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jeffcarlin merged 1 commit into from
Jul 24, 2026
Merged

SP-3307: implement JBs comments on 205 1 NB #179

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10 changes: 5 additions & 5 deletions DP2/200_Data_products/204_Calibrations/204_2_The_Monster.ipynb
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Expand Up @@ -22,7 +22,7 @@
"Data Release: [Data Preview 2](https://dp2.lsst.io/)\\\n",
"Container Size: Large\\\n",
"LSST Science Pipelines version: r30.0.10\\\n",
"Last verified to run: 2026-07-20\\\n",
"Last verified to run: 2026-07-23\\\n",
"Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\\n",
"DOI: [10.11578/rubin/dc.20250909.20](https://doi.org/10.11578/rubin/dc.20250909.20)"
]
Expand Down Expand Up @@ -525,11 +525,11 @@
"outputs": [],
"source": [
"query = (\n",
" \"SELECT coord_ra, coord_dec \"\n",
" \"SELECT ra, dec \"\n",
" \"FROM dp2.Source \"\n",
" f\"WHERE visit = {visit_id} \"\n",
" \"AND calib_psf_used = 1 \"\n",
" \"AND CONTAINS(POINT('ICRS', coord_ra, coord_dec), \"\n",
" \"AND CONTAINS(POINT('ICRS', ra, dec), \"\n",
" f\"CIRCLE('ICRS', {ra_cen}, {dec_cen}, {radius})) = 1\"\n",
")\n",
"job = service.submit_job(query)\n",
Expand All @@ -549,8 +549,8 @@
"metadata": {},
"outputs": [],
"source": [
"source_ra = np.asarray(source_table['coord_ra'])\n",
"source_dec = np.asarray(source_table['coord_dec'])\n",
"source_ra = np.asarray(source_table['ra'])\n",
"source_dec = np.asarray(source_table['dec'])\n",
"print(len(source_ra))"
]
},
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55 changes: 28 additions & 27 deletions DP2/200_Data_products/205_Point_spread_function/205_1_PSF_for_deep_coadd_images.ipynb
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Expand Up @@ -22,7 +22,7 @@
"Data Release: [Data Preview 2](https://dp2.lsst.io/)\\\n",
"Container Size: Large\\\n",
"LSST Science Pipelines version: r30.0.10\\\n",
"Last verified to run: 2026-07-22\\\n",
"Last verified to run: 2026-07-23\\\n",
"Repository: [github.com/lsst/tutorial-notebooks](https://github.com/lsst/tutorial-notebooks)\\\n",
"DOI: [10.11578/rubin/dc.20250909.20](https://doi.org/10.11578/rubin/dc.20250909.20)"
]
Expand Down Expand Up @@ -59,7 +59,7 @@
"This notebook demonstrates how to visualize the PSF model of a `deep_coadd` at a particular location, and how to explore the PSF model methods to calculate and visualize its properties.\n",
"The final section shows an example of PSF model residual visualization.\n",
"\n",
"In DP2, a `deep_coadd` is stored in the new `lsst.images` data model as a `CellCoadd` object. Its PSF is accessed through the `deep_coadd.psf` attribute, and its astrometric solution through `deep_coadd.sky_projection` (see the DP2 tutorial on `deep_coadd` images and astrometric calibration).\n",
"In DP2, a `deep_coadd` is stored in the new `lsst.images` data model as a `CellCoadd` object. Its PSF is accessed through the `deep_coadd.psf` attribute, and its astrometric mapping through `deep_coadd.sky_projection` (see the DP2 tutorial on `deep_coadd` images and astrometric calibration).\n",
"The PSF modeling was performed with the [\"PSF in the Full Field of View\"](https://ui.adsabs.harvard.edu/abs/2021ascl.soft02024J/abstract) (PIFF) software; the coadd PSF is a position-dependent weighted sum of the contributing single-visit PSF models.\n",
"\n",
"The new PSF object provides `compute_kernel_image` and `compute_stellar_image` methods.\n",
Expand Down Expand Up @@ -253,8 +253,11 @@
"metadata": {},
"outputs": [],
