{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using RateFilter to Search Rates in a Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Library` class in `pynucastro` provides a high level interface for reading files containing one or more Reaclib rates and then filtering these rates based on user-specified criteria for the nuclei involved in the reactions. We can then use the resulting rates to build a network.\n",
    "\n",
    "This example uses a Reaclib snapshot downloaded from:\n",
    "\n",
    "https://groups.nscl.msu.edu/jina/reaclib/db/library.php?action=viewsnapshots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a rate snapshot\n",
    "\n",
    "The `Library` class will look for the library file in the working directory or in the `pynucastro/library` subdirectory of the `pynucastro` package.\n",
    "\n",
    "When the constructor is supplied a file name, `pynucastro` will read the contents of this file and interpret them as Reaclib rates in either the Reaclib 1 or 2 formats. The `Library` then stores the rates from the file as `Rate` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pynucastro as pyna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [],
   "source": [
    "library_file = '20180201ReaclibV2.22'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [],
   "source": [
    "mylibrary = pyna.rates.Library(library_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering the Library\n",
    "\n",
    "This example introduces the `RateFilter` class which allows us to define a set of reactants and products to search for in a `Library` object.\n",
    "\n",
    "### Inexact Filtering\n",
    "\n",
    "Inexact filtering is like using wildcards: in the following example, the rate filter we define will match any rates in which $\\mathrm{^{12}C}$ is a reactant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [],
   "source": [
    "c12_inexact_filter = pyna.rates.RateFilter(reactants=['c12'], exact=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we construct a `RateFilter` object, we can apply it to our `Library` by passing it to the `Library.filter` function.\n",
    "\n",
    "`Library.filter` returns a new `Library` object containing the rates that match our `RateFilter`.\n",
    "\n",
    "We can print a `Library` to see the rates it contains. In parentheses the rate identifier is printed, showing the Reaclib rate label as well as whether the rate is forward or reverse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c12 --> n + c11    (c12 --> n + c11 <bb92_reaclib_reverse>)\n",
      "c12 --> p + b11    (c12 --> p + b11 <nw00_reaclib_reverse>)\n",
      "c12 --> he4 + he4 + he4    (c12 --> he4 + he4 + he4 <fy05_reaclib_reverse>)\n",
      "c12 + n --> c13    (c12 + n --> c13 <ka02_reaclib>)\n",
      "c12 + p --> n13    (c12 + p --> n13 <ls09_reaclib>)\n",
      "c12 + he4 --> o16    (c12 + he4 --> o16 <nac2_reaclib>)\n",
      "c12 + n --> p + b12    (c12 + n --> p + b12 <wag_reaclib_reverse>)\n",
      "c12 + n --> he4 + be9    (c12 + n --> he4 + be9 <cf88_reaclib_reverse>)\n",
      "c12 + he4 --> n + o15    (c12 + he4 --> n + o15 <cf88_reaclib_reverse>)\n",
      "c12 + he4 --> p + n15    (c12 + he4 --> p + n15 <nacr_reaclib_reverse>)\n",
      "c12 + c12 --> n + mg23    (c12 + c12 --> n + mg23 <cf88_reaclib_reverse>)\n",
      "c12 + c12 --> p + na23    (c12 + c12 --> p + na23 <cf88_reaclib>)\n",
      "c12 + c12 --> he4 + ne20    (c12 + c12 --> he4 + ne20 <cf88_reaclib>)\n",
      "o16 + c12 --> n + si27    (o16 + c12 --> n + si27 <cf88_reaclib_reverse>)\n",
      "o16 + c12 --> p + al27    (o16 + c12 --> p + al27 <cf88_reaclib>)\n",
      "o16 + c12 --> he4 + mg24    (o16 + c12 --> he4 + mg24 <cf88_reaclib>)\n",
      "ne20 + c12 --> n + s31    (ne20 + c12 --> n + s31 <rolf_reaclib>)\n",
      "ne20 + c12 --> p + p31    (ne20 + c12 --> p + p31 <rolf_reaclib>)\n",
      "ne20 + c12 --> he4 + si28    (ne20 + c12 --> he4 + si28 <rolf_reaclib>)\n"
     ]
    }
   ],
   "source": [
    "c12_inexact_library = mylibrary.filter(c12_inexact_filter)\n",
    "print(c12_inexact_library)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rate identifiers above can be used to access individual `Rate` objects within a `Library` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [],
   "source": [
    "cago = c12_inexact_library.get_rate('c12 + he4 --> o16 <nac2_reaclib>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
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l+yqqmLdlP++HwsrGouNvWx7cPYMpBblMGdiVKQW55Gd1jEClEi8UXkREpEXqwsp7m4KB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\nJGlUDC+SJGlU/gaOx8GXUScxFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cago.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exact Filtering\n",
    "\n",
    "Exact filtering is useful when you have a specific rate in mind or a specific combination of reactants or products. In the following example, we look for all rates of the form $\\mathrm{^{12}C + ^{12}C \\rightarrow \\ldots}$\n",
    "\n",
    "To use exact filtering, omit the `exact` keyword to the `RateFilter` constructor, as it is turned on by default.\n",
    "\n",
    "Exact filtering does not mean all the nuclei involved in the rate must be specified, it means that all filtering options passed to the `RateFilter` constructor are strictly applied. In this case, the filter will return rates with exactly two reactants, both of which are $\\mathrm{^{12}C}$. However, the filter places no constraint on the products or number of products in the rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c12 + c12 --> n + mg23    (c12 + c12 --> n + mg23 <cf88_reaclib_reverse>)\n",
      "c12 + c12 --> p + na23    (c12 + c12 --> p + na23 <cf88_reaclib>)\n",
      "c12 + c12 --> he4 + ne20    (c12 + c12 --> he4 + ne20 <cf88_reaclib>)\n"
     ]
    }
   ],
   "source": [
    "c12_exact_filter = pyna.rates.RateFilter(reactants=['c12', 'c12'])\n",
    "c12_exact_library = mylibrary.filter(c12_exact_filter)\n",
    "print(c12_exact_library)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Building an Alpha Capture Network\n",
    "\n",
    "In the next example, we use rate filtering to iteratively construct a `Library` containing the alpha capture rates linking $\\mathrm{^{12}C}$ to $\\mathrm{^{56}Ni}$.\n",
    "\n",
    "After finding each successive link in the alpha capture chain, we call `Library.heaviest()` to find the heaviest nucleus in the filtered rates. This corresponds to the nucleus with the largest mass number, and in case of a tie between isobars, this returns the isobar with the smallest atomic number. We use this feature to find the reverse rate for each alpha capture reaction.\n",
    "\n",
    "In the example below, we add each filtered library to our alpha capture library `alpha_library`, initialized as an empty `Library`. The `Library` class supports the addition operator by returning a new library containing the rates in the two libraries we added together.\n",
    "\n",
    "This example also introduces the `max_products` keyword, which specifies we are looking for reactions producing at most `max_products` product nuclei.\n",
    "\n",
    "Similarly, the `RateFilter` constructor supports the following keywords constraining the number of reactants and products:\n",
    "\n",
    "- `min_reactants`\n",
    "- `max_reactants`\n",
    "- `min_products`\n",
    "- `max_products`\n",
    "\n",
    "Because we have omitted the argument `exact=False`, the filter constraints we apply are exact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o16\n",
