ppxf_example_legac_photometry.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "ca7b3f6e",
   "metadata": {},
   "source": [
    "\n",
    "# pPXF: Fitting photometry and spectra of LEGA-C galaxies at $z\\approx0.8$\n",
    "\n",
    "![pPXF](https://www-astro.physics.ox.ac.uk/~cappellari/software/ppxf_logo.svg)\n",
    "\n",
    "## Description\n",
    "\n",
    "Usage example for the procedure pPXF originally described in \n",
    "[Cappellari & Emsellem (2004)](http://adsabs.harvard.edu/abs/2004PASP..116..138C),\n",
    "substantially upgraded in \n",
    "[Cappellari (2017)](http://adsabs.harvard.edu/abs/2017MNRAS.466..798C) \n",
    "and with the inclusion of photometry and linear constraints in \n",
    "[Cappellari (2023)](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C).\n",
    "\n",
    "### MODIFICATION HISTORY\n",
    "\n",
    "* V1.0.0: Michele Cappellari, Oxford, 8 December 2023: Created\n",
    "* V1.1.0: Demonstrate use of ``sps_util.mass_to_light`` and\n",
    "      ``ppxf_util.mag_sun`` in pPXF v9.1. MC, Oxford, 18 January 2024\n",
    "* V1.1.1: Updated for pPXF 9.2. MC, Oxford, 11 April 2024\n",
    "* V1.2.0: Updated for pPXF 9.3. MC, Oxford, 28 June 2024\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5092a5d",
   "metadata": {},
   "source": [
    "## Imports and function definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50e8f50f",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "from urllib import request\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "from astropy import constants, cosmology\n",
    "from scipy import ndimage, stats\n",
    "\n",
    "import ppxf.ppxf_util as util\n",
    "import ppxf.sps_util as lib\n",
    "from ppxf.ppxf import ppxf, robust_sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9082df8",
   "metadata": {
    "lines_to_next_cell": 1
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   "outputs": [],
   "source": [
    "###############################################################################\n",
    "\n",
    "def noise_spectrum(residuals, width=101, sigma=1):\n",
    "    \"\"\"\n",
    "    Robust determination of the 1sigma error spectrum as the 68th percentile\n",
    "    of the absolute residuals from a fit in a given running window.\n",
    "    \"\"\"\n",
    "    assert width % 2 == 1, f\"width = {width} must be odd\"\n",
    "    assert width > 10, f\"width = {width} too small\"\n",
    "\n",
    "    p = 200*stats.norm.cdf(sigma) - 100  # sigma = 1 --> 68 %\n",
    "\n",
    "    # The median residual should be nearly zero already, but I redo it anyway\n",
    "    med = ndimage.median_filter(residuals, size=width)\n",
    "    res = abs(residuals - med)\n",
    "    noise = ndimage.percentile_filter(res, p, size=width)\n",
    "\n",
    "    return noise/sigma  # always returns 1sigma noise spectrum\n",
    "\n",
    "################################################################################\n",
    "\n",
    "def clip_outliers(galaxy, noise, bestfit, mask):\n",
    "    \"\"\"\n",
    "    Repeat the fit after clipping pixels deviating more than 3*sigma in\n",
    "    relative error. Continue until there are no further changes in the bad\n",
    "    bins. \n",
    "    \"\"\"\n",
    "    while True:\n",
    "        d = galaxy[mask]/noise[mask]\n",
    "        m = bestfit[mask]/noise[mask]\n",
    "        scale = (d @ m)/(m @ m)   # eq. (34) Cappellari (2023)\n",
    "        resid = scale*bestfit[mask] - galaxy[mask]\n",
    "        err = np.zeros_like(galaxy)\n",
    "        err[mask] = noise_spectrum(resid)\n",
    "        mask_old = mask.copy()\n",
    "        mask = np.abs(bestfit - galaxy) < 3*err  # 3sigma\n",
    "        if np.array_equal(mask, mask_old):\n",
    "            break\n",
    "\n",
    "    return mask\n",
    "\n",
    "###############################################################################\n",
    "\n",
    "def ppxf_clip_outliers(templates, galaxy, noise, velscale, start, mask=None, phot=None, plot=False, \n",
    "                       lam=None, lam_temp=None, component=None, gas_component=None, **kwargs):\n",
    "    \"\"\"\n",
    "    Wrapper for pPXF: \n",
    "    - Perform an initial pPXF fit;\n",
    "    - Find outliers and mask pixels;\n",
    "    - Rescale spectral and photometric uncertainties;\n",
    "    - Perform second pPXF fit, with new mask and uncertainties.\n",
    "    \"\"\"\n",
    "    if mask is None:\n",
    "        mask = np.ones(galaxy.size, dtype=bool)\n",
    "\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start, mask=mask, phot=phot,lam=lam, \n",
    "              lam_temp=lam_temp, component=component, gas_component=gas_component, **kwargs)\n",
    "\n",
    "    if plot:\n",
    "        plt.clf()\n",
    "        plt.subplot(211)\n",
    "        pp.plot()\n",
    "\n",
    "    # Clip outliers and include clipped pixels to the previous mask\n",
    "    mask &= clip_outliers(galaxy, noise, pp.bestfit, mask)\n",
    "\n",
    "    # Scale spectrum errors to have chi2/DOF=1 after masking\n",
