{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supernovae plus extended source simulation\n",
    "In this notebook, we simulate population of lensed supernovae and simulate image of a random lensed supernovae. It follows following steps:\n",
    "\n",
    "1. Simulate lensed supernovae population\n",
    "2. Choose a lens at random\n",
    "3. Set observation time and other image configuration\n",
    "4. Simulate image of a selected lens\n",
    "5. Visualize it\n",
    "\n",
    "Before running this notebook, please download the \"scotch_SNIa_host_galaxies.fits\"\n",
    "\n",
    "file from the following link: https://github.com/LSST-strong-lensing/data_public.git. \n",
    "\n",
    "This file contains type Ia supernovae host galaxies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate lensed supernovae population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from astropy.cosmology import FlatLambdaCDM\n",
    "from astropy.units import Quantity\n",
    "from slsim.lens_pop import LensPop\n",
    "import numpy as np\n",
    "from slsim.image_simulation import lens_image_series\n",
    "from slsim.Plots.plot_functions import create_image_montage_from_image_list\n",
    "from slsim.image_simulation import point_source_coordinate_properties\n",
    "import matplotlib.pyplot as plt\n",
    "import corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = os.path.abspath(\"/Users/sanchez/Devel/DESC/data_public\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a cosmology\n",
    "cosmo = FlatLambdaCDM(H0=70, Om0=0.3)\n",
    "\n",
    "\n",
    "# define a sky area\n",
    "sky_area = Quantity(value=1, unit=\"deg2\")\n",
    "\n",
    "\n",
    "# define limits in the intrinsic deflector and source population (in addition to the\n",
    "# skypy config\n",
    "# file)\n",
    "kwargs_deflector_cut = {\"z_min\": 0.01, \"z_max\": 2.5}\n",
    "kwargs_source_cut = {}\n",
    "## create a point plus extended source lens population.\n",
    "supernova_lens_pop = LensPop(\n",
    "    deflector_type=\"elliptical\",  # type of the deflector. It could be elliptical or\n",
    "    # all-galaxies.\n",
    "    source_type=\"supernovae_plus_galaxies\",  # keyword for source type. it can be\n",
    "    # galaxies, quasar, quasar_plus_galaxies, and supernovae_plus_galaxies.\n",
    "    kwargs_deflector_cut=kwargs_deflector_cut,  # cuts that one wants to apply for the\n",
    "    # deflector.\n",
    "    kwargs_source_cut=kwargs_source_cut,  # cuts that one wants to apply for the\n",
    "    # source.\n",
    "    variability_model=\"light_curve\",  # keyword for the variability model.\n",
    "    kwargs_variability={\"supernovae_lightcurve\", \"i\"},  # specify kewords for\n",
    "    # lightcurve. \"i\" is a band for the lightcurve.\n",
    "    sn_type=\"Ia\",  # supernovae type.\n",
    "    sn_absolute_mag_band=\"bessellb\",  # Band used to normalize to absolute magnitude\n",
    "    sn_absolute_zpsys=\"ab\",  # magnitude system. It can be Optional, AB or Vega.\n",
    "    kwargs_mass2light=None,  # mass-to-light relation for the deflector galaxy.\n",
    "    skypy_config=None,  # Sky configuration for the simulation. If None, lsst-like\n",
    "    # configuration will be used.\n",
    "    sky_area=sky_area,  # Sky area for the simulation\n",
    "    cosmo=cosmo,  # astropy cosmology\n",
