{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c3fe07f-2161-42a4-88b0-3ad3a6db9396",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/casangi/examples/blob/CAS-13662/community/ALMAephemimagingVenusMfsMosaic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478b34b7-fac8-4127-96c1-e9ab7e74bb62",
   "metadata": {},
   "source": [
    "# ALMA ephemeris object (21P/Giacobini-Zinner) cube mosaic imaging:\n",
    "\n",
    "## Use 'TRACKFIELD' in phasecenter parameter\n",
    "\n",
    "Original Author(s): ttsutsum@nrao.edu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e21b32a-151b-464f-a99b-0bace43b11f8",
   "metadata": {},
   "source": [
    "## Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a37d16-966c-441b-b810-036b14d469ff",
   "metadata": {},
   "source": [
    "The following demo describes cube imaging of ALMA observation of Comet Giacobini-Zinner. \n",
    "For imaigng of the sources that move significantly during the observations, proper ephemeris data need to be used. For the current ALMA data, the epehemeris tables used to track the source during the observation generally included in the ASDM. The task, importasdm attaches the ephemeris table(s) in FIELD sub-table in the generated MS. Here we starts from such MS data and also uses a short cut option called 'TRACKFIELD' in tclean to use the attached ephemeris table(s)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36a831e-a2de-4659-a259-bee144fe1057",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f8fc59-7b55-4de3-b234-466dc4b12ba0",
   "metadata": {},
   "source": [
    "The comet dataset used here consists of the two MSes from Band 7 observations separated by 6 days using ACA. Each MS has an ephemeris table approriated for the observation attached. \n",
    "The data is available in the [casatestdata-large repository](https://open-bitbucket.nrao.edu/projects/CASA/repos/casatestdata-large/browse), under [stateholders/alma/](https://open-bitbucket.nrao.edu/projects/CASA/repos/casatestdata-large/browse/stakeholder/alma)\n",
    "\n",
    "Or at NRAO, casatestdata is accessible at /home/casa/data/casatestdata-large."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda0f605-7e6b-40e8-b0b5-1cc13fb44be0",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42aaf45-1d5b-4765-91a7-3b6e1df15681",
   "metadata": {},
   "source": [
    "### Install CASA\n",
    "Skip this step if CASA is already installed"
   ]
  },
  {
   "cell_type": "raw",
   "id": "33a216e5-80c2-49a5-bdd6-56741c7f2347",
   "metadata": {},
   "source": [
    "You may want to check the latest release by\n",
    "os.system('pip show casatasks') \n",
    "and replace the version number below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e874abbf-4c4f-49e3-a781-c715de4e6d5a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 101865,
     "status": "ok",
     "timestamp": 1775673256518,
     "user": {
      "displayName": "Takahiro Tsutsumi",
      "userId": "05523560740067383788"
     },
     "user_tz": 360
    },
    "id": "1b5adef3-8d51-4deb-bd29-d8fb824284b0",
    "outputId": "e59087de-f7ad-4cd8-8062-33dcd7ca5c10"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "print(\"Installing CASA ...\")\n",
    "os.system('pip install casaconfig')\n",
    "os.system('pip install casatools==6.7.0.31')\n",
    "os.system('pip install casatasks==6.7.0.31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "aaaba1a6-95cf-42db-be29-a5c29293abca",
   "metadata": {
    "id": "cee9219e-b4b9-4159-b3e9-37be7c048fb3"
   },
   "outputs": [],
   "source": [
    "# for Colab\n",
    "mydatapath = '/content/.casa/data'\n",
    "#\n",
    "#mydatapath='data'\n",
    "\n",
    "import pathlib\n",
    "from casaconfig import config\n",
    "if not pathlib.Path(mydatapath).exists():\n",
    "  pathlib.Path(mydatapath).mkdir(parents=True)\n",
    "config.measurespath=mydatapath"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4975db72-4124-40d6-9661-b976b884eed7",
   "metadata": {},
   "source": [
    "### Install additional modules used in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f496da4-ac65-4e39-96e8-68c99076deff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.system(\"pip install aplpy\")\n",
    "os.system(\"pip install ipympl\")\n",
    "print(\"complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4125c5-28e8-41f2-8f79-911e1b3c218a",
   "metadata": {
    "id": "bb0a5a54-d780-44a7-8771-bc137bec40d8"
   },
   "source": [
    "<b> This notebook uses functions from EphemerisObjectImagingDemoFunctions.py so that file must be present in the same directory running this notebook and need to import them </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fab88cd4-ab69-434e-bfaa-61765a4f4393",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 527,
     "status": "ok",
     "timestamp": 1775676382705,
     "user": {
      "displayName": "Takahiro Tsutsumi",
      "userId": "05523560740067383788"
     },
     "user_tz": 360
    },
    "id": "X8nLEFX9Yd49",
    "outputId": "836fdb81-3e8e-4768-9f41-7ab62fc86f10"
   },
   "outputs": [],
   "source": [
    "# if you haven't downloaded EphemerisObjectImagingDemoFuntions.py\n",
    "!wget https://raw.githubusercontent.com/casangi/examples/refs/heads/CAS-13662/community/EphemerisObjectImagingDemoFunctions.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ada6be1-4a8e-4da7-a4fd-5f49d13d750a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 55242,
     "status": "ok",
     "timestamp": 1775676447791,
     "user": {
      "displayName": "Takahiro Tsutsumi",
      "userId": "05523560740067383788"
     },
     "user_tz": 360
    },
    "id": "c1323aa8-2eae-40a9-834b-90326a4c1114",
    "outputId": "39679259-b3d2-4339-d635-25fa57a6e1f8"
   },
   "outputs": [],
   "source": [
    "from EphemerisObjectImagingDemoFunctions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d1bca02-0dda-4fac-aef2-d677aca4e948",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9825647d-e987-4e18-83ff-ce6f66bd8ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from casatasks import getephemtable, tclean, exportfits, listobs, imstat, imhead\n",
    "from casatools import measures, quanta, table, image\n",
    "import os\n",
    "from astropy.io import fits\n",
    "from astropy.wcs import WCS\n",
    "from matplotlib import pylab as pl\n",
    "import numpy as np\n",
    "import aplpy\n",
    "me = measures()\n",
    "qa = quanta()\n",
    "tb = table()\n",
    "ia = image()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd11ecc-d53d-442c-b362-ccdd9e2c8be4",
   "metadata": {},
   "source": [
    "## Imaging with epehemeris data attached to the MS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20849272-44c7-4cd7-a446-3c0b85033857",
   "metadata": {},
   "source": [
    "In order to track the ephemeris source during the observations, the proper ephemeris data is used. \n",
    "Ususally such ephemeris data table(s) are attached to the Measurement Set automatically during importadm when they are available. In tclean, the phasecenter parameter can be set to point to the ephemeris table, which can be one attached to the MS (inside FIELD sub-table directory) or an external one. In this demo, we use a shortcut option, 'TRACKFIELD' to use the internal ephemeris table without explicitly specified by ephemeris table name. The spectral frame is set to 'cubesource', which a frame of the source. Note that the frame in the resultant image will be labelled as 'REST'. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ddbca5c-f51e-4214-b7c3-a3099beaa64a",
   "metadata": {},
   "source": [
    "### Obtain data\n",
    "This demo uses two MS data, 2017.1.00750.T_tclean_exe1.ms (1.4GB) and 2017.1.00750.T_tclean_exe2.ms(1.1GB)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "892e77f2-a72d-4060-84d8-42a21250ebb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# At NRAO, point to the casatestdata-large directory\n",
    "#inputdatapath='/home/casa/data/casatestdata-large/stakeholder/alma/'\n",
    "# or Use local clone ofnn tyendata repository, e.g.\n",
    "#inputdatapath='/Volumes/ssd1/casatestdata/stakeholder/alma/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ef852d-26d6-4229-a22f-59c45d7f2d1e",
   "metadata": {},
   "source": [
    "#### For Colab or for downloading the input data locally, execute 4 cells, otherwise skip them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "63bbc089-a3bc-4bbf-9ef1-f7b5e35d2e5f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22817,