    "    resid = ((pp.bestfit - galaxy)/noise)[mask]\n",
    "    chi2 = resid @ resid\n",
    "    noise *= np.sqrt(chi2/mask.sum())\n",
    "\n",
    "    if phot is not None:\n",
    "        # Scale photometry errors to have chi2/DOF=1\n",
    "        phot_resid = (pp.phot_bestfit - phot[\"galaxy\"])/phot[\"noise\"]\n",
    "        phot_chi2 = phot_resid @ phot_resid\n",
    "        phot[\"noise\"] *= np.sqrt(phot_chi2/phot[\"galaxy\"].size)\n",
    "\n",
    "    start = pp.sol   # start from previous pPXF solution\n",
    "\n",
    "    # Repeat the fit after masking\n",
    "    pp = ppxf(templates, galaxy, noise, velscale, start, mask=mask, phot=phot, lam=lam, \n",
    "                lam_temp=lam_temp, component=component, gas_component=gas_component, **kwargs)\n",
    "\n",
    "    # Estimate S/N in output\n",
    "    resid = (galaxy - pp.bestfit)[mask]\n",
    "    rms = robust_sigma(resid, zero=1)\n",
    "    flx = np.median(galaxy[mask])\n",
    "    pp.sn = flx/rms\n",
    "\n",
    "    if plot:\n",
    "        plt.subplot(212)\n",
    "        pp.plot()\n",
    "\n",
    "    return pp\n",
    "\n",
    "###############################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9aac43a",
   "metadata": {},
   "source": [
    "## Read the LEGA-C spectrum\n",
    " \n",
    "Selects one object, and the corresponding redshift, from the LEGA-C catalogue\n",
    "by [van der Wel et al. (2021)](https://ui.adsabs.harvard.edu/abs/2021ApJS..256...44V/abstract).\n",
    "\n",
    "In a real application, this will be done with a specific function reading\n",
    "from the catalogue files, but I avoided including the whole catalogue for\n",
    "this example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aeb93ff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "id = 56670          # ID of the UltraVISTA catalogue by Muzzin+13 (see later) -> ID = 414 in LEGA-C DR3\n",
    "redshift = 0.6686   # Initial redshift estimate from the LEGA-C catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa68bda",
   "metadata": {},
   "source": [
    "Read a LEGA-C galaxy spectrum taken (with permission) from the catalogue of\n",
    "[van der Wel et al. (2021)](https://ui.adsabs.harvard.edu/abs/2021ApJS..256...44V/abstract). \n",
    "The wavelength range outside the observed limits is filled with `NaN`, which\n",
    "I remove below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f76ae5fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_dir = Path(lib.__file__).parent / 'spectra'\n",
    "filename = file_dir.glob(f'legac_*_{id}_v3.0.fits')  \n",
    "filename = list(filename)[0]  # get first of possible duplicates\n",
    "t = fits.open(filename)[1].data\n",
    "flx = np.squeeze(t['flux'])\n",
    "finite = np.isfinite(flx)\n",
    "galaxy_lin = flx[finite]\n",
    "wave_lin = np.squeeze(t['wave'])[finite]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f585fa",
   "metadata": {},
   "source": [
    "<font color='red'>IMPORTANT:</font> Bring the observed spectrum to the\n",
    "rest-frame, using the redshift estimate provided by the LEGA-C catalogue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "926f4a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "wave_lin /= 1 + redshift"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec76b08c",
   "metadata": {},
   "source": [
    "Logarithmically rebin the spectrum to a velocity scale per spectral pixel\n",
    "equal to the instrumental dispersion $\\sigma_{\\rm inst}$ of VIMOS. This\n",
    "ensures Nyquist sampling without loss of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d629b81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lam_range_gal = [np.min(wave_lin), np.max(wave_lin)]\n",
    "c_kms = constants.c.to('km/s').value  # speed of light in km/s\n",
    "R = 3500    # LEGA-C approximate instrumental dispersion (Sec.3.2 van der Wel+21)\n",
    "sigma_instr_gal = c_kms/R/2.355   # 36 km/s\n",
    "galaxy, ln_lam, velscale = util.log_rebin(lam_range_gal, galaxy_lin, velscale=sigma_instr_gal)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67231302",
   "metadata": {},
   "source": [
    "Normalizes the galaxy spectrum to average unity. Adopt as initial estimate of\n",
    "the noise a crude estimate of the true value. This choice has little effect\n",
    "on the final result, as the noise will be rescaled after an initial `pPXF`\n",
    "fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8eabfa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "galaxy /= np.median(galaxy)  # Normalize spectrum to avoid numerical issues\n",
    "noise = np.full_like(galaxy, 0.1)   # S=1, N=0.1 --> S/N = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9941c874",
   "metadata": {},
   "source": [
    "## Setup stellar templates \n",
    "\n",
    "pPXF can be used with any set of SPS population templates. However, I am\n",
    "currently providing (with permission) ready-to-use template files for four\n",
    "SPS. One can just uncomment one of the four models below. The included files\n",
