Skip to content

feat: add point-source imaging simulator #208

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Jammy2211 merged 1 commit into from
Aug 9, 2026
Merged

feat: add point-source imaging simulator #208

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions llms-full.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,8 @@ AUTO-GENERATED by PyAutoHands — do not edit by hand; regenerate with generate.
- Contents: Setup, Fit Imaging, Reconstruction, Inversion Plots, Mapper, Mesh Grids
- [Simulator: Clumpy Galaxy](scripts/imaging/features/pixelization/simulator.py): This script simulates `Imaging` of a galaxy whose light has two distinct components:
- Contents: Dataset Paths, Grid, Over Sampling, Galaxies, Output, Visualize, Mask Extra Galaxies, Plane Output
- [Simulator: Point Source](scripts/imaging/features/point_source/simulator.py): This script simulates an `Imaging` dataset containing a galaxy with:
- Contents: Dataset Paths, Grid, PSF, Galaxies, Simulation, Output
- [Modeling Features: Shapelets](scripts/imaging/features/shapelets/fit.py): A shapelet is a basis function that is appropriate for capturing the exponential / disk-like features of a galaxy. It has been employed in galaxy structure studies to model the light of the galaxy, because it can represent features of disky star forming galaxies that a single Sersic function cannot.
- Contents: Advantages & Disadvantages, Dataset & Mask, Basis, Coefficients, Linear Light Profiles, Fit, Intensities, Model, Search & Analysis, Run Time, Model-Fit, Result, Cartesian Shapelets
- [Modeling Features: Shapelets](scripts/imaging/features/shapelets/modeling.py): A shapelet is a basis function that is appropriate for capturing the exponential / disk-like features of a galaxy. It has been employed in galaxy structure studies to model the light of the galaxy, because it can represent features of disky star forming galaxies that a single Sersic function cannot.
Expand Down
331 changes: 331 additions & 0 deletions notebooks/imaging/features/point_source/simulator.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,331 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simulator: Point Source\n",
"=======================\n",
"\n",
"This script simulates an `Imaging` dataset containing a galaxy with:\n",
"\n",
" - An extended `Sersic` host galaxy.\n",
" - An unresolved point source represented by `ag.lp.PointSource`.\n",
"\n",
"A point source is a delta function in the image plane. Its `intensity` is the\n",
"total flux of the source, not a surface-brightness value evaluated independently\n",
"at every grid coordinate.\n",
"\n",
"Accurate point-source imaging requires an over-sampled PSF. The source is placed\n",
"on the nearest sub-pixel, convolved with the PSF at that finer resolution and\n",
"then binned back to the detector pixels while conserving total flux. Setting\n",
"`convolve_over_sample_size=1` remains a valid pixel-centred approximation, but\n",
"does not retain the source's sub-pixel position.\n",
"\n",
"For model fitting, `ag.lp_linear.PointSource` provides the same spatial model\n",
"with its total flux solved by the linear inversion. The source centre remains a\n",
"non-linear parameter.\n",
"\n",
"__Contents__\n",
"\n",
"- **Dataset Paths:** Define where the simulated dataset is written.\n",
"- **Grid:** Create a detector grid with sampling compatible with the PSF.\n",
"- **PSF:** Define a Gaussian PSF sampled at twice the detector resolution.\n",
"- **Galaxies:** Combine an extended host and unresolved point source.\n",
"- **Simulation:** Simulate noisy imaging using fine-grid PSF convolution.\n",
"- **Output:** Write the dataset, plots and input galaxies to disk."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Google Colab Setup__\n",
"\n",
"This cell sets up the environment when the notebook is run on Google Colab: it installs the\n",
"required PyAuto packages, clones the workspace (configuration files and example datasets) and\n",
"points the configuration at it. If you are running the notebook elsewhere (e.g. locally via\n",
"your own installation) it does nothing, and you can run it safely.\n",
"\n",
"Colab tip: model-fits run much faster on a GPU \u2014 enable one via \"Runtime\" -> \"Change runtime\n",
"type\" -> \"Hardware accelerator\" before running the notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" import google.colab\n",
"except ImportError:\n",
" from autogalaxy import setup_colab as _setup_colab\n",
"else:\n",
" import importlib\n",
" import subprocess\n",
" import sys\n",
"\n",
" subprocess.check_call(\n",
" [sys.executable, \"-m\", \"pip\", \"install\", \"autonerves\", \"--no-deps\"]\n",
" )\n",