     "status": "ok",
     "timestamp": 1775676525206,
     "user": {
      "displayName": "Takahiro Tsutsumi",
      "userId": "05523560740067383788"
     },
     "user_tz": 360
    },
    "id": "6f9f5273-0604-482c-bfcc-fdd3420a137b",
    "outputId": "92647038-a49f-4685-f92f-b318899e7c42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated Git hooks.\n",
      "Git LFS initialized.\n",
      "Cloning into 'mytestdata'...\n",
      "remote: Enumerating objects: 10606, done.\u001b[K\n",
      "remote: Counting objects: 100% (10606/10606), done.\u001b[K\n",
      "remote: Compressing objects: 100% (5657/5657), done.\u001b[K\n",
      "remote: Total 10606 (delta 4973), reused 10556 (delta 4948), pack-reused 0\u001b[K\n",
      "Receiving objects: 100% (10606/10606), 1.89 MiB | 1.74 MiB/s, done.\n",
      "Resolving deltas: 100% (4973/4973), done.\n",
      "remote: Enumerating objects: 2, done.\u001b[K\n",
      "remote: Counting objects: 100% (2/2), done.\u001b[K\n",
      "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
      "remote: Total 2 (delta 0), reused 1 (delta 0), pack-reused 0\u001b[K\n",
      "Receiving objects: 100% (2/2), 1.06 KiB | 1.06 MiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "# If running on Colab\n",
    "\n",
    "# sparse-checkout of the data repository\n",
    "\n",
    "# install git lfs\n",
    "os.system('pip install git-lfs -q')\n",
    "os.system('git-lfs install')\n",
    "# clone the casatestdata to mytestdata\n",
    "os.system('git clone --filter=blob:none --sparse https://open-bitbucket.nrao.edu/scm/casa/casatestdata-large.git mytestdata')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "29c77543-acbc-4de3-a0b5-4f42102b031d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22817,
     "status": "ok",
     "timestamp": 1775676525206,
     "user": {
      "displayName": "Takahiro Tsutsumi",
      "userId": "05523560740067383788"
     },
     "user_tz": 360
    },
    "id": "6f9f5273-0604-482c-bfcc-fdd3420a137b",
    "outputId": "92647038-a49f-4685-f92f-b318899e7c42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/ttsutsum/SWDevel/casa_mod/examples/community/mytestdata\n",
      "remote: Enumerating objects: 211, done.\u001b[K\n",
      "remote: Counting objects: 100% (211/211), done.\u001b[K\n",
      "remote: Compressing objects: 100% (211/211), done.\u001b[K\n",
      "remote: Total 211 (delta 0), reused 209 (delta 0), pack-reused 0\u001b[K\n",
      "Receiving objects: 100% (211/211), 24.58 KiB | 8.19 MiB/s, done.\n",
      "https://git-lfs.github.com/spec/v1 is not a valid attribute name: stakeholder/alma/.gitattributes:1\n",
      "sha256:79f7502d0bda204c2994be5715262ceaae5a846268106e6e76e5c2e2cd6c3615 is not a valid attribute name: stakeholder/alma/.gitattributes:2\n",
      "Filtering content: 100% (316/316), 2.43 GiB | 1.99 MiB/s, done.\n"
     ]
    }
   ],
   "source": [
    "%cd mytestdata\n",
    "os.system('git sparse-checkout set stakeholder/alma/2017.1.00750.T_tclean_exe1.ms stakeholder/alma/2017.1.00750.T_tclean_exe2.ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b7cf2df0-6466-4af7-8794-78dc3f8495df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/ttsutsum/SWDevel/casa_mod/examples/community\n"
     ]
    }
   ],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e646e43d-adea-47fe-ad6f-69ebb3598366",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputdatapath='mytestdata/stakeholder/alma/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d9966ed-3686-4b5e-b6a6-88b4d4086486",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7f6431ec-e2ff-479c-9cb2-35f84255eaf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "msfile=[inputdatapath+'2017.1.00750.T_tclean_exe1.ms',inputdatapath+'2017.1.00750.T_tclean_exe2.ms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8a9b1d5f-5724-48bc-baee-8073c9181486",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the data exists\n",
    "from casatasks import listobs\n",
    "for vis in msfile:\n",
