{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
   "metadata": {},
   "source": [
    "# Example use case: Zero-age stellar luminosity function in binaries\n",
    "\n",
    "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of binary stars using binary_c. \n",
    "\n",
    "Before you go through this notebook, you should look at notebook_luminosity_function.ipynb which is for the - conceptually more simple - single stars.\n",
    "\n",
    "We start by loading in some standard Python modules and the binary_c module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf6b8673-a2b5-4b50-ad1b-e90671f57470",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "from binarycpython.utils.grid import Population\n",
    "\n",
    "# help(Population) # Uncomment this line to see the public functions of this object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
   "metadata": {},
   "source": [
    "## Setting up the Population object\n",
    "To set up and configure the population object we need to make a new instance of the `Population` object and configure it with the `.set()` function.\n",
    "\n",
    "In our case, we only need to set the maximum evolution time to something short, because we care only about zero-age main sequence stars which have, by definition, age zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79ab50b7-591f-4883-af09-116d1835a751",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adding: max_evolution_time=0.1 to BSE_options\n",
      "verbosity is 1\n"
     ]
    }
   ],
   "source": [
    "# Create population object\n",
    "population = Population()\n",
    "\n",
    "# If you want verbosity, set this before other things\n",
    "population.set(verbosity=1)\n",
    "\n",
    "# Setting values can be done via .set(<parameter_name>=<value>)\n",
    "# Values that are known to be binary_c_parameters are loaded into bse_options.\n",
    "# Those that are present in the default grid_options are set in grid_options\n",
    "# All other values that you set are put in a custom_options dict\n",
    "population.set(\n",
    "    # binary_c physics options\n",
    "    max_evolution_time=0.1,  # maximum stellar evolution time in Myr\n",
    " )\n",
    "\n",
    "# We can access the options through \n",
    "print(\"verbosity is\", population.grid_options['verbosity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
   "metadata": {},
   "source": [
    "## Adding grid variables\n",
    "The main purpose of the Population object is to handle the population synthesis side of running a set of stars. The main method to do this with binarycpython, as is the case with Perl binarygrid, is to use grid variables. These are loops over a predefined range of values, where a probability will be assigned to the systems based on the chosen probability distributions.\n",
    "\n",
    "Usually we use either 1 mass grid variable, or a trio of mass, mass ratio and period (other notebooks cover these examples). We can, however, also add grid sampling for e.g. eccentricity, metallicity or other parameters. \n",
    "\n",
    "To add a grid variable to the population object we use `population.add_grid_variable`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68c84521-9ae8-4020-af7a-5334173db969",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(population.add_grid_variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd75cebe-2152-4025-b680-dc020b80889b",
   "metadata": {},
   "source": [
    "All the distribution functions that we can use are stored in the `binarycpython.utils.distribution_functions` or `binarycpython/utils/distribution_functions.py` on git. If you uncomment the help statement below you can see which functions are available now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "048db541-3e92-4c5d-a25c-9c5a34b9c857",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import binarycpython.utils.distribution_functions\n",
    "# help(binarycpython.utils.distribution_functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9104fc-4136-4e53-8604-f24ad52fbe56",
   "metadata": {},
   "source": [
    "First let us set up some global variables that will be useful throughout. \n",
    "* The resolution is the number of stars we simulate in our model population.\n",
    "* The massrange is a list of the min and max masses\n",
    "* The total_probability is the theoretical integral of a probability density function, i.e. 1.0.\n",
    "* The binwidth sets the resolution of the final distribution. If set to 0.5, the bins in log*L* are 0.5dex wide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aba3fe4e-18f2-4bb9-8e5c-4c6007ab038b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set resolution and mass range that we simulate\n",
