Merge branch 'toy_models' into develop

# Conflicts:
#	autoarray/dataset/imaging.py
#	autoarray/mask/mask.py
#	autoarray/plotters/array_plotters.py
#	autoarray/plotters/fit_imaging_plotters.py
#	autoarray/plotters/fit_interferometer_plotters.py
#	autoarray/plotters/grid_plotters.py
#	autoarray/plotters/imaging_plotters.py
#	autoarray/plotters/interferometer_plotters.py
#	autoarray/plotters/inversion_plotters.py
#	autoarray/plotters/line_yx_plotters.py
#	autoarray/plotters/mapper_plotters.py
#	autoarray/plotters/plotter_util.py
#	autoarray/plotters/quantity_radii_plotters.py
#	test_autoarray/unit/mask/test_mask.py
#	test_autoarray/unit/operators/test_convolver.py
#	test_autoarray/unit/plotters/test_mapper_plotters.py
#	test_autoarray/unit/structures/test_grids.py

Author: Jammy2211

autoarray/dataset/abstract_dataset.py

@@ -11,20 +11,20 @@ def __init__(self, data, noise_map, exposure_time_map=None):
         Parameters
         ----------
         data : arrays.Array
-            The array of the image data_type, in units of electrons per second.
+            The array of the image data_type, in unit_label of electrons per second.
         pixel_scales : float
             The size of each pixel in arc seconds.
         psf : PSF
             An array describing the PSF kernel of the image.
         noise_map : NoiseMap | float | ndarray
-            An array describing the RMS standard deviation error in each pixel, preferably in units of electrons per
+            An array describing the RMS standard deviation error in each pixel, preferably in unit_label of electrons per
             second.
         background_noise_map : NoiseMap
             An array describing the RMS standard deviation error in each pixel due to the background sky noise_map,
-            preferably in units of electrons per second.
+            preferably in unit_label of electrons per second.
         poisson_noise_map : NoiseMap
             An array describing the RMS standard deviation error in each pixel due to the Poisson counts of the source,
-            preferably in units of electrons per second.
+            preferably in unit_label of electrons per second.
         exposure_time_map : arrays.Array
             An array describing the effective exposure time in each imaging pixel.
         background_sky_map : aa.Scaled
@@ -81,7 +81,7 @@ def potential_chi_squared_max(self):
 
     def array_from_electrons_per_second_to_counts(self, array):
         """
-        For an array (in electrons per second) and an exposure time mappers, return an array in units counts.
+        For an array (in electrons per second) and an exposure time mappers, return an array in unit_label counts.
 
         Parameters
         ----------
@@ -92,7 +92,7 @@ def array_from_electrons_per_second_to_counts(self, array):
 
     def array_from_counts_to_electrons_per_second(self, array):
         """
-        For an array (in counts) and an exposure time mappers, convert the array to units electrons per second
+        For an array (in counts) and an exposure time mappers, convert the array to unit_label electrons per second
 
         Parameters
         ----------
@@ -106,7 +106,7 @@ def array_from_counts_to_electrons_per_second(self, array):
 
     def array_from_adus_to_electrons_per_second(self, array, gain):
         """
-        For an array (in counts) and an exposure time mappers, convert the array to units electrons per second
+        For an array (in counts) and an exposure time mappers, convert the array to unit_label electrons per second
 
         Parameters
         ----------
@@ -120,7 +120,7 @@ def array_from_adus_to_electrons_per_second(self, array, gain):
 
     @property
     def image_counts(self):
-        """The image in units of counts."""
+        """The image in unit_label of counts."""
         return self.array_from_electrons_per_second_to_counts(self.data)
 
 

autoarray/dataset/imaging.py

@@ -262,7 +262,7 @@ def signal_to_noise_limited_from_signal_to_noise_limit(self, signal_to_noise_lim
 
     @property
     def background_noise_map_counts(self):
-        """ The background noise_maps mappers in units of counts."""
+        """ The background noise_maps mappers in unit_label of counts."""
         return self.array_from_electrons_per_second_to_counts(self.background_noise_map)
 
     @property
@@ -380,18 +380,18 @@ def __init__(
         Parameters
         ----------
         image : aa.Array
-            The array of the image data_type, in units of electrons per second.
+            The array of the image data_type, in unit_label of electrons per second.
         psf : PSF
             An array describing the PSF kernel of the image.
         noise_map : NoiseMap | float | ndarray
-            An array describing the RMS standard deviation error in each pixel, preferably in units of electrons per
+            An array describing the RMS standard deviation error in each pixel, preferably in unit_label of electrons per
             second.
         background_noise_map : NoiseMap
             An array describing the RMS standard deviation error in each pixel due to the background sky noise_map,
-            preferably in units of electrons per second.
+            preferably in unit_label of electrons per second.
         poisson_noise_map : NoiseMap
             An array describing the RMS standard deviation error in each pixel due to the Poisson counts of the source,
-            preferably in units of electrons per second.
+            preferably in unit_label of electrons per second.
         exposure_time_map : aa.Array
             An array describing the effective exposure time in each imaging pixel.
         background_sky_map : aa.Scaled
@@ -468,7 +468,7 @@ def from_fits(
         """Factory for loading the imaging data_type from .fits files, as well as computing properties like the noise-map,
         exposure-time map, etc. from the imaging-data.
 
