Spec_Int_Reader_Contrast_Curve_Generator-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b31f7d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import gaussian_filter\n",
    "import numpy  as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from utils import *\n",
    "import copy\n",
    "from hcipy import *\n",
    "import os.path\n",
    "import csv\n",
    "from astropy.io import fits\n",
    "from processing import *\n",
    "from IPython.lib.display import Audio\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046ab5ee",
   "metadata": {},
   "source": [
    "# Import Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9117d887",
   "metadata": {},
   "source": [
    "## Input File Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24b9dd21",
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_file_name = \"Viper_Speckle_Contrast_Mag_5_Band_I_02\"\n",
    "# Input the name of the fits files as a list of strings. Used for loading FITS data.\n",
    "fits_names = [#\"ProEM_Mag_5_Band_V.fits\",\n",
    "             # \"ProEM_Mag_12_5_Band_I.fits\",\n",
    "              #\"iXon_Mag_5_Band_V.fits\",\n",
    "              #\"iXon_Mag_12_5_Band_I.fits\"\n",
    "              #\"ORCA_Quest_Mag_5_Band_V.fits\",\n",
    "             # \"ORCA_Quest_Mag_12_5_Band_I.fits\"\n",
    "              #\"Kinetix_Mag_5_Band_V.fits\",\n",
    "             # \"Kinetix_Mag_12_5_Band_I.fits\",\n",
    "              \"Prime_BSI_Mag_5_Band_V.fits\",\n",
    "             # \"Prime_BSI_Mag_12_5_Band_I.fits\"\n",
    "              ] \n",
    "\n",
    "# Input the sigma for the contrast curves\n",
    "sigma = 5 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7aabb2",
   "metadata": {},
   "source": [
    "## Load Fits Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84eaded8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fits_files = []\n",
    "for i in fits_names:\n",
    "    fits_files.append(fits.open(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0311a42",
   "metadata": {},
   "source": [
    "## Load Header Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3bc5ead6",
   "metadata": {},
   "outputs": [],
   "source": [
    "wavelengths = []\n",
    "pupil_diameters = []\n",
    "mags = []\n",
    "qs = []\n",
    "shutters = []\n",
    "fpss = []\n",
    "read_noises = []\n",
    "dark_currents = []\n",
    "seeings = []\n",
    "outer_scales = []\n",
    "velocitys = []\n",
    "quantum_efficiencys = []\n",
    "exposure_times = []\n",
    "exposure_numbers = []\n",
    "filter_names=[]\n",
    "EM_gains = []\n",
    "EM_saturates = []\n",
    "full_well_depths = []\n",
    "for i in fits_files:\n",
    "    wavelength = i[0].header['Wavelnth']\n",
    "    wavelength = wavelength.split(' ')\n",
    "    wavelengths.append(float(wavelength[0]))\n",
    "    pupil_diameter = i[0].header['PUPDIAMT']\n",
    "    pupil_diameter = pupil_diameter.split(' ')\n",
    "    pupil_diameters.append(float(pupil_diameter[0]))\n",
    "    mag = i[0].header['PRIMAG']\n",
    "    mag = mag.split(' ')\n",
    "    mags.append(float(mag[0]))\n",
    "    q = i[0].header['Q']\n",
    "    q = q.split(' ')\n",
    "    qs.append(float(q[0]))\n",
    "    fps = i[0].header['FPS']\n",
    "    fps = fps.split(' ')\n",
    "    fpss.append(fps[0])\n",
    "    read_noise = i[0].header['RdNoise']\n",
    "    read_noise = read_noise.split(\" \")\n",
    "    read_noises.append(read_noise[0])\n",
    "    dark_current = i[0].header['DarkCurr']\n",
    "    dark_current = dark_current.split(\" \")\n",
    "    dark_currents.append(dark_current[0])\n",
    "    quantum_efficiency = i[0].header['QE']\n",
    "    quantum_efficiency = quantum_efficiency.split(\" \")\n",
    "    quantum_efficiencys.append(quantum_efficiency[0])\n",
    "    seeing = i[0].header['Seeing']\n",
    "    seeing = seeing.split(\" \")\n",
    "    seeings.append(seeing[0])\n",
    "    outer_scale = i[0].header['OutScale']\n",
    "    outer_scale = outer_scale.split(' ')\n",
    "    outer_scales.append(outer_scale[0])\n",
    "    velocity = i[0].header['Velocity']\n",
    "    velocity = velocity.split(' ')\n",
    "    velocitys.append(velocity[0])\n",
    "    exposure_time = i[0].header['ExpoTime']\n",
    "    exposure_time = exposure_time.split(\" \")\n",
    "    exposure_times.append(exposure_time[0])\n",
    "    exposure_number = i[0].header['NumExpos']\n",
