def test_export2thredds_longlat_list(self):
d = Domain("+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs",
"-te 27 70 31 72 -ts 200 200")
n = Nansat(domain=d)
n.add_band(np.ones(d.shape(), np.float32),
parameters={'name': 'L_469'})
n.set_metadata('time_coverage_start', '2016-01-19')
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_export2thredds_longlat.nc')
n.export2thredds(tmpfilename, ['L_469'])
ncI = netcdf_file(tmpfilename, 'r')
ncIVar = ncI.variables['L_469']
self.assertTrue(ncIVar.grid_mapping in ncI.variables.keys())
def test_export2thredds_longlat_list(self):
d = Domain("+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs",
"-te 27 70 31 72 -ts 200 200")
n = Nansat(domain=d)
n.add_band(np.ones(d.shape(), np.float32),
parameters={'name': 'L_469'})
n.set_metadata('time_coverage_start', '2016-01-19')
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_export2thredds_longlat.nc')
n.export2thredds(tmpfilename, ['L_469'])
ncI = netcdf_file(tmpfilename, 'r')
ncIVar = ncI.variables['L_469']
self.assertTrue(ncIVar.grid_mapping in ncI.variables.keys())
def test_export2thredds_longlat_dict(self):
d = Domain("+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs",
"-te 27 70 31 72 -ts 200 200")
n = Nansat.from_domain(d)
n.add_band(np.ones(d.shape(), np.float32),
parameters={'name': 'L_469'})
n.set_metadata('time_coverage_start', '2016-01-19')
tmpfilename = os.path.join(self.tmp_data_path,
'nansat_export2thredds_longlat.nc')
n.export2thredds(tmpfilename, {'L_469': {'type': '>i1'}})
ncI = Dataset(tmpfilename, 'r')
ncIVar = ncI.variables['L_469']
self.assertTrue(ncIVar.grid_mapping in ncI.variables.keys())
self.assertEqual(ncIVar[:].dtype, np.int8)
def test_export2thredds_longlat_dict(self):
d = Domain("+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs",
"-te 27 70 31 72 -ts 200 200")
n = Nansat.from_domain(d)
n.add_band(np.ones(d.shape(), np.float32),
parameters={'name': 'L_469'})
n.set_metadata('time_coverage_start', '2016-01-19')
tmpfilename = os.path.join(self.tmp_data_path,
'nansat_export2thredds_longlat.nc')
n.export2thredds(tmpfilename, {'L_469': {'type': '>i1'}})
ncI = Dataset(tmpfilename, 'r')
ncIVar = ncI.variables['L_469']
self.assertTrue(ncIVar.grid_mapping in ncI.variables.keys())
self.assertEqual(ncIVar[:].dtype, np.int8)
relation between pixel/line coordinates and geographical coordinates,
type of data projection (e.g. geographical or stereographic)
'''
# Create Domain object. It describes the desired grid of reprojected image:
# projection, resolution, size, etc. In this case it is geographic projection;
# -10 - 30 E, 50 - 70 W; 2000 x 2000 pixels
d = Domain("+proj=latlong +datum=WGS84 +ellps=WGS84 +no_defs",
"-te 25 70 35 72 -ts 2000 2000")
d = Domain(4326, "-te 25 70 35 72 -ts 2000 2000")
d.write_map(oFileName + '01_latlong_map.png')
print 'Latlong Domain:', d, '\n'
# get shape
print 'shape:', d.shape(), '\n'
# Generate two vectors with values of lat/lon for the border of domain
lonVec, latVec = d.get_border()
print 'lonVec :', lonVec, '\n'
print 'latVec :', latVec, '\n'
# Get upwards azimuth direction of domain.
bearing_center = d.upwards_azimuth_direction()
print 'bearing_center :', bearing_center, '\n'
# Create domain with stereographic projection
# -- Get corners of the image
lons, lats = d.get_corners()
meanLon = sum(lons, 0.0) / 4.
meanLat = sum(lats, 0.0) / 4.
def test_init_from_lonlat(self):
lat, lon = np.mgrid[-90:90:0.5, -180:180:0.5]
d = Domain(lon=lon, lat=lat)
self.assertEqual(type(d), Domain)
self.assertEqual(d.shape(), lat.shape)
def test_shape(self):
d = Domain(4326, "-te 25 70 35 72 -ts 500 500")
shape = d.shape()
self.assertEqual(shape, (500, 500))
def test_init_from_lonlat(self):
lat, lon = np.mgrid[-90:90:0.5, -180:180:0.5]
d = Domain(lon=lon, lat=lat)
self.assertEqual(type(d), Domain)
self.assertEqual(d.shape(), lat.shape)
def test_shape(self):
d = Domain(4326, "-te 25 70 35 72 -ts 500 500")
shape = d.shape()
self.assertEqual(shape, (500, 500))