def test_apply_dimension_spatiotemporal(self):
input = Pyramid({0: self.tiled_raster_rdd})
imagecollection = GeopysparkDataCube(
pyramid=input,
metadata=GeopysparkCubeMetadata({
"cube:dimensions": {
# TODO: also specify other dimensions?
"bands": {
"type": "bands",
"values": ["2"]
}
},
"summaries": {
"eo:bands": [{
"name": "2",
"common_name": "blue",
"wavelength_nm": 496.6,
"res_m": 10,
"scale": 0.0001,
"offset": 0,
"type": "int16",
"unit": "1"
}]
}
}))
udf_code = """
def rct_savitzky_golay(udf_data:UdfData):
from scipy.signal import savgol_filter
print(udf_data.get_datacube_list())
return udf_data
"""
result = imagecollection.apply_tiles_spatiotemporal(udf_code)
local_tiles = result.pyramid.levels[0].to_numpy_rdd().collect()
print(local_tiles)
self.assertEquals(len(TestMultipleDates.layer), len(local_tiles))
ref_dict = {
e[0]: e[1]
for e in imagecollection.pyramid.levels[0].convert_data_type(
CellType.FLOAT64).to_numpy_rdd().collect()
}
result_dict = {e[0]: e[1] for e in local_tiles}
for k, v in ref_dict.items():
tile = result_dict[k]
assert_array_almost_equal(np.squeeze(v.cells),
np.squeeze(tile.cells),
decimal=2)
def test_apply_spatiotemporal(udf_code):
udf_code = textwrap.dedent(udf_code)
input = Pyramid({0: TestMultipleDates.tiled_raster_rdd})
imagecollection = GeopysparkDataCube(
pyramid=input,
metadata=GeopysparkCubeMetadata({
"cube:dimensions": {
# TODO: also specify other dimensions?
"bands": {
"type": "bands",
"values": ["2"]
}
},
"summaries": {
"eo:bands": [{
"name": "2",
"common_name": "blue",
"wavelength_nm": 496.6,
"res_m": 10,
"scale": 0.0001,
"offset": 0,
"type": "int16",
"unit": "1"
}]
}
}))
result = imagecollection.apply_tiles_spatiotemporal(udf_code)
stitched = result.pyramid.levels[0].to_spatial_layer().stitch()
print(stitched)
assert stitched.cells[0][0][0] == 2
assert stitched.cells[0][0][5] == 6
assert stitched.cells[0][5][6] == 4