def test_n_pixels(self):
"""
Test that the correct number of random pixels are returned.
The default return value is 100 pixels.
"""
dims = (100, 100)
idx = ut.random_pixel_locations(dims)
n = idx[0].shape[0]
self.assertTrue(n == 100)
def test_lon_array(self):
"""
Test that the interpolated longitude array has sensible
values.
:notes:
The default image that is used is a 1000 by 1000. This might
be too small for a recursive depth of 7 (default within the
NBAR framework). As such a depth of 3 is used and produced
correct results. If a larger image is used, then the depth
level should probably be increased.
"""
acq = acquisitions(LS5_SCENE1).get_acquisitions()[0]
geobox = acq.gridded_geo_box()
fid = create_lon_lat_grids(acq, depth=5)
dataset_name = ppjoin(GroupName.LON_LAT_GROUP.value,
DatasetName.LON.value)
lon = fid[dataset_name][:]
ids = ut.random_pixel_locations(lon.shape)
# We'll transform (reproject) to WGS84
sr = osr.SpatialReference()
sr.SetFromUserInput(CRS)
# Get a list of x reprojected co-ordinates
reprj = []
for i in range(ids[0].shape[0]):
# Get pixel (x,y)
xy = (ids[1][i], ids[0][i])
# Convert pixel to map
mapXY = geobox.convert_coordinates(xy, centre=True)
# Transform map to another crs
x, _ = geobox.transform_coordinates(mapXY, to_crs=sr)
reprj.append(x)
reprj = numpy.array(reprj)
lon_values = lon[ids]
# A decimal degree co-ordinate should be correct up to the 6th dp
self.assertIsNone(npt.assert_almost_equal(lon_values, reprj,
decimal=5))