notebook_luminosity_function_binaries.ipynb

{
 "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",
    "\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\": 4*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",
    "    samplerfunc=\"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",
    "     samplerfunc=\"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",
    "    samplerfunc=\"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 `num_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": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adding: num_cores=4 to grid_options\n",
      "Running the population now, this may take a little while...\n",
      "Creating and loading custom logging functionality\n",
      "Doing dry run to calculate total starcount and probability\n",
      "Generating grid code\n",
      "Generating grid code\n",
      "Saving grid code to grid_options\n",
      "Writing grid code to /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py [dry_run = True]\n",
      "Symlinked grid code to /tmp/binary_c_python-izzard/binary_c_grid-latest0 \n",
      "Loading grid code function from /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py\n",
      "Grid code loaded\n",
      "Dry run of the grid\n",
      "Grid has handled 4000 stars with a total probability of 0.648566\n",
      "****************************************\n",
      "* Total starcount for this run is 4000 *\n",
      "*     Total probability is 0.648566    *\n",
      "****************************************\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 09:56:53,685 DEBUG    Process-2] --- Setting up processor: process-0\n",
      "[2021-11-01 09:56:53,690 DEBUG    Process-3] --- Setting up processor: process-1\n",
      "[2021-11-01 09:56:53,694 DEBUG    Process-4] --- Setting up processor: process-2\n",
      "[2021-11-01 09:56:53,698 DEBUG    MainProcess] --- setting up the system_queue_filler now\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating grid code\n",
      "Generating grid code\n",
      "Saving grid code to grid_options\n",
      "Writing grid code to /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py [dry_run = False]\n",
      "Symlinked grid code to /tmp/binary_c_python-izzard/binary_c_grid-latest1 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 09:56:53,707 DEBUG    Process-5] --- Setting up processor: process-3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading grid code function from /tmp/binary_c_python-izzard/binary_c_grid_25014bc73b334765a1c09a4e4a97ed66.py\n",
      "Grid code loaded\n",
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      "2048/4000  51.2% complete 09:58:29 ETA=    4.3m tpr=5.24e-01 ETF=10:02:45 mem:617.9MB M1=2.9 M2=1.3 P=5e+4\n",
      "2071/4000  51.8% complete 09:58:34 ETA=    4.3m tpr=5.31e-01 ETF=10:02:51 mem:619.7MB M1=2.9 M2=2.2 P=9\n",
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      "2122/4000  53.0% complete 09:58:45 ETA=    4.3m tpr=5.55e-01 ETF=10:03:05 mem:620.5MB M1=3.5 M2=0.92 P=31\n",
      "2143/4000  53.6% complete 09:58:50 ETA=    4.3m tpr=5.59e-01 ETF=10:03:09 mem:623.3MB M1=3.5 M2=1.6 P=1.1e+2\n",
      "2164/4000  54.1% complete 09:58:55 ETA=    4.4m tpr=5.70e-01 ETF=10:03:16 mem:623.9MB M1=3.5 M2=2.3 P=3.6e+2\n",
      "2183/4000  54.6% complete 09:59:00 ETA=    4.4m tpr=5.80e-01 ETF=10:03:23 mem:624.0MB M1=3.5 M2=3 P=1.1e+2\n",
      "2206/4000  55.1% complete 09:59:05 ETA=    4.4m tpr=5.91e-01 ETF=10:03:30 mem:624.0MB M1=4.2 M2=0.27 P=4.2e+3\n",
