Feature/images refactor

autoarray/config/visualize/plots.yaml

@@ -5,19 +5,11 @@
 
 dataset:                                   # Settings for plots of all datasets (e.g. ImagingPlotter, InterferometerPlotter).
   subplot_dataset: true                    # Plot subplot containing all dataset quantities (e.g. the data, noise-map, etc.)?
-  data: false                              # Plot the individual data of every dataset?
-  noise_map: false                         # Plot the individual noise-map of every dataset?
-  signal_to_noise_map: false               # Plot the individual signal-to-noise-map of every dataset?
-  over_sample_size_lp: false               # Plot the over-sampling size, used to evaluate light profiles, of every dataset?
-  over_sample_size_pixelization: false     # Plot the over-sampling size, used to evaluate pixelizations, of every dataset?
 imaging:                                   # Settings for plots of imaging datasets (e.g. ImagingPlotter)
    psf: false
 fit:                                       # Settings for plots of all fits (e.g. FitImagingPlotter, FitInterferometerPlotter).
   subplot_fit: true                        # Plot subplot of all fit quantities for any dataset (e.g. the model data, residual-map, etc.)?
   subplot_fit_log10: true                  # Plot subplot of all fit quantities for any dataset using log10 color maps (e.g. the model data, residual-map, etc.)?
-  all_at_end_png: true                     # Plot all individual plots listed below as .png (even if False)?
-  all_at_end_fits: true                    # Plot all individual plots listed below as .fits (even if False)?
-  all_at_end_pdf: false                    # Plot all individual plots listed below as publication-quality .pdf (even if False)?
   data: false                              # Plot individual plots of the data?
   noise_map: false                         # Plot individual plots of the noise-map?
   signal_to_noise_map: false               # Plot individual plots of the signal-to-noise-map?
@@ -30,9 +22,6 @@ fit_imaging: {}                            # Settings for plots of fits to imagi
 inversion:                                 # Settings for plots of inversions (e.g. InversionPlotter).
   subplot_inversion: true                  # Plot subplot of all quantities in each inversion (e.g. reconstrucuted image, reconstruction)?
   subplot_mappings: true                   # Plot subplot of the image-to-source pixels mappings of each pixelization?
-  all_at_end_png: true                     # Plot all individual plots listed below as .png (even if False)?
-  all_at_end_fits: true                    # Plot all individual plots listed below as .fits (even if False)?
-  all_at_end_pdf: false                    # Plot all individual plots listed below as publication-quality .pdf (even if False)?
   data_subtracted: false                   # Plot individual plots of the data with the other inversion linear objects subtracted?
   reconstruction_noise_map: false          # Plot image of the noise of every mesh-pixel reconstructed value?
   sub_pixels_per_image_pixels: false       # Plot the number of sub pixels per masked data pixels?
@@ -41,22 +30,6 @@ inversion:                                 # Settings for plots of inversions (e
   reconstructed_image: false               # Plot image of the reconstructed data (e.g. in the image-plane)?
   reconstruction: false                    # Plot the reconstructed inversion (e.g. the pixelization's mesh in the source-plane)?
   regularization_weights: false            # Plot the effective regularization weight of every inversion mesh pixel?
-interferometer:                            # Settings for plots of interferometer datasets (e.g. InterferometerPlotter).
-  amplitudes_vs_uv_distances: false
-  phases_vs_uv_distances: false
-  uv_wavelengths: false
-  dirty_image: false
-  dirty_noise_map: false
-  dirty_signal_to_noise_map: false
 fit_interferometer:                        # Settings for plots of fits to interferometer datasets (e.g. FitInterferometerPlotter).
   subplot_fit_dirty_images: false          # Plot subplot of the dirty-images of all interferometer datasets?
-  subplot_fit_real_space: false            # Plot subplot of the real-space images of all interferometer datasets?
-  amplitudes_vs_uv_distances: false
-  phases_vs_uv_distances: false
-  uv_wavelengths: false
-  dirty_image: false
-  dirty_noise_map: false
-  dirty_signal_to_noise_map: false
-  dirty_residual_map: false
-  dirty_normalized_residual_map: false
-  dirty_chi_squared_map: false
+  subplot_fit_real_space: false            # Plot subplot of the real-space images of all interferometer datasets?

autoarray/inversion/inversion/mapper_valued.py

@@ -259,7 +259,7 @@ def magnification_via_mesh_from(
 
     def magnification_via_interpolation_from(
         self,
-        shape_native: Tuple[int, int] = (401, 401),
+        shape_native: Tuple[int, int] = (201, 201),
         extent: Optional[Tuple[float, float, float, float]] = None,
     ) -> float:
         """

autoarray/operators/transformer.py

@@ -47,7 +47,7 @@ def pylops_exception():
         "\n--------------------\n"
         "You are attempting to perform interferometer analysis.\n\n"
         "However, the optional library PyLops (https://github.com/PyLops/pylops) is not installed.\n\n"
-        "Install it via the command `pip install pylops==1.18.3`.\n\n"
+        "Install it via the command `pip install pylops==2.3.1`.\n\n"
         "----------------------"
     )
 

autoarray/plot/multi_plotters.py

@@ -1,3 +1,5 @@
+import os
+from pathlib import Path
 from typing import List, Optional, Tuple
 
 from autoarray.plot.wrap.base.ticks import YTicks
@@ -104,29 +106,6 @@ def plot_via_func(self, plotter, figure_name: str, func_name: str, kwargs):
         else:
             func(**{**{figure_name: True}, **kwargs})
 
-    def output_subplot(self, filename_suffix: str = ""):
-        """
-        Outplot the subplot to a file after all figures have been plotted on the subplot.
-
-        The multi-plotter requires its own output function to ensure that the subplot is output to a file, which
-        this provides.
-
-        Parameters
-        ----------
-        filename_suffix
-            The suffix of the filename that the subplot is output to.
-        """
-
-        if self.plotter_list[0].mat_plot_1d is not None:
-            self.plotter_list[0].mat_plot_1d.output.subplot_to_figure(
-                auto_filename=f"subplot_{filename_suffix}"
-            )
-        if self.plotter_list[0].mat_plot_2d is not None:
-            self.plotter_list[0].mat_plot_2d.output.subplot_to_figure(
-                auto_filename=f"subplot_{filename_suffix}"
-            )
-        self.plotter_list[0].close_subplot_figure()
-
     def subplot_of_figure(
         self, func_name: str, figure_name: str, filename_suffix: str = "", **kwargs
     ):
@@ -266,6 +245,99 @@ def subplot_of_multi_yx_1d(self, filename_suffix="", **kwargs):
         )
         self.plotter_list[0].plotter_list[0].close_subplot_figure()
 
+    def output_subplot(self, filename_suffix: str = ""):
+        """
+        Outplot the subplot to a file after all figures have been plotted on the subplot.
+
+        The multi-plotter requires its own output function to ensure that the subplot is output to a file, which
+        this provides.
+
+        Parameters
+        ----------
+        filename_suffix
+            The suffix of the filename that the subplot is output to.
+        """
+
+        plotter = self.plotter_list[0]
+
+        if plotter.mat_plot_1d is not None:
+            plotter.mat_plot_1d.output.subplot_to_figure(
+                auto_filename=f"subplot_{filename_suffix}"
+            )
+        if plotter.mat_plot_2d is not None:
+            plotter.mat_plot_2d.output.subplot_to_figure(
+                auto_filename=f"subplot_{filename_suffix}"
+            )
+        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

@@ -61,6 +61,7 @@ def __init__(
         self.format_folder = format_folder
         self.bypass = bypass
         self.bbox_inches = bbox_inches
+        self._tag_fits_multi = None
 
         self.kwargs = kwargs
 
@@ -105,6 +106,7 @@ def savefig(self, filename: str, output_path: str, format: str):
             plt.savefig(
                 path.join(output_path, f"{filename}.{format}"),
                 bbox_inches=self.bbox_inches,
+                pad_inches=0,
             )
         except ValueError as e:
             logger.info(
@@ -151,6 +153,17 @@ 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):
         """

autoarray/plot/wrap/two_d/voronoi_drawer.py

@@ -81,7 +81,7 @@ def draw_voronoi_pixels(
 
             cmap = plt.get_cmap(cmap.cmap)
 
-            if colorbar is not None:
+            if colorbar is not None and colorbar is not False:
                 cb = colorbar.set_with_color_values(
                     units=units,
                     norm=norm,

autoarray/structures/arrays/array_2d_util.py

@@ -862,3 +862,61 @@ def header_obj_from(file_path: Union[Path, str], hdu: int) -> Dict:
     hdu_list = fits.open(file_path)
 
     return hdu_list[hdu].header
+
+
+def update_fits_file(
+    arr: np.ndarray,
+    file_path: str,
+    tag: Optional[str] = None,
+    header: Optional[fits.Header] = None,
+):
+    """
+    Update a .fits file with a new array.
+
+    This function is used by the `fits_multi` output interface so that a single .fits file with groups of data
+    in hdu's can be created.
+
+    It may receive a `tag` which is used to set the `EXTNAME` of the HDU in the .fits file and therefore is the name
+    of the hdu seen by the user when they open it with DS9 or other .fits software.
+
+    A header may also be provided, which by default has the pixel scales of the array added to it.
+
+    Parameters
+    ----------
+    arr
+        The array that is written to the .fits file.
+    file_path
+        The full path of the file that is output, including the file name and ``.fits`` extension.
+    tag
+        The `EXTNAME` of the HDU in the .fits file.
+    header
+        The header of the .fits file that the array is written to, which if blank will still contain the pixel scales
+        of the array.
+    """
+
+    if header is None:
+        header = fits.Header()
+
+    try:
+        y, x = map(str, arr.pixel_scales)
+        header["PIXSCAY"] = y
+        header["PIXSCAX"] = x
+    except AttributeError:
+        pass
+
+    if conf.instance["general"]["fits"]["flip_for_ds9"]:
+        arr = np.flipud(arr)
+
+    if os.path.exists(file_path):
+        with fits.open(file_path, mode="update") as hdul:
+            hdul.append(fits.ImageHDU(arr, header))
+            if tag is not None:
+                hdul[-1].header["EXTNAME"] = tag.upper()
+            hdul.flush()
+
+    else:
+        hdu = fits.PrimaryHDU(arr, header)
+        if tag is not None:
+            hdu.header["EXTNAME"] = tag.upper()
+        hdul = fits.HDUList([hdu])
+        hdul.writeto(file_path, overwrite=True)