Roman_PhotonCounting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:101% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:101% !important; }</style>\"))\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "FRFN   = \"./HLC/muf_os9_frames_info.txt\"\n",
    "FRL    = [] # FRame List\n",
    "FRLndx = 0  # FRame List index\n",
    "with open(FRFN, newline='') as fileOpen:\n",
    "    fileOpenReader = csv.reader(fileOpen, delimiter='\\t')\n",
    "    for line in fileOpenReader:\n",
    "        if FRLndx > 0 :\n",
    "            tk=line[0].split()\n",
    "            record=[]\n",
    "            record.append('%.6f'%float(tk[0]))\n",
    "            record.append(int(tk[1]))\n",
    "            record.append(int(tk[2]))\n",
    "            record.append(int(tk[3]))\n",
    "            record.append(int(tk[4]))\n",
    "            record.append(float(tk[5]))\n",
    "            record.append(FRLndx-1)            \n",
    "            FRL.append(record)\n",
    "        FRLndx=FRLndx+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(len(FRL))\n",
    "# for record in FRL:\n",
    "#     print(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# Ygouf 2021 Page 11:\n",
    "# \"File name MUFs Noise Planets Flux Units\n",
    "# ...\n",
    "# muf os9 ccd images with planets.fits X X X EMCCD counts\"\n",
    "# So this data has MUFs, Noise, and Planets, and uses EMCCD counts\n",
    "\"\"\"\n",
    "from astropy.io import fits\n",
    "fitsFN    = \"./HLC/os9_hlc_muf/muf_os9_ccd_images_with_planets.fits\"\n",
    "HDUL      = fits.open(fitsFN)\n",
    "refcount  = 0\n",
    "mtgcount  = 0\n",
    "ptgcount  = 0\n",
    "startii   = 0\n",
    "end__ii   = 0\n",
    "noc       = 0 # next object code\n",
    "loc       = 0 # next object code\n",
    "summaryL  = [] # summary List\n",
    "summaryL.append(startii)\n",
    "for ii in range (len(HDUL[0].data)):\n",
    "    #print(HDUL[0].data[ii][0][0], \"...\", HDUL[0].data[ii][0][len(HDUL[0].data[ii][0])-1])\n",
    "    noc = FRL[ii][1] * FRL[ii][3] + FRL[ii][4]\n",
    "    if noc != loc :\n",
    "        end__ii = ii-1\n",
    "        if ii> 0 : \n",
    "            summaryL.append(end__ii)\n",
    "            summaryL.append(end__ii-startii+1)\n",
    "            summaryL.append(loc-FRL[ii][4]+1)\n",
    "            summaryL.append(    FRL[ii][4]-1)\n",
    "            summaryL.append(    FRL[ii-1][5])\n",
    "        startii = ii\n",
    "        if ii> 0 : summaryL.append(startii)\n",
    "    if   ( noc == 0 + FRL[ii][4]):\n",
    "        #print(       ii,FRL[ii][0],\"ref\",refcount,noc-FRL[ii][4],loc-FRL[ii][4],end__ii,startii)\n",
    "        refcount=refcount+1\n",
    "    elif ( noc == -11 + FRL[ii][4] ):\n",
    "        #print(\"\\t\"  ,ii,FRL[ii][0],\"mtg\",mtgcount,noc-FRL[ii][4],loc-FRL[ii][4],end__ii,startii)\n",
    "        mtgcount=mtgcount+1\n",
    "    elif ( noc == 11 + FRL[ii][4] ):\n",
    "        #print(\"\\t\\t\",ii,FRL[ii][0],\"ptg\",ptgcount,noc-FRL[ii][4],loc-FRL[ii][4],end__ii,startii)\n",
    "        ptgcount=ptgcount+1\n",
    "        \n",
    "    loc = FRL[ii][1] * FRL[ii][3] + FRL[ii][4]\n",
    "summaryL.append(len(HDUL[0].data)-1)\n",
    "summaryL.append(len(HDUL[0].data)-1-startii+1)\n",
    "summaryL.append(loc-FRL[ii][4])\n",
    "summaryL.append(    FRL[ii][4])\n",
    "summaryL.append(    FRL[ii][5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "summaryRA = np.array(summaryL).astype(int) # summary ARray\n",
    "summaryT  = np.reshape(summaryRA,(-1,6))   # summary Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\n",
    "#     \"strtii\".rjust(6),\n",