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      "2245/4000  56.1% complete 09:59:16 ETA=    4.4m tpr=6.06e-01 ETF=10:03:41 mem:624.1MB M1=4.2 M2=1.9 P=1.2e+3\n",
      "2258/4000  56.5% complete 09:59:21 ETA=    4.5m tpr=6.21e-01 ETF=10:03:51 mem:624.1MB M1=4.2 M2=2.3 P=5e+4\n",
      "2269/4000  56.7% complete 09:59:26 ETA=    4.6m tpr=6.36e-01 ETF=10:04:01 mem:626.1MB M1=4.2 M2=2.7 P=1.7e+5\n",
      "2282/4000  57.0% complete 09:59:31 ETA=    4.7m tpr=6.51e-01 ETF=10:04:11 mem:626.1MB M1=4.2 M2=3.6 P=31\n",
      "2300/4000  57.5% complete 09:59:36 ETA=    4.7m tpr=6.63e-01 ETF=10:04:18 mem:626.1MB M1=5 M2=0.32 P=2.6\n",
      "2329/4000  58.2% complete 09:59:41 ETA=    4.7m tpr=6.72e-01 ETF=10:04:22 mem:626.1MB M1=5 M2=1.3 P=1.7e+5\n",
      "2348/4000  58.7% complete 09:59:46 ETA=    4.6m tpr=6.73e-01 ETF=10:04:25 mem:626.2MB M1=5 M2=2.3 P=5e+4\n",
      "2365/4000  59.1% complete 09:59:52 ETA=    4.6m tpr=6.81e-01 ETF=10:04:30 mem:626.4MB M1=5 M2=3.3 P=1.2e+3\n",
      "2383/4000  59.6% complete 09:59:57 ETA=    4.7m tpr=6.90e-01 ETF=10:04:36 mem:626.4MB M1=5 M2=4.3 P=1.1e+2\n",
      "2400/4000  60.0% complete 10:00:02 ETA=    4.7m tpr=6.99e-01 ETF=10:04:42 mem:626.4MB M1=6 M2=0.37 P=2.6\n",
      "2423/4000  60.6% complete 10:00:07 ETA=    4.6m tpr=7.07e-01 ETF=10:04:46 mem:626.4MB M1=6 M2=1.6 P=1.1e+2\n",
      "2438/4000  61.0% complete 10:00:12 ETA=    4.6m tpr=7.10e-01 ETF=10:04:50 mem:628.5MB M1=6 M2=2.1 P=5e+4\n",
      "2454/4000  61.4% complete 10:00:18 ETA=    4.6m tpr=7.20e-01 ETF=10:04:56 mem:629.6MB M1=6 M2=3.3 P=3.6e+2\n",
      "2466/4000  61.6% complete 10:00:23 ETA=    4.7m tpr=7.29e-01 ETF=10:05:03 mem:629.6MB M1=6 M2=3.9 P=4.2e+3\n",
      "2477/4000  61.9% complete 10:00:28 ETA=    4.7m tpr=7.40e-01 ETF=10:05:10 mem:629.6MB M1=6 M2=4.5 P=1.5e+4\n",
      "2492/4000  62.3% complete 10:00:33 ETA=    4.7m tpr=7.51e-01 ETF=10:05:16 mem:630.7MB M1=6 M2=5.7 P=31\n",
      "2516/4000  62.9% complete 10:00:38 ETA=    4.7m tpr=7.59e-01 ETF=10:05:20 mem:630.7MB M1=7.2 M2=1.1 P=4.2e+3\n",
      "2537/4000  63.4% complete 10:00:44 ETA=    4.6m tpr=7.61e-01 ETF=10:05:22 mem:630.7MB M1=7.2 M2=2.6 P=1.5e+4\n",
      "2554/4000  63.9% complete 10:00:49 ETA=    4.6m tpr=7.65e-01 ETF=10:05:25 mem:630.8MB M1=7.2 M2=4 P=3.6e+2\n",
      "2570/4000  64.2% complete 10:00:54 ETA=    4.6m tpr=7.71e-01 ETF=10:05:29 mem:630.9MB M1=7.2 M2=5.4 P=2.6\n",
      "2590/4000  64.8% complete 10:00:59 ETA=    4.6m tpr=7.78e-01 ETF=10:05:33 mem:630.9MB M1=7.2 M2=6.8 P=2.6\n",
      "2622/4000  65.5% complete 10:01:04 ETA=    4.5m tpr=7.81e-01 ETF=10:05:33 mem:630.9MB M1=8.6 M2=2.2 P=31\n",
      "2636/4000  65.9% complete 10:01:09 ETA=    4.4m tpr=7.78e-01 ETF=10:05:34 mem:632.1MB M1=8.6 M2=3.1 P=4.2e+3\n",
      "2652/4000  66.3% complete 10:01:14 ETA=    4.4m tpr=7.87e-01 ETF=10:05:39 mem:632.1MB M1=8.6 M2=4.8 P=31\n",
      "2666/4000  66.7% complete 10:01:19 ETA=    4.7m tpr=8.37e-01 ETF=10:05:59 mem:634.6MB M1=8.6 M2=5.6 P=4.2e+3\n",
      "2691/4000  67.3% complete 10:01:25 ETA=    4.7m tpr=8.59e-01 ETF=10:06:06 mem:634.6MB M1=8.6 M2=8.2 P=9\n",
      "2733/4000  68.3% complete 10:01:31 ETA=    4.6m tpr=8.75e-01 ETF=10:06:08 mem:634.6MB M1=10 M2=3.7 P=1.1e+2\n",
      "2746/4000  68.7% complete 10:01:36 ETA=    4.6m tpr=8.83e-01 ETF=10:06:13 mem:634.6MB M1=10 M2=4.7 P=4.2e+3\n",
      "2757/4000  68.9% complete 10:01:42 ETA=    4.7m tpr=9.04e-01 ETF=10:06:22 mem:634.6MB M1=10 M2=5.7 P=1.5e+4\n",
      "2775/4000  69.4% complete 10:01:47 ETA=    4.8m tpr=9.37e-01 ETF=10:06:34 mem:634.8MB M1=10 M2=7.8 P=1.2e+3\n",
      "2795/4000  69.9% complete 10:01:52 ETA=    4.8m tpr=9.48e-01 ETF=10:06:38 mem:635.1MB M1=10 M2=9.8 P=1.2e+3\n",
      "2831/4000  70.8% complete 10:01:57 ETA=    4.7m tpr=9.71e-01 ETF=10:06:41 mem:636.3MB M1=12 M2=4.4 P=9\n",
      "2845/4000  71.1% complete 10:02:04 ETA=    4.7m tpr=9.67e-01 ETF=10:06:43 mem:636.3MB M1=12 M2=5.6 P=1.2e+3\n",
      "2861/4000  71.5% complete 10:02:09 ETA=    4.7m tpr=9.92e-01 ETF=10:06:52 mem:636.3MB M1=12 M2=8.1 P=9\n",
      "2875/4000  71.9% complete 10:02:16 ETA=    4.7m tpr=1.01e+00 ETF=10:07:01 mem:636.5MB M1=12 M2=9.3 P=1.2e+3\n",
      "2891/4000  72.3% complete 10:02:21 ETA=    4.8m tpr=1.03e+00 ETF=10:07:07 mem:636.5MB M1=12 M2=12 P=9\n",
      "2930/4000  73.2% complete 10:02:26 ETA=    4.7m tpr=1.05e+00 ETF=10:07:07 mem:636.7MB M1=15 M2=5.2 P=2.6\n",
      "2947/4000  73.7% complete 10:02:31 ETA=    4.6m tpr=1.05e+00 ETF=10:07:07 mem:636.7MB M1=15 M2=6.7 P=1.5e+4\n",
      "2965/4000  74.1% complete 10:02:37 ETA=    4.5m tpr=1.05e+00 ETF=10:07:09 mem:636.8MB M1=15 M2=9.7 P=1.2e+3\n",
      "2982/4000  74.5% complete 10:02:42 ETA=    4.5m tpr=1.06e+00 ETF=10:07:11 mem:637.3MB M1=15 M2=13 P=31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:02:46,515 DEBUG    MainProcess] --- Signaling stop to processes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3010/4000  75.2% complete 10:02:47 ETA=    4.4m tpr=1.07e+00 ETF=10:07:12 mem:637.8MB M1=18 M2=2.7 P=2.6\n",
      "3035/4000  75.9% complete 10:02:52 ETA=    4.3m tpr=1.07e+00 ETF=10:07:11 mem:637.8MB M1=18 M2=6.3 P=1.2e+3\n",
      "3059/4000  76.5% complete 10:02:57 ETA=    4.2m tpr=1.07e+00 ETF=10:07:09 mem:637.8MB M1=18 M2=9.8 P=1.7e+5\n",
      "3078/4000  77.0% complete 10:03:02 ETA=    4.1m tpr=1.07e+00 ETF=10:07:08 mem:638.7MB M1=18 M2=13 P=5e+4\n",
      "3100/4000  77.5% complete 10:03:08 ETA=    4.0m tpr=1.08e+00 ETF=10:07:10 mem:638.9MB M1=21 M2=1.1 P=2.6\n",
      "3124/4000  78.1% complete 10:03:13 ETA=    3.9m tpr=1.08e+00 ETF=10:07:09 mem:639.1MB M1=21 M2=5.4 P=3.6e+2\n",
      "3148/4000  78.7% complete 10:03:18 ETA=    3.8m tpr=1.08e+00 ETF=10:07:08 mem:639.7MB M1=21 M2=9.7 P=5e+4\n",
      "3174/4000  79.3% complete 10:03:24 ETA=    3.7m tpr=1.07e+00 ETF=10:07:05 mem:640.1MB M1=21 M2=16 P=3.6e+2\n",
      "3197/4000  79.9% complete 10:03:29 ETA=    3.6m tpr=1.07e+00 ETF=10:07:04 mem:640.4MB M1=21 M2=20 P=1.5e+4\n",
      "3231/4000  80.8% complete 10:03:34 ETA=    3.4m tpr=1.07e+00 ETF=10:07:00 mem:640.9MB M1=26 M2=9 P=9\n",
      "3256/4000  81.4% complete 10:03:39 ETA=    3.3m tpr=1.05e+00 ETF=10:06:55 mem:640.9MB M1=26 M2=14 P=4.2e+3\n",
      "3273/4000  81.8% complete 10:03:44 ETA=    3.1m tpr=1.04e+00 ETF=10:06:53 mem:640.9MB M1=26 M2=19 P=1.1e+2\n",
      "3294/4000  82.3% complete 10:03:50 ETA=    3.0m tpr=1.03e+00 ETF=10:06:51 mem:641.4MB M1=26 M2=24 P=3.6e+2\n",
      "3321/4000  83.0% complete 10:03:55 ETA=    2.9m tpr=1.02e+00 ETF=10:06:48 mem:641.6MB M1=31 M2=7.7 P=9\n",
      "3348/4000  83.7% complete 10:04:00 ETA=    2.7m tpr=1.01e+00 ETF=10:06:45 mem:641.9MB M1=31 M2=14 P=5e+4\n",
      "3373/4000  84.3% complete 10:04:05 ETA=    2.6m tpr=1.01e+00 ETF=10:06:44 mem:641.9MB M1=31 M2=23 P=1.1e+2\n",
      "3394/4000  84.8% complete 10:04:10 ETA=    2.5m tpr=1.01e+00 ETF=10:06:43 mem:642.2MB M1=31 M2=29 P=3.6e+2\n",
      "3422/4000  85.5% complete 10:04:15 ETA=    2.4m tpr=1.00e+00 ETF=10:06:40 mem:642.5MB M1=37 M2=9.3 P=31\n",
      "3445/4000  86.1% complete 10:04:21 ETA=    2.3m tpr=9.93e-01 ETF=10:06:38 mem:642.7MB M1=37 M2=17 P=1.2e+3\n",
