move zoom array to Zoom2D

Author: Jammy2211

autoarray/mask/derive/zoom_2d.py

@@ -1,7 +1,11 @@
 from __future__ import annotations
 import numpy as np
-from typing import List, Tuple, Union
+from typing import TYPE_CHECKING, List, Tuple, Union
 
+if TYPE_CHECKING:
+    from autoarray.structures.arrays.uniform_2d import Array2D
+
+from autoarray.structures.arrays import array_2d_util
 from autoarray.structures.grids import grid_2d_util
 
 
@@ -164,4 +168,38 @@ def shape_native(self) -> Tuple[int, int]:
         The shape of the zoomed in region in pixels.
         """
         region = self.region
-        return (region[1] - region[0], region[3] - region[2])
+        return (region[1] - region[0], region[3] - region[2])
+
+    def array_2d_from(self, array : Array2D, buffer: int = 1) -> Array2D:
+        """
+        Extract the 2D region of an array corresponding to the rectangle encompassing all unmasked values.
+
+        This is used to extract and visualize only the region of an image that is used in an analysis.
+
+        Parameters
+        ----------
+        buffer
+            The number pixels around the extracted array used as a buffer.
+        """
+        from autoarray.structures.arrays.uniform_2d import Array2D
+        from autoarray.mask.mask_2d import Mask2D
+
+        extracted_array_2d = array_2d_util.extracted_array_2d_from(
+            array_2d=np.array(array.native),
+            y0=self.region[0] - buffer,
+            y1=self.region[1] + buffer,
+            x0=self.region[2] - buffer,
+            x1=self.region[3] + buffer,
+        )
+
+        mask = Mask2D.all_false(
+            shape_native=extracted_array_2d.shape,
+            pixel_scales=array.pixel_scales,
+            origin=array.mask.mask_centre,
+        )
+
+        arr = array_2d_util.convert_array_2d(
+            array_2d=extracted_array_2d, mask_2d=mask
+        )
+
+        return Array2D(values=arr, mask=mask, header=array.header)

autoarray/plot/mat_plot/two_d.py

@@ -12,6 +12,7 @@
 from autoarray.plot.mat_plot.abstract import AbstractMatPlot
 from autoarray.plot.auto_labels import AutoLabels
 from autoarray.plot.visuals.two_d import Visuals2D
+from autoarray.mask.derive.zoom_2d import Zoom2D
 from autoarray.structures.arrays.uniform_2d import Array2D
 
 from autoarray.structures.arrays import array_2d_util
@@ -259,9 +260,11 @@ def plot_array(
             "zoom_around_mask"
         ]:
 
+            zoom = Zoom2D(mask=array.mask)
+
             buffer = 0 if array.mask.is_all_false else 1
 
-            array = array.zoomed_around_mask(buffer=buffer)
+            array = zoom.array_2d_from(array=array, buffer=buffer)
 
         extent = array.geometry.extent
 

autoarray/plot/wrap/two_d/array_overlay.py

@@ -19,7 +19,8 @@ class ArrayOverlay(AbstractMatWrap2D):
     def overlay_array(self, array, figure):
         aspect = figure.aspect_from(shape_native=array.shape_native)
 
-        array_zoom = array.zoomed_around_mask(buffer=0)
+        zoom = Zoom2D(mask=array.mask)
+        array_zoom = zoom.array_2d_from(array=array, buffer=0)
         extent = array_zoom.geometry.extent
 
         plt.imshow(X=array.native, aspect=aspect, extent=extent, **self.config_dict)

autoarray/structures/arrays/uniform_2d.py

@@ -306,7 +306,13 @@ def native_for_fits(self) -> "Array2D":
         If it is already stored in its `native` representation it is return as it is. If not, it is mapped from
         `slim` to `native` and returned as a new `Array2D`.
         """
+        if conf.instance["visualize"]["plots"]["fits_are_zoomed"]:
 
+            zoom = Zoom2D(mask=self.mask)
+
+            buffer = 0 if self.mask.is_all_false else 1
+
+            return zoom.array_2d_from(array=self, buffer=buffer)
 
         return Array2D(
             values=self, mask=self.mask, header=self.header, store_native=True
@@ -471,40 +477,6 @@ def brightest_sub_pixel_coordinate_in_region_from(
             pixel_coordinates_2d=(subpixel_y, subpixel_x)
         )
 
