def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
'''
Mapping for the global 30 arc-second elevation (see
https://lta.cr.usgs.gov/GTOPO30).
Parameters:
-----------
fileName : string
Either the name of a gtopo30 DEM file, or <path>/gtopo30.vrt. The
latter is an aggregation of the DEM-files available with gtopo30
except the Antarctic one, which is in polarstereographic
projection. You can create your own gtopo30.vrt file with gdal:
> gdalbuildvrt gtopo30.vrt [E,W]*.DEM
'''
bn = os.path.basename(fileName)
if not bn=='gtopo30.vrt' and not os.path.splitext(bn)[1]=='.DEM':
raise WrongMapperError
metaDict = [{'src': {'SourceFilename': fileName, 'SourceBand': 1},
'dst': {'wkv': 'height_above_reference_ellipsoid'}}]
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalDataset)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
def test_copy_vrt_pixel_func(self):
vrt1 = VRT()
vrt1_xml = '''
<VRTDataset rasterXSize="200" rasterYSize="200">
<VRTRasterBand dataType="Byte" band="1">
<ComplexSource>
<SourceFilename relativeToVRT="0">%s</SourceFilename>
<SourceBand>1</SourceBand>
<SourceProperties RasterXSize="200" RasterYSize="200" DataType="Byte" BlockXSize="200" BlockYSize="13" />
<SrcRect xOff="0" yOff="0" xSize="200" ySize="200" />
<DstRect xOff="0" yOff="0" xSize="200" ySize="200" />
</ComplexSource>
</VRTRasterBand>
<VRTRasterBand dataType="Float32" band="2" subClass="VRTDerivedRasterBand">
<ComplexSource>
<SourceFilename relativeToVRT="0">%s</SourceFilename>
<SourceBand>1</SourceBand>
<SourceProperties RasterXSize="200" RasterYSize="200" DataType="Byte" BlockXSize="128" BlockYSize="128" />
<SrcRect xOff="0" yOff="0" xSize="200" ySize="200" />
<DstRect xOff="0" yOff="0" xSize="200" ySize="200" />
</ComplexSource>
<PixelFunctionType>sqrt</PixelFunctionType>
</VRTRasterBand>
</VRTDataset>
''' % (self.test_file_gcps, vrt1.filename)
vrt1.write_xml(vrt1_xml)
vrt2 = vrt1.copy()
self.assertFalse(os.path.basename(vrt1.filename) in vrt2.xml)
def test_create_band(self):
array = gdal.Open(self.test_file_gcps).ReadAsArray()[1, 10:, :]
vrt1 = VRT.from_array(array)
vrt2 = VRT(x_size=array.shape[1], y_size=array.shape[0])
self.assertEqual(vrt2.dataset.RasterCount, 0)
vrt2.create_band({'SourceFilename': vrt1.filename})
self.assertEqual(vrt2.dataset.RasterCount, 1)
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create VRT '''
##############
# Get time
##############
if fileName[0:len(keywordBase)] != keywordBase:
raise AttributeError("Wrong mapper")
timestr = fileName[len(keywordBase)+1::]
time = datetime.strptime(timestr, '%Y%m%d%H%M')
######################################################
# Find windFileName corresponding to a Nansat-readable
# file in your local (or remote) file archive
######################################################
windFileName = localFolder + <.......>
######################################################
# Open file with any other Nansat mapper
######################################################
w = Nansat(windFileName)
VRT.__init__(self, vrtDataset=w.vrt.dataset)
return
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create NCEP VRT '''
if not gdalDataset:
raise WrongMapperError
geotransform = gdalDataset.GetGeoTransform()
if (geotransform != (-0.25, 0.5, 0.0, 90.25, 0.0, -0.5) or
gdalDataset.RasterCount != 2): # Not water proof
raise WrongMapperError
metaDict = [{'src': {'SourceFilename': fileName,
'SourceBand': 1},
'dst': {'wkv': 'eastward_wind',
'height': '10 m'}},
{'src': {'SourceFilename': fileName,
'SourceBand': 2},
'dst': {'wkv': 'northward_wind',
'height': '10 m'}},
{'src': [{'SourceFilename': fileName,
'SourceBand': 1,
'DataType': gdalDataset.GetRasterBand(1).DataType
},
{'SourceFilename': fileName,
'SourceBand': 2,
'DataType': gdalDataset.GetRasterBand(2).DataType
}],
'dst': {'wkv': 'wind_speed',
'PixelFunctionType': 'UVToMagnitude',
'name': 'windspeed',
'height': '2 m'
}},
{'src': [{'SourceFilename': fileName,
'SourceBand': 1,
'DataType': gdalDataset.GetRasterBand(1).DataType
},
{'SourceFilename': fileName,
'SourceBand': 2,
'DataType': gdalDataset.GetRasterBand(2).DataType
}],
'dst': {'wkv': 'wind_from_direction',
'PixelFunctionType': 'UVToDirectionFrom',
'name': 'winddirection',
'height': '2 m'
}
}]
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalDataset)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# Adding valid time from the GRIB file to dataset
validTime = gdalDataset.GetRasterBand(1).\
GetMetadata()['GRIB_VALID_TIME']
self._set_time(datetime.datetime.
utcfromtimestamp(int(validTime.strip().split(' ')[0])))
return
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
title_correct = False
if not gdalMetadata:
raise WrongMapperError
for key, val in gdalMetadata.iteritems():
if "title" in key:
if not val == "Daily AMSR-E Arctic lead area fraction [in percent]":
raise WrongMapperError
else:
title_correct = True
if not title_correct:
raise WrongMapperError
# initiate VRT for the NSIDC 10 km grid
VRT.__init__(
self,
srcGeoTransform=(-3850000, 6250, 0.0, 5850000, 0.0, -6250),
srcProjection=NSR(3411).wkt,
srcRasterXSize=1216,
srcRasterYSize=1792,
)
src = {"SourceFilename": 'NETCDF:"%s":lf' % fileName, "SourceBand": 1}
dst = {"name": "leadFraction", "long_name": "AMSRE sea ice lead fraction"}
self._create_band(src, dst)
self.dataset.FlushCache()
def test_create_band_name_wkv(self):
short_name='sigma0'
wkv = dict(short_name=short_name)
self.mock_pti['get_wkv_variable'].return_value=wkv
vrt = VRT()
self.assertEqual(vrt._create_band_name({'wkv': short_name}), (short_name, wkv))
self.assertEqual(vrt._create_band_name({'wkv': short_name, 'suffix': 'HH'}),
(short_name + '_HH', wkv))
def test_make_filename(self):
filename1 = VRT._make_filename()
filename2 = VRT._make_filename(extention='smth')
filename3 = VRT._make_filename(nomem=True)
self.assertTrue(filename1.startswith('/vsimem/'))
self.assertTrue(filename2.startswith('/vsimem/'))
self.assertTrue(filename2.endswith('.smth'))
self.assertTrue(os.path.exists(filename3))
def test_get_projection_raises_NansatProjectionError(self, dataset):
dataset.GetProjection.return_value = ''
dataset.GetGCPProjection.return_value = ''
dataset.GetMetadata.return_value = {}
vrt = VRT()
with self.assertRaises(NansatProjectionError):
proj = vrt.get_projection()
def test_get_projection_dataset(self, dataset):
proj = 'SOME_PROJECTION'
dataset.GetProjection.return_value = proj
dataset.GetGCPProjection.return_value = ''
dataset.GetMetadata.return_value = {}
vrt = VRT()
proj_src = vrt.get_projection()
self.assertEqual(proj_src, (proj, 'dataset'))
def test_get_projection_geolocation(self, dataset):
proj = 'SOME_PROJECTION'
dataset.GetProjection.return_value = ''
dataset.GetGCPProjection.return_value = ''
dataset.GetMetadata.return_value = {'SRS': proj}
vrt = VRT()
proj_src = vrt.get_projection()
self.assertEqual(proj_src, (proj, 'geolocation'))
def set_gcps(self, lon, lat, gdal_dataset):
""" Set gcps """
self.band_vrts['new_lon_VRT'] = VRT.from_array(lon)
self.dataset.SetGCPs(VRT._lonlat2gcps(lon, lat, n_gcps=400), NSR().wkt)
# Add geolocation from correct longitudes and latitudes
self._add_geolocation(
Geolocation(self.band_vrts['new_lon_VRT'], self, x_band=1, y_band=self._latitude_band_number(gdal_dataset))
)
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, 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 test_add_swath_mask_band(self, create_band):
vrt = VRT()
vrt.filename = '/temp/filename.vrt'
vrt._add_swath_mask_band()
src = [{'SourceFilename': '/temp/filename.vrt',
'SourceBand': 1,
'DataType': 1}]
dst ={'dataType': 1,
'wkv': 'swath_binary_mask',
'PixelFunctionType': 'OnesPixelFunc'}
create_band.assert_called_once_with(src=src, dst=dst)
def test_find_complex_band(self):
a = np.random.randn(100,100)
vrt1 = VRT.from_array(a)
vrt2 = VRT.from_array(a.astype(np.complex64))
vrt3 = VRT.from_gdal_dataset(vrt1.dataset)
vrt3.create_bands([{'src': {'SourceFilename': vrt1.filename}},
{'src': {'SourceFilename': vrt2.filename}}])
self.assertEqual(vrt1._find_complex_band(), None)
self.assertEqual(vrt2._find_complex_band(), 1)
self.assertEqual(vrt3._find_complex_band(), 2)
def test_from_filenames(self):
lon, lat = np.meshgrid(np.linspace(0,5,10), np.linspace(10,20,30))
x_vrt = VRT.from_array(lon)
y_vrt = VRT.from_array(lat)
g = Geolocation.from_filenames(x_vrt.filename, y_vrt.filename)
self.assertIsInstance(g, Geolocation)
self.assertEqual(g.data['X_DATASET'], x_vrt.filename)
self.assertEqual(g.data['Y_DATASET'], y_vrt.filename)
self.assertEqual(g.data['LINE_OFFSET'], '0')
self.assertEqual(g.data['LINE_STEP'], '1')
self.assertEqual(g.data['PIXEL_OFFSET'], '0')
self.assertEqual(g.data['PIXEL_STEP'], '1')
def test_copy_empty_vrt(self):
vrt1 = VRT()
vrt2 = vrt1.copy()
self.assertIsInstance(vrt2, VRT)
self.assertIsInstance(vrt2.filename, str)
self.assertEqual(vrt2.dataset.RasterXSize, vrt1.dataset.RasterXSize)
self.assertEqual(vrt2.dataset.RasterYSize, vrt1.dataset.RasterYSize)
self.assertEqual(vrt2.dataset.GetProjection(), vrt1.dataset.GetProjection())
self.assertEqual(vrt2.dataset.GetGeoTransform(), vrt1.dataset.GetGeoTransform())
self.assertEqual(vrt2.dataset.GetGCPProjection(), vrt1.dataset.GetGCPProjection())
self.assertIn('filename', list(vrt2.dataset.GetMetadata().keys()))
def test_init_from_gdal_dataset(self, _add_geolocation):
vrt = VRT()
ds = gdal.Open(self.test_file_gcps)
vrt._init_from_gdal_dataset(ds)
self.assertEqual(vrt.dataset.RasterXSize, ds.RasterXSize)
self.assertEqual(vrt.dataset.RasterYSize, ds.RasterYSize)
self.assertEqual(vrt.dataset.GetProjection(), ds.GetProjection())
self.assertEqual(vrt.dataset.GetGeoTransform(), ds.GetGeoTransform())
self.assertEqual(vrt.dataset.GetGCPProjection(), ds.GetGCPProjection())
self.assertIn('filename', list(vrt.dataset.GetMetadata().keys()))
self.assertTrue(_add_geolocation.called_once())
def __init__(self, srs=None, ext=None, ds=None, **kwargs):
"""Create Domain from GDALDataset or string options or lat/lon grids"""
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise ValueError('Ambiguous specification of both dataset, srs- and ext-strings.')
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT.from_gdal_dataset(ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmp_vrt = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmp_vrt is None:
raise NansatProjectionError('Could not warp the given dataset to the given SRS.')
else:
self.vrt = VRT.from_gdal_dataset(tmp_vrt)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extent_dict = Domain._create_extent_dict(ext)
# convert -lle to -te
if 'lle' in extent_dict.keys():
extent_dict = self._convert_extentDic(srs, extent_dict)
# get size/extent from the created extent dictionary
geo_transform, raster_x_size, raster_y_size = self._get_geotransform(extent_dict)
# create VRT object with given geo-reference parameters
self.vrt = VRT.from_dataset_params(x_size=raster_x_size, y_size=raster_y_size,
geo_transform=geo_transform,
projection=srs.wkt,
gcps=[], gcp_projection='')
elif 'lat' in kwargs and 'lon' in kwargs:
warnings.warn('Domain(lon=lon, lat=lat) will be deprectaed!'
'Use Domain.from_lonlat()', NansatFutureWarning)
# create self.vrt from given lat/lon
self.vrt = VRT.from_lonlat(kwargs['lon'], kwargs['lat'])
else:
raise ValueError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
def test_fix_global_metadata(self):
ds = gdal.Open(self.test_file_gcps)
vrt = VRT.copy_dataset(ds)
vrt.dataset.SetMetadataItem(str('test'), str('"test"'))
vrt.fix_global_metadata(['AREA_OR_POINT'])
self.assertNotIn('AREA_OR_POINT', vrt.dataset.GetMetadata())
self.assertEqual('"test"', vrt.dataset.GetMetadataItem(str('test')))
def from_lonlat(cls, lon, lat, add_gcps=True):
"""Create Domain object from input longitudes, latitudes arrays
Parameters
----------
lon : numpy.ndarray
longitudes
lat : numpy.ndarray
latitudes
add_gcps : bool
Add GCPs from lon/lat arrays.
Returns
-------
d : Domain
Examples
--------
>>> lon, lat = np.meshgrid(range(10), range(10))
>>> d1 = Domain.from_lonlat(lon, lat)
>>> d2 = Domain.from_lonlat(lon, lat, add_gcps=False) # add only geolocation arrays
"""
d = cls.__new__(cls)
d.vrt = VRT.from_lonlat(lon, lat, add_gcps)
return d
def test_set_gcps_geolocation_geotransform_with_geolocation(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_band({str('SourceFilename'): vrt.geolocation.x_vrt.filename})
vrt._set_gcps_geolocation_geotransform()
self.assertFalse('<GeoTransform>' in vrt.xml)
self.assertEqual(vrt.dataset.GetGCPs(), ())
def test_get_super_vrt_geolocation(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt2 = vrt1.get_super_vrt()
vrt1 = None
self.assertTrue(vrt2.geolocation.x_vrt is not None)
self.assertTrue(vrt2.geolocation.y_vrt is not None)
def test_set_fake_gcps_empty(self):
ds = gdal.Open('NETCDF:"%s":UMass_AES' % self.test_file_arctic)
vrt = VRT.copy_dataset(ds)
dst_wkt = vrt._set_fake_gcps(self.nsr_wkt, [], 1)
self.assertEqual(dst_wkt, self.nsr_wkt)
self.assertEqual(len(vrt.dataset.GetGCPs()), 0)
def __init__(self, *args, **kwargs):
filename = args[0]
gdal_metadata = VRT._remove_strings_in_metadata_keys(args[2],
['NC_GLOBAL#', 'NANSAT_', 'GDAL_'])
gcmd_keywords_mapping = get_gcmd_keywords_mapping()
for key, val in list(gcmd_keywords_mapping.items()):
if 'source' in list(gdal_metadata.keys()) and key in gdal_metadata['source']:
instrument = gcmd_keywords_mapping[key]['instrument']
platform = gcmd_keywords_mapping[key]['platform']
if not 'instrument' in locals():
raise WrongMapperError
super(Mapper, self).__init__(*args, **kwargs)
time_coverage_start, time_coverage_end = self.time_coverage()
self.dataset.SetMetadataItem('time_coverage_start',
(time_coverage_start.isoformat()))
self.dataset.SetMetadataItem('time_coverage_end',
(time_coverage_end.isoformat()))
self.dataset.SetMetadataItem('instrument', instrument)
self.dataset.SetMetadataItem('platform', platform)
def test_leave_few_bands(self):
ds = gdal.Open(self.test_file_gcps)
vrt = VRT.copy_dataset(ds)
vrt.leave_few_bands([1, 'L_469'])
self.assertEqual(vrt.dataset.RasterCount,2)
self.assertEqual(vrt.dataset.GetRasterBand(1).GetMetadataItem(str('name')), 'L_645')
self.assertEqual(vrt.dataset.GetRasterBand(2).GetMetadataItem(str('name')), 'L_469')
def vrts_from_arrays(self, data, variable_names, pol='', resize=True, resample_alg=2):
""" Convert input dict with arrays into dict with VRTs
Parameters
----------
data : dict
2D arrays with data from LUT
variable_names : list of str
variable names that should be converted to VRTs
pol : str
HH, HV, etc
resize : bool
Shall VRT be zoomed to full size?
resample_alg : int
Index of resampling algorithm. See VRT.get_resized_vrt()
Returns
-------
vrts : dict with (resized) VRTs
"""
vrts = {}
for var_name in variable_names:
vrts[var_name+pol] = VRT.from_array(data[var_name+pol])
if resize:
vrts[var_name+pol] = vrts[var_name+pol].get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
resample_alg)
return vrts
def test_set_gcps_geolocation_geotransform_with_geotransform(self):
ds = gdal.Open('NETCDF:"%s":UMass_AES' % self.test_file_arctic)
vrt = VRT.copy_dataset(ds)
vrt._set_gcps_geolocation_geotransform()
self.assertEqual(vrt.dataset.GetGeoTransform(),
(-1000000.0, 25000.0, 0.0, 5000000.0, 0.0, -25000.0))
self.assertEqual(vrt.dataset.GetMetadata(str('GEOLOCATION')), {})
self.assertEqual(vrt.dataset.GetGCPs(), ())
def test_fix_band_metadata(self):
ds = gdal.Open(self.test_file_gcps)
vrt = VRT.copy_dataset(ds)
self.assertIn('standard_name', vrt.dataset.GetRasterBand(1).GetMetadata())
self.assertIn('time', vrt.dataset.GetRasterBand(1).GetMetadata())
vrt.fix_band_metadata(['standard_name', 'time'])
self.assertNotIn('standard_name', vrt.dataset.GetRasterBand(1).GetMetadata())
self.assertNotIn('time', vrt.dataset.GetRasterBand(1).GetMetadata())
def __init__(self, filename, gdal_dataset, gdal_metadata, *args, **kwargs):
if not filename.endswith('nc'):
raise WrongMapperError
self.input_filename = filename
if not gdal_metadata:
raise WrongMapperError
if 'NC_GLOBAL#GDAL_NANSAT_GCPY_000' in list(gdal_metadata.keys()) or \
'NC_GLOBAL#GDAL_NANSAT_GCPProjection' in list(gdal_metadata.keys()):
# Probably Nansat generated netcdf of swath data - see issue #192
raise WrongMapperError
metadata = VRT._remove_strings_in_metadata_keys(
gdal_metadata, ['NC_GLOBAL#', 'NANSAT_', 'GDAL_'])
# Set origin metadata (TODO: agree on keyword...)
origin = ''
nans = 'NANSAT'
if 'origin' in list(metadata.keys()):
origin = metadata['origin'] + ' '
for key in list(metadata.keys()):
if nans in key:
metadata['origin'] = origin + nans
# else: Nothing needs to be done, origin stays the same...
# Check conventions metadata
if 'Conventions' not in list(
metadata.keys()) or 'CF' not in metadata['Conventions']:
