dva_h5_to_SDFITS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "50fa2b65",
   "metadata": {},
   "source": [
    "# Convert DVA scans into SPASS format\n",
    "## A. Ordog, March 2023\n",
    "#### March 24: added code to test new DVA files\n",
    "### (1) Explore data structure in example file\n",
    "### (2) Write out example DVA file in SPASS format\n",
    "### (3) Test final DVA files in SPASS format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "31e046ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tabulate import tabulate\n",
    "import h5py\n",
    "from astropy.time import Time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065e4301",
   "metadata": {},
   "source": [
    "# (1) Explore data/metadata structure in SPASS files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "064c4bba",
   "metadata": {},
   "source": [
    "## Example of Ettore's data files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ab216c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIMPLE  =                    T / file does conform to FITS standard             \n",
      "BITPIX  =                    8 / number of bits per data pixel                  \n",
      "NAXIS   =                    0 / number of data axes                            \n",
      "EXTEND  =                    T / FITS dataset may contain extensions            \n",
      "COMMENT   FITS (Flexible Image Transport System) format is defined in 'Astronomy\n",
      "COMMENT   and Astrophysics', volume 376, page 359; bibcode: 2001A&A...376..359H \n",
      "COMMENT   Created by CALSCANS v1.1                                              \n",
      "COMMENT   N. McClure-Griffiths & D. McConnell                                   \n",
      "HISTORY  # Bandpass parameters determined from 0407_DEC                         \n",
      "HISTORY  # from data file 2008-07-18_2056_0407_RADEC_GAL.rpf                    \n",
      "HISTORY # NO POLARIZATION PARAMETER FILE FOUND                                  \n",
      "HISTORY # Unit matrix will be applied                                           \n",
      "HISTORY correction for instrumental phase                                       \n",
      "DATE    = '2008-08-26T15:27:44' / file creation date (YYYY-MM-DDThh:mm:ss UT)   \n",
      "\n",
      "XTENSION= 'BINTABLE'           / binary table extension                         \n",
      "BITPIX  =                    8 / 8-bit bytes                                    \n",
      "NAXIS   =                    2 / 2-dimensional binary table                     \n",
      "NAXIS1  =                  594 / width of table in bytes                        \n",
      "NAXIS2  =                 1888 / number of rows in table                        \n",
      "PCOUNT  =                    0 / size of special data area                      \n",
      "GCOUNT  =                    1 / one data group (required keyword)              \n",
      "TFIELDS =                   19 / number of fields in each row                   \n",
      "TTYPE1  = 'OBJECT  '           / label for field   1                            \n",
      "TFORM1  = '20A     '           / data format of field: ASCII Character          \n",
      "TELESCOP= 'PKS     '                                                            \n",
      "IF      =                    1                                                  \n",
      "TTYPE2  = 'BANDWID '           / label for field   2                            \n",
      "TFORM2  = '1D      '           / data format of field: 8-byte DOUBLE            \n",
      "TUNIT2  = 'Hz      '           / physical unit of field                         \n",
      "TTYPE3  = 'DATE-OBS'           / label for field   3                            \n",
      "TFORM3  = '10A     '           / data format of field: ASCII Character          \n",
      "TTYPE4  = 'EXPOSURE'           / label for field   4                            \n",
      "TFORM4  = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT4  = 's       '           / physical unit of field                         \n",
      "TTYPE5  = 'TSYS    '           / label for field   5                            \n",
      "TFORM5  = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT5  = 'K       '           / physical unit of field                         \n",
      "TTYPE6  = 'SCAN    '           / label for field   6                            \n",
      "TFORM6  = '1I      '           / data format of field: 2-byte INTEGER           \n",
      "TTYPE7  = 'CYCLE   '           / label for field   7                            \n",
      "TFORM7  = '1I      '           / data format of field: 2-byte INTEGER           \n",
      "TTYPE8  = 'TIME    '           / label for field   8                            \n",
      "TFORM8  = '1D      '           / data format of field: 8-byte DOUBLE            \n",
      "TUNIT8  = 's       '           / physical unit of field                         \n",
      "TTYPE9  = 'DATA    '           / label for field   9                            \n",
      "TFORM9  = '120E    '           / data format of field: 4-byte REAL              \n",
      "TUNIT9  = 'Jy      '           / physical unit of field                         \n",
      "TDIM9   = '(30,4,1,1)'         / size of the array                              \n",
      "CTYPE1  = 'FREQ-OBS'           / DATA array axis 1: frequency in Hz.            \n",
      "CRPIX1  =              1.0E+00                                                  \n",
      "TTYPE10 = 'CRVAL1  '           / label for field  10                            \n",
      "TFORM10 = '1D      '           / data format of field: 8-byte DOUBLE            \n",
      "TUNIT10 = 'Hz      '           / physical unit of field                         \n",
      "TTYPE11 = 'CDELT1  '           / label for field  11                            \n",
      "TFORM11 = '1D      '           / data format of field: 8-byte DOUBLE            \n",
      "TUNIT11 = 'Hz      '           / physical unit of field                         \n",
      "CTYPE2  = 'STOKES  '           / DATA array axis 2: polarization code           \n",
      "CRPIX2  =              1.0E+00 / Polarization code reference pixel              \n",
      "CDELT2  =              1.0E+00 / Polarization code axis increment               \n",
      "CTYPE3  = 'RA      '           / DATA array axis 3 (degenerate): RA (mid-int)   \n",
      "CRPIX3  =              1.0E+00                                                  \n",
      "CDELT3  =              1.0E+00                                                  \n",
      "TTYPE12 = 'CRVAL3  '           / label for field  12                            \n",
      "TFORM12 = '1D      '           / data format of field: 8-byte DOUBLE            \n",
      "TUNIT12 = 'degree  '           / physical unit of field                         \n",
      "CTYPE4  = 'DEC     '           / DATA array axis 4 (degenerate): Dec (mid-int)  \n",
      "CRPIX4  =              1.0E+00                                                  \n",
      "CDELT4  =              1.0E+00                                                  \n",
      "TTYPE13 = 'CRVAL4  '           / label for field  13                            \n",
      "TFORM13 = '1D      '           / data format of field: 8-byte DOUBLE            \n",
      "TUNIT13 = 'degree  '           / physical unit of field                         \n",
      "TTYPE14 = 'AZIMUTH '           / label for field  14                            \n",
      "TFORM14 = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT14 = 'degree  '           / physical unit of field                         \n",
      "TTYPE15 = 'ELEVATIO'           / label for field  15                            \n",
      "TFORM15 = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT15 = 'degree  '           / physical unit of field                         \n",
      "TTYPE16 = 'PARANGLE'           / label for field  16                            \n",
      "TFORM16 = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT16 = 'degree  '           / physical unit of field                         \n",
      "TTYPE17 = 'FOCUSAXI'           / label for field  17                            \n",
      "TFORM17 = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT17 = 'm       '           / physical unit of field                         \n",
