docs(simulators): mirror autogalaxy_workspace simulators + retarget tutorial subprocess paths

scripts/chapter_1_introduction/tutorial_3_fitting.py

@@ -60,7 +60,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/simulator.py"],
+        [sys.executable, "scripts/simulators/simple.py"],
         check=True,
     )
 

scripts/chapter_2_modeling/tutorial_3_realism_and_complexity.py

@@ -55,7 +55,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/simulator.py"],
+        [sys.executable, "scripts/simulators/simple.py"],
         check=True,
     )
 

scripts/chapter_2_modeling/tutorial_4_dealing_with_failure.py

@@ -56,7 +56,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/simulator.py"],
+        [sys.executable, "scripts/simulators/simple.py"],
         check=True,
     )
 

scripts/chapter_2_modeling/tutorial_5_linear_profiles.py

@@ -70,7 +70,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/simulator.py"],
+        [sys.executable, "scripts/simulators/simple.py"],
         check=True,
     )
 

scripts/chapter_3_search_chaining/tutorial_1_search_chaining.py

@@ -76,7 +76,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/simulator.py"],
+        [sys.executable, "scripts/simulators/simple.py"],
         check=True,
     )
 

scripts/chapter_3_search_chaining/tutorial_2_prior_passing.py

@@ -55,7 +55,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/simulator.py"],
+        [sys.executable, "scripts/simulators/simple.py"],
         check=True,
     )
 

scripts/chapter_3_search_chaining/tutorial_3_x2_galaxies.py

@@ -58,7 +58,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/guides/plot/simulator.py"],
+        [sys.executable, "scripts/simulators/sersic_x2.py"],
         check=True,
     )
 

scripts/chapter_4_pixelizations/tutorial_2_mappers.py

@@ -1,166 +1,166 @@
-"""
-Tutorial 2: Mappers
-===================
-
-In the previous tutorial, we used a pixelization to create made a `Mapper`. However, it was not clear what a `Mapper`
-does, why it was called a mapper and whether it was mapping anything at all!
-
-Therefore, in this tutorial, we'll cover mappers in more detail.
-
-__Contents__
-
-- **Initial Setup:** Load the dataset for illustration.
-- **Mappers:** Understand how mappers map image-plane pixels to pixelization pixels.
-- **Mask:** Apply a mask and see how it affects the mapper.
-- **Wrap Up:** Summary of mapper concepts.
-"""
-
+"""
+Tutorial 2: Mappers
+===================
+
+In the previous tutorial, we used a pixelization to create made a `Mapper`. However, it was not clear what a `Mapper`
+does, why it was called a mapper and whether it was mapping anything at all!
+
+Therefore, in this tutorial, we'll cover mappers in more detail.
+
+__Contents__
+
+- **Initial Setup:** Load the dataset for illustration.
+- **Mappers:** Understand how mappers map image-plane pixels to pixelization pixels.
+- **Mask:** Apply a mask and see how it affects the mapper.
+- **Wrap Up:** Summary of mapper concepts.
+"""
+
 # from autoconf import setup_notebook; setup_notebook()
-
-from pathlib import Path
-import autogalaxy as ag
-import autogalaxy.plot as aplt
-import autoarray.plot as aaplt
-
-"""
-__Initial Setup__
-
-we'll use complex galaxy data, where:
-
- - The galaxy's bulge is an `Sersic`.
- - The galaxy's disk is an `Exponential`.
- - The galaxy's has four star forming clumps which are `Sersic` profiles.
-"""
-dataset_name = "simple"
-dataset_path = Path("dataset") / "imaging" / dataset_name
-
-"""
-__Dataset Auto-Simulation__
-
-If the dataset does not already exist on your system, it will be created by running the corresponding
-simulator script. This ensures that all example scripts can be run without manually simulating data first.
-"""
-if not dataset_path.exists():
-    import subprocess
-    import sys
-
-    subprocess.run(
-        [sys.executable, "scripts/imaging/simulator.py"],
-        check=True,
-    )
-
-dataset = ag.Imaging.from_fits(
-    data_path=dataset_path / "data.fits",
-    noise_map_path=dataset_path / "noise_map.fits",
-    psf_path=dataset_path / "psf.fits",
-    pixel_scales=0.1,
-)
-
-"""
-Now, lets set up our `Grid2D` (using the image above).
-"""
-grid = ag.Grid2D.uniform(
-    shape_native=dataset.shape_native, pixel_scales=dataset.pixel_scales
-)
-
-"""
-__Mappers__
-
-We now setup a `Pixelization` and use it to create a `Mapper` via the plane`s source-plane grid, just like we did in
-the previous tutorial.
-
-We will make its pixelization resolution half that of the grid above.
-"""
-mesh = ag.mesh.RectangularAdaptDensity(
-    shape=(dataset.shape_native[0] / 2, dataset.shape_native[1] / 2)
-)
-
-pixelization = ag.Pixelization(mesh=mesh)
-
-interpolator = mesh.interpolator_from(
-    source_plane_data_grid=grid, source_plane_mesh_grid=None
-)
-
-mapper = ag.Mapper(interpolator=interpolator)
-
-"""
-We now plot the `Mapper` alongside the image we used to generate the source-plane grid.
-
-Using the `Visuals2D` object we are also going to highlight specific grid coordinates certain colors, such that we
-can see how they map from the image grid to the pixelization grid. 
-"""
-indexes = [range(250), [150, 250, 350, 450, 550, 650, 750, 850, 950, 1050]]
-
-aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
-
-
-"""
-Using a mapper, we can now make these mappings appear the other way round. That is, we can input a pixelization pixel
-index (of our rectangular grid) and highlight how all of the image-pixels that it contains map to the image-plane. 
-
-Lets map source pixel 313, the central source-pixel, to the image. We observe that for a given rectangular pixelization
-pixel, there are four image pixels.
-"""
-pix_indexes = [[312]]
-
-indexes = mapper.slim_indexes_for_pix_indexes(pix_indexes=pix_indexes)
-
-aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
-
-"""
-Okay, so I think we can agree, mapper's map things! More specifically, they map pixelization pixels to multiple pixels 
-in the observed image of a galaxy.
-
-__Mask__
-
-Finally, lets repeat the steps that we performed above, but now using a masked image. By applying a `Mask2D`, the 
-mapper only maps image-pixels that are not removed by the mask. This removes the (many) image pixels at the edge of the 
-image, where the galaxy is not present.
-
-Lets just have a quick look at these edges pixels:
-
-Lets use an circular `Mask2D`, which will capture the central galaxy light and clumps.
-"""
-mask = ag.Mask2D.circular(
-    shape_native=dataset.shape_native, pixel_scales=dataset.pixel_scales, radius=2.0
-)
-
-dataset = dataset.apply_mask(mask=mask)
-aplt.plot_array(array=dataset.data, title="Data")
-
-"""
-We can now use the masked grid to create a new `Mapper` (using the same rectangular 25 x 25 pixelization 
-as before).
-"""
-interpolator = mesh.interpolator_from(
-    source_plane_data_grid=dataset.grids.pixelization, source_plane_mesh_grid=None
-)
-mapper = ag.Mapper(interpolator=interpolator)
-
-"""
-Lets plot it.
-"""
-aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
-
-"""
-First, We can see a red circle of dots in both the image and pixelization, showing where the edge of the mask
-maps too in the pixelization.
-
-Now lets show that when we plot pixelization pixel indexes, they still appear in the same place in the image.
-"""
-pix_indexes = [[312], [314], [316], [318]]
