def convert_cfunits(from_value,
from_unit,
wanted_unit=None,
suppress_unit=False):
""" Use the cfunits-python package to handle the interaction with udunits2 """
try:
from cfunits import Units
except ImportError:
raise Exception("cfunits option relies on cfunits-python package")
ZERO = False
if float(from_value) == 0:
# special Zero handling
if from_unit != '1':
warnings.warn("""Found a 0, udunits2 breaks,
so we are assuming that this is not a non-zero based conversion like temperature"""
)
ZERO = True
from_value = 1.
if wanted_unit:
return Units.conform(from_value, Units(from_unit), Units(wanted_unit))
out = Units(" ".join((str(from_value), from_unit))).format()
split_out = out.split(' ')
if len(split_out) == 1:
out = (1., split_out[0])
else:
if ZERO:
out = (0.0, split_out[1])
else:
out = (float(split_out[0]), split_out[1])
if suppress_unit:
return out[0]
return out
def gallon2liter(value):
if value:
return Units.conform(int(value),
Units('gallon / day'),
Units('liter / second'),
inplace=True)
return 0.0
def get_value(self,
name,
out=None,
units=None,
angle=None,
at=None,
method=None):
if out is None:
grid = self.get_var_grid(name)
dtype = self.get_var_type(name)
if dtype == "":
raise ValueError("{name} not understood".format(name=name))
loc = self.get_var_grid_loc(name)
out = np.empty(self.get_grid_dim(grid, loc), dtype=dtype)
self.bmi.get_value(name, out)
if name in self._interpolators and at is not None:
out[:] = self._interpolators[name].interpolate(at)
from_units = Units(self.get_var_units(name))
if units is not None:
to_units = Units(units)
else:
to_units = from_units
if units is not None and from_units != to_units:
Units.conform(out, from_units, to_units, inplace=True)
# if units is not None:
# try:
# from_units = self.get_var_units(name)
# except AttributeError, NotImplementedError:
# pass
# else:
# Units.conform(out, Units(from_units), Units(units),
# inplace=True)
if angle not in ("azimuth", "math", None):
raise ValueError("angle not understood")
if angle == "azimuth" and "azimuth" not in name:
transform_math_to_azimuth(out, to_units)
elif angle == "math" and "azimuth" in name:
transform_azimuth_to_math(out, to_units)
return out
def get_value(self,
name,
out=None,
units=None,
angle=None,
at=None,
method=None):
if out is None:
grid = self.get_var_grid(name)
dtype = self.get_var_type(name)
if dtype == '':
raise ValueError('{name} not understood'.format(name=name))
out = np.empty(self.get_grid_size(grid), dtype=dtype)
bmi_call(self.bmi.get_value, name, out)
if name in self._interpolators and at is not None:
out[:] = self._interpolators[name].interpolate(at)
from_units = Units(self.get_var_units(name))
if units is not None:
to_units = Units(units)
else:
to_units = from_units
if units is not None and from_units != to_units:
Units.conform(out, from_units, to_units, inplace=True)
# if units is not None:
# try:
# from_units = self.get_var_units(name)
# except AttributeError, NotImplementedError:
# pass
# else:
# Units.conform(out, Units(from_units), Units(units),
# inplace=True)
if angle not in ('azimuth', 'math', None):
raise ValueError('angle not understood')
if angle == 'azimuth' and 'azimuth' not in name:
transform_math_to_azimuth(out, to_units)
elif angle == 'math' and 'azimuth' in name:
transform_azimuth_to_math(out, to_units)
return out
def wrap(self, units=None):
time = val_or_raise(func, (self._base, ))
if units is not None:
try:
from_units = Units(self.get_time_units())
to_units = Units(units)
except AttributeError, NotImplementedError:
pass
else:
if not from_units.equals(to_units):
time = Units.conform(time, from_units, to_units)
def wrap(self, name, out=None, units=None):
"""Get a value by name.
Parameters
----------
name : str
CSDMS standard name.
out : ndarray, optional
Buffer to place values.
units : str, optional
Convert units of the returned values.
Returns
-------
ndarray
Array of values (or *out*, if provided).
