def test_export_netcdf_complex_remove_meta(self):
n = Nansat(self.test_file_complex, mapper=self.default_mapper)
self.assertEqual(n.get_metadata('PRODUCT_TYPE'), 'SLC')
n.export(self.tmp_filename, rm_metadata=['PRODUCT_TYPE'])
exported = Nansat(self.tmp_filename, mapper=self.default_mapper)
with self.assertRaises(ValueError):
exported.get_metadata('PRODUCT_TYPE')
self.assertTrue((n[1] == exported[1]).any())
def test_export_netcdf_complex_remove_meta(self):
''' Test export of complex data with pixelfunctions
'''
n = Nansat(self.test_file_complex)
self.assertEqual(n.get_metadata('PRODUCT_TYPE'), 'SLC')
n.export(self.tmpfilename, rmMetadata=['PRODUCT_TYPE'])
exported = Nansat(self.tmpfilename)
with self.assertRaises(OptionError):
exported.get_metadata('PRODUCT_TYPE')
self.assertTrue((n[1] == exported[1]).any())
os.unlink(self.tmpfilename)
def test_time_coverage_metadata_of_exported_equals_original(self):
orig = Nansat(self.test_file_gcps, mapper=self.default_mapper)
orig.set_metadata('time_coverage_start', '2010-01-02T08:49:02.347809')
orig.set_metadata('time_coverage_end', '2010-01-02T08:50:03.599373')
orig.export(self.tmp_filename)
copy = Nansat(self.tmp_filename, mapper=self.default_mapper)
self.assertEqual(orig.get_metadata('time_coverage_start'),
copy.get_metadata('time_coverage_start'))
self.assertEqual(orig.get_metadata('time_coverage_end'),
copy.get_metadata('time_coverage_end'))
def test_special_characters_in_exported_metadata(self):
orig = Nansat(self.test_file_gcps, mapper=self.default_mapper)
orig.vrt.dataset.SetMetadataItem('jsonstring', json.dumps({'meta1':
'hei', 'meta2': 'derr'}))
orig.export(self.tmp_filename)
copy = Nansat(self.tmp_filename, mapper=self.default_mapper)
dd = json.loads(unescape(copy.get_metadata('jsonstring'), {'"':
'"'}))
self.assertIsInstance(dd, dict)
def test_add_band(self):
d = Domain(4326, "-te 25 70 35 72 -ts 500 500")
arr = np.random.randn(500, 500)
n = Nansat(domain=d, logLevel=40)
n.add_band(arr, {'name': 'band1'})
self.assertEqual(type(n), Nansat)
self.assertEqual(type(n[1]), np.ndarray)
self.assertEqual(n.get_metadata('name', 1), 'band1')
self.assertEqual(n[1].shape, (500, 500))
def test_init_domain_array(self):
d = Domain(4326, "-te 25 70 35 72 -ts 500 500")
n = Nansat(domain=d,
array=np.random.randn(500, 500),
parameters={'name': 'band1'},
logLevel=40)
self.assertEqual(type(n), Nansat)
self.assertEqual(type(n[1]), np.ndarray)
self.assertEqual(n.get_metadata('name', 1), 'band1')
self.assertEqual(n[1].shape, (500, 500))
def test_get_metadata_bandid(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata(bandID=1)
self.assertEqual(type(m), dict)
self.assertTrue('name' in m)
def test_set_metadata_bandid(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
n1.set_metadata('newKey', 'newVal', 1)
m = n1.get_metadata('newKey', 1)
self.assertEqual(m, 'newVal')
def get_n(filename,
bandName='sigma0_HV',
factor=0.5,
denoise=False,
dB=True,
mask_invalid=True,
landmask_border=20,
correct_hh=False,
correct_hh_factor=-0.27,
remove_spatial_mean=False,
vmin=None,
vmax=None,
pmin=10,
pmax=99,
**kwargs):
""" Get Nansat object with image data scaled to UInt8
Parameters
----------
filename : str
input file name
bandName : str
name of band in the file
factor : float
subsampling factor
denoise : bool
apply denoising of sigma0 ?
dB : bool
apply conversion to dB ?
