Issue #111 move all examples to "archive" folder

to better signal their unmaintained/outdated state

Author: soxofaan

docs/examples/udf/smooth_savitzky_golay.py

@@ -0,0 +1,16 @@
+import xarray
+from scipy.signal import savgol_filter
+
+from openeo.udf import XarrayDataCube
+
+
+def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
+    """
+    Apply Savitzky-Golay smoothing to a timeseries datacube.
+    This UDF preserves dimensionality, and assumes an input
+    datacube with a temporal dimension 't' as input.
+    """
+    array: xarray.DataArray = cube.get_array()
+    filled = array.interpolate_na(dim="t")
+    smoothed_array = savgol_filter(filled.values, 5, 2, axis=0)
+    return XarrayDataCube(array=xarray.DataArray(smoothed_array, dims=array.dims, coords=array.coords))

docs/examples/udf/udf_modify_spatial.py

@@ -0,0 +1,55 @@
+import numpy as np
+import xarray
+
+from openeo.metadata import CollectionMetadata
+from openeo.udf import XarrayDataCube
+from openeo.udf.debug import inspect
+
+
+def apply_metadata(input_metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
+
+    xstep = input_metadata.get("x", "step")
+    ystep = input_metadata.get("y", "step")
+    new_metadata = {
+        "x": {"type": "spatial", "axis": "x", "step": xstep / 2.0, "reference_system": 4326},
+        "y": {"type": "spatial", "axis": "y", "step": ystep / 2.0, "reference_system": 4326},
+        "t": {"type": "temporal"},
+    }
+    return CollectionMetadata(new_metadata)
+
+
+def fancy_upsample_function(array: np.array, factor: int = 2) -> np.array:
+    assert array.ndim == 3
+    return array.repeat(factor, axis=-1).repeat(factor, axis=-2)
+
+
+def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
+    array: xarray.DataArray = cube.get_array()
+
+    cubearray: xarray.DataArray = cube.get_array().copy() + 60
+
+    # We make prediction and transform numpy array back to datacube
+
+    # Pixel size of the original image
+    init_pixel_size_x = cubearray.coords["x"][-1] - cubearray.coords["x"][-2]
+    init_pixel_size_y = cubearray.coords["y"][-1] - cubearray.coords["y"][-2]
+
+    if cubearray.data.ndim == 4 and cubearray.data.shape[0] == 1:
+        cubearray = cubearray[0]
+    predicted_array = fancy_upsample_function(cubearray.data, 2)
+    inspect(predicted_array, "test message")
+    coord_x = np.linspace(
+        start=cube.get_array().coords["x"].min(),
+        stop=cube.get_array().coords["x"].max() + init_pixel_size_x,
+        num=predicted_array.shape[-2],
+        endpoint=False,
+    )
+    coord_y = np.linspace(
+        start=cube.get_array().coords["y"].min(),
+        stop=cube.get_array().coords["y"].max() + init_pixel_size_y,
+        num=predicted_array.shape[-1],
+        endpoint=False,
+    )
+    predicted_cube = xarray.DataArray(predicted_array, dims=["bands", "x", "y"], coords=dict(x=coord_x, y=coord_y))
+
+    return XarrayDataCube(predicted_cube)