{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "phaseshift.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slhyyFkQl1dQ"
      },
      "source": [
        "# Numerical Accuracy of Task phaseshift\n",
        "Original author: dmehring@nrao.edu\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWUQOYI9mU6V"
      },
      "source": [
        "## Description\n",
        "\n",
        "This notebook demonstrates the determination of the numercial accuracy of task **phaseshift**.\n",
        "Much of the simulation-related code is adapted from that of rurvashi \n",
        "https://gitlab.nrao.edu/rurvashi/simulation-in-casa-6/-/blob/master/Simulation_Script_Demo.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlhUJYkK2Ef0",
        "outputId": "3f2b7ac0-0abb-415e-8cb9-be2714dd3ca2"
      },
      "source": [
        "import os\n",
        "\n",
        "print(\"installing pre-requisite packages...\")\n",
        "os.system(\"pip install scipy matplotlib\")\n",
        "\n",
        "print(\"installing casa...\")\n",
        "# make sure there is a version that contains phaseshift\n",
        "os.system(\"pip install casatasks==6.3.0.48\")\n",
        "os.system(\"pip install casadata\")\n",
        "print(\"complete\")\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "installing pre-requisite packages...\n",
            "installing casa...\n",
            "complete\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZa_MvVrxXKn"
      },
      "source": [
        "# imports and set globals\n",
        "\n",
        "from casatools import componentlist, ctsys, measures, quanta, simulator\n",
        "from casatasks import flagdata, imfit, imstat, phaseshift, tclean\n",
        "from casatasks.private import simutil\n",
        "import glob\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "cl = componentlist()\n",
        "me = measures()\n",
        "qa = quanta()\n",
        "sm = simulator()\n",
        "\n",
        "comp_list = 'sim_onepoint.cl'\n",
        "orig_ms = 'sim_data.ms'\n",
        "orig_im = 'im_orig'\n",
        "pshift_ms = 'sim_data_pshift.ms'\n",
        "pshift_im = 'im_post_phaseshift'\n",
        "tshift_im = 'im_post_tclean_shift'\n",
        "# simulated source flux density\n",
        "exp_flux = 5\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvoimnuYlWwS"
      },
      "source": [
        "def cleanup():\n",
        "  \"\"\"clean up from any previous runs\"\"\"\n",
        "  for f in (comp_list, orig_ms, pshift_ms):\n",
        "    if os.path.exists(f):\n",
        "      shutil.rmtree(f)\n",
        "  for x in (orig_ms, orig_im, pshift_im, tshift_im):\n",
        "    for path in glob.glob(x + '.*'):\n",
        "      shutil.rmtree(path)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAlCpBo0l1KK"
      },
      "source": [
        "def __direction_string(ra, dec, frame):\n",
        "  \"\"\"helper function for often needed string\"\"\"\n",
        "  return ' '.join([frame, ra, dec])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8W-kIzwmF1v"
      },
      "source": [
        "def __makeMSFrame(antenna_file, spwname, freq, radir, decdir, dirframe):\n",
        "  \"\"\"\n",
        "  Construct an empty Measurement Set that has the desired\n",
        "  observation setup.\n",
        "  \"\"\"\n",
        "  # Open the simulator\n",
        "  sm.open(ms=orig_ms)\n",
        "  # Read/create an antenna configuration.\n",
        "  # Canned antenna config text files are located at\n",
        "  # /home/casa/data/trunk/alma/simmos/*cfg\n",
        "  # antennalist = os.path.join(ctsys.resolve(\"alma/simmos\"), \"vla.d.cfg\")\n",
        "  # Fictitious telescopes can be simulated by specifying x, y, z, d,\n",
        "  # an, telname, antpos.\n",
        "  # x,y,z are locations in meters in ITRF (Earth centered)\n",
        "  # coordinates.\n",
        "  # d, an are lists of antenna diameter and name.\n",
        "  # telname and obspos are the name and coordinates of the\n",
        "  # observatory.\n",
        "  (x, y, z, d, an, an2, telname, obspos) = (\n",
        "    simutil.simutil().readantenna(antenna_file)\n",
        "  )\n",
        "  # Set the antenna configuration\n",
        "  sm.setconfig(\n",
        "  telescopename=telname, x=x, y=y, z=z, dishdiameter=d,\n",
        "  mount=['alt-az'], antname=an, coordsystem='global',)\n",
        "  referencelocation=me.observatory(telname)\n",
        "  # Set the polarization mode (this goes to the FEED subtable)\n",
        "  sm.setfeed(mode='perfect R L', pol=[''])\n",
        "  # Set the spectral window and polarization (one\n",
        "  # data-description-id).\n",
        "  # Call multiple times with different names for multiple SPWs or\n",
        "  # pol setups.\n",
        "  sm.setspwindow(\n",
        "    spwname=spwname, freq=freq, deltafreq='0.1GHz',\n",
        "    freqresolution='0.2GHz', nchannels=1, stokes='RR LL'\n",
        "  )\n",
        "  # Setup source/field information (i.e. where the observation phase\n",
        "  # center is) Call multiple times for different pointings or source\n",
        "  # locations.\n",
        "  sm.setfield(\n",
        "    sourcename=\"fake\", sourcedirection=me.direction(\n",
        "      rf=dirframe, v0=radir, v1=decdir\n",
        "    )\n",
        "  )\n",
        "  # Set shadow/elevation limits (if you care). These set flags.\n",
        "  sm.setlimits(shadowlimit=0.01, elevationlimit='1deg')\n",
        "  # Leave autocorrelations out of the MS.\n",
        "  sm.setauto(autocorrwt=0.0)\n",
        "  # Set the integration time, and the convention to use for timerange\n",
        "  # specification\n",
        "  # Note : It is convenient to pick the hourangle mode as all times\n",
        "  # specified in sm.observe() will be relative to when the source\n",
        "  # transits.\n",
        "  sm.settimes(\n",
        "    integrationtime='60s', usehourangle=True,\n",
        "    referencetime=me.epoch('UTC', '2019/10/4/00:00:00')\n",
        "  )\n",
        "  # Construct MS metadata and UVW values for one scan and ddid\n",
        "  # Call multiple times for multiple scans.\n",
        "  # Call this with different sourcenames (fields) and spw/pol\n",
        "  # settings as defined above.\n",
        "  # Timesteps will be defined in intervals of 'integrationtime',\n",
        "  # between starttime and stoptime.\n",
        "  sm.observe(\n",
        "    sourcename=\"fake\", spwname=spwname, starttime='-5.0h',\n",
        "    stoptime='+5.0h'\n",
        "  )\n",
        "  # Close the simulator\n",
        "  sm.close()\n",
        "  # Unflag everything (unless you care about elevation/shadow flags)\n",
        "  flagdata(vis=orig_ms, mode='unflag')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAVTe2Vvm9GY"
      },
      "source": [
        "def __makeCompList(ra, dec, frame):\n",
        "  \"\"\"make a componentlist of point sources\"\"\"\n",
        "  # Add sources, one at a time.\n",
        "  # Call multiple times to add multiple sources.\n",
        "  # ( Change the 'dir', obviously )\n",
        "  cl.addcomponent(\n",
        "    dir=__direction_string(ra, dec, frame),\n",
        "    flux=exp_flux,\n",
        "    fluxunit='Jy', freq='1.5GHz', shape='point',\n",
        "    spectrumtype=\"constant\"\n",
        "  )\n",
        "  # Save the file\n",
        "  cl.rename(filename=comp_list)\n",
        "  cl.done()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbXbftSgnNqn"
      },
      "source": [
        "def __predictSimFromComplist():\n",
        "  sm.openfromms(orig_ms)\n",
        "  # Predict from a component list\n",
        "  sm.predict(complist=comp_list, incremental=False)\n",
        "  sm.close()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GzxUK1repi_O"
      },
      "source": [
        "def __summarize(imagename, imfit_box, sim_source_dir, label, prec):\n",
        "  # get the image statistics, and print the world coordinates of the maxposf,\n",
        "  # and note that they are within one 8\" pixel of the simulated source position,\n",
        "  # as expected.\n",
        "  x = imstat(imagename)\n",
        "  # to be even more accurate, fit a 2-D gaussian to the source to show that, to\n",
        "  # approximately within the fit errors, the position is coincident to the\n",
        "  # expected position\n",
        "  y = imfit(imagename, box=imfit_box)\n",
        "  poserr = y['deconvolved']['component0']['shape']['direction']['error']\n",
        "  print(label)\n",
        "  print('Simulated source position', sim_source_dir)\n",
        "  print(\"coordinates of max position from imstat\", x['maxposf'])\n",
        "  cl.fromrecord(y['deconvolved'])\n",
        "  rd = cl.getrefdir(0)\n",
        "  cl.done()\n",
        "  ra_err = qa.div(\n",
        "    qa.div(qa.quantity(poserr['longitude']), 15),\n",
        "    qa.cos(qa.quantity(rd['m1']))\n",
        "  )\n",
        "  ra_err['unit'] = 's'\n",
        "  dec_err = qa.quantity(poserr['latitude'])\n",
        "  print(\n",
        "    \"fitted position from imfit\",\n",
        "    qa.time(qa.totime(qa.quantity(rd['m0'])), prec=6+prec, form='hms')[0], '\\u00b1',\n",
        "    qa.tos(ra_err, prec=prec),\n",
        "    qa.angle(qa.totime(qa.quantity(rd['m1'])), prec=6+prec)[0], '\\u00b1',\n",
        "    qa.tos(dec_err, prec=prec),\n",
        "  )"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37AJt1gEqKIk"
      },
      "source": [
        "def verify():\n",
        "  def __create_input_ms():\n",
        "    \"\"\"create the input MS\"\"\"\n",
        "    __makeMSFrame(antenna_file, spwname, freq, fra, fdec, fframe)\n",
        "    # Make the component list\n",
        "    __makeCompList(radir, decdir, dirframe)\n",
        "    # Predict Visibilities\n",
        "    __predictSimFromComplist()\n",
        "\n",
        "  for observatory in ('VLA', 'ALMA'):\n",
        "    cleanup()\n",
        "    print(observatory, 'simulation:')\n",
        "    # This is the source position\n",
        "    if observatory == 'VLA':\n",
        "      radir = '19h49m43'\n",
        "      decdir = '38d45m15'\n",
        "      dirframe = 'J2000'\n",
        "      ant_cfg = \"vla.d.cfg\"\n",
        "      spwname = 'LBand'\n",
        "      freq = '1.0GHz'\n",
        "      cell = '8.0arcsec'\n",
        "      imfit_box = '1870, 165, 1890, 185'\n",
        "      prec = 3\n",
        "    elif observatory == 'ALMA':\n",
        "      radir = '19h59m33.2'\n",
        "      decdir = '40d40m53.2'\n",
        "      dirframe = 'J2000'\n",
        "      antenna_file = ant_cfg = 'alma.cycle8.8.cfg'\n",
        "      spwname = 'Band4'\n",
        "      freq = '150GHz'\n",
        "      cell = '0.06arcsec'\n",
        "      imfit_box = '123, 1876, 143, 1896'\n",
        "      prec = 5\n",
        "    antenna_file = os.path.join(ctsys.resolve(\"alma/simmos\"), ant_cfg)\n",
        "    dirstring = __direction_string(radir, decdir, dirframe)\n",
        "    # this is the original phase center\n",
        "    fra = '19h59m28.5'\n",
        "    fdec = '+40.40.01.5'\n",
        "    fframe = 'J2000'\n",
        "    print(\"Create the simulated MS.\\n\")\n",
        "    __create_input_ms()\n",
        "    print(\n",
        "      \"Image simulated MS with no phase shift. The source is offset from\\n\"\n",
        "      \"the phase center of the image. We use wproject and wprojplanes\\n\"\n",
        "      \"to correctly account for the non-negligible values of the w\\n\"\n",
        "      \"coordinate because the source is far from the phase center.\\n\"\n",
        "    )\n",
        "    tclean(\n",
        "      vis=orig_ms, imagename=orig_im, datacolumn='data',\n",
        "      imsize=2048, cell=cell, gridder='wproject',\n",
        "      niter=20, gain=0.2, wprojplanes=128, pblimit=-0.1\n",
        "    )\n",
        "    print(\"Summarize results:\\n\")\n",
        "    __summarize(\n",
        "      orig_im + '.image', imfit_box, dirstring, 'Image with no shift applied', 5\n",
        "    )\n",
        "    print(\n",
        "        \"\\nNow use phaseshift to shift the phase center of the\\n\"\n",
        "        \"MS to the source position.\\n\"\n",
        "    )\n",
        "    phaseshift(vis=orig_ms, outputvis=pshift_ms, phasecenter=dirstring)\n",
        "    print(\n",
        "        \"Image the phase shifted MS. The image can be significantly\\n\"\n",
        "        \"smaller because the source will now be at the image center.\\n\"\n",
        "    )\n",
        "    tclean(\n",
        "      vis=pshift_ms, imagename=pshift_im, datacolumn='data',\n",
        "      imsize=256, cell=cell, gridder='wproject',\n",
        "      niter=20, gain=0.2, wprojplanes=128, pblimit=-0.1\n",
        "    )\n",
        "    print(\"Summarize the results:\\n\")\n",
        "    __summarize(\n",
        "      pshift_im + '.image', '118, 118, 138, 138', dirstring,\n",
        "      'Phase shifted image using phaseshift to set the phase center', prec + 3\n",
        "    )\n",
        "    print(\"\\nNow image the original, unshifted MS using a phase shift in tclean\\n\")\n",
        "    tclean(\n",
        "      vis=orig_ms, imagename=tshift_im, datacolumn='data',\n",
        "      imsize=256, cell=cell, gridder='wproject',\n",
        "      niter=20, gain=0.2, wprojplanes=128, pblimit=-0.1,\n",
        "      phasecenter=__direction_string(radir, decdir, dirframe)\n",
        "    )\n",
        "    print(\"Summarize the results\\n\")\n",
        "    __summarize(\n",