      "ne20\n",
      "mg24\n",
      "si28\n",
      "s32\n",
      "ar36\n",
      "ca40\n",
      "ti44\n",
      "cr48\n",
      "fe52\n",
      "ni56\n"
     ]
    }
   ],
   "source": [
    "alpha_library = pyna.rates.Library()\n",
    "\n",
    "capture = pyna.rates.Nucleus('he4')\n",
    "seed = pyna.rates.Nucleus('c12')\n",
    "\n",
    "while True:\n",
    "    ac_filter = pyna.rates.RateFilter(reactants=[capture, seed], max_products=1)\n",
    "    ac_library = mylibrary.filter(ac_filter)\n",
    "    alpha_library = alpha_library + ac_library\n",
    "\n",
    "    heavy = ac_library.heaviest()\n",
    "    ac_filter_inv = pyna.rates.RateFilter(reactants=[heavy], products=[capture, seed])\n",
    "    ac_inv_library = mylibrary.filter(ac_filter_inv)\n",
    "    alpha_library = alpha_library + ac_inv_library\n",
    "\n",
    "    print(heavy)\n",
    "    if heavy.A == 56:\n",
    "        break\n",
    "    else:\n",
    "        seed = heavy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will next print out the library we constructed, seeing that we have both forward and reverse rates for the alpha chain.\n",
    "\n",
    "Note that at this time `pynucastro` has not yet implemented nuclear partition functions, so these reverse rates are calculated only from detailed balance in the Reaclib library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c12 + he4 --> o16    (c12 + he4 --> o16 <nac2_reaclib>)\n",
      "o16 --> he4 + c12    (o16 --> he4 + c12 <nac2_reaclib_reverse>)\n",
      "o16 + he4 --> ne20    (o16 + he4 --> ne20 <co10_reaclib>)\n",
      "ne20 --> he4 + o16    (ne20 --> he4 + o16 <co10_reaclib_reverse>)\n",
      "ne20 + he4 --> mg24    (ne20 + he4 --> mg24 <il10_reaclib>)\n",
      "mg24 --> he4 + ne20    (mg24 --> he4 + ne20 <il10_reaclib_reverse>)\n",
      "mg24 + he4 --> si28    (mg24 + he4 --> si28 <st08_reaclib>)\n",
      "si28 --> he4 + mg24    (si28 --> he4 + mg24 <st08_reaclib_reverse>)\n",
      "si28 + he4 --> s32    (si28 + he4 --> s32 <ths8_reaclib>)\n",
      "s32 --> he4 + si28    (s32 --> he4 + si28 <ths8_reaclib_reverse>)\n",
      "s32 + he4 --> ar36    (s32 + he4 --> ar36 <ths8_reaclib>)\n",
      "ar36 --> he4 + s32    (ar36 --> he4 + s32 <ths8_reaclib_reverse>)\n",
      "ar36 + he4 --> ca40    (ar36 + he4 --> ca40 <ths8_reaclib>)\n",
      "ca40 --> he4 + ar36    (ca40 --> he4 + ar36 <ths8_reaclib_reverse>)\n",
      "ca40 + he4 --> ti44    (ca40 + he4 --> ti44 <chw0_reaclib>)\n",
      "ti44 --> he4 + ca40    (ti44 --> he4 + ca40 <chw0_reaclib_reverse>)\n",
      "ti44 + he4 --> cr48    (ti44 + he4 --> cr48 <ths8_reaclib>)\n",
      "cr48 --> he4 + ti44    (cr48 --> he4 + ti44 <ths8_reaclib_reverse>)\n",
      "cr48 + he4 --> fe52    (cr48 + he4 --> fe52 <ths8_reaclib>)\n",
      "fe52 --> he4 + cr48    (fe52 --> he4 + cr48 <ths8_reaclib_reverse>)\n",
      "fe52 + he4 --> ni56    (fe52 + he4 --> ni56 <ths8_reaclib>)\n",
      "ni56 --> he4 + fe52    (ni56 --> he4 + fe52 <ths8_reaclib_reverse>)\n"
     ]
    }
   ],
   "source": [
    "print(alpha_library)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Next we can create a reaction network from our filtered alpha capture library by passing our library to a network constructor using the `libraries` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [],
   "source": [
    "alpha_network = pyna.networks.PythonNetwork(libraries=alpha_library)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we can print an overview of the network as well as a Z-N plot of the nuclei linked via the reactions we selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "he4\n",
      "  consumed by:\n",
      "     c12 + he4 --> o16\n",
      "     o16 + he4 --> ne20\n",
      "     ne20 + he4 --> mg24\n",