    "are only a subset of the SPS that can be produced with the models, and one\n",
    "should use the relevant software/website to produce different sets of SPS\n",
    "templates if needed.\n",
    "\n",
    "1. If you use the [fsps v3.2](https://github.com/cconroy20/fsps) SPS model\n",
    "   templates, please also cite in your paper \n",
    "   [Conroy et al. (2009)](https://ui.adsabs.harvard.edu/abs/2009ApJ...699..486C) and\n",
    "   [Conroy et al. (2010)](https://ui.adsabs.harvard.edu/abs/2010ApJ...712..833C).\n",
    "\n",
    "2. If you use the [GALAXEV v2020](http://www.bruzual.org/bc03/) SPS model \n",
    "   templates, please also cite in your paper \n",
    "   [Bruzual & Charlot (2003)](https://ui.adsabs.harvard.edu/abs/2003MNRAS.344.1000B).\n",
    "\n",
    "3. If you use the [E-MILES](http://miles.iac.es/) SPS model templates,\n",
    "   please also cite  in your paper \n",
    "   [Vazdekis et al. (2016)](https://ui.adsabs.harvard.edu/abs/2016MNRAS.463.3409V).\n",
    "   <font color=\"red\">WARNING: The E-MILES models only include SPS with age > 63 Myr and\n",
    "   are not recommended for highly star forming galaxies.</font>\n",
    "\n",
    "4. If you use the [X-Shooter Spectral Library (XSL)](http://xsl.u-strasbg.fr/) \n",
    "   SPS model templates, please also cite in your paper \n",
    "   [Verro et al. (2022)](https://ui.adsabs.harvard.edu/abs/2022A%26A...661A..50V). \n",
    "   <font color=\"red\">WARNING: The XSL models only include SPS with age > 50 Myr and\n",
    "   are not recommended for highly star forming galaxies.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c776b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "sps_name = 'fsps'\n",
    "# sps_name = 'galaxev'\n",
    "# sps_name = 'emiles'\n",
    "# sps_name = 'xsl'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90672245",
   "metadata": {},
   "source": [
    "Read the SPS models file from my GitHub if not already in the pPXF package\n",
    "dir. I am not distributing the templates with pPXF anymore. The SPS model\n",
    "files are also available [this GitHub\n",
    "page](https://github.com/micappe/ppxf_data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63f55bdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ppxf_dir = Path(lib.__file__).parent\n",
    "basename = f\"spectra_{sps_name}_9.0.npz\"\n",
    "filename = ppxf_dir / 'sps_models' / basename\n",
    "if not filename.is_file():\n",
    "    url = \"https://raw.githubusercontent.com/micappe/ppxf_data/main/\" + basename\n",
    "    request.urlretrieve(url, filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac1d7a3",
   "metadata": {},
   "source": [
    "## Setup spectral templates: stars and gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48b50634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emission lines included in gas templates:\n",
      "['Balmer' '[NeIII]3968' '[NeIII]3869' 'HeII4687' '[OIII]5007_d']\n"
     ]
    }
   ],
   "source": [
    "norm_type = 'lbol'          # Normalize spectra by their bolometric luminosity\n",
    "norm_range=[0.1e4, 3e4]     # Use only observable range for bolometric integral\n",
    "libs = lib.sps_lib(filename, velscale, norm_range=norm_range, norm_type=norm_type)\n",
    "\n",
    "# The stellar templates are reshaped below into a 2-dim array with each\n",
    "# spectrum as a column; however, we save the original array dimensions,\n",
    "# which are needed to specify the regularization dimensions\n",
    "reg_dim = libs.templates.shape[1:]\n",
    "stars_templates = libs.templates.reshape(libs.templates.shape[0], -1)\n",
    "\n",
    "# Construct a set of Gaussian emission line templates.\n",
    "# The `emission_lines` function defines the most common lines, but additional\n",
    "# lines can be included by editing the function in the file ppxf_util.py.\n",
    "vacuum = False if sps_name == 'emiles' else True\n",
    "gas_templates, gas_names, line_wave = util.emission_lines(\n",
    "    libs.ln_lam_temp, lam_range_gal, FWHM_gal=0, tie_balmer=1, vacuum=vacuum)\n",
    "\n",
    "# Combines the stellar and gaseous templates into a single array. During\n",
    "# the pPXF fit they will be assigned a different kinematic COMPONENT value\n",
    "templates = np.column_stack([stars_templates, gas_templates])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88367727",
   "metadata": {},
   "source": [
    "## Input galaxy photometry\n",
    "\n",
    "This is the most delicate part of the analysis. One has to associate the\n",
    "measured photometry to the right filter names. For this example, I took the\n",
    "photometric measurements and the filters associations from the [UltraVISTA\n",
    "catalogue](https://local.strw.leidenuniv.nl/galaxyevolution/ULTRAVISTA/)\n",
    "described in [Muzzin et al.\n",
    "(2013)](https://ui.adsabs.harvard.edu/abs/2013ApJ...777...18M/abstract). The\n",
    "association between the columns with flux values and the corresponding\n",
    "precise filter names is generally included as a file in the catalogue, as is\n",