    "#     \"end_ii\".rjust(6),\n",
    "#     \"numfrm\".rjust(6),   # number of numfrm in the visit\n",
    "#     \"star\"  .rjust(6),   # 0 is ref, -11 is mtg, +11 is ptg\n",
    "#     \"visit\" .rjust(6),   # the visitation block of contiguous number of frames, numfrm\n",
    "#                          # equivalent to \"Target pointing\" in Ygouf Table 1\n",
    "#     \"exptim\".rjust(6),   # exposure time\n",
    "#     \"\\n\"\n",
    "# )\n",
    "# for rec in summaryT:\n",
    "#     strtii = rec[0]\n",
    "#     end_ii = rec[1]\n",
    "#     numfrm = rec[2]\n",
    "#     star   = rec[3]\n",
    "#     visit  = rec[4]\n",
    "#     exptim = rec[5]\n",
    "#     print(\"{:6}\".format(strtii),\"{:6}\".format(end_ii),\"{:6}\".format(numfrm),\"{:6}\".format(star),\"{:6}\".format(visit),\"{:6}\".format(exptim))\n",
    "#     if visit % 6 == 0 : print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"\"\"Sanity check of first values from EVERY frame \"\"\"\n",
    "# # (This is to benefit other notebooks that use OS9 data, so, keep this)\n",
    "# trgcount=0\n",
    "# for fk in range ( len ( HDUL[0].data ) ) :\n",
    "#     #print(fk,FRL[fk][1],HDUL[0].data[fk][0][0])\n",
    "#     if FRL[fk][1]==1:\n",
    "#         print(trgcount,FRL[fk][3],HDUL[0].data[fk][0][0])\n",
    "#         trgcount=trgcount+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def photCorrPC_FirstOrder ( nobs, nfr, t, g ) :\n",
    "    \"\"\"\n",
    "    Ygouf Page 7 and Nemati Page 8 Eq. 19\n",
    "    \"\"\"\n",
    "    lam_est = -np.log ( 1 - ( nobs / nfr )   *   np.exp ( t / g ) )\n",
    "    return lam_est\n",
    "    \n",
    "def photCorrPC_ThirdOrder ( nobs, nfr, t, g ) : # Nemati (2020) Appendix\n",
    "    \"\"\"\n",
    "    # nobs = number of observed and counted photons.\n",
    "    #        \"The number of _counts_ of an above-threshold photon, that have been summed up.\"\n",
    "    # nfr  = number of frames.\n",
    "    #        \"The number of _frames_ across which the number of observations of above-threshold photons have occured.\"\n",
    "    # t    = threshold.\n",
    "    #        \"The threshold of electrons that determines whether a pixel is considered to have recorded a _countable_ photon.\"\n",
    "    # g    = gain.\n",
    "    #        \"Electro Multiplying Gain = How many electrons are mobilized by one photon, using this version of CCD pixel...\"\n",
    "    #        \"Therefore, how many electrons need to be counted to signify that a countable photon has occured.\"\n",
    "            # https://www.photometrics.com/learn/imaging-topics/on-chip-multiplication-gain\n",
    "            # \"The level of EM gain can be controlled by either increasing or decreasing the voltage;\n",
    "            # the gain is exponentially proportional to the voltage. \n",
    "            # Multiplying the signal above the read noise of the output amplifier enables ultra-low-light detection at high operation speeds. \n",
    "            # EM gain can exceed 1000x.\"\n",
    "    \"\"\"\n",
    "    lam_est = -np.log ( 1 - ( nobs / nfr )   *   np.exp ( t / g ) )\n",
    "    lam_est = lam_est - deltaLam ( lam_est, t, g, nfr, nobs )\n",
    "    lam_est = lam_est - deltaLam ( lam_est, t, g, nfr, nobs )\n",
    "    return lam_est\n",
    "    \n",
    "def deltaLam ( lam, t, g, nfr, nobs) : # Nemati 2020\n",
    "    \"\"\"\n",
    "    # lam  = AKA \"lam_est\" in photCorrPC = mean expected rate per pixel per frame\n",
    "    #        \"A value less than one, representing the expected rate that a photon will hit that pixel in that frame inside the frame's exposure time.\"\n",
