Feature/fix simulator over sampling

autolens/__init__.py

@@ -113,6 +113,11 @@
 from . import util
 
 from autoconf import conf
+from autoconf.fitsable import ndarray_via_hdu_from
+from autoconf.fitsable import ndarray_via_fits_from
+from autoconf.fitsable import header_obj_from
+from autoconf.fitsable import output_to_fits
+from autoconf.fitsable import hdu_list_for_output_from
 
 conf.instance.register(__file__)
 

autolens/aggregator/__init__.py

@@ -27,4 +27,4 @@
 from autolens.aggregator.subplot import SubplotFit as subplot_fit
 from autolens.aggregator.subplot import SubplotFitLog10 as subplot_fit_log10
 from autolens.aggregator.subplot import FITSTracer as fits_tracer
-from autolens.aggregator.subplot import FITSFit as fits_fits
+from autolens.aggregator.subplot import FITSFit as fits_fit

autolens/aggregator/fit_imaging.py

@@ -18,12 +18,13 @@ def _fit_imaging_from(
     settings_inversion: aa.SettingsInversion = None,
 ) -> List[FitImaging]:
     """
-    Returns a list of `FitImaging` object from a `PyAutoFit` sqlite database `Fit` object.
+    Returns a list of `FitImaging` object from a `PyAutoFit` loaded directory `Fit` or 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 results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
+    attributes of the fit:
 
     - The imaging data, noise-map, PSF and settings as .fits files (e.g. `dataset/data.fits`).
-    - The mask used to mask the `Imaging` data structure in the fit (`dataset/mask.fits`).
+    - The mask used to mask the `Imaging` data structure in the fit (`dataset.fits[hdu=0]`).
     - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
 
     Each individual attribute can be loaded from the database via the `fit.value()` method.
@@ -41,7 +42,8 @@ def _fit_imaging_from(
     Parameters
     ----------
     fit
-        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry in a sqlite database.
+        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
+        an output directory or from an 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).
@@ -87,41 +89,42 @@ def __init__(
         settings_inversion: Optional[aa.SettingsInversion] = None,
     ):
         """
-        Interfaces with an `PyAutoFit` aggregator object to create instances of `FitImaging` objects from the results
-        of a model-fit.
-
-        The results of a model-fit can be stored in a sqlite database, including the following attributes of the fit:
-
-        - The imaging data, noise-map, PSF and settings as .fits files (e.g. `dataset/data.fits`).
-        - The mask used to mask the `Imaging` data structure in the fit (`dataset/mask.fits`).
-        - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
-
-        The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
-        can load them all at once and create an `FitImaging` object via the `_fit_imaging_from` method.
-
-        This class's methods returns generators which create the instances of the `FitImaging` objects. This ensures
-        that large sets of results can be efficiently loaded from the hard-disk and do not require storing all
-        `FitImaging` instances in the memory at once.
-
-        For example, if the `aggregator` contains 3 model-fits, this class can be used to create a generator which
-        creates instances of the corresponding 3 `FitImaging` objects.
-
-        If multiple `FitImaging` objects were fitted simultaneously via analysis summing, the `fit.child_values()` method
-        is instead used to load lists of the data, noise-map, PSF and mask and combine them into a list of
-        `FitImaging` objects.
-
-        This can be done manually, but this object provides a more concise API.
-
-        Parameters
-        ----------
-        aggregator
-            A `PyAutoFit` aggregator object which can load the results of model-fits.
-        settings_inversion
-            Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
-        use_preloaded_grid
-            Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
-            grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
-            used as the fit.
+            Interfaces with an `PyAutoFit` aggregator object to create instances of `FitImaging` objects from the results
+            of a model-fit.
+
+            The results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
+        attributes of the fit:
+
+            - The imaging data, noise-map, PSF and settings as .fits files (e.g. `dataset/data.fits`).
+            - The mask used to mask the `Imaging` data structure in the fit (`dataset.fits[hdu=0]`).
+            - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
+
+            The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
+            can load them all at once and create an `FitImaging` object via the `_fit_imaging_from` method.
+
+            This class's methods returns generators which create the instances of the `FitImaging` objects. This ensures
+            that large sets of results can be efficiently loaded from the hard-disk and do not require storing all
+            `FitImaging` instances in the memory at once.
+
+            For example, if the `aggregator` contains 3 model-fits, this class can be used to create a generator which
+            creates instances of the corresponding 3 `FitImaging` objects.
+
+            If multiple `FitImaging` objects were fitted simultaneously via analysis summing, the `fit.child_values()` method
+            is instead used to load lists of the data, noise-map, PSF and mask and combine them into a list of
+            `FitImaging` objects.
+
+            This can be done manually, but this object provides a more concise API.
+
+            Parameters
+            ----------
+            aggregator
+                A `PyAutoFit` aggregator object which can load the results of model-fits.
+            settings_inversion
+                Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
+            use_preloaded_grid
+                Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
+                grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
+                used as the fit.
         """
         super().__init__(aggregator=aggregator)
 