-        This factory also includes a number of routines for converting the imaging-data from units not supported by PyAutoLens \
+        This factory also includes a number of routines for converting the imaging-data from unit_label not supported by PyAutoLens \
         (e.g. adus, electrons) to electrons per second.
 
         Parameters
@@ -550,12 +550,12 @@ def from_fits(
         background_sky_map_hdu : int
             The hdu the background_sky_map is contained in the .fits file specified by *background_sky_map_path*.
         convert_from_electrons : bool
-            If True, the input unblurred_image_1d are in units of electrons and all converted to electrons / second using the exposure \
+            If True, the input unblurred_image_1d are in unit_label of electrons and all converted to electrons / second using the exposure \
             time map.
         gain : float
             The image gain, used for convert from ADUs.
         convert_from_adus : bool
-            If True, the input unblurred_image_1d are in units of adus and all converted to electrons / second using the exposure \
+            If True, the input unblurred_image_1d are in unit_label of adus and all converted to electrons / second using the exposure \
             time map and gain.
         """
 
@@ -865,7 +865,7 @@ def generate_poisson_noise(image, exposure_time_map, seed=-1):
     """
     Generate a two-dimensional poisson noise_maps-mappers from an image.
 
-    Values are computed from a Poisson distribution using the image's input values in units of counts.
+    Values are computed from a Poisson distribution using the image's input values in unit_label of counts.
 
     Parameters
     ----------
@@ -904,7 +904,7 @@ def load_noise_map(
 ):
     """Factory for loading the noise-map from a .fits file.
 
-    This factory also includes a number of routines for converting the noise-map from from other units (e.g. \
+    This factory also includes a number of routines for converting the noise-map from from other unit_label (e.g. \
     a weight map) or computing the noise-map from other unblurred_image_1d (e.g. the imaging image and background noise-map).
 
     Parameters
@@ -941,12 +941,12 @@ def load_noise_map(
         If True, the noise-map is computed from the observed image and background noise-map \
         (see NoiseMap.from_image_and_background_noise_map).
     convert_from_electrons : bool
-        If True, the input unblurred_image_1d are in units of electrons and all converted to electrons / second using the exposure \
+        If True, the input unblurred_image_1d are in unit_label of electrons and all converted to electrons / second using the exposure \
         time map.
     gain : float
         The image gain, used for convert from ADUs.
     convert_from_adus : bool
-        If True, the input unblurred_image_1d are in units of adus and all converted to electrons / second using the exposure \
+        If True, the input unblurred_image_1d are in unit_label of adus and all converted to electrons / second using the exposure \
         time map and gain.
     """
     noise_map_options = sum(
@@ -1027,7 +1027,7 @@ def load_background_noise_map(
 ):
     """Factory for loading the background noise-map from a .fits file.
 
-    This factory also includes a number of routines for converting the background noise-map from from other units (e.g. \
+    This factory also includes a number of routines for converting the background noise-map from from other unit_label (e.g. \
     a weight map).
 
     Parameters
@@ -1100,7 +1100,7 @@ def load_poisson_noise_map(
 ):
     """Factory for loading the Poisson noise-map from a .fits file.
 
-    This factory also includes a number of routines for converting the Poisson noise-map from from other units (e.g. \
+    This factory also includes a number of routines for converting the Poisson noise-map from from other unit_label (e.g. \
     a weight map) or computing the Poisson noise_map from other unblurred_image_1d (e.g. the imaging image).
 
     Parameters
@@ -1127,12 +1127,12 @@ def load_poisson_noise_map(
     exposure_time_map : ndarray
         The exposure-time map, which the Poisson noise-map can be calculated using.
     convert_from_electrons : bool
-        If True, the input unblurred_image_1d are in units of electrons and all converted to electrons / second using the exposure \
+        If True, the input unblurred_image_1d are in unit_label of electrons and all converted to electrons / second using the exposure \
         time map.
     gain : float
         The image gain, used for convert from ADUs.
     convert_from_adus : bool
-        If True, the input unblurred_image_1d are in units of adus and all converted to electrons / second using the exposure \
+        If True, the input unblurred_image_1d are in unit_label of adus and all converted to electrons / second using the exposure \
         time map and gain.
     """
     poisson_noise_map_options = sum(

autoarray/dataset/interferometer.py

@@ -132,7 +132,7 @@ def from_fits(
         """Factory for loading the interferometer data_type from .fits files, as well as computing properties like the noise-map,
         exposure-time map, etc. from the interferometer-data_type.
 
-        This factory also includes a number of routines for converting the interferometer-data_type from units not supported by PyAutoLens \
+        This factory also includes a number of routines for converting the interferometer-data_type from unit_label not supported by PyAutoLens \
         (e.g. adus, electrons) to electrons per second.
 
         Parameters
@@ -212,12 +212,12 @@ def from_fits(
         background_sky_map_hdu : int
             The hdu the background_sky_map is contained in the .fits file specified by *background_sky_map_path*.
         convert_from_electrons : bool
-            If True, the input unblurred_image_1d are in units of electrons and all converted to electrons / second using the exposure \
+            If True, the input unblurred_image_1d are in unit_label of electrons and all converted to electrons / second using the exposure \
             time map.
         gain : float
             The image gain, used for convert from ADUs.
         convert_from_adus : bool
-            If True, the input unblurred_image_1d are in units of adus and all converted to electrons / second using the exposure \
+            If True, the input unblurred_image_1d are in unit_label of adus and all converted to electrons / second using the exposure \
             time map and gain.
         """