{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "casa6_demo.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6agG0dwApruX"
      },
      "source": [
        "# Modular CASA Demo\n",
        "\n",
        "Original Author: rraba@nrao.edu\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4plpG0ksqJF7"
      },
      "source": [
        "## Description\n",
        "\n",
        "This notebook shows how to install the modular CASA 6 packages with some basic orientation:\n",
        "\n",
        "- locate the casadata folder\n",
        "- list the available tasks\n",
        "- find and print the log file\n",
        "- run a simple tclean command\n",
        "- view the output images with Astropy\n",
        "- view the output images with CARTA\n",
        "\n",
        "WARNING: when running this Notebook in Google Colab, CASA pip-wheel occasionally experience incompatibilities with recently upgraded Colab environments. Please check the Known Issues Section in CASA Docs when experiencing installation problems, or contact the CASA team at casa-feedback_*at*_nrao.edu\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xr_RX4CA876u"
      },
      "source": [
        "## Installation\n",
        "\n",
        "First the system must be configured with the appropriate pre-requisite libraries to create a virtual display necessary for later plots/images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0TPcJeUnydT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8365b025-e960-4386-c487-db125c6e2d77"
      },
      "source": [
        "# prerequisite system setup\n",
        "import os\n",
        "os.system('apt-get install xvfb')\n",
        "os.system('pip install pyvirtualdisplay')\n",
        "\n",
        "from pyvirtualdisplay import Display\n",
        "display = Display(visible=0,size=(1024,768))\n",
        "display.start( )\n",
        "\n",
        "print('completed system setup')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "completed system setup\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lii5t7pToKuL"
      },
      "source": [
        "Then we can choose from the available CASA packages to install: ```casatools, casatasks, casaplotms, casaviewer, almatasks, casampi, casashell, casadata, casampi, casaplotserver```\n",
        "\n",
        "The pip installer generally handles dependencies automatically (for example casatasks needs casatools), however casadata is the exception that must be explicitly installed and updated by the user."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIr5sp0VgR2m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d51dfc3-fce8-4de0-d6c1-d3dbc3128f2c"
      },
      "source": [
        "import os\n",
        "print(\"installing casa packages...\\n\")\n",
        "os.system(\"pip install casatasks==6.3.0.48\")\n",
        "os.system(\"pip install casaviewer==1.2.14\")\n",
        "os.system(\"pip install casadata\")\n",
        "\n",
        "print(\"downloading MeasurementSet from CASAguide First Look at Imaging...\\n\")\n",
        "os.system(\"wget https://bulk.cv.nrao.edu/almadata/public/working/sis14_twhya_calibrated_flagged.ms.tar\")\n",
        "os.system(\"tar -xvf sis14_twhya_calibrated_flagged.ms.tar\")\n",
        "\n",
        "print(\"make a config file for Google Colab...\\n\")\n",
        "!mkdir ~/.casa\n",
        "!echo \"home     = '/content/'\" > ~/.casa/config.py\n",
        "!echo \"datapath = ['`find / -type d -name casadata`']\" >> ~/.casa/config.py\n",
        "!more ~/.casa/config.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "installing casa packages...\n",
            "\n",
            "downloading MeasurementSet from CASAguide First Look at Imaging...\n",
            "\n",
            "make a config file for Google Colab...\n",
            "\n",
            "home     = '/content/'\n",
            "datapath = ['/usr/local/lib/python3.7/dist-packages/casadata']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxuECb4D8su0"
      },
      "source": [
        "## Getting Started\n",
        "\n",
        "We can inspect the contents of a package, or better yet, read its corresponding API section in [CASAdocs](https://casadocs.readthedocs.io/en/stable/api.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0B_KBb09ORJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f0d0bad-5f9e-4162-c9ee-590365759b04"
      },
      "source": [
        "import casatasks\n",
