reduction_trt_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reduction for the TRT data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The script to work with the TRT data. This code will perform the reduction and susequently aperture photometry on the TRT data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following packages are necessary to import:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mp\n",
    "import ccdproc,os,sys,time,random\n",
    "from astropy import units as u\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "from astropy.time import Time\n",
    "from glob import glob\n",
    "#from astroquery.astrometry_net import AstrometryNet\n",
    "from astropy.wcs import WCS\n",
    "import astroalign as aa\n",
    "from astropy.nddata import CCDData\n",
    "from astropy.stats import sigma_clipped_stats, SigmaClip\n",
    "from astropy.visualization import ImageNormalize, LogStretch\n",
    "from matplotlib.ticker import LogLocator\n",
    "from astropy.stats import SigmaClip, mad_std\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "80\n",
      "(2048, 2048)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path='/mnt/eac99553-b108-449e-bf41-0716c975df8b/TRT-data/NGC 5947/band_wise/I_band/'\n",
    "calibration_path='/mnt/eac99553-b108-449e-bf41-0716c975df8b/TRT-data/calibration/'\n",
    "filename='flat_files/MF-1-I*fits'\n",
    "flats=sorted(glob(os.path.join(calibration_path,filename)))\n",
    "print(len(flats))\n",
    "for i in range(5,6):\n",
    "    flat_file=fits.open(flats[i])\n",
    "    flat_image=flat_file[0].data\n",
    "    flat_image=flat_image/np.median(flat_image)\n",
    "    fits.writeto('normalised_flat.fits',flat_image,overwrite=True)\n",
    "\n",
    "sfilename='*fits'\n",
    "s=sorted(glob(os.path.join(image_path,sfilename)))\n",
    "print(len(s))\n",
    "for k in range(0,1):\n",
    "    im_file=fits.open(s[k])\n",
    "    flat=fits.open('normalised_flat.fits')\n",
    "    image=im_file[0].data\n",
    "    fl_im=flat[0].data\n",
    "    print(image.shape)\n",
    "    flat_corrected=image/fl_im\n",
    "    #flat_corrected = ccdproc.flat_correct(image, flat_image)\n",
    "    mean,std=np.mean(flat_corrected),np.std(flat_corrected)\n",
    "    plt.imshow(image,cmap='gray_r',vmin=mean-0.2*std,vmax=mean+0.2*std)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Aligning the science frames using astroalign for all the bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80\n"
     ]
    },
    {
     "ename": "MaxIterError",
     "evalue": "Max iterations exceeded while trying to find acceptable transformation.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMaxIterError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-335a342447d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0msource_image\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mimage_aligned\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfootprint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_image\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreference_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0maligned_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.fits'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/astroalign.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(source, target, fill_value, propagate_mask)\u001b[0m\n\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m     \"\"\"\n\u001b[0;32m--> 426\u001b[0;31m     \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    427\u001b[0m     aligned_image, footprint = apply_transform(\n\u001b[1;32m    428\u001b[0m         \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpropagate_mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/astroalign.py\u001b[0m in \u001b[0;36mfind_transform\u001b[0;34m(source, target)\u001b[0m\n\u001b[1;32m    305\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         best_t, inlier_ind = _ransac(\n\u001b[0;32m--> 307\u001b[0;31m             \u001b[0mmatches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minv_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPIXEL_TOL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_matches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m         )\n\u001b[1;32m    309\u001b[0m     \u001b[0mtriangle_inliers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatches\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minlier_ind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/astroalign.py\u001b[0m in \u001b[0;36m_ransac\u001b[0;34m(data, model, min_data_points, max_iter, thresh, min_matches)\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbestfit\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         raise MaxIterError(\n\u001b[0;32m--> 532\u001b[0;31m             \u001b[0;34m\"Max iterations exceeded while trying to find \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m             \u001b[0;34m\"acceptable transformation.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m         )\n",
      "\u001b[0;31mMaxIterError\u001b[0m: Max iterations exceeded while trying to find acceptable transformation."
