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Last active August 9, 2022 02:43
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PRIISM imaging demo: HL Tau #python #PRIISM #ALMA
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# HL Tau Imaging\n",
"\n",
"PRIISM imaging demol using public ALMA Science Verification dataset. \n",
"\n",
"https://almascience.org/alma-data/science-verification\n",
"\n",
"This demo creates synthesis image of protoplanetary disk HL Tau (ALMA Band 6). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Data\n",
"\n",
"Download calibrated (and concatenated) MeasurementSet. Note that self-calibration will not be applied in this demo. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import subprocess\n",
"\n",
"# download data\n",
"# please uncomment one of the following base_url depending on your location\n",
"base_url = 'https://alma-dl.mtk.nao.ac.jp/ftp/alma/sciver/HLTauBand6/'\n",
"#base_url = 'https://almascience.nrao.edu/almadata/sciver/HLTauBand6/'\n",
"#base_url = 'https://almascience.eso.org/almadata/sciver/HLTauBand6/'\n",
"\n",
"data_dir = 'HLTau_Band6_CalibratedData'\n",
"data_tgz = 'HLTau_Band6_CalibratedData.tgz'\n",
"vis = 'HLTau_B6cont.calavg'\n",
"\n",
"if not os.path.exists(data_tgz):\n",
" subprocess.run(['wget', base_url + data_tgz])\n",
"\n",
"# expand tar file\n",
"if not os.path.exists(data_dir):\n",
" subprocess.run(['tar', 'xf', data_tgz])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LOG: initialize sakura...\n",
"<module 'priism.external.sakura.libsakurapy' from '/remote/home/nakazato/pyenv/priism/lib64/python3.6/site-packages/priism-0.7.16-py3.6.egg/priism/external/sakura/libsakurapy.so'>\n",
"Executing ~/.casa/config.py...\n",
"datapath=['/nfsstore/nakazato/share/casatestdata']\n",
"Executing ~/.casa/config.py...\n",
"datapath=['/nfsstore/nakazato/share/casatestdata']\n"
]
}
],
"source": [
"import priism\n",
"import priism.alma\n",
"\n",
"print(f'PRIISM version {priism.__version__}')\n",
"worker = priism.alma.AlmaSparseModelingImager(solver='mfista_nufft')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Selection\n",
"\n",
"Here, only select spw 0."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"worker.selectdata(\n",
" vis=f'{data_dir}/{vis}',\n",
" spw='0',\n",
" intent='OBSERVE_TARGET#ON_SOURCE',\n",
" datacolumn='data'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image Configuration\n",
"\n",
"Configuring 1600 x 1600 images with 0.005 arcsec resolution. All channels in spw 0 are combined into one image channel."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"worker.defineimage(\n",
" imsize=[1600, 1600],\n",
" cell=['0.005arcsec', '0.005arcsec'],\n",
" phasecenter='0', # HL Tau\n",
" nchan=1,\n",
" start=0,\n",
" width=4\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Visibility Data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"***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"
]
}
],
"source": [
"worker.readvis()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imaging\n",
"\n",
"Imaging with fixed hyper-parameters for penalty terms. To make execution duration short, max iteration is limited to 100. Note that resulting image is scientifically optimized. You should perform cross-validation to obtain more reliable image."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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: 13859004\n",
"X-dim of image: 1600\n",
"Y-dim of image: 1600\n",
"\n",
"\n",
"IO files of libmfista_nufft.so.\n",
"\n",
"\n",
" FFTW file: visibility_working_set\n",
" x was initialized with 1.0\n",
"\n",
"\n",
"\n",
"Output of libmfista_nufft.so.\n",
"\n",
"\n",
" Size of the problem:\n",
"\n",
"\n",
" size of input vector: 6929502\n",
" size of output vector: 2560000\n",
"size of image: 1600 x 1600\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: 1.613078e+11\n",
" computation time[sec]: 1.487708e+02\n",
"\n",
" # of nonzero pixels: 335640\n",
" Squared Error (SE): 3.209273e+11\n",
" Mean SE: 2.315659e+04\n",
" L1 cost: 8.441666e-01\n",
" TSV cost: 8.398957e-06\n",
"\n",
" LOOE: Could not be computed because Hessian was not positive definite.\n",
"2min 31s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
]
}
],
"source": [
"%%timeit -n 1 -r 1\n",
"worker.solve(l1=1e9, ltsv=1e9, maxiter=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export Image to FITS"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Use PHASE_DIR for FIELD 0\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=[800, 800], 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': 1000000000.0}\n",
"\n",
"Direction reference : J2000\n",
"Spectral reference : LSRK\n",
"Velocity type : RADIO\n",
"Rest frequency : 1e+09 Hz\n",
"Telescope : ALMA\n",
"Observer : violette\n",
"Date observation : 2014/10/24/06:43:08\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 04:31:38.426 800.00 -5.000000e-03 arcsec\n",
"1 0 Direction Declination SIN +18.13.57.047 800.00 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 -6.70786e+07 0.00 1.498962e+05 km/s\n"
]
}
],
"source": [
"imagename = 'HLTau_demo.fits'\n",
"worker.exportimage(imagename=imagename, overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Display Image with Astropy"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from astropy.visualization import astropy_mpl_style\n",
"from astropy.io import fits\n",
"\n",
"plt.style.use(astropy_mpl_style)\n",
"\n",
"image_data = fits.getdata(imagename, ext=0)\n",
"\n",
"plt.figure()\n",
"plt.imshow(image_data[0,0], cmap='jet')\n",
"plt.colorbar()\n",
"plt.xlim([600, 1000])\n",
"plt.ylim([650, 1050])\n",
"plt.clim([0, 0.0005])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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