      "TTYPE18 = 'FOCUSTAN'           / label for field  18                            \n",
      "TFORM18 = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT18 = 'm       '           / physical unit of field                         \n",
      "TTYPE19 = 'FOCUSROT'           / label for field  19                            \n",
      "TFORM19 = '1E      '           / data format of field: 4-byte REAL              \n",
      "TUNIT19 = 'degree  '           / physical unit of field                         \n",
      "EXTNAME = 'SINGLE DISH'        / name of this binary table extension            \n"
     ]
    }
   ],
   "source": [
    "# Change directory as needed on elephant\n",
    "scan = fits.open('/home/ordoga/Python/DVA2/east.2008-07-19_1350_spass_39D_1934.sdfits')\n",
    "print(repr(scan[0].header))\n",
    "print('')\n",
    "print(repr(scan[1].header))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "513b194f",
   "metadata": {},
   "source": [
    "## Organize metadata by indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8ed9a41",
   "metadata": {},
   "outputs": [],
   "source": [
    "headers1 = ['scan name', \n",
    "            'bandwidth (Hz)',\n",
    "            'scan date', \n",
    "            'dt', \n",
    "            'Tsys',\n",
    "            '?', \n",
    "            'f0 (Hz)',\n",
    "            'df (Hz)',\n",
    "            'focus tan',\n",
    "            'focus rot']\n",
    "idx1 = np.array([0,1,2,3,4,5,9,10,17,18])\n",
    "\n",
    "headers2 = ['t bin',\n",
    "            't [s past MJD start]',\n",
    "            'RA',\n",
    "            'dec',\n",
    "            'az',\n",
    "            'el']\n",
    "idx2 = np.array([6,7,11,12,13,14])\n",
    "\n",
    "headers3 = ['par. angle',\n",
    "            'focus axi']\n",
    "idx3 = np.array([15,16])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0701509",
   "metadata": {},
   "source": [
    "## Print out metadata for 5 different timestamps\n",
    "## Indices indicate: scan[1].data[t_idx][idx], where idx lists different metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84f8a054",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values that stay constant with time within the aziuth scan:\n",
      "--------------  --------------  ----------  ----  ----  -  ------------  ---------  -------------------  ---------\n",
      "0               1               2           3     4     5  9             10         17                   18\n",
      "scan name       bandwidth (Hz)  scan date   dt    Tsys  ?  f0 (Hz)       df (Hz)    focus tan            focus rot\n",
      "spass_39D_1934  240000000.0     2008-07-19  0.25  1.0   1  2180000000.0  8000000.0  0.08800000697374344  0.0\n",
      "spass_39D_1934  240000000.0     2008-07-19  0.25  1.0   1  2180000000.0  8000000.0  0.08800000697374344  0.0\n",
      "spass_39D_1934  240000000.0     2008-07-19  0.25  1.0   1  2180000000.0  8000000.0  0.08800000697374344  0.0\n",
      "spass_39D_1934  240000000.0     2008-07-19  0.25  1.0   1  2180000000.0  8000000.0  0.08800000697374344  0.0\n",
      "spass_39D_1934  240000000.0     2008-07-19  0.25  1.0   1  2180000000.0  8000000.0  0.08800000697374344  0.0\n",
      "--------------  --------------  ----------  ----  ----  -  ------------  ---------  -------------------  ---------\n",
      "\n",
      "Values that change with time within the azimuth scan (understood)\n",
      "-----  --------------------  ------------------  ------------------  ------------------  ------------------\n",
      "6      7                     11                  12                  13                  14\n",
      "t bin  t [s past MJD start]  RA                  dec                 az                  el\n",
      "1      49811.08984375        31.38985824584961   -89.91378021240234  179.6807861328125   33.000816345214844\n",
      "2      49811.33984375        31.080677032470703  -89.9059066772461   179.6807861328125   33.000816345214844\n",
      "3      49811.58984375        30.77840805053711   -89.89756774902344  179.6807861328125   33.000816345214844\n",
      "501    49936.08984375        15.97268295288086   -65.8399658203125   150.54539489746094  33.00386428833008\n",
      "1888   50282.83984375        344.3750915527344   0.5661685466766357  64.2699966430664    33.00492477416992\n",
      "-----  --------------------  ------------------  ------------------  ------------------  ------------------\n",
      "\n",
      "Values that change with time within the azimuth scan (NOT understood)\n",
      "-------------------  -------------------\n",
      "15                   16\n",
      "par. angle           focus axi\n",
      "-90.03549194335938   0.4053000211715698\n",
      "-90.03836822509766   0.4053000211715698\n",
      "-90.04138946533203   0.4053000211715698\n",
      "-98.00621032714844   0.4053000211715698\n",
      "-131.02444458007812  0.40540000796318054\n",
      "-------------------  -------------------\n"
     ]
    }
   ],
   "source": [
    "print('Values that stay constant with time within the aziuth scan:')\n",
    "table = [idx1, headers1, np.array(scan[1].data[0],dtype='object')[idx1],\n",
    "                         np.array(scan[1].data[1],dtype='object')[idx1],\n",
    "                         np.array(scan[1].data[2],dtype='object')[idx1],\n",
    "                         np.array(scan[1].data[500],dtype='object')[idx1],\n",
    "                         np.array(scan[1].data[1887],dtype='object')[idx1]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('Values that change with time within the azimuth scan (understood)')\n",
    "table = [idx2, headers2, np.array(scan[1].data[0],dtype='object')[idx2],\n",
    "                         np.array(scan[1].data[1],dtype='object')[idx2],\n",
    "                         np.array(scan[1].data[2],dtype='object')[idx2],\n",
    "                         np.array(scan[1].data[500],dtype='object')[idx2],\n",
    "                         np.array(scan[1].data[1887],dtype='object')[idx2]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('Values that change with time within the azimuth scan (NOT understood)')\n",
    "table = [idx3, headers3, np.array(scan[1].data[0],dtype='object')[idx3],\n",
    "                         np.array(scan[1].data[1],dtype='object')[idx3],\n",
    "                         np.array(scan[1].data[2],dtype='object')[idx3],\n",
    "                         np.array(scan[1].data[500],dtype='object')[idx3],\n",
    "                         np.array(scan[1].data[1887],dtype='object')[idx3]]\n",
    "print(tabulate(table))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10efae53",
   "metadata": {},
   "source": [
    "## Print out Stokes data for single time stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42c0bc72",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Data in 30 frequency channels at single timestamp (120)')\n",
    "print('')\n",
    "print('Stokes I')\n",
    "print(scan[1].data[120][8][0][0][0])\n",
    "print('')\n",
    "print('Stokes Q')\n",
    "print(scan[1].data[120][8][0][0][1])\n",
    "print('')\n",
    "print('Stokes U')\n",
    "print(scan[1].data[120][8][0][0][2])\n",
    "print('')\n",
    "print('Stokes V')\n",
    "print(scan[1].data[120][8][0][0][3])\n",
    "print(scan[1].data[120][8][0][0][3][0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b88d4fc",
   "metadata": {},
   "source": [
    "## Plot Stokes I,Q,U,V,PI vs t at single frequency for sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "898c24f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): t[i] = scan[1].data[i][7]\n",
    "    \n",
    "StokesI = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): StokesI[i] = scan[1].data[i][8][0][0][0][10]  \n",
    "StokesQ = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): StokesQ[i] = scan[1].data[i][8][0][0][1][10]   \n",
    "StokesU = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): StokesU[i] = scan[1].data[i][8][0][0][2][10]   \n",
    "StokesV = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): StokesV[i] = scan[1].data[i][8][0][0][3][10]\n",
    "    \n",
    "plt.figure(figsize=(12,4))\n",
    "plt.scatter(t,StokesI,s=5,label='Stokes I')\n",
    "plt.scatter(t,StokesQ,s=5,label='Stokes Q')\n",
    "plt.scatter(t,StokesU,s=5,label='Stokes U')\n",
