{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Running RADMC-3D with AggScatVIR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One primary application of ``AggScatVIR`` is to model total-intensity and polarized scattered light from protoplanetary disks and debris disks. Our database has been designed to allow users to easily conduct radiative transfer simulations with [RADMC-3D](https://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/), a publicly available radiative transfer simulation code. To use one of our dust models with RADMC-3D, place the corresponding file (dustkapscatmat_XXX.inp) in a directory that RADMC-3D will be running. Once the other necessary input files have been properly prepared (as described below), you should be ready to run RADMC-3D." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quick demo using radmc3dPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please ensure that RADMC-3D has been installed on your system before proceeding. If not, refer to [the RADMC-3D manual](https://www.ita.uni-heidelberg.de/~dullemond/software/radmc-3d/) for installation instructions. Once RADMC-3D is installed, go to the directory where you wish to run RADMC-3D. Please select one of our dust model files (dustkapscatmat_XXX.inp) and place it in your working directory. For example, we have created a local copy of ``dustkapscatmat_FA19_Nmax2048_100nm_amc_chop5.inp`` in ``python/notebooks/``. To prepare the other necessary files for RADMC-3D, we use radmc3dPy, which is also part of RADMC-3D. \n", "\n", "We first import the radmc3dPy package:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from radmc3dPy import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then create a default parameter set for a typical protoplanetary disk:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing problem_params.inp\n" ] } ], "source": [ "analyze.writeDefaultParfile('ppdisk')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It would be safe to set the wavelength grid in the radiative transfer calculation (specified in wavelength.inp) to be consistent with the wavelength coverage of the dust file provided by this database. Therefore, let's set the short and long wavelength ends to be 0.554 and 3.78 $\\mu$m, respectively." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "nw='[1,1,1,1,1,2]' \n", "wbound='[0.554,0.735,1.040,1.250,1.630,2.180,3.780]' " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create a scattered-light image using RADMC-3D, we need a dust temperature file (dust_temperature.dat or dust_temperature.bdat) in the working directory. RADMC-3D has a function (mctherm) to determine the radiation equilibrium temperature of dust particles, and the dust temperature file will be generated after running it. **However, the wavelength coverage of this database is too narrow to determine the correct temperature**. Therefore, it is recommended that each user creates the dust temperature file manually.\n", "\n", "In this section, we simply omit the contribution of thermal emission, and therefore we set the dust temperature to 0 K everywhere in the simulation cells. To make such a dust temperature file, we will run mctherm without lauching any photon packets (``nphoto='int(0)'``).\n", "\n", "We can create the necessary input files for RADMC-3D by running the following command:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing problem_params.inp\n", "Writing dustopac.inp\n", "Writing wavelength_micron.inp\n", "Writing amr_grid.inp\n", "Writing stars.inp\n", "-------------------------------------------------------------\n", "Luminosities of radiation sources in the model :\n", "Reading wavelength_micron.inp\n", "As calculated from the input files :\n", "Stars : \n", " Star #0 + hotspot : 3.044969e+33\n", "Continuous starlike source : 0.000000e+00\n", " \n", "-------------------------------------------------------------\n", "Writing dust_density.binp\n", "Writing radmc3d.inp\n" ] } ], "source": [ "setup.problemSetupDust('ppdisk', mdisk='1e-4*ms',rin='10*au',\n", " nw=nw,wbound=wbound,xbound='[10.0*au,10.5*au, 100.0*au]',\\\n", " nz='[64]',\\\n", " dustkappa_ext =\"['FA19_Nmax2048_100nm_amc_chop5']\",\\\n", " nphot='int(0)',nphot_scat='int(1e6)',scattering_mode_max=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Please make sure to (i) specifiy the dust model name (``dustkappa_ext =\"['FA19_Nmax2048_100nm_amc_chop5']\"``) and (ii) include ``scattering_mode_max=5``.\n", "\n", "Now we are ready to run RADMC-3D. Let's start running mctherm first (Again, no photon packets will be launched in this step. This is just to create a dust temperature file that is needed to proceed to the scattering Monte Carlo stage)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "os.system('radmc3d mctherm')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This command ends up with creating a dust_temperature file with 0 K everywhere (``dust_temperature.bdat``).\n", "\n", "We are now ready to create a scattered-light image of the disk by performing a scattering Monte Carlo simulation. Let's do imaging at an optical wavelength (0.6 $\\mu\\mathrm{m}$) and a disk inclination angle of 55 degrees:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Executing RADMC-3D Command:\n", "radmc3d image npix 300 incl 55 sizeau 250.0 lambda 0.6 pointau 0.0 0.0 0.0 fluxcons stokes\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image.makeImage(npix=300., wav=0.6, incl=55, sizeau=250.,stokes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It will take about 50 seconds to complete using a single core, and ``image.out`` will be created in the end. The file contains intensity of the four Stokes components at each image pixel. The resultant total-intensity disk image looks like this:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading image.out\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "{'implot': ,\n", " 'cbar': }" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pylab as plb\n", "im = image.readImage()\n", "image.plotImage(im, au=True, log=True, saturate=1e-5,maxlog=10,cmap=\"magma\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try other parameters: at a wavelength of $\\lambda=2.2~\\mu$m and a disk inclination angle of 85 degrees." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Executing RADMC-3D Command:\n", "radmc3d image npix 300 incl 85 sizeau 250.0 lambda 2.2 pointau 0.0 0.0 0.0 fluxcons stokes\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image.makeImage(npix=300., wav=2.2, incl=85, sizeau=250.,stokes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After completing another round of the second hand of the clock, we obtain the following image:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading image.out\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "{'implot': ,\n", " 'cbar': }" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "im = image.readImage()\n", "image.plotImage(im, au=True, log=True, maxlog=3,cmap=\"magma\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the model images of scattered light of a disk containing fractal dust aggregates with sub-micron grains, although more photon packets/finer spatial grids would be needed to improve the image quality. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparation of input files and caveats\n", "\n", "For those who are not using radmc3dPy, here we summarize the points to consider when generating the input files. Of the RADMC-3D input files, the following three are relevant to using our dust model: ``radmc3d.inp``, ``dustopac.inp``, ``wavelength.inp``.\n", "\n", "``dustopac.inp`` sets how RADMC-3D will read dust input files. To use the dustkapscatmat_XXX.inp file, the inputstype in ``dustopac.inp`` must be set to 10. The extension XXX of the dust file must also be set in this file.\n", "\n", "``radmc3d.inp`` is where the scattering Monte Carlo calculation mode can be set. To run the scattering Monte Carlo calculations taking into account all Stokes parameters, scattering_mode_max = 5 must be set.\n", "\n", "``wavelength.inp`` contains the wavelength grids used in the radiative transfer calculation. Since the scattering Monte Carlo calculation is monochromatic, there will be no problem if the wavelength used for imaging is within the wavelength range of the dust file. However, if mctherm runs in advance as done in the above example, the wavelength grid is better to have the same wavelength coverage as a dust file; otherwise, RADMC-3D might cause some internal errors when reading dust opacity files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".. important:: **Thermal Monte Carlo runs**: As already mentioned in the above example, dust files provided in AggScatVIR have a wavelength coverage too narrow to determine radiation equilibrium temperature via thermal Monte Carlo calculations (``mctherm`` in RADMC-3D). *Therefore, it is recommended to use the package for monochromatic scattering Monte Carlo radiative transfer simulations.*\n", "\n", ".. warning:: **Models with incomplete wavelength coverage**: The following models only have the wavelength coverage of 1.04-3.78 microns. In other words, the data sets at 0.554 and 0.735 microns are missing: ``FA19_(Nmax)4096_100nm_amc``, ``FA19_(Nmax)4096_100nm_org``, ``FA15_(Nmax)1024_100nm_amc``, ``FA15_(Nmax)1024_100nm_org``, ``FA13_(Nmax)512_100nm_amc``, ``FA13_(Nmax)512_100nm_org``, ``FA11_(Nmax)256_100nm_amc``, ``FA11_(Nmax)256_100nm_org``.\n", "\n", ".. seealso:: **Chopping forward scattering**: Dust particles much larger than the wavelength may exhibit a strong forward scattering, which may cause some issues in your simulations, such as poor image quality. One approximate way to mitigate this issue is to chop the forward scattering peak off. For this purpose, ``AggScatVIR`` offers the dustkapscatmat_XXX.inp file with two different chopping angles: 5 and 10 degrees. When you encounter such an issue, please consider using those files." ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }