Merge branch 'tickets/DM-43906'

pyproject.toml

@@ -31,8 +31,8 @@ keywords = [
 dependencies = [
     "astropy",
     "galsim",
-    "gauss2d",
-    "gauss2dfit",
+    "lsst-gauss2d",
+    "lsst-gauss2dfit",
     "lsst-multiprofit",
     "matplotlib",
     "numpy",

python/lsst/meas/extensions/multiprofit/fit_coadd_multiband.py

@@ -24,8 +24,8 @@
 from functools import cached_property
 from typing import Any, Iterable, Mapping, Sequence
 
-import gauss2d as g2
-import gauss2d.fit as g2f
+import lsst.gauss2d as g2
+import lsst.gauss2d.fit as g2f
 import lsst.pex.config as pexConfig
 import lsst.pipe.base as pipeBase
 import lsst.pipe.tasks.fit_coadd_multiband as fitMB
@@ -319,7 +319,7 @@ def get_psf_model(self, source):
                     param.value = math.sqrt(param.value**2 - sigma_subtract_sq)
         return psf_model
 
-    def get_source_observation(self, source, **kwargs) -> g2f.Observation:
+    def get_source_observation(self, source, **kwargs) -> g2f.ObservationD:
         if not kwargs.get("skip_flags"):
             if (not source["detect_isPrimary"]) or source["merge_peak_sky"]:
                 raise NotPrimaryError(f"source {source[self.config_fit.column_id]} has invalid flags for fit")
@@ -374,7 +374,7 @@ def get_source_observation(self, source, **kwargs) -> g2f.Observation:
 
         coordsys = g2.CoordinateSystem(1.0, 1.0, x_min_bbox, y_min_bbox)
 
-        obs = g2f.Observation(
+        obs = g2f.ObservationD(
             image=g2.ImageD(img, coordsys),
             sigma_inv=g2.ImageD(sigma_inv, coordsys),
             mask_inv=g2.ImageB(mask, coordsys),
@@ -450,7 +450,7 @@ def get_model_radec(self, source: Mapping[str, Any], cen_x: float, cen_y: float)
 
     def initialize_model(
         self,
-        model: g2f.Model,
+        model: g2f.ModelD,
         source: Mapping[str, Any],
         catexps: list[CatalogExposureSourcesABC],
         values_init: Mapping[g2f.ParameterD, float] | None = None,

python/lsst/meas/extensions/multiprofit/fit_coadd_psf.py

@@ -19,7 +19,7 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
-import gauss2d.fit as g2f
+import lsst.gauss2d.fit as g2f
 import lsst.pex.config as pexConfig
 import lsst.pipe.base as pipeBase
 import lsst.pipe.tasks.fit_coadd_psf as fitCP
@@ -89,12 +89,12 @@ def check_source(self, source, config):
 
     def initialize_model(
         self,
-        model: g2f.Model,
+        model: g2f.ModelD,
         config_data: CatalogPsfFitterConfigData,
         limits_x: g2f.LimitsD = None,
         limits_y: g2f.LimitsD = None,
     ) -> None:
-        """Initialize a Model for a single source row.
+        """Initialize a ModelD for a single source row.
 
         Parameters
         ----------

python/lsst/meas/extensions/multiprofit/plots.py

@@ -38,7 +38,7 @@
     "ObjectTableBase",
     "TruthSummaryTable",
     "ObjectTable",
-    "ObjectTableCModel",
+    "ObjectTableCModelD",
     "ObjectTableMultiProFit",
     "ObjectTablePsf",
     "downselect_table",
@@ -52,7 +52,7 @@
 FigureAxes = tuple[Figure, Axes]
 
 
-class ObjectTableBase(ABC, pydantic.BaseModel):
+class ObjectTableBase(ABC, pydantic.BaseModelD):
     """Base class for retrieving columns from tract-based object tables."""
 
     model_config = pydantic.ConfigDict(arbitrary_types_allowed=True, frozen=True)
@@ -155,11 +155,11 @@ def get_y(self):
         return self.table["y"]
 
 
-class ObjectTableCModel(ObjectTable):
-    """Class for retrieving CModel fluxes from objectTable_tract."""
+class ObjectTableCModelD(ObjectTable):
+    """Class for retrieving CModelD fluxes from objectTable_tract."""
 
     def get_flux(self, band: str) -> np.ndarray:
-        return self.table[f"{band}_cModelFlux"]
+        return self.table[f"{band}_cModelDFlux"]
 
 
 class ObjectTableMultiProFit(ObjectTableBase):
@@ -360,7 +360,7 @@ def plot_blend(
     objects_primary = rebuilder.objects[rebuilder.objects["detect_isPrimary"] == True]  # noqa: E712
     kwargs_annotate_obs = dict(color="white", fontsize=14, ha="right", va="top")
     kwargs_scatter_obs = dict(c="white", marker="x", s=70)
-    table_within_cmodel = downselect_table_axis(ObjectTableCModel(table=objects_primary), ax_rgb)
+    table_within_cmodel = downselect_table_axis(ObjectTableCModelD(table=objects_primary), ax_rgb)
     labels_extended_model = ("C", "E")
     plot_objects(
         table_within_cmodel,
@@ -390,7 +390,7 @@ def plot_blend(
 
     for idx_child in idx_children:
         for name, matched in rebuilder.matches.items():
-            print(f"Model: {name}")
+            print(f"ModelD: {name}")
             rebuilder_child = matched.rebuilder
             is_dataloader = isinstance(rebuilder_child, DataLoader)
             is_scarlet = is_dataloader and (name == "scarlet")

python/lsst/meas/extensions/multiprofit/rebuild_coadd_multiband.py

@@ -19,14 +19,14 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
-__all__ = ["ModelRebuilder", "PatchModelMatches", "PatchCoaddRebuilder"]
+__all__ = ["ModelDRebuilder", "PatchModelDMatches", "PatchCoaddRebuilder"]
 
 from functools import cached_property
 from typing import Iterable
 
 import astropy.table
 import astropy.units as u
-import gauss2d.fit as g2f
+import lsst.gauss2d.fit as g2f
 import lsst.afw.table as afwTable
 import lsst.daf.butler as dafButler
 import lsst.geom as geom
@@ -142,7 +142,7 @@ def get_radec_unit(table: astropy.table.Table, coord_ra: str, coord_dec: str, de
     return unit_ra
 
