feat: weak shear-profile + convergence-map demos; should_simulate adoption

llms-full.txt

@@ -431,7 +431,7 @@ AUTO-GENERATED by PyAutoBuild — do not edit by hand; regenerate with generate.
 ## weak
 
 - [Fit: Weak Lensing](scripts/weak/fit.py): This script shows how to fit a strong-lens mass model to a weak gravitational lensing shear catalogue. Where the `imaging` and `interferometer` workflows fit a 2D image of a lensed source, the weak-lensing workflow fits a set of (gamma_2, gamma_1) shear measurements at the (y, x) positions of background source galaxies — a ``WeakDataset`` produced by the simulator script in `scripts/weak/simulator.py`.
-  - Contents: Dataset, Model, Fit, Visualization, Notes
+  - Contents: Dataset, Model, Fit, Visualization, Shear Profile, Notes
 - [Modeling: Weak Lensing](scripts/weak/modeling.py): This script fits a lens mass model to a weak gravitational lensing shear catalogue using a non-linear search. It is the weak-lensing analogue of `scripts/imaging/modeling.py`: where that script infers a lens model from a 2D image of a lensed source, this script infers one from the (gamma_2, gamma_1) shear measurements of background source galaxies — a `WeakDataset` produced by `scripts/weak/simulator.py`.
   - Contents: Dataset, Model, Search, Analysis, Run Times, Model-Fit, Output Folder Layout, Result
 - [Simulator: Weak Lensing](scripts/weak/simulator.py): This script simulates a weak gravitational lensing shear catalogue. Unlike the imaging simulator (which produces a 2D image of the lensed source) the weak-lensing simulator produces a *catalogue* of (gamma_2, gamma_1) shear measurements at the (y, x) positions of a population of background source galaxies.

scripts/weak/fit.py

@@ -18,6 +18,7 @@
 - **Model:** Build a lens-model `Tracer` whose mass profiles produce the model shear field.
 - **Fit:** Construct a `FitWeak` and inspect its derived quantities (residuals, chi-squared, log-likelihood).
 - **Visualization:** Plot the fit as a 2x2 mosaic of data, model, overlay, and chi-squared map.
+- **Shear Profile:** Bin the tangential/cross shear about the lens centre and compare data to model.
 - **Notes:** What a "good" fit looks like and how this script relates to the upcoming modeling tutorial.
 """
 
@@ -38,10 +39,22 @@
 vector and per-galaxy noise standard deviation 0.3. The shear field carries the signature of the
 foreground lens's mass distribution.
 
-If `dataset.json` does not yet exist, run `scripts/weak/simulator.py` first.
+__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.
 """
 dataset_path = Path("dataset") / "weak" / "simple"
 
+if al.util.dataset.should_simulate(str(dataset_path)):
+    import subprocess
+    import sys
+
+    subprocess.run(
+        [sys.executable, "scripts/weak/simulator.py"],
+        check=True,
+    )
+
 dataset = al.from_json(file_path=dataset_path / "dataset.json")
 
 print(dataset.info)
@@ -123,6 +136,28 @@
     output_format="png",
 )
 
+"""
+__Shear Profile__
+
+The panels above show the fit galaxy by galaxy; cluster weak-lensing results are usually shown instead as
+the azimuthally averaged *tangential shear profile* `gamma_t(r)` — the mean tangential stretching of
+background galaxies in radial bins about the lens centre, which traces the projected mass profile (this is
+the standard observable of analyses such as the Sloan Giant Arcs Survey and the Frontier Fields clusters).
+
+`aplt.plot_shear_profile` bins the catalogue about a chosen centre and, because we pass it the `FitWeak`,
+overlays the model shear's tangential profile as a line. The plot also shows the *cross* component
+`gamma_x` (the 45-degree rotated component): gravitational lensing produces none at leading order, so the
+cross points scattering around zero is the standard "B-mode" null test — a systematic contaminating the
+measurement would show up here.
+"""
+aplt.plot_shear_profile(
+    fit,
+    centre=(0.0, 0.0),
+    bins=8,
+    output_path=dataset_path,
+    output_format="png",
+)
+
 """
 __Notes__
 

scripts/weak/modeling.py

@@ -69,7 +69,7 @@
 dataset_name = "simple"
 dataset_path = Path("dataset") / "weak" / dataset_name
 
-if not (dataset_path / "dataset.json").exists():
+if al.util.dataset.should_simulate(str(dataset_path)):
     import subprocess
     import sys
 

scripts/weak/simulator.py

@@ -121,5 +121,27 @@
     output_format="png",
 )
 
+"""
+__Convergence Map__
+
+The shear field can be inverted directly into a map of the convergence `kappa` (the dimensionless projected
+mass density) using the Kaiser-Squires (1993) technique: in Fourier space shear and convergence are related
+algebraically, so two FFTs turn the catalogue into a "dark matter map" with no mass model assumed. This is
+the classic visualization used for merging clusters (e.g. the Bullet cluster) and survey mass maps.
+
+`aplt.plot_convergence_map` bins the irregular catalogue onto a regular grid, applies a small Gaussian
+smoothing (raw per-cell shears are shape-noise dominated) and plots the E-mode reconstruction. For this
+Isothermal lens the map peaks at the lens centre at (0.0", 0.0"). Two caveats to remember: the mean of the
+map is unconstrained (the mass-sheet degeneracy) and FFT periodicity causes artefacts near the field edges —
+for quantitative masses, fit a mass model with `scripts/weak/modeling.py` instead.
+"""
+aplt.plot_convergence_map(
+    shear_yx=dataset.shear_yx,
+    shape_native=(30, 30),
+    smoothing_sigma_pixels=1.0,
+    output_path=dataset_path,
+    output_format="png",
+)
+
 print(dataset.info)
 print(f"Wrote dataset to {dataset_path}")

workspace_index.json

@@ -5791,6 +5791,7 @@
       "Model",
       "Fit",
       "Visualization",
+      "Shear Profile",
       "Notes"
     ],
     "cross_refs": [
@@ -5836,7 +5837,9 @@
       "Output",
       "Visualize"
     ],
-    "cross_refs": [],
+    "cross_refs": [
+      "scripts/weak/modeling.py"
+    ],
     "notebook": "notebooks/weak/simulator.ipynb",
     "path": "scripts/weak/simulator.py",
     "summary": "This script simulates a weak gravitational lensing shear catalogue. Unlike the imaging simulator (which produces a 2D image of the lensed source) the weak-lensing simulator produces a *catalogue* of (gamma_2, gamma_1) shear measurements at the (y, x) positions of a population of background source galaxies.",