    "    exposure_number = exposure_number.split(\" \")\n",
    "    exposure_numbers.append(exposure_number[0])\n",
    "    filter_name = i[0].header['FiltName']\n",
    "    filter_name = filter_name.split(\" \")\n",
    "    filter_names.append(filter_name[0])\n",
    "    try:\n",
    "        EM_gain = i[0].header['EMGAIN']\n",
    "        EM_gain = EM_gain.split(\" \")\n",
    "        EM_gains.append(EM_gain[0])\n",
    "    except KeyError:\n",
    "        EM_gains.append(\"\")\n",
    "    try:\n",
    "        EM_saturate = i[0].header['EMSAT']\n",
    "        EM_saturate = EM_saturate.split(\" \")\n",
    "        EM_saturates.append(EM_saturate[0])\n",
    "    except KeyError:\n",
    "        EM_saturates.append(\"\")\n",
    "    try:\n",
    "        full_well_depth = i[0].header['FullWell']\n",
    "        full_well_depth = full_well_depth.split(\" \")\n",
    "        full_well_depths.append(full_well_depth[0])\n",
    "    except KeyError:\n",
    "        full_well_depths.append(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d930f7",
   "metadata": {},
   "source": [
    "# Combine Image Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7eabd4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combining Image Data for  Prime_BSI_Mag_5_Band_V\n",
      "Image Combining Complete\n"
     ]
    }
   ],
   "source": [
    "ims_out_as = []\n",
    "for i in fits_files:\n",
    "    print(\"Combining Image Data for \", i[0].header['Title'])\n",
    "    npix = int(np.sqrt(np.prod(i[1].data.shape)))\n",
    "    ims_out = []\n",
    "    for j in range(1, len(i)):\n",
    "        im = i[j].data\n",
    "        im_out = im.copy().reshape([npix,npix])\n",
    "        ims_out.append(np.array(im_out))\n",
    "    ims_out_as.append(np.array(ims_out))\n",
    "print(\"Image Combining Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a37c64d1",
   "metadata": {},
   "source": [
    "# Image Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af70bf01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Prime_BSI_Mag_5_Band_V\n",
      "Performing Image Preprocessing\n",
      "Taking Fourier Transform\n",
      "Taking Power Spectrum\n",
      "Performing Auto Correlation Function\n",
      "Prime_BSI_Mag_5_Band_V_Updated_Processing_Contrast_Curve_Data.csv written.\n",
      "\n",
      "Processing Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-4d94c2ccab99>:64: RuntimeWarning: invalid value encountered in sqrt\n",
      "  ACF_cc = -2.5 * np.log10((1. - np.sqrt(1. - (2 * (sigma * rad_ACF.std)) ** 2)) / (2 * (sigma * rad_ACF.std)))\n",
      "<ipython-input-9-4d94c2ccab99>:64: RuntimeWarning: invalid value encountered in true_divide\n",
      "  ACF_cc = -2.5 * np.log10((1. - np.sqrt(1. - (2 * (sigma * rad_ACF.std)) ** 2)) / (2 * (sigma * rad_ACF.std)))\n"
     ]
    }
   ],
   "source": [
    "## Image Processing\n",
    "# includes preprocessing, taking FTs, power spectra, and ACFs\n",
    "\n",
    "# Function Parameters - see processing.py for more detail\n",
    "# ims           - input image array\n",
    "# ims_ft        - input FT array\n",
    "# gsigma        - std deviation for the Gaussian kernel\n",
    "# subframe_size - final image size in pixels\n",
    "# HWHM          - half-wavelength at half maximum for supergaussian window\n",
    "# m             - order of supergaussian window\n",
    "# scaling       - determines radial cutoff (fcut) for PS\n",
    "ims_ps=[]\n",
    "FTs =[]\n",
    "PSs = []\n",
    "Av_PSs = []\n",
    "ACFs = []\n",
    "ACF_ccs = []\n",
    "ACF_xaxs = []\n",
    "spixs = []\n",
    "sigmas = []\n",
    "sg_hwhms = []\n",
    "sg_ms = []\n",
    "for i in range(len(fits_files)):\n",
    "    plate_scale = wavelengths[i] / (pupil_diameters[i] * qs[i]) * 206265.\n",
    "    \n",
    "    # setting subframe size to be 2.5x the seeing disk FWHM\n",
    "    spix = int(1.0 * (float(seeings[i])/plate_scale)) \n",
    "    spixs.append(spix)\n",
    "    \n",
    "    # setting gsigma to be 10% of the seeing disk FWHM\n",
    "    sigma = 0.1 * (float(seeings[i])/plate_scale)\n",
    "    sigmas.append(sigma)\n",
    "    \n",
    "    #setting the supergaussian HWHM to be 90% of spix HWHM\n",
    "    sg_hwhm = int(0.9 * (spix/2))\n",
    "    sg_hwhms.append(sg_hwhm)\n",
    "    \n",
    "    #order of supergaussian window , larger m --> wider window, too small--> blurring in FTs\n",
    "    sg_m = 4\n",
