remove PLane.output_to_json

autogalaxy/analysis/analysis.py

@@ -520,7 +520,7 @@ def save_results(self, paths: af.DirectoryPaths, result: ResultDataset):
         try:
             output_to_json(
                 obj=result.max_log_likelihood_plane,
-                file_path=paths._files_path / "plane.json"
+                file_path=paths._files_path / "plane.json",
             )
         except AttributeError:
             pass

autogalaxy/analysis/mock/mock_result.py

@@ -19,7 +19,7 @@ def __init__(
         adapt_galaxy_image_path_dict: Dict[ag.Galaxy, ag.Array2D] = None,
         adapt_model_image: ag.Array2D = None,
         max_log_likelihood_plane: ag.Plane = None,
-        max_log_likelihood_tracer = None,
+        max_log_likelihood_tracer=None,
     ):
         super().__init__(
             samples=samples,

autogalaxy/plane/plane.py

@@ -54,10 +54,6 @@ def __init__(
         self.run_time_dict = run_time_dict
         self.run_time_dict = run_time_dict
 
-    def output_to_json(self, file_path: Union[Path, str]):
-        with open(file_path, "w+") as f:
-            json.dump(self.dict(), f, indent=4)
-
     @property
     def galaxy_redshifts(self) -> List[float]:
         return [galaxy.redshift for galaxy in self.galaxies]

autogalaxy/profiles/mass/__init__.py

@@ -28,7 +28,7 @@
     NFWMCRScatterLudlowSph,
     NFWMCRLudlow,
     gNFWMCRLudlow,
-    NFWVirialMassConcSph
+    NFWVirialMassConcSph,
 )
 from .stellar import (
     Gaussian,

autogalaxy/profiles/mass/dark/__init__.py

@@ -6,4 +6,4 @@
 from .nfw_truncated import NFWTruncatedSph
 from .nfw_truncated_mcr import NFWTruncatedMCRLudlowSph, NFWTruncatedMCRDuffySph
 from .nfw_truncated_mcr_scatter import NFWTruncatedMCRScatterLudlowSph
-from .nfw_virial_mass_conc import NFWVirialMassConcSph
+from .nfw_virial_mass_conc import NFWVirialMassConcSph

autogalaxy/profiles/mass/dark/abstract.py

@@ -264,9 +264,9 @@ def concentration(
         redshift_of_cosmic_average_density="profile",
         cosmology: LensingCosmology = Planck15(),
     ):
-        '''
+        """
         Computes the NFW halo concentration, `c_{200m}`
-        '''
+        """
         delta_concentration = self.delta_concentration(
             redshift_object=redshift_profile,
             redshift_source=redshift_source,
@@ -299,9 +299,9 @@ def radius_at_200(
         redshift_of_cosmic_average_density="profile",
         cosmology: LensingCosmology = Planck15(),
     ):
-        '''
+        """
         Returns `r_{200m}` for this halo in **arcseconds**
-        '''
+        """
         concentration = self.concentration(
             redshift_profile=redshift_object,
             redshift_source=redshift_source,
@@ -318,9 +318,9 @@ def mass_at_200_solar_masses(
         redshift_of_cosmic_average_density="profile",
         cosmology: LensingCosmology = Planck15(),
     ):
-        '''
+        """
         Returns `M_{200m}` of this NFW halo, in solar masses, at the given cosmology.
-        '''
+        """
         if redshift_of_cosmic_average_density == "profile":
             redshift_calc = redshift_object
         elif redshift_of_cosmic_average_density == "local":

autogalaxy/profiles/mass/dark/mcr_util.py

@@ -7,12 +7,12 @@
 
 
 def kappa_s_and_scale_radius_for_duffy(mass_at_200, redshift_object, redshift_source):
-    '''
+    """
     Computes the AutoGalaxy NFW parameters (kappa_s, scale_radius) for an NFW halo of the given
     mass, enforcing the Duffy '08 mass-concentration relation.
 
     Interprets mass as *`M_{200c}`*, not `M_{200m}`.
-    '''
+    """
     cosmology = Planck15()
 
     cosmic_average_density = (
@@ -58,12 +58,12 @@ def kappa_s_and_scale_radius_for_duffy(mass_at_200, redshift_object, redshift_so
 def kappa_s_and_scale_radius_for_ludlow(
     mass_at_200, scatter_sigma, redshift_object, redshift_source
 ):
-    '''
+    """
     Computes the AutoGalaxy NFW parameters (kappa_s, scale_radius) for an NFW halo of the given
     mass, enforcing the Ludlow '16 mass-concentration relation.
 
     Interprets mass as *`M_{200c}`*, not `M_{200m}`.
-    '''
+    """
     from colossus.cosmology import cosmology as col_cosmology
     from colossus.halo.concentration import concentration as col_concentration
 

autogalaxy/profiles/mass/dark/nfw_virial_mass_conc.py

@@ -10,7 +10,9 @@
 from autogalaxy.cosmology.wrap import Planck15
 
 
-def kappa_s_and_scale_radius(virial_mass, concentration, virial_overdens, redshift_object, redshift_source):
+def kappa_s_and_scale_radius(
+    virial_mass, concentration, virial_overdens, redshift_object, redshift_source
+):
     cosmology = Planck15()
 
     cosmic_average_density = (
@@ -45,7 +47,7 @@ def kappa_s_and_scale_radius(virial_mass, concentration, virial_overdens, redshi
     scale_radius = scale_radius_kpc / kpc_per_arcsec  # scale radius in arcsec
 
     return kappa_s, scale_radius, virial_radius
-        
+
 
 class NFWVirialMassConcSph(NFWSph):
     def __init__(
@@ -57,7 +59,6 @@ def __init__(
         redshift_object: float = 0.5,
         redshift_source: float = 1.0,
     ):
-
         """
         Spherical NFW profile initialized with the mass and concentration of the halo.
 
@@ -75,7 +76,7 @@ def __init__(
         self.virial_mass = virial_mass
         self.concentration = concentration
         self.virial_overdens = virial_overdens
-        
+
         (
             kappa_s,
             scale_radius,
@@ -87,6 +88,5 @@ def __init__(
             redshift_object=redshift_object,
             redshift_source=redshift_source,
         )
-        
+
         super().__init__(centre=centre, kappa_s=kappa_s, scale_radius=scale_radius)
-

docs/advanced/database.rst

@@ -21,7 +21,7 @@ to the non-linear search:
     session = af.db.open_database("database.sqlite")
 
     emcee = af.Emcee(
-        session=session,  # This instructs the search to write to the .sqlite database.
+        session=session,  # This can instruct the search to write to the .sqlite database.
     )
 
 When a model-fit is performed, a unique identifier is generated based on the model and non-linear search. However,
@@ -40,7 +40,7 @@ alongside the model and search to create the unique identifier:
     emcee = af.Emcee(
         path_prefix=path.join("features", "database"),
         unique_tag=dataset_name,  # This makes the unique identifier use the dataset name
-        session=session,  # This instructs the search to write to the .sqlite database.
+        session=session,  # This can instruct the search to write to the .sqlite database.
     )
 
 Lets suppose that we have performed 100 model-fits to 100 strong galaxies, and when we ran **PyAutoGalaxy** we told it

test_autogalaxy/aggregator/conftest.py

@@ -36,7 +36,6 @@ def clean(database_file):
     value=True,
 )
 def aggregator_from(database_file, analysis, model, samples):
-
     result_path = path.join(conf.instance.output_path, database_file)
 
     clean(database_file=database_file)

test_autogalaxy/analysis/test_analysis.py

@@ -91,8 +91,8 @@ def test__modify_before_fit__kmeans_pixelization_upper_limit_ajusted_based_on_ma
         9, 1.0e-4
     )
 
-def test__save_results__plane_output_to_json(analysis_imaging_7x7):
 
+def test__save_results__plane_output_to_json(analysis_imaging_7x7):
     galaxy = ag.Galaxy(redshift=0.5)
 
     model = af.Collection(galaxies=af.Collection(galaxy=galaxy))
@@ -102,12 +102,11 @@ def test__save_results__plane_output_to_json(analysis_imaging_7x7):
     paths = af.DirectoryPaths()
 
     analysis_imaging_7x7.save_results(
-        paths=paths,
-        result=ag.m.MockResult(max_log_likelihood_plane=plane, model=model)
+        paths=paths, result=ag.m.MockResult(max_log_likelihood_plane=plane, model=model)
     )
 
     plane = from_json(file_path=paths._files_path / "plane.json")
 
     assert plane.galaxies[0].redshift == 0.5
 
-    os.remove(paths._files_path / "plane.json")
+    os.remove(paths._files_path / "plane.json")