     ]
    }
   ],
   "source": [
    "image_path='/mnt/eac99553-b108-449e-bf41-0716c975df8b/TRT-data/NGC 5947/band_wise/I_band/'\n",
    "calibration_path='/mnt/eac99553-b108-449e-bf41-0716c975df8b/TRT-data/calibration/'\n",
    "filename='*I.fits'\n",
    "files=sorted(glob(os.path.join(image_path,filename)))\n",
    "print(len(files))\n",
    "\n",
    "ref_image=fits.open(files[2])\n",
    "reference_image=ref_image[0].data\n",
    "\n",
    "for i in range(0,1):\n",
    "    image_data=fits.open(files[i])\n",
    "    source_image=image_data[0].data\n",
    "    header=image_data[0].header\n",
    "    image_aligned,footprint=aa.register(source_image,reference_image)\n",
    "\n",
    "    aligned_file=files[i].replace('.fits','')\n",
    "    fits.writeto(aligned_file+'_aligned'+'.fits',image_aligned,header,overwrite=True)\n",
    "    #os.system(\"mv *aligned* aligned/\")\n",
    "    print('No. %i alignment done'%i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Aligning the images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Astro-Align package is used to alighn the images\n",
    "\n",
    "\n",
    "INPUT:\n",
    "\n",
    "    path: The directory where the images are kept (string)\n",
    "    filename: The first few characters and the extension of the images (string). Example:\n",
    "    j0946*fits, HD1104*fits etc.\n",
    "    ref_image: Reference image which will be used to align the images.\n",
    "\n",
    "OUTPUT:\n",
    "\n",
    "    aligned images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. 0 done\n",
      "No. 1 done\n",
      "No. 2 done\n",
      "No. 3 done\n",
      "No. 4 done\n",
      "No. 5 done\n",
      "No. 6 done\n",
      "No. 7 done\n",
      "No. 8 done\n",
      "No. 9 done\n",
      "No. 10 done\n",
      "No. 11 done\n",
      "No. 12 done\n",
      "No. 13 done\n",
      "No. 14 done\n",
      "No. 15 done\n",
      "No. 16 done\n",
      "No. 17 done\n",
      "No. 18 done\n",
      "No. 19 done\n",
      "No. 20 done\n",
      "No. 21 done\n",
      "No. 22 done\n",
      "No. 23 done\n",
      "No. 24 done\n"
     ]
    }
   ],
   "source": [
    "filename=\"*sliced*fits\"\n",
    "ref_image=\"j0947_i_1_sliced.fits\"\n",
    "nfiles=sorted(glob(path+filename))\n",
    "image_data=fits.open(path+ref_image)\n",
    "reference_image=image_data[0].data\n",
    "for i in range(len(nfiles)):\n",
    "    image_data=fits.open(nfiles[i])\n",
    "    source_image=image_data[0].data\n",
    "    header=image_data[0].header\n",
    "    image_aligned,footprint=aa.register(source_image,reference_image)\n",
    "\n",
    "    aligned_file=nfiles[i].replace('.fits','')\n",
    "    fits.writeto(aligned_file+'_aligned'+'.fits',image_aligned,header,overwrite=True)\n",
    "\n",
    "    print('No. %i alignment done'%i)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning the images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This module is meant for cleaning the images. The tasks to be included are: bias correction, flat correction, trimming, overscan as well as the cosmic ray removal from the science cases. (For the time we are skipping the overscan and trimming part.)\n",
    "\n",
    "INPUT:\n",
    "\n",
    "    path: The directory where the images are kept (string)\n",
    "    filename: The first few characters and the extension of the images (string). Example:\n",
    "    j0946*fits, HD1104*fits etc.\n",
    "\n",
    "OUTPUT:\n",
    "\n",
    "\n",
    "    cleaned images in the new directory: path/cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# a) BIAS correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: using the unit adu passed to the FITS reader instead of the unit adu in the FITS file. [astropy.nddata.ccddata]\n",
