Feature/point smbh

autogalaxy/aggregator/abstract.py

@@ -25,7 +25,7 @@ def __init__(self, aggregator: af.Aggregator):
         self.aggregator = aggregator
 
     @abstractmethod
-    def make_object_for_gen(self, fit: af.Fit, galaxies: List[Galaxy]) -> object:
+    def object_via_gen_from(self, fit: af.Fit, galaxies: List[Galaxy]) -> object:
         """
         For example, in the `PlaneAgg` object, this function is overwritten such that it creates a `Plane` from a
         `ModelInstance` that contains the galaxies of a sample from a non-linear search.
@@ -55,7 +55,7 @@ def max_log_likelihood_gen_from(self) -> Generator:
         """
 
         def func_gen(fit: af.Fit) -> Generator:
-            return self.make_object_for_gen(fit=fit, galaxies=fit.instance.galaxies)
+            return self.object_via_gen_from(fit=fit, galaxies=fit.instance.galaxies)
 
         return self.aggregator.map(func=func_gen)
 
@@ -117,7 +117,7 @@ def func_gen(fit: af.Fit, minimum_weight: float) -> List[object]:
                     instance = sample.instance_for_model(model=samples.model)
 
                     all_above_weight_list.append(
-                        self.make_object_for_gen(fit=fit, galaxies=instance.galaxies)
+                        self.object_via_gen_from(fit=fit, galaxies=instance.galaxies)
                     )
 
             return all_above_weight_list
@@ -150,7 +150,7 @@ def func_gen(fit: af.Fit, total_samples: int) -> List[object]:
             samples = fit.value(name="samples")
 
             return [
-                self.make_object_for_gen(
+                self.object_via_gen_from(
                     fit=fit,
                     galaxies=samples.draw_randomly_via_pdf().galaxies,
                 )

autogalaxy/aggregator/fit_imaging.py

@@ -32,7 +32,7 @@ def _fit_imaging_from(
     Parameters
     ----------
     fit
-        A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
+        A PyAutoFit database Fit object containing the generators of the results of model-fits.
     galaxies
         A list of galaxies corresponding to a sample of a non-linear search and model-fit.
 
@@ -99,15 +99,15 @@ def __init__(
         self.settings_inversion = settings_inversion
         self.use_preloaded_grid = use_preloaded_grid
 
-    def make_object_for_gen(self, fit, galaxies) -> FItImaging:
+    def object_via_gen_from(self, fit, galaxies) -> FItImaging:
         """
         Creates a `FitImaging` object from a `ModelInstance` that contains the galaxies of a sample from a non-linear
         search.
 
         Parameters
         ----------
         fit
-            A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
+            A PyAutoFit database Fit object containing the generators of the results of model-fits.
         galaxies
             A list of galaxies corresponding to a sample of a non-linear search and model-fit.
 

autogalaxy/aggregator/fit_interferometer.py

@@ -33,7 +33,7 @@ def _fit_interferometer_from(
     Parameters
     ----------
     fit
-        A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
+        A PyAutoFit database Fit object containing the generators of the results of model-fits.
     galaxies
         A list of galaxies corresponding to a sample of a non-linear search and model-fit.
 
@@ -95,15 +95,15 @@ def __init__(
         self.use_preloaded_grid = use_preloaded_grid
         self.real_space_mask = real_space_mask
 
-    def make_object_for_gen(self, fit, galaxies) -> FitInterferometer:
+    def object_via_gen_from(self, fit, galaxies) -> FitInterferometer:
         """
         Creates a `FitInterferometer` object from a `ModelInstance` that contains the galaxies of a sample from a non-linear
         search.
 
         Parameters
         ----------
         fit
-            A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
+            A PyAutoFit database Fit object containing the generators of the results of model-fits.
         galaxies
             A list of galaxies corresponding to a sample of a non-linear search and model-fit.
 

autogalaxy/aggregator/imaging.py

@@ -9,15 +9,15 @@ def _imaging_from(fit: af.Fit, settings_imaging: Optional[aa.SettingsImaging] =
     """
     Returns a `Imaging` object from an aggregator's `SearchOutput` class, which we call an 'agg_obj' to describe
     that it acts as the aggregator object for one result in the `Aggregator`. This uses the aggregator's generator
-    outputs such that the function can use the `Aggregator`'s map function to to create a `Imaging` generator.
+    outputs such that the function can use the `Aggregator`'s map function to create a `Imaging` generator.
 