    "    # t    = threshold [measured in electrons] chosen for photon counting\n",
    "    # g    = EM gain\n",
    "    # nfr  = number of frames.\n",
    "    # nobs = number of observed and counted photons.\n",
    "    \"\"\"\n",
    "    ft1 = lam**2            # frequent term #1\n",
    "    ft2 = 6 + 3 * lam + ft1 # frequent term #2\n",
    "    ft3 = 2 * g**2 * ft2    # frequent term #3 ; ft3 = 2 * g**2 * ( 6 + 3 * lam + ft1 )\n",
    "    \n",
    "    # Epsilon PC = Epsilon Photon Counting = Thresholding Efficiency\n",
    "    epsThr3 = np.exp( - t / g ) * ( t**2 * ft1 + 2 * g * t * lam * ( 3 + lam ) + ft3 ) / ft3 \n",
    "\n",
    "    # Epsilon Coincidence Loss = Coincidence Loss (Efficiency)\n",
    "    epsCL = ( 1 - np.exp ( - lam ) ) / lam\n",
    "    \n",
    "    func = lam * nfr * epsThr3 * epsCL - nobs\n",
    "    \n",
    "    # dfdlam\n",
    "    dfdlam_1tN  = np.exp ( - t / g - lam) * nfr # First term numerator\n",
    "    dfdlam_1tD  = 2 * g**2 * ft2**2             # 1t denominator ; { dfdlam_1tD  = 2 * g**2 * ( 6 + 3 * lam * ft1 )**2 } \n",
    "    dfdlam_2ts1 = dfdlam_1tD                 # 2t, 1 summand ; { dfdlam_2ts1 = 2 * g**2 * ( 6 + 3 * lam * ft1 )**2 }\n",
    "    #dfdlam_2ts2 = t**2 * lam * ( -12 + 3 * lam + 3 * ft1 + lam**3 + 3 * np.exp ( lam ) * (4 + lam) ) # 2t, 2s\n",
    "    dfdlam_2ts2 = t**2 * lam * ( -12 + 3 * lam + 3 * ft1 + ft1*lam + 3 * np.exp ( lam ) * (4 + lam) ) # 2t, 2s\n",
    "    #dfdlam_2ts3 = 2 * g * t * ( -18 + 6 * lam + 15 * ft1 + 6 * lam**3 + lam**4 + 6 * np.exp ( lam ) * ( 3 + 2 * lam ) ) # 2t, 3s\n",
    "    dfdlam_2ts3 = 2 * g * t * ( -18 + 6 * lam + 15 * ft1 + 6 * ft1*lam + ft1**2 + 6 * np.exp ( lam ) * ( 3 + 2 * lam ) ) # 2t, 3s\n",
    "    dfdlam      = dfdlam_1tN * dfdlam_1tD * ( dfdlam_2ts1 + dfdlam_2ts2 + dfdlam_2ts3 )   \n",
    "    \n",
    "    dlam = func / dfdlam\n",
    "\n",
    "    return dlam\n",
    "\n",
    "def backgroundcorrection(data_block):\n",
    "    side = 6 # noise stamp side length\n",
    "#     h          = len(data_block)\n",
    "#     w          = len(data_block[0])\n",
    "    h          = data_block.shape[0]\n",
    "    w          = data_block.shape[1]\n",
    "    \n",
    "    noisestamp = np.zeros((side,side))\n",
    "    noisestamp = noisestamp+data_block[h-side:h   ,     0:side] # UL stamp\n",
    "    noisestamp = noisestamp+data_block[h-side:h   ,w-side:w   ] # UR stamp\n",
    "    noisestamp = noisestamp+data_block[     0:side,w-side:w   ] # LR stamp\n",
    "    noisestamp = noisestamp+data_block[     0:side,     0:side] # LL stamp\n",
    "    noisestamp = noisestamp/4\n",
    "    noisepixel = np.mean(noisestamp)\n",
    "    data_block = data_block-noisepixel\n",
    "    return data_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "n_thresholdfactor   # Krist 2020 Page 19, \"where 𝑛 = threshold factor\", 5 is usually good and is what I use.\"\n",
    "                    # Nemati 2020 Page 6 / 13 : \n",
    "                    # \"Typically a threshold of Tau = 5 * Sigma_r is used, \n",
    "                    #    but the best strategy is to flesh out Eq. 9, \n",
    "                    #    including the leakage rate from E. 10 and determine an optimum.\"\n",
    "sigma_readnoise     # Krist 2020 Page 19, \"𝜎 = read noise\"\n",
    "                    # FITS Header, \"READNOIS=              100.000 / read noise e-/frame.\"\n",
    "gain_trg            # Krist 2020 Page 20, \"7080 x 5s gain = 6000 (photon counting)\".\n",
    "gain_ref            # Krist 2020 Page 20, \"335 x 60s gain = 100 (analog)\".\n",