    "the case for UltraVISTA. In a real application, these photometric\n",
    "measurements are extracted automatically from a large catalogue file. But I\n",
    "extracted a single line from the file, to avoid redistributing the full\n",
    "catalogue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b94178c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "bands0 = np.array(['VISTA/Ks', 'VISTA/H', 'VISTA/J', 'VISTA/Y', \n",
    "                   'IRAC/irac_tr4_2004-08-09', 'IRAC/irac_tr3_2004-08-09',\n",
    "                   'IRAC/irac_tr2_2004-08-09', 'IRAC/irac_tr1_2004-08-09',\n",
    "                   'COSMOS/SUBARU_filter_z', 'COSMOS/SUBARU_filter_i',\n",
    "                   'COSMOS/SUBARU_filter_r', 'COSMOS/SUBARU_filter_V',\n",
    "                   'COSMOS/SUBARU_filter_g', 'COSMOS/SUBARU_filter_B',\n",
    "                   'megaprime/cfht_mega_u', 'Subaru_MB/IA484', 'Subaru_MB/IA527',\n",
    "                   'Subaru_MB/IA624', 'Subaru_MB/IA679', 'Subaru_MB/IA738',\n",
    "                   'Subaru_MB/IA768', 'Subaru_MB/IA427', 'Subaru_MB/IA464',\n",
    "                   'Subaru_MB/IA505', 'Subaru_MB/IA574', 'Subaru_MB/IA709',\n",
    "                   'Subaru_MB/IA827', 'CAPAK/galex1500', 'CAPAK/galex2500'])\n",
    "phot_galaxy0 = np.array([256.732, 204.159, 148.225, 110.063,  57.896,  98.691, 130.98,\n",
    "                         207.56,  86.484,  58.093,  21.61,   9.848,   4.404,   2.702,\n",
    "                         1.189,   4.866,   8.026,  18.472,  35.406,  51.61,  63.231,\n",
    "                         1.403,   2.667,   6.188,  12.902,  40.663,  71.183,   0., 0.547])\n",
    "Ks_tot = 445.509\n",
    "phot_galaxy0 *= Ks_tot/phot_galaxy0[0]  # Aperture flux to total one (eq.4 Muzzin+13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a30722",
   "metadata": {},
   "source": [
    "The documentation by Muzzin+13 states that one should multiply the tabulated\n",
    "fluxes as below, to obtain CGS units. `3631 Jy = 3.631e-20 erg/cm2/s/Hz` is\n",
    "the constant flux of the AB system, but Muzzin+13 includes an extra factor\n",
    "`1e-10`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa9b69e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "phot_galaxy = phot_galaxy0*3.631e-30  # erg/cm2/s/Hz  inside 2.1 arcsec aperture"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b33f66c",
   "metadata": {},
   "source": [
    "## Compute photometric templates\n",
    "\n",
    "Compute the synthetic flux of each filter-template combination at the\n",
    "estimated redshift of the galaxy. \n",
    "\n",
    "Note that, that `synthetic_photometry` does **not** account for the $(1+z)$\n",
    "dimming, due to the stretching of the spectrum. \n",
    "\n",
    "The output `phot_templates` array is the photometric equivalent of the\n",
    "spectroscopic `templates` that are used as input to pPXF. This class also\n",
    "computes the effective wavelength of each filter-template combination. If the\n",
    "band falls outside of the wavelength range of the spectral templates provided\n",
    "by the SPS library, the procedure returns `p1.ok=False` and prints \"Outside\n",
    "template\" in the verbose output below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64900d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1: VISTA/Ks\n",
      "  2: VISTA/H\n",
      "  3: VISTA/J\n",
      "  4: VISTA/Y\n",
      "  5 --- Outside template: IRAC/irac_tr4_2004-08-09\n",
      "  6: IRAC/irac_tr3_2004-08-09\n",
      "  7: IRAC/irac_tr2_2004-08-09\n",
      "  8: IRAC/irac_tr1_2004-08-09\n",
      "  9: COSMOS/SUBARU_filter_z\n",
      " 10: COSMOS/SUBARU_filter_i\n",
      " 11: COSMOS/SUBARU_filter_r\n",
      " 12: COSMOS/SUBARU_filter_V\n",
      " 13: COSMOS/SUBARU_filter_g\n",
      " 14: COSMOS/SUBARU_filter_B\n",
      " 15: megaprime/cfht_mega_u\n",
      " 16: Subaru_MB/IA484\n",
      " 17: Subaru_MB/IA527\n",
      " 18: Subaru_MB/IA624\n",
      " 19: Subaru_MB/IA679\n",
      " 20: Subaru_MB/IA738\n",
      " 21: Subaru_MB/IA768\n",
      " 22: Subaru_MB/IA427\n",
      " 23: Subaru_MB/IA464\n",
      " 24: Subaru_MB/IA505\n",
      " 25: Subaru_MB/IA574\n",
      " 26: Subaru_MB/IA709\n",
      " 27: Subaru_MB/IA827\n",
      " 28: CAPAK/galex1500\n",
      " 29: CAPAK/galex2500\n"
     ]
    }
   ],
   "source": [
    "p1 = util.synthetic_photometry(libs.lam_temp, templates, bands=bands0, redshift=redshift)\n",
    "\n",
    "# Extract only the bands falling within the templates wavelength range\n",
    "phot_lam, phot_lam_piv, phot_templates, phot_galaxy, bands = \\\n",
    "    p1.lam_eff[p1.ok], p1.lam_piv[p1.ok], p1.flux[p1.ok], phot_galaxy[p1.ok], bands0[p1.ok]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e89c38",
   "metadata": {},
   "source": [
    "## Convert observed photometry to flux/A\n",
    "\n",
    "I convert exactly the observed galaxy values from `flux/Hz` into `flux/A`\n",
    "using the pivot wavelength $\\lambda_{\\rm piv}$ of each filter (eq. 16 of\n",
    "[Cappellari 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C)). \n",
