def rct_pytorch_ml(udf_data: UdfData):
"""Apply a pre-trained pytorch machine learn model on the first tile
The model must be a pytorch model that has expects the input data in the constructor
The prediction method must accept a torch.autograd.Variable as input.
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.
"""
tile = udf_data.raster_collection_tiles[0]
# This is the input data of the model.
input = torch.autograd.Variable(torch.Tensor(tile.data))
# Get the first model
mlm = udf_data.get_ml_model_list()[0]
m = mlm.get_model()
# Predict the data
pred = m(input)
# Create the new raster collection tile
rct = RasterCollectionTile(id=mlm.name,
extent=tile.extent,
data=numpy.array(pred.tolist()),
start_times=tile.start_times,
end_times=tile.end_times)
# Insert the new tiles as list of raster collection tiles in the input object. The new tiles will
# replace the original input tiles.
udf_data.set_raster_collection_tiles([
rct,
])
def rct_time_sum(udf_data: UdfData):
"""Reduce the time dimension for each tile and compute sum for each pixel
over time.
Each raster tile in the udf data object will be reduced by time. Sum is
computed for each pixel over time.
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 list of tiles that were created
tile_results = []
# Iterate over each tile
for tile in udf_data.raster_collection_tiles:
tile_sum = numpy.sum(tile.data, axis=0)
# We need to create a new 3D array with the correct shape for the computed aggregate
rows, cols = tile_sum.shape
array3d = numpy.ndarray([1, rows, cols])
array3d[0] = tile_sum
# Extract the start and end time to set the temporal extent for each tile
if tile.start_times is not None and tile.end_times is not None:
starts = pandas.DatetimeIndex([tile.start_times[0]])
ends = pandas.DatetimeIndex([tile.end_times[-1]])
else:
starts = None
ends = None
# Create the new raster collection tile
rct = RasterCollectionTile(id=tile.id + "_sum", extent=tile.extent, data=array3d,
start_times=starts, end_times=ends)
tile_results.append(rct)
# Insert the new tiles as list of raster collection tiles in the input object. The new tiles will
# replace the original input tiles.
udf_data.set_raster_collection_tiles(tile_results)
def rct_ndvi(udf_data: UdfData):
"""Compute the NDVI based on RED and NIR tiles
Tiles with ids "red" and "nir" are required. The NDVI computation will be applied
to all time stamped 2D raster tiles that have equal time stamps.
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.
"""
red = None
nir = None
# Iterate over each tile
for tile in udf_data.raster_collection_tiles:
if "red" in tile.id.lower():
red = tile
if "nir" in tile.id.lower():
nir = tile
if red is None:
raise Exception("Red raster collection tile is missing in input")
if nir is None:
raise Exception("Nir raster collection tile is missing in input")
if red.start_times is None or red.start_times.tolist() == nir.start_times.tolist():
# Compute the NDVI
ndvi = (nir.data - red.data) / (nir.data + red.data)
# Create the new raster collection tile
rct = RasterCollectionTile(id="ndvi", extent=red.extent, data=ndvi,
start_times=red.start_times, end_times=red.end_times)
# Insert the new tiles as list of raster collection tiles in the input object. The new tiles will
# replace the original input tiles.
udf_data.set_raster_collection_tiles([rct,])
else:
raise Exception("Time stamps are not equal")