HST.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df9eed7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "from astropy.wcs import WCS\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe29b0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"/home/kawahara/hst/color_hst_12182_46_wfc3_ir_f139m_f127m_sci.fits\"\n",
    "hdu = fits.open(filename)\n",
    "dbname = \"/home/kawahara/hst/db/gns_catalog.csv\"\n",
    "db = pd.read_csv(dbname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5907813a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ohsawa code\n",
    "__keywords_naxis3 = (\n",
    "    'NAXIS3', 'CTYPE3', 'CRPIX3', 'CRVAL3', 'CUNIT3',\n",
    "    'CD1_3', 'CD2_3', 'CD3_3', 'CD3_2', 'CD3_1',\n",
    ")\n",
    "\n",
    "def drop_naxis3_keywords(header):\n",
    "    header.set('NAXIS', 2)\n",
    "    for key in __keywords_naxis3: header.remove(key, ignore_missing=True)\n",
    "    return header\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e7fd8c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = hdu[0].data\n",
    "new_header= drop_naxis3_keywords(hdu[0].header)\n",
    "wcs = WCS(new_header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64831c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "138f03a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53660 53660\n"
     ]
    }
   ],
   "source": [
    "ra = db[\"ra\"].values\n",
    "dec = db[\"dec\"].values\n",
    "jmag = db[\"Jmag\"].values\n",
    "hmag = db[\"Hmag\"].values\n",
    "hwmag = 0.8*jmag + 0.2*hmag\n",
    "mask = hwmag < 16.5  \n",
    "print(len(ra[mask]),len(dec[mask]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9233fcb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3882/2356762234.py:7: RuntimeWarning: invalid value encountered in log10\n",
      "  a=plt.imshow(np.log10(img[j,:,:]), origin='lower')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib \n",
    "\n",
    "j=0\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = plt.subplot(projection=wcs)\n",
    "#ax.set_autoscale_on(False)\n",
    "a=plt.imshow(np.log10(img[j,:,:]), origin='lower')\n",
    "plt.scatter(ra[mask],dec[mask],marker=\"+\",alpha=0.5,color=\"C3\",transform=ax.get_transform('fk5'))\n",
    "plt.colorbar(a)\n",
    "plt.savefig(\"tmp.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c19838b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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