{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "924af82b",
   "metadata": {},
   "source": [
    "# Tabulated Example\n",
    "\n",
    "To run this tutorial, you should install NEoST following the install guide. Some extra data files are also required, these are the `ap4_new.dat` file and is included in the GitHub repository along with this notebook.\n",
    "\n",
    "Before continuing with this tutorial, please read the inference process overview to familiarise yourself with the way NEoST parametrises the equation of state.\n",
    "\n",
    "The following block of code will properly import NEoST and its prerequisites, furthermore it also defines a name for the inference run, this name is what will be prefixed to all of NEoST's output files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7efe93e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using c code\n"
     ]
    }
   ],
   "source": [
    "import neost\n",
    "import kalepy\n",
    "from neost.eos import speedofsound, tabulated\n",
    "from neost.Prior import Prior\n",
    "from neost.Star import Star\n",
    "from neost.Likelihood import Likelihood\n",
    "from neost import PosteriorAnalysis\n",
    "from scipy.stats import multivariate_normal\n",
    "from scipy.stats import gaussian_kde\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot\n",
    "from pymultinest.solve import solve\n",
    "import time\n",
    "import os\n",
    "\n",
    "import neost.global_imports as global_imports\n",
    "\n",
    "# Some physical constants\n",
    "c = global_imports._c\n",
    "G = global_imports._G\n",
    "Msun = global_imports._M_s\n",
    "pi = global_imports._pi\n",
    "rho_ns = global_imports._rhons\n",
    "\n",
    "# Define name for run, extra - at the end is for nicer formatting of output\n",
    "run_name = \"tabulated_AP4_test_2D_gaussian\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026b62c6",
   "metadata": {},
   "source": [
    "Defining and loading in the AP4 nuclear equation of state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d332a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "eos_name = 'tabulated'\n",
    "\n",
    "baryondensity, pressure_B, energydensity_B = np.loadtxt('../../examples/ap4_new.dat', unpack=True) #in units of g/cm^3 for all values\n",
    "\n",
    "pressure_B = pressure_B*c**2 #(in units of g/(cm s^2))\n",
    "\n",
    "energydensity_B = energydensity_B"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18fb4398",
   "metadata": {},
   "source": [
    "## Equation of state object\n",
    "With NEoST properly imported the equation of state needs to be defined. For the CS parametrisation this is done by creating a `tabulated.TabulatedEoS()` object. This object takes can take an input the `crust` parameter and the `rho_t` parameter. However, none of these parameters are required if you wish to use only a tabulated equation of state. \n",
    "\n",
    "Valid input for the crust parameter consists of one of the following values: `'ceft-Hebeler'`, `'ceft-Tews'`, `'ceft-Lynn'`, `'ceft-Drischler'`, `'ceft-old'` or `None`. This instructs NEoST on which cEFT model to use, in order of listing these would be: the band based on the work by Hebeler et al., Tews et al., Lynn et al., Drischler et al., an old implementation of the Hebeler band from Raaijmakers et al., or no cEFT at all.\n",
    "\n",
    "The `rho_t` parameter tells NEoST at which density to transition between the cEFT crust parametrisation and the core parametrisation. This value must not exceed a value of twice the nuclear saturation density, although for the currently implemented cEFT models it should not exceed 1.1 times the nuclear saturation density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25f199e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the base EoS model\n",
    "tabulated_example = tabulated.TabulatedEoS(energydensity_B,pressure_B)\n",
    "tabulated_example.update({}, max_edsc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a970e6e8",
   "metadata": {},
   "source": [
    "This next block of code defines the measurements used to create the likelihood function for the Bayesian analysis. It shows you how to define measurements using the `Scipy` library, as well as what parameters need to be past for a mass-radius measurement synthetic data.\n",
    "\n",
    "To define a mass-radius measurement you need to have your mean mass and radius mesurements (muM and muR, respectively), and the uncertainties (`sigM` and `sigR`, respectively). Since we are using a synthetic case, we define the probability density function of these sources using the `multivariate_normal` function in `Scipy` to define the two-dimension gaussian of the mass-radius measurements.\n",
