From 44ccba50cf8ee6884242f6fd222ea15dd02201d7 Mon Sep 17 00:00:00 2001
From: dh00601 <dh00601@surrey.ac.uk>
Date: Sat, 18 Jun 2022 15:57:22 +0100
Subject: [PATCH] rebuilt docs after merge with development_0.9.4

---
 docs/build/doctrees/environment.pickle        | Bin 981572 -> 976242 bytes
 .../notebook_massive_remnants.doctree         | Bin 83665 -> 0 bytes
 docs/build/doctrees/index.doctree             | Bin 45204 -> 45624 bytes
 .../examples/notebook_massive_remnants.ipynb  | 761 --------------
 ...xamples_notebook_massive_remnants_20_1.png | Bin 31898 -> 0 bytes
 ...xamples_notebook_massive_remnants_25_1.png | Bin 31453 -> 0 bytes
 ...xamples_notebook_massive_remnants_33_0.png | Bin 39614 -> 0 bytes
 docs/build/doctrees/readme_link.doctree       | Bin 41915 -> 42335 bytes
 ...xamples_notebook_massive_remnants_20_1.png | Bin 31898 -> 0 bytes
 ...xamples_notebook_massive_remnants_25_1.png | Bin 31453 -> 0 bytes
 ...xamples_notebook_massive_remnants_33_0.png | Bin 39614 -> 0 bytes
 docs/build/html/_images/test_coverage.svg     |   4 +-
 .../utils/custom_logging_functions.html       |   2 +-
 .../_modules/binarycpython/utils/dicts.html   |   2 +-
 .../binarycpython/utils/ensemble.html         |   2 +-
 .../binarycpython/utils/functions.html        |   2 +-
 .../_modules/binarycpython/utils/grid.html    |   2 +-
 .../binarycpython/utils/plot_functions.html   |   2 +-
 .../utils/population_extensions/HPC.html      |   2 +-
 .../Moe_di_Stefano_2017.html                  |   2 +-
 .../population_extensions/analytics.html      |   2 +-
 .../utils/population_extensions/cache.html    |   2 +-
 .../utils/population_extensions/condor.html   |   2 +-
 .../utils/population_extensions/dataIO.html   |   2 +-
 .../distribution_functions.html               |   2 +-
 .../population_extensions/grid_logging.html   |   2 +-
 .../grid_options_defaults.html                |   2 +-
 .../utils/population_extensions/gridcode.html |   2 +-
 .../utils/population_extensions/metadata.html |   2 +-
 .../utils/population_extensions/slurm.html    |   2 +-
 .../spacing_functions.html                    |   2 +-
 .../population_extensions/version_info.html   |   2 +-
 .../utils/run_system_wrapper.html             |   2 +-
 .../binarycpython/utils/useful_funcs.html     |   2 +-
 docs/build/html/_modules/index.html           |   2 +-
 .../notebook_massive_remnants.ipynb.txt       | 761 --------------
 docs/build/html/binary_c_parameters.html      |   2 +-
 docs/build/html/custom_logging_functions.html |   2 +-
 docs/build/html/dicts.html                    |   2 +-
 docs/build/html/ensemble.html                 |   2 +-
 docs/build/html/example_notebooks.html        |   2 +-
 docs/build/html/examples/notebook_BHBH.html   |   2 +-
 docs/build/html/examples/notebook_HRD.html    |   2 +-
 .../examples/notebook_api_functionality.html  |   2 +-
 .../notebook_common_envelope_evolution.html   |   2 +-
 .../examples/notebook_custom_logging.html     |   2 +-
 .../html/examples/notebook_ensembles.html     |   2 +-
 .../examples/notebook_extra_features.html     |   2 +-
 .../examples/notebook_individual_systems.html |   2 +-
 ...notebook_luminosity_function_binaries.html |   2 +-
 .../notebook_luminosity_function_single.html  |   2 +-
 .../examples/notebook_massive_remnants.html   | 942 ------------------
 .../examples/notebook_massive_remnants.ipynb  | 761 --------------
 .../html/examples/notebook_population.html    |   2 +-
 .../html/examples/notebook_solar_system.html  |   2 +-
 .../html/examples/old/basic_example.html      |   2 +-
 .../old/workshop_example_notebook.html        |   2 +-
 docs/build/html/functions.html                |   2 +-
 docs/build/html/genindex.html                 |   2 +-
 docs/build/html/grid.html                     |   2 +-
 .../build/html/grid_options_descriptions.html |   2 +-
 docs/build/html/index.html                    |   3 +-
 docs/build/html/modules.html                  |   2 +-
 docs/build/html/objects.inv                   | Bin 11965 -> 11835 bytes
 docs/build/html/plot_functions.html           |   2 +-
 .../build/html/population_extensions/HPC.html |   2 +-
 .../Moe_di_Stefano_2017.html                  |   2 +-
 .../html/population_extensions/analytics.html |   2 +-
 .../html/population_extensions/cache.html     |   2 +-
 .../html/population_extensions/condor.html    |   2 +-
 .../html/population_extensions/dataIO.html    |   2 +-
 .../distribution_functions.html               |   2 +-
 .../population_extensions/grid_logging.html   |   2 +-
 .../grid_options_defaults.html                |   2 +-
 .../html/population_extensions/gridcode.html  |   2 +-
 .../html/population_extensions/metadata.html  |   2 +-
 .../html/population_extensions/slurm.html     |   2 +-
 .../spacing_functions.html                    |   2 +-
 .../population_extensions/version_info.html   |   2 +-
 docs/build/html/py-modindex.html              |   2 +-
 docs/build/html/readme_link.html              |   3 +-
 docs/build/html/run_system_wrapper.html       |   2 +-
 docs/build/html/search.html                   |   2 +-
 docs/build/html/searchindex.js                |   2 +-
 docs/build/html/stellar_types.html            |   2 +-
 docs/build/html/useful_funcs.html             |   2 +-
 docs/source/_templates/footer.html            |   2 +-
 docs/source/badges/test_coverage.svg          |   4 +-
 88 files changed, 77 insertions(+), 3300 deletions(-)
 delete mode 100644 docs/build/doctrees/examples/notebook_massive_remnants.doctree
 delete mode 100644 docs/build/doctrees/nbsphinx/examples/notebook_massive_remnants.ipynb
 delete mode 100644 docs/build/doctrees/nbsphinx/examples_notebook_massive_remnants_20_1.png
 delete mode 100644 docs/build/doctrees/nbsphinx/examples_notebook_massive_remnants_25_1.png
 delete mode 100644 docs/build/doctrees/nbsphinx/examples_notebook_massive_remnants_33_0.png
 delete mode 100644 docs/build/html/_images/examples_notebook_massive_remnants_20_1.png
 delete mode 100644 docs/build/html/_images/examples_notebook_massive_remnants_25_1.png
 delete mode 100644 docs/build/html/_images/examples_notebook_massive_remnants_33_0.png
 delete mode 100644 docs/build/html/_sources/examples/notebook_massive_remnants.ipynb.txt
 delete mode 100644 docs/build/html/examples/notebook_massive_remnants.html
 delete mode 100644 docs/build/html/examples/notebook_massive_remnants.ipynb

diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
index d44af2094a2cec55151d115a3ca70b52ec5bf81f..4c381d515cef510a9c2686e78ef968bd07b8f715 100644
GIT binary patch
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diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree
index b0f8d923e6b9ddb0339b819824a7e432b632a587..85010eee044d533a2317a8a4fb1d628e4c28d96d 100644
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diff --git a/docs/build/doctrees/nbsphinx/examples/notebook_massive_remnants.ipynb b/docs/build/doctrees/nbsphinx/examples/notebook_massive_remnants.ipynb
deleted file mode 100644
index 28227d1a1..000000000
--- a/docs/build/doctrees/nbsphinx/examples/notebook_massive_remnants.ipynb
+++ /dev/null
@@ -1,761 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Example use case: Massive star luminosity\n",
-    "\n",
-    "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of single stars using binary_c. \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",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from binarycpython.utils.functions import temp_dir\n",
-    "from binarycpython.utils.grid import Population\n",
-    "\n",
-    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_luminosity\")\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",
-      "adding: tmp_dir=/tmp/binary_c_python-izzard/notebooks/notebook_luminosity to grid_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",
-    "    tmp_dir=TMP_DIR,\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' : 0.5 }"
-   ]
-  },
-  {
-   "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": [
-    "# Mass\n",
-    "population = Population()\n",
-    "population.set(\n",
-    "    tmp_dir=TMP_DIR,\n",
-    ")\n",
-    "population.add_grid_variable(\n",
-    "    name=\"M_1\",\n",
-    "    longname=\"Primary mass\",\n",
-    "    valuerange=massrange,\n",
-    "    samplerfunc=\"const({min}, {max}, {res})\".format(min = massrange[0], max = massrange[1], res = resolution[\"M_1\"]),\n",
-    "    probdist=\"{probtot}/({max} - {min})\".format(probtot = total_probability, min = massrange[0], max = massrange[1]), # dprob/dm1 : all stars are equally likely so this is 1.0 / (Mmax - Mmin)\n",
-    "    dphasevol=\"dM_1\",\n",
-    "    parameter_name=\"M_1\",\n",
-    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\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 Printf statement does the outputting: note that the header string is ZERO_AGE_MAIN_SEQUENCE_STAR\n",
-    "\n",
-    "custom_logging_statement = \"\"\"\n",
-    "if(stardata->model.model_number == 0 &&\n",
-    "   stardata->star[0].stellar_type <= MAIN_SEQUENCE &&\n",
-    "   stardata->model.time == 0)\n",
-    "{\n",
-    "   /* Note that we use Printf - with a capital P! */\n",
-    "   Printf(\"ZERO_AGE_MAIN_SEQUENCE_STAR %30.12e %g %g %g %g\\\\n\",\n",
-    "          stardata->model.time, // 1\n",
-    "          stardata->common.zero_age.mass[0], // 2\n",
-    "          stardata->star[0].mass, // 3\n",
-    "          stardata->star[0].luminosity, // 4\n",
-    "          stardata->model.probability // 5\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",
-    "\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",
-    "        # obtain the line of data in dictionary form \n",
-    "        linedata = datalinedict(line,parameters)\n",
-    "        \n",
-    "        # Check the header and act accordingly\n",
-    "        if linedata['header'] == \"ZERO_AGE_MAIN_SEQUENCE_STAR\":\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'][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": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 1\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*       Total probability is 1       *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-b6213f2eb7f94d3196cf966b7b76b9f9 finished! *\n",
-      "*               The total probability is 1.              *\n",
-      "*  It took a total of 6.99s to run 40 systems on 2 cores *\n",
-      "*                  = 13.98s of CPU time.                 *\n",
-      "*              Maximum memory use 472.211 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# set number of threads\n",
-    "population.set(\n",
-    "    # verbose output is not required    \n",
-    "    verbosity=0,\n",
-    "    # set number of threads (i.e. number of CPU cores we use)\n",
-    "    num_cores=2,\n",
-    "    )\n",
-    "\n",
-    "# Evolve the population - this is the slow, number-crunching step\n",
-    "analytics = population.evolve()  \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': 'b6213f2eb7f94d3196cf966b7b76b9f9', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.9999999999999998, 'total_count': 40, 'start_timestamp': 1635760806.5066257, 'end_timestamp': 1635760813.4966016, 'total_mass_run': 2001.3999999999996, 'total_probability_weighted_mass_run': 50.03499999999999, '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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\n",
-      "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",
-    "# this saves a lot of typing! \n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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": [
-    "Does this look like a reasonable stellar luminosity function to you? The implication is that the most likely stellar luminosity is 10<sup>5.8</sup> L<sub>☉</sub>! Clearly, this is not very realistic... let's see what went wrong."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e32c3bbf-390f-45da-ad9c-cc3e7c9449dc",
-   "metadata": {},
-   "source": [
-    "## ZAMS Luminosity distribution with the initial mass function\n",
-    "\n",
-    "In the previous example, all the stars in our grid had an equal weighting. This is very unlikely to be true in reality: indeed, we know that low mass stars are far more likely than high mass stars.  So we now include an initial mass function as a three-part power law based on Kroupa (2001). Kroupa's distribution is a three-part power law: we have a function that does this for us (it's very common to use power laws in astrophysics).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update the probability distribution to use the three-part power law IMF \n",
-    "population.update_grid_variable(\n",
-    "    name=\"M_1\",\n",
-    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 0.218222\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*    Total probability is 0.218222   *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-4b8c7f4a86e445099d73f27dffaad94b finished! *\n",
-      "*           The total probability is 0.218222.           *\n",
-      "*  It took a total of 7.95s to run 40 systems on 2 cores *\n",
-      "*                  = 15.89s of CPU time.                 *\n",
-      "*              Maximum memory use 587.984 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Clean and re-evolve the population \n",
-    "population.clean()\n",
-    "analytics = population.evolve()  \n",
-    "\n",
-    "# Show the results (debugging)\n",
-    "# print (population.grid_results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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IDeZdAQDgwk5bVv3Xtm3bit8/cuSIDhw4IA8PD0VERCgkJKRCwgEAAKD6cnf7Z97V5aqv47k2bU9KLyqv9qbp4NGieVcn8mxat/OI1u08IkkKCfBS89ii4qpZ3RAFMu8KAACX4nRZJUmbNm3SM888o40bN5bYfsEFF+ixxx5Tq1atyjUcAAAAzk1VXlBUw9tdbRuFq22jf+Zdbd2XXjysPSMn/+/teVq+8aCWbyyad1U73PfvVVchalkvRFZrFf4kAQBQBThdVm3fvl3XXXedJOnqq69WgwYNZLfbtWfPHv34448aPXq0vvnmGzVq1KjCwgIAAAAnhQR4q2urKHVtFSWHw6EDR3K0+e9VV/+ed7U/NUf7U3M0/48kNa4dqNsvb8VqKwAATMzpsuq1116Tr6+vpk+frujo6BL77rjjDl155ZV666239Prrr5d7SKAiOVx4KC2Aqof1HsD5sVgsig73U3S4nwZ0jJGt0K49BzKLh7XvSc6U3eHQjv0ZembKH7ozrpXqRQUYHRsAAJyC07dN+fPPPzVy5MhSRZUkRUZGasSIEVqzZk25hgMAoLqhPwfKh7ubVY1jgjSse309em17vXFPd3VuESlJSs/K04tfrtXyvw4anBIAAJyK02VVfn6+fH19T7vfz89Pubm55RIKAAAAKE81vN1188XNNKJfI1ktFtkK7fpkzlZ9OX+7bIV2o+MBAIB/cbqsatasmX766SfZbLZS+woKCvTjjz+qcePG5RoOAAAAZ+dgTZ5TLBaL+neI0YMj2si/hockadHaZE2atq54ODsAADCe02XVzTffrI0bN+raa6/VL7/8ou3bt2v79u2aO3eurr32Wm3evFk33XRTRWYFAAAAyqxJnWA9eX1HxUb6S1LxHKs9BzINTgYAAKRzGLDer18/PfHEE5o0aZLuvffe4u0Oh0NeXl56+OGHNWjQoIrICAAAACdZGNPvlNBAb40f1U5f/LJdKzalKD0rTy9Njdd1A5qo+wW1jI4HAEC15nRZJUmjRo3S0KFDtWrVKu3fv18Oh0O1a9dWly5dFBQUVEERAQAAgPLn6eGmm4Y2U92oAH29cKdshQ59Oneb9qZkaUS/RnJ3c/oiBAAAUI7OqaySpKCgIA0ePLgisgAAAACVymKxqG/72qod7qt3Zm9S1vECLV6XrP2p2bpjWEsF+nkZHREAgGqHPxcBAGAiXMAFGKNJnWA9dUNH1f17jtXO/RmaMOUP7T6QYXAyAACqH8oqAABMhHu6AcYJCfDW+GvbqWurSEnSsex8TZy6Vks3HDA4GQAA1QtlFQAAgKuj5Sw3Hu5uumlIM43q31huVotshQ5NmbtNn/+yXbZCu9HxAACoFpwuq+x2/nMGAABA1XdyjtWDI9oqoIaHJGnJumT976t1OpadZ3A6AACqPqfLqssuu0yfffZZRWYBAAAATKNxTJCevKGj6kUVzbHalZyhZ6b8od3JzLECAKAiOV1W7d27Vz4+PhWZBQAAAGXFlP5yFRLgrUdGtVO3VlGSiuZYvTR1rX5bn2xwMgAAqi6ny6pu3bpp/vz5ys/Pr8g8AAAAgKl4uLvpxiFNde2AojlWhXaHPpu3XZ/P26YCG6MyAAAob+7OHti0aVN99tln6t69u1q1aqXQ0FBZrSW7LovFohdeeKHcQwIVycFUWgAmwqIYwJwsFov6tKut2uF+emf2JmXm5GvJ+gNKSs3WuMtbKcjPy+iIAABUGU6XVe+++27x+8uXLz/lMZRVAACUDfU5YG6NY4L01A0d9fasjdpzIFO7kzM1YcofGjeslRrWDjQ6HgAAVYLTZdW2bdsqMgcAAADgEoL9vfTwyHb6cv52LfvroDKy8zXxq7UaNaCxerWJNjoeAAAuz+mZVf9mt9t15MgR5lcBAACYACvyKp+Hu1U3DG6q6wY2KZ5j9fm87ZoylzlWAACU1TmVVfv27dNdd92l9u3bq3v37oqPj9eqVat01VVX6c8//6yojAAAAIDpWCwW9W4brYdGtlWgr6ckaemGA/rfV2uVnpVncDoAAFyX02XV3r17ddVVV+n3339X9+7di7e7ublpz549uummm7R+/fqKyAgAAAAnMaS/8jWqHaQnb+ioBrUCJEm7D2TqmSl/aOf+Y8YGAwDARTldVr366qvy9vbWnDlz9PTTT8vhKFpw3qlTJ82ZM0dhYWF66623KiwoAAAAYFbB/l56aGQ79bigliQpIydf//tqnRavSy7+uRkAADjH6bJq9erVGjFihEJDQ2WxlPybXUREhEaOHKlNmzaVe0CgovHzIwAAKA8n51iNHvTPHKsvftmuz+YxxwoAgHPhdFmVn5+vgICA0+738PBQXh7X5gMAUBZcwgW4vl5tovXwyHb/mmN1UBOZYwUAgNOcLquaNm2qRYsWnXKfzWbTDz/8oCZNmpRbMAAAqiMWewJVQ8PagUVzrKKL/ti750CmJkz5QzuSjhkbDAAAF+B0WTV27FitXLlSDzzwgFavXi1JSk5O1sKFCzV69Ght2bJFN954Y4UFBQAAwKlxSbs5Bft76aER7dSrTdEcq8ycfL08bZ0Wr93PHCsAAM7A3dkDe/fureeff14vvPCCfv75Z0nSE088IYfDIS8vLz388MMaOHBghQUFAAAAXI2Hu1WjBzVVbKS/vpy/o2iO1fwdSkjJ0nUDGhsdDwAAU3K6rJKkuLg4DRgwQCtWrFBSUpLsdruio6PVpUsXBQcHV1RGAAAAwKX1bBOt6HA/vT1rozKy87X8r4NKTs3RkzdfZHQ0AABM55zKKkny8/PTgAEDlJaWJqvVSkkFAABgJkzpN62G0YF66oaOemfWJu1KzlDCwUz93+TfdNtlLdQ4JsjoeAAAmIbTM6skaffu3br77rvVvn17devWTV26dFGnTp00fvx4paSkVFRGAAAAoEoI8vPSQyPbqnfbaEnSsew8vTxtnRbGM8cKAICTnF5ZtXHjRo0ePVoFBQXq0aOH6tSpI4fDoYSEBP3www9aunSppk2bpjp16lRkXgAAqjQWxQBVn7ubVdcNbFI8x8pWaNfUX3doX0qWrhvYWB7ubkZHBADAUE6XVZMmTZKfn5+mTp1aqpDasWOHRo8erYkTJ+rtt98u95AAAABAVdPjglpq0Shcz3+yRsey87V840ElH8nWuMtbKSTA2+h4AAAYxunLADds2KDRo0efcuVU48aNNXr0aK1atapcwwEAUN1wERBQvTSNDdFTN3RUw9qBkqSEg1l6Zsof2p6YbnAyAACM43RZFRAQoMLCwtPu9/X1lbc3fwECAACofNScrizQz0sPjfhnjlXm8QJN+no9c6wAANWW02XVqFGjNGXKFO3atavUvkOHDumLL77Q1VdfXa7hAAAAgOrg5ByrGwY3lbubRYV2h6b+ukOf/LxVBbbT/8EYAICq6LQzq8aPH19qW15enoYNG6bu3burXr16slgsSk5O1tKlS+Xl5VWhQQEAAHB2Fsb0u7QeF9RS7XA/vT1ro9Kz8rRiU4qSj+TozjjmWAEAqo/TllWzZs067YMWL16sxYsXl9h2/Phxvf/++7r33nvLLRwAAABQ3dSvFaAnr++gd2Zv0s79GdqbkqUJU/7QHcNaqkmdYKPjAQBQ4U5bVm3btq0ycwCGYRQEAAAwm0A/Lz04oq2+XrhTi9YmK+t4gV6etl7X9G2ofu1ry2JhBR0AoOpyemYVAACoePz6CeAkdzerrh3QRDcOaSp3N6vsDoemLdipj3/eqvwC5lgBAKqu066sOpXZs2drxYoVSk1Nld1uL7XfYrHos88+K7dwAABUNyz2BPBf3VsXzbF667uiOVYrT86xuryVQgOZYwUAqHqcLqsmT56s999/Xx4eHgoNDZXVyqIsAAAAM+CS9qqvXlSAnryho96dvUk7ko5p37/mWDWNZY4VAKBqcbqsmjVrlrp166Y333xTPj4+FZkJAAAAwH8E+nrqgeFtNH3RLi2M36/sEwWa9PV6XdOnofp1YI4VAKDqcHp5VHZ2tgYOHEhRBQAAABjE3c2qUf0ba8zQZv/MsVq4Ux/9xBwrAEDV4XRZ1b17d61evboiswAAAKCMWFxTPXRtFaXx17ZTsL+XJGnV5hS98GW8jmScMDgZAABl5/RlgE888YRuvPFG3X///erXr59CQ0NPudS4Y8eO5RoQqGgOxhkDAAAXVC8qQE/9Pcdqe9IxJR7K1jNT/tTtw1qqGXOsAAAuzOmy6sCBA8rKytLPP/+sOXPmlNrvcDhksVi0devWcg0IAEB1wqIYAOciwNdT9w9vo28W7dKCv+dYvfL1el3du4H6d4xhjhUAwCU5XVY988wzyszM1JgxY1S3bl25uzv9UAAAAAAVxN3NqpH9Gys20l+f/7JdBTa7vl60S3sPZen6QU3l5eFmdEQAAM6J043Tzp07deedd+qWW26pyDwAAFRrXJgM4Hx1bRWl6HBfvfXdRqVl5mn15kM6kJqjO+NaKSyImyQBAFyH0wPWIyMjZbU6fTgAAAAqCSUnTqobGaAnb+iopnWCJEmJh7P1zGd/asveNGODAQBwDpxun26++WZ99tln2rVrV0XmAQAAAFAGATWK5lj17xAjSUVzrKav1y+/J8rhoNoEAJif05cBbtu2TRaLRZdeeqliYmIUFhYmN7eS179bLBZ99tln5R4SAAAAgPPcrFaN6NdIdSP9NWXeNhXY7Jq+aJf2pmTphsHMsQIAmJvTZdXixYvl5uamyMhIFRQU6ODBgxWZCwAAAEAZdW4ZqVphvnrru790NDNPa7Yc0oEjRXOswpljBQAwKafLqkWLFlVkDsA4rIYHYCLcZB5AeYuN9NcTN3TUe7M3aVviMSUdztYzU/7QbcNaqkXdEKPjAQBQChPTAQAAgCru5ByrAR2L5ljl5Nr06vT1mreGOVYAAPNxemXV6NGjnTru888/P+8wAABUd/zKCKCiuFmtGt63kWIj/TVlbtEcq28W79K+Q8yxAgCYi9Nl1f79+0tts9vtSk9PV15enqKjo9WoUaNyDQcAAACgfHVuEalaob5667uNOpqZyxwrAIDplHlmVWFhoRYuXKjHH39cY8aMKbdgAAAAcBJL8nCOYiP99eQNHfTe95u1dV968RyrUf0bq2HtQIUGeMtiYYoeAMAYTpdVp+Pm5qYBAwZow4YNmjRpkqZPn14euQAAAABUIP8anrrvmgs0c8lu/fJ7knJybfrgxy2SJG9PN9UK81WtMF9Fn3wL91OQnyclFgCgwpW5rDqpbt26+vLLL8vr6QAAAHAe6BFwLtysVl3Tp2iO1WdztyuvoFCSlJtfqD0HMrXnQGaJ43283BX9rxKrVrivaof5KsCXEgsAUH7KpazKz8/XDz/8oNDQ0PJ4OqBSceUEAACo7i5qHqnW9cOUeChLyUdydOBIjpKP5Cg5NVs5ubbi407k2bQrOUO7kjNKPN7X++8SK9yveCVWrXBfBdTwrOwPBQBQBZT5boD5+flKSEhQZmam7rrrrnILBgBAdcS6BABGqeHtrqaxwWoaG1y8zeFwKDMnv6i4KlFi5ehE3j8lVk6uTTv2Z2jH/pIlln8Nj39WYv1dZNUK85Wfj0elfVwAANdTprsBSkUzq+rXr6+LL75YI0eOLLdgAAAAAIxlsVgU6OelQD8vNa8bUrzd4XDoWHa+ko9k60BqTokyKze/sPi4rOMF2pZ4TNsSj5V43kBfz3/mYYX7KjrMT7XCfFXDu9ymlAAAXFiZ7wYIAADKD5cmA3AFFotFwf5eCvb3Ust6/4wCcTgcSsvM+2cVVmp20ftHc5RfYC8+LiMnXxk5+dq6L73E8wb7e5UY6l4r3Fe1Qn3l40WJBQDVCd/1AQAAXJyDmhMmYbFYFBrordBAb7Vu8E+JZXc4dDQjt3gO1snLCQ8ePa4C2z8lVnpWntKz8rQ5Ia3E84YGeCk63O+fwe5hRSWWl6dbpX1sAIDKc9qy6q233jqvJ7zzzjvPOwwAAACAqsdqsSg8yEfhQT5q0zCseLvd7lBqxgklp/5rJlZqjlLScmQr/KeEPZqZp6OZefpr99HibRZJYUHexZcQniyxokJryNODEgsAXFmZy6r/3qKWsgoAAMA4Fsb0w4VYrRZFBNdQRHANtWscXry90G7X4fSiEuvAv+ZhpaQdV6G9qMRySEo9lqvUY7lav+tI8WMtFqlmkM/fQ93/Hu4e5qfIkBrycLdW9ocIADgPpy2rFi5ceNYHZ2dna/LkyVqyZInc3d1Pe8dAAAAAAHCWm9WqqFBfRYX6lthuK7TrUNrxEncmPHAkR4fSTsju+LvEckiH0k/oUPoJrdv5T4lltVgUEeJTYhVWdLifIoJ95O5GiQUAZnLasio6OvqMD5wzZ45eeuklHT58WO3atdPTTz+txo0bl3tAoMIx5gOAibAmBgBOz93NquhwP0WH+5XYXmCzKyXteNHdCf++lPDAkRwdTj9R/KOe3eHQwaPHdfDoccVvTy1+rJvVosiQGv8psXxVM9hHblZKLAAwwjkPWE9KStKECRO0YsUKBQYG6rnnntOVV15ZEdkAAAAA4Kw83K2KqemnmJolS6z8gkIdPHq8xCqs/anZOpKRW3xMod1RNPj9SI7++Ndj3d0sigz551LC2mG+ql3TT+FBPpX0UQFA9eV0WVVQUKAPPvhAH374ofLy8nT55ZfrwQcfVHBwcEXmAwCgWmGxJwCUH08PN8VG+is20r/E9rz8Qh04+s+lhEUrsbJ1NDOv+BhboUP7U7O1PzW7xGNjavqpa8tIXdQiUgG+npXycQBAdeNUWbV69WpNmDBBCQkJatSokZ566il16NChorMBAAAAQLnz8nRTvagA1YsKKLH9RJ6tqMT6++6EJ1djpWf9U2IlHc7W14t26ZvFu9W6Qai6tIzUBQ3DGN4OAOXojGVVWlqaXnjhBf3888/y9vbW/fffrxtvvFHu7ud89aBhXn/9dc2bN08Wi0U9evTQQw89JCvXngMAgKqEJXlAufDxcleDWoFqUCuwxPbjuQU6cOS4tu5L04pNKTqcXjTQff2uI1q/64h8vd3VqXmEurWKUt1I/1J3TAcAnJvTtk7Tpk3Ta6+9pszMTPXp00ePP/64oqKiKjNbmf32229asWKFfvjhB1mtVo0aNUoLFy5U//79jY4GAAAAwEXU8PZQw9qBalg7UBd3qavdyZlavvGg/th2SCfyCpWTa9PitclavDZZUaE11LVVlDq3iFSwv5fR0QHAJZ22rJowYULx+4sWLdKiRYvO+mQWi0Vbtmwpn2TloGfPnurSpYs8PDyUlpamrKwsBQYGnv2BAAAAAHAKFouluLga2a+R1u08ohWbDmpzQpocDung0eOauWS3vv1tt1rUDVGXVpFq1yhcnh5uRkcHAJdx2rJq2LBhLrF8dfbs2Xr00UdLbV+zZo38/f3l4eGhDz/8UO+8845at26tNm3aVH5ImJqDaycAAABwHjw93HRh8whd2DxC6Vl5Wr05RSs2pejAkRw5HNKmhDRtSkiTj5ebOjatqS4to9SodqBL/J4FAEY6bVn10ksvVWaO8zZs2DANGzbsjMfccsstuuGGG/TII4/o5Zdf1mOPPVY54QAAOEf8+gIArinY30uDL4rVoAvraG9KllZsPKg1Ww4pJ9emE3mFWrrhoJZuOKiawT7q0jJSXVpGKizQx+jYAGBKrjMp/Tzs2LFDNptNzZs3l4eHhy6++GJ98sknRscCAAAAUEVZLJbiOw1e06eR/tp9RCs2pmjjnqMqtDt0OP2EZi9L0OxlCWpaJ0hdW0WpfZNweXtW6V/NAOCcVOnviHv27NEHH3ygr7/+Wm5ubpozZ446duxodCwAAE6LC5MBoOrwcLeqfZOaat+kpjJz8rV6yyGt3HhQiYezJUnbEo9pW+IxfTl/h9o3CVfXlpFqEhssK5cJAqjmTFNWbd26VVdeeaUWLlyoyMjIEvt++uknvfvuu0pKSlJ0dLTGjh171kv/JGnQoEHavn27hg0bJjc3N3Xo0EG33XZbBX0EAAAAxqDkBMwvwNdTAzrGaEDHGCUeytLKTSlavTlFmccLlFdQqJWbUrRyU4pCA7zUuWWUuraMVERIDaNjA4AhTFFW7d69W2PHjpXNZiu1b86cOXrggQd0/fXXq1u3blqwYIEefvhheXt7a9CgQWd97nvuuUf33HNPRcQGAAAAgHNWJ8JfdSL8dWWvBtqUkKaVGw9q/a4jshU6dDQzTz+t3KufVu5Vw+hAdWkVqU5Na6qGt4fRsQGg0hhaVtlsNk2fPl2vvPKKPDxO/c138uTJGjx4sMaPHy9J6t69uzIyMvT66687VVaVh9BQv0p5neooPNzf6AhKzc4vft/qZjVFJjiPrxfKygznkL+/d/H7Pt4epsgE55nh6xWQkl38fkiIrykywXl8vaq3qMhA9e9cT1nH87V0XbIW/ZmoHYnHJEm7kjO0KzlDXy/YqYtaRqlPxxi1aVxTbtaSlwlyDqGsOIdQFhVx/hhaVsXHx2vSpEkaM2aMIiIi9Pjjj5fYn5SUpMTERN13330ltg8cOFBz585VUlKSYmJiKjzn0aPZsttZYF/ewsP9lZqaZXQMpacfL37fXmg3RSY4xyznEFyXWc6hrKzc4vdP5BaYIhOcY5ZzKDPzRPH76ek5SvW0GpgG58Is5xDMoVPjMHVqHKYDR3L+vizwoI5l5yvfZtfS9clauj5ZgX6e6tIiUl1aRSk6zJdzCGXGOYSyON/zx2q1nHFhkKFlVYMGDbRgwQKFhobqu+++K7V/z549kqR69eqV2B4bGytJSkhIqJSyCgCAysJIXQBArTBfXdmrgeJ61NeWfWlauTFF8TtSVWCzKyM7X3PXJGrumkTVjfTXwM511aJOkPx8uEwQQNVhaFkVFhZ2xv1ZWUXtnJ9fybbN19dXkpSdnV3qMQAAAABQFVitFrWsF6qW9UJ1PNemP7cf1oqNB7Vzf4YkaW9Klt6ftVFuVovaNAxTl1aRalU/VO5urK4E4NpMMWD9dByOM196Z7XyTRgAAABA1VfD2109LqilHhfU0uH041q5KUUrNqboaGauCu0Oxe9IVfyOVPnX8NCFzSPUrVWU6kQwhwiAazJ1WeXvX/TNNScnp8T2kyuqTu4HAKCqYEIiAOBsagbX0LDu9XVpt3o6nJmvn5fv1p/bUpVXUKis4wVa8Od+Lfhzv2qH+6lrq0hd1CJSgb6eRscGAKeZuqw6OasqMTFRTZo0Kd6+b9++EvsBAACqs7MsRgdQRVktFrVqGKbIQC+N6m9T/PZUrdyUom370uWQtD81W9MX7dKMxbvVqn6IuraK0gUNw+ThzhUqAMzN1GVVbGysateurXnz5ql///7F2+fPn6+6deuqVq1aBqYDAAAAAHPw9nRX11ZR6toqSkczcrVyc4pWbDyow+knZHc4tGH3UW3YfVS+3u7q1CxCXVtFqV6UvywWbu0BwHxMXVZJ0rhx4zR+/HgFBgaqV69eWrhwoebOnavJkycbHQ0AAAAATCc00FuXdKmrizvHandyplZsOqjftx7WiTybcnJtWrwuWYvXJSsqtIa6tIxUl5ZRCvb3Mjo2ABQzfVkVFxen/Px8ffLJJ5oxY4ZiYmI0ceJEDRkyxOhoAAAAAGBaFotFDWsHqmHtQI3o20jrdx3R8o0HtTkhTQ6HdPDocX372x5999seNa8Xoq4tI9W2cbi8PNyMjg6gmjNNWRUXF6e4uLhT7hs+fLiGDx9eyYkAAKh8XIwBAKgInh5u6tQsQp2aRSg9K0+rt6Ro5cYUJR/JkUPS5oQ0bU5Ik7enmzo2ramuraLUqHYglwkCMIRpyioAAAAAQMUL9vfS4AtjNahTHe1NydLKjSlavSVFObk25eYXatlfB7Xsr4MKD/JW15ZR6tIyUmFBPkbHBlCNUFYBAGAi3NQNAFBZLBaL6kUFqF5UgK7p21Abdh3Vio0HtXHPURXaHUo9lqvZyxM0e3mCmsQEqWurKLVvEi4fL36NBFCx+C4DAADg8qg5AZSNu5tV7ZuEq32TcGXm5GvNlkNasemgEg9lS5K2Jx3T9qRj+vLX7WrfuKa6topU09hgWblMEEAFoKwCAAAAABQL8PVU/44x6t8xRkmHs7Vi40Gt3nJImTn5yi+wa9XmFK3anKLQAC91bhmpri2jFBFSw+jYAKoQyioAAIAqhGHIAMpTTE0/De/bSFf1bqBNe9K0YuNBrd91RLZCh45m5umnlfv008p9ahAdoK4to9SpWYRqePNrJoCy4bsIqj2Hg0snAAAAgDNxs1p1QcMwXdAwTNknCvTH1kNasSlFew5kSpJ2J2dqd3KmZizZpUGd6qh/xxh5e/LrJoDzw3cPAABMhDUxAACz8/PxUO92tdW7XW0dPJqjFRuLLgtMz8rTibxCzVqWoIXx+zW0S131ahMtD3er0ZEBuBjKKgAAAADAeYkK9dWVvRoorkd9bdh9RLOWJmh/arYyjxdo2oKdmv97ki7rVk9dWkbKauVPMgCcQ1kFAAAAACgTq9Wito3CdUHDMP2+9ZBmL03Q4WMndDQzV5/M2aq5a/bp8u711b5JOLP1AJwVZRUAACbCFD0AgCuzWiy6qHmkOjSpqeV/HdQPKxJ0LDtfB48e1zuzN6lupL+u6NlAzesGU1oBOC3KKgAAABfHvUIAmI27m1W92karS8tILVqbrJ9X7VVOrk17U7L0yvT1alonSFf0bKAG0YFGRwVgQky6AwAAAABUCE8PNw26sI4m3tZFl3SpKy8PN0nStsRjev6LeL357V/an5ptcEoAZsPKKgAAgCqEi2oAmFENb3dd3qO++ravrZ9W7dWSdcmyFTq0bucRrd95RBe1iNBl3eurZpCP0VEBmABlFQAAJkLRAACoygJ8PTWyX2MN6BijH1bs1YqNB+VwSKs2H9LvWw+rR5tauqRLXQX5eRkdFYCBuAwQAAAAAFCpwgJ9dNOQZnru5gvVoUm4JKnQ7tDitcl65L1Vmrlkt3JyCwxOCcAorKwCAAAAABgiKtRXd1zeSntTMvXdb3u0KSFN+Ta75qzep8XrkjX4wjrq3yFGXp5uRkcFUIlYWQUAgIlwUzcAQHVUNzJA913TRg+PbKsG0QGSpBN5Nn23dI8efn+VFsbvl63QbnBKAJWFsgoAAAAAYApN6gTr0Wvb6+4rWqt2uK8kKTMnX1N/3aFHP1itFRsPym7nTztAVcdlgAAAAAAA07BYLGrTKEytG4ZqzZZDmr1sj1KP5epIRq4+/nmr5q1J1OU96qttozBZLNyaBKiKKKsAAACqEn5vA1BFWC0WdW4RqY5Na2rZXwf1w4oEZWTnK/lIjt76bqPqRQXoip711bxuiNFRAZQzyipUew5WEQMAAACm5e5mVe+20erSMlKL4vdrzup9ysm1KeFgpiZ9vV7NYoN1Rc8Gql8rwOioAMoJM6sAADARFsUAAHBqXh5uGnxRrCbe1lkXd4mVl0fRHQK37kvXc5//qbe+26jkIzkGpwRQHlhZBQAAAABwGTW8PRTXo4H6to/Rzyv3asn6ZNkKHVq7I1Xrdqaqc4tIDetWT2FBPkZHBXCeKKsAAAAAAC4n0NdTI/s31oBOMfp+eYJWbkqRwyGt3JSiNVsOqVebaF3cta4CfT2NjgrgHHEZIAAAJsIYPQAAzk1YoI/GDG2uZ8ZcqPaNwyVJhXaHFq7dr4ffW6lvf9ut47kFBqcEcC5YWQUAAODiKDkBQIoO89W4uFZKOJip737brc1705VfYNfPq/ZpybpkDb4oVn3b1y6edQXAvFhZBQAAAACoMupFBej+4W314Ii2xXcIzMm1aeaS3XrkvVVatHa/bIV2g1MCOBPKKgAAgCqEO0oCQJFmscF67Lr2uiuulaLDfCVJGTn5+nL+Dj324Wqt2pQiu521qYAZcRkgqj3+ewJgJhQNAACUH4vForaNw3VBwzCt3pKi2csSdCQjV6nHcvXhT1s0Z80+xfWorzYNw2Sx8L8wYBaUVQAAAACAKs1qtahLyyh1ahahpRsO6McVe5WRk6/k1By9+e1GNagVoLieDdQsNtjoqABEWQUAAAAAqCbc3azq0662uraM0oL4JM1dnajjeTbtPpCpl6etU4u6wYrr2UD1ogKMjgpUa5RVAACYCJcmAwBQ8bw83TS0c131ahuteWsS9eufScovsGvz3nRt3vun2jcO1+U96qvW37OuAFQuyioAAAAX53BQcwLA+fD19tAVPRuoX/va+mnlPi1Zn6xCu0PxO1K1dmequrSM1GXd6iks0MfoqEC1QlkFAAAAAKjWAv28NGpAYw3oFKPvlydo1aYUORzSio0pWrPlkHq1idbFXeoqwNfT6KhAtWA1OgAAAADKEXezAoDzFh7ko5svbq5nxnRSu8bhkiRboUML4vfr4fdW6bulu3U812ZwSqDqY2UVwKUTAAAAAP4lOtxPd8a10p4Dmfr2t93aui9deQWF+mnlPi1em6whF8WqT/va8vJwMzoqUCWxsgoAABNhTQwAAOZRv1aAHhzRVg8Mb1N8h8CcXJtmLNmtR95fpcXrkmUrtBucEqh6WFkFAAAAAMAZNK8bomaxwVq744hmLdujA0dylJGdry9+2a5f1iRqWPd66tQ8QlYuxQbKBWUVAAAAAABnYbFY1L5JuNo2CtOqzSn6fnmCjmTk6vCxE/rgxy2as3qf4no00AUNQ2WhtALKhLIKAAATYYoeAADmZrVa1LVVlDo1i9DSDQf048q9yszJ1/7UHL3x7V9qGB2oK3rWV5M6wUZHBVwWM6sAAAAAADhHHu5W9W1fWxPHdlZcj/ry8SpaC7IrOUMTv1qnV6ev176ULINTAq6JlVUAAAAAAJwnL083Xdylrnq3i9bc1Yla8GeS8m12bUpI06aENHVoEq7Le9RXVKiv0VEBl0FZhWqPS24AAFUJU1IAwBi+3h66slcD9etQWz+u3Kul6w+o0O7Qn9tTFb8jVV1bRemyrvUUGuhtdFTA9LgMEAAAE6FoAADAtQX5eem6AU30/K0XqXOLCFkkORzS8r8OavwHq/XX7iNGRwRMj7IKAAAAAIByVjPIR7dc0kITbuqkto3CJEm2QrumzN2m3HybwekAc6OsAgAAAACggtSu6ae7rmit0QObSJKOZefrx5V7jQ0FmBxlFQAAAAAAFaxHm1qqXytAkjT/9ySlpB03OBFgXpRVAACYCDd9wPlwcOIAgOlZLRaN6t9YFkmFdoe+WrBDDr6BA6dEWQUAAAAAQCWoFxWg7hfUkiRt2pOm9bsYtg6cCmUVAABAFWLhlpIAYGpxPeurhpe7JGnagp3KLyg0OBFgPpRVqPZYeAvATOgZAACo2gJqeOryHvUlSUcycjVvTaLBiQDzoawCAAAAAKAS9WpbSzE1/SRJP6/epyPHThicCDAXyioAAAAAACqRm9WqUf0bS5IKbHZNX7TL4ESAuVBWAQAAAABQyRrHBOmiFhGSpPgdqdqckGZwIsA8KKsAADAR5ugBAFB9XNWrobw83SRJXy3YIVuh3eBEgDlQVgEAALg4BzUnALikYH8vXdq1riTp4NHjWvDnfmMDASZBWQUAAAAAgEH6d4hRZEgNSdL3KxJ0LDvP4ESA8SirAP4YDQAAAMAg7m5WjezfSJKUl1+oGYsZtg5QVgEAYCIWowMAAIBK17JeqNo1Dpckrdp8SDuSjhkbCDAYZRUAAAAAAAYb3qehPNyLfkWf+usO2e1cAoLqi7IKAAAAAACDhQX5aMhFsZKkpMPZWrI+2eBEgHEoqwAAAAAAMIHBF9ZRWKC3JGnW0j3KOp5vcCLAGJRVAACYCAv+cV44cQCgSvD0cNOIvkXD1nNybfpu6R6DEwHGoKwCAAAAAMAk2jQKU8t6IZKkpesPKOFgpsGJgMpHWQUAAFCFWCzcUxIAXJnFYtGIfo3kZrXIIemrX3fI7mAJLaoXyipUew6unQBgItQMAAAgKtRXAzrGSJJ2H8jUyo0pBicCKhdlFQAAAAAAJnNxl7oK8vOUJM1cskvHc20GJwIqD2UVAAAAAAAm4+Plrqv7NJQkZR4v0PfLEwxOBFQeyioAAAAAAEzowmYRahwTJElaGL9f+1OzjQ0EVBLKKgAATIQpegAA4CSLxaJR/RvLYpHsDoe++nWHHAxbRzVAWQUAAODi+LUFAKqumJp+6tOutiRpW+Ix/bHtsMGJgIpHWQUAAAAAgIkN615Pfj4ekqTpi3YpL7/Q4ERAxaKsAvhzNACgCrEYHQAAUO58vT10Za8GkqT0rDz9tGqvsYGACkZZBQCAiVA0AACAU+nWOkr1ovwlSb/8nqhDaccNTgRUHMoqAAAAAABMzmqxaFT/JpIkW6FD0xbuNDgRUHEoqwAAAAAAcAH1awWoe+soSdJfu49q/a4jBicCKgZlFQAAAAAALuKKng3k4+UuSZq2YIcKbAxbR9VDWQUAgIlwzwcAAHAmAb6eurx7PUlS6rFczfs9yeBEQPmjrAIAAHB1tJwAUK30bhet2uG+kqSfV+7V0YxcgxMB5YuyCtUeP98DAKoUbikJAFWem9WqUf0bS5LybXZNX8SwdVQtlFUAAJgIPQMAAHBGkzrBurB5hCTpz+2p2rI3zeBEQPmhrAIAAAAAwAVd3buhvDzcJElTf90hW6Hd4ERA+aCsAgAAAADABQX7e+mSrnUlSQePHtei+P3GBgLKCWUVAAAAAAAuqn+HGEWE1JAkzV6eoIzsPIMTAWVHWQUAgIlw0wcAAHAuPNytGtmvkSQpN79QM5bsNjgRUHaUVQAAAC7OQc0JANVaq/qhatsoTJK0clOKdu3PMDgRUDaUVQAAAAAAuLjhfRvJ3a3oV/wvf90uu50/ZMB1UVah2nPwPRwAUIVYjA4AADBEeJCPhlxUR5KUeChbv204YHAi4PxRVgEAYCIUDQAA4HwNvihWoQHekqTvftut7BMFBicCzg9lFQAAAAAAVYCXh5uG920oScrJtem7pXsMTgS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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot luminosity distribution\n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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": "0546f2f3-4732-4841-8ef3-565fbf6b9961",
