GridFire/validation/ManuscriptFigures/pynucastro/figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-04-14T14:53:20.072496Z",
     "start_time": "2026-04-14T14:53:19.712799Z"
    }
   },
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from plot import discover_runs, load_run_data, get_pynuc_eps\n",
    "\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "plt.style.use(\"../ManuscriptFigures/utils/pub.mplstyle\")"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-14T14:53:22.408612Z",
     "start_time": "2026-04-14T14:53:22.393261Z"
    }
   },
   "cell_type": "code",
   "source": "base_dir = \"GF_Validation_Output\"",
   "id": "6b80320acc86fb9e",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-14T14:53:23.299257Z",
     "start_time": "2026-04-14T14:53:23.282646Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def collect_species(steps, sp):\n",
    "    abundances = []\n",
    "    for step in steps:\n",
    "        abundances.append(step[\"Composition\"][sp])\n",
    "    return np.array(abundances)\n",
    "\n",
    "def collect_time(steps):\n",
    "    times = []\n",
    "    for step in steps:\n",
    "        times.append(step[\"t\"])\n",
    "    return np.array(times)"
   ],
   "id": "8d4660c043840d00",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-14T16:13:37.752792Z",
     "start_time": "2026-04-14T16:13:37.735551Z"
    }
   },
   "cell_type": "code",
   "source": "runs = discover_runs(base_dir)",
   "id": "ff332b29e27a5d75",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-14T16:14:42.864567Z",
     "start_time": "2026-04-14T16:14:42.851953Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_run_abundance(run_id, species=(\"H-1\", \"He-3\", \"He-4\", \"C-12\", \"N-14\", \"O-16\")):\n",
    "    fig, axs = plt.subplots(2, 1, figsize=(10, 10), sharex=True, gridspec_kw={'hspace': 0})\n",
    "    data = load_run_data(base_dir, run_id)\n",
    "\n",
    "    for spID, sp in enumerate(species):\n",
    "        gf_t, gf_y = collect_time(data[0][\"Steps\"]), collect_species(data[0][\"Steps\"], sp)\n",
    "        py_t, py_y = collect_time(data[1][\"Steps\"]), collect_species(data[1][\"Steps\"], sp)\n",
    "\n",
    "        axs[0].loglog(gf_t, gf_y, color=f\"C{spID}\", label=f\"GF {sp}\")\n",
    "        axs[0].loglog(py_t, py_y, color=f\"C{spID}\", linestyle=\"dashed\")\n",
    "\n",
    "        el = sp.split('-')[0]\n",
    "        A = sp.split('-')[1]\n",
    "        axs[0].text(gf_t[0], gf_y[0]*0.45, rf\"$^{{{A}}}${el}\", color=f\"C{spID}\", fontsize=12)\n",
    "\n",
    "        f_pync = interp1d(py_t, py_y, fill_value=\"extrapolate\", bounds_error=False)\n",
    "        f_gf = interp1d(gf_t, gf_y, fill_value=\"extrapolate\", bounds_error=False)\n",
    "\n",
    "        t_common = np.logspace(np.log10(max(min(gf_t), min(py_t))), np.log10(min(max(gf_t), max(py_t))), 100)\n",
    "        py_interp = f_pync(t_common)\n",
    "        gf_interp = f_gf(t_common)\n",
    "\n",
    "        log_diff = np.abs(np.log10(py_interp) - np.log10(gf_interp))\n",
    "\n",
    "        axs[1].loglog(t_common, log_diff, color=f\"C{spID}\")\n",
    "        # axs[1].set_xlim(t_common[0], 3.1e17)\n",
    "\n",
    "    axs[0].set_ylabel(\"Abundance [mol/g]\")\n",
    "    axs[1].set_xlabel(\"Time [s]\")\n",
    "    axs[1].set_ylabel(r\"$\\left|\\Delta\\log_{10}\\right|$ [dex]\")\n",
    "\n",
    "    return fig, axs\n"
   ],
   "id": "32114ccdfbdc0c30",
   "outputs": [],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-14T16:22:25.941637Z",
     "start_time": "2026-04-14T16:22:23.679453Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for run in runs:\n",
    "    if \"No_QSE\" in run:\n",
    "        fig, axs = plot_run_abundance(run);\n",
    "        axs[0].set_ylim(1e-8)\n",
    "        plt.savefig(f\"{run}_abundance.pdf\", bbox_inches='tight')\n",
    "        plt.close()"
   ],
   "id": "bd04488fead04880",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v3/xfq17d692dg_gzdwv4yzmrkw0000gp/T/ipykernel_42214/2916075565.py:23: RuntimeWarning: divide by zero encountered in log10\n",
      "  log_diff = np.abs(np.log10(py_interp) - np.log10(gf_interp))\n",
      "/var/folders/v3/xfq17d692dg_gzdwv4yzmrkw0000gp/T/ipykernel_42214/2916075565.py:23: RuntimeWarning: invalid value encountered in log10\n",
      "  log_diff = np.abs(np.log10(py_interp) - np.log10(gf_interp))\n"
     ]
    }
   ],
   "execution_count": 44
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-14T15:30:39.211161Z",
     "start_time": "2026-04-14T15:30:38.434430Z"
    }
   },
   "cell_type": "code",
   "source": [
    "num_species = []\n",
    "py_time = []\n",
    "gf_time = []\n",
    "for run in runs:\n",
    "    if \"Depth\" in run:\n",
    "        data = load_run_data(base_dir, run)\n",
    "        num_species.append(data[0][\"Metadata\"][\"NumSpecies\"])\n",
    "        gf_time.append(data[0][\"Metadata\"][\"ElapsedTime\"])\n",
    "        py_time.append(data[1][\"Metadata\"][\"RunTime1\"])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "ax.semilogy(num_species, gf_time, 'o', label='GridFire', color='C2')\n",
    "ax.semilogy(num_species, py_time, 'D', label='pynucastro', color='black')\n",
    "\n",
    "ax.legend(fontsize=17, frameon=False)\n",
    "ax.set_xlabel(\"Number of Species\", fontsize=25)\n",
    "ax.set_ylabel(\"Wall Time [s]\", fontsize=25)\n",
    "\n",
    "plt.savefig(\"GF_PyNuc_WallTime.pdf\", bbox_inches='tight')\n",
    "\n"
   ],
   "id": "e64c82995a57c114",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ],
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     },
     "metadata": {
      "image/png": {
       "width": 896,
       "height": 622
      }
     },
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-14T15:30:42.930128Z",
     "start_time": "2026-04-14T15:30:42.888964Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for ns, t in zip(num_species, py_time):\n",
    "    print(f\"pynucastro: {ns} species, {t:.2e} seconds\")"
   ],
   "id": "b0b9ceebfa5538b3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pynucastro: 357 species, 8.47e-03 seconds\n",
      "pynucastro: 457 species, 1.07e-02 seconds\n",
      "pynucastro: 568 species, 1.17e-02 seconds\n",
      "pynucastro: 61 species, 8.80e-03 seconds\n",
      "pynucastro: 580 species, 1.05e-02 seconds\n",
      "pynucastro: 98 species, 8.55e-03 seconds\n",
      "pynucastro: 136 species, 8.49e-03 seconds\n",
      "pynucastro: 177 species, 7.85e-03 seconds\n",
      "pynucastro: 219 species, 1.21e-02 seconds\n",
      "pynucastro: 261 species, 1.21e-02 seconds\n"
     ]
    }
   ],
   "execution_count": 27
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}