raise WrongMapperError
# OBS: at this point, generic mapper fails...
#if metadata.has_key('GCPProjection'):
# # Probably Nansat generated netcdf of swath data - see issue #192
# raise WrongMapperError
# Create empty VRT dataset with geo-reference
self._create_empty(gdal_dataset, metadata)
# Add bands with metadata and corresponding values to the empty VRT
self.create_bands(
self._band_list(gdal_dataset, metadata, *args, **kwargs))
# Check size?
#xsize, ysize = self.ds_size(sub0)
# Create complex bands from *_real and *_imag bands (the function is in
# vrt.py)
self._create_complex_bands(self._get_sub_filenames(gdal_dataset))
# Set GCMD/DIF compatible metadata if available
self._set_time_coverage_metadata(metadata)
def test_transform_coordinates_2d_array(self):
src_srs = NSR()
dst_srs = NSR(str('+proj=stere'))
src_points = (np.array([[1,2,3,4],[1,2,3,4]]),
np.array([[5,6,7,8],[5,6,7,8]]),
np.array([[5,6,7,8],[5,6,7,8]]),)
dst_x, dst_y, dst_z = VRT.transform_coordinates(src_srs, src_points, dst_srs)
# check if shape of the result matches the expected shape (2x4 array)
self.assertEqual(dst_x.shape, (2,4))
self.assertEqual(dst_y.shape, (2,4))
self.assertEqual(dst_z.shape, (2,4))
def test_set_add_band_options(self):
# case 1
srcs = [{'SourceFilename': 'filename', 'SourceBand': 1}]
dst = []
options = VRT._set_add_band_options(srcs, dst)
self.assertEqual(options, [])
# case 2
srcs = [{'SourceFilename': 'filename',
'SourceBand': 0,
'ImageOffset': 0,
'PixelOffset': 0,
'LineOffset': 0,
'ByteOrder': 'i'}]
options = VRT._set_add_band_options(srcs, dst)
self.assertIn('subclass=VRTRawRasterBand', options)
self.assertIn('SourceFilename=filename', options)
self.assertIn('ImageOffset=0', options)
self.assertIn('PixelOffset=0', options)
self.assertIn('LineOffset=0', options)
self.assertIn('ByteOrder=i', options)
def test_from_dataset_params(self):
ds = gdal.Open(self.test_file_gcps)
vrt = VRT.from_dataset_params(ds.RasterXSize, ds.RasterYSize,
ds.GetGeoTransform(), ds.GetProjection(),
ds.GetGCPs(), ds.GetGCPProjection())
self.assertEqual(vrt.dataset.RasterXSize, ds.RasterXSize)
self.assertEqual(vrt.dataset.RasterYSize, ds.RasterYSize)
self.assertEqual(vrt.dataset.GetProjection(), ds.GetProjection())
self.assertEqual(vrt.dataset.GetGeoTransform(), ds.GetGeoTransform())
self.assertEqual(vrt.dataset.GetGCPProjection(), ds.GetGCPProjection())
self.assertIn('filename', list(vrt.dataset.GetMetadata().keys()))
def test_set_fake_gcps(self):
ds = gdal.Open('NETCDF:"%s":UMass_AES' % self.test_file_arctic)
gcps = gdal.Open(self.test_file_gcps).GetGCPs()
vrt = VRT.copy_dataset(ds)
dst_wkt = vrt._set_fake_gcps(self.nsr_wkt, gcps, 1)
self.assertEqual(dst_wkt, None)
self.assertEqual(len(vrt.dataset.GetGCPs()), len(gcps))
self.assertEqual([gcp.GCPPixel for gcp in gcps],
[gcp.GCPX for gcp in vrt.dataset.GetGCPs()])
self.assertEqual([gcp.GCPLine for gcp in gcps],
[gcp.GCPY for gcp in vrt.dataset.GetGCPs()])
def test_transform_coordinates_1d_array(self):
src_srs = NSR()
dst_srs = NSR(str('+proj=stere'))
src_points = (np.array([1, 2, 3,
4]), np.array([5, 6, 7,
8]), np.array([5, 6, 7, 8]))
dst_x, dst_y, dst_z = VRT.transform_coordinates(
src_srs, src_points, dst_srs)
# check if shape of the result matches the expected shape (list with four points)
self.assertEqual(dst_x.shape, (4, ))
self.assertEqual(dst_y.shape, (4, ))
self.assertEqual(dst_z.shape, (4, ))
def test_get_dst_band_data_type(self):
self.assertEqual(VRT._get_dst_band_data_type([], {'dataType': 'Float32'}), 'Float32')
self.assertEqual(VRT._get_dst_band_data_type([1, 2, 3], {}), gdal.GDT_Float32)
self.assertEqual(VRT._get_dst_band_data_type([{'ScaleRatio': 2}], {}), gdal.GDT_Float32)
self.assertEqual(VRT._get_dst_band_data_type([{'LUT': [1, 2, 3]}], {}), gdal.GDT_Float32)
self.assertEqual(VRT._get_dst_band_data_type([{}], {}), gdal.GDT_Float32)
self.assertEqual(VRT._get_dst_band_data_type([{'DataType': 'Float32'}], {}), 'Float32')
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create VRT '''
fileBaseName = os.path.basename(fileName)
if not fileBaseName == 'MOD44W.vrt':
raise WrongMapperError
metaDict = [{
'src': {
'SourceFilename': fileName,
'SourceBand': 1
},
'dst': {
'wkv': 'land_binary_mask'
}
}]
# create empty VRT dataset with geolocation only
VRT.__init__(self, gdalDataset)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
def init_from_manifest_only(self, manifestXML, annotXML):
''' Create fake VRT and add metadata only from the manifest.safe '''
X, Y, lon, lat, inc, ele, numberOfSamples, numberOfLines = self.read_geolocation_lut(annotXML)
VRT.__init__(self, srcRasterXSize=numberOfSamples, srcRasterYSize=numberOfLines)
doc = ET.fromstring(manifestXML)
gcps = []
for i in range(len(X)):
gcps.append(gdal.GCP(lon[i], lat[i], 0, X[i], Y[i]))
self.dataset.SetGCPs(gcps, NSR().wkt)
self.dataset.SetMetadataItem('time_coverage_start',
doc.findall(".//*[{http://www.esa.int/safe/sentinel-1.0}startTime]")[0][0].text)
self.dataset.SetMetadataItem('time_coverage_end',
doc.findall(".//*[{http://www.esa.int/safe/sentinel-1.0}stopTime]")[0][0].text)
self.dataset.SetMetadataItem('platform', json.dumps(pti.get_gcmd_platform('SENTINEL-1A')))
self.dataset.SetMetadataItem('instrument', json.dumps(pti.get_gcmd_instrument('SAR')))
self.dataset.SetMetadataItem('Entry Title', 'Sentinel-1A SAR')
self.dataset.SetMetadataItem('Data Center', 'ESA/EO')
self.dataset.SetMetadataItem('ISO Topic Category', 'Oceans')
self.dataset.SetMetadataItem('Summary', 'S1A SAR data')
def test_export(self):
array = gdal.Open(self.test_file_gcps).ReadAsArray()[1, 10:, :]
vrt = VRT.from_array(array)
vrt.export(self.tmp_filename)
self.assertTrue(self.tmp_filename)
tree = ET.parse(self.tmp_filename)
root = tree.getroot()
self.assertEqual(root.tag, 'VRTDataset')
self.assertIn('rasterXSize', list(root.keys()))
self.assertIn('rasterYSize', list(root.keys()))
self.assertEqual([e.tag for e in root], ['Metadata', 'VRTRasterBand'])
def test_remove_strings_in_metadata_keys(self):
gdal_metadata = {
'aaa': 'bbb',
'NC_GLOBAL#ccc': 'ddd',
'NANSAT_eee': 'fff'
}
rm_strings = ['NC_GLOBAL#', 'NANSAT_']
new_metadata = VRT._remove_strings_in_metadata_keys(
gdal_metadata, rm_strings)
self.assertEqual(new_metadata, {
'aaa': 'bbb',
'ccc': 'ddd',
'eee': 'fff'
})
def test_init(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
x_vrt = VRT.from_array(lon)
y_vrt = VRT.from_array(lat)
ga = Geolocation(x_vrt, y_vrt)
self.assertIsInstance(ga, Geolocation)
self.assertEqual(ga.data['X_DATASET'], x_vrt.filename)
self.assertEqual(ga.data['Y_DATASET'], y_vrt.filename)
self.assertEqual(ga.data['LINE_OFFSET'], '0')
self.assertEqual(ga.data['LINE_STEP'], '1')
self.assertEqual(ga.data['PIXEL_OFFSET'], '0')
self.assertEqual(ga.data['PIXEL_STEP'], '1')
srs = osr.SpatialReference()
status = srs.ImportFromWkt(ga.data['SRS'])
self.assertEqual(status, 0)
self.assertEqual(srs.ExportToProj4().strip(),
'+proj=longlat +datum=WGS84 +no_defs')
self.assertEqual(ga.data['X_BAND'], '1')
self.assertEqual(ga.data['Y_BAND'], '1')
self.assertEqual(ga.x_vrt, x_vrt)
self.assertEqual(ga.y_vrt, y_vrt)
def test_transform_points(self):
ds = gdal.Open(self.test_file_gcps)
vrt1 = VRT.from_gdal_dataset(ds, metadata=ds.GetMetadata())
vrt1.tps = True
lon, lat = vrt1.transform_points([1, 2, 3], [4, 5, 6])
self.assertTrue(
np.allclose(lon, np.array([28.23549571, 28.24337106,
28.25126129])))
self.assertTrue(
np.allclose(lat, np.array([71.52509848, 71.51913744,
71.51317568])))
lon, lat = vrt1.transform_points([], [])
self.assertTrue(np.allclose(lon, np.array([])))
self.assertTrue(np.allclose(lat, np.array([])))
def test_hardcopy_bands(self):
ds = gdal.Open(self.test_file_gcps)
vrt = VRT.copy_dataset(ds)
vrt.hardcopy_bands()
self.assertTrue(
np.allclose(vrt.dataset.ReadAsArray(), ds.ReadAsArray()))
band_nodes = Node.create(str(vrt.xml)).nodeList('VRTRasterBand')
self.assertEqual(band_nodes[0].node('SourceFilename').value,
vrt.band_vrts[1].filename)
self.assertEqual(band_nodes[1].node('SourceFilename').value,
vrt.band_vrts[2].filename)
self.assertEqual(band_nodes[2].node('SourceFilename').value,
vrt.band_vrts[3].filename)
def test_init(self, mock_make_filename):
metadata={'key': 'value'}
vrt = VRT(metadata=metadata)
self.assertIsInstance(vrt, VRT)
self.assertEqual(vrt.filename, '/vsimem/filename.vrt')
self.assertIsInstance(vrt.dataset, gdal.Dataset)
self.assertIsInstance(vrt.logger, logging.Logger) # just for testing mocking
self.assertIsInstance(vrt.driver, gdal.Driver)
self.assertEqual(vrt.band_vrts, {})
self.assertEqual(vrt.tps, False)
self.assertTrue(vrt.vrt is None)
self.assertTrue(vrt.xml.startswith('<VRTDataset rasterXSize="1" rasterYSize="1"'))
self.assertTrue(mock_make_filename.called_once())
self.assertEqual(vrt.dataset.GetMetadata(), metadata)
def test_create_geolocation_bands(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_geolocation_bands()
self.assertEqual(vrt.dataset.RasterCount, 2)
self.assertEqual(
vrt.dataset.GetRasterBand(1).GetMetadataItem(str('name')),
'longitude')
self.assertEqual(
vrt.dataset.GetRasterBand(2).GetMetadataItem(str('name')),
'latitude')
self.assertTrue(
np.allclose(vrt.dataset.GetRasterBand(1).ReadAsArray(), lon))
self.assertTrue(
np.allclose(vrt.dataset.GetRasterBand(2).ReadAsArray(), lat))
def __init__(self,
filename,
gdal_dataset,
metadata,
quartile=0,
*args,
**kwargs):
super(Mapper, self).__init__(filename, gdal_dataset, metadata, *args,
**kwargs)
intervals = [0, 1, 2, 3]
if not quartile in intervals:
raise ValueError('quartile must be one of [0,1,2,3]')
y_size = self.dataset.RasterYSize / 4
y_offset = [y_size * qq for qq in intervals][quartile]
# Crop
self.set_offset_size('y', y_offset, y_size)
# Add quartile to metadata
self.dataset.SetMetadataItem('quartile', str(quartile))
# Create band of times
# TODO: resolve nansat issue #263 (https://github.com/nansencenter/nansat/issues/263)
#import ipdb; ipdb.set_trace()
tt = self.times()[int(y_offset):int(y_offset + y_size)]
self.dataset.SetMetadataItem('time_coverage_start',
tt[0].astype(datetime).isoformat())
self.dataset.SetMetadataItem('time_coverage_end',
tt[-1].astype(datetime).isoformat())
time_stamps = (tt - tt[0]) / np.timedelta64(1, 's')
self.band_vrts['time'] = VRT.from_array(
np.tile(time_stamps, (self.dataset.RasterXSize, 1)).transpose())
self.create_band(src={
'SourceFilename': self.band_vrts['time'].filename,
'SourceBand': 1,
},
dst={
'name':
'timestamp',
'time_coverage_start':
tt[0].astype(datetime).isoformat(),
'units':
'seconds since time_coverage_start',
})
def test_from_lonlat(self):
geo_keys = ['LINE_OFFSET', 'LINE_STEP', 'PIXEL_OFFSET', 'PIXEL_STEP', 'SRS',
'X_BAND', 'X_DATASET', 'Y_BAND', 'Y_DATASET']
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat, n_gcps=25)
self.assertEqual(vrt.dataset.RasterXSize, 10)
self.assertEqual(vrt.dataset.RasterYSize, 30)
self.assertIn('filename', list(vrt.dataset.GetMetadata().keys()))
geo_metadata = vrt.dataset.GetMetadata(str('GEOLOCATION'))
for geo_key in geo_keys:
self.assertEqual(vrt.geolocation.data[geo_key], geo_metadata[geo_key])
self.assertIsInstance(vrt.geolocation.x_vrt, VRT)
self.assertIsInstance(vrt.geolocation.y_vrt, VRT)
self.assertEqual(vrt.geolocation.x_vrt.filename, geo_metadata['X_DATASET'])
self.assertEqual(vrt.geolocation.y_vrt.filename, geo_metadata['Y_DATASET'])
self.assertEqual(len(vrt.dataset.GetGCPs()), 25)
def create_VRT_from_ADS(self, adsName, zoomSize=500):
""" Create VRT with a band from Envisat ADS metadata
Read offsets of the <adsName> ADS.
Read 2D matrix of binary values from ADS from file.
Zoom array with ADS data to <zoomSize>. Zooming is needed to create smooth matrices. Array
is zoomed to small size because it is stred in memory. Later the VRT with zoomed array is
VRT.get_resized_vrt() in order to match the size of the Nansat object.
Create VRT from the ADS array.
Parameters
----------
adsName : str
name of variable from ADS to read. should match allADSParams
zoomSize : int, optional, 500
size, to which original matrix from ADSR is zoomed using
scipy.zoom
Returns
-------
adsVrt : VRT, vrt with a band created from ADS array
"""
adsHeight = self.dsOffsetDict["NUM_DSR"]
adsParams = self.allADSParams['list'][adsName]
array = self.get_array_from_ADS(adsName)
if not IMPORT_SCIPY:
raise NansatReadError(
'ENVISAT data cannot be read because scipy is not installed...'
)
# zoom the array
array = scipy.ndimage.interpolation.zoom(array,
zoomSize / float(adsHeight),
order=1)
# create VRT from the array
adsVrt = VRT.from_array(array=array)
# add "name" and "units" to band metadata
bandMetadata = {"name": adsName, "units": adsParams['units']}
adsVrt.dataset.GetRasterBand(1).SetMetadata(bandMetadata)
return adsVrt
def test_update_warped_vrt_xml(self):
dataset = gdal.Open('NETCDF:"%s":UMass_AES' % self.test_file_arctic)
warped_dataset = gdal.AutoCreateWarpedVRT(dataset, None, str(self.nsr_wkt), 0)
warped_vrt = VRT.copy_dataset(warped_dataset)
x_size = 100
y_size = 200
geo_transform = (0.0, 1.0, 0.0, 200.0, 0.0, -1.0)
block_size = 64
working_data_type = 'Float32'
warped_vrt._update_warped_vrt_xml(x_size, y_size, geo_transform, block_size,
working_data_type)
self.assertEqual(warped_vrt.dataset.RasterXSize, x_size)
self.assertEqual(warped_vrt.dataset.RasterYSize, y_size)
self.assertEqual(warped_vrt.dataset.GetGeoTransform(), geo_transform)
self.assertEqual(warped_vrt.dataset.GetRasterBand(1).GetBlockSize(),
[block_size, block_size])
self.assertIn('<WorkingDataType>Float32</WorkingDataType>', warped_vrt.xml)
def create_VRT_from_ADS(self, adsName, zoomSize=500):
''' Create VRT with a band from Envisat ADS metadata
Read offsets of the <adsName> ADS.
Read 2D matrix of binary values from ADS from file.
Read 'last_line_...' ADS (in case of ASAR).
Zoom array with ADS data to <zoomSize>. Zooming is needed to create
smooth matrices. Array is zoomed to small size because it is stred in
memory. Later the VRT with zoomed array is VRT.
get_resized_vrt() in order to match the size of the Nansat onject.
Create VRT from the ADS array.
Parameters
----------
adsName : str
name of variable from ADS to read. should match allADSParams
fileType: string, 'ASA_' or 'MER_'
type of file (from GDAL metadata)
zoomSize : int, optional, 500
size, to which original matrix from ADSR is zoomed using
scipy.zoom
Returns:
---------
adsVrt : VRT, vrt with a band created from ADS array
'''
adsHeight = self.dsOffsetDict["NUM_DSR"]
adsParams = self.allADSParams['list'][adsName]
array = self.get_array_from_ADS(adsName)
# zoom the array
array = scipy.ndimage.interpolation.zoom(array,
zoomSize / float(adsHeight),
order=1)
# create VRT from the array
adsVrt = VRT(array=array)
# add "name" and "units" to band metadata
bandMetadata = {"name": adsName, "units": adsParams['units']}
adsVrt.dataset.GetRasterBand(1).SetMetadata(bandMetadata)
return adsVrt
def get_LUT_VRTs(self, XML, vectorListName, LUT_list):
n = Node.create(XML)
vecList = n.node(vectorListName)
X = []
Y = []
LUTs = {}
for LUT in LUT_list:
LUTs[LUT] = []
xLengths = []
for vec in vecList.children:
xVec = map(int, vec['pixel'].split())
xLengths.append(len(xVec))
X.append(xVec)
Y.append(int(vec['line']))
for LUT in LUT_list:
LUTs[LUT].append(map(float, vec[LUT].split()))
# truncate X and LUT to minimum length for all rows
minLength = np.min(xLengths)
X = [x[:minLength] for x in X]
for LUT in LUT_list:
LUTs[LUT] = [lut[:minLength] for lut in LUTs[LUT]]
X = np.array(X)
for LUT in LUT_list:
LUTs[LUT] = np.array(LUTs[LUT])
Ym = np.array([
Y,
] * np.shape(X)[1]).transpose()
lon, lat = self.transform_points(X.flatten(), Ym.flatten())
longitude = lon.reshape(X.shape)
latitude = lat.reshape(X.shape)
LUT_VRTs = {}
for LUT in LUT_list:
LUT_VRTs[LUT] = VRT(array=LUTs[LUT], lat=latitude, lon=longitude)
return LUT_VRTs, longitude, latitude
def add_incidence_angle_band(self):
# Get GCP variables
pixel = self.ds['GCP_pixel_' + self.ds.polarisation[:2]][:].data
line = self.ds['GCP_line_' + self.ds.polarisation[:2]][:].data
inci = self.ds['GCP_incidenceAngle_' +
self.ds.polarisation[:2]][:].data
inci = inci.reshape(
np.unique(line[:].data).shape[0],
np.unique(pixel[:].data).shape[0])
# Add incidence angle band
inciVRT = VRT.from_array(inci)
inciVRT = inciVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize, 1)
self.band_vrts['inciVRT'] = inciVRT
src = {
'SourceFilename': self.band_vrts['inciVRT'].filename,
'SourceBand': 1
}
dst = {'wkv': 'angle_of_incidence', 'name': 'incidence_angle'}
self.create_band(src, dst)
self.dataset.FlushCache()
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 correct_geolocation_data(data, max_height=5):
""" Correct lon/lat values in geolocation data for points high above ground (incorrect)
Each GCP in Sentinel-1 L1 image (both in the GeoTIF files and Annotation LUT) have five
coordinates: X, Y, Z (height), Pixel and Line. On some scenes that cover Greenland (and
probably other lands) some GCPs have height above zero even over ocean. This is incorrect,
because the radar signal comes actually from the surface and not from a point above the
ground as stipulated in such GCPs. This function provides correction of such GCPs.