    "    source_light_profile=\"double_sersic\",  # light profile for the source galaxy\n",
    "    catalog_type=\"scotch\",  # catalog type. It can be None or scotch\n",
    "    lightcurve_time=np.linspace(\n",
    "        -20, 100, 1000\n",
    "    ),  # array of light curve observation time.\n",
    "    catalog_path=os.path.join(\n",
    "        data_path, \"SupernovaeHostcatalog/scotch_SNIa_host_galaxies.fits\"\n",
    "    ),\n",
    "    # path for catalog. If not provided, small size catalog from\n",
    "    # /slsim/Source/SupernovaeCatalog will be used for\n",
    "    # source_type=\"supernovae_plus_galaxies\" case. For other cases, we do not need to\n",
    "    # provide outside catalog. One can download scotch_SNIa_host_galaxies.fits from\n",
    "    # https://github.com/LSST-strong-lensing/data_public.git\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose a random lens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# specifying cuts of the population\n",
    "kwargs_lens_cuts = {}\n",
    "# drawing population\n",
    "supernovae_lens_population = supernova_lens_pop.draw_population(\n",
    "    kwargs_lens_cuts=kwargs_lens_cuts\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of lenses: 26\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sanchez/Devel/DESC/slsim/slsim/Sources/source.py:362: RuntimeWarning: divide by zero encountered in log10\n",
      "  mag_source0 = -2.5 * np.log10(w0 * flux)\n",
      "/Users/sanchez/.virtualenvs/slsim/lib/python3.12/site-packages/sncosmo/models.py:189: RuntimeWarning: divide by zero encountered in log10\n",
      "  result[i] = -2.5 * np.log10(f / zpf)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of lenses:\", len(supernovae_lens_population))\n",
    "\n",
    "lens_samples = []\n",
    "labels = [\n",
    "    r\"$\\sigma_v$\",\n",
    "    r\"$\\log(M_{*})$\",\n",
    "    r\"$\\theta_E$\",\n",
    "    r\"$z_{\\rm l}$\",\n",
    "    r\"$z_{\\rm s}$\",\n",
    "    r\"$m_{\\rm host}$\",\n",
    "    r\"$m_{\\rm ps}$\",\n",
    "    r\"$m_{\\rm lens}$\",\n",
    "]\n",
    "\n",
    "for supernovae_lens in supernovae_lens_population:\n",
    "    vel_disp = supernovae_lens.deflector_velocity_dispersion()\n",
    "    m_star = supernovae_lens.deflector_stellar_mass()\n",
    "    theta_e = supernovae_lens.einstein_radius\n",
    "    zl = supernovae_lens.deflector_redshift\n",
    "    zs = supernovae_lens.source_redshift\n",
    "    source_mag = supernovae_lens.extended_source_magnitude(band=\"i\", lensed=True)\n",
    "    ps_source_mag = supernovae_lens.point_source_magnitude(band=\"i\")\n",
    "    deflector_mag = supernovae_lens.deflector_magnitude(band=\"i\")\n",
    "    lens_samples.append(\n",
    "        [\n",
    "            vel_disp,\n",
    "            np.log10(m_star),\n",
    "            theta_e,\n",
    "            zl,\n",
    "            source_mag,\n",
    "            ps_source_mag,\n",
    "            deflector_mag,\n",
