 * i + 1,
'ScaleRatio': np.sqrt(Ftot)
}, {
'SourceFilename': self.fileName,
'DataType': gdal.GDT_Float32,
'SourceBand': 2 * i + 2,
'ScaleRatio': np.sqrt(Ftot)
}]
dst = {
'wkv':
'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType':
'RawcountsToSigma0_CosmoSkymed_SBI',
'polarisation':
gdalMetadata[fileNames[i][-7:-4] + '_Polarisation'],
'name':
'sigma0_%s' %
gdalMetadata[fileNames[i][-7:-4] + '_Polarisation'],
'SatelliteID':
gdalMetadata['Satellite_ID'],
'dataType':
gdal.GDT_Float32
}
#'pass': gdalMetadata['']
# - I can't find this in the metadata...
self._create_band(src, dst)
self.dataset.FlushCache()
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
latlonGrid=None,
mask='',
**kwargs):
''' Create VRT
Parameters
-----------
fileName : string
gdalDataset : gdal dataset
gdalMetadata : gdal metadata
latlonGrid : numpy 2 layered 2D array with lat/lons of desired grid
'''
# test if input files is ASCAT
iDir, iFile = os.path.split(fileName)
iFileName, iFileExt = os.path.splitext(iFile)
try:
assert iFileName[0:6] == 'ascat_' and iFileExt == '.nc'
except:
raise WrongMapperError
# Create geolocation
subDataset = gdal.Open('NETCDF:"' + fileName + '":lat')
self.GeolocVRT = VRT(srcRasterXSize=subDataset.RasterXSize,
srcRasterYSize=subDataset.RasterYSize)
GeolocMetaDict = [{
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":lon'),
'SourceBand': 1,
'ScaleRatio': 0.00001,
'ScaleOffset': -360
},
'dst': {}
}, {
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":lat'),
'SourceBand': 1,
'ScaleRatio': 0.00001,
'ScaleOffset': 0
},
'dst': {}
}]
self.GeolocVRT._create_bands(GeolocMetaDict)
GeolocObject = GeolocationArray(
xVRT=self.GeolocVRT,
yVRT=self.GeolocVRT,
# x = lon, y = lat
xBand=1,
yBand=2,
lineOffset=0,
pixelOffset=0,
lineStep=1,
pixelStep=1)
# create empty VRT dataset with geolocation only
VRT.__init__(self,
srcRasterXSize=subDataset.RasterXSize,
srcRasterYSize=subDataset.RasterYSize,
gdalDataset=subDataset,
geolocationArray=GeolocObject,
srcProjection=GeolocObject.d['SRS'])
# Scale and NODATA should ideally be taken directly from raw file
metaDict = [{
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":wind_speed'),
'ScaleRatio': 0.01,
'NODATA': -32767
},
'dst': {
'name': 'wind_speed',
'wkv': 'wind_speed'
}
}, {
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":wind_dir'),
'ScaleRatio': 0.1,
'NODATA': -32767
},
'dst': {
'name': 'wind_direction',
'wkv': 'wind_direction'
}
}]
self._create_bands(metaDict)
