{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell01",
   "metadata": {},
   "source": [
    "# Maximum Likelihood Estimation for Earth Interior Evolution\n",
    "\n",
    "This tutorial demonstrates how to use **maximum likelihood estimation (MLE)** with `vplanet_inference`\n",
    "to find initial conditions for Earth's thermal interior evolution that are consistent with present-day geophysical observations\n",
    "used in Gilbert-Janizek et al. (2026): [A whole-planet model of the Earth without life for terrestrial exoplanet studies](https://arxiv.org/abs/2602.02267)\n",
    "\n",
    "We use VPLanet's `thermint` (thermal interior) and `radheat` (radiogenic heating) modules to model:\n",
    "\n",
    "- Mantle and core temperature evolution over 4.5 Gyr\n",
    "- Heat flow from the core-mantle boundary (CMB) and upper mantle surface\n",
    "- Growth of the inner core radius\n",
    "- Mantle viscosity from a self-consistent parameterized convection scheme\n",
    "\n",
    "We then apply MLE to find the combination of planetary parameters that best reproduce the modern Earth. See [Gilbert-Janizek et al. (2026)](https://arxiv.org/abs/2602.02267) for more details.\n",
    "\n",
    "**What we cover:**\n",
    "\n",
    "1. **Earth model setup** — VPLanet infiles for `thermint` + `radheat`, and the `VplanetModel` interface\n",
    "2. **Observational constraints** — present-day geophysical measurements and the Gaussian log-likelihood\n",
    "3. **Fiducial model** — verifying the setup by running at known-good parameters\n",
    "4. **Single optimization** — `scipy.optimize.minimize` with the Nelder-Mead algorithm\n",
    "5. **Multi-start MLE** — parallel optimization from random starting points to handle non-convexity\n",
    "6. **Results analysis** — comparing best-fit outputs to observations\n",
    "\n",
    "---\n",
    "\n",
    "**Dependencies:** `vplanet_inference`, `scipy`, `numpy`, `matplotlib`, `astropy`, `pandas`, `tqdm`\n",
    "\n",
    "**VPLanet modules used:** `thermint`, `radheat`, `stellar` (for the host Sun)\n",
    "\n",
    "**References:**\n",
    "- [VPLanet documentation](https://virtualplanetarylaboratory.github.io/vplanet/)\n",
    "- [Driscoll & Barnes (2015)](https://doi.org/10.1017/S1473550415000130) — `thermint` module\n",
    "- [vplanet_inference GitHub repository](https://github.com/jbirky/vplanet_inference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cell02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.optimize\n",
    "import multiprocessing as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import astropy.units as u\n",
    "import astropy.constants as const\n",
    "from functools import partial\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "\n",
    "import vplanet_inference as vpi\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell03",
   "metadata": {},
   "source": [
    "## 1. Earth Model Setup\n",
    "\n",
    "### 1.1 VPLanet Input Files\n",
    "\n",
    "The Earth interior model requires three input files:\n",
    "\n",
    "| File | Purpose |\n",
    "|------|---------|\n",
    "| `vpl.in` | System control file — units, timing, list of bodies |\n",
    "| `sun.in` | Host star — fixed luminosity and mass (no stellar evolution) |\n",
    "| `earth.in` | Earth body — `radheat` + `thermint` modules, initial conditions |\n",
    "\n",
    "The `radheat` module tracks the decay of long-lived radiogenic isotopes\n",
    "(${}^{40}\\text{K}$, ${}^{232}\\text{Th}$, ${}^{235}\\text{U}$, ${}^{238}\\text{U}$) and computes their contribution to mantle\n",
    "and core heating over geological time.\n",
    "\n",
    "The `thermint` module uses a parameterized convection model to evolve the\n",
    "mantle and core temperatures, computing heat flows, viscosity, inner core growth,\n",
    "and magnetic moment self-consistently from the thermal state.\n",
    "\n",
    "We create the infiles in a local `earth_infiles/` directory so this notebook is self-contained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cell04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infiles written to: earth_infiles/\n",
      "  dStopTime  = 1.420e+17 sec\n",
      "  dOutputTime= 3.156e+15 sec\n",
      "  sun dMass  = 1.988e+30 kg\n",
      "  sun dRadius= 2.020e+08 m\n",
      "  dObliquity = 0.410152 rad\n"
     ]
    }
   ],
   "source": [
    "INFILE_PATH = \"earth_infiles/\"\n",
    "os.makedirs(INFILE_PATH, exist_ok=True)\n",
    "\n",
    "# SI conversion constants\n",
    "STOP_TIME_SEC  = (4.5e9 * u.yr).si.value   # 4.5 Gyr in seconds\n",
    "OUT_TIME_SEC   = (1e8  * u.yr).si.value    # 100 Myr in seconds\n",
    "M_SUN_KG       = const.M_sun.si.value      # solar mass in kg\n",
    "R_SUN_0135_M   = (0.00135 * u.AU).si.value # 0.00135 AU in metres\n",
    "OBL_RAD        = float((23.5 * u.deg).to(u.rad).value)  # obliquity in radians\n",
    "\n",
    "# ---- vpl.in ----\n",
    "# Units will be converted to SI by vplanet_inference (sUnitMass->kg, sUnitLength->m,\n",
    "# sUnitTime->sec, sUnitAngle->rad). dStopTime and dOutputTime are therefore written\n",
    "# in seconds so VPLanet interprets them correctly after the unit substitution.\n",
    "vpl_in = f\"\"\"\\\n",
    "# Primary input file: Earth interior evolution\n",
    "sSystemName     earth\n",
    "iVerbose        0\n",
    "bOverwrite      1\n",
    "\n",
    "saBodyFiles     sun.in earth.in\n",
    "\n",
    "sUnitMass       solar\n",
    "sUnitLength     aU\n",
    "sUnitTime       YEARS\n",
    "sUnitAngle      d\n",
    "sUnitTemp       K\n",
    "\n",
    "bDoLog          1\n",
    "iDigits         6\n",
    "dMinValue       1e-10\n",
    "\n",
    "bDoForward      1\n",
    "bVarDt          1\n",
    "dEta            0.1\n",
    "dStopTime       {STOP_TIME_SEC:.6e}\n",
    "dOutputTime     {OUT_TIME_SEC:.6e}\n",
    "\"\"\"\n",
    "\n",
    "# ---- sun.in ----\n",
    "# All values in SI because vplanet_inference sets sUnitMass=kg, sUnitLength=m.\n",
    "sun_in = f\"\"\"\\\n",
    "# Host star: present-day Sun (fixed, no evolution)\n",
    "sName           sun\n",
    "saModules       stellar\n",
    "dMass           {M_SUN_KG:.6e}\n",
    "dRadius         {R_SUN_0135_M:.6e}\n",
    "dLuminosity     3.846e26\n",
    "dSemi           0\n",
    "dEcc            0\n",
    "sStellarModel   none\n",
    "\"\"\"\n",
    "\n",
    "# ---- earth.in ----\n",
    "# dObliquity in radians (vplanet_inference sets sUnitAngle=rad).\n",
    "# Negative values (-1) tell VPLanet to use its built-in Earth defaults.\n",
    "earth_in = f\"\"\"\\\n",
    "# Earth body: radiogenic heating + thermal interior\n",
    "sName           earth\n",
    "saModules       radheat thermint\n",
    "\n",
    "# Physical properties (negative -> VPLanet Earth defaults, unit-independent)\n",
    "dMass           -1.0\n",
    "dRadius         -1.0\n",
    "dRotPeriod      -1.0\n",
    "dObliquity      {OBL_RAD:.6f}\n",
    "dRadGyra        0.5\n",
    "dEcc            0.0167\n",
    "dSemi           -1\n",
    "\n",
    "# Radiogenic heating — 40K (substituted by vplanet_inference)\n",
    "d40KPowerMan    -1\n",
    "d40KPowerCore   -1\n",
    "d40KPowerCrust  -1\n",
    "\n",
    "# Radiogenic heating — other isotopes (default Earth values)\n",
    "d232ThPowerMan    -1\n",
    "d232ThPowerCore   -1\n",
    "d232ThPowerCrust  -1\n",
    "d235UPowerMan     -1\n",
    "d235UPowerCore    -1\n",
    "d235UPowerCrust   -1\n",
    "d238UPowerMan     -1\n",
    "d238UPowerCore    -1\n",
    "d238UPowerCrust   -1\n",
    "\n",
    "# Thermint initial conditions and rheological parameters\n",
    "dTMan           3000\n",
    "dTCore          6500\n",
    "dViscJumpMan    2.4\n",
    "dActViscMan     3e5\n",