    "        ]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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iFstWLJ4nM/99qMTERLvvvvvMzP+57bPPPrMpU6bYjBkzbOPGjQ5HWD6LFy+2WrVq2fXXX29PP/20nXDCCXbBBRcU2seN7dCvv/5qRx55pPl8PmvevLmtX7/ezNxxzROqu+++24YOHWqVK1e2IUOG2K+//up0SKUKPEQxZswY27dvn3Xp0sUuvvhip8MKOzokUKTRo0dbnz59Cm174oknrHbt2nbnnXfan3/+6VBkB9u7d6917NjxoA/cy5cvtzp16tjZZ5/tmosML8VqZnb99ddb//79g9/v27fPRo0aZQkJCfbEE08U+dQ8SrZy5Uo7+uijbc6cOcFtw4YNs65du1q/fv2sf//+tmHDBjNz/qLk6quvtrvuusvMzI477jjr3r27denSxZo2bWrNmze3NWvWmJl7L0guuugi+/jjj61v377Wu3dv++ijj8zM7LfffnM4Mv8Ijh49ehz0pOaRRx5pY8eOdSgq52RmZlqPHj2sZs2awW3nnHOOdejQwXw+n/Xq1csef/xxByMM3fDhw23AgAHB79euXWsTJkwwn89nDz74oIORhW7FihXm8/mCHwZzc3Nt2rRpdt9999lbb71lc+fOdTbAMli7dq0dddRRNmvWrOC2zMxMe+utt6xFixZ23nnnORhd+OzevduuvPJKu/rqqwt1ug4cONAeeOABu++++1zXMTFw4MBgXnfp0sW6detmRxxxhB1xxBHWuXPnYNvidFv4zTffWPv27QuNZF2yZIk1btzY0tPTC3VKOGn9+vXWqlWrYLttZvbiiy9arVq1XPXhO9r1y++//25VqlSxyy67zMz8D4Lcdddddt5559nJJ59sU6ZMsT/++MPMnC9rZZGfn2/dunWz66+/vtD2ZcuW2XXXXWedOnWyDz74wKHoyi4rK8suuOACS09Pt59++snMzM4880xr3bq11alTxypVqmS33367rV692uFIyy43N9c6dOhg48ePL7T9u+++s/79+1u/fv3s+++/dyi6g3kt3tLEatmKtfNk5r/XcMcdd1hqaqp9/PHHZmZ26qmnWuvWra1BgwaWmppqF198sX333XcOR1o2e/bssbPPPttuvvnm4LYPPvjABgwYYBs3biw0StRN7dDevXtt9OjRNmDAAPviiy+se/fu1rRpU890SgTiu/nmm+2pp56yxYsXW0pKil1yySW2e/due+SRR4L3M9xk5cqV5vP5bNSoUcFt06ZNs5SUFPvqq6+cCywCYmwCKoTL3r17g/8PrB9w44036oEHHtA///lPvffee5LcMYdcXl6eNm7cqOXLlwe35eTk6KijjtIXX3yhzz//XBMmTHAwQr+8vDzPxBqwfft2paenS/Kf66SkJI0fP1733XefbrvtNn3++efBnyE0NWrUUHZ2tiZNmqStW7dq3LhxevHFF3XqqaeqT58+2rhxo04++WTl5OQ4PqekmWn+/Pl68803lZ6ero8++khffPGFfvjhBx1yyCE699xzJblvLsP8/Hzl5+dryZIlSklJ0RNPPKHExEQ9/fTT6tSpk3r37q28vDxHy+2qVauUkpKiSy+9VJK0b98+SdKxxx5bqP6NF5UqVdJVV12lOnXqaMCAAerXr5/27dunu+++W99++60aNGig1157Ta+++qrToZbIzLRmzRolJycHtzVp0kQ33nijHn30UY0ZM0YvvPCCgxGWzsw0c+ZMSVLz5s0lSSeffLIeeOABvfzyyxo5cqSuuuoqffDBB06GGbK0tDTl5ORo1qxZwW3VqlVT//79dffdd2v58uV65plnHIwwPBISEtS/f39dd911SkxMlM/n0/3336933nlHixcv1meffaYHHnhAt912m9OhBuXm5mrevHl6+umnVb16db333nv64Ycf9Nprr2nnzp06++yzJTk/v3J2drb++usv/f7774W21a9fX2PHjtWKFSv01ltvORihP2+//PJLtWzZUsOGDQu2b0OGDFH9+vW1Zs0aR+MLcKJ++f3333Xsscdq9uzZWrVqlc455xx9++23qlOnjlJTUzVp0iSNGTNGW7dudbyshcr+f02sY445Rps3b9Zff/0V/FmLFi10zTXXqHbt2nr33XcL7e9mKSkpuvrqq9W7d2+NGDFCzZs3V35+vl544QWtWLFCL7zwgp599lm98sorkrxxTJI/zkB9sWXLFkn752Lv0qWLRowYoXXr1un9998P7u8kr8UbilgsW7F4niQpKSlJF198sYYNG6bhw4eradOmSkpK0htvvKH169fr3Xff1dy5c/Xiiy9K8s5xpaSkaOvWrcH7KpL07bffau7cuerYsaPOOussjRw5UpLz1zwFpaamqk2bNho8eLBOOukkvfzyy2rSpIlOPPFE/fbbb0pISHD1faDAPYp+/frp559/VqtWrfTtt9/qzTffVOvWrfXYY4+5Mv7U1FQ9/fTTGj9+vCR/Oe/atasyMjL04YcfSoqh+29R7wKBJzzxxBOWlpYWfFK74Jzm9957r1WrVq3Q/GtOe/TRR61Ro0bBJ6DNLPj0/vjx461z5862detWR3qc//rrr0Lf//3vf3dtrAe6++67rV69erZ9+3Yzs0IjIoYNG2aNGze2LVu2OBWeJ+3bt88mT55sjRo1sj59+ljlypULPbW6evVqq1mzpr355puOxRgoe19//bUdf/zx1rVrV7vqqqvMbP+TBj/88IM1btw4+KSRmwRivOOOO4JP327YsMHq169v1apVswkTJgT3dTLPCs7xHBgZdfPNN9t1111XaL+srKxohuWYvXv32rRp06xZs2bWtWvX4BOrZmZbt261E044wS688EIHIwzNP/7xDzv66KNtyZIlhbZv27bNbrnlFuvatWuwbXWrzMxMmzhxovl8PmvYsKGde+65tnz5cjPz5/6QIUOsV69erhotWZysrCy79NJLrV+/frZgwYJCP9u9e7f179/fBg8e7FB04VXwWm3+/PlWpUqV4BPSeXl5duedd1pGRoZt2rTJqRCDsZiZvfTSS3byySfbKaecctDIsKlTp1qrVq1c8cTq2rVr7bDDDrNLL73Upk6dat98843VqFEj+OTacccd54qpzaZPn37Q1HB79+61ww47zN544w1ngiqCE/XLnDlzrE+fPubz+ezUU0+1zZs3B3/26KOPWrNmzex///tf2P5etLz11ltWuXJlmzJlykHXM9OmTbPExERX5FBZfPvtt8E1MX755ZdCP3vwwQetZs2atnXrVoeiK7/JkydbcnJycE2pgk8WT5o0ydLS0hyvmwvyWryhiMWyFYvnycw/CvHqq6+2U0899aDr6eeff96SkpJcdS+qJHl5ebZjxw7r27evDRgwwJ566ikbOXKkVa5c2V544QX79NNP7d5777WOHTu6flRbfn6+/fLLL8GREoFZB7Kysuznn3921ZTeBdvEL774wlq0aGF79uwxM/+om4SEBDv11FMLfd50u7Fjx1qtWrWC99/ccL+wouiQgJn5hwX9+OOPwe/37NljPXv2tC5dugQL/N69e83Mv+5B48aNHZuX8Pfff7cffvjBPvvss+BNvF9//dXOO+8869at20GLhz799NPWsmVLRyrIuXPn2oknnmjz588Pblu2bJkNGjTIdbEWZeXKlda5c2e74IILbMeOHWa2v1Pip59+skaNGhWaBgOhycnJsa1bt9qiRYusdevWwQuq/Px8W758ubVs2dIVw/E2b95sl156