    "resolution = {\"M_1\": 40} # start with resolution = 10, and increase later if you want \"more accurate\" data\n",
    "massrange = (0.07, 100.0) # we work with stars of mass 0.07 to 100 Msun\n",
    "total_probability = 1.0 # theoretical integral of the mass probability density function over all masses    \n",
    "# distribution binwidths : \n",
    "# (log10) luminosity distribution\n",
    "binwidth = { 'luminosity' : 1.0 }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3a007b-5c17-42a7-a981-7e268e6f545c",
   "metadata": {},
   "source": [
    "The next cell contains an example of adding the mass grid variable, sampling the phase space in linear mass *M*_1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47979841-2c26-4b26-8945-603d013dc93a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the binary grid in \"cubic\" M1 - M2=q*M1 - log10 period space\n",
    "\n",
    "population = Population()\n",
    "\n",
    "# resolution on each side of the cube, with more stars for the primary mass\n",
    "nres = 10\n",
    "resolution = {\"M_1\": 2*nres,\n",
    "              \"q\": nres,\n",
    "              \"per\": nres}\n",
    "\n",
    "massrange = [0.07,100]\n",
    "logperrange = [0.15, 5.5]\n",
    "\n",
    "population.add_grid_variable(\n",
    "    name=\"lnm1\",\n",
    "    longname=\"Primary mass\",\n",
    "    valuerange=massrange,\n",
    "    resolution=\"{}\".format(resolution[\"M_1\"]),\n",
    "    spacingfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
    "    precode=\"M_1=math.exp(lnm1)\",\n",
    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
    "    dphasevol=\"dlnm1\",\n",
    "    parameter_name=\"M_1\",\n",
    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
    ")\n",
    "\n",
    "# Mass ratio\n",
    "population.add_grid_variable(\n",
    "     name=\"q\",\n",
    "     longname=\"Mass ratio\",\n",
    "     valuerange=[\"0.1/M_1\", 1],\n",
    "     resolution=\"{}\".format(resolution['q']),\n",
    "     spacingfunc=\"const({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
    "     probdist=\"flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
    "     dphasevol=\"dq\",\n",
    "     precode=\"M_2 = q * M_1\",\n",
    "     parameter_name=\"M_2\",\n",
    "     condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
    " )\n",
    "\n",
    "# Orbital period\n",
    "population.add_grid_variable(\n",
    "    name=\"log10per\", # in days\n",
    "    longname=\"log10(Orbital_Period)\",\n",
    "    valuerange=[0.15, 5.5],\n",
    "    resolution=\"{}\".format(resolution[\"per\"]),\n",
    "    spacingfunc=\"const({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
    "    precode=\"\"\"orbital_period = 10.0 ** log10per\n",
    "sep = calc_sep_from_period(M_1, M_2, orbital_period)\n",
    "sep_min = calc_sep_from_period(M_1, M_2, 10**{})\n",
    "sep_max = calc_sep_from_period(M_1, M_2, 10**{})\"\"\".format(logperrange[0],logperrange[1]),\n",
    "    probdist=\"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
    "    parameter_name=\"orbital_period\",\n",
    "    dphasevol=\"dlog10per\",\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
   "metadata": {},
   "source": [
    "## Setting logging and handling the output\n",
    "By default, binary_c will not output anything (except for 'SINGLE STAR LIFETIME'). It is up to us to determine what will be printed. We can either do that by hardcoding the print statements into `binary_c` (see documentation binary_c) or we can use the custom logging functionality of binarycpython (see notebook `notebook_custom_logging.ipynb`), which is faster to set up and requires no recompilation of binary_c, but is somewhat more limited in its functionality. For our current purposes, it works perfectly well.\n",
    "\n",
    "After configuring what will be printed, we need to make a function to parse the output. This can be done by setting the parse_function parameter in the population object (see also notebook `notebook_individual_systems.ipynb`). \n",
    "\n",
    "In the code below we will set up both the custom logging and a parse function to handle that output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create custom logging statement\n",
    "#\n",
    "# we check that the model number is zero, i.e. we're on the first timestep (stars are born on the ZAMS)\n",
    "# we make sure that the stellar type is <= MAIN_SEQUENCE, i.e. the star is a main-sequence star\n",
    "# we also check that the time is 0.0 (this is not strictly required, but good to show how it is done)\n",