-   "metadata": {},
-   "source": [
-    "This distribution is peaked at low luminosity, as one expects from observations, but the resolution is clearly not great because it's not smooth - it's spiky! \n",
-    "\n",
-    "If you noticed above, the total probability of the grid was about 0.2. Given that the total probability of a probability distribution function should be 1.0, this shows that our sampling is (very) poor. \n",
-    "\n",
-    "We could simply increase the resolution to compensate, but this is very CPU intensive and a complete waste of time and resources. Instead, let's try sampling the masses of the stars in a smarter way."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "673031c9-7d80-45d4-b209-301c127d3edf",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## A better-sampled grid\n",
-    "\n",
-    "The IMF has many more low-mass stars than high-mass stars. So, instead of sampling M1 linearly, we can sample it in log space. \n",
-    "\n",
-    "To do this we first rename the mass grid variable so that it is clear we are working in (natural) logarithmic phase space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Rename the old variable (M_1) because we want it to be called lnM_1 now\n",
-    "population.rename_grid_variable(\"M_1\",\"lnM_1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "532f691c-c1f6-46cc-84f2-970ec1216e40",
-   "metadata": {},
-   "source": [
-    "Next, we change the spacing function so that it works in the log space. We also adapt the probability calculation so that it calculates dprob/dlnM = M * dprob/dM. Finally, we set the precode to compute M_1 because binary_c requires the actual mass, not the logarithm of the mass."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# update the sampling, note that the IMF is dprob/dM1, and the phase \n",
-    "# space is now sampled in lnM1, so we multiply by M_1 to \n",
-    "# because  M * dprob/dM = dprob/dlnM\n",
-    "population.update_grid_variable(\n",
-    "    name=\"lnM_1\",\n",
-    "    samplerfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min = massrange[0], max = massrange[1], res = resolution[\"M_1\"]),\n",
-    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
-    "    dphasevol=\"dlnM_1\",\n",
-    "    parameter_name=\"M_1\",\n",
-    "    precode=\"M_1=math.exp(lnM_1)\",\n",
-    ")\n",
-    "# print(population.grid_options[\"_grid_variables\"]) # debugging"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 0.995631\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*    Total probability is 0.995631   *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-7a2e4301f5224b2cb8939d2297df0aad finished! *\n",
-      "*           The total probability is 0.995631.           *\n",
-      "*  It took a total of 4.77s to run 40 systems on 2 cores *\n",
-      "*                   = 9.55s of CPU time.                 *\n",
-      "*              Maximum memory use 621.930 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Clean and re-evolve the population \n",
-    "population.clean()\n",
-    "analytics = population.evolve()  \n",
-    "\n",
-    "# Show the results (debugging)\n",
-    "# print (population.grid_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "182b1094-5057-4ccf-bac6-9b0e560ad4f6",
-   "metadata": {},
-   "source": [
-    "You should see that the total probability is very close to 1.0, as you would expect for a well-sampled grid. The total will never be exactly 1.0, but that is because we are running a simulation, not a perfect copy of reality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "68ee1e56-21e5-48f4-b74c-50e48685ae94",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot luminosity distribution\n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31fe91bb-177d-4e4e-90cf-298a3f8a8b61",
-   "metadata": {},
-   "source": [
-    "Most stars are low mass red dwarfs, with small luminosities. Without the IMF weighting, our model population would have got this completely wrong! \n",
-    "\n",
-    "As you increase the resolution, you will see this curve becomes even smoother. The wiggles in the curve are (usually) sampling artefacts because the curve should monotonically brighten above about log(*L*/L<sub>☉</sub>)=-2. \n",
-    " \n",
-    "Remember you can play with the binwidth too. If you want a very accurate distribution you need a narrow binwidth, but then you'll also need high resolution (lots of stars) so lots of CPU time, hence cost, CO<sub>2</sub>, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba032bd8-b4a2-4558-9fd9-8e1e03d7d162",
-   "metadata": {},
-   "source": [
-    "Things to try:\n",
-    "\n",
-    " * Change the resolution to make the distributions smoother: what about error bars, how would you do that?\n",
-    " * Different initial distributions: the Kroupa distribution isn't the only one out there\n",
-    " * Change the metallicity and mass ranges\n",
-    " * What about a non-constant star formation rate? This is more of a challenge!\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?\n",
-    " * Binary stars! (see notebook_luminosity_function_binaries.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5abd0935-3957-4859-80c1-6f5d7ce4b614",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/build/doctrees/nbsphinx/examples_notebook_massive_remnants_20_1.png b/docs/build/doctrees/nbsphinx/examples_notebook_massive_remnants_20_1.png
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diff --git a/docs/build/doctrees/readme_link.doctree b/docs/build/doctrees/readme_link.doctree
index 40863031f5e6966fc89995465e95bd701facd102..142b88abc7c54eb8bd59ca1258496bd1aa06087d 100644
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+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/Moe_di_Stefano_2017.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/Moe_di_Stefano_2017.html
index 685664a1b..092b28ec1 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/Moe_di_Stefano_2017.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/Moe_di_Stefano_2017.html
@@ -1318,7 +1318,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/analytics.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/analytics.html
index e696f61ab..72654f29a 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/analytics.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/analytics.html
@@ -208,7 +208,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/cache.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/cache.html
index 0799b491d..b6be39630 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/cache.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/cache.html
@@ -454,7 +454,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/condor.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/condor.html
index 20fd81c70..9d93a620f 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/condor.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/condor.html
@@ -655,7 +655,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/dataIO.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/dataIO.html
index beaac5daf..65a6b6504 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/dataIO.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/dataIO.html
@@ -902,7 +902,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/distribution_functions.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/distribution_functions.html
index 2b4f99f11..a4427a202 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/distribution_functions.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/distribution_functions.html
@@ -2403,7 +2403,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_logging.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_logging.html
index 455af7acb..77ec01cf1 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_logging.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_logging.html
@@ -555,7 +555,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_options_defaults.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_options_defaults.html
index 3fbf8de0e..cc76d9d6c 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_options_defaults.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/grid_options_defaults.html
@@ -717,7 +717,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/gridcode.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/gridcode.html
index ab13f9367..747bc9478 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/gridcode.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/gridcode.html
@@ -1236,7 +1236,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/metadata.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/metadata.html
index ddecddcdf..8cf18bfd5 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/metadata.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/metadata.html
@@ -229,7 +229,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/slurm.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/slurm.html
index 8d4487530..c99f085bd 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/slurm.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/slurm.html
@@ -553,7 +553,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/spacing_functions.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/spacing_functions.html
index 77daf649d..941ae1d17 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/spacing_functions.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/spacing_functions.html
@@ -776,7 +776,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/population_extensions/version_info.html b/docs/build/html/_modules/binarycpython/utils/population_extensions/version_info.html
index 542d6340f..4ca4cd979 100644
--- a/docs/build/html/_modules/binarycpython/utils/population_extensions/version_info.html
+++ b/docs/build/html/_modules/binarycpython/utils/population_extensions/version_info.html
@@ -522,7 +522,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/run_system_wrapper.html b/docs/build/html/_modules/binarycpython/utils/run_system_wrapper.html
index 5a0df8e7b..5987941d5 100644
--- a/docs/build/html/_modules/binarycpython/utils/run_system_wrapper.html
+++ b/docs/build/html/_modules/binarycpython/utils/run_system_wrapper.html
@@ -206,7 +206,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/useful_funcs.html b/docs/build/html/_modules/binarycpython/utils/useful_funcs.html
index 6e2bc47a6..3d8da4100 100644
--- a/docs/build/html/_modules/binarycpython/utils/useful_funcs.html
+++ b/docs/build/html/_modules/binarycpython/utils/useful_funcs.html
@@ -472,7 +472,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html
index 8be7d4c0f..b43b3f1a8 100644
--- a/docs/build/html/_modules/index.html
+++ b/docs/build/html/_modules/index.html
@@ -110,7 +110,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/_sources/examples/notebook_massive_remnants.ipynb.txt b/docs/build/html/_sources/examples/notebook_massive_remnants.ipynb.txt
deleted file mode 100644
index 28227d1a1..000000000
--- a/docs/build/html/_sources/examples/notebook_massive_remnants.ipynb.txt
+++ /dev/null
@@ -1,761 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Example use case: Massive star luminosity\n",
-    "\n",
-    "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of single stars using binary_c. \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",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from binarycpython.utils.functions import temp_dir\n",
-    "from binarycpython.utils.grid import Population\n",
-    "\n",
-    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_luminosity\")\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",
-      "adding: tmp_dir=/tmp/binary_c_python-izzard/notebooks/notebook_luminosity to grid_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",
-    "    tmp_dir=TMP_DIR,\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' : 0.5 }"
-   ]
-  },
-  {
-   "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": [
-    "# Mass\n",
-    "population = Population()\n",
-    "population.set(\n",
-    "    tmp_dir=TMP_DIR,\n",
-    ")\n",
-    "population.add_grid_variable(\n",
-    "    name=\"M_1\",\n",
-    "    longname=\"Primary mass\",\n",
-    "    valuerange=massrange,\n",
-    "    samplerfunc=\"const({min}, {max}, {res})\".format(min = massrange[0], max = massrange[1], res = resolution[\"M_1\"]),\n",
-    "    probdist=\"{probtot}/({max} - {min})\".format(probtot = total_probability, min = massrange[0], max = massrange[1]), # dprob/dm1 : all stars are equally likely so this is 1.0 / (Mmax - Mmin)\n",
-    "    dphasevol=\"dM_1\",\n",
-    "    parameter_name=\"M_1\",\n",
-    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\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 Printf statement does the outputting: note that the header string is ZERO_AGE_MAIN_SEQUENCE_STAR\n",
-    "\n",
-    "custom_logging_statement = \"\"\"\n",
-    "if(stardata->model.model_number == 0 &&\n",
-    "   stardata->star[0].stellar_type <= MAIN_SEQUENCE &&\n",
-    "   stardata->model.time == 0)\n",
-    "{\n",
-    "   /* Note that we use Printf - with a capital P! */\n",
-    "   Printf(\"ZERO_AGE_MAIN_SEQUENCE_STAR %30.12e %g %g %g %g\\\\n\",\n",
-    "          stardata->model.time, // 1\n",
-    "          stardata->common.zero_age.mass[0], // 2\n",
-    "          stardata->star[0].mass, // 3\n",
-    "          stardata->star[0].luminosity, // 4\n",
-    "          stardata->model.probability // 5\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",
-    "\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",
-    "        # obtain the line of data in dictionary form \n",
-    "        linedata = datalinedict(line,parameters)\n",
-    "        \n",
-    "        # Check the header and act accordingly\n",
-    "        if linedata['header'] == \"ZERO_AGE_MAIN_SEQUENCE_STAR\":\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'][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": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 1\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*       Total probability is 1       *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-b6213f2eb7f94d3196cf966b7b76b9f9 finished! *\n",
-      "*               The total probability is 1.              *\n",
-      "*  It took a total of 6.99s to run 40 systems on 2 cores *\n",
-      "*                  = 13.98s of CPU time.                 *\n",
-      "*              Maximum memory use 472.211 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# set number of threads\n",
-    "population.set(\n",
-    "    # verbose output is not required    \n",
-    "    verbosity=0,\n",
-    "    # set number of threads (i.e. number of CPU cores we use)\n",
-    "    num_cores=2,\n",
-    "    )\n",
-    "\n",
-    "# Evolve the population - this is the slow, number-crunching step\n",
-    "analytics = population.evolve()  \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': 'b6213f2eb7f94d3196cf966b7b76b9f9', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.9999999999999998, 'total_count': 40, 'start_timestamp': 1635760806.5066257, 'end_timestamp': 1635760813.4966016, 'total_mass_run': 2001.3999999999996, 'total_probability_weighted_mass_run': 50.03499999999999, '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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\n",
-      "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",
-    "# this saves a lot of typing! \n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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": [
-    "Does this look like a reasonable stellar luminosity function to you? The implication is that the most likely stellar luminosity is 10<sup>5.8</sup> L<sub>☉</sub>! Clearly, this is not very realistic... let's see what went wrong."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e32c3bbf-390f-45da-ad9c-cc3e7c9449dc",
-   "metadata": {},
-   "source": [
-    "## ZAMS Luminosity distribution with the initial mass function\n",
-    "\n",
-    "In the previous example, all the stars in our grid had an equal weighting. This is very unlikely to be true in reality: indeed, we know that low mass stars are far more likely than high mass stars.  So we now include an initial mass function as a three-part power law based on Kroupa (2001). Kroupa's distribution is a three-part power law: we have a function that does this for us (it's very common to use power laws in astrophysics).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update the probability distribution to use the three-part power law IMF \n",
-    "population.update_grid_variable(\n",
-    "    name=\"M_1\",\n",
-    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 0.218222\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*    Total probability is 0.218222   *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-4b8c7f4a86e445099d73f27dffaad94b finished! *\n",
-      "*           The total probability is 0.218222.           *\n",
-      "*  It took a total of 7.95s to run 40 systems on 2 cores *\n",
-      "*                  = 15.89s of CPU time.                 *\n",
-      "*              Maximum memory use 587.984 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Clean and re-evolve the population \n",
-    "population.clean()\n",
-    "analytics = population.evolve()  \n",
-    "\n",
-    "# Show the results (debugging)\n",
-    "# print (population.grid_results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot luminosity distribution\n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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": "0546f2f3-4732-4841-8ef3-565fbf6b9961",
-   "metadata": {},
-   "source": [
-    "This distribution is peaked at low luminosity, as one expects from observations, but the resolution is clearly not great because it's not smooth - it's spiky! \n",
-    "\n",
-    "If you noticed above, the total probability of the grid was about 0.2. Given that the total probability of a probability distribution function should be 1.0, this shows that our sampling is (very) poor. \n",
-    "\n",
-    "We could simply increase the resolution to compensate, but this is very CPU intensive and a complete waste of time and resources. Instead, let's try sampling the masses of the stars in a smarter way."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "673031c9-7d80-45d4-b209-301c127d3edf",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## A better-sampled grid\n",
-    "\n",
-    "The IMF has many more low-mass stars than high-mass stars. So, instead of sampling M1 linearly, we can sample it in log space. \n",
-    "\n",
-    "To do this we first rename the mass grid variable so that it is clear we are working in (natural) logarithmic phase space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Rename the old variable (M_1) because we want it to be called lnM_1 now\n",
-    "population.rename_grid_variable(\"M_1\",\"lnM_1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "532f691c-c1f6-46cc-84f2-970ec1216e40",
-   "metadata": {},
-   "source": [
-    "Next, we change the spacing function so that it works in the log space. We also adapt the probability calculation so that it calculates dprob/dlnM = M * dprob/dM. Finally, we set the precode to compute M_1 because binary_c requires the actual mass, not the logarithm of the mass."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# update the sampling, note that the IMF is dprob/dM1, and the phase \n",
-    "# space is now sampled in lnM1, so we multiply by M_1 to \n",
-    "# because  M * dprob/dM = dprob/dlnM\n",
-    "population.update_grid_variable(\n",
-    "    name=\"lnM_1\",\n",
-    "    samplerfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min = massrange[0], max = massrange[1], res = resolution[\"M_1\"]),\n",
-    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
-    "    dphasevol=\"dlnM_1\",\n",
-    "    parameter_name=\"M_1\",\n",
-    "    precode=\"M_1=math.exp(lnM_1)\",\n",
-    ")\n",
-    "# print(population.grid_options[\"_grid_variables\"]) # debugging"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 0.995631\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*    Total probability is 0.995631   *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-7a2e4301f5224b2cb8939d2297df0aad finished! *\n",
-      "*           The total probability is 0.995631.           *\n",
-      "*  It took a total of 4.77s to run 40 systems on 2 cores *\n",
-      "*                   = 9.55s of CPU time.                 *\n",
-      "*              Maximum memory use 621.930 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Clean and re-evolve the population \n",
-    "population.clean()\n",
-    "analytics = population.evolve()  \n",
-    "\n",
-    "# Show the results (debugging)\n",
-    "# print (population.grid_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "182b1094-5057-4ccf-bac6-9b0e560ad4f6",
-   "metadata": {},
-   "source": [
-    "You should see that the total probability is very close to 1.0, as you would expect for a well-sampled grid. The total will never be exactly 1.0, but that is because we are running a simulation, not a perfect copy of reality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "68ee1e56-21e5-48f4-b74c-50e48685ae94",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot luminosity distribution\n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31fe91bb-177d-4e4e-90cf-298a3f8a8b61",
-   "metadata": {},
-   "source": [
-    "Most stars are low mass red dwarfs, with small luminosities. Without the IMF weighting, our model population would have got this completely wrong! \n",
-    "\n",
-    "As you increase the resolution, you will see this curve becomes even smoother. The wiggles in the curve are (usually) sampling artefacts because the curve should monotonically brighten above about log(*L*/L<sub>☉</sub>)=-2. \n",
-    " \n",
-    "Remember you can play with the binwidth too. If you want a very accurate distribution you need a narrow binwidth, but then you'll also need high resolution (lots of stars) so lots of CPU time, hence cost, CO<sub>2</sub>, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba032bd8-b4a2-4558-9fd9-8e1e03d7d162",
-   "metadata": {},
-   "source": [
-    "Things to try:\n",
-    "\n",
-    " * Change the resolution to make the distributions smoother: what about error bars, how would you do that?\n",
-    " * Different initial distributions: the Kroupa distribution isn't the only one out there\n",
-    " * Change the metallicity and mass ranges\n",
-    " * What about a non-constant star formation rate? This is more of a challenge!\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?\n",
-    " * Binary stars! (see notebook_luminosity_function_binaries.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5abd0935-3957-4859-80c1-6f5d7ce4b614",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/build/html/binary_c_parameters.html b/docs/build/html/binary_c_parameters.html
index 93a63cd8a..e67ed4e6b 100644
--- a/docs/build/html/binary_c_parameters.html
+++ b/docs/build/html/binary_c_parameters.html
@@ -3555,7 +3555,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/custom_logging_functions.html b/docs/build/html/custom_logging_functions.html
index 15c05e49d..d32f75ac4 100644
--- a/docs/build/html/custom_logging_functions.html
+++ b/docs/build/html/custom_logging_functions.html
@@ -330,7 +330,7 @@ I recommend using this in function in combination with a function that generates
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/dicts.html b/docs/build/html/dicts.html
index d1dbed5b9..cfea40d7d 100644
--- a/docs/build/html/dicts.html
+++ b/docs/build/html/dicts.html
@@ -526,7 +526,7 @@ so that they don’t have to be calculated each time.</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/ensemble.html b/docs/build/html/ensemble.html
index 933d10648..afb38efb3 100644
--- a/docs/build/html/ensemble.html
+++ b/docs/build/html/ensemble.html
@@ -276,7 +276,7 @@ and return its contents to as a Python dictionary.</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/example_notebooks.html b/docs/build/html/example_notebooks.html
index 81ecc725e..3ffc73970 100644
--- a/docs/build/html/example_notebooks.html
+++ b/docs/build/html/example_notebooks.html
@@ -210,7 +210,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_BHBH.html b/docs/build/html/examples/notebook_BHBH.html
index 0d48698de..2720eb88d 100644
--- a/docs/build/html/examples/notebook_BHBH.html
+++ b/docs/build/html/examples/notebook_BHBH.html
@@ -937,7 +937,7 @@ Found bound BHBH system
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_HRD.html b/docs/build/html/examples/notebook_HRD.html
index 144063807..a8f72aef5 100644
--- a/docs/build/html/examples/notebook_HRD.html
+++ b/docs/build/html/examples/notebook_HRD.html
@@ -945,7 +945,7 @@ very start.</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_api_functionality.html b/docs/build/html/examples/notebook_api_functionality.html
index ae8ea57ea..fb06a9396 100644
--- a/docs/build/html/examples/notebook_api_functionality.html
+++ b/docs/build/html/examples/notebook_api_functionality.html
@@ -721,7 +721,7 @@ MAXIMUM MASS RATIO 0.0141
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_common_envelope_evolution.html b/docs/build/html/examples/notebook_common_envelope_evolution.html
index 8a8719953..2f498e8dc 100644
--- a/docs/build/html/examples/notebook_common_envelope_evolution.html
+++ b/docs/build/html/examples/notebook_common_envelope_evolution.html
@@ -739,7 +739,7 @@ Text(0, 0.5, &#39;Number of stars&#39;)
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_custom_logging.html b/docs/build/html/examples/notebook_custom_logging.html
index 009670e82..545746509 100644
--- a/docs/build/html/examples/notebook_custom_logging.html
+++ b/docs/build/html/examples/notebook_custom_logging.html
@@ -793,7 +793,7 @@ EXAMPLE_SN             1.050651207308e+01 1.59452 9.34213 20 12 13 5 1 6.55458 4
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_ensembles.html b/docs/build/html/examples/notebook_ensembles.html
index 5a42e0703..3c55c5713 100644
--- a/docs/build/html/examples/notebook_ensembles.html
+++ b/docs/build/html/examples/notebook_ensembles.html
@@ -151,7 +151,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_extra_features.html b/docs/build/html/examples/notebook_extra_features.html
index 932432ecd..fe5d781d2 100644
--- a/docs/build/html/examples/notebook_extra_features.html
+++ b/docs/build/html/examples/notebook_extra_features.html
@@ -526,7 +526,7 @@ dict_keys([&#39;networks&#39;, &#39;isotopes&#39;, &#39;argpairs&#39;, &#39;ense
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_individual_systems.html b/docs/build/html/examples/notebook_individual_systems.html
index b4d0b7fe0..58de6967b 100644
--- a/docs/build/html/examples/notebook_individual_systems.html
+++ b/docs/build/html/examples/notebook_individual_systems.html
@@ -832,7 +832,7 @@ SINGLE_STAR_LIFETIME 15 14.9927
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_luminosity_function_binaries.html b/docs/build/html/examples/notebook_luminosity_function_binaries.html
index b399028e0..2de613734 100644
--- a/docs/build/html/examples/notebook_luminosity_function_binaries.html
+++ b/docs/build/html/examples/notebook_luminosity_function_binaries.html
@@ -935,7 +935,7 @@ Done population run!
