From ff26272927f27c208bfc82dda912cb447bf06f5b Mon Sep 17 00:00:00 2001
From: David Hendriks <davidhendriks93@gmail.com>
Date: Sun, 12 Sep 2021 17:48:30 +0100
Subject: [PATCH] rebuilt docs

---
 docs/Makefile                                 |    4 +-
 docs/build/doctrees/environment.pickle        |  Bin 563041 -> 570896 bytes
 docs/build/doctrees/example_notebooks.doctree |  Bin 4511 -> 4573 bytes
 .../doctrees/nbsphinx/notebook_HRD.ipynb      |  818 ++++++++++++
 .../doctrees/nbsphinx/notebook_HRD_14_2.png   |  Bin 0 -> 105670 bytes
 .../doctrees/nbsphinx/notebook_HRD_19_2.png   |  Bin 0 -> 109795 bytes
 .../doctrees/nbsphinx/notebook_HRD_23_2.png   |  Bin 0 -> 98964 bytes
 .../doctrees/nbsphinx/notebook_HRD_26_2.png   |  Bin 0 -> 32632 bytes
 .../notebook_common_envelope_evolution.ipynb  |  708 ++++++++++
 ...otebook_common_envelope_evolution_14_2.png |  Bin 0 -> 58548 bytes
 .../notebook_individual_systems.ipynb         |    4 +-
 ...otebook_luminosity_function_binaries.ipynb |  292 ++--
 ...book_luminosity_function_binaries_20_1.png |  Bin 57125 -> 73535 bytes
 .../notebook_luminosity_function_single.ipynb |   79 +-
 ...tebook_luminosity_function_single_20_1.png |  Bin 25029 -> 31898 bytes
 ...tebook_luminosity_function_single_25_1.png |  Bin 23856 -> 31453 bytes
 ...tebook_luminosity_function_single_33_0.png |  Bin 30847 -> 39614 bytes
 .../nbsphinx/notebook_population.ipynb        |    4 +-
 docs/build/doctrees/notebook_HRD.doctree      |  Bin 0 -> 79398 bytes
 ...notebook_common_envelope_evolution.doctree |  Bin 0 -> 90797 bytes
 ...ebook_luminosity_function_binaries.doctree |  Bin 130050 -> 82476 bytes
 ...otebook_luminosity_function_single.doctree |  Bin 72824 -> 71009 bytes
 docs/build/html/_images/notebook_HRD_14_2.png |  Bin 0 -> 105670 bytes
 docs/build/html/_images/notebook_HRD_19_2.png |  Bin 0 -> 109795 bytes
 docs/build/html/_images/notebook_HRD_23_2.png |  Bin 0 -> 98964 bytes
 docs/build/html/_images/notebook_HRD_26_2.png |  Bin 0 -> 32632 bytes
 ...otebook_common_envelope_evolution_14_2.png |  Bin 0 -> 58548 bytes
 ...book_luminosity_function_binaries_20_1.png |  Bin 57125 -> 73535 bytes
 ...tebook_luminosity_function_single_20_1.png |  Bin 25029 -> 31898 bytes
 ...tebook_luminosity_function_single_25_1.png |  Bin 23856 -> 31453 bytes
 ...tebook_luminosity_function_single_33_0.png |  Bin 30847 -> 39614 bytes
 .../utils/custom_logging_functions.html       |    2 +-
 .../utils/distribution_functions.html         |    2 +-
 .../binarycpython/utils/functions.html        |    2 +-
 .../_modules/binarycpython/utils/grid.html    |    2 +-
 .../utils/grid_options_defaults.html          |    2 +-
 .../binarycpython/utils/plot_functions.html   |    2 +-
 .../utils/run_system_wrapper.html             |    2 +-
 .../utils/spacing_functions.html              |    2 +-
 .../binarycpython/utils/useful_funcs.html     |    2 +-
 docs/build/html/_modules/index.html           |    2 +-
 .../html/_sources/example_notebooks.rst.txt   |    4 +-
 .../html/_sources/notebook_HRD.ipynb.txt      |  818 ++++++++++++
 ...tebook_common_envelope_evolution.ipynb.txt |  708 ++++++++++
 .../notebook_individual_systems.ipynb.txt     |    4 +-
 ...ook_luminosity_function_binaries.ipynb.txt |  292 ++--
 ...ebook_luminosity_function_single.ipynb.txt |   79 +-
 .../_sources/notebook_population.ipynb.txt    |    4 +-
 docs/build/html/binary_c_parameters.html      |    6 +-
 docs/build/html/custom_logging_functions.html |    2 +-
 docs/build/html/distribution_functions.html   |    2 +-
 docs/build/html/example_notebooks.html        |   23 +-
 docs/build/html/functions.html                |    2 +-
 docs/build/html/genindex.html                 |    2 +-
 docs/build/html/grid.html                     |    2 +-
 docs/build/html/grid_options_defaults.html    |    2 +-
 .../build/html/grid_options_descriptions.html |    2 +-
 docs/build/html/hpc_functions.html            |    2 +-
 docs/build/html/index.html                    |    6 +-
 docs/build/html/modules.html                  |    2 +-
 docs/build/html/notebook_HRD.html             | 1176 +++++++++++++++++
 docs/build/html/notebook_HRD.ipynb            |  818 ++++++++++++
 .../html/notebook_api_functionality.html      |    6 +-
 .../notebook_common_envelope_evolution.html   | 1132 ++++++++++++++++
 .../notebook_common_envelope_evolution.ipynb  |  708 ++++++++++
 docs/build/html/notebook_custom_logging.html  |    6 +-
 docs/build/html/notebook_extra_features.html  |    6 +-
 .../html/notebook_individual_systems.html     |    6 +-
 .../html/notebook_individual_systems.ipynb    |    4 +-
 ...notebook_luminosity_function_binaries.html |  291 ++--
 ...otebook_luminosity_function_binaries.ipynb |  292 ++--
 .../notebook_luminosity_function_single.html  |   65 +-
 .../notebook_luminosity_function_single.ipynb |   79 +-
 docs/build/html/notebook_population.html      |    6 +-
 docs/build/html/notebook_population.ipynb     |    4 +-
 docs/build/html/objects.inv                   |  Bin 5992 -> 6222 bytes
 docs/build/html/plot_functions.html           |    2 +-
 docs/build/html/py-modindex.html              |    2 +-
 docs/build/html/readme_link.html              |    2 +-
 docs/build/html/run_system_wrapper.html       |    2 +-
 docs/build/html/search.html                   |    2 +-
 docs/build/html/searchindex.js                |    2 +-
 docs/build/html/spacing_functions.html        |    2 +-
 docs/build/html/stellar_types.html            |    2 +-
 docs/build/html/useful_funcs.html             |    2 +-
 docs/source/_templates/footer.html            |    2 +-
 docs/source/example_notebooks.rst             |    4 +-
 docs/source/notebook_HRD.ipynb                |  818 ++++++++++++
 .../notebook_common_envelope_evolution.ipynb  |  708 ++++++++++
 docs/source/notebook_individual_systems.ipynb |    4 +-
 ...otebook_luminosity_function_binaries.ipynb |  292 ++--
 .../notebook_luminosity_function_single.ipynb |   79 +-
 docs/source/notebook_population.ipynb         |    4 +-
 examples/notebook_HRD.ipynb                   |    6 +-
 .../notebook_common_envelope_evolution.ipynb  |   10 +-
 ...otebook_luminosity_function_binaries.ipynb |    6 +-
 .../notebook_luminosity_function_single.ipynb |    4 +-
 97 files changed, 9152 insertions(+), 1293 deletions(-)
 create mode 100644 docs/build/doctrees/nbsphinx/notebook_HRD.ipynb
 create mode 100644 docs/build/doctrees/nbsphinx/notebook_HRD_14_2.png
 create mode 100644 docs/build/doctrees/nbsphinx/notebook_HRD_19_2.png
 create mode 100644 docs/build/doctrees/nbsphinx/notebook_HRD_23_2.png
 create mode 100644 docs/build/doctrees/nbsphinx/notebook_HRD_26_2.png
 create mode 100644 docs/build/doctrees/nbsphinx/notebook_common_envelope_evolution.ipynb
 create mode 100644 docs/build/doctrees/nbsphinx/notebook_common_envelope_evolution_14_2.png
 create mode 100644 docs/build/doctrees/notebook_HRD.doctree
 create mode 100644 docs/build/doctrees/notebook_common_envelope_evolution.doctree
 create mode 100644 docs/build/html/_images/notebook_HRD_14_2.png
 create mode 100644 docs/build/html/_images/notebook_HRD_19_2.png
 create mode 100644 docs/build/html/_images/notebook_HRD_23_2.png
 create mode 100644 docs/build/html/_images/notebook_HRD_26_2.png
 create mode 100644 docs/build/html/_images/notebook_common_envelope_evolution_14_2.png
 create mode 100644 docs/build/html/_sources/notebook_HRD.ipynb.txt
 create mode 100644 docs/build/html/_sources/notebook_common_envelope_evolution.ipynb.txt
 create mode 100644 docs/build/html/notebook_HRD.html
 create mode 100644 docs/build/html/notebook_HRD.ipynb
 create mode 100644 docs/build/html/notebook_common_envelope_evolution.html
 create mode 100644 docs/build/html/notebook_common_envelope_evolution.ipynb
 create mode 100644 docs/source/notebook_HRD.ipynb
 create mode 100644 docs/source/notebook_common_envelope_evolution.ipynb

diff --git a/docs/Makefile b/docs/Makefile
index e51d09f09..ec7d84a15 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -27,7 +27,9 @@ help:
 	cp ../examples/notebook_api_functionality.ipynb source/
 	cp ../examples/notebook_luminosity_function_single.ipynb source/
 	cp ../examples/notebook_luminosity_function_binaries.ipynb source/
-
+	cp ../examples/notebook_HRD.ipynb source/
+	cp ../examples/notebook_common_envelope_evolution.ipynb source/
+	
 	# Copy the badges
 	cp -r ../badges/ source/
 	
diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
index 0c86a1f151d02ae1fd6a3cd784675069e3119ee7..61181afdb4f5e62b0fa4b32c33d50998e3a99a0a 100644
GIT binary patch
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diff --git a/docs/build/doctrees/example_notebooks.doctree b/docs/build/doctrees/example_notebooks.doctree
index 48df9e4b38f0fc2c76ecb9eae315ac298d9746cd..a352e95a0d22a41b8f80e39ff4308c96db027b27 100644
GIT binary patch
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diff --git a/docs/build/doctrees/nbsphinx/notebook_HRD.ipynb b/docs/build/doctrees/nbsphinx/notebook_HRD.ipynb
new file mode 100644
index 000000000..52590f8a2
--- /dev/null
+++ b/docs/build/doctrees/nbsphinx/notebook_HRD.ipynb
@@ -0,0 +1,818 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Hertzsprung-Russell diagrams\n",
+    "\n",
+    "In this notebook we compute Hertzsprung-Russell diagrams (HRDs) of single and binary stars.\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_HRD\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {},
+   "source": [
+    "## Setting up the Population object\n",
+    "First we set up a new population object. Our stars evolve to $13.7\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. These are rough approximations: a real population was born some finite time ago, so cannot possibly evolve to $13.7\\mathrm{Gyr}$, and stars are not really born with a metallicity of $0.02$. These approximations only affect very low mass stars, so we assume all our stars have mass $M\\geq 1 \\mathrm{M}_\\odot$, and metallicity does not change evolution too much except in massive stars through the dependence of their winds on metallicity, so we limit our study to $M\\leq 10 \\mathrm{M}_\\odot$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\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=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    tmp_dir=TMP_DIR,\n",
+    "    verbosity=1\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $10\\mathrm{M}_\\odot$ in nine steps (so the masses are integers). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"M_1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        11\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1,2,1)\",\n",
+      "    \"precode\": null,\n",
+      "    \"probdist\": \"1\",\n",
+      "    \"dphasevol\": \"dM_1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"edge\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10} \n",
+    "massrange = (1, 11) \n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"M_1\",\n",
+    "    longname=\"Primary mass\", # == single-star mass\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{res}\".format(res = resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(1,2,1)\", # space by unit masses\n",
+    "    probdist=\"1\", # dprob/dm1 : we don't care, so just set it to 1\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",
+    "    gridtype=\"edge\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Setting logging and handling the output\n",
+    "\n",
+    "We now construct the HRD output.\n",
+    "\n",
+    "We choose stars prior to and including the thermally-pulsing asymptotic giant branch (TPAGB) phase that have $>0.1\\mathrm{M}_\\odot$ of material in their outer hydrogen envelope (remember the core of an evolved star is made of helium or carbon/oxygen/neon). This prevents us showing the post-AGB phase which is a bit messy and we avoid the white-dwarf cooling track."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "Foreach_star(star)\n",
+      "{\n",
+      "    if(star->stellar_type <= TPAGB &&\n",
+      "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+      "    {\n",
+      "         double logTeff = log10(Teff_from_star_struct(star));\n",
+      "         double logL = log10(star->luminosity); \n",
+      "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+      "         Printf(\"HRD%d %30.12e %g %g %g %g\\n\",\n",
+      "                star->starnum, // 0\n",
+      "                stardata->model.time, // 1\n",
+      "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+      "                logTeff, // 3\n",
+      "                logL, // 4\n",
+      "                loggravity // 5\n",
+      "                );\n",
+      "\n",
+      "    }\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "Foreach_star(star)\n",
+    "{\n",
+    "    if(star->stellar_type <= TPAGB &&\n",
+    "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+    "    {\n",
+    "         double logTeff = log10(Teff_from_star_struct(star));\n",
+    "         double logL = log10(star->luminosity); \n",
+    "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+    "         Printf(\"HRD%d %30.12e %g %g %g %g\\\\n\",\n",
+    "                star->starnum, // 0\n",
+    "                stardata->model.time, // 1\n",
+    "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+    "                logTeff, // 3\n",
+    "                logL, // 4\n",
+    "                loggravity // 5\n",
+    "                );\n",
+    "\n",
+    "    }\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "population.set(\n",
+    "    C_logging_code=custom_logging_statement\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae1f1f0c-1f8b-42d8-b051-cbf8c6b51514",
+   "metadata": {},
+   "source": [
+    "The parse function must now catch lines that start with \"HRD*n*\", where *n* is 0 (primary star) or 1 (secondary star, which doesn't exist in single-star systems), and process the associated data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14565763dca0> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import datalinedict\n",
+    "import re\n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"zams_mass\", \"logTeff\", \"logL\", \"logg\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        match = re.search('HRD(\\d)',line) \n",
+    "        if match:\n",
+    "            nstar = match.group(1) \n",
+    "            \n",
+    "            # obtain the line of data in dictionary form \n",
+    "            linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "            # first time setup of the list of tuples\n",
+    "            if(len(self.grid_results['HRD'][nstar][linedata['zams_mass']])==0):\n",
+    "                self.grid_results['HRD'][nstar][linedata['zams_mass']] = []\n",
+    "\n",
+    "            # make the HRD be a list of tuples\n",
+    "            self.grid_results['HRD'][nstar][linedata['zams_mass']].append((linedata['logTeff'],\n",
+    "                                                                           linedata['logL']))\n",
+    "    \n",
+    "    # verbose reporting\n",
+    "    #print(\"parse out results_dictionary=\",self.grid_results)\n",
+    "    \n",
+    "# Add the parsing function\n",
+    "population.set(\n",
+    "    parse_function=parse_function,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91509ce5-ffe7-4937-aa87-6d7baac9ac04",
+   "metadata": {},
+   "source": [
+    "## Evolving the grid\n",
+    "Now that we configured all the main parts of the population object, we can actually run the population! Doing this is straightforward: `population.evolve()`\n",
+    "\n",
+    "This will start up the processing of all the systems. We can control how many cores are used by settings `amt_cores`. By setting the `verbosity` of the population object to a higher value we can get a lot of verbose information about the run, but for now we will set it to 0.\n",
+    "\n",
+    "There are many grid_options that can lead to different behaviour of the evolution of the grid. Please do have a look at those: [grid options docs](https://ri0005.pages.surrey.ac.uk/binary_c-python/grid_options_descriptions.html), and try  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: verbosity=0 to grid_options\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-20bee5b0c58d49c5bc47eced240685bb finished! The total probability was: 10.0. It took a total of 0.543649435043335s to run 10 systems on 4 cores\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",
+    "    amt_cores=4,\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": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': '20bee5b0c58d49c5bc47eced240685bb', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 10.0, 'total_count': 10, 'start_timestamp': 1631304519.45189, 'end_timestamp': 1631304519.9955394, 'total_mass_run': 55.0, 'total_probability_weighted_mass_run': 55.0, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 8,
+     "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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "hrd = population.grid_results['HRD']\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    for zams_mass in sorted(hrd[nstar]):\n",
+    "        print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "        # get track data (list of tuples)\n",
+    "        track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "        # convert to Pandas dataframe\n",
+    "        data = pd.DataFrame(data=track, \n",
+    "                            columns = ['logTeff','logL'])\n",
+    "        \n",
+    "        # make seaborn plot\n",
+    "        p = sns.lineplot(data=data,\n",
+    "                         sort=False,\n",
+    "                         x='logTeff',\n",
+    "                         y='logL',\n",
+    "                         estimator=None)\n",
+    "        \n",
+    "        # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "        p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "        \n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "We now have an HRD. It took longer to make the plot than to run the stars with *binary_c*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44586e42-b7cb-4a55-be0a-330b98b20de4",
+   "metadata": {},
+   "source": [
+    "## Binary stars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71d0fc4e-c72f-444a-93ab-19f52086b86d",
+   "metadata": {},
+   "source": [
+    "Now we put a secondary star of mass $0.5\\mathrm{M}_\\odot$ at a distance of $10\\mathrm{R}_\\odot$ to see how this changes things. Then we rerun the population. At such short separations, we expect mass transfer to begin on or shortly after the main sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "478e8005-e144-4e6f-80c9-0cf368a9bcb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-cff93424298e4862bb72096e72b98a2d finished! The total probability was: 10.0. It took a total of 0.9686374664306641s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 0.5, # Msun\n",
+    "    separation = 10, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9c433e6a-fe22-4494-b1a9-fce9676a9f40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "\n",
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3557b6d5-6c54-467c-b7a1-b1903493c441",
+   "metadata": {},
+   "source": [
+    "We plot here the track for the primary star only. You can see immediately where stars merge on the main sequence: the tracks move very suddenly where usually evolution on the main sequence is smooth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59335030-dd99-4c2f-afff-207a3fcbbb70",
+   "metadata": {},
+   "source": [
+    "If we now set the separation to be longer, say $100\\mathrm{R}_\\odot$, mass transfer should happen on the giant branch. We also set the secondary mass to be larger, $1\\mathrm{M}_\\odot$, so that the interaction is stronger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dee92b20-ad6b-4c97-80dc-71d3bd937c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-2ea4759ed05544ef8f1b7a887f0f36d2 finished! The total probability was: 10.0. It took a total of 0.7215321063995361s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 1, # Msun\n",
+    "    separation = 100, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    "    alpha_ce = 1.0, # make common-envelope evolution quite efficient\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e0ac2573-bc35-43be-8f20-5c85364fde11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f8e061-a65e-47f2-a777-93de0d5045ea",
+   "metadata": {},
+   "source": [
+    "You now see the interaction in the jerky red-giant tracks where the stars interact. These probably, depending on the mass ratio at the moment of interaction, go through a common-envelope phase. The system can merge (most of the above do) but not all. The interaction is so strong on the RGB of the $1\\mathrm{M}_\\odot$ star that the stellar evolution is terminated before it reaches the RGB tip, so it never ignites helium. This is how helium white dwarfs are probably made."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "698d0a63-11ba-4b3e-a713-35c3e972492f",
+   "metadata": {},
+   "source": [
+    "We can also plot the secondary stars' HRD. Remember, the primary is star 0 in binary_c, while the secondary is star 1. That's because all proper programming languages start counting at 0. We change the parsing function a little so we can separate the plots of the secondaries according to their primary mass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2b0b7c2b-6e43-48ed-9257-9dfc141b3d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "star  1\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '1': # choose only secondaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "            \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c46319-5629-4125-a284-b5d521ed33fc",
+   "metadata": {},
+   "source": [
+    "Remember, all these stars start with a $1\\mathrm{M}_\\odot$ binary, which begins at $\\log_{10}(T_\\mathrm{eff}/\\mathrm{K})\\sim 3.750$, $\\log_{10}L/\\mathrm{L}_\\odot \\sim 0$. The $1\\mathrm{M}_\\odot$-$1\\mathrm{M}_\\odot$ binary evolves like two single stars until they interact up the giant branch at about $\\log_{10} (L/\\mathrm{L}_\\odot) \\sim 2.5$, the others interact long before they evolve very far on the main sequence: you can just about see their tracks at the very start."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53145356-abbb-4880-996f-dedd80de7540",
+   "metadata": {},
+   "source": [
+    "This is, of course, a very simple introduction to what happens in binaries. We haven't talked about the remnants that are produced by interactions. When the stars do evolve on the giant branch, white dwarfs are made which can go on to suffer novae and (perhaps) thermonuclear explosions. The merging process itself leads to luminosus red novae and, in the case of neutron stars and black holes, kilonovae and gravitational wave events. "
+   ]
+  }
+ ],
+ "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/notebook_HRD_14_2.png b/docs/build/doctrees/nbsphinx/notebook_HRD_14_2.png
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diff --git a/docs/build/doctrees/nbsphinx/notebook_common_envelope_evolution.ipynb b/docs/build/doctrees/nbsphinx/notebook_common_envelope_evolution.ipynb
new file mode 100644
index 000000000..526320ccf
--- /dev/null
+++ b/docs/build/doctrees/nbsphinx/notebook_common_envelope_evolution.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Common-envelope evolution\n",
+    "\n",
+    "In this notebook we look at how common-envelope evolution (CEE) alters binary-star orbits. We construct a population of low- and intermediate-mass binaries and compare their orbital periods before and after CEE. Not all stars evolve into this phase, so we have to run a whole population to find those that do. We then have to construct the pre- and post-CEE distributions and plot them.\n",
+    "\n",
+    "First, we import a few required Python modules. "
+   ]
+  },
+  {
+   "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",
+    "from binarycpython.utils.functions import temp_dir\n",
+    "from binarycpython.utils.grid import Population\n",
+    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_comenv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Setting up the Population object\n",
+    "We set up a new population object. Our stars evolve to $13.7\\text{ }\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. We also set the common-envelope ejection efficiency $\\alpha_\\mathrm{CE}=1$ and the envelope structure parameter $\\lambda=0.5$. More complex options are available in *binary_c*, such as $\\lambda$ based on stellar mass, but this is just a demonstration example so let's keep things simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: log_dt=10 to grid_options\n",
+      "adding: max_evolution_time=13700 to BSE_options\n",
+      "adding: metallicity=0.02 to BSE_options\n",
+      "adding: alpha_ce=1.0 to BSE_options\n",
+      "adding: lambda_ce=0.5 to BSE_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\n",
+    "population.set(\n",
+    "    # grid options\n",
+    "    tmp_dir = TMP_DIR,\n",
+    "    verbosity = 1,\n",
+    "    log_dt = 10, # log every 10 seconds\n",
+    "\n",
+    "    # binary-star evolution options\n",
+    "    max_evolution_time=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    alpha_ce = 1.0,\n",
+    "    lambda_ce = 0.5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $6\\text{ }\\mathrm{M}_\\odot$. We avoid massive stars for now, and focus on the (more common) low- and intermediate-mass stars. We also limit the period range to $10^4\\text{ }\\mathrm{d}$ because systems with longer orbital periods will probably not undergo Roche-lobe overflow and hence common-envelope evolution is impossible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"lnm1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        6\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(math.log(1), math.log(6), 10)\",\n",
+      "    \"precode\": \"M_1=math.exp(lnm1)\",\n",
+      "    \"probdist\": \"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+      "    \"dphasevol\": \"dlnm1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"q\",\n",
+      "    \"longname\": \"Mass ratio\",\n",
+      "    \"valuerange\": [\n",
+      "        \"0.1/M_1\",\n",
+      "        1\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1/M_1, 1, 10)\",\n",
+      "    \"precode\": \"M_2 = q * M_1\",\n",
+      "    \"probdist\": \"flatsections(q, [{'min': 1/M_1, 'max': 1.0, 'height': 1}])\",\n",
+      "    \"dphasevol\": \"dq\",\n",
+      "    \"parameter_name\": \"M_2\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 1\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"log10per\",\n",
+      "    \"longname\": \"log10(Orbital_Period)\",\n",
+      "    \"valuerange\": [\n",
+      "        0.15,\n",
+      "        5.5\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(0.15, 4, 10)\",\n",
+      "    \"precode\": \"orbital_period = 10.0 ** log10per\\nsep = calc_sep_from_period(M_1, M_2, orbital_period)\\nsep_min = calc_sep_from_period(M_1, M_2, 10**0.15)\\nsep_max = calc_sep_from_period(M_1, M_2, 10**4)\",\n",
+      "    \"probdist\": \"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**0.15), math.log10(10**4), -0.55)\",\n",
+      "    \"dphasevol\": \"dlog10per\",\n",
+      "    \"parameter_name\": \"orbital_period\",\n",
+      "    \"condition\": null,\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 2\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10, \"q\" : 10, \"per\": 10} \n",
+    "massrange = [1, 6] \n",
+    "logperrange = [0.15, 4]\n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"lnm1\",\n",
+    "    longname=\"Primary mass\",\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{}\".format(resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
+    "    precode=\"M_1=math.exp(lnm1)\",\n",
+    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+    "    dphasevol=\"dlnm1\",\n",
+    "    parameter_name=\"M_1\",\n",
+    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    ")\n",
+    "\n",
+    "# Mass ratio\n",
+    "population.add_grid_variable(\n",
+    "     name=\"q\",\n",
+    "     longname=\"Mass ratio\",\n",
+    "     valuerange=[\"0.1/M_1\", 1],\n",
+    "     resolution=\"{}\".format(resolution['q']),\n",
+    "     spacingfunc=\"const({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
+    "     probdist=\"flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
+    "     dphasevol=\"dq\",\n",
+    "     precode=\"M_2 = q * M_1\",\n",
+    "     parameter_name=\"M_2\",\n",
+    "     condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    " )\n",
+    "\n",
+    "# Orbital period\n",
+    "population.add_grid_variable(\n",
+    "    name=\"log10per\", # in days\n",
+    "    longname=\"log10(Orbital_Period)\",\n",
+    "    valuerange=[0.15, 5.5],\n",
+    "    resolution=\"{}\".format(resolution[\"per\"]),\n",
+    "    spacingfunc=\"const({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
+    "    precode=\"\"\"orbital_period = 10.0 ** log10per\n",
+    "sep = calc_sep_from_period(M_1, M_2, orbital_period)\n",
+    "sep_min = calc_sep_from_period(M_1, M_2, 10**{})\n",
+    "sep_max = calc_sep_from_period(M_1, M_2, 10**{})\"\"\".format(logperrange[0],logperrange[1]),\n",
+    "    probdist=\"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
+    "    parameter_name=\"orbital_period\",\n",
+    "    dphasevol=\"dlog10per\",\n",
+    " )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Logging and handling the output\n",
+    "\n",
+    "We now construct the pre- and post-common envelope evolution data for the first common envelope that forms in each binary. We look at the comenv_count variable, we can see that when it increases from 0 to 1 we have found our object. If this happens, we stop evolution of the system to save CPU time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "\n",
+      "/*\n",
+      " * Detect when the comenv_count increased \n",
+      " */\n",
+      "if(stardata->model.comenv_count == 1 && \n",
+      "   stardata->previous_stardata->model.comenv_count == 0)\n",
+      "{\n",
+      "   /*\n",
+      "    * We just had this system's first common envelope:\n",
+      "    * output the time at which this happens, \n",
+      "    * the system's probability (proportional to the number of stars),\n",
+      "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+      "    * the current timestep (post-comenv) orbital period (days)\n",
+      "    */\n",
+      "    Printf(\"COMENV %g %g %g %g\\n\",\n",
+      "           stardata->model.time,\n",
+      "           stardata->model.probability,\n",
+      "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+      "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+      "           \n",
+      "    /*\n",
+      "     * We should waste no more CPU time on this system now we have the\n",
+      "     * data we want.\n",
+      "     */\n",
+      "    stardata->model.evolution_stop = TRUE;\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "\n",
+    "/*\n",
+    " * Detect when the comenv_count increased \n",
+    " */\n",
+    "if(stardata->model.comenv_count == 1 && \n",
+    "   stardata->previous_stardata->model.comenv_count == 0)\n",
+    "{\n",
+    "   /*\n",
+    "    * We just had this system's first common envelope:\n",
+    "    * output the time at which this happens, \n",
+    "    * the system's probability (proportional to the number of stars),\n",
+    "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+    "    * the current timestep (post-comenv) orbital period (days)\n",
+    "    */\n",
+    "    Printf(\"COMENV %g %g %g %g\\\\n\",\n",
+    "           stardata->model.time,\n",
+    "           stardata->model.probability,\n",
+    "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+    "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+    "           \n",
+    "    /*\n",
+    "     * We should waste no more CPU time on this system now we have the\n",
+    "     * data we want.\n",
+    "     */\n",
+    "    stardata->model.evolution_stop = TRUE;\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 \"COMENV\" and process the associated data. We set up the parse_data function to do just this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14736bebc040> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import bin_data,datalinedict\n",
+    "import re\n",
+    "\n",
+    "# log-period distribution bin width (dex)\n",
+    "binwidth = 0.5 \n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"probability\", \"pre_comenv_period\", \"post_comenv_period\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        # obtain the line of data in dictionary form \n",
+    "        linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "        # choose COMENV lines of output\n",
+    "        if linedata[\"header\"] == \"COMENV\":\n",
+    "            # bin the pre- and post-comenv log10-orbital-periods to nearest 0.5dex\n",
+    "            binned_pre_period = bin_data(math.log10(linedata[\"pre_comenv_period\"]), binwidth)\n",
+    "            \n",
+    "            # but check if the post-comenv period is finite and positive: if \n",
+    "            # not, the system has merged and we give it an aritifical period\n",
+    "            # of 10^-100 days (which is very much unphysical)\n",
+    "            if linedata[\"post_comenv_period\"] > 0.0:\n",
+    "                binned_post_period = bin_data(math.log10(linedata[\"post_comenv_period\"]), binwidth)\n",
+    "            else:\n",
+    "                binned_post_period = bin_data(-100,binwidth) # merged!\n",
+    "                \n",
+    "            # make the \"histograms\"\n",
+    "            self.grid_results['pre'][binned_pre_period] += linedata[\"probability\"]\n",
+    "            self.grid_results['post'][binned_post_period] += linedata[\"probability\"]\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 we actually run the population. This may take a little while. You can set amt_cores higher if you have a powerful machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: amt_cores=4 to grid_options\n",
+      "Creating and loading custom logging functionality\n",
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "Grid has handled 1000 stars\n",
+      "with a total probability of 0.0645905996773004\n",
+      "Total starcount for this run will be: 1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:39,950 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-12 18:07:39,953 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-12 18:07:39,959 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-12 18:07:39,962 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-12 18:07:39,965 DEBUG    Process-5] --- Setting up processor: process-3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 started at 2021-09-12T18:07:39.965721.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee47e0>\n",
+      "Process 1 started at 2021-09-12T18:07:39.970949.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n",
+      "Process 2 started at 2021-09-12T18:07:39.978355.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4f30>\n",
+      "Process 3 started at 2021-09-12T18:07:39.983689.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:40,066 DEBUG    MainProcess] --- Signaling stop to processes\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "163/1000  16.3% complete 18:07:49 ETA=   51.5s tpr=6.16e-02 ETF=18:08:41 mem:594.9MB\n",
+      "322/1000  32.2% complete 18:07:59 ETA=   42.9s tpr=6.33e-02 ETF=18:08:42 mem:538.2MB\n",
+      "465/1000  46.5% complete 18:08:09 ETA=   38.1s tpr=7.12e-02 ETF=18:08:47 mem:538.2MB\n",
+      "586/1000  58.6% complete 18:08:19 ETA=   34.3s tpr=8.29e-02 ETF=18:08:54 mem:540.0MB\n",
+      "682/1000  68.2% complete 18:08:30 ETA=   34.0s tpr=1.07e-01 ETF=18:09:04 mem:540.1MB\n",
+      "784/1000  78.4% complete 18:08:40 ETA=   21.2s tpr=9.81e-02 ETF=18:09:01 mem:541.8MB\n",
+      "872/1000  87.2% complete 18:08:50 ETA=   15.0s tpr=1.17e-01 ETF=18:09:05 mem:546.1MB\n",
+      "963/1000  96.3% complete 18:09:00 ETA=    4.2s tpr=1.14e-01 ETF=18:09:04 mem:546.9MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,366 DEBUG    Process-5] --- Process-3 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.964604, done at 2021-09-12T18:09:06.370832 (total: 86.406228s of which 86.24177551269531s interfacing with binary_c).\n",
+      "\tRan 222 systems with a total probability of 0.014137215791516371.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,374 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-12 18:09:06,979 DEBUG    Process-3] --- Process-1 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.953039, done at 2021-09-12T18:09:06.982866 (total: 87.029827s of which 86.82909393310547s interfacing with binary_c).\n",
+      "\tRan 273 systems with a total probability of 0.01877334232598154.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,985 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-12 18:09:07,174 DEBUG    Process-2] --- Process-0 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.949775, done at 2021-09-12T18:09:07.176660 (total: 87.226885s of which 87.02672934532166s interfacing with binary_c).\n",
+      "\tRan 268 systems with a total probability of 0.016469813170514686.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,179 DEBUG    Process-2] --- Process-0 is finished.\n",
+      "[2021-09-12 18:09:07,233 DEBUG    Process-4] --- Process-2 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.958802, done at 2021-09-12T18:09:07.236252 (total: 87.27745s of which 87.0905077457428s interfacing with binary_c).\n",
+      "\tRan 237 systems with a total probability of 0.015210228389288167.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,238 DEBUG    Process-4] --- Process-2 is finished.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population-ad303100d719457c83256568f9a9887c finished! The total probability was: 0.06459059967730076. It took a total of 87.54819011688232s to run 1000 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set number of threads\n",
+    "population.set(\n",
+    "    # set number of threads (i.e. number of CPU cores we use)\n",
+    "    amt_cores=4,\n",
+    "    )\n",
+    "\n",
+    "# Evolve the population - this is the slow, number-crunching step\n",
+    "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. We check this, and then set about making the plot of the orbital period distributions using Seaborn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': 'ad303100d719457c83256568f9a9887c', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.06459059967730076, 'total_count': 1000, 'start_timestamp': 1631462859.9342952, 'end_timestamp': 1631462947.4824853, 'total_mass_run': 4680.235689312421, 'total_probability_weighted_mass_run': 0.22611318083528567, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'merged': 0.035263029200000025, 'unmerged': 0.019388724199999995}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Number of stars')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# make a plot of the distributions\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import copy\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "# remove the merged objects\n",
+    "probability = { \"merged\" : 0.0, \"unmerged\" : 0.0}\n",
+    "\n",
+    "# copy the results so we can change the copy\n",
+    "results = copy.deepcopy(population.grid_results)\n",
+    "\n",
+    "for distribution in ['post']:    \n",
+    "    for logper in population.grid_results[distribution]:\n",
+    "        dprob = results[distribution][logper]\n",
+    "        if logper < -90:\n",
+    "            # merged system\n",
+    "            probability[\"merged\"] += dprob\n",
+    "            del results[distribution][logper]\n",
+    "        else:\n",
+    "            # unmerged system\n",
+    "            probability[\"unmerged\"] += dprob\n",
+    "print(probability)\n",
+    "    \n",
+    "# pad the final distribution with zero\n",
+    "for distribution in population.grid_results:    \n",
+    "    pad_output_distribution(results[distribution],\n",
+    "                            binwidth)\n",
+    "    \n",
+    "# make pandas dataframe \n",
+    "plot_data = pd.DataFrame.from_dict(results, orient='columns')\n",
+    "\n",
+    "# make the plot\n",
+    "p = sns.lineplot(data=plot_data)\n",
+    "p.set_xlabel(\"$\\log_{10} (P_\\mathrm{orb} / \\mathrm{day})$\")\n",
+    "p.set_ylabel(\"Number of stars\")\n",
+    "#p.set(xlim=(-5,5)) # might be necessary?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4740c93-d01e-4ca1-8766-c2fb4ddca2e4",
+   "metadata": {},
+   "source": [
+    "You can see that common-envelope evolution shrinks stellar orbits, just as we expect. Pre-CEE, most orbits are in the range $10$ to $1000\\text{ }\\mathrm{d}$, while after CEE the distribution peaks at about $1\\text{ }\\mathrm{d}$. Some of these orbits are very short: $\\log_{10}(-2) = 0.01\\text{ }\\mathrm{d}\\sim10\\text{ }\\mathrm{minutes}$. Such systems are prime candidates for exciting astrophysics: novae, type Ia supernovae and gravitational wave sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57faf043-3809-427a-b378-2355ce8c2691",
+   "metadata": {},
+   "source": [
+    "Things to try:\n",
+    "* Extend the logging to output more data than just the orbital period.\n",
+    "* What are the stellar types of the post-common envelope systems? Are they likely to undergo novae or a type-Ia supernova?\n",
+    "* What are the lifetimes of the systems in close ($<1\\text{ }\\mathrm{d}$) binaries? Are they likely to merge in the life of the Universe?\n",
+    "* How much mass is lost in common-envelope interactions?\n",
+    "* Extend the grid to massive stars. Do you see many NS and BH compact binaries?\n",
+    "* Try different $\\alpha_\\mathrm{CE}$ and $\\lambda_\\mathrm{CE}$ options...\n",
+    "* ... and perhaps increased resolution to obtain smoother curves.\n",
+    "* Why do long-period systems not reach common envelope evolution?"
+   ]
+  }
+ ],
+ "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/notebook_common_envelope_evolution_14_2.png b/docs/build/doctrees/nbsphinx/notebook_common_envelope_evolution_14_2.png
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diff --git a/docs/build/doctrees/nbsphinx/notebook_individual_systems.ipynb b/docs/build/doctrees/nbsphinx/notebook_individual_systems.ipynb
index e6451e762..85aef1e39 100644
--- a/docs/build/doctrees/nbsphinx/notebook_individual_systems.ipynb
+++ b/docs/build/doctrees/nbsphinx/notebook_individual_systems.ipynb
@@ -566,7 +566,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -580,7 +580,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/doctrees/nbsphinx/notebook_luminosity_function_binaries.ipynb b/docs/build/doctrees/nbsphinx/notebook_luminosity_function_binaries.ipynb
index 47a96d093..c6b5f1e64 100644
--- a/docs/build/doctrees/nbsphinx/notebook_luminosity_function_binaries.ipynb
+++ b/docs/build/doctrees/nbsphinx/notebook_luminosity_function_binaries.ipynb
@@ -5,7 +5,7 @@
    "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
    "metadata": {},
    "source": [
-    "# Example use case: Zero-age stellar luminosity function in binaries\n",
+    "# Zero-age stellar luminosity function in binaries\n",
     "\n",
     "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of binary stars using binary_c. \n",
     "\n",
@@ -168,7 +168,7 @@
     "\n",
     "# resolution on each side of the cube, with more stars for the primary mass\n",
     "nres = 10\n",
-    "resolution = {\"M_1\": 2*nres,\n",
+    "resolution = {\"M_1\": 4*nres,\n",
     "              \"q\": nres,\n",
     "              \"per\": nres}\n",
     "\n",
@@ -379,10 +379,6 @@
    "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -399,229 +395,74 @@
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
       "Grid code loaded\n",
-      "Grid has handled 2000 stars\n",
-      "with a total probability of 0.6495098935846658\n",
-      "Total starcount for this run will be: 2000\n"
+      "Grid has handled 256 stars\n",
+      "with a total probability of 0.6149734610296649\n",
+      "Total starcount for this run will be: 256\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:14:08,077 DEBUG    Process-2] --- Setting up processor: process-0[2021-09-10 15:14:08,080 DEBUG    Process-3] --- Setting up processor: process-1[2021-09-10 15:14:08,086 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
-      "\n",
-      "[2021-09-10 15:14:08,084 DEBUG    Process-4] --- Setting up processor: process-2\n",
-      "\n",
-      "[2021-09-10 15:14:08,117 DEBUG    Process-5] --- Setting up processor: process-3"
+      "[2021-09-10 22:26:10,473 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-10 22:26:10,475 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-10 22:26:10,478 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-10 22:26:10,481 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-10 22:26:10,482 DEBUG    Process-5] --- Setting up processor: process-3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 started at 2021-09-10T15:14:08.119437.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>Process 0 started at 2021-09-10T15:14:08.126435.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>\n",
-      "Process 2 started at 2021-09-10T15:14:08.138353.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>"
+      "Process 0 started at 2021-09-10T22:26:10.491896.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf510>Process 1 started at 2021-09-10T22:26:10.491948.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf480>\n",
+      "\n",
+      "Process 2 started at 2021-09-10T22:26:10.496677.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf3f0>\n",
+      "Process 3 started at 2021-09-10T22:26:10.498669.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf180>\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "[2021-09-10 22:26:10,510 DEBUG    MainProcess] --- Signaling stop to processes\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "Process 3 started at 2021-09-10T15:14:08.186492.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>\n",
       "Generating grid code\n",
       "Generating grid code\n",
       "Constructing/adding: lnm1\n",
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
       "Grid code loaded\n",
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      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:16:25,175 DEBUG    MainProcess] --- Signaling stop to processes\n"
+      "[2021-09-10 22:26:27,631 DEBUG    Process-3] --- Process-1 is finishing.\n"
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-      "\n",
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-      "1956/2000  97.8% complete 15:26:59 ETA=   25.1s tpr=5.70e-01 ETF=15:27:24 mem:589.2MB\n",
-      "1966/2000  98.3% complete 15:27:04 ETA=   19.1s tpr=5.62e-01 ETF=15:27:24 mem:589.5MB\n",
-      "1976/2000  98.8% complete 15:27:10 ETA=   14.4s tpr=6.01e-01 ETF=15:27:25 mem:589.5MB\n",
-      "1987/2000  99.3% complete 15:27:16 ETA=    6.4s tpr=4.92e-01 ETF=15:27:22 mem:589.5MB\n",
-      "1998/2000  99.9% complete 15:27:21 ETA=    1.0s tpr=4.85e-01 ETF=15:27:22 mem:589.6MB\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-09-10 15:27:22,382 DEBUG    Process-5] --- Process-3 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Process 3 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.117391, done at 2021-09-10T15:27:22.400722 (total: 794.283331s of which 792.6935975551605s interfacing with binary_c).\n",
-      "\tRan 499 systems with a total probability of 0.17005450973840136.\n",
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.475399, done at 2021-09-10T22:26:27.634804 (total: 17.159405s of which 17.104907512664795s interfacing with binary_c).\n",
+      "\tRan 61 systems with a total probability of 0.1439494161909395.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -630,17 +471,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,435 DEBUG    Process-5] --- Process-3 is finished.\n",
-      "[2021-09-10 15:27:22,480 DEBUG    Process-3] --- Process-1 is finishing.\n"
+      "[2021-09-10 22:26:27,639 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-10 22:26:27,698 DEBUG    Process-5] --- Process-3 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.080367, done at 2021-09-10T15:27:22.505288 (total: 794.424921s of which 793.1943278312683s interfacing with binary_c).\n",
-      "\tRan 474 systems with a total probability of 0.15740832333567983.\n",
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.482470, done at 2021-09-10T22:26:27.701828 (total: 17.219358s of which 17.162050247192383s interfacing with binary_c).\n",
+      "\tRan 67 systems with a total probability of 0.17251417460118773.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -649,17 +490,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,531 DEBUG    Process-3] --- Process-1 is finished.\n",
-      "[2021-09-10 15:27:22,846 DEBUG    Process-2] --- Process-0 is finishing.\n"
+      "[2021-09-10 22:26:27,705 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-10 22:26:27,769 DEBUG    Process-4] --- Process-2 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 0 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.077117, done at 2021-09-10T15:27:22.851971 (total: 794.774854s of which 793.4976091384888s interfacing with binary_c).\n",
-      "\tRan 507 systems with a total probability of 0.16018641159091498.\n",
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.478464, done at 2021-09-10T22:26:27.771291 (total: 17.292827s of which 17.243471384048462s interfacing with binary_c).\n",
+      "\tRan 56 systems with a total probability of 0.14306289954535925.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -668,17 +509,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,872 DEBUG    Process-2] --- Process-0 is finished.\n",
-      "[2021-09-10 15:27:22,976 DEBUG    Process-4] --- Process-2 is finishing.\n"
+      "[2021-09-10 22:26:27,774 DEBUG    Process-4] --- Process-2 is finished.\n",
+      "[2021-09-10 22:26:27,865 DEBUG    Process-2] --- Process-0 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 2 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.084369, done at 2021-09-10T15:27:22.981706 (total: 794.897337s of which 793.4600214958191s interfacing with binary_c).\n",
-      "\tRan 520 systems with a total probability of 0.1618606489196724.\n",
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.473000, done at 2021-09-10T22:26:27.867175 (total: 17.394175s of which 17.331928491592407s interfacing with binary_c).\n",
+      "\tRan 72 systems with a total probability of 0.1554469706921749.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -687,14 +528,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,986 DEBUG    Process-4] --- Process-2 is finished.\n"
+      "[2021-09-10 22:26:27,869 DEBUG    Process-2] --- Process-0 is finished.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Population-0fa295ee5c76444bace8fd0ee17a3e11 finished! The total probability was: 0.6495098935846686. It took a total of 795.1383104324341s to run 2000 systems on 4 cores\n",
+      "Population-bc3a5f915411445699f8cf6438817ff1 finished! The total probability was: 0.6149734610296613. It took a total of 17.603368997573853s to run 256 systems on 4 cores\n",
       "There were no errors found in this run.\n",
       "Done population run!\n"
      ]
@@ -728,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -736,7 +577,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '0fa295ee5c76444bace8fd0ee17a3e11', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6495098935846686, 'total_count': 2000, 'start_timestamp': 1631283248.057525, 'end_timestamp': 1631284043.1958354, 'total_mass_run': 41112.220964392276, 'total_probability_weighted_mass_run': 0.6452116023479681, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'bc3a5f915411445699f8cf6438817ff1', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6149734610296613, 'total_count': 256, 'start_timestamp': 1631305570.458824, 'end_timestamp': 1631305588.062193, 'total_mass_run': 5246.190724478048, 'total_probability_weighted_mass_run': 0.6347400152389439, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -746,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -756,13 +597,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -777,8 +618,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# set up seaborn for use in the notebook\n",
+    "sns.set(rc={'figure.figsize':(20,10)})\n",
+    "sns.set_context(\"notebook\",\n",
+    "                font_scale=1.5,\n",
+    "                rc={\"lines.linewidth\":2.5})\n",
+    "\n",
     "\n",
     "titles = { 0 : \"Primary\",\n",
     "           1 : \"Secondary\",\n",
@@ -805,11 +650,36 @@
     "p.set_ylabel(\"Number of stars\")\n",
     "p.set(yscale=\"log\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "You can see that the secondary stars are dimmer than the primaries - which you expect given they are lower in mass (by definition q=M2/M1<1). \n",
+    "\n",
+    "Weirdly, in some places the primary distribution may exceed the unresolved distribution. This is a bit unphysical, but in this case is usually caused by limited resolution. If you increase the number of stars in the grid, this problem should go away (at a cost of more CPU time). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99e25a72-54e6-4826-b0e5-4a02460b857d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Things to try:\n",
+    "* Massive stars: can you see the effects of wind mass loss and rejuvenation in these stars?\n",
+    "* Alter the metallicity, does this make much of a difference?\n",
+    "* Change the binary fraction. Here we assume a 100% binary fraction, but a real population is a mixture of single and binary stars.\n",
+    "* How might you go about comparing these computed observations to real stars?\n",
+    "* What about evolved stars? Here we consider only the *zero-age* main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -823,7 +693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/doctrees/nbsphinx/notebook_luminosity_function_binaries_20_1.png b/docs/build/doctrees/nbsphinx/notebook_luminosity_function_binaries_20_1.png
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diff --git a/docs/build/doctrees/nbsphinx/notebook_luminosity_function_single.ipynb b/docs/build/doctrees/nbsphinx/notebook_luminosity_function_single.ipynb
index 5980adf6d..cdae316f9 100644
--- a/docs/build/doctrees/nbsphinx/notebook_luminosity_function_single.ipynb
+++ b/docs/build/doctrees/nbsphinx/notebook_luminosity_function_single.ipynb
@@ -54,8 +54,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "adding: max_evolution_time=0.1 to BSE_options\n",
+      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "verbosity is 1\n"
      ]
     }
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "aba3fe4e-18f2-4bb9-8e5c-4c6007ab038b",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "47979841-2c26-4b26-8945-603d013dc93a",
    "metadata": {},
    "outputs": [],
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
    "metadata": {},
    "outputs": [],
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "fd197154-a8ce-4865-8929-008d3483101a",
    "metadata": {},
    "outputs": [],
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
     "tags": []
@@ -321,9 +321,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-08f8230453084e4ca6a2391d45ce658b finished! The total probability was: 1.0000000000000002. It took a total of 1.5262682437896729s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.025), (3.75, 0.05), (4.25, 0.05), (0.25, 0.025), (3.25, 0.025), (5.25, 0.2), (4.75, 0.1), (5.75, 0.39999999999999997), (6.25, 0.125)]))])\n"
+      "Population-e6c082aabe0849a0811761a06e50476b finished! The total probability was: 1.0000000000000002. It took a total of 2.3021209239959717s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -353,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -361,7 +360,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '08f8230453084e4ca6a2391d45ce658b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631124829.303065, 'end_timestamp': 1631124830.8293333, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'e6c082aabe0849a0811761a06e50476b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631461389.3681686, 'end_timestamp': 1631461391.6702895, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -371,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -381,13 +380,13 @@
        "[None]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -402,8 +401,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# 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",
@@ -442,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
    "metadata": {},
    "outputs": [],
@@ -456,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
    "metadata": {},
    "outputs": [
@@ -471,9 +474,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-92de7c9221c54206ab4dd10e58e09a34 finished! The total probability was: 0.21822161894107872. It took a total of 1.5900418758392334s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.0164166), (3.25, 0.00515685), (0.25, 0.189097), (3.75, 0.0037453900000000004), (4.25, 0.0014346559999999999), (5.25, 0.0007493004), (4.75, 0.001171479), (5.75, 0.00039801020000000003), (6.25, 5.2369339999999996e-05)]))])\n"
+      "Population-1bc714cffdb344589ea01692f7e1ebd1 finished! The total probability was: 0.21822161894107872. It took a total of 2.335742950439453s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -488,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
    "metadata": {},
    "outputs": [
@@ -498,13 +500,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -559,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
    "metadata": {},
    "outputs": [],
@@ -578,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
    "metadata": {},
    "outputs": [],
@@ -599,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
    "metadata": {},
    "outputs": [
@@ -614,9 +616,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: lnM_1\n",
-      "Population-83f80d829dbd418aa2bc745c99b71991 finished! The total probability was: 0.9956307907476224. It took a total of 0.9961590766906738s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(0.25, 0.0212294), (2.75, 0.00321118), (-0.25, 0.0268827), (1.25, 0.0104553), (3.75, 0.00283037), (6.25, 7.34708e-05), (-0.75, 0.0771478), (0.75, 0.030004499999999996), (2.25, 0.00921541), (3.25, 0.0045385), (1.75, 0.014776889999999999), (4.25, 0.002380189), (4.75, 0.000869303), (5.25, 0.0007310379999999999), (5.75, 0.00036002859999999996), (-2.75, 0.1961345), (-1.75, 0.2181597), (-3.25, 0.0), (-2.25, 0.2568974), (-1.25, 0.11973310000000001)]))])\n"
+      "Population-4f3ee0143c0548338494d2f1fbacc915 finished! The total probability was: 0.9956307907476225. It took a total of 1.5107016563415527s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -639,13 +640,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "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>"
       ]
@@ -684,6 +685,20 @@
     " \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",
+    "* 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)"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/build/doctrees/nbsphinx/notebook_luminosity_function_single_20_1.png b/docs/build/doctrees/nbsphinx/notebook_luminosity_function_single_20_1.png
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diff --git a/docs/build/doctrees/nbsphinx/notebook_population.ipynb b/docs/build/doctrees/nbsphinx/notebook_population.ipynb
index fff337533..a24638c0b 100644
--- a/docs/build/doctrees/nbsphinx/notebook_population.ipynb
+++ b/docs/build/doctrees/nbsphinx/notebook_population.ipynb
@@ -1109,7 +1109,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1123,7 +1123,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/doctrees/notebook_HRD.doctree b/docs/build/doctrees/notebook_HRD.doctree
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diff --git a/docs/build/doctrees/notebook_luminosity_function_binaries.doctree b/docs/build/doctrees/notebook_luminosity_function_binaries.doctree
index 2a46154e50e4b4f53bbb760a728dab35a6862f09..2ba67e6401857e3f174a1bf75b985b33345d6d90 100644
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diff --git a/docs/build/html/_modules/binarycpython/utils/custom_logging_functions.html b/docs/build/html/_modules/binarycpython/utils/custom_logging_functions.html
index 00bdbbf88..d22fad99c 100644
--- a/docs/build/html/_modules/binarycpython/utils/custom_logging_functions.html
+++ b/docs/build/html/_modules/binarycpython/utils/custom_logging_functions.html
@@ -644,7 +644,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/distribution_functions.html b/docs/build/html/_modules/binarycpython/utils/distribution_functions.html
index fc7733923..30d9ff1c9 100644
--- a/docs/build/html/_modules/binarycpython/utils/distribution_functions.html
+++ b/docs/build/html/_modules/binarycpython/utils/distribution_functions.html
@@ -2482,7 +2482,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/functions.html b/docs/build/html/_modules/binarycpython/utils/functions.html
index ab66d8fec..742305585 100644
--- a/docs/build/html/_modules/binarycpython/utils/functions.html
+++ b/docs/build/html/_modules/binarycpython/utils/functions.html
@@ -2276,7 +2276,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/grid.html b/docs/build/html/_modules/binarycpython/utils/grid.html
index b0960a6bd..28d95d888 100644
--- a/docs/build/html/_modules/binarycpython/utils/grid.html
+++ b/docs/build/html/_modules/binarycpython/utils/grid.html
@@ -4442,7 +4442,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/grid_options_defaults.html b/docs/build/html/_modules/binarycpython/utils/grid_options_defaults.html
index cb847c3ec..d727ebca9 100644
--- a/docs/build/html/_modules/binarycpython/utils/grid_options_defaults.html
+++ b/docs/build/html/_modules/binarycpython/utils/grid_options_defaults.html
@@ -1008,7 +1008,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/plot_functions.html b/docs/build/html/_modules/binarycpython/utils/plot_functions.html
index 93bc2be9c..75f0703e9 100644
--- a/docs/build/html/_modules/binarycpython/utils/plot_functions.html
+++ b/docs/build/html/_modules/binarycpython/utils/plot_functions.html
@@ -829,7 +829,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">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 0745bdead..7b8a8cdb0 100644
--- a/docs/build/html/_modules/binarycpython/utils/run_system_wrapper.html
+++ b/docs/build/html/_modules/binarycpython/utils/run_system_wrapper.html
@@ -298,7 +298,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_modules/binarycpython/utils/spacing_functions.html b/docs/build/html/_modules/binarycpython/utils/spacing_functions.html
index 3d13dc489..ca3d72a41 100644
--- a/docs/build/html/_modules/binarycpython/utils/spacing_functions.html
+++ b/docs/build/html/_modules/binarycpython/utils/spacing_functions.html
@@ -209,7 +209,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">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 49ec9f799..975a7c700 100644
--- a/docs/build/html/_modules/binarycpython/utils/useful_funcs.html
+++ b/docs/build/html/_modules/binarycpython/utils/useful_funcs.html
@@ -566,7 +566,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html
index 7e9cfd039..b050c7170 100644
--- a/docs/build/html/_modules/index.html
+++ b/docs/build/html/_modules/index.html
@@ -189,7 +189,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/_sources/example_notebooks.rst.txt b/docs/build/html/_sources/example_notebooks.rst.txt
index d15ea559a..ce09bb2af 100644
--- a/docs/build/html/_sources/example_notebooks.rst.txt
+++ b/docs/build/html/_sources/example_notebooks.rst.txt
@@ -14,4 +14,6 @@ The order of the notebooks below is more or less the recommended order to read.
     notebook_extra_features.ipynb
     notebook_api_functionality.ipynb
     notebook_luminosity_function_single.ipynb
-    notebook_luminosity_function_binaries.ipynb
\ No newline at end of file
+    notebook_luminosity_function_binaries.ipynb
+    notebook_HRD.ipynb
+    notebook_common_envelope_evolution.ipynb
\ No newline at end of file
diff --git a/docs/build/html/_sources/notebook_HRD.ipynb.txt b/docs/build/html/_sources/notebook_HRD.ipynb.txt
new file mode 100644
index 000000000..52590f8a2
--- /dev/null
+++ b/docs/build/html/_sources/notebook_HRD.ipynb.txt
@@ -0,0 +1,818 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Hertzsprung-Russell diagrams\n",
+    "\n",
+    "In this notebook we compute Hertzsprung-Russell diagrams (HRDs) of single and binary stars.\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_HRD\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {},
+   "source": [
+    "## Setting up the Population object\n",
+    "First we set up a new population object. Our stars evolve to $13.7\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. These are rough approximations: a real population was born some finite time ago, so cannot possibly evolve to $13.7\\mathrm{Gyr}$, and stars are not really born with a metallicity of $0.02$. These approximations only affect very low mass stars, so we assume all our stars have mass $M\\geq 1 \\mathrm{M}_\\odot$, and metallicity does not change evolution too much except in massive stars through the dependence of their winds on metallicity, so we limit our study to $M\\leq 10 \\mathrm{M}_\\odot$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\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=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    tmp_dir=TMP_DIR,\n",
+    "    verbosity=1\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $10\\mathrm{M}_\\odot$ in nine steps (so the masses are integers). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"M_1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        11\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1,2,1)\",\n",
+      "    \"precode\": null,\n",
+      "    \"probdist\": \"1\",\n",
+      "    \"dphasevol\": \"dM_1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"edge\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10} \n",
+    "massrange = (1, 11) \n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"M_1\",\n",
+    "    longname=\"Primary mass\", # == single-star mass\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{res}\".format(res = resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(1,2,1)\", # space by unit masses\n",
+    "    probdist=\"1\", # dprob/dm1 : we don't care, so just set it to 1\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",
+    "    gridtype=\"edge\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Setting logging and handling the output\n",
+    "\n",
+    "We now construct the HRD output.\n",
+    "\n",
+    "We choose stars prior to and including the thermally-pulsing asymptotic giant branch (TPAGB) phase that have $>0.1\\mathrm{M}_\\odot$ of material in their outer hydrogen envelope (remember the core of an evolved star is made of helium or carbon/oxygen/neon). This prevents us showing the post-AGB phase which is a bit messy and we avoid the white-dwarf cooling track."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "Foreach_star(star)\n",
+      "{\n",
+      "    if(star->stellar_type <= TPAGB &&\n",
+      "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+      "    {\n",
+      "         double logTeff = log10(Teff_from_star_struct(star));\n",
+      "         double logL = log10(star->luminosity); \n",
+      "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+      "         Printf(\"HRD%d %30.12e %g %g %g %g\\n\",\n",
+      "                star->starnum, // 0\n",
+      "                stardata->model.time, // 1\n",
+      "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+      "                logTeff, // 3\n",
+      "                logL, // 4\n",
+      "                loggravity // 5\n",
+      "                );\n",
+      "\n",
+      "    }\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "Foreach_star(star)\n",
+    "{\n",
+    "    if(star->stellar_type <= TPAGB &&\n",
+    "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+    "    {\n",
+    "         double logTeff = log10(Teff_from_star_struct(star));\n",
+    "         double logL = log10(star->luminosity); \n",
+    "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+    "         Printf(\"HRD%d %30.12e %g %g %g %g\\\\n\",\n",
+    "                star->starnum, // 0\n",
+    "                stardata->model.time, // 1\n",
+    "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+    "                logTeff, // 3\n",
+    "                logL, // 4\n",
+    "                loggravity // 5\n",
+    "                );\n",
+    "\n",
+    "    }\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "population.set(\n",
+    "    C_logging_code=custom_logging_statement\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae1f1f0c-1f8b-42d8-b051-cbf8c6b51514",
+   "metadata": {},
+   "source": [
+    "The parse function must now catch lines that start with \"HRD*n*\", where *n* is 0 (primary star) or 1 (secondary star, which doesn't exist in single-star systems), and process the associated data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14565763dca0> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import datalinedict\n",
+    "import re\n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"zams_mass\", \"logTeff\", \"logL\", \"logg\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        match = re.search('HRD(\\d)',line) \n",
+    "        if match:\n",
+    "            nstar = match.group(1) \n",
+    "            \n",
+    "            # obtain the line of data in dictionary form \n",
+    "            linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "            # first time setup of the list of tuples\n",
+    "            if(len(self.grid_results['HRD'][nstar][linedata['zams_mass']])==0):\n",
+    "                self.grid_results['HRD'][nstar][linedata['zams_mass']] = []\n",
+    "\n",
+    "            # make the HRD be a list of tuples\n",
+    "            self.grid_results['HRD'][nstar][linedata['zams_mass']].append((linedata['logTeff'],\n",
+    "                                                                           linedata['logL']))\n",
+    "    \n",
+    "    # verbose reporting\n",
+    "    #print(\"parse out results_dictionary=\",self.grid_results)\n",
+    "    \n",
+    "# Add the parsing function\n",
+    "population.set(\n",
+    "    parse_function=parse_function,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91509ce5-ffe7-4937-aa87-6d7baac9ac04",
+   "metadata": {},
+   "source": [
+    "## Evolving the grid\n",
+    "Now that we configured all the main parts of the population object, we can actually run the population! Doing this is straightforward: `population.evolve()`\n",
+    "\n",
+    "This will start up the processing of all the systems. We can control how many cores are used by settings `amt_cores`. By setting the `verbosity` of the population object to a higher value we can get a lot of verbose information about the run, but for now we will set it to 0.\n",
+    "\n",
+    "There are many grid_options that can lead to different behaviour of the evolution of the grid. Please do have a look at those: [grid options docs](https://ri0005.pages.surrey.ac.uk/binary_c-python/grid_options_descriptions.html), and try  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: verbosity=0 to grid_options\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-20bee5b0c58d49c5bc47eced240685bb finished! The total probability was: 10.0. It took a total of 0.543649435043335s to run 10 systems on 4 cores\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",
+    "    amt_cores=4,\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": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': '20bee5b0c58d49c5bc47eced240685bb', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 10.0, 'total_count': 10, 'start_timestamp': 1631304519.45189, 'end_timestamp': 1631304519.9955394, 'total_mass_run': 55.0, 'total_probability_weighted_mass_run': 55.0, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 8,
+     "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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "hrd = population.grid_results['HRD']\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    for zams_mass in sorted(hrd[nstar]):\n",
+    "        print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "        # get track data (list of tuples)\n",
+    "        track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "        # convert to Pandas dataframe\n",
+    "        data = pd.DataFrame(data=track, \n",
+    "                            columns = ['logTeff','logL'])\n",
+    "        \n",
+    "        # make seaborn plot\n",
+    "        p = sns.lineplot(data=data,\n",
+    "                         sort=False,\n",
+    "                         x='logTeff',\n",
+    "                         y='logL',\n",
+    "                         estimator=None)\n",
+    "        \n",
+    "        # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "        p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "        \n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "We now have an HRD. It took longer to make the plot than to run the stars with *binary_c*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44586e42-b7cb-4a55-be0a-330b98b20de4",
+   "metadata": {},
+   "source": [
+    "## Binary stars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71d0fc4e-c72f-444a-93ab-19f52086b86d",
+   "metadata": {},
+   "source": [
+    "Now we put a secondary star of mass $0.5\\mathrm{M}_\\odot$ at a distance of $10\\mathrm{R}_\\odot$ to see how this changes things. Then we rerun the population. At such short separations, we expect mass transfer to begin on or shortly after the main sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "478e8005-e144-4e6f-80c9-0cf368a9bcb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-cff93424298e4862bb72096e72b98a2d finished! The total probability was: 10.0. It took a total of 0.9686374664306641s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 0.5, # Msun\n",
+    "    separation = 10, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9c433e6a-fe22-4494-b1a9-fce9676a9f40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "\n",
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3557b6d5-6c54-467c-b7a1-b1903493c441",
+   "metadata": {},
+   "source": [
+    "We plot here the track for the primary star only. You can see immediately where stars merge on the main sequence: the tracks move very suddenly where usually evolution on the main sequence is smooth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59335030-dd99-4c2f-afff-207a3fcbbb70",
+   "metadata": {},
+   "source": [
+    "If we now set the separation to be longer, say $100\\mathrm{R}_\\odot$, mass transfer should happen on the giant branch. We also set the secondary mass to be larger, $1\\mathrm{M}_\\odot$, so that the interaction is stronger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dee92b20-ad6b-4c97-80dc-71d3bd937c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-2ea4759ed05544ef8f1b7a887f0f36d2 finished! The total probability was: 10.0. It took a total of 0.7215321063995361s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 1, # Msun\n",
+    "    separation = 100, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    "    alpha_ce = 1.0, # make common-envelope evolution quite efficient\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e0ac2573-bc35-43be-8f20-5c85364fde11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f8e061-a65e-47f2-a777-93de0d5045ea",
+   "metadata": {},
+   "source": [
+    "You now see the interaction in the jerky red-giant tracks where the stars interact. These probably, depending on the mass ratio at the moment of interaction, go through a common-envelope phase. The system can merge (most of the above do) but not all. The interaction is so strong on the RGB of the $1\\mathrm{M}_\\odot$ star that the stellar evolution is terminated before it reaches the RGB tip, so it never ignites helium. This is how helium white dwarfs are probably made."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "698d0a63-11ba-4b3e-a713-35c3e972492f",
+   "metadata": {},
+   "source": [
+    "We can also plot the secondary stars' HRD. Remember, the primary is star 0 in binary_c, while the secondary is star 1. That's because all proper programming languages start counting at 0. We change the parsing function a little so we can separate the plots of the secondaries according to their primary mass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2b0b7c2b-6e43-48ed-9257-9dfc141b3d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "star  1\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 13,
+     "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": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '1': # choose only secondaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "            \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c46319-5629-4125-a284-b5d521ed33fc",
+   "metadata": {},
+   "source": [
+    "Remember, all these stars start with a $1\\mathrm{M}_\\odot$ binary, which begins at $\\log_{10}(T_\\mathrm{eff}/\\mathrm{K})\\sim 3.750$, $\\log_{10}L/\\mathrm{L}_\\odot \\sim 0$. The $1\\mathrm{M}_\\odot$-$1\\mathrm{M}_\\odot$ binary evolves like two single stars until they interact up the giant branch at about $\\log_{10} (L/\\mathrm{L}_\\odot) \\sim 2.5$, the others interact long before they evolve very far on the main sequence: you can just about see their tracks at the very start."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53145356-abbb-4880-996f-dedd80de7540",
+   "metadata": {},
+   "source": [
+    "This is, of course, a very simple introduction to what happens in binaries. We haven't talked about the remnants that are produced by interactions. When the stars do evolve on the giant branch, white dwarfs are made which can go on to suffer novae and (perhaps) thermonuclear explosions. The merging process itself leads to luminosus red novae and, in the case of neutron stars and black holes, kilonovae and gravitational wave events. "
+   ]
+  }
+ ],
+ "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/_sources/notebook_common_envelope_evolution.ipynb.txt b/docs/build/html/_sources/notebook_common_envelope_evolution.ipynb.txt
new file mode 100644
index 000000000..526320ccf
--- /dev/null
+++ b/docs/build/html/_sources/notebook_common_envelope_evolution.ipynb.txt
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Common-envelope evolution\n",
+    "\n",
+    "In this notebook we look at how common-envelope evolution (CEE) alters binary-star orbits. We construct a population of low- and intermediate-mass binaries and compare their orbital periods before and after CEE. Not all stars evolve into this phase, so we have to run a whole population to find those that do. We then have to construct the pre- and post-CEE distributions and plot them.\n",
+    "\n",
+    "First, we import a few required Python modules. "
+   ]
+  },
+  {
+   "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",
+    "from binarycpython.utils.functions import temp_dir\n",
+    "from binarycpython.utils.grid import Population\n",
+    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_comenv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Setting up the Population object\n",
+    "We set up a new population object. Our stars evolve to $13.7\\text{ }\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. We also set the common-envelope ejection efficiency $\\alpha_\\mathrm{CE}=1$ and the envelope structure parameter $\\lambda=0.5$. More complex options are available in *binary_c*, such as $\\lambda$ based on stellar mass, but this is just a demonstration example so let's keep things simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: log_dt=10 to grid_options\n",
+      "adding: max_evolution_time=13700 to BSE_options\n",
+      "adding: metallicity=0.02 to BSE_options\n",
+      "adding: alpha_ce=1.0 to BSE_options\n",
+      "adding: lambda_ce=0.5 to BSE_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\n",
+    "population.set(\n",
+    "    # grid options\n",
+    "    tmp_dir = TMP_DIR,\n",
+    "    verbosity = 1,\n",
+    "    log_dt = 10, # log every 10 seconds\n",
+    "\n",
+    "    # binary-star evolution options\n",
+    "    max_evolution_time=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    alpha_ce = 1.0,\n",
+    "    lambda_ce = 0.5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $6\\text{ }\\mathrm{M}_\\odot$. We avoid massive stars for now, and focus on the (more common) low- and intermediate-mass stars. We also limit the period range to $10^4\\text{ }\\mathrm{d}$ because systems with longer orbital periods will probably not undergo Roche-lobe overflow and hence common-envelope evolution is impossible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"lnm1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        6\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(math.log(1), math.log(6), 10)\",\n",
+      "    \"precode\": \"M_1=math.exp(lnm1)\",\n",
+      "    \"probdist\": \"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+      "    \"dphasevol\": \"dlnm1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"q\",\n",
+      "    \"longname\": \"Mass ratio\",\n",
+      "    \"valuerange\": [\n",
+      "        \"0.1/M_1\",\n",
+      "        1\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1/M_1, 1, 10)\",\n",
+      "    \"precode\": \"M_2 = q * M_1\",\n",
+      "    \"probdist\": \"flatsections(q, [{'min': 1/M_1, 'max': 1.0, 'height': 1}])\",\n",
+      "    \"dphasevol\": \"dq\",\n",
+      "    \"parameter_name\": \"M_2\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 1\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"log10per\",\n",
+      "    \"longname\": \"log10(Orbital_Period)\",\n",
+      "    \"valuerange\": [\n",
+      "        0.15,\n",
+      "        5.5\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(0.15, 4, 10)\",\n",
+      "    \"precode\": \"orbital_period = 10.0 ** log10per\\nsep = calc_sep_from_period(M_1, M_2, orbital_period)\\nsep_min = calc_sep_from_period(M_1, M_2, 10**0.15)\\nsep_max = calc_sep_from_period(M_1, M_2, 10**4)\",\n",
+      "    \"probdist\": \"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**0.15), math.log10(10**4), -0.55)\",\n",
+      "    \"dphasevol\": \"dlog10per\",\n",
+      "    \"parameter_name\": \"orbital_period\",\n",
+      "    \"condition\": null,\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 2\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10, \"q\" : 10, \"per\": 10} \n",
+    "massrange = [1, 6] \n",
+    "logperrange = [0.15, 4]\n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"lnm1\",\n",
+    "    longname=\"Primary mass\",\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{}\".format(resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
+    "    precode=\"M_1=math.exp(lnm1)\",\n",
+    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+    "    dphasevol=\"dlnm1\",\n",
+    "    parameter_name=\"M_1\",\n",
+    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    ")\n",
+    "\n",
+    "# Mass ratio\n",
+    "population.add_grid_variable(\n",
+    "     name=\"q\",\n",
+    "     longname=\"Mass ratio\",\n",
+    "     valuerange=[\"0.1/M_1\", 1],\n",
+    "     resolution=\"{}\".format(resolution['q']),\n",
+    "     spacingfunc=\"const({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
+    "     probdist=\"flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
+    "     dphasevol=\"dq\",\n",
+    "     precode=\"M_2 = q * M_1\",\n",
+    "     parameter_name=\"M_2\",\n",
+    "     condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    " )\n",
+    "\n",
+    "# Orbital period\n",
+    "population.add_grid_variable(\n",
+    "    name=\"log10per\", # in days\n",
+    "    longname=\"log10(Orbital_Period)\",\n",
+    "    valuerange=[0.15, 5.5],\n",
+    "    resolution=\"{}\".format(resolution[\"per\"]),\n",
+    "    spacingfunc=\"const({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
+    "    precode=\"\"\"orbital_period = 10.0 ** log10per\n",
+    "sep = calc_sep_from_period(M_1, M_2, orbital_period)\n",
+    "sep_min = calc_sep_from_period(M_1, M_2, 10**{})\n",
+    "sep_max = calc_sep_from_period(M_1, M_2, 10**{})\"\"\".format(logperrange[0],logperrange[1]),\n",
+    "    probdist=\"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
+    "    parameter_name=\"orbital_period\",\n",
+    "    dphasevol=\"dlog10per\",\n",
+    " )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Logging and handling the output\n",
+    "\n",
+    "We now construct the pre- and post-common envelope evolution data for the first common envelope that forms in each binary. We look at the comenv_count variable, we can see that when it increases from 0 to 1 we have found our object. If this happens, we stop evolution of the system to save CPU time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "\n",
+      "/*\n",
+      " * Detect when the comenv_count increased \n",
+      " */\n",
+      "if(stardata->model.comenv_count == 1 && \n",
+      "   stardata->previous_stardata->model.comenv_count == 0)\n",
+      "{\n",
+      "   /*\n",
+      "    * We just had this system's first common envelope:\n",
+      "    * output the time at which this happens, \n",
+      "    * the system's probability (proportional to the number of stars),\n",
+      "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+      "    * the current timestep (post-comenv) orbital period (days)\n",
+      "    */\n",
+      "    Printf(\"COMENV %g %g %g %g\\n\",\n",
+      "           stardata->model.time,\n",
+      "           stardata->model.probability,\n",
+      "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+      "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+      "           \n",
+      "    /*\n",
+      "     * We should waste no more CPU time on this system now we have the\n",
+      "     * data we want.\n",
+      "     */\n",
+      "    stardata->model.evolution_stop = TRUE;\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "\n",
+    "/*\n",
+    " * Detect when the comenv_count increased \n",
+    " */\n",
+    "if(stardata->model.comenv_count == 1 && \n",
+    "   stardata->previous_stardata->model.comenv_count == 0)\n",
+    "{\n",
+    "   /*\n",
+    "    * We just had this system's first common envelope:\n",
+    "    * output the time at which this happens, \n",
+    "    * the system's probability (proportional to the number of stars),\n",
+    "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+    "    * the current timestep (post-comenv) orbital period (days)\n",
+    "    */\n",
+    "    Printf(\"COMENV %g %g %g %g\\\\n\",\n",
+    "           stardata->model.time,\n",
+    "           stardata->model.probability,\n",
+    "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+    "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+    "           \n",
+    "    /*\n",
+    "     * We should waste no more CPU time on this system now we have the\n",
+    "     * data we want.\n",
+    "     */\n",
+    "    stardata->model.evolution_stop = TRUE;\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 \"COMENV\" and process the associated data. We set up the parse_data function to do just this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14736bebc040> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import bin_data,datalinedict\n",
+    "import re\n",
+    "\n",
+    "# log-period distribution bin width (dex)\n",
+    "binwidth = 0.5 \n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"probability\", \"pre_comenv_period\", \"post_comenv_period\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        # obtain the line of data in dictionary form \n",
+    "        linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "        # choose COMENV lines of output\n",
+    "        if linedata[\"header\"] == \"COMENV\":\n",
+    "            # bin the pre- and post-comenv log10-orbital-periods to nearest 0.5dex\n",
+    "            binned_pre_period = bin_data(math.log10(linedata[\"pre_comenv_period\"]), binwidth)\n",
+    "            \n",
+    "            # but check if the post-comenv period is finite and positive: if \n",
+    "            # not, the system has merged and we give it an aritifical period\n",
+    "            # of 10^-100 days (which is very much unphysical)\n",
+    "            if linedata[\"post_comenv_period\"] > 0.0:\n",
+    "                binned_post_period = bin_data(math.log10(linedata[\"post_comenv_period\"]), binwidth)\n",
+    "            else:\n",
+    "                binned_post_period = bin_data(-100,binwidth) # merged!\n",
+    "                \n",
+    "            # make the \"histograms\"\n",
+    "            self.grid_results['pre'][binned_pre_period] += linedata[\"probability\"]\n",
+    "            self.grid_results['post'][binned_post_period] += linedata[\"probability\"]\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 we actually run the population. This may take a little while. You can set amt_cores higher if you have a powerful machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: amt_cores=4 to grid_options\n",
+      "Creating and loading custom logging functionality\n",
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "Grid has handled 1000 stars\n",
+      "with a total probability of 0.0645905996773004\n",
+      "Total starcount for this run will be: 1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:39,950 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-12 18:07:39,953 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-12 18:07:39,959 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-12 18:07:39,962 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-12 18:07:39,965 DEBUG    Process-5] --- Setting up processor: process-3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 started at 2021-09-12T18:07:39.965721.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee47e0>\n",
+      "Process 1 started at 2021-09-12T18:07:39.970949.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n",
+      "Process 2 started at 2021-09-12T18:07:39.978355.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4f30>\n",
+      "Process 3 started at 2021-09-12T18:07:39.983689.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:40,066 DEBUG    MainProcess] --- Signaling stop to processes\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "163/1000  16.3% complete 18:07:49 ETA=   51.5s tpr=6.16e-02 ETF=18:08:41 mem:594.9MB\n",
+      "322/1000  32.2% complete 18:07:59 ETA=   42.9s tpr=6.33e-02 ETF=18:08:42 mem:538.2MB\n",
+      "465/1000  46.5% complete 18:08:09 ETA=   38.1s tpr=7.12e-02 ETF=18:08:47 mem:538.2MB\n",
+      "586/1000  58.6% complete 18:08:19 ETA=   34.3s tpr=8.29e-02 ETF=18:08:54 mem:540.0MB\n",
+      "682/1000  68.2% complete 18:08:30 ETA=   34.0s tpr=1.07e-01 ETF=18:09:04 mem:540.1MB\n",
+      "784/1000  78.4% complete 18:08:40 ETA=   21.2s tpr=9.81e-02 ETF=18:09:01 mem:541.8MB\n",
+      "872/1000  87.2% complete 18:08:50 ETA=   15.0s tpr=1.17e-01 ETF=18:09:05 mem:546.1MB\n",
+      "963/1000  96.3% complete 18:09:00 ETA=    4.2s tpr=1.14e-01 ETF=18:09:04 mem:546.9MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,366 DEBUG    Process-5] --- Process-3 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.964604, done at 2021-09-12T18:09:06.370832 (total: 86.406228s of which 86.24177551269531s interfacing with binary_c).\n",
+      "\tRan 222 systems with a total probability of 0.014137215791516371.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,374 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-12 18:09:06,979 DEBUG    Process-3] --- Process-1 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.953039, done at 2021-09-12T18:09:06.982866 (total: 87.029827s of which 86.82909393310547s interfacing with binary_c).\n",
+      "\tRan 273 systems with a total probability of 0.01877334232598154.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,985 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-12 18:09:07,174 DEBUG    Process-2] --- Process-0 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.949775, done at 2021-09-12T18:09:07.176660 (total: 87.226885s of which 87.02672934532166s interfacing with binary_c).\n",
+      "\tRan 268 systems with a total probability of 0.016469813170514686.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,179 DEBUG    Process-2] --- Process-0 is finished.\n",
+      "[2021-09-12 18:09:07,233 DEBUG    Process-4] --- Process-2 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.958802, done at 2021-09-12T18:09:07.236252 (total: 87.27745s of which 87.0905077457428s interfacing with binary_c).\n",
+      "\tRan 237 systems with a total probability of 0.015210228389288167.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,238 DEBUG    Process-4] --- Process-2 is finished.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population-ad303100d719457c83256568f9a9887c finished! The total probability was: 0.06459059967730076. It took a total of 87.54819011688232s to run 1000 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set number of threads\n",
+    "population.set(\n",
+    "    # set number of threads (i.e. number of CPU cores we use)\n",
+    "    amt_cores=4,\n",
+    "    )\n",
+    "\n",
+    "# Evolve the population - this is the slow, number-crunching step\n",
+    "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. We check this, and then set about making the plot of the orbital period distributions using Seaborn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': 'ad303100d719457c83256568f9a9887c', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.06459059967730076, 'total_count': 1000, 'start_timestamp': 1631462859.9342952, 'end_timestamp': 1631462947.4824853, 'total_mass_run': 4680.235689312421, 'total_probability_weighted_mass_run': 0.22611318083528567, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'merged': 0.035263029200000025, 'unmerged': 0.019388724199999995}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Number of stars')"
+      ]
+     },
+     "execution_count": 8,
+     "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 distributions\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import copy\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "# remove the merged objects\n",
+    "probability = { \"merged\" : 0.0, \"unmerged\" : 0.0}\n",
+    "\n",
+    "# copy the results so we can change the copy\n",
+    "results = copy.deepcopy(population.grid_results)\n",
+    "\n",
+    "for distribution in ['post']:    \n",
+    "    for logper in population.grid_results[distribution]:\n",
+    "        dprob = results[distribution][logper]\n",
+    "        if logper < -90:\n",
+    "            # merged system\n",
+    "            probability[\"merged\"] += dprob\n",
+    "            del results[distribution][logper]\n",
+    "        else:\n",
+    "            # unmerged system\n",
+    "            probability[\"unmerged\"] += dprob\n",
+    "print(probability)\n",
+    "    \n",
+    "# pad the final distribution with zero\n",
+    "for distribution in population.grid_results:    \n",
+    "    pad_output_distribution(results[distribution],\n",
+    "                            binwidth)\n",
+    "    \n",
+    "# make pandas dataframe \n",
+    "plot_data = pd.DataFrame.from_dict(results, orient='columns')\n",
+    "\n",
+    "# make the plot\n",
+    "p = sns.lineplot(data=plot_data)\n",
+    "p.set_xlabel(\"$\\log_{10} (P_\\mathrm{orb} / \\mathrm{day})$\")\n",
+    "p.set_ylabel(\"Number of stars\")\n",
+    "#p.set(xlim=(-5,5)) # might be necessary?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4740c93-d01e-4ca1-8766-c2fb4ddca2e4",
+   "metadata": {},
+   "source": [
+    "You can see that common-envelope evolution shrinks stellar orbits, just as we expect. Pre-CEE, most orbits are in the range $10$ to $1000\\text{ }\\mathrm{d}$, while after CEE the distribution peaks at about $1\\text{ }\\mathrm{d}$. Some of these orbits are very short: $\\log_{10}(-2) = 0.01\\text{ }\\mathrm{d}\\sim10\\text{ }\\mathrm{minutes}$. Such systems are prime candidates for exciting astrophysics: novae, type Ia supernovae and gravitational wave sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57faf043-3809-427a-b378-2355ce8c2691",
+   "metadata": {},
+   "source": [
+    "Things to try:\n",
+    "* Extend the logging to output more data than just the orbital period.\n",
+    "* What are the stellar types of the post-common envelope systems? Are they likely to undergo novae or a type-Ia supernova?\n",
+    "* What are the lifetimes of the systems in close ($<1\\text{ }\\mathrm{d}$) binaries? Are they likely to merge in the life of the Universe?\n",
+    "* How much mass is lost in common-envelope interactions?\n",
+    "* Extend the grid to massive stars. Do you see many NS and BH compact binaries?\n",
+    "* Try different $\\alpha_\\mathrm{CE}$ and $\\lambda_\\mathrm{CE}$ options...\n",
+    "* ... and perhaps increased resolution to obtain smoother curves.\n",
+    "* Why do long-period systems not reach common envelope evolution?"
+   ]
+  }
+ ],
+ "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/_sources/notebook_individual_systems.ipynb.txt b/docs/build/html/_sources/notebook_individual_systems.ipynb.txt
index e6451e762..85aef1e39 100644
--- a/docs/build/html/_sources/notebook_individual_systems.ipynb.txt
+++ b/docs/build/html/_sources/notebook_individual_systems.ipynb.txt
@@ -566,7 +566,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -580,7 +580,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/html/_sources/notebook_luminosity_function_binaries.ipynb.txt b/docs/build/html/_sources/notebook_luminosity_function_binaries.ipynb.txt
index 47a96d093..c6b5f1e64 100644
--- a/docs/build/html/_sources/notebook_luminosity_function_binaries.ipynb.txt
+++ b/docs/build/html/_sources/notebook_luminosity_function_binaries.ipynb.txt
@@ -5,7 +5,7 @@
    "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
    "metadata": {},
    "source": [
-    "# Example use case: Zero-age stellar luminosity function in binaries\n",
+    "# Zero-age stellar luminosity function in binaries\n",
     "\n",
     "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of binary stars using binary_c. \n",
     "\n",
@@ -168,7 +168,7 @@
     "\n",
     "# resolution on each side of the cube, with more stars for the primary mass\n",
     "nres = 10\n",
-    "resolution = {\"M_1\": 2*nres,\n",
+    "resolution = {\"M_1\": 4*nres,\n",
     "              \"q\": nres,\n",
     "              \"per\": nres}\n",
     "\n",
@@ -379,10 +379,6 @@
    "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -399,229 +395,74 @@
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
       "Grid code loaded\n",
-      "Grid has handled 2000 stars\n",
-      "with a total probability of 0.6495098935846658\n",
-      "Total starcount for this run will be: 2000\n"
+      "Grid has handled 256 stars\n",
+      "with a total probability of 0.6149734610296649\n",
+      "Total starcount for this run will be: 256\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:14:08,077 DEBUG    Process-2] --- Setting up processor: process-0[2021-09-10 15:14:08,080 DEBUG    Process-3] --- Setting up processor: process-1[2021-09-10 15:14:08,086 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
-      "\n",
-      "[2021-09-10 15:14:08,084 DEBUG    Process-4] --- Setting up processor: process-2\n",
-      "\n",
-      "[2021-09-10 15:14:08,117 DEBUG    Process-5] --- Setting up processor: process-3"
+      "[2021-09-10 22:26:10,473 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-10 22:26:10,475 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-10 22:26:10,478 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-10 22:26:10,481 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-10 22:26:10,482 DEBUG    Process-5] --- Setting up processor: process-3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 started at 2021-09-10T15:14:08.119437.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>Process 0 started at 2021-09-10T15:14:08.126435.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>\n",
-      "Process 2 started at 2021-09-10T15:14:08.138353.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>"
+      "Process 0 started at 2021-09-10T22:26:10.491896.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf510>Process 1 started at 2021-09-10T22:26:10.491948.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf480>\n",
+      "\n",
+      "Process 2 started at 2021-09-10T22:26:10.496677.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf3f0>\n",
+      "Process 3 started at 2021-09-10T22:26:10.498669.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf180>\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "[2021-09-10 22:26:10,510 DEBUG    MainProcess] --- Signaling stop to processes\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "Process 3 started at 2021-09-10T15:14:08.186492.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>\n",
       "Generating grid code\n",
       "Generating grid code\n",
       "Constructing/adding: lnm1\n",
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
       "Grid code loaded\n",
-      "624/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB625/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.04e-03 ETF=15:14:23 mem:800.5MB\n",
-      "626/2000  31.3% complete 15:14:12 ETA=   11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB\n",
-      "\n",
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+      "158/256  61.7% complete 22:26:15 ETA=    3.2s tpr=3.22e-02 ETF=22:26:18 mem:509.0MB\n",
+      "199/256  77.7% complete 22:26:20 ETA=    7.3s tpr=1.28e-01 ETF=22:26:27 mem:476.9MB\n",
+      "238/256  93.0% complete 22:26:25 ETA=    2.3s tpr=1.28e-01 ETF=22:26:27 mem:481.7MB\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:16:25,175 DEBUG    MainProcess] --- Signaling stop to processes\n"
+      "[2021-09-10 22:26:27,631 DEBUG    Process-3] --- Process-1 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "\n",
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-      "\n",
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-      "\n",
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-      "1725/2000  86.2% complete 15:24:31 ETA=    2.6m tpr=5.57e-01 ETF=15:27:04 mem:586.7MB\n",
-      "1735/2000  86.8% complete 15:24:38 ETA=    3.0m tpr=6.76e-01 ETF=15:27:37 mem:586.7MB\n",
-      "1745/2000  87.2% complete 15:24:44 ETA=    2.7m tpr=6.40e-01 ETF=15:27:27 mem:586.9MB\n",
-      "1755/2000  87.8% complete 15:24:51 ETA=    2.8m tpr=6.88e-01 ETF=15:27:40 mem:586.9MB\n",
-      "1763/2000  88.2% complete 15:24:56 ETA=    2.6m tpr=6.59e-01 ETF=15:27:32 mem:586.9MB\n",
-      "1767/2000  88.3% complete 15:25:02 ETA=    5.3m tpr=1.36e+00 ETF=15:30:18 mem:586.9MB\n",
-      "1776/2000  88.8% complete 15:25:09 ETA=    2.9m tpr=7.71e-01 ETF=15:28:01 mem:586.9MB\n",
-      "1785/2000  89.2% complete 15:25:14 ETA=    2.1m tpr=5.90e-01 ETF=15:27:21 mem:586.9MB\n",
-      "1793/2000  89.7% complete 15:25:19 ETA=    2.2m tpr=6.29e-01 ETF=15:27:29 mem:587.1MB\n",
-      "1801/2000  90.0% complete 15:25:24 ETA=    2.2m tpr=6.59e-01 ETF=15:27:35 mem:587.1MB\n",
-      "1812/2000  90.6% complete 15:25:29 ETA=    1.5m tpr=4.68e-01 ETF=15:26:57 mem:587.1MB\n",
-      "1822/2000  91.1% complete 15:25:35 ETA=    1.6m tpr=5.54e-01 ETF=15:27:14 mem:587.4MB\n",
-      "1830/2000  91.5% complete 15:25:41 ETA=    2.1m tpr=7.49e-01 ETF=15:27:48 mem:587.4MB\n",
-      "1839/2000  92.0% complete 15:25:47 ETA=    1.7m tpr=6.21e-01 ETF=15:27:27 mem:587.4MB\n",
-      "1847/2000  92.3% complete 15:25:52 ETA=    1.8m tpr=7.10e-01 ETF=15:27:41 mem:587.4MB\n",
-      "1855/2000  92.8% complete 15:25:59 ETA=    2.0m tpr=8.17e-01 ETF=15:27:57 mem:587.6MB\n",
-      "1864/2000  93.2% complete 15:26:05 ETA=    1.5m tpr=6.79e-01 ETF=15:27:37 mem:587.8MB\n",
-      "1873/2000  93.7% complete 15:26:10 ETA=    1.3m tpr=6.07e-01 ETF=15:27:27 mem:588.0MB\n",
-      "1884/2000  94.2% complete 15:26:16 ETA=   57.0s tpr=4.91e-01 ETF=15:27:13 mem:588.1MB\n",
-      "1895/2000  94.8% complete 15:26:21 ETA=   48.7s tpr=4.63e-01 ETF=15:27:09 mem:588.8MB\n",
-      "1907/2000  95.3% complete 15:26:27 ETA=   45.6s tpr=4.91e-01 ETF=15:27:12 mem:588.9MB\n",
-      "1916/2000  95.8% complete 15:26:33 ETA=   57.5s tpr=6.84e-01 ETF=15:27:30 mem:589.1MB\n",
-      "1926/2000  96.3% complete 15:26:39 ETA=   46.5s tpr=6.28e-01 ETF=15:27:26 mem:589.1MB\n",
-      "1936/2000  96.8% complete 15:26:46 ETA=   42.0s tpr=6.57e-01 ETF=15:27:28 mem:589.1MB\n",
-      "1946/2000  97.3% complete 15:26:53 ETA=   40.1s tpr=7.42e-01 ETF=15:27:33 mem:589.2MB\n",
-      "1956/2000  97.8% complete 15:26:59 ETA=   25.1s tpr=5.70e-01 ETF=15:27:24 mem:589.2MB\n",
-      "1966/2000  98.3% complete 15:27:04 ETA=   19.1s tpr=5.62e-01 ETF=15:27:24 mem:589.5MB\n",
-      "1976/2000  98.8% complete 15:27:10 ETA=   14.4s tpr=6.01e-01 ETF=15:27:25 mem:589.5MB\n",
-      "1987/2000  99.3% complete 15:27:16 ETA=    6.4s tpr=4.92e-01 ETF=15:27:22 mem:589.5MB\n",
-      "1998/2000  99.9% complete 15:27:21 ETA=    1.0s tpr=4.85e-01 ETF=15:27:22 mem:589.6MB\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-09-10 15:27:22,382 DEBUG    Process-5] --- Process-3 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Process 3 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.117391, done at 2021-09-10T15:27:22.400722 (total: 794.283331s of which 792.6935975551605s interfacing with binary_c).\n",
-      "\tRan 499 systems with a total probability of 0.17005450973840136.\n",
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.475399, done at 2021-09-10T22:26:27.634804 (total: 17.159405s of which 17.104907512664795s interfacing with binary_c).\n",
+      "\tRan 61 systems with a total probability of 0.1439494161909395.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -630,17 +471,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,435 DEBUG    Process-5] --- Process-3 is finished.\n",
-      "[2021-09-10 15:27:22,480 DEBUG    Process-3] --- Process-1 is finishing.\n"
+      "[2021-09-10 22:26:27,639 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-10 22:26:27,698 DEBUG    Process-5] --- Process-3 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.080367, done at 2021-09-10T15:27:22.505288 (total: 794.424921s of which 793.1943278312683s interfacing with binary_c).\n",
-      "\tRan 474 systems with a total probability of 0.15740832333567983.\n",
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.482470, done at 2021-09-10T22:26:27.701828 (total: 17.219358s of which 17.162050247192383s interfacing with binary_c).\n",
+      "\tRan 67 systems with a total probability of 0.17251417460118773.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -649,17 +490,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,531 DEBUG    Process-3] --- Process-1 is finished.\n",
-      "[2021-09-10 15:27:22,846 DEBUG    Process-2] --- Process-0 is finishing.\n"
+      "[2021-09-10 22:26:27,705 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-10 22:26:27,769 DEBUG    Process-4] --- Process-2 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 0 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.077117, done at 2021-09-10T15:27:22.851971 (total: 794.774854s of which 793.4976091384888s interfacing with binary_c).\n",
-      "\tRan 507 systems with a total probability of 0.16018641159091498.\n",
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.478464, done at 2021-09-10T22:26:27.771291 (total: 17.292827s of which 17.243471384048462s interfacing with binary_c).\n",
+      "\tRan 56 systems with a total probability of 0.14306289954535925.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -668,17 +509,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,872 DEBUG    Process-2] --- Process-0 is finished.\n",
-      "[2021-09-10 15:27:22,976 DEBUG    Process-4] --- Process-2 is finishing.\n"
+      "[2021-09-10 22:26:27,774 DEBUG    Process-4] --- Process-2 is finished.\n",
+      "[2021-09-10 22:26:27,865 DEBUG    Process-2] --- Process-0 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 2 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.084369, done at 2021-09-10T15:27:22.981706 (total: 794.897337s of which 793.4600214958191s interfacing with binary_c).\n",
-      "\tRan 520 systems with a total probability of 0.1618606489196724.\n",
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.473000, done at 2021-09-10T22:26:27.867175 (total: 17.394175s of which 17.331928491592407s interfacing with binary_c).\n",
+      "\tRan 72 systems with a total probability of 0.1554469706921749.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -687,14 +528,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,986 DEBUG    Process-4] --- Process-2 is finished.\n"
+      "[2021-09-10 22:26:27,869 DEBUG    Process-2] --- Process-0 is finished.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Population-0fa295ee5c76444bace8fd0ee17a3e11 finished! The total probability was: 0.6495098935846686. It took a total of 795.1383104324341s to run 2000 systems on 4 cores\n",
+      "Population-bc3a5f915411445699f8cf6438817ff1 finished! The total probability was: 0.6149734610296613. It took a total of 17.603368997573853s to run 256 systems on 4 cores\n",
       "There were no errors found in this run.\n",
       "Done population run!\n"
      ]
@@ -728,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -736,7 +577,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '0fa295ee5c76444bace8fd0ee17a3e11', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6495098935846686, 'total_count': 2000, 'start_timestamp': 1631283248.057525, 'end_timestamp': 1631284043.1958354, 'total_mass_run': 41112.220964392276, 'total_probability_weighted_mass_run': 0.6452116023479681, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'bc3a5f915411445699f8cf6438817ff1', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6149734610296613, 'total_count': 256, 'start_timestamp': 1631305570.458824, 'end_timestamp': 1631305588.062193, 'total_mass_run': 5246.190724478048, 'total_probability_weighted_mass_run': 0.6347400152389439, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -746,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -756,13 +597,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -777,8 +618,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# set up seaborn for use in the notebook\n",
+    "sns.set(rc={'figure.figsize':(20,10)})\n",
+    "sns.set_context(\"notebook\",\n",
+    "                font_scale=1.5,\n",
+    "                rc={\"lines.linewidth\":2.5})\n",
+    "\n",
     "\n",
     "titles = { 0 : \"Primary\",\n",
     "           1 : \"Secondary\",\n",
@@ -805,11 +650,36 @@
     "p.set_ylabel(\"Number of stars\")\n",
     "p.set(yscale=\"log\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "You can see that the secondary stars are dimmer than the primaries - which you expect given they are lower in mass (by definition q=M2/M1<1). \n",
+    "\n",
+    "Weirdly, in some places the primary distribution may exceed the unresolved distribution. This is a bit unphysical, but in this case is usually caused by limited resolution. If you increase the number of stars in the grid, this problem should go away (at a cost of more CPU time). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99e25a72-54e6-4826-b0e5-4a02460b857d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Things to try:\n",
+    "* Massive stars: can you see the effects of wind mass loss and rejuvenation in these stars?\n",
+    "* Alter the metallicity, does this make much of a difference?\n",
+    "* Change the binary fraction. Here we assume a 100% binary fraction, but a real population is a mixture of single and binary stars.\n",
+    "* How might you go about comparing these computed observations to real stars?\n",
+    "* What about evolved stars? Here we consider only the *zero-age* main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -823,7 +693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/html/_sources/notebook_luminosity_function_single.ipynb.txt b/docs/build/html/_sources/notebook_luminosity_function_single.ipynb.txt
index 5980adf6d..cdae316f9 100644
--- a/docs/build/html/_sources/notebook_luminosity_function_single.ipynb.txt
+++ b/docs/build/html/_sources/notebook_luminosity_function_single.ipynb.txt
@@ -54,8 +54,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "adding: max_evolution_time=0.1 to BSE_options\n",
+      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "verbosity is 1\n"
      ]
     }
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "aba3fe4e-18f2-4bb9-8e5c-4c6007ab038b",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "47979841-2c26-4b26-8945-603d013dc93a",
    "metadata": {},
    "outputs": [],
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
    "metadata": {},
    "outputs": [],
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "fd197154-a8ce-4865-8929-008d3483101a",
    "metadata": {},
    "outputs": [],
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
     "tags": []
@@ -321,9 +321,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-08f8230453084e4ca6a2391d45ce658b finished! The total probability was: 1.0000000000000002. It took a total of 1.5262682437896729s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.025), (3.75, 0.05), (4.25, 0.05), (0.25, 0.025), (3.25, 0.025), (5.25, 0.2), (4.75, 0.1), (5.75, 0.39999999999999997), (6.25, 0.125)]))])\n"
+      "Population-e6c082aabe0849a0811761a06e50476b finished! The total probability was: 1.0000000000000002. It took a total of 2.3021209239959717s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -353,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -361,7 +360,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '08f8230453084e4ca6a2391d45ce658b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631124829.303065, 'end_timestamp': 1631124830.8293333, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'e6c082aabe0849a0811761a06e50476b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631461389.3681686, 'end_timestamp': 1631461391.6702895, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -371,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -381,13 +380,13 @@
        "[None]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -402,8 +401,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# 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",
@@ -442,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
    "metadata": {},
    "outputs": [],
@@ -456,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
    "metadata": {},
    "outputs": [
@@ -471,9 +474,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-92de7c9221c54206ab4dd10e58e09a34 finished! The total probability was: 0.21822161894107872. It took a total of 1.5900418758392334s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.0164166), (3.25, 0.00515685), (0.25, 0.189097), (3.75, 0.0037453900000000004), (4.25, 0.0014346559999999999), (5.25, 0.0007493004), (4.75, 0.001171479), (5.75, 0.00039801020000000003), (6.25, 5.2369339999999996e-05)]))])\n"
+      "Population-1bc714cffdb344589ea01692f7e1ebd1 finished! The total probability was: 0.21822161894107872. It took a total of 2.335742950439453s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -488,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
    "metadata": {},
    "outputs": [
@@ -498,13 +500,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -559,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
    "metadata": {},
    "outputs": [],
@@ -578,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
    "metadata": {},
    "outputs": [],
@@ -599,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
    "metadata": {},
    "outputs": [
@@ -614,9 +616,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: lnM_1\n",
-      "Population-83f80d829dbd418aa2bc745c99b71991 finished! The total probability was: 0.9956307907476224. It took a total of 0.9961590766906738s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(0.25, 0.0212294), (2.75, 0.00321118), (-0.25, 0.0268827), (1.25, 0.0104553), (3.75, 0.00283037), (6.25, 7.34708e-05), (-0.75, 0.0771478), (0.75, 0.030004499999999996), (2.25, 0.00921541), (3.25, 0.0045385), (1.75, 0.014776889999999999), (4.25, 0.002380189), (4.75, 0.000869303), (5.25, 0.0007310379999999999), (5.75, 0.00036002859999999996), (-2.75, 0.1961345), (-1.75, 0.2181597), (-3.25, 0.0), (-2.25, 0.2568974), (-1.25, 0.11973310000000001)]))])\n"
+      "Population-4f3ee0143c0548338494d2f1fbacc915 finished! The total probability was: 0.9956307907476225. It took a total of 1.5107016563415527s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -639,13 +640,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "68ee1e56-21e5-48f4-b74c-50e48685ae94",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -684,6 +685,20 @@
     " \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",
+    "* 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)"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/build/html/_sources/notebook_population.ipynb.txt b/docs/build/html/_sources/notebook_population.ipynb.txt
index fff337533..a24638c0b 100644
--- a/docs/build/html/_sources/notebook_population.ipynb.txt
+++ b/docs/build/html/_sources/notebook_population.ipynb.txt
@@ -1109,7 +1109,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1123,7 +1123,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/html/binary_c_parameters.html b/docs/build/html/binary_c_parameters.html
index b7fbb9cf3..a3432514b 100644
--- a/docs/build/html/binary_c_parameters.html
+++ b/docs/build/html/binary_c_parameters.html
@@ -40,7 +40,7 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="Population grid code options" href="grid_options_descriptions.html" />
-    <link rel="prev" title="Example use case: Zero-age stellar luminosity function in binaries" href="notebook_luminosity_function_binaries.html" /> 
+    <link rel="prev" title="Example use case: Common-envelope evolution" href="notebook_common_envelope_evolution.html" /> 
 </head>
 
