pixel scale dict works in 1d and 2d

Author: Jammy2211

autoarray/__init__.py

@@ -89,7 +89,11 @@
 from .structures.visibilities import VisibilitiesNoiseMap
 
 from autoconf import conf
-from autoconf.fitsable import ndarray_via_hdu_from, ndarray_via_fits_from, header_obj_from
+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__)
 

autoarray/mask/abstract_mask.py

@@ -87,10 +87,6 @@ def pixel_scale_header(self) -> Dict:
         -------
         A dictionary containing the pixel scale of the mask, which can be output to a .fits file.
         """
-        return {
-            "PIXSCAY": self.pixel_scales[0],
-            "PIXSCAX": self.pixel_scales[1],
-        }
 
     @property
     def dimensions(self) -> int:

autoarray/mask/mask_1d.py

@@ -4,7 +4,7 @@
 import logging
 import numpy as np
 from pathlib import Path
-from typing import List, Tuple, Union
+from typing import Dict, List, Tuple, Union
 
 from autoconf.fitsable import output_to_fits
 
@@ -163,6 +163,22 @@ def shape_native(self) -> Tuple[int]:
     def shape_slim(self) -> Tuple[int]:
         return self.shape
 
+    @property
+    def pixel_scale_header(self) -> Dict:
+        """
+        Returns the pixel scales of the mask as a header dictionary, which can be written to a .fits file.
+
+        A 2D mask has different pixel scale variables for each dimension, the header therefore contain both pixel
+        scales as separate y and x entries.
+
+        Returns
+        -------
+        A dictionary containing the pixel scale of the mask, which can be output to a .fits file.
+        """
+        return {
+            "PIXSCA": self.pixel_scales[0],
+        }
+
     @property
     def hdu_for_output(self) -> fits.PrimaryHDU:
         """

autoarray/mask/mask_2d.py

@@ -1,10 +1,9 @@
 from __future__ import annotations
 from astropy.io import fits
-import copy
 import logging
 import numpy as np
 from pathlib import Path
-from typing import TYPE_CHECKING, List, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Tuple, Union
 
 from autoarray.structures.abstract_structure import Structure
 
@@ -706,6 +705,23 @@ def unmasked_blurred_array_from(self, padded_array, psf, image_shape) -> Array2D
             padded_array=blurred_image, image_shape=image_shape
         )
 
+    @property
+    def pixel_scale_header(self) -> Dict:
+        """
+        Returns the pixel scales of the mask as a header dictionary, which can be written to a .fits file.
+
+        A 2D mask has different pixel scale variables for each dimension, the header therefore contain both pixel
+        scales as separate y and x entries.
+
+        Returns
+        -------
+        A dictionary containing the pixel scale of the mask, which can be output to a .fits file.
+        """
+        return {
+            "PIXSCAY": self.pixel_scales[0],
+            "PIXSCAX": self.pixel_scales[1],
+        }
+
     @property
     def hdu_for_output(self) -> fits.PrimaryHDU:
         """

autoarray/plot/multi_plotters.py

@@ -270,74 +270,6 @@ def output_subplot(self, filename_suffix: str = ""):
             )
         plotter.close_subplot_figure()
 