First, Lon/Lat are converted to X/Y in meters. Second, Pixel coordinates are approximated
by a 2nd order polynomial of input X,Y,Z. Third, this polynomial is used to calculate new
Pixel coordinate for ocean surface (Z=0). Fourth, a temporary VRT from original X, Y, Line
and corrected Z,Pixel coordinates is created. Fifth, the temporary VRT is used for
converting original Pixel/Line coordinates into correct Lon/Lat
Parameters
----------
data : dict
Original geolocation data from Mapper.read_annotation()
max_height : int
Maximum afordable height (meters)
Returns
-------
data : dict
Corrected geolocation data with new longitude and latitude
"""
# don't correct geolocation data if only few points are affected
if (data['height'] > max_height).sum() < 10:
return data
# convert XY from degrees to meters in stereographic projection
central_point = int(data['shape'][0] / 2), int(data['shape'][1] / 2)
dst_srs = '+proj=stere +datum=WGS84 +ellps=WGS84 +lat_0=%f +lon_0=%f +no_defs' % (
data['latitude'][central_point], data['longitude'][central_point])
x, y, z = VRT.transform_coordinates(
NSR(), (data['longitude'].flat, data['latitude'].flat,
data['height'].flat), NSR(dst_srs))
# create training data
a = np.vstack(
[np.ones(x.size), x, x**2, y, y**2, x * y, z, z**2, x * z,
y * z]).T
# calculate polynomial coefficients for values of Pixel (using least squares)
b = np.linalg.lstsq(a, data['pixel'].flat)[0]
# pixel_test = np.dot(a, b) # for debugging
# set height to zero (ocean surface)
a[:, 6:] = 0
# calculate Pixel at ocean surface
pixel_ocean = np.dot(a, b)
high_pixels_idx = data['height'] > max_height
tmp_pixel = np.array(data['pixel'])
tmp_pixel[high_pixels_idx] = pixel_ocean[high_pixels_idx.flat]
new_height = np.zeros(data['height'].shape)
# create temporary VRT with correct GCPs for converting original pixel/line into lon/lat
tmp_gcps = Mapper.create_gcps(x, y, new_height, tmp_pixel,
data['line'])
tmp_vrt = VRT(data['x_size'], data['y_size'])
tmp_vrt.dataset.SetGCPs(tmp_gcps, NSR(dst_srs).wkt)
tmp_vrt.tps = True
new_lon, new_lat = tmp_vrt.transform_points(data['pixel'].flatten(),
data['line'].flatten())
data['latitude'] = new_lat.reshape(data['shape'])
data['longitude'] = new_lon.reshape(data['shape'])
data['height'] = new_height
return data
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):
''' 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,
srs=None,
ext=None,
ds=None,
lon=None,
lat=None,
name='',
logLevel=None):
'''Create Domain from GDALDataset or string options or lat/lon grids
d = Domain(srs, ext)
Size, extent and spatial reference is given by strings
d = Domain(ds=GDALDataset):
Size, extent and spatial reference is copied from input
GDAL dataset
d = Domain(srs, ds=GDALDataset):
Spatial reference is given by srs, but size and extent is
determined
from input GDAL dataset
d = Domain(lon=lonGrid, lat=latGrid)
Size, extent and spatial reference is given by two grids
Parameters
----------
srs : PROJ4 or EPSG or WKT or NSR or osr.SpatialReference()
Input parameter for nansat.NSR()
ext : string
some gdalwarp options + additional options
[http://www.gdal.org/gdalwarp.html]
Specifies extent, resolution / size
Available options: (('-te' or '-lle') and ('-tr' or '-ts'))
(e.g. '-lle -10 30 55 60 -ts 1000 1000' or
'-te 100 2000 300 10000 -tr 300 200')
-tr resolutionx resolutiony
-ts sizex sizey
-te xmin ymin xmax ymax
-lle lonmin latmin lonmax latmax
ds : GDAL dataset
lat : Numpy array
Grid with latitudes
lon : Numpy array
Grid with longitudes
name : string, optional
Name to be added to the Domain object
logLevel : int, optional, default=30
level of logging
Raises
-------
ProjectionError : occurs when Projection() is empty
despite it is required for creating extentDic.
OptionError : occures when the arguments are not proper.
Modifies
---------
self.vrt.datasetset : dataset in memory
dataset is created based on the input arguments
See Also
---------
Nansat.reproject()
[http://www.gdal.org/gdalwarp.html]
[http://trac.osgeo.org/proj/]
[http://spatialreference.org/]
[http://www.gdal.org/ogr/osr_tutorial.html]
'''
# set default attributes
self.logger = add_logger('Nansat', logLevel)
self.name = name
self.logger.debug('ds: %s' % str(ds))
self.logger.debug('srs: %s' % srs)
self.logger.debug('ext: %s' % ext)
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise OptionError('Ambiguous specification of both '
'dataset, srs- and ext-strings.')
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# lon and lat
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT(gdalDataset=ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmpVRT = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmpVRT is None:
raise ProjectionError('Could not warp the given dataset'
'to the given SRS.')
else:
self.vrt = VRT(gdalDataset=tmpVRT)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extentDic = self._create_extentDic(ext)
# convert -lle to -te
if 'lle' in extentDic.keys():
extentDic = self._convert_extentDic(srs, extentDic)
# get size/extent from the created extet dictionary
[geoTransform, rasterXSize,
rasterYSize] = self._get_geotransform(extentDic)
# create VRT object with given geo-reference parameters
self.vrt = VRT(srcGeoTransform=geoTransform,
srcProjection=srs.wkt,
srcRasterXSize=rasterXSize,
srcRasterYSize=rasterYSize)
self.extentDic = extentDic
elif lat is not None and lon is not None:
# create self.vrt from given lat/lon
self.vrt = VRT(lat=lat, lon=lon)
else:
raise OptionError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
self.logger.debug('vrt.dataset: %s' % str(self.vrt.dataset))
class Domain(object):
'''Container for geographical reference of a raster
A Domain object describes all attributes of geographical
reference of a raster:
* width and height (number of pixels)
* pixel size (e.g. in decimal degrees or in meters)
* relation between pixel/line coordinates and geographical
coordinates (e.g. a linear relation)
* type of data projection (e.g. geographical or stereographic)
The core of Domain is a GDAL Dataset. It has no bands, but only
georeference information: rasterXsize, rasterYsize, GeoTransform and
Projection or GCPs, etc. which fully describe dimentions and spatial
reference of the grid.
There are three ways to store geo-reference in a GDAL dataset:
* Using GeoTransfrom to define linear relationship between raster
pixel/line and geographical X/Y coordinates
* Using GCPs (set of Ground Control Points) to define non-linear
relationship between pixel/line and X/Y
* Using Geolocation Array - full grids of X/Y coordinates for
each pixel of a raster
The relation between X/Y coordinates of the raster and latitude/longitude
coordinates is defined by projection type and projection parameters.
These pieces of information are therefore stored in Domain:
* Type and parameters of projection +
* GeoTransform, or
* GCPs, or
* GeolocationArrays
Domain has methods for basic operations with georeference information:
* creating georeference from input options;
* fetching corner, border or full grids of X/Y coordinates;
* making map of the georeferenced grid in a PNG or KML file;
* and some more...
The main attribute of Domain is a VRT object self.vrt.
Nansat inherits from Domain and adds bands to self.vrt
'''
def __init__(self,
srs=None,
ext=None,
ds=None,
lon=None,
lat=None,
name='',
logLevel=None):
'''Create Domain from GDALDataset or string options or lat/lon grids
d = Domain(srs, ext)
Size, extent and spatial reference is given by strings
d = Domain(ds=GDALDataset):
Size, extent and spatial reference is copied from input
GDAL dataset
d = Domain(srs, ds=GDALDataset):
Spatial reference is given by srs, but size and extent is
determined
from input GDAL dataset
d = Domain(lon=lonGrid, lat=latGrid)
Size, extent and spatial reference is given by two grids
Parameters
----------
srs : PROJ4 or EPSG or WKT or NSR or osr.SpatialReference()
Input parameter for nansat.NSR()
ext : string
some gdalwarp options + additional options
[http://www.gdal.org/gdalwarp.html]
Specifies extent, resolution / size
Available options: (('-te' or '-lle') and ('-tr' or '-ts'))
(e.g. '-lle -10 30 55 60 -ts 1000 1000' or
'-te 100 2000 300 10000 -tr 300 200')
-tr resolutionx resolutiony
-ts sizex sizey
-te xmin ymin xmax ymax
-lle lonmin latmin lonmax latmax
ds : GDAL dataset
lat : Numpy array
Grid with latitudes
lon : Numpy array
Grid with longitudes
name : string, optional
Name to be added to the Domain object
logLevel : int, optional, default=30
level of logging
Raises
-------
ProjectionError : occurs when Projection() is empty
despite it is required for creating extentDic.
OptionError : occures when the arguments are not proper.
Modifies
---------
self.vrt.datasetset : dataset in memory
dataset is created based on the input arguments
See Also
---------
Nansat.reproject()
[http://www.gdal.org/gdalwarp.html]
[http://trac.osgeo.org/proj/]
[http://spatialreference.org/]
[http://www.gdal.org/ogr/osr_tutorial.html]
'''
# set default attributes
self.logger = add_logger('Nansat', logLevel)
self.name = name
self.logger.debug('ds: %s' % str(ds))
self.logger.debug('srs: %s' % srs)
self.logger.debug('ext: %s' % ext)
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise OptionError('Ambiguous specification of both '
'dataset, srs- and ext-strings.')
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# lon and lat
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT(gdalDataset=ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmpVRT = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmpVRT is None:
raise ProjectionError('Could not warp the given dataset'
'to the given SRS.')
else:
self.vrt = VRT(gdalDataset=tmpVRT)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extentDic = self._create_extentDic(ext)
# convert -lle to -te
if 'lle' in extentDic.keys():
extentDic = self._convert_extentDic(srs, extentDic)
# get size/extent from the created extet dictionary
[geoTransform, rasterXSize,
rasterYSize] = self._get_geotransform(extentDic)
# create VRT object with given geo-reference parameters
self.vrt = VRT(srcGeoTransform=geoTransform,
srcProjection=srs.wkt,
srcRasterXSize=rasterXSize,
srcRasterYSize=rasterYSize)
self.extentDic = extentDic
elif lat is not None and lon is not None:
# create self.vrt from given lat/lon
self.vrt = VRT(lat=lat, lon=lon)
else:
raise OptionError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
self.logger.debug('vrt.dataset: %s' % str(self.vrt.dataset))
def __repr__(self):
'''Creates string with basic info about the Domain object
Modifies
---------
Print size, projection and corner coordinates
'''
outStr = 'Domain:[%d x %d]\n' % (self.vrt.dataset.RasterXSize,
self.vrt.dataset.RasterYSize)
outStr += '-' * 40 + '\n'
try:
corners = self.get_corners()
except:
self.logger.error('Cannot read projection from source!')
else:
outStr += 'Projection:\n'
outStr += (NSR(self.vrt.get_projection()).ExportToPrettyWkt(1) +
'\n')
outStr += '-' * 40 + '\n'
outStr += 'Corners (lon, lat):\n'
outStr += '\t (%6.2f, %6.2f) (%6.2f, %6.2f)\n' % (
corners[0][0], corners[1][0], corners[0][2], corners[1][2])
outStr += '\t (%6.2f, %6.2f) (%6.2f, %6.2f)\n' % (
corners[0][1], corners[1][1], corners[0][3], corners[1][3])
return outStr
def write_kml(self, xmlFileName=None, kmlFileName=None):
'''Write KML file with domains
Convert XML-file with domains into KML-file for GoogleEarth
or write KML-file with the current Domain
Parameters
-----------
xmlFileName : string, optional
Name of the XML-file to convert. If only this value is given
- kmlFileName=xmlFileName+'.kml'
kmlFileName : string, optional
Name of the KML-file to generate from the current Domain
'''
# test input options
if xmlFileName is not None and kmlFileName is None:
# if only input XML-file is given - convert it to KML
# open XML, get all domains
xmlFile = file(xmlFileName, 'rb')
kmlFileName = xmlFileName + '.kml'
xmlDomains = ElementTree(file=xmlFile).getroot()
xmlFile.close()
# convert domains in XML into list of domains
domains = []
for xmlDomain in list(xmlDomains):
# append Domain object to domains list
domainName = xmlDomain.attrib['name']
domains.append(Domain(srs=xmlFileName, ext=domainName))
elif xmlFileName is None and kmlFileName is not None:
# if only output KML-file is given
# then convert the current domain to KML
domains = [self]
else:
# otherwise it is potentially error
raise OptionError('Either xmlFileName(%s)\
or kmlFileName(%s) are wrong' % (xmlFileName, kmlFileName))
# open KML, write header
kmlFile = file(kmlFileName, 'wt')
kmlFile.write('<?xml version="1.0" encoding="UTF-8"?>\n')
kmlFile.write('<kml xmlns="http://www.opengis.net/kml/2.2" '
'xmlns:gx="http://www.google.com/kml/ext/2.2" '
'xmlns:kml="http://www.opengis.net/kml/2.2" '
'xmlns:atom="http://www.w3.org/2005/Atom">\n')
kmlFile.write('<Document>\n')
kmlFile.write(' <name>%s</name>\n' % kmlFileName)
kmlFile.write(' <Folder><name>%s</name><open>1</open>\n' %
kmlFileName)
# get border of each domain and add to KML
for domain in list(domains):
kmlEntry = domain._get_border_kml()
kmlFile.write(kmlEntry)
# write footer and close
kmlFile.write(' </Folder></Document></kml>\n')
kmlFile.close()
def write_kml_image(self, kmlFileName=None, kmlFigureName=None):
'''Create KML file for already projected image
Write Domain Image into KML-file for GoogleEarth
Parameters
-----------
kmlFileName : string, optional
Name of the KML-file to generate from the current Domain
kmlFigureName : string, optional
Name of the projected image stored in .png format
Examples
---------
# First of all, reproject an image into Lat/Lon WGS84
(Simple Cylindrical) projection
# 1. Cancel previous reprojection
# 2. Get corners of the image and the pixel resolution
# 3. Create Domain with stereographic projection,
# corner coordinates and resolution 1000m
# 4. Reproject
# 5. Write image
# 6. Write KML for the image
n.reproject() # 1.
lons, lats = n.get_corners() # 2.
srsString = '+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs'
extentString = '-lle %f %f %f %f -ts 3000 3000'
% (min(lons), min(lats), max(lons), max(lats))
d = Domain(srs=srsString, ext=extentString) # 3.
n.reproject(d) # 4.
n.write_figure(fileName=figureName, bands=[3], clim=[0,0.15],
cmapName='gray', transparency=0) # 5.
n.write_kml_image(kmlFileName=oPath + fileName + '.kml',
kmlFigureName=figureName) # 6.
'''
# test input options
if kmlFileName is None:
raise OptionError('kmlFileName(%s) is wrong' % (kmlFileName))
if kmlFigureName is None:
raise OptionError('kmlFigureName(%s) is not specified' %
(kmlFigureName))
# open KML, write header
kmlFile = file(kmlFileName, 'wt')
kmlFile.write('<?xml version="1.0" encoding="UTF-8"?>\n')
kmlFile.write('<kml xmlns="http://www.opengis.net/kml/2.2" '
'xmlns:gx="http://www.google.com/kml/ext/2.2" '
'xmlns:kml="http://www.opengis.net/kml/2.2" '
'xmlns:atom="http://www.w3.org/2005/Atom">\n')
kmlFile.write('<GroundOverlay>\n')
kmlFile.write(' <name>%s</name>\n' % kmlFileName)
kmlFile.write(' <Icon>\n')
kmlFile.write(' <href>%s</href>\n' % kmlFigureName)
kmlFile.write(' <viewBoundScale>0.75</viewBoundScale>\n')
kmlFile.write(' </Icon>\n')
# get corner of the domain and add to KML
domainLon, domainLat = self.get_corners()
kmlFile.write(' <LatLonBox>\n')
kmlFile.write(' <north>%s</north>\n' % max(domainLat))
kmlFile.write(' <south>%s</south>\n' % min(domainLat))
kmlFile.write(' <east>%s</east>\n' % max(domainLon))
kmlFile.write(' <west>%s</west>\n' % min(domainLon))
kmlFile.write(' </LatLonBox>\n')
# write footer and close
kmlFile.write('</GroundOverlay>\n')
kmlFile.write('</kml>')
kmlFile.close()
def get_geolocation_grids(self, stepSize=1, dstSRS=NSR()):
'''Get longitude and latitude grids representing the full data grid
If GEOLOCATION is not present in the self.vrt.dataset then grids
are generated by converting pixel/line of each pixel into lat/lon
If GEOLOCATION is present in the self.vrt.dataset then grids are read
from the geolocation bands.
Parameters
-----------
stepSize : int
Reduction factor if output is desired on a reduced grid size
Returns
--------
longitude : numpy array
grid with longitudes
latitude : numpy array
grid with latitudes
'''
X = range(0, self.vrt.dataset.RasterXSize, stepSize)
Y = range(0, self.vrt.dataset.RasterYSize, stepSize)
Xm, Ym = np.meshgrid(X, Y)
if len(self.vrt.geolocationArray.d) > 0:
# if the vrt dataset has geolocationArray
# read lon,lat grids from geolocationArray
lon, lat = self.vrt.geolocationArray.get_geolocation_grids()
longitude, latitude = lon[Ym, Xm], lat[Ym, Xm]
else:
# generate lon,lat grids using GDAL Transformer
lonVec, latVec = self.transform_points(Xm.flatten(),
Ym.flatten(),
dstSRS=dstSRS)
longitude = lonVec.reshape(Xm.shape)
latitude = latVec.reshape(Xm.shape)
return longitude, latitude
def _convert_extentDic(self, dstSRS, extentDic):
'''Convert -lle option (lat/lon) to -te (proper coordinate system)
Source SRS from LAT/LON projection and target SRS from dstWKT.
Create osr.CoordinateTransformation based on these SRSs and
convert given values in degrees to the destination coordinate
system given by WKT.
Add key 'te' and the converted values into the extentDic.