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_luminosity_function_single.html b/docs/build/html/examples/notebook_luminosity_function_single.html
index 507722285..ca5c2a571 100644
--- a/docs/build/html/examples/notebook_luminosity_function_single.html
+++ b/docs/build/html/examples/notebook_luminosity_function_single.html
@@ -949,7 +949,7 @@ Added analytics to metadata
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_massive_remnants.html b/docs/build/html/examples/notebook_massive_remnants.html
deleted file mode 100644
index 396e1e18e..000000000
--- a/docs/build/html/examples/notebook_massive_remnants.html
+++ /dev/null
@@ -1,942 +0,0 @@
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-<html class="writer-html5" lang="en" >
-<head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
-
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Example use case: Massive star luminosity &mdash; binary_c-python  documentation</title>
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-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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-    <link rel="search" title="Search" href="../search.html" /> 
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-        <div class="wy-side-nav-search" >
-            <a href="../index.html" class="icon icon-home"> binary_c-python
-          </a>
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-  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
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-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../readme_link.html">Python module for binary_c</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../modules.html">Binarycpython code</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../example_notebooks.html">Example notebooks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../binary_c_parameters.html">Binary_c parameters</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../grid_options_descriptions.html">Population grid code options</a></li>
-<li class="toctree-l1"><a class="reference external" href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python">Visit the GitLab repo</a></li>
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-      <div class="wy-nav-content">
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-          <div role="navigation" aria-label="Page navigation">
-  <ul class="wy-breadcrumbs">
-      <li><a href="../index.html" class="icon icon-home"></a> &raquo;</li>
-      <li>Example use case: Massive star luminosity</li>
-      <li class="wy-breadcrumbs-aside">
-            <a href="../_sources/examples/notebook_massive_remnants.ipynb.txt" rel="nofollow"> View page source</a>
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-div.nboutput.container div.output_area > div[class^='highlight']{
-    overflow-y: hidden;
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-  background: rgba(66, 165, 245, 0.2);
-}
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-/* CSS overrides for sphinx_rtd_theme */
-
-/* 24px margin */
-.nbinput.nblast.container,
-.nboutput.nblast.container {
-    margin-bottom: 19px;  /* padding has already 5px */
-}
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-/* ... except between code cells! */
-.nblast.container + .nbinput.container {
-    margin-top: -19px;
-}
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-.math {
-    text-align: unset;
-}
-</style>
-<section id="Example-use-case:-Massive-star-luminosity">
-<h1>Example use case: Massive star luminosity<a class="headerlink" href="#Example-use-case:-Massive-star-luminosity" title="Permalink to this heading"></a></h1>
-<p>In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of single stars using binary_c.</p>
-<p>We start by loading in some standard Python modules and the binary_c module.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="kn">from</span> <span class="nn">binarycpython.utils.functions</span> <span class="kn">import</span> <span class="n">temp_dir</span>
-<span class="kn">from</span> <span class="nn">binarycpython.utils.grid</span> <span class="kn">import</span> <span class="n">Population</span>
-
-<span class="n">TMP_DIR</span> <span class="o">=</span> <span class="n">temp_dir</span><span class="p">(</span><span class="s2">&quot;notebooks&quot;</span><span class="p">,</span> <span class="s2">&quot;notebook_luminosity&quot;</span><span class="p">)</span>
-
-<span class="c1"># help(Population) # Uncomment this line to see the public functions of this object</span>
-</pre></div>
-</div>
-</div>
-<section id="Setting-up-the-Population-object">
-<h2>Setting up the Population object<a class="headerlink" href="#Setting-up-the-Population-object" title="Permalink to this heading"></a></h2>
-<p>To set up and configure the population object we need to make a new instance of the <code class="docutils literal notranslate"><span class="pre">Population</span></code> object and configure it with the <code class="docutils literal notranslate"><span class="pre">.set()</span></code> function.</p>
-<p>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.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create population object</span>
-<span class="n">population</span> <span class="o">=</span> <span class="n">Population</span><span class="p">()</span>
-
-<span class="c1"># If you want verbosity, set this before other things</span>
-<span class="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">verbosity</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># Setting values can be done via .set(&lt;parameter_name&gt;=&lt;value&gt;)</span>
-<span class="c1"># Values that are known to be binary_c_parameters are loaded into bse_options.</span>
-<span class="c1"># Those that are present in the default grid_options are set in grid_options</span>
-<span class="c1"># All other values that you set are put in a custom_options dict</span>
-<span class="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-    <span class="c1"># binary_c physics options</span>
-    <span class="n">max_evolution_time</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>  <span class="c1"># maximum stellar evolution time in Myr</span>
-    <span class="n">tmp_dir</span><span class="o">=</span><span class="n">TMP_DIR</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># We can access the options through</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;verbosity is&quot;</span><span class="p">,</span> <span class="n">population</span><span class="o">.</span><span class="n">grid_options</span><span class="p">[</span><span class="s1">&#39;verbosity&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-adding: max_evolution_time=0.1 to BSE_options
-adding: tmp_dir=/tmp/binary_c_python-izzard/notebooks/notebook_luminosity to grid_options
-verbosity is 1
-</pre></div></div>
-</div>
-</section>
-<section id="Adding-grid-variables">
-<h2>Adding grid variables<a class="headerlink" href="#Adding-grid-variables" title="Permalink to this heading"></a></h2>
-<p>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.</p>
-<p>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.</p>
-<p>To add a grid variable to the population object we use <code class="docutils literal notranslate"><span class="pre">population.add_grid_variable</span></code></p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># help(population.add_grid_variable)</span>
-</pre></div>
-</div>
-</div>
-<p>All the distribution functions that we can use are stored in the <code class="docutils literal notranslate"><span class="pre">binarycpython.utils.distribution_functions</span></code> or <code class="docutils literal notranslate"><span class="pre">binarycpython/utils/distribution_functions.py</span></code> on git. If you uncomment the help statement below you can see which functions are available now:</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">binarycpython.utils.distribution_functions</span>
-<span class="c1"># help(binarycpython.utils.distribution_functions)</span>
-</pre></div>
-</div>
-</div>
-<p>First let us set up some global variables that will be useful throughout.</p>
-<ul class="simple">
-<li><p>The resolution is the number of stars we simulate in our model population.</p></li>
-<li><p>The massrange is a list of the min and max masses</p></li>
-<li><p>The total_probability is the theoretical integral of a probability density function, i.e. 1.0.</p></li>
-<li><p>The binwidth sets the resolution of the final distribution. If set to 0.5, the bins in log<em>L</em> are 0.5dex wide.</p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set resolution and mass range that we simulate</span>
-<span class="n">resolution</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;M_1&quot;</span><span class="p">:</span> <span class="mi">40</span><span class="p">}</span> <span class="c1"># start with resolution = 10, and increase later if you want &quot;more accurate&quot; data</span>
-<span class="n">massrange</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.07</span><span class="p">,</span> <span class="mf">100.0</span><span class="p">)</span> <span class="c1"># we work with stars of mass 0.07 to 100 Msun</span>
-<span class="n">total_probability</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="c1"># theoretical integral of the mass probability density function over all masses</span>
-<span class="c1"># distribution binwidths :</span>
-<span class="c1"># (log10) luminosity distribution</span>
-<span class="n">binwidth</span> <span class="o">=</span> <span class="p">{</span> <span class="s1">&#39;luminosity&#39;</span> <span class="p">:</span> <span class="mf">0.5</span> <span class="p">}</span>
-</pre></div>
-</div>
-</div>
-<p>The next cell contains an example of adding the mass grid variable, sampling the phase space in linear mass <em>M</em>_1.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Mass</span>
-<span class="n">population</span> <span class="o">=</span> <span class="n">Population</span><span class="p">()</span>
-<span class="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-    <span class="n">tmp_dir</span><span class="o">=</span><span class="n">TMP_DIR</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">population</span><span class="o">.</span><span class="n">add_grid_variable</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;M_1&quot;</span><span class="p">,</span>
-    <span class="n">longname</span><span class="o">=</span><span class="s2">&quot;Primary mass&quot;</span><span class="p">,</span>
-    <span class="n">valuerange</span><span class="o">=</span><span class="n">massrange</span><span class="p">,</span>
-    <span class="n">samplerfunc</span><span class="o">=</span><span class="s2">&quot;const(</span><span class="si">{min}</span><span class="s2">, </span><span class="si">{max}</span><span class="s2">, </span><span class="si">{res}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">min</span> <span class="o">=</span> <span class="n">massrange</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">max</span> <span class="o">=</span> <span class="n">massrange</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">res</span> <span class="o">=</span> <span class="n">resolution</span><span class="p">[</span><span class="s2">&quot;M_1&quot;</span><span class="p">]),</span>
-    <span class="n">probdist</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">{probtot}</span><span class="s2">/(</span><span class="si">{max}</span><span class="s2"> - </span><span class="si">{min}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">probtot</span> <span class="o">=</span> <span class="n">total_probability</span><span class="p">,</span> <span class="nb">min</span> <span class="o">=</span> <span class="n">massrange</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">max</span> <span class="o">=</span> <span class="n">massrange</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="c1"># dprob/dm1 : all stars are equally likely so this is 1.0 / (Mmax - Mmin)</span>
-    <span class="n">dphasevol</span><span class="o">=</span><span class="s2">&quot;dM_1&quot;</span><span class="p">,</span>
-    <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;M_1&quot;</span><span class="p">,</span>
-    <span class="n">condition</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>  <span class="c1"># Impose a condition on this grid variable. Mostly for a check for yourself</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Setting-logging-and-handling-the-output">
-<h2>Setting logging and handling the output<a class="headerlink" href="#Setting-logging-and-handling-the-output" title="Permalink to this heading"></a></h2>
-<p>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 <code class="docutils literal notranslate"><span class="pre">binary_c</span></code> (see documentation binary_c) or we can use the custom logging functionality of binarycpython (see notebook <code class="docutils literal notranslate"><span class="pre">notebook_custom_logging.ipynb</span></code>), 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.</p>
-<p>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 <code class="docutils literal notranslate"><span class="pre">notebook_individual_systems.ipynb</span></code>).</p>
-<p>In the code below we will set up both the custom logging and a parse function to handle that output.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create custom logging statement</span>
-<span class="c1">#</span>
-<span class="c1"># we check that the model number is zero, i.e. we&#39;re on the first timestep (stars are born on the ZAMS)</span>
-<span class="c1"># we make sure that the stellar type is &lt;= MAIN_SEQUENCE, i.e. the star is a main-sequence star</span>
-<span class="c1"># we also check that the time is 0.0 (this is not strictly required, but good to show how it is done)</span>
-<span class="c1">#</span>
-<span class="c1"># The Printf statement does the outputting: note that the header string is ZERO_AGE_MAIN_SEQUENCE_STAR</span>
-
-<span class="n">custom_logging_statement</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">if(stardata-&gt;model.model_number == 0 &amp;&amp;</span>
-<span class="s2">   stardata-&gt;star[0].stellar_type &lt;= MAIN_SEQUENCE &amp;&amp;</span>
-<span class="s2">   stardata-&gt;model.time == 0)</span>
-<span class="s2">{</span>
-<span class="s2">   /* Note that we use Printf - with a capital P! */</span>
-<span class="s2">   Printf(&quot;ZERO_AGE_MAIN_SEQUENCE_STAR </span><span class="si">%30.12e</span><span class="s2"> </span><span class="si">%g</span><span class="s2"> </span><span class="si">%g</span><span class="s2"> </span><span class="si">%g</span><span class="s2"> </span><span class="si">%g</span><span class="se">\\</span><span class="s2">n&quot;,</span>
-<span class="s2">          stardata-&gt;model.time, // 1</span>
-<span class="s2">          stardata-&gt;common.zero_age.mass[0], // 2</span>
-<span class="s2">          stardata-&gt;star[0].mass, // 3</span>
-<span class="s2">          stardata-&gt;star[0].luminosity, // 4</span>
-<span class="s2">          stardata-&gt;model.probability // 5</span>
-<span class="s2">      );</span>
-<span class="s2">};</span>
-<span class="s2">&quot;&quot;&quot;</span>
-
-<span class="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-    <span class="n">C_logging_code</span><span class="o">=</span><span class="n">custom_logging_statement</span>
-<span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<p>The parse function must now catch lines that start with “ZERO_AGE_MAIN_SEQUENCE_STAR” and process the associated data.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import the bin_data function so we can construct finite-resolution probability distributions</span>
-<span class="c1"># import the datalinedict to make a dictionary from each line of data from binary_c</span>
-<span class="kn">from</span> <span class="nn">binarycpython.utils.functions</span> <span class="kn">import</span> <span class="n">bin_data</span><span class="p">,</span><span class="n">datalinedict</span>
-
-<span class="k">def</span> <span class="nf">parse_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Example parse function</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># list of the data items</span>
-    <span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;header&quot;</span><span class="p">,</span> <span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="s2">&quot;zams_mass&quot;</span><span class="p">,</span> <span class="s2">&quot;mass&quot;</span><span class="p">,</span> <span class="s2">&quot;luminosity&quot;</span><span class="p">,</span> <span class="s2">&quot;probability&quot;</span><span class="p">]</span>
-
-    <span class="c1"># Loop over the output.</span>
-    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">splitlines</span><span class="p">():</span>
-        <span class="c1"># obtain the line of data in dictionary form</span>
-        <span class="n">linedata</span> <span class="o">=</span> <span class="n">datalinedict</span><span class="p">(</span><span class="n">line</span><span class="p">,</span><span class="n">parameters</span><span class="p">)</span>
-
-        <span class="c1"># Check the header and act accordingly</span>
-        <span class="k">if</span> <span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;header&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;ZERO_AGE_MAIN_SEQUENCE_STAR&quot;</span><span class="p">:</span>
-
-            <span class="c1"># bin the log10(luminosity) to the nearest 0.1dex</span>
-            <span class="n">binned_log_luminosity</span> <span class="o">=</span> <span class="n">bin_data</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;luminosity&#39;</span><span class="p">]),</span>
-                                             <span class="n">binwidth</span><span class="p">[</span><span class="s1">&#39;luminosity&#39;</span><span class="p">])</span>
-
-            <span class="c1"># append the data to the results_dictionary</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;luminosity distribution&#39;</span><span class="p">][</span><span class="n">binned_log_luminosity</span><span class="p">]</span> <span class="o">+=</span> <span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;probability&#39;</span><span class="p">]</span>
-
-            <span class="c1">#print (self.grid_results)</span>
-
-    <span class="c1"># verbose reporting</span>
-    <span class="c1">#print(&quot;parse out results_dictionary=&quot;,self.grid_results)</span>
-
-<span class="c1"># Add the parsing function</span>
-<span class="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-    <span class="n">parse_function</span><span class="o">=</span><span class="n">parse_function</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</section>
-<section id="Evolving-the-grid">
-<h2>Evolving the grid<a class="headerlink" href="#Evolving-the-grid" title="Permalink to this heading"></a></h2>
-<p>Now that we configured all the main parts of the population object, we can actually run the population! Doing this is straightforward: <code class="docutils literal notranslate"><span class="pre">population.evolve()</span></code></p>
-<p>This will start up the processing of all the systems. We can control how many cores are used by settings <code class="docutils literal notranslate"><span class="pre">num_cores</span></code>. By setting the <code class="docutils literal notranslate"><span class="pre">verbosity</span></code> 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.</p>
-<p>There are many grid_options that can lead to different behaviour of the evolution of the grid. Please do have a look at those: <a class="reference external" href="https://ri0005.pages.surrey.ac.uk/binary_c-python/grid_options_descriptions.html">grid options docs</a>, and try</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># set number of threads</span>
-<span class="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-    <span class="c1"># verbose output is not required</span>
-    <span class="n">verbosity</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="c1"># set number of threads (i.e. number of CPU cores we use)</span>
-    <span class="n">num_cores</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-    <span class="p">)</span>
-
-<span class="c1"># Evolve the population - this is the slow, number-crunching step</span>
-<span class="n">analytics</span> <span class="o">=</span> <span class="n">population</span><span class="o">.</span><span class="n">evolve</span><span class="p">()</span>
-
-<span class="c1"># Show the results (debugging)</span>
-<span class="c1"># print (population.grid_results)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Doing dry run to calculate total starcount and probability
-Generating grid code
-Grid has handled 40 stars with a total probability of 1
-**************************************
-* Total starcount for this run is 40 *
-*       Total probability is 1       *
-**************************************
-
-Generating grid code
-**********************************************************
-*  Population-b6213f2eb7f94d3196cf966b7b76b9f9 finished! *
-*               The total probability is 1.              *
-*  It took a total of 6.99s to run 40 systems on 2 cores *
-*                  = 13.98s of CPU time.                 *
-*              Maximum memory use 472.211 MB             *
-**********************************************************
-
-There were no errors found in this run.
-</pre></div></div>
-</div>
-<p>After the run is complete, some technical report on the run is returned. I stored that in <code class="docutils literal notranslate"><span class="pre">analytics</span></code>. As we can see below, this dictionary is like a status report of the evolution. Useful for e.g. debugging.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">analytics</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;population_name&#39;: &#39;b6213f2eb7f94d3196cf966b7b76b9f9&#39;, &#39;evolution_type&#39;: &#39;grid&#39;, &#39;failed_count&#39;: 0, &#39;failed_prob&#39;: 0, &#39;failed_systems_error_codes&#39;: [], &#39;errors_exceeded&#39;: False, &#39;errors_found&#39;: False, &#39;total_probability&#39;: 0.9999999999999998, &#39;total_count&#39;: 40, &#39;start_timestamp&#39;: 1635760806.5066257, &#39;end_timestamp&#39;: 1635760813.4966016, &#39;total_mass_run&#39;: 2001.3999999999996, &#39;total_probability_weighted_mass_run&#39;: 50.03499999999999, &#39;zero_prob_stars_skipped&#39;: 0}
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># make a plot of the luminosity distribution using Seaborn and Pandas</span>
-<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">binarycpython.utils.functions</span> <span class="kn">import</span> <span class="n">pad_output_distribution</span>
-
-<span class="c1"># set up seaborn for use in the notebook</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">rc</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">:(</span><span class="mi">20</span><span class="p">,</span><span class="mi">10</span><span class="p">)})</span>
-<span class="n">sns</span><span class="o">.</span><span class="n">set_context</span><span class="p">(</span><span class="s2">&quot;notebook&quot;</span><span class="p">,</span>
-                <span class="n">font_scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
-                <span class="n">rc</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;lines.linewidth&quot;</span><span class="p">:</span><span class="mf">2.5</span><span class="p">})</span>
-
-
-<span class="c1"># this saves a lot of typing!</span>