# This should not be necessary
# - should be provided by GeolocationArray!
self.dataset.SetProjection(GeolocObject.d['SRS'])
# Add time
startTime = datetime.datetime(int(iFileName[6:10]),
int(iFileName[10:12]),
int(iFileName[12:14]),
int(iFileName[15:17]),
int(iFileName[17:19]),
int(iFileName[19:21]))
# Adding valid time to dataset
self.dataset.SetMetadataItem('time_coverage_start',
startTime.isoformat())
self.dataset.SetMetadataItem('time_coverage_end',
startTime.isoformat())
# set SADCAT specific metadata
self.dataset.SetMetadataItem('sensor', 'ASCAT')
self.dataset.SetMetadataItem('satellite', 'Metop-A')
warnings.warn("Setting satellite to Metop-A - update mapper if it is" \
" e.g. Metop-B")
self.dataset.SetMetadataItem('mapper', 'ascat_nasa')
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
latlonGrid=None,
mask='',
**kwargs):
''' Create VRT
Parameters
-----------
fileName : string
gdalDataset : gdal dataset
gdalMetadata : gdal metadata
latlonGrid : numpy 2 layered 2D array with lat/lons of desired grid
'''
# test if input files is ASCAT
iDir, iFile = os.path.split(fileName)
iFileName, iFileExt = os.path.splitext(iFile)
try:
assert iFileName[0:6] == 'ascat_' and iFileExt == '.nc'
except:
raise WrongMapperError
# Create geolocation
subDataset = gdal.Open('NETCDF:"' + fileName + '":lat')
self.GeolocVRT = VRT(srcRasterXSize=subDataset.RasterXSize,
srcRasterYSize=subDataset.RasterYSize)
GeolocMetaDict = [{
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":lon'),
'SourceBand': 1,
'ScaleRatio': 0.00001,
'ScaleOffset': -360
},
'dst': {}
}, {
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":lat'),
'SourceBand': 1,
'ScaleRatio': 0.00001,
'ScaleOffset': 0
},
'dst': {}
}]
self.GeolocVRT._create_bands(GeolocMetaDict)
GeolocObject = GeolocationArray(
xVRT=self.GeolocVRT,
yVRT=self.GeolocVRT,
# x = lon, y = lat
xBand=1,
yBand=2,
lineOffset=0,
pixelOffset=0,
lineStep=1,
pixelStep=1)
# create empty VRT dataset with geolocation only
VRT.__init__(self,
srcRasterXSize=subDataset.RasterXSize,
srcRasterYSize=subDataset.RasterYSize,
gdalDataset=subDataset,
geolocationArray=GeolocObject,
srcProjection=GeolocObject.d['SRS'])
# Scale and NODATA should ideally be taken directly from raw file
metaDict = [{
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":wind_speed'),
'ScaleRatio': 0.01,
'NODATA': -32767
},
'dst': {
'name': 'windspeed',
'wkv': 'wind_speed'
}
}, {
'src': {
'SourceFilename': ('NETCDF:"' + fileName + '":wind_dir'),
'ScaleRatio': 0.1,
'NODATA': -32767
},
'dst': {
'name': 'winddirection',
'wkv': 'wind_from_direction'
}
}]
self._create_bands(metaDict)
# This should not be necessary
# - should be provided by GeolocationArray!
self.dataset.SetProjection(GeolocObject.d['SRS'])
# Add time
startTime = datetime.datetime(int(iFileName[6:10]),
int(iFileName[10:12]),
int(iFileName[12:14]),
int(iFileName[15:17]),
int(iFileName[17:19]),
int(iFileName[19:21]))
# Adding valid time to dataset
self.dataset.SetMetadataItem('time_coverage_start',
startTime.isoformat())
self.dataset.SetMetadataItem('time_coverage_end',
startTime.isoformat())
# Get dictionary describing the instrument and platform according to
# the GCMD keywords
mm = pti.get_gcmd_instrument('ascat')
ee = pti.get_gcmd_platform('metop-a')
# TODO: Validate that the found instrument and platform are indeed what
# we want....
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
def __init__(self,
filename,
gdalDataset,
gdalMetadata,
fast=False,
**kwargs):
if kwargs.get('manifestonly', False):
fast = True
NansatFutureWarning(
'manifestonly option will be deprecated. Use: fast=True')
if not os.path.split(filename.rstrip('/'))[1][:3] in ['S1A', 'S1B']:
raise WrongMapperError('%s: Not Sentinel 1A or 1B' % filename)
if not IMPORT_SCIPY:
raise NansatReadError(
'Sentinel-1 data cannot be read because scipy is not installed'
)
if zipfile.is_zipfile(filename):
zz = zipfile.PyZipFile(filename)
# Assuming the file names are consistent, the polarization
# dependent data should be sorted equally such that we can use the
# same indices consistently for all the following lists
# THIS IS NOT THE CASE...
mds_files = [
'/vsizip/%s/%s' % (filename, fn) for fn in zz.namelist()
if 'measurement/s1' in fn
]
calibration_files = [
'/vsizip/%s/%s' % (filename, fn) for fn in zz.namelist()
if 'annotation/calibration/calibration-s1' in fn
]
noise_files = [
'/vsizip/%s/%s' % (filename, fn) for fn in zz.namelist()
if 'annotation/calibration/noise-s1' in fn
]
annotation_files = [
'/vsizip/%s/%s' % (filename, fn) for fn in zz.namelist()
if 'annotation/s1' in fn
]
manifest_files = [
'/vsizip/%s/%s' % (filename, fn) for fn in zz.namelist()
if 'manifest.safe' in fn
]
zz.close()
else:
mds_files = glob.glob('%s/measurement/s1*' % filename)
calibration_files = glob.glob(
'%s/annotation/calibration/calibration-s1*' % filename)
noise_files = glob.glob('%s/annotation/calibration/noise-s1*' %
filename)
annotation_files = glob.glob('%s/annotation/s1*' % filename)
manifest_files = glob.glob('%s/manifest.safe' % filename)
if (not mds_files or not calibration_files or not noise_files
or not annotation_files or not manifest_files):
raise WrongMapperError(filename)
