Add load_stac to localprocessing

CHANGELOG.md

@@ -8,8 +8,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added `load_stac` for Client Side Processing, based on the [openeo-processes-dask implementation](https://github.com/Open-EO/openeo-processes-dask/pull/127)
+
 ### Changed
 
+- Updated docs for Client Side Processing with `load_stac` examples, available at https://open-eo.github.io/openeo-python-client/cookbook/localprocessing.html
+
 ### Removed
 
 ### Fixed

docs/cookbook/localprocessing.rst

@@ -9,15 +9,15 @@ Background
 ----------
 
 The client-side processing functionality allows to test and use openEO with its processes locally, i.e. without any connection to an openEO back-end.
-It relies on the projects `openeo-pg-parser-networkx <https://github.com/Open-EO/openeo-pg-parser-networkx>`_, which provides an openEO process graph parsing tool, and `openeo-processes-dask <https://github.com/Open-EO/openeo-processes-dask>`_, which provides an Xarray and Dask implementation of most openEO processes. 
+It relies on the projects `openeo-pg-parser-networkx <https://github.com/Open-EO/openeo-pg-parser-networkx>`_, which provides an openEO process graph parsing tool, and `openeo-processes-dask <https://github.com/Open-EO/openeo-processes-dask>`_, which provides an Xarray and Dask implementation of most openEO processes.
 
 Installation
 ------------
 
 .. note::
-    This feature requires ``Python>=3.9`` and has been tested
-    with ``openeo-pg-parser-networkx==2023.3.1`` and
-    ``openeo-processes-dask==2023.3.2``
+    This feature requires ``Python>=3.9``.
+    Tested with ``openeo-pg-parser-networkx==2023.5.1`` and
+    ``openeo-processes-dask==2023.7.1``.
 