-<span class="n">ldist</span> <span class="o">=</span> <span class="n">population</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;luminosity distribution&#39;</span><span class="p">]</span>
-
-<span class="c1"># pad the distribution with zeros where data is missing</span>
-<span class="n">pad_output_distribution</span><span class="p">(</span><span class="n">ldist</span><span class="p">,</span>
-                        <span class="n">binwidth</span><span class="p">[</span><span class="s1">&#39;luminosity&#39;</span><span class="p">])</span>
-
-<span class="c1"># make pandas dataframe from our sorted dictionary of data</span>
-<span class="n">plot_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">({</span><span class="s1">&#39;ZAMS luminosity distribution&#39;</span> <span class="p">:</span> <span class="n">ldist</span><span class="p">})</span>
-
-<span class="c1"># make the plot</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">lineplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">plot_data</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;$\log_</span><span class="si">{10}</span><span class="s2">$ ($L_\mathrm</span><span class="si">{ZAMS}</span><span class="s2">$ / L$_{☉}$)&quot;</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Number of stars&quot;</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">yscale</span><span class="o">=</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[None]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_notebook_massive_remnants_20_1.png" src="../_images/examples_notebook_massive_remnants_20_1.png" />
-</div>
-</div>
-<p>Does this look like a reasonable stellar luminosity function to you? The implication is that the most likely stellar luminosity is 105.8 L☉! Clearly, this is not very realistic… let’s see what went wrong.</p>
-</section>
-<section id="ZAMS-Luminosity-distribution-with-the-initial-mass-function">
-<h2>ZAMS Luminosity distribution with the initial mass function<a class="headerlink" href="#ZAMS-Luminosity-distribution-with-the-initial-mass-function" title="Permalink to this heading"></a></h2>
-<p>In the previous example, all the stars in our grid had an equal weighting. This is very unlikely to be true in reality: indeed, we know that low mass stars are far more likely than high mass stars. So we now include an initial mass function as a three-part power law based on Kroupa (2001). Kroupa’s distribution is a three-part power law: we have a function that does this for us (it’s very common to use power laws in astrophysics).</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Update the probability distribution to use the three-part power law IMF</span>
-<span class="n">population</span><span class="o">.</span><span class="n">update_grid_variable</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;M_1&quot;</span><span class="p">,</span>
-    <span class="n">probdist</span><span class="o">=</span><span class="s2">&quot;three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Clean and re-evolve the population</span>
-<span class="n">population</span><span class="o">.</span><span class="n">clean</span><span class="p">()</span>
-<span class="n">analytics</span> <span class="o">=</span> <span class="n">population</span><span class="o">.</span><span class="n">evolve</span><span class="p">()</span>
-
-<span class="c1"># Show the results (debugging)</span>
-<span class="c1"># print (population.grid_results)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Doing dry run to calculate total starcount and probability
-Generating grid code
-Grid has handled 40 stars with a total probability of 0.218222
-**************************************
-* Total starcount for this run is 40 *
-*    Total probability is 0.218222   *
-**************************************
-
-Generating grid code
-**********************************************************
-*  Population-4b8c7f4a86e445099d73f27dffaad94b finished! *
-*           The total probability is 0.218222.           *
-*  It took a total of 7.95s to run 40 systems on 2 cores *
-*                  = 15.89s of CPU time.                 *
-*              Maximum memory use 587.984 MB             *
-**********************************************************
-
-There were no errors found in this run.
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plot luminosity distribution</span>
-<span class="n">ldist</span> <span class="o">=</span> <span class="n">population</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;luminosity distribution&#39;</span><span class="p">]</span>
-
-<span class="c1"># pad the distribution with zeros where data is missing</span>
-<span class="n">pad_output_distribution</span><span class="p">(</span><span class="n">ldist</span><span class="p">,</span>
-                        <span class="n">binwidth</span><span class="p">[</span><span class="s1">&#39;luminosity&#39;</span><span class="p">])</span>
-
-<span class="c1"># make pandas dataframe from our sorted dictionary of data</span>
-<span class="n">plot_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">({</span><span class="s1">&#39;ZAMS luminosity distribution&#39;</span> <span class="p">:</span> <span class="n">ldist</span><span class="p">})</span>
-
-<span class="c1"># make the plot</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">lineplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">plot_data</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;$\log_</span><span class="si">{10}</span><span class="s2">$ ($L_\mathrm</span><span class="si">{ZAMS}</span><span class="s2">$ / L$_{☉}$)&quot;</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Number of stars&quot;</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">yscale</span><span class="o">=</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[None]
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_notebook_massive_remnants_25_1.png" src="../_images/examples_notebook_massive_remnants_25_1.png" />
-</div>
-</div>
-<p>This distribution is peaked at low luminosity, as one expects from observations, but the resolution is clearly not great because it’s not smooth - it’s spiky!</p>
-<p>If you noticed above, the total probability of the grid was about 0.2. Given that the total probability of a probability distribution function should be 1.0, this shows that our sampling is (very) poor.</p>
-<p>We could simply increase the resolution to compensate, but this is very CPU intensive and a complete waste of time and resources. Instead, let’s try sampling the masses of the stars in a smarter way.</p>
-</section>
-<section id="A-better-sampled-grid">
-<h2>A better-sampled grid<a class="headerlink" href="#A-better-sampled-grid" title="Permalink to this heading"></a></h2>
-<p>The IMF has many more low-mass stars than high-mass stars. So, instead of sampling M1 linearly, we can sample it in log space.</p>
-<p>To do this we first rename the mass grid variable so that it is clear we are working in (natural) logarithmic phase space.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Rename the old variable (M_1) because we want it to be called lnM_1 now</span>
-<span class="n">population</span><span class="o">.</span><span class="n">rename_grid_variable</span><span class="p">(</span><span class="s2">&quot;M_1&quot;</span><span class="p">,</span><span class="s2">&quot;lnM_1&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<p>Next, we change the spacing function so that it works in the log space. We also adapt the probability calculation so that it calculates dprob/dlnM = M * dprob/dM. Finally, we set the precode to compute M_1 because binary_c requires the actual mass, not the logarithm of the mass.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># update the sampling, note that the IMF is dprob/dM1, and the phase</span>
-<span class="c1"># space is now sampled in lnM1, so we multiply by M_1 to</span>
-<span class="c1"># because  M * dprob/dM = dprob/dlnM</span>
-<span class="n">population</span><span class="o">.</span><span class="n">update_grid_variable</span><span class="p">(</span>
-    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;lnM_1&quot;</span><span class="p">,</span>
-    <span class="n">samplerfunc</span><span class="o">=</span><span class="s2">&quot;const(math.log(</span><span class="si">{min}</span><span class="s2">), math.log(</span><span class="si">{max}</span><span class="s2">), </span><span class="si">{res}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">min</span> <span class="o">=</span> <span class="n">massrange</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">max</span> <span class="o">=</span> <span class="n">massrange</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">res</span> <span class="o">=</span> <span class="n">resolution</span><span class="p">[</span><span class="s2">&quot;M_1&quot;</span><span class="p">]),</span>
-    <span class="n">probdist</span><span class="o">=</span><span class="s2">&quot;three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1&quot;</span><span class="p">,</span>
-    <span class="n">dphasevol</span><span class="o">=</span><span class="s2">&quot;dlnM_1&quot;</span><span class="p">,</span>
-    <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;M_1&quot;</span><span class="p">,</span>
-    <span class="n">precode</span><span class="o">=</span><span class="s2">&quot;M_1=math.exp(lnM_1)&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># print(population.grid_options[&quot;_grid_variables&quot;]) # debugging</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Clean and re-evolve the population</span>
-<span class="n">population</span><span class="o">.</span><span class="n">clean</span><span class="p">()</span>
-<span class="n">analytics</span> <span class="o">=</span> <span class="n">population</span><span class="o">.</span><span class="n">evolve</span><span class="p">()</span>
-
-<span class="c1"># Show the results (debugging)</span>
-<span class="c1"># print (population.grid_results)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Doing dry run to calculate total starcount and probability
-Generating grid code
-Grid has handled 40 stars with a total probability of 0.995631
-**************************************
-* Total starcount for this run is 40 *
-*    Total probability is 0.995631   *
-**************************************
-
-Generating grid code
-**********************************************************
-*  Population-7a2e4301f5224b2cb8939d2297df0aad finished! *
-*           The total probability is 0.995631.           *
-*  It took a total of 4.77s to run 40 systems on 2 cores *
-*                   = 9.55s of CPU time.                 *
-*              Maximum memory use 621.930 MB             *
-**********************************************************
-
-There were no errors found in this run.
-</pre></div></div>
-</div>
-<p>You should see that the total probability is very close to 1.0, as you would expect for a well-sampled grid. The total will never be exactly 1.0, but that is because we are running a simulation, not a perfect copy of reality.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># plot luminosity distribution</span>
-<span class="n">ldist</span> <span class="o">=</span> <span class="n">population</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;luminosity distribution&#39;</span><span class="p">]</span>
-
-<span class="c1"># pad the distribution with zeros where data is missing</span>
-<span class="n">pad_output_distribution</span><span class="p">(</span><span class="n">ldist</span><span class="p">,</span>
-                        <span class="n">binwidth</span><span class="p">[</span><span class="s1">&#39;luminosity&#39;</span><span class="p">])</span>
-
-<span class="c1"># make pandas dataframe from our sorted dictionary of data</span>
-<span class="n">plot_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">({</span><span class="s1">&#39;ZAMS luminosity distribution&#39;</span> <span class="p">:</span> <span class="n">ldist</span><span class="p">})</span>
-
-<span class="c1"># make the plot</span>
-<span class="n">p</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">lineplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">plot_data</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;$\log_</span><span class="si">{10}</span><span class="s2">$ ($L_\mathrm</span><span class="si">{ZAMS}</span><span class="s2">$ / L$_{☉}$)&quot;</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Number of stars&quot;</span><span class="p">)</span>
-<span class="n">p</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">yscale</span><span class="o">=</span><span class="s2">&quot;log&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/examples_notebook_massive_remnants_33_0.png" src="../_images/examples_notebook_massive_remnants_33_0.png" />
-</div>
-</div>
-<p>Most stars are low mass red dwarfs, with small luminosities. Without the IMF weighting, our model population would have got this completely wrong!</p>
-<p>As you increase the resolution, you will see this curve becomes even smoother. The wiggles in the curve are (usually) sampling artefacts because the curve should monotonically brighten above about log(<em>L</em>/L☉)=-2.</p>
-<p>Remember you can play with the binwidth too. If you want a very accurate distribution you need a narrow binwidth, but then you’ll also need high resolution (lots of stars) so lots of CPU time, hence cost, CO2, etc.</p>
-<p>Things to try:</p>
-<ul class="simple">
-<li><p>Change the resolution to make the distributions smoother: what about error bars, how would you do that?</p></li>
-<li><p>Different initial distributions: the Kroupa distribution isn’t the only one out there</p></li>
-<li><p>Change the metallicity and mass ranges</p></li>
-<li><p>What about a non-constant star formation rate? This is more of a challenge!</p></li>
-<li><p>What about evolved stars? Here we consider only the <em>zero-age</em> main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?</p></li>
-<li><p>Binary stars! (see notebook_luminosity_function_binaries.ipynb)</p></li>
-</ul>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2021, David Hendriks, Robert Izzard.</p>
-  </div>
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-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-  
-<br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
-<br><br>
-Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
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diff --git a/docs/build/html/examples/notebook_massive_remnants.ipynb b/docs/build/html/examples/notebook_massive_remnants.ipynb
deleted file mode 100644
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--- a/docs/build/html/examples/notebook_massive_remnants.ipynb
+++ /dev/null
@@ -1,761 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Example use case: Massive star luminosity\n",
-    "\n",
-    "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of single stars using binary_c. \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",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from binarycpython.utils.functions import temp_dir\n",
-    "from binarycpython.utils.grid import Population\n",
-    "\n",
-    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_luminosity\")\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",
-      "adding: tmp_dir=/tmp/binary_c_python-izzard/notebooks/notebook_luminosity to grid_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",
-    "    tmp_dir=TMP_DIR,\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' : 0.5 }"
-   ]
-  },
-  {
-   "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": [
-    "# Mass\n",
-    "population = Population()\n",
-    "population.set(\n",
-    "    tmp_dir=TMP_DIR,\n",
-    ")\n",
-    "population.add_grid_variable(\n",
-    "    name=\"M_1\",\n",
-    "    longname=\"Primary mass\",\n",
-    "    valuerange=massrange,\n",
-    "    samplerfunc=\"const({min}, {max}, {res})\".format(min = massrange[0], max = massrange[1], res = resolution[\"M_1\"]),\n",
-    "    probdist=\"{probtot}/({max} - {min})\".format(probtot = total_probability, min = massrange[0], max = massrange[1]), # dprob/dm1 : all stars are equally likely so this is 1.0 / (Mmax - Mmin)\n",
-    "    dphasevol=\"dM_1\",\n",
-    "    parameter_name=\"M_1\",\n",
-    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\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 Printf statement does the outputting: note that the header string is ZERO_AGE_MAIN_SEQUENCE_STAR\n",
-    "\n",
-    "custom_logging_statement = \"\"\"\n",
-    "if(stardata->model.model_number == 0 &&\n",
-    "   stardata->star[0].stellar_type <= MAIN_SEQUENCE &&\n",
-    "   stardata->model.time == 0)\n",
-    "{\n",
-    "   /* Note that we use Printf - with a capital P! */\n",
-    "   Printf(\"ZERO_AGE_MAIN_SEQUENCE_STAR %30.12e %g %g %g %g\\\\n\",\n",
-    "          stardata->model.time, // 1\n",
-    "          stardata->common.zero_age.mass[0], // 2\n",
-    "          stardata->star[0].mass, // 3\n",
-    "          stardata->star[0].luminosity, // 4\n",
-    "          stardata->model.probability // 5\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",
-    "\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",
-    "        # obtain the line of data in dictionary form \n",
-    "        linedata = datalinedict(line,parameters)\n",
-    "        \n",
-    "        # Check the header and act accordingly\n",
-    "        if linedata['header'] == \"ZERO_AGE_MAIN_SEQUENCE_STAR\":\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'][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": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 1\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*       Total probability is 1       *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-b6213f2eb7f94d3196cf966b7b76b9f9 finished! *\n",
-      "*               The total probability is 1.              *\n",
-      "*  It took a total of 6.99s to run 40 systems on 2 cores *\n",
-      "*                  = 13.98s of CPU time.                 *\n",
-      "*              Maximum memory use 472.211 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# set number of threads\n",
-    "population.set(\n",
-    "    # verbose output is not required    \n",
-    "    verbosity=0,\n",
-    "    # set number of threads (i.e. number of CPU cores we use)\n",
-    "    num_cores=2,\n",
-    "    )\n",
-    "\n",
-    "# Evolve the population - this is the slow, number-crunching step\n",
-    "analytics = population.evolve()  \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': 'b6213f2eb7f94d3196cf966b7b76b9f9', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.9999999999999998, 'total_count': 40, 'start_timestamp': 1635760806.5066257, 'end_timestamp': 1635760813.4966016, 'total_mass_run': 2001.3999999999996, 'total_probability_weighted_mass_run': 50.03499999999999, '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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UFOTz/PPPHnWHv/Xr19GsWfMKFVQAKSkpbNuWc9ixTz/9pEKPrWw9epxOQkIC06e/edjxWbNmUFJSQs+eZ7BtWw6XXjqOWbOmA9CuXQcmTPg6ffsOOOLrOORoUyYBxo27iHnz3mfmzOmce+7YY94PoE+ffhQVFfHee2+XHysuLmbBgnnHfMz//d/DfOtb1xCJREhKSmLw4KHcdNP3Acqzxsae/FvNhx8uoLT03wXjoe/JoXW26tVLISfn+D/b433NZc/Vh08/XXTY9zYcDjNz5jS6detevtaWJEmSdCLCkQhPvrqEz9fsYvKMFezZXxR0JKlGcySVyr3wwhTef/89LrvsKgoLi1i8+LMj7pOSksLq1avYt2/vUUdKAZx33jgmT36Gl19+kf/5nx8ccX7UqDE8++wkNm3ayB//+ORRn6NBgwbceOP3uP/+e7nppuu58MJLaNWqNXl5ebzzzizeeOM1fvWruyv8tQ0aNJT33nuHRx55gMGDh/Lpp5/wxhuvVfjxlalBg4ZcffV/8be//YW4uDgGDhzMmjWr+fOfH6dXrzMZMGAQMTExtGjRkgcfvI/9+/fTunUbli1byrx5c/j616876vOmppaN0Jo27Q1OO60nLVu2AmDEiNE8+OB9ZGcv4447fn3cbH379qd//4Hcffev+fa3d5Kens7zzz/L7t25NG3a7KiP6devP5MnP8Ndd/2Kc88dS3FxCZMm/Y20tDR69+5Tnm3Roo9ZuPCDE15Havv2HH75y9u4+OLLWLEimz/96VEuuOAi2rVrD5QtcP73vz/FM888RY8ep/Hee2+zcOHhOyAe63tzyJVXTuCNN17je9/7DtdeewP16qXw4ovPs27dWv73fx86obySJEnSIXMXb2X15rLlPiIR+HDZNkb1afMlj5LqLksqlcvOXgbACy88ywsvPHvU+/TqdSaJiUmkpaXRt2//o94nI6MzmZlZvPHGq3z72zcdcf6003qSnt6CmJjYwxYp/0+XXno57dq154UXnuXxx//Anj17qFcvhe7de/DQQ4+WFyAVMW7cRWzatJHXX3+Vl156gV69+vDb397Ld75z9MKnqn3rW9+hcePG/OMfz/Hiiy/QqFFjvvKVS7n22m8TE1M26uiuu/7fwZ3lHmPPnt00b57OtdfewIQJXz/qcw4dOpypU1/hrrt+xUUXXcIPf/gTAOrVq0fv3meSm5tLx46dvjTb3Xf/L48++jBPPvkohYVFjBo1hosuupT333/3qPfv1+8sfvWru5g48W/87Gc/JhQKccYZvXj44cfKpzZecsllfP75Z9xyy83cfvudR90t8lguvvgy9u3by223/YjExCQuv/wqvvOdG8vPX3PNtezevZtJk/5GSUkJgwYN5qc/vYOf/vSHX/q9OaRp06Y8+uifefTRh7nvvnsIh8N07dqdBx7441F3npQkSZK+TEFhCc/PXnXYsXlLtlpSSccRihxazVlH2Lkzj3D42N+erVvX0aJF+2pMVDvExcVQUuJC09UlPz+fSy4Zy003fZ+LLrok6DiVIhqvId8vapZmzeqzffu+L7+jdAxeQzpVXkM6VV5DNd+UmSt4c8EGANo0S2Xj9rKNf+7974E0SzuxjZmqgteQTtXJXEMxMSGaNDn60kLgmlRSrbVly2b++tc/8YMf3ERSUhLnnDM26EiSJElSnbBl536mf7gRgM5tGnL9Bf/eTGjB0qOvMSvJkkqqtUKhGJ5//ll27drJL395F0lJSUFHkiRJkmq9SCTCpOkrKA1HCAETRmfRtnkqrZumADB/iSWVdCyuSSXVUi1atGDq1BlBx5AkSZLqlE9W7uDzNbsAGNarFe1blK3T2r97Oi++s5qN2/ezcXsebZode8qTVFc5kkqSJEmSpEpQXFLKszNWAJCSFMclZ/9746IB3dPLbzuaSjo6SypJkiRJkirBmws2sH33AQAuHtqJ+vUSys81T0umU6sGQFlJ5R5m0pEsqU6RbyySvozvE5IkSbXfrr0HeHXuWgDaNEtheO9WR9zn0GiqHXsOsHrz3uqMJ0UFS6pTEBsbR3FxUdAxJNVwxcVFxMa6BKAkSVJt9tyslRQVhwEYPzqL2JgjP27379qcUKjs9jyn/ElHsKQ6BampaezevZ2iokJHSkg6QiQSoaiokN27t5OamhZ0HEmSJFWR5etzWbB0GwD9ujana/tGR71fw9REuh0898GybZSGw9WWUYoG/tP+KUhOLttCdM+eHZSWlgScJnrExMQQ9s1YpyCarqHY2Djq129U/n4hSZKk2qU0HGbitLLF0hPiYrhiROfj3n9At3SWrM1l7/4ilq3bTY+OjasjphQVLKlOUXJyih8+T1CzZvXZvn1f0DEUxbyGJEmSVFO888lmNm7PA+D8ge1p0jDpuPfv06UZz7y1nJLSCPOX5FhSSV/gdD9JkiRJkk5CXkEx/3xnNQBNGyZxXv92X/qYeknxnN6pCQALs7dRXFJapRmlaGJJJUmSJEnSSXjx3dXsP1C29MtVozJJiI+t0OPO6tECgILCUj5dtavK8knRxpJKkiRJkqQTtD5nH7M/3gRAjw6N6J3ZtMKPPSOjCYkJZYXW/CVbqySfFI0sqSRJkiRJOgGRSIRJ07KJRCA2JsTVo7MIhUIVfnxCfCxnZjYDYNGqnRQUuhGXBJZUkiRJkiSdkAVLt5G9cQ8Ao/q0oVXTE99Ma0D3dACKS8J8lL29UvNJ0cqSSpIkSZKkCiosKuW5WSsBaFAvnosGdzyp5+neoRGpyfEAzF+aU2n5pGhmSSVJkiRJUgW9Nm8tufsKAfjqsAzqJcWd1PPExcbQr2tzAJasyWVvflGlZZSilSWVJEmSJEkVsC03nzfmrwegY8v6DO7Z8pSe79CUv3AkwofLtp1yPinaWVJJkiRJklQBz85YSUlpBIDxY7KIOYHF0o+mc5uGNG6QCMC8JU75kyypJEmSJEn6Ep+t3sknK3cAMPj0FmS0anjKzxkTCtG/W9loqpUb97Bzz4FTfk4pmllSSZIkSZJ0HCWlYSZPXwFAUkIslw3LqLTnPuvglD+ABS6grjrOkkqSJEmSpOOY/uFGtu7KB+CiwR1pmJpYac/dtnkqLZvUA5zyJ1lSSZIkSZJ0DHvyCnllzhoAWjSux+i+bSr1+UOhUPkC6hu25bFpx/5KfX4pmlhSSZIkSZJ0DC+8vYoDRaUAjB+dSVxs5X+MHvCFKX/zHU2lOsySSpIkSZKko1i1aQ9zPtsKQO/MppzWqUmVvE56o3p0bFkfgAVLcohEIlXyOlJNZ0klSZIkSdJ/CEciTJyWDUBcbAxXjsqs0tcbcHCXv227C1izZV+VvpZUU1lSSZIkSZL0H+Z8uoW1W8vKovMGtKV5WnKVvl6/bumEDt52yp/qKksqSZIkSZK+IP9AMS+8vQqARvUTGXdWhyp/zUb1E+nSLg2ABUtzCIed8qe6x5JKkiRJkqQveGXOWvblFwNwxYjOJCbEVsvrntWjBQB79hexfH1utbymVJNYUkmSJEmSdNCmHfuZsXAjAFlt0+jfrXm1vXafLs2IjSmb9DfPKX+qgyypJEmSJEkCIpEIk6dnUxqOEArB+NGZhEKhL39gJUlJiuf0gzsILly+neKScLW9tlQTWFJJkiRJkgR8lL2DJWvLptkN792adun1qz3DgO5lu/zlF5awePXOan99KUiWVJIkSZKkOq+ouJQpM1cAkJIUxyVDOwWSo1fnpiTGl62BNX+pU/5Ut1hSSZIkSZLqvDcWrGfHngMAXHp2J1KT4wPJkZgQS+/MpgB8smIHB4pKAskhBcGSSpIkSZJUp+3cc4Cpc9cB0LZ5KsN6tQ40z6Epf0UlYT5esSPQLFJ1sqSSJEmSJNVpz81aSdHBRcrHj84kJqb6Fks/mh4dG5OSFAfAfHf5Ux1iSSVJkiRJqrOWrcvlg2XbgLIRTF3aNQo4EcTFxtCva3MAPl+zi335RQEnkqqHJZUkSZIkqU4qDYeZND0bgIT4GC4fnhFwon87NOWvNBzhw+XbA04jVQ9LKkmSJElSnTT7481s3L4fgAsGdqBxg6SAE/1bZts0GtVPBJzyp7rDkkqSJEmSVOfsyy/ixXdWA9AsLYlz+7cNONHhYkIh+ncrm/KXvWE3u/YeCDiRVPUsqSRJkiRJdc6L76wmv7AEgKtGZRIfFxtwoiOd1b1F+e0FS7cFmESqHpZUkiRJkqQ6Zd3Wfbz9yWYATuvYmF6dmwac6OjapaeS3rgeAPOWbA04jVT1LKkkSZIkSXVGJBJh4vRsIkBsTIirR2cSCoWCjnVUoVCIsw4uoL4+J48tO/cHnEiqWpZUkiRJkqQ6Y96SHFZu3APAmL5tadkkJeBEx3dolz9wAXXVfpZUkiRJkqQ6oaCwhOdmrQSgQUoCFw7uEGygCmjRuB7tW9QHykqqSCQScCKp6lhSSZIkSZLqhNfmrmNPXhEAlw/PIDkxLuBEFTOgW9loqpzcAtZu3RdwGqnqWFJJkiRJkmq9nF35vPXBegA6tWrAwNNafMkjao7+3ZpzaNUsp/ypNrOkkiRJkiTVes/OWEFJadlUuQljsoipoYulH03jBklktU0DYMHSHMJhp/ypdrKkkiRJkiTVap+u2sGiVTsBGNKzJR1bNgg40Ykb0KNsyt/uvCKyN+wONoxURSypJEmSJEm1VklpmMnTVwCQnBjLZcMyAk50cvp2aU5sTNnor3lO+VMtZUklSZIkSaq1pn2wgZzcAgC+MqQTDVISAk50clKT4zmtY2MAFi7fRklpOOBEUuWzpJIkSZIk1Uq5+wp55f21ALRsUo+RZ7YONtApGtC9bMrf/gMlLF69K+A0UuWzpJIkSZIk1UovzF5FYVEpAONHZxEXG90fgXtlNiUhruxrmL/UKX+qfaL7/1BJkiRJko5i5cY9zP18KwBnZjWjx8GpctEsKSGOXplNAfh4xfbyAk6qLSypJEmSJEm1SjgcYeL0bADiYmO4cmTngBNVnkNT/oqKw3y8cnvAaaTKZUklSZIkSapV3vtsC+u27gNg7IB2NEtLDjhR5Tm9UxNSkuIAWLBkW8BppMplSSVJkiRJqjX2HyjmhdmrAGjcIJHzB7YPOFHliouNoU+XZgB8tnoneQXFASeSKo8llSRJkiSp1nj53TXlxc2VIzNJjI8NOFHlG9C9BQCl4QgLlzuaSrWHJZUkSZIkqVbYuD2PmR9tAqBruzT6HhxxVNt0aZtGw9QEAOYvcZc/1R6WVJIkSZKkqBeJRJg8fQXhSIRQCMaPziIUCgUdq0rExIQY0K1sAfXl63eTu68w4ERS5bCkkiRJkiRFvYXLt7N0XS4AI3u3oU3z1IATVa1Du/xFgAVLHU2l2sGSSpIkSZIU1QqLS5kycwUAqcnxfGVox4ATVb0OLerTvFHZroVO+VNtYUklSZIkSYpqr89bx869ZVPeLh3WidTk+IATVb1Q6N9T/tZu3UfOrvyAE0mnzpJKkiRJkhS1duwu4PX56wFol57K2T1bBZyo+hya8geOplLtYEklSZIkSYpaU2atpLgkDMCEMVnExNTOxdKPplXTFNodXHtr3pIcIpFIwImkU2NJJUmSJEmKSkvW7mLh8u0AnNUjncw2acEGCsCAHmWjqbbuymd9Tl7AaaRTY0klSZIkSYo6JaVhJk8vWyw9MT6Wy4d3DjhRMPp3dcqfag9LKkmSJElS1Jn18SY27dgPwAWD2tOofmLAiYLRpGESWW0aAjB/aQ5hp/wpillSSZIkSZKiyt78Il56dw0AzRslc06/dgEnCtahBdRz9xWyYsPuYMNIp8CSSpIkSZIUVf759ioKCksAuHpUJvFxdfujbd+uzYk9uGD8/KXbAk4jnby6/X+yJEmSJCmqrNmyl3cXbQGgZ0YTzujcNOBEwatfL4HuHRoD8OGybZSUhgNOJJ0cSypJkiRJUlQIRyJMmp5NBIiNCXHVqMygI9UYZx2c8pdXUMyStbsCTiOdHEsqSZIkSVJUmPf5VlZt2gvAOf3a0qJxvYAT1Ry9MpuWT3uc5y5/ilKWVJIkSZKkGq+gsITnZ60CoGFKAhcM6hBsoBomOTGOXgenPn6cvYPC4tKAE0knzpJKkiRJklTjvfr+WvbsLwLg8hEZJCfGBZyo5jm0y19hcSmLVu4IOI104iypJEmSJEk12pad+3nrgw0AZLRuwMAeLQJOVDOd3qlJeXk33yl/ikKWVJIkSZKkGisSiTB5xgpKwxFCwIQxWYRCoaBj1UjxcTH06dIMgE9X7WT/geKAE0knxpJKkiRJklRjLVq1k8Wry3arG3pGKzq0aBBwoprt0C5/peEIC5dvDziNdGIsqSRJkiRJNVJxSZhnp68AyhYGv3RYp4AT1Xxd2zWiYUoC4JQ/RR9LKkmSJElSjfTWB+vZtrsAgIuHdqRBvYSAE9V8MTEh+nVrDsCydbnszisMOJFUcZZUkiRJkqQaJ3dfIa++vw6A1k1TGNG7dcCJosehXf4iwIKl24INI50ASypJkiRJUo3z/KyVFBaXAjB+dCZxsX58rahOLRvQLC0JcMqfoov/l0uSJEmSapTsDbuZd7Bc6dulGd06NA44UXQJhULlo6nWbNlLTm5+wImkirGkkiRJkiTVGOFwhEnTsgGIj4vhipGdA04UnQZ0b1F+e4GjqRQlLKkkSZIkSTXGO4s2s35bHgDnn9Wepg2TA04UnVo3TaFNs1QA5i3JIRKJBJxI+nKWVJIkSZKkGiGvoJh/vrMagCYNkhg7oF3AiaLbgO5lu/xt2ZnPhoPFn1STWVJJkiRJkmqEl99dQ15BMQBXjuxMQnxswImi24Bu6eW35y91yp9qPksqSZIkSVLgNm7LY+bHGwHo1r4Rfbo0CzhR9Gualkzn1g2BsnWpwk75Uw1nSSVJkiRJClQkEmHitGwiEYgJhRg/OpNQKBR0rFrh0C5/O/cWsmrTnoDTSMdnSSVJkiRJCtQHy7axfMNuAEb2aU3rgwt+69T169qcmIOF3zx3+VMNZ0klSZIkSQpMYVEpz81aCUBqcjwXD+kYcKLapUFKAt07NALgw2XbKCkNB5xIOjZLKkmSJElSYKbOW8euvYUAXDY8g3pJ8QEnqn0OTfnbl1/M0nW5AaeRjs2SSpIkSZIUiO27C3h9/noA2reoz5DTWwacqHY6M6sZcbFlH//nO+VPNZgllSRJkiQpEFNmriyffjZhdBYxMS6WXhWSE+M4o3MTABZmb6eouDTgRNLRWVJJkiRJkqrd52t28VH2dgAG9mhB5zYNA05Uu511cMpfYVEpn67aGXAa6egsqSRJkiRJ1aqkNMyk6dkAJCbEcvmIjIAT1X49M5qQnBgLuMufai5LKkmSJElStZq5cCNbduYDcNHgDqSlJgacqPaLj4vlzKxmAHy6aid5BcUBJ5KOZEklSZIkSao2e/YX8fKcNQCkN67HmL5tA05Udxza5a+kNMy8zzYHnEY6kiWVJEmSJKna/OPtVRQUli3cffWozPJd51T1urVvRIN68QC8/fGmgNNIR/LdQJIkSZJULVZv3st7n24B4IyMJvTMaBJworolNiaGfl3LRlN9umI7e/IKA04kHc6SSpIkSZJU5cKRCBOnlS2WHhcb4qrRmQEnqpsG9CgrqcIR+GDZtoDTSIezpJIkSZIkVbn3P9vKmi17ATi3fzvSG9ULOFHdlNGqAU0bJgEw313+VMNYUkmSJEmSqlT+gRJeeHsVAGmpCYwb2D7gRHVXKBQqX0B91ea9bNtdEHAi6d8sqSRJkiRJVepf769h7/4iAK4Y0ZmkhLiAE9VtA7qll99e4Ggq1SCWVJIkSZKkKrNl536mf7gRgM5tGpaP4lFw2jRPpX2L+gDMX2pJpZrDkkqSJEmSVCUikQiTpq+gNBwhBEwYnUUoFAo6loCze7cBYNP2/WzclhdwGqmMJZUkSZIkqUp8smIHn6/ZBcCwXq3KR+8oeGf3bl1+29FUqiksqSRJkiRJla64pJTJM1YAkJIUxyVndwo4kb6oRZMUMlo1AMp2+YtEIgEnkiypJEmSJElV4I0FG9ix5wAAFw/tRP16CQEn0n86tD7Yjj0HWLV5b8BpJEsqSZIkSVIl27X3AK/NXQtAm2YpDO/dKthAOqp+3dI5tETY/M+d8qfgWVJJkiRJkirVc7NWUlQcBmD86CxiY/zoWRM1TEmge/tGAHywLIfScDjgRKrrfKeQJEmSJFWa5etzWbB0GwD9ujan68ESRDVT/4NT/vbmF7N0XW7AaVTXWVJJkiRJkipFaTjMxGlli6UnxMVwxYjOASfSl+mT1Zy42LJqYP4Sp/wpWJZUkiRJkqRK8fYnm9m4PQ+A8we2p0nDpIAT6cvUS4qjZ0YTAD7K3k5xSWnAiVSXWVJJkiRJkk5ZXkExL76zGoCmDZMYO6BdwIlUUWcdnPJXUFjKp6t2BpxGdZkllSRJkiTplL34zmr2HygB4KpRmcTHxQacSBXVM6MJSQllP695TvlTgCypJEmSJEmnZH3OPmZ/sgmAHh0a0TuzacCJdCIS4mM5M6sZAItW7qSgsCTgRKqrLKkkSZIkSSctEokwaVo2kQjExoS4enQWoVAo6Fg6QQMOTvkrKQ3zUfb2gNOorrKkkiRJkiSdtAVLt5G9cQ8Ao/q0oVXTlIAT6WR0a9+I+vXiAXf5U3AsqSRJkiRJJ+VAUQnPzVoJQIN68Vw0uGPAiXSy4mJj6Nu1OQBL1uayd39RwIlUF1lSSZIkSZJOymtz15G7rxCArw7LoF5SXMCJdCoGdCub8heORPhg2baA06gusqSSJEmSJJ2wbbn5vLlgPQAdW9ZncM+WASfSqercpiFNGiQCMH+pU/5U/SypJEmSJEkn7NkZKykpjQAwfkwWMS6WHvViQiH6HxxNtXLjHnbsKQg4keoaSypJkiRJ0gn5bPVOPlm5A4DBp7cgo1XDgBOpshza5Q/KFsWXqpMllSRJkiSpwkpKw0yevgKApIRYLhuWEXAiVaa2zVNp2aQeAPM+d8qfqpcllSRJkiSpwqZ/uJGtu/IBuGhwRxqmJgacSJUpFApx1sHRVBu357Fpe17AiVSXWFJJkiRJkipkd14hL89ZA0CLxvUY3bdNwIlUFfp/YcqfC6irOllSSZIkSZIq5B+zV1FYVArA+DGZxMX6kbI2Sm9Uj44tGwAwf0kOkUgk4ESqK3xHkSRJkiR9qVWb9jBn8VYAemc25bSOTQJOpKp0aAH17bsPsHrL3oDTqK6wpJIkSZIkHVc4EmHitGwA4mJjuHJUZsCJVNX6d2tO6ODt+Uuc8qfqYUklSZIkSTquOZ9uYe3WfQCcN6AtzdOSA06kqpaWmkjX9o0A+GDpNsJhp/yp6llSSZIkSZKOKf9AMS+8vQqARvUTGXdWh2ADqdocmvK3Z38Ry9bnBpxGdYEllSRJkiTpmF5+by378osBuGJEZxITYgNOpOrSp0szYmPKJv3Nc8qfqoEllSRJkiTpqDbt2M+MhRsByGqbRv9uzQNOpOqUkhRPz4yyBfIXLt9OcUk44ESq7SypJEmSJElHiEQiTJqWTTgSIRSC8aMzCYVCX/5A1SqHpvwVFJbw2eqdAadRbWdJJUmSJEk6wkfZO1i6rmwdouG9W9MuvX7AiRSEMzo3JTG+bIqnu/ypqllSSZIkSZIOU1RcypSZKwBISYrjkqGdAk6koCTGx9I7qykAn6zcQUFhScCJVJtZUkmSJEmSDvPGgvXs2HMAgEvP7kRqcnzAiRSksw5O+SsuCfPJih0Bp1FtZkklSZIkSSq3Y08BU+euA6Bt81SG9WodcCIFrXuHxuVFpbv8qSpZUkmSJEmSyj03axVFB3dxmzAmi5gYF0uv6+JiY+jbtWxnx8/X7GJvflHAiVRbWVJJkiRJkgBYui6XD5dtA8p2dctqmxZsINUYA7qVlVThSISFB68RqbJZUkmSJEmSKA2HmTQ9G4CE+BguH54RcCLVJJlt02hUPxFwlz9VHUsqSZIkSRKzP97Mpu37AbhgYAcaN0gKOJFqkphQiAHdyhZQz964h50HF9aXKpMllSRJkiTVcfvyi3jxndUANEtL4tz+bQNOpJpowMFd/gAWLHM0lSqfJZUkSZIk1XH/fGc1+YUlAFw1KpP4uNiAE6kmapeeSovG9QCY/7kllSqfJZUkSZIk1WHrtu7jnU82A3Bap8b06tw04ESqqUKhEGcdHE21flsem3fsDziRahtLKkmSJEmqoyKRCBOnZRMBYmNCXD0qk1AoFHQs1WBfnPLnAuqqbJZUkiRJklRHzVuSw8pNewAY07ctLZukBJxINV1643p0aFEfgPlLc4hEIgEnUm1iSSVJkiRJdVBBYQnPzVoJQIOUBC4c3CHYQIoah0ZTbcstYO3WfQGnUW1iSSVJkiRJddBrc9exJ68IgMuHZ5CcGBdwIkWL/t3SOTQp1Cl/qkyWVJIkSZJUx+TsyufNBesByGjVgIGntQg4kaJJo/qJdGmXBsCCpTmEw075U+WwpJIkSZKkOmbyjBWUhiOEgPFjsohxsXSdoENT/nbnFbF8w+5gw6jWsKSSJEmSpDpk0codfLpqJwBDerakY8sGASdSNOrTpTmxMWXlplP+VFksqSRJkiSpjiguCfPsjBUAJCfG8tVhGQEnUrRKTY7ntI6NAVi4fBslpeGAE6k2sKSSJEmSpDpi+ocbyMktAOArQzrRICUh4ESKZgN6lE3523+ghMWrdwWcRrWBJZUkSZIk1QG5+wp55f21ALRsUo+RZ7YONpCiXu/OzUiIL6sV5i3ZGnAa1QaWVJIkSZJUB7wweyWFRaUAjB+dRVysHwd1ahITYumd2QyAT1bu4EBRScCJFO18V5IkSZKkWm7Fxt3M/bxsceszs5rR4+BaQtKpGtCtbMpfUXGYT1bsCDiNop0llSRJkiTVYuFwhEnTyhZLj4+L4aqRnQNOpNrktE6NSUmKA9zlT6fOkkqSJEmSarF3P93Mupx9AIwd0I6mackBJ1JtEhcbQ58uzQFYvGYXeQXFASdSNLOkkiRJkqRaav+BYv7x9moAGjdIZOxZ7QNOpNrorO5lU/5KwxE+XL4t4DSKZpZUkiRJklRLvfzumvKRLVeOzCQxPjbgRKqNstqmkZaaAMD8z53yp5NnSSVJkiRJtdDG7XnM/GgTAF3bpdG3S7OAE6m2iokJ0f/gAurZG3aza++BgBMpWllSSZIkSVItE4lEmDQtm3AkQkwoxPjRWYRCoaBjqRYbcHDKXwRYsNQpfzo5llSSJEmSVMssXL6dZet3AzDizNa0aZ4abCDVeh1a1Ce9Udmi/POXOuVPJ8eSSpIkSZJqkcLiUqbMXAFAanI8Fw/tGHAi1QWhUKh8NNW6rfvYuis/4ESKRpZUkiRJklSLvD5vHTv3FgJw6bBOpCTFB5xIdcWhkgpg/hJHU+nEWVJJkiRJUi2xY3cBr89fD0C79FTO7tkq4ESqS1o2SaFdetnU0nlLcohEIgEnUrSxpJIkSZKkWmLKrJUUl4QBmDAmi5gYF0tX9TqrewsAcnblsz4nL+A0ijaWVJIkSZJUCyxZu4uFy7cDcFaPdDLbpAUbSHVS/27Ny2/PW7I1wCSKRpZUkiRJkhTlSkrDTJpetlh6Ynwslw/vHHAi1VWNGySR1TYNgAVLtxF2yp9OgCWVJEmSJEW5WR9tYvOO/QBcOLgDjeonBpxIddmhBdRz9xWyYsPuYMMoqlhSSZIkSVIU27u/iJfeWwNA80bJjOnbNuBEquv6dmlG7MH10NzlTyfCkkqSJEmSotg/31lFQWEJAFePyiQ+zo95Clb9egn06NgYgA+WbaOkNBxwIkUL370kSZIkKUqt2bKXdxdtAaBnRhPO6Nw04ERSmUNT/vYfKOHzNbsCTqNoYUklSZIkSVEoHIkwaVo2ESA2JsRVozKDjiSV653ZlISDo/qc8qeKsqSSJEmSpCg0d/FWVm3eC8A5/dvSonG9gBNJ/5aUEEevzLKRfR+v2EFhUWnAiRQNLKkkSZIkKcrkHyjmhdmrAGiYmsAFAzsEG0g6igHdyqb8FRaX8snKHQGnUTSwpJIkSZKkKDNlWjZ79hcBcMXwziQnxgWcSDrSaZ2aUO/gtemUP1WEJZUkSZIkRZEtO/fzyrtlo6gyWjfgrB7pASeSji4+LoY+XZoB8NnqneQVFAecSDWdJZUkSZIkRYlIJMLkGSsoKY0QAiaMySIUCgUdSzqmsw7u8lcajvBR9vaA06imO+GSqqCgoPx2bm4uEydOZPLkyezevbsyc0mSJEmS/sOilTtZvHoXAEPPaEWHFg0CTiQdX5d2jWiYmgDAvM+3BpxGNV2FJy7v3buXH/zgB+zdu5fnn3+evLw8vvrVr7JlyxYikQj/93//x6RJk2jbtm1V5pUkSZKkOqm4pJTJM7IBSEmO59JhnQJOJH25mJgQ/bumM+3DDSxfv5vcfYU0qp8YdCzVUBUeSfXggw8yf/58hg4dCsALL7zA5s2bufXWW/nb3/5GTEwMDz74YFXllCRJkqQ67a0PNrB99wEAJpzblQb1EgJOJFXMgINT/iLAB8u2BRtGNVqFS6qZM2fyta99jZtvvhmA6dOn06RJE6699lr69+/PhAkTeP/996ssqCRJkiTVVbv2HuBf768FoHXTFM4f1CHQPNKJ6NiyPs3TkgGYv8Qpfzq2CpdUO3fuJDMzE4B9+/bxySefMHjw4PLzjRo1Omy9KkmSJElS5Xhh9iqKisMAjB+dSWyse2ApeoRCIfofHE21Zss+cnLzA06kmqrC72zp6els2LABKBtFVVpayvDhw8vPf/TRR7Rs2bLSA0qSJElSXZa9YTfzluQA0LdLM7p1aBxwIunEHZryBzD/4PUs/acKL5w+YsQInn76afLy8njttddo2LAhI0eOJCcnhz/96U+8/PLL3HjjjVWZVZIkSZLqlHA4wqRpZYulJ8TFcMXIzgEnkk5O66YptG2eyoZtecxfksOFgzoQCoWCjqUapsIjqW699VbGjRvHCy+8QIMGDXjggQdISkoiJyeHiRMncuGFF3LDDTdUZVZJkiRJqlPeXrSZ9dvyADj/rPY0bZgccCLp5B0aTbVlZz4bDl7X0hdVeCTVunXr+M1vfsNvf/vbw4537dqVt99+m+bNm1d6OEmSJEmqq/IKivnn26sAaNIgifMGtAs4kXRq+ndrzguzy67p+UtyaJdeP+BEqmkqPJLqG9/4Br///e+POJ6QkGBBJUmSJEmV7KV3V7P/QAkAV43qTEJ8bMCJpFPTtGEynds0BGD+0hzCkUjAiVTTVLikys/Pp02bNlWZRZIkSZIEbNiWx6yPNwHQrX0jzsxqFnAiqXKcdXDK3669hazcuCfgNKppKlxSff3rX+evf/0rn332WVXmkSRJkqQ6LRIpWyw9EoGYUIjxozNdYFq1Rt+uzYk5eD27y5/+U4XXpFq8eDHbtm3jiiuuICkpibS0NGJiDu+4QqEQ06dPr/SQkiRJklRXfLBsG8s37AZgZJ/WtG6WGmwgqRI1qJdA946NWLx6Fx8s28bVozOJi63w+BnVchUuqQoLCznttNOqMoskSZIk1WmFRaVMmbkSgPr14rl4SMeAE0mVb0C3dBav3kVeQTFL1ubSM6NJ0JFUQ1S4pHrmmWeqMockSZIk1XmvzVtH7r5CAL46LIN6SfEBJ5Iq35lZzfjbm8spLgkzf0mOJZXKVeqYuiVLllTm00mSJElSnbFtdwFvzF8PQIcW9RnSs2XAiaSqkZwYxxkHi6mPVmynsLg04ESqKSo8kqqoqIiHH36Yd999l/z8fMLhcPm50tJS9u/fT15eHkuXLq2SoJIkSZJUm02ZsYKS0rLPWePHZJUvLi3VRgO6t+DD5dspLCrl01U76de1edCRVANUeCTVQw89xJNPPsmePXtITk5m06ZNtGzZkri4OLZu3UpxcTE///nPqzKrJEmSJNVKi9fs5OMVOwAYdFoLOrduGHAiqWr1zGhMcmIsAPM+3xpwGtUUFS6p3njjDfr378/MmTP505/+BMAvfvEL3nzzTR5//HFKSkqIj3e+tCRJkiSdiJLSMJOnrwAgMSGWy4ZnBJxIqnrxcbH0ySobPfXZ6p3kHygOOJFqggqXVDk5OZxzzjnExMSQnp5OkyZN+PjjjwEYNmwYl1xyCc8991yVBZUkSZKk2mjGwo1s2ZkPwEWDO5CWmhhwIql6DOieDkBJaYSFy7cHnEY1QYVLqqSkpMNGSrVr147s7OzyP/fs2ZMNGzZUbjpJkiRJqsX25BXy8ntrAEhvXI8xfdsGnEiqPl3bp9EgJQGA+UtzAk6jmqDCJVW3bt145513yv/cqVOn8pFUUDbSKuTCfpIkSZJUYf94ezUHisp2Nhs/OpO42ErdgF2q0WJjYsoXTF+6Lpc9eYUBJ1LQKvwOOH78eGbMmMH48ePJy8tj3LhxLFmyhNtuu40//elPPPXUU5x++ulVmVWSJEmSao3Vm/fy3mdbAOjVuSmnd2oScCKp+h2a8heJwKJVOwNOo6DFVfSOY8eOJS8vj7/+9a8kJyczaNAgJkyYwMSJEwFo1aoVP/3pT6ssqCRJkiTVFuFIhInTypZPiYsNceWozgEnkoLRqkm98tuHRhWq7qpwSQVw+eWXc/nll5f/+Y477uC6665jz549ZGRkkJCQUOkBJUmSJKm2ef+zrazZsheAc/u3I71RvS95hCTVfhWe7nfNNdcwd+7cI463atWKbt268d577zFu3LhKDSdJkiRJtU3+gRJemL0SgEb1Exk3sH3AiSSpZjjmSKqCggJyc3PL/7xgwQLGjBlD+/ZHvoGGw2HeeecdNm7cWDUpJUmSJKmWeGXOGvbmFwNw+YgMkhJOaIKLJNVaxy2pLr74Yvbt2wdAKBTi7rvv5u677z7q/SORCIMHD66alJIkSZJUC2zesZ8ZC8v+cT+zTUMGdEsPOJEk1RzHLKkaN27M//7v//LZZ58RiUT44x//yJgxY+jSpcsR942JiaFx48ZO95MkSZKkY4hEIkyesYLScIRQCCaMySIUCgUdS5JqjOOOKx02bBjDhg0DYPPmzVx11VWcccYZ1RJMkiRJkmqTT1bs4PM1uwAY1qs17dLrB5xIkmqWCk9+vueee456fMWKFcTExJCRkVFpoSRJkiSpNikuKWXyjBUApCTFccnQjgEnkqSap8K7+wE88cQT3HbbbUDZYuk33HADF110ERdccAHXXXcd+/fvr5KQkiRJkhTN3pi/nh17DgBw8dBO1K+XEHAiSap5KlxSPfnkk9x///3s2LEDgNdff5133nmHc845h5tuuokPP/yQP/7xj1UWVJIkSZKi0a69B3ht7joA2jRLZXjvVgEnkqSaqcLT/V588UXGjBnDI488AsDUqVNJTk7m3nvvJSkpif379/PGG2/w4x//uMrCSpIkSVK0eW7WSopKwgBMGJNJbMwJTWiRpDqjwu+OGzZs4OyzzwaguLiYuXPn0r9/f5KSkgDIyMgoH2UlSZIkSYLl63NZsHQbAP27NadLu0YBJ5KkmqvCJVWDBg3Iy8sDYP78+eTn55eXVgDr16+nadOmlZ9QkiRJkqJQaTjMxGlli6UnxMVwxYjOASeSpJqtwtP9evfuzd///ndat27NY489RlxcHOeccw7FxcXMmjWLyZMnM3r06KrMKkmSJElR4+1PNrNxe9k/9I8b2J7GDZICTiRJNVuFR1L97Gc/IzExkZtvvpmlS5fyox/9iGbNmvHRRx9x880306xZM773ve9VZVZJkiRJigr78ot48Z3VADRtmMR5A9oFnEiSar4Kj6Rq2bIlr7zyCkuWLCE9PZ309HQAunbtyv3338+IESNITk6usqCSJEmSFC1efHcN+w+UAHD1qEzi42IDTiRJNV+FSyqAuLg4evbsedixhg0bcv7551dqKEmSJEmKVuu27uPtjzcB0KNjY3plunavJFWEe59KkiRJUiWJRCJMmp5NBIiNCXH1qExCoVDQsSQpKlhSSZIkSVIlmb80hxUb9wAwqk8bWjVNCTiRJEUPSypJkiRJqgQHikp4ftYqABrUi+eiwR0DTiRJ0eWYJdXs2bPZsWNHdWaRJEmSpKj12tx15O4rBOCrwzOol3RCSwBLUp13zJLqlltuYfbs2eV/vuaaa5g7d251ZJIkSZKkqJKTm8+bC9YD0LFlAwaf3jLgRFIUikSCTqCAHbOkikQiLFy4kIKCAgAWLFjAzp07qy2YJEmSJEWLKTNWUlJa9gF7wpgsYlwsXZJO2DHHn55zzjm8+OKLvPTSS+XHbr31Vm699dZjPlkoFGLJkiWVGlCSJEmSarJPV+3kk5VlS6UMOb0lnVo1CDiRFE0sdPVvxyyp7rzzTnr06EF2djZFRUW8/PLL9OnTh7Zt21ZnPkmSJEmqsUpKw0yesQKA5MRYvjo8I+BEkhS9jllSJSQk8LWvfa38zy+99BJXXnklF154YbUEkyRJkqSabvqHG8nZlQ/ARYM70jAlIeBEkhS9KrzdxLJly8pv79ixg82bNxMfH096ejqNGzeuknCSJEmSVFPtzivk5TlrAGjZpB6j+rQJOJEkRbcT2hN18eLF/PrXv+azzz477PgZZ5zBz3/+c04//fRKDSdJkiRJNdULs1dRWFQKwNWjM4mLPea+VJKkCqhwSbV8+XL+67/+C4ArrriCjIwMwuEwq1ev5l//+hfXXHMNzz33HJmZmVUWVpIkSZJqgpWb9vD+4q0A9M5symkdmwScSJKiX4VLqgcffJCUlBSmTJlC69atDzt34403ctlll/GHP/yBhx56qNJDSpIkSVJNEY5EmDgtG4C42BiuHOU/1EtSZajweNQPP/yQ8ePHH1FQAbRo0YKrr76a+fPnV2q4ylJUVMQ3v/lNZs2aFXQUSZIkSVHuvU+3sG7rPgDOG9CO5mnJASeSpNqhwiVVUVERKSkpxzyfmprKgQMHKiVUZVq2bBkTJkzgo48+CjqKJEmSpCiXf6CYf7y9CoBG9RMZd1b7gBNJUu1R4ZKqW7duvPrqq5SUlBxxrri4mH/9619kZWVVarjKMHnyZG688UZ69uwZdBRJkiRJUe7l99ayL78YgCtHdiYxITbgRJJUe1S4pLr++uv57LPP+NrXvsabb77J8uXLWb58Oa+//jpf+9rX+Pzzz7n22murMutRvfTSS3Tv3v2I//btKxt+e+eddzJixIhqzyVJkiSpdtm0PY8ZCzcC0KVtGv26Ng84kSTVLhVeOH306NHccccd3HfffXz/+98vPx6JREhMTOQnP/kJ5513XlVkPK6LL76Yiy++uNpfV5IkSVLdEYlEmDR9BeFIhFAIxo/JIhQKBR1LkmqVCpdUABMmTGDcuHHMnTuXjRs3EolEaNOmDYMGDSItLa2KIkqSJElSsD7K3s7SdbkAjOjdmrbNUwNOJEm1zwmVVABpaWmMHTu2KrJIkiRJUo1TVFzKszNWApCSFMfFQzsFnEiSaqcKr0lV1ZYuXUqPHj3YunXrEedeffVVxo0bR8+ePRk7diwvvfRS9QeUJEmSVCe9MX89O/eW7WR+6bAMUpPjA04kSbXTCY+kqgqrVq3i29/+9lF3Dpw6dSq33HILX//61xkyZAjTp0/nJz/5CUlJSSe0BtYzzzxTmZElSZIk1QE79hTw2rx1ALRtnsqwM1oFnEiSaq9AS6qSkhKmTJnC73//e+Ljj/6vEQ888ABjx47ltttuA2Do0KHs2bOHhx56qMoXam/SxHnmVaVZs/pBR1CU8xrSqfIa0qnyGtKp8hqKDn+euozikjAAN13ei/T0BgEn+jevIZ2qmnAN7S8oLr+dkppUIzKp4ir751XhkiocDhMTU7mzAxcuXMh9993HddddR3p6Orfffvth5zds2MD69ev54Q9/eNjxc889l9dff50NGzbQtm3bSs30RTt35hEOR6rs+euqZs3qs337vqBjKIp5DelUeQ3pVHkN6VR5DUWHpWt3MefTzQAM6J5O8/oJNebn5jWkU1VTrqH8A/+eUbU/70CNyKSKOZlrKCYmdNwBQRVunb7yla/w9NNPn9CLf5mMjAymT5/Od7/7XWJjY484v3r1agA6dux42PH27dsDsGbNmkrNI0mSJEkApeEwk6avACAxPpYrRnQOOJEk1X4VHkm1du1akpOTK/XFmzZtetzz+/aVNXKpqYe3bCkpKQDk5eVVah5JkiRJApj10SY27dgPwAWD2tOofmLAiSSp9qvwSKohQ4bw1ltvUVRUVJV5DhOJHH+qXWVPP5QkSZKkvflFvPRu2ayN5mnJnNOv6pYYkST9W4VHUnXt2pWnn36aoUOHcvrpp9OkSZMjSqJQKMTdd99daeHq1y9bgGv//v2HHT80gurQeUmSJEmqLC++s5r8wrJ1cq4alUl83JFLk0iqfK4IrQqXVI8++mj57ffee++o96nskurQWlTr16+nS5cu5cfXrVt32HlJkiRJqgxrt+7lnU/KFks/rVNjzujcJOBEUu0WCgWdQDVJhUuqZcuWVWWOo2rfvj1t2rThjTfeYMyYMeXH33rrLTp06ECrVq2qPZMkSZKk2ikSiTBxWjYRIDYmxNWjMgn5CVqSqk2FS6ovCofD7Nq1iwYNGpCQkFDZmQ5z0003cdttt9GwYUOGDx/OjBkzeP3113nggQeq9HUlSZIk1S3zPs9h1aa9AIzp15aWTVICTiRJdcsJlVTr1q3jvvvu47333uPAgQP85S9/AeD+++/nJz/5CX379q30gJdeeilFRUX85S9/4fnnn6dt27bce++9nH/++ZX+WpIkSZLqpoLCEp6bvRKAhikJXDioQ7CBJKkOqnBJtXbtWq644gpCoRBDhw5l2rRpAMTGxrJ69WquvfZa/va3v9GrV6+TCnLppZdy6aWXHvXcVVddxVVXXXVSzytJkiRJX+bVuWvZk1e2k/llwzNITjypSSeSpFMQ8+V3KXP//feTlJTE1KlT+dWvfkUkUrbufv/+/Zk6dSpNmzblD3/4Q5UFlSRJkqSqkLMrn7cWbAAgo1UDBp7WIuBEklQ3VbikmjdvHldffTVNmjQ5YvHA9PR0xo8fz+LFiys9oCRJkiRVpckzVlAajhACxo/JIsbF0iUpEBUuqYqKimjQoMExz8fHx1NYWFgpoSRJkiSpOixauYNPV+0EYEjPlnRseezPPJKkqlXhkqpr167MnDnzqOdKSkp45ZVX6NKlS6UFkyRJkqSqVFwSZvKMFQAkJ8bx1WEZASeSpLqtwiXVt7/9bd5//31uueUW5s2bB8CmTZuYMWMG11xzDUuWLOGb3/xmlQWVJEmSpMo07cMNbMstAODiIR1pkJIQcCJJqtsqvGXFiBEjuOuuu7j77rt57bXXALjjjjuIRCIkJibyk5/8hHPPPbfKgkqSJElSZcndV8i/5qwFoFXTFEac2TrYQJKkipdUAJdeeinnnHMOc+bMYcOGDYTDYVq3bs2gQYNo1KhRVWWUJEmSpEr1wuyVFBaXAnD16EziYis8yUSSVEVOqKQCSE1N5ZxzzmHXrl3ExMRYTkmSJEmKKis27mbu5zkA9MlqRo8OjQNOJEmCEyypVq1axUMPPcR7771HQUHZ3O369eszatQovve979GiRYsqCSlJkiRJlSEcjjBxWjYA8XExXDmyc8CJJEmHVLik+uyzz7jmmmsoLi7m7LPPpl27dkQiEdasWcMrr7zCO++8w+TJk2nXrl1V5pUkSZKkk/bOp5tZn5MHwNgB7WialhxwIknSIRUuqe677z5SU1OZOHHiEUVUdnY211xzDffeey9//OMfKz2kJEmSJJ2q/QeK+efbqwFo0iCRsWe1DziRJOmLKrw64KJFi7jmmmuOOlIqKyuLa665hrlz51ZqOEmSJEmqLC+9u4a8gmIArhyZSWJ8bMCJJElfVOGSqkGDBpSWlh7zfEpKCklJSZUSSpIkSZIq08Ztecz6aBMAXdul0adLs4ATSZL+U4VLqgkTJvDUU0+xcuXKI87l5OTwzDPPcMUVV1RqOEmSJEk6VZFIhEnTswlHIsSEQowfnUUoFAo6liTpPxxzTarbbrvtiGOFhYVcfPHFDB06lI4dOxIKhdi0aRPvvPMOiYmJVRpUkiRJkk7Gh8u3s2z9bgBGnNmaNs1Tgw0kSTqqY5ZUL7744jEfNGvWLGbNmnXYsfz8fB5//HG+//3vV1o4SZIkSToVhcWlTJm5AoDU5HguHtox4ESSpGM5Zkm1bNmy6swhSZIkSZXu9Xnr2LW3EICvDutESlJ8wIkkHUskEnQCBa3Ca1JJkiRJUjTZvruAqfPWA9A+vT5De7YKOJEk6XiOOZLqaF566SXmzJnD9u3bCYfDR5wPhUI8/fTTlRZOkiRJkk7WczNXUlJa9rll/JhMYmJcLF2SarIKl1QPPPAAjz/+OPHx8TRp0oSYGAdhSZIkSaqZPl+7i4XZ2wEY2COdzDZpwQaSJH2pCpdUL774IkOGDOGRRx4hOTm5KjNJkiRJ0kkrKQ0zaVo2AIkJsVw2vHPAiSRJFVHh4VB5eXmce+65FlSSJEmSarSZH21iy858AC4a1IFG9RMDTiRJqogKl1RDhw5l3rx5VZlFkiRJkk7J3v1FvPzeagDSGyUzum/bgBNJkiqqwtP97rjjDr75zW/yox/9iNGjR9OkSRNCoSMXHuzXr1+lBpQkSZKkivrH26soKCwF4OrRmcTHuZauJEWLCpdUmzdvZt++fbz22mtMnTr1iPORSIRQKMTSpUsrNaAkSZIkVcSaLXt579MtAPTMaELPjKYBJ5IknYgKl1S//vWv2bt3L9dddx0dOnQgLq7CD5UkSZKkKhWORJg0LZsIEBsT4upRmUFHkiSdoAo3TStWrOC73/0u3/rWt6oyjyRJkiSdsLmLt7Jq814AzunflvTG9QJOJEk6URWeoN2iRQtiYpzPLUmSJKlmKSgs4fnZqwBIS03gwkEdgg0kSTopFW6drr/+ep5++mlWrlxZlXkkSZIk6YT8a85a9u4vAuDyEZ1JSnBpEkmKRhV+9162bBmhUIiLLrqItm3b0rRpU2JjYw+7TygU4umnn670kJIkSZJ0NFt27mfahxsA6Ny6IWd1Tw84kSTpZFW4pJo1axaxsbG0aNGC4uJitmzZUpW5JEmSJOm4IpEIk6evoDQcIQRMGJNFKBQKOpYk6SRVuKSaOXNmVeaQJEmSpBOyaOVOFq/ZBcDZvVrRvkX9gBNJkk6FK6FLkiRJijrFJaVMnpENQL3EOC45u1PAiSRJp6rCI6muueaaCt3vb3/720mHkSRJkqSKeHPBBrbvPgDAJWd3okG9hIATSZJOVYVLqo0bNx5xLBwOk5ubS2FhIa1btyYzM7NSw0mSJEnSf9q19wCvzl0LQOtmKQzv3SrYQJKkSnHKa1KVlpYyY8YMbr/9dq677rpKCyZJkiRJR/P87FUUFYcBGD86i9gYVzGRpNrglN/NY2NjOeecc7j88su57777KiOTJEmSJB1V9obdzF+SA0Dfrs3p1r5RwIkkSZWl0v7JoUOHDixbtqyynk6SJEmSDhMOR5g4rWyx9IS4GK4YkRFwIklSZaqUkqqoqIhXXnmFJk2aVMbTSZIkSdIR3l60mQ3b8gA4/6z2NG2YHHAiSVJlOuXd/YqKilizZg179+7lf/7nfyotmCRJkiQdkldQzD/fXgVA04ZJnDegXcCJJFWGUCjoBKpJTml3Pyhbk6pTp05ccMEFjB8/vtKCSZIkSdIhL767mv0HSgC4cmQmCfGxASeSJFW2U97dT5IkSZKq0vqcfcz+eBMA3Ts04syspgEnkiRVBfdqlSRJklRjRSIRJk1fQSQCMaEQV4/OIuT8IEmqlY45kuoPf/jDST3hd7/73ZMOI0mSJElf9MGybWRv2A3AqD5taN00JdhAkqQqc8ol1X/+K4YllSRJkqTKUFhUypSZKwGoXy+erwzpEGwgSVKVOmZJNWPGjC99cF5eHg888ACzZ88mLi7umDsASpIkSdKJem3eWnL3FQLw1WEZ1EuKDziRJKkqHbOkat269XEfOHXqVH73u9+xbds2zjzzTH71q1+RlZVV6QElSZIk1T3bcvN5Y/56ADq0qM+Qni0DTiRJqmoV3t3vkA0bNnDnnXcyZ84cGjZsyG9/+1suu+yyqsgmSZIkqY6aMnMlJaURACaMySLGxdIlqdarcElVXFzME088wZ/+9CcKCwu55JJLuPXWW2nUqFFV5pMkSZJUxyxevZOPV+wAYPBpLcho3TDgRJKk6lChkmrevHnceeedrFmzhszMTH75y1/St2/fqs4mSZIkqY4pKQ0zafoKAJISYvnq8IyAE0mSqstxS6pdu3Zx991389prr5GUlMSPfvQjvvnNbxIXd8KzBCVJkiTpS81YuJGtu/IBuGhwR9JSEwNOJEmqLsdsmyZPnsyDDz7I3r17GTlyJLfffjstW7pYoSRJkqSqsSevkJffWwNAi8b1GN23TcCJJEnV6Zgl1Z133ll+e+bMmcycOfNLnywUCrFkyZLKSSZJkiSpTnnh7VUcKCoF4OrRmcTFxgScSJJUnY5ZUl188cWE3EFDkiRJUjVYtXkPcz7bCkCvzk05vVOTgBNJkqrbMUuq3/3ud9WZQ5IkSVIdFY5EmDQtG4C42BBXjeoccCJJUhAcPytJkiQpUHM+28KaLfsAOLd/O5o3qhdwIklSECypJEmSJAUm/0AJ/5i9CoBG9RMZN7B9wIkkSUGxpJIkSZIUmFfmrGFvfjEAl4/IICnhmCuSSJJqOUsqSZIkSYHYvGM/MxZuBCCrTUMGdEsPOJEkKUiWVJIkSZKqXSQSYdL0bErDEUIhGD8my93FJamOs6SSJEmSVO0+XrGDJWtzARjeqzXt0usHnEhS0CJEgo6ggFlSSZIkSapWRcWlPDtjBQApSXFccnangBNJkmoCSypJkiRJ1erNBevZsecAAJec3YnU5PiAE0kKSgin+erfLKkkSZIkVZtdew/w2tx1ALRplsqwXq0CTiRJqiksqSRJkiRVmykzV1JUEgZgwphMYmP8SCJJKuPfCJIkSZKqxbJ1uXywbBsA/bs1p0u7RgEnkiTVJJZUkiRJkqpcaTjMpOnZACTEx3DFiM4BJ5Ik1TSWVJIkSZKq3OyPN7Nx+34Axg3sQOMGSQEnkiTVNJZUkiRJkqrUvvwiXnp3NQBNGyZxXv+2ASeSJNVEllSSJEmSqtSL765h/4ESAK4elUl8XGzAiSRJNZEllSRJkqQqs27rPt7+eBMAPTo2pldm04ATSZJqKksqSZIkSVUiEokwaXo2ESA2JsT40ZmEQqGgY0mSaihLKkmSJElVYv6SHFZs3APA6L5taNkkJeBEkqSazJJKkiRJUqU7UFTCc7NWAtAgJYGLBncMOJEkqaazpJIkSZJU6V6bu47deUUAXDYsg+TEuIATSZJqOksqSZIkSZUqJzefNxesB6BjywYMOr1FwIkkSdHAkkqSJElSpZoyYyUlpREAJozJIsbF0iVJFWBJJUmSJKnSfLpqJ5+s3AHAkJ4t6dSqQcCJJEnRwpJKkiRJUqUoKQ0zeXo2AMmJsXx1WEbAiSRJ0cSSSpIkSVKlmPbhBnJyCwD4yuCONExJCDiRJCmaWFJJkiRJOmW78wp5Zc5aAFo2qcfIPm2CDSRJijqWVJIkSZJO2QuzV1FYVArA+NFZxMX6UUOSdGL8m0OSJEnSKVm5aQ/vL94KQO/MpvTo2DjgRJKkaGRJJUmSJOmkhcMRJk4rWyw9LjaGq0ZlBpxIUtSKBB1AQbOkkiRJknTS3vtsC+u27gNg7IB2NEtLDjiRpKgSCjqAahJLKkmSJEknJf9AMS/MXgVA4waJnD+wfcCJJEnRzJJKkiRJ0kl56b015BUUA3DFiM4kxscGnEiSFM0sqSRJkiSdsE3b85i5cBMAXdqm0a9r84ATSZKinSWVJEmSpBMSiUSYNH0F4UiEUAjGj8kiFHJhGUnSqbGkkiRJknRCFi7fztJ1uQCM7N2Gts1TA04kSaoNLKkkSZIkVVhhcSlTZq4AIDU5nq8M7RhwIklSbWFJJUmSJKnC3pi/np17CwG49OxOpCbHB5xIklRbWFJJkiRJqpAdewqYOm8dAO2ap3L2Ga0CTiRJqk0sqSRJkiRVyHMzV1JcEgbKFkuPiXGxdElS5bGkkiRJkvSllq7dxYfLtwNwVvd0stqmBRtIklTrWFJJkiRJOq7ScJhJ08sWS0+Mj+XyEZ0DTiRJqo0sqSRJkiQd18yPNrFpx34ALhjUnkb1EwNOJEmqjSypJEmSJB3T3vwiXnp3DQDN05I5p1+7gBNJkmorSypJkiRJx/TPt1dTUFgCwFWjM4mP8yOEJKlq+DeMJEmSpKNau3Uv7y7aDMDpnZpwRkaTgBNJkmozSypJkiRJR4hEIkyclk0EiI0JcdWozoRCoaBjSZJqMUsqSZIkSUeY93kOqzbtBWBMv7a0bJIScCJJUm1nSSVJkiTpMAWFJTw3eyUADVMSuHBQh2ADSZLqBEsqSZIkSYd59f217MkrAuDyERkkJ8YFnEiSVBdYUkmSJEkqt3VXPm99sAGAjNYNOKtHi4ATSZLqCksqSZIkSeWenbGC0nCEEDB+dBYxLpYuqZpEgg6gwFlSSZIkSQJg0codfLpqJwBDz2hJx5YNAk4kqbazBtcXWVJJkiRJorgkzOQZKwBITozj0rMzAk4kSaprLKkkSZIk8dYH69mWWwDAxUM70iAlIeBEkqS6xpJKkiRJquNy9xXy6vvrAGjdNIURvVsHnEiSVBdZUkmSJEl13POzV1JYXArA1aMziYv1Y4IkqfrFBR1AkiRJqkq79h5gz/6ioGMcZveBEnJz84OOAcD23QXM+zwHgD5dmtG9Q+OAE0mS6ipLKkmSJNVaS9bu4v4piwhH3Nj8y8THxXDliM5Bx5Ak1WGO45UkSVKtVFIa5pm3si2oKuiCQR1ompYcdAxJUh3mSCpJkiTVStM/3EjOrrIpdef0a0vX9o0CTvRvDRsms2dPQdAxytVLjCOzTcOgY0iS6jhLKkmSJNU6u/MKeXnOGgBaNqnHZcMzatRi4M2a1Wf79n1Bx5AkqUapOX9TS5IkSZXkH7NXUVjkbnWSJEUT/7aWJElSrbJq0x7mLN4KQO/MppzWsUnAiSRJUkVYUkmSJKnWCEciTJyWDUBcbAxXjsoMOJEkSaooSypJkiTVGu99uoW1W8vWejpvQDuau1udJElRw5JKkiRJtUL+gWL+8fYqABrVT2TcWe0DTiRJkk6EJZUkSZJqhZffW8u+/GIArhzZmcSE2IATSZKkE2FJJUmSpKi3acd+ZizcCEBW2zT6dW0ecCJJknSiLKkkSZIU1SKRCJOmZROORAiFYPzoTEKhUNCxJEnSCbKkkiRJUlT7KHsHS9flAjCid2vapdcPOJEkSToZllSSJEmKWkXFpUyZuQKAlKQ4Lh7aKeBEkiTpZFlSSZIkKWq9MX89O/YcAODSYRmkJscHnEiSJJ0sSypJkiRFpR17Cnht3joA2jZPZdgZrQJOJEk6FZFIJOgICpgllSRJkqLSc7NWUVwSBmDCmCxiYlwsXZKijftc6IssqSRJkhR1lq7L5cNl2wAY0D2drLZpwQaSJEmnzJJKkiRJUaU0HGbS9GwAEuNjuWJE54ATSZKkymBJJUmSpKgy66NNbNq+H4ALBrWnUf3EgBNJkqTKYEklSZKkqLE3v4iX3l0DQPO0ZM7p1zbgRJIkqbJYUkmSJClqvPjOavILSwC4alQm8XGxASeSJEmVxZJKkiRJUWHd1n2888lmAE7r1JgzOjcJOJEkSapMllSSJEmq8SKRCBOnZRMBYmNCXD0qk5D7lkuSVKtYUkmSJKnGm7ckh5Wb9gAwpl9bWjZJCTiRJEmqbJZUkiRJqtEKCkt4btZKABqmJHDhoA7BBpIkSVXCkkqSJEk12qtz17InrwiAy4ZnkJwYF3AiSZJUFSypJEmSVGPl7MrnrQUbAMho1YCBp7UIOJEkSaoqllSSJEmqsSbPWEFpOEIIGD8mixgXS5ckqdaypJIkSVKNtGjlDj5dtROAIT1b0rFlg4ATSZKkqmRJJUmSpBqnuCTMszNWAJCcGMdXh2UEnEiSJFU1SypJkiTVONM+3EBObgEAFw/pSIOUhIATSZKkqmZJJUmSpBold18h/5qzFoBWTVMYcWbrYANJkqRqYUklSZKkGuWF2SspLC4F4OrRmcTF+iurJEl1gX/jS5IkqcZYsXE3cz/PAaBPVjN6dGgccCJJklRdLKkkSZJUI4TDESZNK1ssPT4uhitHdg44kSRJqk6WVJIkSaoR3v10M+ty9gEwdkA7mqYlB5xIkiRVJ0sqSZIkBW7/gWL+8fZqAJo0SGTsWe0DTiRJkqqbJZUkSZIC99K7a8grKAbgypGZJMbHBpxIklQ9QkEHUA1iSSVJkqRAbdyWx6yPNgHQtV0afbo0CziRJEkKgiWVJEmSAhOJRJg0PZtwJEJMKMT40VmEQv6ruiRJdZEllSRJkgKzcPl2lq3fDcCIM1vTpnlqsIEkSVJgLKkkSZIUiMLiUqbMXAFAanI8Fw/tGHAiSZIUJEsqSZIkBeL1eevYubcQgK8O60RKUnzAiSRJUpAsqSRJklTttu8uYOq89QC0T6/P0J6tAk4kSZKCZkklSZKkavfczJWUlIYBGD8mk5gYF0uXJKmus6SSJElStfp87S4WZm8HYGCPdDLbpAUbSJIk1QiWVJIkSao2JaVhJk8vWyw9MT6Wy4Z3DjiRJEmqKSypJEmSVG1mfbSJzTv2A3Dh4A40qp8YcCJJklRTWFJJkiSpWuzdX8RL760BIL1RMmP6tg04kSRJqkksqSRJklQt/vH2KgoKSwC4enQm8XH+KipJkv7N3wwkSZJU5dZs2ct7n24BoGdGE3pmNA04kSRJqmksqSRJklSlwpEIk6ZlEwFiY0JcPSoz6EiSJKkGsqSSJElSlZq7eCurNu8F4Jz+bUlvXC/gRJIkqSaypJIkSVKVKSgs4YXZqwBomJrABQM7BBtIkiTVWJZUkiRJqjL/mrOWPfuLALhiRGeSE+MCTiRJkmoqSypJkiRViS079zPtww0AdG7dkLO6pwecSJIk1WSWVJIkSap0kUiEydNXUBqOEAImjMkiFAoFHUuSJNVgllSSJEmqdItW7mTxml0AnN2rFe1b1A84kSRJquksqSRJklSpiktKmTwjG4B6iXFccnangBNJkqRoYEklSZKkSvXWBxvYvvsAABcP7UiDegkBJ5IkRYNIJOgECpollSRJkirNrr0H+Nf7awFo3SyFEWe2DjaQJKlGc7lCfZEllSRJkirN87NXUVQcBmD86CxiY/x1U5IkVYy/NUiSJKlSZG/YzfwlOQD07dqcbu0bBZxIkiRFE0sqSZIknbJwOMLEaWWLpSfExXDFiIyAE0mSpGhjSSVJkqRT9vaizWzYlgfA+We1p2nD5IATSZKkaGNJJUmSpFOSV1DMP99eBUDThkmcN6BdwIkkSVI0sqSSJEnSKXnx3dXsP1ACwJUjM0mIjw04kSRJikaWVJIkSTpp63P2MfvjTQB079CIM7OaBpxIkiRFK0sqSZIknZRIJMKk6SuIRCAmFOLq0VmEQqGgY0mSpChlSSVJkqST8sGybWRv2A3AqD5taN00JdhAkiQpqllSSZIk6YQVFpUyZeZKAOrXi+crQzoEG0iSJEU9SypJkiSdsNfmrSN3XyEAXx2WQb2k+IATSZKkaGdJJUmSpBOybXcBb8xfD0CHFvUZ0rNlwIkkSVJtYEklSZKkEzJlxgpKSsMATBiTRYyLpUuSpEpgSSVJkqQKW7x6Jx+v2AHA4NNakNG6YcCJJElSbWFJJUmSpAopKQ0zafoKAJISYvnq8IyAE0mSpNrEkkqSJEkVMmPhRrbuygfgosEdSUtNDDiRJEmqTSypJEmS9KX25BXy8ntrAGjRuB6j+7YJOJEkSaptLKkkSZL0pV54exUHikoBuHp0JnGx/hopSZIql79dSJIk6bhWbd7DnM+2AtCrc1NO79Qk4ESSJKk2sqSSJEnSMYUjESZNywYgLjbEVaM6B5xIkiTVVpZUkiRJOqY5n21hzZZ9AJzbvx3NG9ULOJEkqbaKBB1AgbOkkiRJ0lHlHyjhH7NXAdCofiLjBrYPOJEkSarNLKkkSZJ0VK/MWcPe/GIALh+RQVJCXMCJJElSbWZJJUmSpCNs3rGfGQs3ApDVpiEDuqUHnEiSJNV2llSSJEk6TCQSYdL0bErDEUIhGD8mi1AoFHQsSZJUy1lSSZIk6TAfr9jBkrW5AAzv1Zp26fUDTiRJkuoCSypJkiSVKyou5dkZKwBISYrjkrM7BZxIkiTVFZZUkiRJKvfmgvXs2HMAgEvO7kRqcnzAiSRJUl1hSSVJkiQAdu09wGtz1wHQplkqw3q1CjiRJEmqSyypJEmSBMCUmSspKgkDMGFMJrEx/qooSZKqj795SJIkiWXrcvlg2TYA+ndrTpd2jQJOJEmS6hpLKkmSpDquNBxm0vRsABLiY7hiROeAE0mSpLrIkkqSJKmOm/3xZjZu3w/AuIEdaNwgKeBEkiSpLrKkkiRJqsP25Rfx0rurAWjaMInz+rcNOJEkSaqrLKkkSZLqsBffXcP+AyUAXD0qk/i42IATSZKkusqSSpIkqY5at3Ufb3+8CYAeHRvTK7NpwIkkSVJdZkklSZJUB0UiESZOzyYCxMaEGD86k1AoFHQsSZJUh1lSSZIk1UHzl+SwcuMeAEb3bUPLJikBJ5IkSXWdJZUkSVIdc6CohOdmrQSgQUoCFw3uGHAiSZIkSypJkqQ657W569idVwTAZcMySE6MCziRJEmSJZUkSVKdkpObz5sL1gPQsWUDBp3eIuBEkiRJZSypJEmS6pBnp6+gpDQCwIQxWcS4WLokSaohLKkkSZLqiE9X7WDRqp0ADOnZkk6tGgScSJKkL4hEgk6ggFlSSZIk1QElpWEmT18BQHJiLF8dlhFwIkmSwAG9+iJLKkmSpDpg2ocbyMktAOArgzvSMCUh4ESSJEmHs6SSJEmq5XbnFfLKnLUAtGxSj5F92gQbSJIk6SgsqSRJkmq5F2avorCoFIDxo7OIi/VXQEmSVPP4G4okSVIttnLjHt5fvBWA3plN6dGxccCJJEmSjs6SSpIkqZYKhyNMnJ4NQFxsDFeNygw4kSRJ0rFZUkmSJNVS7322hXVb9wEwdkA7mqUlB5xIkiTp2CypJEmSaqH8A8W8MHsVAI0bJHL+wPYBJ5IkSTo+SypJkqRa6KX31pBXUAzAFSM6kxgfG3AiSZKk47OkkiRJqmU2bc9j5sJNAHRpm0a/rs0DTiRJkvTlLKkkSZJqkUgkwqTpKwhHIoRCMH5MFqFQKOhYkiRJX8qSSpIkqRZZuHw7S9flAjCydxvaNk8NOJEkSVLFWFJJkiTVEoXFpUyZuQKA1OR4vjK0Y8CJJEmSKs6SSpIkqZZ4Y/56du4tBODSszuRmhwfcCJJkqSKs6SSJEmqBXbsKWDqvHUAtGueytlntAo4kSRJ0omxpJIkSaoFnpu5kuKSMFC2WHpMjIulS5Kk6GJJJUmSFOWWrN3Fh8u3A3BW93Sy2qYFG0iSJOkkWFJJkiRFsZLSMJOnly2Wnhgfy+UjOgecSJIk6eRYUqlO2767gMnTV7B2696go0iqo9bn7GPy9BXk5OYHHUVRatbHm9i0Yz8AFwxqT6P6iQEnkiRJOjmWVKrT/t+kj5j24QZ+/dSHQUeRVEf96q8fMO3DDdz9zMKgoyhKvbNoMwDN05I5p1+7gNNIkiSdPEsq1WmHtumWpKDtyy8OOoKiVGFRKQCd2zQkPs5f7SRJUvTyNxlJkiRJkhS4SNABFDhLKkmSJEmSFIgQoaAjqAaxpJIkSZIkSVLgLKkkSZIkSZIUOEsqSZIkSZIkBc6SSpIkSZIkSYGzpJIkSZIkSVLgLKkkSZIkSZIUOEsqSZIkSZIkBc6SSpIkSZIkSYGzpJIkSZIkSVLgLKkkSZIkSZIUOEsqSZIkSZIkBc6SSpIkSZIkSYGzpJIkSZIkSVLgLKkkSZIkSZIUOEsqSZIkSZIkBc6SSpIkSZIkSYGzpJIkSZIkSVLgLKkkSZIkSZIUOEsqSZIkSZIkBc6SSpIkSZIkSYGLCzpATRYTEwo6Qq1VU763zRsll9+uKZlUMf68dKpqyjXk+1D0qik/ryYNk4iJCdEwNaHGZFLF+PPSqfIa0qmqKdfQod+HUpPja0wmVcyJ/ry+7P6hSCQSOZVAkiRJkiRJ0qlyup8kSZIkSZICZ0klSZIkSZKkwFlSSZIkSZIkKXCWVJIkSZIkSQqcJZUkSZIkSZICZ0klSZIkSZKkwFlSSZIkSZIkKXCWVJIkSZIkSQqcJZUkSZIkSZICZ0mlavPqq68ybtw4evbsydixY3nppZeCjqQotXTpUnr06MHWrVuDjqIoEg6HmTx5MhdeeCG9e/dm9OjR3HPPPeTl5QUdTVEiEonw1FNPce6559KzZ08uuugi/vWvfwUdS1Hsu9/9LmPGjAk6hqJISUkJPXv2pEuXLof917t376CjKYp88MEHXH311ZxxxhkMGTKE3/zmN+zfvz/oWIoC8+fPP+L954v/vfjii6f8GnGVkFP6UlOnTuWWW27h61//OkOGDGH69On85Cc/ISkpifPOOy/oeIoiq1at4tvf/jYlJSVBR1GUefLJJ3nwwQe57rrrGDhwIGvWrOHhhx9m5cqV/PnPfw46nqLA448/zsMPP8z//M//0KtXL9555x1uueUWYmNjOf/884OOpyjz8ssvM23aNNq1axd0FEWRNWvWUFhYyL333kuHDh3Kj8fEOPZAFfPJJ5/wzW9+k5EjR/Loo4+ybt067r//fnbt2sUDDzwQdDzVcD169GDKlCmHHYtEIvz85z8nPz+fYcOGnfJrWFKpWjzwwAOMHTuW2267DYChQ4eyZ88eHnroIUsqVUhJSQlTpkzh97//PfHx8UHHUZSJRCI8+eSTXHnllfzoRz8CYNCgQTRq1Igf/OAHLF26lG7dugWcUjVZcXExf/nLX7j66qv5zne+A8DAgQNZvHgxf//73y2pdEJycnK46667aNGiRdBRFGWWLVtGTEwM5557LsnJyUHHURS677776NWrFw899BChUIhBgwYRDof561//SkFBgdeVjis1NZVevXodduzpp59mzZo1PPvsszRu3PiUX8PKXVVuw4YNrF+/nnPOOeew4+eeey6rV69mw4YNASVTNFm4cCH33Xcf1157LbfcckvQcRRl9u/fz0UXXcQFF1xw2PFOnToBsH79+iBiKYrExsbyzDPPcMMNNxx2PD4+nsLCwoBSKVrdfvvtDB48mIEDBwYdRVFm6dKltGvXziJBJ2XXrl18+OGHXH311YRCofLjEyZMYPr06V5XOmHbt2/noYceKp8+WhksqVTlVq9eDUDHjh0PO96+fXugbNiy9GUyMjKYPn063/3ud4mNjQ06jqJMamoqt99+O3369Dns+PTp0wHo3LlzELEURWJiYujSpQvp6elEIhF27NjBE088wfvvv8+VV14ZdDxFkeeff57PP/+cO+64I+goikLLly8nISGB6667jt69e9OvXz9+8YtfuL6iKiQ7O5tIJELDhg35/ve/T69evejTpw+//OUvOXDgQNDxFIUeeeQRYmJi+P73v19pz+l0P1W5ffv2AWUfEr8oJSUFwL9UVSFNmzYNOoJqmUWLFvHEE08wevRoMjIygo6jKPLWW29x8803AzB8+HAuuuiigBMpWmzatIl77rmHe+65p1KmRKjuWbZsGXl5eVx++eX893//N4sXL+aRRx5hzZo1/O1vfztsdIz0n3bt2gXAT3/6U8aMGcOjjz7K8uXLefDBByksLOR3v/tdwAkVTXbu3MlLL73EtddeS4MGDSrteS2pVOUikchxz7vQo6TqtnDhQv77v/+bNm3a8Nvf/jboOIoy3bt35+9//zvLly/noYce4oYbbuDpp5/2w6GOKxKJ8LOf/Yxhw4Zx7rnnBh1HUeqBBx6gYcOGdOnSBYB+/frRpEkTbr31Vt5//30GDx4ccELVZMXFxQCceeaZ/PKXvwTK1leMRCLce++93HTTTbRt2zbIiIoizz//POFwmGuuuaZSn9d2QFWufv36AEdsa3poBNWh85JUHaZOnco3v/lNWrZsyVNPPUWjRo2CjqQo07ZtW/r168fXvvY1fv7znzN//nw+/vjjoGOphps4cSLLly/nZz/7GSUlJZSUlJT/Q94Xb0vH079///KC6pDhw4cDZaOspOM5NJPl7LPPPuz4kCFDiEQiLF++PIhYilJvvvkmQ4cOrfSRwZZUqnKH1qL6z4WJ161bd9h5Sapqf/3rX/nhD39Ir169mDhxIs2bNw86kqLE7t27eemll8jJyTnsePfu3QHYtm1bELEURd58801yc3MZMmQIPXr0oEePHrz00kusX7+eHj168OKLLwYdUTXczp07ef7554/YdOjQWkL+o4u+TIcOHQAoKio67PihEVaOCFZF5eTksGTJEsaOHVvpz21JpSrXvn172rRpwxtvvHHY8bfeeosOHTrQqlWrgJJJqkuef/55fve73zF27FiefPJJR3HqhITDYX76058yZcqUw47PmTMHgKysrCBiKYrceeedvPDCC4f9N2LECFq0aFF+WzqeUCjEL37xC/7+978fdnzq1KnExsYesTmI9J8yMjJo3bo1U6dOPez4rFmziIuLo3fv3gElU7RZtGgRQJW877gmlarFTTfdxG233UbDhg0ZPnw4M2bM4PXXX+eBBx4IOpqkOmDnzp3cddddtG7dmgkTJrBkyZLDzrdr185FjHVcjRs3Zvz48TzxxBMkJSVx+umns3DhQh5//HEuv/xyOnXqFHRE1XBHu0bS0tJISEjg9NNPDyCRok3jxo2ZMGECzzzzDKmpqfTt25eFCxfy2GOPMWHChPKds6VjCYVC3HLLLfzwhz/klltu4dJLL2Xx4sU8+uijfO1rX/N3IVVYdnY2ycnJtG7dutKf25JK1eLSSy+lqKiIv/zlLzz//PO0bduWe++9l/PPPz/oaJLqgHfffZeCggI2bdrEhAkTjjj///7f/+MrX/lKAMkUTW677TZatmzJCy+8wCOPPEKLFi24+eabue6664KOJqmO+MlPfkJ6ejr/+Mc/eOKJJ0hPT+fmm2/m+uuvDzqaosT5559PQkICf/zjH/n2t79NkyZNuOmmm/j2t78ddDRFkR07dlTqjn5fFIq4SqMkSZIkSZIC5ppUkiRJkiRJCpwllSRJkiRJkgJnSSVJkiRJkqTAWVJJkiRJkiQpcJZUkiRJkiRJCpwllSRJkiRJkgJnSSVJkiRJkqTAWVJJkiRJkiQpcJZUkiQpKsyfP58uXbrwz3/+M+gopywnJ4cBAwawYcOGoKNUmSlTpjBq1Khjnv/pT39Kly5d2LhxY6W+7s9//nPuueeeSn1OSZJUPSypJEmSqtldd93FuHHjaNu2bfmx3bt306VLF66//voAk1WeOXPmMGjQoGp/3ZtuuokpU6awbNmyan9tSZJ0aiypJEmSqtEHH3zAjBkz+Na3vnXY8SVLlgDQo0ePIGJVqnA4zPz58xk4cGC1v3arVq0YN26co6kkSYpCllSSJEnV6KmnnqJPnz60bNnysOOff/45AN27dw8iVqVasmQJe/bsCaSkArj88suZN2+eo6kkSYoyllSSJCmq7dq1izvvvJNhw4Zx2mmnMWzYMO68805yc3OPuO//b++OY6Ks/ziAv+8CptwpeBdTukw4resEzQPsVNaWhDWRcqNjgOicmgMzlpuODWzBnG0WtlY5Q07aKovVnYSJCqTeqgm4ceCcu8PUAd4xLAacosEl3P3+aNx4fBDOfuJFvV8bG/s83+d5Ps/DP+y97/f7OJ1O5OXlIS4uDnFxcdi2bRscDgeSkpKwYcOGSe+1q6sLFosFycnJomMjM6n+DSFVfX09tFotZs2aFZD7L1myBHPmzMHXX38dkPsTERHR3xMU6AaIiIiI/q7+/n5kZWWho6MDr7/+OhYuXAi73Y6Kigo0NjbCZDJBLpcDAPr6+pCdnY2enh5kZmZCrVbDarVi48aN+OOPPx5Jv7/88guGh4fx4osvio7ZbDaEhYUJ9qmaqurr6wM2i2rE0qVL8fPPPwe0ByIiInowDKmIiIhoyjp8+DDa29vx7rvvIjs721fXarXYs2cPDh8+jB07dgAAjEYjbty4gZKSErz22msAgHXr1uGDDz5AeXn5I+nXarUiNDRUFETdvn0bHR0d0Ov1j6SPyeR2u9Hc3BzwDeCfeeYZHD9+HA6H418R/BEREf0XcLkfERERTVk//vgjFAoFMjIyBPWMjAwoFAqcPn3aV7NYLIiIiEBqaqpg7JYtWx5JrwDgcDigUqkgkUgEdbvdDq/X+69Y6me1WuH1epGQkPBQr9vd3Q2j0YiCggLs378fly5dGnf8SDDldDofah9EREQ0eRhSERER0ZTldDoRHR2NoCDh5PCgoCBERUXB4XAIxs6bNw9SqfDfH6VSiZkzZwpqJ0+eRFZWFnQ6HZKSkkT3HRoawt69e/H8888jISEBhYWFcLvdE/brcrl8yw9HG9k0fbwv+zU1NUGn04l+YmNjodVqBWOLi4uh0WjQ0tIius6GDRug0Wjw008/iZ5Zo9EgJyfHV2tra8Obb76JZcuWQafTYdWqVRN+Ne/cuXPQ6XSYNm3auOMeRG1tLQ4ePIgVK1agqKgIWVlZOHfuHD788EN4vd4xzxm9zJOIiIimBoZURERERPcICwvD+vXrfUsF71VaWorz58/j+PHjqKurw7Vr11BSUjLhdaVSKTwej6juz5f9EhIS0NLSIvipqalBeHg43n77bd+4wcFBVFdXIzw8HCaTacxrqdVqHD16VFAzm81Qq9WCWk5ODqKjo3HmzBlYrVYYjUZoNJpxn7GhoQErVqwYd8yD+PXXX9HZ2YmioiLExMRg2rRpUKlUyMnJwUsvvYSKiooxzxt5z4899thD64WIiIgmF0MqIiIimrLmzp2LtrY2DA0NCepDQ0Nob28X7EWkUqnQ0dEhCol6enpw69YtQS0xMRFr1qyBSqUa875msxm5ubmYPXs2FAoF3nrrLVRWVmJ4eHjcfpVKJVwul6hus9kQGhqK6Ojocc8f7c8//0ReXh7i4+ORm5vrq9fU1EAqlaKgoACnTp3CnTt3ROeuXr0ajY2N6O3tBQB0dnbCbrcLvjrY29uLjo4OZGZmQiaTQSqVIioqCmlpafftqa+vD3a7/aGGVHV1ddi0adOYx5YsWYK+vj7R3x+A7z0rlcqH1gsRERFNLoZURERENGUlJyejt7dXNGPou+++Q29vryB0WblyJbq7u1FdXS0Y+6Cbpt+6dQtdXV149tlnfbWYmBjcuXMHnZ2d4577xBNP4PfffxeEWQMDA2hra4NWqxXtVTWeoqIiuN1u7Nu3T1A3mUxISUlBSkoKgoODcfLkSdG5MpkMycnJqKqqAvBX6JaamoqQkBDfGIVCgfnz56OwsBAnTpzA9evXJ+ypoaEBcrkcsbGxfj/HRKZPn+57Ly0tLdDr9Th48KDveGxsLNrb20Xn/fbbbwD+eudEREQ0NfDrfkRERDRlvfHGG6ipqcGePXtgs9mg1Wpht9thNpsRHR0t+MLc1q1bUV1djcLCQly8eBFqtRpWqxUtLS2YNWuW3/ccmZk0eh+rGTNmCI7dz7Jly1BZWYkrV674Qq7W1lYMDw/D7XajrKxMdE5oaCjWr18vqH355ZewWCwwm82YPn26r97W1oampibk5+cjJCQEKSkpMJvNSE9PF13XYDDgnXfewcaNG/H999/j0KFDqKurE4z56quvUF5ejtLSUly7dg2RkZHYuXMnUlJSxny+hoYG6PV60b5f4/noo48gk8lE9dWrV2P58uWCWmtrK1wuF5qbm301mUw25nu/cOEC5s2bx5CKiIhoCmFIRURERFPWjBkzUFFRgU8++QRnz55FZWUllEolMjMzkZeXJ9ikXKFQ4JtvvsH777+Po0ePQiKRQK/X44svvoDBYPB7o++RQKW/vx8RERG+30cfu58XXngBUqkUTU1NvpDKZrMBAC5dujTmF+uWLl0qCKkaGxuxf/9+GI1GPPnkk4KxJpMJarUazz33HAAgLS0N6enpuHLlCp5++mnB2Li4OHi9Xnz66ad4/PHHodFoRCGVUqlEfn4+8vPzcfv2bXz77bfYtWsXNBoN5s+fL+q1vr4emzdvHvcd3OvemW0j1Go1li9fjsHBQV8tPT0dERER0Ol0vtrly5exZs0awbkejwcXLly4b5hGRERE/0wMqYiIiGhK0Ov1uHz5sqiuUChQXFyM4uLiCa8xd+5cHDhwQFDr6+uDy+VCZGSkX33MnDkTkZGRaG1t9W00brPZIJPJ7ruH1ehek5KScOLECV/wlJ2djezsbL/u7XQ6sWPHDuTn50Ov1wuO3b17F8eOHUN/fz8SExMFx8xmMwoKCkTXMxgMKCkp8evdyeVybNmyBWVlZbh69eqYIdWZM2f8eg4A2Ldvn2ip4lhUKhWam5sRFxeHoKAgwRLO/v5+OJ1OhIeHC85paGhAT08PDAaD3/0QERFR4DGkIiIiov+MwcFB0YypkSV2o4Od4eFhDA0N4e7du/B6vXC73ZBIJL49mwwGAw4dOoT4+HgEBwfjwIEDSEtL8+tLcps3b8a6detw/fp1PPXUU373PjAwgO3btyMpKUm0/A8ALBYLbt68iaqqKoSFhfnqP/zwA4xGI3bu3CnYcwoAMjIyoNVqBTOTRty8eRPl5eV49dVXERUVBa/Xi8rKSgwMDCAmJsbvvv9fa9euxXvvvYeBgQHB38jhcODjjz8eM3yrqqpCYmKiYN8wIiIi+udjSEVERET/GVu3boVKpcLChQvh8XjQ2NgIi8UCnU4nmKFz7NgxQfixePFiqFQqnD17FgCQm5sLl8uF1NRUeDwevPLKK9i1a5dfPcTHx2PlypUoKyvD3r17/e69trYWra2taG9vx6lTp0THFy1ahNTUVCxYsEBQz8zMRGlpKU6fPi1a/iaXy+/7Jb7g4GB0d3dj27Zt6OnpQUhICBYsWIDPPvtMtMxwMkkkEhQWFuLIkSMwmUyQSqXweDyIiIjA7t27RfuJORwO1NbW4siRI4+sRyIiIno4JF6v1xvoJoiIiIgehc8//xxVVVXo7OyE2+3G7Nmz8fLLL2P79u2C/asmW1dXF9auXQuz2fxAs6loYgUFBZDL5di9e3egWyEiIqIHxJCKiIiIiIiIiIgCzv/vAxMREREREREREU0ShlRERERERERERBRwDKmIiIiIiIiIiCjgGFIREREREREREVHAMaQiIiIiIiIiIqKAY0hFREREREREREQBx5CKiIiIiIiIiIgCjiEVEREREREREREF3P8AWcJbCPaEpSMAAAAASUVORK5CYII=\n",
-      "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",
-    "# this saves a lot of typing! \n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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": [
-    "Does this look like a reasonable stellar luminosity function to you? The implication is that the most likely stellar luminosity is 10<sup>5.8</sup> L<sub>☉</sub>! Clearly, this is not very realistic... let's see what went wrong."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e32c3bbf-390f-45da-ad9c-cc3e7c9449dc",
-   "metadata": {},
-   "source": [
-    "## ZAMS Luminosity distribution with the initial mass function\n",
-    "\n",
-    "In the previous example, all the stars in our grid had an equal weighting. This is very unlikely to be true in reality: indeed, we know that low mass stars are far more likely than high mass stars.  So we now include an initial mass function as a three-part power law based on Kroupa (2001). Kroupa's distribution is a three-part power law: we have a function that does this for us (it's very common to use power laws in astrophysics).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Update the probability distribution to use the three-part power law IMF \n",
-    "population.update_grid_variable(\n",
-    "    name=\"M_1\",\n",
-    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 0.218222\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*    Total probability is 0.218222   *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-4b8c7f4a86e445099d73f27dffaad94b finished! *\n",
-      "*           The total probability is 0.218222.           *\n",
-      "*  It took a total of 7.95s to run 40 systems on 2 cores *\n",
-      "*                  = 15.89s of CPU time.                 *\n",
-      "*              Maximum memory use 587.984 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Clean and re-evolve the population \n",
-    "population.clean()\n",
-    "analytics = population.evolve()  \n",
-    "\n",
-    "# Show the results (debugging)\n",
-    "# print (population.grid_results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot luminosity distribution\n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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": "0546f2f3-4732-4841-8ef3-565fbf6b9961",
-   "metadata": {},
-   "source": [
-    "This distribution is peaked at low luminosity, as one expects from observations, but the resolution is clearly not great because it's not smooth - it's spiky! \n",
-    "\n",
-    "If you noticed above, the total probability of the grid was about 0.2. Given that the total probability of a probability distribution function should be 1.0, this shows that our sampling is (very) poor. \n",
-    "\n",
-    "We could simply increase the resolution to compensate, but this is very CPU intensive and a complete waste of time and resources. Instead, let's try sampling the masses of the stars in a smarter way."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "673031c9-7d80-45d4-b209-301c127d3edf",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## A better-sampled grid\n",
-    "\n",
-    "The IMF has many more low-mass stars than high-mass stars. So, instead of sampling M1 linearly, we can sample it in log space. \n",
-    "\n",
-    "To do this we first rename the mass grid variable so that it is clear we are working in (natural) logarithmic phase space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Rename the old variable (M_1) because we want it to be called lnM_1 now\n",
-    "population.rename_grid_variable(\"M_1\",\"lnM_1\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "532f691c-c1f6-46cc-84f2-970ec1216e40",
-   "metadata": {},
-   "source": [
-    "Next, we change the spacing function so that it works in the log space. We also adapt the probability calculation so that it calculates dprob/dlnM = M * dprob/dM. Finally, we set the precode to compute M_1 because binary_c requires the actual mass, not the logarithm of the mass."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# update the sampling, note that the IMF is dprob/dM1, and the phase \n",
-    "# space is now sampled in lnM1, so we multiply by M_1 to \n",
-    "# because  M * dprob/dM = dprob/dlnM\n",
-    "population.update_grid_variable(\n",
-    "    name=\"lnM_1\",\n",
-    "    samplerfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min = massrange[0], max = massrange[1], res = resolution[\"M_1\"]),\n",
-    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
-    "    dphasevol=\"dlnM_1\",\n",
-    "    parameter_name=\"M_1\",\n",
-    "    precode=\"M_1=math.exp(lnM_1)\",\n",
-    ")\n",
-    "# print(population.grid_options[\"_grid_variables\"]) # debugging"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Doing dry run to calculate total starcount and probability\n",
-      "Generating grid code\n",
-      "Grid has handled 40 stars with a total probability of 0.995631\n",
-      "**************************************\n",
-      "* Total starcount for this run is 40 *\n",
-      "*    Total probability is 0.995631   *\n",
-      "**************************************\n",
-      "\n",
-      "Generating grid code\n",
-      "**********************************************************\n",
-      "*  Population-7a2e4301f5224b2cb8939d2297df0aad finished! *\n",
-      "*           The total probability is 0.995631.           *\n",
-      "*  It took a total of 4.77s to run 40 systems on 2 cores *\n",
-      "*                   = 9.55s of CPU time.                 *\n",
-      "*              Maximum memory use 621.930 MB             *\n",
-      "**********************************************************\n",
-      "\n",
-      "There were no errors found in this run.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Clean and re-evolve the population \n",
-    "population.clean()\n",
-    "analytics = population.evolve()  \n",
-    "\n",
-    "# Show the results (debugging)\n",
-    "# print (population.grid_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "182b1094-5057-4ccf-bac6-9b0e560ad4f6",
-   "metadata": {},
-   "source": [
-    "You should see that the total probability is very close to 1.0, as you would expect for a well-sampled grid. The total will never be exactly 1.0, but that is because we are running a simulation, not a perfect copy of reality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "68ee1e56-21e5-48f4-b74c-50e48685ae94",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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VmnrgiLlS9VsBJ2r16hU6eDBFEydOPurKqMjILqqsrNAXX3yqYcNGHDHcPTX1oAIDg5pUVEmSu7u7UlKSGx3buXN7kz62ufXvP0BOTk5atuz7RkXdypXLZbVaNXDgIOXm5ujGG6/W7bffoTPOmKguXbppzpxuiovbpayszKM+79G2UkrSjBnn6oUXnlFGRrrOPHP6MR8nScOGDdeHH76rtWtXN2yxrK2t1caNG464W+OvXnnlP9q2bbPeeON9ubi4aPTosQoKCtZVV81Wbm6O+vTpK4vFrLqTXGy4efNG1dXVNeReuXKZJDXM4XJzc1dOTk6jj/njn+2fvef65xqm//3vc+Xm5igoKFhSfXm4YsWP6tu3X8MsLqNQVgGQJGUVlGtTfK42JuQqM7/8iPNdgz1VWW1VbvFvP40pKKlWQUmetuzNazgW7OuqbqFeigqpX33VJdhDLk78VQMAAIC258svP9PPP6/VBRdcourqGsXF7TriMe7u7kpOTlJpaclRV05J0rRpMzR//odauPBr/fWvfz/i/MSJU/T55/OVkZGul19+66jP4eXlpZtvvk3PPfe0brnlWp1zzvkKCwtXWVmZ1qxZqaVLv9O8eU80+b2NGjVWa9eu0X//+7xGjx6rnTu3a+nS75r88c3Jy8tbl156uT744B05ODho5MjRSklJ1ttvv67Bg4fqtNNGyWw2KyQkVM89938qKSlVeHiEEhLitWHDOl1xxTVHfV4Pj/oVWz/+uFQxMQMVGhomSZowYbJeeOEZJSYm6IEHHvnTbLGxIzRixEg98cQjuuGGAgUHB+uLLz5VcXGRAgICj/oxw4eP0Pz5H+rxx+fpzDOnq7bWqk8++UA+Pj4aMmRYQ7YdO7Zpy5ZNJzxnKi8vRw89dI9mzrxA+/Yl6s03X9VZZ52jLl26SqofhP7RR+/pww/fU//+MVq7drW2bGl8x8RjfW5+dfHFc7R06Xe67babdPXV18vNzV1ff/2FDh48oH//+8UTytsS+A4S6MRyiirqC6r4XKXnHbn/uWuIp0b0DdLw3kEK8Knfs15WWauD2aU6kF2ilKz6/y0sqf7dc1Yqp6hSv+ypb/pNJinM313dDpdX3UI91SXIQ44Of970AwAAAC0tMTFBkvTll5/qyy8/PepjBg8eKmdnF/n4+Cg2dsRRHxMd3UM9e/bS0qWLdMMNtxxxvn//AQoODpHZbGk0zPyPZs26UF26dNWXX36q119/SYcOHZKbm7v69euvF198taEIaYoZM85VRka6lixZpAULvtTgwcP02GNP66abjl78tLTrrrtJfn5++t//PtfXX38pX18/nXfeLF199Q0Nq8gef/z/9PrrL+mtt17ToUPFCgoK1tVXX685c6446nOOHTteixd/o8cfn6dzzz1f//jH3ZIkNzc3DRkyVEVFRYqK6n7cbE888W+9+up/9NZbr6q6ukaTJk3RuefO0s8//3TUxw8ffrrmzXtcH3/8ge69958ymUwaNGiw/vOf1xq2PJ5//gXavXuX7rzzb7r//oePenfJY5k58wKVlpbonnvukLOziy688JJGd96bO/dqFRcX65NPPpDVatWoUaP1r389oH/96x/H/dz8KiAgQK+++rZeffU/euaZJ2Wz2dSnTz89//zLR1092NpM9l8nnuGYCgrKZLO1/KcpMNBTeXmlx38gcAryiiu1OaG+oDqYc+R/b5FBHvUFVZ8gBfkef/ifJB0qr9GBrBKlZJXoQHapDmSVqKSi9piPt5hNCg90b9g6GBXqpbAAd4WGeHMNoFPj3wF0dlwDQNu+DrKzDyokpKvRMdDBOTiYZbWe2vDuiooKnX/+dN1yy+0699zzmykZTtSf/Z1hNpvk73/0bbcSK6uATqGwpKphBlVKVskR58MD3TW8T31BFervfsLP7+3upEE9AjSoR/0dM+x2u4pKqxtWXh04XGKVV1klSXU2u1JzypSaU6bVh5/DwWJWdLi3IgLcGwa4h/q5yWxuvjudAAAAAOi4srIytXTpd9qw4We5uLho6tTpRkfCSaKsAjqootLq+hVUCTlKyjiyoArxc2tYQRUeeOxG+2SYTCb5ebnIz8tFw3rX7/O22+3KK65sKLBSskp1MKdU1YdvcWyts2lvapH2phY1PI+zk0Vdgz1/G+Ae6qkgH9dmvVUvAAAAgI7BZDLriy8+lbu7ux566HG5uLgYHQknibIK6EAOlddoc0KuNsXnaF/6If1x82qQj6uG9w3SiL7Bigh0b9XSx2QyKcjXTUG+bjqt3693m7Aru7CiYftgen65kjMOqfbwst/qmjolphUrMa244XncnB3qV16FeCnq8P/6eTlTYAEAAACdXEhIiBYvXm50DDQDyiqgnSupqNHWvXnaGJ+jvWnF+uMUugBvl/qCqk+wugR7tKlSx2w2KSzAXWEB7ho9IFSBgZ7Kyj6kzPzyhtlXKVmlSs8rU93huXEV1VbtOVCkPQd+W4Hl5eZYP7z98BD3qFAvebsbe6tVAAAAAMDJoawC2qGyylptTczTpvgcxR8slu0PDZWfl/PhGVTBigr1bFMF1fE4WMzqEuypLsGeOmNQ/e1Va611SsstP7x9sH4VVmZ+eUMxV1JRq51JBdqZVNDwPL6ezo22D3YL8ZKHq6MRbwkAAAAAcAIoq4B2oqKqVtv25WtjfK72HChsWGn0Kx8PJ8X2qd/i1z3MS+Z2VFAdj6ODRd3DvNQ9zKvhWFWNVak5ZQ3D21OyS5VTWNFwvqi0WkWl1dq2L7/hWHS4l64+q+9JDZEHAABA/RzS9vSDUADGsP9xy88JoqwC2rDKaqu278vXpoRcxaUUyFrX+IL3cndSbO9AjegbrB4R3h2qoDoeFycH9Yr0Ua9In4ZjFVW1Oni4uPp1C2FBSVXD+aSMEj3y3mZdMa23Tu8fYkBqAACA9sticVRtbbWcnBhaDeDP1dZWy8Hh5He2UFYBbUxVjVU79hdoY3yOdiUXylpna3Tew9VRsb0DNbxvsHpH+shs7jwF1fG4uTiqbzc/9e3m13CspKJGB7JKtTMpXyu2Zqi6tk5vfLtHCanFmj25p5wcLQYmBgAAaD88PLxVXJwvd3dvubi4ymy2sMoKQAO73S6brU5VVZUqLz8kT0/fk34uyiqgDaiurdOupPqCamdSgWqsjQsqdxcHDe1Vv4KqT1cfWcxmg5K2P15uThoY7X/4V4DeWrRHZZW1WrMjU8mZJbr5/BiF+LkZHRMAAKDNc3V1l4ODo8rKilVefkg2W53RkdABmc1m2Wy24z8QbZLZbJGjo5N8fYPk6HjyN70y2U91I2EnUFBQJput5T9NgYGeyssrbfHXQdtQa63TruRCbYzP0Y79BaqubfyPvauzg4b2CtDwPsHq181XDpaOX1C1xjVQWFKl177Zrf3phyRJzk4WXTmtj07rF9yirws0Bf8OoLPjGgC4DgCugc7BbDbJ39/jmOdZWQW0olqrTbtTCrUpIUfb9uWrqqZxQeXiZNGQnvUFVf8oPzk6dPyCqrX5ebnon5cO0dc/JWvJhlRV19Tp9W92a29asS6d1EOODmwLBAAAAAAjUVYBrSCroFyLNxzU1sR8VVZbG51zdrRoUA9/jegbrAHd/ShLWoGDxawLx/dQrwgfvbVoj8qrrFq1LUPJGYd008wYBbMtEAAAAAAMQ1kFtIJXF+xWel5Zw++dHMwaGH24oIr2lzNDvg0xqEeAHr56hF5dGKekjBKl5pbp4fc26crpfTSiL9sCAQAAAMAIlFVAKygpr5YkhQW465xR3TSoh79cnLj82gI/LxfdPXuovlqTrKW/pKqqpk6vLazfFnjJRLYFAgAAAEBrYyAO0Ip6RfrotH7BFFVtjIPFrIsm9NDf/jJQ7i71fzYrt2boiQ+3KreowuB0AAAAANC5UFYBwGGDewbooauGq3uYlyTpYE6pHn5vkzYn5BqcDAAAAAA6D8oqAPidAG9X/WvOUE0dHilJqqyu0ysL4vTxD4mqtdoMTgcAAAAAHR9lFQD8gYPFrEsm9dRfZw2Qm3P9tsDlW9P1xEdblFtcaXA6AAAAAOjYKKsA4BiG9ArUvKuGKyr08LbA7FI9/O4mbdnLtkAAAAAAaCmUVQDwJwJ8XHXPZUM1JfbXbYFWvfx1nD75MVHWOrYFAgAAAEBzo6wCgONwsJh16eSeunXWALke3ha4bEu6nvxoi/LYFggAAAAAzYqyCgCaaOjhbYHdQjwlSSlZ9dsCtybmGZwMAAAAADoOyioAOAGBPq6657JhmjwsQpJUUW3VS1/t0qfL97EtEAAAAACaAWUV0ArsRgdAs3J0MGv2lF665fyYhm2BP2xK01Mfb1X+IbYFAgAAAMCpoKwCgJM0rHeQHrpquLoe3haYnFmiee9s0rZ9bAsEAAAAgJNFWQW0IpPRAdDsgnxcde9lwzRp6G/bAv/7v136bAXbAgEAAADgZFBWAcApcnQwa87UXrp5ZoxcnS2SpO83punpj7eq4FCVwekAAAAAoH2hrAKAZhLbJ0gPXjlcXYI9JElJmSWa9+5Gbd+fb3AyAAAAAGg/KKsAoBkF+7rpvsuHacLQcElSeZVV//lypz5fuZ9tgQAAAADQBJRVANDMHB0sunxqb914Xn+5ONVvC1z6S6r+75NtKixhWyAAAAAA/BnKKgBoISP6BuuhK4erS1D9tsD9GYc0791N2pnEtkAAAAAAOBbKKgBoQcF+brpv7jCNH1K/LbCsslYvfLFTX6zarzob2wIBAAAA4I8oqwCghTk6WDT3zN66/tx+cj68LXDJBrYFAgAAAMDRUFYBQCs5vV+IHrpyuCIC67cF7kuv3xa4K7nA4GQAAAAA0HZQVgFAKwrxc9P9c4dp3OAwSfXbAp//fIf+tzqJbYEAAAAAIMoqoFXY7UYnQFvi5GjRFdP66Lpz+snZsX5b4HfrD+rf87erqLTa4HQAAAAAYCzKKqA1mYwOgLZkZP8QPXhlrMID3SVJiWnFmvfuRsWlsC0QAAAAQOdFWQUABgr1d9f9c2M1dmCoJKm0olbPf7ZDX61Jls3GkjwAAAAAnQ9lFQAYzNnRoqvO6qtrz+4rJ0ez7JIW/XxAz3y6TcVlbAsEAAAA0LlQVgFAGzEqJlQPXjFc4QH12wITUos1752N2n2g0OBkAAAAANB6KKsAoA0JC3DX/VfEasyA+m2BJRW1eu7T7VrwE9sCAQAAAHQOlFUA0MY4O1p09Yy+umbGb9sCv1lXvy0wt7jS6HgAAAAA0KIoqwCgjRo9IFQPXDFcYb/bFnjfGxs0f9k+lVXWGpwOAAAAAFoGZRUAtGHhAe56YG6szhhUvy2wzmbXj5vTdPdr67Xkl4OqtdYZnBAAAAAAmhdlFQC0cc5OFl05va8euCJWfbr4SJIqq636YmWS7n3jF63fnS2bnXlWAAAAADqGTlVW1dTU6KqrrtLKlSuNjgIAJywq1Et3XTpEt10wUKH+bpKkgpIqvfntHj36/mbFHywyOCEAAAAAnLpOU1YlJCRozpw52rp1q9FRAOCkmUwmDeoRoEeuGaG503rLy91JknQwu1T/nr9NL3yxQxn55QanBAAAAICT12nKqvnz5+vmm2/WwIEDjY4CAKfMYjZr/OBwPXXD6Tp3dDc5Odb/db4zqUAPvv2L3l+aoOKyaoNTAgAAAMCJ6zBl1YIFC9SvX78jfpWWlkqSHn74YU2YMMHglOjsTEYHQIfj4uSgmWO766kbRuqMQWEymSS7XVq9PVP3vL5BC9emqKrGanRMAAAAAGgyB6MDNJeZM2dq5syZRscAAEP4eDjryul9NCU2Ql+sStLOpAJV19Zp4doUrdqWoZljozRmYKgs5g7zMwoAAAAAHRTftQBABxIe6KHbLxykuy4doq7BnpKkQ+U1en/pXj30zibt2J8vO3cOBAAAANCGUVYBQAfUt6uvHrgyVted00/+Xs6SpMz8cr345U79e/42HcwuNTghAAAAABxdmyur4uPj1b9/f2VnZx9xbtGiRZoxY4YGDhyo6dOna8GCBa0fEADaCbPJpJH9Q/TE9afrwgnRcnWu3/mdkFqsh9/bpDe+3a38Q5UGpwQAAACAxtrUzKqkpCTdcMMNslqPHAa8ePFi3Xnnnbriiis0ZswYLVu2THfffbdcXFw0bdq0Jr/Ghx9+2JyRAaDNc3SwaPppXTV2YJi+WZeilVszVGeza8PuHG1OyNPk2AidPbKr3FwcjY4KAAAAADLZ28DwEqvVqs8++0zPPvusHB0dVVxcrNWrVyskJKThMVOmTFFMTIyef/75hmO333679u7dqyVLlhgRG2iy2Q8sUWlFjWaMjtKNswYaHQedXFZ+ud5fvEfrdmQ2HPN0c9TFU3rrrFFRcnRoc4tuAQAAAHQibWJl1ZYtW/TMM8/ommuuUXBwsO6///5G59PS0pSamqp//OMfjY6feeaZWrJkidLS0hQZGdli+QoKymSztXynFxjoqbw85sh0RL92wpWVNfwZ/wmugdbhIOma6X00fmCoPlu5X/vTD6m0olZvLYzTwtX79Zdx0RreJ0gmk8noqJ0O1wA6O64BgOsA4BroHMxmk/z9PY59vhWzHFN0dLSWLVumW2+9VRaL5YjzycnJkqSoqKhGx7t27SpJSklJafmQANDBRId76545Q3XL+QMU7OsqScorrtJrC3fr8Q+3KDGt2NiAAAAAADqlNrGyKiAg4E/Pl5bWt6oeHo1bN3d3d0lSWVlZywQDmkkb2G0LHJXJZNKw3oEa1MNfq7dnauHaFJVV1io5s0RPfbxVQ3sF6oLx0QrxczM6KgAAAIBOok2UVcdzvG/0zeY2sUAMANotB4tZk4ZFaFRMiBZvOKgfNqWp1mrT1sQ87difr3GDw3TumCh5uTkZHRUAAABAB9cuWh5PT09JUnl5eaPjv66o+vU80NaZxAwgtG2uzg76y7hoPXn96Ro9IEQmSXU2u1ZszdC/XluvRT8fUHVtndExAQAAAHRg7aKs+nVWVWpqaqPjBw8ebHQeANA8/LxcdM2MfnroquHqH+UnSaqqqdNXa5J17xsbtHZnVqvceAIAAABA59MuyqquXbsqIiJCS5cubXT8hx9+ULdu3RQWFmZQMgDo2LoEe+qOiwfrHxcPUkRg/dzAotJqvbM4XvPe3aS4lAKDEwIAAADoaNrFzCpJuuWWW3TPPffI29tb48eP1/Lly7VkyRI9//zzRkcDgA4vJspf/a7y089x2fpqTZKKy2qUnlem5z7bof5RfrpoQg9FBh371rMAAAAA0FTtpqyaNWuWampq9M477+iLL75QZGSknn76aZ111llGRwOATsFsNmnMwFAN7xukHzalacmGg6qqqdPulELNS9moUQNCdP7Y7vLzcjE6KgAAAIB2zGQ/3q32oIKCslaZzRIY6Km8vNIWfx20vr++sEblVVZNGhqhOVN7GR2nzeIaaF9Kymu0cF2KVm/LlO3wPyVODmZNGR6ps07vKlfndvPzkDaDawCdHdcAwHUAcA10DmazSf7+x96Z0S5mVgEA2h4vdyddPrW3Hr12hIb0DJAk1Vht+m79Qf3r9fVasTVd1jqbwSkBAAAAtDeUVQCAUxLq766//mWg/jVnqLqHeUmSSitq9dEPiXrg7Y3ampgnFvECAAAAaCrKKgBAs+gV6aP7Lh+mG8/rr0Cf+rlVOYUVeumrXXrq461KyjxkcEIAAAAA7QFlFQCg2ZhMJo3oG6zHrj1dl0zqKXeX+rlV+9IP6fEPtuh/q5MMTggAAACgraOsAgA0O0cHs6YOj9RTN47UtNO6yMFikiR9t/6gNsbnGJwOAAAAQFtGWQW0JpPRAYDW5e7iqIsm9NAj15zWsMrqvSUJyimsMDgZAAAAgLaKsgoA0OJC/Nx07dn9JElVNXV6ZUGcamrrDE4FAAAAoC2irAIAtIpBPQI0/fQukqS03DJ9smyfwYkAAAAAtEWUVQCAVjPrjO7qFeEtSVqzI1M/x2UZnAgAAABAW0NZBQBoNRazWTecFyNPN0dJ0gff71VGfrnBqQAAAAC0JZRVAIBW5evprOvO6SeTpJpam15dEKfqGuZXAQAAAKhHWQUAaHUxUf46Z3Q3SVJmfrk++H6v7Ha7saEAAAAAtAmUVQAAQ5w7Okp9u/pKktbvztZPO5lfBQAAAICyCgBgELPZpOvP6SdvdydJ0sc/Jiott8zgVAAAAACMRlkFtAJ2NwFH5+3hrBvO7S+TSaq12vTK17tUWW01OhYAAAAAA1FWAQAM1aerr2aO7S5Jyimq1PtLE5hfBQAAAHRilFVAKzIZHQBoo2aM7KqYKD9J0sb4XK3almFwIgAAAABGoawCABjObDLpunP6ydfTWZI0f/k+HcguMTgVAAAAACNQVgEA2gRPNyfdeF5/mU0mWevseuXrOFVU1RodCwAAAEAro6wCALQZPSN8dMH4aElS/qEqvbOY+VUAAABAZ0NZBQBoU84cEanBPQIkSVsT8/Tj5nSDEwEAAABoTZRVAIA2xWQy6Zqz+8rfy0WS9MXK/UrKOGRwKgAAAACthbIKANDmuLs46qaZMbKYTaqz2fXqwjiVVTK/CgAAAOgMKKsAAG1S9zAvXTyxhySpsKRaby3aIxvzqwAAAIAOj7IKANBmTRoWodjegZKknUkFWvpLqsGJAAAAALQ0yiqgFbAWBDg5JpNJV07vqyAfV0nSV6uTlZhWbGwoAAAAAC2KsgoA0Ka5uTjoppkxcrCYZbPb9drCOJWU1xgdCwAAAEALoawCWpPJ6ABA+9Q1xFOzp/SUJBWX1eiNb3fLZmPNIgAAANARUVYBANqFcYPCdHq/YEnSngNFWvTzAWMDAQAAAGgRlFUAgHbBZDJp7rTeCvV3kyQtXJuiPQcKDU4FAAAAoLlRVgEA2g0Xp/r5VU4OZtklvfHNbhWXVRsdCwAAAEAzoqwCALQrEYEeumxqb0lSSUWtXl+4W3U2m8GpAAAAADQXyioAQLszZmCoxgwIlSTtTSvWwrUpBicCAAAA0FwoqwAA7dKcqb0UHuguSVr080HtTCowOBEAAACA5kBZBQBol5wdLbp5ZoycHS2SpLcW7VFhSZXBqQAAAACcKsoqAEC7Fervrium1c+vKqus1asL42StY34VAAAA0J5RVgEA2rXT+4do/JBwSVJSRom+Wp1scCIAAAAAp4KyCmgVdqMDAB3apZN6qEuwhyRp6cZUbUvMMzgRAAAAgJNFWQW0IpNMRkcAOiRHB4tumhkjV+f6+VVvfxevvOJKg1MBAAAAOBmUVQCADiHY101XTe8rSaqotuq1hXGqtTK/CgAAAGhvKKsAAB1GbJ8gTR4WIUlKySrV5yv3G5wIAAAAwImirAIAdCgXTeyhqFAvSdLyLenalJBrcCIAAAAAJ4KyCgDQoThYzLrpvP5yd3GQJL27OF45RRUGpwIAAADQVJRVAIAOJ8DHVdfM6CdJqqqp06tfx6mmts7gVAAAAACagrIKANAhDe4ZoGmndZEkpeaWaf7yfQYnAgAAANAUlFUAgA5r1hnd1SPCW5K0enum1u/ONjgRAAAAgOOhrAIAdFgOFrNuPLe/PFwdJUkfLN2rzPxyg1MBAAAA+DOUVQCADs3Py0XXndNPJknVtXV6dUGcqmuYXwUAAAC0VZRVQCuw241OAHRuA7r7a8aobpKkjPxyffTDXmMDAQAAADgmyiqgFZlMRicAOq+ZY6LUp4uPJGldXLZ+2plpbCAAAAAAR0VZBQDoFMxmk244t7+83J0kSR/9kKi03DKDUwEAAAD4I8oqAECn4e3hrBvO7S+TSaq12vTKgjhVVluNjgUAAADgdyirAACdSt+uvpo5JkqSlFNYofeXJsjOYDkAAACgzaCsAgB0OjNGdVP/KD9J0sb4XK3azvwqAAAAoK2grAIAdDpmk0nXndNPvp7OkqT5yxJ1MLvU4FQAAAAAJMoqAEAn5eXmpBvO7S+zySRrnV2vLNiliirmVwEAAABGo6wCAHRavSJ99Jdx3SVJecVVendxPPOrAAAAAINRVgEAOrUzT+uiQdH+kqQtiXlatjnd4EQAAABA50ZZBQDo1Mwmk645u5/8vernV32+cr+SMg8ZnAoAAADovCirgFbApiKgbfNwddSNM2NkMZtUZ7PrtQVxKqusNToWAAAA0ClRVgEAICk6zFsXTeghSSooqdbbi/bIxvyqNqGsslZLfjmo5z/fobjkAqPjAAAAoIU5GB0AAIC2YnJshBLTirUlMU87kgr0/S+pmn56V6NjdVoZ+eVavjlNP+/OVk2tTZK0N61ID1wxXOEB7ganAwAAQEuhrAIA4DCTyaSrzuqr1NxS5RVX6X+rkxUd7q1ekT5GR+s0bHa7diUVaNmWdO1OKTzifE2tTa98vUsPXjFczk4WAxICAACgpbENEACA33FzcdDNMwfIwWKSzW7XawvjVFJRY3SsDq+y2qplm9N03xsb9OKXOxsVVQOj/XXHxYN1zqhukqSsggp98P1e2dmmCQAA0CGxsgoAgD/oGuKpSyf30off71VxWY3e/HaP/n7RIJlNJqOjdTi5xZVasSVdP+3MVGV1XcNxZyeLxgwI1eRhEQr2c5Mk9e3qq/0ZhxR/sEjrd2erdxcfnTEozKjoAAAAaCGUVQAAHMX4wWHam1qkjfG52p1SqO9+PqBzRkcZHatDsNvtSkgt1rLNadq+L7/RHVMDfVw0aVikxgwIlZtL4y9TzGaTrj+3v+a9s1GHymv08Y+J6hbiqS7Bnq37BgAAANCiKKsAADgKk8mkK6b10cGcMuUUVmjB2hT1CPdW325+Rkdrt2pq67RhT46WbU5Tel55o3N9u/pqcmyEBkUHyGw+9go2b3cn3Xhef/3f/G2qtdr06oI4PXjlcLk68yUNAABAR8FXdgAAHIOrs4NumRmjRz/YrFqrTc9/sUN9uvpqUHSABkX7K8DH1eiI7UJRabVWbE3X6u2ZKqusbTju6GDWyP7BmjwsUhFBHk1+vt5dfHX+2O76ak2ycooq9f7SBN1wbn+Z2KYJAADQIVBWAQDwJyKCPHTZ1F56d3GCrHV2xSUXKi65UB//KIUHuGtgD38Nig5QdLiXLGbuW/J7SRmH9OPmNG3Zm6c622+b/Xw9nTVxaLjOGBQmTzenk3rus0Z2VWJ6seKSC7UxPle9In00cWhEc0UHAACAgSirAAA4jrEDwxTk46pf9uRoR1KBikqrJUkZ+eXKyC/Xkg2pcndx0IDu/hrYw18xUf7ycHU0OLUxrHU2bU7I1Y+b05WSVdLoXHS4l6bERmpor0A5WE6t2DObTLru7H6a9+4mFZVW69Pl+xQV6qWoUK9Tel4AAAAYj7IKaA3cXR1o93p38VXvLr6y2+1Kyy3TjqQC7dyfr+TMEtkllVdZtWFPjjbsyZHZZFKPcC8N6hGggT0CFObv1uG3qJVU1Gj1tgyt2JahQ2U1DcctZpOG9w3SlNjIZi+SPN2cdNN5MXr6k62y1tn16oI4zbtquNxcOmdRCAAA0FFQVgEAcAJMJpO6BNffge6cUd1UUl6jXckF2pFUoN0pBaqsrpPNbldi+iElph/SF6uSFODtUj/nqoe/enfxkaODxei30WxSc0q1bHO6NuzJkbXO1nDc081R4weHa/yQcPl6OrfY6/eI8NZfxkXr85X7lX+oSm9/F69bZw3o8OUgAABAR0ZZBbQivncCOh4vdyeNHhCq0QNCZa2zaV9asXYk1ZdXOYUVkqT8Q1VavjVdy7emy9nRon7dfOtXXUX7y8ej5YqclmKz2bVtX76WbU7T3rTiRue6BHlocmykTusX1Gql3JkjIrUvvVjb9uVr2758/bgpTVNHdGmV1wYAAEDzo6wCAKCZOFjM6tvNT327+emSST2VXVihnfvztSOpQIlpxaqz2VVdW9dQqkhS1xBPDYr216AeAfL3b/od8YxQUVWrNTuytGJruvIPVTUcN5mkIT0DNSU2Qr0ifVp9VZPJZNLVM/rq4Xc3Kf9Qlb5YlaTocG9Fh3u3ag4AAAA0D8oqAABaSIifm0JGdNHUEV1UUWXV7gOFDeVVWWWtJOlgdqkOZpfqm3UH5OPprJgoPw2KDlC/br5ydW4b/0xnFZRr2ZZ0/bwrW9W1dQ3HXZ0ddMagUE0aGqEAH1cDE0ruLo66aWaMnvhwi+psdr26ME7zrhrRaQfdAwAAtGdt46tgAAA6ODcXBw3vE6ThfYJks9mVklWiHUn52rm/QKm5ZZKk4tJqrd2ZpbU7s+RgMal3pI8G9gjQoB4BCmrlMshmt2t3SqF+3JymuOTCRudC/Nw0OTZCo2JC5OLUdr6UiAr10iWTeurjHxNVWFKttxbt0d8uGCgze7ABAADalbbzFSYAAJ2E2Wxq2KY264xoFZZUaWdSgeLTirUjMU81VpusdXbtPlCk3QeKNH/ZPoX6uzUMaY8O95aDxdwi2apqrPo5LlvLNqcr+/DMrV/FdPfTlNhI9Y/ya7MF0MSh4dqbVqzNCbnamVSgJRsOasbIbkbHAgAAwAmgrAIAwGB+Xi4aPyRcF07to4zMYiWkFmnH/gLtSMpXYUm1JCmroEJZBalaujFVbs4Oiulev11wQLR/s2x1yy+u1PKt6VqzI0uV1daG486OFo0aEKLJwyIU6u9+yq/T0kwmk66c1kepOaXKLarU12tS1CPcW727+BodDQAAAE1EWQUAQBvi5GjRwOgADYwO0GX2XsrIK9eOpHzt2F+gpIxDskuqqLZqY3yuNsbnymSSosO964e0RwcoPNC9yQPO7Xa7EtOK9ePmdG3blye7/bdzAd4umjg0QmcMCpWbS/ua++Tm4qCbZ8bosQ+2yFpn02vf7NbDV42Ql7uT0dEAAADQBJRVAAC0USaTSRFBHooI8tCMkd1UWlGjXckF2plUoF3Jhaqstspul/anH9L+9EP63+pk+Xs518+5ig5Qny4+cnK0HPG8tdY6bdiTo2Wb05V2eF7Wr3pH+mhybKSG9AyQ2dw2t/o1RZdgT82e0lMfLN2rQ2U1euPb3frHRYPb9XsCAADoLCirAABoJzzdnDQqJlSjYkJlrbNpf/qhhlVXv86XKiip1sqtGVq5NUNODmb16+angT3qV11J0sptGVq9PUOlFbUNz+tgMev0fsGaHBuhLsGehry3ljBuUJgS04q1YXeO9hwo0rc/H9B5Y6KMjgUAAIDjoKwCWoFd9uM/CABOgIPFrD5dfdWnq68unthTOUUV2nl4ztXe1GLV2eyqsdq0fX++tu/Pl7RXZpNJtt/t9fP2cNLEIeEaNyRcXm4db4ucyWTS3DN762B2qbIKKvTN2hT1jPBWv25+RkcDAADAn6CsAgCgAwj2ddOU4W6aMjxSldVW7TlQqB37C7QzKV8lh1dR/VpURYV6aUpshGL7BLXYXQXbChen+vlVj76/WTVWm974ZrceumqEfD2djY4GAACAY6CsAlqRScxKAdDyXJ0dNKx3kIb1DpLNbteBrFLtTMpXZXWdRvQNUnS4t9ERW1V4oIcuP7O33v4uXiUVtXr9m92669LBspg7dlEHAADQXlFWAQDQgZlNJnUP81L3MC+joxhq9IBQ7U0r1tqdWUpMK9aCn1L0l3HRRscCAADAUfAjRQAA0CnMmdJLEYHukqTv1h/UzqQCgxMBAADgaCirAABAp+DsaNFNM2Pk7GSRJL357W4VllQZnAoAAAB/RFkFAAA6jVB/d10xrbckqbzKqlcXxslaZzM4FQAAAH6PsgoAAHQqp/cL0YQh4ZKkpIwS/W91ksGJAAAA8HuUVQAAoNO5ZFIPdQn2kCR9vzFN2xLzDE4EAACAX1FWAQCATsfRwaKbZ8bI1bl+ftVb38Urr7jS4FQAAACQKKsAAEAnFeTrpqvP6itJqqy26tUFcaq1Mr8KAADAaJRVQGuwGx0AAHA0w3oHaUpspCTpQHapPl+x3+BEAAAAoKwCAACd2oUTohUV6iVJWr41XRvjcwxOBAAA0LlRVgGtyWR0AADAHzlYzLppZn+5uzhIkt5bkqCcwgqDUwEAAHRelFUAAKDTC/B21TVn95MkVdXU6ZUFcaqprTM4FQAAQOdEWQUAACBpcI8ATT+tiyQpLbdMnyzbZ3AiAACAzomyCgAA4LDzz+iunhHekqQ1OzK1Pi7b4EQAAACdD2UVAADAYQ4Ws248L0Yero6SpPe/T1BGfrnBqQAAADoXyioAAIDf8fV01vXn9pNJUk2tTa8uiFN1DfOrAAAAWgtlFQAAwB/ERPnr7FHdJEmZ+eX68Ie9stvtxoYCAADoJCirAAAAjuK8MVHq08VHkvRzXLbW7swyNhAAAEAnQVkFAABwFGazSTec21/e7k6SpI9+TFRabpnBqQAAADo+yioAAIBj8PZw1g3n9pfJJNVabXplQZwqq61Gx2pVNrtd2/fn6/nPd+jR9zdrx/58oyMBAIAOzsHoAEBnwJQTAGi/+nT11cyx3fX1mmTlFFbo/aUJhwssk9HRWlRltVXrdmVp2ZZ05RZVNhx/8cudGhjtr0sn91Swr5uBCQEAQEdFWQW0oo79bQ0AdFwzRnbVvrRixaUUamN8rnpH+mjC0AijY7WI3KIKLd+SobW7MlVZ/dtdEF2cLDKZTKqstmpnUoH2HCjUmSO66OyR3eTsZDEwMQAA6GgoqwAAAI7DbDLpunP6ad67m1RUWq35y/cpKsxL3UK8jI7WLOx2uxIOFunHzenasT+/0YrgIB9XTRoWoTEDQ1Vrtel/q5P0084sWevs+m79Qf0cl61LJvVUbO/ADr/aDAAAtA7KKgAAgCbwdHPSjef119Mfb5O1zq5XF8TpoSuHy83F0ehoJ62mtk4b9uTox81pysgrb3Sub1dfTYmN1MBof5nN9SWUq7N01Vl9NW5wuD7+ca9SskpVVFqtVxfEqU8XH82Z0kvhgR5GvBUAANCBUFYBAAA0Uc8IH10wPlqfr9yvvOIqvbM4QbecH9PuVhQVllRp5bYMrd6eqbLK2objjg5mjewfosmxEYr4k9Kpe5iX7psbq7U7s/TlqiSVVdYqIbVYD72zSZOGRei8MVFyc+HLTAAAcHL4KgIAAOAEnDkiUolpxdq+P19bE/P04+Z0TR0eaXSs47Lb7UrKLNGyzWnanJAnm/23zX6+ns6aODRc4waHy8O1aSvFzCaTzhgUpmG9A7XgpxSt2Joum92uHzen6Zc92bpgfA+NGhAiczsr8gAAgPEoqwAAAE6AyWTS1TP66uF3N6mgpEpfrNyv6DAvRYd7Gx3tqKx1Nm1KyNWyzWlKySptdK5HuLcmx0ZoaK9AOVjMJ/X87i6OmjOll84YFKaPf0xUYlqxSipq9c7ieK3enqE5U3t1mNleAACgdVBWAQAAnCAPV0fdNDNGT360RXU2u15bGKeHrhrR5FVJraGkvEartmdo5bYMHSqraThuMZs0om+QJsdGKiq0+UqkyCAP3T17iH6Jz9HnK/aruKxGSZklevS9zTpjcJhmndFdnm5OzfZ6AACg46KsAgAAOAndw7x08cQe+mTZPhWUVOutRXv0twsGGr7tLTWn9PBWvFxZ62wNx73cHDV+SLjGDwmXj4dzi7y2yWTS6f1CNLhHgL79+YB+2JimOptdq7dnanNCrs4/o7vGDw5vGNgOAABwNJRVAAAAJ2nSsAglphVr89487Uwq0NJfUnXW6V1bPYfNZte2ffXzsxLTihud6xLsoSmxkRrRN0iODpZWyePi5KALx/fQmAGhmr98n+KSC1VeZdVHPyRqzfZMzZ7SS70ifVolCwAAaH8oqwAAAE6SyWTSldP7KjWnTLnFlfpqdbJ6hHu3WhFTXlWrn3ZkafmWdBWUVP0ulzS0V6CmxEaqZ4S3YXcrDPV3198vHKTt+/M1f9k+5R+qUmpumZ76eKtG9g/WhRN6tNgqLwAA0H5RVgGt4Hc3XAIAdDBuLg66aWaMHv9wi6x1Nr22ME7zrhohL/eWm8+UVVCuZZvTtS4uSzW1v231c3N20BmDwzRxaLgCvF1b7PVPhMlk0pCegerfzU9Lf0nVdxsOqtZq0/rdOdq6L1/njY7S5NiIkx7wDgAAOp4TLqsqKyvl6lr/xU9RUZEWL14ss9ms6dOny8fHp7nzAR0LIzoAoEPqGuKp2ZN76oPv96q4rEZvfrtbf79ocLPOZrLZ7YpLLtSyzWmKSylsdC7U302Th0VoVEyonJ1aZ6vfiXJytOjcMVEaFROiz1bs15bEPFXX1Onzlfu1ZkemZk/pqQmBnkbHBAAAbUCTy6qSkhL9/e9/V0lJib744guVlZXpL3/5i7KysmS32/XKK6/ok08+UWRkZEvmBQAAaJPGDQ5TYlqxNuzJ0e4DRVq0/oDOHR11ys9bVWPVul3ZWr4lXdmFFY3ODYz21+RhEeoX5Wf4YPemCvBx1S2zBmh3SqE+WZaorIIKZRdW6LnPdmj9nlydP7qbAnzaxqowAABgjCavt37hhRf0yy+/aOzYsZKkL7/8UpmZmbrrrrv0wQcfyGw264UXXmipnAAAAG2ayWTS3Gm9FervJkla+FOK9hwoPM5HHVtecaU+Xb5Pd7z8sz7+MbGhqHJ2tGjS0Ag9cf3puv3CQYrp7t9uiqrf6x/lp4evHqGLJvRoWA22fleW7nvrFy1cm6Ka2jqDEwIAAKM0eWXVihUrdNlll+lvf/ubJGnZsmXy9/fX1VdfLUmaM2eO3n333ZZJeQree+89ffnllzKZTOrSpYsee+wx+fr6Gh0LAAB0QC5O9fOrHnt/s2qsNr3xzW7Nu3pEk4eI2+12JaYV64dNadq+P7/RzMMAbxdNGhahsQND5ebi2ELvoHU5WMyadloXnd4/WF+sTNL63dmqtdq0cG2K1u3K0qWTempwzwDDBsQDAABjNHllVUFBgXr27ClJKi0t1fbt2zV69OiG876+vqqsrGz+hKdgy5Yt+vLLL/XZZ5/p22+/Vffu3fXss88aHQsAAHRgEYEeumxqb0lSSUWtXl+4W3U2259+TK21Tj/tzNS8dzfp6U+2adu+34qqPl18dOusAXrqhpE6c0SXDlNU/Z6Ph7OuO6efnr51jLoEeUiS8g9V6b9f7dLzn+9QVkG5wQkBAEBravLKquDgYKWlpUmqX1VVV1en8ePHN5zfunWrQkNDmz3gqfDx8dGDDz4od3d3SVK/fv30+eefG5wKAAB0dGMGhioxrVhrd2Vpb1qxFq5N0awzoo94XFFptVZuy9Dq7RkqrahtOO5gMev0/sGaPCxCXYI7z9DxflH+evDK4Vq9PUNfrUlWeZVVcSmFevDtjZo6PFJnj+omV2duZg0AQEfX5H/tJ0yYoPfff19lZWX67rvv5O3trYkTJyonJ0dvvvmmFi5cqJtvvrklsx7VggULdO+99x5x/JdfflF0dLSio+u/MCwrK9Mrr7yi2bNnt3ZEAADQCc2Z2ksp2SXKyCvXop8Pqke4jwZG+0uSkjNLtGxzmjYl5KrO9ttePx8PJ00YGqFxg8Pk5eZkVHRDmc0mTRgaodg+Qfp6TbJWb89Unc2uJb+kav3ubF00oYdO6xfM1kAAADqwJpdVd911lyorK/Xll18qODhY8+bNk4uLixITE/Xxxx/r3HPP1fXXX9+SWY9q5syZmjlz5p8+JicnRzfddJOGDh2qSy+9tHWCAQCATs3Z0aKbZ8bokfc2q7q2Tm8t2qO/jOuutTuzlJRZ0uix3cO8NDk2QrG9g+RgafKUhg7N081Jc6f10RmDw/TxD4lKyixRcVmN3vh2j1Zty9Ccqb0VeXjLIAAA6FhMdvvvR3ce2759+9SjR48jfopVU1Oj4uJiBQUFtUjAU5WQkKAbb7xRF1100Umv/CooKJPN1qRP0ykJDPRUXl5pi78OWt/1/14la51N00/vogvH9zA6TpvFNYDOjmugY9qwO1tvfLvniOMWs0mxfYI0OTZC0WHeBiRre451Ddjsdq2Py9YXq5JUUl4jSTKZpIlDIjTzjCi5d8A5Xui8+LcAnR3XQOdgNpvk73/sHzo1eWXVlVdeqfPPP1933nlno+NOTk5ttqjKyMjQlVdeqQceeEAzZswwOg4AAOiETu8fosT0Q1q1LUOS5OHqqPFDwjRhSIR8PZt2l8DOzmwyafSAUA3pGahv1qVo2eZ02ex2Ld+arl/ic3TB+GiNGRgqM1sDAQDoEJpcVlVUVCgiIqLFgsTHx+uCCy7Q8uXLFRIS0ujcokWL9OqrryotLU3h4eG64YYbjrv1T5Lee+89VVZW6o033tAbb7whSerRowd3BIQBWn5lHgCg7Zo9uafCA9zl6mxRbO8gOTlajI7ULrm5OOiSST01dmCoPlm2T/EHi1RWWav3liQc3hrYi1VqAAB0AE0uq6644gq9++676t+/vwYMGNCsIZKSknTDDTfIarUecW7x4sW68847dcUVV2jMmDFatmyZ7r77brm4uGjatGl/+rz33Xef7rvvvmbNCpwKk/iJLwB0Rg4WsyYNa7kf+nU24YEeuvOSwdqyN0+frtinwpJqHcgu1eMfbNGYAaG6YHy0vNw754B6AAA6gibPrLr22mu1ZcsWVVVVycXFRT4+PjKbGw8ANZlMWrZsWZNf3Gq16rPPPtOzzz4rR0dHFRcXa/Xq1Y1WVk2ZMkUxMTF6/vnnG47dfvvt2rt3r5YsWdLk1wKMdP4/v5W1zqYLJvbUFTP6GR0HAIAOo6raqi9W7NNXK/fLWmeTJLm7OGj2mX00Y3SULAysBwCg3Wnyyqrq6mrFxMQ064tv2bJFzzzzjK655hoFBwfr/vvvb3Q+LS1Nqamp+sc//tHo+JlnnqklS5YoLS1NkZGRzZrpaBiwjlNX/99PRUUNf8Z/gmsAnR3XADq7k70GpsVGaEi0nz5dtk87kgpUXmXVmwvjtPjnFM2Z3Et9uvq2QFqgZfBvATo7roHOodkGrH/44YfNEuj3oqOjtWzZMvn7++urr7464nxycrIkKSoqqtHxrl27SpJSUlJapawCAABA2xbs66bbLhykHfvzNX/5PuUWVSojr1z/N3+bRvQN0kUTesjPy8XomAAAoAmadV30nj1H3pb5zwQEBMjf3/+Y50tL69tUD4/GbZu7u7skqays7AQTAgAAoCMb1CNAj15zmv4yrrucHOu/1N0Yn6t739yg79YfaNgqCAAA2q4mr6yqqanRf/7zH/3000+qqKiQzfbbP/R1dXUqLy9XWVmZ4uPjmy3c8cZp/XFmFgAAAODoYNaMkd00sn+IPl+5Xxvjc1VTa9P/VifrQHapbp4ZI5OJm54AANBWNbntefHFF/XWW2/p0KFDcnV1VUZGhkJDQ+Xg4KDs7GzV1tY2+533PD09JUnl5eWNjv+6ourX8wAAAMAf+Xm56MbzYnTXpUMUFlC/Mn/L3jz9uDnd4GQAAODPNLmsWrp0qUaMGKEVK1bozTfflCQ9+OCD+v777/X666/LarXK0dGxWcP9OqsqNTW10fGDBw82Og8AAAAcS9+uvvrXnKHyPzyz6ouV+5WUccjgVAAA4FiaXFbl5ORo6tSpMpvNCg4Olr+/v7Zt2yZJGjdunM4//3x9/vnnzRqua9euioiI0NKlSxsd/+GHH9StWzeFhYU16+sBAACgY/JwddSNM/vLYjapzmbXawvjVFZZa3QsAABwFE0uq1xcXBqtnOrSpYsSExMbfj9w4EClpaU1bzpJt9xyixYtWqRHHnlEa9as0UMPPaQlS5botttua/bXAgAAQMcVHeatiyb2kCQVlFTrrUV7ZDvOjFQAAND6mlxW9e3bV2vWrGn4fffu3RtWVkn1K69aYlDlrFmz9PDDD2vt2rW65ZZbtGnTJj399NM666yzmv21gJbC18EAALQNk4dFaFjvQEnSzqQCLf0l9TgfAQAAWluT7wY4e/Zs/f3vf9fs2bP1xhtvaMaMGfrf//6ne+65R927d9d7772nwYMHn3SQWbNmadasWUc9d8kll+iSSy456ecGAAAAJMlkMumq6X2VmlOqvOIqfbU6WT3CvdUr0sfoaAAA4LAmr6yaPn26Hn30URUXF8vV1VWjRo3SnDlz9PXXX+vZZ5+Vl5eX/vWvf7VkVqDd4y7ZAAAYz83FQTfPHCAHi1k2e/38qpLyGqNjAQCAw5pcVknShRdeqMWLF8tisUiSHnjgAa1YsUJff/21li5dqh49erRISAAAAKA5dQ3x1OzJPSVJxWU1evPb3bLZ2LcPAEBb0OSyau7cuVq/fv0Rx8PCwtS3b1+tXbtWM2bMaNZwAAAAQEsZNzhMp/ULliTtPlCkResPGBsIAABI+pOZVZWVlSoqKmr4/caNGzVlyhR17dr1iMfabDatWbNG6enpLZMSAAAAaGYmk0lzz+ytg9mlyi6s0MKfUtQj3Fv9uvkZHQ0AgE7tT8uqmTNnqrS0VFL9P+ZPPPGEnnjiiaM+3m63a/To0S2TEgAAAGgBrs4OunlmjB77YLNqrDa98c1uzbt6hHw8nI2OBgBAp3XMssrPz0///ve/tWvXLtntdr388suaMmWKevfufcRjzWaz/Pz82AYIAACAdiciyENzpvbSu4sTVFJRq9cX7tadlw6WxXxC410BAEAzOWZZJUnjxo3TuHHjJEmZmZm65JJLNGjQoFYJBgAAALSWsQPDlJhWrHW7srU3rVgL16Zo1hnRRscCAKBTavKPi5588smjFlX79u1TUlJSs4YCAAAAWttlU3srPNBdkrTo54PamVRgcCIAADqnE1rb/MYbb+iee+6RVD9U/frrr9e5556rs88+W9dcc43Ky8tbJCQAAADQ0pwdLbp5ZoycHS2SpLcW7VFhSZXBqQAA6HyaXFa99dZbeu6555Sfny9JWrJkidasWaOpU6fqlltu0ebNm/Xyyy+3WFAAAACgpYX6u+uKafUzWssqa/Xawt2y1tkMTgUAQOfS5LLq66+/1pQpU/Tmm29KkhYvXixXV1c9/fTTuvXWWzV79mwtXbq0xYICAAAAreH0/iEaPzhMkrQ/45C+Wp1scCIAADqXJpdVaWlpOuOMMyRJtbW1Wr9+vUaMGCEXFxdJUnR0dMOqKwAAAKA9u3RyT3UJ8pAkLd2Yqm378gxOBABA59HkssrLy0tlZWWSpF9++UUVFRUN5ZUkpaamKiAgoPkTAgAAAK3M0cGim86PkYtT/fyqtxfFK7+40uBUAAB0Dk0uq4YMGaKPPvpIP/zwg5577jk5ODho6tSpqq2t1Q8//KD58+frtNNOa8msAAAAQKsJ9nXT1Wf1lSRVVFv16sI41VqZXwUAQEtrcll17733ytnZWX/7298UHx+vO+64Q4GBgdq6dav+9re/KTAwULfddltLZgUAAABaVWyfIE0eFiFJSskq1ecr9xucCACAjs+hqQ8MDQ3VN998oz179ig4OFjBwcGSpD59+ui5557ThAkT5Orq2mJBAQAAACNcNLGHkjIPKSWrVMu3pKtXpI+G9wkyOhYAAB1Wk1dWSZKDg4MGDhzYUFRJkre3t8466yyKKgAAAHRIDhazbjovRm7O9T/nfXdxvHKKKgxOBQBAx3VCZRUAAADQGQX4uOqas+vnV1XV1OnVr+NUU1tncCoAADomyioAAACgCYb0DNS0EV0kSam5ZZq/fJ/BiQAA6JgoqwAAAIAmmjWuu3qEe0uSVm/P1Prd2QYnAgCg4zlmWbVq1Srl5+e3Zhagw7LbjU4AAACag4PFrBvP6y8PV0dJ0gdL9yozv9zgVAAAdCzHLKvuvPNOrVq1quH3c+fO1fr161sjEwAAANBm+Xm56Lpz+kmSqmvr9OqCOFXXML8KAIDmcsyyym63a8uWLaqsrJQkbdy4UQUFBa0WDOiITCajEwAAgOYwoLu/zh7VVZKUkV+uj37ca3AiAAA6DodjnZg6daq+/vprLViwoOHYXXfdpbvuuuuYT2YymbRnz55mDQgAAAC0ReeNidL+9ENKSC3Wul3Z6hXpo7EDw4yOBQBAu3fMsurhhx9W//79lZiYqJqaGi1cuFDDhg1TZGRka+YDAAAA2iSL2azrz+2vee9uUkl5jT76IVHdQrwUGeRhdDQAANq1Y5ZVTk5Ouuyyyxp+v2DBAl188cU655xzWiUYAAAA0Nb5eDjrhnP66ZnPtqvWatMrC+L04BWxcnU+5pfZAADgOI45s+qPEhISGoqq/Px87dy5U/Hx8SosLGyxcAAAAEBb17ebn84bEyVJyims0PtLE2TnVsAAAJy0E/qRT1xcnB555BHt2rWr0fFBgwbpvvvu04ABA5o1HAAAANAenD2qm/alH9LulEJtjM9V7y6+mjAk3OhYAAC0S00uq/bu3avLL79cknTRRRcpOjpaNptNycnJ+vbbbzV37lx9/vnn6tmzZ4uFBQAAANois8mk687pp3nvbFRxWY3mL0tU91AvdQ3xNDoaAADtTpPLqhdeeEHu7u767LPPFB7e+KdEN998sy644AK99NJLevHFF5s9JAAAANDWebk56cbzYvR/n2yTtc6uVxbs0kNXjpCbC/OrAAA4EU2eWbV582bNnj37iKJKkkJCQnTppZfql19+adZwAAAAQHvSK9JHfxnXXZKUV1yldxfHM78KAIAT1OSyqqamRu7u7sc87+HhoaqqqmYJBXQ0dvFFKgAAncWZp3XRwGh/SdKWxDwt25xucCIAANqXJpdVffv21aJFi2S1Wo84V1tbq2+//Va9evVq1nAAAABAe2M2mXTt2f3k7+UsSfp85X4lZR4yOBUAAO1Hk8uqa6+9Vrt27dJll12m77//Xnv37tXevXu1ZMkSXXbZZdq9e7euvvrqlswKdAAmowMAAIBW4OHqqBtnxshiNqnOZtdrC+JUVllrdCwAANqFJk97nDx5sh544AE988wzuv322xuO2+12OTs76+6779a0adNaIiMAAADQ7kSHeeuiCT00f/k+FZRU6+1Fe/TXCwbKbOKHVwAA/JkTujXJnDlzNGPGDK1fv17p6emy2+2KiIjQqFGj5OPj00IRAQAAgPZpcmyEEtOKtSUxTzuSCvT9L6mafnpXo2O1Orvdrn3ph1RjrVP/bn4yUdgBAP7ECd9H18fHR9OnT2+JLAAAAECHYjKZdNVZfZSaW6q84ir9b3WyosO91SvSx+horaKmtk7rd2dr2ZZ0ZeSVS5J6RXhr9pRe6hLsaXA6AEBb1eSZVQAAAABOnJuLo26eOUAOFpNsdrteWxinkooao2O1qMKSKn25Kkl3vLxO7y/d21BUSVJi+iE9/N4mffTDXuZ4AQCOirIKAAAAaGFdQzx16aSekqTishq9+e0e2ex2g1M1L7vdrv0Zh/Tawjj989X1WrzhoMqr6u8k7uvprL+M666pwyNlNplkt0srtmbo3jc2aPX2DNlsHetzAQA4NSe8DRAAAADAiRs/JFx704q1MT5Xu1MK9d3PB3TO6CijY50ya51NmxJytWxzmlKyShud6xHurcmxERraK1AOlvqfk48dFKZPfkxU/MEilVXW6v2le7V6e6bmTO2l6DBvI94CAKCNaXJZZbPZZDazEAsAAAA4GSaTSVdM66ODOWXKKazQgrUp6hHurb7d/IyOdlJKymu0anuGVm7L0KGy37Y1WswmjegbpMmxkYoK9Tri48ID3HXnJYO1ZW+ePl2xT4Ul1TqQXarHP9iiMQNC9Zfx0fJ2d2rNtwIAaGOaXFadd955uuCCC3TFFVe0ZB4AAACgw3J1dtDNM2P02AebVWu16fVv9+jhq4bL28PZ6GhNlppTqh83p+mXPTmy1v22fc/LzVHjh4Rr/JBw+Rzn/ZhMJsX2CdKA7v76bsNBLf0lVdY6m9buytKWxFzNHNNdE4eFy8IPywGgU2pyWXXgwAG5urq2ZBYAAACgw4sM8tBlU3rp3SUJKimv0evf7NadlwyR2WwyOtox2Wx2bduXpx83pysxrbjRuS7BHpoSG6kRfYPl6HBi5ZKzk0WzzuiuMQNC9Ony/dq+P1+V1XWav3yf1uzM1JzJvdSnq28zvhMAQHvQ5LJqzJgx+uGHHzRz5kw5ObEsFzghzAwFAAC/M2ZgqBLTirUuLlsJqcVasDZFs87obnSsI5RX1eqnHVlaviVdBSVVDcdNJmlor0BNiY1UzwhvmUynVrQF+brpbxcM1M6kfH2ybJ9yiyqVkVeu/5u/TSP6BumiCT3k5+Vyqm8HANBONLms6tOnj95//32NHTtWAwYMkL+//xEzrEwmk5544olmDwl0FG3356UAAKA1mUwmXTa1t1KyS5WZX65FPx9QzwhvDejub3Q0SVJWQbmWbU7Xurgs1dTaGo67OTvojMFhmjg0XAHezb/rYmB0gPp29dMPm1L17c8HVFNr08b4XG3fn69zRnXT1OFdTnj1FgCg/THZ7U27Z26fPn2O/2Qmk+Lj4085VFtTUFDWKrfTDQz0VF5e6fEfiHbnmqdWyC7pnFHddH4b/KlpW8E1gM6OawCdXWe8BjLzy/XI+5tUU2uTh6uj5l013LAVRDa7XXHJhVq2OU1xKYWNzoX6u2lybKRG9Q+Rs5OlVfIUllTp85X7tTE+t+FYkK+rZk/uqYHRAa2SwQid8ToAfo9roHMwm03y9/c45vkmr6xKSEholkAAAAAA6oUFuOuKM/vozUV7VFZZq9e+2a1/XjpEDpbWWz1UVWPVul3ZWr4lXdmFFY3ODYz21+TYCPXv5nfKW/1OlJ+Xi248L0bjBhfpk2WJysgrV25RpV74YqcGRfvr0sk9FeTr1qqZAACto8ll1e/ZbDYVFhbKy8uL+VUAAADAKRgZE6LE9GKt3p6p/emH9NWaZF00oUeLv25ecaWWb0nXTzuzVFltbTju7GjRmAGhmhQboRA/48ugvl19Ne+q4VqxNUMLfkpWZXWddiQVaPeBQk07rYtmnN6t1VZ7AQBaxwmVVQcPHtQzzzyjtWvXqqqqSu+8844k6bnnntPdd9+t2NjYFgkJAAAAdGSXTuqp5MwSpeWWaekvqeoV4aPBPZt/q5vdbtfe1GL9uDlN2/fn6/cDQQK8XTR5WITGDAyVm4tjs7/2qbCYzQ13HPzfqiSt3ZUla51di34+qJ/jsnXxxJ6K7R3Y6qu/AAAto8nriw8cOKALL7xQGzdu1NixYxuOWywWJScn6+qrr9b27dtbIiMAAADQoTk5WnTzzBi5HF4h9PZ3e5RfXNlsz19rrdNPOzI1791N+r/527Rt329FVZ8uPvrrrAF66oaRmjqiS5srqn7P291JV8/oq/vmDlO3EE9JUmFJtV5dEKdnPt2ujPxygxMCAJpDk8uq5557Ti4uLlq8eLHmzZunX+eyjxgxQosXL1ZAQIBeeumlFgsKAAAAdGTBfm666qy+kqTyKqteXRgna53tOB/154pKq/XVmiTd8fLPendJgtJyyyRJDhazxgwM1cNXj9A/Zw/VkF6BMpvbz6qk6DBv3X9FrK6c3kcervXlWvzBIs17Z6M+Xb5PFVXW4zwDAKAta/I2wA0bNuiqq66Sv7+/ioqKGp0LDg7W7Nmz9dZbbzV7QAAAAKCzGN4nSIlDI7R8a7pSskr1+Yr9mj2l1wk/T3JmiZZtTtOmhFzV/e6u1j4eTpowNELjBofJy619z541m0w6Y1CYhvUO1II1KVqxLV11Nrt+2JSmDXtydOH4aI2MCZGZrYEA0O40uayqqamRl5fXMc87Ojqqurq6WUIBAAAAndVFE3soKfOQDmSXatmWdPWK9FFsn6Djfpy1zqYte/O0bHOakjJLGp3rHualybERiu0d1Kp3GmwN7i6OmjO1l8YOCtUnPyYqMf2QSspr9PZ38Vq1PUOXTemtroe3DAIA2ocml1V9+vTRihUrNGfOnCPOWa1WffPNN+rdu3ezhgMAAAA6G0cHs26aGaOH392kimqr3l0Sr8hgDwX7Hv3OfKUVNVq9PVMrtqaruKym4bjFbFJsnyBNjo1QdJh3a8U3TJdgT909Z6h+2ZOjz1fuV3FZjZIySvTIe5s0bnCYZo2LbtgyCABo25pcVt1www26+eabdeedd2rSpEmSpIyMDC1fvlxvv/229uzZoxdeeKGlcgLtmv34DwEAAGgQ6OOqa2b01X+/2qXK6jq9+nWc7ps7TI4OlobHpOeW6cfN9Vveaq2/zbbycHXU+CFhmjAkQr6ezkbEN4zJZNLp/UM0qEeAFv18QD9sSlOdza5V2zO1KSFXs87ornGDw9vVfC4A6IxMdru9yd9Hf/XVV3riiSdUXl4uu90uk8kku90uZ2dn/f3vf9eVV17ZglGNU1BQJput5euGwEBP5eWVtvjroPVd/dQKSdK5o7tp5tjuBqdpu7gG0NlxDaCz4xo40mcr9un7jWmSpPGDw3TZ1N7asT9fP25OU0JqcaPHRgR6aEpshE7rFywnR8tRnq3zySoo1yfL9ml3SmHDsS5BHpoztZd6RvgYF+xPcB2gs+Ma6BzMZpP8/T2Oeb7JK6skadasWZo6darWrVuntLQ02Ww2hYeHa9SoUfL19T3lsAAAAAB+85dx0dqfcUhJGSVatT1TO5IKVFT625xYk6TBPQM0JTZSvbv4yMQw8UZC/d31j4sGadu+fH26fJ/yD1UpNbdMT360VSP7h+jCCdHy8ehcq88AoD04obJKkjw8PDR16lQVFhbKbDZTUgEAAAAtxMFi1k3nxWjeu5tUVlnbUFS5Ojto7MBQTRoWoUAfV4NTtm0mk0lDewUqJspPS35J1eINB1VrtWn97mxt25enc0dHaXJsRIcbPA8A7dkJlVVJSUl68cUXtXbtWlVWVkqSPD09NWnSJN12220KCQlpkZAAAABAZ+Xn5aIbzuuvV7+Ok7eHkyYOjdDoASFycTrhnzt3ak6OFp03JkqjY0L06Yr92pqYp6qaOn2+cr9+2pmp2ZN7qX+Un9ExAQA6gbJq165dmjt3rmpra3XGGWeoS5custvtSklJ0TfffKM1a9Zo/vz56tKlS0vmBQAAADqd/t389N/bx7LNrxkE+Ljq1lkDFJdSoE9+3KfswgplFVTo2c+2a1jvQF08sYcCvFmtBgBGanJZ9cwzz8jDw0Mff/zxEYVUYmKi5s6dq6efflovv/xys4cEAAAAOjuKquYVE+WvR67x1bLN6Vq4LkXVNXXasjdPu5IKdNbpXTXttC4MqgcAgzR5Y/aOHTs0d+7co66c6tWrl+bOnav169c3azgAAAAAaCkOFrOmndZFT1x3uk7vHyxJqrHatGBtiu5/6xdt25enE7h5OgCgmTS5rPLy8lJdXd0xz7u7u8vFxaVZQgEAAABAa/H1dNb15/TXv+YMVWRQ/a3U8w9V6b//26Xnv9ih7MIKgxMCQOfS5LJqzpw5eu+997R///4jzuXk5OjDDz/URRdd1KzhAAAAAKC19Ir00YNXxuqyqb3k7lI/MSUuuVAPvPWLvli1X1U1VoMTAkDncMyZVffcc88Rx6qrqzVz5kyNHTtWUVFRMplMysjI0Jo1a+Ts7NyiQQEAAACgpVnMZk0cGqHhfYL01ZpkrdmeqTqbXUs2pOrnuGwN6Rmo6DAvdQ/zUrCfm8zMEgOAZmeyH2MTdp8+fU78yUwmxcfHn3KotqagoEw2W8vvVQ8M9FReXmmLvw5a39VPrZAknTu6m2aO7W5wmraLawCdHdcAOjuuAbRFKVkl+vjHRCVnlhxxzs3ZQd0PF1fdw7zVPcxLHq6Op/R6XAfo7LgGOgez2SR/f49jnj/myqqEhIQWCQQAAAAA7UVUqJfuvXyY1sdl66edWTqQXaKaWpskqaLaqriUQsWlFDY8PtjXtaG4ig73UkSghxwsTZ6+AgDQn5RVAAAAAADJbDJp9IBQjR4QqjqbTRl55UrOLFFS5iElZ5Yoq+C3Aew5RZXKKarU+t3ZkiRHB7O6hniqe6iXosO9FR3mJV9PZ5nYPggAx3RCZdWCBQu0bt065eXlyWazHXHeZDLp/fffb7ZwAAAAANCWWMxmdQn2VJdgT40fEi5JqqiqVXJWiZIzf/tVVlkrSaq12rQ//ZD2px+SNqVJkrw9nBQd5t0w+6pbiJecnSyGvScAaGuaXFY9//zzev311+Xo6Ch/f3+ZzSxlBQAAAAA3F0fFRPkrJspfkmS325VbXKnkjJKGFVhpuWWqOzwH91BZjbYm5mlrYp6k+pVb4YHuig7z0qDewQr0dFKIP8PbAXReTS6rvv76a40ZM0b//e9/5erq2pKZAAAAAKDdMplMCvZ1U7Cvm0bGhEiSamrrlJpT1rB1MDnzkApKqiVJNrtdabllSsst06rtmZIkV2cHdQ/1bJh/1T3MS55uToa9JwBoTU0uq8rKynTmmWdSVAEAAADACXJytKhHhLd6RHg3HCsuq25YeZWSWaKUrFJV19ZJkiqrrdp9oEi7DxQ1PD7I17V+cPvhAisyiOHtADqmJpdVY8eO1YYNG3ThhRe2ZB4AAAAA6BR8PJw1tFeghvYKlCTV2WyqrJM2786q30KYVaLM/PKGx+cWVSq3qFIbdudIkhwsZnUL8WxYedU9zEv+Xi4MbwfQ7jW5rHrggQd01VVX6Y477tDkyZPl7+9/1L8Ehw8f3qwBAQAAAKAzsJjNigr2lIejWeMH/za8PSWrVMmZh5T0h+Ht1jqb9mcc0v6MQw3P4e3u1FBcRYd5q1uop1ycuAk8gPalyX9rZWZmqrS0VN99950WL158xHm73S6TyaT4+PhmDQgAAAAAnZWbi6P6R/mpf5SfpPrvu/KKKxuKq+TMQ0rN+d3w9vIabduXr2378iVJJpMUHuBxuLzyUmyfILk6U14BaNua/LfUI488opKSEl1zzTXq1q2bHBz4Cw4AAAAAWpPJZFKQr5uCfN00sn/98PZaa50O5pQpOeOQkrNKlJRRooKSKkmS3S6l55UpPa9Ma3ZkatH6A7p/bizD2gG0aU1unPbt26dbb71V1113XUvmATocu91udAQAAAB0YI4OFvUI91aP8N+Gtx9qGN5ev/rq1+HtecVVev2b3fr7RYNkMTOcHUDb1OSyKiQkRGb+MgMAAACANs/bw1lDegVqyOHh7TabXW98u1sb43O150CRvlqdrAsn9DA4JQAcXZPbp2uvvVbvv/++9u/f35J5gA6NO7MAAADACGazSVdN76vwQHdJ0pJfUrUpIdfgVABwdE1eWZWQkCCTyaRzzz1XkZGRCggIkMViafQYk8mk999/v9lDAgAAAABOjbOTRbfOGqBH3tusymqr3vkuXmH+bgoP9DA6GgA00uSVVStXrpTFYlFISIhqa2uVlZWl9PT0Rr/S0tJaMisAAAAA4BQE+7rp+nP6ySSpurZOL321SxVVtUbHAoBGmryyasWKFS2ZAwAAAADQCgb1CNB5Y6K0YG2Kcooq9ea3e/TXCwbKzMgKAG0EE9MBAAAAoJM5e3Q3De4RIEnakVSgResOGBsIAH6nySur5s6d26THffDBBycdBgAAAADQ8swmk649u58efX+TcooqtXBtirqGeGrQ4QILAIzU5LIqPT39iGM2m01FRUWqrq5WeHi4evbs2azhAAAAAAAtw83FQbfOGqDHPtii6to6vfHtHj14RayC/dyMjgagkzvlmVV1dXVavny57r//fl1zzTXNFgwAAAAA0LLCAz10zYy+emVBnCqrrXrp61267/JhcnFq8reKANDsTnlmlcVi0dSpU3XhhRfqmWeeaY5MAAAAAIBWEtsnSNNP7yJJysgr17uLE2S32w1OBaAza7YB6926dVNCQkJzPR3QYfDPPAAAANq6v5wRrX7dfCVJmxJy9f3GNIMTAejMmqWsqqmp0TfffCN/f//meDoAAAAAQCsym0268bwY+Xu5SJK+WLVfew4UGpwKQGd1yncDrKmpUUpKikpKSvTXv/612YIBHZHJ6AAAAADAMXi4OurWWQP0xEdbVGu16bWFu/XglbEK8HY1OhqATuaU7gYo1c+s6t69u84++2zNnj272YIBAAAAAFpX1xBPXTGtt95aFK+yylq9/HWc7pkzVE6OFqOjAehETvlugAAAAACAjmNUTKhSskq1fEu6DmaX6sMf9urqs/rKZGKfAIDW0WwD1gEAAAAAHcPFE3uoZ4S3JGndrmyt3JZhcCIAnckxV1a99NJLJ/WEt95660mHAQAAAAAYz8Fi1s0zY/Twe5tUXFaj+cv2qUuQp3ocLrAAoCWdcln1x6WglFUAAAAA0P55ezjr5vMH6OmPt6rOZtfLX+/SQ1cNl4+Hs9HRAHRwxyyrli9fftwPLisr0/PPP69Vq1bJwcHhmHcMBAAAAAC0Pz3CvTVnSi998P1eHSqv0Stfx+mfs4fIwcJEGQAt55hlVXh4+J9+4OLFi/XUU08pNzdXQ4cO1bx589SrV69mDwgAAAAAMM64wWFKzirR2p1Z2p9xSJ8u36fLpvY2OhaADqzJdwP8VVpamh5++GGtW7dO3t7eeuyxx3TBBRe0RDYAAAAAgMFMJpMun9pLGXllSskq1YqtGYoK9dLoAaFGRwPQQTV57WZtba1efvllnXPOOVq3bp3OP/98LVmyhKIKOB670QEAAACAU+PoYNEt5w+Qp5ujJOn9pXt1ILvE4FQAOqomlVUbNmzQueeeq//+97+KjIzUhx9+qCeeeEK+vr4tnQ8AAAAA0Ab4ebnoxvNiZDaZZK2z6eWvdqmkosboWAA6oD8tqwoLC3XnnXfqqquuUnZ2tu644w59/fXXio2Nba18QMdiOv5DAAAAgLaqb1dfXTghWpJUUFKt1xfuVp3NZnAqAB3NMcuq+fPna/r06fruu+80ceJELV68WNddd50cHE54zBUAAAAAoIOYOjxSI/oGSZLiDxbpf6uTDU4EoKM5ZvP08MMPN/z/FStWaMWKFcd9MpPJpD179jRPMgAAAABAm2MymXTV9L7KzK9Qel6Zlv6Sqm4hnhrRN9joaAA6iGOWVTNnzpTJxJ4lAAAAAEBjzk4W3TorRo+8t1kV1Va9uzhBYQHuigj0MDoagA7gmGXVU0891Zo5AAAAAADtSJCvm64/t79e/GKHqmvr9NJXu/TgFbFyc3E0OhqAdq5JdwMEAAAAAOCPBkb7a+bYKElSblGl3vx2j2x2u8GpALR3lFUAAAAAgJM2Y1Q3DekZIEnakVSgb9amGJwIQHtHWQUAAAAAOGlmk0nXnt1PwX5ukqRv1h3Q9n35BqcC0J5RVgEAAAAATomrs4P+OmuAnJ0skqQ3F+1WTmGFwakAtFeUVQAAAACAUxYW4K5rZ/SVJFVW1+m/X+1SVY3V4FQA2iPKKqCF2cWASQAAAHQOw3oHacbIrpKkzPxyvfNdvOwMXAdwgiirgFZkMjoAAAAA0MLOH9td/aP8JEmb9+Zp6cZUgxMBaG8oqwAAAAAAzcZsNumGc/srwNtFkvTlqiTtPlBocCoA7QllFQAAAACgWXm4OurWWQPk5GCW3S69vnC38osrjY4FoJ2grAIAAAAANLsuwZ66YnofSVJZZa1e+nqXamrrDE4FoD2grAIAAAAAtIiR/UM0OTZCkpSaU6YPvt/LwHUAx0VZBQAAAABoMRdN6KFekT6SpJ/jsrVia4axgQC0eZRVAAAAAIAW42Ax66aZMfL1dJYkfbp8nxLTio0NBaBNo6wCAAAAALQob3cn3Xx+jBwsJtXZ7Hp1QZyKSquNjgWgjerwZdWLL76o6dOn66yzztJTTz0lm81mdCQAAAAA6HSiw7w1Z0ovSdKh8hq9smCXrHV8fwbgSB26rFq9erXWrVunb775Rt9++622b9+u5cuXGx0LAAAAADqlcYPDdcagUElSUkaJ5i/bZ3AiAG1Rhy6rxo0bp48//liOjo46dOiQSktL5e3tbXQsdDLc7AQAAAD4zZwpvRUV6iVJWrktQz/tzDQ4EYC2pt2XVQsWLFC/fv2O+FVaWipJcnR01JtvvqlJkyYpICBAgwcPNjYwOjeTyegEAAAAgKEcHcy65fwYebk5SpI+/D5RKVklBqcC0Ja0+7Jq5syZ2rNnzxG/PD09Gx5z3XXXaePGjQoICNC///1vA9MCAAAAAPy8XHTTzBiZTSZZ62x6+etdKqmoMToWgDai3ZdVfyYxMVF79uyRVL/C6uyzz1ZCQoLBqQAAAAAAvbv46qKJPSRJhSXVen3hbtVxQywA6uBlVXJysu6//37V1NSorq5Oixcv1