 <body class="wy-body-for-nav">
@@ -3486,7 +3486,7 @@
         <a href="grid_options_descriptions.html" class="btn btn-neutral float-right" title="Population grid code options" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
-        <a href="notebook_luminosity_function_binaries.html" class="btn btn-neutral float-left" title="Example use case: Zero-age stellar luminosity function in binaries" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+        <a href="notebook_common_envelope_evolution.html" class="btn btn-neutral float-left" title="Example use case: Common-envelope evolution" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
       
     </div>
   
@@ -3509,7 +3509,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/custom_logging_functions.html b/docs/build/html/custom_logging_functions.html
index 8f854c226..b4b340d1d 100644
--- a/docs/build/html/custom_logging_functions.html
+++ b/docs/build/html/custom_logging_functions.html
@@ -420,7 +420,7 @@ 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: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/distribution_functions.html b/docs/build/html/distribution_functions.html
index 3af662f67..566951b34 100644
--- a/docs/build/html/distribution_functions.html
+++ b/docs/build/html/distribution_functions.html
@@ -911,7 +911,7 @@ 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: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/example_notebooks.html b/docs/build/html/example_notebooks.html
index c2985b191..f4596c160 100644
--- a/docs/build/html/example_notebooks.html
+++ b/docs/build/html/example_notebooks.html
@@ -96,7 +96,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -240,13 +242,28 @@
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html#A-better-sampled-grid">A better-sampled grid</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a><ul>
+<li class="toctree-l1"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html#Setting-up-the-Population-object">Setting up the Population object</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html#Adding-grid-variables">Adding grid variables</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html#Setting-logging-and-handling-the-output">Setting logging and handling the output</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html#Evolving-the-grid">Evolving the grid</a></li>
 </ul>
 </li>
+<li class="toctree-l1"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html#Setting-up-the-Population-object">Setting up the Population object</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html#Stellar-Grid">Stellar Grid</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html#Setting-logging-and-handling-the-output">Setting logging and handling the output</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html#Evolving-the-grid">Evolving the grid</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html#Binary-stars">Binary stars</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html#Setting-up-the-Population-object">Setting up the Population object</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html#Stellar-Grid">Stellar Grid</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html#Logging-and-handling-the-output">Logging and handling the output</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html#Evolving-the-grid">Evolving the grid</a></li>
+</ul>
+</li>
 </ul>
 </div>
 </div>
@@ -285,7 +302,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/functions.html b/docs/build/html/functions.html
index f2e044437..e27f55452 100644
--- a/docs/build/html/functions.html
+++ b/docs/build/html/functions.html
@@ -1027,7 +1027,7 @@ 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: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index e4179d7d5..0c51efc2f 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -782,7 +782,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/grid.html b/docs/build/html/grid.html
index 94641c943..f94395fa7 100644
--- a/docs/build/html/grid.html
+++ b/docs/build/html/grid.html
@@ -633,7 +633,7 @@ like m1,m2,sep, orb-per, ecc, probability etc.</p>
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/grid_options_defaults.html b/docs/build/html/grid_options_defaults.html
index 7c016a8cf..8bb1e5cb9 100644
--- a/docs/build/html/grid_options_defaults.html
+++ b/docs/build/html/grid_options_defaults.html
@@ -308,7 +308,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/grid_options_descriptions.html b/docs/build/html/grid_options_descriptions.html
index 2ddcc302d..17724a27e 100644
--- a/docs/build/html/grid_options_descriptions.html
+++ b/docs/build/html/grid_options_descriptions.html
@@ -482,7 +482,7 @@ q extrapolation (below 0.15) method
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/hpc_functions.html b/docs/build/html/hpc_functions.html
index d88ba0dd9..902834f26 100644
--- a/docs/build/html/hpc_functions.html
+++ b/docs/build/html/hpc_functions.html
@@ -239,7 +239,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 10521d44c..f57caabf7 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -318,7 +318,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a><ul>
@@ -385,7 +387,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/modules.html b/docs/build/html/modules.html
index 8b5127acb..8c5dccc91 100644
--- a/docs/build/html/modules.html
+++ b/docs/build/html/modules.html
@@ -250,7 +250,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_HRD.html b/docs/build/html/notebook_HRD.html
new file mode 100644
index 000000000..e0d4a842f
--- /dev/null
+++ b/docs/build/html/notebook_HRD.html
@@ -0,0 +1,1176 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8">
+  
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  
+  <title>Example use case: Hertzsprung-Russell diagrams &mdash; binary_c-python  documentation</title>
+  
+
+  
+  <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+
+  
+  
+  
+  
+
+  
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+    
+      <script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
+        <script src="_static/jquery.js"></script>
+        <script src="_static/underscore.js"></script>
+        <script src="_static/doctools.js"></script>
+        <script src="_static/language_data.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+        <script type="text/x-mathjax-config">MathJax.Hub.Config({"tex2jax": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true, "ignoreClass": "document", "processClass": "math|output_area"}})</script>
+    
+    <script type="text/javascript" src="_static/js/theme.js"></script>
+
+    
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Example use case: Common-envelope evolution" href="notebook_common_envelope_evolution.html" />
+    <link rel="prev" title="Zero-age stellar luminosity function in binaries" href="notebook_luminosity_function_binaries.html" /> 
+</head>
+
+<body class="wy-body-for-nav">
+
+   
+  <div class="wy-grid-for-nav">
+    
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+          
+
+          
+            <a href="index.html" class="icon icon-home" alt="Documentation Home"> binary_c-python
+          
+
+          
+          </a>
+
+          
+            
+            
+          
+
+          
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+
+          
+        </div>
+
+        
+        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
+          
+            
+            
+              
+            
+            
+              <p class="caption"><span class="caption-text">Contents:</span></p>
+<ul class="current">
+<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 current"><a class="reference internal" href="example_notebooks.html">Example notebooks</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="notebook_individual_systems.html">Tutorial: Running individual systems with binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_custom_logging.html">Tutorial: Using custom logging routines with binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_population.html">Tutorial: Running populations with binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Example use case: Hertzsprung-Russell diagrams</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Setting-up-the-Population-object">Setting up the Population object</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Stellar-Grid">Stellar Grid</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Setting-logging-and-handling-the-output">Setting logging and handling the output</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Evolving-the-grid">Evolving the grid</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Binary-stars">Binary stars</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
+</ul>
+</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>
+<li class="toctree-l1"><a class="reference external" href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/issues/new">Submit an issue</a></li>
+</ul>
+
+            
+          
+        </div>
+        
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
+
+      
+      <nav class="wy-nav-top" aria-label="top navigation">
+        
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">binary_c-python</a>
+        
+      </nav>
+
+
+      <div class="wy-nav-content">
+        
+        <div class="rst-content">
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+
+
+<div role="navigation" aria-label="breadcrumbs navigation">
+
+  <ul class="wy-breadcrumbs">
+    
+      <li><a href="index.html" class="icon icon-home"></a> &raquo;</li>
+        
+          <li><a href="example_notebooks.html">Example notebooks</a> &raquo;</li>
+        
+      <li>Example use case: Hertzsprung-Russell diagrams</li>
+    
+    
+      <li class="wy-breadcrumbs-aside">
+        
+            
+            <a href="_sources/notebook_HRD.ipynb.txt" rel="nofollow"> View page source</a>
+          
+        
+      </li>
+    
+  </ul>
+
+  
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+            
+  
+<style>
+/* CSS for nbsphinx extension */
+
+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
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+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
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+
+/* input prompt */
+div.nbinput.container div.prompt pre {
+    color: #307FC1;
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+/* output prompt */
+div.nboutput.container div.prompt pre {
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+    width: 4.5ex;
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+}
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+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
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+@media (max-width: 540px) {
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+    div.nboutput.container div.prompt {
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+
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+
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+.prompt a.copybtn {
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+
+/* Some additional styling taken form the Jupyter notebook CSS */
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+}
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+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+div.rendered_html th {
+  font-weight: bold;
+}
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
+/* CSS overrides for sphinx_rtd_theme */
+
+/* 24px margin */
+.nbinput.nblast.container,
+.nboutput.nblast.container {
+    margin-bottom: 19px;  /* padding has already 5px */
+}
+
+/* ... except between code cells! */
+.nblast.container + .nbinput.container {
+    margin-top: -19px;
+}
+
+.admonition > p:before {
+    margin-right: 4px;  /* make room for the exclamation icon */
+}
+
+/* Fix math alignment, see https://github.com/rtfd/sphinx_rtd_theme/pull/686 */
+.math {
+    text-align: unset;
+}
+</style>
+<div class="section" id="Example-use-case:-Hertzsprung-Russell-diagrams">
+<h1>Example use case: Hertzsprung-Russell diagrams<a class="headerlink" href="#Example-use-case:-Hertzsprung-Russell-diagrams" title="Permalink to this headline">¶</a></h1>
+<p>In this notebook we compute Hertzsprung-Russell diagrams (HRDs) of single and binary stars.</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_HRD&quot;</span><span class="p">)</span>
+
+</pre></div>
+</div>
+</div>
+<div class="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 headline">¶</a></h2>
+<p>First we set up a new population object. Our stars evolve to <span class="math notranslate nohighlight">\(13.7\mathrm{Gyr}\)</span>, the age of the Universe, and we assume the metallicity <span class="math notranslate nohighlight">\(Z=0.02\)</span>. These are rough approximations: a real population was born some finite time ago, so cannot possibly evolve to <span class="math notranslate nohighlight">\(13.7\mathrm{Gyr}\)</span>, and stars are not really born with a metallicity of <span class="math notranslate nohighlight">\(0.02\)</span>. These approximations only affect very low mass stars, so we assume all our stars have mass <span class="math notranslate nohighlight">\(M\geq 1 \mathrm{M}_\odot\)</span>, and metallicity
+does not change evolution too much except in massive stars through the dependence of their winds on metallicity, so we limit our study to <span class="math notranslate nohighlight">\(M\leq 10 \mathrm{M}_\odot\)</span>.</p>
+<div class="nbinput nblast 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"># 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="mi">13700</span><span class="p">,</span>  <span class="c1"># maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)</span>
+    <span class="n">metallicity</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span> <span class="c1"># 0.02 is approximately Solar metallicity</span>
+    <span class="n">tmp_dir</span><span class="o">=</span><span class="n">TMP_DIR</span><span class="p">,</span>
+    <span class="n">verbosity</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+
+</pre></div>
+</div>
+</div>
+</div>
+<div class="section" id="Stellar-Grid">
+<h2>Stellar Grid<a class="headerlink" href="#Stellar-Grid" title="Permalink to this headline">¶</a></h2>
+<p>We now construct a grid of stars, varying the mass from <span class="math notranslate nohighlight">\(1\)</span> to <span class="math notranslate nohighlight">\(10\mathrm{M}_\odot\)</span> in nine steps (so the masses are integers).</p>
+<div class="nbinput 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="kn">import</span> <span class="nn">binarycpython.utils.distribution_functions</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">10</span><span class="p">}</span>
+<span class="n">massrange</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</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="c1"># == single-star mass</span>
+    <span class="n">valuerange</span><span class="o">=</span><span class="n">massrange</span><span class="p">,</span>
+    <span class="n">resolution</span><span class="o">=</span><span class="s2">&quot;</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="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">spacingfunc</span><span class="o">=</span><span class="s2">&quot;const(1,2,1)&quot;</span><span class="p">,</span> <span class="c1"># space by unit masses</span>
+    <span class="n">probdist</span><span class="o">=</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="c1"># dprob/dm1 : we don&#39;t care, so just set it to 1</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="n">gridtype</span><span class="o">=</span><span class="s2">&quot;edge&quot;</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>
+Added grid variable: {
+    &#34;name&#34;: &#34;M_1&#34;,
+    &#34;longname&#34;: &#34;Primary mass&#34;,
+    &#34;valuerange&#34;: [
+        1,
+        11
+    ],
+    &#34;resolution&#34;: &#34;10&#34;,
+    &#34;spacingfunc&#34;: &#34;const(1,2,1)&#34;,
+    &#34;precode&#34;: null,
+    &#34;probdist&#34;: &#34;1&#34;,
+    &#34;dphasevol&#34;: &#34;dM_1&#34;,
+    &#34;parameter_name&#34;: &#34;M_1&#34;,
+    &#34;condition&#34;: &#34;&#34;,
+    &#34;gridtype&#34;: &#34;edge&#34;,
+    &#34;branchpoint&#34;: 0,
+    &#34;grid_variable_number&#34;: 0
+}
+</pre></div></div>
+</div>
+</div>
+<div class="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 headline">¶</a></h2>
+<p>We now construct the HRD output.</p>
+<p>We choose stars prior to and including the thermally-pulsing asymptotic giant branch (TPAGB) phase that have <span class="math notranslate nohighlight">\(&gt;0.1\mathrm{M}_\odot\)</span> of material in their outer hydrogen envelope (remember the core of an evolved star is made of helium or carbon/oxygen/neon). This prevents us showing the post-AGB phase which is a bit messy and we avoid the white-dwarf cooling track.</p>
+<div class="nbinput 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="n">custom_logging_statement</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">Foreach_star(star)</span>
+<span class="s2">{</span>
+<span class="s2">    if(star-&gt;stellar_type &lt;= TPAGB &amp;&amp;</span>
+<span class="s2">       star-&gt;mass - Outermost_core_mass(star) &gt; 0.1)</span>
+<span class="s2">    {</span>
+<span class="s2">         double logTeff = log10(Teff_from_star_struct(star));</span>
+<span class="s2">         double logL = log10(star-&gt;luminosity);</span>
+<span class="s2">         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star-&gt;mass/Pow2(star-&gt;radius*R_SUN));</span>
+<span class="s2">         Printf(&quot;HRD</span><span class="si">%d</span><span class="s2"> </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">                star-&gt;starnum, // 0</span>
+<span class="s2">                stardata-&gt;model.time, // 1</span>
+<span class="s2">                stardata-&gt;common.zero_age.mass[0], // 2 : note this is the primary mass</span>
+<span class="s2">                logTeff, // 3</span>
+<span class="s2">                logL, // 4</span>
+<span class="s2">                loggravity // 5</span>
+<span class="s2">                );</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>
+
+</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: C_logging_code=
+Foreach_star(star)
+{
+    if(star-&gt;stellar_type &lt;= TPAGB &amp;&amp;
+       star-&gt;mass - Outermost_core_mass(star) &gt; 0.1)
+    {
+         double logTeff = log10(Teff_from_star_struct(star));
+         double logL = log10(star-&gt;luminosity);
+         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star-&gt;mass/Pow2(star-&gt;radius*R_SUN));
+         Printf(&#34;HRD%d %30.12e %g %g %g %g\n&#34;,
+                star-&gt;starnum, // 0
+                stardata-&gt;model.time, // 1
+                stardata-&gt;common.zero_age.mass[0], // 2 : note this is the primary mass
+                logTeff, // 3
+                logL, // 4
+                loggravity // 5
+                );
+
+    }
+}
+ to grid_options
+</pre></div></div>
+</div>
+<p>The parse function must now catch lines that start with “HRD*n*”, where <em>n</em> is 0 (primary star) or 1 (secondary star, which doesn’t exist in single-star systems), and process the associated data.</p>
+<div class="nbinput 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="kn">from</span> <span class="nn">binarycpython.utils.functions</span> <span class="kn">import</span> <span class="n">datalinedict</span>
+<span class="kn">import</span> <span class="nn">re</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">    Parsing function to convert HRD data into something that Python can use</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;logTeff&quot;</span><span class="p">,</span> <span class="s2">&quot;logL&quot;</span><span class="p">,</span> <span class="s2">&quot;logg&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="n">match</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="s1">&#39;HRD(\d)&#39;</span><span class="p">,</span><span class="n">line</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">match</span><span class="p">:</span>
+            <span class="n">nstar</span> <span class="o">=</span> <span class="n">match</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">1</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"># first time setup of the list of tuples</span>
+            <span class="k">if</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;HRD&#39;</span><span class="p">][</span><span class="n">nstar</span><span class="p">][</span><span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;zams_mass&#39;</span><span class="p">]])</span><span class="o">==</span><span class="mi">0</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;HRD&#39;</span><span class="p">][</span><span class="n">nstar</span><span class="p">][</span><span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;zams_mass&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="c1"># make the HRD be a list of tuples</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;HRD&#39;</span><span class="p">][</span><span class="n">nstar</span><span class="p">][</span><span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;zams_mass&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;logTeff&#39;</span><span class="p">],</span>
+                                                                           <span class="n">linedata</span><span class="p">[</span><span class="s1">&#39;logL&#39;</span><span class="p">]))</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>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+adding: parse_function=&lt;function parse_function at 0x14565763dca0&gt; to grid_options
+</pre></div></div>
+</div>
+</div>
+<div class="section" id="Evolving-the-grid">
+<h2>Evolving the grid<a class="headerlink" href="#Evolving-the-grid" title="Permalink to this headline">¶</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">amt_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>[6]:
+</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">amt_cores</span><span class="o">=</span><span class="mi">4</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>
+adding: verbosity=0 to grid_options
+Generating grid code
+Constructing/adding: M_1
+Grid has handled 10 stars
+with a total probability of 10.0
+Total starcount for this run will be: 10
+Generating grid code
+Constructing/adding: M_1
+Population-20bee5b0c58d49c5bc47eced240685bb finished! The total probability was: 10.0. It took a total of 0.543649435043335s to run 10 systems on 4 cores
+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>[7]:
+</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;20bee5b0c58d49c5bc47eced240685bb&#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;: 10.0, &#39;total_count&#39;: 10, &#39;start_timestamp&#39;: 1631304519.45189, &#39;end_timestamp&#39;: 1631304519.9955394, &#39;total_mass_run&#39;: 55.0, &#39;total_probability_weighted_mass_run&#39;: 55.0, &#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>[8]:
+</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="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</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="n">hrd</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;HRD&#39;</span><span class="p">]</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">nstar</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;star &quot;</span><span class="p">,</span><span class="n">nstar</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">zams_mass</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">]):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;zams mass &quot;</span><span class="p">,</span><span class="n">zams_mass</span><span class="p">)</span>
+
+        <span class="c1"># get track data (list of tuples)</span>
+        <span class="n">track</span> <span class="o">=</span> <span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">][</span><span class="n">zams_mass</span><span class="p">]</span>
+
+        <span class="c1"># convert to Pandas dataframe</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">track</span><span class="p">,</span>
+                            <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span><span class="s1">&#39;logL&#39;</span><span class="p">])</span>
+
+        <span class="c1"># make seaborn 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">data</span><span class="p">,</span>
+                         <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                         <span class="n">x</span><span class="o">=</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span>
+                         <span class="n">y</span><span class="o">=</span><span class="s1">&#39;logL&#39;</span><span class="p">,</span>
+                         <span class="n">estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="c1"># set mass label at the zero-age main sequence (ZAMS) which is the first data point</span>
+        <span class="n">p</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">track</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span><span class="n">track</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span><span class="nb">str</span><span class="p">(</span><span class="n">zams_mass</span><span class="p">))</span>
+
+<span class="n">p</span><span class="o">.</span><span class="n">invert_xaxis</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;$\log_</span><span class="si">{10}</span><span class="s2"> (T_\mathrm</span><span class="si">{eff}</span><span class="s2"> / \mathrm</span><span class="si">{K}</span><span class="s2">)$&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;$\log_</span><span class="si">{10}</span><span class="s2"> (L/$L$_{☉})$&quot;</span><span class="p">)</span>
+
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+star  0
+zams mass  1.0
+zams mass  2.0
+zams mass  3.0
+zams mass  4.0
+zams mass  5.0
+zams mass  6.0
+zams mass  7.0
+zams mass  8.0
+zams mass  9.0
+zams mass  10.0
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;$\\log_{10} (L/$L$_{☉})$&#39;)
+</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/notebook_HRD_14_2.png" src="_images/notebook_HRD_14_2.png" />
+</div>
+</div>
+<p>We now have an HRD. It took longer to make the plot than to run the stars with <em>binary_c</em>!</p>
+</div>
+<div class="section" id="Binary-stars">
+<h2>Binary stars<a class="headerlink" href="#Binary-stars" title="Permalink to this headline">¶</a></h2>
+<p>Now we put a secondary star of mass <span class="math notranslate nohighlight">\(0.5\mathrm{M}_\odot\)</span> at a distance of <span class="math notranslate nohighlight">\(10\mathrm{R}_\odot\)</span> to see how this changes things. Then we rerun the population. At such short separations, we expect mass transfer to begin on or shortly after the main sequence.</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="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
+    <span class="n">M_2</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span> <span class="c1"># Msun</span>
+    <span class="n">separation</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="c1"># Rsun</span>
+    <span class="n">multiplicity</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="c1"># binaries</span>
+<span class="p">)</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>
+</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>
+Generating grid code
+Constructing/adding: M_1
+Grid has handled 10 stars
+with a total probability of 10.0
+Total starcount for this run will be: 10
+Generating grid code
+Constructing/adding: M_1
+Population-cff93424298e4862bb72096e72b98a2d finished! The total probability was: 10.0. It took a total of 0.9686374664306641s to run 10 systems on 4 cores
+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>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+
+<span></span><span class="n">hrd</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;HRD&#39;</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">nstar</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;star &quot;</span><span class="p">,</span><span class="n">nstar</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">nstar</span> <span class="o">==</span> <span class="s1">&#39;0&#39;</span><span class="p">:</span> <span class="c1"># choose only primaries</span>
+
+        <span class="k">for</span> <span class="n">zams_mass</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">]):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;zams mass &quot;</span><span class="p">,</span><span class="n">zams_mass</span><span class="p">)</span>
+
+            <span class="c1"># get track data (list of tuples)</span>
+            <span class="n">track</span> <span class="o">=</span> <span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">][</span><span class="n">zams_mass</span><span class="p">]</span>
+
+            <span class="c1"># convert to Pandas dataframe</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">track</span><span class="p">,</span>
+                                <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span><span class="s1">&#39;logL&#39;</span><span class="p">])</span>
+
+            <span class="c1"># make seaborn 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">data</span><span class="p">,</span>
+                             <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                             <span class="n">x</span><span class="o">=</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span>
+                             <span class="n">y</span><span class="o">=</span><span class="s1">&#39;logL&#39;</span><span class="p">,</span>
+                             <span class="n">estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+            <span class="c1"># set mass label at the zero-age main sequence (ZAMS) which is the first data point</span>
+            <span class="n">p</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">track</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span><span class="n">track</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span><span class="nb">str</span><span class="p">(</span><span class="n">zams_mass</span><span class="p">))</span>
+
+<span class="n">p</span><span class="o">.</span><span class="n">invert_xaxis</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;$\log_</span><span class="si">{10}</span><span class="s2"> (T_\mathrm</span><span class="si">{eff}</span><span class="s2"> / \mathrm</span><span class="si">{K}</span><span class="s2">)$&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;$\log_</span><span class="si">{10}</span><span class="s2"> (L/$L$_{☉})$&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+star  0
+zams mass  1.0
+zams mass  2.0
+zams mass  3.0
+zams mass  4.0
+zams mass  5.0
+zams mass  6.0
+zams mass  7.0
+zams mass  8.0
+zams mass  9.0
+zams mass  10.0
+star  1
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;$\\log_{10} (L/$L$_{☉})$&#39;)
+</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/notebook_HRD_19_2.png" src="_images/notebook_HRD_19_2.png" />
+</div>
+</div>
+<p>We plot here the track for the primary star only. You can see immediately where stars merge on the main sequence: the tracks move very suddenly where usually evolution on the main sequence is smooth.</p>
+<p>If we now set the separation to be longer, say <span class="math notranslate nohighlight">\(100\mathrm{R}_\odot\)</span>, mass transfer should happen on the giant branch. We also set the secondary mass to be larger, <span class="math notranslate nohighlight">\(1\mathrm{M}_\odot\)</span>, so that the interaction is stronger.</p>
+<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="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
+    <span class="n">M_2</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="c1"># Msun</span>
+    <span class="n">separation</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="c1"># Rsun</span>
+    <span class="n">multiplicity</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="c1"># binaries</span>
+    <span class="n">alpha_ce</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span> <span class="c1"># make common-envelope evolution quite efficient</span>
+<span class="p">)</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>
+</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>
+Generating grid code
+Constructing/adding: M_1
+Grid has handled 10 stars
+with a total probability of 10.0
+Total starcount for this run will be: 10
+Generating grid code
+Constructing/adding: M_1
+Population-2ea4759ed05544ef8f1b7a887f0f36d2 finished! The total probability was: 10.0. It took a total of 0.7215321063995361s to run 10 systems on 4 cores
+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>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="n">hrd</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;HRD&#39;</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">nstar</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;star &quot;</span><span class="p">,</span><span class="n">nstar</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">nstar</span> <span class="o">==</span> <span class="s1">&#39;0&#39;</span><span class="p">:</span> <span class="c1"># choose only primaries</span>
+
+        <span class="k">for</span> <span class="n">zams_mass</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">]):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;primary zams mass &quot;</span><span class="p">,</span><span class="n">zams_mass</span><span class="p">)</span>
+
+            <span class="c1"># get track data (list of tuples)</span>
+            <span class="n">track</span> <span class="o">=</span> <span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">][</span><span class="n">zams_mass</span><span class="p">]</span>
+
+            <span class="c1"># convert to Pandas dataframe</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">track</span><span class="p">,</span>
+                                <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span><span class="s1">&#39;logL&#39;</span><span class="p">])</span>
+
+            <span class="c1"># make seaborn 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">data</span><span class="p">,</span>
+                             <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                             <span class="n">x</span><span class="o">=</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span>
+                             <span class="n">y</span><span class="o">=</span><span class="s1">&#39;logL&#39;</span><span class="p">,</span>
+                             <span class="n">estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+            <span class="c1"># set mass label at the zero-age main sequence (ZAMS) which is the first data point</span>
+            <span class="n">p</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">track</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span><span class="n">track</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span><span class="nb">str</span><span class="p">(</span><span class="n">zams_mass</span><span class="p">))</span>
+
+<span class="n">p</span><span class="o">.</span><span class="n">invert_xaxis</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;$\log_</span><span class="si">{10}</span><span class="s2"> (T_\mathrm</span><span class="si">{eff}</span><span class="s2"> / \mathrm</span><span class="si">{K}</span><span class="s2">)$&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;$\log_</span><span class="si">{10}</span><span class="s2"> (L/$L$_{☉})$&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+star  0
+primary zams mass  1.0
+primary zams mass  2.0
+primary zams mass  3.0
+primary zams mass  4.0
+primary zams mass  5.0
+primary zams mass  6.0
+primary zams mass  7.0
+primary zams mass  8.0
+primary zams mass  9.0
+primary zams mass  10.0
+star  1
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;$\\log_{10} (L/$L$_{☉})$&#39;)
+</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/notebook_HRD_23_2.png" src="_images/notebook_HRD_23_2.png" />
+</div>
+</div>
+<p>You now see the interaction in the jerky red-giant tracks where the stars interact. These probably, depending on the mass ratio at the moment of interaction, go through a common-envelope phase. The system can merge (most of the above do) but not all. The interaction is so strong on the RGB of the <span class="math notranslate nohighlight">\(1\mathrm{M}_\odot\)</span> star that the stellar evolution is terminated before it reaches the RGB tip, so it never ignites helium. This is how helium white dwarfs are probably made.</p>
+<p>We can also plot the secondary stars’ HRD. Remember, the primary is star 0 in binary_c, while the secondary is star 1. That’s because all proper programming languages start counting at 0. We change the parsing function a little so we can separate the plots of the secondaries according to their primary mass.</p>
+<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="n">hrd</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;HRD&#39;</span><span class="p">]</span>
+
+<span class="k">for</span> <span class="n">nstar</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;star &quot;</span><span class="p">,</span><span class="n">nstar</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">nstar</span> <span class="o">==</span> <span class="s1">&#39;1&#39;</span><span class="p">:</span> <span class="c1"># choose only secondaries</span>
+
+        <span class="k">for</span> <span class="n">zams_mass</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">]):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;primary zams mass &quot;</span><span class="p">,</span><span class="n">zams_mass</span><span class="p">)</span>
+
+            <span class="c1"># get track data (list of tuples)</span>
+            <span class="n">track</span> <span class="o">=</span> <span class="n">hrd</span><span class="p">[</span><span class="n">nstar</span><span class="p">][</span><span class="n">zams_mass</span><span class="p">]</span>
+
+            <span class="c1"># convert to Pandas dataframe</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">track</span><span class="p">,</span>
+                                <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span><span class="s1">&#39;logL&#39;</span><span class="p">])</span>
+
+            <span class="c1"># make seaborn 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">data</span><span class="p">,</span>
+                             <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                             <span class="n">x</span><span class="o">=</span><span class="s1">&#39;logTeff&#39;</span><span class="p">,</span>
+                             <span class="n">y</span><span class="o">=</span><span class="s1">&#39;logL&#39;</span><span class="p">,</span>
+                             <span class="n">estimator</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+
+<span class="n">p</span><span class="o">.</span><span class="n">invert_xaxis</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;$\log_</span><span class="si">{10}</span><span class="s2"> (T_\mathrm</span><span class="si">{eff}</span><span class="s2"> / \mathrm</span><span class="si">{K}</span><span class="s2">)$&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;$\log_</span><span class="si">{10}</span><span class="s2"> (L/$L$_{☉})$&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+star  0
+star  1
+primary zams mass  1.0
+primary zams mass  2.0
+primary zams mass  3.0
+primary zams mass  4.0
+primary zams mass  5.0
+primary zams mass  6.0
+primary zams mass  7.0
+primary zams mass  8.0
+primary zams mass  9.0
+primary zams mass  10.0
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;$\\log_{10} (L/$L$_{☉})$&#39;)
+</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/notebook_HRD_26_2.png" src="_images/notebook_HRD_26_2.png" />
+</div>
+</div>
+<p>Remember, all these stars start with a <span class="math notranslate nohighlight">\(1\mathrm{M}_\odot\)</span> binary, which begins at <span class="math notranslate nohighlight">\(\log_{10}(T_\mathrm{eff}/\mathrm{K})\sim 3.750\)</span>, <span class="math notranslate nohighlight">\(\log_{10}L/\mathrm{L}_\odot \sim 0\)</span>. The <span class="math notranslate nohighlight">\(1\mathrm{M}_\odot\)</span>-<span class="math notranslate nohighlight">\(1\mathrm{M}_\odot\)</span> binary evolves like two single stars until they interact up the giant branch at about <span class="math notranslate nohighlight">\(\log_{10} (L/\mathrm{L}_\odot) \sim 2.5\)</span>, the others interact long before they evolve very far on the main sequence: you can just about see their tracks at the
+very start.</p>
+<p>This is, of course, a very simple introduction to what happens in binaries. We haven’t talked about the remnants that are produced by interactions. When the stars do evolve on the giant branch, white dwarfs are made which can go on to suffer novae and (perhaps) thermonuclear explosions. The merging process itself leads to luminosus red novae and, in the case of neutron stars and black holes, kilonovae and gravitational wave events.</p>
+</div>
+</div>
+
+
+           </div>
+           
+          </div>
+          <footer>
+  
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="notebook_common_envelope_evolution.html" class="btn btn-neutral float-right" title="Example use case: Common-envelope evolution" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
+      
+      
+        <a href="notebook_luminosity_function_binaries.html" class="btn btn-neutral float-left" title="Zero-age stellar luminosity function in binaries" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+      
+    </div>
+  
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>
+        
+        &copy; Copyright 2021, David Hendriks, Robert Izzard
+
+    </p>
+  </div>
+    
+    
+    
+    Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a
+    
+    <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a>
+    
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+<br><br>
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+<br><br>
+Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
+
+
+
+</footer>
+
+        </div>
+      </div>
+
+    </section>
+
+  </div>
+  
+
+  <script type="text/javascript">
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script>
+
+  
+  
+    
+   
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/notebook_HRD.ipynb b/docs/build/html/notebook_HRD.ipynb
new file mode 100644
index 000000000..52590f8a2
--- /dev/null
+++ b/docs/build/html/notebook_HRD.ipynb
@@ -0,0 +1,818 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Hertzsprung-Russell diagrams\n",
+    "\n",
+    "In this notebook we compute Hertzsprung-Russell diagrams (HRDs) of single and binary stars.\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_HRD\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {},
+   "source": [
+    "## Setting up the Population object\n",
+    "First we set up a new population object. Our stars evolve to $13.7\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. These are rough approximations: a real population was born some finite time ago, so cannot possibly evolve to $13.7\\mathrm{Gyr}$, and stars are not really born with a metallicity of $0.02$. These approximations only affect very low mass stars, so we assume all our stars have mass $M\\geq 1 \\mathrm{M}_\\odot$, and metallicity does not change evolution too much except in massive stars through the dependence of their winds on metallicity, so we limit our study to $M\\leq 10 \\mathrm{M}_\\odot$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\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=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    tmp_dir=TMP_DIR,\n",
+    "    verbosity=1\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $10\\mathrm{M}_\\odot$ in nine steps (so the masses are integers). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"M_1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        11\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1,2,1)\",\n",
+      "    \"precode\": null,\n",
+      "    \"probdist\": \"1\",\n",
+      "    \"dphasevol\": \"dM_1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"edge\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10} \n",
+    "massrange = (1, 11) \n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"M_1\",\n",
+    "    longname=\"Primary mass\", # == single-star mass\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{res}\".format(res = resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(1,2,1)\", # space by unit masses\n",
+    "    probdist=\"1\", # dprob/dm1 : we don't care, so just set it to 1\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",
+    "    gridtype=\"edge\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Setting logging and handling the output\n",
+    "\n",
+    "We now construct the HRD output.\n",
+    "\n",
+    "We choose stars prior to and including the thermally-pulsing asymptotic giant branch (TPAGB) phase that have $>0.1\\mathrm{M}_\\odot$ of material in their outer hydrogen envelope (remember the core of an evolved star is made of helium or carbon/oxygen/neon). This prevents us showing the post-AGB phase which is a bit messy and we avoid the white-dwarf cooling track."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "Foreach_star(star)\n",
+      "{\n",
+      "    if(star->stellar_type <= TPAGB &&\n",
+      "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+      "    {\n",
+      "         double logTeff = log10(Teff_from_star_struct(star));\n",
+      "         double logL = log10(star->luminosity); \n",
+      "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+      "         Printf(\"HRD%d %30.12e %g %g %g %g\\n\",\n",
+      "                star->starnum, // 0\n",
+      "                stardata->model.time, // 1\n",
+      "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+      "                logTeff, // 3\n",
+      "                logL, // 4\n",
+      "                loggravity // 5\n",
+      "                );\n",
+      "\n",
+      "    }\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "Foreach_star(star)\n",
+    "{\n",
+    "    if(star->stellar_type <= TPAGB &&\n",
+    "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+    "    {\n",
+    "         double logTeff = log10(Teff_from_star_struct(star));\n",
+    "         double logL = log10(star->luminosity); \n",
+    "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+    "         Printf(\"HRD%d %30.12e %g %g %g %g\\\\n\",\n",
+    "                star->starnum, // 0\n",
+    "                stardata->model.time, // 1\n",
+    "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+    "                logTeff, // 3\n",
+    "                logL, // 4\n",
+    "                loggravity // 5\n",
+    "                );\n",
+    "\n",
+    "    }\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "population.set(\n",
+    "    C_logging_code=custom_logging_statement\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae1f1f0c-1f8b-42d8-b051-cbf8c6b51514",
+   "metadata": {},
+   "source": [
+    "The parse function must now catch lines that start with \"HRD*n*\", where *n* is 0 (primary star) or 1 (secondary star, which doesn't exist in single-star systems), and process the associated data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14565763dca0> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import datalinedict\n",
+    "import re\n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"zams_mass\", \"logTeff\", \"logL\", \"logg\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        match = re.search('HRD(\\d)',line) \n",
+    "        if match:\n",
+    "            nstar = match.group(1) \n",
+    "            \n",
+    "            # obtain the line of data in dictionary form \n",
+    "            linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "            # first time setup of the list of tuples\n",
+    "            if(len(self.grid_results['HRD'][nstar][linedata['zams_mass']])==0):\n",
+    "                self.grid_results['HRD'][nstar][linedata['zams_mass']] = []\n",
+    "\n",
+    "            # make the HRD be a list of tuples\n",
+    "            self.grid_results['HRD'][nstar][linedata['zams_mass']].append((linedata['logTeff'],\n",
+    "                                                                           linedata['logL']))\n",
+    "    \n",
+    "    # verbose reporting\n",
+    "    #print(\"parse out results_dictionary=\",self.grid_results)\n",
+    "    \n",
+    "# Add the parsing function\n",
+    "population.set(\n",
+    "    parse_function=parse_function,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91509ce5-ffe7-4937-aa87-6d7baac9ac04",
+   "metadata": {},
+   "source": [
+    "## Evolving the grid\n",
+    "Now that we configured all the main parts of the population object, we can actually run the population! Doing this is straightforward: `population.evolve()`\n",
+    "\n",
+    "This will start up the processing of all the systems. We can control how many cores are used by settings `amt_cores`. By setting the `verbosity` of the population object to a higher value we can get a lot of verbose information about the run, but for now we will set it to 0.\n",
+    "\n",
+    "There are many grid_options that can lead to different behaviour of the evolution of the grid. Please do have a look at those: [grid options docs](https://ri0005.pages.surrey.ac.uk/binary_c-python/grid_options_descriptions.html), and try  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: verbosity=0 to grid_options\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-20bee5b0c58d49c5bc47eced240685bb finished! The total probability was: 10.0. It took a total of 0.543649435043335s to run 10 systems on 4 cores\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",
+    "    amt_cores=4,\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": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': '20bee5b0c58d49c5bc47eced240685bb', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 10.0, 'total_count': 10, 'start_timestamp': 1631304519.45189, 'end_timestamp': 1631304519.9955394, 'total_mass_run': 55.0, 'total_probability_weighted_mass_run': 55.0, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 8,