-    def zoomed_around_mask(self, buffer: int = 1) -> "Array2D":
-        """
-        Extract the 2D region of an array corresponding to the rectangle encompassing all unmasked values.
-
-        This is used to extract and visualize only the region of an image that is used in an analysis.
-
-        Parameters
-        ----------
-        buffer
-            The number pixels around the extracted array used as a buffer.
-        """
-
-        zoom = Zoom2D(mask=self.mask)
-
-        extracted_array_2d = array_2d_util.extracted_array_2d_from(
-            array_2d=np.array(self.native),
-            y0=zoom.region[0] - buffer,
-            y1=zoom.region[1] + buffer,
-            x0=zoom.region[2] - buffer,
-            x1=zoom.region[3] + buffer,
-        )
-
-        mask = Mask2D.all_false(
-            shape_native=extracted_array_2d.shape,
-            pixel_scales=self.pixel_scales,
-            origin=self.mask.mask_centre,
-        )
-
-        array = array_2d_util.convert_array_2d(
-            array_2d=extracted_array_2d, mask_2d=mask
-        )
-
-        return Array2D(values=array, mask=mask, header=self.header)
-
     def resized_from(
         self, new_shape: Tuple[int, int], mask_pad_value: int = 0.0
     ) -> "Array2D":

test_autoarray/mask/derive/test_zoom_2d.py

@@ -109,3 +109,106 @@ def test__quantities():
 
     assert zoom.centre == (6, 2)
     assert zoom.offset_pixels == (3, 1)
+
+
+def test__array_2d_from():
+    array_2d = [
+        [1.0, 2.0, 3.0, 4.0],
+        [5.0, 6.0, 7.0, 8.0],
+        [9.0, 10.0, 11.0, 12.0],
+        [13.0, 14.0, 15.0, 16.0],
+    ]
+
+    mask = aa.Mask2D(
+        mask=[
+            [True, True, True, True],
+            [True, False, False, True],
+            [True, False, False, True],
+            [True, True, True, True],
+        ],
+        pixel_scales=(1.0, 1.0),
+    )
+
+    arr = aa.Array2D(values=array_2d, mask=mask)
+    zoom = aa.Zoom2D(mask=mask)
+    arr_zoomed = zoom.array_2d_from(array=arr, buffer=0)
+
+    assert (arr_zoomed.native == np.array([[6.0, 7.0], [10.0, 11.0]])).all()
+
+    mask = aa.Mask2D(
+        mask=np.array(
+            [
+                [True, True, True, True],
+                [True, False, False, True],
+                [False, False, False, True],
+                [True, True, True, True],
+            ]
+        ),
+        pixel_scales=(1.0, 1.0),
+    )
+
+    arr = aa.Array2D(values=array_2d, mask=mask)
+    zoom = aa.Zoom2D(mask=mask)
+    arr_zoomed = zoom.array_2d_from(array=arr, buffer=0)
+
+    assert (arr_zoomed.native == np.array([[0.0, 6.0, 7.0], [9.0, 10.0, 11.0]])).all()
+
+    mask = aa.Mask2D(
+        mask=np.array(
+            [
+                [True, False, True, True],
+                [True, False, False, True],
+                [True, False, False, True],
+                [True, True, True, True],
+            ]
+        ),
+        pixel_scales=(1.0, 1.0),
+    )
+
+    arr = aa.Array2D(values=array_2d, mask=mask)
+    zoom = aa.Zoom2D(mask=mask)
+    arr_zoomed = zoom.array_2d_from(array=arr, buffer=0)
+
+    assert (arr_zoomed.native == np.array([[2.0, 0.0], [6.0, 7.0], [10.0, 11.0]])).all()
+
+    array_2d = np.ones(shape=(4, 4))
+
+    mask = aa.Mask2D(
+        mask=np.array(
+            [
+                [True, True, True, True],
+                [True, False, False, True],
+                [True, False, False, True],
+                [True, True, True, True],
+            ]
+        ),
+        pixel_scales=(1.0, 1.0),
+    )
+
+    arr = aa.Array2D(values=array_2d, mask=mask)
+    zoom = aa.Zoom2D(mask=mask)
+    arr_zoomed = zoom.array_2d_from(array=arr, buffer=0)
+
+    assert arr_zoomed.mask.origin == (0.0, 0.0)
+
+    array_2d = np.ones(shape=(6, 6))
+
+    mask = aa.Mask2D(
+        mask=np.array(
+            [
+                [True, True, True, True, True, True],
+                [True, True, True, True, True, True],
+                [True, True, True, False, False, True],
+                [True, True, True, False, False, True],
+                [True, True, True, True, True, True],
+                [True, True, True, True, True, True],
+            ]
+        ),
+        pixel_scales=(1.0, 1.0),
+    )
+
+    arr = aa.Array2D(values=array_2d, mask=mask)
+    zoom = aa.Zoom2D(mask=mask)
+    arr_zoomed = zoom.array_2d_from(array=arr, buffer=0)
+
+    assert arr_zoomed.mask.origin == (0.0, 1.0)

test_autoarray/plot/wrap/base/test_ticks.py

@@ -60,7 +60,8 @@ def test__yticks__set():
     units = aplt.Units(use_scaled=True, ticks_convert_factor=None)
 
     yticks = aplt.YTicks(fontsize=34)
-    array_zoom = array.zoomed_around_mask(buffer=1)
+    zoom = aa.Zoom2D(mask=array.mask)
+    array_zoom = zoom.array_2d_from(array=array, buffer=0)
     extent = array_zoom.geometry.extent
     yticks.set(min_value=extent[2], max_value=extent[3], units=units)
 