-
-indexes = mapper.slim_indexes_for_pix_indexes(pix_indexes=pix_indexes)
-
-aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
-
-"""
-__Wrap Up__
-
-In this tutorial, we learnt about mappers, and we used them to understand how the image and pixelization map to one 
-another. Your exercises are:
-        
- 1) Think about how this could help us actually model galaxies. We have said we're going to reconstruct our galaxies 
- on the pixel-grid. So, how does knowing how each pixel maps to the image actually help us? If you`ve not got 
- any bright ideas, then worry not, that exactly what we're going to cover in the next tutorial.
-"""
+
+from pathlib import Path
+import autogalaxy as ag
+import autogalaxy.plot as aplt
+import autoarray.plot as aaplt
+
+"""
+__Initial Setup__
+
+we'll use complex galaxy data, where:
+
+ - The galaxy's bulge is an `Sersic`.
+ - The galaxy's disk is an `Exponential`.
+ - The galaxy's has four star forming clumps which are `Sersic` profiles.
+"""
+dataset_name = "simple"
+dataset_path = Path("dataset") / "imaging" / dataset_name
+
+"""
+__Dataset Auto-Simulation__
+
+If the dataset does not already exist on your system, it will be created by running the corresponding
+simulator script. This ensures that all example scripts can be run without manually simulating data first.
+"""
+if not dataset_path.exists():
+    import subprocess
+    import sys
+
+    subprocess.run(
+        [sys.executable, "scripts/simulators/simple.py"],
+        check=True,
+    )
+
+dataset = ag.Imaging.from_fits(
+    data_path=dataset_path / "data.fits",
+    noise_map_path=dataset_path / "noise_map.fits",
+    psf_path=dataset_path / "psf.fits",
+    pixel_scales=0.1,
+)
+
+"""
+Now, lets set up our `Grid2D` (using the image above).
+"""
+grid = ag.Grid2D.uniform(
+    shape_native=dataset.shape_native, pixel_scales=dataset.pixel_scales
+)
+
+"""
+__Mappers__
+
+We now setup a `Pixelization` and use it to create a `Mapper` via the plane`s source-plane grid, just like we did in
+the previous tutorial.
+
+We will make its pixelization resolution half that of the grid above.
+"""
+mesh = ag.mesh.RectangularAdaptDensity(
+    shape=(dataset.shape_native[0] / 2, dataset.shape_native[1] / 2)
+)
+
+pixelization = ag.Pixelization(mesh=mesh)
+
+interpolator = mesh.interpolator_from(
+    source_plane_data_grid=grid, source_plane_mesh_grid=None
+)
+
+mapper = ag.Mapper(interpolator=interpolator)
+
+"""
+We now plot the `Mapper` alongside the image we used to generate the source-plane grid.
+
+Using the `Visuals2D` object we are also going to highlight specific grid coordinates certain colors, such that we
+can see how they map from the image grid to the pixelization grid. 
+"""
+indexes = [range(250), [150, 250, 350, 450, 550, 650, 750, 850, 950, 1050]]
+
+aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
+
+
+"""
+Using a mapper, we can now make these mappings appear the other way round. That is, we can input a pixelization pixel
+index (of our rectangular grid) and highlight how all of the image-pixels that it contains map to the image-plane. 
+
+Lets map source pixel 313, the central source-pixel, to the image. We observe that for a given rectangular pixelization
+pixel, there are four image pixels.
+"""
+pix_indexes = [[312]]
+
+indexes = mapper.slim_indexes_for_pix_indexes(pix_indexes=pix_indexes)
+
+aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
+
+"""
+Okay, so I think we can agree, mapper's map things! More specifically, they map pixelization pixels to multiple pixels 
+in the observed image of a galaxy.
+
+__Mask__
+
+Finally, lets repeat the steps that we performed above, but now using a masked image. By applying a `Mask2D`, the 
+mapper only maps image-pixels that are not removed by the mask. This removes the (many) image pixels at the edge of the 
+image, where the galaxy is not present.
+
+Lets just have a quick look at these edges pixels:
+
+Lets use an circular `Mask2D`, which will capture the central galaxy light and clumps.
+"""
+mask = ag.Mask2D.circular(
+    shape_native=dataset.shape_native, pixel_scales=dataset.pixel_scales, radius=2.0
+)
+
+dataset = dataset.apply_mask(mask=mask)
+aplt.plot_array(array=dataset.data, title="Data")
+
+"""
+We can now use the masked grid to create a new `Mapper` (using the same rectangular 25 x 25 pixelization 
+as before).
+"""
+interpolator = mesh.interpolator_from(
+    source_plane_data_grid=dataset.grids.pixelization, source_plane_mesh_grid=None
+)
+mapper = ag.Mapper(interpolator=interpolator)
+
+"""
+Lets plot it.
+"""
+aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
+
+"""
+First, We can see a red circle of dots in both the image and pixelization, showing where the edge of the mask
+maps too in the pixelization.
+
+Now lets show that when we plot pixelization pixel indexes, they still appear in the same place in the image.
+"""
+pix_indexes = [[312], [314], [316], [318]]
+
+indexes = mapper.slim_indexes_for_pix_indexes(pix_indexes=pix_indexes)
+
+aaplt.subplot_image_and_mapper(mapper=mapper, image=dataset.data)
+
+"""
+__Wrap Up__
+
+In this tutorial, we learnt about mappers, and we used them to understand how the image and pixelization map to one 
+another. Your exercises are:
+
+ 1) Think about how this could help us actually model galaxies. We have said we're going to reconstruct our galaxies 
+ on the pixel-grid. So, how does knowing how each pixel maps to the image actually help us? If you`ve not got 
+ any bright ideas, then worry not, that exactly what we're going to cover in the next tutorial.
+"""