tutorial_hltau.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JW2_lad820_s"
   },
   "source": [
    "# Imaging Tutorial with the Sparse Modeling Technique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rRyt8_55edcl"
   },
   "source": [
    "This notebook is a tutorial of synthesis imaging based on the sparse modeling technique using PRIISM. \n",
    "**PRIISM** is an imaging tool for radio interferometry based on the sparse modeling technique ([PRIISM](https://github.com/tnakazato/priism): Python Module for Radio Interferometry Imaging with Sparse Modeling). \n",
    "\n",
    "In this tutorial, we try end-to-end synthesis imaging for ALMA interferometry data using `runner` module with parameter file. For interactive processing using `priism.alma`, please see `tutorial_twhya.ipynb`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j_g7z3Mw20_v"
   },
   "source": [
    "## Setup and Initialize PRIISM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you start this tutorial, you should install PRIISM and packages that PRIISM depends on (casatools, casatasks, etc.). Please see README.md on how to install PRIISM. In addition, you need to install `astropy` because this tutorial uses `astropy` to display final image.\n",
    "\n",
    "There are two fundamental submodules, `priism.core` and `priism.alma`, which are mainly for interactive use. For batch mode, we have `priism.runner` submodule. Here, we demonstrate how to use `runner` submodule as well as show the format of parameter file for `runner`.\n",
    "\n",
    "First, we import PRIISM to show the version information. This is not mandatory in terms of processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOG: initialize sakura...\n",
      "<module 'priism.external.sakura.libsakurapy' from '/Users/nakazato/pyenv/priism/lib/python3.8/site-packages/priism-0.11.5-py3.8.egg/priism/external/sakura/libsakurapy.so'>\n",
      "PRIISM version 0.11.5\n"
     ]
    }
   ],
   "source": [
    "import priism.alma\n",
    "print(f'PRIISM version {priism.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we make matplotlib figures to be displayed in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vpw7MdpSefcc"
   },
   "source": [
    "## ALMA Data : HL Tau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TPNB9Rt-20_u"
   },
   "source": [
    "Sample data for the tutorial is the protoplanetary disk \"**HL Tau**\" observed at Band 6 with long baselines of ALMA 12m array as part of [science verification](https://almascience.nao.ac.jp/alma-data/science-verification). The calibration information can be obtained from [CASA Guide](https://casaguides.nrao.edu/index.php?title=ALMA2014_LBC_SVDATA#HL_Tau_-_Protoplanetary_Disk), and the data can be downloaded from the link below: \n",
    "\n",
    "https://almascience.nao.ac.jp/almadata/sciver/HLTauBand6/HLTau_Band6_CalibratedData.tgz\n",
    "\n",
    "After you download the data, you should extract the MS from the file. You may run the following cell to obtain the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Zfa-9wJC20_y",
    "outputId": "cfa2c070-1461-4305-8aa5-d92bbb3a2d74"
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "# download if necessary\n",
    "[ -e HLTau_Band6_CalibratedData.tgz ] || wget -nv https://almascience.nao.ac.jp/almadata/sciver/HLTauBand6/HLTau_Band6_CalibratedData.tgz\n",
    "\n",
    "# extract data from tar-ball\n",
    "[ -e HLTau_Band6_CalibratedData ] || tar xf HLTau_Band6_CalibratedData.tgz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DCwm3dBq20_y"
   },
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we downloaded calibrated data, input MS is already available at `HLTau_Band6_CalibratedData/HLTau_B6cont.calavg`. Here we just define some variables for convenience. Also, we run `listobs` to get data summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "0uqszxQq20_z",