 
-class DataLoader(pydantic.BaseModel):
+class DataLoader(pydantic.BaseModelD):
     """A collection of data that can be used to rebuild models."""
 
     model_config = pydantic.ConfigDict(arbitrary_types_allowed=True, frozen=True)
@@ -238,7 +238,7 @@ def from_butler(
     def load_deblended_object(
         self,
         idx_row: int,
-    ) -> list[g2f.Observation]:
+    ) -> list[g2f.ObservationD]:
         """Load a deblended object from catexps.
 
         Parameters
@@ -257,7 +257,7 @@ def load_deblended_object(
         return observations
 
 
-class ModelRebuilder(DataLoader):
+class ModelDRebuilder(DataLoader):
     """A rebuilder of MultiProFit models from their inputs and best-fit
     parameter values.
     """
@@ -292,7 +292,7 @@ def from_quantumGraph(
         Returns
         -------
         rebuilder
-            A ModelRebuilder instance initialized with the necessary kwargs.
+            A ModelDRebuilder instance initialized with the necessary kwargs.
         """
         if dataId is None:
             quantum = next(iter(quantumgraph.outputQuanta)).quantum
@@ -343,8 +343,8 @@ def make_model(
         idx_row: int,
         config_data: CatalogSourceFitterConfigData = None,
         init: bool = True,
-    ) -> g2f.Model:
-        """Make a Model for a single row from the originally fitted catalog.
+    ) -> g2f.ModelD:
+        """Make a ModelD for a single row from the originally fitted catalog.
 
         Parameters
         ----------
@@ -398,22 +398,22 @@ def set_model(self, idx_row: int, config_data: CatalogSourceFitterConfigData = N
             param.value = row[f"{prefix}{key}"] + offsets.get(type(param), 0.0)
 
 
-class PatchModelMatches(pydantic.BaseModel):
+class PatchModelDMatches(pydantic.BaseModelD):
     """Storage for MultiProFit tables matched to a reference catalog."""
 
     model_config = pydantic.ConfigDict(arbitrary_types_allowed=True, frozen=True)
 
     matches: astropy.table.Table | None = pydantic.Field(doc="Catalogs of matches")
     quantumgraph: QuantumGraph | None = pydantic.Field(doc="Quantum graph for fit task")
-    rebuilder: DataLoader | ModelRebuilder | None = pydantic.Field(doc="MultiProFit object model rebuilder")
+    rebuilder: DataLoader | ModelDRebuilder | None = pydantic.Field(doc="MultiProFit object model rebuilder")
 
 
-class PatchCoaddRebuilder(pydantic.BaseModel):
+class PatchCoaddRebuilder(pydantic.BaseModelD):
     """A rebuilder for patch-level coadd catalog/exposure fits."""
 
     model_config = pydantic.ConfigDict(arbitrary_types_allowed=True, frozen=True)
 
-    matches: dict[str, PatchModelMatches] = pydantic.Field("Model matches by algorithm name")
+    matches: dict[str, PatchModelDMatches] = pydantic.Field("ModelD matches by algorithm name")
     name_model_ref: str = pydantic.Field(doc="The name of the reference model in matches")
     objects: astropy.table.Table = pydantic.Field(doc="Object table")
     objects_multiprofit: astropy.table.Table | None = pydantic.Field(doc="Object table for MultiProFit fits")
@@ -553,13 +553,13 @@ def from_butler(
             matched["patch"][np.where(unmatched)[0]] = patches_unmatched
             matched = matched[matched["patch"] == patch]
             rebuilder = (
-                ModelRebuilder.from_quantumGraph(butler, quantumgraph, dataId=dataId)
+                ModelDRebuilder.from_quantumGraph(butler, quantumgraph, dataId=dataId)
                 if is_mpf
                 else DataLoader.from_butler(
                     butler, data_id=dataId, bands=bands, collections=[collection_merged]
                 )
             )
-            matches_name[name] = PatchModelMatches(
+            matches_name[name] = PatchModelDMatches(
                 matches=matched, quantumgraph=quantumgraph, rebuilder=rebuilder
             )
         return cls(

tests/test_fit_coadd.py

@@ -21,7 +21,7 @@
 
 import os
 
-import gauss2d.fit as g2f
+import lsst.gauss2d.fit as g2f
 import lsst.meas.extensions.multiprofit.fit_coadd_multiband as fitCMB
 import lsst.meas.extensions.multiprofit.fit_coadd_psf as fitCP
 import numpy as np
@@ -240,7 +240,6 @@ def source_fit_ser_shapelet_psf_results(
     source_fit_ser_config.action_psf = fitCMB.SourceTablePsfComponentsAction()
     task = fitCMB.MultiProFitSourceTask(config=source_fit_ser_config)
     results = task.run(catalog_multi=catalog, catexps=[catexp])
-    source_fit_ser_config.action_psf = fitCMB.PsfComponentsAction
     return results.output.to_pandas()