+     "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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "hrd = population.grid_results['HRD']\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    for zams_mass in sorted(hrd[nstar]):\n",
+    "        print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "        # get track data (list of tuples)\n",
+    "        track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "        # convert to Pandas dataframe\n",
+    "        data = pd.DataFrame(data=track, \n",
+    "                            columns = ['logTeff','logL'])\n",
+    "        \n",
+    "        # make seaborn plot\n",
+    "        p = sns.lineplot(data=data,\n",
+    "                         sort=False,\n",
+    "                         x='logTeff',\n",
+    "                         y='logL',\n",
+    "                         estimator=None)\n",
+    "        \n",
+    "        # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "        p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "        \n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "We now have an HRD. It took longer to make the plot than to run the stars with *binary_c*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44586e42-b7cb-4a55-be0a-330b98b20de4",
+   "metadata": {},
+   "source": [
+    "## Binary stars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71d0fc4e-c72f-444a-93ab-19f52086b86d",
+   "metadata": {},
+   "source": [
+    "Now we put a secondary star of mass $0.5\\mathrm{M}_\\odot$ at a distance of $10\\mathrm{R}_\\odot$ to see how this changes things. Then we rerun the population. At such short separations, we expect mass transfer to begin on or shortly after the main sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "478e8005-e144-4e6f-80c9-0cf368a9bcb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-cff93424298e4862bb72096e72b98a2d finished! The total probability was: 10.0. It took a total of 0.9686374664306641s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 0.5, # Msun\n",
+    "    separation = 10, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9c433e6a-fe22-4494-b1a9-fce9676a9f40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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OSzQJwLrqUh6fUz3h+0md7iJ6sYuPOU1Mc/e1evVdrFy5ZsR+xz98jVMHdgDgC4S497EXaVx8J0o5pBIxzh7axan97wJgphKc/ORtTn7yNgtXb2TNQ88SWFKFb145iaNtJE91gALzYh9mywD+ZdX419aj+yZWrOnoaKOnp2tCxwRob28lGo1QVlY+4WMLIyPikiAIgiAIgiAIgiAUQCnFLy62cbbPdR1bVh7iSwvqJzz2j9URY+DAFfZxmgEtDsDSpStYt+7eEfudPbQ7IywFS8rZ/OV/Tnl1AwCaZhAIlbLmoWe588GnuX7hFEd2v8xATzsAF07s48KJfcxetJo1Dz1L+b2z8a+sIf7pNcyLfWA5JE92kGrqJnDXLHzLqtH0iTnuefMWsn59nEgkkikr/CfVMnXRaJRLl5pHHPfhh7eLsDRNiLgkCIIgCIIgCIIgCAV4p6WLw12u69acsJ9vLG7AmCCBZRAnYdG/q4lP1Gl6tSgACxYs4r77HhpRxLp6/hiHd/0CgEC4jK1f+2NKygtbVGmazuxFq6ifv5wLJ/bRfOJjetquAHCt+QTXL5xkyV2bWHnfY4Q3L8BcFiFx5Dp2ewyVtIl/cpXk6U6C62fjaSy9aXHN4/GMapElzCxEXBIEQRAEQRAEQRCEHJRSvHetm92tPQBU+718a+ls/IY+4fuJ7rnE0dg5urS0iDVnHg89tBVdL76vruuX+PiN7wEKj9fHpud/r6iwlItheFiy9iGWrH2I6xdOcnj3ywx0t6GUw7nDuzl3eDdzl9/N2k3PUfLEEsxLfSQOXseJpHD6kkR3XsDTUELw3tkYlcGJ+jMItwEiLgmCIAiCIAiCIAhCGlspXr3UzoGOfgBKPAa/tayREu/EPz4nT7Rz/loTV7ROwA1IvXnz9hGFpWhfF3te+S62ZaJpGvc//VtU1c+94X03LFxJw8KV9HZc5fD7P6e95TwAV84c4tr54yy75xGWrH2I0ueXkzzVSeJYG5gO1vUIA6+dxbe0isBds9CDkxP0W5hZiLgkCIIgCIIgCIIgCEDKdvhJUyun+1z3tAqfh99c1khVICugKKXob9tDpOsIlY2PEqq4Y1z7Mq9HuHb4HCe4BEAwGGLz5u0YRvHHdDOVYM8rf0cy5lo53b31K8xetGpc+x+koraRLV/5X7jadJyTn7xNT9sVbNvk1P53OH1gB0vWPsSq+5+kbMkdJI60kjrbBQpSZ7tJNfcSuLMO/8paNM/EWnUJMwsRlwRBEARBEARBEITPPRHT4vvnrmWywjWE/PzG0tmU+bKPzcqx6Lr8GrGe4wDE+88PE5eUUrS3txGPR1FKDfsAOEmL+LFWzqorKE2hazpbtjxKMBgqOj+lHD558wf0dV4DyFgWTQSapjFnyRrmLFlDR0sTh3b9nN72Ftdd7sgHXDr1Kcvu3syiOx+gdIUb9Nu6OgCWQ+JwK8kzXQTXzcK7qHLCgn4LMwsRlwRBEARBEARBEITPNV2JFP9w9hpdSROAJWUhXlwyi4BhZNo4VoKOC/9EMuJaGnn8VZTXb8obx7YtPvxw16hZzTKkdZj1995PbW39iE1PfPQmV5tcUWvWgjtYu+kLY9vHDVI7ZzGPvvi/0nbpNKc/3Un7lXOkkjFOfPwmpw7s5I57t7F881ZUe4r4wWs4PQlUzCS29wrGqQ4CdzfgmX3zQb+FmYWIS4IgCIIgCIIgCMLnlpZogu+dvUbUsgFYV13KFxfU48mxwLFSfXQ0/Qgz0QGALzyH2kVfx/BkLY2SySS7dr1DW9v1G9r/0qUrWL585YhtLp85xMlP3gagtLKO+5/6jRHjMt0suq7TsHAlsxbcwdWm4xz94BUivR3YVooTH79J0/GPWHLXJhZtewDtWorE4euouIXdnSC64wJGXYjgugY8s0ombY7CrYWIS4IgCIIgCIIgCMLnkjO9UX7cdJ2U47qrbW6o5LHG6jyrm1TsOh1NP8a2IgAEy1dQveCL6Ho2DlMkEmHnzjfo6+sF3MDcGzY8iGEYaJqWGc9ujRL/6AoaGnp5gLLHlhAIjZx1rbvtMvvf+iEAXn+QTc//Lr5Acfe5iWTQXa5x8Z20XjzFsQ9fo7fjKvFIH8c/fJ3T+3ewfP02lj7zMM65fhIn2sFysNtjRN5uwjO7lMDds/BUT818helDxCVBEARBEARBEAThc8enHX388mI7Dq532rPza9lYV5HXJt5/ns4LL6GcFACltfdR0fgompa1Guru7mLnzjeJx2MAzJ+/kIceemRYYG47kiLy6WWC+MGrU/rIMoyQf8Q5xqP9fPjK32Hbbma4B57+TUor62762G8UTdNoWLiS+vkruHhyP599/CaxgR7MVIITH/2Kc4d3s/K+x1j4hfuwzvSQPNUJtsK6NkDk2gDe+eUE1s3CKA9M+dyFqUHEJUEQBEEQBEEQBOFzg1KKnde6ee9aNwAeTeNri2exqjLfhSvSdYTuy68BrlVTReNjlNVtzGtz7VoLu3e/i2m6sZruuGM169ffPyzekLIdYrsuolKu613ogbkY5SMLS7ZlsvfV/0480gfA2s3PM2vB+DLT3ShKKdquDXDtci8tF3sorwqy9I46fH4PC1fdx8JV93H9wkmO732d3o6rJOMRDu/6BWcOvs+qjU8w7/l1pE50ZjLLmZf6MC/34VtcRWBtPXqJb0qOQ5g6RFwSBEEQBEEQBEEQPhfYjuKVS+182tkPQNDQ+dbS2cwvzbqmKaXob/2AvtbdboFmULPgS8OywjU3n2Pv3l2ZDHDr129k5co1BfcbP3ANuysOgG9FDb4FFSPOUynFpzv+ia7rFwFYuGojy9ZtubGDHQe27XDq6HVOHLpGT2csU371Ui8nD7uxpJbcUcvmJ5Yxe9EqGhbewZWzRzi+91dEejuIDfRw4N0fc6piB0vWPsiCZ9ZjfdaL2dwDClLnu0k19+BbXk3gzjr0oLfYVIQZhohLgiAIgiAIgiAIwm1P0nb4cdN1zva5okmlz8NvLmukNpi1olHKofvKG0S7DgGgGwFqF30df8m8nDaKEyeOcvjwfreNrvPgg4+wcOHigvtNXeghdaYLAKMmRHB9w6hzPXPwfS6edMevmb2Ie7Z9ZVKzrymlaD7TySe7L9DXEx+x7flTHZw/1UFVbZhNjy1h3vK7mbN0LRc++4TPPn6LeKSXSG8HR3b/kpOfvMPy9VtZ/OR9mCe6sa70g6NIneokda6bwOpa/Kvq0DyTF5xcmBpEXBIEQRAEQRAEQRBuawZMi++fvcbVWBKAxpCfby2bTak3+0js2Ck6L75Eov88AIavnLrF38QbqMm2cRz27/+Is2dPAuD1+njkkceYNWt2wf3afQliH7UAoPkNwpvnoxkjCynXL5zk2J5XAAiVVvLgs/8MwzN5Fj6tV/vZu+M87dcHMmWl5QHuWDuLRctqKK8K0d0R5exnbVxu7s5YNHV3RHn1R0dZfucslq+uZ9Hq+1lwx700HdvLmYPvExvoIZWIcfzD1znz6XssX7+VRY/ei3W8G6s1ApZD4kgbybPdBO9pwLuwYlIFNGFy0dSgDd9tRFdXBMeZ+YdVW1tKR8fA6A0FQZDzRRBuADlfBGHsyPkiCGPnVj1fOhMp/ufZq/QkLQCWlYf4xuIG/Dkij21G6Gj+CanYNQC8wVnULf4Ghrc008ayLPbs2cmVK5cACIXCbN/+JBUVVQX3q0ybgTfO4/QmAAhvW4h3TtmIc+3vbmPHj/4CM5XA8PjY9vU/prJuzvgPfgQScZN9u5o5dbQ1UxYIernnwXmsums2RgFrIqUUR/e3cOzTq0QHknl1C5fV8MhTy/EHPCjl0HLuGJ/te4u+zmuZNr5AiOV3b2VRw92Yx7qwuxOZOqMmRPDe2XjqwpNwtLcet+r5Ugxd16iuLilaL+LSLcxM+7IJwnQi54sgjB05XwRh7Mj5Ighj51Y8Xy5H4nz/3DVilgPAPTVlPD+/DkPPWsiYiS46mn6EleoBIFC6mJqFL6Ab2YDbiUSC9957i87OdgAqK6vYtu1JQqHCQohSitiHV9xYQ4D/zjqCd4/sDpdKxHj3R39BpLcDgAee+S3mLls3ziMvjlKK08da2bermUTcFdwMQ2Pthrms2zgXn39sDk4drQO888uT9PdmBSJNg8Urarl30wIqqkIo5XD1/HFOfPzmMJFpyZpNLCi7E+fUACphZeq8CyoI3tNw2wf9vhXPl5EQcWkGM9O+bIIwncj5IghjR84XQRg7cr4Iwti51c6Xkz0RftLUipV+5N06u4pts6vyXK+S0RY6mn6MY7txhsJVd1E172k0zci0GRjoZ8eONxkYcLO2zZrVyJYtj+LzFRc/kme7iH/susN5ZpUQfnQRml7c5ctxbD54+W9ou3QGgFUbn2D1A0+N88iL09Ue4YO3z9F6tT9TNndhJZseW0p5ZXCEnoVJJS3On2rn1NHWPLc6r89g8xPLWLqyDqCoyKTpOgtXbGRZ2b2opigMPscbGv6VtQTurEPzGtyO3Grny2iIuDSDmWlfNkGYTuR8EYSxI+eLIIwdOV8EYezcSufLvvZeXrvUgQJ04PkFdayvLc9rE+s9Q9fFn6OUazVTNuthymdtzhOfOjs7eO+9t0gkXPFp4cIlPPDAZgyjuOBhdcWIvHEeHIUW9FD67LJRs6Idev/nnDvsZqebs3QtDzzzW2jaxAW5Nk2bg3svcXR/S+ZZOVzq48FtS1i0vOamYx0ppTiyv4V97zfnlfv8Bvc8MJ+1G+agaVpGZPps31v0dlzNtNM0jcVLN7LQuxa91cyWBz0E1s3Ct7hqRHFuJnIrnS9jQcSlGcxM+7IJwnQi54sgjB05XwRh7Mj5Ighj51Y4XxyleKeliw9aXXc0r67x4uIGllfku68NdHxKT8ubgAI0quY+TUnN3Xltrl69zO7dO7AsV3xavfou1q27d0QhxknZRF47ixNJgQYljy3GM6v4AzlA8/GPOfDujwGoqG1k29f/GI/XP2KfG+FyczcfvH2OgT7XfU3TYM36Oax/aP6YXeBuhEvnu3jvV6czLncAcxZUsu2ZFYRyXN26rl/i1P53udp0LK//vFlrWRZYj5HzVTKqAvhX1YGuge2gbJVZJg5eH31SHp3SZ5ZilAdu+vgmilvhfLkRRFyawcy0L5sgTCdyvgjC2JHzRRDGjpwvgjB2pvt8sRzFLy60caTbnUPYY/Aby2YzJ5wVFJRS9F1/j/62vQBoupeaBV8mWL4sb6zz58/w8ccfMPi4vGHDg6xYsWrE/TsJi+iui9htUQACdzcQuLNuxD4dV5vY9bPv4Dg2/mAJj37zfyVcVjhA+I0Si6bYu7OJ8yfbM2V1DaVsfmIZNfUjC143S6Q/yYfvnuPCua688llzytj8xDKqarJiX2/HNU7uf4crZw7jin0ui6vXs8i3Bj01MRZLgbtnEbizfkLGmgim+3y5UURcmsHMtC+bIEwncr4IwtiR80UQxo6cL4IwdqbzfElYNj9suk5Tv+u+Vu338lvLGqkKZN3RlGPTdflVYj3HAdA9IWoXfQN/uDHbRilOnDjC4cMHADAMg02btjFv3oIR92/3JojuvOBaLAGeOWWEty4Y0cop2t/Nuz/8jyTjEXTdYMtX/gW1jYvHdfy5KKU4dbSVj99vJpXOkOf1Gdy3eSGr1s1Gn0L3slTSYs875zn7WVumzPDobNyykDvvacz7+0T7umg6tpfzRz/ETLlWVjoGyys3MtezHM0Z/7x9y6sJrK0f1T1xKplp1xcRl2YwM+3LJgjTiZwvgjB25HwRhLEj54sgjJ3pOl+6Eil+eP46rXFX2JkbDvCtpbMJ5wSCduwknRd+SmLgAgAefxW1i1/E689aCSmlOHDgI06f/gwAn8/P1q2PU1c3a8T9my39RD+4BKabkc67oILQg3PRPMVjJllmkp0/+U+ZuEP3PvoNFt15/ziOPp/uzii73zpLa0s2YPfCZTU8tH0JJWUT52p3o5w72c6hjy/T3RHNlHm8OmvuncM9D8zHk/O3SiXjnD+6h7MHd5GMR9y2mo/qsrksu3cLdQuWoxsGGBqaoYGh58Vjikf6aDq2NyNQ2ZaZ/dgmynGYNf8OFq7eiNc3fX+TmXZ9EXFpBjPTvmyCMJ3I+SIIY0fOF0EYO3K+CMLYmerzRSnFoc5+XrvcQSr9/HdHRZivLZqFz8iKFbYZob3pR5jxVgB8oUZqF38DwxPKtrFtPvzwfS5dcgNSh0Jhtm9/koqK4i5qSimSJztJHLyW8eYK3DUL/5q6ES2WlHL46PX/Scu5owAsXbeZux/58vj+CGks0+bQx5c5vO9KTsBuP5seXcLCZTU3NXYhlOPgJBKgFInmJjzV1fhnN47a79qVXt57/Uwm/hNARXWIR55azqzGsry2lpmi+cTHnPn0PWIDPZnyyro51MxehGWlsE0T20pl1i0rRW97y5iOYe7yu3ng6d8c2wFPAjPt+jKauDTx0bsEQRAEQRAEQRAEYRKJWTYvX2zns55Ipuyh+gqemFuDniPsmIku2pt+iJ3qBSBYvozqBV9G17PuUalUivfff5u2NjcwdEVFJdu2PUk4XPxBWtkO8X1XSZ3vdgs8OqGH5uKbXzHq3D/7+K2MsFQ/fzl3bX5+jEddmJaLPXzw9jn6elyXQE2DO+9p5N5NCyY8YLfZ3UXvzh307dmNE4vl1YXvWkfdi7+Ot6q4IDd7bgVf/Wf3cPiTK3x26BrJhEVvV4yXf3CYuoZS7rpvbiZ7ncfrY9m6zSxe8yBNR/dy4uM3MJNxetpb6BmjgDQSpRW1Nz2GkEUsl25hZpqSKQjTiZwvgjB25HwRhLEj54sgjJ2pOl/O98V46UIr/aYNQKnX4IWF9Swtz88Il4xepaP5xziWK4KEq++mau5TaFrWqikWi7Fz55v09LiBp+vqZvHII4/j9xd3l3ISFtH3L2K3uy5eWshLeOsCPNWhon0GuXL2MB+9/j8BKKmo5dEX/w2+wOj9ChGPpfhoZ3NePKOa+hK2PLmM2lml4xqz6L6am+l99y0GDn4KjlO8oabhra8ntHwFNV/6CkY4XLSp4yiOfHKFAx9exLGzz+/zFlWx6bEllFUE89on41HOH/mA5hP7sFJJDK8Xw+PD4/VheLzppQ/D68Pj8eLxBfAHQnh8ATw+Px6vH8PjRdd1giUVVNQ2jmhhNtnMtOuLuMXNYGbal00QphM5XwRh7Mj5IghjR84XQRg7k32+WI7DOy1dfNjWmylbWRHm2fm17LzaTcDQeWxONR5dJ953js6LL6EcE4CyWQ9TPmtznpjQ19fLjh1vEI261k9z5y5g06ateDzFrX3snjjR9y5mAncbNSHCjyxAD40eKLqnvYWdP/lP2FYKry/A9hf/DWVVN569TCnFmeNtfPReE8mEG7Db49XZ8LAbJHsiA3YnLl+i6+WfEz1+LK88uGw54TVrUZaFEQ4TP3+OgU/25bXxVFZS/5vfJrxq9Yj76O6M8umHl2g63ZHt69FZtKKWlWsbaJhbPmHHcysx064v4hYnCIIgCIIgCIIgzGja4kl+2tTK9XTQbq+u8cy8WpaVh/izoxcz7VZUhKkzz9F9+VXcQEgalXOfpLRmfd54HR3tvPfemySTSQCWLbuDDRseRNeLB+E2r6QDd1vpwN2LKgg9MBfNKN5nkHikjw9f+TtsK4Wmadz/9G+OS1jq6YrxwVtnuXalL1M2f0k1mx5dQml54IbHK0bq+jU6X3mZyKcHsoWGQem9G6h89HEC8xfkta94ZBul922k40c/xOx0RSKrp4er/+9/pOzBTdS+8FW0gB/d6xu2r6qaMI89v5J4zGTf+82cPt6KZTmcPdHG2RNtLF5Ry/2PLJrQ4xMmHrFcuoWZaUqmIEwncr4IwtiR80UQxo6cL4VRSg3GL0apwVjGbtng04UaLMnUk6lXmR657dWQvqT7qiF9R2qvhvQt3nbwMWjovnKPK9M3Z7zsnIfvKzPXkeaV8zcY/jcc27yyY6khf5Mh7YfWFfw7ZI8lf4wh5Sp3nML/t36/h0TCyvs/G20OQ+edV54zj4sDCax0xzlhP19dNIuoafO3p/Nj7/yvc9uJXN/pbmgGNQu+TKhiRV6blpbLfPDBDizLtfpZu/Ye1qy5u6iLlFKK5GcdJA5ez5QF7p6Ff/XIgbsHuX7hJPvf/iGJmPtbsvbh51mxfuuo/XKxLYdDH1/m0L7LGReycImPh9IBuyfKvcvs6qTr1Vfo/+jD7H+6YVD+0MNUPf3siPGUBlFK0bdnNx3/9BNUMhu4W/P5qHryaSqfeLKgyDTItcu97N3ZRGdbNp6W4dFZdVcDy1bXU1NfclPH23ymk4vnO9E0DU3TSCUtkgmL+tmlLL9zFuWVwdEHmQBm2vVF3OJmMDPtyyYI04mcL4IwduR8EW4EZ/ChOL10Bh9c0w99Tm55pi046TZOTlulFA45D9aK9LbKLIfuY+j+8tfz9zm0nlHq8/qT81Cf0zYY9BGNpQq0zRcSitUX2he59WOYG6PN/SaPPXc+5Mw/s15ARBCE6UADNjdUsm12NYe7+vnFxfa8+j+ou4jq/thtawSoXfR1AiXz8tqcP3+Gjz/+AKUUmqZx330PsWzZHUX3qWyH2MctmE09boFHJ7RpHr555aPO17ZMju55lXOHd2fKFq95kHu2ffWGxJGrl3r54O2z9HbHM2Wr757NfZsXTljAbmugn+7XXqV39/tgu7Gs0DRK79tI9XNfxFdXd8Njml1dtP3gH4idOJ5X7q2rp+7FXyO8+s4R+0f6E3y86wLnT+b/PzfOr2DjloXUNWSzy5kpm2gkiZmyMU0bM2VjmQ5myiYeN+nrjmGmbJIJiysXeorus7Q8wK/9wX03fKzjYabdj4m4NIOZaV82QZhO5HwRhLEj58twHKWwlcJRYKfXbWfIdrreUiqnvVuWWVJ4W42h3s4IMTltIH8fZMeyM0KMyggwzjARp4AglCf8FBaEcscSBGFmoaU/aOklGoMyhpYpS5fmbWfb5PXJlA0Zb2hfDTyGjm2rAvvL75PfTysyh+wsNCDkMXi4oZIFpUF+ebGd/R1ZtzCAP6o+RqrvMwAMbxm1i1/EF8wKIkopTpw4yuHD+902hsGmTduYN29Bgb+iixM33cDdHW5AcC3spWTrQoyq0S1bejuvse+N79PXeQ0Aj9fP3VtfYMHKDWMWlhJxk4/fc93EBqmuC7P5iWXUzy4boefYcZJJet59m5633sBJZK2MStbdQ/XzX8TfOOemxldKMfDJxwx8eoDkpUtYPd3ZfdyznpoXvoq3ugZtBHfE61f62LvzPB2tkbzy8qogjuVgWg7JuJkRxW+G+UuqeeqFkWNETRQz7X5MxKUZzEz7sgnCdCLniyCMnVv5fLGVImU7JG2HpOOQslV66ZZZSmE6CstRWMpxl47CVCqzPlhuOgorr9wVjIaKRYMijfD5Jv9hWMs8UOuaBir/4VpLPwxrue1zHpoz9TkPyJoGemYfBfrnth+hXmPI/nKEgGL1Q/tTZL5QYMx0YfbvM3LbvL9TAfFi8KG6kGCh5/VlmOiQ9/fMmVehYxoqrBSbV+5xQe7/0Qj7yvn/KdR29H3liCZD2uZvD+63kDg0fF7TmfVqkMm+vjhK8VefXaY9HXcJoMJn8K3gh6SiFwHwBmqpXfwiHl/WskgpxYEDH3H6tCs++Xw+HnnkCerrZxXdl90dJ/LeBVTUDQhu1KYDdwdHDtytlOL8kT0c+eCXOLbrdlc1az4bn/wWpZW1YzpOpRRnP2vno51NJOLu/j0enfWbFrBmfSPGGGI8jboP26Zv7x66Xvkldl9vpjy44g5qvvQVgosW3fQ+Cu2z9/2ddP3yF3lCFoZBxSNbqXr6WTylhUUzpRStLf2c/ayN08dab/h53x/wEAh68foMvF4Dr99d+vweqmvDhEt9hEr81DWUTmhA9JG4le/HCiHi0gxmpn3ZBGE6kfNFEMbOdJ4vlqO4HInTFk/RnkjRn7KImjZRy/0k7BHSG9+GGOkHRz0tYuga6Hnb7gOkrmluW01DJ6dtTr2efsgcLNMyddl9FCrXhtRn+2f3p6XnNbTt6PvIFTXS9Xlj5e4ju69iY5HZ1+QILIWQ64sgjJ3JPF9StsP/dagpr2xtZYCHrTcw420A+MNzqVn0dQxP1rLItm0+/PB9Ll1qBiAUCrN9+5NUVBSPHWRe7iO653I2cPfiSkL3zxk1cHci2s/+d37E9QsnAfd35Y4Nj7Fq4xPohjGm4+ztjvHB2+e4eqk3UzZvURWbHltKWcXNB7RWShE9eoTOn/+M1PVrmXLfnLnUvvBVQqtWT7pQafX20vGznwzLLqcHAlQ+8RSVjz6O7vcX7d/fG+fYgavEoik8Hh3Dq+P3e6isCePzGXh9Bh6vgderu2KSzyAQ9N4SAmwuM+36IuLSDGamfdkEYTqR80UQxs50nS99KYvvnr5CT9KasDENDTyajkfX3I+m4c1Z9+h6zrqGkRZpXLEm+9E13HV9yPbQ+gLbGZEnXTYonBQWi8irF2595PoiCGNnss6XvpTFnx29kFf2REOQxX0/x071ARAsX071gi+h61nLItNM8f7779LaehWA8vJKtm9/knC48AOyUorkiXYSh7JuaIF7GvCvqh1VmLjW/Bn73/4hybjruhUqrWTjk9+ids7iMR2jbTsc+eQKB/dewk4H7A6FfTy4fTGLV4y+/7EQbzpP50s/JX7ubKbMU1VFzfNfpnTj/SO6pk0GsdOnGNi/j8TFiyQvX8qUG+UVVD/3Bcof3ITmuX0T3M+068to4tLt+z8lCIIgCMItRXN/rKCwNCvoozbgI+w1CHkMAoaO39Dx6zq+zLqGz9DxDRGLRKARBEG4vWmJJPivp67klb04R6ei48fYthvguqT6birnPoWmZcWRRCLOzp1v0tXVCUBtbT1btz6O31/Y+kfZDrGPWjCbs4G7ww/Pwzt35MDdlpni6J5XOX/kg0zZvOX3cM+2r+ALhMZ0jNev9LH7rbP0dMUyZSvXNbBx8yL8gZt/ZE+1ttL58ktEDn6aKdNDIaqefpaKrdtGzNw2mYRW3EFoxR0opYh9dpyOl35GquUKdl8v7T/4Hu0/+B7++Qsou/9Byjc9PKI1kzD9iLgkCIIgCMKUsLwiTGPIz9VYMq+8NZ4ibjtU+73UBHz40yJSideg1Ouh1Gfg1/VbzpxdEARBmFyOdPXz0+a2vLJvz+rA27oTJx0tr3zWZspmPZx3jYhEBtix4w36+12rpsbGeWzevB1PESuYoYG79RIf4a0LMEZJSd/bcZWP3/g+/V3XAfD4/Nyz9avMv2P9mK5ZyYTJx+9f4NTR65myypoQW55Yxqw5o2ejGw2rr4+u11+hb/cucFwXP83joWLbdqqefAajpLgVylSiaRrh1WsIrVzNwCcf0/nLX2B1dQGQvHSRjksX6X79VSq2bafikW2TMm+lFH0f7CZ55TKaYYBSOKkkVm8fmq7R8Hv/XMStURC3uFuYmWYmJwjTiZwvgjB2pvN8cZTiTG+Ug539nOmLYo/xcu3VNUo8BsG0ZVPQoxMwDIKGTsDjLvPLdIKGQdCj49E0EaaEcSPXF0EYOxN1viileLuliw9a81PG/075IYzoGQA0zUPl3Kcoqb4rr01vbzc7drxJLBYFYNGipTzwwGb0Ii5fVleM6PsXs4G768Ju4O4RLIaUcjh3+AOO7nk1E7S7umEBG5/8FiUVNWM6vvOnOti74zzxWHq/Hp31D85n7YY5Nx2w20kk6HnnLbrffguVTAfO1jRKN95PzfNfwls9+hynE8c06d+7h8jhQyQvXcKOZL9Tmt9P+YObKN/8CP7Gxky5UgpsGyeVQqVSOGYKlTJRqSSOaaJSKZSZyqlPl6VSpDraiRw6iEomC00nw7L//g8Tepwz7foibnGCIAiCINwy6JrGHZUl3FFZQtJ2aIkmuBSJ0x5P0ZUw6UyaJAsE9TYdRU/Koid14/GaNEi71Gn40q522XV36dV1/IbmLnUdb7o8t83Qfl5NTweoFuFKEARhorAcxT+cvUrzQDyv/HcCb2JEe9NbOtULvkyoYnlem46ONnbufItUyhUJVq68k3vu2Vj0dzp1qZfYh1cygbt9S6oIbmwcMXB3PNLH/rd/SOul04B7DVh53+Os3Pg4uj560O7+3jgfvH2OKxeywtmcBZU8/PhSykexlBoNZVn0ffgBXa/+Eru/P1MeWrWa2he+in/uvJsaf6rQvV4qtmylYstWlOMQOfgp3W/+iuTlS6hkkt73dtD73g78CxaiBwIkL110s89Not2MVsSdUsgi4pIgCIIgCNOC39BZXBZicVk2JoVSirjt0J+yGDAtIqbNgGkxkM4ol7Ac4rZN3HZIWO7SHMVaWQFJ2yFpA9gTegwapAOI6+4yHVDcm44NlS3Lbmfap+NGZdrnBCPPa6/ljJUJOC6CliAItx8xy+Y/HruYlzm01mvyvPMyhpX7++3QeeFnNK7+VxjeMABXr15h9+53sSz3JcS6dRtYvXptQWFJKUXyWDuJI+nA3RoE7pmNf2XNiC8MrjYd58A7PyIZd62iwmVVbHzqW9TMXjTqsVmWw7EDLRzcewkrLWYFQl4e3LaYpSvrbupFhVKKyOFDdP78Z5ht2WDk/nnzqXnhq4RXrhr32NONpuuU3ruBkvX3Ejv5GT1vv0ns5GcAJC9eGKX3GPfhDxCYPx+jtBQ9FMIIhdCDIYxwGNBwUkkqtm6bkH3dzoi4JAiCIAjCLYOmaYQ8bmDvWYwttoHlKBK2TcJ2iKfFp0ERKmE7JG2HlKNI2Q4pJ7tuOoqk42AO1jujC1VDUZDuO7Gi1WgY6cx0g8HNjcEg54PrabEqry4tTOUGRC9YP1g3ZLtYXxG7BEGYCLoSKf7i+KW8sjt8nTxsv0vhnxgHTXMthS5cOM+HH76PUgpN09i4cRNLl64ouB9lOcQ+uoJ5odct8OqEH56Pd05Z0blZZoojH/ySpqMfZsrm37Geu7d+BZ9/ZGsjpRRNpzvYt+sCA32J7LGtncXGLYsIBL0j9B6d+LlzdLz0TySazmfKPDU11Dz/JUo3bJzyDHCThaZphFetJrxqNan2dvo+2EXss+Og6fjnzcNTXoHm9aL7fGg+H5rXl1nXfT40rzdn3Yfm86L7/G75bfI3mm5EXBIEQRAEYUbj0TVKdA8lN3d/Drgxocy00JQaIkqZjsoIVWZ623QcLEdhKuUu0+XZsnR95uNgpdvejPG+rcBWitQtEmNyqNjl0fURxS/vCKKXUUT8ymuTWz5kHPcj7oqCMJO4OBDnu6db8sru95xirXMENNB0H8pJ5dVXzXsO3RPg9OkT7N//EQC6rrNp0zbmz19YcD9OzCT63gXsLtflTi/xEd62EKOiuMtTT3sL+974Hv3dbmBxry/APdvcoN2j0X69n707m2htybqoVdaEePjxpcyeWzFq/5FIXb9Gxy9eInr4UKZMD4epfvo5yh/Ziu6dgIviLYqvro7aF74KL3x1uqci5CDikiAIgiAIQhpd0/AbGn5Dh0m8L1dKYaeFLEupPKEqI0apHKHKSQtVaWHKUgo7vbRylnah+sG6nO3B9YliuNg1tZZchRguQpEncBlavnXWUBFrsLysZ4Bk3CxYX0gUc8dmmDWYWHkJtxPZnFBqsABQOLaJ45g5sW/y69VgWU790e44L13uzRt/u/4RS3CtmDz+Kqxkd159/bLfwheaw5Ejn3LsmCuueL1eHnnkcWbNml1wzlZXjOh7F1GDAbTrw4S3FA/crZTD2UO7OPbhazi2+5tWM3sRG5/8dcLl1SP+fSL9ST7ZfYGzn2Uz3QWCXjY8vIA71jag6+P/HbB6e+l67Zf07fkgmwHO66Vi26NUPfU0Rig87rEnGtuxSdopQOHRvXh0A10TK6HbFRGXBEEQBEEQphgt4242fXNwBS6wlFNcmCqwbecIVoWELDunvFBfs8i+7AkOxDq4f4bHh59WdI2iIpUrRJGzTbZMH6wjY52Vu51dd8vd/ahhZYam8OAujcFtDTQUGoOP/yojBgCg1LByNUQ0yGuXKVNDRIgh/XPqVE6f3DGHlmfnQZHyQfkif/yRxI38/RSpH1am0v+K9VHpzeF9hv/9ctqpnDkOPc7MPPLLssdPwT7D6jP7Lf73GD7mkPoiXBmxNh+l4FNnNQfVnXnlzxk7mK11uO5O4XkkIxfz6mev+pfonlI++WQvZ8+eBCAQCLBt21NUF8mClrrYS+zDywymKPUtrSJ4X/HA3fFIH5+8/Y+0XRrMTKez6v4nuGPDoyMG7TZTNkf2X+HIviuZuEq6rnHn+kbueWA+/hEy0I2Gk4jT/dab9LzzFiqVtuLSNMruf5Dq57+It2pkwWuyMW2TMz3nOdNznisDV7kauU7Mig9r5zd8VPgrqApUUOkvp2JwmfMJegJifToDEXFJuK2JWzZ7WnvoTppU+31UBbxU+Dz4hgRWHQyk6tHljaIgCILw+UBLCxIeDCjwrOQ+bDvuQ6ZyAMddKoXCAeV+3OfYtDigHNyH6vz2brvB9ul2Q8ZVg26EykkLTmA7DpbCXU9/rLQolt3OXddwMmUaNullZlvLbqOl2+eUo+e007HRcJhYBdBRkBoUMqbfwCsHhYGNgYOOk1kftq3ZOXXuuidn3dBy1nHwuH/VbBst29etc9vkjim3Yrc/ttLZ6dxPs8rPXvZV4w2qtD4MXwW+YB3xvrN59XPW/B8oDPbseY9Ll5oBKCkpZfv2pygrKx+2H6UUiaNtJI+mLYg0CN47G9+K4oG7W84f48A7PyaVSAftLq9m45PfomZ2YVe7wf2c/aydT3Y3Ex3Iuu8tXFbD/Y8suqkscMqy6PtgF12vvYI9kE1bH75zDTVf/gr+OXPHPfbNopTiYv9l9l3/lIPtR4lbiVH7JO0UbbF22mLtRdv4dC8VgXIqfOWU+8vxe3yUeMOU+koo85VS6g1T6iulzFdC0BMUIeoWQcQl4bbmFxfb+KwnOq6+pV4Dbzr7T25mn0ExKjejT6aNll+XL2LpBduImCUIgvD5wBVfbJSyUI6VXuZvM7it7Jw2+dvuGGmhJrOev1TKztTnLx0Uw+sGx8sVgW4FNFzvxEmNHKINWQ7BNX7S0xKJkZZDsuv2oPSictZz2jtD11WR8txtNUJdznrRSY/zD+HKPaMwmoHZBBig6eQLUEPFrEEBy5MWvjzpssy25mSEMY/mpNunt3Fciy5s13oLJ91XYWg2Phx0jbTApbkfDbS8L4qW/tO76+7qkC9Selsr0H6wnTa0TMuua4X6aNqQeeT3cccsXD84aqF55o2pZeu0Ye1Hry8p8RONpor0cfcUc3R+3FpCu519FNVQ/OHcKGWeTWiGj0jnwSHCksbcu/4/WJbNrl1vcf36VQAqKirZvv0pQgVcwZTlEPvwMualPrfAqxPePB9vY+HA3ZaZ5PCul2k+/lGmbMHKDdz9yJfxjhC0+3pLH3t3NNHRmhV+aupLeGDrYhrnVxTtNxpKKSIHP6XzFy9htmfd6/wLFlL7wlcJrbhj3GPfLI5yONbxGW9deo8rA1fz6gzNYHbJLOaWzKYyUEHA8KNpOqZjYjoWMTNGT7KPnkQvPYleBszIsPFTjkl7rJP2WOeoc/FoBiW+Esp8JZT6SocJUCW+MIam4yiF5VjYysFWNrZjYykbJ70cLMuty5Y52Om+Q8ewlY3l2DjK5kL/5YJzNDSDP9n4b6kJVo3vDz5DEHFJuK2pD/rHLS4NmDZT8UrR0IZYUKUDnQ5NZV1M6PLklOcLX7ltsuUiZgmCIIwfpRSOFcNKdWObURwrhmPHsa0YjhXHcZIoO4VyUjhOCuWYOOntocFohZtFA03PecDV0g/Kes4D9WCdnn2IHqzTdLIPv1rRcXw+L6ZpZ/aXfYAfMk5m3ML7y8x1WNvBcUmPBa4vn0LTnLw5QvaBXikNB9fKylFaxuLKSi+dtDWWlWOpZavcDxnLLmvQysshp22uVZiWdofMWotZCixnsI3KrE+AtsSgFGQWkxUL7WQidpxGgyH3Xtl7Ld+Ql4jF2nl1Hd8o7W7Hl4y1taV0dAwUre9MpPhvn13OS0ZQG/Dx+3fMIegxUMrm6on/F8eKZer94XnULf0NkskkO3e+SVdXR3pf9Wzd+gR+//DMok40RfS9i9jd6cDdpenA3eWFA3d3t11h3xvfY6DHtabx+oOs3/415i2/u+ix9Pcm2LermabTHZmyUNjHfZsXsmx1/U3FVYqdPUPnS/9Eork5U+atraXmiy9Qsv7eactuZjs2B9uP8val92mNZgUvXdNZWbWMDbPuYVX1cgKe4gHSh2I6Fn3JfnqTfdlPImc92Y/pmETNWI6rZhZL2Zm2tyq2smnuuyjikiDMZLY3VrOqsoTWWJKupEl30qQ7YdKVNIlat4Ytuq0Utq1I2DA1YhZ4cm54vNpwK6w8QUsrXJ5Z14aIXkPaGLfZTZMgCJ9PEgPN9LV+SCp+HWUnp3k2uitCaEbeUtMMV8DILAuVZZfDxkBP1w0KIXqOmKJnxszdducypH2OMKOl22TGRc8RY/TsuIMCS2acHJFoqICUZ50x+Yz2sCxkUUql3RIHY2o5eQHnrSGxvKwCgepz25mZOF1OXsyuwVhdg1kas+uOe191k0KTAjdTpDP592UeLStaeXQNn6ETGPrxGMPLjHSZx932zoBU6hcG4vzdkIxwy8tDvLikAa+u41gJWo7/eV59ae1GKuc8RiQSYefON+jr6wWgsXEumzc/iscz/HHW6ogRff8CKm4B4JlVQmjLfHT/8LZKOZz59H2O730dJ/3/Xdu4mPue/HXCZYWFgFTS4tDHlzl2oAU7/WUzPDprN8zh7o3z8PqKx2QajeS1q3T+/GdEjx7JlBklpVQ98xwVWx5BK3C8U4GjHPa3HuLNizvpjHdlysOeEFvmPsiDszdS7i8d19he3UNNsGpU4cVRDhEzykAqQn9qIG+Z/QzQn4owYEZwbtICV9d0PJqBoRsYWvqjG3g0Az29NDL1OoZmkLSTBa2XNs95gLtqV9/UfGYCIi4Jtz0NIT8NoeFvNHIxHYcB02YgZdFvWgyYNv0piwEzvZ2y6TctEvbEugl4dY2gYVAd8FLl96ZvjpxMyurhaa2zdePFVmDbDskp0tZ0Dbxa+oapgKXVUCssX0Ghq7AVVm6bEtPGdhTGTbwlEgRBKIRjp2hv+jGoEX44NQPDE0Iz/Oi6D033oRvpZXpbM7xomhdN96BpHjTdSC89abHHA4PrQ9uk27likPzOCbcmWjrQuIGG34CCwbymAGdIAPl88aqw4DWYkXHwPsvMrDuk8rYLr483+6KlFJatiN/kPaahaUMEqSEi1BiEqsm8hzrc2c/PLrTlld1XW86z82vRNQ0r2cO1k/8lr75q3nOUVN9Fb28PO3a8QSzmeiMsWrSEBx7Ygl5AUEs19xD76Eo2cPfyaoIbGtEKHFtsoJdP3vpH2q+47nearrP6/qdYce/2gmM7juL08Vb2f3CBeNTMlC9ZWcfGzQspLWIVNRas3h46X3mZ/g/3ZIKnaz4fldsfo/KJpzBCoXGPfTM4yuFIxwl+1fwOrTkxkkp9JWyb+zCbGjfekJXSzaBrOmW+Usp8pTTSMGJbRznErDgDqQhRM4ajHPS0AOTRc0UhVxjy6B5XIBpcyrV2XIi4JAiAV9ep8utU+UeO6pCynbTgZDNgWmkxKleUcreTY7xBcG9K3L4t0QQNIT9zwwHmhAPMCfup8nsL/rCpnEw95jABKv+GKNsmZ1vl3xhZQ9eVGlY3XjnLUZBUDkkHxuegeGPoMCwW1lBLq6HB3IdZXWn520MtsnLbGBpy8RGE2xxN9+DxlmGlevLKPf5qAqULCZQswBduxPCW5rg1CYIwXeiahs9wr83jD6N8YwwKWqkiAlVx0WpIma1I2DYJ28l8krYz6n2YrRRRy74py3yvrg23jEoLVYMvQ+uDPuqCPgLG2IRDpRQ7r3Xz3rXuvPItDZU82liNpmkkI5dpO/cPefV1S75FoHQBnZ3t7Nz5JsmkazF6xx2rWb/+/mH3XkopEkdaSR5LCyAaBDc04l9ROHtcy7mjbtDupOt+V1JRy8Ynv0V1w/zC7S/28NF7TXS1Z+9m62aX8uC2xcxqHB5IfKzYsRg9b71Bz4538jPAPbiJ6i98EW9l5bjHvhmUUpzsPsNrzW/nxVQq95Xx2PxHeGD2BnzGpEbDuyl0TafEG6bEOzwWlzB5iLgkCDeAz9CpNnxUjyLQJwdFqJRrBTW4PmgVNbid629uOorLkQSXI9ksC0FDTwtNrtg0pyRAqdeDpg3GZZqamyalsm//CllRFXrTl9vGKnJjlSt0DRXKxi1mMXWm7JCNyzBMgNKGuBcOtcIyhgpdw9sMDwrvuhmKmCUIU4um6dQu+Sb9bXuJdR93g2oDVrKLSLKLSOengy0xvGV4fGUYvnIMTxjdE0T3hDCMUHbdE0I3QmgjpLMWBGFmMSho+Yqktr8ZHOWKVgnLyROdMiKUNWR78JNTnnvPWQz3PsxOxx01R2xb4fNQF/RRH/RRH/RTF/RRF/DltbEch19cbOdIV75b6RNzani4wRVNIl1H6b78Sl59wx1/iDdQzbVrLeza9Q6W5f7mrlt3L6tX3zVcWDJtYh9ewbzsxtzRfAahzfPxzh7upmWmkhze9XMunNiXKVu4aiPrHvkyXt9wT4fe7hgfv9fMxfNZV7CSMj/3bV7I0pV1474nU5ZF76736X79VexITga4tXdR86Wv4G9sHNe4E8H53gu82vQmTX0Xs/Pyhnhs/iM83PjALS0qCdOLptQ4bThvYbq6Ijg34TZ0qyA+/rc/SduhJ2lyLZakJZqgJZrgeiyFPcJpWe71MKfEnxGdGkN+Ap7b6wFFpeMl5FtR5QtQ7tvBrKWVP+ijZyAxzAorNYrQNbh9a+RFGh0NhllhDXU3zFvXClhdDXU91PLrcoUuj4hZtyVyfRk/thUn1nuSRH8TyegVHGv8Npma7suITZruRzd86IY/s56/TLvbDWvjE2upSUbOF+F2wFaKZJ7wNESIGlKWtB3iabEqaTvELGfE+1Nw71FqQj5qfF5KvAYHOvqHtXl+fh0b6spRStF77V0G2vfl1Teu/tcY3hIuXGhi7973cRz3Dm3jxk0sWzY8O5obuPsCdrf7clYv8xPeuhCjfLhQ1N16mY/f+B6RXjcAt88fYv2jX2PusnXD2iYTJp/uvcSJg9cyz5Uer866jfNYu2EOXu/47r2V4xD59ACdL7+E2ZENBB5YuIiar3yN0LLl4xp3Irjc38KrzW9xqjubpS9gBNg2bxOPzN1EcIrc3z5PzLTri65rVFeXFK0XyyVBmEb8hs6skJ9ZIT9317hpUS3HoTWWoiWWoCWSoCWapCORyljy9JkWfT1WJgueBtQEvDkWTgEaQj48MyCwYzE0TcOjgUc3GOtl7GZ/nO1hAtQQQUs5mHZhS6tCbfKtuXK2MwFHxzdPBZkxmQJJbFDMKiRA5bkXDrHCKpjlUBuhf47QJWKWcCtjeIKU1txDac09ANhmFDPRhpnswU71YaX6sM0+rFQ/jhUbMUOcclLYqRR2qvem5qTp3owApeledN2bLsv/6JonZ9uX09YzvG3utnZ7vcAQhM8jhqYR8hiExvlC0lGK7qRJWzyV/iRpj6foTKQy9zQK6Iil6IgV/t372qJZrK0uRSmb9vM/JBm5mFc/Z83/QTJlcfjAbs6fP5NXt3TpimHjWR1Rou9dRCXSgbsbSghtHh6423Eczny6k+Mf/QqVFqvq5izhvid/nVBpvtuZbTucPHKdTz+8SCIdEBxg+Z313PfwQsKlI8dxHYnY6VN0vPRTkhcvZMq8dfXUfOkFSu5ZP233P9ejbbze/DZHOk5k56V72TLnQbbP3yyuZcKYuaXFpX/xL/4FZ86c4d13353uqQjClOHRdeaUBJhTEoA6tyxh21yNutZN5/piNA/EM+0V0JEw6UiYHM4xO15RHuZri2fhnwTz7NsRQ9cwMAhM0TOUrQpZUeULUKkhllZ5roMF+g8VunLjbY32trEYGTGLmw82OlYGM+cUi3eVK3QNja/l0VyXhOwYBVwPh/S/3dJAC1OL4Q1jeBcRKJIkRzkWth3HsWLpTxzbiuHYOetWDMdJouxUepnEsVOMVUBWjolyTBwrMnEHlodeQLTyZISnbPBxD+QGKB8sHyzLbGfrhgcwz19ms8wJgjCd6JpGTcBHTcDHqhw9xnYUnUlXcGqPp9jf2U8kZQ3rv76mjLXVpTh2gmuf/RWOnQ0D4Qs2ULPkNzh56hTHjx/CNIe75Jmmic+XdbtLNaUDd6etinwragjeO3tY4O7YQA+fvPkD2lvOA27Q7jsfeJrl67cNC9p9qamLj99rpqcrlilrmFvOg9sWUztrfJnQAJItV9wMcMePZcqM0lKqn/0C5Q9vmbYMcJ3xLt64sIP9rYcYDEhhaAYPzt7A4wu2UuEffywp4fPJLSsuvfLKK7z77rvMmzdvuqciCJOKUu5D+4BpETHt9Ce9buWsmzYRyxqzxcvpviht8STzSqYqlKVwIxiahmFoUyb+OYXcC4dZWBUXugrGy8prk1833qw5kJs5B2Bq0kAPs7rShghSw9wKR85yOLxNtk7ErM8Xmu7Bo5eC98YeTJRSoGwcO5kVnJxURnhSjrscVpcWmtyPhVJp4SldNmLGu6I4KCeJ7STH0ffmyYpQBYSrgpn3CohWowlcORn7ho59G0aQEIQJw9A16oN+6oN+Dnf2EzezvzF3VISZXxKkLuhjRUUYK9nLtZN/ldc/XHUXUW0Nr732CwYGhrvRAcyfvygjLCmlSBxqJXkiJ3D3fXPwL68e1u/K2cMcePcnmEn3pWxpZR0bn/oWVfX5z5c9XTH27jjPlQvZpA2l5QHuf2QRi5bXjEvgtqNRIgc/pf+Tj4mfPZOfAe7xJ6l6/An0wPTco/clB3jr4g4+vPYJjnJfYmhobJh1N08tfJSaYNW0zEuY+dyS4lJbWxv/4T/8B2bNmjXdUxGEmyJlO/SbFn0pN4B3X8qiz8yuR0yLiGUPvnS5KQKGTsrOxg56rLGaOWHxjRZcdE3DP8ViVp7roBpBnBrWpnCQ96FCV976BIhZiSkSs4w8yyzXpbCQgJUvdOW2GVnoGtrfEDFrRqJpGmgeNy0yE+eSoJSTIz6ZQ8QoE6WsnPJUnlBVuK2FUnZ6aUF6mdmekDlbYFvjTvRws1xBc10DRxG0RhW4Ci6NMQlcYsEl3MoopXjvWjc70xnhDA2+uKA+E/IBIBm9StvZv8/r5696kENnk1y//k7Rse+6az1r1tzt7se0ie65jHXFFaE0n0FoywK8DfkxYMxUgsPv/5wLn32SKVt05wOs2/JFPN58t7arl3p58+cnMFPu9d/rM7jnwfmsuacRw3Nj90xOKkX02BH6P9lH7PgxlJXzG6jrlG96mOpnn8dTUXFD404UcSvBzsu72Xn5A1JO1jpsXe2dPLPoMWaF66dlXsLtwy0pLv2f/+f/yYMPPojf7+fgwYPTPR1BKIjlKPpSJt1J0xWLhopIKeum3Ih0IOw1KPF6KPEYlA6uew3343HXw16DkGFg6HLTKdw6ZLPmAEy+r+GgmGWpwgHbC4tYw4WuoZkQ80Wu/P7jxVYKOyNmTT6GxjABKugz0Bw1XJAqIHQVynJYuI1bJ79FtzaapqMZfjDGHzdkrLgWP44rNOWJTnZmPSNG5QhUhZf2EOGqUHs7X9iaMIFLTbvABRRwPSxmyTV2t8PCAlexpbjZC8OxHMUvL7ZxKB2aIeQ1eHHRLBaVhTJtYr2n6Lzws7x+vfadHP2geUTLwM2btzN//iIAnEiKyHsXcHrSgbvL04G7y/J/y7quX2LfG98j0tcJgC8Q4t5Hv8GcpWuHjX/pfBdv//IktuXer6+8q4F7Ny0gFPYNa1sMZdvETp9i4JOPiRw6iJNI5NUb5RWUbriPioc342uYPeZxJxLTsfjw6j7euriTiJlNQnFH1TKeW/QE88rmTMu8hNuPW05c+tnPfsZnn33G66+/zp//+Z9P93SEzzFKKaKWTU/SojtpZj496WVfanw3mQFDp9znoczrodSXFYlKvAalg+KRx0PQo4v7jCCMkYyYBUyFmKWUyrO0Guo6OFTAGt5GYamR3Q2HZjkc70OtrdwApXkOTYlirW8eHQoGbL+RLIderUi8rVyrrXQbQ0MsOm5R3P8XA80wpkTMKoTrXujcmGg1uJ4uDwUNIpFoRtCiwBjFtidO4HItuJQ9MWONDz3ftTB3OazMO0Ts8maEMT1XICs4lleErRlC3LL54fnrmVigVX4v/+q+pRhx1ypGKcVA+0f0XtuZ1+/E5Xo6+9xMaZqmUVVVQ1dXR16bp5/+EtXVNQBY7VGi7+cE7m4sJfzwfDRf9nrvOA6nD7zLiY/eRKVdvernLWPD479GqLRi2NzPn2pn52uncRyFpsHWZ1awbNXYLHeUUiQuXGBg/8cM7P8Euz/fnU8PBim5Zz1l991PcPkKtGlKsuMoh0/bjvB689t0JbIuf/NL5/L8kidZVrlkWuYl3L7cUuLS1atX+dM//VP+9E//lKoq8fUUpoaU7dCRSNGRcAMRdiRMuhIpupMmqRu0TgiQoERLUGrYlHl1yjyKUg+UejTKvFDmNQh4vPk3UJmbKC+aTvqjoyE3UoJwq6Jpg+5qU7M/pdyg7AXFqAIZDIdZXg1po3l0ogkzK3oVEMrGK2Y5QNJxSE5N/Hc0KCxAaYXdCAsJXSPF18pv47oZipg1c3DdCw00xi9w3Ww20tEFLguGWF0Vtc4aLBsUucYgdE2cwOWkXSWLZ0CcPMYqbHmL1g0VtopmVfycZUkctBzKXyqUyl8fuuxNmfz4YicdSfe71Rj08pW5lYScFF2RCI5jE+vYSar/s7z97T9bQyzp/obW1zdQX9/AsWOH8tq88MKvEQq5lk+p893EPm7JBO7231FDYH1+4O5ofzefvPkDOq42AaDrBnc+9CzL79lSUJg8efQ6u988C4BhaDz2/EoWLK0Z9W/lJBL0vPs2/R9/hNnelleneTyE16yl9L6NhNesRfeO3fppolFKcbL7DK80vcnVyPVMeV2whmcXP8G62jvlOiZMCreMuKSU4t/9u3/H5s2befzxx29qrOrqktEbzRBqa8efmUDIJ27atAzEuRaJcz2SoDWS5HokQXdi7DdJpT4PlV4LX+wiZUQo06KUEqFUixIijkfLeZoy058counPWNF0D7rhQ9e96IY3s9SGbOet6160zPZg3+w42pC2mT76zL+JkvNFECaGQTErZTukbFeYStkOpu1mMTRtJ1PmlitSzmB9ejunjbtUOfVDxnWccceeU0DKUaScqfEz1ACvoeMz3EDuXkPHq7vbXkPDp+tuvZ7ezqyn26T7ZMu0TH9fZqxsG48uYtatwEy8vgyKBY7jpD8Wjm1iWyl36Zg49uDHjanl2GamnZOJtZW15Mpdd+NuFbbaYlAIS4tbY818WJypF7aU0lDoKAx3qXQcDJQaXE8v0x+ltMy6M7juaDnrOraj4SgDy9EH4zvniTqDnxspv9n18ZAIlHBl7hpsjyvYlvR3ED59krcOuf/PuuawZkEPFSX5N8IfnaolZRmUlZWxefNmuru72bt3b16bP/qjP8Lr9aIcRdeei8QOXCU9KHXbF1O+Jj8m79mjn7Drl98jlXCtpyprG3js679H7ez5Bef+8e7mjLDk9Rl8/Z/dy8IxCEvmwAAn//wviJw9ly3UNMrvXE3t5k1Ub9yIp2Ti4uONl/NdF/nhsZf5rP1spqwiUMZXVj3DI4sewHMb3O8r5bhWnJaJsi2UlULZJsqy3KVtojJ1Zk6ZlbeevHaW2NkDw8Zv+LV/T3D+6ik7npl4fSnGLSMu/fCHP+TMmTO89tprWOngZ4M/epZlYRjGmG+uuroiOBMRIXmaudk3ZZ9XlFIMmDbXYkmux5KZZXdyeFrToWhApd9LTcBLtd9Lpd9LVfpT6ffiN3Qcx6Sn5TTRruOTfyyOhe1YUxBiGAZTTQ9aVOn6SBZWuWmoc9oPKRvad/BtINrYz+exIueLIIyd8ZwvvvTHvXXWQDfcj3di5mQPWl0Vymw4JMthMXfD3CyHxYLHD7ob3pSYlRbVpgIN8OgaHk3LiE3DLLCKWGENdTf0aMNdDYe3ufXFLKVUjmBiZ9YHy7NLB8dRecuR2gyvc8uDQS+RSOIGx1WZ/q4FyGBZ7vroH1B5+yhcN2hdotL3v/lixNSh4T5aFHq8UOga6LpC11R2qYGRVza0TeE+g+vZvhRtM16PJE1TaNhkEj1M8GlhO2DbOpajYTsalq2nlxq2o6eXI9dZtp7+LZu6czZSUs3VxpUo3f1/ruy6Ql37+cwMfB6buxd3EfBlfyNjSYOD56vRdB933XUXK1euYe/e97l06UKmjaZpfPOb36a3N4Eyo0Q/uIzVkg7c7TcIb1lAalY4c+2yLZNPd/6UizlBuxeveZC7Nn8RvL5h1zilFAc+vMTBvZcA8Ac8PP3VOymp8I96PbT6emn5y/9I6mqLe4yNcyh/8CFKN9yHp6ISgJ64A/Hpuw9ti7bzavPbHOnIPp8EDD+Pzt/CI3M34Td89HTFpmQuSjmoRAQV7XaXZgLMBCqVQJnx7LqVAtsE2xWysS133bbAMd04d+ky7LTFpm3CJL9M6tjzS4KhwuLkRDPTnl90XRvRkEdTt0h+1V//9V9n//79Rev/9E//lC996UtjGkvEpc8XSduhJZrgUiTB5Uicq9EkUWvkHx2PplEb8FIb9FEb8GWWNQEv3nHehTh2CtuKYKf6sVJ92Gafu0z1YSa7sFN94xq3GJrmQfeEQdPysvnc/NvBqaGgWDVk2xW4ckWqQZP3/DJd81JZVUZfv1m4ncRqEIQ85PriBla3HNfiqrCIlRWnxtIm3x0xX+iyHAd7Bt2WeHIyGua5C2quW6ChKQzASNt0GMq179CVk/lojoPm2JkPjg22hWZb4DhojgW2jaZsHDtfJMpfd7dt2xV1bNueBtFEmGloGZFUQ9MUho4rZBmu2GRouEsjK1AZOui6g6HnC16G5uSIVoPrTnZ78KNPz/fStbAycPCglCd9VnqGfRx8OFoImxBKCwHZDIRajqhceKmhaXDW9HAg5UGhAYp7/TYr/W7MItDQ1QC+2NuQ41jtGHU44S1oupf6+gYCgQAvv/wTotFIpk1tbT1PPPEcmqZhDySJvncRpzcncPe2hRilWbdWy0yx99X/Tuul0wD4AmE2PPYNGpesKfI3Uny0s4ljn7pWUMGwl2e/tobqutG9XcyuLlr+8s8x21w3uJL1G2j47d9F89waNhp9yX7euPAuH10/gJOONeXRDB6e8wCPz99KiW/yramUmcC6chy79Rx22zmc7iuuUDQD0YLlBB/7XzDqpyYe1Uy7H5sx4lJzczPRaL7D0F//9V9z6tQpvvOd7zBnzhwqKyvHNJaIS7c3/SmL5oEYlwYSXI4maI0lR4wNUuHz0BDyMzvkpyHkZ1bQT4XfM+XBspVSOFYMy3QFp0HhyUr1YqX6sVO9OHb8hsfVjSCGrxyPr9xdekvRPWEMTwjdE0bXfShlpk3ZzbTZupk1ac9JPT1Y5uSVmVkTeGXmtZuoIKWTTiZTzhALq9z4DMOss3LiMdyACDYZVlmCMNHI9WXqGRSzClpdqeFB4bOiVdaia2jA99QQoSs3s6F9a9zejQnNsdGUg+446aW7rTkOusqts9NlOXU5Sy0jbNlZgUvZw8e92flqGrquo2k6etpt0V3XMw/puXXug7lWYFtPj5fb70Y+Opo23v5j7zs4X01jyHZhYSK/39A+5PUfuj28z/B95vcZ2n/6Avy71mK5905m9p7KMVFOCsdOouwkjpPEsVMoJ4ljux81WJapT4CarBeGGh5fBR5/pfvxVeWsV6Ib+fGCHKV4u6WLPa1uUGivrvG1RbNYWZl9yEwMXKT9/Pfz+oUqV1M9//nMSz7TNPnxj/9nXpsVK1axYcODAFitEaK7LqKS7kviQoG7zWScPb/8bia+Us3sRTzwzG8RLCkveKSOo9j91llOH2sFoKTMz7NfX0NFVahg+1xSra20/OWfY3V3A1C26WHqf/03py1Ady5xK867l3bz3pU9mI7rnaGhce+sdTyz8DGqg5Mfv9huO0/q1PtYzZ+ClRy9wyCaAb4AmscPhgfN8IDuza4bXjd7ZXpdM9w6d93NbJlZHyxPl+WOkemnD2mbu49pvmefafdjo4lLt4bkCixatGhYWUVFBT6fjzvvvHMaZiTcKiQsm+aBOE39Mc73x+kYIUZStd/L3HCA2WFXSGoI+Ql5bg3fYk3TMLxhDG8YQoVTkTp2Ks/iyUrlr9tmPwyR0hw7jhOPY8Zbi+zXky8++coxvOXuTYWvHMNXxniDVubfSBUSqwbFLCvn5mpQmMrtky9cOQXbmMOOfewTtVG2jSI5BXZdWo57YGEhSx9BqMrtpxe11soRvETIEoQZgaFpGIaG35iahxKnkGXWEBGrq7eHU2dPEUskcDQDpes5Sx2l6yjNwNF1lKbj6Ia71HTU4Hq6Tt1EHA+luzZQzhRdrg3Ao7sWWp7cwO/a0IDu7rKsJICVtLKxtfICxKfdCrXsum9IYHjJ/Pr5wBW4vO5D8gShHMsVn5y0KJVZT+UIUvniVG75oFClnKGhIRRWqgcr1QMFnmsNT0labKoCXyVv9NRxKuqeoGGPwbeWzmZuSSDTPtp9nK5LL+eNUVK7gcrGxzP3KfF4jJ/97B/z2tx330MsX74SgOS5LuL7rmYDd6+sJXBPQ17g7mQ8yu5f/Dd62i4DMGv+Ch587rfxFAmebdsOO187TdNpNxNdeVWQZ7+2htLyQMH2uSSvXKblL/8j9oDrmlfx6OPUfvXr037fZdomH1z9mLcvvkfUyrq5rapewRcWP0ljScOkz8Fubyb56S+wW07kV+gGes18jLrF6GV1aOFKtEApmjeA5guCN4DmDbgCj/wu3pbcMuKSIOTSmUhxsifKqd4IlyOJgpKCR9OYUxJgXjjA/JIAc0sClHhn9ldaN3zoRi3eQG3BeqUcbHMAK9Wbdr/rzYpQaYuooTcQSllYyS6sZFfR/Rre0rTV06AIVYHHV4Y31IDHWzzI3GTcSI2EGyi0sIVVWamX3p7+whZWjokzogiWtcQaFMFQ4/XnVjli2BSQTvGsjxAPKy+WVpF4WCPFyBoUuNB0uRkQhBmCrmn40gHFi/HO/vfxtV5j6GOZz+cnGAwRCATwen34fN70UsfrNfB6fXg8HgzDwDAGlwZK97iClG7gaJorVGk6FuRbXaWDxVtDMiDmuR6qfCuuYfG1bsIyy8aNd+PaPY9hnK7I6G1GwNAYJkAVdDtMx84qmLVwSJbDkdoY8jt926DpHgzdg8HNuTa5948RrGS3Kyolu7GSPVjJHsxUN8rOtzqxrQi2FaE30sZb9sO04QpLFfTztP4R3pYAnWlLp1jPSVekyqFs1sOUz9qcuWfo6enmtddeymuzfftTzJ49B+UoEgevkTzZ6VboGsGNc/Avzbe8iUf72f3SX9PX5WY/a1x8J/c//ZsYnsL3oKZp887LJ7nc7FodVdeFeeZrawiFR8/iFm86z9X//Jc4MVe8qX7ueaqe/cK03gM5ymF/6yFeb36HnmRvpnxB2TyeX/wkSysXT/ocVCpO8pN/wjy1K1uoG3jmr8O77CGMxpVonunLkidMP7eMW9xEIm5xMw+lFNdiSY53RzjZG6EzMfzB3NBgXkmQxWVBFpeGaAwH8OhyA5WLUgrHjg+xeurNWD1ZqV4c60aC+WlUNj5GSe2GW15UmOjzRSlVxAor31XQKWBhNdT10ClQNnSscVtlTSnaDVpYFXY71IuKW0OEsVv8OzeT+TxdX4TiHD9+hMOH8+NdhkJhZs2aTXV1LeXlFVRUVBIMhm6589FJZzQcLkAN2VbF3A1z1oe0yXUvNB3HzZw4g+4rdY0hAd6zwpOvgKVVocDu3rw2Q0WwnHaajiH3YjOawXtHV2waFJ966IzF+GV0BX3KdYFpoJ3HjT0EtJEz9s1Z/hx66K7M9tWrV9i58828Ns8++wKVlVWolE30g0tYV93rkeY3CD+yAE99vttNtL+bXS/9NZFe1wJp3op7uO/xX0M3Cps7ppIWb/zsBNdb3Hin9Y1lPP2V1fgDo78MjZ06ydXv/GdU0hXcar/6DSofu7lM5jeDUorPuk7zStObXItmvRTqQ7U8t+gJ1taunpLfZ+vyURJ7voeKumIduoF3xRZ8655BD48tdI0wnJl2PzZjYi5NJCIu3ThKqWm5cexJmhztGuBw10BBd7dqv5c7KsIsKQ+xoCQ44htYYWw4jjkk5lPfkADk/QwNCh6uXkfVnCddf+ZblJn245yLUsp13csRoJxi1lU5ZVnRamh8rAJuh46Jky4bv1XW1FI8W+HwsqzAVdzN0G1XzM3w8/XbMpPPF2HiUEpx+fIFTp48TkdHW9F2uq4TCoUJBkOEQmFCocFlOG3hFMTn8+P3+zGKPOzNZGprS2lv78fKuBoOj5GVa2llOcNFr6FCV9EYWzlB4mfKnawOhQWotJthfpbD4VZYo2U5HNrG0KYvrtLnhcuRON8/d51YOkHOneUenq5JgumKT6lYK6nY1WH9guUrWHnftzPXl9OnT7B//0d5bV544ZuEQmGcpEX03WbsLjfeqF4ZILx1IUZJvuXLQE8Hu176DrEB1zpq0er7uWf71zIxy4YSj5n86qfH6Wh159A4v4Inv7war2/036bIkcNc/5u/RlkWaBr13/pNyjdtHrXfZHGx/zIvn/8V53uzWfXKfWU8vfBRNjasx7gJd+SxoswEyY9+hHnmg0yZMfsOAg//FnpZ3aTv/3Znpt2PzZiYS8L0YDoOO692c6Cjj/vrK9jeWD0l+zzeHeFARx+XIolh9fPCAe6oDHNHRQm1AfHJnWh03YseqMEbqElb56SwzQi2FcWxotjmAKlYK8noZayk+3Yi2nUYO9VH7eJvyv/HJKBpGqSFFBg9DsDNopST5wo4VJhyRrCwGmqZNRYRbPzzHExBO/x3YuLR88WmdBbCkS2sskKVfgMimAR9F24VNE1j/vxFzJ+/iIGBfi5daqat7TodHW2kUtkXPo7jEIkMEImMfgPs8Xjw+fwZsanQ0v348Hq9eL1ePB7X7c7r9d6y4pSmaWlLHghOwf5UnmXWUAFqJBHLGbWNlQ4QP1ToGq+Y5QApR5Ga5PTgg2hQQIAayd1QLxBPa6Q2+XWenGDhnwdOdA/w0+a2jOvploZKtjdWZ2KHWal+Opp+nGmvGQFKa+7FXzKPQKkbQ1cpxb59ezh37nTe2F//+m/i8/lw4iaRd5txerLX90LCUl/XdXa99Nckom7co2V3b+GuzV8s+v8RHUjy2j8do6fTtdJfsLSaR7+wEo9n9BdI/Z98TOvf/x04DhgGDd/+XUo33Ddqv8mgM97Nq01vcrD9aKYs6Anw2LxH2DL3QXzG1Lie2e1NxN/7W1R/u1vgDeK//+t4lz/8uTonhLEj4tLnmMuROD+/0EZH2gVtT2sPmxsq8U5SBoTupMn+9j4+7ewjZuVbxswK+lhbXcraqlIq/FMTv+d2RjmWG5vJ7Mc2I2nRKC0gmdG0L727PtaMb4mBZhw7juEZPbuGcGujaTqa4YMpuDnJWmXlxr26kWyFxUWwYYHilXkTmXUclJNCOSOb+08URV0Bh8S50nOFrGFB4ouIYEOtsz5nVlnC+CgtLWP16rtYvfoulFLE4zF6e3vo6+slGo0Qi0WJx2PEYlFisSi2XVhIsCwLy7KIxaIF60dD1/W02JQVnAqJUNltb04MKE8mFtTQ7WJWDrcqWibY+FSKWWSFJzvfXdAqKk4NdzUc3d3QXR/vr7Vi6sWsYpZWQy2yvLqGRysQ8H1Ym+KWWtMlZiml2NvWy5tXOlG4FmlfWFDHvbXZLGypeBsdTT/CNl2hOVi2jOoFX8rLLmfbNm+++Us6OzsyZcFgkC996UUMw8CJpoi804zTnx/nKbbnMqVPZtO/d7ddYffP/yuphPtbsvK+x1n9wFNF/zb9vXFe/fExBvpcwWrZqjq2PLUcYwxeD727d9H+j98DpdC8Xhr+4A8pWXPXqP0mmpgZ462L77G7ZS9W2srcoxk8POcBHl+wlRLvzcXeGivKsUkdfp3UoVcy91XG7DsIbPlt9JLJN0QQZi4iLn0OSdkOO652sbetN+8tlekomvvjLK+Y2B+uK5EEu693c6o3mre/Uq/Buuoy1laX0hDyT+g+b2fcjHL96cDe/Zl1O9WfFpMGcKzx3dTnohtBDG8JuieM4QkTLF8mwpJww+RaZU3Fo51rlTXcPXAsFlZZcWska618cWv88xy0yopP4NEXQdOHxcjqbPJj23oRC6scV8MRRbBs3WCgeLHKuj3QNC3j9jZ79pxh9UopTDOVFppiJJMJUqlUepkkmUwWXBYTpHJxHIdUym0/kei6nhGbiglQ+dvZ8oqKEuJxa0xtPZ6ZGS/OFbPAoxuu/ewUvOezh7gBpgoIUOYQK6yhLomZdVXY1TBX6BpvxAwFmbGGhg2YLAoLUMXFqXyhK7dNfvbDYe6JaWstgNcvd7Cv3Y1R5NM1XlzSwLLy7DNBvL+Jzgs/y7yEKam5l8o5j+e9wDDNFP/lv/x93rk+e/Yctm17Ek3TsPuTRN5pQkULWDVb2T6d15r54Bd/g5lyhaI1m57jjnu3F/17dXdGef0nx4hG3LmtWjebTY8tGdO52P3WG3S+9FMANH+Axj/6Y0LLV4zabyIxHYsPWj7irYs7iVnZ+4J76tby3OInqQlWjdB7YnH624m//12ctvNugW7gv/cFvGsel5dVwqiIuDQD+bM/+w9cuXKJ73znu3nl165d5Tvf+U8cPnwQgAceeIh/8S/+FZWV2SBrFwbi/OJCG11J90fdo2lsbqjkzROnOffLf+QPm0/h1fWCfW8EpRTNA3F2Xe+mqT//4WlBSYCN9RWsqiiRIJAFcKxEOj1srxtcMdWDlezNWCKpcbsIaeieEIYnjOENo3tK8te9roike0swPCE07dZ0TRCEkXCtsvxgTL5grZRKC1OFhKrhVldFA7sPiZHlDGszmH1wvE9GDspOonAf1m3AnERPw6JuhEPjYxWx1sp1O9QLWWvl9pmBD/G3A5qmZdzbKirG/tBjWVZGbDLNFKZpYlkmpjn4KVRmYlmpIdsmNxoy1HEcHCeFaU6+deJgFr2xiVjFhCoPHo8x4rZhzGwx19A1DAwCU3S7YavhFlb5IlZhK6zibYqIXmmhy76JoFmmozBRxO2pEbMMjcx8y7wefmPZ7LwXv5Guw3Rffp3B61DF7EcprduY9/2LxaK89NIP88ZduXIN69dvBMDuTbjCUrzwi5mSJ1yrpbbLZ9jzy7/Dttxz9e6tL7D0roeLzr2jdYDX/+kYifS46zbO5b7NC0c9N5RSdL3yC7pffw0APRym8V/+G4KLFo3YbyJRSnGo/RivNL1JV6I7U764fCFfWvo0C8rmTelcrHN7Sez9x8xNgl4xm8DW38OomT9l8xBmNiIuzTBef/2XvPbay9x119155X19vfzRH/0+pmnyzW9+C9u2+fGPf0BT03n+7u++h9IN3m7pYl971lppbjjAlxfW40/F+H/+6/+PlGmyePsXeKiuLK+v13tjr6+uRBK81dLJhYGsqGRocFd1GQ/UV3zurZSUcnKEo6yAZCd7sVI9OOMQj3QjgOEtw/CWYvjK8HjLMHzpbW8phqcE3ROUNw6CMIFomoameUH3MhWOK2ow6PsQV8CMaFUwmHuu+2C2zOuFZCKeM87QcW/CKisjhk0BmpFnOVUoHtbYsxXmC175IpjXtQCbwQ/ytwKDVkOh0M2mVVdp97us4GTbrjtedmmPsm1hWXaBftlyxxnfg71t29i2PeHWV4UYzYrqRsSqkfrr+sz//huahmFo+KcoOYyjRheg8gK+D7W8GiKGDRO9VH5mQ/smcjQNCkuzgj5+Y9lsyn3uvb9Sir7W3fS3poM5awY1879IqHJlXv/e3h5effVneWUbN25i2bI7ALC6YkTfbUYlC1svlv/6GjRd42rTcT56/X/i2BaapnHvYy+ycFXxuEfXrvTy5ksnSKXHvW/zQu6+f3RBRjkOHf/0Y3p3vguAUV7OnH/9b/E3DrfUnCzO917g5fO/4mL/5UxZXaiG5xc/zZqalVN6vqlEhMSef8C68GmmzLtqG/77vobmmZr4TsLtgYhLMwTbtvn+9/8H/+N/fLdg/U9+8kM6Otr53vd+woIFCwFYuXI1/+pf/SHff/kX9Ky4j+4ca6VH51TzYH0Fuqbxt9//O2I9Xdz9v/05ofpGHl81L9P3zTdf57nnvjimOXbEU7xztZPPerIuWV5dY0NtOQ/NqshcqD4vOHYKK9mFmejETHZiJjqxEp2Yye4bytalGX48vko8vrKsgOQty9vWpyiwnyAI04emGWiGMSFWWaNlJ8laZY2chdAZxVprrCLY+K2ybJRtM1WRV8ZnYTWSuFVcBJvpD/KTiaZpmbhLwUnUdR3HwbYtyssDtLf3jUGsypYXa1tM1Bpv8uZBIQsmX8i6cfFqpPL8/ll3Rc+Mi49VDF3T8E+xmDUsgHsBd8E8qyuVFb1KvB7urS0jkA6qr5RN95U3iHYddo/HCFK76Ov4S+bm7bet7Tpvv/1aXtm2bU/S2Oi2s9qjRHY0gzlcrNXL/JR90XVBu3zmEPve/D7KcdB0nfuf+g3mLltX9HgvN3fz9i8+w0rHcd302BJW39046t9J2TZt3/8H+vfuAcBTXc2cf/2/4auvH7XvRNAW6+CVpjc52nEiU1biDfP0wkd5cPZ9U5IBLhfr6kkSu/4OFXWz8WnBMgKbfxvPvDVTOg/h9kDEpRlAMpnkd3/3N2lqOscTTzzNwYMHhrXZufMd7rrrnoywBLDm7nupapjDy2++wZqFrqXT/BLXWqkm4Mvru3rt3YTq3R/kM31RNt97H/PmzWfnzndGFZfils2Oq1180t6X8UQ3NI3768p5uKGSEu/t/TVTysZMdJKKtWLGWzNikp3qG+MIOh5/BR5fhSsi+bPLvohNImkTKClLp3i+fd4gCoJw65JvlTW5uEHfnbyYVkVjZA0RrYqLW0OErJyA8jci7g+Z6TQEfS8cD6uQS2HBGFljEcF0j7hBF0HXdXTdRzgcpqRkct2THMcZUawaWbwq3rZQm/EKWYP7TU6yjqXrxjDBafi2N6+sUDuPx1ukbGa7ExZD1zR8hobPALi5c9qxU3RefIlEvxt3x/BVULf4m3gD+cGcL1xoYs+enXllv/Zrv4auuzE6zesDRN+7CNbw88e7sILww667VfOJfXz67o9RSqEbHh589tvMXrSq6PyaTnew49VTOI5C02Dr0ytYtnp0cUhZFq1//10GDux35zBrFnP+9f+Gt2ryYxoNpCK8eXEHe67uw0kHyfbqHrbOfZhH528h6Jn8bMG5KNskeeDnmMfeypR55q/D//BvoQfLpnQuwu3D7f3Uf5uQSrlBM//9v/9Ttm17lBdeeDavvr+/n2vXrrJly7ZM2fm+GL+42IbRMI++k0fw6hqPz6lhY115JpXo0L5xv5eupMnp3iibG6pYtmwF+/btLTovRymOdA3w5pVOoukgfBqwrrqUbY3VVN6GWd8cO4UZbyMVbyUVb8WMtZJKtI/pYcXjq8QTqMYbqMHrr8Hjr8Ljr8TwlhZ0Vzt+/AiHD+8f07y8Xh+lpaXD3gyOHs8hW1/sjeLt8gZREIRbEzfou4GGAcbk31y7Qd/HZ2E1VPDKF8EKx94a/zwHg75PYoCsDHrBjIP6iBZW2TL9BkQwscoqjK7r+Hw+YHItkZVSGYuskSywbta1cLDteHAcm1Rqct0K3fsc7w2JWNnMhEMzGGazFt4O90y2GaGj6cek4tcB8AUbqF38DQxvSV67EyeOcOhQ/n3q889/lfr6ejo6BjBb+om+f5FCkdQD62YRWOOKQWcP7+bw+z8HwOP18dAXfpf6ecuKzu/sZ2289/pplALd0HjsCytZuKxm1ONyTJPr3/1vRA8fAsA/dy6N/+rf4imbXCElZZvsuvIhb196n0T691xDY8Osu3l20eNUBiomdf+FcPpaie/8bzidl9wCw4f//m/gvWOL/D4LN4WISzOAcDjMj3/8Czyewv9dnZ3tANTW1hGzbN680snBzn4AfGUV2IkY355fxbyaihH7hirC7G3r5XIkQcyyqa6uIRKJEIlEKCnJv6C0xpK8cqmdS5HsTe+i0iDPzKtl1m0SU0kphZ3qJRm9QjLaQjLaghlvY0T3DU3H66/FG6hJC0m1eP3VeALV6DdoAWDbY78pM80U3d1dNzT+WMnNsDOaEDV28er2C0wqCMLMwA367oMpcCd2rbLcWFnOsAyExbMXOnmC11iDxJuZlNE3jjMNVllDLayGxr4aIm4VtboadDUs0k5iDeahaVo64LiBzze592uukGUXFKaKlVmWWaBsqPCV32Y8sbEG3Qkn2gpL1w28Xs8wEWqoEOXx+PLqfD4/fn8Av99dGsb0WBOaiU7am36EneoFIFC2hJoFL+SFX1BKsW/fHs6dO53X98tffpFw2H1eSF3sJfbBpYK3zKHN8/EtqADg5P53OP7h6wB4/UEe/uLvUzN74fBOaU4dvc6uN88C4PHoPPHlVcxdOLrVkWOmuP5fv0P0+DEA/AsWMueP/w3GkOebicRRDgdaD/Na89v0JHsz5Ssql/L8kqeZWzp70vY9Eua5j0h8+P1s0O6aBQS3/h56RcO0zEe4vRBxaQbgmmUXvzmKxWIAtFuKvzx+iVjaisina6yoqeAqECoSkWKwbyAQYEVaXFLA2b4ofr9705FIxDPikq0Ue673sPNaV05WCYMn59aypqpkXOKAUgor2Y3HXzmtN4FKKcxEO4mBCyQjl0hGW3CsaNH2mu7HF6rHF2zAG5yFL1iPN1CLNkG+0mvX3kMoFOLKlUsMDPQzMNA/blP2m2GqM+yMJWtOobrr1wunii403u3wZlEQhFsf1yor7ZLGVFtlDc04OChwDbfWUjnWWs4o1lq5ZeOf56BVVnz0xjeLpueJTfnugyMHdh8UrXSzhFjUHi6KDdany9HkJUkurpDlXnsnk0GXwqzgZBYRpswiYtXobcZ6/+U4NsmkTfImVSuPx5sRmrLL/PVAIJDXxuO5uQyayegVOpp+gpM+L8PV66ia+1Se26xtW+zc+RatrdcyZYZh8OUvf5NAwP2N6z/RVlRYKnlyCZ66MEopju/9Faf2vwOAPxhm85f/OZV1c4d3SvPZ4Wt88PY5ALw+g6deWM3seRWjHpeTTHLtO39F7NRnAAQWL6HxX/5rjFBo1L7j5Uz3eV4+/zpXItm/0+zwLJ5f8jQrq5ZNy++EMhMkPvwB1rmsV4p3zRP4730BbZLPUeHzg3yTbgM64+5D/6edAzSkhaVl5SG+ML+Onx0cvJkt/CM2eLHUNJhfEsSv6yQdh9O9uaKK27ctnuSl5jauxpKZ0gfrK9jWWD2uYIVKKeL9Z+m79j5mop1Q5Z3ULBhb8PCJwkr1kRi4QGKgmcTAhaJikqZ58IVm4wvPwR9uxBecheGrmNSLg6ZpLFu2kmXLVhastyyLSGSASGSAgYH+zHok4q6b5vjdMQKBIMFgML0M4fX6MrEbxhL34VYPTKppWkHRaagl1o1aXxUaSx40BEGYKqbDKmt4fKzCMbKcEeJhDYulVUAEg3FaZSkHpZIox72ujCfiVveVsbct6ApYKAthEWut3DK9WLD3zBjyogSyLoWuW+HEM2iBNZiZcPBjWamc9RupM0e9T3JFLpNoNDLmeeq6jt/nx+/z4/MFMut+nx+/14/P58Pv9RPyhygvKUc3DFAKHIhHz9LT8ysGz5CSwH2EzXtJne8DxwFHkUgm2XF6F9FULLPPUl+YbXMewP60naitUJZDb0t/wfmVfmkFRqkfpRSHd/2Cc4d3AxAIl7HlhT+kvLq45cyxAy3s3dkEgM9v8PRX72RWY/mofxMnEefqX/0n4mfPABBcvoLG/+WP0QOTI/Zfi7Tyy6Y3+Kwra9VV7ivlmUWPs7FhPfo0nbN25yXiO/8bqq8VAC1QSmDL70jQbmHCEXFpBtObNNlxtYs913oB1+Sz1GvwzLxaVle6VkSDb07C4cJpf4NBV7VPJpN4dI2l5SFO9EQ41xejPOmaSwZCIXZf72bH1e5MmtPagI8XFtYzt2R8P86peDs9LW+TjFzIlLkuZ5OLUgozfp1Y3xnivWcxE4X3aXhL8YfnpcWkOfiCsybMImmi8Hg8VFRUUlFROaxOKUUymcwITcMFqIERb2wSiTiJRPaNsq4blJSUUFpaRklJafrjrpeWlg4zrS8WmPRGYjaMniraSgtRN45SKnOTN9m47gdjy5ozHvEqNzaWCFmCIEwVg1ZZhu4BJjFlWxqVdi8cGrjdyS0r6npYKEj8SNkNb8IqyzFRmFNklWUUEaFGyEKo57gRjlkE86YtwG7uGqOUcuPvKMBRRbZJCx5j2U6vq/QYKrcsf6mcIuXFxhicF7nzA10p/Ar86fZK6aD8oHwj708H5VPgBeU42MrBciwsZWMqC9OxSCkz+3FMUliklEWK7LqlFb/vcRyHeCJOPDH6d09TGiUEKCdMffUAJbMvoWmA0gi0rEXvmUWcrKoaI8lOjqByvgK1qpz7kstwmvpHlX7Lvr4K3e/BcRw+3fETLpzYB0CorIpHXvhDSipqi/Y9vO8y+3a5zwv+gIdnvraGuobSUY/RjsW4+p//kkSTG5Q8tHIVs//wj9D9E+8O2pfs51cX3uGjawdQaZMtn+Hj0Xmb2TZvM/5pyuqslML8bAfJff8Ejvu7Zsy+g8DW30MPVUzLnITbGxGXZiCOUnQnTf7y+CUspfBXukHsau04f7x6PkFPVgTp7OygpKSUYJFcvfX1s9LtOgFYXhHmRE+EuO3Q19pGuKSU71/spCWatVbaNKuSbY1VeMfhWmRbcfqu7yLS+SlD7WV1z+TcnCqlSEWvEO35jHjfaWxzePptzfATKFlIoHQhgdJFePxVM/pBXdM0AgHXZLqmpm5YveM4xGKxPPEpEulnYMBdj8djQ9rb9Pf30d9fOAOez+fPiE6lpfnCUzhcOmmxA5RSGbGqoiJAe3vvCEJVsbTRY0sfPZ6YDpC1xprMwKSQdT8YTyrosbgi5vYRt0JBEKYaTTPQDAOMyY/rqJTKcwWsrPDR1dk7SrbCQu6I+YLXcOutwaDv43R5VzbKtrEn2drX3ZcGykBLfwbXcQosHQMc3V23hyxz26jBtoPlera8iMX97YKOG7rdh4Gb2W1s32tHKUwsV2wiR3jCGlKerTOx8kQhAKUpBohRW99OaZ1rtW/bGucu18BAknJaKCdMGSFMLHZrJ/L6z6OWtcZidI8Ouga6hmZoOP3DQyiU/9qdaIaOY9t88tYPuHzGDahdWlnHlhf+kFDp8Jekg3y69xIH9lwEIBD08uzX11BTP3qcJDsSoeU//QXJi64oFV6zloY/+EN078SKPEk7xY7Lu9lxeTcp2z12DY0HZm/g6YWPUu6fvqxrTmKAxK6/x758xC3QdHzrv4hv7dNoch8nTBIiLs0QlFJcjSb5uL2XvpRFMmVhpS1P1jTU0TRrNqr1Sp6wBHDu3BlWrLij6LilpaU0NDRyNm0uuqw8hIZ7m/PZ6dP4Zi/ICEu1AS9fXljPvJIbF4GUcoh0HqTv+q6MLzdolNSsJ9J1CJSNx1dxw+OOhBnvINpzjGjPCezUcFHEG2wgVL6MQNkSfKGGz5V5ua7rlJSUDAvUPkiuy12uq50rPvUPs/hJpZJ0dyfp7u4sOF4oFM6xeCrNs4AKhcLjFvIG3ds8Hg+lpaUkJjGp0kjZdcYvXhUeazwopTJm9JONrutjEqJuJOB7oT66Lm6FgiBMPZqmoWleSCfiCIRL8cVcS22lFKTdf5TlgGm7sa6UA+mybF163XbAVmA7KDttdZMuU7aDsm1XcBrMOqhMFIMWVDZKt0G3UVp6qdugDZY7eeXD2zh5fdHH6V6oKdAsd14T9HceEaeQGOWKT8WFKU+mzXCxy0BTemFhS9Pd+BAamaWma+46uOW6lrbWY3jbAuWaPrwdWuExtPT47r60nP0VHiNUbIxC7dN39aZjk7SSpOwUkUSUnmgXQe8JysOusJQydY5fqmQgboDWQys9Rf9r7rxzHXfdtT7v+qyUIrb7Up64pPkMyr62Ck3XsC2Tj371P7nW5IpU5TWz2fLlf04gXFh8UUqxf89FDn10GYBg2MtzX19LVW1hT4xcrIF+rv7l/0Pyimt5VbLuHhp+7w/QiiRGGg+Ocvj4+gF+1fwOfansS+vV1Sv4wuKnmF0ya8L2NR6sa6dJvP+3qKj7/6iVVBPc+vsYs5ZO67yE2x8Rl25x+lMWx7sHONjZT2s8/23AotIgjzZWM780SN8j2/jpT3/EpUsXmT9/AQAHDnzC5cuXePHFXx9xH1u2bM3rOycc4NihAwy0XWXplqfRgIdmVbJ9nNZKiYEL9LS8jZloz5T5SxZQOecJvIGatBWT64p2syjHItZ7kkjnQZLRoUESNAKlCwiWryBYvgyPb3Rf7c8ro7ncpVLJPLEpuz5ANDowzMonFosSi0Vpb28dNp4rdOW62pVQXl5JY+PcW8pCxg2s78M7wW+9hjKYJvrGXAdHEreKW2Q5zvjcCh3HIZVKAZMf5D03m+DNug6O1OdW+q4JgjB1qJSNEzMzHxUzUUkblbJwkjYqaRNzFGY0hTJdAWny0NHwoTF51xmFygpTHgdlOGiGu8SwwXBcQcoYFLQclOEKVYMildJyxC3NcstIr5NugyuMoY1TitIdVzjDTM97MtEKuAcOyVaY52ZYJHthxs3QKFCWzW44HS9N/MDg60THTtDR/FOSEVd4MLyV+Ku2sjiQpLu7i56eLgYGCsdNuvvuDaxefVdemVKKgVfO4PRlLeg8s0oIP7YITdOwzBQfvvp3tF1yX2RX1c/j4S/9Af5gYaFIKcXH7zdzdH8LAOESH89+Yy2V1aMH4Lb6emn5iz8ndc0NpF264T5m/bPfmTBhSSnFye4z/PL8G1yLZu9p55bM5otLnmF51ZIJ2c94UY5N6tCrpA6/6rpjAp6F6wk8/Fto/tGFOUG4WURcugVxlOKT9j5Onr9Gc08074KqAT5dZ3bIz2+vmJMpf/HFb/HWW7/iX/7LP+DrX/8mqVSKH/3o+yxffgePPfZUpt3Vqy2cOHGM1avX0Ng4p2Df1s4+Tv7ynyiZs5CVDz7CV5fOYX7pjVsrOVaCnqtvE+0+mikzfBVUNj5GsHw5mqalXdTcI7wZcclK9jDQcYBo99EcyygXX3gO4crVhCpWYngnL+Xo5wVN0zKZSqqrh/vIK6WIxaJFYz3FYvlB0x3HKehyt2LFajZseGBSj+VWJDdN9FjN5MeLa41lj2KRNVr8q3zxqthY4w3yfjPWXDeCruvjzFZ4oxZbYo0lCNOJEzdJne/Gaoti9yRQsdGtPccnw6fRNTA0NENPLzUwdNcyxtDT2+l6fUg7PVs/rM+gK9KQ5VjXMxY3k8hg0PdBayynSDysoYHdnRHjYeW7HeZmN0SN939KoZwUypn8FyZAgcyEhYO768PKhgpVuVkQiwle+S9OrFQ/HU0/yrzw9YXnULvo6xieEIN52pRSHDq0n88+O5rXd/Xqu4YLS7ZD3z8ezysrv6sBba0bksFMJdnzy7+lo8WNe1TbuJhNz/8uXn/h5wqlFHt3NHH84FUASsr8PPeNtZRXjv4cYnZ30/IXf47Z5oo+ZQ88SP1vfnvCXMCuDFzj5fOvc6bnfKas0l/Bs4se595Z66YtWPcgTqSbxPt/i33dFfEwPPjvfxHvHY/IfYcwZYi4dAuyr72P1y935JVV+b2sryljXU0Z3/Yaw7KzVVZW8td//V3+6q/+kr//+7/F7w+wadMW/vk//5d52TOOHj3M//1//3v+3b/7/2bEpUJ9l62/n2d+43d5avnCcVkrxfvO0X3l9Ux8I033Ula/ibK6jWh69muXG/9oPOJSKnad/raPiPWeJPe9lu4JU1J9F+HqdXj9VTc8rjB+NE0jHC4hHC6hvj6b+cNxHBKJOJHIAJ2dHXR2ttPZ2U4kMjwGFoDX652qKX9uca2x9En/Ww9aY43XdXAsolaum+F4cBwHx3GmPMj7SC6CY3EdHMndUIK8C0I+TsJi4LWzqPgogrVHR/MZaH4DzWcQKPVjAppXB6+O5jHQPLrbzqujedwPeetuG03//J6Dg0Hf3fu+AJOdFkUpp0jsq0JZCAsFgM+KV8MDwA8XwcY/TwtlWzepWo4VPU+gcuw4ynYtjILly6le8CV0PXsP4DgOBw58xJkzJwE3puYjjzyeidGadxymTd+P8mMxBe6dTd3mRXR0DGCmEnzw8t/QebUZgPr5y3noud/BU8QCXCnFB2+f4+SR6wCUVQR47htrKS0fPXmQ2dlBy3/8c8xO9/mp/OHN1P3ab0yIsNST6OW15rfZ33ooE6w7YAR4fP4jbJn7ED5j+u9XrYuHie/+75B0X+DqFbMJbP8DjKq5o/QUhIlFU+N9nXwL09UVwXFm7mE19cf4wblrlAd8rCgLsaoyzJxwAH0GPCQ4doKeq+8S7TqcKQuULqZq3rN4fMP9qmN9Z+hs/icA6pd9G3+4cUz7SUQu09+6m8TAhbxyf8kCSmruIVS+4pbL7na7Ypom8XiUeDxOPB5Lf3LX3e3EGLKXDLJmzd2sWXP3Dbkq1daW0tFRWKgSPj/kBnkfa2D3GxO1suOMN8j7VDEYk6yQBVYw6MdxtBGEqmIWWYXFLRGxhJmA1RYh8lZTXplnThm+hRXoIS9ayOsuPfnXHrm+CEPJWmWZOIUyEBYQpgoHgC+cvdAZYq3lpp+bOEpq7qVyzuN5lk22bfHhh+9z6ZJ7bx0Khdm+/amCIRKcuEn/T0/mlYW2zMc3v4La2lKutrTzwS/+G13XLwLQsGAlDz73bQxPYSHGcRS73jzDmeNuFufyqiDPfX0tJWWjW3Cn2tpo+Ys/x+ruAqBi63Zqv/HNm74uxa0E717axXtXPsBMZ1rTNZ1Njffz5IJtlPqm3xtCWSmSn/wU87MdmTLvis34H3gRzTP5yQ+ELEopTMvhtY8ucuRcJ5VlflYtqGL7+jkYIzzPzLTri65rVFcX/+6L5dItyOKyEP/XPUum7Mt29OgRvvvdv+b06ZOUlpaxadMWvv3t36OiomLEfteuXeU73/lPHD58EID77l3L156sJxxIZ0vQfVTOeZxw1V1Ff+BtM5JZH4vlUirWSu/190j0n88p1QhVrqas/gF8wfpRxxDGhlIK00wRjUYzMZOi0QixWCy9HSEWi6Zj74wfXTcIBoMEAkFKS8tYu/YeyssrJuYghM8duUHeJ5uxxsYqLl6NFk8rWz4e3HPYnBJrLF038kSn8VtkjR4bS4QsYbwYtWG8CyowL/ZmyqyWfuyeON45ZXgby1yLJY/EYBNGJtcqayq+La5V1vCshGOxsMqKWxYoi0DZUsJVa/J+S1OpFO+//zZtba7VUEVFJf9/9t47PI7y3Pv/zMz2XfVqFffeKzbFxvQSWiihh5CEBAgl9T3nd86b9/Rzcgi9hAAJJEAwxdTQqykGG2Pce5NsFau37Tvl98esV1rtrrSSJVm2n8917bUzzzzTVHae/T73/b3POOM83O7EL5FaW5CO13bEtXnOG4+l0PT0CQZ8fLr8EZrrTDPukrHTOemCG3sUlj5+czu7tpqpejn5Li68aiZuTxrCUm0NB+65C6211dz3nHPJv/zKw3pOaLrGyprVvLXvA7yRTiuH2QXTuWjceRS5Ei0hjgR6ay2Bjx5FbzJ/zlidOJb8AOu4hUf2wo4SdN0gFNEIhjWCYZVgWCMUjq5Huq2H1S7L0fVI13Wzr94lZqe60cfmvc04bAqnzk4veOJYQIhLxznffvsNv/rV7Xg8GVx//Y0oisKLLy7j22/X8OijT5KZmbyKQ1tbK3fccTORSIRrrrkWX8sOXvn7Z+za4eI/fr0ET874aLRSz6bZ8WlxqY3m1FALrTUf42/dEmuTJAvu/LlkFi4a8EpzxwuRSLhbVbhDL2/SqnDpIkkSTqcr+nJ2We5cdzicOBwOLBar+LIoOCqRZTmadjwUJu9aL0JV6iirZBFbkmQQDIYT+vY3GkvXNcLhIcnxSCpE9Td1sCdRS5i8H3tIsoRryUjUCbmEdzQROdAGBhi+COEdTYR3mJEPktuKkutEybIjZzoIhA10TUVyiCg9wZFBkmQkxQ7KwEej+P0+PvroHVpamgEoLCzmtNPOwW5PPJda78P7zu64tozvTkaJRhiFAj4+fv6PMWGpsGw8kiSx8Yu/M2vJJQmfq5qm8+Eb29i7w6w2nFfg5sKrZ+J09f5cDVVXUXX3XWhR8/HcCy4k7+JL+/0/ahgGGxu38Nqet6n3d1Y/Hp05ku+O/w7js8f067gDjWEYqLtWEvziGVDNFEe5YCzOM25BzhwewtdQohsGrR0h6pr9NLYF8QYisVcgJhCpcUJRMKIRjgx+9LlFkRlZdPgFq44mhLh0nHP//b9HlmX++McnYx5MS5acxg03XMXTTz/Jbbf9POl+zz//Nxoa6nn8D3fj4Vu0sIfyggX8zyNfsXZXNpenGY56SFySLW4kKTGNTdcjtNd9QXvdl12MGiU8eXPJLF6cNNVO0IlhGAQCftraWmlvb4uZa/t8ZnW3cDjU+0G64XA4cbvdOJ1u3G43Lpf56iog2e12MQAXCAYI0+TdFD4GilSRsam9sfomXvW8j9l+dJi89yRE9SxepZtyKEzehxZJkrCWZGAtyUD3R4hUthI50I5a54OopYLhi6D6IqjRorNVX0YXZKkzfc5tptDJbiuSy2auOyymACUinwRHCW1trXz44dv4fGYmQXn5aBYvPj1p9G+4ohX/p5VxbZnfm4rsNCOSQgEvK5Y/QmuDacadWzSS+qpOIWr0tIXkFHRGcGiqzvuvbaVitynqFhR7uODKmTicvXsYBfdXUnXv79G95nXnXXIpeRdc1Jdbj6OifT+v7HqLPW2ddhv5jlwuGncecwtnDpvPaCMcIPjF06i7v4q12Wadj23BpXGetsc6ze1BVm+tY/v+VnZXtxEIDe6YwGaRcdgU7DYFh80Sv2xVuqx32W5VGF2cQX5234tiHc0cP3+FggRqa2vYu3cPF1303ZiwBDBq1GhOPnkx7777Zkpx6aOP3mP6lFE4w5/E/Ajnz19AeXkVX6zewRXXpPchfEhc6p4SZxgGgbYdtFS/hxburCLmyplO1oilwqS7G6qq0tHRFhORzHdzuS/RR4qi4PFk4vF48HgycLszoubcnQKSWcVMIBAciwy9yfvhpw721vfwTN7DRCKDX0Gqf5UKk6cOpo7IEtFY3ZFdVuxTCrBPKcCIaKgNfrQmP1pjAK01iN4RIq5kr26ge8PgDffsxWyRkewKsj0qNjm6LNu7r5um4ZIifjeCoaWhoZ6PP36HUMicaJw4cQonnHBy0s+J4NYGgmtq4tqyrpmOZDXHhEF/ByuWP0Jbo9knI6cwFr10iOz8ziIvakTj3Ve3cGBvCwBFJRl853szsTt6/2oa2LuX6vvvRvf7Aci/4kpyzzkv3duOozHQzBt73mFtfWdlPJfFyXmjz2Bx2UlYh5FgozVUEPjoUYx205dKcmbiOO0nWMqmH+ErGxoiqs66XQ18sbGWLfua6Wl6SpElPE4rTrslKvh0F4VM8edQW3fh6FB/e1Q4ko/jwgx9Zfj8xwiGnIYGM7d53LjxCdtKS8v59NNPqKs7mFAhoqF2KzU1Ncybau4nyVayS87Ekz+fSZN2sGrVyrSvIZm4FAk103LgHYIdnaabVucIcsvPxe4emqoHgUCAfft2s3XrRiwWC0uXnp3U0HCoMQyDjo52WlqaaGlpjr2nqrjWHVmWcbsz8HgyogJSZty7w+EcNrMzAoHg2MWMxlJQFAWbbXBNRw3D6EGISvTGSiVe9S5q9T8ay/TX0oC+R5P2heQm74lRVummDvZ0rKPtWSJZlVhE0yEM3UDvCOFBprWmDd0XQfdH0H0RDL+5TLICMqqOoepovj6kliuSKTJ1fVmVxDabnKRNiFOCvlFVtZ/PPvswFgU6a9Y8Zs6cm/T/1v91NeFtjXFtWdfNiP3NBXztrFj+MO1NBwFwuD10tNTH9f/uz/43Zh4eCWu88/JmqitbASguy+Q7V8zAZk9DWNq1i+oH7kEPBgEouOY6ck4/sw93Hr2niJ93Kz7m06qVqNHMCIukcGrZyZw7+nRcVlefj9kftKYDoCgo2SUp+xiGTmTT+4S+fgl081qV0mk4TrsJ2ZU9JNd5JImoGp+ur+GtVZW0eeMnewqznUwoy2L0iEyKc10U5DjJdFmxW4++Z9CxghCXjmOcTjNMzx9V/rvS3m5GCzU3N8XEJV0L0lrzCTs3fgRATpYDu3skuaMuikUS5eXlR/16vHg8vVdR6CouGYZBR8PXtNV+HCvxKitOskpOx5M3J66ixWBgGAb19QfZvn0z+/dXxH1JaGpqGHJxSdM0WlqaaWpq6CImNaOqvQ9WXS43mZlZZGVlk5mZHVt2uz3iw1YgEBxXSJKE1Wod9GgsSGXy3rMglVzU6r1aYX8YSpN3UzxMpwJhetUKU+0zmCbvkiyhZDnwFGQQyEr0gDEMAyOoovsjGL4IelDFCKkYQQ09pGIEo6+Qhh5UQe3B40MzMAIqRqCf6R2KZIpRVhnJKoPl0HKXNqtpVp68X2c7FmGcfyyze/cOvvrqMwzDQJIkFi48hYkTpyT0MwwD38cVqFXtsTY5007GJZNifx8BbxufvPRQnJgU9HnjjnP5nffG0rrDIZW3X9pMbZX5PaN0VDbnXTYdq633qHj/9m1UP3Q/RigEkkThdTeQferSPt17RFf5vOpL3qn4CL/aWcV4XuEsLhp3HvnOocmMMAwD3zN3YAQ7QFJwX3cfsjPR6kMPtBNc8Se0AxvNBknBtuAybLPOHfTvRUeaiKrz+cYa3vqqkpaOzokXt8PCoqnFnDJzBKOKjy8/o6MBIS4dx4wePRa3282KFR9z3XU/iD0oQqEQX3+9CjCrRxiGQaB1Gy1V76Kp3lhea3bhTAonfD/uw+2Q+V8wGOhVXDJ0DV31R5cj1O/6CyHfgdh2d95csktOR7EM7uyBqqrs27eb7du30NLSlLRPefnoQb0GwzDw+300NNTT2FhHQ0M9zc2NvX6ByMjIJCcnj+zsHDIzs6NiUtaQfIkSCAQCQTxDafKeGFXVl9TB3qoZdu7TX5P3Q9FY/fH26wuHPMlSC1J9Sx1Mtk84bEfX9YR0IUmSkJxW03Mmr/drNVQdI6RGRSjNFJ7CWlqvHnNAwBSnNBUj2P+fZRwWOVGc6ipERZexdhOnLDJ03c8ixKrhgmEYbN68nnXr1gCmALx48RmMHDk6sa9u0PHGDvS2zv9fS1kG7tPHxH6X/o4WPnnpYbytDSnP+b1fPND5/SKo8taLG6mrMSeWy8fkcO6l07BYexeWfJs3UfPIgxiRCEgSRT/4EVknn9Kne/+2fiOv73mHpmBzrH189hguHX8BozKHJjMCwNB1vH/6YZcGDcmaxDy9dgfBjx7F8LcCIGXk4zz9ZpSixIyTYwnDMFi/u5HnP9pFQ2vnB1phtpMLThrNwqmFWC3ComO4IsSl4xir1cqVV17Lk08+zr/92//l+utvRNc1nnjiUQIBU83XI63U736WkLfT4M7qMCOZXFlje1DN0zDzVjtnNvwtm2LLFlsOuSMvxJExuu831Qe8Xi87d25l165tsXxzML8cdB1ML158evTLwsBxKL3t4MEaDh6soa6ulkAgMYLsEBaLlZyc3Ogrj9zcPLKzcwdNRNJag3S8vQu6V1KQJZQcB0qu0/SKUKKDRouMZJHMZaXLYFKRwSJFt8ugyEgib1kgEAgOi0PpbclMdweaZN5Yhy9eJT9WfzDTHiNpRfUeLrIspyVepV2tMOPQuiNuH1mOT+kwDMNMtQvrptgUSSFCRTSMiB59aRDRMdTOth4jp7oTTe3rdyRVVySiYlWn4CRZlagI1SlUxbXFoqyi7V2XFUmIVX3EMAzWrPmS7dvNqss2m43TTjs3wfoCTBG07blNcYKmbXIeroWd/qy+9mY+eekhfG3JJ2Uzcgo5/8b/G1sPBiK8+cJGGg6aY/9R4/M4+5KpWNIwv/duWE/tow9jqCrIMsU//gmZJyxK674Bdrfu49Xdb1HR3ukBVeQq4OJx5zMzf+qQ/i0Zahjvkz+Ja3Oc/lMkS6e4ZBg64fVvEf7mFYhmUVjGLsCx+AdI9tSVtY8Fapt8LPtoF5v3dgqA+VkOLjp5DCdOL0IRvoHDHiEuHef84Ac/xuvtYPnyF/jww/cAOPnkxVx95RU8/qc/E2p8h5BiRiDJipPs0rMJeZzA8jhB5hCH2tzu3j/8DqXEdSWjYCFZI05DVgZv1re9vY2NG9eyb9+euNQ3p9PFpElTyc3N5+OP3wWguLiU0aPHDch5AwE/NTVV1NZWc/BgDX6/L2k/SZLIzc0nP7+AgoIi8vMLycjIHNKHX6SyNVFYAtANtKYAWlMgcVu6yFKn+GSRTMGpq/jUrT0mXimd+3QVrw4tqw4bekg112Ux8BQIBIKBwDR5t2G1Dn40Vqq0wtSpgz2lEKaOyNL1/pu8h8NhYPBN3nuvTJgkIsveXdyyoijOLl5aMoqhoBgyigGyLiNpQEQzhaQu4lTSNrWbeKWl6TFmYApdEb3XAKy0kOgUouLS/LqKUF3T/BJT/ySrAraogHWMT3ppmsYXX3xCZeVewLROOPPM88jOTkwB00Mq7c9viWtzzBuBY3phbN3b1sQnLz2Ev725++4AjJ06lwXn/iC2HvCH+fvzG2mqN8e9Yyflc+ZFU1DS8AnrWLuG2sf/CJoGisKIn95Kxtx5ve4HUOdv4PU977ChYXOszWN1850xZ3FyyUIUeWijX4ygF+/Tt8W1Oc/7JZbymbF1PdhB8JPH0Q5EJ90VC/YTr8U6ZekxPa4NhFT+/mUFH6w5gBb1sXPZLVyyeAxL55RiEZ5yRw1CXDrOkWWZO+74Fddd9wMOHDhAbrYFF7t58q8vIMsS+blOQMaTP4+sEaeiWFwU20xRqLGxMeF4jY0NeDwZMT+nnukcYljsueSOvAiHZ+QA3VkiHR3tbNz4LXv37ooTlQoKipgyZTojR44B4O23XwPMn82iRSf3+8PcMAyam5uoqqqkuno/jY3Jw4ZtNjvFxSMoLCwmP7+Q3Nz8IZmN7gnbhDzCe1vQ2wdhAK0bZoh/WBuYQWaUOKlSIl60sshIitQlyqoXISu2PbmQFTvWMfygFwgEgqGkq8k7DK7JuxmNpfUaXXXo3eGw0Nbm61G8StXeX5P3Q8dIMo83oMiy3Hu1QrsFizsx8kqRLSiSjEL0ZcjIhmSKV7qEokvIuoysGciahKTqceKUoepR0ckUrZKaoyfDYGDHEd1TAKNm6iRp61yW4/vYlGEpUoXDYVaseJ+DB80qbllZOZx55nm43YnWFbovTPvybXFtrlPKsY3rFKE6WhpYsfxh/B0tSc83ef4ZnHnZdTQ0mKMyvzfMG89voKXRjM4fP7WQMy6YnFb1rfbVqzj458dB15EsFkbcehuembN73a8j7OWdig/5vHoVumFOlFplC6eXL+GsUUtxWhy9HmOg0Tsa8S37dVyb86J/xlI8IbauHtxF8KM/YPjMn62UWYTzzFtR8kcN6bUOJYZh8NWWg7z0yR7afOZ3DglYMruE7y4ZS6ZrcCc1BAOPEJeOcz744F3ycnOZPNbDCPcmgs378AHbdzcxpjyLrPypZJecgdWRH9snIyODESNK2blzR8Lxdu3aweTJiaaAybC5SskacTqSJOMpWIAsD06KVzAYYMOGtezcuS1ukDdq1FimT59NXl7nvW3fvpnmZlM0mz59NpmZ2X06l2EYNDbWU1Gxh8rKfUmjk6xWK0VFIyguLqG4uJScnNxhJ1LILiuZ3+38PRqGKQjp3jB6ewi9PYzWHkJvDaI1H0YUEyA5LcguKygSqIY5U6rpsZD8tGdGu2LQGdJ/WFfXC4rUTaiKpgEqPQhVvQlZ3UWxYThYFQgEgqMZMxpLTju1vKAgI/ZluS8cisbqb+pg+hFZ/Td513UdXdeH3OQ9JlS5ukRmyQqKrGCRFBRJQe4mXClR4UrWJRRDQtYkFE1C1jCFLFVCjhhIETONMO0BwEClAHY1Ve9JoLJHXzYLcmxZGfBJK7/fz0cfvRPzEy0oKOL008/Bbk8UV7SWIB1vxI/r3WeOwVraaTLd0VLPJy8+RMDXlvR8c0+/ggmzF8fWvR0h3li2gbboOHHS9CKWnj8pPWHpy5UcfOpPYBhINhslP7sD97TpPe4T1iKsOPAF71V+QlAzvXokJE4onsuFY88hx5Hd63kHA61pP/6X/19cm+vSf4uJRoahE97wLuE1yyEqhlnGnoBjyY1ItnQm649Oqhu8PPP+TnYeaI21jSvN5NqzJjK6ONHcXHB0IMSl4xTD0Ah2VPDcM48SCHj5n39YHAtPXbe5jh17m/nH3/yCgrFXJt1/6dLTefHF56isrGDUqNEArFmzmv37K7nmmuvTugZJkskqTt+Mr69omsb27ZvZuHEdkUhnBM7IkaOZNWseOTnx7pt+vz9mcujxZDB9+uy0z9Xa2syePTupqNiLr1uVDIDs7FzKykZSVjaS/PzCBFPQ4Y4kSUh2C7LdAnnxBuuGYZiVcNpDaG2hqPgUMsWn9lCvM5FGQEULqEgOC3KmDUuuCznTjpxpR8m0I2fYwTAwNKNzANhFfDJUAzQdj9NGR4sfQ4uKUtFtpkB1qG+0Pcmx+qVCaQaGpgEDG4UVxyGvirjUwBRCVvfUwaRCVjcPrENtw0zgFAgEgqOdrtFYA+3d2J3uJu/peGP1TdTqPFZ/o7GG0uTdYrV0illR0UqRTdFKkZRO0SomXkmmaKVHxauoaCWroGhSYv/oS+rqMXq4puqyFBOaJLsFya4gd1k227tsc9vM9STP7/b2Vj788B28XlMULSsbxZIlZySNjFfrvHjf3RPX5jl/PJaCTouL9qaDfLL8YYK+9u67A3DKxTdROm5GbN3bHuT15zbQHjVknjJrBKeeOyGtsUbbyi+o+8ufTWHJbqf09p/j6mHiWjd01hxcx9/3vkdLqDXWPjlnApeM/w7lGSW9nnOwUKu3Enjrrrg21xX/hZJTCpipcoEVT6Dt32BulC3YT7wa69TT+zwuW7Guml1VbVx08miKcge3GNLhEAprvLFyH+93SYHLctu44rRxnDitWIxHj3KEuHQcEmjbRVPlq+hakO+cNoL7//wNv39sNSfMGUmbz8Nrb3/LCSecyHkXXAVAdXUVmzdvZPr0mZSWmmZ+11zzfd599y3uvPMWrrrqWsLhMM899zSTJk3h7LPPP5K3B0BNTRWrV39BR0fnQ3DEiDLmzj0hLlKpK99881Vs5m7hwpN7TU2LRCJUVOxh167tNDbWx22TJImiohGMHDmGsrKReDzHbqlMSZJMYchhwVIY77Vl6Aa6P4LeFi846e0hdG98yp0RVNGCKlp9orG55LaaQlOmHSXL3ik+uW2xyJ6sggzC/ZhZhqhZqh4VolQjXrxKImR1ilRRHwothZAVbY9FYaUb8h93cQysV0UquqcOHq6QJczcBQKBYMgYapP3w4my6t1Pq/NY/cEwDCKRyMBFY/Xw6JIl2RSvJAVFllHoFK5kOqOtzBTB6HIKoUrRZZSAghww1y2HjoEclbGSXIhVRsmwIWfYkTNsyB47LXoHn274lFBUxJswYTILF56SdGIzXNmGf0VFXFvGxZNQsjujm1oba1ix/BFC/uRjrDOv/hV5IzpTt9paAnHC0vS5JZxy1vh+CEsOyn7+S5wTJqbsv6N5N6/ufpMD3pq49hJ3MT7Vz2u73+LHM64/Iqlwkd2rCH78x7g29/d+h5xtmqhrdbsJfPgHDJ/pXSVlFOA882coBaP7dB7DMLj7+fVsqzTT6bIzbFyxdPhVlDMMg3W7Gnnuw500t5t/m5IEZ8wt45LFY3E5hCxxLCB+i8chvpYt6NFw0RPmlPHLWwp57d1NPPvKFnJy8rjmmu9z/fU3Rn0PYMOGdfz3f/8b//RP/xITl3Jycnjkkcd58MF7+fOfH8Nud7B48VJuvfXOQZ+d64lQKMg336xiz56dsbasrGzmzVtEaWl5ygdbTU0VFRXmrM3IkWMoLU3t/dTa2sy2bZvZt29PQnWawsJiRo8ey6hRY3E6h++swVAhyRKKx4bisUFpvMBmaDp6RzhOcDoU+WQE4weUhi+C6otAbbeoMFkyB1OZdqTiDEJWKSZCSQ5L2rMfkiSZ0TuKPKhWH4ZudIuiSiVkxQtV8duNbuJVZ/rgob79QtUxVCA0iFFYh8zcFSmJENWbmbuUQvSK3y7M3AUCgWBwkWU5OtYbCpN3rRehqucoq+TRXIn7dK0S3Bd0Qyes6UCaQlZ/H08GyQWpiIzSHH1FY6lqaEaTzPuZZB3J1PYSgqtrzPFShg0lKkSF97QQWF0dd5qMy6aYY7YoLQ3VfLr8YUKB5EVovvPD3+LJLoite9uDvPnCppiwNHN+KSedMa6fwtKvcE6YkLRvra+O13a/xeam7Um31/gOxpbr/Q2Myizv9fwDSXjjO4RWvRDX5r7yf5Gzikzxc9O7hFYvB8NMZ7WMmY/j1B8i2fr23UHXDe5/aUNMWAKYNS75JPqRpKE1wN8+2MnGPZ3VBceWZHL92ZMYVXzsTsAfj0hGf2NbhzFNTV70/kQIDDP6m+PfG2qoBW/Tt1gdBTgzJyIfATV/MKis3Mvq1SsJBs3cbovFypw585k0aVqPaWiapvH3vy+nvb0Ni8XKxRdfkWB0aBgGtbXVbN26iZqaA3HbnE4X48dPZPz4yWRkiBzhgcAIazHRSWsPxUU+pS2eWOXOaKfou5xlR8mwm94GxyjxUVjJxacEIUvr1re7kNV9u2b0LwprKOhu5j7gQtaxYeY+WM8XgeBYRPy/HPuk9sZKFKJ6irzquV07LJP3PmHATEYzmqK0d8n83lRkp+lDZhgGLfVVfPryI4SDiRHlAJfc8t/YnZ3jZW97iNefWx8TlmbML+XkARaW2kLtvLXvfb6sWYORxlTYgqI53DD1qiF7ZhuGTmjVC0Q2vRfX7r7qLuTMQoyQj+CKP6FWrjM3yAr2RVdhnXZmn68xomo8sHwjWys6haULThrFpUsGpsr1QBBRdd79ej9vfllBJDp+dzssXL50HItnlSAf5WOpgeBoe77IskReXmJBgEOIyKXjEIs9h+ySM470ZQwYfr+fr7/+gv37K2JtpaXlLFq0OGk1jO5s2bKB9nbTnHD27Hlx+xiGwf79+9i4cV3MEPEQZWUjmTBhCqWl5Uedh9JwR7IpWPJdkJ/E3ymgxqXYxSKfOsLxgkdER2sKoDUFEuYTJaclTnRSosKT7LGZgsJRzJBGYSWkBiZJHexNqOph+9Fl5t5NqFLM6kM9mrn3JmSJNEKBQCAYEvpq8t4fdF1HUyOokTDhcIhIOEQkEjbfw2Eiahg1EkFVI/FilqahqyqqrsV8q0wxzDRj16PG8bpuoBsGFmTKrJm40WnVmrBpdmy6HQup7+2LwKuEn3wOXdMwdA1d79kg/rLb78Zi7YxwOjxh6XPq/vJkr8LSlzVreGnX64S15JWM3RYXPrVTCJtfNJvvT71y6IQlLULwkydQ934df11X/x45owCtfi+BDx/B8JrfJ6SMfJxn3IpSOLbP5/IHVR5YvoFdVZ3m6idPLx5WwtLWimaefX8nB5s7fyenzBjB5aeNE1XgjmGEuCQYcrZv38Yf//gQmzdvRJYVZs+ey2233cnIkaN73K+mppqHH76fdevWAnDSSadw/vkXsGvXVsJh80Fjt9tZsOAkxoxJL7e7o6OdTZvM2YPs7FwmTzYrURwSlTZs+JbW1uZYf4vFwrhxE5kyZQaZmVn9uX3BYSBJEpLLiuyyYimOFw7z8zzU7WtKMBTX2kMYvnh56ZCJuFbXLcxbAtljizMUt47JNo3MBXFIsgQ2BQkFBqmYiWEYPURcpfDAUnsTso4FM/dUEVcphKyowNW1r+aOmD8TYeYuEAiOA3RNQ1PDaGok9lLVCLoWSWgzl8PoUYFH16LvapdlLX451qYm365rGobRz7T1NJEABfORdiD66opNcrIw60JcSmcaUn24ko0dn6Klm9YHXPHz+5Dlzgjw7sLSwsVjmHNSaiuKrqQrLAG8tvutOGHJaXEyKWc8U3InMDKjjOe2L8fnNYWM+UWzuWHqVcjS0EwYGmE/gfceQKvtUnFPknFfdReSJ4/wpvcJrX4BoqKdZdQcHEt/jGR3pzhialo6Qtz34gaqGjqtIiaPzObG76RXrXuwafWGeOHj3azeWhdrKy1wc/3Zk5hYnn3kLkwwJIi0uGHM0RYmlw7791fwox99H4fDwZVXXgPA88//DTD4y1+WkZ9fkHS/trZWfvSj64lEIlxxxVX4/X5eeOE5PB43l1xyCYqiMHr0OBYsOAmnM71vuoZh8PHH71JdbT5+zz33IgoLizl4sIa1a1fR1NQY62uz2Zk6dQaTJk1NWsJVcORJ9f9iRDS0jjBanRe13oda5+tTuWGlyE3GucPPGFEwMMTSCLuITv0SsgbDzH2oSGnmHm/i3l8hS0RhCY52jsXx2HDCMIyYoKOGQ0QiIdRwKH49EkaLtquRMGqk872rOBT/CqOpKpoaGXRh50ghSTKyoiDLCrKiIMkKsmKJrcuy+bLIdsarM8nQswEIyn72Z+1Ft+rxfRVLbHnLqnfjzmVzuLjklv+JE4287SHeWLaBthbTkmLGvFIuuXo2jY2JlZO70xdhCeDz6q/Y0rSd0ZkjmRwVlGRJxhfx89C6x2Om3vOLZvP9KVeiyENjgaD7Wgi8fQ96S1Vno92N+7J/R7I6CH76JGqFOTGOpGBf+D2sM87u18TOwWY/9zy/nqb2zpKEI/Jc/L8bFmA/wpYPmq7zybfVvPr5XgIhU0SzWxUuPmUMZ84vw3KUZwYMFkfb80WkxQmGFS++uIxAwM8jjzzOxImTAZg3bwE33XQDL7zwHD/72Z1J93v++b/R0FDPU089RzDoZ/36NZxxxum8/fbbVFRU8OMf30J5+eg+Xcv+/ftiwtL48ZNwOBx88sn7HDhQEetjs9mZNm0mkydPw2oVIZzDBcMwMMIahj+C7lcxAhGa97bib/SZbQGzTQ+o/Te4jqJkCTHxWKYzjRAYxIFZnJl7j0JWEjP3bkLVsWnm3pOQlZg6mEzIEmbuAsGRQVMjhEMBIqEA4aDffA8FiIT85nswcT0SDkaFIvM1nOe640QXxYKiWKLr0WVLkraky9HjpOjf2aYgy5beRSNFQUojMkf3R/B+uBe9xRQklEI3hadPo9h+YtL+hmHw2SuPxrXljRjNGVf9Il5Y6kgUlk4+s5+pcL/4Fc7xqYUlgMWlJ7K4NP6aj7SwpLXUEHj77ljFNwDJnYvrkt9i+Fvx//13GB0NZrsnD+cZt6AU9W/CsuJgO/e+sAFvoDPKzOO08qsrZx9xYWlPTRvPvLeD/XWdouK8SQVcfcYEcjPFOPp4QohLgiGlpqaa7OzsmLAEMGXKNLKystizZ3fK/T766H2mT5/J9u2baGysB6CsrIyCgkKam1v7LCxFImHWrPkKMPPsdV3j9ddfig1uFEVh2rRZTJ0684hWvzteMSIauj+C7oug+8IYvkh0PRxtiyR8kU5uN5kaya4gOa3IDotZWc5hMZedls42pwUlYxCNiwTHDZIsgawgWQdRwEowc483a+8qZHkcNjpaA4nbuwtZA2XmrpuCMPQvCzEt0jZz74+Q1SVKS0RhCY4DDMMgEgrg62jB396Mv6OFoK+doN9LyN9BMPoK+b2okdCQXpuiWLHYbFisdhSrDcViQ7FYUCzW6MvWZdmKxWJFVqwJbd37yxZLtD26rlhQLJa0BJzhitYewvfBXnSvmU5mLc/EtWSU+XmWBMMwePup/8Tb2hBrGzVlPovO+35cP29HiDeeGzphKRlHWlhSD+4k8O79EO4cgUpZRbgu+EfUirWEvnoedDNSXhk5C+fSm5AcvXvBJmNbZQsPvryRULjTC8uiyNx5+cwjKt54AxFe+XQPn66viT3bC7IdXHvWJGaOyzti1yU4cghxSTCklJWV8803X9PS0kJOTg4A7e1teL1e8vOTl85sa2ulpqaawsKCmLCUkZHJiScuYe/eSlatWtnn69iw4Vv8ftNvR9d19u7tFLbGjZvI7Nnz0zIDH25oHSHUGi/hvS1o9T4ku4L7rLFY8vpW2nSwMSIaekcYrSOM3hFC94ZjwpHhi8S+hPYZq4zstJiikdNqLrusXdqiopHdIr4gCo45+mLmnl2QQaSfYdhxZu5qDxFXPZm19+KRddSbuVt6EKrSELJEFJZgKImEQzRW76G1oZq2plramg7ibW1ADQ+MaCQrCja7C6vdidXuxGZ3YnW4sEbFIavNjsVqj4lFqddtKFa7KKKSJmqTH9+H+zCCpsBhm5iLc2FZyvGPruu8/OCv4sy8py48hxknfyeuX3dhafrckiMuLM0rnDWkwlJk31qCHz8KWqfNgpw9AufZdxL66jnUvWvMRknGfsLlWGee22+R8tudDfzx9S2oWvyk6g/Pn8y40iPj/2oYBl9uPsiLn+ymw29GUlkUifMXjeL8RaOwDeJEmmB4I8QlwZByzTU3sHLl5/zrv/4zt99uhtc+8sj9WCwWLr/8yoT+TU2NvP76cgBcLheSJDFlynRmz16AxWIhLy8fr9eL1+vF40lPDGppaWLbtk0J7UVFI5g//0Ty8pKLXMMRI6KhHvQSqe5Are1Ab4+voGGENDMMeojFJcMwMIIqekc4+gp1Ckkd4dhApy9IDguy24rstiG5TVNvOfouOa0UlufQ1NbX+CWBQNBX4szcB4kEM/fuolUfzNyJVjQ8Js3cexOyUnlgHYrsEgLWcYthGFTtWs/uDStprN7Ta3UwMAVsm9ODw+nB7srA4crA7vLgcHmw2l3Y7E5sDlengBR9WYStwJCj1nnxfrQPIqYgYZ9VhGNWUcr/eU2NsPzBX8W1zTvje4yfdUpcWzJh6ZSz0iui0/7lyoETltY/EScs3TD1qiETlsJbPiK08lm6PkCkrCLsi67C/+69GO3mRLjkzjXT4Ir7fo+H+GJjLU+9s43uWaMXnDSKRdOK+33cw6Gqwcuz7+1gZ5dKdVNH53Dd2ZMozh1ek9mCoUeIS4Ihpbi4mOuvv5H77ruLH/zgasBMQfuP//jfuFQ5TVPZuPFbNm/eQFOTGZqbkZHJuedeTEFBYayf3W5OzweDgbTEJcMwWLXqi7jcfrvdwfz5ixg7dsKwH2gbhoHWEkStbidS3YHW4O8xRUXOdmAdmzOo12P4I2itQbTWEHpbEK01iN4W6lv0kVU2q7S5rciu6LvbiuS2xUQkqRcjQPkI55sLBIKBQ5KkmGAyWKQ0cz8MIWvAzNwNIBIVxQb8zrvQl9TBrkKWIiNZhZn70cy2NR+w6Ys3E9od7kyy8orJyCnCnZmLKzMHV0YO7sxc7K4METV0FBCpase3oiIWAeo8oQT7lOQFcwDUSIiXH/pNXNtJF/yQ8omz49p8hyMsfb2Kg0/96bCFJX8kwMPrn+BARzUwtMKSYRiE17xMeH38/42UUYB17AkEPngoFsmklM/EcdpNyI6MZIdKi/e+3s8LHydahsydWMAli8f2+7j9JRhWeWNlBR+sOYAWfa5leWxcfcYEFkwuHPbfoQRDgxCXBEPKE088yl//+mdmz57LRRddiq5rvPbay/y///eP/Od/3sXJJy9m//59rF27Gq83PmVj1qx5ccJSPOl9oDU1NdDQ0Fkac/z4ycydewIOx/A1mzskKEUqWolUtKJ3hHvfSZZwnliGfXzugF2HHlLRmgNoTQH01iBaWwitNZi2ibDktCBn2FEybMgZduRD75k2ZLv4KBIIBEPLsDBz7y21sKtQlSB6DYCZ+6EorEE2cz8kYvVNyJK6iVrCzH0gObBzfWw5I6eQWUsuJr9kDHbn0WcJIOgkvLcF/xf7TYFaAtfJI7GNSz3JGA76efUP/xjXdtoVt1NYHi/8+LwhXu9i3j1tbgkLTx2DGtGx9vL52bF2DQf/9HhUWLJT9vNf9ktYCqgBHt7wJ/YfCWFJVwl+9hTqzm5WHHY3kiuL8Lq/m+uSjG3Bpdhmnd/vNDjDMHj18728+WVlwraRhR5uumAq8hB+3hmGwbc7G1n20U6a281UWUmCM+eVc8niMTjFGF7QBfHXIBgyOjo6WLbsGSZPnsoDDzyKopgPgzPPPIcf//j7/O53/8Ett9wai1QCyM3NY9q02bz++utEIomiSihkfsi53e60riEzM5u8vHxkWWHu3IUUFR2ZkNJ00FqDhPe1EKloQ29P9D2QM+1YSjKQXRZC2xoxAuZsieyx4TptNJZcZ7/PrQciaE2mkKQ1m69DZpA9IdkU5GwHSrYdOdMRJyQNZgSCQCAQDFeGm5l7yu1pCFn9NXNHNwY/CqubkXtaQlZX36xkqYNRDyw9rGHoxjEVhVU+YRat9Wbp9I6WejZ+8SbjZpzE6KkLsDlEasvRSGh7I4HVpvCCLOFeOgpreWpPnoC3jTce/21c29nX/YacwvL4fv4wf39+I23NUWFpTglqROPP963E6bJy7c0LUwpM3vXrqH38j6DrSFYrpbf/HOeEiX2+t6Aa5JH1T1LZblZ5nlMwY+iEpUiQwAcPo1VtTtwYDqLXmdFFkisbxxm3YBkxqd/n0nWDZz/YyYp11QnbMt027rh85pBWhqtvDfDcBzvZuKcp1jauJJPrz5nEyKL+R2UJjl2EuCQYMqqq9hMOhznzzLNjwhKYht3jxo1l9+6d7Ny5nby8POx2O7NnL2DChMn4fKbxdmNjY8IxGxsb8HgycDrTE1JsNhvf+c6lA3NDg4Ch6kQqWwntaDJT3rqh5Luwjs7GOjITJcNOeE8z/q+qYqHPlrJMXKeU9ykSyNANtJYAWr0Ptd6HWu/H8Ed63EdyWJCz7CjZDpQsB3K2uSw5LGL2WCAQCIaYvpi5Hw4pzdxTClk9mbknF7L6ZeYOnfsz8BUJ2w8tyF3TCKX46oS9eWBFo62Si15Db+Y+5YSzkSSZravfQ42EaW+qZd2Kl9n4+RuUT5rDqMnzKBw5EXmIfGwE/ccwDEIb6wmuP2g2WGXcp4/BWpw6Cq2jpZ63n/rPuLbv/PC3eLLj0+eCgQh/X7aRlkZzTDpl1gjaWvxUVbQCEPBH0DQdaxIPPt/mjdT+8RHQNCSLhZLb7sQ1eUqf7y+khfnDhqfY125G8szKn8aN064ZEmFJ97cRePc+9MaK5B0M0wJCKZuO47SfIDsz+30uVdP505tb+XpbfcI2iyJz+2UzhqwyXETVeXd1JW9+VUkk+rnqdli4fOk4Fs8qGdLIKcHRhRCXBEOGNWroqOs6hmFQU1PF9u2bqa4+QEtLc6zflCkzmDlzbsxPKSMjgxEjStm5c0fCMXft2sHkfjyohhtaa5DQziYie1oSvIqUPKcpKI3ORvGYP0ND0/F/VUV4Z3QmQQLH7GLsM3rPeTY0HbXBj1rrNQWlRn+PKRWyx4aS60TJc8beZaf18G5YIBAIBEcdR97MPVXEVQ9m7qqRNHqrXwqUbpjP6PAQmLkr3UQnywAKWVEz9yknnMWY6YvYt3kVezZ9ia+tCU2LULH1ayq2fo3d6aFswiyKR02msHyCiGgahhiGQfCbGkJbzQnYdKoEN9ft54O/3R3XdtFP/xOnO14YCQVV3nxhI00N5iTvpOlF1BxojUUwgem75EgyJvRv20rNIw9hqCooCiNuvQ33tOl9vr+wFuaPG55iT9s+83x5k/nh9GuHRlhqq8P/zj0xg+6kSBK2ed/FNueCfqfBAYTCGo+8uonN+8zvQzkZdgIhlWD0O8EPz5/MuJKhqQy3paKZZ9/fSV1z5yT3KTNHcPnScWS6hDm/oGeEuCQYMsaMGUteXh6vvvoSFotEMGg+nFRVZefOnbjdbm644Sfk5uYl7Lt06em8+OJzVFZWMGrUaADWrFnN/v2VXHPN9UN5GwOGYRioNV5Cm+tRD3rjtkkOC7bxudgm5KJkxk9D694wvk8r0aKzSJJdwbVkFNaS5OGphmGgtwSJ1HSg1npR67wpZ4YlhwVLoRulwBUTk4QfkkAgEAiGiuFh5m7EiVpuuxVvW6DPQtbRYuZeqpRTWngVam4Iv78Vv78V1YigGxrabpX63Rs4aKzFnpGJOycPV3Yu7tw87J4MJIuSWsg6htIIhyOGbhD48gDhPS0ASC4rnrPHomSljm6p27+DFcsfiWv77s/+F5s9PgMgHFJ568WNNETHp+OnFrJraz16l7/pE5aMZt5JoxLO0bZlK9UP3Y8RiYAsM+Knt+KZObvP9xfRIjy28a/sbN0DwJTcifx4+vVY5MEfl2r1ewm8ex9GsCNlH8mZheOMm7GUHN4kty8Y4f6XNrCn2oyRHF2cgQG0dJiWGENVGa7VG+L5j3bFRU6VFbi5/pxJTCjLHvTzC44NxLdGwaDj9/vYs2cnlZX7mDt3Lh9++CHPPvsMkyZNQpIk9uzZS1tbG7/97b+Tm5tHdXUVmzdvZPr0mZSWlgFwzTXf59133+LOO2/hqquuJRwO89xzTzNp0hTOPvv8I3yHfcPQDSL72whtqkfrMvsDYBnhwTYxD2t5ZtLqaJHqDvyfV2KEomG4+S7cS0chu+NnEgxVR631EjnQRqSqPebH1B0524Gl0BUVlNymN9IwD3U1DGPYX6NAIBAIhi99NXPPKchAbUj9JTMV6Zm5R4WsSPpm7ociuwbazF0GPGThsaWIkNCARvOl0YSfpuT9DpHMzL171cGezNq7b0/ip3W8mrkbmo7/00oiB0xBQs604zlrLLIndWTJ/h3f8tVbf4lru+z2u7FY4/eJhDXeemkTdTXm3/yYifns3hofvXPqeROZOmtEwjkCe3az+767McJhkCRG3HQzGXPn9fn+IrrK45ueZnvLLgAm50zgJzNuwKoMfuS8un8DgQ8fATW116hSOhXHaT9Fdh1eNFGrN8S9L6ynKhodNm1MLhlOK6u2dhYfGuzKcJqu8/G31bz62d5YpJTdpnDJKWM4Y14Zll6qNQsEXRHikmBQ0XWdt956hUDAFFHGjBnD+eefz8aNG1m7di2yLDNx4mR+85t/YtGikwDYsGEd//3f/8Y//dO/xMSlnJwcHnnkcR588F7+/OfHsNsdLF68lFtvvROb7egI0TR0g/CeFkKb69DbuzywLDL2ibnYJuUnRCnF9u2eTw/YJuXhXFASE6GMiEbkQDuRyjYiNR1JB5yS24q1JANLSQaWYg+y4+j6CKg/sItPXnoo5XanO4vM/GIcrkwsVhuK1YbFYsNitWGx2s316EuxmG2xfrF262GFNgsEAoFAAMPIzL2niKs0zNwNVUfqZxrhETNzj/PDSpU6mELIUrrsc6jvMIrCMiIavo8rYlHvSq4T95ljerQs2L1xJWs/fCGu7Yo770NW4v821YjGOy9v5mCVKVqVj8lh3854z9NzL53GmIn5CecIVuyj+v570INBkCSKf3gTGQtO6PP9qbrKnzY9w9Zm0w5jQvZYfjrzBmxDICxFdnxO8LOnwEgl2krY5l2Mbc5FSPLhjRUbWwPc/fx66lvN70gLJhdSku/m9S/2xfosmlY0qP5Ge6rbeOa9Heyv78ygmD+pgKvOmDBk/k6CY4uj65ul4KjDMAwUxfwzy8rKYdSoMVx44WVkZ+emnGk6//wLOf/8CxPaR44czd13Pzio1zsYGIaBeqCdwLe16G2dVd8ku4J9Sj62yfk9pp7pIRX/F/tRq6KzpoqE66RybGNzMDSd8P42IvtaiRxoS0x3kyUsIzxYyzLNynJHQWRSTzTW7Otxe8DXRsDXdtjnMYUnK0pUfDokUB0SoRSrvYtolby9U7CydxGzbMiKMD0XCAQCwcAw1Gbu4UCQ9vpa2htq8TbV42tuJNjRjoyCggVZsqB0fWG+y5KCIlmwSDYsFjtW2YYiW81tKEi6hNTPIKzBNHOP0ZOZe1ehSpGRrL14YKUQstKJwtKDKr4P96I1mYKEUuTGc/oYpB4i8LZ+/T6bvngztm6x2rn0tv9NmEjTVJ13X91CdWUrAMVlmRzY1xLX5+JrZlEyMjvhHMH9lVTdezd6dDK56IYbyTzxpB7vJRmarvHklufY3LQNgHFZo7l55o3YlMGdSDYMg/C6vxP+5pWUfSRnJo7Tb8ZSOvWwz1fb5OPu59fHUt9OnV3CmBGZ/OWd7XH9vrMoMe1wIPAGIrz86R4+W18T+58pzHZy7dkTmTE20Z5EIEgXIS4JBhVFUbjwwssJh0O43amrVhyrqHVeAmtr4yq/SS4rjmkF2Cbk9jqbqTb58a+oRPeakU5ypg330tEgSwTW1BDe0xxLkYsd36ZgKc/EWp6JtSRjUGdMh5qJc5fS0VJPxdavB/U8mhpGU8MQ8A34sSVJigpPphAVFzVlNQfdCdFU0XbFao2JVZ1iljUuKktU9hEIBALBQHPIzN1uc1OQNZ6CCeNj2zQ1gretkY6WBjqa6+hoaaC9tZ6OlgZC/r6lE8oo2KwuXK4snM4snI5MHA4Pdrsbm9WNzerEZnVgVRwostUUpNLxwOoSkXU0m7lrzQH0DnNMaCnLxH3qqB79ydZ9+io7134SW8/IKeS8H/xzgoilaTrvvbaVA3tNMSm/0BOLXjrEFTfOI78ocSwfqq6i+t670f3mmGnszTdhmX9yn29f0zWe2rqMDQ2bARiTOYpbZ/0Qh2UQVVPA0HVCK58hsu2TlH2UEZNxnHEzsiv7sM+3v66De19YT3u0MvO5J4xk9IgM/vj6lrh+/3T9PEoLBva7k24YrNxUy0uf7MEbMM9vUSTOXzSK8xeNwnYMfWcQHBkkwzAGNVL1SNDU5I0znDtaKSjIoKEfOf6CI4/mDRP4uhr1QOeDWbIp2GcUYp+cn5ZRaWhXE4FV1TFDUEtZBtbSTML7WtHqu4keFhlreSa2MTlYSjxJ/ZqOVQxDJ+jrwCoHqdlfha+9GV9HC/62Znwdzfjamk2haIBQLFasdie6pqJGwuhacj+rI4WsKHFCVEykssRHUylJIrK6R1kpXdpEyuCxhXi+CATpI/5f+k846Mfb1oi/vQV/Ryv+jhb8Hc3mcnsLAV87hxNvZHO4cLgzcbgysbs82B1u7E63uez0mMtOc9nmdKMoFlNoStPMvVOoStPM/dC+KQqnDBTWsdm4Th7ZY7reqneepnLbN7H1gtJxnPa9OxKEJV03+OD1rezdYaa/ZWY7aG8NxvW59uYTyMyON/0GCB+s5cBd/4PWbo53C666holXX9bn/xfd0Pnr1uf5pm49AKMyyrl9zo9xWhLPOZAYapjgR4+iVq5L2cc29yJscy857DQ4MNPQ7ntxA/6QOXa8ZPEYinJcPPZGvLB0+6UzmDOx4LDP15Wqei/PvL+DXVWdEf7TxuRy3VkTKcoVlSCPFEfb80WWJfLyUoueInJJIBhADN0gtKWB4IaDnQMLRcI+JR/79MK0Kq8Zmk5gdTXhXc2xNsmuoDUFOlPjoihFbuwTcrGOzDqmIpT6giTJOD1ZFBSUYXEVJWw3DINw0IevvRl/e4spPrU344+++9qbiYQCSY6cHE2NoGsaTk8WWXkjcGZkY3d6cLg82JweHE4PNocLw9BRI+HoK4QWXdbUcGJ7tE3r2q5GUCMhDL1veQK6phHW/BDqvW9/UCzWzqipLkJUT9FUqdvt0ePZRcqgQCAQHIPYHC5yHSPJLRqZdLumqQS9beakUHsLAW8rQX8HQV8HQX87QV87QV8H4ZA/6f7hoJ9w0E9708Gk27tjtTmwdRGcOsWnLu8uD/YsDzaHG7vD2a9JlT6Zuavpe2BhgHVMNo5ZRSmfl4Zh8Okrf6CuckesrWzCLE6+8EcJfXXd4KM3t8eEJYfTmiAs3XDbibiSGIWH6+o4cPf/xoSl/Mu+R86ZZ/f9Z2UYLNv+SkxYKs8o5bbZPxp8YSnoxf/e/eh1u1P2cZ7/ayxl0wfkfNsrW3hg+UZCETPj4KrTx2OzKgnC0lWnjx9QYSkYVnnjiwreX3MAPRpTku2xcfWZE5k/qUCMuwQDihCXBIIBQq334f+qCr3LQ9k6LgfnnOKEam6p0DpC+FdUJlSR65r6Jjks2MblYJuQh5I1uKHCxwKSJMUGkakGt5FQAF+7OZvqa4uKT4eWO1oSwvoNQ4/OvrYkPR6Aw52JOzMXV2Yu7owc3Fm5ZGeXxtqstvR+d5qmRkWnUII4FWtXI2jR7XHb1HBie0zICqFGIvR1xlhTI2hqZHBTBi1JoqZStSeJsooXvETKoEAgEAxXFMWCOysPd1bPPi+aGkkiOrUT8HfElkMBH6GAt8cJo0g4SCQcxNfWS6W7KJIkR8WoRBHK5nRjd3RZjrZbrPYhMXNPhmHovPfMXbQ11sTaxs08mflnXpmkr8GKd3bEKsHJskQwmip1iB/+/GTsSYq/RBobqLrnf9FaWwHIu/i75J7X9+rNhmGwfNcbfFlr2h2UuIu5bfaPcVkHN5JG72gk8M496K21Kfu4r70P2Z0zIOfbuKeRR17dTETVkYDvnzuJ5vYQz38cL2ydOruEsxaUD8g5DcNg7Y4Gln20K+btJEsSZ84v4+JTxuBMY8JbIOgr4q9KIDhM9JBKcG1tXKSRnGXHtagMS3H6udKRqnb8n+83c/qTIGc7sE8rwDYm+7hKexsKrHYn2QVOsgtKkm5XI2H8HS1dIp5a8LU3xSKhAt42uos0hwa7TbUVSY9pd3ooHDmR0rHTGTFmKjZH8oGUolhQFEvK7YeDYRhRsagzYqprBFWqdlO06tauxgteWiSCpkV6v4hu16OGQ6jhwQm7kmUlrdTArtFUiWmF9ngxy3LI+F2kDAoEAsFgoVisuDNzcWfm9tpX1zTCQR/BgJdwVHAyXz7z5fcSCno7lwPelCnuhqET8nf0yTtKVhTsDk8XUcqNzXkoZa+rGNW5rlgOv+CKrmv8/Yl/IejrtGSYcsLZzDzlgiT3ZfDZe7vYsamuy/7x45gf/+oUrEnEsUhzE1V334XabI57c79zIXkXXtyva/773vdYUbUSgEJXPrfNvgmP1d2vY6WL1lhJ4N37MPytSbfL2SW4Lv8PpAGakFqzvZ7H39iCphvIksSN509me2ULKzfHR9uNL8vi2rMmDkgkUX2Ln799sItNeztF1HGlmVx/9iRGFmUc9vEFglQIcUlw3FBbW8MVV1zUY58HH/wjc+fOT7qtpaWFRx99kK++WkkoFGLu3HncfPGPyNkPRjA6KFEkHDOLsE8rSFsAMnSD4IY6Qhvrkm63jPBgn1aApSRDhK4eISxWG5m5RWTmJqbdgRldFOhojUY8teBra4qG+DfH2rqnt4UCXg7s+JYDO75FkmQKSsdSMm4GJWOnk5EzsHn2qZAkKRbhYx+E6HNd17tFSqVKDYygqqEuaYFdhaxugldMyOpHyqCuoYcCfUqD7AuxlEFLl1TAbhFW3aOpkrYnicYSKYMCgUCQHrKimD5M7sy0+psTLeE4sSkUE6U6xalwoFOwCgd9pLKt1TWtz9VrFcWaIEY5XBk43Zk4PVk4PVk43Jk43VlY7c5EQ241wvIHf03Xia5ZSy5m8vwzkt7vyg/3sHV96qidn/xmMUqScaza2krVPXcRaWwAIOecc8m75NK077Mr71Z8zHuVHwOQ68jhjtk/Ics+uMKHWr2VwPsPQiSYdLtl4ik4l/54wM73xcZannpnG4ZhGmffeN4UVm6uZWtFfOS7067w88tnYTnMyeOIqvPO6kre+qqSSLSCotth4YrTxnPKzBHIYhwhGGSEofcw5mgz+BruBAIBPvsssRJEKBTi/vt/T3Z2Dn/5yzIyMxMHI+FwmFtu+REHDuznyiuvwanYeeGFvyHp8NCl/0qGw4OlJAPnwlKUzPRT1fSgiu/jfXHV5A5hKcvEMbsIS54w2UuH4fz/ous6QV9bXMRTa0M1Byu2EQknDnAyc4soGTeD0nHTyS0ejTwAJpLHIp0pg4cEq1AScaqbsNVT+2GmDA4mkiQlpAB2jbBKHk0Vn1LYVfAqLMqhrT0SE7VkRaQMCgSpGM7PF8GRwTB0wsEA4aAvTogKd13uvi3oZyCeK4rFitMdFZw8WVhtDvZu+jKuz/yzrmLcjJOSXLfBV5/sZcPXVSmP/9P/swQ5iVG42t5O1e9/R7jWTLnLPuMsCq66JkHoSuf/5ZMDX7B81xsAZNky+cXcWyhw9ZwaebhEdq8iuOIJ0JNnCNhOuBz77MQor/7y0doq/vbBTvPYVpnrz57Ee18foKrBm9D3rptPJD+JYXpf2LKvmWff30FdS+cE2uKZI7h86TgyXOnZcwiGnqPt+SIMvQWCKE6nk3POScwHf+CBe1BVlX/5l/9MKiwBvPvuW+zYsY17736IGfYxhDbXM+PcYm5b/i+8uu1Dbr7zDqyjs/sUWaDW+/C+k2giaCnNwDG7GEu+EJWOFWRZxpWRgysjh4LSsbF2TVNprN5D9Z7N1OzdHPOAaG+uo725ju1rPsTudDNi7HRKx06naNTktL2ajgeGS8qgFol0CltqfGqg6YkVToi+6lfKYCSEGhn8lMHOaCp7nDiVqqpgz4KXSBkUCATHHpIkx6KMMtK05dF1nUgo0EWI6oyMCgf9CRFTQX970jRxTY3gbWvE29aY9DwnfucHjJw0N+m2rz+v6FFYuvkfliQdy2peL1X33BUTlrJOXZpUWEqHlTWrY8KSx+rmjjk3DbqwFN74DqFVL6Tcbl/8A2xTlg7Y+d76qoKXP90LgNNu4Yql43jls70x76Ou/NP18w5LWGrpCPHCx7v4elt9rK2swM3150xiQll2v48rEPQHIS4Jjmv27NnNyy+/wHnnXcCsWXNS9vvoo/cpKSphck02oQ4zfa08ewRzJs7ki+pvuWNM+oZ/hmHg/6SCyIH2uHY524HrxDIshYObay4YPiiKhaKRkygaOYmZp1xI/YFd7N38FTV7N8dSvkIBHxVbVlOxZTWyYmHMtIXMO+N7IkVqkBnalMGuVQKTpwYe9SmDijWN6oHxwlaq9q5eWIrFhmKxiv8HgUAw7JHlTkEqXSLhoGlc7m0jEHtvI+hto6W+io6W+rj+J5x7XUphae3KSr79cn/SbYoicdOvFycXlnw+qu79PeFqU5TKPHkxhdd+v1+fu2sOrmPZ9lcAcFqc3Db7JordyS0HBgLD0AmteoHIpvdS9rGfcsOACUuGYfDKZ3t566tKADxOK+cvGsVLK3YTCCVGTN188TTGl2b161yarvPx2mpe/Xwvwahfq92m8N1TxnDG/DIUEfUuOAIIcUlwXPP4449gt9u56aZbU/bR/RG2b9nC3JJp6B1hAJRcB85F5UwJzuabZ56ivb09ZdRTd8LbGhOEJdeSkX2OfDre0TWNUNCHwzV8vagMQycU8BHwtsUPDn1tBL3meyjgJeT3phWRomsqezauZPpJ5+NwCUPGoxlZlpFtDqw2x6AcP1XK4KE2l1Omubk9lgIYV1VQ7UwNNCOwuhu2h+lzlUHNNHgPBwepyqDFliTKKj7yKj6aKlV7VPDqImaJlEGBQHCksEafExk5hXHtvvZmVix/JLaeN2I0S757c8pI3nWrD/D15xVJt3ky7Vx/66Kk27RAgOr77yG03xRLMhaeSNENNyL1Q7jY0LCZp7e9gIGBXbHxs1k/pDwjeSGVgcDQIgQ/eQJ179cp+9hPuhbb1NMG5Hy6YbDsw118tNYU4bI8NhbPLOHlT/egJbFrueSUMZwwpX/C2u7qNp55bwcH6jtT7OZPLuSq08eTmzk44wqBIB2EuCQ4btm9excrV37OVVddR35+fsJ2QzcIb2+keU0FvqCfXFc2WGQcc4qxT85HkiXy8sww3rq6g2mLS1qXkFhLeSbuU0eJ6m+9EA76aW2ojr1aGqppb6pF1zQmzDmVuaddNqTXYxgGkVAAX0cLgY5WUzjymcJRsMvsYtDf3ucIku7IigWHKwO7y4PDmcGIMVOFsCTold5SBg8nx797ymDXyKvuhu1dqwp2pgx2aVfDCVFZwy1lUJLlqJdVt6qCVisWS7eqgjHRqrsX1qHoq/h2xWITnmoCgaBPtDfXsWL5IwS8rQCMmb6I+WdelfBZYhgGvo4QO7fUs/rTfUmPlV/k4Yob5yXdpgeDVN9/D8F9ZnqXZ/4Cin/4434JS1ubdvDk5r+hGzpW2cLNM29kTNaoPh8nXYywn8B7D6LVbk/Zx77oSmzTzxqQ8+m6wVPvbGPlJrMCXH6Wg9kT8nnzy4qk/edNKuDCk0f3+TzeQITlK3bz2YZOM/bCHCfXnTWR6WMHN7VQIEgHIS4Jjltee205iqJw+eVXJmxTD3oJrKlBaw4Q8Jtm2668TDIvmYTs7jTFs9vN2YFgMP1UEuf8EiwjMrDkOeOOJTAjfXxtTbTUR4Wkxmpa66vxd7Sk3CcUSDRGPFwOGXD7O1rwtUervsWqv5nvh/NF1mp3mtVf3JnYnVHhyNXl3enB4fJgd2VgsdqHbWSW4PhkqFIGu4pOXVMG49rVeM8rraf26DH1FGauqTCiPilDkTIYE6ks3aoKxkVZRYWtbt5XyTyxRMqgQHBs0dpQzYrlj8TGPhPnnsbYWedSs7+NtpYAbc0B8701QHtLAE1LHWVaNjqHC6+amXSbHg5T/fADBPeY3qDu2XMY8eOfIvUjknNXyx4e3/RXVENDkRRumnEDE3PG9fk46aL7Wgi8cw96c2pvKduCy7HNPG9AzqdqOo//fSvfbDdTFItzXYwryeTDb5KfPz/LwU8unNanz2bdMFi5sZaXVuzBGzAnYCyKzHdOHMX5i0ZitYgIW8HwQIhLguOSUCjIe++9w8knL6G4eESsXWsJEPi2FrWqc0ZfclsBsI3OTikG9eUBISkytpH9y68+1gh422iqraCxZh9NBytora9OW7SxO92Mn72ESfP6F84cCQfxtjbQ0dJgvrc24m9rikUj9fULKIDN4cLhzsLpMcsFO92ZsXVHbD0Ti1WIigJBKgY7ZVDXtBRRVt1TAxPbu/bvmjLYVfAaTimDIGGxdqkeaOkeZWXvUkmw9/auKYOK1YaiiGGkQDAUaJrOgd27+eb9P6NFokK3YzZrvslj1arUaV+pGD+lgLMunpp0m6Gq1P7xEQLbtwHgmj6TET+9FcnS9//3fW37eXTjU0R0FVmS+eH0a5mWN6nPx0kXraWGwNt3Y/iaU/axzb0Y+5yBqQoXjmj84bXNbNxjFmQpLXBTkOVk5eaDKff57Q3zsVrSj/46UO/lmfd3sLuqLdY2fUwu1549kaIcUfxHMLwQowLBccm3335DIODntNPOAEBrDRLcXE9kb0vn9wJFwjG9kIKxLngcQqFE0SMUMsvIu1zChDsdvG1N1O3fQf2BXTTV7MPXnvrhnwpPVj6T5p/G6KkLexVp1EiYjtYGvC310fcG8721kaCvvcd9u2O1O3Fn5uLKzMGVkYs7MydaAS4bpzsLhzsTxWLt8/0IBIKhRVYUbIprSKoMppMyqKldqg3GpQzGe2H1J2UQjNj+g5E0mCxlsDP6Kj7CqmvKYEJ7Mk8skTIoOE7x+8I01nlpqvdG3314W6pwK58jSyoAPnUmofZxdBezZUUiK9tJZo6TrBwHWTlOPn8/vjLxtDklLDlnQtJzG7rOwSefwLdxAwDOyVMoufU2ZGvfxzdVHTU8suHPhLQwEhLXT/keswum9/k46aIe3EngnXshEkzZxzrzXGzzLhmQ8wVCKg+9vJHt+1sBGFnowWm3sH63WcXP7bDgC6px+/zXTQvJcKU3wRgIqbz+xT4+/KYK3TB/zzkZdq4+YwLzJhWIqFTBsESIS4Ljkq++WonNZuOECXPxfrwPtavBtgS28bk4Zhcju8yHqceTQVNTYsnXxkazLT+/YEiu+2gj4Gunfv9O6g7spH7/zpRikiTJZBeWkls0EjUSpm7/jgTxJ7d4FJPnn0Hp+JkJXzg0TaXpYBX7d+2mrekgbY21tDXV4mtrxDDSiyJwuDJwZ+XhyszFnZFjvkfFJHdGDtbByP8RCATHFIOdMmgYeqIBexeBKs6/Su0mbCVEWQ3/lEFZsSRWD0zwvrIlibJKnTKoHKpSKFIGBcOAgD9MXU0HdTXtNBzsoKnOh98XjutjkRrIsKxEksz/T586G4t7KoX5LrLzXOTkucjKcZKV48SdYUeWO/+un//TmrhjzT1xJAtPHZP0WgzDoP7Zp+n4ejUA9tFjKL3tDmRb36OtD/rqeWj9EwRU87Ph6kmXckJx8ip2A0Fk31qCHzzUrVWiq/hmnXwq9oVXDsj/vS8Y4b4XN7C3xhyrjirKwMBgx4FWwIxgamiN/1z8zdVzGJHX+2S0YRis3dHAso920RL1aZUliTPnl3HxKWNw2sXXd8HwRfx1Co479KDKhjVrGV8wGuPTWrrOKVhHZeGYXYySHZ+OMXHiJHbuTDQF3LlzB2Vl5WmbeR/r6LpGY80+avZsprZiK+1NycOCbXYX+WVjyR8xhrySMeQUllNXuZ3t33xEU21FXN+ScdOZPO8M8kvHIkkS4aCflvoDNNcdoKXuAG1NtXS01KdlnG13ZeDJzicju4CMnAI82eYrIztfiEcCgWDYI0kyVpsdq80+KMfvNWUwwYA9eXsqwauvKYO6phLWVMJB/yDcbXzKYNfoqc7UQHN7ZpaHcIRu/ezxqYTd2kXKoKA7hmHQ2uSnen8rddXt1NV00NbSszDrcTdhU1eCYQpLk074LlMXLMbWi8BgGAZ/eegrgv7OaMdFS8cwZ9HIlP0bl79I22crALCVllH2818hO/o+NqrzNvDgusfxRsxU38smXMjJpQv7fJx0CW/5iNDKZ+La5JwSjEgIw2umq1nGLcR+yg0DIiy1+cLc8/x6qhpM36tRRRkEwyp10d/l5JHZ2K0K1Q2dqc4/OG8yU0bl9HrsuhY/f/tgJ5v3dk7Gji/N4vpzJlFe6DnsaxcIBhvx5BMcF+iBCJH97UT2txGsbqWyej/nTl5ibpQlbONysE8rQMlK7vFx6qmnc999d7FmzWoWLDAfkJWVFaxd+zXXXfeDIbqL4YkaCVO7byvVezZSu29r0i8BisVGQdk4ikZOpLB8ItkFpciyjBoJU7FtDWveX4a3tSHWX1YURk85gXEzTyYSDtJUW8Gu9Z/RUncAb1tiBFk8Ep7sfLLyRpCVP4LMvOKYkGQTApJAIBCkZLBTBnVNjfer6ik1UO2SIpjE86q7F5amDsOUwViUVZeqgnHRVImpgUqSyKtkgpdIGTw68HWEqKpooaqilarKFvzecMq+Ofku8os85Bd6yC/yEAlU8s37r6EbGpIks/Dc6xg1ZX6v5zQMgz/+72dxbUvOmcC0OSUp92l++01a3nsHAGtBIWW/+DWKp+9iRkuwlQdWP0Zb2IzouXDsOZxevrjPx0kHwzAIrX6RyMZ34tqtk5eidzSgV28BQBk5G8dpN/Wryl13Wr0hfr9sHbVN5lh3ZJGHdn84FmE0b2IBY0syeWnFntg+Z8wrY8ms1D97gIiq8faq/bz1VSWqZk6WepxWrlg6jpNnjkAWUZaCowQhLgmOecKVbfg/rYhNmDZ0NKHqKoX5hTjmjsA2PgfZ2ZlL3tzcxJo1qxk3bgLjx5s56RdeeAmvvPIiv/3tP3D11dfjcDhYtuxZCgoK+d73rj4Cd3Vk0TWNg5Xb2b9jLdW7NyWYcEuSRO6I0RSPmkzRyInkFo+Km8UNBXzs3vA5u9Z9llDtzZNdQE5hGS31B/hw2T09prU53VlkF5aSlTeCsjFjkGw5ZOYWCcNsgUAgGGZIkoRisaJYrNidA+9TmJAy2C0F8JBg1ZMXVirBS1PD6Fo/UgbDQSLh1P4vh0NcyqDFmiSaqjMFsGtqYNcIq1SCl0gZPDzaW4Ps3dHA3p2N1FUn93e0OywUlWZSVJJJcWkmhSMy4qKRDuxcz5r3/oKh68iywqLzb6B84uxez63rBo/dFS8snXnRFCZMLUy5T8vHH9L06ssAWHJyKPvVb7BkZ/d+o91oD3fw4PrHafCb0UJnjzqNc0ad3ufjpIOhqwTeuRetemtcu+P0m1ErvkU7JCyVTMF55q1I8uF/5W3pCHHXsnXUNZvCUlmBh6a2YMxXaensEuZOLODeFzfE9hlbksnVZyb3tzrE5n1NPPv+Tuq7RLEtmTWCy5eOx+MUXp6CowshLgmOebRmf0xYkpwWAg6zXGfuCWNwzEh82FZU7OM//uP/ceONN8XEJZvNxgMPPMpDD93Hc889jSwrzJkzj9tu+zlZWdlDdStHFMMwaKrdR8XWNRzYuT6hspHFaqN41BRKxk1nxJipOFwZCcfwtjWxY+0n7Nu8Ck1NPnvnbW2Ii2I6hNOdRU5ROblF5eQUjSSnqBynuzMdsaAgg4aGjoT9BAKBQHDsM5gpgwUFGdQdbE0eTRU1X+/J80pLEnl1VKQMWrpETPVgvn4omsqSRNhKrDZoRVYsx5x4pak6e3Y0sOXbGg4mEZRsdoWS8mxKR2dTNiqHnHxXyp9B5bZvWP3usxiGjqwonHzhjygZ27sRtqbpPP77z+PazrtsGqMn5Kfcp/3LlTQ89ywAiieD0l/8Bms/fER9ET8PrXuCer8ZXX5q2clcNPbcQfk9G5Eg3qdujm+0OnB/918Jb3gbda9ZPU8uHIvz7DuQLIc/4djcHuSu59ZRH/VRKspx0tAaIBQxReeLTh7NwqlF/PMTq+P2+83Vc1JGHbV0hHj+o12s2V4faysv9HD9OZMYXyqqSguOTiQjXbfbo4imJi+6fvTflviyPDAYqk5kfxuyx4ZSkPphLkhOKOCjctsa9mz6ivam2rhtimJlxNhpjJo8jxFjpqasltZ8cD/bv/mIAzvXpXVOWVHIKRpJQclY8kvHkls0Eqen5wet+H8RCNJH/L8IBOkz2P8vSVMG46KpUlcbTBStOgUvLba9rymDg4skyQmRUsmiqRJSB+OirFILXkOZMhgJa2xYU8WmtdVx/kYAmdkOxk0uYPSEPApHZMYZbadi7+avWPP+84CBYrFyysU3UTxqcq/7JROWLrxqJmWjU/v8dHy7ltpHHwbDQHY6Kfv1P+AYNbrXc3UnqIZ4cP3jVLYfAOC0MSdx6eiLkKWB/z3ovhZ8f/tFXJtl9Dwcp/+U0NfLiWx+HwA5twzXBf+I5Dh8n6LGtgB3PbeOxjYzCjHTZcUXVNF0Awm45qyJLJpWxO33x//8773tZLI9iWK3put89E0Vr36xj1DYFKfsNoXvLh7LGfNKUUTK63HF0TYek2WJvLzU/1cicklwzCNZZGxjezfRE8TTUl/Fzm9XsH/Ht+hap+25JMsUj5rMyEnzKB0/A6stuU+VYejU7tvG5i/foqW+qsdzWaw2CkrHU1A2LiYmpRKqBAKBQCA4lhjSlMGEKKtuKYCH2rtFafXU3ucqg8YQpAzGVRVMFU11yNsqsb1rSmH3tMJDk5R7tjfw+fu7CHQRlRwuK1NmFjN+SiF5he4+TWjuWv853378EgAWq53Fl/yEwvKeU6rAjJp6/O54YePia2dRUp6dch/fls0cfPxRMAwkm43SO36BJMvUP/8c7unTcU+fmdY1R3SVJzY9HROW5hXO4qfzr6WpydfLnn1HrdlO4M3fxbXZl9yIbfKphNa+HhOWpKwinOf/ekCEpYZWU1hqajf/VhVZoj36+1ZkiZsunMrciQX8/MEv4vb7tx+ekFRY2l3VxtPv7YiZgQMsmFzIVWdMICdjcAo1CARDiRCXBAJBjEOC0I61n1B/YGfcNk92AWOnL2LMtIU43Kmr42lqhN0bvmD9p6+m7CNJErnFoygaOYmiUZPIGzFaVNYRCAQCgWAQGJIqg91FpxQpgEnbe6k22Ncki0Mpg4QGI2XQLFJiGAoRVcJqWLBaFCw2O5k5HjKzPcihfVRutlHdLX2wa+RV95TB/Tu+ZcNnrwNgtTlYcukt5JeM6fVaVFXniW7C0qXfn0NRSepxWmD3LmoeeRBDVUFRKLn1dpAkKv/t/wHg/XYtY++6p9dz64bO01ufZ3vLLgCm5k3ihqlXDUrkmKGGE4Ql1+X/hZJbSnjrx4TXmmNOyZ2L6zv/B9mVfdjnrG/xc9eydTS3d/qKatHMGLtN4fZLZzB1dC4PLt+IP9Q5CXvn5TMTKrt5AxGWr9jNZxs6MwAKc5xcd/ZEpo/JO+xrFQiGC+LbnEAgQFMjVGxdw85vP6G9uS7WLkkyZRNmMm7mKRSWj0fqIcQ56Gvn89efoPlgZco+5RNnUzJuBiVjpg1KNSKBQCAQCARDi1ll0AmDUJE1IWUwIcoqPgUwMcoqRFzKoNoldbCfKYOHPCMVCTgUmKRCe4P5OhxsDhenXnYruUUje+0biWj86Z74iJnLfzCXguJEz8tDhA7sp/qBezHCYZAkRtx0M7LdzoHf/Vesj6T0Lg4ZhsGLO1/n2/qNAIzJHMWPp1+PIiu97tsv1DBIChhmlJznh48hWexE9nxN6ItnzOt2ZOD6zm+QPYcv1tQ1m8LSoSpwXclwWfnF92YxujiTt1dVsn53ZxXjy04dy6zxnR5XumHwxcZalq/Ygzdg/q1ZFJkLThzFeYtGYrUM0s9LIDhCCHFJIDiO0dQIezd/xbbVHxDwtcXaLTY7Y6efyMS5S3Fn5vZ4jJq9W/j8tcdSbh85aS6jp55AYfkEkeomEAgEAoEgbYYiZdAUrHqKsuoUs7wdPrZ8ewAJFbsDikvdSIaa0iOrL1UG7U4PSy//GdkFpb32jYQ1/nRvvLB0xY3zyC9KnQoWPniQqnvvRg9ETalv+CFKRgYH/ve/4/qN/q//7fX8b+37gM+rvwJghLuIW2bdiF0ZvEq9ksOD67J/BzWEUjgWALVqC8FPHgMMsDpwnvdL5OwRh32u2iYfdy1bR5s3sfBMXqadX101h+JcF5v3NrF8xZ7YtjkT8vnOiaNj6wfqvTzz3g52V3eOr6ePzeW6syZSmCMmWAXHJkJcEgiOQ3RNY9+W1Wxd/R7+jpZYu9OTzcS5pzJ2xknYepiBVCNhNn35FjvXfpKyz+JLfkrx6ClDaqw5nAkFI6xbdYBQUMVqVbDaury6rFusCrZu22RFEkb0AoFAIBAMMKa5uB2LNb2UwQ1fHyCgmT6eF1w5j7zCnn19ElIGu0VZmWJWBDAoGTcjrgpuKsIhlT/ftzKu7cofzSe3ILX4Fmlupureu9A6zEp2BVddgzU/n6rfx6eaTXjiqV7HGysOrOSdig8ByHXkcNvsH+O2Dr5YouR2im5a/V4C7z8IugayBedZt6MU9J5G2BvVjT5+v2wd7b5EYako18VvrppNbqaD2iYf9764IbbN47Ry63fNin6BkMrrX+zjw2+q0KMpnTkZdq4+YwLzJhWI8ZzgmEaISwLBcYSua1Ru+4Ytq97F19YUa3dn5TFt0bmMmjwfWUkdouvvaOHz1x6ntaE66fbR0xYy7/QrsFgHb/bqaGX7xjrWrTrQr31lWcLSRYCyRUWoeHFK7iJSWeLb4wQsc5siBCuBQCAQCPqEquqxZZen97HOQKcMhoIqT94fLyxdddMCcvJSizua10v1/XejNjcDkHfRJdhLy6i6Oz5CKR1hac3Bdby0y/SG8ljd3D77x2Tbe67mO9BorTUE3rkX1BAg4Tj9J1jKph32casavNy9bF3MsLsr5YUefnnlbLLcNryBCP/8xOq47f9784nIksSa7fUs+3AnrdGoJ1mSOGtBGRedPAanXXztFhz7iL9ygeA4obZiG+s/fY32pk4zQVdGDlMXnsOYaQt7FJWa6/bz4XP3pDTVPOmCGymfOGfAr/lYYuS4XHZvq6exzouu99GcVDcIh1TCXQwjDxdJIjFyqoeIKqtVwWLrElXVLdrKalOwWGQhWAkEAoHgmGVEWaeQsmrFPpaeN3HInnvBQISnHvgyru2an55AVk5q4UoPhah++AHCNTUAZJ9+Bo5x46m65664fukIS1uatvP0thcAcCh2fjb7RxS6CvpzK/1G9zYTePsejJBZbc1+yvVYx55w2Mc9UO/l98vWxXyRujKuJJOff28WbocVVdO544F4A/W7bjmRdn+YR1/byeZ9zbH28WVZfP/sSZT1Et0mEBxLCHFJIBhGtLS08Pjjj/DFF58RCoWYOHESN998O9Onz+hxv5qaah5++H7WrVsLwEknncJtt/2CnJwcWhtr2PDpaxys3B7r73RnMWXh2YydvqhHH6Taim189sqjSbc53JmcdsXtZOYW9eNOjz9y8lxcdsNcdN3A1xGivTVIe2uA9rZg53JrkGCSGbPBwDAgHNIIh/pWQronYoJVVIjqLk4lFa56E7SsQrASCAQCwfBgRHkWJeVZ1BxoY/vGg1gsMiefOR5ZHtznVMAf4S8PxgtL1958ApnZqYUlQ9OofewPBHeb1dw880/ANX0m1ffdHdcvHWFpb1slT2x6Bt3QsUgKP515AyMzyvp5N/3DCHoJvHM3hteMvLfN+y62qacf9nErD3Zw9/Pr8AUTJ/CmjMrh9stm4LCZX5nvfn593PZ/uGYOX2ys5e1V+1E1M6rN47RyxWnjOHnGCGQxfhEcZwhxSSAYJvj9Pm677SYaGxv43veuISMjk1deeZE777yZJ574K2PHjk+6X1tbK3fccTORSIRrr/0+mqaxbNkz7N61g1uuuZj921bHIo4sNjtTTzibCXNO7TF17cDOdXz55lNJt5WMm84JZ187KMaaxwOyLJGR5SAjy0HpqOyE7eGQSkc3wamrEKVrfYt6AlAUCafbhtNlxemy4XBZcTqt6LpBJKIRCWud7+HE9XQZDMEKSCo69Spa2Uxh6lCbzW6JbROClUAgEAj6gyRJnHHRFF59Zh3e9hCbv62hsd7L6d+Z3GME0eEQ8If5y4NfxbVdd8tCMrIcKfcxDIO6p/+Cb6PpC+SaMpXMhQupefC+uH7pCEs13oM8uuFJInoECYkbp1/LxJzkY9LBwoiE8L97H3qLGYFlnXYGtrkXHfZxKw62c8/z65MKS7PH53PLJdNiFd1e/WwvOw+0xrYvmlrEU29vp741EGtbMquEy5eOw+MUBWwExydCXBIIhgnPPvtX9u+v5KGHHmP27LkAnHHGWXzvexfzt789zW9/++9J93v++b/R0FDPX//6PKNHj0FTI3iUMPf98c+89dbrTB9ThCRJjJ1xEtNPOh+HK3WJ2r2bv2LN+8uSbpuy4EymLjpX+CkNMja7hbxCT1KTUMMw8HWEu4hO8ZFPAV/yqCdNM/C2h/C2x5fUdWfYyMxykpntIDffRWa2uZyZ7cTptiJJEoZhoKo6kbCGGjGFozghKqKhdhGjwtF2NZJEqOrSliLDMikxkcvXpx9lj8QJTzYLlu7eVL1EVCVbFoKVQCAQHPt4Mux89/o5vPXiJpobfBysaufFP3/D/FNGMWN+GRbLwBUySSYsXf+zRXgyejYgb3r1ZdpXmulb9pGjyDxlMTWPPBTXJx1hqSnQzMPr/4RfNQWUayZfxuyC6X29jcPC0FUCHz6MXm9WZrOMW4j9pGsP+5m7t6ade15YTyCJ5cDCqUX86DtTsCjm73Ldrgb+/mVFXJ9VW+tiy+WFHr5/ziTGlQ6t/5RAMNwQ4pJAMAwwDIN33nmTE088JSYsAeTl5fOzn/0ciyX1v+pHH73P7NnzGDVqNFW7N7L+01dR25rI9jjZVdXIWaedxqwlF5OVn7o8q65pvPTAL5Jum3v6FYybebKo+jYMkCQJT6YdT6adkpGJ2yNhLSo2meJTR9fop7YgWhcjUgBfRxhfR5jaqraEY1ksMhlRockUnDqX83M9WK2pPbp6wzAMNFVPEKlSRU6ZyzqRsBq/HreP2ifBSo3oqBGdAAOXhmixyn3yrzpkzp7Mv+pQ22CnWggEAoGg73gy7Fx6/RxWf7qPTWurUVWdVSv2sfnbGhacMpqJ04sO+/PbTIWLF5a+f9si3J6ehaWWD9+n+e03AbAWFpG19DQOPvFYXJ90hKWOsJeH1/+JtrBZYe7icedxUsnh+xv1BcPQCa74E9qBTQAoZdNxLL0JSTq8Meme6jbufXE9gSSR1ktmlfD9cybFfn+1TT4eenlT0uM4bArfXTyW0+eVoohxskAgxCWBYDhQW1tDQ0M911zzfcD88h0IBHC5XFx66RUp92tvb6emppoTFy5kxfJHqD+wM7attCiPfbXNLLn05l7P31JfldAmTLqPPqw2hbwCN3lJyhEbhoHfG05Mt2szl/3e+LK7qqrT0uinpdGf9Fwuj80UnLISxSeXx9bjoFWSzOp3FquCc4CqFxuGgaYZCeJU1wiqcNf2bgJWuGvfLu19MV+PCVYD6Jtlscip/ausClZ7imgrq9xZNbDbNiFYCQQCweFjtSmcctZ4xkzM57P3d9Ha5MfbHuKTt3fw7Vf7mXVCGZOmF2Hpx2RMMo+l7/+sd2GpffUqGp5/DgAlM5OsU5dS//RfOjvIMhMe+3OvwlJADfLIhj9TH2gE4IzyJZw1cmmf7+NwMAyD0FfLUHevAkAuGIvzrNuQlMP7+rqrqpX7XtxAMEna/zknlPO908bHfj7JKsMd4oQphVx5+gRyeokiEwiOJ4S4JBAMA6qqzBL1OTk5PPLIA7zxxiv4fD5KS8u4/fZfcsopS5LuV1NVAUDD3nXUS2Zkks3uYvpJ51Nn+YYtLz6H1+vF4+m5UkV2YSmTF5xJ1c71zD/rSopGThq4mxMMCyRJwp1hx51hZ0R5Yth2JKKl9HrqaA3GlV8G8HvD+L1hDla1JxzL7rAwbU4JM+aX4nIPTRqlJElYLBIWi4zTNXBeB5qm9xJR1XP6X7I+ffHNUlUdVdUJDmCElaJI2OyWLlFS8SmBljQiqmJVA6PbFEXM2AoEguOT0lHZXPmj+WzfdJBvvqjA1xGmrSXAZ+/t4uvPK5gxt4Rpc0vTfjYlE5au/9ki3L2IGL4tmzn45BMAyA4HWacsofGlF2LbJauV8X94vFdhKaJFeGzjXzjQUQ3AwuJ5fHf8d4Y89Tu87u9ENn8AgJw9Aud5v0CypvaZSocd+1u4/6WNhCKJwtIli8dw4UmjY/eZrDIcQFGOk+vOnsS0MbmHdS0CwbGIEJcEgmFAR0cHAH/60x+xWCzceeevkWWZZcue4Z/+6dfcc89DLFiwMNZfUyPs3vgFH/7dHDRYZBlJkhk36xSmn3gedqebT9ZsBiAYDPQqLimKhVmLL2LW4sM3RxQcnVitCrn5bnLzk0c9BXyReK+nLpFPvo74qKdQUOXbr/azYU0VU2YWM3theY/Go8MZRZFRnDKOATTn1DQ9MaIqRbRVJBKNqkoQsA6JXmaqoNYHwUrTjGh01cAJVrIipedTla6XlU0IVgKB4OhBliWmzhrBxKmFbN1Qy8avq+hoDxH0R1jzRSXffrWfcZMLmDq7hOKyzJRCTVJh6dbePZaC+/ZS84eHQNOQLBYyFi6KpcaBKTaNe+jRXgUiTdd4ausydrXuBWBG/hSunXz50AtL21YQ/uYVACR3Ls7zf43sSO0Zmg7bKlt4YPkGwhE9YdvVZ0zgrAXlcW3/9tSahH6XLB7DeQtHxky+BQJBPEJcEgiGAZGI+eXc6+3guedeITMzE4CTT17ClVdewmOPPcKCBQvRdZ3KbWvY/OXb+DtaUMNBADLziznn+n9I4askUmAEh4ckSbg8NlweG8VliVFPqqpHo54CtLcE2bW1jrqaDjRVZ/O3NWxdX8v4qYXMWVSeVLw63lAUGUWRsTsGWrDSk0RLqV2EqM5tFkWmvT2YMiJLDWsJ0Wo9oWsGIU0llKTiTn+RZSlBeLJY46OmuvpXdY+q6h5tZQpWkjBeFwgEg4bFqjBzfhnT55ayZ3sD61cfoLHOi6YZ7NxSz84t9eTku5g6ewSTphfFPQeSCUvX3bIQT2bPwlL44EGqH7gPIxQCScI9cxZtn66IbZddbsY/+Eiv124YBs/veIUNDebk5LisMfxw2nUo8tAKKZG9awh98Vdzxe42hSVP3mEdc0tFMw8t30i423NNkuAH505m8aySuPZ1uxqobuysImK3KvzbDxdQmDNAufwCwTGKEJcEgmGA02mWr12y5LSYsASQkZHBKacs4Z133mT3ptXs+vYj2psOxrZn5eQDMHraSQnCUihkVgZzu8WXecHgoigSLrcNWZawO6xkZNnZs6ORnZvNSiq6brBzcx07N9cxeWYxp547Ufj+DDCdglV6j/WCggwaGjp67KPrRq8RVcn8q3pKF1STzBj3dP5QcOAFK0tCtFQXI3abpdt679FWikUWgpVAIIhDliUmTC1k/JQCava3sXV9DXt3NKLrBi2NflZ+uIdVK/YxfnIBU+eMICvHmWDefd0tC3uN+lVbW6i67/doXvPz3DlhIt5v18a2KxkZjLvvoVS7x/HG3nf5staM1in1jODmmT/ApgzcJEg6qNVbCX78GBgGWOy4zvslSk5J7zv2wOZ9TTz08iYi3YQlWZK46cKpLJxaFNfuDUT467s7Yuuzx+dz+2UzxOe8QJAGw0pcMgyDv/71ryxbtoza2lpGjx7NTTfdxIUXXnikL00gGFTy8wsByMmJz9/W1AiSGsAwDD77+1O4HaZ/jdOdxbQTzyN/5FT+/NoKmpoaE47Z2NiAx5MRE64ExzaGYaBGdKy2gZlhNAxTWPD7TG+lpO++MEF/hEAgkraP0PaNBzlhyeheTUkFRx5TLLSkLVilg64bpkjVa3XA1F5WalgnHFa7iF59E6zCIZVwktLT/UWS6DH9z9Ldp+pQtFU3kcpm7+xrEYKVQHBMIEkSpaOyKR2Vjd8XZsemg2xdX0t7q1nBdcfmOnZsrkvY79qbT+hVWNL8Pqruvxe1qQkAW2kZgZ2dooiSlc24e+5P6zo/2v8Z71d+AkC+I5efzfoxLuvQjh+1hgoC7z8IugqygvPs21EKxx3WMbdWNCcVlhRZ4qcXTWP+5MK4dlXT+cOrm2j3mRkFF5w0ikuXHN41CATHE8NKXHrsscd48MEHuf3225k9ezafffYZv/71r1EUhfPPP/9IX55AMGiMHTsOm83Gvn1mjruvvZl9W1azZ+NKdmxZhyLLOO1WbHYXU044i/GzF2OxmkLTiBGl7OwymDjErl07mDx5ypDeh2Bw0VSdjvZOo+3W5gC7ttYT7FKdbOK0Is64cHLKYxiGQTik4W0P4u0I4fOGCXjD+KKiUcDXKSD1JS0qFYoi4XDZcLqsOF1WysfkCmHpOEaWTTNxm33ghh+HhNWuAlQ46kWlJkkJ7FHQ6tKW/vkhHNIIJylpfTj0GjmVrn9VLKVQCFYCwZHE5bYxZ9FIZi8sp6qiha3ra9m7I3Fy8JqfnkBmds/Cjh4OU/Pwg4SjBWEsObmEqzsr/ypZWWkLS6tr1/LKbtOfKcPm4bbZN5FlPzx/o76itx0k8M49EAkCEo6lN2Epm35Yx9yxv4UHX96YVFi69ZLpzJlYENduGAZPv7uD7ftbAVgwuZBLFo89rGsQCI43ho24FIlEePLJJ7n66qu55ZZbADjxxBPZvHkzzz77rBCXBMc0TqeTExedzBcrP+PFx/8bw1sHGLT5guyrbWZCeTFzllzC2JknYbPHDziWLj2dF198jsrKCkaNGg3AmjWr2b+/kmuuuX7ob0bQbwzDIBhQkxhnm+/e9lCvx6g/2EFLkx9fR4iONlNA8raH8EXfvR2hPn1x7o4sSzjdVlxuGy63DafLhiMqHDmc0ffoutNlE19oBYOOJHV6Mw0UMcGqj9UB1S7pgl39qw71MdL3Xe+zyJUO3cUpi02ORlVZulQLlOP6mJUF5ZSilfj/Fgj6hiRJlI/JpXBEJnU17XFFMa7+yQKycnoWlgxdp/aJP8ailCS7A7WlObZddjgYe/f9aV3LtqadPLv9JQCcFge3zfoxBa7D8zfqK7qvBf/bd2MEzdQ++0nXYh2/6LCOubuqjftf2phg3m1RJG797gxmj89P2OftVZV8sakWgLElmfzoO1OQxeebQNAnho24pCgKzzzzDNnZ2XHtVqsVv99/ZC5KIBhkdF2nYutqDuxcz1i3l68tEk+88AazxpWgyBIb99Vjdzj4l989THn5KKqrq9i8eSPTp8+ktLQMgGuu+T7vvvsWd955C1dddS3hcJjnnnuaSZOmcPbZQpQdjhiGga8jRHOjn5ZGn/neZC4fbvRDa5Of559IrHDSGza7xTTtdtui71EByWOPW3Y4LeLLpOCYJ06wGiDbOsMwUFU9qX9VWimCKZb7JVj5eu+bLj0JT32pDtjVtF14sgmOdcIhlTdf3BgTlsZPKeCMC6f0+rdvGAb1zz6Nb923nW2hYGcHSUqrKhzAgY5qntj8NLqhY5EUfjrjB5RlHJ6/UV8xQj4Cb9+D0WFGcNnmXoxt+pmHdcy9Ne3c99J6QpH48ZRFkbn9shnMGJsonn2zvZ6XPzWzB/IyHdx+2UxsVlERTiDoK8NGXJJlmUmTJgHmB2dTUxOvvPIKX375Jf/+7/9+hK9OIBgctnz1DltXvweAx2nliqUzWbW1ig1765Bkhdmz53HrrXdSXj4KgA0b1vHf//1v/NM//UtMXMrJyeGRRx7nwQfv5c9/fgy73cHixUu59dY7sdlsR+zeBCYBf4TGug6a6n20NPlpbvTR0ugf8IiEnnC6rHgy7Xgy7HgyHbhjy3bcHjsujw2LRZR9FwgGE0mSTAFlAL+wGIaBphmdlQHjhCc9ProqdMinSk+ItjJTCTsrDPZFsFIjZqXCAJHeO6eJxSJjOeRTlcS/KhZVZUusFphKtBKClWC4EA6pvPXiJuprzEidsZPy0xKWAJpef5W2z1ak3D7h8SfTEpaaAs38YcOThLQwEhI3TLuaCTlDmwJmaCqBDx5GbzHT+axTTsM275LDOmblwQ7ufWE9gW4TdTaLzO2Xz2Ta6NyEffbWtPPEm1sBcNgU7rxiJlluMX4WCPqDZBh9GUIMDe+99x533HEHAEuXLuWBBx7A4ejZ1E4gOBrZ9NXHfPrGM7g8mYydNo+x0+ZROnYSijJsdF9BH/B1hKipaqO2qo2D1eZ7W0tgUM8pyxJZOU5y8txk5zrJynGSme0kM9tBVraTzCwHFjH7JhAI0sQUrPSoj5RKOKzFTNDDIdPPqnNbl+WufcOmmHVoPRRS0zb9HywsFjnq92WmAcaW7RZsNvPdGn232S3Yo/2scfuY73aH2VdRhCgv6BvhkMpzf/qa/XvNNLZJ04q4/IZ5af0t1b71Dnsf/1PK7Se9tjwtYakj5OW3H91NTYdpJP6DOVdw/sTT07yDgcEwDBreeBDv5s8AcE1aSNGlv0KS+z9e2VfTxj8/upIOf7zQbbcp/L8fLWTm+IKEfepb/Pzqgc9o7QghyxL/8qNFzO1m8i0QCNJnWIpLBw4c4ODBg+zYsYMHHniAKVOm8Ne//jXtVIymJi+6Puxuq8+kUypacHRjVuQKYrHakWUxSD0chvr/RVN1Guo6OFjVzsHqdupr430TBhqn28qIsqxO4SjHfPdkOsSMvKDPiOeLYKjRtL6bq6tdI6pi1QI7l7UjLFgpihRfAbBLRJXV1q0qYJopgkKwOnaJRDTeWb6Z6spWAEaNy+Wc705D6SVyWA+FaF/1FfXP/pVUYYUTHn8SKY1xZFiL8ND6x9nbVgnAGeVLuHTCBX27kV5I5/kSWvMy4XV/B0AuHIfrgv+DZOl/sY/qRh93PfdtorBkVfj5FTOZNDInYZ9ASOV/nl1LVYOZJ3z92RM5bW5Zv69BIOgPR9t4TJYl8vI8KbcPy/CI8vJyysvLWbBgAR6Ph3/4h39g3bp1zJ0790hfmkAwoEiSlGDQLRieRMIaNQdaqdnfysGqduoPdgzqTPyEaYVMnlFMboEbp8sqfI4EAsFRjaLIKE4Zh9M6YMfUND21f1WsUmDXdEG9Wz+1Sz+zXetDlUxNM9ACKsGAOmD3JCvSgPlXHVqWFUk8Q44wqqrz7stbYsJS+Zgczk4hLBmqSmDvHvzbthLYvo3A3j2gpU6lT1dY0g2dv2xdFhOW5hXO4pLxQ+/NGd7+aUxYkjILcZ5z52EJS7VNPn6/bF2CsOSwKfzye7MZX5aVsI+m6/zx9S0xYenM+WVCWBIIBoBhIy61trayYsUKTjzxRIqKimLtU6dOBaC+vv5I2CDFCAAArERJREFUXZpAIDgO0TSduup2qipbqa5sob6mY9AiIjOyHMxcUMrYSQV4Mvo/wBIIBILjCUWRURQZu2PgBCtdNxKEKrfLRkODNyGCKpykWmBXoSoc7af2QbDSNYOQphIKDqBgJUtYrAo2e7xxerwYJWO1WZIIWl0N2y1dIqyEYJUumqrz3itbqKpoAaB0VDbnXjotzusw0tCAb/NGfJs24t++DSOcXiR0usKSYRgs3/UGGxo2m/tlj+X6qVciS0MbKafu30jo878CINk9uM77JbIzs9/Hq2vx8/tl62j3xf+8nHYLv7xyFuNKEoUlgOc/3M2mvU0AzByXx1WnT+j3NQgEgk6Gjbik6zr/+I//yK233hrzWwJYuXIlABMnTjxSlyYQCI4TvO0hKvc0UbGriZoDraiR9L8Q9IXcAjcjx+YyalwuRaWZIg1CIBAIhgmyLGF3mL5KhygoyMCd1X/hX9eNTuEp3eqA3VIEu7f15fmk60bMF2ugkCS6iVMWU4jqQ0RVV6N2m01BscjHnGClaTrvvbY15rFUUp7FeZdPR0bDt2Ubvk0b8W3eSOTgwaT720rLcE2ZgjUvn4YXlsVtm/DYn9MSlgA+3P8pn1Z9aV6Du5ifzLgBqzy0XwO1xgoCHz4Chg6KFee5P0fOKu738RpbA/x+2TpavfHCktth4ZdXzmbMiOSi1YffHOCjb00T8fJCDz+9aJqwFxAIBohhIy7l5uZyzTXX8Pjjj+NwOJgxYwZr167lscce44orrmDs2KGtYCAQCI59DMOgsc5Lxa4mKnY30VjnHZTzWG0KZaNzGDk2l5Fjc/BkigIFAoFAcLwgy1LMKHygiAlWaYpTapeUwHA0VVDtXlGwD1VMDYOomfvAVT6NCVbWbn5V0cqBidFWvQtYFuuRE6w0TeeD17dRuduMkCka4eGUUT4anvgDvs2bkkYnKVnZuKfPwDVtGq5JU7BkZRFpamLfP/wqrt+EP/4JSUnP/HrNwXW8tudtALLtWdw664e4rENryaB3NBJ45z5QQ4CE4/SfohSN7/fxmtuD3LVsHc3tobh2j9PKr66czajijKT7bdzTyLKPdgGQ5bFx5+UzcQ7g/6VAcLwzrP6b/r//7/9jxIgRLF++nIceeoji4mLuuOMOfvSjHx3pSxMIBMcIhwSlXVvq2b29AV9HKKGPxSojyzK6rqNrRr/S4XLyXbHopOKyLBGdJBAIBIIBYzAEK8MwUCPdfanSN2BP1Sf98w+8YAXdI6z6ElElmxUDu21LR7DSdYOP/r6dfTsbAciRvEz+ahlNnwfjO8oyznHjcc+YiWv6DOzlI+OOrba1JghL4x99HMmS3u99Z8tuntn2IgBOi4OfzfoROY7stPYdKIyQj8C792IE2gCwn3g11jHz+328lo4Qdy1bR2Nb/M/S47Tym6vnUF6Y3Gz4QL2XR1/fgmGAzSpz5+UzyRWTfQLBgDKsxCWr1cpNN93ETTfddKQvRSAQHGO0NPnZtbWe3VvraWsJJGx3uq0Yhul3EQmrqEbfUuIsVrlLdFIuGVliwCIQCASCowdJkmJCykBhGAaqqsc8qcKhRHFKjUVUpfav6r7cl1rXfRW50sFilaMRVZZopFWnN5VFgbaaJupazYvMCDYws+Y9LLppOC27XLhnzcYzazauqdNQXO6k59C8Xvb+6udxbeMfeQzZakvrGg/66nh809NohoZFUvjJjBso8fQ/Da0/GFqEwPsPobfUAGCdfja2GWf3+3htvjC/X7aO+m7juEy3jd9cNZvSguTCUqs3xAPLNxAKa0jATRdMY3Rx/72eBAJBcoaVuCQQCAQDSSgYYdeWerZtPJg05S0rxwwL1zWdjvbECKbeyMkzo5NGjstlRFlWr+WEBQKBQCA4npCkaPU768AKVpqq9z26Km692/5htU+ClRrRUSM6AV+kx34ZwSbm1LyP3e3EM/dkPHPn45o0udfII83vZ8/Pb4trG//wo8j29Ly/2sMd/GHDkwRUM7rnuinfY2LOuLT2HSgMwyD46Z/RarcDYBk9D/uiq/p9vHZ/mLuXreNgsz+uPdNt4/9cPYeS/OQiXSii8dDLG2MpdJefNo55kwr6fR0CgSA1QlwSCATHFIZhUHugjW0batmzozGhrHRWrhOLIqPpBq1N/hRHSY7FKlM6qjM6KTNbRCcJBAKBQDCUSJJZ/c5iVXC6BuaYhmGgaUYS43TzPdy9/VC0lT+E/2A9waYWVA1U2YomWdBkKzmRJhaOM8i75lc4x09I23xbD4XYc8etcW3j7n8Y2ZGeT1JYC/PHjX+hKWhWp7tgzDksKJ7Ttx/IABBe8zLq7lUAyEXjcZz+07R/Bt3xBiLc8/x6qht9ce0ZLiu/uWp2SmFJNwz+9OZW9tV2ALBk1gjOPWFkv65BIBD0jhCXBALBMUE4pLJ5bTWb1lbT2hwfLu3JtOPOsKNrBi1Nvj5V2cnOdXZGJ5Vnx5UOFggEAoFAcPQjSRIWi4TFIuN0WXvtH9xfSevHK+hYvQoj0hm9JFksuGfNJvPEk3FPPz1tb6RD6OEwu3/207i2sffcj+JJnu6VsL+h85etz1PZfgCARcXzOXf06X26hoEgvG0F4fVvAiBlFuE8504kS3rpfN3xByPc88J6DtTHR6B7nFZ+c9WclKlwAK98upe1OxoAmDIqh+vOnnTMVSQUCIYTQlwSCARHNe2tATavrWH7poOEgp1llmVZorgsC1mGtpYgddXtaR3PYpEpHZUdE5Qys4e2oopAIBAIBILhSWDvHppefxX/ls1x7baSErJPO4OMBQvTFoK6o0ci7L71J3FtY/73HixZ2Wkf49Xdb7Ghwby2iTnjuXrypUMupvh3ryX0xdMASI4MXOf9EtmRvHpbbwTDKve9uIHKgx1x7W6HhV9fNZuyFObdAJ9vqOHtVZUAFOe6uPW707GI4ioCwaAixCWBQHDUcSj1beM31VTsaozzScjIcpBb4EZTNWr2t6VV6S0rpzM6qaQ8C8sAekMIBAKBQCA4uglW7KPp9VfxbdrY2ShJuGfPIef0M3FOnnJYIo6haey+Jb6g0ej/+h3WvLy0j/FZ1Zd8fOBzAIrdRdw0/XossoWmQDMGBvnO9I/VX7SGCurevBcMHRQbznPuRM4q6texIqrGQy9vYk9N/OSgy27h11fNYWRRasFqW2ULT7+3AzAjnH5+xUzcjt4j0gQCweEhxCWBQHDUYBgGlXuaWbuykvra+FmsstE5WK0yjXVeKnc39XgcxSJTOrIzOumQsbdAIBAIBALBITSvl4aXX6T9889ibZLFQubiJeSecx7W/MM3hjZ0nV0//VFc26h//Q9sRelXdtvcuI0Xd74OQIbNw60zf4jL6uSZrS+y6uA3yJLMv5/4j+Q4sg/7elOhdzQQePc+jEgQkHCc8VOUovH9Opam6/zx9S1sq2yJa3faLfz66tmMKk4tLNU2+fjDq5vQdAOLInHbpTMozBkgcy6BQNAjQlwSCAQDxk03fZ9t27YmtC9dejr/+Z93pdyvpqaahx++n3Xr1gJw0kmncNttvyAnJwcwRaV9O5tY+2VlXNU3RZEoH5NLe1uQqoqWpMc+RGa2IyYmlY7MFtFJAoFAIBAIkmIYBh2rvqLhhWVo3uhklqKQdcpics+/sE8RRb2dZ/ft8ebd5f/0W+xl5WkfY39HFX/e8jcMDKyylVtm3kieM4e/73mXVQe/AUwvJrvSP8+jdDBCPgLv3IcRaAPAftI1WEfP69exdMPgqbe3s25XY1y7067wqytnM7o4M+W+Hf4wD7y0EV/UJuHG86cwsTy7X9chEAj6jhCXBALBgGAYBhUV+1i8eClLl8abRxYXj0i5X1tbK3fccTORSIRrr/0+mqaxbNkz7Nmzm8cf/wsH9rbxzcpKmhs6K4TY7ArT55ayd0cDFT1EKZWPyYkJStm5YtZKIBAIBAJBz+ihEHXP/pWOr76MtbmmTafwmuuxFfUvxSsVFb/9/zBCwdh66S9+jXPsuLT3bwm28scNTxHWwkhI3DjtGkZllvNp1Ze8W/lxrN9pZafgsg7OOMjQVQIf/gG9tQaArIUXok8/q3/HMgyWfbiLLzcfjGu3WxV+ccVsxpakFpYiqs4jr2yivtUs6nLRyaM5cVr60V8CgeDwEeKSQCAYEGprawgEAixefCrnnHN+2vs9//zfaGio569/fZ7Ro8cAMGXKNH75y9v4j//7CCW5c2N97Q4LMxeUMWNeKU31Xr79an/C8abMGsGYCXmUjMrGKqKTjhj1y/5G60cfJG5QFOylZdjLyrEWFCA7XchOJ4rLiexwxtZjL6vwSBAIBALB0KC2t1N9/z2E9ptG0EpmJoVXX4dn/oIBN8Y+8PvfETnYKaKMuPlnuKdNT3v/gBrkDxuepC1sRlZdNuFCZhVM49v6jby487VYv9kFM7h84kUDdt1dMQyD0Mpn0aq3AGAZPY/cM75PY6Ovlz2T8/oX+/hobVVcm0WRueOyGYwvy+rxOv7yznZ2VpmRUwunFnHxKWP6dQ0CgaD/CHFJIBAMCPv27QVg1Ki+Pcw/+uh9Zs+ex+jRY9B1g11b6ti9XibTXcDWHaspOXEuDqeFWSeUM31uCTa7+bFVUJzBuMkF7NnewKjxeXzn0hnoki5KzA4DDMOgfdWXyTdqGqH9lbGBe29IFktUaIoXnRRnVIxyRbc5om3JRCq7HUkWFWIEAoFAkBrN66Xqrv8hfLAWAOfkKYy46WYsWalFjf5S+8QfCezYHlsv/P4PyJi/IP1r1TX+vPlZanymOHVq2cmcVn4KO5p38+fNz8b6jcwo46YZ1w/chXcjsvl9IttWACDnj8Jx2k+QpP49b9/7ej9vrKyIa1Nkidsunc6U0bk97vvmV5V8tcX8WYwrzeSH508W40GB4AjQL3Fp37597N69m6amJiRJIjc3lwkTJjB69OgBvjyBQHC0sG/fHoDY50AgEMDp7Nkou729nZqaak499XS2bajl26/2095qhofnZJVR27CdRaeNZfqcEqy2+Cgkq03h7EumxtbzCzw0NMSbfAuODJIkUXr7zznwu/867GMZqorW0YHWcRi/W0lCdjjiRSpHNFrqkEjldCK7XCgOZ3zklNNlilZOJ5JFzMcIBALBsYih69T88ZGYsJR58mKKvv8DJGXgI6Ablr9Ix+pVsfX8y64ge8nSPh3j1T1vsa15JwAz8qdw+YQLOdBRzYPrH4/1cVmc/MOCOwbkmpOhVq4n9NXzAEjuHJzn/BzJau/XsT7bUMMLH++Oa5MliZsvnsbMcfk97vv1tjpe/cyc4MzPcnD7ZTOxWkTkukBwJEh7pLxnzx6WLVvGe++9R2OjabBmROt/H1KG8/LyOO+887jqqqsYNy79fGGBQHD0s2/fHlwuNw89dB8fffQBgYCfkpJSfvKTWznzzHOS7lN3sA6A/TsDrOjYGWt3uW2Mm1hOZc06JkzLSRCWBMMf5/gJTPzTX2LrhmGg+3xEGhuJNDWiNjURrq8jXF1FqOoAeiAweBdjGOiBQPQczf0+jGS1JopOMWGqc1npntrXpb9kt4vZVIFAIBhmtH26gsD2bQBkLDiBohtuHJSI15YP3qPl3bdj69lnnUPued/p0zG+rFnDJwe+AKDUM4IfTL2GpkALv1vzQFy/uxb/62Ffbyq0pv0EPnoUMMBiw3nOncjunH4da832ev767va4Ngn40QVTmDepsMd9d1e38ac3zd+b027hzitmkekaPONygUDQM72KS/v37+fuu+/mgw8+wOFwMG/ePK688kpGjhxJdnY2hmHQ1tbG/v37Wb9+PcuXL+fZZ5/lrLPO4je/+Q3l5elXOxAIBEcv+/btxe/34fV28H//77/h9Xbw0kvP86//+s+oqsq553YOntSIxtYNtbz31prouvll251hZ86icqbMLObJp9YBEAwG8Hg8Q39DggFFkiQUjwfF48GRIspVDwaINDXFxKdIUxORxkbU5kYiTU1obW19Pq+SmYm1oBBrQQGSLKNFRabOlx89EMBQ1V6PZUQiaJEIWnt7n68jhiR1Ck4OJ4rLFR891TXtL4lIpThdyA6HiKISCASCAcLQdZrfeQsAS34+RTf8cFCEpfbVZvW5Q3jmzafwyqv7dIzdrft4fscr5v5WNz+d8QPCeph/XfW/cf0eOu13gzaRoftbCbx7P6ghQMJx+k9R8kf361ib9jbx+BtbiMYrxLjhvMm9mnE3tAZ46OWNqJqOLEnc+t3plOa7+3UdAoFgYOh1dHr++eczceJE/ud//oezzz4bl6vnSgN+v5/33nuPp59+mvPPP59NmzYN2MUKBILhy0UXfRdN07nssu/F2s4882yuv/5K/vCHBznrrHPRNdiyrob1Xx8g4IsQ8IUBcDitLDlnApNnFKNYug/oRJTH8YLscJpm36VlSbfrkTBqczORpibUxkYiUdFJbWwkVF2N7k80ENXa29Ha2wnu2Y0lPx/HyFG4Jk/BPnIUjlGjsGRlR48dSRSdggE0f3Q92ClE6YFAcpEqGEw4fwKGgf7/s3ff4XFVd/7H33d6US+WZMm23CsumGaqMS2G0CEhkJBCQiCBEJJskt1ksz822bRlk0AgJJQESOg99OLQqzHGvRfZlqzey9R7f3+MPNJYsjSSJav483oePZq55dwz4BnNfOac72ltxWxtPZj/VBguV2JI5fUljJ7qCKgSQ6p926KpTizL0igqETnsBUt2EqmNrTybddZSbB7PgF+jZd1ayu/6S/y+u3giY6+9rk9t1LTVcdea+4laUeyGnW8ccSU+p5cfvPWzhONuWfxLbP2se9QbKxKk7eVbsFpio4Ddx16Ks3hhv9ravLue259cQ9RMTJa+cPpUTp43tsdzWwMRbnl8NU2tYQC+eNY0ZvdSl0lEBl+v4dItt9zCaaedlnSDPp+PCy+8kAsvvJDXXnvtoDonIiPHBRdc0mWb2+3hrLPO5m9/u4tnHn2bunInoWDHCJGMzNiSsvOPG8vsBYlvJILBIAB+v76Fkhib04UrLx9XXtdvMy3LIlJbQ6CkhOCunQRLSgjsKkkY7RSprqa5uprmT1bEt9nTM/BMmIB7/IR44OSeMKFfoYtlmpiBQEcY1dpGtD2kMtti9/eFVF3DqX2hVStEo71fKxQiGgr1azQXwHYAmy1eFD1hil83YVTn7fZOoZbN6x2UmiQiIofKvjpLAN7pMwa8/cDOHZT+/ub4fVtKCuN/8rMezuimjUiQv6y5l+Zw7EuUy6ZfyIS0cXz3jf9IOO7/Tv45DtvgjGy1LJPA63dhVu0AwDn9ZJxzl/arrZLyJm55fBWhiJmw/eJTJnHGUT3PeomaJnc8s5ay9hXpzjpmHIvnF/arHyIysHp99elLsLS/008/vd/nisjIZlkWZbvqqdgdC4nWflpCbmYxABnZPhYuGk/eOC+PPv9Latu/MeysurqKlJTUXouCi0Bs2p0zOwdndg6pR3Z8ixqpryfQvjpdsKSEwO4SIu11AwGiDfW0rK6nZfWq+Dab349nfDHu8eNxT5iAZ3wxzjFjep0mYdhs2H0+7L2M8O2JZVlYkXAsiIqPmGoj2toppOoURHUJqNpHW1nBJEZRmSZmawtmawu9Two8MMPt7ma1vm7CqM4h1X4F1Q2XS6OoRGRodH7tiZoHPq4fQuXl7PrFTQnbJv/u1j693pmWyd83PEJpcywEO7XoRI4rOIrrX/9xwnG/PvFneBz9K6idjNDHTxHZ8TEA9rEzcZ94Zb9et/fWtPC7Rz+lLZj4Rcq5xxdzzqLiHs+1LIsHXt3Cuh2xkVMLpuZw6eIpfe6DiAwOFW0QkYNWVVXJjTdex2mnncHFF36JTWvL2bSmgqaGANu2xlbwSPFmUVCUztyjCymemoPNFntDUlBQyObNm7q0uWXLJmbMmHlIH4eMPo6MDFIyMkiZOy++LdrcTHD3LgIlOwnu2kVg107CFRXsK/pgtrTQumEdrRvWxc8x3B4848cnjHByFYwd8FE7hmFgOF3Y0l1wEMtfx0ZRJY6Y6hxGeW0mTdX17SOouoZU0fZzkxpFFQwSDQaJNtT3u7/Y7bEgqstqfYkr9nWtP9UeaO0LqQahToqIjG7uoo6RMi1rVuEeoHqxkfo6dv40MQCa+ue7+/w69cKO1/i0ai0AMzKncuGUc7oESzct+jGprsGrTxne+gGhlc8CYKTn4T392xj2vn+MrGkI8H+PfBqfzrbPmUeP44KTJvZ6/qsf7+GNlaUAjM9L4epzZ8ffT4rI0BuwcMk0TXbs2EFLSwvFxcWkpaUNVNMiMoxZloXd8FNXW88jDz9K057xOJ2xegUtbXVs37OcieNn8qVrFpObn9rl/MWLl/Doow9SUrKTCROKAVi+/EN27Srh8su/dCgfihwm7Ckp+GbOwjdzVnybGWgjuHt3bJRT+5S6UFkpmLFvsa1ggLYtm2nb0rGqoeFw4Coa1z6trhjP+PG4ioqwOYd+pZrYKCo/dp8fsrvuz81Npaqqqcc2LMvCCoU6jZ7qWnsqsf5U635TAmO3rfYprj2KRjGbmzGbm/v5iGMMtyc2IqqbkKrLKn7dFVT3eTEcTo2iEjmMuAqLcI0tJFRWSu1LL5J63CKcWd28cPZBtK2N7T+4MWHblD/d2efFGD6pXM2LO2NlRsb4crhqzhX84O3/Sjjm34/+Ljnewas3FK3aQeDNe2J3XD58Z92I4el7kNXQEuLmh1dS25j4N2HxgkI+v2RKr6+7n26p5pFlWwDISHFxwyXzcGs1YZFhxbCs/evz991DDz1EfX09s2fPxu/3s3HjRsrKyrjqqqvIyjr0xdVqapoxzYN+WEMumTf/IkOpqryJ157dSH1NK7vL1/DWx/eSnprPlHHH4vJYrNvyFhZR7rjjrxQXT6S0dA9r165mzpy5FLYXba6rq+PKKz+P3W7nssuuIBQK8eCD91NYOI477rgHlyu5D+p6vshAM8MhQqWl8TpOgZISQnt297yynN2Oq2AsnvET2qfUTcA9bhw2z/Ca3nkony9WNJo4xW//MKpLgfSOkCraaeTVvqBv0NntXYOo/Vfx22/EVJeC6h6PRlGNIvr7Mvo1r/yEsttvBWLFtou+/0Ps/ZyWb0UibLnm6wnbJt9yO/Y+1pDc1bSH3624g7AZxuvw8G8Lr+Pe9Q+xq6k0fsx3F3yTqZmT+9XPZJgtdbQ+dRNWaz0YNrxLv4ejaE6P53T3fGkNhPnNgyvZXZn4BcIJc/L56jkzsfUSLO2qaOJX//iEYDiKy2nj369YyIRuvrAUGWlG2t8Xm80gO/vA4fJBh0u///3vOe2005g7d27C9ubmZv77v/+bn/zkJ6QfxND+/lC4JHJovPvaVlZ/3PEmp651C2s3v8beihLcbg8LFizkmmuui49IeuGFZ/nlL2/iP/7jvzj77HPj5+3atZNbb/0dq1atxO32sGjRCXzrWzeQmZmZdF/0fJFDwYpECJXvbQ+cYj+BXbuSq3EEjP329aQs6N/KOgNppD1f4qOo2oOn7sKohG3dFVRva8MKhQ5Zn20eT0Jx9G4LpLeHUR2jrfYLtZzOQ9ZfObCR9nyR/qn4x/00vPEvIBYwFV53A46MjD61YZkmW67+WsK2ib/9Hc4+ftneGGriN8tvpT7YgIHBtfO+xkflK/i44tP4MV+f8yUWjDmiT+32hRUJ0frsr+IFvN3HX4Frzhm9nrf/8yUcifJ/j6xi8+76hOMWTs/lmvNnY+8liK9rCvKL+z+mrimIAVx38REsmJrb58cjMhyNtL8vgxourVu3jnfffZerr7662/179uzh73//O//+7//e30v0i8IlkUOjqSHAyg92k5bhoXhqNhlZ/S9kfLD0fJHBYIbDRJuaiDY3Jf7udDvS2Ehg29Z4zabeTPq/W3Ac4i9d9ne4Pl+sSAQzEOi+GHp3q/gdoKB6sv+vD5bhcHRZwS8eUsVHT/m6Kaje6Xi3W6OoDtLh+nw53FiRCGV/vp2WT1cCYE9NI//rV+Of3fNInfj5lsW2G76N2doa3zbhpv/BXdi3lczCZoRbPvkLOxpLALhoymdpi7Tx4s5l8WMunXo+i8ed0Kd2+8KyLAKv/4XI1g8AcM44BfdJX0lqynDn54tpWtzxzFpWbKpKOGbmhEy+e+k8nI6eX5uCoSi/fuATSipi7V22ZApnHjO+Pw9JZFgaaX9feguXDqrm0ssvv8wXvvCF+P0///nPrFy5kl/96ldkZWVRVFREZWXlwVxCRIax1HQPJ581dai7IdInZjhEtLGRSH09kYYGog2x35GGeqINDfHwKNLUlPSIpGT5Zs/Bnqqh/EPFcDiwp6RgT+l/4VvLsmJFzA9UDH2/2lMJU/w6HW+Fw71fKxKJ/3vsN8NoH0XVKXTab7W+WL0pXzcF1TuKqfe1VozISGM4HIy95ttU/OM+Gt95m2hTI6W/v5m0408k5+JLe/1SYM9vf5UQLBX94Ed9DpYsy+LhjU/Gg6Xj8o/C6/Dy5Nbn4scsGXfSoAZLAKFPn48HS/aC6bhP+FKfa9FZlsUDr23uEiwV56dy3UVH9BosmabFnc+uiwdLi+eP5YyjB6bYuogMjoN6p1BdXU12dkfBu3vvvZeGhgZWrlzJaaedBoBN35aJiMghYEWjscCotpZwXQ3R+noiDfVE6tuDo8YGIvUNmK0tB38xw2gPKVKxp7b/pKRiT03BnpKGPcWPPSUFmy8WZNj9/tgHdP1NHPEMw8DweLB5PNCHqbv7syKRTtP59iuSHmjDbG3db/RUW6fRU/v2BXofRWVZHSOuqO13fw2ns2vo1KkQenyKX7f1qmLHG263iqXLsGY4HOR/5Sp802ZQ8cD9WMEgje+9Q/PKFWR99jwyFi/B5nZ3Oa/i/r8lLPiQ/41r8PVjxdt/7X6bD8o/BmBS+gSOzJvLn1b9Nb7/iJxZXDz13AOdPiDCOz8htPxxAIzUHDxnXNevleGee7+E1z8pTdiWn+Xjxs/Nw+vuvb3H39jGyi3VAMwuzuTyM6bp9UNkmDuocGnSpEls27aNmTNjL55//vOf2bhxI6eeemr8GPNQFeAUEZFRy7Ks2Gii2hrCtbVEams7bte136+vG5TpSqnHLSJlwcJ4gORITcXm9ysokoNiOBzxYLK/LNPEDAYT60/tt1qf2dYaL4reZdpfe0jVY5H6fdcKh4mGw0QbG/vdXwyj59X69i+c3t2Kfx6PRlHJoEs7/gQ8U6dS9chDtHy6ErOtjerHHqHupRfIPPMzZJy6JL5QQ+1LL9Dw1pvxc3MuvpS0Y4/r8zXX1Wzkqa3PA5DpzuDcSZ/hlpV/6WjXm801c79ycA+sF9HaUgKv3xm74/TgPesGbJ6+v0a9vaqMp97anrAtM9XN9z8/n1Rf7wu1vPlpKS99tAuAsTl+rr1gDg67/uaKDHcH9df5ggsu4I477uA///M/AZg/fz7z58+P73///fc58sgjD6qDIiJyeDDDISLV1YSqqghXVRKuqiJcXRX7XVV5SIsxdxauqiJ14VFDcm2Rnhg2G/b2MOZgmOFw19Ap0D5aqnNI1e3qfvuOT2IKqWXFRmR1mjrUH4bLlRhS7b9aX0LxdF+32wyXS6MgpEeu3DEUXncDLWtWU/nIg4TLy4k2NVH9xGPUvvg8aSechCMtneonHo2fk37yKWQtPafP1ypvqeSvax/EwsJlc3LFjEsSgiWAmxb96KAfU0+sYAttr9wK4dhz2XvqN7Fn9X0a2kfry7nvpU0J21K8Tr7/+flkp3t6PX/dzlr+8UpsFFiqz8kNl8zF59HiBiIjwUGFSzk5ORx77LHce++9fOUrX0nYt3LlSpYtW8ZPf/rTg7mEiIiMIpZpEq6uJlReRri8nFD5XkLl5YSrKonU1Q1197qVeVrvq+OIjGQ2pzO2Ml1aWr/bsEwTMxA48Gp9rR0hVddwqqNeFdFo79cKhYiGQkQbGvrdX2y2eFH0hCl+3YRR1pgsWiJGYkDVfrxht/e/DzIi+I+YS/HsOTR9/BG1zz1LqKwUs7WV+ldfTjjOO3UaeVd+tc/tt4Zb+cvqewlEY6HOF2ZczG2r7k445o+n/rr/DyAJlmnS9q+/YDVWAOBaeCGO4gV9bmdbaQP/+/CnmJ1GEbuddr576TzG5vh7Pb+suoU/PbWWqGnhsNu4/uK55GYcXHguIofOQa0Wt8+WLVt45ZVX8Pl8sdUFAgGKioo477zzBqKPfabV4kQOP3q+DC9WNEqoooLgnl2E9uzpCJEqK5KaggOAYUD71DPDMLAsC0xzUFfq8kyajG/WLHwzZ+OZUByrqzMK6fkiw5FlWViRcEIh9O5W60sonL7f6n7R1rYBL8TfE8Pt7ma1vo6Qyr7f1L746n6dCqprFNXIYZkmLatWUv3kE4T2lsW327xepvzxjj63FzWj/GnVX9lYtwWApcWnJawKB/C7U36B2977VLKDEfzocUKfxoqGO4qPjNVZMvo2DW1vTQu//PsKWgIdf+PtNoPvfm4es4uzej2/sTXEL+77mOqG2PP3m+fN5thZeX3qg8hIM9Lejw3qanH7TJ06lalTtWKUiMjhKNrURHDP7tjP7tjvUFlpUiFSrH6KE8s0saKR2DmWFQuWotH4KIaBipNsPh/uonG4CotwFxbiGluIe2zhQa0eJiIDwzAMDKcLW7oLelmZqyexUVSJI6a6C6MOuOJf+7lJjaIKBokGg0Qb6vvdX+z2WBDVZbW+xBX7utafag+09oVUqgM36AybDe+MWRiOZ+LbbF4vk2/9U7/ae3Lrc/FgaX7uEV2Cpf854SeDHiyFty+PB0u2jAI8i7/R52CprinI7x5ZlRAsGcQComSCpXAkym1PrIkHSxecNFHBksgINOAVET/88EM2bdrElVdeOdBNi4jIEIu2thDYsYPAju0Edu4gsHMH0fr6nk8yDBwZmViWGRt5BPG0KOlaLX1kuD24C8fiGtseIrWHSfa0dI0QEBnlDJsNu8+P3eeH7N6P745lWVihEGagjXSPjerS6i61pxLrT3Va3a9TQXUrGOz9YtEoZnMzZnNz/zrbznB7YiOiugmpuqzi111BdZ8Xw+HUa2QPzHCYsttvJbg7Vmw67aSTybvyq/36b/Zu2Ye8seddAApTCihtLkvY/+9Hf5cMd/9D1mREa/cQeKN9Cp7Ti/fM72C4+jYNrTUQ4fePrqKmMfFv+ZfOms5RM8b0er5lWfzthY1sLY1Nc100O49zjy/uUx9EZHgY8HDphRde4NFHH1W4JCIywlnRKMFdJbTt2E5g+zYCO3YQrijv8Ryb348jNQ0Lqz1AsiBqEq6t6QiWBpDhcOAqGIursBB3YVH8tyMzS9/ii0i/GYYRm/LmduPLTcXr7l89KisaTZzit38Y1aVAekdIFe008iqZ108rGCASDAAHUb/Obu8aRO2/it9+I6a6FFT3eEbl669lmlT89S7aNm4AwD9vPnlf/HK/gqWt9Tt4ZNPTAKQ4/fgcXkqb98b3Xzv3qxSljh2Qfh9IvIB3JAgYeE/7JraMgj61EY5Eue3J1eypSgxGLzx5EosXFCbVxj/f3ckH62O1nqYWpfOVpTMVcIqMUFrLVUREgNg3ssGdO2jdvIm2zZto27q1x9olztzc2IikaAQrHMaKRIk2NREq33vAc/rNZsM1Jq9LiOTMHaOCuiIybBl2O/aUlIOaehsfRdUePHUXRiVs666geltbcituRqNEm5uINh9cDRCbx5NQHL1r/SlfPIzqGG21X6jlHD4rhFmWRdWjD9G0/CMAPJOnUHD1tf36+7O1fgd3rbmfqBXFbtgpThvP2poN8f2XTjufOTkzB6zv3YkV8P4zVmMlAK6jLsQxfn6f2jBNi7ueXc/GXfUJ2887eRKfXTQhqTbeX1fOM+/sAGBMhpfrLjoCp2P0BZMihwuFSyIihynLNAns3Enr+rW0rl9HYPu2A9ZJsqWk4C4YG6uFBGBZhKuraNuyecD75czJTQyRxhbhzM8fVh80REQOlc6jqMjI7Hc7ViSCGQh0Xwy9u1X8DlBQPZlFFcxAIDbl+SAGURkOR5cV/OIhVXz0lK+bguqdjne7B2QUVd1LL1L/2qsAuArGUnj9d2P/P/qgKdTMU1uf58PyFfFtM7KmJgRLpxQdz+KiEw66v70Jffwk0d1rAHAUL8S14LN9Ot+yLB56bQsfb6pK2L5odj5XnTuHmprep3hu2VPP316IPXaf28ENl84l1Te49aVEZHApXBIROYyEa2poXb+WlnXraN2wDrOlpdvjnHl5uIvGxe6YFuG6Wtq2bhnQldrsGRmxAGlsYXtx7SLcY8eO2hXaRESGkuFwDMwoqmDwwMXQ96s9lTDFr9PxVjjc+7UiEaJNTUSbDmIUlWG0j6LqFDrtt1pfrN6Ur5uC6rFi6s2rP6X6iUcBcGRmUvjd7/fpv6Fpmbxftpynt71Aa6QNAKfNyZFj5iYETVMyJvK5aRf0/7EmKaGAd+ZYPIu/3ucC3i99tItln+xJ2DZ3cjZfPXsGNlvvU9oq69v44xNriEQt7DaDb184h4Jsf5/6ICLDj8IlEZFRzDJNAtu30fzpSlpWfZqwdHJnroKxuMePj90xTUIVFTR/smJAwiSb358wCin2Wyu0iYiMNIZhYHg8sS8BMg9yFNUBVuyLBtowW1v3Gz3V1mn01L59gd7/RllWx4gravvdX4itClf43e/jzE6+SnxVaw0PbHyMLfXb49vmZM/kM8VLuHnF7fFtTpuTG4+89qD6l4xo7e6OAt6u/hXw/nhjJY+9vi1h28SCVK49fw4Oe+8hVWsgzC2PraK5LRYwfums6cxMYkU5ERn+eg2Xysq6/yByIC0H+BZcREQODTMYpHXDepo//YSWVauINjV2Ocbm9+ObOQu7PwUsk2BpKU0ffXhQYZLhdneMQNq3QtvYQuzpWqFNREQ6GA4H9tRU7Kmp/W7DMk3MYDCx/tR+q/WZba3xouhdpv21h1QHmg6+f3/HXv9d3IVFSfXNtEze2P0O/9z+MmEzFqJkuNP53LTzmZU1ne+++ZOE4393ys/7/h+gj6xQK22v/LGjgPeSa7Cl5/epjW1lDdz13PqEbbkZHr5zyTzcrt7rT0WiJrc/tZa9Na0ALD1uPCfPG9zC5SJy6PQaLi1ZsqRPHwosy9KHCBGRQ8wMh2hZs4bm5R/SvOrTroVbDQPPxEl4Jk8ByyRSX0/zx8v7da3YCm0FiSFSYSGOrOxRuUKQiIgMP4bNhr29BtPBMMPhrqFToH20VFsbZjCAb+ZsvJMmJdXe3pYKHtjwGDsad8W3nVS4iPMnL8Vjd3Pd6z9KOP73p/wPtj5OS+sry7IIvHXvfgW85/Wpjar6Nv74+GrCkY6VC1O8Tm783HzS/b3XSrIsi3+8sokNJbFCXAun5XLxKZP71AcRGd56DZcuuOAChUUiIsOQFYnQsn4tTcs/omXlJ7EpAp0YTmdsdFJKCsHduwhs30Zg+7YDtNYNrdAmIiKjnM3pjC0YkZZ2UO2Ylsm/dr/Ns9teImJFAcj1ZnPFjEuYmhkLUf7jncQRSr884ae47IO/WEV401tEtsdWurOPm9vnAt4tgTB/eGwVja0dtbKcDhvfuXgu+Vm+pNp4+aPdvLUqtppscX4qXz93FjZ9xhQZVXoNl379618fin6IiEgSLMsisHULDe+9Q/OKFZitB56KbIXDtKxelVS7+1Zo21dc2124b4U2rdwiIiLSk7pAPfevf4TN9bEvcAwMlow7ic9OOhOXPfZ39N51D9MQ6ihO/sOjrifdfXCBVjKitaUE330g1i9fRp8LeEeiJn/qNJUNwACuPncWU4rSk2rjk81VPPb6VgAyU91855K5uJ36kkpktEmqoPcpp5zC6aefzumnn84xxxyDXd9Yi4gcUuGaGhrff5fG994lXFlxcI3Z7WScshj3+AlaoU1EROQgrKj4lIc2PUVb+0pwud5svjzrMiamT4gf8+ae91he8Un8/pUzP8+EtHGD3jcrEiSw7E8QDQEGniXfxOZNPtCyLIv7X+qYyrbPZadPZeH0MUm1sbO8kTufXYcFuF12brhkLhkp7j48ChEZKZIKl0477TRee+01HnjgAdLT0zn55JM588wzOfHEE/Ee5DxnERHpnhkMUv3U49S/9upBtWPz+8k+5zzSjj9BK7SJiIgMgGA0xCObnuLD8hXxbccXHMPFU8/F4+gIT7bUbePRzU/H759SdDzHFiw8NH187yHMulIAXEeei2PszD6d//z7JbyzZm/CtjOPHscZRyUXjNU2Brjl8dWEwiaGAdecN5vxef0v4i4iw1tS4dLPfvYzfvazn7F69WpeffVVXnvtNZ599lk8Hg+LFi3ijDPO4NRTTyXzIJYkFRGRDpZlseuXPydUuqfP59q8XjLP/AypRx+DK79gEHonIiJy+CpvqeTutX9nb0tsJLHf6eOKGZcwL3dOwnE1bXX8YeVf4vfz/Xl8btoFh6SP4W0fEd74BgD2/Gm4jjy/T+d/uL6CJ9/anrDtqBlj+NySKUmdHwhFuPXx1TQ0xxYYuey0qcybktOnPojIyJJUuLTP3LlzmTt3Lt///vfZtm0br732Gq+99ho/+clPsNlsHHnkkZxxxhmcfvrpjB2rZSVF5PCydesWvv71L/GlL32Vq676Zo/HlpWVctttf2Dlytg3nscffyLXXXdjPKS3IhEi9XU9NZHAkZlF6jHHknrscbjHjddCDCIiIoPg44pPeXDj4wSjsdBkWsZkvjL7C13qJwWjIX72/q8Stv30mO8dkj6ajZUE3vpb7I7bj2fJNzFsyZc12bKnnnue35CwbWpROt/47MykinCbpsWd/1zPrspmAJYcWcjpC4uSfwAiMiL1KVzqbPLkyUyePJlvfvObVFRUxEc0/fa3v+VXv/oVM2bM4MYbb+Tkk08eyP6KiAxLkUiEX/7y/xGJRHo9tqGhnu985xrC4TBXXHEl0WiUhx76O9u2beWuu+7D2b5yzfif/Bctq1cRbWkmsHULrRs3gGXF27F5vaQefSypxy3CO2Uqhm1wlzIWERE5XEXNKE9ufY439rwb3/aZ4tM4Z+IZ2PYrkG1aJt9786cJ225Z/MtD8sWPFY3QtuzPEI7VgPKe8nVsKdlJn19R18ofn1hDJGrGtxVk+7j+4rk4HckFVI++vpVPt1YDMGdSFl84faq+9BI5DPQ7XOosLy+PL37xi3zxi1+koaGB119/nddee40tW7YoXBKRw8I//nEvO3Zs7/1A4OGHH6CqqpL77nuY4uKJAMyaNYcbb/w2L774HOeddyGhqkoa33mLhnfeItrYmHC+Z9Jk0k9eTOrRx2BzqyimiIjIYGoNt3HP2n+wsW4LAD6Hly/Puow5Od3XMPrxO/+dcP9XJ/4nDtuAfOzqVXD5E5hVsfcjzjln4ChekPS5zW1h/vDoKprbwvFtaX4XN146jxSvM6k2XnhvB68s3w1AYa6fa8+fg11ffokcFgb8VS49PZ0LLriACy64YKCbFhEZlrZt28p9993Dl798FXff/edej1+27BXmz18YD5YAjj76WMaPm8DLTz3OkVu30rp+XcI5Nq+XtEXHk37SYtzjBn+FGREREYHK1mr+vPpeKlorAShKGcvVR1xJtjer2+PvWfsPWsKt8fs/Ouo7pLkOTRHryO7VhFe/CIAtewLuYz+X9LnhiMltT66hoq4tvs3ttPPdS+eSk5HcAk5rt9fwl6fWALFQ6oZL5uJ1H5pQTUSG3oA/2x944AFefvll7r///oFuWkRk2IlNh7uJo48+lrPOOrvXcKmxsZGyslIWLz4tvi1UUUHD228ytqWFVaW7abV3fDvomTwlNkrpqKM1SklEROQQ2t5Qwp9X/Y2WSCwsmpczmy/P/gJuu6vb49/Y/S6fVK6O3//yrMsYn3Zoag2ZLXUEXr8rdsfpwXv6tRj25EYbWZbFvS9uYPPu+vg2A/jm+bMpzk874HmdlVY1c8czazFNC6fDxvUXH0FOulYVFzmcDHi41NLSwvLlywe6WRGRYemBB+5jz55d/OpXNxONRns9vro69s1nbnY2Tcs/ouGtN2jdsB6ANMuizTQJuF3kn3gK6SefgrtQBTBFREQOtQ01m7lzzX2EzNgUsTMnnMq5k87qUl9pnx0Nu3hsyzPx+6cWncgx+Ucekr5apkng9TuxAk0AeE68Elt6ftLn//Pdnby/riJh22WnTWV+kqu7NbSE+MNjq2kLxt4Hff2zs5g8Nj3p64vI6KBxiiIi/bR9+zbuvfdubrzxh4wZk8fevWW9ntOwO1aHoPHZZ9jr8SXs8+XkQF0N+T/5L8aMLRyUPouIiEjPPqlczb3rHiJqRTEw+MKMizhh7LEHPL453MLNK26L38/zjeGSaecdiq4CEPr0OaJlsdXdHNNOwDn1+KTPXb6xkmfe2ZGw7dQFhZx+VHJfboXCUW57YjU1jQEArjx7JkfPGJP09UVk9FC4JCLSD9FolF/+8ibmzp3Peedd2OOxZjBI84qPaXj7Tfau+hQAKxAAjw+bz0/a8SeQfvIppD33DGzZhM2l6W8iIiJD4eOKT7l33UNYWDgMO1+ZfTkLxhxxwONNy+RHb9+UsO0/j/3+YHczLlK+mdCKpwGwpefjOeFLSZ9bUt7EPc+tT9g2e2IWl5+R3OpupmXx1xc2sK0stvDICUfkc8mSqVRXNyf/AERk1EgqXLr66quZPXs2s2bNYtasWRQW6ht1ETm8Pfjg39m2bQt/+tPd1NfXA9DUFHtzFQwGqKurw11XS9O7b9P00YeYbbECme59K6bk5JL/5atIWXgUNper/bwgAH6//9A+GBEREeHTqrXct/5hLCxcNidXz/0yM7Om9XjOv731/xLu/9/JP08qmBkIVqCZwLI/g2WC3YHntGsxnJ6kzm1sCfHHJ1cTipjxbWNz+ra629Nv7+CjDbHp/tPHZfDlz8w4ZI9dRIafpMKlt956i7feeiv+YpGWlhYPmvaFTsXFxYPZTxGRYeXDD98jHA7zjW98ucu+Bx/8Ow8++Hd+O2kaOa6Oop/21DQmL14Mf76N6IIjSVuUOGy9urqKlJRUvF4VwDxcvbnnPVZXrcPr9JLi9JPi9OF3+ttv+/Hvu+/y47I59SZeRGSArK/ZxF/XPoBpmThtDq6d9zWmZU7u8Zx/bHiMQDQQv//TY7+Px3FoRh9blkXgzXuwWmoBcB93GfacCUmdG4ma3P7UGmobg/FtqT4nN1wyF58nuYkt767Zy3Pv7QQgL9PLty86Aoc9uVBKREanpF49PvzwQ9avX8+6devivz/44APef//9+Btbn8/HzJkz49+8i4iMZtddd2P7SCWLcHUNge1bKV+zij+tXc2itAyOT88g3eEAw8A/5wjSTjqFlLnzMBwOCp55is2bN3Vpc8uWTcyYMfPQPxgZFkzL5KmtzxNuLx7bG6fN0SV4SnH5O23rFEy5/PgdPpxJrhwkInI4KW3ey91r/07UiuIw7Fx9xJd7DZY+Ll/J+3s7FjH68qzLKPDnDWo/zZY6whvfwjHxKKJlG4iUrATAUXwkzlmn9XJ2jGVZ/OOVTWzZ0xDf5rAbXH/xXHIzkvtya9OuOu59cSMAfo+D7146jxSv/r6IHO6SCpfS09NZtGgRixYtim9raWlhw4YNrF27lvXr17N+/XpWrlxJNBrVN6kiMqqZ4TBFwSAtmzbRsmYVkZoaADyhEAC5TidHzphF6tHHkHrc8TizshLOX7x4CY8++iAlJTuZMKEYgOXLP2TXrhIuvzz5WgkyutgMGxdN+SzvlH1ARUslEavn1QfDZoT6YAP1wYYej+vMZXeR4vST4U3FbXg6BVN+Ulz7j5KKBVR2m/1gH5qIyLDVFGrmz6vvJRiN/Q3/yuzLmZU9vcdzylsq+Nv6h+L3j81fOOgrw5mBJloeuBGA8PplWMFWAIyUbDwnfy3pz1//+qSUt1btTdj2laUzmFKY3OpuFXWt3PbkGqKmhd1mcN1FR5CX5ev9RBEZ9fpd0Nvv93PUUUdx1FFHxbcFAgE2btzIunXrBqRzIiLDUenvb6atm5FHzjF5sH0zGUtOZ8INsWKepaV7WPvyB8yZM5fCwtjKK5dffiUvvfQ8N9xwLZdddgWhUIgHH7yf6dNncuaZZx/SxyLDy8lFizi5aBFRM0p1Ww1lLRWUtZSzt7mcspYKqtqqMS2z94YOIBQNURsNURuoS/ocj90TGwXl2m+U1L7brsSRUn6n74BLdYuIDCemZXLP2n/EXxPPn7S0x+LdAIFIkJ9/+H/x+y67iytnfX5Q+2mZJi33X99xvy1W4xHDhmfJNRielKTaWb+zlgdf3Zyw7TPHjuf4OQVJnd/cFuYPj62mJRABYqHU9PGZSZ0rIqPfgK4W5/F4mD9/PvPnzx/IZkVEhpVIXay+AXY7vmkz8B8xF//ceVSbUfjc29hTOt7krVq1kl/+8ib+4z/+Kx4uZWZmcvvtd3Lrrb/jnnv+gtvt4aSTFvOtb92Aq1ONJjl82W128vxjyPOPYQEdH3TCZoTK1irKmstjoVNLOXubK6gO1PbvOoYdh80e/8a+O4FogEA0kPQ1DAx8Dm+n+lDdj4jqPIXP6/AokBKRQ+6VkjfYUr8dgGPyj+SMCYt7PN60TL7/1n8mbLv5pJsOcPTAaXv+N91udx11IY78qUm1UVnXyh1Pr8XqtO2ISdlcckrP0//2iURN/vTUGipqYyOmzlk0gROOSC6UEpHDg2FZltXTAe+//37CdLi+eO+99zj++ON7P3CA1dQ0Y5o9PqwRITc3laqqpqHuhsiIcCifL5H6OkLl5XiKi7F5VHxbhl4gEqSitbJT6FTB3paKpKbMeR0ecr05pLtTSXWmkuZOJc2VigE0h1toDrfSEm6hOdQS+x1upTncknRtqGQYGPHRUP7O9aJc+42SiteQ8uGxezQNXw4JvR8bnXY17eF/P74N0zLJ8+Xyo6NvwG3v+Que//nwd5S1lMfv/+bE/yLFNbgrvIY2vknwrb912W4vnIV36Q8wkljZrS0Y4X/+voKy6pb4tvwsHz+98qikCnhblsXfXtzIO6tj0+mOmjGGa86fja2b12A9X0SSN9KeLzabQXb2gUdK9vpq8vWvf52FCxfy1a9+lZNPPhm7vefaC+FwmDfeeIP77ruPTz/9lLVr1/a91yIiw5gjIxNHhoaBy/DhcbiZkDaOCWnjEra3hlspaw+a9raUx8OnlnBr/Ji2SIBdTXtgv/c2qc4UClLyKfDnMTVjEmPbb3sdsUA1FA3R0h40NYdbaAl1BE8t7dv2D6YOVEfKwoq3kyybYesyTS/+27XfKKn22267S4GUiGBaJo9sehrTMrEZNr46+/Jeg6Vnt72UECx9f+G3Bz1YilaXdBssGZ5UPKdenVSwZFoWdz27PiFY8rodfKcPK8O9+OGueLA0aWwaXz9nZrfBkogc3np9RXnqqaf49a9/zbXXXktWVhaLFi1i7ty5jB8/nvT0dCzLoqGhgZKSEj799FM++OADGhsbOeGEE3j66acPwUMQERGR7vicPqZkTGRKxsT4NsuycKXB2pJt8al1Zc2xAKrzktpN4Waa6rayuW5rQpsZ7nTG+vMpSMmL/fbnMTm9GFcvH8wsyyIYDXYETvHf+4KpxG2xkKr1gDWmTMukMdREYyj5b/wcNkfXmlEJK+vtX1vKj0sr7ImMOsvLV7KzcRcAp407mXGphT0ev6F2My+V/Ct+/+Kp5zIpfcKg9tFsa6T1yf/qdp/n1Kux+TKSaufpt7fz6dbqhG3Xnj+b/CSLcH+8sZLH39gGQHaah+svnovLqYUeRKSrXsOladOm8de//pWVK1fy4IMPsmzZMp5//vku3/xZlkVKSgpnnHEGX/jCF5g7d+6gdVpERET6xzAMMjypTM+awvSsKfHtlmVRH2ygrH2E077RTntbKhOmwO1boW59bUdRewODbG8WY/35jPXnUeDPoyAlnzxfLg6bI35dj8ODx+Ehx5u4guKBWJZFWySQMBqqJT46qrXTVL2OYKol3IpF91PjI/1ZYc/mjE/R6zaYcvnxO3zxKXx+px+nbUBLWorIADItk5d2LgMg3ZXKZ4pP6/H4plAzt316d/z+jMypLBl30qD20TIjtD37q273ueadjWNcz0XH9/loQwXPvVeSsO2yJVOYMyk7qfN37G3krufWA+Bx2bnh0rmk+1UbUkS6l/S7nwULFrBgwQKi0Sjr1q1j69at1NbWYhgGWVlZTJ06lVmzZmFLYnimiIiIDC+GYZDpySDTk8Hs7Bnx7aZlUt1W22mEUyx4Km+tjI8qsrCobquhuq2G1dUdK8baDBtjfLkU+PMY628f6ZSST643O6kC3oZh4HN68Tm9QE5Sj8O0TFojbd1O02vpfD/UMUqqNdJ2wPZCZphQsJ66YH1S1wfw2N3x+lB+V9dRUgnBlNOH3+HDbtNIAJFDYXX1eirbYiN5zphwKh6H+4DHmpbJj9/574Rt183/+qD2DyD43oOY9Xu7bLeNmYTr6IuSaqOkvIl7nt+QsO2EI/I54+hxBzgjUU1DgFsfX004YmIYcO0FcyjKTW5VOhE5PPX5qzW73c7cuXM1MklEROQwEAuIchjjy2Fe7pz49ogZobK1uqOeU0sFe5vLqWqriY8cMi2T8pYKylsqWNmpTYfNQb5vDAX+fMZ2ml6X6ck46FXjOtdiykvynKgZpTXSFhsBdYDRUPvXluo8hXB/gWiQQDRITR9W8fM6vInBU7fBVMfUPZ/DqxX2RPrho/JPAPDYPRw/9pgej/35Bzcn3P/9Kb8Y9Lpt4U1vE17/r647XF68S67FSGJkZHNbmNueXEM40jGteHJhGleeNSOp/rcFI9zy+CoaWmIriV5xxjSOSHK0k4gcvjRuW0RERPrMYXMwNiWfsSn5wLz49lA0HF+5bm9LRXz1utpAXfyYiBlhT3MZe5rLoKKjTbfdFQucOk2tG+vPj61eN4gf6Ow2O6muFFJdKZBkfd6IGUkcCdVlml4slOq8wl4oGjpge22RNtoibVS11SR1fYPYqK4uwVN8RJS/S1jldWiFPTm8haNh1tVsBGB+7pwei3i/tHNZfIQTwH8cc2OvteUOVrRqJ4E37+l2n+fkr2JLy+21DdO0+Ms/11HT2BGAZ6a6ue7CI3A6eg+ko6bJn59Zx56qWAHw048qYsmRRUk+AhE5nA1YuGRZFnv27KGlpQW/309RUZHewIiIiBxmXHYn41ILuxTIbYsEKN8XNrUXEC9rKU8oyB2MhtjZuCteaHcfv8NHvj8vFmb581iYNx+/M7litIPFYXOQ7k4j3Z2W9DmhaLhLMfPOo6H2n77XHG4hYka6bcvCag+vWoGqpK5vM2zxulD7B0+dg6nOU/jcdrfez8mosaupNP6cmpU9/YDHlTTu5tntL8fvXzL1PApTCga1b2agibZXbul2n3PmYpyTeh5ltc/T7+xg3Y6OUZNOh43rLz6C9JQDT//r7OFlW1mzPRZyz52czWVLpiZ1nojIQYdLoVCI//3f/+Wpp56iubk5vj0lJYWLLrqIH/zgB7hcKvwmIiJyOLIsi5AZpjXcht2wk+3Jwuvwkucfw/TwFCrbqtlWvyNhhMD+WiKtbGvYwbaGHQB8WP4J/3bUdYfqIQwYl92Jyx6ra5WMff/tuhsRlRhMJRY6j1rRbtszLZOmUDNNoeZu93fHYdgTCpd3rKbn6xRMJU7hG+zRHSL9VdFaGb89PrX70ThtkQC//fiPCcedOu7EQe2XZUYJLLsDq6Wuyz5bZhHuRZcn1c7KLVU8997OhG1fPXsGxfnJheDLVuxh2Yo9ABTlpvDN82ZjsylcFpHkHHS49N///d9s3bqVP/zhD8yaNYu0tDQaGxtZv349t912Gz//+c/5+c9/PhB9FRERkSG2rz5RU6iZlnALTZ0KZDeHm/erWxTbFj7A6Jv+Sna1uZHOMAzcdhdur4tsb2ZS51iWRSAa7AijQvuFUd1M4etxhT0rSkOokYZQY9L9dtqcCQGU3+nrNFVvv2CqPbRy2p1Jty/SX83hlvjtVFfX4tSWZfGDt36WsO1QBNmh5U8QLV3fdYfdhef0azEcvQe2FbWt3P1cYhtnHj2O42blJ9WH1duqefC1zQCk+11899K5eN2qoCIiyTvoV4xXXnmFl19+mczMjjc9WVlZnHjiicyaNYuzzjpL4ZKIiMgwZlomreE2GkNNXX+CzTR1ut9TENEfDsOOz+mL/Ti8+Bxe/PtuO734HL723158Th8pTh+53uRWjjscGYaB1+HB6/CQ402uAK9pmbRFAolT9kLdjJKKj5ZqpSXSesD2wmaYuj6usOe2uxKLmTv9pLh8+B3tvzuPkmoPqLTCnvSV09YRYobNMB4Sp4rd+uldCff/96T/N+iF88PbPyK06oVu97lPuAJ7ZmG3+zoLhqLc9tQa2oIdoxanFqVzyeLJSfVhd2UzdzyzDssCl8PGdy6ZS1aaJ7kHICLS7qDDJcMwiES6/0YyEolonr6IiMgQCpsRGoKN1AcbqA/UUxdsILi7jbL6KuoDDTSEGmkMNWFaZu+N9cJjd7dPkfKT2rmeT7yOjw+fwxf73R4YOW1OvVcYYh21mHyMSfKcfSPY9q8h1RLqZpRU++22yIFX2AtGQwSjIWoCXacFHYjX4el+NFT7v7X9p/D5nT6tsHeYS3Olxm9XtlYnjF56t+xDNtdtjd+/8chr8Q1ybTezfi+BN//a7T7H5GNxTj+51zYsy+LelzZSWtUxKivd7+LaC+bgsPf+772hOcgtj68iGIpiAN84dzYTC5KvJSciss9Bh0vnnnsuX//617nmmmuYMWMGaWlpNDU1sWHDBu68807OP//8geiniIiI7Me0TBpDTdS01VETqKU+0EBdsCEWJAVjQVJf6uvsz2FzkOZKJdWVQportf0nhRRXSvxDfHxqk9OPM4klsmV0SFhhL0lRM9pt4fKOKXyd98V+B3tcYS9AWyRAdV9W2HN44/Wh9g+jOv4t+xJW2JPRY3JGcfz26up18fvlLZU8uPGJ+L6zJixhSsbEQe2LFQnS9urtEO4auhqpuXhO+nJSwftrH+/hw/Udy24awLUXzCEjiQLewXCUW59YTW1jEIBLFk9m4fTeV6QTEenOQb8L/Pd//3fuuOMOfvvb37J3714Mw8CyLAoKCrjkkku45pprBqKfIiIih6XYh+daagK1VLfVxIOk6rZaagO1/apn5Ha4yXClk+lOj694lhggxX60dL0MJLvNTro7lXR3au8HtwtHw7REWjtN0+s8UqpjCl8yNb4sLFoisSl9lRy4gHxnBgapbj8+e+fV9LqOkuo8Os9j1/NmuMpwpzMpvZjtDTt5p/RDFhedQIrTz88/vDl+TJorlfMmf2ZQ+2FZFoF37ses29N1p82O97RrMVy9j5pau72Gh5dtSdj2+dOmMm1cRq/nmpbFPc+tZ8fe2IqdJ80t4DPHjk+q/yIi3TEsyxqwwglNTU20tLTg9/tJTU3+jcNAq6lpxjQHrh7EUMnNTaWqqqn3A0VEzxcZ0cJmhKrWaipbq6jo9FPVWt1jbZvueB0eMtzpZLozyHCnk+GJhUgZ7T+ZnnTG5edSXd3/EU0iw1koGuq0ml7iNL2OGlKJtaUOtMJef9gMW5cRUR1T9DqNkorXlvLj0vTQQ2ZdzUb+tCo2FW1f0NTZrYt/Nej1vEIb3yT41t+63ec+7jJccw8cblmWxfayRp59byertyWO2jtm5hi+ed7spP4tPfHmNp5/vwSAmRMyufFz85KaRtcbvR8TSd5Ie77YbAbZ2QcesTyg49dTU1OHNFQSEREZztoiAfa2VFDWvJfy1koqWquobKmiJlCXdJFsj91NtjeLHE8W2d6shNuZ7nQ8SUzj0YdYGc1cdhdZdhdZnuRX2AtGgwlT9jqPhoraQ1Q31e9X3Lz1gHXK9k1XbQwl/4HBYXN0Hzwl1I7qtM3px6UV9vpldvYMFhUczft7l3cJlv7nhJ8MerAUrdlF8N1/dLvPPm4uziPO7HZfKBzlow2VLPtkDyXlXf9tjc3x85WlM5J6fX97dVk8WMrP8vGtC5OrzyQi0pNBLY4QCoVYunQpy5YtG8zLiIiIDCsRM0JFaxVlzeWUtZTHf9cmWaw41ZVCni+XMd5ccr3ZZHszyfFmk+3Jwu/0KRzqxLKs2NTBQE376K8aqtqqqWqrpjhtPEuLT8fn9A51N2UYMwwDj8ODx+Ehx5vVZX933yyblkkgEkysGdXtNL2OEVM9rbQYMSPttdIaku63K77Cnu+AI6I6T+HzO304VBcNgM9Nu4AtdduoDtTGt119xJfJcKcP6nWtUGuszlI03GWf4cvAs/jrGPsVnW9oCbFsxR7eWFlKc1vX8wA8LjvfvnAOHlfv/383lNRx/0ubAEjxOvnupXPxexRUisjBG/S/MKWlpYN9CRERkSETNiOUNe9lV1Mpu5v2sKuplLLm8l6n2ThsDsZ4cxjjyyVv348/FigdrmFIxIzQFGqOjejab4pgssFcZ9sbSoiYUT4//YKB76wc1myGLbbiodML5CR1jmmZsRX2QonB0/5T+DoXPG+NtB2wvVA0RG001KfnhsfuSRgN1RFMxYKoxGDKj8/hHfSRPEPDwt4paDux8Djm5c4e3CtaFoE3/4rVWNHNXgPPkm9i83as0lZa1czLy3fzwbpyItGOUDI9xcWi2fm8s3pvPGy66pyZFGT7e+1DeW0rf3pqDVHTwmE3uO6iIxiTObgr4onI4eOgw6XTTjvtgPssy9K3qyIiMmpEzSilLXvZ2bC7I0hqKT/g9BiIFQQe48tlbEo+Y/15jE0pYKw/jxxv9qhfFj0QCVDeWsne5gr2tlawt6WC8pbKfgVF/VWcNu6QXUukJ51rMeUleU7UjNIaadtvql5L4hS+/YKpQDR4wPYC0QCBaCBhxE5vfA5vp9ApNhKq6/S9jil8Pod32L+2Pbr5GSpaKwE4c8KpnD956aBfM7z2VSI7Pu52n+vI83CMnQnA1tIGnuumntLEgjTOOmYcR07L5a8vbIgHS0uPHc/C6WN6vX5zW5g/PLaKlkCs4P1Xl85MqvC3iEiyDjpcqq2t5Xvf+x4FBQVd9oXDYb73ve8d7CVERESGRFOomR0NJexo3MWOhhJKGncTMruflgCxekjjUgsZl1pIUcpYxqYUkO/LxTkKaqOEzUh8yk95ayWlzXspa95LWUvFIQ2LuuO0Ocj15jDGl5PwuyAljxRn79/miwxXdpudVFcKqa4USPKfctiM0JowMqp1v2CqU2Hz9u09va61RtpiI6jakl9hzx+fjpc4Gqrb7U7/IV2Z8oO9H/P+3uUATMmYyGcndl/jaCBFK7YS/OCRbvfZC6bjOvI8Nu+u55/v7mD9zo7XUwOYPzWHs44Zz9SidAzD4I1PS/lgXWz004zxGVx0yqRerx+OmNz2xGoq62Ij4c47oZhFc/IP/oGJiHRy0OHSzJkzyc7O5vTTT++yLxQKMYCL0YmIiAyqmrY6ttRvY0vddrY17KCqreaAx3odHsalFDIurZDxqUWMSy0kd4SMRgpFQtQF6uOjH/bVhKkPNlLaXEZpczl1wfoh6VuqM4Uxvlzy/bnk+cbEak/5ckl3p2lFLZEkOG0O0t1ppLvTej+4XSgaigdOCYXLQ92Mkgq30hxqJnKAqb8WVvzY7iaAdcdm2LotZt55pb39p/C57e4+vx6UNZfz8KanAEhx+vnq7MsHfdqfGWii7bU/QTf/vRxTj6d04nk8+fAqNu6q79huNzhx7ljOOnoceVkd09Z2Vzbz0GtbgNj0uG+ePwe7ree/OZZlcd9LG9m8J1bP69hZeZx/4sQBeGQiIokOOlz60pe+REZGRveNOxz86le/OthLiIiIDIq6QD2b67axuT1QqjnAVBEDg7Ep+UxMn8CktAlMTB9PrjdnyIMOy7IImeGEGi0toRaaI7HfLZF9IxZiHw7rgg00h1sOWf/shp0Cf17CT75/DBnuDK10JTKMuOwuXHYXmZ6MpI6PrbAX6jJNr2swlbitpxX2mkLNNIWak+6zw7B3GhHVdTTUvml6+0Ipp83JPWv/QdgMY2Dw1dmXD34Bb8sk8K+/YLV0/dvSdtSXeWRHNis/XB/f5nTYOGXeWJYeN4HMVHfC8YFQhD8/s5ZwxMQw4JrzZpPud/Xah+feL+G9teUATC5M42tnJ7einIhIXx10uLR06YHnKNtsNi688MKDvYSIiMiAaAm3srF2Mxtqt3RZKagzj93NpIxiJqUVMzF9PMVp4/A4PIPat33Loe8/eqCly4e2jvst4RbCZmRQ+7W/bE8WRSkFFLb/FKTkk+XOGBVT/0QkObEV9tx4HG6yu1lhrzuWZRGIBmgOdVPMPD46KnEKX48r7FlRGkKNNIQa+9z/pcWnMSNrap/P66vQp88T3bO2y/aX867ixVfDWFZsqqHTYePUBYUsPXY86SnuLscDPPDKZvbWtAJw/gkTmT4+s9frf7Shgqfe2g5ATrqH6y+ai9MxGgu0i8hwoPVIRURk1DItk5LGPayv3cSGmk3sbNzd7QcVt93F5IyJTMuYzNTMSYxLKTyoqRL7PkQlBEXtI4n2/za/c3h0oGkmgy3F6Y/Xisr15pDpTifdnUaGO/2Q1kIRkdHLMAy8Di9eh5dcspM6x7RM2iKBjjBqv2l6LeH9VtgLtdISae2xzemZU1g6sWs5j4EW2buJ0PInumy/pe18tm+I1bgyDDjhiAIuOHEiWWkH/gLj3TV7ebd99NGM8Rl89vjiXq+/rbSBu5/bAIDXbeeGS+eRlsRIJxGR/upzuHTllVf2uN8wDDweDwUFBZx44omcdtppelMqIiKHTGu4lbU1G1lXs5ENtZtpCXf9oOGyOWNhUuZkpmZMZnzqgcMk0zIJRAIH+ECTOKqo8++eVpA7VGyGjUx3BtneLLI9mWR5Msj2ZDEpvxBbwE2GO22ULjMuIqPBvlpMfqcPyE3qnH0r7MVHQnWaomcBJxcuGvTaeNHaUtqe7Voa5PGWo9kejE3Fmzs5m0sWT6YoN6XHtvbWtPCPVzYDkOpzcvV5s7HZev5sVV3fxh+fWE0kamIzDL51wREU5mhxAxEZXH0Ol/bs2UMgEKC2NjaVIC0tViywsTE2JDUrKwvTNHnzzTd55JFHOPLII7nrrrvw+XwHbFNERORg1AbqWF21nlXV69hav73bYGesP58ZWVOZmD6BPF8uwWgwtvJZSwVb67cfMChqjbQNaFDUsZJSx5LePoeXpnAz9YEG6oMNsZWZ+tBeliejY5U0Xw5jvLHfOZ6sbsOj3NxUqqqaBuwxiYgMFwkr7A2BSNlG2p77dZftG8IFvBOcwZhML5efPpW5k3N6bSsUjnLH0+sIhmOjWr9x7iwyDjBtbp/WQIRbHl9NY2tsdNQXz5zG7InJTV0UETkYfQ6X7r//fq688kquuuoqrrrqKrKyYi9WtbW13H333bz88svcf//9+P1+/vKXv/C3v/2N22+/nX/7t38b8M6LiMjhybIs9jSX8f7e5bxX9lGvdYdcdhcNoUZe3/0O/9r99oD1w2bY8Dt8Cctr7wuMUlx+/A5fvNjsvqKydsNGaXM5e5rL2NNUxp7mMjbUbibSy2Nw2pzk+8eQ78tjrD+PPP8Y8nw5ZHuzcdo0y11EZChZlkV43WsE33ugy74W08VjgRO54OTJfOaYcUnXPXr4X1vZUxUrcn7OognMmdjzdMKoaXLHM2sprY4t3HDm0eNYvKCwj49ERKR/DMuyuq+SdwDf/va38Xq93Hzzzd3u//73v08wGOS2224D4JprrmHbtm28+uqrB9/bJNXUNGOafXpYw5K+WRZJnp4vh49gNMTvV/yJ3c1lA9quzbDtt9y1LyEU6rwM9r7bHoe7x+kVpmWyt6WCnY27KGnczc7G3ZQ1lx+wQC3EVljL94+JFcvutMpalidzwKZy6Pkikjw9X6Q3ViRE4J37iGx+t9v9LzrP4qTzzqMgO/mpacs3VnLH07Fi4FOK0vnR5Quw2w78N8CyLP7xymZeX1kKwPwpOVx30RG9TqEbaHq+iCRvpD1fbDaD7OwDjwrt81edH3zwQY+jkI466ij+7//+L35/0aJFvPtu9y+0IiIifVUfbOg1WNq3RHVCUOTyk+JoX5q6fURR5xDJY3cfdI3AhmATOxp2srNxdyxQatpDKBo64PFeh4eilLGxn9TY73z/GBwaiSQiMiKYzTW0vfJHzOqd3e6vzJjLJZdc1qeQp7K+jXtfjBXj9nscXHPe7B6DJYDXPt4TD5bG56Vw9XmzDnmwJCKHt369e92+fXuP+zoPhrLZbHg8g7t8s4iIHD7yfLl8bfYVvL77bYrTxjM+rYgUZ2JQ5La7DsliEjVttWyt38HW+u1srd9BZVv1AY91211MSB1Hcfp4JqQWMS61kCxPpha9EBEZoSJlGwm8djtWoPuRB1FPJpPO/yZGH0KeSNTkz0+vpS0Yq7N01TmzelxJDuDTLdU8vGwLABkpLm64ZB4el76kEJFDq8+vOscffzwPPfQQ8+bN45xzzknY99xzz/Hwww9z6qmnxretX7+ewsLk5vqapskjjzzCgw8+yJ49e8jOzua0007j+uuvJyVlaIryiYjI8LMwbx4L8+Yd0mtalkVlWzVb6ra1B0o7qAvWd3uszbBR4M+jOG18+8848v1jBn2FIhEROTRCG98k+Pb9YEUPeEzK6VdjuPu2SttTb29nZ3ksrDrz6HHMn9pz4e9dFU385Z/rsACX08YNl8wjM7Xnot8iIoOhz+HSj3/8Y1avXs0PfvADfvOb3zBhwgQASkpKqKqqIjc3lx/96EcABINBSktLueCCC5Jq++677+YPf/gDV111FYsWLWLHjh3ceuutbN26lXvuuaevXRURETkoLeFWNtVtZWPtZjbUbqE2UNftcS67i8npxUzJmMjk9ImMTyvCbXcd4t6KiMhgsyyT0EePE1r1Qo/HOY84C8fYmX1qe9OuOl76YBcAE/JSuWTx5B6Pr2sKcsvjqwmGoxjAN8+dzYT81D5dU0RkoPQ5XCosLOSZZ57hzjvv5I033mDVqlXx7Z/97Gf5xje+QWZmJgBut5v7778/qXYty+Luu+/m85//PN///veB2CipzMxMbrzxRjZs2MDMmX17gRYRkZFnxYrl3H33n9m6dQt+v59TTz2db3zjWnw+X4/nlZWVctttf2DlyhUAHH/8iVx33Y3xv0nJiJgRdjTsioVJdVvY1bin2+LbPoeXyRkTmZIxkakZkyhKGYvdltzqPyIiMjJZkSCBf91JZGfs74zl8hMIhvEaibX1bJmFuI++uE9ttwbC3PXc+tgIJIeNq8+bhcN+4NGuwVCUW59YTV1TEIDPLZnCgmm5fXtAIiIDqF+TcTMyMvjhD3/ID3/4wwHrSEtLC+eddx5Lly5N2D5p0iQAdu3apXBJRGSUW7FiOTfe+G2mT5/BNddcR2VlBY899jAbN67n9tvvwnaAgqYNDfV85zvXEA6HueKKK4lGozz00N/Ztm0rd911H06n84DXbAo1s65mI2uq17OhdjPBbgpwO21OpmRMZGbWNGZkTaXAn6cpbiIihxGztYG2l/+AWbUjtiE9n+UNuRxjrEk80GbHc+rVGI6+jV79xyubqW3sCIp6WlnOtCzufHYdJe3T5xbPH8uZR4/r0/VERAbasKn0lpKSwk9/+tMu21977TUApkyZcqi7JCIih9jtt99CXl4+t912J253rIBpXl4+v/vdb/jww/dZtOiEbs97+OEHqKqq5L77Hqa4eCIAs2bN4cYbv82LLz7HeeddGD/WsiwqWqtYU72e1dXr2dFQ0u3opHEpY5nRHiZNTi/GaT9wQCUiIqOX2VBB6ws3YzVVAWAfO5Pnmo/gdOtx2K9Wt+uoi7DnTOhT+x+sK+eD9RUAzJ2czakLeq5X+/gb21i5JbaAxOziTC4/Y5oWhxCRIdevcKm1tZW7776bV199lT179gBQVFTEmWeeyVVXXdXr1IVkrVq1ijvvvJPTTz+dyZN7nnMsIiIjWzAYJCMjk8WLl8SDJYD5848EYNu2LQcMl5Yte4X58xfGgyWAo48+lvHjJ7Bs2Suce+4F7Graw4rKVaypWt/tqm5+h49Z2TOYkz2d6VlTSXVpIQkRkcNdtGonbS/9DqutEQDHtBNZnXkG8979PU6HmXCsPX8arrlLu2vmgGoaAvz9lc0ApPqcfPXsmT0GRW+tKuOlD2N1mQqyfVx7wZwep8+JiBwqfQ6X6uvrueKKK9i2bRtZWVnxqWo7d+7k9ttv56WXXuKBBx4gIyPjoDq2YsUKrrnmGoqKivjFL37Rp3Ozs0fPB4LcXBXlE0mWni8jXSp///u9Xba+/37sTfTUqRO7/X/c0NBAWVkpZ5+9NGG/ZVlMnjGZ9955j59/9L9UtHQNlApSxnBU4VwWjp3L9JxJh1XdJD1fRJKn58vhqXX7Kiqe/y1WKABAxgkX4z72Eqp++2tmOeoTjjVcXsZefCPOjPSk24+aFr97bBVtwQgAN3x+AVOKsw94/KrNVfz95U0ApKe4+O9vHk9+D9PnhoqeLyLJG03Plz6HS7feeivbt2/nP//zP7nsssuw22NvxKPRKI888gi/+MUvuO2227qd4pasF154gR//+McUFxdz991396kYK0BNTTOm2XWKw0iTm5tKVVXTUHdDZETQ82X0KS/fyyeffMxtt/2BSZMmM3/+cd3+P96+fTsAfn8GVVVNVLRWsaLiU1ZUrmZt8xaCrQHKasqxexwYGExMn8DcnFnMzZlFnn9MvJ3amtZD9tiGmp4vIsnT8+XwFN6xgsCyP4EZBQzcx19BdPbpvPT0a5xkX93lePeiy6kPe6EP/1Ze/KCEtdtqADhl/lgm5aUc8N9aWXUL//P3FURNC4fdxrcvOAK7aQ67f5t6vogkb6Q9X2w2o8eBPH0Ol/71r39x6aWXcsUVVyRst9vtXH755WzYsIHXXnut3+HS3/72N37zm99wzDHHcPvtt5OaOnqSPBERSU5jYwOXXHIuAB6Ph+9+999wu93dHtvaGguFtreU8OuP/sDu5rL4PsMRmyow1p3PiVMXceSYuWS4k/9WWUREDj/hre8TeP0usMxYge4l38Q56RiiwQDjtj6GzZb4JbajeCGOaSf26Rol5U08+Vbsy5G8TC+XLZl6wGMbW0Pc8njHCKevnTODKUX6WyYiw0ufJ+hWV1f3uGrbrFmzqK7uOvUgGY899hi//vWvWbp0KXfffbeCJRGRw5bBTTf9kp/+9CaKiydx443f5o03liUcUR9s4PXd7/DAhscBWFm1JiFYKkwpYFpmbDGIb837GkvGnaRgSUREehTa+CaBf90ZC5bsTrxnfRfnpGMAqHz9AXJsjQnHG9403Cd9uU8FtYPhKHc+u46oaWEzDK4+bzZuV/fTssMRk9ueXENVfWxq3gUnTuS4Wfn9fHQiIoOnzyOXcnJy2LBhwwH3b9iwgZycnD53pKamhv/5n/+hsLCQK664gvXr1yfsHz9+PFlZWX1uV0RERp60tDROO+1MAE499TS+9KXPc+utv2Ph8cfyadUaVlSsYmv9Diws2sLNAFgRkzzfGBbmzWPhmHnk+8fwxw9/D4DfP/xqUoiIyPAS2vAGwbfvjd1xuPF+5rs4xsa+VI+Uridl19tdzvGcchU2b1qfrvP469vY2z4V+/wTi5lY0P35lmXxtxc3sHVPAwCLZudx7gnFfbqWiMih0udw6dRTT+WRRx5h1qxZfO5zn8Nmiw1+Mk2Txx57jCeeeILPf/7zfe7I22+/TVtbG6WlpV2m3AH89re/5fzzz+9zuyIiMrJFbCYT503l3Rff4Iev/AybL/FPV15ePpuBo1Pm8f1jv5/w7XF1dRUpKal4vd5D3GsRERlJwpvfJfj2fbE7Li++pd/Hnhcb/WqFWgm8cXeXc5wzT8Uxfl6frrOhpI5ln8RW255SmM7ZiyYc8Nhn393JB+sqAJhalM5Xlva8kpyIyFDqc7j0ne98h/fee4+bbrqJP/7xj0ycGFv2eceOHdTW1jJ+/Hiuv/76Pnfkggsu4IILLujzeSIiMjqUlOzk+9+/nssvv5Kzzj2HNdXrWVG5io21W9hZvgkMsNpnDaS70mIjlPLmMSF1HJ/78wr27izt8qZ7y5ZNzJhx4KncIiIi4W0fEnjzbsACpwff2f+Gfcyk+P7Aew9htdQmnGOk5eE+7rI+XScQivC3F2IzQFwOG1//7Ezstu6rlHywrpyn39kBQG6Gh+suOgKno88VTUREDpk+h0uZmZk88cQT3HXXXbz22musWbMGgHHjxnHJJZfwjW98g5SUA1cQFxER6Y4ry0t9Yz33PHwny1KXQ3uQFKpvo2F9JekTszl10skcOWYukzOKsRkdb7IXL17Co48+SEnJTiZMKAZg+fIP2bWrhMsv/9IQPBoRERkJwjtXEPjXX8CywOHCu/R7CcFSpGQlkc2J0+FMDFKWXI3h7H6hiQN54o3tVDfEaiddvHgyYzJ93R63ZU89f20PoXxuB9+9dB6pPlefriUicqgZlmVZvR82stTUNGOaI/9hjbSlCUWGkp4vI9eupj3cv/4R9rZUULeqnF1PrMdXlEbmvHxsQYPqD3eDCXf86R6mTJ5Kaeke1q5dzZw5cyksLAKgrq6OK6/8PHa7ncsuu4JQKMSDD95PYeE47rjjHlwuvSnvTM8XkeTp+TJ6RUrX0/bi/4EZjRXv/syNOApnxfebgSZaH/sJVltiEe83zSP57DXf6dO1Nu2q4zcPrgRgWlE6P7ziSGzdTHGrrG/jF/d9THNbGLvN4Hufm8fM4pFTd1bPF5HkjbTni81mkJ194IFEGlspIiJD6t3SD9nbEqspkTkvn9lXHEuK3U/Fy9up/3Avxy5cxD13/Z0pk2PLNK9atZKf//xnrFq1Mt5GZmYmt99+J1OmTOWee/7Co48+xEknLebmm29VsCQiIl1Ea/fQ9uofY8GSzY73zOsTgiWA4Dv3dwmWSiLZPF0/i6r6tqSvFQxF+dsLG4HYdLivnj2z22CpNRDmlsdW0dwWBuBLZ00fUcGSiBzeep0W9/TTT/erYdVPEhGRZCwZfzKmZZLtzWZuziwKTs3DuPbABUvPPvtczj773C7bx48v5uabbx3MroqIyChgttTR9uLvIBQLiDyLv45j3NyEY8I7PiayfXnCNsvu5B/1J2JiY/nGSs4+7sDFuDt74q1tVLaHURedMpm8rK7T4SJRkz89vTa+itzSY8dz8ryxfX5sIiJDpddw6cc//jGGYdCX2XOGYShcEhGRpOT5crli5qVD3Q0RETkMWKE22l76fbxAt+uYS3BOWZR4TLCF4Lv/6HKu57jLsL/nh5pWXl2+mzOOKsLpsPd4vc2761n2ccfqcKcvLOraJ8viH69sZv3OOgAWTsvl4sWT+/X4RESGSq/h0v33338o+iEiIiIiIjJoLMsi8MbdmDW7AHDOXIxr3jldjgt++ChWa33CNnvRHJyzlnCOVc7dz22goSXEs++VcNHJk7qcH28nHOVvL2zAApwOG189ewY2W9eRuS9/tJu3VpUBMCE/la+fO6vbaXMiIsNZr+HSMccccyj6ISIiIiIiMmhCq54nsnMFAPZxR+A+4UsY+4U4kbINhDe+mXii24/nlKswDINjZ+Xxyke72VXZzAvvlzB3cjZTCtO7vd5Tb22noi42He7CkyZRkO3vcszKzVU89vpWADJT3Xzn4rm4nT2PhhIRGY5U0FtEREREREa1yJ51hJY/AYCRmot3yTUYtsQQx4qECLx1b5dzPSd9BZs/EwC7zcbXzpmJ3WZgWha3PbmG6oauxb237mng1eW7AZg8No0zjx7X5ZiS8ib+8uw6LMDtsnPDJXPJTHUf5CMVERkaCpdERERERGTUMlvqCCy7AywL7C68Z16P4e46iij0yTNYjRUJ2xxTT8A56eiEbePzUrn8jGkANLaEuPmhT6lpCHS0E47y1/bpcA57LIzafzpcbWOAWx5fRShsYhhwzXmzGZ+XOkCPWETk0FO4JCIiIiIio5JlmQTeuBsr2AyA5+SvYM8e3+W4aHUJoVUvJmwzUrLxnHBFt+2euqCQs46JjUaqrG/jNw9+QnltbKW3p9/ZEb994UkTu0yHC4Qi3Pr4auqbQwBcdtpU5k3JOYhHKSIy9BQuiYiIiIjIqBRet4xo6ToAHNNOxDn1+C7HWGaUwFt/BcvstNXAs/gbGC7fAdv+3KlT4tPdqhsC/Py+j3nhgxJe+Sg2HW5iQRpnHpM4Hc40Le7853p2VcbCrlOPLOx2BTkRkZFG4ZKIiIiIiIw6ZlM1wY8eA9pHIR3f/Sik8JqXMatLErY5556FY+yMHts3DIPPL5nCeScUA9AWjPD4G9swLQuAry6dgd2W+HHr0de38unWagDmTMri8tOndikqLiIyEilcEhERERGRUSf43gMQiU0985xyFYbL2+UYs6GC4MdPJWyzZRXhPvripK5hGAYXnDSJa86f3bXt9pBpn9dXlvJKe5Hvwlw/154/p0v4JCIyUunVTERERERERpXIzpVESlYC4Jh6PI7CWV2OsSyLwNv3QjTcsdHmwHPqNzHszj5db2pRRpdtP7/vY55+ezuRqMnaHTU88MpmANJ8Tm64ZC5et6NP1xARGc70iiYiIiIiIqOGZUYJfvhI7I7Lh/u4y7o9LrLlXaJlGxK2uY++GHv2uG6P78lDy7bEb08sSGVXRTNR0+Kf7+7k+fdLiJqxUUxOh43rL5lLTnrXUVQiIiOZwiURERERERk1Ilvfx2woB8B95HnYvGldjrGCLQQ/eCRhm71gOs4jzurz9VZvq+bjjZUAHD1jDNdeMIeS8ibueX4De6qa48ESwFXnzGTy2PQ+X0NEZLjTtDgRERERERkVLDNKcMUzABi+DJyzlnR7XHD5k1iBpo4NTk9sdbg+1kAKhqP8o326m9dt5wunTwVgQn4qP/vKUZzVabW4pceN55iZeX1qX0RkpNDIJRERERERGRUiu1ZhNVUB4Jr/WQyHq8sx0eqdhNf/K2Gb54QvYUvN6fP1/vnuDqobAgBcfMpkMlLc8X0Ou43PL5nKgqm5BEIRjpiU3ef2RURGCoVLIiIiIiIyKsRDI6cH5/QTu+y3LJPAO/cDHVPVHBOPwjH1+D5fa09lM698FFv9bdLYNBbPL+z2uGnjMvrctojISKNpcSIiIiIiMuKZLXVE96wFwDn1BAynp8sx4U1vY1Zuj983fBl4TvoKhmH07VqWxf0vbyJqWtgMgyvPmo7N1rc2RERGE4VLIiIiIiIy4kV2rYrfdk5d1GW/FWjuUsTbc8rXMDwpfb7W+2vL2VraAMAZRxcxPi+1z22IiIwmCpdERERERGTEi+5eDYDhTcM2ZlKX/cGPHoNQa/y+c9YSHOPm9vk6rYEwj72+FYCMFBfnnTCxnz0WERk9FC6JiIiIiMiIF60uAcCePw3DSPyYE63cRnjjm/H7Rno+7uM+36/rPP3ODhpbwwB8bskUvG6VsRURUbgkIiIiIiIjmhVswWquAcCWPT5xn2kSePOehG3eU6/GcLjpqz2VzfxrRSkQK9R97My8fvZYRGR0UbgkIiIiIiIjmtlSF79tSxuTsC+86S3MurL4fdfCC7F3M22uN5Zl8cCrmzGtWBHvK86Y1udC4CIio5XCJRERERERGdkiofjNzqvEWaFWgm/fG79vyyrCteCz/brE8o2VbNpdD8CSIwsZN6bvhcBFREYrhUsiIiIiIjKyOTumuFnhQPx28P2HEw7znn4dhs3e5+bDkSiPv7ENgBSvkwtOUhFvEZHOFC6JiIiIiMiIZvNlxG+bjZWx3/XlhDe9Fd/uPvFKbBn5/Wr/leW7qW6IhVYXnjQRn8fZ/86KiIxCCpdERERERGREM9x+jLRYce1o6ToAWh79ccd+bzrOmaf2q+2GlhDPvx9bia4wx8/J88ceZG9FREYfhUsiIiIiIjLiOSbMByC6dxOB9x5I2Oe7+KZ+F99+6q3tBEJRAD6/ZAp2mz5CiYjsT6+MIiIiIiIy4rlmLQEjVk8pvPbV+Hb3iVcmTJvri92Vzby9OrbS3BGTspkzKfug+ykiMhopXBIRERERkRHPlp6Ha97SLttds5b0qz3Lsnh42RYsC2yGweeXTDnYLoqIjFoKl0REREREZFRwzT874b7/i7f0u61VW2vYUFIHwOIFYxmb4z+ovomIjGaOoe6AiIiIiAwsKxIkvOltzNpSHBMXYi+YgWHX2z4Z/UJr9p8Ol96vdiJRk0de3wqA1+3g/BMnDkj/RERGK73LEBERERllQp++QOiTZwAIb3g9cadh4CheGAud8qdh+LP6XehYZDgxW+sJrXoBAFv2eJwzF/e7rddXllJR2wrAeScUk+pzDUQXRURGLYVLIiIiIqOMLavowDsti8iOj4ns+LjLLnvBdBzFR2LPm4ItPR/DrWlAMnKEVjwNkSAA7uMuwzD6VwGkNRDhn+/sAGBMppfTFvbwfBIREUDhkoiIiMio45x0NPbLfkt4+0dEdqzArNqR1HnRvZuI7t3UZbstowBH8ZHYcoqxZRRgSxuD4dBIDhk+zIYKwhvfBsA+bi6Owln9buvFD0toCUQAuOSUyTjsKlMrItIbhUsiIiIio5AtbQzu+Z/FPf+z8W2WZWG11BHZvZrIzk+I7l6dVFtm/V5Cnz7fZbvhTccxYQG2rEJs6Xmx0U4pORg2fRiXQyu44mmwogC4j7mk3+3UNwd5dfluACaNTWPh9NyB6J6IyKincElERETkMGEYBkZKFq6Zi3F1qkdjWSZWcw3Rqp1ESj4lsnMFhAO9tme1NRDe+EbX6/gysI+ZFAub2kMnW3o+hjdN9Z1kwEVrdxPZ+gEAjsnHYs8e3++2nn13J6GICcRGLenfq4hIchQuiYiIiBzmDMOGkZqLLTUX56SjgW8AYJkmVmMl0bpSohVbiOxYgdVU1Wt7Vms9kZ2fdN3h9GLLyI+Pcur4ycNweQf4UcnhIrT8ScACw4Z74YX9bqeirpW3VpUBMGdiFjMmZA5QD0VERj+FSyIiIiLSLcNmw8jIx5aRj3PiQjjuMgCsaASzvgyzZhfR6l1E96zBrN/be4PhNsyqHd3WgDJ8GV1CJyMjD1vqGAy73rJK96KV24iUrATAOf1EbBn5/W7rqbe2EzUtAC4+ZfKA9E9E5HChv9QiIiIi0ieG3YE9ezz27PE4p8W2xeo51WLW7CZas6s9eCpJaqQTxEY7RVvruxYUN4zYqKp9oVNGp9FO/sx+rwgmo0Nw+ROxGzYHriPP73c7uyqa+GhDJQDHzspjQn7qQHRPROSwoXBJRERERA5arJ5TNraUbBwT5se3W6G2WNhUvZNodQlm9c7YKCfLSq5hy4pNzWus7FqA3O5qH+2Ut1/wlI/hSRm4ByfDUqR0PdHS9QA4Zy3BlpLd77aefjs2ms5mGFxw4sQB6Z+IyOFE4ZKIiIiIDBrD5cVRMB0Kpse3WeEgZu1uolU7iVbtwKzchtlQ3vfGoyHM2t2Ytbu7XtedEpvS16W+0xgMh/tgHpIMA5ZlEVz+eOyOw41rwWd7PqEH28sa+XRrNQAnHJFPXpZvILooInJYUbgkIiIiIoeU4XRjz5uCPW9KfJsVbCFatYNo5Xaildsxq7ZjtTX2oVEjYTSUFWzGqtiKWbG166H+LGwZBYnBU0Y+Rko2hs1+UI9NDo1oyaeYldsBcB1xJjZvWr/bevrtWDt2m8G5JxQPRPdERA47CpdEREREZMgZbj+Oojk4iuYA7TWcmmtiYVPVdqIVW2OFwM1o9+d707Gl5mKkZGH4MyHUitlQgVlfjtXWkHCs1VJLtKWWaOm6xEZsdmxpY2LT6tqDp7bWSZikYXjTtSz9MGFZZketJbcf19zP9LutzbvrWbujFoBT5o8lJ12rFoqI9IfCJREREREZdgzDwEjNwZaag3PyMQBYkVBsdNPeTUQrthAt3wLhQGxfe0FwKmLn23KKcUxYgHvR5djScjEbKjEbyjv9VMRqP7WfD4AZxazfm7Dy3d632284PZ2m1uUl1ndyKZA4lCLbPsKs2wOAa97ZGG5/v9vaN2rJ6bBxzqLigeieiMhhSeGSiIiIiIwIhsOFo2B6rIYTYJlmrHZT2YZYcee9GyESAsCs3kmoeiehFU9hpOXhnLoI58zFOKccF2/PsiystoZY0NRQHhvltC94aqxIHCUVDsSKkVfv7Novb1pCQXFjXwiVlothdw7qf5PDjWWZhFY+B8T+u7tmn97vtjbvrmfjrnoATl1QSGaqanGJiPSXwiURERERGVEsy4JwACvUCoaBLacYZ9oYHMVHEt29msjOTxKPb6wgtOJpQiuexnv2D+JT7wzDwPBlYPNlJBQcB7DMKFZzDak0UleyvWO0U0M5VnNN4rFtjUTbGomWb07sqGFgpOR0GuW0b1W7Agx/JoZhG/D/NqNdpGRlfNSS84jPYDj7Hwg9995OABx2G0uPHT8Q3RMROWwpXBIRERGRIWFFI1jBlvgPgeZYIe5O26xAp/uh1thxodaE4t19Edn5STxc6olhs2OkjcGXO5mWtCkJ+6xIELOxErO+I3AyG8qx6suxgs2dDrSwmqqINlUR3b0m8QJ2534r2cVuGxn5sZXuVN+pC8uyCH3ybOyO249r1qn9bmvH3saOWkvzxpKeolFLIiIHQ+GSiIiIiBwUyzIh2IoVaMIMNGEFmiHYEguKAi2JgVGn+wn1jgaa04vh9sV+XD6wLGzZ43DNXXrQTRsON/ascdizxnXZZwWaE0Y5xX/qKyAa6jgwGsas3YNZu6frBdz+9rCpILG+U1reQY3UGemiu9fEpyW65px5ULWu9o1astsMPqNRSyIiB03hkoiIiIgk2BcWmYFGrLYmrEBT++/O9xs7bW8Gq/tV3PrNMDBcfvD4YyN53D4Mlz8eFhluH7j97bf98W2GywcuH4ZtaKacGZ4U7J4p2PP2G+1kmVgt9Z3Cpo7C4lZTFVhmx8HBFszK7ZiV27u2789KHPGU0T7iKTUXw2Yf7Ic3ZCzLIrjyn7E7Tg+uOf2vtbS7spmVW6oBOOGIfLLTPQPRRRGRw5rCJREREZHDhBUOYrXWY7bWY7U1YLU2dNxvbb/f1tAeFpm9N5gMwxYLf9x+8KS0327/7fHvdz+l41iXd1TVJDIMG0ZKFraULCiclbDPikawmqoSRjnFp9q11ice21JLtKWWaNmG/S5gjxUQ71RYPD7Vzpcx4qfZRfduxKzYCoBr9ukHtULcvlFLNsPg7OMmDET3REQOewqXREREREY4KxLEaq7DbKnFaqlrD4vq42HRvvBoQKahOb0Y3tTYCmmeVAxPauy+J639d/tPe3iE0zvig43BZtgdGBkF2DIKuuyzQm2YjRVd6juZ9eUQbut0YBSzoRwayonu2q8Rh7sjbMoo6DTyKe+gQppDKfRJ+6gluwvnEWf2u529NS18vLESgGNn5TEm0zcQ3RMROewpXBIREREZxhKDo1rM5s6/Y9sJtvT/AoYNw5eO4U3H8KVj86ZjeNM6QqN9t/fdtzsH7sFJrwyXF3tOMfac4oTtlmXFalw1lGPW78VKqPFUCWak4+BIELOmBLOmpGv73rSOoCleXDwfW/qYYfP/Olq+JT5SyznrVGzetH639fz7JViAAZyzSKOWREQGisIlERERkSFkRYKYTdWxaVFN1bHbje23m6v7Hxw5PRjedGy+9Ni0KF+n8MiX0bHNkzKqpp8dLgzDiAVD3jTIn5awzzJNrOaaxILiDRWxEKq5FuhYac9qayTa1ki0fPP+V8BIzYmNckrLw5aaE7vf/vtQrmgXXNm+QpzNgWvuZ/rdTmV9Gx+sqwBg4YwxjM0ZGaO2RERGAoVLIiIiIoPIsqzY1LT6cszGivbgKPZjNVVjtTX2uU3DnYKRkhkr7uzPitXy2ffbl4nhz8Bwqkjx4cqw2TDScrGl5cK4IxL2WZEQZmNlQn0na199p0BT5yOxmqqINlURZW3Xizjc2FKzMVJigVM8fEppD588qQMSPkVr9xDdvRoA5/STsPkz+93WSx/uwrRiwdpnNWpJRGRAKVwSERERGQBWqLWjJs5+9XH6VOvI5oiPELGl5LQXgc7G8GdiS8nC8GdiOA7f5ejl4BgOF/asIuxZRV32WcGWTv+G97bf3ovZWNX133AkiFlXBnVldLtOoN3VKXDK7vg3nZqDkZITm26ZRPgUWv3ivp7jmre0z493n6bWEO+t2QvA3MnZjM9L7XdbIiLSlcIlERERGdY+/PB97rvvHjZt2oDNZmPWrCP4xjeuZc6cI3o8r6yslNtu+wMrV64A4PjjT+S6624kM7P/Ix+gvcByXSnR2j2Y+37qy5IfgbRv1bDU3Nioj7Sc2O3U3NgHb1+6pqnJkDDcfuxjJmEfMylhu2VZEGrtmLbZPmVz32+zsTqxuDhANIRZXwb1BwqfnBi+DGydpmwm3PemAxDZ8j4AjokLsaWN6fdje+PTMkKR2AqIZx0zvt/tiIhI9xQuiYiIyLC1cuUKfvCD7zBx4iSuvvpbRKNRnnrqca6//mpuv/0uZs2a0+15DQ31fOc71xAOh7niiiuJRqM89NDf2bZtK3fddR9OZ++Fii3TjI3eqN2FWbsnHiZZzTVJ9d1IyU5YDn7f8vBGSjaGzd6n/w4iQ8kwDHD7sbv92HO6n05mBVva64TVxEKnpmqs5ur4ti61w6Lh+LS7ZER2fEzr8//bXlw+JXFlwviKhamxKaO2xHA2HDH514o9AIwfk8KM8Rl9/m8gIiI9U7gkIiIiw9att/4fY8bkceed9+HxxGoIfeYz53DFFZdy551/4g9/+FO35z388ANUVVVy330PU1w8EYBZs+Zw443f5sUXn+O88y5MON6yTKyGSqJV24lW7cSs3km0eidEQj130O7CllWILXMstvT2Jd4z8rGl5WE4XAf9+EVGCqO38CnUitlU0zHaqakaq7UBq7Ueq7Ues7Wh6+in/URL1yXdH0fxQjynX4thc/DRhgoaWmLP5TOPGXfICpGLiBxOFC6JiIjIsNTY2MjWrVu47LIvxoMlgKysbObPP5Llyz844LnLlr3C/PkL48ESwNFHH8v48RNYtuwVzj37bKKV22NLnJdvJlqxrecPtoYtFhxlFWHLLMLWXrPGSM3tMkpCRLoyXD7s2T7IHnfAY6xIEKu1AbO1geietYQ+eabf14vsXIHZUIEtYywvf7QbgPQUF8fMzOt3myIicmAKl0RERGRY8vv9PPjgE3i93i77Ghrqsdu7n1rW2NhIWVkpixefFt9mRUJEyzczOdvHR2tX0vy3b4HVbSUYsDmwZY/DnjsRe04xtpwJ2DIKNBJJZJAZDjdG2hhsaWOw503B8KTERhB2JxrBCjRjBZpiP21NYEbiu93HfxFbxlg2lNSxp6oZgNOOLMJhVxgsIjIYFC6JiIjIsGS32xk3rmvh3a1bt7BmzSqOOWZRt+dVV1cCkO13EVr9MpE9a4ju3QTRMJnBSlqCEVqCIfyuWDhlpOdjz5vaXsh4IrbMIgy73iKJDCXDMHDNOeOg23lleWzUksthY/GCwoNuT0REuqd3TiIiIjJitLa28otf/BcAX/zilxP2WZaJWbmd+o/+CYBt7QsEg1kJx7jaRy1EppyMZ/oC7PnTsHnTDkHPReRQK6tuYfW2WAH+E44oIMXbeyF/ERHpH4VLIiIiMiIEAgF+/OPvsXXrZr70pa+yYMFCLMvCrCkhvPldIjtWYLXUEq5sBWBfyV4jJRtH0RzsRXNwRd+DNX/Hc9SFOHNyhu7BiMige/Xj3fHbZxx94FpPIiJy8BQuiYiIyLDX1NTED3/4XdasWcU555zH17/0RUJrXia86R3M2t0Jx3qdsdFJ0XFH4v/cNzHS8+OrQwUj7wCxek4iMno1tYZ4b205APOn5JCf5RviHomIjG4Kl0RERGRYq6ur5Xvfu44tWzZz7llncP1xBbQ++P2E4r1gYB87A8fEo5iQOx2evYB6dy62jIKEtqqrq0hJSe22SLiIjB7vriknHDEBjVoSETkUFC6JiIjIsNXa2hIPli46eirfyNtLdMve+H4jLQ/ntBNwTjsBW0o2AC6goKCQzZs3dWlvy5ZNzJgx81B1X0SGgGlZvPFpKQAF2T5mjM8Y2g6JiBwGFC6JiIjIsGSFA/zvf36XLVs2c/7MbL4x0x3bYRg4ihfinHMG9vxp8SlvnS1evIRHH32QkpKdTJhQDMDy5R+ya1cJl1/+pUP4KETkUNtQUkdlXRsAi+cXdvsaISIiA0vhkoiIiAwrlmkSWvEUW995llc/XEuKy8bkTA//KmnBXjADx7g5GGYqrN7GWQXTKS3dw9q1q5kzZy6FhUUAXH75lbz00vPccMO1XHbZFYRCIR588H6mT5/JmWeePcSPUEQG0xsrY6OWnA4bxx+RP8S9ERE5PChcEhERkWElsnMFoZXPsmZPLQDNIZPfvVfavncH8GL82LPOOptVq1byy1/exH/8x3/Fw6XMzExuv/1Obr31d9xzz19wuz2cdNJivvWtG3C5XIf4EYnIoVLfHGTl5moAjpk5Br/HOcQ9EhE5PChcEhERkWHFPmYSttxJnFc4m4uvPw3H+PkYNtsBjz/77HM5++xzu2wfP76Ym2++dTC7KiLDzNur92JaFgCLFxQOcW9ERA4fCpdERERkWLGlZOO/8GdD3Q0RGWFM0+Kt9kLe48ekMKkgbYh7JCJy+Djw14AiIiIiIiIjxJrtNdQ0BgE4ZYEKeYuIHEoKl0REREREZMTbV8jb7bJz3Ky8Ie6NiMjhReGSiIiIiIiMaDUNAVZvrwFg0aw8vG5V/xAROZQULomIiIiIyIj27pq9tNfx5pT5KuQtInKoKVwSEREREZERy7Is3ltXDsD4vBQm5KcOcY9ERA4/CpdERERERGTE2lbWSGVdGwDHz84f4t6IiByeFC6JiIiIiMiI9f7a2Kglm2FwrAp5i4gMCYVLIiIiIiIyIoUjJh9tqABg9sQs0lPcQ9wjEZHDk8IlEREREREZkVZvq6ElEAHg+DmaEiciMlQULomIiIiIyIj03tq9AHhcdhZMzRni3oiIHL4ULomIiIiIyIjT3BZm9bYaAI6aMQaX0z7EPRIROXwpXBIRERERkRHnow0VRE0L0CpxIiJDbdiGSxs2bGD27NmUl5cPdVdERERERGSY2bdKXHaam2njM4a2MyIih7lhGS5t27aNb37zm0QikaHuioiIiIiIDDMVda1sK2sE4LjZ+dgMY4h7JCJyeBtW4VIkEuGBBx7g0ksvJRgMDnV3RERERERkGFqxqSp++7hZeUPYExERgWEWLq1YsYKbb76Zr33ta/zgBz8Y6u6IiIiIiMgw9PHGSgAKsn0U5qYMcW9ERMQx1B3obPLkybz22mtkZ2fz5JNPDnV3RERERERkmKluaGNneRMAC6ePGeLeiIgIDLNwKScnZ6i7ICIiIiIiw1jnKXFHTc8dwp6IiMg+wypcGijZ2aNnaGxubupQd0FkxNDzRSR5er6IJE/Pl+Fl9fZaAAqy/Rw5uwBDxbyHFT1fRJI3mp4vozJcqqlpxjStoe7GQcvNTaWqqmmouyEyIuj5IpI8PV9Ekqfny/BS1xRkw85YuDR/SjbV1c1D3CPpTM8XkeSNtOeLzWb0OJBnWBX0FhEREREROZBPNndMiVO9JRGR4UPhkoiIiIiIjAgrNsVWictKczOxYPRMJxERGekULomIiIiIyLDX2BJi0+56ABZOG6NaSyIiw4jCJRERERERGfY+3VqN1V5W9agZWiVORGQ4Gbbh0kUXXcSmTZvIz88f6q6IiIiIiMgQW7W1GoBUn5PJhelD3BsREels2IZLIiIiIiIiAOFIlHXtq8TNnZyNTVPiRESGFYVLIiIiIiIyrG3cVU8obAIwb3LOEPdGRET2p3BJRERERESGtX1T4uw2g9kTs4a4NyIisj+FSyIiIiIiMmxZlsWqrTUATBuXgdftGOIeiYjI/hQuiYiIiIjIsLW3ppWaxgAA8yZnD3FvRESkOwqXRERERERk2FrfXsgbYPYkhUsiIsORwiURERERERm21u+sAyAjxcXYbN8Q90ZERLqjcElERERERIalSNRk465YuDSrOAvDMIa4RyIi0h2FSyIiIiIiMixtL2skEIoCMLtYq8SJiAxXCpdERERERGRY6lxvaVZx5hD2REREeqJwSUREREREhqV99ZaKcv2kp7iHuDciInIgCpdERERERGTYaQ1E2F7WCMTqLYmIyPClcElERERERIadTbvqMC0LULgkIjLcKVwSEREREZFhZ0NJbEqc3WYwfVzG0HZGRER6pHBJRERERESGnS17GgCYODYNt8s+xL0REZGeKFwSEREREZFhpS0YYVdlEwBTC9OHuDciItIbhUsiIiIiIjKsbC9rpL3cElOLMoa0LyIi0juFSyIiIiIiMqxs2VMfvz2lSCOXRESGO4VLIiIiIiIyrOyrt1SQ7SPF6xzi3oiISG8ULomIiIiIyLARNU22lzUCmhInIjJSKFwSEREREZFhY3dlM8FwFICpmhInIjIiOIa6AyIiIiIiIvts2d0Qv719byO1TcEBa3taUTrTx2cOWHsiIhKjcElERERERIaNXRVN8duvf1I6oG3bbQa/v/5E1XESERlgmhYnIiIiIiLDxqziLJyOwfmYYloWTrs+AomIDDSNXBIRERERkWFj0Zx8jp2Vh2lZA9bm7x9dxYaSOsbm+HG77APWroiIxChcEhERERGRYcVmM7BhDEhblmXFp9oV56cOSJsiIpJIY0JFRERERGTUqmoI0BKIAFCcnzbEvRERGZ0ULomIiIiIyKi1c29j/HZxgUYuiYgMBoVLIiIiIiIyau0sj02Js9sMxuWmDHFvRERGJ4VLIiIiIiIyau0buVSY48flVDFvEZHBoHBJRERERERGJcuy2F3ZDMB4FfMWERk0CpdERERERGRUqmsKxot5jxujKXEiIoNF4ZKIiIiIiIxKe6qa47eLVG9JRGTQKFwSEREREZFRad+UOICiXP8Q9kREZHRTuCQiIiIiIqNSaVULAOkpLlJ9riHujYjI6KVwSURERERERqWy6li4pClxIiKDS+GSiIiIiIiMOqZpsbe2FYCx2ZoSJyIymBQuiYiIiIjIqFPd0EY4YgIwNsc3xL0RERndFC6JiIiIiMioU1bdGr89Nkcjl0REBpPCJRERERERGXXKalritws0LU5EZFApXBIRERERkVGnor3eUorXSYrXOcS9EREZ3RQuiYiIiIjIqFNV3wbAmEzvEPdERGT0U7gkIiIiIiKjTuW+cClD4ZKIyGBTuCQiIiIiIqNKOGJS1xgEIFfhkojIoFO4JCIiIiIio0p1QxtW+21NixMRGXwKl0REREREZFSprGuL39bIJRGRwadwSURERERERpV99ZZAI5dERA4FhUsiIiIiIjKqVLWPXHI5baT7XUPcGxGR0U/hkoiIiIiIjCr7Ri7lZngxDGOIeyMiMvopXBIRERERkVGlpiEAQG66psSJiBwKjqHugIiIiIiIyMFqDUQor22lvLaFqobYyKXsNM8Q90pE5PCgcElEREREREaESNSkuiFAeU1rPEgqr2mlvK6NxpZQl+Oz0xUuiYgcCgqXRERERERk2LAsi8bWMOU1LZTXtlJR20Z5bSt7a1uprm8jalq9tmEA4/NTOWbmmMHvsIiIKFwSEREREZFDLxiOUlkXC472BUnl7UFSWzCSVBt+j4P8LB/5WT7y2n/nZ/vIy/TidNgH+RGIiMg+CpdERERERGRQmJZFbWOgYwRSTftUttpWahqDSbVhtxmMyfTGQ6R4gJTlI9Xr1GpwIiLDgMIlERERERE5KK2BcPuoo/YRSDWxUUiVda2EImZSbaSnuCjoFCDltYdIOeke7DYtci0iMpwpXBIRERERkV5FoiZV9W2d6iC1xAtrN7aGk2rD7bSTl9XNKKRMH163PpqIiIxUegUXEREREZE407TYXdnMzvLGjlFIdW3JF9M2ICfdE6+BVNBpJFJmqlvT2ERERiGFSyIiIiIih7m2YITV22pYuaWKtdtraU2ioLbf4yA/25c4CinLxxgV0xYROewoXBIREREROQy1BSN8vKmSjzdWsaGklki066gkh91gTOa+kUfe9pFIfvKzfaR4nUPQaxERGY4ULomIiIiIHCZM02J9SS3vrSnnk81VXYpte1x25k7OZlZxFlOL0snL9GGzaRqbiIj0TOGSiIiIiMgoV9sY4PWVpby7Zi/1zaGEfWk+Jwum5bJgai4zJ2TidGhlNhER6RuFSyIiIiIio5BlWWzZ08BrK/bwyaYqTKtj2pvTYWPB1ByOn1PA7ImZ2G0KlEREpP8ULomIiIiIjCKWZbF+Zx3PvLODraUNCfumFKZz4twCjpo+Bp9HHwVERGRg6C+KiIiIiMgosWlXHU+8uT0hVHLYDY6ZmcfpRxVRnJ82hL0TEZHRSuGSiIiIiMgIV93QxqOvb+PjjZXxbW6nndMWFnHG0eNI97uGsHciIjLaKVwSERERERmhTMti2Yo9PPHGtvjKby6HjdOOKuIzx4wn1adQSUREBp/CJRERERGREaiuKcjdz61nQ0ldfNuxs/K4dPFkstI8Q9gzERE53ChcEhEREREZYbaVNnDbU2toaA4BkJPu4Wtnz2TGhMwh7pmIiByOFC6JiIiIiIwgq7ZWc/tTa4lEY9PgTjyigC+cPhWvW2/tRURkaOgvkIiIiIjICLFmew23PbmGqGlhtxlcfsY0Tl1QONTdEhGRw5zCJRERERGREWBPVTN3PL2WqGnhsBt864IjmD81Z6i7JSIigm2oOyAiIiIiIj0LR0z+8s91BEJRAL553mwFSyIiMmwoXBIRERERGeZeWb6L0qoWAM49vpiF08cMcY9EREQ6KFwSERERERnGgqEoL324C4CxOX7OPaF4aDskIiKyH4VLIiIiIiLD2MebKmkJRAA4/8SJOOx6Cy8iIsOL/jKJiIiIiAxja7bXAJDidbJAdZZERGQYUrgkIiIiIjKMbS9rBGBqUbpGLYmIyLCkv04iIiIiIsOUZVnUNgaBWL0lERGR4UjhkoiIiIjIMBUMRzEtCwCf2zHEvREREemewiURERERkWHK5bRjtxkAtAYjQ9wbERGR7ilcEhEREREZpmyGQYrPCUB9U3CIeyMiItI9hUsiIiIiIsPY2OxYraXtexuHuCciIiLdU7gkIiIiIjKMTS1KB2BvTStV9W1D3BsREZGuFC6JiIiIiAxjC6ePid/+aEPFEPZERESkewqXRERERESGsaJcP2NzYlPj3lhZRtQ0h7hHIiIiiYZduPTcc89xzjnnMHfuXJYuXcrTTz891F0SERERERkyhmFw2sIiAGoaAyzfUDnEPRIREUk0rMKlF154gR/84AeceOKJ3H777RxzzDH86Ec/4qWXXhrqromIiIiIDJnj5+ST2r5q3JNvbScciQ5xj0RERDoMq3Dp97//PUuXLuXf//3fOemkk7jppptYunQpt9xyy1B3TURERERkyLidds4/cSIA1Q0BXlm+e4h7JCIi0mHYhEu7d+9m165dnHnmmQnbzzrrLLZv387u3foDKiIiIiKHr1Pmj43XXnr23Z2U17YOcY9ERERihk24tH37dgAmTpyYsH3ChAkA7Nix45D3SURERERkuLDbbHzpzGkYQChics9z6zFNa6i7JSIigmOoO7BPU1MTACkpKQnb/f7YtzPNzc1Jt5WdndL7QSNEbm7qUHdBZMTQ80UkeXq+iCRvOD1fcnNT2bCngX++tZ1tZY38a9VevnDm9KHulkjccHq+iAx3o+n5MmzCJcvq+VsXmy35QVY1Nc2j4luc3NxUqqqahrobIiOCni8iydPzRSR5w/H5cvbR4/hobTnlta089PJGclNdzJuSM9TdEhmWzxeR4WqkPV9sNqPHgTzDZlpcamossWtpaUnYvm/E0r79IiIiIiKHM5fTzrcunIPbaccC7nx2PRUHWX/pN7/5H6677uqkji0rK+U//uPfWLp0CUuXLuHnP/8ZdXV1B3V9EREZ2YZNuLSv1tKuXbsStpeUlCTsFxERERE53BXlpnDVOTMBaAtGuPWJ1bQEwv1q67nnnubZZ59K6tiGhnq+851rWLduDVdccSWXXXYF7777Fjfe+G3C4f5dX0RERr5hEy5NmDCBoqIiXnrppYTtr7zyCsXFxYwdO3aIeiYiIiIiMvwcNWMM5yyKLX6zt6aVPzy2imAomvT50WiUv/3tLn7zm/9J+pyHH36AqqpKbrnlDr74xa/w5S9fxc9//hu2bt3Miy8+1+fHICIio8OwCZcAvv3tb/Pcc8/x3//937z11lv813/9Fy+++CI33HDDUHdNRERERGTYufCkSRw1YwwA20obue3J1YQjZq/nBYNBvva1L3LPPX/hrLPOJjd3TFLXW7bsFebPX0hxccesgqOPPpbx4yewbNkr/XsQIiIy4g2rcOmiiy7ipptu4p133uHb3/42y5cv5ze/+Q1nn332UHdNRERERGTYsdkMrj53FnMmZgGwbmcddz67jqjZc8AUCoVobW3hppt+xU9/ehN2u73XazU2NlJWVsr06TO67Js2bQabNm3o34MQEZERb9isFrfPZZddxmWXXTbU3RARERERGREcdhvfvvAI/u/RT9m6p4EVm6q478VNfOXsGdgMo9tz/H4/Dz30JA5H8h8HqqsrAbod5ZSdnUNzczPNzc2kpBx4NSERERmdhtXIJRERERER6Tu3y853L5nL+DGxYOedNXt59F9bsSyr2+NtNlufgiWA1tbYinQej6fr9d1uAAKBtj61KSIio4PCJRERERGRUcDncXLj5+eTl+kF4JXlu3n2vZ0D1v6+oOoAg6Ha9bhTRERGKYVLIiIiIiKjRLrfxQ8uW0Bmamwk0dNv72DZij0D0rbX6wNixcD3t2+b3+8fkGuJiMjIonBJRERERGQUyU738IPL5pPqcwLwwKubeX9d+UG3m5eXD0B1dXWXfdXVVaSkpOL1eg/6OiIiMvIoXBIRERERGWUKsv1873Pz8bhiq8D99fkNrNraNRTqi9TUVAoKCtm8eVOXfVu2bGLGjJkH1b6IiIxcCpdEREREREahCfmp3HDJXBx2G1HT4s/PrGNPVfNBtbl48RI+/vhDSkp2xrctX/4hu3aVcPrpZx5kj0VEZKRSuCQiIiIiMkpNH5/JtRfMxgCC4Si3PbmGtmAkqXNLS/fw8ssvUFraUbPp8suvJC0tnRtuuJaHH/4H99//V/7zP3/E9OkzOfPMswfpUYiIyHCncElEREREZBRbMDWXC06aCEBlXRsbd9Uldd6qVSv5+c9/xqpVK+PbMjMzuf32O5kyZSr33PMXHn30IU46aTE333wrLpdrUPovIiLDn2HtW1N0FKmpacY0R/7Dys1Npaqqaai7ITIi6Pkikjw9X0SSN1qeL6Zl8cQb2yiv/f/t3X90THf+x/HXiCbZJoiwfmxEmygh0WwJRRINK8Ru1e5WNQ27trt+lP5QtVRiays5qEVXS6JChfiRVGOJpqrI8avltJZiq360UkFU1ZqkSHZJZL5/OJmvaRKd3JWZmDwf5ziHz/3cO+8b530m9zX3fqZEIx8N1r2eDZ1dElyQq/QL4Ah3W780aGBSs2be1W7nXQUAAABwcQ1MJg3t+4CzywAAuCgeiwMAAAAAAIBhhEsAAAAAAAAwjHAJAAAAAAAAhhEuAQAAAAAAwDDCJQAAAAAAABhGuAQAAAAAAADDCJcAAAAAAABgGOESAAAAAAAADCNcAgAAAAAAgGGESwAAAAAAADCMcAkAAAAAAACGES4BAAAAAADAMMIlAAAAAAAAGEa4BAAAAAAAAMMIlwAAAAAAAGAY4RIAAAAAAAAMI1wCAAAAAACAYYRLAAAAAAAAMIxwCQAAAAAAAIYRLgEAAAAAAMAwwiUAAAAAAAAY1tDZBdSGBg1Mzi7hjnGlcwFqG/0C2I9+AexHvwD2o18A+91N/fJjtZosFovFQbUAAAAAAADAxfBYHAAAAAAAAAwjXAIAAAAAAIBhhEsAAAAAAAAwjHAJAAAAAAAAhhEuAQAAAAAAwDDCJQAAAAAAABhGuAQAAAAAAADDCJcAAAAAAABgGOESAAAAAAAADCNcqgOef/559e/f3+7558+fV1hYmBYtWlSLVQF1kz39cvHiRb3yyivq27evunTposcff1ybN292UIVA3WFPvxQXFysxMVERERHq0qWLRo8erfz8fMcUCDiRxWLRihUrFBMTo9DQUA0ePFg5OTm33cdsNishIUGRkZF6+OGH9cwzz9AvqBeM9Et5ebneeust9evXT6GhoXrssce0adMmB1UMOI+RfrnV3Xq939DZBdR3Gzdu1LZt29S2bVu75lssFk2dOlVXr16t5cqAuseefrl+/bpGjRqlK1euaPz48WrRooW2bNmiCRMm6MaNGxo0aJADKwacx973l5deekmff/65Xn75ZXl5eSk5OVkjRozQpk2b1KhRIwdVCzheamqqFixYoBdeeEEPPfSQdu/erUmTJsnNzU2/+tWvKs23WCx67rnndObMGU2ePFk+Pj5asGCBRowYoZycHDVp0sQJZwE4Rk37RZJmzZqltWvXauLEierYsaM2bdqkP//5z/L29lZUVJSDzwBwHCP9UuFuvt4nXHKiCxcuaObMmWrVqpXd+2RkZOjrr7+uxaqAusneftm9e7eOHz+urKwshYaGSpIiIiL0zTffaOnSpYRLqBfs7Zf9+/dr165dWrp0qR555BFJUrdu3dSvXz9lZmZqzJgxjigXcLjS0lKlpaUpLi5O48aNkyT16tVLR44c0erVq6v85T8/P1+fffaZ/va3v+k3v/mNJKldu3aKjo7W9u3b9dvf/taRpwA4jJF+OXPmjNasWaOkpCQNHTrUuk9+fr4++ugjwiW4LCP9cqu7+XqfcMmJXnnlFUVERMjDw0MHDhz40flnz57VvHnz9Oabb2r06NEOqBCoO+ztFy8vL8XGxurBBx+0GQ8MDLSrzwBXYG+/7NmzR15eXoqIiLCO+fr6qnv37tq9ezfhElyWm5ubVq1aJR8fH5vxe+65RyUlJVXuc+3aNUk332cqVNytVFRUVCt1AnWBkX7Jzc2Vp6enNYitsHr16lqqEqgbjPRLhbv9ep81l5wkKytLX3zxhaZNm2bX/PLycsXHx+uXv/yl9dNloL6oSb/06tVLSUlJMplM1rHS0lLt2rVL7du3r80ygTqhJv3y9ddf67777pObm5vNeNu2bXXq1KnaKhFwugYNGigoKEgtW7aUxWLRv//9by1ZskR79+5VbGxslft07NhRPXr0UEpKivLy8mQ2mzVjxgzde++9io6OdvAZAI5jpF9OnDihgIAA7d27V4MHD1ZwcLAGDBigDz74wMHVA45lpF8k17je584lJzh37pxee+01vfbaa/L19bVrn/T0dBUUFGjx4sW1XB1Qtxjplx+aO3eu8vPzlZKScoerA+qWmvbL1atX5e3tXWncy8vrrnzWHzBi69atGj9+vCSpT58+Gjx4cLVzp0+frlGjRlkfa3B3d1dKSor8/f0dUivgbPb2i9ls1vnz5zV16lS9+OKLatOmjbKysvTSSy/J19dXPXv2dGTZgFPU5P3FFa73uXPJwSoW6IqKilJMTIxd++Tl5emNN95QUlISi6uiXjHSLz/cf86cOUpPT9fIkSP5ZBkuzUi/WCyWarc1aMCvCKgfgoODtXr1ak2bNk2fffaZxowZU2Vv5OXlKTY2Vk2bNlVKSoqWLVumvn37avz48dq/f78TKgccz95+KS0ttd7d9+STTyo8PFyvv/66OnbsqOTkZCdUDjheTd5fXOF6nzuXHGzNmjU6ceKEcnJyVFZWJun/f7kvKyuTm5ubzeM8N27cUEJCggYOHKiIiAjrPtLNW+fKysrUsCH/jXBNNe2XW12/fl3x8fHatGmTRo4cqZdfftlhdQPOYKRfvL29VVBQUOlYxcXFVd7RBLgif39/+fv7q3v37vL29taUKVN08OBBde3a1WbeihUrJElpaWnWtZYiIiI0bNgwzZo1S+vXr3d06YDD2dsvXl5ecnNzs1nTr0GDBgoPD9e6descXTbgFPb0iytd7/OxpINt2bJFhYWFioyMVEhIiEJCQpSdna0zZ84oJCREGzZssJl//vx5HT58WNnZ2db5ISEhkqSFCxda/w64opr2S4WrV6/qj3/8ozZv3qypU6cSLKFeMNIvAQEBOnv2bKVP0U6fPq2AgABHlQ44XFFRkbKzs3XhwgWb8eDgYEnSd999V2mfb775Ru3atbMGS5JkMpkUFhamkydP1m7BgBMZ6Zf77rvPemF8q9LS0mo/GARcQU37xZWu9++OCMyFJCYmqri42GYsJSVFx44dU3Jystq0aWOzrUWLFlWm+0888YTi4uI0ZMiQWq0XcKaa9ot0M/0fN26cDh8+rPnz52vgwIGOKhdwKiP9EhkZqcWLF2vv3r3WT5fNZrP279+vZ555xiF1A85QsXDqs88+a10PQ7r5DYqS1KFDh0r7BAQEaMOGDbp8+bIaN25sHT98+LD8/Pxqv2jASYz0S+/evbVs2TJt3rzZer1SVlamjz76SGFhYY4pHHCCmvaLK13vEy45WGBgYKUxHx8fubu7W7863Ww268yZM3rggQfk7e1d6SvVK7Ro0aLabYArMNIv77zzjvbt26fY2Fi1atVKhw4dsu5rMpn085//3FHlAw5lpF+6d++uhx9+WBMnTtSkSZPk4+OjhQsXqlGjRoqLi3P0KQAO4+vrq2HDhmnJkiXy9PTUgw8+qAMHDig1NVVDhw5VYGBgpX55+umn9d577+lPf/qTxowZI09PT23cuFH79u3T/PnznX1KQK0x0i+9evVSVFSUZsyYoZKSEt1///3KyMjQuXPn9Prrrzv7lIBaY6RfXOV6n3CpDtq5c6cSEhK0cuVK9ejRw9nlAHXaD/tly5YtkqS1a9dq7dq1NnPd3Nx09OhRZ5QJ1AlVvb8kJydr9uzZmjNnjsrLyxUWFqY33njD5tEfwBUlJCSodevWWrdunRYuXKhWrVpp/PjxGjlypKTK/dKmTRtlZmZq3rx5SkhIkMlkUocOHbR8+XKFh4c7+WyA2lXTfpGkBQsW6M0339SSJUv0/fffKzg4WGlpaercubMzTwWodUb6xRWYLLf7qhgAAAAAAADgNljQGwAAAAAAAIYRLgEAAAAAAMAwwiUAAAAAAAAYRrgEAAAAAAAAwwiXAAAAAAAAYBjhEgAAAAAAAAwjXAIAAAAAAIBhhEsAAAAAAAAwjHAJAADcdT799FMFBQVp/fr1zi6lRr788ksFBwdrz549zi7lf5abm6vOnTsrPz/f2aUAAAAnI1wCAABwkNmzZ6tr166KiIiQJBUXF6tTp04KCgqy609RUdEdrScpKUm9e/eWxWKxBnbLli2rNG/fvn0KCwtTZGSkjh8/LkmKjo5Whw4dNG/evDtaEwAAuPs0dHYBAAAA9cHBgwe1Z88epaSkWMdu3Lih2bNn28zLzMzUwYMHNWXKFDVr1sw67u7uLh8fnztWj8ViUW5urvr16yeTyVTtvB07dujFF19U8+bNtWLFCrVt29a6bcSIEZoyZYq++uortW/f/o7VBgAA7i6ESwAAAA6QkZGhpk2bKioqyjrWuHFj/frXv7aZl56eLg8PD40YMUING9ber2qff/65Lly4oOjo6Grn5OTkKD4+XgEBAVq2bJlatmxps71///6aPn263nnnHU2bNq3WagUAAHUbj8UBAACXYTablZiYqKioKHXu3FlRUVFKTExUYWFhpbkFBQV64YUX1LVrV3Xt2lXjxo3T2bNn9Ytf/EK///3v72hdZWVlys3NVXh4uO65555q55WWlurLL79UUFBQrQZLkrRt2zY1btxYPXr0qHJ7RkaGJk+erODgYK1evbpSsCRJXl5eCgsL05YtW2q1VgAAULdx5xIAAHAJV65cUVxcnE6fPq0hQ4YoODhYx44dU2Zmpj755BNlZWXJ29tbklRYWKjhw4fr0qVLeuqppxQYGKgDBw7oD3/4g0pKSu54bV988YVKSkoUGhp623knT55UaWmpOnXqdMdr+KFt27bpkUceqTLsSk1N1d///nf17NlTixYtkpeXV7XH6dKliz7++GPl5eWpXbt2tVkyAACoowiXAACAS3j77beVn5+vv/71rxo+fLh1vFOnTkpKStLbb7+tCRMmSJKWLl2qb7/9VnPnztXgwYMlScOGDdOcOXOqXND6f3Xy5ElJkr+//23nHT16VJIUEhJyx2u4VV5enk6dOmX9edwqMzNTZ8+eVXR0tObPny93d/fbHqvinE6ePEm4BABAPcVjcQAAwCVs27ZNvr6+io2NtRmPjY2Vr6+vcnNzrWM7duzQT3/6Uw0aNMhm7siRI2ulNrPZLElq0qTJbedVhEtG7lwym816+umnFRYWprFjx1Y7Jkm5ubny8PBQ7969Kx3n4sWLkqS2bdv+aLAkybrI+KVLl2pcMwAAcA3cuQQAAFxCQUGBOnfuXGmtooYNG+r++++3BjcVc0NDQ9Wgge3nbM2aNVPjxo1txj744AOtWrVKx48fV9OmTbV9+3ab7WVlZZo9e7bee+89lZeXa8CAAXr11Vfl4eFhnXO7b2O71dGjR9WwYUMFBQXZNf9Wa9euVXl5ufbt2yc3N7dqx6SbQVx4eHiVj7uNHj1a//znP5WWliaLxaL4+Hi7Xt/ecwQAAK6HO5cAAABuo0mTJvrd735X5SNkkrR48WJ9+umnysnJ0datW5WXl6e5c+fazPH19ZUkFRUVVfs65eXlOn78uAIDA22CKXsVFBTogQcesAmRqhr79ttvdeTIEfXr16/K4/zkJz9RamqqevXqpeXLl2vWrFm3fd2Kc6o4RwAAUP8QLgEAAJfg7++vU6dOqayszGa8rKxM+fn5Nusd+fn56fTp0yovL7eZe+nSJV2+fNlmLCIiQo8++qj8/PyqfN1169Zp7NixatmypXx9ffX8889r/fr1unHjhnVO+/btJUmnT5+utv78/HyVlJTcdr2lkpISzZgxQ3369FHPnj01YcIEmc1mjR8/XtnZ2Xr33XfVpUsXrVmzpsox6eYjcSaTqdpwSZI8PT21ePFihYeHKz09XTNnzqx27pkzZ2zOEQAA1D+ESwAAwCVER0fLbDYrKyvLZvzdd9+V2WxWdHS0daxv3766ePGi3n//fZu5NV3M+/Llyzp//rw6duxoHQsJCVFxcbHOnTtnHQsODpa3t7cOHz5c7bHsWW/pL3/5i7777jtt2LBBO3fulJeXlxISErRgwQI99thjevLJJ3Xw4EENHz68yjHp5iNxYWFhP3qnkaenp9566y1FRERo5cqVmjFjRpXzDh06pObNmyswMPC2xwMAAK6LNZcAAIBLGDVqlD788EMlJSXp6NGj6tSpk44dO6Z169YpICBAo0aNss4dPXq03n//fU2dOlX/+te/FBgYqAMHDujgwYNq2rSp3a9ZXFwsSTbrNDVq1MhmmyS5ublpwIABys3N1fXr16tcKPvHvinObDZr8+bN2rt3r7XGiRMnKjw8XN9//71d9RYVFWn//v2aPHmyXfMrAqZnn31Wq1atksVi0bRp06zbi4uLdeDAAQ0ZMsSu4wEAANfEnUsAAMAlNGrUSJmZmYqNjdWuXbs0c+ZM7dq1S0899ZQyMjLk7e1tnevr66uMjAz16dNH//jHPzRv3jyVlJQoPT1dFotFnp6edr1mxYLYV65csY5V/P2Hi2XHxcXp8uXL2rFjR5XHOnbsmEwmk81dULcqKCiQxWLRgAED1K1bN3Xr1k0xMTFyd3fX+fPn7ap3586dKisrs7mL68d4eHho0aJFioyM1OrVq5WYmCiLxSJJ2rp1q/7zn/9U+oY+AABQv3DnEgAAuOv06NFDJ06cqDTu6+ur6dOna/r06T96DH9/fyUnJ9uMFRYWqqioSK1bt7arjsaNG6t169bWhbilm3cgeXl5VVqjKTQ0VJGRkUpPT1dMTEylYy1fvvy2r/Wzn/1MJpNJO3futAnKamLbtm3q2LGj2rRpU2lbdT9T6WbAVNUjgytXrlT//v3VoUMHQ/UAAADXwJ1LAACgXvrvf/9baWzJkiWSbi7iXeHGjRu6du2aSktLZbFYdO3aNV2/ft26/YknnlBqaqouXLggs9ms5ORkPf744zbf0FYhPj5ehw4d0scff1zjeps3b66YmBglJibq0qVLkm4uQP7hhx/afYyHHnpIEydOrPFrVyU3N1dfffWVJk2adEeOBwAA7l7cuQQAAOql0aNHy8/PT8HBwSovL9cnn3yiHTt2qEuXLjaPjW3cuFEJCQnWf4eGhsrPz0/bt2+XJI0dO1ZFRUUaNGiQysvLFRMTU23g0r59e+vaSkbMmjVLycnJGjp0qAoLC9WsWTP17dtXAwcOtPuc75To6GgdOXLkjh0PAADcvUyWiofmAQAA6pG0tDRlZ2fr3Llzunbtmlq2bKkBAwboueeeM/zYGQAAQH1EuAQAAAAAAADDWHMJAAAAAAAAhhEuAQAAAAAAwDDCJQAAAAAAABhGuAQAAAAAAADDCJcAAAAAAABgGOESAAAAAAAADCNcAgAAAAAAgGGESwAAAAAAADDs/wBsWXTYwYRhXwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3557b6d5-6c54-467c-b7a1-b1903493c441",
+   "metadata": {},
+   "source": [
+    "We plot here the track for the primary star only. You can see immediately where stars merge on the main sequence: the tracks move very suddenly where usually evolution on the main sequence is smooth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59335030-dd99-4c2f-afff-207a3fcbbb70",
+   "metadata": {},
+   "source": [
+    "If we now set the separation to be longer, say $100\\mathrm{R}_\\odot$, mass transfer should happen on the giant branch. We also set the secondary mass to be larger, $1\\mathrm{M}_\\odot$, so that the interaction is stronger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dee92b20-ad6b-4c97-80dc-71d3bd937c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-2ea4759ed05544ef8f1b7a887f0f36d2 finished! The total probability was: 10.0. It took a total of 0.7215321063995361s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 1, # Msun\n",
+    "    separation = 100, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    "    alpha_ce = 1.0, # make common-envelope evolution quite efficient\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e0ac2573-bc35-43be-8f20-5c85364fde11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f8e061-a65e-47f2-a777-93de0d5045ea",
+   "metadata": {},
+   "source": [
+    "You now see the interaction in the jerky red-giant tracks where the stars interact. These probably, depending on the mass ratio at the moment of interaction, go through a common-envelope phase. The system can merge (most of the above do) but not all. The interaction is so strong on the RGB of the $1\\mathrm{M}_\\odot$ star that the stellar evolution is terminated before it reaches the RGB tip, so it never ignites helium. This is how helium white dwarfs are probably made."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "698d0a63-11ba-4b3e-a713-35c3e972492f",
+   "metadata": {},
+   "source": [
+    "We can also plot the secondary stars' HRD. Remember, the primary is star 0 in binary_c, while the secondary is star 1. That's because all proper programming languages start counting at 0. We change the parsing function a little so we can separate the plots of the secondaries according to their primary mass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2b0b7c2b-6e43-48ed-9257-9dfc141b3d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "star  1\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 13,
+     "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": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '1': # choose only secondaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "            \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c46319-5629-4125-a284-b5d521ed33fc",
+   "metadata": {},
+   "source": [
+    "Remember, all these stars start with a $1\\mathrm{M}_\\odot$ binary, which begins at $\\log_{10}(T_\\mathrm{eff}/\\mathrm{K})\\sim 3.750$, $\\log_{10}L/\\mathrm{L}_\\odot \\sim 0$. The $1\\mathrm{M}_\\odot$-$1\\mathrm{M}_\\odot$ binary evolves like two single stars until they interact up the giant branch at about $\\log_{10} (L/\\mathrm{L}_\\odot) \\sim 2.5$, the others interact long before they evolve very far on the main sequence: you can just about see their tracks at the very start."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53145356-abbb-4880-996f-dedd80de7540",
+   "metadata": {},
+   "source": [
+    "This is, of course, a very simple introduction to what happens in binaries. We haven't talked about the remnants that are produced by interactions. When the stars do evolve on the giant branch, white dwarfs are made which can go on to suffer novae and (perhaps) thermonuclear explosions. The merging process itself leads to luminosus red novae and, in the case of neutron stars and black holes, kilonovae and gravitational wave events. "
+   ]
+  }
+ ],
+ "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/notebook_api_functionality.html b/docs/build/html/notebook_api_functionality.html
index c10f7f17e..cdd5ae1fc 100644
--- a/docs/build/html/notebook_api_functionality.html
+++ b/docs/build/html/notebook_api_functionality.html
@@ -103,7 +103,9 @@
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -1385,7 +1387,7 @@ MAXIMUM MASS RATIO 0.0141
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_common_envelope_evolution.html b/docs/build/html/notebook_common_envelope_evolution.html
new file mode 100644
index 000000000..6d041737c
--- /dev/null
+++ b/docs/build/html/notebook_common_envelope_evolution.html
@@ -0,0 +1,1132 @@
+
+
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" >
+<head>
+  <meta charset="utf-8">
+  
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  
+  <title>Example use case: Common-envelope evolution &mdash; binary_c-python  documentation</title>
+  
+
+  
+  <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+
+  
+  
+  
+  
+
+  
+  <!--[if lt IE 9]>
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+<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 current"><a class="reference internal" href="example_notebooks.html">Example notebooks</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="notebook_individual_systems.html">Tutorial: Running individual systems with binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_custom_logging.html">Tutorial: Using custom logging routines with binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_population.html">Tutorial: Running populations with binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Example use case: Common-envelope evolution</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Setting-up-the-Population-object">Setting up the Population object</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Stellar-Grid">Stellar Grid</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Logging-and-handling-the-output">Logging and handling the output</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Evolving-the-grid">Evolving the grid</a></li>
+</ul>
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+<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>
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+.math {
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+</style>
+<div class="section" id="Example-use-case:-Common-envelope-evolution">
+<h1>Example use case: Common-envelope evolution<a class="headerlink" href="#Example-use-case:-Common-envelope-evolution" title="Permalink to this headline">¶</a></h1>
+<p>In this notebook we look at how common-envelope evolution (CEE) alters binary-star orbits. We construct a population of low- and intermediate-mass binaries and compare their orbital periods before and after CEE. Not all stars evolve into this phase, so we have to run a whole population to find those that do. We then have to construct the pre- and post-CEE distributions and plot them.</p>
+<p>First, we import a few required Python modules.</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_comenv&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="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 headline">¶</a></h2>
+<p>We set up a new population object. Our stars evolve to <span class="math notranslate nohighlight">\(13.7\text{ }\mathrm{Gyr}\)</span>, the age of the Universe, and we assume the metallicity <span class="math notranslate nohighlight">\(Z=0.02\)</span>. We also set the common-envelope ejection efficiency <span class="math notranslate nohighlight">\(\alpha_\mathrm{CE}=1\)</span> and the envelope structure parameter <span class="math notranslate nohighlight">\(\lambda=0.5\)</span>. More complex options are available in <em>binary_c</em>, such as <span class="math notranslate nohighlight">\(\lambda\)</span> based on stellar mass, but this is just a demonstration example so let’s keep things simple.</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="n">population</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
+    <span class="c1"># grid options</span>
+    <span class="n">tmp_dir</span> <span class="o">=</span> <span class="n">TMP_DIR</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="n">log_dt</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="c1"># log every 10 seconds</span>
+
+    <span class="c1"># binary-star evolution options</span>
+    <span class="n">max_evolution_time</span><span class="o">=</span><span class="mi">13700</span><span class="p">,</span>  <span class="c1"># maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)</span>