"""
if out is None:
grid = self.get_var_grid(name)
dtype = self.get_var_type(name)
if dtype == '':
print self.get_output_var_names()
raise ValueError('{name} not understood'.format(name=name))
out = np.empty(self.get_grid_size(grid), dtype=dtype)
val_or_raise(func, (self._base, name, out))
if units is not None:
try:
from_units = self.get_var_units(name)
except AttributeError, NotImplementedError:
pass
else:
Units.conform(out,
Units(from_units),
Units(units),
inplace=True)
def time_from(self, time, units):
if units is None:
return time
try:
# units_str = self.time_units
units_str = self.time_units
except (AttributeError, NotImplementedError):
pass
else:
to_units = Units(units_str)
from_units = Units(units)
if not from_units.equals(to_units):
time = Units.conform(time, from_units, to_units)
return time
def test_get_field_with_overloaded_units(self):
rd = self.test_data.get_rd('cancm4_tas', kwds={'conform_units_to': 'celsius'})
preload = [False, True]
for pre in preload:
field = rd.get()
# conform units argument needs to be attached to a field variable
self.assertEqual(field.variables['tas']._conform_units_to, Units('celsius'))
sub = field.get_time_region({'year': [2009], 'month': [5]})
if pre:
# if we wanted to load the data prior to subset then do so and manually perform the units conversion
to_test = Units.conform(sub.variables['tas'].value, sub.variables['tas'].cfunits, Units('celsius'))
# assert the conform attribute makes it though the subset
self.assertEqual(sub.variables['tas']._conform_units_to, Units('celsius'))
value = sub.variables['tas'].value
self.assertAlmostEqual(np.ma.mean(value), 5.921925206338206)
self.assertAlmostEqual(np.ma.median(value), 10.745431900024414)
if pre:
# assert the manually converted array matches the loaded value
self.assertNumpyAll(to_test, value)
def convert_units(input_vals, input_unit, output_unit):
"""
Convert the units of an array of values
Params
-------
input_vals : Numpy array
Values to convert
input_unit : str
Unit corresponding to the input values
output_unit : str
Desired unit to covert the values to
Return
-------
Numpy array
"""
conv_vals = Units.conform(input_vals, Units(input_unit),
Units(output_unit))
return conv_vals
def conform(*args, **kwargs):
return cfUnits.conform(*args, **kwargs)
def test_Units_conform(self):
self.assertEqual(Units.conform(0.5, Units('km'), Units('m')), 500)
self.assertEqual(Units.conform(360, Units('second'), Units('minute')),
6)
x = Units.conform([360], Units('second'), Units('minute'))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, 6))
x = Units.conform((360, 720), Units('second'), Units('minute'))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, [6, 12]))
x = Units.conform([360.0, 720.0], Units('second'), Units('minute'))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, [6, 12]))
x = Units.conform([[360, 720]], Units('second'), Units('minute'))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, [[6, 12]]))
v = numpy.array([360.0, 720.0])
x = Units.conform(v, Units('second'), Units('minute'))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, [6, 12]), x)
v = numpy.array([360, 720])
x = Units.conform(v, Units('second'), Units('minute'), inplace=True)
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, [6, 12]))
self.assertTrue(numpy.allclose(x, v))
x = Units.conform(35, Units('degrees_C'), Units('degrees_F'))
self.assertIsInstance(x, float)
self.assertTrue(numpy.allclose(x, 95))
x = Units.conform([35], Units('degrees_C'), Units('degrees_F'))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, 95))
x = Units.conform(35,
Units('degrees_C'),