qSUlJR00hduCBQusZcuWtnjxYoeiK93EiRNt4MCBZmZ2xRVX2CGHHGJdunSx0047zV5//XXH4jpweGvB76+55ppgzGZmjz32mD322GOuHxIbLnv27LF///vf9umnnwbr98DX66+/3rp37+769+Lbb7+1Y4891u64447gkOaAzz//3NLS0mz27NkORRe6vXv32qOPPmrdunU7KN733nvPUlNTC7VtbhaYV//8888/aK2mRx991Dp27OiadjecAlMMBXJmypQp1qpVq+BDBk5bu3at9ejRw3w+30Hz4n799dfWokULx4fSF5yb/5hjjrHDDz/cGjVqVKgDYtCgQQfNA+20QNz5+fnWoUOHQusovf76644/TBCt+qXgh/Xvv//eRowYYZ9//rmZWaEpUmvUqOGZKfUONGbMGEtJSbFXX3210AMM8+bNs9atW3umQ6LguZo9e7Z99NFHwfWjAnXY5MmT7ZhjjvHUlLEFj+vKK6+0tLQ0mzlzZqF9/vOf/1jbtm1dcTPca/GGIhbLViyepwOtWLHCvvzyy+A5CZyrd955x1XXMsU5cBru77//3vr162cXXHCBtWjRwv71r38Ff/bnn39akyZNCj0055Rdu3bZzp07g/d+Agp+/lq1alWwU2L16tV2/fXXW0ZGxkEP4kZbcbH//vvvwek1hw4dao0aNbIXX3zRqlWrZv3793fFdM7FxW62P983b95sLVu2tLvuuismOiPMmLIJkubNm6dOnTpp3rx5wW2VK1fWiBEj5PP5NGjQIG3btk2pqamS/EPOqlatqqSkpKjHumDBAnXt2lUXX3yxBg0apNatW2vq1Klq2LCh7rjjDtWoUUOjR4/W1KlTJfmnQ1q9erUOPfTQ4DDGaJk/f76OO+44de3aVcccc0xwe4sWLXTFFVeoVq1arolV8k8h8+CDD2rkyJF64403tHfvXh155JG68sortWLFCt12223avn178LzXq1dPKSkpwSm9ELrExESlp6erQYMGSk5O1tdffy3JP0TzjTfekM/n01FHHeVojGamOnXq6N5779UZZ5yhb775Rtdee60kadu2bXr//feVmpqqQw45xNE4i2L/P3y3Xbt2kqQrr7xSn3zyiX788Ue98sor2rp1q6ZNm6bMzExH4jtwiquCw12rVq2qtLQ0SdLYsWM1fPhw9e7d23XTYkVK5cqVdcopp+jkk09WpUqVJCn4dcuWLWrfvr3r34sTTzxRQ4YM0ZtvvqkpU6Zo9erVwZ+1bdtWTZo0UXZ2toMRhiY1NVVXXnmlnnzySbVt21bS/uHBdevW1eGHH67q1as7GWJI8vPz1aZNG33wwQf6+OOPddddd+mrr74K/nzZsmVq1KiREhMTHYwyMurVqydpf52zcOFCtWnTRikpKU6GJclfTzdp0kRTpkxR+/bt9fHHHwenrMzOztYXX3yh2rVrO17GfD6f8vPzdeyxx+rzzz/XjBkz9Pnnn+vBBx+U5J92aufOnWrdurWjcR4oMOWDz+dT1apVVblyZUnSyJEjde2116pWrVpOhhe1+sXn8wWvCTp37qwbbrhB3bt3l+RvW/Ly8vTnn3/q6KOPdt05LE3guO677z7ddNNNuuSSS/TII4/op59+0s6dO/Xaa69p3759wWsKtyt4rjp16qTTTjstWC8H6rClS5fqyCOPVG5urmemaik4/cqDDz6o/v37q0+fPnr55Ze1Zs0a5eXlafr06UpISHDF9Y3X4i1OwfIRC2XrwJhi5TyVpHnz5urZs2fw3kPgOGbNmqVGjRoFPx+40YoVK/TYY4/pjz/+CG7r3Lmz3n77bb388stKT09XtWrVgj9LT09XixYtgu2eU2VwyZIlOuecc9SjRw+1bNlSr732WjCeguXoiCOO0AsvvKBmzZrpiCOO0IsvvqhJkyapZs2ajsQtFR+7JB166KHKzMxUgwYN9Mknn+i9997TpZdeqk8++UQ//PCD49NklfS+S/vzvWbNmurSpYu+/vrr2LkH50QvCNwjsHr7rbfeetDPAovMde7c2Zo1a2bTp0+3L7/80kaPHm316tWztWvXRjXWTZs22dFHH2133323/fLLL7ZhwwYbNGiQHXXUUXbvvfdaVlaWzZs3z6655hpLTEy0du3aWZcuXaxWrVpRX4Bz0aJFVrly5eAUBPn5+bZ169ZCT2Z+9913rog1EG/NmjWtR48e1r17d0tMTLQBAwbYN998Y2b+p7QDixovXrzYFi5caKNHj7YmTZq4fuoRtyiqF3vHjh123nnnBRfzHDx4sNWuXTuqZSAvL6/YJ/YDX9evX2+333671a9f32rWrGkdO3a0unXr2s8//xy1OMtj06ZNVqtWLWvQoEGhWJctWxb1+qs0gado7rzzThsxYoRNmDDBUlNTPfEkfVnl5eUd9NRQSaMe9uzZY3fffbfVr1/fli1bFunwKqTgcTzwwAPWokULu+CCC+w///mPrV692m6//XZr1KiRp4YHF+X222+3rl27Ov4kVFGKqmsD5W327NnWvn1769ixo7Vr187OOussq169us2bNy/aYVZIWXNo9+7ddvfdd9shhxxiixYtinR4hZQUa+Dr8uXLbeDAgda4cWOrX7++de/e3dLT06N+PVRSrEWVqw0bNtioUaOsTp06tmLFiqjEWFb79u2zli1b2nvvvWf333+/Va5c2fHREaWJdv0ybtw4a9GihWem/yjOP/7xD2vVqpWlp6dbu3btrF69eq6/TjM7+Cnioqxbt85Gjx5tNWrUiHodVhaBp+5LsmfPHrvrrrssPT3dmjRpYhkZGVa7dm1HzpXX4g1FWesNL5StNWvW2KeffmpmxeeL185Teaxatcruvvtuq1mzpi1cuNDpcIq1cuVKS09PN5/PZyNHjiw0RWBeXp7t2rXLOnfubGPGjLG//vrLMjMzbcyYMVa/fn1HR7QtXrzYateubcOHD7fXXnvNbr31VktKSir2Wiw7O9sGDx5s6enpjs+YUFrsOTk5Nnr0aOvZs6fNmTPHzPbnUmAWGKeE+r4HrkNXr15tPp/PnnnmGQeiDT86JOLYihUrLCUlJTgH7r59++zDDz+0KVOm2Lvvvhu8SFm0aJENGTLEDjnkEDvqqKOsdevWwUSOpsWLF9thhx120A26O++801q3bm0TJ060/Px827Vrl3333Xd2//3329NPP20rV66MapxbtmyxI4880jp06BDcNnToUOvUqZPVr1/fTjzxxODNj8zMTEdjNfNfwJxxxhl2/fXXB7fNmTPHMjIyrFevXsFppT766CM7+eSTLTk52Y4++mg7/PDDHSkHXrB+/Xp788037Z133jlozvKAQKOyZs0ae+SRR+z888+3O++8M6o3XBcvXmwXX3yx9erVy6666qpCc0wHGunAzZjdu3fb77//bs8995x98sknjk+jUZQDbxjl5eXZf/7zH1u6dGmhbW42YsQI8/l8VrVqVdffNCqPxYsX24UXXmi9e/e2a665xv79738Hf1bUh6x3333XhgwZYvXr13fVh6qSbqAULGMvvviinX322ZaQkGBt27a1pk2beuY4irJs2TIbPny41apVy1XTNZU0zDkgcKxr1661d99912644QZ76KGHCtUPXlDWHPrggw/s0ksvtcaNG0e97IUSayBftmzZYvPmzbMJEybYa6+9dtDUWm6ItaDVq1cHO0qj/b6WJW9zcnLs+OOPt5YtW0a9M8IN9UtJbf6nn35qN9xwg9WoUcORh4FCcWCeFKXgz5YtW2ZfffWVTZ8+3RVTUBRly5YttnTpUvvuu++C20oqK3PnzrWePXtas2bNXHuezPzv/Z133hnyZ7lZs2bZtGnT7LXXXrNff/01ssEVwWvxhqKoqZJL29/tZWvhwoWWmJhobdq0CWl/L5wnM7NffvnF/vGPf9itt95q3377bXBu/+IsWrTITjnlFGvRooVrz5WZ/3r08ssvt8suu8yeeuop8/l8dvvttxfqlDAze/PNN83n89lRRx1lnTt3dvzzwdatW61Pnz520003Fdres2dPu/HGG82s8OfsvLw8e/LJJ61SpUqOf64JJXYz/7TzgalMC3Jy6qPyvO87d+60G2+80bUPwpQVHRJxKicnx2666SarXbu2TZs2zczMTjvtNDvmmGPssMMOs4SEBDvnnHMK3UxdunSpbdiw4aAKNVrmzZtnjRo1Cj61X7Dhuummm6xp06auuUFyww032Iknnmjjxo2zY4891vr162dTpkyx9957z7p27WqNGzd2pPOhOMcff7yNGzfOzPZ/sFm6dKn17NnTTjnllEI3bH744QdbunSp55/wjZQFCxZY06ZNLSMjw+rWrWtnnnnmQTdWDnzi8sB5TKNh6dKlVqtWLbviiivs0Ucftb59+9qRRx5pN9xwQ3Cfggukusny5cvtjjvusMsuu8wee+yxQg3ygU/eOnmRsXHjxlKf1DrwnI8bN86qVat20AJusWDZsmVWo0YNGzx4sN11113Wrl07y8jIsFtuuSW4z4Fl7tdff7X777/fVRddy5cvt4kTJxZ5URtQ8KnDXbt22cKFC23x4sWuWgQ6lOMomD8LFy60YcOGWYcOHVw1omDx4sXWp08f69ChgzVo0MBeffVVMzv4Av7AbV5Unhxas2aN/f3vf4/6Df7yxOqU8sS6a9cumzNnTtSfqi9r3u7Zs8eOP/54q1OnTlSvkaNdvyxbtszuuusuu+iii+yRRx4pdMPqwOuCgIceesj69+/v2idtFy5caD179gyWsaKuEd3+kMWBFixYYMcdd5y1aNHCDj30UOvbt2/wZ4HyUFTnxCeffHLQIsRukZ+fb3v27LFjjz3WfD6fXXPNNYXqhQOPy+l2yGvxhmrevHmWlJRkt99++0E/C+SJ18rW3LlzrWrVqnb66afbEUccYS+//PJB+3jtPJn564FDDz3UTj31VDv88MPtsMMOO6h9Kupcffvtt64b4X6gPXv22FNPPWVTp041s/0dD0V1SsycOdPGjx9vTz/9tOOdR3/++acdd9xxwftsgZwZOnSoXXjhhUX+zocffuiKz2ehxO7WtrI877uZFVovyuvokIhjK1assKuvvtq6dOlijRs3ttNOO82WLl1qe/bssdmzZ1vDhg0LLTLohobu2GOPtV69egW/L5iMGRkZNnjwYCfCCipY2d16661Wt25dO/300w+6CdW6dWu79NJLoxxd0TIzM61Xr152zTXXmJn/AiBwM23x4sXWqFGjQr3LKN6aNWusYcOGdtddd9muXbvsk08+sXr16tkPP/xQ5P7PP/98oQuraOVYVlaWXXjhhYV64/fu3WsdOnQwn89nQ4YMOShOt0xlsHjxYqtRo4b169fPzj33XKtRo4adfPLJ9uyzzwb3KXgz2KkO1CVLllhycrINHDiwxKe2AwqOOHH7xXZ55Ofn2913323nn39+cNvOnTtt/Pjx1r59e7vqqqsK7f/BBx8EOz3L+pRtJJU0DDvADW1lacp7HHPmzHFVZ3RZh5e///77tnHjxugGGSYVyaFofxArT6ybNm2KaowB5YnVqTJU3rx96aWXbPny5dEI0cyiX78sXrzYatasaeedd55dc8011rhxY+vYsaNNnjw5uE/B64KCN37cuijqr7/+akceeaT5fD5r3ry5rV+/3syKz+Unnngi+ICZWy1btszq1Kljd911l3333Xc2ffp0O/zww23kyJFF7j9p0iRPjRS9++67bejQoVa5cmUbMmRIsTcYJ0+e7IrpOL0Wb0mKmyq5uOlvvFC2AtNqjxkzxvbt22ddunQpdF/mQF44T2b+xYVbtmxp99xzT/D6vlWrVjZp0qQi93/iiSfsrbfeimaIFbZr165C30+dOtV8Pp+NGDEi2B7u27fPsc+oxSnYuRBYSHz06NEHlbudO3dGNa5QhBp7ZmZmVOMKhZdjDwc6JOLMgTd2Vq1aZRdffLGdfvrpB00V8+GHH5rP54vqh5iCipqC4eeff7ZDDz200A3TwIeMW2+91c4888yox2lW/HQREydOtHfeeeegp03OPfdcGzhwYNTjDNi6dastXbo0eG4/+ugj8/l89s4775iZ/wNPoEJ8/fXXrVatWrZ27VpP3Ghz0jPPPGM9e/Ys9D6ddtpp9swzz9hLL71kX375ZXD7N998Y82bN7eLLrrIkRuuvXv3tnvuucfM9s+deMcdd9i5555rHTt2tEceecTM/E+jOBlnQdnZ2XbRRRcVukG0cuVKGzRokHXp0sUef/zxQvuPGzfOLr/88qg/+fTnn3/a8ccfbyeddJLVqVPHzjvvvBI7JSZOnGgnn3xyoekLYtFll11m3bt3L7Rt586dNnHiRMvIyLAJEyaYmdm///1va9Sokd19992Wl5fnmnon1GHYAQ8//LDdd999UY6ydOU5jkBd4SZlHeb80UcfWaNGjWzUqFGufVKqNF7KobLG6uR58UKsXsnbaMeZmZlpffv2tTvuuCO47bfffrPatWtb3bp17YEHHii0f6C9Le5BETfYu3evjR492gYMGGBffPGFde/e3Zo2bVpsp8TWrVutWbNm1q9fP9fesMjMzLTzzz/frrvuuuC2vLw8u/HGG61///4H7b9x40bz+Xx21llnuf5J0MD5uPnmm+2pp56yxYsXW0pKil1yySW2e/due+SRR4IPnbjhuLwWb2lKmyq5e/fuNnfu3GA76IVjWrlypfl8vuC02mZm06ZNs5SUFPvqq68O2t8LxxQwc+ZMa9OmTaGbsIMGDbIRI0bYRRddVOghuM2bN7u+bitJbm5usNy98cYbwfZww