docs(simulator): mirror autolens_workspace no_lens_light simulator + retarget tutorial subprocess paths

scripts/chapter_4_pixelizations/tutorial_10_brightness_adaption.py

@@ -58,7 +58,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/features/no_lens_light/simulator.py"],
+        [sys.executable, "scripts/simulator/no_lens_light.py"],
         check=True,
     )
 

scripts/chapter_4_pixelizations/tutorial_11_adaptive_regularization.py

@@ -50,7 +50,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/features/no_lens_light/simulator.py"],
+        [sys.executable, "scripts/simulator/no_lens_light.py"],
         check=True,
     )
 

scripts/chapter_4_pixelizations/tutorial_2_mappers.py

@@ -49,7 +49,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/features/no_lens_light/simulator.py"],
+        [sys.executable, "scripts/simulator/no_lens_light.py"],
         check=True,
     )
 

scripts/chapter_4_pixelizations/tutorial_3_inversions.py

@@ -51,7 +51,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/features/no_lens_light/simulator.py"],
+        [sys.executable, "scripts/simulator/no_lens_light.py"],
         check=True,
     )
 

scripts/chapter_4_pixelizations/tutorial_4_bayesian_regularization.py

@@ -54,7 +54,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/features/no_lens_light/simulator.py"],
+        [sys.executable, "scripts/simulator/no_lens_light.py"],
         check=True,
     )
 

scripts/chapter_4_pixelizations/tutorial_5_borders.py

@@ -48,7 +48,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/features/no_lens_light/simulator.py"],
+        [sys.executable, "scripts/simulator/no_lens_light.py"],
         check=True,
     )
 

scripts/chapter_4_pixelizations/tutorial_6_lens_modeling.py

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

scripts/chapter_4_pixelizations/tutorial_7_adaptive_pixelization.py

@@ -50,7 +50,7 @@
     import sys
 
     subprocess.run(
-        [sys.executable, "scripts/imaging/features/no_lens_light/simulator.py"],
+        [sys.executable, "scripts/simulator/no_lens_light.py"],
         check=True,
     )
 