mask_invalid : bool
mask invalid pixels (land, inf, etc) with 0 ?
landmask_border : int
border around landmask
correct_hh : bool
perform angular correction of sigma0_HH ?
correct_hh_factor : float
coefficient in the correction factor sigma0_HH_cor = sigma0_HH + correct_hh_factor * incidence_angle
remove_spatial_mean : bool
remove spatial mean from image ?
vmin : float or None
minimum value to convert to 1
vmax : float or None
maximum value to convert to 255
pmin : float
lower percentile for data scaling if vmin is None
pmax : float
upper percentile for data scaling if vmax is None
**kwargs : dummy parameters for
get_denoised_object()
Returns
-------
n : Nansat object with one band scaled to UInt8
"""
if denoise:
# run denoising
n = get_denoised_object(filename, bandName, factor, **kwargs)
else:
# open data with Nansat and downsample
n = Nansat(filename)
if factor != 1:
n.resize(factor, resample_alg=-1)
# get matrix with data
img = n[bandName]
# convert to dB
if not denoise and dB:
img[img <= 0] = np.nan
img = 10 * np.log10(img)
if correct_hh:
img = hh_angular_correction(n, img, bandName, correct_hh_factor)
if mask_invalid:
mask = get_invalid_mask(img, n, landmask_border)
img[mask] = np.nan
if remove_spatial_mean:
img -= get_spatial_mean(img)
# convert to 0 - 255
img = get_uint8_image(img, vmin, vmax, pmin, pmax)
# create Nansat with one band only
nout = Nansat.from_domain(n, img, parameters={'name': bandName})
nout.set_metadata(n.get_metadata())
# improve geolocation accuracy
if len(nout.vrt.dataset.GetGCPs()) > 0:
nout.reproject_gcps()
nout.vrt.tps = True
return nout
def test_get_metadata_key(self):
n1 = Nansat(self.test_file_stere, log_level=40, mapper=self.default_mapper)
m = n1.get_metadata('filename')
self.assertEqual(type(m), str)
def test_get_metadata_wrong_key(self):
n1 = Nansat(self.test_file_stere, log_level=40, mapper=self.default_mapper)
with self.assertRaises(ValueError):
n1.get_metadata('some_crap')
def test_get_metadata_key(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata('fileName')
self.assertEqual(type(m), str)
def test_set_metadata_bandid(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
n1.set_metadata('newKey', 'newVal', 1)
m = n1.get_metadata('newKey', 1)
self.assertEqual(m, 'newVal')
def test_get_metadata_wrong_key(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata('some_crap')
self.assertTrue(m is None)
def test_get_metadata(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata()
self.assertEqual(type(m), dict)
self.assertTrue('fileName' in m)
def test_get_metadata_band_id(self):
n1 = Nansat(self.test_file_stere, log_level=40, mapper=self.default_mapper)
m = n1.get_metadata(band_id=1)
self.assertEqual(type(m), dict)
self.assertTrue('name' in m)
def test_set_metadata_band_id(self):
n1 = Nansat(self.test_file_stere, log_level=40, mapper=self.default_mapper)
n1.set_metadata('newKey', 'newVal', band_id=1)
m = n1.get_metadata('newKey', 1)
self.assertEqual(m, 'newVal')
def test_get_metadata_key(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata('fileName')
self.assertEqual(type(m), str)
def _get_normalized_attributes(self, dataset_info, *args, **kwargs):
"""Gets dataset attributes using nansat"""
normalized_attributes = {}
n_points = int(kwargs.get('n_points', 10))
nansat_options = kwargs.get('nansat_options', {})
url_scheme = urlparse(dataset_info).scheme
if not 'http' in url_scheme and not 'ftp' in url_scheme:
normalized_attributes['geospaas_service_name'] = FILE_SERVICE_NAME
normalized_attributes['geospaas_service'] = LOCAL_FILE_SERVICE
elif 'http' in url_scheme and not 'ftp' in url_scheme:
normalized_attributes['geospaas_service_name'] = DAP_SERVICE_NAME