Units('degrees_F'),
inplace=True)
self.assertIsInstance(x, float)
self.assertTrue(numpy.allclose(x, 95))
x = Units.conform([35],
Units('degrees_C'),
Units('degrees_F'),
inplace=True)
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype('float64'))
self.assertTrue(numpy.allclose(x, 95))
with self.assertRaises(ValueError):
Units.conform(1, Units('m'), Units('second'))
def _convert(val, from_units, to_units):
from_units, to_units = Units(from_units), Units(to_units)
if from_units.equals(to_units):
return val
else:
return Units.conform(val, from_units, to_units)
def __init__(self, inputFileName, gdalDataset, gdalMetadata, logLevel=30,
rmMetadatas=['NETCDF_VARNAME', '_Unsigned',
'ScaleRatio', 'ScaleOffset', 'dods_variable'],
**kwargs):
# Remove 'NC_GLOBAL#' and 'GDAL_' and 'NANSAT_'
# from keys in gdalDataset
tmpGdalMetadata = {}
geoMetadata = {}
origin_is_nansat = False
if not gdalMetadata:
raise WrongMapperError
for key in gdalMetadata.keys():
newKey = key.replace('NC_GLOBAL#', '').replace('GDAL_', '')
if 'NANSAT_' in newKey:
geoMetadata[newKey.replace('NANSAT_', '')] = gdalMetadata[key]
origin_is_nansat = True
else:
tmpGdalMetadata[newKey] = gdalMetadata[key]
gdalMetadata = tmpGdalMetadata
fileExt = os.path.splitext(inputFileName)[1]
# Get file names from dataset or subdataset
subDatasets = gdalDataset.GetSubDatasets()
if len(subDatasets) == 0:
fileNames = [inputFileName]
else:
fileNames = [f[0] for f in subDatasets]
# add bands with metadata and corresponding values to the empty VRT
metaDict = []
xDatasetSource = ''
yDatasetSource = ''
firstXSize = 0
firstYSize = 0
for _, fileName in enumerate(fileNames):
subDataset = gdal.Open(fileName)
# choose the first dataset whith grid
if (firstXSize == 0 and firstYSize == 0 and
subDataset.RasterXSize > 1 and subDataset.RasterYSize > 1):
firstXSize = subDataset.RasterXSize
firstYSize = subDataset.RasterYSize
firstSubDataset = subDataset
# get projection from the first subDataset
projection = firstSubDataset.GetProjection()
# take bands whose sizes are same as the first band.
if (subDataset.RasterXSize == firstXSize and
subDataset.RasterYSize == firstYSize):
if projection == '':
projection = subDataset.GetProjection()
if ('GEOLOCATION_X_DATASET' in fileName or
'longitude' in fileName):
xDatasetSource = fileName
elif ('GEOLOCATION_Y_DATASET' in fileName or
'latitude' in fileName):
yDatasetSource = fileName
else:
for iBand in range(subDataset.RasterCount):
subBand = subDataset.GetRasterBand(iBand+1)
bandMetadata = subBand.GetMetadata_Dict()
if 'PixelFunctionType' in bandMetadata:
bandMetadata.pop('PixelFunctionType')
sourceBands = iBand + 1
# sourceBands = i*subDataset.RasterCount + iBand + 1
# generate src metadata
src = {'SourceFilename': fileName,
'SourceBand': sourceBands}
# set scale ratio and scale offset
scaleRatio = bandMetadata.get(
'ScaleRatio',
bandMetadata.get(
'scale',
bandMetadata.get('scale_factor', '')))
if len(scaleRatio) > 0:
src['ScaleRatio'] = scaleRatio
scaleOffset = bandMetadata.get(
'ScaleOffset',
bandMetadata.get(
'offset',
bandMetadata.get(
'add_offset', '')))
if len(scaleOffset) > 0:
src['ScaleOffset'] = scaleOffset
# sate DataType
src['DataType'] = subBand.DataType
# generate dst metadata
# get all metadata from input band
dst = bandMetadata
# set wkv and bandname
dst['wkv'] = bandMetadata.get('standard_name', '')
# first, try the name metadata
if 'name' in bandMetadata:
bandName = bandMetadata['name']
else:
# if it doesn't exist get name from NETCDF_VARNAME
bandName = bandMetadata.get('NETCDF_VARNAME', '')
if len(bandName) == 0:
bandName = bandMetadata.get(
'dods_variable', ''
)