    "\n",
    "NB: `synthetic_photometry` computes $\\lambda_{\\rm piv}$ in the restframe\n",
    "wavelength and I must shift it to the observed redshift as $\\lambda^{\\rm\n",
    "obs}_{\\rm piv}=\\lambda^{\\rm rest}_{\\rm piv}(1+z)$ below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd47f5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "phot_lam_piv *= 1 + redshift                # Use observed wavelength in flux conversion\n",
    "c_as = constants.c.to('Angstrom/s').value   # speed of light in A/s\n",
    "phot_galaxy *= c_as/phot_lam_piv**2         # Exact conversion flux/Hz --> flux/A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf157d88",
   "metadata": {},
   "source": [
    "## Scale photometry to match galaxy spectrum\n",
    "\n",
    "The photometry was not measured in the same aperture as the spectra.\n",
    "Moreover, the *relative* calibration of spectra and photometry is never\n",
    "sufficiently accurate. For this reason, I scale the photometry to match the\n",
    "spectrum flux, and save the scaling factor for later. \n",
    "\n",
    "For this, I compute the synthetic photometry on the LEGA-C spectrum, for the\n",
    "few filters that fall within the spectrum wavelength range, and match the\n",
    "synthetic predictions to the observed photometry from Muzzin+13. (see Sec.\n",
    "6.4 of [Cappellari 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C)). \n",
    "\n",
    "The procedure `synthetic_photometry` checks if a filters fall within the\n",
    "wavelength range of the spectrum and returns `p2.ok=False`, and prints\n",
    "\"Outside template\" in the verbose output, for filters where this is not the\n",
    "case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "598b35e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1 --- Outside template: VISTA/Ks\n",
      "  2 --- Outside template: VISTA/H\n",
      "  3 --- Outside template: VISTA/J\n",
      "  4 --- Outside template: VISTA/Y\n",
      "  5 --- Outside template: IRAC/irac_tr3_2004-08-09\n",
      "  6 --- Outside template: IRAC/irac_tr2_2004-08-09\n",
      "  7 --- Outside template: IRAC/irac_tr1_2004-08-09\n",
      "  8 --- Outside template: COSMOS/SUBARU_filter_z\n",
      "  9: COSMOS/SUBARU_filter_i\n",
      " 10 --- Outside template: COSMOS/SUBARU_filter_r\n",
      " 11 --- Outside template: COSMOS/SUBARU_filter_V\n",
      " 12 --- Outside template: COSMOS/SUBARU_filter_g\n",
      " 13 --- Outside template: COSMOS/SUBARU_filter_B\n",
      " 14 --- Outside template: megaprime/cfht_mega_u\n",
      " 15 --- Outside template: Subaru_MB/IA484\n",
      " 16 --- Outside template: Subaru_MB/IA527\n",
      " 17 --- Outside template: Subaru_MB/IA624\n",
      " 18: Subaru_MB/IA679\n",
      " 19: Subaru_MB/IA738\n",
      " 20: Subaru_MB/IA768\n",
      " 21 --- Outside template: Subaru_MB/IA427\n",
      " 22 --- Outside template: Subaru_MB/IA464\n",
      " 23 --- Outside template: Subaru_MB/IA505\n",
      " 24 --- Outside template: Subaru_MB/IA574\n",
      " 25: Subaru_MB/IA709\n",
      " 26: Subaru_MB/IA827\n",
      " 27 --- Outside template: CAPAK/galex1500\n",
      " 28 --- Outside template: CAPAK/galex2500\n"
     ]
    }
   ],
   "source": [
    "# Compute synthetic photometry on LEGA-C galaxy spectrum, which is already in rest-frame\n",
    "p2 = util.synthetic_photometry(np.exp(ln_lam), galaxy, bands=bands, redshift=redshift)\n",
    "\n",
    "# Extract the bands that fall inside the galaxy spectrum\n",
    "# Scale photometry to match the synthetic one from the LEGA-C spectrum\n",
    "err = 1                     # Assume constant uncertainties\n",
    "d = p2.flux[p2.ok]/err      # photometry predicted from spectrum\n",
    "m = phot_galaxy[p2.ok]/err  # photometry observed\n",
    "scale = (d @ m)/(m @ m)     # eq. (34) Cappellari (2023)\n",
    "phot_galaxy *= scale        # Match photometry to spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "436afd50",
   "metadata": {},
   "source": [
    "## pPXF input parameters\n",
    "\n",
    "Create the input `phot` dictionary for `pPXF`.\n",
    "I assign an initial uncertainties of 3% of the maximum flux. This choice has\n",
    "little influence on the final result, as the uncertainties will be rescaled\n",
    "after an initial `pPXF` fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5674cf58",
   "metadata": {},
   "outputs": [],
   "source": [
    "phot_noise = np.full_like(phot_galaxy, np.max(phot_galaxy)*0.03)   # assume 1sigma uncertainties of 3%\n",
    "phot = {\"templates\": phot_templates, \"galaxy\": phot_galaxy, \"noise\": phot_noise, \"lam\": phot_lam}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6564d50a",
   "metadata": {},
   "source": [
    "I adopt two kinematic components, one for the stars ``component=0`` and one\n",
    "for the gas ``component=1``. For each component I fit two moments $(V, \\sigma)$. \n",
    "I start the fit from a feasible guess and place reasonable bounds on the\n",
    "parameters.\n",
    "\n",