+    <span class="n">metallicity</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span> <span class="c1"># 0.02 is approximately Solar metallicity</span>
+    <span class="n">alpha_ce</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+    <span class="n">lambda_ce</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</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: log_dt=10 to grid_options
+adding: max_evolution_time=13700 to BSE_options
+adding: metallicity=0.02 to BSE_options
+adding: alpha_ce=1.0 to BSE_options
+adding: lambda_ce=0.5 to BSE_options
+</pre></div></div>
+</div>
+</div>
+<div class="section" id="Stellar-Grid">
+<h2>Stellar Grid<a class="headerlink" href="#Stellar-Grid" title="Permalink to this headline">¶</a></h2>
+<p>We now construct a grid of stars, varying the mass from <span class="math notranslate nohighlight">\(1\)</span> to <span class="math notranslate nohighlight">\(6\text{ }\mathrm{M}_\odot\)</span>. We avoid massive stars for now, and focus on the (more common) low- and intermediate-mass stars. We also limit the period range to <span class="math notranslate nohighlight">\(10^4\text{ }\mathrm{d}\)</span> because systems with longer orbital periods will probably not undergo Roche-lobe overflow and hence common-envelope evolution is impossible.</p>
+<div class="nbinput 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="kn">import</span> <span class="nn">binarycpython.utils.distribution_functions</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">10</span><span class="p">,</span> <span class="s2">&quot;q&quot;</span> <span class="p">:</span> <span class="mi">10</span><span class="p">,</span> <span class="s2">&quot;per&quot;</span><span class="p">:</span> <span class="mi">10</span><span class="p">}</span>
+<span class="n">massrange</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
+<span class="n">logperrange</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.15</span><span class="p">,</span> <span class="mi">4</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;lnm1&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">resolution</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</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">spacingfunc</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">precode</span><span class="o">=</span><span class="s2">&quot;M_1=math.exp(lnm1)&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;dlnm1&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>
+
+<span class="c1"># Mass ratio</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;q&quot;</span><span class="p">,</span>
+     <span class="n">longname</span><span class="o">=</span><span class="s2">&quot;Mass ratio&quot;</span><span class="p">,</span>
+     <span class="n">valuerange</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;0.1/M_1&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
+     <span class="n">resolution</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">resolution</span><span class="p">[</span><span class="s1">&#39;q&#39;</span><span class="p">]),</span>
+     <span class="n">spacingfunc</span><span class="o">=</span><span class="s2">&quot;const(</span><span class="si">{}</span><span class="s2">/M_1, 1, </span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">massrange</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">resolution</span><span class="p">[</span><span class="s1">&#39;q&#39;</span><span class="p">]),</span>
+     <span class="n">probdist</span><span class="o">=</span><span class="s2">&quot;flatsections(q, [{{&#39;min&#39;: </span><span class="si">{}</span><span class="s2">/M_1, &#39;max&#39;: 1.0, &#39;height&#39;: 1}}])&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">massrange</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
+     <span class="n">dphasevol</span><span class="o">=</span><span class="s2">&quot;dq&quot;</span><span class="p">,</span>
+     <span class="n">precode</span><span class="o">=</span><span class="s2">&quot;M_2 = q * M_1&quot;</span><span class="p">,</span>
+     <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;M_2&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>
+
+<span class="c1"># Orbital period</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;log10per&quot;</span><span class="p">,</span> <span class="c1"># in days</span>
+    <span class="n">longname</span><span class="o">=</span><span class="s2">&quot;log10(Orbital_Period)&quot;</span><span class="p">,</span>
+    <span class="n">valuerange</span><span class="o">=</span><span class="p">[</span><span class="mf">0.15</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">],</span>
+    <span class="n">resolution</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">resolution</span><span class="p">[</span><span class="s2">&quot;per&quot;</span><span class="p">]),</span>
+    <span class="n">spacingfunc</span><span class="o">=</span><span class="s2">&quot;const(</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">logperrange</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">logperrange</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">resolution</span><span class="p">[</span><span class="s2">&quot;per&quot;</span><span class="p">]),</span>
+    <span class="n">precode</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;orbital_period = 10.0 ** log10per</span>
+<span class="s2">sep = calc_sep_from_period(M_1, M_2, orbital_period)</span>
+<span class="s2">sep_min = calc_sep_from_period(M_1, M_2, 10**</span><span class="si">{}</span><span class="s2">)</span>
+<span class="s2">sep_max = calc_sep_from_period(M_1, M_2, 10**</span><span class="si">{}</span><span class="s2">)&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">logperrange</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">logperrange</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+    <span class="n">probdist</span><span class="o">=</span><span class="s2">&quot;sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**</span><span class="si">{}</span><span class="s2">), math.log10(10**</span><span class="si">{}</span><span class="s2">), </span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">logperrange</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">logperrange</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="o">-</span><span class="mf">0.55</span><span class="p">),</span>
+    <span class="n">parameter_name</span><span class="o">=</span><span class="s2">&quot;orbital_period&quot;</span><span class="p">,</span>
+    <span class="n">dphasevol</span><span class="o">=</span><span class="s2">&quot;dlog10per&quot;</span><span class="p">,</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>
+Added grid variable: {
+    &#34;name&#34;: &#34;lnm1&#34;,
+    &#34;longname&#34;: &#34;Primary mass&#34;,
+    &#34;valuerange&#34;: [
+        1,
+        6
+    ],
+    &#34;resolution&#34;: &#34;10&#34;,
+    &#34;spacingfunc&#34;: &#34;const(math.log(1), math.log(6), 10)&#34;,
+    &#34;precode&#34;: &#34;M_1=math.exp(lnm1)&#34;,
+    &#34;probdist&#34;: &#34;three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1&#34;,
+    &#34;dphasevol&#34;: &#34;dlnm1&#34;,
+    &#34;parameter_name&#34;: &#34;M_1&#34;,
+    &#34;condition&#34;: &#34;&#34;,
+    &#34;gridtype&#34;: &#34;centred&#34;,
+    &#34;branchpoint&#34;: 0,
+    &#34;grid_variable_number&#34;: 0
+}
+Added grid variable: {
+    &#34;name&#34;: &#34;q&#34;,
+    &#34;longname&#34;: &#34;Mass ratio&#34;,
+    &#34;valuerange&#34;: [
+        &#34;0.1/M_1&#34;,
+        1
+    ],
+    &#34;resolution&#34;: &#34;10&#34;,
+    &#34;spacingfunc&#34;: &#34;const(1/M_1, 1, 10)&#34;,
+    &#34;precode&#34;: &#34;M_2 = q * M_1&#34;,
+    &#34;probdist&#34;: &#34;flatsections(q, [{&#39;min&#39;: 1/M_1, &#39;max&#39;: 1.0, &#39;height&#39;: 1}])&#34;,
+    &#34;dphasevol&#34;: &#34;dq&#34;,
+    &#34;parameter_name&#34;: &#34;M_2&#34;,
+    &#34;condition&#34;: &#34;&#34;,
+    &#34;gridtype&#34;: &#34;centred&#34;,
+    &#34;branchpoint&#34;: 0,
+    &#34;grid_variable_number&#34;: 1
+}
+Added grid variable: {
+    &#34;name&#34;: &#34;log10per&#34;,
+    &#34;longname&#34;: &#34;log10(Orbital_Period)&#34;,
+    &#34;valuerange&#34;: [
+        0.15,
+        5.5
+    ],
+    &#34;resolution&#34;: &#34;10&#34;,
+    &#34;spacingfunc&#34;: &#34;const(0.15, 4, 10)&#34;,
+    &#34;precode&#34;: &#34;orbital_period = 10.0 ** log10per\nsep = calc_sep_from_period(M_1, M_2, orbital_period)\nsep_min = calc_sep_from_period(M_1, M_2, 10**0.15)\nsep_max = calc_sep_from_period(M_1, M_2, 10**4)&#34;,
+    &#34;probdist&#34;: &#34;sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**0.15), math.log10(10**4), -0.55)&#34;,
+    &#34;dphasevol&#34;: &#34;dlog10per&#34;,
+    &#34;parameter_name&#34;: &#34;orbital_period&#34;,
+    &#34;condition&#34;: null,
+    &#34;gridtype&#34;: &#34;centred&#34;,
+    &#34;branchpoint&#34;: 0,
+    &#34;grid_variable_number&#34;: 2
+}
+</pre></div></div>
+</div>
+</div>
+<div class="section" id="Logging-and-handling-the-output">
+<h2>Logging and handling the output<a class="headerlink" href="#Logging-and-handling-the-output" title="Permalink to this headline">¶</a></h2>
+<p>We now construct the pre- and post-common envelope evolution data for the first common envelope that forms in each binary. We look at the comenv_count variable, we can see that when it increases from 0 to 1 we have found our object. If this happens, we stop evolution of the system to save CPU time.</p>
+<div class="nbinput 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="n">custom_logging_statement</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
+
+<span class="s2">/*</span>
+<span class="s2"> * Detect when the comenv_count increased</span>
+<span class="s2"> */</span>
+<span class="s2">if(stardata-&gt;model.comenv_count == 1 &amp;&amp;</span>
+<span class="s2">   stardata-&gt;previous_stardata-&gt;model.comenv_count == 0)</span>
+<span class="s2">{</span>
+<span class="s2">   /*</span>
+<span class="s2">    * We just had this system&#39;s first common envelope:</span>
+<span class="s2">    * output the time at which this happens,</span>
+<span class="s2">    * the system&#39;s probability (proportional to the number of stars),</span>
+<span class="s2">    * the previous timestep&#39;s (pre-comenv) orbital period (days) and</span>
+<span class="s2">    * the current timestep (post-comenv) orbital period (days)</span>
+<span class="s2">    */</span>
+<span class="s2">    Printf(&quot;COMENV </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,</span>
+<span class="s2">           stardata-&gt;model.probability,</span>
+<span class="s2">           stardata-&gt;previous_stardata-&gt;common.orbit.period * YEAR_LENGTH_IN_DAYS,</span>
+<span class="s2">           stardata-&gt;common.orbit.period * YEAR_LENGTH_IN_DAYS);</span>
+
+<span class="s2">    /*</span>
+<span class="s2">     * We should waste no more CPU time on this system now we have the</span>
+<span class="s2">     * data we want.</span>
+<span class="s2">     */</span>
+<span class="s2">    stardata-&gt;model.evolution_stop = TRUE;</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>
+
+</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: C_logging_code=
+
+/*
+ * Detect when the comenv_count increased
+ */
+if(stardata-&gt;model.comenv_count == 1 &amp;&amp;
+   stardata-&gt;previous_stardata-&gt;model.comenv_count == 0)
+{
+   /*
+    * We just had this system&#39;s first common envelope:
+    * output the time at which this happens,
+    * the system&#39;s probability (proportional to the number of stars),
+    * the previous timestep&#39;s (pre-comenv) orbital period (days) and
+    * the current timestep (post-comenv) orbital period (days)
+    */
+    Printf(&#34;COMENV %g %g %g %g\n&#34;,
+           stardata-&gt;model.time,
+           stardata-&gt;model.probability,
+           stardata-&gt;previous_stardata-&gt;common.orbit.period * YEAR_LENGTH_IN_DAYS,
+           stardata-&gt;common.orbit.period * YEAR_LENGTH_IN_DAYS);
+
+    /*
+     * We should waste no more CPU time on this system now we have the
+     * data we want.
+     */
+    stardata-&gt;model.evolution_stop = TRUE;
+}
+ to grid_options
+</pre></div></div>
+</div>
+<p>The parse function must now catch lines that start with “COMENV” and process the associated data. We set up the parse_data function to do just this.</p>
+<div class="nbinput 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="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="kn">import</span> <span class="nn">re</span>
+
+<span class="c1"># log-period distribution bin width (dex)</span>
+<span class="n">binwidth</span> <span class="o">=</span> <span class="mf">0.5</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">    Parsing function to convert HRD data into something that Python can use</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;probability&quot;</span><span class="p">,</span> <span class="s2">&quot;pre_comenv_period&quot;</span><span class="p">,</span> <span class="s2">&quot;post_comenv_period&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"># choose COMENV lines of output</span>
+        <span class="k">if</span> <span class="n">linedata</span><span class="p">[</span><span class="s2">&quot;header&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;COMENV&quot;</span><span class="p">:</span>
+            <span class="c1"># bin the pre- and post-comenv log10-orbital-periods to nearest 0.5dex</span>
+            <span class="n">binned_pre_period</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="s2">&quot;pre_comenv_period&quot;</span><span class="p">]),</span> <span class="n">binwidth</span><span class="p">)</span>
+
+            <span class="c1"># but check if the post-comenv period is finite and positive: if</span>
+            <span class="c1"># not, the system has merged and we give it an aritifical period</span>
+            <span class="c1"># of 10^-100 days (which is very much unphysical)</span>
+            <span class="k">if</span> <span class="n">linedata</span><span class="p">[</span><span class="s2">&quot;post_comenv_period&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.0</span><span class="p">:</span>
+                <span class="n">binned_post_period</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="s2">&quot;post_comenv_period&quot;</span><span class="p">]),</span> <span class="n">binwidth</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">binned_post_period</span> <span class="o">=</span> <span class="n">bin_data</span><span class="p">(</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span><span class="n">binwidth</span><span class="p">)</span> <span class="c1"># merged!</span>
+
+            <span class="c1"># make the &quot;histograms&quot;</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;pre&#39;</span><span class="p">][</span><span class="n">binned_pre_period</span><span class="p">]</span> <span class="o">+=</span> <span class="n">linedata</span><span class="p">[</span><span class="s2">&quot;probability&quot;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="s1">&#39;post&#39;</span><span class="p">][</span><span class="n">binned_post_period</span><span class="p">]</span> <span class="o">+=</span> <span class="n">linedata</span><span class="p">[</span><span class="s2">&quot;probability&quot;</span><span class="p">]</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>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+adding: parse_function=&lt;function parse_function at 0x14736bebc040&gt; to grid_options
+</pre></div></div>
+</div>
+</div>
+<div class="section" id="Evolving-the-grid">
+<h2>Evolving the grid<a class="headerlink" href="#Evolving-the-grid" title="Permalink to this headline">¶</a></h2>
+<p>Now we actually run the population. This may take a little while. You can set amt_cores higher if you have a powerful machine.</p>
+<div class="nbinput 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"># 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"># set number of threads (i.e. number of CPU cores we use)</span>
+    <span class="n">amt_cores</span><span class="o">=</span><span class="mi">4</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 docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+adding: amt_cores=4 to grid_options
+Creating and loading custom logging functionality
+Generating grid code
+Generating grid code
+Constructing/adding: lnm1
+Constructing/adding: q
+Constructing/adding: log10per
+Saving grid code to grid_options
+Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py
+Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py
+Grid code loaded
+Grid has handled 1000 stars
+with a total probability of 0.0645905996773004
+Total starcount for this run will be: 1000
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[2021-09-12 18:07:39,950 DEBUG    Process-2] --- Setting up processor: process-0
+[2021-09-12 18:07:39,953 DEBUG    Process-3] --- Setting up processor: process-1
+[2021-09-12 18:07:39,959 DEBUG    Process-4] --- Setting up processor: process-2
+[2021-09-12 18:07:39,962 DEBUG    MainProcess] --- setting up the system_queue_filler now
+[2021-09-12 18:07:39,965 DEBUG    Process-5] --- Setting up processor: process-3
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Process 0 started at 2021-09-12T18:07:39.965721.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x14736bee47e0&gt;
+Process 1 started at 2021-09-12T18:07:39.970949.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x14736bee4870&gt;
+Process 2 started at 2021-09-12T18:07:39.978355.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x14736bee4f30&gt;
+Process 3 started at 2021-09-12T18:07:39.983689.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x14736bee4870&gt;
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[2021-09-12 18:07:40,066 DEBUG    MainProcess] --- Signaling stop to processes
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Generating grid code
+Generating grid code
+Constructing/adding: lnm1
+Constructing/adding: q
+Constructing/adding: log10per
+Saving grid code to grid_options
+Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py
+Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py
+Grid code loaded
+163/1000  16.3% complete 18:07:49 ETA=   51.5s tpr=6.16e-02 ETF=18:08:41 mem:594.9MB
+322/1000  32.2% complete 18:07:59 ETA=   42.9s tpr=6.33e-02 ETF=18:08:42 mem:538.2MB
+465/1000  46.5% complete 18:08:09 ETA=   38.1s tpr=7.12e-02 ETF=18:08:47 mem:538.2MB
+586/1000  58.6% complete 18:08:19 ETA=   34.3s tpr=8.29e-02 ETF=18:08:54 mem:540.0MB
+682/1000  68.2% complete 18:08:30 ETA=   34.0s tpr=1.07e-01 ETF=18:09:04 mem:540.1MB
+784/1000  78.4% complete 18:08:40 ETA=   21.2s tpr=9.81e-02 ETF=18:09:01 mem:541.8MB
+872/1000  87.2% complete 18:08:50 ETA=   15.0s tpr=1.17e-01 ETF=18:09:05 mem:546.1MB
+963/1000  96.3% complete 18:09:00 ETA=    4.2s tpr=1.14e-01 ETF=18:09:04 mem:546.9MB
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[2021-09-12 18:09:06,366 DEBUG    Process-5] --- Process-3 is finishing.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Process 3 finished:
+        generator started at 2021-09-12T18:07:39.964604, done at 2021-09-12T18:09:06.370832 (total: 86.406228s of which 86.24177551269531s interfacing with binary_c).
+        Ran 222 systems with a total probability of 0.014137215791516371.
+        This thread had 0 failing systems with a total probability of 0.
+        Skipped a total of 0 systems because they had 0 probability
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[2021-09-12 18:09:06,374 DEBUG    Process-5] --- Process-3 is finished.
+[2021-09-12 18:09:06,979 DEBUG    Process-3] --- Process-1 is finishing.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Process 1 finished:
+        generator started at 2021-09-12T18:07:39.953039, done at 2021-09-12T18:09:06.982866 (total: 87.029827s of which 86.82909393310547s interfacing with binary_c).
+        Ran 273 systems with a total probability of 0.01877334232598154.
+        This thread had 0 failing systems with a total probability of 0.
+        Skipped a total of 0 systems because they had 0 probability
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[2021-09-12 18:09:06,985 DEBUG    Process-3] --- Process-1 is finished.
+[2021-09-12 18:09:07,174 DEBUG    Process-2] --- Process-0 is finishing.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Process 0 finished:
+        generator started at 2021-09-12T18:07:39.949775, done at 2021-09-12T18:09:07.176660 (total: 87.226885s of which 87.02672934532166s interfacing with binary_c).
+        Ran 268 systems with a total probability of 0.016469813170514686.
+        This thread had 0 failing systems with a total probability of 0.
+        Skipped a total of 0 systems because they had 0 probability
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[2021-09-12 18:09:07,179 DEBUG    Process-2] --- Process-0 is finished.
+[2021-09-12 18:09:07,233 DEBUG    Process-4] --- Process-2 is finishing.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Process 2 finished:
+        generator started at 2021-09-12T18:07:39.958802, done at 2021-09-12T18:09:07.236252 (total: 87.27745s of which 87.0905077457428s interfacing with binary_c).
+        Ran 237 systems with a total probability of 0.015210228389288167.
+        This thread had 0 failing systems with a total probability of 0.
+        Skipped a total of 0 systems because they had 0 probability
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[2021-09-12 18:09:07,238 DEBUG    Process-4] --- Process-2 is finished.
+</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>
+Population-ad303100d719457c83256568f9a9887c finished! The total probability was: 0.06459059967730076. It took a total of 87.54819011688232s to run 1000 systems on 4 cores
+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. We check this, and then set about making the plot of the orbital period distributions using Seaborn.</p>
+<div class="nbinput 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="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;ad303100d719457c83256568f9a9887c&#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;: 0.06459059967730076, &#39;total_count&#39;: 1000, &#39;start_timestamp&#39;: 1631462859.9342952, &#39;end_timestamp&#39;: 1631462947.4824853, &#39;total_mass_run&#39;: 4680.235689312421, &#39;total_probability_weighted_mass_run&#39;: 0.22611318083528567, &#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>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
+<span></span><span class="c1"># make a plot of the distributions</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">import</span> <span class="nn">copy</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</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="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+<span class="c1"># remove the merged objects</span>
+<span class="n">probability</span> <span class="o">=</span> <span class="p">{</span> <span class="s2">&quot;merged&quot;</span> <span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s2">&quot;unmerged&quot;</span> <span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+
+<span class="c1"># copy the results so we can change the copy</span>
+<span class="n">results</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">population</span><span class="o">.</span><span class="n">grid_results</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">distribution</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;post&#39;</span><span class="p">]:</span>
+    <span class="k">for</span> <span class="n">logper</span> <span class="ow">in</span> <span class="n">population</span><span class="o">.</span><span class="n">grid_results</span><span class="p">[</span><span class="n">distribution</span><span class="p">]:</span>
+        <span class="n">dprob</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">distribution</span><span class="p">][</span><span class="n">logper</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">logper</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">90</span><span class="p">:</span>
+            <span class="c1"># merged system</span>
+            <span class="n">probability</span><span class="p">[</span><span class="s2">&quot;merged&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="n">dprob</span>
+            <span class="k">del</span> <span class="n">results</span><span class="p">[</span><span class="n">distribution</span><span class="p">][</span><span class="n">logper</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># unmerged system</span>
+            <span class="n">probability</span><span class="p">[</span><span class="s2">&quot;unmerged&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="n">dprob</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">probability</span><span class="p">)</span>
+
+<span class="c1"># pad the final distribution with zero</span>
+<span class="k">for</span> <span class="n">distribution</span> <span class="ow">in</span> <span class="n">population</span><span class="o">.</span><span class="n">grid_results</span><span class="p">:</span>
+    <span class="n">pad_output_distribution</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">distribution</span><span class="p">],</span>
+                            <span class="n">binwidth</span><span class="p">)</span>
+
+<span class="c1"># make pandas dataframe</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="n">results</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;columns&#39;</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;$\log_</span><span class="si">{10}</span><span class="s2"> (P_\mathrm</span><span class="si">{orb}</span><span class="s2"> / \mathrm</span><span class="si">{day}</span><span class="s2">)$&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="c1">#p.set(xlim=(-5,5)) # might be necessary?</span>
+
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;merged&#39;: 0.035263029200000025, &#39;unmerged&#39;: 0.019388724199999995}
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;Number of stars&#39;)
+</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/notebook_common_envelope_evolution_14_2.png" src="_images/notebook_common_envelope_evolution_14_2.png" />
+</div>
+</div>
+<p>You can see that common-envelope evolution shrinks stellar orbits, just as we expect. Pre-CEE, most orbits are in the range <span class="math notranslate nohighlight">\(10\)</span> to <span class="math notranslate nohighlight">\(1000\text{ }\mathrm{d}\)</span>, while after CEE the distribution peaks at about <span class="math notranslate nohighlight">\(1\text{ }\mathrm{d}\)</span>. Some of these orbits are very short: <span class="math notranslate nohighlight">\(\log_{10}(-2) = 0.01\text{ }\mathrm{d}\sim10\text{ }\mathrm{minutes}\)</span>. Such systems are prime candidates for exciting astrophysics: novae, type Ia supernovae and gravitational wave sources.</p>
+<p>Things to try: * Extend the logging to output more data than just the orbital period. * What are the stellar types of the post-common envelope systems? Are they likely to undergo novae or a type-Ia supernova? * What are the lifetimes of the systems in close (<span class="math notranslate nohighlight">\(&lt;1\text{ }\mathrm{d}\)</span>) binaries? Are they likely to merge in the life of the Universe? * How much mass is lost in common-envelope interactions? * Extend the grid to massive stars. Do you see many NS and BH compact binaries? *
+Try different <span class="math notranslate nohighlight">\(\alpha_\mathrm{CE}\)</span> and <span class="math notranslate nohighlight">\(\lambda_\mathrm{CE}\)</span> options… * … and perhaps increased resolution to obtain smoother curves. * Why do long-period systems not reach common envelope evolution?</p>
+</div>
+</div>
+
+
+           </div>
+           
+          </div>
+          <footer>
+  
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="binary_c_parameters.html" class="btn btn-neutral float-right" title="Binary_c parameters" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
+      
+      
+        <a href="notebook_HRD.html" class="btn btn-neutral float-left" title="Example use case: Hertzsprung-Russell diagrams" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+      
+    </div>
+  
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>
+        
+        &copy; Copyright 2021, David Hendriks, Robert Izzard
+
+    </p>
+  </div>
+    
+    
+    
+    Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a
+    
+    <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a>
+    
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+<br><br>
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+<br><br>
+Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
+
+
+
+</footer>
+
+        </div>
+      </div>
+
+    </section>
+
+  </div>
+  
+
+  <script type="text/javascript">
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script>
+
+  
+  
+    
+   
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+</body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/notebook_common_envelope_evolution.ipynb b/docs/build/html/notebook_common_envelope_evolution.ipynb
new file mode 100644
index 000000000..526320ccf
--- /dev/null
+++ b/docs/build/html/notebook_common_envelope_evolution.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Common-envelope evolution\n",
+    "\n",
+    "In this notebook we look at how common-envelope evolution (CEE) alters binary-star orbits. We construct a population of low- and intermediate-mass binaries and compare their orbital periods before and after CEE. Not all stars evolve into this phase, so we have to run a whole population to find those that do. We then have to construct the pre- and post-CEE distributions and plot them.\n",
+    "\n",
+    "First, we import a few required Python modules. "
+   ]
+  },
+  {
+   "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",
+    "from binarycpython.utils.functions import temp_dir\n",
+    "from binarycpython.utils.grid import Population\n",
+    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_comenv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Setting up the Population object\n",
+    "We set up a new population object. Our stars evolve to $13.7\\text{ }\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. We also set the common-envelope ejection efficiency $\\alpha_\\mathrm{CE}=1$ and the envelope structure parameter $\\lambda=0.5$. More complex options are available in *binary_c*, such as $\\lambda$ based on stellar mass, but this is just a demonstration example so let's keep things simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: log_dt=10 to grid_options\n",
+      "adding: max_evolution_time=13700 to BSE_options\n",
+      "adding: metallicity=0.02 to BSE_options\n",
+      "adding: alpha_ce=1.0 to BSE_options\n",
+      "adding: lambda_ce=0.5 to BSE_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\n",
+    "population.set(\n",
+    "    # grid options\n",
+    "    tmp_dir = TMP_DIR,\n",
+    "    verbosity = 1,\n",
+    "    log_dt = 10, # log every 10 seconds\n",
+    "\n",
+    "    # binary-star evolution options\n",
+    "    max_evolution_time=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    alpha_ce = 1.0,\n",
+    "    lambda_ce = 0.5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $6\\text{ }\\mathrm{M}_\\odot$. We avoid massive stars for now, and focus on the (more common) low- and intermediate-mass stars. We also limit the period range to $10^4\\text{ }\\mathrm{d}$ because systems with longer orbital periods will probably not undergo Roche-lobe overflow and hence common-envelope evolution is impossible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"lnm1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        6\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(math.log(1), math.log(6), 10)\",\n",
+      "    \"precode\": \"M_1=math.exp(lnm1)\",\n",
+      "    \"probdist\": \"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+      "    \"dphasevol\": \"dlnm1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"q\",\n",
+      "    \"longname\": \"Mass ratio\",\n",
+      "    \"valuerange\": [\n",
+      "        \"0.1/M_1\",\n",
+      "        1\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1/M_1, 1, 10)\",\n",
+      "    \"precode\": \"M_2 = q * M_1\",\n",
+      "    \"probdist\": \"flatsections(q, [{'min': 1/M_1, 'max': 1.0, 'height': 1}])\",\n",
+      "    \"dphasevol\": \"dq\",\n",
+      "    \"parameter_name\": \"M_2\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 1\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"log10per\",\n",
+      "    \"longname\": \"log10(Orbital_Period)\",\n",
+      "    \"valuerange\": [\n",
+      "        0.15,\n",
+      "        5.5\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(0.15, 4, 10)\",\n",
+      "    \"precode\": \"orbital_period = 10.0 ** log10per\\nsep = calc_sep_from_period(M_1, M_2, orbital_period)\\nsep_min = calc_sep_from_period(M_1, M_2, 10**0.15)\\nsep_max = calc_sep_from_period(M_1, M_2, 10**4)\",\n",
+      "    \"probdist\": \"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**0.15), math.log10(10**4), -0.55)\",\n",
+      "    \"dphasevol\": \"dlog10per\",\n",
+      "    \"parameter_name\": \"orbital_period\",\n",
+      "    \"condition\": null,\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 2\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10, \"q\" : 10, \"per\": 10} \n",
+    "massrange = [1, 6] \n",
+    "logperrange = [0.15, 4]\n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"lnm1\",\n",
+    "    longname=\"Primary mass\",\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{}\".format(resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
+    "    precode=\"M_1=math.exp(lnm1)\",\n",
+    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+    "    dphasevol=\"dlnm1\",\n",
+    "    parameter_name=\"M_1\",\n",
+    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    ")\n",
+    "\n",
+    "# Mass ratio\n",
+    "population.add_grid_variable(\n",
+    "     name=\"q\",\n",
+    "     longname=\"Mass ratio\",\n",
+    "     valuerange=[\"0.1/M_1\", 1],\n",
+    "     resolution=\"{}\".format(resolution['q']),\n",
+    "     spacingfunc=\"const({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
+    "     probdist=\"flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
+    "     dphasevol=\"dq\",\n",
+    "     precode=\"M_2 = q * M_1\",\n",
+    "     parameter_name=\"M_2\",\n",
+    "     condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    " )\n",
+    "\n",
+    "# Orbital period\n",
+    "population.add_grid_variable(\n",
+    "    name=\"log10per\", # in days\n",
+    "    longname=\"log10(Orbital_Period)\",\n",
+    "    valuerange=[0.15, 5.5],\n",
+    "    resolution=\"{}\".format(resolution[\"per\"]),\n",
+    "    spacingfunc=\"const({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
+    "    precode=\"\"\"orbital_period = 10.0 ** log10per\n",
+    "sep = calc_sep_from_period(M_1, M_2, orbital_period)\n",
+    "sep_min = calc_sep_from_period(M_1, M_2, 10**{})\n",
+    "sep_max = calc_sep_from_period(M_1, M_2, 10**{})\"\"\".format(logperrange[0],logperrange[1]),\n",
+    "    probdist=\"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
+    "    parameter_name=\"orbital_period\",\n",
+    "    dphasevol=\"dlog10per\",\n",
+    " )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Logging and handling the output\n",
+    "\n",
+    "We now construct the pre- and post-common envelope evolution data for the first common envelope that forms in each binary. We look at the comenv_count variable, we can see that when it increases from 0 to 1 we have found our object. If this happens, we stop evolution of the system to save CPU time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "\n",
+      "/*\n",
+      " * Detect when the comenv_count increased \n",
+      " */\n",
+      "if(stardata->model.comenv_count == 1 && \n",
+      "   stardata->previous_stardata->model.comenv_count == 0)\n",
+      "{\n",
+      "   /*\n",
+      "    * We just had this system's first common envelope:\n",
+      "    * output the time at which this happens, \n",
+      "    * the system's probability (proportional to the number of stars),\n",
+      "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+      "    * the current timestep (post-comenv) orbital period (days)\n",
+      "    */\n",
+      "    Printf(\"COMENV %g %g %g %g\\n\",\n",
+      "           stardata->model.time,\n",
+      "           stardata->model.probability,\n",
+      "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+      "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+      "           \n",
+      "    /*\n",
+      "     * We should waste no more CPU time on this system now we have the\n",
+      "     * data we want.\n",
+      "     */\n",
+      "    stardata->model.evolution_stop = TRUE;\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "\n",
+    "/*\n",
+    " * Detect when the comenv_count increased \n",
+    " */\n",
+    "if(stardata->model.comenv_count == 1 && \n",
+    "   stardata->previous_stardata->model.comenv_count == 0)\n",
+    "{\n",
+    "   /*\n",
+    "    * We just had this system's first common envelope:\n",
+    "    * output the time at which this happens, \n",
+    "    * the system's probability (proportional to the number of stars),\n",
+    "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+    "    * the current timestep (post-comenv) orbital period (days)\n",
+    "    */\n",
+    "    Printf(\"COMENV %g %g %g %g\\\\n\",\n",
+    "           stardata->model.time,\n",
+    "           stardata->model.probability,\n",
+    "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+    "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+    "           \n",
+    "    /*\n",
+    "     * We should waste no more CPU time on this system now we have the\n",
+    "     * data we want.\n",
+    "     */\n",
+    "    stardata->model.evolution_stop = TRUE;\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 \"COMENV\" and process the associated data. We set up the parse_data function to do just this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14736bebc040> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import bin_data,datalinedict\n",
+    "import re\n",
+    "\n",
+    "# log-period distribution bin width (dex)\n",
+    "binwidth = 0.5 \n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"probability\", \"pre_comenv_period\", \"post_comenv_period\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        # obtain the line of data in dictionary form \n",
+    "        linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "        # choose COMENV lines of output\n",
+    "        if linedata[\"header\"] == \"COMENV\":\n",
+    "            # bin the pre- and post-comenv log10-orbital-periods to nearest 0.5dex\n",
+    "            binned_pre_period = bin_data(math.log10(linedata[\"pre_comenv_period\"]), binwidth)\n",
+    "            \n",
+    "            # but check if the post-comenv period is finite and positive: if \n",
+    "            # not, the system has merged and we give it an aritifical period\n",
+    "            # of 10^-100 days (which is very much unphysical)\n",
+    "            if linedata[\"post_comenv_period\"] > 0.0:\n",
+    "                binned_post_period = bin_data(math.log10(linedata[\"post_comenv_period\"]), binwidth)\n",
+    "            else:\n",
+    "                binned_post_period = bin_data(-100,binwidth) # merged!\n",
+    "                \n",
+    "            # make the \"histograms\"\n",
+    "            self.grid_results['pre'][binned_pre_period] += linedata[\"probability\"]\n",
+    "            self.grid_results['post'][binned_post_period] += linedata[\"probability\"]\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 we actually run the population. This may take a little while. You can set amt_cores higher if you have a powerful machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: amt_cores=4 to grid_options\n",
+      "Creating and loading custom logging functionality\n",
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "Grid has handled 1000 stars\n",
+      "with a total probability of 0.0645905996773004\n",
+      "Total starcount for this run will be: 1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:39,950 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-12 18:07:39,953 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-12 18:07:39,959 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-12 18:07:39,962 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-12 18:07:39,965 DEBUG    Process-5] --- Setting up processor: process-3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 started at 2021-09-12T18:07:39.965721.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee47e0>\n",
+      "Process 1 started at 2021-09-12T18:07:39.970949.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n",
+      "Process 2 started at 2021-09-12T18:07:39.978355.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4f30>\n",
+      "Process 3 started at 2021-09-12T18:07:39.983689.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:40,066 DEBUG    MainProcess] --- Signaling stop to processes\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "163/1000  16.3% complete 18:07:49 ETA=   51.5s tpr=6.16e-02 ETF=18:08:41 mem:594.9MB\n",
+      "322/1000  32.2% complete 18:07:59 ETA=   42.9s tpr=6.33e-02 ETF=18:08:42 mem:538.2MB\n",
+      "465/1000  46.5% complete 18:08:09 ETA=   38.1s tpr=7.12e-02 ETF=18:08:47 mem:538.2MB\n",
+      "586/1000  58.6% complete 18:08:19 ETA=   34.3s tpr=8.29e-02 ETF=18:08:54 mem:540.0MB\n",
+      "682/1000  68.2% complete 18:08:30 ETA=   34.0s tpr=1.07e-01 ETF=18:09:04 mem:540.1MB\n",
+      "784/1000  78.4% complete 18:08:40 ETA=   21.2s tpr=9.81e-02 ETF=18:09:01 mem:541.8MB\n",
+      "872/1000  87.2% complete 18:08:50 ETA=   15.0s tpr=1.17e-01 ETF=18:09:05 mem:546.1MB\n",
+      "963/1000  96.3% complete 18:09:00 ETA=    4.2s tpr=1.14e-01 ETF=18:09:04 mem:546.9MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,366 DEBUG    Process-5] --- Process-3 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.964604, done at 2021-09-12T18:09:06.370832 (total: 86.406228s of which 86.24177551269531s interfacing with binary_c).\n",
+      "\tRan 222 systems with a total probability of 0.014137215791516371.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,374 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-12 18:09:06,979 DEBUG    Process-3] --- Process-1 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.953039, done at 2021-09-12T18:09:06.982866 (total: 87.029827s of which 86.82909393310547s interfacing with binary_c).\n",
+      "\tRan 273 systems with a total probability of 0.01877334232598154.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,985 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-12 18:09:07,174 DEBUG    Process-2] --- Process-0 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.949775, done at 2021-09-12T18:09:07.176660 (total: 87.226885s of which 87.02672934532166s interfacing with binary_c).\n",
+      "\tRan 268 systems with a total probability of 0.016469813170514686.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,179 DEBUG    Process-2] --- Process-0 is finished.\n",
+      "[2021-09-12 18:09:07,233 DEBUG    Process-4] --- Process-2 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.958802, done at 2021-09-12T18:09:07.236252 (total: 87.27745s of which 87.0905077457428s interfacing with binary_c).\n",
+      "\tRan 237 systems with a total probability of 0.015210228389288167.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,238 DEBUG    Process-4] --- Process-2 is finished.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population-ad303100d719457c83256568f9a9887c finished! The total probability was: 0.06459059967730076. It took a total of 87.54819011688232s to run 1000 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set number of threads\n",
+    "population.set(\n",
+    "    # set number of threads (i.e. number of CPU cores we use)\n",
+    "    amt_cores=4,\n",
+    "    )\n",
+    "\n",
+    "# Evolve the population - this is the slow, number-crunching step\n",
+    "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. We check this, and then set about making the plot of the orbital period distributions using Seaborn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': 'ad303100d719457c83256568f9a9887c', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.06459059967730076, 'total_count': 1000, 'start_timestamp': 1631462859.9342952, 'end_timestamp': 1631462947.4824853, 'total_mass_run': 4680.235689312421, 'total_probability_weighted_mass_run': 0.22611318083528567, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'merged': 0.035263029200000025, 'unmerged': 0.019388724199999995}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Number of stars')"
+      ]
+     },
+     "execution_count": 8,
+     "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 distributions\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import copy\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "# remove the merged objects\n",
+    "probability = { \"merged\" : 0.0, \"unmerged\" : 0.0}\n",
+    "\n",
+    "# copy the results so we can change the copy\n",
+    "results = copy.deepcopy(population.grid_results)\n",
+    "\n",
+    "for distribution in ['post']:    \n",
+    "    for logper in population.grid_results[distribution]:\n",
+    "        dprob = results[distribution][logper]\n",
+    "        if logper < -90:\n",
+    "            # merged system\n",
+    "            probability[\"merged\"] += dprob\n",
+    "            del results[distribution][logper]\n",
+    "        else:\n",
+    "            # unmerged system\n",
+    "            probability[\"unmerged\"] += dprob\n",
+    "print(probability)\n",
+    "    \n",
+    "# pad the final distribution with zero\n",
+    "for distribution in population.grid_results:    \n",
+    "    pad_output_distribution(results[distribution],\n",
+    "                            binwidth)\n",
+    "    \n",
+    "# make pandas dataframe \n",
+    "plot_data = pd.DataFrame.from_dict(results, orient='columns')\n",
+    "\n",
+    "# make the plot\n",
+    "p = sns.lineplot(data=plot_data)\n",
+    "p.set_xlabel(\"$\\log_{10} (P_\\mathrm{orb} / \\mathrm{day})$\")\n",
+    "p.set_ylabel(\"Number of stars\")\n",
+    "#p.set(xlim=(-5,5)) # might be necessary?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4740c93-d01e-4ca1-8766-c2fb4ddca2e4",
+   "metadata": {},
+   "source": [
+    "You can see that common-envelope evolution shrinks stellar orbits, just as we expect. Pre-CEE, most orbits are in the range $10$ to $1000\\text{ }\\mathrm{d}$, while after CEE the distribution peaks at about $1\\text{ }\\mathrm{d}$. Some of these orbits are very short: $\\log_{10}(-2) = 0.01\\text{ }\\mathrm{d}\\sim10\\text{ }\\mathrm{minutes}$. Such systems are prime candidates for exciting astrophysics: novae, type Ia supernovae and gravitational wave sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57faf043-3809-427a-b378-2355ce8c2691",
+   "metadata": {},
+   "source": [
+    "Things to try:\n",
+    "* Extend the logging to output more data than just the orbital period.\n",
+    "* What are the stellar types of the post-common envelope systems? Are they likely to undergo novae or a type-Ia supernova?\n",
+    "* What are the lifetimes of the systems in close ($<1\\text{ }\\mathrm{d}$) binaries? Are they likely to merge in the life of the Universe?\n",
+    "* How much mass is lost in common-envelope interactions?\n",
+    "* Extend the grid to massive stars. Do you see many NS and BH compact binaries?\n",
+    "* Try different $\\alpha_\\mathrm{CE}$ and $\\lambda_\\mathrm{CE}$ options...\n",
+    "* ... and perhaps increased resolution to obtain smoother curves.\n",
+    "* Why do long-period systems not reach common envelope evolution?"
+   ]
+  }
+ ],
+ "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/notebook_custom_logging.html b/docs/build/html/notebook_custom_logging.html
index 47a303d16..aa4d05806 100644
--- a/docs/build/html/notebook_custom_logging.html
+++ b/docs/build/html/notebook_custom_logging.html
@@ -108,7 +108,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -882,7 +884,7 @@ 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: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_extra_features.html b/docs/build/html/notebook_extra_features.html
index 5edb46523..407cf0316 100644
--- a/docs/build/html/notebook_extra_features.html
+++ b/docs/build/html/notebook_extra_features.html
@@ -103,7 +103,9 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -648,7 +650,7 @@ get_defaults(filter_values:bool=False) -&gt; dict
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_individual_systems.html b/docs/build/html/notebook_individual_systems.html
index 9d13cae76..3c61e7be6 100644
--- a/docs/build/html/notebook_individual_systems.html
+++ b/docs/build/html/notebook_individual_systems.html
@@ -101,7 +101,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -929,7 +931,7 @@ SINGLE_STAR_LIFETIME 15 14.9947
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_individual_systems.ipynb b/docs/build/html/notebook_individual_systems.ipynb
index e6451e762..85aef1e39 100644
--- a/docs/build/html/notebook_individual_systems.ipynb
+++ b/docs/build/html/notebook_individual_systems.ipynb
@@ -566,7 +566,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -580,7 +580,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/html/notebook_luminosity_function_binaries.html b/docs/build/html/notebook_luminosity_function_binaries.html
index 505d42726..026d2e78e 100644
--- a/docs/build/html/notebook_luminosity_function_binaries.html
+++ b/docs/build/html/notebook_luminosity_function_binaries.html
@@ -7,7 +7,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Example use case: Zero-age stellar luminosity function in binaries &mdash; binary_c-python  documentation</title>
+  <title>Zero-age stellar luminosity function in binaries &mdash; binary_c-python  documentation</title>
   