    "plt.scatter(t,StokesV,s=5,label='Stokes V')\n",
    "plt.scatter(t,np.sqrt(StokesU**2+StokesQ**2),s=5,label='PI')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c185c398",
   "metadata": {},
   "outputs": [],
   "source": [
    "az = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): az[i] = scan[1].data[i][13] \n",
    "el = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): el[i] = scan[1].data[i][14]  \n",
    "RA = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): RA[i] = scan[1].data[i][11] \n",
    "dec = np.empty(len(scan[1].data))\n",
    "for i in range(0,len(t)): dec[i] = scan[1].data[i][12]\n",
    "\n",
    "    \n",
    "i1 = 0\n",
    "i2 = 1888\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.plot(t[i1:i2],az[i1:i2],label='az')\n",
    "plt.plot(t[i1:i2],el[i1:i2],label='el')\n",
    "plt.plot(t[i1:i2],RA[i1:i2],label='RA')\n",
    "plt.plot(t[i1:i2],dec[i1:i2],label='dec')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.ylim(-100,200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd9fd23",
   "metadata": {},
   "source": [
    "# Questions:\n",
    "### (1) Why does azimuth value increment in jumps while RA/dec change smoothly?\n",
    "### (2) Is it correct that most of the metadata is not used in the map-making code?\n",
    "### (3) Why are RA and dec used directly from data to compute az/el rather than telescope generated az/el?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eba29eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1c2b8781",
   "metadata": {},
   "source": [
    "# (2) Generate example DVA file in SPASS format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33389f11",
   "metadata": {},
   "source": [
    "## Read in example SPASS file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e263b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change directory as needed on elephant\n",
    "spass_file = fits.open('/home/ordoga/Python/DVA2/east.2008-07-19_1350_spass_39D_1934.sdfits')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cf953d",
   "metadata": {},
   "source": [
    "## Read in example DVA file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cfff0abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1803, 3)\n",
      "(3,)\n",
      "(1803,)\n"
     ]
    }
   ],
   "source": [
    "dva_file = h5py.File('/media/ordoga/15m_band1_survey/dva_phase1/survey_phase1_day01/dva_survey_phase1_raw_0709.h5','r')\n",
    "dva_dataset = dva_file['data']['beam_0']['band_SB0']['scan_0']\n",
    "\n",
    "# Coordinates, times and noise flag:\n",
    "dec = dva_dataset['metadata']['declination']\n",
    "ra = dva_dataset['metadata']['right_ascension']\n",
    "el = dva_dataset['metadata']['elevation']\n",
    "az = dva_dataset['metadata']['azimuth']\n",
    "t = dva_dataset['metadata']['utc']\n",
    "noise = dva_dataset['metadata']['noise_state']\n",
    "\n",
    "# Time in MJD:\n",
    "t_mjd = Time(t, format='isot',scale='utc').mjd\n",
    "\n",
    "# For the binned version, the following would be replaced\n",
    "# by the medians calculated for each selected frequency.\n",
    "# Here I am just randomly grabbing channels 100 to 103:\n",
    "RR = dva_dataset['data'][:,0,100:103]\n",
    "LL = dva_dataset['data'][:,1,100:103]\n",
    "reRL = dva_dataset['data'][:,2,100:103]\n",
    "imRL = dva_dataset['data'][:,3,100:103]\n",
    "freq = dva_file['data']['beam_0']['band_SB0']['frequency'][100:103]\n",
    "\n",
    "print(RR.shape)\n",
    "print(freq.shape)\n",
    "print(t_mjd.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb07fc6",
   "metadata": {},
   "source": [
    "## Set up values from DVA to go into SPASS file\n",
    "### The only one that needs to be changed here is 'scanname' to include East or West"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac48187f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dim       = len(t)    # number of datapoints (timestamps)\n",
    "scannum   = 1    # Don't know what this is. Don't think it matters.\n",
    "scanname  = 'phase1_0906'    # Just a descriptive string. Should include East or West.\n",
    "bandwidth = 680e6    # Full survey bandwidth\n",
    "date      = str(t[0])[2:12]    # Take date from first datapoint (extract just date, not time)\n",
    "t_samp    = (t_mjd[1]-t_mjd[0])*24*3600    # Time sampling in seconds\n",
    "Tsys      = 1.0    # Don't know what this is. Don't think it matters.\n",
    "time_s    = (t_mjd-np.floor(t_mjd[0]))*24*3600\n",
    "freq0     = 350e6    # Leave as this for now. Doesn't get used.\n",
    "dfreq     = 1e6    # Leave as this for now. Doesn't get used.\n",
    "StokesI   = 0.5*(RR+LL)\n",
    "StokesQ   = reRL.copy()\n",
    "StokesU   = imRL.copy()\n",
    "StokesV   = 0.5*(RR-LL)    # Something like that. Good enough for now\n",
    "\n",
    "# A few sanity checks:\n",
    "print(dim,date,t_samp,StokesI.shape,time_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d1b40e",
   "metadata": {},
   "source": [
    "## Replace data in SPASS example file with DVA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9447f95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy SPASS file and trim it down to DVA length:\n",
    "dva_new_file = spass_file.copy()\n",
    "dva_new_file[1].data = spass_file[1].data[0:dim]\n",
    "print(dva_new_file[1].data.shape)\n",
    "\n",
    "# Loop through time data points and populate new file:\n",
    "for i in range(0,dim):\n",
    "    dva_new_file[1].data[i][0]  =  scanname\n",
    "    dva_new_file[1].data[i][1]  =  bandwidth\n",
    "    dva_new_file[1].data[i][2]  =  date\n",
    "    dva_new_file[1].data[i][3]  =  t_samp\n",
    "    dva_new_file[1].data[i][4]  =  Tsys\n",
    "    dva_new_file[1].data[i][5]  =  scannum\n",
    "    dva_new_file[1].data[i][7]  =  time_s[i]\n",
    "    \n",
    "    # Fill the first three channels with our Stokes values:\n",
    "    dva_new_file[1].data[i][8][0][0][0][0:3] = StokesI[i,:]\n",
    "    dva_new_file[1].data[i][8][0][0][1][0:3] = StokesQ[i,:]\n",
    "    dva_new_file[1].data[i][8][0][0][2][0:3] = StokesU[i,:]\n",
    "    dva_new_file[1].data[i][8][0][0][3][0:3] = StokesV[i,:]\n",
    "    \n",
    "    # I don't know how to reduce the size of the data array from\n",
    "    # 30 frequency channels to 3, so for now fill the rest with\n",
    "    # zero (we will just not make maps with these channels).\n",
    "    dva_new_file[1].data[i][8][0][0][0][3:30] = 0\n",
    "    dva_new_file[1].data[i][8][0][0][1][3:30] = 0\n",
    "    dva_new_file[1].data[i][8][0][0][2][3:30] = 0\n",
    "    dva_new_file[1].data[i][8][0][0][3][3:30] = 0\n",
    "    \n",
    "    dva_new_file[1].data[i][9]  =  freq0\n",
    "    dva_new_file[1].data[i][10] =  dfreq\n",
    "    dva_new_file[1].data[i][11] = RA[i]\n",
    "    dva_new_file[1].data[i][12] = dec[i]\n",
    "    dva_new_file[1].data[i][13] = az[i]\n",
    "    dva_new_file[1].data[i][14] = el[i]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b956bbc0",
   "metadata": {},
   "source": [
    "## Write out new file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eb4bbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dva_filename = '/home/ordoga/Python/DVA2/test_dva_spass_file.fits'\n",
    "dva_new_file.writeto(new_dva_filename,overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b0515b",
   "metadata": {},
   "source": [
    "## Check to see how new file was populated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aa4db46",
   "metadata": {},
   "outputs": [],
   "source": [
    "headers1 = ['scan name', \n",
    "            'bandwidth (Hz)',\n",
    "            'scan date', \n",
    "            'dt', \n",
    "            'Tsys',\n",
    "            '?', \n",
    "            'f0 (Hz)',\n",
    "            'df (Hz)',\n",
    "            'focus tan',\n",
    "            'focus rot']\n",
    "idx1 = np.array([0,1,2,3,4,5,9,10,17,18])\n",
    "\n",
    "headers2 = ['t bin',\n",
    "            't [s past MJD start]',\n",
    "            'RA',\n",
    "            'dec',\n",
    "            'az',\n",
    "            'el']\n",
    "idx2 = np.array([6,7,11,12,13,14])\n",
    "\n",
    "headers3 = ['par. angle',\n",
    "            'focus axi']\n",
    "idx3 = np.array([15,16])\n",
    "\n",
    "print('Values that stay constant with time within the aziuth scan:')\n",
    "table = [idx1, headers1, np.array(dva_new_file[1].data[0],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[1],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[2],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[200],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[500],dtype='object')[idx1]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('Values that change with time within the azimuth scan (understood)')\n",
    "table = [idx2, headers2, np.array(dva_new_file[1].data[0],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[1],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[2],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[200],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[500],dtype='object')[idx2]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('Values that change with time within the azimuth scan (NOT understood)')\n",
    "table = [idx3, headers3, np.array(dva_new_file[1].data[0],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[1],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[2],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[200],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[500],dtype='object')[idx3]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('')\n",
    "print('Data in 30 frequency channels at single timestamp (120)')\n",
    "print('')\n",
    "print('Stokes I')\n",
    "print(dva_new_file[1].data[120][8][0][0][0])\n",
    "print('')\n",
    "print('Stokes Q')\n",
    "print(dva_new_file[1].data[120][8][0][0][1])\n",
    "print('')\n",
    "print('Stokes U')\n",
    "print(dva_new_file[1].data[120][8][0][0][2])\n",
    "print('')\n",
    "print('Stokes V')\n",
    "print(dva_new_file[1].data[120][8][0][0][3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe72c0ba",
   "metadata": {},
   "source": [
    "# (3) Test final DVA files in SPASS format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "936e66e8",
   "metadata": {},
   "source": [
    "## Read in example DVA East and West files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dac43a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values that stay constant with time within the aziuth scan:\n",
      "----------------  --------------  ----------  ------------------  ----  -  -----------  ---------\n",
      "0                 1               2           3                   4     5  9            10\n",
      "scan name         bandwidth (Hz)  scan date   dt                  Tsys  ?  f0 (Hz)      df (Hz)\n",
      "phase1_0041_Jy_E  680000000.0     2022-06-07  0.5999999046325684  1.0   1  350000000.0  1000000.0\n",
      "phase1_0041_Jy_E  680000000.0     2022-06-07  0.5999999046325684  1.0   1  350000000.0  1000000.0\n",
      "phase1_0041_Jy_E  680000000.0     2022-06-07  0.5999999046325684  1.0   1  350000000.0  1000000.0\n",
      "phase1_0041_Jy_E  680000000.0     2022-06-07  0.5999999046325684  1.0   1  350000000.0  1000000.0\n",
      "phase1_0041_Jy_E  680000000.0     2022-06-07  0.5999999046325684  1.0   1  350000000.0  1000000.0\n",
      "----------------  --------------  ----------  ------------------  ----  -  -----------  ---------\n",
      "\n",
      "Values that change with time within the azimuth scan (understood)\n",
      "-----  --------------------  ------------------  -----------------  ----------------------  -----------------\n",
      "6      7                     11                  12                 13                      14\n",
      "t bin  t [s past MJD start]  RA                  dec                az                      el\n",
      "1      15365.800004149787    0.0                 89.99988554033179  1.8021534060608246e-06  49.32011413574219\n",
      "2      15366.400004038587    0.0                 89.98216533476064  0.027218952775001526    49.3203125\n",
      "3      15367.000003927387    0.0                 89.9206685605262   0.12170109897851944     49.32096862792969\n",
      "451    15635.800003772601    16.94412691066025   35.3713965584056   89.4832763671875        49.31175231933594\n",
      "900    15905.200004135258    13.514931929225618  8.708747452168689  179.21380615234375      49.38636016845703\n",
      "-----  --------------------  ------------------  -----------------  ----------------------  -----------------\n",
      "\n",
      "Values that change with time within the azimuth scan (NOT understood)\n",
      "-------------------  -------------------\n",
      "15                   16\n",
      "par. angle           focus axi\n",
      "-90.03549194335938   0.4053000211715698\n",
      "-90.03836822509766   0.4053000211715698\n",
      "-90.04138946533203   0.4053000211715698\n",
      "-97.11711883544922   0.40540000796318054\n",
      "-105.50535583496094  0.40540000796318054\n",
      "-------------------  -------------------\n",
      "\n",
      "\n",
      "Data in 30 frequency channels at single timestamp (120)\n",
      "\n",
      "Stokes I\n",
      "[2275.8389 1586.8156 1677.023  1859.1964    0.        0.        0.\n",
      "    0.        0.        0.        0.        0.        0.        0.\n",
      "    0.        0.        0.        0.        0.        0.        0.\n",
      "    0.        0.        0.        0.        0.        0.        0.\n",
      "    0.        0.    ]\n",
      "\n",
      "Stokes Q\n",
      "[237.715     41.83735   16.896723  18.859695   0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.      ]\n",
      "\n",
      "Stokes U\n",
      "[37.486496 27.123274 29.76956  26.334724  0.        0.        0.\n",
      "  0.        0.        0.        0.        0.        0.        0.\n",
      "  0.        0.        0.        0.        0.        0.        0.\n",
      "  0.        0.        0.        0.        0.        0.        0.\n",
      "  0.        0.      ]\n",
      "\n",
      "Stokes V\n",
      "[47.304634   5.563878  -5.0338697 26.270958   0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.       ]\n"
     ]
    }
   ],
   "source": [
    "dva_E_file = fits.open('/media/ordoga/15m_band1_survey/Mini_survey_SPASS_with_nans_highel/dva_survey_phase1_0041_Jy_E.fits')\n",
    "#dva_W_file = fits.open('/home/ordoga/Python/DVA2/dva_survey_phase1_1775_Jy_W.fits')\n",
    "\n",
    "####################################\n",
    "dva_new_file = dva_E_file.copy()\n",
    "####################################\n",
    "\n",
    "headers1 = ['scan name', \n",
    "            'bandwidth (Hz)',\n",
    "            'scan date', \n",
    "            'dt', \n",
    "            'Tsys',\n",
    "            '?', \n",
    "            'f0 (Hz)',\n",
    "            'df (Hz)']\n",
    "idx1 = np.array([0,1,2,3,4,5,9,10])\n",
    "\n",
    "headers2 = ['t bin',\n",
    "            't [s past MJD start]',\n",
    "            'RA',\n",
    "            'dec',\n",
    "            'az',\n",
    "            'el']\n",
    "idx2 = np.array([6,7,11,12,13,14])\n",
    "\n",
    "headers3 = ['par. angle',\n",
    "            'focus axi']\n",
    "idx3 = np.array([15,16])\n",
    "\n",
    "print('Values that stay constant with time within the aziuth scan:')\n",
    "table = [idx1, headers1, np.array(dva_new_file[1].data[0],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[1],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[2],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[200],dtype='object')[idx1],\n",
    "                         np.array(dva_new_file[1].data[899],dtype='object')[idx1]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('Values that change with time within the azimuth scan (understood)')\n",
    "table = [idx2, headers2, np.array(dva_new_file[1].data[0],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[1],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[2],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[450],dtype='object')[idx2],\n",
    "                         np.array(dva_new_file[1].data[899],dtype='object')[idx2]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('Values that change with time within the azimuth scan (NOT understood)')\n",
    "table = [idx3, headers3, np.array(dva_new_file[1].data[0],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[1],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[2],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[450],dtype='object')[idx3],\n",
    "                         np.array(dva_new_file[1].data[899],dtype='object')[idx3]]\n",