    "dViscRef        6e7\n",
    "dEruptEff       0.10\n",
    "dDTChiRef       0\n",
    "\n",
    "# Output time series for visualization (overridden by vplanet_inference)\n",
    "saOutputOrder   -Time -TMan -TUMan -TCMB -TCore -HflowUMan -HflowCMB -RIC -ViscUMan -ViscLMan -FMeltUMan\n",
    "\"\"\"\n",
    "\n",
    "with open(os.path.join(INFILE_PATH, \"vpl.in\"),   \"w\") as f: f.write(vpl_in)\n",
    "with open(os.path.join(INFILE_PATH, \"sun.in\"),   \"w\") as f: f.write(sun_in)\n",
    "with open(os.path.join(INFILE_PATH, \"earth.in\"), \"w\") as f: f.write(earth_in)\n",
    "\n",
    "print(\"Infiles written to:\", INFILE_PATH)\n",
    "print(f\"  dStopTime  = {STOP_TIME_SEC:.3e} sec\")\n",
    "print(f\"  dOutputTime= {OUT_TIME_SEC:.3e} sec\")\n",
    "print(f\"  sun dMass  = {M_SUN_KG:.3e} kg\")\n",
    "print(f\"  sun dRadius= {R_SUN_0135_M:.3e} m\")\n",
    "print(f\"  dObliquity = {OBL_RAD:.6f} rad\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell05",
   "metadata": {},
   "source": [
    "**Key infile options for Earth interior modelling:**\n",
    "\n",
    "| Option | Value | Description |\n",
    "|--------|-------|-------------|\n",
    "| `dStopTime` | `4.5e9` | Simulate 4.5 Gyr — the age of the solar system |\n",
    "| `d40KPowerMan` | `-1` | Placeholder: `-1` tells VPLanet to use the primordial Earth value; vplanet_inference will substitute the real value |\n",
    "| `dTMan` | `3000` | Initial mantle temperature [K] — substituted by vplanet_inference |\n",
    "| `dViscJumpMan` | `2.4` | Mantle viscosity jump across the transition zone |\n",
    "| `dActViscMan` | `3e5` | Mantle activation viscosity [m²/s] |\n",
    "| `dEruptEff` | `0.10` | Melt eruption efficiency (fraction of melt that erupts) |\n",
    "| `saOutputOrder` | `...` | Variables written to the time-series `.forward` file |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell06",
   "metadata": {},
   "source": [
    "### 1.2 Input Parameters\n",
    "\n",
    "We optimize **9 parameters** that control the initial radiogenic heat budget, starting temperatures,\n",
    "and rheological properties of Earth's interior.\n",
    "\n",
    "| Parameter | Symbol | Units | Physical Meaning |\n",
    "|-----------|--------|-------|------------------|\n",
    "| `earth.d40KPowerMan` | ${}^{40}\\text{K}$ mantle power | W | Initial ${}^{40}\\text{K}$ heating in mantle |\n",
    "| `earth.d40KPowerCore` | ${}^{40}\\text{K}$ core power | W | Initial ${}^{40}\\text{K}$ heating in core |\n",
    "| `earth.dTMan` | $T_{\\rm man}$ | K | Initial mantle temperature |\n",
    "| `earth.dTCore` | $T_{\\rm core}$ | K | Initial core temperature |\n",
    "| `earth.dEruptEff` | $\\epsilon_{\\rm erupt}$ | — | Melt eruption efficiency |\n",
    "| `earth.dDTChiRef` | $\\Delta T_{\\chi}$ | — | CMB temperature offset parameter |\n",
    "| `earth.dViscRef` | $\\eta_{\\rm ref}$ | — | Mantle reference viscosity |\n",
    "| `earth.dViscJumpMan` | $\\Delta\\eta$ | — | Mantle viscosity jump |\n",
    "| `earth.dActViscMan` | $\\eta_{\\rm act}$ | m²/s | Mantle activation viscosity |\n",
    "\n",
    "**Note:** In this example, other radiogenic isotopes (${}^{232}\\text{Th}$, ${}^{238}\\text{U}$, ${}^{235}\\text{U}$) are held at their primordial Earth values (`-1` in the infile). Only the ${}^{40}\\text{K}$ budget is varied, since ${}^{40}\\text{K}$ has the largest uncertainty among the major radiogenic heat producers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cell07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of free parameters: 9\n",
      "\n",
      "Parameter            User unit             VPLanet unit          Status\n",
      "-----------------------------------------------------------------------------------\n",
      "earth.d40KPowerMan   W                     W                     OK\n",
      "earth.d40KPowerCore  W                     W                     OK\n",
      "earth.dTMan          K                     K                     OK\n",
      "earth.dTCore         K                     K                     OK\n",
      "earth.dEruptEff                                                  OK\n",
      "earth.dDTChiRef      K                     K                     OK\n",
      "earth.dViscRef       m2 / s                m2 / s                OK\n",
      "earth.dViscJumpMan                                               OK\n",
      "earth.dActViscMan    m2 / s                m2 / s                OK\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'consistent': [('earth.d40KPowerMan', Unit(\"W\"), Unit(\"W\")),\n",
       "  ('earth.d40KPowerCore', Unit(\"W\"), Unit(\"W\")),\n",
       "  ('earth.dTMan', Unit(\"K\"), Unit(\"K\")),\n",
       "  ('earth.dTCore', Unit(\"K\"), Unit(\"K\")),\n",
       "  ('earth.dEruptEff', Unit(dimensionless), Unit(dimensionless)),\n",
       "  ('earth.dDTChiRef', Unit(\"K\"), Unit(\"K\")),\n",
       "  ('earth.dViscRef', Unit(\"m2 / s\"), Unit(\"m2 / s\")),\n",
       "  ('earth.dViscJumpMan', Unit(dimensionless), Unit(dimensionless)),\n",
       "  ('earth.dActViscMan', Unit(\"m2 / s\"), Unit(\"m2 / s\"))],\n",
       " 'inconsistent': [],\n",
       " 'unknown': []}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# =====================================================\n",
    "# Input parameters: names and astropy units\n",
    "# =====================================================\n",
    "inparams = {\n",
    "    \"earth.d40KPowerMan\":  u.W,   \n",
    "    \"earth.d40KPowerCore\": u.W,   \n",
    "    \"earth.dTMan\":         u.K,\n",
    "    \"earth.dTCore\":        u.K,\n",
    "    \"earth.dEruptEff\":     u.dimensionless_unscaled,\n",
    "    \"earth.dDTChiRef\":     u.K,\n",
    "    \"earth.dViscRef\":      u.m**2 / u.s,\n",
    "    \"earth.dViscJumpMan\":  u.dimensionless_unscaled,\n",
    "    \"earth.dActViscMan\":   u.m**2 / u.s,\n",
    "}\n",
    "\n",
    "# Human-readable labels for plots\n",
    "inlabels = [\n",
    "    r\"${}^{40}\\mathrm{K}$ Mantle Power [W]\",\n",
    "    r\"${}^{40}\\mathrm{K}$ Core Power [W]\",\n",
    "    r\"$T_{\\rm man}$ [K]\",\n",
    "    r\"$T_{\\rm core}$ [K]\",\n",
    "    r\"Erupt. Efficiency\",\n",
    "    r\"$\\Delta T_{\\chi}$\",\n",
    "    r\"Visc. Reference\",\n",
    "    r\"Visc. Jump\",\n",
    "    r\"Act. Viscosity [m$^2$/s]\",\n",
    "]\n",
    "\n",
    "# Reference values for K-40 radiogenic power\n",
    "K40_man_ref  = 3.615780e13   # W\n",
    "K40_core_ref = 3.385730e13   # W\n",
    "\n",
    "# Prior bounds — uniform sampling between these limits\n",
    "bounds = [\n",
    "    [0.8 * K40_man_ref,  1.5 * K40_man_ref],    # d40KPowerMan\n",
    "    [0.8 * K40_core_ref, 1.5 * K40_core_ref],   # d40KPowerCore\n",
    "    [2500,  3000],    # dTMan [K]\n",
    "    [5800,  6800],    # dTCore [K]\n",
    "    [0.05,  0.15],    # dEruptEff\n",
    "    [0.0,   0.001],   # dDTChiRef\n",
    "    [4e7,   9e8],     # dViscRef\n",
    "    [1.1,   2.4],     # dViscJumpMan\n",
    "    [2.5e5, 3.1e5],   # dActViscMan [m^2/s]\n",
    "]\n",
    "bounds = np.array(bounds)\n",
    "\n",
    "# Fiducial (reference) parameter values\n",
    "theta_fiducial = np.array([\n",
    "    K40_man_ref,   # d40KPowerMan\n",
    "    K40_core_ref,  # d40KPowerCore\n",
    "    3000,          # dTMan [K]\n",
    "    6500,          # dTCore [K]\n",
    "    0.10,          # dEruptEff\n",
    "    0.0,           # dDTChiRef\n",
    "    6e7,           # dViscRef\n",
    "    2.4,           # dViscJumpMan\n",
    "    3e5,           # dActViscMan [m^2/s]\n",
    "])\n",
    "\n",
    "print(f\"Number of free parameters: {len(inparams)}\")\n",
    "\n",