4YNNnz4cBswYIDt2rXLNZ9zAgq2MaNGjSo0ku1vf/ubPfrooyGtP+MEYvceOiTiSHHDp9euXWuffvpp8AZ0oFJ8++237eijj3Zk0cripmDYu3evvfHGG1anTh0bOHCg7du3L5i8F110kQ0ePNhycnKiWrEXFWvBG7cHDvPPz8+3gQMHFnqKI5oWLlxoHTp0sLZt21pSUlJwQfAbb7zRUlJS7KOPPiq0/yeffGItW7a0LVu2RDVOL3r66aft8MMPD86lOH78ePP5fHbyySfbsccea4ceeqi98MILwf2fffbZqC9elZ+fb7t377Zu3brZxRdfHGzYfvvtN2vatKk9//zzdtFFFxUaieREnMU55ZRT7Oqrrzaz/bmzdu1au+yyy6xbt26Fyu9DDz1kLVq0iPo0OZ9++qldcMEF9tNPP9kPP/xg6enpJXZKvPDCC9a9e/fgzYZYEzhPTzzxhJ1wwgkHdX5v27bNrrrqKjv++OOD9eXYsWNdN4S+LMOwt27daoMGDbLOnTvb1q1bnQi3WOU9Dre1AeUZ5jxmzBjXlatQeCmHiDUyvJK30Y5z9+7dduyxx9rrr78e/N7M7LzzzrPevXvb8ccfb5988klw/5deeskT7e3UqVPtzTffNDP/CMpu3boV2SlRcJFLN9dtmZmZdsUVV9iUKVMKbX/zzTetbdu2lp2dfdDn0OXLl0d1XbWK+vTTT+2KK64wM7Mff/zRUlJS7LDDDrOGDRsWuoZ2y3F5Ld6SlDZVctOmTQtNlez2Y1q/fn2hBWvz8/Ptt99+sxNOOMGGDx9uZgfXAW4/poCPP/7YDj30UPv0008tKyvLHn74YUtKSrIbb7zRTj31VDv22GPtxhtvDD6Fv2bNGlfXbaXJz88PnqupU6daUlKStWjRwhITE129FkagXI0aNcpOPfVUM/NfQ/t8PldN21oUYvcWOiTiRGnDp4u6KT5ixAjr27dvSNONhFNxUzAEbvLu3r3bPvzwQ2vUqJEdffTRdvbZZ9v5559vVatWjfo8sGWdLiInJ8dGjx5t9evXd2QNiUC8I0aMsMWLF9vEiRPN5/PZhg0bbMOGDXbVVVdZUlKSTZ482f744w/bu3dvcD7lbdu2RT1er1m9erUdf/zxduSRR9q5555rPp/P3n//fcvPz7eNGzfaTTfdZD179nTFlCEzZ860hIQE6969u1188cVWtWpVu/LKK83M32mVlpZmixcvdjjK/XJzc23fvn02dOhQGzhwoGVlZRW6yPvll1+sa9euNmjQoEK/50S53bRpU6EnmL777rtgp0TB6SEKdlxGu551wqpVq6xOnTp2+eWXB590CrQ969atM5/Pd1CHqNuUNAw7cEMtNzfX/vrrL9u6dWuJ86c7KVaOI9Rhzk48WBEJXsohYg0/r+RttOIMXFs1aNAgOKrTzH8zr1WrVvbSSy/ZMcccE7y2CfBae5ufn2+//PJLcKREYLHqrKwsmzNnjiunzzhQXl5eoakpA/kV6JAo6MDy42YFPz9/8cUX1qJFi+Aah6eeeqolJCTYqaeeGpyKzOknob0Wb2nKM1Wyl58wHjt2rNWqVStYj5a07oqb9erVy+rXr2+9e/e2lJQU+/TTT4M/e/DBB61JkyYxNX1tfn5+8FyddNJJlp6eXmidVjcK5Na4cePs6quvtkceecRSUlJszpw5DkdWOmL3Fjok4kAow6cLXnAsWrTIRo0aZdWrV496ZRnKFAwBO3futDvuuMOuvPJKu+GGG6J+87Ss00X85z//sTPPPNPq1asX7FyJps2bN1v37t3t5ptvDm7Lz8+3vn372vfff28LFiywH3/80SZNmmTJycnWrFkzO+aYY+yQQw5xJF6vWr16tb355ps2bty4g6blevDBB61du3bBKZKc9uOPP9pFF11kV155pT311FPB7R988IG1bNnSFXMrH3iRPWPGDKtUqVKh6ZkC+8yYMcMSEhJs0aJFUV/EtrgPA4E4vv/++0IjJfbt22eTJk0KXoS7/UNfuHz55ZeWkpJi119/faE26I8//rB27drZrFmzHIwudCUNw77lllvs7LPPdv2webPYOY54GubspRwi1sjwSt5GKs4D29t//vOf5vP57PLLL7fRo0dbtWrVglM7Tps2zQ477DDbsmWLq+uA4qZ+LVi3rVq1KtgpsXr1arv++ustIyPDtR2uoRzTtGnTrHXr1sHvb731VjvjjDNcfYO1uOP6/fff7YwzzjAz/0i9Ro0a2YsvvmjVqlWz/v37BzuSos1r8YbCa1Mlh6K4YzLb/xlh8+bN1rJlS7vrrrs887mhuOOaOXOmvf/++9apU6dC9fOsWbPsyCOPdGzK8EjJzc214cOHm8/nO2gBbzcLzPZQo0YN16+7ciBi9wY6JOJAWYZP//rrr9avXz87/PDDHRlGFsoUDAWfig5wYt7hskwXkZ+fbytXrrQ777zTli5dGvVYzfxzbP7tb38r9ETpfffdZz6fz4455hhr0qSJ9evXz5YsWWLLli2zN99806ZOnVroiSaE7tlnn7XTTz+90JRdw4cPt7POOstVT38VNzqqZ8+ejj9FWNw0cxMnTrSEhIRCC1mb+RfEbNmyZbEL80VKcXEeKDB90/nnn29Dhw61pKQkW7VqVZSidI8PP/zQUlJS7JxzzrGpU6fakiVL7K677rL69eu7fhqNgkoahu2lTtxYOg6z+Bjm7KUcItbI8ErehjvOotrbvLw8e/HFF4NTtTz00EPBnz355JPWoUMHV9+8K26a2qJi/uWXX6xnz57m8/msatWq9uOPP0Y73JCEekwff/yxtWjRwszMRo4caZUrV3b1elrFHZeZ/4Zjjx49rH79+la3bt3gTaRvvvnG6tataxs2bCDeMPDaVMmhCDVfcnJybOjQoda1a9fgqFA3K+q4CnYMf/rppweNkBoxYoRlZGTE3MwMubm59txzz7l6mqai/PTTT+bz+Vw1a0KoiN0b6JCIE6EOn960aZP9+uuvjg6TC3UKhoI3S526uAg11sCctk4/8VNwWHfgabU333zTtm7dajNmzLCMjIzgBRsqZvHixVajRg17+OGH7eWXX7Y77rjDatas6eohmgsWLLDrrrvOqlev7vgNvJKmmdu9e7fde++95vP5bPTo0fbzzz/b1q1b7a677rIjjzzSNm3a5Io4izJz5kzz+XyWnp4e08MvSzNnzhzr0aOHNW3a1I444gg76qijXHUTLVReHIZdlFg4jngb5uylHCLWyPBK3oYrztLa27179x404uKGG26wgQMH2t69e117I7IsU79mZ2fb4MGDLT093bU3KspyTB988IF16dLF7r77bktOTnZ1fV3acQWm5e3Zs2fwOAKf+5wYGe21eEPhtamSQxHqMRVcL8bn8xVaY8KNQjmu7du3W8OGDa1bt242ZswYu+KKK6x27dqOfwaNFDe2QaFw04OUZUXs7keHRJwpafj08OHD7eyzz3bNRYiXpmAoLdaJEye6JtaANWvWHHThf/rppweH76LivvzySzviiCOsefPm1rNnT1cP0czKyrJ3333XBg8e7HicxU0zV7CjIS8vz1566SWrV6+eNWzY0I4++mhr0KBBVD/MhjIdXkHZ2dl2zTXXuG59Dqfs2LHDfv31V1uwYEGpHTlu5tVh2AeKleOIp2HOXsohYo0Mr+RtReMs6/SzS5cutVtuucXS0tJc2UljVvapX/Py8uzJJ5+0SpUqubaTrKzHFBi1X6tWLZs9e3ZUYy2LUKcU/uOPP4ocLRvtG5FeizcUXpsqORTlqQN27txpN954Y6GHIt0mlOMK3BdZvHix9erVy7p27WrnnXcen4+AOJMoxJVKlSrJzJSfn6/BgwfL5/Pp4osv1ocffqhffvlFP/74o1JTU50OU5KUkJAgM5PP5wt+L0ljx47V+PHjNXfuXCUmuqMIeynWgKZNm6pp06aSpPz8fO3bt0/VqlXTMccc43BksaNXr1768ccflZOTo5SUFNWsWdPpkIqVkpKi0047TX369FHVqlUdjSUhIUGdOnVS7dq1NWjQINWpU0eDBw+WJN1+++065JBDlJCQoEsuuUTdu3fXunXrtGfPHrVt21YNGzZ0RZx33HGH6tSpU2j/+fPn69tvv9UXX3yhVq1aRS1Ot6pevbqqV6/udBhh0bp1a/3888+erz9j4Tj69u2rMWPGaNasWTGfZ17KIWKNHK/kbUXiDKW9DVyDZ2Zm6vPPP9fcuXP1zTffqG3btmE9jnDJycnR9u3bNXDgQEn+a/GEhAQ1a9ZM27Ztk6TgMUn+96Bp06ZaunSpmjdv7kjMpSnrMXXq1EknnniinnrqKdeeJym048rPz1e9evWK/P2CxxwNXos3FGUpW2amZs2aqVWrVnr44Yd19NFHOxZ3ScpTB6SlpemRRx5RSkqKIzGHIpTjCtwXadWqlb788ktlZ2fL5/MpOTnZsbgBRJ/PzMzpIBB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",
      "text/plain": [
       "<Figure size 1600x1600 with 49 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist2dkwargs = {\n",
    "    \"plot_density\": False,\n",
    "    \"plot_contours\": False,\n",
    "    \"plot_datapoints\": True,\n",
    "    \"color\": \"b\",\n",
    "    \"data_kwargs\": {\"ms\": 5},\n",
    "}\n",
    "corner.corner(\n",
    "    np.array(lens_samples), labels=labels, label_kwargs={\"fontsize\": 20}, **hist2dkwargs\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose a lens to simulate an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs_lens_cut = {\"min_image_separation\": 1, \"max_image_separation\": 10}\n",
    "rgb_band_list = [\"i\", \"r\", \"g\"]\n",
    "lens_class = supernovae_lens_population[-5]\n",
    "# (\n",
    "#     lens_class.source.source_dict[\"z\"],\n",
    "#     lens_class.einstein_radius,\n",
    "#     lens_class.source.source_dict[\"mag_i\"],\n",
    "#     lens_class.source.source_dict[\"ps_mag_i\"],\n",
    "#     lens_class._deflector_dict[\"mag_i\"],\n",
    "#     lens_class._deflector_dict[\"z\"],\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sanchez/Devel/DESC/slsim/slsim/Sources/source.py:362: RuntimeWarning: divide by zero encountered in log10\n",
      "  mag_source0 = -2.5 * np.log10(w0 * flux)\n"
     ]
    }
   ],
   "source": [
    "pix_coord = point_source_coordinate_properties(\n",
    "    lens_class,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=27,\n",
    "    delta_pix=0.2,\n",
    "    num_pix=32,\n",
    "    transform_pix2angle=np.array([[0.2, 0], [0, 0.2]]),\n",
    ")[\"image_pix\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14.00699877, 16.4677447 ],\n",
       "       [17.07628824, 15.42222337]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pix_coord"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## See the light curve of a selected supernovae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "light_curve = lens_class.source.variability_class.kwargs_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-22.0, 100.0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(light_curve[\"MJD\"], light_curve[\"ps_mag_i\"])\n",
    "# plt.ylim(12, 18)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ylabel(\"Magnitude\")\n",