@@ -106,7 +107,8 @@ def test__xticks__set():
     array = aa.Array2D.ones(shape_native=(2, 2), pixel_scales=1.0)
     units = aplt.Units(use_scaled=True, ticks_convert_factor=None)
     xticks = aplt.XTicks(fontsize=34)
-    array_zoom = array.zoomed_around_mask(buffer=1)
+    zoom = aa.Zoom2D(mask=array.mask)
+    array_zoom = zoom.array_2d_from(array=array, buffer=0)
     extent = array_zoom.geometry.extent
     xticks.set(min_value=extent[0], max_value=extent[1], units=units)
 

test_autoarray/structures/arrays/test_uniform_2d.py

@@ -354,109 +354,6 @@ def test__trimmed_after_convolution_from():
     ).all()
     assert new_arr.mask.pixel_scales == (1.0, 1.0)
 
-
-def test__zoomed_around_mask():
-    array_2d = [
-        [1.0, 2.0, 3.0, 4.0],
-        [5.0, 6.0, 7.0, 8.0],
-        [9.0, 10.0, 11.0, 12.0],
-        [13.0, 14.0, 15.0, 16.0],
-    ]
-
-    mask = aa.Mask2D(
-        mask=[
-            [True, True, True, True],
-            [True, False, False, True],
-            [True, False, False, True],
-            [True, True, True, True],
-        ],
-        pixel_scales=(1.0, 1.0),
-    )
-
-    arr_masked = aa.Array2D(values=array_2d, mask=mask)
-
-    arr_zoomed = arr_masked.zoomed_around_mask(buffer=0)
-
-    assert (arr_zoomed.native == np.array([[6.0, 7.0], [10.0, 11.0]])).all()
-
-    mask = aa.Mask2D(
-        mask=np.array(
-            [
-                [True, True, True, True],
-                [True, False, False, True],
-                [False, False, False, True],
-                [True, True, True, True],
-            ]
-        ),
-        pixel_scales=(1.0, 1.0),
-    )
-
-    arr_masked = aa.Array2D(values=array_2d, mask=mask)
-    arr_zoomed = arr_masked.zoomed_around_mask(buffer=0)
-
-    assert (arr_zoomed.native == np.array([[0.0, 6.0, 7.0], [9.0, 10.0, 11.0]])).all()
-
-    mask = aa.Mask2D(
-        mask=np.array(
-            [
-                [True, False, True, True],
-                [True, False, False, True],
-                [True, False, False, True],
-                [True, True, True, True],
-            ]
-        ),
-        pixel_scales=(1.0, 1.0),
-    )
-
-    arr_masked = aa.Array2D(values=array_2d, mask=mask)
-    arr_zoomed = arr_masked.zoomed_around_mask(buffer=0)
-    assert (arr_zoomed.native == np.array([[2.0, 0.0], [6.0, 7.0], [10.0, 11.0]])).all()
-
-
-def test__zoomed_around_mask__origin_updated():
-    array_2d = np.ones(shape=(4, 4))
-
-    mask = aa.Mask2D(
-        mask=np.array(
-            [
-                [True, True, True, True],
-                [True, False, False, True],
-                [True, False, False, True],
-                [True, True, True, True],
-            ]
-        ),
-        pixel_scales=(1.0, 1.0),
-    )
-
-    arr_masked = aa.Array2D(values=array_2d, mask=mask)
-
-    arr_zoomed = arr_masked.zoomed_around_mask(buffer=0)
-
-    assert arr_zoomed.mask.origin == (0.0, 0.0)
-
-    array_2d = np.ones(shape=(6, 6))
-
-    mask = aa.Mask2D(
-        mask=np.array(
-            [
-                [True, True, True, True, True, True],
-                [True, True, True, True, True, True],
-                [True, True, True, False, False, True],
-                [True, True, True, False, False, True],
-                [True, True, True, True, True, True],
-                [True, True, True, True, True, True],
-            ]
-        ),
-        pixel_scales=(1.0, 1.0),
-    )
-
-    arr_masked = aa.Array2D(values=array_2d, mask=mask)
-
-    arr_zoomed = arr_masked.zoomed_around_mask(buffer=0)
-
-    assert arr_zoomed.mask.origin == (0.0, 1.0)
-
-
 def test__binned_across_rows():
     array = aa.Array2D.no_mask(values=np.ones((4, 3)), pixel_scales=1.0)