Changes for page kg-spatial-search
Last modified by oschmid on 2023/08/22 11:23
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... ... @@ -28,6 +28,7 @@ 28 28 29 29 Simply use the API endpoint at [[https:~~/~~/spatial.kg.ebrains.eu/api/>>https://spatial.kg.ebrains.eu/api/]] by running queries according to the following examples: 30 30 31 + 31 31 == Get started: Query by a "bounding box" (hyperrectangle) == 32 32 33 33 {{code language="bash" layout="LINENUMBERS"}} ... ... @@ -42,6 +42,7 @@ 42 42 43 43 As you can see, you're sending a POST request to the endpoint at **https:~/~/spatial.kg.ebrains.eu/spatial-search/cores/ebrains/spatial_objects **with a payload defining a geometry of interest. In this case, we're looking for all objects that are **inside **a **hyperrectangle **defined by its lowest and highest point in the coordinate system of the coordinate space **AMB-CCF_v3-RAS**. This query will return you an array of ids of the objects located within the geometry which you then can use to conveniently query the KG either through the [[Instance API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/2%20Advanced/getInstancesByIds]] or the [[Query API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/1%20Basic/runDynamicQuery]] to access detailed meta information. 44 44 46 + 45 45 == Query by hypersphere == 46 46 47 47 Alongside the possibility to use hyperrectangles for querying the spatial search, you can also use hyperspheres: ... ... @@ -54,6 +54,8 @@ 54 54 55 55 It is defined by the center of the sphere with its coordinates, the radius in coordinate units and the coordinate space of the given coordinates (in this case again AMB-CCF_v3-RAS) 56 56 59 + 60 + 57 57 == Advanced use: Union geometries for complex queries == 58 58 59 59 To build arbitrarily complex geometries, it is possible to combine hyperrectangles and hyperspheres via (nested) unions: ... ... @@ -65,17 +65,8 @@ 65 65 {{/code}} 66 66 67 67 68 -= Technical details of the ingestion pipeline = 69 69 70 -The current pipeline is regularly parsing locareJSON files registered in the KG. 71 71 72 -* Points are translated to the data structure of spatial search without manipulation 73 -* Polyhedrons are translated via the [[vtk>>https://pypi.org/project/vtk/]] library into a point cloud of (currently) a density of 1 coordinate unit (by ray-casting on an obb-tree) 74 -* Other geometries are work-in-progress 75 - 76 -The resulting files (all representing point-clouds) as well as the space definitions are uploaded to the [[bucket of this collab>>https://wiki.ebrains.eu/bin/view/Collabs/kg-spatial-search/Bucket]]. A cron job running on the spatial search server pulls the files from the repo and rebuilds the databases index in regular intervals. 77 - 78 - 79 79 80 80 ))) 81 81