-    def output_to_fits(
-        self,
-        func_name_list: List[str],
-        figure_name_list: List[str],
-        filename: str,
-        tag_list: Optional[List[str]] = None,
-        remove_fits_first: bool = False,
-        **kwargs,
-    ):
-        """
-        Outputs a list of figures of the plotter objects in the `plotter_list` to a single .fits file.
-
-        This function takes as input lists of  function names and figure names and then calls them via
-        the `plotter_list` with an interface that outputs each to a .fits file.
-
-        For example, if you have multiple `ImagingPlotter` objects and want to output the `data` and `noise_map` of
-        each to a single .fits files, you would input:
-
-        - `func_name_list=['figures_2d', 'figures_2d']` and
-        - `figure_name_list=['data', 'noise_map']`.
-
-        The implementation of this code is hacky, with it using a specific interface in the `Output` object
-        which sets the format to `fits_multi`  to call a function which outputs the .fits files. A major visualuzation
-        refactor is required to make this more elegant.
-
-        Parameters
-        ----------
-        func_name_list
-            The list of function names that are called to plot the figures on the subplot.
-        figure_name_list
-            The list of figure names that are plotted on the subplot.
-        filenane
-            The filename that the .fits file is output to.
-        tag_list
-            The list of tags that are used to set the `EXTNAME` of each hdu of the .fits file.
-        remove_fits_first
-            If the .fits file already exists, it is removed before the new .fits file is output, else it is updated
-            with the figure going into the next hdu.
-        kwargs
-            Any additional keyword arguments that are passed to the function that plots the figure on the subplot.
-        """
-
-        output_path = self.plotter_list[0].mat_plot_2d.output.output_path_from(
-            format="fits_multi"
-        )
-        output_fits_file = Path(output_path) / f"{filename}.fits"
-
-        if remove_fits_first:
-            output_fits_file.unlink(missing_ok=True)
-
-        for i, plotter in enumerate(self.plotter_list):
-            plotter.mat_plot_2d.output._format = "fits_multi"
-
-            plotter.set_filename(filename=f"{filename}")
-
-            for j, (func_name, figure_name) in enumerate(
-                zip(func_name_list, figure_name_list)
-            ):
-                if tag_list is not None:
-                    plotter.mat_plot_2d.output._tag_fits_multi = tag_list[j]
-
-                self.plot_via_func(
-                    plotter=plotter,
-                    figure_name=figure_name,
-                    func_name=func_name,
-                    kwargs=kwargs,
-                )
-
 
 class MultiYX1DPlotter:
     def __init__(

autoarray/plot/wrap/base/output.py

@@ -153,17 +153,6 @@ def to_figure(
                             file_path=path.join(output_path, f"{filename}.fits"),
                             overwrite=True,
                         )
-                elif format == "fits_multi":
-                    if structure is not None:
-                        from autoarray.structures.arrays.array_2d_util import (
-                            update_fits_file,
-                        )
-
-                        update_fits_file(
-                            arr=structure.native,
-                            file_path=path.join(output_path, f"{filename}.fits"),
-                            tag=self._tag_fits_multi,
-                        )
 
     def subplot_to_figure(self, auto_filename: Optional[str] = None):
         """

test_autoarray/mask/test_mask_2d.py

@@ -363,7 +363,7 @@ def test__from_fits__output_to_fits():
         file_path=path.join(test_data_path, "mask.fits"), hdu=0
     )
 
-    assert header["PIXSCALE"] == 1.0
+    assert header["PIXSCAY"] == 1.0
 
 
 def test__from_fits__with_resized_mask_shape():

test_autoarray/structures/arrays/test_array_2d_util.py

@@ -286,6 +286,7 @@ def test__resized_array_2d_from__padding_with_new_origin():
         )
     ).all()
 
+
 def test__replace_noise_map_2d_values_where_image_2d_values_are_negative():
     image_2d = np.ones(shape=(2, 2))
 

test_autoarray/structures/arrays/test_uniform_1d.py

@@ -165,7 +165,7 @@ def test__output_to_fits():
         file_path=path.join(test_data_path, "array.fits"), hdu=0
     )
 
-    assert header_load["PIXSCALE"] == 1.0
+    assert header_load["PIXSCA"] == 1.0
 
 
 def test__recursive_shape_storage():

test_autoarray/structures/arrays/test_uniform_2d.py

@@ -224,7 +224,7 @@ def test__output_to_fits():
     )
 
     assert (array_from_fits.native == np.ones((3, 3))).all()
-    assert array_from_fits.header.header_sci_obj["PIXSCALE"] == 1.0
+    assert array_from_fits.header.header_sci_obj["PIXSCAY"] == 1.0
 
 
 def test__manual_native__exception_raised_if_input_array_is_2d_and_not_shape_of_mask():