def test_export_gcps_complex_to_netcdf(self):
''' Should export file with GCPs and write correct complex bands'''
n0 = Nansat(self.test_file_gcps, logLevel=40)
b0 = n0['L_469']
n1 = Nansat(domain=n0)
n1.add_band(b0.astype('complex64'), parameters={'name': 'L_469'})
tmpfilename = os.path.join(ntd.tmp_data_path,
'nansat_export_gcps_complex.nc')
n1.export(tmpfilename)
ncf = netcdf_file(tmpfilename)
self.assertTrue(os.path.exists(tmpfilename))
self.assertTrue('GCPX' in ncf.variables)
self.assertTrue('GCPY' in ncf.variables)
self.assertTrue('GCPPixel' in ncf.variables)
self.assertTrue('GCPLine' in ncf.variables)
n2 = Nansat(tmpfilename)
b2 = n2['L_469']
np.testing.assert_allclose(b0, b2)
lon0, lat0 = n0.get_geolocation_grids()
lon2, lat2 = n1.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon2)
np.testing.assert_allclose(lat0, lat2)
def test_export_gcps_complex_to_netcdf(self):
''' Should export file with GCPs and write correct complex bands'''
n0 = Nansat(self.test_file_gcps, logLevel=40)
b0 = n0['L_469']
n1 = Nansat(domain=n0)
n1.add_band(b0.astype('complex64'),
parameters={'name': 'L_469'})
tmpfilename = os.path.join(ntd.tmp_data_path, 'nansat_export_gcps_complex.nc')
n1.export(tmpfilename)
ncf = netcdf_file(tmpfilename)
self.assertTrue(os.path.exists(tmpfilename))
self.assertTrue('GCPX' in ncf.variables)
self.assertTrue('GCPY' in ncf.variables)
self.assertTrue('GCPPixel' in ncf.variables)
self.assertTrue('GCPLine' in ncf.variables)
n2 = Nansat(tmpfilename)
b2 = n2['L_469']
np.testing.assert_allclose(b0, b2)
lon0, lat0 = n0.get_geolocation_grids()
lon2, lat2 = n1.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon2)
np.testing.assert_allclose(lat0, lat2)
def test_geolocation_of_exportedNC_vs_original(self):
""" Lon/lat in original and exported file should coincide """
orig = Nansat(self.test_file_gcps, mapper=self.default_mapper)
orig.export(self.tmp_filename)
copy = Nansat(self.tmp_filename, mapper=self.default_mapper)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
def test_geolocation_of_exportedNC_vs_original(self):
""" Lon/lat in original and exported file should coincide """
orig = Nansat(self.test_file_gcps, mapper=self.default_mapper)
orig.export(self.tmp_filename)
copy = Nansat(self.tmp_filename, mapper=self.default_mapper)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
def geolocation_of_exportedNC_vs_original(self, file):
orig = Nansat(file)
testFile = 'test.nc'
orig.export(testFile)
copy = Nansat(testFile)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
os.unlink(ncfile)
def geolocation_of_exportedNC_vs_original(self, file):
orig = Nansat(file)
testFile = "test.nc"
orig.export(testFile)
copy = Nansat(testFile)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
os.unlink(ncfile)
def test_geolocation_of_exportedNC_vs_original(self):
''' Lon/lat in original and exported file should coincide '''
orig = Nansat(self.test_file_gcps)
tmpfilename = os.path.join(ntd.tmp_data_path, 'nansat_export_gcps.nc')
orig.export(tmpfilename)
copy = Nansat(tmpfilename)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
def test_geolocation_of_exportedNC_vs_original(self):
''' Lon/lat in original and exported file should coincide '''
orig = Nansat(self.test_file_gcps)
orig.export(self.tmpfilename)
copy = Nansat(self.tmpfilename)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
os.unlink(self.tmpfilename)
def test_geolocation_of_exportedNC_vs_original(self):
''' Lon/lat in original and exported file should coincide '''
orig = Nansat(self.test_file_gcps)
orig.export(self.tmpfilename)
copy = Nansat(self.tmpfilename)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