@@ -138,7 +141,8 @@ def object_via_gen_from(
         Parameters
         ----------
         fit
-            A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry in a sqlite database.
+            A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
+        an output directory or from an 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).

autolens/aggregator/fit_interferometer.py

@@ -15,13 +15,13 @@
 def _fit_interferometer_from(
     fit: af.Fit,
     instance: Optional[af.ModelInstance] = None,
-    real_space_mask: Optional[aa.Mask2D] = None,
     settings_inversion: aa.SettingsInversion = None,
 ) -> List[FitInterferometer]:
     """
-    Returns a list of `FitInterferometer` objects from a `PyAutoFit` sqlite database `Fit` object.
+    Returns a list of `FitInterferometer` objects from a `PyAutoFit` loaded directory `Fit` or 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 results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
+    attributes of the fit:
 
     - The interferometer data, noise-map, uv-wavelengths and settings as .fits files (e.g. `dataset/data.fits`).
     - The real space mask defining the grid of the interferometer for the FFT (`dataset/real_space_mask.fits`).
@@ -43,7 +43,8 @@ def _fit_interferometer_from(
     Parameters
     ----------
     fit
-        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry in a sqlite database.
+        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
+        an output directory or from an 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).
@@ -52,7 +53,6 @@ def _fit_interferometer_from(
     """
     dataset_list = _interferometer_from(
         fit=fit,
-        real_space_mask=real_space_mask,
     )
     tracer_list = _tracer_from(fit=fit, instance=instance)
     dataset_model_list = _dataset_model_from(fit=fit, instance=instance)
@@ -87,45 +87,44 @@ def __init__(
         self,
         aggregator: af.Aggregator,
         settings_inversion: Optional[aa.SettingsInversion] = None,
-        real_space_mask: Optional[aa.Mask2D] = None,
     ):
         """
-        Interfaces with an `PyAutoFit` aggregator object to create instances of `FitInterferometer` objects from the
-        results of a model-fit.
+            Interfaces with an `PyAutoFit` aggregator object to create instances of `FitInterferometer` objects from the
+            results of a model-fit.
 
-        The results of a model-fit can be stored in a sqlite database, including the following attributes of the fit:
+            The results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
+        attributes of the fit:
 
-        - The interferometer data, noise-map, uv-wavelengths and settings as .fits files (e.g. `dataset/data.fits`).
-        - The real space mask defining the grid of the interferometer for the FFT (`dataset/real_space_mask.fits`).
-        - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
+            - The interferometer data, noise-map, uv-wavelengths and settings as .fits files (e.g. `dataset/data.fits`).
+            - The real space mask defining the grid of the interferometer for the FFT (`dataset/real_space_mask.fits`).
+            - The settings of inversions used by the fit (`dataset/settings_inversion.json`).
 
-        The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
-        can load them all at once and create an `FitInterferometer` object via the `_fit_interferometer_from` method.
+            The `aggregator` contains the path to each of these files, and they can be loaded individually. This class
+            can load them all at once and create an `FitInterferometer` object via the `_fit_interferometer_from` method.
 
-        This class's methods returns generators which create the instances of the `FitInterferometer` objects. This ensures
-        that large sets of results can be efficiently loaded from the hard-disk and do not require storing all
-        `FitInterferometer` instances in the memory at once.
+            This class's methods returns generators which create the instances of the `FitInterferometer` objects. This ensures
+            that large sets of results can be efficiently loaded from the hard-disk and do not require storing all
+            `FitInterferometer` instances in the memory at once.
 
-        For example, if the `aggregator` contains 3 model-fits, this class can be used to create a generator which
-        creates instances of the corresponding 3 `FitInterferometer` objects.
+            For example, if the `aggregator` contains 3 model-fits, this class can be used to create a generator which
+            creates instances of the corresponding 3 `FitInterferometer` objects.
 
-        This can be done manually, but this object provides a more concise API.
+            This can be done manually, but this object provides a more concise API.
 