# remove digits added by gdal in
# exporting to netcdf...
if (len(bandName) > 0 and origin_is_nansat and
fileExt == '.nc'):
if bandName[-1:].isdigit():
bandName = bandName[:-1]
if bandName[-1:].isdigit():
bandName = bandName[:-1]
# if still no bandname, create one
if len(bandName) == 0:
bandName = 'band_%03d' % iBand
dst['name'] = bandName
# remove non-necessary metadata from dst
for rmMetadata in rmMetadatas:
if rmMetadata in dst:
dst.pop(rmMetadata)
# append band with src and dst dictionaries
metaDict.append({'src': src, 'dst': dst})
# create empty VRT dataset with geolocation only
VRT.__init__(self, firstSubDataset, srcMetadata=gdalMetadata)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# Create complex data bands from 'xxx_real' and 'xxx_imag' bands
# using pixelfunctions
rmBands = []
for iBandNo in range(self.dataset.RasterCount):
iBand = self.dataset.GetRasterBand(iBandNo + 1)
iBandName = iBand.GetMetadataItem('name')
# find real data band
if iBandName.find("_real") != -1:
realBandNo = iBandNo
realBand = self.dataset.GetRasterBand(realBandNo + 1)
realDtype = realBand.GetMetadataItem('DataType')
bandName = iBandName.replace(iBandName.split('_')[-1],
'')[0:-1]
for jBandNo in range(self.dataset.RasterCount):
jBand = self.dataset.GetRasterBand(jBandNo + 1)
jBandName = jBand.GetMetadataItem('name')
# find an imaginary data band corresponding to the real
# data band and create complex data band from the bands
if jBandName.find(bandName+'_imag') != -1:
imagBandNo = jBandNo
imagBand = self.dataset.GetRasterBand(imagBandNo + 1)
imagDtype = imagBand.GetMetadataItem('DataType')
dst = imagBand.GetMetadata()
dst['name'] = bandName
dst['PixelFunctionType'] = 'ComplexData'
dst['dataType'] = 10
src = [{'SourceFilename': fileNames[realBandNo],
'SourceBand': 1,
'DataType': realDtype},
{'SourceFilename': fileNames[imagBandNo],
'SourceBand': 1,
'DataType': imagDtype}]
self._create_band(src, dst)
self.dataset.FlushCache()
rmBands.append(realBandNo + 1)
rmBands.append(imagBandNo + 1)
# Delete real and imaginary bands
if len(rmBands) != 0:
self.delete_bands(rmBands)
if len(projection) == 0:
# projection was not set automatically
# get projection from GCPProjection
projection = geoMetadata.get('GCPProjection', '')
if len(projection) == 0:
# no projection was found in dataset or metadata:
# generate WGS84 by default
projection = NSR().wkt
# fix problem with MET.NO files where a, b given in m and XC/YC in km
if ('UNIT["kilometre"' in projection and
',SPHEROID["Spheroid",6378273,7.331926543631893e-12]' in
projection):
projection = projection.replace(
',SPHEROID["Spheroid",6378273,7.331926543631893e-12]',
'')
# set projection
self.dataset.SetProjection(self.repare_projection(projection))
# check if GCPs were added from input dataset
gcps = firstSubDataset.GetGCPs()
gcpProjection = firstSubDataset.GetGCPProjection()
# if no GCPs in input dataset: try to add GCPs from metadata
if not gcps:
gcps = self.add_gcps_from_metadata(geoMetadata)
# if yet no GCPs: try to add GCPs from variables
if not gcps:
gcps = self.add_gcps_from_variables(inputFileName)
if gcps:
if len(gcpProjection) == 0:
# get GCP projection and repare
gcpProjection = self.repare_projection(geoMetadata.