      The `Imaging` is created following the same method as the PyAutoGalaxy `Search` classes, including using the
     `SettingsImaging` instance output by the Search to load inputs of the `Imaging` (e.g. psf_shape_2d).
 
     Parameters
     ----------
     fit
-        A PyAutoFit aggregator's SearchOutput object containing the generators of the results of PyAutoGalaxy model-fits.
+        A PyAutoFit aggregator's SearchOutput object containing the generators of the results of model-fits.
     """
 
     data = fit.value(name="data")
@@ -52,7 +52,7 @@ class ImagingAgg:
     def __init__(self, aggregator: af.Aggregator):
         self.aggregator = aggregator
 
-    def imaging_gen_from(self, settings_imaging: Optional[aa.SettingsImaging] = None):
+    def dataset_gen_from(self, settings_imaging: Optional[aa.SettingsImaging] = None):
         """
         Returns a generator of `Imaging` objects from an input aggregator, which generates a list of the
         `Imaging` objects for every set of results loaded in the aggregator.
@@ -64,7 +64,7 @@ def imaging_gen_from(self, settings_imaging: Optional[aa.SettingsImaging] = None
         Parameters
         ----------
         aggregator
-            A PyAutoFit aggregator object containing the results of PyAutoGalaxy model-fits.
+            A PyAutoFit aggregator object containing the results of model-fits.
         """
 
         func = partial(_imaging_from, settings_imaging=settings_imaging)

autogalaxy/aggregator/interferometer.py

@@ -13,7 +13,7 @@ def _interferometer_from(
     """
     Returns a `Interferometer` object from an aggregator's `SearchOutput` class, which we call an 'agg_obj' to
     describe that it acts as the aggregator object for one result in the `Aggregator`. This uses the aggregator's
-    generator outputs such that the function can use the `Aggregator`'s map function to to create a
+    generator outputs such that the function can use the `Aggregator`'s map function to create a
     `Interferometer` generator.
 
     The `Interferometer` is created following the same method as the PyAutoGalaxy `Search` classes, including
@@ -51,7 +51,7 @@ class InterferometerAgg:
     def __init__(self, aggregator: af.Aggregator):
         self.aggregator = aggregator
 
-    def interferometer_gen_from(
+    def dataset_gen_from(
         self,
         real_space_mask: Optional[aa.Mask2D] = None,
         settings_interferometer: Optional[aa.SettingsInterferometer] = None,
@@ -67,7 +67,7 @@ def interferometer_gen_from(
         Parameters
         ----------
         aggregator : ImaginAggregator
-            A PyAutoFit aggregator object containing the results of PyAutoGalaxy model-fits.
+            A PyAutoFit aggregator object containing the results of model-fits.
         """
 
         func = partial(

autogalaxy/aggregator/plane.py

@@ -21,7 +21,7 @@ def _plane_from(fit: af.Fit, galaxies: List[Galaxy]) -> Plane:
     Parameters
     ----------
     fit
-        A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
+        A PyAutoFit database Fit object containing the generators of the results of model-fits.
     galaxies
         A list of galaxies corresponding to a sample of a non-linear search and model-fit.
 
@@ -62,15 +62,15 @@ class PlaneAgg(AbstractAgg):
     search model-fit.
     """
 
-    def make_object_for_gen(self, fit, galaxies) -> Plane:
+    def object_via_gen_from(self, fit, galaxies) -> Plane:
         """
         Creates a `Plane` object from a `ModelInstance` that contains the galaxies of a sample from a non-linear
         search.
 
         Parameters
         ----------
         fit
-            A PyAutoFit database Fit object containing the generators of the results of PyAutoGalaxy model-fits.
+            A PyAutoFit database Fit object containing the generators of the results of model-fits.
         galaxies
             A list of galaxies corresponding to a sample of a non-linear search and model-fit.
 

autogalaxy/analysis/analysis.py

@@ -324,7 +324,7 @@ def instance_with_associated_adapt_images_from(
     def save_attributes_for_aggregator(self, paths: af.DirectoryPaths):
         """
         Before the model-fit via the non-linear search begins, this routine saves attributes of the `Analysis` object
-        to the `pickles` folder such that they can be load after the analysis using PyAutoFit's database and aggregator
+        to the `pickles` folder such that they can be loaded after the analysis using PyAutoFit's database and aggregator
         tools.
 
         For this analysis the following are output:

autogalaxy/analysis/model_util.py

@@ -158,7 +158,7 @@ def clean_model_of_adapt_images(model):
     return model
 
 
-def hyper_model_from(
+def adapt_model_from(
     setup_adapt,
     result: af.Result,
     pixelization_overwrite=None,
@@ -231,7 +231,7 @@ def adapt_fit(
             "The analysis class does not have a adapt_model_image attribute, which is required for hyper fitting."
         )
 
-    hyper_model_pix = hyper_model_from(
+    adapt_model_pix = adapt_model_from(
         setup_adapt=setup_adapt,
         result=result,
     )
@@ -245,10 +245,10 @@ def adapt_fit(
     )
 
     set_upper_limit_of_pixelization_pixels_prior(
-        model=hyper_model_pix, pixels_in_mask=result.mask.pixels_in_mask
+        model=adapt_model_pix, pixels_in_mask=result.mask.pixels_in_mask
     )
 
-    adapt_result = search.fit(model=hyper_model_pix, analysis=analysis)
+    adapt_result = search.fit(model=adapt_model_pix, analysis=analysis)
 
     result.adapt = adapt_result
 

autogalaxy/analysis/preloads.py

@@ -46,7 +46,7 @@ def __init__(
         Parameters
         ----------
         blurred_image
-            The preloaded array of values containing the blurred image of a lens model fit (e.g. that light profile of
+            The preloaded array of values containing the blurred image of a model fit (e.g. that light profile of
             every galaxy in the model). This can be preloaded when no light profiles in the model vary.
         w_tilde
             A class containing values that enable an inversion's linear algebra to use the w-tilde formalism. This can

autogalaxy/config/README.rst

@@ -3,7 +3,7 @@ The ``config`` folder contains configuration files which customize default **PyA
 Folders
 -------
 
-- ``priors``: Configs defining default priors assumed on every lens model component and set of parameters.
+- ``priors``: Configs defining default priors assumed on every model component and set of parameters.
 - ``visualize``: Configs defining what images are output by a lens model fit.
 
 Files

autogalaxy/config/grids.yaml

@@ -56,4 +56,5 @@ radial_minimum:
     ShapeletPolarEll: 1.0e-8
     ShapeletExponential: 1.0e-8
     ShapeletExponentialEll: 1.0e-8
+    SMBH: 1.0e-8
     EllProfile: 1.0e-08

autogalaxy/config/notation.yaml

@@ -26,6 +26,7 @@ label:
     intensity: I_{\rm b}
     kappa: \kappa
     kappa_s: \kappa_{\rm s}
+    mass: M
     mass_at_200: M_{\rm 200}
     mass_to_light_gradient: \Gamma
     mass_to_light_ratio: \Psi
@@ -57,6 +58,7 @@ label:
     ExternalShear: ext
     Mesh: mesh
     Point: point
+    SMBH: smbh
     Redshift: z
     Regularization: reg
     HyperBackgroundNoise: hyper