        "      tshift_im + '.image', '118, 118, 138, 138', dirstring,\n",
        "      'Phase shifted image using tclean to set phase center', prec + 3\n",
        "    )\n",
        "    print()\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjQnMfcFrqM-",
        "outputId": "42940cc6-b44e-4a87-d050-83162cfafb1a"
      },
      "source": [
        "if __name__ == '__main__':\n",
        "  verify()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VLA simulation:\n",
            "Create the simulated MS.\n",
            "\n",
            "Image simulated MS with no phase shift. The source is offset from\n",
            "the phase center of the image. We use wproject and wprojplanes\n",
            "to correctly account for the non-negligible values of the w\n",
            "coordinate because the source is far from the phase center.\n",
            "\n",
            "Summarize results:\n",
            "\n",
            "Image with no shift applied\n",
            "Simulated source position J2000 19h49m43 38d45m15\n",
            "coordinates of max position from imstat 19:49:42.907, +38.45.13.185, I, 999980842.28Hz\n",
            "fitted position from imfit 19:49:42.99118 ± 0.01171s +038.45.15.21608 ± 0.10346arcsec\n",
            "\n",
            "Now use phaseshift to shift the phase center of the\n",
            "MS to the source position.\n",
            "\n",
            "Image the phase shifted MS. The image can be significantly\n",
            "smaller because the source will now be at the image center.\n",
            "\n",
            "Summarize the results:\n",
            "\n",
            "Phase shifted image using phaseshift to set the phase center\n",
            "Simulated source position J2000 19h49m43 38d45m15\n",
            "coordinates of max position from imstat 19:49:43.000, +38.45.15.000, I, 999983449.88Hz\n",
            "fitted position from imfit 19:49:43.000008 ± 0.001796s +038.45.14.999908 ± 0.016103arcsec\n",
            "\n",
            "Now image the original, unshifted MS using a phase shift in tclean\n",
            "\n",
            "Summarize the results\n",
            "\n",
            "Phase shifted image using tclean to set phase center\n",
            "Simulated source position J2000 19h49m43 38d45m15\n",
            "coordinates of max position from imstat 19:49:43.000, +38.45.15.000, I, 999980842.28Hz\n",
            "fitted position from imfit 19:49:42.989652 ± 0.001851s +038.45.14.869487 ± 0.016600arcsec\n",
            "\n",
            "ALMA simulation:\n",
            "Create the simulated MS.\n",
            "\n",
            "Image simulated MS with no phase shift. The source is offset from\n",
            "the phase center of the image. We use wproject and wprojplanes\n",
            "to correctly account for the non-negligible values of the w\n",
            "coordinate because the source is far from the phase center.\n",
            "\n",
            "Summarize results:\n",
            "\n",
            "Image with no shift applied\n",
            "Simulated source position J2000 19h59m33.2 40d40m53.2\n",
            "coordinates of max position from imstat 19:59:33.200, +40.40.53.214, I, 1.49997e+11Hz\n",
            "fitted position from imfit 19:59:33.20002 ± 0.00002s +040.40.53.20107 ± 0.00038arcsec\n",
            "\n",
            "Now use phaseshift to shift the phase center of the\n",
            "MS to the source position.\n",
            "\n",
            "Image the phase shifted MS. The image can be significantly\n",
            "smaller because the source will now be at the image center.\n",
            "\n",
            "Summarize the results:\n",
            "\n",
            "Phase shifted image using phaseshift to set the phase center\n",
            "Simulated source position J2000 19h59m33.2 40d40m53.2\n",
            "coordinates of max position from imstat 19:59:33.200, +40.40.53.200, I, 1.49997e+11Hz\n",
            "fitted position from imfit 19:59:33.20000000 ± 0.00000483s +040.40.53.19999711 ± 0.00007946arcsec\n",
            "\n",
            "Now image the original, unshifted MS using a phase shift in tclean\n",
            "\n",
            "Summarize the results\n",
            "\n",
            "Phase shifted image using tclean to set phase center\n",
            "Simulated source position J2000 19h59m33.2 40d40m53.2\n",
            "coordinates of max position from imstat 19:59:33.200, +40.40.53.200, I, 1.49997e+11Hz\n",
            "fitted position from imfit 19:59:33.20007211 ± 0.00000624s +040.40.53.20079618 ± 0.00010252arcsec\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyXaDEy71rUx"
      },
      "source": [
        "## Overview\n",
        "The script performs two simulations, the first based on the VLA and the second based on ALMA.\n",
        "For each, an MS is created using the simulator tool. In order to make the verification process\n",
        "more transparent, no noise is introduced into the simulated data. The visibilities are then\n",
        "predicted using a 5 Jy point source that has a significant offset (so that the w coordinate is non-\n",
        "negligible) from the phase center. The MS is imaged by tclean using no phase center shift, to\n",
        "illustrate that the source is indeed significantly offset from the phase center. Because proper\n",
        "account of the w coordinate must be made, gridder=’wproject’ and wprojectplanes are set in\n",
        "tclean. The peak pixel coordinates are computed via the task imstat and a two dimensional\n",
        "Gaussian fit using the task imfit is performed to further constrain the source position to illustrate\n",
        "that it is indeed located at the coordinates specified when creating the MS. The phaseshift task is\n",
        "then run on the original MS, shifting the phase center to the position of the source. The resulting\n",
        "MS is imaged using tclean. Both imstat and imfit are run as before to verify the source\n",
        "coordinates. Finally, tclean is run using the original MS and specifying the phasecenter\n",
        "parameter to be coincident with the source position, to illustrate that tclean can also properly\n",
        "shift the phase center. Results, described in the next sections, indicate that both phaseshift and\n",
        "tclean properly shift the phase center to within a small fraction of a pixel, and that phaseshift is\n",
        "slightly more accurate, although both are very close to the expected result.\n",
        "\n",
        "## Results\n",
        "The output of the script, when run on an RHEL 7 machine, is\n",
        "### VLA simulation\n",
        "Image with no shift applied:\n",
        "```\n",
        "Simulated source position: J2000 19h49m43 38d45m15\n",
        "coordinates of max position from imstat: 19:49:42.907, +38.45.13.185, I, 999980842.28Hz\n",
        "fitted position from imfit 19:49:42.99118 ± 0.01171s +038.45.15.21608 ± 0.10346arcsec\n",
        "```\n",
        "Phase shifted image using phaseshift to set the phase center:\n",
        "\n",
        "```\n",
        "Simulated source position J2000 19h49m43 38d45m15\n",
        "coordinates of max position from imstat 19:49:43.000, +38 45.15.000, I, 999983449.88Hz\n",
        "fitted position from imfit 19:49:43.000008 ± 0.001796s +038.45.14.999908 ± 0.016103arcsec\n",
        "```\n",
        "Phase shifted image using tclean to set phase center:\n",
        "```\n",
        "Simulated source position J2000 19h49m43 38d45m15\n",
        "coordinates of max position from imstat 19:49:43.000, +38.45.15.000, I, 999980842.28Hz\n",
        "fitted position from imfit 19:49:42.989652 ± 0.001851s +038.45.14.869487 ± 0.016600arcsec\n",
        "```\n",
        "\n",
        "### ALMA simulation\n",
        "Image with no shift applied:\n",
        "```\n",
        "Simulated source position J2000 19h59m33.2 40d40m53.2\n",
        "coordinates of max position from imstat 19:59:33.200, +40.40.53.214, I, 1.49997e+11Hz\n",
        "fitted position from imfit 19:59:33.20002 ± 0.00002s +040.40.53.20107 ± 0.00038arcsec\n",
        "```\n",
        "Phase shifted image using phaseshift to set the phase center:\n",
        "```\n",
        "Simulated source position J2000 19h59m33.2 40d40m53.2\n",
        "coordinates of max position from imstat 19:59:33.200, +40.40.53.200, I, 1.49997e+11Hz\n",
        "fitted position from imfit 19:59:33.20000000 ± 0.00000442s +040.40.53.19999711 ± 0.00007270arcsec\n",
        "```\n",
        "Phase shifted image using tclean to set phase center:\n",
        "```\n",
        "Simulated source position J2000 19h59m33.2 40d40m53.2\n",
        "coordinates of max position from imstat 19:59:33.200, +40.40.53.200, I, 1.49997e+11Hz\n",
        "fitted position from imfit 19:59:33.20007214 ± 0.00000593s +040.40.53.20079661 ± 0.00009744arcsec\n",
        "```\n",
        "## VLA Simulation Details\n",
        "The VLA simulation uses antenna positions in the D configuration and a frequency of 1.0 GHz.\n",
        "The resulting images have 8.0” pixels. The phase center and the source in the original MS were\n",
        "separated by about 2.7 degrees. Figure 1 shows the full image created from the original MS. The\n",
        "point source can be seen in the lower right corner of the image. Figure 2 shows the image created\n",
        "from the MS after running phaseshift to shift the phase center to the position of the source. The\n",
        "contours represent the image created from the original MS, using tclean to shift the phase center\n",
        "to the source position using the phasecenter parameter. Figure 3 is the central portion of Figure 2.The results (see above) indicate that source position in the image that is not phase shifted is\n",
        "indeed as expected. The somewhat large fit errors are likely due to the fact that the source is not\n",
        "located at the center of a pixel. The results for the image created from the MS that has been\n",
        "produced by phaseshift show excellent agreement with the expected source position. The fit\n",
        "uncertainty provides an upper limit of about 30 marcsec (0.003 pixels) of the offset of the source\n",
        "from the phase center, and the best fit results are two orders of magnitude better than that at about\n",
        "0.1 marcsec (0.00002 pixels). The results of the image created from the unshifted MS by\n",
        "applying the phase shift in tclean using the phasecenter parameter are also very good, although\n",
        "not as good as the image made from the MS created using phaseshift. In this case, the offset\n",
        "between source and phase center falls significantly outside the fit uncertainty, with a separation of\n",
        "about 0.2arcsec (0.02 pixels).\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Figure 1: VLA simulated data image prior to phase shift. The source is in the lower right corner.\n",
        "\n",
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArQAAAKUCAYAAAD1kqvsAAAABHNCSVQICAgIfAhkiAAAIABJREFUeF7t3W2sdNtBF/CZ9lJsgZYCFkibAgFpSwlFqanFD5wHjSiY8AGaCDHBagx8MIIfMBhjjPKFRBLQqJEYXzARQoIvJBCioOdqIglSohAJECUIqeFFXkNrobntuNc5s55nP6tr7bfZM3vvtX+nuZ0ze+/19lv7zPnfddfMOR4Oh1Pzjy8CBAgQIECAAAECmxR42SZ7rdMECBAgQIAAAQIEzgICrVuBAAECBAgQIEBg0wIvbLr3G+n86WRXx0amSjcJECBAgACBEQLH43HE1de7VKC9nu1zNR+P66E+nV46rKk/N5qC55ph0Gye3/l9sPfxhx+IvRvsffzugcdfC3u/Dy4Zfyi7li9bDtYyE/pBgAABAgQIECAwSUCgncSmEAECBAgQIECAwFoEBNq1zIR+ECBAgAABAgQITBIQaCexKUSAAAECBAgQILAWAYF2LTOhHwQIECBAgAABApMEBNpJbAoRIECAAAECBAisRUCgXctM6AcBAgQIECBAgMAkAYF2EptCBAgQIECAAAECaxEQaNcyE/pBgAABAgQIECAwSUCgncSmEAECBAgQIECAwFoEBNq1zIR+ECBAgAABAgQITBIQaCexKUSAAAECBAgQILAWAYF2LTOhHwQIECBAgAABApMEjk2p06SSCg0WOJ1Oh+PxhcHXu5AAAQIECBAgsHaB0+mlJt+EKLn8lxXa5edADwgQIECAAAECBC4QEGgvwFOUAAECBAgQIEBgeQGBdvk50AMCBAgQIECAAIELBATaC/AUJUCAAAECBAgQWF5AoF1+DvSAAAECBAgQIEDgAgGB9gI8RQkQIECAAAECBJYXEGiXnwM9IECAAAECBAgQuEDAh6NegKcoAQIECBAgQOC2Ah/qae7lPefrPG2Fts55NSoCBAgQIECAwG4EBNrdTLWBEiBAgAABArUJhL9G6utwEGjdBQQIECBAgAABApsWEGg3PX06T4AAAQIECBAgINC6BwgQIECAAAECBDYtINBuevp0ngABAgQIECBAwMd29dwDuc3Wx+Oxp5TTBAgQIECAAAECtxIQaDukY5hNA2w4nh7rqMYpAgQIECBAgACBKwrYcjARN7dyO7EqxQgQIECAAAECBC4QEGh78HIrsbljPdU4TYAAAQIECBAgcCWBTW456FsdLQXOUrnc9aVrwzx0nbvSPKmWAAECBAgQIECgILDJQJsLoGF8XUGztB82lkvrjM/T/bJd9RSMHSZAgAABAgQIELiiQDVbDrqCZte5YBvC65AwfMV5UDUBAgQIECBAgMBEgU2u0KZj7Qus6fWl5+lqbLguht124E1Xc0v1tY+fTi9lLzseq5iC7NgcJECAAAECBLYvUMowaxrZ5tPU0DDbF0K7Vmn7yg6ZUMF1iJJrCBAgQIAAgbUJlDLMmoJuVVsOLtk20FV2bTeW/hAgQIAAAQIECDwT2HygDaun8Z8wLMHU7U2AAAECBAgQ2JfA5gNte7pKWwPi8VLYLR3f161gtAQIECBAgMDWBErZZ2vjuLS/VQXaiJELqO1QG8+Hx/i9G+LSW0l5AgQIECBAgMAyAtUF2q5gmtua0D62zBRolQABAgQIECBA4BKBzX/KwZTB50JvblV3St3KECBAgAABAgSuJ/Dy61W94ZqrW6G9JJjmgu6G51bXCRAgQIAAAQK7ENhMoG3vd+2bmVww7Qq6Xef62nKeAAECBAgQIEBgWYHNBNr2m7pKZH3BNHfem8JKmo4TIECAAAECBLYhsKk9tENCbW51NkxFOF5a5S2V2cYU6iUBAgQIECBAYN8Cmwq0caqmBtCp5fZ9ixg9AQIECBAgQGDdApvZcrBuRr0jQIAAAQIECBBYSkCgXUpeuwQIECBAgAABArMICLSzMKqEAAECBAgQIEBgKQGBdil57RIgQIAAAQIECMwiINDOwqgSAgQIECBAgACBpQQE2qXktUuAAAECBAgQIDCLgEA7C6NKCBAgQIAAAQIElhIQaJeS1y4BAgQIECBAgMAsAgLtLIwqIUCAAAECBAgQWEpAoF1KXrsECBAgQIAAAQKzCAi0szCqhAABAgQIECBAYCkBgXYpee0SIECAAAECBAjMIiDQzsKoEgIECBAgQIAAgaUEBNql5LVLgAABAgQIECAwi4BAOwujSggQIECAAAECBJYSEGiXktcuAQIECBAgQIDALAIC7SyMKiFAgAABAgQIEFhKQKBdSl67BAgQIECAAAECswgItLMwqoQAAQIECBAgQGApAYF2KXntEiBAgAABAgQIzCIg0M7CqBICBAgQIECAAIGlBATapeS1S4AAAQIECBAgMIuAQDsLo0oIECBAgAABAgSWEhBol5LXLgECBAgQIECAwCwCAu0sjCohQIAAAQIECBBYSkCgXUpeuwQIECBAgAABArMICLSzMKqEAAECBAgQIEBgKQGBdil57RIgQIAAAQIECMwiINDOwqgSAgQIECBAgACBpQQE2qXktUuAAAECBAgQIDCLgEA7C6NKCBAgQIAAAQIElhIQaJeS1y4BAgQIECBAgMAsAgLtLIwqIUCAAAECBAgQWEpAoF1KXrsECBAgQIAAAQKzCAi0szCqhAABAgQIECBAYCkBgXYpee0SIECAAAECBAjMIiDQzsKoEgIECBAgQIAAgaUEBNql5LVLgAABAgQIECAwi4BAOwujSggQIECAAAECBJYSEGiXktcuAQIECBAgQIDALAIC7SyMKiFAgAABAgQIEFhKQKBdSl67BAgQIECAAAECswgItLMwqoQAAQIECBAgQGApAYF2KXntEiBAgAABAgQIzCIg0M7CqBICBAgQIECAAIGlBATapeS1S4AAAQIECBAgMIuAQDsLo0oIECBAgAABAgSWEhBol5LXLgECBAgQIECAwCwCAu0sjCohQIAAAQIECBBYSkCgXUpeuwQIECBAgAABArMIvDBLLQtVcjqdOls+Ho+d58PJtI6uMuHarvO9jbmAAAECBAgQIEBgdoFNB9qgMTVgtoNsrCMcE1pnv8dUSIAAAQIECBC4qsBmA226sjpGKZZNw3A72KbnxtTvWgIECBAgQIAAgdsJbDbQTiUqhdl2fdcIs6fTS9kuH4+7m4Ksg4MECBAgQIDAOgVKGWZNvd1dmgph9ZLV3amTJ7hOlVOOAAECBAgQWFKglGHWFHQ3HWhL4fQaK6xL3kjaJkCAAAECBAgQKAtsOtDmtg/EN3aFIXcF265V2lK50vEyrzMECBAgQIAAAQLXFthsoC2Fy3g8BtvSdQE2d25IuWtPivoJECBAgAABAgSGC1T7hxXawbbNkVvVbZ/PhdzhnK4kQIAAAQIECBC4tUC1gTZA5sJp7lgOvWtLQu56xwgQIECAAAECBJYRqDrQTiUdGnqn1q8cAQIECBAgQIDAfAIC7XyWaiJAgAABAgQIEFhAYLOBdsiWgCHX5MynlsvV5RgBAgQIECBAgMB1BTYbaPtY+t78VQqtfeX62nWeAAECBAgQIEDgtgKb/diuwJQLn+2gWtoLG46H69rlh5S77dRojQABAgQIECBAYIjAZgNt+2O50tXWUpBtg5TKDyk7BNY1BAgQIECAAAECtxHYbKCNPJcG0EvL32aatEKAAAECBAgQIFASqHYPbWnAjhMgQIAAAQIECNQlINDWNZ9GQ4AAAQIECBDYnYBAu7spN2ACBAgQIECAQF0CAm1d82k0BAgQIECAAIHdCQi0u5tyAyZAgAABAgQI1CUg0NY1n0ZDgAABAgQIENidgEC7uyk3YAIECBAgQIBAXQICbV3zaTQECBAgQIAAgd0JCLS7m3IDJkCAAAECBAjUJSDQ1jWfRkOAAAECBAgQ2J2AQLu7KTdgAgQIECBAgEBdAgJtXfNpNAQIECBAgACB3QkItLubcgMmQIAAAQIECNQlINDWNZ9GQ4AAAQIECBDYnYBAu7spN2ACBAgQIECAQF0CAm1d82k0BAgQIECAAIHdCQi0u5tyAyZAgAABAgQI1CUg0NY1n0ZDgAABAgQIENidgEC7uyk3YAIECBAgQIBAXQICbV3zaTQECBAgQIAAgd0JCLS7m3IDJkCAAAECBAjUJSDQ1jWfRkOAAAECBAgQ2J2AQLu7KTdgAgQIECBAgEBdAgJtXfNpNAQIECBAgACB3QkItLubcgMmQIAAAQIECNQlINDWNZ9GQ4AAAQIECBDYnYBAu7spN2ACBAgQIECAQF0CAm1d82k0BAgQIECAAIHdCQi0u5tyAyZAgAABAgQI1CUg0NY1n0ZDgAABAgQIENidgEC7uyk3YAIECBAgQIBAXQICbV3zaTQECBAgQIAAgd0JCLS7m3IDJkCAAAECBAjUJSDQ1jWfRkOAAAECBAgQ2J2AQLu7KTdgAgQIECBAgEBdAgJtXfNpNAQIECBAgACB3QkItLubcgMmQIAAAQIECNQlINDWNZ9GQ4AAAQIECBDYnYBAu7spN2ACBAgQIECAQF0CAm1d82k0BAgQIECAAIHdCQi0u5tyAyZAgAABAgQI1CUg0NY1n0ZDgAABAgQIENidgEC7uyk3YAIECBAgQIBAXQICbV3zaTQECBAgQIAAgd0JCLS7m3IDJkCAAAECBAjUJSDQ1jWfRkOAAAECBAgQ2J2AQLu7KTdgAgQIECBAgEBdAgJtXfNpNAQIECBAgACB3QkItLubcgMmQIAAAQIECNQlINDWNZ9GQ4AAAQIECBDYncALWx7x6XTq7P7xeMyev6Rcqc5sQw4SIECAAAECBAhcXWDTgTbojA2YMczmyvUF3avPhgYIECBAgAABAgRGC2x2y8HU8JkLslEtnAv/TK17tL4CBAgQIECAAAECFwtsfoX2YoEbVXA6vZRt6Xg0BVkYBwkQIECAAIFVCJQyzCo6d+7ELtNU1yrttVZnBdc13fb6QoAAAQIECAwVKGWYNQXdzQfaUgDtCq2lCezaX1sq4zgBAgQIECBAgMCyApsMtEOCZ7hmSqjtmo656+tqyzkCBAgQIECAAIFhApt8U1h881bXEKe+uUto7VJ1jgABAgQIECCwPoFNBtoxjKUtCWkdQ69Ly3lOgAABAgQIECCwrEDVgXboauuQLQzLTpPWCRAgQIAAAQIESgJVB9rSoNvHhdkhSq4hQIAAAQIECKxXoOpA27eNQJhd742pZwQIECBAgACBoQKbDbSXhlVhdugt4joCBAgQIECAwLoFNhlohdl131R6R4AAAQIECBC4pcAmP4c2vtkrt8qaO9YG7Tt/S3xtESBAgAABAgQIXC6wyUAbhx0/azZdsR3y6QZpmRzlkHpy5RwjQIAAAQIECBC4ncCmA21gGhs6x15/u6nQEgECBAgQIECAwBSBTe6hnTJQZQgQIECAAAECBOoUEGjrnFejIkCAAAECBAjsRkCg3c1UGygBAgQIECBAoE6B2ffQerNVnTeKUREgQIAAAQIE1iowW6BtB1lvvFrrdOsXAQIECBAgQKA+gVkCrc92re/GMCICBAgQIECAwFYEZtlDa0V2K9OtnwQIECBAgACB+gRmCbT1sRgRAQIECBAgQIDAVgRmCbRD3gi2FRD9JECAAAECBAgQ2JbALIE2bjkQbLc1+XpLgAABAgQIEKhBYJZAGyBCqA3/CLU13BbGQIAAAQIECBDYjsAsn3IQhtsOsn2h1pvItnOD6CkBAgQIECBAYO0CswVaIXXtU61/BAgQIECAAIE6BWbbclAnj1ERIECAAAECBAisXUCgXfsM6R8BAgQIECBAgECngEDbyeMkAQIECBAgQIDA2gVm20PbHmjuTWH22K79VtA/AgQIECBAgMA2BWZdoQ1Bth1m40d5BZpcyN0mmV4TIECAAAECBAisSWC2FdoYWHMrse0/vJA7vyYQfSFAgAABAgQIENiWwKwrtEOGbqV2iJJrCBAgQIAAAQIEhgrMGmj7Vl/7zg/ttOsIECBAgAABAgQIRIFZAy1WAgQIECBAgAABArcWmCXQDt1GMPS6WyNojwABAgQIECBAYLsCswTa+GkGXYG1601j2+XTcwIECBAgQIAAgaUFZgm07UF0hdqlB6t9AgQIECBAgACB+gRm+9iuQBNWakOgLYVabwqr7wYyIgIECBAgQIDA0gKzBtowGKF16SnVPgECBAgQIEBgXwKzbznYF5/REiBAgAABAgQILC0g0C49A9onQIAAAQIECBC4SECgvYhPYQIECBAgQIAAgaUFJu2hzX0EV+mNYLkB2mebU3GMAAECBAgQIEBgisDoQJsLs6FhIXUKvzIECBAgQIAAAQKXCowOtILrpeTKEyBAgAABAgQIzClgD+2cmuoiQIAAAQIECBC4uYBAe3NyDRIgQIAAAQIECMwpMFugHfqmsKHXzTlIdREgQIAAAQIECNQrMFugHUIkzA5Rcg0BAgQIECBAgMAYgZsF2tKnI4zprGsJECBAgAABAgQIpAKjP+UgVFBaaS0dj436hISU33MCBAgQIECAAIFLBSYF2lwwDWE2d/zSDipPgAABAgQIECBAoEvgZlsOujrhHAECBAgQIECAAIGpArMFWquzU6dAOQIECBAgQIAAgUsEZgu0l3RCWQIECBAgQIAAAQJTBQTaqXLKESBAgAABAgQIrEJg0pvCcj3v+4SDdhnbE3KCjhEgQIAAAQIECEwRmC3QhpDa9VmzXeemdFwZAgQIECBAgAABAkFgti0HAqsbigABAgQIECBAYAmBWQLtkDAbVnDbq7hLDFabBAgQIECAAAEC9QnMEmjtia3vxjAiAgQIECBAgMBWBGbbQ7umAQ/5q2WlN7F1hfMh9a7JQV8IECBAgAABAnsQmGWFNkKVQuLQ83OA9/UhtNHeIhG3QsQgO6T8HP1UBwECBAgQIECAwDwCswbari4N2WfbVX7IuTFhNLcSa5/vEGXXECBAgAABAgTWJTDbloPcCmd6LBci5+YIbYwJtnO3rz4CBAgQIECAAIHbCswWaGO324GyHSyvHWbnCrFz1ZNO4+n0Unro4fnxOPsUZNtxkAABAgQIECAwRaCUYabUda0yV0lT1w6vKcaU7QyhTNrPKfWkfSk9F1xLMo4TIECAAAECaxYoZZg1Bd2rBNpbTsqUEBpXkXOrsWnIbY+l69wtx6wtAgQIECBAgACBZwI3e1PYNdHnDJq5kHvNvqubAAECBAgQIEDgMoFNr9BODZ9dq7rhXG47wmXMShMgQIAAAQIECFxLYPZAOyRkzrGi2hVKu7Cmluuq0zkCBAgQIECAAIHlBGYLtO0gO0dgHUpSCtC5/gwJs7HvVmmHzoDrCBAgQIAAAQLLCswSaIcExbmH2RWaS2E0vhls7r6ojwABAgQIECBAYDmBWd4U1hUulxualgkQIECAAAECBPYgMEug3RpUaZtCHEff+a2NV38JECBAgAABAjULzLLlIAKV/lP/mgDjtoNcX9tB1qrzmmZNXwgQ6BcY8nKe/4uF/XW7ggABAusWGPIKuO4RTOhdO9SmxQXZVMRzAgQIECBAgMC6BWYLtGv6dIAhoXTINeueOr0jQGA/AnO9VA+pxyrufu4rIyVQj8CQV7dBo23/5/q+PajC5CBSFxEgQIAAAQIECAwQmC3QCqkDtF1CgACBToHZXpI7W+k+mfbBim23l7MECKxBYJefcrAGeH0gQIAAAQIECBCYRyD9V/F5alULAQIECIwQGPNSPObaIV3oW4Ftt9d37ZD2XEOAAIH5BazQzm+qRgIECBAgQIAAgRsKTPpX/fimr/a+2b43grXHZL/tDWdYUwQIECBAgACBygVGB9pcmA1GQmrld4rhESAwk8CQl930mvR5uytd59Iuj9kykLs2bSt3Tdqm5wQIELi+QPrq1Nui4NpL5AICBAgQIECAAIEbCowOtDfsm6YIECBQkUDXy216Lj5PHwNH6dpI1T6frqDG5+3jpWtifen59pTEtrquqWgKDYUAgdUKeFPYaqdGxwgQIECAAAECBIYIpP+qP6SMawgQIEBgsEDpZbZ9PF2Jjc9/37mV9rXxWHx8eeaa2Ll05fT3zifax3/3fCw+xnPxeW6gab1dq8K58o4RIEBgXoHSK21nK2M+0SBXkX24ORXHCBAgQIAAAQIEpghMCrQC6RRqZQgQ2I9A10truhobVNIV2bj6+rFnso9p0cVj8TFdxc2tlsYV1fed64mP4Wn8Pq7exuexnvZKbboymz5vjyV3rjUM3xIgQGBGAXtoZ8RUFQECBAgQIECAwO0FBNrbm2uRAAECBAgQIEBgRoGu/y42YzOqIkCAwB4Eul5S060GcatAcEm3GLzmjPXx58dPauF9YnIuXpO+SSxc9qHztelWg19r1ffr5+/jsY8+P//t1jXx29IbxXLbC+J4c+cyVTtEgACBCwRmXaENbxbLvWGsdPyCfitKgAABAgQIECBA4EGgazlhFFHpT+KGSuKbyMI13lA2itXFBAhsQqDrpTRdmY3P2yu08Q1ecfX1U86jfv358Q0thU9Ljn3y4f4+XP/48V13dx91ePHFuDL74Ydrnzz5hXOZXzk/vrdVXzwXj/2f87m03+FwutLbqubpt1ZkcyqOESBwXYGuV+GrtCzUXoVVpQQI7Ezg/v4tDyO+u3t1s1Dwv5vv0k89CGcftwicTp/VhNwPNM9/fxNu/8dDOV8ECBCoSeDYDOY0x4CGBtWh183Rp7XU8Tjmm/+7w1qGrx8EKhYo/Vznjqd/ECHufQ08cWU2rsTGVdg3ne0+9+Hx/v6zm///2CbEHg/Hd51P/fHz4xvPjzHXhqe/cT72i+fHf9e84H//4WEF98mTEHDD10+dH2PQ/dnz83TlNhyO+23jSm36RxnCNaUV2tLxc3MeCBDYnMDp9NJq/st77lV3c6A6TIAAgdoFTqcvaH5x/HgzzLc9DvUrp718H/90U/wHPtCE41c2wfjlTZ21yxkfAQJ7EJhlhbZr/2wbceh1tcFboa1tRo2HQBQohcr28fh9XKGNK7NxVTbUFVdmP/Nc8Tm0Hr7g4XnYMnD84vOprz8//onz4yvf8/DN/XmV9+7wGYcXDz9zPvmqw5PD9zTfny/+zXO9P3w+3TycmpXeYzj8k7HMj55PxpXbn3t28eG95+9LK7XhdFyJLa3Ilo63mvEtAQKbEKhuhXbIm772GmY3cUfqJAECqxS4v399s4r6ymdhNunl/SvCgTcd7g4fdzge/sb5bAyd4ennNHvKvrEJuI9bBJ5kRhnC7OknwjaENzbbEOLehMyFDhEgQGDFAqXlhcld3uMe2clYChIgsAOBdIU2/czZQBBXa5+t0N7fNwHzW195OHxrc/rbWkxve0+zGvsYYt/QxNh/25wKa7Qf/uD5mt9qXdssBp8Of/Xwiofg23z/2lMTbl88PHnXJz+76I1vORzDouxfe1WzEvzmZgtC3Bf7/vM1cb9seBq/j38mN17b/lViBfYZru8IELiVwKyfQ9teqQ3Btv1PGJCP7LrVtGqHAIGtC9zdvSo7hNPh7c02gp9tNilkNr++cH84vq55n+/H3z/+k9TQvJWsKfukCbmPn5CQNnB8Z3jj2Welhz0nQIDA6gVmX6EVWlc/5zpIgMDKBR5XSps9rV/25ud6emq2Bxwf1mOffb2pWa8NX8dX3B1OHzweTq0V2hBuTx9+8fDW5vxPNUE2fh0PP/0QasNj+nV31/6YhPSs5wQIEFinwOyBdp3D1CsCBAjcQiC+pLZfWtNj8U/Ltj+261POnfv0ZoX0Dx2OYQfCJzRh9q+cDzfbDMLKbAizf/nwhx8OftvPHw4v+4wmsH7zeaX220PYbb7iR3WF718f1mQPh1f/pcPhC7/p1GxLePFw+K0nhz//us85vLs53pQ+HN/RbKANX9/0+Iax8KkHp9PnN/9FLf4Rhl9+PP/w//HNYDE1x+0T7W0G6a8VWxBagL4lQOBKArNuObhSH1VLgACB3Qjc3b32I8Yaw2z7RAizH/75zLaD+8cV27SS07c01zZh9mFLwvnr3U3cPcWPAWsVCKH2/v4xOKf1eE6AAIE1CqT/Kr3GPuoTAQIEViYw5qUzXaHNvSnskx7Gd3//zmZltNlq8A3nrQZf2Bxr3tDV/C2w5nMMHr++/VebldgQSr/0eHhYkfjB73o8cf9Vj493zUPzHobDjzWP73/807fHJ//48dx7vq55PB7+6Xc3ofd9Tx6OHV8bQm2zUhs/FuxrHy89HMJKcvjTu3H1OByPfxY3jmGMQ6y3XcbqbVTxSIDAZQJTXo2KLcaP5mpfEPbU+siuIpkTBAgQeCqQeyNY87cPnvt62Bf7q49bCR5O3DdJ9K751IK4WPt5rct/8hwYT01KPX7H0xPHj2r227YuC7Xdv/J0ePrHw5pzd3cf81y7nhAgQGDNArMF2lxoTY/5SK813wr6RoDAdQXiy21c3WwHxo9pVme/pFmd/bWmC81q7TmU3r/y/U3U/MXDeQ328MXNG8A+/J2PyfX4g1/RhNm/dTh8/fkjuP7RufdvaI3ivefP6/oDzbGHUPtDDyc/9F3Hw8u/+vTw+F1ffb7+heZjuj6u+RzaP/j4CQihL/f3b2o+mzb8dbL4Fff/piu0s/0qabXlWwIECAwXuPke2twq7vDuupIAAQL7Ffjkw93zg78Ln18w8CuE2hCAz18hzL7sjz3bb3t3sCI7UNJlBAisUGDWf63u+8iu9vaDFVroEgECBGYSGPvSGq4PK6/nlc+nn5wVtgz85uHTmv9/a7M6+yPNNe/8pXMX7/9BU+S7D4e/eN47+9nn449bYx+fxLz69FjYq/sVh5f94r96PN/8fYRQd/g6NrXfN608+YTzgYe+hDWP9liGjCteY39slPRIgMD1BW6+Qnv9IWmBAAECdQjcHV4z70DCXltfBAgQqFBglkA7dBvB0OsqdDYkAgQIdArcJWHz/ouev/w1H7HdYGI4DW8iO38d33j3XCN+epvnAAAgAElEQVRpgL5L35HWOQInCRAgsJzAkP9+1Nu7uJWg601f6RvEeit1AQECBDYrMOQ/t7eveenw4ou/3Iz2/JL8Gx97ePJvmi2vX9u8Uevw2sMvNP//mYcXHx7f8cZHlJe/+FPNN83WgfhxXV9wxvp/LbT3nr//0fNjKPPkbx5O//z8/JebOp9+Ktdrmxaa9t4X99L+btOn8CawIWNptTn6+nZZ3xMgQGCawCwrtKHpEGrbwba9GivMTpscpQgQIBAF/mPzp2u/qv1hW00wPZy+dzag8KdwnzSfqOCLAAECWxSYZYW2PfD0jV/C7BZvC30mQGA+gbjCGR9/91x1846sp1/vbz4e6183H5P1Nc3jzx8O/+3xDy0cXmpWSz/wlsNXn/+qwqc2a6hf9KfOhb6iWZ39yuaNZP+z+UTZ8BmzX/fuxxOfd/6orvDsJ2MDzZvHTs2bx770cw+Hpvwx/HP+PNvmw7wevj71Q83/fbD5yK6nZcJHiL2q+SesEsev+H0cw9jV21ZVviVAgMCMArOt0Lb7FFdr248z9llVBAgQqFIg/WMG4Q8dnOKfCGtG/KT53/G1TYBtf4UwGz5jtvQVLg/7ZsMnIpy/YpgtFXGcAAECWxOYfYV2awD6S4AAgfECcWVyzEtoukL7vlazYf/sodmz+hvN/zd/XvY7mpXU8NWspL74R5vHT/rvj8+b/3938wFb/6wJtad/GD7mq/l6e/PnbL/l6w7HHzl/Rtc770JFj+dC9z7xyeH0Dc3j325y7599PPxzzUrv33/d4/e/0/yN3NPh7YfjucjhXzweP53e3Gwj+w/Nk7BSG79in9MV2jErtWOubTXtWwIECHQIjHk17qjGKQIECBC4VODJkyZcnsJfDHtW05P/0gTRL39bE2N/4unBh1B73jLw9OC7zh822+Thp19PP1P28cjLPqMJwh9owmyz0hu/HsLs4T3N07e3CoZM/JvPPfeEAAECaxYIL5vJf7+a1t2uj+Tq+4ML01rcTqnHT3/w7w7bmTE9JTBUoPRz3T4e/0xsfDzvjz20P3brM88Nfm6zj/bLm320//fx+R/5/MfHb29eqN/R7BoIn1bwjp8+X/s5zR9CuG8+zOvu8Q8ufPB8+Ldaff/4x+9/vNlW+/bmpf4ND3+8IawB/9jD40OYjTn5r5/L/cDPNKE6rM7+k/OB9p++/bnzsV85P8bG2qvN6ert+dKnD1ZoUxHPCWxV4HR66eEDAdbwdfEe2hDW2m/8SvfPhkF2hd01IOgDAQIE1iLw5Mn3Na+Z5yDb6lQIsyHUtr/Cntrw18PCXxE7vHB/OL7i+fWJh+fN8Tc1599zDrOx/LOV2efrfAyzP7MWDv0gQIDAIIHJK7TtkNqXzvf+SQdWaAfdiy4isEGBISu08Zq4Qhv/ru0ntsb7hvP3caX2rU2o/Zpm5eMcLD/vzU+vvf+hw+Gu2f96jDsCXhuXWP/9wzWnwzc2O2Qfyz05/MtzuS9/fPzA47aC0yub8t93PvV3z4/3/+vhm/v7VzcrxGE/bqw3rsqGs+89X/zr58e4QhtXZcPhuAJbWoktHT9X6YEAgc0IrGmFtvRq3Ik5NqDGwPsY7NaxNN05QCcJECCwsMDx+J3Ph9pzf578p8dvTu9qhdpWX4/N28aefcWgfC7TBNkXm4/nehpmkzGeTp/VhOjvWXjkmidAgMB4gUmBdnwzz0oItZfoKUuAwJ4EwgrtwxaAt33kqMMK7f3DYu/jqu5dszJ7PPydj7jwdPjmZsX2dx6OH5uPASt9PYbZx1Xa0jWOEyBAYK0Ck7YcXBJKLym7VsS+ftly0CfkPIGtC3StDaRbDtKtB2HscftBXFH9tDPImx4eT6e/0KychneEha+3Pj582XnrwpecDzd/Eve++Yivu+Y9Zy/GnQDNauyT/9qcj38A7IfP137vs//sfzq90Hyiwe88/GGHx6+fPT/+wvkxbjMIT0tbDdpbDs7Fin8C15aDKOSRwNYFNr/lYOsToP8ECBDYkkAIs/f3bznc3b26CbbtTxR4fhThI74O7Y/qKl/6UDCE2eOx/SkGW1LRVwIECDwTsEJ7g7vBCu0NkDVBYBUCuZXaeCx9jG8OCx2P37/mPIpPOT++/vz4bC/s/f2fa4Lt65ogGtJr+Iof/3X+jK5DXAEO5+LKaUy2v9KE2Hc0K7KPHwv25Ml/bq5JV2LjB9k+/rGHZ6uyob5YT3yMq63tVdfSCmzp+MMgfBEgsEEBK7QbnDRdJkCAwBoEHj+BIHwawZ88d+cVTcB9ZRNw23/R6/menk6f3oTYEG4/4bx1ob2NYA2j0gcCBAhcJpBbTrisxhWUnrpPt69c3/kVDF0XCBBYnUBpZbK9HyBd6YzP4zVx72oY3GMYffIk7nV9XN29v/8zDyO/azbRvvjiL50VHj+X9nj8e+fncXNt+68vxLpjII7nfvtcpt3PuOJb6u+5yHMPpfHnrnWMAAEC0wQmB9oQ7tb4NbVfU8ut0UCfCBDYn8CTJ99/HvTLW4Nv3hXmiwABAjsQmBRo+/6QwlJuU0Pp1HJLjVO7BAisVSCuRuZeWrtWKtNPCYjX/t55oO0V1bi3Ne67jY/pHt1QNF1JTffUhmviCuz7z23F5/Hadt/S+rrG1HVurfOnXwQIbFUg96q71bE89DuE7SkBdWq5oVhh43Tu63isbgpyw3SMAAECBAgQ2KhAKcOsaTiTPuVgTQOIfYkhNgbToavIY8qFa4fW2zZ6LCe4rvG+0ScC1xco/eznjsdj8ZMK0ueht+m5j06GkKs3/gt13ILQXnVN98Wmz9v/Mp6u0Ob08v/ynrvSMQIEti2wpk85eNm2KR973w6lY8YztdyYNlxLgAABAgQIECBwXYHNB9qpoXRKuSmrs9edPrUTIECAAAECBAjk/tvU5lSmBs2p5TYHpMMECCwoEP8TfPpy2/Wf5uN/9o9l2lsE4rH0MQ6x3U7aRvo8lEm3EaTP23S58u06FmTWNAECuxbY9AptXGUdO4NTy41tx/UECBAgQIAAAQLXF0iXDK7f4kwtTNkyEJqeWm6mbquGAIHdCpRWatsg6Qpo+jxcm75sp8+7gHP1xevTc+nzXL1DrsmVc4wAAQLzCox5JZy35ZlqK622to/nthZMLTdTt1VDgAABAgQIECAwk8BmA20upEaTEFZL50vHQ9mucjN5q4YAgd0L5FY105fi3DUluDHX5uoYU37Mtbm2HCNAgMB1BDa9h/Y6JGolQIAAAQIECBDYkkC6LLClvusrAQIEKhGIK59DXpJvvUp66/YqmVLDIEDgpgJWaG/KrTECBAgQIECAAIG5BQTauUXVR4AAAQIECBAgcFOBIf9966YdmqOxrjd+ddU/tVxXnc4RIEBguED6n/dv+RKdtj28164kQIDA0gJWaJeeAe0TIECAAAECBAhcJHDRv/6XPss11yOrnzkVxwgQINAlMGTVdMjL+JB6uvrhHAECBNYtMOSVMDuCsX9xy2e8ZhkdJECAAAECBAgQuFBgUqAdG2ZDH8MKrVB74WwpToAAgY8QsPr6ESQOECCwO4Gb76Eds01hd7NhwAQIECBAgAABAqMFJgfaKXtip5QZPSIFCBAgQIAAAQIEdiUwOdDuSslgCRAgQIAAAQIEViswOdBO2Towpcxq5XSMAAECBAgQIEBgFQKTA+3Y3k95I9nYNlxPgAABAgQIECCwP4FJgTbshY2fWjCETJgdouQaAgQIECBAgACBKQKTPrYrNpSG2vRNX+0tBum5KZ1VhgABAgQIECBAgEAqcFGgDZXFoBrCa2mPrDCbsntOgAABAgQIECAwl8DFgTZ2RGida0rUQ4AAAQIECBAgMEZg0h7aMQ24lgABAgQIECBAgMA1BQTaa+qqmwABAgQIECBA4OoCF205KO2ZzfXaloScimMECBAgQIAAAQKXCkwOtGM/iitcL9ReOl3KEyBAgAABAgQIpAKTthyMDbOh0fQjvtKOeE6AAAECBAgQIEBgisCkQDuloVhmzDaFS9pRlgABAgQIECBAYB8CkwPtlO0DU8rsYxqMkgABAgQIECBAYKrA5EA7tUHlCBAgQIAAAQIECMwpMDnQTtk6MKXMnINVFwECBAgQIECAQH0CkwNtfRRGRIAAAQIECBAgsEWBSYE27oUds+I65ZMRtgiqzwQIECBAgAABArcVmPw5tPFjuNqhNn3TVxp40/O3HarWCBAgQIAAAQIEahSYHGgDRjughvCaBtgIJsjWeOsYEwECBAgQIEBgHQIXBdr2EITWdUyoXhAgQIAAAQIE9iYwaQ/t3pCMlwABAgQIECBAYL0CAu1650bPCBAgQIAAAQIEBgjMtuWgtH829MF2hAEz4RICBAgQIECAAIFJAhcF2jTEpsE1nveRXZPmRiECBAgQIECAAIEBApMD7ZCQmvsUhDT0DuijSwgQIECAAAECBAgUBSbtoR0SZtMWY5BNV3XT6zwnQIAAAQIECBAgMEZgUqANDUxZaZ1SZsxgXEuAAAECBAgQILA/gcmBdn9URkyAAAECBAgQILBGAYF2jbOiTwQIECBAgAABAoMFJgfaKXthp5QZPBIXEiBAgAABAgQI7FJgUqCd8gavKW8k2+WMGDQBAgQIECBAgMAogckf2xVCbQip7VXX9E1f6Ypsen5UT11MgAABAgQIECBAICMwOdCGutoBNQ237bYE2Yy8QwQIECBAgAABArMIXBRo2z0QWmeZD5UQIECAAAECBAiMFJi0h3ZkGy4nQIAAAQIECBAgcDUBgfZqtComQIAAAQIECBC4hcDNA236RrFbDFIbBAgQIECAAAEC9QpctIc2Daf20dZ7oxgZAQIECBAgQGCtApMDbe5zZXPH1jpw/SJAgAABAgQIEKhDYNKWg1JwnfIHF+pgNAoCBAgQIECAAIGlBCYF2tDZ0vaCcDz8k25HWGqA2iVAgAABAgQIEKhbYHKg7WMRavuEnCdAgAABAgQIEJhD4GqBNnRuqVA7dHU4/nWz9mMX6tB6u+pwjgABAgQIECBAYF6ByYF2aLi7dagd2q/2PuD29omh5eedBrURIECAAAECBAhMFZgcaMc0eKtQOzSMtsNsHEfc+xueD61njIFrCRAgQIAAAQIEriMwKdC23/gVwt+QAHirUNtebe0iK11XOt5Vl3MECBAgQIAAAQLLCUz+HNrQ5bHh75qhdkioXo45rPq+lG3+eLxoCrJ1OkiAAAECBAgQmEuglGHmqn+Oem6epsaG4CGDzG0hKJUbGnzDdXP2VXAtzYjjBAgQIECAwJoFShlmTUF30paDNaGPCbOx331BtXS+dHxNHvpCgAABAgQIENibwOYDbZgwQXNvt63xEiBAgAABAgSeCWw60A7dPmDCCRAgQIAAAQIE6hWYtId2apCccyV1ylaDeqfRyAgQIECAAAEC+xWYFGinBNMQQOd+o1WYtlK4bh9P+9vXj1Kd+71NjJwAAQIECBAgsF6BSYF2ynBCqJwz1KYhtd2nvsA6pf/KECBAgAABAgQIrFPgpntou0LorXiG9mHodbfqt3YIECBAgAABAgTyAjcNtPkuLHO0tK2gdHyZXmqVAAECBAgQIECgT+CmgXYtYTGuvrb7E7dDBDCrs323jfMECBAgQIAAgfUI3CzQxvC4lrDYDrXtYLuW/q3nFtETAgQIECBAgMC6Ba76prB0RfZWYXFoO0OvW/cU6h0BAgQIECBAYN8CVw20AuO+by6jJ0CAAAECBAjcQuBmWw5uMRhtECBAgAABAgQI7E9AoN3fnBsxAQIECBAgQKAqAYG2quk0GAIECBAgQIDA/gQE2v3NuRETIECAAAECBKoSEGirmk6DIUCAAAECBAjsT0Cg3d+cGzEBAgQIECBAoCoBgbaq6TQYAgQIECBAgMD+BATa/c25ERMgQIAAAQIEqhIQaKuaToMhQIAAAQIECOxPQKDd35wbMQECBAgQIECgKgGBtqrpNBgCBAgQIECAwP4EBNr9zbkREyBAgAABAgSqEhBoq5pOgyFAgAABAgQI7E9AoN3fnBsxAQIECBAgQKAqAYG2quk0GAIECBAgQIDA/gQE2v3NuRETIECAAAECBKoSEGirmk6DIUCAAAECBAjsT0Cg3d+cGzEBAgQIECBAoCoBgbaq6TQYAgQIECBAgMD+BATa/c25ERMgQIAAAQIEqhIQaKuaToMhQIAAAQIECOxPQKDd35wbMQECBAgQIECgKgGBtqrpNBgCBAgQIECAwP4EBNr9zbkREyBAgAABAgSqEhBoq5pOgyFAgAABAgQI7E9AoN3fnBsxAQIECBAgQKAqAYG2quk0GAIECBAgQIDA/gQE2v3NuRETIECAAAECBKoSEGirmk6DIUCAAAECBAjsT0Cg3d+cGzEBAgQIECBAoCoBgbaq6TQYAgQIECBAgMD+BATa/c25ERMgQIAAAQIEqhIQaKuaToMhQIAAAQIECOxPQKDd35wbMQECBAgQIECgKgGBtqrpNBgCBAgQIECAwP4EBNr9zbkREyBAgAABAgSqEhBoq5pOgyFAgAABAgQI7E9AoN3fnBsxAQIECBAgQKAqAYG2quk0GAIECBAgQIDA/gQE2v3NuRETIECAAAECBKoSEGirmk6DIUCAAAECBAjsT0Cg3d+cGzEBAgQIECBAoCoBgbaq6TQYAgQIECBAgMD+BATa/c25ERMgQIAAAQIEqhIQaKuaToMhQIAAAQIECOxPQKDd35wbMQECBAgQIECgKgGBtqrpNBgCBAgQIECAwP4EBNr9zbkREyBAgAABAgSqEhBoq5pOgyFAgAABAgQI7E/ghS0P+XQ69Xb/eDx2XpPW0XV9uLbrfGdDThIgQIAAAQIECFxFYNOBtitcpkE11Wufj/WEY0JrKuU5AQIECBAgQGDdApsOtCXaGFZLgbd0vh1sS2VLbTpOgAABAgQIECCwjECVgTZQlgJpKcy2+UtlL5mi0+mlbPHjsdopyI7XQQIECBAgQGBbAqUMs6ZRVJem+rYahLDad801JkhwvYaqOgkQIECAAIFrC5QyzJqCblWfcjBk9fXak65+AgQIECBAgACB2wpUt0Ib+IYE265V2tKWg9Lx206Z1ggQIECAAAECBNoCVQXaNHCG0Joeaw8+dy6U6SvnFiJAgAABAgQIEFiPQFVbDnKs6Ups3+ptLuTm6nWMAAECBAgQIEBgHQJVB9pcOM0dy01FGoRz1zhGgAABAgQIECCwvEDVgXYq79DQO7V+5QgQIECAAAECBOYTEGjns1QTAQIECBAgQIDAAgKbDbRDtgQMuSZnPrVcri7HCBAgQIAAAQIEriuwyUA7JHD2vfmrVEdfuetOh9oJECBAgAABAgTGCmzyY7viHtdc+GwH1dJe2HA8XNcuP6TcWFzXEyBAgAABAgQIXF9gk4E2sqTBtH28j64dioXZPi3nCRAgQIAAAQLrFdh0oA2spVXYoeSXlh/ajusIECBAgAABAgSuI7DJPbTXoVArAQIECBAgQIDAFgUE2i3Omj4TIECAAAECBAg8FRBo3QwECBAgQIAAAQKbFhBoNz19Ok+AAAECBAgQICDQugcIECBAgAABAgQ2LSDQbnr6dJ4AAQIECBAgQECgdQ8QIECAAAECBAhsWkCg3fT06TwBAgQIECBAgIBA6x4gQIAAAQIECBDYtIBAu+np03kCBAgQIECAAAGB1j1AgAABAgQIECCwaQGBdtPTp/MECBAgQIAAAQICrXuAAAECBAgQIEBg0wIC7aanT+cJECBAgAABAgQEWvcAAQIECBAgQIDApgUE2k1Pn84TIECAAAECBAgItO4BAgQIECBAgACBTQsItJuePp0nQIAAAQIECBAQaN0DBAgQIECAAAECmxYQaDc9fTpPgAABAgQIECAg0LoHCBAgQIAAAQIENi0g0G56+nSeAAECBAgQIEBAoHUPECBAgAABAgQIbFpAoN309Ok8AQIECBAgQICAQOseIECAAAECBAgQ2LSAQLvp6dN5AgQIECBAgAABgdY9QIAAAQIECBAgsGkBgXbT06fzBAgQIECAAAECAq17gAABAgQIECBAYNMCAu2mp0/nCRAgQIAAAQIEBFr3AAECBAgQIECAwKYFBNpNT5/OEyBAgAABAgQICLTuAQIECBAgQIAAgU0LCLSbnj6dJ0CAAAECBAgQEGjdAwQIECBAgAABApsWEGg3PX06T4AAAQIECBAgINC6BwgQIECAAAECBDYtINBuevp0ngABAgQIECBAQKB1DxAgQIAAAQIECGxaQKDd9PTpPAECBAgQIECAgEDrHiBAgAABAgQIENi0gEC76enTeQIECBAgQIAAAYHWPUCAAAECBAgQILBpAYF209On8wQIECBAgAABAgKte4AAAQIECBAgQGDTAgLtpqdP5wkQIECAAAECBARa9wABAgQIECBAgMCmBQTaTU+fzhMgQIAAAQIECAi07gECBAgQIECAAIFNC7yw5d6fTqfe7h+Px+w1pbKl60MloUzX+WxDDhIgQIAAAQIECFxVYNOBtitclgJr0Izn0vLhuNA6/n47nV56Wuh43PQtNX7wShAgQIAAAQKLC1SZPkqBNWh3nVt8NjbSgXaADV1uh9iucxsZnm4SIECAAAECGxOoMtCGOUhXX+O8hOOl1dtYxipt/i6OYbVrFTY9F8qkx/K1O0qAAAECBAgQmCZQXaAthdVpPPOVSlcuY81bCXtTg2kY35AgPJ+0mggQIECAAIE5BUoZZs42Lq2rqkA7dDtBafU2YF4rEG8luOZuqKlhNtYVx35pPbm+OUaAAAECBAhcV6CUYdYUdKv82K6pobQvEHcF4eveSsvVPmcIba/WLjciLRMgQIAAAQK1CVQVaEPgbO+DrW2ybj2eOcNs7LtQe+tZ1B4BAgQIEKhfoKpAG6crBtsxK7V9q7P13wrPj/AaYXZvhsZLgAABAgQI3EagykDbphsSaoXZ29xssRWrtLf11hoBAgQIEKhdoOpAO2TPqzBb+y1ufAQIECBAgEDtAlUH2r7JE2b7hJwnQIAAAQIECKxfYLOBdsxWgtw0CLM5lcdj9s+WbZwhQIAAAQIE1iew2UDbR9kVWLvO9dW7h/P2uO5hlo2RAAECBAjUI7DJQNu3OtsVWLvO1TOtRkKAAAECBAgQ2I/AJv9S2JDPms29IawdhPtCcbgFcnXs59YwUgIECBAgQIDANgQ2GWgj7djAOfb6bUzh9nppj+725kyPCRAgQIDAmgU2ueVgzaC19M0+2lpm0jgIECBAgED9AgJt/XO8qhFanV3VdOgMAQIECBCoQkCgrWIarzOIuVdphdnrzJNaCRAgQIDA3gUE2r3fAT3jj6E2hNFLvoTZS/SUJUCAAAECBLoEBNouHeceBEKovWS1Vph1IxEgQIAAAQLXFNj0pxxcE0bdHymQhtrwvPTVXtHtuq5U3nECBAgQIECAwFCBciIZWoPrdiXQDqdd2xCE2F3dFgZLgAABAgQWFRBoF+XfduNC67bnT+8JECBAgEAtAvbQ1jKTxkGAAAECBAgQ2KmAQLvTiTdsAgQIECBAgEAtAgJtLTNpHAQIECBAgACBnQoItDudeMMmQIAAAQIECNQiINDWMpPGQYAAAQIECBDYqYBAu9OJN2wCBAgQIECAQC0CAm0tM2kcBAgQIECAAIGdCgi0O514wyZAgAABAgQI1CIg0NYyk8ZBgAABAgQIENipgEC704k3bAIECBAgQIBALQICbS0zaRwECBAgQIAAgZ0KCLQ7nXjDJkCAAAECBAjUIiDQ1jKTxkGAAAECBAgQ2KmAQLvTiTdsAgQIECBAgEAtAgJtLTNpHAQIECBAgACBnQoItDudeMMmQIAAAQIECNQiINDWMpPGQYAAAQIECBDYqYBAu9OJN2wCBAgQIECAQC0CAm0tM2kcBAgQIECAAIGdCgi0O514wyZAgAABAgQI1CIg0NYyk8ZBgAABAgQIENipgEC704k3bAIECBAgQIBALQICbS0zaRwECBAgQIAAgZ0KCLQ7nXjDJkCAAAECBAjUIiDQ1jKTxkGAAAECBAgQ2KmAQLvTiTdsAgQIECBAgEAtAgJtLTNpHAQIECBAgACBnQoItDudeMMmQIAAAQIECNQiINDWMpPGQYAAAQIECBDYqYBAu9OJN2wCBAgQIECAQC0CAm0tMzlgHKfTSwOuqvsSBo/zy+FwYMAg/DS4Dxi4D+r4vSDQ1p3fjI4AAQIECBAgUL2AQFv9FBsgAQIECBAgQKBuAYG27vk1OgIECBAgQIBA9QICbfVTbIAECBAgQIAAgboFBNq659foCBAgQIAAAQLVCwi01U+xARIgQIAAAQIE6hYQaOueX6MjQIAAAQIECFQvINBWP8UGSIAAAQIECBCoW0CgrXt+jY4AAQIECBAgUL2AQFv9FBsgAQIECBAgQKBuAYG27vk1OgIECBAgQIBA9QICbfVTbIAECBAgQIAAgboFBNq659foCBAgQIAAAQLVCxybEZ6qH+XCAzydEC88BZonQIAAAQIEriBwPIYoufyXQLv8HNysByFYr+XGu9mgNUSAAAECBAhUL2DLQfVTbIAECBAgQIAAgboFBNq659foCBAgQIAAAQLVCwi01U+xARIgQIAAAQIE6hYQaOueX6MjQIAAAQIECFQvINBWP8UGSIAAAQIECBCoW0CgrXt+jY4AAQIECBAgUL2Aj+2qfooNkAABAgQIECBQt4AV2rrn1+gIECBAgAABAtULCLTVT7EBEiBAgAABAgTqFhBo655foyNAgAABAgQIVC8g0K5gisOfpPVFgAABAgQIECAwTUCgneamFAECBAgQIECAwEoEBNqVTIRuECBAgAABAgQITBMQaKe5KUWAAAECBAgQILASgRdW0o/dd6O0j/Z4DB8VvNxXV79y55bsb64/UW7Jfi0xeyWL4Ge+F+oAAA72SURBVJA7t6RPrj97nbe575WS7RrvgzD2rv7ObaO+6QKleQo1LvlaMn1E+ynZNXc5hSXns6uvuX4JtLkZXOhYOkFdk3nrLrb7FvoV+5Yev3W/Ynu5/qTnlurbku2m85NzWvI+y/XHvM1/xwy9D8J8pK9D8/emu8Zc+2voV3ev93PWz2wdc52+JoRRpT97W/vdYMvBwvdm14vDwl172nx6k8fn6fEl+xv7kvsBXFM/b2mUjtu83VJ/+bZKry2l+yC9X249gtzPbuzD0n27tcWa2/Nau+bZGda39s9T6XViWE3Xu2rKfWaF9nrzMajmMGldL+SDKnHRg0DpB8Avw3XfIObtOvOztdcW98F17oNr1GqurqG6TJ1rfp0Ye58JtAPvoTH/FlMKqHMFq1L9cShD2hkznoFExcu6+tvV165yobFS2fR4qCc9VuzsFU9MHU9XuWuO65J2p5RNx7KWebviLfFc1UPGW3JN7W7R50v6O/TnN7QxpJ1bjHcLbZTujy7vcG5qufS+M1fz3SVDLLvmrW/OL+lpV7vpPdFuZ2q5tM6SjUA7YFa7JiEtHq9NJyC+aOSOp3UMeX5JPWPGM6QvXdd0eXSZDClXuqm7+rP0ubHzNsRhbJ1DDLraDee67C8pO6RvNV4TzbrGVnLtm4+uOqeeu6S/oc2hfY73dtf9NnUMtZUr3R9xnCXDqeVq81vTeC79+RrzMzZ23F33S9fPdVe52N9Lf5fZQ9sxm3FywiVjoEvXhuNDbtSOLs1WvtTHrrbHnuu7gaNrajK0XLs/7bka289bXJ+OcUibQx2m1N3Vfl+74d7pu5dL91d6fO3z1uU017kx85f6xZ+hufoypJ5L+9vV51zduWND+rmna/p+ZqN5ajmlnJ/Z695Z6Rx1tZZ7PYjXd53rqrPrXN/9Uvrd0Fcud39Ouc+s0HbM3tgbYsyN2NHsVU+NHdMlnQltTTGZUi6Oa0p7l4zxmmWnOMzRn1u2W+O8TZmDPvMh93W45lY/3339nWIQy+TGeqtxXdLvJctOnY8p5fzMXn+mp8zL9Xv1uLCX+/nsa3vKeKbcZwJt30yMPD/lhbdUpnR8ZJcuurzUh9zx3LGhjU/9ZZyWu6QPQ/u6hetKDrnjQ4+1x116USsdT83M26PIUK9wdW6eUtf0eanM2OOx3qH9HXNduy+lfqXj8vz2An5mr28+9Oem/fNY+pkp1ZW7PncstJE7njvWlim1O1TvkvtMoB2qvLLrSjdN3822smE8dCf0uTServ5OLddV57XPlca5pXmLYyj1uXQ82m5x3q5xX/Q5jmnzFqZj++s+GDOD6772FvfXugWu37spP1+hTOl3Suhx38/g3KMaO4a0/UvvM4E2Fb3weZjQa91EQ26Wa7Z/Ic1ui5u33U59ceBD7om0cNfPdtcvtbSeKc+n9HdKO8oQ2KPA1J+vmDXSn/9rZZC1z41Ae8MZSm+6sU0PuUnjv+EMuXZs+66fJjBkLrY2b0PGNE1rP6XmMrz0dWWo+Fz9Hdqe6wjsSWDKz1f7Zz+WD8emBuQ5vKeMY452Qx0C7VySTT3tGypUmz6fsSlVEVhE4FbhaZHB3ajRKYbpa0nu+ZR6hwz5WvUOads1BGoXmPrzVQqtabC9VcCcOo4559fHds2pea4rvaHC4XDsVjfWFYakSgKL/lt/LfylX0JDx1d6bRlafux1l/Z3bHuuJ7Angak/X0PK3TJvDOnPLebVCu2VlG95M11pCDerduq/2U0td7OBVdTQmBescG3X/W/eyn+ZqW1TMswdv7Zpqf70eLtv7oN6XgDSea5nZOsYSck3Pd7++cq9DiwxmtjHOfqTjnfseATasWKuv4rA1B+GqeWuMohKKx3zghXmY8iL0l7nrWvcfQGw7/bqqruvbOl8V51d/XUflES3e7zrXtjuqJbteZdp18/Xsr1+1vqY3w1D+9xl0leHLQd9QiPPd/0y7zrX10woO6T8kGv62prr/JC+5K7JHZurT7eu55J5G+Iw5JpLxhzrv+RF5pL2lX0mcO25Zr1dgSH3Ru6a3LFUYcg1aRnPryewlvkY87thSJ+HXNOnKtD2CU04n5uYMZOfazIGilzd8fpL28i1e8mx0OeulZpSf/vKhT51OVzS57nLXjJvfQ4lv7nGcEn9pfkpHZ+rz3usZ82mpb6Vju9x/uYY89TXiqnl5uizOqYJDPmdEmq+5s/Y2N8Nt7rPjmHc01j3VypMYryZukafTnb6vKvskHO5+nLH+uoaOp6+eoacT/uXPi/VkV4Xn4frh8xFqd4ljqdjCX3IHcv1Lb0ufZ4rc8mxS+tPy2953i5xHFM2GHXd06npmPtnTD+GXtvX31L/cuMY2qbr+gVS3/R5qYb0uvR5qZzj1xGY+vNV+rmbq5eX3hdp+fT5Jf0UaEfoDbnBYnVxkuLzrl9UI7rw9NK0/nBibBtjxjOlj2mZtM9D+5uWmzLWtC9LPb9kLGnZoX5Txpq21VVHqR+5OkrXdtW/l3NDfh7XZDqkv2Hu1tTnPd1L7bEO/blL52poub243nKcl/x8hX5ea+7Se6Rk0tV+WkfXtaX6c8cF2pyKYwQIECBAgAABApsRsId2M1OlowQIECBAgAABAjkBgTan4hgBAgQIECBAgMBmBATazUyVjhIgQIAAAQIECOQEBNqcimMECBAgQIAAAQKbERBoNzNVOkqAAAECBAgQIJATEGhzKo4RIECAAAECBAhsRkCg3cxU6SgBAgQIECBAgEBOQKDNqThGgAABAgQIECCwGQGBdjNTpaMECBAgQIAAAQI5AYE2p+IYAQIECBAgQIDAZgQE2s1MlY4SIECAAAECBAjkBATanIpjBAgQIECAAAECmxEQaDczVTpKgAABAgQIECCQExBocyqOESBAgAABAgQIbEZAoN3MVOkoAQIECBAgQIBATkCgzak4RoAAAQIECBAgsBkBgXYzU6WjBAgQIECAAAECOQGBNqfiGAECBAgQIECAwGYEBNrNTJWOEiBAgAABAgQI5AQE2pyKYwQIELhA4HQ6HcI/vvYpYP73Oe9GvazAC8s2r3UCBJYQCL9wj8djselcGOu6PlZ063Kh3bTNIf0sDjw5kdadlsu11VcmrWPI8775KtUxtVypvnj8WvX2tbuF89eY/y2MWx8JLC0g0C49A9onsCKB9i/jdlgLx+O5XIgLQ8idj+VKZdrlwvfxumuWG8td6nvJpD2GsW1t4fo4z+GxZLOFcVyrj7XP/7Xc1EvgUgFbDi4VVJ5AZQLhF3IaVHLH2sPOhdlwvu+Xe7tcu83YXjyfEk8tl9ZzyfO2Sa6fqeElbSm7PQHzv7050+NtCwi0254/vSewGoHSL/DS8djxrvPhXBoWS+G5DZErdy2orv5fq82l693jmJc21z4BAt0CAm23j7MECPQIpIGzdHl6Xfq8VM7x9Qikc5Y+X09P9YQAgb0J2EO7txk3XgIdAlbeOnAKp0qhLhwveebKxFXlUpnQfKlc2rX2de3vu+pO6+h7PnQVfGif2+2lZfr6nV4f6iqVac9LWq5UJvYtvX5oO6nlmHpC2djnseXSdj0nULOAQFvz7BobgRkFcr9MY/V9QaAUfvrKlbo/tVypvinH2x5j+hPLtcuEY12+oX9d5dL24/MYhKaMr1QmbavURsknjjWtpz3G8H3qkx6L/ety6SuTnp9zLCW/rv7mTIaMs6tcqR+OE6hNQKCtbUaNh8AVBHK/hK/QzCqrbAeztINjg0TJsR1A0zbaz9P2wvOu/nXVNfZcrp1S+6VxhjbbY22Pp69MOB/+GVJmiGdqGT2GttE1lpJtaYwlk7SetM/RP+1zWs5zAnsQsId2D7NsjAQuECj9Er6gyk0VDaGh/U/sfBouhg5qbLkt+rcDWsllrEOcg7S+MfX0WebqmnssuTbaY4p9jMem9Dk18pzAHgSs0O5hlo2RwESBvl+mE6vddLEQSILLrVbFYntrQCuFsZxFLgiWysex9Z3PGaQBMHdNu/4x16f9aped0teuvoVzubnOHeurx3kCexQQaPc468a8C4FcyBgz8DFhtq+tUojoK1fq79RypfrGHt9byIjzV5rHLr92sB1zT3XV2T53jWBZavvaYym16zgBAv0Cthz0G7mCwO4ExgSPoYEivS59PhR5armh9Y+5bkrAG1P/2q4N9rl/Qj/7LGK5Ideubdxpf2oaSzo2zwlsVUCg3erM6TeBAQK5kJE71q5qTJgd0IWLLsn1NXcsbWTINWmZsc/XFKzH9n3K9V3j7TqXthXDYG6OcsfS8mt63jWWKf3c2vinjFEZAtcSEGivJateAgsLdIWM0rlLwmzpl3HpeOQpnS/1pS9ElMpdazpK/S+1V7q+dLxUz9Djl9Y7pXwo01Wu61xpXKU6S3WVri/VXzreV0+p/a76cudiPaWfzVwZxwgQeCYg0LobCFQu0P6F2/XL95JfqPGXcFp/X53tcrl+dv1yD+dCmbHl5pzu0rhLbZSuT91K5dd+vD3fufkJ/S/dE+H6XJn2HKf3Q59nev0Uv1K/usZSaucW/S217TiB2gW8Kaz2GTa+XQvkAkLul3wuFHbB5erItRXqyF3brjuWC8fa/egrF+tOQ+2Qcl1jG3uu3f8hZWP/0hA7tp4pbU21GVIu1/94LDfWUv9zZbra7/IstTHleK5f8R4cU9+t+jumT64lUIPAsRnEqYaBGAMBAgS2LhCCX1d42/r49J8AAQLXErDl4Fqy6iVAgMAIgXQVc0RRlxIgQGD3AgLt7m8BAAQIrEXA6uxaZkI/CBDYmoBAu7UZ018CBDYtkO75DYOxOrvpKdV5AgRWIGAP7QomQRcIENifQBpirc7u7x4wYgIE5hMQaOezVBMBAgQIECBAgMACArYcLICuSQIECBAgQIAAgfkEBNr5LNVEgAABAgQIECCwgIBAuwC6JgkQIECAAAECBOYTEGjns1QTAQIECBAgQIDAAgIC7QLomiRAgAABAgQIEJhPQKCdz1JNBAgQIECAAAECCwgItAuga5IAAQIECBAgQGA+AYF2Pks1ESBAgAABAgQILCAg0C6ArkkCBAgQIECAAIH5BATa+SzVRIAAAQIECBAgsIDA/wdJXMJfLXVuZQAAAABJRU5ErkJggg==)\n",
        "\n",
        "Figure 2: VLA simulated data full image after running phaseshift. The contours\n",
        "represent the image created by setting the phasecenter parameter in tclean.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Figure 3: VLA simulated data image central portion after running phaseshift. The\n",
        "contours represent the image created by setting the phasecenter parameter in tclean.\n",
        "\n",
        "### ALMA Simulation Details\n",
        "\n",
        "The ALMA simulation uses antenna positions in the eighth configuration of cycle 8 and a\n",
        "frequency of 150 GHz. The resulting images have 60 marcsec pixels. The phase center and the\n",
        "source in the original MS were separated by about 1.2 arcmin. Figure 4 shows the full image\n",
        "created from the original MS. The point source can be seen in the upper left corner of the image.\n",
        "Figure 5 shows the image created from the MS after running phaseshift to shift the phase center\n",
        "to the position of the source. The contours represent the image created from the original MS, and\n",
        "using tclean to shift the phase center to the source position using the phasecenter parameter.\n",
        "Figure 6 is the central portion of Figure 5.\n",
        "The results (see above) indicate that source position in the image that is not phase shifted is\n",
        "indeed as expected. The somewhat large fit errors are likely due to the fact that the source is not\n",
        "located at the center of a pixel. The results for the image created from the MS that has been\n",
        "produced by phaseshift show excellent agreement with the expected source position. The fit\n",
        "uncertainty provides an upper limit of about 90 μarcsec (0.001 pixels) of the offset of the sourcefrom the phase center, and the best fit results are an order of magnitude better than that at about 3\n",
        "μarcsec (0.00005 pixels). The results of the image created from the unshifted MS by applying the\n",
        "phase shift in tclean using the phasecenter parameter are also very good, although not as good as\n",
        "the image made from the MS created using phaseshift. In this case, the offset between source\n",
        "and phase center falls significantly outside the fit uncertainty, with a separation of about 1\n",
        "marcsec (0.02 pixels).\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Figure 4: ALMA simulated data image prior to phase shift. The source is in the upper left corner.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Figure 5: ALMA simulated data full image after running phaseshift. The contours represent\n",
        "the image created by setting the phasecenter parameter in tclean.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Figure 6: ALMA simulated data image central portion after running phaseshift.\n",
        "The contours represent the image created by setting the phasecenter parameter in\n",
        "tclean."
      ]
    }
  ]
}