get('GCPProjection', ''))
# add GCPs to dataset
self.dataset.SetGCPs(gcps, gcpProjection)
self.dataset.SetProjection('')
self._remove_geotransform()
# Find proper bands and insert GEOLOCATION ARRAY into dataset
if len(xDatasetSource) > 0 and len(yDatasetSource) > 0:
self.add_geolocationArray(GeolocationArray(xDatasetSource,
yDatasetSource))
elif not gcps:
# if no GCPs found and not GEOLOCATION ARRAY set:
# Set Nansat Geotransform if it is not set automatically
geoTransform = self.dataset.GetGeoTransform()
if len(geoTransform) == 0:
geoTransformStr = geoMetadata.get('GeoTransform',
'(0|1|0|0|0|0|1)')
geoTransform = eval(geoTransformStr.replace('|', ','))
self.dataset.SetGeoTransform(geoTransform)
subMetadata = firstSubDataset.GetMetadata()
### GET START TIME from METADATA
time_coverage_start = None
if 'start_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_time'])
elif 'start_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_date'])
elif 'time_coverage_start' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_start'])
### GET END TIME from METADATA
time_coverage_end = None
if 'stop_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['stop_time'])
elif 'stop_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['stop_date'])
elif 'time_coverage_stop' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_stop'])
elif 'end_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['end_time'])
elif 'end_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['end_date'])
elif 'time_coverage_end' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_end'])
### GET start time from time variable
if (time_coverage_start is None and cfunitsInstalled and
'time#standard_name' in subMetadata and
subMetadata['time#standard_name'] == 'time' and
'time#units' in subMetadata and
'time#calendar' in subMetadata):
# get data from netcdf data
ncFile = netcdf_file(inputFileName, 'r')
timeLength = ncFile.variables['time'].shape[0]
timeValueStart = ncFile.variables['time'][0]
timeValueEnd = ncFile.variables['time'][-1]
ncFile.close()
try:
timeDeltaStart = Units.conform(timeValueStart,
Units(subMetadata['time#units'],
calendar=subMetadata['time#calendar']),
Units('days since 1950-01-01'))
except ValueError:
self.logger.error('calendar units are wrong: %s' %
subMetadata['time#calendar'])
else:
time_coverage_start = (datetime.datetime(1950,1,1) +
datetime.timedelta(float(timeDeltaStart)))
if timeLength > 1:
timeDeltaEnd = Units.conform(timeValueStart,
Units(subMetadata['time#units'],
calendar=subMetadata['time#calendar']),
Units('days since 1950-01-01'))
else:
timeDeltaEnd = timeDeltaStart + 1
time_coverage_end = (datetime.datetime(1950,1,1) +
datetime.timedelta(float(timeDeltaEnd)))
## finally set values of time_coverage start and end if available
if time_coverage_start is not None:
self.dataset.SetMetadataItem('time_coverage_start',
time_coverage_start.isoformat())
if time_coverage_end is not None:
self.dataset.SetMetadataItem('time_coverage_end',
time_coverage_end.isoformat())
if 'sensor' not in gdalMetadata:
self.dataset.SetMetadataItem('sensor', 'unknown')
if 'satellite' not in gdalMetadata:
self.dataset.SetMetadataItem('satellite', 'unknown')
if 'source_type' not in gdalMetadata:
self.dataset.SetMetadataItem('source_type', 'unknown')
if 'platform' not in gdalMetadata:
self.dataset.SetMetadataItem('platform', 'unknown')
if 'instrument' not in gdalMetadata:
self.dataset.SetMetadataItem('instrument', 'unknown')
self.logger.info('Use generic mapper - OK!')
def __init__(self,
inputFileName,
gdalDataset,
gdalMetadata,
logLevel=30,
rmMetadatas=[
'NETCDF_VARNAME', '_Unsigned', 'ScaleRatio',
'ScaleOffset', 'dods_variable'
],
**kwargs):
# Remove 'NC_GLOBAL#' and 'GDAL_' and 'NANSAT_'
# from keys in gdalDataset
tmpGdalMetadata = {}
geoMetadata = {}
origin_is_nansat = False
if not gdalMetadata:
raise WrongMapperError
for key in gdalMetadata.keys():
newKey = key.replace('NC_GLOBAL#', '').replace('GDAL_', '')
if 'NANSAT_' in newKey:
geoMetadata[newKey.replace('NANSAT_', '')] = gdalMetadata[key]
origin_is_nansat = True
else:
tmpGdalMetadata[newKey] = gdalMetadata[key]
gdalMetadata = tmpGdalMetadata
fileExt = os.path.splitext(inputFileName)[1]
# Get file names from dataset or subdataset
subDatasets = gdalDataset.GetSubDatasets()
if len(subDatasets) == 0:
fileNames = [inputFileName]
else:
fileNames = [f[0] for f in subDatasets]
# add bands with metadata and corresponding values to the empty VRT
metaDict = []
xDatasetSource = ''
yDatasetSource = ''
firstXSize = 0
firstYSize = 0
for _, fileName in enumerate(fileNames):
subDataset = gdal.Open(fileName)
# choose the first dataset whith grid
if (firstXSize == 0 and firstYSize == 0
and subDataset.RasterXSize > 1
and subDataset.RasterYSize > 1):
firstXSize = subDataset.RasterXSize
firstYSize = subDataset.RasterYSize
firstSubDataset = subDataset
# get projection from the first subDataset
projection = firstSubDataset.GetProjection()
