Changes for page kg-spatial-search
Last modified by oschmid on 2023/08/22 11:23
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... ... @@ -28,7 +28,6 @@ 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 - 32 32 == Get started: Query by a "bounding box" (hyperrectangle) == 33 33 34 34 {{code language="bash" layout="LINENUMBERS"}} ... ... @@ -43,7 +43,6 @@ 43 43 44 44 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. 45 45 46 - 47 47 == Query by hypersphere == 48 48 49 49 Alongside the possibility to use hyperrectangles for querying the spatial search, you can also use hyperspheres: ... ... @@ -56,8 +56,6 @@ 56 56 57 57 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) 58 58 59 - 60 - 61 61 == Advanced use: Union geometries for complex queries == 62 62 63 63 To build arbitrarily complex geometries, it is possible to combine hyperrectangles and hyperspheres via (nested) unions: ... ... @@ -69,8 +69,11 @@ 69 69 {{/code}} 70 70 71 71 68 += Technical details of the ingestion pipeline = 72 72 70 +The current pipeline is regularly parsing locareJSON files registered in the KG and translates complex geometries via the [[vtk>>https://pypi.org/project/vtk/]] library into a point cloud of a density of 1 coordinate unit (by ray-casting on an obb-tree) and uploads the resulting files to the [[bucket of this collab>>https://wiki.ebrains.eu/bin/view/Collabs/kg-spatial-search/Bucket]]. A cron job on the spatial search server pulls the files from the repo and rebuilds the databases index. 73 73 72 + 74 74 75 75 ))) 76 76