    "    sg_ms.append(sg_m)   \n",
    "    \n",
    "    print(\"Processing\", fits_files[i][0].header['Title'] )\n",
    "    print(\"Performing Image Preprocessing\")\n",
    "    ims_p = image_preprocessing(ims_out_as[i], sigma, spix)\n",
    "    #              parameters: (ims, gsigma, subframe_size)\n",
    "    ims_ps.append(ims_p)\n",
    "    \n",
    "    print(\"Taking Fourier Transform\")\n",
    "    FT = fourier_transform(ims_p, sg_hwhm, sg_m)\n",
    "    #              parameters: (ims, HWHM, m)\n",
    "    FTs.append(FT)\n",
    "    \n",
    "    print(\"Taking Power Spectrum\")\n",
    "    PS, Av_PS = power_spectrum(FT, qs[i], wavelengths[i], pupil_diameters[i], 1.)\n",
    "    #              parameters: (ims_ft, wavelength, pupil_diameter, scaling, HWHM, m)\n",
    "    PSs.append(PS)\n",
    "    Av_PSs.append(Av_PS)\n",
    "    \n",
    "    print(\"Performing Auto Correlation Function\")\n",
    "    ACF = generate_ACF(Av_PS)\n",
    "    #              parameters: (ims_ps)\n",
    "    ACFs.append(ACF)    \n",
    "    rad_ACF = radial_data(np.abs(ACFs[i]), annulus_width=2)\n",
    "    ACF_cc = -2.5 * np.log10((1. - np.sqrt(1. - (2 * (sigma * rad_ACF.std)) ** 2)) / (2 * (sigma * rad_ACF.std)))\n",
    "    ACF_ccs.append(ACF_cc)\n",
    "    ACF_xax = np.array(range(len(rad_ACF.mean))) * plate_scale # arcsec \n",
    "    ACF_xaxs.append(ACF_xax)\n",
    "    \n",
    "    data = np.column_stack([ACF_xaxs[i],ACF_ccs[i]])\n",
    "    csv_name = fits_files[i][0].header['Title']  + '_Contrast_Curve_Data.csv'\n",
    "    with open(csv_name, 'w', newline='') as csvfile:\n",
    "        writer = csv.writer(csvfile)\n",
    "        writer.writerows(data)\n",
    "    print(csv_name, \"written.\")\n",
    "    print()\n",
    "print(\"Processing Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5c6c11",
   "metadata": {},
   "source": [
    "# Play Sound when Processing Complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bfde2cb",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "framerate = 4410\n",
    "play_time_seconds = 1\n",
    "\n",
    "t = np.linspace(0, play_time_seconds, framerate*play_time_seconds)\n",
    "audio_data = np.sin(2*np.pi*590*t) + np.sin(2*np.pi*240*t)\n",
    "Audio(audio_data, rate=framerate, autoplay=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d053e4",
   "metadata": {},
   "source": [
    "# Generate Speckle Contrast Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88dc3402",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Viper_Speckle_Contrast_Mag_5_Band_I_02\\nPrimary Magnitude = 5.0, Exposure Time = 0.02, Number of Exposures = 100\\nSeeing = 1.75, Outer Scale = 40.0, Wind Velocity = 30.0')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(15,12))\n",
    "for i in range(len(fits_files)):\n",
    "    label = fits_files[i][0].header['Title'] + \", FPS = \"+ fpss[i] + \", Read Noise = \" + read_noises[i] + \", Dark Current = \" + dark_currents[i] + \", Filter = \" + filter_names[i] + \", QE = \" + quantum_efficiencys[i]\n",
    "    if EM_gains[i]:\n",
    "        label = label + \"\\nEM Gain = \" + EM_gains[i] + \", EM Saturate = \" + EM_saturates[i] + \", Full Well Depths = \" + full_well_depths[i]\n",
    "    plt.plot(ACF_xaxs[i], ACF_ccs[i], label = label , lw=3)\n",
    "title = \"\\nPrimary Magnitude = \" + str(mags[0]) +  \", Exposure Time = \" + exposure_times[0] + \", Number of Exposures = \" + exposure_numbers[0] + \"\\nSeeing = \" + seeings[0] + \", Outer Scale = \" + outer_scales[0] + \", Wind Velocity = \" + velocitys[0]\n",
    "plt.xlim(0.0, 0.5)\n",
    "ax1.set_xlabel(r'Separation (arcsec)')\n",
    "ax1.set_ylabel(r'' + str(sigma) + ' $\\sigma$ Contrast (mag)') \n",
    "ax1.tick_params(axis='y')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.legend(loc='lower left')\n",
    "ax2 = ax1.twinx() # second y-axis\n",
    "for i in range(len(fits_files)):\n",
    "    plt.plot(ACF_xaxs[i], 10**(0.4*ACF_ccs[i]) , lw=3)\n",
    "ax2.set_ylabel('Flux Ratio')  \n",
    "ax2.tick_params(axis='y')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.yscale(\"log\") \n",
    "plt.title(saved_file_name + title)\n",
    "#plt.savefig(saved_file_name + \".jpg\", bbox_inches = 'tight',  dpi = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64574e00",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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