os.unlink(self.tmpfilename)
def test_geolocation_of_exportedNC_vs_original(self):
''' Lon/lat in original and exported file should coincide '''
orig = Nansat(self.test_file_gcps)
tmpfilename = os.path.join(ntd.tmp_data_path, 'nansat_export_gcps.nc')
orig.export(tmpfilename)
copy = Nansat(tmpfilename)
lon0, lat0 = orig.get_geolocation_grids()
lon1, lat1 = copy.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
def test_get_auto_ticks_vector(self):
n = Nansat(self.test_file_gcps)
lon, lat = n.get_geolocation_grids()
f = Figure(lon)
lonTicks = f._get_auto_ticks([28, 29, 30, 100], lon)
self.assertEqual(len(lonTicks), 3)
def test_get_tick_index_from_grid(self):
''' Should return indeces of pixel closest to ticks '''
n = Nansat(self.test_file_gcps)
lon, lat = n.get_geolocation_grids()
f = Figure(lon)
lonTicksIdx = f._get_tick_index_from_grid([28.5, 29], lon, 1, lon.shape[1])
latTicksIdx = f._get_tick_index_from_grid([71, 71.5], lat, lat.shape[0], 1)
n.logger.error(str(lonTicksIdx))
n.logger.error(str(latTicksIdx))
def test_get_auto_ticks_number(self):
n = Nansat(self.test_file_gcps)
lon, lat = n.get_geolocation_grids()
f = Figure(lon)
lonTicks = f._get_auto_ticks(5, lon)
latTicks = f._get_auto_ticks(5, lat)
self.assertEqual(len(lonTicks), 5)
n.logger.error(str(lonTicks))
n.logger.error(str(latTicks))
def test_export_gcps_to_netcdf(self):
''' Should export file with GCPs and write correct bands'''
n0 = Nansat(self.test_file_gcps, logLevel=40)
tmpfilename = os.path.join(ntd.tmp_data_path, 'nansat_export_gcps.nc')
n0.export(tmpfilename)
ncf = netcdf_file(tmpfilename)
self.assertTrue(os.path.exists(tmpfilename))
self.assertTrue('GCPX' in ncf.variables)
self.assertTrue('GCPY' in ncf.variables)
self.assertTrue('GCPPixel' in ncf.variables)
self.assertTrue('GCPLine' in ncf.variables)
n1 = Nansat(tmpfilename)
b0 = n0['L_469']
b1 = n1['L_469']
np.testing.assert_allclose(b0, b1)
lon0, lat0 = n0.get_geolocation_grids()
lon1, lat1 = n1.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
def test_add_latlon_grids_auto(self):
''' Should create figure with lon/lat gridlines spaced automatically '''
tmpfilename = os.path.join(ntd.tmp_data_path, 'figure_latlon_grids_auto.png')
n = Nansat(self.test_file_gcps)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(clim='hist', lonGrid=lon, latGrid=lat)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_export_gcps_to_netcdf(self):
''' Should export file with GCPs and write correct bands'''
n0 = Nansat(self.test_file_gcps, logLevel=40)
tmpfilename = os.path.join(ntd.tmp_data_path, 'nansat_export_gcps.nc')
n0.export(tmpfilename)
ncf = netcdf_file(tmpfilename)
self.assertTrue(os.path.exists(tmpfilename))
self.assertTrue('GCPX' in ncf.variables)
self.assertTrue('GCPY' in ncf.variables)
self.assertTrue('GCPPixel' in ncf.variables)
self.assertTrue('GCPLine' in ncf.variables)
n1 = Nansat(tmpfilename)
b0 = n0['L_469']
b1 = n1['L_469']
np.testing.assert_allclose(b0, b1)
lon0, lat0 = n0.get_geolocation_grids()
lon1, lat1 = n1.get_geolocation_grids()
np.testing.assert_allclose(lon0, lon1)
np.testing.assert_allclose(lat0, lat1)
def test_add_latlon_grids_number(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path,
'figure_latlon_grids_number.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
n.resize(3)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(cmax=100, lonGrid=lon, latGrid=lat, lonTicks=7, latTicks=7)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_add_latlon_grids_list(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path, 'figure_latlon_grids_list.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(clim='hist', lonGrid=lon,