      "Master bias generated\n",
      " Mean and median of the masterbias:  515.4987641731899 515.5\n"
     ]
    }
   ],
   "source": [
    "gain = 2 * u.electron / u.adu  # gain and readout noise are properties of the CCD and will change for different CCDs.\n",
    "readnoise = 7.5 * u.electron\n",
    "\n",
    "#ra=input('Enter the RA of the source:   ')\n",
    "#dec=input('Enter the DEC of the source: ')\n",
    "\n",
    "\n",
    "bias_files = sorted(glob(os.path.join(path,'bias*.fits')))\n",
    "biaslist = []\n",
    "for i in range (0,len(bias_files)):\n",
    "    data= ccdproc.CCDData.read(bias_files[i],unit='adu')\n",
    "        #data = ccdproc.create_deviation(data, gain=gain, readnoise=readnoise)\n",
    "        #data= data-(data.uncertainty.array)\n",
    "    biaslist.append(data)\n",
    "    \n",
    "masterbias = ccdproc.combine(biaslist,method='average',sigma_clip=True, sigma_clip_low_thresh=5, sigma_clip_high_thresh=5,\n",
    "                             sigma_clip_func=np.ma.median, sigma_clip_dev_func=mad_std)\n",
    "masterbias.write('masterbias.fits', overwrite=True)\n",
    "mbias=ccdproc.CCDData.read('masterbias.fits',unit='adu')\n",
    "print('Master bias generated')\n",
    "print(\" Mean and median of the masterbias: \",np.mean(masterbias), np.median(masterbias))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# b) Flat correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: using the unit adu passed to the FITS reader instead of the unit adu in the FITS file. [astropy.nddata.ccddata]\n",
      "Master flat generated\n",
      " Mean and median of the masterflat:  0.9992749321566833 0.9999320144129445\n"
     ]
    }
   ],
   "source": [
    "flat_files=sorted(glob(os.path.join(dir,'flat*.fits')))\n",
    "flatlist = []\n",
    "for j in range(0,len(flat_files)):\n",
    "    flat=ccdproc.CCDData.read(flat_files[j],unit='adu')\n",
    "    flat_bias_removed=ccdproc.subtract_bias(flat,masterbias)\n",
    "    flatlist.append(flat_bias_removed)\n",
    "\n",
    "    def inv_median(a):\n",
    "        return 1 / np.median(a)\n",
    "\n",
    "masterflat = ccdproc.combine(flatlist,method='median', scale=inv_median,\n",
    "                                 sigma_clip=True, sigma_clip_low_thresh=5, sigma_clip_high_thresh=5,\n",
    "                                 sigma_clip_func=np.ma.median, sigma_clip_dev_func=mad_std)\n",
    "masterflat.write('masterflat.fits', overwrite=True)\n",
    "mflat=ccdproc.CCDData.read('masterflat.fits',unit='adu')\n",
    "print('Master flat generated')\n",
    "print(\" Mean and median of the masterflat: \",np.mean(masterflat), np.median(masterflat))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# c) Cosmic Ray removal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename=\"*aligned*fits\"\n",
    "file_names = sorted(glob(os.path.join(dir,filename)))\n",
    "for i in range(0,len(file_names)):\n",
    "    image=ccdproc.CCDData.read(file_names[i],unit='adu')\n",
    "    header=fits.getheader(file_names[i],0)\n",
    "    bias_subtracted = ccdproc.subtract_bias(image, masterbias)\n",
    "    flat_corrected = ccdproc.flat_correct(bias_subtracted, masterflat)\n",
    "    cr_cleaned = ccdproc.cosmicray_lacosmic(flat_corrected,readnoise=7.5, sigclip=5,satlevel=65535,niter=20,cleantype='meanmask',gain_apply=True)\n",
    "        #print('Cosmic rays removed')\n",
    "    clean_file=file_names[i].replace('.fits','')\n",
    "    fits.writeto(clean_file+'_cleaned.fits',cr_cleaned,header,overwrite=True)\n",
    "    print('Image no-%i has been cleaned'%i)"
   ]
  }
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