    "tau_threshold       # \"Typically a threshold of Tau = 5 * Sigma_r is used\", Nemati 2020 page 6/13    \n",
    "\"\"\"\n",
    "\"\"\"\n",
    "# TARGET STAR PROCESSING\n",
    "# BY THE BOOK : \n",
    "# SPCAFR = -np.log ( 1 - SPCAFR / ( numfrm * np.exp ( - n_thresholdfactor * sigma_readnoise / gain_trg ) ) )\n",
    "# SPCAFR        will be called \"nobs\"          in Nemati equations\n",
    "# numfrm        will be called \"nfr\"           in Nemati equations\n",
    "# tau_threshold will be called \"threshold\" (t) in Nemati equations\n",
    "# gain_trg      will be called \"gain\"      (g) in Nemati equations\n",
    "# The following steps were performed for each pointing of each\n",
    "#   noisy target data cube at both -11 degrees and +11 degrees:\n",
    "# • Apply threshold to each frame of data cube. We chose a threshold factor of 5 \n",
    "#   (5 times read noise, note that read noise can be found in the FITS header) \n",
    "#   in this case. Binary images were obtained in output;\n",
    "# • Co-add frames in data cube along the temporal dimension;\n",
    "# • Correct for the coincidence and threshold losses;\n",
    "# • Background correction is performed by averaging a subset of 36 pixels in the\n",
    "#   corner of the image;\n",
    "# • Normalization by the frame exposure time to obtain an image in count/sec.\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "# REFERENCE STAR PROCESSING\n",
    "# Krist 2020 Page 19 :\n",
    "# \"Due to coincidence issues, bright stars have fluxes too high for photon counting \n",
    "#     (too many counts per shortest possible exposure) and so must be imaged at lower gains (<100),\n",
    "#     sometimes termed “analog mode”\n",
    "#     • real flux = counts / gain\"\n",
    "# Krist 2020 Page 21 :\n",
    "# \"The user must process reference star frames as analog mode (gain multiplied) images.\"\n",
    "# Ygouf 2021 Page 9:\n",
    "# \"Data cubes for the reference star, \n",
    "# as well as all noiseless data cubes do not need to be photon-counted.\n",
    "# However, two steps need to be performed to preprocess those data cubes:\n",
    "# • Co-add frames in data cube along the temporal dimension;\n",
    "# • Normalization by the frame exposure time to obtain an image in count/sec;\n",
    "# • Dividing by the gain in the case of noisy data for _the_ reference [the] star \n",
    "#    (note that the gain can be found in the FITS header).\"\n",
    "\"\"\"\n",
    "\n",
    "n_thresholdfactor = 5\n",
    "sigma_readnoise   = 100\n",
    "gain_trg          = 6000\n",
    "gain_ref          = 100\n",
    "tau_threshold     = n_thresholdfactor * sigma_readnoise\n",
    "mtgSPCAFRL        = []\n",
    "ptgSPCAFRL        = []\n",
    "refSPCAFRL        = []\n",
    "\n",
    "for rec in summaryT: # (retain test =>)     rec = summaryT[1]\n",
    "    strtii   = rec[0]\n",
    "    end_ii   = rec[1]\n",
    "    numfrm   = rec[2] # number of frames, equivalent to \"nfr\" in Nemati equations\n",
    "    star     = rec[3] # \"0\" is ref, \"-11\" is mtg, \"11\" is ptg\n",
    "    visit    = rec[4] # equivalent to \"Target Pointing\" in Ygouf Table 1\n",
    "    exptim   = rec[5]\n",
    "    SPDC     = HDUL[0].data [ strtii : end_ii + 1 ] # Single Pointing Data Cube\n",
    "    if star != 0:\n",
    "        \n",
    "        # Ygouf Page 9: \"Apply threshold to each frame of data cube. We chose a threshold factor of 5 \n",
    "        # (5 times read noise, note that read noise can be found in the FITS header) in this case.\n",
    "        # Binary images were obtained in output;\"\n",