    "vpi.check_units(inparams)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell08",
   "metadata": {},
   "source": [
    "### 1.3 Output Parameters and Observational Constraints\n",
    "\n",
    "We constrain the model using **8 present-day geophysical observables**:\n",
    "\n",
    "| Output | Observable | Value | Uncertainty |\n",
    "|--------|------------|-------|-------------|\n",
    "| `FMeltUMan` | Upper mantle melt fraction | 0.06 | 0.04 |\n",
    "| `HflowCMB` | Core-mantle boundary heat flow | 11 TW | 6 TW |\n",
    "| `HflowUMan` | Upper mantle surface heat flow | 38 TW | 3 TW |\n",
    "| `RIC` | Inner core radius | 1224.1 km | 1 km |\n",
    "| `TCMB` | CMB temperature | 4000 K | 200 K |\n",
    "| `TUMan` | Upper mantle potential temperature | 1587 K | 34 K |\n",
    "| `ViscLMan` | Lower mantle viscosity | $1.5 \\times 10^{18}$ m²/s | $1.4 \\times 10^{18}$ m²/s |\n",
    "| `ViscUMan` | Upper mantle viscosity | $2.3 \\times 10^{18}$ m²/s | $2.3 \\times 10^{18}$ m²/s |\n",
    "\n",
    "> Note: Some variables like viscosity observables have relatively high uncertainties comparable to their central values. The inner core radius, by contrast, is very well constrained by seismology and will strongly influence the likelihood function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f260d76c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Parameter              User unit             VPLanet unit          Status\n",
      "-------------------------------------------------------------------------------------\n",
      "final.earth.ViscLMan   m2 / s                m2 / s                OK\n",
      "final.earth.ViscUMan   m2 / s                m2 / s                OK\n",
      "final.earth.FMeltUMan                                              OK\n",
      "final.earth.HflowCMB   W                     TW                    OK\n",
      "final.earth.HflowUMan  W                     TW                    OK\n",
      "final.earth.RIC        m                     km                    OK\n",
      "final.earth.TCMB       K                     K                     OK\n",
      "final.earth.TUMan      K                     K                     OK\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'consistent': [('final.earth.ViscLMan', Unit(\"m2 / s\"), Unit(\"m2 / s\")),\n",
       "  ('final.earth.ViscUMan', Unit(\"m2 / s\"), Unit(\"m2 / s\")),\n",
       "  ('final.earth.FMeltUMan', Unit(dimensionless), Unit(dimensionless)),\n",
       "  ('final.earth.HflowCMB', Unit(\"W\"), Unit(\"TW\")),\n",
       "  ('final.earth.HflowUMan', Unit(\"W\"), Unit(\"TW\")),\n",
       "  ('final.earth.RIC', Unit(\"m\"), Unit(\"km\")),\n",
       "  ('final.earth.TCMB', Unit(\"K\"), Unit(\"K\")),\n",
       "  ('final.earth.TUMan', Unit(\"K\"), Unit(\"K\"))],\n",
       " 'inconsistent': [],\n",
       " 'unknown': []}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unit conversion: TW -> W (SI)\n",
    "TW_TO_W = (u.TW).to(u.W)\n",
    "\n",
    "# Output parameters: names and astropy units for VPI to convert to.\n",
    "outparams = {\n",
    "    \"final.earth.ViscLMan\":  u.m**2 / u.s,\n",
    "    \"final.earth.ViscUMan\":  u.m**2 / u.s,\n",
    "    \"final.earth.FMeltUMan\": u.dimensionless_unscaled,\n",
    "    \"final.earth.HflowCMB\":  u.W,\n",
    "    \"final.earth.HflowUMan\": u.W,\n",
    "    \"final.earth.RIC\":       u.m,\n",
    "    \"final.earth.TCMB\":      u.K,\n",
    "    \"final.earth.TUMan\":     u.K,\n",
    "}\n",
    "outlabels = [k.replace(\"final.earth.\", \"\") for k in outparams.keys()]\n",
    "\n",
    "vpi.check_units(outparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cell09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "like_data shape: (8, 2)\n",
      "\n",
      "Observable   |     Mean      |    Std\n",
      "--------------------------------------------------\n",
      "  ViscLMan           1.5e+18       1.4e+18\n",
      "  ViscUMan         2.275e+18      2.27e+18\n",
      "  FMeltUMan             0.06          0.04\n",
      "  HflowCMB           1.1e+13         6e+12\n",
      "  HflowUMan          3.8e+13         3e+12\n",
      "  RIC              1.224e+06          1000\n",
      "  TCMB                  4000           200\n",
      "  TUMan                 1587            34\n"
     ]
    }
   ],
   "source": [
    "# Observational data: (mean, 1-sigma) in SI units matching VPLanet output\n",
    "outparams_data = {\n",
    "    \"final.earth.FMeltUMan\": [0.06,               0.04],\n",
    "    \"final.earth.HflowCMB\":  [11.0 * TW_TO_W,     6.0 * TW_TO_W],   # W\n",
    "    \"final.earth.HflowUMan\": [38.0 * TW_TO_W,     3.0 * TW_TO_W],   # W\n",
    "    \"final.earth.RIC\":       [1224.1e3,            1e3],              # m\n",
    "    \"final.earth.TCMB\":      [4000,                200],              # K\n",
    "    \"final.earth.TUMan\":     [1587,                34],               # K\n",
    "    \"final.earth.ViscLMan\":  [1.5e18,              1.4e18],           # m^2/s\n",
    "    \"final.earth.ViscUMan\":  [2.275e18,            2.27e18],          # m^2/s\n",
    "}\n",
    "\n",
    "# like_data: shape (n_obs, 2) — rows in same order as outparams\n",
    "like_data = np.array([outparams_data[key] for key in outparams.keys()])\n",
    "\n",
    "print(\"like_data shape:\", like_data.shape)\n",
    "print(\"\\nObservable   |     Mean      |    Std\")\n",
    "print(\"-\" * 50)\n",
    "for label, (mean, std) in zip(outlabels, like_data):\n",
    "    print(f\"  {label:<12s}  {mean:12.4g}  {std:12.4g}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell10",
   "metadata": {},
   "source": [
    "### 1.4 Initializing VplanetModel\n",
    "\n",
    "We create two `VplanetModel` instances:\n",
    "\n",
    "- **`vpm_final`** — runs a single VPLanet simulation and returns the **final-state** values\n",
    "  from the log file. This is fast and used for MLE optimization.\n",
    "- **`vpm_evol`** — runs with `timesteps` set, returning **time-series** data from the `.forward`\n",
    "  file at every recorded output interval. Used for visualizing the evolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cell11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input parameters:\n",
      "  earth.d40KPowerMan\n",
      "  earth.d40KPowerCore\n",
      "  earth.dTMan\n",
      "  earth.dTCore\n",
      "  earth.dEruptEff\n",
      "  earth.dDTChiRef\n",
      "  earth.dViscRef\n",
      "  earth.dViscJumpMan\n",
      "  earth.dActViscMan\n",
      "\n",
      "Output parameters (sorted alphabetically):\n",
      "  final.earth.ViscLMan\n",
      "  final.earth.ViscUMan\n",
      "  final.earth.FMeltUMan\n",
      "  final.earth.HflowCMB\n",
      "  final.earth.HflowUMan\n",
      "  final.earth.RIC\n",
      "  final.earth.TCMB\n",
      "  final.earth.TUMan\n"
     ]
    }
   ],
   "source": [
    "# Fast model: final-state values only (used for MLE)\n",
    "vpm_final = vpi.VplanetModel(\n",
    "    inparams=inparams,\n",
    "    outparams=outparams,\n",
    "    inpath=INFILE_PATH,\n",
    "    outpath=\"output/earth_mle\",\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "# Evolution model: time series at every 100 Myr (used for visualization)\n",
    "vpm_evol = vpi.VplanetModel(\n",
    "    inparams=inparams,\n",
    "    outparams=outparams,\n",
    "    inpath=INFILE_PATH,\n",
    "    outpath=\"output/earth_mle\",\n",
    "    timesteps=1e8 * u.yr,\n",
    "    verbose=False,\n",
    ")\n",
    "\n",
    "print(\"Input parameters:\")\n",
    "for p in vpm_final.inparams:\n",
    "    print(f\"  {p}\")\n",
    "\n",
    "print(\"\\nOutput parameters (sorted alphabetically):\")\n",
    "for p in vpm_final.outparams:\n",