Parameters
-----------
dstSRS : NSR
Destination Spatial Reference
extentDic : dictionary
dictionary with 'lle' key
Returns
--------
extentDic : dictionary
input dictionary + 'te' key and its values
'''
coorTrans = osr.CoordinateTransformation(NSR(), dstSRS)
# convert lat/lon given by 'lle' to the target coordinate system and
# add key 'te' and the converted values to extentDic
x1, y1, _ = coorTrans.TransformPoint(extentDic['lle'][0],
extentDic['lle'][3])
x2, y2, _ = coorTrans.TransformPoint(extentDic['lle'][2],
extentDic['lle'][3])
x3, y3, _ = coorTrans.TransformPoint(extentDic['lle'][2],
extentDic['lle'][1])
x4, y4, _ = coorTrans.TransformPoint(extentDic['lle'][0],
extentDic['lle'][1])
minX = min([x1, x2, x3, x4])
maxX = max([x1, x2, x3, x4])
minY = min([y1, y2, y3, y4])
maxY = max([y1, y2, y3, y4])
extentDic['te'] = [minX, minY, maxX, maxY]
return extentDic
def _create_extentDic(self, extentString):
'''Create a dictionary from extentString
Check if extentString is proper.
* '-te' and '-lle' take 4 numbers.
* '-ts' and '-tr' take 2 numbers.
* the combination should be ('-te' or '-lle') and ('-ts' or '-tr')
If it is proper, create a dictionary
Otherwise, raise the error.
Parameters
-----------
extentString : string
'-te xMin yMin xMax yMax',
'-tr xResolution yResolution',
'-ts width height',
'-lle minlon minlat maxlon maxlat'
Returns
--------
extentDic : dictionary
has key ('te' or 'lle') and ('tr' or 'ts') and their values.
Raises
-------
OptionError : occurs when the extentString is improper
'''
extentDic = {}
# Find -re text
str_tr = re.findall('-tr\s+[-+]?\d*[.\d*]*\s+[-+]?\d*[.\d*]*\s?',
extentString)
if str_tr != []:
# Check the number of -tr elements
elm_str = str(str_tr[0].rstrip())
elms_str = elm_str.split(None)
if len(elms_str) != 3 or elms_str[2] == '-':
raise OptionError('Domain._create_extentDic():'
'-tr is used as'
'"-tr xResolution yResolution"')
# Add the key and value to extentDic
extentString = extentString.replace(str_tr[0], '')
trElem = str(str_tr).split(None)
trkey = trElem[0].translate(string.maketrans('', ''), "[]-'")
if trkey != '':
elements = []
for i in range(2):
elements.append(
float(trElem[i + 1].translate(string.maketrans('', ''),
"'[]'")))
extentDic[trkey] = elements
# Find -ts text
str_ts = re.findall('-ts\s+[-+]?\d*[.\d*]*\s+[-+]?\d*[.\d*]*\s?',
extentString)
if str_ts != []:
# Check the number of -ts elements
elm_str = str(str_ts[0].rstrip())
elms_str = elm_str.split(None)
if len(elms_str) != 3 or elms_str[2] == '-':
raise OptionError('Domain._create_extentDic(): '
'"-ts" is used as "-ts width height"')
# Add the key and value to extentDic
extentString = extentString.replace(str_ts[0], '')
tsElem = str(str_ts).split(None)
tskey = tsElem[0].translate(string.maketrans('', ''), "[]-'")
if tskey != '':
elements = []
for i in range(2):
elements.append(
float(tsElem[i + 1].translate(string.maketrans('', ''),
"[]'")))
extentDic[tskey] = elements
# Find -te text
str_te = re.findall(
'-te\s+[-+]?\d*[.\d*]*\s+[-+]?\d*[.\d*]*\s'
'+[-+]?\d*[.\d*]*\s+[-+]?\d*[.\d*]*\s?', extentString)
if str_te != []:
# Check the number of -te elements
elm_str = str(str_te[0].rstrip())
elms_str = elm_str.split(None)
if len(elms_str) != 5:
raise OptionError('Domain._create_extentDic():'
'-te is used as "-te xMin yMin xMax yMax"')
# Add the key and value to extentDic
extentString = extentString.replace(str_te[0], '')
teElem = str(str_te).split(None)
tekey = teElem[0].translate(string.maketrans('', ''), "[]-'")
if tekey != '':
elements = []
for i in range(4):
elements.append(
float(teElem[i + 1].translate(string.maketrans('', ''),
"[]'")))
extentDic[tekey] = elements
# Find -lle text
str_lle = re.findall(
'-lle\s+[-+]?\d*[.\d*]*\s+[-+]?\d*[.\d*]*\s'
'+[-+]?\d*[.\d*]*\s+[-+]?\d*[.\d*]*\s?', extentString)
if str_lle != []:
# Check the number of -lle elements
elm_str = str(str_lle[0].rstrip())
elms_str = elm_str.split(None)
if len(elms_str) != 5:
raise OptionError('Domain._create_extentDic():'
'-lle is used as '
'"-lle minlon minlat maxlon maxlat"')
# Add the key and value to extentDic
extentString = extentString.replace(str_lle[0], '')
lleElem = str(str_lle).split(None)
llekey = lleElem[0].translate(string.maketrans('', ''), "[]-'")
if llekey != '':
elements = []
for i in range(4):
elements.append(
float(lleElem[i + 1].translate(
string.maketrans('', ''), "[]'")))
extentDic[llekey] = elements
result = re.search('\S', extentString)
# if there are unnecessary letters, give an error
if result is not None:
raise OptionError(
'Domain._create_extentDic():'
'extentString is not redable :', extentString)
# check if one of '-te' and '-lle' is given
if ('lle' not in extentDic) and ('te' not in extentDic):
raise OptionError('Domain._create_extentDic():'
'"-lle" or "-te" is required.')
elif ('lle' in extentDic) and ('te' in extentDic):
raise OptionError('Domain._create_extentDic():'
'"-lle" or "-te" should be chosen.')
# check if one of '-ts' and '-tr' is given
if ('ts' not in extentDic) and ('tr' not in extentDic):
raise OptionError('Domain._create_extentDic():'
'"-ts" or "-tr" is required.')
elif ('ts' in extentDic) and ('tr' in extentDic):
raise OptionError('Domain._create_extentDic():'
'"-ts" or "-tr" should be chosen.')
return extentDic
def get_border(self, nPoints=10):
'''Generate two vectors with values of lat/lon for the border of domain
Parameters
-----------
nPoints : int, optional
Number of points on each border
Returns
--------
lonVec, latVec : lists
vectors with lon/lat values for each point at the border
'''
# prepare vectors with pixels and lines for upper, left, lower
# and right borders
sizes = [self.vrt.dataset.RasterXSize, self.vrt.dataset.RasterYSize]
rcVector1 = [[], []]
rcVector2 = [[], []]
# loop for pixels and lines
for n in range(0, 2):
step = max(1, sizes[n] / nPoints)
rcVector1[n] = range(0, sizes[n], step)[0:nPoints]
rcVector1[n].append(sizes[n])
rcVector2[n] = rcVector1[n][:]
rcVector2[n].reverse()
# coumpund vectors of pixels (col) and lines (row)
colVector = (rcVector1[0] + [sizes[0]] * len(rcVector1[1]) +
rcVector2[0] + [0] * len(rcVector1[1]))
rowVector = ([0] * len(rcVector1[0]) + rcVector1[1] +
[sizes[1]] * len(rcVector1[0]) + rcVector2[1])
return self.transform_points(colVector, rowVector)
def _get_border_kml(self, *args, **kwargs):
'''Generate Placemark entry for KML
Returns
--------
kmlEntry : String
String with the Placemark entry
'''
domainLon, domainLat = self.get_border(*args, **kwargs)
# convert Border coordinates into KML-like string
coordinates = ''
for lon, lat in zip(domainLon, domainLat):
coordinates += '%f,%f,0 ' % (lon, lat)
kmlEntry = ''
# write placemark: name, style, polygon, coordinates
kmlEntry += ' <Placemark>\n'
kmlEntry += ' <name>%s</name>\n' % self.name
kmlEntry += ' <Style>\n'
kmlEntry += ' <LineStyle><color>ffffffff</color>'\
'</LineStyle>\n'
kmlEntry += ' <PolyStyle><fill>0</fill>'\
'</PolyStyle>\n'
kmlEntry += ' </Style>\n'
kmlEntry += ' <Polygon><tessellate>1</tessellate>'\
'<outerBoundaryIs><LinearRing><coordinates>\n'
kmlEntry += coordinates + '\n'
kmlEntry += ' </coordinates></LinearRing>'\
'</outerBoundaryIs></Polygon></Placemark>\n'
return kmlEntry
def get_border_wkt(self, *args, **kwargs):
'''Creates string with WKT representation of the border polygon
Returns
--------
WKTPolygon : string
string with WKT representation of the border polygon
'''
lonList, latList = self.get_border(*args, **kwargs)
# apply > 180 deg correction to longitudes
for ilon, lon in enumerate(lonList):
lonList[ilon] = copysign(
acos(cos(lon * pi / 180.)) / pi * 180, sin(lon * pi / 180.))