# take bands whose sizes are same as the first band.
if (subDataset.RasterXSize == firstXSize
and subDataset.RasterYSize == firstYSize):
if projection == '':
projection = subDataset.GetProjection()
if ('GEOLOCATION_X_DATASET' in fileName
or 'longitude' in fileName):
xDatasetSource = fileName
elif ('GEOLOCATION_Y_DATASET' in fileName
or 'latitude' in fileName):
yDatasetSource = fileName
else:
for iBand in range(subDataset.RasterCount):
subBand = subDataset.GetRasterBand(iBand + 1)
bandMetadata = subBand.GetMetadata_Dict()
if 'PixelFunctionType' in bandMetadata:
bandMetadata.pop('PixelFunctionType')
sourceBands = iBand + 1
# sourceBands = i*subDataset.RasterCount + iBand + 1
# generate src metadata
src = {
'SourceFilename': fileName,
'SourceBand': sourceBands
}
# set scale ratio and scale offset
scaleRatio = bandMetadata.get(
'ScaleRatio',
bandMetadata.get(
'scale', bandMetadata.get('scale_factor', '')))
if len(scaleRatio) > 0:
src['ScaleRatio'] = scaleRatio
scaleOffset = bandMetadata.get(
'ScaleOffset',
bandMetadata.get(
'offset', bandMetadata.get('add_offset', '')))
if len(scaleOffset) > 0:
src['ScaleOffset'] = scaleOffset
# sate DataType
src['DataType'] = subBand.DataType
# generate dst metadata
# get all metadata from input band
dst = bandMetadata
# set wkv and bandname
dst['wkv'] = bandMetadata.get('standard_name', '')
# first, try the name metadata
if 'name' in bandMetadata:
bandName = bandMetadata['name']
else:
# if it doesn't exist get name from NETCDF_VARNAME
bandName = bandMetadata.get('NETCDF_VARNAME', '')
if len(bandName) == 0:
bandName = bandMetadata.get(
'dods_variable', '')
# remove digits added by gdal in
# exporting to netcdf...
if (len(bandName) > 0 and origin_is_nansat
and fileExt == '.nc'):
if bandName[-1:].isdigit():
bandName = bandName[:-1]
if bandName[-1:].isdigit():
bandName = bandName[:-1]
# if still no bandname, create one
if len(bandName) == 0:
bandName = 'band_%03d' % iBand
dst['name'] = bandName
# remove non-necessary metadata from dst
for rmMetadata in rmMetadatas:
if rmMetadata in dst:
dst.pop(rmMetadata)
# append band with src and dst dictionaries
metaDict.append({'src': src, 'dst': dst})
# create empty VRT dataset with geolocation only
VRT.__init__(self, firstSubDataset, srcMetadata=gdalMetadata)
# add bands with metadata and corresponding values to the empty VRT
self._create_bands(metaDict)
# Create complex data bands from 'xxx_real' and 'xxx_imag' bands
# using pixelfunctions
rmBands = []
for iBandNo in range(self.dataset.RasterCount):
iBand = self.dataset.GetRasterBand(iBandNo + 1)
iBandName = iBand.GetMetadataItem('name')
# find real data band
if iBandName.find("_real") != -1:
realBandNo = iBandNo
realBand = self.dataset.GetRasterBand(realBandNo + 1)
realDtype = realBand.GetMetadataItem('DataType')
bandName = iBandName.replace(iBandName.split('_')[-1],
'')[0:-1]
for jBandNo in range(self.dataset.RasterCount):
jBand = self.dataset.GetRasterBand(jBandNo + 1)
jBandName = jBand.GetMetadataItem('name')
# find an imaginary data band corresponding to the real
# data band and create complex data band from the bands
if jBandName.find(bandName + '_imag') != -1:
imagBandNo = jBandNo
imagBand = self.dataset.GetRasterBand(imagBandNo + 1)
imagDtype = imagBand.GetMetadataItem('DataType')
dst = imagBand.GetMetadata()
dst['name'] = bandName
dst['PixelFunctionType'] = 'ComplexData'
dst['dataType'] = 10
src = [{
'SourceFilename': fileNames[realBandNo],
'SourceBand': 1,
'DataType': realDtype
}, {