normalized_attributes['geospaas_service'] = OPENDAP_SERVICE
elif 'ftp' in url_scheme:
raise ValueError(
f"Can't ingest '{dataset_info}': nansat can't open remote ftp files"
)
# Open file with Nansat
nansat_object = Nansat(nansat_filename(dataset_info),
log_level=self.LOGGER.getEffectiveLevel(),
**nansat_options)
# get metadata from Nansat and get objects from vocabularies
n_metadata = nansat_object.get_metadata()
# set compulsory metadata (source)
normalized_attributes['entry_title'] = n_metadata.get(
'entry_title', 'NONE')
normalized_attributes['summary'] = n_metadata.get('summary', 'NONE')
normalized_attributes['time_coverage_start'] = dateutil.parser.parse(
n_metadata['time_coverage_start']).replace(tzinfo=tzutc())
normalized_attributes['time_coverage_end'] = dateutil.parser.parse(
n_metadata['time_coverage_end']).replace(tzinfo=tzutc())
normalized_attributes['platform'] = json.loads(n_metadata['platform'])
normalized_attributes['instrument'] = json.loads(
n_metadata['instrument'])
normalized_attributes['specs'] = n_metadata.get('specs', '')
normalized_attributes['entry_id'] = n_metadata.get(
'entry_id', 'NERSC_' + str(uuid.uuid4()))
# set optional ForeignKey metadata from Nansat or from defaults
normalized_attributes['gcmd_location'] = n_metadata.get(
'gcmd_location', pti.get_gcmd_location('SEA SURFACE'))
normalized_attributes['provider'] = pti.get_gcmd_provider(
n_metadata.get('provider', 'NERSC'))
normalized_attributes['iso_topic_category'] = n_metadata.get(
'ISO_topic_category', pti.get_iso19115_topic_category('Oceans'))
# Find coverage to set number of points in the geolocation
if nansat_object.vrt.dataset.GetGCPs():
nansat_object.reproject_gcps()
normalized_attributes['location_geometry'] = GEOSGeometry(
nansat_object.get_border_wkt(n_points=n_points), srid=4326)
json_dumped_dataset_parameters = n_metadata.get(
'dataset_parameters', None)
if json_dumped_dataset_parameters:
json_loads_result = json.loads(json_dumped_dataset_parameters)
if isinstance(json_loads_result, list):
normalized_attributes['dataset_parameters'] = [
get_cf_or_wkv_standard_name(dataset_param)
for dataset_param in json_loads_result
]
else:
raise TypeError(
f"Can't ingest '{dataset_info}': the 'dataset_parameters' section of the "
"metadata returned by nansat is not a JSON list")
else:
normalized_attributes['dataset_parameters'] = []
return normalized_attributes
def test_get_metadata_bandid(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata(bandID=1)
self.assertEqual(type(m), dict)
self.assertTrue('name' in m)
if S == 98 and F == 10:
CC = 98 + 10 # color code for iceberg
CT = ft.GetFieldAsInteger('CT')
else:
CC = 2 # color code for bergy water
if CT == 1:
CC = 1 # color code for open water
if CT == 98:
CC = 0 # color code for ice free
ft.SetField('classID', int(CC))
oLayer.SetFeature(ft)
sigma0 = gdal.Open(ifile).ReadAsArray()
fp_classID = gdal.Open(ifile)
gdal.RasterizeLayer(fp_classID, [1], oLayer, options=["ATTRIBUTE=classID"])
classID = fp_classID.ReadAsArray()
classID[classID == sigma0] = 255
classID[np.isnan(classID)] = 99
nansatObjSigma0 = Nansat(ifile)
nansatObjIceChart = Nansat.from_domain(array=classID.astype(np.uint8),
domain=nansatObjSigma0)
nansatObjIceChart.set_metadata(nansatObjSigma0.get_metadata())
nansatObjIceChart.set_metadata('entry_title',
'REPROJECTED_%s_ICE_CHART' % cfg.sourceType)
nansatObjIceChart = add_colortable(nansatObjIceChart, cfg.sourceType)
nansatObjIceChart.export(ofile, bands=[1], driver='GTiff')
if cfg.quicklook:
rgb = np.zeros((classID.shape[0], classID.shape[1], 3), 'uint8')
for k in colorDict[cfg.sourceType].keys():
rgb[classID == k, :] = colorDict[cfg.sourceType][k]