        "casatasks.__all__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['casalog',\n",
              " 'version',\n",
              " 'version_string',\n",
              " 'imhead',\n",
              " 'immoments',\n",
              " 'imhistory',\n",
              " 'applycal',\n",
              " 'bandpass',\n",
              " 'blcal',\n",
              " 'calstat',\n",
              " 'concat',\n",
              " 'split',\n",
              " 'listobs',\n",
              " 'flagdata',\n",
              " 'flagcmd',\n",
              " 'setjy',\n",
              " 'cvel',\n",
              " 'cvel2',\n",
              " 'importuvfits',\n",
              " 'importfits',\n",
              " 'exportfits',\n",
              " 'exportuvfits',\n",
              " 'partition',\n",
              " 'listpartition',\n",
              " 'flagmanager',\n",
              " 'mstransform',\n",
              " 'tclean',\n",
              " 'immath',\n",
              " 'vishead',\n",
              " 'uvsub',\n",
              " 'spxfit',\n",
              " 'splattotable',\n",
              " 'specsmooth',\n",
              " 'specflux',\n",
              " 'smoothcal',\n",
              " 'specfit',\n",
              " 'imstat',\n",
              " 'slsearch',\n",
              " 'delmod',\n",
              " 'imsubimage',\n",
              " 'accor',\n",
              " 'asdmsummary',\n",
              " 'clearcal',\n",
              " 'conjugatevis',\n",
              " 'exportasdm',\n",
              " 'importasdm',\n",
              " 'clearstat',\n",
              " 'fixplanets',\n",
              " 'fixvis',\n",
              " 'phaseshift',\n",
              " 'fluxscale',\n",
              " 'ft',\n",
              " 'gaincal',\n",
              " 'gencal',\n",
              " 'uvcontsub3',\n",
              " 'testconcat',\n",
              " 'apparentsens',\n",
              " 'hanningsmooth',\n",
              " 'imcollapse',\n",
              " 'imcontsub',\n",
              " 'imdev',\n",
              " 'imfit',\n",
              " 'impbcor',\n",
              " 'importasap',\n",
              " 'importatca',\n",
              " 'importfitsidi',\n",
              " 'importgmrt',\n",
              " 'importnro',\n",
              " 'importvla',\n",
              " 'impv',\n",
              " 'imrebin',\n",
              " 'imreframe',\n",
              " 'imregrid',\n",
              " 'imsmooth',\n",
              " 'imtrans',\n",
              " 'imval',\n",
              " 'initweights',\n",
              " 'listcal',\n",
              " 'listfits',\n",
              " 'listhistory',\n",
              " 'listsdm',\n",
              " 'listvis',\n",
              " 'makemask',\n",
              " 'polcal',\n",
              " 'polfromgain',\n",
              " 'predictcomp',\n",
              " 'rerefant',\n",
              " 'rmfit',\n",
              " 'rmtables',\n",
              " 'sdatmcor',\n",
              " 'sdbaseline',\n",
              " 'sdcal',\n",
              " 'sdfit',\n",
              " 'sdfixscan',\n",
              " 'sdgaincal',\n",
              " 'sdimaging',\n",
              " 'sdsmooth',\n",
              " 'tsdimaging',\n",
              " 'nrobeamaverage',\n",
              " 'sdtimeaverage',\n",
              " 'simanalyze',\n",
              " 'simobserve',\n",
              " 'feather',\n",
              " 'simalma',\n",
              " 'statwt',\n",
              " 'virtualconcat',\n",
              " 'uvcontsub',\n",
              " 'uvmodelfit',\n",
              " 'visstat',\n",
              " 'widebandpbcor',\n",
              " 'importmiriad',\n",
              " 'plotweather',\n",
              " 'plotants',\n",
              " 'fringefit',\n",
              " 'plotbandpass',\n",
              " 'sdintimaging',\n",
              " 'sdpolaverage',\n",
              " 'sdsidebandsplit',\n",
              " 'plotprofilemap']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQMP7v8VxGD0"
      },
      "source": [
        "We execute tasks just like normal Python functions. Many times they will write information to the log or a specified output file, which we then must display."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6KsW1NvgsQs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f460857b-e5e6-4bc6-aa66-84d9bd68ea46"
      },
      "source": [
        "from casatasks import listobs\n",
        "\n",