    "#\n",
    "# The \n",
    "#\n",
    "# The Printf statement does the outputting: note that the header string is ZERO_AGE_MAIN_SEQUENCE_STARn\n",
    "#\n",
    "# where:\n",
    "#\n",
    "# n = PRIMARY    = 0 is star 0 (primary star)\n",
    "# n = SECONDARY  = 1 is star 1 (secondary star)\n",
    "# n = UNRESOLVED = 2 is the unresolved system (both stars added)\n",
    "\n",
    "PRIMARY = 0\n",
    "SECONDARY = 1\n",
    "UNRESOLVED = 2\n",
    "\n",
    "custom_logging_statement = \"\"\"\n",
    "// select ZAMS\n",
    "if(stardata->model.model_number == 0 &&\n",
    "   stardata->model.time == 0)\n",
    "{\n",
    "    // loop over the stars individually (equivalent to a resolved binary) \n",
    "    Foreach_star(star)\n",
    "    {\n",
    "        // select main-sequence stars\n",
    "        if(star->stellar_type <= MAIN_SEQUENCE)\n",
    "        {\n",
    "            /* Note that we use Printf - with a capital P! */\n",
    "           Printf(\"ZERO_AGE_MAIN_SEQUENCE_STAR%d %30.12e %g %g %g %g\\\\n\",\n",
    "                  star->starnum,\n",
    "                  stardata->model.time, // 1\n",
    "                  stardata->common.zero_age.mass[0], // 2\n",
    "                  star->mass, // 3\n",
    "                  star->luminosity, // 4\n",
    "                  stardata->model.probability // 5\n",
    "           );\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    // unresolved MS-MS binary\n",
    "    if(stardata->star[0].stellar_type <= MAIN_SEQUENCE &&\n",
    "       stardata->star[1].stellar_type <= MAIN_SEQUENCE) \n",
    "    {\n",
    "        Printf(\"ZERO_AGE_MAIN_SEQUENCE_STAR%d %30.12e %g %g %g %g\\\\n\",\n",
    "                  2,\n",
    "                  stardata->model.time, // 1\n",
    "                  stardata->common.zero_age.mass[0] + stardata->common.zero_age.mass[1], // 2\n",
    "                  stardata->star[0].mass + stardata->star[1].mass, // 3\n",
    "                  stardata->star[0].luminosity + stardata->star[1].luminosity, // 4\n",
    "                  stardata->model.probability // 5\n",
    "           );\n",
    "    }\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "population.set(\n",
    "    C_logging_code=custom_logging_statement\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1f1f0c-1f8b-42d8-b051-cbf8c6b51514",
   "metadata": {},
   "source": [
    "The parse function must now catch lines that start with \"ZERO_AGE_MAIN_SEQUENCE_STAR\" and process the associated data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd197154-a8ce-4865-8929-008d3483101a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the bin_data function so we can construct finite-resolution probability distributions\n",
    "# import the datalinedict to make a dictionary from each line of data from binary_c\n",
    "from binarycpython.utils.functions import bin_data,datalinedict\n",
    "import re\n",
    "\n",
    "def parse_function(self, output):\n",
    "    \"\"\"\n",
    "    Example parse function\n",
    "    \"\"\"\n",
    "    \n",
    "    # list of the data items\n",
    "    parameters = [\"header\", \"time\", \"zams_mass\", \"mass\", \"luminosity\", \"probability\"]\n",
    "    \n",
    "    # Loop over the output.\n",
    "    for line in output.splitlines():\n",
    "        \n",
    "        # check if we match a ZERO_AGE_MAIN_SEQUENCE_STAR\n",
    "        match = re.search('ZERO_AGE_MAIN_SEQUENCE_STAR(\\d)',line) \n",
    "        if match:\n",
    "            nstar = match.group(1) \n",
    "            #print(\"matched star\",nstar)\n",
    "\n",
    "            # obtain the line of data in dictionary form \n",
    "            linedata = datalinedict(line,parameters)\n",
    "\n",
    "            # bin the log10(luminosity) to the nearest 0.1dex\n",
    "            binned_log_luminosity = bin_data(math.log10(linedata['luminosity']),\n",
    "                                             binwidth['luminosity'])\n",
    "            \n",
    "            # append the data to the results_dictionary \n",
    "            self.grid_results['luminosity distribution'][int(nstar)][binned_log_luminosity] += linedata['probability'] \n",
    "            \n",
    "            #print (self.grid_results)\n",
    "    \n",
    "    # verbose reporting\n",
    "    #print(\"parse out results_dictionary=\",self.grid_results)\n",
    "    \n",
    "# Add the parsing function\n",
    "population.set(\n",
    "    parse_function=parse_function,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91509ce5-ffe7-4937-aa87-6d7baac9ac04",
