ppxf_example_population_bootstrap.ipynb

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    "# pPXF: Bootstrapping non-parametric stellar population\n",
    "\n",
    "Usage example for the procedure pPXF originally described in [Cappellari & Emsellem (2004)](http://adsabs.harvard.edu/abs/2004PASP..116..138C) and substantially upgraded in [Cappellari (2017)](http://adsabs.harvard.edu/abs/2017MNRAS.466..798C).\n",
    "\n",
    "Michele Cappellari, Oxford, 29 March 2022\n",
    "\n",
    "![pPXF](https://www-astro.physics.ox.ac.uk/~cappellari/software/ppxf_logo.svg)"
   ]
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   "source": [
    "from time import perf_counter as clock\n",
    "from os import path\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "\n",
    "from ppxf.ppxf import ppxf\n",
    "import ppxf.ppxf_util as util\n",
    "import ppxf.miles_util as lib"
   ]
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   "source": [
    "## Function to boostrap the spectrum residuals"
   ]
  },
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   "source": [
    "def bootstrap_residuals(model, resid, wild=True):\n",
    "    \"\"\"\n",
    "    https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#Resampling_residuals\n",
    "    https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#Wild_bootstrap\n",
    "\n",
    "    Davidson & Flachaire (2008) eq.(12) gives the reccomended form\n",
    "    of the wild bootrsapping probability used here.\n",
    "\n",
    "    https://doi.org/10.1016/j.jeconom.2008.08.003\n",
    "\n",
    "    :param spec: model (e.g. best fitting spectrum)\n",
    "    :param res: residuals (best_fit - observed)\n",
    "    :param wild: use wild bootsrap to allow for variable errors\n",
    "    :return: new model with bootstrapped residuals\n",
    "\n",
    "    \"\"\"\n",
    "    if wild:    # Wild Bootstrapping: generates -resid or resid with prob=1/2\n",
    "        eps = resid*(2*np.random.randint(2, size=resid.size) - 1)\n",
    "    else:       # Standard Bootstrapping: random selection with repetition\n",
    "        eps = np.random.choice(resid, size=resid.size)\n",
    "\n",
    "    return model + eps"
   ]
  },
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   "source": [
    "## Read the spectrum\n",
    "\n",
    "Read SDSS DR8 galaxy spectrum taken from here http://www.sdss3.org/dr8/\n",
    "The spectrum is *already* log rebinned by the SDSS DR8 pipeline and `ppxf_util.log_rebin` should not be used in this case."
   ]
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   "source": [
    "ppxf_dir = path.dirname(path.realpath(lib.__file__))\n",
    "file = ppxf_dir + '/spectra/NGC3522_SDSS_DR8.fits'\n",
    "hdu = fits.open(file)\n",
    "t = hdu[1].data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "z = float(hdu[1].header[\"Z\"])               # SDSS redshift estimate\n",
    "galaxy = t['flux']/np.median(t['flux'])     # Normalize spectrum to avoid numerical issues\n",
    "wave = t['wavelength']"
   ]
  },
  {
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   "source": [
    "The SDSS wavelengths are in vacuum, while the MILES ones are in air. For a rigorous treatment, the SDSS vacuum wavelengths should be converted into air wavelengths and the spectra should be resampled. To avoid resampling, given that the wavelength dependence of the correction is very weak, I approximate it with a constant factor.\n"
   ]
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   "source": [
    "wave *= np.median(util.vac_to_air(wave)/wave)"
   ]
  },
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   "source": [
    "I choose the noise to give $\\chi^2/{\\rm DOF}=1$ without regularization (`regul=0`).\n",
    "A constant noise is not a bad approximation in the fitted wavelength range and reduces the noise in the fit.\n"
   ]
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   "source": [
    "noise = np.full_like(galaxy, 0.01635)  # Assume constant noise per pixel here"
   ]
  },
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   "source": [
    "The velocity step per spectral pixel was already chosen by the SDSS pipeline and I convert it below to km/s.\n"
   ]
  },
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   "source": [
    "c = 299792.458                          # speed of light in km/s\n",
    "velscale = c*np.log(wave[1]/wave[0])    # eq. (8) of Cappellari (2017)\n",
    "FWHM_gal = 2.76                         # SDSS has an approximate instrumental resolution FWHM of 2.76A."