    "print(tabulate(table))\n",
    "\n",
    "print('')\n",
    "print('')\n",
    "print('Data in 30 frequency channels at single timestamp (120)')\n",
    "print('')\n",
    "print('Stokes I')\n",
    "print(dva_new_file[1].data[120][8][0][0][0])\n",
    "print('')\n",
    "print('Stokes Q')\n",
    "print(dva_new_file[1].data[120][8][0][0][1])\n",
    "print('')\n",
    "print('Stokes U')\n",
    "print(dva_new_file[1].data[120][8][0][0][2])\n",
    "print('')\n",
    "print('Stokes V')\n",
    "print(dva_new_file[1].data[120][8][0][0][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1132a7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(2,2,figsize=(18,10))  \n",
    "\n",
    "#dva_E_file = fits.open('/home/ordoga/Python/DVA2/dva_survey_phase1_1363_Jy_E.fits')\n",
    "#dva_W_file = fits.open('/home/ordoga/Python/DVA2/dva_survey_phase1_1363_Jy_W.fits')\n",
    "\n",
    "dva_E_file = fits.open('/media/ordoga/15m_band1_survey/Mini_survey_SPASS_with_nans_highel/dva_survey_phase1_0042_Jy_E.fits')\n",
    "dva_W_file = fits.open('/media/ordoga/15m_band1_survey/Mini_survey_SPASS_with_nans_highel/dva_survey_phase1_0042_Jy_W.fits')\n",
    "\n",
    "t = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): t[i] = dva_E_file[1].data[i][7]\n",
    "StokesI = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): StokesI[i] = dva_E_file[1].data[i][8][0][0][0][0]  \n",
    "StokesQ = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): StokesQ[i] = dva_E_file[1].data[i][8][0][0][1][0]   \n",
    "StokesU = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): StokesU[i] = dva_E_file[1].data[i][8][0][0][2][0]   \n",
    "StokesV = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): StokesV[i] = dva_E_file[1].data[i][8][0][0][3][0]\n",
    "    \n",
    "az = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): az[i] = dva_E_file[1].data[i][13] \n",
    "el = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): el[i] = dva_E_file[1].data[i][14]  \n",
    "RA = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): RA[i] = dva_E_file[1].data[i][11] \n",
    "dec = np.empty(len(dva_E_file[1].data))\n",
    "for i in range(0,len(t)): dec[i] = dva_E_file[1].data[i][12]\n",
    "    \n",
    "axs[0,0].scatter(t,StokesI,s=2,label='Stokes I',color='k')\n",
    "ax2 = axs[0,0].twinx()\n",
    "ax2.scatter(t,StokesQ,s=2,label='Stokes Q')\n",
    "ax2.scatter(t,StokesU,s=2,label='Stokes U')\n",
    "ax2.scatter(t,StokesV,s=2,label='Stokes V')\n",
    "ax2.set_ylim(-800,800)\n",
    "ax2.legend(loc='upper left')\n",
    "        \n",
    "axs[1,0].scatter(t,az,label='az')\n",
    "axs[1,0].scatter(t,el,label='el')\n",
    "axs[1,0].scatter(t,RA,label='RA',s=1)\n",
    "axs[1,0].scatter(t,dec,label='dec')\n",
    "\n",
    "\n",
    "t = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): t[i] = dva_W_file[1].data[i][7]\n",
    "StokesI = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): StokesI[i] = dva_W_file[1].data[i][8][0][0][0][0]  \n",
    "StokesQ = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): StokesQ[i] = dva_W_file[1].data[i][8][0][0][1][0]   \n",
    "StokesU = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): StokesU[i] = dva_W_file[1].data[i][8][0][0][2][0]   \n",
    "StokesV = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): StokesV[i] = dva_W_file[1].data[i][8][0][0][3][0]\n",
    "    \n",
    "az = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): az[i] = dva_W_file[1].data[i][13] \n",
    "el = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): el[i] = dva_W_file[1].data[i][14]  \n",
    "RA = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): RA[i] = dva_W_file[1].data[i][11] \n",
    "dec = np.empty(len(dva_W_file[1].data))\n",
    "for i in range(0,len(t)): dec[i] = dva_W_file[1].data[i][12]\n",
    "    \n",
    "axs[0,1].scatter(t,StokesI,s=2,label='Stokes I',color='k')\n",
    "ax2 = axs[0,1].twinx()\n",
    "ax2.scatter(t,StokesQ,s=2,label='Stokes Q')\n",
    "ax2.scatter(t,StokesU,s=2,label='Stokes U')\n",
    "ax2.scatter(t,StokesV,s=2,label='Stokes V')\n",
    "ax2.set_ylim(-800,800)\n",
    "ax2.legend(loc='upper left')\n",
    "\n",
    "axs[1,1].scatter(t,az,label='az')\n",
    "axs[1,1].scatter(t,el,label='el')\n",
    "axs[1,1].scatter(t,RA,label='RA',s=1)\n",
    "axs[1,1].scatter(t,dec,label='dec')\n",
    "\n",
    "\n",
    "for i in range(0,2):\n",
    "    for j in range(0,2):\n",
    "        axs[i,j].legend(loc='upper right')\n",
    "        axs[i,j].grid()\n",
    "        axs[0,j].set_ylim(0,8000)\n",
    "        axs[1,j].set_ylim(-30,370)\n",
    "        #axs[1,j].set_ylim(23,25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "568df68d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd91e6ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04abd073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1804, 3)\n",
      "[418.77083333 418.85416667 418.9375    ]\n",
      "(1804,)\n"
     ]
    }
   ],
   "source": [
    "dva_file = h5py.File('/home/ordoga/Python/DVA2/dva_survey_phase1_1775_Jy.h5','r')\n",
    "dva_dataset = dva_file['data']['beam_0']['band_SB0']['scan_0']\n",
    "\n",
    "# Coordinates, times and noise flag:\n",
    "dec = dva_dataset['metadata']['declination']\n",
    "ra = dva_dataset['metadata']['right_ascension']\n",
    "el = dva_dataset['metadata']['elevation']\n",
    "az = dva_dataset['metadata']['azimuth']\n",
    "t = dva_dataset['metadata']['utc']\n",
    "noise = dva_dataset['metadata']['noise_state']\n",
    "\n",
    "# Time in MJD:\n",
    "t_mjd = Time(t, format='isot',scale='utc').mjd\n",
    "\n",
    "# For the binned version, the following would be replaced\n",
    "# by the medians calculated for each selected frequency.\n",
    "# Here I am just randomly grabbing channels 100 to 103:\n",
    "\n",
    "i1 = 900\n",
    "i2 = 903\n",
    "\n",
    "RR = dva_dataset['data'][:,0,i1:i2]\n",
    "LL = dva_dataset['data'][:,1,i1:i2]\n",
    "reRL = dva_dataset['data'][:,2,i1:i2]\n",
    "imRL = dva_dataset['data'][:,3,i1:i2]\n",
    "freq = dva_file['data']['beam_0']['band_SB0']['frequency'][i1:i2]\n",
    "\n",
    "print(RR.shape)\n",
    "print(freq/1e6)\n",
    "print(t_mjd.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "220a2426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(2,2,figsize=(18,10))  \n",
    "\n",
    "t_use = t_mjd[0:900]\n",
    "StokesI = 0.5*(RR+LL)[0:900,0]\n",
    "StokesQ = reRL[0:900,0]\n",
    "StokesU = imRL[0:900,0]\n",
    "StokesV = 0.5*(RR-LL)[0:900,0]\n",
    "    \n",
    "axs[0,0].scatter(t_use,StokesI,s=2,label='Stokes I',color='k')\n",
    "ax2 = axs[0,0].twinx()\n",
    "ax2.scatter(t_use,StokesQ,s=2,label='Stokes Q')\n",
    "ax2.scatter(t_use,StokesU,s=2,label='Stokes U')\n",
    "ax2.scatter(t_use,StokesV,s=2,label='Stokes V')\n",
    "ax2.set_ylim(-800,800)\n",
    "ax2.legend(loc='upper left')\n",
    "        \n",
    "axs[1,0].plot(t_use,az[0:900],label='az')\n",
    "axs[1,0].plot(t_use,el[0:900],label='el')\n",
    "axs[1,0].plot(t_use,ra[0:900],label='RA')\n",
    "axs[1,0].plot(t_use,dec[0:900],label='dec')\n",
    "\n",
    "\n",
    "t_use = t_mjd[900:1803]\n",
    "StokesI = 0.5*(RR+LL)[900:1803,0]\n",
    "StokesQ = reRL[900:1803,0]\n",
    "StokesU = imRL[900:1803,0]\n",
    "StokesV = 0.5*(RR-LL)[900:1803,0]\n",
    "    \n",
    "axs[0,1].scatter(t_use,StokesI,s=2,label='Stokes I',color='k')\n",
    "ax2 = axs[0,1].twinx()\n",
    "ax2.scatter(t_use,StokesQ,s=2,label='Stokes Q')\n",
    "ax2.scatter(t_use,StokesU,s=2,label='Stokes U')\n",
    "ax2.scatter(t_use,StokesV,s=2,label='Stokes V')\n",
    "ax2.set_ylim(-800,800)\n",
    "ax2.legend(loc='upper left')\n",
    "\n",
    "axs[1,1].plot(t_use,az[900:1803],label='az')\n",
    "axs[1,1].plot(t_use,el[900:1803],label='el')\n",
    "axs[1,1].plot(t_use,ra[900:1803],label='RA')\n",
    "axs[1,1].plot(t_use,dec[900:1803],label='dec')\n",
    "\n",
    "\n",
    "for i in range(0,2):\n",
    "    for j in range(0,2):\n",
    "        axs[i,j].legend(loc='upper right')\n",
    "        axs[i,j].grid()\n",
    "        axs[0,j].set_ylim(0,8000)\n",
    "        axs[1,j].set_ylim(-30,370)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "23ce5663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(2,2,figsize=(18,10))  \n",
    "\n",
    "t_use = t_mjd[0:900]\n",
    "RR_plt = RR[0:900,0]\n",
    "LL_plt = RR[0:900,0]\n",