 
   
@@ -39,7 +39,7 @@
     
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Binary_c parameters" href="binary_c_parameters.html" />
+    <link rel="next" title="Example use case: Hertzsprung-Russell diagrams" href="notebook_HRD.html" />
     <link rel="prev" title="Example use case: Zero-age stellar luminosity function" href="notebook_luminosity_function_single.html" /> 
 </head>
 
@@ -96,13 +96,15 @@
 <li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Example use case: Zero-age stellar luminosity function in binaries</a><ul>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Zero-age stellar luminosity function in binaries</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Setting-up-the-Population-object">Setting up the Population object</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Adding-grid-variables">Adding grid variables</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Setting-logging-and-handling-the-output">Setting logging and handling the output</a></li>
 <li class="toctree-l3"><a class="reference internal" href="#Evolving-the-grid">Evolving the grid</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -157,7 +159,7 @@
         
           <li><a href="example_notebooks.html">Example notebooks</a> &raquo;</li>
         
-      <li>Example use case: Zero-age stellar luminosity function in binaries</li>
+      <li>Zero-age stellar luminosity function in binaries</li>
     
     
       <li class="wy-breadcrumbs-aside">
@@ -446,8 +448,8 @@ div.rendered_html tbody tr:hover {
     text-align: unset;
 }
 </style>
-<div class="section" id="Example-use-case:-Zero-age-stellar-luminosity-function-in-binaries">
-<h1>Example use case: Zero-age stellar luminosity function in binaries<a class="headerlink" href="#Example-use-case:-Zero-age-stellar-luminosity-function-in-binaries" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="Zero-age-stellar-luminosity-function-in-binaries">
+<h1>Zero-age stellar luminosity function in binaries<a class="headerlink" href="#Zero-age-stellar-luminosity-function-in-binaries" title="Permalink to this headline">¶</a></h1>
 <p>In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of binary stars using binary_c.</p>
 <p>Before you go through this notebook, you should look at notebook_luminosity_function.ipynb which is for the - conceptually more simple - single stars.</p>
 <p>We start by loading in some standard Python modules and the binary_c module.</p>
@@ -556,7 +558,7 @@ verbosity is 1
 
 <span class="c1"># resolution on each side of the cube, with more stars for the primary mass</span>
 <span class="n">nres</span> <span class="o">=</span> <span class="mi">10</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">2</span><span class="o">*</span><span class="n">nres</span><span class="p">,</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">4</span><span class="o">*</span><span class="n">nres</span><span class="p">,</span>
               <span class="s2">&quot;q&quot;</span><span class="p">:</span> <span class="n">nres</span><span class="p">,</span>
               <span class="s2">&quot;per&quot;</span><span class="p">:</span> <span class="n">nres</span><span class="p">}</span>
 
@@ -779,12 +781,12 @@ Constructing/adding: lnm1
 Constructing/adding: q
 Constructing/adding: log10per
 Saving grid code to grid_options
-Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py
-Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py
+Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py
+Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py
 Grid code loaded
-Grid has handled 2000 stars
-with a total probability of 0.6495098935846658
-Total starcount for this run will be: 2000
+Grid has handled 256 stars
+with a total probability of 0.6149734610296649
+Total starcount for this run will be: 256
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -792,11 +794,11 @@ Total starcount for this run will be: 2000
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-[2021-09-10 15:14:08,077 DEBUG    Process-2] --- Setting up processor: process-0[2021-09-10 15:14:08,080 DEBUG    Process-3] --- Setting up processor: process-1[2021-09-10 15:14:08,086 DEBUG    MainProcess] --- setting up the system_queue_filler now
-
-[2021-09-10 15:14:08,084 DEBUG    Process-4] --- Setting up processor: process-2
-
-[2021-09-10 15:14:08,117 DEBUG    Process-5] --- Setting up processor: process-3
+[2021-09-10 22:26:10,473 DEBUG    Process-2] --- Setting up processor: process-0
+[2021-09-10 22:26:10,475 DEBUG    Process-3] --- Setting up processor: process-1
+[2021-09-10 22:26:10,478 DEBUG    Process-4] --- Setting up processor: process-2
+[2021-09-10 22:26:10,481 DEBUG    MainProcess] --- setting up the system_queue_filler now
+[2021-09-10 22:26:10,482 DEBUG    Process-5] --- Setting up processor: process-3
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -804,8 +806,10 @@ Total starcount for this run will be: 2000
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Process 1 started at 2021-09-10T15:14:08.119437.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x7f351ff53810&gt;Process 0 started at 2021-09-10T15:14:08.126435.    Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x7f351ff539f0&gt;
-Process 2 started at 2021-09-10T15:14:08.138353.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x7f351ff539f0&gt;
+Process 0 started at 2021-09-10T22:26:10.491896.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x154d03cdf510&gt;Process 1 started at 2021-09-10T22:26:10.491948.    Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x154d03cdf480&gt;
+
+Process 2 started at 2021-09-10T22:26:10.496677.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x154d03cdf3f0&gt;
+Process 3 started at 2021-09-10T22:26:10.498669.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x154d03cdf180&gt;
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -813,7 +817,7 @@ Process 2 started at 2021-09-10T15:14:08.138353.        Using store memaddr &lt;
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-
+[2021-09-10 22:26:10,510 DEBUG    MainProcess] --- Signaling stop to processes
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -821,177 +825,18 @@ Process 2 started at 2021-09-10T15:14:08.138353.        Using store memaddr &lt;
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-
-
-Process 3 started at 2021-09-10T15:14:08.186492.        Using store memaddr &lt;capsule object &#34;STORE&#34; at 0x7f351ff53810&gt;
 Generating grid code
 Generating grid code
 Constructing/adding: lnm1
 Constructing/adding: q
 Constructing/adding: log10per
 Saving grid code to grid_options
-Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py
-Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py
+Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py
+Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py
 Grid code loaded
-624/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB625/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.04e-03 ETF=15:14:23 mem:800.5MB
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-</div>
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-<div class="highlight"><pre>
-[2021-09-10 15:16:25,175 DEBUG    MainProcess] --- Signaling stop to processes
-</pre></div></div>
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+158/256  61.7% complete 22:26:15 ETA=    3.2s tpr=3.22e-02 ETF=22:26:18 mem:509.0MB
+199/256  77.7% complete 22:26:20 ETA=    7.3s tpr=1.28e-01 ETF=22:26:27 mem:476.9MB
+238/256  93.0% complete 22:26:25 ETA=    2.3s tpr=1.28e-01 ETF=22:26:27 mem:481.7MB
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -999,7 +844,7 @@ Grid code loaded
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-[2021-09-10 15:27:22,382 DEBUG    Process-5] --- Process-3 is finishing.
+[2021-09-10 22:26:27,631 DEBUG    Process-3] --- Process-1 is finishing.
 </pre></div></div>
 </div>
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@@ -1007,9 +852,9 @@ Grid code loaded
 </div>
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-Process 3 finished:
-        generator started at 2021-09-10T15:14:08.117391, done at 2021-09-10T15:27:22.400722 (total: 794.283331s of which 792.6935975551605s interfacing with binary_c).
-        Ran 499 systems with a total probability of 0.17005450973840136.
+Process 1 finished:
+        generator started at 2021-09-10T22:26:10.475399, done at 2021-09-10T22:26:27.634804 (total: 17.159405s of which 17.104907512664795s interfacing with binary_c).
+        Ran 61 systems with a total probability of 0.1439494161909395.
         This thread had 0 failing systems with a total probability of 0.
         Skipped a total of 0 systems because they had 0 probability
 </pre></div></div>
@@ -1019,8 +864,8 @@ Process 3 finished:
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-[2021-09-10 15:27:22,435 DEBUG    Process-5] --- Process-3 is finished.
-[2021-09-10 15:27:22,480 DEBUG    Process-3] --- Process-1 is finishing.
+[2021-09-10 22:26:27,639 DEBUG    Process-3] --- Process-1 is finished.
+[2021-09-10 22:26:27,698 DEBUG    Process-5] --- Process-3 is finishing.
 </pre></div></div>
 </div>
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@@ -1028,9 +873,9 @@ Process 3 finished:
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Process 1 finished:
-        generator started at 2021-09-10T15:14:08.080367, done at 2021-09-10T15:27:22.505288 (total: 794.424921s of which 793.1943278312683s interfacing with binary_c).
-        Ran 474 systems with a total probability of 0.15740832333567983.
+Process 3 finished:
+        generator started at 2021-09-10T22:26:10.482470, done at 2021-09-10T22:26:27.701828 (total: 17.219358s of which 17.162050247192383s interfacing with binary_c).
+        Ran 67 systems with a total probability of 0.17251417460118773.
         This thread had 0 failing systems with a total probability of 0.
         Skipped a total of 0 systems because they had 0 probability
 </pre></div></div>
@@ -1040,8 +885,8 @@ Process 1 finished:
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-[2021-09-10 15:27:22,531 DEBUG    Process-3] --- Process-1 is finished.
-[2021-09-10 15:27:22,846 DEBUG    Process-2] --- Process-0 is finishing.
+[2021-09-10 22:26:27,705 DEBUG    Process-5] --- Process-3 is finished.
+[2021-09-10 22:26:27,769 DEBUG    Process-4] --- Process-2 is finishing.
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -1049,9 +894,9 @@ Process 1 finished:
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Process 0 finished:
-        generator started at 2021-09-10T15:14:08.077117, done at 2021-09-10T15:27:22.851971 (total: 794.774854s of which 793.4976091384888s interfacing with binary_c).
-        Ran 507 systems with a total probability of 0.16018641159091498.
+Process 2 finished:
+        generator started at 2021-09-10T22:26:10.478464, done at 2021-09-10T22:26:27.771291 (total: 17.292827s of which 17.243471384048462s interfacing with binary_c).
+        Ran 56 systems with a total probability of 0.14306289954535925.
         This thread had 0 failing systems with a total probability of 0.
         Skipped a total of 0 systems because they had 0 probability
 </pre></div></div>
@@ -1061,8 +906,8 @@ Process 0 finished:
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-[2021-09-10 15:27:22,872 DEBUG    Process-2] --- Process-0 is finished.
-[2021-09-10 15:27:22,976 DEBUG    Process-4] --- Process-2 is finishing.
+[2021-09-10 22:26:27,774 DEBUG    Process-4] --- Process-2 is finished.
+[2021-09-10 22:26:27,865 DEBUG    Process-2] --- Process-0 is finishing.
 </pre></div></div>
 </div>
 <div class="nboutput docutils container">
@@ -1070,9 +915,9 @@ Process 0 finished:
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Process 2 finished:
-        generator started at 2021-09-10T15:14:08.084369, done at 2021-09-10T15:27:22.981706 (total: 794.897337s of which 793.4600214958191s interfacing with binary_c).
-        Ran 520 systems with a total probability of 0.1618606489196724.
+Process 0 finished:
+        generator started at 2021-09-10T22:26:10.473000, done at 2021-09-10T22:26:27.867175 (total: 17.394175s of which 17.331928491592407s interfacing with binary_c).
+        Ran 72 systems with a total probability of 0.1554469706921749.
         This thread had 0 failing systems with a total probability of 0.
         Skipped a total of 0 systems because they had 0 probability
 </pre></div></div>
@@ -1082,7 +927,7 @@ Process 2 finished:
 </div>
 <div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-[2021-09-10 15:27:22,986 DEBUG    Process-4] --- Process-2 is finished.
+[2021-09-10 22:26:27,869 DEBUG    Process-2] --- Process-0 is finished.
 </pre></div></div>
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@@ -1090,14 +935,14 @@ Process 2 finished:
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-Population-0fa295ee5c76444bace8fd0ee17a3e11 finished! The total probability was: 0.6495098935846686. It took a total of 795.1383104324341s to run 2000 systems on 4 cores
+Population-bc3a5f915411445699f8cf6438817ff1 finished! The total probability was: 0.6149734610296613. It took a total of 17.603368997573853s to run 256 systems on 4 cores
 There were no errors found in this run.
 Done population 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>[12]:
+<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>
@@ -1110,11 +955,11 @@ Done population run!
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;population_name&#39;: &#39;0fa295ee5c76444bace8fd0ee17a3e11&#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;: 0.6495098935846686, &#39;total_count&#39;: 2000, &#39;start_timestamp&#39;: 1631283248.057525, &#39;end_timestamp&#39;: 1631284043.1958354, &#39;total_mass_run&#39;: 41112.220964392276, &#39;total_probability_weighted_mass_run&#39;: 0.6452116023479681, &#39;zero_prob_stars_skipped&#39;: 0}
+{&#39;population_name&#39;: &#39;bc3a5f915411445699f8cf6438817ff1&#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;: 0.6149734610296613, &#39;total_count&#39;: 256, &#39;start_timestamp&#39;: 1631305570.458824, &#39;end_timestamp&#39;: 1631305588.062193, &#39;total_mass_run&#39;: 5246.190724478048, &#39;total_probability_weighted_mass_run&#39;: 0.6347400152389439, &#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>[13]:
+<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>
@@ -1123,8 +968,12 @@ Done population run!
 <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 the figure size (for a Jupyter notebook in a web browser)</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="p">)</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="n">titles</span> <span class="o">=</span> <span class="p">{</span> <span class="mi">0</span> <span class="p">:</span> <span class="s2">&quot;Primary&quot;</span><span class="p">,</span>
            <span class="mi">1</span> <span class="p">:</span> <span class="s2">&quot;Secondary&quot;</span><span class="p">,</span>
@@ -1154,7 +1003,7 @@ Done population run!
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -1169,6 +1018,22 @@ Done population run!
 <img alt="_images/notebook_luminosity_function_binaries_20_1.png" src="_images/notebook_luminosity_function_binaries_20_1.png" />
 </div>
 </div>
+<p>You can see that the secondary stars are dimmer than the primaries - which you expect given they are lower in mass (by definition q=M2/M1&lt;1).</p>
+<p>Weirdly, in some places the primary distribution may exceed the unresolved distribution. This is a bit unphysical, but in this case is usually caused by limited resolution. If you increase the number of stars in the grid, this problem should go away (at a cost of more CPU time).</p>
+<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>Things to try:
+* Massive stars: can you see the effects of wind mass loss and rejuvenation in these stars?
+* Alter the metallicity, does this make much of a difference?
+* Change the binary fraction. Here we assume a 100% binary fraction, but a real population is a mixture of single and binary stars.
+* How might you go about comparing these computed observations to real stars?
+* 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?
+</pre></div>
+</div>
+</div>
 </div>
 </div>
 
@@ -1180,7 +1045,7 @@ Done population run!
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="binary_c_parameters.html" class="btn btn-neutral float-right" title="Binary_c parameters" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="notebook_HRD.html" class="btn btn-neutral float-right" title="Example use case: Hertzsprung-Russell diagrams" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="notebook_luminosity_function_single.html" class="btn btn-neutral float-left" title="Example use case: Zero-age stellar luminosity function" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
@@ -1206,7 +1071,7 @@ Done population run!
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_luminosity_function_binaries.ipynb b/docs/build/html/notebook_luminosity_function_binaries.ipynb
index 47a96d093..c6b5f1e64 100644
--- a/docs/build/html/notebook_luminosity_function_binaries.ipynb
+++ b/docs/build/html/notebook_luminosity_function_binaries.ipynb
@@ -5,7 +5,7 @@
    "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
    "metadata": {},
    "source": [
-    "# Example use case: Zero-age stellar luminosity function in binaries\n",
+    "# Zero-age stellar luminosity function in binaries\n",
     "\n",
     "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of binary stars using binary_c. \n",
     "\n",
@@ -168,7 +168,7 @@
     "\n",
     "# resolution on each side of the cube, with more stars for the primary mass\n",
     "nres = 10\n",
-    "resolution = {\"M_1\": 2*nres,\n",
+    "resolution = {\"M_1\": 4*nres,\n",
     "              \"q\": nres,\n",
     "              \"per\": nres}\n",
     "\n",
@@ -379,10 +379,6 @@
    "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -399,229 +395,74 @@
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
       "Grid code loaded\n",
-      "Grid has handled 2000 stars\n",
-      "with a total probability of 0.6495098935846658\n",
-      "Total starcount for this run will be: 2000\n"
+      "Grid has handled 256 stars\n",
+      "with a total probability of 0.6149734610296649\n",
+      "Total starcount for this run will be: 256\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:14:08,077 DEBUG    Process-2] --- Setting up processor: process-0[2021-09-10 15:14:08,080 DEBUG    Process-3] --- Setting up processor: process-1[2021-09-10 15:14:08,086 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
-      "\n",
-      "[2021-09-10 15:14:08,084 DEBUG    Process-4] --- Setting up processor: process-2\n",
-      "\n",
-      "[2021-09-10 15:14:08,117 DEBUG    Process-5] --- Setting up processor: process-3"
+      "[2021-09-10 22:26:10,473 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-10 22:26:10,475 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-10 22:26:10,478 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-10 22:26:10,481 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-10 22:26:10,482 DEBUG    Process-5] --- Setting up processor: process-3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 started at 2021-09-10T15:14:08.119437.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>Process 0 started at 2021-09-10T15:14:08.126435.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>\n",
-      "Process 2 started at 2021-09-10T15:14:08.138353.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>"
+      "Process 0 started at 2021-09-10T22:26:10.491896.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf510>Process 1 started at 2021-09-10T22:26:10.491948.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf480>\n",
+      "\n",
+      "Process 2 started at 2021-09-10T22:26:10.496677.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf3f0>\n",
+      "Process 3 started at 2021-09-10T22:26:10.498669.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf180>\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "[2021-09-10 22:26:10,510 DEBUG    MainProcess] --- Signaling stop to processes\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "Process 3 started at 2021-09-10T15:14:08.186492.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>\n",
       "Generating grid code\n",
       "Generating grid code\n",
       "Constructing/adding: lnm1\n",
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
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-      "[2021-09-10 15:16:25,175 DEBUG    MainProcess] --- Signaling stop to processes\n"
+      "[2021-09-10 22:26:27,631 DEBUG    Process-3] --- Process-1 is finishing.\n"
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-      "\n",
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-      "1936/2000  96.8% complete 15:26:46 ETA=   42.0s tpr=6.57e-01 ETF=15:27:28 mem:589.1MB\n",
-      "1946/2000  97.3% complete 15:26:53 ETA=   40.1s tpr=7.42e-01 ETF=15:27:33 mem:589.2MB\n",
-      "1956/2000  97.8% complete 15:26:59 ETA=   25.1s tpr=5.70e-01 ETF=15:27:24 mem:589.2MB\n",
-      "1966/2000  98.3% complete 15:27:04 ETA=   19.1s tpr=5.62e-01 ETF=15:27:24 mem:589.5MB\n",
-      "1976/2000  98.8% complete 15:27:10 ETA=   14.4s tpr=6.01e-01 ETF=15:27:25 mem:589.5MB\n",
-      "1987/2000  99.3% complete 15:27:16 ETA=    6.4s tpr=4.92e-01 ETF=15:27:22 mem:589.5MB\n",
-      "1998/2000  99.9% complete 15:27:21 ETA=    1.0s tpr=4.85e-01 ETF=15:27:22 mem:589.6MB\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-09-10 15:27:22,382 DEBUG    Process-5] --- Process-3 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Process 3 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.117391, done at 2021-09-10T15:27:22.400722 (total: 794.283331s of which 792.6935975551605s interfacing with binary_c).\n",
-      "\tRan 499 systems with a total probability of 0.17005450973840136.\n",
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.475399, done at 2021-09-10T22:26:27.634804 (total: 17.159405s of which 17.104907512664795s interfacing with binary_c).\n",
+      "\tRan 61 systems with a total probability of 0.1439494161909395.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -630,17 +471,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,435 DEBUG    Process-5] --- Process-3 is finished.\n",
-      "[2021-09-10 15:27:22,480 DEBUG    Process-3] --- Process-1 is finishing.\n"
+      "[2021-09-10 22:26:27,639 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-10 22:26:27,698 DEBUG    Process-5] --- Process-3 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.080367, done at 2021-09-10T15:27:22.505288 (total: 794.424921s of which 793.1943278312683s interfacing with binary_c).\n",
-      "\tRan 474 systems with a total probability of 0.15740832333567983.\n",
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.482470, done at 2021-09-10T22:26:27.701828 (total: 17.219358s of which 17.162050247192383s interfacing with binary_c).\n",
+      "\tRan 67 systems with a total probability of 0.17251417460118773.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -649,17 +490,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,531 DEBUG    Process-3] --- Process-1 is finished.\n",
-      "[2021-09-10 15:27:22,846 DEBUG    Process-2] --- Process-0 is finishing.\n"
+      "[2021-09-10 22:26:27,705 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-10 22:26:27,769 DEBUG    Process-4] --- Process-2 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 0 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.077117, done at 2021-09-10T15:27:22.851971 (total: 794.774854s of which 793.4976091384888s interfacing with binary_c).\n",
-      "\tRan 507 systems with a total probability of 0.16018641159091498.\n",
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.478464, done at 2021-09-10T22:26:27.771291 (total: 17.292827s of which 17.243471384048462s interfacing with binary_c).\n",
+      "\tRan 56 systems with a total probability of 0.14306289954535925.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -668,17 +509,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,872 DEBUG    Process-2] --- Process-0 is finished.\n",
-      "[2021-09-10 15:27:22,976 DEBUG    Process-4] --- Process-2 is finishing.\n"
+      "[2021-09-10 22:26:27,774 DEBUG    Process-4] --- Process-2 is finished.\n",
+      "[2021-09-10 22:26:27,865 DEBUG    Process-2] --- Process-0 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 2 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.084369, done at 2021-09-10T15:27:22.981706 (total: 794.897337s of which 793.4600214958191s interfacing with binary_c).\n",
-      "\tRan 520 systems with a total probability of 0.1618606489196724.\n",
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.473000, done at 2021-09-10T22:26:27.867175 (total: 17.394175s of which 17.331928491592407s interfacing with binary_c).\n",
+      "\tRan 72 systems with a total probability of 0.1554469706921749.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -687,14 +528,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,986 DEBUG    Process-4] --- Process-2 is finished.\n"
+      "[2021-09-10 22:26:27,869 DEBUG    Process-2] --- Process-0 is finished.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Population-0fa295ee5c76444bace8fd0ee17a3e11 finished! The total probability was: 0.6495098935846686. It took a total of 795.1383104324341s to run 2000 systems on 4 cores\n",
+      "Population-bc3a5f915411445699f8cf6438817ff1 finished! The total probability was: 0.6149734610296613. It took a total of 17.603368997573853s to run 256 systems on 4 cores\n",
       "There were no errors found in this run.\n",
       "Done population run!\n"
      ]
@@ -728,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -736,7 +577,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '0fa295ee5c76444bace8fd0ee17a3e11', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6495098935846686, 'total_count': 2000, 'start_timestamp': 1631283248.057525, 'end_timestamp': 1631284043.1958354, 'total_mass_run': 41112.220964392276, 'total_probability_weighted_mass_run': 0.6452116023479681, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'bc3a5f915411445699f8cf6438817ff1', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6149734610296613, 'total_count': 256, 'start_timestamp': 1631305570.458824, 'end_timestamp': 1631305588.062193, 'total_mass_run': 5246.190724478048, 'total_probability_weighted_mass_run': 0.6347400152389439, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -746,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -756,13 +597,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -777,8 +618,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# set up seaborn for use in the notebook\n",
+    "sns.set(rc={'figure.figsize':(20,10)})\n",
+    "sns.set_context(\"notebook\",\n",
+    "                font_scale=1.5,\n",
+    "                rc={\"lines.linewidth\":2.5})\n",
+    "\n",
     "\n",
     "titles = { 0 : \"Primary\",\n",
     "           1 : \"Secondary\",\n",
@@ -805,11 +650,36 @@
     "p.set_ylabel(\"Number of stars\")\n",
     "p.set(yscale=\"log\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "You can see that the secondary stars are dimmer than the primaries - which you expect given they are lower in mass (by definition q=M2/M1<1). \n",
+    "\n",
+    "Weirdly, in some places the primary distribution may exceed the unresolved distribution. This is a bit unphysical, but in this case is usually caused by limited resolution. If you increase the number of stars in the grid, this problem should go away (at a cost of more CPU time). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99e25a72-54e6-4826-b0e5-4a02460b857d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Things to try:\n",
+    "* Massive stars: can you see the effects of wind mass loss and rejuvenation in these stars?\n",
+    "* Alter the metallicity, does this make much of a difference?\n",
+    "* Change the binary fraction. Here we assume a 100% binary fraction, but a real population is a mixture of single and binary stars.\n",
+    "* How might you go about comparing these computed observations to real stars?\n",
+    "* What about evolved stars? Here we consider only the *zero-age* main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -823,7 +693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/html/notebook_luminosity_function_single.html b/docs/build/html/notebook_luminosity_function_single.html
index a8b6ec93a..8f7cb72ab 100644
--- a/docs/build/html/notebook_luminosity_function_single.html
+++ b/docs/build/html/notebook_luminosity_function_single.html
@@ -39,7 +39,7 @@
     
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Example use case: Zero-age stellar luminosity function in binaries" href="notebook_luminosity_function_binaries.html" />
+    <link rel="next" title="Zero-age stellar luminosity function in binaries" href="notebook_luminosity_function_binaries.html" />
     <link rel="prev" title="Tutorial: Using the API functionality of binary_c-python" href="notebook_api_functionality.html" /> 
 </head>
 
@@ -104,7 +104,9 @@
 <li class="toctree-l3"><a class="reference internal" href="#A-better-sampled-grid">A better-sampled grid</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -505,8 +507,8 @@ div.rendered_html tbody tr:hover {
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options
 adding: max_evolution_time=0.1 to BSE_options
+adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options
 verbosity is 1
 </pre></div></div>
 </div>
@@ -538,7 +540,7 @@ verbosity is 1
 </div>
 <p>First let us set up some global variables that will be useful throughout. * The resolution is the number of stars we simulate in our model population. * The massrange is a list of the min and max masses * The total_probability is the theoretical integral of a probability density function, i.e. 1.0. * 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>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+<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>
@@ -554,7 +556,7 @@ verbosity is 1
 </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>[5]:
+<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>
@@ -585,7 +587,7 @@ 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>[18]:
+<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>
@@ -622,7 +624,7 @@ it works perfectly well.</p>
 </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>[7]:
+<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>
@@ -672,7 +674,7 @@ it works perfectly well.</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">amt_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>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
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 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
@@ -704,14 +706,13 @@ with a total probability of 1.0000000000000004
 Total starcount for this run will be: 40
 Generating grid code
 Constructing/adding: M_1
-Population-08f8230453084e4ca6a2391d45ce658b finished! The total probability was: 1.0000000000000002. It took a total of 1.5262682437896729s to run 40 systems on 2 cores
+Population-e6c082aabe0849a0811761a06e50476b finished! The total probability was: 1.0000000000000002. It took a total of 2.3021209239959717s to run 40 systems on 2 cores
 There were no errors found in this run.
-OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.025), (3.75, 0.05), (4.25, 0.05), (0.25, 0.025), (3.25, 0.025), (5.25, 0.2), (4.75, 0.1), (5.75, 0.39999999999999997), (6.25, 0.125)]))])
 </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>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
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 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
@@ -724,11 +725,11 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.025), (3.
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-{&#39;population_name&#39;: &#39;08f8230453084e4ca6a2391d45ce658b&#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;: 1.0000000000000002, &#39;total_count&#39;: 40, &#39;start_timestamp&#39;: 1631124829.303065, &#39;end_timestamp&#39;: 1631124830.8293333, &#39;total_mass_run&#39;: 2001.4, &#39;total_probability_weighted_mass_run&#39;: 50.035000000000004, &#39;zero_prob_stars_skipped&#39;: 0}
+{&#39;population_name&#39;: &#39;e6c082aabe0849a0811761a06e50476b&#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;: 1.0000000000000002, &#39;total_count&#39;: 40, &#39;start_timestamp&#39;: 1631461389.3681686, &#39;end_timestamp&#39;: 1631461391.6702895, &#39;total_mass_run&#39;: 2001.4, &#39;total_probability_weighted_mass_run&#39;: 50.035000000000004, &#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>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
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 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
@@ -737,8 +738,12 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.025), (3.
 <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 the figure size (for a Jupyter notebook in a web browser)</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="p">)</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>
@@ -759,7 +764,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.025), (3.
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -780,7 +785,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.025), (3.
 <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 headline">¶</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>[11]:
+<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>
@@ -793,7 +798,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.025), (3.
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+<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>
@@ -818,13 +823,12 @@ with a total probability of 0.2182216189410787
 Total starcount for this run will be: 40
 Generating grid code
 Constructing/adding: M_1
-Population-92de7c9221c54206ab4dd10e58e09a34 finished! The total probability was: 0.21822161894107872. It took a total of 1.5900418758392334s to run 40 systems on 2 cores
+Population-1bc714cffdb344589ea01692f7e1ebd1 finished! The total probability was: 0.21822161894107872. It took a total of 2.335742950439453s to run 40 systems on 2 cores
 There were no errors found in this run.
-OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.0164166), (3.25, 0.00515685), (0.25, 0.189097), (3.75, 0.0037453900000000004), (4.25, 0.0014346559999999999), (5.25, 0.0007493004), (4.75, 0.001171479), (5.75, 0.00039801020000000003), (6.25, 5.2369339999999996e-05)]))])
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+<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>
@@ -847,7 +851,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.0164166),
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -871,7 +875,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.0164166),
 <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>[14]:
+<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>
@@ -882,7 +886,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.0164166),
 </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>[15]:
+<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>
@@ -902,7 +906,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(2.25, 0.0164166),
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+<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>
@@ -927,14 +931,13 @@ with a total probability of 0.9956307907476224
 Total starcount for this run will be: 40
 Generating grid code
 Constructing/adding: lnM_1
-Population-83f80d829dbd418aa2bc745c99b71991 finished! The total probability was: 0.9956307907476224. It took a total of 0.9961590766906738s to run 40 systems on 2 cores
+Population-4f3ee0143c0548338494d2f1fbacc915 finished! The total probability was: 0.9956307907476225. It took a total of 1.5107016563415527s to run 40 systems on 2 cores
 There were no errors found in this run.
-OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(0.25, 0.0212294), (2.75, 0.00321118), (-0.25, 0.0268827), (1.25, 0.0104553), (3.75, 0.00283037), (6.25, 7.34708e-05), (-0.75, 0.0771478), (0.75, 0.030004499999999996), (2.25, 0.00921541), (3.25, 0.0045385), (1.75, 0.014776889999999999), (4.25, 0.002380189), (4.75, 0.000869303), (5.25, 0.0007310379999999999), (5.75, 0.00036002859999999996), (-2.75, 0.1961345), (-1.75, 0.2181597), (-3.25, 0.0), (-2.25, 0.2568974), (-1.25, 0.11973310000000001)]))])
 </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>[17]:
+<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>
@@ -967,6 +970,8 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(0.25, 0.0212294),
 <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: * Change the resolution to make the distributions smoother: what about error bars, how would you do that? * Different initial distributions: the Kroupa distribution isn’t the only one out there * Change the metallicity and mass ranges * What about a non-constant star formation rate? This is more of a challenge! * 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? * Binary stars! (see notebook_luminosity_function_binaries.ipynb)</p>
 </div>
 </div>
 