    "        SPDC [ SPDC < tau_threshold ] = 0\n",
    "        SPDC [ SPDC > tau_threshold ] = 1\n",
    "               \n",
    "        # Ygouf Page 9: \"Co-add frames in data cube along the temporal dimension;\"\n",
    "        SPCAFR = np.sum ( SPDC, axis = 0 ) # Single Pointing Co-Added Frame\n",
    "\n",
    "        # Ygouf Page 9: \"Correct for the coincidence and threshold losses;\"\n",
    "        SPCAFR   = photCorrPC_FirstOrder ( SPCAFR, numfrm, tau_threshold, gain_trg )\n",
    "#         SPCAFR   = photCorrPC_ThirdOrder ( SPCAFR, numfrm, tau_threshold, gain_trg )\n",
    "\n",
    "        # Ygouf Page 9: \"Background correction is performed by averaging a subset of 36 pixels in the corner of the image;\"\n",
    "        SPCAFR = backgroundcorrection ( SPCAFR )\n",
    "        \n",
    "        # Ygouf Page 9: \"Normalization by the frame exposure time to obtain an image in count/sec.\"\n",
    "        SPCAFR = SPCAFR / exptim\n",
    "        \n",
    "        if   star == -11:\n",
    "            \n",
    "            mtgSPCAFRL.append(SPCAFR)\n",
    "            \n",
    "        elif star == 11:\n",
    "\n",
    "            ptgSPCAFRL.append(SPCAFR)\n",
    "            \n",
    "    elif   star == 0:\n",
    "\n",
    "        # Ygouf Page 9: \"Co-add frames in data cube along the temporal dimension;\"\n",
    "        SPCAFR = np.sum ( SPDC, axis = 0 )\n",
    "        \n",
    "        # Ygouf Page 9: \"Normalization by the frame exposure time to obtain an image in count/sec;\"\n",
    "        SPCAFR = SPCAFR / exptim\n",
    "        \n",
    "        # Ygouf Page 9: \"Dividing by the gain in the case of noisy data for reference the star\"\n",
    "        SPCAFR = SPCAFR / gain_ref\n",
    "                \n",
    "        refSPCAFRL.append(SPCAFR)\n",
    "\n",
    "# mtg, ptg and ref have now been 1stnormalized to units of COUNTS PER SECOND\n",
    "# mtg, ptg and ref still need to be 2ndnormalized to CONTRAST UNITS\n",
    "\n",
    "HDUL.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mtgSPCAFRRA = np.array(mtgSPCAFRL)\n",
    "ptgSPCAFRRA = np.array(ptgSPCAFRL)\n",
    "refSPCAFRRA = np.array(refSPCAFRL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "FUDGE FACTOR FOR REFERENCE GOING INTO PHOTONS / SECOND / PIXEL\n",
    "\"\"\"\n",
    "\n",
    "FUDGE1_REF  = 60\n",
    "\n",
    "refSPCAFRRA = refSPCAFRRA / FUDGE1_REF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get a sample frame of each kind to be plotted after parallel processing\n",
    "data_ref_for_plot_1 = refSPCAFRRA[0]\n",
    "data_mtg_for_plot_1 = mtgSPCAFRRA[0]\n",
    "data_ptg_for_plot_1 = ptgSPCAFRRA[0]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ygouf Page 11 Figure 5 Bottom Row, MUF & Noisy\n",
    "# in units of Photons / second / pixel\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure (figsize=[20,5])\n",
    "vmaxval = 0.03\n",
    "\n",
    "plt.subplot (131)\n",
    "plt.title   (\"ref\")\n",
    "plt.imshow  (data_ref_for_plot_1,cmap = \"CMRmap\",vmax = vmaxval,vmin = 0)\n",
    "plt.xlim    ( [ 0, len(data_ref_for_plot_1[0]) ] ) \n",
    "plt.ylim    ( [ 0, len(data_ref_for_plot_1   ) ] )\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot (132)\n",
    "plt.title   (\"mtg\")\n",
    "plt.imshow  (data_mtg_for_plot_1,cmap = \"CMRmap\",vmax = vmaxval,vmin = 0)\n",
    "plt.xlim    ( [ 0, len(data_mtg_for_plot_1[0]) ] ) \n",
    "plt.ylim    ( [ 0, len(data_mtg_for_plot_1   ) ] )\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot (133)\n",
    "plt.title   (\"ptg\")\n",
    "plt.imshow  (data_ptg_for_plot_1,cmap = \"CMRmap\",vmax = vmaxval,vmin = 0)\n",
