def hyper_min_median_max(udf_data: UdfData):
"""Compute the min, median and max of the time dimension of a hyper cube
Hypercubes with time dimensions are required. The min, median and max reduction of th time axis will be applied
to all hypercube dimensions.
Args:
udf_data (UdfData): The UDF data object that contains raster and vector tiles as well as hypercubes
and structured data.
Returns:
This function will not return anything, the UdfData object "udf_data" must be used to store the resulting
data.
"""
# Iterate over each tile
cube_list = []
for cube in udf_data.get_datacube_list():
min = cube.array.min(dim="t")
median = cube.array.median(dim="t")
max = cube.array.max(dim="t")
min.name = cube.id + "_min"
median.name = cube.id + "_median"
max.name = cube.id + "_max"
cube_list.append(XarrayDataCube(array=min))
cube_list.append(XarrayDataCube(array=median))
cube_list.append(XarrayDataCube(array=max))
udf_data.set_datacube_list(cube_list)
def rct_stats(udf_data: UdfData):
"""Compute univariate statistics for each hypercube
Args:
udf_data (UdfData): The UDF data object that contains raster and vector tiles
Returns:
This function will not return anything, the UdfData object "udf_data" must be used to store the resulting
data.
"""
# The dictionary that stores the statistical data
stats = {}
# Iterate over each raster collection cube and compute statistical values
for cube in udf_data.get_datacube_list():
# make sure to cast the values to floats, otherwise they are not serializable
stats[cube.id] = dict(sum=float(cube.array.sum()),
mean=float(cube.array.mean()),
min=float(cube.array.min()),
max=float(cube.array.max()))
# Create the structured data object
sd = StructuredData(
data=stats,
type="dict",
description=
"Statistical data sum, min, max and mean for each raster collection cube as dict",
)
# Remove all collections and set the StructuredData list
udf_data.set_datacube_list(None)
udf_data.set_structured_data_list([
sd,
])
def hyper_map_fabs(udf_data: UdfData):
"""Compute the absolute values of each hyper cube in the provided data
Args:
udf_data (UdfData): The UDF data object that contains raster and vector tiles as well as hypercubes
and structured data.
Returns:
This function will not return anything, the UdfData object "udf_data" must be used to store the resulting
data.
"""
# Iterate over each tile
cube_list = []
for cube in udf_data.get_datacube_list():
result = numpy.fabs(cube.array)
result.name = cube.id + "_fabs"
cube_list.append(XarrayDataCube(array=result))
udf_data.set_datacube_list(cube_list)
def hyper_ndvi(udf_data: UdfData):
"""Compute the NDVI based on RED and NIR hypercubes
Hypercubes with ids "red" and "nir" are required. The NDVI computation will be applied
to all hypercube dimensions.
Args:
udf_data (UdfData): The UDF data object that contains raster and vector tiles as well as hypercubes
and structured data.
Returns:
This function will not return anything, the UdfData object "udf_data" must be used to store the resulting
data.
"""
red = None
nir = None
# Iterate over each tile
for cube in udf_data.get_datacube_list():
if "red" in cube.id.lower():
red = cube
if "nir" in cube.id.lower():
nir = cube
if red is None:
raise Exception("Red hypercube is missing in input")
if nir is None:
raise Exception("Nir hypercube is missing in input")
ndvi = (nir.array - red.array) / (nir.array + red.array)
ndvi.name = "NDVI"
hc = XarrayDataCube(array=ndvi)
udf_data.set_datacube_list([
hc,
])