    "    print(f\"  {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell12",
   "metadata": {},
   "source": [
    "## 2. The Log-Likelihood Function\n",
    "\n",
    "We assume each observable $y_i$ is measured with Gaussian uncertainty $\\sigma_i$, so the\n",
    "log-likelihood of the data given model parameters $\\boldsymbol{\\theta}$ is:\n",
    "\n",
    "$$\n",
    "\\ln \\mathcal{L}(\\boldsymbol{\\theta}) = -\\frac{1}{2} \\sum_{i=1}^{N_{\\rm obs}}\n",
    "\\left( \\frac{m_i(\\boldsymbol{\\theta}) - y_i}{\\sigma_i} \\right)^2\n",
    "$$\n",
    "\n",
    "where $m_i(\\boldsymbol{\\theta})$ is the VPLanet model prediction for observable $i$.\n",
    "\n",
    "This is a standard chi-squared statistic (up to a constant normalization).\n",
    "**Maximizing the log-likelihood** is equivalent to minimizing the sum of squared residuals\n",
    "weighted by the observational uncertainties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cell13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-likelihood function defined.\n",
      "Note: expects theta values in units defined by inparams\n"
     ]
    }
   ],
   "source": [
    "def lnlike(theta, data):\n",
    "    \"\"\"\n",
    "    Gaussian log-likelihood for the Earth interior model.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    theta : array-like, shape (n_params,)\n",
    "        Parameter vector in the order defined by inparams.\n",
    "    data : array-like, shape (n_obs, 2)\n",
    "        Observational data; data[:, 0] = means, data[:, 1] = 1-sigma uncertainties.\n",
    "        Must be in the same units as vpm_final output (sorted outparams order).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        Log-likelihood value (always <= 0 for this form).\n",
    "    \"\"\"\n",
    "    mdl = vpm_final.run_model(theta, remove=True)\n",
    "    lnl = -0.5 * np.sum(((mdl - data[:, 0]) / data[:, 1])**2)\n",
    "    return lnl\n",
    "\n",
    "\n",
    "print(\"Log-likelihood function defined.\")\n",
    "print(\"Note: expects theta values in units defined by inparams\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell14",
   "metadata": {},
   "source": [
    "## 3. Fiducial Model\n",
    "\n",
    "Before running the optimization, we verify the setup by evaluating the model at the\n",
    "fiducial (reference) parameters and checking that the outputs are physically reasonable.\n",
    "\n",
    "The fiducial parameters are based on best-guess Earth values from the literature.\n",
    "They are **not** the MLE solution — the optimization will improve upon them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cell15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running fiducial model...\n",
      "\n",
      "Fiducial log-likelihood: -100798.23\n",
      "\n",
      "Fiducial model outputs vs. observations:\n",
      "Observable               Model        Observed       (Obs σ)    Residual/σ\n",
      "------------------------------------------------------------------------\n",
      "  ViscLMan           1.105e+18         1.5e+18       1.4e+18         -0.28\n",
      "  ViscUMan            4.04e+17       2.275e+18      2.27e+18         -0.82\n",
      "  FMeltUMan            0.05074            0.06          0.04         -0.23\n",
      "  HflowCMB           1.396e+13         1.1e+13         6e+12          0.49\n",
      "  HflowUMan          3.482e+13         3.8e+13         3e+12         -1.06\n",
      "  RIC                7.751e+05       1.224e+06          1000       -448.99\n",
      "  TCMB                    4020            4000           200          0.10\n",
      "  TUMan                   1585            1587            34         -0.05\n"
     ]
    }
   ],
   "source": [
    "# Run the final-state model at fiducial parameters\n",
    "print(\"Running fiducial model...\")\n",
    "mdl_fid = vpm_final.run_model(theta_fiducial, remove=True)\n",
    "lnl_fid = lnlike(theta_fiducial, like_data)\n",
    "\n",
    "print(f\"\\nFiducial log-likelihood: {lnl_fid:.2f}\")\n",
    "print(\"\\nFiducial model outputs vs. observations:\")\n",
    "print(f\"{'Observable':<14}  {'Model':>14}  {'Observed':>14}  {'(Obs σ)':>12}  {'Residual/σ':>12}\")\n",
    "print(\"-\" * 72)\n",
    "for label, m, (obs, sig) in zip(outlabels, mdl_fid, like_data):\n",
    "    resid = (m - obs) / sig\n",
    "    print(f\"  {label:<12s}  {m:14.4g}  {obs:14.4g}  {sig:12.4g}  {resid:12.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell16",
   "metadata": {},
   "source": [
    "### 3.1 Visualizing the Thermal Evolution at Fiducial Parameters\n",
    "\n",
    "The `vpm_evol` instance returns a time series for each output, allowing us to visualize\n",
    "how Earth's interior has evolved since formation.\n",
    "The horizontal dashed lines mark the present-day observed values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cell17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final time recorded: 4.50 Gyr\n"
     ]
    }
   ],
   "source": [
    "# Run the evolution model to get time series\n",
    "evol = vpm_evol.run_model(theta_fiducial, remove=True)\n",
    "\n",
    "time_gyr = evol[\"Time\"].to(u.Gyr).value\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(14, 8), sharex=True)\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Data to plot: (key, label, observed mean, observed std, y-scale)\n",
    "evol_keys = [\n",
    "    (\"final.earth.TUMan\",    r\"$T_{\\rm UMan}$ [K]\",       1587,              34,           \"linear\"),\n",
    "    (\"final.earth.TCMB\",     r\"$T_{\\rm CMB}$ [K]\",        4000,              200,          \"linear\"),\n",
    "    (\"final.earth.HflowUMan\",r\"$H_{\\rm UMan}$ [W]\",       38.0 * TW_TO_W,   3 * TW_TO_W,  \"linear\"),\n",
    "    (\"final.earth.HflowCMB\", r\"$H_{\\rm CMB}$ [W]\",        11.0 * TW_TO_W,   6 * TW_TO_W,  \"linear\"),\n",
    "    (\"final.earth.RIC\",      r\"$R_{\\rm IC}$ [m]\",          1224.1e3,          1e3,          \"linear\"),\n",
    "    (\"final.earth.FMeltUMan\",r\"Melt fraction\",              0.06,              0.04,         \"linear\"),\n",
    "]\n",
    "\n",
    "for ax, (key, ylabel, obs_mean, obs_std, yscale) in zip(axes, evol_keys):\n",
    "    yvals = evol[key]\n",
    "    # Quantities (non-None units) need .value for matplotlib\n",
    "    if hasattr(yvals, 'value'):\n",
    "        yvals = yvals.value\n",
    "    ax.plot(time_gyr, yvals, color=\"steelblue\", lw=2)\n",
    "    ax.axhline(obs_mean, color=\"firebrick\", ls=\"--\", lw=1.5, label=\"Observed\")\n",
    "    ax.axhspan(obs_mean - obs_std, obs_mean + obs_std,\n",
    "               alpha=0.15, color=\"firebrick\", label=r\"$\\pm1\\sigma$\")\n",
    "    ax.set_ylabel(ylabel, fontsize=18)\n",
    "    ax.set_yscale(yscale)\n",
    "\n",
    "for ax in axes[3:]:\n",
    "    ax.set_xlabel(\"Time [Gyr]\", fontsize=18)\n",
    "axes[0].legend(fontsize=18, loc=\"upper right\")\n",
    "fig.suptitle(\"Earth Interior Evolution — Fiducial Parameters\", fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(f\"\\nFinal time recorded: {time_gyr[-1]:.2f} Gyr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell18",
   "metadata": {},
   "source": [
    "## 4. Single Optimization Run\n",
    "\n",
    "We minimize the **negative log-likelihood** using `scipy.optimize.minimize` with the\n",
    "**Nelder-Mead** algorithm.\n",
    "\n",
    "Nelder-Mead is a gradient-free simplex method — it does not require derivatives of the\n",
    "objective function, which is important here because VPLanet is a black-box numerical code\n",
    "with no analytical gradients.\n",
    "The `adaptive=True` option scales the algorithm's step sizes to the problem dimensionality.\n",