polyCont = ','.join(
str(lon) + ' ' + str(lat) for lon, lat in zip(lonList, latList))
# outer quotes have to be double and inner - single!
# wktPolygon = "PolygonFromText('POLYGON((%s))')" % polyCont
wkt = 'POLYGON((%s))' % polyCont
return wkt
def get_border_geometry(self, *args, **kwargs):
''' Get OGR Geometry of the border Polygon
Returns
-------
OGR Geometry, type Polygon
'''
return ogr.CreateGeometryFromWkt(self.get_border_wkt(*args, **kwargs))
def overlaps(self, anotherDomain):
''' Checks if this Domain overlaps another Domain
Returns
-------
overlaps : bool
True if Domains overlaps, False otherwise
'''
return self.get_border_geometry().Intersects(
anotherDomain.get_border_geometry())
def contains(self, anotherDomain):
''' Checks if this Domain fully covers another Domain
Returns
-------
contains : bool
True if this Domain fully covers another Domain, False otherwise
'''
return self.get_border_geometry().Contains(
anotherDomain.get_border_geometry())
def get_border_postgis(self):
''' Get PostGIS formatted string of the border Polygon
Returns
-------
str : 'PolygonFromText(PolygonWKT)'
'''
return "PolygonFromText('%s')" % self.get_border_wkt()
def get_corners(self):
'''Get coordinates of corners of the Domain
Returns
--------
lonVec, latVec : lists
vectors with lon/lat values for each corner
'''
colVector = [
0, 0, self.vrt.dataset.RasterXSize, self.vrt.dataset.RasterXSize
]
rowVector = [
0, self.vrt.dataset.RasterYSize, 0, self.vrt.dataset.RasterYSize
]
return self.transform_points(colVector, rowVector)
def get_min_max_lat_lon(self):
'''Get minimum and maximum lat and long values in the geolocation grid
Returns
--------
minLat, maxLat, minLon, maxLon : float
min/max lon/lat values for the Domain
'''
allLongitudes, allLatitudes = self.get_geolocation_grids()
maxLat = -90
minLat = 90
for latitudes in allLatitudes:
for lat in latitudes:
if lat > maxLat:
maxLat = lat
if lat < minLat:
minLat = lat
maxLon = -180
minLon = 180
for longitudes in allLongitudes:
for lon in longitudes:
if lon > maxLon:
maxLon = lon
if lon < minLon:
minLon = lon
return minLat, maxLat, minLon, maxLon
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 _get_geotransform(self, extentDic):
'''
the new coordinates and raster size are calculated based on
the given extentDic.
Parameters
-----------
extentDic : dictionary
includes 'te' key and 'ts' or 'tr' key
Raises
-------
OptionError : occurs when maxX - minX < 0 or maxY - minY < 0
OptionError : occurs when the given resolution is larger than
width or height.
Returns
--------
coordinate : list with 6 float
GeoTransform
rasterSize : list with two int
rasterXSize and rasterYSize
'''
# recalculate GeoTransform based on extent option
minX = extentDic['te'][0]
minY = extentDic['te'][1]
maxX = extentDic['te'][2]
maxY = extentDic['te'][3]
cornerX = minX
cornerY = maxY
width = maxX - minX
height = maxY - minY
if width <= 0 or height <= 0:
raise OptionError('The extent is illegal. '
'"-te xMin yMin xMax yMax" ')
if 'tr' in extentDic.keys():
resolutionX = extentDic['tr'][0]
resolutionY = -(extentDic['tr'][1])
if (width < resolutionX or height < resolutionY):
raise OptionError('"-tr" is too large. '
'width is %s, height is %s ' %
(str(width), str(height)))
rasterXSize = width / resolutionX
rasterYSize = abs(height / resolutionY)
else:
rasterXSize = extentDic['ts'][0]
rasterYSize = extentDic['ts'][1]
resolutionX = width / rasterXSize
resolutionY = -abs(height / rasterYSize)
# create a list for GeoTransform
coordinates = [cornerX, resolutionX, 0.0, cornerY, 0.0, resolutionY]
return coordinates, int(rasterXSize), int(rasterYSize)
def transform_points(self, colVector, rowVector, DstToSrc=0, dstSRS=NSR()):
'''Transform given lists of X,Y coordinates into lon/lat or inverse
Parameters
-----------
colVector : lists
X and Y coordinates in pixel/line or lon/lat coordinate system
DstToSrc : 0 or 1
0 - forward transform (pix/line => lon/lat)
1 - inverse transformation
dstSRS : NSR
destination spatial reference
Returns
--------
X, Y : lists
X and Y coordinates in lon/lat or pixel/line coordinate system
'''
return self.vrt.transform_points(colVector,
rowVector,
DstToSrc,
dstSRS=dstSRS)
def azimuth_y(self, reductionFactor=1):
'''Calculate the angle of each pixel position vector with respect to
the Y-axis (azimuth).
In general, azimuth is the angle from a reference vector (e.g., the
direction to North) to the chosen position vector. The azimuth
increases clockwise from direction to North.
http://en.wikipedia.org/wiki/Azimuth
Parameters
-----------
reductionFactor : integer
factor by which the size of the output array is reduced
Returns
-------
azimuth : numpy array
Values of azimuth in degrees in range 0 - 360
'''
lon, lat = self.get_geolocation_grids(reductionFactor)
a = initial_bearing(lon[1:, :], lat[1:, :], lon[:-1:, :], lat[:-1:, :])
# Repeat last row once to match size of lon-lat grids
a = np.vstack((a, a[-1, :]))
return a
def shape(self):
'''Return Numpy-like shape of Domain object (ySize, xSize)
Returns
--------
shape : tuple of two INT
Numpy-like shape of Domain object (ySize, xSize)
'''
return self.vrt.dataset.RasterYSize, self.vrt.dataset.RasterXSize
def write_map(self,
outputFileName,
lonVec=None,
latVec=None,
lonBorder=10.,
latBorder=10.,
figureSize=(6, 6),
dpi=50,
projection='cyl',
resolution='c',
continetsColor='coral',
meridians=10,
parallels=10,
pColor='r',
pLine='k',
pAlpha=0.5,
padding=0.,
merLabels=[False, False, False, False],
parLabels=[False, False, False, False],
pltshow=False,
labels=None):
''' Create an image with a map of the domain
Uses Basemap to create a World Map
Adds a semitransparent patch with outline of the Domain
Writes to an image file
Parameters
-----------
outputFileName : string
name of the output file name
lonVec : [floats] or [[floats]]
longitudes of patches to display
latVec : [floats] or [[floats]]
latitudes of patches to display
lonBorder : float
10, horisontal border around patch (degrees of longitude)
latBorder : float
10, vertical border around patch (degrees of latitude)
figureSize : tuple of two integers
(6, 6), size of the generated figure in inches
dpi: int
50, resolution of the output figure (size 6,6 and dpi 50
produces 300 x 300 figure)
projection : string, one of Basemap projections
'cyl', projection of the map
resolution : string, resolution of the map
'c', crude
'l', low
'i', intermediate
'h', high
'f', full
continetsColor : string or any matplotlib color representation
'coral', color of continets
meridians : int
10, number of meridians to draw
parallels : int
10, number of parallels to draw
pColor : string or any matplotlib color representation
'r', color of the Domain patch
pLine : string or any matplotlib color representation
'k', color of the Domain outline
pAlpha : float 0 - 1
0.5, transparency of Domain patch
padding : float
0., width of white padding around the map
merLabels : list of 4 booleans
where to put meridian labels, see also Basemap.drawmeridians()
parLables : list of 4 booleans
where to put parallel labels, see also Basemap.drawparallels()
labels : list of str
labels to print on top of patches
'''
# if lat/lon vectors are not given as input
if lonVec is None or latVec is None or len(lonVec) != len(latVec):
lonVec, latVec = self.get_border()
# convert vectors to numpy arrays
lonVec = np.array(lonVec)
latVec = np.array(latVec)
# estimate mean/min/max values of lat/lon of the shown area
# (real lat min max +/- latBorder) and (real lon min max +/- lonBorder)
minLon = max(-180, lonVec.min() - lonBorder)
maxLon = min(180, lonVec.max() + lonBorder)
minLat = max(-90, latVec.min() - latBorder)
maxLat = min(90, latVec.max() + latBorder)
meanLon = lonVec.mean()
meanLat = latVec.mean()
# generate template map (can be also tmerc)
plt.figure(num=1, figsize=figureSize, dpi=dpi)
bmap = Basemap(projection=projection,
lat_0=meanLat,
lon_0=meanLon,
llcrnrlon=minLon,
llcrnrlat=minLat,
urcrnrlon=maxLon,
urcrnrlat=maxLat,
resolution=resolution)
# add content: coastline, continents, meridians, parallels
bmap.drawcoastlines()
bmap.fillcontinents(color=continetsColor)
bmap.drawmeridians(np.linspace(minLon, maxLon, meridians),
labels=merLabels,
fmt='%2.1f')
bmap.drawparallels(np.linspace(minLat, maxLat, parallels),
labels=parLabels,
fmt='%2.1f')
# convert input lat/lon vectors to arrays of vectors with one row
# if only one vector was given
if len(lonVec.shape) == 1:
lonVec = [lonVec]
latVec = [latVec]
for i in range(len(lonVec)):
# convert lat/lons to map units
mapX, mapY = bmap(list(lonVec[i].flat), list(latVec[i].flat))
# from x/y vectors create a Patch to be added to map
boundary = Polygon(zip(mapX, mapY),
alpha=pAlpha,
ec=pLine,
fc=pColor)
# add patch to the map
plt.gca().add_patch(boundary)
plt.gca().set_aspect('auto')
if labels is not None and labels[i] is not None:
plt.text(np.mean(mapX),
np.mean(mapY),
labels[i],
va='center',
ha='right',
alpha=0.5,
fontsize=10)
# save figure and close
plt.savefig(outputFileName,
bbox_inches='tight',
dpi=dpi,
pad_inches=padding)
if pltshow:
plt.show()
else:
plt.close('all')
def reproject_GCPs(self, srsString=''):
'''Reproject all GCPs to a new spatial reference system
Necessary before warping an image if the given GCPs
are in a coordinate system which has a singularity
in (or near) the destination area (e.g. poles for lonlat GCPs)
Parameters
----------
srsString : string
SRS given as Proj4 string. If empty '+proj=stere' is used
Modifies
--------
Reprojects all GCPs to new SRS and updates GCPProjection
'''
if srsString == '':
lon, lat = self.get_border()
srsString = '+proj=stere +datum=WGS84 +ellps=WGS84 +lat_0=%f +lon_0=%f +no_defs' % (
np.nanmedian(lat), np.nanmedian(lon))
self.vrt.reproject_GCPs(srsString)
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')