    "reRL_plt = reRL[0:900,0]\n",
    "imRL_plt = imRL[0:900,0]\n",
    "    \n",
    "axs[0,0].scatter(t_use,RR_plt,s=5,label='RR',color='k')\n",
    "axs[0,0].scatter(t_use,LL_plt,s=2,label='LL',color='r')\n",
    "ax2 = axs[0,0].twinx()\n",
    "ax2.scatter(t_use,reRL_plt,s=2,label='reRL')\n",
    "ax2.scatter(t_use,imRL_plt,s=2,label='imRL')\n",
    "ax2.set_ylim(-800,800)\n",
    "ax2.legend(loc='upper left')\n",
    "        \n",
    "axs[1,0].plot(t_use,az[0:900],label='az')\n",
    "axs[1,0].plot(t_use,el[0:900],label='el')\n",
    "axs[1,0].plot(t_use,ra[0:900],label='RA')\n",
    "axs[1,0].plot(t_use,dec[0:900],label='dec')\n",
    "\n",
    "\n",
    "t_use = t_mjd[900:1803]\n",
    "RR_plt = RR[900:1803,0]\n",
    "LL_plt = RR[900:1803,0]\n",
    "reRL_plt = reRL[900:1803,0]\n",
    "imRL_plt = imRL[900:1803,0]\n",
    "    \n",
    "axs[0,1].scatter(t_use,RR_plt,s=5,label='RR',color='k')\n",
    "axs[0,1].scatter(t_use,LL_plt,s=2,label='LL',color='r')\n",
    "ax2 = axs[0,1].twinx()\n",
    "ax2.scatter(t_use,reRL_plt,s=2,label='reRL')\n",
    "ax2.scatter(t_use,imRL_plt,s=2,label='imRL')\n",
    "ax2.set_ylim(-800,800)\n",
    "ax2.legend(loc='upper left')\n",
    "\n",
    "axs[1,1].plot(t_use,az[900:1803],label='az')\n",
    "axs[1,1].plot(t_use,el[900:1803],label='el')\n",
    "axs[1,1].plot(t_use,ra[900:1803],label='RA')\n",
    "axs[1,1].plot(t_use,dec[900:1803],label='dec')\n",
    "\n",
    "\n",
    "for i in range(0,2):\n",
    "    for j in range(0,2):\n",
    "        axs[i,j].legend(loc='upper right')\n",
    "        axs[i,j].grid()\n",
    "        axs[0,j].set_ylim(0,8000)\n",
    "        axs[1,j].set_ylim(-30,370)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0aae441b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZDpapsXc0L2z2B9t4dMVGBWEiIkVAgZdIEeupXEVPrYyieWGNfJ8VqxuVnC8iUgQUeImUiNQE/Wgro2hjb6PrlqSZp03OVy9JEZH8U+AlUqKiAVO0sXdqXhhk10tSRVtFROKnwEukH4huSUbzwi4sW0WdG61Ol+T81Or50BmEqUyFiEh8FHiJ9DOpQdiG1zfy+d1Leu0lGS3amixTEWcPSRGRgUiBl0g/NrtuNI2NW3mpfmJW1fOha5mKaA9JrYSJiOROgZfIANFT9fx0QVhqD8noSpiCMBGRfaPAS2SAigZN0SAsWaYi9erI5EpYahCmYq0iItlT4CUiXYKwaQuHcOXmnd16SGaqmq8m3iIi2csq8DKzWmAC8B5gF7AeeMjdd8Q4NhEpgGRJiWQPyc07dmVs5N1TE2/1jhQR6a7HwMvMPgdcAfwZeAzYCOwHfBT4ppmtB77r7i/HPE4RybNoD8nURt6ZtiOjTbyTvSNn+ijOPUH5YCIi0PuK1wHAae6+K92DZnYSMBJQ4CXSj6U28k5uR/bUxDvaO1LlKUREAj0GXu6+oJfHn+zT0YhISUhuR6YWa00XhGUqT6GkfBEZiMqyOcnMbjCzg8yswsxWmdlrZvbZuAcnIsVtdt1o1n//HF66/nwOO/0yTvF7GLF7CVe1zGC3J2h3cA8DMIOEBeUpXqis51E+y+urb6NmzgOccu1DrGzaVuiPIyISu6wCL+Bsd38LmAJsAUYBs2MblYiUnNQg7MS2ezh29xKWtZ3WEYBBZxA22IKk/D9X1vPfe2awcPESjpnzALPuebywH0REJEbZlpOoCH+fB9zr7jss2mtERCQi2rYoWp4i2bIoNSk/XT6YSlOISH+UbeB1v5k9S1BKYpaZDQXejW9YItJfRMtTJFsW9ZYP1lC+vEtpiuGH7s93poxRaQoRKXm9Bl5mVgb8N3AD8Ja7t5nZO8DH4x6ciPQf0ZZFqUn511TcSQVtHSth0Fma4joWsaV5KF9dfDlfZJRaFYlISes18HL3djO70d3/IXLs78DfYx2Z9Gpl0zbWbNrOhJFDtRIgJSW6FTl/xQhOXDOZd1vb09YHSwDDbXu4Fdl5VaQKtIpIKcp2q/E3ZjYNWOaeTJGVQlrZtI0r7n2CXS1t/GLdFm6+6GR9AUlJ2pt8sESkaXeyQKtWwUSklGQbeH2NoJhqq5m9Cxjg7n5QbCOTHq3ZtJ1dLW0A7GppY82m7Qq8pOSlywf7RmIJM8uX91CgtXMVTAn5IlLssgq83P3AuAcie2fCyKH8Yt0WdrW0MbgiwYSRQws9JJE+E80Hm3XPMEatr+ck0jftjq6CKSFfRIpdtk2yP5buuLv/tm+HI9maPKaamy86WTle0u8ltxCjTbszrYKlS8ifVTaamR87TqtgIlIUst1qjBZL3Q/4EEHT7DP6fESStdT+eSL9WfTPe++rYJ0J+VoFE5Fiku1W4z9G75vZ0QTlJURE8i7dKlimhHyVpRCRYpLtileqLcDYvhyIiMjeiq6C9ZaQr7IUIlIMss3x+jcgWUaiDDgJeCqmMYmI7LXUhPyRT9dz8l6UpfiCj2L88CEdV1aKiMQh2xWvdZHbrQT9GtfGMB4RkZxFtyKzLUsBsOyV06iZc7lWwUQkNtnmeN1lZoOAUeGhjfENSUSkb+xNWQqAqYm1TE2s7VgFm+mjOPeEYXzmqMJ9BhHpX8qyOcnMJgKbgAXArcBzmUpMRJ7zUzN71czWR45dbWavmNmT4c95+z50EZHs3XrxOF6cdz4Nl9Rz6YF3MGL3Eha2TqHVoc3BvTMIO7IsWAV7vrKesU038vQrbzJ27q+Zv0L/5hSR3GS71XgjcLa7bwQws1HAvcD4Hp5zJ3ALcHfK8Zvc/Ud7OU4RkT6RWpbi/c/U09pOxoT8hvLlrLZTeJRruWb1pSpLISI5yTbwqkgGXQDu/pyZVfT0BHf/rZnV5DI4EZE4RctJzLpnGCOerufCslVcU3EnFbR1JORjMNhaVJZCRHKWdXK9mS0CFof3LyYooLovvmRmlxIk7F/p7jv38XVERPpMMniav2IEJ66ZzLut7dxYvoBPJtZ2XNOtshQikitz995PMqsELgc+StAg+7fAre6+u5fn1QDL3X1seL8aeI3gr7EfAMPc/Z8yPHcmMBOgurp6/NKlS7P8SMWjubmZqqqqQg+jpGkOc6c53HcvbP877+xp5ajBbQx592Us+YBFTgr/Cm0lwWav5h0qOXhwBe87dP88j7a46c9h7jSHucvXHE6aNOkxd69N91hWgde+Sg28sn0sVW1tra9bt66304pOY2MjEydOLPQwSprmMHeaw9z9x/0rmPvHdt5tbe+WCxblDu3Aba1TuKGtnv3Ky5gx4Vj1iUR/DvuC5jB3+ZpDM8sYeGVbQPU04GpgePQ57n7sXg5kmLtvDe9+Eljf0/kiIsWg+qBKnr12IpBdn8hkcVb1iRSRVNnmeC0CvkqQ19WWzRPM7F5gInC4mW0B5gITzewkgsX5l4DL9m64IiKFla5PZDIhfxBtXeqCpesTqQr5IgNbtoHXm+7+q715YXe/KM3hRXvzGiIixSpalmL+ihGMXX0WJ/jGjKtgyYR86KyQX1WZYPqpx2grUmQA6THwMrPkNdIPm9l8YBnQkVDv7o/HODYRkZIwu240s+tGs7JpfMcq2LgMfSIhqJD/icRa9lDRURtMV0WKDAy9rXjdmHI/mijmwBl9OxwRkdIVXQVL1yfSCNqFJFfBBhPUBpvHom5tilQbTKR/6jHwcvdJ+RqIiEh/ktonMlkhP1kbLHUVLNmsux24rWkKNXN0VaRIf9TbVuNngXs8Q80JMzuOoBbX7+IYnIhIfxBdvZq2cAhXbt7JhWWrmFtxFxW0ZnVVpPLBRPqH3rYaDwOeNLPHCK5o3A7sB4wATicohjon1hGKiPQjyasZ568YwSlrz6Z5T1v6NkV0vSpS+WAi/UNvW43/ama3EORynQZ8ENgFbAAucfeX4x+iiEj/k0zIh/RtiqJbkZnywVSaQqT09FpOwt3bgJXhj4iI9LFoEJbciuzpqshkPhh0lqYoA45WkVaRopdtHS8REcmD5OpV9KrITPlgEJSmmJpYSzuoSKtICVDgJSJShKJXRWaTD5apSCugIEykiOxz4GVm1e6+rS8HIyIi3WXKB4s27Ib0K2HQGYQlDOrGqkaYSCHtVeBlZgcD04B64APAUXEMSkRE0osGYcmG3e1O2iKtSdHtyGSNMAPGaSVMJO96DbzMbDBwAUGwNQ44EPgE8NtYRyYiIj2KrlxFi7SmWwmDzhphl5UvpwxtR4oUQm8FVO8BPgb8BrgF+L/A8+7eGP/QREQkW6lBWOpKWDQIS95Otx2pqyNF4tXbitdYYCdB3a5n3b3NzNJWsRcRkeKQKQhL1ghzet6OjF4dqWKtIn2rtwKqJ5rZ+wm2GR8ys1eBA83sSHf/W15GKCIi+yy1XdHXN+/E6Xk7Mnp1ZLRYq4IwkdxlU0D1WeB7wPfMrJYgCPujmW1xdyUEiIiUiGgOV2/bkemKtUaDsPIyOPt4XSEpsrf26qpGd18HrDOzOcBn4hmSiIjErbecsOjVkalBWDsAxm1N51Mzp15lKkT2Qm/J9QcBlxOUjbifoG3Q5cDXgaeAn8U9QBERiVemqyPTFWvtTM53GsqX01C+vEuZCiXni/SstxWvxQTJ9b8H/jcwGxgEfMLdn4x3aCIikm/RICxarDVTEAadZSqSQVg0OX+/8jJmTDiWUyoL8WlEik9vgdex7n4CgJn9O/Aa8D53fzv2kYmISEFlqpg/zp7jpvJbOKrstV6T8/dQztzV03n6+NP5/JwHVLRVBrzeAq+W5I2wlMSfFXSJiAw80SBsZVMtly4/kZd37MLpLFORrE4RXQ2rpJV5FYtYbe/jxcq5XYq26ipJGYh6C7xONLO3wtsGDA7vG+DuflCsoxMRkaITNPDuDJamLRzClZt3Aj0k54e/pybW8onEWsrQVZIyMPVWxyvR0+MiIiKpZSqSyfnj7DkWlN9Eddmb4ODetXJ+pqskDTigMsH0U4/pWGUT6S/2qpyEiIhIT6IrViubarlg2Vi2N+/hSm/l9bbTulXOz3SV5B4quGb1pdQ8fKYaeku/osBLRERiEWxJTgagsbGRf3vrOx2V83u7SnIwLcyrWMR1LOrS0FurYVLqFHiJiEheRFessrlKMl1D79TVMIDxWg2TEqLAS0RE8q6nqySjq2GpDb2jq2HzWARoNUxKiwIvEREpqNSrJKOrYdBzL0nIvBqmKvpSjBR4iYhIUYmuhkHXXpIXlq1ibsVdVNCatnhrdDUstYq+SlZIMVDgJSIiRS21jdEpa8+meU8b0PNqWLSKfmrJCq2GSaEo8BIRkZKxL6thmUpWRFfDEgZ1Y7UaJvFT4CUiIiWrL1bDANqB25qmqICrxC62wMvMfgpMAV5197HhsUOB/wBqgJeAz7j7zrjGICIiA0dPq2E9layAIBDLVMBV25LSl+Jc8boTuAW4O3JsDrDK3a83sznh/W/GOAYRERmgUqvoR0tWZOwpSe9J+lVaDZMcxBZ4uftvzawm5fDHgYnh7buARhR4iYhIzFJLVqT2lOxtNSyapK/VMMlFvnO8qt19K4C7bzWzI/L8/iIiIj2uhvWWpN/Tath+5WXMmHCsVsMkI3P3+F48WPFaHsnxesPdD4k8vtPdh2R47kxgJkB1dfX4pUuXxjbOuDQ3N1NVVVXoYZQ0zWHuNIe50xzmrpTmcNtbu3m9eTdt4ffjkbaDobzZeYKleZInfxl/9cPYwYEYcNDgCt536P59Mq5SmsNila85nDRp0mPuXpvusXyveG0zs2Hhatcw4NVMJ7r77cDtALW1tT5x4sQ8DbHvNDY2UorjLiaaw9xpDnOnOcxdKc/hrHse59frt/ZawDUpuZ7RDtzWOoXPtdUDMLSqkh9OPWGftyRLeQ6LRTHMYb4Dr/uB6cD14e9f5vn9RURE9ko2JSsyJek3lC/nsvLllAHb9hzCrMVf0ZbkABdnOYl7CRLpDzezLcBcgoDr52Y2A3gZ+HRc7y8iItLX0pWsSCbpZ1oNS94+0t7oqBu2h3Lmrp5OzcNnqpXRABPnVY0XZXjozLjeU0REJJ96Wg27sXwBn0ysxem+GlZJK/MqFnEdi1Aro4FFletFRET6QOpq2LSFQ/j65p0dV0peU3EnFbRhZN/KaLiCsH5HgZeIiEgM7ms4teP2/BUjOHHNZN5tbWecPceC8puoLnuzSxAG3WuGRYOwb57YTkvTNgVhJU6Bl4iISMyiq2Erm2q5YNlYtjfv6bF4a2o/yVV+Lbcufl4rYSVOgZeIiEgeBVX0JwOZWxmlq6Bfbm1pV8IUhJUWBV4iIiIFkq6VUbKxd2qpCui+EpYahI0fPqTLFqcUHwVeIiIiRSJ6lWRqqYo6h3anW3J+dDty2SunUTPnchIGdWNVoqIYKfASEREpQqmlKppe38jndy/pcTtyamItUxNrg6r5TVOomVOfc8V86VtlvZ8iIiIihTS7bjTHv+cgXrr+fNaPuZJRe5ZwzO4lLGydQqtDmwetijpWwiyomv/nynr+e88MFi5eQs2cBzj9hodZ2bSt0B9nQNOKl4iISAnJtB2ZbiUMOivmR/PBvsgobUUWiAIvERGREpUahCUT85NV8zPlg7VHquVrKzK/FHiJiIj0A9EgbNrCIVy5eWcPFfM7q+Unm3fPKhvNzI8dp8bdMVPgJSIi0s8kS0pEK+ZHtyKjCfnJrcho426VpYiPkutFRET6qdl1o3n22nO7JOVP23M1m9sOpzVMyIcgCKu0oHH3nyvrqX/lWmrmPMAp1z6kZPw+phUvERGRASC5FZmslr95x66OrchBtKUtS6FtyL6nwEtERGQAiVbLn79iBGMaz+TTtoq5FXdRQWvHViSk34Y87wRdDZkLbTWKiIgMULPrRvPCvPM57PTLOMXvYUSkNli6bcgXKusZ23QjNXMeYNY9jxd28CVKgZeIiMgAN7tuNOu/f05HLtiI3Uu4qmUGuz3RJQBLFmZ9ofLijgBs2sL/KezgS4wCLxEREelw68XjeOn6YBVsbMvPmLbnara2HZwSgHlHZfxkIr4CsOwo8BIREZFuZteNZtMPz6PhknrqEnd0XA3ZHgnAzIJE/GgApi3IninwEhERkYwmj6nmT1fX0XBJPZ+q/AmfSilHEQ3AkluQx855gPkrNhZ66EVJgZeIiIj0avKYah79zmQaLqnn0gPv6JaIH92C3FB5Ka+vvo3jrlIAlkqBl4iIiGRt8phqVn/jjC6J+Atbp3TZgkxeBdk0KAjAPvDdX6kQa0iBl4iIiOyTZCL+qvdezqfSJOEnA7CH7YssXLyEM29sLOh4i4EKqIqIiEhO7ms4lZVNx1H38zGM2N3EgvKbOLLszY78r2Qh1mU7T6Nmzt8HdC9IrXiJiIhIzqJJ+MmrIFNXwKJXQI741gMDcvtRgZeIiIj0mWgAdga3dSnEGr0C8nflswbk9qMCLxEREelzk8dUs+EH53LY6Zfxgd2Lu10BeWRZsP04a+cNA6r+lwIvERERic3sutG8GLkCclnbad22H5P1vwZC+QkFXiIiIhK