   "metadata": {},
   "source": [
    "## Evolving the grid\n",
    "Now that we configured all the main parts of the population object, we can actually run the population! Doing this is straightforward: `population.evolve()`\n",
    "\n",
    "This will start up the processing of all the systems. We can control how many cores are used by settings `amt_cores`. By setting the `verbosity` of the population object to a higher value we can get a lot of verbose information about the run, but for now we will set it to 0.\n",
    "\n",
    "There are many grid_options that can lead to different behaviour of the evolution of the grid. Please do have a look at those: [grid options docs](https://ri0005.pages.surrey.ac.uk/binary_c-python/grid_options_descriptions.html), and try  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adding: amt_cores=4 to grid_options\n",
      "Running the population now, this may take a little while...\n",
      "Creating and loading custom logging functionality\n",
      "Generating grid code\n",
      "Generating grid code\n",
      "Constructing/adding: lnm1\n",
      "Constructing/adding: q\n",
      "Constructing/adding: log10per\n",
      "Saving grid code to grid_options\n",
      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
      "Grid code loaded\n",
      "Grid has handled 2000 stars\n",
      "with a total probability of 0.6495098935846658\n",
      "Total starcount for this run will be: 2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-09-10 15:14:08,077 DEBUG    Process-2] --- Setting up processor: process-0[2021-09-10 15:14:08,080 DEBUG    Process-3] --- Setting up processor: process-1[2021-09-10 15:14:08,086 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
      "\n",
      "[2021-09-10 15:14:08,084 DEBUG    Process-4] --- Setting up processor: process-2\n",
      "\n",
      "[2021-09-10 15:14:08,117 DEBUG    Process-5] --- Setting up processor: process-3"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Process 1 started at 2021-09-10T15:14:08.119437.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>Process 0 started at 2021-09-10T15:14:08.126435.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>\n",
      "Process 2 started at 2021-09-10T15:14:08.138353.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Process 3 started at 2021-09-10T15:14:08.186492.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>\n",
      "Generating grid code\n",
      "Generating grid code\n",
      "Constructing/adding: lnm1\n",
      "Constructing/adding: q\n",
      "Constructing/adding: log10per\n",
      "Saving grid code to grid_options\n",
      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
      "Grid code loaded\n",
      "624/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB625/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.04e-03 ETF=15:14:23 mem:800.5MB\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-09-10 15:16:25,175 DEBUG    MainProcess] --- Signaling stop to processes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
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      "\n",
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      "\n",
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      "1987/2000  99.3% complete 15:27:16 ETA=    6.4s tpr=4.92e-01 ETF=15:27:22 mem:589.5MB\n",
      "1998/2000  99.9% complete 15:27:21 ETA=    1.0s tpr=4.85e-01 ETF=15:27:22 mem:589.6MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-09-10 15:27:22,382 DEBUG    Process-5] --- Process-3 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Process 3 finished:\n",
      "\tgenerator started at 2021-09-10T15:14:08.117391, done at 2021-09-10T15:27:22.400722 (total: 794.283331s of which 792.6935975551605s interfacing with binary_c).\n",
      "\tRan 499 systems with a total probability of 0.17005450973840136.\n",
      "\tThis thread had 0 failing systems with a total probability of 0.\n",
      "\tSkipped a total of 0 systems because they had 0 probability\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-09-10 15:27:22,435 DEBUG    Process-5] --- Process-3 is finished.\n",
      "[2021-09-10 15:27:22,480 DEBUG    Process-3] --- Process-1 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Process 1 finished:\n",
      "\tgenerator started at 2021-09-10T15:14:08.080367, done at 2021-09-10T15:27:22.505288 (total: 794.424921s of which 793.1943278312683s interfacing with binary_c).\n",
      "\tRan 474 systems with a total probability of 0.15740832333567983.\n",
      "\tThis thread had 0 failing systems with a total probability of 0.\n",