vDhw42OBQAAAACQNCU2Qqf3C5YkxR8s0perkgxOBKAtaDNlVXx8vPr376/s7Owjzi1atEgzZszQwIEDNX36dC1YsKBJzzlt2jSNGzdOM2fO1MyZM+Xh4aEbb7yxmZMDAAAAAE6GyWTSFdP7KDLIQ5L0/cY0bYzPMTgVAKM5GB1AkpKSknTDDTfIarUecW7x4sW68847dcUVV2jMmDFatmyZ7r77brm4uGjatGnHfe7bbrtNt912W0vEBgAAAACcImdHi26ZNUCPvrdJ5VVWvbM4XmH+7oo4XGAB6HxMdrvdbtSLW61WffbZZ3r22Wfl6Oio4uJirV69WiEhIQ2PmTJlimJiYvT88883HLv99tu1d+9eLVmyxIjYwAmx1tl0/j+/lSRdNr2PLp7c2+BEAAAAQNuzJSFHD7+1QXa7FOrvruduP0Mebk5GxwJgAENXVm3ZskXPPPOMrrnmGgUHB+v+++9vdD4tLU2pqan6xz/+0ej4mWeeqSVLligtLU2RkZEtnrOgoEw2W8t3eoGBnsrLK23x10Hrstb9NiSyvLyGP+M/wTWAzo5rAJ0d1wDQua+DLv5uOn9sd321JllZBeV68r2N+tsFA2U2mYyOhlbUma+BzsRsNsnf/9irJw2dWRUdHa1ly5bp1ltvlcViOeJ8cnKyJCkqKqrR8a5du0qSUlJSWj4kAAAAAKBVzBjZVUN7BUqSdiYV6Ju1fM8HdEaGllUBAQHy9/c/5vnS0vo21cOjcdvm7u4uSSorK2u5cAAAAACAVmUymXTNjL4K9XeTJH2z7oC27cszOBWA1tZm7gZ4NMcbp2U2t+n4AAAAAIAT5OrsoFtnDZCLU/3um7cW7VFWQbnBqQC0pjbd9nh6ekqSyssb/8X064qqX88D7QW77QEAAIDjC/V317Vn95MkVVbX6eWv41RZfeTd4wF0TG26rPp1VlVqamqj4wcPHmx0HgAAAADQsQztFaizR9XPK87ML9e7i+OPu/sGQMfQpsuqrl27KiIiQkuXLm10/IcfflC3bt0UFhZmUDIAAAAAQEubOaa7YqL8JEmb9+bpm3UHVFNbZ3AqAC3NwegAx3PLLbfonnvukbe3t8aPH6/ly5dryZIlev75542OBgAAAABoQWazSdef21+Pvr9JecVVWrg2RYt+PqCIIA9Fh3kpOsxb3cO8FOTrKpOJoRtAR9Hmy6pZs2appqZG77zzjr744gtFRkbq6aef1llnnWV0NAAAAABAC/NwddQt5w/Qv+dvU3mVVXU2uw5ml+pgdqlWbM1oeExUqJeiw7zUPdxL3UO95ObiaHByACfLZGfT73EVFJTJZmv5T1NgoKfy8kpb/HXQuqx1Nl3/71WSpFlndNfZo7oZmqct4xpAZ8c1gM6OawDgOvgzZZW1SjhYpKTMQ0rOLNGB7FLVWm3HfHyov5u6h3qpe7i3osO8FB7oLgt3lG/zuAY6B7PZJH9/j2Oeb/MrqwAAAAAA8HB1VGyfIMX2CZJU/0Ph9LwyJWeWKCmjRMlZJcoprGh4fFZBhbIKKrQuLluS5ORoVrcQL3UPO7wCK8xbvp7OhrwXAH+OsgoAAAAA0O44WOrLp24hXpo4tP5YWWWtkjNLlHx49VVyZokqqq2SpJpamxLTipWYVtzwHL6ezg3FVfcwL3UL8ZSTo8WAdwPg9yirAAAAAAAdgoerowZG+2tgtL8kyWa3K6ewoqG4Sso8pPTcctkOT8MpKq3W5r152rw3T5JkMZsUEeih7uG/rb4KZng70OooqwAAAAAAHZLZZFKov7tC/d01ekCoJKm6tk4Hs0sbZl8lZ5aoqLRakuqHt+eU6mBOqVYeHt7u7uKgqMN3HowO81JUmJfcGd4OtCjKKqCFcQsDAAAAoO1wdrSoV6SPekX6NBwrLKmqL66ySpSccUgHsktVc3h4e3mVVXHJhYpLLmx4fIifW6PZVxFBDG8HmhNlFdCKWD0MAAAAtD1+Xi7y83JpNLw9I6+8YfZVUmaJsn83vD27sELZhRX6+dfh7Q5mdQvxbJh9FR3O8HbgVFBWAQAAAADwOw4Ws7qGeKpriKcmHB7eXl5Vq5TDxdWvQ9zLqw4Pb7falJh+SInphxqew9fTWd3Dfr37oLe6hnjKmeHtQJNQVgEAAAAAcBzuLo6K6e6vmO71w9vtdrtyiiqVnHmoocBKzy1Tne234e1b9uZpy+Hh7WaTSd1CPXXJxJ7qEeFt2PsA2gPKKgAAAAAATpDJZFKIn5tC/Nw0Kqbx8PZfV14lZ5WosKR+eLvNbldyZon+/ek2XX9Ofw3rHWhkfKBNo6wCAAAAAKAZHG14e1FptZIzDykx7ZCWb0lXrdWmV77epdlTemnSsAjjwgJtGLcrAAAAAACghfh6OmtY7yBdOrmnbrtwoJwdLbJL+vjHRH2xar9s3D4cOAJlFQAAAAAArWBAd3/dPWeIvNwcJUlLNqTqrUV7ZK2zGZwMaFsoqwAAAAAAaCXdQrx079xYBfu6SpI27M7R85/vUGW11eBkQNtBWQUAAAAAQCsK8nHVvZcPU3SYlyQp/mCRnvp4q4pKqw1OBrQNlFUAAAAAALQyTzcn3XnpEA3uESBJSsst0xMfblZGfrnByQDjUVYBLY6BiQAAAACO5Oxo0S2zYjR+SLgkqaCkWk9+uEWJacXGBgMMRlkFAAAAAIBBLGazLp/aS7PO6C5Jqqi26plPt2tzQq7ByQDjUFYBAAAAAGAgk8mks0d10zUz+spiNslaZ9OrC+L04+Y0o6MBhqCsAgAAAACgDRg9IFS3XThQzk4W2SXNX7ZPn6/cL5ud0SLoXCirAAAAAABoI2Ki/PWv2UPl7e4kSVr6S6re/HaPaq02g5MBrYeyCgAAAACANqRriKfuu3yYQvzcJEm/7MnR859vV0WV1eBkQOugrAIAAAAAoI0J8HHVvZcPU49wb0lSQmqxnvp4i4pKqw1OBrQ8yioAAAAAANogD1dH3XnJYA3pGSBJSs8r1+MfblZGXpnByYCWRVkFAAAAAEAb5eRo0S3nD9CEoeGSpMKSaj350VbtTS0yOBnQciirAAAAAABow8xmky6b0kt/GdddklRRbdWzn23XpoRcg5MBLYOyCgAAAACANs5kMmnGyG669uy+sphNstbZ9dqCOP2wKc3oaECzo6wCWpjdbnQCAAAAAB3FqJhQ3X7hILk4WWSX9OnyffpsxT7Z+MYDHQhlFQAAAAAA7Uj/KD/9a85Qebs7SZK+35imN77ZrVqrzeBkQPOgrAJakclkMjoCAAAAgA6gS7Cn7ps7TKH+bpKkjfG5ev7z7aqoqjU4GXDqKKsAAAAAAGiHArxddc9lw9QjwluSlJBarCc/3qrCkiqDkwGnhrIKAAAAAIB2ysPVUXdePFjDegVKkjLyyvX4h1uUnldmcDLg5FFWAQAAAADQjjk5WnTTzBhNGhYhSSoqrdaTH21VwsEig5MBJ4eyCgAAAACAds5sNmn25J66cEK0JKmy2qrnPt+ujfE5BicDThxlFQAAAAAAHYDJZNL007rq+nP6yWI2yVpn12sLd+v7jalGRwNOCGUVAAAAAAAdyOn9Q/T3iwbJ1dkiSfpsxX7NX7ZPNrvd4GRA01BWAQAAAADQwfTr5qd/zRkmHw8nSdKPm9P0+sLdqrXWGZwMOD7KKgAAAAAAOqDIIA/dd3msQv3dJEmbEnL17Gc7VF5Va3Ay4M9RVgEAAAAA0EH5e7vo3suHqVeEtyQpMa1YT320VYUlVQYnA46NsgpoYewKBwAAAGAkdxdH3XHJYMX2DpQkZeSX67EPNistt8zgZMDRUVYBrchkdAAAAAAAnZKjg0U3zozR5NgISVJxWY2e+niL4g8WGZwMOBJlFQAAAAAAnYDZZNKlk3rqogk9JEmV1XX/396dR1Vd538cf10EFEERcEcEsYYQNREVEZ3MHdTskP7UrOOUdbCxZhzzmJiTtnjUtDEr01zapnIcyME0tzR/LW6TuMxPWUplT80BITRFgfv7AyFuuKBy7/fCfT7O8RzO53sv98Wlr8mL9/fz1d/WHda+5NMGJwMsUVYBAAAAAOAgTCaThoW3V+wDIWrgZFJpmVkrP0vW1v1ZMpvZxAT2gbIKAAAAAAAHE96plaaN7Sa3hg0kSf/cdVxrd/ygsjIKKxiPsgoAAAAAAAcU7O+luAlh8mrSUJK0IylHyzcc1ZWSUoOTwdFRVgEAAAAA4KDatfTQ84+Gybe5uyQpKe2sXvvHYZ2/eMXgZHBklFUAAAAAADgw76aNFPdIdwX5NZMkfZ9TqPkfJSmv8JKxweCwKKsAAAAAAHBwjRu5aNrYbup5T0tJ0qm8X/TK3w8o60yRwcngiCirAAAAAACAXJydFDsqREN6+kmSCs9f1oKPDyo5I9/gZHA0lFUAAAAAAECS5GQyadzAuzVuwF2SpEuXS7Xkn0e099hpg5PBkVBWAdbGnV8BAAAA1DFDerXX5FEhcm5gUmmZWas2JmvzvkyZzfyAA+ujrAJsyWR0AAAAAAComV7BrTTtf7rJraGzJCnhf0/oX9+cNDgVHAFlFQAAAAAAuKZ7/L0U90h3eTVpKEnavDdLZ879YnAq1HeUVQAAAAAA4LratfDQn0d3lSSVmc3atDvD2ECo9yirAAAAAADADbVv1URhQS0kSXuPnWG6ClZFWQUAAAAAAG7qgcgOksqnqzYyXQUroqwCAAAAAAA35dfSQz0qp6tO60w+01WwDsoqAAAAAABQIxXTVWaz9BnTVbASyioAAAAAAFAj7Vp6qMc9LSVJ+5JP6zTTVbACyioAAAAAAFBjD0QGyKTy6aqNu9ONjoN6iLIKAAAAAADUWLsWVaerzuhU3gWDE6G+oawCrMwss9ERAAAAAKBWVZ2u2rQnw+g4qGcoqwAbMslkdAQAAAAAuGO+LTzUM5jpKlgHZRUAAAAAALhlIyM7/Lp3FdNVqEWUVQAAAAAA4Jb5NnevnK7az3QVahFlFQAAAAAAuC0W01W7M4yOg3qCsgoAAAAAANwW3+bu6tWplaTy6aof/8t0Fe4cZRUAAAAAALhtI/tcvTOg2LsKtYOyCgAAAAAA3La2zd0VfnW66t/JZ5TLdBXuEGUVAAAAAAC4IyMjA2QyXZ2u2p1udBzUcZRVAAAAAADgjrTx+XW66ruUn5R79rzBiVCXUVYBAAAAAIA7NrJPlekq9q7CHaCsAqzMbDY6AQAAAABYXxsfd/Vmugq1gLIKAAAAAADUipGRHSqnqz7bnWF0HNRRlFUAAAAAAKBWtPZurN6dWkuSDqT+pBymq3AbKKsAAAAAAECtqXpnQKarcDsoqwAAAAAAQK1p7d1YESFVpqt+YroKt4ayCgAAAAAA1KqRfQLkZDJJkj7bnW5wGtQ1lFUAAAAAAKBWtfJurIiQ8jsDHkg7q2ymq3ALKKsAAAAAAECtGxHJdBVuD2UVAAAAAACoda28Giuic/l0VVLaWWWdKTI4EeoKyioAAAAAAGAVVfeu2sidAVFDlFUAAAAAAMAqWno1Vp/O5XcGTPqe6SrUDGUVAAAAAACwmhF9/KvsXZVhbBjUCZRVgA1d/fsZAAAAABxG1emqg0xXoQYoqwAAAAAAgFVVvTPghm+5MyBujLIKAAAAAABYVctmburTpXy66tAP/1XmaaarcH2UVQAAAAAAwOpG9AlQA6eKvauYrsL1UVYBAAAAAACra9nMrXLvKqarcCOUVQAAAAAAwCaqTlexdxWuh7IKAAAAAADYRItmboq8unfV4eP/Vcbpnw1OBHtEWQUAAAAAAGxmRESVvau+zTA2DOwSZRUAAAAAALCZ5s3cFNmljaTy6ar0U0xXwRJlFQAAAAAAsKkRffyrTFexdxUsUVYBVmY2G50AAAAAAOxLc0839e1aPl115EQe01WwQFkFAAAAAABsbniEP3cGxDVRVgE2ZDI6AAAAAADYieaebup3dbrqPyfydPJHpqtQjrIKAAAAAAAYYnjVOwPuZroK5SirAAAAAACAIXw8G6nfvW0llU9XpWXmG5wI9oCyCgAAAAAAGGZElb2r1m5PMzgN7AFlFQAAAAAAMIx300b6/dXpqqTUn3Qit9DgRDAaZRUAAAAAADDU8Ah/OTe4emdA9q5yeJRVAAAAAADAUN5Nf9276ujJfB1nusqhUVYBAAAAAADDDe/tL+cG5TXFZ98yXeXIKKsAAAAAAIDhvJs20tDe/pKko+lMVzkyyioAAAAAAGAXxgy8+9e9q5iucliUVYDVmY0OAAAAAAB1go+nm+6711eSdCw9X8dzmK5yRJRVgC2ZTEYnAAAAAAC7Fh3x695VG749aXAaGIGyCgAAAAAA2A2vJg11X7fyOwMeyzinH3IKjA0Em6OsAgAAAAAAdiW6d9XpKvaucjSUVQAAAAAAwK54NWmo/lenq5Izzun77AJjA8GmKKsAAAAAAIDdiY7wl4sz01WOiLIKAAAAAADYnWYev+5dlZLJdJUjoawCAAAAAAB2Kbo301WOiLIKAAAAAADYpWYeDdW/m6+k8umqtKxzBieCLVBWAQAAAAAAuxXduz3TVQ6GsgoAAAAAANgtT4+Guj+0fLoqNauA6SoHQFkFWJnZ6AAAAAAAUMdFhbeXK9NVDoOyCrAhk9EBAAAAAKAO8vRoqP5VpqtSM5muqs8oqwAAAAAAgN2L6u3PdJWDoKwCAAAAAAB2z9PdVfd3L5+uSstmuqo+c5iy6sMPP1RMTIzRMQAAAAAAwG0aFv7rdFXit+kym9kluD5yiLIqOTlZq1atMjoGAAAAAAC4A57urhrQvZ0k6fvsAqVmFRgbCFZR78uqCxcu6IUXXtC0adOMjgIAAAAAAO7QsPD2cnW5unfVNyeZrqqH6nxZlZiYqE6dOlX7U1RUJEmaO3euHn/8cbVt29bgpAAAAAAA4E41rTpdlVPI3lX1kLPRAe7Ugw8+qAcffPCax9avXy8XFxdFR0dr//79tg0GAAAAAACsYliv9vryYI4uXylT4rfpusffSyaTyehYqCV1frLqRjZu3KgjR45o1KhRmj17tk6cOKE//OEPRscCAAAAAAB3oKm7qwZena76IadQKUxX1St1frLqRt57773Kj/fv36+FCxfq/fffNy4QAAAAAACoFUPD2+vLg7kqvlKqxG/TFcx0Vb1hN5NVKSkpCgkJ0enTp6sd27Rpk4YPH66uXbsqKipKiYmJtg8I3Cb2+gMAAACA2te0sasGhPlKko7nFCqZ6ap6wy7KqhMnTig2NlYlJSXVjm3evFnTp09X3759tWzZMvXq1UvPPfectm7dekuvER4ervXr19dWZOD2UPIDAAAAQK0Z1qu9Gro0kCRt+CadOwPWE4ZeBlhSUqJ169bptddek4uLyzUfs2TJEkVFRSkuLk6S1K9fPxUWFmrp0qUaNmyYTXL6+HjY5HUkqUWLJjZ7LdhGo18uV37s4dGQ7/FN8P7A0XEOwNFxDgCcB8CtnAMtJI3sF6iEL3/Q8dxC5Z67pNCgltYLB5swtKxKSkrS4sWLNWnSJLVq1UqzZ8+2OJ6dna2srCxNmzbNYn3o0KHasmWLsrOz5efnZ/WceXnnVVZm/Xa2RYsmOnu2yOqvA9s6f/HKrx+fL+Z7fAOcA3B0nANwdJwDAOcBcDvnQL/OrbTx25MqvlyqDz4/Jl+vRuxdZeecnEw3HAwy9DLAjh07aseOHXr66afVoEGDasdPnjwpSerQoYPFur+/vyQpPT3d+iEBAAAAAIDdatLYVYPCyu8MeCL3Zx3LyDc4Ee6UoWVV8+bN5ePjc93jRUXlbaqHh2Xb5u7uLkk6f/689cIBAAAAAIA6YWiv9mroyt5V9YVdbLB+PTf7j8v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-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot luminosity distribution\n",
-    "ldist = population.grid_results['luminosity distribution']\n",
-    "\n",
-    "# pad the distribution with zeros where data is missing\n",
-    "pad_output_distribution(ldist,\n",
-    "                        binwidth['luminosity'])\n",
-    "\n",
-    "# make pandas dataframe from our sorted dictionary of data\n",
-    "plot_data = pd.DataFrame.from_dict({'ZAMS luminosity distribution' : ldist})\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\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31fe91bb-177d-4e4e-90cf-298a3f8a8b61",
-   "metadata": {},
-   "source": [
-    "Most stars are low mass red dwarfs, with small luminosities. Without the IMF weighting, our model population would have got this completely wrong! \n",
-    "\n",
-    "As you increase the resolution, you will see this curve becomes even smoother. The wiggles in the curve are (usually) sampling artefacts because the curve should monotonically brighten above about log(*L*/L<sub>☉</sub>)=-2. \n",
-    " \n",
-    "Remember you can play with the binwidth too. If you want a very accurate distribution you need a narrow binwidth, but then you'll also need high resolution (lots of stars) so lots of CPU time, hence cost, CO<sub>2</sub>, etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba032bd8-b4a2-4558-9fd9-8e1e03d7d162",
-   "metadata": {},
-   "source": [
-    "Things to try:\n",
-    "\n",
-    " * Change the resolution to make the distributions smoother: what about error bars, how would you do that?\n",
-    " * Different initial distributions: the Kroupa distribution isn't the only one out there\n",
-    " * Change the metallicity and mass ranges\n",
-    " * What about a non-constant star formation rate? This is more of a challenge!\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?\n",
-    " * Binary stars! (see notebook_luminosity_function_binaries.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5abd0935-3957-4859-80c1-6f5d7ce4b614",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/build/html/examples/notebook_population.html b/docs/build/html/examples/notebook_population.html
index 0b2d2d390..96975f411 100644
--- a/docs/build/html/examples/notebook_population.html
+++ b/docs/build/html/examples/notebook_population.html
@@ -1412,7 +1412,7 @@ time mass_1 zams_mass_1 mass_2 zams_mass_2 stellar_type_1 prev_stellar_type_1 st
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/notebook_solar_system.html b/docs/build/html/examples/notebook_solar_system.html
index 1b4fa431d..d5186edd8 100644
--- a/docs/build/html/examples/notebook_solar_system.html
+++ b/docs/build/html/examples/notebook_solar_system.html
@@ -657,7 +657,7 @@ div.rendered_html tbody tr:hover {
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/old/basic_example.html b/docs/build/html/examples/old/basic_example.html
index c61dcdcf1..9954a2b4a 100644
--- a/docs/build/html/examples/old/basic_example.html
+++ b/docs/build/html/examples/old/basic_example.html
@@ -458,7 +458,7 @@ div.rendered_html tbody tr:hover {
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/examples/old/workshop_example_notebook.html b/docs/build/html/examples/old/workshop_example_notebook.html
index 55ef3f357..500aaf10b 100644
--- a/docs/build/html/examples/old/workshop_example_notebook.html
+++ b/docs/build/html/examples/old/workshop_example_notebook.html
@@ -889,7 +889,7 @@ Took 14.214274644851685s
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/functions.html b/docs/build/html/functions.html
index dce673ad7..ef1abd20b 100644
--- a/docs/build/html/functions.html
+++ b/docs/build/html/functions.html
@@ -733,7 +733,7 @@ of all the binary_c parameters.</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index 8aa8d8bb6..f96b236d6 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -1088,7 +1088,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/grid.html b/docs/build/html/grid.html
index 988f50bae..4c0a274a9 100644
--- a/docs/build/html/grid.html
+++ b/docs/build/html/grid.html
@@ -378,7 +378,7 @@ in the self.grid_options</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/grid_options_descriptions.html b/docs/build/html/grid_options_descriptions.html
index 9a8a2c676..0037fc2d4 100644
--- a/docs/build/html/grid_options_descriptions.html
+++ b/docs/build/html/grid_options_descriptions.html
@@ -647,7 +647,7 @@ q extrapolation (below 0.15) method
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 1f80a8ff1..27c00fa1f 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -108,6 +108,7 @@
 <p>We present our package <a class="reference external" href="https://ri0005.pages.surrey.ac.uk/binary_c-python/">binary-c-python</a>, a population synthesis code which is aimed to provide a convenient and easy-to-use interface to the <a class="reference external" href="http://personal.ph.surrey.ac.uk/~ri0005/doc/binary_c/binary_c.html">binary_c</a> framework, allowing the user to rapidly evolve single stellar systems and populations of star systems. Based on a early work by Jeff Andrews. Updated and extended for Python3 by David Hendriks, Robert Izzard.</p>
 <p>binary_c-python is developed for students and scientists in the field of stellar astrophysics, who want to study the evolution of individual or populations of single and binary star systems (see the example use-case notebooks in the <a class="reference external" href="https://ri0005.pages.surrey.ac.uk/binary_c-python/example_notebooks.html">online documentation</a>.</p>
 <p>The current release is version <a class="reference external" href="VERSION">version</a>, and is designed and tested to work with binary_c version 2.2.1 (for older or newer versions we can’t guarantee correct behaviour).</p>
+<p>The current development branch is <code class="docutils literal notranslate"><span class="pre">development_0.9.5/2.2.1</span></code>.</p>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2>
 <p>To install binary_c-python we need to make sure we meet the requirements of installation, and</p>
@@ -335,7 +336,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/modules.html b/docs/build/html/modules.html
index c65f10a95..9961ab613 100644
--- a/docs/build/html/modules.html
+++ b/docs/build/html/modules.html
@@ -174,7 +174,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
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diff --git a/docs/build/html/plot_functions.html b/docs/build/html/plot_functions.html
index 531211e81..31775e892 100644
--- a/docs/build/html/plot_functions.html
+++ b/docs/build/html/plot_functions.html
@@ -378,7 +378,7 @@ This is not included in all the plotting routines.</p></li>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/HPC.html b/docs/build/html/population_extensions/HPC.html
index 6eac60d04..4995e61e4 100644
--- a/docs/build/html/population_extensions/HPC.html
+++ b/docs/build/html/population_extensions/HPC.html
@@ -375,7 +375,7 @@ and (if given) the string passed in.</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/Moe_di_Stefano_2017.html b/docs/build/html/population_extensions/Moe_di_Stefano_2017.html
index da844f5e7..bc5a51dd1 100644
--- a/docs/build/html/population_extensions/Moe_di_Stefano_2017.html
+++ b/docs/build/html/population_extensions/Moe_di_Stefano_2017.html
@@ -193,7 +193,7 @@ then set the distribution functions</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/analytics.html b/docs/build/html/population_extensions/analytics.html
index 7ba8c7a04..d7897bedb 100644
--- a/docs/build/html/population_extensions/analytics.html
+++ b/docs/build/html/population_extensions/analytics.html
@@ -182,7 +182,7 @@ the time elapsed if otherwise or if force is True</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/cache.html b/docs/build/html/population_extensions/cache.html
index ee1e8d5d6..7d2c7c6d0 100644
--- a/docs/build/html/population_extensions/cache.html
+++ b/docs/build/html/population_extensions/cache.html
@@ -216,7 +216,7 @@ given caches of size self.grid_options[‘function_cache_size’][func]</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/condor.html b/docs/build/html/population_extensions/condor.html
index fbf83d2a2..35e886946 100644
--- a/docs/build/html/population_extensions/condor.html
+++ b/docs/build/html/population_extensions/condor.html
@@ -235,7 +235,7 @@ TODO: Comment this function better</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/dataIO.html b/docs/build/html/population_extensions/dataIO.html
index e3a8fd30c..232d3c6e9 100644
--- a/docs/build/html/population_extensions/dataIO.html
+++ b/docs/build/html/population_extensions/dataIO.html
@@ -394,7 +394,7 @@ passed to json.dumps)</p></li>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/distribution_functions.html b/docs/build/html/population_extensions/distribution_functions.html
index d7fc6de81..32fec1717 100644
--- a/docs/build/html/population_extensions/distribution_functions.html
+++ b/docs/build/html/population_extensions/distribution_functions.html
@@ -834,7 +834,7 @@ and is be given by dp/dlogP ~ (logP)^p for all other binary configurations (defa
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/grid_logging.html b/docs/build/html/population_extensions/grid_logging.html
index 6da7aee98..4b38425e1 100644
--- a/docs/build/html/population_extensions/grid_logging.html
+++ b/docs/build/html/population_extensions/grid_logging.html
@@ -180,7 +180,7 @@ system_number: the system number</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/grid_options_defaults.html b/docs/build/html/population_extensions/grid_options_defaults.html
index 753e9b0ca..e4f6a9eaa 100644
--- a/docs/build/html/population_extensions/grid_options_defaults.html
+++ b/docs/build/html/population_extensions/grid_options_defaults.html
@@ -243,7 +243,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/gridcode.html b/docs/build/html/population_extensions/gridcode.html
index b97d558f7..7e063b248 100644
--- a/docs/build/html/population_extensions/gridcode.html
+++ b/docs/build/html/population_extensions/gridcode.html
@@ -299,7 +299,7 @@ of the self.grid_options[“_grid_variables”] dictionary.</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/metadata.html b/docs/build/html/population_extensions/metadata.html
index add7db083..6d5ab84dc 100644
--- a/docs/build/html/population_extensions/metadata.html
+++ b/docs/build/html/population_extensions/metadata.html
@@ -168,7 +168,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/slurm.html b/docs/build/html/population_extensions/slurm.html
index ddf4f0e59..d5bc70a5b 100644
--- a/docs/build/html/population_extensions/slurm.html
+++ b/docs/build/html/population_extensions/slurm.html
@@ -236,7 +236,7 @@ TODO: Comment this function better</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/spacing_functions.html b/docs/build/html/population_extensions/spacing_functions.html
index e3db921fd..a39d45529 100644
--- a/docs/build/html/population_extensions/spacing_functions.html
+++ b/docs/build/html/population_extensions/spacing_functions.html
@@ -323,7 +323,7 @@ not that the integral is 1.0</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/population_extensions/version_info.html b/docs/build/html/population_extensions/version_info.html
index 348f01c47..5d423b5f1 100644
--- a/docs/build/html/population_extensions/version_info.html
+++ b/docs/build/html/population_extensions/version_info.html
@@ -200,7 +200,7 @@ dictionary</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index d2840cb25..16dab6e8c 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -223,7 +223,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/readme_link.html b/docs/build/html/readme_link.html
index baf3c5c76..e62e89399 100644
--- a/docs/build/html/readme_link.html
+++ b/docs/build/html/readme_link.html
@@ -134,6 +134,7 @@
 <p>We present our package <a class="reference external" href="https://ri0005.pages.surrey.ac.uk/binary_c-python/">binary-c-python</a>, a population synthesis code which is aimed to provide a convenient and easy-to-use interface to the <a class="reference external" href="http://personal.ph.surrey.ac.uk/~ri0005/doc/binary_c/binary_c.html">binary_c</a> framework, allowing the user to rapidly evolve single stellar systems and populations of star systems. Based on a early work by Jeff Andrews. Updated and extended for Python3 by David Hendriks, Robert Izzard.</p>
 <p>binary_c-python is developed for students and scientists in the field of stellar astrophysics, who want to study the evolution of individual or populations of single and binary star systems (see the example use-case notebooks in the <a class="reference external" href="https://ri0005.pages.surrey.ac.uk/binary_c-python/example_notebooks.html">online documentation</a>.</p>
 <p>The current release is version <a class="reference external" href="VERSION">version</a>, and is designed and tested to work with binary_c version 2.2.1 (for older or newer versions we can’t guarantee correct behaviour).</p>
+<p>The current development branch is <code class="docutils literal notranslate"><span class="pre">development_0.9.5/2.2.1</span></code>.</p>
 <section id="installation">
 <h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h2>
 <p>To install binary_c-python we need to make sure we meet the requirements of installation, and</p>
@@ -282,7 +283,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/run_system_wrapper.html b/docs/build/html/run_system_wrapper.html
index 514c3aba4..0e7202022 100644
--- a/docs/build/html/run_system_wrapper.html
+++ b/docs/build/html/run_system_wrapper.html
@@ -194,7 +194,7 @@ and returns what the parse_function returns</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/search.html b/docs/build/html/search.html
index dcaa7b14a..1d0efe756 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -101,7 +101,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 497d25460..45468d049 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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diff --git a/docs/build/html/stellar_types.html b/docs/build/html/stellar_types.html
index cc3dd3d95..b34277ea9 100644
--- a/docs/build/html/stellar_types.html
+++ b/docs/build/html/stellar_types.html
@@ -154,7 +154,7 @@
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
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+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
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diff --git a/docs/build/html/useful_funcs.html b/docs/build/html/useful_funcs.html
index 5ec042793..30b97845d 100644
--- a/docs/build/html/useful_funcs.html
+++ b/docs/build/html/useful_funcs.html
@@ -351,7 +351,7 @@ determine if two stars collide on the ZAMS</p>
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
   
 <br><br>
-Generated on binarycpython git branch: development_0.9.4/2.2.1 git revision c48f2256195b6290caa705a905bd276b3c042622 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/development_0.9.4/2.2.1">git url</a>.
+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
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diff --git a/docs/source/_templates/footer.html b/docs/source/_templates/footer.html
index 2ca0040ea..8ffdad5e6 100644
--- a/docs/source/_templates/footer.html
+++ b/docs/source/_templates/footer.html
@@ -2,7 +2,7 @@
 
 {%- block extrafooter %}
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+Generated on binarycpython git branch: master git revision abda8c4f6d329da2e0d918786588c29f46dff1c5 url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch branch_david: git revision: "6118:20220617:8463fa650" url: <a href="https://gitlab.surrey.ac.uk/ri0005/binary_c/-/tree/branch_david">git url</a>.
 
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index a4262d340..9f708fe59 100644
--- a/docs/source/badges/test_coverage.svg
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-- 
GitLab