Feature/data offset

autogalaxy/__init__.py

@@ -2,6 +2,7 @@
 from autoarray.dataset import preprocess  # noqa
 from autoarray.dataset.imaging.dataset import Imaging  # noqa
 from autoarray.dataset.interferometer.dataset import Interferometer  # noqa
+from autoarray.dataset.dataset_model import DatasetModel
 from autoarray.inversion.pixelization import mesh  # noqa
 from autoarray.inversion import regularization as reg  # noqa
 from autoarray.inversion.pixelization import image_mesh

autogalaxy/abstract_fit.py

@@ -17,7 +17,6 @@ class AbstractFitInversion:
     def __init__(
         self,
         model_obj,
-        sky: Optional[LightProfile],
         settings_inversion: aa.SettingsInversion,
     ):
         """
@@ -32,14 +31,10 @@ def __init__(
             The object which contains the model components (e.g. light profiles, galaxies, etc) which are used to
             create the model-data that fits the data. In PyAutoGalaxy this is a list of galaxies and PyAutoLens
             it is a `Tracer`.
-        sky
-            Contains model components which specifically fit the background sky emission in an image (e.g. a `Sky`
-            light profile).
         settings_inversion
             Settings controlling how an inversion is fitted for example which linear algebra formalism is used.
         """
         self.model_obj = model_obj
-        self.sky = sky
         self.settings_inversion = settings_inversion
 
     @property
@@ -68,7 +63,7 @@ def perform_inversion(self) -> bool:
             A bool which is True if an inversion is performed.
         """
 
-        return self.model_obj.perform_inversion or isinstance(self.sky, Basis)
+        return self.model_obj.perform_inversion
 
     @property
     def w_tilde(self) -> Optional[aa.WTildeImaging]:
@@ -205,31 +200,6 @@ def model_obj_linear_light_profiles_to_light_profiles(self):
 
         return model_instance.model_obj
 
-    @property
-    def sky_linear_light_profiles_to_light_profiles(self):
-        """
-        The model object may contain linear light profiles, which solve for the `intensity` during the `Inversion`.
-
-        This means they are difficult to visualize, because they do not have a valid `intensity` parameter.
-
-        To address this, this property creates a new `model_obj` where all linear light profiles are converted to
-        ordinary light profiles whose `intensity` parameters are set to the results of the Inversion.
-
-        Returns
-        -------
-        A `model_obj` (E.g. galaxies or `Tracer`) where the light profile intensity values are set to the results
-        of those inferred via the `Inversion`.
-        """
-
-        if self.linear_light_profile_intensity_dict is None:
-            return self.sky
-
-        model_instance = self.append_linear_light_profiles_to_model(
-            model_instance=ModelInstance(dict(sky=self.sky))
-        )
-
-        return model_instance.sky
-
     def append_linear_light_profiles_to_model(self, model_instance):
         """
         For a model instance, this function replaces all linear light profiles with instances of their standard

autogalaxy/aggregator/dataset_model.py

@@ -0,0 +1,62 @@
+from __future__ import annotations
+import logging
+from typing import List
+
+import autofit as af
+import autoarray as aa
+
+logger = logging.getLogger(__name__)
+
+
+def _dataset_model_from(
+    fit: af.Fit, instance: af.ModelInstance
+) -> List[aa.DatasetModel]:
+    """
+    Returns a `DatasetModel` object from a `PyAutoFit` sqlite database `Fit` object.
+
+    The results of a model-fit can be stored in a sqlite database, including the following attributes of the fit:
+
+    - The model and its best fit parameters (e.g. `model.json`).
+
+    Each individual attribute can be loaded from the database via the `fit.value()` method.
+
+    This method combines all of these attributes and returns a `DatasetModel` object for a given non-linear search
+    sample (e.g. the maximum likelihood model).
+
+    If multiple `DatasetModel` objects were fitted simultaneously via analysis summing, the `fit.child_values()` method
+    is instead used to load lists of dataset models. This is necessary if each analysis has different dataset
+    models (e.g. certain parameters vary across each dataset and `Analysis` object).
+
+    Parameters
+    ----------
+    fit
+        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry in a sqlite database.
+    instance
+        A manual instance that overwrites the max log likelihood instance in fit (e.g. for drawing the instance
+        randomly from the PDF).
+    """
+
+    if instance is not None:
+        try:
+            dataset_model = instance.dataset_model
+        except AttributeError:
+            dataset_model = None
+    else:
+        try:
+            dataset_model = fit.instance.dataset_model
+        except AttributeError:
+            dataset_model = None
+
+    if len(fit.children) > 0:
+        logger.info(
+            """
+            Using database for a fit with multiple summed Analysis objects.
+
+            DatasetModel objects do not fully support this yet (e.g. variables across Analysis objects may not be correct)
+            so proceed with caution!
+            """
+        )
+
+        return [dataset_model] * len(fit.children)
+
+    return [dataset_model]

autogalaxy/aggregator/fit_imaging.py

@@ -11,6 +11,7 @@
 
 from autogalaxy.aggregator.imaging import _imaging_from
 from autogalaxy.aggregator.galaxies import _galaxies_from
+from autogalaxy.aggregator.dataset_model import _dataset_model_from
 from autogalaxy.aggregator import agg_util
 
 
@@ -62,6 +63,8 @@ def _fit_imaging_from(
 
     galaxies_list = _galaxies_from(fit=fit, instance=instance)
 
+    dataset_model_list = _dataset_model_from(fit=fit, instance=instance)
+
     adapt_images_list = agg_util.adapt_images_from(fit=fit)
 
     settings_inversion = settings_inversion or fit.value(name="settings_inversion")
@@ -72,8 +75,12 @@ def _fit_imaging_from(
 
     fit_dataset_list = []
 
-    for dataset, galaxies, adapt_images, mesh_grids_of_planes in zip(
-        dataset_list, galaxies_list, adapt_images_list, mesh_grids_of_planes_list
+    for dataset, galaxies, dataset_model, adapt_images, mesh_grids_of_planes in zip(
+        dataset_list,
+        galaxies_list,
+        dataset_model_list,
+        adapt_images_list,
+        mesh_grids_of_planes_list,
     ):
         preloads = agg_util.preloads_from(
             preloads_cls=Preloads,
@@ -86,6 +93,7 @@ def _fit_imaging_from(
             FitImaging(
                 dataset=dataset,
                 galaxies=galaxies,
+                dataset_model=dataset_model,
                 adapt_images=adapt_images,
                 settings_inversion=settings_inversion,
                 preloads=preloads,

autogalaxy/aggregator/fit_interferometer.py

@@ -11,6 +11,7 @@
 
 from autogalaxy.aggregator import agg_util
 from autogalaxy.aggregator.interferometer import _interferometer_from
+from autogalaxy.aggregator.dataset_model import _dataset_model_from
 from autogalaxy.aggregator.galaxies import _galaxies_from
 
 
@@ -66,6 +67,8 @@ def _fit_interferometer_from(
 
     galaxies_list = _galaxies_from(fit=fit, instance=instance)
 
+    dataset_model_list = _dataset_model_from(fit=fit, instance=instance)
+
     adapt_images_list = agg_util.adapt_images_from(fit=fit)
 
     settings_inversion = settings_inversion or fit.value(name="settings_inversion")
@@ -76,8 +79,12 @@ def _fit_interferometer_from(
 
     fit_dataset_list = []
 
-    for dataset, galaxies, adapt_images, mesh_grids_of_planes in zip(
-        dataset_list, galaxies_list, adapt_images_list, mesh_grids_of_planes_list
+    for dataset, galaxies, dataset_model, adapt_images, mesh_grids_of_planes in zip(
+        dataset_list,
+        galaxies_list,
+        dataset_model_list,
+        adapt_images_list,
+        mesh_grids_of_planes_list,
     ):
         preloads = agg_util.preloads_from(
             preloads_cls=Preloads,
@@ -90,6 +97,7 @@ def _fit_interferometer_from(
             FitInterferometer(
                 dataset=dataset,
                 galaxies=galaxies,
+                dataset_model=dataset_model,
                 adapt_images=adapt_images,
                 settings_inversion=settings_inversion,
                 preloads=preloads,

autogalaxy/aggregator/galaxies.py

@@ -50,17 +50,18 @@ def _galaxies_from(fit: af.Fit, instance: af.ModelInstance) -> List[Galaxy]:
         if hasattr(fit.instance, "clumps"):
             galaxies = galaxies + fit.instance.clumps
 
-    if len(fit.children) > 0:
-        logger.info(
-            """
-            Using database for a fit with multiple summed Analysis objects.
-
-            Galaxy objects do not fully support this yet (e.g. variables across Analysis objects may not be correct)
-            so proceed with caution!
-            """
-        )
-
-        return [galaxies] * len(fit.children)
+    if fit.children is not None:
+        if len(fit.children) > 0:
+            logger.info(
+                """
+                Using database for a fit with multiple summed Analysis objects.
+
+                Galaxy objects do not fully support this yet (e.g. variables across Analysis objects may not be correct)
+                so proceed with caution!
+                """
+            )
+
+            return [galaxies] * len(fit.children)
 
     return [galaxies]
 

autogalaxy/analysis/analysis/analysis.py

@@ -67,11 +67,11 @@ def galaxies_via_instance_from(
 
         return Galaxies(galaxies=instance.galaxies, run_time_dict=run_time_dict)
 
-    def sky_via_instance_from(
+    def dataset_model_via_instance_from(
         self, instance: af.ModelInstance
-    ) -> Union[lp.Sky, lp_linear.Sky]:
+    ) -> aa.DatasetModel:
         """
-        Create a sky from a model instance, which is used to fit the dataset.
+        Create a dataset model from a model instance, which is used to fit the dataset.
 
         Parameters
         ----------
@@ -81,10 +81,10 @@ def sky_via_instance_from(
 
         Returns
         -------
-        A sky that is used to then fit the dataset.
+        A dataset_model that is used to then fit the dataset.
         """
-        if hasattr(instance, "sky"):
-            return instance.sky
+        if hasattr(instance, "dataset_model"):
+            return instance.dataset_model
 
     def make_result(
         self,

autogalaxy/analysis/chaining_util.py

@@ -157,24 +157,6 @@ def source_from(
     return source_custom_model_from(result=result, source_is_model=True)
 
 
-def sky_from(result: Result) -> Optional[af.Model]:
-    """
-
-
-    Parameters
-    ----------
-    result
-
-    Returns
-    -------
-
-    """
-    try:
-        return result.model.sky
-    except AttributeError:
-        return None
-
-
 def clumps_from(
     result: Result,
     light_as_model: bool = False,

autogalaxy/analysis/maker.py

@@ -63,8 +63,6 @@ def fit_via_model_from(
         fit
             A fit object where an instance of the model has been fitted to the data.
         """
-        self.fit_unit_instance_from(unit_value=unit_value)
-
         try:
             try:
                 return self.fit_unit_instance_from(unit_value=unit_value)

autogalaxy/config/priors/dataset_model.yaml

@@ -0,0 +1,10 @@
+DatasetModel:
+  background_sky_level:
+    type: Constant
+    value: 0.0
+  grid_offset_0:
+    type: Constant
+    value: 0.0
+  grid_offset_1:
+    type: Constant
+    value: 0.0

autogalaxy/galaxy/to_inversion.py

@@ -23,7 +23,6 @@ class AbstractToInversion:
     def __init__(
         self,
         dataset: Optional[Union[aa.Imaging, aa.Interferometer, aa.DatasetInterface]],
-        sky: Optional[Basis] = None,
         adapt_images: Optional[AdaptImages] = None,
         settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
         preloads=Preloads(),
@@ -43,7 +42,6 @@ def __init__(
 
         self.dataset = dataset
 
-        self.sky = sky
         self.adapt_images = adapt_images
 
         self.settings_inversion = settings_inversion
@@ -97,18 +95,6 @@ def linear_obj_galaxy_dict(
 
     @cached_property
     def linear_obj_list(self) -> List[aa.LinearObj]:
-        if isinstance(self.sky, Basis):
-            sky_linear_obj_list = LightProfileLinearObjFuncList(
-                grid=self.dataset.grid,
-                blurring_grid=self.dataset.blurring_grid,
-                convolver=self.dataset.convolver,
-                light_profile_list=self.sky.light_profile_list,
-                regularization=None,
-                run_time_dict=self.run_time_dict,
-            )
-
-            return list(self.linear_obj_galaxy_dict.keys()) + [sky_linear_obj_list]
-
         return list(self.linear_obj_galaxy_dict.keys())
 
     @cached_property
@@ -121,7 +107,6 @@ def __init__(
         self,
         dataset: Optional[Union[aa.Imaging, aa.Interferometer, aa.DatasetInterface]],
         galaxies: List[Galaxy],
-        sky: Optional[LightProfile] = None,
         adapt_images: Optional[AdaptImages] = None,
         settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
         preloads=aa.Preloads(),
@@ -131,7 +116,6 @@ def __init__(
 
         super().__init__(
             dataset=dataset,
-            sky=sky,
             adapt_images=adapt_images,
             settings_inversion=settings_inversion,
             preloads=preloads,