75BWQ8/f/Wrftx2T9r/WDpvP66tv69fajAi8RERHJmz9866wu24/R+l+DrYV5FYuYtuOOflv9XoGXiIiI5FVy+zFZ/2tz2+Fdth+j1e/72+qXAi8REREpiPsaTqXhknr+MXErV7XMYJeXd2w/JouvTttxB6O+/WC/KT2hwEtEREQKJll+4rDTL+P43Xd3S75vKF/ObxMNLFy8hGkL/6ewg+0DBQm8zOwlM3vazJ40s3WFGIOIiIgUj+T2YzL5Prr6dWTZG/znoKs5c8uCku/7WMgVr0nufpK71xZwDCIiIlJEksn3qatfZeHq16/tyyW9+qWtRhERESkqqatfbZGtx+Fl27kvXP066ZoVhR3oPihU4OXAb8zsMTObWaAxiIiISBH7w7fO4q3jP8unM1z5+N2Wfy25no/myU+Rzzc1e4+7/9XMjgBWAl9299+mnDMTmAlQXV09funSpXkfZ66am5upqqoq9DBKmuYwd5rD3GkOc6c5zN1AnsO3323l5R3vcASvM5Q3g4MGOLSSYLNX01a+P6Oqe56ffM3hpEmTHsuUSlWQwKvLAMyuBprd/UeZzqmtrfV160ovB7+xsZGJEycWehglTXOYO81h7jSHudMc5k5zCLPueZwHn97KjeULmJpYi1lw3B2WtZ3GVf4lFlw8nsljqtM+P19zaGYZA6/y2N+9+2AOAMrc/e3w9tnA9/M9DhERESktt148DoDjv1cGbXQEX8mej7X+HF9dfDn/Nfb0jnOLTSFyvKqB35nZU8AfgQfc/dcFGIeIiIiUoGe+f27ano/Dy7bzi0HXcNAzP+MjP3yo0MNMK++Bl7u/6O4nhj/Hu/t1+R6DiIiIlLbUno/JzKmEOfMqFjH7nR8XZcV7lZMQERGRkpQsO3H3AZ/v1nB7amItKxNXsHDxEmbd83hhBxqhwEtERERK2h++dVZHw+2tbQen3XoslmbbCrxERESk5CUbbp/e/pO0W4/TdtzB+r++VfCtRwVeIiIi0i9MHlPNc9ed17H1mFpw9f1sZuHiJcxfsbFgY1TgJSIiIv3KH751Fvcd+oVuzbbLrY27B83j96sLl3SvwEtERET6nVVXTkzbbLvKdnNFYhlrNm0vyLjyXkC1GK1s2sa3lv2J7c17SBjUjR1WtIXXREREJDuz60Yzu240H/lhJY82v5+zvYyN7UexoH0aM0cOLciYBvyK18qmbSxcvIT7d8/gxcp6nhtUz9imG0uy47mIiIh0l6z5tansGK4cejszL74oY1uhuA34Fa81m7ZzRfkyhiXe7DjWUL6c6padHDunlYZJI5hdN7qAIxQREZFcza4bTWPjVi678KMFHceAX/GaMHIoN7dOZWvbwbQ7HQl4UxNreaZyOq+vvq1oan+IiIhIaRvwgdfkMdU0XFLPBZWLOHb3ko4EPDMYbC0dtT+Kse2AiIiIlJYBv9UIQfA1ecxkAE66ppxte4bQUL68o+N5Q/lypvrvmLX4K0Xd8VxERESK24Bf8Ur15Ny6tLU/jix7o+jaDoiIiEhpUeCVRqbaH9G2Ax/47q8KvvW4smkb3/vl+oKPQ0RERLKjwCuDZMfzH1T8c7eO5w3ly/m1fbmgHc9XNm3jinuf4O7fb+aKe59Q8CUiIlICFHj14sm5dR0dz3d5BdC94/lHfvhQ3se1ZtN2drW0AbCrpa1gFXhFREQkewq8snBfw6n8w8TzuHjPt9ncdni3rcfZ7/yYY+c8kNemmxNGDmVwRQKAwRUJJhSoAq+IiIhkT4FXlmbXjabhknrO4ZZuW4+FqPk1eUw1N190Mpf+w3BuvujkglXgFRERkeypnMRemDymmg0/OJdpCw/moZdrWVB+E0eWvRnU/CKo+bVwxzY+8N1d3HzROCryMB4FXCIiIqVDK1774L6GU2m4pJ7T23/CwtYpHVuPqYn3L+94p7ADFRERkaKiwGsfTR5TzXPXnddR86stEnwlE+8Tu3YURdkJERERKQ4KvHK06sqJvHX8Z/n0nqu7Jd4fZa/xsH2RhYuXMG3h/xR2oCIiIlJwCrz6wK0Xj0ubeE9Y8f4/B13NmVsWcNxV+b3yUURERIqLAq8+kky8T9b82tp2MIQBWFmY+7V+UH6vfBQREZHiosCrjyUT78/gNl7xw7vkfg22lo6WQ1r9EhERGXgUeMUgufrVNvjQbrlfptUvERGRAUuBV4zed+j+NFxSzz8mbu125WN09SvfVe9FRESkMBR4xWzymGr+dHVd2isfk6tfGyov5fXVt6n0hIiISD+nwCtPklc+Jle/dnk57kHwVWmtzKtY1FF64oNXr1AAJiIi0g8p8Mqj5OrXYadfxvG772ZZ22ldVr+OLHuD+wZdzW/aZ6r2l4iISD+kwKsAZteN5sXrz+fWId9gWlh6Il0AduaWBcr/EhER6UcUeBXQqisndpSeuKplBrs9kTH/S+UnRERESp8CrwJLlp447PTL+MDuxSxsnUKr0y3/q2mQAjAREZFSV5DAy8zOMbONZva8mc0pxBiKTXL7cf2YKxmxe0m3/C8FYCIiIqUv74GXmSWABcC5wBjgIjMbk+9xFKtbLx7HSz3kf6UGYCO//aACMBERkRJRiBWvDwHPu/uL7r4HWAp8vADjKGrJ/K+6xB09BmAPJa7g940KvkREREpBIQKvo4C/RO5vCY9JimT5iZ4CsOFl27mifBkPNf2tsIMVERGRXpknv8Xz9YZmnwbq3P1/h/cvAT7k7l9OOW8mMBOgurp6/NKlS/M6zr7Q3NxMVVVVn73e2++28vKOdzjE3+I99jqGs4dy/uJHUHXgwVQfVNln71Us+noOByLNYe40h7nTHOZOc5i7fM3hpEmTHnP32nSPlcf+7t1tAY6O3H8v8NfUk9z9duB2gNraWp84cWJeBteXGhsbiWPc81dsZN7aP/NOSxv7D0ow/dRjmFE3us/fpxjENYcDieYwd5rD3GkOc6c5zF0xzGEhAq9HgZFmdgzwCnAhUF+AcZSs2XWjmd1PAy0REZH+LO+Bl7u3mtmXgBVAAvipuz+T73GIiIiI5FshVrxw9weBBwvx3iIiIiKFosr1IiIiInmiwEtEREQkTxR4iYiIiOSJAi8RERGRPFHgJSIiIpInCrxERERE8kSBl4iIiEie5L1X474ws+3A5kKPYx8cDrxW6EGUOM1h7jSHudMc5k5zmDvNYe7yNYfD3X1ougdKIvAqVWa2LlOTTMmO5jB3msPcaQ5zpznMneYwd8Uwh9pqFBEREckTBV4iIiIieaLAK163F3oA/YDmMHeaw9xpDnOnOcyd5jB3BZ9D5XiJiIiI5IlWvERERETyRIFXlszsHDPbaGbPm9mcNI+bmd0cPv4nMxsXHj/azB42sw1m9oyZ/XOa537dzNzMDs/HZymUuObQzL4cvu4zZnZDvj5PIcQxh2Z2kpn9wcyeNLN1ZvahfH6mfMthDvczsz+a2VPhHF4Tec6hZrbSzDaFv4fk8zPlW0xzON/Mng3P/y8zOySPHynv4pjDyHP1ncK+z2Hs3ynurp9efoAE8AJwLDAIeAoYk3LOecCvAAM+AjwSHh8GjAtvHwg8F30ucDSwgqBO2eGF/qylNofAJOAhoDK8f0ShP2sJzuFvgHMjz28s9Gct0jk0oCq8XQE8AnwkvH8DMCe8PQf4l0J/1hKcw7OB8vD2v2gO934Ow2P6Tsntz2Hs3yla8crOh4Dn3f1Fd98DLAU+nnLOx4G7PfAH4BAzG+buW939cQB3fxvYABwVed5NwDeA/p5sF9ccNgDXu/vu8PFX8/FhCiSuOXTgoPD2wcBf4/4gBZTLHLq7N4fnVIQ/HnnOXeHtu4BPxPkhCiyWOXT337h7a/jYH4D3xv5JCieuP4eg75SofZnD2L9TFHhl5yjgL5H7W+gaPGV1jpnVACcTRNeY2QXAK+7+VB+PtxjFMofAKGCCmT1iZqvN7JS+HHSRiWsOvwLMN7O/AD8CruqzERefnObQzBJm9iTwKrDS3ZNzWO3uWwHC30f0/dCLRlxzGPVPBCsV/VUsc6jvlD75cxj7d4oCr+xYmmOp/5ro8RwzqwLuA77i7m+Z2f7At4Hv9dkoi1ufz2F4uBwYQrCMPBv4uZmle53+IK45bAC+6u5HA18FFvXBWItVTnPo7m3ufhLBasyHzGxs3w6vJMQ6h2b2baAVuCf3oRatPp9DfacAffPnMPbvFAVe2dlCsG+e9F66b8dkPMfMKgi+7O5x92Xh48cBxwBPmdlL4fmPm9mRfT764hDHHCafsyxcOv4j0E7Qi6s/imsOpwPJ+78gWMLvr3KawyR3fwNoBM4JD20zs2EA4e/+vOUd1xxiZtOBKcDF7t6ft8rimEN9p/TNn8PYv1MUeGXnUWCkmR1jZoOAC4H7U865H7g0vIriI8Cb7r41jJQXARvc/cfJk939aXc/wt1r3L2G4D/2OHf/W14+Uf71+RyG/g9wBoCZjSJIsuyvTWTjmsO/AqeHt88ANsX3EQoulzkcauGVdmY2GDgLeDbynOnh7enAL2P+HIUUyxya2TnAN4EL3P2dPH2WQunzOdR3Sp/9v/x/iPs7xYvg6oRS+CG4OuI5gqsovh0e+yLwRe+8SmJB+PjTQG14/KMES5t/Ap4Mf85L8/ov0Y+vQIlrDsP/KX4GrAceB84o9OcswTn8KPAYwVVBjwDjC/05i3QOPwg8Ec7heuB7kdc8DFhFELSuAg4t9OcswTl8niAfJ/nn8yeF/pylNocpr/8S+k7Zlz+HsX+nqHK9iIiISJ5oq1FEREQkTxR4iYiIiOSJAi8RERGRPFHgJSIiIpInCrxEREQGKDO72sxeMbMnw5/zMpz3z2a23oLG0V+JHP+PyHNfCqvBY2Y1ZrYr8thPIs/5tXU2qP6JmSXC418zsyYLGlqvMrPhkedMt6AJ/aaw3lvy+DEWVJnfFI5lUHjcLE2D7PCxtM21bR+b3ZvZQeEc3pLN+Qq8RKSomNlhkb+s/xb5Umg2s1tjes+vmNml4e07zewdMzsw8vi/mpmb2eHh/ebwd/LL5Qkz22Bmf0z5UphiZtfEMWaRvWVmE83szjQP3eTuJ4U/D6Z53ljgCwTFlU8EppjZSAB3/1/J5xIUaI4WZ34h8rpfjBz/jLufCIwFhgKfDo8/QVDy4YPAfxI0n8fMDgXmAh8OxzA3EhT9Szj+kcBOYEZ4/FxgZPgzE1gYvlaCoMTEucAY4CIzGxM+Zw6wKnytVeH9bPwAWJ3luQq8RKS4uPvrkb/If0Lnl0KVu8/q6/czs3KC3oBLIoefJ2y4a2ZlwCTglQwv8YK7n+zuHyAo4vhVM/t8+NgDwAUWtHMRKVUfAP7g7u940Mh8NfDJ6AlmZsBngHt7ezHv2vJtEJ1tfB72zuK50UbpdQT9FHe4+05gJXBO+J5nEARp0LVBfdoG2fTcXDtts3sL+jrON7NHw9WzyyKfezxQDfymt8+dpMBLREpC+K/15eHtq83sLjP7Tbi9MdXMbjCzp8NtjIrwvPEWNLp9zMxWhH/xpjoDeDz8Qkm6F/hf4e2JwFqC/oE9cvcXga8BV4T3naAdyZR9+tAi+fGlMKD4aYbttfXAx8LV6P0JCpcenXLOBGCbu0c7XxwTrgavNrMJ0ZPNbAVBa6236QycombQ2Sg9U7Prw4A3Iv/vRhtlZ3pOT821MzW7n0FQ9f4U4BTgC+EWZxlwI0FPx6wp8BKRUnUccD7Bv1J/Bjzs7icAu4Dzw+Dr34BPuft44KfAdWle5zSCyv1Rm4Ch4ZfQRQT/Ks7W48D7I/fXEXwpiRREmAP1JPDvBCuwya38OoItuOOAk4CtBIFEF+6+gWBLbyXwa4IuF6n/ELmIrqtdW4H3ufvJBP8YWWJmB0Vesw4YBlQStuiJjPezQC0wP3kozcfyHo7v63MyOZug9dCTBN09DiPYwpwFPOjuf+nhud2U783JIiJF5Ffu3mJmTwMJgi8ECFqD1ACjCXJIVgY7EiQIvgxSDQM2pDm+jGDr8MPAZWkezyT1L/ZXgffsxfNF+pS7fxiCVWPgc+7+uXTnmdkdwPIMr7GIoN8rZvZDgpWi5PPKganA+Mj5u4Hd4e3HzOwFYBTBP0SS57xrZvcT/ONpZfhaZwHfBk4PX4PwvSZGhvNegpXk1wi2EMvDVa9oE+xMDbIHZTgOYbN7D/o5RpvdG/Bld1+RMl/TgQlmNguoAgaZWbO795gbphUvESlVyb/U24EW7+x/1k7wj0oDnokk957g7meneZ1dwH5pji8lSJpdGb5Htk6mayC3X/geIkUnZfv9kwTbiunOOyL8/T6CICu6upVs1B0NxoZa59WKxxKsEL1oZlXJ9wwDtvPobJR+MnAbQaP0VyOvvwI428yGhKvQZwMrwv/nHwY+FZ4XbVCftkE2PTfXztTsfgXQEElhGGVmB7j7xe7+Pg+akn+dIKes14R8rXiJSH+1kWC78B/c/ffhX5qj3P2ZlPM2ACNSn+zuL5vZt4GHsn1DM6sBfkSwxZk0igxfZiJF4AYzO4lgu+0lwtVdM3sP8O/uniwvcZ+ZHQa0AJeHSe5JF9I9qf5jwPfNrBVoI2hcvcPMqoH7zaySYBX6/xJcRAPB1mIV8Itwlfpld78gfN4PCIImgO+7+47w9jeBpWZ2LcFVkYvC4w8SBHXPA+8Anwdw91Yz+xJBMJUAfhr5O+F64OdmNgN4mc6rLf+dYBX98TChfzudSfx7TU2yRaRomdnVQLO7/yjcJvm6u0+JHg/Pa3b3qjTPOQm4GTiY4B+a/5+735HyHsOBxe7+sfD+ncByd//PlPNeIsg7eYMgifiwMNDaQPAv9v0IEoUXuvv/H3necuAqd3+6TyZFREqaAi8RGfDM7L+Ab6RckZXp3BOBO9z9Q1mcWw0scfcz+2CYItIPKMdLRCQolJiu1EQXZvZFgi2V72T5uu8DrsxhXCLSz2jFS0RERCRPtOIlIiIikicKvERERETyRIGXiIiISJ4o8BIRERHJEwVeIiIiInmiwEtEREQkT/4f+xYAcnkhfg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RAflag = dva_dataset['metadata']['noise_state']\n",
    "\n",
    "print(dva_dataset['metadata']['trim_scan_flag'])\n",
    "ra_good = ra.copy()\n",
    "ra_good[dva_dataset['metadata']['trim_scan_flag']==1] = np.nan\n",
    "\n",
    "fig,axs = plt.subplots(1,1,figsize=(10,5))  \n",
    "axs.scatter(t_mjd,ra,s=10)\n",
    "axs.scatter(t_mjd,ra_good,s=2)\n",
    "axs.set_xlabel('Time (MJD)')\n",
    "axs.set_ylabel('RA (hours)')\n",
    "axs.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eebbb1d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f6dbb08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "23266460",
   "metadata": {},
   "outputs": [],
   "source": [
    "hduQ = fits.open('/home/ordoga/spassMap_AO_BB/hmap_ALL_AbsCal_des_128_64_3_Q.fits')\n",
    "hdrQ = hduQ[1].header\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0136c2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XTENSION= 'BINTABLE'           / Written by IDL:  Wed Apr  5 16:45:09 2023      \n",
       "BITPIX  =                    8 /                                                \n",
       "NAXIS   =                    2 / Binary table                                   \n",
       "NAXIS1  =                 4096 / Number of bytes per row                        \n",
       "NAXIS2  =                  192 / Number of rows                                 \n",
       "PCOUNT  =                    0 / Random parameter count                         \n",
       "GCOUNT  =                    1 / Group count                                    \n",
       "TFIELDS =                    1 / Number of columns                              \n",
       "TFORM1  = '1024E   '           / Real*4 (floating point)                        \n",
       "TTYPE1  = 'UNKNOWN1'           /unknown content                                 \n",
       "PIXTYPE = 'HEALPIX '           / HEALPIX pixelisation                           \n",
       "ORDERING= 'RING    '           / Pixel ordering scheme, either RING or NESTED   \n",
       "NSIDE   =                  128 / Healpix resolution parameter                   \n",
       "NPIX    =               196608 / Total number of pixels                         \n",
       "OBJECT  = 'FULLSKY '           / Sky coverage, either FULLSKY or PARTIAL        \n",
       "FIRSTPIX=                    0 / First pixel # (0 based)                        \n",
       "LASTPIX =               196607 / Last pixel # (zero based)                      \n",
       "INDXSCHM= 'IMPLICIT'           / indexing : IMPLICIT or EXPLICIT                \n",
       "COORDSYS= 'C       '           / Pixelization coordinate system                 \n",
       "COMMENT         G = Galactic, E = ecliptic, C = celestial = equatorial          \n",
       "BAD_DATA=         -1.63750E+30 / Sentinel value given to bad pixels             "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdrQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c4b1d6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "this = np.empty([192,1024])\n",
    "for i in range(0,192):\n",
    "    this[0,:] = np.array(dataQ[i])\n",
    "    #print(len(dataQ[i][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "97bbec87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f81a9ceda00>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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07bEUWO/aqVl2PXKyxT7gn4CvuMd1tMHjwB+44SXAVXWyA7AGOAVc5h4/Cfx+HWwA/BrwIeC4Ly5zvYGjwIcBAb4KfDSp7LJn+LcAU6r6uqpeBA4D20vWqRBU9S1V/U83fAF4DafTb8cZ/Lj/P+GGtwOHVfVnqnoKmMKx10AjImuB3wI+74uumw2uxBn0XwBQ1Yuq+kNqZgec54AuE5FFwOXAGWpgA1X9OvBOIDpTvUVkFXClqv6HOt7/73x5Iinb4a8B3vQdz7hxQ42IrAM+CHwDWKmqb4FzUQCuc5MNq20eAu4DWr64utlgA3Ae+KK7tPV5EbmCGtlBVb8HPAi8AbwF/EhV/5Ua2SBA1nqvccPB+FjKdvhha05DvW1IRH4O+Gdgj6r+T1zSkLiBto2IfAw4p6rfTJslJG6gbeCyCOcn/SOq+kHgxzg/46MYOju4a9TbcZYpVgNXiMgdcVlC4gbaBimJqndP9ijb4c8A/remrcX5WTeUiMhiHGf/j6r6tBv9tvvzDPf/OTd+GG3zK8DHReQ0zvLdb4jIP1AvG4BTrxlV/YZ7/BTOBaBOdvhN4JSqnlfVS8DTwC9TLxv4yVrvGTccjI+lbIf/EjAmIutFZAmwE3i2ZJ0Kwb2D/gXgNVUd9516FrjTDd8JPOOL3ykiS0VkPTCGc5NmYFHV+1V1raquw2nrf1fVO6iRDQBU9Szwpoi8z426FXiVetnhDWCLiFzujo1bce5r1ckGfjLV2132uSAiW1z7fcqXJ5oK3LG+HWfHyjTw2bL1KbCev4rzk+tbwDH373bgGuAF4KT7f7kvz2ddu5wgxR34QfoDPkJnl07tbABsAibd/vAvwNV1swPw58C3gePA3+PsRBl6GwBP4Ny3uIQzU7+rl3oDm13bTQN/jfsgbdyfPWlrGIZRE8pe0jEMwzAWCHP4hmEYNcEcvmEYRk0wh28YhlETzOEbhmHUBHP4hmEYNcEcvmEYRk0wh28YhlET/h+k8o9sMkKqQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(this,vmin=-10,vmax=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "531b02d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f81aa2f7070>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(this2[100,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1060f7f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192,)\n",
      "1024\n"
     ]
    }
   ],
   "source": [
    "dataQ= hduQ[1].data\n",
    "print(dataQ.shape)\n",
    "print(len(dataQ[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "773267f8",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "bad number of pixels",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_595066/343547785.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mordering\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhduQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ORDERING'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Visualize the map in Mollweide projection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mhp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmollview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhealpix_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'HEALPix Map'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'jet'\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     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\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~/.local/lib/python3.8/site-packages/healpy-1.16.1-py3.8-linux-x86_64.egg/healpy/visufunc.py\u001b[0m in \u001b[0;36mmollview\u001b[0;34m(map, fig, rot, coord, unit, xsize, title, nest, min, max, flip, remove_dip, remove_mono, gal_cut, format, format2, cbar, cmap, badcolor, bgcolor, notext, norm, hold, reuse_axes, margins, sub, nlocs, return_projected_map, alpha)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;31m# Ensure that the nside is valid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m     \u001b[0mnside\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpixelfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_nside\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\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    197\u001b[0m     \u001b[0mpixelfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_nside\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnside\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/healpy-1.16.1-py3.8-linux-x86_64.egg/healpy/pixelfunc.py\u001b[0m in \u001b[0;36mget_nside\u001b[0;34m(m)\u001b[0m\n\u001b[1;32m   1871\u001b[0m     \u001b[0ma\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0mexception\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mraised\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1872\u001b[0m     \"\"\"\n\u001b[0;32m-> 1873\u001b[0;31m     \u001b[0mtyp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaptype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\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   1874\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtyp\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1875\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnpix2nside\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\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~/.local/lib/python3.8/site-packages/healpy-1.16.1-py3.8-linux-x86_64.egg/healpy/pixelfunc.py\u001b[0m in \u001b[0;36mmaptype\u001b[0;34m(m)\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\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[0;32m--> 238\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bad number of pixels\"\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    239\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    240\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misnpixok\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\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;31mTypeError\u001b[0m: bad number of pixels"
     ]
    }
   ],
   "source": [
    "#import matplotlib.pyplot as plt\n",
    "import healpy as hp\n",
    "from astropy.io import fits\n",
    "\n",
    "\n",
    "# Extract the HEALPix map data from the binary table extension\n",
    "healpix_map = hduQ[1].data#['TTYPE1']\n",
    "# Extract the HEALPix parameters from the header\n",
    "nside = hduQ[1].header['NSIDE']\n",
    "ordering = hduQ[1].header['ORDERING']\n",
    "# Visualize the map in Mollweide projection\n",
    "hp.mollview(healpix_map, title='HEALPix Map', cmap='jet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "541f1c18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196608 192\n",
      "Error: bad number of pixels\n"
     ]
    }
   ],
   "source": [
    "# Extract the HEALPix map data from the binary table extension\n",
    "healpix_map = hduQ[1].data['UNKNOWN1']\n",
    "# Extract the HEALPix parameters from the header\n",
    "nside = hduQ[1].header['NSIDE']\n",
    "ordering = hduQ[1].header['ORDERING']\n",
    "npix = len(healpix_map)\n",
    "print(hp.nside2npix(nside),npix)\n",
    "# Check the number of pixels\n",
    "if npix != hp.nside2npix(nside):\n",
    "      print(\"Error: bad number of pixels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71ffefdf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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