# convert list of MDS files into dictionary. Keys - polarizations in upper case.
mds_files = {
os.path.basename(ff).split('-')[3].upper(): ff
for ff in mds_files
}
polarizations = list(mds_files.keys())
# read annotation files
annotation_data = self.read_annotation(annotation_files)
if not fast:
annotation_data = Mapper.correct_geolocation_data(annotation_data)
# read manifest file
manifest_data = self.read_manifest_data(manifest_files[0])
# very fast constructor without any bands only with some metadata and geolocation
self._init_empty(manifest_data, annotation_data)
# skip adding bands in the fast mode and RETURN
if fast:
return
# Open data files with GDAL
gdalDatasets = {}
for pol in polarizations:
gdalDatasets[pol] = gdal.Open(mds_files[pol])
if not gdalDatasets[pol]:
raise WrongMapperError('%s: No Sentinel-1 datasets found' %
mds_files[pol])
# Check metadata to confirm it is Sentinel-1 L1
metadata = gdalDatasets[polarizations[0]].GetMetadata()
# create full size VRTs with incidenceAngle and elevationAngle
annotation_vrts = self.vrts_from_arrays(
annotation_data, ['incidenceAngle', 'elevationAngle'])
self.band_vrts.update(annotation_vrts)
# create full size VRTS with calibration LUT
calibration_names = ['sigmaNought', 'betaNought']
calibration_list_tag = 'calibrationVectorList'
for calibration_file in calibration_files:
pol = '_' + os.path.basename(calibration_file).split(
'-')[4].upper()
xml = self.read_vsi(calibration_file)
calibration_data = self.read_calibration(xml, calibration_list_tag,
calibration_names, pol)
calibration_vrts = self.vrts_from_arrays(calibration_data,
calibration_names, pol,
True, 1)
self.band_vrts.update(calibration_vrts)
# create full size VRTS with noise LUT
for noise_file in noise_files:
pol = '_' + os.path.basename(noise_file).split('-')[4].upper()
xml = self.read_vsi(noise_file)
if '<noiseVectorList' in xml:
noise_list_tag = 'noiseVectorList'
noise_name = 'noiseLut'
elif '<noiseRangeVectorList' in xml:
noise_list_tag = 'noiseRangeVectorList'
noise_name = 'noiseRangeLut'
noise_data = self.read_calibration(xml, noise_list_tag,
[noise_name], pol)
noise_vrts = self.vrts_from_arrays(noise_data, [noise_name], pol,
True, 1)
self.band_vrts.update(noise_vrts)
#### Create metaDict: dict with metadata for all bands
metaDict = []
bandNumberDict = {}
bnmax = 0
for pol in polarizations:
dsPath, dsName = os.path.split(mds_files[pol])
name = 'DN_%s' % pol
# A dictionary of band numbers is needed for the pixel function
# bands further down. This is not the best solution. It would be
# better to have a function in VRT that returns the number given a
# band name. This function exists in Nansat but could perhaps be
# moved to VRT? The existing nansat function could just call the
# VRT one...
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
band = gdalDatasets[pol].GetRasterBand(1)
dtype = band.DataType
metaDict.append({
'src': {
'SourceFilename': mds_files[pol],
'SourceBand': 1,
'DataType': dtype,
},
'dst': {
'name': name,
},
})
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
'''
Calibration should be performed as
s0 = DN^2/sigmaNought^2,
where sigmaNought is from e.g.
annotation/calibration/calibration-s1a-iw-grd-hh-20140811t151231-20140811t151301-001894-001cc7-001.xml,
and DN is the Digital Numbers in the tiff files.
Also the noise should be subtracted.