    "\n",
    "> **Why Nelder-Mead?** The Earth interior likelihood landscape is non-convex and potentially\n",
    "> multi-modal (multiple local maxima), so we need a method that can explore broadly without\n",
    "> getting stuck near the starting point. The next section addresses this more systematically\n",
    "> with multi-start optimization.\n",
    "\n",
    "> **Note:** If optimizer is failing to converge, try increasing ``maxiter`` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cell19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running single optimization from fiducial starting point...\n",
      "\n",
      "Optimization status: Not converged\n",
      "Function evaluations: 401\n",
      "\n",
      "Log-likelihood improvement: -100798.23 -> -1.29\n",
      "\n",
      "Best-fit parameters (single run):\n",
      "  ${}^{40}\\text{K}$ Mantle Power [W]   fiducial=3.616e+13  MLE=3.729e+13\n",
      "  ${}^{40}\\text{K}$ Core Power [W]     fiducial=3.386e+13  MLE=3.366e+13\n",
      "  $T_{\\rm man}$ [K]                    fiducial=3000  MLE=2977\n",
      "  $T_{\\rm core}$ [K]                   fiducial=6500  MLE=6368\n",
      "  Erupt. Efficiency                    fiducial=0.1  MLE=0.09925\n",
      "  $\\Delta T_{\\chi}$                    fiducial=0  MLE=0.0001465\n",
      "  Visc. Reference                      fiducial=6e+07  MLE=6.025e+07\n",
      "  Visc. Jump                           fiducial=2.4  MLE=2.325\n",
      "  Act. Viscosity [m$^2$/s]             fiducial=3e+05  MLE=2.978e+05\n"
     ]
    }
   ],
   "source": [
    "neg_lnlike = lambda theta: -lnlike(theta, like_data)\n",
    "\n",
    "# Start from the fiducial parameters for this single run\n",
    "print(\"Running single optimization from fiducial starting point...\")\n",
    "\n",
    "result_single = scipy.optimize.minimize(\n",
    "    neg_lnlike,\n",
    "    x0=theta_fiducial,\n",
    "    bounds=bounds.tolist(),\n",
    "    method=\"nelder-mead\",\n",
    "    options={\"maxiter\": 200, \"adaptive\": True},\n",
    ")\n",
    "\n",
    "theta_opt_single = result_single.x\n",
    "lnl_opt_single   = -result_single.fun\n",
    "\n",
    "print(f\"\\nOptimization status: {'Converged' if result_single.success else 'Not converged'}\")\n",
    "print(f\"Function evaluations: {result_single.nfev}\")\n",
    "print(f\"\\nLog-likelihood improvement: {lnl_fid:.2f} -> {lnl_opt_single:.2f}\")\n",
    "print(f\"\\nBest-fit parameters (single run):\")\n",
    "for label, val_fid, val_opt in zip(inlabels, theta_fiducial, theta_opt_single):\n",
    "    print(f\"  {label:35s}  fiducial={val_fid:.4g}  MLE={val_opt:.4g}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell20",
   "metadata": {},
   "source": [
    "## 5. Multi-Start MLE\n",
    "\n",
    "A single optimization from one starting point may converge to a **local** maximum of the\n",
    "likelihood rather than the global one. To mitigate this:\n",
    "\n",
    "1. We draw `n_starts` random starting points uniformly from the prior bounds\n",
    "2. We run a separate optimization from each starting point in **parallel**\n",
    "3. We select the solution with the **highest log-likelihood** across all runs\n",
    "\n",
    "This multi-start strategy increases confidence that we have found the global MLE.\n",
    "The `analysis.py` script in this directory runs 100 optimizations on 32 cores;\n",
    "here we use fewer starts to keep the notebook runtime manageable.\n",
    "\n",
    "> **Practical tip:** For production runs, use at least 50–100 starts.\n",
    "> Here we use 16 starts as a demonstration; increase `N_STARTS` as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cell21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-start MLE: 16 starts on 8 cores\n",
      "Starting points shape: (16, 9)\n"
     ]
    }
   ],
   "source": [
    "def uniform_prior_sampler(nsample, bounds):\n",
    "    \"\"\"Draw uniform samples within the prior bounds.\"\"\"\n",
    "    bounds = np.array(bounds)\n",
    "    return np.random.uniform(bounds[:, 0], bounds[:, 1], size=(nsample, bounds.shape[0]))\n",
    "\n",
    "\n",
    "def opt_parallel(x0):\n",
    "    \"\"\"Run a single Nelder-Mead optimization from starting point x0.\"\"\"\n",
    "    result = scipy.optimize.minimize(\n",
    "        neg_lnlike,\n",
    "        x0=x0,\n",
    "        bounds=bounds.tolist(),\n",
    "        method=\"nelder-mead\",\n",
    "        options={\"maxiter\": 200, \"adaptive\": True},\n",
    "    )\n",
    "    return result.x\n",
    "\n",
    "\n",
    "N_STARTS = 16    # increase to 50–100 for more thorough exploration\n",
    "N_CORES  = min(8, mp.cpu_count())\n",
    "\n",
    "np.random.seed(42)\n",
    "starting_points = uniform_prior_sampler(N_STARTS, bounds)\n",
    "\n",
    "print(f\"Multi-start MLE: {N_STARTS} starts on {N_CORES} cores\")\n",
    "print(f\"Starting points shape: {starting_points.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cell22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running 16 optimizations in parallel...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimizing: 100%|██████████| 16/16 [11:26<00:00, 42.89s/it]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Done. Evaluating log-likelihood at each solution...\n",
      "Log-likelihood range: [-2.49, -0.84]\n",
      "Best log-likelihood:  -0.84\n"
     ]
    }
   ],
   "source": [
    "print(f\"Running {N_STARTS} optimizations in parallel...\")\n",
    "\n",
    "with mp.Pool(N_CORES) as pool:\n",
    "    opt_results = list(tqdm(\n",
    "        pool.imap(opt_parallel, starting_points),\n",
    "        total=N_STARTS,\n",
    "        desc=\"Optimizing\",\n",
    "    ))\n",
    "\n",
    "print(\"\\nDone. Evaluating log-likelihood at each solution...\")\n",
    "\n",
    "# Evaluate likelihood at each optimized solution\n",
    "lnl_values = [lnlike(theta, like_data) for theta in opt_results]\n",
    "\n",
    "print(f\"Log-likelihood range: [{min(lnl_values):.2f}, {max(lnl_values):.2f}]\")\n",
    "print(f\"Best log-likelihood:  {max(lnl_values):.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cell23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLE results saved to mle_earth_results.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run</th>\n",
       "      <th>lnlike</th>\n",
       "      <th>earth.d40KPowerMan</th>\n",
       "      <th>earth.d40KPowerCore</th>\n",
       "      <th>earth.dTMan</th>\n",
       "      <th>earth.dTCore</th>\n",
       "      <th>earth.dEruptEff</th>\n",
       "      <th>earth.dDTChiRef</th>\n",
       "      <th>earth.dViscRef</th>\n",
       "      <th>earth.dViscJumpMan</th>\n",
       "      <th>earth.dActViscMan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.563355</td>\n",
       "      <td>3.893072e+13</td>\n",
       "      <td>4.957678e+13</td>\n",
       "      <td>2898.415445</td>\n",
       "      <td>6359.310809</td>\n",
       "      <td>0.066165</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>8.978487e+07</td>\n",
       "      <td>2.213111</td>\n",
       "      <td>285441.080525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.858103</td>\n",
       "      <td>4.565375e+13</td>\n",
       "      <td>2.819600e+13</td>\n",
       "      <td>2963.016897</td>\n",
       "      <td>6632.468070</td>\n",
       "      <td>0.071242</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>2.014352e+08</td>\n",
       "      <td>1.534283</td>\n",
       "      <td>291384.310267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.025054</td>\n",
       "      <td>3.892838e+13</td>\n",
       "      <td>3.408225e+13</td>\n",
       "      <td>2750.667482</td>\n",