'SourceFilename': fileNames[imagBandNo],
'SourceBand': 1,
'DataType': imagDtype
}]
self._create_band(src, dst)
self.dataset.FlushCache()
rmBands.append(realBandNo + 1)
rmBands.append(imagBandNo + 1)
# Delete real and imaginary bands
if len(rmBands) != 0:
self.delete_bands(rmBands)
if len(projection) == 0:
# projection was not set automatically
# get projection from GCPProjection
projection = geoMetadata.get('GCPProjection', '')
if len(projection) == 0:
# no projection was found in dataset or metadata:
# generate WGS84 by default
projection = NSR().wkt
# fix problem with MET.NO files where a, b given in m and XC/YC in km
if ('UNIT["kilometre"' in projection
and ',SPHEROID["Spheroid",6378273,7.331926543631893e-12]'
in projection):
projection = projection.replace(
',SPHEROID["Spheroid",6378273,7.331926543631893e-12]', '')
# set projection
self.dataset.SetProjection(self.repare_projection(projection))
# check if GCPs were added from input dataset
gcps = firstSubDataset.GetGCPs()
gcpProjection = firstSubDataset.GetGCPProjection()
# if no GCPs in input dataset: try to add GCPs from metadata
if not gcps:
gcps = self.add_gcps_from_metadata(geoMetadata)
# if yet no GCPs: try to add GCPs from variables
if not gcps:
gcps = self.add_gcps_from_variables(inputFileName)
if gcps:
if len(gcpProjection) == 0:
# get GCP projection and repare
gcpProjection = self.repare_projection(
geoMetadata.get('GCPProjection', ''))
# add GCPs to dataset
self.dataset.SetGCPs(gcps, gcpProjection)
self.dataset.SetProjection('')
self._remove_geotransform()
# Find proper bands and insert GEOLOCATION ARRAY into dataset
if len(xDatasetSource) > 0 and len(yDatasetSource) > 0:
self.add_geolocationArray(
GeolocationArray(xDatasetSource, yDatasetSource))
elif not gcps:
# if no GCPs found and not GEOLOCATION ARRAY set:
# Set Nansat Geotransform if it is not set automatically
geoTransform = self.dataset.GetGeoTransform()
if len(geoTransform) == 0:
geoTransformStr = geoMetadata.get('GeoTransform',
'(0|1|0|0|0|0|1)')
geoTransform = eval(geoTransformStr.replace('|', ','))
self.dataset.SetGeoTransform(geoTransform)
subMetadata = firstSubDataset.GetMetadata()
### GET START TIME from METADATA
time_coverage_start = None
if 'start_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_time'])
elif 'start_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['start_date'])
elif 'time_coverage_start' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_start'])
### GET END TIME from METADATA
time_coverage_end = None
if 'stop_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['stop_time'])
elif 'stop_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['stop_date'])
elif 'time_coverage_stop' in gdalMetadata:
time_coverage_start = parse_time(
gdalMetadata['time_coverage_stop'])
elif 'end_time' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['end_time'])
elif 'end_date' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['end_date'])
elif 'time_coverage_end' in gdalMetadata:
time_coverage_start = parse_time(gdalMetadata['time_coverage_end'])
### GET start time from time variable
if (time_coverage_start is None and cfunitsInstalled
and 'time#standard_name' in subMetadata
and subMetadata['time#standard_name'] == 'time'
and 'time#units' in subMetadata
and 'time#calendar' in subMetadata):
# get data from netcdf data
ncFile = netcdf_file(inputFileName, 'r')
timeLength = ncFile.variables['time'].shape[0]
timeValueStart = ncFile.variables['time'][0]
timeValueEnd = ncFile.variables['time'][-1]
ncFile.close()