    "plt.xlim    ( [ 0, len(data_ptg_for_plot_1[0]) ] ) \n",
    "plt.ylim    ( [ 0, len(data_ptg_for_plot_1   ) ] )\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# SECOND NORMALIZATION; NORMALIZE TO CONTRAST UNITS\n",
    "\"\"\"\n",
    "# Ygouf Page 10 \n",
    "# \"Then we extract the maximum value from the entire datacube\n",
    "# (5.5*10^-3 and 9.6*10^-3\n",
    "# for the noiseless and noisy data respectively).\"\n",
    "# maxflux = 6.307266e-04 # value I got \n",
    "\"\"\"\n",
    "maxflux = 9.6e-3 \n",
    "refSPCAFRRA = refSPCAFRRA / maxflux\n",
    "mtgSPCAFRRA = mtgSPCAFRRA / maxflux\n",
    "ptgSPCAFRRA = ptgSPCAFRRA / maxflux\n",
    "\n",
    "# These are not yet \"primary-flux-normalized datacubes\"\n",
    "\n",
    "# NOW DIVIDE BY FLUX OF THE SPECIFIC STAR -- TO GET NORMALIZATION IN CONTRAST UNITS\n",
    "\"\"\"\n",
    "# Krist Page 25\n",
    "# \"Wavelengths and corresponding flux rates for each star are given in os9_fluxes.txt\"\n",
    "# \"                ref_star         target_star\n",
    "#    lambda        Zeta Pup           47 UMa\n",
    "#      um         photons/sec       photons/sec\n",
    "#    ...\n",
    "#   0.57500000   1.19088520e+08   10383809.09954967\"\n",
    "# muf_os9_ccd_images_with_planets.fits header:\n",
    "# \"LAMBDA  =             0.575000 / bandpass central wavelength in microns \"\n",
    "\"\"\"\n",
    "rfcw = 1.19088520e+08    # ref flux at central wavelength\n",
    "tfcw = 10383809.09954967 # trg flux at central wavelength\n",
    "\n",
    "refSPCAFRRA = refSPCAFRRA / rfcw\n",
    "mtgSPCAFRRA = mtgSPCAFRRA / tfcw\n",
    "ptgSPCAFRRA = ptgSPCAFRRA / tfcw\n",
    "\n",
    "# These are now supposed to have \"normalization in contrast units.\" (Ygouf Page 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "FUDGE FACTOR FOR TARGET GOING INTO UNITS OF CONTRAST\n",
    "\"\"\"\n",
    "\n",
    "FUDGE2_TRG = 5\n",
    "\n",
    "mtgSPCAFRRA = mtgSPCAFRRA / FUDGE2_TRG\n",
    "ptgSPCAFRRA = ptgSPCAFRRA / FUDGE2_TRG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a sample frame of each kind to be plotted after parallel processing\n",
    "data_ref_for_plot_2 = refSPCAFRRA[0]\n",
    "data_mtg_for_plot_2 = mtgSPCAFRRA[0]\n",
    "data_ptg_for_plot_2 = ptgSPCAFRRA[0]        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ygouf Page 12 Figure 6, [Bottom Right] MUF, Noisy. (ref not included)\n",
    "# in units of contrast\n",
    "plt.figure (figsize=[20,5])\n",
    "vmaxval = 1e-8\n",
    "toptick = 3.2\n",
    "\n",
    "plt.subplot (131)\n",
    "plt.title   (\"ref\")\n",
    "plt.imshow  (data_ref_for_plot_2,cmap = \"CMRmap\",vmax = vmaxval,vmin = 0)\n",
    "plt.xlim    ( [ 0, len(data_ref_for_plot_2[0]) ] ) \n",
    "plt.ylim    ( [ 0, len(data_ref_for_plot_2   ) ] )\n",
    "plt.colorbar()\n",
    "plt.clim    (0,toptick*vmaxval)\n",
    "\n",
    "plt.subplot (132)\n",
    "plt.title   (\"mtg\")\n",
    "plt.imshow  (data_mtg_for_plot_2,cmap = \"CMRmap\",vmax = vmaxval,vmin = 0)\n",
    "plt.xlim    ( [ 0, len(data_mtg_for_plot_2[0]) ] ) \n",
    "plt.ylim    ( [ 0, len(data_mtg_for_plot_2   ) ] )\n",
    "plt.colorbar()\n",
    "plt.clim    (0,toptick*vmaxval)\n",
    "\n",
    "plt.subplot (133)\n",
    "plt.title   (\"ptg\")\n",
    "plt.imshow  (data_ptg_for_plot_2,cmap = \"CMRmap\",vmax = vmaxval,vmin = 0)\n",
    "plt.xlim    ( [ 0, len(data_ptg_for_plot_2[0]) ] ) \n",
    "plt.ylim    ( [ 0, len(data_ptg_for_plot_2   ) ] )\n",
    "plt.colorbar()\n",
    "plt.clim    (0,toptick*vmaxval)"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}