scripts/simulator/no_lens_light.py

@@ -0,0 +1,231 @@
+"""
+Simulator: No Lens Light
+========================
+
+This script simulates `Imaging` of a 'galaxy-scale' which is identical to the `simple` simulated in the `start_here.py`
+script, but where the lens galaxy's light is omitted.
+
+It is used in `autolens_workspace/notebooks/modeling/features/no_lens_light.ipynb` to illustrate how to fit a
+lens model to data where the lens galaxy's light is not present (e.g. because it is too faint to be detected).
+
+__Contents__
+
+- **Model:** Compose the lens model fitted to the data.
+- **Dataset Paths:** The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a.
+- **Simulate:** Simulate the image using a (y,x) grid with the adaptive over sampling scheme.
+- **Ray Tracing:** Setup the lens galaxy's light, mass and source galaxy light for this simulated lens.
+- **Output:** Output the simulated dataset to the dataset path as .fits files.
+- **Visualize:** Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset.
+- **Mask Extra Galaxies:** Save an empty `mask_extra_galaxies.fits` so noise-scaling tutorials can load it.
+- **Tracer json:** Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass.
+
+__Model__
+
+This script simulates `Imaging` of a 'galaxy-scale' strong lens where:
+
+ - The lens galaxy's total mass distribution is an `Isothermal` and `ExternalShear`.
+ - The source galaxy's light is an `Sersic`.
+
+__Start Here Notebook__
+
+If any code in this script is unclear, refer to the `simulators/start_here.ipynb` notebook.
+"""
+
+from autoconf import jax_wrapper  # Sets JAX environment before other imports
+
+# from autoconf import setup_notebook; setup_notebook()
+
+from pathlib import Path
+import autolens as al
+import autolens.plot as aplt
+
+"""
+__Dataset Paths__
+
+The `dataset_type` describes the type of data being simulated and `dataset_name` gives it a descriptive name. 
+"""
+dataset_type = "imaging"
+dataset_name = "simple__no_lens_light"
+dataset_path = Path("dataset", dataset_type, dataset_name)
+
+"""
+__Simulate__
+
+Simulate the image using a (y,x) grid with the adaptive over sampling scheme.
+"""
+grid = al.Grid2D.uniform(
+    shape_native=(100, 100),
+    pixel_scales=0.1,
+)
+
+over_sample_size = al.util.over_sample.over_sample_size_via_radial_bins_from(
+    grid=grid,
+    sub_size_list=[32, 8, 2],
+    radial_list=[0.3, 0.6],
+    centre_list=[(0.0, 0.0)],
+)
+
+grid = grid.apply_over_sampling(over_sample_size=over_sample_size)
+
+"""
+Simulate a simple Gaussian PSF for the image.
+"""
+psf = al.Convolver.from_gaussian(
+    shape_native=(11, 11), sigma=0.1, pixel_scales=grid.pixel_scales
+)
+
+"""
+Create the simulator for the imaging data, which defines the exposure time, background sky, noise levels and psf.
+"""
+simulator = al.SimulatorImaging(
+    exposure_time=300.0,
+    psf=psf,
+    background_sky_level=0.1,
+    add_poisson_noise_to_data=True,
+)
+
+"""
+__Ray Tracing__
+
+Setup the lens galaxy's light, mass and source galaxy light for this simulated lens.
+
+the `lens_galaxy` below does not include a `bulge` or `disk` component and therefore has no lens light.
+"""
+lens_galaxy = al.Galaxy(
+    redshift=0.5,
+    mass=al.mp.Isothermal(
+        centre=(0.0, 0.0),
+        einstein_radius=1.6,
+        ell_comps=al.convert.ell_comps_from(axis_ratio=0.9, angle=45.0),
+    ),
+    shear=al.mp.ExternalShear(gamma_1=0.05, gamma_2=0.05),
+)
+
+source_galaxy = al.Galaxy(
+    redshift=1.0,
+    bulge=al.lp.SersicCore(
+        centre=(0.0, 0.0),
+        ell_comps=al.convert.ell_comps_from(axis_ratio=0.8, angle=60.0),
+        intensity=4.0,
+        effective_radius=0.1,
+        sersic_index=1.0,
+    ),
+)
+
+"""
+Use these galaxies to setup a tracer, which will generate the image for the simulated `Imaging` dataset.
+"""
+tracer = al.Tracer(galaxies=[lens_galaxy, source_galaxy])
+
+"""
+Lets look at the tracer`s image, this is the image we'll be simulating.
+"""
+aplt.plot_array(array=tracer.image_2d_from(grid=grid), title="Image")
+
+"""
+Pass the simulator a tracer, which creates the image which is simulated as an imaging dataset.
+"""
+dataset = simulator.via_tracer_from(tracer=tracer, grid=grid)
+
+"""
+Plot the simulated `Imaging` dataset before outputting it to fits.
+"""
+aplt.subplot_imaging_dataset(dataset=dataset)
+
+"""
+__Output__
+
+Output the simulated dataset to the dataset path as .fits files.
+"""
+aplt.fits_imaging(
+    dataset=dataset,
+    data_path=dataset_path / "data.fits",
+    psf_path=dataset_path / "psf.fits",
+    noise_map_path=dataset_path / "noise_map.fits",
+    overwrite=True,
+)
+
+"""
+__Visualize__
+
+Output a subplot of the simulated dataset, the image and the tracer's quantities to the dataset path as .png files.
+"""
+
+aplt.subplot_imaging_dataset(dataset=dataset)
+aplt.plot_array(array=dataset.data, title="Data")
+
+aplt.subplot_tracer(
+    tracer=tracer, grid=grid, output_path=dataset_path, output_format="png"
+)
+aplt.subplot_galaxies_images(
+    tracer=tracer, grid=grid, output_path=dataset_path, output_format="png"
+)
+
+"""
+__Mask Extra Galaxies__
+
+This dataset has no extra galaxies, but pixelization tutorials that load it (e.g.
+`imaging/features/pixelization/modeling.py`, `imaging/features/pixelization/fit.py`) demonstrate the
+noise-scaling API by applying a `mask_extra_galaxies` mask to the dataset. Output an empty (all-False,
+no-pixels-masked) mask so those tutorials can call `apply_noise_scaling(mask=...)` without crashing on a
+missing FITS file. The mask shape tracks `dataset.shape_native`, so `PYAUTO_SMALL_DATASETS=1` is honoured
+automatically.
+"""
+mask_extra_galaxies = al.Mask2D.all_false(
+    shape_native=dataset.shape_native,
+    pixel_scales=dataset.pixel_scales,
+)
+
+aplt.fits_array(
+    array=mask_extra_galaxies,
+    file_path=dataset_path / "mask_extra_galaxies.fits",
+    overwrite=True,
+)
+
+"""
+__Tracer json__
+
+Save the `Tracer` in the dataset folder as a .json file, ensuring the true light profiles, mass profiles and galaxies
+are safely stored and available to check how the dataset was simulated in the future.
+
+This can be loaded via the method `tracer = al.from_json()`.
+"""
+al.output_to_json(
+    obj=tracer,
+    file_path=Path(dataset_path, "tracer.json"),
+)
+
+"""
+__Multiple Images__
+
+Lens modeling can use a "positions likelihood penalty", whereby mass models which traces the (y,x) 
+coordinates of multiple images of a source galaxy to positions which are far apart from one another 
+in the source plane are penalized in the lens model's overall likelihood.
+
+This speeds up lens modeling, helps the non-linear search avoid local maxima and is vital for inferred 
+accurate solutions when using pixelized source reconstructions.
+
+For real data, the multiple image positions are determined by eye from the data, for example
+using a Graphical User Interface (GUI) to mark them with mouse clicks. For simulated data, we can save
+ourselves time by using the `PointSolver` to determine the multiple image positions automatically and
+output to a .json file.
+
+If you have not looked in the `point_source` package, the point solver is the core tool used to find
+multiple image positions for point source lens modeling (e.g. lensed quasars).
+"""
+solver = al.PointSolver.for_grid(
+    grid=grid, pixel_scale_precision=0.001, magnification_threshold=0.1
+)
+
+positions = solver.solve(
+    tracer=tracer, source_plane_coordinate=source_galaxy.bulge.centre
+)
+
+al.output_to_json(
+    file_path=dataset_path / "positions.json",
+    obj=positions,
+)
+
+"""
+The dataset can be viewed in the folder `autolens_workspace/imaging/simple__no_lens_light`.
+"""