    "When studying stellar population, one should not use additive polynomials, to\n",
    "avoid changing the line strength of the  spectra. Instead, I use\n",
    "multiplicative polynomials of ``mdegree=2`` (see Sec. 6.2 of \n",
    "[Cappellari 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66fa0141",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_stars = [0, 200.]     # (km/s), starting guess for [V, sigma]\n",
    "start_gas = [1, 100.]       # ensures feasible start: sigma_gas < sigma_stars\n",
    "start = [start_stars, start_gas]\n",
    "\n",
    "bounds = [[[-500, 500], [1, 500]],   # Important: sigma lower bound cannot be zero\n",
    "          [[-500, 500], [1, 500]]]\n",
    "\n",
    "n_stars = stars_templates.shape[1]\n",
    "n_gas = len(gas_names)\n",
    "\n",
    "# I fit two kinematics components, one for the stars and one for the gas.\n",
    "# Assign component=0 to the stellar templates, component=1 to the gas.\n",
    "component = [0]*n_stars + [1]*n_gas    \n",
    "gas_component = np.array(component) > 0  # gas_component=True for gas templates\n",
    "\n",
    "# Fit (V, sig) moments=2 for both the stars and the gas\n",
    "moments = [2, 2]\n",
    "\n",
    "mdegree = 2   # Adopt a degree=2 multiplicative polynomial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e0ca6a9",
   "metadata": {},
   "source": [
    "In my initial tests, I noticed that in some galaxies without gas, pPXF\n",
    "introduces an extremely broad spurious gas emission, which is essentially\n",
    "acting like an additive polynomial rather than a genuine emission line. I use\n",
    "linear constraints to prevent this from happening. \n",
    "\n",
    "Given that I am not interested in the gas kinematics, I set the constraint\n",
    "that the gas velocity $\\sigma_{\\rm gas}$ dispersion (`component=1`) must be\n",
    "smaller than the stellar velocity dispersion $\\sigma_*$ (`component=0`). This\n",
    "is not always true, but appears to work well in this case.\n",
    "\n",
    "In formulas, I want to have:\n",
    "\n",
    "$$\\sigma_{\\rm gas} < \\sigma_*$$\n",
    "\n",
    "Following standard practice in numerical optimization (e.g.\n",
    "[HERE](https://uk.mathworks.com/help/optim/ug/linear-constraints.html)), I\n",
    "express this inequality as a linear constraint as this leads to an efficient\n",
    "optimization algorithm, with guaranteed local convergence:\n",
    "\n",
    "$$\\mathbf{A}_{\\rm ineq}\\cdot \\mathbf{p} < \\mathbf{b}_{\\rm ineq}.$$\n",
    "\n",
    "In this example, the vector of nonlinear kinematic parameters is\n",
    "$\\mathbf{p}=[V_0, \\sigma_0, V_1, \\sigma_1]$ (following the same order of the\n",
    "`start` parameters of `pPXF`) and I can write the above matrix equation\n",
    "explicitly as (in this case the matrix $\\mathbf{A}_{\\rm ineq}$ has a single\n",
    "row)\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "0 & -1 & 0 & 1 \\\\\n",
    "\\end{bmatrix}\n",
    "\\cdot\n",
    "\\begin{bmatrix}\n",
    "V_0 \\\\\n",
    "\\sigma_0 \\\\\n",
    "V_1 \\\\\n",
    "\\sigma_1\n",
    "\\end{bmatrix}\n",
    "<\n",
    "\\begin{bmatrix}\n",
    "0\\\\\n",
    "\\end{bmatrix}.\n",
    "$$\n",
    "\n",
    "This translates into the following Python code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "338d72e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constrain the gas dispersion to be smaller than the stellar one\n",
    "A_ineq = [[0, -1, 0, 1]]       # -sigma1 + sigma2 < 0\n",
    "b_ineq = [0]\n",
    "constr_kinem = {\"A_ineq\": A_ineq, \"b_ineq\": b_ineq}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a438cdbe",
   "metadata": {},
   "source": [
    "Finally I specify the attenuation. I use two different attenuation curves for\n",
    "the stars and the gas:\n",
    "- For the stars I use a two-parameter attenuation, parametrized by the\n",
    "  $V$-band attenuation $A_V$ and the slope $\\delta$ (eq. 23 of [Cappellari\n",
    "  2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C)).\n",
    "- For the gas I adopt a single-parameter [Calzetti et al.\n",
    "  (2000)](https://ui.adsabs.harvard.edu/abs/2000ApJ...533..682C) curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a64b0cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dust = [{\"start\": [0.11, -0.2], \"bounds\": [[0, 4], [-1, 0.4]], \"component\": ~gas_component},\n",
    "        {\"start\": [0.12], \"bounds\": [[0, 8]], \"component\": gas_component}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4e364d",
   "metadata": {},
   "source": [
    "## pPXF fit with iterative clipping\n",
    "\n",
    "Adopt a regularization parameter and start the pPXF fit, including\n",
    "iteratively clipping of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3b5bf2ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Best Fit:       Vel     sigma\n",
      " comp.  0:       -39       218\n",
      " comp.  1:       -79        41\n",
      "Attenuation Parameters 0: 0.000 0.393\n",
      "Attenuation Parameters 1: 7.974\n",
      "chi2/DOF: 0.1054; DOF: 2727; degree = -1; mdegree = 2\n",