"source": [
"query = f\"band.name='{my_band}' AND patch.region OVERLAPS POINT({ra_cen}, {dec_cen})\"\n",
"dataset_refs = butler.query_datasets(\"deep_coadd\", where=query)\n",
"query = f\"band.name=:band AND patch.region OVERLAPS POINT(:ra, :dec)\"\n",
"bind_params = {'band':my_band, 'ra':ra_cen, 'dec':dec_cen}\n",
"\n",
"dataset_refs = butler.query_datasets(\"deep_coadd\", where=query,\n",
" bind=bind_params)\n",
"deep_coadd = butler.get(dataset_refs[0])\n",
"deep_coadd"
]
Expand Down Expand Up @@ -432,7 +435,7 @@
"source": [
"### 3.2. PSF size and shape with GalSim HSM\n",
"\n",
"An independent way to characterize the PSF size and shape is with adaptive moments, using the HSM algorithm ([Hirata & Seljak 2003](https://ui.adsabs.harvard.edu/abs/2003MNRAS.343..459H/abstract); [Mandelbaum et al. 2005](https://ui.adsabs.harvard.edu/abs/2005MNRAS.361.1287M/abstract)) as implemented in [GalSim](https://github.com/GalSim-developers/GalSim). `galsim.hsm.FindAdaptiveMom` fits an elliptical Gaussian to the image and returns the adaptive-moments size `moments_sigma` (the determinant radius $|\\det M|^{1/4}$, in pixels) and the `observed_shape` (a `Shear` with ellipticity components `e1` and `e2`)."
"An independent way to characterize the PSF size and shape is with adaptive moments, using the HSM algorithm ([Hirata & Seljak 2003](https://ui.adsabs.harvard.edu/abs/2003MNRAS.343..459H/abstract); [Mandelbaum et al. 2005](https://ui.adsabs.harvard.edu/abs/2005MNRAS.361.1287M/abstract)) as implemented in [GalSim](https://github.com/GalSim-developers/GalSim). `galsim.hsm.FindAdaptiveMom` fits an elliptical Gaussian to the image and returns the adaptive-moments size `moments_sigma` (the determinant radius $|\\det M|^{1/4}$, in pixels) and the `observed_shape` (a `Shear` with ellipticity components `e1` and `e2`). The HSM algorithm is the same algorithm that is used to measure PSF moments that are tabulated in various DP2 catalogs."
]
},
{
Expand Down Expand Up @@ -485,42 +488,24 @@
"## 4. PSF radial profile"
]
},
{
"cell_type": "markdown",
"id": "d91387cc",
"metadata": {},
"source": [
"Compute the center of the PSF image from its dimensions."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ba9af6d",
"metadata": {},
"outputs": [],
"source": [
"center_pix = np.array([xlen / 2, ylen / 2])"
]
},
{
"cell_type": "markdown",
"id": "88ca3c8f",
"metadata": {},
"source": [
"Create a coordinate grid and calculate the distance from each pixel to the center."
"Create a coordinate grid and calculate the distance from each pixel to the center. Using the `meshgrid` method associated with the psf bbox retrieves coordinates with (0, 0) at the center of the kernel image."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7ca37fb",
"id": "d74a40fd-6560-41f8-8fd7-3e40e9633cc4",
"metadata": {},
"outputs": [],
"source": [
"yy, xx = np.indices((ylen, xlen))\n",
"xx, yy = psf_kernel_image.bbox.meshgrid()\n",
"coords = np.stack((xx, yy), axis=-1)\n",
"distances = np.linalg.norm(coords - center_pix, axis=-1)"
"distances = np.linalg.norm(coords, axis=-1)"
]
},
{
Expand Down Expand Up @@ -1038,6 +1023,22 @@
"source": [
"> **Figure 6:** Measured star used for PSF modeling (left), PSF model (center), and residuals (right)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4fcf988d-c407-449b-8d96-0b9f1b1cbd00",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "843d80a4-3227-41f9-93a9-29420319e7cc",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand Down
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