See
https://sentinel.esa.int/web/sentinel/sentinel-1-sar-wiki/-/wiki/Sentinel%20One/Application+of+Radiometric+Calibration+LUT
The noise correction/subtraction is implemented in an independent package "sentinel1denoised"
See
https://github.com/nansencenter/sentinel1denoised
'''
# Get look direction
longitude, latitude = self.transform_points(
calibration_data['pixel'].flatten(),
calibration_data['line'].flatten())
longitude.shape = calibration_data['pixel'].shape
latitude.shape = calibration_data['pixel'].shape
sat_heading = initial_bearing(longitude[:-1, :], latitude[:-1, :],
longitude[1:, :], latitude[1:, :])
look_direction = scipy.ndimage.interpolation.zoom(
np.mod(sat_heading + 90, 360),
(np.shape(longitude)[0] / (np.shape(longitude)[0] - 1.), 1))
# Decompose, to avoid interpolation errors around 0 <-> 360
look_direction_u = np.sin(np.deg2rad(look_direction))
look_direction_v = np.cos(np.deg2rad(look_direction))
look_u_VRT = VRT.from_array(look_direction_u)
look_v_VRT = VRT.from_array(look_direction_v)
lookVRT = VRT.from_lonlat(longitude, latitude)
lookVRT.create_band([{
'SourceFilename': look_u_VRT.filename,
'SourceBand': 1
}, {
'SourceFilename': look_v_VRT.filename,
'SourceBand': 1
}], {'PixelFunctionType': 'UVToDirectionTo'})
# Blow up to full size
lookVRT = lookVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize, 1)
# Store VRTs so that they are accessible later
self.band_vrts['look_u_VRT'] = look_u_VRT
self.band_vrts['look_v_VRT'] = look_v_VRT
self.band_vrts['lookVRT'] = lookVRT
metaDict = []
# Add bands to full size VRT
for pol in polarizations:
name = 'sigmaNought_%s' % pol
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': (self.band_vrts[name].filename),
'SourceBand': 1
},
'dst': {
'name': name
}
})
name = 'noise_%s' % pol
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename':
self.band_vrts['%s_%s' % (noise_name, pol)].filename,
'SourceBand': 1
},
'dst': {
'name': name
}
})
name = 'look_direction'
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.band_vrts['lookVRT'].filename,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': name
}
})
for pol in polarizations:
dsPath, dsName = os.path.split(mds_files[pol])
name = 'sigma0_%s' % pol
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
metaDict.append({
'src': [{
'SourceFilename': self.filename,
'SourceBand': bandNumberDict['DN_%s' % pol],
}, {
'SourceFilename':
self.band_vrts['sigmaNought_%s' % pol].filename,
'SourceBand':
1
}],
'dst': {
'wkv':
'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol,
'suffix': pol,
},
})
name = 'beta0_%s' % pol
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
metaDict.append({
'src': [{
'SourceFilename': self.filename,
'SourceBand': bandNumberDict['DN_%s' % pol]
}, {
'SourceFilename':
self.band_vrts['betaNought_%s' % pol].filename,
'SourceBand':
1
}],
'dst': {
'wkv':
'surface_backwards_brightness_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol,
'suffix': pol,
},
})
self.create_bands(metaDict)
# Add incidence angle as band
name = 'incidence_angle'
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
src = {
'SourceFilename': self.band_vrts['incidenceAngle'].filename,
'SourceBand': 1
}
dst = {'wkv': 'angle_of_incidence', 'name': name}
self.create_band(src, dst)
self.dataset.FlushCache()
# Add elevation angle as band
name = 'elevation_angle'
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
src = {
'SourceFilename': self.band_vrts['elevationAngle'].filename,
'SourceBand': 1
}
dst = {'wkv': 'angle_of_elevation', 'name': name}
self.create_band(src, dst)
self.dataset.FlushCache()
# Add sigma0_VV
if 'VV' not in polarizations and 'HH' in polarizations:
name = 'sigma0_VV'
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
src = [{
'SourceFilename': self.filename,
'SourceBand': bandNumberDict['DN_HH'],
}, {
'SourceFilename': (self.band_vrts['sigmaNought_HH'].filename),
'SourceBand':
1,
}, {
'SourceFilename': self.band_vrts['incidenceAngle'].filename,
'SourceBand': 1
}]
dst = {
'wkv':
'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Sigma0HHToSigma0VV',
'polarization': 'VV',
'suffix': 'VV'
}
self.create_band(src, dst)
self.dataset.FlushCache()
def __init__(self,
fileName,
gdalDataset,
gdalMetadata,
GCP_COUNT=10,
**kwargs):
''' Create VRT
Parameters
----------
GCP_COUNT : int
number of GCPs along each dimention
'''
titles = [
'HMODISA Level-2 Data', 'MODISA Level-2 Data',
'MERIS Level-2 Data', 'GOCI Level-2 Data', 'VIIRSN Level-2 Data'
]
# should raise error in case of not obpg_l2 file
try:
title = gdalMetadata["Title"]
except:
raise WrongMapperError
if title not in titles:
raise WrongMapperError
# get subdataset and parse to VRT.__init__()
# for retrieving geo-metadata
# but NOT from longitude or latitude because it can be smaller!
subDatasets = gdalDataset.GetSubDatasets()
for subDataset in subDatasets:
if ('longitude' not in subDataset[1]
and 'latitude' not in subDataset[1]):
gdalSubDataset = gdal.Open(subDataset[0])
break
if title is 'GOCI Level-2 Data':
# set GOCI projection parameters
rasterXSize = 5567
rasterYSize = 5685
proj4 = '+proj=ortho +lat_0=36 +lon_0=130 units=m +ellps=WGS84 +datum=WGS84 +no_defs'
srs = osr.SpatialReference()
srs.ImportFromProj4(proj4)
projection = srs.ExportToWkt()
geoTransform = (-1391500.0, 500.0, 0.0, 1349500.0, 0.0, -500.0)
# create empty VRT dataset with georeference only
VRT.__init__(self,
srcGeoTransform=geoTransform,
srcProjection=projection,
srcRasterXSize=rasterXSize,
srcRasterYSize=rasterYSize)
else:
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalSubDataset)
# parts of dictionary for all Reflectances
#dictRrs = {'wkv': 'surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_air', 'wavelength': '412'} }
# dictionary for all possible bands
allBandsDict = {
'Rrs': {
'src': {},
'dst': {
'wkv':
'surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_air'
}
},
'Kd': {
'src': {},
'dst': {
'wkv':
'volume_attenuation_coefficient_of_downwelling_radiative_flux_in_sea_water'
}
},
'chlor_a': {
'src': {},
'dst': {
'wkv': 'mass_concentration_of_chlorophyll_a_in_sea_water',
'case': 'I'
}
},
'cdom_index': {
'src': {},
'dst': {
'wkv':
'volume_absorption_coefficient_of_radiative_flux_in_sea_water_due_to_dissolved_organic_matter',
'case': 'II'
}
},
'sst': {
'src': {},
'dst': {
'wkv': 'sea_surface_temperature'
}
},
'sst4': {
'src': {},
'dst': {
'wkv': 'sea_surface_temperature'
}
},
'l2_flags': {
'src': {
'SourceType': 'SimpleSource',
'DataType': 4
},
'dst': {
'wkv': 'quality_flags',
'dataType': 4
}
},
'qual_sst': {
'src': {
'SourceType': 'SimpleSource',
'DataType': 4
},
'dst': {
'wkv': 'quality_flags',
'name': 'qual_sst',
'dataType': 4
}
},
'qual_sst4': {
'src': {
'SourceType': 'SimpleSource',
'DataType': 4
},
'dst': {
'wkv': 'quality_flags',
'name': 'qual_sst',
'dataType': 4
}
},
'latitude': {
'src': {},
'dst': {
'wkv': 'latitude'
}
},
'longitude': {
'src': {},
'dst': {
'wkv': 'longitude'
}
}
}
# loop through available bands and generate metaDict (non fixed)
metaDict = []
bandNo = 0
for subDataset in subDatasets:
# get sub dataset name
subDatasetName = subDataset[1].split(' ')[1]
self.logger.debug('Subdataset: %s' % subDataset[1])
self.logger.debug('Subdataset name: "%s"' % subDatasetName)
# get wavelength if applicable, get dataset name without wavelength
try:
wavelength = int(subDatasetName.split('_')[-1])
except:
wavelength = None
subBandName = subDatasetName
else:
subBandName = subDatasetName.split('_')[0]
self.logger.debug('subBandName, wavelength: %s %s' %
(subBandName, str(wavelength)))
if subBandName in allBandsDict:
# get name, slope, intercept
self.logger.debug('name: %s' % subBandName)
tmpSubDataset = gdal.Open(subDataset[0])
tmpSubMetadata = tmpSubDataset.GetMetadata()
slope = tmpSubMetadata.get('slope', '1')
intercept = tmpSubMetadata.get('intercept', '0')
self.logger.debug('slope, intercept: %s %s ' %
(slope, intercept))
# create meta entry
metaEntry = {
'src': {
'SourceFilename': subDataset[0],
'sourceBand': 1,
'ScaleRatio': slope,
'ScaleOffset': intercept
},
'dst': {}
}
# add more to src
for srcKey in allBandsDict[subBandName]['src']:
metaEntry['src'][srcKey] = (
allBandsDict[subBandName]['src'][srcKey])
# add dst from allBandsDict
for dstKey in allBandsDict[subBandName]['dst']:
metaEntry['dst'][dstKey] = (
allBandsDict[subBandName]['dst'][dstKey])
# add wavelength, band name to dst
if wavelength is not None:
metaEntry['dst']['suffix'] = str(wavelength)
metaEntry['dst']['wavelength'] = str(wavelength)
# append band metadata to metaDict
self.logger.debug('metaEntry: %d => %s' %
(bandNo, str(metaEntry)))
metaDict.append(metaEntry)
bandNo += 1
if subBandName == 'Rrs':
metaEntryRrsw = {
'src': [{
'SourceFilename': subDataset[0],
'SourceBand': 1,
'ScaleRatio': slope,
'ScaleOffset': intercept,
'DataType': 6
}],
'dst': {
'wkv':
'surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_water',
'suffix':
str(wavelength),
'wavelength':
str(wavelength),
'PixelFunctionType':
'NormReflectanceToRemSensReflectance',
}
}
# append band metadata to metaDict
self.logger.debug('metaEntry: %d => %s' %
(bandNo, str(metaEntryRrsw)))
metaDict.append(metaEntryRrsw)
bandNo += 1
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# set TIME
startYear = int(gdalMetadata['Start Year'])
startDay = int(gdalMetadata['Start Day'])
startMillisec = int(gdalMetadata['Start Millisec'])
startDate = datetime(startYear, 1, 1) + timedelta(
startDay - 1, 0, 0, startMillisec)
self._set_time(startDate)
# skip adding georeference for GOCI
if title is 'GOCI Level-2 Data':
return
self._remove_geotransform()
# add geolocation
geoMeta = self.geolocationArray.d
if len(geoMeta) > 0:
self.dataset.SetMetadata(geoMeta, 'GEOLOCATION')
# add GCPs
geolocationMetadata = gdalSubDataset.GetMetadata('GEOLOCATION')
xDatasetSource = geolocationMetadata['X_DATASET']
xDataset = gdal.Open(xDatasetSource)
yDatasetSource = geolocationMetadata['Y_DATASET']
yDataset = gdal.Open(yDatasetSource)
longitude = xDataset.ReadAsArray()
latitude = yDataset.ReadAsArray()
# estimate pixel/line step of the geolocation arrays
pixelStep = int(
ceil(
float(gdalSubDataset.RasterXSize) /
float(xDataset.RasterXSize)))
lineStep = int(
ceil(
float(gdalSubDataset.RasterYSize) /
float(xDataset.RasterYSize)))
self.logger.debug('pixel/lineStep %f %f' % (pixelStep, lineStep))
# ==== ADD GCPs and Pojection ====
# estimate step of GCPs
step0 = max(1, int(float(latitude.shape[0]) / GCP_COUNT))
step1 = max(1, int(float(latitude.shape[1]) / GCP_COUNT))
if str(title) == 'VIIRSN Level-2 Data':
step0 = 64
self.logger.debug('gcpCount: >%s<, %d %d %f %d %d', title,
latitude.shape[0], latitude.shape[1], GCP_COUNT,
step0, step1)
# generate list of GCPs
dx = .5
dy = .5
gcps = []
k = 0
for i0 in range(0, latitude.shape[0], step0):
for i1 in range(0, latitude.shape[1], step1):
# create GCP with X,Y,pixel,line from lat/lon matrices
lon = float(longitude[i0, i1])
lat = float(latitude[i0, i1])
if (lon >= -180 and lon <= 180 and lat >= -90 and lat <= 90):
gcp = gdal.GCP(lon, lat, 0, i1 * pixelStep + dx,
i0 * lineStep + dy)
self.logger.debug('%d %d %d %f %f', k, gcp.GCPPixel,
gcp.GCPLine, gcp.GCPX, gcp.GCPY)
gcps.append(gcp)
k += 1
# append GCPs and lat/lon projection to the vsiDataset
self.dataset.SetGCPs(gcps, NSR().wkt)
