Add CLAUDE.md with development commands and architecture overview

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: 2 people

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Commands
+
+### Install
+```bash
+pip install -e ".[dev]"
+```
+
+### Run Tests
+```bash
+# All tests
+python -m pytest test_autogalaxy/
+
+# Single test file
+python -m pytest test_autogalaxy/galaxy/test_galaxy.py
+
+# Single test
+python -m pytest test_autogalaxy/galaxy/test_galaxy.py::TestGalaxy::test_name
+
+# With output
+python -m pytest test_autogalaxy/imaging/test_fit_imaging.py -s
+```
+
+### Formatting
+```bash
+black autogalaxy/
+```
+
+## Architecture
+
+**PyAutoGalaxy** is a Bayesian galaxy morphology fitting library. It depends on two sibling packages:
+- **`autoarray`** – low-level data structures (grids, masks, arrays, imaging/interferometer datasets, inversions/pixelizations)
+- **`autofit`** – non-linear search / model-fitting framework (defines `af.Analysis`, `af.ModelInstance`, `af.Result`)
+
+### Core Class Hierarchy
+
+```
+GeometryProfile (geometry_profiles.py)
+├── SphProfile
+└── EllProfile
+    ├── LightProfile (profiles/light/abstract.py) + OperateImage mixin
+    │   ├── lp.Sersic, lp.Exponential, lp.Gaussian, etc. (profiles/light/standard/)
+    │   ├── LightProfileLinear (profiles/light/linear/) - intensity solved via inversion
+    │   ├── LightProfileOperated (profiles/light/operated/) - PSF already applied
+    │   └── Basis (profiles/basis.py) - collection of profiles for MGE / shapelets
+    └── MassProfile (profiles/mass/abstract/abstract.py) + OperateDeflections mixin
+        ├── mp.Isothermal, mp.NFW, mp.PowerLaw, etc.
+        └── stellar, dark, total subdirectories
+
+Galaxy (galaxy/galaxy.py) - inherits af.ModelObject, OperateImageList, OperateDeflections
+  └── holds arbitrary kwargs: light profiles, mass profiles, pixelizations, etc.
+
+Galaxies (galaxy/galaxies.py) - a List[Galaxy] with group operations
+```
+
+### Fit Classes
+
+Each dataset type has a `Fit*` class that orchestrates the full fitting pipeline:
+
+- `FitImaging` (`imaging/fit_imaging.py`) – CCD imaging
+- `FitInterferometer` (`interferometer/fit_interferometer.py`) – ALMA/interferometry
+- `FitQuantity` (`quantity/fit_quantity.py`) – arbitrary quantity datasets
+- `FitEllipse` (`ellipse/fit_ellipse.py`) – isophote/ellipse fitting
+
+All inherit from `AbstractFitInversion` (`abstract_fit.py`), which handles the linear algebra inversion step when `LightProfileLinear` or pixelization-based profiles are present.
+
+### Analysis Classes (autofit integration)
+
+Each dataset type has an `Analysis*` class that implements `log_likelihood_function`:
+
+- `AnalysisImaging` (`imaging/model/analysis.py`)
+- `AnalysisInterferometer` (`interferometer/model/analysis.py`)
+- `AnalysisQuantity` (`quantity/model/analysis.py`)
+- `AnalysisEllipse` (`ellipse/model/analysis.py`)
+
+These inherit from `AnalysisDataset` → `Analysis` (in `analysis/analysis/`), which inherits `af.Analysis`. The `log_likelihood_function` builds a `Fit*` object from the `af.ModelInstance` and returns its `figure_of_merit`.
+
+### JAX Support
+
+JAX is integrated via the `xp` parameter pattern throughout the codebase. Fit classes accept `xp=np` (NumPy, default) or `xp=jnp` (JAX). The `AbstractFitInversion.use_jax` property tracks which backend is active. The `AnalysisImaging.__init__` has `use_jax: bool = True`. The conftest.py forces JAX backend initialization before tests run.
+
+### Linear Light Profiles & Inversions
+
+`LightProfileLinear` subclasses do not take an `intensity` parameter—it is solved via a linear inversion (provided by `autoarray`). The `GalaxiesToInversion` class (`galaxy/to_inversion.py`) handles converting galaxies with linear profiles or pixelizations into the inversion objects needed by `autoarray`.
+
+### Adapt Images (Multi-stage fitting)
+
+`AdaptImages` (`analysis/adapt_images/`) stores per-galaxy model images from a previous search. These are passed to subsequent searches to drive adaptive mesh/regularization schemes. `galaxy_name_image_dict_via_result_from` extracts adapt images from a `Result`.
+
+### Configuration
+
+Default priors, visualization settings, and general config live in `autogalaxy/config/`. Tests push a local config directory via `conf.instance.push(...)` in `test_autogalaxy/conftest.py`.
+
+### Operate Mixins
+
+- `OperateImage` (`operate/image.py`) – provides `blurred_image_2d_from`, `visibilities_from`, etc. on light objects
+- `OperateDeflections` (`operate/deflections.py`) – provides deflection-related operations on mass objects
+
+Both are mixin classes inherited by `LightProfile`, `MassProfile`, `Galaxy`, and `Galaxies`.
+
+### Workspace (Examples & Notebooks)
+
+The `autogalaxy_workspace` at `/mnt/c/Users/Jammy/Code/PyAutoJAX/autogalaxy_workspace` contains runnable examples and tutorials. Key locations:
+
+- `start_here.ipynb` / `start_here.py` – entry point overview of the API
+- `scripts/imaging/` – end-to-end scripts: `simulator.py`, `fit.py`, `modeling.py`, `likelihood_function.py`, `features/`
+- `scripts/interferometer/`, `scripts/ellipse/`, `scripts/multi/` – same structure for other dataset types
+- `scripts/howtogalaxy/` – chapter-by-chapter tutorial scripts (chapters 1–4 + optional)
+- `notebooks/` – Jupyter notebook equivalents of all `scripts/`
+- `scripts/guides/` – topic guides (e.g. linear profiles, pixelizations, chaining)
+- `config/` – workspace-level config that overrides package defaults when running workspace scripts
+
+### Namespace Conventions
+
+When importing `autogalaxy as ag`:
+- `ag.lp.*` – standard light profiles
+- `ag.lp_linear.*` – linear light profiles
+- `ag.lp_operated.*` – operated light profiles
+- `ag.lp_basis.*` / `ag.lp_snr.*` – basis and SNR profiles
+- `ag.mp.*` – mass profiles
+- `ag.lmp.*` – light+mass profiles
+- `ag.ps.*` – point sources
+- `ag.Galaxy`, `ag.Galaxies`
+- `ag.FitImaging`, `ag.AnalysisImaging`, `ag.SimulatorImaging`