      "\tSkipped a total of 0 systems because they had 0 probability\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-09-10 15:27:22,531 DEBUG    Process-3] --- Process-1 is finished.\n",
      "[2021-09-10 15:27:22,846 DEBUG    Process-2] --- Process-0 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Process 0 finished:\n",
      "\tgenerator started at 2021-09-10T15:14:08.077117, done at 2021-09-10T15:27:22.851971 (total: 794.774854s of which 793.4976091384888s interfacing with binary_c).\n",
      "\tRan 507 systems with a total probability of 0.16018641159091498.\n",
      "\tThis thread had 0 failing systems with a total probability of 0.\n",
      "\tSkipped a total of 0 systems because they had 0 probability\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-09-10 15:27:22,872 DEBUG    Process-2] --- Process-0 is finished.\n",
      "[2021-09-10 15:27:22,976 DEBUG    Process-4] --- Process-2 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Process 2 finished:\n",
      "\tgenerator started at 2021-09-10T15:14:08.084369, done at 2021-09-10T15:27:22.981706 (total: 794.897337s of which 793.4600214958191s interfacing with binary_c).\n",
      "\tRan 520 systems with a total probability of 0.1618606489196724.\n",
      "\tThis thread had 0 failing systems with a total probability of 0.\n",
      "\tSkipped a total of 0 systems because they had 0 probability\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-09-10 15:27:22,986 DEBUG    Process-4] --- Process-2 is finished.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population-0fa295ee5c76444bace8fd0ee17a3e11 finished! The total probability was: 0.6495098935846686. It took a total of 795.1383104324341s to run 2000 systems on 4 cores\n",
      "There were no errors found in this run.\n",
      "Done population run!\n"
     ]
    }
   ],
   "source": [
    "# set number of threads\n",
    "population.set(\n",
    "    # verbose output is not required    \n",
    "    verbosity=1,\n",
    "    # set number of threads (i.e. number of CPU cores we use)\n",
    "    amt_cores=4,\n",
    "    )\n",
    "\n",
    "# Evolve the population - this is the slow, number-crunching step\n",
    "print(\"Running the population now, this may take a little while...\")\n",
    "analytics = population.evolve()  \n",
    "print(\"Done population run!\")\n",
    "\n",
    "# Show the results (debugging)\n",
    "# print (population.grid_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ab45c7-7d31-4543-aee4-127ab58e891f",
   "metadata": {},
   "source": [
    "After the run is complete, some technical report on the run is returned. I stored that in `analytics`. As we can see below, this dictionary is like a status report of the evolution. Useful for e.g. debugging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'population_name': '0fa295ee5c76444bace8fd0ee17a3e11', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6495098935846686, 'total_count': 2000, 'start_timestamp': 1631283248.057525, 'end_timestamp': 1631284043.1958354, 'total_mass_run': 41112.220964392276, 'total_probability_weighted_mass_run': 0.6452116023479681, 'zero_prob_stars_skipped': 0}\n"
     ]
    }
   ],
   "source": [
    "print(analytics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot of the luminosity distribution using Seaborn and Pandas\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from binarycpython.utils.functions import pad_output_distribution\n",
    "\n",
    "# set the figure size (for a Jupyter notebook in a web browser) \n",
    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
    "\n",
    "titles = { 0 : \"Primary\",\n",
    "           1 : \"Secondary\",\n",
    "           2 : \"Unresolved\" }\n",
    "\n",
    "# choose to plot the \n",
    "# PRIMARY, SECONDARY or UNRESOLVED\n",
    "nstar = UNRESOLVED\n",
    "\n",
    "plots = {}\n",
    "\n",
    "# pad the distribution with zeros where data is missing\n",
    "for n in range(0,3):\n",
    "    pad_output_distribution(population.grid_results['luminosity distribution'][n],\n",
    "                            binwidth['luminosity'])\n",
    "    plots[titles[n] + ' ZAMS luminosity distribution'] = population.grid_results['luminosity distribution'][n]\n",
    "\n",
    "# make pandas dataframe from our sorted dictionary of data\n",
    "plot_data = pd.DataFrame.from_dict(plots)\n",
    "\n",
    "# make the plot\n",
    "p = sns.lineplot(data=plot_data)\n",
    "p.set_xlabel(\"$\\log_{10}$ ($L_\\mathrm{ZAMS}$ / L$_{☉}$)\")\n",
    "p.set_ylabel(\"Number of stars\")\n",
    "p.set(yscale=\"log\")"
   ]
  }
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