    "   listobs(vis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "514f9d77-1824-4934-a0fa-f84320ae6c7c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "imgname='21PGZComet-cubesrc-mosaic-trackfield'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f780831c-49ce-4801-87b9-59905ef52bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "delete_tcleanimages(imgname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "00e829d8-7162-419c-bfd2-1d12f232a797",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0%....10....20....30....40....50....60....70....80....90....100%\n",
      "\n",
      "0%....10....20....30....40....50....60....70....80....90....100%\n",
      "\n",
      "0%....10....20....30....40....50....60....70....80....90....100%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "270"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret=tclean(vis=msfile, field='21PGiacobini-Zinner', spw=['0', '0'], \\\n",
    "            antenna=['0,1,2,3,4,5,6,7,8,9,10,11', '0,1,2,3,4,5,6,7,8,9'], \\\n",
    "            scan=['7,11,15,19,23', '8,12,16,20,24'], \\\n",
    "            intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='data', \\\n",
    "            imagename=imgname, imsize=[80, 80], cell=['0.66arcsec'], \\\n",
    "            phasecenter='TRACKFIELD', stokes='I', specmode='cubesource', \\\n",
    "            nchan=1000, start='354.4452721710GHz', width='0.1221004MHz', perchanweightdensity=True, \\\n",
    "            gridder='mosaic', mosweight=False, \\\n",
    "            usepointing=True, pblimit=0.2, deconvolver='hogbom', \\\n",
    "            restoration=True, restoringbeam='common', pbcor=True, \\\n",
    "            #weighting='briggsbwtaper', robust=0.5, npixels=0, niter=30000, \\\n",
    "            weighting='briggsbwtaper', robust=0.5, npixels=0, niter=100, \\\n",
    "            threshold='0.274Jy', nsigma=0.0, usemask='auto'\n",
    "            '-multithresh', sidelobethreshold=1.25, noisethreshold=5.0, \\\n",
    "            lownoisethreshold=2.0, negativethreshold=0.0, \\\n",
    "            minbeamfrac=0.1, growiterations=75, dogrowprune=True, \\\n",
    "            minpercentchange=1.0, fastnoise=False, restart=True, \\\n",
    "            calcres=True, calcpsf=True, savemodel='none', \\\n",
    "            parallel=False, verbose=True)\n",
    "ret['iterdone']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa35e49-ff52-40ed-a134-d40375753131",
   "metadata": {},
   "source": [
    "### Examination of the resultant image\n",
    "For displaying the resultant image, first find a peak channel and dispay that channel image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c2af0981-3465-447c-9e64-0ef038a624f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "488\n"
     ]
    }
   ],
   "source": [
    "# Find a peak channel\n",
    "imstatres = imstat(imgname+'.image')\n",
    "maxchan = imstatres['maxpos'][3]\n",
    "print(maxchan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9fc1b0ff-c4f3-4c81-ac33-66ade5a93693",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to   -67.754929 from OBSGEO-[XYZ].\n",
      "Set OBSGEO-B to   -23.022886 from OBSGEO-[XYZ].\n",
      "Set OBSGEO-H to     5053.796 from OBSGEO-[XYZ]'. [astropy.wcs.wcs]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dispImage(f'{imgname}.image',chanslice=maxchan, useAstropy=True)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ea6b57f7-63c1-4948-89d0-6641f6e36a02",
   "metadata": {},
   "source": [
    "The image is stopped with respect to the first time of the MS after the data selection applied. The reference time can be found in the image header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "19890077-0949-4626-b134-2c9e37e5d9e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018/09/16/10:15:58.895999\n"
     ]
    }
   ],
   "source": [
    "obsdate = imhead(f'{imgname}.image', mode='list')['date-obs']\n",
    "print(obsdate)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e468e9a6-039c-4ad7-9248-c152a596ba2e",
   "metadata": {},
   "source": [
    "Using the attached ephemeris table from the first MS in the list, determine the inferred position at the reference time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b78b9ae8-fcc1-4f68-86f5-7042a713a2fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ephemtab = get_attachedEphemtablepath(msfile[0],'21PGiacobini-Zinner')\n",
