{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5d86efd-a0da-4876-b74b-111de01130e1",
   "metadata": {},
   "source": [
    "# ExoPie: Quickstart Guide\n",
    "\n",
    "Welcome to **ExoPie**, an open-source Python library designed for modeling and simulating the interior structures of exoplanets. \n",
    "\n",
    "This quickstart tutorial will walk you through the basics of the library, including:\n",
    "1. Calculating Radius given Mass and composition.\n",
    "2. Inferring the interior structure of exoplanets based on its mass and radius error.\n",
    "3. Applying specific compositional constraints to the core and mantle.\n",
    "4. Saving your models and visualizing the parameter space.\n",
    "\n",
    "Let's start by importing the necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2d956410-51fe-4990-8afc-256db509db50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import exopie\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8108312-6b66-48da-8e48-2796a9800bbc",
   "metadata": {},
   "source": [
    "## 1. Calculating Radius \n",
    "This is the main function used in exopie, to infer planet composition and can be used in variety of ways. \n",
    "\n",
    "For example, we can get different Mass-Radius curves for a given composition and plot these isocomposition lines to visialize the parameter space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b3d1fb08-a1fb-493c-97b5-3626e27023a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Mass,Mass_err = 5, 0.5\n",
    "Radius,Radius_err = 1.6,0.05\n",
    "\n",
    "plt.errorbar(Mass,Radius,xerr=Mass_err,yerr=Radius_err)\n",
    "\n",
    "#plotting the isocomposition curves\n",
    "M = np.linspace(0.5,18,100)\n",
    "CMF = [0,0.3,0.7,1] # Core-Mass fractions to try out\n",
    "for cmf in CMF:\n",
    "    R = exopie.get_radius(M,cmf=cmf) # finding radius given Mass and cmf\n",
    "    plt.plot(M,R,label=f'CMF={cmf}')\n",
    "plt.xlabel('Mass [Me]')\n",
    "plt.ylabel('Radius [Re]')\n",
    "plt.xlim(1,15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40145fc-7860-46a7-b1f3-ed1ed50612c3",
   "metadata": {},
   "source": [
    "## 2. Calculating interior of an exoplanet\n",
    "First we need to initiallize the model using exopie.planet()\n",
    "\n",
    "The main parameters to take in is Mass and Radius, additional parameters include:\n",
    "* planet_type (can be 'rocky', 'water', or 'envelope')\n",
    "* xSi, xFe (for purely rocky planets)\n",
    "* CMF (for planets with volatiles)\n",
    "* Teq (for planets with volatiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "21477e2e-3f15-4017-9252-c3a5aa37f566",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 5000\n",
    "Mass_arr = np.random.normal(Mass,Mass_err,N)\n",
    "Radius_arr = np.random.normal(Radius,Radius_err,N)\n",
    "planet = exopie.planet(Mass=Mass_arr,Radius=Radius_arr,N=N,planet_type='rocky')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98e380c-74b4-423f-9172-e075987fc2c8",
   "metadata": {},
   "source": [
    "After initializing we can run the code and find out what the results are, the output is stored in the original class\n",
    "\n",
    "Although, the initialization can also work without specifying specific planet type case and will suggest one based on physical parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b22078e1-f95f-4d92-a3c5-a30b539fdf4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Planet is inside the rocky region, using purely rocky model.\n"
     ]
    }
   ],
   "source": [
    "planet = exopie.planet(Mass=Mass_arr,Radius=Radius_arr,N=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8a7058f5-a427-4825-bf08-20383e62812b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running the model\n",
    "planet_results = planet.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750dd4f8-c55b-41e6-9582-4f533502242d",
   "metadata": {},
   "source": [
    "The output generated can be accessed by interacting with the result class or simply viewed using the summary table by printing the results.\n",
    "\n",
    "The important variables to look at is how many sampled points have been rejected due to physical constraints and what are the posterior distribution mean/variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8ecb2238-7f7d-46f8-a119-e20687dec98b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for rocky, N = 37324\n",
      "Accepted samples 37324 out of 50000 (74.65%)\n",
      "Param        mean  se_mean       sd     2.5%      25%      50%      75%    97.5%\n",
      "--------------------------------------------------------------------------------\n",
      "Mass         5.14     0.00     0.44     4.31     4.84     5.13     5.43     6.03\n",
      "Radius       1.58     0.00     0.04     1.50     1.56     1.59     1.61     1.66\n",
      "FeMF         0.23     0.00     0.10     0.05     0.15     0.22     0.30     0.44\n",
      "CMF          0.21     0.00     0.13     0.01     0.10     0.20     0.31     0.48\n",
      "xSi          0.10     0.00     0.06     0.01     0.05     0.10     0.15     0.20\n",
      "xFe          0.10     0.00     0.06     0.00     0.05     0.09     0.14     0.19\n"
     ]
    }
   ],
   "source": [
    "print(planet_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b32a7f9e-daea-46d6-8080-6429b8897b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.27897271, 0.32924472, 0.1106662 , ..., 0.11454908, 0.37383063,\n",
       "       0.23610064], shape=(37324,))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_results.FeMF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6820d1-63a7-4c57-bbd5-9a77cd5734d3",
   "metadata": {},
   "source": [
    "## 3. Applying specific compositional constraints to the core and mantle.\n",
    "\n",
    "We can also apply specific constraints for the interior inference model to use, specifically on the refractory ratio inside the rocky interior.\n",
    "\n",