        "rc = listobs(vis='sis14_twhya_calibrated_flagged.ms', listfile='obslist.txt', verbose=False, overwrite=True)\n",
        "!cat obslist.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "           MeasurementSet Name:  /content/sis14_twhya_calibrated_flagged.ms      MS Version 2\n",
            "================================================================================\n",
            "   Observer: cqi     Project: uid://A002/X327408/X6f  \n",
            "Observation: ALMA(26 antennas)\n",
            "Data records: 80563       Total elapsed time = 5647.68 seconds\n",
            "   Observed from   19-Nov-2012/07:36:57.0   to   19-Nov-2012/09:11:04.7 (UTC)\n",
            "\n",
            "Fields: 5\n",
            "  ID   Code Name                RA               Decl           Epoch   SrcId      nRows\n",
            "  0    none J0522-364           05:22:57.984648 -36.27.30.85128 J2000   0           4200\n",
            "  2    none Ceres               06:10:15.950590 +23.22.06.90668 J2000   2           3800\n",
            "  3    none J1037-295           10:37:16.079736 -29.34.02.81316 J2000   3          16000\n",
            "  5    none TW Hya              11:01:51.796000 -34.42.17.36600 J2000   4          53161\n",
            "  6    none 3c279               12:56:11.166576 -05.47.21.52464 J2000   5           3402\n",
            "Spectral Windows:  (1 unique spectral windows and 1 unique polarization setups)\n",
            "  SpwID  Name                           #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz) BBC Num  Corrs  \n",
            "  0      ALMA_RB_07#BB_2#SW-01#FULL_RES    384   TOPO  372533.086       610.352    234375.0 372649.9688        2  XX  YY\n",
            "Antennas: 21 'name'='station' \n",
            "   ID=   1-4: 'DA42'='A050', 'DA44'='A068', 'DA45'='A070', 'DA46'='A067', \n",
            "   ID=   5-9: 'DA48'='A046', 'DA49'='A029', 'DA50'='A045', 'DV02'='A077', \n",
            "   ID= 10-15: 'DV05'='A082', 'DV06'='A037', 'DV08'='A021', 'DV10'='A071', \n",
            "   ID= 16-19: 'DV13'='A072', 'DV15'='A074', 'DV16'='A069', 'DV17'='A138', \n",
            "   ID= 20-24: 'DV18'='A053', 'DV19'='A008', 'DV20'='A020', 'DV22'='A011', \n",
            "   ID= 25-25: 'DV23'='A007'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVxAgJHyynlR"
      },
      "source": [
        "Another example, lets do channel averaging with MSTransform. Here we need to make sure we've deleted the previous output file if/when running multiple times.  Since this task doesn't return anything, we can look at the end of the log file to see what happened."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mzz2dtFtyyAj",
        "outputId": "604d0ce3-c17f-4bbc-f405-6bbb6545d415"
      },
      "source": [
        "from casatasks import mstransform\n",
        "\n",
        "os.system(\"rm -fr chanavg.ms\")\n",
        "mstransform(vis='sis14_twhya_calibrated_flagged.ms', outputvis='chanavg.ms',\n",
        "            datacolumn='DATA', chanaverage=True, chanbin=3)\n",
        "!tail casa-202*.log"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-10-14 17:43:24\tINFO\tMSTransformManager::parseMsSpecParams\tTile shape is [0]\n",
            "2021-10-14 17:43:24\tINFO\tMSTransformManager::parseChanAvgParams\tChannel average is activated\n",
            "2021-10-14 17:43:24\tINFO\tMSTransformManager::parseChanAvgParams\tChannel bin is [3]\n",
            "2021-10-14 17:43:24\tINFO\tMSTransformManager::colCheckInfo\tAdding DATA column to output MS from input DATA column\n",
            "2021-10-14 17:43:24\tINFO\tMSTransformManager::open\tSelect data\n",
            "2021-10-14 17:43:24\tINFO\tMSTransformManager::createOutputMSStructure\tCreate output MS structure\n",
            "2021-10-14 17:43:24\tINFO\tParallelDataHelper::::casa\tApply the transformations\n",
            "2021-10-14 17:43:29\tINFO\tmstransform::::casa\tTask mstransform complete. Start time: 2021-10-14 17:43:23.610120 End time: 2021-10-14 17:43:29.323998\n",
            "2021-10-14 17:43:29\tINFO\tmstransform::::casa\t##### End Task: mstransform          #####\n",
            "2021-10-14 17:43:29\tINFO\tmstransform::::casa\t##########################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0hpzf1D8zcD"
      },
      "source": [
        "## Running tclean\n",
        "\n",
        "Tclean works in non-interactive mode only (interactive=False)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ucruiVYxvUJh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f48635f8-d3fb-4bc0-97eb-d7d6e9ccc84e"
      },
      "source": [
        "from casatasks import tclean\n",
        "\n",
        "print(\"running tclean, may take a bit...\")\n",
        "\n",
        "tclean(vis='sis14_twhya_calibrated_flagged.ms', imagename='first_image',\n",
        "       field='5', spw='', specmode='mfs', deconvolver='hogbom', nterms=1,\n",
        "       gridder='standard', imsize=[250,250], cell=['0.1arcsec'],\n",
        "       weighting='natural', threshold='0mJy', niter=5000,\n",
        "       interactive=False, savemodel='modelcolumn')\n",
        "\n",
        "print(\"complete\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "running tclean, may take a bit...\n",
            "complete\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Sh41pgSmkcE"
      },
      "source": [
        "## View Images with Viewer\n",
        "\n",
        "We can use the casaviewer package to view images, but we need to start the viewer manually as a separate process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UwZYF_hgG9g",
        "outputId": "0505a0aa-ac12-4402-c22f-c94ea6d05fef"
      },
      "source": [
        "import subprocess as sp\n",
        "\n",
        "sp.Popen('/usr/local/lib/python3.7/dist-packages/casaviewer/__bin__/casaviewer-x86_64.AppImage',\n",
        "         shell=True, preexec_fn=os.setsid, stdin=sp.PIPE, stdout=sp.PIPE, stderr=sp.STDOUT)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<subprocess.Popen at 0x7fb797158190>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GvsKjHlPp99P"
      },
      "source": [
        "Now call imview and render the image to an output file where it can then be displayed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "tPHVK49kaT82",
        "outputId": "32390bc4-11ee-495b-8bb2-482d3db9797f"
      },
      "source": [
        "from casaviewer import imview\n",
        "from IPython.display import Image\n",
        "\n",
        "imview('first_image.image', out='test.png')\n",
        "\n",
        "Image(filename=\"test.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0) waiting for viewer process...\n",
            "(1) waiting for viewer process...\n",
            "(2) waiting for viewer process...\n",
            "(3) waiting for viewer process...\n",
            "(4) waiting for viewer process...\n",
            "\t...{'id': 'casaviewer:b1fc', 'priority': 0, 'types': array(['shutdown', 'image-view', 'interactive-clean'], dtype='<U18'), 'uri': '0.0.0.0:44403'}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1U3B_5u8qfPF"
      },
      "source": [
        "## View Images with Astropy\n",
        "\n",
        "We can use the image tool from casatools to load raw image data, then feed it to another Python package like Astropy and display it using Matplotlib.\n",
        "\n",
        "Astropy is already installed in Google Colaboratory, but if running this on some other Jupyter Hub system, you'll probably need to ```pip install astropy```.  Also note that we didn't explicitly install casatools either, but it was automatically installed as a dependency of casatasks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3UhdwznVQXe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "b54d35cc-1456-4ebc-b8fd-020b75189d28"
      },
      "source": [
        "from casatools import image as IA\n",
        "from astropy.wcs import WCS\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "ia = IA()\n",
        "ia.open('first_image.image')\n",
        "pix = ia.getchunk()[:,:,0,0]\n",
        "csys = ia.coordsys()\n",
        "ia.close()\n",
        "\n",
        "rad_to_deg =  180/np.pi\n",
        "w = WCS(naxis=2)\n",
        "w.wcs.crpix = csys.referencepixel()['numeric'][0:2]\n",
        "w.wcs.cdelt = csys.increment()['numeric'][0:2]*rad_to_deg\n",
        "w.wcs.crval = csys.referencevalue()['numeric'][0:2]*rad_to_deg\n",
        "w.wcs.ctype = ['RA---SIN', 'DEC--SIN']\n",
        "\n",
        "plt.subplots(1,1, figsize=(10,7))\n",
        "ax = plt.subplot(1, 1, 1, projection=w)\n",
        "p1 = int(pix.shape[0]*0.25)\n",
        "p2 = int(pix.shape[0]*0.75)\n",
        "\n",
        "im = ax.imshow(pix[p1:p2,p1:p2].transpose(), origin='lower',  cmap=plt.cm.viridis)\n",
        "plt.colorbar(im, ax=ax)\n",
        "ax.set_xlabel('Right Ascension')\n",
        "ax.set_ylabel('Declination')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}