{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Running TARDIS with a Custom Packet Source" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, TARDIS generates energy packets using its `BasePacketSource` class, which models the photosphere of the supernova as a perfect blackbody (see [Energy Packet Initialization](../../physics/montecarlo/initialization.ipynb)). However, users may create their own packet source, as will be shown in this notebook. In order to do this, a user must create a class (that can but does not have to inherit from `BasePacketSource`) which contains a method `create_packets` that takes in (in this order):\n", "- the photospheric temperature\n", "- the number of packets\n", "- a random number generator\n", "- the photospheric radius\n", "\n", "and returns arrays of the radii, frequencies, propogation directions, and energies of the ensemble of packets (once again see [Energy Packet Initialization](../../physics/montecarlo/initialization.ipynb) for more information). To use your packet source in a run of TARDIS, you must pass an instance of your class into the `run_tardis` function under the `packet_source` keyword argument.\n", "\n", ".. note:: In both the `BasePacketSource` class and in the example here, all packets are generated at the same radius. This need not be true in general (although the `create_packets` method should only take in a single radius as its argument).\n", "\n", "We show an example of how a custom packet source is used:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import necessary packages\n", "import numpy as np\n", "from tardis import constants as const\n", "from astropy import units as u\n", "from tardis.montecarlo.packet_source import BasePacketSource\n", "from tardis import run_tardis\n", "import matplotlib.pyplot as plt\n", "from tardis.io.atom_data import download_atom_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Download the atomic data used for a run of TARDIS\n", "download_atom_data('kurucz_cd23_chianti_H_He')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a packet source class that contains a create_packets method\n", "class TruncBlackbodySource(BasePacketSource):\n", " \"\"\"\n", " Custom inner boundary source class to replace the Blackbody source\n", " with a truncated Blackbody source.\n", " \"\"\"\n", " \n", " def __init__(self, seed, truncation_wavelength):\n", " super().__init__(seed)\n", " self.rng = np.random.default_rng(seed=seed)\n", " self.truncation_wavelength = truncation_wavelength\n", " \n", " def create_packets(self, T, no_of_packets, rng, radius,\n", " drawing_sample_size=None):\n", " \"\"\"\n", " Packet source that generates a truncated Blackbody source.\n", " \n", " Parameters\n", " ----------\n", " T : float\n", " Blackbody temperature\n", " no_of_packets : int\n", " number of packets to be created\n", " truncation_wavelength : float\n", " truncation wavelength in Angstrom. \n", " Only wavelengths higher than the truncation wavelength\n", " will be sampled.\n", " \"\"\"\n", " \n", " # Makes uniform array of packet radii\n", " radii = np.ones(no_of_packets) * radius\n", "\n", " # Use mus and energies from normal blackbody source.\n", " mus = self.create_zero_limb_darkening_packet_mus(no_of_packets, self.rng)\n", " energies = self.create_uniform_packet_energies(no_of_packets, self.rng)\n", "\n", " # If not specified, draw 2 times as many packets and reject any beyond no_of_packets.\n", " if drawing_sample_size is None:\n", " drawing_sample_size = 2 * no_of_packets\n", "\n", " # Blackbody will be truncated below truncation_wavelength / above truncation_frequency.\n", " truncation_frequency = u.Quantity(self.truncation_wavelength, u.Angstrom).to(\n", " u.Hz, equivalencies=u.spectral()).value\n", " \n", " # Draw nus from blackbody distribution and reject based on truncation_frequency.\n", " # If more nus.shape[0] > no_of_packets use only the first no_of_packets.\n", " nus = self.create_blackbody_packet_nus(T, drawing_sample_size, self.rng)\n", " nus = nus[nus no_of_packets.\n", " while nus.shape[0] < no_of_packets:\n", " additional_nus = self.create_blackbody_packet_nus(\n", " T, drawing_sample_size, self.rng\n", " )\n", " mask = additional_nus < truncation_frequency\n", " additional_nus = additional_nus[mask][:no_of_packets]\n", " nus = np.hstack([nus, additional_nus])[:no_of_packets]\n", " \n", " return radii, nus, mus, energies" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Call an instance of the packet source class\n", "packet_source = TruncBlackbodySource(\n", " 53253, truncation_wavelength=2000\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now run TARDIS both with and without our custom packet source, and we compare the results:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mdl = run_tardis('tardis_example.yml',\n", " packet_source=packet_source)\n", "mdl_norm = run_tardis('tardis_example.yml')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "plt.plot(mdl.runner.spectrum_virtual.wavelength,\n", " mdl.runner.spectrum_virtual.luminosity_density_lambda,\n", " color='red', label='truncated blackbody (custom packet source)')\n", "plt.plot(mdl_norm.runner.spectrum_virtual.wavelength,\n", " mdl_norm.runner.spectrum_virtual.luminosity_density_lambda,\n", " color='blue', label='normal blackbody (default packet source)')\n", "plt.xlabel('$\\lambda [\\AA]$')\n", "plt.ylabel('$L_\\lambda$ [erg/s/$\\AA$]')\n", "plt.xlim(500, 10000)\n", "plt.legend()" ] } ], "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": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 2 }