    "plt.xlabel(\"Time\" \"[Days]\")\n",
    "plt.xlim(-22, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set observation time and image configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = np.array([-19.5, -15, -11.35135135135135, 0, 10, 20, 25, 30, 40, 44.86])\n",
    "# time = sorted(np.random.uniform(-20, 100, 10))\n",
    "# time = np.array([0, 50, 70, 120])\n",
    "repeats = 10\n",
    "# load your psf kernel and transform matrix. If you have your own psf, please provide\n",
    "# it here.\n",
    "path = \"../../tests/TestData/psf_kernels_for_deflector.npy\"\n",
    "psf_kernel = 1 * np.load(path)\n",
    "psf_kernel[psf_kernel < 0] = 0\n",
    "transform_matrix = np.array([[0.2, 0], [0, 0.2]])\n",
    "\n",
    "# let's set up psf kernel for each exposure. Here we have taken the same psf that we\n",
    "# extracted above. However, each exposure can have different psf kernel and user should\n",
    "# provide corresponding psf kernel to each exposure.\n",
    "psf_kernel_list = [psf_kernel]\n",
    "transform_matrix_list = [transform_matrix]\n",
    "psf_kernels_all = psf_kernel_list * repeats\n",
    "# psf_kernels_all = np.array([dp0[\"psf_kernel\"][:10]])[0]\n",
    "\n",
    "# let's set pixel to angle transform matrix. Here we have taken the same matrix for\n",
    "# each exposure but user should provide corresponding transform matrix to each exposure.\n",
    "transform_matrix_all = transform_matrix_list * repeats\n",
    "\n",
    "# provide magnitude zero point for each exposures. Here we have taken the same magnitude\n",
    "#  zero point for each exposure but user should provide the corresponding magnitude\n",
    "# zero point for each exposure.\n",
    "mag_list = [31.0]\n",
    "mag_zero_points_all = mag_list * repeats\n",
    "# mag_zero_points_all = np.array([dp0[\"zero_point\"][:10]])[0]\n",
    "\n",
    "expo_list = [30]\n",
    "exposure_time_all = expo_list * repeats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sanchez/Devel/DESC/slsim/slsim/Sources/source.py:362: RuntimeWarning: divide by zero encountered in log10\n",
      "  mag_source0 = -2.5 * np.log10(w0 * flux)\n"
     ]
    }
   ],
   "source": [
    "# Simulate a lens image\n",
    "image_lens_series = lens_image_series(\n",
    "    lens_class=lens_class,\n",
    "    band=\"i\",\n",
    "    mag_zero_point=mag_zero_points_all,\n",
    "    num_pix=32,\n",
    "    psf_kernel=psf_kernels_all,\n",
    "    transform_pix2angle=transform_matrix_all,\n",
    "    exposure_time=exposure_time_all,\n",
    "    t_obs=time,\n",
    "    with_deflector=True,\n",
    "    with_source=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/61/jz5zv4qn1ml4kvb_mzhpz5500000gn/T/ipykernel_81151/422990630.py:4: RuntimeWarning: divide by zero encountered in log10\n",
      "  log_images.append(np.log10(image_lens_series[i]))\n"
     ]
    }
   ],
   "source": [
    "## Images in log scale\n",
    "log_images = []\n",
    "for i in range(len(image_lens_series)):\n",
    "    log_images.append(np.log10(image_lens_series[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize simulated images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_montage = create_image_montage_from_image_list(\n",
    "    num_rows=2, num_cols=5, images=image_lens_series, time=time, image_center=pix_coord\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}