       "      <td>6123.975675</td>\n",
       "      <td>0.079518</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>4.358965e+08</td>\n",
       "      <td>2.145643</td>\n",
       "      <td>273883.511320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.926928</td>\n",
       "      <td>4.311602e+13</td>\n",
       "      <td>4.092170e+13</td>\n",
       "      <td>2758.781145</td>\n",
       "      <td>6085.287384</td>\n",
       "      <td>0.069936</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>8.833581e+08</td>\n",
       "      <td>2.316191</td>\n",
       "      <td>259588.954306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-1.431515</td>\n",
       "      <td>3.485832e+13</td>\n",
       "      <td>2.964312e+13</td>\n",
       "      <td>2732.755139</td>\n",
       "      <td>6381.378285</td>\n",
       "      <td>0.061550</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>7.068233e+07</td>\n",
       "      <td>2.327582</td>\n",
       "      <td>297874.943285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.348407</td>\n",
       "      <td>4.332892e+13</td>\n",
       "      <td>2.973540e+13</td>\n",
       "      <td>2926.703362</td>\n",
       "      <td>5878.761427</td>\n",
       "      <td>0.074504</td>\n",
       "      <td>0.000958</td>\n",
       "      <td>5.683533e+08</td>\n",
       "      <td>2.326135</td>\n",
       "      <td>274163.365658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-1.251800</td>\n",
       "      <td>4.541234e+13</td>\n",
       "      <td>4.940365e+13</td>\n",
       "      <td>2714.012548</td>\n",
       "      <td>5923.434594</td>\n",
       "      <td>0.057617</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>3.741858e+08</td>\n",
       "      <td>1.489795</td>\n",
       "      <td>275949.097477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>-1.148466</td>\n",
       "      <td>3.747331e+13</td>\n",
       "      <td>3.436708e+13</td>\n",
       "      <td>2692.752564</td>\n",
       "      <td>6045.599365</td>\n",
       "      <td>0.132646</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>8.916665e+08</td>\n",
       "      <td>2.136990</td>\n",
       "      <td>264246.866368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>-1.422873</td>\n",
       "      <td>3.770678e+13</td>\n",
       "      <td>4.109049e+13</td>\n",
       "      <td>2899.956842</td>\n",
       "      <td>5894.255551</td>\n",
       "      <td>0.148081</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>2.816351e+08</td>\n",
       "      <td>1.460465</td>\n",
       "      <td>284087.077141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>-0.841316</td>\n",
       "      <td>4.504730e+13</td>\n",
       "      <td>3.480988e+13</td>\n",
       "      <td>2620.668352</td>\n",
       "      <td>6015.273831</td>\n",
       "      <td>0.085205</td>\n",
       "      <td>0.000753</td>\n",
       "      <td>5.838136e+08</td>\n",
       "      <td>2.238319</td>\n",
       "      <td>269713.243481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>-1.142041</td>\n",
       "      <td>3.077604e+13</td>\n",
       "      <td>4.475529e+13</td>\n",
       "      <td>2857.197722</td>\n",
       "      <td>6442.514549</td>\n",
       "      <td>0.127087</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>4.939935e+08</td>\n",
       "      <td>1.688872</td>\n",
       "      <td>268659.871213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-2.491033</td>\n",
       "      <td>3.243361e+13</td>\n",
       "      <td>2.800948e+13</td>\n",
       "      <td>2878.936605</td>\n",
       "      <td>6080.328883</td>\n",
       "      <td>0.102207</td>\n",
       "      <td>0.000929</td>\n",
       "      <td>2.477820e+08</td>\n",
       "      <td>1.607480</td>\n",
       "      <td>290939.048564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>-1.850890</td>\n",
       "      <td>3.559391e+13</td>\n",
       "      <td>2.909320e+13</td>\n",
       "      <td>2788.869979</td>\n",
       "      <td>5935.182596</td>\n",
       "      <td>0.148698</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>5.829686e+08</td>\n",
       "      <td>2.267291</td>\n",
       "      <td>273405.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>-1.189563</td>\n",
       "      <td>3.287107e+13</td>\n",
       "      <td>4.964914e+13</td>\n",
       "      <td>2765.977927</td>\n",
       "      <td>6617.698031</td>\n",
       "      <td>0.140172</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>1.344867e+08</td>\n",
       "      <td>1.425437</td>\n",
       "      <td>286769.173588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>-1.223201</td>\n",
       "      <td>4.903179e+13</td>\n",
       "      <td>4.613785e+13</td>\n",
       "      <td>2571.065819</td>\n",
       "      <td>6077.389074</td>\n",
       "      <td>0.092369</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>1.410221e+08</td>\n",
       "      <td>1.528535</td>\n",
       "      <td>289728.523228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>-1.034078</td>\n",
       "      <td>3.662420e+13</td>\n",
       "      <td>3.948137e+13</td>\n",
       "      <td>2860.078679</td>\n",
       "      <td>6282.429019</td>\n",
       "      <td>0.147578</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>2.577457e+08</td>\n",
       "      <td>1.760098</td>\n",
       "      <td>280401.338261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    run    lnlike  earth.d40KPowerMan  earth.d40KPowerCore  earth.dTMan  \\\n",
       "0     0 -1.563355        3.893072e+13         4.957678e+13  2898.415445   \n",
       "1     1 -1.858103        4.565375e+13         2.819600e+13  2963.016897   \n",
       "2     2 -1.025054        3.892838e+13         3.408225e+13  2750.667482   \n",
       "3     3 -0.926928        4.311602e+13         4.092170e+13  2758.781145   \n",
       "4     4 -1.431515        3.485832e+13         2.964312e+13  2732.755139   \n",
       "5     5 -1.348407        4.332892e+13         2.973540e+13  2926.703362   \n",
       "6     6 -1.251800        4.541234e+13         4.940365e+13  2714.012548   \n",
       "7     7 -1.148466        3.747331e+13         3.436708e+13  2692.752564   \n",
       "8     8 -1.422873        3.770678e+13         4.109049e+13  2899.956842   \n",
       "9     9 -0.841316        4.504730e+13         3.480988e+13  2620.668352   \n",
       "10   10 -1.142041        3.077604e+13         4.475529e+13  2857.197722   \n",
       "11   11 -2.491033        3.243361e+13         2.800948e+13  2878.936605   \n",
       "12   12 -1.850890        3.559391e+13         2.909320e+13  2788.869979   \n",
       "13   13 -1.189563        3.287107e+13         4.964914e+13  2765.977927   \n",
       "14   14 -1.223201        4.903179e+13         4.613785e+13  2571.065819   \n",
       "15   15 -1.034078        3.662420e+13         3.948137e+13  2860.078679   \n",
       "\n",
       "    earth.dTCore  earth.dEruptEff  earth.dDTChiRef  earth.dViscRef  \\\n",
       "0    6359.310809         0.066165         0.000158    8.978487e+07   \n",
       "1    6632.468070         0.071242         0.000179    2.014352e+08   \n",
       "2    6123.975675         0.079518         0.000364    4.358965e+08   \n",
       "3    6085.287384         0.069936         0.000068    8.833581e+08   \n",
       "4    6381.378285         0.061550         0.000484    7.068233e+07   \n",
       "5    5878.761427         0.074504         0.000958    5.683533e+08   \n",
       "6    5923.434594         0.057617         0.000339    3.741858e+08   \n",
       "7    6045.599365         0.132646         0.000075    8.916665e+08   \n",
       "8    5894.255551         0.148081         0.000077    2.816351e+08   \n",
       "9    6015.273831         0.085205         0.000753    5.838136e+08   \n",
       "10   6442.514549         0.127087         0.000488    4.939935e+08   \n",
       "11   6080.328883         0.102207         0.000929    2.477820e+08   \n",
       "12   5935.182596         0.148698         0.000841    5.829686e+08   \n",
       "13   6617.698031         0.140172         0.000312    1.344867e+08   \n",
       "14   6077.389074         0.092369         0.000224    1.410221e+08   \n",
       "15   6282.429019         0.147578         0.000959    2.577457e+08   \n",
       "\n",
       "    earth.dViscJumpMan  earth.dActViscMan  \n",
       "0             2.213111      285441.080525  \n",
       "1             1.534283      291384.310267  \n",
       "2             2.145643      273883.511320  \n",
       "3             2.316191      259588.954306  \n",
       "4             2.327582      297874.943285  \n",
       "5             2.326135      274163.365658  \n",
       "6             1.489795      275949.097477  \n",
       "7             2.136990      264246.866368  \n",
       "8             1.460465      284087.077141  \n",
       "9             2.238319      269713.243481  \n",
       "10            1.688872      268659.871213  \n",
       "11            1.607480      290939.048564  \n",
       "12            2.267291      273405.008880  \n",
       "13            1.425437      286769.173588  \n",
       "14            1.528535      289728.523228  \n",
       "15            1.760098      280401.338261  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build a DataFrame with all MLE results\n",
    "param_keys = list(inparams.keys())\n",
    "rows = []\n",
    "for ii, (theta_opt, lnl) in enumerate(zip(opt_results, lnl_values)):\n",
    "    row = {\"run\": ii, \"lnlike\": lnl}\n",
    "    for key, val in zip(param_keys, theta_opt):\n",
    "        row[key] = val\n",
    "    rows.append(row)\n",
    "\n",
    "df_mle = pd.DataFrame(rows)\n",
    "df_mle.to_csv(\"mle_earth_results.csv\", index=False)\n",
    "\n",
    "print(\"MLE results saved to mle_earth_results.csv\")\n",
    "df_mle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ced2cc6",
   "metadata": {},
   "source": [
    "Reload table of results (if already run):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c2805b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_keys = list(inparams.keys())\n",
    "df_mle = pd.read_csv(\"mle_earth_results.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell24",
   "metadata": {},
   "source": [
    "## 6. Analyzing the MLE Results\n",
    "\n",
    "### 6.1 Best-Fit Parameters\n",
    "\n",
    "The global MLE solution is the row with the highest log-likelihood value.\n",
    "We compare the best-fit model outputs against the observed Earth values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cell25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best solution: run 9  |  ln L = -0.84\n",
      "\n",
      "Parameter                                       Fiducial        MLE Best                Bounds\n",
      "-----------------------------------------------------------------------------------------------\n",
      "  ${}^{40}\\mathrm{K}$ Mantle Power [W]         3.616e+13       4.505e+13  [2.89e+13, 5.42e+13]\n",
      "  ${}^{40}\\mathrm{K}$ Core Power [W]           3.386e+13       3.481e+13  [2.71e+13, 5.08e+13]\n",
      "  $T_{\\rm man}$ [K]                                 3000            2621  [2.5e+03, 3e+03]\n",
      "  $T_{\\rm core}$ [K]                                6500            6015  [5.8e+03, 6.8e+03]\n",
      "  Erupt. Efficiency                                  0.1          0.0852  [0.05, 0.15]\n",
      "  $\\Delta T_{\\chi}$                                    0       0.0007528  [0, 0.001]\n",
      "  Visc. Reference                                  6e+07       5.838e+08  [4e+07, 9e+08]\n",
      "  Visc. Jump                                         2.4           2.238  [1.1, 2.4]\n",
      "  Act. Viscosity [m$^2$/s]                         3e+05       2.697e+05  [2.5e+05, 3.1e+05]\n"
     ]
    }
   ],
   "source": [
    "# Identify the best solution\n",
    "best_idx   = df_mle[\"lnlike\"].idxmax()\n",
    "theta_best = df_mle.loc[best_idx, param_keys].values.astype(float)\n",
    "lnl_best   = df_mle.loc[best_idx, \"lnlike\"]\n",
    "\n",
    "print(f\"Best solution: run {best_idx}  |  ln L = {lnl_best:.2f}\\n\")\n",
    "print(f\"{'Parameter':<40}  {'Fiducial':>14}  {'MLE Best':>14}  {'Bounds':>20}\")\n",
    "print(\"-\" * 95)\n",
    "for label, key, fid, mle, (lo, hi) in zip(\n",
    "        inlabels, param_keys, theta_fiducial, theta_best, bounds):\n",
    "    print(f\"  {label:<38}  {fid:14.4g}  {mle:14.4g}  [{lo:.3g}, {hi:.3g}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell26",
   "metadata": {},
   "source": [
    "### 6.2 Model Outputs vs. Observations\n",
    "\n",
    "We run the best-fit model and compare its predictions to the observed Earth values.\n",
    "Residuals are expressed in units of the observational $1\\sigma$ uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cell27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running best-fit model...\n",
      "\n",
      "Fiducial chi^2 = 201596.46\n",
      "Best-fit chi^2 = 1.68\n",
      "\n",
      "Observable            Observed        Fiducial             MLE    Res_fid    Res_mle\n",
      "----------------------------------------------------------------------------------\n",
      "  ViscLMan             1.5e+18       1.105e+18       9.831e+17      -0.28      -0.37\n",
      "  ViscUMan           2.275e+18        4.04e+17       3.762e+17      -0.82      -0.84\n",
      "  FMeltUMan               0.06         0.05074         0.05973      -0.23      -0.01\n",
      "  HflowCMB             1.1e+13       1.396e+13       1.409e+13       0.49       0.52\n",
      "  HflowUMan            3.8e+13       3.482e+13       3.573e+13      -1.06      -0.76\n",
      "  RIC                1.224e+06       7.751e+05       1.224e+06    -448.99       0.09\n",
      "  TCMB                    4000            4020            4000       0.10      -0.00\n",
      "  TUMan                   1587            1585            1587      -0.05       0.00\n"
     ]
    }
   ],
   "source": [
    "# Run best-fit model\n",
    "print(\"Running best-fit model...\")\n",
    "mdl_best = vpm_final.run_model(theta_best, remove=True)\n",
    "\n",
    "obs_means = like_data[:, 0]\n",
    "obs_stds  = like_data[:, 1]\n",
    "residuals_fid  = (mdl_fid  - obs_means) / obs_stds\n",
    "residuals_best = (mdl_best - obs_means) / obs_stds\n",
    "\n",
    "print(f\"\\nFiducial chi^2 = {-2*lnl_fid:.2f}\")\n",
    "print(f\"Best-fit chi^2 = {-2*lnl_best:.2f}\")\n",
    "print(f\"\\n{'Observable':<14}  {'Observed':>14}  {'Fiducial':>14}  {'MLE':>14}  {'Res_fid':>9}  {'Res_mle':>9}\")\n",
    "print(\"-\" * 82)\n",
    "for label, obs, fid_m, best_m, r_fid, r_best in zip(\n",
    "        outlabels, obs_means, mdl_fid, mdl_best, residuals_fid, residuals_best):\n",
    "    print(f\"  {label:<12s}  {obs:14.4g}  {fid_m:14.4g}  {best_m:14.4g}  {r_fid:9.2f}  {r_best:9.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "34a1bd6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final time recorded: 4.50 Gyr\n"
     ]
    }
   ],
   "source": [
    "# Run the evolution model to get time series\n",
    "evol_fiducial = vpm_evol.run_model(theta_fiducial, remove=True)\n",
    "evol_best     = vpm_evol.run_model(theta_best, remove=True)\n",
    "\n",
