Tutorial for SSP particles.

Stellar_particles.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stellar particles\n",
+    "This tutorial will teach you how to obtain spectral properties of stellar particles (e.g. representing single stellar populations, SSPs,from a hydrodynamic simulation) from BPASS using `hoki`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prerequisites\n",
+    "In addition to `hoki`, you need a local copy of the BPASS v2.2.1 database (or at least the spectral data for the chab300 IMF). These data are too large to be included in this repository."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# specify the base path to your BPASS database here\n",
+    "base_path = '~/BPASSv2.2.1_release-07-18-Tuatara/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports/settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from hoki import interpolators\n",
+    "from hoki.spec import bin_luminosity\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.style.use('tuto.mplstyle')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The stellar particles\n",
+    "Here we just create a dataframe of three stellar particles, this would normally come from a star formation simulation or similar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star_parts = pd.DataFrame({\n",
+    "    # absolute initial stellar metallicities\n",
+    "    'metallicity': np.array([1e-2, 1e-4, 1e-3]),\n",
+    "    # log of stellar ages in yr\n",
+    "    'log_age': np.array([6.0, 8.3, 10]),\n",
+    "    # stellar masses in 1e6 M_sun\n",
+    "    'mass': np.array([1, 2.5, 3])\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interpolators\n",
+    "Now let's create interpolator objects for the spectra and emissivities/luminosities provided by BPASS. These interpolator objects simply interpolate SSP properties on the BPASS age-metallicity grids."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = os.path.join(base_path, 'bpass_v2.2.1_imf_chab300')\n",
+    "imf = 'imf_chab300'\n",
+    "\n",
+    "spec_interp = interpolators.SpectraInterpolator(data_path, imf, lam_min=2e2, lam_max=2e3)\n",
+    "em_interp = interpolators.EmissivitiesInterpolator(data_path, imf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Results\n",
+    "Let's do the interpolation and see what we get."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# interpolated SEDs and the corresponding wavelengths\n",
+    "wl, spectra = spec_interp(star_parts['metallicity'], star_parts['log_age'], star_parts['mass'])\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "for spec in spectra:\n",
+    "    plt.plot(wl, spec, lw=0.5)\n",
+    "plt.xlabel(r'Wavelength ($\\AA$)')\n",
+    "plt.ylabel(r'SED ($L_\\odot/\\AA$)')\n",
+    "plt.xscale('log')\n",
+    "plt.yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# interpolated emissivities/luminosities\n",
+    "em = em_interp(star_parts['metallicity'], star_parts['log_age'], star_parts['mass'])\n",
+    "# let's add the to our dataframe (there's probably a more efficient way of doing this)\n",
+    "for col, name in zip(em.T, ['Nion', 'L_Halpha', 'L_FUV', 'L_NUV']):\n",
+    "    star_parts[name] = col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is what the dataframe looks like now\n",
+    "star_parts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Binning the spectra\n",
+    "`hoki` provides functionality to bin the interpolated spectra, conserving luminosity. This is useful, e.g., when you want to perform a radiative transfer simulation and need to reduce the number of spectral sampling points from what is provided by BPASS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bins = np.geomspace(2.1e2, 1.9e3, num=20)\n",
+    "wl_binned, spectra_binned = bin_luminosity(wl, spectra, bins=bins)\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "for spec in spectra:\n",
+    "    plt.plot(wl, spec, lw=0.5)\n",
+    "for spec in spectra_binned:\n",
+    "    plt.stairs(spec, edges=bins)\n",
+    "plt.xlabel(r'Wavelength ($\\AA$)')\n",
+    "plt.ylabel(r'SED ($L_\\odot/\\AA$)')\n",
+    "plt.xscale('log')\n",
+    "plt.yscale('log')"
+   ]
+  }
+ ],
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+}