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26	26
27	27	= How do I query the KG Spatial Search? =
28	28
29		-Simply use the API endpoint at [[https:~~/~~/spatial.kg.ebrains.eu/cores/ebrains/spatial_objects>>https://spatial.kg.ebrains.eu/spatial_objects]] by running queries according to the following examples:
	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		-== Get started: Query by a "bounding box" (hyperrectangle) ==
32	32
33		-{{code language="bash" layout="LINENUMBERS"}}
34		-curl -X 'POST' \
35		- 'https://spatial.kg.ebrains.eu/cores/ebrains/spatial_objects' \
36		- -H 'accept: application/json' \
37		- -H 'Content-Type: application/json' \
38		- -d '{
39		- "filters": "inside(hyperrectangle{[50.0,146.0,227.0], [51.0, 149.0, 228.0], \"AMB-CCF_v3-RAS\"})"
40		-}'
41		-{{/code}}
42	42
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
45		-== Query by hypersphere ==
	34	+Theoretically, all data which is anchored in their respective coordinate space can be registered in the KG Spatial Search. We currently provide ingestion pipelines for data annotated with the[[ locareJSON format>>https://github.com/blixilla/LocareJSON]]. Please note that complex, planar and volumetric geometries such as polyhedrons, spheres, etc. are translated into point clouds of a configurable density during the ingestion process to ensure better performance at query time for the cost for potential false-negatives in highly fine-granular queries.
46	46
47		-Alongside the possibility to use hyperrectangles for querying the spatial search, you can also use hyperspheres:
48	48
49		-{{code language="javascript" layout="LINENUMBERS"}}
50		-{
51		- "filters": "inside(hypersphere{[50.0,146.0,227.0], 2, \"AMB-CCF_v3-RAS\"})"
52		-}
53		-{{/code}}
54	54
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		-
57		-== Advanced use: Union geometries for complex queries ==
58		-
59		-To build arbitrarily complex geometries, it is possible to combine hyperrectangles and hyperspheres via (nested) unions:
60		-
61		-{{code language="javascript" layout="LINENUMBERS"}}
62		-{
63		- "filters": "union(union(inside(hyperrectangle{[50.0,146.0,227.0], [51.0, 149.0, 228.0], \"AMB-CCF_v3-RAS\"}), inside(hyperrectangle{[120.0,146.0,227.0], [150.0, 180.0, 300.0], \"AMB-CCF_v3-RAS\"})),inside(hypersphere{[50.0,146.0,227.0], 2, \"AMB-CCF_v3-RAS\"}))"
64		-}
65		-{{/code}}
66		-
67		-== How to get the available spaces? ==
68		-
69		-To get a list of available spaces (to know which coordinate systems are available), please run
70		-
71		-{{code language="bash" layout="LINENUMBERS"}}
72		-curl -X 'POST' \
73		- 'https://spatial.kg.ebrains.eu/spaces' \
74		- -H 'accept: application/json' \
75		- -H 'Content-Type: application/json' \
76		- -d '{}'
77		-{{/code}}
78		-
79		-to retrieve a list of spaces. To get more information about the space, you can then ask for a specific one with
80		-
81		-{{code language="bash" layout="LINENUMBERS"}}
82		-curl -X 'GET' \
83		- 'https://spatial.kg.ebrains.eu/spaces/AMB-CCF_v3-RAS' \
84		- -H 'accept: application/json' \
85		- -H 'Content-Type: application/json'
86		-{{/code}}
87		-
88		-
89		-= Technical details of the ingestion pipeline =
90		-
91		-The current pipeline is regularly parsing locareJSON files registered in the KG.
92		-
93		-* Points are translated to the data structure of spatial search without manipulation
94		-* 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)
95		-* Other geometries are work-in-progress
96		-
97		-The resulting files (all representing point-clouds) as well as the space definitions are uploaded to the [[bucket of this collab>>https://data-proxy.ebrains.eu/kg-spatial-search]]. A cron job running on the spatial search server pulls the files from the repo and rebuilds the databases index in regular intervals.
98		-
99		-
100	100
101	101	)))
102	102