    "time_gyr = evol_fiducial[\"Time\"].to(u.Gyr).value\n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(14, 8), sharex=True)\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Data to plot: (key, label, observed mean, observed std, y-scale)\n",
    "evol_keys = [\n",
    "    (\"final.earth.TUMan\",    r\"$T_{\\rm UMan}$ [K]\",       1587,              34,           \"linear\"),\n",
    "    (\"final.earth.TCMB\",     r\"$T_{\\rm CMB}$ [K]\",        4000,              200,          \"linear\"),\n",
    "    (\"final.earth.HflowUMan\",r\"$H_{\\rm UMan}$ [W]\",       38.0 * TW_TO_W,   3 * TW_TO_W,  \"linear\"),\n",
    "    (\"final.earth.HflowCMB\", r\"$H_{\\rm CMB}$ [W]\",        11.0 * TW_TO_W,   6 * TW_TO_W,  \"linear\"),\n",
    "    (\"final.earth.RIC\",      r\"$R_{\\rm IC}$ [m]\",          1224.1e3,          1e3,          \"linear\"),\n",
    "    (\"final.earth.FMeltUMan\",r\"Melt fraction\",              0.06,              0.04,         \"linear\"),\n",
    "]\n",
    "\n",
    "for ax, (key, ylabel, obs_mean, obs_std, yscale) in zip(axes, evol_keys):\n",
    "    # plot fiducial evolution\n",
    "    yvals = evol_fiducial[key]\n",
    "    # Quantities (non-None units) need .value for matplotlib\n",
    "    if hasattr(yvals, 'value'):\n",
    "        yvals = yvals.value\n",
    "    ax.plot(time_gyr, yvals, color=\"steelblue\", lw=2, label=\"Fiducial\")\n",
    "    \n",
    "    # plot best-fit evolution\n",
    "    yvals = evol_best[key]\n",
    "    if hasattr(yvals, 'value'):\n",
    "        yvals = yvals.value\n",
    "    ax.plot(time_gyr, yvals, color=\"green\", lw=2, label=\"Best-fit\")\n",
    "    \n",
    "    ax.axhline(obs_mean, color=\"firebrick\", ls=\"--\", lw=1.5, label=\"Observed\")\n",
    "    ax.axhspan(obs_mean - obs_std, obs_mean + obs_std,\n",
    "               alpha=0.15, color=\"firebrick\", label=r\"$\\pm1\\sigma$\")\n",
    "    ax.set_ylabel(ylabel, fontsize=18)\n",
    "    ax.set_yscale(yscale)\n",
    "\n",
    "for ax in axes[3:]:\n",
    "    ax.set_xlabel(\"Time [Gyr]\", fontsize=18)\n",
    "\n",
    "axes[0].legend(fontsize=18, loc=\"upper right\")\n",
    "fig.suptitle(\"Earth Interior Evolution — Fiducial Parameters\", fontsize=18)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(f\"\\nFinal time recorded: {time_gyr[-1]:.2f} Gyr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1113b17c",
   "metadata": {},
   "source": [
    "### 6.3 Analyze Local Sensitivity Around the MLE Solution\n",
    "\n",
    "Here we will perform local sensitivity analysis of the model by estimating the the gradient around the MLE solution. Here we will plot how much each output parameter varies (the percent change to the output parameter relative to the MLE value) when a single input parameter is varied by +/- `delta_percent`. This give us an idea of which input parameters affect the output parameters the most: in this case `dActViscMan`  is the most influential input parameter to all of the input parameters (at this MLE point)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "881bcbf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing baseline outputs...\n",
      "Performing sensitivity analysis for 9 parameters...\n",
      "  Parameter 1/9: earth.d40KPowerMan\n",
      "  Parameter 2/9: earth.d40KPowerCore\n",
      "  Parameter 3/9: earth.dTMan\n",
      "  Parameter 4/9: earth.dTCore\n",
      "  Parameter 5/9: earth.dEruptEff\n",
      "  Parameter 6/9: earth.dDTChiRef\n",
      "  Parameter 7/9: earth.dViscRef\n",
      "  Parameter 8/9: earth.dViscJumpMan\n",
      "  Parameter 9/9: earth.dActViscMan\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta_percent = 0.01\n",
    "\n",
    "# Run the sensitivity analysis\n",
    "sensitivity_matrix, param_names, output_names = vpi.local_sensitivity_analysis(\n",
    "    vpm_final, theta_best, inparams, outparams, delta_percent=delta_percent\n",
    ")\n",
    "\n",
    "# Plot the results\n",
    "fig = vpi.plot_sensitivity_heatmap(sensitivity_matrix, param_names, output_names, \n",
    "                                   delta_percent=delta_percent, label_fs=20)\n",
    "# fig.savefig(\"local_sensitivity_heatmap.png\", dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18171f12",
   "metadata": {},
   "source": [
    "### 6.4 Compute Fischer Information Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8466db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "FI = vpi.compute_fisher_information(lnlike, theta_best, like_data, method='hessian')\n",
    "results = vpi.analyze_fisher_information(FI, param_names=inlabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d92670b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'standard_errors': {'${}^{40}\\\\mathrm{K}$ Mantle Power [W]': np.float64(100000.0),\n",
       "  '${}^{40}\\\\mathrm{K}$ Core Power [W]': np.float64(100000.0),\n",
       "  '$T_{\\\\rm man}$ [K]': np.float64(91287.09373326863),\n",
       "  '$T_{\\\\rm core}$ [K]': np.float64(70710.67601670453),\n",
       "  'Erupt. Efficiency': np.float64(0.01243852030909686),\n",
       "  '$\\\\Delta T_{\\\\chi}$': np.float64(91287.09346368705),\n",
       "  'Visc. Reference': np.float64(67545.45274672522),\n",
       "  'Visc. Jump': np.float64(0.00189359681397719),\n",
       "  'Act. Viscosity [m$^2$/s]': np.float64(91287.09375973474)},\n",
       " 'correlation_matrix': array([[ 1.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "        [ 0.00000000e+00,  1.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "        [-0.00000000e+00, -0.00000000e+00,  1.00000000e+00,\n",
       "          4.47213604e-01,  4.34345823e-01,  1.99999979e-01,\n",
       "          1.73192397e-16, -3.76658645e-01,  1.99999979e-01],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  4.47213600e-01,\n",
       "          1.00000000e+00,  9.64795130e-01, -4.47213608e-01,\n",
       "          1.59113218e-16, -8.42445309e-01, -4.47213608e-01],\n",
       "        [-0.00000000e+00, -0.00000000e+00,  4.34345818e-01,\n",
       "          9.64795130e-01,  1.00000000e+00, -4.25716914e-01,\n",
       "          9.20637064e-15, -9.04921009e-01, -4.34345821e-01],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  1.99999981e-01,\n",
       "         -4.47213604e-01, -4.25716909e-01,  1.00000000e+00,\n",
       "          1.33795574e-12,  3.76941749e-01, -1.99999982e-01],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  1.73190594e-16,\n",
       "          1.59118282e-16,  9.20637635e-15,  1.33795574e-12,\n",
       "          1.00000000e+00,  9.27611711e-16, -1.33759358e-12],\n",
       "        [ 0.00000000e+00,  0.00000000e+00, -3.76658641e-01,\n",
       "         -8.42445311e-01, -9.04921011e-01,  3.76941754e-01,\n",
       "          9.27617038e-16,  1.00000000e+00,  3.76658643e-01],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  1.99999983e-01,\n",
       "         -4.47213609e-01, -4.34345821e-01, -1.99999978e-01,\n",
       "         -1.33759358e-12,  3.76658642e-01,  1.00000000e+00]]),\n",
       " 'condition_number': np.float64(1.7375540344199836e+16),\n",
       " 'determinant': np.float64(6.81655730923163e-51),\n",
       " 'eigenvalues': array([3.90189009e-11, 9.92186470e-11, 1.00000000e-10, 1.00000000e-10,\n",
       "        1.06866498e-10, 2.19183454e-10, 1.99762155e-02, 8.95995419e+04,\n",
       "        1.73755403e+06])}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "881ed62d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Fisher Information Matrix'}, xlabel='Parameters', ylabel='Parameters'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vpi.plot_fisher_heatmap(FI, param_names=inlabels)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vpi",
   "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": "3.10.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}