    "\n",
    "These PDFs are then passed to NEoST. This is done through the `likelihood_functions` and `likelihood_params` lists. The first one is a list of all the (callable) PDFs, and the second one is a list of as many instances of `['Mass', 'Radius']` as you have mass-radius measurements. The ordering of this list matters insofar as that you need to put any mass-radius measurements first in the `likelihood_functions` list and any gravitational wave events second.\n",
    "\n",
    "You will also need to define a `chirp_mass` list containing the median values of the chirp masses of your events, in case your event is a mass-radius measurement, enter `None` instead.\n",
    "\n",
    "Finally you will also need to define how many events you pass to NEoST, a quick and easy way to do this is shown in the example code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea7bde44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here just a simple 2D gaussian.\n",
    "\n",
    "muM = 2.0947   #synthetic source with log_10(central energy density) = 15.240180657118929\n",
    "muR = 10.808\n",
    "sigM = 0.05*muM # 5% uncertainty in mass \n",
    "sigR = 0.05*muR # 5% uncertainty in radius\n",
    "test = multivariate_normal(mean=[muM, muR], cov=[[sigM, 0.0], [0.0, sigR]])\n",
    "\n",
    "muM2 = 1.7090   #synthetic source with log_10(central energy density) = 15.07681219601247\n",
    "muR2 = 11.312\n",
    "sigM2 = 0.05*muM2 # 5% uncertainty in mass \n",
    "sigR2 = 0.05*muR2 # 5% uncertainty in radius\n",
    "test2 = multivariate_normal(mean=[muM2, muR2], cov=[[sigM2, 0.0], [0.0, sigR2]])\n",
    "\n",
    "muM3 = 1.0814   #synthetic source with log_10(central energy density) = 14.913443734906012\n",
    "muR3 = 11.4587\n",
    "sigM3 = 0.05*muM3 # 5% uncertainty in mass \n",
    "sigR3 = 0.05*muR3 # 5% uncertainty in radius\n",
    "test3 = multivariate_normal(mean=[muM3, muR3], cov=[[sigM3, 0.0], [0.0, sigR3]])\n",
    "\n",
    "likelihood_functions = [test.pdf,test2.pdf,test3.pdf]\n",
    "likelihood_params = [['Mass', 'Radius'],['Mass', 'Radius'],['Mass', 'Radius']]\n",
    "\n",
    "# This is not a GW event so we set chirp mass to None\n",
    "chirp_mass = [None, None, None]\n",
    "number_stars = len(chirp_mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f91a832",
   "metadata": {},
   "source": [
    "With our data defined, the next step is to define both the prior and the likelihood function.\n",
    "\n",
    "The prior is defined through a pair of dictionaries, `variable_params` and `static_params`. Here `variable_params` takes in the equation of state parameters that will be allowed to vary, and `static_params` will take in those that won't. Entries into `variable_params` should be formatted as follows: `'param_name':[lower_bound,upper_bound]`\n",
    "\n",
    "Additionally, for each of the measurements you must also append a dictionary item `'rhoc_i':[14.6, 16]` to the end of `variable_params`, this parameter covers the central density of star i and needs to be appended for each star individually. Entries into `static_params` should be formatted in the following manner: `'param_name':value`.\n",
    "\n",
    "Finally, the prior object must be created using the following function call:`neost.Prior.Prior(EOS, variable_params, static_params, chirp_masses)` where the `EOS` argument is the equation of state object that was created in the previous step. When this prior is called it will then uniformly sample sets of parameters from the defined parameter ranges.\n",
    "\n",
    "The likelihood is defined by providing both the previously defined prior object and the likelihood functions defined in the previous codeblock. This is done with the following code: `likelihood = Likelihood(prior, likelihood_functions, likelihood_params, chirp_mass)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f4a2a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Only vary the central densities on the sources because there are no parameters to vary in a tabulated equation of state\n",
    "variable_params = {}\n",
    "for i in range(number_stars):\n",
    "\tvariable_params.update({'rhoc_' + str(i+1):[14.6, 16]})\n",
    "\n",
    "# and set the rest to static parameters in the sampling\n",
    "static_params = {}\n",
    "\n",
    "# Then we define the prior and likelihood accordingly\n",
    "prior = Prior(tabulated_example, variable_params, static_params, chirp_mass)\n",
    "likelihood = Likelihood(prior, likelihood_functions, likelihood_params, chirp_mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2c9d23",
   "metadata": {},
   "source": [
