updated comparison of photometry

spacetelescope · Oct 28, 2024 · 190ff7d · 190ff7d
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diff --git a/notebooks/psf_photometry/MIRI/miri_phot_comparison.ipynb b/notebooks/psf_photometry/MIRI/miri_phot_comparison.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5e6982fe-42fe-42d7-984b-226ae08a01d0",
+   "metadata": {},
+   "source": [
+    "# MIRI Photometry Comparison of Photutils vs. Space_Phot\n",
+    "\n",
+    "**Author**: Ori Fox<br>\n",
+    "\n",
+    "**Submitted**: August, 2023<br>\n",
+    "**Updated**: November, 2023<br>\n",
+    "\n",
+    "**Use case**: A comparison of the photometry produced by the two other notebooks in this folder.<br>\n",
+    "**Data**: MIRI Data PID 1028 (Calibration Program; Single Star Visit 006 A5V dwarf 2MASSJ17430448+6655015) and MIRI Data PID 1171 (LMC; Multiple Stars).<br>\n",
+    "**Tools**: photutils, space_phot drizzlepac, jupyter <br>\n",
+    "**Cross-Instrument**: NIRCam, MIRI.<br>\n",
+    "**Documentation**: This notebook is part of a STScI's larger post-pipeline Data Analysis Tools Ecosystem and can be downloaded directly from the JDAT Notebook Github directory.<br>\n",
+    "**Pipeline Version**: JWST Pipeline<br>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18ff6a53-9e2d-4bef-aa2b-e2abd0758ec7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import astropy.units as u\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "from pandas import DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99887406-cc2a-4e62-8b8e-38de616e643d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read space_phot data\n",
+    "\n",
+    "sphot = pd.read_csv('miri_photometry_space_phot_lvl2.txt')  \n",
+    "sphot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "672f0fde-cd6e-4e85-9518-a07b53f5f581",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read photutils data\n",
+    "\n",
+    "phot = pd.read_csv('miri_photometry_photutils.txt')  \n",
+    "phot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bf4436f-47ad-4f59-9992-1ede269724cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphot['dec'].to_numpy()*u.deg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a88bf03-50a0-4c9b-bc1d-1b9d93032ed0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Find closest neighbors\n",
+    "\n",
+    "c1 = SkyCoord(sphot['ra'].to_numpy()*u.deg, sphot['dec'].to_numpy()*u.deg, frame='icrs')\n",
+    "c2 = SkyCoord(phot['RA'].to_numpy()*u.deg, phot['DEC'].to_numpy()*u.deg, frame='icrs')\n",
+    "idx, d2d, d3d = c2.match_to_catalog_3d(c1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b32e3c67-397a-4515-94d6-afd8da0b850c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate Delta Mag\n",
+    "\n",
+    "delta_mag = sphot['mag'][idx].to_numpy() - phot['Mag']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34f6ed58-d8dc-4af8-8f2a-d7498e118fd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cross match Catalogs and Look at Delta Mags\n",
+    "\n",
+    "df = DataFrame({\"photutils_skycoord\": c1[idx], \"spacephot_skycoord\": c2, \"skycoord_separation\": d3d, \"photutils_mag\": phot['Mag'], \"spacephot_mag\": sphot['mag'][idx].to_numpy(), \"delta_mag\": delta_mag})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d0b5394-c11b-4fa0-922b-567225c7dc65",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+}