      "     mg24 + he4 --> si28\n",
      "     si28 + he4 --> s32\n",
      "     s32 + he4 --> ar36\n",
      "     ar36 + he4 --> ca40\n",
      "     ca40 + he4 --> ti44\n",
      "     ti44 + he4 --> cr48\n",
      "     cr48 + he4 --> fe52\n",
      "     fe52 + he4 --> ni56\n",
      "  produced by:\n",
      "     o16 --> he4 + c12\n",
      "     ne20 --> he4 + o16\n",
      "     mg24 --> he4 + ne20\n",
      "     si28 --> he4 + mg24\n",
      "     s32 --> he4 + si28\n",
      "     ar36 --> he4 + s32\n",
      "     ca40 --> he4 + ar36\n",
      "     ti44 --> he4 + ca40\n",
      "     cr48 --> he4 + ti44\n",
      "     fe52 --> he4 + cr48\n",
      "     ni56 --> he4 + fe52\n",
      "\n",
      "c12\n",
      "  consumed by:\n",
      "     c12 + he4 --> o16\n",
      "  produced by:\n",
      "     o16 --> he4 + c12\n",
      "\n",
      "o16\n",
      "  consumed by:\n",
      "     o16 --> he4 + c12\n",
      "     o16 + he4 --> ne20\n",
      "  produced by:\n",
      "     c12 + he4 --> o16\n",
      "     ne20 --> he4 + o16\n",
      "\n",
      "ne20\n",
      "  consumed by:\n",
      "     ne20 --> he4 + o16\n",
      "     ne20 + he4 --> mg24\n",
      "  produced by:\n",
      "     o16 + he4 --> ne20\n",
      "     mg24 --> he4 + ne20\n",
      "\n",
      "mg24\n",
      "  consumed by:\n",
      "     mg24 --> he4 + ne20\n",
      "     mg24 + he4 --> si28\n",
      "  produced by:\n",
      "     ne20 + he4 --> mg24\n",
      "     si28 --> he4 + mg24\n",
      "\n",
      "si28\n",
      "  consumed by:\n",
      "     si28 --> he4 + mg24\n",
      "     si28 + he4 --> s32\n",
      "  produced by:\n",
      "     mg24 + he4 --> si28\n",
      "     s32 --> he4 + si28\n",
      "\n",
      "s32\n",
      "  consumed by:\n",
      "     s32 --> he4 + si28\n",
      "     s32 + he4 --> ar36\n",
      "  produced by:\n",
      "     si28 + he4 --> s32\n",
      "     ar36 --> he4 + s32\n",
      "\n",
      "ar36\n",
      "  consumed by:\n",
      "     ar36 --> he4 + s32\n",
      "     ar36 + he4 --> ca40\n",
      "  produced by:\n",
      "     s32 + he4 --> ar36\n",
      "     ca40 --> he4 + ar36\n",
      "\n",
      "ca40\n",
      "  consumed by:\n",
      "     ca40 --> he4 + ar36\n",
      "     ca40 + he4 --> ti44\n",
      "  produced by:\n",
      "     ar36 + he4 --> ca40\n",
      "     ti44 --> he4 + ca40\n",
      "\n",
      "ti44\n",
      "  consumed by:\n",
      "     ti44 --> he4 + ca40\n",
      "     ti44 + he4 --> cr48\n",
      "  produced by:\n",
      "     ca40 + he4 --> ti44\n",
      "     cr48 --> he4 + ti44\n",
      "\n",
      "cr48\n",
      "  consumed by:\n",
      "     cr48 --> he4 + ti44\n",
      "     cr48 + he4 --> fe52\n",
      "  produced by:\n",
      "     ti44 + he4 --> cr48\n",
      "     fe52 --> he4 + cr48\n",
      "\n",
      "fe52\n",
      "  consumed by:\n",
      "     fe52 --> he4 + cr48\n",
      "     fe52 + he4 --> ni56\n",
      "  produced by:\n",
      "     cr48 + he4 --> fe52\n",
      "     ni56 --> he4 + fe52\n",
      "\n",
      "ni56\n",
      "  consumed by:\n",
      "     ni56 --> he4 + fe52\n",
      "  produced by:\n",
      "     fe52 + he4 --> ni56\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(alpha_network.network_overview())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tzouHvIxGA1yZW97T9WadiPj5wmNj3N8f2vK6Aa4t5Pb1DBG5N7UliIjIkVaq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\nnxEO+DQ6KyIiLUfhVmQT5xwX7i5VPXasJ8Jjg530RoNkCyW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0/nRN60/XtP50TetP17T+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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha_network.plot()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}