    "ineph_dir = ephem_dir(ephemtab, obsdate,'ALMA')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fd6b2c5d-1c4f-41ae-a651-f17ca30f363d",
   "metadata": {},
   "source": [
    "Compare this poisiton with the center of the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "68a5445f-0438-4a9e-a26b-a22d24417af5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Internal ephem dir =  06:12:57.770 +022.34.20.236\n",
      "Image center: ('06:12:57.770', '+022.34.20.236')\n"
     ]
    }
   ],
   "source": [
    "print(\"Internal ephem dir = \",qa.time(ineph_dir['m0'],prec=9)[0],qa.angle(ineph_dir['m1'],prec=9)[0])\n",
    "print('Image center:',printImageCenter(imgname+'.image'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd81e7b-1dca-4034-ad72-93f79cc8c19d",
   "metadata": {},
   "source": [
    " ### Spectral information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ab61dd-b1e9-4bb4-a377-a55708f8c930",
   "metadata": {},
   "source": [
    "Determine image channel frequency at the peak "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "444b68ff-e670-4bf7-b983-bbf15ddd1cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "restfreq: 354505473000.0\n"
     ]
    }
   ],
   "source": [
    "ia.open(f'{imgname}.image')\n",
    "csys = ia.coordsys()\n",
    "csysrec = csys.torecord()\n",
    "ia.done()\n",
    "restfreq = csysrec['spectral2']['restfreq']\n",
    "print('restfreq:',restfreq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f3fba15e-704d-4f38-a9c0-9aa37adbd632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find image frequency at the peak\n",
    "specaxis=csys.findcoordinate('spectral')['pixel'][0]\n",
    "pixel=csys.referencepixel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "18657217-bf83-4afa-b083-7db42e9e04d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ar_type': 'absolute', 'numeric': array([ 40.,  40.,   0., 488.]), 'pw_type': 'pixel'}\n",
      "354504857166.1997 Hz\n"
     ]
    }
   ],
   "source": [
    "pixel['numeric'][specaxis] = maxchan\n",
    "print(pixel)\n",
    "f_peak = csys.toworld(pixel)['numeric'][specaxis]\n",
    "# this is different from what I read from the Viewer\n",
    "print(f_peak,'Hz')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f45a8c87-6d9d-4e15-9ee3-e87bbced61de",
   "metadata": {},
   "source": [
    "The spectrum is centered around the rest freqeuncy. This roughly indicates that the 'SOURCE' \n",
    "frame trasformation is done correctly. \n",
    "Now, from the radial velocity provided in the ephemeris table, check if the frequency shift is done correctly,."
   ]
  },
  {
   "cell_type": "raw",
   "id": "ab1237aa-4686-467a-9e8d-a968bfebe734",
   "metadata": {},
   "source": [
    "Get the radial velocity at the reference time from the ephemeris table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "536f92c9-df98-4e91-9657-3b0cf9e71d48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58377.427764999986\n"
     ]
    }
   ],
   "source": [
    "# reference time in mjd\n",
    "mjd0=qa.quantity(obsdate,'d')['value']\n",
    "print(mjd0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2fd2e2cf-856f-471e-bd22-1bc6cb02fb20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access ephemeris table data\n",
    "tb.open(ephemtab)\n",
    "mjdcol = tb.getcol('MJD')\n",
    "radc = tb.getcol('RA')\n",
    "decdc = tb.getcol('DEC')\n",
    "rhoc = tb.getcol('Rho')\n",
    "radvel=tb.getcol('RadVel')\n",
    "tb.done()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "80c9a433-0530-43ee-9d97-92e8ef265827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'unit': 'km/s', 'value': 3.35206559623668}\n",
      "['06:12:57.610'] ['+022.34.25.174']\n"
     ]
    }
   ],
   "source": [
    "# Do linear interpolation to get the radial velocity at the reference time\n",
    "radv = np.interp(mjd0,mjdcol,radvel)\n",
    "qradv = qa.convert(qa.quantity(radv,'AU/d'),'km/s')\n",
    "raval = np.interp(mjd0,mjdcol,radc)\n",
    "decval= np.interp(mjd0,mjdcol,decdc)\n",
    "rhoval = np.interp(mjd0,mjdcol,rhoc)\n",
    "print(qradv)\n",
    "print(qa.time(qa.quantity(raval,'deg'),prec=9),qa.angle(qa.quantity(decval,'deg'),prec=9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "42977fe7-e4e6-48b6-a676-448bb7b66241",
   "metadata": {},
   "outputs": [],
   "source": [
    "vradkmps = qradv['value']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac0d26b-a8db-4fb6-9605-c87fc248b4e0",
   "metadata": {},
   "source": [
    "Run imaging with outfrmae = TOPO to compare the frequency at the peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cbd1322d-66fc-482c-bff7-f78c51041995",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "img2name = '21PGZComet-cube-topo-mosaic-trackfield'\n",
    "# remove images from the previous run\n",
    "delete_tcleanimages(img2name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d22cc4cf-9569-425b-8945-3838af494675",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0%....10....20....30....40....50....60....70....80....90....100%\n",
      "\n",
      "0%....10....20....30....40....50....60....70....80....90....100%\n",
      "\n",
      "0%....10....20....30....40....50....60....70....80....90....100%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "183"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret=tclean(vis=msfile, field='21PGiacobini-Zinner', spw='0', \\\n",
    "            antenna='0,1,2,3,4,5,6,7,8,9,10,11', \\\n",
    "            scan='7,11,15,19,23', \\\n",
    "            intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='data', \\\n",
    "            imagename=img2name, imsize=[80, 80], cell=['0.66arcsec'], \\\n",
    "            phasecenter='TRACKFIELD', stokes='I', specmode='cube', \\\n",
    "            nchan=1000, start='354.4452721710GHz', width='0.1221004MHz', perchanweightdensity=True, outframe='TOPO',\\\n",
    "            gridder='mosaic', mosweight=False, \\\n",
    "            usepointing=True, pblimit=0.2, deconvolver='hogbom', \\\n",
    "            restoration=True, restoringbeam='common', pbcor=True, \\\n",
    "            #weighting='briggsbwtaper', robust=0.5, npixels=0, niter=30000, \\\n",
    "            weighting='briggsbwtaper', robust=0.5, npixels=0, niter=100, \\\n",
    "            threshold='0.274Jy', nsigma=0.0, usemask='auto'\n",
    "            '-multithresh', sidelobethreshold=1.25, noisethreshold=5.0, \\\n",
    "            lownoisethreshold=2.0, negativethreshold=0.0, \\\n",
    "            minbeamfrac=0.1, growiterations=75, dogrowprune=True, \\\n",
    "            minpercentchange=1.0, fastnoise=False, restart=True, \\\n",
    "            calcres=True, calcpsf=True, savemodel='none', \\\n",
    "            parallel=False, verbose=True)\n",
    "ret['iterdone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8c51d0fa-77b7-44c5-a95d-d3f3d6dec3d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maxchan in topo image:457\n"
     ]
    }
   ],
   "source": [
    "imstatres = imstat(img2name+'.image')\n",
    "maxchantopo = imstatres['maxpos'][3]\n",
    "print (f'maxchan in topo image:{maxchantopo}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "32bf3120-f840-4222-9026-de5d2d1ec1de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: 'obsfix' made the change 'Set OBSGEO-L to   -67.754929 from OBSGEO-[XYZ].\n",
      "Set OBSGEO-B to   -23.022886 from OBSGEO-[XYZ].\n",
      "Set OBSGEO-H to     5053.796 from OBSGEO-[XYZ]'. [astropy.wcs.wcs]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dispImage(f'{img2name}.image',chanslice=maxchantopo, useAstropy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3021f5f6-ff56-4f3a-9391-0ca036c68aa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ar_type': 'absolute', 'numeric': array([ 40.,  40.,   0., 457.]), 'pw_type': 'pixel'}\n",
      "354501072053.79895 Hz\n"
     ]
    }
   ],
   "source": [
    "# Find a peak channel frequency in the TOPO image\n",
    "ia.open(f'{img2name}.image')\n",
    "csys = ia.coordsys()\n",
    "ia.done()\n",
    "pixel=csys.referencepixel()\n",
    "pixel['numeric'][specaxis] = maxchantopo\n",
    "print(pixel)\n",
    "ftopo_peak = csys.toworld(pixel)['numeric'][specaxis]\n",
