{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze iRAWD mppnp simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: module://ipympl.backend_nbagg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab \n",
    "from nugridpy import nugridse as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching files, please wait.......\n",
      "Reading preprocessor files\n",
      "File search complete.\n"
     ]
    }
   ],
   "source": [
    "cmp=mp.se('/data/ASDR/NuGrid/iRAWDyields/mppnp_hif/H5_out_C_mesa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What quantities are available in this data type, and what are the units?\n",
    "Each of the _se_ file sets (in fact each of the dozens of hdf5 files that make up the data set for one mass/metallicty combination, or stellar evolution track) has three types of data contained in them:\n",
    "\n",
    "data type access | content \n",
    "----------------|---------\n",
    " `pt.se.hattrs` |  a header section that holds the _header attributes_, including units in the form of factors so that if applied with the quantities the result is in cgs units \n",
    "`pt.se.cattrs` | for each cycle (or time step) the _cycle attributes_ are a number of scalar global quantities, such as total mass or star age\n",
    "`pt.se.dcols` | again, for each time step these are the vector quantities available, i.e. the data table columns; one of the data columns, _iso_massf_ is in fact a matrix where the matrix columns are different species, i.e. a radial vector of species vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HDF5_version',\n",
       " 'SE_version',\n",
       " 'numcodev',\n",
       " 'codev',\n",
       " 'modname',\n",
       " 'mini',\n",
       " 'zini',\n",
       " 'rotini',\n",
       " 'overini',\n",
       " 'zisnb',\n",
       " 'age_unit',\n",
       " 'mass_unit',\n",
       " 'radius_unit',\n",
       " 'rho_unit',\n",
       " 'temperature_unit',\n",
       " 'dcoeff_unit']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp.se.hattrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['shellnb', 'age', 'deltat', 'total_mass', 'cycle']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp.se.cattrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mass', 'radius', 'rho', 'temperature', 'dcoeff', 'iso_massf']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp.se.dcols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temperature profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmp.get('temperature_unit')/1.e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61639ccec6194944a5cd226782150a2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " reading ['mass']...100%0.14117026329040527\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$m_r/M_\\\\odot$')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ifig=122;close(ifig);figure(ifig)\n",
    "cmp.plot('mass','temperature',fname=1000,shape=':')\n",
    "ylabel('$T / 10^9 \\mathrm{K}$')\n",
    "xlabel('$m_r/M_\\odot$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diffusion coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3729ba82022d4b8da10fbffc1f1232d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " reading ['radius']...100%0.033908843994140625\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$radius / [R_\\\\odot]$')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ifig=123;close(ifig);figure(ifig)\n",
    "cmp.plot('radius','dcoeff',fname=1000,shape='--',logy=True)\n",
    "ylabel('$ \\\\log D / \\mathrm{[cm^2/s}]$')\n",
    "#xlabel('$m_r/M_\\odot$')\n",
    "xlabel('$radius / [R_\\odot]$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nugridpy import astronomy as ast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mixing time scale\n",
    "Let's assume there are three pressure scale heights and a mixing length is $0.01R_\\odot$. Then the mixing time scale is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mixing time scale = 80.73 minutes.\n"
     ]
    }
   ],
   "source": [
    "print(\"Mixing time scale = %.2f minutes.\"%((0.01*ast.rsun_cm)**2/1e14/60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abundance profiles "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bf305f5721d438188529818401b58b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " reading ['iso_massf']...100%"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-14, -3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species=['H-1','He-3','Be-7','Li-7','C-13','C-14','N-13']\n",
    "ifig=108;close(ifig);figure(ifig)\n",
    "cmp.abu_profile(isos=species, ifig=ifig, fname=5500, logy=True)\n",
    "ylim(-14,-3)\n",
    "#xlim(0.552,0.556)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "273e07ea73404ef6a45e2fa26d04f650",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " reading ['iso_massf']...100%"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-14, -3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species=['Kr-84','Kr-85','Kr-86','Kr-87','Kr-88','Kr-89']\n",
    "ifig=109;close(ifig);figure(ifig)\n",
    "cmp.abu_profile(isos=species, ifig=ifig, fname=5500, logy=True)\n",
    "ylim(-14,-3)\n",
    "#xlim(0.552,0.556)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " reading ['mass']...100%"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ebd612781b249859c9277f97d112db7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureCanvasNbAgg()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ifig=1233;close(ifig);figure(ifig)\n",
    "cmp.abu_chart(6000,mass_range=[0.707824, 0.7209746], plotaxis=[48, 59, 35, 43],\\\n",
    "             ilabel=True,imlabel=True,boxstable=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can extract the trajectory and initial abundances from this model at the key mass coordinate for use in a 1-zone PPN simulation in order to study, for example, the impact of a new rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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