def test_from_sentinel_2(self):
dataset = create_s2plus_dataset()
gm = GridMapping.from_dataset(dataset)
# Should pick the projected one which is regular
self.assertEqual("Projected CRS", gm.crs.type_name)
self.assertEqual(True, gm.is_regular)
gm = GridMapping.from_dataset(dataset, prefer_is_regular=True)
# Should pick the projected one which is regular
self.assertEqual("Projected CRS", gm.crs.type_name)
self.assertEqual(True, gm.is_regular)
gm = GridMapping.from_dataset(dataset, prefer_is_regular=False)
# Should pick the geographic one which is irregular
self.assertEqual('Geographic 2D CRS', gm.crs.type_name)
self.assertEqual(False, gm.is_regular)
gm = GridMapping.from_dataset(dataset, prefer_crs=GEO_CRS)
# Should pick the geographic one which is irregular
self.assertEqual('Geographic 2D CRS', gm.crs.type_name)
self.assertEqual(False, gm.is_regular)
gm = GridMapping.from_dataset(dataset,
prefer_crs=GEO_CRS,
prefer_is_regular=True)
# Should pick the geographic one which is irregular
self.assertEqual('Geographic 2D CRS', gm.crs.type_name)
self.assertEqual(False, gm.is_regular)
def test_rectify_dataset(self):
source_ds = create_s2plus_dataset()
expected_data = np.array([
[nan, nan, nan, nan, nan, nan, nan, nan, nan],
[
nan, 0.019001, 0.019001, 0.008999, 0.012001, 0.012001,
0.022999, nan, nan
],
[
nan, 0.021, 0.021, 0.009998, 0.009998, 0.008999, 0.022999, nan,
nan
],
[
nan, 0.022999, 0.022999, 0.007999, 0.007999, 0.008999,
0.023998, nan, nan
],
[nan, 0.022999, 0.022999, 0.007, 0.007, 0.009998, 0.021, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan, nan]
])
source_gm = GridMapping.from_dataset(source_ds, prefer_crs=CRS_WGS84)
target_ds = rectify_dataset(source_ds,
source_gm=source_gm,
tile_size=None)
self.assertEqual(None, target_ds.rrs_665.chunks)
print(repr(target_ds.rrs_665.values))
np.testing.assert_almost_equal(target_ds.rrs_665.values,
expected_data,
decimal=3)
target_ds = rectify_dataset(source_ds,
source_gm=source_gm,
tile_size=5)
self.assertEqual(((5, 1), (5, 4)), target_ds.rrs_665.chunks)
np.testing.assert_almost_equal(target_ds.rrs_665.values,
expected_data,
decimal=3)
target_ds = rectify_dataset(source_ds,
source_gm=source_gm,
tile_size=None,
is_j_axis_up=True)
self.assertEqual(None, target_ds.rrs_665.chunks)
np.testing.assert_almost_equal(target_ds.rrs_665.values,
expected_data[::-1],
decimal=3)
target_ds = rectify_dataset(source_ds,
source_gm=source_gm,
tile_size=5,
is_j_axis_up=True)
self.assertEqual(((5, 1), (5, 4)), target_ds.rrs_665.chunks)
np.testing.assert_almost_equal(target_ds.rrs_665.values,
expected_data[::-1],
decimal=3)
def test_transform_s2(self):
dataset = create_s2plus_dataset()
gm = GridMapping.from_dataset(dataset, prefer_is_regular=True)
# Assert we've picked the projected one which is regular
self.assertEqual("Projected CRS", gm.crs.type_name)
self.assertEqual(True, gm.is_regular)
gm_t = gm.transform(CRS_CRS84)
self.assertEqual(CRS_CRS84, gm_t.crs)
gm_t = gm.transform(CRS_WGS84)
self.assertEqual(CRS_WGS84, gm_t.crs)
def test_reproject_crs_to_wgs84_s2plus(self):
dst_width = 6
dst_height = 4
dataset = create_s2plus_dataset()
proj_params = dataset.transverse_mercator.attrs
wkt = get_projection_wkt(
"Some S2+ Tile",
"Transverse_Mercator",
latitude_of_origin=proj_params["latitude_of_projection_origin"],
central_meridian=proj_params["longitude_of_central_meridian"],
scale_factor=proj_params["scale_factor_at_central_meridian"],
false_easting=proj_params["false_easting"],
false_northing=proj_params["false_northing"])
proj_dataset = reproject_crs_to_wgs84(dataset,
src_projection=wkt,
dst_region=(
0.2727627754211426,
51.3291015625,
0.273336261510849,
51.329463958740234,
),
dst_size=(dst_width, dst_height))
self.assertIsNotNone(proj_dataset)
self.assertEqual(dict(lon=dst_width, lat=dst_height, bnds=2),
proj_dataset.sizes)
self.assertIn('lon', proj_dataset)
self.assertEqual(proj_dataset.lon.shape, (dst_width, ))
self.assertIn('lat', proj_dataset)
self.assertEqual(proj_dataset.lat.shape, (dst_height, ))
self.assertIn('lon_bnds', proj_dataset)
self.assertEqual(proj_dataset.lon_bnds.shape, (dst_width, 2))
self.assertIn('lat_bnds', proj_dataset)
self.assertEqual(proj_dataset.lat_bnds.shape, (dst_height, 2))
expected_rrs_665 = np.array(
[[0.025002, 0.019001, 0.019001, 0.008999, 0.012001, 0.012001],
[0.028, 0.021, 0.021, 0.009998, 0.008999, 0.008999],
[0.036999, 0.022999, 0.022999, 0.007, 0.009998, 0.009998],
[0.033001, 0.018002, 0.018002, 0.007999, 0.008999, 0.008999]],
dtype=np.float32)
self.assertIn('rrs_665', proj_dataset)
self.assertEqual((dst_height, dst_width), proj_dataset.rrs_665.shape)
self.assertEqual(np.float32, proj_dataset.rrs_665.dtype)
assert_array_almost_equal(expected_rrs_665, proj_dataset.rrs_665)
def test_transform_no_op(self):
dataset = create_s2plus_dataset()
gm = GridMapping.from_dataset(dataset, prefer_is_regular=True)
# Assert we've picked the projected one which is regular
self.assertEqual("Projected CRS", gm.crs.type_name)
self.assertEqual(True, gm.is_regular)
gm_t = gm.transform(gm.crs)
self.assertIs(gm, gm_t)
# Almost no op
gm = GridMapping.regular(size=(3, 3), xy_min=(10, 53), xy_res=0.1, crs=CRS_CRS84)
gm_t = gm.transform(crs=gm.crs, xy_var_names=('x', 'y'))
self.assertEqual(('x', 'y'), gm_t.xy_var_names)
def test_post_process(self):
ds1 = create_s2plus_dataset()
ds2 = self.processor.post_process(ds1)
self.assertIs(ds1, ds2)
def test_get_time_range(self):
ds = create_s2plus_dataset()
t1, t2 = self.processor.get_time_range(ds)
self.assertEqual(to_time_in_days_since_1970("2018-08-02T10:59:38.888000Z"), t1)
self.assertEqual(to_time_in_days_since_1970("2018-08-02T10:59:38.888000Z"), t2)
def setUp(self):
clean_up()
dataset = create_s2plus_dataset()
dataset.to_netcdf(INPUT_FILE)
dataset.close()