Hi all,
When renaming some single band datacubes, I think I may have run into a bug, as I keep getting "MNDWI" back as band name, resulting is errors when merging:
dc_merged \
.filter_bands(["MNDWI"]) \
.apply(lambda _: processes.int(1)) \
.rename_labels("bands", target=["water"], source=["MNDWI"])
I also tried the band method of Datacube, getting the same result.
When I do the same thing from the source data, using the same code, I am getting the correct result. Maybe something with UDFs?
Process Graph (warning, very long due to udfs):
{'loadcollection1': {'process_id': 'load_collection',
'arguments': {'bands': ['B03', 'B08', 'B11', 'CLM', 'CLP'],
'id': 'SENTINEL2_L1C_SENTINELHUB',
'spatial_extent': {'west': 589947.873967385,
'east': 596088.3303869166,
'south': 5489655.1321837725,
'north': 5494855.214093619,
'crs': 'EPSG:32633'},
'temporal_extent': ['2021-05-01', '2021-08-01']}},
'renamelabels1': {'process_id': 'rename_labels',
'arguments': {'data': {'from_node': 'loadcollection1'},
'dimension': 'bands',
'source': ['B03', 'B08', 'B11', 'CLM', 'CLP'],
'target': ['green', 'nir', 'swir', 'cloudmask', 'cloudp']}},
'chunkpolygon1': {'process_id': 'chunk_polygon',
'arguments': {'chunks': {'type': 'MultiPolygon',
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'context': {'cutoff_percentile': 35,
'quality_band': 'cloudp',
'score_percentile': 75},
'data': {'from_node': 'renamelabels1'},
'process': {'process_graph': {'runudf1': {'process_id': 'run_udf',
'arguments': {'data': {'from_parameter': 'data'},
'runtime': 'Python',
'udf': 'from typing import Optional\n\nfrom openeo.udf import XarrayDataCube\nimport numpy as np\nfrom xarray import DataArray\n\ndef apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:\n """\n Filter the datacube based on the quality band and the quantile of the band values.\n \n This function assumes a DataCube with Dimension \'t\' as an input.\n \n Args:\n cube (XarrayDataCube): datacube to apply the udf to.\n context (dict): key-value arguments.\n """\n\n # Load kwargs from context\n cutoff_percentile: Optional[float] = context.get("cutoff_percentile")\n if not cutoff_percentile:\n cutoff_percentile = 35\n cutoff_percentile = cutoff_percentile / 100.\n\n score_percentile: Optional[float] = context.get("score_percentile")\n if not score_percentile:\n score_percentile = 75.\n score_percentile = score_percentile / 100.\n\n quality_band: Optional[str] = context.get("quality_band")\n if not quality_band:\n quality_band = "cloudp"\n\n array: DataArray = cube.get_array()\n # Need to get band index, as bands are not dims here\n index = np.where(array["bands"].values == quality_band)[0][0]\n score: DataArray = array.isel(bands=index).quantile([score_percentile], dim=["x", "y"])\n filtered: DataArray = array.sel(t=score.where(score / np.max(score) < cutoff_percentile, drop=True).t)\n print(filtered.shape)\n return XarrayDataCube(\n array=filtered\n )\n'},
'result': True}}}}},
'chunkpolygon2': {'process_id': 'chunk_polygon',
'arguments': {'chunks': {'type': 'MultiPolygon',
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'context': {'minimum_filled_fraction': 'from typing import Optional\n\nfrom openeo.udf import XarrayDataCube\nimport numpy as np\nfrom xarray import DataArray\n\ndef apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:\n """\n Filter the datacube based on the quality band and the quantile of the band values.\n \n This function assumes a DataCube with Dimension \'t\' as an input.\n \n Args:\n cube (XarrayDataCube): datacube to apply the udf to.\n context (dict): key-value arguments.\n """\n\n # Load kwargs from context\n cutoff_percentile: Optional[float] = context.get("cutoff_percentile")\n if not cutoff_percentile:\n cutoff_percentile = 35\n cutoff_percentile = cutoff_percentile / 100.\n\n score_percentile: Optional[float] = context.get("score_percentile")\n if not score_percentile:\n score_percentile = 75.\n score_percentile = score_percentile / 100.\n\n quality_band: Optional[str] = context.get("quality_band")\n if not quality_band:\n quality_band = "cloudp"\n\n array: DataArray = cube.get_array()\n # Need to get band index, as bands are not dims here\n index = np.where(array["bands"].values == quality_band)[0][0]\n score: DataArray = array.isel(bands=index).quantile([score_percentile], dim=["x", "y"])\n filtered: DataArray = array.sel(t=score.where(score / np.max(score) < cutoff_percentile, drop=True).t)\n print(filtered.shape)\n return XarrayDataCube(\n array=filtered\n )\n',
'quality_check_bands': ['green', 'nir', 'swir']},
'data': {'from_node': 'chunkpolygon1'},
'process': {'process_graph': {'runudf2': {'process_id': 'run_udf',
'arguments': {'data': {'from_parameter': 'data'},
'runtime': 'Python',