try:
timeDeltaStart = Units.conform(
timeValueStart,
Units(subMetadata['time#units'],
calendar=subMetadata['time#calendar']),
Units('days since 1950-01-01'))
except ValueError:
self.logger.error('calendar units are wrong: %s' %
subMetadata['time#calendar'])
else:
time_coverage_start = (
datetime.datetime(1950, 1, 1) +
datetime.timedelta(float(timeDeltaStart)))
if timeLength > 1:
timeDeltaEnd = Units.conform(
timeValueStart,
Units(subMetadata['time#units'],
calendar=subMetadata['time#calendar']),
Units('days since 1950-01-01'))
else:
timeDeltaEnd = timeDeltaStart + 1
time_coverage_end = (datetime.datetime(1950, 1, 1) +
datetime.timedelta(float(timeDeltaEnd)))
## finally set values of time_coverage start and end if available
if time_coverage_start is not None:
self.dataset.SetMetadataItem('time_coverage_start',
time_coverage_start.isoformat())
if time_coverage_end is not None:
self.dataset.SetMetadataItem('time_coverage_end',
time_coverage_end.isoformat())
if 'sensor' not in gdalMetadata:
self.dataset.SetMetadataItem('sensor', 'unknown')
if 'satellite' not in gdalMetadata:
self.dataset.SetMetadataItem('satellite', 'unknown')
if 'source_type' not in gdalMetadata:
self.dataset.SetMetadataItem('source_type', 'unknown')
if 'platform' not in gdalMetadata:
self.dataset.SetMetadataItem('platform', 'unknown')
if 'instrument' not in gdalMetadata:
self.dataset.SetMetadataItem('instrument', 'unknown')
self.logger.info('Use generic mapper - OK!')
def test_Units_conform(self):
"""Tests the `conform` class method on `Units`."""
self.assertEqual(Units.conform(0.5, Units("km"), Units("m")), 500)
self.assertEqual(Units.conform(360, Units("second"), Units("minute")),
6)
x = Units.conform([360], Units("second"), Units("minute"))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, 6))
x = Units.conform((360, 720), Units("second"), Units("minute"))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, [6, 12]))
x = Units.conform([360.0, 720.0], Units("second"), Units("minute"))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, [6, 12]))
x = Units.conform([[360, 720]], Units("second"), Units("minute"))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, [[6, 12]]))
v = numpy.array([360.0, 720.0])
x = Units.conform(v, Units("second"), Units("minute"))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, [6, 12]), x)
v = numpy.array([360, 720])
x = Units.conform(v, Units("second"), Units("minute"), inplace=True)
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, [6, 12]))
self.assertTrue(numpy.allclose(x, v))
x = Units.conform(35, Units("degrees_C"), Units("degrees_F"))
self.assertIsInstance(x, float)
self.assertTrue(numpy.allclose(x, 95))
x = Units.conform([35], Units("degrees_C"), Units("degrees_F"))
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, 95))
x = Units.conform(35,
Units("degrees_C"),
Units("degrees_F"),
inplace=True)
self.assertIsInstance(x, float)
self.assertTrue(numpy.allclose(x, 95))
x = Units.conform([35],
Units("degrees_C"),
Units("degrees_F"),
inplace=True)
self.assertIsInstance(x, numpy.ndarray)
self.assertEqual(x.dtype, numpy.dtype("float64"))
self.assertTrue(numpy.allclose(x, 95))
with self.assertRaises(ValueError):
Units.conform(1, Units("m"), Units("second"))
def test_Units_conform(self):
self.assertTrue(Units.conform(0.5, Units('km'), Units('m')) == 500)
self.assertTrue(
Units.conform(360, Units('second'), Units('minute')) == 6)
with self.assertRaises(ValueError):
Units.conform(1, Units('m'), Units('second'))