autogalaxy/config/priors/mass/point/smbh.yaml

@@ -0,0 +1,55 @@
+SMBH:
+  centre_0:
+    type: Gaussian
+    mean: 0.0
+    sigma: 0.1
+    lower_limit: -inf
+    upper_limit: inf
+    width_modifier:
+      type: Absolute
+      value: 0.05
+    gaussian_limits:
+      lower: -inf
+      upper: inf
+  centre_1:
+    type: Gaussian
+    mean: 0.0
+    sigma: 0.1
+    lower_limit: -inf
+    upper_limit: inf
+    width_modifier:
+      type: Absolute
+      value: 0.05
+    gaussian_limits:
+      lower: -inf
+      upper: inf
+  mass:
+    type: LogUniform
+    lower_limit: 1000000.0
+    upper_limit: 10000000000000.0
+    width_modifier:
+      type: Relative
+      value: 0.25
+    gaussian_limits:
+      lower: 0.0
+      upper: inf
+  redshift_object:
+    type: Uniform
+    lower_limit: 0.0
+    upper_limit: 1.0
+    width_modifier:
+      type: Relative
+      value: 0.5
+    gaussian_limits:
+      lower: 0.0
+      upper: inf
+  redshift_source:
+    type: Uniform
+    lower_limit: 0.0
+    upper_limit: 1.0
+    width_modifier:
+      type: Relative
+      value: 0.5
+    gaussian_limits:
+      lower: 0.0
+      upper: inf

autogalaxy/cosmology/lensing.py

@@ -140,7 +140,7 @@ def critical_surface_density_between_redshifts_from(
         D_l = angular_diameter_distance_of_lens_redshift_to_earth
 
         This function returns the critical surface density in units of solar masses, which are convenient units for
-        converting the inferred masses of a lens model from angular units (e.g. dimensionless units inferred from
+        converting the inferred masses of a model from angular units (e.g. dimensionless units inferred from
         data in arcseconds) to solar masses.
 
         Parameters
@@ -177,7 +177,7 @@ def critical_surface_density_between_redshifts_solar_mass_per_kpc2_from(
         D_l = Angular diameter distance of lens redshift to earth
 
         This function returns the critical surface density in units of solar masses / kpc^2, which are convenient
-        units for converting the inferred masses of a lens model from angular units (e.g. dimensionless units inferred
+        units for converting the inferred masses of a model from angular units (e.g. dimensionless units inferred
         from data in arcseconds) to solar masses.
 
         Parameters

autogalaxy/imaging/model/analysis.py

@@ -247,7 +247,7 @@ def visualize_before_fit(self, paths: af.DirectoryPaths, model: af.Collection):
             The PyAutoFit model object, which includes model components representing the galaxies that are fitted to
             the imaging data.
         """
-        if paths.is_complete or os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
+        if not self.should_visualize(paths=paths):
             return
 
         visualizer = VisualizerImaging(visualize_path=paths.image_path)
@@ -295,7 +295,7 @@ def visualize(
             which may change which images are output.
         """
 
-        if os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
+        if not self.should_visualize(paths=paths):
             return
 
         instance = self.instance_with_associated_adapt_images_from(instance=instance)
@@ -374,7 +374,7 @@ def make_result(
     def save_attributes_for_aggregator(self, paths: af.DirectoryPaths):
         """
          Before the non-linear search begins, this routine saves attributes of the `Analysis` object to the `pickles`
-         folder such that they can be load after the analysis using PyAutoFit's database and aggregator tools.
+         folder such that they can be loaded after the analysis using PyAutoFit's database and aggregator tools.
 
          For this analysis, it uses the `AnalysisDataset` object's method to output the following:
 

autogalaxy/interferometer/model/analysis.py

@@ -318,7 +318,7 @@ def visualize_before_fit(self, paths: af.DirectoryPaths, model: af.Collection):
             the imaging data.
         """
 
-        if paths.is_complete or os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
+        if not self.should_visualize(paths=paths):
             return
 
         visualizer = VisualizerInterferometer(visualize_path=paths.image_path)
@@ -364,7 +364,7 @@ def visualize(self, paths: af.DirectoryPaths, instance, during_analysis):
             which may change which images are output.
         """
 
-        if os.environ.get("PYAUTOFIT_TEST_MODE") == "1":
+        if not self.should_visualize(paths=paths):
             return
 
         instance = self.instance_with_associated_adapt_images_from(instance=instance)
@@ -449,7 +449,7 @@ def make_result(
     def save_attributes_for_aggregator(self, paths: af.DirectoryPaths):
         """
          Before the model-fit begins, this routine saves attributes of the `Analysis` object to the `pickles` folder
-         such that they can be load after the analysis using PyAutoFit's database and aggregator tools.
+         such that they can be loaded after the analysis using PyAutoFit's database and aggregator tools.
 
          For this analysis, it uses the `AnalysisDataset` object's method to output the following: