Abstraction of data groups (e.g. imaging) renamed from 'data' to 'dataset'

Author: Jammy2211

autoarray/__init__.py

@@ -9,9 +9,9 @@
 )
 from autoarray.structures.kernel import Kernel as kernel
 from autoarray.structures.visibilities import Visibilities as visibilities
-from autoarray.data.imaging import Imaging as imaging
-from autoarray.data.interferometer import Interferometer as interferometer
-from autoarray.data import data_converter
+from autoarray.dataset.imaging import Imaging as imaging
+from autoarray.dataset.interferometer import Interferometer as interferometer
+from autoarray.dataset import data_converter
 from autoarray.operators.convolution import Convolver as convolver
 from autoarray.operators.fourier_transform import Transformer as transformer
 from autoarray.operators.inversion.mappers import mapper

autoarray/data/__init__.py autoarray/dataset/__init__.pyautoarray/data/__init__.py renamed to autoarray/dataset/__init__.py

@@ -4,7 +4,7 @@
 from autoarray import exc
 
 
-class AbstractData(object):
+class AbstractDataset(object):
     def __init__(self, data, noise_map, exposure_time_map=None):
         """A collection of abstract 2D for different data_type classes (an image, pixel-scale, noise-map, etc.)
 

autoarray/data/abstract_data.py autoarray/dataset/abstract_dataset.pyautoarray/data/abstract_data.py renamed to autoarray/dataset/abstract_dataset.py

@@ -8,12 +8,12 @@
 from autoarray.mask import mask as msk
 from autoarray.structures import kernel, arrays
 from autoarray.masked import masked_structures
-from autoarray.data import abstract_data, data_converter
+from autoarray.dataset import abstract_dataset, data_converter
 
 logger = logging.getLogger(__name__)
 
 
-class AbstractImaging(abstract_data.AbstractData):
+class AbstractImagingSet(abstract_dataset.AbstractDataset):
     @property
     def image(self):
         return self.data
@@ -360,7 +360,7 @@ def output_to_fits(
             )
 
 
-class Imaging(AbstractImaging):
+class Imaging(AbstractImagingSet):
     def __init__(
         self,
         image,
@@ -373,7 +373,7 @@ def __init__(
         name=None,
         **kwargs
     ):
-        """A collection of 2D imaging data(an image, noise-map, psf, etc.)
+        """A collection of 2D imaging dataset(an image, noise-map, psf, etc.)
 
         Parameters
         ----------
@@ -557,7 +557,7 @@ def from_fits(
             time map and gain.
         """
 
-        image = abstract_data.load_image(
+        image = abstract_dataset.load_image(
             image_path=image_path, image_hdu=image_hdu, pixel_scales=pixel_scales
         )
 
@@ -574,7 +574,7 @@ def from_fits(
         else:
             inverse_noise_map = None
 
-        exposure_time_map = abstract_data.load_exposure_time_map(
+        exposure_time_map = abstract_dataset.load_exposure_time_map(
             exposure_time_map_path=exposure_time_map_path,
             exposure_time_map_hdu=exposure_time_map_hdu,
             pixel_scales=pixel_scales,

autoarray/data/data_converter.py autoarray/dataset/data_converter.pyautoarray/data/data_converter.py renamed to autoarray/dataset/data_converter.py

@@ -4,13 +4,13 @@
 import autoarray as aa
 
 from autoarray import exc
-from autoarray.data import abstract_data
+from autoarray.dataset import abstract_dataset
 
 
 logger = logging.getLogger(__name__)
 
 
-class AbstractInterferometer(abstract_data.AbstractData):
+class AbstractInterferometerSet(abstract_dataset.AbstractDataset):
     @property
     def visibilities(self):
         return self.data
@@ -74,7 +74,7 @@ def output_to_fits(
             )
 
 
-class Interferometer(AbstractInterferometer):
+class Interferometer(AbstractInterferometerSet):
     def __init__(
         self,
         visibilities,

autoarray/data/imaging.py autoarray/dataset/imaging.pyautoarray/data/imaging.py renamed to autoarray/dataset/imaging.py

@@ -1,24 +1,24 @@
 import numpy as np
 
-from autoarray.masked import masked_data as md
+from autoarray.masked import masked_dataset as md
 from autoarray.util import fit_util
 
 
-def fit(masked_data, model_data, inversion=None):
+def fit(masked_dataset, model_data, inversion=None):
 