    "outputId": "d39b4e4d-2331-43e1-f335-e00921d973a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectral Windows:  (4 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_06#BB_1#SW-01#FULL_RES      4   TOPO  224750.000   -500000.000   2000000.0 224000.0000        1  XX  YY\n",
      "  1      ALMA_RB_06#BB_2#SW-01#FULL_RES      4   TOPO  226750.000   -500000.000   2000000.0 226000.0000        2  XX  YY\n",
      "  2      ALMA_RB_06#BB_3#SW-01#FULL_RES      4   TOPO  239250.000    500000.000   2000000.0 240000.0000        3  XX  YY\n",
      "  3      ALMA_RB_06#BB_4#SW-01#FULL_RES      4   TOPO  241250.000    500000.000   2000000.0 242000.0000        4  XX  YY\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from casatasks import listobs\n",
    "\n",
    "data_dir = 'HLTau_Band6_CalibratedData'\n",
    "vis = 'HLTau_B6cont.calavg'\n",
    "listfile = 'HLTau_B6cont.calavg.listobs'\n",
    "imagename = 'hltau_b6cont_spw0.fits'\n",
    "    \n",
    "listobs(vis=f'{data_dir}/{vis}', listfile=listfile, verbose=True, overwrite=True)\n",
    "\n",
    "# extract spw setup from data summary\n",
    "os.system(f'grep -E \"^Spectral Windows:\" -A 5 {listfile}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End-to-end Processing using a Parameter File\n",
    "\n",
    "Imaging and cross-validation can be processed using parameters stored in a parameter file. PRIISM has `runner` submodule for this purpose. For reading the parameter file, `loadParam` is used. And also, the used parameters can be stored by `saveParam`. You can use `runner` module interactively, too. In the interactive use case, `runner` submodule saves processing parameters in a parameter file of *.param. (The parameters in cross-validations are automatically stored, such as \"cvname.param\".) You can manually make the parameter file and apply that for imaging and cross-validation as well.\n",
    "\n",
    "For example, you can perform imaging or cross-validation using the parameter file \"`test.param`\" as follows,\n",
    "\n",
    "```\n",
    "from priism import runner\n",
    "hh=runner.Session.loadParam('test.param')\n",
    "hh.run(overwrite=True)\n",
    "```\n",
    "\n",
    "or\n",
    "\n",
    "```\n",
    "from priism import runner\n",
    "hh=runner.Session.loadParam('test.param')\n",
    "hh.crossValidation(overwrite=True)\n",
    "```\n",
    "\n",
    "The sample of the parameter file for the classical cross-validation is given below. We create the parameter file first. Then we perform end-to-end imaging using `runner` submodule.\n",
    "\n",
    "First, create the param file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = f'''vis:            {data_dir}/{vis}\n",
    "field:          0\n",
    "spw:            0\n",
    "ch:             0~3\n",
    "datacolumn:     data\n",
    "imname:         {imagename}\n",
    "imsize:         [800, 800]\n",
    "cell:           ['0.005arcsec']\n",
    "nchan:          1\n",
    "start:          0\n",
    "width:          4\n",
    "cvname:         hltau_b6cont_spw0.cv\n",
    "solver:         mfista_nufft\n",
    "l1:             1000000000.0\n",
    "ltsv:           1000000000.0\n",
    "maxiter:        100\n",
    "numFold:        4\n",
    "imagePolicy:    full\n",
    "l1List:         [1000.0, 10000.0, 100000.0, 1000000.0]\n",
    "ltsvList:       [100000000.0, 1000000000.0, 10000000000.0, 100000000000.0]\n",
    "optimizer:      classical\n",
    "bayesopt_maxiter:       15\n",
    "'''\n",
    "\n",
    "with open('test.param', 'w') as f:\n",
    "    f.write(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contents of the param file is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vis:            HLTau_Band6_CalibratedData/HLTau_B6cont.calavg\n",
      "field:          0\n",
      "spw:            0\n",
      "ch:             0~3\n",
      "datacolumn:     data\n",
      "imname:         hltau_b6cont_spw0.fits\n",
      "imsize:         [800, 800]\n",
      "cell:           ['0.005arcsec']\n",
      "nchan:          1\n",
      "start:          0\n",
      "width:          4\n",
      "cvname:         hltau_b6cont_spw0.cv\n",
      "solver:         mfista_nufft\n",
      "l1:             1000000000.0\n",
      "ltsv:           1000000000.0\n",
      "maxiter:        100\n",
      "numFold:        4\n",
      "imagePolicy:    full\n",
      "l1List:         [1000.0, 10000.0, 100000.0, 1000000.0]\n",
      "ltsvList:       [100000000.0, 1000000000.0, 10000000000.0, 100000000000.0]\n",
      "optimizer:      classical\n",
      "bayesopt_maxiter:       15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('test.param', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U7E7ogSs20_z"
   },
   "source": [
    "Then, feed `test.param` to `runner` submodule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "id": "1DXeyuu020_z",
    "outputId": "8e5370b7-ec85-4bf1-df6d-cde168f95ffa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Reading parameter file : test.param\n",
      "#############################\n",
      "(runner.Session): vis = HLTau_Band6_CalibratedData/HLTau_B6cont.calavg\n",
      "###### Start PRIISM\n",
      "***WARN*** refocusing is disabled even if distance to the source is known.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "read 8064 visibility chunks\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DONE reading visibility chunks\n",
      "## Image name : hltau_b6cont_spw0.fits\n",
      "lambda_l1 = 1000000000.0\n",
      "lambda_tv = 0.0\n",
      "lambda_tsv = 1000000000.0\n",
      "c = 5e+10\n",
      "\n",
      "number of u-v points: 3464751\n",
      "X-dim of image:       800\n",
      "Y-dim of image:       800\n",
      "compute Fourier Transform\n",
      "Memory allocation and preparations.\n",
      "\n",
      "Preparation for FFT.Done\n",
      "Cleaning fftw plan.\n",
      "\n",
      "  wieghted chi-sq 8.56663e+10\n",
      "lambda_l1:        1e+09\n",
      "  L1 cost         0\n",
      "lambda_tsv:       1e+09\n",
      "  TSV cost        0\n",
      "total cost        4.28332e+10\n",
      "\n",
      "Memory allocation and preparations.\n",
      "\n",
      "(OPENMP): Running in multi-thread with 4 threads.\n",
      "\n",
      "Preparation for FFT. Done.\n",
      "\n",
      "Computing image with MFISTA using NUFFT.\n",
      "Stop if iter = 100 or Delta_cost < 3.05209e-07\n",
      "\n",
      "    1 cost = 42833174065.60546, c = 50000000000.00000\n",
      "   11 cost = 12591386590.56093, c = 19277164471476572.00000\n",
      "   21 cost = 12227982326.32982, c = 7432181401207171.00000\n",
      "   31 cost = 12193873026.51490, c = 2865427665058396.00000\n",
      "   41 cost = 12186124348.97121, c = 4614799908853200.00000\n",
      "   51 cost = 12184189739.45305, c = 4195272644411999.50000\n",
      "   61 cost = 12182763281.19999, c = 4195272644411999.50000\n",
      "   71 cost = 12181965127.86949, c = 3813884222192726.50000\n",
      "   81 cost = 12181457593.98275, c = 4195272644411999.50000\n",
      "   91 cost = 12181119601.16932, c = 3813884222192726.50000\n",
      "  100 cost = 12180889985.49190\n",
      "\n",
      "Cleaning fftw plan.\n",
      "\n",
      " computation time: 145.5\n",
      "compute Fourier Transform\n",
      "Memory allocation and preparations.\n",
      "\n",
      "Preparation for FFT.Done\n",
      "Cleaning fftw plan.\n",
      "\n",
      "\n",
      "\n",
      "IO files of libmfista_nufft.so.\n",
      "\n",
      "\n",
      " FFTW file:              visibility_working_set\n",
      " x was initialized with 0.0\n",
      "\n",
      "\n",
      "\n",
      "Output of libmfista_nufft.so.\n",
      "\n",
      "\n",
      " Size of the problem:\n",
      "\n",
      "\n",
      " size of input vector:  3464751\n",
      " size of output vector: 640000\n",
      "size of image:          800 x 800\n",
      "\n",
      "\n",
      " Problem Setting:\n",
      "\n",
      "\n",
      " x is a nonnegative vector.\n",
      "\n",
      "\n",
      " Lambda_l1: 1.000000e+09\n",
      " Lambda_tsv: 1.000000e+09\n",
      " MAXITER: 100\n",
      " Results:\n",
      "\n",
      " # of iterations:       100\n",
      " cost:                  1.218087e+10\n",
      " computation time[sec]: 1.455031e+02\n",
      "\n",
      " # of nonzero pixels:   70688\n",
      " Squared Error (SE):    2.292709e+10\n",
      " Mean SE:               6.617241e+03\n",
      " L1 cost:               7.173204e-01\n",
      " TSV cost:              1.432553e-06\n",
      "\n",
      " LOOE:    Could not be computed because Hessian was not positive definite.\n",
      "Use PHASE_DIR for FIELD 0\n",
      "  wieghted chi-sq 2.2927e+10\n",
      "lambda_l1:        1e+09\n",
      "  L1 cost         0.71732\n",
      "lambda_tsv:       1e+09\n",
      "  TSV cost        1.4326e-06\n",
      "total cost        1.2181e+10\n",
      "\n",
      "Use Freuquency for channel 0 spw 0\n",
      "DEBUG phasecenter={'m0': {'unit': 'rad', 'value': 1.1852550088444016}, 'm1': {'unit': 'rad', 'value': 0.3182173842968282}, 'refer': 'J2000', 'type': 'direction'}\n",
      "DEBUG refpix=[400, 400], refval=[1.1852550088444016, 0.3182173842968282]\n",
      "start {'unit': 'Hz', 'value': 224750000000.0} width {'unit': 'Hz', 'value': -500000000.0}\n",
      "f = [2.2475e+11]\n",
      "set increment for spectral axis: {'unit': 'Hz', 'value': -500000000.0}\n",
      "rest_frequency={'unit': 'Hz', 'value': 90500000000.1}\n",
      "\n",
      "Direction reference : J2000\n",
      "Spectral  reference : LSRK\n",
      "Velocity  type      : RADIO\n",
      "Rest frequency      : 9.05e+10 Hz\n",
      "Telescope           : ALMA\n",
      "Observer            : violette\n",
      "Date observation    : 2014/10/24/06:43:07.946999\n",
      "Telescope position: [2.22514e+06m, -5.44031e+06m, -2.48103e+06m] (ITRF)\n",
      "\n",
      "Axis Coord Type      Name             Proj   Coord value at pixel    Coord incr Units\n",
      "------------------------------------------------------------------------------------- \n",
      "0    0     Direction Right Ascension   SIN  04:31:38.426   400.00 -5.000000e-03 arcsec\n",
      "1    0     Direction Declination       SIN +18.13.57.047   400.00  5.000000e-03 arcsec\n",
      "2    1     Stokes    Stokes                            I\n",
      "3    2     Spectral  Frequency                2.2475e+11     0.00 -5.000000e+08 Hz\n",
      "                     Velocity                    -444720     0.00  1.656312e+03 km/s\n"
     ]
    }
   ],
   "source": [
    "from priism import runner\n",
    "hh=runner.Session.loadParam('test.param')\n",
    "hh.run(overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mw9TzSoA20_1"
   },
   "source": [
    "## Display Final Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BJ0hbXiqazCt"
   },
   "source": [
    "The created image is displayed here using the `matplotlib` module. Please note that intensity unit of resulting image will be **Jy/pixel** if input visibility data is correctly calibrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "oYi0zv7p20_1",
    "outputId": "dca1d243-982b-412f-df28-8f6ddcf5e05f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "pixs=800; imwd=500; exwd = (pixs-imwd) // 2\n",
    "ff,aa=plt.subplots(1,1, figsize=(6,6))\n",
    "#im=aa.imshow(fits.getdata(imname)[0,0,exwd:((pixs-1)-exwd),exwd:((pixs-1)-exwd)], \n",
    "im=aa.imshow(fits.getdata(imagename)[0,0,exwd+40:((pixs-1)-exwd)+40,exwd:((pixs-1)-exwd)], \n",
    "             origin='lower', cmap='jet')\n",
    "#             vmin=0, vmax=0.0005, origin='lower', cmap='jet')\n",
    "cbar=plt.colorbar(im, ax=aa)\n",
    "aa.set_title(imagename)\n",
    "aa.set_xlabel(\"x [pix]\",size=11)\n",
    "aa.set_ylabel(\"y [pix]\",size=11)\n",
    "plt.savefig(imagename.replace('fits','png'), bbox_inches=\"tight\", pad_inches=0.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgements\n",
    "\n",
    "This tutorial makes use of the following ALMA data: ADS/JAO.ALMA#2011.0.00015.SV. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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