latGrid=lat,
lonTicks=[28, 29, 30],
latTicks=[70.5, 71, 71.5, 73])
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_add_latlon_grids_list(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path, 'figure_latlon_grids_list.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(clim='hist', lonGrid=lon,
latGrid=lat,
lonTicks=[28, 29, 30],
latTicks=[70.5, 71, 71.5, 73])
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_add_latlon_grids_number(self):
''' Should create figure with lon/lat gridlines given manually '''
tmpfilename = os.path.join(self.tmp_data_path, 'figure_latlon_grids_number.png')
n = Nansat(self.test_file_gcps, mapper=self.default_mapper)
n.resize(3)
b = n[1]
lon, lat = n.get_geolocation_grids()
f = Figure(b)
f.process(cmax=100, lonGrid=lon,
latGrid=lat,
lonTicks=7,
latTicks=7)
f.save(tmpfilename)
self.assertEqual(type(f), Figure)
self.assertTrue(os.path.exists(tmpfilename))
def test_reproject_pure_geolocation(self):
n0 = Nansat(self.test_file_gcps)
b0 = n0[1]
lon0, lat0 = n0.get_geolocation_grids()
d1 = Domain.from_lonlat(lon=lon0, lat=lat0)
d2 = Domain.from_lonlat(lon=lon0, lat=lat0, add_gcps=False)
d3 = Domain(NSR().wkt, '-te 27 70 31 72 -ts 500 500')
n1 = Nansat.from_domain(d1, b0)
n2 = Nansat.from_domain(d2, b0)
n1.reproject(d3)
n2.reproject(d3)
b1 = n1[1]
b2 = n2[1]
self.assertTrue(np.allclose(b1, b2))
# get array with watermask (landmask) b
# it must be done after reprojection!
# 1. Get Nansat object with watermask
# 2. Get array from Nansat object. 0 - land, 1 - water
#wm = n.watermask(mod44path='/media/magDesk/media/SOLabNFS/store/auxdata/coastline/mod44w/')
wm = n.watermask(mod44path='/media/data/data/auxdata/coastline/mod44w/')
wmArray = wm[1]
n.write_figure(oFileName + '_proj.png', clim='hist', bands=[3], \
mask_array=wmArray, mask_lut={0: [204, 153, 25]}) # 5.
# write figure with lat/lon grid
# 1. Get lat/lon arrays from Nansat object (may take some time)
# 2. Make figure with lat/lon grids
lonGrid, latGrid = n.get_geolocation_grids()
n.write_figure(oFileName + '_latlon.png', bands=[1], \
latGrid=latGrid, lonGrid=lonGrid, \
latlonGridSpacing=10, latlonLabels=10, \
mask_array=wmArray, mask_lut={0: [0, 0, 0]})
# make KML file with image borders (to be opened in Googe Earth)
n.write_kml(kmlFileName=oFileName + '_preview.kml')
# make image with map of the file location
n.write_map(oFileName + '_map.png')
# Reprojected image into stereographic projection
# 1. Cancel previous reprojection
# 2. Get corners of the image
# 3. Create Domain with stereographic projection, corner coordinates and resolution 1000m
fig.save(oFileName + '03_title.png')
# Create a Figure object (fig)
fig = Figure(array)
# add logo to image to the lower left corner
# (make sure file is in the current folder)
fig.process(cmin=10, cmax=60, logoFileName='nansat_logo_s.png',
logoLocation=[10, -35], logoSize=[20, 20],
legend=True, LEGEND_HEIGHT=0.3)
# Save the figure
fig.save(oFileName + '04_logo.png')
# Create a Figure object (fig)
fig = Figure(array)
# Get lat/lon arrays from Nansat object (may take some time)
lonGrid, latGrid = n.get_geolocation_grids()
# Make figure with lat/lon grids
fig.process(cmin=10, cmax=60, latGrid=latGrid, lonGrid=lonGrid,
latlonGridSpacing=10, latlonLabels=10)
# save the fig
fig.save(oFileName + '05_latlon.png', )
# Create a Figure object (fig)
fig = Figure(array)
# Get Nansat object with watermask
wm = n.watermask()
# Get array from Nansat object. 0 - land, 1 - water
wmArray = wm[1]
# Compute min and max valuse from ratio
clim = fig.clim_from_histogram(ratio=1.0)
# Make figure with land overlay (gray) and apply brightness gamma correction