      "Photometry chi2/n_bands: 0.4353; n_bands: 28\n",
      "method = capfit; Jac calls: 6; Func calls: 62; Status: 2\n",
      "linear_method = lsq_box; Nonzero Templates (>0.1%): 3/392\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "gas_component           name        flux       err      V     sig\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Comp:  1                Balmer    0.01714      0.27     -79    41\n",
      "Comp:  1           [NeIII]3968    0.07280      0.20     -79    41\n",
      "Comp:  1           [NeIII]3869      0.000      0.20     -79    41\n",
      "Comp:  1              HeII4687      0.000      0.20     -79    41\n",
      "Comp:  1          [OIII]5007_d     0.5211      0.26     -79    41\n",
      "-----------------------------------------------------------------\n",
      " Best Fit:       Vel     sigma\n",
      " comp.  0:       -40       221\n",
      " comp.  1:       -79        41\n",
      "Attenuation Parameters 0: 0.000 0.188\n",
      "Attenuation Parameters 1: 8.000\n",
      "chi2/DOF: 0.9620; DOF: 2657; degree = -1; mdegree = 2\n",
      "Photometry chi2/n_bands: 1.752; n_bands: 28\n",
      "method = capfit; Jac calls: 3; Func calls: 32; Status: 2\n",
      "linear_method = lsq_box; Nonzero Templates (>0.1%): 2/392\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "gas_component           name        flux       err      V     sig\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Comp:  1                Balmer      0.000     0.072     -79    41\n",
      "Comp:  1           [NeIII]3968    0.06846     0.055     -79    41\n",
      "Comp:  1           [NeIII]3869      0.000     0.055     -79    41\n",
      "Comp:  1              HeII4687      0.000     0.055     -79    41\n",
      "Comp:  1          [OIII]5007_d     0.5223     0.068     -79    41\n",
      "-----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "regul = 10  # A fixed `regul` can be used because galaxy and templates are normalized\n",
    "pp = ppxf_clip_outliers(templates, galaxy, noise, velscale, start,\n",
    "    moments=moments, degree=-1, mdegree=mdegree, lam=np.exp(ln_lam), lam_temp=libs.lam_temp, \n",
    "    regul=regul, reg_dim=reg_dim, component=component, dust=dust, gas_component=gas_component,\n",
    "    gas_names=gas_names, phot=phot, plot=0, constr_kinem=constr_kinem, bounds=bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11d7de8",
   "metadata": {},
   "source": [
    "## Population, stellar mass and redshift\n",
    "\n",
    "Extract the weights for the stars only (no gas) and compute the\n",
    "luminosity-weighted age and metallicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ad6d83eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted <lg_age> [yr]: 9.53\n",
      "Weighted <[M/H]>: 0.166\n"
     ]
    }
   ],
   "source": [
    "light_weights = pp.weights[~gas_component]      # Exclude weights of the gas templates\n",
    "light_weights = light_weights.reshape(reg_dim)  # Reshape to (n_ages, n_metal)\n",
    "\n",
    "# Given that the templates are normalized to L_bol, the pPXF weights represent\n",
    "# L_bol light fractions and the computed ages and metallicities below are also\n",
    "# light weighted by L_bol.\n",
    "libs.mean_age_metal(light_weights);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44c30ee",
   "metadata": {},
   "source": [
    "### Stellar mass using all bands (recommended)\n",
    "\n",
    "This is the recommended approach, using all bands for the overall normalization.\n",
    "\n",
    "- Convert the light weights into mass weights, using the ``libs.flux``\n",
    "  attribute;\n",
    "- Multiply by the mass fraction ``libs.mass_no_gas_grid`` in stars and\n",
    "  remnants (i.e. excluding gas loss);\n",
    "- Multiply by the area of a sphere of radius $D_L$ to convert observed fluxes\n",
    "  into intrinsic luminosity;\n",
    "- Multipy by $(1+z)$ to correct for the decrease with redshift of the\n",
    "  spectrum flux density in $\\mathrm{cgs/\\AA}$;\n",
    "- Divide by $L_\\odot$ to convert fluxes to Solar luminosities;\n",
    "- Divide by the ``scale`` to undo the scaling previously applied to match the\n",
    "  photometry to the the spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ef44567b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total stellar mass: lg(M*/Msun) = 11.64\n"
     ]
    }
   ],
   "source": [
    "cosmo = cosmology.FlatLambdaCDM(H0=70, Om0=0.3)     # Adopt standard flat cosmology\n",
    "lum_dist = cosmo.luminosity_distance(redshift).to('cm').value  # in cm as fluxes are erg/s/cm^2/A\n",
    "area_sphere = 4*np.pi*lum_dist**2                   # Convert observed flux/cm^2 to luminosity\n",
    "dist_fact = area_sphere*(1 + redshift)              # Correct for flux/A ~ 1/(1 + z)\n",
    "Lsun = constants.L_sun.to('erg/s').value            # erg/s (IAU definition) - Spectra are in units of Lsun/A\n",
    "mass_weights = light_weights/libs.flux              # Convert from light weights to mass weights\n",
