def get_pixelsize_meters(self):
'''Returns the pixelsize (deltaX, deltaY) of the domain
For projected domains, the exact result which is constant
over the domain is returned.
For geographic (lon-lat) projections, or domains with no geotransform,
the haversine formula is used to calculate the pixel size
in the center of the domain.
Returns
--------
deltaX, deltaY : float
pixel size in X and Y directions given in meters
'''
srs = osr.SpatialReference(self.vrt.dataset.GetProjection())
if srs.IsProjected:
if srs.GetAttrValue('unit') == 'metre':
geoTransform = self.vrt.dataset.GetGeoTransform()
deltaX = abs(geoTransform[1])
deltaY = abs(geoTransform[5])
return deltaX, deltaY
# Estimate pixel size in center of domain using haversine formula
centerCol = round(self.vrt.dataset.RasterXSize / 2)
centerRow = round(self.vrt.dataset.RasterYSize / 2)
lon00, lat00 = self.transform_points([centerCol], [centerRow])
lon01, lat01 = self.transform_points([centerCol], [centerRow + 1])
lon10, lat10 = self.transform_points([centerCol + 1], [centerRow])
deltaX = haversine(lon00, lat00, lon01, lat01)
deltaY = haversine(lon00, lat00, lon10, lat10)
return deltaX[0], deltaY[0]
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create VRT '''
ThreddsBase = 'http://thredds.met.no/thredds/dodsC/myocean/siw-tac/siw-metno-svalbard/'
# First check if mapper is called with keyword syntax:
# filename = metno_hires_seaice:YYYYmmdd
keywordBase = 'metno_hires_seaice'
foundDataset = False
if fileName[0:len(keywordBase)] == keywordBase:
keywordTime = fileName[len(keywordBase)+1:]
requestedTime = datetime.strptime(keywordTime, '%Y%m%d')
# Search for nearest available file, within the closest 3 days
for deltaDay in [0, -1, 1, -2, 2, -3, 3]:
validTime = (requestedTime + timedelta(days=deltaDay) +
timedelta(hours=15))
fileName = (ThreddsBase +
validTime.strftime(
'%Y/%m/ice_conc_svalbard_%Y%m%d1500.nc'))
try:
urllib2.urlopen(fileName + '.dds')
foundDataset = True
# Data is found for this day
break
except:
# No data for this day
pass
if not foundDataset:
raise WrongMapperError
# Then check if a valid OPeNDAP URL is given
# (or has been constructed from keyword)
if fileName[0:len(ThreddsBase)] != ThreddsBase:
AttributeError("Not Met.no Svalbard-ice Thredds URL")
else:
timestr = fileName[-15:-3]
validTime = datetime.strptime(timestr, '%Y%m%d%H%M')
fileName = fileName + '?ice_concentration[0][y][x]'
srcProjection = osr.SpatialReference()
srcProjection.ImportFromProj4('+proj=stere lon_0=0.0 +lat_0=90 +datum=WGS84 +ellps=WGS84 +units=km +no_defs')
srcProjection = srcProjection.ExportToWkt()
# From thredds web, with manual shift
srcGeotransform = (-1243.008 - 1, 1, 0, -3190.026 - 7, 0, 1)
# create empty VRT dataset with geolocation only
VRT.__init__(self,
srcGeoTransform=srcGeotransform,
srcProjection=srcProjection,
srcRasterXSize=3812,
srcRasterYSize=2980)
metaDict = [{'src': {'SourceFilename': fileName,
'sourceBand': 1},
'dst': {'name': 'sea_ice_area_fraction',
'wkv': 'sea_ice_area_fraction'}}]
# Add band
self._create_bands(metaDict)
# Set time
self.logger.info('Valid time: %s', str(validTime))
self._set_time(validTime)
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()