" _setup_colab = importlib.import_module(\"autonerves.setup_colab\")\n",
"\n",
"_setup_colab.setup(\"autogalaxy\")"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"\n",
"from autogalaxy import setup_notebook; setup_notebook()\n",
"\n",
"from pathlib import Path\n",
"\n",
"import autogalaxy as ag\n",
"import autogalaxy.plot as aplt"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Dataset Paths__\n",
"\n",
"The simulated data are written to `dataset/imaging/point_source`."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"dataset_path = Path(\"dataset\", \"imaging\", \"point_source\")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Grid__\n",
"\n",
"The detector pixels are 0.1 arcseconds across. A uniform over-sample size of 2\n",
"gives four sub-pixels per detector pixel and is compatible with the PSF\n",
"convolution factor defined below."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"over_sample_size = 2\n",
"\n",
"grid = ag.Grid2D.uniform(\n",
" shape_native=(101, 101),\n",
" pixel_scales=0.1,\n",
" over_sample_size=over_sample_size,\n",
")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__PSF__\n",
"\n",
"The PSF kernel is sampled on pixels that are twice as fine as the detector\n",
"pixels. Therefore its `pixel_scales` are `0.1 / 2 = 0.05` arcseconds and its\n",
"`convolve_over_sample_size` is 2.\n",
"\n",
"During simulation PyAutoGalaxy evaluates the point source on this fine grid,\n",
"convolves it with the fine PSF and bins the result back to the 0.1 arcsecond\n",
"detector grid."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"psf = ag.Convolver.from_gaussian(\n",
" shape_native=(21, 21),\n",
" sigma=0.12,\n",
" pixel_scales=grid.pixel_scales[0] / over_sample_size,\n",
" normalize=True,\n",
" convolve_over_sample_size=over_sample_size,\n",
")\n",
"\n",
"simulator = ag.SimulatorImaging(\n",
" exposure_time=300.0,\n",
" psf=psf,\n",
" background_sky_level=0.1,\n",
" add_poisson_noise_to_data=True,\n",
" noise_seed=1,\n",
")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Galaxies__\n",
"\n",
"The point source is offset by a quarter of a detector pixel in each direction.\n",
"At over-sample size 2 this selects one of the central pixel's four sub-pixels,\n",
"so the blurred image retains the offset instead of forcing the source onto the\n",
"detector-pixel centre.\n",
"\n",
"The point source `intensity=25.0` is its total flux before noise and background\n",
"sky are added. It is conserved when the fine-grid image is binned back to the\n",
"detector resolution."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"point_source_centre = (0.025, -0.025)\n",
"\n",
"galaxy = ag.Galaxy(\n",
" redshift=0.5,\n",
" host=ag.lp.Sersic(\n",
" centre=(0.0, 0.0),\n",
" ell_comps=ag.convert.ell_comps_from(axis_ratio=0.8, angle=45.0),\n",
" intensity=0.5,\n",
" effective_radius=0.8,\n",
" sersic_index=2.0,\n",
" ),\n",
" point_source=ag.lp.PointSource(\n",
" centre=point_source_centre,\n",
" intensity=25.0,\n",
" ),\n",
")\n",
"\n",
"galaxies = ag.Galaxies(galaxies=[galaxy])"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Simulation__\n",
"\n",
"Simulate the dataset. The simulator automatically uses the over-sampled PSF\n",
"path because `convolve_over_sample_size=2`."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"dataset = simulator.via_galaxies_from(galaxies=galaxies, grid=grid)\n",
"\n",
"aplt.subplot_imaging_dataset(dataset=dataset)"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Output__\n",
"\n",
"Write the image, PSF and noise map as FITS files."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"aplt.fits_imaging(\n",
" dataset=dataset,\n",
" data_path=dataset_path / \"data.fits\",\n",
" psf_path=dataset_path / \"psf.fits\",\n",
" noise_map_path=dataset_path / \"noise_map.fits\",\n",
" overwrite=True,\n",
")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write PNG visualizations of the simulated dataset and its input galaxy."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"aplt.subplot_imaging_dataset(\n",
" dataset=dataset,\n",
" output_path=dataset_path,\n",
" output_format=\"png\",\n",
")\n",
"aplt.subplot_galaxies(\n",
" galaxies=galaxies,\n",
" grid=grid,\n",
" output_path=dataset_path,\n",
" output_format=\"png\",\n",
")"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Save the exact input galaxy as JSON for reproducibility."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"ag.output_to_json(\n",
" obj=galaxies,\n",
" file_path=dataset_path / \"galaxies.json\",\n",
")\n",
"\n",
"print(f\"Point-source dataset written to {dataset_path}\")\n"
],
"outputs": [],
"execution_count": null
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Loading
Loading