 .. code:: bash
 
@@ -26,18 +26,68 @@ Installation
 Usage
 -----
 
+Every openEO process graph relies on data which is typically provided by a cloud infrastructure (the openEO back-end).
+The client-side processing adds the possibility to read and use local netCDFs, geoTIFFs, ZARR files, and remote STAC Collections or Items for your experiments.
+
+STAC Collections and Items
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. warning::
+    The provided examples using STAC rely on third party STAC Catalogs, we can't guarantee that the urls will remain valid.
+
+With the `load_stac` process it's possible to load and use data provided by remote or local STAC Collections or Items.
+The following code snippet loads Sentinel-2 L2A data from a public STAC Catalog, using specific spatial and temporal extent, band name and also properties for cloud coverage.
+
+.. code-block:: pycon
+
+    >>> from openeo.local import LocalConnection
+    >>> local_conn = LocalConnection("./")
+
+    >>> url = "https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a"
+    >>> spatial_extent = {"west": 11, "east": 12, "south": 46, "north": 47}
+    >>> temporal_extent = ["2019-01-01","2019-06-15"]
+    >>> bands = ["red"]
+    >>> properties = {"eo:cloud_cover": dict(lt=50)}
+    >>> s2_cube = local_conn.load_stac(url=url,
+    ...    spatial_extent=spatial_extent,
+    ...    temporal_extent=temporal_extent,
+    ...    bands=bands,
+    ...    properties=properties)
+    >>> s2_cube.execute()
+    <xarray.DataArray 'stackstac-08730b1b5458a4ed34edeee60ac79254' (time: 177,
+                                                                    band: 1,
+                                                                    y: 11354,
+                                                                    x: 8025)>
+    dask.array<getitem, shape=(177, 1, 11354, 8025), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>
+    Coordinates: (12/53)
+      * time                                     (time) datetime64[ns] 2019-01-02...
+        id                                       (time) <U24 'S2B_32TPR_20190102_...
+      * band                                     (band) <U3 'red'
+      * x                                        (x) float64 6.52e+05 ... 7.323e+05
+      * y                                        (y) float64 5.21e+06 ... 5.096e+06
+        s2:product_uri                           (time) <U65 'S2B_MSIL2A_20190102...
+        ...                                       ...
+        raster:bands                             object {'nodata': 0, 'data_type'...
+        gsd                                      int32 10
+        common_name                              <U3 'red'
+        center_wavelength                        float64 0.665
+        full_width_half_max                      float64 0.038
+        epsg                                     int32 32632
+    Attributes:
+        spec:        RasterSpec(epsg=32632, bounds=(600000.0, 4990200.0, 809760.0...
+        crs:         epsg:32632
+        transform:   | 10.00, 0.00, 600000.00|\n| 0.00,-10.00, 5300040.00|\n| 0.0...
+        resolution:  10.0
+
 Local Collections
 ~~~~~~~~~~~~~~~~~
 
-Every openEO process graph relies on data, which was always provided by a cloud infrastructure (the openEO back-end) until now.
-The client-side processing adds the possibility to read and use local netCDFs, geoTIFFs and ZARR files for your experiments.
-
 If you want to use our sample data, please clone this repository:
 
 .. code:: bash
 
    git clone https://github.com/Open-EO/openeo-localprocessing-data.git
-   
+
 With some sample data we can now check the STAC metadata for the local files by doing:
 
 .. code:: python
@@ -80,9 +130,8 @@ Let's start with the provided sample netCDF of Sentinel-2 data:
     Attributes:
         Conventions:  CF-1.9
         institution:  openEO platform - Geotrellis backend: 0.9.5a1
-        description:  
-        title:        
-    ...
+        description:
+        title:
 
 As you can see in the previous example, we are using a call to execute() which will execute locally the generated openEO process graph.
 In this case, the process graph consist only in a single load_collection, which performs lazy loading of the data. With this first step you can check if the data is being read correctly by openEO.
@@ -102,3 +151,27 @@ We can now do a simple processing for demo purposes, let's compute the median ND
    result_ndvi.plot.imshow(cmap="Greens")
 
 .. image:: ../_static/images/local/local_ndvi.jpg
+
+We can perform the same example using data provided by STAC Collection:
+
+.. code:: python
+
+    from openeo.local import LocalConnection
+    local_conn = LocalConnection("./")
+
+    url = "https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a"
+    spatial_extent =  {"east":11.40,"north":46.52,"south":46.46,"west":11.25}
+    temporal_extent = ["2022-06-01","2022-06-30"]
+    bands = ["red","nir"]
+    properties = {"eo:cloud_cover": dict(lt=80)}
+    s2_datacube = local_conn.load_stac(url=url,
+                        spatial_extent=spatial_extent,
+                        temporal_extent=temporal_extent,
+                        bands=bands,