    "After defining your prior and likelihood function, it is best practice to test your prior and likelihood function. This is done with the short loop in the code block below. This loop will for each iteration first take a sample from the prior, and then compute the corresponding likelihood of said prior sample and print the likelihood as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a535568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bounds of prior are\n",
      "{'rhoc_1': [14.6, 16], 'rhoc_2': [14.6, 16], 'rhoc_3': [14.6, 16]}\n",
      "number of parameters is 3\n",
      "Testing prior and likelihood\n",
      "Testing done\n"
     ]
    }
   ],
   "source": [
    "print(\"Bounds of prior are\")\n",
    "print(variable_params)\n",
    "print(\"number of parameters is %d\" %len(variable_params))\n",
    "\n",
    "# First we test if everything is working as expected\n",
    "print(\"Testing prior and likelihood\")\n",
    "cube = np.random.rand(50, len(variable_params))\n",
    "for i in range(len(cube)):\n",
    "    par = prior.inverse_sample(cube[i])\n",
    "print(\"Testing done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f501723",
   "metadata": {},
   "source": [
    "When finished with testing your likelihood and prior you can proceed to the actual inference process. This is done in the code block below. Warning: depending on the performance of your platform, this might be a very slow process. However, here we only show a relatively fast example with a decreased number of live points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40b1b0fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analysing data from ../../examples/chains/tabulated_AP4_test_2D_gaussian.txt\n",
      "843.1442375183105\n"
     ]
    }
   ],
   "source": [
    "# Then we start the sampling with MultiNest\n",
    "start = time.time()\n",
    "result = solve(LogLikelihood=likelihood.call, Prior=prior.inverse_sample, n_live_points=500, evidence_tolerance=0.1,\n",
    "               n_dims=len(variable_params), sampling_efficiency=0.8, outputfiles_basename='../../examples/chains/' + run_name, verbose=True)\n",
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15fc65bc",
   "metadata": {},
   "source": [
    "Finally, NEoST also includes functionality to perform the first steps of posterior analysis. The first step in this process is to call the `PosteriorAnalysis.compute_auxiliary_data()` function with the code block below. This will generate as output a set of files that can subsequently be used with several additional plotting routines included in NEoST, or you can analyse these files on your own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "082c8206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples is 2327\n",
      "|████████████████████████████████████████| 2327/2327 [100%] in 9:29.0 (4.09/s) \n"
     ]
    }
   ],
   "source": [
    "# Compute auxiliary data for posterior analysis\n",
    "PosteriorAnalysis.compute_auxiliary_data('../../examples/chains/' + run_name, tabulated_example, \n",
    "                                         variable_params, static_params, chirp_mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163e9abd",
   "metadata": {},
   "source": [
    "This following plotting routine will create a cornerplot of all the parameters you have included in the `variable_params` dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "939bc1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make some analysis plots\n",
    "PosteriorAnalysis.cornerplot('../../examples/chains/' + run_name, variable_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772190ff",
   "metadata": {},
   "source": [
    "This will plot the data you have used to define the likelihood. So these are the masses and radii of the neutron stars that have been included in the analysis. Note that this will also plot the masses and radii of any gravitational wave events included in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44deee69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 760x760 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PosteriorAnalysis.mass_radius_posterior_plot('../../examples/chains/' + run_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46faa8bd",
   "metadata": {},
   "source": [
    "This routine will plot the posterior on the mass-radius relationship of neutron stars according to the inference process, the `label_name` parameter will be the label used in the legend. Note, for the tabulated equation of state with no varying equation of state parameters, one cannot make the below `PosteriorAnalysis.py` files since EoS is tabulated and cannot be varied. If you do try to run these, the code will throw an `Error` message stating that you cannot do this. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}