    "For example we can use stellar values to constrain the composition and remove parameter degeneracy. For this exopie.star() need to be used to initiallize and impose constraints for planet model to use.\n",
    "\n",
    "Thus, let us assume that the Fe/Mg and Mg/Si ratios of the plaent follow the star. We thus pass these ratios as a string to the prior function to constrain possible rocky compositions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "94a73a9c-b9e0-4b24-8551-971a2b3a175f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using stellar prior to constrain rocky interior:\n",
      "Fe/Mg~𝒩(2.05, 0.27), Mg/Si~𝒩(0.79, 0.10), \n",
      "Planet is assumed to be rocky, using xSi and xFe to match the stellar abundances.\n"
     ]
    }
   ],
   "source": [
    "# Initialize planet parameters\n",
    "planet = exopie.planet(Mass=Mass_arr,Radius=Radius_arr,N=N,planet_type='rocky')\n",
    "\n",
    "# Stellar abundances normalized to the sun\n",
    "Fe = np.random.normal(0,0.04,N)\n",
    "Mg = np.random.normal(0,0.04,N)\n",
    "Si = np.random.normal(0,0.04,N)\n",
    "# Initialize star parameters\n",
    "star = exopie.star(Fe=Fe,Mg=Mg,Si=Si,\n",
    "            N=N,prior=['Fe/Mg','Mg/Si'])\n",
    "\n",
    "# Running the model by passing the star data\n",
    "planet_results = planet.run(host_star=star)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e0f173cc-4303-46d6-929f-4951d82dac2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference for rocky, N = 90\n",
       "Accepted samples 90 out of 100 (90.00%)\n",
       "Param        mean  se_mean       sd     2.5%      25%      50%      75%    97.5%\n",
       "--------------------------------------------------------------------------------\n",
       "Mass         2.99     0.03     0.32     2.34     2.81     3.00     3.23     3.56\n",
       "Radius       1.29     0.01     0.08     1.13     1.23     1.30     1.34     1.44\n",
       "FeMF         0.41     0.02     0.16     0.10     0.27     0.42     0.53     0.65\n",
       "CMF          0.42     0.02     0.20     0.07     0.25     0.41     0.57     0.78\n",
       "xSi          0.09     0.01     0.06     0.00     0.03     0.09     0.14     0.19\n",
       "xFe          0.11     0.01     0.06     0.01     0.06     0.10     0.16     0.19"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_planet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74067887-14c8-413a-a890-f7a6b7ba7cae",
   "metadata": {},
   "source": [
    "We can also define the prior to be a bit more complicated or adding other chemical constraints:\n",
    "- Using Ca, Al or Ni\n",
    "- Adding a functional relationship\n",
    "- Constraining the possible CMF, xSi, xFe ... boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d25657ab-9872-44e5-aa2e-dc78886d5bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For example if the refractory ratios of planets have a linear relationship with host star\n",
    "# e.g. Fe/Mg_planet = Fe/Mg_star*2+0.1\n",
    "# We can modify the prior to be as following\n",
    "prior=['Fe/Mg*2+0.1','Mg/Si'] \n",
    "star = exopie.star(Fe=Fe,Mg=Mg,Si=Si,\n",
    "            N=N,prior=prior)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b1f8c8-fffc-4e12-a22b-77d606b994be",
   "metadata": {},
   "source": [
    "## 4. Saving your models and visualizing the parameter space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "32b4a9d4-eeb5-4c1d-a01c-919fb71b3413",
   "metadata": {},
   "outputs": [],
   "source": [
    "import corner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "444590fa-5d6c-4874-9664-26aa89d9a8e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Too few points to create valid contours\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x550 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corner.corner(np.array([results_planet.Mass,results_planet.Radius]).T);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aad9fa9-2424-4a50-9de6-6b170116549c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('output',[results_planet.Mass,results_planet.Radius])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d95c9c-963c-451b-a1bd-dc51ae84fc9d",
   "metadata": {},
   "source": [
    "### Using chemistry function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d340ec0-0f09-4a97-ad7c-5711f44e3974",
   "metadata": {},
   "outputs": [],
   "source": [
    "femf,simf,mgmf,camf,almf,nimf = exopie.chemistry(0.3,xSi=0,xFe=0,xWu=0,xSiO2=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a90f759-966d-468a-805f-d0cfb6294bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6923460306158774"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mgmf / simf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a110ddb-4a37-4bc8-b55e-24c33f41fe8a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import exopie\n",
    "import numpy as np\n",
    "\n",
    "# find the rocky threshold radius (RTR, cmf=0) at 5 Earth masses\n",
    "R = exopie.get_radius(5,xSi=0,xFe=0,cmf=0.)\n",
    "print(f'Radius Threshold (at 5Me) = {R[0]:.2f} Re')\n",
    "\n",
    "# Find the interior of M=5 +/-0.5, R=1.4 +/-0.05 planet\n",
    "planet = exopie.rocky(N=50000,Mass=[5,0.5],Radius=[1.4,0.05]) #model input\n",
    "planet.run() # run the model\n",
    "\n",
    "print(f'CMF = {np.mean(planet.CMF):.2f}', \n",
    "      f'FeMF = {np.mean(planet.FeMF):.2f}')\n",
    "\n",
    "# Case where no Si in the core (xSi=0) and no Fe in the mantle (xFe=0)\n",
    "planet = exopie.rocky(N=50000,Mass=[5,0.5],Radius=[1.4,0.05],\n",
    "                        xSi=[0,0],xFe=[0,0]) # model input\n",
    "planet.run() # run the model\n",
    "print(f'CMF = {np.mean(planet.CMF):.2f}', \n",
    "      f'FeMF = {np.mean(planet.FeMF):.2f}')\n",
    "\n",
    "planet.save_data('rocky.pkl') # save the model\n",
    "\n",
    "\n",
    "# To make a nice corner plot use (need corner.py)\n",
    "# fig,axs = planet.corner()\n",
    "\n"
   ]
  }
 ],
 "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}