   ]
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   "source": [
    "## Setup the stellar templates"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "pathname = ppxf_dir + '/miles_models/Eun1.30*.fits'"
   ]
  },
  {
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   "source": [
    "I normalize the templates to `mean=1` within the FWHM of the V-band. In this way the weights returned by pPXF and mean values are light-weighted quantities"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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   "source": [
    "miles = lib.miles(pathname, velscale, FWHM_gal, norm_range=[5070, 5950])"
   ]
  },
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   "source": [
    "I reshape the stellar templates into a 2-dim array with each spectrum as a column, however we save the original array dimensions, which are needed to specify the regularization dimensions\n"
   ]
  },
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   "source": [
    "reg_dim = miles.templates.shape[1:]\n",
    "stars_templates = miles.templates.reshape(miles.templates.shape[0], -1)"
   ]
  },
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   "source": [
    "I want the first `pPXF` fit to be regularized because this suppresses the noise makes it more representative of the underlying galaxy spectrum. See the `pPXF` documentation for the keyword `regul`"
   ]
  },
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    "regul_err = 0.1  # Large regularization error"
   ]
  },
  {
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   "source": [
    "## Setup the gas emission lines templates\n",
    "\n",
    "This is the estimated wavelength fitted range in the rest frame. It is needed to find which gas emission lines fall within the observed range."
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
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   "source": [
    "lam_range_gal = np.array([np.min(wave), np.max(wave)])/(1 + z)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "I construct a set of Gaussian emission line templates. The `ppxf_util.emission_lines` function defines the most common lines, but `pPXF` allows complete freedom to add additional lines by simply editing the function in the file `ppxf_util.py`. I fix the ratios of the Balmer lines using the `tie_balmer=True` keyword."
   ]
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     "text": [
      "Emission lines included in gas templates:\n",
      "['Balmer' '[SII]6716' '[SII]6731' '[OIII]5007_d' '[OI]6300_d'\n",
      " '[NII]6583_d']\n"
     ]
    }
   ],
   "source": [
    "gas_templates, gas_names, line_wave = util.emission_lines(miles.ln_lam_temp, lam_range_gal, FWHM_gal, tie_balmer=1)"
   ]
  },
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   "source": [
    "I combines the stellar and gaseous templates into a single array of templates. During the PPXF fit they will be assigned a different kinematic `component` value.\n"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "templates = np.column_stack([stars_templates, gas_templates])"
   ]
  },
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   "source": [
    "## Input parameters for pPXF\n",
    "\n",
    "The expression below is the **definition** of velocities in `pPXF`. One should always use this precise expression rather than other approximations. E.g. **never** use $z\\approx V/c$. See Sec. 2.3 of [Cappellari (2017)](https://ui.adsabs.harvard.edu/abs/2017MNRAS.466..798C/abstract) for details"
   ]
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   "source": [
    "c = 299792.458\n",
    "vel = c*np.log(1 + z)   # eq. (8) of Cappellari (2017)\n",
    "start = [vel, 180.]     # (km/s), starting guess for [V, sigma]"
   ]
  },
  {
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   "source": [
    "In this example I consider two gas components, one for the Balmer and another for the forbidden lines."
   ]
  },
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   "source": [
    "n_temps = stars_templates.shape[1]\n",
    "n_forbidden = np.sum([\"[\" in a for a in gas_names])  # forbidden lines contain \"[*]\"\n",
    "n_balmer = len(gas_names) - n_forbidden"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Assign `component=0` to the stellar templates, `component=1` to the Balmer gas emission lines templates and `component=2` to the gas forbidden lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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   "source": [
    "component = [0]*n_temps + [1]*n_balmer + [2]*n_forbidden\n",
    "gas_component = np.array(component) > 0  # gas_component=True for gas templates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "Fit two moments (V, sig) `moments=2` for the stars and for the two gas kinematic components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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   "source": [
    "moments = [2, 2, 2]"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Adopt the same starting value for the stars and the two gas components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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   "source": [
    "start = [start, start, start]"
   ]
  },
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   "source": [
    "## The first pPXF fit with regularization\n",
    "\n",
    "I enforce some regularization on the solution with `regul` and I fit for a different reddening for the stars and the gas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Best Fit:       Vel     sigma\n",
      " comp.  0:      1212        99\n",
      " comp.  1:      1208        88\n",
      " comp.  2:      1198        80\n",
      "Stars Reddening E(B-V): 0.070\n",
      "Gas Reddening E(B-V): 0.000\n",
      "chi2/DOF: 1.236; DOF: 3839; degree = -1; mdegree = 0\n",
      "method = capfit; Jac calls: 4; Func calls: 38; Status: 2\n",
      "linear_method = lsq_box; Nonzero Templates (>0.1%): 28/156\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "gas_component           name        flux       err      V     sig\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Comp:  1                Balmer      2.619     0.070    1208    88\n",
      "Comp:  2             [SII]6716     0.3373     0.037    1198    80\n",
      "Comp:  2             [SII]6731     0.2724     0.037    1198    80\n",
      "Comp:  2          [OIII]5007_d      1.273     0.049    1198    80\n",
      "Comp:  2            [OI]6300_d    0.03054     0.047    1198    80\n",
      "Comp:  2           [NII]6583_d     0.5206     0.046    1198    80\n",
      "-----------------------------------------------------------------\n",
      "Elapsed time in PPXF: 2.69 s\n"
     ]
    }
   ],
   "source": [
    "t = clock()\n",
    "pp = ppxf(templates, galaxy, noise, velscale, start,\n",
    "          moments=moments, degree=-1, mdegree=-1, lam=wave, lam_temp=miles.lam_temp,\n",
    "          regul=1/regul_err, reg_dim=reg_dim, component=component, gas_component=gas_component,\n",
    "          gas_names=gas_names, reddening=0, gas_reddening=0)\n",
    "print('Elapsed time in PPXF: %.2f s' % (clock() - t))"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Plot fit results for stars and gas."