+                        properties=properties)
+
+    b04 = s2_datacube.band("red")
+    b08 = s2_datacube.band("nir")
+    ndvi = (b08-b04)/(b08+b04)
+    ndvi_median = ndvi.reduce_dimension(dimension="time",reducer="median")
+    result_ndvi = ndvi_median.execute()

openeo/local/connection.py

@@ -4,13 +4,16 @@
 from typing import Callable, Dict, List, Optional, Union
 
 import xarray as xr
+import numpy as np
 from openeo_pg_parser_networkx.graph import OpenEOProcessGraph
+from openeo_pg_parser_networkx.pg_schema import BoundingBox, TemporalInterval
+from openeo_processes_dask.process_implementations.cubes import load_stac
 
+from openeo.metadata import CollectionMetadata, SpatialDimension, TemporalDimension, BandDimension, Band
 from openeo.internal.graph_building import PGNode, as_flat_graph
 from openeo.internal.jupyter import VisualDict, VisualList
 from openeo.local.collections import _get_geotiff_metadata, _get_local_collections, _get_netcdf_zarr_metadata
 from openeo.local.processing import PROCESS_REGISTRY
-from openeo.metadata import CollectionMetadata
 from openeo.rest.datacube import DataCube
 
 _log = logging.getLogger(__name__)
@@ -88,7 +91,155 @@ def load_collection(
             fetch_metadata=fetch_metadata,
         )
 
-    def execute(self, process_graph: Union[dict, str, Path]) -> xr.DataArray:
+    def datacube_from_process(self, process_id: str, namespace: Optional[str] = None, **kwargs) -> DataCube:
+        """
+        Load a data cube from a (custom) process.
+
+        :param process_id: The process id.
+        :param namespace: optional: process namespace
+        :param kwargs: The arguments of the custom process
+        :return: A :py:class:`DataCube`, without valid metadata, as the client is not aware of this custom process.
+        """
+        graph = PGNode(process_id, namespace=namespace, arguments=kwargs)
+        return DataCube(graph=graph, connection=self)
+
+    def load_stac(
+        self,
+        url: str,
+        spatial_extent: Optional[Dict[str, float]] = None,
+        temporal_extent: Optional[List[Union[str, datetime.datetime, datetime.date]]] = None,
+        bands: Optional[List[str]] = None,
+        properties: Optional[dict] = None,
+    ) -> DataCube:
+        """
+        Loads data from a static STAC catalog or a STAC API Collection and returns the data as a processable :py:class:`DataCube`.
+        A batch job result can be loaded by providing a reference to it.
+
+        If supported by the underlying metadata and file format, the data that is added to the data cube can be
+        restricted with the parameters ``spatial_extent``, ``temporal_extent`` and ``bands``.
+        If no data is available for the given extents, a ``NoDataAvailable`` error is thrown.
+
+        Remarks:
+
+        * The bands (and all dimensions that specify nominal dimension labels) are expected to be ordered as
+          specified in the metadata if the ``bands`` parameter is set to ``null``.
+        * If no additional parameter is specified this would imply that the whole data set is expected to be loaded.
+          Due to the large size of many data sets, this is not recommended and may be optimized by back-ends to only
+          load the data that is actually required after evaluating subsequent processes such as filters.
+          This means that the values should be processed only after the data has been limited to the required extent
+          and as a consequence also to a manageable size.
+
+
+        :param url: The URL to a static STAC catalog (STAC Item, STAC Collection, or STAC Catalog)
+            or a specific STAC API Collection that allows to filter items and to download assets.
+            This includes batch job results, which itself are compliant to STAC.
+            For external URLs, authentication details such as API keys or tokens may need to be included in the URL.
+
+            Batch job results can be specified in two ways:
+
+            - For Batch job results at the same back-end, a URL pointing to the corresponding batch job results
+              endpoint should be provided. The URL usually ends with ``/jobs/{id}/results`` and ``{id}``
+              is the corresponding batch job ID.
+            - For external results, a signed URL must be provided. Not all back-ends support signed URLs,
+              which are provided as a link with the link relation `canonical` in the batch job result metadata.
+        :param spatial_extent:
+            Limits the data to load to the specified bounding box or polygons.
+
+            For raster data, the process loads the pixel into the data cube if the point at the pixel center intersects
+            with the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).
+
+            For vector data, the process loads the geometry into the data cube if the geometry is fully within the
+            bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).
+            Empty geometries may only be in the data cube if no spatial extent has been provided.
+
+            The GeoJSON can be one of the following feature types:
+
+            * A ``Polygon`` or ``MultiPolygon`` geometry,