      "3464/4000  86.6% complete 10:04:26 ETA=    2.2m tpr=9.90e-01 ETF=10:06:39 mem:642.8MB M1=37 M2=24 P=3.6e+2\n",
      "3483/4000  87.1% complete 10:04:31 ETA=    2.1m tpr=9.96e-01 ETF=10:06:40 mem:642.8MB M1=37 M2=31 P=1.1e+2\n",
      "3509/4000  87.7% complete 10:04:37 ETA=    2.0m tpr=9.91e-01 ETF=10:06:38 mem:642.8MB M1=44 M2=2.3 P=1.7e+5\n",
      "3533/4000  88.3% complete 10:04:42 ETA=    1.9m tpr=9.81e-01 ETF=10:06:36 mem:642.8MB M1=44 M2=16 P=1.1e+2\n",
      "3550/4000  88.8% complete 10:04:47 ETA=    1.8m tpr=9.71e-01 ETF=10:06:36 mem:642.8MB M1=44 M2=24 P=2.6\n",
      "3568/4000  89.2% complete 10:04:52 ETA=    1.7m tpr=9.65e-01 ETF=10:06:37 mem:642.8MB M1=44 M2=29 P=5e+4\n",
      "3588/4000  89.7% complete 10:04:58 ETA=    1.7m tpr=9.64e-01 ETF=10:06:37 mem:643.1MB M1=44 M2=38 P=5e+4\n",
      "3622/4000  90.5% complete 10:05:03 ETA=    1.5m tpr=9.68e-01 ETF=10:06:34 mem:643.2MB M1=53 M2=13 P=313623/4000  90.6% complete 10:05:03 ETA=    1.5m tpr=9.68e-01 ETF=10:06:34 mem:643.2MB M1=53 M2=13 P=1.1e+2\n",
      "\n",
      "3640/4000  91.0% complete 10:05:08 ETA=    1.4m tpr=9.52e-01 ETF=10:06:33 mem:643.6MB M1=53 M2=24 P=2.6\n",
      "3657/4000  91.4% complete 10:05:13 ETA=    1.4m tpr=9.50e-01 ETF=10:06:34 mem:643.7MB M1=53 M2=29 P=1.5e+4\n",
      "3676/4000  91.9% complete 10:05:18 ETA=    1.3m tpr=9.53e-01 ETF=10:06:35 mem:643.7MB M1=53 M2=40 P=4.2e+3\n",
      "3706/4000  92.7% complete 10:05:23 ETA=    1.2m tpr=9.66e-01 ETF=10:06:34 mem:643.9MB M1=64 M2=3.2 P=4.2e+3\n",
      "3726/4000  93.2% complete 10:05:29 ETA=    1.1m tpr=9.50e-01 ETF=10:06:34 mem:644.5MB M1=64 M2=16 P=4.2e+3\n",
      "3746/4000  93.7% complete 10:05:34 ETA=    1.0m tpr=9.48e-01 ETF=10:06:34 mem:644.5MB M1=64 M2=29 P=4.2e+3\n",
      "3763/4000  94.1% complete 10:05:39 ETA=   55.8s tpr=9.42e-01 ETF=10:06:35 mem:644.5MB M1=64 M2=41 P=1.1e+2\n",
      "3786/4000  94.7% complete 10:05:45 ETA=   50.8s tpr=9.49e-01 ETF=10:06:35 mem:644.6MB M1=64 M2=54 P=4.2e+3\n",
      "3811/4000  95.3% complete 10:05:50 ETA=   45.5s tpr=9.63e-01 ETF=10:06:35 mem:645.0MB M1=76 M2=11 P=9\n",
      "3832/4000  95.8% complete 10:05:55 ETA=   39.9s tpr=9.51e-01 ETF=10:06:35 mem:645.0MB M1=76 M2=27 P=31\n",
      "3849/4000  96.2% complete 10:06:00 ETA=   35.5s tpr=9.42e-01 ETF=10:06:35 mem:645.1MB M1=76 M2=34 P=1.7e+5\n",
      "3875/4000  96.9% complete 10:06:05 ETA=   29.4s tpr=9.40e-01 ETF=10:06:34 mem:645.4MB M1=76 M2=57 P=1.2e+3\n",
      "3905/4000  97.6% complete 10:06:10 ETA=   22.2s tpr=9.36e-01 ETF=10:06:32 mem:645.5MB M1=91 M2=4.6 P=1.2e+3\n",
      "3930/4000  98.2% complete 10:06:15 ETA=   16.5s tpr=9.41e-01 ETF=10:06:32 mem:645.5MB M1=91 M2=32 P=2.6\n",
      "3931/4000  98.3% complete 10:06:15 ETA=   16.2s tpr=9.41e-01 ETF=10:06:31 mem:645.5MB M1=91 M2=32 P=9\n",
      "3954/4000  98.8% complete 10:06:20 ETA=   10.6s tpr=9.19e-01 ETF=10:06:31 mem:645.8MB M1=91 M2=50 P=3.6e+2\n",
      "3977/4000  99.4% complete 10:06:25 ETA=    5.2s tpr=9.06e-01 ETF=10:06:30 mem:645.8MB M1=91 M2=69 P=1.5e+4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,167 DEBUG    Process-3] --- Process-1 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 1 free memory and return \n",
      "****************************************************\n",
      "*                Process 1 finished:               *\n",
      "*  generator started at 2021-11-01T09:56:53.690194 *\n",
      "* generator finished at 2021-11-01T10:06:29.176751 *\n",
      "*                 total: 9m 35.49s                 *\n",
      "*         of which 9m 34.96s with binary_c         *\n",