   ]
  },
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   "execution_count": 21,
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "pp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Plot stellar population for first regularized fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "weights = pp.weights[~gas_component]                # Exclude weights of the gas templates\n",
    "weights = weights.reshape(reg_dim)/weights.sum()    # Normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Compute the light-weighted stellar population parameters and the stellar M/L."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted <lg_age> [yr]: 9.87\n",
      "Weighted <[M/H]>: -0.0782\n",
      "M/L_r: 3.131\n"
     ]
    }
   ],
   "source": [
    "miles.mean_age_metal(weights)\n",
    "miles.mass_to_light(weights, band=\"r\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Plot stellar population mass fraction distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,3))\n",
    "miles.plot(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Start bootstrapping\n",
    "\n",
    "IMPORTANT: I store the residuals from the first fit. I will use them to bootstrap the spectrum by [reasampling residuals](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)#Resampling_residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "bestfit0 = pp.bestfit.copy()\n",
    "resid = galaxy - bestfit0\n",
    "start = pp.sol.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Here below I start the bootstrapping loop. All `pPXF` parameters are the same as before, with the exception that now I don't include regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/10: Elapsed time in pPXF: 3.38 s\n",
      "2/10: Elapsed time in pPXF: 3.27 s\n",
      "3/10: Elapsed time in pPXF: 3.51 s\n",
      "4/10: Elapsed time in pPXF: 3.30 s\n",
      "5/10: Elapsed time in pPXF: 3.06 s\n",
      "6/10: Elapsed time in pPXF: 3.24 s\n",
      "7/10: Elapsed time in pPXF: 3.23 s\n",
      "8/10: Elapsed time in pPXF: 3.26 s\n",
      "9/10: Elapsed time in pPXF: 3.37 s\n",
      "10/10: Elapsed time in pPXF: 3.88 s\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1584x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "\n",
    "plt.subplots(2, 5, sharex=True, sharey=True, figsize=(22, 3))\n",
    "plt.subplots_adjust(hspace=0.04)\n",
    "plt.subplots_adjust(wspace=0.02)\n",
    "\n",
    "nrand = 10\n",
    "weights_array = np.empty((nrand, pp.weights.size))\n",
    "for j in range(nrand):\n",
    "\n",
    "    galaxy1 = bootstrap_residuals(bestfit0, resid)\n",
    "\n",
    "    t = clock()\n",
    "    pp = ppxf(templates, galaxy1, noise, velscale, start,\n",
    "              moments=moments, degree=-1, mdegree=-1, lam=wave, lam_temp=miles.lam_temp,\n",
    "              component=component, gas_component=gas_component, gas_names=gas_names,\n",
    "              reddening=0, gas_reddening=0, quiet=1)\n",
    "    print(f\"{j + 1}/{nrand}: Elapsed time in pPXF: {clock() - t:.2f} s\")\n",
    "\n",
    "    weights_array[j] = pp.weights\n",
    "\n",
    "    # Plot stellar population mass fraction distribution\n",
    "    weights = pp.weights[~gas_component]                # Exclude weights of the gas templates\n",
    "    weights = weights.reshape(reg_dim)/weights.sum()    # Normalized\n",
    "\n",
    "    plt.subplot(2, 5, j + 1)\n",
    "    ylabel=\"[M/H]\" if j in [0, 5] else \"\"\n",
    "    miles.plot(weights, colorbar=False, title=\"\", ylabel=ylabel)\n",
    "\n",
    "pp.weights = weights_array.sum(0)\n",
    "weights_err = weights_array.std(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Plot the average stellar population mass fraction distribution and uncertainties.\n",
    "See an example of application of this bootstrapping approach with pPXF in [Kacharov et al. (2018)](https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.1973K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = pp.weights[~gas_component]  # Exclude weights of the gas templates\n",
    "weights = weights.reshape(reg_dim)/weights.sum()  # Normalized\n",
    "\n",
    "plt.figure(figsize=(9,3))\n",
    "miles.plot(weights)\n",
    "plt.title(\"Averaged Weights Fraction\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights_err = weights_err[~gas_component]  # Exclude weights of the gas templates\n",
    "weights_err = weights_err.reshape(reg_dim)/weights_err.sum()  # Normalized\n",
    "\n",
    "plt.figure(figsize=(9,3))\n",
    "miles.plot(weights_err)\n",
    "plt.title(\"Weights Standard Deviation\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}