'udf': 'from typing import List, Optional\n\nfrom openeo.udf import XarrayDataCube\nimport numpy as np\nimport xarray as xr\n\ndef apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:\n """\n Preprocess reservoir polygons to fill the polygon with nodata values.\n \n This function assumes a DataCube with Dimension \'t\' as an input.\n \n Args:\n cube (XarrayDataCube): datacube to apply the udf to.\n context (dict): key-value arguments.\n """\n\n # Load kwargs from context\n minimum_filled_fraction: Optional[float] = context.get("minimum_filled_fraction")\n if not minimum_filled_fraction:\n minimum_filled_fraction = 0.35\n \n quality_check_bands: Optional[List[str]] = context.get("quality_check_bands")\n if not quality_check_bands:\n quality_check_bands = ["green", "nir", "swir"]\n\n masked_value: Optional[int] = context.get("masked_value")\n if not masked_value:\n masked_value = -999999\n \n missing_value: Optional[int] = context.get("missing_value")\n if not missing_value:\n missing_value = -888888\n\n array: xr.DataArray = cube.get_array()\n # Need to get band index, as bands are not dims here\n indexes_used: List[int] = [bandname in quality_check_bands for bandname in array["bands"].values]\n check = array.isel(bands=indexes_used)\n masked = xr.where(check.sel(bands=quality_check_bands[0], drop=True) == masked_value, 1, 0)\n fraction_masked = masked.mean(dim=["x", "y"])\n print(fraction_masked)\n mean = 1 - np.isnan(check).mean(dim=["x", "y"])\n # Because we need all bands, use the most scarcely populated band as a filter criterion\n mean = mean.min(dim="bands")\n # Filter where the are too few observations.\n filtered: xr.DataArray = array.sel(t=mean.where(mean / (1 - fraction_masked) > minimum_filled_fraction, drop=True).t)\n # replace missing observations using missing value\n return XarrayDataCube(\n array=filtered\n )'},
'result': True}}}}},
'reducedimension1': {'process_id': 'reduce_dimension',
'arguments': {'data': {'from_node': 'chunkpolygon2'},
'dimension': 'bands',
'reducer': {'process_graph': {'arrayelement1': {'process_id': 'array_element',
'arguments': {'data': {'from_parameter': 'data'}, 'index': 0}},
'arrayelement2': {'process_id': 'array_element',
'arguments': {'data': {'from_parameter': 'data'}, 'index': 2}},
'subtract1': {'process_id': 'subtract',
'arguments': {'x': {'from_node': 'arrayelement1'},
'y': {'from_node': 'arrayelement2'}}},
'add1': {'process_id': 'add',
'arguments': {'x': {'from_node': 'arrayelement1'},
'y': {'from_node': 'arrayelement2'}}},
'divide1': {'process_id': 'divide',
'arguments': {'x': {'from_node': 'subtract1'},
'y': {'from_node': 'add1'}},
'result': True}}}}},
'adddimension1': {'process_id': 'add_dimension',
'arguments': {'data': {'from_node': 'reducedimension1'},
'label': 'MNDWI',
'name': 'bands',
'type': 'bands'}},
'adddimension2': {'process_id': 'add_dimension',
'arguments': {'data': {'from_node': 'adddimension1'},
'label': 'MNDWI',
'name': 'bands',
'type': 'bands'}},
'loadcollection2': {'process_id': 'load_collection',
'arguments': {'bands': ['occurrence'],
'id': 'GLOBAL_SURFACE_WATER',
'spatial_extent': {'west': 589947.873967385,
'east': 596088.3303869166,
'south': 5489655.1321837725,
'north': 5494855.214093619,
'crs': 'EPSG:32633'},
'temporal_extent': None}},
'filtertemporal1': {'process_id': 'filter_temporal',
'arguments': {'data': {'from_node': 'loadcollection2'},
'extent': ['2019-12-31', '2020-01-02']}},
'dropdimension1': {'process_id': 'drop_dimension',
'arguments': {'data': {'from_node': 'filtertemporal1'}, 'name': 't'}},
'resamplecubespatial1': {'process_id': 'resample_cube_spatial',
'arguments': {'data': {'from_node': 'dropdimension1'},
'method': 'nearest',
'target': {'from_node': 'chunkpolygon2'}}},
'dropdimension2': {'process_id': 'drop_dimension',
'arguments': {'data': {'from_node': 'resamplecubespatial1'},
'name': 'bands'}},
'apply1': {'process_id': 'apply',
'arguments': {'data': {'from_node': 'dropdimension2'},
'process': {'process_graph': {'multiply1': {'process_id': 'multiply',
'arguments': {'x': {'from_parameter': 'x'}, 'y': 1.0},
'result': True}}}}},
'adddimension3': {'process_id': 'add_dimension',
'arguments': {'data': {'from_node': 'apply1'},
'label': 'wo',
'name': 'bands',
'type': 'bands'}},
'mergecubes1': {'process_id': 'merge_cubes',
'arguments': {'cube1': {'from_node': 'adddimension2'},
'cube2': {'from_node': 'adddimension3'}}},
'filterbands1': {'process_id': 'filter_bands',
'arguments': {'bands': ['MNDWI'], 'data': {'from_node': 'mergecubes1'}}},
'apply2': {'process_id': 'apply',
'arguments': {'data': {'from_node': 'filterbands1'},
'process': {'process_graph': {'int1': {'process_id': 'int',
'arguments': {'x': 1},
'result': True}}}}},
'renamelabels2': {'process_id': 'rename_labels',
'arguments': {'data': {'from_node': 'apply2'},
'dimension': 'bands',
'source': ['MNDWI'],
'target': ['water']},
'result': True}}
