Merge pull request #863 from lsst/tickets/DM-34340

DM-34340: Adapt to daf_butler API changes.

python/lsst/pipe/tasks/hips.py

@@ -41,7 +41,7 @@
 
 from lsst.sphgeom import RangeSet, HealpixPixelization
 from lsst.utils.timer import timeMethod
-from lsst.daf.butler import Butler, DataCoordinate, DatasetRef, Quantum, SkyPixDimension
+from lsst.daf.butler import Butler, DataCoordinate, DatasetRef, Quantum
 import lsst.pex.config as pexConfig
 import lsst.pipe.base as pipeBase
 import lsst.afw.geom as afwGeom
@@ -548,16 +548,19 @@ def build_quantum_graph(
         output_dataset_type = dataset_types.outputs[task_def.connections.hips_exposures.name]
         incidental_output_dataset_types = dataset_types.outputs.copy()
         incidental_output_dataset_types.remove(output_dataset_type)
-        (hpx_output_dimension,) = (d for d in output_dataset_type.dimensions
-                                   if isinstance(d, SkyPixDimension))
+        (hpx_output_dimension,) = (
+            registry.dimensions.skypix_dimensions[d] for d in output_dataset_type.dimensions.skypix.names
+        )
 
         constraint_hpx_pixelization = registry.dimensions[f"healpix{constraint_order}"].pixelization
         common_skypix_name = registry.dimensions.commonSkyPix.name
         common_skypix_pixelization = registry.dimensions.commonSkyPix.pixelization
 
         # We will need all the pixels at the quantum resolution as well
-        task_dimensions = registry.dimensions.extract(task_def.connections.dimensions)
-        (hpx_dimension,) = (d for d in task_dimensions if d.name != "band")
+        task_dimensions = registry.dimensions.conform(task_def.connections.dimensions)
+        (hpx_dimension,) = (
+            registry.dimensions.skypix_dimensions[d] for d in task_dimensions.names if d != "band"
+        )
         hpx_pixelization = hpx_dimension.pixelization
 
         if hpx_pixelization.level < constraint_order:
@@ -608,7 +611,7 @@ def build_quantum_graph(
             bind=bind
         ).expanded()
         inputs_by_patch = defaultdict(set)
-        patch_dimensions = registry.dimensions.extract(["patch"])
+        patch_dimensions = registry.dimensions.conform(["patch"])
         for input_ref in input_refs:
             inputs_by_patch[input_ref.dataId.subset(patch_dimensions)].add(input_ref)
         if not inputs_by_patch:
@@ -664,7 +667,7 @@ def build_quantum_graph(
             raise RuntimeError("Given constraints yielded empty quantum graph.")
 
         # Define initOutputs refs.
-        empty_data_id = DataCoordinate.makeEmpty(registry.dimensions)
+        empty_data_id = DataCoordinate.make_empty(registry.dimensions)
         init_outputs = {}
         global_init_outputs = []
         if config_dataset_type := dataset_types.initOutputs.get(task_def.configDatasetName):
@@ -862,7 +865,7 @@ class GenerateHipsTask(pipeBase.PipelineTask):
     def runQuantum(self, butlerQC, inputRefs, outputRefs):
         inputs = butlerQC.get(inputRefs)
 
-        dims = inputRefs.hips_exposure_handles[0].dataId.names
+        dims = inputRefs.hips_exposure_handles[0].dataId.dimensions.names
         order = None
         for dim in dims:
             if "healpix" in dim:

python/lsst/pipe/tasks/postprocess.py

@@ -1022,7 +1022,7 @@ def runQuantum(self, butlerQC, inputRefs, outputRefs):
             raise ValueError("config.functorFile is None. "
                              "Must be a valid path to yaml in order to run Task as a PipelineTask.")
         result = self.run(handle=inputs['inputCatalog'], funcs=self.funcs,
-                          dataId=outputRefs.outputCatalog.dataId.full)
+                          dataId=dict(outputRefs.outputCatalog.dataId.mapping))
         outputs = pipeBase.Struct(outputCatalog=result)
         butlerQC.put(outputs, outputRefs)
 
@@ -1071,7 +1071,7 @@ def transform(self, band, handles, funcs, dataId):
         if dataId and self.config.columnsFromDataId:
             for key in self.config.columnsFromDataId:
                 if key in dataId:
-                    df[str(key)] = dataId[key]
+                    df[key] = dataId[key]
                 else:
                     raise ValueError(f"'{key}' in config.columnsFromDataId not found in dataId: {dataId}")
 
@@ -1400,7 +1400,7 @@ def __init__(self, **kwargs):
 
     def runQuantum(self, butlerQC, inputRefs, outputRefs):
         dataRefs = butlerQC.get(inputRefs.calexp)
-        visit = dataRefs[0].dataId.byName()['visit']
+        visit = dataRefs[0].dataId['visit']
 
         self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
                        len(dataRefs), visit)
@@ -1770,7 +1770,7 @@ def runQuantum(self, butlerQC, inputRefs, outputRefs):
         # Add ccdVisitId to allow joining with CcdVisitTable
         idGenerator = self.config.idGenerator.apply(butlerQC.quantum.dataId)
         inputs['ccdVisitId'] = idGenerator.catalog_id
-        inputs['band'] = butlerQC.quantum.dataId.full['band']
+        inputs['band'] = butlerQC.quantum.dataId['band']
         outputs = self.run(**inputs)
         butlerQC.put(outputs, outputRefs)
 
@@ -1870,7 +1870,7 @@ def runQuantum(self, butlerQC, inputRefs, outputRefs):
             raise ValueError("config.functorFile is None. "
                              "Must be a valid path to yaml in order to run Task as a PipelineTask.")
         outputs = self.run(inputs['inputCatalogs'], inputs['referenceCatalog'], funcs=self.funcs,
-                           dataId=outputRefs.outputCatalog.dataId.full)
+                           dataId=dict(outputRefs.outputCatalog.dataId.mapping))
 
         butlerQC.put(outputs, outputRefs)
 

python/lsst/pipe/tasks/skyCorrection.py

@@ -27,7 +27,6 @@
 import lsst.afw.math as afwMath
 import lsst.pipe.base.connectionTypes as cT
 import numpy as np
-from lsst.daf.butler import DimensionGraph
 from lsst.pex.config import Config, ConfigField, ConfigurableField, Field, FieldValidationError
 from lsst.pipe.base import PipelineTask, PipelineTaskConfig, PipelineTaskConnections, Struct
 from lsst.pipe.tasks.background import (
@@ -59,7 +58,7 @@ def _skyFrameLookup(datasetType, registry, quantumDataId, collections):
     results : `list` [`lsst.daf.butler.DatasetRef`]
         List of datasets that will be used as sky calibration frames.
     """
-    newDataId = quantumDataId.subset(DimensionGraph(registry.dimensions, names=["instrument", "visit"]))
+    newDataId = quantumDataId.subset(registry.dimensions.conform(["instrument", "visit"]))
     skyFrames = []
     for dataId in registry.queryDataIds(["visit", "detector"], dataId=newDataId).expanded():
         skyFrame = registry.findDataset(