plt.imsave(ofile.replace('.tif', '.png'), rgb)
def save_ice_map(inp_filename,
raw_filename,
classifier_filename,
threads,
source,
quicklook=False,
force=False):
""" Load texture features, apply classifier and save ice map """
# get filenames
out_filename = inp_filename.replace('_texture_features.npz',
'_classified_%s.tif' % source)
if os.path.exists(out_filename) and not force:
print('Processed file %s already exists.' % out_filename)
return out_filename
# import classifier
plk = pickle.load(open(classifier_filename, "rb"))
if type(plk) == list:
scaler, clf = plk
else:
class dummy_class(object):
def transform(self, x):
return (x)
scaler = dummy_class()
clf = plk
clf.n_jobs = threads
# get texture features
npz = np.load(inp_filename)
features = np.vstack([
npz['textureFeatures'].item()['HH'],
npz['textureFeatures'].item()['HV'],
npz['incidenceAngle'][np.newaxis, :, :]
])
imgSize = features.shape[1:]
features = features.reshape((27, np.prod(imgSize))).T
gpi = np.isfinite(features.sum(axis=1))
result = clf.predict(scaler.transform(features[gpi, :]))
classImage = np.ones(np.prod(imgSize)) * 255
classImage[gpi] = result
classImage = classImage.reshape(imgSize)
img_shape = classImage.shape
# open original file to get geometry
raw_nansat = Nansat(raw_filename)
# crop and resize original Nansat to match the ice map
raw_shape = raw_nansat.shape()
crop = [rshape % ishape for (rshape, ishape) in zip(raw_shape, img_shape)]
raw_nansat.crop(0, 0, raw_shape[1] - crop[1], raw_shape[0] - crop[0])
raw_nansat.resize(height=img_shape[0])
raw_nansat.reproject_gcps()
# create new Nansat object and add ice map
ice_map = Nansat.from_domain(domain=raw_nansat,
array=classImage.astype(np.uint8))
ice_map.set_metadata(raw_nansat.get_metadata())
ice_map.set_metadata('entry_title', 'S1_SAR_ICE_MAP')
ice_map = add_colortable(ice_map)
ice_map.export(out_filename, bands=[1], driver='GTiff')
if quicklook:
rgb = colorcode_array(classImage)
plt.imsave(out_filename.replace('.tif', '.png'), rgb)
return out_filename
def test_get_metadata_wrong_key(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata('some_crap')
self.assertTrue(m is None)
def test_get_metadata(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
m = n1.get_metadata()
self.assertEqual(type(m), dict)
self.assertTrue('fileName' in m)
def test_get_metadata_wrong_key(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
with self.assertRaises(OptionError):
n1.get_metadata('some_crap')
def test_get_metadata_wrong_key(self):
n1 = Nansat(self.test_file_stere, logLevel=40)
with self.assertRaises(OptionError):
n1.get_metadata('some_crap')
# Open an input file, specify which Mapper to use, set logging level
n = Nansat(iFileName, mapperName='generic', logLevel=10)
# list bands and georeference of the object
print 'Raw Nansat:', n, '\n'
# get dictionary with metadata from all bands
print 'Bands:', n.bands(), '\n'
# get time of the image aquisition
print 'Time:', n.get_time()[0], '\n'
# set GlobalMetadata
n.set_metadata(key='GlobalKey', value='GlobalVal')
# get Global Metadata
print 'Global Metadata:', n.get_metadata(), '\n'
# set BandMetadata to the 1st band
n.set_metadata(key='BandKey', value='BandVal', bandID=1)
# get 1st Band Metadata
print '1st Band Metadata:', n.get_metadata(bandID=1), '\n'
# add a band from file (copy the 2nd band to the end (4th band)
n.add_band(fileName=n.fileName, bandID=2)
# add a band from numpy array (copy the 1st band to the end (5th band))
n.add_band(array=n[1], parameters={'name': 'Name1',
'info': 'copy from the 1st band array'})
# print band list
n.list_bands()
# get GDAL raster band (2nd band)
band = n.get_GDALRasterBand(bandID=2)