      "*                 Ran 1001 systems                 *\n",
      "*       with a total probability of 0.160675       *\n",
      "*         This thread had 0 failing systems        *\n",
      "*       with a total failed probability of 0       *\n",
      "*   Skipped a total of 0 zero-probability systems  *\n",
      "*                                                  *\n",
      "****************************************************\n",
      "\n",
      "process 1 queue put output_dict \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,186 DEBUG    Process-3] --- Process-1 is finished.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 1 return \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,342 DEBUG    Process-5] --- Process-3 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 3 free memory and return \n",
      "****************************************************\n",
      "*                Process 3 finished:               *\n",
      "*  generator started at 2021-11-01T09:56:53.706780 *\n",
      "* generator finished at 2021-11-01T10:06:29.345842 *\n",
      "*                 total: 9m 35.64s                 *\n",
      "*         of which 9m 35.06s with binary_c         *\n",
      "*                 Ran 1001 systems                 *\n",
      "*       with a total probability of 0.155662       *\n",
      "*         This thread had 0 failing systems        *\n",
      "*       with a total failed probability of 0       *\n",
      "*   Skipped a total of 0 zero-probability systems  *\n",
      "*                                                  *\n",
      "****************************************************\n",
      "\n",
      "process 3 queue put output_dict \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,350 DEBUG    Process-5] --- Process-3 is finished.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 3 return \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,429 DEBUG    Process-2] --- Process-0 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 0 free memory and return \n",
      "****************************************************\n",
      "*                Process 0 finished:               *\n",
      "*  generator started at 2021-11-01T09:56:53.684890 *\n",
      "* generator finished at 2021-11-01T10:06:29.433207 *\n",
      "*                 total: 9m 35.75s                 *\n",
      "*         of which 9m 35.15s with binary_c         *\n",
      "*                 Ran 1025 systems                 *\n",
      "*       with a total probability of 0.162454       *\n",
      "*         This thread had 0 failing systems        *\n",
      "*       with a total failed probability of 0       *\n",
      "*   Skipped a total of 0 zero-probability systems  *\n",
      "*                                                  *\n",
      "****************************************************\n",
      "\n",
      "process 0 queue put output_dict \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,437 DEBUG    Process-2] --- Process-0 is finished.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 0 return \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,449 DEBUG    Process-4] --- Process-2 is finishing.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 2 free memory and return \n",
      "****************************************************\n",
      "*                Process 2 finished:               *\n",
      "*  generator started at 2021-11-01T09:56:53.694517 *\n",
      "* generator finished at 2021-11-01T10:06:29.453059 *\n",
      "*                 total: 9m 35.76s                 *\n",
      "*         of which 9m 35.25s with binary_c         *\n",
      "*                  Ran 973 systems                 *\n",
      "*       with a total probability of 0.169775       *\n",
      "*         This thread had 0 failing systems        *\n",
      "*       with a total failed probability of 0       *\n",
      "*   Skipped a total of 0 zero-probability systems  *\n",
      "*                                                  *\n",
      "****************************************************\n",
      "\n",
      "process 2 queue put output_dict \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021-11-01 10:06:29,456 DEBUG    Process-4] --- Process-2 is finished.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process 2 return \n",
      "****************************************************************\n",
      "*     Population-25014bc73b334765a1c09a4e4a97ed66 finished!    *\n",
      "*              The total probability is 0.648566.              *\n",
      "*  It took a total of 9m 35.99s to run 4000 systems on 4 cores *\n",
      "*                   = 38m 23.97s of CPU time.                  *\n",
      "*                 Maximum memory use 856.406 MB                *\n",
      "****************************************************************\n",
      "\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",
    "    num_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": 10,
   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'population_name': '25014bc73b334765a1c09a4e4a97ed66', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6485656144116352, 'total_count': 4000, 'start_timestamp': 1635760613.6602514, 'end_timestamp': 1635761189.652638, 'total_mass_run': 82563.09295167374, 'total_probability_weighted_mass_run': 0.6438124832773024, 'zero_prob_stars_skipped': 0}\n"
     ]
    }
   ],
   "source": [
    "print(analytics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 11,
     "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 up seaborn for use in the notebook\n",
    "sns.set(rc={'figure.figsize':(20,10)})\n",
    "sns.set_context(\"notebook\",\n",
    "                font_scale=1.5,\n",
    "                rc={\"lines.linewidth\":2.5})\n",
    "\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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
   "metadata": {},
   "source": [
    "You can see that the secondary stars are dimmer than the primaries - which you expect given they are lower in mass (by definition q=M2/M1<1). \n",
    "\n",
    "Weirdly, in some places the primary distribution may exceed the unresolved distribution. This is a bit unphysical, but in this case is usually caused by limited resolution. If you increase the number of stars in the grid, this problem should go away (at a cost of more CPU time). "
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    "Things to try:\n",
    "\n",
    " * Massive stars: can you see the effects of wind mass loss and rejuvenation in these stars?\n",
    " * Alter the metallicity, does this make much of a difference?\n",
    " * Change the binary fraction. Here we assume a 100% binary fraction, but a real population is a mixture of single and binary stars.\n",
    " * How might you go about comparing these computed observations to real stars?\n",
    " * What about evolved stars? Here we consider only the *zero-age* main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?"
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