+            * a ``Feature`` with a ``Polygon`` or ``MultiPolygon`` geometry, or
+            * a ``FeatureCollection`` containing at least one ``Feature`` with ``Polygon`` or ``MultiPolygon`` geometries.
+
+            Set this parameter to ``None`` to set no limit for the spatial extent.
+            Be careful with this when loading large datasets. It is recommended to use this parameter instead of
+            using ``filter_bbox()`` or ``filter_spatial()`` directly after loading unbounded data.
+
+        :param temporal_extent:
+            Limits the data to load to the specified left-closed temporal interval.
+            Applies to all temporal dimensions.
+            The interval has to be specified as an array with exactly two elements:
+
+            1.  The first element is the start of the temporal interval.
+                The specified instance in time is **included** in the interval.
+            2.  The second element is the end of the temporal interval.
+                The specified instance in time is **excluded** from the interval.
+
+            The second element must always be greater/later than the first element.
+            Otherwise, a `TemporalExtentEmpty` exception is thrown.
+
+            Also supports open intervals by setting one of the boundaries to ``None``, but never both.
+
+            Set this parameter to ``None`` to set no limit for the temporal extent.
+            Be careful with this when loading large datasets. It is recommended to use this parameter instead of
+            using ``filter_temporal()`` directly after loading unbounded data.
+
+        :param bands:
+            Only adds the specified bands into the data cube so that bands that don't match the list
+            of band names are not available. Applies to all dimensions of type `bands`.
+
+            Either the unique band name (metadata field ``name`` in bands) or one of the common band names
+            (metadata field ``common_name`` in bands) can be specified.
+            If the unique band name and the common name conflict, the unique band name has a higher priority.
+
+            The order of the specified array defines the order of the bands in the data cube.
+            If multiple bands match a common name, all matched bands are included in the original order.
+
+            It is recommended to use this parameter instead of using ``filter_bands()`` directly after loading unbounded data.
+
+        :param properties:
+            Limits the data by metadata properties to include only data in the data cube which
+            all given conditions return ``True`` for (AND operation).
+
+            Specify key-value-pairs with the key being the name of the metadata property,
+            which can be retrieved with the openEO Data Discovery for Collections.
+            The value must be a condition (user-defined process) to be evaluated against a STAC API.
+            This parameter is not supported for static STAC.
+
+        .. versionadded:: 0.21.0
+        """
+        arguments = {"url": url}
+        # TODO: more normalization/validation of extent/band parameters and `properties`
+        if spatial_extent:
+            arguments["spatial_extent"] = spatial_extent
+        if temporal_extent:
+            arguments["temporal_extent"] = DataCube._get_temporal_extent(temporal_extent)
+        if bands:
+            arguments["bands"] = bands
+        if properties:
+            arguments["properties"] = properties
+        cube = self.datacube_from_process(process_id="load_stac", **arguments)
+        # detect actual metadata from URL
+        # run load_stac to get the datacube metadata
+        arguments["spatial_extent"] = BoundingBox.parse_obj(spatial_extent)
+        arguments["temporal_extent"] = TemporalInterval.parse_obj(temporal_extent)
+        xarray_cube = load_stac(**arguments)
+        attrs = xarray_cube.attrs
+        for at in attrs:
+            # allowed types: str, Number, ndarray, number, list, tuple
+            if not isinstance(attrs[at], (int, float, str, np.ndarray, list, tuple)):
+                attrs[at] = str(attrs[at])
+        metadata = CollectionMetadata(
+            attrs,
+            dimensions=[
+                SpatialDimension(name=xarray_cube.openeo.x_dim, extent=[]),
+                SpatialDimension(name=xarray_cube.openeo.y_dim, extent=[]),
+                TemporalDimension(name=xarray_cube.openeo.temporal_dims[0], extent=[]),
+                BandDimension(name=xarray_cube.openeo.band_dims[0],
+                              bands=[Band(x) for x in xarray_cube[xarray_cube.openeo.band_dims[0]].values])
+            ],
+        )
+        cube.metadata = metadata
+        return cube
+
+    def execute(self, process_graph: Union[dict, str]) -> xr.DataArray:
         """
         Execute locally the process graph and return the result as an xarray.DataArray.
 

setup.py

@@ -40,7 +40,7 @@
     "rioxarray>=0.13.0",
     "pyproj",
     "openeo_pg_parser_networkx>=2023.5.1",
-    "openeo_processes_dask[implementations]>=2023.5.1",
+    "openeo_processes_dask[implementations]>=2023.7.1",
 ]
 
 jupyter_require = [