@@ -978,7 +983,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(0.25, 0.0212294),
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="notebook_luminosity_function_binaries.html" class="btn btn-neutral float-right" title="Example use case: Zero-age stellar luminosity function in binaries" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
+        <a href="notebook_luminosity_function_binaries.html" class="btn btn-neutral float-right" title="Zero-age stellar luminosity function in binaries" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
       
       
         <a href="notebook_api_functionality.html" class="btn btn-neutral float-left" title="Tutorial: Using the API functionality of binary_c-python" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
@@ -1004,7 +1009,7 @@ OrderedDict([(&#39;luminosity distribution&#39;, OrderedDict([(0.25, 0.0212294),
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_luminosity_function_single.ipynb b/docs/build/html/notebook_luminosity_function_single.ipynb
index 5980adf6d..cdae316f9 100644
--- a/docs/build/html/notebook_luminosity_function_single.ipynb
+++ b/docs/build/html/notebook_luminosity_function_single.ipynb
@@ -54,8 +54,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "adding: max_evolution_time=0.1 to BSE_options\n",
+      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "verbosity is 1\n"
      ]
     }
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "aba3fe4e-18f2-4bb9-8e5c-4c6007ab038b",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "47979841-2c26-4b26-8945-603d013dc93a",
    "metadata": {},
    "outputs": [],
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
    "metadata": {},
    "outputs": [],
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "fd197154-a8ce-4865-8929-008d3483101a",
    "metadata": {},
    "outputs": [],
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
     "tags": []
@@ -321,9 +321,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-08f8230453084e4ca6a2391d45ce658b finished! The total probability was: 1.0000000000000002. It took a total of 1.5262682437896729s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.025), (3.75, 0.05), (4.25, 0.05), (0.25, 0.025), (3.25, 0.025), (5.25, 0.2), (4.75, 0.1), (5.75, 0.39999999999999997), (6.25, 0.125)]))])\n"
+      "Population-e6c082aabe0849a0811761a06e50476b finished! The total probability was: 1.0000000000000002. It took a total of 2.3021209239959717s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -353,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -361,7 +360,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '08f8230453084e4ca6a2391d45ce658b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631124829.303065, 'end_timestamp': 1631124830.8293333, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'e6c082aabe0849a0811761a06e50476b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631461389.3681686, 'end_timestamp': 1631461391.6702895, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -371,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -381,13 +380,13 @@
        "[None]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -402,8 +401,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# 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",
@@ -442,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
    "metadata": {},
    "outputs": [],
@@ -456,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
    "metadata": {},
    "outputs": [
@@ -471,9 +474,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-92de7c9221c54206ab4dd10e58e09a34 finished! The total probability was: 0.21822161894107872. It took a total of 1.5900418758392334s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.0164166), (3.25, 0.00515685), (0.25, 0.189097), (3.75, 0.0037453900000000004), (4.25, 0.0014346559999999999), (5.25, 0.0007493004), (4.75, 0.001171479), (5.75, 0.00039801020000000003), (6.25, 5.2369339999999996e-05)]))])\n"
+      "Population-1bc714cffdb344589ea01692f7e1ebd1 finished! The total probability was: 0.21822161894107872. It took a total of 2.335742950439453s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -488,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
    "metadata": {},
    "outputs": [
@@ -498,13 +500,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -559,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
    "metadata": {},
    "outputs": [],
@@ -578,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
    "metadata": {},
    "outputs": [],
@@ -599,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
    "metadata": {},
    "outputs": [
@@ -614,9 +616,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: lnM_1\n",
-      "Population-83f80d829dbd418aa2bc745c99b71991 finished! The total probability was: 0.9956307907476224. It took a total of 0.9961590766906738s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(0.25, 0.0212294), (2.75, 0.00321118), (-0.25, 0.0268827), (1.25, 0.0104553), (3.75, 0.00283037), (6.25, 7.34708e-05), (-0.75, 0.0771478), (0.75, 0.030004499999999996), (2.25, 0.00921541), (3.25, 0.0045385), (1.75, 0.014776889999999999), (4.25, 0.002380189), (4.75, 0.000869303), (5.25, 0.0007310379999999999), (5.75, 0.00036002859999999996), (-2.75, 0.1961345), (-1.75, 0.2181597), (-3.25, 0.0), (-2.25, 0.2568974), (-1.25, 0.11973310000000001)]))])\n"
+      "Population-4f3ee0143c0548338494d2f1fbacc915 finished! The total probability was: 0.9956307907476225. It took a total of 1.5107016563415527s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -639,13 +640,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "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>"
       ]
@@ -684,6 +685,20 @@
     " \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",
+    "* 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)"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/build/html/notebook_population.html b/docs/build/html/notebook_population.html
index c45b33a56..055662c64 100644
--- a/docs/build/html/notebook_population.html
+++ b/docs/build/html/notebook_population.html
@@ -104,7 +104,9 @@
 <li class="toctree-l2"><a class="reference internal" href="notebook_extra_features.html">Tutorial: Extra features and functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_api_functionality.html">Tutorial: Using the API functionality of binary_c-python</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_single.html">Example use case: Zero-age stellar luminosity function</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Example use case: Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_luminosity_function_binaries.html">Zero-age stellar luminosity function in binaries</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_HRD.html">Example use case: Hertzsprung-Russell diagrams</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebook_common_envelope_evolution.html">Example use case: Common-envelope evolution</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="binary_c_parameters.html">Binary_c parameters</a></li>
@@ -1469,7 +1471,7 @@ 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: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/notebook_population.ipynb b/docs/build/html/notebook_population.ipynb
index fff337533..a24638c0b 100644
--- a/docs/build/html/notebook_population.ipynb
+++ b/docs/build/html/notebook_population.ipynb
@@ -1109,7 +1109,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1123,7 +1123,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
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\ No newline at end of file
diff --git a/docs/build/html/spacing_functions.html b/docs/build/html/spacing_functions.html
index 1033359ea..43bf98fea 100644
--- a/docs/build/html/spacing_functions.html
+++ b/docs/build/html/spacing_functions.html
@@ -262,7 +262,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/stellar_types.html b/docs/build/html/stellar_types.html
index 1418be289..c31a11e0d 100644
--- a/docs/build/html/stellar_types.html
+++ b/docs/build/html/stellar_types.html
@@ -243,7 +243,7 @@
     
     provided by <a href="https://readthedocs.org">Read the Docs</a>.
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/build/html/useful_funcs.html b/docs/build/html/useful_funcs.html
index 6a1794092..a123310d9 100644
--- a/docs/build/html/useful_funcs.html
+++ b/docs/build/html/useful_funcs.html
@@ -441,7 +441,7 @@ 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: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/source/_templates/footer.html b/docs/source/_templates/footer.html
index 308df1118..5ce206833 100644
--- a/docs/source/_templates/footer.html
+++ b/docs/source/_templates/footer.html
@@ -2,7 +2,7 @@
 
 {%- block extrafooter %}
 <br><br>
-Generated on binarycpython git branch: master git revision 0e871bc098f163940c2256300af798a29ad36e29 url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
+Generated on binarycpython git branch: master git revision 0886cd4f1d7f43222aac21d6ed91e917b67b20fc url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c-python/-/tree/master">git url</a>.
 <br><br>
 Using binary_c with bit branch newmaster: git revision: "6185:20210910:1621c23a5" url: <a href="https://gitlab.eps.surrey.ac.uk/ri0005/binary_c/-/tree/newmaster">git url</a>.
 
diff --git a/docs/source/example_notebooks.rst b/docs/source/example_notebooks.rst
index d15ea559a..ce09bb2af 100644
--- a/docs/source/example_notebooks.rst
+++ b/docs/source/example_notebooks.rst
@@ -14,4 +14,6 @@ The order of the notebooks below is more or less the recommended order to read.
     notebook_extra_features.ipynb
     notebook_api_functionality.ipynb
     notebook_luminosity_function_single.ipynb
-    notebook_luminosity_function_binaries.ipynb
\ No newline at end of file
+    notebook_luminosity_function_binaries.ipynb
+    notebook_HRD.ipynb
+    notebook_common_envelope_evolution.ipynb
\ No newline at end of file
diff --git a/docs/source/notebook_HRD.ipynb b/docs/source/notebook_HRD.ipynb
new file mode 100644
index 000000000..52590f8a2
--- /dev/null
+++ b/docs/source/notebook_HRD.ipynb
@@ -0,0 +1,818 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Hertzsprung-Russell diagrams\n",
+    "\n",
+    "In this notebook we compute Hertzsprung-Russell diagrams (HRDs) of single and binary stars.\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_HRD\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {},
+   "source": [
+    "## Setting up the Population object\n",
+    "First we set up a new population object. Our stars evolve to $13.7\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. These are rough approximations: a real population was born some finite time ago, so cannot possibly evolve to $13.7\\mathrm{Gyr}$, and stars are not really born with a metallicity of $0.02$. These approximations only affect very low mass stars, so we assume all our stars have mass $M\\geq 1 \\mathrm{M}_\\odot$, and metallicity does not change evolution too much except in massive stars through the dependence of their winds on metallicity, so we limit our study to $M\\leq 10 \\mathrm{M}_\\odot$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\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=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    tmp_dir=TMP_DIR,\n",
+    "    verbosity=1\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $10\\mathrm{M}_\\odot$ in nine steps (so the masses are integers). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"M_1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        11\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1,2,1)\",\n",
+      "    \"precode\": null,\n",
+      "    \"probdist\": \"1\",\n",
+      "    \"dphasevol\": \"dM_1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"edge\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10} \n",
+    "massrange = (1, 11) \n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"M_1\",\n",
+    "    longname=\"Primary mass\", # == single-star mass\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{res}\".format(res = resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(1,2,1)\", # space by unit masses\n",
+    "    probdist=\"1\", # dprob/dm1 : we don't care, so just set it to 1\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",
+    "    gridtype=\"edge\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Setting logging and handling the output\n",
+    "\n",
+    "We now construct the HRD output.\n",
+    "\n",
+    "We choose stars prior to and including the thermally-pulsing asymptotic giant branch (TPAGB) phase that have $>0.1\\mathrm{M}_\\odot$ of material in their outer hydrogen envelope (remember the core of an evolved star is made of helium or carbon/oxygen/neon). This prevents us showing the post-AGB phase which is a bit messy and we avoid the white-dwarf cooling track."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "Foreach_star(star)\n",
+      "{\n",
+      "    if(star->stellar_type <= TPAGB &&\n",
+      "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+      "    {\n",
+      "         double logTeff = log10(Teff_from_star_struct(star));\n",
+      "         double logL = log10(star->luminosity); \n",
+      "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+      "         Printf(\"HRD%d %30.12e %g %g %g %g\\n\",\n",
+      "                star->starnum, // 0\n",
+      "                stardata->model.time, // 1\n",
+      "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+      "                logTeff, // 3\n",
+      "                logL, // 4\n",
+      "                loggravity // 5\n",
+      "                );\n",
+      "\n",
+      "    }\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "Foreach_star(star)\n",
+    "{\n",
+    "    if(star->stellar_type <= TPAGB &&\n",
+    "       star->mass - Outermost_core_mass(star) > 0.1)\n",
+    "    {\n",
+    "         double logTeff = log10(Teff_from_star_struct(star));\n",
+    "         double logL = log10(star->luminosity); \n",
+    "         double loggravity = log10(TINY+GRAVITATIONAL_CONSTANT*M_SUN*star->mass/Pow2(star->radius*R_SUN));\n",
+    "         Printf(\"HRD%d %30.12e %g %g %g %g\\\\n\",\n",
+    "                star->starnum, // 0\n",
+    "                stardata->model.time, // 1\n",
+    "                stardata->common.zero_age.mass[0], // 2 : note this is the primary mass\n",
+    "                logTeff, // 3\n",
+    "                logL, // 4\n",
+    "                loggravity // 5\n",
+    "                );\n",
+    "\n",
+    "    }\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "population.set(\n",
+    "    C_logging_code=custom_logging_statement\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae1f1f0c-1f8b-42d8-b051-cbf8c6b51514",
+   "metadata": {},
+   "source": [
+    "The parse function must now catch lines that start with \"HRD*n*\", where *n* is 0 (primary star) or 1 (secondary star, which doesn't exist in single-star systems), and process the associated data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14565763dca0> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import datalinedict\n",
+    "import re\n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"zams_mass\", \"logTeff\", \"logL\", \"logg\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        match = re.search('HRD(\\d)',line) \n",
+    "        if match:\n",
+    "            nstar = match.group(1) \n",
+    "            \n",
+    "            # obtain the line of data in dictionary form \n",
+    "            linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "            # first time setup of the list of tuples\n",
+    "            if(len(self.grid_results['HRD'][nstar][linedata['zams_mass']])==0):\n",
+    "                self.grid_results['HRD'][nstar][linedata['zams_mass']] = []\n",
+    "\n",
+    "            # make the HRD be a list of tuples\n",
+    "            self.grid_results['HRD'][nstar][linedata['zams_mass']].append((linedata['logTeff'],\n",
+    "                                                                           linedata['logL']))\n",
+    "    \n",
+    "    # verbose reporting\n",
+    "    #print(\"parse out results_dictionary=\",self.grid_results)\n",
+    "    \n",
+    "# Add the parsing function\n",
+    "population.set(\n",
+    "    parse_function=parse_function,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91509ce5-ffe7-4937-aa87-6d7baac9ac04",
+   "metadata": {},
+   "source": [
+    "## Evolving the grid\n",
+    "Now that we configured all the main parts of the population object, we can actually run the population! Doing this is straightforward: `population.evolve()`\n",
+    "\n",
+    "This will start up the processing of all the systems. We can control how many cores are used by settings `amt_cores`. By setting the `verbosity` of the population object to a higher value we can get a lot of verbose information about the run, but for now we will set it to 0.\n",
+    "\n",
+    "There are many grid_options that can lead to different behaviour of the evolution of the grid. Please do have a look at those: [grid options docs](https://ri0005.pages.surrey.ac.uk/binary_c-python/grid_options_descriptions.html), and try  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: verbosity=0 to grid_options\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-20bee5b0c58d49c5bc47eced240685bb finished! The total probability was: 10.0. It took a total of 0.543649435043335s to run 10 systems on 4 cores\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",
+    "    amt_cores=4,\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": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': '20bee5b0c58d49c5bc47eced240685bb', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 10.0, 'total_count': 10, 'start_timestamp': 1631304519.45189, 'end_timestamp': 1631304519.9955394, 'total_mass_run': 55.0, 'total_probability_weighted_mass_run': 55.0, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 8,
+     "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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "hrd = population.grid_results['HRD']\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    for zams_mass in sorted(hrd[nstar]):\n",
+    "        print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "        # get track data (list of tuples)\n",
+    "        track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "        # convert to Pandas dataframe\n",
+    "        data = pd.DataFrame(data=track, \n",
+    "                            columns = ['logTeff','logL'])\n",
+    "        \n",
+    "        # make seaborn plot\n",
+    "        p = sns.lineplot(data=data,\n",
+    "                         sort=False,\n",
+    "                         x='logTeff',\n",
+    "                         y='logL',\n",
+    "                         estimator=None)\n",
+    "        \n",
+    "        # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "        p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "        \n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "We now have an HRD. It took longer to make the plot than to run the stars with *binary_c*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44586e42-b7cb-4a55-be0a-330b98b20de4",
+   "metadata": {},
+   "source": [
+    "## Binary stars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71d0fc4e-c72f-444a-93ab-19f52086b86d",
+   "metadata": {},
+   "source": [
+    "Now we put a secondary star of mass $0.5\\mathrm{M}_\\odot$ at a distance of $10\\mathrm{R}_\\odot$ to see how this changes things. Then we rerun the population. At such short separations, we expect mass transfer to begin on or shortly after the main sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "478e8005-e144-4e6f-80c9-0cf368a9bcb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-cff93424298e4862bb72096e72b98a2d finished! The total probability was: 10.0. It took a total of 0.9686374664306641s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 0.5, # Msun\n",
+    "    separation = 10, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9c433e6a-fe22-4494-b1a9-fce9676a9f40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "zams mass  1.0\n",
+      "zams mass  2.0\n",
+      "zams mass  3.0\n",
+      "zams mass  4.0\n",
+      "zams mass  5.0\n",
+      "zams mass  6.0\n",
+      "zams mass  7.0\n",
+      "zams mass  8.0\n",
+      "zams mass  9.0\n",
+      "zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "\n",
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3557b6d5-6c54-467c-b7a1-b1903493c441",
+   "metadata": {},
+   "source": [
+    "We plot here the track for the primary star only. You can see immediately where stars merge on the main sequence: the tracks move very suddenly where usually evolution on the main sequence is smooth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59335030-dd99-4c2f-afff-207a3fcbbb70",
+   "metadata": {},
+   "source": [
+    "If we now set the separation to be longer, say $100\\mathrm{R}_\\odot$, mass transfer should happen on the giant branch. We also set the secondary mass to be larger, $1\\mathrm{M}_\\odot$, so that the interaction is stronger."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dee92b20-ad6b-4c97-80dc-71d3bd937c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Grid has handled 10 stars\n",
+      "with a total probability of 10.0\n",
+      "Total starcount for this run will be: 10\n",
+      "Generating grid code\n",
+      "Constructing/adding: M_1\n",
+      "Population-2ea4759ed05544ef8f1b7a887f0f36d2 finished! The total probability was: 10.0. It took a total of 0.7215321063995361s to run 10 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "population.set(\n",
+    "    M_2 = 1, # Msun\n",
+    "    separation = 100, # Rsun\n",
+    "    multiplicity = 2, # binaries\n",
+    "    alpha_ce = 1.0, # make common-envelope evolution quite efficient\n",
+    ")\n",
+    "population.clean()\n",
+    "analytics = population.evolve()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e0ac2573-bc35-43be-8f20-5c85364fde11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n",
+      "star  1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '0': # choose only primaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "        \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "            # set mass label at the zero-age main sequence (ZAMS) which is the first data point\n",
+    "            p.text(track[0][0],track[0][1],str(zams_mass))\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f8e061-a65e-47f2-a777-93de0d5045ea",
+   "metadata": {},
+   "source": [
+    "You now see the interaction in the jerky red-giant tracks where the stars interact. These probably, depending on the mass ratio at the moment of interaction, go through a common-envelope phase. The system can merge (most of the above do) but not all. The interaction is so strong on the RGB of the $1\\mathrm{M}_\\odot$ star that the stellar evolution is terminated before it reaches the RGB tip, so it never ignites helium. This is how helium white dwarfs are probably made."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "698d0a63-11ba-4b3e-a713-35c3e972492f",
+   "metadata": {},
+   "source": [
+    "We can also plot the secondary stars' HRD. Remember, the primary is star 0 in binary_c, while the secondary is star 1. That's because all proper programming languages start counting at 0. We change the parsing function a little so we can separate the plots of the secondaries according to their primary mass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2b0b7c2b-6e43-48ed-9257-9dfc141b3d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "star  0\n",
+      "star  1\n",
+      "primary zams mass  1.0\n",
+      "primary zams mass  2.0\n",
+      "primary zams mass  3.0\n",
+      "primary zams mass  4.0\n",
+      "primary zams mass  5.0\n",
+      "primary zams mass  6.0\n",
+      "primary zams mass  7.0\n",
+      "primary zams mass  8.0\n",
+      "primary zams mass  9.0\n",
+      "primary zams mass  10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\log_{10} (L/$L$_{☉})$')"
+      ]
+     },
+     "execution_count": 13,
+     "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": [
+    "hrd = population.grid_results['HRD']\n",
+    "\n",
+    "for nstar in sorted(hrd):\n",
+    "    print(\"star \",nstar)\n",
+    "    \n",
+    "    if nstar == '1': # choose only secondaries\n",
+    "\n",
+    "        for zams_mass in sorted(hrd[nstar]):\n",
+    "            print(\"primary zams mass \",zams_mass)\n",
+    "        \n",
+    "            # get track data (list of tuples)\n",
+    "            track = hrd[nstar][zams_mass]\n",
+    "        \n",
+    "            # convert to Pandas dataframe\n",
+    "            data = pd.DataFrame(data=track, \n",
+    "                                columns = ['logTeff','logL'])\n",
+    "            \n",
+    "            # make seaborn plot\n",
+    "            p = sns.lineplot(data=data,\n",
+    "                             sort=False,\n",
+    "                             x='logTeff',\n",
+    "                             y='logL',\n",
+    "                             estimator=None)\n",
+    "\n",
+    "\n",
+    "p.invert_xaxis()\n",
+    "p.set_xlabel(\"$\\log_{10} (T_\\mathrm{eff} / \\mathrm{K})$\")\n",
+    "p.set_ylabel(\"$\\log_{10} (L/$L$_{☉})$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92c46319-5629-4125-a284-b5d521ed33fc",
+   "metadata": {},
+   "source": [
+    "Remember, all these stars start with a $1\\mathrm{M}_\\odot$ binary, which begins at $\\log_{10}(T_\\mathrm{eff}/\\mathrm{K})\\sim 3.750$, $\\log_{10}L/\\mathrm{L}_\\odot \\sim 0$. The $1\\mathrm{M}_\\odot$-$1\\mathrm{M}_\\odot$ binary evolves like two single stars until they interact up the giant branch at about $\\log_{10} (L/\\mathrm{L}_\\odot) \\sim 2.5$, the others interact long before they evolve very far on the main sequence: you can just about see their tracks at the very start."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53145356-abbb-4880-996f-dedd80de7540",
+   "metadata": {},
+   "source": [
+    "This is, of course, a very simple introduction to what happens in binaries. We haven't talked about the remnants that are produced by interactions. When the stars do evolve on the giant branch, white dwarfs are made which can go on to suffer novae and (perhaps) thermonuclear explosions. The merging process itself leads to luminosus red novae and, in the case of neutron stars and black holes, kilonovae and gravitational wave events. "
+   ]
+  }
+ ],
+ "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/source/notebook_common_envelope_evolution.ipynb b/docs/source/notebook_common_envelope_evolution.ipynb
new file mode 100644
index 000000000..526320ccf
--- /dev/null
+++ b/docs/source/notebook_common_envelope_evolution.ipynb
@@ -0,0 +1,708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Example use case: Common-envelope evolution\n",
+    "\n",
+    "In this notebook we look at how common-envelope evolution (CEE) alters binary-star orbits. We construct a population of low- and intermediate-mass binaries and compare their orbital periods before and after CEE. Not all stars evolve into this phase, so we have to run a whole population to find those that do. We then have to construct the pre- and post-CEE distributions and plot them.\n",
+    "\n",
+    "First, we import a few required Python modules. "
+   ]
+  },
+  {
+   "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",
+    "from binarycpython.utils.functions import temp_dir\n",
+    "from binarycpython.utils.grid import Population\n",
+    "TMP_DIR = temp_dir(\"notebooks\", \"notebook_comenv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Setting up the Population object\n",
+    "We set up a new population object. Our stars evolve to $13.7\\text{ }\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. We also set the common-envelope ejection efficiency $\\alpha_\\mathrm{CE}=1$ and the envelope structure parameter $\\lambda=0.5$. More complex options are available in *binary_c*, such as $\\lambda$ based on stellar mass, but this is just a demonstration example so let's keep things simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79ab50b7-591f-4883-af09-116d1835a751",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: log_dt=10 to grid_options\n",
+      "adding: max_evolution_time=13700 to BSE_options\n",
+      "adding: metallicity=0.02 to BSE_options\n",
+      "adding: alpha_ce=1.0 to BSE_options\n",
+      "adding: lambda_ce=0.5 to BSE_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create population object\n",
+    "population = Population()\n",
+    "population.set(\n",
+    "    # grid options\n",
+    "    tmp_dir = TMP_DIR,\n",
+    "    verbosity = 1,\n",
+    "    log_dt = 10, # log every 10 seconds\n",
+    "\n",
+    "    # binary-star evolution options\n",
+    "    max_evolution_time=13700,  # maximum stellar evolution time in Myr (13700 Myr == 13.7 Gyr)\n",
+    "    metallicity=0.02, # 0.02 is approximately Solar metallicity \n",
+    "    alpha_ce = 1.0,\n",
+    "    lambda_ce = 0.5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a65554-36ab-4a04-96ca-9f1422c307fd",
+   "metadata": {},
+   "source": [
+    "## Stellar Grid\n",
+    "We now construct a grid of stars, varying the mass from $1$ to $6\\text{ }\\mathrm{M}_\\odot$. We avoid massive stars for now, and focus on the (more common) low- and intermediate-mass stars. We also limit the period range to $10^4\\text{ }\\mathrm{d}$ because systems with longer orbital periods will probably not undergo Roche-lobe overflow and hence common-envelope evolution is impossible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "47979841-2c26-4b26-8945-603d013dc93a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Added grid variable: {\n",
+      "    \"name\": \"lnm1\",\n",
+      "    \"longname\": \"Primary mass\",\n",
+      "    \"valuerange\": [\n",
+      "        1,\n",
+      "        6\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(math.log(1), math.log(6), 10)\",\n",
+      "    \"precode\": \"M_1=math.exp(lnm1)\",\n",
+      "    \"probdist\": \"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+      "    \"dphasevol\": \"dlnm1\",\n",
+      "    \"parameter_name\": \"M_1\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 0\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"q\",\n",
+      "    \"longname\": \"Mass ratio\",\n",
+      "    \"valuerange\": [\n",
+      "        \"0.1/M_1\",\n",
+      "        1\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(1/M_1, 1, 10)\",\n",
+      "    \"precode\": \"M_2 = q * M_1\",\n",
+      "    \"probdist\": \"flatsections(q, [{'min': 1/M_1, 'max': 1.0, 'height': 1}])\",\n",
+      "    \"dphasevol\": \"dq\",\n",
+      "    \"parameter_name\": \"M_2\",\n",
+      "    \"condition\": \"\",\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 1\n",
+      "}\n",
+      "Added grid variable: {\n",
+      "    \"name\": \"log10per\",\n",
+      "    \"longname\": \"log10(Orbital_Period)\",\n",
+      "    \"valuerange\": [\n",
+      "        0.15,\n",
+      "        5.5\n",
+      "    ],\n",
+      "    \"resolution\": \"10\",\n",
+      "    \"spacingfunc\": \"const(0.15, 4, 10)\",\n",
+      "    \"precode\": \"orbital_period = 10.0 ** log10per\\nsep = calc_sep_from_period(M_1, M_2, orbital_period)\\nsep_min = calc_sep_from_period(M_1, M_2, 10**0.15)\\nsep_max = calc_sep_from_period(M_1, M_2, 10**4)\",\n",
+      "    \"probdist\": \"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**0.15), math.log10(10**4), -0.55)\",\n",
+      "    \"dphasevol\": \"dlog10per\",\n",
+      "    \"parameter_name\": \"orbital_period\",\n",
+      "    \"condition\": null,\n",
+      "    \"gridtype\": \"centred\",\n",
+      "    \"branchpoint\": 0,\n",
+      "    \"grid_variable_number\": 2\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import binarycpython.utils.distribution_functions\n",
+    "# Set resolution and mass range that we simulate\n",
+    "resolution = {\"M_1\": 10, \"q\" : 10, \"per\": 10} \n",
+    "massrange = [1, 6] \n",
+    "logperrange = [0.15, 4]\n",
+    "\n",
+    "population.add_grid_variable(\n",
+    "    name=\"lnm1\",\n",
+    "    longname=\"Primary mass\",\n",
+    "    valuerange=massrange,\n",
+    "    resolution=\"{}\".format(resolution[\"M_1\"]),\n",
+    "    spacingfunc=\"const(math.log({min}), math.log({max}), {res})\".format(min=massrange[0],max=massrange[1],res=resolution[\"M_1\"]),\n",
+    "    precode=\"M_1=math.exp(lnm1)\",\n",
+    "    probdist=\"three_part_powerlaw(M_1, 0.1, 0.5, 1.0, 150, -1.3, -2.3, -2.3)*M_1\",\n",
+    "    dphasevol=\"dlnm1\",\n",
+    "    parameter_name=\"M_1\",\n",
+    "    condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    ")\n",
+    "\n",
+    "# Mass ratio\n",
+    "population.add_grid_variable(\n",
+    "     name=\"q\",\n",
+    "     longname=\"Mass ratio\",\n",
+    "     valuerange=[\"0.1/M_1\", 1],\n",
+    "     resolution=\"{}\".format(resolution['q']),\n",
+    "     spacingfunc=\"const({}/M_1, 1, {})\".format(massrange[0],resolution['q']),\n",
+    "     probdist=\"flatsections(q, [{{'min': {}/M_1, 'max': 1.0, 'height': 1}}])\".format(massrange[0]),\n",
+    "     dphasevol=\"dq\",\n",
+    "     precode=\"M_2 = q * M_1\",\n",
+    "     parameter_name=\"M_2\",\n",
+    "     condition=\"\",  # Impose a condition on this grid variable. Mostly for a check for yourself\n",
+    " )\n",
+    "\n",
+    "# Orbital period\n",
+    "population.add_grid_variable(\n",
+    "    name=\"log10per\", # in days\n",
+    "    longname=\"log10(Orbital_Period)\",\n",
+    "    valuerange=[0.15, 5.5],\n",
+    "    resolution=\"{}\".format(resolution[\"per\"]),\n",
+    "    spacingfunc=\"const({}, {}, {})\".format(logperrange[0],logperrange[1],resolution[\"per\"]),\n",
+    "    precode=\"\"\"orbital_period = 10.0 ** log10per\n",
+    "sep = calc_sep_from_period(M_1, M_2, orbital_period)\n",
+    "sep_min = calc_sep_from_period(M_1, M_2, 10**{})\n",
+    "sep_max = calc_sep_from_period(M_1, M_2, 10**{})\"\"\".format(logperrange[0],logperrange[1]),\n",
+    "    probdist=\"sana12(M_1, M_2, sep, orbital_period, sep_min, sep_max, math.log10(10**{}), math.log10(10**{}), {})\".format(logperrange[0],logperrange[1],-0.55),\n",
+    "    parameter_name=\"orbital_period\",\n",
+    "    dphasevol=\"dlog10per\",\n",
+    " )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163f13ae-fec1-4ee8-b9d4-c1b75c19ff39",
+   "metadata": {},
+   "source": [
+    "## Logging and handling the output\n",
+    "\n",
+    "We now construct the pre- and post-common envelope evolution data for the first common envelope that forms in each binary. We look at the comenv_count variable, we can see that when it increases from 0 to 1 we have found our object. If this happens, we stop evolution of the system to save CPU time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: C_logging_code=\n",
+      "\n",
+      "/*\n",
+      " * Detect when the comenv_count increased \n",
+      " */\n",
+      "if(stardata->model.comenv_count == 1 && \n",
+      "   stardata->previous_stardata->model.comenv_count == 0)\n",
+      "{\n",
+      "   /*\n",
+      "    * We just had this system's first common envelope:\n",
+      "    * output the time at which this happens, \n",
+      "    * the system's probability (proportional to the number of stars),\n",
+      "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+      "    * the current timestep (post-comenv) orbital period (days)\n",
+      "    */\n",
+      "    Printf(\"COMENV %g %g %g %g\\n\",\n",
+      "           stardata->model.time,\n",
+      "           stardata->model.probability,\n",
+      "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+      "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+      "           \n",
+      "    /*\n",
+      "     * We should waste no more CPU time on this system now we have the\n",
+      "     * data we want.\n",
+      "     */\n",
+      "    stardata->model.evolution_stop = TRUE;\n",
+      "}\n",
+      " to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_logging_statement = \"\"\"\n",
+    "\n",
+    "/*\n",
+    " * Detect when the comenv_count increased \n",
+    " */\n",
+    "if(stardata->model.comenv_count == 1 && \n",
+    "   stardata->previous_stardata->model.comenv_count == 0)\n",
+    "{\n",
+    "   /*\n",
+    "    * We just had this system's first common envelope:\n",
+    "    * output the time at which this happens, \n",
+    "    * the system's probability (proportional to the number of stars),\n",
+    "    * the previous timestep's (pre-comenv) orbital period (days) and\n",
+    "    * the current timestep (post-comenv) orbital period (days)\n",
+    "    */\n",
+    "    Printf(\"COMENV %g %g %g %g\\\\n\",\n",
+    "           stardata->model.time,\n",
+    "           stardata->model.probability,\n",
+    "           stardata->previous_stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS,\n",
+    "           stardata->common.orbit.period * YEAR_LENGTH_IN_DAYS);\n",
+    "           \n",
+    "    /*\n",
+    "     * We should waste no more CPU time on this system now we have the\n",
+    "     * data we want.\n",
+    "     */\n",
+    "    stardata->model.evolution_stop = TRUE;\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 \"COMENV\" and process the associated data. We set up the parse_data function to do just this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd197154-a8ce-4865-8929-008d3483101a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: parse_function=<function parse_function at 0x14736bebc040> to grid_options\n"
+     ]
+    }
+   ],
+   "source": [
+    "from binarycpython.utils.functions import bin_data,datalinedict\n",
+    "import re\n",
+    "\n",
+    "# log-period distribution bin width (dex)\n",
+    "binwidth = 0.5 \n",
+    "\n",
+    "def parse_function(self, output):\n",
+    "    \"\"\"\n",
+    "    Parsing function to convert HRD data into something that Python can use\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # list of the data items\n",
+    "    parameters = [\"header\", \"time\", \"probability\", \"pre_comenv_period\", \"post_comenv_period\"]\n",
+    "    \n",
+    "    # Loop over the output.\n",
+    "    for line in output.splitlines():\n",
+    "        \n",
+    "        # obtain the line of data in dictionary form \n",
+    "        linedata = datalinedict(line,parameters)\n",
+    "            \n",
+    "        # choose COMENV lines of output\n",
+    "        if linedata[\"header\"] == \"COMENV\":\n",
+    "            # bin the pre- and post-comenv log10-orbital-periods to nearest 0.5dex\n",
+    "            binned_pre_period = bin_data(math.log10(linedata[\"pre_comenv_period\"]), binwidth)\n",
+    "            \n",
+    "            # but check if the post-comenv period is finite and positive: if \n",
+    "            # not, the system has merged and we give it an aritifical period\n",
+    "            # of 10^-100 days (which is very much unphysical)\n",
+    "            if linedata[\"post_comenv_period\"] > 0.0:\n",
+    "                binned_post_period = bin_data(math.log10(linedata[\"post_comenv_period\"]), binwidth)\n",
+    "            else:\n",
+    "                binned_post_period = bin_data(-100,binwidth) # merged!\n",
+    "                \n",
+    "            # make the \"histograms\"\n",
+    "            self.grid_results['pre'][binned_pre_period] += linedata[\"probability\"]\n",
+    "            self.grid_results['post'][binned_post_period] += linedata[\"probability\"]\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 we actually run the population. This may take a little while. You can set amt_cores higher if you have a powerful machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding: amt_cores=4 to grid_options\n",
+      "Creating and loading custom logging functionality\n",
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "Grid has handled 1000 stars\n",
+      "with a total probability of 0.0645905996773004\n",
+      "Total starcount for this run will be: 1000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:39,950 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-12 18:07:39,953 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-12 18:07:39,959 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-12 18:07:39,962 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-12 18:07:39,965 DEBUG    Process-5] --- Setting up processor: process-3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 started at 2021-09-12T18:07:39.965721.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee47e0>\n",
+      "Process 1 started at 2021-09-12T18:07:39.970949.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n",
+      "Process 2 started at 2021-09-12T18:07:39.978355.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4f30>\n",
+      "Process 3 started at 2021-09-12T18:07:39.983689.\tUsing store memaddr <capsule object \"STORE\" at 0x14736bee4870>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:07:40,066 DEBUG    MainProcess] --- Signaling stop to processes\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating grid code\n",
+      "Generating grid code\n",
+      "Constructing/adding: lnm1\n",
+      "Constructing/adding: q\n",
+      "Constructing/adding: log10per\n",
+      "Saving grid code to grid_options\n",
+      "Writing grid code to /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Loading grid code function from /tmp/binary_c_python/notebooks/notebook_comenv/binary_c_grid_ad303100d719457c83256568f9a9887c.py\n",
+      "Grid code loaded\n",
+      "163/1000  16.3% complete 18:07:49 ETA=   51.5s tpr=6.16e-02 ETF=18:08:41 mem:594.9MB\n",
+      "322/1000  32.2% complete 18:07:59 ETA=   42.9s tpr=6.33e-02 ETF=18:08:42 mem:538.2MB\n",
+      "465/1000  46.5% complete 18:08:09 ETA=   38.1s tpr=7.12e-02 ETF=18:08:47 mem:538.2MB\n",
+      "586/1000  58.6% complete 18:08:19 ETA=   34.3s tpr=8.29e-02 ETF=18:08:54 mem:540.0MB\n",
+      "682/1000  68.2% complete 18:08:30 ETA=   34.0s tpr=1.07e-01 ETF=18:09:04 mem:540.1MB\n",
+      "784/1000  78.4% complete 18:08:40 ETA=   21.2s tpr=9.81e-02 ETF=18:09:01 mem:541.8MB\n",
+      "872/1000  87.2% complete 18:08:50 ETA=   15.0s tpr=1.17e-01 ETF=18:09:05 mem:546.1MB\n",
+      "963/1000  96.3% complete 18:09:00 ETA=    4.2s tpr=1.14e-01 ETF=18:09:04 mem:546.9MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,366 DEBUG    Process-5] --- Process-3 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.964604, done at 2021-09-12T18:09:06.370832 (total: 86.406228s of which 86.24177551269531s interfacing with binary_c).\n",
+      "\tRan 222 systems with a total probability of 0.014137215791516371.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,374 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-12 18:09:06,979 DEBUG    Process-3] --- Process-1 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.953039, done at 2021-09-12T18:09:06.982866 (total: 87.029827s of which 86.82909393310547s interfacing with binary_c).\n",
+      "\tRan 273 systems with a total probability of 0.01877334232598154.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:06,985 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-12 18:09:07,174 DEBUG    Process-2] --- Process-0 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.949775, done at 2021-09-12T18:09:07.176660 (total: 87.226885s of which 87.02672934532166s interfacing with binary_c).\n",
+      "\tRan 268 systems with a total probability of 0.016469813170514686.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,179 DEBUG    Process-2] --- Process-0 is finished.\n",
+      "[2021-09-12 18:09:07,233 DEBUG    Process-4] --- Process-2 is finishing.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-12T18:07:39.958802, done at 2021-09-12T18:09:07.236252 (total: 87.27745s of which 87.0905077457428s interfacing with binary_c).\n",
+      "\tRan 237 systems with a total probability of 0.015210228389288167.\n",
+      "\tThis thread had 0 failing systems with a total probability of 0.\n",
+      "\tSkipped a total of 0 systems because they had 0 probability\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2021-09-12 18:09:07,238 DEBUG    Process-4] --- Process-2 is finished.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population-ad303100d719457c83256568f9a9887c finished! The total probability was: 0.06459059967730076. It took a total of 87.54819011688232s to run 1000 systems on 4 cores\n",
+      "There were no errors found in this run.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set number of threads\n",
+    "population.set(\n",
+    "    # set number of threads (i.e. number of CPU cores we use)\n",
+    "    amt_cores=4,\n",
+    "    )\n",
+    "\n",
+    "# Evolve the population - this is the slow, number-crunching step\n",
+    "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. We check this, and then set about making the plot of the orbital period distributions using Seaborn."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'population_name': 'ad303100d719457c83256568f9a9887c', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.06459059967730076, 'total_count': 1000, 'start_timestamp': 1631462859.9342952, 'end_timestamp': 1631462947.4824853, 'total_mass_run': 4680.235689312421, 'total_probability_weighted_mass_run': 0.22611318083528567, 'zero_prob_stars_skipped': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(analytics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'merged': 0.035263029200000025, 'unmerged': 0.019388724199999995}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Number of stars')"
+      ]
+     },
+     "execution_count": 8,
+     "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 distributions\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import copy\n",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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",
+    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\n",
+    "\n",
+    "# remove the merged objects\n",
+    "probability = { \"merged\" : 0.0, \"unmerged\" : 0.0}\n",
+    "\n",
+    "# copy the results so we can change the copy\n",
+    "results = copy.deepcopy(population.grid_results)\n",
+    "\n",
+    "for distribution in ['post']:    \n",
+    "    for logper in population.grid_results[distribution]:\n",
+    "        dprob = results[distribution][logper]\n",
+    "        if logper < -90:\n",
+    "            # merged system\n",
+    "            probability[\"merged\"] += dprob\n",
+    "            del results[distribution][logper]\n",
+    "        else:\n",
+    "            # unmerged system\n",
+    "            probability[\"unmerged\"] += dprob\n",
+    "print(probability)\n",
+    "    \n",
+    "# pad the final distribution with zero\n",
+    "for distribution in population.grid_results:    \n",
+    "    pad_output_distribution(results[distribution],\n",
+    "                            binwidth)\n",
+    "    \n",
+    "# make pandas dataframe \n",
+    "plot_data = pd.DataFrame.from_dict(results, orient='columns')\n",
+    "\n",
+    "# make the plot\n",
+    "p = sns.lineplot(data=plot_data)\n",
+    "p.set_xlabel(\"$\\log_{10} (P_\\mathrm{orb} / \\mathrm{day})$\")\n",
+    "p.set_ylabel(\"Number of stars\")\n",
+    "#p.set(xlim=(-5,5)) # might be necessary?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4740c93-d01e-4ca1-8766-c2fb4ddca2e4",
+   "metadata": {},
+   "source": [
+    "You can see that common-envelope evolution shrinks stellar orbits, just as we expect. Pre-CEE, most orbits are in the range $10$ to $1000\\text{ }\\mathrm{d}$, while after CEE the distribution peaks at about $1\\text{ }\\mathrm{d}$. Some of these orbits are very short: $\\log_{10}(-2) = 0.01\\text{ }\\mathrm{d}\\sim10\\text{ }\\mathrm{minutes}$. Such systems are prime candidates for exciting astrophysics: novae, type Ia supernovae and gravitational wave sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57faf043-3809-427a-b378-2355ce8c2691",
+   "metadata": {},
+   "source": [
+    "Things to try:\n",
+    "* Extend the logging to output more data than just the orbital period.\n",
+    "* What are the stellar types of the post-common envelope systems? Are they likely to undergo novae or a type-Ia supernova?\n",
+    "* What are the lifetimes of the systems in close ($<1\\text{ }\\mathrm{d}$) binaries? Are they likely to merge in the life of the Universe?\n",
+    "* How much mass is lost in common-envelope interactions?\n",
+    "* Extend the grid to massive stars. Do you see many NS and BH compact binaries?\n",
+    "* Try different $\\alpha_\\mathrm{CE}$ and $\\lambda_\\mathrm{CE}$ options...\n",
+    "* ... and perhaps increased resolution to obtain smoother curves.\n",
+    "* Why do long-period systems not reach common envelope evolution?"
+   ]
+  }
+ ],
+ "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/source/notebook_individual_systems.ipynb b/docs/source/notebook_individual_systems.ipynb
index e6451e762..85aef1e39 100644
--- a/docs/source/notebook_individual_systems.ipynb
+++ b/docs/source/notebook_individual_systems.ipynb
@@ -566,7 +566,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -580,7 +580,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebook_luminosity_function_binaries.ipynb b/docs/source/notebook_luminosity_function_binaries.ipynb
index 47a96d093..c6b5f1e64 100644
--- a/docs/source/notebook_luminosity_function_binaries.ipynb
+++ b/docs/source/notebook_luminosity_function_binaries.ipynb
@@ -5,7 +5,7 @@
    "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
    "metadata": {},
    "source": [
-    "# Example use case: Zero-age stellar luminosity function in binaries\n",
+    "# Zero-age stellar luminosity function in binaries\n",
     "\n",
     "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of binary stars using binary_c. \n",
     "\n",
@@ -168,7 +168,7 @@
     "\n",
     "# resolution on each side of the cube, with more stars for the primary mass\n",
     "nres = 10\n",
-    "resolution = {\"M_1\": 2*nres,\n",
+    "resolution = {\"M_1\": 4*nres,\n",
     "              \"q\": nres,\n",
     "              \"per\": nres}\n",
     "\n",
@@ -379,10 +379,6 @@
    "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [
@@ -399,229 +395,74 @@
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
       "Grid code loaded\n",
-      "Grid has handled 2000 stars\n",
-      "with a total probability of 0.6495098935846658\n",
-      "Total starcount for this run will be: 2000\n"
+      "Grid has handled 256 stars\n",
+      "with a total probability of 0.6149734610296649\n",
+      "Total starcount for this run will be: 256\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:14:08,077 DEBUG    Process-2] --- Setting up processor: process-0[2021-09-10 15:14:08,080 DEBUG    Process-3] --- Setting up processor: process-1[2021-09-10 15:14:08,086 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
-      "\n",
-      "[2021-09-10 15:14:08,084 DEBUG    Process-4] --- Setting up processor: process-2\n",
-      "\n",
-      "[2021-09-10 15:14:08,117 DEBUG    Process-5] --- Setting up processor: process-3"
+      "[2021-09-10 22:26:10,473 DEBUG    Process-2] --- Setting up processor: process-0\n",
+      "[2021-09-10 22:26:10,475 DEBUG    Process-3] --- Setting up processor: process-1\n",
+      "[2021-09-10 22:26:10,478 DEBUG    Process-4] --- Setting up processor: process-2\n",
+      "[2021-09-10 22:26:10,481 DEBUG    MainProcess] --- setting up the system_queue_filler now\n",
+      "[2021-09-10 22:26:10,482 DEBUG    Process-5] --- Setting up processor: process-3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 started at 2021-09-10T15:14:08.119437.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>Process 0 started at 2021-09-10T15:14:08.126435.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>\n",
-      "Process 2 started at 2021-09-10T15:14:08.138353.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff539f0>"
+      "Process 0 started at 2021-09-10T22:26:10.491896.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf510>Process 1 started at 2021-09-10T22:26:10.491948.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf480>\n",
+      "\n",
+      "Process 2 started at 2021-09-10T22:26:10.496677.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf3f0>\n",
+      "Process 3 started at 2021-09-10T22:26:10.498669.\tUsing store memaddr <capsule object \"STORE\" at 0x154d03cdf180>\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "[2021-09-10 22:26:10,510 DEBUG    MainProcess] --- Signaling stop to processes\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "Process 3 started at 2021-09-10T15:14:08.186492.\tUsing store memaddr <capsule object \"STORE\" at 0x7f351ff53810>\n",
       "Generating grid code\n",
       "Generating grid code\n",
       "Constructing/adding: lnm1\n",
       "Constructing/adding: q\n",
       "Constructing/adding: log10per\n",
       "Saving grid code to grid_options\n",
-      "Writing grid code to /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
-      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_0fa295ee5c76444bace8fd0ee17a3e11.py\n",
+      "Writing grid code to /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
+      "Loading grid code function from /tmp/binary_c_python/binary_c_grid_bc3a5f915411445699f8cf6438817ff1.py\n",
       "Grid code loaded\n",
-      "624/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB625/2000  31.2% complete 15:14:12 ETA=   11.1s tpr=8.04e-03 ETF=15:14:23 mem:800.5MB\n",
-      "626/2000  31.3% complete 15:14:12 ETA=   11.1s tpr=8.05e-03 ETF=15:14:23 mem:800.5MB\n",
-      "\n",
-      "713/2000  35.6% complete 15:14:17 ETA=    1.3m tpr=6.00e-02 ETF=15:15:34 mem:547.8MB\n",
-      "728/2000  36.4% complete 15:14:22 ETA=    7.1m tpr=3.37e-01 ETF=15:21:30 mem:548.1MB\n",
-      "743/2000  37.1% complete 15:14:27 ETA=    7.0m tpr=3.34e-01 ETF=15:21:26 mem:549.5MB\n",
-      "759/2000  38.0% complete 15:14:33 ETA=    7.7m tpr=3.73e-01 ETF=15:22:16 mem:550.5MB\n",
-      "774/2000  38.7% complete 15:14:38 ETA=    6.9m tpr=3.35e-01 ETF=15:21:29 mem:551.1MB\n",
-      "787/2000  39.4% complete 15:14:43 ETA=    7.8m tpr=3.88e-01 ETF=15:22:33 mem:551.1MB\n",
-      "799/2000  40.0% complete 15:14:48 ETA=    8.5m tpr=4.24e-01 ETF=15:23:17 mem:552.5MB\n",
-      "812/2000  40.6% complete 15:14:54 ETA=    8.4m tpr=4.23e-01 ETF=15:23:16 mem:554.8MB\n",
-      "830/2000  41.5% complete 15:14:59 ETA=    5.5m tpr=2.80e-01 ETF=15:20:26 mem:555.2MB\n",
-      "847/2000  42.4% complete 15:15:05 ETA=    6.8m tpr=3.52e-01 ETF=15:21:50 mem:555.2MB\n",
-      "864/2000  43.2% complete 15:15:10 ETA=    6.2m tpr=3.28e-01 ETF=15:21:23 mem:557.0MB\n",
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-      "934/2000  46.7% complete 15:15:42 ETA=    6.4m tpr=3.60e-01 ETF=15:22:06 mem:561.7MB\n",
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-      "956/2000  47.8% complete 15:15:53 ETA=   11.1m tpr=6.39e-01 ETF=15:27:01 mem:561.7MB\n",
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-      "988/2000  49.4% complete 15:16:16 ETA=    9.7m tpr=5.76e-01 ETF=15:25:59 mem:562.0MB\n",
-      "995/2000  49.8% complete 15:16:21 ETA=   12.3m tpr=7.37e-01 ETF=15:28:42 mem:562.2MB\n"
+      "158/256  61.7% complete 22:26:15 ETA=    3.2s tpr=3.22e-02 ETF=22:26:18 mem:509.0MB\n",
+      "199/256  77.7% complete 22:26:20 ETA=    7.3s tpr=1.28e-01 ETF=22:26:27 mem:476.9MB\n",
+      "238/256  93.0% complete 22:26:25 ETA=    2.3s tpr=1.28e-01 ETF=22:26:27 mem:481.7MB\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:16:25,175 DEBUG    MainProcess] --- Signaling stop to processes\n"
+      "[2021-09-10 22:26:27,631 DEBUG    Process-3] --- Process-1 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1003/2000  50.1% complete 15:16:26 ETA=   11.2m tpr=6.76e-01 ETF=15:27:40 mem:563.0MB\n",
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-      "\n",
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-      "\n",
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-      "\n",
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-      "1725/2000  86.2% complete 15:24:31 ETA=    2.6m tpr=5.57e-01 ETF=15:27:04 mem:586.7MB\n",
-      "1735/2000  86.8% complete 15:24:38 ETA=    3.0m tpr=6.76e-01 ETF=15:27:37 mem:586.7MB\n",
-      "1745/2000  87.2% complete 15:24:44 ETA=    2.7m tpr=6.40e-01 ETF=15:27:27 mem:586.9MB\n",
-      "1755/2000  87.8% complete 15:24:51 ETA=    2.8m tpr=6.88e-01 ETF=15:27:40 mem:586.9MB\n",
-      "1763/2000  88.2% complete 15:24:56 ETA=    2.6m tpr=6.59e-01 ETF=15:27:32 mem:586.9MB\n",
-      "1767/2000  88.3% complete 15:25:02 ETA=    5.3m tpr=1.36e+00 ETF=15:30:18 mem:586.9MB\n",
-      "1776/2000  88.8% complete 15:25:09 ETA=    2.9m tpr=7.71e-01 ETF=15:28:01 mem:586.9MB\n",
-      "1785/2000  89.2% complete 15:25:14 ETA=    2.1m tpr=5.90e-01 ETF=15:27:21 mem:586.9MB\n",
-      "1793/2000  89.7% complete 15:25:19 ETA=    2.2m tpr=6.29e-01 ETF=15:27:29 mem:587.1MB\n",
-      "1801/2000  90.0% complete 15:25:24 ETA=    2.2m tpr=6.59e-01 ETF=15:27:35 mem:587.1MB\n",
-      "1812/2000  90.6% complete 15:25:29 ETA=    1.5m tpr=4.68e-01 ETF=15:26:57 mem:587.1MB\n",
-      "1822/2000  91.1% complete 15:25:35 ETA=    1.6m tpr=5.54e-01 ETF=15:27:14 mem:587.4MB\n",
-      "1830/2000  91.5% complete 15:25:41 ETA=    2.1m tpr=7.49e-01 ETF=15:27:48 mem:587.4MB\n",
-      "1839/2000  92.0% complete 15:25:47 ETA=    1.7m tpr=6.21e-01 ETF=15:27:27 mem:587.4MB\n",
-      "1847/2000  92.3% complete 15:25:52 ETA=    1.8m tpr=7.10e-01 ETF=15:27:41 mem:587.4MB\n",
-      "1855/2000  92.8% complete 15:25:59 ETA=    2.0m tpr=8.17e-01 ETF=15:27:57 mem:587.6MB\n",
-      "1864/2000  93.2% complete 15:26:05 ETA=    1.5m tpr=6.79e-01 ETF=15:27:37 mem:587.8MB\n",
-      "1873/2000  93.7% complete 15:26:10 ETA=    1.3m tpr=6.07e-01 ETF=15:27:27 mem:588.0MB\n",
-      "1884/2000  94.2% complete 15:26:16 ETA=   57.0s tpr=4.91e-01 ETF=15:27:13 mem:588.1MB\n",
-      "1895/2000  94.8% complete 15:26:21 ETA=   48.7s tpr=4.63e-01 ETF=15:27:09 mem:588.8MB\n",
-      "1907/2000  95.3% complete 15:26:27 ETA=   45.6s tpr=4.91e-01 ETF=15:27:12 mem:588.9MB\n",
-      "1916/2000  95.8% complete 15:26:33 ETA=   57.5s tpr=6.84e-01 ETF=15:27:30 mem:589.1MB\n",
-      "1926/2000  96.3% complete 15:26:39 ETA=   46.5s tpr=6.28e-01 ETF=15:27:26 mem:589.1MB\n",
-      "1936/2000  96.8% complete 15:26:46 ETA=   42.0s tpr=6.57e-01 ETF=15:27:28 mem:589.1MB\n",
-      "1946/2000  97.3% complete 15:26:53 ETA=   40.1s tpr=7.42e-01 ETF=15:27:33 mem:589.2MB\n",
-      "1956/2000  97.8% complete 15:26:59 ETA=   25.1s tpr=5.70e-01 ETF=15:27:24 mem:589.2MB\n",
-      "1966/2000  98.3% complete 15:27:04 ETA=   19.1s tpr=5.62e-01 ETF=15:27:24 mem:589.5MB\n",
-      "1976/2000  98.8% complete 15:27:10 ETA=   14.4s tpr=6.01e-01 ETF=15:27:25 mem:589.5MB\n",
-      "1987/2000  99.3% complete 15:27:16 ETA=    6.4s tpr=4.92e-01 ETF=15:27:22 mem:589.5MB\n",
-      "1998/2000  99.9% complete 15:27:21 ETA=    1.0s tpr=4.85e-01 ETF=15:27:22 mem:589.6MB\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2021-09-10 15:27:22,382 DEBUG    Process-5] --- Process-3 is finishing.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Process 3 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.117391, done at 2021-09-10T15:27:22.400722 (total: 794.283331s of which 792.6935975551605s interfacing with binary_c).\n",
-      "\tRan 499 systems with a total probability of 0.17005450973840136.\n",
+      "Process 1 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.475399, done at 2021-09-10T22:26:27.634804 (total: 17.159405s of which 17.104907512664795s interfacing with binary_c).\n",
+      "\tRan 61 systems with a total probability of 0.1439494161909395.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -630,17 +471,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,435 DEBUG    Process-5] --- Process-3 is finished.\n",
-      "[2021-09-10 15:27:22,480 DEBUG    Process-3] --- Process-1 is finishing.\n"
+      "[2021-09-10 22:26:27,639 DEBUG    Process-3] --- Process-1 is finished.\n",
+      "[2021-09-10 22:26:27,698 DEBUG    Process-5] --- Process-3 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 1 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.080367, done at 2021-09-10T15:27:22.505288 (total: 794.424921s of which 793.1943278312683s interfacing with binary_c).\n",
-      "\tRan 474 systems with a total probability of 0.15740832333567983.\n",
+      "Process 3 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.482470, done at 2021-09-10T22:26:27.701828 (total: 17.219358s of which 17.162050247192383s interfacing with binary_c).\n",
+      "\tRan 67 systems with a total probability of 0.17251417460118773.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -649,17 +490,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,531 DEBUG    Process-3] --- Process-1 is finished.\n",
-      "[2021-09-10 15:27:22,846 DEBUG    Process-2] --- Process-0 is finishing.\n"
+      "[2021-09-10 22:26:27,705 DEBUG    Process-5] --- Process-3 is finished.\n",
+      "[2021-09-10 22:26:27,769 DEBUG    Process-4] --- Process-2 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 0 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.077117, done at 2021-09-10T15:27:22.851971 (total: 794.774854s of which 793.4976091384888s interfacing with binary_c).\n",
-      "\tRan 507 systems with a total probability of 0.16018641159091498.\n",
+      "Process 2 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.478464, done at 2021-09-10T22:26:27.771291 (total: 17.292827s of which 17.243471384048462s interfacing with binary_c).\n",
+      "\tRan 56 systems with a total probability of 0.14306289954535925.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -668,17 +509,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,872 DEBUG    Process-2] --- Process-0 is finished.\n",
-      "[2021-09-10 15:27:22,976 DEBUG    Process-4] --- Process-2 is finishing.\n"
+      "[2021-09-10 22:26:27,774 DEBUG    Process-4] --- Process-2 is finished.\n",
+      "[2021-09-10 22:26:27,865 DEBUG    Process-2] --- Process-0 is finishing.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Process 2 finished:\n",
-      "\tgenerator started at 2021-09-10T15:14:08.084369, done at 2021-09-10T15:27:22.981706 (total: 794.897337s of which 793.4600214958191s interfacing with binary_c).\n",
-      "\tRan 520 systems with a total probability of 0.1618606489196724.\n",
+      "Process 0 finished:\n",
+      "\tgenerator started at 2021-09-10T22:26:10.473000, done at 2021-09-10T22:26:27.867175 (total: 17.394175s of which 17.331928491592407s interfacing with binary_c).\n",
+      "\tRan 72 systems with a total probability of 0.1554469706921749.\n",
       "\tThis thread had 0 failing systems with a total probability of 0.\n",
       "\tSkipped a total of 0 systems because they had 0 probability\n"
      ]
@@ -687,14 +528,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2021-09-10 15:27:22,986 DEBUG    Process-4] --- Process-2 is finished.\n"
+      "[2021-09-10 22:26:27,869 DEBUG    Process-2] --- Process-0 is finished.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Population-0fa295ee5c76444bace8fd0ee17a3e11 finished! The total probability was: 0.6495098935846686. It took a total of 795.1383104324341s to run 2000 systems on 4 cores\n",
+      "Population-bc3a5f915411445699f8cf6438817ff1 finished! The total probability was: 0.6149734610296613. It took a total of 17.603368997573853s to run 256 systems on 4 cores\n",
       "There were no errors found in this run.\n",
       "Done population run!\n"
      ]
@@ -728,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -736,7 +577,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '0fa295ee5c76444bace8fd0ee17a3e11', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6495098935846686, 'total_count': 2000, 'start_timestamp': 1631283248.057525, 'end_timestamp': 1631284043.1958354, 'total_mass_run': 41112.220964392276, 'total_probability_weighted_mass_run': 0.6452116023479681, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'bc3a5f915411445699f8cf6438817ff1', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 0.6149734610296613, 'total_count': 256, 'start_timestamp': 1631305570.458824, 'end_timestamp': 1631305588.062193, 'total_mass_run': 5246.190724478048, 'total_probability_weighted_mass_run': 0.6347400152389439, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -746,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -756,13 +597,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -777,8 +618,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# set up seaborn for use in the notebook\n",
+    "sns.set(rc={'figure.figsize':(20,10)})\n",
+    "sns.set_context(\"notebook\",\n",
+    "                font_scale=1.5,\n",
+    "                rc={\"lines.linewidth\":2.5})\n",
+    "\n",
     "\n",
     "titles = { 0 : \"Primary\",\n",
     "           1 : \"Secondary\",\n",
@@ -805,11 +650,36 @@
     "p.set_ylabel(\"Number of stars\")\n",
     "p.set(yscale=\"log\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d7b275e-be92-4d59-b44d-ef6f24023cc3",
+   "metadata": {},
+   "source": [
+    "You can see that the secondary stars are dimmer than the primaries - which you expect given they are lower in mass (by definition q=M2/M1<1). \n",
+    "\n",
+    "Weirdly, in some places the primary distribution may exceed the unresolved distribution. This is a bit unphysical, but in this case is usually caused by limited resolution. If you increase the number of stars in the grid, this problem should go away (at a cost of more CPU time). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99e25a72-54e6-4826-b0e5-4a02460b857d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Things to try:\n",
+    "* Massive stars: can you see the effects of wind mass loss and rejuvenation in these stars?\n",
+    "* Alter the metallicity, does this make much of a difference?\n",
+    "* Change the binary fraction. Here we assume a 100% binary fraction, but a real population is a mixture of single and binary stars.\n",
+    "* How might you go about comparing these computed observations to real stars?\n",
+    "* What about evolved stars? Here we consider only the *zero-age* main sequnece. What about other main-sequence stars? What about stars in later phases of stellar evolution?"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -823,7 +693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebook_luminosity_function_single.ipynb b/docs/source/notebook_luminosity_function_single.ipynb
index 5980adf6d..cdae316f9 100644
--- a/docs/source/notebook_luminosity_function_single.ipynb
+++ b/docs/source/notebook_luminosity_function_single.ipynb
@@ -54,8 +54,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "adding: max_evolution_time=0.1 to BSE_options\n",
+      "adding: tmp_dir=/tmp/binary_c_python/notebooks/notebook_luminosity to grid_options\n",
       "verbosity is 1\n"
      ]
     }
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "aba3fe4e-18f2-4bb9-8e5c-4c6007ab038b",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "47979841-2c26-4b26-8945-603d013dc93a",
    "metadata": {},
    "outputs": [],
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "id": "0c986215-93b1-4e30-ad79-f7c397e9ff7d",
    "metadata": {},
    "outputs": [],
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "fd197154-a8ce-4865-8929-008d3483101a",
    "metadata": {},
    "outputs": [],
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "8ea376c1-1e92-45af-8cab-9d7fdca564eb",
    "metadata": {
     "tags": []
@@ -321,9 +321,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-08f8230453084e4ca6a2391d45ce658b finished! The total probability was: 1.0000000000000002. It took a total of 1.5262682437896729s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.025), (3.75, 0.05), (4.25, 0.05), (0.25, 0.025), (3.25, 0.025), (5.25, 0.2), (4.75, 0.1), (5.75, 0.39999999999999997), (6.25, 0.125)]))])\n"
+      "Population-e6c082aabe0849a0811761a06e50476b finished! The total probability was: 1.0000000000000002. It took a total of 2.3021209239959717s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -353,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "e1f0464b-0424-4022-b34b-5b744bc2c59d",
    "metadata": {},
    "outputs": [
@@ -361,7 +360,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'population_name': '08f8230453084e4ca6a2391d45ce658b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631124829.303065, 'end_timestamp': 1631124830.8293333, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
+      "{'population_name': 'e6c082aabe0849a0811761a06e50476b', 'evolution_type': 'grid', 'failed_count': 0, 'failed_prob': 0, 'failed_systems_error_codes': [], 'errors_exceeded': False, 'errors_found': False, 'total_probability': 1.0000000000000002, 'total_count': 40, 'start_timestamp': 1631461389.3681686, 'end_timestamp': 1631461391.6702895, 'total_mass_run': 2001.4, 'total_probability_weighted_mass_run': 50.035000000000004, 'zero_prob_stars_skipped': 0}\n"
      ]
     }
    ],
@@ -371,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "05c6d132-abee-423e-b1a8-2039c8996fbc",
    "metadata": {},
    "outputs": [
@@ -381,13 +380,13 @@
        "[None]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -402,8 +401,12 @@
     "import pandas as pd\n",
     "from binarycpython.utils.functions import pad_output_distribution\n",
     "\n",
-    "# set the figure size (for a Jupyter notebook in a web browser) \n",
-    "sns.set( rc = {'figure.figsize':(20,10)} )\n",
+    "# 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",
@@ -442,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "1f37d2c0-1108-4ab9-a309-20b1e6b6e3fd",
    "metadata": {},
    "outputs": [],
@@ -456,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "6f4463e8-1935-45f2-8c5f-e7b215f8dc47",
    "metadata": {},
    "outputs": [
@@ -471,9 +474,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: M_1\n",
-      "Population-92de7c9221c54206ab4dd10e58e09a34 finished! The total probability was: 0.21822161894107872. It took a total of 1.5900418758392334s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(2.25, 0.0164166), (3.25, 0.00515685), (0.25, 0.189097), (3.75, 0.0037453900000000004), (4.25, 0.0014346559999999999), (5.25, 0.0007493004), (4.75, 0.001171479), (5.75, 0.00039801020000000003), (6.25, 5.2369339999999996e-05)]))])\n"
+      "Population-1bc714cffdb344589ea01692f7e1ebd1 finished! The total probability was: 0.21822161894107872. It took a total of 2.335742950439453s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -488,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "cfe45a9e-1121-43b6-b6b6-4de6f8946a18",
    "metadata": {},
    "outputs": [
@@ -498,13 +500,13 @@
        "[None]"
       ]
      },
-     "execution_count": 13,
+     "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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9zgEAgGvgboAAAMDl3XzzzZo3b56eeeYZbdmyRc2aNdPWrf/f3v2FNLnHcRz/TFKozf5oI8b6a4ZUVKjEEukiL4rMEGQjySAwhEKCQBmoXXhRUBREJaQTg0qKcK0ZRRjl7tILM4nwTCyKNLoIaWIwh7mdK0fPmZ6zwzmdFuf9uhrf58++e3b34ff7Pr/J6/Vqw4YNhjfS1dTU6OHDh2psbNSrV6+Uk5OjFy9e6OXLl1qxYkXS3zm3Uun7OVeZmZmGYwvZtWuXfD6fRkdH42FXMBjU7OysIpGIPB5PwjVLlizRkSNHDLWbN28qEAjI6/Vq8eLF8fq7d+80MDAgt9utjIwMlZaWyuv1yuVyJdzX6XTq9OnTOnr0qO7fv6+2tjY9efLEcM6tW7fU0dGh1tZWvX37VjabTXV1dSotLZ339/X19cnhcCTMBfszly5dktlsTqjv379fRUVFhlowGFQoFNLg4GC8Zjab533uQ0NDWrduHWEVAAC/EMIqAADwy8vMzNSdO3d05coV9fb2yufzKTs7W5WVlTp58qRhmHlWVpZu376t8+fP6969ezKZTHI4HLpx44acTmfSA8HngpWpqSlZrdb45++PLWT37t1KS0vTwMBAPKwaHh6WJL1+/XreN9zt3LnTEFb19/fr4sWLam9v1+rVqw3ndnV1KScnRzt27JAkVVRUyOVyaXR0VJs2bTKcW1BQoFgspqtXr2rlypXKy8tLCKuys7Pldrvldrv19etX3b17V/X19crLy9PGjRsTen3+/Lmqq6v/9Bn80R9Xus3JyclRUVGRpqen4zWXyyWr1ar8/Px4bWRkRAcOHDBcG41GNTQ0tGCoBgAAUhNhFQAA+KU4HA6NjIwk1LOystTc3Kzm5ua/vMeaNWvU0tJiqH358kWhUEg2my2pPpYuXSqbzaZgMBgfSD48PCyz2bzgjKvvey0pKdGjR4/iAVRVVZWqqqqS+u7x8XGdOnVKbrdbDofDcGxmZkbd3d2amppScXGx4ZjX61VDQ0PC/ZxOpy5cuJDUs7NYLDp27Jg8Ho/evHkzb1j17NmzpH6HJJ07dy5hC+N87Ha7BgcHVVBQoEWLFhm2dk5NTWl8fFzLly83XNPX16eJiQk5nc6k+wEAAD8fYRUAAPjfmZ6eTlhBNbf17vuAZ3Z2Vt++fdPMzIxisZgikYhMJlN8ppPT6VRbW5sKCwuVnp6ulpYWVVRUJPXmuerqah0+fFgfPnzQ2rVrk+49HA6rtrZWJSUlCdsCJSkQCGhyclJ+v1/Lli2L1x88eKD29nbV1dUZZlJJ0qFDh7R582bDSqU5k5OT6ujo0MGDB7V+/XrFYjH5fD6Fw2Ft3bo16b7/qfLycp09e1bhcNjwH42Njeny5cvzhnB+v1/FxcWGuWIAACD1EVYBAID/nZqaGtntdm3ZskXRaFT9/f0KBALKz883rNjp7u42hCDbt2+X3W5Xb2+vJOn48eMKhUIqKytTNBrVvn37VF9fn1QPhYWF2rNnjzwej86cOZN07z09PQoGg3r//r0eP36ccHzbtm0qKytTbm6uoV5ZWanW1lY9ffo0YVucxWJZ8M196enp+vz5s06cOKGJiQllZGQoNzdX165dS9h++COZTCY1Njaqs7NTXV1dSktLUzQaldVqVVNTU8K8sbGxMfX09Kizs/M/6xEAAPw7TLFYLPazmwAAAPgvXb9+XX6/Xx8/flQkEtGqVau0d+9e1dbWGuZb/WifPn1SeXm5vF7v31pdhb/W0NAgi8Wipqamn90KAAD4mwirAAAAAAAAkDKSf58wAAAAAAAA8IMRVgEAAAAAACBlEFYBAAAAAAAgZRBWAQAAAAAAIGUQVgEAAAAAACBlEFYBAAAAAAAgZRBWAQAAAAAAIGUQVgEAAAAAACBl/A4xkHKhZ+CmQQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -559,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "5956f746-e3b9-4912-b75f-8eb0af66d3f6",
    "metadata": {},
    "outputs": [],
@@ -578,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "108d470a-bb21-40b0-8387-2caa7ab0f923",
    "metadata": {},
    "outputs": [],
@@ -599,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "fb8db646-f3d0-4ccd-81ba-7fde23f29c79",
    "metadata": {},
    "outputs": [
@@ -614,9 +616,8 @@
       "Total starcount for this run will be: 40\n",
       "Generating grid code\n",
       "Constructing/adding: lnM_1\n",
-      "Population-83f80d829dbd418aa2bc745c99b71991 finished! The total probability was: 0.9956307907476224. It took a total of 0.9961590766906738s to run 40 systems on 2 cores\n",
-      "There were no errors found in this run.\n",
-      "OrderedDict([('luminosity distribution', OrderedDict([(0.25, 0.0212294), (2.75, 0.00321118), (-0.25, 0.0268827), (1.25, 0.0104553), (3.75, 0.00283037), (6.25, 7.34708e-05), (-0.75, 0.0771478), (0.75, 0.030004499999999996), (2.25, 0.00921541), (3.25, 0.0045385), (1.75, 0.014776889999999999), (4.25, 0.002380189), (4.75, 0.000869303), (5.25, 0.0007310379999999999), (5.75, 0.00036002859999999996), (-2.75, 0.1961345), (-1.75, 0.2181597), (-3.25, 0.0), (-2.25, 0.2568974), (-1.25, 0.11973310000000001)]))])\n"
+      "Population-4f3ee0143c0548338494d2f1fbacc915 finished! The total probability was: 0.9956307907476225. It took a total of 1.5107016563415527s to run 40 systems on 2 cores\n",
+      "There were no errors found in this run.\n"
      ]
     }
    ],
@@ -639,13 +640,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "68ee1e56-21e5-48f4-b74c-50e48685ae94",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -684,6 +685,20 @@
     " \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",
+    "* 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)"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/source/notebook_population.ipynb b/docs/source/notebook_population.ipynb
index fff337533..a24638c0b 100644
--- a/docs/source/notebook_population.ipynb
+++ b/docs/source/notebook_population.ipynb
@@ -1109,7 +1109,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1123,7 +1123,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_HRD.ipynb b/examples/notebook_HRD.ipynb
index 50e37f1b7..52590f8a2 100644
--- a/examples/notebook_HRD.ipynb
+++ b/examples/notebook_HRD.ipynb
@@ -7,7 +7,7 @@
     "tags": []
    },
    "source": [
-    "Hertzsprung-Russell diagrams\n",
+    "# Example use case: Hertzsprung-Russell diagrams\n",
     "\n",
     "In this notebook we compute Hertzsprung-Russell diagrams (HRDs) of single and binary stars.\n"
    ]
@@ -796,7 +796,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -810,7 +810,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_common_envelope_evolution.ipynb b/examples/notebook_common_envelope_evolution.ipynb
index 4c80b2d02..526320ccf 100644
--- a/examples/notebook_common_envelope_evolution.ipynb
+++ b/examples/notebook_common_envelope_evolution.ipynb
@@ -7,7 +7,7 @@
     "tags": []
    },
    "source": [
-    "## Common-envelope evolution\n",
+    "# Example use case: Common-envelope evolution\n",
     "\n",
     "In this notebook we look at how common-envelope evolution (CEE) alters binary-star orbits. We construct a population of low- and intermediate-mass binaries and compare their orbital periods before and after CEE. Not all stars evolve into this phase, so we have to run a whole population to find those that do. We then have to construct the pre- and post-CEE distributions and plot them.\n",
     "\n",
@@ -32,7 +32,9 @@
   {
    "cell_type": "markdown",
    "id": "f268eff3-4e08-4f6b-8b59-f22dba4d2074",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "## Setting up the Population object\n",
     "We set up a new population object. Our stars evolve to $13.7\\text{ }\\mathrm{Gyr}$, the age of the Universe, and we assume the metallicity $Z=0.02$. We also set the common-envelope ejection efficiency $\\alpha_\\mathrm{CE}=1$ and the envelope structure parameter $\\lambda=0.5$. More complex options are available in *binary_c*, such as $\\lambda$ based on stellar mass, but this is just a demonstration example so let's keep things simple."
@@ -684,7 +686,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -698,7 +700,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_luminosity_function_binaries.ipynb b/examples/notebook_luminosity_function_binaries.ipynb
index c6b5f1e64..e50463e6e 100644
--- a/examples/notebook_luminosity_function_binaries.ipynb
+++ b/examples/notebook_luminosity_function_binaries.ipynb
@@ -5,7 +5,7 @@
    "id": "bbbaafbb-fd7d-4b73-a970-93506ba35d71",
    "metadata": {},
    "source": [
-    "# Zero-age stellar luminosity function in binaries\n",
+    "# Example use case: Zero-age stellar luminosity function in binaries\n",
     "\n",
     "In this notebook we compute the luminosity function of the zero-age main-sequence by running a population of binary stars using binary_c. \n",
     "\n",
@@ -679,7 +679,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -693,7 +693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_luminosity_function_single.ipynb b/examples/notebook_luminosity_function_single.ipynb
index acab6b2d0..cdae316f9 100644
--- a/examples/notebook_luminosity_function_single.ipynb
+++ b/examples/notebook_luminosity_function_single.ipynb
@@ -703,7 +703,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -717,7 +717,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
-- 
GitLab