-    if isinstance(masked_data, md.MaskedImaging):
+    if isinstance(masked_dataset, md.MaskedImaging):
         return ImagingFit(
-            mask=masked_data.mask,
-            image=masked_data.image,
-            noise_map=masked_data.noise_map,
+            mask=masked_dataset.mask,
+            image=masked_dataset.image,
+            noise_map=masked_dataset.noise_map,
             model_image=model_data,
             inversion=inversion,
         )
-    elif isinstance(masked_data, md.MaskedInterferometer):
+    elif isinstance(masked_dataset, md.MaskedInterferometer):
         return InterferometerFit(
-            visibilities_mask=masked_data.visibilities_mask,
-            visibilities=masked_data.visibilities,
-            noise_map=masked_data.noise_map,
+            visibilities_mask=masked_dataset.visibilities_mask,
+            visibilities=masked_dataset.visibilities,
+            noise_map=masked_dataset.noise_map,
             model_visibilities=model_data,
             inversion=inversion,
         )
@@ -28,7 +28,7 @@ class DataFit(object):
 
     # noinspection PyUnresolvedReferences
     def __init__(self, mask, data, noise_map, model_data, inversion=None):
-        """Class to fit simulator where the data structures are any dimension.
+        """Class to fit simulator where the dataset structures are any dimension.
 
         Parameters
         -----------
@@ -37,7 +37,7 @@ def __init__(self, mask, data, noise_map, model_data, inversion=None):
         noise_map : ndarray
             The noise_map-map of the observed simulator.
         mask: msk.Mask
-            The masks that is applied to the data.
+            The masks that is applied to the dataset.
         model_data : ndarray
             The model simulate the fitting image is fitted with.
 
@@ -48,14 +48,14 @@ def __init__(self, mask, data, noise_map, model_data, inversion=None):
         chi_squared_map : ndarray
             The chi-squared map of the fit ((datas - model_data) / noise_maps ) **2.0
         chi_squared : float
-            The overall chi-squared of the model's fit to the data, summed over every simulator-point.
+            The overall chi-squared of the model's fit to the dataset, summed over every simulator-point.
         reduced_chi_squared : float
             The reduced chi-squared of the model's fit to simulate (chi_squared / number of datas points), summed over \
             every simulator-point.
         noise_normalization : float
             The overall normalization term of the noise_map-map, summed over every simulator-point.
         likelihood : float
-            The overall likelihood of the model's fit to the data, summed over evey simulator-point.
+            The overall likelihood of the model's fit to the dataset, summed over evey simulator-point.
         """
         self.mask = mask
         self.data = data
@@ -83,7 +83,7 @@ def chi_squared_map(self):
 
     @property
     def signal_to_noise_map(self):
-        """The signal-to-noise_map of the data and noise-map which are fitted."""
+        """The signal-to-noise_map of the dataset and noise-map which are fitted."""
         signal_to_noise_map = np.divide(self.data, self.noise_map)
         signal_to_noise_map[signal_to_noise_map < 0] = 0
         return signal_to_noise_map
@@ -138,7 +138,7 @@ def figure_of_merit(self):
 
 class ImagingFit(DataFit):
     def __init__(self, mask, image, noise_map, model_image, inversion=None):
-        """Class to fit simulator where the data structures are any dimension.
+        """Class to fit simulator where the dataset structures are any dimension.
 
         Parameters
         -----------
@@ -147,7 +147,7 @@ def __init__(self, mask, image, noise_map, model_image, inversion=None):
         noise_map : ndarray
             The noise_map-map of the observed simulator.
         mask: msk.Mask
-            The masks that is applied to the data.
+            The masks that is applied to the dataset.
         model_data : ndarray
             The model simulate the fitting image is fitted with.
 
@@ -158,14 +158,14 @@ def __init__(self, mask, image, noise_map, model_image, inversion=None):
         chi_squared_map : ndarray
             The chi-squared map of the fit ((datas - model_data) / noise_maps ) **2.0
         chi_squared : float
-            The overall chi-squared of the model's fit to the data, summed over every simulator-point.
+            The overall chi-squared of the model's fit to the dataset, summed over every simulator-point.
         reduced_chi_squared : float
             The reduced chi-squared of the model's fit to simulate (chi_squared / number of datas points), summed over \
             every simulator-point.
         noise_normalization : float
             The overall normalization term of the noise_map-map, summed over every simulator-point.
         likelihood : float
-            The overall likelihood of the model's fit to the data, summed over evey simulator-point.
+            The overall likelihood of the model's fit to the dataset, summed over evey simulator-point.
         """
 
         super(ImagingFit, self).__init__(
@@ -194,7 +194,7 @@ def __init__(
         model_visibilities,
         inversion=None,
     ):
-        """Class to fit simulator where the data structures are any dimension.
+        """Class to fit simulator where the dataset structures are any dimension.
 
         Parameters
         -----------
@@ -203,7 +203,7 @@ def __init__(
         noise_map : ndarray
             The noise_map-map of the observed simulator.
         visibilities_mask: msk.Mask
-            The masks that is applied to the data.
+            The masks that is applied to the dataset.
         model_data : ndarray
             The model simulate the fitting image is fitted with.
 
@@ -214,14 +214,14 @@ def __init__(
         chi_squared_map : ndarray
             The chi-squared map of the fit ((datas - model_data) / noise_maps ) **2.0
         chi_squared : float
-            The overall chi-squared of the model's fit to the data, summed over every simulator-point.
+            The overall chi-squared of the model's fit to the dataset, summed over every simulator-point.
         reduced_chi_squared : float
             The reduced chi-squared of the model's fit to simulate (chi_squared / number of datas points), summed over \
             every simulator-point.
         noise_normalization : float
             The overall normalization term of the noise_map-map, summed over every simulator-point.
         likelihood : float
-            The overall likelihood of the model's fit to the data, summed over evey simulator-point.
+            The overall likelihood of the model's fit to the dataset, summed over evey simulator-point.
         """
 
         super(InterferometerFit, self).__init__(

autoarray/data/interferometer.py autoarray/dataset/interferometer.pyautoarray/data/interferometer.py renamed to autoarray/dataset/interferometer.py

@@ -340,7 +340,7 @@ def unmasked_blurred_array_from_padded_array_psf_and_image_shape(
     ):
         """For a padded grid and psf, compute an unmasked blurred image from an unmasked unblurred image.
 
-        This relies on using the lens data's padded-grid, which is a grid of (y,x) coordinates which extends over the \
+        This relies on using the lens dataset's padded-grid, which is a grid of (y,x) coordinates which extends over the \
         entire image as opposed to just the masked region.
 
         Parameters

autoarray/fit/fit.py

@@ -1,5 +1,5 @@
 from .masked_structures import MaskedArray as array, MaskedGrid as grid
-from .masked_data import (
+from .masked_dataset import (
     MaskedImaging as imaging,
     MaskedInterferometer as interferometer,
 )

autoarray/mask/mapping.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 
-class AbstractMaskedData(object):
+class AbstractMaskedDataset(object):
     def __init__(
         self,
         mask,
@@ -38,7 +38,7 @@ def __init__(
         self.inversion_uses_border = inversion_uses_border
 
 
-class MaskedImaging(AbstractMaskedData):
+class MaskedImaging(AbstractMaskedDataset):
     def __init__(
         self,
         imaging,
@@ -49,10 +49,10 @@ def __init__(
         inversion_uses_border=True,
     ):
         """
-        The lens data is the collection of data_type (image, noise-map, PSF), a mask, grid, convolver \
+        The lens dataset is the collection of data_type (image, noise-map, PSF), a mask, grid, convolver \
         and other utilities that are used for modeling and fitting an image of a strong lens.
 
-        Whilst the image, noise-map, etc. are loaded in 2D, the lens data creates reduced 1D arrays of each \
+        Whilst the image, noise-map, etc. are loaded in 2D, the lens dataset creates reduced 1D arrays of each \
         for lensing calculations.
 
         Parameters
@@ -183,7 +183,7 @@ def signal_to_noise_limited_from_signal_to_noise_limit(self, signal_to_noise_lim
         )
 
 
-class MaskedInterferometer(AbstractMaskedData):
+class MaskedInterferometer(AbstractMaskedDataset):
     def __init__(
         self,
         interferometer,
@@ -194,10 +194,10 @@ def __init__(
         inversion_uses_border=True,
     ):
         """
-        The lens data is the collection of data_type (image, noise-map, primary_beam), a mask, grid, convolver \
+        The lens dataset is the collection of data_type (image, noise-map, primary_beam), a mask, grid, convolver \
         and other utilities that are used for modeling and fitting an image of a strong lens.
 
-        Whilst the image, noise-map, etc. are loaded in 2D, the lens data creates reduced 1D arrays of each \
+        Whilst the image, noise-map, etc. are loaded in 2D, the lens dataset creates reduced 1D arrays of each \
         for lensing calculations.
 
         Parameters