-        Parameters
-        ----------
-        aggregator
-            A `PyAutoFit` aggregator object which can load the results of model-fits.
-        settings_inversion
-            Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
-        use_preloaded_grid
-            Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
-            grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
-            used as the fit.
+            Parameters
+            ----------
+            aggregator
+                A `PyAutoFit` aggregator object which can load the results of model-fits.
+            settings_inversion
+                Optionally overwrite the `SettingsInversion` of the `Inversion` object that is created from the fit.
+            use_preloaded_grid
+                Certain pixelization's construct their mesh in the source-plane from a stochastic KMeans algorithm. This
+                grid may be output to hard-disk after the model-fit and loaded via the database to ensure the same grid is
+                used as the fit.
         """
         super().__init__(aggregator=aggregator)
 
         self.settings_inversion = settings_inversion
-        self.real_space_mask = real_space_mask
 
     def object_via_gen_from(
         self, fit, instance: Optional[af.ModelInstance] = None
@@ -138,7 +137,8 @@ def object_via_gen_from(
             Parameters
             ----------
             fit
-                A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry in a sqlite database.
+                A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
+        an output directory or from an 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).

autolens/aggregator/tracer.py

@@ -12,9 +12,10 @@ def _tracer_from(
     fit: af.Fit, instance: Optional[af.ModelInstance] = None
 ) -> List[Tracer]:
     """
-    Returns a list of `Tracer` objects from a `PyAutoFit` sqlite database `Fit` object.
+    Returns a list of `Tracer` objects from a `PyAutoFit` loaded directory `Fit` or 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 results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
+    attributes of the fit:
 
     - The model and its best fit parameters (e.g. `model.json`).
     - The adapt images associated with adaptive galaxy features (`adapt` folder).
@@ -31,7 +32,8 @@ def _tracer_from(
     Parameters
     ----------
     fit
-        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry in a sqlite database.
+        A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
+        an output directory or from an 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).
@@ -79,7 +81,8 @@ class TracerAgg(af.AggBase):
     Interfaces with an `PyAutoFit` aggregator object to create instances of `Tracer` objects from the results
     of a model-fit.
 
-    The results of a model-fit can be stored in a sqlite database, including the following attributes of the fit:
+    The results of a model-fit can be loaded from hard-disk or stored in a sqlite database, including the following
+    attributes of the fit:
 
     - The model and its best fit parameters (e.g. `model.json`).
     - The adapt images associated with adaptive galaxy features (`adapt` folder).
@@ -117,7 +120,8 @@ def object_via_gen_from(
         Parameters
         ----------
         fit
-            A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry in a sqlite database.
+            A `PyAutoFit` `Fit` object which contains the results of a model-fit as an entry which has been loaded from
+        an output directory or from an sqlite database..
         galaxies
             A list of galaxies corresponding to a sample of a non-linear search and model-fit.
         """

autolens/analysis/plotter_interface.py

@@ -1,4 +1,4 @@
-from os import path
+from autoconf.fitsable import hdu_list_for_output_from
 
 import autoarray as aa
 import autogalaxy.plot as aplt
@@ -65,25 +65,26 @@ def should_plot(name):
             tracer_plotter.subplot_galaxies_images()
 
         if should_plot("fits_tracer"):
-            number_plots = 4
-
-            multi_plotter = aplt.MultiFigurePlotter(
-                plotter_list=[tracer_plotter] * number_plots
-            )
-
-            multi_plotter.output_to_fits(
-                func_name_list=["figures_2d"] * number_plots,
-                figure_name_list=[
+            hdu_list = hdu_list_for_output_from(
+                values_list=[
+                    grid.mask.astype("float"),
+                    tracer.convergence_2d_from(grid=grid).native,
+                    tracer.potential_2d_from(grid=grid).native,
+                    tracer.deflections_yx_2d_from(grid=grid).native[:, :, 0],
+                    tracer.deflections_yx_2d_from(grid=grid).native[:, :, 1],
+                ],
+                ext_name_list=[
+                    "mask",
                     "convergence",
                     "potential",
                     "deflections_y",
                     "deflections_x",
                 ],
-                tag_list=["convergence", "potential", "deflections_y", "deflections_x"],
-                filename="tracer",
-                remove_fits_first=True,
+                header_dict=grid.mask.header_dict,
             )
 
+            hdu_list.writeto(self.image_path / "tracer.fits", overwrite=True)
+
     def image_with_positions(self, image: aa.Array2D, positions: aa.Grid2DIrregular):
         """
         Visualizes the positions of a model-fit, where these positions are used to resample lens models where