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Jammy2211 · Mar 29, 2024 · a43b7a2 · a43b7a2
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diff --git a/autolens/imaging/model/result.py b/autolens/imaging/model/result.py
@@ -5,41 +5,9 @@
 from autolens.lens.tracer import Tracer
 from autolens.imaging.fit_imaging import FitImaging
 from autolens.analysis.result import ResultDataset
-from autolens.analysis.preloads import Preloads
 
 
 class ResultImaging(ResultDataset):
-    """
-    After the non-linear search of a fit to an imaging dataset is complete it creates this `ResultImaging`, object
-    which includes:
-
-    - The samples of the non-linear search (E.g. MCMC chains, nested sampling samples) which are used to compute
-      the maximum likelihood model, posteriors and other properties.
-
-    - The model used to fit the data, which uses the samples to create specific instances of the model (e.g.
-      an instance of the maximum log likelihood model).
-
-    - The non-linear search used to perform the model fit.
-
-    This class contains a number of methods which use the above objects to create the max log likelihood `Plane`,
-    `FitImaging`, adapt-galaxy images,etc.
-
-    Parameters
-    ----------
-    samples
-        A PyAutoFit object which contains the samples of the non-linear search, for example the chains of an MCMC
-        run of samples of the nested sampler.
-    model
-        The PyAutoFit model object, which includes model components representing the galaxies that are fitted to
-        the imaging data.
-    search
-        The non-linear search used to perform this model-fit.
-
-    Returns
-    -------
-    ResultImaging
-        The result of fitting the model to the imaging dataset, via a non-linear search.
-    """
 
     @property
     def max_log_likelihood_fit(self) -> FitImaging:

diff --git a/autolens/interferometer/model/result.py b/autolens/interferometer/model/result.py
@@ -1,7 +1,4 @@
-import numpy as np
-
 import autoarray as aa
-import autogalaxy as ag
 
 from autogalaxy.analysis.adapt_images.adapt_images import AdaptImages
 from autolens.lens.tracer import Tracer
@@ -10,37 +7,6 @@
 
 
 class ResultInterferometer(ResultDataset):
-    """
-    After the non-linear search of a fit to an interferometer dataset is complete it creates
-    this `ResultInterferometer` object, which includes:
-
-    - The samples of the non-linear search (E.g. MCMC chains, nested sampling samples) which are used to compute
-      the maximum likelihood model, posteriors and other properties.
-
-    - The model used to fit the data, which uses the samples to create specific instances of the model (e.g.
-      an instance of the maximum log likelihood model).
-
-    - The non-linear search used to perform the model fit.
-
-    This class contains a number of methods which use the above objects to create the max log likelihood `Tracer`,
-    `FitInterferometer`, adapt-galaxy images,etc.
-
-    Parameters
-    ----------
-    samples
-        A PyAutoFit object which contains the samples of the non-linear search, for example the chains of an MCMC
-        run of samples of the nested sampler.
-    model
-        The PyAutoFit model object, which includes model components representing the galaxies that are fitted to
-        the interferometer data.
-    search
-        The non-linear search used to perform this model-fit.
-
-    Returns
-    -------
-    ResultInterferometer
-        The result of fitting the model to the interferometer dataset, via a non-linear search.
-    """
 
     @property
     def max_log_likelihood_fit(self) -> FitInterferometer: