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Extract all SpatialZones of a given type for a Bundle

Feature Group: Extract | PostgreSQL

Extraction of all SpatialZones

The intent is to extract in bulk all defined Apartments, Commons, Parkings,... from a bundle

By All SpatialZones of a given type, it is meant all Units of a Bundle that have been defined as a SpatialZone using one of the following types:

  • IfcSpatialZone (preferred)
  • IfcBuildingStorey
  • IfcZone
  • IfcGroup
  • IfcSpace

Submit the processing request

The extract is started by sending a POST with a Json Body as defined in pydantic with:

  • the bundleId (bundle_id in PostgreSQL) of the bundle that contains the data of the IfcJSON from the conversion of the IFC file
  • the choice of using or not the useRepresentationsCache for each unit
  • the elementType as listed hereunder
  • withIFC as True or False; if True, an IFC will also be produced
#
# Extract all spatial units from an IFC
#   
class ExtractAllSpatialUnits_Instruction(BaseModel):
    bundleId: str | None = "1"
    useRepresentationsCache: bool | None = False
    elementType: Literal['IfcBuildingStorey', 'IfcZone', 'IfcSpatialZone', 'IfcSpace', 'IfcGroup'] | None = 'IfcSpatialZone'
    withIFC: bool | None = True

    @computed_field()
    def includeRelationshipTypes(self) -> list[str]:
        if self.elementType == 'IfcBuildingStorey':
            return ['IfcRelAggregates','IfcRelContainedInSpatialStructure','IfcRelFillsElement','IfcRelVoidsElement']
        elif self.elementType == 'IfcSpatialZone' or self.elementType == 'IfcSpace':
            return ['IfcRelAggregates','IfcRelContainedInSpatialStructure','IfcRelFillsElement','IfcRelVoidsElement','IfcRelSpaceBoundary','IfcRelReferencedInSpatialStructure']
        elif self.elementType == 'IfcZone' or self.elementType == 'IfcGroup':
            return ['IfcRelAggregates','IfcRelContainedInSpatialStructure','IfcRelFillsElement','IfcRelVoidsElement','IfcRelSpaceBoundary','IfcRelReferencedInSpatialStructure']
        else:
            return ['IfcRelAggregates','IfcRelContainedInSpatialStructure','IfcRelFillsElement','IfcRelVoidsElement']   

class ExtractAllSpatialUnits_ResultItem(BaseModel):
    unitId: str
    result: dict[
        ExtractSpatialUnit_Result | None , 
        ConvertIfcJsonToIfc_Result | None
    ]
class ExtractAllSpatialUnits_Result(BaseModel):
    result: list[ExtractAllSpatialUnits_ResultItem]

Processing

The processing is done by using a Celery Workflow of type chord

This type of workflow makes it possible to execute all extractions in parallel and then journalize and notify the result when all parallel tasks have individually completed or failed.

%%{init: {"flowchart": {"htmlLabels": true}} }%%
flowchart
    direction TB
    start
    subgraph fastapi ["header"]
        direction TB
        task-1
        task-2
        task-n
    end
    callback
    start --> task-1
    start --> task-2
    start --> task-n
    task-1 --> callback
    task-2 --> callback
    task-n --> callback

So, the total processing time is the runtime of the longest running task and not the sum of all runtimes.