    "print(ftopo_peak,'Hz')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e71babef-1026-420c-a473-b425d538ef16",
   "metadata": {},
   "source": [
    "To shift the frequency in topo to the corresponding frequency in the source frame, we need radial velocity components of the observatory as well as the source's velocity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "12b4a563-61c1-4bfb-a230-226ba742e747",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'m0': {'unit': 'm/s', 'value': 1.128021837932235}, 'refer': 'GEO', 'type': 'radialvelocity'}\n"
     ]
    }
   ],
   "source": [
    "# geocetric radial velocity of the observatory\n",
    "me.doframe(me.observatory('ALMA'))\n",
    "me.doframe(me.epoch(str(mjd0)+'d'))\n",
    "vobsgeo = me.measure(me.radialvelocity('TOPO', '0.0m/s'),'GEO')\n",
    "print(vobsgeo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1d3d21b1-f027-4e9f-857d-78c284c1d21d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vrad= -3350.937574398748\n",
      " expected SOURCE frequency (Hz) at peak shifting the topo peak frequency (354501072053.79895) = 354505034520.3982\n",
      " actual peak frequency in cubesrc image = 354504857166.1997\n",
      "f_peak - exp_fpeak =  -177354.19848632812\n"
     ]
    }
   ],
   "source": [
    "# Add the source and observatory velocities. Note for the source's velocity flip the vector so it\n",
    "# point back to the source\n",
    "totvelmps=-1.*vradkmps*1000. + vobsgeo['m0']['value']\n",
    "print('vrad=',totvelmps)\n",
    "# expected shift in freq\n",
    "#v/c = (f0**2 - f**2)/(f0**2 + f**2)\n",
    "velperc = totvelmps/qa.constants('C')['value']\n",
    "foverf0 = np.sqrt((1-velperc)/(1+velperc))\n",
    "exp_fpeak = ftopo_peak*foverf0\n",
    "print(f' expected SOURCE frequency (Hz) at peak shifting the topo peak frequency ({ftopo_peak}) = {exp_fpeak}')\n",
    "print(f' actual peak frequency in cubesrc image = {f_peak}')\n",
    "print(\"f_peak - exp_fpeak = \",f_peak - exp_fpeak)\n",
    "# note that channel width is ~122kHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "20ebd26a-bce4-4467-a193-25dcf36e8499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qvrad= {'unit': 'km/s', 'value': 3.35206559623668}\n",
      "vel={'m0': {'unit': 'm/s', 'value': -3350.9375743988885}, 'refer': 'GEO', 'type': 'radialvelocity'}\n",
      "dop={'m0': {'unit': 'm/s', 'value': -3350.9563022008015}, 'refer': 'RADIO', 'type': 'doppler'}\n",
      "mfrq={'m0': {'unit': 'Hz', 'value': 354501072053.79895}, 'refer': 'TOPO', 'type': 'frequency'}\n",
      "expected SOURCE frequecy at the peak in lsrk frame using cubesrc peak frequency: {'m0': {'unit': 'Hz', 'value': 354505034520.3982}, 'refer': 'REST', 'type': 'frequency'}\n",
      "compared to freq_peak from the cubesrc image = 354504857166.1997 Hz\n",
      "-177354.19848632812\n"
     ]
    }
   ],
   "source": [
    "# The same excersise using measure tool\n",
    "# imaging with TOPO\n",
    "me.doframe(me.observatory('ALMA'))\n",
    "me.doframe(me.epoch(str(mjd0)+'d'))\n",
    "print(\"qvrad=\",qradv)\n",
    "modvrad = -1.*qradv['value'] + 1.1280218377914935/1000.\n",
    "mradv = me.radialvelocity('GEO',str(modvrad)+'km/s')\n",
    "dop=me.todoppler('radio', mradv)\n",
    "print(f'vel={mradv}')\n",
    "print(f'dop={dop}')\n",
    "#mfrq = me.frequency('LSRK', qa.quantity(flsrk_peak,'Hz'))\n",
    "mfrq = me.frequency('TOPO', qa.quantity(ftopo_peak,'Hz'))\n",
    "print(f'mfrq={mfrq}')\n",
    "# use 'REST' frame to convert frequency to that of SOURCE frame\n",
    "exp_f_peak = me.tofrequency('REST', dop, me.frequency('REST', qa.quantity(ftopo_peak,'Hz')))\n",
    "#print('expected frequecy at the peak in rest(source) frame using LSRK peak frequency:',exp_f_peak)\n",
    "print('expected SOURCE frequecy at the peak in lsrk frame using cubesrc peak frequency:',exp_f_peak)\n",
    "print(f'compared to freq_peak from the cubesrc image = {f_peak} Hz')\n",
    "print(f_peak - exp_f_peak['m0']['value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18bbc61e-3db5-42d3-9a2c-646b69598635",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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