    "lg_mpop = np.log10((libs.mass_no_gas_grid*mass_weights).sum()*dist_fact/Lsun/scale)  # This is survived stellar mass\n",
    "print(f\"Total stellar mass: lg(M*/Msun) = {lg_mpop:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6638a70",
   "metadata": {},
   "source": [
    "### Alternative stellar mass using a single band and $(M_*/L)_{\\rm pop}$\n",
    "\n",
    "This approach is useful if one has the luminosity in a single band and the\n",
    "population comes from spectrum fitting. I give it here for illustration and\n",
    "cross check.\n",
    "\n",
    "The stellar mass can be obtained using a single band for normalization as\n",
    "follows:\n",
    "\n",
    "- Convert the galaxy flux into apparent $x$-band AB magnitudes $m_x = 25 -2.5\n",
    "  \\lg(f_x)$ following [Muzzin et al.\n",
    "  (2013)](https://ui.adsabs.harvard.edu/abs/2013ApJ...777...18M/abstract);\n",
    "- Convert the apparent into absolute magnitude $M_x$ ($d=10$ pc);\n",
    "- Also correct as $1/(1+z)$ for the increase with redshift of the spectrum\n",
    "  flux density in $\\mathrm{cgs/Hz}$;\n",
    "- Compute the absolute magnitude of the Sun $M_{\\odot x}$ in the redshifted\n",
    "  $x$ band;\n",
    "- Convert the galaxy $M_x$ absolute magnitudes into $L_x$ solar luminosities.\n",
    "  Up to here the process is general and does not depend on SED fitting;\n",
    "- Now compute the galaxy stellar $(M_*/L_x)_{\\rm pop}$ from the ``pPXF``\n",
    "  weights;\n",
    "- Finally convert the galaxy luminosity into total stellar mass\n",
    "  $M_*=L_x\\times(M_*/L_x)_{\\rm pop}$.\n",
    "\n",
    "This method yields similar but not identical stellar masses when using\n",
    "different $x$-bands, as one can verify by uncommenting different bands below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d28d10f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(M*/L)=3.540 (COSMOS/SUBARU_filter_z at z=0.6686)\n",
      "Total stellar mass: lg(M*/Msun) = 11.64\n"
     ]
    }
   ],
   "source": [
    "# band_x = 'VISTA/Ks'\n",
    "band_x = 'COSMOS/SUBARU_filter_z'\n",
    "# band_x = 'megaprime/cfht_mega_u'\n",
    "# band_x = 'CAPAK/galex2500'\n",
    "w = bands0 == band_x\n",
    "mag_x = 25 - 2.5*np.log10(phot_galaxy0[w])  # AB mag\n",
    "dist_ratio = cosmo.luminosity_distance(redshift).to('pc').value/10\n",
    "abs_mag_x = mag_x - 2.5*np.log10(dist_ratio**2/(1 + redshift))  # Correct for flux/Hz ~ (1 + z)\n",
    "abs_mag_x_sun = util.mag_sun(band_x, redshift=redshift)\n",
    "lum_x = 10**(0.4*(abs_mag_x_sun - abs_mag_x))\n",
    "ml = libs.mass_to_light(light_weights, band=band_x, redshift=redshift)\n",
    "lg_mpop = np.log10(lum_x*ml).item()\n",
    "print(f\"Total stellar mass: lg(M*/Msun) = {lg_mpop:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e1e3ba",
   "metadata": {},
   "source": [
    "### Redshift\n",
    "\n",
    "Compute the redshift and its uncertainty (eq. 5 of \n",
    "[Cappellari 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9df087fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best-fitting redshift: z = 0.668380 +/- 0.000016\n"
     ]
    }
   ],
   "source": [
    "errors = pp.error[0]*np.sqrt(pp.chi2)               # Assume the fit is good: make chi2/DOF=1\n",
    "vpec = pp.sol[0][0]                                 # Stellar velocity is 1st element of 1st kinematics component\n",
    "vcosm = c_kms*np.log(1 + redshift)                  # eq. 5a of Cappellari (2023)\n",
    "vtot = vcosm + vpec                                 # eq. 5b \n",
    "redshift_ppxf = np.exp(vtot/c_kms) - 1              # eq. 5c\n",
    "redshift_err = (redshift_ppxf + 1)*errors[0]/c_kms  # eq. 5d\n",
    "\n",
    "prec = int(1 - np.floor(np.log10(redshift_err)))    # two digits of uncertainty\n",
    "print(f\"Best-fitting redshift: z = {redshift_ppxf:#.{prec}f} +/- {redshift_err:#.{prec}f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8cd1d9",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "Plot results: photometry and spectroscopy fit, and luminous weight\n",
    "distribution (like Figs. 5 and 6 of \n",
    "[Cappellari 2023](https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.3273C))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0b47b092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 815x815 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot fit results for stars and gas.\n",
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "# Plot photometry fit\n",
    "plt.subplot(311)\n",
    "pp.plot(gas_clip=1, spec=0, lam_flam=False)\n",
    "plt.title(f\"ID: {id}; {sps_name}; z={redshift_ppxf:.4f}; $S/N$={pp.sn:.0f}; regul={regul}, $\\\\lg M_*$={lg_mpop:.2f}\")\n",
    "\n",
    "# Plot spectrum fit\n",
    "plt.subplot(312)\n",
    "pp.plot(gas_clip=1, phot=0, lam_flam=False)\n",
    "\n",
    "# Plot stellar population light-fraction distribution\n",
    "plt.subplot(313)\n",
    "libs.plot(light_weights, title=\"\", colorbar_label=\"light fraction\")\n",
    "plt.tight_layout()\n",
    "plt.pause(5)"
   ]
  }
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